ipynb/richard_w_evans-gmmest.ipynb

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                "# Generalized Method of Moments Estimation\n",
                "### by [Richard W. Evans](https://sites.google.com/site/rickecon/), July 2017\n",
                "The code in this Jupyter notebook was written using Python 3.6. It also uses data file `Econ381totpts.txt` and the image file `MLEplots.png`. This data file and image file are stored along with the Jupyter notebook file `GMMest.ipynb` in the public GitHub repository [https://github.com/rickecon/Notebooks/tree/master/GMM](https://github.com/rickecon/Notebooks/tree/master/GMM)."
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                "## 1. MLE vs. GMM: Strengths and weaknesses\n",
                "A paper by Fuhrer, et al (1995) studies the accuracy and efficiency of the maximum likelihood (ML) estimator versus the generalized method of moments (GMM) estimator in the context of a simple linear-quadratic inventory model. They find that ML has some very nice properties over GMM in small samples when the model is simple. In the spirit of the Fuhrer, et al (1995) paper, we list the strengths and weaknesses of MLE vs. GMM more generally. I recommend you read the introduction to Fuhrer, et al (1995)."
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                "### 1.1. MLE strengths\n",
                "* More statistical significance. In general, MLE provides more statistical significance for parameter estimates than does GMM. This comes from the strong distributional assumptions that are necessary for the ML estimates.\n",
                "* ML estimates are less sensitive to parameter or model normalizations than are GMM estimates.\n",
                "* ML estimates have nice small sample properties. ML estimates have less bias and more efficiency with small data samples than GMM estimates in many cases."
            ]
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                "### 1.2. MLE weaknesses\n",
                "* MLE requires strong distributional assumptions. For MLE, the data generating process (DGP) must be completely specified. This assumes a lot of knowledge about the DGP. This assumption is likely almost always wrong.\n",
                "* MLE is very difficult in rational expectations models. This is because the consistency of beliefs induces a nonlinearity in the likelihood function that makes it difficult to find the global optimum.\n",
                "* MLE is very difficult in nonlinear models. The likelihood function can become highly nonlinear in MLE even if the model is linear when the data are irregular. This difficulty is multiplied when the model itself is more complicated and nonlinear."
            ]
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                "### 1.3. GMM strengths\n",
                "* GMM allows for most flexible identification. GMM estimates can be identified by any set of moments from the data as long as you have at least as many moments as you have parameters to estimate and that those moments are independent enough to identify the parameters. (And the parameters are independent enough of each other to be separately identified.)\n",
                "* Good large sample properties. The GMM estimator is strongly consistent and asymptotically normal. GMM will likely be the best estimator if you have a lot of data.\n",
                "* GMM requires minimal assumptions about the DGP. In GMM, you need not specify the distributions of the error terms in your model of the DGP. This is often a strength, given that most error are not observed and most models are gross approximations of the true DGP."
            ]
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                "### 1.4. GMM weaknesses\n",
                "* GMM estimates are usually less statistically significant than ML estimates. This comes from the minimal distributional assumptions. GMM parameter estimates usually are measured with more error.\n",
                "* GMM estimates can be sensitive to normalizations of the model or parameters.\n",
                "* GMM estimates have bad small sample properties. GMM estimates can have large bias and inefficiency in small samples."
            ]
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                "### 1.5. Key questions to answer when deciding between MLE and GMM\n",
                "1. How much data is available for the estimation? Large data samples will make GMM relatively more attractive than MLE because of the nice large sample properties of GMM and fewer required assumptions on the model\n",
                "2. How complex is the model? Linear models or quadratic models are much easier to do using MLE than are more highly nonlinear models. Rational expectations models (macroeconomics) create an even more difficult level of nonlinearity that pushes you toward GMM estimation.\n",
                "3. How comfortable are you making strong distributional assumptions? MLE requires a complete specification of all distributional assumptions of the model DGP. If you think these assumptions are too strong, you should use GMM."
            ]
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                "## 2. The GMM estimator\n",
                "GMM was first formalized by Hansen (1982). A strength of GMM estimation is that the econometrician can remain completely agnostic as to the distribution of the random variables in the DGP. For identification, the econometrician simply needs at least as many moment conditions from the data as he has parameters to estimate.\n",
                "\n",
                "A *moment* of the data is broadly defined as any statistic that summarizes the data to some degree. A data moment could be as narrow as an individual observation from the data or as broad as the sample average. GMM estimates the parameters of a model or data generating process to make the model moments as close as possible to the corresponding data moments. See Davidson and MacKinnon (2004, ch. 9) for a more detailed treatment of GMM. The estimation methods of linear least squares, nonlinear least squares, generalized least squares, and instrumental variables estimation are all specific cases of the more general GMM estimation method.\n",
                "\n",
                "Let $m(x)$ be an $R\\times 1$ vector of moments from the real world data $x$. And let $x$ be an $N\\times K$ matrix of data with $K$ columns representing $K$ variables and $N$ observations. Let the model DGP be characterized as $F(x,\\theta)$, where $F$ is a vector of equations, each of which is a function of the data $x$ and the $K\\times 1$ parameter vector $\\theta$. Then define $m(x|\\theta)$ as a vector of $R$ moments from the model that correspond to the real-world moment vector $m(x)$. Note that GMM requires both real world data $x$ and deterministic moments from the model $m(x|\\theta)$ in order to estimate $\\hat{\\theta}_{GMM}$. There is also a stochastic way to generate moments from the model, which we discuss later in our section on Simulated Method of Moments (SMM).\n",
                "\n",
                "The GMM approach of estimating the parameter vector $\\hat{\\theta}_{GMM}$ is to choose $\\theta$ to minimize some distance measure of the data moments $m(x)$ from the model moments $m(x|\\theta)$.\n",
                "\n",
                "$$ \\hat{\\theta}_{GMM}=\\theta:\\quad \\min_{\\theta}\\: ||m(x|\\theta)-m(x)|| $$\n",
                "\n",
                "The distance measure $||m(x|\\theta)-m(x)||$ can be any kind of norm. But it is important to recognize that your estimates $\\hat{\\theta}_{GMM}$ will be dependent on what distance measure (norm) you choose. The most widely studied and used distance metric in GMM estimation is the $L^2$ norm or the sum of squared errors in moments. Define the moment error function $e(x|\\theta)$ as the percent difference in the vector of model moments from the data moments.\n",
                "\n",
                "$$ e(x|\\theta) \\equiv \\frac{m(x|\\theta)-m(x)}{m(x)} $$\n",
                "\n",
                "It is important that the error function $e(x|\\theta)$ be a percent deviation of the moments (given that none of the data moments are 0). This puts all the moments in the same units, which helps make sure that no moments receive unintended weighting simply due to its units. This ensures that the problem is scaled properly and does not suffer from ill conditioning.\n",
                "\n",
                "In this case, the GMM estimator is the following,\n",
                "\n",
                "$$ \\hat{\\theta}_{GMM}=\\theta:\\quad \\min_{\\theta}\\:e(x|\\theta)^T \\, W \\, e(x|\\theta) $$\n",
                "\n",
                "where $W$ is a $R\\times R$ weighting matrix in the criterion function. For now, think of this weighting matrix as the identity matrix. But we will show in Section 3 a more optimal weighting matrix. We call the quadratic form expression $e(x|\\theta)^T \\, W \\, e(x|\\theta)$ the *criterion function* because it is a strictly positive scalar that is the object of the minimization in the GMM problem statement. The $R\\times R$ weighting matrix $W$ in the criterion function allows the econometrician to control how each moment is weighted in the minimization problem. For example, an $R\\times R$ identity matrix for $W$ would give each moment equal weighting, and the criterion function would be a simply sum of squared percent deviations (errors). Other weighting strategies can be dictated by the nature of the problem or model."
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                "## 3. The Weighting Matrix (W)\n",
                "In the GMM criterion function in the problem statement above, some weighting matrices $W$ produce precise estimates while others produce poor estimates with large variances. We want to choose the optimal weighting matrix $W$ with the smallest possible asymptotic variance. This is an efficient of optimal GMM estimator. The optimal weighting matrix is the inverse variance covariance matrix of the moments at the optimal moments,\n",
                "\n",
                "$$ W^{opt} \\equiv \\Omega^{-1}(x|\\hat{\\theta}_{GMM}) $$\n",
                "\n",
                "where $\\Omega(x|\\theta)$ is the variance covariance matrix of the moment condition errors $e(x|\\theta)$. The intuition for using the inverse variance covariance matrix $\\Omega^{-1}$ as the optimal weighting matrix is the following. You want to downweight moments that have a high variance, and you want to weight more heavily the moments that are generated more precisely.\n",
                "\n",
                "Notice that this definition of the optimal weighting matrix is circular. $W^{opt}$ is a function of the GMM estimates $\\hat{\\theta}_{GMM}$, but the optimal weighting matrix is used in the estimation of $\\hat{\\theta}_{GMM}$. This means that one has to use some kind of iterative fixed point method to find the true optimal weighting matrix $W^{opt}$. Below are some examples of weighting matrices to use."
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                "### 3.1. The identity matrix (W = I)\n",
                "Many times, you can get away with just using the identity matrix as your weighting matrix $W = I$. This changes the criterion function to a simple sum of squared error functions such that each moment has the same weight.\n",
                "\n",
                "$$ \\hat{\\theta}_{GMM}=\\theta:\\quad \\min_{\\theta}\\:e(x|\\theta)^T \\, e(x|\\theta) $$\n",
                "\n",
                "If the problem is well conditioned and well identified, then your GMM estimates $\\hat{\\theta}_{GMM}$ will not be greatly affected by this simplest of weighting matrices."
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                "### 3.2. Two-step variance covariance estimator of W\n",
                "The most common method of estimating the optimal weighting matrix for GMM estimates is the two-step variance covariance estimator. The name \"two-step\" refers to the two steps used to get the weighting matrix.\n",
                "\n",
                "The first step is to estimate the GMM parameter vector $\\hat{\\theta}_{1,GMM}$ using the simple identity matrix as the weighting matrix $W = I$.\n",
                "\n",
                "$$ \\hat{\\theta}_{1, GMM}=\\theta:\\quad \\min_{\\theta}\\:e(x|\\theta)^T \\, I \\, e(x|\\theta) $$\n",
                "\n",
                "You then use the vector of moment error functions from thet Step 1 GMM estimate $e(x|\\hat{\\theta}_{1,GMM})$ to get a new estimate of the variance covariance matrix\n",
                "\n",
                "$$ \\hat{\\Omega}_2 = \\frac{1}{N}e(x|\\hat{\\theta}_{1,GMM})\\,e(x|\\hat{\\theta}_{1,GMM})^T $$\n",
                "\n",
                "The optimal weighting matrix is the inverse of the two-step variance covariance matrix.\n",
                "\n",
                "$$ \\hat{W}^{two-step} \\equiv \\hat{\\Omega}_2^{-1} $$\n",
                "\n",
                "Lastly, re-estimate the GMM estimator using the optimal two-step weighting matrix.\n",
                "\n",
                "$$ \\hat{\\theta}_{2, GMM}=\\theta:\\quad \\min_{\\theta}\\:e(x|\\theta)^T \\, \\hat{W}^{two-step} \\, e(x|\\theta) $$\n",
                "\n",
                "$\\hat{\\theta}_{2, GMM}$ is called the two-step GMM estimator."
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                "### 3.3. Iterated variance covariance estimator of W\n",
                "The truly optimal weighting matrix $W^{opt}$ is the iterated variance-covariance estimator of $W$. This procedure is to just repeat the process described in the two-step GMM estimator until the estimated weighting matrix no longer changes between iterations. Let $i$ index the $i$th iterated GMM estimator,\n",
                "\n",
                "$$ \\hat{\\theta}_{i, GMM}=\\theta:\\quad \\min_{\\theta}\\:e(x|\\theta)^T \\, \\hat{W}_{i} \\, e(x|\\theta) $$\n",
                "\n",
                "and the $i+1$th estimate of the optimal weighting matrix is defined as the following.\n",
                "\n",
                "$$ \\hat{W}_{i+1} \\equiv \\hat{\\Omega}_{i+1} = \\frac{1}{N}e(x|\\hat{\\theta}_{i,GMM})\\,e(x|\\hat{\\theta}_{i,GMM})^T $$\n",
                "\n",
                "The iterated GMM estimator is the $\\hat{\\theta}_{i,GMM}$ such that $\\hat{W}_{i+1}$ is very close to $\\hat{W}_{i}$ for some distance metric (norm).\n",
                "\n",
                "$$ \\hat{\\theta}_{it,GMM} = \\hat{\\theta}_{i,GMM}: \\quad || \\hat{W}_{i+1} - \\hat{W}_{i} || < \\varepsilon $$"
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                "### 3.4. Newey-West consistent estimator of $\\Omega$ and W\n",
                "The Newey-West estimator of the optimal weighting matrix and variance covariance matrix is consistent in the presence of heteroskedasticity and autocorrelation in the data (See Newey and West, 1987). Adda and Cooper (2003, p. 82) have a nice exposition of how to compute the Newey-West weighting matrix $\\hat{W}_{nw}$. The asymptotic representation of the optimal weighting matrix $\\hat{W}^{opt}$ is the following:\n",
                "\n",
                "$$ \\hat{W}^{opt} = \\lim_{N\\rightarrow\\infty}\\frac{1}{N}\\sum_{i=1}^N \\sum_{l=-\\infty}^\\infty e(x_i|\\theta)e(x_{i-l}|\\theta)^T $$\n",
                "\n",
                "The Newey-West consistend estimator of $\\hat{W}^{opt}$ is:\n",
                "\n",
                "$$ \\hat{W}_{nw} = \\Gamma_{0,N} + \\sum_{v=1}^q \\left(1 - \\left[\\frac{v}{q+1}\\right]\\right)\\left(\\Gamma_{v,N} + \\Gamma^T_{v,N}\\right) $$\n",
                "\n",
                "where\n",
                "\n",
                "$$ \\Gamma_{v,N} = \\frac{1}{N}\\sum_{i=v+1}^N e(x_i|\\theta)e(x_{i-v}|\\theta)^T $$\n",
                "\n",
                "Of course, for autocorrelation, the subscript $i$ and the sample size $N$ can be changed to $t$ and $T$, respectively."
            ]
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                "## 4. Examples\n",
                "In this section, we will use GMM to estimate parameters of the models from the [maximum likelihood estimation notebook](https://github.com/rickecon/Notebooks/blob/master/MLE/MLest.ipynb). We will also go through the standard moment conditions in most econometrics textbooks in which the conditional and unconditional expectations provide moments for estimation."
            ]
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                "### 4.1. Fitting a truncated normal to intermediate macroeconomics test scores\n",
                "Let's revisit the problem from the MLE notebook of fitting a truncated normal distribution to intermediate macroeconomics test scores. The data are in the text file `Econ381totpts.txt`. Recall that these test scores are between 0 and 450. The figure below shows a histogram of the data, as well as three truncated normal PDF's. The black line is the ML estimate of $\\mu$ and $\\sigma$ of the truncated normal pdf. The red and the green lines are just the PDF's of two \"arbitrarily\" chosen combinations of the truncated normal parameters $\\mu$ and $\\sigma$."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
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                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<IPython.core.display.Image object>"
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "import requests\n",
                "from IPython.display import Image\n",
                "\n",
                "# Download and save the data file MLEplots.png\n",
                "url = ('https://raw.githubusercontent.com/rickecon/Notebooks/' +\n",
                "       'master/GMM/images/MLEplots.png')\n",
                "image_file = requests.get(url, allow_redirects=True)\n",
                "open('MLEplots.png', 'wb').write(image_file.content)\n",
                "Image(\"MLEplots.png\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Let's try estimating the parameters $\\mu$ and $\\sigma$ by GMM. What moments should we use? Let's try the mean and variance of the data. These two statistics of the data are defined by:\n",
                "\n",
                "$$ mean(scores_i) = \\frac{1}{N}\\sum_{i=1}^N scores_i $$\n",
                "\n",
                "$$ var(scores_i) = \\frac{1}{N-1}\\sum_{i=1}^{N} \\left(scores_i - mean(scores_i)\\right)^2 $$\n",
                "\n",
                "So the data moment vector $m(x)$ for GMM is the following.\n",
                "\n",
                "$$ m(scores_i) \\equiv \\begin{bmatrix} mean(scores_i) \\\\ var(scores_i) \\end{bmatrix} $$\n",
                "\n",
                "And the model moment vector $m(x|\\theta)$ for GMM is the following.\n",
                "\n",
                "$$ m(scores_i|\\mu,\\sigma) \\equiv \\begin{bmatrix} mean(scores_i|\\mu,\\sigma) \\\\ var(scores_i|\\mu,\\sigma) \\end{bmatrix} $$\n",
                "\n",
                "Define the error vector as the vector of percent deviations of the model moments from the data moments.\n",
                "\n",
                "$$ e(scores_i|\\mu,\\sigma) \\equiv \\frac{m(scores_i|\\mu,\\sigma) - m(scores_i)}{m(scores_i)} $$\n",
                "\n",
                "The GMM estimator for this moment vector is the following.\n",
                "\n",
                "$$ (\\hat{\\mu}_{GMM},\\hat{\\sigma}_{GMM}) = (\\mu,\\sigma):\\quad \\min_{\\mu,\\sigma} e(scores_i|\\mu,\\sigma)^T \\, W \\, e(scores_i|\\mu,\\sigma) $$"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Import packages and load the data\n",
                "import numpy as np\n",
                "import numpy.linalg as lin\n",
                "import scipy.stats as sts\n",
                "import scipy.integrate as intgr\n",
                "import scipy.optimize as opt\n",
                "import matplotlib\n",
                "import matplotlib.pyplot as plt\n",
                "from mpl_toolkits.mplot3d import Axes3D\n",
                "from matplotlib import cm\n",
                "cmap1 = matplotlib.cm.get_cmap('summer')\n",
                "\n",
                "# This next command is specifically for Jupyter Notebook\n",
                "%matplotlib notebook"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Download and save the data file Econ381totpts.txt\n",
                "url = ('https://raw.githubusercontent.com/rickecon/Notebooks/' +\n",
                "       'master/GMM/data/Econ381totpts.txt')\n",
                "data_file = requests.get(url, allow_redirects=True)\n",
                "open('Econ381totpts.txt', 'wb').write(data_file.content)\n",
                "\n",
                "# Load the data as a NumPy array\n",
                "pts = np.loadtxt('Econ381totpts.txt')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Define function that generates values of a truncated normal pdf\n",
                "def trunc_norm_pdf(xvals, mu, sigma, cutoff):\n",
                "    '''\n",
                "    --------------------------------------------------------------------\n",
                "    Generate pdf values from the truncated normal pdf with mean mu and\n",
                "    standard deviation sigma. If the cutoff is finite, then the PDF\n",
                "    values are inflated upward to reflect the zero probability on values\n",
                "    above the cutoff. If there is no cutoff given or if it is given as\n",
                "    infinity, this function does the same thing as\n",
                "    sp.stats.norm.pdf(x, loc=mu, scale=sigma).\n",
                "    --------------------------------------------------------------------\n",
                "    INPUTS:\n",
                "    xvals  = (N,) vector, values of the normally distributed random\n",
                "             variable\n",
                "    mu     = scalar, mean of the normally distributed random variable\n",
                "    sigma  = scalar > 0, standard deviation of the normally distributed\n",
                "             random variable\n",
                "    cutoff = scalar or string, ='None' if no cutoff is given, otherwise\n",
                "             is scalar upper bound value of distribution. Values above\n",
                "             this value have zero probability\n",
                "    \n",
                "    OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION: None\n",
                "    \n",
                "    OBJECTS CREATED WITHIN FUNCTION:\n",
                "    prob_notcut = scalar \n",
                "    pdf_vals = (N,) vector, normal PDF values for mu and sigma\n",
                "               corresponding to xvals data\n",
                "    \n",
                "    FILES CREATED BY THIS FUNCTION: None\n",
                "    \n",
                "    RETURNS: pdf_vals\n",
                "    --------------------------------------------------------------------\n",
                "    '''\n",
                "    if cutoff == 'None':\n",
                "        prob_notcut = 1.0\n",
                "    else:\n",
                "        prob_notcut = sts.norm.cdf(cutoff, loc=mu, scale=sigma)\n",
                "            \n",
                "    pdf_vals    = ((1/(sigma * np.sqrt(2 * np.pi)) *\n",
                "                    np.exp( - (xvals - mu)**2 / (2 * sigma**2))) /\n",
                "                    prob_notcut)\n",
                "    \n",
                "    return pdf_vals"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Now let's define a criterion function that takes as inputs the parameters and the estimator for the weighting matrix $\\hat{W}$."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [],
            "source": [
                "def data_moments(xvals):\n",
                "    '''\n",
                "    --------------------------------------------------------------------\n",
                "    This function computes the two data moments for GMM\n",
                "    (mean(data), variance(data)).\n",
                "    --------------------------------------------------------------------\n",
                "    INPUTS:\n",
                "    xvals = (N,) vector, test scores data\n",
                "    \n",
                "    OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION: None\n",
                "    \n",
                "    OBJECTS CREATED WITHIN FUNCTION:\n",
                "    mean_data = scalar, mean value of test scores data\n",
                "    var_data  = scalar > 0, variance of test scores data\n",
                "    \n",
                "    FILES CREATED BY THIS FUNCTION: None\n",
                "    \n",
                "    RETURNS: mean_data, var_data\n",
                "    --------------------------------------------------------------------\n",
                "    '''\n",
                "    mean_data = xvals.mean()\n",
                "    var_data = xvals.var()\n",
                "    \n",
                "    return mean_data, var_data\n",
                "\n",
                "\n",
                "def model_moments(mu, sigma, cutoff):\n",
                "    '''\n",
                "    --------------------------------------------------------------------\n",
                "    This function computes the two model moments for GMM\n",
                "    (mean(model data), variance(model data)).\n",
                "    --------------------------------------------------------------------\n",
                "    INPUTS:\n",
                "    mu     = scalar, mean of the normally distributed random variable\n",
                "    sigma  = scalar > 0, standard deviation of the normally distributed\n",
                "             random variable\n",
                "    cutoff = scalar or string, ='None' if no cutoff is given, otherwise\n",
                "             is scalar upper bound value of distribution. Values above\n",
                "             this value have zero probability\n",
                "    \n",
                "    OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:\n",
                "        trunc_norm_pdf()\n",
                "        xfx()\n",
                "        x2fx()\n",
                "    \n",
                "    OBJECTS CREATED WITHIN FUNCTION:\n",
                "    mean_model = scalar, mean value of test scores from model\n",
                "    m_m_err    = scalar > 0, estimated error in the computation of the\n",
                "                 integral for the mean of the distribution\n",
                "    var_model  = scalar > 0, variance of test scores from model\n",
                "    v_m_err    = scalar > 0, estimated error in the computation of the\n",
                "                 integral for the variance of the distribution\n",
                "    \n",
                "    FILES CREATED BY THIS FUNCTION: None\n",
                "    \n",
                "    RETURNS: mean_model, var_model\n",
                "    --------------------------------------------------------------------\n",
                "    '''\n",
                "    xfx = lambda x: x * trunc_norm_pdf(x, mu, sigma, cutoff)\n",
                "    (mean_model, m_m_err) = intgr.quad(xfx, -np.inf, cutoff)\n",
                "    x2fx = lambda x: ((x - mean_model) ** 2) * trunc_norm_pdf(x, mu, sigma, cutoff) \n",
                "    (var_model, v_m_err) = intgr.quad(x2fx, -np.inf, cutoff)\n",
                "    \n",
                "    return mean_model, var_model\n",
                "\n",
                "\n",
                "def err_vec(xvals, mu, sigma, cutoff, simple):\n",
                "    '''\n",
                "    --------------------------------------------------------------------\n",
                "    This function computes the vector of moment errors (in percent\n",
                "    deviation from the data moment vector) for GMM.\n",
                "    --------------------------------------------------------------------\n",
                "    INPUTS:\n",
                "    xvals  = (N,) vector, test scores data\n",
                "    mu     = scalar, mean of the normally distributed random variable\n",
                "    sigma  = scalar > 0, standard deviation of the normally distributed\n",
                "             random variable\n",
                "    cutoff = scalar or string, ='None' if no cutoff is given, otherwise\n",
                "             is scalar upper bound value of distribution. Values above\n",
                "             this value have zero probability\n",
                "    simple = boolean, =True if errors are simple difference, =False if\n",
                "             errors are percent deviation from data moments\n",
                "    \n",
                "    OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:\n",
                "        data_moments()\n",
                "        model_moments()\n",
                "    \n",
                "    OBJECTS CREATED WITHIN FUNCTION:\n",
                "    mean_data  = scalar, mean value of data\n",
                "    var_data   = scalar > 0, variance of data\n",
                "    moms_data  = (2, 1) matrix, column vector of two data moments\n",
                "    mean_model = scalar, mean value from model\n",
                "    var_model  = scalar > 0, variance from model\n",
                "    moms_model = (2, 1) matrix, column vector of two model moments\n",
                "    err_vec    = (2, 1) matrix, column vector of two moment error\n",
                "                 functions\n",
                "    \n",
                "    FILES CREATED BY THIS FUNCTION: None\n",
                "    \n",
                "    RETURNS: err_vec\n",
                "    --------------------------------------------------------------------\n",
                "    '''\n",
                "    mean_data, var_data = data_moments(xvals)\n",
                "    moms_data = np.array([[mean_data], [var_data]])\n",
                "    mean_model, var_model = model_moments(mu, sigma, cutoff)\n",
                "    moms_model = np.array([[mean_model], [var_model]])\n",
                "    if simple:\n",
                "        err_vec = moms_model - moms_data\n",
                "    else:\n",
                "        err_vec = (moms_model - moms_data) / moms_data\n",
                "    \n",
                "    return err_vec\n",
                "\n",
                "\n",
                "def criterion(params, *args):\n",
                "    '''\n",
                "    --------------------------------------------------------------------\n",
                "    This function computes the GMM weighted sum of squared moment errors\n",
                "    criterion function value given parameter values and an estimate of\n",
                "    the weighting matrix.\n",
                "    --------------------------------------------------------------------\n",
                "    INPUTS:\n",
                "    params = (2,) vector, ([mu, sigma])\n",
                "    mu     = scalar, mean of the normally distributed random variable\n",
                "    sigma  = scalar > 0, standard deviation of the normally distributed\n",
                "             random variable\n",
                "    args   = length 3 tuple, (xvals, cutoff, W_hat)\n",
                "    xvals  = (N,) vector, values of the truncated normally distributed\n",
                "             random variable\n",
                "    cutoff = scalar or string, ='None' if no cutoff is given, otherwise\n",
                "             is scalar upper bound value of distribution. Values above\n",
                "             this value have zero probability\n",
                "    W_hat  = (R, R) matrix, estimate of optimal weighting matrix\n",
                "    \n",
                "    OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:\n",
                "        norm_pdf()\n",
                "    \n",
                "    OBJECTS CREATED WITHIN FUNCTION:\n",
                "    err        = (2, 1) matrix, column vector of two moment error\n",
                "                 functions\n",
                "    crit_val   = scalar > 0, GMM criterion function value\n",
                "    \n",
                "    FILES CREATED BY THIS FUNCTION: None\n",
                "    \n",
                "    RETURNS: crit_val\n",
                "    --------------------------------------------------------------------\n",
                "    '''\n",
                "    mu, sigma = params\n",
                "    xvals, cutoff, W = args\n",
                "    err = err_vec(xvals, mu, sigma, cutoff, simple=False)\n",
                "    crit_val = np.dot(np.dot(err.T, W), err) \n",
                "    \n",
                "    return crit_val"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Now we can perform the GMM estimation. Let's start with the identity matrix as our estimate for the optimal weighting matrix $W = I$."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "mu_GMM1= 556.6070097457138  sig_GMM1= 176.26235549380954\n"
                    ]
                }
            ],
            "source": [
                "# Note that this takes a little time because the intgr.quad() commands\n",
                "# are a little slow\n",
                "mu_init = 400\n",
                "sig_init = 60\n",
                "params_init = np.array([mu_init, sig_init])\n",
                "W_hat = np.eye(2)\n",
                "gmm_args = (pts, 450.0, W_hat)\n",
                "results = opt.minimize(criterion, params_init, args=(gmm_args),\n",
                "                       method='L-BFGS-B', bounds=((1e-10, None), (1e-10, None)))\n",
                "mu_GMM1, sig_GMM1 = results.x\n",
                "print('mu_GMM1=', mu_GMM1, ' sig_GMM1=', sig_GMM1)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Mean of points = 341.90869565217395 , Variance of points = 7827.997292398056\n",
                        "Mean of model = 341.8085155314267 , Variance of model = 7829.0500135680995\n",
                        "Error vector= [-0.000293    0.00013448]\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "      fun: array([[1.03935782e-07]])\n",
                            " hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>\n",
                            "      jac: array([-1.35273256e-06,  4.92242982e-06])\n",
                            "  message: b'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL'\n",
                            "     nfev: 75\n",
                            "      nit: 16\n",
                            "   status: 0\n",
                            "  success: True\n",
                            "        x: array([556.60700975, 176.26235549])"
                        ]
                    },
                    "execution_count": 21,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "mean_data, var_data = data_moments(pts)\n",
                "mean_model, var_model = model_moments(mu_GMM1, sig_GMM1, 450.0)\n",
                "err1 = err_vec(pts, mu_GMM1, sig_GMM1, 450.0, False).reshape(2,)\n",
                "print('Mean of points =', mean_data, ', Variance of points =', var_data)\n",
                "print('Mean of model =', mean_model, ', Variance of model =', var_model)\n",
                "print('Error vector=', err1)\n",
                "results"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Let's plot the PDF implied by these results against the histogram."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/javascript": [
                            "/* Put everything inside the global mpl namespace */\n",
                            "window.mpl = {};\n",
                            "\n",
                            "\n",
                            "mpl.get_websocket_type = function() {\n",
                            "    if (typeof(WebSocket) !== 'undefined') {\n",
                            "        return WebSocket;\n",
                            "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
                            "        return MozWebSocket;\n",
                            "    } else {\n",
                            "        alert('Your browser does not have WebSocket support.' +\n",
                            "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
                            "              'Firefox 4 and 5 are also supported but you ' +\n",
                            "              'have to enable WebSockets in about:config.');\n",
                            "    };\n",
                            "}\n",
                            "\n",
                            "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
                            "    this.id = figure_id;\n",
                            "\n",
                            "    this.ws = websocket;\n",
                            "\n",
                            "    this.supports_binary = (this.ws.binaryType != undefined);\n",
                            "\n",
                            "    if (!this.supports_binary) {\n",
                            "        var warnings = document.getElementById(\"mpl-warnings\");\n",
                            "        if (warnings) {\n",
                            "            warnings.style.display = 'block';\n",
                            "            warnings.textContent = (\n",
                            "                \"This browser does not support binary websocket messages. \" +\n",
                            "                    \"Performance may be slow.\");\n",
                            "        }\n",
                            "    }\n",
                            "\n",
                            "    this.imageObj = new Image();\n",
                            "\n",
                            "    this.context = undefined;\n",
                            "    this.message = undefined;\n",
                            "    this.canvas = undefined;\n",
                            "    this.rubberband_canvas = undefined;\n",
                            "    this.rubberband_context = undefined;\n",
                            "    this.format_dropdown = undefined;\n",
                            "\n",
                            "    this.image_mode = 'full';\n",
                            "\n",
                            "    this.root = $('<div/>');\n",
                            "    this._root_extra_style(this.root)\n",
                            "    this.root.attr('style', 'display: inline-block');\n",
                            "\n",
                            "    $(parent_element).append(this.root);\n",
                            "\n",
                            "    this._init_header(this);\n",
                            "    this._init_canvas(this);\n",
                            "    this._init_toolbar(this);\n",
                            "\n",
                            "    var fig = this;\n",
                            "\n",
                            "    this.waiting = false;\n",
                            "\n",
                            "    this.ws.onopen =  function () {\n",
                            "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
                            "            fig.send_message(\"send_image_mode\", {});\n",
                            "            if (mpl.ratio != 1) {\n",
                            "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
                            "            }\n",
                            "            fig.send_message(\"refresh\", {});\n",
                            "        }\n",
                            "\n",
                            "    this.imageObj.onload = function() {\n",
                            "            if (fig.image_mode == 'full') {\n",
                            "                // Full images could contain transparency (where diff images\n",
                            "                // almost always do), so we need to clear the canvas so that\n",
                            "                // there is no ghosting.\n",
                            "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
                            "            }\n",
                            "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
                            "        };\n",
                            "\n",
                            "    this.imageObj.onunload = function() {\n",
                            "        fig.ws.close();\n",
                            "    }\n",
                            "\n",
                            "    this.ws.onmessage = this._make_on_message_function(this);\n",
                            "\n",
                            "    this.ondownload = ondownload;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_header = function() {\n",
                            "    var titlebar = $(\n",
                            "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
                            "        'ui-helper-clearfix\"/>');\n",
                            "    var titletext = $(\n",
                            "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
                            "        'text-align: center; padding: 3px;\"/>');\n",
                            "    titlebar.append(titletext)\n",
                            "    this.root.append(titlebar);\n",
                            "    this.header = titletext[0];\n",
                            "}\n",
                            "\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
                            "\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
                            "\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_canvas = function() {\n",
                            "    var fig = this;\n",
                            "\n",
                            "    var canvas_div = $('<div/>');\n",
                            "\n",
                            "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
                            "\n",
                            "    function canvas_keyboard_event(event) {\n",
                            "        return fig.key_event(event, event['data']);\n",
                            "    }\n",
                            "\n",
                            "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
                            "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
                            "    this.canvas_div = canvas_div\n",
                            "    this._canvas_extra_style(canvas_div)\n",
                            "    this.root.append(canvas_div);\n",
                            "\n",
                            "    var canvas = $('<canvas/>');\n",
                            "    canvas.addClass('mpl-canvas');\n",
                            "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
                            "\n",
                            "    this.canvas = canvas[0];\n",
                            "    this.context = canvas[0].getContext(\"2d\");\n",
                            "\n",
                            "    var backingStore = this.context.backingStorePixelRatio ||\n",
                            "\tthis.context.webkitBackingStorePixelRatio ||\n",
                            "\tthis.context.mozBackingStorePixelRatio ||\n",
                            "\tthis.context.msBackingStorePixelRatio ||\n",
                            "\tthis.context.oBackingStorePixelRatio ||\n",
                            "\tthis.context.backingStorePixelRatio || 1;\n",
                            "\n",
                            "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
                            "\n",
                            "    var rubberband = $('<canvas/>');\n",
                            "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
                            "\n",
                            "    var pass_mouse_events = true;\n",
                            "\n",
                            "    canvas_div.resizable({\n",
                            "        start: function(event, ui) {\n",
                            "            pass_mouse_events = false;\n",
                            "        },\n",
                            "        resize: function(event, ui) {\n",
                            "            fig.request_resize(ui.size.width, ui.size.height);\n",
                            "        },\n",
                            "        stop: function(event, ui) {\n",
                            "            pass_mouse_events = true;\n",
                            "            fig.request_resize(ui.size.width, ui.size.height);\n",
                            "        },\n",
                            "    });\n",
                            "\n",
                            "    function mouse_event_fn(event) {\n",
                            "        if (pass_mouse_events)\n",
                            "            return fig.mouse_event(event, event['data']);\n",
                            "    }\n",
                            "\n",
                            "    rubberband.mousedown('button_press', mouse_event_fn);\n",
                            "    rubberband.mouseup('button_release', mouse_event_fn);\n",
                            "    // Throttle sequential mouse events to 1 every 20ms.\n",
                            "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
                            "\n",
                            "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
                            "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
                            "\n",
                            "    canvas_div.on(\"wheel\", function (event) {\n",
                            "        event = event.originalEvent;\n",
                            "        event['data'] = 'scroll'\n",
                            "        if (event.deltaY < 0) {\n",
                            "            event.step = 1;\n",
                            "        } else {\n",
                            "            event.step = -1;\n",
                            "        }\n",
                            "        mouse_event_fn(event);\n",
                            "    });\n",
                            "\n",
                            "    canvas_div.append(canvas);\n",
                            "    canvas_div.append(rubberband);\n",
                            "\n",
                            "    this.rubberband = rubberband;\n",
                            "    this.rubberband_canvas = rubberband[0];\n",
                            "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
                            "    this.rubberband_context.strokeStyle = \"#000000\";\n",
                            "\n",
                            "    this._resize_canvas = function(width, height) {\n",
                            "        // Keep the size of the canvas, canvas container, and rubber band\n",
                            "        // canvas in synch.\n",
                            "        canvas_div.css('width', width)\n",
                            "        canvas_div.css('height', height)\n",
                            "\n",
                            "        canvas.attr('width', width * mpl.ratio);\n",
                            "        canvas.attr('height', height * mpl.ratio);\n",
                            "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
                            "\n",
                            "        rubberband.attr('width', width);\n",
                            "        rubberband.attr('height', height);\n",
                            "    }\n",
                            "\n",
                            "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
                            "    // upon first draw.\n",
                            "    this._resize_canvas(600, 600);\n",
                            "\n",
                            "    // Disable right mouse context menu.\n",
                            "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
                            "        return false;\n",
                            "    });\n",
                            "\n",
                            "    function set_focus () {\n",
                            "        canvas.focus();\n",
                            "        canvas_div.focus();\n",
                            "    }\n",
                            "\n",
                            "    window.setTimeout(set_focus, 100);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_toolbar = function() {\n",
                            "    var fig = this;\n",
                            "\n",
                            "    var nav_element = $('<div/>')\n",
                            "    nav_element.attr('style', 'width: 100%');\n",
                            "    this.root.append(nav_element);\n",
                            "\n",
                            "    // Define a callback function for later on.\n",
                            "    function toolbar_event(event) {\n",
                            "        return fig.toolbar_button_onclick(event['data']);\n",
                            "    }\n",
                            "    function toolbar_mouse_event(event) {\n",
                            "        return fig.toolbar_button_onmouseover(event['data']);\n",
                            "    }\n",
                            "\n",
                            "    for(var toolbar_ind in mpl.toolbar_items) {\n",
                            "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
                            "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
                            "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
                            "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
                            "\n",
                            "        if (!name) {\n",
                            "            // put a spacer in here.\n",
                            "            continue;\n",
                            "        }\n",
                            "        var button = $('<button/>');\n",
                            "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
                            "                        'ui-button-icon-only');\n",
                            "        button.attr('role', 'button');\n",
                            "        button.attr('aria-disabled', 'false');\n",
                            "        button.click(method_name, toolbar_event);\n",
                            "        button.mouseover(tooltip, toolbar_mouse_event);\n",
                            "\n",
                            "        var icon_img = $('<span/>');\n",
                            "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
                            "        icon_img.addClass(image);\n",
                            "        icon_img.addClass('ui-corner-all');\n",
                            "\n",
                            "        var tooltip_span = $('<span/>');\n",
                            "        tooltip_span.addClass('ui-button-text');\n",
                            "        tooltip_span.html(tooltip);\n",
                            "\n",
                            "        button.append(icon_img);\n",
                            "        button.append(tooltip_span);\n",
                            "\n",
                            "        nav_element.append(button);\n",
                            "    }\n",
                            "\n",
                            "    var fmt_picker_span = $('<span/>');\n",
                            "\n",
                            "    var fmt_picker = $('<select/>');\n",
                            "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
                            "    fmt_picker_span.append(fmt_picker);\n",
                            "    nav_element.append(fmt_picker_span);\n",
                            "    this.format_dropdown = fmt_picker[0];\n",
                            "\n",
                            "    for (var ind in mpl.extensions) {\n",
                            "        var fmt = mpl.extensions[ind];\n",
                            "        var option = $(\n",
                            "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
                            "        fmt_picker.append(option)\n",
                            "    }\n",
                            "\n",
                            "    // Add hover states to the ui-buttons\n",
                            "    $( \".ui-button\" ).hover(\n",
                            "        function() { $(this).addClass(\"ui-state-hover\");},\n",
                            "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
                            "    );\n",
                            "\n",
                            "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
                            "    nav_element.append(status_bar);\n",
                            "    this.message = status_bar[0];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
                            "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
                            "    // which will in turn request a refresh of the image.\n",
                            "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.send_message = function(type, properties) {\n",
                            "    properties['type'] = type;\n",
                            "    properties['figure_id'] = this.id;\n",
                            "    this.ws.send(JSON.stringify(properties));\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.send_draw_message = function() {\n",
                            "    if (!this.waiting) {\n",
                            "        this.waiting = true;\n",
                            "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
                            "    }\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
                            "    var format_dropdown = fig.format_dropdown;\n",
                            "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
                            "    fig.ondownload(fig, format);\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
                            "    var size = msg['size'];\n",
                            "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
                            "        fig._resize_canvas(size[0], size[1]);\n",
                            "        fig.send_message(\"refresh\", {});\n",
                            "    };\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
                            "    var x0 = msg['x0'] / mpl.ratio;\n",
                            "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
                            "    var x1 = msg['x1'] / mpl.ratio;\n",
                            "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
                            "    x0 = Math.floor(x0) + 0.5;\n",
                            "    y0 = Math.floor(y0) + 0.5;\n",
                            "    x1 = Math.floor(x1) + 0.5;\n",
                            "    y1 = Math.floor(y1) + 0.5;\n",
                            "    var min_x = Math.min(x0, x1);\n",
                            "    var min_y = Math.min(y0, y1);\n",
                            "    var width = Math.abs(x1 - x0);\n",
                            "    var height = Math.abs(y1 - y0);\n",
                            "\n",
                            "    fig.rubberband_context.clearRect(\n",
                            "        0, 0, fig.canvas.width, fig.canvas.height);\n",
                            "\n",
                            "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
                            "    // Updates the figure title.\n",
                            "    fig.header.textContent = msg['label'];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
                            "    var cursor = msg['cursor'];\n",
                            "    switch(cursor)\n",
                            "    {\n",
                            "    case 0:\n",
                            "        cursor = 'pointer';\n",
                            "        break;\n",
                            "    case 1:\n",
                            "        cursor = 'default';\n",
                            "        break;\n",
                            "    case 2:\n",
                            "        cursor = 'crosshair';\n",
                            "        break;\n",
                            "    case 3:\n",
                            "        cursor = 'move';\n",
                            "        break;\n",
                            "    }\n",
                            "    fig.rubberband_canvas.style.cursor = cursor;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
                            "    fig.message.textContent = msg['message'];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
                            "    // Request the server to send over a new figure.\n",
                            "    fig.send_draw_message();\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
                            "    fig.image_mode = msg['mode'];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.updated_canvas_event = function() {\n",
                            "    // Called whenever the canvas gets updated.\n",
                            "    this.send_message(\"ack\", {});\n",
                            "}\n",
                            "\n",
                            "// A function to construct a web socket function for onmessage handling.\n",
                            "// Called in the figure constructor.\n",
                            "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
                            "    return function socket_on_message(evt) {\n",
                            "        if (evt.data instanceof Blob) {\n",
                            "            /* FIXME: We get \"Resource interpreted as Image but\n",
                            "             * transferred with MIME type text/plain:\" errors on\n",
                            "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
                            "             * to be part of the websocket stream */\n",
                            "            evt.data.type = \"image/png\";\n",
                            "\n",
                            "            /* Free the memory for the previous frames */\n",
                            "            if (fig.imageObj.src) {\n",
                            "                (window.URL || window.webkitURL).revokeObjectURL(\n",
                            "                    fig.imageObj.src);\n",
                            "            }\n",
                            "\n",
                            "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
                            "                evt.data);\n",
                            "            fig.updated_canvas_event();\n",
                            "            fig.waiting = false;\n",
                            "            return;\n",
                            "        }\n",
                            "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
                            "            fig.imageObj.src = evt.data;\n",
                            "            fig.updated_canvas_event();\n",
                            "            fig.waiting = false;\n",
                            "            return;\n",
                            "        }\n",
                            "\n",
                            "        var msg = JSON.parse(evt.data);\n",
                            "        var msg_type = msg['type'];\n",
                            "\n",
                            "        // Call the  \"handle_{type}\" callback, which takes\n",
                            "        // the figure and JSON message as its only arguments.\n",
                            "        try {\n",
                            "            var callback = fig[\"handle_\" + msg_type];\n",
                            "        } catch (e) {\n",
                            "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
                            "            return;\n",
                            "        }\n",
                            "\n",
                            "        if (callback) {\n",
                            "            try {\n",
                            "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
                            "                callback(fig, msg);\n",
                            "            } catch (e) {\n",
                            "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
                            "            }\n",
                            "        }\n",
                            "    };\n",
                            "}\n",
                            "\n",
                            "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
                            "mpl.findpos = function(e) {\n",
                            "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
                            "    var targ;\n",
                            "    if (!e)\n",
                            "        e = window.event;\n",
                            "    if (e.target)\n",
                            "        targ = e.target;\n",
                            "    else if (e.srcElement)\n",
                            "        targ = e.srcElement;\n",
                            "    if (targ.nodeType == 3) // defeat Safari bug\n",
                            "        targ = targ.parentNode;\n",
                            "\n",
                            "    // jQuery normalizes the pageX and pageY\n",
                            "    // pageX,Y are the mouse positions relative to the document\n",
                            "    // offset() returns the position of the element relative to the document\n",
                            "    var x = e.pageX - $(targ).offset().left;\n",
                            "    var y = e.pageY - $(targ).offset().top;\n",
                            "\n",
                            "    return {\"x\": x, \"y\": y};\n",
                            "};\n",
                            "\n",
                            "/*\n",
                            " * return a copy of an object with only non-object keys\n",
                            " * we need this to avoid circular references\n",
                            " * http://stackoverflow.com/a/24161582/3208463\n",
                            " */\n",
                            "function simpleKeys (original) {\n",
                            "  return Object.keys(original).reduce(function (obj, key) {\n",
                            "    if (typeof original[key] !== 'object')\n",
                            "        obj[key] = original[key]\n",
                            "    return obj;\n",
                            "  }, {});\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.mouse_event = function(event, name) {\n",
                            "    var canvas_pos = mpl.findpos(event)\n",
                            "\n",
                            "    if (name === 'button_press')\n",
                            "    {\n",
                            "        this.canvas.focus();\n",
                            "        this.canvas_div.focus();\n",
                            "    }\n",
                            "\n",
                            "    var x = canvas_pos.x * mpl.ratio;\n",
                            "    var y = canvas_pos.y * mpl.ratio;\n",
                            "\n",
                            "    this.send_message(name, {x: x, y: y, button: event.button,\n",
                            "                             step: event.step,\n",
                            "                             guiEvent: simpleKeys(event)});\n",
                            "\n",
                            "    /* This prevents the web browser from automatically changing to\n",
                            "     * the text insertion cursor when the button is pressed.  We want\n",
                            "     * to control all of the cursor setting manually through the\n",
                            "     * 'cursor' event from matplotlib */\n",
                            "    event.preventDefault();\n",
                            "    return false;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
                            "    // Handle any extra behaviour associated with a key event\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.key_event = function(event, name) {\n",
                            "\n",
                            "    // Prevent repeat events\n",
                            "    if (name == 'key_press')\n",
                            "    {\n",
                            "        if (event.which === this._key)\n",
                            "            return;\n",
                            "        else\n",
                            "            this._key = event.which;\n",
                            "    }\n",
                            "    if (name == 'key_release')\n",
                            "        this._key = null;\n",
                            "\n",
                            "    var value = '';\n",
                            "    if (event.ctrlKey && event.which != 17)\n",
                            "        value += \"ctrl+\";\n",
                            "    if (event.altKey && event.which != 18)\n",
                            "        value += \"alt+\";\n",
                            "    if (event.shiftKey && event.which != 16)\n",
                            "        value += \"shift+\";\n",
                            "\n",
                            "    value += 'k';\n",
                            "    value += event.which.toString();\n",
                            "\n",
                            "    this._key_event_extra(event, name);\n",
                            "\n",
                            "    this.send_message(name, {key: value,\n",
                            "                             guiEvent: simpleKeys(event)});\n",
                            "    return false;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
                            "    if (name == 'download') {\n",
                            "        this.handle_save(this, null);\n",
                            "    } else {\n",
                            "        this.send_message(\"toolbar_button\", {name: name});\n",
                            "    }\n",
                            "};\n",
                            "\n",
                            "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
                            "    this.message.textContent = tooltip;\n",
                            "};\n",
                            "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
                            "\n",
                            "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
                            "\n",
                            "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
                            "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
                            "    // object with the appropriate methods. Currently this is a non binary\n",
                            "    // socket, so there is still some room for performance tuning.\n",
                            "    var ws = {};\n",
                            "\n",
                            "    ws.close = function() {\n",
                            "        comm.close()\n",
                            "    };\n",
                            "    ws.send = function(m) {\n",
                            "        //console.log('sending', m);\n",
                            "        comm.send(m);\n",
                            "    };\n",
                            "    // Register the callback with on_msg.\n",
                            "    comm.on_msg(function(msg) {\n",
                            "        //console.log('receiving', msg['content']['data'], msg);\n",
                            "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
                            "        ws.onmessage(msg['content']['data'])\n",
                            "    });\n",
                            "    return ws;\n",
                            "}\n",
                            "\n",
                            "mpl.mpl_figure_comm = function(comm, msg) {\n",
                            "    // This is the function which gets called when the mpl process\n",
                            "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
                            "\n",
                            "    var id = msg.content.data.id;\n",
                            "    // Get hold of the div created by the display call when the Comm\n",
                            "    // socket was opened in Python.\n",
                            "    var element = $(\"#\" + id);\n",
                            "    var ws_proxy = comm_websocket_adapter(comm)\n",
                            "\n",
                            "    function ondownload(figure, format) {\n",
                            "        window.open(figure.imageObj.src);\n",
                            "    }\n",
                            "\n",
                            "    var fig = new mpl.figure(id, ws_proxy,\n",
                            "                           ondownload,\n",
                            "                           element.get(0));\n",
                            "\n",
                            "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
                            "    // web socket which is closed, not our websocket->open comm proxy.\n",
                            "    ws_proxy.onopen();\n",
                            "\n",
                            "    fig.parent_element = element.get(0);\n",
                            "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
                            "    if (!fig.cell_info) {\n",
                            "        console.error(\"Failed to find cell for figure\", id, fig);\n",
                            "        return;\n",
                            "    }\n",
                            "\n",
                            "    var output_index = fig.cell_info[2]\n",
                            "    var cell = fig.cell_info[0];\n",
                            "\n",
                            "};\n",
                            "\n",
                            "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
                            "    var width = fig.canvas.width/mpl.ratio\n",
                            "    fig.root.unbind('remove')\n",
                            "\n",
                            "    // Update the output cell to use the data from the current canvas.\n",
                            "    fig.push_to_output();\n",
                            "    var dataURL = fig.canvas.toDataURL();\n",
                            "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
                            "    // the notebook keyboard shortcuts fail.\n",
                            "    IPython.keyboard_manager.enable()\n",
                            "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
                            "    fig.close_ws(fig, msg);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.close_ws = function(fig, msg){\n",
                            "    fig.send_message('closing', msg);\n",
                            "    // fig.ws.close()\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
                            "    // Turn the data on the canvas into data in the output cell.\n",
                            "    var width = this.canvas.width/mpl.ratio\n",
                            "    var dataURL = this.canvas.toDataURL();\n",
                            "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.updated_canvas_event = function() {\n",
                            "    // Tell IPython that the notebook contents must change.\n",
                            "    IPython.notebook.set_dirty(true);\n",
                            "    this.send_message(\"ack\", {});\n",
                            "    var fig = this;\n",
                            "    // Wait a second, then push the new image to the DOM so\n",
                            "    // that it is saved nicely (might be nice to debounce this).\n",
                            "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_toolbar = function() {\n",
                            "    var fig = this;\n",
                            "\n",
                            "    var nav_element = $('<div/>')\n",
                            "    nav_element.attr('style', 'width: 100%');\n",
                            "    this.root.append(nav_element);\n",
                            "\n",
                            "    // Define a callback function for later on.\n",
                            "    function toolbar_event(event) {\n",
                            "        return fig.toolbar_button_onclick(event['data']);\n",
                            "    }\n",
                            "    function toolbar_mouse_event(event) {\n",
                            "        return fig.toolbar_button_onmouseover(event['data']);\n",
                            "    }\n",
                            "\n",
                            "    for(var toolbar_ind in mpl.toolbar_items){\n",
                            "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
                            "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
                            "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
                            "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
                            "\n",
                            "        if (!name) { continue; };\n",
                            "\n",
                            "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
                            "        button.click(method_name, toolbar_event);\n",
                            "        button.mouseover(tooltip, toolbar_mouse_event);\n",
                            "        nav_element.append(button);\n",
                            "    }\n",
                            "\n",
                            "    // Add the status bar.\n",
                            "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
                            "    nav_element.append(status_bar);\n",
                            "    this.message = status_bar[0];\n",
                            "\n",
                            "    // Add the close button to the window.\n",
                            "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
                            "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
                            "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
                            "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
                            "    buttongrp.append(button);\n",
                            "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
                            "    titlebar.prepend(buttongrp);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._root_extra_style = function(el){\n",
                            "    var fig = this\n",
                            "    el.on(\"remove\", function(){\n",
                            "\tfig.close_ws(fig, {});\n",
                            "    });\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._canvas_extra_style = function(el){\n",
                            "    // this is important to make the div 'focusable\n",
                            "    el.attr('tabindex', 0)\n",
                            "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
                            "    // off when our div gets focus\n",
                            "\n",
                            "    // location in version 3\n",
                            "    if (IPython.notebook.keyboard_manager) {\n",
                            "        IPython.notebook.keyboard_manager.register_events(el);\n",
                            "    }\n",
                            "    else {\n",
                            "        // location in version 2\n",
                            "        IPython.keyboard_manager.register_events(el);\n",
                            "    }\n",
                            "\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
                            "    var manager = IPython.notebook.keyboard_manager;\n",
                            "    if (!manager)\n",
                            "        manager = IPython.keyboard_manager;\n",
                            "\n",
                            "    // Check for shift+enter\n",
                            "    if (event.shiftKey && event.which == 13) {\n",
                            "        this.canvas_div.blur();\n",
                            "        event.shiftKey = false;\n",
                            "        // Send a \"J\" for go to next cell\n",
                            "        event.which = 74;\n",
                            "        event.keyCode = 74;\n",
                            "        manager.command_mode();\n",
                            "        manager.handle_keydown(event);\n",
                            "    }\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
                            "    fig.ondownload(fig, null);\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.find_output_cell = function(html_output) {\n",
                            "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
                            "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
                            "    // IPython event is triggered only after the cells have been serialised, which for\n",
                            "    // our purposes (turning an active figure into a static one), is too late.\n",
                            "    var cells = IPython.notebook.get_cells();\n",
                            "    var ncells = cells.length;\n",
                            "    for (var i=0; i<ncells; i++) {\n",
                            "        var cell = cells[i];\n",
                            "        if (cell.cell_type === 'code'){\n",
                            "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
                            "                var data = cell.output_area.outputs[j];\n",
                            "                if (data.data) {\n",
                            "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
                            "                    data = data.data;\n",
                            "                }\n",
                            "                if (data['text/html'] == html_output) {\n",
                            "                    return [cell, data, j];\n",
                            "                }\n",
                            "            }\n",
                            "        }\n",
                            "    }\n",
                            "}\n",
                            "\n",
                            "// Register the function which deals with the matplotlib target/channel.\n",
                            "// The kernel may be null if the page has been refreshed.\n",
                            "if (IPython.notebook.kernel != null) {\n",
                            "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
                            "}\n"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Javascript object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<img 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\" width=\"640\">"
                        ],
                        "text/plain": [
                            "<IPython.core.display.HTML object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/plain": [
                            "<matplotlib.legend.Legend at 0x1a1d032630>"
                        ]
                    },
                    "execution_count": 22,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# Plot the histogram of the data\n",
                "num_bins = 30\n",
                "count, bins, ignored = plt.hist(pts, num_bins, density=True,\n",
                "                                edgecolor='k')\n",
                "plt.title('Econ 381 scores: 2011-2012', fontsize=20)\n",
                "plt.xlabel(r'Total points')\n",
                "plt.ylabel(r'Percent of scores')\n",
                "plt.xlim([0, 550])  # This gives the xmin and xmax to be plotted\"\n",
                "\n",
                "# Plot the estimated GMM PDF\n",
                "dist_pts = np.linspace(0, 450, 500)\n",
                "plt.plot(dist_pts, trunc_norm_pdf(dist_pts, mu_GMM1, sig_GMM1, 450),\n",
                "         linewidth=2, color='k', label='1: $\\mu_{GMM1}$,$\\sigma_{GMM1}$')\n",
                "plt.legend(loc='upper left')"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "That looks just like the ML estimate from the previous notebook. Let's see what the criterion function looks like for different values of $\\mu$ and $\\sigma$."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/javascript": [
                            "/* Put everything inside the global mpl namespace */\n",
                            "window.mpl = {};\n",
                            "\n",
                            "\n",
                            "mpl.get_websocket_type = function() {\n",
                            "    if (typeof(WebSocket) !== 'undefined') {\n",
                            "        return WebSocket;\n",
                            "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
                            "        return MozWebSocket;\n",
                            "    } else {\n",
                            "        alert('Your browser does not have WebSocket support.' +\n",
                            "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
                            "              'Firefox 4 and 5 are also supported but you ' +\n",
                            "              'have to enable WebSockets in about:config.');\n",
                            "    };\n",
                            "}\n",
                            "\n",
                            "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
                            "    this.id = figure_id;\n",
                            "\n",
                            "    this.ws = websocket;\n",
                            "\n",
                            "    this.supports_binary = (this.ws.binaryType != undefined);\n",
                            "\n",
                            "    if (!this.supports_binary) {\n",
                            "        var warnings = document.getElementById(\"mpl-warnings\");\n",
                            "        if (warnings) {\n",
                            "            warnings.style.display = 'block';\n",
                            "            warnings.textContent = (\n",
                            "                \"This browser does not support binary websocket messages. \" +\n",
                            "                    \"Performance may be slow.\");\n",
                            "        }\n",
                            "    }\n",
                            "\n",
                            "    this.imageObj = new Image();\n",
                            "\n",
                            "    this.context = undefined;\n",
                            "    this.message = undefined;\n",
                            "    this.canvas = undefined;\n",
                            "    this.rubberband_canvas = undefined;\n",
                            "    this.rubberband_context = undefined;\n",
                            "    this.format_dropdown = undefined;\n",
                            "\n",
                            "    this.image_mode = 'full';\n",
                            "\n",
                            "    this.root = $('<div/>');\n",
                            "    this._root_extra_style(this.root)\n",
                            "    this.root.attr('style', 'display: inline-block');\n",
                            "\n",
                            "    $(parent_element).append(this.root);\n",
                            "\n",
                            "    this._init_header(this);\n",
                            "    this._init_canvas(this);\n",
                            "    this._init_toolbar(this);\n",
                            "\n",
                            "    var fig = this;\n",
                            "\n",
                            "    this.waiting = false;\n",
                            "\n",
                            "    this.ws.onopen =  function () {\n",
                            "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
                            "            fig.send_message(\"send_image_mode\", {});\n",
                            "            if (mpl.ratio != 1) {\n",
                            "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
                            "            }\n",
                            "            fig.send_message(\"refresh\", {});\n",
                            "        }\n",
                            "\n",
                            "    this.imageObj.onload = function() {\n",
                            "            if (fig.image_mode == 'full') {\n",
                            "                // Full images could contain transparency (where diff images\n",
                            "                // almost always do), so we need to clear the canvas so that\n",
                            "                // there is no ghosting.\n",
                            "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
                            "            }\n",
                            "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
                            "        };\n",
                            "\n",
                            "    this.imageObj.onunload = function() {\n",
                            "        fig.ws.close();\n",
                            "    }\n",
                            "\n",
                            "    this.ws.onmessage = this._make_on_message_function(this);\n",
                            "\n",
                            "    this.ondownload = ondownload;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_header = function() {\n",
                            "    var titlebar = $(\n",
                            "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
                            "        'ui-helper-clearfix\"/>');\n",
                            "    var titletext = $(\n",
                            "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
                            "        'text-align: center; padding: 3px;\"/>');\n",
                            "    titlebar.append(titletext)\n",
                            "    this.root.append(titlebar);\n",
                            "    this.header = titletext[0];\n",
                            "}\n",
                            "\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
                            "\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
                            "\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_canvas = function() {\n",
                            "    var fig = this;\n",
                            "\n",
                            "    var canvas_div = $('<div/>');\n",
                            "\n",
                            "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
                            "\n",
                            "    function canvas_keyboard_event(event) {\n",
                            "        return fig.key_event(event, event['data']);\n",
                            "    }\n",
                            "\n",
                            "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
                            "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
                            "    this.canvas_div = canvas_div\n",
                            "    this._canvas_extra_style(canvas_div)\n",
                            "    this.root.append(canvas_div);\n",
                            "\n",
                            "    var canvas = $('<canvas/>');\n",
                            "    canvas.addClass('mpl-canvas');\n",
                            "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
                            "\n",
                            "    this.canvas = canvas[0];\n",
                            "    this.context = canvas[0].getContext(\"2d\");\n",
                            "\n",
                            "    var backingStore = this.context.backingStorePixelRatio ||\n",
                            "\tthis.context.webkitBackingStorePixelRatio ||\n",
                            "\tthis.context.mozBackingStorePixelRatio ||\n",
                            "\tthis.context.msBackingStorePixelRatio ||\n",
                            "\tthis.context.oBackingStorePixelRatio ||\n",
                            "\tthis.context.backingStorePixelRatio || 1;\n",
                            "\n",
                            "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
                            "\n",
                            "    var rubberband = $('<canvas/>');\n",
                            "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
                            "\n",
                            "    var pass_mouse_events = true;\n",
                            "\n",
                            "    canvas_div.resizable({\n",
                            "        start: function(event, ui) {\n",
                            "            pass_mouse_events = false;\n",
                            "        },\n",
                            "        resize: function(event, ui) {\n",
                            "            fig.request_resize(ui.size.width, ui.size.height);\n",
                            "        },\n",
                            "        stop: function(event, ui) {\n",
                            "            pass_mouse_events = true;\n",
                            "            fig.request_resize(ui.size.width, ui.size.height);\n",
                            "        },\n",
                            "    });\n",
                            "\n",
                            "    function mouse_event_fn(event) {\n",
                            "        if (pass_mouse_events)\n",
                            "            return fig.mouse_event(event, event['data']);\n",
                            "    }\n",
                            "\n",
                            "    rubberband.mousedown('button_press', mouse_event_fn);\n",
                            "    rubberband.mouseup('button_release', mouse_event_fn);\n",
                            "    // Throttle sequential mouse events to 1 every 20ms.\n",
                            "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
                            "\n",
                            "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
                            "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
                            "\n",
                            "    canvas_div.on(\"wheel\", function (event) {\n",
                            "        event = event.originalEvent;\n",
                            "        event['data'] = 'scroll'\n",
                            "        if (event.deltaY < 0) {\n",
                            "            event.step = 1;\n",
                            "        } else {\n",
                            "            event.step = -1;\n",
                            "        }\n",
                            "        mouse_event_fn(event);\n",
                            "    });\n",
                            "\n",
                            "    canvas_div.append(canvas);\n",
                            "    canvas_div.append(rubberband);\n",
                            "\n",
                            "    this.rubberband = rubberband;\n",
                            "    this.rubberband_canvas = rubberband[0];\n",
                            "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
                            "    this.rubberband_context.strokeStyle = \"#000000\";\n",
                            "\n",
                            "    this._resize_canvas = function(width, height) {\n",
                            "        // Keep the size of the canvas, canvas container, and rubber band\n",
                            "        // canvas in synch.\n",
                            "        canvas_div.css('width', width)\n",
                            "        canvas_div.css('height', height)\n",
                            "\n",
                            "        canvas.attr('width', width * mpl.ratio);\n",
                            "        canvas.attr('height', height * mpl.ratio);\n",
                            "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
                            "\n",
                            "        rubberband.attr('width', width);\n",
                            "        rubberband.attr('height', height);\n",
                            "    }\n",
                            "\n",
                            "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
                            "    // upon first draw.\n",
                            "    this._resize_canvas(600, 600);\n",
                            "\n",
                            "    // Disable right mouse context menu.\n",
                            "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
                            "        return false;\n",
                            "    });\n",
                            "\n",
                            "    function set_focus () {\n",
                            "        canvas.focus();\n",
                            "        canvas_div.focus();\n",
                            "    }\n",
                            "\n",
                            "    window.setTimeout(set_focus, 100);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_toolbar = function() {\n",
                            "    var fig = this;\n",
                            "\n",
                            "    var nav_element = $('<div/>')\n",
                            "    nav_element.attr('style', 'width: 100%');\n",
                            "    this.root.append(nav_element);\n",
                            "\n",
                            "    // Define a callback function for later on.\n",
                            "    function toolbar_event(event) {\n",
                            "        return fig.toolbar_button_onclick(event['data']);\n",
                            "    }\n",
                            "    function toolbar_mouse_event(event) {\n",
                            "        return fig.toolbar_button_onmouseover(event['data']);\n",
                            "    }\n",
                            "\n",
                            "    for(var toolbar_ind in mpl.toolbar_items) {\n",
                            "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
                            "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
                            "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
                            "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
                            "\n",
                            "        if (!name) {\n",
                            "            // put a spacer in here.\n",
                            "            continue;\n",
                            "        }\n",
                            "        var button = $('<button/>');\n",
                            "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
                            "                        'ui-button-icon-only');\n",
                            "        button.attr('role', 'button');\n",
                            "        button.attr('aria-disabled', 'false');\n",
                            "        button.click(method_name, toolbar_event);\n",
                            "        button.mouseover(tooltip, toolbar_mouse_event);\n",
                            "\n",
                            "        var icon_img = $('<span/>');\n",
                            "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
                            "        icon_img.addClass(image);\n",
                            "        icon_img.addClass('ui-corner-all');\n",
                            "\n",
                            "        var tooltip_span = $('<span/>');\n",
                            "        tooltip_span.addClass('ui-button-text');\n",
                            "        tooltip_span.html(tooltip);\n",
                            "\n",
                            "        button.append(icon_img);\n",
                            "        button.append(tooltip_span);\n",
                            "\n",
                            "        nav_element.append(button);\n",
                            "    }\n",
                            "\n",
                            "    var fmt_picker_span = $('<span/>');\n",
                            "\n",
                            "    var fmt_picker = $('<select/>');\n",
                            "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
                            "    fmt_picker_span.append(fmt_picker);\n",
                            "    nav_element.append(fmt_picker_span);\n",
                            "    this.format_dropdown = fmt_picker[0];\n",
                            "\n",
                            "    for (var ind in mpl.extensions) {\n",
                            "        var fmt = mpl.extensions[ind];\n",
                            "        var option = $(\n",
                            "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
                            "        fmt_picker.append(option)\n",
                            "    }\n",
                            "\n",
                            "    // Add hover states to the ui-buttons\n",
                            "    $( \".ui-button\" ).hover(\n",
                            "        function() { $(this).addClass(\"ui-state-hover\");},\n",
                            "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
                            "    );\n",
                            "\n",
                            "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
                            "    nav_element.append(status_bar);\n",
                            "    this.message = status_bar[0];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
                            "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
                            "    // which will in turn request a refresh of the image.\n",
                            "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.send_message = function(type, properties) {\n",
                            "    properties['type'] = type;\n",
                            "    properties['figure_id'] = this.id;\n",
                            "    this.ws.send(JSON.stringify(properties));\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.send_draw_message = function() {\n",
                            "    if (!this.waiting) {\n",
                            "        this.waiting = true;\n",
                            "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
                            "    }\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
                            "    var format_dropdown = fig.format_dropdown;\n",
                            "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
                            "    fig.ondownload(fig, format);\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
                            "    var size = msg['size'];\n",
                            "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
                            "        fig._resize_canvas(size[0], size[1]);\n",
                            "        fig.send_message(\"refresh\", {});\n",
                            "    };\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
                            "    var x0 = msg['x0'] / mpl.ratio;\n",
                            "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
                            "    var x1 = msg['x1'] / mpl.ratio;\n",
                            "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
                            "    x0 = Math.floor(x0) + 0.5;\n",
                            "    y0 = Math.floor(y0) + 0.5;\n",
                            "    x1 = Math.floor(x1) + 0.5;\n",
                            "    y1 = Math.floor(y1) + 0.5;\n",
                            "    var min_x = Math.min(x0, x1);\n",
                            "    var min_y = Math.min(y0, y1);\n",
                            "    var width = Math.abs(x1 - x0);\n",
                            "    var height = Math.abs(y1 - y0);\n",
                            "\n",
                            "    fig.rubberband_context.clearRect(\n",
                            "        0, 0, fig.canvas.width, fig.canvas.height);\n",
                            "\n",
                            "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
                            "    // Updates the figure title.\n",
                            "    fig.header.textContent = msg['label'];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
                            "    var cursor = msg['cursor'];\n",
                            "    switch(cursor)\n",
                            "    {\n",
                            "    case 0:\n",
                            "        cursor = 'pointer';\n",
                            "        break;\n",
                            "    case 1:\n",
                            "        cursor = 'default';\n",
                            "        break;\n",
                            "    case 2:\n",
                            "        cursor = 'crosshair';\n",
                            "        break;\n",
                            "    case 3:\n",
                            "        cursor = 'move';\n",
                            "        break;\n",
                            "    }\n",
                            "    fig.rubberband_canvas.style.cursor = cursor;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
                            "    fig.message.textContent = msg['message'];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
                            "    // Request the server to send over a new figure.\n",
                            "    fig.send_draw_message();\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
                            "    fig.image_mode = msg['mode'];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.updated_canvas_event = function() {\n",
                            "    // Called whenever the canvas gets updated.\n",
                            "    this.send_message(\"ack\", {});\n",
                            "}\n",
                            "\n",
                            "// A function to construct a web socket function for onmessage handling.\n",
                            "// Called in the figure constructor.\n",
                            "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
                            "    return function socket_on_message(evt) {\n",
                            "        if (evt.data instanceof Blob) {\n",
                            "            /* FIXME: We get \"Resource interpreted as Image but\n",
                            "             * transferred with MIME type text/plain:\" errors on\n",
                            "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
                            "             * to be part of the websocket stream */\n",
                            "            evt.data.type = \"image/png\";\n",
                            "\n",
                            "            /* Free the memory for the previous frames */\n",
                            "            if (fig.imageObj.src) {\n",
                            "                (window.URL || window.webkitURL).revokeObjectURL(\n",
                            "                    fig.imageObj.src);\n",
                            "            }\n",
                            "\n",
                            "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
                            "                evt.data);\n",
                            "            fig.updated_canvas_event();\n",
                            "            fig.waiting = false;\n",
                            "            return;\n",
                            "        }\n",
                            "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
                            "            fig.imageObj.src = evt.data;\n",
                            "            fig.updated_canvas_event();\n",
                            "            fig.waiting = false;\n",
                            "            return;\n",
                            "        }\n",
                            "\n",
                            "        var msg = JSON.parse(evt.data);\n",
                            "        var msg_type = msg['type'];\n",
                            "\n",
                            "        // Call the  \"handle_{type}\" callback, which takes\n",
                            "        // the figure and JSON message as its only arguments.\n",
                            "        try {\n",
                            "            var callback = fig[\"handle_\" + msg_type];\n",
                            "        } catch (e) {\n",
                            "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
                            "            return;\n",
                            "        }\n",
                            "\n",
                            "        if (callback) {\n",
                            "            try {\n",
                            "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
                            "                callback(fig, msg);\n",
                            "            } catch (e) {\n",
                            "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
                            "            }\n",
                            "        }\n",
                            "    };\n",
                            "}\n",
                            "\n",
                            "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
                            "mpl.findpos = function(e) {\n",
                            "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
                            "    var targ;\n",
                            "    if (!e)\n",
                            "        e = window.event;\n",
                            "    if (e.target)\n",
                            "        targ = e.target;\n",
                            "    else if (e.srcElement)\n",
                            "        targ = e.srcElement;\n",
                            "    if (targ.nodeType == 3) // defeat Safari bug\n",
                            "        targ = targ.parentNode;\n",
                            "\n",
                            "    // jQuery normalizes the pageX and pageY\n",
                            "    // pageX,Y are the mouse positions relative to the document\n",
                            "    // offset() returns the position of the element relative to the document\n",
                            "    var x = e.pageX - $(targ).offset().left;\n",
                            "    var y = e.pageY - $(targ).offset().top;\n",
                            "\n",
                            "    return {\"x\": x, \"y\": y};\n",
                            "};\n",
                            "\n",
                            "/*\n",
                            " * return a copy of an object with only non-object keys\n",
                            " * we need this to avoid circular references\n",
                            " * http://stackoverflow.com/a/24161582/3208463\n",
                            " */\n",
                            "function simpleKeys (original) {\n",
                            "  return Object.keys(original).reduce(function (obj, key) {\n",
                            "    if (typeof original[key] !== 'object')\n",
                            "        obj[key] = original[key]\n",
                            "    return obj;\n",
                            "  }, {});\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.mouse_event = function(event, name) {\n",
                            "    var canvas_pos = mpl.findpos(event)\n",
                            "\n",
                            "    if (name === 'button_press')\n",
                            "    {\n",
                            "        this.canvas.focus();\n",
                            "        this.canvas_div.focus();\n",
                            "    }\n",
                            "\n",
                            "    var x = canvas_pos.x * mpl.ratio;\n",
                            "    var y = canvas_pos.y * mpl.ratio;\n",
                            "\n",
                            "    this.send_message(name, {x: x, y: y, button: event.button,\n",
                            "                             step: event.step,\n",
                            "                             guiEvent: simpleKeys(event)});\n",
                            "\n",
                            "    /* This prevents the web browser from automatically changing to\n",
                            "     * the text insertion cursor when the button is pressed.  We want\n",
                            "     * to control all of the cursor setting manually through the\n",
                            "     * 'cursor' event from matplotlib */\n",
                            "    event.preventDefault();\n",
                            "    return false;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
                            "    // Handle any extra behaviour associated with a key event\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.key_event = function(event, name) {\n",
                            "\n",
                            "    // Prevent repeat events\n",
                            "    if (name == 'key_press')\n",
                            "    {\n",
                            "        if (event.which === this._key)\n",
                            "            return;\n",
                            "        else\n",
                            "            this._key = event.which;\n",
                            "    }\n",
                            "    if (name == 'key_release')\n",
                            "        this._key = null;\n",
                            "\n",
                            "    var value = '';\n",
                            "    if (event.ctrlKey && event.which != 17)\n",
                            "        value += \"ctrl+\";\n",
                            "    if (event.altKey && event.which != 18)\n",
                            "        value += \"alt+\";\n",
                            "    if (event.shiftKey && event.which != 16)\n",
                            "        value += \"shift+\";\n",
                            "\n",
                            "    value += 'k';\n",
                            "    value += event.which.toString();\n",
                            "\n",
                            "    this._key_event_extra(event, name);\n",
                            "\n",
                            "    this.send_message(name, {key: value,\n",
                            "                             guiEvent: simpleKeys(event)});\n",
                            "    return false;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
                            "    if (name == 'download') {\n",
                            "        this.handle_save(this, null);\n",
                            "    } else {\n",
                            "        this.send_message(\"toolbar_button\", {name: name});\n",
                            "    }\n",
                            "};\n",
                            "\n",
                            "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
                            "    this.message.textContent = tooltip;\n",
                            "};\n",
                            "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
                            "\n",
                            "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
                            "\n",
                            "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
                            "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
                            "    // object with the appropriate methods. Currently this is a non binary\n",
                            "    // socket, so there is still some room for performance tuning.\n",
                            "    var ws = {};\n",
                            "\n",
                            "    ws.close = function() {\n",
                            "        comm.close()\n",
                            "    };\n",
                            "    ws.send = function(m) {\n",
                            "        //console.log('sending', m);\n",
                            "        comm.send(m);\n",
                            "    };\n",
                            "    // Register the callback with on_msg.\n",
                            "    comm.on_msg(function(msg) {\n",
                            "        //console.log('receiving', msg['content']['data'], msg);\n",
                            "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
                            "        ws.onmessage(msg['content']['data'])\n",
                            "    });\n",
                            "    return ws;\n",
                            "}\n",
                            "\n",
                            "mpl.mpl_figure_comm = function(comm, msg) {\n",
                            "    // This is the function which gets called when the mpl process\n",
                            "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
                            "\n",
                            "    var id = msg.content.data.id;\n",
                            "    // Get hold of the div created by the display call when the Comm\n",
                            "    // socket was opened in Python.\n",
                            "    var element = $(\"#\" + id);\n",
                            "    var ws_proxy = comm_websocket_adapter(comm)\n",
                            "\n",
                            "    function ondownload(figure, format) {\n",
                            "        window.open(figure.imageObj.src);\n",
                            "    }\n",
                            "\n",
                            "    var fig = new mpl.figure(id, ws_proxy,\n",
                            "                           ondownload,\n",
                            "                           element.get(0));\n",
                            "\n",
                            "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
                            "    // web socket which is closed, not our websocket->open comm proxy.\n",
                            "    ws_proxy.onopen();\n",
                            "\n",
                            "    fig.parent_element = element.get(0);\n",
                            "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
                            "    if (!fig.cell_info) {\n",
                            "        console.error(\"Failed to find cell for figure\", id, fig);\n",
                            "        return;\n",
                            "    }\n",
                            "\n",
                            "    var output_index = fig.cell_info[2]\n",
                            "    var cell = fig.cell_info[0];\n",
                            "\n",
                            "};\n",
                            "\n",
                            "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
                            "    var width = fig.canvas.width/mpl.ratio\n",
                            "    fig.root.unbind('remove')\n",
                            "\n",
                            "    // Update the output cell to use the data from the current canvas.\n",
                            "    fig.push_to_output();\n",
                            "    var dataURL = fig.canvas.toDataURL();\n",
                            "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
                            "    // the notebook keyboard shortcuts fail.\n",
                            "    IPython.keyboard_manager.enable()\n",
                            "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
                            "    fig.close_ws(fig, msg);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.close_ws = function(fig, msg){\n",
                            "    fig.send_message('closing', msg);\n",
                            "    // fig.ws.close()\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
                            "    // Turn the data on the canvas into data in the output cell.\n",
                            "    var width = this.canvas.width/mpl.ratio\n",
                            "    var dataURL = this.canvas.toDataURL();\n",
                            "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.updated_canvas_event = function() {\n",
                            "    // Tell IPython that the notebook contents must change.\n",
                            "    IPython.notebook.set_dirty(true);\n",
                            "    this.send_message(\"ack\", {});\n",
                            "    var fig = this;\n",
                            "    // Wait a second, then push the new image to the DOM so\n",
                            "    // that it is saved nicely (might be nice to debounce this).\n",
                            "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_toolbar = function() {\n",
                            "    var fig = this;\n",
                            "\n",
                            "    var nav_element = $('<div/>')\n",
                            "    nav_element.attr('style', 'width: 100%');\n",
                            "    this.root.append(nav_element);\n",
                            "\n",
                            "    // Define a callback function for later on.\n",
                            "    function toolbar_event(event) {\n",
                            "        return fig.toolbar_button_onclick(event['data']);\n",
                            "    }\n",
                            "    function toolbar_mouse_event(event) {\n",
                            "        return fig.toolbar_button_onmouseover(event['data']);\n",
                            "    }\n",
                            "\n",
                            "    for(var toolbar_ind in mpl.toolbar_items){\n",
                            "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
                            "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
                            "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
                            "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
                            "\n",
                            "        if (!name) { continue; };\n",
                            "\n",
                            "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
                            "        button.click(method_name, toolbar_event);\n",
                            "        button.mouseover(tooltip, toolbar_mouse_event);\n",
                            "        nav_element.append(button);\n",
                            "    }\n",
                            "\n",
                            "    // Add the status bar.\n",
                            "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
                            "    nav_element.append(status_bar);\n",
                            "    this.message = status_bar[0];\n",
                            "\n",
                            "    // Add the close button to the window.\n",
                            "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
                            "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
                            "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
                            "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
                            "    buttongrp.append(button);\n",
                            "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
                            "    titlebar.prepend(buttongrp);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._root_extra_style = function(el){\n",
                            "    var fig = this\n",
                            "    el.on(\"remove\", function(){\n",
                            "\tfig.close_ws(fig, {});\n",
                            "    });\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._canvas_extra_style = function(el){\n",
                            "    // this is important to make the div 'focusable\n",
                            "    el.attr('tabindex', 0)\n",
                            "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
                            "    // off when our div gets focus\n",
                            "\n",
                            "    // location in version 3\n",
                            "    if (IPython.notebook.keyboard_manager) {\n",
                            "        IPython.notebook.keyboard_manager.register_events(el);\n",
                            "    }\n",
                            "    else {\n",
                            "        // location in version 2\n",
                            "        IPython.keyboard_manager.register_events(el);\n",
                            "    }\n",
                            "\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
                            "    var manager = IPython.notebook.keyboard_manager;\n",
                            "    if (!manager)\n",
                            "        manager = IPython.keyboard_manager;\n",
                            "\n",
                            "    // Check for shift+enter\n",
                            "    if (event.shiftKey && event.which == 13) {\n",
                            "        this.canvas_div.blur();\n",
                            "        event.shiftKey = false;\n",
                            "        // Send a \"J\" for go to next cell\n",
                            "        event.which = 74;\n",
                            "        event.keyCode = 74;\n",
                            "        manager.command_mode();\n",
                            "        manager.handle_keydown(event);\n",
                            "    }\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
                            "    fig.ondownload(fig, null);\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.find_output_cell = function(html_output) {\n",
                            "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
                            "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
                            "    // IPython event is triggered only after the cells have been serialised, which for\n",
                            "    // our purposes (turning an active figure into a static one), is too late.\n",
                            "    var cells = IPython.notebook.get_cells();\n",
                            "    var ncells = cells.length;\n",
                            "    for (var i=0; i<ncells; i++) {\n",
                            "        var cell = cells[i];\n",
                            "        if (cell.cell_type === 'code'){\n",
                            "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
                            "                var data = cell.output_area.outputs[j];\n",
                            "                if (data.data) {\n",
                            "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
                            "                    data = data.data;\n",
                            "                }\n",
                            "                if (data['text/html'] == html_output) {\n",
                            "                    return [cell, data, j];\n",
                            "                }\n",
                            "            }\n",
                            "        }\n",
                            "    }\n",
                            "}\n",
                            "\n",
                            "// Register the function which deals with the matplotlib target/channel.\n",
                            "// The kernel may be null if the page has been refreshed.\n",
                            "if (IPython.notebook.kernel != null) {\n",
                            "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
                            "}\n"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Javascript object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
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\" width=\"640\">"
                        ],
                        "text/plain": [
                            "<IPython.core.display.HTML object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/plain": [
                            "Text(0.5,0,'Crit. func.')"
                        ]
                    },
                    "execution_count": 23,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# Note that this will take a few minutes because the intgr.quad() commands\n",
                "# are a little slow\n",
                "mu_vals = np.linspace(60, 700, 50)\n",
                "sig_vals = np.linspace(20, 200, 50)\n",
                "# mu_vals = np.linspace(540, 572, 50)\n",
                "# sig_vals = np.linspace(170, 182, 50)\n",
                "crit_vals = np.zeros((50, 50))\n",
                "for mu_ind in range(50):\n",
                "    for sig_ind in range(50):\n",
                "        crit_vals[mu_ind, sig_ind] = \\\n",
                "            criterion(np.array([mu_vals[mu_ind], sig_vals[sig_ind]]),\n",
                "                      pts, 450.0, W_hat)\n",
                "\n",
                "mu_mesh, sig_mesh = np.meshgrid(mu_vals, sig_vals)\n",
                "\n",
                "fig = plt.figure()\n",
                "ax = fig.gca(projection='3d')\n",
                "ax.plot_surface(sig_mesh, mu_mesh, crit_vals, rstride=8,\n",
                "                cstride=1, cmap=cmap1)\n",
                "ax.set_title('Criterion function for values of mu and sigma')\n",
                "ax.set_xlabel(r'$\\sigma$')\n",
                "ax.set_ylabel(r'$\\mu$')\n",
                "ax.set_zlabel(r'Crit. func.')"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Similar to the ML problem, it looks like the criterion function is roughly equal for a specific portion increase of $\\mu$ and $\\sigma$ together. That is, with these two moments probably have a correspondence of values of $\\mu$ and $\\sigma$ that give roughly the same criterion function value. This issue has two possible solutions.\n",
                "\n",
                "1. Maybe we need the two-step variance covariance estimator to calculate a \"more\" optimal weighting matrix $W$.\n",
                "2. Maybe our two moments aren't very good moments for fitting the data.\n",
                "\n",
                "Let's first try the two-step weighting matrix."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "[[ 5.33232895e-10 -2.44741839e-10]\n",
                        " [-2.44741839e-10  1.12330969e-10]]\n",
                        "[[ 1.27949475e+09 -5.87259153e+08]\n",
                        " [-5.87259153e+08  2.69538669e+08]]\n"
                    ]
                }
            ],
            "source": [
                "err1 = err_vec(pts, mu_GMM1, sig_GMM1, 450.0, False)\n",
                "VCV2 = np.dot(err1, err1.T) / pts.shape[0]\n",
                "print(VCV2)\n",
                "# Use the pseudo-inverse calculated by SVD because\n",
                "# VCV2 is ill-conditioned\n",
                "W_hat2 = lin.pinv(VCV2)\n",
                "print(W_hat2)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "mu_GMM2= 556.7246900639263  sig_GMM2= 176.25245649937506\n"
                    ]
                }
            ],
            "source": [
                "# Note that this takes a little time because the intgr.quad() commands\n",
                "# are a little slow\n",
                "mu_init = 400\n",
                "sig_init = 60\n",
                "params_init = np.array([mu_GMM1, sig_GMM1])\n",
                "# W_hat3 = np.array([[1. / VCV2[0, 0], 0.], [0., 1. / VCV2[1, 1]]])\n",
                "gmm_args = (pts, 450.0, W_hat2)\n",
                "results = opt.minimize(criterion, params_init, args=(gmm_args),\n",
                "                       method='L-BFGS-B', bounds=((1e-10, None), (1e-10, None)))\n",
                "mu_GMM2, sig_GMM2 = results.x\n",
                "print('mu_GMM2=', mu_GMM2, ' sig_GMM2=', sig_GMM2)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Using a better weighting matrix didn't improve our estimates or fit very much. This means that we did not choose good moments for fitting the data. Let's try some different moments. How about four moments to match.\n",
                "\n",
                "1. The percent of observations greater than 430 (between 430 and 450)\n",
                "2. The percent of observations between 320 and 430\n",
                "3. The percent of observations between 220 and 320\n",
                "4. The percent of observations less than 220 (between 0 and 220)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [],
            "source": [
                "def data_moments4(xvals):\n",
                "    '''\n",
                "    --------------------------------------------------------------------\n",
                "    This function computes the four data moments for GMM\n",
                "    (binpct_1, binpct_2, binpct_3, binpct_4).\n",
                "    --------------------------------------------------------------------\n",
                "    INPUTS:\n",
                "    xvals = (N,) vector, test scores data\n",
                "\n",
                "    OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION: None\n",
                "\n",
                "    OBJECTS CREATED WITHIN FUNCTION:\n",
                "    bpct_1_dat = scalar in [0, 1], percent of observations\n",
                "                 0 <= x < 220\n",
                "    bpct_2_dat = scalar in [0, 1], percent of observations\n",
                "                 220 <= x < 320\n",
                "    bpct_3_dat = scalar in [0, 1], percent of observations\n",
                "                 320 <= x < 430\n",
                "    bpct_4_dat = scalar in [0, 1], percent of observations\n",
                "                 430 <= x <= 450\n",
                "\n",
                "    FILES CREATED BY THIS FUNCTION: None\n",
                "\n",
                "    RETURNS: bpct_1, bpct_2, bpct_3, bpct_4\n",
                "    --------------------------------------------------------------------\n",
                "    '''\n",
                "    bpct_1_dat = xvals[xvals < 220].shape[0] / xvals.shape[0]\n",
                "    bpct_2_dat = (xvals[(xvals >=220) & (xvals < 320)].shape[0] /\n",
                "                  xvals.shape[0])\n",
                "    bpct_3_dat = (xvals[(xvals >=320) & (xvals < 430)].shape[0] /\n",
                "                  xvals.shape[0])\n",
                "    bpct_4_dat = xvals[xvals >= 430].shape[0] / xvals.shape[0]\n",
                "\n",
                "    return bpct_1_dat, bpct_2_dat, bpct_3_dat, bpct_4_dat\n",
                "\n",
                "\n",
                "def model_moments4(mu, sigma, cutoff):\n",
                "    '''\n",
                "    --------------------------------------------------------------------\n",
                "    This function computes the four model moments for GMM\n",
                "    (binpct_1, binpct_2, binpct_3, binpct_4).\n",
                "    --------------------------------------------------------------------\n",
                "    INPUTS:\n",
                "    mu     = scalar, mean of the normally distributed random variable\n",
                "    sigma  = scalar > 0, standard deviation of the normally distributed\n",
                "             random variable\n",
                "    cutoff = scalar or string, ='None' if no cutoff is given, otherwise\n",
                "             is scalar upper bound value of distribution. Values above\n",
                "             this value have zero probability\n",
                "    \n",
                "    OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:\n",
                "        trunc_norm_pdf()\n",
                "        xfx()\n",
                "    \n",
                "    OBJECTS CREATED WITHIN FUNCTION:\n",
                "    bpct_1_mod = scalar in [0, 1], percent of model observations in\n",
                "                 bin 1\n",
                "    bp_1_err   = scalar > 0, estimated error in the computation of the\n",
                "                 integral for bpct_1_mod\n",
                "    bpct_2_mod = scalar in [0, 1], percent of model observations in\n",
                "                 bin 2\n",
                "    bp_2_err   = scalar > 0, estimated error in the computation of the\n",
                "                 integral for bpct_2_mod\n",
                "    bpct_3_mod = scalar in [0, 1], percent of model observations in\n",
                "                 bin 3\n",
                "    bp_3_err   = scalar > 0, estimated error in the computation of the\n",
                "                 integral for bpct_3_mod\n",
                "    bpct_4_mod = scalar in [0, 1], percent of model observations in\n",
                "                 bin 4\n",
                "    bp_4_err   = scalar > 0, estimated error in the computation of the\n",
                "                 integral for bpct_4_mod\n",
                "    \n",
                "    FILES CREATED BY THIS FUNCTION: None\n",
                "    \n",
                "    RETURNS: bpct_1_mod, bpct_2_mod, bpct_3_mod, bpct_4_mod\n",
                "    --------------------------------------------------------------------\n",
                "    '''\n",
                "    xfx = lambda x: trunc_norm_pdf(x, mu, sigma, cutoff)\n",
                "    (bpct_1_mod, bp_1_err) = intgr.quad(xfx, 0.0, 220)\n",
                "    (bpct_2_mod, bp_2_err) = intgr.quad(xfx, 220, 320)\n",
                "    (bpct_3_mod, bp_3_err) = intgr.quad(xfx, 320, 430)\n",
                "    (bpct_4_mod, bp_4_err) = intgr.quad(xfx, 430, 450)\n",
                "    \n",
                "    return bpct_1_mod, bpct_2_mod, bpct_3_mod, bpct_4_mod\n",
                "\n",
                "\n",
                "def err_vec4(xvals, mu, sigma, cutoff, simple):\n",
                "    '''\n",
                "    --------------------------------------------------------------------\n",
                "    This function computes the vector of moment errors (in percent\n",
                "    deviation from the data moment vector) for GMM.\n",
                "    --------------------------------------------------------------------\n",
                "    INPUTS:\n",
                "    xvals  = (N,) vector, test scores data\n",
                "    mu     = scalar, mean of the normally distributed random variable\n",
                "    sigma  = scalar > 0, standard deviation of the normally distributed\n",
                "             random variable\n",
                "    cutoff = scalar or string, ='None' if no cutoff is given, otherwise\n",
                "             is scalar upper bound value of distribution. Values above\n",
                "             this value have zero probability\n",
                "    simple = boolean, =True if errors are simple difference, =False if\n",
                "             errors are percent deviation from data moments\n",
                "    \n",
                "    OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:\n",
                "        data_moments4()\n",
                "        model_moments4()\n",
                "    \n",
                "    OBJECTS CREATED WITHIN FUNCTION:\n",
                "    mean_data  = scalar, mean value of data\n",
                "    var_data   = scalar > 0, variance of data\n",
                "    moms_data  = (2, 1) matrix, column vector of two data moments\n",
                "    mean_model = scalar, mean value from model\n",
                "    var_model  = scalar > 0, variance from model\n",
                "    moms_model = (2, 1) matrix, column vector of two model moments\n",
                "    err_vec    = (2, 1) matrix, column vector of two moment error\n",
                "                 functions\n",
                "    \n",
                "    FILES CREATED BY THIS FUNCTION: None\n",
                "    \n",
                "    RETURNS: err_vec\n",
                "    --------------------------------------------------------------------\n",
                "    '''\n",
                "    bpct_1_dat, bpct_2_dat, bpct_3_dat, bpct_4_dat = \\\n",
                "        data_moments4(xvals)\n",
                "    moms_data = np.array([[bpct_1_dat], [bpct_2_dat], [bpct_3_dat],\n",
                "                          [bpct_4_dat]])\n",
                "    bpct_1_mod, bpct_2_mod, bpct_3_mod, bpct_4_mod = \\\n",
                "        model_moments4(mu, sigma, cutoff)\n",
                "    moms_model = np.array([[bpct_1_mod], [bpct_2_mod], [bpct_3_mod],\n",
                "                          [bpct_4_mod]])\n",
                "    if simple:\n",
                "        err_vec = moms_model - moms_data\n",
                "    else:\n",
                "        err_vec = 100 * ((moms_model - moms_data) / moms_data)\n",
                "    \n",
                "    return err_vec\n",
                "\n",
                "\n",
                "def criterion4(params, *args):\n",
                "    '''\n",
                "    --------------------------------------------------------------------\n",
                "    This function computes the GMM weighted sum of squared moment errors\n",
                "    criterion function value given parameter values and an estimate of\n",
                "    the weighting matrix.\n",
                "    --------------------------------------------------------------------\n",
                "    INPUTS:\n",
                "    params = (2,) vector, ([mu, sigma])\n",
                "    mu     = scalar, mean of the normally distributed random variable\n",
                "    sigma  = scalar > 0, standard deviation of the normally distributed\n",
                "             random variable\n",
                "    args   = length 3 tuple, (xvals, cutoff, W_hat)\n",
                "    xvals  = (N,) vector, values of the truncated normally distributed\n",
                "             random variable\n",
                "    cutoff = scalar or string, ='None' if no cutoff is given, otherwise\n",
                "             is scalar upper bound value of distribution. Values above\n",
                "             this value have zero probability\n",
                "    W_hat  = (R, R) matrix, estimate of optimal weighting matrix\n",
                "    \n",
                "    OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:\n",
                "        err_vec4()\n",
                "    \n",
                "    OBJECTS CREATED WITHIN FUNCTION:\n",
                "    err        = (4, 1) matrix, column vector of four moment error\n",
                "                 functions\n",
                "    crit_val   = scalar > 0, GMM criterion function value\n",
                "    \n",
                "    FILES CREATED BY THIS FUNCTION: None\n",
                "    \n",
                "    RETURNS: crit_val\n",
                "    --------------------------------------------------------------------\n",
                "    '''\n",
                "    mu, sigma = params\n",
                "    xvals, cutoff, W = args\n",
                "    err = err_vec4(xvals, mu, sigma, cutoff, simple=False)\n",
                "    crit_val = np.dot(np.dot(err.T, W), err) \n",
                "    \n",
                "    return crit_val"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "(0.08695652173913043, 0.17391304347826086, 0.6894409937888198, 0.049689440993788817)\n",
                        "(0.0934956999323638, 0.329494338155561, 0.5091375802184772, 0.06774764171590582)\n",
                        "[[10064.19118871]]\n"
                    ]
                }
            ],
            "source": [
                "print(data_moments4(pts))\n",
                "print(model_moments4(356, 96, 450))\n",
                "params = np.array([356, 96])\n",
                "print(criterion4(params, pts, 450.0, np.eye(4)))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "mu_GMM1_4= 361.66916898463234  sig_GMM1_4= 92.18003343703371\n"
                    ]
                }
            ],
            "source": [
                "# Note that this takes a little time because the intgr.quad() commands\n",
                "# are a little slow\n",
                "mu_init = 400\n",
                "sig_init = 70\n",
                "params_init = np.array([mu_init, sig_init])\n",
                "W_hat = np.eye(4)\n",
                "# W_hat[1, 1] = 2.0\n",
                "# W_hat[2, 2] = 2.0\n",
                "gmm_args = (pts, 450.0, W_hat)\n",
                "results_4 = opt.minimize(criterion4, params_init, args=(gmm_args),\n",
                "                       method='L-BFGS-B', bounds=((1e-10, None), (1e-10, None)))\n",
                "mu_GMM1_4, sig_GMM1_4 = results_4.x\n",
                "print('mu_GMM1_4=', mu_GMM1_4, ' sig_GMM1_4=', sig_GMM1_4)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "(0.08695652173913043, 0.17391304347826086, 0.6894409937888198, 0.049689440993788817)\n",
                        "(0.07474865015171069, 0.31702556092458795, 0.5356212786085315, 0.07255200401950533)\n",
                        "[[9583.46421288]]\n"
                    ]
                }
            ],
            "source": [
                "print(data_moments4(pts))\n",
                "print(model_moments4(mu_GMM1_4, sig_GMM1_4, 450))\n",
                "params = np.array([mu_GMM1_4, sig_GMM1_4])\n",
                "print(criterion4(params, pts, 450.0, np.eye(4)))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/javascript": [
                            "/* Put everything inside the global mpl namespace */\n",
                            "window.mpl = {};\n",
                            "\n",
                            "\n",
                            "mpl.get_websocket_type = function() {\n",
                            "    if (typeof(WebSocket) !== 'undefined') {\n",
                            "        return WebSocket;\n",
                            "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
                            "        return MozWebSocket;\n",
                            "    } else {\n",
                            "        alert('Your browser does not have WebSocket support.' +\n",
                            "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
                            "              'Firefox 4 and 5 are also supported but you ' +\n",
                            "              'have to enable WebSockets in about:config.');\n",
                            "    };\n",
                            "}\n",
                            "\n",
                            "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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                            "\n",
                            "    this.ws = websocket;\n",
                            "\n",
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                            "        var warnings = document.getElementById(\"mpl-warnings\");\n",
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                            "            warnings.style.display = 'block';\n",
                            "            warnings.textContent = (\n",
                            "                \"This browser does not support binary websocket messages. \" +\n",
                            "                    \"Performance may be slow.\");\n",
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                            "    }\n",
                            "\n",
                            "    this.imageObj = new Image();\n",
                            "\n",
                            "    this.context = undefined;\n",
                            "    this.message = undefined;\n",
                            "    this.canvas = undefined;\n",
                            "    this.rubberband_canvas = undefined;\n",
                            "    this.rubberband_context = undefined;\n",
                            "    this.format_dropdown = undefined;\n",
                            "\n",
                            "    this.image_mode = 'full';\n",
                            "\n",
                            "    this.root = $('<div/>');\n",
                            "    this._root_extra_style(this.root)\n",
                            "    this.root.attr('style', 'display: inline-block');\n",
                            "\n",
                            "    $(parent_element).append(this.root);\n",
                            "\n",
                            "    this._init_header(this);\n",
                            "    this._init_canvas(this);\n",
                            "    this._init_toolbar(this);\n",
                            "\n",
                            "    var fig = this;\n",
                            "\n",
                            "    this.waiting = false;\n",
                            "\n",
                            "    this.ws.onopen =  function () {\n",
                            "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
                            "            fig.send_message(\"send_image_mode\", {});\n",
                            "            if (mpl.ratio != 1) {\n",
                            "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
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                            "                // Full images could contain transparency (where diff images\n",
                            "                // almost always do), so we need to clear the canvas so that\n",
                            "                // there is no ghosting.\n",
                            "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
                            "            }\n",
                            "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
                            "        };\n",
                            "\n",
                            "    this.imageObj.onunload = function() {\n",
                            "        fig.ws.close();\n",
                            "    }\n",
                            "\n",
                            "    this.ws.onmessage = this._make_on_message_function(this);\n",
                            "\n",
                            "    this.ondownload = ondownload;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_header = function() {\n",
                            "    var titlebar = $(\n",
                            "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
                            "        'ui-helper-clearfix\"/>');\n",
                            "    var titletext = $(\n",
                            "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
                            "        'text-align: center; padding: 3px;\"/>');\n",
                            "    titlebar.append(titletext)\n",
                            "    this.root.append(titlebar);\n",
                            "    this.header = titletext[0];\n",
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                            "\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
                            "\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
                            "\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_canvas = function() {\n",
                            "    var fig = this;\n",
                            "\n",
                            "    var canvas_div = $('<div/>');\n",
                            "\n",
                            "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
                            "\n",
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                            "        return fig.key_event(event, event['data']);\n",
                            "    }\n",
                            "\n",
                            "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
                            "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
                            "    this.canvas_div = canvas_div\n",
                            "    this._canvas_extra_style(canvas_div)\n",
                            "    this.root.append(canvas_div);\n",
                            "\n",
                            "    var canvas = $('<canvas/>');\n",
                            "    canvas.addClass('mpl-canvas');\n",
                            "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
                            "\n",
                            "    this.canvas = canvas[0];\n",
                            "    this.context = canvas[0].getContext(\"2d\");\n",
                            "\n",
                            "    var backingStore = this.context.backingStorePixelRatio ||\n",
                            "\tthis.context.webkitBackingStorePixelRatio ||\n",
                            "\tthis.context.mozBackingStorePixelRatio ||\n",
                            "\tthis.context.msBackingStorePixelRatio ||\n",
                            "\tthis.context.oBackingStorePixelRatio ||\n",
                            "\tthis.context.backingStorePixelRatio || 1;\n",
                            "\n",
                            "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
                            "\n",
                            "    var rubberband = $('<canvas/>');\n",
                            "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
                            "\n",
                            "    var pass_mouse_events = true;\n",
                            "\n",
                            "    canvas_div.resizable({\n",
                            "        start: function(event, ui) {\n",
                            "            pass_mouse_events = false;\n",
                            "        },\n",
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                            "            fig.request_resize(ui.size.width, ui.size.height);\n",
                            "        },\n",
                            "        stop: function(event, ui) {\n",
                            "            pass_mouse_events = true;\n",
                            "            fig.request_resize(ui.size.width, ui.size.height);\n",
                            "        },\n",
                            "    });\n",
                            "\n",
                            "    function mouse_event_fn(event) {\n",
                            "        if (pass_mouse_events)\n",
                            "            return fig.mouse_event(event, event['data']);\n",
                            "    }\n",
                            "\n",
                            "    rubberband.mousedown('button_press', mouse_event_fn);\n",
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                            "\n",
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                            "\n",
                            "    canvas_div.on(\"wheel\", function (event) {\n",
                            "        event = event.originalEvent;\n",
                            "        event['data'] = 'scroll'\n",
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                            "    });\n",
                            "\n",
                            "    canvas_div.append(canvas);\n",
                            "    canvas_div.append(rubberband);\n",
                            "\n",
                            "    this.rubberband = rubberband;\n",
                            "    this.rubberband_canvas = rubberband[0];\n",
                            "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
                            "    this.rubberband_context.strokeStyle = \"#000000\";\n",
                            "\n",
                            "    this._resize_canvas = function(width, height) {\n",
                            "        // Keep the size of the canvas, canvas container, and rubber band\n",
                            "        // canvas in synch.\n",
                            "        canvas_div.css('width', width)\n",
                            "        canvas_div.css('height', height)\n",
                            "\n",
                            "        canvas.attr('width', width * mpl.ratio);\n",
                            "        canvas.attr('height', height * mpl.ratio);\n",
                            "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
                            "\n",
                            "        rubberband.attr('width', width);\n",
                            "        rubberband.attr('height', height);\n",
                            "    }\n",
                            "\n",
                            "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
                            "    // upon first draw.\n",
                            "    this._resize_canvas(600, 600);\n",
                            "\n",
                            "    // Disable right mouse context menu.\n",
                            "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
                            "        return false;\n",
                            "    });\n",
                            "\n",
                            "    function set_focus () {\n",
                            "        canvas.focus();\n",
                            "        canvas_div.focus();\n",
                            "    }\n",
                            "\n",
                            "    window.setTimeout(set_focus, 100);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_toolbar = function() {\n",
                            "    var fig = this;\n",
                            "\n",
                            "    var nav_element = $('<div/>')\n",
                            "    nav_element.attr('style', 'width: 100%');\n",
                            "    this.root.append(nav_element);\n",
                            "\n",
                            "    // Define a callback function for later on.\n",
                            "    function toolbar_event(event) {\n",
                            "        return fig.toolbar_button_onclick(event['data']);\n",
                            "    }\n",
                            "    function toolbar_mouse_event(event) {\n",
                            "        return fig.toolbar_button_onmouseover(event['data']);\n",
                            "    }\n",
                            "\n",
                            "    for(var toolbar_ind in mpl.toolbar_items) {\n",
                            "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
                            "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
                            "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
                            "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
                            "\n",
                            "        if (!name) {\n",
                            "            // put a spacer in here.\n",
                            "            continue;\n",
                            "        }\n",
                            "        var button = $('<button/>');\n",
                            "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
                            "                        'ui-button-icon-only');\n",
                            "        button.attr('role', 'button');\n",
                            "        button.attr('aria-disabled', 'false');\n",
                            "        button.click(method_name, toolbar_event);\n",
                            "        button.mouseover(tooltip, toolbar_mouse_event);\n",
                            "\n",
                            "        var icon_img = $('<span/>');\n",
                            "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
                            "        icon_img.addClass(image);\n",
                            "        icon_img.addClass('ui-corner-all');\n",
                            "\n",
                            "        var tooltip_span = $('<span/>');\n",
                            "        tooltip_span.addClass('ui-button-text');\n",
                            "        tooltip_span.html(tooltip);\n",
                            "\n",
                            "        button.append(icon_img);\n",
                            "        button.append(tooltip_span);\n",
                            "\n",
                            "        nav_element.append(button);\n",
                            "    }\n",
                            "\n",
                            "    var fmt_picker_span = $('<span/>');\n",
                            "\n",
                            "    var fmt_picker = $('<select/>');\n",
                            "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
                            "    fmt_picker_span.append(fmt_picker);\n",
                            "    nav_element.append(fmt_picker_span);\n",
                            "    this.format_dropdown = fmt_picker[0];\n",
                            "\n",
                            "    for (var ind in mpl.extensions) {\n",
                            "        var fmt = mpl.extensions[ind];\n",
                            "        var option = $(\n",
                            "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
                            "        fmt_picker.append(option)\n",
                            "    }\n",
                            "\n",
                            "    // Add hover states to the ui-buttons\n",
                            "    $( \".ui-button\" ).hover(\n",
                            "        function() { $(this).addClass(\"ui-state-hover\");},\n",
                            "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
                            "    );\n",
                            "\n",
                            "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
                            "    nav_element.append(status_bar);\n",
                            "    this.message = status_bar[0];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
                            "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
                            "    // which will in turn request a refresh of the image.\n",
                            "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.send_message = function(type, properties) {\n",
                            "    properties['type'] = type;\n",
                            "    properties['figure_id'] = this.id;\n",
                            "    this.ws.send(JSON.stringify(properties));\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.send_draw_message = function() {\n",
                            "    if (!this.waiting) {\n",
                            "        this.waiting = true;\n",
                            "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
                            "    }\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
                            "    var format_dropdown = fig.format_dropdown;\n",
                            "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
                            "    fig.ondownload(fig, format);\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
                            "    var size = msg['size'];\n",
                            "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
                            "        fig._resize_canvas(size[0], size[1]);\n",
                            "        fig.send_message(\"refresh\", {});\n",
                            "    };\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
                            "    var x0 = msg['x0'] / mpl.ratio;\n",
                            "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
                            "    var x1 = msg['x1'] / mpl.ratio;\n",
                            "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
                            "    x0 = Math.floor(x0) + 0.5;\n",
                            "    y0 = Math.floor(y0) + 0.5;\n",
                            "    x1 = Math.floor(x1) + 0.5;\n",
                            "    y1 = Math.floor(y1) + 0.5;\n",
                            "    var min_x = Math.min(x0, x1);\n",
                            "    var min_y = Math.min(y0, y1);\n",
                            "    var width = Math.abs(x1 - x0);\n",
                            "    var height = Math.abs(y1 - y0);\n",
                            "\n",
                            "    fig.rubberband_context.clearRect(\n",
                            "        0, 0, fig.canvas.width, fig.canvas.height);\n",
                            "\n",
                            "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
                            "    // Updates the figure title.\n",
                            "    fig.header.textContent = msg['label'];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
                            "    var cursor = msg['cursor'];\n",
                            "    switch(cursor)\n",
                            "    {\n",
                            "    case 0:\n",
                            "        cursor = 'pointer';\n",
                            "        break;\n",
                            "    case 1:\n",
                            "        cursor = 'default';\n",
                            "        break;\n",
                            "    case 2:\n",
                            "        cursor = 'crosshair';\n",
                            "        break;\n",
                            "    case 3:\n",
                            "        cursor = 'move';\n",
                            "        break;\n",
                            "    }\n",
                            "    fig.rubberband_canvas.style.cursor = cursor;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
                            "    fig.message.textContent = msg['message'];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
                            "    // Request the server to send over a new figure.\n",
                            "    fig.send_draw_message();\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
                            "    fig.image_mode = msg['mode'];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.updated_canvas_event = function() {\n",
                            "    // Called whenever the canvas gets updated.\n",
                            "    this.send_message(\"ack\", {});\n",
                            "}\n",
                            "\n",
                            "// A function to construct a web socket function for onmessage handling.\n",
                            "// Called in the figure constructor.\n",
                            "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
                            "    return function socket_on_message(evt) {\n",
                            "        if (evt.data instanceof Blob) {\n",
                            "            /* FIXME: We get \"Resource interpreted as Image but\n",
                            "             * transferred with MIME type text/plain:\" errors on\n",
                            "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
                            "             * to be part of the websocket stream */\n",
                            "            evt.data.type = \"image/png\";\n",
                            "\n",
                            "            /* Free the memory for the previous frames */\n",
                            "            if (fig.imageObj.src) {\n",
                            "                (window.URL || window.webkitURL).revokeObjectURL(\n",
                            "                    fig.imageObj.src);\n",
                            "            }\n",
                            "\n",
                            "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
                            "                evt.data);\n",
                            "            fig.updated_canvas_event();\n",
                            "            fig.waiting = false;\n",
                            "            return;\n",
                            "        }\n",
                            "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
                            "            fig.imageObj.src = evt.data;\n",
                            "            fig.updated_canvas_event();\n",
                            "            fig.waiting = false;\n",
                            "            return;\n",
                            "        }\n",
                            "\n",
                            "        var msg = JSON.parse(evt.data);\n",
                            "        var msg_type = msg['type'];\n",
                            "\n",
                            "        // Call the  \"handle_{type}\" callback, which takes\n",
                            "        // the figure and JSON message as its only arguments.\n",
                            "        try {\n",
                            "            var callback = fig[\"handle_\" + msg_type];\n",
                            "        } catch (e) {\n",
                            "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
                            "            return;\n",
                            "        }\n",
                            "\n",
                            "        if (callback) {\n",
                            "            try {\n",
                            "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
                            "                callback(fig, msg);\n",
                            "            } catch (e) {\n",
                            "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
                            "            }\n",
                            "        }\n",
                            "    };\n",
                            "}\n",
                            "\n",
                            "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
                            "mpl.findpos = function(e) {\n",
                            "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
                            "    var targ;\n",
                            "    if (!e)\n",
                            "        e = window.event;\n",
                            "    if (e.target)\n",
                            "        targ = e.target;\n",
                            "    else if (e.srcElement)\n",
                            "        targ = e.srcElement;\n",
                            "    if (targ.nodeType == 3) // defeat Safari bug\n",
                            "        targ = targ.parentNode;\n",
                            "\n",
                            "    // jQuery normalizes the pageX and pageY\n",
                            "    // pageX,Y are the mouse positions relative to the document\n",
                            "    // offset() returns the position of the element relative to the document\n",
                            "    var x = e.pageX - $(targ).offset().left;\n",
                            "    var y = e.pageY - $(targ).offset().top;\n",
                            "\n",
                            "    return {\"x\": x, \"y\": y};\n",
                            "};\n",
                            "\n",
                            "/*\n",
                            " * return a copy of an object with only non-object keys\n",
                            " * we need this to avoid circular references\n",
                            " * http://stackoverflow.com/a/24161582/3208463\n",
                            " */\n",
                            "function simpleKeys (original) {\n",
                            "  return Object.keys(original).reduce(function (obj, key) {\n",
                            "    if (typeof original[key] !== 'object')\n",
                            "        obj[key] = original[key]\n",
                            "    return obj;\n",
                            "  }, {});\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.mouse_event = function(event, name) {\n",
                            "    var canvas_pos = mpl.findpos(event)\n",
                            "\n",
                            "    if (name === 'button_press')\n",
                            "    {\n",
                            "        this.canvas.focus();\n",
                            "        this.canvas_div.focus();\n",
                            "    }\n",
                            "\n",
                            "    var x = canvas_pos.x * mpl.ratio;\n",
                            "    var y = canvas_pos.y * mpl.ratio;\n",
                            "\n",
                            "    this.send_message(name, {x: x, y: y, button: event.button,\n",
                            "                             step: event.step,\n",
                            "                             guiEvent: simpleKeys(event)});\n",
                            "\n",
                            "    /* This prevents the web browser from automatically changing to\n",
                            "     * the text insertion cursor when the button is pressed.  We want\n",
                            "     * to control all of the cursor setting manually through the\n",
                            "     * 'cursor' event from matplotlib */\n",
                            "    event.preventDefault();\n",
                            "    return false;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
                            "    // Handle any extra behaviour associated with a key event\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.key_event = function(event, name) {\n",
                            "\n",
                            "    // Prevent repeat events\n",
                            "    if (name == 'key_press')\n",
                            "    {\n",
                            "        if (event.which === this._key)\n",
                            "            return;\n",
                            "        else\n",
                            "            this._key = event.which;\n",
                            "    }\n",
                            "    if (name == 'key_release')\n",
                            "        this._key = null;\n",
                            "\n",
                            "    var value = '';\n",
                            "    if (event.ctrlKey && event.which != 17)\n",
                            "        value += \"ctrl+\";\n",
                            "    if (event.altKey && event.which != 18)\n",
                            "        value += \"alt+\";\n",
                            "    if (event.shiftKey && event.which != 16)\n",
                            "        value += \"shift+\";\n",
                            "\n",
                            "    value += 'k';\n",
                            "    value += event.which.toString();\n",
                            "\n",
                            "    this._key_event_extra(event, name);\n",
                            "\n",
                            "    this.send_message(name, {key: value,\n",
                            "                             guiEvent: simpleKeys(event)});\n",
                            "    return false;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
                            "    if (name == 'download') {\n",
                            "        this.handle_save(this, null);\n",
                            "    } else {\n",
                            "        this.send_message(\"toolbar_button\", {name: name});\n",
                            "    }\n",
                            "};\n",
                            "\n",
                            "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
                            "    this.message.textContent = tooltip;\n",
                            "};\n",
                            "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
                            "\n",
                            "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
                            "\n",
                            "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
                            "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
                            "    // object with the appropriate methods. Currently this is a non binary\n",
                            "    // socket, so there is still some room for performance tuning.\n",
                            "    var ws = {};\n",
                            "\n",
                            "    ws.close = function() {\n",
                            "        comm.close()\n",
                            "    };\n",
                            "    ws.send = function(m) {\n",
                            "        //console.log('sending', m);\n",
                            "        comm.send(m);\n",
                            "    };\n",
                            "    // Register the callback with on_msg.\n",
                            "    comm.on_msg(function(msg) {\n",
                            "        //console.log('receiving', msg['content']['data'], msg);\n",
                            "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
                            "        ws.onmessage(msg['content']['data'])\n",
                            "    });\n",
                            "    return ws;\n",
                            "}\n",
                            "\n",
                            "mpl.mpl_figure_comm = function(comm, msg) {\n",
                            "    // This is the function which gets called when the mpl process\n",
                            "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
                            "\n",
                            "    var id = msg.content.data.id;\n",
                            "    // Get hold of the div created by the display call when the Comm\n",
                            "    // socket was opened in Python.\n",
                            "    var element = $(\"#\" + id);\n",
                            "    var ws_proxy = comm_websocket_adapter(comm)\n",
                            "\n",
                            "    function ondownload(figure, format) {\n",
                            "        window.open(figure.imageObj.src);\n",
                            "    }\n",
                            "\n",
                            "    var fig = new mpl.figure(id, ws_proxy,\n",
                            "                           ondownload,\n",
                            "                           element.get(0));\n",
                            "\n",
                            "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
                            "    // web socket which is closed, not our websocket->open comm proxy.\n",
                            "    ws_proxy.onopen();\n",
                            "\n",
                            "    fig.parent_element = element.get(0);\n",
                            "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
                            "    if (!fig.cell_info) {\n",
                            "        console.error(\"Failed to find cell for figure\", id, fig);\n",
                            "        return;\n",
                            "    }\n",
                            "\n",
                            "    var output_index = fig.cell_info[2]\n",
                            "    var cell = fig.cell_info[0];\n",
                            "\n",
                            "};\n",
                            "\n",
                            "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
                            "    var width = fig.canvas.width/mpl.ratio\n",
                            "    fig.root.unbind('remove')\n",
                            "\n",
                            "    // Update the output cell to use the data from the current canvas.\n",
                            "    fig.push_to_output();\n",
                            "    var dataURL = fig.canvas.toDataURL();\n",
                            "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
                            "    // the notebook keyboard shortcuts fail.\n",
                            "    IPython.keyboard_manager.enable()\n",
                            "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
                            "    fig.close_ws(fig, msg);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.close_ws = function(fig, msg){\n",
                            "    fig.send_message('closing', msg);\n",
                            "    // fig.ws.close()\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
                            "    // Turn the data on the canvas into data in the output cell.\n",
                            "    var width = this.canvas.width/mpl.ratio\n",
                            "    var dataURL = this.canvas.toDataURL();\n",
                            "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.updated_canvas_event = function() {\n",
                            "    // Tell IPython that the notebook contents must change.\n",
                            "    IPython.notebook.set_dirty(true);\n",
                            "    this.send_message(\"ack\", {});\n",
                            "    var fig = this;\n",
                            "    // Wait a second, then push the new image to the DOM so\n",
                            "    // that it is saved nicely (might be nice to debounce this).\n",
                            "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_toolbar = function() {\n",
                            "    var fig = this;\n",
                            "\n",
                            "    var nav_element = $('<div/>')\n",
                            "    nav_element.attr('style', 'width: 100%');\n",
                            "    this.root.append(nav_element);\n",
                            "\n",
                            "    // Define a callback function for later on.\n",
                            "    function toolbar_event(event) {\n",
                            "        return fig.toolbar_button_onclick(event['data']);\n",
                            "    }\n",
                            "    function toolbar_mouse_event(event) {\n",
                            "        return fig.toolbar_button_onmouseover(event['data']);\n",
                            "    }\n",
                            "\n",
                            "    for(var toolbar_ind in mpl.toolbar_items){\n",
                            "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
                            "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
                            "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
                            "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
                            "\n",
                            "        if (!name) { continue; };\n",
                            "\n",
                            "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
                            "        button.click(method_name, toolbar_event);\n",
                            "        button.mouseover(tooltip, toolbar_mouse_event);\n",
                            "        nav_element.append(button);\n",
                            "    }\n",
                            "\n",
                            "    // Add the status bar.\n",
                            "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
                            "    nav_element.append(status_bar);\n",
                            "    this.message = status_bar[0];\n",
                            "\n",
                            "    // Add the close button to the window.\n",
                            "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
                            "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
                            "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
                            "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
                            "    buttongrp.append(button);\n",
                            "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
                            "    titlebar.prepend(buttongrp);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._root_extra_style = function(el){\n",
                            "    var fig = this\n",
                            "    el.on(\"remove\", function(){\n",
                            "\tfig.close_ws(fig, {});\n",
                            "    });\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._canvas_extra_style = function(el){\n",
                            "    // this is important to make the div 'focusable\n",
                            "    el.attr('tabindex', 0)\n",
                            "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
                            "    // off when our div gets focus\n",
                            "\n",
                            "    // location in version 3\n",
                            "    if (IPython.notebook.keyboard_manager) {\n",
                            "        IPython.notebook.keyboard_manager.register_events(el);\n",
                            "    }\n",
                            "    else {\n",
                            "        // location in version 2\n",
                            "        IPython.keyboard_manager.register_events(el);\n",
                            "    }\n",
                            "\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
                            "    var manager = IPython.notebook.keyboard_manager;\n",
                            "    if (!manager)\n",
                            "        manager = IPython.keyboard_manager;\n",
                            "\n",
                            "    // Check for shift+enter\n",
                            "    if (event.shiftKey && event.which == 13) {\n",
                            "        this.canvas_div.blur();\n",
                            "        event.shiftKey = false;\n",
                            "        // Send a \"J\" for go to next cell\n",
                            "        event.which = 74;\n",
                            "        event.keyCode = 74;\n",
                            "        manager.command_mode();\n",
                            "        manager.handle_keydown(event);\n",
                            "    }\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
                            "    fig.ondownload(fig, null);\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.find_output_cell = function(html_output) {\n",
                            "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
                            "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
                            "    // IPython event is triggered only after the cells have been serialised, which for\n",
                            "    // our purposes (turning an active figure into a static one), is too late.\n",
                            "    var cells = IPython.notebook.get_cells();\n",
                            "    var ncells = cells.length;\n",
                            "    for (var i=0; i<ncells; i++) {\n",
                            "        var cell = cells[i];\n",
                            "        if (cell.cell_type === 'code'){\n",
                            "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
                            "                var data = cell.output_area.outputs[j];\n",
                            "                if (data.data) {\n",
                            "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
                            "                    data = data.data;\n",
                            "                }\n",
                            "                if (data['text/html'] == html_output) {\n",
                            "                    return [cell, data, j];\n",
                            "                }\n",
                            "            }\n",
                            "        }\n",
                            "    }\n",
                            "}\n",
                            "\n",
                            "// Register the function which deals with the matplotlib target/channel.\n",
                            "// The kernel may be null if the page has been refreshed.\n",
                            "if (IPython.notebook.kernel != null) {\n",
                            "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
                            "}\n"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Javascript object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
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\" width=\"640\">"
                        ],
                        "text/plain": [
                            "<IPython.core.display.HTML object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/plain": [
                            "<matplotlib.legend.Legend at 0x1a1ece82b0>"
                        ]
                    },
                    "execution_count": 30,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# Plot the histogram of the data\n",
                "count, bins, ignored = plt.hist(pts, num_bins, density=True,\n",
                "                                edgecolor='k')\n",
                "plt.title('Econ 381 scores: 2011-2012', fontsize=20)\n",
                "plt.xlabel(r'Total points')\n",
                "plt.ylabel(r'Percent of scores')\n",
                "plt.xlim([0, 550])  # This gives the xmin and xmax to be plotted\"\n",
                "\n",
                "# Plot the estimated GMM PDF\n",
                "dist_pts = np.linspace(0, 450, 500)\n",
                "plt.plot(dist_pts, trunc_norm_pdf(dist_pts, mu_GMM1_4, sig_GMM1_4, 450),\n",
                "         linewidth=2, color='k', label='1: $\\mu_{GMM1}$,$\\sigma_{GMM1}$')\n",
                "\n",
                "# Plot the estimated GMM PDF\n",
                "dist_pts = np.linspace(0, 450, 500)\n",
                "plt.plot(dist_pts, trunc_norm_pdf(dist_pts, 400, 70, 450),\n",
                "         linewidth=2, color='g', label='1: $\\mu=400$,$\\sigma=70$')\n",
                "plt.legend(loc='upper left')"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Let's see how much things change if we use the two-step estimator for the optimal weighting matrix $W$ instead of the identity matrix."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "[[  1.22419249  -7.17558615   1.94548021  -4.01210898]\n",
                        " [ -7.17558615  42.05959205 -11.40340346  23.51691745]\n",
                        " [  1.94548021 -11.40340346   3.09174683  -6.37602232]\n",
                        " [ -4.01210898  23.51691745  -6.37602232  13.14909108]]\n",
                        "[[ 0.00034551 -0.00202518  0.00054908 -0.00113235]\n",
                        " [-0.00202518  0.01187058 -0.00321841  0.00663723]\n",
                        " [ 0.00054908 -0.00321841  0.00087259 -0.00179952]\n",
                        " [-0.00113235  0.00663723 -0.00179952  0.0037111 ]]\n"
                    ]
                }
            ],
            "source": [
                "err1_4 = err_vec4(pts, mu_GMM1_4, sig_GMM1_4, 450.0, False)\n",
                "VCV2_4 = np.dot(err1_4, err1_4.T) / pts.shape[0]\n",
                "print(VCV2_4)\n",
                "# Use the pseudo-inverse calculated by SVD because\n",
                "# VCV2 is ill-conditioned\n",
                "W_hat2_4 = lin.pinv(VCV2_4)\n",
                "print(W_hat2_4)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "mu_GMM2_4= 361.66916900398843  sig_GMM2_4= 92.18003346800349\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "      fun: array([[161.]])\n",
                            " hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>\n",
                            "      jac: array([-1.70530257e-05,  2.84217094e-06])\n",
                            "  message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n",
                            "     nfev: 24\n",
                            "      nit: 1\n",
                            "   status: 0\n",
                            "  success: True\n",
                            "        x: array([361.669169  ,  92.18003347])"
                        ]
                    },
                    "execution_count": 32,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# Note that this takes a little time because the intgr.quad() commands\n",
                "# are a little slow\n",
                "mu_init = 356\n",
                "sig_init = 96\n",
                "params_init = np.array([mu_GMM1_4, sig_GMM1_4])\n",
                "gmm_args = (pts, 450.0, W_hat2_4)\n",
                "results2_4 = opt.minimize(criterion4, params_init, args=(gmm_args),\n",
                "                       method='L-BFGS-B', bounds=((1e-10, None), (1e-10, None)))\n",
                "mu_GMM2_4, sig_GMM2_4 = results2_4.x\n",
                "print('mu_GMM2_4=', mu_GMM2_4, ' sig_GMM2_4=', sig_GMM2_4)\n",
                "results2_4"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "No real change. The criterion function for different values of $\\mu$ and $\\sigma$ here has a clear minimum in a certain area. But it also has some really interesting nonlinearities."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/javascript": [
                            "/* Put everything inside the global mpl namespace */\n",
                            "window.mpl = {};\n",
                            "\n",
                            "\n",
                            "mpl.get_websocket_type = function() {\n",
                            "    if (typeof(WebSocket) !== 'undefined') {\n",
                            "        return WebSocket;\n",
                            "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
                            "        return MozWebSocket;\n",
                            "    } else {\n",
                            "        alert('Your browser does not have WebSocket support.' +\n",
                            "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
                            "              'Firefox 4 and 5 are also supported but you ' +\n",
                            "              'have to enable WebSockets in about:config.');\n",
                            "    };\n",
                            "}\n",
                            "\n",
                            "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
                            "    this.id = figure_id;\n",
                            "\n",
                            "    this.ws = websocket;\n",
                            "\n",
                            "    this.supports_binary = (this.ws.binaryType != undefined);\n",
                            "\n",
                            "    if (!this.supports_binary) {\n",
                            "        var warnings = document.getElementById(\"mpl-warnings\");\n",
                            "        if (warnings) {\n",
                            "            warnings.style.display = 'block';\n",
                            "            warnings.textContent = (\n",
                            "                \"This browser does not support binary websocket messages. \" +\n",
                            "                    \"Performance may be slow.\");\n",
                            "        }\n",
                            "    }\n",
                            "\n",
                            "    this.imageObj = new Image();\n",
                            "\n",
                            "    this.context = undefined;\n",
                            "    this.message = undefined;\n",
                            "    this.canvas = undefined;\n",
                            "    this.rubberband_canvas = undefined;\n",
                            "    this.rubberband_context = undefined;\n",
                            "    this.format_dropdown = undefined;\n",
                            "\n",
                            "    this.image_mode = 'full';\n",
                            "\n",
                            "    this.root = $('<div/>');\n",
                            "    this._root_extra_style(this.root)\n",
                            "    this.root.attr('style', 'display: inline-block');\n",
                            "\n",
                            "    $(parent_element).append(this.root);\n",
                            "\n",
                            "    this._init_header(this);\n",
                            "    this._init_canvas(this);\n",
                            "    this._init_toolbar(this);\n",
                            "\n",
                            "    var fig = this;\n",
                            "\n",
                            "    this.waiting = false;\n",
                            "\n",
                            "    this.ws.onopen =  function () {\n",
                            "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
                            "            fig.send_message(\"send_image_mode\", {});\n",
                            "            if (mpl.ratio != 1) {\n",
                            "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
                            "            }\n",
                            "            fig.send_message(\"refresh\", {});\n",
                            "        }\n",
                            "\n",
                            "    this.imageObj.onload = function() {\n",
                            "            if (fig.image_mode == 'full') {\n",
                            "                // Full images could contain transparency (where diff images\n",
                            "                // almost always do), so we need to clear the canvas so that\n",
                            "                // there is no ghosting.\n",
                            "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
                            "            }\n",
                            "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
                            "        };\n",
                            "\n",
                            "    this.imageObj.onunload = function() {\n",
                            "        fig.ws.close();\n",
                            "    }\n",
                            "\n",
                            "    this.ws.onmessage = this._make_on_message_function(this);\n",
                            "\n",
                            "    this.ondownload = ondownload;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_header = function() {\n",
                            "    var titlebar = $(\n",
                            "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
                            "        'ui-helper-clearfix\"/>');\n",
                            "    var titletext = $(\n",
                            "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
                            "        'text-align: center; padding: 3px;\"/>');\n",
                            "    titlebar.append(titletext)\n",
                            "    this.root.append(titlebar);\n",
                            "    this.header = titletext[0];\n",
                            "}\n",
                            "\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
                            "\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
                            "\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_canvas = function() {\n",
                            "    var fig = this;\n",
                            "\n",
                            "    var canvas_div = $('<div/>');\n",
                            "\n",
                            "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
                            "\n",
                            "    function canvas_keyboard_event(event) {\n",
                            "        return fig.key_event(event, event['data']);\n",
                            "    }\n",
                            "\n",
                            "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
                            "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
                            "    this.canvas_div = canvas_div\n",
                            "    this._canvas_extra_style(canvas_div)\n",
                            "    this.root.append(canvas_div);\n",
                            "\n",
                            "    var canvas = $('<canvas/>');\n",
                            "    canvas.addClass('mpl-canvas');\n",
                            "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
                            "\n",
                            "    this.canvas = canvas[0];\n",
                            "    this.context = canvas[0].getContext(\"2d\");\n",
                            "\n",
                            "    var backingStore = this.context.backingStorePixelRatio ||\n",
                            "\tthis.context.webkitBackingStorePixelRatio ||\n",
                            "\tthis.context.mozBackingStorePixelRatio ||\n",
                            "\tthis.context.msBackingStorePixelRatio ||\n",
                            "\tthis.context.oBackingStorePixelRatio ||\n",
                            "\tthis.context.backingStorePixelRatio || 1;\n",
                            "\n",
                            "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
                            "\n",
                            "    var rubberband = $('<canvas/>');\n",
                            "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
                            "\n",
                            "    var pass_mouse_events = true;\n",
                            "\n",
                            "    canvas_div.resizable({\n",
                            "        start: function(event, ui) {\n",
                            "            pass_mouse_events = false;\n",
                            "        },\n",
                            "        resize: function(event, ui) {\n",
                            "            fig.request_resize(ui.size.width, ui.size.height);\n",
                            "        },\n",
                            "        stop: function(event, ui) {\n",
                            "            pass_mouse_events = true;\n",
                            "            fig.request_resize(ui.size.width, ui.size.height);\n",
                            "        },\n",
                            "    });\n",
                            "\n",
                            "    function mouse_event_fn(event) {\n",
                            "        if (pass_mouse_events)\n",
                            "            return fig.mouse_event(event, event['data']);\n",
                            "    }\n",
                            "\n",
                            "    rubberband.mousedown('button_press', mouse_event_fn);\n",
                            "    rubberband.mouseup('button_release', mouse_event_fn);\n",
                            "    // Throttle sequential mouse events to 1 every 20ms.\n",
                            "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
                            "\n",
                            "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
                            "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
                            "\n",
                            "    canvas_div.on(\"wheel\", function (event) {\n",
                            "        event = event.originalEvent;\n",
                            "        event['data'] = 'scroll'\n",
                            "        if (event.deltaY < 0) {\n",
                            "            event.step = 1;\n",
                            "        } else {\n",
                            "            event.step = -1;\n",
                            "        }\n",
                            "        mouse_event_fn(event);\n",
                            "    });\n",
                            "\n",
                            "    canvas_div.append(canvas);\n",
                            "    canvas_div.append(rubberband);\n",
                            "\n",
                            "    this.rubberband = rubberband;\n",
                            "    this.rubberband_canvas = rubberband[0];\n",
                            "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
                            "    this.rubberband_context.strokeStyle = \"#000000\";\n",
                            "\n",
                            "    this._resize_canvas = function(width, height) {\n",
                            "        // Keep the size of the canvas, canvas container, and rubber band\n",
                            "        // canvas in synch.\n",
                            "        canvas_div.css('width', width)\n",
                            "        canvas_div.css('height', height)\n",
                            "\n",
                            "        canvas.attr('width', width * mpl.ratio);\n",
                            "        canvas.attr('height', height * mpl.ratio);\n",
                            "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
                            "\n",
                            "        rubberband.attr('width', width);\n",
                            "        rubberband.attr('height', height);\n",
                            "    }\n",
                            "\n",
                            "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
                            "    // upon first draw.\n",
                            "    this._resize_canvas(600, 600);\n",
                            "\n",
                            "    // Disable right mouse context menu.\n",
                            "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
                            "        return false;\n",
                            "    });\n",
                            "\n",
                            "    function set_focus () {\n",
                            "        canvas.focus();\n",
                            "        canvas_div.focus();\n",
                            "    }\n",
                            "\n",
                            "    window.setTimeout(set_focus, 100);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_toolbar = function() {\n",
                            "    var fig = this;\n",
                            "\n",
                            "    var nav_element = $('<div/>')\n",
                            "    nav_element.attr('style', 'width: 100%');\n",
                            "    this.root.append(nav_element);\n",
                            "\n",
                            "    // Define a callback function for later on.\n",
                            "    function toolbar_event(event) {\n",
                            "        return fig.toolbar_button_onclick(event['data']);\n",
                            "    }\n",
                            "    function toolbar_mouse_event(event) {\n",
                            "        return fig.toolbar_button_onmouseover(event['data']);\n",
                            "    }\n",
                            "\n",
                            "    for(var toolbar_ind in mpl.toolbar_items) {\n",
                            "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
                            "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
                            "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
                            "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
                            "\n",
                            "        if (!name) {\n",
                            "            // put a spacer in here.\n",
                            "            continue;\n",
                            "        }\n",
                            "        var button = $('<button/>');\n",
                            "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
                            "                        'ui-button-icon-only');\n",
                            "        button.attr('role', 'button');\n",
                            "        button.attr('aria-disabled', 'false');\n",
                            "        button.click(method_name, toolbar_event);\n",
                            "        button.mouseover(tooltip, toolbar_mouse_event);\n",
                            "\n",
                            "        var icon_img = $('<span/>');\n",
                            "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
                            "        icon_img.addClass(image);\n",
                            "        icon_img.addClass('ui-corner-all');\n",
                            "\n",
                            "        var tooltip_span = $('<span/>');\n",
                            "        tooltip_span.addClass('ui-button-text');\n",
                            "        tooltip_span.html(tooltip);\n",
                            "\n",
                            "        button.append(icon_img);\n",
                            "        button.append(tooltip_span);\n",
                            "\n",
                            "        nav_element.append(button);\n",
                            "    }\n",
                            "\n",
                            "    var fmt_picker_span = $('<span/>');\n",
                            "\n",
                            "    var fmt_picker = $('<select/>');\n",
                            "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
                            "    fmt_picker_span.append(fmt_picker);\n",
                            "    nav_element.append(fmt_picker_span);\n",
                            "    this.format_dropdown = fmt_picker[0];\n",
                            "\n",
                            "    for (var ind in mpl.extensions) {\n",
                            "        var fmt = mpl.extensions[ind];\n",
                            "        var option = $(\n",
                            "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
                            "        fmt_picker.append(option)\n",
                            "    }\n",
                            "\n",
                            "    // Add hover states to the ui-buttons\n",
                            "    $( \".ui-button\" ).hover(\n",
                            "        function() { $(this).addClass(\"ui-state-hover\");},\n",
                            "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
                            "    );\n",
                            "\n",
                            "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
                            "    nav_element.append(status_bar);\n",
                            "    this.message = status_bar[0];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
                            "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
                            "    // which will in turn request a refresh of the image.\n",
                            "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.send_message = function(type, properties) {\n",
                            "    properties['type'] = type;\n",
                            "    properties['figure_id'] = this.id;\n",
                            "    this.ws.send(JSON.stringify(properties));\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.send_draw_message = function() {\n",
                            "    if (!this.waiting) {\n",
                            "        this.waiting = true;\n",
                            "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
                            "    }\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
                            "    var format_dropdown = fig.format_dropdown;\n",
                            "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
                            "    fig.ondownload(fig, format);\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
                            "    var size = msg['size'];\n",
                            "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
                            "        fig._resize_canvas(size[0], size[1]);\n",
                            "        fig.send_message(\"refresh\", {});\n",
                            "    };\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
                            "    var x0 = msg['x0'] / mpl.ratio;\n",
                            "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
                            "    var x1 = msg['x1'] / mpl.ratio;\n",
                            "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
                            "    x0 = Math.floor(x0) + 0.5;\n",
                            "    y0 = Math.floor(y0) + 0.5;\n",
                            "    x1 = Math.floor(x1) + 0.5;\n",
                            "    y1 = Math.floor(y1) + 0.5;\n",
                            "    var min_x = Math.min(x0, x1);\n",
                            "    var min_y = Math.min(y0, y1);\n",
                            "    var width = Math.abs(x1 - x0);\n",
                            "    var height = Math.abs(y1 - y0);\n",
                            "\n",
                            "    fig.rubberband_context.clearRect(\n",
                            "        0, 0, fig.canvas.width, fig.canvas.height);\n",
                            "\n",
                            "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
                            "    // Updates the figure title.\n",
                            "    fig.header.textContent = msg['label'];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
                            "    var cursor = msg['cursor'];\n",
                            "    switch(cursor)\n",
                            "    {\n",
                            "    case 0:\n",
                            "        cursor = 'pointer';\n",
                            "        break;\n",
                            "    case 1:\n",
                            "        cursor = 'default';\n",
                            "        break;\n",
                            "    case 2:\n",
                            "        cursor = 'crosshair';\n",
                            "        break;\n",
                            "    case 3:\n",
                            "        cursor = 'move';\n",
                            "        break;\n",
                            "    }\n",
                            "    fig.rubberband_canvas.style.cursor = cursor;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
                            "    fig.message.textContent = msg['message'];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
                            "    // Request the server to send over a new figure.\n",
                            "    fig.send_draw_message();\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
                            "    fig.image_mode = msg['mode'];\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.updated_canvas_event = function() {\n",
                            "    // Called whenever the canvas gets updated.\n",
                            "    this.send_message(\"ack\", {});\n",
                            "}\n",
                            "\n",
                            "// A function to construct a web socket function for onmessage handling.\n",
                            "// Called in the figure constructor.\n",
                            "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
                            "    return function socket_on_message(evt) {\n",
                            "        if (evt.data instanceof Blob) {\n",
                            "            /* FIXME: We get \"Resource interpreted as Image but\n",
                            "             * transferred with MIME type text/plain:\" errors on\n",
                            "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
                            "             * to be part of the websocket stream */\n",
                            "            evt.data.type = \"image/png\";\n",
                            "\n",
                            "            /* Free the memory for the previous frames */\n",
                            "            if (fig.imageObj.src) {\n",
                            "                (window.URL || window.webkitURL).revokeObjectURL(\n",
                            "                    fig.imageObj.src);\n",
                            "            }\n",
                            "\n",
                            "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
                            "                evt.data);\n",
                            "            fig.updated_canvas_event();\n",
                            "            fig.waiting = false;\n",
                            "            return;\n",
                            "        }\n",
                            "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
                            "            fig.imageObj.src = evt.data;\n",
                            "            fig.updated_canvas_event();\n",
                            "            fig.waiting = false;\n",
                            "            return;\n",
                            "        }\n",
                            "\n",
                            "        var msg = JSON.parse(evt.data);\n",
                            "        var msg_type = msg['type'];\n",
                            "\n",
                            "        // Call the  \"handle_{type}\" callback, which takes\n",
                            "        // the figure and JSON message as its only arguments.\n",
                            "        try {\n",
                            "            var callback = fig[\"handle_\" + msg_type];\n",
                            "        } catch (e) {\n",
                            "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
                            "            return;\n",
                            "        }\n",
                            "\n",
                            "        if (callback) {\n",
                            "            try {\n",
                            "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
                            "                callback(fig, msg);\n",
                            "            } catch (e) {\n",
                            "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
                            "            }\n",
                            "        }\n",
                            "    };\n",
                            "}\n",
                            "\n",
                            "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
                            "mpl.findpos = function(e) {\n",
                            "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
                            "    var targ;\n",
                            "    if (!e)\n",
                            "        e = window.event;\n",
                            "    if (e.target)\n",
                            "        targ = e.target;\n",
                            "    else if (e.srcElement)\n",
                            "        targ = e.srcElement;\n",
                            "    if (targ.nodeType == 3) // defeat Safari bug\n",
                            "        targ = targ.parentNode;\n",
                            "\n",
                            "    // jQuery normalizes the pageX and pageY\n",
                            "    // pageX,Y are the mouse positions relative to the document\n",
                            "    // offset() returns the position of the element relative to the document\n",
                            "    var x = e.pageX - $(targ).offset().left;\n",
                            "    var y = e.pageY - $(targ).offset().top;\n",
                            "\n",
                            "    return {\"x\": x, \"y\": y};\n",
                            "};\n",
                            "\n",
                            "/*\n",
                            " * return a copy of an object with only non-object keys\n",
                            " * we need this to avoid circular references\n",
                            " * http://stackoverflow.com/a/24161582/3208463\n",
                            " */\n",
                            "function simpleKeys (original) {\n",
                            "  return Object.keys(original).reduce(function (obj, key) {\n",
                            "    if (typeof original[key] !== 'object')\n",
                            "        obj[key] = original[key]\n",
                            "    return obj;\n",
                            "  }, {});\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.mouse_event = function(event, name) {\n",
                            "    var canvas_pos = mpl.findpos(event)\n",
                            "\n",
                            "    if (name === 'button_press')\n",
                            "    {\n",
                            "        this.canvas.focus();\n",
                            "        this.canvas_div.focus();\n",
                            "    }\n",
                            "\n",
                            "    var x = canvas_pos.x * mpl.ratio;\n",
                            "    var y = canvas_pos.y * mpl.ratio;\n",
                            "\n",
                            "    this.send_message(name, {x: x, y: y, button: event.button,\n",
                            "                             step: event.step,\n",
                            "                             guiEvent: simpleKeys(event)});\n",
                            "\n",
                            "    /* This prevents the web browser from automatically changing to\n",
                            "     * the text insertion cursor when the button is pressed.  We want\n",
                            "     * to control all of the cursor setting manually through the\n",
                            "     * 'cursor' event from matplotlib */\n",
                            "    event.preventDefault();\n",
                            "    return false;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
                            "    // Handle any extra behaviour associated with a key event\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.key_event = function(event, name) {\n",
                            "\n",
                            "    // Prevent repeat events\n",
                            "    if (name == 'key_press')\n",
                            "    {\n",
                            "        if (event.which === this._key)\n",
                            "            return;\n",
                            "        else\n",
                            "            this._key = event.which;\n",
                            "    }\n",
                            "    if (name == 'key_release')\n",
                            "        this._key = null;\n",
                            "\n",
                            "    var value = '';\n",
                            "    if (event.ctrlKey && event.which != 17)\n",
                            "        value += \"ctrl+\";\n",
                            "    if (event.altKey && event.which != 18)\n",
                            "        value += \"alt+\";\n",
                            "    if (event.shiftKey && event.which != 16)\n",
                            "        value += \"shift+\";\n",
                            "\n",
                            "    value += 'k';\n",
                            "    value += event.which.toString();\n",
                            "\n",
                            "    this._key_event_extra(event, name);\n",
                            "\n",
                            "    this.send_message(name, {key: value,\n",
                            "                             guiEvent: simpleKeys(event)});\n",
                            "    return false;\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
                            "    if (name == 'download') {\n",
                            "        this.handle_save(this, null);\n",
                            "    } else {\n",
                            "        this.send_message(\"toolbar_button\", {name: name});\n",
                            "    }\n",
                            "};\n",
                            "\n",
                            "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
                            "    this.message.textContent = tooltip;\n",
                            "};\n",
                            "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
                            "\n",
                            "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
                            "\n",
                            "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
                            "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
                            "    // object with the appropriate methods. Currently this is a non binary\n",
                            "    // socket, so there is still some room for performance tuning.\n",
                            "    var ws = {};\n",
                            "\n",
                            "    ws.close = function() {\n",
                            "        comm.close()\n",
                            "    };\n",
                            "    ws.send = function(m) {\n",
                            "        //console.log('sending', m);\n",
                            "        comm.send(m);\n",
                            "    };\n",
                            "    // Register the callback with on_msg.\n",
                            "    comm.on_msg(function(msg) {\n",
                            "        //console.log('receiving', msg['content']['data'], msg);\n",
                            "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
                            "        ws.onmessage(msg['content']['data'])\n",
                            "    });\n",
                            "    return ws;\n",
                            "}\n",
                            "\n",
                            "mpl.mpl_figure_comm = function(comm, msg) {\n",
                            "    // This is the function which gets called when the mpl process\n",
                            "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
                            "\n",
                            "    var id = msg.content.data.id;\n",
                            "    // Get hold of the div created by the display call when the Comm\n",
                            "    // socket was opened in Python.\n",
                            "    var element = $(\"#\" + id);\n",
                            "    var ws_proxy = comm_websocket_adapter(comm)\n",
                            "\n",
                            "    function ondownload(figure, format) {\n",
                            "        window.open(figure.imageObj.src);\n",
                            "    }\n",
                            "\n",
                            "    var fig = new mpl.figure(id, ws_proxy,\n",
                            "                           ondownload,\n",
                            "                           element.get(0));\n",
                            "\n",
                            "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
                            "    // web socket which is closed, not our websocket->open comm proxy.\n",
                            "    ws_proxy.onopen();\n",
                            "\n",
                            "    fig.parent_element = element.get(0);\n",
                            "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
                            "    if (!fig.cell_info) {\n",
                            "        console.error(\"Failed to find cell for figure\", id, fig);\n",
                            "        return;\n",
                            "    }\n",
                            "\n",
                            "    var output_index = fig.cell_info[2]\n",
                            "    var cell = fig.cell_info[0];\n",
                            "\n",
                            "};\n",
                            "\n",
                            "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
                            "    var width = fig.canvas.width/mpl.ratio\n",
                            "    fig.root.unbind('remove')\n",
                            "\n",
                            "    // Update the output cell to use the data from the current canvas.\n",
                            "    fig.push_to_output();\n",
                            "    var dataURL = fig.canvas.toDataURL();\n",
                            "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
                            "    // the notebook keyboard shortcuts fail.\n",
                            "    IPython.keyboard_manager.enable()\n",
                            "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
                            "    fig.close_ws(fig, msg);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.close_ws = function(fig, msg){\n",
                            "    fig.send_message('closing', msg);\n",
                            "    // fig.ws.close()\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
                            "    // Turn the data on the canvas into data in the output cell.\n",
                            "    var width = this.canvas.width/mpl.ratio\n",
                            "    var dataURL = this.canvas.toDataURL();\n",
                            "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.updated_canvas_event = function() {\n",
                            "    // Tell IPython that the notebook contents must change.\n",
                            "    IPython.notebook.set_dirty(true);\n",
                            "    this.send_message(\"ack\", {});\n",
                            "    var fig = this;\n",
                            "    // Wait a second, then push the new image to the DOM so\n",
                            "    // that it is saved nicely (might be nice to debounce this).\n",
                            "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._init_toolbar = function() {\n",
                            "    var fig = this;\n",
                            "\n",
                            "    var nav_element = $('<div/>')\n",
                            "    nav_element.attr('style', 'width: 100%');\n",
                            "    this.root.append(nav_element);\n",
                            "\n",
                            "    // Define a callback function for later on.\n",
                            "    function toolbar_event(event) {\n",
                            "        return fig.toolbar_button_onclick(event['data']);\n",
                            "    }\n",
                            "    function toolbar_mouse_event(event) {\n",
                            "        return fig.toolbar_button_onmouseover(event['data']);\n",
                            "    }\n",
                            "\n",
                            "    for(var toolbar_ind in mpl.toolbar_items){\n",
                            "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
                            "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
                            "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
                            "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
                            "\n",
                            "        if (!name) { continue; };\n",
                            "\n",
                            "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
                            "        button.click(method_name, toolbar_event);\n",
                            "        button.mouseover(tooltip, toolbar_mouse_event);\n",
                            "        nav_element.append(button);\n",
                            "    }\n",
                            "\n",
                            "    // Add the status bar.\n",
                            "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
                            "    nav_element.append(status_bar);\n",
                            "    this.message = status_bar[0];\n",
                            "\n",
                            "    // Add the close button to the window.\n",
                            "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
                            "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
                            "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
                            "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
                            "    buttongrp.append(button);\n",
                            "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
                            "    titlebar.prepend(buttongrp);\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._root_extra_style = function(el){\n",
                            "    var fig = this\n",
                            "    el.on(\"remove\", function(){\n",
                            "\tfig.close_ws(fig, {});\n",
                            "    });\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._canvas_extra_style = function(el){\n",
                            "    // this is important to make the div 'focusable\n",
                            "    el.attr('tabindex', 0)\n",
                            "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
                            "    // off when our div gets focus\n",
                            "\n",
                            "    // location in version 3\n",
                            "    if (IPython.notebook.keyboard_manager) {\n",
                            "        IPython.notebook.keyboard_manager.register_events(el);\n",
                            "    }\n",
                            "    else {\n",
                            "        // location in version 2\n",
                            "        IPython.keyboard_manager.register_events(el);\n",
                            "    }\n",
                            "\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
                            "    var manager = IPython.notebook.keyboard_manager;\n",
                            "    if (!manager)\n",
                            "        manager = IPython.keyboard_manager;\n",
                            "\n",
                            "    // Check for shift+enter\n",
                            "    if (event.shiftKey && event.which == 13) {\n",
                            "        this.canvas_div.blur();\n",
                            "        event.shiftKey = false;\n",
                            "        // Send a \"J\" for go to next cell\n",
                            "        event.which = 74;\n",
                            "        event.keyCode = 74;\n",
                            "        manager.command_mode();\n",
                            "        manager.handle_keydown(event);\n",
                            "    }\n",
                            "}\n",
                            "\n",
                            "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
                            "    fig.ondownload(fig, null);\n",
                            "}\n",
                            "\n",
                            "\n",
                            "mpl.find_output_cell = function(html_output) {\n",
                            "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
                            "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
                            "    // IPython event is triggered only after the cells have been serialised, which for\n",
                            "    // our purposes (turning an active figure into a static one), is too late.\n",
                            "    var cells = IPython.notebook.get_cells();\n",
                            "    var ncells = cells.length;\n",
                            "    for (var i=0; i<ncells; i++) {\n",
                            "        var cell = cells[i];\n",
                            "        if (cell.cell_type === 'code'){\n",
                            "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
                            "                var data = cell.output_area.outputs[j];\n",
                            "                if (data.data) {\n",
                            "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
                            "                    data = data.data;\n",
                            "                }\n",
                            "                if (data['text/html'] == html_output) {\n",
                            "                    return [cell, data, j];\n",
                            "                }\n",
                            "            }\n",
                            "        }\n",
                            "    }\n",
                            "}\n",
                            "\n",
                            "// Register the function which deals with the matplotlib target/channel.\n",
                            "// The kernel may be null if the page has been refreshed.\n",
                            "if (IPython.notebook.kernel != null) {\n",
                            "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
                            "}\n"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Javascript object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/html": [
                            "<img 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CUhAAhKQgAQkIAEJSEACEpCABCQgAQl0mIACYIcH165JQAISkIAEJCABCUhAAhKQgAQkIAEJSEAB0DkgAQlIQAISkIAEJCABCUhAAhKQgAQkIIEOE1AA7PDg2jUJSEACEpCABCQgAQlIQAISkIAEJCABCSgAOgckIAEJSEACEpCABCQgAQlIQAISkIAEJNBhAgqAHR5cuyYBCUhAAhKQgAQkIAEJSEACEpCABCQgAQVA54AEJCABCUhAAhKQgAQkIAEJSEACEpCABDpMQAGww4Nr1yQgAQlIQAISkIAEJCABCUhAAhKQgAQkoADoHJCABCQgAQlIQAISkIAEJCABCUhAAhKQQIcJKAB2eHDtmgQkIAEJSEACEpCABCQgAQlIQAISkIAEFACdAxKQgAQkIAEJSEACEpCABCQgAQlIQAIS6DABBcAOD65dk4AEJCABCUhAAhKQgAQkIAEJSEACEpCAAqBzQAISmCsCu+66a7j99ttjn2+99dbA/7tkf/3Xfx1+6Id+KHbpzW9+c+D/82QPPfRQ+NCHPhQ+85nPhBtuuCE89thj4dlnn40I/uqv/ir84A/+4DzhGKqvv/M7vxPe8Y53xH28/e1vD/x/nmzt2rXhgx/8YDj99NPDLbfcEr785S+H5557LiI488wzw0knnTRPOCbW17POOiucfPLJ8Xgnnnhi4P/a9Al4bZ3+GMxCC9r4jMW1+uyzz/baPQsTyDZKQAJjJ6AAOHbEHkACEsgJPProo+Gzn/1sXFRfcskl4f777w8PPPBA2HjjjcOWW24Z9t5773D44YeHb/qmbwpHH330yOG18eF0lJ2cZwHwtttuCyeccEK48847S5EqADabafMsAF588cXh1a9+dXj44YdLoSkANptLTbZWAGxCazLbem2dDOcuHKWNz1gKgF2YWfZBAhIYFQEFwFGRdD8SkEBfAk888UR4//vfH9773vf2XFQXd7DXXntFr6Pv/u7vDkuWLBkJ4ToPp3iJ/c3f/E083qyJRvMsAL7mNa8Jn//85+O4vfjFLw6vetWrwo477hhe9KIXxb99//d/fzjyyCNHMo9mbSeDiHmDfGfWuJS1Fy+//fbbL1x33XXx4y222CKccsopYdtttw0bbLBB/NvP//zPhz333LML3W1dHxQAWzckwWtr+8akrS2q84w16bYrAE6auMeTgATaTEABsM2jY9sk0BECd9xxR/Tou/LKKxf1aOeddw4HHXRQ2GabbcLXvva1cM8994Qrrrgi3HvvvYu2e/e73x1+9Vd/dSQ06jycKgCOBPVEd8Lc2X777eMxN9lkk3DVVVcp0GQjMIiYN8h3JjroYzrY+eefv+B9zLXpmmuuCVtvvfWYjuZuiwQUANs1J7y2tms82t6aOs9Yk+6DAuCkiXs8CUigzQQUANs8OrZNAh0gQOgQobwsIjA8+d70pjeF3/iN3wj777//ej3E+4bwuz/90z8N//AP/xDzt006/9gsC4AdmDIDdeF///d/w+tf//r4XTz/CDHXXiAwr2LeIHPgz/7sz8JP//RPx6/+6I/+aPj4xz8+yG78zoAEFAAHBDemr3ltHRPYju62jQJgR1HbLQlIQAIDEVAAHAibX5KABOoQ+MpXvhKOPfbYKOhhm266afinf/qn8IY3vKHO18OXvvSlKBZ++7d/+0QLECgA1hqeVm2EWPx93/d9sU2E+v7t3/5tq9o37cYoANYfgd///d8Pv/VbvxW/8P/+3/8Lv/u7v1v/y245NAEFwKERjnQHXltHirPzO1MA7PwQ20EJSGDGCSgAzvgA2nwJtJnAO9/5zriATvYv//Iv4Tu/8zsbNfnxxx8Pl19+eRQSJ2UKgJMiPbrjzHPuwzoUFQDrUHp+G1nVZzWOLRUAx0F18H16bR2c3Tx+UwFwHkfdPktAArNEQAFwlkbLtkpghgg8+eSTYZdddolVfrFv+7ZvC//+7/8+sh6U5XRZt25dLNrxn//5n4G8gxz75S9/eXjkkUcWjtvv4TT/rKqh/cKSn3nmmfDP//zP4X/+53+i9yPtIJR52bJl4aijjooiKF6Q/Qqb9FoEE471d3/3d3G/9BeB9E/+5E/CL/zCL8QmN12sEXL9b//2b+FTn/pUuPDCCxfyL1LwgIIZjBsemFVFWMrG46GHHorhk+z/1ltvjW0lT9/JJ58cfvEXfzEccMABVZj7fp4zqtpRPl5NGRHGvttuu8VDMKf5f9F6bcM4/fmf/3k455xzwtq1a2N+QopHMP4UknjpS19a1fSFzx977LFYnIZCJ+Q4pHo2c22rrbYK++67b6yA/B3f8R2LQuvzcak60Jvf/OY4f5I1FcJoy9///d+H//qv/wqXXXZZuO+++8JGG20Ux/y4446LxXxOPfXUqmaEsnMUdh/96EfDf//3f4fbb7899nunnXaK+/vlX/7lOC7DWD4nqvbTqzDQOPt/8803h7/8y78MnP+wePDBB2P+VF6O1DWuv8wPjGrrqchJ1ffhzfznWsF4ct1hzuVGdXfaxjlJm2gv8xWvb657RxxxRPjWb/3WeC1JhVR6HbeOAFhnm+L+82sYfakyGDMvTjvttMiK6zj92WGHHeI17Id+6IfCYYcdVrWbyI1z4l//9V9j5Xv4PfXUU7HqPbkl2d+JJ54YC22sXLmykk/lAb++wf/93//FOfOZz3wmXH311fF6QXEkCiNxXehXFGnQa2vdtrFd2Ys27hmf+MQnwic/+cl4z/jyl78cz21yCL/tbW9bLxcn9/kPf/jDcYy4BsOauf293/u9MYyf+drLxnEfaNJ/tuWaccYZZ4QvfvGL4aKLLgrXX399gAFzlXOMc5x58SM/8iPhZS97Wd/dj/seRJ5mrn3/+I//GKMzGBuu7YcffnhMlZCu7eMQAGHDvW/16tVxXjC3ebbj/EnXFzhxD2SOF61pDkCOk+7bpK+hENQee+wR72Gc94xFnftj2TYUxGPuEQ1z4403xvHmGkDV+d/8zd9c717Gecu97z/+4z9i37l27L777vFazr2vzjPEtddeG8+Rc889N14L7r777rgf+sX5dfzxx4e3vOUtsfiVJgEJdJ+AAmD3x9geSmAqBBCpfuAHfmDh2KtWrRqpF1/xgY4FKA9mDz/88KL+br755hMVAFk48TDMArifIQQijLEYK7PiApcFJP3jIbBogwqAPHx+13d9VxRr+hmLUrw3eQDuZcXxYOHFvu+6667Sr1CZ9yMf+Uh86BzUBl2kjmPhV1x88aDOwz9esIi/ZYao8oUvfCE+zFcZixHyZhbnd9n3PvvZz4bXvva18aNJCYAXXHBBXHRXzXsWiYQUUlyjlxUXkAhKiAWc42XGgg/BIOWArGJZ9vmwAuA4+4/gi1jMgi23FStWNBIAn3766bDddtstXA9ZVNcRsP7gD/4gLkyxb/7mb45iVm68PPie7/mewP6r7OCDD47XMMa4l9UR9+psU9x/EwEQUYk+95pz7Jv9cU3mOrbxxhuXdoeCVgifa9asqUITPyd3KTlMh7VPf/rT8dqacu/22h/jxkual7zkJYs2GfTa2qTdRQGQF0K8cLrzzjtLd4Pgz4uUNHdo98/+7M/2nHdEDXzuc5/rKZCM4z7QpP/085BDDolifpUhdCG89XuBMs57EPfxb/mWb4kCdi/juYfzZq+99oovaTDug/3O9ap+f/WrX41C7sc+9rGqTePnnLPcc4tWVwBEQObl5Ac/+MEoJpfZPvvsE69hvOR9xzveETfp9UK4KACSpoTrAQJqmfG8iiB86KGHxo95iYxQ3+s6hNh99tlnxwr1vYwXztwfq4zrGfeZ9773vYHnM00CEuguAQXA7o6tPZPAVAmwMEreRFT7TQ+Eo2pU/kBHzi4etJI3FG+BeWDGAwlxKz92v7fTPMTxMM7b+OQd88pXvjLwwFe0173udYGf3HjIQgShHRjeIgh9HJMHqhtuuCEuBHmoxVjQsAgve3jLF2D0Z7PNNgss6nhI4407Hl88oPIgidDKgxtWd1HDG2G8TpKHJt898MADAwt0jgE3vMyS0UYWXzzcl1k+HhRwwVuDt/S8neftMt4MLCJ4uMU7FMMTCGGYIjGDGALmBz7wgfhVxotxwxgvxi23fLzqMkrfH8QDkMVtWhzAFLaIoohZl1566ULT+DuLqn6eKj/3cz8Xi+IkYy4xB/AkZI4xhuw3eSayOEl5NuEDJ7w7mWsY38Ujq2jM1ZRHkc/qeDiwHfPiG77hGwKeDcnYP0V+yANKVd1cGGQOMe69RMD8HMUb6Md//MdjlXCuI8wVzgUWlpwj6VxCwOBcSJ6aTecTAh4vLbAqVkXPqXH2/4/+6I8WKqDzsuCYY44JLBLx4MBzpK6wlHggCsEUw2uYlwdVxjhSCRnDi+2Nb3zjoq8gTv/kT/5k/Nvy5cujFwlCI2PCNYBrDXM+LajZhvla9CJMO60j7tXZptivugIgAsD73//+ha/TTs4NvHQQYbk25gt4xHa87IqejcxZvKhz0QSRix88b9gXAh2V7/EKxEYhAPKyhvsQx8e4XuCB+4pXvCKOB15AzJ9knFNcl7mWJBv02lo1l/LPcwGQPJuILng7ca/hngcj2sH5lV6icG2HPd5TnIcYf0PIRoTF4/rKK69cOEy/Ij7juA806T/3LO7jGN6gnGd4Y+FdxnWTaxzXziT8b7jhhlHs4RpQZsX71KjuQVxnOCbeicm493CNT/e05InMNYX7z6gEQPaX7vEcm2sgx+X5jusJz2tcm1LbhhUAi/da7jWnnHJKvFdxzpx55pnxPsd9BkE0XSfqCIBcV3iGY04zt3lmYr8IwZx/jDlG32666aZ4zcSrkedJ5gXnKXMDD778ul9V9IxrF/c35g/XZsaO43Nd4BmZ54L8RS3XcgphaRKQQHcJKAB2d2ztmQSmSoDFRlr0s2Bk4ThKywUnHmxY7LCI+LVf+7VFYgpeKYRdJqsTnjJIDkAeyngwTSIID67kP1y6dOmibt9yyy2BUEsEEAzhhNC5ouULXPqH0IFYhPcUv3PL+1hnUcODJm1l4YnxEEroJiEoueF5xEKSRRnGW2kWJGViVT4e8Ka9iBc8UNP+ZDzsIsalBTShdDz8Dmt1+p2O0WRbvtNUAGQhykM7nn14bRTFtqJQTGhT7i2bs8jFFf7O2/w//uM/jkJL0WCKVwyecMWxrCvm5fus8x08EpmPaQGBlyieEUXPMuYt4lMSfwnpI5y3zPJzlLnE/IEDczEXcTjnWCClY/PSgZDHYa1Ov9Mxxt1/+o6w9KEPfSh6Fuf9L17b6vQbAYFzFUOkI5y4n7cHYlfyRmExjEdbLhSxH7xUGAtC0rjulxliBgtLPLIwQhqTEFncvo64V2eb4n7rCIDMH9qGsdh+z3veE374h394PQ8/hAAEqDT33v3udy8Item4pKLA2wcjVBJhBEGwzOCHAI0HXJk4X2ds2YZ7Li8cEPowzkOuQSz6kyGmIVz8yq/8yoKwhicdAlyZNb1e1m1rfp9N18zf+73fi/fw/J5x3nnnxfskIacYL/ze9a53xXkLM64lufGyhPsOxpjDpOzFQNN+1bkP1O072yGScS3npQvjVBYaTxg9TPDKwhhHhMOybfP2jfIelI8T5z7nLdfi3PBkp2Abzwo8H6SXoMN4ALIvzhueJRjrv/iLv4j3ybJ0JAjoRFTw0iGdv3n76ngA8rzD/SQZ5z3iYx56jRjK/jm3uTclr+c6AiBjwrMXHo08G+Vet4wpLy2TMM+5iciNQIcYR1vyfuN1TYRFegHG9ZAXumXGy1iu4fSNa3jREFIRJulXeiHMSwJeGmgSkEA3CSgAdnNc7ZUEpk6Ah8D0cMKCmgekUVoxtJGwjxSm1u844xIAeXhLQlZVW8iFhxcWnjEYolpxYVgMwWKxjkceb4f7WZ1FDXl8eKDEGCfeJhPmW2a8HebtfxrLXmJVcTzIWfNjP/ZjpftEqCK3EQ+ePNSyiOZBfxir0++0/ybb8p06C798G20VWtgAACAASURBVL6D1xCeKHgNlRkP+GlRhwcRYbtFQ1zizX9a+P7ET/xEDDccxJqIWmn/db7DeZ2q5OJVgKiMp16ZIYAgcCRDjMLTp2j5Ocr8QCBPIc3FbfG8+sZv/Mb4ZxZqMMvFg3GzGnf/aT/ifHHBPUi/+A7nHHzJnYYhyBXF4nzf5JhCpMC4ZrAIH9QQBRA6OC8QEljs4vlUtDriXp1tivutEgA5z5i75IxFcOB6XjY/0365frOwxkOL8x2m+aI+Z4cwj4A7buPlUqqAzgsIvA85L8sM789f+qVfih8hKOF1NAqhrG4fc2GJ7/R7TvjDP/zD6FWejPYyPr1ED8QOBB2M7yIqFm0c94G6fW+6HeI5L0EwrocIokUbxz0Iz7o8AqLftYi8eXj75ykvhhEAEaWSuMv1j2MPanUEQJ7J8CDFSHWAyFcmNnIdY3/0N1kdAZBtmfM8f5UZL84QUXND4M698vPP8IxPodHDPBukfeIliLcgxotGPIk1CUigmwQUALs5rvZKAlMlwFtrwtSS4W2QQlRH1bD8gY6wEB5+6yz8xyEAInrgdYER3khISlUOlfxhr8z7oigA8hY4hdn1Y1hnUZNCQthPP8+PdJyf+qmfWhCe+G5Z2GE+HniE5WFYZe1F8CTUEsODKIk4g86POv1O+26yLd8ZRABENEmL67I+IR6khNuIB8nLMt8Wr6Jf//Vfj39CCGQxlnuzNmFVR8wr7q/qO4hJCJwpz1hVn9k/3p9J7CShOl4ORcvP0X6egnyv2AZE8mGLy1T1O7V3Ev3HG4yF2SgNIQVRBMOjBlG/zFjIk6YgeaUguOCxO4zh+ZLEGDxAi95b7LuOuFdnm2I7qwRAvH1SMaV+Hor5fll487IDo8hKLnDzAgThD0NMIGRwnIZwyYui5JVUbE/x2IwvL2LwPsS41uBZV7Sm18u6fcwFQMJ+EVB75VJESMpzpVYVFctfclF4Bu+wYftV5z5Qt+9Nt+NemV4Scl9Jony+n6IAWHU9rnMP+tVf/dXoBYv1uvfnbeB6klIp8PdhBEA8V9OLj7rpCnpxrRIAeSmZIiu4ThCm2y/nMeIfOSaT1REAmdtEQJAWpcxSUY50/vJyoV/ORbyQCU/GeIGbxMumcyvfnmcS5kWvZ5Jh9u13JSCB9hBQAGzPWNgSCXSGAB5deYgiISNlYRnDdDh/oCPcJ88T02+/4xAAWTRRoAHrlYOm2CZCO1LuPxKB53nh2DZf4PJAikBUDCcu62fVYo3QMDxCUn4oQvySeNmLG21LHoIImySkLlaey8cDjzDCn/tZvnAmZOtnfuZnhpkOtXMfcpAqRsWG1Fn4FRdfLGYRT3oZ4hEMU0gs3kfFKo+ILcwDrMqrtApeXVEr30/VdxC6yVuF4ZHDnO6V1y3tl9AlFuQYYkXKfZYfNz9H8WZKub569RHvwBRaOgoxuarfqR2T6P/73ve+mJR+lJa3m0qahPWWVc4kp2YqSMH1nJDFqgq+iFB4NCMqkZ+L603uEZTn6ux1nagj7tXZpsisSgDMvcbq5uIjtQWheBjjxHgl45xN10E8thA867ykGnSs8QxLhXA4DxHmq45He9/61rfGQyIwMXZFa3q9rNv+XADsl6sv7Y/rI97zGCkFKGDSy/BcT6HUvYSUpv2qcx+o2/fidniVIfTzMpFx436QvO7Zlv8nz05En5TvNt/POO5BuVccBT54GdjPCAXOC5UMIwCSJgWPQox7KSIo94xBrEoAJMUCL0OxOkIn2+Etm/Lu1hEAq3L1sc883yqh7um5sqzPhOsmMZFnwzrFZMhDjVBIWDzPcYiNebETvPTTC5+qZ5hBxsHvSEAC7SCgANiOcbAVEugUgUl7APbLoVYEOw4BkHARhAeMh/OU2LvfoPLQlRItE9abF+Pge/kClwdNcgfWsapFDfmUUm4XFlQ8BFYt6lnAkzsmLb7YRzERef6AnReh6NVmhFKqi2LDilvso6rfeTuabMv36iz88m2Klad7McBzNT1sk4utWBGacMIkEJYxrzMf0jZ1Ra18n1XfYRGewpPwHEieRP3ahdiUL+J4WVAMk87P0ToCNV4ieItgowiXrep36t8k+t8rTLrJ2Jdty0uHlLgfL0y8MYuWF3LCEwiP1F7G/MWDDE+rOtWA2U+vFzd1xL062xTbWiUAco1L4faIzmX5sor7pN+pKjI5EPNqm4idLOiTAEr+NpgiBuJ5V3XdbTrGCAa/9Vu/Fb+Gpy3h8VWWh/3hoYQXUjHssen1suqY6fNcAMwr2ff6PiJ0yrlYdV0gnDnlPSQvJR5dRWvarzr3gbp9T9txfec+SHhvmRd42f6YOyl/b/75qO9BPKMQqp+KUyAc9UoVktrBsx8vGJOoNIwAiCjKGKaCIohchLjjNYxIV/bSohf/KgEQATqlN6j7QjmvrltHACQKJi8uVNZWns2412NVz1EIxCkfM0J/yrtYtl+uBbyM4Lypa7z45T6hSUAC3SOgANi9MbVHEmgFgUnmAOyVE6cMxDgEQPJa9QvVqBoQvOryt/1sny9wm4QBVi1q8sT0CJWpumdVG9k2VUZmwYvomVv+gF0nVLCuyFLVrvR5Vb/z/TTZlu/VWfjl29Stet1vLhZFdDzlBvV+oA+D8K76DouZ5J3Wq5hN2fixcEuVLVnIsqDNLeeC8F1V2XeQoj395lVVv9N3J9F/zs86LxTqnidpO8IDyVGHEX6fXmCkzxkfPJSZhxgh/cXiQ2lbFpXkQCX/YhPrVbSljrhXZ5tiW/oJgHgq4g05jMEAD6jcmCOEbOZeNnyOuEhVT66bFArZe++9hzl0/G5eMbVuJc+iIM8LoaLw2fR6Wbcj+XlL8RXmQz9rcl2oc81u2q86+6zbd7bjfOGFYRLi63631wvBUd+D8ObNc3Ti4d2rcnvedr7Dd7FhBEC+jxcgXq3pOpSOg1iNZyc5OvkcT8GyfH1p+yoB8A1veMOCkE+aAnL0VlleLbyOAPjbv/3b4R3veEff3TZ9jqp6qcHB8vtZVZ/yz8f18qlJG9xWAhIYDwEFwPFwda8SmHsCeRXgcSQUrnqg6zUA4xAAeUuNx8EwVlwgDrLA5fhVixo8pFJIJcIloVJ1LBc5y7ysmo5HXZGlTtvq9DvfTxWj4jHrLPzqbFPcb7+5WAyjLwsRrsumuAjotVgp7q9qjPIQx6L3U7+24fGawpVY4OW5lPhenXM03/+0BMC29L/JPEjbIijjUYV3Gi9r+H8evp2HtvbyOGJfePshULLQxxANCe8nfJ0cWnjtIPimhWqdc6/Ota/ONkUu/RbLxfNtEKYUpEgh+/n3mePMFcKK83DofBuEIMTCXiJrnfbkXkyIuyl3W7/vFoXPMk/kOmNWp33FbZqet02uC3Wux037VWefTTgQwpxyoJLbFR6IWZxPvOzhvEm5hOscu842Te5BzIU8jQVzpZj6o6y/uafmsAIg+6dfVEKmKEWKQigel9zL5DVNVbeLn1c9nxCem8KqCQemUm+V4W2L1y1WRwCsc9+tameTaxrbcs3JizzhYYgXJc9zjC1RIHle4abHr2Lk5xKQQDsJKAC2c1xslQRmnkD+cE8Bg5QrZVQdG/RBpc4iounChBx6KSRnVMneB1ngwrZqUTMJD0CSUzM+/axKXGo6T6r6ne+vybZ8Dy+0lBC811we9eJLD8Db45DVWUA2PV+r5lbduTkJD8A6/a/qT6/PydWVPNaKRYYoWEHOOqyfR0yeqJ+FP2GCKbdp2XHJ90mIHcZClHOxaHWufXW2yfeL8JYXZiq+cCl6O/H/vJDVoIzz7xHiiVfNueeeG3/w/MoFQQQfFuxFQbzusWfZA5CiHZzH/azOvTt9v871eBz3gbpjheCMAMM8rFNxmuJGyVN6UvegNngA5jwJl6b4xjnnnBPPH/JVphQZabtehU+qnhfrXu/y9jT1AJyGAIj4xzUFoyhRKljUa54S4p3yUdd5jqs7391OAhJoFwEFwHaNh62RQGcIkLCaBV6yYXOYFcFUPdD1AllnEdFUUMiTx+N1kULrhhnMpgvcdKyqRU0xByBCU7/QGfbbNAdgnQfHuiJLXYZV/c73k+duI38cHo39jBBHwo2wSS2+OFaeA5CFDyGDg9ogvKu+k3Mk1xmVFKssL37DtlU5AOsIYE3P16o2VvU7fb8t/a/qT6/PyZ2aRBdyeqbcUw899FDYfvvtY+4v8tSRDL6YnzLtk+JOhG9iVVVH2YZrY6pgOowAmF/HEMzwsutnhFvmRZSKAiDfJd9Zyl+IZzReMuM0BEE8wCiGkvK/1T2PytqV5wDEk+zTn/50ZfPz6rLTzAE4DQFwHPeBSuBf34BKuVTMxaoqnbMNY5kqZk/qHjTtHIBVLBH/qCjP+ZNewDKHeWFXvF5VPS/m17Fx5QCctABIoTeeIdJ1nOIyVSHcfJ6uRXWe46rGyM8lIIF2ElAAbOe42CoJzDwBHs7IhZYeJqj8SYL4UVnVA12v49QRAPPk93UWJuR2IUQFq1PprQ6DcQmAxSrAZTnYiu3LBbA6VYDrPDjWFVnqsGKbJgIgnk288ceoIMsiop/lYvakFl+0Z5RVgMk9BHOszkKE7arGKK8my7xA3KuqVE1i82/7tm+L7ahTBbjNAmBb+l/3HClux7UAb70nnngifpTyLVKQgBxyWK+Ko2lf5H487bTT4n/5zcuQfkY+05R2YBgBkJyEK1asiIfaZ599wrXXXtv3uHgNEaKbrEwAJJ8YnkXYKAoT1R2XNWvWLCqqRIXO3Xffve7XF7bLqwCzkCesO/d6LNshxTfIUYhNswpwnftsnXt36mMdD8Bx3AfqDhoFdSiag/H7Xe96V9+vUoQnhXRP8h6UVwGuExo7yirAdVki7lOECoELo5jHD//wDy/6etXzYu6ZPK4qwHXuu1XtLDLpl9Ygz+/Z616b7y+/n/H3Os9xdcfI7SQggXYRUABs13jYGgl0igCiGOJYMgRAhMAmRs4XRKp+VWebPKjUWUSw+GURjH3sYx8Lb3nLW/o2Ofei4IGM9g6Ty4mDjUsAZN884FL9ESOM5X3ve1/f/vFGnAdkDC80vNGK1vTBtUpcajJH2LaJAJiHU+HphBdaPy9IiiSkipqTXHwRevlrv/ZrEQXHvf766xfl62nCKF9w/sZv/MZC7qJ++6gaI0QUKvimhdcHPvCBhfDOXvvNK2a/6U1vWqjem29f5xzNt5+WB2Bb+t9kHhS3ZQz++Z//Of45iV55Jcqq4gx4miE8YZ/61Kd65uDicwol5V51wwiAebVRKmAiApDPqpf9zM/8TPjwhz+88HGZAJifb4QzUzkWr8BJGPkX8bzEBvX2JWSThX7yYiwr1pT3BQZU+UzeU29729sWKrPn2zW5tjZh1fS8bXJdqCMAjuM+ULf/iHmIeliVxxkCPYIwgk66F5SlVKnT52L7qpjmwmMdYYz8wrlHfZ0XOHWZ9duO50quPxhVlZnLuVU9n+RzgWcBcjr3E+E5R/NQ/TbmALz//vvDsmXLIgbSGXCN7Pec87M/+7MBkTdZk+fqUYyh+5CABCZHQAFwcqw9kgTmjgALEYS7lFOEHEckck6hLFVAeChjgUqBgeS9lL5T9UDXa99VD7x8r05y5+L+c28tcvXgSVKspljWJsIzEDnzantsN04BEG+L9IackBk8coqVWFNb8f7DM+SZZ56JfyJsMIUuNXnALva9SlyqmhvFz5ssUqm4jKcahTWwfp5LCH8IgMkmKQDywI4XLZ5aGMUVPvKRjzRFE7f/xCc+sSBk9xJeBhkjFj6EYGGIGIgJvcJFiyx7VRmsc47mbW0qJFQBbDI329D/qv70+zwfE4oPIOax8E3hf4gO/a5j+aKx37xCxKBSZ7oX0KZhBEC+n1cm54XNj//4j5d2lWsY4gXX2mRlAiAVcJl7qYIpnuB4E1WlSGCfeLpzDc897vgbBW+qjPMc4S61D+GRIlqDGEzxWMYojEBOxl7VjVnsM34Yod6IHmUVt5tcW5u0uel52+S6UEcMG8d9oG7///3f/z0+12Dkl73uuusCQnaZ5S8k+XyS9yDalVchLysAltqMKMY5nue1HEYApFBUXpioH9vcU/HjH/94oCBObnWeF/NCZ0QI4K1edu4zb/Amzl+EtlEAZBy4JqUKyv0EPVIq0CfChpMpANY9m91OArNHQAFw9sbMFktgpggQVobXGOGBaaFB3jXe0OYPlqlTLMxYtLA44WGTh5iyh6s6D3RloOosIhApv/u7vzt+nTAzPPwQyvoZ+c/oZxJr9t5779gHQoLLjMUWxyH5PomZc5GJ7ccpALLQJBQveX6w+CSZPyJmblTFo1JhGjvy4JF4m6qhRWs6Hk1EljoTvukiNc/5QzJ2REDCiPJ5yPxDdOOhOHnVTHLxRVuYH3lFQipqk0MND6WiXX311dFjFa+svPIf2+HxiQiCMd4s7KqKHNQZI8QLvF3xosSY93iUURgnN+Y6zFMVx355r+qco/m+mwoJVfOpTr/TPtrQ/6r+9PucxSxenHiLYIRnJ0+aOtXbP/e5z8UweozFMp5NFKPIhTCudYwRi0yqiKY5MKwAmHuYb7HFFuF//ud/At6LuRHezwsLxP50DvN5mQDI3/PrCP/nXHrve98bw4yLxj64HpJLjpcqiKW5FyLh07z0ou+ve93rSiuokl8RD/PPf/7zcfdUlL/hhhsGHlLut9yz0n2IexLXsdybiXsq3pB4f6cFP0LgBz/4wdLjNr221m180/O2yXWhjgBIO0d9H6jbd64bXMNT+D3PRDwvMI+TIdyQMxNBKz9vJn0P4vwhZyGGRywvk2hvbjwr8MyE6M3zQXphOIwAyHWYomUUr6C6L976ReO8xnMZ712M6w7HzKsX8/c6zydcKzhPk/GSFK/2/JzGS5c5Q7uonpuuKW0UAOlH7uHN+cN9mGe/3Kj4zjWI+ZbPMwXAumez20lg9ggoAM7emNliCcwcAR7GWfAXiwTwQILnGV4SLEQIJaQyYgp1SR1lAfbWt751Ub/rPNCVgaqziOBBiIfN9HDO4onj8XCe3ggjsBRFFhJ1f9d3fdfC9zg+D6K8naaPCG8stBHe1q5du9A8Fq6TFAA5MDmzeOObFv78jYVjEm4YhyQQ8hmhJHg14lVSZk3Ho4nIUmfCN12kMicPOOCABTGCRQs8GGvGn7f7LM5ZUCDQJo+CSS++6PtP/dRPLfL8o03MKcaCBRljiJdTCgvDc+ENb3jDImws+vHuoU8YIiDzl3mZ5jT7ZP4mqztG5FcjF1w6X9gfXqMIqsx5xEe8mpIhciAG9UpIXucczTvXVEiomk91+532M+3+V/Wn6vNi6Ffanhxpdby183Of7zLPeFmAwMy4cy5xfccz9Od//ucXQh+HFQDx2GOO3X333bHJzDvC8hDrnnrqqfgiCaEbQ6DDoy9ZLwGQz/Ocrmm/XCv4wRsSARPBm3MueQuyHWJELhbkXLi+8H3OWbiw7e233x4FxOQxxXnNPSQJqlXj1utzFvkINEncw7MMzyw8zWg71/H8/sOLARb7vcKdm15b67a76Xnb5LpQVwAc9X2gbt/ZLvce5v94pXPd5DwhfyMvARkv5gWh+Kmo2qTvQXjiMUcQ8vNrOH+jbTwncC5giP/cf5jb2LACILlr0znI/OUc4p6FwMh5z7UlvVBgu9/8zd+MgmDR6j6f8LKNl27JON8R8rlXcTzOE+5zXOPwEqQSPEY783Q36ftN7yV125n23y8HINuQ1w/PxlQtme15KcB1iHsz+UcZIwwRkJcPeOZjCoBNzma3lcBsEVAAnK3xsrUSmFkCeCSQcJx8c/miqV+HEKR4SObt76APdMXv1V1E8NYd7688nCXfV683vjwM84aYfFd1jPbwwFz0mBqnB2BqFw97vLVPD++92stinrfEPID3sqYPrk0fjKtYDrJIxeuP3EFJuCoeg4d/hAP6n0LjJr34Sm3CE4EFRgrn6cWDB3z6VRSn2Z7wTjy8ck+ofD9FQabJGCFkIDrggdTP8IjF27RfNcK652g6TlMhoWouNel32tc0+1/Vn6rPc+/QtC2hd4gQZd6+xf3xwgbPmTy8t7gNQt0nP/nJ6E2dhLhhBUCOwTEpPJKKTRWPi+c29x1E9KrFcv5drnd4yCVxsYohXjUIwXgFJUM8rVOJl+15wYL3bipOVHW8qs85Li8tii/Tit/DQwiPLqqF9rJBrq1V7ePzpudtk+tCXQGQdozyPlCn32kbBFq8zFLIdtl3eenIPYjng2neg+688844N/s9K3BeE4qPuDQKARBPdzwg6xjnOeJfmQjH9+s+n/C8x0sKPGR7vSTAyx0PQLwiyTeI8VzL9aJoTe8ldduZjlPnmkYuUCI5ej3nsC+8LPEA5lqqAFhnxrmNBGabgALgbI+frZfAzBHAa4O8U6effnoUyfBeIqyCBzjegOO9wVtwPJgQXnpZ0weltJ8miwjejvJAy+Iejw8eoNJDYVVFN0K6eEjE04lFJKIni0OEDx6QeXvOwxZvY8vyzExCAIQJD7wUZyEnEYvzFO7LgpRxIE8RIllVHqym49H0wbhqog+6SGVhg4cpoYz8G48G8u6xeCf3Ev+us5iss02xD03mIt/FE4N+0lbe7CfRA48IwunxYMSDDw+7XkaYMKFmq1atios0hPk0p4cRADkeHgWEGzLv8SBlLiEg4W1IaCZiQ5kwOSyXpkJC1VwadG5Oq/9V/anzOYvaPPQUwSwvmlG1D0RlhCTCv/H05lrJNYT9MicRhxGZ6pynTa99nBcswPGkxpuFaxrhlaeeemoU/lJof53Fct5P+kR/ON/Ik8q9ivOFMDm8tDjn8KxD/CzzjOa8QjAhPBKRFa9rPO/wWEr3Ajzg+T4L9Do5Y6vGIf+ctuI5hhjIec/1gpBkQr5J90BoJ9f4KqszZlX7KPu86Xnb5HrZ9Ho8qvvAIBwYH8Rf5gihweRt476D4IZAyHjV6U+dbYa51pIuADGSkHfOceYXkRIrV66MLz3xAseajFMVL/rEsyKefuSE5v+8BCNnJeIo5yAeesxlXs6N6nmR+yPPfnjLIqLjtcsLUF6YMiZ4+ua5GXsVS2p6L2n6HFX3msZ1kWskz6VEAeAVzLzCY5rzkAroWNPjV42fn0tAAu0koADYznGxVRKQgAQkIAEJSEACEpCABCTQMgKIZ6kQCC+LU47dljXT5khAAhJYj4ACoJNCAhKQgAQkIAEJSEACEpCABCRQQQAvOvIFE8ZN9AoRHnjXahKQgARmgYAC4CyMkm2UgAQkIAEJSEACEpCABCQggakRILSftCjkbsbe+MY3xhzJmgQkIIFZIaAAOCsjZTslIAEJSEACEmhMgAUbnhrkd6zKZdl4535BAhKQgAQ6QYD8uHjzUdCEPJ9Fw/OPYh+f+tSn4kfkIiTPs+G/nRh+OyGBuSGgADg3Q21HJSABCUhAAvNDAOGPghDPPPNMLFBCqBYiIIs2fhQD52cu2FMJSEACVQRS0Q7uD/vvv38sIESxEYoaUSSJwnUUQ0lG5eF3vvOdVbv1cwlIQAKtIqAA2KrhsDESkIAEJCABCQxDIAl/eP3lAiCVDxH9kvDHIi8XBPUQHIa635WABCQw2wTyqr39ekIV77e//e3hbW9722x32NZLQAJzSUABcC6H3U5LQAISkIAEukcAwQ8PDUTAJATi/cdP8vpLImDxNzSSd2AuDuaiYfeI2SMJSEACEoDA448/Hj7zmc+E008/PVx55ZXhvvvuCw888EC8fyxdujTsueee4ZRTTgk/+qM/GpYvXy40CUhAAjNJQAFwJofNRktAAhKQgAQkkAgg/CWPvyT88Rvxjr8TBoyoh6XP07/5nUS+okiYPit6CxpC7NyTgAQkIAEJSEACEpg1AgqAszZitlcCEpCABCQggQUxD48/BMCi8IdIlwqAIAAWQ3yTl2DZ71wUzMXB9Hd+G0LsJJSABCQgAQlIQAISmCUCCoCzNFq2VQISkIAEJCCBBWEP77483LcY1ps8A8sEwDKM7AtLgmIuDpaJgvnxkiiYwohTfkFDiJ2wEpCABCQgAQlIQAJtIKAA2IZRsA0SkIAEJCABCVQSSF5+xTx/Zfn8kpCXQoCHKfJR9BJEIMSSYGgIceXQuYEEJCABCUhAAhKQwJQJKABOeQA8vAQkIAEJSEAC/QkUK/smQY5vFb3w8j019QBsMg7DhhAXKxCbV7AJfbeVgAQkIAEJSEACEmhKQAGwKTG3l4AEJCABCUhgYgTyyr4pNLdK+EuNG6cAWAbAEOKJTQsPJAEJSEACEpCABCTQkIACYENgbi4BCUhAAhKQwPgJ9KvsW9dbbtICYC8qdUKIixWI8WxMQqdViMc/3zyCBCQgAQlIQAIS6DoBBcCuj7D9k4AEJCABCcwQAcSyfpV9kzBWp0ttEQDL2jpICHHed0OI68wAt5GABCQgAQlIQAISSAQUAJ0LEpCABCQgAQlMnQCCGAU7qir7NmlomwXAXqIgfx+2CnEuDlqFuMmMcVsJSEACEpCABCTQXQIKgN0dW3smAQlIQAISaD2BcQh/qdOzJgD2GixDiFs/jW2gBCQgAQlIQAISaD0BBcDWD5ENlIAEJCABCXSPwKCVfZuQ6IoAWNbnOiHEt99+e/So3HHHHcPLXvayWDE5mSHETWaS20pAAhKQgAQkIIHZJ6AAOPtjaA8kIAEJSEACM0MgCVd5nr9UPTeFqzbJ89ev4wiA/HzlK18JCF6j2m9bYRerEK9evTrmU9x3333D0qVLY/97/dAnio0UC44YQtzW0bZdEpCABCQgAQlIoBkBBcBmvNxaAhKQgAQkIIEBCYyism+TQ8+bAFhkc+6554ZnnnkmHHjggWGrrbYKSXxNOQbT9oh8dasQJyG162Jqk3nmthKQgAQkIAEJSGAWCCgAzsIo2UYJSEAC4fGlIAAAIABJREFUEpDADBMYZWXfJhgUAF8QALfZZptF6OqEECfvv1wcTDsxhLjJTHRbCUhAAhKQgAQkMH0CCoDTHwNbIAEJSEACEugkgWKBj+R5ljzO6PQ4PckUAHsLgGUTrokoWAwlZn9lIcT8TZOABCQgAQlIQAISmD4BBcDpj4EtkIAEJCABCXSKwDgr+zYBlRcBSeJUk+/P+rZ5CHDRA7Bu31JewWL4cPp7EnENIa5L1O0kIAEJSEACEpDAdAgoAE6Hu0eVgAQkIAEJdI5AWWVfRLgyb7FJdH7eBcBVq1bFAigHHHBAWLZs2UiRN/EWNIR4pOjdmQQkIAEJSEACEhiIgALgQNj8kgQkIAEJSEACiUCZ8Deuyr5NqCsAjk8ALBuHJqKgIcRNZrLbSkACEpCABCQggeEJKAAOz9A9SEACEpCABOaWACLbV7/61fUqzOaVZacFRwFwsgJgL1GQvw8TQlwsOJLEw2nNK48rAQlIQAISkIAEZpGAAuAsjpptloAEJCABCUyZQBLX+H3bbbeFe++9NyxdujTsvvvusRjEOIt71O36vAuA5513Xnj66afD/vvvH7bddtu62CayXRNvwWIIcRKX+XsuDrZhzk0EngeRgAQkIAEJSEACAxBQABwAml+RgAQkIAEJzCuBssq+1157bVi7dm3Yfvvtw0EHHdQK8Y/xUQBsrwBYdv40EQUNIZ7XK5D9loAEJCABCUhgUAIKgIOS83sSkIAEJCCBOSLQr7Lv9ddfH+64446w3XbbhYMPPrg1VBAA+aEQxjxWAU4egPvtt18cm1m0OlWIyyoQJ29AfhtCPIsjb5slIAEJSEACEhg1AQXAURN1fxKQgAQkIIEOEUgFPop5/nIPLARAwoCpNHvooYe2pvfzLgCuXr06PPXUU2GWBcBek6mJt6AhxK05JW2IBCQgAQlIQAJTJKAAOEX4HloCEpCABCTQVgJNKvvecMMN4ZZbbgnbbLNNWLlyZWu6pADYXQGwbJJViYKPPvpoWLduXdh4443DXnvtFUPV84IiyUs0eQzy27yCrTmdbYgEJCABCUhAAkMSUAAcEqBfl4AEJCABCXSNQK/KvkVPqtTvG2+8Mdx8881h6623DocddlhrcCgAzpcA2EsU5O+Ig3fffXdArN50000XCdWGELfmlLUhEpCABCQgAQmMkYAC4BjhumsJSEACEpDALBHIK/smD0B+p6qrvbyhbrrppsAPVYCPOOKI1nQ5CYDPPPPMQh9a07gJNGTNmjXhySefDPvuu28s0DLvhgB43XXXhZe85CXhyCOPjKJg2Q+ckmdgURxMnxW9BdtS+Xrex9j+S0ACEpCABCTQm4ACoLNDAhKQgAQkMOcEEEHI8YdgVib8JdGjFybCf/Gs2nLLLaOw0hZLhUsUABUAmZO5AHjUUUetN02rQoiLlYfLQojLCo605XywHRKQgAQkIAEJzDcBBcD5Hn97LwEJSEACc0ygX2XforjRD9Ott94aKASy+eabh6OPPro1RBUA9QDMJ2OVAFg2cfMqxEkgT0Jh2t4Q4tac8jZEAhKQgAQkIIE+BBQAnR4SkIAEJCCBOSNQp7Jvk+IHVAAmtHKzzTYLxxxzTGtozrsAeP7554cnnngi7LPPPmGHHXZozbhMqyF33XVXFKpf+tKXDu2pWtdb0BDiaY22x5WABCQgAQlIoEhAAdA5IQEJSEACEpgTAk0q+zZBcscdd4RrrrkmvPzlLw/HHntsk6+OdVsFQAXAfIKNUgAsm7h1RcE8vyD7SWI7YqEhxGO9JLhzCUhAAhKQwFwTUACc6+G38xKQgAQkMA8EkjBBnr88x18SH5IgMSiLO++8M1x99dXRs+r4448fdDcj/54CoALgJAXAXqIgf8/PO0OIR36qu0MJSEACEpCABGoQUACsAclNJCABCUhAArNKYNDKvk36u3bt2vClL30pVlc94YQTmnx1rNvOuwB4wQUXhMcffzzsvffeYccddxwr61nYOfOUYjUve9nLpl6tuq63oCHEszCzbKMEJCABCUhgNggoAM7GONlKCUhAAhKQQCMCVZV9m+T4qzowxRWuvPLK8OIXvziceOKJVZtP7HMFQAXAfLK1SQAsOwnqioKGEE/sEuKBJCABCUhAAp0ioADYqeG0MxKQgAQkMO8EipV9U+XSvKrvKMU/eK9bty5cccUVYZNNNgknn3xya4ZAAVABcJYEwF6iIH/vFUJcPK+L1bvxIEw/Kb/gsCH/rTnBbYgEJCABCUhAAo0IKAA2wuXGEpCABCQggXYSKAp/STAYp/CXSNx7773hsssuCxtvvHE45ZRTWgMoMSH3IYYQMk924YUXhv/7v/8Le+21V1i+fPk8db20r233AGwyQHW9BQ0hbkLVbSUgAQlIQALdJqAA2O3xtXcSkIAEJNBxAmWVffH6m4Twl9Ded9994dJLLw0bbbRReOUrX9ka4gqACoD5ZKRYzY033hirVR9++OGtmaejakhdUdAQ4lERdz8SkIAEJCCB2SKgADhb42VrJSABCUhAApFAXtk3hfnyN6wYBjhuZPfff3+45JJLAiGGp5566rgPV3v/CoAKgPMkAJadGOma0C+E+KabbgpcQ3baaacojuYhwoYQ177cuKEEJCABCUig9QQUAFs/RDZQAhKQgAQksJjAJCr7NmH+4IMPhosuuiiG2L761a9u8tWxbovo8eUvfzk8+uijsUIx1V9Hnf9wrB0YcueMCf3fc889o7gz79Z1D8Am45t7C65atSp87WtfC/vtt1/YcsstF71E6BdCnHIKJpFwns6tJqzdVgISkIAEJNAWAgqAbRkJ2yEBCUhAAhKoINA24S8196GHHgrkm8Ne+9rXtmIcv/KVrwQ8mxB9khcUQgUiIF5O/PBvfrqaG1ABcPFUvOOOO+Kc2GyzzcJhhx3WinnahkacddZZ0QNwxYoVUQAsCyWmncW0Aum8ScIf/89FQf6tKNiGEbYNEpCABCQggecJKAA6EyQgAQlIQAItJ9Cvsm9xET6Nrjz88MPhggsuiId+zWteM9VFP55Mt99+e7jllltCKv6BCJFEwCIfPnvpS1+6IAwmgXDDDTecBsqRHlMBUAGwzoRKAuAhhxyy4AGYvpfOmzzNQBIIy0RBqxDXIe42EpCABCQggekQUACcDnePKgEJSEACEqgkMM3KvpWNyzYgxHbNmjXxL4QAT8OjDlbr1q0LN9xwQ3jqqadiWyhKsvvuu4dtt9024BH4xBNPxJBYfqiOm7Yr6+uLX/zi9bwFN9lkkyZYpr7txRdfHB577DFDgL8+EnoAlk/JM888MwrkZQJgr0lc9BJEIMSKeUgNIZ76ZcAGSEACEpCABBYIKAA6GSQgAQlIQAItI5AS9iNaIVLh1bbppptGYW3SBT7qoEFkWr16ddz0Va96VZi09xw5CK+//voodmFw2nXXXeMP/8YTEKaEJOb2zDPPLIiBSRR8/PHHe3Z54403XggdTiHECIVtDXNMAuArXvGKsPPOO9cZyk5vkwTAzTffPKxcubLTfa3bOc4LBEAMJrAZ1IatQlwMH07Xu0Hb4/ckIAEJSEACElhMQAHQGSEBCUhAAhJoCYEk/CH44VHzyCOPLITWIqy1NacW3nQUEsBe+cpXRs+7SRjHRfijCnGyHXbYIXq8IczlVYDLBMCyNsKe/fKTvAURBZOHU/E7iJ0pbDj9puDINLwgi21TAFxMhNDwm2++OYpcCoDPs8kFQPIikh9xlGYI8Shpui8JSEACEpDAcAQUAIfj57clIAEJSEACIyGAwJQ81ZIQiAB1/vnnx/1Pw7OubscQyM4999y4+SmnnBLwlBunPf3007GYw9q1axdCDpcuXRr23nvvRR5MeQg1fIsegHXbyHdT+HASBvmdcgwW94P4R17B5CWYfg96/LrtLG6nALiYiALg+jOJuU0OQGwcAmCvuVsnhLjo8Zw8bfldLDiit+CgVwm/JwEJSEAC80RAAXCeRtu+SkACEpBA6wj0q+z75JNPLnjWTUJYGxQO4tg555wTv37SSSfFcOVxGN55t912Wyzwwb8xhDaEv2222Wa9UNxBPADrtpt9E56d5xTk34Rt9zI8A/MKxPx7nN6Sl1xySSA/4x577BF22WWXul3r7HZp7ugB+MIQcx6dffbZ8Q+HH354nJ/TskFCiPPwe0OIpzVyHlcCEpCABGaFgALgrIyU7ZSABCQggU4RYLGLB1mqrpl+J+8WOosAOAlhbViwCGHJi+jEE0+M4bejNFjdfffd4cYbb1wo3IGXIbntli9f3jPcdpwCYK/+IQAWRUHGsZdRWKQoCvK3UeQVVABcTD0JgFtssUU49NBDRzlFZ3ZfXIPSNeaII46I4extslGFEOfiYMqj2qZ+2hYJSEACEpDAJAgoAE6CsseQgAQkIAEJfJ1Ak8q+4xbWRjUohOSmQgInnHBCwNNtVEaBj+uuuy6Kahihfrvttlv8qSo2Mg0BsKzfiCx5TkH+Tdh0EjeK38ErMOUTTOHDMG0qCiYBkErIFESZd1MAXH8GUAgnhe8feeSR0aN2FswQ4lkYJdsoAQlIQAJtI6AA2LYRsT0SkIAEJNBJAimvXzHPX17Vtyjw4E12xhlnRB7HH398axfneTuPO+64kXgRIfjdcMMNiwp87LjjjrHAR90Q47YIgGUTGo9PRMDcWxBhMIU2F7+DBxOiYC4MItb0KzZy6aWXxkIyCoDP07z11lvjjx6AL8wuzt1UwOeoo44aqXg/6Qt5kxDiPL9gaqchxJMeMY8nAQlIQAKTJqAAOGniHk8CEpCABOaKQLGyb1qkAiEX/8qgIBZ+4QtfiB8de+yxU83P1W/QRtlOvAkJ9aXAR7Ktttoq5vlrWqG0zQJgGU/aW1ZsBC+tMmP+IAIWvQWTZ6QC4GJqSQDccsstwyGHHDJX16FeneV8O++88+LHRx999MjD96cN2RDiaY+Ax5eABCQggTYRUABs02jYFglIQAIS6BSBvLJvyvFXR/hLEPjO5z//+YXFOcUL2mh4rZ1++ulDtRMRkRBNRJrkBYewhfC39dZbNw5/pTGjqgI8Teb0AZEGT8E8jJi/9TJyMBI6/Nhjj8WciTvvvHPMlzjvpgC4/gxgfqxevTp+cMwxx9T2rp3luZREwdxjkGttumakazQFdBDkEdl5CdGrCjGeg+YVnOUZYdslIAEJzA8BBcD5GWt7KgEJSEACEyLQr7JvCj2r0xQWqJ/73OfipuTnwnOpjZa3kzBCQizrGt+96667otdfErUogoFgRchvvxDXOsdATEzFVliod8XwCiyKgogVvYyiKcViI4RSN80rOMv8FADXHz0K1KxZsyZ+gJcx5968WjGEmBQE99xzT1i2bFlMPZB7bBtCPK+zxH5LQAISmG0CCoCzPX62XgISkIAEWkSgqrLvIGILAiD7pULn0qVLW9TbxU057bTTGguVDzzwQCzwgWcbhkBHsYo6BT7qguiqAFjWf/qavAT5fe+99/bMKcj3CRVO4cPpN8VGhhVd647NpLe75ZZbopepIcAvkEc0Pv/88+MfyN+JUKw9T+Caa66JAuD2228f9tprr3gdzn/YppjDtZjWgXOJnzy/YFfPL+eNBCQgAQm0n4ACYPvHyBZKQAISkEDLCTSp7Nu0K4TWIuwcdthhMRS2rZaEysMPPzyGy/UzPNeuv/76gACYbPny5dHrr26Bj7oc5kkALDK57LLLwsMPPxw9KcmfmIcQ9yo2gjhByGPuLYg42AXvySQAIqQffPDBdadQp7ejEM0FF1wQ+0ihISpQa88TuPrqq6OIzvlDKoJkxRDilN4hr+qNEJh7CeYhwumzJAoaQuyMk4AEJCCBSRFQAJwUaY8jAQlIQAKdIzBO4S/B+uIXvxgI9zz00ENjKFpbrY5QSb6xm266aVGBD0RNFtcITuOweRYAL7/88vDQQw9Fr0oqAecCBqGfubcgoiwVYXtZXmwkeQvOmlikALj+6DIHLrzwwvjBCSecEL1CtecJXHXVVbEK+U477RRDgKvMKsRVhPxcAhKQgASmTUABcNoj4PElIAEJSGDmCAxT2bdpZ88444wozOCxtN122zX9+sS2p1oxufZWrlwZttlmm0XH5e/kXyP8MnmeIfilAh/jbKQC4PoCYC/e5GDMvQT5N0JhL8NbM69AzJgSQjpIqPs450Da98033xxuv/326KG6YsWKSRyy9cdA+L3oootiO0888cROeHqOCvqVV14ZvZR32WWXsMceewy02yaiYDGcmAMaQjwQdr8kAQlIQAI9CCgAOjUkIAEJSEACNQmkxVwqKpH+z9eLuZ9q7rJys7POOitWcj3ooIPCDjvsULn9tDZInoqHHHJI2HbbbWMz4LN27dro9ZcX+MCbhrC6SQhFCoD1BcCyucNcz4uNIAoSNpqHO+bfwysQITAXBqlKPImxrpr7CoDrE6JS9MUXXxw/OOmkkzqb/7FqbpR93suDdpB95d8ZRQhxnlPQEOJhR8TvS0ACEpgfAgqA8zPW9lQCEpCABIYgMKrKvk2bcM455wQS9R944IFRNGurnXnmmVHkw1MRARDPGfL85QU+KO5BOOokwwznWQC84oorwoMPPjiUB1PZfIMpImDRW5BzpMwQKIrFRggpnnQxBAXA/gLgySef3Aqhti3XuEsvvTQ88sgjMXye69a4rYm3YLEKccorWCw40gbhfdzc3L8EJCABCdQnoABYn5VbSkACEpDAHBIYR2XfJhhXrVoVhZb9998/5qJqqyVPRbz7yDuH8JSMAh/8fZNNNpl48xUARy8Alg0i5wlCde4tyL/xICwzhIlUbCQXB8cpDicBkLyTeNRqITz66KPhkksuiSgUABfPCLjAh/BfwoCnYU1EQUOIpzFCHlMCEpDAbBFQAJyt8bK1EpCABCQwIQLFAh+p0mPytKAZk/CuWL16dSBMb999953aIrQO8iQA5tuSC3CvvfYaW4GPOu1CAEw/XahkW6fPaZtxeQDWbQPnEF6hCIG5MJjCwcv2Q7hwMYSYvIKjMELR77jjjlhNWwHweaJUiaZaNNcyBEDtBQLkRmTe8vKiTS9f6oQQl1UgTvcrfhtC7EyXgAQkMJ8EFADnc9zttQQkIAEJ9CAwicq+TeCvWbMmeqFQMIMQ2rZZKvCBd1UyBJx99tknFluYts2zAJiKGOy8887hFa94xbSHYuH4FLUpViDGe7CX4Tma5xTk3xQgaSrAKwCuTzgJgAhG5ADUXiBAdWTmKdfeNqdfSC1u4i1oCLEzXQISkMB8ElAAnM9xt9cSkIAEJFAgUFbZF6+/srCqScK74IILopcOnnTkomqLwSYV+EDQSUahEvIVNhVnxtWvJAAiVI4zvHRc7R9mv20VAMv6xDgVcwry/17FRhjLoij4kpe8pG9eQQXA9ckTrk+xCzzCqAKsvUDg/PPPj2HtvMxocwGmfmPWRBSsE0KcCo44TyQgAQlIYDYJKADO5rjZaglIQAISGBGBMuEviQ7jquzbpOmEoZFPDw+uNnhxweb++++PBT4oBIGxKESQIbSzbaHKCoAPhLZ5ANad/4jMZcVGGNMyw6sJUTAXBskzmEK/b7zxxnDnnXcGQtMRqbUQry2EiisArj8b8L5+8sknw3777Re22267zkwXQ4g7M5R2RAISkEBjAgqAjZH5BQlIQAIS6AoBBAY8w5KXRDHPXxu82EhEj+CG9x9egNM0QpER/vAawuBDgQ+ESSpm8jneMpOomFmXwzwLgFdddVWcO+QvI49ZF4xzFVGm6C2Ye6Hm/WSO4hmIKIhATVVXQtNXrFjRBRxD94Fq3XiKIuCfcMIJQ++vSzs477zz4pyhABOVzbtudb0FEdrTtimXoFWIuz477J8EJNAVAgqAXRlJ+yEBCUhAArUJIPQhDCXBr43CX+oMCfrvvffeKKohrk3DEFzwnrr77rsXDo8XFbmxEFYwwuUQV9oWqqwA2C0BsNf8T8VGcmHwqaee6nm6kEOwrNhIG0T/SZ7jCMQIxRtttFE4/vjjJ3no1h/r3HPPDc8880z0FuV6N4/WSxRMXuAU6DnyyCPXS5WRziPEQn6SUGgI8TzOIvssAQm0iYACYJtGw7ZIQAISkMBYCaQCH3j9IfrlOf5YpGBtEwDIz3XPPffEME5C0SZpcLrlllvCbbfdFllhm222WRT+igU+SJiPZyCeZnvsscckm9n3WAqA8yEAlk0C5m9efZhwVwSdXoYIhiiYC4NUJW7bNWGUJ9d9990XvvSlLwWEnOOOO26Uu575fZ1zzjnRQ5yK0VSO1p4nwH2Ue9K1114bi/EcfvjhCx6BiZFViJ0tEpCABNpJQAGwneNiqyQgAQlIYIQEipV9EasorEHoKqG1KdffCA85sl0RnofnHWGchKJNwsoKfLDQw7tv++23LxVEUq5CxL82hZvOswCIsIPAQ5j2tMPHJzFvq45xww03xMI1W265ZVi2bNmiSsRJ4C7uA4+llFMw/SavYHphUHXMtn+Od/HVV18dqLR87LHHtr25E23fWWedFV98HHzwwWHp0qUTPXbbD8Y96brrrgucC3gAJmGwl8cgn6f7bFEcTJ8VvQXTdm1nYfskIAEJzBIBBcBZGi3bKgEJSEACjQikAh/FPH/kq8NbrW1iVVnnEHEQLXbcccexFy4oK/BBbjBE0l122WWhmEJZO9uUqzBvXy4Azlv4mQLg4pmaBEDEvwMOOGDhQ+Y91V7xFsw9BrlulBlCBsJH7i2IODiLVabx5LrmmmuiJ9cxxxzT6Pra9Y3PPPPM6Nl26KGHhi222KLr3W3UP4rpkBaCcwAPwF5WN69gLg4mQZDfefhw+neXPXIbDYIbS0ACEhiAgALgAND8igQkIAEJtJtAVWVf8uqRw2i33XaL4axtNhbnd9xxR/S8G2fhAgp44NGBZ2RagOF1iJck4YFVhqiKt1nbmCoA6gGY5m4vAbBsbnMNIYdgyimYfpNrsJelYiO5MFjn3Kk6t8b5+bp162IoJ6HORx999DgPNVP7ZvwRALGVK1eGzTfffKbaP+7G3n777eHmm2+OXODTxPIqxCn/bhIK034MIW5C1G0lIAEJ1CegAFiflVtKQAISkMAMEOhV2TdfUKTCGni17bvvvq3uFYtzFltUoTzkkENG3lYKfCCMIAQkw0OKkNFU4KPOQdvKdJ4FQEI7CfE0BPj5GUwF67vuuiueS4OG01NtuFiBGO/BXkZobZ5TkHMKb7u2eDGlUE7Ey6OOOqrOqT4X23DdOPvss2Nf8XBjDLUXCNx6662BH8LpR3VfqustaAixM1ECEpDA4AQUAAdn5zclIAEJSKBFBJpU9p1GXr1BUSFasNCiCmVTT4t+x6QYAgU+EBdT/jO8OfCIHCTf1TSLlfTrpwLgvTF8vO2eroOeH02+NwoBsOx4hAo//vjjCyHEhBHz/+TpVPwOocLFCsQIcNMQBRFE4ZLncmvCtKvbMqYUAcGOOOKIRi9Dusok7xfef9w7KI5CkZRxWV1R0BDicY2A+5WABLpGQAGwayNqfyQgAQnMGQEWCKmqbwr95TcLgl6VfSeZV2/Y4SDPEostFlqHHXbYsLuLYh/5m2666aaFiqh4JCEQbbfddgOLEG0VVRUAFQDTSTMuAbDspOQ8S6JgHkbMfCwzrlVlxUbIWzlOI78oHsAcG6FLe54Anp6rVq2K/8YzEoFWe4FAk3D6UXOrCiFOYmDZb9qSio3k+QXbXAhs1PzcnwQkMN8EFADne/ztvQQkIIGZJVCs7JvEv+JDf1kHJ5VXbxRwEf8QAfHKG2aBDh9y9CGCpJBFPJEohLLzzjv3LfBRpx9XXXVVDK+cRLGSOu1J2ygAKgCmuUCOS0JeEbr322+/JtNoJNtyDhJynxca4d9445YZ1zKEpzynIP8eZbGRusUcRgJghnZCrsfzzjsvtpjiKLwk0V4gMO1zqWws6noLGkLsTJaABOaZgALgPI++fZeABCQwgwR6VfatI/zlQsBtt902trx6o8RK+C+iHVUoB83R9cgjj8R95AU+EP0Q/0ZVpIB8c4gJO+yww1hDwpqynWcBEKGbKq+MyT777NMUXee2b6togbdZsQIxBUh6GWJUURQk1+AgRoEhvIE322yzkXgYD9KGNn4HoXbNmjWxaccee2wYlG8b+zaKNs3KtaWuKGgI8ShmhfuQgARmgYAC4CyMkm2UgAQkIIGYT6uY5y+FAuXiXx1UhC+R/25UYbV1jjnoNgiVCBcs0PFEaWJ4+tFXRKBkFECgwAc5v0ZpyasS76qDDz54lLseal8KgAqAaQK1UQDsNbnxCixWIOZ87pVXECE/hRCn/IJU9q3KKzhMNdehTsyWfxnW559/fmzl8ccfHzbaaKOWt3iyzSONBh7lVIrfc889J3vwIY9mCPGQAP26BCQw0wQUAGd6+Gy8BCQgge4TSG/wyfOX5/ij502Fv0RrVGG1k6CPhw7iGot6PFHqGOJBStKeFjsU+MALjKqN47BxVysetM2pKjTzh3xqVYLIoMdp4/eSl87222/f+mrXk+DHHKXa9azyQMzOi40gEPKTivgUGTLfi6Igwn/Kjcr2vGDgZQgexoceeugkhmEmjgHXCy+8MLb1hBNOGGnY9UwAqGjkFVdcER588MGwyy67RE/yLlhdb8F+IcScc3mOwXm633RhDtgHCcwDAQXAeRhl+ygBCUhgRgk0qezbpIujCKttcrxhtiVJP94WLNzxROln8EIwRPxLecXwAqLAB55/41yMpGrFy5Yta5WQkOYQPOZNAJx1wWuY86bsu13kwfzGWy15C6b8ggjeZcY1AFEwCYNsjyiqALiY1mOPPRYuvvji+MeTTjppkWg66nk5i/u77LLLYkqJ3XbbLf501eqKgoYQd3UG2C8JdI+AAmD3xtQeSUACEph5AlWVfYcVsgh7QwwYJKx20nDEjSrgAAAgAElEQVQpWkCFXYS8E088sfTw8Lr33ntjuG8q8EHIWirwkXv8jKv9Kax6m222CStXrhzXYRrvVwFwdj3eGg92xRe6KACWdZnrATkEi6IghS16GdcIzt3cY3Cew14fffTRcMkll0RcJ5988lhfnox6nk9if7CBEfcYvADnyeqEEOOtS1EsXjpRGItzKT23WIV4nmaLfZVA+wgoALZvTGyRBCQggbklMExl3ybQKFZB0Yo6XnVN9juObcnfd/nll8ck9CxEi4YXBt53FPrAWGSwINt9991HVuCjTr+oVIzn4VZbbRUOP/zwOl+ZyDYKgOumVvV2IgPc4CDzHhKdio3kuQXTC4MyjFxzUj7BVHSEvw37AqbBkE1tU66reLnR17Lr7tQa1pIDX3TRRbFwDflkly9f3pJWTbcZubcg5xWMsCOOOGJBAGQ+GUI83XHy6BKYdwIKgPM+A+y/BCQggRYQmJTwl7pax6uuBVhiE/DsYyGKB8ErX/nKhWaxwED44/NkFOBgQfaSl7xk4s1va17FeRYAZ6noxSQm7KxULp0Ei3QMhHteiHDNIE8oog55BnsVG+E6lLwE02++2zVR8KGHHoovXvDg6uV5PclxatuxKJDCPYi8slQZ1xYT4DxKAiCpOxD9iuHEfCPPY5zEwfR3fuc5BdO/u3auOXckIIHJElAAnCxvjyYBCUhAAhmBssq+CDbFh+JRQ6vyqhv18YbZ3/333x9D0TbccMPwqle9KuDFg9hGrr+0SCd/F3n+xlXgo077KSRAGDBtOeqoo+p8ZSLbKADerQfg12eaAuD6p9xNN90UryVURD/ooIPiBpwzqdhI7i1IWGOZIW4Ui43w/0mkHhjXReSBBx6IqRe47lIERFtMYPXq1THMfL/99ovXF20xATzyL7300vjHYgh5um9zntURBYvFzgwhdrZJQALDEFAAHIae35WABCQggYEI5JV984dgdjZoZd8mDbnvvvviw3nRq67JPia1LZUW8SSAC959iH8pwT+eN/xt3AU+6vSVaqJ4nOFFdPTRR9f5ykS2mWcBEA9R8lCxQGehPu9G2D8es+TkQjDXQkgegOT/O/DAA3si4Zr95JNPRg/BVGiE36nYUPGLXK9IsVD0FkRQmwXjHkHxpY033jgcd9xxs9DkibZx1apV8WXUAQccECj8pC0mwH2bSslNPEiLYmCq7p0Ew7Lw4eQNmD6zCrEzUQISqCKgAFhFyM8lIAEJSGCkBMZV2bdJI/HuoMIjD8unnnpqk69OfFtC0S688MJFx510gY86nW5rYRUFwLuiQLz//vvXGcZOb6MAuP7w4rVLpXFEHMScJoYwkfIK5qIgnmG9jGJGRW9B8gq2zRCKmS+07dhjj21b86bennPOOSe+iFqxYkXM+6otJjAqAXmQKsR5iLAhxM5MCUigSEAB0DkhAQlIQAITITDuyr5NOkGC9wsuuCB+5TWveU1r81fRThahhOFhPNjvuuuuscBH2yp0EkZIiCWL+zZ5zCgAKgCma4MC4PpXyeQlOkqRGK/AYgViQop7GV52ebERriEIhdPMdbZu3bpYKZ52tMmjucl9bpzbnnnmmTF89ZBDDplq6olx9nGYfaf5s+mmm4ZjjjlmmF2t991RhRDn4mCKvBhpQ92ZBCTQSgIKgK0cFhslAQlIoDsEigU+UshvWcLrSfX60UcfDWvWrImHe/WrX926XFUslvHMyQt80FY8UVgot9HwIiJkrm2VlRUAFQAVAHtfMSZVKIb8gYiCuTDIdS6FORZbiDhRrEBMyoNJ5RWkUBRsuJ4deeSRbbzkTq1N3NMRALGVK1fGtA/aYgLcD7mHI2ZTBXgSZgjxJCh7DAnMPgEFwNkfQ3sgAQlIoJUEJl3ZtwkEwtXOO++8+BUKa7QlLxXhdCTlpypnesu/2Wabhcceeyy2lSrAbfP8S9zJNXfVVVfFaqJtSpqPNxJtgy0LVUSFtox3kzk7yLbDhHcOcry2fweBmtC85cuXx9yZWohebngrbb/99mHfffedKBLEPyrJ5uHDCIQpx2mxMYh/CHJFb0HEwlHbNAScUfdhXPtDzD377LPj7g8//PDWvpQaV//r7BePeO7l3L8PO+ywOl8ZyzZNQog5v4qegIYQj2VY3KkEpkpAAXCq+D24BCQgge4RmFZl3yYk8Tw599xz41dOOeWUmOh9msaCihx6VNLNC3xQqAAPgtRWqgm2MV8W7PCYoWomIU8nnXTSNHHGYzMPETYIcXz66acXtYewPkSE/KetwuowIBUAF9NTAFx/NrWtMjLnLTkEc1GQfyPg9zJeOqRzOeUXHPZ8bouAM8z5P67v8lIl3ZOo+A5/bTGBW2+9NfCz5ZZbxjDpNpkhxG0aDdsigckTUACcPHOPKAEJSKCTBMqEv7x6XZtyzFDNMnkwnHjiiTHP0zQsiVQINSlxPgvXV7ziFWGnnXaK4W78/ayzzorNm2Zbq/jcc8894fLLL48CJULlNI3ciYTvEeqNwRFhEm+jXsbnRVFw2sLwsAwVABcTVABcf0bNSl7EsmIjXMd7GdehoijI3+rmFeSFDBXX8RomzFV7gQD3pNWrV8c/kN+Oa6e2mADef4jIW2+9dTjooINmAo8hxDMxTDZSAkMTUAAcGqE7kIAEJCABQrnwXEsPkCnPX1lISRtosZg844wzYlOOP/74GFY2aaO6LyJVCu+F1S677LJegQ+811K+pWm1tQ4b8hVedtllMUSZUOVpGIIAHn+IkckIbdxjjz0Wcofh/YlHUfrh/0moLrY5FxGSmIAoWFdEmAaD/Jg33nhjDCffZpttwoEHHjjt5kz9+ISo33///VFc33PPPafenjY0YJZFUe45ZcVGep3PXJuSh2D6jfda2fncZg+uac8bXqScf/75sRkUfJr1FyXj4DmO4jrjaGfVPg0hriLk5xKYPQIKgLM3ZrZYAhKQQGsIpAILLAgefPDBmNAdwSUV+GirUMLC8Qtf+ELkOOnCGixY8cwiF1myHXbYIQoSZZ6IhFt98YtfXFhssXBtoyGsXHLJJTG/HnkVJ2mMJ+HTt91220JRgS222CLss88+MQcTIdZwRGQtFhFgzuYiAsJgv+IEiAhFT0E8YNo41xUAF89CBcD1z8quMeF8zkX+VHiEa0CZkeOMa2oSBDm3eSHEtYSfrbbaKqxYsWKSl7PWHwumF154YWwn+V7nJadqk4FpW2h9k7ZXbTtoCPE//uM/hk9+8pOBsPF3vvOdrbxnVvXdzyXQBQIKgF0YRfsgAQlIYMIEigU+ELPw/mIxlcSfNgoiCROLxM9//vPxv0cfffREqhjiyUdIWV7gY+nSpYE8f/2qKLJwPf3002NbCbdC0GqjPfDAA+Hiiy+Oc+DUU0+dSBOZhxT4QOhKef4Q42C63XbbxQVGXgW4TAAsa2henCD3FuxVsZQFcFEURMyd9jmgAKgAWHUikreTc3fnnXeOqQe6aFwneElV9BbkpUCZcd5yTvM5YmDKxarQ9TwtvNa51mPke51UZeZZmptdE9ar2CdRMPcYTJEg6bvveMc7woc+9KHw+te/Pnz605+u2qWfS0ACYyKgADgmsO5WAhKQQBcJ9Krs+8gjj8QFAQun17zmNa3vOv343Oc+F9t55JFHxkTd4zIEPDxJ8FBLXihpUUloZpVIlLeVN+d4trXR8AC96KKLJjYHOB4h1Ah0GMLj7rvvHnbdddf472SDCIBlfJOIkAuC/LufZ1ExBxnjXjXeoxzbWcxDNcr+F/c1D2JXU35XXHFF9N4m/QCh8vNinM+8NCiKgikXaxmHVDwo9xacx/BXcqzywo9r2bTzvbZ1vpIPlzQf3I+4L82r5YLgL/zCL4S/+Zu/CW9605sC3oCaBCQwHQIKgNPh7lElIAEJzBSBVOCjmOcvFfbAIyDlBHr1q189Ex4BeNUh3hx22GExUfeoDWZUxsULKy0qWSziZbN8+fJGjBAr2d8RRxwR8Bpso7EovOCCC2LTXvva146tiYT3kV8pD6GGJyHUZRWSRyUA9hIFyTtYFAVTJefid/CUycWDFG44Lg8aBcDFI6AAuP4sTgLgvAsViQxef5zPvLDhvsbLhF4iP9/hms55nJ/XbU0JMKqLMoIx8wY2FKbS1idAOgyKUCGqI65rIfzgD/5g+NSnPhV+7Md+LHz0ox8ViQQkMCUCCoBTAu9hJSABCcwCgbqVffGiWLVqVewSBSDIk9Z2I68ei71DDz00LFu2bKTNZYGESJUX+EgL7EHCyAhXRsgal1g5is7jBZpEYLxAR+3pxlgRQk11zhRuhBia8vz16sM4BcBeoiCCbzGvIIVnygxOZaJg7sU46PgoACoAVs0dPLkQ73fbbbf4oz1P4Nprrw3r1q2LOW332muvhfM5ndf98oRyjS8rNjIuoX/SY0a+V0Jcuc9TmEpbnwA5EpkrzB1eUM27cc/+ju/4jpjO5Jd/+ZfDe97znnlHYv8lMDUCCoBTQ++BJSABCbSbQF7ZN+Vy4SGurLIv+ZXOOeec2CFCgso8sdrWWyrrEgJ28MEHx3xxozAe+BH+WCAlo8AHiwC8QgY1CpbgVbZy5cpY0bWNhrfDmjVrYtPIATgKAYt9MffWrl0bPSlTzi4qd5KXC+G2SmictADYa2yYa0VPwZS3sPgd+kS4cJ5XEEGhKdMkmFrI4HnCegCuPzsvvfTSgHivALiYTSrisOOOO8ZrTdHyPKFJFOR3P+/f/JxOhUeantNtuPZTZR0+3OcpoqWtT4B7Id7h++67bxSR5914diQyAC7kAvzt3/7teUdi/yUwNQIKgFND74ElIAEJtJNAEkyS6Jd+96vsi5CBoIZRFRCBpu129tlnxwf0gw46KCDSDWP0H28rhKom3ml1j3nGGWcEPMgOOeSQsO2229b92kS3Q9w677zz4jEpBDOIp2OxwQipCKosrDH2SQg1BQvqetMwf/mBX90iIJMCR5uKomC/HGScV8ViI/04KwAuHsl5zXfXbz4nATDlz5zU3G/7cb70pS/FNAN4b/ECp45x7ef8Ted0EgZ7ef+yT0TBordg2z3oSW1B/lWuR+Sl1dYnQEQE437AAQeMPMJgFnlzbiAWc169733vC7/4i784i92wzRLoBAEFwE4Mo52QgAQkMDwBHtDwXmgi/KWj8j281DAe8hAp2m7nnntuIIxr//33DzvttNNAzS0r8MFiDo8R8gpWeafVPehZZ50VF5YrVqxorTfBKMPA2RcLTKqTYnBkjBD/mibdb7MAWDb+KQdZHkKMh20vS4UJcmEwCQgKgIupKQCuP4tSrrIkrNe9JnV9u1TFdRTVkYtCP+c2L596Gd7ieVoA/o233ajuJ8OOHS+6brjhhthG8tJq6xMgIoLnIu7ZeGDPu/F8CQsKon3iE58IP/IjPzLvSOy/BKZGQAFwaug9sAQkIIF2EOhV2TcV+Ei/+7UWkYU8ddjRRx8dNt9883Z0rk8rVq9eHXP0EaLTNEk3zO66664YlprCOBGmKERByFhd77S6kFhMIAIdeOCBcf9tNMRURFVs0DBwFsp4Ut55550LnpSEPCOostgcxGZNACzrIwvJYk5BePcTEBAE4UloNlWu8R6dd1MAXH8GUL2d66AC4GI2454rnNN4Cubhw5zTyYO8OFKI+sViI4j/0xAF77jjjnid3myzzWJeWm0xAcaQl3b85rrL9XfejfswBVHIj/yv//qv4Y1vfOO8I7H/EpgaAQXAqaH3wBKQgASmS2AUwl/eg1moVJu3l4IV5L5CXGqS/B6vNMJSWbxhiH0pf9Yowl7LZsUovBXHPdvwaCGsGjvppJMa5TxkcUBxDzzWUg6t5Ek5bM7DLgiAZWOH92mZKNhLQEjVSnNPwTZ5FY17frL/yy+/PDz00EPBircv0L7ooovitYyXF4N6Qk9i7CZ9jGkUR+GcRgTMz2v+zTWszMgfWAwfJqR41C+gise+9dZbAz9bbLFFLKKlLSbAeCEAYgikCKXzbjAhZy8vpU477bRAoTBNAhKYDgEFwOlw96gSkIAEpkagbmXfpg2kuhsLmDYXqsj7RJU+xAAWvryZrjIWyQh/KSyV7fHG4/vDFPioOi6fk1uP4++3334x/10bjRDltOipmweSuUieLbimMFc8XWBK7q1RLGS7KgCWzQH6ioCQcpCRQ7Ff/rHkVZSLgszlaXgVTWJOKwCuT9lqpeUzry25EblGcm0segumgkjF1ucFhHJxcJQvp0wt0P9qxdgkb/gjjzwy5nmcd+MFYcpfTCEQc0fO+4yw/9MkoAA4TfoeWwISkMAECbCQSHn+kgiYvIXycN9BmzQLhSryvhH6hpiH+Ifg1MsQtlKBj7QNOX322WefieU65IGZUE6OifdSGw2hiTmAHX/88ZWLHsIOyfOHCIsxBwnFZjxGmQQ/ebqyKEuFbNrIbxxtuuWWW2LOJTxQEKvz4gSI9WWGUJAEwSQgkOy/C6KgAuD6I37BBRdE0RhP6LamFxjHuVG1zzaHRnNNS1XFc2/BXlXF6WvKFZrnFmyaTzUxI/UFaRrwziYthbaYAM8MpBjBjjnmmLG/IJwF/rzoI80ARn5NiqNoEpDAdAgoAE6Hu0eVgAQkMFECg1T2bdrAUVbVbXrsQbYnxOvee++NghrCWtEIRUU8IdQpiSWjCktt2l4W6Q8//HCsRkm1zjYaAtsXv/jF2LTjjjuuZ84+FqksIEkknwzPAPo2Dk+JeRYAU6heMQdg7lWUi4Ip/Lo4v/JQwyQOIgqOwkNzknN5GmGdk+zfIMciFQIeZlwDh62GPsjx2/qdFBrNdQlv5FmwVEAo9xbsV0ComBaA+1sdD2A8tsmBy3WbIlraYgII6tyzMV6GjfKF1qyyxms05aElh6TpBmZ1JG13FwgoAHZhFO2DBCQggR4Ehqns2xTqqlWrYu6iYarqNj3mMNuT5H3dunUxpJbQ2mRlBT7IlZYKfEzDEyqFK/MGPb1FH6bv4/huXgkar4di3qOyislsg+fROKskKgDeWqsICJwI0yrmFewVaoj4l3sTIQxOIv/YMHNXAXB9engXM+4UQ9p+++2Hwdup73bFM7KYK5Tzm59euULxAC5WIC6K/ddcc02455574nxh3miLCeDdjgcpRj7cWXtRMo7xxPua1CAYuZdnoVDcODi4Twm0gYACYBtGwTZIQAISGDGBYoEPPAD5W5PKvk2blKrqtjlMNe8TYSh4MeDdkcJRyJmGdwMLJAzPJwp84CU4yhxKTdnWDVduut9Rbt+rEjTzjsUiXAmNwiYpqCoADp6sPw81TJ6C/O6VV5DrS5koyHnUBlMAXH8UuG5zXvISZLvttmvDMLWiDckzsovCKNfqlFcw9xbslRYgif3p3CacE4907p14SGqLCcCGaw3Xw5NPPlk8IYRzzjknfOM3fmN8puKFg16RTgsJTI+AAuD02HtkCUhAAiMnMOrKvk0aOAthqnl/rr766pjHiLA3RD7y0T344IMLm7C4wdtu3AU+6jAmIT2LLtqJx1wbjblHJWiMBN9UiORNP1z5jbGQTBVYJyWozrMASAg7eQBHXa0z5R/LRcFe+cdYBONBlBcaQUiY1Pjn50oq7JCqdrfxPJp0mygwxNjhuZ2S9E+6DW083rwJo0UP4OQJXFVEaOnSpYtyhirshJhb+Morr4zXuOT11sY5Psk2feYznwlvetOb4r0IPm15KTRJBh5LAm0hoADYlpGwHRKQgASGIFBW2Ze3/OP0+Cs2l5xJCGhVRTWG6GaDrz4bQvhyWLLkgbBkyYMh8BO+EkJ4PCxZ8lgI4anw9NOPhq8+83R44smXh2ef/VrYdNMnw7PPvii86EWbhE023TFsuOGyEJ7Dc2mzEMLm4bnntgwhbBOWhA0btGM0mxI+gxcdRTLaHHJ12mmnxQ6vWLEiCpaEWCcjXAxvEZLRT9IUAEcvAJaNH0IBogHhb0k8wNOjl5WJguMWD9pS2XWS87/qWKRuYOzwgl62bFnV5nPzuVyeH+qyYiPJk7tsMvDCDLG/WGxkGqkzpjVZyS3MC0Y83Y899thpNaNVx/2Xf/mX8Ja3vCXm/uPFlGHRrRoeGzNnBBQA52zA7a4EJNAtAmXC3ygr+zahNT0vta+FJUvuCUuW3B422OC2sGTJ2vDccy8OYclDYYMNXvDooy/PPbckPPfc8vDssxuEEO4ML3oRQuHz9tRTLw5LlmwTNtzoqbBkyX3xb+znuee2C88992wIS+4KIbw8hOd2Cs89t/XX/03I3K5hSXhpE1SNt8Wb4O67744Pz21Ouo4HIPOPh3sEaIxcP4SFU4hiGqYAeEscg5UrV04cP/kDizkF+xUlGGWl0rLOKgCuT+Xcc88NjBPVXKnqqj1PIHE56KCDwtZbc73XEoFUIZncrYj2eAJzXvfKK8g2xcrinOtdFQW5V+P5zksOvOG1ED7+8Y+Ht771rTHVAOlXFACdFRKYHgEFwOmx98gSkIAEhiKAwELhBR66kxCY8vzxcDXph+teRTWG6mTpl8lluDZssME1YcmSu6KH3wYb3Fm65bPPbheeCyw01oYlS55ZtM1Xv/qi8OijLw8bbPBs2Pz/s/ceUJJc9fXwfa+6e9LuhM1ZWkWEhCSEQCiQhBDYPjbh2MbYIIM/c4wNB2wMtrBxPiR/GPsQHPiwiQ4cG8Mf/rYJMiIpEBQQEhJC2mVXq9VqtWFmdyd1qPed+3v1uqt7OlTV9Gz3zLwHo5mdqZd+71V11a37u3fsBJSq/ZlsP2PWw2AKSj0hfzAmB5htMMjB4JBlFJqtMCgAZhDAFsCcA2A3FIa7Om2nV7h9+3Z5UO+3wn3Hhx6O0xUyQcj4I/PvdO/FeHxWMwC4b98+0H2xVwBgs33Ka1YzULAVeEAWTTx9mD/zd1mKBwAXRo3aXFwTD3TVx4au9tTEI5t5KU2KsuzjXtdxDsk0xnJurowV3W/jmoI8z92LoMYxx53FHVuw302Eksad0iJ0uue16ulPf3rSaiv6uPe973340z/9U1xxxRWg8VAv7wlWdKD95HwEEkTAA4AJguQP8RHwEfAR6KcI8IaaN9vO2CNu8NEL4M/F5t5778WBAwewNCDVHLR+EFrfFwF/U3VLEoZMXVsDpfZDqfKC5TImj2JxC4rFKaxZyxTg+mLMWhhp4wSUtoCfK8asg3xhUlKKWcgktODfEAyOQqlJmyIsx5UFJIQwB9dD4Xxo7FzUForrFfJBvZ/KsWPHhO3A1E9XCPoxpbAfdH4cAEiQg2U1MQ/6EQBstnfj4IHTFWznVFooFJqCgp0eKu+44w5MTU2JTAHT6X0BPNDVfBd87Wtfk8/Ypz71qT1jL/fr/kzqkMxrrzMbiYP+7lrcOD+evwQB42zBXumFLib2S6W9upgx9brun/3Zn+Gv/uqvcN111+HLX/6yBwB7vSC+/1UdAQ8Arurl95P3EfARWE4RaOfs60CNTg/ASznfH/7wh9i/f784SV566aVd6IpMvz0Igm9D67sAjMCYcSj1yAI2Xw2sG4Qx2wEw/fd40zFMTw8hDMcxOHQMudz8gmPCcCOMGYVSj0Mp6wZca3+9jMHgOJQ6Fvs964zBEEBUhyO2oGUH8lgggAmZPrwJSl0MhQuhEKMcdojW/fffD4I53Yvt4peHD3Z09qXekSsu9ZcPzf1iKOABwP5iACbdeQRfHKMoDgq2YhTF0wydBlljmqEHABdG3wFdvGbT0MEXvuAxuPnmmyUUTJ0ng9aXWgTI4KK+ZxaH5EZncQcMtjIRYq9MpY1rCvJnvgTo10LWNT+vyRwlg9QX4C1veQs+/OEP42Uvexk+85nP+JD4CPgI9DACHgDsYfB91z4CPgI+Akki0Etn3yTjc8cQDNq7d68IyV922WVpqjYcewxB8F0B/hzjLn6AMRbkI9Cm1MmW/VQq2zA7W8LIyBN16b2ugjEax4+vRRBorB0loFfflNUL3A4w9VdSjetTiE24EaEZFa1BpWpgI8FDagQaMw+og1CKKdpkB04gNCcBYQsWBDBUZjOgLoDCZdBMJW5RuhfbRSxLVJV6YXSWJcvBpW1S3486f0yx5IMcwQSClf1QVjMASED+oYcewujoKC6//PJ+WI5FjYHgn2MUxUFBMgibFbpwxoEDxoOAg2cA1qLlmW4Ldw73GePCwvOG548vtQgshUGKMxGKn9ft9EKdNIA7v/mdshO9fAnqIsT0X6YBU1OzHyU7erGXX/e61+Ff/uVfcMMNN+DjH/94L4bg+/QR8BGIIuABQL8VfAR8BHwE+jQC/eDsmyY0vOnlm2++9c6ie8P03SD4KrT+gRh1AFPQusayaxwLATxjdgGYhdY1FlrjcXNzA5idHcXo6CkEQXNXUoJ2odkYuQbXpxezPaYQEww0phzpCdb3UgMDbTqwKxas3BqZiBAMLElbZAKGmBOmom1/AEYMRjZBmXOh1OXQ4j5si4stxeh7BeTwoZgp3gSU+LDGQobV+eefL2w/PnjxoZkOkUxT3rZtW5rts2THegBw5QCAzTZJ2jRDtkGggOeSSzUkw2g1pYbH40imG2PoU11rUSGgzNRolmc84xkCIvtSi8Dp0o1kqnCcBUzwvp00AAH/RgdintunGxSkJAY1cfkSjKYXvgC/8iu/gi984Qt4wxvegA984AM+JD4CPgI9jIAHAHsYfN+1j4CPgI9AqwfaRp2/Xjn7plkhssIefPBB0Uui0HOywjTfB5DL/a9o/MWLZeAR4KP+X2uAj3XCkCBbAVrva8r24zE1wLAIrQ+2HF4Y7hANP6snWHMJdhWMWQMTbm6qF2jHshk8BorMw5ouXs1EJIAB04tnpEkLPoYwpgilijAgsBkAhkYi6wQQ3L9vC370wBFJ0eMD6ekuR44cEZ0/Pnyx8EHLsajiwAkfDMnaIOuBWpD9UOIAIH/uB13C0xWXlcYATBo3rjOBaMcmcuzuTKMAACAASURBVN/JXm1WuIfjTEGCCCvFkKBdzHyqa/PocJ/QBZiFn2XcC77UItDLtHEnDRDXFOTPrVjA8XM7bjaylJ8D1OylNAY/A/mCbLUXXmde/OIXywvCP/iDP8A73vGO1R4SP38fgZ5GwAOAPQ2/79xHwEfAR6A+As7ZlyAKH0Koa8UvZ+5xut9kp1kfat5Qq47pUldddVWHqgZa3yPafvximmy7EoYEkxS0PtD2uNnZAUxPD4urbz7fPC2QDYThBhSLQ8jlHkUutxDk4zHGDCE0ZLFNQuujTfttpxdo+6ERyAjQkK5cbyJyBEpZ1iF/XxFXZwhrQYE/F1AJDUplg3J5GMNDT4E2l0Grc1LpCKZZS3csH6yYfvzEEzVjFLo+nnPOOU2dWPnQTMbGhRdeWHWHzNJvN+usZgDQu1HWdhL3Ad1Luaf5koLXVAKDjs3auOcaDQkcs2gpgYNu7vskbcUBQJ/qWosY9wTTXFmuvPJKYTr7YiMQ3zOU+hgfH+95aDgmahI2uou3Avx5bpMZGDcb4c98sdWNcs8994AvzXbt2iWflau9cH2uvfZaUIP1Xe96F2688cbVHhI/fx+BnkbAA4A9Db/v3EfAR8BHwEagkfF3yy23yM0sRbZ5E9nPwJ9bQ6aH0gmYbIlnPetZLZeWxh653P+B1nujB4pxcdlVam8CIJBOvUzp+UlLpp99SKHG3k4oVZ+S2ziocjnA1NQYJiYILjYH+ez6bIGRfpsbkHTSC7RtkKU4BIi5yHTdUIyxxiPWRKQGtlVCLexAyhM6jcLQUCtQQyBTMwhgi00bxlMRqA1dOaX4AMx0bjLIHPuUqd3U+eODUqvCfUtQhWlP3Lf9UDwA+GNZsyxp+f2wft0cAwFA7s9zzz23ClBTs7IROCB7sFlpBA4cKNgt4KCbc03Slte6ax4lrv+tt94qf7z66qubvuxIEt+VeEx8z/Ca0u7zoJfz53Wfn2NxTUH+3Orc5lgpDdAIClJrMG256667cPz4cezevVu+VnvhWnCvMEPkQx/6EH7rt35rtYfEz99HoKcR8ABgT8PvO/cR8BFY7RHgjRF1bnhT7TT/HFNlampK0keWyw0kNW/45ptsiec85zkLlpauukHwBQTBPU2XXVx0zfoI3GvOynMV6eJ76tQARkYOIwhaswdracRM+32s7XYLw22xNOLmbVpgkSnCM9D6UIt5UC9wB4wpNdcLlNRm9jUAqEPVVGDXmLgMm/UIjQUDq8BfCJTKOUlhVWpefh+aKLUZZC7wH3xYYf0tCPAkKFwCrZI/wHAfEvQj+OfYEwR0CfxRM60TEM2H5hMnTsjxZ555Zl+c3h4A9ACg24jNAMBmm5R7vzF9mAyjVqUZm4jM7X4vca27fgZzTnccycC//fbbpdtrrrmmrx1nT3dslnt6NMfvAH/3nevtXnQ1xpNuw40OxI3u4o11vve978nnINl//fIi7HTvk3h/vK/gS0HeI37yk5/EK1/5yl4Ox/ftI7DqI+ABwFW/BXwAfAR8BHoRgU7OvryBPHbsmNxALpcUEmre8M03b5iZ7lErswiCL0Hr+zuCcKzjXHQty689EDg/X8DMzBjGxpim21zfqwYaUrdvCEpRJ7AdaLgWhmYcwtSzmnfNCtOIxclX09yjlbnIWpiQrMUpKH1kQTNWl3AbDAgWPAal6plHxgyL03ClcgRBQAOSWhOVkABgIGnAStm/hYaMQaYNG2hFoJGAJQXs10NhK7QAgmcvSB3m8Uzzpc6fc14kgMG9x5TfpAYJt912G/oNuF7NACBZuWRd8AG2F9qRvbi2tuszKQDYrA2+qImDggQPmO7eqsTZRI5VxGtjPxXOibqdLN7sorYyXNdvf/vb8otnP/vZXUsN7ae1zzoWMmbJ9GZZKenRBMKd2UgcHCRw1azwJVwjKBjXDOXeYXt8gdsvWrhZ17sb9RhH3kfw+vm5z31O9AB98RHwEehdBDwA2LvY+559BHwEVmEEHMuPD1782f1b9N5iX9RKISBD9t9yEZHmeDlu3hy/4AUvEEaa0ncjl/tM1QwjDHdBCXtuIRjWuB2MWYsw3AStCQS21vNjPXHRlZRfgmitH8rtsbZdpR6F1s1T/exxBOd2Aii1NQ2hYQePM3Lcoy13Nftk30pYfwvHaPvbDoMAAEFF67TrSqWiUSwHCAKNQFsGoCtkAhIQJKwZKJsuTB1BOg6HbE/ci7nnaEAyABg6DG+AxjmYPXU+HnjgII4etSnQ3IdkLdDkIy1gwQcfpj6dd955OOuss/riDI+D7XwQWUkabp0C7AHA+gg5AJD7c8cOOo0vrhA4aEwf5oN/KzYR0wkdGOi+Z0kxXNyoa7WXO5urW3FobIdABfcKC9nsq+ma0SmmZMLyRQ/LSk6P5mcF59qYQsx7t2aFn5sEBfnFeyEeRyb8tm3UEV7dhbGghAivi1/96lfxvOc9b3UHxM/eR6DHEfAAYI8XwHfvI+AjsDoi4IA+PjC6dN92zr533303Dh06JEAMUyeWQyFj8Tvf+Y4ASC984dORy/07dHDfgqHbtNwzoNUklJrsOLVKZRhTU0MYGzvWNt2XDdFplyCj1sc6ts1jjx5di6GhWYyMtAYCbbu8eR2PNADrgbn4BHgMmYFKkz14sunc7Px3AHT6VQcEmGssHJtlBmoAj0KpenYjwb2yMACBQAM6BgbybxUTIDQV0Q7k3+0cLENQ3IgVdQWZeq4FOGRq8fxcAaXiZoyteSaGBp8GrdILonP9uQ+osUYAsR+KZwB6BqDbh9yfBOy6BQA2299xNlEcPGiXYhgHBQkgkD3YKd2+G+dW3Ozimc98phgj+AJJ3yQLn4VgxelYi+US99XMjnTu4o0OxGRFtipOHiDOGFwO8gDd3I/MCiADkIUviWke44uPgI9A7yLgAcDexd737CPgI7BKIuCcfeMaf5x6nPHXGAqaaZC9w/SRpzzlKcsiUrzJu+22W7Fr135ccumPFzDYFoJcBKPOhBadu+ZgWbzO/HwepdIGjIzw+OZv4d3xDmQEpqF1zVSjVSDL5R3Qms7ErRl8rMuU2jDcIa697UxDHMjH70rTOKSVpiCZi9QDnG/JMiSLr1jcKClFa0dPNAVBSwQDQ8v8y0WAn5urpAWHefoJQ6tizEzE1nEjI1AYCvBIlqCdq8I6KFmjJ0PjPGhFdmLrQtYMmYQE/wgC9kNZzQDgo48+Ki7OTE+74oor+mE5ejqG0wEANpsgPwNcimEcFGyVYkiAIA4YECDspDuWJbArMZ0zSxwa60xOTuLOO++Uz2jPVqqPThwcfe5zn5tYHqIb69KvbTizEQcMHj58uO1QHRM4fo7zdysVaKYb/YUXXigx+fGPf7xsZG36db/5cfkILDYCHgBcbAR9fR8BHwEfgRYRaHT2dcw/3uRRU63dzd7999+Pffv2YcuWLbj00kuXRYynpx9FqfwRrFt3DMBuBAHTWNsz6yyoxhRagkxM353pOFfRxQu3Quv9C5hxzR+++ea50jaN19ULw40AmKbbWX+QpiFAHkpxHO00BdcgNFsiR+LjLecnJijhBkAdh1KM4cJC12Ktd8IYon1kDy7UKCIzkKy+ZmBgnDlIdmAuhueFIVmDCgZlYRRaTUGF0FCfkKxFjofpwxPQIAh6LgJcBK1qumYudZ3pv2RZ9UPxAKAHAN0+7BUA2Py6ZFMMCajEGUVkEDYrTENtTB8mu2gxoEHc7faqq64S5qEvEBYzWfj8nCbI5UstAg4c5W88O3LhzuDnzc033yx/oHwL/+1A/3byAHQSb3QgXuz53S/7lvez7gUUtaI3baIusi8+Aj4CvYqABwB7FXnfr4+Aj8CKjUArZ98kwJ8LCoX79+zZg40bN+JpT3ta38dKqe9DB/8KrWsAnjFDwm7Tem9HMw9OkEy36ekNGBw4jHy+veafPX4IYbg9AgJbp+W64IXhZhgziCDY1zGexowgDLeIaUknUJImG8Zs7mgawk45XoNCBBq2niP75hiUamcwMghjtsKYOUDA04XTqoQBKiEBvAoCTVfi+mMIFPKLucJOO9DG1rIB+aUQCpvTuQ5b52GmL7Mxig3SZGQcymzCI/sC7H14GDt3nCf6R/1QPADoAUC3D/tdnJ97Na475oCDVrpjBKiagYJJTXtWi55b2usQWczf//73xfyDJiC+1CLgwdH2u6GdsU6cCRw3G2kF+vM8dixB951s7uWmSckXL9ddd50Ejtcc/6LBX1F8BHobAQ8A9jb+vncfAR+BFRSBTs6+LuU3yZQffvhhSZWYmJjo89S9eejgswiCW1tOS5htZI2JmUfn2ZdKOZw8OY6JcTr7tk/1rQGBBBqZatsZCDRmnXXvFTfg9i7DVouPDMITbVN+7TiSmYbYYwcFDIRiu61TlG2bO1AqVZDL0bSkefzIiiyHdAw+IinAzcFAgKnCzO/NO/wu1hzTlcvCAiRYyHTh2h+FOSjAHyE/JX93c7Zpw6EwB1nKlQCmMohcfgOUGI2chZy6GFqNdV78JThiNQOABw8eFGdnMkmo8bbaS78DgM3Wx+mOxR2I+TMNPJoVggYECRp1BZuBgnT8vv3226WZa665JrXpz0rdTzRx+MEPfgCmYj/rWc9aqdPMNK8jR47gnnvu8bFpEb20upqNoL8DBlud37yP5PndCAwSrO7XctNNN+FlL3uZyBiQ8dzPY+3XGPpx+Qh0MwIeAOxmNH1bPgI+AqsyAt0E/lwAf/KTn8iD++joKJia1Z/lUQS5f4bWBxINjyw5gCmsBxMePwIjLLykDEIy4raLu2+y1OO1OHo0h/HxKeRy7YFADpjafyxJ5pvUNMS2uxkGZPsxpbc1gFks5nDixFpMTAxA60MtY0jWXym0BiAE+poVsvhKFav/R8AwDva54wUMDHMCkmpVzx6sug4bxqNWn78vl7U4GYv5iCpFwCh/x/TCtdBmvaQP53AxAr0+0V5YzEGr2QXYA4D1O2c5AoDN9j73tNMdiwODrcwIHGjQCAoyBZgxYSHQtdrMCVpdV6jhRh1e6rLR6daXWgSYwnnffff52LTYFN1g1Tae3w4U5PnaqhBci2sK8udeOozHx/nZz34Wv/qrvyqpv/xMWm4MRn/++wistAh4AHClraifj4+Aj8Bpi0AzZ1+meMTNPbLqM1E0mTfZfNPbjwwEpe6ECv4ZYDorgT29pymI1BR8CqlhN41crrnOXWMdY0ZhzAZoRSCwtdaeq2cMjTWoUZcMCCSwRuCwUKC5SBINQjoCOwZh+1TlmmkImX5H2u5NpkCTbWgw0xbgYyMmnEBo1kGpI2JI0qqUygrFciDGIYVc87GS1Ucw0DkKO9fgeJtME6a2YCvAsBIyXThAuUJWUggVcx6urhjTieUP9o+iYwjOeQRabYTGLih1DvI4C1q3NxtJe5IzxYqpWTw/V9PDhwcA63cK2W5kvTE9fds2aniurNIMFOwEGhCwYLn44osxPj7u2TkADh06hB/+8IeSqti/L+B6s3cfe+wxUNONgNOVV17Zm0H0ca8E65jyysL08W6y3cgKbHQgpq5gq1IoFIQJHGcLLoWZUKfl+MQnPoE3vOENYg5GeZukEgWd2vV/9xHwEcgWAQ8AZoubr+Uj4COwiiNA4K9R54//Zmnn7JsmZHxwZ5oNH0D6S4Sc4M7/hQ5uqpuOMZuYGCqaeUkKw3X82ASGR6YxNNQ5bZdtMo3YmHHLCEzQCcE3AmrtdPTizVgAjiAUgcATHXtg2i01DpXo77W+CXcNJTUNsXNdjzAch0qgQSjtmkEYHIDWrWNJkI8pwNT6a8UMtCYhBTH/IOsv0AuZkXQOno8AQx5PBiDXwxmH8HcVSRe2RjdaVaopyVZbkC7DFUkndsxDA6Y6U6vQpihDcS3GoLFZgMG8ugCB3tpxTVod4AFAnwLMvbHSAcBm+5+gQZwlSACBIGirwnTxRgfi1cYM9MB560utdxZv/zE0NTUFmmGxnA6TFH62xR3GeX7zq5XDuDMTip/jPOeXEpT70Ic+hLe97W146lOfKrHJ+mI88w2Ar+gj4CNQFwEPAPoN4SPgI+AjkCICi3H2TdENmIJ05513SkrW85///DRVl/DYGejgY1D6gZZ9TB4fx9DQTApQj+y7MxLr9wk+FG4AwTcd7E8JBHY29LDgG8GoMxLp/tnj6WK8C8Cptnp+LmhpTENs2ztRCYvI5dqnTlcqGlOTo4AKMT5+oq3eogMDc6reCbhxYYsVaxIixh/QUNDQugIdY2LSPbhiyOqrIFBkwNa34pyHBddjfzEdQwsIEuwj469mNmJ/zz4VlBiOsDYdiNdAmXEotR5a7UKeLsS6s7bgagUAPVunfi+uRgCw2cWabFiCgtRzI9u8U+GLqEazEbKLVmpxIBdBkmc84xkrdZqZ5sX9Qn1iypNcfvnlmdpYyZX6wSSF96kE+XmOx81G2pkJOd3QOFuwW2z5d7/73XjnO98p2Sxf//rXPQC4kk8AP7dlEQEPAC6LZfKD9BHwEeh1BLrh7JtmDnQh/O53vytvZa+//vo0VZfo2IPQuY9IymmnQiCqXN6BgYHHhEWWpFg23VZovSdRmi/bpFMuWYdKP5oQCHQMv2RAoO2DwF6xY0qum6O4/Bqm3O7vOO00piFsbHZ2EKdODWPd+nlo3Z5xyLRa6ifSvES1MRlhuwT5mN5LYM6BcwT95suE9GoagTyGvydrrx2DkO2ZUCMXoDmD0Chpx1ApsEF/0LEHCf7RbIRAowMUrdkItQTJSixHaz4ImAJghqHUOKA2IYdzkMMF0HqgugYeAPTpetwMHgCsvyzF2UpMV4wziQge8N+O3d54QaO+WDNQcCWwexzIxfk9/elP73gtX00HUJ94z549ki5+2WWXraapJ5prvxrIxM2E4mnElA1oVcgMdOe4AwazsIH/8A//EB/4wAfw0z/90/iv//qvRHH0B/kI+AgsXQQ8ALh0sfUt+wj4CKyACDQafPDNKn8nqY2RJetSPPBMTk5W3Rlf+MIX9vaNqXoQKvg0YEag6OSbcF0rlWGcOjmI8fFjidx/2SzTfGFGoTQdepN1RNBNoZIYpKul+h5KlLprgcBtEbDXmS1j50GnYTLV9kOpzk7GYbgBwGhHIxDbtsL8/GZQ22t0bLJjnMJwo6TTQj0KrazeV7PC1N6Z0iAqhsBcser02+xYpwnINcq3cB1mPQcacilbuxPTeZgOhrbf+LrHAUG2R/CxmjYsLEGCgoHdk2KgogQkpM6goT4l1kCDe2onUDkbJtyJfLBymUuNa+UYgF7LzEbmtttuAzXvLrjgAmzdmj2lPNmVqf+P6pSuyM+7OFjgQMFW6YUEBxpBQe69pfiMXMro7t+/Hw899BDGxsbwtKc9bSm7WnZtE/wjCLhu3Tpceumly278Sz3g5aYfGdcNdee60wVtFqs48O9AQf6u3Tn+xje+ER/72MfwS7/0S/jXf/3XpV6CuvaZTUNNRn7xpTq/+IKdhcYkHFfawrZY7+abbwbZwrwebt68WbRlmbHzqle9Chs3blzQLE2a7rrrLhmDGxPZtO4lS6uXLWnH54/3EegUAQ8AdoqQ/7uPgI/AqozAUjj7pgkkH7RuueUWqXLdddd1VUg6zTig7oAKPgWlrIGEIdimZqFUMgMP1jl5cg0K+TyGho4n7lrYawKyJtMUZMNhuBMKc4nScGUuJofJSaYsn8DgYDIdQoJpEMfen3QE3mwfQ5EzMcHGUx3nb01DqFs4Ca3tTWq7Mj9fQLm8EYNDTD9ubQZix6IQmq0oVo6gEBSrQNpc2bL9CNA5AxAxBokYf2T7NTMGceMKjUYlpH5fiJyudwuOj10cgjs4D7t0YR5LoI8swniJpwyTCahUqS4dWZiF1BIUsxHOkXqdTOnOSQoz9QUN8lAga3AjFFmnOAMa50CLS/HKKsvtYXSpo+8BwPoIHz9+XB5I+fBOvbIkJZ5eGNcWbAUK0gShERTshRFBkrm5YzzLrXW0CIwSIN2wYYMYx/hSH4GVoJHIVOF46nAnNjCB/3jqMBn3BMScAcprXvMafOYzn8FrX/tafPjDHz6tW6YdMJkWACSARzOTf/zHf2zJjObk6Hr8kpe8ZME8GYd2gKMHAE/r1ljVnXkAcFUvv5+8j4CPQGMEltLZN020qd/yjW98Q6rQBIQsitNe9Fehg88t6FYYVmZ3xG5LluLLRkx4JpQ+Cq1OJp4KU3C1OgWlkwOOIfvBFLROBjgyZXl+fqsAgSrh2Mjuo1GHBQLbOwHL3Ku6gsl0AlnHphPnoDVZhJ3dj+V4glvCOlxo3hEPeiXUODkfis7eYM5q87UqBN2KkQ5gIagx8Jodz/aoL8jRtjtWmH1hXsxGGL+cbh5DAQ0F1LMgZDMgssYyJGBs6tyoGXfHLjSGZiRWo9ACiQQJc5HpCBeJaOOIZaCadQC2IcDZ0Ngm6cjLsXgAsH7Vbr31VmHOegagjYsDAMlmX4zZFD83neZYHBQkENCsOCMC51BKPTmmG/YLU9Cz3Fpf7ejieuDAAQF4LrzwwuV4WVzSMTv26ErTSCTA38xspNk5/o53vAM/+MEPcNZZZ+HJT36yXGeoaX3DDTfgb/7mb5Y0/o2Nx68pO3fulGv/l7/8ZTksDQBIpiRBvf/5n/+RutQz5HzYHoHOffv24fvf/z7+/d//HX/5l3/ZFAB89atfjY9//ONSn9c+ptD/6Ec/Etdxe5/Y+T7vtAbPd7ZiI+ABwBW7tH5iPgI+Amki0Az467azb5rx8E0j0wvcjQYFmk9fodPv/4EKbP+tinFASYq0YGFn0WlX74NOkBprb4rI4DoTWj+eOGWXbDepo44mcvS1/QQgeKj14RRA4IhoEWp9AErNJ1oim07sgL3OVUqlIUxNDWHNmpMYHOwMuJJ1GNKdGJMCuMZLGCqUwzNRCScBZQFSAmj8aucO7NoQMDAC+MgMdOm4zWZRd2wL8M7VkzFEWAGBw1aAZCXMieGIQohAl5se5wBBts3xxUFDC/xxbzDlmKBeCK1tirZlSEYag1Gav00pJltwCAANSMagsD5iDZ4LDf6+f4sDAJmidfXVV/fvQE/TyBwAyIfSLVuokbm6izMsICD3nOc8p6vB4OcnUwnjgCB/bmdE0Og+zM+9pXQnbTXhhx9+WB7oPcttYYTuv/9+UFqAKfQEP3ypj8DevXvBr4mJCXG9XcnFAf9xmQD+TGDMpdnG589z+aKLLpK48IsA2CWXXCKGMktV/uRP/kR0PPlF0Jrs3t27d0t3aQDAP/7jP8Zf/MVfSL33vve9+N3f/d2WQ6bzejOtxE9/+tNyTeRYeO64Fy80RrH3IB4AXKp94Nutj4AHAP2O8BHwEVj1EeCbTT6U8MPXAYFxnb9esBI4nptuuknW5qqrrlrSG6T4BjAoQQX/Cq2/l3hfnJgaQy43j5GRucR1HHgomoIJyVUEYuiIaxlxnYEwe0MVCPNQ6+R6fxYIpDPxkRTg4QCM2QGlCFJ2TvW1Y5uQL6UeSTQfgneTk2NYu7aAfP5wolgTnDQCUh1COdyGSvgEoE60vnGtkJUXIB/M16XWNqtAdh7BQJZ2oJ2dq00pZp242UirdoVFGLH+4q7BdXs11qZWCjldMwyJH1epBBErkaChWZBWzDGJ43B0703Q0O1JCwoStCZbMAQEtKYOKIHCIDIgGYJSI1BmAzR2RunEm/uCNfj444/jvvvugwcA7Y7wAGD9GceHdLJWlgIAbHZuOyOCRl3BVkYE/OxtBgp2y5201YWQulw0AqGO11Oe8pRE19rVchCvJ7yubN++Heeff/5qmXbiea72FGme42TQUueO8gL33nsvfvjDHzYFBF1QzznnHAEEf/EXfxE///M/nzjWWQ7MAgByPtT3I7BHFt9HP/rRLF03rUPmtQcAuxZO31DCCHgAMGGg/GE+Aj4CKy8CBP6YvuCMPRoNPnoB/Lko8ybqS1/6kvzziiuukLfJS10I/kHzxoYgVpDYVIPjIjg1P78dA4WDCIL26af1IM7WyMAjGaDFusasgTGbIsfgZFFxxh9aHRQNwyRFwENzhjgfqzagWf18XJ3jUBHDrlNfxgxGOoHJmYdWi3BtR/CQxh6l8CyUyfjDOIBTgDrcMaGVYBi1AYnNDnRIEZb1FzDQptMWcs2ZefE4lCo5YeJpVUG+w34hGEhWn7gPtzEciacfk/XXCjhkW6Wysk7FuUoLnUEak3D2BBUrdUzHesBQR07FfHnAtQ9YAxCWodUaVBL3jdDYDo0dklKsCR6ehuIBwPogewCwPh5HjhzBPffcIylsdAHuVSHjvZEpyN81K/xcJjMwritIkLCboKBPc229E5jaSadbplOee+65vdoyfdsvUzqpA+hTpO0S8V6WLD8Cb29+85uFOUpgkF+MVVw79O1vf3uVZbdUC5wFALzxxhvxnve8RyQKyA52DMJujNEDgN2Iom8jbQQ8AJg2Yv54HwEfgWUfgV45+6YN3Fe+8hUBKC+//HJJRVrKYsG/fwLUfdFdG8GPswRg0jqZQQYrFovDmJnOY3xiqiPI5OZDlhUM9QEPQ6vpxNOkBp91JiYjMFk1YwZgwh0pWYQ2BVlJOnF7o434nAgeAjOSUpykkFV28iR1BWewdm2yOMh8zA4AxxZoHpYq21CsEMVr6J/mF1gLg0cTpS3TWINMPwJwBAM7FR4/X7EsuoE26byunTjA16n9MNQoi+EIU3vJUmw9GpfaLA7EbdKVyxUmFBPAW5gy7Fon6EfdROoEUo+Rzs71bsXUGuQ+YUtWZ5BjY5uSji5sQYJ+LsUnD2OGxaWYacVQ6xFgBwLshsaaTiFO/Hc6IJKB4RmANmQ0ViKwRO0yPqCv9kIgh4AO09WoadVPJe5O6sBB6je2KgQFG9mCzoQg7bweeOABHDx4UNLEmS7uSy0CZIySOXrmmWeKxpsvFm7YGQAAIABJREFU9REg243SCz5F2saFAB8ZfnzZwBRYsvxcoW4oX0A4QPAVr3hFYjOirPsuCwB49tlnC6uRqbt073Xz4jWCrEBeJ2hslKV4ADBL1HydxUbAA4CLjaCv7yPgI7BsItBrZ9+0gfrqV78KPgQxNWIpH1Yt+PcRQN2/cIiGLLN1UGpfYpCNjcxMb8DwME0XkhlxsI7VWtsJpfdCdzCxiA/UhNsAsrT044lDTPDFhFuh1F5onYyx6Iw8OCe69CYtYUiAjlpzB5NWwfT0OAqFEeRyBOmSVWM/NjX1JIohAb797SuKDt5OAHOAeiwRYEuwjqm8TPltxbCLd+rAwKTgobDzIrAxSVoxgckK04rJDmxDqrPuwnkB8bi3ckFrt2KOWdyEm2gIurmJiQlTh6Oto/VCPUQyCMOQg2L6MAFRgoa2QlVvUPKO7e9oNGLElIQA56CA29Qd1BiHxlYEahcU6FqcnD3oAMBCoYBrrrkm2UZawUd5ALB+cZfb/uDDNsHAeAoxQYRWhQ/ljQ7EzbS5Gus7EGfbtm2S+udLLQI0c5icnBTwjyCgL/UR4AsXnlc7duzAeeedt+rDQwCQ96988UIDjRe96EU9jUlaAJAvSTZt2iRjfuMb3ygMReoBfuITnxBzExa+aKBUz+/93u/hZ37mZ1LNzwOAqcLlD+5SBDwA2KVA+mZ8BHwE+jcCTtevUeePdP74V7/NgLogFAy++OKLwQeRpSgGxQj8e6Bt81OTYxgamsPQUDKjCzbGdEhh9gl4aI0WkhQ67CqMpQYdrYHHZCpwjgYbJ04MYt3EcRDESVJqjr6TiZ2G2a7o8ZkBBMG+JN3IMdQIDMN1iRiLTMMuVpjue0xAW4PHhE2YqJh1UJhIXIe4FVl+/E7WXjsmnuuf4F5aJiGPT5qG7MBJLmMn8NDpFzqdwXbgYSVUNmWYKcs6RD7XXKibx5GhaCRtOJSYNIK3lklY4wHyGBc7yxZ0acRWc1Ah0hwEf89IsH1qEg5CqWFxK9bYLNqDOdD0pp6FsNwAnkR7dREHeQCwPngrIUWcn+uNmoJ0K21VBgcHF4CCBMjjxenceRBnYRS/973v4cSJE8Lq2rVr1yLOxpVZ1TEkzzjjDJA5ttoLWbsOQOP1l0BZL0taAJD34c4h/fd///fxH//xH5IG3Kr8zu/8Dt73vvclnqIHABOHyh/YxQh4ALCLwfRN+Qj4CPRXBPrN2TdtdL71rW/Jgw1TkJbiRtuCfx8G1IOJhkYzhbCyHYXC/sRgmQWxxmyaI1N1E/VkDzLhdihVhNZPJK5lARQCgcm1/tj4/PwI8vlN0GpvYsaddRpmmu8JaH0s8RgJ6DFVulA4kDiO1Ak8fnwNhofpBLwQhC1VNqFYYfrpkdo4mK6KnTCKaXM0QUkwRElT3YFyeBS54FSi9SKoNV/OixbeQC4Z0FsOc+BXoMrIBZ3rCGBXpgCgQSEwHQFHe7wF2jqZjjAqLg2ZPxc6AJoE8AhmsjCm7UxKeBxBQaU0tKaW4ELwkKYrFjhkIXBI5mxsGSOTEs6JB/FP/LshGGhykl5M7iB/Y92N+TUAhUEU5/N44vEyisUxnHfOsxAIk3AwwUZYmYfwmkpWNZ0o3UPpypxpslk5l2iCYr1+ME824mRHUTqjGSjYymWTAGCcKUgNNzoke527hfFmCiRjS3YbAVJf6iPgGZL18SCDzgGhTPfttalOWgDws5/9LF72spfJpCilQSYjr5Xvete7JCWYACeZjW95y1vEHZvl7/7u7/C6170u0anhAcBEYfIHdTkCHgDsckB9cz4CPgL9EQHn7Mt0Id7Ia61Fp4PfHeuvP0baehS33XYbpqamxGmvm6LD7JFpv6H6VORamzw1VUZrNluGUwpgTqqFO8UhN11aMJGO3VD6EHRSNpuAjlYbT4vLcGRVm2DBTbgBxoxAB/sSgV8yryoQeBJaH03Qiz1kbm4Ap04NY2LiBIIg2RiF5VfcikLBpjyHYQ7z5TNQMXsEHGtZzCZxqjWg43BnwI3tVMIBzJXnRcevHUMu3ieBNLL2aNZBIC1JIVDHtGLq9JG516lYwNGyD9lHklTkUiWPchggQBn5DiCldSwmkJyDVmUEuh6QaxyfAwRdynC78Uh6ceQ4TOYfzUqaFXccCAySqQwtKcKWWci9YsE+CwIS/uMYawCjczWWVGOaF7MFdiZbhGynAcAMAxiBNtQcHEWgN4lRicKWVGnGndarn/7uAcD61eAD6/333y/6VVdeeWU/LVXXx8J7AjID42YjBLLiJgSNnRIYZfqiAwf5716ag3U9KBkavP3228G0a6ZGL1VmQoZh9U0Vut9yj9EghQDyai/Uzrv00kslDPv27VuSl9lpYpwWAPzUpz6FV73qVdUunva0p4GfI7wWxAudwynXw2sM3cM51yS6gB4ATLN6/thuRcADgN2KpG/HR8BHoC8i0Ojsu3//fnnAoUA4NbCW080737QTvGSqDb+6VWhSEKqPAepu26SxZh9KlZJ3IemIrEc2YPJ62dOCCVjsgNJ7mrKoWg3cmFHQLETr5Mw+thWGm6GQh9IHlhwINGYIxmyDSuFQzDHOl85FmQCPeTAZu0/WeggK22DE0CSZjqFzBGb1oQSOwG4t6CJMIGsw1xroalw3V4eAYxLQ0Y1NwEDqEiYAEMnKI4DoUos7pS9Ty69UsSYh4i7coQ/qDJbpchwaAQ9zudYakwTnyiGZpGT1MXnYAptk+LGv+NiqoGDEAuTfm6UYO5dipgsTE1bapiPL8gtY7YBD+Y2kGRNYtEYlRG2JSvJnCxQqcM9MQGFDlG68A1rx30kopckvKafjSA8A1keZIvY0vBgeHsYzn/nM07EEfdUH7xcIZjWCgmQQNivU+nJgIA1HRkdH5SF/Od1XLHYBfBp9+wjWANLzsXXbBIBp0SgWrVfRfOXPvM7yBQyNoOrTzxe7Pv1W/+677646jFMzb3x8vKdDTAsAMuX3F37hF6pj/uIXv4gXvvCFTefw1re+Fe9973vlb5///Ofxsz/7sx3n6gHAjiHyByxBBDwAuARB9U36CPgInP4IMLWHWkC8oXepv/zOFCeKMvNtndPxOP2jy9bjHXfcAaZPkP1HFmA3CiEGo/4NRt1W1xxTUudmA4yPn0zXDdN71Si0fiRVPdH5M6Pp04JFq24kAiyTd2nMRgEztD6QvJIAgdskvVJrm9qRtIQhU4OnoXUsJbdDZWPoDLsLSj0BpU60PNqCXmejHO4FaCohmonrI3ZfcsdmMVyRVFKCv8lm5sAzgm1J2HpsleOdjQA3goFJ+yIYyP6oMZiE5ce+4gBikvE5NiG/k7WYBHR07sLsj8d3AhAJ3JHhSHSPx8YBRPZL1qRLVeYy2LRhuyAWcGzO7GRcQ0Og0SKSlgFY707sVpVjcKxA4nZsPc4+tG1FrsXyVzIOOV46GhP8DCKA0G2UyLBEwMIClADLa0S7E5iAxjoEIIjOtPqEVNBkW3BRR33zm98U10amoZGlsdoLU11/9KMfyQuyK664YrWHQ+bP+wbq3BEUHBvj55uSlFfeXzQrQRAscB8moMpsg5VYVvc5xDuo4zCKn+tHATUJhWnLxJYrZhmHD9O4q4jx8UEUCrxulgA1S2u0iHlNc6eCHG8MPxjn5BhlNsr1UpktkmWhwXuPgWW/hbhfaIzB84HpsklMeJZy0mkBwC996UtV4xJKBfC60KgZ6sZ700034QUveIH884/+6I/w53/+5x2n4gHAjiHyByxBBDwAuARB9U36CPgInL4IdHL2JYB21113yU3H85///NM3sC70xDenBDCp/0cdwG6UUH0eRt3UsqlTJzZizdopaJ0CSJKnJpp9PAGtW4uvN+vUpgWfTMxGc22YkGzAWWiVPOWWdemUq9R86vTlkOPEXOp6k5MTCIJ5rF2b0IxDHkCZ1kl3Ra5D/fzm5vOYKU4gP3B4YTgN2Vo7YNAeQFxQkQCiIoCYUjexzDRZMvzap8jG+6NRxnxFI1AhBlqYaTSOz7H8hE2YT84mJMuPYBrBQ4KISYpLRxbX4qAzWGnThS14xsc/AoKdAE4HoiYxIOGYG9OGOZ9WfVi3YzseFuHytTjeuRTz4dUmCRPsW+iOHMp+pPEIjyMIwvXmsbYPMS+J2IW2R4KDfLx1YCEZLqxv2YTicEwQXxyOaWKy0YKFamzJWYWrG7xYeAYcOHAADz74oABYz3jGM5KcIqviGL58o/wGtcto5sD7DBpyNeoKEkxuVgh2MKZxXUGCrCsBFKQpAhmSl1xyCdavX7+s94O8EMVUpJ17BEbx55MwmBHJEX7mKzUHpYvWkCmSEwnFUX4AZVNAaEKEmJV6ZE3PzeZRms9hZHhcAEDRddWUk5gFeL/C73ItHQVEfoHXR75kmUfINvjFS6do+O5AgN3Q5iIEWJ6Oy9THe/nLXy7MvyNHjoCAeS9LWgCQjuAXXnihDJkp3cwqalX4MsW5hv/Gb/wG/v7v/77jVD0A2DFE/oAliIAHAJcgqL5JHwEfgaWPQCfgz+n8HT16FNRk4b+vv/76ZZWqQ+YiH9C2b9/eFeHkEDfB6M93XhyzFkoRFErH6uMDfrG4EYODBzqCIPFBiHGBOQNKpdTr40206AM+Ci1GF8mKTYOkUQjTYFsz7Zq1RqdhldL0g+3MzGzGwECIIEhuaMJ6lklIwPIQJk9MoKKmURjoAM4aQi+sNweoQ8mCIkhODgq7YESnsQnA2KIlAk5k3hVSAG1sigAdwbM0GoMEtsgmJOg0lO/MvnNDLlaYxqsQaIPBhMAjwcpihaxMGo+UWur1xcNCUK1cIbsD0LqMnK6lEjItmDqETJkns84VGoBU5BwgsEdTlNYpw7JMDuTjYZEJSSstwbrjHSjI9OImDsXuWGoUVtmCwgJsDrpWWYMchOw5NkrnYrog29lZfUKmE5MJI0+1NhVO0uD43TFnIuBQTGgKUVpcvsoqVBiHjpiFStiFE5mu5R4ArD+JH3nkEVC7ikAVBe19sRFIouPG6wINAeLpw/yZJjPNCu9BmoGCvQZE0q75zTffLNdE6p1NTDDFtb9KRUA0fn4dhVGTAvBZUI33CDTQmgdE7sR9USS1AoWcNWtSyrK65XrJjAlmlVRQDg3KFb5gWQuFtRa4k+taGQazAt7xe7woSfHliw2+9OB9RwkGc8jpNQgIJqonInZgVMsMicyCorO72WK/y7/JrF6e5dOf/jRe+9rXimEMdfF6DYKnBQAJ8hO853dqXpI13arEwcLXv/71+OAHP9hx0TwA2DFE/oAliIAHAJcgqL5JHwEfgaWLQFpnX77Fp5kGCwHAXt98pIkMtQt5w0TzEieinKZ+/NgKbgH0p9NVF1bf41CSvpK8nDyxBvl8iJGR5Kw3udWWtOC1UKnTiYcAsxVK702pD5jD3NwW5POPIZ9PZsJhxxkBiOoYlOLDRfIShrsEmNM6OcAmKbTFC3Bi+nEMj7DP5P1BHiIGYUBwNWU9xXr7he2VtMyWLHg0nAKccynCaUE9AQMjAg6ZgZ1Scd0cmHJL8JGgWdKU5CoL0QA5sgMTMgrZFwFLFtZJMsZqyjDZeyphWrI8mNq9mdNG0ozbrTePowsz2aYszuyk1TpbtmCOXBZBIBVoQtOc+VkDBiPJq8i1WCC+Bndjp3novrN/HlN1OnYAovQr0GLNCjlKVbZahQQNqRPqWIbDYnqjMYYANkVeYz1u+dbtPgU4tshkszz00EOiZXf55ZcnPc1X/HHO6ZbSG3wBl6Y0AwX5u2aFoCDTheNMQYKE1Brsx0KJla997WsyNO4X7pulKCEIoh4DJN2Wn7Gn5MsoZhiQbefAvKKVwYh0TOXFgoioyic1qHdsXzbwV/Ylg/2/dU1X4AuZCvK6xmh28xF2d2UY86U1mOeLIMwgBF8YNt4r5KDlGjMs+qqTU8cR5EoYHGbqb4t1x5CwnnNqCwpqJwJF2QR+Vi9NPJdijZK2+ZGPfARvfvObccEFF4gcT6/vwdMCgJznc57zHHzjG98Q9iKfKQgINitf+MIX8HM/93Pyp3e+851429ve1jFMHgDsGCJ/wBJEwAOASxBU36SPgI9A9yNA4M/p/MU1/uTGji6Z0Vdjz3TkIuuD5dprr22p3dH9ES++RaZm0UGNWlV0HstaQtyH+cp/olKextBQOlAOhnp71MpKByBRw0wrmoT8RG6w0xQTUgdvKjW4RrMPphim1fkrFvOYnh7DxATBteRgl+iikRGoDqVOfWZKMVAUJ992haDLTGk7QuyTw6ZPjSAI1mBwiMBsiqhSqxEbMugEksGwFaaDLmHjSAhiEfjKK5u2m7SwHtmEBMrI8EtaXH8MCesljU3VEITjTAjQcUx0Oma6cE4XUAiKC804onkoavhFUmDVdOHQzi9JujD7ygIIEhy1WoI2Fkm0Cp0eoUtnFv3BNjJmnA/ZghwfhextWnC5JchpgUFR1BQwzxYjrsaN62XTipliTkYgAXcez47IMKzpHLrjLDho27PfeayDC8lQ5EQ0ymWDSkUhFwwiCKivRZahNTvRomFIds9Y9EBP1uGo/J4pfCux8AXTww8/LFp3i/mMWWmx6bbTLVmBTB8+ceJENY2YKcWtSiMoSICwH0BB6iASCGFhyjjBylYlFLkAGk0dsyw8RRYegTyCeNS9IxOPQB+/CJbxeL7R4f1CBNpVne0dc7i+t4ikF0kYkE0cgXzRiwICgHxZ4a4KrC2MZmH62WtLlG8r1wweSzukuVLENK92R36zZf7xRYN9YWFTdi0o2Lzw5VuALQjUZuSi7wT+ArV8GX1pz/W//uu/xp/8yZ/IfuF51WvDnCwA4Pvf/3686U1vkqn/8z//M375l3+5aRhe85rX4GMf+5j8jc8dNB7sVDwA2ClC/u9LEQEPAC5FVH2bPgI+Al2NQKOzrzP64I0E3ya2u6Gg6LB7Y/3sZz9b3rYvl8IHM6ZnMc0mq0D7qekfIxj6OwRBGWFF48TUGNatO54YIKnGSlJ0j0DJG/gUxTBVj4LordMmmrVWSwume29yUI5tia6gPgWtjqcYqGUg8kvrdGBnpaJx/PgYxkYpDt1cKL7VQCwQWJIU38ZSCUcxUyILrxlbkC6sExGzL0V8qjqBXMsU7EUBYKjXyMejdCnezpiDDsLtUlUb50+Qbb4CDGigkAJ/cSnJfNBLyvBj3w585M9p0osJahXLVNALBTDjuMXso4MHQNUAhKxCOhgn9AzIAgi6tGECdizsK0l/7EuYhXJi2Tqd1lDYgsZUdQitmUh7UNY5F4tJZvRI3q5O9fhoXG7vNKsTNzcx0VzkhZHmQ70FH22/BBypdxjYF0ri2qlgiOIyxVnmwAOpZ0h0mpuSaYAEEQehRbDffmle8xRZIgQQCRqMRuBBGtQ+1eUr08F8GOZLJupzXXbZZZnaWImVbr31VjEsoPYuGfhLUZhS2KgpSEfiVoVuw41MwVZmBFnHa0E7AlpMoz0FiKbvDGBmYNQcwnAGR48ehA4qGB8flu8WvLNMOzlnBLwjC8+9+IvcheR3kRsSZQGqYJ09iS2E7/4ubxWq1wK5Jizi1JEXCKaAvEg5NP+85HVuupjDfJmfrTx3NUKCh2YGFfBeovVnuwX6NgPhBuzfM4O5mTW46ILnYXhwU9alWDH1aIRBZ1xqcH/lK19ZlgAgz1PqgR4+fFg0Qb/97W9j8+bNdWvE54zrrrtO9DEvuugi3HPPPYnm6gHAFbPVl9VEPAC4rJbLD9ZHYHVFoJWzbxLgz0WKb6zpzMVy9dVXyw30cil8OHvggQckzeaqq65KNWymHO3Z+31sP/M/MDhUr4+nzDYo0XlLp38HcfrcAiiCcqmGY7X61OPQOh0D0RBANGsypAU7fcAD0C3ScFrNwISbAcXUoIOpJlmpEBTYhUDTVTediQrNSfjg5NyGS+UtmK3wIawT4ErW0uYMzD4CHExHnodRh6p2DYkmTCdmRfBxnzC+khYCMXPlHAJlMEDjkKQVRS+QYusahWBedPySFmruzbNPXcFALv1Y+eA4mOus/ydpzKUBYbSQ/UoQjEYn+YR6g5wP04XLobXOKOTIjEs2S3ElrljjDQJ0SZyM2TLriQMx3Ymp35jAwMTWY/qw0/1junHYcayW/aflSx7GlWX+6SrDp/lcbR0LzDmQwLIMW+8B20fOpgsLcM1jCfTVOySzXepe8mLGsdSOd2Mho9GCGvWGJ2w/0i50w+B4+LOwIMkksgxGaoRVkYyqNQrrWgCEKYgEHvllU5h5DbGMRGFVxkBF/mx/RwMVXov5RaBiKGIzJdsv8aP27t0LfvElEzXdfLERuOWWW0Tfj+L/jQ/6Sxkj3q80AwV5L1QrIXK5MnSuhKEhjTVrcxhZk8PQsMbgUGDlLNQslEuV5eefIUvZ6m6K0Y+AdtzbPNYx7giMWZYti+PURhY/1e6rIH3D9cmOMGLURdICrpJj4El/Tl6v+kdeD8SuKHau8I982cQL1ELAzoL2vFa688c2ZvupMQj5O8ooxA2Z7bDd+adQMQGmiwOYqxQlzbdd4TloGXxbkIu+23+PSzVmnBAcYnnWs57Vc8fbpdyrSdt+61vfin/4h3/AS1/6Uvznf/5n0mpdO+5b3/qWyBy4QiMSjomFzwS//uu/XtfXq1/96qZ9U8vwFa94hWQi0QzkxhtvFFYjXxTQ6IRMR7J6ydQlGMi2GwtN/b74xS/W/frd7363OLGzfPSjH637GxmE55xzTtdi4RvyEXAR8ACg3ws+Aj4CfReBRoOPOOOvXbpvs4mwrS996UvyJ7Lo+lG0utUCUKD9vvvuE70R3kwmKXyAIHC4b9+Pccnlt2J0vAXLyxSgFUGnPRnAPDLBJsW9N1UhgKi2QGUAEE1IBiKZi+lAS2OGITp4ek9HkKJxLtTr02oaqsGJt9Oc2acJt0LrdAAZ2w3D7Zgvr0HJ8IY1Ter0IBS2w+AxKJUOZLU6gUxr4ng7zS72d5NHOZxAxRxOlebLFsgKJPhErcAkTDTXK0HEmUj3byRFqi/rU/ePjEL2lya9mA+87JN9k1HYCLBNF20qLP/WWBzIZkG9ZBqAbIN9kknIPgkE0kwy6dq4PtlOGkCwMQWY8+zE9nPzteClfbLnwzb7TQJgsk+yDKuuxXxY78AWlPMkMipx9fg753jcage7vqpxjMkJcsytxus0DWV6DocRsM/CHc0MVZwzco196HQNCeCxbjliS9l1de06EMWmPLc6GeNgkDNPcU7OLg0ySoUUAJJAo7ZMRgEa+Z1AYx6z00WcPDWLQm4I69ZT6oGbmMfY7+5nE/1by3cyHmkYZNuwKdT23/Zn+3utUtB2U1x2lvpQgkjfuuVrqFQIAJ6H9euZrkmjB75A4PeI9SYAFS9G/M71JIhsXbLtMa6OTXO1x5ZgjDvWGeHUvtt6EQhn7M/8nWjZmSiVNZJD4TpqbeQcFfJwtB/le5Wtyp8dgBYZ9cT2ld2LcsWJAHYX3doeS3rdWep1SdO+lT5QKAQ1Q6LG+rzunCwC002NnAsRwLdZUnhzyn6nVl+7QhMYGsiwUDduuRm8pIlx0mN/8zd/U9JmX/WqV+ETn/hE0mpdO46A3sc//vHE7dWD7fXVPvShD4meYSujH6bEf+pTn8KLX/zipv0RGHze856XeCwEBFsBkokb8Qf6CDSJgAcA/bbwEfAR6JsIJHX2TTtgph2Qlk+NI+rpLZdy8OBBSSMYHBwE0wTaFcaO7mRMGZ6fn8NFT70Tm7a015eTe39DMI/ad52YZg29SyrpdkBlARC3C0ilddoUXTqlUnOPfaZIe5UH943ygJqW1WcNPxx7MV2MjFkr/WoBPDuz1iyL7GwUw4ehsC0CCtKxEO2D+BkwSJniK8+AGXUCmSQWAWQjhWTAj9tNDtDjA+xwoTXk0WzvO4ONfEpAj20RgCTARmAtqaGHhChyIebDI4GfYmhBzCSFc2WfbIPgWhrgk/2xLgvny5ThpCUrIMj2q8AeoSCmYifs1+odKtBJWcYsjNpSIhDTAm45yxaMmEhkLSUBFHm+urrVvGVoBLosMgjtiugTRv2yLvtmEqDS7RmHbLOO3Rhxp5zRAA1W2oGLknocmQvF2YJ2rAQwLLLaTCfR6SNatUO5olc9EAw1EAkWiRZiBC1Gh1nA0R5rwUqro1tfLJ9KyI0RSFRjhfFI5+DM4TnAsjaOaPTWHTpSaWMf1GlzSm2OMcmx1NpubMuNikCmZY8KSBZdUu23+PW1fgy1OcXRWwt41eq5n22swwhZti8cG3dN/Bf252r8q8OI92WPsNF0X6wXzaeqQxdrV1Hz2M3LMVgjsC8OkEeMWreONgwOCWTKrV17+fxpiicLhB2tkdMgsGxil45LINcy95oiZVU2XS2l183RAaFtzjoZL/uvsf9c3xYMXfi5uXA9aixA7nfL6OW1pvVnLq//p4psPY8Am8SMw5ly8LsGWe5p3obZOR4/fhx33XWX1OU9W5Y22l6kluEfX/nKV+Lzn/88krridnuK3QQAOTa+lCcQyOcK3nMT5D3rrLPwohe9CL/927+NrVu3tpyCBwC7vbq+vawR8ABg1sj5ej4CPgJdi0AzZ1+y/uJsv8XcSN18882SzkMn3aXS8+laMGINUW/kzjvvlDQS6qe0KkxpYAoB3z6znPOkB7Frdy3loePYDFPINkEpazSRqpgdwgRMpSfHDqizhTMlnVi30ORpNQ6bFjwCpQ+kGqp0SyahnoJWFCdPXqzO3zgmJiaRy6UFH6ktOBEBgc37FJZZ8WyUzMN1B1ggkA9kjyYCT2qV+XDJFN9TgDqSfKISJLKUdmYCEQkY8eGKxh9JwSI3ODL0CHJRKzBpGqur6wC9LHXpJFwKLRCYZMxcq8k5ezznS3CO400DJHLcnCvrEhxKW7dcCVAJSQtk38VE4JiLFdOiyxU+1BNQKEsWaDVWAAAgAElEQVQKb9IiQGQ5YiYybTglq7ES5lE2FkiQx3adnNnIvsm0jLMFm4GoYoBSsW27feQ0EIWRF9MYTNp/I1OR4xcIh4YCCbQb44zFeDana6MT69HWsey+GjvQARQWrLFA4UI30/jaygsNacMOmkAXv4h1aCLa0o8zUGnNnoozFuvZi7ErUDQ8sV+JKGoC7UXmLM3wlSowKenbFgCuJqNGayeXKAtZCnBkgR7LxKuWOJYWC0BSTKcu43ZBymoclnJgZdx8wqGstuM48Gddadufb8LQjExytA6glUZOHLctIOaALRvLpGfuyj7OsXQ7XY+K5bWYmbsMCrvFlMMCfQlO4IThO3r0KL7//e8LKEQG4GovvLcnG47AFx1x6Yzri4+Aj0DvI+ABwN6vgR+Bj8CqjUAz4M/R79Om+rYLIl3rKK5NYd4dO5j2ujwKbyaZTkKjk+uvv37BoAn4EfgjAOjKk548iy07f9jRXbZZBJSw6+gu29qdsGnkRAR/F6AeTv9AQudeVahq36VZmexpwdTmOlN0BXVKrb5KeRBKb4PWyVh98fnQpdiEa6CDfXVkDGrGzZTORNnsbTl9OvHOz5UwMEhmX5oo8fGZQCA1nx5LV1E0lli3CKMeS6XZxwcyplYRU0jjyisPzFFdTjNtmm81XTeqm4Q1Fg/KbInAmhKNwlywkEFCPUEafhRyC9kwpUoOpTBATlVQyNXrzXUKPFl6BAQ5Z4KQSUAl1ybnzNRmAmQExNKCiQQxCYCykF2YBnx1qcocP/clx56G2Rh3K+bkuV/S1ncpxA4Y7AQC1J+TBBXJGrTAmvxPhaIZmfQ8Yz3qiFkDEcueom4gdQWT6lUyjjVwMy8cOXFJjUAegZcSnPcu9Tj+3TZiUSybGurSkTvtSscO5PUycm+uOjLLmVqdq8gVqED0Pa0mZGv2les1nh5dBRFjrD7LHLRfLnlVGG0CILYHOlvNrBlYWQP7rFM0+ytLPjsQBDmboi3/IgLJfjnvelZmlXOYYI06RV3S/lNIBtRA03gquWMQ2t6qZMIGMNMyBzVMWOWIRvuDg3AswJpPruzBaI0CXRCGrIl0YB2LzzFfLVMzxk3tQmyaxc5d99pdt/i5Wyq+GGHl0jap9Z1WpvPf+dL23nvvBc1ZkjjAdm5xeR/B+3m+vP7e974n4B9BQF98BHwEeh8BDwD2fg38CHwEVmUEFuPsmzZgdPQ7ceIEnvSkJ+HMM89MW71nx09OTuL222+X/l/4whdW00koOkxR4wMHagy4DRs24PwL1iA3/P9ZMwmcBeAhq62dppg10IrOvY+kqRU9ZWwT8fG07rv2CeUsKHUQWtcblnQahKFovtkV6QomZzJJl2YNwsp6BLl9qRhUtu46wKyFEp2/TqOs/7sJ6Qw4IACkCXOYLm5FBcniPX1yDYKggKFhpm2n65cgIrW5DPanrguzWUwHrE5g54f7+MhE7y+0YF4aYIttlCoKxUoOA0EZ+SaAXLsIEMibLeegVYhhCuOnKA6E5MMlU5N5GpH1l9QdWByMyzZVl6zENGvFvhkzAgF8qE3CSoxPzYGJfFCn7mAaQM3GPAIEo/7TAIKN9V3acJr5MxW3EuYiphtZduWWzp3SX7kgoF0uSvG1YJoWMxbLEwuFZZwmDg6Qq+oTkiFHpbsgfYq7a8MBTcK2Swl0ujRdK0nggCq5EkUpnukAZwcQOhDOthTLEo3G2A5Ad3XjOpO1djlOm+ZqISD35fh8lrHIczPN3ojvc8f6EpgpYgc6MMxqFBK8qxlaWGDWOtFmBRA7XULiDEaCH878wn5mNHwONDQmZhWK6eoW8pT/VsHO1mzMTmPqp7/XANhoPwiwbHefBVUjnLVh0As+cSSjmYZSgwi0deOualBGOesudZ4GW+XiiygyseSheOyxx3D//feDjs1XXnnlkvfX7x3wHKBRBl9Uf/CDH5Q0YF98BHwEeh8BDwD2fg38CHwEVlUETifw5wJLVzZqs5x77rk4++yzl028yfCjGyHLddddJ9/p2EiTD2oastDV+Pzzz8f69UOYx/8Lg2PV+RHwUZiRlNe0RQkgRzfbVro/LVo0ORw/PoqJdcdSg2pgWq/aCKV+kvqhkICcMsOZ0oJPnRyRh9WxsVNpwyRmH+Joqg+lrlup7MJMcQihsg5waQrdmPnAY8A1SlOTx26AwloYMM7pwDyYUShshMEBqJTuygTk5sp55AM68mYA5ESzCVhLrcCUcyYYJ6nJufQMOeobUi+KfQ7nsoCYZOfRIXhe+k87doKJBOUIDCR16o3vCNYnKJiGVRSvX65oYTYSlCDIlkuZsk8AbL6sQGF+mhaQoZcWCGYbBJEJIHDpCTrx3wRNkgKUZNjxsuleEzi2Ydr1YCw5HnfmyHhSAoOMrzAHwyBiH1oIxAJilVRgpVsrl2ZsWV0xKbgoVTktG9aOkeMis5E6hUxNtVREUa+jNqKAq3RzTnsNqh0fH7esTSzVV3qiDl/EgGRsFtNXfJQONHT9WaCSc9QRA1BJKqdlAFrQ0H63YFw3UnAtIJZHISDI3fla3MiatAB35CodB9NM5KDboKHnxs1e42zQTino2Vd3aWqqcCdyuaNtDa9oxlWa/0WElYuWZhBNWuVL2QcffBA0gyDwtdoLAcAnP/nJopVHI44bbrhhtYfEz99HoC8i4AHAvlgGPwgfgZUfgXbOvkxxtTeki3iKaBPCO+64A0888QR2794tYNlyKUxbZvoyC8dN4I9ahiwDAwMCaG7fvl2emIr4e4R4oMnUBhBgG0wG512YcWg1LMy8tIWg2vBQAbl8OqMP6Ud0BU9B63Q6fVI1o1sw604en8DQ0DSGhujamK6E4RnQ6gRUQmOTMCzg5Pw6hDgEDaalE2E6nK5TecYnM48OvhlYfXCmH+ndims6gXwIS79OdM7lw+xa4mIpT3uCYQTlCKY1c97tFERx7Y2YfZ1YYcfnbFqsA6ycaQmHTPOPtGBE3MyDOonZ6tuA0eEyLZAmRiRlGmwo0RUrpNSzZL8OkLRjSA7AuXVxacME8DgTxpdMxSRF0p3LtfWIMjVtO5lAOIKjWkA4MWVgCitNOxKAMY3j5Vj41ahRmHaN2G48NZo/O+Amp8kQS8d0rsWdqc5RqrJo7BmEkdMswad243QgXav09HgKcyPTzY1d3GpTnuvN9kRjX44B6I6tgYW2v6UEt2oMysh9WfZRxH4UJpoFDWloIXGI1pJj5b95/epGTJKcO52OaWSFLmRWNlhyONZl6MxMIn1HZRCoACqg23G6lPPOYxwQ7T6y9tuVSuVclOZ/SYytTmfZt28fHn74YYyNjYnp3GovfOG/a9cuycD57Gc/i5e85CWrPSR+/j4CfREBDwD2xTL4QfgIrNwILJWzb5qIUZSZqRm8EeHbyOVSmOpL8eR4ISOBQCZTmXM5+9RcMv8XZXy57bQ0DTfwGJS2AGLiIrpYZAOSLZaWtUUlq92RU3BndkPdmIx1s6XLcNqH8cWkBdPso1jchqGhx8VJME2xGmC7Rc+QLsetShz8ix+jOV8x7Tiapls5dnFA4LC4DltWX7oUbGqDZdUJ5LgJJpGZRzAtKZMrHhxxcgyBNQPpH6T58C7MPgBrGliFNJGYnAfoatyqEMybLdv6abUK2SZBuFLFdlDIzacGAlyqMJltBNHSav+xX86T+oEsrN8JEG0WC65hXL8wyzpyHMUy2VbU3zOyFxqB4XmejpHOYLNxCDAUEty0rrrU4MvpdKmxrl0H5jkwSwxFUhiWxMfHfeKAwYhAJ3HOEmu265yO7Xers8a0VrLxsoCNbqwupTbOMnO6gc61Oi1Y37hOcTOVOHDnQEJnqtJNUMzNi+eJ+1nSRzX3Se3z0KVmd4PZ1+qa4fp3wJ+NtTVlkQTliMVnBDisuVa7uLuxSZzibsCpPzFOf4X6NG1aANE+nfrBA4AZII8WolkitwoOKY7uG8iMNXQSJ0uWn1G88BJgpPYkLwx8accLWYBy6UWolGjAkVb/ZPEx2bNnj7yoXbdunZjOrfbCTBXGgs8B//u//4trr712tYfEz99HoC8i4AHAvlgGPwgfgZUXgaV29k0TMYoyMzVj27ZtuPjii9NU7dmxfGP6wAMP4NixWkovDUzI+iP7z5WKuQdFfCThOMcQUAdHpzSDkKfO9dB8q5+BpQbDtF7qGaVnuNm6vPFPn2K7mLRgQ30/GpToLOnIBRizC1rq1h7iGMYwzOPk/AaEaLUGTOgiWHscyMCsY3quwnCk1ZdwW1QPc+6/NIJJnw49fWoE5VIeo2OTketn8v4dGEdQhGBgWqCBAMtM0TLSqNOXthDAohuwA69KFZ2KHVcOFeZLFsAazqfXNeP8CYTycZfgQBZwyAFxfLQmuzBtDBkzjsE56KYx04jHmy7BzlQkC0NQzhNDbT9tU22J8DL9ug0Y2w5wEadmZ1wQpQxniY2MiSnEUVsEYbKwDt1YCQCLm7OAPjbF1IGfafevO77KjmtIUU7qVlwdW0jwhOn689V9FG9bLDCidOy4pmGWuDabK2MTuvToiK1omXRWN3ApgML4OJiu7hyjnYFG/BVWPAU4KcOwUhnAQI6pvulepLXbCxZQtACiHa9ldVZNP2KMS2ckI4nOERuzm0Br1j3bqR7nqM1ZCHJ72h5qwjGU5m+AMXyR1pvy4x//GI888gg2btyIpzzlKb0ZRB/1yvtYZ7xHIxDPiuyjxfFDWdUR8ADgql5+P3kfge5H4HQ5+6YZOYE0vpXdvHkznvrUp6apetqPJeuPN5HUTIkX3kzadN9aCc3jmMd7gRiLofOACTDRIGQPVNo0N8MUud1QrJtaO46PH7szmXWQZWbrHoDW6dNzF5MWbAy19vjAlAWAXANjNkFrxisJ+BdfPbInz4TBE1DqZOdlbThicUAg9d7OgMFxKJU+hXt+bgAz0yOYWDedWieQ0yAQRwCJrLwsjCbr5KsxUiilZtUdnbEgHAHFrKzEKphIMJPMwpSr55h9fKAnCJeFUUfgjGAeS1ZmXxWUNCpK003J4o3mzZRdYQgqq2GYdD7sn3vB6SYSfHMpv854Im1sOaRGIM8ZlaRcpurhBPEIABOe4n9zEYMxa3tufI6xxrgthoHoxmHBopyYo1itPQJR1PCrscmcYUkWrUo2yfrOzdixJ2lsYQE7C9x1szT2F9dldIYawSKMRpKO1eouBoym1U1kerUw0uyInN6kS0lmzLsciqRDrTvO7gkySfmd4KEdcWMaNytVQc8YgLjULERjhhBgHXRQfz/UONmwcgZK878KYDRTHLpVifeaBw8exJYtW5ZVtkm35t/YDl+8u6wbGoGcd955S9WVb9dHwEcgRQQ8AJgiWP5QHwEfgfYRoN5HuVyWm18HBPI7Nf4sA6w3t7wE1KjLsn79ejz96U/vy2Vk3GjwwS/GkYUGH9PT0/Lvyy+/HHT6rT3MFTEb/hOUegzIANLYlNFiYs26+qeGTZJepzKkqoJmHYqunekBNXHdVayfwXnX5DE7swlDQ4+mZqfJs3J4JpQ+Cp0BjDNmPcLKGpwqFWG4XqlKDkwNNkzfbpNW3KpJJYYfayLDj1QdWxdKSUueyaRPyFRsjV0IzSFoPZ22c1jTkALyQTm1aQg7I1h0at4CTkzlbXf14QPvEzMWdKydY8B0yT4MZ0kxZjvOfMQxE7NcAQnkEeigTl5aR2DZu2QXlsgyy4mBRyGlAYuLh3M2dtplWViKbEtcoQkIRuBkM0CQGo8aQduxxo1BCKwwNlkBJpuOTUMOjpAgXpgYqGy2sd3Y+F0AvGh8i/kItLqAUYqzpN5bAw5qOS6mXZmxMC4t0CiagxHgw393E+RxgF2jiYpzRc4C+Ce5sDjjlrr05miOLp2W+3mxcYyPhedd2TR3Aa9Pt2YqcgB50cXAi3sw17QiSaxxgDbJXE/3Mcbwosk0Xn4n/ToHGPKQIz1EGZBNU4cpIzRFGDMPmCK0LiFocT06dXINhgZLGBhoL1tSKV+BcvGltt8el/vuuw+PP/64vKxdTnrTSxU2AqLODOXQoUPyEt4XHwEfgd5HwAOAvV8DPwIfgWUfgV44+6YJGkE1vn2kMPOVV16ZpuqSH8vY8S3pQw89hGLRstsGBwcl1ZcpyzfffLP8nszF+M3TXPhplAwdggvIqW2A+kmGseYRYCeMsgy1VIV6PIqpqg9nqMvOqCuYwXxCniV2ifGE1idSDZkHz0wPoVwpYGw0vTNyVVtQ74NuSO1tN5Aw1Dg5vw2hsQ6fUI+nHjfXuVzcDK0PIpdPn0KmsB4KozDYm369BAqkUQkf46gTmHL4THUM16EUnsBQvpSJ+XKSz4vkd2QwDeFoafxB0ImmI43AFVNej83Vg3+NM5QU45KtmyVFme3Nlaze3mBgU3SzFOrkVUwQAVTZ9O0ckEfgg+nSWUAzx1IkUzMXtZN6X0QBiAOCBAMJmgpgm3KfcUylSk7AUoJ4hSAUd96shWvuUn4dwLgYgErGF+kBUqtMUoi1TftdTHGMQY5XIUCgybgrCZiXJIbcD1zDVsxMp60oqcoOnCJkFekYJukj6fysRqAzK4kWj0YeqERg5+Ji1W4cFpQje1ihXDbCkFeR4YqwwCNDkSR7gPuG8UzKdk0SH8fOjIOYkoat+JKIRhz82X7GBIpj7w+WYae5UfeQ4GElzKNSDlAuA7MzBeTz9fIZbn5BAAQ5g1zOYPrkM6HwLAwNDfXsBXN8fvfccw+OHDkietPnnHNOp6mv+L9/97vfxfOf/3yZJ03tuE6++Aj4CPQ+Ah4A7P0a+BH4CCzbCPBhgMw1glhxxh+Zfkvt7JsmaNRk4ZvZNWvW4JprrklTdcmOZbzoTExgkiw/Fhp8nHXWWWLwwZ9Zvv71r2N2dla0CwkIspTM3ZgL/6lubIGYbTyyQHMuyQSs+cRhKD2b5PC6Y5TZJsy0LE6wMBNQijfu6V2GnQst1MMZGX1nQKljmdJrjRmFIpMxgT4gH9BOzp+JUlgDaAMBTiczafyVSznMTI9hbHwq01orrIPCWGYgUHT+yrmo/9TbRXT6CFQQiMsCPBEsIpBGRl6Wh2s+RJ8sWsCDbbAtAntpdAMJUPGLIF4W4w1GbY5AXiWHQq4kQFWW4lJrGYesKZsuzdalG2dhGHLszhCF+51tZInLXMkyeIQ5FVo9RcYnK9NQrpWVSI/QWPCWLMqUuGLd0rj2CEM5Vl+WfRxv1LVJ4jfBNBlnExOUtHvEafbV2HaBjJkadOyHafLlssbwQD3QkqYfu1Z2/d0uJu9rKcDB2t6oTzOW1NSIZZnVqCXtnJ0rskuZZX2CrxYozIupz2KB3TRjanZs3HClkfnI450mIB17rUPz4pmkix2zAJoJ9P7K5QB3fe8yHD2yUbqkIRrv75g14b6Gh4dPOyh411134fjx43IPx3u51V5o/PHSl75UXmqfPHmyaly32uPi5+8j0OsIeACw1yvg+/cRWIYR6Adn3zRhoyYL38zyJuS5z31umqpLcuzU1JQAf87gg4Dpzp07cfbZZ9cZfLDzb33rWzh16hQuvPBCOSY0xzAdvodKaQvGRpZXIE9DRzKMey00RqF0e62dpg2bArQiQywDk1Ce6sgGJLMsvb4fzGbRI9Q6vcGIY/SRxcg0q7TFmK3iQtiqbz7MnJo/G8Xw4SZN82GcGn8HoVR64BUYgcaWCMhLDyApTEBhPEoNTs+qCcNxBHpdVD99/2QdEQxkym0WII8PtGQFkuEVT9tNs4bU+6NhBdlmWcbAvmZLORHgH8wXM+kVsg06EcPkMZgrZR6HY/XxQT4rQ9HOxwI6BKHSgKKNcWc6balCh0+CeO1jw/Nkas6uQzMwzWlCDugC8h3a6rT+BKoYKzEWoCZhLhsQ7fphO2yPMeNZ5MC7xYKC3N+uXfblwMYk7LNOMeCYGVO2JVpvDjxbRAp1sz4tOMiUZTL6+JLQAkzd0DNsNUcLeloWn2gohmXJrHWg5GJMWzrF1Z037lpimZMxsDJqQJhsEaNQUqwzfPZ0GkuWvwsTD4OAGbQpvdV03ggyVyHm5k6iUpnDQMEgXyiJA+9iAPX4OI0hk3ErdPBI2+FXysPYt/en8MRhCyrxJXSzwpfQcUCQPxMUdC+ns8SoUx0aXdD4guw/sgBXe/nc5z6HG264QUxRHnvsseqL7dUeFz9/H4FeR8ADgL1eAd+/j8AyjABvuJzWH9l/Tt+vlzp/7cJ4+PBh3Hnnncjn89V0hF6EnUw+6hESkHRl06ZNIozMt9fNym233QYChk960pNwxhk7MRO+HyH2thl+Djm1CyYjGKfBtBUahKQHdZTZAaXITEtvWgGy6tQYlGp/89904vLgQhBxv2gKpS2zsyMozWuMjWcYt+gD7obST0A3OOdOz5+DucpDHYYzgJzaKWsq6cEpC9N6NegqnA18XSwQCKyFwiYY7IdS6WPvGHlMi80KODG9lw/fo4PJwZzJWXus0zybK+Xl35KinOGJlvMgkEcgeW2G9FUuuwNmCP4wFlmZb4wFmZKcRpZUWrcFxdm4bOMyEFCvK+XmjA7nvMiydABJHKAkyEUWJseZpNg0WqaU03DDAniLAVAsSJVHma63hrp/2UFYN/5G8I5sU44zy75qjAmBVbIjadhgUz3DVGxL7lOuQ6sUdP5NwMy40/ESgFQuHdqltXKe7nxcahafm2OjDqEYdQTZzjvu46zAuwUKNSrGyJeLvYvJUusjJjnvWh1D4xNgOAINC4Ahi9dq/1ndvwqUsUAh1JzoyZLZ21iMGYFWIx1f5JlwA0rzrwXMemmCL6NpnEYgMP5VKjX/LCL4NzIyUgcM8t6rW6Dgd77zHXlhy3s65367mPgu97qf/OQn8frXv17YkLz37Vacl3tc/Ph9BHodAQ8A9noFfP8+AsswAgT9eNPFEgf/+nUqR48eBbVIONbrr7/+tKeFECzds2ePOBE7g4/R0VERiaYxSbvCG0oyBflGeedZD6BovpwozAHOBBTZZelZdQR0FMpQ+liivuoOMoOYmxnD8Mjj2R54Dd1+aXhh91eaMj8/iPnZAUysS6/vx37mZrdicPBUJgBThNDNTkkLZirVbPEszJT3JB4+gbxArY+AwMTVqgcSyNMYQ4ifZIo72YBMD7YagekZgWSOUCfQMhpn0k8AwIl5++jI1NwMGJyke86UNIbyBgO51nM4NhMgH1Sass0InDG1lg/zWdJYOXFhN5as4y1dgLMUl5rLWQwvAuSS1Fw+e0NheMBkSrvm+DkezontMd04a2zYFgEYYfWVLfMya4zYlgMEqd3GOTJleLHsO0nJJUswYt5lTa+Orzvjx31FVhr3dj5QKARZzrOFu4lrQm1Jq31oAaxmzMb5skY+oOZg+h3pQLPIn0rAYAJ1ojOYvrm2NRxz0LL4OKmK9OHcmrsBpLYbQNzAxZrCWPDW6kAqcXl2xaUCZ315kSZ0LqXXuSyT2khDLY08tA7oPwyti5k+O9OMY7HHGkN24RAg3/NAOAQtLxwl/xfK8O0FX8QUY6AhEFZ2ojz///ATosP+MaKb3AgKzs83NxPhfWEzUNDJsKSZL1/Y8kXvBRdcgK1bt6apuiKP/du//VvceOONuPTSS+UlfK+MAFdkcP2kfAQWEQEPAC4ieL6qj8BqjkAcAOz3OJBBxxszFgKAp+stZCuDD74d5s1hkpuhO+64Q7QCz39yHhu2f97eJCcsBHW0GoDKZDrBdJxdQBaDEABTk2MYXTuLXC49AAmzBkqtF5OQLGVudjOGhqah9anU1atAnsw7eaxdR8aMoVjaiunyAxmBuI0I1ADCLGYbwrug6+9wxMhLPX2US8OYPjmAsYnjGRlfeUxNjmJw6CQGBzOsPTkiBIcq1vAjC5hDAIFgIh/aG01DDp9KxtSR9G0+f0aAZJZxMPpiPhJmT3VmGw4M5M8EJrOOhcAGYyvtFLIxndyOIoAn5hEZUo4Z20maruTtGommYsXOi/PLAlDVzr8aGObcihfTnou/A+/IEiOwu9g22S5BNbYrrruRS3BWDcbGM50xZkzZh9V/UwKsx8Gr9FeHhTUcWCZAWTQPAoNk0nUbGGTvjjko8yJmFAFzzsRkqcFBtx8IOJcqZGC6MdDURQsj02ksdiO+WdpgKi0lImBouMAXU2TpURqE1M6IiSe6vVmkJ7KMqHUdE25EoKc7vjQKK09GpfjLNkU5Y2kGCrr72GZNNgMFqTXYrlCyhf1cdNFFYHbHai/vec978I53vEO0t7/xjW8kuudd7THz8/cROB0R8ADg6Yiy78NHYAVGoNXb1H6cKk02vvnNb8rQrr32WhQKGWk5CSfXzOCDN45MgzjjjDNS6aDcfffdOHJkPy5++g8xtCaDWQb4ULI7m1uvJPPsBHAUSqdndZVLAyjkqdG3P2HkGg4zNOp4ouPDQdPGzQAUtguAmSV10dDkwwyl1kQsVTZhav44AmyEQglGPZFp7hrbxcnTqPTahuxQYYuI0hs8mgmILBUHodUm5PLUZkyfDm6YplnahsIAkbgsmpQWHJku2ZTarMAIgarZsm2DTr/8nrY4Rh/BKbLBshQCJKL1h+wGKKzrQDyCZSMRgJZ1PAQDOS7OK6sGIvt2Dsn8mW21S13m+KeLOQw3OHy6Odh0YZviK+zARcyRbTogjP12CxBku3Qbpr4d04ZzQVmYdd0AvAh8c79xXZxGYdZUcI5zvkz9PVNNr7euxgVUDJliNfffbgNn1P0r05FZnIPpfF4RxiDXtBtxarbnHXOQqevUHAyNZfl2y1Ql3udcqYDhAtmmC18SOfOVGvhq5xzXP8zymZTlPG9Xx6bvxoFCXtw4Ul7vCRTy2k2gMP1nf5KxmnAzAk3JkPZs/7ByHirFV1sws8uFqcJxpiDTd+lW26pQQ7DRbITSMq7QtK1SqeCSSy7pmN3R5an0ZXNvf/vb8XBZzGoAACAASURBVP73vx8/9VM/hf/+7//uyzH6QfkIrMYIeABwNa66n7OPQBciwLecBLqWQ+Fb3q997Wsy1Gc/+9kiBL1UhWzDBx54QJzgWJzBB1N4swCP9957LwbHv4B1mw4hr8+GwUOZAB0CeUodyfjWn4YTE1D6QKawKUnrZWpo8xScto0ShMNmQGVLbbUmISG0zgbEmfBMKHU0UVpwOVyDqTkFA6clqJCj0Yc5DCjr9Jy2WMfgY4A6kbaq3X/YBo0QRh3KVJ8af1o0/hj/9BqFdgwUQ+cD5WOZxkDwhow+Mq+SasXFO+Jl6jGSQY0FILK0wfbYDgFJfs/qYsx2qDdHzcFAlzGUz+68WWaqcdmCOIsBypxGH1MLF5vey7bmynmEIdOsyyjkagL9zj2ZY01ayJ4kiMtPmqFFph67NXTMOGHzLSK9Oj6HOHBHwIlxzMrUbIwNAUFq//F1jDUDKSV6qUG3a8asE4BIsJHj5/qzsA+eJ91gOTbOxekjunRlwk1LAdA19uuYgxYAtX+NOy6nWSuaD02QXLeIYs1BAkmDVeDLFqbyBlDUsBUtWQJvi+igi1VpcuSAwunpMkolg8HBIQwOEJBjui6p0gTNyORLeE8YbpPP5E66sWHlSagUfzUyJOnipNo0RckWgoIEAx04yJfIrQrN5WgwQmBw716rz3zZZZdhfHz89Ay4j3t505vehI9+9KN4+ctfjn/7t3/r45H6ofkIrK4IeABwda23n62PQNcisJwAQN7Q3XTTTTL3q6++Wm7Wul2o+/Lggw+K05krmzdvFjFoppJkLXsf+QLGt3ylWj1QBFOor5ceTFNYi0CtFW3ALEXjbIBAkM4ABJlRaLUWSmVwGeZgzU6Y8CiCXAY2gjzA0CTkJ9C6uWNgu3gkSQsOwxym5tejYh5v0lTBGn0Y6uulZ9MBgQCJoTkApdOvOwdkmZxzQEZGInWXNDbDYB+USh9DjkGBmkj5zOnJbINusXwupuFHkudjPvg/fsrqCrrCtFyCAIsC8SpKgK5CUMFgPsP5EA3GMRQJii1GR8yZaRC4YTtJYtNqzzvDDgKurcwikl4/aPAxR3CpYttim4spknocWnCNDMjFglQWsLQsRrZF4G6xbXJ+3F9sl0CPNVFZHMsyHjPHahQ3YwJoYl5TDzhOzQOjGc1oXF80G6mIiy/3U0UAa2HwLWZztVh8B0KGkYkE9RwDsgaD8pL01xhPzpXAfDmsZw7GtRRdKv5Y9izUxFvfpvGuBQxfVtJNm59hvM4QIJyWF0KJwbbEvS7uQOsiTEMQjpkXXDJkOW7uUrJA+dk1C4UxaM0Xgu2vm2Hlggj8y0i7Xtx06mqT1RcHBAkMEhRs9RKcrMCxsbE6sxG+AE4i+9LFYfe8qV/7tV/7/9l7DyBJrvp+/PM6TNgcLue9E0mSQSIYhAgSGfGzwVAGTCETBA6CIkeXBbbBJBNMMhiMDAaBMRQubIPAwggUAAkkkEQQfxTvpMu3eXdSd79/fV5P383uTuh+r2fDbb+qq0Nc9wvf1zPT/elPwNe//nW84hWvwGc/+9kVn082gawCWQXCCmQAYHYlZBXIKqBVAUonokALrQ6W8STepH33u99VIz760Y/G8PBwaqOzDgz4uPfee0/Wgzd+DPgYGRkxGieQU5iovBuWvRD0obefLfiUr8NqIyttLyTu1HqwosecpYyXdMbmj84+5e2nAyJ5NQezM30YHpnUmjskU4YHYFkaScOKAdZcFqw858q7UZPtPQvDfRtEILl+jUtDFjA9WcTA8CTsBiP6JD0JuRueNw43p5d4TCaIha11RqAeEAjlU9hXBxNjMkYWLZIgHgGQoTbJv9yXw3OtZb9haIhZ4AenRUYQ2VODeTPwiCxHAkYENzuxttrtecS0SwMMJOBGxhwBLAUsaly3TFwmsBYxDTl3U9kx+wgBGco9bSURzzvmYFG3AEHOt+rb8H1HwSGu5cPlfJN8eNscG4WMcK/ojUewlew/nf1qN6XGgAweR7C0WeBISstS1ww/55GcVjEGbSDXhWTiVnOOwHX10BR5DqYIFuvUKgTbKN/ly0UikgzB4ZUVefzNaL0k1JlLknNksBOWxZeA9CYkUFiAlAQ4aVfBZG++IKuCzH2/9sIQRFyljfe/jaDg9PS0+u9WjQBgxBTk3/xD9uDpDAo+//nPx3e+8x284Q1vwIc+9KFVupPZtLIKrL8KZADg+tvzbMVZBVKpwFoCALngq666SnmzPOIRj8DGjRuNa8CbvwMHDuCOO+4Aa8FWLBYV42/Lli3GN3UELWf9f4Ynf91irg5csQsBGFaRfDn0mBOC/jvJgzL4sGGplGE9EBFyuB5OoidLlUo+RLmRXtovJGW9x1QfOm2xLHi2sg9l/87YXdnYZuQPWCnnUchvgbD0gEQFWE4Oo6enhHwhedpyuNCeOhC4v6OMq3VhBlVoiQmrkA/ns5WQzdfoE6iYf3Nh+EanFoWGEMygR6DO54ljEKQgGEhAJM64realJM/VUIq5OMSk01oW/3vFs1GuCMVSLOT0wNaoTzLa+IdebpRRx6nT+Hxzr0LWnN6MlPjmLX1wsXG9IVCURyArCnA08TWM+u0mIMjrhfUkYMdrj4CdCfswCiqJmKRKbuu5odQUAVzhI0fJedKLqMPxJ4M56ozEKNCEIHaca0R3OqE0uh6iEoGRXZAus66Rf+TiufqBCyn5UqRXyXiVfx7DNcQULA2Wvm4tmp2n0nYl1Q4ECAmycefJxCONmkx6goRpjti+Lxlsh2UdjsH8exCC6stWNfjXbKVUgURhc7R8ofVMJCFu9bKc3tARGBj9zfvI0wEU5D3sRRddhOuvvx7vfOc78Td/8zfLd7FlI2UVyCrQtgIZAJhdIFkFsgpoVWCtAYBXX301GFxyzjnnKIBOt/Gm5ujRo/jtb3970iyaN3H79u1TAR9pJQxXgh9j3v9Kx2k6YgwSB7QYdQRxHDECCE1vP+yAwCSERtouJB9HKMvVlMUq1gBTinVByILyxxOWHoBK1gLkLpQ9H/PePR33aekBZGKOQcrD9Yex5F3Qm08oJqiurNqCRY9CHNUEgjnnIixsq1+Deqm/vA4pDw6wXwWf6DSCHXPVAlzlq+fh6JyLHjd5XxHjjcCbCQsvYihSLmjSTxSEQkBL17swqifZjlUvBNt0w0yivqJgFK6NYOdiIIGg0HgpfugKPe6qvqMk8ty3JJ5sra6XSNrLvhZLZHWuMZ4TMtJc+AFTX33knJoRaNc4jwhsJChKXIasS4LJcdp8xYJtS+Sc9iAvwWUCrwQfKc5k//zTDRyIY0SBJlwDr5VusgWjOkV+jMpnUIRAOl8O6ASQhIC3pUJeEjem8MqBkwy9U+DgDMQq8PgLvf366ixCgpcRi5CefgQxCRAm/w5tVicZbK17/rVnjUt/D/zqK+sS4sQVX9ETyP678cYb1RzoNR0lBvOekcEijWEj/N98Id2s2ba9BBSkb/VaAwW57sc//vG49dZbFfuPLMCsZRXIKrA6KpABgKtjH7JZZBVYcxWgr16rG5jVuJhrrrlG3YSdffbZ2LFjh9YUJycnFfDXGPCxa9cuBf7pBHy0moQvT2Daex8zHGPN06Isl346IgweSdpsevsJAmE6DCECQBshLN2kX85dKEaeVpNbldRJiHGt0yG3oubNoZBPHrJR9TdhpiwV8yOAJgiHPByxw8AfkA/yOxTbRDdxmDIrC7shccgg8ZHXwfY6EBjvul26YTmUakVY1pQRQLV/MgQbTNhzBA8iNp8J8Kb6qYasuWZAWZKLlkxHCRdFt2bMbiNASYCGUlzdhOVo7pGUmkCb8lqUwATBvwbfxSTrVEnB9bAVzi8uCNZujMiTj39zLxTolQLqFfWrPATrQKMJi2/xGkKWG/XTNhzbR76JJx6l46yT7rjcP+VXyBAYOwfHqhpfX632ojGVmb82vGYicC7JNaJzbARI8m9eo0ziPXktNOmwNN+Hnh7KynVZ0m1mKQm4DQIECZXMlagvU3dnIZaZmdeullIWANmnXvRwnlOT9B700dvnwHH5Pd+ZRSiDLbAsBmm1BxPJ7Pcrf1kfS2eHV/YcBsDddNNNahIXXnhhW8CO4BgZg4tBQd5XN2t8sbw4fZje0mm9cO5G5bhGvnBnMMpnPvMZvPKVBHazllUgq8BqqEAGAK6GXcjmkFVgDVZgrQGAP/rRj0CPlgc/+MHYs4fJqvEbgUMGfBw+fEqymkbAR7MZSBlg1v8EPHlH/AmqI3NwxVZI0d6HrlWnFrbAqkuXEg6sDrewFyATkSmGSZu04NV2wM2RAZb0ZD7MORBKkqyXkEwmxOT4IIZHp2Bb8ZgeflDERInAXyhDDtN+TyjWhE4TGFYBLYGkpFavhzAxmHNIDmaGIzKJcqchm6+AuZkBFIon4GqGYxBUmSiHksik4NvhmVPnqMAIPwQCdcERVoVAIOdENp/W9VnfznLNRi2w0evW4Ng6YHvYEUGU2TrZkoCbyZyiZGOCbgzVMJXNEkyaKJFZGijA06Tu0adAMRf9eoLzorALnU8Kz2HgAxmHLCZZfKbrjubRbUDwJIPPDwFHT4Y+mHrfGa2rp0IxfAu+pC+bpwBTEyZrp33yAgHPZ+AIWWhMba8h78jU19X8N9eBkExs7VVedOXKHErzVQwNT8O29QN+Oq255b+rABCCg/TIq1NAVdIu7TooLdbuOfUTQy9ChpXQi7CRRVj3IuTLJWscgvNv02SwAX7lVWFfa7SNj4/jF7/4hQLlLrjggsSrIGBG2fDisBEG7jVrZAQ2AwXJIFwNjbLnBzzgATh27JhKAGYScNayCmQVWB0VyADA1bEP2SyyCqy5Cqw1APCGG25QzD3ekJCxF6dR5nznnXeqgI8o7Y0BHwQR0wwSaZxLybse5eAqQJPR5oh9kMoXUAdgKMARmwFNEFFgBBYfWKyjccq75Ji52V44VoCe3pLW+TQOp4RQl01YqfSgkB+BZbWXRNOza6q8EzW5OEzEVb6MvnbaL0VY2yFANt9xrRpIaWNuehi9fRNwNAE4sj3oUxgYJP56ng2/ugWFHj786e0nwanJcsjS6Y+RvHl0NpS2LgZDCEqRUUaAy4RNRhCKzDmCkiYSWoJETDMmOMbgEBPwhmwmzonBCFyfSV+hjDoEGHXAO4JT3K+I+Rf1x7/TABf5gYjYhlwn55gWKKX8COugWk/ODFRt/OCeAgSFkpLSizEtAGdiPgS3mbLMa1PJhp3mnwGtL5NFJ0Wee7w+CD5yrDQA3lZzi4JNeI3z1ywaMy2wttW4ldIm5PLH1GeJL2ZCBhxfMrHITOBlCJVuAJLhTijZ7kB9Tm6ocWZoBj39lO/gCgCWLZYkg2FYFtmTNIcksFdnO3IN/J2mwkF5EfK34i9Bb+C13Ah03XbbbWACMKWvaTVa1yxmCvL/awUKkhnY6CtIkHAlQEECgLTbIaj57W9/G8985jPTKknWT1aBrAKGFcgAQMMCZqdnFVivFaD8t5VcYTXWhNIM3qCNjY2phN52jTcu+/fvV+BfY8AHzyPzr1teLL6cwHj1PWpqrrUZEnqyWhsMIeGDih4bzSabTyX16jxMWAi8XbCde5TEKmmT0oEtdgOaKcVQD0hj+t6CnLAKCTkKy+LDydI2W96H+TahHxZGYIseBNDzVuRDnak/IBOTA28jCsUjEDFZjUtXSnP7LZDQ9GlUHUasQhN5cQiYsZGF1wzkYtgEwYh24AoBBbL5KDlMyixsrI1izVVz6vPRl/ONQDeyFMlwI2hmAipyfpRx0jOQCbCmfRHMUyxDGc6tE9CzGPxbfC1FTEPPN09djvoOg0RcJWXO2zXl85dGC9OFQ0ksr5VmoLLuOFHf9PkjeKkj3VXAeCn8LDRr7JvXApnRri1QcGpa38Vx1sjrjePxOuE1wlqlBXC2Gp9AZOQtyO8CviBIa49EsBNS3Nf5BZokQ4+gFr1o5cngj67IheNsxMkPBdl4BCypv+dvIRNM6Oc3vayhJCoYRdDTs31QFyXGQfXVgNT3ZU5Snm4eS4XIr3/9a+TzeZx//vndHApkBS4GBQm0tWoEBRezBSOPwm5NlCBlFLh37bXX4nGPe1y3hsr6zSqQVSBhBTIAMGHBssOzCmQVCCuw1gDAW265BYcOHQI9+84888ym20iW35EjR5Tcl7JfNr7NJWOQ53XTb4VjT3mfQTX41cm55cQZCOQdEBpAmqgHfPBhRqcJbIStfAX1vPVmZ/qRz1VRKOr5wQm5TYGYnR4gWq5NboAQNoQ4orN8QBYgBOewMCSkXNuJ6epi5l/zIRyxG1JOGEhyI39AXUYnocRBWGKkDuLplUJgABZGEeCezg/GLYdw6z6DBw18BsPE32oAjBRPAYEEBwmsJGGDhX56Zj6BXCrBO4ItlGEmGX9xmTj/aT6/ibAvUxBlripANmhPzusI3nW6KiI/RM6JYODiuXUC/5r1rzwI/RC4IWCZRiNoRyYfGWJMczatYTSnyCOPe0NZepoMtAgQVB6CMQDBiBVL5mjcFo3Bzw1ZdGkAxK3GDj3+BGpET/mZrAOoae1F6+97SmVHIJRsltQ9sqjJPo7v30fwTwUqiXg2EM1vIiiFJUOPIVOcCllufBHX2Ssv7n5qH7cklIS/7+H8BPhbq93zghOl5EufQQirva8vrTeC2iuB4AHpDLzCvRw8eBC33347GNjxmMc8Ztlnw5fVBAUbJcTRfWyzyTBteHECMe9302rHjx/H3r20hgF4//3Qhz40ra6zfrIKZBUwrEAGABoWMDs9q8B6rcBaAwB/+ctf4r777sO2bdua3ohQHsyADwZ9qPt2IVSqL8G/NG+KWl0vZf9mTHufX/LPVrANgTyi6adGJtleSF02HVwVUBEywJJf6WShufY2WI4ekxF8kBAMbNFL6mXSMFTSMNmMmpItFTJSVSbmnj+IiXIVEkkkreayYAvDsIz8AckFMUwMVo/Vw+rBLgQCk18P4RkEAndB4nDIUtVsijnnhUmpZAPpBlmwHwJ4pmm9BHAYxkBAi9JUk8b5ECBjP6astojJF4F3+vsWrojrJHhKIIn+gxEgpRv4wT7LnoOqx6RVsvjoOWfe1Dwjn8Sc/vXRbCZRUjQBXzJJ0wS32jEElfdjJY/enN5Llca1kLnH64wgeM4Besjc03jRFGenGqW8PJ51I5Bqei3GGTtk6Q1CKH86H1KBcQQGF56dCvjXbkIq9GNIhX7QZxAMxFCefpQT69h1xFp9/IOYaq+ASwKo/N9kNVJazO9oSovjzZGegEyFF1bnUKyg9gJI/1Hx57jKjzxw4AB+97vfKVDtUY9aHeuiSmexp+DcXOvf3UKhsAQU1A24Y/jHwx72MLVr99xzj7qfzlpWgawCq6MCGQC4OvYhm0VWgTVXAcpkI3nsWpg838zyJoQS3nPPPffklPmGlMAfmX9Ro2/JAx/4QPUmdzlaIOdwovoeSDSX7FbKBVhSaHvj2WIHBMbrfjvJV2SrgI37O6b4teo58HfCsg7BtvVAOCF3KCag0JQ001tIiCKEOJh88TyDTIVgDNPVefg4pNWHuSyYTkk7UC5NoNCjD5wx5MMSZYPEYAKBG0CGKSXq+g/xUfLwUe19JYhxokQQykGfIdONLC+m9RJ0M5XPEggk6JEGk2+2Evp89eWrxkBTlcBiLfRuM5FARx8AAkhM+yUImxYQxj7JqswxNZn+iFqftoUnRfJjAsU9joWiS5/QFDompBTJhYMQZCRgm1bfnCGvo/layKjjNUrmayc5ts7KCBSz7gROXdtGjxsYBdV0mkNjWAq3gvudVjJzp7EpjW5kC0pYEJbZC4mOY7Y6QDKwgXLiRp9B+vlN6L+00p5MixOVrUY/Ar+IaX65ccaDvRD091O+gw3BX8FuWHbnILLAewqk94y0Z7qi/fH+8q677sLQ0BAe/vCHr+hc2g3Ol/fNQMHI53rxuQQABwYGFkiIKXPuZIVD1l/khciAlG75Zjdb69GjR3HjjTeqPz/96U/VnxMnTqhDX/KSl+Dzn1/6sr3ThrEvnnf11Vfj/vvvB5+B+DxBT/AnP/nJuPjii09Knpv1RTD2c5/7HK644gr85je/UXuwfft2POUpT8FrXvOalsqkTvPK/j2rgE4FMgBQp2rZOVkFsgqoH7+1BADyzSw9/UZHR9XbWXqo8L/p9Rfd+PDGjT/m/Hs523TtCpSDG9oO6fsW8vZ2CCue/HRxZ5Rx0pcO4lSScZI1him1pG20l/W06rNSzsOvuegfJOtBo8kCLLEV0GQjqhHlPghB0Cp5UvF0eR8q/jgci95+ndkNrVZoKgtWLKBJhnzMwM3pAaqEVcwTg9nLZgi4kKBnlsaeqlNsWNgDCZrux08vJmBxZE6gNxcyUwhc0N+P0k+ToA8lxa2ELCUGLJg0ApQE3NIAFQleUepMoM2UYcg1kWFISSj70mFPMghnvGyhLxfKJWu+jYrnwLE89LhmvohRzVk/glJkGzaTHuvuTbVmoRIQqAtUOEmagBr3SfnvMfLA1avt4nXxM0+glSzVEBAM5dNR8Eua828cm+NwD4jCEhSnX6H+5zzGbimW3CgEpaqKrTcdAmExTtU+RPI35RhAhrjsV5YJ83Meal4ZPT0+3NwKSnclGXlkMObqcuIQcAPmu7sPSYtJ1qCa50jdZ5AfLs51FgKzS+Ya+OdC1l5UDzBJOtjqPT4KjOM9ZsR8W72zXTgz3s+TGdjoK0iAiv9/s0ZVzGL5MNmDjaDgddddh4suukj9f/QDXA4lTTTXduBkUgCQc3/1q1+twLtWICnH/c///E885znPaVovgo/PetazwDDCZo2A6j/90z/h5S9/+Vq5ZLJ5rvEKZADgGt/AbPpZBVaqAvwhJIi2VhrlCGT68U3m1q1bFfgXhZiQ6UfGXzcDPlrVqRr8FpO1T8Yuo/IFxB2aDwAWXDGGQFsSbNeBozu1xqc8yBb0hLkLwoonKVpcGCF3q5RfodIDNZocgBD8E98bsVLbhcnqKRkzayhxRJtRSYmVK3bDl5Q26/lN+QRbxHYIm4CmXi2ZbEkgMATw4ntlLdkTbIMFCSn02JFhfwQCd0PieEcgkIDIfdPAQJMQhMhLj2CFqXyWgCL7I/BiIvNUoG01B1tI9OZqWp+dxpoTCGTwAtlgJr6D7JPri/wQ44R98Byu59icwECh+XVHAKwcMQ1TAo1UkjMViQQDc+mBdhGgxr+VHyHJYSm2smfBC/g5o/dechYnaz1eCtmkzVojIEggkACx6TXRavncg6rvIpC1k95+3QIfT85BeemNKE89BlpIMak8VVMBBeVGAAQZ20iqFUOP3oL09gu9BUN2XhIbiBQvKPUBZC0oJ2ZtOKdGOXHKY8XtrgXzj+xsMDAFPeplEeQQpPd8lap8ujV6R9NmZtOmTTj77LPX/PII/lEhsxgUJIOwWbv33nvxhS98Qa2dKhuCcK973evUPTcZgMuZRNwIAO7cuRMPechD8L//+79q2kkAQD7jENS78sor1blkNP7pn/6p6o8hKlwzmY5f+9rX8IEPfKApAMh6PelJT8I111yj+njuc5+LV77ylRgZGVGA4Lvf/W6Qscj6fOtb38LTn/70NX/tZAtY/RXIAMDVv0fZDLMKrMoKrDUAkEw/JrTxxiB6i7dcAR+tNlDKKsar74OP44n2mL58AifCdD+NFqbsMpVVD8C1sUsxAXUfgkLmWBXCmtCYPR+AemCJjcrbT7tJegMykKI98OX7fThRDiCxEHAUKMK1ttZBPL1ZhN5+vQqA021p+AMCBVAmLpXXoh4gyflTXgzwAfmo7nJULyEjkD5doR/n4kbwr5PnHIETMvmiQAiDCSlGF9lQpkEfnAN96djfcME8TIKsx8lyKMElW9GUneUHAvO1HGwrQI/bGqg8Mts6gXZxnSO/vKIN9Bh6I0Z9c56lWthZ0a2kCnhxvmTx8bohiGwC/C6uBcE6FX7C1OKYjLoT863Bv2bXNK/7sufCD2xYlq8SgLsFCKqx/HA9BOQKjoBrS+PrsONnVfnoERQkRZegIIOWGGSRoEmy/AmcadopyD7FFgzTdhmmwX5OGLyMSTD3ljcUBCsJDBKsdCDUvJZBThxsgrD4fd2ekS4JBFZfp+TEp2OjrJNBc3zBTIDodGy8d24GCvJl+ne/+1186lOfWrBsMtt4zl/8xV/gkY98pJJGP+hBD1LgWTfbO9/5TqX04R++2Kc8e2xsTA2ZBAB8xzvegXe9613qvA9+8IN44xvf2HLaVEQ1YzlSNvyyl71MnXfppZfik59c+ML/jjvuwCMe8QhMT0/jAQ94gHpO6XZ9uln7rO+1UYEMAFwb+5TNMqvAqqvAWgIAGfBx2223nUz2JQi4Z88elVC2nLKExZs4630H8/63tfaWYQyOyENqgi0hcKQv6Q0lxb2ANusrpzzt5KKU3STFEHIMQhDI1GSv8SFOjLYEEiUBlsp2VIPWkl+HScFg4mToL6PTHLELkJOh5E2zsZYC9PZLBiY3DpdGYjD7q5Q2QcoZbc/KcE4EAglUk/FzCig+Pm/BsZJ5uJHJR3DAFCTzCJBVXBRcDwVXHyjl6iLwjmEIncDMOJcE10gwZrQgUvFui0JWGA7RCNwdnW3OvIwzR4Ko9CFMg50ZjRdJtqlUo5zZRP69eA1kvDFVmMAqpcJpA2kEGlkTXptkqy6e+/h8fKC1Vf0jhiDHcurBLWmvY8HYsheCMl7+tmAulPZbZp+VONcWQS/aVDA4isBemADcAtyrA2ShnDbFprzyIrYggz5IWeVLjKUy2BRHjdfVEjkxvxS5fsP0X/Ubys9Ie2sPxQSsvQqQfEl0ejYGzZHJtWPHDqUoWS+NzwKlUgk//vGP8V//9V9gHai2IaDVrDF9mBJpgoH8Q/CL/93JU9CknjoAIP0caQlEYO+lBv4J6wAAIABJREFUL30p/vVf/1VrCmeddZYC9eiBSIZoM2/x973vfXj729+u+v/617+O5z3veVpjZSdlFYhbgQwAjFup7LisAlkFllSA3hirudHThLKMxoAP3mSQxr9cAR+t6lMLDuNY5QPIW7vhST1JLd/y58ROSHG35jZQRroT0D5fwAr2ANbd2gmSIchzFMLSYzPSe8gSg4kkvYuL5Xs7EARHkM8v9Aacq+zDrHdnjNrayFlj8CXTkpvLYzp34tRlwexD94GZqc9jkPKwgTyZ0BvZlaSU6TMTFTtofiN6e4mgaDI9VdGYKrlHJVEG0sJ0lamZet6HZF/ROzANSS+Zd5FPoCnzjpJehklQ0mvKOOP6yrWCYn+1Y/F1vh5PHUFwkZJjgpasXRot3AsLva5EztGVsC+cCa85BrkQuCNgZyoBb+w9ChKh915c9l7SOinPPT/0POQahmmFl3JrDERRKcauORO17RSlfTI0CMIPmb2Wpg9s0loolh5BQQbpVENQkKw4MtAMXpQknUaY/DtMjiRF98rDD4JsQd3fisQzaH2CYlFG6cRW6IWowrZipBOrvR2FsDozvmXtBUCwOpJxU6zegq4oBaXXG9Nu9+3b161h1kS/lA+/973vxWc+8xkQ8KMk+Oabb1YMycVtw4YNCjhdbQDg2972Nrz//e9X86JlUMQgTLIB9B6PwGCyIBczJKO+Dh8+rJijbC960YtUUEjWsgp0swIZANjN6mZ9ZxU4zSuwWgFA+naQVn/gwIGTcl8aFtPLhD/mT3va07p6s9Fp2/nG9ET1k6gG/586NASP7tUGflyxF1KFY+g9SDvqfI6v90AyO92PYrGCfF5PUswHMgsDEJZ+uIZQkt4DWgEf3INa1cXcXC+Gh/ngA9S8zRivMPAkPhhnCwalmIWEVMsFeNUc+gf12YCUpVEmHiiPQb1rgjWxsEOZukvN4JfwcxAx+ShZ119TIHtQrm1DgAlIyt8NWsWzUfMd9OUrCmwxaQocYzprjzl4x8AFAlj0uEsDvCKLjww202AU1oc+dAwLoYSZnw+yKU3ByqjulGoTuExjno17ybmypvT1ozee4VYvuEzYL2ubdkAJB5mp5JB3ayjVLFR9X9WdQF1a9W5cCAFBxcwk8Ggx1EXC0fRnjf05og8rmMxOxlzEEtT/noo7Lj1oyUATkKEfnQIFCcRpvnyKO3Cz4zgXDEEQjCQ7Tvn4kZE33X0JdZx5KzZjlE7sQiBAzSeTcQKuG94nKD9eq7MNh/QeB/jNwxHiTGWtHEOAa3JyUilLqDBZ743S2X/4h39Q/nff+9731D03AUDWqfEPWXaUD3ez6TAACeKSBUgZMROA2QhsHjx4ULECt2zZosDNdu3yyy/HJZdcog75yle+ghe+8IUtD6c0moSFXbt2KW/BrGUV6GYFMgCwm9XN+s4qcJpXgEBbu1Ss5V4+zXb5w8kf7caAD/6w0ovkJz/5iZoSAUDL4g3uyrSSdzMmap9fMLgjtkBgBlLTkyiUok5pexqR9WWpt/96bC0ZFODaG4xYYxb41vxufcmYHIIlepS3n26TwQ7FnpsoB/Blcw+6Tn27KljjqBELL6htQc4tQRrI1NLxByTIwYcZ+myZSOaitN/DraV5LQpLkKJU24lqECZgUzI9792PYv1BtNN+tPp3gjgEidLw46OvH1ltZMjppOo2zpGyzamygGNZGMibJ+pGMllCDjrsR9aI8EyjdFSFctTCtRKcMmVBcv2RbyPnS4DRtI6NNS3VBCqeC0d46CsEqYKBBC9ZC86f8mYTGTLBUJ6/OFwjqjc3grLxNADipp8LyViJDRDoQ8jYIzNH02Ih7gdTEmDimEUFhKkgIF1GeJvvkOmJYQwMN/l9U4AkwTjKlivwgqNwnORJ8XGX2/Y4JWEegSBbkCAhwUkVOrJ61Ba1Wg45e0xJnENQvQYoVufkEpBaBnuB2p+rkKfTvf30pz9VL5np48bgifXe3vKWt+DTn/60CsZgQm7L3+EKXx6Tidq9lhQAPHbsmApzYXvNa16jfADpB/hv//ZvoKWQug9xHDz2sY8F18mE32btzW9+s/IOZPv5z3+Oc845p+Uin/3sZysJNYFSXke9vfTyzFpWge5UIAMAu1PXrNesAuuiAqsFACQIyTeLfHtWLocPK/T2O+OMM9SNGME+yoGvvfZa9W98I5nLpeRIn3CnA1nB0fK7ERCsW9TIgnOsAgKCRxrNQj8ci6EShzXO5il5uGILpEG4ho0zAEFJsx6jQxCI5FOuZeBlJ/dBiHu0GY1TpTPgywCeYtDplTKNkBBKvMO0YP21cPY2ttd9CvVryl7CxGAGp5gwZlxY2JWon0ptH0r+Uin2XMWFLXz0FuKzNJvtJqWtlOESdDJNgSUYRHkwQZq+FJ5pCL6RoUV5cBqAGEFPglVxJbIENsl0a1eXcs1CyWNwSHrAFEFAsisJqnFfGMqRVuOa2Ldr6QGineYRMerYP5mHcZl782RXWvHWSnYn10EGIr0Pu+nrJ+Qo6PkaAoJM6dZMYO9UuIZ/F5KSWaa18wt4tg4KJuhg0aGTJ0YwODIeu4NKuQBLDCGf6wGUtyo9BbsMhLabneyvh47wvoXhHpTpEhjU+52NXYgmB87PDaK3l+nJi8ZWickRa5AhJA6E9yenbejH4tLwBTMDMsho27Ztm0mJT4tz//Iv/1JJWV/84hfji1/84oquKSkA+MMf/hAXXHCBmvNb3/pW5ctHGXCr9vrXvx4f/vCHl/wzGX9f/epX1f9PUJFy51bt1a9+9cmAkNtvv12FpWQtq0C3KpABgN2qbNZvVoF1UAHS4EmJX8k2Pj4O/lhGhsME++jBsjjgg8DgD37wAzXVJzzhCSvmAThd+yZmvf9rUzIHeWsHfNyjWVb60e2uyz/1unDEvnoirN7DhYUt8GpTyOV1gSInDIBQQKLeGiBHYAkXQhxJ1MF8ZTumaqEU2SWrUswjYBCFZksjJMTCEGwxAF/u168HH8dS8Aekj5UjtiOAGShJsJkSYwnKtltLxz1/J2ZrIfOvVSN4p/z4DP3pCN6xLwJZBG5M20QpBH+YHqx9HdcnQZCS/RGIS8uHj2wzAm2cXzOQivWYVqEn8dhQkQdf5LOYFigVMRhZiqH8UnacyT5R+so6qH1KuW/OKwr64P+mxLkVkMnj+KdAZWrCpoI+6H3IdGHbRo/rxwYdEw6lDl8JQJDhHiFLkB+mah0QjAnIBTvDpHVTsEwOKhAuZAquBlCQgNswhAo1YegIgz1os0CrE52d7XxOpZyDzbRntwMjUVoQ3qUQcv1IYa+//nrQFufMM89U8tD13i6++GJ885vfbJp8u9y1SQoAkrH43Oc+V02T7ETuK9l+9DWkJJjPE1deeSXe9KY3nfQ1pL8fff4aG5mB3/52GPTHoJRCofVNCoHGD3zgA+rYn/3sZyocJWtZBbpVgQwA7FZls36zCqyDCqwkADg7O6sYfzQPjhrfulJ+0cyXg5Jg+pCwnX/++aAn4HK3WnAExyrvjeUrl7f2GYSDELwaq/v66QG0FrZCML3QItMgefN9F6XZHgwN68tGhUq2nYCwWqQ5dpqWkrPRGzBesEal4mCiymTlxvEIyJoEtXCSaYSEUPq6E9XqONycZj3C29m6P2C8mrQqcZgCPYpAeU922oh2/16EDXr70YNyYbhHEAxiplqBRLwHfYJ3lE6aAmQEssjiixhiJqvjuWSbMdxhtJgOeMW5ERBMqz/PF5j3cshbPoq5cA9YgyNz+rUkCMh1E1gjMBuXBdep1n4gMFu1IKWvgMvFUtlO57f7d9Z0pkLpNUE0L3VWHYE6ysQ5DoFcAs387PC/Z2shky+VJgmub1JAlQTTxSdSlTwvniPZegTGCLDRn7NTImwqa1RA5EgDS3CqHjCyqHe5EZJWDvTY60aTlA5TQkwwjn6pK8wUVB9eggzcE8qIWSiGjpAtqOvRGxZO+UXO9qNvoPM9gfCeBRFc2I2Kr9o+qTDhPfHv/d7vYePGjat2nssxMapy/uiP/gjf//73wTANAmcr2ZICgF/60pdAADNqBOOuu+66JQAeQz4YcEKFEfecFkSNzx9PfvKTVQ3YaFHUznqIEmNKjdl4LT3ucY9byZJlY5/mFcgAwNN8g7PlZRXoZgVWAgDkmzhS8RsDPkZGRhRdfnBwsOVyeUMSGQ0/+tGPxvAwJUbL18Lgj39CNfht7EFdQankIe2HF1tshqUSBzvfsDeblFdzUS0XMWAQSFEtbUWx55h2civQA4vMD2t/7LotOVDS35AP2wz1aN3uPjSEQn9ztp/DWgoPgUEARa3SA79mx3qAajVLGViYnRrEwNAULEsP3GXfafkDcm8sUUSgmHz6WwT0gszRMMwmgJQ25qqj8GRyOXzE6KJ01GxOISNQ2fWnwOKj/JZz47zS8HBjf1PsLwf0pAQecX70myMbjcEmabRIdkzQKzWQi85jdfYeQUAFBhpdfwtXGoF1UlrocSVcW48N3a5+Sl7NAI4glHinCWY2jivkACwQLAsQgN/FuszseFeDUKDYkPrghICg3u9PvNEajpJ5xRKE4N8VBHIutIFYrvGjqZxkCq4iUJBzU/MaILe9LiOmbx8By3jXdnl+K4q9S1Ncl+xT8BAI72Uqv309NSpMqIihzxvvSddz4/3uU57yFNAX8e///u/xV3/1VytajqQAICW/f/zHf3xyzt/5znfw9Kc/vekaGn3+6OH3B3/wByePyxiAK7rt2eBtKpABgNnlkVUgq4B2Bciq41ut5Wgchz/iDPiIxqRJLoE/vnkLvYLat6uuukqdy7d5y/2GtuT/HBPVf+00xSX/bosN8L0SHFeP9SXQC9caVJ5rOo1v/f3qduQL92uDKXwos/mQIfTTWy3sBQgyWZpMDmnDEmMA7mi6jsPHBiGLndiKNvLWnro3YLyHpmY1r5U2o1icASx9Ly2/1oNCbmM9PVpnZ8NzQn9ApmG2B0c7jWCBcmk+bCeTXC/uV7Fp5GiYTCrv7jRs238nmMWWBnhXqjmo+hJDBXN5JcElpurmbX2GXePC2V8kNyaQFOOrsG3djs2FQRT0H6RkldJV0z6jAcmAI7jYnxPIOfqfocULYJ9kHFJ23ErSbHIxsRYEHNMO4Dg2H863MUikNyeQT7E2C9atWNH0We2DlCUEOAxrGVJ/+cKBv9GKkYgwab2bjcAtJFlYNQhBhh7bdJ2h182RW/S92uTD0TSVb18kI7Ya0ogXyYiDnRB2eysG1SXXWXsDeN+xnhoBr6uvvlotmfeX7V5Gr4e6sB6Pecxj8Jvf/AYf//jHQX+7lWxJAUCSBZ7xjGeoKdMvnKEcrXzDqSx66lOfqo697LLL8Hd/93cnl5p5AK7krmdjt6tABgBm10dWgawC2hVYDgCQNxIHDx4EqfZRwAd/iBnwsWPHjkRpvrxBI4OQb2iX06OFwR/HK/8ETxPQCPw8aqUcemNIb5pvpoWcNYZA6nvq2YJy3HGDZFsmcNLzTV8yKjAMC3TV1weZhNwMIfhQeMoQ3g96cHCqBiem15kjyHqztMNauEeUZ7nWNvgGQSPshzWFnFMyP/2Wlj9gmBjs+8dhO2SX6DXPH8NcbQLVUg5u8aixfFSFSaQEBBKY5B/Kb9PwuKOclwAegbs0ZLIEqviH89NJo41A08a1cb2UMKeZzMsXC2QvRh6EadQyutqqDPkgeNnFkA+y9wjg0itSF8siCNzfIijGwigs8MXNvAp1YjhIN5rvWyjP9aGvd1hZLUhxXHs9secn+0NGokIBKd2lPDX22fEODMYQiCYvEJQ8OmQJUrILlTi8Qgm7i9KHQ/lwd9mZ8YoXyYjJ5CwC9POzyKg8Attu8wJuHfr+RfXkvfA111yj/vP3f//30dfXF7vUp+OBvG8/66yzcN999+Hzn/88XvKSl6zoMpMCgL/+9a/V/NkYJLh/f2sFym9/+1sV/ML253/+5yr5OGr0CPzQhz6k/jNLAV7RSyAbfFEFMgAwuySyCmQV0K5AtwHAEydOgD+ujQEfY2Nj4B/HoRF3ssYbNKa0nX322Qo8XK42Wb0SU9Xvo2BvhE8zco0mpYBX2ohC71HthyVXpbjepy3HFeiHLfoAEUMG1GKNNvYAgkmyun5EFkI24B36D8XSgSV2AwhT3SbLu1H27024KwJ5ay88udS7LklHjqDXIhl4+uxI8vi4t2FIiCZDUk06HX9Asm9mJgfRNzALNyaoGtUsCIYxWZk9uY5KqQgR9KDQSylhksouPXa+RraLjb5c1bgvyloJLA6nlM5LZlyU9ptG4i195U7Mh15znGOcFgFnreTJURiH8llMQV4dzYlzJfDopOwXyP65T6VaDjlboi9H4D9OJeIfE8l4CWAmYUp6wSCknAMW+V42H5k+mfT0o4j3KITVRZBIUorPsA2y9Y4DQh/Ij11FNeYoIPj5nK7LhmOfveRABk8kCdGil+ApluCMsnmwugS4dlyVSvsl8EaZLn8XCArqM8U7jtfpgDpjFFZoxVCt5BD4/SgWaLcSAPTLVXP0sR59/6Ly8cUyQ0DYzjvvvKY+1J1KfTr9O6XQDOObmprCN77xDeUHuJItKQBIeyMqjPg3vcXvvz8MhmvWGsHCV73qVfjEJz5x8rDLL78cl1xyifrvr3zlKyAjsFWjmom+5rt27VJeglnLKtDNCmQAYDerm/WdVeA0rwDltAQB024M+CDwd+zYKVni9u3bVcBHuxStTvP40Y9+pMBEvq3bs2d50um8YBL3z78bEgS8BIr2GHzc1WmqLf89Z+2FL8mi05PP2WIjbNQghW6yrRWmyUKfTcgHHIs+TQZyUaFCSuYhrE6y3dalFnIbyl4vpqq/094PWwzDVh54ehLrcGAyNLmvTNbV/zxZKphj2FgWzP2xxSACBW5qlwa+byPv7Gka8NGs1yAQOD7dB7ew1DPMEZtgiwJqgUkScjgqgTsVotFjLm1lP5N1JhdDHUybx5CLSg5Ft4aiq+/x2DgPsgwJhG3sbc1ajEI/yPKL06q+jarHhOAq8k468+S4BNTIYGQt0/QLZN/02yMzsFiXXptc281qRBkygVwVfJJvzeiUQQ98FdDBxNakjc5qm2ApieU0AjL2DD6jnUZX4JhKvqX/6RGDFzedRmr4d1mss/T4gZqBVL6FMc+XmxDIEwDtCDSb79kozfeipzgEx2U/JyCseEFEmkO2P60OCtK7L3xZxN9uviTpymgLOhVyL2B1uF8hSBicCyt44brz/YuKxRfLP/nJT9R/MryhlVy0+zu2Okbgs8Ho6KjyRKRElmEYK9mSAoCc6xOf+ETF6rRtWwGZBASbtf/+7//GH/7hH6p/es973oO3v/3tJw8joEdgj40JwUwKbtYOHz6MrVu3qn/6kz/5E3z5y19eyXJlY6+DCmQA4DrY5GyJWQW6VYG0AUC+Rb3jjjuUbIASAjbeRPAHdGCA5tVm7YYbbsDExIQCEvft22fWWcyzj5evwKx3w4KjC/Y++AZyXEdsI29NX44r86iWiujt0wUBKfXcBagHQl35lDmQSMYaE2SlSvmNuSENh1H6e3zehWuNohbcpdVH1J1X3gIndxSOARhCnyzH6kWgyRKN5qLk2rJkyCokV24bhAKLzfwBBfpgi00I0L7Gh44OozDQXsrsiC2whA0v0PekjOpEoImgDeWypoyfyIePHnGtZJ1JrlD2RwYfmWVp+PpxbIKAE+UwNKRvEdB3aEbPjzCS8rL/tFN5CdQqv8B8yGRMsxGom6sBPU4I1ul8f7Sdj8ypUBu+9Alw6CRwJgP+92blvZdG42crZOz5qk9tj9Q4k5FkX28Cg5n48iUEBPVeRMUZ7uQxSra7ERBMupmDJAuyybhSUq7qQgodYLX9jFTSsSDrjdxIgpKUDidaRboHy17QEgPI1b+j+SJsOtXrWATbADvGiy3ZA9t7Y5gEvU4bPeIYeMFG4Iig0XpurAdf2rOxLo985CNXtBw6AODHPvYxvPa1r1XzvuKKK/CiF72o6Rpe9rKXKZkzW7P03jPPPFN5ITIYhuGFPT1L07Xe9773nQQO/+M//mNBAMmKFi4b/LStQAYAnrZbmy0sq0D3K8C3e6TIm7ZmAR/0UCHwt2HDhlgBH3HmcNNNNylWISXE0Vu5OOfpHlPxD+BQ6YM01FnSRc7aqQA0aEph6Q9lW24o09JofHCfmxrB0DDlOxodqEchMsVcwAAgsrATQlDeqS9zsigrVg+/yVgak6UxzHuhT1R1fgiuW4Kb1wU0gVolD/g9KPaZ+PFByXnJelHyKu1mwRVjKciC6Q+4B5JpvEbz4fUyoiTkAZay+MYnBiCK8R/cXbFdyc58aQ6kEAyix10aQCC3i95uBO6GC9qbt+BEMvgIhG3qTccnkJ1Pl10EsoahYjjfNNiLnCNBVYKgiwFGk0oQDJ2v5kAbhL58JfW0XM6Z8us+Nx3wdslaGUAEsjsc+IEDX9xhUo4253KcLbCQh8S4YorpfrfHmqACOTcruaoK96AUNNaJhgdJMuE2QSgfP3okUp4aAJIvg2IAVobDq9PV2uklWAhZeUzTNQh1SmNKkEX1HcsXY4AHKCBU8xpgX5Rkx5CAW95LYMnfS2UJa7WTyclJ3HzzzWr6F154YWr3rGu1HvTtjnzxbr/99mW5325XKx0AkEokEgWOHj2q5MwkEGzevHnBMEx+Ztoxn2FoLXTrrbcu2ftGGfBiiTA7u/POO/Hwhz9cqZM4HuulY3G0Vq+VbN4rU4EMAFyZumejZhU4LSpgCgCS5UdvDQZ8kP3HRukEGXp8e2iZ0nIWVfmWW27BoUOHlMcG38p1s3Fth0sfRyVo/bDHMAnb4sNDfOCjcc4CObjWViWx1G2OYvIdNpB2OXDFro7srnbzCxlig4Bo7bPSeX0DKtlSWPEeACu1HThRXujH6HsOHMrrXHoUdh6x1RFh4MphwMDQnSEhOWtbPXFYfy70bXTEaF1erN8PmSa8VgIlP9eX13EGBEMC34PthszCctnFbE3AdpP7QrqCQHrFKJAlqgpBIEpQybijz51pozTYl8DGFKTGnAslrPTKo6dfWoy46bKD6WqA/lygFRrSqkZKZi1DVmCaAR9M4iUgyoAP1sHkc9ps7pw3r4HeLoCBMtiNqtwPW4FmZIGEIF23mrJaYMIrqiEL0UpPqt1szkJJVUeVe2BAhpzhC4PYdZGkh5LRz5eR5TpDUN9KIfa4iw4UcqjOErQgFFPxGES3E5Y7TVYSoCQoyLcRTGmnvULnl35C7gSszqm/IngUbP8FnWZx2v87/ap5f0nmHxmA673RwudRj3qUKgPBwEjeulx1ue6665SaKGrHjx/Hm9/8ZvWf559/Pl7xilcsmMpLX/rSplP76le/qiS5vJ9nGMjb3vY2FfLCQMIrr7wSH/nIR1AqlRRgRzCQfS9uBAd5TUQekc973vPwyle+EsPDw7jxxhvxrne9S4GMfN75n//5Hzzzmc9crjJl46zjCmQA4Dre/GzpWQVMK2ACAPIHmTcJlAqw8caJvny6AR9x1vLLX/5SyYtp6vvQhz40zinax8x5t+JY+V86nm+JPuSU7FM/2TZv7YNnIClmIqPNJ2mDIIrQF/CAgYedgM1wD6HvLUiOWRgQcnfbh90gcHB8vg+ebP7wnbN2QcoTkAYPsPTocq0N9ZCQjpdBywMoeaUvoC7TM+o4lPPSP0qPMRr1Q5kXfQYD5Veovy6eOTM1iFyugnLQB+GazStn7VahCgQeTBuBtlKKQCBBJcpvN/RSOm86u9C/kPJgJtDG9exrNqofkP1noScXqDTeqXII1nUj4CNnpy+1jTz9ugHWsV6WZNr3gAqlgND3GmVfMhhFVfJFz0LGPFlk9O5Ufn4KNDO/Ppr34MLGFgjlPXgcsJb6bKY9spBRkjFfcvElU5dAObkN/gIPVkqVN0Ion0SOTQ9BfWa3dl2kA8F5CDLz+B1OluAyhKp0mrBiUI4CTPilOkGx/MjCD5UKQo4BVpME5cX9ylHY3utVov16bwRweH/JF9j0AFzv7Wc/+xme9KQnqTKQSdfKP69bdSKg94UvfCF295HlULMTPvnJT+INb3gDqtXmLyipVvrSl76EZz/72S3H4/PORRdddFImvvhAXjcMDyEwmLWsAstRgQwAXI4qZ2NkFThNK8AfzVY/iq2WTMCPwB9/EKPGRN4zzjjDKOAjTolJracUgDT+c889N84pWsdI6eH++ffCk/G80/hQVrC3wzdg8rliNwLQF03vIStiE0qDOfBh1haBkoLpNrLDLJUsqMeKVA8wKjHTh7CaJ+vOlPdhphYmALdqForI2ZsUgGfSQmCKYKLJgx9DQijnNUscJvculAUzCdrsgZj7RDczGAS5sK7lyh74wkIgjxrWKNz5sN5TCGAmw2ZvFU+g4rkYKFRTYQRGgNWGIpBG0i/nSCCQbYMGy/DuyVBWvLhREk05b9rsvXLNQc230ZOvgIBgmo3MSIKsg4X02JGN86P3HZnKUMm4yb6bZODAkwMI0P57UTDERwEz5bpvYJoVWtgXU3cDvxel0jh6+maNPTA7zlT5B5L5SNCJQH1zH7+O/Sw6QMoeSMU5bGeXwBdD9Ensq3slEmjVt5xIOscFx8v+kJUpyFqkfJksQTNGtdF8opOlHYKCisVI+XAdFGw1Nylg+68CE5ezBqUsoc8bQ+oe+9jHrvuSfP/738dznvMc5PN5BQAut6Q1TQCQm/mrX/0KBAKvuuoqpVoiYWHv3r14xjOegde97nWxGI4MTPzsZz+rAj54rczNzSkyAgNS6DV41llnrfvrJivA8lUgAwCXr9bZSFkFTrsKJAEASZmPAj6iQtDfj158/f39y1IbSo3pt8FgkUie0I2Bp6pXY6L6n4m7LtpmTD4m/EKWIQzYHTmxD4FBwi+loq6gF9OYJrljAAAgAElEQVQ9idd/6oQCmPoKsd+gDwcWdgFiYfBEzduIYyUCg/HkcHlrD7yA8jl9wEzJee2t8AK9sJKoCPR9DLwCnJw+W5R9kRnD+oZp0gYlJnNXjAGSoEhyRlEQDGOqSkN9T/mI5e1d8IID2r6Yp1ZCIJD7RpDBxEcx7JGSUAJM7VJ0k1SR4NpEKfQILKaQHMyxOT8CdwT04oCLB2OEfpBpyH4pN+5n/kJKTQWmlEM2JGuQptMD+6ZEmB6nlAinKT8+9TkkkEXklL6Y7YF9ziMIdsNL+GKFQBmlwpRtMmHcVHbfbutofZBzyBC2Qj+95fCyU8EeBFXJSJzS8g8Mc8K2qxdfSRtlsfw+DX8HxrW+v5KO2fR4SbkwfY4Z6hGoWqDb3o0tJs56+t4wHLf+8kTNjfJhAqcC86Vx5AszKuhK+E+BHTwjlRKcDp1QWcLEV7LBKBFd7+2b3/wmLr74YuXhzYTb9R6Kst6vh2z9q68CGQC4+vYkm1FWgTVTgTgAIN96kXV39913K6NctijgY+PGjcu6Vs6B7MPBwUGcd955XRnbC2ZxcP5dCFDS6r9AphfI9IoHUC0exKu58Kt59PbrM84cpsgq2ZveGjgn6e2E5dwH28ADyaavk0r41asF58GQEeV5ZM1BUjpZ2oxqkAxA86o5+NUiegfMZIAu6ypmEPAhz6DNTQ+hp6cEN6cPSnJ4W7H4POOUX8CFA/pAxmcoqhCa2hZUg4UhHhZ6FFhaU2Cp/r6H5bVRqfVDiMlUgDYyzAiIjfbEA9k6bTEluCdKQL+bXmgG58g+CaxRItysRenH+QTJupQx089QJf0agsaNcyIrcrYWJhP3pAgyqu8g6YahGMLrGqMuDN0ohsBZE7BZBmOoyhhyyrYXCz9f/KyS18zk3WRhR52uw8X/TmCOfqoSs6GfXgp+mJ3moOUfKPfCx12duo7177Q2IDMv8ANUakdRKJSNX47EGrjZQbIIMjSFyEGC3/FMHDb7ro8zl6nxEQyOdGbv18p70ev+OWjakbWwAvv371cvuAcGBlY88XY17AklsZdeeqmy9GFd0vbzXg1rXG9zoB8iWYu8xtkoaW6WaLze6rJW15sBgGt157J5ZxVYJRWIwjsWT6dZwAflAFHAhzClHmms/8CBA4rKTwCyWz4tx0r/iWqwH9VA38uO4Q+ghFETgGNapiN3Qjj6DDqyIxw+gBgk/JbmeuE6HgpF/YcXejlZDJ0wkBUDPeqBar6Ww1S1vfS33WVFmTW9rGAgnyXTjR6DJp6NnKPvOyi6u+DLhQzH5B8LpvyOQcqDgCGwQAljGDjSmVlY9fZh1mu9F7YYgmsN14HA5KtqPCOQAjMVF71uNZWgi6oHTFZC2W0ctl2n2RMMJWhXdGwM5NORA5IJd2wOSmpLwDJqBB0PzYbsOJ1W8y2Uay7yThVFd2m6uU6fPIc1UMw9pBfC0jgXhuHAH0GlchSFXv0XG63XR/4cwcCC8nNVEtNgAyqSbKp09jQc21Jefkz6VX6XGszbJHvEsJLQT49cuSOJk9aTjLVgv2TE0CN4y3ou8t+Sm+EzKT0mkzvpPGrVHHLuZtiCDEWylLvpz9h5dkKOQAg+eBMGrs8nRWC2Vh2C406d9AFsNaMgELj7N8/EQN8DlXKD91IZuwu466671ItuBjt0016m85WyOo741Kc+hbe+9a3Ka/vnP/95BgCujm3RmgW93gngMsjlyJEjys+RQCCl0Msd7qK1gOykphXIAMDswsgqkFXAqAL0AFxsoHvs2DHFtKP3BxtvEPkmkCEfy+0F0rg4ppHdeuutyqflggsuMFp3s5NrwTjunXm3kjRSzlszCOZg0IJj0duo8xv5VgvJWfvqAJHug7qDnNhhJOf1PAcFdysgOicKtt4QF47YCQl9sMsL+jFV2gxP3gNo+iSqa1kQ5BqAj4UJwkkvpjDcoxYGDBg0W2yGRd9F9TCs3/igzzmZA4pAaa5PITl9LVioQbABk9V4wIgrNsEWLjwNmd/ialDSSuktU37T8KCjlJegFftLwqZrt0uRp19aycEca7wEMD13cx+wfyqUMpu2CLCzRMgK5N9pNdaVTEvKo01CTlrNpzzXi96eTZCiOWvPfB0CltwJP8iH3xOaL3LizINsPVux9eh7ySCHOGfpHhOBnESPZxAIhmvo9pXgPOUfSLkwkex5+IpBTXacGZM6wQxUim4IhFKzz4TfdDwMk82h4WgV5EHpMANG+DsyoR8wIokkDscKAdt/5wNw4K4HnJwIX+QSECAYGP1Zj6AgWW5kAVLy2u2AOe1rZhlP/MAHPoB3v/vdKhX32muvxUq88F/G5Z7WQ/H5jvu3adOmBd7t4+PjGBoaOq3XfjovLgMAT+fdzdaWVWAZKtAIADLgg0EbJ06cAjWWK+AjzlKZ1HbzzTfDdV1lvJt2OzL/ZUzXfnKy24K9G768Xxtwondc3h7V8jiKJuGK7SGIaPAQ6oq9kEzWracE6tTNFWfUvQV1wUgKOvcAgn5YzdPY2s1rYn4P5rx74IgROJYDn5I9g1aw9sKT9KtbmOqZrEsbymNQsfj06xKGX+yFL/crUNGk0XeMD/XSIJU6Gn9magiDg76SPUeN7NTZ2gbUgmSAZc6iLL1SZ/2YrDAEwwjcjaYUxsH+6GlHVh398tJonB/lvATt0pLc3jcd9klGYFqAJddKGS/9BwkEptkv+54v5+HXcujrnYfjpMmkY+8UMdL7jt8H+gFKzfY78PegxpcNsEL5rnARKPZw9+S7FobARHeCZByr2+AcQzXCcI0gHM9K/r2s81mRAYMnQt9QYBaBSvnV6cnkHMrLCQgyAbdUTxrWC+AymcWCc08GjFCayzlROtx5TkLuhRSdpdRzM/245YbzMTQ0Cno6l0rNmbTrERTkC28yohgwl4U5AJdddhk++tGPqpCMb3/72xkAmNqHfOU6+uAHP6hIHVR98W/ubybtXrn9MB05AwBNK5idn1VgnVeAACBvBBmwwRugqNHf74EPDGUiq6URmPzpT3+qbkae9rSnpXpTUvEPYf/s+yhmW7DcnLWFFv2Q2qmDFooEEqHvI2WJYdiCBu/6bDNbbIWF6aY+V3H3V3nOEQwySMQVGIYlXAiyd2K2qrcNR+cPNhxtocfZi2pwh9GDI2WqjuiBj8a+Y06q4TBHUObMx+j4a2o2ikUJrjWEAPrS76hfgr6BJGBhmJYpHTjWbgR1cLLqnYFZ747kRaqfQcCUbBdTH0V2R6bZVDmU8qYRFkGGIaXG/XkfecfUvzBcMIE1+u9t6gNcA8kfZcsEKemz5wcCs5U88k4NfSlJjjlXyosJXFIWPZSH0WdryQUiHQS1TZAow82Pp9u3GiynwECgVk9T175EgWAXKkGzzyDBwG0qATbAISMrgU6zY0iJjU319fClSTrXY+txydILJdAgG1F0h40ogl2oLfp+C2XKBCLt8DcuYUpzp1rG+3eun4Ag6bXVumS4e2BvrDmpEA96CTJghI3S4RMLPR0lr5FjQKcXUNLCL244D3MzgypAjfd19HfmS1/+mZ6eVn+vV1Dw17/+tQq7YKrrgx/84FjbczofxGTcyy+/HM9//vNB77isnT4VoJc7/+RyKZv2nj4lWhMryQDANbFN2SSzCqzOCvBHgIy/xoAP3hgy2ZdSiNXWpqam8OMf/1hNiwBgmm+vDs79C+a8W5su2SEAZ9GtqJ6up1GY0tQI+ofGtRMzBdN5rS1G4BAZH65FudnC4IYkywmlpsOQInly46lxrNC3LkZaMRlnR+dGUQuOL8UUamTLzCFX0PcoZKcFyr2D+AEYzeslkFesQtN+AFfsAjAJafggTHaLI7bDN5CyR2vltSPldkxWfpcCGGGjYO+BHxwyANZP7UIU7kHZLT+npo1A2PF5YLBNEEfSMZhETPkywUodluG94zZG+pYy6DhPggNMD06TRcWgEdaV8ug0wNXGeoVMt+G6J50hQN1kI0JmGwOqNNhlsh9Vn5LMTuCPXQcDLfg4pMVqjn8N5eBgSz1EhMxAs++7OOPSD9TGqGLqEeyMw0Tr2G/QBx8+ZIeArSjQI0zVJbjVDc/HjrNV4BtfyoQ+hQQm9YO5Oo8W8wiVvjwKCAK1VcXehBXjBZZ3Ia6/OjQOffSjH61kv81arVZT7CAdUJDhAux3LXoK3nbbbaD1zc6dO5XP9Xpvl1xyCb72ta+Bf//Lv/zLei/Hql0/n+FIiEjzWWjVLjab2IIKZABgdkFkFcgqoF0BmsN+//vfV4awDPgg449vQFer3wfnST8Stic96UmpvcEqe/fgwNyH29bREj3IWfSO0wfPSjOD6OufgW3rszny1hnwpAnzzULO2oPAKHyCwROUFesHpbDYTPgVimnSGgSYq+zDRKV12ETg2wgqQyj0mTFWbCUtzsEna86g0fvRFkUEhqxCJvMybCQN8I5gCM3wFWtJsxGInanypQB5Og484/Wxpzzy9k54gZmvY7SkMhNpq8CG3nRktxEQSB+7vrxm4RadRpbh8TlgMEGflP4SjGzXyDKcr4WS4zQBO863VC2gxxXIOWkDMRTvMlmbMRv3Gcrom1eHYAlBnDiBG/RFDIJt8Biok6g5dZkwwUCy9cxk/O2HZogI2dw5eMERWHb6AOrS8e06O5C+fXwxMZEYbGZtIbcrqXbSpoKkGAKDSj1QpLMsNukYcY6PgMnw2ElVizRB9zhzaDyG0t9A3AUhByAwpJiplI+HqcOnaiTkZsjSpbj++hvU6eeddx6KxfgpQhEoGLEEkzAF1woo+Itf/AL0RKPP9d69e5NuxWl3/Ate8AJceeWVeP3rX48Pf7j9vfFpt/hsQVkF1kAFMgBwDWxSNsWsAqu5ApT9klnHG5/V/uaWvjU/+MEPVDmf8IQnpBJhr9KO5z6Okt9Z1sjH1YLDB0QDiaY/jLzrQRqkPxLA8yUfmPUfhFzFwKPnnL4vly3o60Z/Qv2HULJ1bDEINGEU+kEvDs96kIjBeKltgbCPw3H1a0JIqmDvrSfX6oO0vD7JBuQeQcPvsPH7whYE7yz14GvabLkbfnAEttOJ3bR0pJq3D9O1U0Bs3t4NKRlcMGk6LVjoQ87ahJoRKH1qGmXPQtVzMViopBJuESXy9uWAgQ5AXNxiqBCOUk5Jjfvzra/Z+Sow58VnDZK1p/wMU2QvRmtS6bUiD18eSIEFurBSZBbTvxIiBJi60UK/QAZ7EKRb+p0iA9oKdPZSaz83goHhS7S0fQmbjctAlMArYGCQPDWzlyBxax4CYSMqvCL0DozxnRuMwTOwwTg1tzBQxKoHioQJwyb+q3FXvfQ4yoWVRBcE3ijPXUYvQ7mh7g3c5HdKMml4FJboUy9sXPn/UC1tOqmeYLADX/iaNIKCkXw4+juufHg1goI33XSTug/et28fdu/ebVKaNX8u74mf9axn4brrrsM73vEO/O3f/u2aX9PpuAAqtz73uc8p4sYLX/jC1AgRp2OtTsc1ZQDg6bir2ZqyCixjBegDQxr5Wmic6/e+9z01Vd7EpuFPOFf7DQ7OfyrB8oXyn2NCsG6zRT9ciywxfc84RzBVsWwkE7WZ0GrYh0A/bD5oCH12Wcgq2wuQzdDwMDdRGsNcLb53olfNwZI9yPeYgVKO2KBAN9OgETKPXGsQPkwSlHmVMSRkDIEKpIkBhra5MH3PRmlmAP3DE7HBsSAYxmSFD7iLH/RtFO09qAUEOs3mxSmHAS/9dTm27qfr1HlkAzLggvLYNFJuCQRSctvjdmbkJZn90TmyYZfKeAkS3jMZMhqTNs51usL07ZpiD6bJVCJY54J2BMeNvn9arYlAoCWKdQCtG2w6px4eIk8xD+UGVPx4ydbx9yICA8lwJOgYAyiL3/mSIwnMhbLd5QkRCSfgwOJ+Ia8AKSmasOKCIXiYU36G6Td6P/K3cCUDRaJV5eo+ggTXGOLBpOH07634vSBQT8HuUFBHPh55PFvJem+88UZ1NF+eOk5KaUcN4+uCggQEeS/H9OGVkjKyNqwRwRQG363nRgCQ18gtt9wCBke88Y1vXM/lWJVrn5+fx/vf/368613vUuDfl7/8ZURpv60mfM011+BVr3qVUk8xACRra7sCGQC4tvcvm31WgRWvwFoCAPkD993vflfVjD42w8PDRvWTMsCB2Q+iQgAjYSvSN87AW40PLAV7K3yDwAcLvXAsytv0wTeBIlxrBFJDmnWqZPT0oyTYRJpMEGQrLDGnTOCr3lYcnddbV7g3TD02YfFZKNhjqAWmCb+oS66PGjElWWsy5RxrA3x5jzGgQwZPKFVuL8njw+ZcbTsqfuvjKI/PW1sUeyoNoMkVW2BZAr7U2//FH2XKY8mMS9Mnj6Bd0QGG4ivpOn7D0COw4gNb6snB904CIz0dT+t4ANdPOTNTjtPwSDw1ILlP25UHYSjh7TiVhAe4qn8m73YrFVcGeYhgKwIh4KnvjIRTjH0418IQpeUCA4shoxK+siPoBhDVbOkL2YH8/NLzb3MqDOY4pZZBAfMzBQhhoX+AHoIrKdOldJrhJmQKki1JhmBy9vXidQs5hkB0fjEm5CCKeLNKOqZ892c/+5nq6oILLlg2oG2tgIL0liaD8SEPeQi2bt0a51I7bY/hffa5556Lu+66C//8z/+MP/uzPztt17rWFhaBfAytec5znqN8Kz/+8Y/jxS9+MWjp1AxAj8751a9+hSc+8Ykg4E7A8DGPecxaW34234YKZABgdjlkFcgqYFQBsv8IAq6VdtVVVynG4iMe8QgwqdikzVRvxuHS57W7KNi74Skprv5bfoJVngGQCNjI0yvOSF5Fhtk+BEbegvRdY3gFWQ8mDzkFWHITDs/MILD0pYCutUGBHb5cGh6SZMPDhF8ZeogZNErWXGtzKiCDo6TXs4ptY9ocQSnvRMvkzXJ1F+b8eJJ3R4zCsYrqM5FGowciQ14obUyjMTGYnnYMC0kL6Jko2cjZAgOF9L5DCdiNz4e+g0USm1JqBJBdsSn0MTWQ7TebjkqwFgSn0++b4zE0RPr9KjjGzafLJBs/MYziwAT8Wg9EMAQ3NwthT6dU9WbdkJlJOTIZiPcb/X7EmyTBR6bZM2X3yDKGajgQ8oy6hQOTv5cfjAvDTBgWJVWyL/iCaQVbGCxCP0PKlschMZPsu0gOhCEqMXwm8/KlcHC2Wu3k5CRuvvlm9b8vvPDCFfV5XgwKEpykvUuzRik9g0XIEOwmU5By12q1irPPPhubNjFZef02AkkMQiG49JWvfEUxzLK2OioQgXz/93//h6c+9akKxCOYRwunTu3o0aO49NJL8Y1vfAMf+chH8NrXvrbTKdm/r+IKZADgKt6cbGpZBdZCBdYaAHj11VejUqngnHPOwZYtfKjRa2T/3TX9QQXueJRWarZaeQC53LyR9xyZZiGrS9/LKKc858hg0e/DJRhkmGhJM3Jb5AChL28+fGIDZss+iv2TsC399fCBt8fZg2pgFlbCfphaWzPuh4K5HajWjiGXN5XMOshZu+Er3zyTGvHCd+AK+kqyr1OsyUrFxZxvQSaU9+atnUqG6KcC3Ank7TFIeRwB0gFlJssMewgTedMCAo/Nhey6jRpy3cVfPWRd3jsF9LoA5zqqmRzc+ivNQU7sgFQgshmwvXQMCy4ooavV03E1v1hbnMYwGkrYBwbJfDYPDpmb7YfILQVhKnN9kEEOhd5ZOE413UUs6C0HR2xVaa6hTFj/ZVK8SdJuYTPI/AYmUnmJ0HJcOYyanFFJwmyWkiiPKPAqZHSmB5rHWzvnMKLmEaYbp8PIizt2s+NooRH6KVoKDCRI2eo7id/yltyOoIlf7uK+bXk2Cnjpyf+bARcMuiBDiAzA1dZ0QMEIEExDPvzDH/5QvVh+2MMehtFRyujXbyPIxDBAyky/9a1v4aKLLlq/xVhlK+c1Sq/2L37xi3jJS16C5z73ufjSl76EQqGzOTEB7re97W34x3/8R+XreNlll62y1WXTSVKBDABMUq3s2KwCWQWWVGCtAYD0seCNCd/Umni1TFZ+ivvnvqhYEb3uTtSYRKrZapUi8q4N253V7IEy0e0AARMD9pwrtteNwfXTOukhRVAUYlx7LawpwcQAySWh1ZqNg9MWbLcGWetHIe8iYNCIQcuztmIaAfgwqt9csVklfJrOhz58lbkB9A8lT9Nc8qCHUdhWPoXU4ZBlRX9KXzIcBjh4dAj5fl2WoaX8Ab3gMGQqbDMnZLoyodXgM9JYP0pu2QgEptXoEchmIjem9Jdy3aiRtXhiPllycNz1UJZqWU492CPuWfGOI7DhiMH6npkC3kvHpLTSwWYVGqITHCKljZrHZPfWTGMCjuXZAQgpUOyfgm2bgu3tarfcYCA/82SkERCbVZ60aQHiBLGl3BKyTZs2+iPy+5R+eWQHmn8XxrsqG48i7MaEYYZlrGzC8KlZFeqy4VzdR5AhJ+FLGSF3IxD3dl6mzCvpr4Whk8eSzXXbbbfBdV08/vGP79zHKjiiERSMEojbMQXpIUgwMPoT11OQEkmGy0XSV1NrmVVQOqMpECjasGGD6oP322vlejFa9Bo5OQIAP/GJT+A1r3mNkv5+5jOfiQUA8vp+85vfrFKd//qv/xp/93d/t0ZWnU2zWQUyADC7LrIKZBUwqgDf9vFGa620H/3oR8rP5sEPfnAs2nuzdUnp446p96IanGKp9bn7FFNMt9EHLWfxYbLVA0/nnhmCQMabbPNA2qkXS5CB54RyJ80mkINrbYVEjIeNNmM4Yo/yFiRoFrfde2gEVu8pwI8uYz3OLlT85GBi45gWiig4TJo1WxPZcpR+p8EGtORm2HbVaL+jNVbmNyKfnzBiokZ9OWInjhybg91nBryyPwsF5O3tqAamnozh7Ohn5YrQOzMtttR4CXAtC0MFE8/IhVc4+/QlsLk3GctwrgrM14B8E49+gioWtsC1PAQpM/cIArliRElSTZOrl37WXbjgy4l5SANmcLvvkDChuJAoOCTw96KSIPU38G2U5wZg2x6KvTOwmNzStbb8YKBKZAdtNarKV9bEQ5U+dVXZ2acuKt9qYAfyxVWYMEz0nUEqK5cwfOqy4pw2ApLUYnoaHoKw2lts5OSz4WIhyHfkyBHQA4zpvwxQW6stAgUjQJAJxKagIEEVMgDZHvnIRyqp8XpuJ06cwNjYmCoBWaNkRWZtdVRgMQD4zGc+E5/+9Kexc+fOjiEgTLlmCAglwwx3ecMb3rA6FpXNQqsCGQCoVbbspKwCWQWiCqw1APCGG27AxMSE8ijZt2+f1kaeYv8tPL2XIKCvLxclWFVwtsGT8TzTmk2eDx85e9iI0RUCeEzp1J8H51ae3YTevqNGD7pKciUsIAZgMV/K40TVA5rI4Ir2bviKTWbGJArl1uYgBwFSMEHZAKwN999BwdptFCgTXUdezYWsDaHQS2mb1kdDnSSlg8MTLvxKEb2DUxCWuSzREcNwrQFUg3uN5nYKMOiHKzbAQ3rBDZTxujYwmnKwRy0Igz3i7MldE/EYiTl6yKngCgLs+nu9+Ez13aGYxN2RhzKx1RY9de/UbkhAo+AQMroIYrWoTbAZJZ8vgPQYfX4tj8p8L9xcBYWeuVT3YOmMlx8MBJiuuwUWBHwFhCXwdZUDqEkeryudttXYtJIIMKVY7Wle4/E/LW49YZgJx5Tnmn2vxh936ZEEVCOf31AyPKhEw+HndOpkfSy5AwW8RkmKG9uhQ4fwm9/8BsViEeedd57JVFbduaagIEHR66+/Xq2L4XL0HFzP7e677z4J+vF/x/GXW8/1Ws61Rx6AX//61xUDkGDtN7/5TTzjGc9QXu6UB/O+oLFF5xDkJgDIAJF///d/x/Of//zlnHo2VsoVyADAlAuadZdVYL1VYK0BgDfddJMyJ+Ybygc96EGJtytk/70H1eBY03N7HCa/mvjxCfQ4exWgo98on9xtGOwB5C3TgBFgfmYAfX0V2LYJ8EbT+x1Ah+TC8fndmPNaM/QoJ8xZRdQYNGDQHDEEl2EVBunJHD5Mct6Vgscg4IhN9bCR5tdlkuWGAQMVSKEXnlGp7cN0Nbx+LdmHojuKSkrAXc7aBkt48KS+R2RjLegpZtXTjNMCCZjwW7AXynCT1L/ZsQwgKXshENiKOVaqbkRNJtt/htQ4ogc1JdvWA7Narc1l2IwIlIQ3rdpGY5HJSfkxMIXAyG6g9c6Ekna+TGEC9yl7hlD6Owjf0FogGllSRlwrwnam4OYTAGVaF9UpMJDgr2Wlx1ptV8kQDKTVAG0qWntxkqUKuQMe0gkCUt9B9SAPdS0q70BdYFGr4A0nFRRDkt/76rrFidQ/F81mKCSDe1p/X5K5GYKCDvL4A5DBvbjdf//9+O1vf6vALYJcp3tLCgpSHsnGl8ojIyOqTs0SVU/3unF9t956Kx73uMeppRJgYj2ytroqQAYs2X9Mr2YYCCXBJEW0amQOvvSlL8UVV1yBRz3qUUo2nDE7V9eeJp1NBgAmrVh2fFaBrAILKsAbH3p+rJV2yy23gG+zd+3ahTPPPDPxtCcrN+L+uS+1Pa9o74QnyR7RZ6gw3ZcgoMmDc8HeB9+wj9CLjw/w8WW4i4sTeL0ouL3G8j0bY0pW3OyhteZtweH5OMCehV5nDBUDpma4vhCorQbmIRquRcCNabVTia/HhSdYyFtj8FQgjCnrzkLOItuRjLv413EQDGO8NK3ySRsbZbyWKMOTeqDiwnUKkIkZyKMIoO+b2din7w9izpvCSIrsvSOzYRDHYGd/7dj7PlsVCIIiBgvzC4BAKfOYq9LvUq8eNgbA5GvFPk6w33EmTl9QR/TDw4EUrsulIxIIFCJX9yHsBqBFaGS7Ykb4uA8yGEsk/Y1To+gYAmUIXEjrCCyru7+rMnBQmu2D49go9DJh1/Q7I95KLWxQoFwz30D61FWNbRbazcNSISYE/eoziFYAACAASURBVCX4PdU6NCPeavSPogclaxEGeExAdiHlmLiUwEYVWNKp5XAeiuJ5TQ/bv38/7rjjDiVvpcx1PTaCgo3S4STyYdZtvYCCZEMSXOL3JeXVuRz9KLO2WirAZzbuzcc+9jG86U1vUsy/Jz/5ybj44otx7rnnYuPGjcoTkMcR+Dt+/Dje8pa34Morr1T/zRAQsgeztrYrkAGAa3v/stlnFVjxCqw1APCXv/wl7rvvPpVS9tCHPjRR/Tqx/xo7q833IV8oG3mq0SvOk0yq1H8wy1tM5qVkNT6Is7gotqDRORlhJimqTA7dCdmBxddpQ0rzPXCsAMWeU0wZPuQcn9+Csh8HAAxHKNjbISVZGHqASTRP1+LDpHmwB6WTeXsHagpQ7FSF9v9u0wtSkG1zyKwjxZ4ZgmP1qcTUTo37MFfdiZJ3oMWhFsiQ9YL7jKXYHCBkUNJP8R6j67txssfngGKKoB1rQkZgXw4YYF5BSo0SPnp++iBAG6BaG0M5iO+Z1moaTHfNW1vhy7TCV06NxL7pv0gppBRmgTrN5i/QAxe0Ljhu+F3VZpOC7fCDvEq/NQ3z6fAphquYuEJZDqQho28/3krIhPkZ7oetQLAqfDkFHz4k9EOokn68Qt/CDeozJBXTs9sMzNYzFHWmIo+Qiilp/hmx5JiyOejU+Nnpw1thieby1XvuuQd33XUXhoaG8PCHP7xTd+vm3/nym/6Iv/vd79SaKQeuVJqrHQi6NAaNnK6g4He+8x0lD2WYCu12KCvN2uqrAAHtt771rQrQYyNQS3Cf6ihem7y2Kfunb3rUuK88fsuWLatvQdmMElUgAwATlSs7OKtAVoHFFVhrAODtt98O3sxu3rxZve1K0iYrN+D+uStinxJUe1HMC0hLH2TKKa+4KaMkVFfJQ0tGD93q4doaMvIWVDcZYi8CkKGmz9SRgQvX3gbUEw3LtT04Vkqewkwvsbw9ilrQCrCKt9UEoorOzjobMN45rY7KWTsAMHHYBGwNe6eE25cHUgllIBM0fChtfS37/m6MlzuHpNiiFwV7s2JRmYKdXKctBpCzRlBV0nuz+vPsQAL08yNzj8BdGo19Hp0NQcD+VIHAfkBuw2ztztRA0HC9DvJiR+ihJlqn3OrVRsAVO0PQR0ky9XppfVadsaf858g6TKd/grk1fyO8usyasntH9MJTydImFgft5yeQV8Apw1W6IadeOjotFwg+ksdL9rf+y6NElffHIIWvQE8GaCw/GCfgVYZQKQkUe3y4+VPeeInWkdLBlKAT6Oc+EDRH0jR02YMA/J3tDGoW8DzkRWtvP4J/vG+inPOcc85JaYWnRzcEuX7+858rVtWFF16ogBOyAxv/dAoaIeASpQ+vdabg1772NVxyySXqJTuZoxkAuHqvc0q0P/e5zylpL1O+mzWC2mQJvvzlL8d73/veTNK9ercz0cwyADBRubKDswpkFWhWgVZvPFdjtShj4Z/R0VHlZRG3hey/v0c1SJaOa4t+5Ow8fJnsvMZ5MQAB0oOw9RkBnAd965SflXajxHQPfNyl3QNPtAX9oGbbgklxBnDEPgRyP47MMRRAXz7b6+xDxTdPmc3bO5XhvMRcnOm3PIYP/JTMpsEG9GsF1Mo59A2aA4oKiLB2NJWVM/hjskQwJP4+5Cwa9QfwjK7JU2UkG9MWlpLfp9H8ADg2D2zsaZ6qqzMGgUBKg4cLQG8K4KKUFuZrgwhkBXlrC2ryvlQA31NrE8gp/00mux7RWXLbcxxsBAFhT6Uy678UaDWIhX7Q59CnZ2Qb8DrOwmSwFyW/2XcfwdLtCoBNO1Rl8byi9fAlgb8s8lWGolBija6CgUJuRSVo/NxacE5KdU+ooIrlbmSsMnSGwE7IDpxf7iksGM/CaF067SlJb6dgFSH5W935xZiF7ejDayEYttWi8Z6JYM6GDRsSKydWtGjLMDglkvS9cxwHT3jCE5qOuBgUpJS4E1NwrYKCl19+OV73utcpj20GRqxXL8RluPSMhoikwOzk2muvxTXXXKOSvo8eParAa16fvKYJ+F966aUZ89eo2qvv5AwAXH17ks0oq8CaqwBvbiIT5NU+eaaS0cx6cHAwUZrdROUnODj3Za3lWaKAgj1sBEwISSDHQbFPn00YSia3KdmgSctZe+FLAmb6oQH0P3KtQUgcNJkKStUHY6Z2SMnHTBpZkrXKNNxCZ7ZEu3EsxSqse6mZTIhsSYuAIr2h9Pc8moJX3oRCcQYQ5vI6W1D2TFbKKTC5UjsD09U7NFZML0X6Fh6GRDoP2JQFB0rePakxn6WnVH1gvBQGcDitn5GTjSVtuGJXPWBCHzD2/b2Y9U6BUkwBp4S3ppKq02WluQTuhQtPpseqi4pm8ftAbFYgoxT69Wi9CRbgbUG5PIOevpnkrEDZj5LH1O72Xqi2kmaPKm+5wMgyofPlxAAbBhJ1VfK8YBrdAQMJYgcBQ1VaM03pI2mLQeUbyECLtFidnat86ggGeBCAJRDI69Tk9y/JuM2PpXMgvRT7ANCCgjU55Rkp5BbFro3TevFqOGJP20N5z8QgEConzjrrrDjdrptjKAEmcEKm1Pnnnx973UlAQYJoZAauBVDwox/9KC677DIlJ73xxhuXpMrGLlB24IpUgOA05cG83ugFmLXTswIZAHh67mu2qqwCy1qBtQQAHjhwQN2s0YslSirrVCyy/+6d+VfMebd2OrTlv1OU1uNsU2mbui0ILHilAfQOmAEbDBjxDMNBKBEDARYjVkQYNBFoziWQBRyskyKLziZUA/3ack8C34Y334/eQbP6sq+ivVcBJTAIT2E/TDvN21vqydK6V054nqVk3JvqISFmfXFmBIIDeQCB7G0a/JFkBILkRXtbarJgwMb/z96bQElylFejNyIza+91lp7u6Z7umRESEpIQi5BYJRAIjjGyzWJjDJjNLMY2xscGbMD2MYtZjm0OIBb/z2D/LM/4YWOEAYPAEgIhVgkhWUhondHsSy/TS225vHMjO2equ6u6siqiqrun8zunTo9UmRkRX0RVZd64371ZawJVn4w4M0DYQhWYqwBDecAyBQTChoOxxZLH1gDQIOjFbGUBAVaWaCoWqdzZES0/akw6onfROdg0a88KGYcowhc6bOXGq69ayiKXHYZPpmgMQJylv54/igrXUgtBDb8QMNXTcY3TpC12cMWHoE+MMcW55urH2LAxsmiMomcSJfw9KAfxWeUEi1l+TeDLA8222tfIbTcP/HyF7EBbgb0m9Pra7Ut4HgFBApQF+AE3sTwEojlj18HjkBO/3bRpaoHRPG14eBjnn39+0+M30wGHDh0CpWVyuRwuv/xyraG3CgrWagqyhHg9lA+/+93vxgc+8AFVDv3tb387AQC1VkTnT/Z9X5l78C8Zf1HJdmQCQvA5YXF2fh663UICAHY740l7SQbOwgxsJACQN2ss1+DO1pVXXhlrNiZLP8FDs/8XhdOur7FOq3OQQN7ZrUo72w0+jLJktYr7272EOi9Dh9dFA4F2LyTJdpGpWA6Dq7Uh/TFAHoQlWwMTZst7MVU6k4eCsxclT9+VNwWalfBBun3nY46Xpdu2TMdmYqyWo7TcBT9gKZw+O8qRY0BAbTcDZcFBARV3DLPVXxhh5aTkNliSoIkeMzTKZcjI3IGKKvFun7FaOzezZaDsAkMFGBlzeG0HXHfUXIujfcbvgYo7ilITUIqs39JsL9LZWdgps66yBGJSckiVvZoCWWvzHIJaqUX3YDNzt/QzZsEBwUZqERJIavAJ9HdhwWt/c4FgUUpyw2QebtBp9ppU5iH0unXReeAxzBjBQJYJy8U8trDOgl5UfALf7eoMOiAzlRtsoW5gayB6u799y8+TGISFfvU58JWuZfdBydPfecFuVIN9CqDkpg8B4ZAhuLxPafQo4w86Mq8e3DQl023nzp2qtDOJMxngpjJNQAjAtSIrEzeHtaBg5ELcqHyYQM1ag4Jve9vb8LGPfQzXXHMNvvzlL8cdZnLcGmSgtgx4DZpPmlzDDCQA4BomP2k6ycDZkgHSxbl7tBGC+ha33norHMfBVVdd1bTLQeDjF1PvR8kL9YmqcwPI905pPfgTwKv492tdgyy+apvMuWjQ4UPplCZjxEFa6pcVlxZycCwfmWy8ElzPL+DQbHlFSV7GosvoHHy0r5fI/DhyALZi7+iykFjiGs23HojB8s6UNWSEDahch+UoqoGeEYfnjeFk8WFlghJgFl6gz55k/rP2hAI8decxWussy7RFVmnkmSofnCs7SNkp5Bx9UDbqJ+fFBs03VgfVfG8XZt34oFTgC9jBTlj2DHwD7qK1X5zsc0rSVftkR8peWXbpUMePrtYdYreFJaa9ymRjCZgZpFByucmhX4bPnLFsl2ZKnXBZXv5jxnkJzUPc0EnYkBnKaj+aLOeV/g7YlgNfbaI0Zt8SxEbA76DWmJWN2ycMSMOrPAJQAuBkV8a8sj8ERCmTQCf2GWWe1I3cq34EGfgBNQuXSz1Yi4BgdhEQPIqseC7S4oqm90A8gAYBx48fx9jYGB7xiEfEOmezHLQWDskRKBgBgpFmW72cdxsUpF7cZz/7WfzO7/yO+pvE+s7AT37yE1XezzXFMvYIQOZfvshszWaz6kU91CTOjgwkAODZMY/JKJIMrGkGNhIAODk5eVqX5Oqrr276g3bg5PdxzP/XJfm1vR2Qjp4GUM7eo23yQK0zPtjxAa/dsMQAHEnXxcl2L6HOo+OsblkxghRS1g4EaA5snCruwXSlPpOS7r4Zm66wug+WFvL2BMqeHljL/JApRY0mfxWdq7gTUF3YAid1CnZKj6HI9kKAgEBq6/MfBAIzReb5pOo6H7/zzsQiC1N/QyB0V96Fiq9v0BLlNqhuhZQVWI4++zG6pqNKVksIRPtGP8vnnqXfZHJ5dUw9aLgyX83BC9oZg0SGbFJMG9NIPNN3ibQge5YaacfjLucWjrMXy4Pn4BvM9dIOOHCwU+lRkjUV+LtR8h9soY9xD5VIiZ3q9yfUVNTbHGjWqnJxF0OKhdgtDT2Czl5lCyzLgeVMQsilYKAIxlDWdGBfbdwS/bDFoConD92mO5vjRn0RNKLBFkD4i2Yi8Ta5ms1pvfeFvxsumq9XC8PoFX8EKZ1Yzdx+++2gY+jExAT27NkT65zNctD999+Pffv2KWO5Rz/60Ws2bB1QkNqCBHpMlHq+7GUvU8y/N7zhDYoJmMT6zMAPf/hD/N3f/R2oi04WK8kRtcHfplQqpV4EBgkAkjjBTYAbbrhhfQ4q6VXsDCQAYOxUJQcmGUgy0CgDtIinhsRGiJmZGdxyyy2qqwQAG93wLCws4O577kZ14Etw8isNJnL2GDy6FmqAb1l7HB4fgDTcL+mkSkdIPnS3G6HO3JaQdaQRNDUIlAZVCyVgddpL0d0XjUE3zxvAwTnOyeogU8E5ByXvPm32RYbuvoqN1n6OOcwQ0BpDxddj3fFabtWBW8qh0KdnfhKm30Ja0oiDOY//kOy6ezBZWgnCpuQgKiUfTtYMG5Cl1CmrgCo1FQ0EmUel2QFkc3NGQNSwS3TLHQ9doA2UVkfDpBOpLYbhBXTJDYF+5n2+rhttK8kRYFl5aKoQArgmwxE7lVEMS7k7QRpgTizF0GVeTPb8zLWkz00ajoHf0e1vsjTrXajNOYQApxRrrdMRAWOV6jHYKTPMxmZ9JhhYWuiBFCmkc6dUOarnZxFoOjM3a/fM5ygDWwyBS4Ulup0oWY/XF7HIxMsDYn7RTCTemc2OEgGNxpr/LvI6PfKVSIn4Zh6smpienlbgH0HAJM5k4Je//CUOHDiAbdu24aKLLlpXqek2KMiS0uc///lK+++tb30r3ve+962rfCSdCTPAZ6Df/M3fVMy/KOjwTQ1APs9Fz3R8ruO/WeEVaQJSBoCAYRIbOwMJALix5y/pfZKBdZGBjQQAzs/PK8t7xjOe8Qy1u1UbZDNGO7p24TgGzv1RwxzTtCAITiDQALzS1gigtN3aNyogQEJTAj12mVSmCR7a1ydkoiyxBRI+AtHY0THOorXFGARZRGIlW2K6OIFTlYfiXEa5HoflYHplmpYoIC37QndVzchYZGCdMOJ665eHkMrMxNKNa9ZtW2yDFJy95uLxQZDB5IKAFzR2Fa7M9SNXKEJY7a/t2j4TiA0BK721FV2Tpi/luT7k+qZgyfjA5+p5pIkF59esGQMZXNTE87xZnHIJEukzLKNxpKkJibJihpkOW2yFLfKLhiGmcnymlxK9cMTW2NqJcccXaixuR5WafcpQhXIJs/Bo+NDBoLkF8+Uqg5LOMcWiIZQX8uB3WzY33zUwjrmdOrEN0naRyc3BMcBkbm1KpPosSWRUeW4gTGyitNaD6GhuvimtPvW9e1LLTET4o0r3sVnY2Ite+fqm1Q+112GZIMtNzznnHOzaxY2DJKIMRAYpO3bswAUXXLDuE0NQsLZ0uJXy4WZMQYJEz3zmM/HjH/8YNAN5+9vf3rV8kMFG12G+2D5fZK0yfvd3fxf//M//3LQvPOaVr3xl0+N4wKc//Wm84hWvWPXY//qv/1Lt/uAHP1Al9NSJZAn9C1/4QsWQJOuy28E8/d7v/R6+8pWvoL+/X5Vqk7nKvnH+SH6ofRWLRfXfpVIJU1NT6vP/93//993udtKe4QwkAKDhhCaXSzKwGTOwkQBA/ojdeOONapqe9rSnnf4B5g4Xd3Ep5kwQkDH4yB/Dzq9eypa2KLRNzbn22WEpuRVSlLR0pqTIIi374VInSyMy1l54mtqCqnxRboUf42Fkta6GTBUHgTgzB663DYfmWisvtAne2RSb1zeWoH5j2YDRCHWqyLokg0k3pCiArDsT1yKTLS3pYLzvNOOsXv/K1b2YKccwovFTKKRHF8uCdUfK3llKH7BKx2dNk5aoN76bhVfOIlOYNMYmU7p4YhQe9J2go34qUKo6rhibFPmHYaMBlqRK4cEleGk4qONHgxc64uoyhOt3jeXB1KEkG9oAi86fwJy7cpOBZbS2yCyOo5OsdwLJLBH2lMlKp1iOZ3IpFNOUpiuh1qIZ0L7eXFUrebhYgJAB1Jpe6EHg2cjk5uGk9djj7Sxb6jJaoh/AAtzgCKQxh+/We9OumYgIdoZmPE1DoE/+MWyxs+mRtQcQVJmbm8O5556L0VFKHiQRZeBsMEhpFxQkIMhNdYJC3EwngPTEJz4Rd911Fz784Q/jD//wD7u2UFbTp+s2AEhQlcAaQbZGQSDwuuuuwyMf+ciu5Cgy/CAwetlll6Gvrw9vfOMbFVAbNyLHYJYCJ7GxM5AAgBt7/pLeJxlYFxmIaOLrojNNOkGw8lvf+pY66slPfrLa9Tpx4gTuvvtudYPLIA1+/BE5zOaWav81ujTBF0u68NvS5AqvaoteOJYNL2hdiy3qFzXYMtZOuNinNRVkBAXKUVHnYUzAKw0jldUtAVx8sBehrtHJhTHMV9spPxAIXYL1S4LTquyazor6zJGsRbDNDEjEMl7PEHOID8RkInl1ysKDYADH5+OVmkULkU68ClwKWgNvGy1kGjZkrEGU/YeMgSM0EKDpj7DMsbxUeacYggv2U48BF3hjmK6Ea18xUq1tqPgEas2xAXntFNlRgiCjeeApdMTdCT841jHGGbUThbDgKW29Nr4KgzQWqtaqmzpkj6XlsPoO0NVPbdbDCDz1giltZnWztsL3LaU/yQfq0DzELNDpe2OoNCjnL88XEHgOMrkFOOnOgZCN8uC5KZTn88jl0rBSNO/Q11mNl/N6R9mwFs1EFLCNE3XXM7VYEQzEWodpcSkK8rda7hIZTGQBnX/++RgeHm75/LP5hJ///OfqHpIgGBmSZ0vEBQX/+I//GIcOHVJjv/DCC/HAAw+o++kPfehDePWrX921dNQCgNSp41r95je/qdpvBwD8xje+gZERMr/rB4FwMuiWB4G25z73ufj617+u3nrc4x6HN7/5zQroIzD41a9+FR/5yEdAJ2eW05NdS/3ITgfBO0oesV/s3+WXX45/+7d/U4A+55rPPbUR5TMx/uj0zKzN9RMAcG3ynrSaZOCsysBGAgD548wfdsbFF1+Mw4cPK2p+FJHL3cPFf8ZM5c7Y8+TIXqSkBTdovzyRLL6M1adYCDpxxnW2/auwdM+SFcWo0Yn5mQEU+qa1SywdsQcVr4Ij881LnFbrb9YehR9MajE2eX2JNLL2MCp+vFLk1frEEm5bppVQvW5I0YOUIBO0HZB0ZespuRv+MlBxvrwL89V2mIsSBWc3yp459h5L6PmQ7hkCFpmBtNKynDbqaCvRB0fNCwG71meZ7qpz5Z5Fja8z51uiB2lrq1qHugDj8l45YjsskVbai+30efVRWouGIbMd070Lc74lBLFbYLT53m4seM2NFKLxpZQeYWoxT2bB2JVzQnA2s+hWrLNBE28NErANjYL4GdMHhMlUK3pxmGqAW+6FV7GRSs8jle0+GEggNCwVTi2WCuv9FsbLeOOjBAqLZiJAoOQxwqoDGexRbu7Nw8GAfBuk6Gt+6LIjbr75ZgVYPOpRj8LQEM1kkogycNttt6nSyN27d6vX2RxcAwSxohfvnV/wghcojbjlQUYgwa/o9fjHP16BYMuBJlP5+qu/+itceuml6sU1SnfmaD7aAQBpjtGO3uUXv/hFvOhFL1LDetazngWWAS+XGiIJ4TnPeY7STidLkmzJTkcEAH7pS19Sc/a85z0PX/jCF5DJZDrddHL9dZiBBABch5OSdCnJwEbLwEYCAJlb7gouv2GhAO55552nGIFF9xB+MdW6eLEl8shaOVQ1wAgaReTsHdqGB2SW8aFA58FdIo+U1RPqmWlEab6AQoE+Ce0LzrNU7FTpXJS8Q/CD9q/DYdgEyZSphF65NK+Vs3ej6rGsUZcpIkFn6IrfmhFHo2lJW3vg+a0BH42uJVGAI1muvA9BMIITC3ql1LbsRdoaQNlrDwxb2U8BVIYgnOOwbVNsJRqjRC7b5sAHC1sV2NtqWafn7sGpauOHfFv0IS0HUQ7MA4G22AJHFFBRZeEaXwQNTmWpdFjqergj1+d3Kp2aPX8SwmrC2vW3YM4lC7t1tqZEFik5rIxg6LLcyQjHxBLhcsfytrz//D2gRiFLZT0cbXmuyFRz/YH2WO7eAHw3Ayln4GTal9vQmRN+dsk8DnVIj7U8fp22651rYRtE0IsAVXg40JQJnBVXIyevbqsb1E2mNApNLmh2kcSZDGx2fcT9+/crjbuf/vSnuOOOO3DnnXcqw5h6QSfZSy65RIGC1MDrpGbiWgGABNYI+jFoEMNS33rx0pe+FJ/73OcUOHjkyBEMDAx09GMVAYD/8z//g2uuuUaVan/mM58BtSuT2HwZSADAzTfnyYiTDBjPAH9YIt084xc3eEH2kzcrLE+IIp/Pq13J2pvaB0/9C6bKP22rZSnSyNmDqNIhuO2QKNjjqATxWSj1mkpbY/DJJtQCpyxkrTF40GO6kb3gyELbYGLVHcWhuQOqLDVj9xnQ8wvZaCWvsdtw3OlzWAKuSg71y1urxR7YVhWprL4BAB9WWVrOh0MTQZfnU6UARc8Mu5COyGSYesq5Uj881wGqfUjnTkAY0vEKQR0C8maBNa88CMcGpBOj5D/IY7pURYDmjC9b9CMl+1FR/dXPae0VeG2H11Zlx60DZM16Q3DJEtlF/clmR7f3fmmuFxIS2Z7pFfnhJkPVI6tX57s77FdKjMAS9iIr0HyuakdvoQ8EaVki6ht0oF4tw5GTMJmygYixhrn/4+9G0df7TWOfLGyBDPhbMgNhrZzH9lZGa2cJ5OEIAmGe0kyMHLpbu4r+0WfYf45yOZZwFk2vluqZCvRiQL4VQqTbavQ73/mOYivRLKAb5YptdXKNTvrhD3+odPC4gUyH1M0clNGJymZpFEGzEQKDfLFMuDa+973vKRmeTsVaAYAE1I4ePapKoqkp3ig++9nP4mUve5l6m0YhZCl2I7hWX/ziF6tSZJYDP/vZz1bPb5ZlnTYGSsp+uzETa9tGAgCubf6T1pMMnBUZWO8AIMt+WapA4I86NlFQy4Y72tTFiILH3jfzMcxW72l7bqjFV3D4IKkHlOSdc1DR1KxLySHFvAs0XXCz1l64muYgfHRLy13w0NpDIB/MT8wPY8GNHswlelO7UXT1wbucPQaXTs5o7GYbZyHQnCJnT6BsAFD0PQk72AGZ0tVPDHueoc6gr2++4HkTmCwdRt7eiaKrxy6NckomU97ZtWgSYgYoScntsKTULqWvnXdLDMIWOWX+YBJYK872IZWqIJ1dxU25OoH5OoYUq61LlpWnZG9HWHssO06JrcrZ17QRCcdk0dlXbjWmjVkvT2F58OCie/Aiw9Mfx5yrp5+6vC1qQFIz1KPbrAHN0GbfRSzXZTmyMoQQbrPDjbxPJhrXBMtSAzFb/5pBFmUvQAD9jY2ln8sBcK2HDs3HjX424yeHmonD4HdZ6CrcnVJhEVASgPczK1nP1I20xBYI+PBxDHn5a8jIS+MPadmRN9xwgzJ4eOxjH1tX96ztC58FJ37/+99XDqlks212NhUldQiEMuiOXGtwwfciMJCsyX/9138FN+A7FWsFAJLRR0CN4CZBzkZx/fXX4+qrQ0YunYc/9alPdSoVp68bsQBpXPMbv/EbinVI8JF6iUlsrgwkAODmmu9ktEkGOpKB9QwAUquEwN/Jk6EzJHe26GBF0VvenNTT+PCDCh6Y+Secqv5CI18SPc4ubY24vCoL1QNbLNEHR9rqIVQn0nICPqgHpldmmZKtOQ1X3FEcnlvJYis4E6h6BxFoMRxDA5aUKt3WK3VmbjOKdcmHff0yNTI4WU6oC96yX2QDOqIHbh1Tjzhrghp0U8UeVP2QrZe1KI5d1NK8rG03JbfAttKoGnBqjq6btXbDx3Etd+3luSGzi+Y4PswZhRDgLs0OggYzXgAAIABJREFUIJNdaXxQLg1gQUNXlCBXeR5IGXQ4jnLCktC03K7MQvQMg+qvQLqJp+UIvOAoAjEfZ5m2fExUHuz7cyh6JXhoAGC1fOWVJ9DVl0zhUFPRDNjdqFuhCzXXakkB4SZB69VSEToJZ0I9U1EDavsTKBnQS12tbQLHttyi2vW6OOblfTpTKsxS6c7lXgbjoRt4k7AxggHrTZDSanZo3fd5f3fjjTeq96jjRufXJM5kICmPPpMLlrxyjTAOHjy4qolGp9eQLgB4xRVXKBCT+o5c82T0PfOZz1Sly6sxPQmqsQSahigsiW4UtVqBdOVlGXUnI3IBJuuPz0Rkrv77v/87HvOYxyjHYup7Dg4OIpfLKV1AvtLptHoR1OyUdmMnx5xcu3EGEgAwWR1JBpIMaGeAPywE1NZTUKyY9PsDB84AR9u3b1e7k9z9mpycVNoce/furX/TG1TBUuCZys81hiXQS9MDP45Ad+NmsvY4PLIJNdw+6VqZshYZLxojYplR4M/AsvWYHNTkIltiyUNinX6F7L8dWHDrg3Npawss4RsAoiREeRusbOvaVsu7rRxaZV8IjGiGFDll8uCSbWUgMrI912HX3YuTpfuX9CBkuo4vsgHNABp5ezfc4Ki2SUvUUQIhZHmW/QebamTFT69AWowrcNEkMBX4EqW5fmTzp2A7LtTanyrAzuppXnJcbimHTKoXsM0DEqEj7gjc4BACofe9UH8OOm8YEnj8XJSU+zGZ250EzELgdAgemcddYItZYOn2IPzgOPxGDL34iz/mkVI5CUtQGqGMkn+0LV3FmI2tOCzULd0GgRJcVaLb7pX0zhPIKd3EkIlHUNTMfZII+JsQbxOi33o10rJ9ho/rurjppptUIp7whCegoAR9k4gyQHCUICm17QigbOYgw+/pT3+6SgHLgTvJ8GuWZ10AsNH1CYzR4fh1r3td3UOe9KQn4ZZbblEltWQ9NtLM/KM/+iPlBsxgNdLyEulm42v1fX6OCeK95CUvUe6/LNXmc1BUFcX/Znk/wT6+CPzVgoD83PPYt771rUoKIImNnYEEANzY85f0PsnAusjAegIAqVOzb98+3H///UqzhkFjD7L9Iu0a3qSwJJgOYVG5Qr1EBoGHh2Y/27YeYHTNHucclDVLeTPWCFzvGKSlU9bFMtUxpWemE24lDeFbyOb1WG5S9CudLGpXNYpI+2+1/lJ3Me8Mhe6ymlGZ60O2sABp6Zp6CJC9WfbI3tQHx8hmY/mpnp5jmBzFCBWF2GzAIEjh5IINT5WbrYy0tQ02BCrBMc3sh6fTDTtnDy+WBRu5pAJAbBmW8JoKssfSkiXkLIXV+Vwu61GQhnC3oVipomoTODEXLK11ZLYjIBfB1ozcCTc4ZhQYrR19aBhCwN9Meby6dtCD+WoRAcI5pKEKGalko3YG0IxGRCB5BFJIVFVpuf73RLOVQlAudCxmewbXbIOGCWLDHwWEpUAwbopw/roZ1PF0KIWhzDIIBna3/TNjjVyF0yGzW7RnFMOcSvBz1nyDyRF7MGC94bS2Vzt55+ZuVMp4+eWXK4ZQEmEGeO/L8mgGjS36+lp3WD6bcslc/Nqv/ZoCkKg1t5assXYBwHe96114/vOfr0wyxsbG1PQ88MADii1H1h7nnPHJT34Sr33ta1dM3/vf/3687W1vU/+f7/O45UFyAsvpCZIyCK6xWqmTwechgpJXXXXV6TXbSnucU34X0ESRTMgkNnYGEgBwY89f0vskA+smA2TcrWXwR5nCu/fccw+KxbD0iD9YZPmNjvKh8QwF4Pbbb1c7c7t27WrqQhYEPvbP/StOlvTo+T3OXm19uMDthRQlOCk9FkHO3rvoNqsxY4GDjL1DlQTrBEEDRw7Dx8pSppD9N4QFNx4I0pvaa0QXEG4OuQxdgvUBLep/sVSWgvW6EZowZOBqujJH/chI6joSvFod7Ky6ezG5jP23ciwCPc4elKihZghYUNpp7gKEY05TKyPH4OMUAqE/H1EOqLeVkoOqHM8U0ygILCxU6BicQ9kza0CivhvlNtgijYphTcMwJzZUngOaUnTmocYW7D/L9vdrA2e+N4EFb+WmCAHejDUKP5iBh3hGF+1+xi0UQO1KGgk11NFr9+J1zuP3bmWBYEURmfycsXW7vCkZjGKBLumLEZZ18zuxrFzYTX1e4qZGIK1MfYSCewlG6slZxG233nEWaB7Vr+YgLBWOBwDLYGdsdvmA9YdIyXGdbqr7KTKaGNQ1IzMoiTADCTty6Uq47rrrQHdbbrbzfpyA01pFOwDgzMyMKvdtZIJBd1+Cg9T4IxBOosFy3Ucan7CcNqo+Yj7+7M/+TJEQCPJ97Wtfw1ve8hbl/BuBaswT11I3gmAm2yabj+Nlf9kvvvjv6MX/JkBJIJcvgn8c749+9CMFdiexsTOQAIAbe/6S3icZWDcZWEsAkD9i1LSgVgeDph7U9tuzZ0/dHcg777xT/TiT8n7xxRc3zSFBwAPz/4HjxbAMpt2g62zFYyliu1cAyL5LWzaslJ4mFhll+s6mAlkaTGibgxCQWKkLWHXHcGiuNYAxZ/OBfVK7hJSmHnln3AwLLXDgzheQ7Q3Xp14ISHcEwj6INiWdljTPB1ACKR6WOvRFBwV+HicWqHkXjxHpyAGkLYJWzdkpcfIQBAJVlsX2nIK0TD2sW8jKXXAJXhsCKzkWglJktJJlpBueuxsz1dAsx1HOvn0oK/dd3SsvPV8ZppxmhJm9NnlKGblLgd8+TKz9lf0Lwddti+zY1jdGRLAds9XmQH9aDkMKuyPMyaWjIitwJ6QQXWMFBl4BWWe78RJhbuB4/ha4QX3tWZbpcv0FmFOsUdNru9lqDvUfOa8i/MwaKs9t1m699wmMhi6+tPQ4qrQb6wVzyvJfL4YGaVpciH77Fe10Z8k5fPinXhjjqU99qtJQTiLMAO97b775ZvXvhB0JfP7zn8frX/96df9NsKjWYK/ba6YdADBOH9/znvfgHe94hzr03e9+N97+9revOO22227Dr/zKryigrVH8/u//viqt5/NIf3//6eeXOH1Yq2O43glaJi7BazUD5tpNAEBzuUyulGRgU2eAu0MRNb5biaDzGkWHa7UzuBt37rnnrlqmQrCQNwdDQ0NKADdOcGyH5r+Co8VvxTm84TF5exyupp5f4KWQTw9qu5ym5ahyCNR98MlYu+EF+iwlR4wjIBAlqkr/7Pj8EIox2X+1CXckTT2yqCrdKb3Iqfk6jMDAw2FlbhC5wikIqb/TWynmkbIDOBm9MuwoO43YgOXqXkyXl2r/xcmo5Q4jkMdg2WZAO1v0IGMPouSZA8GUm61h5h5zkxI0b5lDINoDvYIgi5kSNcOWsqpPs/Z8s07E7LMjtsORDiqqRDPODLdyjFCl0gBNEeLplrVydR5LZlda7myJQcfvGNfbgbIf3/yHZjppuRVV9Z2g5xzebIzdZgWG68BcibAMdmPBi+f4zo0IGtaEYLGeWVWzvNZ/34KDYczNziGdn4Pj6H9Ht9ePcDXb2KGkEOgczXxEn0krmEAlloSHwBb7TxWoqBtkAv34xz9Wl6ExwlqyunTHYvp8sqgi84anPOUpChzZzPGJT3xCsdsuuugi/OxnPzsrAcBjx44p1h+fCZ71rGepkth6weP+9m//Fl/4whdUxVEUzA0ZgS972cuwdetWZVBIOSIy87oR7Hf0itpb/uwWAXz8G7260bekje5lIAEAu5frpKUkA2d1BroJAJIqTwCPP5gUX2ZQe4UUezpwNYv77rsPfLFM4dJLL212+On3+SN5ZOEbOLzwtdjn1Dswa+9E4FMnKx6rqt41yF7I2TuUo6ROUCidZcW+pvtlStIVdqqpqUezvlpiKyRclL1eHJ5rX9OPDL6eVGRQ0azV1d8nq40uytXguN6F+IAtB2ALRzFedIOmEfnUhLZLdNQPxQaUOXhByAYMgn4cm2f5bXuaWX7VgeX3wsqceYDVHXPWJrh2Cl5groSXJYFSePCgP79nxieRluPwgyMtA0Wuuwenqo0fBjKKjUY9NX2Ae/l8BJV+OJYDOMc7AAQCaUE9ODd0ie1ISKTFLgRirqlTs/DHMOu29/1JiIblwUEwD9fouqn/bU9WIB/EqGEZt1RUJ71nXITLcNsp1Q1sVLw0fLTOVLfFFqXDSKdtlut3O8g6tjEEx8ouGv3om/DojEHS3Vhsge+Tib0Qa2MhI56APvs3dZo9fS4rLKibzKDBQ8L+OZNWlkiyJJLxtKc9bU0174xMtuZFPvjBD4IaejTCoG7kWq6VTjEAmSIaClJH/IILLlCmgs2CYCCdgWkIEj2jEBRkFRLjuc99LlhenESSgW5lIAEAu5XppJ0kA2d5BqiJEYFxnRqqYuEdOqRYf1HJMfVoaORBF624NxsPPvig0gokaEih31bj6ML/4OD8f7Z62pLjM9YQBE7Bhw6LRIKMwmoQj2XRqMNkQkk4gNTTubLEAGxJdSW96yDI4FRpDLPVe7VyzJN76MJMcxBNrSejJcEsL7YntDUho+SkrVEA0/Bh5kE1Y+2F6+9DsTqKUxU9wxj2MWfvgh9MwQvM6MHx8TzvTCyWZ7cHTq5cWAJZOaGAKZPMrpCZNrKoDxijr34vpsrzCNCcOZm1yKqbhxtoft7qfMqqCz2QAkjnZzsCBKZU6SXNLzrBOAwHlFoCmi0dZBBIlN1euEF7Rgy1VyOAzDLq0D04no5bu19sa8EKtNCnACiaNfkxXYuFvwdFX5/R4oghJVHQSWOZZnPBPlgir5h4JrVDm7W7/H0Le9QaswXXm600NuvrRdrYar9tUV+w1VZWHk+n0IjNdeWVV+pf8Cy6QgKOLp3Mv/zLv1QOuc9+9rPx9a9/PfY9eSeWRCcBQAJ5J06ciA0A1hsfnXh/67d+S731N3/zN3jnO9/ZiTSsuCaf09ayNLsrg0waaZqBBABsmqLkgCQDSQbiZKDTACD1/X7xi18ogVoGy1BIm+er1ZKUhx9+WO3a0XmLZRvtxPHid/Hw3P/Xzqmnz6HTpCW4q68HjOSdc1DRdBn2PQtuKY98rx7jQom8W1vgo30NODr/Hpg9iP70HsxX79cGIBTYKorwAr2xceJy9gRc5Q7aut7Y8sUSMoim2mLJLL8WnS7T1na4dcxU2lmkIhjHbKWCsqevZ8f26dRcsEex4OrPZzQelu86VgYVv75+YTvjlmr9DitQ3WQprAJRZJ8yXVntuuXqOOZppBI7BHLWODyle6m/vpc3GwGBmYLed1Sj4bD02BJpxWQ2me/a9kJGWQ+q1FCUiwCdvxtzrt7GyfIxcSMlLbeh6hNENlOa33gZRA7C1mLuOgs8Rv2gZh7nSznQNmCwiyCPkldBEFM3NN5SFyBobMmUym8jjbx412r/qNB8pkeV5no1pbntXzHemZ4n4fkOpLVUFsAWW2GhFxDz8IKj6jOUk1egx3pevAvHOIpAx89//nPFbiPLLYkzGUjA0aWr4c1vfjP+6Z/+CS960YtAgGsto1MAYG0JMN1wr7/++raG+eu//uv48pe/rM7lsw0rmDodrJ56/OMfr6qf8vm8MjyJXj09Perf/MsXn494TPTKZrPKAIjsxyQ2fgYSAHDjz2EygiQD6yIDnQIAqbFCxl+tmC5p89T5y2QybY2dLELe0PJ8nR3tk6UfYt/s51ks2VY/eJIj+5CSEm7Qnl5Y1HDegLMvS5/S1C+z2i+9DfsjkbXIpmqPAXJ8bgRz1RDU6U3tRsnVZ/BZIousvQUV/4wjZbuT5shB2NKCa6AkmMyStDWAqoF+cTw0d3FZwiv0XLnny7twqvIwelNmnX2z1rByADXJWiMoS/fUdsoNG60BRxCcT8ETZgDQqB2yifiQ7lPof3n42zFZbq80nAxVAoFucETbAKdeTtxiH2iVkCmYYZkub8MWgyFIZ8DVt+Hn2s+hOJtBOluEKwP4qG+00O73wpnzaDIzpkBAE6X+zfpzhhVIRph5ELhe+6GBxgiEqKrvmyXgrT+Bkq/PHG48bpbXj0CC4GeoGbsWoRjvYgAQc/A6bGIyOzWIbN/qTF+BLFJyFIPW78CSBWMpIeBBowLq27W7YWqsM+vsQiwDveOOO5QxCg1SNnu85jWvUcDfq171KgUErmV0CgCk8UfE1mO5c2QI0spYaarD6iNWNV111VX41rf0tMXjtk0iBcG/doO66bV6hu1eJzlv7TOQAIBrPwdJD5IMnBUZ4M6S5zUvW4s7WF6PLmL79u07XVpM7QzukrF0Vyd4Q3vrrbeqmzb++OrEVOlWPDj7f9vWSWPbtgKBWOakp0GWtcfhaRqMsD9Z+xxU/fu0GTlhKSmd4OJn2PWG8fCppaBL1qaQ+YKBElKB3tReFF39sbEM1XK3AanD2nlidgjglpVDdIwy0SbppFGBIwsNnX2bzUbgD+HI/BmAKiUHkLIzqBhiA1K70J3vR6ZnsqW1sVq/pcggZ4+YcW2uaYgutgHLqw0DKymxC6DAvwi1DGlIUaoOo6iZY4kUstZoCHQbYKkuz7nlbwcCFzJlvuyYbVmiDykxiEpAsxf9z0K9NTMztRWFnh74mIQHc1qS9dpKsYRVZlBW5cGdGU9tuyx7tkR3WYF0YqaWLMv8aWBR9Pi3/U2xZt9Pte8TiEzJYdC3pqr0ETuf43r9C3OwFUBJOQqbZLMGfgquDwjZnHU+d/RRkKUnKwYP2Ty5XE67DJObr3fddZfaMKW2WxJnMpDkZulqePGLX4yvfe1reNOb3qRKgdcyWgUAeTwBstWMAanT94IXvADUPOfngVriO3fuXDHM/fv3Y9cu/savDJ5DLc0DBw4oUJ2OwdQS7EbQ0IeuxSwBprt3sVgESRb8W/uitiVzwRJ3Hkv2L8dM8JCgdxIbPwMJALjx5zAZQZKBdZEBUwAgd8T4w3jvvfeqHxwGqefU+ePuU1ydv9WSwrINCjfzWldffbX2NafLd+DBU5+KpdvVqF8skczZg8pdUicy1giCgFpFegywjDWxWK6o90DllbbASU/DjukGe3J+TDHPloctC8jaBZQ9ffOAvL0LbnAUwTKX1XbyXpnrR7ZwCtLSyxPbTsntkKIKT5MNGo0ja+9F1W8dVJxT+ovL54DgqVk2oPT6kEllUQ305zQac9qiXhfBAHMGGaHhw65FHT9zmxyAhYwka+8ggmAbpsr67NQoD5bIISvpcPsQ0AFQhI67Urhw6zEZ2/kgLTtHooC03B6aHBlkeJXLaZT8KoTk55Xl09RRLHdsHNGwJHLIWDsUI9CUVudqaZbIIy2H4GFSAdjdCumfq8ytQpkEvd+gVvtMvc0QDHQXtSW7A0Iu7yflGBxJjV9+PsiO1PttsII9KAfN2fRe1cGDP70Svuec7hLlUaKSvqi8j/dTrdxHHTx4UGkmsxTwsssua3Vazurjk9ycmV7eu//qr/4qvvvd7yqGHHXtuhk0HSG4FgVL1+m2y3jyk58MshNr4xWveMWS/77xxhsVMEdm3vOe9zxccsklqtyV46Lh4Be/+EX1ihxzP/rRj+KNb3xj3SEyDyQvvPzlL1clt/39/SDx4Bvf+AY++clPKtCNn0GyJF/5yld2M00guEeyBp/Z+GL1Fl983uKL+up0Jv7JT36Cz3/+82rs1HR84QtfqPr50pe+tKv9TRrrTAYSALAzeU2ummRg02XABADIH527774b3KVi8OZ17969GB8fb1nnb7UJ4K7WLbfcog4hAGhCEPdU5W7cP/N/tHSPyGYoODuU0LdOpORWSFHW1haMnH11TREqxSwyaQnbWd0R0vW24eFTjXcXWebYS2dfr/nDULP8kdXmSDJlTjQ7tOn7YUkwy7j1rxW6O4+h7D1ghEVCVoolhSqRjROBvwNH5hsDcixXTlkZY9qABGF6nD0ou/u1XLGXjk0g7+xB1TtgFIRQ7DTZt1imGieb8Y4RQRZVbwzz7r3GTSRs0YvqggM7e9IY27J2VBk5CiEqcNFe6XKzDCltSzmsykwDoV+yOzk5ADu/Um4hLXfAkc4iU69Zr3TelwhzVkXFN8sUa9SrlBhRphGdZsjJYDvm3HAdhKD5CAToVk1mnk7OWj+X64bmLGTkVdtxMW69ybpn0E3ZEXQZZwH94dZZuQFd2V0EaM7+s8tPQ/HkRUonmfdQ0Qbq8o6RzVOr90VgkNpejUBBaiZzQ5bnXHrppYYyc3Zchkwvgk7MIYGezRwExq644gplGEM34D/90z/tajoI6P3Lv/xL7DYjIC86IQIAm12ArNp/+Id/wGtf+9qGhxIA/OpXv9rw/cHBQRBA/O3f/u1mza3p+6VSSfXxv//7v3Httdeq0m7mrZUNhDUdQNJ4wwwkAGCyOJIMJBkwkoFoR6mdi5GKzh1m7pBFMTo6ikc84hHqxtR0sD3uUjKe8YxnKBq+iZir3I/7Tn0CfqDDfJDocXahoqmhxAd/x3LgBSe1hkZtLkv68DVZJL6XQj61JXwIahBTC+OYLjc3QOhL7cWCS4BMj+FBsK0ntRMlT1+vKnSmHUNJlfFqpVydrDQU/cOGACxLmZdU/OYGHLOlUcxVmzHRzLMBHdmLtOxTAIypoL5i1t6m5tfEnET9IjAuBUv9zJTBBt44Tpb3ISX7kZa9KPmrG4W0kx+vlEXG6UVgh0YBpqPTQCCBlLQchU8GnWhPh9BzezHnnlp1/I7oV5qcahNGuKbTtOR6jtgKR/Z0pS02TBYigU4fNB7S05xdnhiWsPv+CMp1DHmUzqkcOm2c0dGk1rk42aRkVweYUwzMTqz/eGOylJGJpIMvjsUyirGC3SgHzc1qOLc7Un8BSiEw+JBOJg+BwAgQ5F9u1NYLyqFEDMHIFCC6LyKTiXIslF553OMeF2+om+SoBx98EHxRnma10tHNkA6uucc+9rFqrXziE5/A6173uq4OWxcA5GfluuuuU+QAst+odUcWIT8znN9HPepRSjKITMJmRhg333wz/uM//kM9ZxBAJ7mBLEASGq655hp1DToJr6eIAFGCe/w3n+m4WUAGIFmR7C+BQD6bJbHxM5AAgBt/DpMRJBlYFxloBwAk7Zy7p9xFjX58qDFBnT/uNncquKvF3T4GXe24o2cq5qv7cN/Mx+EFOi6QZEXtRsXXY7qpUqBqGpl8ew/MUU6kyCEle+FBt1TTQtbaBQ8rH2h8fxD7ZuJrR+XtEfjBNDzo5DkcIRloIXCnByjyWnmb80b9NX1helsxzsyVyCpGp5hXD+L1IvBHcGQ+vqOueTYgkHcm4Hosk9Sf12iMGWuncoE2aTxCjlNWTsBVrJ72mWk03pkt96HinynTzFpkDJE5ZZ5VRyDGFhKVQE9qoNH3ZaeBQASWAgIDMQ2/Rf2+YnEHqjLedxgZZFl7WDmrmjSXqZe30Hl6ZNHJuTvlugSiFubKSOWnYEXOyBo/ghI7MVdt7vxOrcCU6FFmNZ13Sl45ILJ4afAT4JRibK8dGChAQyCaU/mgZMdK8xYRFFAN+N3SHIjus56LHvvpq84g77F471MLCBL0aKTdzM1XgoEEQKgHlgCAK9Mb3b9u3boVF198scYnaOOf6vu+MufjRj5LR9c7u23jZ7w7I2DJ8Nve9jZ87GMfw4c//GH8wR/8QXcaTlrpaAYSALCj6U0unmRg82SgFQCQNwrcFePNE0FABkE4An/cZeo0vZw3tJHrFrVBTIONC+5B3Dd9LdxAD3jrcc5B2dMzrKDhguVtgZ2JVwLaaMWS4Za1dyodNN2gOYgXLGWjzRR3Y7LUnOlQ2zbLMVNWChVfb2y8Jh/2A/8UfLF6mXKcsafkFlgScDXZl2FbEnl7j/Y6iPpNJlXWpknE0hJjMnhmyyOYX3RfjjPO8BjzbEA+FOfsHSi6ZtiUYS8t5JwJVMj2FOZ0/AgUUdutErTXV9+bwGS5PgM1b48rYwUP5p1dM5KgKB1czYOMzDeBQJqQeB0qDVaMs/I2OE4F0mlu5CGCIcxU2tGFpLsyHX3p9Kpf4t/sc6UAVACVLpXNeq6NlNgB2ykqIKqd4Fx4/nZU/FbWEkuhR5T8Bh3QdXXy2ul36Dzd3xFGZKv9scU25YLNzRkPJxUwaQUTKAfN2ek0INmR+nNI0XolA0FB6pHVMgX5b96j1QsaH0QMwUhbkCyhzRqsXKEOIPWpyRDbzME1MzIyotbTV77yFaUHmMTZkYG//uu/VpqOLOv+wAc+cHYMapOPIgEAN/kCSIafZMBUBvjjH4F5ja7Jm01S6qnzxzJcBktPSIunY5YJLb4442E/KMbLoKg16f2mo+Qexb0zH0XVb/5wulrbPQ7dYZuXbq7ef6GYadUYQuLNrpOz98QqJW2Wz7RyVyV7qgrf78W+GYKlrYuly8Uy3qKBMl4LObglC+l8qEGpEywvzjujKPutgZqN2iRDCMGstq5jdP2MRXDp+GkWTuDvxJH55gyeRv2z0Qe/GsDOmAOrcvYogoBle+ZYUWRV0iHZRftjrZcDhw/w0oLbAks2CCROlfKoBo3XG4HLgjOOinfQUDn40t7nLH4OZ+EaMp5ZnpsQCCzDgz5I32jtlef6YFleQ6YzAaqqO4ySrsGS3KmA/Yp/sOPMMQJTjhzoqpkGgUBLZOAGZDA3Z51F82EFY5h12y/dJwOSIDpQVOY9a8HKs8VWBG4GrnccTkZHwkPnVyM810I/bGyDJ/jd11wnss/6NfTYT9VvePEKvJcjiBMxBcnoWu3ejpu3kaYgwcFCoWBUs9nYwDpwIboj0wl4eHgY559/fgda2DiX5BphBQ/jO9/5jqquSWL9ZaAR4zciXkR/+ZwUuQUT+KN5CZmA733ve9ffoJIetZyBBABsOWXJCUkGkgzUy0AzAJC7ygT+qIXB4I/M2NgYzjnnHGMafK3MzPXXX69KX6hp0yktjrJ3AvdOfxQVX08rrMByYKVjpleimrf3GgHvstZeVJcpwYVEAAAgAElEQVQx+FrJfXQsWQ+WLGOmtBUni3rlzqEuoC5QSu0kAW9+ANneSSMPomTvVfz9LT1QN8qlFGSbbUOV1zMQktpcFp2nH8ZseRjzVb2yUMXKmh9Epmd60WVVv5NknhIAKxrQfKztTUaS1cUyUn2wd+l1xxFgEr5ofl3f243JcjyAOGJFhnqGet8DK2eFbrjjSi/UdD6itphvlkp3EgiszPdAQCBTWKrzVy5uRVmaY++R4ZtS2n38XLe+adHKp4IbCRmLQPgsXJgbw2p9oJtumnIBmIXXpE1+5l1vC6pG2M5QjLy0HFTt+h1gvsbJvVvqQSG3TW2QxPkcx7lmq8fYmEDJf4j8b6VfCNBoh2Dg0vUm0Yvh1J9DiDPOv6221ex43rcdOnRIgTssdY2AQW7iLjdSiO7t6BhcazRCULBbG7zNxmPy/TvuuAPHjx9X97LUrN7MMTk5iYmJCZUCGoE8+tGP3szpOGvG/qUvfQl/8id/olyNP/KRjzR0Pj5rBrxJBpIAgJtkopNhJhnodAZ4I1jPdY7/jw5yLPmNgoDbeeedp3aK1ypuuOEGJZJ9ySWXYMcOsg86ExVvCvfOXIuy10p51Mq+5Oxd8PyD2uWLISCl7zCbtsbgB0e0te5EQPAvhaIbX3uu0UwVnDG4/nH4aF+TLbq27Q3BcqYQiObui81WjtmSYJZX0tBjnxHtLvbdFufj6Px9Wg7WtTmgNqAMHHhCb80vveZ2WAKotlRmuPrMnHZc9s3oP0athaDNmCqXF43KjQMb06U03KC1knO6V6dlASX/YSMAdW2GVJm0Na702UxqMNa20Q0gkACO70pke0L29fSpPKxMa3lu9pnm+8pkxhpCVZn1FOOconUM3ZClcJRpiHkQuH7XHLEdtsjDDfjbs/K7UAbjmHP1ZSHqtc7x2iKDaoO2tZIZ8+SQFZlVpfKBAXmIOM0S1Kv4KxnxlG+gszEdhckM5Hz027+BgvXkOJdt+5iI5cbyTsq0RMENVOqD1WoKkjlYL7jhy/u9WqMRMgc3Oih4++23q03t8fFxVcmymeOhhx46rYNI44jdu3dv5nSsq7Hzs2pZFj7+8Y8rd2KuV37+onJ+6nvWfj75b0ol0fSD5xw4cAAXXXSR+veTnvSkdTW2pDPtZSABANvLW3JWkoEkA8sysBwAJCMwco+LnOf4o8IbSO4ir3XcdNNNqszlwgsv7LirVdU/hXunP4aSpwdyZa0RVL2jqtxNJ5QRR3BIm5XmyO2QYgE+2tc6XKjsxdH5fehPj2LBba531GzcZLTZUqDqt6dnVXt9MlEsS8A1cC3TJcHVUg6WCJDO6gEPofbfMKp+CSkrjbIXzySh2TzwfTIp0/lpSMsUY43mOHtQIuDQQolis7461AKT6dDQw2DQcIA6ldVgpauv5+7GVCUe+69elzppFCKpE2mNKm02EwB4vf53AwiUfh8q5X6U5D5IaXBil12KLNWczQ0RmhKZddet12tLFJCW2+H6R41olsbJDL+/0nKnKtN1F8t0yZauen1GS/TrgkewoYx84CowcC1KhKkiGjr4puEpAxO9793Vch6x/1afFwtZeQ6G0q+EEJ3V4GuF5cZ7PVZ71GoKFov1c0XwL9IRjNiCBCU6rQEdZ73HPebWW2/F9PQ09uzZc5r9Fvfcs+04rhNqajMo9ROVA59t49yI4+HnklqdL3zhC5U7cTtx7bXX4g1veEM7pybnrMMMJADgOpyUpEtJBjZiBiIAkH+PHj2KX/7ylwpgY6RSKVXqS/v49bLj+/3vf1/tXBOQjMoWOpl3159X7sALrl75plcqIJUqw3b0XGbTSnNpBgH0HmQs0QvHclT5YKsRBDYennHgBmEfBtPnYK6qZ3rC6/AhrccZQtHTy7W6FlLIO8Mo+2ZYLqFL8MNGACwavGStMbgg06zV7C8eH4zg0FwETAv0p/dgofoAYKjM1K9k0JsfQEVTf612dI7sR9oqoOwdaHPQ9U/LLuoi+sKcA7H6/qPZgSp/DaUAgiCN6ZKEt7judQbRSaMQGpzkrMW1b9A4pXa8IRBYbFpq2k6OCG6XqgMIAg8ZewuK7n4Iqbd50qwf/DxK4ar13vZnslkjp9+nkQbzV0bVP9KF9sKGbbEFjuiD5wPznp50Q+yhLh5I1iWdrFke7HepJHplH2UIBkoHLr/XhDnNQBn0oKJYwc1Ly7c4v4Fep7PsP45dl+VGbbhaQJD3Xay+qBdkKdWWDpOhROOR9QoK/vjHP1ZjY/kvy4A3c/Ce+jnPeY5KAeeX9/1JrI8MkJDBZ68///M/x9e+9jVks1nlCM4KLX4++SJIyL9kC/I5jp9FnkfCxnve8x48//nPX7efw/WR5Y3ViwQA3FjzlfQ2ycC6zgDFoqkXMzUVsiB400ZwjbujNPtYT/GjH/0I1CzhjVu3Sjc8v4j7Zj6JeVfvockrZ+HYPpyU3oOHQ4abcLW1lqgZlbG3hmViLUS5uhcH5+5fckZfahxll4YH8UXoGzXZl96Lhaq+LiCv35vaa0yHLiW3qvJdMy7BQMbaBT84gQCtA1ez5VHMVpYCaVmb5bYeKgaYj+HcmHcK5lWpjVn1WK6qB2LXrh+p1vIIKqosuIXF3PRQgaycgIfDcL0RTFX0vgNqm1NGIfa4MqcIDIIRURt0J81YWxDqDzYdaFsHdAIIlP44pipngHsRZFCdzyJdmIW09L9fVhtoSm5DSuZQ9sn+NMWAbdyiI7bCUbqEZjYXmk0iDWxcrxeO7INABeWuAJ5Le+XIrXBED/zgWNeYkCvzYoGlyvwMRmW5zXK32vvx2H8sP+/FaIbOv52/r7rtttvUPR1LOk2VdRJ4qC0d5r8bGY2QuVRrMsJ/p9PpdQFG/OAHP1Ab3dxIZon0Zg4a673oRS9SpaRkRRJASmJtMsDPF/O/fA7IzORnmSAfAUCyc/niGo7+8t8E/gi+U8fx4osvXptBJK12NAMJANjR9CYXTzKweTLAHxWW1UYxNDSkdP5Y0rEe46c//akSb+YNLfvZrfCCMh6Y+X8wW71Hq0nh55BNZeAFegYjLCdLWVl4ga5Lp0TOHkc1iFfWSHbO4dl+lLyVDq85ewjAPLyg/dLiKLk9dFD1DxnRt8vadOKdgQd9PTHTJcGW6EFa9oZ6WTEjCIZweO5o3aNZ1tiXHse8IQCVjSj9OjuDsmeu1NYSOXC9FF2zgB2F96k56MKcjiFzINGPcnUL5rWdvetAESKLvD2MomdW0zBqyREDyvwiBLViLrIWDzMFBLI8tVTtQ8Vf+f0S+DZ60rtUCa2Jz/KqgI4CT2nccwiB0NcmbZZOOurSMZy/Cz7MuWcvb9fCbsxWz3zXOzTvUIZCR+Eb+H5sNs6l7wtkFMvWXtQq7Cy427hvBANHICFRVRIbrbH0ZdCLivrNa87+G3SuQZ/THZdV3ivNzMyojVJqh3UiyDgiayxiCkbgYCQfs7xNssuWMwXXgnF28803q34/6lGPAu95N3N88YtfxKte9SrliEzN7wQAXLvV8M53vhP33Xcf3vGOd6i1mUSSgeUZSADAZE0kGUgyYCQDvIH77ne/q3aWuBs6ODho5LqdugjLWg4fPoxdu3bhggsu6FQzda/rB1U8eOrTmKncqdUuwY+M1aM0mXSCrKesTSdHPY1C9iFnn4OK37yMt+pO4OHZxpp/ZLNk7ay2eQr7ZPl9CPwy7LT+A7gtCsjYZNqYAbFMugRzrHR6Lns0eWnOOpov78JMZfUyab/YAydVgaXJNq1dn2RTmtbxy9mj8INZeEFo/GAmQndcmmKYAm98bw8myw8gLbfAsVKKOWU6UiyRlr0odQioI7vNEWmU/QMdBgLbdw0W/gSmK6tripK1lbfH4AZT8GBy3aycUWU4Y43CB9vqHDBX2zI1+yT5eYHZeSL7r+rRGKSe0zVlCdhu0NH10egzQ6OMjDWiWIn8PesUUN3sM8sNFJb+Eyd3Y+rt2tiNkt98A43OwGPZt0OK7pRYslqCZh/nnntux/WSa/PKe0qylGqZggQIeY9ZL8gKrDUZIUDY6coTbnoTpCRLaj1oWzdbl518/9Of/jTe9KY3qXXyi1/8Yt3I/XRyzOv12nyuoXEHWZnPetazVEkvK7Je85rXKEfvD37wgwoY5P9v5OS9Xsvu12vON1q/EgBwo81Y0t8kA+s4A7w54w3XRvjhuPPOO9UPJMs21oLiTl2qh2Y/g6nyrVozGmrUbVPlf3phIe+MoerrG3FkrAm4yvigPpOB7L/j80OYq64OXLK8qS81ggUDLpOBZ0O6eTg5Ew/6Er2p3Si6ZsqLTZcEp+QQpKjACxobEgTBNhyei8n69G0UqIMYmHOeDdmABHj1Qedo3RNkKTi7jM1LdF0pcsha21H2NUtggzSmSgBZwFEU6O6NabjBKb2Pb52zQ6OQAFXNDYJGHcsoV1pe35xxzPK22jELIfuvWO1F1Y/7WRfI27sQYB7V4ITxeVhywYAl+6NhiX3QDZ1AavbRjIbsPDPmFcvZf40SxtLxtNwGLzgBD+bXd7OJClnR2xCArO3WNWqbXT/u+/xeCsFAyj7QTXklgBVq/1HCoblG5YDzK+h3nhG3ee3j1lOZK8EKlihGoGBkOMKSxXpBrbNapiD/zZJiU3HDDTcoAOUxj3kMBgYGTF12Q17nwx/+sGKcPe5xjwO1ETfCc8CGTHSMTpONygqn733ve8q1NwIAaczCaq3vfOc7eOpTnxrjSskhZ2sGEgDwbJ3ZZFxJBtYgA9RwaXQjtgbdWbVJahU+9NBDqmyDN29rEUHgY//c/4uTpR9qNa80wJxRVAwYVRScvaj4S3X52ukcS6ECTNV1S/S8Yew7FZ/5NJDea6gUVSDUBWzOUIwz5oIzgapH3bXWSr3qXdt0STCZMDl7J8qqHHRliwuVCUyXWwN7e1PjqHjHjGrukQ1YdvcbyWE0yoxFANRH1Y8JcMaZbABpSUCtCrdNMCFi/y1vjmyhHgKXNK0xoH25YjjlLZDWvBEGbN2HbGuUsu9wtWUEGk9EK0Cg9Ccw1YT916glsvQETTw6CGqebrvaB0fmAPsIhGzO2I25TFc5jM6xo+o7ueofa4sdtzr7b7X52wlL2Isahc1LXPXHuvQKIWuVbHmCoK1rpZrqD7+XU0oz0F90NA5zEZ/9l8NY9i8gRcZUl5peh+YOZOKxUmLHDpqHra/gPWctKEhwkIzFeswm9pyyNLVMQWrWtVOuynZvvPFGlYzHP/7x6pqbOd773vfife97H57+9Kfj29/+dgIAruFi2LZtG06ePIlvfvObeOYzn6mey2gCwrXOz8oPf/hDBdSuuBdZvFlMwNs1nLwuNZ0AgF1KdNJMkoHNkIGNBABSH4Mv7ohdeumlazY9BAEfmvk3TFW/r9kHgV5nAuUYJUTNGmIZKUFA3fIpWwxCCg+BWMrEmVrYhalyaw69fakJlFwK3DdnSDQbX09qAmX3gBGwJW1tCQ09fD0txqjPpkuCs9YEPP/wUnOIYBCH5sgObB10cGQBWacfJebPUKiSVYuGCebYgFTb63H2oOQ+ZGSezwxVImdNoBpw/VRiZyAIUovOv43L0AlQZO1BLHgars4NekRWXMrbASs9bRTArW0up8xoTnW0xDUjR1XevQbajIr9V+lBVZNRmbGGYQmrK2WsfjUFv1xAunAKVofNSaL5crAN87NVZHpmIFsAH+Oy/xp9MOgsnbWGlT6hq6lfG/vDt+RA6gUSjLQWP8P6vyft9YPWSOkQDPSBkmIGNt9I6neejQHnWe022dZ5ZBHRVODCCy/E9u3b27pGt08i4EEQsNZ9eH5+vmG5Yz6fX8IUJFBCwGS14P0uZW8Yl112GXiNzRx0mL322mvxvOc9D9ddd91mTsWajp3ANwHpn/3sZ3j1q1+Nf/zHfzzdH65pvvbv37/pTWvWdJLWQeMJALgOJiHpQpKBsyUDGwkAfPDBB3HPPfegr68PT3ziE9dkCqhlw348+OADyOy4C/kd+s6gvc5elA0w+HL2BKo+gYjWQaLaZErkUClKZAuhoYfvD+KhmfbAsry9AwGo86ZvwkGnW6AETxMo4JioxUTzhbIBBiavZ7okmCWAjswq9gujVN2NyVJzranVPhR9qT0oeQ8bMVeJ2lFsQG+/0Ws6ClwsoOyZAyzZX5rnpOUgKqrUvfnXRyP2X70zw9JdloiaZTCGazWDvDWiypkV8mA8CJCOq7JPH/U04sw0SCBQiMoKkxYR7MZ0WW9t1/YwTadZK49SB0DZ5ZkIPAl3oQ9OpohURl+vNE6mfddG1t6JQM40NQ1pl/3XqB9pOaS+l8pePPArznhaOeaMXmAZ1S6VY9frny12KykEugnTdijULlz52RTIKO0/S2RbGab2sSwX5L0KHUG5YbpRg2MgKEiGYFRCTDZUvSADiiBgLVOQzMFaUJCsSLIjGSyzzGS6x8pcj3Pwxje+EZ/5zGfwkpe8BJ/73OfWYxc3RZ8Ifr/nPe/BX/3VXylm6znnnKP0/rh+P/vZzyqZJs4VdQK5xvkieB395b95LMvn+aK25npx3d4UE9ilQSYAYJcSnTSTZGAzZIBiyI0Emtfb+OlS9r//+7/qR+8pT3lKV7vHHbqjR4+CZci8iWRkMmmMPPIk5qwzTsrtdqqHIKABl1FqVfnBsZaYTvX67Pt8sO1Bvm8Gs6U9OF5sH+hMyV5kLBoQ6IMjtsgj6/QZ06EjgFV04xlwNJtb0yXB5JsodqF3EofmqMelD/6krQGkOBeeOQ24lOxDxiqgpK1puTTDBWc3qh4dSs2W/5FNBDG/KustZP9Z8IJis2mveV+ADtYs1fQM95mNOKIXGXugY8AWZQkiAxXTOa9NYi0QSICqWMmjWtecooXU1znUEX3I2IMoqTLtzrLGqJFKINCWPlL52VgAs97owrPTchTkPDUyDdFl/zXqI4G4rDUCoIiKf7Rr463tD/UCvVIeEDNIZVv5nOplXgRZVAN3yaaHcnKWLLUlGHjw9CZcv/1MDKSeo9dgG2efzTp3vGeNdAQjYDC6J1ueKoJ/1BCMNAWpJfjzn/9cHUY9tU4bjrQxdV095eUvfzn+8z//E69//evx8Y9/vKttJ40tzcDk5KQy/7jttttOv0FQO9ICjJiABAj5b/7leuaL65iO2nwR+COwzf9Pd2c6PSdxdmQgAQDPjnlMRpFkYF1kYCMBgHTC4s0bf9yuvPLKruWPN5t0SOMPNIM/vrt378aePXvUj/CxhRtwYP5L2v0pOAR7CEbpXYraSVIswIc+6872d+PBWf0HaJqD9Do7UPQe1hscCItJsLx4XgF32pdD1h4BAorO6+eLvcnbu1HxWW7avDwsTu9d95GYWtgPWKaAMIF+pat4P6DJFq3tf29qj2LtBYhfZtts/GTOZK0hlDxNM48VDVnIW+OoBPvqgkOtsP9WPHSKNAr2ThS9zjD2MnI7bGkpQ4pOhNKitEZR9amVecb8xHRbBGKDoICpyj2mL73kepbII2cPKdZaJ8cTNSq8fsC1YWVOoElForFxh6YhW1BV0gEhGGaa/deosykxCEoCkJHno3tA3JL+uH3IpQfV5pcvzHyPNxqvI/ZgwWu8IabAQDKCEWBH5lXg+utmEDAgAMigZhgrJs72YCVLrckI/10ur/7dNTo6qnJDxiDvKTebhhrXyQte8AJ861vfwlve8ha8//3vP9uXybofX7FYBJ2ZOSc0PKQmICuOCOb19/crXcDomY3EDf539Ir0M/mXzyj8/2QMUjc9ibMjAwkAeHbMYzKKJAPrIgMbCQA8duwYbr31VrXbddVVV3U8f9TQuffee0HmYRQU1D7vvPMUzb42ThS/j/1zX2hLo632Onl7HK5P3Tw9tpcqIbXkqq6ycRJYKe/CnGdjrmoGbMPCIFKFSSPAHV19S3Qb1swV82CLAjJ2Dyq+GVCFD+OWBNxAz8nS97I4NFtWD/NWNQ8nH9cltfnssqRaOZv6en2sbclRbM9epcNmMjKSbCOy9syNP5z3PqSsXlSDGmA6sDFdcuC2xP5bOVrlmmzlUfQOGFnvy1sgSAex0DFdtkj7TRkVdYBBR+2/hWoBZAhL4aLsr+4wrrueJNLIOTvh+keNgf2r9Ykl5ylsgYcjgOwckLq0DzQNGVMgYODnMOuaK61unn8ySHcqV1w63JvYnGne5vIjBGhmZQtHMfFMbcKcbiVIwQskfDQv9x5wrsC29PNaH4LmGQQGWALMoFYy2W+bMQgA1uoJEhQkUFgvCLDUlg4zZ2d7CSWBoquvvlqZS7zrXe9SbsBJrI8MkNHK55ybbrpJPevwmeNjH/uYAqlZEk9tTL7479pX9P95/okTJxRRgSXeSZwdGUgAwLNjHpNRJBlYFxngzSJBwI0QZOD96Ec/Uj+CvHHp1I4td84ouEvDkSg3vCE8//zzMTg42DBVk6Uf46FZ6qjogXc5exS+f1TbZVWKLDJW32kduVbnOPAFDsxkUA6K6EuNoeIfoQ9iq5dZcXwmGEZgHTUC3OXsHai60xBW8wey5h2XIKhYdPXNVNhWVBJcauDq27w/wNFjg6hmzugv9qcJepJlpz8PYR9t9KfHMVc1M+ZoTJ1gA7KveXsXwnzq6Vwuzz0da2l84+MUfH8PJkvtl7wvv3aeem1YQDWgiYvpECjY1O873jFQi0BW1tq2yMI0l3cZTGCyxtWaDthKR9E3V55eL9ssdeY6cjEFL5g2PSErrsd1m7PHEAQzcNGelmqrnSS4Gvijp01ROuJUvUqnbNELajHSZbqTupKr5YXfvxlrBOJ0WW6rWVx5fDP235kzLOzO/QUc2X32HTcuaQLCuPzyy5U2WBJkxAY4cuSIqubgvSMZVQQIG93/spwyKh2O/vL/nS3BfFAHkbI6H/rQh/CmN73pbBnaWTOOm2++WZWqP/KRj8Rdd93V8rgiJ+GWT0xOWJcZSADAdTktSaeSDGzMDGwkAHBmZga33HKLSjS1Mlh+azq4a0adP+6qMbgL94hHPAJjY2OxAMfp8u148NQ/I4Ce5lTGGoIgGKFZUsWHIAWS1TKcYiZtdrofR/0zD8g5exuELMH19Y0C8s4OBIEZcxB4GVVyl8qFc6YbBeq4eYe0AdioH3l7QjFiWmWjuK7EkVkJLHMZzVhkljkoecd0h3r6/LwzgiCYg2vAYCW6KNmAWbsXJcNmHjRcsYVElVqXBoNATVqO4WTpBFzjmnQSvc64ck32YZ4NJkFTm1GUfZbrd2ZDh2zJtBxQZiS67C5q5hWr/ajUfL9EU5mzRyCF33EgMNTYHFsEZ08YXEmNL5W1CHKi4ww5av/NVEL2X2Qi42ESbhcAz6Wjp4PvCCxho2KA2d7uJFkipzT6AvA77lh76zew4AeZWEB7r/0E7Mj8Zrvd1TovMbponD5Wkdx5551KK4060gTBWHZZyxTkvxvpYpMVuJwpuFF1BDn2iy66SG12f+pTn8IrX/lKrXWXnGw+AywB/sY3voGhoSFcccUVK1yuo7LfWjIE/90pcoT5ESZXbCUDCQDYSraSY5MMJBlYNQPcIWpUFrHeUkd6+3e/+13VrWc84xnqJs5U8Np0GOYNIoM/oNTPoBtXqzd4M5W78MDMP2mztFQZqagYYFCQJbQblaA1VtPDx/MoW0v1lFJWD7J2BiVP39CDpX9pK42KAXMQshXT/hCQNsMeolusZQm4hspjU3IQbqUKKx0fPJ2c3IoFuz4wQR3EgcwezFXua+9hts4HxxJpeAs5OPkpY9dkMz008/CpD2aCpRl1XCBvUWuR5fJm2JC8cuDvxqnKNDJWzripCa9PTcO8PYQF45qGYV6USQ7ZegZAukbfrdR8c5RLczw35XrXkcE4Jsv7Vv36zlnD8NwKfOuk0fVYr9EQmAsUQKsLbsb5TeJ3e0pScoDrV2+zaHl7BFer3gAq/krGKUt0LSFR8h/uyjhr+xaWlO9Qv2du0B3Atd5cOGIALM/3QNdrmivFC0fsxoIXp6RaYCL3Z0hJutZ3P+iS+4Mf/EA1TJDL5H1S90djtsXDhw8rBiAlXJ74xCfWvThBFeYw0hTkX24I81657ndHNruEKUi2IEuK13twPCwRnZqaUkYR1ANMIslAkoH1m4EEAFy/c5P0LMnAhsvARgIAubN94403qhw/7WlPM1LawvKP+++/XwnlRrtpW7ZsUeW+dBtuN2Yr9+L+U/8IP9Bj+5BF5UgbXqBfOpa3z0HFjwcYVYp92F+sr7dmiRR600NYcPUNPWgO0pcaxoK7v91ULzmvb9HV14S5BVlVBWdEgSkmIvAtuAu9yPY2B9gCHzhRpNvx6pp3Pamd8P0ZuIEZ9iPH6S/0IVeowF80EzAxdkcSOKaDrZl5jvpkix6krb7QdEUzWDY5UyYoE4ICvc4uuAHLROODtnG7kLG2wpYOyoY0J5e3S8A5LQlidkZ/kO2lJTUkUyr3rYBmBKhK1UGU/XjfaX6xBynbgkhPt9RO3LmoPS40WEl3DSAjM42gmBvQ7dqMecX8TD+C7OqlzfzcZO1tqPrm2m0l31w7tswvMq31fiNbaXf5sWQF2jIL1z9y2jyl3vX43QD0wg2aa5AWrIswkv1dnW5pnUuwilIp0X3SRgCjtAbcwsk0VvjlL3+p7u2e8IQnxD6T98ncJK5lCjLP0T3j8gux7LqWKcj2OlGxEnsAdQ4ky3Hbtm2qBPqb3/ymqqpJIslAkoH1m4EEAFy/c5P0LMnAhsvARgIAeaNCdyzGk5/8ZC1xa9640VWYN4ORWxxv2qi1wZsiExT6+eo+3DfzcXiBnnsrHxIzVkGVLulGzt6Dqt/c0OPE5BZMYzVzCIEtmd2Yc1tjFTbq/0B6L+YN6dCxhDAIpuFBL+9RX3tSe1FSjsNm9M9EdQhOegqBaOyWG/gTODgXD3jkA2yPs80YiMpxW8iikNqGomHArpjvzPUAACAASURBVMeZWAQdzLqF5qxd8IKTeiBKMIFjxaU5lyBAPYYFmikYmv/az0DB3gUP00ZLr2uvn7WGla6e6XLp2jYycocqa60G8di3ErswWWodCCZQRvfjktd5gwkCqGQohyXVepqu8b6zaaAxBgiWqLbPjiO4OjuXg0zH/e6TqnRcoKrA6FaA3HjjWv0olt2TfSlQWZP2z/ROgq7UlhCL5iFLWZmOmFCs3Tgxlv0jZK1dcQ7tyDGUSvnpT3+qrv30pz/dyL1MRzq6Bhfdt2+f2vCl+y8dknWC984EAWuZggQJ6wXvJ/P5/BKmIEFBurWuVbCvw8PDqnkagbQCiK5Vn5N2kwxs5gwkAOBmnv1k7EkGDGeAQBhFozdCsK/Uw2BcdtllGBgYaKvb09PTqgyEN8oM7szu3bsXExMTxm/IFtyDuHfqo7F0g1YbjBQp5OytqPqH2hpz7UlZawxecLihVpglenHPiVOUyGoaeexE1TLzQN5n0NWXzMm0lUHF1wdNmYSsPaz0o3xDbDAr6IPnenAyK5l7fIifLm7DvNtamfVgeg8WqvsRGNSAU3PiHUQAc98RtiwgZ29ByVu9BLTp4lt2gEQGWXsHym2U1yrgpDKIkleflZaxCAilO8LYIwjS49Dc5GFjupPLc0d3cZ9sxhZKHlvNf2YRyKHxQ6NgnssuDUVaW9tLv7+GYEu7K0CgYspZW5WrdWCw1Hy13GbkMCwZaua1CshZwShmqu2xYR0MIOP0o6L0T7vPynOoMWltQdVfO+MQzotAatE8pIJqEP7eCgyiGoOFn7X2Yiz7hlY/OkaPZ0nnbbfdpoA/AoBJnMnAAw88oKo9aOZ2ySWXGE8NWXVkCdYyBVlOXC84PwQBa5mC3ITuFihIQ5Rzzz1XdY1GIBdccIHxfCQXTDKQZMBcBhIA0FwukyslGdj0GdhIACAn6/rrr1cCzdy9JVOvlWAJMRl/ZP5FsXPnTnUTRHHnTsX03D7cO3MtrJSeBhrdKwvOKCq+PnCSlkOAmFUC+MvD9/figen746ej2IdU7hSEpc+QM2kOQmClNzWGYizdpubDpb5axma5qT4Iy9aoW+guDCDbO7n0Qd8fxYG59h7is/YWOFKirAGwLM9ESvYhY+dR8syMO7p+yAY8Bt8QUzO6LhlpQpRV+W7cEMEYjhabl7TTyIMMLS8wU65Z2z9bhMDogrevZeAnzjjV94c9rkANs3qMS1snGzM0W1gJphKgOllub20vH2PWYhlpSpnMtAqUxclX7TE00shZI4rlaHq9NupLCIgNLhoINQfgCa76/hCK3tFWh7f0eN+C7W+DbZfhW/E/Q3qN1p4tFCtQQirgVXSFgVm/93TAzogxVHBCAZPN1tnOzO8hb59nLhVtXInGAbfffrva2KRxQBJnMnDvvffi4YcfVveONMDoRrBypRYQJGOQ96L1guAfNQRr3YcJCpqoSFneHnMRsSBZGs174SSSDCQZWL8ZSADA9Ts3Sc+SDGy4DGw0APCGG25QJbvcvd2xY0esfBMw5K4vd38jdzeWgHDHk387Hezvd27+Kvof8SPYmbilWY16JdDrTKDsxxEjX31kjhyAJXz4OKNrRNBs/4yDstdaiSaNDQJBV1/d8UGV3pkyB2EGqAu4YKyEV6A3tQdF9/6mD4Nx11XOHofrHz5dEjxb2omZysG4p684jkDPQGYCcxVzfWQj/em9WKiyFNZcSaQtcsir8mXDoFcgkQpG4Fs0dVi9vwROFqpDmHfjASc0S+l1RjDv0g1XH/RePoFZawjVSgki1VxvrJ1FErnClrmRYNiA4kx/BHIWWYeTp1mHypzCpQFKvDzHHVvG2oaUzKDotc6Yi9tGdBy/H+kc7AYnO8qmrO2XRFoBYgSgIBvrUVoYwUzFLEjvlQqwAgd2dgbSMve5j5v3yDjEA39b2i+NjtteXVAGO1D2j8BRupr9SmrAq/nNjM5Jy53Ylf3jjoA1rfT/+PHjuOOOO5R52VOf+tRWTj3rj7377rvVBjDvHdeS8UbjvdrSYQKEkRTN8kkgkBsBghFbMJPJaK+zW2+9FVdeeaVqju3raF6f9QsnGWCSgXWQgQQAXAeTkHQhycDZlIFGNx7rcYw33XSTcmi78MILMTo6umoXCW7S1Zc3fcViCGiR6Xfeeecp7ZNO7KrW61CkXSidEkYefScqBrT8epzQ0EM3LJFH2sqf1he0sAf3TLan65ex+pGyLJQNOOeaNgfJ2yPwDeoC5m0aRBxFADOlco7shyNTqPrAgVkzZctkP7r+pFG2Gs0rFMPQUGl1tH4LZNap0j99ALn2M+GVs5CBoxiqjUIEwzhaPNzyRylr0cnVQcmPp33XagPeQj8yuRJg6TGHG7XriF5kbJY9E8hstXdxj5cKCPSC4xDoxcmSWYCqthdcmymZU7qVnRtP1KIAvwPIoK6uUvIcN0uxjiOAutAH2/KRys2uGKPv7cSC1/7GwWp9CDwbXrEHTroIJ9OZ9dgsBzQOcWQeFY+bJd3pgy3qMyrJoHdkDi5NVET4nTWcfil6HPNlpc3ysvx9lnbeddddIEj0pCc9qdXTz+rjWep69OhRxXbjfeB6Ct6HL2cKEiisFwR3a5mCBAbp9tzKPS0N9a655hoFFFMPkH+TSDKQZGD9ZiABANfv3CQ9SzKwITNADcD/n733gJLkKq/Hb+WOk+PuzM7OrIIREkIiCYksITDBIIJ/IGwEBiMyPgRzjBBgg8FgBDYCRBAIiyQwyYQ/FggUEBLZQkgkaXe1Oe/EzhX+577e2u2d7Z6u7n7V0z1b3zl9NnT1q/e+V9X96r773VvLzazTBnTHHXeInVOadVCzr1ZwIUXgj+UwDJZW8PiZmRm02xWvUrvwEY86C/vxReTs1svg0sYmFJzWWV7UPEoYIyi5O3EwM4S5QvNMC12JIWUNImfLeRCVaQ7CUlZTo2tp8/pjldebqfYLh2ZZAACZe677F9iXvVcagMEH5pQxIMWx+Rj0oaLXmkamJM8YhW17tgGvmISVku/2Wlrqh5VYgKYfL+7P8y7lx5BtAcTrNaYEO0uW6cxx32mehh5zCjnqJYbE1rPUYRiqgeIRvbMwvvPJnFO9U0BjJAeNsYsb7Y+lDsLSUiJn4QOBKJerKm5bTSxMlazHpDApIcNVxQgWinI2Durl28n2QKFSXnIeq+Fh4BuHgMYlzu5Q+6ArlJBYSRpARVxbB1NNYyL+EiiKVi99ob9PhhvXPiwdPe+880I/Xzed4O6778bBgwexYcMGnHLKKR3dda4bCQouZwpyQ7laEAD0GYI+Y5D/Vyu+853v4EUvepHQQ+RGebtcinkuulTz9ctf/lK8/HX6ZZddhs997nN154XHvPSlL617HA+47rrr8JKXvGTFY+mCzONohkIAnQYvLBNnifSll16K5z//+Q2Bq4E6Fh0UZaDBDEQAYIMJiw6PMhBlYOUMdBMAyEXD4cOHceqppwrjjuXBsdx///1C58UHNUdHR8VuLxfEqxVcYHBR8YhHPAK9/XFsnv8kMnQWbTFSxjSKzlYJD7pk6pyOPx7+Q4s9omC6ioHYFJYkjI+d6TU3Im/LceMUuoDWBHJ2MEfHeskQBg7mJPISdAbJkNqxsIReawIFZz8cTx7LZSC2CZniA1JBpIQ+CkUpouTK1QmzMA5VZ8nfiQYp9eZjpfc9x4Kbjx8HMOaySSyidT2/Y2XBMu7FE0dBUxsybMNktyW09YDQTmx+A6BW/glQHcrtB82MeowJYeDhSmLP1jqnxZJNLd02ILDMUIshL0C5Vq7U4J8lg5sl4wXbRSagS23w1usc6cSAYgqquQDdrK9RKO28FQ25JQtOPolkugRvhfLoZs6toR95d45bE3U/Ph67BIPWBXWPa8cB1HOj1jFLOiNn1+MzTnMUmqT4m8HtmA+Z5+CaltUslUxB/t2Xtll+Lla8+KAgGX7899DQkDjsy1/+Mi6//HKRCzojt8t8ZCWWYrsBQD4v/O3f/i2++tWvrjhNLJX+1re+1RbJIJnXS9TW2spABACurfmMRhNlYNUz0E0A4K9//WtQ42Z6evq4Eg6Ca1z4UtjYL5vgAvhBD3oQBgcHVz3HP/rRj0S/zjnnHBCQdLwCtsx/GoulP7fcN1GO6u5sWZstV5xC3taxIEk7bjC2CYul1hmKTJBMcxC2V9YFlNM3tpc2ZoT7bCv6eCVnE/ZmyuYrMa0Xlh5HzpZXXprQh6ErHgquPIBHhY5eawpLkubZvxk0MkmNcWRt+YCaiTF4WATUDA7NDsCxqjv/NnNjxjWy6VgiLVfnzu9LQqPBiYNiaGWnijAKocadyxxJCtfegPnS9qOtcX7TxjrkbLmu1dW6a6p9R8BTMgLrgzmtDpnl/DGtH3mHmxYnMk5bbX/55wlULRQXhUGU5y2hGAKAu3KfFVi8p1wbnl7fKEP2+P32Yuo66IohjENk5N1UppEJsLGjKXGcnn47VCU8I7FGcrZ9+3axCUrg5+EPf3gjH13zx/7qV78SjDqy/8gCXAtBUJCyOJVMwaWlJbHhXBl33nkn3v/+92PdunV48IMfjL6+PvD/yBa85557VgUAnJycFGt0bpAzmgEAb7zxRjGmWkGpII61Wrz61a/GNddcI94aGRnBP/7jP+Lcc88V5dDU0WS+tm0rm+497WlPw/e+9721cMlEY+jSDEQAYJdOXNTtKAOdmgECU8sXC53aV7rb7dmzRyzefBFnlg/84Q9/ABc9DP54c4HHxUW7djXr5Yt6K3R+e8hDHnJ0seJ6JWxduA7zxXvqfbzu+yxD8ryDR40k6n5g2QGaksYfDy7BgwejMAg1fgiK2mgrJx7fb21EVhJ7j+YgMc1CQVIJb1JfD8c7DFdSSaJi01AmD8NqQhfQM7Ano8F2j7H+yKQcjE9jviAPqCRg1xcCYEfwwfXmYXvyQCNeTUl9Ai4W4Hi1NfyauUoVGIipG7GbgKtEUxO/L71HHI5DKQuGgvTRsuPW2YvV8kdma8rYgKKzC57SxPVc0aiCPhzO0dDkRPCNztopYxRZMnJDmIfKsVECIK6zHD4c85bleRTsPH1USg5XusZVbyMWSscYzQltHLqqt8UUZXm/BPip0rmYOn3hlnrXyknZNGWd0GcsuvuaYmOqSKDoFuGherll5bmHrCdhLPa0Zr6GQvnM1q1bwVd/f7/YcIziWAZYQcJ14mmnnVZXQ7qb88b1PHX9KpmCBLq+9rWvVR0W19IEi1mhwj/PPvtsxOPxUFLwzne+U5yHL26G06CPG/qMZgBAXusryQHVGgRLkakFzlzxXmF5+HJdcYKqzAX7yCABgQBhFFEGViMDEQC4GlmPzhllYA1noJsAQO5UkunHHT+CfH/605+EqLMfvrbLStonqzGVt99+u1h4cueVwKQfnufggcXPY7bwm5a7ZWkjULHUlJGCY09j8/yxkmS10AcjNg9Fa50xQ6aPg1k4XusPhDQH6THJHCrvyrYasnUBXVuH7qWhxxori3XdGexaqm6+0mdNougchC0hf36+ygYhh6S4Nvttsgw2ba5DpiSXtceyUZq4yDarcJxpZO0l6IqCvGRTE+akzHILh8XI9pmXlD6BXIvM05XuIbKbCCgV3BYAOmcas8WV5Q4MNY2kPoiMcINu/TtnpTGxnDqhDR7RVQzf2Valxqo+IYyWHMgta1eRxGIxDw8nMg0F4KnR5IUgbntLdKlnSvAeKLZVG3H5vBvKAJgH5t5toNTfVGaQceqbYXGcp6XfBkPl5k9nBMs5yVpi5QPBiyiOZYAa0tyIJeuM4M/JFAQEyYAkCMpS6Lvuugu7du2qqv9NnWwa7REMvPjii4UGXlixWgDgt7/9bTzrWc8Sw3rjG9+Iq666quoQP/KRj+ANb3iDeO/qq6/Ga1/72rBS0XK7fJYj6aFdeo4tdzhqoKEMRABgQ+mKDo4yEGWgXga6CQCkuDUXDMlkUmih+MxFChlzUcdyhk4MllrMz89XNS/xPBfbl27AofzPWu66qQ5AV+3GGFOegs0HDTjq8Q+JSjEF08pD0eqzIOp1PK73Q1eBoiS9OJnmICoMpM31ZSBFUvSYm5ALWGLsecDhHJlJtUtRLa0HcT2JrN24W22tIYVhEMJz0bSi5Mg3xYhr44CSheORTdZaKIhhf9YBWbhUrew3NyIndOnkAyUxdQiGpgk2UhhBgIMl49kQ3W8JcFCDMN+gsYaCBGZzwZhUzI1FBpnei6wAAsPI1rE2DSWNhD4kdBVllIzW760qnINZWs0SaxmhYwZzxZWBqjJQTLbzLEoeNe3aGzRlMbWeI0xI+fdXsNHQrINmLUDB2bkyyOxpcDwzEDO8z3g4JhIvCNaFNh1FGRRqINPE4KyzzmrTWbvjNNyIpeQNwS2WfJ7MQdDr2muvBfXtCIQRHOSLm+qV8cIXvhBf+tKXQkvVagGAZEP6wOZHP/pRvOY1r6k6Rpb9PuMZzxDvffCDH8Sb3vSm0HLRasPvfe97xbMR2Zt8Ud8wirWTgQgAXDtzGY0kykBHZICuYrVEhDuig0c6Qa0T7lpWMv74I0dHYC7mVhIXXu1x1DMv4dh2Zb6J/blbWu6qrqRhaWbgh8zMfB922dUfDFU7gWRCR8ltvQRTV+NIGTQykANilc1Bdkh7eJetC0jWjyu01OowH71J7FhcyWnSvyQUDMVmMC9Jo9FvdcDaJFh7MkswdTUpGF0CXJEYLN1NG2S9tcYy9NwZ7M8dD5zQyTOp9yGzoutn84MxSiNQzXlAba2ktlYPyNRTFDtEfUACdNQ41FH0gt3DqjuDQ4X6TKrlY4ppA4hpKWTs8A01WIacNEaF0ZCntL7ZEeQKSWiTUJQSCu7epoFOxTOQtQlWBTULUkDZAzoW553dTZ83yPiqHcONFn4nUqewBDlO7M30hazWmDp2BIg9UQ/VUqcDG1idknojYlpt/bFm+tfqZwjgkNnF8kpWHERxLAO33nqrWOuSGdkJ2tCrOTeveMUrcMMNNwg33c9+9rNHu8KN6t/85jfCnZeAILXv6rnotjKO1QIAWfLrM2SDMgDJGnzmM5/ZynBD+yzLvXt7jzGRSYbgXEaxdjIQAYBrZy6jkUQZ6IgMdAMAyB8y6vzNzR0DqugETO2PbqC7++YlMzMzQn+mWhAE3JP9/7A3e2PL1wUfcuJaL0pefROJXQd6kFFrA3yWlkLciCNvt/7QxpKpgfgklio0q1oZLJ1oPXcRrpJtpZmjn00aE3DcAKBdwLOx3NDSqCdVex4WCxOYK+wM2CINTCZQcg/D9uSMmScmE6psECKHmeQPxs30w0wuQNXkmiHEhRkGHWsbK7UW/fJUHM7HUXKr6+f1GpMoebOwJbsQi1M7OtRCGnpyFqoEjc0TLxrqA25Eyd2HcPQHy2cM5Bjs6VgoGC2V/mtuL9yiBz2xEDpgxe/MlD6OvLOzbSWzMW0UumI15RxsoH5pda0vFbLy6JCcJ9jdBqOS5f2ICcfkxBHDjvaArtVyYRLQVlIouXuFZiHZ2NSsDMKUTOqnYTr5isDf2+068Pe//z327t0rZFK4ORpFOQNcX918883i79Rxq2UMcbLki8w+stte//rX4z//8z9XbditAoCPf/zjxbMB3Z1pfENpoIsuugivetWrsH79+hXHdf755wsjFFYQ0fRjuZkIQTWChL7OICuQ6KTciUFzRIL+BP7Yb5Ii+D0QxdrJQAQArp25jEYSZaAjMsAdUYKAnRiFQgF//vOfxY52ZSQSCTzucY/rxC5X7ROZi/wxnpqaEqXKK8Xe7E3Ynfl2y2MjW4oC+0W6ItYIt5TG/Qv1jRs0xUSfNYyMffw8NNvJwfgmLEpisulKCl7Bgx6XY4hA11BTM1CUZDZC0DNtTiFnbzkBxFC8IWxfbNyV11RTSJq9yJTkzAfnkQYhemkAsPZLBVsUJ46k1ReYNRb0mvKNKvLOiXldqQ3F24i92ZXLvaln2Guuw6JkPUO/X14hCV2BtGt2+XjpSMqyzzD1AQmVpPQNorTUwYkbCJo3g4P5xtl/1eZOtXuRiqdEmXbYpcHUbqTmZNFlSXg4bM3lYzTVflH+nHe3BWLiep6CkpNGsUVmNkHPpD6OkncAjmQDnyD3MQ07mGsHNBCS58Yd5NzHH6MJUFtBHEv2nwJdY1OJv0faOL3xU4X8Ceok0+CAhga1NhtD7kJHNs91LhmADBpQdKpcTDuSRzCUTLbbbrsNb3/72/Hud7+7Haeteo5WAcBaHY/FYviP//gPXH755TXHRkDvqU99qtDMJHjmuwBTA5H30Qc+8AEB/pEtSvYfAcNODWqMk8lJDUASJQiGEtyNYu1kIAIA185cRiOJMtARGehEAJDafvxRvv/++4+WJ5PePjQ0BIpc88ed2iXdEtxdJIjJRTn1Z+rFgdxPsGPpv+sdFuB9FWm6eVLEv0o4ziZsntscoB0+7qsYik8JUERG9MemkS0Fe+Ctdz7PVQWzykzJKXkIQxfQwnrY2ANNO2Y8kLdncCDbLEiiYDC2CQvF+wM9sNbL4dH3cz2w4gVAkwt+9Fmbjsy33M2GGM1vFDsQgECGz1JxEFk7GNMxqbNtFwW3cZA2SL5ZFgxjFqpGLUL5QTCbTK+cs0PuNVLR1WqOwQSossXWAarlGSFYpQqmavjMBh9EpaNt3TJ+SVPHcmQ6BxfquC8byhRmC3KMkMpdV4WhDJQiCk7zZcmtpCGmjcFQLaHRtxqsxHIWxmC7i4hrIyuCkpY6ilNSb+5I2RGWNh48eBC+IVorc7KWPkvtP2oAMs477zxwE/lkDQKAXD/TDOT973+/AL5WK5oFAAlaPuc5z8GjH/3oo8Z6W7Zswde//nXhdswxMj75yU+C5c61gvfKxz/+cfz7v/+7MOqrDMMwBIjGF++ndgefzShtRFAviigDEQAYXQNRBqIMSM1AJwGA/NEmlZ07c9lsucSRjr7cySadn+9Rn4Q/zBdeeKHUPITZGMtytm/fjrGxMTz0oQ8NdKpD+V9g2+IXWbwS6PiVDrKX+pHsPb5ckgzBrXMqik5jQM9I4hTMF+SATnSNddzDcBBUx2rlVPQa0y3rw1WewcsOwkgeklau6RQS0FUPRiwHeAnsWirC9VoDxOiKTPZOya3P5Ax6IaleDL2xYWSpsSgxqO1maAYKjlxDDMGyNKaOzH3t+0Xx1mNvtjHWJIHvPnMjsiFpxInSU20EOTc844uEvg4Kiih64QCZvETInEto64RjsIZJHMzLBKiOvwjJbixr6O2XeHVWb4qbASmWhYuyajks43qdLjsHr0fJOyi06o77TvIAxx1G3mldkqFaP2LaMCw1ecQcJXyX5OV9KDtPj8HBXHMl/vWSW+N9p5CGox+fa1GqrCVRdPbAU479Rq2P/zX6zUc2eaZwP0ZQh+WQlEeh5EgU5QzQNI7lnowLLrigY0s52zFfXGc/7GEPExvs11xzDV75yle247RVz9EMAEhJIDLcaul+f/e73xXgIA0OCfSSNMC1d7W4/vrrBQjKNXq1YNXO6173OuEU3Mk64+w757XT+7hqF9oaOHEEAK6BSYyGEGWgkzJAth1/KFc7uPtG4I87cgz+kHERu2nTJpCSzzh8+DBoqMH3Lr744q75saMwN0sJ6MzHhVfQmC3chQcW/gseWtdQ00rj0Kw9R5lAmjKDPxxsjn02JLGEN64PQFNdlFw5DpW95lSZQYPWgDV/jtx8L2LxvDTzBs/R0BObQLakY3cmGPuy3vVCPa20MYglyYAd3Zaz1GuUqhOmoMwG3AworYPblbmx1CHoqiJKGqtFrrgeC02WTVtqL+J6ClmnMQCx3tz575N1ZKgIEdRqjz6gjjQ8bxzzxT+Hxjr0c5Y2JuF5WRQluequNFdkOvJ8JZcO18czRYLOcePHnegcXMz1oajJ+a5cqT+6kkBSH0PB3Qe3TcDn8v7Q+VtXTRSEVmG4YGR2oQ96skZePQ1xbZ34nXK8BZzecwVUpbwm6bSg3jABEoJ/XD9FUc4A15dcOzIoH+OvKU/G/HDNT31IytJ88YtfxKWXXrpqaWgGAAzS2X/9138V5c2M97znPbjiiitO+Nib3/xmXHXVVeL/n/3sZ+Mtb3mL0Pyjrjh1Ba+++mpcd9114n06BtM0JWw2HueG52B59qtf/Wr8zd/8jehXUK1z/1mOJIko1lYGIgBwbc1nNJooA6uegdUGAPmDxZ1IMuR82j6BMi5QaGlfGVzY+ru4T37ykwP/KK52krkDed999wmx4Uc+sjHmwELxD9g8fy08tA7Spo0ZFI5oph3MDONwvnkWSX9sCnl7pxRwkgBW0uhBzpFT2pfQR6AgB1uSrpVsXUAaURTsUzBXuE8qCDYYO0VoK8oE1uL6EAyFwJRc9lhcH4GmuChKbpfF6mljGgWHwOUx0ED1hrAn2/oYek2W1B+C44XDBus1NiJf2g1oxVC+tsLWB9SxDvtzu5HQhmBpltANDVu3r8fYAAdkwTZhCtNglsts0w3CZd32WndHD3p6zRmBXcqh5OnwzPDH6feLDNiUPgkPORS98BmX1fKhKQkk9DFR5u948sFPxaUhST4QyKjMnY0+5TGCgcRXpz1o072VJgA0SZucnAx6ea3547h2JDjKeOITn9g1m8dhTAzX/KyoyWQy+M53voNnPOMZYZwmUJthAYDUwSTrj88UfFb4wQ9+cFx/yBL0HX3pcuwDfcs7/Y53vOOoRuJHP/pRvOY1rwk0rmYPYkUWwT7q+b385S8XTMavfOUrgZ91PvaxjwnG4lvf+la8733va7Yb0ec6MAMRANiBkxJ1KcpAN2dgtQBA/jDv3LlTmHz4u1YE/Aj8EQCsFlyw/OQnPxFvPelJTxLlwd0QXOSQ3UgdQ2qWNBqLxfuxeeGTcL3GynWrnSepkxVQxO8P7m60Gycc32OOw/VYqpVruS0+WPfHFIIatwAAIABJREFUJpGxVzZoCHoiQ00JxhY1rWSEVF1AbwrbFraB+fOwJLV8N2Ww1CXbskFAZc5oEELAV5Zxi98257zP2oil0mbpIJGpDsBUDRS9crlxyZ7CYUm6aSx37TXHQzMJUTwTyCVhJGehhCT/Y6q9sDSC7nL1AR1nAvPFY8ZDaWMdVNjIh16uq6DHmBKmJCVPjhboyt8bZX1Vfv8FcY2V8R1UysVRcAz0pPpESXrYwOryPse1URhqTJQHK5LZu0HzQ7MOTdGQd3ndymEQW8oMFu36bHhqzS78/onw7GNOoNQj9sFA/klziaBsnaBjbuS4n//85wLYOf300+u6oDbSbrcfy+oRmrGRXdVN+tFh5J3rbRpbMG655RbQSXe1IiwAkOOhEy5lg8444wzce++9xw3xkksuwbe+9S3xf3wOqeUYnM/nxfMIGaSU72GJfZjhA4A0MGHZ8cte9jJ86lOfCgxYv/Od7xSA5Zve9CahaxjF2slABACunbmMRhJloCMyQCCOAsntDC7GSLHnTjWD5RinnHKKENpdiWLPH2MuWBgs4+gWIecdO3aIBUgqlcJjHvOYplKdKW3H/fMfh+OVtRFbCdU7A9sX7ofjtT7vCX0Auuqg6Mp56BYstpIcjcEywLQBWVuOcQlz3mtuQtZuDbCaz49jrrBHTKHJ8l2zX5rDsriflBh6rFEs0WRFYiiFPuhGBqrROhu1sltJY5wqTdLKwI+1rSCtT6PkHsbe7AI8yC0jZImkSuMENxwHU83pQTIWF2WYYYVmD0DXXbgSyko19ONArjo7jcxJlk/KKvWvlQ8y1sgILAmGnjxdzNr5L7she2QghuxkuzjbDy9Rzi9L0vndSwDXU+TIHQS9xui8ToOcorsXDlr/PQp63srjVCRQWrJgxLLQrRY2xjwNjmfCCbCJFXfOgDb7GCwsLAhAgJun1YIbmZWgIP8ddumg34877rgDXCc96EEPwvg4v1ejYAYIBNGMjYzNxz72sSd1Urj+9svDCWgF1aUOI2lhAoAE7igpVA0AJNGA0jx0/2Up9EpB0xgC6wT7qSUZZhCc5TXqA3lk833oQx8SAOByjb/len/8N0FDgocEAauVPYfZ96jtcDMQAYDh5jdqPcrASZeBdgKA/PHkj27lDy7LVFiuEoTNZ9s2brrpJjFHFHLmbns3xO7du0F3vng83tJua87ejfvmPw7bbb70TFViuP+QjaQxeOSBvPUFjamlkDDiyNvNlxRXzmO/NY0smYCSGB7UsstIZJkljQk47qGm3EGzmQQO2MsfmhWMJGYwX2gNWFx+L9AleLG4NVBpW+D7yDGQ0PrhaHLLAVXFQI85iUxpi3Rmk+qdipy9hKxTBl1lhjAJsTYKh2NPqlbisV6yLLjk7g8NbKFhopfthx7PQDOa3xRQ3GkcKtQG28uGKtTopKFGuMARwf8eUa69P7Ry7eXXEUtlgTyKNTQoW7nuPMfCQrEERT0edNKVONLGuHBGDjuny/vPHKeMCXje6pUHs09lVqCKvLuzYVagpUxjMeAG0V/0/AMSdEsGBPhHph3BQP/Ff1cLgn/c/PNBQa5buHkZhmA/nW65oXvmmWcKBlQU5QxwzUmjB8uyxNrxZI5t27bhrLPOEimgPM1qmsWEBQBWlgBfdNFF+OEPf3jclPP+4KY8mZC+7nita4K63TQf5D3skxZkXz8+889vl87MH/zgB4V+4dve9rZAp+Pz1Utf+lJ89atfxSc+8YkV3Y8DNRgd1FEZiADAjpqOqDNRBro/A+0AAPnjRhOMLVu2HN017+/vF7vUXBQHDfb1xhtvFIc/6lGPAtvohti3b58oHSDIydLlVoLuj386/BE4aI5xZygzuPeI+UfKGICq2Cg4zQOK/lg0xUSfNYKMfaz8r5Vxps31AmST5RAswAC6OUrQUuS4mtUF3HugH4UaGl4sgS45B6WUVPu5TxrDogSzIFkfbcCaQba0XTr7iICC683LY295Gg7lTJTcHAYsukTvgovmQa5a17Sl9SKuhWcSoikWWE5LNmtYpZ80qPHyvaL0WNUaK69UEMNs3oHr1WeH8rui15woOytLMuupNS8sX+e9TymAdgFkSX0CCkpHy89b+R70P2vnx5FVagPYNClhTm1vHiWvfRqBfv86oTxYV5JgP2zvIBzU/00j8K1iEAX3UN0ponTG6T2vXfE4blCSGVgJCpKNVy1Y9UAgsJIpSHCq1aB5APvxkIc8BENDQ602t2Y+v2vXLrH5TOCVjK6TOe655x6cf/75IgVkRq7mdRIWAEjg7MorrxRjJBvONwTx5536f9QBZBAY5rNItSBbkkxaguoETbmRLzN84I9ag9/73vfEufhc87Of/UyAs6x04jMDwUcyifmdwT/54v/xeiYzkZ/59re/jXe9613gtc6xPe1pT5PZ1aitVc5ABACu8gREp48ysBYzUCi0UEKzQkII2HHnlQsvfyHMHyvS70m9b2YHnDt5/NHkrlwtrcBOmyPuMP7qV78S2kAUJG42stmsKJ0+NLcdfaf9AnqscSOCQ9lhHModY+rFtB7EdQNZu/5DUL1+k+EzlKBWnJySW9kOwYqdhqYUoZlyrvdGdQHtkoZdS3zqrF2KGtcJJFnIOvIYdmVwdr3QrJMZcX0Qhqqi4Mhhfvp9o8Ze2hxHptQ62KV6G7E7c0xX0lLTSBq9yDhygOrl+aQLdZFOsRJK9avNVVwbgqnqyLtytC2rXg92HChZwhU1KNioeTM4kK+vo1Z5PkNNIm2MYKn0QMPMrUavY1Ux0WNMIO/shovqoEyjbdY7PqGvg6Z4yDvH3Nfrfaba+56nIFOIwVWCsLWphTgBiNL08ErHa43DUFKgCdNqlgfTCKjMClRWZAWa6jos0XAnQEyn/hb95tkBjjz+EAIHZA1VgoK+5vHyxrhBuFxPsFGTEUqkkJ3Isk6ajkVRzgBlWGjERgDlEY94xEmdFhrpPeUpTxE54LpcBvDcbEIbBQB5/OzsLM4555yapyT49dznPleAdnzeoMngco0/6updfvnloo2LL75YmKEsr0LifXTZZZfhC1/4gjjun/7pn/De97632aFW/6m1bSGB9PSnPx3f//73xd8J4FeW/JJB7L/4DOG/eCy/H/gngVz/GYsmN2QAsrIqirWTgQgAXDtzGY0kykDHZIA/lL4Dr6xOccFLsIo/1gz+gLHUYHp6uiWR7JtvvhkELLnApctXNwRzQA0RBhdejQKfXBCQPUkW5VGn5NEUYpO3oeAFBwJMdRT3HDjxodBU4+gxe7FYCt7WSnkfiZ+C+aIcHT/ZDsGebcBSUlAkOmkG1QU8dKgfS3p9do4KTQCp8wW55bADsRlQS1Im66ps3rIRS0W55cu8vtLGFGyCaU2Wi5LhkykOY6l0IkDZb02h6OyHjSCgSmPfMgQwafCyJAHArHXmXmMKJTKdQnIj5nktZQTwbLjayhqHlEKbz8bgac0BazGtH3E9La7NoIBjYzNy7Ogyk3I9cg4dzOUzQav1q8yOM8Q5mxmfoUzhUL5xPU9qVPK8WclGL0Fy3ynlwSuxAnVMIBtgI8BQe3Fm79ugKFqQoa94DH+/uX6pBAQJEHJTs+q1E48fBwqS9VPLZIRtc33E4AYpTceiKGeAwBHXUH19fTj33HNP6rTQEfd5z3ueYJHRHbmdpjUsUScg5wc3x9/ylreIf7I0m863lUGH3sogwE2Ai2Z6ZPHxOYCl7rz2Ob9f+9rXxMtfJ9dy7uUzz9lnny3M+Rhk91Fvj//HfJAVeM0114BgKYOEBTInZbMlCTLy2YimHTQ49AFAPjtRUoAgPkE+PgP4L36G3xf80x8nwUt+lmDuF7/4xZaIBif1zdHBg48AwA6enKhrUQa6NQMyAUC2RWdfOmv5QaCOrnTUwGs1WOJCJhw1PCYmyno8nR5c7FOcm0E9Ev5QBwn+uLN8mIsUf3ePlH8yKLnosd0s7p+/Blk72MOh40xj81x1FpimGBiMjR7n4Bmkj7WOGYrPYLEoB8CS7RBMpmK/tRGZAM6PQXOQ1NfD9WZrglUEo3YcjsHTg4MkQzH2kUCFPJH/hD4IXVWQdw4GHVqg41iy7boL8kp3j5xVVxJIGIPIOzsC9aPyIA3j2LVUu2yS4HKPOYTFkICnsklIKVCJYcOD46YKS2mNCWREWXBjJbuNnC8lgNjDcFDdVCMz34eCOddIk1WPpbGEoWjIufK1GpefULBMjXXI2TTRqF+23PLgAMS0YXCzpeyiG6xFfm847giydvOMYEvrR0IvOwcjJJ3KlUbTCeXB7F+lgzBBvawd7JpdF/9LjMUvDDZhTRzF33muaSpBQZYSV9uU5eZhpcmIXxJIEIGgwK233ip6QJZbt2gkN5Gyhj/Cckpq31HzjSDPyRxf//rXhVYc1+Vcp7cTACSg91//9V+B07/8HvABwHoNcJ384Q9/eEUdPF4Pz3rWs/Db3/52xeZIWvjGN77RFrMU6vjxOenBD36wIFDQAIQuzfw+8F8EBiv/zg0FAn80Unz2s58dAf/1Lo4ufT8CALt04qJuRxno5AzIAAC5G7V9+3axu8edKgbLWQhWySxFIZDGhTLb9Z3MOjm37BsX9wQuGdy9DFJywR94Ue57qFyaywX+pk2bxJgrF2yOm8fmhU9jqXTfimlQYWLzYQ+lFTS6FCgYTUxhtnCsZLKV3PZbG0TJnSwAazBOUwt5TDPZ5iCm2gtTM0UJ6AnhTWDbQuNlp0ljCIbqIu/Ic5pVFR2cmwVJAK0/VhoS9JjUgQwGSDdybaX0KdjeXrgIXr5dKm3AocL2uqfpMyeFg6sdApuuHSYhMW0AlmohHyJwxnL3lDEpwCtUuM4SoMoVBpCX6IDLElaPZhquXJC62oVAgDlljJU3UdoEjlnqACwtjZyzrS4QaChjOJSXw8zmWNPmGArOHjghMF/r3Whk4yX0UZRcmsA0Ll9Rr/2g77MfMXUKeXc3bG9lEJDaimf1XQldTQZtXspxXE8t1xPkOqJacG1AsI/sQOp/MR75yEeKf0dRzoC/Kc2NU24en8zxuc99Dq9//etFiSg3l9vlUM2ctwoAki1LrTsy8yirs2fPHmHiwWcO6uAROLvwwgsFkzCICQ7L8W+44QbBGqTRB0tpCTrymYU6mgTUXvziFwvQvZ3xspe9DL/+9a8FWHqyA9btzHsnnysCADt5dqK+RRno0gzwR5ALzmaDP5pcSPgueKSjc3FBhl6j5a71+vCLX/wCFOZl+wTEuiG4Q+eX5lDUl7uTtYILGYKo3J30dz/rMSgpvL9l4bNYKN5bs91K8496ORtPzOBwoTE9r1ptps1RuN6iNE20/ti0cFyF0vz1WtnXtDEB2z0oTReMD4x0s805xzMtlwqTOJRvnMXGvlLDbyA2jkWOW2LEvTHYygEoWvXys2ZP1W/NlJlVkgxX/H5QX4waiXm3/JC7UihIY+9SBh6CXSe6EkOvNYZFoUdXr/XG3w/bJIQ96jE2wCYL1avO1Gu81yd+wlDSoPZj1innScMYDuTkAFTHn02B6QzDwxxUM/xSXUNJImmQaSfvu6VevmkkFNf7kVvpnB6B+vogdr1zVb5PMLdHGIbMolQHAGuk3aDHEhT3HZPz7r5Q7rcVvxs8btIArlcEmdu6qiHPUuAqvymD1iMxlfzroEML9TiuDZbrCdbSb2aVQaXrMP8eZOMx1AGsYuPcTCVYRJOFWoYPq9i9tp766quvxhVXXCFKoQmiyV6jt3Uwa/RkLM32S4B53y5nQvr/9ueOf0bzuEYvhiPDigDAtT2/0eiiDKxKBpoFAPkDReCPACCDP0BTU1MCmGtUvDrowLkrxvORls+y4m4ILtxvuukm0VXqnFQrzeEP+u7du4VhChmZDO7gc7HKspV64XkOHlj8PGYLv6l66FxuFPuzwUXhk84IbLP5srPKTvAh11QhzYk2ba6DI4AOOfptZFDpqoeSRKfcsi4gS6A9KOjDA/N0bW6tTHNYMCC3ADLLPUsxGIoBxZILGlHXzdJM5J3g11y9a9x/v8eYRoEabiuUbnruDPZlGwexe1nKjAUUXbn58PveSzdquluHwDbkOcrgziRy9gPSQPJq8xLXRqCpKnIlA3PF5oDtQPPtKrDcUWjWfFtYawZNYvShtgKBBFUT+tARduUxMF5DD2YLvA5b+96onWcahkwCSmFVDEPYr5g2IsqiRYm/pE2detdVXJ3G3DKjKrIjk/oobMwexwr8i543gmYunRpcK/ilw3Nzc+CrVhBIIBBY6T4cVI6kU8cftF/Ub9u/f7/YlD7ttNOCfmxNHve+970PfD3hCU/Aj3/84wg46uBZJjFjOTmjEvTzn7si8K+DJ1FS1yIAUFIio2aiDEQZOJYBAlS1RKir5YnHU1OFwsr+ThTFcVmWG3bZCfU6uJO7YcMGnHHGGV0xjczRjTfeKPp63nnnCSHqyuBuH3eo/cU7S3zJcOQYGynP8DwX25e+gkP5snCxH6Y6jHuOgLSNJMws9EOJz9JUseUwVZbapZGVBAjRIZigXVESaFd2nh1Czm68TLdWcpL6OrjeHPLOOHYvbW45h2ygx6TxTRZFd0FKe2IBCRWDsWnMSyyv9tslYzMMgxCWW1taHIVqbriehkM5EyW3OYCYjMt+az0WQjLxoBFFr7EOi0K7T9o0HteQpfYjpiUCsSWb7YGKHpTsPtiYk3o9VuuP4ulIKKOw1b1tKdU11R5QLzNjh+9Q7I9X6F3qLNEluF2EhhnMSmJi15vj1TQMYd80JY6UPo6iRwftcMB3nocl64o3gIJbW1IhoY/DUE3xOrWn7BTaDcGSYVZIMLgW4wYtwUGyBmtVeLAaoRIUXMlkpBtyUKuPXDdSTsXfoO7msbTa97e97W2gOcYznvEM4X4bxeplgO7UvOdYYrzchXj1ehWduRMzEAGAnTgrUZ+iDHR5BoICgASyqDFDPRWfpVZpStGONHAnl8LF69atExod3RJ0XuMinOLcPqOPObzvvvvARYAfHBeZjc2W64g5ynwL+3NlN0CG687g/tnG2VD8bAojsLVD8NB6maiqGBiIjWGpJIcxpHoWVNuAFl+SdBkoYPlqpiRPZ9BQ+7FQSGKuIA9YpHlFrzWApZK8NplAAixkp6m6PNMRcQ0Z4wK0LLlkQcqNHmMGeed4/TbV24jdmdZ1LNPGGBTBjqrv3NzMqMg4UhUHhRC17tLGJFxvHrYnDzD2x6p6M9if2wKa9AzEppC1d8IN21nXNqGWktATs1DUZrLe2GcstVeU6ZaBwMY+2+zR3IxIaOswVzwUyj2zUr8stQ8Jo3/VDEO4HcHyYEUpIe/skZ5zS1mHhdLuQFNzWs+LMRjrnjUGwT6WdDLI7vI3D7nuqDQZISC4kskIAYlKUJDgRLczjKjvxg3WmZmZrtGODnSRNnHQa1/7Wlx//fV44QtfiC996UtNtBB9REYGeB/6Tt0E/3yjPxqBsPKHzsO8F0kY8F88nn+n3iH/zpd/r/qGQHwma4Q4IGMsURvhZyACAMPPcXSGKAMnXQaCAICzs7OCpcZFJoMsNbpOcUe1nT82LDkm85A/juecc07XzNWPfvQjsNSauivDw8MC9CP4x/9j8EecP/r8YW81CALuzf4v9mS/D2rSPTCno+AEd59dfv6B2HrkbTIzZGhxKRhOTJdLWWWEq0Ar9kJPBXN0DHLKPnN6ZV2uII0cOUbxNmLbwg6MJjbgcKG6A3MDzVUcqmAkPiOdtQfbhIEYFEsuYCRYb9Y6LBXls95oqmCoOorefsHwyRSHsVSqYsTSRKJV6BiIbcBiaavc0mv/+oCKPmujAFxkmeUsHya/A1h6XNaaax3IF+17GuYKJuwKlqUAps0RLISko3jcuEoxaI4FLTEvHSSqdpkIRqXeI0qD2wEEmso0Dhd2odecQMHZF4pBzUq3Aw190sa4YNg6qG4+0cTt1NBHYuogLK1HlAd7FcYzDTWy7GBDmQy0AWWoPXjY4BVQFK2V07X1swS4CHQxaDa2EmjHio9KkxGCEbVMRrjW88uG/T9jsVhXgYK//OUvBROSlRWTk5NtnZdOO9lll12Gb37zm7j88svxiU98otO6d9L0h1JG1KSkXBKfCWiiyGCpOnW/mw3eo6wqimJtZSACANfWfEajiTLQERngYtB37l3eIe5KUZeOZbd+rF+/XuioNMtSa2XQNMjgiyw6sum6JW655Raxw0d9RP7w+0Aqf/z9RansXfZ92ZtxIHcX7jnQOvBEd1fPy6DoynFvHE5swkJBHtNOyfXDSM1KezhPGuPwvPmWzUvm8+OYzZfvnVhpEF7ssFQgqc+aQMk93HI/j7uPPICOywuSS4J5DoJdBZtuvs0D0tXveQW9xjSKbh67loIxfBr57kjqw9A1IO/IARaXn9tSe4TBSZZaaCEFy6YTPIe9veX7xFCmsSdT/XuFpbMxLYaMU9+spdWhKsUUVG5zxMMrG63so3Bc1tKhAoEEsUvOAHJHnL/JsDTtQbjqHFRDxiZM8Kz7pkaON4eSFw4Ttl5vyIiknILjHW7JtERTepAtBdNUnEhejMnkxfW61lHv0xztrrvuEhuyZAA2GtyM9E1G/D9rmYxw3bJcT7CTSxh/9rOfCYCTpdGssjhZg5vDz33uc4Um9Vve8hZ84AMfOFlTserjJgD/2c9+Vjx7kWVLQJbB+5guzbyfeM3yuYH3YbUXq4j8F9vhcwVJGVu3tr7mX/UERR04LgMRABhdEFEGogxIz0A1AJD/R6bdli1bjuoDknpOlppPW5fekQAN8oeNgCT78OhHPzrAJzrjkFtvvRWk9lcGd6IJ/oW5cH5g/mf42Z4vSxGST+r90FUXeUfO7iJ15zIlmhXIEbm3nBHAPChNTN7UemGpdIxsDvRRMIitc4eOm3O9lIYVd2BLZNXEtB4kjQQytlw31h5htrKAkiurxLqcCrJ64loKOUc+UAf3VOTsWeScg9Jv/LJW4kbhFByWYQFNGQqlg/C05vQLgww6pa8HFJZkNwfoEKAqlIaxZK98X5C95iG7ot5akP4GOSaOEXgowtPkMYFXOm9MIzstKQVMXX4eQxnHwSObBse95ymIuSMwYkVp2qdBcusfQ8d0lqznQijNDdqPpD4BTYHQam20BDymkjFdn3nO+/zcwSvA7/9uCm4s/u53vxOMosc+9rFSuk7QwTcZ8fUEa20WkxVYCQqSidQpJiM//elPBYBC3ehW2FVSkrqKjRAAfMpTngICov/yL/+CK6+8chV7E526WgY4R2Tz+s9lBOZ98I9gHwHB5S8+W/D/eI+yisgHE6MMr50MRADg2pnLaCRRBjomA5UAIH989u3bJ0A2H7Ai04+6dKSry2apNZoEls7ee++9QhvjMY95TKMfb/vx1N/Ztm2byKcf7QZSty/8Bnfuvh4e3JbHb2kpJI0YMiU5AAsZbEVnvzTtsLSxDg7kOQRTt5BAmCifbDBK9ibsqmL+YSpx9Mb7sFA8xqptsOkTDhfglLUBi5K1yljWmTYGsWTLZqYpGBB6i/JcjRUksXuRwJmC4fiUeNgPo1yTBjR0OM45cgFXf1JpeKHk09BC1Lnj9dJrbkTeZnllWYYgaOgYw95ssLHzPAPWFPLuXtiSXLtX6idLncugdXuAwLg2BFOLI2vvkHatKd4GzBXL5WDVQ0GfSW3HHApNbk4Enetqx8W1YVh64gj4KWfzptH+qE4Sdt6ClVyEogW4fj0VjhuD7dUvZx60HoLTel/caJdW/Xiu27g24nrtggsuCKU/XB9yXeiDgfVMRshsqnQd5rqtnZIxfhJuu+02wbQ666yzRLnlyRqcv/PPP19cJx/+8IfxD//wDydrKqJxRxnoqgxEAGBXTVfU2SgD3ZEBglR++Qd1/khBZ3ChNj09LV6dspO7e/du3H333eBuczNlLu2cEbrO/f73vxdufH5Qu/ChD31o24HU3Uv34qe7PgvHC/CwVCdJhhpDr9WPRUkAVsoYhqLkpDHNYlo/TE2RyjzqtzY1Zg7i6di1qKPkVi91VaFiNLURh/P1GSmNXLNavhd6bAmKLknr7cjJB2ObsFikDl7rIHLleJL6CFTFRnEFV86g41fcGezKHMtn2ii3vZLjZ9C2qx03YE0j59D4ovV7qlr7mt2DZDyGvLu/lW6u+FlDTSGpDzZU0uo5UzhUaAwQ1xULfdb6UNmT/kAJOvaZUyi4++F4ciQL6k0AQTFTs4QRSiugs4Y0DhfIuA0GrPUIVp4dGhi90rhNNY2kMYy8uAfaW5p8tF+ehh4BhmaEg3CtiKkbMV8MZgx0Rt+r0GtuqjflHfc+ZVq4fovH422tjuD60Xcc9gFB/ptg0/LgBjIBwUpQkKYFYW8sU4KF/eTaa2BgoOPmrl0d4pzQPI+b0p/5zGfwd3/3d+06dXSeOhng3PA++L//+z+84x3vEBrnLNc+++yzxbVb7x6p9340Ad2dgQgA7O75i3ofZaAjM0DqOBeOvggtO0mgiqw/Ls46KSiQS6FrlrlceOGFndS1o33hDjnNSrgjz+APMwFL/j9d6KifuBqxP3sfbtv5KdhuoeXTa4qOofg6zBVWYqoEPw3LWC3dQME5vmQ2eAvHH6krMaStAWRteWWmZEwVnJ2BzBoUbxpb5+vrsIwmp7FQ2A5XgsuynwE6lyZME7k6JZqN5jZljAJKAUVHLrtKVXT0WRuw1AJjj8+as7k08s7xWnBseyg2ifkQzEeYP5YzJ/Q0sqHp3SnoNzci5+wKFWRJ6uNQUULRW5nZqyKJA9lc02zimNaLpNGLxVLrOoT1rl+yd8mUY7louwCqhDYCnezQJoFAQ5nBwSY2BWjYoasaMqXWAMh6Oa32vqqY6DHWo+QdhO21R4uxWj8S2hh01ULO4bV1PPikeiPIOfWB9Lg2irMH3lz3YbuZPIX9mV27donANlVkAAAgAElEQVRKA7LuHvWoR4V9uhXbZ1XJcj3B5RIofgOVJiMsIeaLLEZZgAbBEwKAjIc97GGrKmGzqpMCCCCJa1Bu8v/3f/83nve85612l6LzH8kA7xneC1/5yleEQzMJDtdee62YryiiDEQAYHQNRBmIMiA9A3SjJVONwRIN6vzRZKMTgwuXX/ziF2JxePHFF0tbJMoYq6+buHnzZrHQYjCPzCeNS/bu3SsEevnv1YpDuW24dcc1KLr1S6Hq9VGBgtHERsxKcrelU6xhxwBLjsagKD+MTWHJrg/E1Rur/35CH4GCPGxvZZfc+dwYZgvByiR7rVF4yKHgyHPe9YGvBbrXSgzOUZ81XmZySQ4yeWz3UFOGJhomsWOxdplyr0lXvTwKIZWGDlgbkXfomCrb3KScZDL1UsYAlkIFzhRRFlx09tQ0adG8GezLtc5aTRtj0FQ3ECjT6mVGR9secwyZ0nZ5Lsh1OpXQR2EougCGAzMCPRVZO4ZSC9/NZNRaWizk66TW4BX0GBsAhaXJ9cG2Vue11ud1JYmkPooiGaBYgqmMYLEUrD/TqUswlginfDas8frtcgOX6wwCaA9/+MPDPl3D7bPKxNcT9MFBappVC+oiLzcZ4aZvM8HSX5YAMx75yEeKNe7JGlyXsgSac3HjjTeKNXQUnZEBHwD8/Oc/Dzo1P//5zwf/HqZGeGeMPOpFkAxEAGCQLEXHRBmIMtBQBvjDc/vttwt3tImJiVXRaAnaYdrb33nnneLwJz/5yWLHrBOCzESyKP1dbjL+6DhHJiXBSopzc4ee+T3zzDNXtctzhd24ZfvHkZcEOI0nN+FwfrOcMXkKjGIvtKQ8lpkoXy3Jcxw21KRgfRHwqRYqhrBlrjGNREtLoMeiLqA8xiL7NhSn0cqOQKzFRiZwQBi4yG+XuSXQRU21RiJfXI/DhZVdZzXFwGBsIjQ2oKkmkTYHQ2W3pY318EA3bnn3x/I8EzAj2zO7XE/SU7BQSEhzAud5+60plOju6obPHGPJaop6lqUHgoNyjVyEVY5N6mPQFFUY3tQDAk1lCgfyjZVW1+peXBtAXO/BUmnbCWy4FocU6OMct6EawtW63rgDNdjUQSpS+gRUxDFX/FPdfqiKhYcNXgldjTV1ttX+kG/aRo3hc889d7W7U/f8LHn0TUZ8QJAAIdej1cI3GfFZgiwjDrL+4zloAsKgcRxLpE/W4PqUa1IGjUBWmyl6ss5DtXETnKXs0te//nUB/j3nOc/Bpz/9aWHqEUWUgQgAjK6BKANRBqRnwF+IySq5kN7BigapLfOTn/xE/M+TnvSkVd8dY39Y7ksHPoavm0jafuXilAxL7tDTgY46NKsdi8UDuHn7x5C1y3qPrcZYYhNmC5JAQADD8U1YKMoD7fpoRODskgaEKdBE2Wq2CruwZM9g11LjLCkyKsdSM/LA1COTSo1FTWjhNef6WuvaSOiD0FUF+RAcd/utGeTs7YHmS0Ufdi4GB8T6zHVwsYhiSKATy04Jatkh6c+pOFIyHaIbMec8ro3AUKmlWZYyMJQp7MnIAagqrymOpz/Ge6k9pboEx2JaEhmJxh31vj9NbxCGpqOIfTWBKMcdxVKpnGtZQTkAnzkKRa4uaJA+WmofEno/cg7NZuwgH5F6jIoYcrYNlp7H9B5Rmu3V0OwcjT8aM+nnSj1/OxvbsmULCAJS464T1hjNjJ1r0Ww2e5zJyNLS0tGKiuVtks1XqSfI8uflJiNsj2AXg8ZxJzOjirI0p556qsjFPffcgwc/+MHNTFP0mRAzwPv4r//6rwWb98c//rEA8wli+9dtNzynhZiek7bpCAA8aac+GniUgXAzwB+YbgjqFfp6Lo973ONWTaOQZSX8od66detRsWuWVrC8t5puIrV5eCyPoQ5NJ0S2NItbdnwcC0U5D50j8Y1YKBK0kWMUMRSn8YQ8EDBljAngR6YxwInmIDp2LWgotaCzOJLYiKXSLimGLf51RhOG/vgYFotyARyCNwOxDaE47tJt11R15Otod3nONPZkGyt11hQTg7F1whggDIYSNSj7rDEshMg2I5BlUvPMlecmXe17qdeYQsk9iGwphUXJAFXl+cig7DFHwLL1MOZk+dhYJmqQnRdy/irP6+XTMHQV6jKZA0MZwcF8sDLVZn47yKztEazO9oCsy/soWKXmeLks16PJSXsirs1gtnBsM4YSBsIp3ps/wXjoIQNvArUwuzUIGHCTcWhoSBg9rJUgM4ogYCVLsNJYrXKcBP8ICvosQf7JtdqvfvUrcdjjH//4QKzBtZK75ePgNeKzQ3fs2CEqUqLonAz4LMBPfvKTeNWrXoVnPvOZuP76609q3crOmZ3V7UkEAK5u/qOzRxlYsxnoFgCQi7mbbrpJzMMFF1wgdn/bGdyhppYfAT2CkQwCfgT+CO7VCi68+OLuPHVoOiXy9qLQBJwt7JTSpcHYJLL2XrgS3IbZof7YFPK2POaepfXC0gwU3MZKdFdKTtqYgO0eFLppQc0/6iW7xxyGqpaQs4Mz2+q1yffJrFwsbgGWieQH+exKx5BhWbD3SdfAo45jvzWNpVol3J6GA1kTJTfX1BD6rQnY3pw0B+rlnegx1sFTsqGW7PYJ3b79sNG6rmetJJrKGEpOHAt0UpV87Sw/J5ml1LELz1jl+DPSwAIoSP1OqHcxEgjUFA96ogyG5TPDKOi1XWzrtRf0fQJgveb6sl6lF971Uqs/vJ97zA11XXuDjmel42gMpKAfeac68zmlr4Ou6kJuoMfciDP7XyPjtKvWBtcklBlhiedaZ3ZxHUhAsBIU9NdjyyeAlRh+WTHlV3p7e4XJyMkYdJglCMpguXW7188nY84bHTNNDvmMcd111+F73/ueeF4gI5DA7cjIiChh13VdGCHyT17f/osAeMQQbDTj3XF8BAB2xzxFvYwy0HUZoBgzwa1OD/aR4sUM6pe0Ux+Di03q/NGIhMEfXZb6Tk9P19VNJPuPC3QuPqlD00lRdLLCHfigBHF/jqvPHIPtzTcNyizPDQX8ydhwvOZAnhMeCBQLPdawYMPIipg2AF31cDBjBTb/qHdu1TNgOnF4MXnmIDwn8wkQlJLbbrnMLi4AYNlBh09FyZ5gvqJiI3YutmZIQrbeQGwU80XqpcnuOUA32gFrIlRmm290sRgSe051p7EvtxUJfQAJPY4le2W9RRlZNJ0BYY6jWnLu+3p96jWnxPdMKUR9xeV9IBAID8hrGUCVw5yuN06+T+ZurzWJknMIpTqGRkHaa+aYMgCnIuuE41wcU9dhLoCmqqGmcXrvJRiKdTdrjmuTPXv2YHx8fFWNxpq5FmR8hmvYSkCQABfNLqqFbzJSqSfYrMmIjL63q41bb71VsMoIHLE0+mQYc7ty2+p5CGpzXi655BJwnshkpbY4r2vqep5yyimCZEDQlqQDAoF8+X9n+TvbeMELXiD03KNYWxmIAMC1NZ/RaKIMdEwGugUAZMJ++MMfih1dltKuxLqTlVwuIv3yGh8kpZbf6aefHlhQmuUW9957r/hRpw5Np4XtFnD7rs9gb+aPUrpGsX1VKUlztiXwoKuuRCaVgsHYNJbsxrX6aiXIUEaxWDCksSnFeTwgjVEUDTll2n7fDTWOXouGCPJAULZNhs9gbAbzxfulg2lkL/UYY8jYx4C6TIHAnZzcDFiTKLqHYIfEjEoZI1AVBwX3kJR7rFojNF8Q953EcygwcSgLON4xx07miiC/bBD5hDF5QAJjUMyF0Oal8py8fvvMKeFiK1MqYMUJL61HrpSHAhueKccBPegFVh7vhiNzKVcjNGgfYlo/4npvWZ9Pok6gqWzAAp2f6wTLox89ciXonN7NwfUFNd7Wr18v1iYne3CtRlbgzp07wfUXmVFkSNUyGSGYUlk6zLVaEJORbsrzd7/7XVx66aVi45y61WttfN00F8v76rsA0zzwz3/+c9NDoaHj+eef3/Tnow92ZgYiALAz5yXqVZSBrs8AQS7qT3RD3HzzzUIUl0LXBOLCCi4gd+/eLZh7BEgZXBSy3HdwcLCh07Kdu+++WwCGfglGQw204WDHLeHOPddj5+JvpZwtpvVA8zzYihyHT2qEpYwUso4cwIeDlOkQ7DibsGNhM9anN+FgTp4hCvs5nJhCprTnOBBGxiSNJDZhviBPZ9HvU4+5HrY7F4oRRo+xASV3PxSksWNRbtkkgdE+a1g4+YYRBFwGYtR4ZCltON+3AtSxNiIb0ESl3jgNTGN35kSNRQImQ7ENWCxtg4dwDSao2chybbraem0wsxBGK+YG5J3d0svaK/NN0n3B7kPOKZf695rrAK+Egif3uq43x4Tue81JFiPX1dys31ZzR5DFmjbGUfD2tQy+qkggZxcCXZeTycdjU88zm+t0B33qd7/7nQB1Jicnjxo9dFD3Vq0rZEWSHcm113nnnQfqB1YyBakvWK36hYAhWVWVoCDZVstNRlZtYE2c+IYbbsArXvEKbNiwQWhSd/NYmhh+V3zkt7/9rWD++WY4ZLL6GpiV161fAs/3+OIzEUuH+T2w1iUAumIiJXcyAgAlJzRqLspAlIFyBroJALztttvEjyP1XMISMZ6fnwede/kng9R8UvC5cGpm0cSdeeqvsPSE7sWdGq7n4Jd7b8DW+Z9L6aLi6LAQg2fKEX4X5ZSxMSyVdkjpn3joJvPL2Qu3hjtkoBN5KnYtWig65XLF8dQ05gvbwXzKCrp5GhqkOTf7/eqz1gsdPNvNyOqqaIfMmpTRj4zEUmu/g6aahuKtw+7Mn6T22W9swNqAgntAWtn58k6S0WpqBnISwezl52COknofllpwuyVAlS8NYalUWzOTQH/a7BMGQGGUUFeOy9LSwtWWoGPY5+J5NSUmQLlsabtUdpo/Jktdj73ZE8upqdOnKnao10etGydtrBf6hFl7d1tyvLwfdFjvMSfgIoNik1qtCXUGh6l1GiAeOfxWJPTa+r0BmuiIQ+666y4hT7Jx40YhTRJFOQNkAJJRxc3bavrLvskIgRb/xfVlteDar9J1mOBgLBbrGt21T33qU3jzm98sACJuSDezlo2uq87JAIFrlv3y+Y0khVwuJzRAo3ntnDmS1ZMIAJSVyaidKANRBo7LQDcBgHfccYdYqJEqz8WuzOCPKBeLXDT6wZKa0047rSXh6IMHDwonOpZcPPnJT5bZZelteZ6LX+35GjYv3C6lbRUahuLjmC/KKjdVMJyYLptZSAo+AKpqAaUmdfFUTGHz7PEOuwQq6XhZcOSAnxyqrpoYio9htiCXoWZpKST1NLKOfEdZwbKUbTzi6di3pGI4MYH5wja4ITDQTDWBXnMQi7Y8sHn55TpoTQuzCxfHymslXdJHm+kxJuHQ6MRrnIlrKqPYtRSMcUugTFHIICtrpIYZKX0EhqaFcr1W67ehppA2hpERzE15WrmKtwGHV7iXVxMITOojwmU60wKA3Oo1QJ1ATVWRa0AnUEgZe70ouPXLqfvMTXjo4Kta7WZHfJ7mAXNzcwL8k70u6ogBNtmJbdu2YfPmzYLJ9/CHPzxQK77JSCUoWMsojzp6y0FBbvR2Ylx11VX453/+Z8GE5Do6MozoxFkK3qdK5mo0l8Hz1o1HRgBgN85a1OcoA12QAS54ammjdFr3f/GLX4id7lNPPRWbNm2S0j3+kFIn5r777jsqHM0F4xlnnCEEeFuN2dlZ/PznZVbdU57ylI5feBEQ/u6vr0Gh7/5Why4+r0DBaGIKs4XWDBsqO0NH24WivPJVU00hbiSaKoHLFCaxP3siUBTX00ibCSxI0qnzxz+ePAWHCywzlgdGcJb0fB+05Kx05k/KGAWUAopHSh1bvaiW5vowr5bLJmNII2ZoyHtyHZP9Pg7GplBw9sH2yq7fsoOstqTei4wjCyA/sYcqDPRZk1gqbW0MwHI34kAu+D3L8uOh2JQA5pyQ8lU5uj5zEq63iEJIc788kzGtT+jVZSQwEFUlicO5HDzULwXXS32CEajG5G0mBL2O49oA+D2WYVm8RPAz6Pl5nKX1I6GxVHpHXSZmTF2PuWIwk5oH9b0Io/FzGulKxx7LDUYCVn6lQsd2tM0d27JlCx544AGhe3fOOc3PNQHA5SYjXDdXC7oMV5YOEyBkFclqx7ve9S586EMfEpvQNNOLQKPVnpHy+fn8Ec1FZ8xFp/YiAgA7dWaifkUZ6PIMdBMA+Otf/1po3dB9V4bYNcE5lvtyccfgji4ZfywvlvWjzIU5d1wZF110UUcsBle6ZFkW84Mf/AD5ni3ID8gxBuH5xpObcDgvTx9vMLbxyINp/YfoILcoS4z7rHXCaCJoqOjB5lleO9XBOE3RMZacwKF8cCAlyLkH4xMo0MXTleuSqhZ6EE8WpWufUceNuV0ki6rF2H84hZJeAYZ4ChJ2H9x4OEYGlppE2uqXbppSmYZ+awpFdz9sSW7X1VIc1waPlB7Xd2pWEcOBrA3Xq/6Qu9IUUkux3xrDfHGrdDB5+XkJOjJ3BXdvqLmrPC/zGNPiory92VJkU5nBvgad15PKCExTWZXSYEvtAc2dMqX6IFyLt3fNj7MkO22sQ6awC6pRqHqcqUxiIYBExFox//CTwI1RaoH5a5ew5qDb2vUN3IaGhvCQh8hzevZNRipZglxD1tLSpn7gcpORdpdqvulNb8KnP/1pPPe5z8XXvva1bpvKNdlf6jK++93vFtfGG9/4Rjz/+c8PPE7qW7JcnQAzDRFlPa8E7kB0YNsyEAGAbUt1dKIoAydXBroJAKRILn/4qMdHhl6zQYc4lvvSoMMPtskddNklHBSe/slPfiJO88QnPrGlcuJmx9vo57hDzEXuurM0/H7xu9LYZmPJTZiVCAL2UDi+tB+KLk9vbzDOstWA7EL3FGydr8+U7FPGkFf3kmgnLRJ6L+KGiaWSXNMAi/pxZgJZO1j5ZyMDGohNI1PaCa9JzUUdQ9i2UF2TziyloRlFeHp1cKCRflY7luy2nL0XDsJpn8CZ5fWiqO5tGlgKMsY+c6NgNTqoDR4bmMHuTGtl9iljGJamIWPXBxyD9HulY3TFAvUsBcAcksHK8vPTdVlXgbzT2PiEtqLdg7yz0NSwWRqsKQ6yDZ63qZMt+xDNOnrMceTtcA1SVuyrpwD5XiRSGhzlmKu2pqSQKVG/rf6G0Fox//DzdOeddwoNMJqUjY+Py5jqNdEGTdx27doltNHCNkcg+Fdp3kBwkGu/WiYj1CX0QUGCODQdCRPEufzyy/HlL38Zl112GT73uc+tifnt5kEcOnRISBmRhPD0pz8d//M//9PQcDiXb3jDGwQA+NGPfhR/+Zd/2dDno4O7JwMRANg9cxX1NMpAV2WA5b+1yhk6bSD33HOP0Ohbt25dUzu6XKRRF4Y7w37ZM8t8CSZyMRZGEGy85ZZbRNOPe9zjwN3gTo+bbrpJXBMPe9jDkLN24Ge7Py9Na20kPo2FIt1D6z+oBcmTUkrA1B0oNVghQdpYfkx/bBrZOppffJA/kOlDphSs/DRW6gWsJUCVB1aSYUgtPJnl1cwFxfiHExtDcQmO0wRD1ZBzGgcuPae6K60/fxoMJLwelIxjwEAz81/rMyzZTRs9WLKDlRk2c26yMA2rCOjhlB2zT2UgZwyLpRNZeryus8V+ZG05jEqWURddslXDL2G1NDLV+ttmFMJc9hjrARRQCGhaYSkT2JtrveR7NYHAclk5WcgHYHvhz2ut+4ggrKGayDrbkdBnMFsIBlqvFfMPPy8//elPhRMoQS6CXVGUM8DqDrqjcr1IsKXdwTUmmZk+U5AswVomI9SIrtQT5N9lmoxceuml+O53v4vXve51+MhHPtLuVETnq8gAr4vPfvazICjLaiPev3TwbiQILF9yySX49re/fVTXMSonbiSD3XNsBAB2z1xFPY0y0FUZ6CYA8I9//KPQdOEit1FNF5px/OEPfxC7sgxqtbCMmDvmYe68EkgjoMa44IILxCKv0+Pmm28WDxQPfehDMTY2ht1Lv8ftO6+Fi8ZLAquNdSi2ARl7d1MlhlVzZ5voiadR9OQBP2lzHRxvtqYbrIb1uH+2MSDIdJNIJUxp4IqfCzIr5wpbpYGqfrsEb/L2HjiSjSoIMCaUMRSVXcHZbp6G/RkDJbc+MDYSn0KG/fbCMdgYim1ETrCgwmlf8TQMxjdgoQpAJ/O7gwAK9eUqwStTWYddS8eY0TLOpykGBmOTWCw+AK8NDL2UMQJDVdvKkus1N8D1FlB062wIeJOYLcgzl1lNIBCeijhGoepZ2KhvvCHjWqrWhqn0wtSGhWmJW4ehu5bMP/xcsMKA2r1nnXWWKAeMopyB3/3ud0IyhuAKdaM7IThPy/UEaUBXLShJU8kS5N+bqVAhMPRXf/VXuPXWW3HFFVfgPe95Tyek4qTtA6/JF7zgBeA6myy+D3/4w6J8vNGycGp/0t2azzD/+7//K+7/KNZeBiIAcO3NaTSiKAMdkYFuAgDJ3ONrcHAQj3jEIwLlj6UxBA737SuXNBLsm5qaEuW+7RBn5uKLJbUMOrDJMBYJNPAWDuJCkXmjbg77S+B0z8KfsDT6K0CVAwL2WWMouXOwAwA6QYZS1pkbEdpcsoJsNV31UHRPZEMVShuxe6lxTTtTi2EgNoS5grx+crx91jhcLEp1Hma7Cb0flq4jZzfO2Ks3D0q+F3osC0Ur1TsUOqawbSG4PmNc70HaTGKhKN/dmJ2NaWmkQmYD0kClDNDJA7aXJ5o6en3WFLJ2uTTbczbgYF6u07R/zrjeh5SeFjptzern1b1QKg6gUQjduIueHDZjvXOLXJpkPB6EXcV5WUMaB/NkzMk08Cn3ik7MmuK2FfQ8mg8P4L1sGC4Uq3HH6Xp5rfd+QpvA4cJO8DeAgCi/r0s1zGHWkvmHnxf+XnMdd/bZZ4u1URTlDNx1113CNI7OyHRI7tTgZutyPcFaVTlkBVaCgkFMRrgGpfwM3aL/7d/+DW9961vblor9+/eDGpV8/fKXvxQvlr8ygpYjs2T5pS99aaA+X3fddXjJS15ywrE0QaELciPxzne+E/yc7OB6mq7UAwMDohz7wgsvbMoMhOzWZz3rWQLoft/73ifAxCjWXgYiAHDtzWk0oigDHZEB7jxxV7IbYuvWraCuS29vLx796Eev2GUuiHk8neB8cWYujqmTQ/2VdgZNNdgHgpbdsEC//fbbRekK2QRcrPn56xnVsDf5YxTdMouy1aCwvKqUUGhSD2v5+YUTaXxKlDbKCl2NI22yJPIYK0rxYnhgvjmTBPaLUoDr05twMCfPFIXtWnoKPWZaOuilKjqG4hswX9giHbgxqTlopAUjdKXIFsYwW2hMb43tjSemMVfcDg/ySq8r+zlMXUN7F9yw2IDQQCZm2Pp2nIeENoztiw9IZ5Iun1fq9XleFoUqwLqs+9Zvh98JA4LJuhf2CrqHMs9LhmuftQEFoRl5TGuxGfOPRvtFXVRqE9KNeTVCyaehKQrU+IL074pa47HUDZgvVoLWCnrNCQGekwnsg81rzfyD+SC4QyYR49xzz+2KDcZ2XZc0jZufn8emTZvEpm+3BOeUG7CVoCDXY7VMRqgfWAkKcn1bySZjewSc7rvvPnz84x/Hq171qralYqXqmk4HAL/0pS/hhS98obRc+SW6t912G57whCcIiZ1vfvObogy4mSCT9O1vfzuuvvpqvOY1rxF/RrH2MhABgGtvTqMRRRnoiAx0EwC4Y8cO3HvvvQLAe8xjHlM1f/yRJcWeu2xcRDG4a0oNGJYOh1nuW2tCf/SjHwmQlQv0kZGRjpj3lTrBBUqlVk08HhfAKfu+UNiHm3d8FDlbTslXTOtBXDeQteWxnIbjm7AQ1MgjwGz4IMKSXQYWVW8TNs+1Dt6Np6Yxn98uTV+RfVOgYCw1jcP5YHpYAYZ/9JChOLURd8Jt0sSj9rkUDMZmas6Zhn5sX2iexdVjDsFQPWQkXmOVY6FxSsqsD2I2kuvlxyaonagZobrAEqDKlQqCNZdz5Nzftcbsg/XZEkupwzFWqTz3ahiFlBlpE8jZO0U5es5Oo+C0hyEngEANyJSOAWCtXH8Nf7aYhOYaUONzUNWGPx34A5qSQLaUrwlaJ/QhxPUUlkrbMZl6LDb1PDNw291wINdvvsYwQZ6wtIy7IRfL+7iW3JE5z5SvqdQTJChYLQj+XX/99QIEJMjEyhOWnNJA7wtf+AJe9KIXtW06K9fbLMXmOpIb4oxmAEBW01DTsVYQTKtWZUMmIl8rBUkD1OlmjnkfkWHHta+s8AHAb33rW3jOc56Diy++GN/4xjea1gUnS5SMzne84x0il2Q/RrH2MhABgGtvTqMRRRnoiAzwR6mWBklHdLCiE3TtvfvuuwWgxx205cEFEoE/6v0xuACanp4W5R8UWV6t4AKdZiAsqV1p8bJa/fPPW61cmrlbnr+l4iHcsuPj0hxoTTWOHqsXi8XGGV61cjYoHGfpCiqv3G4wtgmLpc2Yy41iriDHJXcgNgbHY+muHFaln4+RxEYsEqzz5JRs++0mjUEYwv1UHmDrt502xuEhg6J7vENqPfOPIPeNqqgYS0zhcEEeO3T5eWP2MGAelmr0svwcg9Y0so58xmHZ/KMPGXsOmqKBOoqzBZr1hMOc9MdlqglRuj9ffKAtjLHVMAqh6Ura2Ig9mfuk6agGueZ5TI8xBkNTsVTa3Zb8Lu+X7qWgOXF4+kEomrzv4qPfR/oMDgXY7DDUBB418jokjbWlkUcQgBt2DOqBtbu6Ieh1uBrHrXV3ZAJWlXqC/DvXcFzTv/jFLxbv+UEgi2DcU5/6VNAQhNcKwbKwN8RZRsvKF764AU8Nb67JGc0AgKzqYUl3GPH9738fT3va00TTL3vZy3DttddKPY0PAN5www1iDjgXBGRZCtxsvPvd7wZz/PKXvxyf+tSnmm0m+lwHZyACADt4cqKuRd8VkDoAACAASURBVBno5gx0EwDIHTzqmFAcmboZfnARvHnzZrG44HgYZKuR9dcJrrsU6SY4SZe+Rt2+2nFtcXeZuWMOfXdknnfDhg3CIbla5OwFAQLOF+QYBtDRdjA+jnmJAvl0qiw6+6WWZ6aNU3D/7DY4nryy+bhOBlkCi0U5oKI/X73mMBS1hJwdzKk46LUmTB3iE1goygfTdCWGHmsUS6Ujen+eigMZC0X3WCll0H5WO24wtk7os4XFxFJsCyZMKLHwmF5kHCbNXql6l6Yyjp1Lx5eNpvQ+JIwk5ouNmd00Mz9pYdyhIOvIvQdq9SWlj8DQtLaVyiruJDL2LPqtYcxJdEEPmmsBBKqacLBuh/7i8n6x/DapDSHv7IGnyDHP4U+9gn7knfrs4D5zGo8afU3QdHXNcdy8pWQHg0yvTljvdErymBfm58wzz+yKygsZeWOlCStg6PT729/+Vjghz85Wvz8IyBEIJDjHP8kgDVuippMBQJb7EpxjUFeTbECZ4QOAX/7ylwULk/frZz7zGcGKbMbBlwzQN7/5zQL4u/LKKxvWOJQ5tqit8DIQAYDh5TZqOcrASZ2BbgIAKejMsg7uWpI+zyBNnyYfFFJmcAHMH9ROcsO74447RFkBAcmwdi+bvYip8cdFou+OTHYlHZKpncO+ss+1ouhkceuOT+BQvnEzjGptsnx1NLERsxJZWiljGIqSR8mVA8gUChtwaGkWqlaAq8l5kGUuCICOJSek5dLPL9mVffEhqcCq3/ZwfEYAdWGwxAZiM8gUt0HFOmxflOeayr6bagyD8RHMFsIxu+A5jHw/1Pg8FM1t9tas+7l+a8MRw4ls3WPrHaB4U9iXrW6yQjYgGZ8Fp3rJWb22G3mfeocZsmu1+m7PjbRb61gahbhYClWPUEcKB3Jk+JY3pxJ6H9Jmr9CmDMMQZKW8pI1RmKq+akCgb9RRcA7A9lq7nuLaOGYLwbQOzxz4f1ifDGYcJuO6alcbZHyR6ca44IILxG93FOUMROYoELqBrJqhiQY3eAnyUUe7kh1Yeb3cc889YqM6rOhUAJDr87GxMcGgJEORuZLNjvRBvh//+MeCscdnF+oMPvvZz27IBdh3DKYc0itf+Ur89Kc/xSc/+Un8/d//fVjTFrW7ihmIAMBVTH506igDaz0DPnjW6eMkKOUvdrl7xoWMv7vJEl+KPRO0qhRA7oQx/fznPxf9PPXUU0UfOyFYkkzglIsQBhc7zB37x4UF9WJWYgD6Yyi5Bdy+81rsy/5J2rDGk5twON+6xp7fIZb9xXUTeadcGt5seC6wbdaAo5agOxZ6YnHkarhNNnsOmoMcylG/T2653HjqFBzO399st2p+jgCrJpxq67NwGj15XB9EodiDfTn5TEP2ZTQ+hYy9D7YXDthkKikYngFHl58bP5dkTPZaY8IkpFlmlwoL+7POiuXiumpiOEa3Vc6F3Gtz+XWheBqMYi/U+CwFNxu9bBo+Xmh8WlPIufvgeK2Dqcs7EFNnsCdzoiZnyhg4wrCUC3AHSQAZl6ZmYqm0s+nrJsh5ah0jjFLMSdjefFWX9SBtlzIDcKzDdQ/VFAtPWPcO6OraA8e4ccf1BeOxj32sqI6I4nhzlHPOOQf9/f0nbVq2b98uWJCM+++/XwBcXDvTkdd356VjMtfQBMJ0XQ8tV50KAJKJR1COQU29Rh2DG0kYK5lY/kw9w9e+9rW46qqrGrpvWaXDufrYxz6G17/+9TjllFMEk7CWLnojfYuO7bwMRABg581J1KMoA2smAyyT8EtnO3lQXOyynJZBwMrvM3fuyFQje60Tg250LMuglt5pp522ql3k7uG2bdvEQtAv96UGCUt9ff0g7gLv3LkT69evx1lnnVW3v45bwp27/ws7l+6ue2zQA8YSmzBbkAcC0gSg1xoSjq3NRmahB3srHIt11cB4chwHc3KZZCOJSWTt/bBdueYIdPLNOfukt6vBhG4nocTkAl0aerBjIYvxxCQOSmSFVs4/y6/TwjlZTil7tWur7BS8I1T9tx5jHTxkUWwCkDaVaexcCgay9pjDMFUVi6XwS3UNpNAX68dCaUdbQCqy0/qtCSzZdI2Wo53JMtWC3buiaVKPOQJLM7BQav67qdnvNAL4lhbDUptyXK2fveYkgCLyDZR/u7aGousBan2G7UTyUXjwwPObTVFHf45MLgI5jMc//vGrqnXcSYni2oYMQAZLXNPpdCd1r619YYUHN8wZBJ+qVceQBLBlyxZRPRNmtAoA8hqnzjc31GnUQfDroosuEs7GXK82G2zX19Lk2jisjXofvCPI+J73vEd095prrsHll18u/s73axEYuHb3tcxZ3s3PEMB9xStegfe+970taQk2m7foc+FnIAIAw89xdIYoAydtBroBACTYx8UDdy79IGDFBUvYuiWtXhjcXSXTbmpqKvQF1kp9ZQk1F4O+e9z/z957QMlyldfCu0J3V8fpyTnLAp5QBkkICWWJ+LABER4YsA0Yg+21DMg26QdbYLwWJqxFlAk2fg8ssACDAQtJoJyzhGRJV9KNcyfnnunc/a995ta9fXs6VHef6jBT31qzbpiqE76q7jq1z7f3Jl2IwCkB1Fy6A4/hrjH//7TTTrM0/Uw2jfum/x371u6zdLyVg2hksRbny3j5lzwr7ZnOo+tJa2BHfptzi+1YV7eDXKOhEzC3Kbe6Luim6ytdPMtXuFiZ+9HPjKsdbl3DRrK2ashCfeqxDmj+ZSiSjFe07AT2rW1VTvX6hhFNLUjTAswff79/Aqvx/VIdmXP78Gpt8Ll82EhZoyxWck3NY1VFR4dnWFQDWjW/EQBVsgtrFd4PvV6CmjNIStJmLDXfsHsAUGKIpeV+For16VYDoHP0Wg1VlWbbhjqA6Q1r4HK7px+qmkGkDuBq/tz9eje8uoH1BgKBNAHSVX43la9KNDCClZS1jZfw8uXo9E0IwIA/fO7JpvdV83mVcQ5ZEdxgZFx00UU7Zl615oZaeOZm8dlnnw2/319rky17PitEL7vsMjF+UlwbuVFeKwBY7CJwTl/+8pePAmmVXCxuiLMqku8YpNGbmpqVtFHpsVNTU8IJmOA9Ky4/8IEPiKrDtra2kk2RucPP+4c+9CFxLt+Bfvazn4nPvhM7MwMOALgzr6szKycDTZGBZgcAucglKMU/zeADm5TaZqP7Frqgjz/+OPjAp+uaScWo54Xn7i6BU7ooM/jyQzCSu6eF6B48lm5rNFI544wzLA81m83g4bmf4pnlrZ13GdFpDCGamkM6K09vr8d3Albjz1ZUVZRJu3BgPYt0EVfdoeAElqL7pIGVzN2WVl03liUao7Bdmnh0+waxHJej3Zh7nYPuXiiIIZE59lmt5j4gMLUcbcNG8lg7Xj2ANncQKwl7QLSgi6Crio2UfHDUzAGrASOpg9IqzArllmCOrtGpeb5s6l1KD6Yic2WPK3SAR/Wiw+jFkg330fb+FHQbY4imp22jbOf36dM7YbA6rgbjDB2jmI8W1lYslnR+52URs/U+LNa3X++EV/djPXmgou/Hqm6gIid5NY4hiA0aARXYTOB3g6Z0YdPK5zQegH7gfFBf1gy3230UDDRBQTtpjzJzk98WN/W4wch10IUXXmhnVy3Vdq424rnnnttQ0KvRibvxxhvxxje+Uehjk+JrVpE1YlzVAoB0uyVg9rKXveyokR4rFn/84x/juuuuO8oGohYeK+IqCbbNijwGDTXs1tIztQCvv/56/PEf/7EoDqBLM01Z+BkmlZ9yPGTm8Frx/WxlZUVUbxL0+9a3vnV0E//zn/88/uIv/gL8TnNiZ2bAAQB35nV1ZuVkoCkywN1Slpc3W/DB98wzzwg6an5wV7dVdF2qqaiTcS240GAl3549e0CnZAZzRrpvKUoMKRD8YWUl6TOVBPt8YvF6/G7hvys5reSxpMhlshEkM/L0uTpZwZSwXimlKRN4Zmm7jlfuwHu8A4inl5DIyNWUGwqegIWo3ApDjrvPP4mV+F6poCXb1VUD7Z4eASJUG251AM8tF66cGgpMYDHGccvXh1Ohos8/ZqvWnZfuurohKujsCwWdnjFspEuDjVp2HNOb1VXEmmMPu3uhKWlErAAyNU7YpXrR7unDamJv3QCqkHsAikKKamXAsAoDy9Fk1VWlnZ5hbMQWkHXJccCuJPU+vQN+PYi15P665Tl/fG41CFKUNwVgfsx1PR3zI6nTVKV8DOoXwbv5QgF88CfX5T73bFaI8ZnIChyCgvx3K2wuLiwsCJMHApiyXUvLZ7d5j3C0EY9dm5/85Cd497vfLQAmbkS3GgDIjX9+JotV7f7iF78Q4CDfYwhy0sCD7BWr8YIXvEC8Z7CKkGBcuSo8q+2WOs4EAUk7fsc73nH0HYdAHtfoHAO/j/hvfmfxc07A0wxW/rEK8G/+5m8EeOjEzs2AAwDu3GvrzMzJQMMz0GwAIMHIgwcPHgdccQFA4Io7YHwgnnnmmU3l9FvqIpoVddRe4bjrEdwxJPDIlx4GFxJc6AwM8GX2WDVEobFwocEFUTgcPqodU+mYn166BQ/P/aTS04oe79fboatZxNIr0toMe4aQSM8hg/LVhasxav2VrzwLuduPUHfl6uH1+8exmjhY0qihmsS0GwNbQvxpay/UlfTR7Z3EeuJ5y3TU3LYz6XFMldCl6/D0IosoojmajJWMrdyxBJmoqRezqX32X49qQDXjhVf1I6UXAq90LG1qSGVr15rkN0qvbwJriUNSq3WLXSdWqhm6Bxspa/Tactfbyu/bPaNIZpcsO4p71XEc3qgNXCXG7UmG4Q6kkZDkZG5lruYxXq0dAVcb1otU41XSVrXH0uiGIGw0OY2MEkUy0oG0UZ4OTqORCwc+CbcWEF3zpXtzc/MoGMhnI+UwCukfE/zjC7hZIcg/CRCUe3ZWO8dqz2NVEDV7+Xx3TACOZZHX9oEHHhD/waqqVgBzq70Hyp33ve99T1SJkfHBtWgjc1FNBWC5+fH3n/3sZ/GJT3xCHEptvY9//ONWTsM999wjqgoZb3nLW3DttddaOk/mQfwMf+lLXxKmHqY8T7H2CWzSnO/DH/4wrrxyZ+qaysztTmjLAQB3wlV05uBkoEkz0EwAICktFPmluDWDrnY0ziB9lovvm2++GaS0Upuukl2+RqberKhjSf9ZZ51l61AKVU1ywUC6tFWHQGqi8BrwpYf0mWrj+ZV7cf/MD6RVank0P/zUUUuWpzZaHbPf1Y1MOoKsWrzKRlXC2LNkHXj0aF50etuxHJMLTrCqjqBXLL312ZAVhh6AltaQ0mqj7RYaT8jNnfhNJDJbQLSVUODG1Brp1seqfgqd51Y96PKSglp9pWGp8QjnW++ALVRps9+takCvrdqA7EuNhaEbESj6MXMLLTOE6ej26mor16jYMfyMtnu6sByvjPZabZ/UPExl12qmnFvtX4WOdmMEm6kpZFAcOCVNNZXuRCS5aLXpksdRv7THN4rN1DwSmYiUNitpxNDaQF3S9Qqqpitp38qxBPSymyFASyLrLp+DXu/JOK3rXSWb5mYi1xpmhSD/pM5WoeDzMxcQ5N+tPlOtzK+aY6anp8WzmuBkLc/qavpu5nNoEvHwww+LNeNu10f76le/io997GOgGzL14xoJYtsFABJE4/sAwXzqHd5www2Wbk9q79GEg/HLX/4Sr371qy2dJ/sgvtOwyo+gNYscyHrivxms2ORmBCsDyXx661vfaqtTs+y5Oe3VlgEHAKwtf87ZTgacDJTIAOmhxagx9UocF93cneSC1oxCwBVL5rmLTy09goKtENTT49xY1m/uNsoeNxc+XDSwco+ALoP9sWqyUkoD22FVAWlQ1COpJQ6uP4q7D/8raBIiIwjKtHu6sZaQ6JiZ8sCtaFA9RSjG2RPw3EplFFwVCoZCY5ivkV6ZnzND96PNE8JqvHw1YkX5zgJGsh1Zn9zKRY6BtM2wp0sYDFgJtzqB55ZL061z2xnwj2E1MVUWMLTSd6FjerzDiKYXbDW8ENWAyQPIKnI+JwXnmnYJt2bVtyJoncsrQSRdcsFks98OD12JY9hMla/Uqva6mOep0NDpHcVGkk7LpUHjWvsyz+c93ebuP2K4sl0+w6P2YGajOm3FUmNUFQ3d3pG6GbBs+/7RQgi4CGxSn6/+siHpjTASrhV0erecgzdSxd2oz+j6E3R7K3c15SZaLiDIv5sSGvn5IP3OBAX5nCU1r54VVqR0cm3BZzXBASe2MuBQo4/dCZ/73OfAH1LEb7nllh0JAHK21Kyen58Xa94nnnii7EeBn/P+/n6w6KAZ6NHmgPl+Qwo7f7iuZ3Uvwc1c6rZJIS47SeeAls+AAwC2/CV0JuBkoHkz0EgAkHRfVpyxSs4EIbnTRXdfLqzz46677hKLc7rXUii3FYI6fKTj8uXADppOvkkKqxJI9x0cHKxqsUezEOoKsapAhrD4zMbTuOPQt5CSZOShKiq6vcNYkVhlpGZdCHt7RGVPbrCSZ24jjEjSegVg7vkjwRMwH32OBDRptyrnP+AfxUKsRnphgREF1T6ktAXpVGOxQPdNYi1enhK8mejHogW6de7wg652GLqOdYnVobntG5ofYU87VhJyK+Zy+3ArQShJQDHsAeXMvtyZTmRSaSxmq7unrd7I1FPs9RGcPWgbOJs7Fo9Gk5gurCb21U23jpVxfkGRPd40w4VxzEXlfz7N+eqKG13eIawlCXzXTuG2ek3N4zzCKbnbftA6b2DxSAgZz7Fq4jZ3HzyaC2t5mwserQ0X9H8ciqJWOrVtx/Nlm6YSuaAgqwYLUYdZXZVPHSZIaFfVlSmVwj4r1eutOTFN3MDs7KwAgej4TGfX3Rykw37lK1/Ba17zGlAvr5FhVwUg50SJHQK/VgFAaiPSHIVBPb0vfOELjUxN2b75rsTvEbu+S8oOwDmgIRlwAMCGpN3p1MnA7shAowBAPqxJX+FOF4OLNQJX3JUr9pC77777xI4dKa2Tk5MtcYFMQI0vAhdccIG0MbPSjwYfBBjNYFUkKdO1uIJx8Uz6DNu4+OKLpYx3IboXtx28BgmJRh40sViOEVyTE6TZdXlHsZ489uKuKwN4eqk2Km+fbxhrsSlkVbkVM4OBSSwJMwy57bozQahKElmXXDMTXiVWTWURKapnpivt2LtSXRXiFjA6hoWY9erBSu+cPt841pPUubOv0syT7ITmXUUGx+i6lY6z3PFuTCCdzWKJlE6J4HShfr1aECF3GCsJaxWg5cZe7vdBVw9cqoLNdPHqsHJtVPr7gOhTxWZ6Bgp0rMVUaRsepcbiolO4MYBVVj/aeE8WG4Nb9aPN3YuNlP3Vl1rWj0iqsFapX+9AwB3CWoKGIWlMhC7B77W9qtLLaPl4voxTr4ugIDfgCAiycqdQ0KAjnzpcy/M5tw9untL0gNWH9dIXtpykBh7INddTTz0lTCHOOeecBo6k8V1T/486gG9729vwgx/8oKEDsgsAzKUAX3rppaDzcbn4/d//ffzsZz8Th9FJ+9RTTy13ivN7JwN1z4ADANY95U6HTgZ2TwZYeVeM4mJHFrhQJm2FQBODYB+r+QjocbFcKqhhwjL/8fFxARa2QsgG1Fh5wAUuc0gaA8M0SaFxR61hapGQckA9FVmxEjuMWw5+XaqxQpsygJhaG0CXP79u7wlYSzwrqoiS6XEcWKu9ksed8sHlSiOjya3W6fIOIJFekQqsis9kRkNAa0dCq8z51Mq94lJ9aPN0IJLcXk2nYRL7VmsDdXu8g4inlxGXCDbnzstP3T6XB+tJ+wAmr9YGH/UuU5Kp3sIMAViNBRBLRwQ1262pWE/Kp6vm3wudniGks+uIpuVrTRa67zqNUSQzdOW2t6Iyt++wexgKDExt7LHyUZB2jEfzHXFH3l+163AtgzEp0dHUFNIltBFr6cOdHsRqprT0A6tAw54enNxxJajvWs/ghlw+ddiU48gfB6vrc0FBVvBV485Kwy6CKmRNUOPNia0MOJWRx+4EOgCz2u1973sfrrnmmobeInYBgDT++OQnPynmdvXVVx81BCk22cXFRVFowM8ngT8CgE44GWjGDDgAYDNeFWdMTgZ2SAbqBQCyH+rhcdHKHXRGZ2enoPuSHmslHn30UaETSH1Alvq3QsgE1FhpQDoxRa4ZBExZ8Tc8PCyNGsAKS1ZaMq644gpp7bK99cS8AAE3JInjs013vB2Kd6Uqt9li90+nMY6N5CEcWFORzMgB7bS0Cx3+NkRSckE1v94Gr8uNiA30135RZciKOnkUZjPnPd5JrCaeO0rXJDC1tBnEZqp2wIZaie2eMJbjErUic24Wut72+yeEQYjsCszce7Lbu3UfytS286hD2L92DHzlXAYCE3Wh6lLDrtc7Kuj7Gdiod3gkiZriQqcxLFxsWRlWj8ik+2HoBqLpecTT5c0qZI7J0IMIu7uwkuB85VYGWxknHXvDngFEU4eRhrwKYn43pFMBJFE+n53GBM7r/4CV4dp6DDfqqG2cTx021z65nXMTlDp+rOQjGMg/Wb1Wju5H6RQyALq6unDKKafYOp9WapxAE9eZ3BA944wzWmnoUsfKe/BNb3qTqIj7yEc+gs9//vNS26+0sUoBQB7PtW4pcJu0ZlJ5uRlOYJ2fCcrflAoao7AykkHqLynATjgZaMYMOABgM14VZ0xOBnZIBrggLbZTLWOKXISwRJ+UDGrpMPigJvBH4d5yi9zcMdCcgiYVAwMDLbPg5QLm3nvvFdOoFlBjhaZJ9zW1h7jIIfhH6rTMIKXp7rvvFk1efvnl0kXNo8lVAQKuJuRVNxnpDiieNanade2eF+DppX3SAEDmU1M0DASGsRAl9VJe6IoLPf4BLMXku692GkOIZxZtMcFo8wwgk11DMhOBWx3Cc8tyNfYGA+NYjh2wDWxqc3dDU9O2ml1IrwbMjGBmc7tzst8VQpsnaBtomnu3s4rS7wpg1UZNxdz+DC2EoDuMtcTxWn3yPoFbLbnUMGY2trQVNUVHn29EzDEtSf/U6nh9eljMl0CgHeB9uXFoCs2ahhBNzyCdLWKuVK6RnN/7tH4sxqw9L07rejNGg2dV0Hr9DuVai5InuaCgKYGSPwpWBOZTh/Of9TT94nqI6ygaozmxlQHSokmP5gbzbqZ2cq34yle+UqznPv3pT+NTn/pUXW+RO+64QwByZnAz/KqrrhL/pDbje97znuPGw2rF3KBpCV2caZ73ute9Dqeddpq41zkvArzXXXed+DHXxAT2PvjBD5ad41lnnSXcdvkZo5EOTUCccDLQjBlwAMBmvCrOmJwM7JAM2AkAUieHwJ9paU+HPNJ3JyYmqqK8sC3uCvKB3SqUFy72aV7CoD5JOZpz7m3FhQ0rHkn3jce3KtFYIcDqR9J+7AheMy7cGJdccgloKiI74ukN3Hrwm1IBq7CnD6nMqjSgKpMZwUpsE4qSwGbqmPC8jFyMhCYxv1kb1bXQOIaCJ2AhWpljsZX5+ESVoQcRG6iibtWHkKcDqzEdhyNygVHOLezphqaksJGqTluwXH5Y1UagZyleO1W8VF90Ct5ITSGDLdp/NaHCwMxGsqQrd79vFJvpeem08kLj7fIMIZldkyoLUCovNI1Q1TSiqflq0lf2HEOdwNTG8RqUpOd2Gb1Yjte/Ki+gd8DvDmAlsR3wLTsZCQeoio4OzzDi6Xkks+Wr94p16dPGsBgr/93Ais8rRj4FaiO2SnBzL586bEp75M+BAGAuKMi1wczMjHAJbRVGRD2uiwmM0hji5JNPrkeXTdkH148E2rhx/sUvfhF/9Vd/VddxEtCj/qDVyDfWMQHAcuezWvZLX/qSoDmXC66laSLIePWrX41f/vKX5U5xfu9koGEZcADAhqXe6djJwM7PgB0AIBe13IUlWGc+1LlzxwcvH9bVBncT+cOd3VZxveMO/+233y6mzN1MqxV7BOJI9yUll8HdSpqfkP5MINWuoEbjbbfdJpqnCzCrNe2IZDqG26e+jbnNZ6Q171HaoGRiyOq10XapafX0YgyZbBY+VwBtHh9W4nJ10gYDY1iJU7xfLjWx1zeCjeSMdBMCQd/0jwjjEdmhwgtgEHMbzyFrA91YZzWWfwgLFkCEaudGMwZq3MXStVOYi42BlWwBVwCRVHW6lx5lHPvXy18/t2agx9uHxXh50KXafJnnKVkVIaUHcW2uTrRVBV3GKGLpOaQkVKeZ8yBNNZoKIVpksyCghxHyhLAcrz8YF3R1wevyCpp3I0KFhnZjBAlRSVzZZooKF2IpxVIV5ZD/DJzZ838aMUVpfXK9xM2+fOowJVSKBddUlAEhOEgqsZ3rA2kTtbEhmssRHN3twCjvJVZAch3+7W9/G3/yJ39iY9a3N10rAEjJm5///OeigvGBBx4Q15TFBHy/4Ab4SSedJDapWUnI9wsrQVfkf/iHfxCHXnvttXjLW95i5TTnGCcDDcmAAwA2JO1Op04GdkcGuEgotuNcaQYKVaxxcUq6L3djaw1qCHIHjxo5pAW0QlAHiDuZjFe84hVlAdBC4CkFi2l6YhcYl5tHvnzcfPPNlsdbyzVIZ5K46/D3MBV5rJZmjjtXSblhaC5k9MKOkVY6Crom8PDssUoel+pCf6AP81G5L9CdRp8AjVgRKTNC7k7oWgabSflVb33+CaxK1nAztAnsWXoePb4BARBEU9VXC5XKIyv1IqlZpCTpOub35VI96PL2iWovO6PLGDuis1ZZNWAs2VMRkN1l9COrxLBpU/Vkbo60lAE33Mh4KgOIqs0zaaqkt68n9iGr1K6X59UGcChSHpht9/TArWlYkyiBYDUHIXcPPJoLa0l7tDHLjYNO6x2eEaSyq4hnrH03BfQxzFuUTDi370/R7f29csNoud9zXZVPHeYGYaEg+GfqCJrVgtx0rERqpeUSlDdgVrxRdoYyKa1iFmdHzrm5T3M9fVmk5wAAIABJREFUml786Ec/wpVXXmlHN06bTgacDNiUAQcAtCmxTrNOBpwM0BVSDgCYb1DBijUuPujwK2tHmu5uTzzxhDANOe+881ri8hHQu+mmm8RYScfg4rxQ8DrQMZg0Z4KGDO7mk9rDisd6hdXxyhoPK+Dun7kWe1e3dBJlBClgbZ52rFf5kh1PDuDg+vEv8zRLGG2bwOzm8RS/Wsfr10Pwu91YT8g1B3GrBjp9XViOydXV43zbPNTMiUqjbsaS/Zjf3NL4MnQfOo0OLNowbrbv04MIuv1YTczUeumKnt/jHREmEMnMluapHWFoQQRcIURS1sAcl9qBg2tb1cSVBHUr+/2jWLLZ8MQckycRhuKK1lzFa3WO1FgMuNtq1gfUlTHMblqvmOw2hpBFFBupRatDlXYc5RJ0VcF60pqunrSOjzakCCBQbH5kSt+TLqXfEljq1cK4bPhjUBT7quPl56H6FlkR+PDDD4tKQW6y8t+mTEh+q263e5vrsB3SHtXPRu6Zjz32mKgUI1vihBNOkNt4C7VGAJCVcdzgv/7664UGtRNOBpwMtE4GHACwda6VM1InAy2XgVoBQBqImAYV5uTtqlg7fPgwuLhjJRzpqa0QzO+vf/1rMdRzzjlHONPlB3f3SVsxtRLtAE+t5oqLxhtuuKHkeK22ZfW4bDaDh+f+E88sb1VKyghSVru9g1ipkHLn0drwxPxq0SGMtU1iVrJ+HysMe319WIzJrTDkJPzJDqS9lQM/5a6BW/Mi7Oms2chBV9qwd2V7vkeCE5iP7rWFEkwwdzAwgcWYPe0zd4bmR9jTjhWbjS62qgGnkUZp2ruOCRyKVA9es6rU53LZCpya95ySUeHPdiDpWpTq7l3qnm5z90NVUgK4rTQUuLEcyyKdTVZ0Ku/DXt8YYulFxG2kjhcbVLtnQGgiRpKzFY1b5sHtnmEAcUTT2yUW3EoYq4ktU5VycWLbJXhRx6vKHbajfk8AkCZj1FXmTz51mOBgMeowQcNcPUFuqsraqG10kvPz0ujxNKp/mu6ZBhfUoW4V1kyj8uX062Sg2TLgAIDNdkWc8TgZ2GEZKLZzXGqaBLbooEVKrukizOo20n07OjpsyRBpHQ899JAwpqD2R6sEATUCa9QtzK3m4+Kcbmb8MbUSuWCjVqLXS120xgQBS46Hbml2Xcv8mbG/JxdvwOML8kSZ+YJNyupSzDrw4dcn8ehcaYOOoeAYlm3Q7xsNTWJOMrjIPPsznci6V5FBSvoNNRCYxGKsekMTlzKJ51cKn086bSpDXT25FGkzCaRgZ7KbiKbto532+caxnqQTbGXgUCUXyqMFhOtrJFmk2jOrYCXmk5JHaleuJadto1HnzpsmFh7VhUi6XgAV9QEJyM1WpA/o1cZwqAYDG1ZZ9vpGsZ6YQipbm35pJffNsc8BqxHj2LDJHMXKmMLuIQHAbqaPVeYGdG4CWPvuvmTobxBw1S4zYmWszXLMgw8+iNXVVcG0GB0d3TYsPlOp6ZurJ0jqcL7ZAk8k+EcQMBcU5BqkFanD1IvjnFn9xyrA3RpcL5sVkI8//rjjFL1bbwRn3i2bAQcAbNlL5wzcyUBrZIAUgUKLwmKjX1lZERVrXHwy6GxLgwoKUdu5i0xDjPvuu08sSi+//PKWWZz+5je/ESDpGWeccVSsmIsz5pC7tAyZWom13nWkLJMKfOaZZ0rRbqxkPHuWb8ODs9dVckrZY/v9J2ApZs0ddzXWifnN8rS8Hl8/4pkVJNJyaZ5DwXEsReU7hoY9PYASQ0yyozGT3yUor5Vr69E4YS3WjrVEcT0wQ/Ohw9uOpZg1qmvZmyHvALfQ7evFUoWVopX049fD8LkMrCftox1zPJ3GKOICvNqSEDDDUIewb00eFdyr+9FhdNqas9zx9xgjiGWW61YlpysedBgDWEvsByzoA2Yy/ViO106ndatedAsNyQPIQq45kJX7tdMYQQYb2GwALdkcX8jdL+jJkcRhKAhZkhlo94ziFQN/YWWKO+oYroUI6J144okYGhqyNDduOvKcXFDQXIPkN8CN1lxAkH9vBerwvffeKzQTqf9HHcDdGjTiO/3008X0Dxw4INbnTjgZcDLQOhlwAMDWuVbOSJ0MtGQGrAKArBQk3ffQoWMvk1x4cgFKnRm7g4AjHcEYl112mXDGbYWgCQh1/U455RRBASbwNz+/RTUjYModfFJ47ARPK8kTTUB4rU877TThpFfv2Ld6P+6d/r5UV9Be3wRW4nRAzRadjk/vxmNz1imA1Bl0a0BEstlGt3cAifQyEpI15AimtRlhrMbLmxVUes39ejsMl45I0nr+PGofnl22BooNByewYBMlmHPt949hLTFlW6WeWY26Eie4ax+441b9CLk7EEkdo5NnMyOY3ZTvPtvjHUIyu2qr87F5H+qKC93eYSzXSYuQ/Xr1MAJ6EGvJg1B4AQuESw1jZsMaTdXqZ8qnhxBQg1jP2AN6lxsHAf1UNoJoSr50QLm+zd+3uyeF868V5+JTO9+EsdA5VpveMcfdc889osKPjIGBgYGq58X1HzWcub4y/+QGYKFgVWA+dbjZ1mGku3K9Rf3kRqxfqr4Qkk985JFHhPEcg5v2NM9zwsmAk4HWyYADALbOtXJG6mSgJTNQDgAkfZUGHAT/zIUhFxNcYNVzUcFd3dtvv13k+OKLL64L6CjjgnLMHDsFmanzx3wy+G8u3ln910xx6623ispEApa1vFjUMqepyO9w59R3kcnKo63yxXYjebhom15tEo/PV0Zn9eo+tHtDWI5ZA7Ks5iTkbodHUxBJyn0JV6BgMDiOBYvUOqvj5XGaoqPbNyRAGiuhYgL7V61R/Nhet7cfSUEJtsclOOgKw9DdWE9u1yOzMh8rx4TcXXCpWdvNH1jNFU8vIJPNYiaSQMYm0FGnQ7ZvGItx+/QUc/MacHXAqxtYS8gHsYtdv1L6gIY6gakN6/ewlXvEPMaV8gkKdNpdXJO0kvYqPbbHOyaqnGNpuQCnlXEEtDHMx/Yh6OqE3xUAgXMUAGFVRccrhz8Fl9Y4yQwr87HjGLuALrJB+PzPpw6b65bcuZCNkU8d5nqmkdRhrrfIuDj55JPrzmCw4zpX2+Ztt92G1772tWKjnNezFao3q52rc56TgZ2YAQcA3IlX1ZmTk4EmygAXS4UWdxwiabdPPvmkoI0wWOnHij9SK+q9yOOuLqvpGNzZbDbgrNgl5UKMO/VmcBedWokEAJsx7rjjDnG9TzrppIbQRngvkrLy5IF7sNZ1H6DKAwHpgJnKrBZ0aJ3fCGE1XrkeHDW8hkJDmNvcL/VyujUD3d4uLNngiDsQmMBKbL8twBB1F8tWumUVzG0YiFdIoRaUYCOMJRuqGHnxBEAaGMdCBbqRlV50VVHR5xvDUpmK1ErbzT/erfoQ0Efx/Nr/1NpU2fPDnm5RDbteQQVo2UZLHNDjHT1inmEPGLy9a+oDss+5o/qApLBHU0FEU+u1TKXsuV0GDUrStgLTxQbBz0O3AAKXEEvXB4hUoSOWUkUFoBmujB962o2ssXKcMcyg/zS8pOcdZXO4Ew/gc5qbty9+8YttX0vwmZxPHc5d0+Tml5Iw1INmpSA3iPlnPRgi5hi4RuR4yWCol4ZxM95fv/rVr/DWt75VsE648dxslZrNmDNnTE4GmikDDgDYTFfDGYuTgR2YgUIAIME2GnxMTx/TNqLQNEWFG7WTyOpD6tMxXv7yl4tFZjMHd12feuopzM5uidgTMJ2YmBA/zbwYY2UBd/9ZnTg2NlbXFJOqQsCZ/TNUfxTrPfcimT0GoNY6oICrE5qSPE5fKuAawCOztVUVjYcnMbNRWQVhubnwBXw4NI75TflVRh1GH9LZCOI2VNQRaM1gE/EiBhuGOoI9y9XTUof8Y1gUdNrilO5yuS31+y7vAFKZNduqDdl3u6cPWURtBVYSqR5Q5zCWJoBjj5mKmUcWaBFYJm3TTtMTs79G0II1uNBhDCGS3A+P3o+pSP1our3eESSza4im6l+Rx+8hVgRG0wuIZ+wFPEOuUcwW2UxxwYsuf5+4x1gdfk7ve9Dre2EtH/WWPZeV+tT0O/XUU48zF6vXhLhuJGXYrBQkhdg0hMsfg2EYx1GHuXazYw3E6kVKmDCoYVxPhkq98m61nx/+8Id473vfKzZx9+3b1zQSM1bH7xznZGC3Z8ABAHf7HeDM38mAzRnIBQC5c8rFAgWEubgUL6rt7YLu22jAjYs7OtQyzj77bDGuZoxCOeQ4uRBjVV2zB0W0l5eXRaUnwcp6BO9BUsxZ+WcGAWeay2xmlnDLga9hM1XcLKLSMRpaUBgzbBypWHIpE/ifxdpBttEQnSvl0yGpgbdoQ7tePQhDcSOaLW98UmmOPdQc9LRjNbEdJMmkRzEVqa1istPTizQ2bAPpPJoXnUYXluLH9PQqzUG54wli9fgGsWSRNl2uvdzfu9V2HFjb+sx4NAM9vj7MR63RsyvpJ/9YnyuIsKcNy3F5xiOlxkNasE83sFpHWrCW9kJJd2JVqc8czfkTiOv3jWEjNYtERt6miNXrrUJFt28Um6l5JDL2VF8a6iCW46WBVX63sAr0ZX1/BEVRrQ5/Rx1HoItrIho9NMNaiGPhxrGpI0hgkH8vRh32+/3HgYL8d62sEm4Sk3HBOOusswQ9ebfGt771LXz4wx8Wa3e6ADeLxvRuvR7OvJ0MVJoBBwCsNGPO8U4GnAxUlAEumgj20ZiCBhUmtcPj8Qgntf7+/poXZhUNqMTBN954oxhrIxxqrcxhcXFRVLBR84/BnW/+sLKNgBapv80eDzzwgKCM0JyEAJydwZcGVpmyUpJ0JgYpQwRKc3fvN5LLuPXg17GW2KqmlBEu1UDY04H1xCwOrbkQTR3vnlptHwOBYeH4msokq22i4Hm+dBvg2kBWIiWaHSlQ4U2FkCG9TnII84vAJJZixyojVRg4uJZEJlu7GQZBug6jHcs2UYKZjgH/+JGqNnlU9Pw0h/U+xNNLyGrHaI+1XgqPOoH9a8eD2r2+ISQyq7bTVjn2Pv+IqDy0o8K0UG7q6RacTatYjgOetA+alkXaZW91Zf58CRz3+oaxmjh0HFW21nvG6vmqoqHbO4qN1AySEoFIl+LHWoLAZvnK3heEL8Bp3a+zOuQddVyrVLoR/ONaKFdP0Fwb5V8QVgSa1GGTPsw1aCVB87I777xTnHLOOee0jExMJXO0euwXv/hFfPrTnxab5TTPqxVctdqvc5yTAScDcjLgAIBy8ui04mTAyUCRDHBx9sQTT2Bubkv8ngsFUj8JAFHPpZmi0Q61xXKRT5nOzSFB1ampKdAxmXo9zR4PP/ywoC3zHiAN2K7giwDBUoKmDN5rrDpkpWShxWostY7bDl2DpVj11NH8uVCPrcf7Qtw//aTUaXZ6e5DFJqIpuVUy7owPfkNHLFO5VmG5CfrTnch4lqW6L5t90oAlmp5FKhMHgalnl2uvtsydz5B//AgleMtgR3bQWdejqVhPLshu+mh7SkaHLxtA2lM7EEt9ukiiDRvJ7bptLtWNfv8g5qJ0xbY3XKoHvf4BLMZYeVge1Kl1NDSi6fGO2O4WrKf7MZs4Jo/hT4aRdW8iIxHAtZILVsJ1Gb1Yjh+w1V262FiE8Y93BJHUdEFdVStzyD0m5JrArEW5g8tHPoR2T/Xut5WOrZmO5yYoKcCMl770pQ1nZ1SSG24451KHuf4kcFcoqB3IjUACg+afpdak3LymOzLjvPPOq6v2YCU5qMexf/d3f4cvfOELuPTSS3HDDTc4AGA9ku704WRAYgYcAFBiMp2mnAw4GdieAbqm0eyD0dXVJarUSMdoxjANNQikEVBrdJiGFaSvmpRpCk+TdmHSTwhykdra19cnhKmbPR599FFRlTcyMiLmITuYp7179+L5558/Sg9ilSmrTVktWSqS6Rhun/oW5jb3SBuWhjEk0hoOR+Tq9wVcIfjdbqwl5IJGgprqbcdyrDbNwkIJpPZdIr1iC73Qr7fD49KxuKFjPnoMPJF1Ibu81DTcsK26jWDxgH/MVoMQ5oLURoKlyUz1FamG1od9q6WdqXuoc5jdwGbKfnOHDqMXmppCJCmfal7o/gno7fC5fAXp5zLuNxWDmI8eT1NVoSGETsS1BWQVe4DoYmP36yGEPe1YoltuA0JTXOj2DmM9NY1UJlr1CHR0WQLZ29x9uGLkw7sW1GC1PE1AGDuh0o0AYK6WIAFCcz2VfzMVog6b9Faed//994tTaBTXbBvYVX8wqjjxqquuwjXXXIM3vOEN+PGPf1xFC84pTgacDDQyAw4A2MjsO307GdgFGSDd86GHHhLAX3d3d1MvqhtpUJF/K1Anj5WTpkMy6SqsmCPQl1vBRjMVAl7MLanLzR6/+93vcOjQIeH0fPLJJ0sdLu81AqImzZxOzgQZCTxbjXQmibsO/yumIo9bPaXocXxxfW4JSGSSmAxPSgcB3ZoHbVoQkaxcEFCFgiGbzEH8eht8bjfWE/M15ze/AbfSgWQmhNlNe7ToDM2LLm8XFmL26fZ1eweRyKwgbqOphqEF0OZpqxrA0jGOQ5HyFX666sKAf6gu1YBbDstjWBEmIfbRqXPvOVanEdCOFTGjqeYGdykBzGyS8lu4otGnB9HmCmM5ad89WGzcbe5u+HQPVhL11SY0x6MrbnR5h7CePIxUtjIA26t1YilmDSA+pfM1eFHHRdVcvh1xDhkHXAsxzj333LIbZ602aVKcC1GH+f/5QfDPpA4T8ONai3HRRRc19VrW7mvy/ve/Hz/4wQ/wzne+E9/73vfs7s5p38mAkwHJGXAAQMkJdZpzMuBk4PgMcKeVO8qtIBJ83333iWpFatORotyI4G41Qb3Dh7cqsAj2sVqOYyq04/zss8+CP6wMpDB1s4cdFYvMGXX+TFfpWh2RqR93/8y12Lt6b03pDLlH8cD0saqZsdA45jbpLiuvgofARygTRsIjz8TEnPRIcBIL0eelu+GS2tfnHzpC3awpxced7FYnsWf5OYwGWUlHJ8/adQALjW4kSDOWfVKvY24/pF52GB22G130+cawnpyqyFVXyapYjLqRqKCCkNWTWcRBrU27w+8Koc0dxHIBcxg7+jZpwSvx/cig9vvNr0/gwHp5CnuHpwcuTcVqonQlph1z7jT6oSlprCe3ZD3qHbrqQZcxKO7dVLYwvTN/TNbpvwpeN/Zx+Fzhek+rafrbjVRXrlPzqcMEQotFZ2fncSYjLperaa5fPQby9re/Hf/1X/+FP//zP8dXvvKVenTp9OFkwMmAxAw4AKDEZDpNORlwMrA9A6Sx0oW1FeLBBx8UZiXj4+OCMlrP4O7zwYMH8cwzz4A6NgwrDsnckSZgSA2bl73sZfUcclV9mRWLPT09OOOMM6pqwzypUM64MGfVX60082w2g58/+BVEA9VTdxWM4unF42lzg4FhrCXkm3h0qj3Y1OS/kPf6hxFNziGZsfaiXckFHQyegMUo81u7fhuLN9biHViJH5Eb8PYAiGEjKV/PkHPsNHqRRcxWiisdWVeTh5CxsaLNr4fhdxlYS1oDkrzaCPauVq6TqSkaBgOjtrhYF7rn+nyjiKUXEM/Ux0CDeQy4AjVXx6XSXVitgNbf7xtBTBiv2E+1zs9zr28Eqcy6VAf1Sr4/qAHZaQxiTYDYxb+f+N2QzQYsGcb0eCdx0dCfVTKMHXcsWQfcDGXsZqorN65zDUZotlbIdZh5ItuA5iLmDyVaWmHTu5qbl+uu17/+9bjlllvwsY99DJ/97GeracY5x8mAk4EGZsABABuYfKdrJwO7IQOtBADarU9X7HpzYcnKOC42GRSnJgA5MDBQlmZC/T+eywUnhambPcyKRQJ1FBivNkxzmdXVrRdf5owUaZmu0jffcjNWPU8g1v5MxcMkXW3PUgbJzHY6Yo+vF8nMOmIpOlLKi+HQGJZsqHxr83TCpaaxkazdQCJ/tt3eIQHU1KJJJ66/2ok9y8dT/EjZ7fZ1YiFqD2WRFGxey4Vo5YCY1asedHXArSnYSFmjL1ptN/+4ft8EVhMWqtiyw5jZqJ5+2unpgaqyesze+WzdEx70+PulV5qWynGXMSRAsWi6ckDOo3XhcKRyOr+pH7mSmBJGOPUMViCzkjSanrcEsNkxNjqudxoDWEsWdi0O6AOYj1rTNH1pz5sx0db8lfR25NFsk89UboYydjvVNTfPZBjQdI3Vfr29vWCeCJYWog6ThZDrOkxg0Ov1ll3P2XldZbXN+V588cXiHvnc5z6Hv/3bv5XVtNOOkwEnA3XKgAMA1inRTjdOBnZrBloJADT16Qi8nXLKKbZfMu4ws+KPmnhmmHRfq5QSUoUfe+wxsbi84IILbB9zrR2YFYvhcFgIjFcarI6kKcr+/ccq6yrNmdU+aWBDraDOFyTxXPymiirVQu4xPDBdXIuuzdMOj5bFekIuqEbAI06jjXT1YvmF8iPMQYx2LMetvUhbzTGPC7jCMHQdkRpccF3KJJ5dKVytOdE2iemN6is5y81lODiBxdh+2yjHBHj6vSNYTNijbWjOj2CjR9cQSRbWZ9TgxXQkjkyNFHbOZygwZiuNOvea1dskRFU09HpHsZI4UFH1plezpq1Y7H406Nrr7RWAZ1ZCVW25+z7396zwZNUl9flqBfMr6Tf3WLfqRYfRvw0IDOhb91q5UKHj9yc+DZdW2iyqXDut/nvqDz/88MMCrCIA6MRWBqampgTbguyCs88+W/wf17f51OFotPCzlxIuuVWC/Ds3LlstCABy85Zr16997Wv4wAc+0GpTcMbrZGDXZ8ABAHf9LeAkwMmAvRngYoFAVysEdeT27dsndndPP/1024bMnBD04wLKpEeTwkvqKv+sJGZnZ8VinQtJ7so2exC44y46F78UGLcazBnnynOp+cfgDvtJJ50Egol2xJ133ikW97wu2fA87jn8fy3rvmUzw9izXLpSyqf7ETb8WI7Jpe4aWT90LY20VplQfrkcbpmDjGF+s7wBRLm28n9Pw4ge3wCWYpU7jZLitxRtQyRZvOqKZhSbqUXEJQOj5jyoyaaoCVuqJM0+uowBJDOrttJaWdHV7x/HUpzX+HhqNgGqvavyrn3Y0wWP0LGTe/8Xuvc4r0CmAynXMqDK0+AsdZ97tSDa3GEsJ8pXTPIejqYCiKYilX50th3f5u6Az+XFcvx4J+GaG7bQgFs10O0dqBj8tNC05UOOAYEHRXVWLKVY0rnsdb8IF47+ieV+duqBi4uLIBtC07SW2FSs13Ug24IMBq5dXvKSlxTtlmu6XOow/15MBscwDNEe1338k0wO5r2Zg58pmvpx8/nf/u3f8Id/+IfNPFxnbE4GnAwUyIADADq3hZMBJwO2ZqCVAEBZ9NRSCSVthJRdk7rKSr8TTzwRQ0NDVdFD6Hz7wAMPiEXjZZddZuu1lNE4gU9WWnIX/fzzz7fUJEXJmTPOlcG50hSFlX926uzcfffd4jqRWjw2NobpyJO4Y+q7SGdLA9q6auCphQQy2fJAg4tOqYFezG3KpalqGR3tRhAbWfnGCzQHmY/SqKB27b78G2AoeAIWKtQFNNQ+PL1cXsMu6Aoh4DawErcHcHKpbvT5+zEfrRzEtPRBACAMQjwdWLbZiTVMqq6SPE7fLZliddms1aFaOk4BMBQcx6Kgrtvv3qtnPOjwhrGekTuPUpOlTl0mu1lSK8+nD+Dgutzq2h7vEDLYRCS5pYtZzzBUHzq8PViJH7C8aSJ7fG7Vh07PGGaieywBgKeH3owTe3c3/ZfXgDrIjz/+uKC6Wn1Gy752zdge2Qv8oTZzJRvEAoSOxY4DBbmxWEhPkFWXBAFzKwWpL8j/b5bguIeHh8Xm6H/+538KPUAnnAw4GWitDDgAYGtdL2e0TgZaLgOtBADaaajBHWBSV7mLbAZBP4J/tdBASNe5994tt9orrriiqRaKhW5Wk7LMne8LL7yw5P3MhSavyXPPPXd0sczqTO4+83y7g3llfnNdoRc2n8eth65BMlOcYtvmnsD90+WdPM3xq4qCsdAYpjfkVVexbdLyhkJDwnlYdvT5h7FpkzlIj28Ym6k5y3pmGibw/Kq1fGuKipHQKGYk5zo3v6QEL8UOIG0joDUYmBDgip198P6h0QOrAV1KGAfX5dLVc3MWcrfD7/JgOV4eyJVxL/cJ84yluunWmVp5qwkaVmzfQPAoozhsw+eU/Q4ExrCemEGixHeWjJwWasOd8SOg+xFV7QHdy43b0AaF+zSdqFfiB4sCgUrajcv6PoL2cEe5Jnf872dmZsSGG5+xlVTp7/TEcIOY67eurq6aJWK4tqG8CDcYzWpBbnQWCm545lOHPR5Pw9JNGRZqOHNt/9vf/tahiTfsSjgdOxmoPgMOAFh97pwznQw4GbCYAZOyafHwhh1GF94nnnhCqqEGF0kEvagdY1KhuZgjrVQGdZWLx7vuukvk7NJLLwV1Zpo5rFKWl5aWxLXgIplBjUPmrLu7u27Tu//++0E61OTkpAABzViJHcYth76OWKqww2w6PYTnViqv6JsMn4DDkWelz288PIkZGzTw7DQHCbraj5hflKleyiqY2/QiWqGhykiQhik0DbCn6oz0VpeawbqN1Vchdyc8uor1RGHNPlk3UqenHxkEsHd1j6wmi7YzHBgX1NVUmSpbGQOhi2yvbwCLcerDya9mLTRGQwsg7OnEcvwYKE/tueWYauucOdc+3yAW4/ZpVZa6Jq6UH25FQ8Zjjyt3ob5dqg+r8ehRPcQtjcTCQKCxPoqLTniXAFp2e3C9QjkUVp5Vo9O7U/PHNRx1ALkJSekR2UFgLZ86XEw+hwBgPihYL+owQUtWADJoBHLGGWfIToXTnpMBJwM2Z8ABAG1OsNO8kwEnAxDAVyGntGbLTSXVaVbGTooEd9JZRcYgOMeKPy6eZFE6CJDRrIJBwe4zuuGSAAAgAElEQVRG7gxbyUk5yjLvFb588FowmKfx8XEBwtVrgWvO46GHHsLc3Jzon67MuRFJLOKWg1/bZlzBl84n54+9dFrJSe4xE20TRyoB5QISo6EJW0wX+FLdYYRtMQfRVTe6jF6slNBQM7QhPL1UOdjKnHcY3dCUFCJJ+TRp8Xknvds/iDkLBgSV3ifm8TTUGBSGGtYqIKvph/p0yVQ3gh4/ZjfLmylU00fuOX49iDYjhKVYfTTsOowe6GoW60XMT2qdT6Hz2z19UJFCJLUAvz6KA+vyq3QL9RvQQ/DAwAYaU5HnSYagaxlk3LVrHZa7Lm3ucRwuUOlbCAgMz56Ll592udj82+1h6hMzF2ed5VCizfuBazlWR/b39wsWgt3BNTM3z/NBwULUYY6F14u6yKaeIGVWZK0zc+fKjXITAKWOde7mqN05cdp3MuBkQE4GHABQTh6dVpwMOBkokYFWAQAJ9hD0ofbNJZdcUvU1NZ1qSRcxgU86CxNEkg3QUVvmlltuEWN9xSteIXbtmzlY2XffffeJIeZSlgsZo1BrhwvNRr2UPfLII2LBPzo6WnDBH02t4daD38BKjth+yD2BByqg/xa6VsOhUSxHSRWUW53WJ4ww5pHMbJmoyIotc5BxzG/aA0KR7roYK9J2dgz716oHpdyaB/2+XsxFj1HzZeXFbIeOt7xHUtmk7KaPttftHRQGIbG0fGDFULuwd21Lf7PfN4hYegWx9FZlrp1BYHMtMVMXV1kqbA0EJrCaYFVofUyr2GevdxzriTRmbbz/Cl0jIx2Ax6UhrhY3zrHz2vrTHYAaRcYl1608d8wuhZsHxbUeCQR2enqxGllA++x5OOfsc4Q27W4Pq2YXuy1P1EWkPiI3cBsFehH8I1WYoCAr8bjJHIkU/s7nhikBwdxKQa4/awUFacRmuiCT0dHT07PbbgVnvk4GWj4DDgDY8pfQmYCTgebPQKsAgCY4xQXS5ZdfXvFCiSAWASNWsJm0Z4JXBLEIZtkRBBtvuukm0fTLX/5yseBr5uCileYaDOaYJh5cxJLuu7KypTFGAJbGGwRNa12s1pKLxx57TFQiUqvxxS9+ccGmEulN3Hbon7FwpAIrmR7E3pXaK5doJhFLLSOeluvk22F0QVES2CxCX64lXzQHYR6yNtApu73DiFIXMHsMvFSgY2pdRTJTO2AzHprA7OZeW8bOnNKZlXTdtcQWkGZHuBQDRtpA0i1Xq8+tjGH/+jGQ1aMZ6PV2Yz5W3t221nl6dT86jQ4s1KEvjtWnB9FuhLEUt39u7E9XfFiJKejxdWNOmMfIrfwtl/9+/yhi6WVwM6PeQQC0yz2EeGYJSRTWP6t2TB61DUtxa+BmaP33EI78Hl72spcJqYndHtWaXez0vHFDkGtEbgiSkdAswTUg11C5lYLFZHeoN51PHa5UNoYbuJSbYUSj0broMTdLrp1xOBnYKRlwAMCdciWdeTgZaOIM0ACjGG2hmYadC07RUbcSyil3YUkR4QKRUS+nWoKOv/71r0Wf1OuRoSto5zVhnu644w7RxQUXXID9+/eLH7NSUoYxiqzx062YdCgCkaecckrRZlOZBO6a+hcsxvbj8Tl5FVgdRqdwYt1Iyn0597kCaPP4bHHD7fUPIZqUX2XI5AddHXBrwEZq6zNmaKN4ekkedZJ6cKyii6bsqW6jqcZgcARzm3LNXvJvzEC6Q2itZVB7BSnpv2vxADZT2+/rAf8wNpILdTGX6PePYDO1iHgdKg+Zz17fMBKiolLuZ2/btdLGsXdt637oNLrh1lWsxOvnUCyeVYoG5nclcdiy8Y6s71i2Qxp7n28UkSSrPeVUBNKI6fCGtYrk/rlXwJUOiA002RX6MvNUr7ZousVnMo0eTj311Hp12/T9UO+Oa8SJiQmMjY019XgLUYfT6XTBMZM1YoKCpA+zCpYbs8WCG85veMMbBFhO0LFSALHaxJGhQ/CRP9RnNjWa2d673vUu/Ou//mvZpnnMH/3RH5U9jgf8y7/8C9797neXPZbvN9///vfxH//xH8I9m1WR3IgnVZyVkmS6XHnllWXbcQ5wMlDPDDgAYD2z7fTlZGCXZqBVAMBcPb2LL77Ykjsvd1+5YN63b99REIsPftJ96+FUy1vqhhtuEADrS1/6UrFob+YgfeW2224TQ+TLVr0qJavJCakufBHq6+vDaaedVrKJTDaNew7/F27a99tquip6jqEYcENFUpNbIeNSXegP9GPeBuphm7sTLi0j3Ddlh4u6gN5eLNPRMz2CQxG51F2f7keHEcJibFr20I+2N+AfwXpyVjoVO3fANFHxulxYS9Sm9ebTB/DcypYeZ6Hw6l50iQq92qteyyWclYc9vj6hZVmP0BUdfYFhLMX2I4uMLV1mM31YiB7vfEzK+EZqvi4069xJkRLb7e3Fgo3zLZVEan72eodqpmETtEY2jM10+UrYdnc/gvtPF8M677zzLD3zbbkRmqjRPXv2gDpvNNw6+eSTm2hkjR0KASdW2pH+a5pgNHZE1nvnBqtJHTYrBbkZW0ibm+BfLnWYf+da1gQFf/rTnwrAjdRfMiQq2Si3PuLtR5ZigzQKACRL5O1vfzu4WVwsCKqa7JZa5u+c62RAZgYcAFBmNp22nAw4GSiYAYJkxXYfmylllejpceHEnT7SfXkegzundKqtNwj3m9/8BgRZ6cbW7HosuVWWzBkXjyeccIKg1ZTadW7EfULXP9KhmFMrTnfZbAa/3vtT3Dd9q9ThalkN7a4gNlD+hbbSju1yCPZoXnR627EcKw4eVTrW3OMH/JN4bH6/dJ1E9qFAwXjbOKYtVhBVM4+gqw1+t2FrtRfnMRSk+QsroaqjlnrUcew7UqFWap7DgVGsJqal0LHL5bPXN4hEZr1utNU2dxcMl4a1Enpy5cZc6PduNYSp9cIVhm7Vjf7AkC3GPeXGSvA46A7UjQadPx5+d3Qb/cIpOYPCVUsl55DwY0OxVsV7cttlWH3KJZqjhm69qpnKXYNG/t5ut9tGzq2Wvu+55x4BopnyJLW01Qznck1OENDUE+Sf5lo2d3zPPvssrr76arG25UYoacTf/va3xcYoTUDqtW7LBQAJwNKIhZvfjGoAQDJnyO4oFmSjlGLUEPyj8R5ZP8wJKwtf9apXCckYAn7cPObanCZ91NV0wslAM2XAAQCb6Wo4Y3EysEMz0CoAoFU9PVYKsjqMjrYMgljUhCEtpF6LodxbhSYgXLiRrsPqw2YMVihyQcTqApMO3tHRISoMmlV3iWNldWdXVxde8pKXWEorgeHbD92AWw780tLxVg/SFBUjoRHMbMivfhptmxQGHrK1+4Q5SJAOtfIpr15tDJFEWlS4JSSbmpjXZCgwIkAf2aYpZvvMz3BoHLM2maeY/XR5B5DORioHzLIKlmIG4mlrtMyAK4SwJ4jFOlQDbjksD2HOhnur2GdyIDCOSJIgpxxdzoA2gb1rpWmqbe52BD2+uuQ0f97dXj5LUnV1R84dg1cPosPTiaV4ZdqISqwbEW3e0lfrZV1/jqcf37oGlKSoVzWTpcE16CDT7ZbgCMEuJ7YycOeddwrGAjWde3t7d2RaqNed7zr885//HP/8z/+8bb5kcLzpTW8SNFe6RRMctJNC/6lPfUqwXPjD/JN1Mz4+LsZVDQDIzd1qqdzmepsAKNfcBCKL6kQnEk5l8Y78tLT2pBwAsLWvnzN6JwMtkYFWAQBz9fS4qMk37uCO6fPPPy9+TOoEFyJcJDcSxOIOI0FJLkybkZqyvLwsTD7y3eqaXbOQ4B9BQN4Hpuud1Q/cA9O341fPX1d15VWxfibDkzgcec7qMCwfNxAgLXUaqYx8p9pAsh1pY5mlddIimR7GofWD6DA64NGA1cSWLqDsaHOH4XO5sBK3BihU0/+WO/MiEhaBtmr6cKsedPv6hE6l1fDrw3h2pXIzjLEQXZsP2nIv5Y+9y+gDlDgiNtDNC+WJNNkubw8W47UD8clUF1bi1kxhqLeYyKxhI2XN2MLqNbZy3IB/TNyfsfS6lcOlH7NVkRjEcrx8FQ3pv9lsEFELY/XEO9C7dM7R8bISnXQ9Uh4bsZEnPXFVNkg6I/XWTD3eKpvZcadRuoRrWeoBc1NwNwTXuVzvspLtgQcewKOPPirWRMxDftC8jSCgCQhSU5N6iXZFIwHAz3zmM/jkJz8ppnbzzTfjwgsvtGuaTrtOBmzJgAMA2pJWp1EnA04GcjNA4KzQgqEZs3TjjTcKuvKZZ54pNHDM4IKYVX90PWNQOJkUhNxjGjWfu+66S+zaEoisdkfTjrFzN5k7pDTSYJDCwfFx55XBXWNWATZrcIFJijdfCukQWWk8sfAQfvrM/wX1AWXGRHgS0zaAgJ3ebmQRQzQl/0XfmwpCdUeRUWs3ptAUL55fiSOT3dJl82huDAb6MLNZHiCo5jrQJGGr+lJ+JaM5HrrPtnkCWI7bpz3Ivvr9Y0eA3mNuysVyoitjOJjj/ltJ7gicBt1eW7UUzfHQRIK6edQGtEurL3/uXUY/oMSwkapO59LQOnBwrTLQmvfhUHBUgKvprHygvtT11RR9yygkfgipbO2u25XcS+axYXc3vLoHK4mt50mhCOj9mI1a+wx1rJ2MwMbwtmb4nMrVQOP3PzXQGulIX02+qj2H1EayG0ZGRoQ8hxNbGSDQQ0Ds9NNP37Y5vJty9NGPfhTf+c53BLjHdfK9994r1nn5QQMNGmnYFY0CAPl+QMmaqakpAfzxvnDCyUCrZcABAFvtijnjdTLQghloJQCQD3PSPLiTSY0Tar4QBCIAyGBlgEn3bRa6EBdgrLKjODXH1ujgIpni0MwbtQkZ1FJhhSJfrOgiR0A4H2Rt9Ljz+6duC+lQHDN3s6uJ55b/Bz966jvStdFGQ2NYiB6UDi76XUEEPQZWbah4c6UNeDQFad0apbRYvr3aOJ5c3A7GncDqyA351ZHmOEaD40fAl9pBzEJzY4HkcGgSc4ISXJ1mn5V71K+HEPT4sVICbFSgY25TqbmKj9WAC9EDtmg15s+13dMFl6ZgLWFftWZunwQeWR23nDiATLaye8KvTWBfGfpvsWvJz2intwPzUevVnFbuCyvHbFVA9mIxRrDVvnu01Fg6PH1wacBa4njzFJ4TdI1jxoLLtgIVV05+Ektzq0fBC27q8XlfKFjdRCDQdEvlnztVL/Dhhx8W6wlu1tlZwWXlfmuWYyhbQqkVBuVAeP13a/zlX/6lcNx961vfin//938XaeD9QpMU06WXa9JPf/rT+LM/+zPb0tQoAJCMG+qFMr72ta/hAx/4gPg7vzu47qUWOJlBu7mK2LaL7jQsLQMOACgtlU5DTgacDBTLQCsBgKR58EFOwWOCV6SBmpp1rPZj1R9fFJopHnzwQczPz4vF+oknntjQoZHmS7ovF4QMvjhxTKQTmRUU+SBrQwdconNWLpIOxQXd+eefX/UwD63vw78/+U1EU3KdfPv9A9hMLkrXwNtyCKbbauX0z3JJ0uCCP+tD0lU9lTGRGsJUpHAV0HBwGOs26gJ2GN3QlCQiSfmGLGbuerwDgu4ZS0fKpbPq3xNsHAxOYjG6t2DVXEAfxZ4VOQBT2NMBn+7Ccny26vFaPZHGJ4OBMWFgUSkoZ7WP/OMCrvARQNWaEzJpqrFkGyLJ6j8DHEOPrx+KksRawhqNuNr5FTqPTt9+lxdL8eLVeDL7K9RWtzF0hP59BPDNqkhk3JY0Gof8L8SlI+8RlW6seCOYx5d6PvNzNdBoWlWMvcDnggkIEhzkv3dClSDXE5w3NxNZ6eQExH1B4IdBiiuv9W4Nml38+Mc/xnvf+96C2oDMCzeBue63EySvFQCk5idZPVyr8nPMatdLL71UgJaDg4NFL+/nP/95/PVf/7X4/UMPPSTuDdKBubGdq2/9B3/wB+L/nc/Qbv2kNPe8HQCwua+PMzonAzsiA3wompVgzT4hk05L4MocM/X9CPw1q8PuI488gpmZGbHQ4DgbEVzsESwlvdfURzRFxOmQlhu33nqroFJTS6eUC1sj5pHbJ3dz+XJI+letGi/zm9P4f098HeuJ2l7683PSYXQimVhHSpVLyyNANNo2YYtBhQCf/GNV6aiR/vvccrwk1bPd0w7DpWA1XhnF0ur95tY86Pf1Yi5qD+WY49CzLnizXiTdhV1irY613HEdnl4oagIbeRp6CoZxOCIPAOY1H2N1Y3Sf9KrVQnMM6uEtl+UCVWLlclLt7/t8I4hllhAvA9z6tD7sX9tevVZNv6JqNDiO1cQMEpnaKmur6b/bSxfNBNaT9QchzfH2ekeQyq5DVwOY3rB2z54/8H8w2XaG2Dh7/PHHxUZVoU0ePsv4rCIgZgKD3OQyn3G5OSMjgNXiuZWCdpoiVHO9rJzDSq719XXBKGhGTWErc5B9DE0fuDZknHvuuWJNsFvjyiuvBB10P/zhD+Of/umfGpaGWgHAYgPntf3yl7+MP/3TPy14CAFQVkAyvv71r4MVkcU2Cch8+dnPfna0YrBhyXI6djKQlwEHAHRuCScDTgZsz0CrAIBc5NHpzQT+uJvPqjr+NAvdt9DF4gsM9UhYZVfMiczOi8yXKFJlTX1E7o6zgrKzs7Ngt3fccYcwBGlW0xJz0ARVCa4SwLz44otrSiE/A7979jFcP/tDxNSNmtrKP9md8cDQVCQ1+QDAWNvkERBQPt1vKDCOpdj+inTbYmvtmM6U111zq24Mhfows2EfSEd669ymvbpzPa4BrGdnKspRpTeXrrhAI5KF2BatWoMXhzeOaSxW2l6p4zuMLkEDt9NUJbf/Qf8oVhPTddOtc6lu9PkHS1Jkveo4DqzL1ZM0NC96/azabQw1t9FGIQRCe70vwnJiqqzbtaa48NYTPw2X6sHs7KyoWCdQZ1XmgZtdBMhMQJDgIGVDCgXBBFYXmaBgIBBo6rUE50D6Jk3FXvCCF5SshJL5vdDsbTEfzAuDQDEB490YBL5f9apXCTCUFF868zYqqgUAr776arzhDW8Qus4mwE2jE1Y1XnfddUfB/WuuuQbve9/7tk3v9a9/PeiMzOD3BrWur7rqKgEYcg1+8OBBfPOb38QXvvAF0RbXwTRPKVVV2KgcOv3u3gw4AODuvfbOzJ0M1C0DzQ4AcnxcTLCCjYt7Bqv+qPXSClQPgm/Uq+vv78epp55at+tKwJQUCr5EMUx9xPHx8ZL6J81qWpKfOAKbpEORxkJqSLWxsrIiqMQEPZNKHHv8DyKiyKWQalkNvf4uLCfk0ywHAyNYs8khuMs7gGR62XL10sJSEOuqdZOSE8ITOCzMO+QDmLwfSMNMZdYRTdlH1+00ercMJ2qkjJa7f3t9I4imFuBSe/HsilyAKrdvVVEwGhrH7Ia94KnZp6H54c14ENXsqQgtlNd2Tzfc1CNMbmnHHo0sEEn4EU3J3QQw26cOIl2rl2w2kyk0ZxqFDPhHsNwAoxANOjaSCrJIg6DvemoW8XRhyYWx4Km4cOgPxRS4ycPnJ4E6VnZVGwQACQjmVgqadMDcNrmpSBAwt0qQa41mog7z+cxnO9kEXFM4AXFt6YLLIBtgt+q7EdA677zzRNUsAa4PfehDDbs9qgEA+fkkIF/s8/aLX/xCgIMsAqDUD98JqAWeG1wL0hXZDFKCP/KRj2zLw+c+9zl87GMfE///wQ9+EF/96lcbliunYycD+RlwAEDnnnAy4GTA9gxw0cBdsmaMxcVF8QLAHV4GF3ZcuHNnkBVqrRBPP/20oN6SonzGGWfYPmTmh4Djnj17jgKmXV1dourPij6iaVpCbcBmFhnnvUE6FO+Jyy+/vOK8chHJHDFXDNMFeXh8CNc98y/Yt7rdOa/iTnJOoCnBaGgYMxtytNtyx9Lp7UEWm7YAXQFXm9CIK0cj1BUfnl2OVmw+MOAbwGZmCYl0rJb0Fj3Xq/vQZYSxEDtsS/ts1K16jlR4yb+2uYM2dD886gD2re2xbS5mw11GN3Q1i9U6adj5U21QjBhSKO+ALGvyg/5xrKUOI5XZ6tOvD2LfqjWtwFrGQNA+ll7BZspeCnmhMR4zCqmsureW+ba7R3EocuyzodO1ODCC1cThbZqAFw+9GyPBF4vuTLMqPrfOOeecWoZw3Ll8RnJNkVslWMpgJFdLsNEGI6zQ53qNbIJmlT2RdqEsNkSdOJqj8BlOALCZAFuLU5ByGNfy3GQm+Patb30L73nPe6S0W00j1QCAVvr57Gc/i0984hPi0M985jP4+Mc/ftxpr33ta/HLX/5S/B8r/jiOQgwh0oLppD09PS2qALmhvFvvGyt5d46pbwYcALC++XZ6czKwKzPQjAAgd7gJnPHhzDDBGe7k86WA2nTUqGuFePbZZ8Gfjo4OnHXWWbYOmdVspEyRAsUgBYKVAnQ9s7q44U46xdcpMk6doWYNLvpN2s8VV1xheX6831kVyepIkxrGig8CyqZ7YCqTxE+e+Tc8tfio9OlP0g03It8NN+AKIeD2YNUGl1VSJ3t8vViKFdfwMrQJ/M8iHXIrD+rp+dw6orBePVhJL0Ljrm0SMza6EIsXjsA4luMHbXPVdak+7F/dxESY9Gb73Xu3QGtWAz5fMbBbyfUxj/VoBnp8W1TZesUWINaNxfh+eJQxHIrUp29N0TAUHMVizP7rWCiXQVc7gu6AMGSxOwL6MGY3t/dD4JyUbI4hnU3CrXrxlt/7FDRVF0Oi0dMzzzwjqvLsfnZyQ4jPzdwqwWLayLkGI3xm8N/1qjqjERrBC65/uLHnBI6axRDooXnEbg0C2zTL4Prthz/8Id785jc3LBV2AYBzc3Oi6o/ruMsuuww33HDDcXN829vehmuvvVb8H/UAv/vd7xbNwTve8Q58//vfF79nNWEzb3g37EI6HTckAw4A2JC0O506Gdh9GSimkVPvTBSqXiNwxuo1vgQ89dRTYkePgNbpp59e7+FV1R+r/whmEmSirokdwRcVvihR38QMmo4QwKvU6Y076QTIxsbG8MIXvtCO4Uppky9qd999t2iLFYBWXsCog8iKUu72MvjCwEpH7gTnA6SZbAa/eu5HeGh2S1xcZkyEJzFtAwhIoK7X34PFmD3unyPBScxHC4OXseQApjeqr7JTsyra1RCiulz6de51G/APYTO1iHhavh6j2U/Y0wmXBqwnFmXeMqKtgGsCTy9tgaydRicMXcVy/IjLqvTejjXY7eUGQhLrifrQdHt9g0hlI7bTqnNT1uUdwlosiaU6uCHn9htwBdFhtGM+Zp8eZqlbo8voh6qmsZbIo0NLup90xY31RLqkuQxBWG4wtBs9OLf/yqM983nGKm2ad7z0pS+VNCJrzZgGI7lVguUMRnL1BO0yGLnlllsEC+K0004Tm4pOAASFKONBPWBSYHdr8L7g2pjr+f/+7//GK1/5yoalwi4AkBNi5SvXcHwv4IZ3btABmLRfBl1+//7v/75oDj760Y/iH//xH8XvuZaUWWXcsMQ7He+IDDgA4I64jM4knAw0fwaaAQBkRRfBmdzqNQpdU+fGBGfMajqW7Nf7haDaq0iKKedFAFP24pQvKaySJDBq0rjzq9kqHTcFkdkmQTEusJo1+DJGOhTjkksuKSn8zTzt37//OFo0F8qsjizlGMjzbj7wS9xx6PhdZhk5GQmOiYq6dHZL11JWsNptJDSOuag9OnEDwrhhCpls6uiQdcWPPcvU9Kpdy68z24GoawngRGyIoCuEoNuLZRuBHlIcB4LDmNuUew0ymeNBVk1RMd42ZquOonkJWLE2EhrFjI2ajbmXW1ddGAiYOaz9vip3KwVdw9i7OoWx0Bjmo/Wvyuv19QNKAms2AMfl5s7f9/tHBS05mpLrhN7utl5V+dqx92I49IKjw+V3Nitz+Ew788wzrUzD1mOoQcznTm6VINkKhYIAoKklyD9lGIyI59HNN4vumA+268Qxqjj1Gu3aZG2FPPNeNGnhXBtZNc6xY252AoDd3d2iyrEQAPi9730P7373u8WUqPFHynCxyAULKSdDXXEnnAw0QwYcALAZroIzBicDuyADBI+4uGxEEHxk9RqdchkE+wg+Fapeq0c1newckLL82GOPCeMSmfQUahgRWKQWHoOVfqxmoz6iVbpvoblyJ53UK7qinXzyybLTIa09ajaRDsW46KKLBN25ULCCg3PinwwCfiYt2upg7jl8M27Y+1Orh1s+rs8/gGjKHv27UbrgChBQ/ue609OLDDYQS2+Za9RC/y2UrF6jD7HMMpJZe/TgtqitY5jZqI6ybPUC03RhPTmL5BGNOavnFTrOrQawd5VaqNuv54B/AElRMWe/phyNVbLZGCLJ8m7PtczXPJcmK5qoUFuQ0VzRNjzKCA5GtqrwQq4Q2r0hzEftqaQtNghi3mF0IaGtIKMeA9htnXhO4/xcbAH809u0+aodg18bwpyFPNIM5l3/6/+DqmhHuyKIQAfQcDhcF/3cauZoGoyYJiPcwDTNynLbk2EwwnZvvfVW0Sw3QFkZ6UR9qeLNnG9WxVG6hcE1ZyPXb3YBgLkUYBp+3HjjjcddEr4jmFTet7zlLUfpwIWu2xvf+Eb85Cc/Eb/iOt0x1Wnmu3t3jc0BAHfX9XZm62SgYRloBABIwJEUH4J/1LRhtLe3i129YgtbHs+Sfzuq6exKPum0pNWSnnLxxRfX3A1fAvhSxB8TtOXChXRdGbQj07WYOiukGTVrcLebdCgGgVUCrLnBe4oVo1yImlEtLVosqOfuw8/2/ABZZKSmpMPogqokbAFvBvzDWE/OIJVNSh0zG/PpQVFJR+pgNNmPmY0tvU5Z0ebe0jS0k+I6HBzFcvQw0pCfHzMPQVcb/G4DKzVWHObSfwvl2NA8GAz2YdoGk5n8/nRVx3BwxHYA1exXhYKh0AQWovtKUkmrvfdUaFiMqkhmjr8PhoPDiKZo1mGPNmWx8WoZHd3eHqxlptk7vUMAACAASURBVOuivZg/DrdqoNc3IHQRMzVUKOuKgbV4wtJ35v/qeBkuGHrjcUPhM47f36S6NvOzKHfQfCZzcy63StA0McvPs8vlErqzuT/8v2JBqY/bb79d/Prss88W2oNOQFT3N1OlaKOuCT8v5ueEOeFGeqPCLgCQxh+k9jKuvvrqo4YgufOk2R7X3Nw4YGFBIfM7AvXc5OafBE25VnTCyUCzZMABAJvlSjjjcDKwwzPAhSX1Q+oVNKsg0GRWZREcI92X5h6lqtfMajpWcdHtrRWCVAUaa1BvjqLFtQTbYt5Mx0IubAiYyhQDr7drcbX5IGj929/+VpxOajVBYTO4S8w8mfQsAsp0TayVMvXM0hO47unvgiYhMoM6YEG3Bytx+VVOnd5uZBG1xSFYo5unbxQPzMo3NWF+CTSNhoYwvWGfMYMn44VHU5DUSGG2JxQoGAlNYHaz+jylM9ZAVlJYV+LTSEioOiyXDYJEmewGIkm5tNFi/ba5O+BzERSWCzYH9CE8v1q42k+AnaQiR+vnmmvOv93D+RpYilevrVnuGpb6vV8PIWy0YzFWnbt1JfTf/z3xfgwGTjhuOHwpp4QGn2+tYvpVKJ/cjMrVEuTfixmM8JmeqyWYazDCasM777xTdEGqa/6mVy3XupXPbUWg2I58P/LII3jFK14hmqakDgGwRkWlACCP55hLaXv/4he/AKv2uPbjOwC/Hwji5cePfvQjsPqP8f73vx/f+MY3th1Dh+TvfOc74v+pGfiRj3ykUaly+nUysC0DDgDo3BROBpwM1CUD9QIA+eBmxR8ppmaYdN9SO9/msQR2HnroIaH3Rt23Vohq3Wpz58aFP3X+cl2RuWs5Pj4ugEWZ0So6i3ypuummm8TUzz33XPHSxDzR3XdmZkb8P3NDVzxW/lkxCbGSxwNrz+PaJ69BTLKRhEfzoNfXhfnoFhVeZvgFwGjY4hDsU8eRzCg4GLGPTjsZnsDMxj5LlUTV5E2FClZ7EeSxM2huEU+vIJYmldd6uNUg9q5ar0LborAGMB+1HzhyqS4MBYfrVg3IrA0HJ7ASn5JCrWZ7bgzj0EZpN1yCcX6Xq+4mIRwfK3kTmTVsSNbns3oHtnu64dFdWKkQiPRqg1iw8H3m1YN454s+CVKQc4MGIKz6p+ZXI+mMVvNk9ThWCXJzKrdKkJVIhWRY+NwyKwQJenD9xOCmFzdOnYDQ9t2J90ml15bVoa95zWvEWof3l5U1daV9FDuemoO5VXTcrL7qqqvE4dQiJOCWG6ZOn/l/ZHNQyoXA9ute9zpRyUg9Q34mCPBed9114sf8jHz1q1/FBz/4waLDZx5+9atfid/z7+973/uEPA43FL75zW/i+uuvF78j4HjXXXeV1IKWlSOnHScDVjPgAIBWM+Uc52TAyUBNGbAbAORDm6AfF6/mzjersVi9VklV1tLSEu677z5RJUjn11q07mpKWAUnc7efCwwGKwArAeyYNy5YuMA1adI0QGHe7KL/mDqL3D1uZlc0VqzecMOWOQfpUHyByqWTs2qEeSpE/6jg8hU8dG7jML7/5DewnpBb+cQX4LHQiC0VbwRq+gJ9WIiWBjoqzU082Y/DkWkx7qX4DJKZRKVNWDq+398vQJBoqjLwzFLjRw7qN4awnp5BOsfgpJLzrRxr6D50iqoq60BvOfpvoX6pJzcZnsTMpj202fw++whuplYRy2zpQtodpKB3eNuxEK3NPVeFCwtRWK7qHQmMYD01b6uTdKHc0fBl6IhxkB2UfivXq8c7hAyiiCTLO1y7VR9WYlFLFOYXd56L8wffsG0IrEYnhY+GTSeddJKVIbbsMabBiKklyD+LGYxwkny+8RlNcJAV7pWsK1o2SUUGzs1RskMoW9LMxmV2553Ov6x8431BAK6e9wQBPRpwWI18sNsEAMudz/Xcl770JQHolQqa9bBa0FwjFjqWOpo///nPxX3jhJOBZsqAAwA209VwxuJkYAdngOBSIeFqGVPmQpa6fdztZnBXkmYVQ0NDFQN4bOPuu+8W7VQKpsmYSzVtUP/H1O2hBqDVXXvOlXnLpUnTvIKLFTuBT2rHsIqOLxasrGvm+PWvfy12hEn/5YKPwfzWI08rsUX8vye+jqXYvPQUEbg5HKmeLlpsQASFRttIRZVTrUf332eXNo++5Hd7u6CoSenAqDkfUqXbDR8WY7PSc2426M344dIzSKpR2/pgwyPBCcxHrVU1ptJ9mN3cqmqtNLq83XBrWVvo5fljUbMqQtk2xN31MQhh/wOBEWwmFyuuqjTHHnKN4tmVyio/Wa074O/DrAAf5ZvslLrGPj2Abl+n7dWqpb5D+v1j2EwtHDUBKnRsh3scByPWXLBfP/kBDPgntjWz24EdMiZyqwT590JSLabBiFkpyE1V0oPtXCdU+j1k5/FcJ1FrmXRQSsns1vjhD3+I9773vWJtTUptKwGA3MAlGMf1PSVzyHYhiMl3E2qDcwOArB9WEppOx1au87XXXiuASdKjaZhHcJTVhW9729vwzne+s645sjJe5xgnA8yAAwA694GTAScDdcmAHQAgK/1YucYKNjO4MCH4ZxUEy598tWBaXZJYpJNcswrqs5SrSOO1YN4IxJlRCU261rmyUpOuuawwPP/882ttzrbzCViTApy7k8z7iy8A9aK+bCTW8YMnv4npMvTBapIwHprAzKY9Lr7Uo1uI7rVUnVMajBjHE/PHv+R7dR96/WHMbtpDP2Ul1HjbKA5vWAMXqsm9mtUQQgBxl9wKz/yx0OEWSgwbJTT0PGoIz6/W5u6rKxrGRM7kAL/lctrnG0AyE8FmqrZxl+vH/L1b84CO2nPi81JZuNURHFqvroqw2+iBi+BqQv4mQLlZdBo9cOtKzeYy5fop9nuh/+kfwUpiCqkCepOG2ofFWHnQmjqDf/iiT0DJo/+yX+q4Us6B2sA0udrtQTkRmhswaPzFzcFiBiO6rh+nJUhwsF7PxXpfJzreEiziOomSH7s1vv3tb+NDH/qQ2ADlGk6W7MluzaczbycDjcqAAwA2KvNOv04GdlkGZAKABGRIxyB9hzvYDC4+Sc2oVZT4/2fvPcAkucqr4dM5x4k7OWyQhHJCCeUA2AQZfrANAkwQn4xtjP8fGzA2fBjszxjb2MY2MhgjgzBBEuEjCQWEclplidVKu9rZ3ck59fTMdPifU6O7qu3tUF1dVV09c9/nmWeknaq6955b3XXr3PO+p1oyzQ7TSCL0jjvuULrCWiilHI6JG3exqb5jLTuBG3c+q0mTrnXMjWC0wp1c7voLMxTWRjrllFOUnWKrYzWTxnf3fBUvzW/UZjIyusLdiqGDGSmp20JdWMpMIFNDyu56phOHF49NZXU6HBiM9+GwRgWQHswGYv2YXDmIbA0upZXaTaIZK65pbsdWOlT33z1OL9pD2zBZov6gnvTfUp3pDHdgNbtoiaut1+lFZ7jzZRJbNzxVndga6Hg5RVWbAtHl8GJiOVfz56sv2q/Ux7PCeKUQkK5wL1KZaaxkrUm9Lmzf5wqgNcjSAq+YpHidYcymtfXnpObX4IKONxWdZ6HsEhuHVd0Mm/BgPveefPJJRbVE53uG2mBEpA9XMhgRJiNqg5FGhoukKMlR1kTmz1YNpsZ+6lOfwtlnn40HH3xwyyhAt+p8y3FvXgQkAbh551aOTCJgKwSophI15mrpGGX83LXnYozBXegdO3YoO7NGpKOojR/KkWm1jMHoc9W16lhTrxgJSiKLuHEXm8EFvsDN6l1ckpBcUFOlyZRlOwUJZZEWpu4XC8QXc4Ozqu90Bf7+3m/g19NPGN5ka6ANa7lFrGaNd6lN+JvhdKzpUmq5HUHsm0kjh9Lu4dsTAxhZ0pbmqge4tmAbckhheV27QUa17bQE2pDJLiGdM6/2IPvUFenHbPowsvmjHabXs22YSBmX8ux3+dEZacPocnVpr9XiJo4n0byWXdB1j+lp0+VwKbXyqAbMl7k3ee2otw8vzhrjMB10B9EeblXMaqwOOhV3hXswlSYhnrG6eaW9sDuOmD+K6fRBJLz9msn/qwf/AO2hvqJ9fvrppzE5OakU7+fzcKsHsSAmVPKVUucLgxG163A5gxFuSHKDUW000mg4M2WU46X6j2vNrRqf+cxn8IUvfEFJlb3tttsMWXNvVSzluCUC9URAEoD1RF+2LRHYQgjUSgCSmKMDGNNWRUom03aYjunz+QxDktdm3TcGjR/qofjSMxgWIiYRyKLDNPEQwX+j6ca+ffuO1PZhjT+mO1HVVo8gCckFNUlI1lm0QwhVKck/oW5IJpOKApCq0HoTgMQol8/hZ/u/h91j9xkOWcyXgNeZx9L6nOHXDrhDiPtDmFudqOraIXc/nilI/y12ga5IB5bXZw13TRZtBT1BtATipjreMq25JZDE5Mor7uVVgaXx4Jg3CZ/bhYWXU0p9zhj2v1w7VeMlNB/WF+vDXHrUEtVaPdSAcV8z/G5X2RRZj6Mbw0vGmuJQzelwrGN+dWMzx8oIe6JI+uOYTOtLaTair0lfKzzOCEaWK9cwDXvieOdxnyia/su+UO1G1Rtd3Ol6v9WDm3NURXJNxQ1QrcF1BmvkinqC/F3KYITXVtcSbASDkYceekhJhRa1pbXistmOo+vu9ddfj6uvvhq33HLLZhueHI9EYMsgIAnALTPVcqASgfoiwAViqbSRcj0jMcMaPSRmRNoqDRmY7kuCxozgziYJyzPOOAMtLS1mNGH4NZkCTHxPP/30IwWM+WJD1Z+o4cOi3cSt3mOiepMLasZVV11V911k4sOXHjpAM6h+IEFKgvm+++5TXmyYJk2VSL2Dn4dfHfoZ7j70c8O7QhIq4Y9g1gQDDCqIOsIdSkqt1shmu3FwQRt5EvfFEfS4MLta2T1Ua/vq45xwoCfcjYlV7f3X005/bBDjy/trrp1Yrm06QXdH+hSjlrBnEM/PVCZS9IyF50Q9USQCYVPJU3XfOkJdWM3OW5KCLNql2crM6uFjUt3dDh/GljPImZBC7oAD/bF+TKWPbVfvXFVzXmuQJGTmCJFczbm1Hut3RTG1soBtwQ3H4IW10p/5U5ovwnkdbyjZJAv383u/r68PAwPHmoTU2tdGO5/GCCwRwrXCueeeW1P3qaZXOw7zv4sZwTFzg6nCapUg6xgbkdFR0wBUJ99///0Kocnad6yNuFXjuuuuw4033ohrrrkG//3f/71VYZDjlgg0PAKSAGz4KZQDkAg0BgJ6CEASL1yMkshiWJW2+stf/lIhG+nkRbVcI8Rdd92lLFBZp47EKOsjstYegwtp1q2hwsFK17ZSuKmdlq+88sq6FZIupo4UxeCFiQwX/nxxISHIl0S7xCOjd+Nn+2823CHU4/SgI9yOiZQ24q1aPPpIcKUqE04u+LB/bl1RPWoNr8uLLiX11Jy+sx/xfBzrngXkHdr7pbX/4jgaXKzl5rGSMTcleFuoG9MrWYwum2OmIsZDZ+jBOJ2hh0ytpyjao2EHaxGOmWjiUjinNJpIBGKYWnnl3ot5+/CCQem/pe4hula3BpMYS1mTbl3Yj+5IPxbXx00pH1BqzE2+AQwtbpjN8N5iOvby+jRWMsem6f/W9j9CW7B0yuZjjz2Gubk5hfyz0/d7td8ZRh1Pg669e/cqhBwzIIwMbl5RUa92HeYar1gIgxFRS7DeBiP33HOPssF64oknVuUQayR+drjWO9/5TsVJ90Mf+hC+9KUv2aFLsg8SAYmADgQkAagDNHmKREAiUD0C1RCATPdlyuqBAweOpPty15Xpvlakrd59993KQpWLPRYHb4TgApVKNhJYrOMj1JZMYaZ6japJuwQX/ffee6/SHdaSqYdzIFWIVP2JFxAqDoiTOn2a/WOha74gMvXHbgqRZycfw/df+IbhCiO+VFNdNGoSgdId7ce0qqB/sfsy5O7DM5PV1zpj33ckB3FosTLJqPfzkPQl4XHnsLhmfLq06FPQHULSH8N02jxyzpULYXIFaA5FMbkyqhcOzec1B5rhdQFzFqWu1kMN2BHuxfL6FFazy3A7ujFicPpvKbA7Qp0vq+E2VMxWBk1mOsJdiklHDlnTm/Y52zBdoFJmXcbuSC9m0sNYf9kxOOJJ4B1K+i+/FYrH7t27FUJqq9d2E+gcPHhQKbVCwu3MM880fS651mP9QDUpKIzdChunKlGtEuSaxqr6xb/61a8U9SI3WAvXCKaDZJMGSOC+6U1vAjebP/7xj+Ov//qvbdIz2Q2JgESgWgQkAVgtYvJ4iYBEQBcCXDyUWtiJC/KYiYkJRfUn6sdwJ5ppq1Yuuuyq+ioHvCAAxTHqNFY7pdKwfyRXSbIyLr74YktIXYELiVGqI6l0YFRSRz7yyCOKApUviPyxW+yb24NvP/sfyML4wvwDsUGMaqizpQeTtmAH0tkZrOXSRU/P53pwYF5/um1/rBdTK8PImGRY4Hf5sC3cgvGUeTX7SFtQMTlm0hxklhIYzc4CeaDd3YwFuhHzf0wMl8OJ/lgfRhRy2dy2OIx6qAF9Lj9aA5349cxLVSlYa4Wdqd390Q3n6kyB0Uut19ZyfsQTQ8wXwfSqeZ8JvzOCqXRpQx6/K4D2ULuCwcnNr8G5236zbNf5/U4CigYgdijxoAVnM4/hpuv+/fsVIzGWE7E6ihmMcJOOG8iFQfKP9QPVKkHWFzR6vcM+MSuEQUyKmaxZjVM92iMO3LBl/WaSfyQBZUgEJAKNiYAkABtz3mSvJQINh0AlApDqNRJ/apdapqwyLceqXV4B6sMPP6zUBeJLgd0LgwtzFC7cRVC1SMWaSGO1283C9GqxoL7wwgtB9Z3ZIWpJ8h4TRDQX8lT98SWiVFAhQkUl1X/E1I5x/zN3467ZHyHjXDO8e73RPkylDpZ14tXbaMybgMedx3KB8YjT4cHQbL5m8q4t2KqoopZMdPDdHh/UZEagFyOeRyVbKjON1exKLZc55tyFxSjmcwtH/j2CMFzudaw5Vw1tp9jFovko4F5FxoK22L7VTsFxXz8W11aRzbFG3YZjvVURdIURyLmx4jZPoVpuLK3+dsC5jsV14+txNvn6MbRI8rh8RDxRvGnwvWgNllfwC3MHZhfU0+W90nis+jvJP64lWEaEJVDsEMJgRF1PcGWl+Hch1zxqlSDJwVrLnlD5RwUggyZr5dYLdsDLrD5wDcW0cNbjZvov04BlSAQkAo2JgCQAG3PeZK8lAg2HQCkCkIsrLjrpVCt2edva2pSaa0z5qEcI0od18/hiYNegY59aLcl+MgX45JNPtmuXlX6RtLz99tuV/6bToNkLaioOiROJPAbrC5HMo+Kjklrg8ccfB3EmEc170m7BzxVrNj134GnsDT2CVYexJBHHuy3UqaQ1itQ6IzGgYqcpGMdM+pUU1LC7D0/rSP8t1q+QJ4SmQASTK2NGdvuoa3VHurG4NmGq2y3dV2O+IGbSxozD64xg3+yxSio33Ei4wlh2mU8eufIudIbbMbU2bNrcqC9spVOw19mNQ4uH4Ha4jigeq6lnaQQgUcTg9eeRyr5C8hpxXS3XoHq1M9yHJdYHzBn3neR3tWNKw2eZbtfvfdXHK36/s8QDnw/C9EnL2DbzMUz/ZRpwc3OzrdcRwmBEkIJUcXJdURjCYEStEqzWYIRtiZIl55xzjiUblna8x7jWYDbO8PAwbrjhBrzrXe+yYzdlnyQCEgENCEgCUANI8hCJgETAGASEi6+4mkj3Fbu5XJjRZa3eLrVPPvkk6IbX09OjLHjsFsSL7r6C0OION7HjIri3t1fB0M5BovcXv/iF0kUuqM1KqWE7Q0NDSk0j4T5IUxe+7GmtJWnXe4GLcfHDdGZF2ehI4/nAw1hxFS+sXss9kfQ3w4FVU9xVN1xpezCx8rKKNdeLl+aNMzVg2ulAvAeHl6qvKagVs7gvobgQm1nfjs6vfbEBQ1KCI54BPDe1YaRQLHrDXZhZG0XWgppuvdEexU3WaIVjqbGZrQZ0O30YW8ocZXjS5G9C0OPRRF5pvee0HMd7vy/WjwnFgMX4MgGV+kDStT3Ugan0QeRRm3FOpfRfdV/ObL0YF3aVT//l8cLdlc/5RjH8qoR5LX9neQwSPNyEpTq+UUIYjKhVguUMRrjpqFYKlsuW4HrrgQceUKDghiXTjLdicD3FNTEx/v73v483v/nNWxEGOWaJwKZAQBKAm2Ia5SAkAo2BAHdSxUKNaQQkABlM8WWKJRV3taZrGIHEM888o9SIs5uajgswpufQIEUQWq2trQrhR5KLC3em/9K8xO5x6623KvfC2WefraQbGR0sKk6TDy5WGST8+EJTLbks7gWmh5100klGd1PX9Ygb7wWhmOW9wJR1mpVMzU3i0cydWHIZn3ZIx9Gw14d5k0wcWO9uMjWEg/MOrOfWdWFT7qQdiQEML72EvEm15+ig3BPtwOiyceRlsfF0hnuwpCgOi9dP1AJcNrsNI0vljT+aA0n4PU7MpKe0XLKmY6jUbAkmMGFiTUV1B81UA8Z9fXh+5liymaq4gfgAplLDpqpFi01E1BtFUyCO8ZT+upq1THDUE0fUF66pPmCTvx9DC5XTf9nP39n1R9gWKu3+K8Zy3333gRuTW93dVeDBjSRuftJ0ze4biZXuR2EwwjWAIAbLGYyoVYJqgxESiSwLw2DJEmYQbMXgOoNrNa4/7rjjDlx66aVbEQY5ZonApkBAEoCbYhrlICQCjYEAd1KZ6ksCS5AXJGS40LSiDpxWlEhOkmjjLvhpp52m9TRTjyt0rSWhRdUCCUAGFYFM3eHCnU51dg+mAHOBfsYZZ1RNypUbG6/5wgsvKMo/EUzfpYGHnoW7nXBVq/4ECcjfJNCZ6iTSmdNrK7j5+a/jwPJew28Dn8uH1mATplbMcaftDB2HJyderLn+X6mBd0c6ldpkq1n95FklUAfjVOkdqFnxVK4d1jiLeAOYXR2v1J1j/u51hrB/NqWJCKWCbHuyD4cWS6sFq+5AmRMG4v2YSh22zMSC6e3ruUUsrxuXJutz9uDgYmmijUR6azCB0WXrybiucBfWcktYKqi7aeQclrtWa7BDURIv6KgPWMz9t1hbEU8c7z/xzyum//JcmmfRGIqbO9VuDlmFmZXtcNOMJS+44WXn8id6MOGzkmSvWiXIrIlKBiNcN3Ddyrjkkks03Vd6+mf3c4gbN5gZNALh2k2GREAi0JgISAKwMedN9loi0HAIkPzjbjvNPhis70fiTxBYdhoQ1XT8ofMwiz7XM7hjzRpvatdaElo0J1ETWkzd4SKVeNbDva9ajGgCwsU4C40blXpFRSkJO+EgzR19Kjv4W2/YhQwuVP3xpYWEnyD/CseXy2fxk/3fxVOTG8oFI8PpcKA32quQXEZHyEPTkRVkcktYXt/4rjA6Yt4YXPkMVmDO9dnfbaEOrOXmsZIxrw2mTvdF+zC6XB05F/X049kpbUoqgX1vrBtL69NYyaSMno5jrpfwJRD2ejGdrp7c1NO5DTVgF8ZS1eFYrC2Xw4vx5exR6b+l+tQT7UFqfRbLJprUFO+jC32xXkykDtYlLZhKyO7IABbWx7Ca1XY/kbSeSWv7LJ3eeiEu7nqjpluBbvTcNOKmGZ/3Wz2efvpppbQI6+PSBG2zh9pgRBCDpQxGiAVrIwqlINOI9WwqNiqmzDARqlCuN+1qitao+Mp+SwSsREASgFaiLduSCGxhBEhgcLHN1Eym+/LHDum+xaaERBoXOKwRc+6559Zl1ogXF1zsBxUKjHKutXYiLbUARlc9LrRpWMJU61qChB9Tl6hcYPC+4ssL6yFWMvmo1C7JV5rUUB1Sjx1vofrji4paAVio+is2Dh5/16Gf4P6ROyoNU9ffB2KDGF3ep+vcYic54MTIohepTBpxXxRRhQQyJ/2UBhQbZhfzhvW/8EJUeiX8IUwbZNxRqqNd4V4srI1pNmnJ5zpxeLF6442IN4zmYAzjqerPrRZkJxxKuuzoMlO2a6sdp7Xt9mAHsvllLK3rvydKpf+W6oPP5UV3pEtJTYdJqeml2iYRnghEMZE6pBUiQ4/zunzoCHdicmUI3LAoFwlvLw4taUutf9uO30dHqE/ZHKkUd911l6IA40aUGaUoKrVvt78/8cQTSjkJbjJyjbYVg+sttUqQpTWKqQSJDVOFSQgKUrBag5FGwpeboSzZwmCauFEbt42EgeyrRGCzICAJwM0yk3IcEoEGQIBprCQlQqGQrXt76NAhpX4cF3cXXHCB5X1lzRm2T7wYHo9HScdhWk4pQssOpGU1QNFVj+NkXT6qDfQE7yWmPZOkK6yJaJSDtCBW+XIoFr96+lrtOWqyT03+lVP9lWrj4dFf4bahH1TbBU3Hd4d6lLpeRpA0Tf5OPDz6CrlEcqQn2oqRpcOa+qLnoJ3JQRxaNI7ELOwDU2j7Y30YqVKlV+1Yot4EQh435lY3nK5LhdsRxNBcGjmdpBrVWzuSAxhZPgArXG2TniQ8njwW142vaVkMI9Zx7Ip0Y0znfPlcPTi4UH1qb3uwHU5nBrMmEd7l7omucDfWcot1SwuOeuOI+6OYXCmNW8DVrsnJO+SO4N07/xROp0sZsvr7shgheOeddyrHUTVvlhlVtZ/leh7/2GOPKbVkSf6RBJSxQXZxg5FqP5aF4SY2M1n4jD7m+97lOooQJDFYzmCkkfB95JFHcNlllyld5vjtVLankXCUfZUI2AEBSQDaYRZkHyQCWwQB7qyW2km1EwQjIyN46qmnFOOIiy++2LKukcRifUSSeWJxKWrxVFpEkghj+mu9SMtqQaL7InfZ6cir50WDtXto0MHFOIPOfExP4QK9VtWfeiyCWOXLIR2LrQh1fb9qVX+l+vfM1G78333fMoW0ieRjyHmWkUNtLqMx7wAeGz86DZOE0/FNAxhaqD09sxQ2/bFeTKVHkDHBeES0SRJwauWwqSmXJBt7In1lmmRkrAAAIABJREFUU1n1pP8Ww6091Ao41rCwNmf6R8KZd6DJmcCye8b0tkQDbcEO5PKpqkgxp8ODyVQemZy+zwFT6wdi/RhfPmhaDcxSALocTAvuq5tbMPvF+oAkQedXN8zBRPicVNEyVfhYwqVwPCcnz8WFHW846p/Fs1TUSVXXS6USnXHmmWfWVCrCshvT5IZY243PZdbMpeOrDBzJxCDhJdYAXKtxDcL1hzAZYUmTYsF1pHAc5m+1wUgj4Uvjj6uvvlpZF3PsWyn9uZHmSfZVIqAFAUkAakFJHiMRkAgYggBr7QilliEXNOkirCXHnXAq78SOp0lNHbks2+Qus6g/Q5Uk1XFa05KYLsz6PVS+XXTRRWZ3t+brP/TQQ4rCkXVkqkk14v1DVR5NWsSLHV9UmPLL+TI6aCbCeeFO/nnnnWf05Y95US2m/NOj+ivW0f1ze3DT3v/Cem7N8HEEciG43OvIOvVfe3Ylgpn0YtG+HZccwOFF8xx8W4MtyGMVSwaaQRQOpCXQAodjraYUUy0T1x3pxfzqaNF5duS7cHDBGEWl1+VBiyeB2dzRhI2WPuo5JpKPwOFeQ8ZZ/EVbzzXLneN2utEd6cHYsrb03Li3F8/PaktTLdduzBdDwhe2JNW6sB9RbwzJOqYFsz/dkT4sr09hJbukdK/ZP4ADGjcA3rr9g+gI9h8pl1AMZ/Hc4GbkAw88oBzC8g6s6aYlbdjo+8xO16PbLZX5fC4Lwwc79a8efWFGCI3FeH+UqwnNUiRqx+FSBiN8nvNaghTk2oKkmpEbl2bg9IMf/ADvete7lHIoVEXatYSPGWOX15QIbDYEJAG42WZUjkciYGMEGoUAZA0cLoS5ILvyyitNXZgV1q/jCwh336mKq+ZlZGxsDKzfQ6XgpZdeauO7YKNrVBpMTU0pZiZai43zeKZGC5KUO+k0+TAzdcuqdPBSJh+C/DNqQoeXhvCdPV8xxZzCDz+8DgfWXStVdzfha8OjY+VNH/piXVhYncRq1hwCKOgJoiUYw0RqtOr+az0h4A6gLdSEiZQxJFypdmPeBAIeF+ZXX6mh6HL4cGg+o8mgQut4eFwT4ljzLiFbowJUS5tuuBFDGCse85WHoj+twXYAq1hcK5+G7Hf1YmihdgJQtNviakYa88g4NmrAWhk0RaERz2Kd3ILdSip2D6ZWhuBztWIiVdl1POgO47pT/zdojiMyDbhhVOieThz5bzTWEm7xp512mpLSWKgSNPr718o51NMWCVE+X6mm37Ztm55LbLpzmAXAH64zqjFY4z3IVFm1SjCVKm56w81LNSFIUtBuCrtvfOMb+NCHPqRs2JIQrWZ9uuluCjkgiUCDIyAJwAafQNl9iUAjIdAoBCAXbEIZcMUVV5iy08nFIdN2uZASqkjurHLhrae2CskxkmrclWWf7R6PP/64YtpBopNpwOWCqTUsQM1dZ4ZeklQPJkJZyTm58MIL9Vyi7DmFL6ei3p8Wkw+9nZlemcC3fv1lLFQgNPRc3+fyI+mNYm69OlVY3NuP3eOVnWlbAk3wME1wTb9RQ7lxMY12IN6Lw4opgznBtObt8UEMG2igUqynTOuk0+yGgg2IePrw3JTxzs28dtwXQ9Tvx9SKNc69ncFOLGWmsJ43hwwuxJNYbjhfF1ehOuDG9IrTcHWtO+9Gq68JszlrcFWPm5+Fvlh/XdOCE95meNxhjGpwHD+l5Txc2ff/lPzQiu9WkjJUdVOtxeBGEs2o1ISGOm2Yx1SqJWjON4X1V73vvvvA5y2zD1hOQwaUsiwkiukSTbfoWkIYjKhNRrguLhbMAhHmIvzN/6+nSvDf/u3f8LGPfUzBgBkykgCs5U6Q50oE6ouAJADri79sXSKwpRAg0VVqsWMnIPiCcM899yhdopquUv29avvOIttUsjFFhGFE/Tqm0zKtlnHVVVfVdaGoBY8nn3xSIfSYvnvCCScUPaWYEzIX4Xw50UOSaulX4TFCWck5uuSSS/RcouQ5pVR/gvwztLGCi7F227d//R+YXDFe7bZRi64bYyntaqjp+SDmcsXVEYU4hDxBtClutMb3XbS1I2G+0UV3pBuL65NYy6bNnGr0RHoxuzqCfG4bDsxXb1ChtXOsYUfcDi2xXmPlem1ar1vquLCHrsRx09WU6vZbAm1wOjJYWJs+qlsxbw/2zpqHbUeoAznqAVetq4MoBhjxRBWcx1Pmja/UHLcE+vHS/EtoDbbB63JiOl36M/+2XdehN7qz7Pctv8+5mSTWIXz+cBOKxIp6M6bYRUrVEtxMKkGue0hSnXTSSUqqpwwoJmNUi7a2tioZB0YG7ykqLtUqQaZgVzIYEcSg0WvTcmP727/9W3zuc59TjPHuvvtu268xjZwneS2JwGZDQBKAm21G5XgkAjZGoFEIQKbl3nXXXQqSVH0ZRTZxYc3FJNNKRfT29iopsLWme3BHmcYaDLNUi0beWjTw4KKaJid82SiMQidkLnSpFGRakpW74GbUgxQvmmp3X/6bmaq/YnO3kknhe8//Jw4tmmOwMRAbxKgGlZsnG8CeherSht0OJ7YnenBo0RxFG/HqCHcgnZ0DcTIr4r644t47q0rVNaMtKqnmV12YSJmvJOuKdGA1t4jl9eL1HI0e30C8XzFYMdPERd1nppj2RftfVgPmlD8F3H04MG/evcg2NtSp/RhdOmB4GreWOekIdyKXX8HCmnUkZMTThdHlV9Lle6K9WMnMHVNHM+AO4feV9N8N99/C4LOXqj+qzhnc0OFGEjeU1CG+k/lb/f1c7JrFVIJqMrARFVI0ReE6jSqvQmy03COb8RjeN9ys5NqDGRpmhzAYUasEyxmMqFWCZtax/OQnP4l//ud/xute9zr89Kc/NRsGeX2JgETARAQkAWgiuPLSEgGJwNEIcEHNhbjdg+qA22+/Xenm+eefrxRsriX4osAFJJUHa2sbJgms98IXEC7ejAizVYtG9FF9DToWMwW6vb0dp5566pE/8R5hys3+/furdkI2uo+8ntGp1fVU/RXDh4YgP3jhm9g7+7QZ8ClEyUTqAPJlFGFN/gE8PKqPhOzxt2E6ax6pFfNGEfH5MJ2eNAUfXtTjdKMnSqLDPAKJCrU90yPYHu/G0IJ56c0CJNY67Iy0YGTZGtVY3JdAxMt5GjNtngov3ORvgdcFzK9OYybtwZpJtSkL2036OdaAprp4RoNBlWd/rB+TCuGq3/BHS788Tj/mV9eOcS4nEdqruGoPYz23kQJ+cvM5uKr/7UUvy5q+3HASJApTW0nkaDWNEmRg4YZNscYKSUH1po7dCUH2/Ze//KUyLNa6M7O2rpb5t8sxvHe4EUhTFJqj1CN476pVgiQHRZ1LdX+EwQjXlYIYNMpg5MMf/jD+67/+C29/+9vx7W9/ux4wyDYlAhIBgxCQBKBBQMrLSAQkApURaBQCkAvhW2+9VRnQq1/9aiQSicqDK3EElWwku/gSwqDSj4vI7u5uQ5VsZqkWdQ+8wonPP/+8UlibaTWisDYxYmo0yUxGtU7IZvRVbQjD1Gq9IYi/wpp/Vqv+ivU/l8/i5y/djMcnNhwxjQ4aCiysjZdUaOVy7XhpXj9x0+yIYdW9iBw21FhGBwm63lgnaKBiZgzGBjC+cuAYwsOINn3OPrwwu0Ew7oj3YSJF8sT8zZgdiX6Mpw4hky9e58qIsYlrsLbiYGIQY8vEMGvkpUtei4RYd/g4PDe913JVXn+sD3PpcaSz1alnjQAm7ImgNZisKs2/2nZbAn14qYyqMuAOoiPcphDnb9l5LfpjR9eS5XqDjvHC6IPPXqrIuelUi4pcrRJUf6+XGh+PEe0JkxFBBtqJFOS4RObDmWeeadjmZLXzbrfjWa5kenoazNagaZkdQhiMqFWC5QxG1CpBvQYj733ve3HTTTfh/e9/P77yla/YAQbZB4mAREAnApIA1AmcPE0iIBGoHoFGIQA5sttuu01JhTnjjDN01cLhuVSxqZVsTCHhCwjTj4wOKivvuOMO5bJGqBaN7l/h9fhixh9RWJuEIA03GHxJotMcF9v1fkFSG8LQEbra/pQy+VAXlTcbay3XZz/vPvxz3Dv8Cy2HV31Mc6AVufzyMe7DAXcYz0wuVX29whOijhCcrnWsO8xTJe1MDr6cLm1efbu2UBuyuRRSGePSZx1wYHzJi7RKodYcSCLgdmJ65RWX4JonocQFmgJNCHgcmEmb3xa70BygMi+POZPTqsVw/ejGYiaFsNeNSYtMUETbJME6w60YXjJPPVruvmgPdcDhWDvKbdqo+4iq1eGlygrSbaFOvPekP4bL6T7SNDfenn76afA3g2o21m8LBAJGde+o62wGlSAzH1jbjXH22Wcr5igyoBhesG4z1ySsF2nXUBuMkBjkT6mMG2EwIohBLQYjb3vb2/Dzn/8cf/Inf4K///u/twQGKi8ffvhh5eeRRx5RfkjGMt797nfj61//esV+8Jjf+73fq3gcD6DC8T3veU/RYy+++GIwRV5LFKvhqOU8eYxEwCoEJAFoFdKyHYmAREBJ6RQpsHaHg6kwTLtgeioVA9UE00ap+hM7sqwhWKzeUDXXrHQsX0B+8YsN8uacc86xffoO1X8k/YgNXzzEfUG1JbGyy8sHjVrojMi4/PLLq6rVWKgOISksFCDid6V5tfrvu8fuw88P3GyKiQPNBIIe91E1xFoCA3hwRF/6byE2AfgQcruw4jCvZl9PtBvzqxNYezn10Iz5CXqCaAnEMbkyYsjlo55OPDO1Qa6rg8pGq1KCmba5PdH3skGIIcMqexGlXl6sD8OKA7J5hG0+B0wvebDuWAcViD2BbZjNTViuBmS9ylxuBfMmOHtXmi0SzEwLnkkPG/a5cDk8WFrLI6tBOXpyy5l48453KN3kdy5r7L7wwgtKiiS/Z7mRJIw+Ko3FqL83okqQz+B77733yBrCqNrHRmFar+uQdOI6gLWambnRKCEMRgQZyM3McgYjLHXD0jQifVi9Uc1rvf71r1fWQp/61Kfw6U9/2hIYyil1JQFoyRTIRjYpApIA3KQTK4clEbAjAo1EAHInnAQeVQOs/aIlmIbLOn90GmRQLcZd4/7+frhcxYuTa7mu1mOYtkyMzzrrLNsX8OYLGmv9iWA9pl27dimmILWkZ2nFSutxemorNorqrxQGv55+Aj988ZumkBg+lw+twSZMvUxuuR1deH7mlSL/Wuel1HFepxvb/ElMrU/UeqmS5zf5k/C485hfnTWtDSWdNT6IEQ0mKpU6EXQNYM9MaZKVKcHjy8PI5M1PCWatw1RmFqnMRpq/2bEttE0xrlhcnzOlKfd6DIfS80ddOwQ/gm4PUk7jVJxaOr9BevYrKbFZi1Kg1f0KuoNoD7cp5ii1RnOgFwfmtaXcv23Xe3Fc00ng85clJES5DRJYNJgyqs5urWMSpCA3gqpxHBZricL04Vr7U3g+3WgfeGCjDASzCMzIVDC6z1Zc78EHH1TWgsze6OjosKJJ09rgvUcSUNQT5O9SBiO33HKLkpXBLBhuKn/mM5/BE088oaj/qAK0ItRrQZKvrN0pNrr1EIBcI5ebQ671S9W+FApApsdTKVgujHaLtgJr2cbWQkASgFtrvuVoJQJ1RaCRCEA66nLnlIu+SmkfHBcNLUhqUc3GYGrrCSecoNSxsyqYAsyUD9bUY209OwZfgg4cOKBgJdIkzEyNrhUDvhSJtA8uAFlQu1zYzeRD7/gPzL+Am/Z+DavZtN5LlDyPLqq90W7MrIzh+RkW+TdWoUXy7PimAQwtGKMsLDYQv8uHjkjLUQ6lhgNFRVm0B/OrY6BZi54gtHPpMBbXyqdZhxCA3+UwVT0p+k+F47Zw0nTsRHtep0cxWRkxgJgqnIOoZwDPlyBXWx0JrLgXkHNYU49Q9K2eJiHsQ2uwDR5XHrNp/SR8wteLQ4uVCUC304OPnvVZzEzNKi6/IuVRGDZYsfGm53PJc4SJgyAExf+XSh80u5YgN7seeughZTivec1rNJuk6B1/o5xH1RtJMq7nqs0GaYQxcmxqlSDVjrwnP/rRjyrrNBGsoUlSmOnh73vf+5T62FTXmrlhS7UhN7T5Q/Merh25oc7QQwAy86TSer7UnAkC8KKLLjpSK7MR5lf2USJQDAFJAMr7QiIgEbAUgVK7jZZ2QkNjrDlCJQHTPsoVfuYOKlUHXEAxuEAyotC4hi4ecwgLeFMFccopp4Ckmt2CdXToqCfqMrF/VP5ddtllduvqkf7wfhXOiHwpKkXollL92cHkQy+4Y8vD+Pae67G8bo6SqT96Au4bfk5v9yqetzNBNdSQKaYabJxE447EgOlprUl/Ej63Q3GbrTbCnnY8N6XNYMUFJwbj3ThsstmJGAOxG0sNWaZW6wp3IZ2dQypTe81JjoHkamotioW1je/+YhF0+BF2erHsKn1MtXOq9XimQJOES2fNS4kv15e+WD8WVmlSUl37TjiRzrixpoH03pU4ESfmzsHIyEa6vNfrVYialpYWrTDZ6jh16rDadbhYJwsdh9V1ZautVcvrk/hhuiuDJIedyVMrJ43ZINzYpZq0Ue+ravDifUfF49e+9jVQ/fjUU08pG9zFghvdJAKpEORvkoNmukdLArCamZTHSgRKIyAJQHl3SAQkApYiwDozjVAgd/fu3ZicnFR2G5maWhhUGnB3VL0w6unpUQhDklr1iHvuuUdx0K0mbdmKfhZixRcVLqRZ4JkvbJdeeqkV3dDVBhf+t99+u3JuKXOVzaL6KwbQXHoa/7PnesykJ3XhV+6kkLsXa1mnovTJsZiaCdET6cByZhbpjHlOqf2xXkytMIXWPKdbqti6ox0KoVlNhN0DeG66OiUkU4InUyOaCJhq+lLs2JZgs2LYMauD3NTTdsAdQEe4tWoci7UV8bRi74w2lVubq0lxql7P61Ny6hkrz+F4O8PtGF6qPS1XTx/8Lj86Ix1KWnBeYy3GJn83hhYOaWruhNxZiK1skH3Nzc1K/Vg+UzZL6FEJcuyFtWYFOVgOF25mct3DuOSSS0xVdjXS/HBjlfPAetDJZLKRum5YX7kWft3rXqeseZkCSyUda10XCypxuQluRkgC0AxU5TW3IgKSANyKsy7HLBGoIwKNQgA++eSTGB0dBUk9KgpEkOxhjT/W+hNqRtYY4osHCyjXM6pJW7ain8RqfHxcSc0SWBEjEpT8/0cffVRRGVxxxRVWdEdXG2pzlXPPPfeoORaqP7VSg//WyKq/YiAtry/hO3u+gtHlyo6cWkFmGvDheQ9SmVUMxLowQ2ONrDnkCFMigx4HZtMzWrtX9XHN/iY4nOtYWjdX6TUYH8DY8gHkUZkwpUJteS2BmXT1tQqbAwkE3W5MrRhP/BaC63a6MBjvNV1JqW63N9qDhbVJrGb1E8Nx7yB+Pf1KHdNKN03EG0Z7OIGRJW3kVqXrVfP3jtA25LGKuVXzPgPl+tMUaEbI7cFUerRit5P+fhxcqExYOvIOnJm6HF6nz5b1YysOVOcBZqkEmfHAGm98fjHdUcZGmjYJQAZr4dV7jVevOeF6TagfudHMzdD9+/crKeNUCfKH9w4zYJjpYZZ6VBKA9boDZLubDQFJAG62GZXjkQjYHIFGIQCZqnr48GGlYPDJJ5+soMq0CLr7ip1PLnJ27typkIRm1kHROqVcjM3OzlZMW9Z6vVqOY+08YsWdY0YhVuynqDd01VVX2QK/YuMlocfC0QymuNCluNFNPvTM61p2FTfv/Tr2z+/Rc/ox57QEOvHA8CvOtM3eOLL5FFZhDgkYcPvQGW7G6PKxbriGDIjp7HkPEt4QFmGO4YTo57ZQO9ZzSxVTWYPuJuyZrj5tWLRDYm5HvNfUWopq7Htj3Vhan8ZKprqUUb3zF/KE0BpMYjylj5BbyyQxvVI9oUayc35t0rJxCnxoEtIf68PY8kFN7rp6cS13Xk+0F6n1GaQyJcoK5B3I5PxIayBmY5lmnOO7XNlQsrLWrhm41HLNQpVgNQYjaqXg9PS0UqKDtd4uvPDCWrq0ac5lBgBTgBlMbw2Hw5tmbNUMhGteGtoxuDku1sTqa7D8DI3duBluVtRKADK1nRvSXH9y43779u24/PLLcd111ykGdOVC1ABkLUIaklAEwOwWKo9JDr/lLW/B7/zO79QtA8gszOV1NycCkgDcnPMqRyURsC0CfGCKBattOwkoD3cuNviwZ009pjxwcSP6zmLQTHOoZAph5RipqBMLNRKT9Qi+fAwNDSmpIiwkzSCGdG9TY8V0I+E4eOWVVyqqA7sGXec47yxEzRQgteJP3A+bTfVXbC6yuSx+sv/beHrq0ZqnKuEbwCOjR6emBuFD2OvGUt4ch1inw4Hjkn0Y0qAu0jtARx5odiaw5K5edVdNmySvmgJRTK2UVlTRoOKZqerSf4v1YXu8F9MrY1jNrlbTRV3HhrwhtIUSGFs2zhm6UkfonDudHq7KaCXkTuLF2erJP9GXkCeIrkiLZfUW1RjEfXHEfWGMp6zDWN2+RzFl6cF46gByBW7FSX8nDi5oI+nPilyEq171Rls/Oyrde2b9XY9KkAQg1z1MoabaXUvasFn9t8t1qXyjCQiDmAQCAbt0zdJ+cP3LdTCD6+Le3l5L2xeN1UoAluo016Zf/OIX8cEPfrDkuAQBWG7gzBa66aablPWuDImAnRGQBKCdZ0f2TSKwCRFoFALwxRdfBH+4S0gii7X1GFwA2rXI+OOPP66k3HJxVo8FCI1QqCAQhihcVBGrYo7ENAK59957FUxpAlKvuolaPmLCXfm0005TdntFzT/hyijIPy3XavRj8vkc7jz4Yzw4+suahrKQjmEiNX/MNfwuL3qiJEa0kQB6OnFccgCHF7XXJNPTRhPoADuHPBlBk8IJBwYS/RhZKk7yra23YiylrUZdpS4m/XFEPF5MrhhzvUrt7UwOKHX6sgUEUaXz9P495o0i7g9jcmXDTKJSJLyDeK6K9N9S1+uPdSOVmcOSSUY75cbRF+3F4tp0RSVpJSz0/j3mjSMRiGBCpcBsDgzgwLw20vojZ34aEW99y27oHXs9ziMpKH4KVYIkup5//nnl2c3nNhVNDD21BOsxNrPaZNYH01sZF1xwwaaqL1kNZlT90QSNwVRxZkLUI/QSgH/1V3+F3/qt31JIXKr3GExhvvnmmxXCTtQlv/7663HttdcWHRprVXOt9/rXv14hQ2mAQuOcxx57DDyPykIGN7xpIsjMIBkSAbsiIAlAu86M7JdEYJMi0CgEIBVsVPyJ4EKYKRD8Mau+Sa1TTrc2uiF2dXUpaVFWBdNkSJZycSaCJCQNUZhOVCy4sBapNdxZtZOSsrC/dAHmCxJdAElm8iVK7bhoFc52aueh0btw+9APdXUp4k7g8YnSCjmXw4FdyV4cWHjlftLVUJmT+qJdL9eAM0/V1hpoUVJ10zn9dea0jJv17OZWx45SsHkdYbw4Z4zbreiD2+HEjgQVlNoIGi19L3eM1QYh7AtrLE6kKqfI5nNtGFser3WIyvl+lw998Q4cWqxc986QBlUX8bl86I50vmwSYh5ZXa7fneEuZPPLmF/ld0IYrDlaKboifXjvSR+udJj8exkEhEqQm4Yk//gcZ/T19SlrCHUUcxxWKwTtrOCv5SaQzsgb6HGzlsQX55xroXpt2OohAJltwo38UmV6fvzjHyvkIN9NgsGgsu5nhk9hsLZhKYdjnvuBD3wAN9xwg3La1VdfjVtuuaWWW0+eKxEwFQFJAJoKr7y4REAiUIgAF5kiNdSO6HChy9p/3M0T6Z1M+6SSze71X1hzj67E27ZtO5KuYTbGdPJlu6z/wohEIgr5WKlYNheRJNYYrDfEhZfdQqgkSFRyfEyN4gJQ/HCsm/XFR8tcPDv1GH6071vHpPFVOteTTuLXqcrpkyc2D2L/vHaThUrtFv69JdAEjyuL+VXzavaFPWEkAiFMrRhDFpUaY8KXgN/jxPzLbrr55QQOZcxJQx6I92BuZQLp7MZn3sxQDEISvTi0aA3pyLEk/UmEPB5Mp4vPmd8ZwUvzJWrY1QBGT7QD67kUFtbMux9Lda/Z1wSv24HZ1eLOnjUMS9OpNAUaiO7ESwv7NaViX9H7RpzbeYmma8uDiiPAddjevXuV9Q6Dzzeuc6juKjS2KnaFQlJQXQZjszwXSfpQ4cXYys7IP/vZz/D2t79dIdKoAKzXJrgeAlDL5/9zn/scPvnJTyqHfvazn8Wf//mfazntqGP4bsNNYqbRM/i5qlRXsOpG5AkSAYMQkASgQUDKy0gEJALaELAzAcj0l2effRbcMRTBnU5K/+1g8lEJYe7is1YLVWqnn356pcNr+jsJMS506IjM4IKQBZWp/NOy+Od9cPvttyvn0lGOZJqdQqT58kWIZDCVlYXBMZPoFIQg/7uU4tFOYzOyL/vnnsfNe/8LazntSrqFhTAmMpVVPuwna/YdXjqIXL6y662ecYU8AbSH4hhbruxOquf6ymeD5Ea8F4eXzFV4uR1uJBxRLLtmsDAfwRyMJ6kEBnFfFHFfABMpc4lN0V5vtAtLmRnLjDMcihpwEGNF6tS50k04vK7fXKXcfeRxurE90aOoAfOwWJGXB1pczVhxziGLDTWYldEWpMP1ONoCSYytlDdm+cPT/xwJf7OV3dtUbVHZ9vTTTx8pbcLSFjRvIAmoDnUtQXXpi1JgiLIY/LtIHxbrAS3rAruBzLqITH/ls54GEls1vvvd7+L973+/QmixxvNmIwC5kU3VH+/fK664Aqz7rCe+8IUv4KMf/ahy6o033ojf/d3f1XMZeY5EwHQEJAFoOsSyAYmARECNAHedRaqJXZARKaxc2IhdbS6IaahBApA16hohRN1C1iahYYUZQXwOHTqkKAfEPBIrKgeqUfHxxUIsss4555ySqRVmjKHcNYXqr9Dog67GJIb5Q1UA/79YUCWqVgkytbkRyOMUZcSoAAAgAElEQVRacB5dPoTv7PkPTal77rwXz8+uVUVt9EU7sLA2Y5rijATdzmQvDppoDkJ8dyQGMLx0AHmYQ2aKOYxlm3Bgbc70+nk0VdmV6LcsJZjGGW3hpKUGIc2BFvhc+aOUcanFCOZNJFc5j9vCbXA7M5hJm0M0lvu8e/NeRBxBpNzWKRHzecDtTGBOSQMGIvkwvG4g7Tx2o6At2IEPnrrxki2jOgTE85slTkQpCxqGsS6a1udUuVqChb1pdJUgiSHWNSYxyhqAWzX+8z//Ex/5yEcU4ztukteLzDVLAch55cb55OSkspblGPXET3/6U/zGb/yGcurnP//5I2SgnmvJcyQCZiIgCUAz0ZXXlghIBI5BwE4EIBenXOBR4aVOYeUCgH9jIV8uiulSq3VxXM8pp/qPKkAq0Vjs2OigaoALIxJgDC6KaTbCnVM9+Nx6660KzmeffbbirlvPUBdFV5N/pWr9MYWZOIgfYiNedtTj8Pl8RykEN2va8Gx6Cv+z53rwd7lo8ffigZGhqqe6NZiEy8l03WONQ6q+WIkTjm8awKEFc5VXHeEOpLNzpirZQo4OLGayyOSZTmqeClDASCOLhdUprGTMrXUo2iOROqahTp9R9wXTU5udSSw5poCcG2OprCXqPBLTO5I0eTlgOplbDKtYPgq41rDmND/VO+xOYDR1bMr69ng/5lYnsJJ5xRn8ou6rcFH3a42a3i1znbW1NeX5zY1NRigUUlIWa1Xfb2aV4OjoqLI+pPmbGWuqRrn56JD7l3/5l8rG8kMPPaRrvWfEWM0kAFtaWpTPRi0E4E9+8hP85m/+pjJUSQAaMePyGmYhIAlAs5CV15UISASKImAXApAmFFzYccePUZjCSqXXAw88oPyNKQH1Snmo5jZi/T/W46MKzcjdas4ZCyOTYBQkF4uE79q1q6Zi0EwBpoqQjoNcfNUr1KlN4r/ZF3VNo0p9I0ZMIVeTgsWUrrxmYdpwvQpqVxpTtX9fXl/Ed/Z8BVQElgq/swfPTB2s9tLK8RFPEM3BCMZNTDsdiHVjdnUca9k1XX3UchIdZyM+P6bT5jjqphfjmMjNwe/woMUfxXTGfBVZzBtBUyBsaiq1GtvmYBP8bgdmKhDOWuZD6zHhfBhBTwIvLJVPT9V6Pa3HtQSbEPK4MWlyHcli/XE5XGj3tGI2N2Yq6elbS2IKxeuC0qiEpj0jyy8ppQA+eMpH0Rbq0AqfPA4AU1mpZCMJyGAqJ5/fZq1rBCnI52Kh43DhhNhZJcg6bsx24JqKG5VbNeii+3d/93dKORyu2/Rs+BqBnVkEoDoF+PLLL8dtt92mq7vE6E//9E+Vc7/5zW/iHe94h67ryJMkAmYjIAlAsxGW15cISASOQoALQzpm1SvYPhcRTJcVJh9tbW1KagN3eUUsLy/jnnvuUf6Xi57C2jj16n+5doeHh5W6PhyHUfVq+OJA1QAJUwYXwqwVxELhtYZw1z311FOLuq7Vev1K56tfTNSpv0Y4/PJ6vIdICIq0YYFhYb+IqZoU5PzVa4FdCbNyf+eYDw4P4cdD/4M517HklsvhxoFZYDWnv76Y1+nGQLwDBxf1kYhaxtcWbIbTsY6FNfPUhm6nG32xLiUl2NDIOzCy4EQG2SOX7XQlMeusbLpSaz+UlODkAIZMNG5R95EKue3JPksNQkKuXvjcLrw0b50pCcfMmoQ7k1Q+HkImZ/3zk8YoYY8Pkyvm1MrMrYax7ChfF7QpkERnuB2/e/z7G/L7sdbPl57zucbh5h3XPAzWqKXCiWseK0OstQQhKP6/mGqe/SpWS7CwpqAV/WdZGOJH84szzzzTiiZt2QZJrS9/+ct405vehB/84Ad166NZBCCNP/7iL/5CGRfJTmEIUs1AC01AuCHP1HoZEgE7IiAJQDvOiuyTRGATI1BPApDuZVTILS1tvGiwPhsXw6z9URhMCb7rrruUf7arS21hn2nI8cQTTyhkJUnLWoJKAZp8CPMLKtcGBwfR399vWP2XX/3qV0otvZNPPhkdHdYqOtQmH8RJ1EMSqr9asCt1LjFVKwSpGCz2AqR2GyYxyJePetXc0YoDPy/8bJEwziGHoeAzmHRtuEuKaA104/7h2tVTJENe1TyA/SaSMEq9uWAU46kNkxuzYkdiEMNL+w1TVyV8XXhy4mjc2fcWZxSrzhQyDv3kq1YMeqOdWF6bQ0qVtqn1XD3HdUc6kc7Na6pBqef64hynw42JJWA9l0FPdBsyuRXMmeggXayvCV8MySCVlsO1DEX3uf2xPsyvTh6Vkqv7Yi+f6Mp6MadRcfuarovxm4NvqrXJLXE+N5y4IcjnDIO1aZnyy3WPHUKdOqwuu1Gsb4UqQfUmnZnPxv379yvkKTc8TzvtNDvAVpc+XHfddYqpxTvf+U584xvfqEsf2Gi1BCCPn52dLTt3P/7xj/GWt7xFUcfys0FxQKF7LzerOf/8DBULiho+8IEP4IYbblD+/IY3vAE/+tGP6oaTbFgiUAkBSQBWQkj+XSIgETAUgXoQgHywszYeFXIMLh77+voUQquUa6vdXWqLTQrTmXfv3q2k9TBtWU9woU3Sj+SfUGqyPh9Vf6wZZGTce++9ChnLa1u1U1qo+hMvHtWk+xqFAdUQrB2oJgWLqWPZN5KAanMRu6QNE09+rpgmxfEwWBOSheXvG/8FHhi58whccW8/Hh0zzgn3hGaqzcyr2edmDbZEDw4uGqzSK7iBuiIdWF6fRTpbew29iGcAz04VV6fFPWEE3S7MZcxTNoqhhT0htIXiGF2yhqgKeoLoCDdjZNk8ZWiTrxvPTL1CYPtcHgzGu3FgwVo1IDHekejHdHoUq1nz6/MVft/53X50hTswrLha1+5UHHW0Y3hVG9H+v075A/THB436Ct6U1+F3MmvX8RnO72SudwYGBpTNOzsrywtVgtWkDgt1oNEqQZql0PSMRmfcqNyqcc011+CHP/whfv/3fx//+q//ahkMXCOSkBPBGn3CZff8889XnInV8Z73vOeo/+cm/iWXXKLUbyQpx2wTbvjz3iK5e9NNNyk/gmD+0pe+hA996EPHjI/Xvfnmm/HGN74RF198sZI+zzUZ169cc19//fVKSSEGr//ggw8qnzcZEgG7IiAJQLvOjOyXRGCTIsAHrahDY/YQBTlB8k8QKyRRSDhVKnzNc2lSwXj1q19tSMqr2ePlTicLNDOuuuqqqhf7TFllui+VkgySTEyNpjrPjBcH1lhkeizbICFrdtRD9VfNmNg/qjYEIUhsOCfFgmSsOm2YDsxmzFG5/rOvVP3xvmPQ8ISmMOp6jg+P/gq3Df1QIQqmUyHMpouPpxqc1Mduj/coaYnrJqZFWmEOEvPFEPZ6aqppRyfVhdUI5ldLG3+4nS7sSvbgpXnjiNhSc0el5nHJQRxcME7hWOk+2Z7ox8TKYVPSZKOefjw/cyxufbFOrGQWsGiB4Yp6/GFvCB3hJgwvmUd6lsO7LUin4hxm0ht1dPWGYzWKBceGSq1chDwhfPLcz4CmLDKKI8B1Dok/ZgMwqGii6q+UcsnuONZbJSg2jpkyzXXjVgyuS66++mrceeed+NjHPoa/+Zu/sQwGEm9CVael0cKMCkEAVjqX66d//Md/xLXXXlv0UK394Gft29/+tpJZJEMiYGcEJAFo59mRfZMIbEIErCIAqaxSkxMks7hrR2m/VqKEhYC5g15vkwqttwFTfe6//37l8GqMS7jIpsEHa92IXXiSfiTmzKx9SLKS5BHVYlQomBXq+n5qVUE9VH/VjpFkuaghSGKQcyzmSH0t3t9qhSAJbrMKvCu1/g4ePKqOJj9XO3bsKGoK89zU47hn+HY8ODxS7fA1Hd8ZbsV6bhlL68aSi+rGB2I9mE2PYS1nnjmIx+lGb6wTw0vVuySzr1FPC56Z0kbGHNdEd1nWkzM/Jbgr0o717LJlBFnSn0DY58HUioEmK3lgcTVU8h7zu30YiHXgwIL5xGrhh2Ig3oOl9RnTU6CLfRhJ8g7GBzCZGsZablXT51V9kDPvxsJaFnlHZSXhqc1n4LdPeIfm53fVnWnwE/h8oNEHy2owSFpxQ8YuanEj4LVaJcg1JMlUsR4yYgyNdg0+72mM8cgjj+Bzn/scPvGJT1g2BK3Em+hQIQHI9wCm4nKz+dFHH1WUsVQRMsOHad0kdS+77DJFSVisFJC4LtV9FATwOrwnmG3DjXJuevJzxvqQb33rWxWi1Kx1l2Wgy4a2BAKSANwS0ywHKRGwDwJmE4Ak7JgywNofYjEgHO+qJbPqbVJR7azpMS4hAUfVn6iLyJ1QLoqampqqbb7q47kg42KMqdgkj4wONdmnrjFkhMmH0X3Vej2OgySgmhQspqjlGAvThqu9/4v1ifcJF8Bsn0HDEu52M028XOydeRFffPQ/sZIxJ2Ux4aOzLkkf8xxvaQ7icKxhca2yWknrfBY7bkdiAIeXqk8rjXsH8dTkPs1Nd4TpvL1uSR27oDuAzkgzhhdrrwGpZYA0JCGOhxQcK5NLla4Z97bj19OV01SZErywRjLOPDK6WF9JQPbFOnBo0XoCkv0JuUNoZwp2leR1a7AX++a0Ed4nr5+Bbm+PonwW6mczNzoq3RN2+Tufc9zAY0oj/5sEBDfvtm3btiXIUjNVgqyhSLKHJUrMWKPY5R4q1w/eU+ecc46S4vov//Iv+IM/+ING6Lbso0RAIlAGAUkAyttDIiARsByB1dXqlQJaOjkxMaEsUsQOON1VtZATpa599913KymZJ554Irq6urR0oa7HVGNcwlQhprccPrxhGEDCiDVLSMZZtYP5+OOPY3x8XEn/5QuLkSHSfQtr/jWC6q8aHDg+3u/qtGFB5hZeh+QuVYLi5ZlpxFrVsMI9W7xk8to9PT3Yvn275vvl4MIw/vGR/8DcqjkEWsDtQxdJJhPrzlllDhLLR5H1pJCtwrRjPduK0aXqVG/ErDfaZqqrsvo+PC45gOHFIWTzr7gUV3O/V3ssXWMzWMFijY7OCd8AnitRW/GYz5knoGA6tGBu7chiWHRHOpDNpzC/NlctVIYc3xnuRCaXwsLaRlmASpH092JooTIB6My7cNH65XDBddQlxUaHmhS0i8lFpbEb8Xc+86n6E2UYSIgyDdHoer1G9NXKa/B5JX5qqSX41FNPKUovrlHMzFKwEptq2yJ+3BTmWvHrX/863v3ud1d7CXm8REAiYDMEJAFoswmR3ZEIbAUEqFgqlOrXMm4ugkn8kUxikOQhMcFFWy0OcUynpdrKqhp1tWDAc0nq3XHHHcplWCC5WJ1D4s6UFuIllGNa6yLW2r/C85988kklJYNEklE1U0qZfDSy6q9a3HkfqBWC/O9SacPqOoJUDBYjf/kZUKtE+XLJ+dJTV2pqZQZ///D1GFuujqjSioHL4cCupj4cMLHGnVXmIBF3GBG/D7OrlVWNflcEe2dK1/6rhB9dlYlZ3gC1XKW2Wv1NcDkzmK+RlKvUjvi73+VDd2wbhpf0E3LrmSR471YT2xM9mEtPIZVJVXNazccylXww0YPDi9bMZ2GHXQ4n2jytmMtOIO/IlRyPA06kM25NafXHN70Kb+37beV7TXy3laqPyrQ8sdHRKC7qeiadG578XmY6I6O3t1dZ99Sy5tHTj0Y4R69KkAQgn3/cHCW+Yn3ZCGM2qo/EjmPn5+6WW25R0lxlSAQkAo2NgCQAG3v+ZO8lAg2JgFEEIBcmrEVGpzbhQEoDAta9odqp1nj44YeV3V+mflAZZ/cgHr/4xS+UbjJlo5CgoZqRxB9TWhh0QGb9Paa3aFWCGYkBlQvcVWaKNlULtUYpkw+1Q2CtbTTi+bwvWAtHTQoWU+Gq1TS8d0gg0+F3aGhIIeyFezZfhmpRiS6tLeOfdn8VL87qJ2QqzcOJzYPYP689HbbS9Yr93QpzEI/Tg94YHVfLq6SSvgE8MVF92rB6XL3RDqxk5k2tpSjac8OFNk8cs/nK5KaeuSl2zjZfG5Zy01WpKnmdsDuBF142uqm2L1SMdkeaLVNYqvvXHmqB1wVM12jSUe2YxfH+nA8xXxgLJea4OdCFl+Y3FOiV4q27fhtntb/6qMPERocgBflbkGHqA0mI8btMTQo2skqQax1hTsFxsrQDsxSsKNtRaZ4a6e+VVIJU1XO9RLyp/lOnVKszCTY74crx894iXrfffrtSM0+GREAi0NgISAKwsedP9l4i0JAIGEEAcnHG3W8SGwzhQMqCvEaRWbt371bIMhIeNBBphGChYpI1Z5111pEXAi7cSOKwNqIgStvb2xVlYz1fhFhLjgQu+3LqqafqhreU6m+zpfvqBqjgROJF1axIG+bvUmnD4lR+vkgWG/X5Wsuu4ctPfAOPjz9j1LCOuc6uRC9Glg+bmm5qhTkIB7ZTqWfH+m7F69m50YX9GsmUcoBHPCG0haMYWRo1bV7UF251JJByziNXRilmZEf8eZJSASxCe4psk38Az0zWRq7uTPRiOj2BlcyGQYNVwVqIOxP9GE0dtMTwpdi4eqI9WFmfw9L60an/LYEB7J+vjKsDDsX9N+wNl4WN32tUBfL7TJCCpVSCfO4J9TN/kyBsBCKH6x3WpRPjam5uVtIzjajvatU9add2hEqQayTWkOaaicGN0tNOO01ZY6pDbIrx38Qmo7iHGuFe0joPvOe4ScugEQgNL2RIBCQCjY2AJAAbe/5k7yUCDYkAd+6LpSRqGQzP3bt3Lw4d2igmz4WXSH3hQs3IMCNF1cj+FbsWU4CJ0emnn664mvFFiEQp01gYfPHhCwOVkvUOqhhYvJz9ZH/1RCnVnyD/9FxzK55D5QzvFSpemZZdqk4nP2PqtGH+t141YDaXxTefvRl3HXrANMh7otuQWp9DykTiheYgTsc6FkxOae2OdGFxfQqr2aONVNwOL4bms4YRnXR2ZUqwFnLGiIkLIwCfy4EVhzWpshzfjuQARpYPIJcvnaIqxubCNhxerJ0QJbm6LZLEYYuMUNRzQ2fkhD+AsZQ5TtyV7gMqWfuiPRhZfknBPK/w2GEsrS9VOhV90X5cd9ofVTyu2AHqcgiCFBSbYOrj+bxgCQQ1KVhI+OjqgEEn8TnHNQ+zHbh24rqnnup9g4Zlu8sU1lTk/cByFyRYq6klyIFtFpXgyMjIkRrNe/bsaZjNcNvdXLJDEgEbISAJQBtNhuyKRGCrIKCHAOTii8QEFyCidh0XZySzuHA3I0SKakdHB04++WQzmjD8mnfddZei7mJKEHduxS42G2JNRNYIMpoo1TsIKhL5w/QSKharCbEYV7v78t+k6q8aFI8+dnp6WnH45f2jvJ6Hw6DChCYjfHkW/64+iy+iPI7pdeKnGlUp5+z/vngbvv/Cz/R3vMKZzYEEfG5gNq3NmEBPRzbMQWIYT9VOFJVrP+6LI+R1YyY9deSwpK8HT0wc1NPtsudsj/dgJj2BdAHhaHhDVNk4XOgJtmNsddiMyxe9ZmuwBW5XFnOrpWv7+VxBDM2tGFobcVeyD5OpMUtwLRz4jkQ/ZtKjdWmbfUn6mxDx+pChKn2hsqsyz3n9wBtxUfclhtwX/L6h2lmUQ+BvlsYoFvweU5sm8XuuHsourne4iTc1tfGZZw1WlswoVuPXEJC26EWIL9d8XJ+K9RJLv6jnXF1LUGw+8thSNa03g0qQG7VifUYykKnQMiQCEoHGRkASgI09f7L3EoGGRIBqo2K78KUGwwU7iQmqkxhW1a4j2chUEKY9MgWkEeKee+5R0oOIkaiHRIKUhKBZRKleXKj+4+KSL1msWag1pOpPK1LajhOqWi7uGcJEh+Ys6nT6YmnDxV58xIuzIAT54lwpLf9Xhx7Efz/zPU2KLG2jOvqooCeAbaE4RpfNI+hofrAz0YODi+bVNuSoCusCBt192DNtTptN/hgiPi8mUht1Q82OQZKOK+NYzZrjFF/Yf/fLhhmHFounojb7+/H0JFOvjY2YN4K2cAyHF7XVwDOy9bA3hI4w3bIru+8a2a76WttjJ+DQ0hCW15crNvHRsz6B5qB5inUSbGpzEarly6kE1aSg2am33JQhKSU2PZmKyXIkelXXFcHeggeQ1Nu3b5+y1lO+Xz0eZb3EzS8toU4d3owqwUcffRSXXnqpAgXX4lvdYVrLPSGPkQjYHQFJANp9hmT/JAKbEAGtBCAX4fv371d+BNFANR4XwFak59SiUKvHtJGguffee48Qf3xJoIEJU6QrETD16C/ViSyyTWLyvPPOq9gFQfwV1vyTqr+K0JU8gE6SakfoZDKp2URHpA2r1TTFivDzPixMGy6mQn1i/Fn8++M3YC23ocAwOtxOJ3YkujG0YC7xcXxyAIcscGDdkRhQnG0nUz6k1s2rLed2urAr2YOXTHRWVs91whdDzOfHRGrD1d2K6I52YSUzi1TmaEIq4OrG/rmNchNmxK5kPyZSI5YRnuox9Me6kcqwNp9+92i9mHidTVhaW0J/rBMHy3xWWoNt+H/P+pjeZnSdR0JHXUuQNQWpgi4WgUDgKHMRo1SC7APXH+o6dExF5WakDOMQ4LyypiKfYYxEIqGQf9Wo2At7I8rbcP2qVyVYWFPQuBFXf6U777wTb37zm5U1NwlAu2SQVD8SeYZEQCIgEJAEoLwXJAISAcsR0EIA0nyDxIRIz6GrL9N9rXS6Ewo1khfnnnuu5ThpbZCLTJppsDaiUC5wl5bFmvmCYtegAzDVDezra17zmpLdLGXywUWyrPWnb3apKKHCdXx8g2Thop5kMRUmeslikV4nzEX4UlXqxVntyklFDV+42O6+uSF88dGvgE7BZoUVDsF90S4srE2aTuz0RLbj19PDptY4FPNwXLIPo8vDWDeJoFXPN80rdiX6MbRQ2STCqPsk6AkqyriR5Y10aqfDjYklYD2XMaqJotehGrA1FMPwkvVqQJ/Li4F4lyWEtRh82BPDyNIG4cJoD7bA73FhInVsSvAl3ZfjtQO/YSr+Wi7O70u1uQi/24rVMeZmBze01I7D1aoEST7yuSjq9vJaTPmthZTSMsatdgw3v5haLTat6PTLH73Pv3L4qVOH1WVLip0jNrtFP9TrnHqkoP/whz/ENddcoygix8bGpPp0q31Q5Hg3JQKSANyU0yoHJRGwNwIkqYophdhrqthITHChobyEOZ3Koow/Vi9+WHSbC0Tu6l9wwQW2BJV1/viyIHawiREXmI3gXMyU06eeekp5sbn44ouL4qtW/fEA3jtid1z8tuXE2LRTxJOfLaZei1pHXNgff/zxprxg8vMsFIJ8geb9WixtmOoCkTK85sviP567EZMr06aheFyyH4eXhkxLOWbH454onPk1rDqONu0wclBxL9WG04j5vBi3IE2XhiceVxYzJtZTVONDInVp7VhlnpEYFl5re6IfkyvDiHra8MyUeeq/wnZ5T47XSQ3YEW6D05nBbNq8z5wYb0doAHtmjiV2N+oTjh3llPyh0/4YPdFeM6db17X5jKUaSk0Kltrs4Oal2lykVEkEdZ1j8ZzjuofPcjNIKV0D3wQnce5opsJNUwYJWhKsVL9bFYUqwWpShwvXP2avi2+88UZcd911yn1IVarZ7Vk1B7IdicBWRkASgFt59uXYJQJ1QqAYAShUbFyYCXKQaj+mvdSr5ogWgqpOECpEGBdjrFsjCBXWbONLCNWTTPslqWPnoPrs8ccfVxbgosaM6K9U/Rk/cyTjqKoVxeRZ64jp9O3t7Za9YPK+Vdfb4gt0sc2AVcc67s7txmSmtEFDrQj1xTqwsDpjqiGCB240e0JYwCuKp1r7rT5/Zb0J48vT8Lrc2JXsxP55c9Ob2bbf5UV/fJvpqdRinGFPCG2s37hknUEIzVYi3lY8M7XXyOmqeK24L4qWYATDFo5VdIo1LHck+zGydMAwR+liA456O3F4sfhcBt0B9ETblbTgiDeKT5zzKTgdzoq42eEAOqcX1hIspRIkISh+uPHBUG98clOMpJT4mx3Gtxn6wIwSbjpyI4rBNSZTfqtVaZqBhV1Vgv/+7/+OP/uzP1OM8LhekwSgGbMvrykRsBYBSQBai7dsTSIgEXhZxaV+6eeimUo7kfJCNdBxxx1nKTFRbGKYIvLYY48pRaEvu+wy28wdCRziJRQHVBRwEcuXBS5uSVx2dXUp/2bn4DhYYJppU1dcccWRrkqTD2NnjXgODw8rqgfxuWMtKX7G6v3iw76JelsidVjc1+v5DO7NPo7RvHkGFK3BJFzOLOZXzSHoOJNKOmuyDwcXjDWTCLpjeG7q6H6f3DqA/XMvGepaW+puPKF5AAcXDpiqohRtOwAclxzEoYWXkEPO2A9Ikavl88DCig8doSaMpUeRQ970NtUNUA04tjyMtdyape2yseZAEhGfDxMmOFp7nT7MpNcr3jPbwm04teVVeP3gGywfv1ENktAh0aSukVrMSZ3tUdUlNvLsREoZhYUdrkPlO83khLpy+/bttq2PTLzsohL8/Oc/j89+9rM4//zzQZM5qUa1w90s+yARqA0BSQDWhp88WyIgEdCBABc2TD/kjzoVg5eiio21yEi61TvoOvzwww8rC54rr7yy7gsfKgyoEhgd3XAyFW6tfX19R3ZlucBlasu2bdtwyimn1BvCsu3Pzs7ioYceUo656qqrlN9q5Z/YEZcmH/qnkWSa2kGbhB+Voa2trfovavKZQklDQnB6dga3z96H/Tnz6qOFPUG0hiIYWzbXdOK45ACGFw8YRmA1+Qewe+zYVMqBeAeW1uewvJ4yeaaArkg7MrkUFtYWTG+LDbT5m7GeWUIa5qVVsx1PJoSh9EYdyoQjBLc7hxTMM1opBh7VgM3BCEbqoAZkf1qdCaRdi8g6jKuBuC3Ui+dntKlUP3jq+3Fi86ssua+sakSURBCkIDc9i5VEYE3WwlqCdlgTWYWTke2Q8GPJC26CMRpZXVkPleBf/MVf4J/+6crzSf8AACAASURBVJ/w2te+Fj/96U/rvg428t6Q15IIbFUEJAG4VWdejlsiUEcEuCBjfT2SWXzZZ3CxS5MPpsXYJbhIf+CBB5TuUKFGpVo9Qii41HXbqBIgXqwvpA4eQ/MSEjynn356Pbqruc1CfHmi2PXmb2nyoRnKYw7kPcPPGAl2gSkdtHfu3GkLcr2akVG1+J1nf4jbh++t5rSqjvU43die6MDQwkZdKLOiN9qBpTVj0o7dji68OFucGE36o0gE/BhbnjBrKEeuG/QE0BVpwuFF80ha9SDccKHFFcW8Y9a0sUVd3dgz/0r9Pxcc6HDHMWNim6UGc1zTy2rArPVqQD+86Ig2Yzw9YgjWzf5eTWnqHqcH/+eiz8Lr8hrSrt0uQiKQtXu5CcZg1gOV/KwrKNZEhX1mKRR1LUH+v1RjlZ9Z4kmXX/5mtLS0KOumzUSm8vkufsyoJfjHf/zH+NrXvoa3ve1t+M53vmO3j5Lsj0RAIqADAUkA6gBNniIRkAjUhgBJCaqSGCTVSEpQ+We3xSxTE5nywGCNunqkS3LhynRf8aLAPjB1kwq/YnixLiB/SBCeddZZtU2UyWdzbPfeu0HqXHTRRYoTrVjAStWffvCJKz9fwhiGigfW0rTSQVt/70uf+cuh+/CNZ282Nb31xOYB7J8313m2yZ9AwOPAbFp/fUOXw4Oh+RwyuWxJwDxOF45v6sa++QNmTMcx13xV8yAOzO83dX7UjdIleHT5EDImuPTm8y0YXT429bzD14R0bhGrsJaMq7casD/Wg1RmFkvrG7XT9IQj78Bq1qep5uZJzSfi2lPfp6cZ259T6D7Ler1MRxW11dTGSfwOL6cSVNcR5H/zGSpjAwGWQmHNW7GZyHVmd3e37daZRs9XrSpBsfHKfvGefN/73ofvfe97yu+vfvWrRndXXk8iIBGoAwKSAKwD6LJJicBWR4AL3DvvvFMhJEhmkaCwY3An/pe//KXStQsvvPAYtZ2ZfeYibt++fdi/f/+RFKHOzk7FtKEcEUn1H1WAfBk499xzzexiTdcm0ceC3IJgJQGcSCSUfnN8diODaxqsRSfznhkaGlLuG5FWxhcevlxulhfDx8afxpcf/wbWc+umoWqFQ7Df7UN3pAUjS/pUc0l/Dx4b06ZWPKllAEMLNHYwv3YejVWW1+exvL6RPmt2tASa4Hc5MJ2eMqypoDuKF+dKpzSHPEF0h5M4pHPuaumoUhswNYy1OqgBfS4vBuJdOLSor8ZkS6AD++a0KQl/5/i347zOc2qBynbnFqai8jnHOr2VNmZ4HmsJqh2HS6kEqSJUqwSZIbDVnqVUjKtLpQQCAcVQxU7ZJVbfnHpVguznNddcg5/97Gf48Ic/jC9+8YtWd122JxGQCJiAgCQATQBVXlIiIBEojwDJCS5omfZi5+BC8vbbb1e6yALIkUjEku6y9iBVf1QgMpjqw7SVZDJZsX3W/6P6iy8CF1xwQcXj63GAMPlgDci77777SIoq+8KXFeJMQxPxY/f7pB4YFrbJzxPvGeFuyBc/qv5Iqm62eHH2AP7p0a9iyUSSqT/WgfnVaaSzGyUKzAgaWxzfRHKuesVhxNOPpye1m4r0xdqRzi5icc18Yi7qDaE1FMXI0katUrNDSd+O9+jCsVjfWoMDeGKi8pyc0EQFovVkXL3VgB3hdricGcxUSbq2Bwewd7YyrpyTz77m04j57FMOpNZ7mN/LTEUVz/Tm5mblma4nq4DPz8Jagrx+sVqCTHVVOw5vdpUgcaARGjcXGTS7Ys3bzZTyW+u9yPPVKkGxHit2/7CG8Jlnnonx8XG84x3vUBSAvGelE7ARsyCvIRGoHwKSAKwf9rJlicCWRqDUDradQOGC6NZbb1W69OpXv9p0MmVtbe2oYtUkwwYGBjA4OKh5wcVC13zR4K4302rtFOr6NGLRSVXD5OSkkq5a6iWGY1ETgrL20SuzyoU8VaIHDhxQXgB5zzCljPdNvWpWWnHPjS1N4B8euR6TK/rTaCv1s8kbBb2IzDa42JlgjbeDyOZLp/Oq+0qH2rl0GPOrG3WttEbcF0ZLKIyRpTGtp+g+juTmqyxIp1Z3cCDeg7mVCU0ppuUGFnB3Yd+cNmVmUyCGpD9oGdmp7jdNZcZSh+uiBqSz9c7kAEaWhpDNVzYJ4T3rcsQxtzpX8Z7qifbgo2d/pOJxjXBAYS1Wfj+bkYpKlSBThfkcFQYjXE8UC24O8nkqiMHNoBJU10nmM5EEFbMlmDWx1RSQej8XhSpBlup573vfq2wsMphRwoyNzbyu0IudPE8i0GgISAKw0WZM9lcisEkQaAQCkFDfdttt4OL6jDPOUApImxFcvNLZl2krYtFO5RYVAlysVxNjY2N44oknlF1a1i20SwjCr5zJB3EWLy8kBvnDfysMoWogRnyRoYHMVtyRJj5UewpVCe8V3jPEYyvE/OoCvvjIV3BgQRtZowcTHzyIe3xYRHVkW7VtdYXbsZZb1JQ6G/W24KmJY+vTaWnT5XDixJY+vDinTYml5ZrljhmMd2NudRIrGXNde0UfYt4omgIhjC3rUx+6HG6MLqOquoIkO09sYf3Dl5CzIM1ajTfVgC3BCIbr5BSc9CeQCAQwtlw+tTfmbcKhxWlNt9NvDLwOrx24UtOxdj6Iz3KSJ1NTG+np3LhiKqoVmQRCJSjShvmbtWHLqQTVpGAjkTzMJGCtP6rUrMbZzvef3r7xHrnpppvwh3/4h8qmLMuHkAj8yEc+opTskSERkAg0PgKSAGz8OZQjkAg0JAJcHBdbjNptMKwBSLLy1FNPRXt7u+HdY6oKXxKmpzdejkhu1bJzTTXd7t27lV1aOhfXO4qp/vhvWkw+SBbypUWQgfxdjDjmtUh6qVWCmznlh6QojV6Y7s0QStG+vr4tR4SmM6v4t8e/jqcn95h2q9MBts0dw5yjsnqplk6QvIr7fZhaKU/uJX0DeGy8NgLvVc39OLx4EBmNqsNaxpXwRxH3BTCe2nhBNztIyFEdd3Ch+lp1zYFuPDX5ivtvNX3tDLfA7cxhakUb0VXNtSsdy9qA46kRrJqYsl6uD9sTfZhbncBKZiP1sjA6QgPYM6Ptnv2zV/9/6Ip0Vhqyrf/O5zldfsWGnqjfW09iTagE1aQgybPC4POksJYgVfh2VNJxw5AZD0xVZdAcjSTVZql5a/VNzvXVxz/+cVx//fVK06zN/K1vfQvnnXeeLeffanxkexKBzYKAJAA3y0zKcUgEGgyBRiEAmfJAhRWLdXd1dRmGMsktpm2SyBGqOLF4raXmHd2CH3roIaWfV111VV0XbVpUf9UAqlY1CFKQBGGxoNpCEIJUCtJoxo4vMNWMn8eyPiRVf+KFh8SnHqVote3a+Xg64d7wzHdx7+GHTe1mt6cZ0zDObKJYZ1nPbiDeicOLQyXHks214dBC7WRad6QVWaRBJaXZQeXh8U19pjssq8fRHe5QVJWLa9qdaxP+fjw7pb22YiFuG87LvdhnkcJS3f6GGjCK4TqYk7AfNEfpirbi8OKxrtNhT7um1POEL47/fcFfNux3NZ/lfKbTjIlBIoq1WFmLzm7B5ymfI0J1L8pwFOsnMwrUjsN87tSTzBSp1Xv37lU2krkJyFp/HR0ddoO5IfpDDHnPvutd71I2kBmve93rcMMNN5iW+dIQwMhOSgQ2KQKSANykEyuHJRGwOwLceRbEl537ev/99yu1dbirTIWVEUHyiuoAQV5xd50vCUakGLOv7DODCsB6LNKF6k8Umhb/r0X1Vy2+vI+EooHkJ8df7L7iC4xIGSYxSIVDI6UNc5ysycMajwz2nbUhWe9vMxCb1c574fG8x37wws/xoxd/Ueulyp6/K9GL4aVDyMFcR90TmgYxtLDvmL74XEE8P11cZaVn4GFvEF2ROA4tanNn1dOG+pydiR5Mroxh1SIX26A7gK5Ii6J2rBSsU7e8HsbCWu3p3iRxV9YXsFAF+Vipf1r/PhDpwuz6ZN3UgD3RTmTyKcyvzipd5j07lVpBHvmKQ7ig63y8/bi3VjzOjgdwo5DPdT6DGHzOMOWXm0+NEjQ+E7UExXO1nEpQpA3zt1WbbOwPsyaY7cDgs/zkk09WUqxlVI8An510+b322mvBNRTXjJ/+9KfxsY99TCopq4dTniERaAgEJAHYENMkOykR2HwINAoB+PDDDyuqqx07diiESy0hSBx16iZJxe3btxtG1PElhKpFBmsA6nEZ1DtGdbqvmvwjQSXIP73X1noe2+ULjDptuNgLDBe5VDQIlaCd3RH5osMaRyL9mUQmCWMWb5dxNAK/OvgAbnjme5rIBr3Y9UU7sLg+Y3pdu8F4D6ZXRrGeeyVNr8nfh91jxyqs9I6F5zFl9uTWAcvqAjb743Aji4WsdmVeLePjubuSAxhVUp5LG1bEfa14bnqi1qaOnB90+9Afb8eB+dJqTsMaK7iQH14kvSHM5TdIOKvD7XRhR6IPh5deQluwF8/PaFNV/q9Tr8Wrmo+3urs1taeu4cs0W1GSob+/v+E3Zzg2lilRm4uUUt1zraEmBFnr0OgNSD7XmfJLF2SGHVKra7p56nwy10af+cxn8A//8A9KT1jm5pvf/KaydpQbi3WeHNm8RMBEBCQBaCK48tISAYlAaQQahQBkOgQJGC7mWZtPT3ARzQLVahKHhBPTio0uCM6F8V133aV088ILL7SMJBLpvmr1H/tghuqvmjkQLzCCEOQOt0ifVV9H1D3iCwwJNs5PvZUbwhWaxi4MvkyRiGYqulycl74Lnhh/Fv/+xH+b6o7aEkzA68xjVoOraTX3a+GxbcFmOB3rWFibV/7kd/Ziz4w5hBJTV2nmsKYiHGvpe7lznXBgmzuOWYd1BFVroAleFzCTLl6jryUwiCcnj1Vd1orBrmQvplfGTSeMi/WzzRnHinMJGUdlp95ax1ns/JZgE2LeJJ6ffaHi5b0uL/7PhZ+Fx+WpeKxdDuA6huZd4juazwyq/vgc2axBlaAgBEX6MP+tMPiM4vpGbS6iVyXI5zhLpuzbt09J+eWzkJtgZtRl3qzzVjiukZERvOc978F9992n/Omiiy7CjTfeqJCqMiQCEoHNjYAkADf3/MrRSQRsiwAXjMUcXu3W4SeffFJx6GUxZC44qw2STazZJtJVuHDduXOncj0zSBy+kNxxxx1KN88//3zDCcbC8ReafAgi0ErVX7VzQiWdWiFIp7tihjRMzVYbizDFyIw5K4YpCWO+WAr1YlNTk3L/1ZuUrBbreh2/f+4gvvjoV7BoQDpnqTGw5ll7KIZRnY6zWrFhO22hGCaWxzC65AWNT8yKbaEmeFw5TKetIeaOS/ZhdHn4KJWjWWPjdVljcXu85/9n70rA7CjK7Zk7+77v+0xWIDskrLIJyCLuoCi74obKQzQKgoigIs+HwkMUEHnIEhQQH8oT5QkRIRBCCCGQdTLJbJnMvu/L+07dV0PPnb5zu/t299x7p/7vm2+Sud3VVafqdlWdOv//42DPzIQUUchDY599CkBtO9LjUlCYQldr5zJW+8ONasCMmET0RHlJZDfNAw+GxrxxLQ8PHMLQuP9s0Mtzl+ELK65ws3pBPctXjcY4f4xDF8kJqPQA0x6ySWLQn0qQ8Y2l8p6/GUswUCgOHoTRtVomSiOpSJdfpYC3NnzZXxs3bsTll1+Olhbv+47uvlQCzrexaw1BdZdCIPwRUARg+PehaoFCICwRCBcCkLFm6uvrRXBpLjqNmgyqzLhtkuiUGwQnSRy6wP7tb944aMcee6yjSgS7k3wYxdbu69g/UskgiUE9cpoB3bWEoBOB0ElOUikqCWM+k8pTJohxg3y0G9u5LK+lvw0/e+PXaBlwLnFHjMcDxrU70GOvW64vblTMLUhfiFea9jgOaWJMPKoy8nCgx1o2XLMVzE+iMm8S7UMdZm+1fH11ehk6h1sxNObNHpoYk4KaruBj/wWq0FE5VWjorXeN8NTWpyKlCN0jbRieHAlUTds+L0guxp4Ob9zS1LhklKbl4GCPvoL1oqUX4rjiY217tlMFcd6rra3F/v37p9RojBGs3tHvI86DKxmKQ5KC/lSCnEe1pKB2fcTwK3T5ldmUeXBKFXwg0tCpvg/3crmuueOOO3DbbbeJWMk8WPztb3+L8847T60vwr1zVf0VAiYQUASgCbDUpQoBhYB9CIQLAUgVFl1PSN6tWrXKEABc+GqDgXNBS/VWXl6eofuDvej5558XG5NjjjlGLPDsNl/Vn4z3N9fuvna1k+2jgoFkIF2G+VvG39M+g4ScbxxBqzEX+Uy65DCrodwocbxwYxlMVmi7MAnXcnqG+3DXmw+gpssZt1mJy1E51djfbb/76DTc+zMATzTaJjocjXEon7kir1pkszWSvCHY8REXHYsFmcU40O0skaqtZ3pcKrITU9Dcfwh5SZXY1mIsTl2wbc1LykRqXBya+4PP5Gy2LinM1Juai7pZskybLXO264uSq7CrY7rasiqjBEPjvegenq5IvO2kHyAtPs3Ox9teFkNscG7nvECjGo0uvyoBxexQc35jfGJtxmH+X8+4XuK8ynlQqv54EMZs926toWwfOCFQIA8VmehDHhCvW7cOjz/+uAhvo0whoBCYXwgoAnB+9bdqrUIgZBDgSaTeiXDIVPD/K7Jv3z7wh0QaCbXZjO3htSQMpTFLK0+suYB1y+gCzBP41atX275gjhTVn9m+oCu31m3Yn4sTN4JalSDdiAMp91g2VX9ys0MSkcQfSWdlwSPAjLO/3vY7vHV4R/CFzVICY7019TVgfHLcked0dCeie2IQBTFpGPEMYgTvJwdx5IEAFmaVoHOozbX4dUuyKtHUX4+xCXdi1jEBypKsKrQOjGJflzuKR/aVJyoKVAPWdtdiYtLZjNJ6Y2NBZhm6h9vRP6pPwtgxnphVOdaTgY6hrhnFxXlisCi7HAd7vO0vTyvDdWv/zY7HOlYG3SXpESDXLZzbmcBLqdGsQc41ija5CA9O/a0JqRKUsXk5v6pDMeOYc832+uuv49JLL0VDgzcEwde+9jX89Kc/VSFFjMOorlQIRBQCigCMqO5UjVEIhA8C4UIA0tVn9+7d4kT6uOOO8wswNweM9Sez01EZwCQfvM9tYxIQ1mPFihXCLckO0yb30CoAI0X1ZxYjuXmRpCA3MlRC+prMjMjNCzcuKSkpUxtG4sgFudZNnP1Fl18Vi8dsj8x+fW9fL369+WG8O7TP3oJ9SitLK8TAaDcGxgZsfU5STBrebe2ZKjM9OgFJMR70Ttr7HL1K5ySmIzU+1lFXau1zC5JzEOMZR4dLcQg9UdGI9xRgaKwPnT6qNFs7UaewkpQ8eDxjaB90z/1ZViMpNhHlafk46JD7ekZ8Fup6Zm8X+zo5LgZr8lfhQ1VnOg23pfK5VuEaoLHR68rMdzrndifU9ZYqGCE36c2H/pomVYIywQjXW4qInYkW1yT33HMPbrzxRnEoTCL117/+NS688MKAB5MRMqxUMxQCCgEdBBQBqIaFQkAhMCcIcGEiExzMSQUMPpTx/3jqT+LmxBNPnHEXiTZtFkAm+aAqgOqAuVqQvvzyy8LdhpsUZowNxrRkn3T15d9COclHMO21ei+xkTGPJCmoN745PkgKM4A53cikGxQ3NAwgn5OTY7UK6j4dBGQsTmaP5EZ+58R+bJvY7ShW2YkZgpyzM6ZdbmIVNjdNd6VMiI5FZVoO6h1OQiJIj+gYLM4qwX6XXHQTouNQmVHoN16cnR2Ym1iCtw43ICkmHguyCrDfYXdx37ozMcnS7DLhbj0XVp1Rit6RTvSN2hsDMctTgAOD3gzmsxlVmDefeB1K00Iv+ygTRDEGnXxP8/1MV1SroR4CYTFfP+f8yXe09J7gAZg8QOV8qs06rBefl2stklvajMPzXSVI3L7yla/gT3/6kxhWjGG9YcMG4V0QyCthvo5D1W6FwHxBQBGA86WnVTsVAiGGQLgQgMwAzEzAJGhOOeWUKRRJLJAc1MZsy83NFSTOXGene/XVVwUZxYVeRUWF5Z6X7r6+Mf/mq+rPDJDazIiSEBwY0FdrcbPD2EZZWVliA+NkkhgzbQj3a7lpJ3nPzSON30tu3nf21+A32x93zFWXz2IijZLUHDT2eVVDwVq8pxS72vVdVFcXVGNPh8PxB/+/Actyq4RibNwlt9UjsitR31vnaF/lJFRhW8v75Nuy3Eo09TWCruNuWmV6EYbH+tA98r7S063nc7xWZhThQLd9cRCjR1LROdkbsAmZCRn491NvCilSQs7vVGdzrULCZNGiRSgtLQ2pegYENwwuYAgMkqzyPU21PMk/vXlQG59XkoL+5lWG39AmF9Gq78MAFstVJEZcs1588cUiUQ2NGX/vuusucZCtTCGgEFAIKAJQjQGFgEJgThAIFwKQrr1bt24VLpmnn366wIqqABILJHZoVAOQ+CsoKAiJzQHjvVBdxtiD1dXVpvvXX5IPpfozDeW0GxjjT+sm7q80bly0cQQZV1Cd2BvHXqv6k27ZzB5JZS4VmLT32vbiP7c+6GhsO8Z5W5pdIeK8BWMeeNDQE43hcf8x/47MKcOhvkMYmXA+LmBFegGGxvvQO2KvYswfRkUpuYjCKDqHZ8aSCwZX3ss4dSNjGWgdnF52flIm0uJjcai/JdhHmLqfRNyCjELXlJa+latILxau0D0j0xN0mGoEgJioWHQOjRlKILM2ZyUuW35hyBx8MOMs5/e2Nm/2cL5/meiDbqbK7EXAN65iVVUV+GNmvmN/aWMJ+gvHwYNLEoJaUjDSlJyc7x566CF861vfEmFgeOh19913CwLQDKb29rIqTSGgEAg1BBQBGGo9ouqjEJgnCHCTzoVbqFtHRwc2b94sFk8kAHmiyriArD+NigAqA0IpZtuWLVvE5oULadbNjPlL8sH2yx8z5alrIRQkHDPacUMXcaozZbZh6eakFwSdCWS0hCBdnSSRpfCdjoCv6o9kKlV/VJX4WkPvIfzHG/ehUydJgZ24HplTJZRVVrPpZicUYcuhpoBVKk3LBTCMziHnFWTpccnIik9Ey7CXJHHaEmLiUJlegIM9dbY+Ki0uE++1eTO6+lp0VBRW5FViT6f7rrmLMsvQNdyG/lHnYzz6tjs+OhbVmaU40G293cUp5djZbizz9knRa1DiyRcEoDar+lwotnhIwyy/cm1SXFwsYrKq962tXzsxJ1JdWVfn/T6TiCPJShV8sMayOa9qSUGqDPVMHrbJcUeyd65CtwTbbrb5mmuuEZl9aRy3dPllLGhF/gWLrrpfIRBZCCgCMLL6U7VGIRA2CIQLAchF5KZNmwSuXCzKhSQ3J/6IhbnuhLfeeguHDx8WcQipTDRi/lR/yt3XCHr+r+H4oZpExpDiBoPjRi85jNa9SboNy6Qy2idwMS/jHUliMNKUDGZRJ3bcTDILtz/Vn16ZJP/ufON+1PcGJtjM1kl7/YKMMrQNWlPoZcVX481mYy6+aXFJKE5NR33voWCqa+hexm4rjctEO/QJNEOFmLzoiJwq1PcctM0lOD+pCm82z050LcgowsBYD3pcUjxKSFJjk1CYlI7GAef7Uq8bStMKMDFJQtl8/xYkVWBP54GAvUt16wUJH0KUTuJsqdjSxnVz6j3HdwbfHQcPeklLHrocccQRKhN7wB40fwFddrdv3y48KWhMpkKXX6f6ls8goSsP2fibIVL0knbJGL1SKcjfTtbLPHoz7+DcxyQ1dPmlhwHtggsuwH333TcnSejsaJMqQyGgEHAWAUUAOouvKl0hoBDwg0C4EIBcLL722mtTreCmhG61lZWVIXtSzMV1U1OTSADChXUg86f6k+RfoPvV5zMRYKByBjWXG0qSdhwzZscNCWdJBvI3T/n1jMSiViVIsnq+nPqTXOXGR7rkz6b608NucHQI/7n1t3ivfY+jQ5murOOTQ+gdCRwXTVuRwZEsNPcbzxLrVa6RgAnO9dgoGNWphWgfbcXoxJjRW4K6riglj1t6dNngEpwQXYx9nYHjNKbEJaEiPRsHuvXjMAbVoAA3F0Vnot/TgzHosGROPhgAE5QsyioXakDDCtZJYHwyyZB68cicxbj2mC+KAxJtRnV/cd3o0igJQf62IzwCn03VH0khGsulGk3FYrV/cDU3N4t3NedHzk8yYZrbc5VUCWpJQb3DNiLAMecbS9Dt+vrrCa7dfv/73+PrX/+6WBuQrLzjjjtw9dVXh+z61P5RpUpUCCgEzCKgCECziKnrFQIKAdsQGB4etq0suwviwookGjP8ymyucmPATUcoGxfYVEMVFhYK94/ZFo96iT6U6i+43qXbOPtAqkUZO4qqPztiSHEsUlWo3SzrKRm4EdASgnx2uLo2zTZ+raj+9MobmxjDb995Aq82bgmu8wPcnR6XiszERLQMGIstlxyThh2t1lx6V+ZVorabarkJR9vEwtOjEhEfC/RD39XO7gowVh5jER7sMeZmqvd8xqlr6JnA2KRxYm1FHpOg1IHjxU3LjE9FblIqGvucVar6axNJ12jPONoH2wM2OyeRmZQDZ/9lQZ854mM4o+IDM8o0qtiiUk/rNkxlNP9mxDj3MckX53hJSDFsBg9pQoXgMdKOcLiG+FKl1tjoJdtJrpJk5RwVKsb1qJxb+duISlCS0XMRAoaE5Xe+8x3cf//9AkKGFKHL79q1a9X4DZVBpeqhEAhRBBQBGKIdo6qlEJgPCHCRL2PphVJ7ZRwxEjlaO/7444XrZagbF9qMN8fMsqtXr9atrlL92d+LjN/HuEYNDQ2icKkWZQIKp8g3kn90pZKEIJO/SMJa20I+X0sIctNsdKNsP1LBl0iFkDYRj1nVn14N+J14Zu9f8d/7/hZ8BWcpIS46FlUZhagzEM8uN7EKm5usx2KrzixC/0gX+lyIJReHaOTFpaATwSWQMAN+MC7BeUll2NpsPqZgQUpYrgAAIABJREFUUXI2EmOjcHjAnfiHWjyW5VShrtd9ApJ1iI7yYEl2JQ721GJiFlK5JKUa77Ubc1n/yck3IC85J2CXa99zkqTxd4DIww6tSpBkky+hx3fkzp07RagMWigSUgFBCZMLqExjll+pXs/NzRUHYnNBmpmBTI45LSnoTyXIQ2FtghE7lKn+6sp56sCBA7jkkktEgjraueeeK5J/5OQE/i6ZwUBdqxBQCEQmAooAjMx+Va1SCIQFAqFGAMpkDXTdlKqqoqIioQSkrVu3TjeZQKiBzVhG/GFsnWOOOWZa9fQUf/ybUv0F14tMusINpdwgcAPKGFJuq0XZlyTHtG7D/tzp5EZZEoPh4PLG9tXX1wuiVX5HmYiHGa/tCtS/sf41PLzjD7OSHMGNFoDx85gcZH+ARAvxnlLsag/O7TQ7MQ2ZCfFo7m8NttqG7q9MyEXLhDvPYoWsugRnJVRhe4s1cjXWE42jcsuxdw4ShOQlZSE1LhbN/V7yym3LT8pBYqzHr4o1KSbX0FgrSM7Dj07+rqXq8z3Ad61WDU2CSe9AUauGJknD9wYPD+S7Oj8/X8TKDXVCyhJQc3wT106cF4k5SVgmJeP7OlwVltoxJ1WCemNOKlOlOpUHx3aMLz7rL3/5C6666iox9jnn3XLLLVi/fr1t898cDxn1eIWAQsAFBBQB6ALI6hEKAYWAPgKhRABSOcVNgTylZtwXnlKTRPv73/8uXITWrFkDnl6HulH9RxUgiZ1jjz1WVNdfkg8uxFWsP+s9SiUJsaYrGY0LcpJRjL8YKpsc6U4nSUG6NultWkgASjKQWXOdVDFYQVxP9UeS1Y7Mkb712d6yE7986yEMjzubqZyKqobeg7pkIxMk1PdEY2R81Apc0+5hZtcl2UWo6bLuMmumElUZRegZ7sDg+JCZ2yxf63UJzjecJZhJ3AdG04LOmLwoqwQ9w53oG+23XHcrN3qionBkTiUOdB9wlKj2VzcS2Euyq9DYN12NmBybikN9xmJcnll5Cj699CNWmq97D+dobeZX/ltPDS1v5vuZ72m6TobD4YdtQLlQENXwdK2W8yIV2nT51Ut+5UJ1HHsEiU3Op9px50+ZKuP0SqWg2fmVY/n73/8+fvGLX4j2MMTLo48+ilNOOSVk1hqOAa0KVggoBGxFQBGAtsKpClMIKATMIMAFjV78MjNlBHutJHCk2yY3BYwDxB+pKHrxxRfBRd3KlStRUFAQ7CMdv59x0RiDjpmKTzzxREH2EGdJ+sh4R2yr/HG8UhH4ALqPcZNDgo1GsphKEm52QtnkRlkbR5AbNl+jikHrNkwVg10qOzP4+FP9MYC8k27MTPjw8y33o3vYGKFhpk3aa8vTitA32onBsenx87ITirHlUOAEFWaeuzq/Gns6jblnmilX79qshDRkJiagud9YvMNgn8f7j8iuQn1v4CzBGfE52NFqjwsvMy+XpWXhQI/X9d9NK07JRYxnAm0GYvM5Ua+cxEykxyfiUL9XJV+SUon32o0ln/nW2q9gac5CJ6olytSqodvb29Ha2up3vcF3ttZtmHNnqBzgOAaQQwUzJAUTkUnl+XxTWFIlKJOLGFEJarNc+5vPGDvx0ksvxaZNm0SvnXbaafjd734HeqgoUwgoBBQCZhFQBKBZxNT1CgGFgG0IzCUByM0BM9LRPUUSOFyI6SVrePnll0WWQmbUpWIg1I2LRcbc4abmpJNOmlL/SRJQqf6C60GSwST+Wlq8xAYX7XRt4mI8HDeN/C5Q+ap1G9aLdcS2kQTUkoJ0r3PSuIkkmU2FLo1KHX5HnVD96bWjbbADd75xH5r6nHW3JJGSEAN0DHnbScuMr8LWZmsuqrP1ydLsUrQOHMaQw+pG8d3weHBETjlquoyRQnaMJWZbjsIoOmfJEpyfVIU3bcbWmyDkIMYmjCcVsaO9AuPsCuzvqjWeqdeOB2vKoJK1ub8RaXFMABI4rmJCTDzu+uCtiPEYS9gRTHX5nqa6Xx5yMDYuFf5StaV3CMmDDt/kIna4cAbTjlC/l/MI1x5UxBNTehYsXrwYxcXFYTkv2oU3D9xknF7psi7XnL7PoHsvw4kw3MzJJ58s5rqNGzfiiiuuEAQ25+Drr79eKAHVeLSrh1Q5CoH5h4AiAOdfn6sWKwRCBoG5IgAlqcCFlpbA8Reb5tVXXxVuHkuWLBHuQqFuJDa3bdsGkjMkAGkyBo9y97XeezJrJDc4cjNJl3Cq/uLj460XHIJ3ShUDSUGSb9I13reqMvg5XYZJDJJ0toMEJdZU5TLWHzdQNJLvdK92UvWn1xX9owO4+80HsbvDWeVcUkwCilOzpjK9Doxk4XD/9EREdg2VopRsxEVTOfY+4WhX2Xrl0F21oa/etey5CTFxqEwv9JslODaqCLXd9mfULU7JQXzMJFoGAmfLtRvv8rRCjE8OonOoy+6iDZWXmZCBlNh0kXk6kK3JX46vrrk80GVBfe6beZbzIQ/xqNSWxnmR7zZtLEF/iR6oCtSSgna964JqZIjc7JtUhfMCXX4ZZ1bZdAR841dy7JEg5N+/+c1vgjGopRE/rtl4DQ/ffvOb3+ATn/iELXOs6heFgEJg/iKgCMD52/eq5QqBOUeAJIrc3LtRGS72Dx48KBJkyOfSpZfE3mwxgDZv3gxmBCb5UF1d7UZVLT+Di0ieFDM7nIxxxE0LCRqSVHaQM5YrF8Y3clNIJRpdyWg8fee4oXvTfMCUGzztJpn/1lPOaAPukxCUGxgzXT84OCgUO3Ol+tOr6+j4GB5853G81uTNuuiURUdFYUl2BVr7O/BOq7PZdFNiE1CWlom6Xm/8SqetJDUPkxhG17Cz7dK244jsStT31mF88n1VXpwnHge6Rx2LnTeXCUIY63FxVilquuxXjgYaH4XJxdjV0Yil2RVoH2wBiXN/dvmyT+Ok0nWBirT8OQkVquCp3KcxOyrVVEYUy0YTPXAOkGpozrFzFSLBMkg23ci5gFjzvU1jbDrOjW4f1NjUnDkphutRHjIzvt9rr70m8OThm6+RDCSxevzxx+O4444TP1yTzoc1yJx0jHqoQiBCEVAEYIR2rGqWQiAcEHCTAOQilaQCF1k06UpoJKnHm2++KUi1yspK4dISqiZj/VHRIGPFaOsqg1Bz00JCkBiohePsvamnRCNpzHFgZDMZqmMl2HqR/JNuTdJ1WM+tiRsWrWqGY8/fxjCUVH96+ExMTuDpPf+Dv9S8ECx8Ae9fknkE/lm/03GXTiZzqIzPQsukM0pD34YmxyaiLD0bdS7Gy8tPzEZMzAQ6/9+9Oi+pDFubA7upBuykABcszipB9xwkCGG1qtKLMTTWi+4R73znhpWmVmFHm5d4TIlNQmVGHvZ3HdB99J2n/wDp8Wm2V8s3XijfPzy4CybzrNaFU8Z203vXcS7VZlbney+Sk4tIrPfs2SPUa3SbJvGn4tJZH9bEkWs3xvtjqBES1zy4JbaMq6h3YM41LMPUhPLa1Doi6k6FgELACQQUAegEqqpMhYBCwBACbhCAfAbdCKn8o3GRXl5eDjMJBN5++22Rza6srAzMOhpqJjP8yhh//D+JTqrVpGpLT63FhaUkA/lbBT6f3rO+8eeIF919jZDGoTZGnK4PxxwVINJlmL9lEHjfZ2s3yRx33CTzXiosqbSl8W/8rmnd9Zxug9HyX6x7Fb/b8aSj5FyCp1RU53C/O7H6lmaWoHmwGaMTM5PBGMXF6HUkHZfnVWGfiyq1GHhQlJCFtvE2pHhKsKvLnYQdTBCSE5OAljF3CFZtHzAz8oLMIhEb0A2L82Si1celfFFmKfpGu6Yl0ilPK8H3T/ym7VUiKcdDPhnawyk3VO27Ts6v/kIk8D2mPQDhHEtSMtyNinBizYNRGtu1fPlykTlemTUEuEa7++67cdNNN4kQIxw3999/Pz75yU+KdSvVrG+88YYgCOUPxzoPIrnei7QwJNZQVHcpBBQCRhBQBKARlNQ1CgGFgCMI8DRTL/uoXQ9j8G+SCjKmD110GAOIv80YF7r19fXiZJuL3FAxSfxps/zy375JPrRqLbpVkpzhAt7XqMyS7sIkZuarSxMxJGHMWDySOGUgcypJVOBt46OfG3JtYhFuUmQmam0pxJTvAfkZv2dUM4SyC9nbLe/il289jBEHEml4EIX6nlgMjY2gLC0XwDA6hpxXcpWn5YkYcl0OZz2Wfb8wqwSdQ20YHBsyPqiCvLLAk4GDA4PonxwOsiRzt5fHZaIL3RjHhLkbbbh6YWYpeoY70DfqdYd1wtLj0lHXq+/anRgTh0WZJVOE7/kLzsRHF51tazV42LVjx46phF58X/Md4lbWcr6/JBkok4vorW20imhJDIbbnMJ3Ol1U5bqKsVmZBMstrG0dOCFSGNdlX/7yl/Hss8+KGq1YsQJPPPGEwNWflwbnS65RqMA855xzQqQlqhoKAYVAOCCgCMBw6CVVR4VAhCLgFAHIhSmz+x4+7M3cyYUpyRsq/6y4vDLj64EDB0S8t1WrVoVEb/iSfpKokkk+Zmsn76U6S5KBXNDL+D3axrEMX/fNcNusmO0sKjm0ruIM9E4lmltZZ83WN5yul3GOJCnI8afn0hQuRPSB7nr8fMv909RNdvRHTkIRXm96P0FFRnwSClNSUd/rbCZi1j09LglFqemodykuYHZCGjISE9Dc782o7bQlIQ1tfWOIjZlE76Q3Zplb5k0QArQMeJNPuWl0va5Iz8MBAwk6rNSrLK0K77TOHnewIr0QExND+PLqS1GVUW7lMTPu4bzHmL5S4c93B9/XnKvn0mRmdW3cVL05lnWUiZR46Mb5lv+3sk5xur1sE9dBJJ34b2JNRTxDYiizhgBxfOutt3DxxRcLbGlXXnmliAWo1JTWMFV3KQQUAoERUARgYIzUFQoBhYBDCHDxrqdEs/o4Lqbq6urEiagkFvLy8sQilUSOVeMGgz90RzzmmGOsFmPLfVrVn5YE9FX9mX2Y0ayvdPWRGV+l+6bZZ4Xi9RyLtbW14kcq0ejyTVdxpWywt8eIb2NjI5hNWRLX/H7K7Ii+T+PYphpVBtzn71CJv9g22IE737gPTX32kXM5CdV4vWl6xuE4TzSOyivBno7AGVaD7a0YjwfLc8uxp9Md11EmzViaU4YaF1xVC5OqsflQDeKjY1CdmouDA+4kQJF9MpcJQliHJVnlaBtsweCYveRnTmIJagy4VWclpOM/z7gR0Z7oYIepcImkEo2xSGmcl6jwD9W4e1IRLUlBKqL1QnOQWJNkoFTiz7UamnWnwlImwWIYB3pDJCUlBd2P87UA9j2z+q5fv17E+yOW99xzj4j/F4oE8HztJ9VuhUAkIqAIwEjsVdUmhUCYIGAnAchNABeoXFzTZLw2O7K0khQiWcHTeWZdmyuThJ/cNPB3sMSfv7bIrK9SJUhc9dw3SdxoiZlQVS/M1mdsG13FZRwntoEqErZLmb0I+Mb64/eUWDPYOU1LRFMpKDf3vrXgZkk77vj/udo0Mdvpf775W+zq2GcLWKNjuajr8cbW8rVjCquws92dDK8r8ypR230Q45PuuK0emVOJhr56jDkYhzAuqhA1Xe+TfivymCW4AcPjM0Mi2NKZfgpZlFWC3pFO9I4455brr/5pcckoTs1CXU+9LU2MjopG70iUofiRJ5WswdVrPhfUczkPMSYvlfk86OP3vqqqSiTpmqt3gJUGGU2kxDbx4E1LCrqZwItxWUm0ysQnPBijR0UkxDK00m923MN57Rvf+IZw86XxkPrxxx8XpGo4jWE7sFBlKAQUAu4joAhA9zFXT1QIKAT+HwE7CEBuAKjOo/uEVrllZ7w2xv+jWygX4SeeeKLr/edP9WfE3deuyvq6b5Kc0YtxRBdhScxQkUGlQKhuFNim/fv3T7necOFdUVEhNpOhWme7+tPtcqTqT6vOZaw/xjiaza1cxtYiET1bQhsqArWEoNvjbnR8DL99ZwM2Nb0ZFLQJ0Ul4t3Vg1jJW5JWjrqfREOESVGWYTTajEINjPa4RVSWpjHk4gs5h/XhywbQnzhOP2q4RTExOTiumKCULSbEeHOp31zU3NS4JFenZqO22h4gzi80R2RU43H8IQ+PBxUMsSinBznZjSVW+tuZzOLFkjdmqTl3PgylteA8SYcuWLYuIwxqpgJaZhvmbh1J6B288ONGG53Difcfncn7kD40qxCOPPBL0qlBmDQFiyvF7ySWXiN+0z3zmM/jVr35lOja1tRqouxQCCgGFAKAIQDUKFAIKgTlDgIsheapspRLMgEZiTsbWIUFHFyC7lVtUGzATMDcbp5xyipWqWr7HTdWfmUrKGEfarK90Y/E1EmlaYoablrl2Z2IdSShR9Scz1XIDxc0NfyuzFwGq+oi1dB/zVf2ZeZpR1Yw22L5Uzjgdv5LfiT/u/R88u+/vZpo07dq8xApsavTGgprNqjLyMTzeh+5h5xVkWQmpyElKRFOfO3H6GK+uLD0bdT3GSKVAWMnPC5PLsLmpTvfyuOgYHJVbit0d7rg9ayuxPK8SDT0NGJlwV4XIOmTEp6IgmTEfrWNdklqFd9sCq1KjEIX7PnQL0uJTjHbZtOt8k08w9tySJUsiOjETD0DoKqwlBf0lF5FhEiQxGEyYBL6z6VHBeZLGMkm0BhNKxVKnR9BNnB82bNgglH90X+c8+LOf/Uwk/1AHjhHU0aopCoEwQEARgGHQSaqKCoFIRcAqAUiiie4/JOZoXDwxVhvVW04spJhNeOvWrWKjcfrpp7vSHVL1R8JDqwB0U/VnpqFSvaBNLMJFrq9RZUeSjaSMjCUYzEbFTB15LTdPVIxS1Uljfaqrq0WCGCfGjtn6RdL1HBNNTU0iJqfctBpR/ZnBgM/gAYA227DeuGOZMn6lzHTtVKywf9a/jod3/N6S62xqbAW2HQ5MALI9OYlpyEqMR1OfvruwGRwDXeuNXVeKvZ3G6haoPCOfr8irRk3XfkxiumLPyL1612QnVGLb4dkJvmW55WjqoypuxOpjLN2Xn5yJtLhYHHIpGYpvJY/MrkJTf4OlrNZxnky0DnqJotlsQWY5bvvANYEum/E5v+MMw0ElGv/NmKwk/goLC+eduyTbz/ebNuOwv/edNjwH33l8/xlxL+XBKsk/GZ+Z6yrOkWp+ND10p27gHPXtb38bDz74oPgb3dVJBjKmtJE+sf5kdadCQCGgEJiJgCIA1ahQCCgE5gwBswSgNnmAXJwyMQeVW04Go2YMnM2bN4uF2plnnunogk1L9mnJP6di/TnZ+TLouVQJMu6NnjsT+06bWIQbFycWxVSgUYlGdQONmyKOHZVtz/5RoKf6Y5yj3Fy6eDpr2nHHsUcFjd64IwGoVaca3SAbqf2O1l24562HMDRm3L2SnqntA8mmVH0J0bFYkl2AfV3uuJGuyq9CTdcBTLgUF3BBRjF6RjvBOIvBGLEdGElFx5A3YcRsRjIuPT4WjS4pHmVdoqOiQDXgvs5a20jPQG3Vfp6dkI7sxGQ09r2fgTrQ/enxmajrCUz+sZxPLj4Ln1ryoUBFTvvcV4nGwyMq0dQ7+32YuBbSKgRJDvpLLsI5TyoEfdX4vIcZfmU2Wh540qNCxmc11XHqYoGAdKNmll96kdDOP/98QQRy7apMIaAQUAjMBQKKAJwL1NUzFQIKgSkE9NxG9eBhLBy6+0qXFKrG3FIBcEG9adMmUa0zzjjDsayw0t1Xq/7jM0NV9Wd2GDPmHrHUJhaR2Zq1ZdE1RhtHMFhihhskqtCoRpN4MkZkaWmpI0SjWVwi6Xo3VH9m8TIav5Ku6dq4Wvx3MBmgGafv51vuR+eQsXh2mfG52HLIvJovCkB1QiaaJ4wRMWbx871+YWYRekZIytmbSdZfvbISqHRMxKF+65mWM+Ozsb2l3XDTmQl5RV4FdnUEdm01XKjBC6vSCzEy0Y+u4R6Dd9h72VE5VWjorceoAZfksrQqvNNqDKNbT7oGC7PKDVeWynvO+VI9TJU2lf5KiTY7hCTyuF7SkoLy0Mv3Ts6tfM/xEI4eFTIRVqhnVDY8iObwQs6Fzz77LL74xS+KQyjOL7feeiuuu+66oOaVOWySerRCQCEQIQgoAjBCOlI1QyEQrghQraOnzpHtkafS0v2Hfy8pKRHJA9xyHaWLzcsvvyyqdNppp9n+XN8kH5IIDEfVn5lx6BvPjcSgVHZqy5HEjFQJMtaRUWKGm0gG25axJrOyskTGPScVo2YwiKRruckk1nQho/H7yQy/bqj+zOAo3eik2zDHnd4Gmd8/GVdLEtJm3zmdQ1248437Ud8bWFWVl1iNTY01Zpoy7dqlaQU4NNyKsclxy2UYvZHuxxkJ8WjuN09YGn2G9rroKA+Oyq3Avi5jZJPvMwqTq7C5yfy9R+SUoW2w1TWyU9Y7PioGZclZODTiTtxFX7xykzKREZ8g3KFns5zEUtQYUJ+mxiWL+H+eKE/A7idhv3v3bjQ2Nk69R6hEU4qpgND5vYDvN63bsD9VNAvg3MhQDXznmZlrrdcu8u7keuOmm27C3XffLRpHPB977DF84AMfUIeOkdfdqkUKgbBDQBGAYddlqsIKgchCYDYCkK63VADIGDd0+6HLJkkcN40qxRdffFE8kgs4O8kjf0k+JPnnZjvn+lnEgkk5tIlFZIIXbd2IjVapxY2Kb4IHjivGiTx82KsaIolI1V9xcbFagNvc0ew3qke4aZdqHcbnWrx4cdgE6OcGWRtHkO7qesbvvtZtmP8P5K4+ODqEX771X9jRtmtW5D2TRdjbGZgonK2QhZmF6B3tRt9IcC6zRoZIfHQMlmYXY1/XQSOX23LN0pwKNPc1mk6YkRBdjL0dXkLJrGXEJSHVE42OicDuw2bLDnR9SWwG+qP6MIqxQJfa/jmVpUflVKOu9yDGJmY+PyYqBj0jMJSN+oSS1fj6mosD1pHfu3feeWdqzqf7Ked8s8R7wAfN8wukGp8uv3zv+TN5CKJ1G3YqdmqkdElDQwMuu+yyKa+RD37wg3j44YdFzEplCgGFgEIgFBBQBGAo9IKqg0JgHiOgRwDyb1oFABehVVVVcxaImqTGCy+8IHrphBNOsCVTrK/qT8b7ixR3X7uGtFFihq5MkpihipCKUakm5CaSqj+1cbGrV94vJ1xUf2Zbzu+8VMxIYlAvrhaJZ627OmOU6bkojk2M45F3n8TG+td0qxIbFYe9HWOWEof4FpiXlI60+Bg09xt3eTWLj/Z6xgV0M25dYXI24mIm0TbYYajaMZ5Y1HdPgH1g1UiGLUjKwaGxNptSkhivSUpUHDJi49EF9wlI1jIviQlK4ma4YBellGJnu7HYk1ev/ixOKj3ab6M5HzIx0969e0X8On6HVJgG42PE7JU8aNu+fTvkQQcPVelezcNW6Tos3YF9y5axUyUpyLlXuWV74/1xnXjllVeKjPdct37ve9/DjTfeGDYHYWbHkbpeIaAQCE8EFAEYnv2maq0QiBgESNLIjbVUElG5JV026fZJBQAXmXNlrNfzzz8vHr9u3TqRsCIY86f6k+RfMGVH+r0cL5KYoesmXZn0iBniwAU4T93LysoMZ0CMdPzsal8kqP7MYOHrrs5NsnxHacvhd9g3jqBUpxKz5/b/A0/u/vOMR+cllmJTozEyxUi9k2LjsDAzDzVdDUYuD/qaRVnF6Bpqx8CYN8GO05YQE4cFmYWo7Q6sPixIKsUbh+zBdlFWEXpHutEzMjPDuZNtJgG5KKUQh0aaMeE6BQkINWBuNeq6D0y5mJekVuHdtsBu1VGIEu6/afH6czi/R1T6y9ABVPoz0QfJdGX2I9Dc3CySYVEFyDmSxB/jK/oqmTnXcn6VByCcd/Vi9mrfefLd56vIt78VoVUiD4x+8pOfiB++5xn2gqq/s846K6BCPLRaomqjEFAIzAcEFAE4H3pZtVEhEMIISAKQJ9LcBPDklMYFJF0IQ8Vl8+9//7tY/K5Zs8ZyTDNtcg+tAlCp/qwPUBIz3JgcPHhQbCD9xZM0qtSyXpP5cydd4rmB1Mb6o8IyLy9v3oDAcUb3dK3bsAxV4AuCVp1KteD2zp14YPtj0xRpmXFV2NIcmEwxA7AnClhTUIWd7faW668OeUkZSI2LweEBbwxIN2xZbhUO9hyYVTmZm1CFrYftwyA9Lgml6Zmo7bbmUhwMLmWpefB4RtE26E7CF9+65idlIyUuGof6WhAXnYG2Qf/uo/LeqoxS/Pjka3Wbzfl+x44dU2Q653vO+0ZjvAaD5Xy71ze2IpV8JFr5TjJifOf5JhfRC9HBskjiag9CjIRKMFKHULyGYUao+pNhYuglwnh/PHhUphBQCCgEQhEBRQCGYq+oOikE5hECJBPorrlv374pJRdVW8zwy2ywoWJc3LGuK1euREFBgalqack+6erLv0V6kg9TIFm8mMQxE08wXiSNRF9lZaVwSZKxBPUyTUvVgkwsws0K4wQq848AxyzVI1Toylh//C7wuzrfFB96KFHJpHUb5r/1CGluvHsTBvHnjhcxOO5VzPUNZ+Bwf2Ayxcr4XJ1fiZqug7a4Fwd6fkJMLBZnFaKmqy7QpbZ9Xp5egJHxfvSMzHSRnZwEhscy0DpgL7ZUxK0uqMaezlpMTE7Y1hYjBcVFx+DInDLs7bSP1DTyXHmNJyoKy7IXYltrDcYNJJz5xKIzccHSs6c9gvMg53we3ND47mXCoPz8fDNVUdcaRIDEHWMrSrdeKtToWRHse5tzq29yEX+hErRxBIPNsG6w2Y5exnf7K6+8gssvvxxNTd7Yrddeey1+/OMfq5iVjiKvClcIKASCRUARgMEiqO5XCCgELCPABdTGjRvFApKWmJgYkllDWTdmAabCh9kImYXYqEl3X9+Yf0r1ZxRB/euIZ11d3TTimJn2mB1au6nhdTKOIF2GSQrqKbVIxlKpJQlB/laB59/Hnhs9Eq2trd6sr8SYkuFZAAAgAElEQVRmvqn+zI5YKm60LnQce5I4ZVk9k314aWwLhicmsKvLWdfZRZlF6BnpRN/ooNlmWLp+dX61IKgmXXJXTYlLQklqJup7p6vy0uIy8W6rc2q5qox8jE4MoWPIO4e5aYuzStEz0oFel92R2caK9Cq09vcgKc4j1ICz2Q9P+gYWZVVMXcL3L8koGX+O71rOqypGqzOjh+QU390k5jjPcY4sLS11xDWVz+A7T3sQohcqQc63Mn4qCUH2f6CESs4gZL5UtvPnP/85br75ZuEZwnY88MAD+PjHPx42bTDfanWHQkAhECkIKAIwUnpStUMhEKYIcGHKwN8VFRUiFk2ouv68+uqrYmFLtRPrGsj8JflQqr9AyAX+nCoGuqBK4pgbB6pHsrOzA98MCHczresm+1VPqSUzvkpSkAR1uGxQDAFh4CJ/qj+66SmC1ACAmkuIpQyyL8df50AXNg7swuY+591JC5IzkBznweF+Y8kzzLVu5tVLskrQPtSGQZfiAlKVtzyvGvu6aqYqU5RchdebnFXKJcfGY0FWPvZ1uqd6lA1MjUtCRXo2arvtiXFotM/zEkuwp7MBVAOuzKtCTRfdsGcmWUmJTcL9Z/8QniiPeMcyWzgVxDL+HJN7UbE9396rRnEO5joeNhBrYk7j/LV8+XKkpaUFU6ype7UHcJIU9JdhnR4fJNKkUtBfQiVTFXDgYnobfPGLX8Rzzz0nSl+9ejU2bNgg1q9qHDsAuCpSIaAQsB0BRQDaDqkqUCGgEDCDAMkYEjChHvB78+bNws2UmQmrq6tnbaK/JB9cHMofMxipa70I8NSdLmM1NTVThB2VDFx4B+O+y80oNydSIegv2DkJL0kGcqPCMRvJC3491R8JcOWmZ983kurUlo42/Mdbf8RWF1w6BVmVmetacpD85EykxHpweMCdjMTsmYWZJegaZkKSQaTEGM9UG2yvrsqvFETc6MRYsEWZvn95biUa+howMj5q+l6zN0RHedA7Ej3tWcUpObpqwOOLV+EbR18iMrLzsI/x0mhm48+ZreN8v54kG7P8MkQGje9sHpIFM0/ahSmJSamMlqSgVhktn0MvCZKVWlJwLg+duK578803cckll0y5rl911VVCCUhyVZlCQCGgEAgXBBQBGC49peqpEIhQBEi+6C3+Qq25XPjR/ZFqBaqf9Myf6k+5+wbfm9wwUPUn1QNU5zGGkdEA5mZqQKKRKkNJCPrL+Eq1qnRhIjHIzUqoKljNtF+q/nbv3i027nIDSfJvLjdgZtoQTtdyvO3dtw8P7ngemwacVasRF7eTg8xFXMCshDRkJyVjW3Mbhl0gxeR4K0nNQWz0BFoG3FFZasd5XlImMhLi0NTnJdmcsnSk46COy7OeGvArqy7CirSFwuWXZDdNxQ11qmcgDsYaGhqwZ88ecWDGtUcoJVPzt26iMlobS9BfQiXO+9rkIkw24sYhHLG8//778Z3vfEd4EPC59957Lz73uc+58nznRowqWSGgEJiPCCgCcD72umqzQiCEEAgXAvDtt98WrjTM7MaTdF/zp/qT5F8IQR5WVeH4qK2txYEDB8Tmhov98vJy0HXMLbKNz6WSYspts7NTZID1NdaNJKBWJRhskHW3O4uqP7qNtbR443qx/oz1p1R/zvQECW1mQZXB+d+dbMEzrVtdSSyxuqASNZ3uJAchem7HBcxPKkJUVALea3OeVNWOjoToWByRW4LdHbXODJpZSiUJtyKPSV8OODaG0sazUDfqn+AsSslGcqwHh/pbcf2SK9Fa3yze3Xxf8xCBSb7cIG1cB3+OH+irsiRJxSy/oe5doQebTKikJQX1kotQ0UhCUJtgxG6VIw8fv/71r+MPf/iDqCrnwyeeeELErVTjeI4HvXq8QkAhYAkBRQBagk3dpBBQCNiFABd1UmVkV5lOlPPuu++ivr4eTDTBODrSpOpPm92Xf1Oqv+B7gYQbVX9SDcAkHVT9uRnDyF8rSJTJLMP87S+uEeusVQmGaqB7jlm655H8U6q/4MduoBJ83dm5kSSpzfiibzTvxk3//C8Mjg0HKiboz91ODrI4qwQdLsUFLEyuxmtNNViVV46G3kMYGh8JGi8zBSzLq0BTb5Prz2Udy9PygagRtA/anwAlFhloHZo9qzLjMS5LLMFp8d65ku9BzpskpZTZjwCJMqos5cEU1ykkW906JLO/RdNLlKp8eQjH9kpFqe+z5ZwrSUGrsXs5J3L9cfHFF4NqeNpnP/tZ/PKXvwyJNYjTmKvyFQIKgchFQBGAkdu3qmUKgbBAIFwIQBIjVKFRCbVq1SqBrVL9OTPEqPrbt2+fyPJL05IjJFZD0ejGrk0swg2KnmKBBCAJQakSdMuFaTbMqLZgfC6l+nNnZJHQ5oGCTGLDMUA1iZbY3tfZiPUv3o/WAeezy+YnpyM1LgbN/e7E6XMrLuDkZC7qerxZq7Njk5AYPYnuCXeyIMuRlJ+cgYyEeDT0OuuWqzdy46NjsTSnBPs67VMipsalor6n19AX5Zj4apyQ6A2XwdAB8iBExk8N1Xe5ocaFyEVcg3CeZCI1rcqSBGCkGwlAbbZhHsLpJfPi2NO6DVMRGYgYZTmPPfYYrrnmGqH+57x95513gjH/1LiN9JGl2qcQiHwEFAEY+X2sWqgQCGkEwoUAJCHFH2aaPfroowW54xvzT6n+gh9q7e3tgoySSgYu3OlyzVP9cDKODxnoXMYS1It1SRdb7caYJJCbGwyq/oi3VP3l5eUJFycV68/+0eYbn4tPoDs7kwrpbUhbB7rwnRcfwN5O5zMEJ8bEYlFWvmvJQbxxAYtQ03XQfqABJMUkY2d7/7SyY6M8OCIzH/v7vFlR3bJoZsrNr8SuDnddkWX7qLrsGelE78h0PKy0vyKtEttajBGKn0o5FmXxuSLjr6/xHad13eQ7MNzCJVjBz857+M7mQQJjE9Pmu8qS48w3uYied4kM1cHx19jYKN6/fA9L49rjuuuuw0MPPST+xM+Z5XfNmjXK5dfOAazKUggoBOYMAUUAzhn06sEKAYUAEeCmmAqkUDfGoaMbCBeNa9eunVL/ybh0KtZfcD3IhTpVDFyQ04gns/sy5mIkxNnhOKHyS5tYRM+FSW6MpUqQ483umEbEl985qlplVk5uvukyxgD9yuxHgH3NzTozidPolkZ3dipBZ7OB0SHc/PLDeK1pp/2V0ilxSUouGka8hIIb5lRcwIKkUrx+qF63CUcz9mFXHcYmZhJTTrZ5SVYJumwi4szWMzUuCRXpOajt9qqqrVphcjl2tgcmbeOjYvHIeT9AYnzCtPipVGz5S/BAJaw8DOF7jwkfIuHdbxXr2e6j2lybWKWkpASLFi0KqGxzoi6hWqaM3auNIyhjrco6f+Mb3xAZfenZsWLFCvFO/t///V+RQZn20Y9+FL/5zW+QlZUVqs1U9VIIKAQUAqYRUASgacjUDQoBhYCdCIQLAUg3G8aD4cZ93bp1YmPi+2MnLvOpLCoYqEJjXD0aSRGq/rgBjGSj0kAbR9DfxpguS9rEIvHx8UHBolR/QcFn6ma+35g8iIcHUgFaXFwsNutGiV0SVXdt+SOe2fOKqWdbvbgyPgPtk92YwKTVIkzdR4Va51AbBsa8WWLtsJjhTNQM+Y9/V5meSxocbYPOu1hr25MRn4yStAzUdjuv6tTDcVluJZr6GjFsJR7iJDA6kYD+0cD9dGzRUbjxhCt1u1ImeJAhE6ja0guX4Ou66bY62o5xaHcZfJ8wFElNTY04hOQ7hIptdXBjDGkeNJIQ5A/nQRJ8/uZdHkBecMEFOPHEE3HssccGPKwxVgN1lUJAIaAQmHsEFAE4932gaqAQmNcIhDoBKN18uYnniTuNxJ8kZSQxo9yXzA9jbgRJjDQ3N4ub6QZJYoQEyXxUfsiNsVQJcmOsF9OIxKg2sYjRIOf+VH9UP8xHvM2PWHN3+MZWJKFBYjs3l+STOeM4+MOuf+KeN/+ESReIuYLYVIx5hjA0OWquohavzktKR1p8LJr72yyW8P5tk5NAe18c+idmV5anxMZjUVYe9nbqKwWDrsgsBawpqMbezlqMT044+RjdsnMT05GdlCQSo5ixguQC7Gr3vqsD2VdWfxLnVp8Q6DLxuTZcglRr6XkFUB1NEpDvPhnTbT6FKiAmzBjOMBk0rkGYWCXSD8oMDSKLF/FQ5vXXXxeJPbgW4WFkW5v+O4jv7uOPP178fOITn1CJQCxirm5TCCgE5h4BRQDOfR+oGigE5j0CUvkVakBok3zw5JhuIdyg6JEyjL8jyUD+DlalFWpY2FkfvYyzOTk5QskQqlly7Wy/0bIY00gb5JyKGb14WjLAvhx/3Bj6Enoyw6/cWDPWH11+1Tg12hvmrmNCFapaJd4kWTm+gz0oeLn+Hdzyr99heNx5Yi47IRXpcdFoCZDx1Rwy/q+Oj47B0uxi7AsyLmDCRBJ29w4Yrtbawirs7jiACZfJuMqMfIxPDqHdZRUigWGW3pX51djfZZyErEirxraWGkO4PnD2DShMyTF0re9FnB+kOlqqBP2ptLSHISQGI9VtmKEDeAAp3ycMjbFw4UJX48Va6swQv4meHZdeeik2b94sanrmmWfiZz/7mYj3/Oqrr4qfLVu2THknyOaQKOSaRZlCQCGgEAhHBBQBGI69puqsEIgwBLio1SPV5qqZvsk9ZMIPKhBkoGmp0vKX7ZWqLBIykpQxqtKaqza79VzGQmPsORm4nITI4sWLhQuTUqHN3gsch4xhpHUb1lPKUEkpFYIkpqle1Wb4VXg7N9qpKKGSpKmpSTzECRe9Xe11IjlIx5CxbKzBtDZBZJItxN7O4GLHmanDqvxq7Ovcb1npmBtXii2t5lR9i7IKMDjah67hPjNVDfrapJg4LM4uxJ7OwHH1gn6YTgElqbmIi55Ay0DgDNDpcQU42BNYAViUkoP7z77B1uryAI7vPW08Nz23Yc4n2oyvVAwGyvhqa0VtLoxrkf3794sf+T5hnDoe4CizjgBx/dvf/obPf/7zIi4r13Y33XQTbrjhhhmhGXhA/dZbb00Rgly7bNy40frD1Z0KAYWAQmCOEVAE4Bx3gHq8QkAh4E1IECoEoFb1x77hJoPElL8kH9J9iYQgf7hB0cv2SqWVTOxAUpABz+cT4UVcSYrs2bNnCh+qokhGKRWatbeADHIuVTL8PTDgX/lEErqqqkq4oAarRLNW48i+ixtJJvqQyV2YMZxuY06oWpv7OrD+pQdQ22XOjdNqD6wtrMZ77cbUX1afob1vYWYReke60DdqXMkn70+KKcbOdvMx9tLjklCZkYmaLvP3BtvmlXmVqOttcEXZ6VvXWE80jsotx95O/1mKE2IScbhvyBApe171ifjy6k8EC8ms93Pe7e3tFaSg/NE7DJEZX7XJRcJlvuF7hC6/XFfQSGwuW7ZMxCFWZh0Brs9uu+023HHHHWLdSTL1d7/7Hc4444x5tSazjqC6UyGgEAh3BBQBGO49qOqvEIgABEKBAJSqP6n2k/+XxJ9Rso73yY2JVAnqbUxIwGjjuNFtk8+KRKM7FxOoyAyodFml+ykJQGX2IkC1AmMYMVD8bGQgCWhtYhG1qbTeD1QF02WM7mQ0t2JZ9o0M4qaXH8KWQ3usV97EnSvyylHf04iRiTETd1m/NDsxDdmJCWjqazFcSHRUDOp7JjFqMcMvXWPXFVXjvTbrCkTDlfW5sCA5E6nxMWjqcy8Ls7YKCzKLMDRGFWTPjCYUJhRip0Gy+aYTrsS6oqOswmDpPs67JMy0hKBvxldZMN91cu7l71A8jOM7nOQflY+0iooKVFdXR+wawVKnW7iJ8YavuOKKKQUfE3w89thjKC0ttVCaukUhoBBQCIQnAooADM9+U7VWCEQUAlzk6rnzuNFIrbuvlvybTfVnpl5SpSXJQP6WCiFtOSQNeMIvSRn+O5xdl9g2tr2+vl6QIzJ2XWFhoVD9KQWamVFk/Frf2HNZWVkoKioSmQ6lC53ed40qNS0hHYqbYuMouHclFb9U/ckYZcSQLnpuBeZnhuA7Nz+JZ/e95kqjK9PzMDoxiK5h592P2SCvOq0UezsPGGpfYXIJXmtqMHTtbBcdkV2MruFO9I6YVyAG8/AYjwcr8iqwq8O/Gi+Y8gPdmxTDxCiF2Nc1He+UsUw0jPnPqizLjYmKxoaP3obEmOAylQeqp5HPZcZX+d7jd1Uvhird9LUKwbmce/luZoZfHuCI8R8bi6OOOkrFmzPS4bNcw7XIv/71L1x22WVTSce+9a1v4dZbb0UkJJJhwrDnnnsOb7zxhohZ2NjYKMKs8PCVY5tK9HPOOQdXXnklqEw3Yoxh++CDD+L5558X6ziuW3loy+zIp512Gi666CJBTAeyF154AY888ojAn+FI+H1jOUxgc/rpp+Piiy8GQ5UoUwgoBNxDQBGA7mGtnqQQUAj4QWCuCEDp7qtV/7GKZlV/ZjtWBjiXpKBegHPpuqRVaYUTacY2UfXHzReNBBOTIKjA2WZHi7Hr+R1i7DkusGlcZJNoJeGqVa9qM27K8afnsi5jacnxx1hakapQNYbw9KuIY21trfjh+4MYc2NUXl7uuhsZn7/hvRdx71vPWmmK6Xsy45NRkJKC+t7Dpu+1egNdZPd3HwyYqKMguRqvN9njqpyTkIrC1BQc6HbHzVqLzdLsEnQOd7hOQMo6HJFThraBFgyMDYFZlUdH4jAwOXtWZd67LHcBfnLKV612s6P3aWOoSqWgXgIyfpepyNeSgk648fs2lusCJvogUUnju5fknxvPdhT4OS6cpO+dd96JW265RRDAxJXE1kc+8hHX39VOQUGSjS7MgYzrL5JxZ511lt9LOZ/ceOONuP3223XD2cgbiek111zjtxyuLy6//HL86U9/mrVajK+4cuXKQFVXnysEFAI2IqAIQBvBVEUpBBQC1hAgAaF3Mm+ttMB3+UvyYZfqL3ANpl9BF2GZ2IGLJroQ65nclMjkIqF4cs1N1sGDB0XQcqk0KykpERkLSUopsx8BnvSTbJWu5mYyKvO7QLJWkoEch3oKVZJ/2uD63BzP1/6kayHd8+T3lN9LbtTnWsXwUt3buPWVRzHiQobgOE80luWViuy5blludAomo0cwBP9E1MREDup722yrUnRUFI4urBQuwW5bRnwyStIyUNvtfkxCtjUpKg45sQkYnhxD47AxJeSly87FBUs+6DZUlp/n6zbsb+7VKqT57uN33WhYECOVo3KbSmJ5GMNYrfyx8xlG6hFp17S3t+Oqq67CX//6V9G0o48+Ghs2bIg4bEkA0rX51FNPxZo1a4RLMw//uAZraGjAk08+iaefflqss7lupFKQCjw9+8IXvoAHHnhAfLRixQqhGuRvus5TCci1BssjufeNb3xDtwyS2FT3vfnmm+Lzc889F5/+9KfFIRnrwDUi68Bynn32WUUARtoXT7Un5BFQBGDId5GqoEIg8hFwkwA0m+RjLtAnHlKhQGKG7h16bpt0M5QKLf7mJmUuNwzcPHETIzdRXDDSHZJ1U2Y/AkZVf2afLBWqcgz6i6WlJaS5KQ6X4Ppm8ZDX893BjQtd9GRyILpAcaMeKurI99oO4rsvPYDOIXey2R5dWIXd7bWGkkNYxV17H0mp7Lg4dE7ObF9ybArea3Om3ctzS9Ey0Ir+0SE7mmGqjDUFVcIFenxywtR9dl28ImcxdrTvN5Sg5K4PfhPVmSV2Pdr1cjj3ajMN8x3oz22YByLyUIS/rRyI8D3CxFgkVmgkZ5jog6EblFlHgO9qEkyXXnrpVGzWL3/5y/iP//iPiFRUcowGChnzzDPP4GMf+5gA9eMf/zieeuqpGQBTGUnCj0Z1389+9jO/cxsPHP0dQl9yySUisQq/E1QcXnjhhbqdyX5i3a18d6yPDnWnQkAhoAhANQYUAgqBOUfADQLQn+rPaXdfO8DlAokkoFalpbcpkSoFSQq6FceNmxgq/hi3SGZzpiskg5YHWpTagc98LCMY1Z9ZvEg0+hLSelm7SfhqXdZJUM8lIW22nbNdT1KUqj/p0s62UfXHjX+o2SFmCH7xPhzodsdF98icUhweOIyhscAuonZgxVRJFfGZaJmcHpMudTIbO3ra7XiEbhn5SWnISUpEXY87uGorUZGeh0mMoG3QG1LBTctLKkbP8ACyEuNR19Ps99EZ8Sn43Yd/AE9U5CSz4nuOByDa5CJ6Cmm+56gK1CYXCeS6yyRN27dvnzowY2w2vlNCUdnv5ngL9llcj/z617/G9ddfL1TxPKi69957Rcy6SJmPrGLEMCy7du0SoVi4htAaD265buM685RTTsGLL75o6TGM9XfSSSeJe2+++WZ8//vft1SOukkhoBBwDgFFADqHrSpZIaAQMIgAySy9OGQGbw94WTio/gI2QnOBjGWkTSwiswVqy5GZhqXLMDcodiuVfJMg8BkMOB2KxIgZjEP1WqdUf2bay++rVMnIjbEeIc2NrDaxiBPjz0y9rVzLd0dTU5OIryjbSPcqurSHMrntdobgktRsxHjGXSWoFqbko2WkDeMYF13rGcpA7bCzBBmTdBxdUIF358AlODEmDkuzi7DbYEIUK+Pd9x4m9egejsLoxBg8UcCa/GqhRhyb9GKutVPL1uC6dZ+z47EhXQYJQO37j8SJ3oEICUBt2ATt+4/ZaOlKyXfKXMYPDWmgLVSO/fK1r31tSt1GD4QnnnhCrEnmO/lHOOkCTbdcjkVfd/df/epXoEqS9tJLL+Hkk0+20AMQrr7EnM9gTOK5Do1hqRHqJoVAhCOgCMAI72DVPIVAOCDgFAGoTe6hVQCGg+rPTL/JOG4yjqC/OG4kLLSEDBM7WCUx2GfM7ltXVyeqysV1ZWWl+LGbZDSDRSRfyxN7ZuaTgeupGOHGJpDSxGlMfIPrk5iW8Qi1zw63TNfEmZv0tjZvTDniTLyNZlF0GvdA5XszBD+FZ/dtCnSpLZ+nxCWiMj0Ttd1NtpRnpJDStFwAI+gY7EbHQDz6xoaN3Bb0NSvyynCo7zAGXXqetsLL8irQ1HcIQy48uyy1GNtbp8cgLEvLRXz0JA71T4+1+M21n8Vp5UcHjW24FeB7IEISSu9Ak+8/zrn8TJIvfKfQ5ZfzsjLrCHANRIU2M8ru3btXFMR/33PPPUIBqAxi7cCxxvFKIpAu0lpjzL5//OMfyMvLw+HDXpUzceW/GSeYmXsDkXmc90l6kyT/1Kc+hd///veiHI55ZibmOrGgoECpXNWAVAjMMQKKAJzjDlCPVwgoBCAWJHYqALVkH8kJ+f+5SvIxF31Ml0WtQpDuRr5GPLhY07ptGonF0tHRIYgRPoPGTQ2JEbXQdqanqfpjnCgq0Wjso0WLFqGoqCgkVQ38vvlmuvY3/jh2tG5zoeL+xk0PN0xSWcuA6syqHE6ZuOUGbsPOl/Crrc+6EqePSTNWF1RiZ7t7STNIPC7MLMELB7wbf7esMDkDGQmxaOid7krnxvPzktKRmZiABoczMVelV2Hr4Zl9GevxYGV+BXZp4j8++uFbkJGgyBbtgZxUSMu50ndsMEyHdv6d6zi+boxdu59BvBlv7tprrxXzDjH8xS9+ASazmO+qP867JN6YaOOnP/3pFLFHvD73uffVulwncx4mMX3++efjD3/4A3784x8LV2qq+GjEcvXq1SLxB+/Vw5ak4tq1a8X1jLf4mc98Bt/97ndFeSQRaewfJiv53ve+h+OPP97u4aDKUwgoBAwgoAhAAyCpSxQCCgFnEeDiQ8+F1cpTpbuvVv3HciJN9WcWG6qZtArB2TINS5dhLgi1hAxJWp6uM6ucxJRx/srKypTqz2yHGLye6jOSraGm+jNY/anL5PiTY9Df+OOGmONObooZV9BN43uIMZLookcj4Udym6qIcLaNddtx6yuPGErkYEc7V+dXoqbroGuJK8pSqzA+CWxvdY94FOPD48Hqgoo5yRLsEWRrFXa173eM3E2NzUX9LATnwsxCjEwMgPH/7jrjOjuGTkSWweRBnDv1XIW1DWYiJb7/pOswD9WUot7/kCCp9M1vflMQgDRmmWWWXxJV85X8e+ihh0SGXn923XXXCTJQiw/HJxNa0T7/+c/jnXfeweuvv+63jAsuuACPPvrojOQd//Vf/4XLLrtM3PejH/1IkIBSQe9bGMc1k4ww2YgyhYBCwF0EFAHoLt7qaQoBhYAOAnYQgL5JPiQROJ9Uf2YGF4kOuipJlSD/rbc5kYQM3ZdIikjXTm5SSIzwc2X2I+Cr+iP+VKCFqurPLAIy26Z2/OllupaJbaRKkC5ITm3sfMnW3NxcMcZDRZVoFmPf63e21YkMwR1DvcEWZej+hZkF6BvtQe/ITPWxoQJMXJQSW4x32xqxKq8MDX3NGHQpIYms4qq8cjT0HsLQuDuJULTQLMwswsBYL7qG7e3XlNhkNPR6VTuzWWJMLC5ffhYuXHp6oEvn3ed8z/FAQaqoeKCxfPlyMW8ysZdUCHL+1TsEJUmizTTM92C4qZCd6HSuVUio0s2Xrr80ZrZ94IEHxOHRfDZ/BODKlSvBOH/r1q2bAc9bb70lSFMaSWge2DFhyB133DEVC5BxAUkeMh4ubf369fjJT34yraw777xTKDG15Zx33nkiGQgT3HCcM/vwd77zHTH+OZf/5S9/wdlnnz2fu0y1XSHgOgKKAHQdcvVAhYBCwBcBLub0YoYZRcpfkg9J/hktZz5fp41jRFKGCzW9xA7EiG6bxcXFYqEdSZleQ6X/fYmorKwsQUS5rYZzEw+Sf1QFajNd622I6f7sG8cyWIWMr7KVzyDZSrdfp8hGN7HVPutwfyfWv3g/9nd53bqcttykNGQmxKGpb3q8ODufGx0VjYbeKIyMj4liS1IzkRQbhaa+DjsfE7CsopRMpMZFO9pWf5WgG3R1Zi72dXpjstphVekV2Hr4gKGi7uHFp2oAACAASURBVDz9K1hdsNDQtfPlIr7PmOVXhj9gDDW+x/XCbHANw+u02Yb1wiYQO3koJ9+DnBci7T012xghVk8//TS++tWvijmDhOjtt98uXFODnQsiYWxyDEkvDbpE19TUiFh8f/zjH0GPjZ///OcgKac1beZe/p2JrrZt2wauPbTGtcmKFStEOBIejO3fv1+sBaXdeuutuPHGG6f+/+EPfxjPPPPMjH7h85hkhPM+iUF+T+bTGI6EcabaEN4IKAIwvPtP1V4hEBEIWCUAfVV/Mt7ffHf3tWNQEEu6hdTW1volAvkcmelVumzSZUkt5Kz1AIkoxvpjzB4aVX+M9ccF9nzD1DeOFolBBhb3NX7XSUibjWMpy+FmiQoSGaNrPpCt/SNDuPlfD+P1pp3WBqrJuxKiY7E0pxB7bSSntFUoSi7Cpv+Pjyn/TlXastwivNd+0GRtg7s8zhONVflleK+9NriCLN69Mr8SB3saMDI+arGE928rS63A9tbABGB8dCz+/KkfIS46JuhnRkIBfHeRgOG7nPMo31E8UDD7HuehKN9PMuMwFVN6Kmk5B0ulIN+HkUqEUZl2ww034N577xVDhUTV448/LmLJzbc50ux3hW7Sl156qcDpN7/5zZSrLsvZsmULjjnmmKkiqRT84he/qPsIJla5+uqrxWd33XWXyLos7d///d/xrW99a+r/VL9y7OsZk4Q8+eST4iMSgExQokwhoBBwBwFFALqDs3qKQkAhMAsCVghAf6o/Sf4pwK0jwEU2F24tLS2iEJl0ghsLbRxBGZdO+yReKxOLkJSJ5M2IdYRn3tne3i5i/UmSaz4QUWbxIzZahWBfX59uESShtXEE6dLka9xIUxlx4ICX4OB7Y+HChWJDOR82kswQfM+bf8JTu1822w2Wr1+TV4HdXYEJJbMPKEmpxiuNNbq3HV9cjR1t+zExOWm22KCuX5yai7bRToxMeFWJblpRchaS46lEtJ6chHBFIRldw4FdgNcWLsEdp+mTBW62OxSeRdUykwfJLKpU65HYsCNBFt9ZWrdhzsX+3Ia1yZU4H4d7GAOu9+rq6nDJJZcIsor2oQ99CA8//DAYqkGZMQQuvPBCoQbkuKyvr59yl6Zb75IlS6YK4eEvYzvr2b59+8RcSaMLNvtAGpOGfOlLXxL/raysFApBf0Z3bSZqoZGQvOKKK4w1Ql2lEFAIBI2AIgCDhlAVoBBQCNiBgB6ZpFeuNrmHVgGoVH/B9wLxZKwiLgZlVmYmP+DC0JdEMZrpVRvDiIQgNyNGMg0H35rwKEGp/qz3Eze/0mWOxCA3x3pxLOkipyUE6dr+7rvvQhKI3CzTDWk+xrN8atc/cfebz7hGkJXFpaMHvRjDhPWO97kzOioP+7u8hxV6dkROEbqGu9Az7HwsQu3zs2ISkRwTha4Jd5/LOsREebAgJRf1w4ct4ZyXlIM9Hcbctr+y+nxcuPRUS8+JpJuo1GPyBKkmZrxWzp1Ucjth0m1YKgT5LpSZVn2fx3ebVAjyXRhOoTvYzr/+9a+CLOJ7nmsKxpRjdlm1ljA3sh577DF89rOfFTcxicdFF10k/s1xQ5Jazp9cl/gbt1yrMzYv7ayzzhJ9I+25557DueeeK/570kkn4Z///KffCj7//POCxKUx4zDjAipTCCgE3EFAEYDu4KyeohBQCARAIBABqCX7pKsv/6aSfNgztLhpoXKBSjQaFQPcvJAANKqI0mYa5kJdT6HFsrjQ1Lpshrs6wWoPKNWfVeT07yOxJxUyMo6lJLL9PYlueXSzns8byU2N7+Hmlx/G4NiwvR3ip7SS5CxEx4yjfbA76OclxiRid/tQwCy4eUlpyEtOxIFua4SY1YrGIApViRk4NN5ptYig7iuKTcNozAgGxme6z89W8IKMamxp1ldV+t734DnfQnVmUVD1DOebpTpNZvklccK5kwSg26Y9FCEh6M9tmHHzZAxB/uac7BRRGQwGbM8Pf/hDkS2WxjiKjzzyCE4//XTD65Jgnh9p9/7973/HmWeeKZrFLL0kUaVRsScV8XQ/95dshrEp5WEZyb4///nPU2VoswnTLfuVV17xC6GWLGSyESYYUaYQUAi4g4AiAN3BWT1FIaAQCIAAFxx66h3eJt19fWP+KdVf8MNKxivi5kUm/WDyA5IiwRJzRhVaXExKQpC/5ely8K0LzRL0VH90qSkpKVGbGhu7jGObJDTJQAYv7+jo0H3HcOMr1TFSpRqKm2EboZlRVE1nk0gO0jLQ5eRjpspOi0tCWXoGDnQ3BfW80tRy/KvBWJy/WI8HRxeW451W9+PzLU3Lx+HhNoxOjgfVXis3E+uKjGzUdNUbvj0/qQS7OxoCXp+VkIqnP/6Defve4hxHNXFrq9fdmlnKZZbfgOC5cIFMrqRNLqKXcI0Hc1q3YZKCwc7/wTaP3giXXXYZmDCC9oEPfECo1jhPKrOGgDZDsG/8PsYHlO68PAzWugRrn0aVK8c47aqrrgLdfrVWXl4u3LVJ1jY3N/ut6N13342vf/3r4nMqEz/zmc9Ya5S6SyGgEDCNgCIATUOmblAIKAScQECPAPSX5EOp/uzpAbp9MO4cNwc0uvkuXbrUsZg6MtOwNo6bXlBzX5fNcHJXCtQzSvUXCCF7P/cNyM/Ss7OzxeaWrnN6mTZDcTNsLyr6pbUNdOP6jQ9iV7t9mWRnq3d0VBRWF1RgZxAJMwqSK/F6kzlC75jCCtR0Nrgen68kNQtJMVE41O9VWbttq/KrUNtdh9EAcQmjozzoHfFMZVWerZ5nVKzB9074nNtNCYnncd4kGSLjtpKY4sFZKB8e8H3I+moJQX+xVDnvkgiUhyM8qDPqDRBMB7GOdB29/PLLp2Iprl+/XigB/anSgnnefLqXij0q72gvvvgiTjnllKnm//d//zc+8pGPiP/fdtttuP7663Wh+cEPfiBcsGlMLPK5z03//l977bW48847xedUAFIJqGennnoqXnrpJfER4xEqYnc+jUTV1rlGQBGAc90D6vkKAYWAQIAn6VoyyF+SDy5A5Y+CzhoCxJauGkyCIDGnKyRVaG4usLVBzSUpqOeySbJGqxCkysKNjYg1dPXvYruosmR2SBo3iUr1ZyfCM8viRpfqHCr/aCSWjzzyyKnA5/wbDx60hHRvb6+uSpCbX+kyJ1Wq4TYGjaA9NDaC2159FBvrthu53JZrVudXoqbrIMYnzccFHB7LQHO/edViVUYuJjGC1oHg3ZDNgBAfHYOV+aV4r80caWnmGbNdW5KajQRBQvqP71eaWox3Wr2ZyAPZd4+7CB+qej97aKDrI+Fzzp90leT8yX8zfAAPzgoKCsKyeVx7yTiC/M0f6Q2gbRDbKd+BJAX5YzfZyecyk+ytt94q1iZMhvXb3/4WH/7wh8NuzndzMFDZ9+lPf3pW7wmSciTnaBUVFWI9og19QbxXrFiBHTt2CDXo5s2bZ2TwpTJw7dq1QlnP5Cv8HpAo1hrVf8z8y/l3zZo12Lhx44z4unTjZgIRmq8bsZu4qWcpBOYrAooAnK89r9qtEAgxBCQB6E/1p9x97ekwLtxIijA2EI2kyBFHHCEW2nNt0mVTZhomMaPnriQ3IpIUDPVMwySgiLlUirDeJKKIvTL7EdBLZmM01h+JWm1QfY5FPZUq1bLaxCLhSEr7Q35icgIPbPsfPPLuC/Z3jp8SF2YVom+kG70jxhNmZMSn4+0W6wRealwiFmVlG3J1tRuI1fkVqO9pwtD4iN1FBywv1hONFfnl2Nmun6GzOr0Kbx72n71T+4CnPnYzcpLSAz4zUi7gfESCRMbK5dzDLL++JEg4t5fvO64TtCpBvRjNMp6vNpagXsZ1o1jQjZoupX/729/ELSSaHn/8cZFNNhIPW4ziYuQ6Eno8vPrEJz6BE088EdXV1cIdnX+jSpWu0zIeHw9U//KXv+CDH/zgjKLpbs34ihznXBNSeXnyyScLoptE3u233y4OzGgbNmwAswrrGWP6ffvb3xYfcX3JfzPRFufWp59+Gr/61a8EyczvD7M6y6zCRtqqrlEIKASCR0ARgMFjqEpQCCgEbECABCAXBFx8yg03fyt3XxvABQSmtbW14kfGWiwrK8OCBQtsP8W3p8be2I9MTiIVWvwtMyxqnyEzDWsTi9itTLDSJl/VH+vJhW5paana0FgB1MA93LhQpdDS4s0Ky80OydacnBwDd8+8RMbQ0qoE+a7yNa06huMw1ElpI2D8T81m3PH67zE24U7cutykNGQmxKOpzxtPLZCVpVXi5frglHRRAI4rrsL2VmOEV6A6mfm8OCUTKXHRaOozlm3XTNlGrl2SVYLukU70jPRPuzw1Lhf1PYH7oDK9AA+dt97IoyLiGh7kkEyRh1KcP/k+53s90o3zLglBeThCYknPZPgO6TZs5GCE8zzVZpdccsmUQv7qq68GSaRIjwds17ghAUivjkBGN9sHH3wQZ5xxht9Ln3rqKeF+7a+P6SXC+IFf+tKXZn0cE4yQMPQX25sJ5p555hkcd9xxgaqtPlcIKARsRkARgDYDqopTCCgEzCPAQME8YWZsLppUASrVn3ks9e7gop2x/mSsH7oz8lSWJ/fhZjJ+kSRk/GUalgHNJSnopmszMeVmkZhLwpJYk4iKJKVIqI0dkn4k/+QGnUHI6ZpnZ9/z3cTYmVIdw3EolZ1aPPju4hiU448bYjvr4Rb22w7X4HsbH0SPCWVeMHWji+yRucXY0xF4M5udWI6tzYGvM1KfFXklaO5vQ/+ouWy5Rsqe7Zo4TzRWFZTNmUuwb4KQlNgkNPQaU2F+askHcPWajwULQcjfz+/8/v37xQ+NZD/f5SQw5qv5KqVncxuWZCAJJar5+F6UxgOWe++9FzfccIMIA8PPmFSCyjKl+jM+uuiO/sILL4i4fpwDDx8+LFSqJFA5D65cuRLnnXceLrjgAkNrELrxMkkHlYKMz8d+4sElVYPXXHONODg2Yps2bRL9+/LLL4NJXVgfxsk8//zz8bWvfU24kStTCCgE3EdAEYDuY66eqBBQCPggcNNNN4kAz1wY8DTwhBNOwEknnSQWHDS1ELQ2ZKio5KaFcVokjjwprqqqihjVAskeLRnjL4YblQjaOILBuCrN1hvEnLF1uGimKdWftbFr5i5uRnfv3o2mJm9GWRJtzGDoVkwubVB9EoL+guprxyAJ4XBRtzT0tmL9P+5HfW9gVZiZfpvt2mMKq7GrfT8mMal/2STQOZSInpFBux6JopQMpMfHutpOWflVeeVo6DsExmCcC1tVUIXarjqUppZg62HvfBHIbj/lCzi2+IhAl4X15/xu0+VXuj2SsKDLrwrfML1bteE75HzsezBy4403ilAYdE9dvXo1jj76aJHs49lnnxWFEdcnnnhCvLvVmi+svzaq8goBhUCII6AIwBDvIFU9hcB8QODss8/GX//61xlNLS8vF2QgY5rwR8aCUYvDwKOCGxYq0GSW09TUVKFa4O9INqlMkApBKhP8ZRomIShJQW7ogh1XfCY3OEr1594I842vSBUx1a1zSa5RyaKNn8UxqOcGJd3lZCzBUM523Ts8gBtffghbm/e61rlH5pSiZaAFg2PDM56Zl5SHNw553bztNCoQV+WXYEebMRLMzmcXJmciPSEGjS4Srdr6FyZlIi8lB1sM9HGMJxp//tRtSIyJtxOCkCqrra1NkH/S5Z+HZySv5oPLrx0doT0YoRrtox/9qG7WdT6Lazu6/TIz7PLly0M2LIkduKgyFAIKAYXAXCOgCMC57gH1fIWAQgAkbRgI+KWXXgKDEL/66qtTJ+5aeIqKinD88cdPkYLypDhY4iaSuoBY7tu3b5oCjYo/kqnzceMiMw1rY7jpZRrWJnUgKUg3aaPjSqn+3P8GEXOOc7oq0RjzkQpiJvsw2m9u1Zp1ZdIdmdyGhKDeGKRyURvHkmR9KH1nh0dHcMs/HsLLrTvdgg7FKVmIj5lEy4A38Ly0stRqvNxQ41g91hVVYndHnWvxD2VDYj0erC6owHtt7scknJwEJkcTUJqShgNDs5OrK/MX4Bcf/Kpj+M9lwZwz6FIplfP8XjKBgdU4onPZllB6Nt97TCTxhz/8AW+//bZQbOvF9KVS+thjjxXrPP5wzcf5WJlCQCGgEFAI2IOAIgDtwVGVohBQCNiEAJUy3Bxv375dxA2hiwhJQWaI8zUuyLk4pDqQC0Uu0kkEhBoBYBM0AYvhKTtVf9L1hu5KVP2pxfP70ElXJRKCkhT0l2lY6zLsj4zRU/1RgaYwDzhcLV/AjSSVlozFRwu3+IraMSiVgnpZNvkuk/GzZKbNuUpuQ9d6qqH4+7WBWvxv3y5/zrmW+9XfjSmx8ajOzEVNV8PUJSmxxXi3rdH2Z2kLXJiZj5GJQbQPejOmu2kr8spwqO+wrvrRqXqkRCWifsDrUl0an4qRqGH0T+q7JH9+xTm4+Cj/iQScqqPT5ZKQYqIPvmNonAPomupUyAin2xNK5TM0wr/927/hscceE9XigQ1Dv3DdwkNfZqllkjJfY2w7ZqZVphBQCCgEFAL2IKAIQHtwVKUoBBQCDiHAzTJP5ElskQwkKcgfGe9L+1hulmUMQZKCDHzM0/tIJwTporRnz54pTFTcOeODkeOLbtLaTMP+kjpIEoabQqoUuFmRCjRizsDYzAwZ6ePNOLr2XumbyZo4E3OqW8MZc45BjjmtSlWSm1oE2UYS0dJlmL+Z5dhJY904xhnXkv9mHegG2RjThx++8ggGXYpZx2y9xxRW4b32/YiOikZDbxRGxsecbLooOz0uCdVZmdjT4SzZqNeQjOh4pMfGoGNiepZepxq9IL0CbzS/7/qc5IlBcUIyDo17yTCtrV/0UawsXijGoh3hE5xqk5lymUSIBwtSnUvlPH/C+d1ipv1OXcv3BmO0XnzxxWIdR/vUpz6F++67b0YiMiaKIBEoCUEeBPPwl/OtMoWAQkAhoBCwBwFFANqDoypFIaAQcAkBSQjSRYfuJCQDqRDUOznmonHdunVCJcikIgw8zdhgkbSg9818mpWVJWKgqSDl1gekJGMkIaNHxmhLZ+w2qk9VRjvrmAe6k+oRqUDjtSTCiHmkbgxlchs5Bv0lt6HSVEtM2/l+4/eAhAjjLNL4LGIus3ju62zE+hcfQOtAV6Dus+3zlXkVGB4fx78avEl23DCSj8cVV2N7q3Mux/7a4QGwOq8cu7vsyXY8G17FKWXY0ep1qdfaURkFaBxqxejkuPhzQlQsrs35IDxRRAaChJZjkL9DzXU90BjhwQIP0GTiJraHqj/OpcqCQ4DrNbr8MuMr3+HE9qc//an4v5HwBlRGK/VlcH2g7lYIKAQUAr4IKAJQjQmFgEIgrBHgAlOqVCQhyBPkXbt2zWgXN8fMPCddhteuXWsq1lsoAUWCgG08fPiwqFZMTIxwqWGcxEgiOEMBc0nGkAgh3nouw6ynrzpLbVyC7z1+tw8ePChicnGjzrEdaZmsjaDEOILaxCL8t15yG20sS5IxJEitvA84zqnWkWooZmRfuHDhjOD8bQPduH7jg9jVPpM4MtIuK9cszV6MXe2H0Oaya+6KvFI097eif3TISrWDumdZbinaBtrQN2pf1mNthUjlDY/Fo390ZsIVXleUkomMxFjU9bTgmOyFuLjwBDEe9d6FJHY49nggQrU0f3N+CkWj+psqMxLsNCYRIsnttLI2FLGwu048QPjud78rlH40KrU3bNggDmWtvJPsrp8qTyGgEFAIzFcEFAE4X3tetVshEKEISEKwublZqANJClIhSPWQbyZOugdTFSjjCDLwNDcrobw4ZRvYNrrUyOyEubm5YEKUucx8GqHDaapZ3OxSDSWzKlP1R4UIVQ1M8KBHxvAabRxBO9VZkY4328d4XPzeEnuaUlq+3+scbyQtZGIR/pbvA+3YIPEiiRiSMlTvzRZHkIQfDxboikcjEUJFMd8x/mx4bAQ/2vQ4Xjy4zZVhGefJR9tALyoy0rG301tPt6wwJR0ZCXGo75kZk9bpOuQkpqEwJRkHuu1vc3FKAXa0Ns/ahOioKBxTVIWzqlbj/IXHi/mU70MtMS3fj74FycMRqRQMhbmK8yhJbpLrkRJOwOkxaKR8eWhDl9+tW7eKW84991w89NBDEZNIhXP+c889hzfeeEMksGtsbBSuypyzOMb5zjznnHNw5ZVXClLZiO3cuRMPPvggnn/+eaFGJYGan58vwlycdtppuOiii8Thl1HjO3zp0qVT8SxPPvlkkWhPmUJAIaAQUASgGgMKAYVAxCPAzTIDTcv4gfy9bds2sfDXGpULK1asmMoyzHiCXLyFCiHIBSEXiW1tbaLaJDBJ/HGRGCp1jLTB5JttlmOEMdC0ced8s7xyQ+w7togL1VlaQtBMpuFIw3W29nADyQ0V3fIkjv4UaPMJl0CYaWNZcgzqZdjk+CUJqHXZ5HuERndjEq4yBiZJP25kjaihJiYn8ND25/HQO39ztEsSouOxp2MEE5OTiPFE4bjiSmxrcTdjbnx0DFbll2BH2/vx8hxttKZwQcIVVuJdm7MEL8qsxutNxlyc//TJG1CSlqPbZKmWlqQgiRLfgzfeSAJQOwatKlWt4M53Cg/Q+I6RdaHLL+ujLDgE2Nckxq666ipBDPOw4ZZbbsH69etnPXgI7qnu383EJGecETgJDhPVPfLIIzjrrLP8VpKY3Xjjjbj99tt1s8PLG++8805cc801hhv7yU9+Ek899dTU9YoANAydulAhEPEIKAIw4rtYNVAhoBDwRYALLi5O6SosYwjyJNdXQUNSjRtgZhjmD12HSbbR3CTcJCHCQPzSJa+goACLFy82tDlXI8AaAr6qP6NZlUk4UxmoTSyip84i8aJN6BBusbOsoTr7XYz5RFWOJLlJFPA7aFRF4USdwrVMEnmSiOFY5JjUM0m+SDdIkoR8txQXF5t+z/2t9k38dNMGjEw4k6CjLLUU/6yfHv/v+JJK7Gyvw9jE9AMdp/ttbWEl9nXWO9bW2ep/RHYxeka60D1sT4KQvMQi7O5oCghZcUoW/vuCGwNeJy/wPRxhdl05h2kLoVJVSwgGUqoaroDPhfwOMMuv/C6Q5D7yyCPFYZqy4BDgHPeDH/wAJKpoXKM8+uijOPXUU02/R4KrifN3kwC84oorRNvWrFkDHlAVFhYKT4CGhgY8+eSTePrpp8UBFg9QuL5cvny5bsW+8IUv4IEHHhCf8QCaqkH+ZhxnKgE5H7K8yy+//P/YOw8oK6qsCx+ajGRoskQR0wgoII4BJIgZ1DEDioGgoCgoyqBiIKhjxIQjoKigIuqgJEXRUUmSR1Ryjk2SnP3Xd/u/z6K6Xr/8+oVz1urVDa/CrX1v1au77z77yH333RfUxX3xxRdy1VVXSYUKFQSfaEIJwKCg040UgbRAQAnAtOhmvUhFQBHIDQEINgo9TJ8+3VdpeNasWZ4KGnywnIQgL35ErAhBVD28ADKBJ1CRofrjxU4jNgjw0o7nHN5zhJfqL5Qz2/HlTNf0qjSMWsJZ0CFWk+BQ2h7PbfGdQ+FqyVImVBBROjmPTi+AKwSMJab9pa7zjCG93ZLTpF6H8nz7X9ZK+ed3w2XnQW/CMZKrqVa8tvy4Lqfi77TylWTP4d2y40B0CLFg21indKb8KYdky76clXKDPUa425UpfILUKFValu+MrEJxwYwCsn3/n0ERqFef3FT6nX9DuE02akDIN2fasNezkPHmVqoGo0TNrWEbNmwwzxfrJYpnLt/foYztsC88xXcE29tuu80sqhIQYyjf8CROxeAdITcrBa75888/l6uvvtpc/jXXXHOcGs9iQsovhB+Buu/555/3WxwFdW0w9wD3F4tmkIcjR46Ujh07muMrAZiKI1GvSREIDwElAMPDTfdSBBSBFEaASQqTElZtbWERyEEvBQ2poLbKMMRg7dq1zYQi0kmFLWyybNkyn78cihwISCVEYjf4mJhCuNrKv0xCUYhEu9os6ZlOhaCXdxZjyOnfBiGTqGb6kfQIxBS+c3hyEYxvJjBKckeCau772ucLqmKbognR7eVlaZWqlhAMRqm6cc92efi7t2Xlzuj61eX7s7ys/CPbAsEdmUWLS5WSJ8iKndmFkeIVJQoVkXplM+X37fGrTGyvjeIdTarUkd+3rTBp0eFEzZLVZN7mdUHtOqh5R7m4dsOgtg12I6dSleevVaK694eItmOQ52KwxLTb1xJlFWosW8062HbqdjkR4NkxdepUo4bDA4/vLAp/9O/fX99TRIwHH99tpAKDjzMY57w/8h7QvHlzg2M04t5775UhQ4YYEvbbb7/1vYsqARgNdPUYikBqIKAEYGr0o16FIqAIxBABXnJZfZ03b55RCPIzbdo0nyrPeWpWvCEEbcowar1QCUGIRkgo1DqEpkHGsHP//9Cs6K9YsUJWrcr29aLPrNcfxEisg9RXZ7qmv0kw5IvTRzAYRUCs2x7J8Un1Zaxz/QSkH5OmZL+uSDCJ9b7uNGsIESqfQqpYdZYlp22/ONtkq7xataq/Kq97Dx2QJ396T6av/zUql3RCgWKyaOu+XI9VKCNDmlStLgu2xN+f7+9V68j/slbInxIeERcJSCeXqSQHj+6T7Qd2hXyYk0rXkZ83Buf/N+Wmp6RM0eIhnyOUHSDsnEpV/vYipnlGONOGvYhpnqNU+bULLFh4sLiQigspoWAcjW35znz22Wdl4MCBpn8guSj0QfGLSBdAo9G+RDhGo0aNZM6cOWYB0f2d/uabb0q3bt1MMynOAUEXaZC5gnc145txj4Le9oUSgJGiq/srAqmDgBKAqdOXeiWKgCIQJwQgBJmk8ILlLCziXuGlObwUOwlBJtqkjni9IDPZ5qWQCYoNUpSoAqcTlth1LhNMKvzGWvUXyhW40zVpo5eZPoVEnD6CkDnJENw/qM/wSyIY30xWSPvV0snL/AAAIABJREFUyWPsepBnFGPdplmzYAHuXs8Xq4S2ZCAEtb1HnC2kv2yVV0tOWwL36LFj8sa8L+Tj3yKvPlmjZA35fk12Wn6gOO/E2rIoa5Uc/fNYoE2j+vkZ5avI9gM7Zdeh3InKqJ70/w9WolBRqVumvCzdEZoSsWTBTFkdRFXjk8tWkdHtHoxF03M9prPitV0kYUHOHRDTkNGWFITMxsqB/SPxtYz7BSfBCfGVw7sOLzyiadOmMnr06JCq1CbBZUbURNLNKS4DUQoRSEaJM1q2bGkUeix6YX9B8Mzlb56zkNWhZB7wncp5FixYYIqKUHyFUAIwom7UnRWBlERACcCU7Fa9KEVAEYgnAry0McnghY+U4R9//NEQg7bKoLMtTFBYobU+gg0bNjSpMigK7777biHlt3v37sbAmdRTrUwYu56kz5gg5pXqL5QrYxIBCWh9BPnbq9IwalGnQjDYNLlQ2hLptlwD1WZtlVr85iC9k4W8jPT682J/d+XTcNOsnVVeIQZRtXgR04w7Ow55hn2zfoG8MOuTiAi5KsVrybR1K4OG72+ZVWTHQQplxJeMyyxWQiqdcIKs/CM7pT3e0aRybVm6Y3VQnn6oKtftDg6f9mc0l/ubtI335eQ4H+ONZ4fTR9CLmLY7Qm7XrFnTLC7wfNQIHwGwnzFjhvH7s4s3pJyiBMQ7NN0DpSnvfRThABNL7L333nvSvn17Hzy8e/Bc5PnJu96YMWNk0KBBMnToUNm4Mds2AeLurLPOMoU/2DfQwtjgwYNN+jWZC3y/2rGuBGC6j0q9fkUgJwJKAOqoUAQUAUUgyghYQhByyXoIYo5Niqk7UHChwJk/f75PqQARSFpNIJPpKDc7rQ7nVv2hYEKdGcqKe14C5lTFWIWWv0rDEDGWjOE6A00kYnVdbsIVVQ6elmrEHyvEs4/LWGdCaNMgqajM4kI0JuyWmLZj0B8xzbm2FNgvw9d8J3uOHAjrgg8fLSPrd2cXQwo2KhcvKeWLFZZVfxzvvxXs/uFuVyBfPjmjTCVZtie6HojBtqdWqUzJyHdEtuzLHa86pWvI7I3BqSpfad1Zzjvx1GCbENftLDEN4YI6zStlmAZBijjThm0F7Lg2NklPBqavvvqqUZehNmMx86233pLrrrsuz75TEgFK0p6p0OsvevfubchA5/cuBcYgpYk777zTVKaeOXOm32Ncf/31pqKyv0wQ3i15f4EYnzx5slx88cW+YykBmAijRNugCCQWAkoAJlZ/aGsUAUUgBRGAELSm+9ZDEEIQc2hnkI5H6ly1atXk/PPPNyrBJk2aCCRhXpE2qdYdloTiBZw+ibfXX6zwtJWGnYVFvPzbmEA4C4vwdzw8DlE6QELZQjqcFxKKsa0RGwQY66hbmRwyPuJBuLrTNRmPTmJ6+5G98uHO2bLtaGjVeksVKikLtoTubweyhfMXkEaVq8nCrPj7AtYpUlp2/LlHDv15JDadnMtRixUoJKdnVpHft/m/7tqlasmcTYFVlQUy8svUWwZIsYKJqfJyF7VhrFevXt0QJlYpCGnlDvs8dPpZ6sJbzkHFfczC5Lhx48yH9evXlw8//PA4j7m4D/AEOaE/ArBBgwbG0uWcc87J0VL8pFH3medT4cLGAxfv2+eee87nBYgvIOTh4sWLzXZ9+vQRVH5e0bp1a5OOfcMNN5h+cYYSgAkyULQZikACIaAEYAJ1hjZFEVAE0gMB0pVI1WA13U7MSdtgou5O6yRVjxdFfAQhBfHagTxRQjD0sZLsqr9Qrtjp32ZJQa9Kw0yUqYZpVYL+CjqEcm7nthBCkK2oYS3hSqVs1A/xIB7DbXey70dfQ7jaQkIonfCjirfClT6nLTZ13fzeu0vG/jFPVhzyrubrhX2tUrVk6urARFVu/XbBiXVkYVb41XLDHRMnligrRQvmkw17toV7iIj2O7tSTVn9x3o5ePTwccehaHChjFKyZV92sancomHF2vL25T0CbZYnn6P+w9eSgkIEY5wqv87FBecCiS0wQvVhd/C9yvPQqRJM54JE4EZ2QseOHX0ZDHfccYe8/PLLunjz/4OHZ5pNh0aBx3fdxx9/LJ999plJx33ppZfkiiuuOG6oYRNzwQUX+P4PFTw4Y4fhDMY0ZOuGDRvM4jDviFWrVj1um5EjR8qtt95qxi2LyqS6O0MJwDx5LOlJFYGERkAJwITuHm2cIqAIpBoC33zzjTHPXrkyezLL6vDw4cOFasHbtm07rqgIL4RuQhDShMmNVQhCDJLSp4Sg/5ECCWUr/KYzCWUrDUMI8mPVeO7Jgr+CDqHeixDdTMydJBSqPyYqGrFBgPGNhxQTQfvsgGxlIpoohCvEy7bt22Xo/8bLd1nBVQiuULS6zNq4JmLQzqxQRbbt3yG7Du2P+FihHKBogYJyZoWqsmhr/FWItLNq8TJSolABWb/nr1TockVLy7LtO4O6jC4NL5HODdsEtW08N4J8oRiXVTujnj/55JODss9gHDp9BP1VXsfP0kkIJqKvaiww53tzxIgR8uCDDxp8uW4WLfH/0/eNwIjj+wcxB1bDhg0zuNmYPXu2NG7c2PdvlIJdunTxPOhrr71mfKGJV155RXr0+IuIhyBEOcjvIUOG+LZzf6fzb60CHLjPdAtFIF0QUAIwXXpar1MRUATyHAHnqi8FDwYMGCAYaHulHDGRZ3JCcRDShtmXl0av6ocUULCEIL+pHkfoS7rIrl27DAllyS7ILUgofqd7kJppJ8AQgmDlr9Kws7BIICN9joEiYsmSJT4vrho1ahgSStPrYjfqeDZQiAgPNIJ+Yqy7VSWxa0HoR/5s8Y/y8uzP5Fgu1XpRqu3YU0h2Hc1Z+TX0M4pUKV5KyhQtKGt2Ba9ADOc8XvucU6WWLN6+JqgCHdE6pz1OwYwMQQ24aGu2F23dMrVl1oacvrRe5x12eQ9pULF2tJsU9vF4xpDebpXFpPLyPWi/+8I5MCnCttCSfS56eQmiyncWuOG7JFHI9XCu22sfyNCePXv60klZoCS1lMVHfa8IHmVSclEDokZdu3atGTcEab1gagOVPCnrXkFhOLxyiQ4dOgiKPxsoMyEaqf6Lh6DXOFQFYPD9pVsqAumCgBKA6dLTep2KgCKQ5wgwaWnTpo0x0P73v/9tCJFgw6YwTZ8+3agEIQVnzZrlq6TqPA4vizZlGEKQ9BIinV7cvVR/tWrVEn5SbbIW7BgKtJ27oAOTYK8JMOQ1ihg7CXYqYlDVQLhu377dnI5tIaHsxCdQG/Tz8BBAPQzuVglVqVIlM8GErEj0mL1xsTz2w7uyx48q7wQpJot2BlepNthrLVKggJxVqar8Lyu4AhjBHjeY7U4qU0GOHDsoW/cHTr0N5nihbnNmZnXJ2pclZYtUkAVbAl9/sQKF5dv2A6RgRv5QTxWT7RnjjHXGPIGimPR2nkPRDLefJc9DrwU4vk+wTnD6CCbDfeeFFe8ZqIchmlhMIG688UbjZcc1aoSGwKhRo+SWW24xO1HE4+abbzZ/o46HOLYLbrwT+lscY7zbRTfeHydNmmSOQVqwTQd+6KGHpGHDhp6Nu+mmm8z/oxR87LHHzN+8B3l5E4Z2dbq1IqAIJCsCSgAma89puxUBRSApEUBhwIQlUjLOerz9/PPPvkrDM2bMEK80JtRXEIJ4zlBYBA82zh9pGxK1A1T1F52esRNgmzLMBNjLSB9vIia/TISzsrJ8qadMTkjH81e5MDqtTO+jQNqiEFmzJjs9Fqwh/tw+UImO0tpdW6TP1Ldl3e6c1Xprlqol30Xo/+fv+i84sbYszFopx5AZxjFKFCoqJ5ctJ4u3r4vjWf86VWbRklKmcCn5ddvagOc//8TT5OXWdwXcLh4bsLBAxVRLxKGaYsErHos6fOfi8eZMG4bI8Qp8CC0hyOJHINV0PLALdA6u76OPPjJZCVwXz/Xnn3/eFP+IB76B2peMn3/99de+irwDBw403s82IOFQsRKMZ3+kMR6q1s/y8ssvly+//NLsw74cI5wgNZniJRqKgCKQnggoAZie/a5XrQgoAimGAC/vvETiG/j9998blSCVhiFv3EG1YQhBSwpCGKQCIQhhhbciP9brT1V/0RvoYEoqtS3owNjyUsRwRkjuChUqGOUff+sEMnr9YI/krqwM1qgtUV0mY+w+uE8e++EdmbNp6XHNL1+kuszeFLn/nz9M6leoKln7t8vuOPsC0p6/V60jv2yNf2GSqsUz5detWfL3qrVl4ZaV8qf4J0B7ndNObj69WZ4OKZ49+LjyQ0B0M9Z5xuRl8PzjeWgLi/izUYAAdPoIQhAm0gIcym1UZHjVEfiGQgbiU5dI7czLvg7n3M4KwW7/Pkg4m86L2tKZEuw8F4Q3qddE586dZejQoeZvJQDD6RHdRxFQBEBACUAdB4qAIqAIpCACTJhQa/HySLowhCA/KLTcUb58eTn33HN9PoKkU5GOkkwv/m4yhAnWGWecoV5/MRzb1utv6dKlOYrVOE9rU+ScvlnqBRh+x4A7ij9wt0T3SSedJCh9k+me9ULgyLGj8srsz+TzJT+Zj43/34GisutgbIt24AtYtmghWb0r5/Mx/J4Kbs/TyleRXQf/kJ0HvdVkwR0ltK1OLVdHpq1bbnbi/PsO75Zt+3d7HuTDdg9K3bJVQjtBFLeGnKKitV3MIhWV76hEJLpR5EICOlWCXqppCEx32nBePBN5frBghpfcvHnzTK9deeWVpvgHxcU0IkMAxd6ECRPMQaZOnSrNmzf3HXDcuHHStm1b82/8oPv27et5sieeeEL69+9vPsPvr3379iE1Sj0AQ4JLN1YE0gIBJQDTopv1IhUBRSDdEeBFH4UcK81OQnD9+vU5oGFi0rRpUx8hiLcM6SmJSC6o6i9vRjYFRCjygQ8RwfhAwYDKxSoE+e2v0jCqQKePYLJ6ZsUbfbfHIqlhEN2pVlmZ4iAQgeWLlpOZG+JDyhXJX0DOqpw3voDlipwg1UqWlOU7N8ZlSJ1Y4kRZuOWv9N9ShYpKvXLl5TdXSnDZIsXlq5uezLNnP9VNIf943hCJVtE6UGdZ714nIUgasTv4buUe5rvXLpSQghvLoG2kk1J9FgUjpOTTTz8tvXv31mJNAYBH2Yc3Ym6p3S+++KI88MADvnHLgo3TDoN3l/r165vxTd/j6VyvXr3jzsz7WpMmTcz3aGZmplH9hep1qQRgLO8iPbYikJwIKAGYnP2mrVYEFAFFICIELCFISpVNGabSsE2xch4ckgHDaJsyfPbZZ5sX37wmBFH9YQZvfQ9R/ZEWlmpkSEQdHYOd8eECd8goAqUIFTi9JkO20jDqHSbB/lLk6DunQjAZPLNiAG2uh9y0aZMh8K2iiOI++J/lhXIoHtdOcZCh86bK5BWL43E63zkalK4gK/dnybFc0mJj0aD8+fLJOVVrycItwVXmDbcNGZJP9h8uKPuO5KyqTErw79tWy+FjR83h29Q+SwY27xDuqcLeD3KECr/WI40FAohu1OrJHjw3nYSgl28v1wjR40wbdhZbihQDUpcff/xxIS2VwBaEIhXNmjXL8+/1SK8tHvtDRNNv1157rVkopaAb32H8H1kXYIkFCwGRO378eGnVqlWOpvHO1bJlS2OlQbX2Pn36mD7g/Yz3smeeecanfKUKM1WFQw0lAENFTLdXBFIfASUAU7+P9QoVAUVAEQiIAC+c/Kxdu/Y4QpCKgO6AnGnUqJEpKMLLLyvUkITxIgS9VH+8kFPcRL3mAnZ12Bu4C05APFHkg2IfwfY95BVqE6sS5G9/lYYtIchv0v2CPUfYF5igO0KiLl68WDZuzFaHMaGE6E4FMiQQ5Mt3bJY7xg+TFTvjowK07alZtITskwOy/1i28iyeUb/CibJpb5bsPZxNsEc7qpesKAs2b/Z72NqlM6VgxlHZsGe7PHb+jdL25HOi3YRcj4dCDhKFZwPB/U/Kb+HChePajnidzPlMtH6CPGvdAQnqJATD9VZdt26d3HbbbTJ9+nRzCogpvOiSrXBQvPrH6zy8b6xeHbiCdrVq1WT48OHSunVrv80dO3asdOrUybOAGzvR7xC1Xbt2DeuSlQAMCzbdSRFIaQSUAEzp7tWLUwQUAUUgfAQgZlAdsUrNajS/mZhBFDqDF9SzzjrLKAQhBfETJJUpFoSNqv7C789I9mQyjurPVr1kIgoJFWo6krsNjDFUgVYhmFulYYgASwommol+JNjmti+4kCJm1ZakgaG2jHV6YKyuJ5zj7jywT+6Z/K78sHZJOLuHvU/ZAoWldOECknUkft58trGVTyglZYoWljW7toTdfn87nlq2jkxbn+3/5y+KFigoDStWk0EXtZfKxctGvQ3+DrhlyxbznLEqVxZ1bNX6uDUij0/EM9FZbIlnolexJeutaklBvnNzs1Lge/ubb76RO+64Q0it5vu5X79+8uijj+a6Xx7DkZCnR506ZcoU4+uHKnvz5s2ybds2o4KvWLGiNGjQQK644gq5/vrrg/qOxNN1yJAhRinIIixjAIU35GzPnj0Fj9dwQwnAcJHT/RSB1EVACcDU7Vu9MkVAEVAEoooAEwgmDhCB+AjyG+Nwt1qBiQlV61AHWkIQtVIkhCAvxKSDkaJsCx+o6i+q3et5MC+1ZSwLTthKwxBflhT0mvzipWQ9BCEFS5QokVLqT3cKJGpL/KFI1YvkPor9iInNGSgO8tSP/5ERC3+IzQn8HLVgvnxyepnysnxvfBWINKdgRoY0qlxT/pcV3ZTgyidUlV+35vR+dUNQvWR5mXLzP+OCN+MdT1HIDwKCG9UfaZHpHjwTUUU604btQowbGxZGUPhh09CiRQtjEUDwHT148GAZNGiQ+f7k+/jdd9+VSy+9NC2fJ+k+pvT6FQFFIL0RUAIwvftfr14RUAQUgbARYCLBpGTatGm+KsOzZ8/2VCugWrIpw/yuVKmSOW8wZAZqCFRQ1iuJdGPUZygeNGKHgBt3SDZ8uJhkxisYY/v27fOlDEMKWjWcsw2QzlYJAyHI2EhWbzwm9yht7XjnWsA9UrVlvPosluf54Jdp8uh/x8qRY8dieZocxz7vxNqyKGuVHP0zvuelIWdXqimrd22QAx6efaGCUDBfftl5MJ8cOnok4K7Xn9pUnm4WuudYwAO7NuD+XrhwoW+84ynKeE8nlWuomLEoYq0UbNqwVea/+eabMmnSJHNIiD4W4yAQbcovSv1Ro0aZquEaioAioAgoAumHgBKA6dfnesWKgCKgCMQEAVvxkInGDz/8YH5mzpxpJh/uQJlgU4YvuOACk+5COAlBa1ROOtjFF19sPk+2KpAxATrGB6Uf8TcizQllDn2SSGpLCECrDuS3lxrGVtV0FhZJ9ErD4I56ByWUxb1WrVrCj3pb/jXop69bJl0mjpCdB/fF+E44/vCnZ1aWXQd3yc6D8U8JPrFEWSlWKEPW794a0TXXKlVF5m7KrtwdKF5o2UGuqHtWoM0i+hyLiV9//dUo1LhnY6kujqihCb4z+LFgwPOwf//+hgD0qsAOqUqRiQsvvNAsyFHcSxcWErxztXmKgCKgCEQZASUAowyoHk4RUAQUAUUgGwEIDcgaVIHfffedSRmGHPSqeli9enUzIeEHQpCJTJcuXUzxA3x13nrrLeOHo6q/2I4uyFrUlqhKCCaHqHESGXeIYltUhN+ML7dPJddiKw1bUjCRigocPHjQeJ/hI0VQ9ATcUTVq5ERg9R9b5fbxb8vS7f6LWcQCt8xiJaRKiWKyYmd8z8u1FMlfQBpUqia/ZK0K+9JOKVtHpgfw/7MH/6njE5JZrGTY58ptRwgrnu3r12enIvOMJ+VXx3t04KZw0NNPPy0TJ0405CrWGf6sFPDvxa6jXbt25rtXQxFQBBQBRSC1EVACMLX7V69OEVAEFIGEQQBShknI/PnzfZWGSR/Gr8gZpG6iBkH5h/IJI+2XXnrJEDjBpAwnzAUnUUPoGybjqM+spyOqTJSayZZKa6tqWh9Biox4VRqG3HT6CEJC5MX4ovABKigm7QRVlamujM+hhn8Edh86ID0mj5RvV/8WV5gKZWRIk6o1ZMGWlXE9rz1Zk8q1ZNnOtUGl8bobmFm0kizZvilgu08qU1Em3PBwwO3C2QBlGinuVqFGYRssHRJdoRvOtebFPnyfduvWTb788ktz+oYNG8pHH31kFnP4vmUh7qeffjL+vc7nYt++fWXAgAF50WQ9pyKgCCgCikAcEVACMI5g66kUAUVAEVAE/kIA0gmyhskg6cJMWFAK2gqQVNMjZQljeHyhSBm2hUVQR0GQ5AVhk2p9iPoMAooCLwREGJ6NYJ4KwSTX+mXZ1GF34RquE0WgVQfyG6/JWI4vtwoKAgTcK1SokAqwx+Uajh47JoOnfylD502Ny/mcJ/l71Vry2/Y1QoGSeEetUuUlf8ZR2bR3R9CnLpK/kGzZeyQoH8P2Z5wvj51/bdDHDmZDnvcbN240VVNtijtENwsNsbzPgmlbKmwDvnPnzpWOHTuaglnEXXfdZRbPvNJ8UUpj0QEZCCnYp08fo7LXUAQUAUVAEUhtBJQATO3+1atTBBQBRSDhEYDwe/755+Wxxx4zCkFUfy1btjQEFBMTfNHcQUpq06ZNfYVFGjRoYMhCnUiG1t2bN282E3KrPqtcubKpNpvKahzIB9RHTh9Be/1O9MDAKgT5Hc1KwxCSpFpTAIFgrKOCSqS05NBGUt5uPea3WfLI1I/lUJzJuFPKVZIDR/fItv174g5AsYKF5IzMyvLr1tVBnbtO6Woye2POZ6nXzkMu7iRtap8Z1HGD2Yhn/O+//24IQIIUd4pTlCwZmxTjYNqUStvwTHv77bfl4YcfFhZ0WLx47bXXDBmo34mp1NN6LYqAIqAIRI6AEoCRY6hHUAQUAUVAEQgTAcin2267TWbNmmWOcMopp8iIESMMuYeigYnNypUrjTIQlSCEIH5G7mDCg6E5KkF8jM4++2yjZNPJj3fHQHgxIceEn0hn9ZmtNOwkBL0qDZMKDfFsVYLhVBpmPKPOYQxzXshuVFDVqlXTsRrmM8TuNmfjKuk8cbhk7dsd4ZFC271ckWJSo3QpWbojm9yKd5ydWU2W794YUIlYr2xtmbE+57PT3d58kk9m3PaUlClyQlQuBaUZVX4t2Y2yG6WrprhHBV7B4uC+++6Tjz/+2Bzw1FNPNSm/qORT5fuPa5wwYYL8/PPPxlMYu4qsrCxTYIzFGcbTZZddJnfccUfQynXePYYPHy6TJ082WQY88xmbFKJp0aKF3Hzzzab4lTtsW7755huZM2eOeZYztvk+YBHniiuukDvvvFP9LKMzvPUoioAiEAMElACMAah6SEVAEVAEFIHgEOjQoYO8//77ZqLSu3dveeKJJ4w6xCsgTPjhZf2///2v+SF9iRd5d6CkatSokS9lGHIw1imdwV1x3m9Fqi8pvyhFCFJOmTSioNTIRoCJpbOwiL9Kw0z6nCrB3EgNJomo/lD/ESgKmaTjbakRHQQ27N4hd04YLr9kBad0i85ZRfLnyyfnnVhL5m0OTLBF65zO41QqdILkz39M/jias+K63a5M4UxZsTMr4OlPK19VPv9H74DbBdrAXdUasht1MR6XqUJMBcIglp+DL89xvkMpqEJAWr3xxhspp6ycMmWKtG7dOiCc5cuXN+8Tbdq08bstuD366KPyzDPP+OxGvDZ+8cUXpWfPnsd9RFGVq6++2vfd6e8kEImjR4+Wiy66KGCbdQNFQBFQBOKNgBKA8UZcz6cIKAKKgCLgQwAy6pprrjEv4+eee27IyKCoIo0VdeD3339vFIJ4CrqrwELMUO3Q+ghyLsibdJqIkoa3dOlSX0o1mDAhJ+03nXAIeZCJmNR0p0LQq5I1x4XUc/oIQqoyFjds2GAm6dZ7EGVJnTp1jAJQI7oI7D98SHp9M1q+XDY/ugcO4mjnVKkhy3dukINHswu6xDOK5Msv1YsWlw1HsglmZxQrUETW7z4QVHNur99cHj63bVDb+tsIhTELMzybCRZfqPLL/aEROQI8UyCYUP6xsMCC1wsvvCBdu3ZNyWcKBODtt99uCDXU/fhG8r3F9z8WIZ988ol8+umn5vnKMxelICnmXoEvIunSRP369Y1qkN8sPLK4CKnK8Tp16mTwdQbkIoQrz20IyUsuucTsyyIQ7fjggw+M+pLAd5EFSuxJNBQBRUARSCQElABMpN7QtigCioAioAhEhAATo23bthlC0P5Q7dBd9IEXeCYI5513nlEJQgiiHkhVIgw1G+ozlG1E2bJlTdqUP7VlRJ2QBjtDpjoVgqj63KSznQQySbUpxaSlo/qDJNSIHQJg/vS3Y+Xt36fF7iR+jlyndKbkyzgsm/fujPu5OeGpJ5ST9Ye3yzH503f+ihml5fcg2/PWpXdJ8xqnhd127gUWYeyzpkqVKsbaIdmqiYcNQIx3BNcHH3zQWGUQtWvXlg8//NAo3lP1+4vv70Dj5/PPPzfqPIJFxbFjx+boCVJ+IfwI1H14D/tbhGHRx62Kh9ybOnWqUDG5evXqnj09ZMgQuffee81npBKTKqyhCCgCikAiIaAEYCL1hrZFEVAEFAFFIKoIQMowIWUl3noIog7g5d4dEGIQgpYUrFSpktkkmSdVECHLly/3VYVkslO3bl2tvBnVUSaGYMYbCpUgP4w5f5WGIV9t2jAqkWQeX1GGMSqH495etGiRqWo9Z/cmGbZpgRyMc3GQEoWLyKnlystv2+KbimwBPKl0phw8sl92HMouTpIpZWTJ/sAVg/Pny5CfOw2Q4oWKhNwXPGvXrFljVMb8DWED8QcBqBE5AmDKsxwFGp6KRNu2bY2PHc8UjWz/Q7xtWczDI9AZqLZr1Khhns/Nmzc3RF6sonHjxsarkO/bLVu2BO1LGKv26HEVAUVAEXAioASgjgdFQBFQBBSBtEGASRR+bjNmzDDxHu7kAAAgAElEQVQegpCCM2fO9KlVnEBgBm4JQQqLkHZEJAthw4QH1R8VbwlrUk46nkbsEID4W7JkiS/VmvHCRNCLEKT4ijNlmBTJZBlfsUMw/COj/mXMW4Kf4ipHyhaXLpNGyLrdgQmw8M+cc898InJB9Toyb/PyaB426GOVKFRE6pXLlN+3rZVi+cvI+j2Br79OsXLyYpN/+MZksFWpnaQrDcTXEoW1PmuC7q5cN+R7a9y4cSbFl4UG7BsGDhwoDzzwQEBlXHRakBxHQQVJYQ7Gn9um4c0335Ru3bqZC6GoWLNmzWJ2USg0//Wvf5njs+BIuzQUAUVAEUgUBJQATJSe0HYoAoqAIqAIxB0BJlakZ7Jaj4cghOD06dNzTB5oGCk/1kOQtGE83CBrEo2wQfW3evVqoxbh+mgfaWL4zqnnXGyHGJNO0h9t0RDIPSpDkvrL/zl9BG0RFmeLUE05i4pA2mqfBe4zxvyyZcvMuCfcVa237tstXSe9I7M2xL9Ix1mVTpQNe7bI3sPZRXfiHedVO0l+XLNCjvx5LOCp25SuI1eVq+fbDosAS1AzLr0Uq6TCo0iz4xnSlcrWgVI2AzZGNzAIQK5StOLVV181/6aIyqhRo0y1+0T77snLLsNzEp9JFlog3CDenNGyZUv59ttvTdEr603J9yN/82ymcEe0CjKRAkwqMAEhif+whiKgCCgCiYKAEoCJ0hPaDkVAEVAEFIE8R4AJAROu+fPn+xSCpA9v3749R9swIYcQtCnDpB/lNSHIRIb0R1tplgkNBFTJkiXzHNtUbgDjBvIJEsqSrihISTnzmqRb4tmmDEOiYObvDsg/+g4Shh8IwdwqDacyxv6ujTEP6WoVP+CEzyKkqzMOHT0ij34/Vkb/OiPuMJ1YooyUKJwha3dvi/u5Ty1bQ7YfOCAHj+6XrH27cj3/oLPbSa38JYy/pZdiFU80S1AzFlFcrlixwox5xiU2ChApGtFBgKIUt912m1GsE61atTJVbhXjbHx5Zq5fv16++OILefbZZ33E3nvvvSft27f3dQILBIxbnhFXXXWVjBkzRgYNGiRDhw6VjRs3mu14TkPUUfiDfSMhVykMAinOPcE9ot+/0bkf9CiKgCIQHQSUAIwOjnoURUARUAQUgRREgIktBR9IK7Qpw6gE8fVxR7ly5Y4jBCEhmABEMpEIFlLaSRVCUk+Z7BCQT6gUVYkTLIrhbYeClPEBmUeEW/EUBZWzsIhXpWHGEmnCTpWg26g+vKtIvr3c1ZXBhvGO0tXfPcc+I//3k/T/4TM5GoQiLpqoFC1QUBpWqiL/y8pWKcYrTi5TS6avXymlCheV08qXl0Vb13qeumBGfplz+0ApUiC7cjXjj/Fox6SXb6o9EKnCeIuirtLnTeQ9C/5ff/213HnnnYZAYiEAFWC/fv3SfgHgnXfeMRV6/UXv3r0NGeh8BrA4w3OBAFMWDLD+8BfXX3+9qegbzmLL+PHj5YorrjCHvvzyy+XLL7+MfEDoERQBRUARiCICSgBGEUw9lCKgCCgCikBqI8DEDIINo3FShn/88UeTNgz55g5W/akubH0EGzZsaKoKRpsQhIBC9WdViqTtofrTSrOxH4ubNm0SUs8giQnSxFH+RYMEOXz48HEEDN5fXpWGIRydPoJu5VvsUYj/GcAG3G0qH2Mewh1iNJj4ad1S6TbxHdl5MKfqMpj9I9nm/BNryy9Zq+JGQBbJX1rW7/6rInGz6nVk4ZaVOc7fpHIdeb9td89LY9xRfRYykDEPKeUVPNt47tEP9iddCepwxwjPEvz9ILHAPTMzU1C0XXzxxVH/7gi3jXm5nz8CsEGDBoLP3znnnJOjefPmzfOl4UJWs9iCYv+5557zeQHiCwh5uHjxYrN/nz59ZPDgwSFdKt/BqAghHPkOIA2Z730NRUARUAQSCQElABOpN7QtioAioAgoAkmFABM0fkiDgxBEJUjKMP577oCoadKkiS9lGJ8iyJpwCUHOS/oSExZLQOEPhf9WOMqFpAI+jxsLAQUJDBlCQHJAQKECjVWQkklqt/URhIyxak/nORlTTkIw1SoNc90oeCC+Cap1U20W379QYvUfW+WO8cNkyfbsPoxnnJFZRXYd+kN2HNgb09OWLVJSlm7PmfZ7WvmKOVKC7210iXRv1MZve+xzjmcdAcGB3x//z5j0UqyyHc89p2IVslbDGwEI7dtvv90UqSDwmkWJxsKCRjYC3P92wQ1Smu/ajz/+WD777DOjAH7ppZd8CjyLGQt1eCbaoKAXNh/u6slUDid9d8OGDeaZzljnOzWY4PmM8m/SpElm88cff1z69+8fzK66jSKgCCgCcUVACcC4wq0nUwQUAUVAEUhlBCwhiHeTTRlm8oFayR0oESABmeShEoQcxLMvGEKQdDyOaVORmayg+itfvnwqw5sQ14bKA8WlJaBIe0RNEm+lE+QfpIvTR9ASwU6gnL5tEIPBjrGEANvRCO6tlStX+jznIKAg/vDiDOae8bqePYcOyH1ffyBfr/wl7pebWayEVC1xgizfGTsC8tRyNeXHtas8r82dEjyqbQ9pVLm257buNHf8/yi44CTzGHsQ1DZlmL/9EdROhWCyjsdoDhjGNgtH+P1ZT7pevXoZJWC8nyvRvK54HguV5K233mqeBcOGDTNY2qDIV+PGjX3/RinYpUsXz+a99tpr0r17thL2lVdekR49egR1GRzvrbfeMtuS+vuf//wnKkrwoE6uGykCioAiEAICSgCGAJZuqggoAoqAIqAIhIoAk2CUHaQKW1IQBZM7nRPVHulDttIw6cNMtN3kBooQKkLiB8XkEEN4CKhQFVChXke6b08/ojZZtSqbUIGAqlevnlSpUiVsAiqamDKe9uzZ4yNgIAa9fNsYZ5aAgRAkZTPRKw2j9MFnEXKJoM0QUKgbI41jfx6T52dOkiGzv470UCHvXzAjQ5pWrSnzt8SmOnHdMrVkxvqVubaLlOBlO9bL9FufkkL5C+TYFlUU2KN6JfBSQ2kVaMw4CWrrJWiP4TxJMo7HkDs6lx3A6cUXX5QnnnjCFF7hnoTAateuXUI8V6J5rbE+1g033GDUgKhOWYSzNhio5FkssEGKrj9VJYWc8LMkOnToICNHjgzY7EceecSXLsyC3ldffXUcOR7wALqBIqAIKAJxREAJwDiCradSBBQBRUARUAQgavDQghDkB4Xg3Llzc1TdZIINyWEVglTY/Oc//ymYjBMYlVPJkBRIjdgiALEGCWLTHCFmSfmNBgEVq5Zb3zabMsxviDR3MM64Hps2DDkYDQ/DaF0X5Pmvv/7qS3MPloAK9fzjls6T3t+MlgNHsomueAYk4LId6+Tg0WwvyWhF4YxSsmHPHwEPd029BvL6Jbcet52b8I40zZ3xSMVmZ2ERq6J1nthZ+doS1alqacD3AMqxiRMnGgjOPvts+fDDDw3BGq6qNWBnp/AGo0aNkltuucVcIQtlN998s/mbcUfxJLvohlrV3zMOf0Dro9qmTRtfSq8/2J555hl5+OGHzccs4H377bfmeaqhCCgCikCiIqAEYKL2jLZLEVAEFAFFIC0QYFJCgQdSwFAIQghiHu5Wb6FqYCJDYCyO1xETRkIni7EZKu7qyuBcu3Zto4IKpICKTYsiOyqEi5OAgdh0h6007PQRzAt1KWoolDvr1683TYyUgAoGuV+y1smd44fJhj1/Fc0IZr9obFOndKbkyzgsm/dG59zlipaUJdty+v95tbXv3y+Xexu38n0EUYxKmTReAq80CG9sC6IZjEenp6XXeOR87srX0W5HNK8pmGPxXJkzZ45RmK1Zs8bs0rVrV3nhhRdUORYMgH62oXIyxVII0qdR5tmoVauWT73Nd6u/Z9q+ffuMgpAIVMX39ddfl3vuucdsiwqf72+14YigA3VXRUARiAsCSgDGBWY9iSKgCCgCioAiEBwCTA6ZhEyfPt2oCfA2sqbnTHwhoKynIBVnbcowSkGb1qSEYHBY57YVShC8/mzFU/zOUGSmkrrDVhq2JIy/SsP4tDkLOcS60jBKSwgoS3gzqcbjMh5+aFv37ZYuE0fIzxtzT52NfITlPEKJQoXltMwK8uvWtREfPjf/P/fBx19/n5xduab5b3xFGffWTxI1GuRJPJ4pzsrXENUQkF6Vr7kXnYrVZCp0g7ISrzjIKYgo7q033njDKNfigXHEAyuBD+CsEOz278Mf0Kbz8v3pTAl2XhLPnTPPPNP8V+fOnWXo0KGeV2w9BxmffCej5scOQkMRUAQUgURHQAnARO8hbZ8ioAgoAopAWiKACrBjx46m2izBpKRVq1YmFRVy0KvqJgSgkxC0qWQ6sQxtCEGCkHZqPcvSpboyqjunQtBfIQcnAQMRw7+jMcaYTOPdtWTJEkP8oLLEj4uqndE4frCj4NDRI/LYfz+VUYumB7tLVLe78MQ6xhfwT/kz7OPWLVNTZqz3LgDiPGixgoVkcZeBkj9fPoM7+BMsNqD6c1dKDbtBYezIeISUdhYWya3QjU0ZRjGYiApdroWiEp988olBA1uHjz76yJDb8RzfYXRFUuyCYm/ChAmmrVOnTpXmzZv72j1u3Dhp27at+feAAQOkb9++nteEF6Ot3gvJ1759+xzbffrpp8aCg/FJJWzIP1ThGoqAIqAIJAMCSgAmQy9pGxUBRUARUATSBgFIJyYoTz/9tJlgoHoinalnz57GtwhihG3mz58v33//vZl8kD5MdVp3UCEVQpCfCy64wKQpMdHUyab3cIJcgASxaaekiTFJp9JvOgZqJUgLZ5qmPwIGItCqssKp7IoaCuUZRScI0vBQXELm5FW897+f5PEfPpUjx47FvQkNKlSTrP3bZNehnL6NgRrz558ihTJKyqa9gVOAL6pxigy7uIMsXLjQt6hQrlw5Q/7FQ3EZ6Fqcn9tCN05fS69CNzwnUepa1Sp/56WvJe1m4YYFHZ4vBMQSKaR5Ob5DwT4vt0XZd+ONN/q8+bzaQiGVBx54wHwEGbd06VJxekfyLKtfv77pB4oIzZo1yxRxcgbKwCZNmphiSpmZmSZl2O3zSoGPK6+80qg3+V4g7dd9nLzESs+tCCgCikAgBJQADISQfq4IKAKKgCKgCMQRASYoePxBtPAbFQIKEX/B5JJt2c9ZaRgVmzuY2EMGnnfeeaa4CJN8JklKCIpRGYGhLZQBVuCe7H5j0Ry6wRIwtrKrJQQDVRomzRrsLZmDqubkk0/OU9LG4jZj/TLpOvEd2X4g238znlG5eEkpX6yIrPoj572cWzsyi5aS37cFLv7BMe49s5k0/bOkWWzgOYCtQI0aNZLimWAL3ViFIL+xT3AH18UYtApBfseL3KSN77//vtx///3m2UL6PP6tpJfqcze4uwlCD8X7tddea763ULazyGCtAij4wSIYQb9SKAu1vDvw123ZsqV5zqBs7dOnjzRr1swsqrGYRkEPyGWCYixUFXbGjBkzzP6MMRaHICZturC/K+FZxnjTUAQUAUUgURBQAjBRekLboQgoAoqAIqAI/D8CVPdlstivX7+QJ6pMZlA7kDqMOsFWG7Y+gk6QmRSfe+65hhDkB8KRCVQ6TUzBauXKlebHpp1CPjFxSyccwrn5rF+lk4DxV2nYki+QglaRBfbLli2T1atXm9MnquJy7a7tcteEYfLr1g3hwBTRPoUyMuScqjVk/pbgPQlPK1dLflgb3PaP1/y7nFSstCGmUFwmO1kBueMcj5BEXj6CKEydY5Lrj/b9DlHUq1cvn/cc5Oro0aNN8aZonyuiQZbgO0MA2mdEbk3lmT18+HBp3bq1383Gjh0rnTp18rTQsM8g/AMpyuIOUoNJEQ4lRowYIbfddlsou+i2ioAioAjEFAElAGMKrx5cEVAEFAFFQBHIWwSY/PIDwfXdd98ZQhAlxPLly3M0jEkxKVBWIchENVr+bnmLgvfZmaCjPLPVTknHQxWJukQjPASclV1R09hCHs6jQX6AMYVWrOoPYhDsY11gJLyrEtl3+KD0+ma0jF+2INxDRLTf36vWksU71gr+hIHipNI1ZeaGwP5/RTLyyxv1WkvlChWN2jUvqj0HupZIP0cdzf1tvS35DfHsDpS+zkI34aSx22PyvIXYpsovRSWIq6++WoYNG2bS5DVCQ4DvqilTphhfP9J0N2/ebIoz8ayoWLGiNGjQQK644grjy+dO2fU6E5WXhwwZYpSCeF4yHvAZRTWI1QZErVcoARhav+nWioAikJgIKAGYmP2irVIEFIE4IsAElDTLMWPGyIIFC4yXGhMhjP8hQkjVadq0aVAtwleGtBBeVPER48WSF1QqzpE6woQAbxkNRSCvELCEIIpA6yEIIWgrCzvbxaS4UaNGPkIQcjCSiXFeXbP7vGCwYcMGWbx4sUl9JFCZkFqWiMUDEgW3cNoRrCKLceX0EUzE1GvGzWtzpshzMyZGVKAjHBzZp27ZTPlTDsnmvf7Te/H/K5CvhGzZtzvgac4snilvt24f9yIrARsWww34TkYV6PS1tMV+nKe1aeyWFAyUxm73ZYx89tlncs899xj/TN4lBg8eLPfdd19CpLTHEFo9tCKgCCgCikASIKAEYBJ0kjZREVAEYocAq79UjrOr9P7OhH/P888/7zdtByVL9+7dzQq/V7qRPS4Tg3bt2sXugvTIikAYCDApRlUBEQgpyG+KArjHMpPis846y+cjSPowE+RkSmeDkKLCb1ZWlkEKFQnKM1XmhDFwQtwFooVCHxZ7yFZLSLsPhZLHki/0TSxSNENsvm/zKSsXyb1fvSd7Dh8M9xBh71eicBE5rVym/Lotu1qvOyoUKyO/bc32MQsUvc9uJb3PvzzQZin9OeMPlapTIegvjR0S0Kaw88wrX778cdjwHoBtA8U9CFRlo0aNMgsoyfSMTOkO14tTBBQBRSDNEVACMM0HgF6+IpDOCJAaBJlhyT/MnKkiR0U3FAKQIJB+NoXt2WeflQcffDAHZBAKkHoTJ040n1FtlWp/VFyFMMG7BmUhCkOOoQRgOo+65Lh2JsWkWHEPWA/BuXPn+tRy9iogcPANw5idSS4FRpgUJ+pklwqzEFA27ZQqydzvqZj6mGgjDYKFZy0pwkSlSpWMMprqrDZF06qyrCrTeQ0oAm1REX6Trp6X42zZjs1y5/hhsmJnNpEc77jwxDoyf8uKHErEUPz/JtzQU86qVCPeTU/48zFGLSHImKQqrDNQ97PgV7t2bePnx7OPRYSHH35Yfv75Z7NpmzZtjPdfulYQT/hO1gYqAoqAIpCmCCgBmKYdr5etCCgCIphB/+Mf/zBQoGSC6GAy6ow5c+aYz1CuMOmksiqknjMee+wxeeqpp8x//etf/zKm3/6C4yjZoKMv2RCAECSdjUqLlhBkomuJNOf1nHbaaYYIhBTkB6KHyEuyBkJp6dKlxu+J4B6GoLdtS7b+SKb2MnZWrFjhK7LCMxbir0qVKp6XwfYswNhCDhAwXimaPEedRRzwb4x3+vYfB/cbJeDU1b/lSZfUr1BVtu7fLrsO7fedv07pmjIrCP+/EwoWlsVdB0iBjOO/8/LkQhL8pIw/p0Lwk08+kVdffdVvq6+66ioZMGCA8VXMy+degsOqzVMEFAFFQBHIAwSUAMwD0PWUioAikBgIoPZ78cUXTWPGjRsnV155pWfDrrnmGuPpQ6BgYaXfBhNbJrNMEKj0RsU3DUUg1RGw1V9nzJjhqzQ8c+ZMoaiGOzBUdxKC1atXN5vEa2IMcUmhD6vkLVu2rJmYJ2qxiVQaO6RSgj3kCUEKJYrRYIz6LQ52rFl1IL+titCJFcQi1YWtStBWGo41nkePHZPnZkyQ1+d+E+tTeR6/0gklpWLxorJi52bB/y9DisvW/ccr1rx2bFHjVBnVrnOetDnZT8qzhIUQ7BLw+6Xgh1faMGpoFkHICuCHYhW6AJjsva/tVwQUAUUguRFQAjC5+09brwgoAhEgQArPa6+9Zo7AJBVSwCtI+0XZR8yePduk/Ngg5eeZZ54xZAaV6mrVqhVBi3RXRSA5EYCkgZRBMUulYVKHp0+fblSD7sAXi5Q5fpgUU3iD+yfahCBtIv2eyTl/c/y6desKBGS0z5WcvRbbVuMpidciVgtENIusQLZYhSC//VUahgS0PoL8HUvyZdySudL72w/lwJHDsQXW4+gFMzKkadUasmnvTlmUFZz/X7/zrpDujVrGva2pcsKNGzdKp06dDBHI84RCYZdddpl5l+D/KDLkDgrdsJ9WGU+VUaDXoQgoAopA8iGgBGDy9Zm2WBFQBKKEwCuvvGIq8xHBKAB5yWeyiYrFBuQFKsDGjRsLFYAJCirw8o8qkBTDokWLRqnFehhFIDkQgHBj/M+fP98oBPkhfZgK2+7gHrH+gahlSCGOlBCEIMLrD7UYwYQb5S5pohqxRYB0a6or45NGFCpUyGBfrly5mJ2YVHSnQpAUYq+g/50+grQtmvFL1jq5a8JwWb87OBIumufmWM2qnyr/Xb1UDh7LJl1zi4k33C8NK2WrcTWCR4BnG88zyD9IbuKhhx4yNiB2PLHNypUrj/NQ5Z7g2cZzSUMRUAQUAUVAEcgrBJQAzCvk9byKgCKQ5whQiZL0RFRKEBCk87g9AOfNm2dW9plg3nTTTaainw32twbf9957r5kA4AeI8bclHvAaI/2RCQLVhjUUgXREgAkxSjCrjrE+gnYC7cQEogjfTVtYhJRR7qNgVXsobH7//Xef8gzFH/e5+95Ox36I9TVDvGGTYBV5pECirI420RboOhhrziIOFBnxqs5OKrKTEIzGYs22/Xvk7knvyvT1ywI1M+qfVy1cXrbt3yuFCohsPfyXL6D7RMULFZbfu6j/X6gdALlNYTC+61now05g+PDhgudfoOcT7wuQ4qQBaygCioAioAgoAnmFgBKAeYW8nlcRUAQSAgG8/W655Rbj39OwYUPp2bOnnHzyyabqH4olXvaZ1PLSTpVfZ9EACMPmzZub6+jTp49gDE4asL+4//775YUXXkiI69ZGKAJ5iQBkDBNoVDHcR7basC3S4WwbilsIQesjyH0KoeSecDPB7tGjhyHauU+pGgv5FEvlWV5imEjnpj/XrFljCq3wN8U4eI5Wq1YtIDESj+uAuGGhx6oEIQe9Kg3jC2lThvkdbqXhw0ePyoBp42T4gv/G4/LMOfD/O3akkOw8ckhOKFBAzqpUReZvWeN5/pY1T5UP2qr/XyidQwXxzp07y+TJk81uqP4//PBDY/sRiPwL5Ty6rSKgCCgCioAiEEsElACMJbp6bEVAEUgKBPCpgphjJd+tEqlYsaIh93jxZzLoDMhDCoQQkA0HDx40JMWgQYPM5ABPNEjD3r17G98f4o033pCuXbsmBS7aSEUgXghw39m0OTwEUQhCwOPf5w7uwyZNmhjVLipBPDm//fZbwdMTErBevXry9ttvm7TTeCvP4oVXIp0HdTTKzm3btplm0T+oNhM53Rry2VlpGELQX6Vhp0KQVPJQKg2P+W2W9P1ujBw8GjglN9I+LZ1RRJbvPXDcYS6qXlvmbV4lR/88dtz/P3r+lXLP2S0iPWVa7M9ziYrnHTt29FURv+eee4wvcKoVEoIknzBhgrle/I5RLPJMZYEUQpwUZnwO77jjjqAXVn777TfzbgVxygIP70W8V6HKbtGihdx8883GHzS3gGilwNrChQsNic8CD/6x9AMZGhqKgCKgCCgCwSOgBGDwWOmWioAikIIIMOl74okn5N///rds2bLF8woh8x5//PEcKbzvv/++dOjQwbcPRARKJvekAFUMqiXS4jIzM01hgmikmqVgd+glKQIGAUsIrlu3zuchyL3FZNIdpOExKWQf7qt+/foZJaCm/MZ+MEH6Qf5BAhIo/lD+JRv2jB2ez04fQRZ03MF1ORWCqFMDXev8zaul84QRsmnvHzHtkJpFysucHVtznONvmZVkz6E9x1UGnnTj/dKgovr/BeoQiOI333xT+vbtawhiSO2hQ4fKjTfemJKqvylTpkjr1q0DwSKk9vP+06ZNG7/bck89+uijpkiaLQTktfGLL75oMi+8ArLwuuuuky+//NLzc8j4/v37m/NoKAKKgCKgCASHgBKAweGkWykCikAKIsCEj9VsDL2ZxPXq1csYe9euXdusUs+cOVOefPJJQ+qR4sOLqi0aAhyk/PJyamPSpEl+X4idlYRzKziSgjDrJSkCUUGAyTgkPepA7tmvv/7apBDbqFGjhiFxUHNBuNtKw6QPQ9poml5UusEchL5AncliBkF1XdRB1hM1emfKmyPZqtZOQnDfvn05GgMBAQloVYKMM/wq3bFl7y7pOukdmb1xZcwu6OSyNWT6+uz+cEe5osXkpDKl5detG6REoSLyW5enpUBG/pi1JRUOjG8kCjOU/gSK4o8++khOPfXUlH2WQADefvvtctFFFxllNRXbK1eubO53FmN45/n0009N+jzqapSCZ555pmd333XXXUaJTdSvX9+oBvnNIg1KQDIvOB7vXM73KufBsGexvsu0ie2qVKlifEYHDhzos1xhAffOO+9MhWGn16AIKAKKQMwRUAIw5hDrCRQBRSBRESA1F48/4p133pFbb701R1NZub744otl6tSpJvWLoiD2hZeUlksuucTsw8swKWX+Ug6dK+usVkMsaigCikDoCEDOvPrqq6awDkQ95D0kH4qtuXPn5vB2474lJdWmDJOmj4JFCcHQsWcPSFYm4LbSLuQX5EiqpUO60WF8QS5bUjBQpWFLCtrvhENHj0j/Hz6T93+ZFh7wueyF/1+h/CVl095dfrfKyCdyUY06UrJQYXm/7V1Rb0OqHJDnC+Mbdb+1IODdgGcOKeCpHBB7gRStn3/+uVx99dUGBixQxo4dmwMSUn4h/AjUfbxn+UudRz3s9d7k9Fi+8sorDRHrbBuejJCUeI9yr61YsS7BmpQAACAASURBVMIs9GgoAoqAIqAI5I6AEoA6QhQBRSAtEeAlHxJg+/btJmXNqSRyA4IXGV5j9mUWJSDBCjZFBghWynkR9Rcc/5RTTjEfd+nSxaQVaSgCikBoCOCliWLEGvFz75KKRpo+9zQeVtOmTTMKQZS7KFS8UjlRq0EEWlIQlQuhpKD//gDfDRs2mGclRAFY1alTx/h3pSNupIQ6Kw0z9rwqDeOJaNOGISo+XT5PHv1+rBw+djS0wZ/L1hWKlZLftgWXYvyvFu2k29nNonbuVDoQ/Tdy5EiTDYDvHWq1V155xZBZ6TjG/fUtKkgqrfMOhUegMyDGUWNDlFMkjcXTcIJiTvgRQvqtWrXK2Au4A2/Am266yfw3noz0m4YioAgoAopA7ggoAagjRBFQBNISgU2bNpnUFuKGG24w1fz8BSoj69mH4o/CHgQTQCZ3/CYtBcNsf+EkC0krQk2goQgoAsEjwOQcpR+p+QTFdJj0uYvz2COyPWmbM2bMMIQgqcPs65XKCZFlU4Yxl69ePdsfTSf92WjyjMN/cfPmzebfPA9RVZYqVSr4DkzxLSFFSRt1VhomddIdKCU3ZRySQb9Nla0H9kYFlTMya8r3a1YFdaz/tr9fzq6s/n9usFC2PvDAA2ZBgWBxYfTo0cZOQJ8Dx6PVqFEjmTNnjlFEupWwLG5269bN7EBBp2bNQieb9+zZY8hFFm+c71zuPkM9iK8y5DsLOizWaigCioAioAjkjoASgDpCFAFFIC0RIH2EF0fi2muvNV40/oIXXHyeiCuuuEK++OIL36a83FoPQSZ//sgI9rnqqqvMfnjXPPLII2mJu160IhAJAtOnTzf3K0b8pIWFEhCCTCipbkl6GYQgx2Py6A4UvUwoUf7yQ8VKSIB0JAJQuZESyUIIQQVOFEBeXneh9Eeqb2srDTt9BJ3FEHYcOSBvrJ8ryw/sjBiKU8rVlJ/WBSYAixcsLOvvHaD+fw7EeS6gaqXK76JFi8wn//jHP0xhME0pzTk0WQiA/IfwhghEZe2Mli1bmqrs+IHaBQMw5m9IVioAB0qlZn+OQwwaNEgefvhhv/cIhUi++uor8zxicQc/Ug1FQBFQBBQB/wgoAaijQxFQBNISASZnpGMx+Ue9h5m9vwktFegs2UB1UVKCbPC3NbD+4IMP5Oabb/bEk7RFfAYJiAebUpyW4OtFKwIRIAARFQ2/OSalKNvmz5/vUwiiIKGyrTsgvZwpw6QQpzohCD74avFDkIqHjQHPS43QEXBWGrak4J79+2T0ll/l+z/Whn5Axx5FCpSS9bsDpwC3qnmK/Oe6LhGdK5V2pk9Y/ON7nYU+yKNnn31W7r33Xr+edal0/cFeC8QaGQ4sZIKPJfbee+89ad++ve8wvFdBmoIlC55jxowxBB4LNtg3EDw3zzrrLPPexL5eiyqvvfaadO/e3WyP91+7du38NpXj2HcyCFyezRqKgCKgCCgC/hFQAlBHhyKgCKQtApB1pPgQ/fv3l8cffzwHFkzUIOtI4SXwHqMoiA1SVUgfpDopvjekGLLC7QzSYFq1amVWzDHLX7hwYUIriUir4cWel/cFCxYYn0QmRlWrVjVpkp07d5amTZvmOm4gVN944w1TqXX58uVm5b9EiRKGQLj00ktN+qZVYKbtANQLTygEIANQaDGJtCnDkPV2sutsbLly5Uw6svUQRBHDAkKqKATxP/vll1+Mxx2BApprLFasWEL1WTI3hvEGzmD8waLp8vqSn+Qo1TxCjHJFS8qS7f6LfzgP1/+Cy+XBpq1CPENqbo4aGCU+5BRB2j/vA9zXqXIfR9JzLFiycOkvKKIGGejEiu99PEEJqvKiHLaWDV7Huf7664WFU/fiK4q/Z555xuyCwhClob/ABuLBBx80H0+aNElQBGooAoqAIqAI+EdACUAdHYqAIpC2CGBiTRU56wmGyo9qf7Vr1zbpbniHvfTSS77iHqSkUM3XHR999JExomZCR+ogL69NmjQxx8AvkKIhTPR4yYUMhDRI1Fi7dq1gvs2Le25x//33m8p+XhOlUaNGyV133eXptWaPCYHy8ccfS4sWLRIVCm1XmiPA/YyihfRASwhSWMSr2A8EGcSBVQmicKGyZTISCRCeLHjYdFUm9Cxy+KvimebDJGqXP2fjKuk8cbhk7dsd0jFPL1dD/rtudVD7fHPzvdK0aq2gtk3VjbivIapI+cXHjmBR6t1339VFKUen+yMAGzRoYIqYnXPOOTmGyLx584y6jyhcuLCxXMAu4LnnnvN5AfIOBHloC6/16dNHBg8efNyx8El+/fXXzf+RcmwLqHmNSRYa7777bvMRak4sIvI6lixZIrzj8KOhCCgCikCiIaAEYKL1iLZHEVAE4ooAhB7kHZ6AuQVEFS+XpA17BSkrGIijnvMKPG8wF2/btm1cry+UkzHh5+Xdkn9nnnmmuaZ69eqZlB7ID0g/1HwEq/925d2eB081iiigdoQwgFDlmkkbhDhhkmU9FPFLRGVkFQOhtFW3VQTijQDEAT8rV640HoKQgqQML1u2LEdTUMqxCIB6GMIfBQuFMxKZEOSeZVJuixlBYKJY1klsfEYaC1HfzZ4pzy35b0i+gJXzl5Ff9uwI2MiiBQrKhnsHSqH8BQJum6obcP9SWbZLly6mWAtp7U888YRAQqmn5fG9jjJ13bp15j9ZwETJz6IdKbksCLA4iieyM3hH4PvfBguiWCyULVv2uO1436pfv76pKs5zBpsBMgxsUHV5+PDh5p+cl0VZf8F2bE+4U5LzYhxDRtKO8ePHy4UXXmi+MxL5uZ8XGOk5FQFFIG8RUAIwb/HXsysCikACIIDn17Bhw4xaj/Q/XnyZDOD71bhxY+Prh59NoJc49oUIJO2VSTSTC15cqWLXs2dPX9XhBLhkzyaMHTvWmJ8TqJlIf+QanIFigs/wToMMJfXZOXFCRYlnIgEWdmXeeYxevXrJCy+8YP7L7amYqNhouxQBNwKWEORehwy0KkEUK+5ADYPa2BKCqGdYFAj0TIkX6hD8EP+W3KcC5+mnn24m5xqxR4Cq9KguIWGP/HlMvjywTr5Y80tQJ844WkSyDmUXaMktmlWvKxNuyFZKpWPwnfXkk0/6vnv4fmdRjsW9RLkPk6FfILdY2AMz3ptuu+02X7MpsMQ7kw2UgpCtXuH0+cPDj3cBG8mqAEQ9jQch5PItt9xiiEANRUARUAQSDQElABOtR7Q9ioAioAjkEQKo/UhXJsaNG+e3yuo111xjVAAEpAEqIRus9PPyi2rIn6qSasm2uiKkCJMGDUUgFRAgZRhSHPIcQhBFDJ6f/L8zIM0bNmzoqzQMqc49EW8iAhITZe7SpUuNUgXV7sknnyzVqlWLe1tSof9DvQa36pLiNngtMhZGLZouj30/Vg4dO+r3sKWLnCDLd2QrsgPFnXUaSq9Gzc3CTSKRz4HaHY3PUZpBVKHYJZo1a2a855yqs2icJ12OccMNNxg1ICp+bENsZgQKYme6LqnWeCt6BcrpunXrmo86dOggI0eO9G2WrB6AkMx4EqIqJRuE9yQyIFQFmC53hl6nIpAcCCgBmBz9pK1UBBQBRSDmCFB1j1V5gtRcFEBeQdovL7kE5B0kng0mlqiISHnEvNtfUAAEghDyMJDfYMwvXE+gCMQIASZ+FNGBCLSE4Ny5c33+eva0EG8QP6QL84OXIPdILAlBJqjc57bqMZN52kCxHo3YI0ABKZ59/Cbob565FFyyMXfTKuky8R3ZvNe7wu/p5WvIf9cG5//39EnnyBnFsz3JIKAhGSFu+I2HZSp6PHL/ka5PMQuIee4nyCUIGifOse/t1DoDPr8o3AiIVLIkCFvsC9wJbEXcWQQWCfwBbTV3CndQwMPGq6++6lMEJlsVYMYblYlZ+CGjgncqLXiWWuNfr0YRSHYElABM9h7U9isCioAiECUESMPhxZUIRgHIZIp0aSaPNvAQxAg8NwXgrl27pFSpUmYX1ISkHmsoAumAABNj0m1RIqES5AeinMmwOzDPt4QgqcOVK1c2m0SDFIT0g/yznqUo/lD++Zusp0PfxOsaGQMbN240xQ1QhtKfYI9fmlffbtm7S7pNekd+3rgyRxNPK19bfli7ImDTC2bklymt2suB3XuMn5s7IP8gAi0pyPM52ccC6kqKTwwYMMDgzHfSiBEjjG9dNO6hgKCn8AbYnFx88cXmCgcOHGiqKduoVauWrFq1yvyT54s/ohXPSxYdCAqPWesQ/v3tt98KRdeIQYMGGdLWX0AefvXVV4bUhoBMBNuCrl27yltvvWUUjtidcH0aioAioAgkCgJKACZKT2g7FAFFQBHIYwSysrLkpJNOEgg6iAdWst2TQMi9pk2bmhd7iqegBHDG0KFDhZdfgup89m/nNk4F4eTJk30TiTy+fD29IhB3BCCDmAhTcdymDc+cOdOzgjbG+ygDIQP5qVGjhmlvKGQGRAipd6TmEUzO8ayqUKFC3K89HU+IIorq8xCABIVhKLbkXETxwuXQ0SPy9I//kXf+9+NxH5csVFZW/rE9IJTnVastX92U7bFGdXpsGli84bf1fXQehDFFm5wqwWRSzPFdRiV6iCoCz83Ro0cL5JRG5Ag4KwS7/fvwB7TpvLlV8EX9ytgnOnfuLLw72GCRBB9S3jPwUMaf2fO+OHTIqOt4Z8FGYdq0aZFfXBBH4N7h3uB56lTO2n/jC8tzlevgHYiKxpoGHASwuokioAjEBQElAOMCs55EEVAEFIHkQIB0G1J7UIngUUbxEtQppKmhWqIKMC+1DRo0MC/lGKk7A9UFEwDSgngxvv32200BFdRLeI1hum79A6m8OHjw4OQARlupCMQBASaJqAFJrYeAJ3WYSS0TXHegGHMSgpD3EDf+CEFS0qi2aVU3pH+Sgm/T8OJweWl9Cp6b9AGEL1GxYkVDEoRSfXbMb7Ok73dj5ODRI1KqcDFZsTP7WIGiz7mt5bHzL/PcDJIFQsMSgrTTpnA6d8DewaYM85vCNokWtBsyHb8/W8GW4hIoAROxvYmGX7DtQdFGNWVi6tSp0rx5c9+uZA/ge0egvuzbt6/nYUnD7t+/v/nMq3rvZZddZt4xuD+ovI5K2R0ffvihWYgknn32WWFxMdbx9NNPy7///W+Tvg9J7iYBeQdi4RSblIceesg0h3cnCEr3trFuqx5fEVAEFAEvBJQA1HGhCCgCioAicBwCVKMkbWX48OE5JoJMWiHuWLG3RIIXfBiEQ+6hGHTHRRddZFJ6bAqRwq8IKALeCEBoYCw/f/58X5VhJpPWt8+5F2Q8hCA/F1xwgZBCDAnPMfDUYsKNapCUvXr16knNmjVDUg9qH4WHAPhDRi1ZssRHAIA/BShCUW/asy/YvEa6TBwhpYuUkh+C9P/74rqu0qJmvaAuAJUihZqsSpC/3UVsOBDqRYhASwry73CuJ6hGBbERbcRv7dFHHzX3DOQMaZjXX399nrYriKYnzCYo+2688cZcFwUoFEbBMIJnCAWEnCQ2/VC/fn1jMUAfzJo1yzxvnIEysEmTJmZhEQUfKcPFihU7bhtnGjCLiJ9++ulxGQl4COM/zMIiajwWN2wxklgBCrkM2Qlp3q9fP1NV2l/MmTNH7r33Xpk+fbq0atXK2Kqw2KJKwFj1jh5XEVAEgkVACcBgkdLtFAFFQBFIAwSYOEEUsMKNabpXNG7cWB5//HG/vjakuEHwjR8/PkexA47HS/C1115rVBnW1ywNoNVLVAQiRoDJIwTNokWLfCnDpA5v3rw5x7GpyE3qI2Qhk3ACkvA///nPcZW7I26UHsAvAjxPWVCxz9JoFVrZtn+P9PtunIz8xX+hJduoAhkZsqHHQDmhUHiKPQgdFKgQgpYUROXkDhR2NmUYIoZrjRchiHrx7rvvNmObILUUdRgVaePVhlS4DSD0UIDy/YzNALYDKD/5PxRvKPttJWW89viOh9xyB8plPPwgyngOsWhI5WVblOWZZ54xY4mgn6gq7BWo+/icYOGQjIQqVaqYtqAuXL58ufnszTfflC5dusS8C6h43Lt3b/n888/NAsvbb79tCp55Ban2qB9ZfGHhlGu2xVJi3lA9gSKgCCgCuSCgBKAOD0VAEVAEFAGDAF5QpN1QrZQUll69epnqibVr1za+UXiTseLNyz2TKpQAtmiIhRAygtV6JmSojUiXad26tZkEQFKwCs7KOS//pPRg3s2LtIYioAiEjgATagiaxYsX+xSC3J+oYtzBfUbasC0sQsEeJvFKkISOezB7oJyDqLBFNyAuIKSiVVzjyLGj8uj3X8ors7/LtTlNKteQqe17BtPkoLaxhWxsyjDPcohOd+AZ6CwsQnXpaFcapi0LFiyQDh06GAUYge3Eyy+/bIgrjdAQgAC0/qC57cl3NxkCfLf7C4p78f4AeegVjA/8A718gu323DtU0rXpxu7jMJ5QfNpU4tCuNrytUUnecccdZnyxENq9e/cchUesyo+K76ixuQ48KSEt8TbUUAQUAUUgLxFQAjAv0ddzKwKKgCKQQAiwso3HH8FLLl5+7kB9ROouvj+8fJPia4288S5DMYABNkojPnN7BHI81EusmkMq8psqqBqKgCIQOQJWIYgXFpNr/s19yn1Jqp4zSLkjDQ9CELUP92Jep3FGjkDeHwHMIWDBm78h/CBfY6V2HvPbXLl78key7/Ahz4t/oEkLearZlTEDxhaycRYW4dnuDnCgurBNGY600jDEN99TjHXOx3hGbYX/n5La4XU3iropU6aY73fSdFm0Q0GMah8VG96/VFEmrdqdsut1Ru6DIUOGGKUg6jn6jEUIVIOo+fAtDSYoNkZfQ/ZCPNMWiDXIN7z14hHO1F3UjWDEs5PUZNKY/aX2skgKBqRDf/PNN2YRACz4raEIKAKKQF4goARgXqCu51QEFAFFIMEQ4OWVlent27eboh8oivwFKUAQBgQv8SgBCdKv2rVrZ/7Ozfybz1kNJ32GwN8MzyANRUARiAwBJtzt27c36cEE6foU3oHYQ9nLDwpB0lLdQQonnlqWECR9GJWLkinB9wkpjyxw4E9GgB8LJLn5pQZ/dP9b/pK1QW76fISs2Jl9Xmd8em1naVM7viprFE9WIcjv3CoNW0IQtWCwlYbxjuO7h8q+BB5zpIryPaLjNRojSo/hhQALoPgdUpgJEpBFT4p94InoJgBtwY833njDkNQU/+F5TIYE/oikBFevXl2BVgQUAUUg7ggoARh3yPWEioAioAgkHgKbNm3yKVTw47G+O14tRW0BoUBccsklplIfQdGPRx55xPzN//GZv8Czp1u3bubj3DyAEg8pbZEikJgITJo0yVTEhHCBBMF3Cz9P0nydwcQUTzpIQksIUp3WXeiBiS6VwCkqAinIb0gaJVi8+x8FHCm/kAIEaZIspkQr5TfQqNt5YL/cMf59mbTiL3I3I18+Wd9joJQsXCTQ7jH93FYatj6C/tJCSRO2PoKQp27iFJKFxSlSfi2JzffV0KFDjbpQQxGIBwKMY3yO8UpG2UvBEkhoJwloqwGPGTPGeByixOYZy/MT4u/rr78OWgEZj2vScygCikD6IKAEYPr0tV6pIqAIKAJ+EUCxQhoLgQH4J5984ndbJm+ksxCkA33xxRfmb1bCWekm+D8+8xekxFAhj+BcnFNDEVAEwkcAv6mmTZsaJe97771nFCrBBJNWlL8oAyEF+eFYqF2cwQT2jDPOMOpfSwjyzEh3QhD8qGJK+iR/Q5yedtppJk0x3nHsz2MyaNpXMnDaZHPqhhWryY8de8W7GQHPx9iCqLYqQfwSwc4ZVKvesGGDUaUy5lq0aGHU4qRUogCE2OY755577om6t2DAC9AN0h4BCqJAApIizXvPY4895kn2v/TSS0YhyDPBktYULOEdyFk9Oe0BVQAUAUUgbggoARg3qPVEioAioAgkLgKsTJOKRbVHzOoxAvf3cvrll1/KlVdme0r16NHDeI0RmH5j2E089NBDJsXFX7Ad2xNz5swRChJoKAKKQGQIUGQHpV4kRvO2yAOp/pYQxKfTKtucLcTbjvNB0PBjfe7ShRQEE9L5IFAJFkb+9re/BeWPFllP5773xOWLjBqw49+ayuCL2sbyVFE5NmopW2kYUhA8SWV3KwXxokOBXq5cOeP3hxddtAuLROWC9CApi4BV+TFer7nmGqP+Y7EFKxQWSJzBogCqbBYIIPzwBUXZSvo64c83MGXB0wtTBBSBhEBACcCE6AZthCKgCCgCeY/AzTff7PNUoqoeFe7cQeoLE327kj158mRTFIRg4la1alXjdUMqFwQCk2F3kB6MOhDSke3xLUuGSRwTTyofQlySMolqBQUUxugdO3Y0aT7BxPTp0+X111835Aqp1xCveFdhXn/jjTcGcwjdRhGIGwK2yANVwEkZZtzOmDHD3OfuoNiIkxCkEjiRioQgyh/IP9JbCdL66tatmzDPsmU7suSPA/vl7MrJ5zOGQpDnJOQK3yMUf0D15w6IbopB8HPhhRea56iqquL2aEjbE1l/P4p62ErIgwYNkjvvvNOQ0zwzKaBCijDvUXyvU8jEGTZFOG1B1AtXBBSBPENACcA8g15PrAgoAopAYiHw+++/m3QrO7FH5Ucl4Nq1axvVBZN+0lkg7AhWvakY6IynnnrKpMIQrHSjEOQFGZKLF2IKhfBSbNMLSVVE6ZHoge9U27Ztcy2Oguch6cy5Gf4/+eSTxpfN7bdmrx/MP/74Y1N1UUMRSEQEmNyifEO5+/333xtCELIGQtwd+OCRLswPJA1VPyEDk5kQ5N5dsWKFrFy50lwuhStOP/10n4VCIvZZMraJcUb12M6dO5uxhbqUqtVUn2XMrVu3Lsdl8Z3Tr18/43+poQjEAwEKmg0bNswsenbq1Ml4H2/cuNFYKowYMcI0gYVDFvgs6afKv3j0jJ5DEVAE/CGgBKCODUVAEVAEFAEfAhB6pKzYKpb+oMGPCbILYs8ZvNjid/Pyyy/n8HRybsekGY+n3r17Jzz6WVlZhhhdu3ataet1111niFFSpfGoevfddwWjbwICjzRMr6DqMZMFAqVU3759jUKSY4DX1KlTzWe33HKLqdyqoQgkAwLc84cPHzb+bLawCKotFHLuqFSpklEIWpUgvliof5OFEGQhhEIfqJ0JClZwDythH92RynhCOcVzkYD8w3OtefPmZqww5rCpsOMNZeqSJUvMtlRd7dq1a3QbpEdTBPwgQEYDC50sChCMTwr/2EXOp59+2nzXaygCioAikCgIKAGYKD2h7VAEFAFFIEEQYOLOijYvtosWLTKTXdKqmLw3btxYSBW+6qqrcp20ow6C8GIVnIkaqkLUGSiAmjVrJphgUyEzGaJ79+7y2muvmaYyKSU92h38P+o+ghRhvIGcAYa1atUyWJIqCD5OnzaUAVdffbWvoArKKlLaNBSBZEMAcobJL88O6yFo093d11K2bFk599xzfYVFzjzzTPOsSURCkIUArglyiuB+Rh2dDPYFyTSG1q9fbxZYUJUSLDahFGfBJbdAYc44oxAOylMNRSAeCGDjcf/99/sWRLEB4LsdEhAi2lqk2LTheLRJz6EIKAKKQG4IKAGo40MRUAQUAUVAEfCDAMQcL/MQd/iZYerNi7072A4ygPToRo0aCUUTnPHcc8+ZwijE6NGjPb3+SGmrWbOmSRNyVlfWzlEEkhkBCEEmvyi0ILZttWFrJeC8NopoQODYtGGKA1HtNS8JQdq+bNkys5BB0B7M/vH60ogeAowTPP/uuOMOgWylz//5z38aSwkU46kUFJCYMGGC+Z6YPXu2QHpyzfv37zeqUpSxl112mcHC3zj77rvv5KKLLgoKFn8LV86dsfjAnoP7k/ZAdEPQ46tI0S58bhn7GjkRwLYDnz/S0wcMGCB33323UalavJT801GjCCgCiYSAEoCJ1BvaFkVAEVAEFIGEQgBfRCqdEhh8M0HyF3yOcpLAHwwyzwaExrRp00yVUCZ6/iZS+AdRWKVw4cImDRvVpIYikEoIMDHmh3uE1E1+SBmmQqY7mFDj+8b9Q/EhyPWiRYvGjRCEkCHl1/obQohA/nF/akQPARY9qBqPLQRjg0UXrBUuvfTSuPV19K4m8JGw2rDFI3LbGhywg2jTpk2OzaJFAFrbDvx9cwtS3SEtVV3pjRIKfjyOUTHTv/QdSmgtShP4ftAtFAFFIL4IKAEYX7z1bIqAIqAIKAJJhACkHeQDgRoFPx9/wedMYAkmrygmCKqEUhiEyQATuUmTJvk9BpUErV8QaphgFR5JBKk2VRE4DgFLCKI6sp5u/LaVxp0bQ5zjxwkZyH2JWhCSPBYKwS1btpiUX+vlhW8nab+xOFc6DwlwZvGEiqoEKeGopG0F6VTEBoLo9ttvN893xvOJJ55ofA5RiqEEx1/3008/NWpwxjxKQYglZzgJQIpMYM/hLypUqCD8eMWzzz7rK5pCIQs8fLm3uK8ofvX888+batcEbcC+Qkmtv5C0BT1QcqLaZOGOdwEKomkoAoqAIpCICCgBmIi9om1SBBQBRUARSAgEFixYIA0aNDBtwQtwyJAhftvl9AqkCuXgwYPNtpAIqIaI++67z1RS9hefffaZzz8Q30FSiTQUgXRDACIEpSzqQOsjuHDhwhzVsyEiuD9tyjDFRUihjISks+nKtugPaj/uX9R/GtFDAOIEnz+qo0L+Ej179jTPzVRXWNpqsLmh+fnnnxtfWAJPWbxlneEkACkgRYGUUIM034oVK8qOHTsM0Thz5kzf9509FgQ4hDufEV4et6GeNxW33759u0lXf/31103aNmpJSFmt+JuKva3XpAgkNwJKACZ3/2nrFQFFQBFQBGKIwN69e02lYyZKqB8gBP0Fn5MuSFBJedSoUeZvFH+kshF4AeZW+RgVgVVyPPzww4IiUEMRSHcEmEQzwbb+gfxGPiu5AwAAIABJREFUiWTVeRYfiD9SFW3KMIRgZmZm0IQgxYogGnfv3m0OyUQe8k+9z6I7AiFZWUyBMKEPS5UqZYpGXXvttUH3VXRblJhHw34CGwrSSSHEnRENApCxjscf4UUy2vNR2b5t27bmn7169ZJ//etfiQlYHrdqzJgxBh/6isU7VIDYGGgoAoqAIpBICCgBmEi9oW1RBBQBRUARSDgEIO9s2i6kHuSeO0hZozqyDWcRDyYF119/vfnojTfeMJUB/cVvv/1mDOCJQIrDhANKG6QIxAkBCEFIOlL0UQlCCM6aNUsOHjyYowWQKBCBkIIXXHCBSbUk3CpB/D1RG3bo0MF8RsVy0lAjURPGCY6kOg1qs27duvkqnqPg/PDDD01VeMX6+K7E8xKim3RcS0rbLaJBADoXnFiYYoHKK5wqdv1e8n+7QWZDlE6cONE8c15++WWT4q2hCCgCikAiIaAEYCL1hrZFEVAEFAFFIOEQgFiAPODlnmqUqFbw94NI2Lhxo4wcOVKefPJJM3nF749o2bKlMQIn3nvvPZ8fIEVC8H7yFytWrBC8xggqQKKK0VAEFIHcEYAQpGAHaYq2sAh/o+B1B9W6bcowhCAqPwgp0u8JKqZ27tzZpBJrRA8B+mjevHmGYF21apU5MN5/WCLgkapxPAIsBqFmJV3Yq7J8NAhAitugcKdvglUAvvLKK9KjRw/tLj8IMMYt6ffmm2+aZ4mGIqAIKAKJhIASgInUG9oWRUARUAQUgYREAJLvrrvu8hF87kbmz5/fTGTtxKhdu3Y+QkEVgAnZpdqoFEYAQgM1IOqp77//3ij78Juz1Xydl06KsE2vRLVD1VUKJqgaLXoDhJRfFj/wRqVfIPzwOGUhRXH+C2dS0PFD/OKLL4TiHJs3bzYfsojUvn374zrESQBSOGXNmjVCQRWwpQI9noAQ2ygrcwsU7Sgw/RUbYeELonzGjBmmiv3y5ctNSrKGfwQgtulLawOiWCkCioAikEgIKAGYSL2hbVEEFAFFQBFIWATw/xswYIBJB7bpWBkZGaaSI//PxAvFBtGpUyehMiOhHoAJ26XasDRBAEIQH0/uYasQpMr2nj17DAIUE4EoofIwRRFsyjDFD0jJ5z5Xoiq8wcKzkuJHH330kTkAKdkQTjwrFVORd955x3xf+AtScyED3Vg5CUB/+zJuH330UaNq9Yf1pk2bjEft/PnzDcGHhx3j31YBfvHFF819U7RoUfnggw98hUnCGw3psRcLDfhaEsEUfEkPVPQqFQFFIFEQUAIwUXpC26EIKAKKgCKQFAjwQk/q74EDB6RKlSo+k29W+2+55RZzDc5iH7/88ouPGNQqwEnRxdrIFEYA0g+DflRVBGomUvYgOSBD3EH1XxRWtrAIxX4gDJW8yn2QQLqSxkrKL4UsCHxS8UGFaNLIRsAfAYg3Iimk55xzjidUEIDgSeouRDWp7YxLlIAoCBnfkN7EI488IgMHDvQLOanyb731ljzzzDM+1aHdmHGObcUDDzzg86fVvgsOAZSvkLAaioAioAgkEgJKACZSb2hbFAFFQBFQBJIWAUgFJrcEKiPSpgh8AakECHHYpk0bX0ERrwul6m/fvn3NRyiUUBdqKAKKQHQQgOSjIM+SJUvMAW+88UYZOnSolChRQpis8//cu6QMU1hk9erVOU4MedW0aVOjkoJ4Oeuss0z6pBKCf0EF+YfKjwUPyKXChQvLCy+8YAogKSFy/JDauXOnrFu3zvwnPpak2H788cfGQgI/WKwlKCrlDnBl3OFL6xV411588cUm7Z2xOXfuXIFU9Irx48cbH1v28QpS4vGk7d+/v1bEjs6jSI+iCCgCikCeIaAEYJ5BrydWBBQBRUARSBUEIPmqVatmvMSqVq1qiAN8AW1AFuBBBnnANkzcvOKSSy6RyZMnmwkz20FMaCgCikDkCFBgh3RePOiKFCkiQ4YMMaSGF3EHgcUPxSqshyCE4NKlS3M0BHK/SZMmvsIijRs3NumS6UoIQmI99NBDPgsElGlUSQeXdMUknNGLgu/WW281mOGfeNttt4V8GFJ2rXcgvnRUunYHlWpR90GAX3jhhSZlmPHMPQIZiZUFacAsYPH5hAkTtGhLyD2hOygCioAikDgIKAGYOH2hLVEEFAFFQBFIUgRI+WXSSzz11FPSr1+/464EDycM8AkmwyiP3IEKBPN2JlqXXXaZoMrQUAQUgeghQEVOiDz86KxfZzBHt4Tghg0bfB6CqATxDHQH5D4pxagDSRsmhRMiP9XJLzCCZCXlF6UlcdVVV8mIESOENGqN0BG44YYbjBoQf9m1a9eair2hBAU8SHFHBVi3bl2f8tUeg35CwQr516pVK6NOdy5c2e0gASHLCTwJ+b7TUAQUAUVAEUhOBJQATM5+01YrAoqAIqAIxBEBfJWqV6/ueUb8lq699lrjt8Qka+HChUY94Yzt27cbjyYmYjVq1DDVScuVK+fbBNLv6quvNt5NhKb/xrFz9VRpgwDqNMgOCJVIg+Og0oUIJG0YYhFChf93Br5spF7alGH8BCFyUokQhPzj2dWlSxfZtWuX8aKjMBIFJbwIpUixT5f9nb6yqPnw/As1UPP9/PPPxoaCtGFn3H///SbFmPjpp5/MGPUXFMlBAQuZu3Xr1pQav6FiqtsrAoqAIpDMCCgBmMy9p21XBBQBRUARiAsCpO4ycb/uuuvk9NNPNym8pAeOGTPGV92SSf0333wjDRs29GwTXmN4YBF4O/3zn/80KiRURUzCpk6daj676aabhIlfsgTFUFCIjB071pCfkJyZmZmG9OjYsaOgYvEXKFS47q+++kpmzJhhigXgiQVBA2HaokUL6datm8FLQxFIZAQgwSD6IVIsIQjRzxh3BsQf9z3qQPvD/ZKshCD2B4899phJqSawQICsIl00Wa8pUcbZ119/bXz8CIp4UMwj1CD1evbs2Z4EoLWc4JiQ4+6FK+e5UK3bSs4Uy6FatoYioAgoAopA8iGgBGDy9Zm2WBFQBBQBRSDOCBQvXjyHesLZBLzF3n//fb/kn9328ccfNynCkAVeQeovRFpuE7E4X3qup1u8eLG0bdtW+O0vmGR+8sknOVRXqKdOPfVU2bZtW67ngGwlhZqCAhqKQLIgwD2+e/dumTZtmlEJ8kORBTwI3XHKKaf4yEBSh6kuTiQ6gYZtAT51kPcEaaR411WqVClZuimh2+msEPzKK69Ijx49Qmov5DNKc1SZJ510Ug4PS4qLWKsJxirfc/4Clfunn35qPkYB6FSwh9Qo3VgRUAQUAUUgTxFQAjBP4deTKwKKgCKgCCQDAlS0RKXGBH7jxo2yZ88eo3I788wz5R//+IfxvfJXjdF9fRACr732miEENm/eLKVLl5b69etLp06djPovWQICD68zvKkI1JGQAZAXqBrfffddo5AkrrzyShk3btxxlwZ5cOKJJ5r/Qy0IkYhfGsoSVIQTJ040qiIUhgQKSjzcNBSBZEQAQhCV1cyZM32VhiHO3GmZXBvqV5syDCGINyiRKIQg1zJlyhTjCweBT7soHsEP6b8a0UHg8ssvN0U3CJTSzZs3D+nALErx3UTQV2+//fZx+0Movvrqq+b/OM+ll17qeXzsLbCu4LuvVKlSsmPHjoQZiyEBohsrAoqAIqAIiBKAOggUAUVAEVAEFAFFIGQEunfvbohMAmVj//79cxyD/3/yySfN/6NsvOaaa3zbrF+/3pCefN60aVPP80OWXHTRRYY4YeIJ2aiVkUPuKt0hARGAREMNOHfuXPnuu++MhyCLA5Df7qDCuJMQxGsU0i0vCEFUZYMHDzY/XAMLISNHjpQ2bdrkSXsSsGsDNgllHym1uSm9qbxLdV4CAhj/PUuuQsDhN5kbIfh/7d0JuKdj/T/wO1dkZxTZhlQIyRJFtoRIibKMMJYiEmnBlOXHWItLyRIuUbaQZVyGFoyQKCbJkGxZI9myK0v/633/ft/zP3OcM3NmnDkz53le93W5ZuZ8v9/n+9yv5/meOu9z359PflmVa5KSCrlP8u9VVlllonPLL7XynIz8Miu/lEq5i55jv/32K0ceeWT98lArUTHZi+EJBAgQaJmAALBlF9x0CRAgQIDAWxVI05J0l8wPl1kZct999/Va7D/Py2qmNFHJD58pRj+lI10njznmmPqybEFLsxSDQNMEEqZlpVWCnU5jkdQTzHbLniOrZBMIpoZgVgimBMFMM800zQO4rFjOSrJOvdK8f+qV9tUgqWnXaKDmk0AvW26zrTbXLzVOs/02X5swYUKtoZhrn5ESCNmmm+3VnZH6s0sssUQN7TbbbLO6EnuhhRaq34PzvTYNWbIVO/dTxj777FPLKPQ21ltvvdp0KiPHTKmFNA5JOHnvvffW+q7pDpyR2qypa7n00ksPFIXjECBAgMAgCwgABxnc2xEgQIAAgaEukGYdqd+XsfPOO5dTTz21zynl8dNOO60+fv/993dtZ+yvQX74Ta2qjKOPProkEDQINF0ggWAC9DvuuKMrEEwwmAYMPUc6s6ZJUScQTDCU1WIDtUIw55JAKit2s70/Ix1+05giAZUxZQIJAB988MHJvigrPxPAbbDBBhM9txMATu4ACQSzLTtNWvq6F7KaMEFkJ9Tt65hZ6Zmwt3sQObn39zgBAgQIzHgCAsAZ75o4IwIECBAgMEMLZKtiwoaMdDM+7LDD+jzfPJ6gICN1AdMZeEpGVv3lB9SMrATsbIubkmN4LoGhLpAQ7o033ih33313V5fhBIK9BUnZxpl6mp0uw1khlqBuagLBvGe6lGeLfwLJ1CxNoJ+VuFNzvKF+HQbi/LNiOjUUE7rdeeedtRZsailm1V1Wd6Yman7psdVWW9XuvT1HOi+npuqNN95Yt/amnEJWiqZeakolZIVetgfnly+d+pGTOu/cWzleAr6s0k7InK3eudbpep/agDlWgmaDAAECBIa2gABwaF8/Z0+AAAECBAZdINsU80NqRmoBpllHX6N7rcBRo0bV2mFTMrIlLR0wM7IaMJ2SDQJtF0hok/+yGuzaa6+tqwSzSi8BYc+REGnVVVftWiGYv88222yTDfCefvrpsuuuu3Y1olh55ZVLGiKlo6zwr+13oPkTIECAwFAUEAAOxavmnAkQIECAwHQUSOfSYcOG1RpT2W6YQLCvkcdT1ypjSgvIp+tkVrOkNlZqDqa+VYILgwCBiQU6gWC26CYMTCiYxiLZQtxzZDVgVgV2tgxntWCa63RCvRwrtd6yWrezwjBBYBpT+Py58wgQIECAwNAVEAAO3WvnzAkQIECAwHQTyLawTnH4bB1LuNdznHvuuWWbbbbp+nK2taVAfX9GQogUuM/WtIwf/vCH5Wtf+1p/Xuo5BAiUUrcMP/HEE101BBMIJqzP17uP1AvMit40Fsl/2aI6evTokq2maU5x0kknlW233daqP3cVAQIECBAY4gICwCF+AZ0+AQIECBCYHgKpPZUVRKkVNfPMM9dC81kxlG6UWbl35plnlkMOOaSGBgkSMtJxMrWv+jMOP/zwcsABB9SnrrvuuvV16XRqECAwdQIJ1dP0IUFgVgnmv6z0y2e4t5Huwueff36tA2fL79SZexUBAgQIEJiRBASAM9LVcC4ECBAgQGAICSTk22WXXboCvp6nni6UaSCw55571oeyom/MmDGTneE555xTRo4cWWucpYh9it0vuOCCk32dJxAg0H+BfL5eeOGFWjuwEwjm71khmC3CaVKRrcEGAQIECBAg0AwBAWAzrqNZECBAgACB6SKQLYVZrZftwKnVl5GVelm1l6/PMcccZfnll69f32mnncrpp58+yfNMo490GE19wXTEzGqlNB0wCBCYtgIJBNMB9sQTT6zNP4YPHz5t39DRCRAgQIAAgUEVEAAOKrc3I0CAAAECzRR4/fXX69bfV155pSy88MIlnUczUh8w9cMyjj766LL33nv3CXDNNdeU1BbMMdJkJP9OExGDAAECBAgQIECAAIG3JiAAfGt+Xk2AAAECBAhMQmD33XevTQQyrrvuurLWWmv1+uzUFEyNwGxJTOOB1PxLd1KDAAECBAgQIECAAIG3LiAAfOuGjkCAAAECBAj0IpDmH4suumjtRLrIIouUBx98sKQuYM9x2223lY9//OO1QcGss85afvnLX9Z/GwQIECBAgAABAgQIDIyAAHBgHB2FAAECBAgQ6CGQLb/77rtv/eqhhx7a1dW3+9Puvvvusvbaa5fHH3+8dhO+5JJLysYbb8ySAAECBAgQIECAAIEBFBAADiCmQxEgQIAAgTYJPPTQQ2WxxRbrdcpjx44tm2++eW3mseSSS5as8svqvu4jr19zzTXLww8/XFcGnnfeeWWLLbZoE6G5EiAwDQSee+658otf/KLcfPPNZfz48eXvf/97XYn88ssvl3nnnbcsu+yy9RcNX/rSl8o73/nOXs8gNUjTzKg/46CDDioHH3zwZJ+aRisXX3xx/V6X80rTldlmm602PErn5ZRB2H777XtdKT3Zg3sCAQIECBCYjIAA0C1CgAABAgQITJXA3HPPXVZfffWy5ZZbluWWW67MMsss5YEHHigXXHBBOf/88+sx08xj3LhxZaWVVproPZ566qn62nvuuad+PSsFR44cOcnzyLGylXiojDQzSdfjiy66qAagzz77bJl//vnLiiuuWH/IHzFixBRP5Y033ihrrLFG+f3vf9/12oQKBgEC/18gNUQ32GCDyZK8613vKmeffXbZcMMN3/TcgQ4A8wuPNERKZ/NJjZRCSEhpECBAgACBgRYQAA60qOMRIECAAIGWCKRZx4svvtjnbLPKJj9c9wz/8oIp+eG68wY77LBD+elPfzokdO+6666y6aablvzZ19hoo43KhRdeWOaYY45+z+mEE04oe+6550TPFwD2m88TWyKQAPCLX/xiXcGXlXXDhw8vCy20UEmA/sgjj9TPXVbipXt5fnGRlYI9O453/x6VIH/VVVftU2+BBRYo+a+vkVXO66yzTrn//vvLTDPNVLbeeuuy2Wablfe85z3lpZdeqvVREwyOGTOm/lJEANiSG9U0CRAgMMgCAsBBBvd2BAgQIECgKQLZxnbFFVeUdPB97LHHagffrHDLD9LZypsVfanr19tocgCYrYYJHfJDf0ZWSCa8XHjhhcujjz5azjjjjLpKMmOTTTYpl156ab9uiWxjTKj6/PPP122LTz75ZH2dALBffJ7UIoEEe701HOpOkHqjn/vc5+qXPv/5z9eVut1H9+9Rv/nNb6a6MVE+n2lqlC7oc801V/2899Xk6LXXXqvn/ba3va1FV8tUCRAgQGCwBASAgyXtfQgQIECAAIFWCOyxxx7lxBNPrHPtqzZYvn7IIYfU5yR4SAAxuZGwIqFFVjbdd9995dprrxUATg7N4wQmIbDMMsuUv/71ryVbgRPcT4sAMKugO+UNsoI5vwwwCBAgQIDA9BAQAE4Pde9JgAABAgQINFIgK48SJvzrX/8qiy++eA3qeluJlOe9973vLakLtsoqq9QtiJMa2a6Ypio5dgKL/F0A2MhbyKQGUSCfvT/+8Y8l5QyysnZaBICrrbZa+cMf/lCWWGKJ+v3A6r5BvMDeigABAgQmEhAAuiEIECBAgAABAgMkkHAuq4oydt5553Lqqaf2eeQ8ftppp9XHUxss9cB6G+lomq2/2QL8k5/8pOy44451C6EAcIAumsO0UuDOO+8syy+/fK0D2FsIPxBbgBPw5xcBGfvss0856qij6t///e9/189z6g8uuOCC5e1vf3srr4FJEyBAgMDgCggAB9fbuxEgQIAAAQINFrjhhhtql96M/fffvxx22GF9zjaPH3HEEfXx1AVMZ+Dexu67715OOumksvbaa3eFfgLABt9EpjbNBNJwI8Hb2LFjaxj3+OOP1/c666yzynbbbTfR+3YPANOxPGHeP//5z9q0J2F9PoNf+cpXylJLLdXn+abW51ZbbVUfzyredEvfb7/96vv/5z//qV/P6sNPfepTtVxAHjcIECBAgMC0EhAATitZxyVAgAABAgRaJ/DnP/+5rLjiinXeqQV4/PHH92nQvVbgqFGjyne/+903PffGG28sa665Zt1GfOutt9aVgBkCwNbdWiY8lQKpu7fTTjv1+eq99967hoE9t+b2p1FROvoeeOCBNbzrbWvv6NGjy8EHH1zf+9hjj63hX0LI3sY73vGOGkSmaZBBgAABAgSmhYAAcFqoOiYBAgQIECDQSoEXX3yxDBs2rLz66qu1G3ICwb5GHp8wYUJ9+Atf+EL52c9+NtFTc4yVV1653H777eU73/lO12pBAWArby2TnkqBvgLABPUnn3xy+ehHP9rrkRMAbrPNNrVBT0L41OzMVt2sBMwKvoR1+Yxm9Px8dg641157leOOO67+MwFftv4mjEzo+P73v7+uKMxxEhJmRWCek3qBK6ywwlTO1ssIECBAgEDfAgJAdwcBAgQIECBAYAAFsp3vV7/6VT1iQr2Eez3HueeeW8OFzvjMZz5TQ4XuI9uHs7oozQPuuOOOMttss3U9bAXgAF4wh2q0QBryPPLII3WOL7/8cm3E8fOf/7yMGTOmvO9976sr8/L56zkS5qdG38wzz9yrz0033VQ++clPlmeffbau/rvlllu6Vv92XtC9zme+9tWvfrWccMIJk/x+8OlPf7pcdtlljb4mJkeAAAEC00dAADh93L0rAQIECBCoAnfddVddUbLAAgvU+k+KwQ/9GyPBQOoAvvbaazU8+J//+Z9a32+hhRYqjz32WDnzzDPLIYccUkODTh2w9dZbr1x11VVdk7/nnnvqCsJXXnmlXH755WXjjTeeCEYAOPTvEzOYvgJZebfDDjvUz2Ga8aS5zpSOc845p6t2YG9Nf7pv80+A/+ijj5Z5552317dZddVVy/jx40u2FT/99NNlnnnmmdLT8XwCBAgQIDBJAQGgG4QAAQIECAyyQIKhSy65pG77yhayrFB54okn6gqvD37wg+UTn/hE2WCDDcpqq61WZp999kE+O283EAIJ+XbZZZeugK/nMVPTLyuP9txzz/rQZpttVlckdUbugd/85jdl8803LxdeeOGbTkkAOBBXyTHaLjBixIi6GjCNPR5++OG6fX9KRr6Xv+td76qrAJdccsly9913T/TybA3u1PZcd911y9VXX93n4Q844IBy+OGH18fHjRtX/3fAIECAAAECAykgABxITcciQIAAAQJ9CLzxxht1ZUdW+x155JHllFNO6XrmoosuWleK3X///W96dVYFJgzMVrOPfexjZe6552Y8RARS/y8/0Gc78PPPP1/POvdAgoB8PaHD8ssvX7+eumCnn356/Xv+/NKXvlTmmmuucuedd5ZFFlnkTTMWAA6Rm8BpztAC2aK/7bbb1nPMar7u2/L7e+If+chHys0331x/WZNtw93Hj370o7rtN2PkyJF19W9fI/+bsNtuu9WHUyJg66237u8peB4BAgQIEOiXgACwX0yeRIAAAQIEpl7gv//9b91m9o9//KN84xvfKOeff349WLaJJvhZZpll6hbgFITPD6Rnn312DYzyuvzXGfPNN1/9QTM14YyhI/D666/Xrb/Zzrvwwgt3rersHj4cffTRtTFAxtJLL11XEqUuWSec6DnbbCFOONgJC/JnAsVNNtlk6MA4UwLTWeDKK6+sv1zJOOKII2ozjykdna27vQWA1157be3YnZFwMSFjX6N7WHjBBReULbbYYkpPxfMJECBAgMAkBQSAbhACBAgQIDBIAl/5yldqx8eXXnqpBn/77bdfLULfGVklmBUkqSWVHwAzUjcuP1imcH1qVHVWiQ3SKXubaSiw++67l5NOOqm+w3XXXVfWWmut+vf3vOc95cEHH5zid1588cXLAw88MMWv8wICbRXo3iE43Xo7W/L765EtwO985zvLc889V7v6pnZn95Hv9dkinOYj6Tb8+9//vs9Df+tb3yrf//736+M33HBDWX311ft7Gp5HgAABAgT6JSAA7BeTJxEgQIAAgbcmkO2gK620UknttxVWWKH+IJiGH50Vflkh2BmpBZcQ8Jlnninf/OY3S7aYTZgwoXz+85+vx+isKHxrZ+TV01MgzT+y9Tu1H7PFN4Ff7o0MAeD0vDLeu00C6bj7i1/8ok45NTc7q/X6a5DV2tnam5Ft+z/+8Y/f9NJ838739Hy+U+Zh+PDhb3pOfvmz1FJL1V/05Bc++d6fDsQGAQIECBAYSAEB4EBqOhYBAgQIEOhFIFt7s2UzW7yyvfN73/te+exnP1s6dQF7viSruN773vfWL48ePboceOCBXBsmkC2/++67b53VoYceWtIAYEqGGoBTouW5bRPIyr7U0Jt11ln7nPoPfvCD+guWjITuWb3X6cKeAC6/tJlUIJhu3xtuuGFt4pRf4OTfq6yyypveL1/P6r+MbOtPGNiz23u+B6RbeEZqBp5wwgltu2TmS4AAAQKDICAAHARkb0GAAAEC7RTorNS7/PLLy7e//e1yxx13lK997Wu11lRv3X07geBvf/vbss4665Q555yzZJtoukimjlxnhVg7NYfWrNPsZbHFFuv1pMeOHVu7+6YDdDqH3nbbbZMMKno7SBMCwNREzJb2iy66qBqkk+r8889fVlxxxbL99tuXdGjt70g9xBzr17/+de3mmmO/+93vrtsy00019dcS8hjtEMi1Th3VfM7WXHPNWmoh30/ztaymTi2+3/3udxUjK+3yPXr99dfvwskvYVJr9UMf+lDt0P3hD3+4lmPI9+B8tvMZTjmHfIYz9tlnn3LUUUf1iZtQL78Aykgzp69//ev1nPLLoRwn9UAzsjrwlltuqduGDQIECBAgMNACAsCBFnU8AgQIECDQQyArvU4++eTywgsvlOuvv77+ANjb6ASAWQly2GGH1YYRCf+22267PlcLwp5oiVlQAAAUdUlEQVQxBdKtOTW8ttxyy5JOzgkZEiqktmOnCcywYcPKuHHj6rbuKR1DPQC86667yqabblryZ19jo402KhdeeGFtbtLXSMieFbJZVZt6bH2NrPZK6GK0Q6C/2+izDT/BcTqtdx+dAHByWgkEc//le3b3Mg49X5df4Hzxi1+cZBfghNWXXXZZXSVuECBAgACBaSEgAJwWqo5JgAABAgT+TyBNPVIj6pJLLqmrvFIUflIj28nWXXfduv0sf5544onlAx/4gLp/Q+yOymqjXPu+xrLLLlu7PU9N+JdjDuUAMHUPs6IqK/UyEpLusMMONfB+9NFHyxlnnNHVBCddjS+99NI+HXfZZZeuumuprZk6bPlzttlmq8f/y1/+UkPENN3Za6+9hthd5HSnViC19K666qpa1y+rQx9//PHy1FNP1e/BWRmaVabZjrvVVlv1uho7NTpz39144411a+/f//738uSTT9aVpfPMM08N6fIZTK3WKVlZ+qtf/arer6kBm89Bvk/kFwRZqbjrrrtO8UrgqfXxOgIECBBop4AAsJ3X3awJECBAYJAE0twh3X/zg1/qQOUHyr628+br2SaWlUpZ2ZSOkHvssceb6kUN0ql7m7cgcN5555UrrriihgePPfZYXf2Z7a3ZUrjFFlvUUHjmmWee6ncYygFg7ukE2xkHHXRQOfjgg9/kkK+nbmZGtginkULPkZVbCfwy8pk55phjykwzzdSraQIdTRWm+nbzQgIECBAgQKABAgLABlxEUyBAgACBGVfg5ZdfruFFapNltdfFF19cFl988a4T7h4Gjh8/vq4EycqlZZZZpoaF2UpqEGiKQO731DfLStd8DrJSq7falnleGuGk3loaK9x8880TEaSWW16fZg0JQ7PSyyBAgAABAgQIEOhbQADo7iBAgAABAtNYICu+Evxl+1lW+O24445v2tKbACN1//JntpSlplSe11en4Gl8yg5PYJoI/PWvf63hdka2T5566ql9vk8eP+200+rj999//0RbLVNTMytrM6655praNMcgQIAAAQIECBAQALoHCBAgQIDAdBNIh9PUnMpI0fkEfelMmvH000+X6667rhx99NFdNdESimSLaLYsdjoJT7eT98YEBlDghhtuKGussUY94v77718/C32NPJ6O2RmpC5jOwJ2x3nrrlauvvrossMACtb5bRj4r+XtqL6bOW+qrGQQIECBAgAABAv8rYAWgO4EAAQIECAyCwDnnnFNrnWXLY8Ziiy1WFllkkVqgPtshM1IjLt1Ms/LPINBEgTS36YThqQV4/PHH9znN7rUCR40aVTtiZ2RV7LzzzluyDfizn/1sbRhy5JFHllNOOaXWW6z/B/dtbysrr7xybfyRLtqT6tDaRGdzIkCAAAECBAj0FBAAuicIECBAgMAgCLz66qvlsssuKz/72c/KlVdeWZ577rkaSiQETA20ESNGlG233bYsv/zytv0OwvXwFtNHIKvzhg0bVvJ5SEOUBIJ9jTw+YcKE+vAXvvCF+tnJSGOdTufVbBPOc/7whz/0eZx0ek0A//a3v336TNq7EiBAgAABAgRmAAEB4AxwEZwCAQIECLRPICv/shpwoYUWKh/84Afrdt8Egq+99pqgon23Q6tm/KlPfap2xc5IqJdwr+c499xzyzbbbNP15c985jNl7Nix9d9/+tOf6uq+jHe84x3l3//+d60rmG30nVqAqQu49957l7vuuqs+r/sKwlZhmywBAgQIECBA4P8EBIBuBQIECBAgMEgC6Wya0VvX00E6BW9DYLoL3HTTTbUOYMLumWeeuTa8SX2/hOHZwnvmmWeWQw45pAbi//nPf+r5pubfVVddVf9+/fXXl7XWWqtrHsOHDy+33nprmW+++Saa25NPPllWWGGF8uijj9aA/W9/+1tdcWsQIECAAAECBNooIABs41U3ZwIECBCY7gJpWJChNtl0vxROYDoIJOTbZZddugK+nqeQkPzYY48te+65Z31os802K2PGjKl/Hz9+fFl11VW7XpKOwLvuumuvszjxxBNLaglmHHfccV3Hmw5T9pYECBAgQIAAgekqIACcrvzenAABAgQIECDQToHU/zv88MPrduA09MiYaaaZyrrrrlu/Psccc9SamBk77bRTOf300+vfs633Ax/4QBdaagKmqU5v49577y1LLrlkfSidtRM8GgQIECBAgACBNgoIANt41c2ZAAECBAgQIDCDCGRrfLb+vvLKK2XhhRcus88+ez2z1AdMY5yM1PdLTb+MNBKZa665SmcVbbYS97WtPvUBZ5111vq6DTfcsKv24AwydadBgAABAgQIEBg0AQHgoFF7IwIECBAgQIAAgf4K7L777uWkk06qT7/uuusmqvu3xBJLlAceeKA+ljqBqSXY23jppZfqSsKMT3/607UTt0GAAAECBAgQaKOAALCNV92cCRAgQIAAAQIzsEBCvUUXXbQ88cQTtXFHtvl2X+W3ww47dG3nTUft7luCu09rwoQJ5UMf+lD90pe//OVyyimnzMCzdmoECBAgQIAAgWknIACcdraOTIAAAQIECBAgMBUC2fK777771lceeuih5YADDpjoKJdeemnZdNNN69dSL3C//fbr9V1Gjx5dDj744PrYWWedVbbbbrupOBsvIUCAAAECBAgMfQEB4NC/hmZAgAABAgQIEBhSAg899FCfjTvGjh1bNt988/Lqq6/WBh633XZbVx2/ziTfeOONssIKK5Tbb7+9zD333OWmm24qSy+99EQGWRn4kY98pLzwwgtl/vnnr1uGO/UFhxSWkyVAgAABAgQIDICAAHAAEB2CAAECBAgQIECg/wIJ7VZfffWy5ZZbluWWW67MMsssNaC74IILyvnnn18PNGzYsDJu3Liy0kor9Xrg66+/vqy33nq1BuB8881XRo0aVdZZZ53aHOTaa68t3/ve98ozzzxTX3veeeeVESNG9P8EPZMAAQIECBAg0DABAWDDLqjpECBAgAABAgRmdIE555yzdvPtayy77LLl7LPP7jP867zuoosuKjvttFN5/vnnez1UmoMcd9xxZbfddpvRSZwfAQIECBAgQGCaCggApymvgxMgQIAAAQIECPQUyIq8K664om7dfeyxx7q26aZhxxZbbFFGjhzZZ2ffnsfKduLjjz++XH755eXhhx8u2R48fPjwsv7665evf/3r5f3vf78LQIAAAQIECBBovYAAsPW3AAACBAgQIECAAAECBAgQIECAAIEmCwgAm3x1zY0AAQIECBAgQIAAAQIECBAgQKD1AgLA1t8CAAgQIECAAAECBAgQIECAAAECBJosIABs8tU1NwIECBAgQIAAAQIECBAgQIAAgdYLCABbfwsAIECAAAECBAgQIECAAAECBAgQaLKAALDJV9fcCBAgQIAAAQIECBAgQIAAAQIEWi8gAGz9LQCAAAECBAgQIECAAAECBAgQIECgyQICwCZfXXMjQIAAAQIECBAgQIAAAQIECBBovYAAsPW3AAACBAgQIECAAAECBAgQIECAAIEmCwgAm3x1zY0AAQIECBAgQIAAAQIECBAgQKD1AgLA1t8CAAgQIECAAAECBAgQIECAAAECBJosIABs8tU1NwIECBAgQIAAAQIECBAgQIAAgdYLCABbfwsAIECAAAECBAgQIECAAAECBAgQaLKAALDJV9fcCBAgQIAAAQIECBAgQIAAAQIEWi8gAGz9LQCAAAECBAgQIECAAAECBAgQIECgyQICwCZfXXMjQIAAAQIECBAgQIAAAQIECBBovYAAsPW3AAACBAgQIECAAAECBAgQIECAAIEmCwgAm3x1zY0AAQIECBAgQIAAAQIECBAgQKD1AgLA1t8CAAgQIECAAAECBAgQIECAAAECBJosIABs8tU1NwIECBAgQIAAAQIECBAgQIAAgdYLCABbfwsAIECAAAECBAgQIECAAAECBAgQaLKAALDJV9fcCBAgQIAAAQIECBAgQIAAAQIEWi8gAGz9LQCAAAECBAgQIECAAAECBAgQIECgyQICwCZfXXMjQIAAAQIECBAgQIAAAQIECBBovYAAsPW3AAACBAgQIECAAAECBAgQIECAAIEmCwgAm3x1zY0AAQIECBAgQIAAAQIECBAgQKD1AgLA1t8CAAgQIECAAAECBAgQIECAAAECBJosIABs8tU1NwIECBAgQIAAAQIECBAgQIAAgdYLCABbfwsAIECAAAECBAgQIECAAAECBAgQaLKAALDJV9fcCBAgQIAAAQIECBAgQIAAAQIEWi8gAGz9LQCAAAECBAgQIECAAAECBAgQIECgyQICwCZfXXMjQIAAAQIECBAgQIAAAQIECBBovYAAsPW3AAACBAgQIECAAAECBAgQIECAAIEmCwgAm3x1zY0AAQIECBAgQIAAAQIECBAgQKD1AgLA1t8CAAgQIECAAAECBAgQIECAAAECBJosIABs8tU1NwIECBAgQIAAAQIECBAgQIAAgdYLCABbfwsAIECAAAECBAgQIECAAAECBAgQaLKAALDJV9fcCBAgQIAAAQIECBAgQIAAAQIEWi8gAGz9LQCAAAECBAgQIECAAAECBAgQIECgyQICwCZfXXMjQIAAAQIECBAgQIAAAQIECBBovYAAsPW3AAACBAgQIECAAAECBAgQIECAAIEmCwgAm3x1zY0AAQIECBAgQIAAAQIECBAgQKD1AgLA1t8CAAgQIECAAAECBAgQIECAAAECBJosIABs8tU1NwIECBAgQIAAAQIECBAgQIAAgdYLCABbfwsAIECAAAECBAgQIECAAAECBAgQaLKAALDJV9fcCBAgQIAAAQIECBAgQIAAAQIEWi8gAGz9LQCAAAECBAgQIECAAAECBAgQIECgyQICwCZfXXMjQIAAAQIECBAgQIAAAQIECBBovYAAsPW3AAACBAgQIECAAAECBAgQIECAAIEmCwgAm3x1zY0AAQIECBAgQIAAAQIECBAgQKD1AgLA1t8CAAgQIECAAAECBAgQIECAAAECBJosIABs8tU1NwIECBAgQIAAAQIECBAgQIAAgdYLCABbfwsAIECAAAECBAgQIECAAAECBAgQaLKAALDJV9fcCBAgQIAAAQIECBAgQIAAAQIEWi8gAGz9LQCAAAECBAgQIECAAAECBAgQIECgyQICwCZfXXMjQIAAAQIECBAgQIAAAQIECBBovYAAsPW3AAACBAgQIECAAAECBAgQIECAAIEmCwgAm3x1zY0AAQIECBAgQIAAAQIECBAgQKD1AgLA1t8CAAgQIECAAAECBAgQIECAAAECBJosIABs8tU1NwIECBAgQIAAAQIECBAgQIAAgdYLCABbfwsAIECAAAECBAgQIECAAAECBAgQaLKAALDJV9fcCBAgQIAAAQIECBAgQIAAAQIEWi8gAGz9LQCAAAECBAgQIECAAAECBAgQIECgyQICwCZfXXMjQIAAAQIECBAgQIAAAQIECBBovYAAsPW3AAACBAgQIECAAAECBAgQIECAAIEmCwgAm3x1zY0AAQIECBAgQIAAAQIECBAgQKD1AgLA1t8CAAgQIECAAAECBAgQIECAAAECBJosIABs8tU1NwIECBAgQIAAAQIECBAgQIAAgdYLCABbfwsAIECAAAECBAgQIECAAAECBAgQaLKAALDJV9fcCBAgQIAAAQIECBAgQIAAAQIEWi8gAGz9LQCAAAECBAgQIECAAAECBAgQIECgyQICwCZfXXMjQIAAAQIECBAgQIAAAQIECBBovYAAsPW3AAACBAgQIECAAAECBAgQIECAAIEmCwgAm3x1zY0AAQIECBAgQIAAAQIECBAgQKD1AgLA1t8CAAgQIECAAAECBAgQIECAAAECBJosIABs8tU1NwIECBAgQIAAAQIECBAgQIAAgdYLCABbfwsAIECAAAECBAgQIECAAAECBAgQaLKAALDJV9fcCBAgQIAAAQIECBAgQIAAAQIEWi8gAGz9LQCAAAECBAgQIECAAAECBAgQIECgyQICwCZfXXMjQIAAAQIECBAgQIAAAQIECBBovYAAsPW3AAACBAgQIECAAAECBAgQIECAAIEmCwgAm3x1zY0AAQIECBAgQIAAAQIECBAgQKD1AgLA1t8CAAgQIECAAAECBAgQIECAAAECBJosIABs8tU1NwIECBAgQIAAAQIECBAgQIAAgdYLCABbfwsAIECAAAECBAgQIECAAAECBAgQaLKAALDJV9fcCBAgQIAAAQIECBAgQIAAAQIEWi8gAGz9LQCAAAECBAgQIECAAAECBAgQIECgyQICwCZfXXMjQIAAAQIECBAgQIAAAQIECBBovYAAsPW3AAACBAgQIECAAAECBAgQIECAAIEmCwgAm3x1zY0AAQIECBAgQIAAAQIECBAgQKD1AgLA1t8CAAgQIECAAAECBAgQIECAAAECBJosIABs8tU1NwIECBAgQIAAAQIECBAgQIAAgdYLCABbfwsAIECAAAECBAgQIECAAAECBAgQaLKAALDJV9fcCBAgQIAAAQIECBAgQIAAAQIEWi8gAGz9LQCAAAECBAgQIECAAAECBAgQIECgyQICwCZfXXMjQIAAAQIECBAgQIAAAQIECBBovYAAsPW3AAACBAgQIECAAAECBAgQIECAAIEmCwgAm3x1zY0AAQIECBAgQIAAAQIECBAgQKD1AgLA1t8CAAgQIECAAAECBAgQIECAAAECBJosIABs8tU1NwIECBAgQIAAAQIECBAgQIAAgdYLCABbfwsAIECAAAECBAgQIECAAAECBAgQaLKAALDJV9fcCBAgQIAAAQIECBAgQIAAAQIEWi8gAGz9LQCAAAECBAgQIECAAAECBAgQIECgyQICwCZfXXMjQIAAAQIECBAgQIAAAQIECBBovYAAsPW3AAACBAgQIECAAAECBAgQIECAAIEmCwgAm3x1zY0AAQIECBAgQIAAAQIECBAgQKD1Av8PMfNaDjuFnGcAAAAASUVORK5CYII=\" width=\"640\">"
                        ],
                        "text/plain": [
                            "<IPython.core.display.HTML object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/plain": [
                            "Text(0.5,0,'Crit. func.')"
                        ]
                    },
                    "execution_count": 33,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# Note that this will take a few minutes because the intgr.quad() commands\n",
                "# are a little slow\n",
                "mu_vals = np.linspace(355, 367, 50)\n",
                "sig_vals = np.linspace(85, 97, 50)\n",
                "# mu_vals = np.linspace(350, 370, 50)\n",
                "# sig_vals = np.linspace(85, 98, 50)\n",
                "crit_vals4 = np.zeros((50, 50))\n",
                "for mu_ind in range(50):\n",
                "    for sig_ind in range(50):\n",
                "        crit_vals4[mu_ind, sig_ind] = \\\n",
                "            criterion4(np.array([mu_vals[mu_ind], sig_vals[sig_ind]]),\n",
                "                      pts, 450.0, W_hat2_4)\n",
                "\n",
                "mu_mesh, sig_mesh = np.meshgrid(mu_vals, sig_vals)\n",
                "\n",
                "fig = plt.figure()\n",
                "ax = fig.gca(projection='3d')\n",
                "ax.plot_surface(sig_mesh, mu_mesh, crit_vals4, rstride=8,\n",
                "                cstride=1, cmap=cmap1)\n",
                "ax.set_title('Criterion function for values of mu and sigma')\n",
                "ax.set_xlabel(r'$\\sigma$')\n",
                "ax.set_ylabel(r'$\\mu$')\n",
                "ax.set_zlabel(r'Crit. func.')"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### 4.2. Unconditional and conditional expectations, instruments, and moments\n",
                "Most standard treatments of the generalized method of moments estimator in econometrics textbooks start with this principle and this selection of moments. However, this notebook follows the progression of starting with the most general treatment of GMM and then covering these special cases.\n",
                "\n",
                "In stochastic models, the assumed data generating process might have one or more characterizing equations that involve an unconditional expectation. The unconditional expectation is a strong assumption with many implications on conditional expectations that can create moments for identifying parameters using GMM. In econometric models, these unconditional expectations often show up as an assumption on the error term of one or more of the equations. Note that this is a minimal assumption and does not require knowledge of the distribution of the error term.\n",
                "\n",
                "$$ y_i = \\beta_0 + \\beta_1 x_{1,i} + \\beta_2 x_{2,i} + \\varepsilon_i \\quad\\text{where}\\quad E\\left[\\varepsilon_i\\right] = 0 $$\n",
                "\n",
                "In a macroeconomic model like the Brock and Mirman (1972) model, unconditional expectations show up in two places. The first is in the Euler equation, which is the first equation below. And the second is on the error term in the law of motion for the productivity shock, which is the last equation below.\n",
                "\n",
                "$$ \\quad\\left(c_t\\right)^{-1} = \\beta E\\left[r_{t+1}\\left(c_{t+1}\\right)^{-1}\\right] $$\n",
                "$$ c_t + k_{t+1} = r_{t+1}k_t + w_t $$\n",
                "$$ \\quad w_t = (1 - \\alpha)e^{z_t}k_{t}^\\alpha $$\n",
                "$$ r_t = \\alpha e^{z_t}k_{t}^{\\alpha-1} $$\n",
                "$$ \\qquad\\qquad\\qquad\\qquad\\qquad\\qquad z_{t} = \\rho z_{t-1} + (1 - \\rho)\\mu + \\varepsilon_t \\quad\\text{where}\\quad E[\\varepsilon_t]=0 $$\n",
                "\n",
                "It is valuable to note first that these unconditional expectations imply minimal restrictions on the stochastic distributions in the model. They only imply a restriction on the first moments of those particular parts of the distributions. Furthermore, because they are unconditional distributions (which is a strong assumption), they also imply restrictions on conditional distributions."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Let $\\mathcal{I}$ be the set of variables that are in the information set of the model at the time the expectations operator in the model is formed. Let $w\\in\\mathcal{I}$ be the typical element (variable) in the information set. In a cross sectional econometric model, the variables in the information set are $w\\in\\mathcal{I}$ that could possibly be related to the dependent variable $y$ and were determined at the time the expectation was formed. In dynamic models or time series models, variables in the information set include any variables that were determined on or before the period in which the expectation was formed.\n",
                "\n",
                "The following sequence shows how an unconditional expectation can lead to moments that can identify parameters.\n",
                "\n",
                "$$ E[x] = 0 \\Rightarrow E[x|\\mathcal{I}] = 0 \\Rightarrow Cov[x,w] = 0 \\Rightarrow E[xw] = 0 $$\n",
                "\n",
                "The first equation states that the unconditional expectation of $x$ is zero. This implies that the conditional expectation of $x$ given anything else in the information set is also zero. This, in turn, implies that the covariance of $x$ and any element $w$ of the information set is zero so that the expectation of $x$ times $w$ is zero. It is this last equation that generates many of the moments used to identify parameters in GMM. Any variable in the instrument set $w\\in\\mathcal{I}$ can generate a moment condition."
            ]
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                "#### 4.2.1. Econometric (linear regression) GMM example using unconditional expectation for moments\n",
                "In the econometric model example above, there are three parameters to be estimated $(\\beta_0, \\beta_1, \\beta_2)$. We need at least three independent moments to identify these parameters. The first moment we can use is the assumption that the unconditional expectation of the error terms is zero. This has an empirical counterpart that the sample mean of the error terms should be zero.\n",
                "\n",
                "$$ \\text{(Theoretical moment 1)}\\quad E[\\varepsilon_i] = 0 \\quad\\Rightarrow\\quad E\\left[y_i - \\beta_0 - \\beta_1 x_{1,i} - \\beta_2 x_{2,i}\\right]=0 $$\n",
                "$$ \\text{(Empirical moment 1)}\\qquad\\qquad\\qquad\\:\\:\\: \\frac{1}{N}\\sum_{i=1}^N\\left[y_i - \\beta_0 - \\beta_1 x_{1,i} - \\beta_2 x_{2,i}\\right]=0 $$\n",
                "\n",
                "The next two moments are formed from the implications that the unconditional expectation on the errors implies that the expected value of the product of the error and any other variable in the information set is also zero.\n",
                "\n",
                "$$ \\text{(Theoretical moment 2)}\\quad E[\\varepsilon_i x_{1,i}] = 0 \\quad\\Rightarrow\\quad E\\Bigl[\\left(y_i - \\beta_0 - \\beta_1 x_{1,i} - \\beta_2 x_{2,i}\\right)x_{1,i}\\Bigr]=0 $$\n",
                "$$ \\text{(Empirical moment 2)}\\qquad\\qquad\\qquad\\qquad \\frac{1}{N}\\sum_{i=1}^N\\Bigl[\\left(y_i - \\beta_0 - \\beta_1 x_{1,i} - \\beta_2 x_{2,i}\\right)x_{1,i}\\Bigr]=0 $$\n",
                "\n",
                "$$ \\text{(Theoretical moment 3)}\\quad E[\\varepsilon_i x_{2,i}] = 0 \\quad\\Rightarrow\\quad E\\Bigl[\\left(y_i - \\beta_0 - \\beta_1 x_{1,i} - \\beta_2 x_{2,i}\\right)x_{2,i}\\Bigr]=0 $$\n",
                "$$ \\text{(Empirical moment 3)}\\qquad\\qquad\\qquad\\qquad \\frac{1}{N}\\sum_{i=1}^N\\Bigl[\\left(y_i - \\beta_0 - \\beta_1 x_{1,i} - \\beta_2 x_{2,i}\\right)x_{2,i}\\Bigr]=0 $$"
            ]
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        {
            "cell_type": "markdown",
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            "source": [
                "#### 4.2.2. Brock and Mirman (1972) estimation by GMM using unconditional expectations for moments\n",
                "Give Brock and Mirman (1972) example.\n",
                "\n",
                "Cite Hansen and Singleton (1982) example."
            ]
        },
        {
            "cell_type": "markdown",
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            "source": [
                "## 5. Identification\n",
                "An issue that we saw in the examples from the previous section is that there is some science as well as some art in choosing moments to identify the parameters in a GMM estimation. Suppose the parameter vector $\\theta$ has $K$ elements, or rather, $K$ parameters to be estimated. In order to estimate $\\theta$ by GMM, you must have at least as many moments as parameters to estimate $R\\geq K$. If you have exactly as many moments as parameters to be estimated $R=K$, the model is said to be *exactly identified*. If you have more moments than parameters to be estimated $R>K$, the model is said to be *overidentified*. If you have fewer moments than parameters to be estimated $R<K$, the model is said to be *underidentified*. There are good reasons to overidentify $R>K$ the model in GMM estimation as we saw in the previous example. The main reason is that not all moments are orthogonal. That is, some moments convey roughly the same information about the data and, therefore, do not separately identify any extra parameters. So a good GMM model often is overidentified $R>K$.\n",
                "\n",
                "One last point about GMM regards moment selection and verification of results. The real world has an infinite supply of potential moments that describe some part of the data. Choosing moments to estimate parameters by GMM requires understanding of the model, intuition about its connections to the real world, and artistry. A good GMM estimation will include moments that have some relation to or story about their connection to particular parameters of the model to be estimated. In addition, a good verification of a GMM estimation is to take some moment from the data that was not used in the estimation and see how well the corresponding moment from the estimated model matches that *outside moment*."
            ]
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            "source": [
                "## 6. Linear regression by GMM and relation to OLS\n",
                "The most common method of estimating the parameters of a linear regression is using the ordinary least squares (OLS) estimator. This estimator is just special type of generalized method of moments (GMM) estimator. A simple regression specification in which the dependent variable $y_i$ is a linear function of two independent variables $x_{1,i}$ and $x_{2,i}$ is the following:\n",
                "\n",
                "$$ y_i = \\beta_0 + \\beta_1 x_{1,i} + \\beta_2 x_{2,i} + \\varepsilon_i \\quad\\text{where}\\quad \\varepsilon_i\\sim N\\left(0,\\sigma^2\\right) $$\n",
                "\n",
                "Note that we can solve for the parameters $(\\beta_0,\\beta_1,\\beta_2)$ in a number of ways. And we can do it without making any assumptions about the distribution of the error terms $\\varepsilon_i$.\n",
                "\n",
                "One way we might choose the parameters is to choose $(\\beta_0,\\beta_1,\\beta_2)$ to minimize the distance between the $N$ observations of $y_i$ and the $N$ predicted values for $y_i$ given by $\\beta_0 + \\beta_1 x_{1,i} + \\beta_2 x_{2,i}$. You can think of the $N$ observations of $y_i$ as $N$ data moments. And you can think of the $N$ observations of $\\beta_0 + \\beta_1 x_{1,i} + \\beta_2 x_{2,i}$ as $N$ model moments. The least squares estimator minimizes the sum of squared errors, which is the sum of squared deviations between the $N$ values of $y_i$ and  $\\beta_0 + \\beta_1 x_{1,i} + \\beta_2 x_{2,i}$.\n",
                "\n",
                "$$ \\varepsilon_i = y_i - \\beta_0 - \\beta_1 x_{1,i} - \\beta_2 x_{2,i} $$\n",
                "\n",
                "$$ \\hat{\\theta}_{OLS} = \\theta:\\quad \\min_{\\theta} \\varepsilon^T\\, I \\, \\varepsilon $$\n",
                "\n",
                "Let the $N\\times 1$ vector of $y_i$'s be $Y$. Let the $N\\times 3$ vector of data $(1, x_{1,i}, x_{2,i})$ be $X$. And let the vector of three parameters $(\\beta_0, \\beta_1, \\beta_2)$ be $\\beta$. It can be shown that the OLS estimator for the vector of parameters $\\beta$ is the following.\n",
                "\n",
                "$$ \\hat{\\beta}_{OLS} = (X^T X)^{-1}(X^T Y) $$\n",
                "\n",
                "But you could also just estimate the coefficients using the criterion function in the GMM statement of the problem above. This method is called nonlinear least squares or generalized least squares. Many applications of regression use a weighting matrix in the criterion function that adjusts for issues like heteroskedasticity and autocorrelation. \n",
                "\n",
                "Lastly, many applications use a different distance metric than the weighted sum of squared errors for the difference in moments. Sum of squared errors puts a large penalty on big differences. Sometimes you might want to maximize the sum of absolute errors, which is sometimes called median regression. You could also minimize the maximum absolute difference in the errors, which is even more extreme than the sum of squared errors on penalizing large differences."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## 7. References\n",
                "* Adda, Jerome and Russell Cooper, *Dynamic Economics: Quantitative Methods and Applications*, MIT Press (2003).\n",
                "* Brock, William A. and Leonard J. Mirman, \"Optimal Economic Growth and Uncertainty: The Discounted Case,\" *Journal of Economic Theory*, 4:3, pp. 479-513 (June 1972).\n",
                "* Davidson, Russell and James G. MacKinnon, *Econometric Theory and Methods*, Oxford University Press (2004).\n",
                "* Fuhrer, Jeffrey C., George R. Moore, and Scott D. Schuh, \"Estimating the Linear-quadratic Inventory Model: Maximum Likelihood versus Generalized Method of Moments,\" *Journal of Monetary Economics*, 35:1, pp. 115-157 (February 1995).\n",
                "* Hansen, Lars Peter, \"Large Sample Properties of Generalized Method of Moments Estimators,\" *Econometrica*, 50:4, pp. 1029-1054 (July 1982).\n",
                "* Hansen, Lars Peter and Kenneth J. Singleton, \"Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models\", *Econometrica*, 50:5, pp. 1269-1286 (September 1982).\n",
                "* Newey, Whitney K. and Kenneth D. West, \"A Simple, Positive, Semi-definite, Heteroskedasticy and Autocorrelation Consistent Covariance Matrix,\" *Econometrica*, 55:3, pp. 703-708 (May 1987)."
            ]
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