{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aeUxtTpEfzbn"
   },
   "source": [
    "# Population Inference on GWTC-3\n",
    "\n",
    "The thrid gravitational-wave transient catalog [GWTC-3](https://arxiv.org/abs/1811.12907) includes all compact binary coalescences observed during Advanced LIGO/Virgo's first three oberving runs.\n",
    "\n",
    "`GWPopulation` builds upon [Bilby](https://git.ligo.org/lscsoft/bilby) ([arXiv:1811.02042](https://arxiv.org/abs/1811.02042)) to provide simple, modular, user-friendly, population inference.\n",
    "\n",
    "There are many [implemented models](https://colmtalbot.github.io/gwpopulation/_autosummary/gwpopulation.models.html#module-gwpopulation.models) and an example of defining custom models is included below.\n",
    "In this example we use:\n",
    "\n",
    "- A mass distribution in primary mass and mass ratio from Talbot & Thrane (2018) ([arXiv:1801:02699](https://arxiv.org/abs/1801.02699)). This is equivalent to the `PowerLaw + Peak` model used in LVK analyses without the low-mass smoothing for computational efficiency.\n",
    "- Half-Gaussian + isotropic spin tilt distribution from Talbot & Thrane (2017) ([arXiv:1704.08370](https://arxiv.org/abs/1704.08370)).\n",
    "- Beta spin magnitude distribution from Wysocki+ (2018) ([arXiv:1805:06442](https://arxiv.org/abs/1805.06442)).\n",
    "- Each of these are also available with independent but identically distributed spins.\n",
    "- Redshift evolution model as in Fishbach+ (2018) ([arXiv:1805.10270](https://arxiv.org/abs/1805.10270)).\n",
    "\n",
    "_Setup_\n",
    "\n",
    "If you're using colab.research.google.com you will want to choose a GPU-accelerated runtime (I'm going to use a T4 GPU).\n",
    "\n",
    "\"runtime\"->\"change runtime type\"->\"Hardware accelerator = GPU\"\n",
    "\n",
    "\n",
    "_Note_\n",
    "\n",
    "This is largely reproduced from the demonstration for [wcosmo](https://wcosmo.readthedocs.io/en/latest/examples/gwtc3.html) except with the cosmology fixed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dc8zj3wngiYu"
   },
   "source": [
    "## Install some needed packages\n",
    "\n",
    "All of the dependencies for this are integrated into `GWPopulation`.\n",
    "These include `Bilby` and `dynesty` for sampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "id": "Tsv8JYinDZv0"
   },
   "outputs": [],
   "source": [
    "!pip install gwpopulation --quiet --progress-bar off"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TdiqeGZtHwRw"
   },
   "source": [
    "## Download data\n",
    "\n",
    "We need to download the data for the events and simmulated \"injections\" used to characterize the detection sensitivity.\n",
    "\n",
    "### Event posteriors\n",
    "\n",
    "We're using the posteriors from the GWTC-3 data release in a pre-processed format.\n",
    "\n",
    "The file was produced by [gwpopulation-pipe](https://docs.ligo.org/ratesAndPopulations/gwpopulation_pipe) to reduce the many GB of posterior sample files to a single ~30Mb file.\n",
    "\n",
    "The choice of events in this file was not very careful and should only be considered qualitatively correct.\n",
    "\n",
    "The data file can be found [here](https://drive.google.com/drive/folders/1wyfR6sYvYVdBefF9_vrVTp0Btu03OlzL?usp=drive_link).\n",
    "The original data can be found at [zenodo:5546663](https://zenodo.org/records/5546663) and [zenodo:6513631](https://zenodo.org/records/6513631) along with citation information.\n",
    "\n",
    "### Sensitivity injections\n",
    "\n",
    "Again I have pre-processed the full injection set using `gwpopulation-pipe` to reduce the filesize.\n",
    "The original data is available at [zenodo:7890398](https://zenodo.org/records/7890398) along with citation information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "s8bqvo26HABd",
    "outputId": "efbf2542-dfb3-40d2-bda8-4e9047f5f8d1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=16gStLIjt65gWBkw-gNOVUqNbZ89q8CLF\n",
      "To: /Users/colmtalbot/modules/gwpopulation/examples/gwtc-3-injections.pkl\n",
      "100%|██████████████████████████████████████| 2.69M/2.69M [00:00<00:00, 75.2MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=10pevUCM3V2-D-bROFEMAcTJsX_9RzeM6\n",
      "To: /Users/colmtalbot/modules/gwpopulation/examples/gwtc-3-samples.pkl\n",
      "100%|██████████████████████████████████████| 36.4M/36.4M [00:00<00:00, 60.6MB/s]\n"
     ]
    }
   ],
   "source": [
    "!gdown https://drive.google.com/uc?id=16gStLIjt65gWBkw-gNOVUqNbZ89q8CLF\n",
    "!gdown https://drive.google.com/uc?id=10pevUCM3V2-D-bROFEMAcTJsX_9RzeM6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I1MQIoc_gvR-"
   },
   "source": [
    "## Imports\n",
    "\n",
    "Import the packages required for the script.\n",
    "We also set the backend for array operations to `jax` which allows us to take advantage of just-in-time (jit) compilation in addition to GPU-parallelisation when available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "mKC8rhSUDmtg"
   },
   "outputs": [],
   "source": [
    "import bilby as bb\n",
    "import gwpopulation as gwpop\n",
    "import jax\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from bilby.core.prior import PriorDict, Uniform\n",
    "from gwpopulation.experimental.jax import JittedLikelihood, NonCachingModel\n",
    "\n",
    "gwpop.set_backend(\"jax\")\n",
    "\n",
    "xp = gwpop.utils.xp\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6MIE6_uMg4vw"
   },
   "source": [
    "## Load posteriors\n",
    "\n",
    "We remove two events from the file that shouldn't be there that have NS-like secondaries as we are just interested in BBHs for this demonstration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "1pAakjJ1DwrK"
   },
   "outputs": [],
   "source": [
    "posteriors = pd.read_pickle(\"gwtc-3-samples.pkl\")\n",
    "del posteriors[15]\n",
    "del posteriors[38]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "82GMaSlJhKm1"
   },
   "source": [
    "## Load injections\n",
    "\n",
    "Load the injections used to characterize the sensitivity of the gravitaitonal-wave survey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "IuHqDXFQE9Mw"
   },
   "outputs": [],
   "source": [
    "import dill\n",
    "\n",
    "with open(\"gwtc-3-injections.pkl\", \"rb\") as ff:\n",
    "    injections = dill.load(ff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I5FzBbnniQfl"
   },
   "source": [
    "## Define some models and the likelihood\n",
    "\n",
    "We need to define `Bilby` `Model` objects for the numerator and denominator independently as these cache some computations interally.\n",
    "\n",
    "We create a model that uses a cosmology fixed to the Planck 2015 values for flat Lambda CDM.\n",
    "\n",
    "The `HyperparameterLikelihood` marginalises over the local merger rate, with a uniform-in-log prior.\n",
    "The posterior for the merger rate can be recovered in post-processing.\n",
    "\n",
    "We provide:\n",
    "\n",
    "- `posteriors`: a list of `pandas` DataFrames.\n",
    "- `hyper_prior`: our population model, as defined above.\n",
    "- `selection_function`: anything which evaluates the selection function.\n",
    "\n",
    "We can also provide:\n",
    "\n",
    "- `conversion_function`: this converts between the parameters we sample in and those needed by the model, e.g., for sampling in the mean and variance of the beta distribution.\n",
    "- `max_samples`: the maximum number of samples to use from each posterior, this defaults to the length of the shortest posterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "7IrVTnK5GbaZ"
   },
   "outputs": [],
   "source": [
    "model = NonCachingModel(\n",
    "    model_functions=[\n",
    "        gwpop.models.mass.two_component_primary_mass_ratio,\n",
    "        gwpop.models.spin.iid_spin,\n",
    "        gwpop.models.redshift.PowerLawRedshift(cosmo_model=\"Planck15\"),\n",
    "    ],\n",
    ")\n",
    "\n",
    "vt = gwpop.vt.ResamplingVT(model=model, data=injections, n_events=len(posteriors))\n",
    "\n",
    "likelihood = gwpop.hyperpe.HyperparameterLikelihood(\n",
    "    posteriors=posteriors,\n",
    "    hyper_prior=model,\n",
    "    selection_function=vt,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lQW_m3bNioih"
   },
   "source": [
    "## Define our prior\n",
    "\n",
    "The mass model has eight parameters that we vary that are described in arXiv:1801:02699. This model is sometimes referred to as \"PowerLaw+Peak\"\n",
    "\n",
    "The spin magnitude model is a `Beta` distribution with the usual parameterization, and the spin orientation model is a mixure of a uniform component and a truncated Gaussian that peaks at aligned spin. This combination is sometimes referred to as \"Default\".\n",
    "\n",
    "For redshift we use a model that looks like\n",
    "\n",
    "$$p(z) \\propto \\frac{d V_{c}}{dz} (1 + z)^{λ - 1}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "IRf7FFQDGda-"
   },
   "outputs": [],
   "source": [
    "priors = PriorDict()\n",
    "\n",
    "# mass\n",
    "priors[\"alpha\"] = Uniform(minimum=-2, maximum=4, latex_label=\"$\\\\alpha$\")\n",
    "priors[\"beta\"] = Uniform(minimum=-4, maximum=12, latex_label=\"$\\\\beta$\")\n",
    "priors[\"mmin\"] = Uniform(minimum=2, maximum=2.5, latex_label=\"$m_{\\\\min}$\")\n",
    "priors[\"mmax\"] = Uniform(minimum=80, maximum=100, latex_label=\"$m_{\\\\max}$\")\n",
    "priors[\"lam\"] = Uniform(minimum=0, maximum=1, latex_label=\"$\\\\lambda_{m}$\")\n",
    "priors[\"mpp\"] = Uniform(minimum=10, maximum=50, latex_label=\"$\\\\mu_{m}$\")\n",
    "priors[\"sigpp\"] = Uniform(minimum=1, maximum=10, latex_label=\"$\\\\sigma_{m}$\")\n",
    "priors[\"gaussian_mass_maximum\"] = 100\n",
    "# spin\n",
    "priors[\"amax\"] = 1\n",
    "priors[\"alpha_chi\"] = Uniform(minimum=1, maximum=6, latex_label=\"$\\\\alpha_{\\\\chi}$\")\n",
    "priors[\"beta_chi\"] = Uniform(minimum=1, maximum=6, latex_label=\"$\\\\beta_{\\\\chi}$\")\n",
    "priors[\"xi_spin\"] = Uniform(minimum=0, maximum=1, latex_label=\"$\\\\xi$\")\n",
    "priors[\"sigma_spin\"] = Uniform(minimum=0.3, maximum=4, latex_label=\"$\\\\sigma$\")\n",
    "\n",
    "priors[\"lamb\"] = Uniform(minimum=-1, maximum=10, latex_label=\"$\\\\lambda_{z}$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ncci_peFisbm"
   },
   "source": [
    "## Just-in-time compile\n",
    "\n",
    "We JIT compile the likelihood object before starting the sampler.\n",
    "This is done using the `gwpopulation.experimental.jax.JittedLikelihood` class.\n",
    "\n",
    "We then time the original likelihood object and the JIT-ed version.\n",
    "Note that we do two evaluations for each object as the first evaluation must compile the likelihood and so takes longer. (In addition to the JIT compilation, `JAX` compiles GPU functionality at the first evaluation, but this is less extreme than the full JIT compilation.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fgkofuRrGfX7",
    "outputId": "990ef6f2-e36f-4fe7-9b7d-95b07ce39441"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-273.6719945829451\n",
      "CPU times: user 111 ms, sys: 36.2 ms, total: 147 ms\n",
      "Wall time: 64 ms\n",
      "-273.671994582945\n",
      "CPU times: user 1.3 s, sys: 257 ms, total: 1.56 s\n",
      "Wall time: 1.1 s\n",
      "-273.671994582945\n",
      "CPU times: user 50.4 ms, sys: 3.6 ms, total: 54 ms\n",
      "Wall time: 15.7 ms\n"
     ]
    }
   ],
   "source": [
    "parameters = priors.sample()\n",
    "likelihood.parameters.update(parameters)\n",
    "likelihood.log_likelihood_ratio()\n",
    "%time print(likelihood.log_likelihood_ratio())\n",
    "jit_likelihood = JittedLikelihood(likelihood)\n",
    "jit_likelihood.parameters.update(parameters)\n",
    "%time print(jit_likelihood.log_likelihood_ratio())\n",
    "%time print(jit_likelihood.log_likelihood_ratio())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fX0FoqX0ixo0"
   },
   "source": [
    "## Run the sampler\n",
    "\n",
    "We'll use the sampler `dynesty` and use a small number of live points to reduce the runtime (total runtime should be approximately 5 minutes on T4 GPUs via Google colab).\n",
    "The settings here may not give publication quality results, a convergence test should be performed before making strong quantitative statements.\n",
    "\n",
    "`bilby` times a single likelihood evaluation before beginning the run, however, this isn't well defined with JAX.\n",
    "\n",
    "**Note:** sometimes this finds a high likelihood mode, likely due to [breakdowns in the approximation](https://arxiv.org/abs/2304.06138) used to estimate the likelihood. If you see `dlogz > -190`, you should interrupt the execution and restart."
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
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    "colab": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "15:30 bilby INFO    : Running for label 'cosmo', output will be saved to 'outdir'\n",
      "15:30 bilby INFO    : Analysis priors:\n",
      "15:30 bilby INFO    : alpha=Uniform(minimum=-2, maximum=4, name=None, latex_label='$\\\\alpha$', unit=None, boundary=None)\n",
      "15:30 bilby INFO    : beta=Uniform(minimum=-4, maximum=12, name=None, latex_label='$\\\\beta$', unit=None, boundary=None)\n",
      "15:30 bilby INFO    : mmin=Uniform(minimum=2, maximum=2.5, name=None, latex_label='$m_{\\\\min}$', unit=None, boundary=None)\n",
      "15:30 bilby INFO    : mmax=Uniform(minimum=80, maximum=100, name=None, latex_label='$m_{\\\\max}$', unit=None, boundary=None)\n",
      "15:30 bilby INFO    : lam=Uniform(minimum=0, maximum=1, name=None, latex_label='$\\\\lambda_{m}$', unit=None, boundary=None)\n",
      "15:30 bilby INFO    : mpp=Uniform(minimum=10, maximum=50, name=None, latex_label='$\\\\mu_{m}$', unit=None, boundary=None)\n",
      "15:30 bilby INFO    : sigpp=Uniform(minimum=1, maximum=10, name=None, latex_label='$\\\\sigma_{m}$', unit=None, boundary=None)\n",
      "15:30 bilby INFO    : alpha_chi=Uniform(minimum=1, maximum=6, name=None, latex_label='$\\\\alpha_{\\\\chi}$', unit=None, boundary=None)\n",
      "15:30 bilby INFO    : beta_chi=Uniform(minimum=1, maximum=6, name=None, latex_label='$\\\\beta_{\\\\chi}$', unit=None, boundary=None)\n",
      "15:30 bilby INFO    : xi_spin=Uniform(minimum=0, maximum=1, name=None, latex_label='$\\\\xi$', unit=None, boundary=None)\n",
      "15:30 bilby INFO    : sigma_spin=Uniform(minimum=0.3, maximum=4, name=None, latex_label='$\\\\sigma$', unit=None, boundary=None)\n",
      "15:30 bilby INFO    : lamb=Uniform(minimum=-1, maximum=10, name=None, latex_label='$\\\\lambda_{z}$', unit=None, boundary=None)\n",
      "15:30 bilby INFO    : gaussian_mass_maximum=100\n",
      "15:30 bilby INFO    : amax=1\n",
      "15:30 bilby INFO    : Analysis likelihood class: <class 'gwpopulation.experimental.jax.JittedLikelihood'>\n",
      "15:30 bilby INFO    : Analysis likelihood noise evidence: nan\n",
      "15:30 bilby INFO    : Single likelihood evaluation took 1.016e-04 s\n",
      "15:30 bilby INFO    : Using sampler Dynesty with kwargs {'nlive': 100, 'bound': 'live', 'sample': 'acceptance-walk', 'periodic': None, 'reflective': None, 'update_interval': 600, 'first_update': None, 'npdim': None, 'rstate': None, 'queue_size': 1, 'pool': None, 'use_pool': None, 'live_points': None, 'logl_args': None, 'logl_kwargs': None, 'ptform_args': None, 'ptform_kwargs': None, 'gradient': None, 'grad_args': None, 'grad_kwargs': None, 'compute_jac': False, 'enlarge': None, 'bootstrap': None, 'walks': 100, 'facc': 0.2, 'slices': None, 'fmove': 0.9, 'max_move': 100, 'update_func': None, 'ncdim': None, 'blob': False, 'save_history': False, 'history_filename': None, 'maxiter': None, 'maxcall': None, 'dlogz': 0.1, 'logl_max': inf, 'n_effective': None, 'add_live': True, 'print_progress': True, 'print_func': <bound method Dynesty._print_func of <bilby.core.sampler.dynesty.Dynesty object at 0x323abb280>>, 'save_bounds': False, 'checkpoint_file': None, 'checkpoint_every': 60, 'resume': False}\n",
      "15:30 bilby INFO    : Checkpoint every check_point_delta_t = 600s\n",
      "15:30 bilby INFO    : Using dynesty version 2.1.2\n",
      "15:30 bilby INFO    : Using the bilby-implemented rwalk sampling with an average of 5 accepted steps per MCMC and maximum length 5000\n",
      "15:30 bilby INFO    : Resume file outdir/cosmo_resume.pickle does not exist.\n",
      "15:30 bilby INFO    : Generating initial points from the prior\n"
     ]
    },
    {
     "data": {
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       "model_id": "f7bf8f85008144668cfdfc91935e8959",
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      },
      "text/plain": [
       "1it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "15:37 bilby INFO    : Written checkpoint file outdir/cosmo_resume.pickle\n",
      "15:37 bilby INFO    : Rejection sampling nested samples to obtain 677 posterior samples\n",
      "15:37 bilby INFO    : Sampling time: 0:07:00.040342\n",
      "15:37 bilby INFO    : Summary of results:\n",
      "nsamples: 677\n",
      "ln_noise_evidence:    nan\n",
      "ln_evidence:    nan +/-  0.398\n",
      "ln_bayes_factor: -199.070 +/-  0.398\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "result = bb.run_sampler(\n",
    "    likelihood=jit_likelihood,\n",
    "    priors=priors,\n",
    "    sampler=\"dynesty\",\n",
    "    nlive=100,\n",
    "    label=\"cosmo\",\n",
    "    sample=\"acceptance-walk\",\n",
    "    naccept=5,\n",
    "    save=\"hdf5\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4WeziuZoi7t_"
   },
   "source": [
    "## Plot some posteriors\n",
    "\n",
    "We can look at the posteriors on some of the parameters, here the cosmology parameters and the location of the mass peak and the redshift evolution.\n",
    "\n",
    "We see that the value of the Hubble constant is strongly correlated with the location of the peak in the mass distribution as has been noted elsewhere.\n",
    "\n",
    "We also include the values of the cosmology parameters reported in the `Planck15` cosmology for reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "vIHkO4UdHyMK",
    "outputId": "3792db0e-d7fb-44cd-f91a-05826c9e9bb4"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 760x760 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = result.plot_corner(save=False, parameters=[\"mpp\", \"sigpp\", \"lamb\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DC-zMTIFMDIZ"
   },
   "source": [
    "## Post-processing checks\n",
    "\n",
    "As mentioned above, hierarchical analyses performed in this way are susceptible to systematic bias due to Monte Carlo error.\n",
    "To ensure we are not suffering from this issue, we compute the variance in each of our Monte Carlo integrals along with the total variance for each posterior sample.\n",
    "We then look at whether there are correlations between the log-likelihood, the variance, and the hyperparameters.\n",
    "If we see significant correlation between the variance and other quantities, it is a sign that our results may not be reliable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 330
    },
    "id": "v2uZN-HQjYTB",
    "outputId": "34791c56-dd55-4b4b-e0d3-84c8106a4a07"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>alpha_chi</th>\n",
       "      <th>amax</th>\n",
       "      <th>beta</th>\n",
       "      <th>beta_chi</th>\n",
       "      <th>gaussian_mass_maximum</th>\n",
       "      <th>lam</th>\n",
       "      <th>lamb</th>\n",
       "      <th>ln_bf_0</th>\n",
       "      <th>ln_bf_1</th>\n",
       "      <th>...</th>\n",
       "      <th>var_66</th>\n",
       "      <th>var_67</th>\n",
       "      <th>var_68</th>\n",
       "      <th>var_69</th>\n",
       "      <th>var_7</th>\n",
       "      <th>var_70</th>\n",
       "      <th>var_8</th>\n",
       "      <th>var_9</th>\n",
       "      <th>variance</th>\n",
       "      <th>xi_spin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.0</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.0</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>677.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.776850</td>\n",
       "      <td>1.712533</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.059456</td>\n",
       "      <td>4.765464</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.036284</td>\n",
       "      <td>2.259357</td>\n",
       "      <td>-10.416216</td>\n",
       "      <td>-10.662652</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001103</td>\n",
       "      <td>0.000925</td>\n",
       "      <td>0.001178</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.000804</td>\n",
       "      <td>0.000955</td>\n",
       "      <td>1.552220</td>\n",
       "      <td>0.645813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.229683</td>\n",
       "      <td>0.441259</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.067155</td>\n",
       "      <td>0.873841</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027913</td>\n",
       "      <td>1.022998</td>\n",
       "      <td>1.306954</td>\n",
       "      <td>1.029802</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000654</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>0.000757</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.000327</td>\n",
       "      <td>0.000378</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.819144</td>\n",
       "      <td>0.240400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.050729</td>\n",
       "      <td>1.001482</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.659842</td>\n",
       "      <td>1.862621</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.004847</td>\n",
       "      <td>-0.784904</td>\n",
       "      <td>-14.955488</td>\n",
       "      <td>-14.237053</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.000215</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.336554</td>\n",
       "      <td>0.001326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.621572</td>\n",
       "      <td>1.369770</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.397719</td>\n",
       "      <td>4.156605</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.019076</td>\n",
       "      <td>1.660442</td>\n",
       "      <td>-11.316283</td>\n",
       "      <td>-11.319475</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000682</td>\n",
       "      <td>0.000642</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>0.000339</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.000570</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>1.036452</td>\n",
       "      <td>0.470641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.784653</td>\n",
       "      <td>1.648453</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.915804</td>\n",
       "      <td>4.899788</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.028897</td>\n",
       "      <td>2.268566</td>\n",
       "      <td>-10.398208</td>\n",
       "      <td>-10.665089</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000914</td>\n",
       "      <td>0.000830</td>\n",
       "      <td>0.001011</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000750</td>\n",
       "      <td>0.000721</td>\n",
       "      <td>0.000870</td>\n",
       "      <td>1.342318</td>\n",
       "      <td>0.691562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.937532</td>\n",
       "      <td>2.008287</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.565101</td>\n",
       "      <td>5.496479</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.046055</td>\n",
       "      <td>2.986201</td>\n",
       "      <td>-9.532608</td>\n",
       "      <td>-10.007681</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001382</td>\n",
       "      <td>0.001077</td>\n",
       "      <td>0.001412</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>0.000951</td>\n",
       "      <td>0.000934</td>\n",
       "      <td>0.001141</td>\n",
       "      <td>1.818107</td>\n",
       "      <td>0.850433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.425676</td>\n",
       "      <td>3.318228</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.709659</td>\n",
       "      <td>5.994280</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.384039</td>\n",
       "      <td>5.247202</td>\n",
       "      <td>-6.867461</td>\n",
       "      <td>-7.933087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005489</td>\n",
       "      <td>0.005247</td>\n",
       "      <td>0.009805</td>\n",
       "      <td>0.004341</td>\n",
       "      <td>0.001919</td>\n",
       "      <td>0.004697</td>\n",
       "      <td>0.004150</td>\n",
       "      <td>0.004305</td>\n",
       "      <td>6.894325</td>\n",
       "      <td>0.996642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 161 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            alpha   alpha_chi   amax        beta    beta_chi  \\\n",
       "count  677.000000  677.000000  677.0  677.000000  677.000000   \n",
       "mean     2.776850    1.712533    1.0    2.059456    4.765464   \n",
       "std      0.229683    0.441259    0.0    1.067155    0.873841   \n",
       "min      2.050729    1.001482    1.0   -0.659842    1.862621   \n",
       "25%      2.621572    1.369770    1.0    1.397719    4.156605   \n",
       "50%      2.784653    1.648453    1.0    1.915804    4.899788   \n",
       "75%      2.937532    2.008287    1.0    2.565101    5.496479   \n",
       "max      3.425676    3.318228    1.0    9.709659    5.994280   \n",
       "\n",
       "       gaussian_mass_maximum         lam        lamb     ln_bf_0     ln_bf_1  \\\n",
       "count                  677.0  677.000000  677.000000  677.000000  677.000000   \n",
       "mean                   100.0    0.036284    2.259357  -10.416216  -10.662652   \n",
       "std                      0.0    0.027913    1.022998    1.306954    1.029802   \n",
       "min                    100.0    0.004847   -0.784904  -14.955488  -14.237053   \n",
       "25%                    100.0    0.019076    1.660442  -11.316283  -11.319475   \n",
       "50%                    100.0    0.028897    2.268566  -10.398208  -10.665089   \n",
       "75%                    100.0    0.046055    2.986201   -9.532608  -10.007681   \n",
       "max                    100.0    0.384039    5.247202   -6.867461   -7.933087   \n",
       "\n",
       "       ...      var_66      var_67      var_68      var_69       var_7  \\\n",
       "count  ...  677.000000  677.000000  677.000000  677.000000  677.000000   \n",
       "mean   ...    0.001103    0.000925    0.001178    0.000479    0.000318   \n",
       "std    ...    0.000654    0.000457    0.000757    0.000260    0.000151   \n",
       "min    ...    0.000221    0.000202    0.000293    0.000154    0.000088   \n",
       "25%    ...    0.000682    0.000642    0.000735    0.000339    0.000228   \n",
       "50%    ...    0.000914    0.000830    0.001011    0.000424    0.000282   \n",
       "75%    ...    0.001382    0.001077    0.001412    0.000544    0.000370   \n",
       "max    ...    0.005489    0.005247    0.009805    0.004341    0.001919   \n",
       "\n",
       "           var_70       var_8       var_9    variance     xi_spin  \n",
       "count  677.000000  677.000000  677.000000  677.000000  677.000000  \n",
       "mean     0.000802    0.000804    0.000955    1.552220    0.645813  \n",
       "std      0.000327    0.000378    0.000413    0.819144    0.240400  \n",
       "min      0.000136    0.000215    0.000267    0.336554    0.001326  \n",
       "25%      0.000601    0.000570    0.000683    1.036452    0.470641  \n",
       "50%      0.000750    0.000721    0.000870    1.342318    0.691562  \n",
       "75%      0.000951    0.000934    0.001141    1.818107    0.850433  \n",
       "max      0.004697    0.004150    0.004305    6.894325    0.996642  \n",
       "\n",
       "[8 rows x 161 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func = jax.jit(likelihood.generate_extra_statistics)\n",
    "\n",
    "full_posterior = pd.DataFrame(\n",
    "    [func(parameters) for parameters in result.posterior.to_dict(orient=\"records\")]\n",
    ").astype(float)\n",
    "full_posterior.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 582
    },
    "id": "vdJOUe7xMuE8",
    "outputId": "2b0c7919-c8d0-40c5-c98c-a953cd7a33a9"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>beta</th>\n",
       "      <th>mmin</th>\n",
       "      <th>mmax</th>\n",
       "      <th>lam</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018332</td>\n",
       "      <td>0.049273</td>\n",
       "      <td>0.165293</td>\n",
       "      <td>-0.458561</td>\n",
       "      <td>-0.096644</td>\n",
       "      <td>0.099342</td>\n",
       "      <td>-0.039909</td>\n",
       "      <td>0.032705</td>\n",
       "      <td>-0.067643</td>\n",
       "      <td>0.012180</td>\n",
       "      <td>0.616722</td>\n",
       "      <td>0.213035</td>\n",
       "      <td>0.082727</td>\n",
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       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>0.018332</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.030397</td>\n",
       "      <td>-0.108583</td>\n",
       "      <td>-0.049465</td>\n",
       "      <td>-0.047072</td>\n",
       "      <td>-0.011391</td>\n",
       "      <td>0.029325</td>\n",
       "      <td>-0.030322</td>\n",
       "      <td>0.028157</td>\n",
       "      <td>-0.041763</td>\n",
       "      <td>-0.138196</td>\n",
       "      <td>-0.157355</td>\n",
       "      <td>0.144190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mmin</th>\n",
       "      <td>0.049273</td>\n",
       "      <td>-0.030397</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.067611</td>\n",
       "      <td>0.026643</td>\n",
       "      <td>0.028545</td>\n",
       "      <td>0.012661</td>\n",
       "      <td>0.004106</td>\n",
       "      <td>-0.055865</td>\n",
       "      <td>0.003692</td>\n",
       "      <td>0.097444</td>\n",
       "      <td>-0.006103</td>\n",
       "      <td>0.035900</td>\n",
       "      <td>-0.037539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mmax</th>\n",
       "      <td>0.165293</td>\n",
       "      <td>-0.108583</td>\n",
       "      <td>0.067611</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025335</td>\n",
       "      <td>-0.076469</td>\n",
       "      <td>0.090215</td>\n",
       "      <td>0.028796</td>\n",
       "      <td>0.035281</td>\n",
       "      <td>0.023455</td>\n",
       "      <td>0.128840</td>\n",
       "      <td>0.069579</td>\n",
       "      <td>-0.168815</td>\n",
       "      <td>-0.039724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lam</th>\n",
       "      <td>-0.458561</td>\n",
       "      <td>-0.049465</td>\n",
       "      <td>0.026643</td>\n",
       "      <td>0.025335</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.552935</td>\n",
       "      <td>0.450844</td>\n",
       "      <td>0.064456</td>\n",
       "      <td>0.012491</td>\n",
       "      <td>-0.030650</td>\n",
       "      <td>0.035253</td>\n",
       "      <td>-0.224907</td>\n",
       "      <td>-0.498883</td>\n",
       "      <td>-0.082098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mpp</th>\n",
       "      <td>-0.096644</td>\n",
       "      <td>-0.047072</td>\n",
       "      <td>0.028545</td>\n",
       "      <td>-0.076469</td>\n",
       "      <td>-0.552935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.811752</td>\n",
       "      <td>-0.147834</td>\n",
       "      <td>-0.025253</td>\n",
       "      <td>0.099803</td>\n",
       "      <td>-0.151705</td>\n",
       "      <td>-0.222503</td>\n",
       "      <td>0.509110</td>\n",
       "      <td>0.343512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigpp</th>\n",
       "      <td>0.099342</td>\n",
       "      <td>-0.011391</td>\n",
       "      <td>0.012661</td>\n",
       "      <td>0.090215</td>\n",
       "      <td>0.450844</td>\n",
       "      <td>-0.811752</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044627</td>\n",
       "      <td>-0.046373</td>\n",
       "      <td>-0.076903</td>\n",
       "      <td>0.129302</td>\n",
       "      <td>0.093172</td>\n",
       "      <td>-0.535496</td>\n",
       "      <td>-0.463671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha_chi</th>\n",
       "      <td>-0.039909</td>\n",
       "      <td>0.029325</td>\n",
       "      <td>0.004106</td>\n",
       "      <td>0.028796</td>\n",
       "      <td>0.064456</td>\n",
       "      <td>-0.147834</td>\n",
       "      <td>0.044627</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.616715</td>\n",
       "      <td>-0.014138</td>\n",
       "      <td>0.295180</td>\n",
       "      <td>0.032030</td>\n",
       "      <td>-0.172487</td>\n",
       "      <td>-0.193389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_chi</th>\n",
       "      <td>0.032705</td>\n",
       "      <td>-0.030322</td>\n",
       "      <td>-0.055865</td>\n",
       "      <td>0.035281</td>\n",
       "      <td>0.012491</td>\n",
       "      <td>-0.025253</td>\n",
       "      <td>-0.046373</td>\n",
       "      <td>0.616715</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.053659</td>\n",
       "      <td>-0.020179</td>\n",
       "      <td>0.085114</td>\n",
       "      <td>0.138990</td>\n",
       "      <td>0.312379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xi_spin</th>\n",
       "      <td>-0.067643</td>\n",
       "      <td>0.028157</td>\n",
       "      <td>0.003692</td>\n",
       "      <td>0.023455</td>\n",
       "      <td>-0.030650</td>\n",
       "      <td>0.099803</td>\n",
       "      <td>-0.076903</td>\n",
       "      <td>-0.014138</td>\n",
       "      <td>0.053659</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047134</td>\n",
       "      <td>-0.020606</td>\n",
       "      <td>0.200745</td>\n",
       "      <td>0.104602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_spin</th>\n",
       "      <td>0.012180</td>\n",
       "      <td>-0.041763</td>\n",
       "      <td>0.097444</td>\n",
       "      <td>0.128840</td>\n",
       "      <td>0.035253</td>\n",
       "      <td>-0.151705</td>\n",
       "      <td>0.129302</td>\n",
       "      <td>0.295180</td>\n",
       "      <td>-0.020179</td>\n",
       "      <td>0.047134</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005804</td>\n",
       "      <td>-0.341574</td>\n",
       "      <td>-0.358150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamb</th>\n",
       "      <td>0.616722</td>\n",
       "      <td>-0.138196</td>\n",
       "      <td>-0.006103</td>\n",
       "      <td>0.069579</td>\n",
       "      <td>-0.224907</td>\n",
       "      <td>-0.222503</td>\n",
       "      <td>0.093172</td>\n",
       "      <td>0.032030</td>\n",
       "      <td>0.085114</td>\n",
       "      <td>-0.020606</td>\n",
       "      <td>-0.005804</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.185433</td>\n",
       "      <td>0.040097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log_likelihood</th>\n",
       "      <td>0.213035</td>\n",
       "      <td>-0.157355</td>\n",
       "      <td>0.035900</td>\n",
       "      <td>-0.168815</td>\n",
       "      <td>-0.498883</td>\n",
       "      <td>0.509110</td>\n",
       "      <td>-0.535496</td>\n",
       "      <td>-0.172487</td>\n",
       "      <td>0.138990</td>\n",
       "      <td>0.200745</td>\n",
       "      <td>-0.341574</td>\n",
       "      <td>0.185433</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.258472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.082727</td>\n",
       "      <td>0.144190</td>\n",
       "      <td>-0.037539</td>\n",
       "      <td>-0.039724</td>\n",
       "      <td>-0.082098</td>\n",
       "      <td>0.343512</td>\n",
       "      <td>-0.463671</td>\n",
       "      <td>-0.193389</td>\n",
       "      <td>0.312379</td>\n",
       "      <td>0.104602</td>\n",
       "      <td>-0.358150</td>\n",
       "      <td>0.040097</td>\n",
       "      <td>0.258472</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   alpha      beta      mmin      mmax       lam       mpp  \\\n",
       "alpha           1.000000  0.018332  0.049273  0.165293 -0.458561 -0.096644   \n",
       "beta            0.018332  1.000000 -0.030397 -0.108583 -0.049465 -0.047072   \n",
       "mmin            0.049273 -0.030397  1.000000  0.067611  0.026643  0.028545   \n",
       "mmax            0.165293 -0.108583  0.067611  1.000000  0.025335 -0.076469   \n",
       "lam            -0.458561 -0.049465  0.026643  0.025335  1.000000 -0.552935   \n",
       "mpp            -0.096644 -0.047072  0.028545 -0.076469 -0.552935  1.000000   \n",
       "sigpp           0.099342 -0.011391  0.012661  0.090215  0.450844 -0.811752   \n",
       "alpha_chi      -0.039909  0.029325  0.004106  0.028796  0.064456 -0.147834   \n",
       "beta_chi        0.032705 -0.030322 -0.055865  0.035281  0.012491 -0.025253   \n",
       "xi_spin        -0.067643  0.028157  0.003692  0.023455 -0.030650  0.099803   \n",
       "sigma_spin      0.012180 -0.041763  0.097444  0.128840  0.035253 -0.151705   \n",
       "lamb            0.616722 -0.138196 -0.006103  0.069579 -0.224907 -0.222503   \n",
       "log_likelihood  0.213035 -0.157355  0.035900 -0.168815 -0.498883  0.509110   \n",
       "variance        0.082727  0.144190 -0.037539 -0.039724 -0.082098  0.343512   \n",
       "\n",
       "                   sigpp  alpha_chi  beta_chi   xi_spin  sigma_spin      lamb  \\\n",
       "alpha           0.099342  -0.039909  0.032705 -0.067643    0.012180  0.616722   \n",
       "beta           -0.011391   0.029325 -0.030322  0.028157   -0.041763 -0.138196   \n",
       "mmin            0.012661   0.004106 -0.055865  0.003692    0.097444 -0.006103   \n",
       "mmax            0.090215   0.028796  0.035281  0.023455    0.128840  0.069579   \n",
       "lam             0.450844   0.064456  0.012491 -0.030650    0.035253 -0.224907   \n",
       "mpp            -0.811752  -0.147834 -0.025253  0.099803   -0.151705 -0.222503   \n",
       "sigpp           1.000000   0.044627 -0.046373 -0.076903    0.129302  0.093172   \n",
       "alpha_chi       0.044627   1.000000  0.616715 -0.014138    0.295180  0.032030   \n",
       "beta_chi       -0.046373   0.616715  1.000000  0.053659   -0.020179  0.085114   \n",
       "xi_spin        -0.076903  -0.014138  0.053659  1.000000    0.047134 -0.020606   \n",
       "sigma_spin      0.129302   0.295180 -0.020179  0.047134    1.000000 -0.005804   \n",
       "lamb            0.093172   0.032030  0.085114 -0.020606   -0.005804  1.000000   \n",
       "log_likelihood -0.535496  -0.172487  0.138990  0.200745   -0.341574  0.185433   \n",
       "variance       -0.463671  -0.193389  0.312379  0.104602   -0.358150  0.040097   \n",
       "\n",
       "                log_likelihood  variance  \n",
       "alpha                 0.213035  0.082727  \n",
       "beta                 -0.157355  0.144190  \n",
       "mmin                  0.035900 -0.037539  \n",
       "mmax                 -0.168815 -0.039724  \n",
       "lam                  -0.498883 -0.082098  \n",
       "mpp                   0.509110  0.343512  \n",
       "sigpp                -0.535496 -0.463671  \n",
       "alpha_chi            -0.172487 -0.193389  \n",
       "beta_chi              0.138990  0.312379  \n",
       "xi_spin               0.200745  0.104602  \n",
       "sigma_spin           -0.341574 -0.358150  \n",
       "lamb                  0.185433  0.040097  \n",
       "log_likelihood        1.000000  0.258472  \n",
       "variance              0.258472  1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_posterior[result.search_parameter_keys + [\"log_likelihood\", \"variance\"]].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YVwaByjPUNMY"
   },
   "source": [
    "The most strongly correlated variables are the ones that control the position and width of the peak in the mass distribution.\n",
    "Below we show a scatter matrix for these variables.\n",
    "The variance for this analysis has a tail up to ~4 and so may be non-trivially biased.\n",
    "The simplest method to resolve this is by using more samples for all of the Monte Carlo integrals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "id": "jJaMvciSNe_l",
    "outputId": "43c60272-940d-49fd-a14f-c26a9d4c067e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gXN4VedLtkcfFh7gd43nSXfmz2jTpnGczIGmdZx71U+tqlVnSbPhBA/Qooehx3bNq0wVo0TgK3V3o7uVaUeiAlJLMCJwT2/HFptuUD2vJq9ZSZJrrWtK5NCoKITIrNLuTnN6nz5JzgaPacmfR0/ULdiYZ1gc+OmmIwG6ZoaVk10hurC1SwFHs8T6yatKWlHcBG2CaSXKdOhFHdcNp3dP0aSS76i37VU6uYNF5Fo1npxS8d3uNRLI3SXq5SZ6KXSPTQkLuBK2N5Cb938p4bp00VJmh0JKu9+xNc7res19lvLVniDHig8b69Lpk6tHvpw+RH9xdcdL0lEZwWnuigMYFfnjU0DtPbZMPT2kUinQTMCnSVp0QguO1YqfKiCEiJXgnEAJigNZ7bi9btBKcDGO8ea4pMoWSAikUuYR5ZVg0joOZwvrIB0cNuU4bpJkSXJ0VQ3cvkClFrpPnUmkUqy51r1wW2C0NhdFcmUZ8iLyzV5GZ1J27tezwITArMu4sOpreo4RIOq5ZWs44fz4ojKIbOnlKCqbFi1cy9C6waC0hRmb5k93EvHjPZmTkOXF9JyO/23N33bHqHftaU/eRzKQTwe40o7MeB/gYmVeG1+c5nUvz8N4H/tGdVfIOGrYt5oXinf2SWwvB3VWLj+mANRpWbeSHd2qUSm3xK5Mc5x13Fz2zoqUqJW/OS24tak5qi4yC3TIjV0n7cXfVc2fVsTfoHNadQynItMJ6NwgXBS6cFSsbkTSklvaqdexNHq2TKIcV6hDTz7zfiu/T4PJ2nH9GhRaAPFfsSCGe+kjvScZqIVx8nuE+T3unMvyJ1+f8w48X9D6wP8lRSm7v0gst+OikxfmIVoJpbiizJDieFcl76rSxwx1+0vqcthYtJaveE+gGrZKiNIr9yrDqHJkSqNRwpFQSaeRWRO3JULJOXZ7gKTPN8arncNVRdx5jJEoETtYOiEgZOZhk9N7x4fEaIyULmzHVKq3SCxDCQwxJ1Ns6cpOK+ePa470gSPiHH53S9I639ydMMoVUAimSe/u8TJ/nGCPh3pfxHu6skjD87rJDCIEIYIxkp9BAYNH29NZjAzStRRsJIXBSB5yP7FQ5mZLEocvT2dQhRgh65ygyxbp3OBfQUnEwS12adRsockHrBUZput6zaB1GSaaFQg46ob0qo9CSaW6QItlfICSdD+xVOZ3zaC1QUnJ1lpMbTSTS2MB+mSGlZK8yGJ2sEFoX6J1PN0pCoJRg12gOJhlXZvk9WrXUGf1k3VHrA6dNKk6muX4m543Nz4ekk8vvd/A8gLEAGhkZKEwSGu5Pcia5QQLWe24vk8bhzZ2cw2VHdIFZIdFC0nlQGlSUnLQdkbTZIohorViuLVGmMUiuNCufLgTOezIp8AK8EIQ+cBQtexPD0brnpLHstjoJFy3cXXQYIygyyawURJJw+vKF8+o059ZpR9s7SpPuqPNLJ7QYk+hRCUE+/IzjusfHyH51/zgDJUUa64WIkk+/WNigh8e7KdIeZ4TxSdmpDGp4bZ7l73kc9iaGZWfph47N7n3GcdYHbIQrszyZZWaaECPzIsOFSOccVyYFrfOE4X169yBtEX14UnPjuObOsmNeGnoXuHHY0XlPlSt2qxIj0wZkoSVaki62hSIAJ03qAE0Lw+vzjJ1SURhNCIHKCG6edoQAbR+ovaftLEfrVLzIGClMwCiNtZHjrklmfCHiQ2Cxtsx3NVpGPIpcBboelkPMRhRJZLw3Mdw4aTldJx1c2ztuHK/ZrwrKLF1cpRQ0vccNhfrkMS64y0EEfnfRs2wte1VGjElHU2SGQjvqdrMtpciCx/o0AsuNpnGB26ctRku0UgjhEQgmmWBeZnx03HBcO3wM7FaGWamYGp30PadJMN4IaHtBmSukhMZ6jJTsVgZRma2zd2kUkzxpfX7s6pRSS24cN+jOslsY3tirsC6Jp+eFRqrUEbYhokIq0GIk+YnF9FnLlOALVyZ8+drsqRtSLhqLH85Ry9Y90OwxhIgQj+fefJl4qT/9JLdMYwE0MjJglOL1nZxlU3G87mmdZ7fK2J8ZTtaWu6ukyXFSEKNgUmqigCqTrJoe65Mj6/Gqp3OeN3YrbIjEEKn7gAuggL3KsD8tIERql1aLtZIE4nbTZFIaAun7D5ue3Ci0iHx02jLJDAfzjNdnJZHI8bpPP0OC94FpofliNk0+H4NPix9s9yFyd91Td45MKWaF5qOThpNh7HSytnz52uS+KcpCpC0THyJ1l0Sxk+z+myAhpE2aECOTTD/RuuxulUYxArYny7o/Wyl/3MyjBwmKNxglmT/BltDjEEJk3acLd3Wf570RM0MqMAAmeRpZfPnqlN5FjJb3fb1O6nSn21rP4aqjyjUHVcbHPo23iEn3YrSktY7SKFyILNqO24uG1jqOVpa69xRaonSk0orcKKa5Yn+a8yWT1qePV33aeKp7bi9arIvsVoYrk4y390r2qxz9o2NuL3tOm57cSLyPHNcdrQsoIZkUCiEFNkaEg9OmprGe3GiCEOwPgv/WeaaF5u66pzCGpvMcrWqWLjIxgnUDCDcU/IAMuF5wZZKDlORGbr1zKqOo+8A012RKUlu/vQDfj2VrOWksi8ZSZAKB5s3dnJPWc+OowYdI5yNIiREBZPpM1n1akpDOIYTmtPNcyVMXSgqJj5G6DcTYUWaKw9oyyTTTTBM8HDcdRgq6GPnopOZPvLGDVJKdyqCFIDcSLSV1a5mVGW/tluhB+9dajxq2v97ar7gySyNiJQRaSRaD7morAI+p25s8oNLIb7M+vz/N0uioNPQ+UMinuwxw+ZW/3zuxccUWwPwTmLtOc70tlstMQf/455qxABoZGditMv7YdUPTRw6GE8ObuyWd87x/pybESJ4putqhlEAA1nmOmoAxgnYZ0DpiMokRSVibZ5Kus9xdtbTW4TxM86TtEWzM6Dwy0xgtsTbgPByetsQQwJsklnSRQgve3i1456AgM5rOBz46ajhtOn503CIkvLNXMi0ydiepS9D2qThadx4pInWf3GydD5hScnfV0w0nyhjTSbNzgTI7K4BWnRt8URSFSUaQm5azdWHYCLt493baWPpBm9LZwJXpkzn9ntchbNx5IZnJ7VUPXymPMXJS222syG6VfWZ+JSfDiAmSTuTKNKe1qejxw7aWUZI7y47GJiH5tLC8e5CKziI7ew4xcuGOPMbIx4uaD+6mkejBNCfGNPIMgwi60JKPThsOVx1KJG3RurUc1hZipLGO3BjuLDvWfdqcUiKNy65OMlofyVXKEjuqe+4uetre0fZp9NI4zwdH67TO7RiKUk2VBQQCox2l83Q2brcRy9LQW8/tVYv3yZtHhcC6DXQ+RdAcrjtChGtV5LjtuFv3VEpSR01uAs4LFq2jMMnfJ6iIA3ZyRaUVRilyIzFaMR2Kg0fR9I6PThruLlt+7+Mlq6bDRUHvHSdryz+6u2LVumE7zGFE6uSuOwcCok/X2hxBpmHZeLzvCVGwWxp8hOOmJxcKpQRSglSCIAJNmwTriy5t331h3/OVNyYAgyt2Ks6KLGWpHdY9b+5WfHzacFpbEHB9XnBlmlNe6nJVmabJkphckMbzpVHJ/JBUePuQDFkzrciURD6mKeaTMs01i8YSgUKre/SGzoftDUEkdYke9RjOCrjUia4yTa7Vdqtw0TeP/fjGAmhkZEBJwf/jjTlfvjaltwEE/PYPj2hd5I2dktO6TSMwoFCCK9OSSS5wQWB9j3WRrk+6h1lumJQKJQQ/6uxWsBqEoHOR09YmjYYGow37laEP6cTUB0/bO8Iisu4cu2WO9x2dMJy0PaeNY1/BzVPHnWXDhyctR2uLkvDxacsXr0z5f5YGSFs/i9ayaHqaLuAjNNYxLww2BFxIDsLL1rFoLUrK7UlICMH6nJ9N5wIx6gsC6MNVn060IolmNxeeTREASTDtQ9KlfBKcv3jf+KiV8sb6bfHlho7M/DNyFT7/vH2I9DYJgV0I3F101NYzKzSHq24Y0wwjSevJhhN/78I2T6vQarudVmaKWyctx7Wlt4EbRzVN77g2L5jkiirXHK86bi/T6vT33z9GS4mQAiPThTuEQKFyYqGockmhDS54bq/bVLCEwHt3lnx80uODx4c0bg3Sc3fZcdpZ3typEFFw0lquTDIOJjlHqwYhwfaRP7i9pHcOAsQYMFKgjcRZSevcsFIPe4Vhr0qjtD4EdIjcWXf0IXKlyjFKMin0tkiz3nN9ViKVHMJIFfPCMM81X7w6xQyfvauzfHvRneYa+4Dit+49def4/ZtLPjhecbSyhBj4wd3UTVk06ZjonMOmiR3RJoF0qWB3ohFBUBYKISUxBBobyE0yoVzbVJD2wZNJySRXFEpSaI0MgvePW3IluD7L2J8a/uSbO0QBvY8crVrWnUfJ5Bc2KzRN71g0LnVRYlpm2CnNPUVFpmUqjmY5ElDn/n5vkvHxScO00FgbCMC81Nt/97RJIzZJGIqTR/Goe6TOeU7r9N5qKbYRKukG58nPL2MBNDJyDiEEudHkJnUbrs9K9qqM0kj+p99rkUqwqw1Xqpy39nK++sYOR03PD26vWbeRpezRKo0TMiU4aXoypXh9p+RoJbcBhGIwHCtzzarraVxg0dhUZPh0539SWxrrkmDRRbSKRB/5XnM4bIFI7q66lNNERCKotcJ5xzTXFEbhfORHxzW3Fh1q0CIpISFPF9/WOq5Nc6wPRAz7k4wIWzff3oUUvjq46oaYtCV+6DiEoakdI9vVeyXFBfv+5Gr7yTswmZbbFfwUBfDiWu9nw/YdpBN0HJ52awNCimEGIDhteopMb92Vz3d6Vp07y/dynsKlsd9kY5AZBUYLms6n3xEj3kcwUBjNNDPcbGva3qGlpsokH562ZCo5Q8+KjMpohE4BpkdrT2MDR4uOw3XHUZ1GX3IIAw4hEj3oTBB94LjpmBhJ1zt8ppgWCjPPOa4tbUxFxdo5pplmbgoiKS8vyySNA0nqPPQhYLxESI+IUOSKukm+RF/YL1n2jkprci04aR1GSmrn+YmDis5HJln6fC97T2sDCEGVpffg6jAWEkJw/ID3qjSK26uW07rn1iKZGxKSr85OoVE6YkOAAGEogOSwIepU0jul7pvAeUfvIloKMik5rnvmuUYaBUiuTDU+Jr2KkZK9HcNJ32Md7E0KAqk7tFvlHNcdhdFcneU4HwcBscIHEOeGSFKIB17ypRTIB/xtbhQ5CvJUpM+KVEQ9iwLoUY9FK8k019uu2qNuVOrOb1+BpHv7dF2rsQAaGXkISgkykbYnrs3yFH44ON5+9c09XtvJaV1kUihe2y2YtpoiV7yxW7JuHRFBrjVSpAKktZFZkdaFZ4UmN4pMClrnybVECYHTULeBxlpaK1h3ySRRDt9X6FRITYqMEEDgiVGitKAwkr1JgRTpwvjenSV3lx23Vx2VMWgpWLYtQQRa53ltp+Tu2pJJyRu7Of3gIJtJwapz/Oi4pu4d8yJjf2IoTAp5rG3yNcrU2fjspE46HUGKI2C4sO9Xny7ocrNS7oYogkcVQKVRWwM3JR9PCPu02K0M9dDS38QXpFXndMG6vpPGVl84qNJ2nkibfefvjn2IrNp0l+t8KkBTAZn0VFdnWSpkcs313QIjJb3zTNFY76htiq1Ydp7XdwxagSYihv9386TljYOCQmqIEa2Tv9BJbbl52uACSesWI7NSUmWaeWlYN6ng9T51agqZLsq3Fx3TwmA9HNY9iHTBq22KZOmjRyrBXqk5rTuMVsQAIabMPCEF12c5V6cFb+wJtEpFb9ZJru+ULBuLNordKqM0ir1pzl6V8dFJAyKNQta923Y6HlfImzRURRILI+icSxdqG1kr6PpA3zt8kv4QBfgIIkLXg8oDAUkMHq0UbXQEJEZphAxcHXLQlJJkOp038izDA1mm+eL+jNp6JplmkmmsC5w2PS5EpEoiZ+uHUWIAFzyZlrhB4H59nj9xDpe8dBwWRj0zP6/HZZLrx34MUqbA6g2fdhdjLIBGRh5AYSRXpxmHQ5L7T7w+4/2jhrb3XJ3lXN8pWPeb7Y4MJSQ7VcYsU0wLzek66TR8jBSZQpIywFwQTHPJrMq4Os14/27D7WWNi4JFa6lyyYe3HUZqEGlsFkNk0Sb/ljyTZEogkRzMcyZFTqUkUkqkgOO15Q9uLWl6x//5wSm7kzQjP6p7Ci0IQQwjs7S6n2kFMsV1LFpLriXHjU3rtUoO2zVQZGp7wp3mGvIzAaP1MSVZkxKqT2rLTmUIg65oc4LrXSpMnvSO80nu8oRIrfEnNXt8GoR4trofSRec/SpjkinWnceFSN07cqPJYiqYzms4ko9N0oWsOoeWgirXHK0tcYimePdKBQia3lFmhnVnWfcBUSfx625uCNNIoZNXzLpPq+6LJjkVz/MM7wXzqSHXgvePam6ettw+7Vhbh5TJd8hIQa4M1+cFy9qx6FsKpSiNxIaYRmRNyqNaNMn3Z9Wm2IaDCmob2KkyCiNRKkXN7E4yvPO4CN5HqtywM0kZZm/vT9gpMhyB4AO3lj2FVvTKo7VM2XmZZrdIW1FXZzkhQqYEQqQR05NEXwC8tptzbafirb2WrrdDYLHE+yHrSkii9MQIIiRfHyFTx3NTDPUhMjHwxl6ZRr1SsZcbcpNCYyWpqNWZ4crUsFvlvLGb4336jJhh87RzgbVN2pxN2Oy81Nhw1veZFoadTyAU3qCkYKdMAc8bo9GXiVmuk/dViCkM9jEXIh7Ey/XsR0Y+Q4QQXJkVHExz+oMJf3hrxbVZAURe263ItaSxAqMkQqSNihRUCIjIlVlOa9PFvtKS28sOFzwuBPIs44sHU2alZneS8fsfa9at482dAg80TXLIrTtP7S3BS7IMnAuUSjApDHmmKJRiN884mOYomUZunQv8wcen3F62fLzouLmUfOX6jM56pkXqxvgQaW1ayRfANDfMS7Pd/toEeQrSZkkxhCdC0rmc1D1176mytLLdu8CdZTdsmYQLd2ZnouAU5bBhtzLkOuU5Ldpk5jgtnp5XyGdd/ADbgEpIxd6VaY6UgjLTlJlm2dptqGVjk7nc7jmn7tQUEhxMc0JMG37A4L2ksCGiZepMViYnzxQhRk7qnuN1R2uTk/TeNGN/miOI3F2LNM48bIhNj1FpFHNUd8QgkjYtwO7EcE1mBNJF/o3dimuTjKPWImWS0K46x+0lXJ2VWO/4o1vrQbytmBlFJhUycyw6OJiWlLlgYjTOQ6Y0b+9qGutYtx6lJI6I86m7UhhJwPPmbsVJbVn16XveOphAjOxWGTul4QsHE/YnGR+fttsRrZKC1+blVkf1uFyZFlyZ5LyxV9HbwMeLdlvwO+eohpiQnojfdB5CKlwiEBCEAIXWXJ8VaC3pXdze7EwKxSQzlEYzLzRFZtgpNbtVjtGSXMnksK0luRYc1Ulvtwkz3qsylu1ZYOnms/BpKIx66mLnzwopxWP5lj0uYwE0MvIIki5I8WPXpixbi1aSeZG2ZLQS7FQZuU6GdJPcYKTg40UKJxTSQYiUpeHOusfHNMrRWpIbxZVZjhIFsyJjVVt+eLjmeN2hlKLMQGlFrKH2lhgiu1XOJNO8vV9QGYVQkllhCMGz6gKtdexVhg9XllWXVt+DTw7CX75WYpTgcNXjQ+Cgynl9XjIpDFKki6gLgcJoSqPQUtD7dEe6N3QxfIgcrTrurvphRBOHbas05T9cdmkUp+V2DX0zstpogjb0Lm2WLVq71bwsW0eh1XMpXj4tcRB7b/Ah3tOF2ly8lBRM82ROePHv0991NkUrZDpt6PQucH1e0FjLR6ctSkoKJdkVhtJIWqOGu/q08Zc2ZGBe5rQ+MMs1PqQiamewGcikoMwkHshzSRYku1XO1WlKDXdhGDtKyXHb0QwBvj86bsilJIhUuGgROVo2dEXOwcSwbgXWp402jeT6TsXxquNgZugt6d+RLvIeMEJyUGZ8+dqMCBzVyfl4b5KxVxp2J2kN3Eex7WAIIXhtXmxXvasH2DE8Dq/t5vzBbZgUGWVr2Rs6l7dPUzcvyxR5DKxjIFOgBPRuU7RICi25OivYrTStBZNFjpc9bUji+zLTHEwz3tqr0IOHlvWRWaGYlZqdIo2m+8GZO5lysi2WK5P0fptU92el1fk8MhZAIyOPSZmpC+Giu2WWpH0xoqXZprrnWpLpHuskb+5WSBFYtYE/dl1y67ghCMFPvDbj2k7BtWkycZsVhlumZdk7fIR3DuBw1WGHO9xUTHkKI/nKGzPemOWs+jROkyJyt7aUJh3OTR+QCGalTuutIaBFYF6YFP9gJHtlRpkpOheZxsBHp6nT4Hzg9Z1k0T8pMkqddCyLNqWQK5EiGDaXeesDyoHOksGZVAIlBEalYml/km1fs0xJmnMD/K2W59mZPX+mCCHonGfVerSC/Sp1f5wPW2HzJE8Xs94nfUx1KaxWCMFelXFS98wKzfV5vl1fnuSa06bH++T3BBCC4AsHFULUZCr5/yBScVVoQZEZILJqHVrapD2KIiW9D+9XoQXXZgWvzXKmRRLCf3Rc88FJg2siuRLUvaexETVoburebfOsjtvkcXWgoR1CfSeloe394IMVuLaTkxlJZz2tz9grDD88XOMjvLVT8vpegQ+RMtN8YX+CdYGvlBpjFLPi/iMfKcWn1q+4ECmM5u29CbtVjoyek8bR+8g7BxUfn3bbLUijHCm7PjLPBdMyKauUVFSZ5EpV0HjPzUVHHwO5Tn5HV6eGa/OC13fLwZQ0FWqdC5QusuqSseKisVQmBbvGeGbOWVvP9XOxKCNPj7EAGhl5ApwP1NYPLrOKq7MCJZMoOYSUml1oyZVpzm6ZLhalUdxethRDx2eWp7DTYji5S5lyfkKAj47SimrwASMlrbXUXXKNnmSSN3Ym/L//H9dxUfB/f7zg5qIlTY8i12cZfVAsm8gXD0pal8S0CMm00PzwsOb6rKA0ySFWCMGqsxzVLSHCJDNoJVl2llmR0fTJq6TuPa31FDrdfUYRKYxM4z0t2SmzNCbwnpN1j5LJrO2tvSoJVDuHHrbeYmR78d9c1GaFSSMw0kX+Zez+QIoZSUVN+tro9DyO6zOrftsErk7zh3YrlEwjMClSB06SDOIgbfBsCD5tHWmVkuHvLDp+eJhsEtado8w0X76a8fpuiQuBqpD84PYaT6BSkigDPib9yWYTSAJ3lz23VsnZeZqnfLTXZopFk/yjpEgbVyFEvItoBUWmeXOvwijJurPUfdpcQgjyXHN1kvPFgynNoHEpjEqdRymGPLqhI3ZurfvqMD582mw0NmYINT1adwiRRlZvHUwo15bbixYf0ghzlil6H1l1Ysib0uRGpuw2KZiYZD5pRUQLyU5hmBWaTCte3yn4E2/ucHWac2WaExAcrjo6FwYjzMi6szTW43yypNibZKxav33uL+fR8HIwFkAjI49JOGclD6kY2q2ybX6VHHwp5kUSl540LjkaC/jqGztpDTbGrXDv/Mm96T2Nc0QZaXvHaed4fZqz7BXtTvINmhWar1yf0QfSWAvBXpERRKCzktalThMi0rmkLZiXOUImAXUIcHfdsj/oS9Zd2hbqXVotNcryzl6FPucGe9pYOpuW3a137MjkWeR8Gu8cTDJ2q4zWeqxL3+dCZNk6bi9bjtYpYmOSKa7Py9RF4+LdfJklg8V15wZtkv/MNQobs0KlBLP8k4WihmHkt+0SDjvw532T4uDK+zg/fm+SDQXHYEEQ4lb8e9pYZrnm9d0SSKLlg6nhD2/FIZE8iUTXvUtFbeNY1pa1dZzWSVNyMIw188HJ/Pay347mrItoKQe9SBrvfilOuLVoOW2SqHrdO7ROWpV5rsi1oTQp/qHuLIernswoZEij0irXfOlaybJNbtRf1jPq3iNJ8R7X58XZBl32bMag1geO12l8KwR01nG47vjhYUPXO3ofqPL0nBeNw7pU5GcqadaMSkVprjWzSTJznOSKidZMsnQu0IMFhJGCN/cqvnRlSiCFq57UfRrbDQaWd1YdnU0Bqm5wcRdCsFNpWps6hzul3hZsI0+XsQAaGXlM/ODOu2Hj9zIt9Nacq9BnY7L9STYEkqbRxoMcLnyILFqL95HrsxIf4PXdihBhLwR+7NqEk9pu84CsSwLrapFs9jMjaG2gbh1lJlECtJbgI1WhOF1bSjO03kVkt9AIKbg6yzhcp5+7Sa6e5ooySyfcVesG35i0NdL75BC96gR3Fh1SCn503CTDulyzNzHsrbNtyOKyddsOwqrzzHp3YYR4nsZ61sPFrx06bJ+V1qF3YZtAv5nQfRLjxEIrapmiF4SAKk/PtTBJ6A2Qa/lEF3atJL1LW1G5lhxM8m20yOUisXORK8PWoRy0V/MiY9UlLUlyH0+REQS2HiqHa8uqdWmEZ1PG1dVphlKSN3Yrvni15Id3GnbKjINZzkfHDc4HbJA4nywgDiYFr+1mzIuck7rjo5MWpZJf0c1V+t+RpNm5Ni8gJsH45nAqs4ujrmd1sW/smY9MjKnbtW49RgpOXMA6hw1JjD4tM75wYNI6fAx458lMRiRghxWwXEZ2S8XruxVv7FWD95fFKEGhNf+vd3aphuNn2Vr88LobJeidZ9k6jBJEnxyNq0xR6BRNcrjuyXRqJ542NoXFjjxVXvoC6J/75/45bt68iZSS2WzGX/trf40/+Sf/JLdv3+ab3/wm7733Hnme86u/+qv89E//9PN+uCMvMVqmi8fmjn7Tycm14upM3hNdADyWAeDm5+khKLA0GinSCEVLydt7JbeXHad1z6J1CJEey0+8PuO47rm7TOOKg1nKVlr3bhhNJcHr1UlO4zwiCjIt+LFrs+ECmdaHTxuLAIKItC5w5+6aD49rdkvDFw4qauvTyV4rciX5oztLNkspk1xT927w8jDMBp2RUXJb+G140IV/3TluLZIT8cZI8VFuz0+Ty1lR/pLz9OMipWCvNPiYuieb57tTmm0g7ZN2tlbnnLiNSqneD9K9SAGZSTlii8ayW6Xvm+Rp1FR36XNVGkVhxLCyD8T0ecoGE83WeYwu+Mk35nzx6pS19czKFGSZG0UuJY0NNNbhvWd/VnAwyZGkgNLGehZNx7JzTDNDkUtsSI7Y01xvR2D7k2zIDTvrmj3rLoe61HqLMbJsHD86aYaRLuyXip1csTPJyLSi6SynjaO1Aa0E7+xXLNo08s6MgCAIMXB31TEvNAcTw5VZiRSCtQ1MB/lOCOn3aZGO67QyL7af+auzYmvg6Hy4II0LIS0p9C51gnYr88Au5f1iVEbuz0tfAP36r/86u7u7APw3/81/w7e+9S3+j//j/+Db3/42X/va1/jud7/L9773Pb7+9a/z3nvvofVL/5RHnhMbf5nG+u2F5PzffdLt1MuJ7XsTg3PJ7Gx3YphkmroPKCmZlYF2CHgsM4VRBTHASdsnMaVLYmejJfPcEEIgz5JVv9FpC8kPgYjTQvH6PMUGKBnJpeT2ouGkSUWUkoI7q47r8xJJ0p/YENBS4YadYOsClTk7pt7eL1l36e+uz3OO657WpnDKvUEc43xI3yOSMHrVpd9V92ksdz97/2dJNmzwbbp7n3T8thntCIZQx3OjxE/6Mzc5SZDGN27IQLofYhiVzQvDNNccTIoUcKkVi9pitOLHr8+wLml4bp00eCJGamalhgh3V4G3Dwr2K0MYssR8hEmmWdSW3dLw1k7Fsrd01nN33ROHOJWdUrMenK3LTBNjcrKusrQa3vaeVee2nwOtJNPPeKwzyVOUS+/C8L4Lrswy7qw1revQUiGFYn+eCnpB5OaJQyl4rcrZnWRcmea8JQRVrli1nmVrOVx1hEFY7uPGsTxtQxZGEUg3OqpLBoydDQgF89wgJNuCcINWkkKrMwd0cbZF2fvAuvdbkfR5Wuu3MSBlpj6zCJiXlZe+GtgUPwCnp6dbH5Nf//Vf5wc/+AEAP/VTP8X169f5rd/6Lf70n/7T9/yMruvoum779WKxeKaPeeTlZaOReNpsEtCluH9sxP4kS52awUNmr0rjtZOmx/mAi5HgI1HLdIERsFcl9+bby555ma7wnY0suz7dmRuNAq7MMoiCZWvpfSSSVm8Bepf8V9yQJK0QXJlmuJAuIvuTjMk5MzWtJDvV2UWtyvSFHKAY4wVR8GqwFSgG12RBWn82Sl4YH54Xrj7tYFMlBQeTfOsn80lGb86HrX7lcUMdH/exhaEjlWJMzp57CHEb+KqVJJJGd4IUvbExZFQyfb58CBQmva65TqGid1c9ArgyzZBScDCxqbMgUsei96nwrnvHrEwmfJmWfOFKRe8jh8uW26uWKjPsVobrUhCiIFeCf3RnSRkNr88zdqcF1+YFO6WhdYHqOU5zZueKgjLT7E9zXt8p2SkNZaaoTFpb359mfHzc8PZBxapNXc7X5iWzQnN1iP6wLlBLybQw+JB0P2kbzzMvzeApdGZ54IYiVkJ6jQc92P0M/XYqQ+VVej8Hh/YN53Vl51m2bts5anpPZdQTu0V/nnjpCyCAb37zm/zmb/4mAN/97nc5PDwkhMDVq1e33/Puu+/ywQcf3Pff//Iv/zJ/4S/8hc/ksY6MPIiHdT02ievnvUDC0ELfm+TMcsPaOu6uOvRwE1Blin7IMKo7z7r1BOERSBoT8DGQK8W699sxxE6lubXoESLdwX7pypT9QYybjPs0cpK2vKRMguEHEWO8JwcoxnTydj7gfBJ/ainSOrJWyZk2Jq8hO2hpZrlmOayRC9LF/Gnrg5QUD9QnPQ6fJurjYeyUhmWbwnQn+ZkwOITI4brfXgh3SjNEgPikE/NpzKRl2kJc955lGwbn77SJdGvRseosWgrevVqxX2bD+CyNWatMMc01zSCC34xNhyeMEBFjFNd3SqxLAu1ZYSi04h/cOGJWZggEszJjlqutgd3TLmA/DVdnOYfrjp1SJ8NSo9mdpGNt3SVd2t4kR8tUeB/MMoxS1J3neNXjYmReKeZl0gEm4XnAB0eZ28E49Yw05n78x7c5J1RCbDu/aQP1/sedSIkpI4/JK1EA/Rf/xX8BwN/4G3+D/+g/+o/4tV/7tXtOSPEBFTPAL/7iL/ILv/AL268XiwVvv/32s3mwIyOfACkFu/ex+Y8xBVoWmea1rEji485RGsVr85Kjuh/CBi2TQtL0aSyhpGDZeChIxVNnmWQp9uDLV6asOk+RKabD3bJWktm5Aq0wKq1B3yfl/Xxi82S48wW2fjing4u0HkZ/12ZJKC2A09ay7hzHQ9J1lWmO1v1ZB4n0+DcFUGv9NiPseRrEbTqD21DH8tOfWjcO2uffdz9EafQu0LkUwKmGrsPeJOPKJMeFyGuzVCyHEDha98SYwmuLLAmj7w5mllJE2hi4c9rR9cnGoTSaK9OcN3fLlGOlHEqCdWdbbkoK1v0m9FUyaHVxPtD6wMGkpLWp+5RGMdm2e/ewovmzZlYYXt8pKLRKDufrpLOxIXBlktLUT9c9uRZkRrJuPUI6Pj5paWzgzZ2cnTLn3SsTVq3lg6OaQLKYiAHWDxH+PwmpO5e6vmowU7z/89FJ0B/TqG3s/jycF+eT+BT42Z/9Wf7tf/vf3n59586dbRfo/fff55133rnvv8vznDwfFfYjLxebjZbNXaKPJD+YVY9RAinTKvW8jLiQsWodrfUcr3uMVhAjVaZYdZ7OBm77lqO6I1OSK7MCqSTeB3oftw7Fm7v3unOsumS6t7n4blg0qQ3vQ2Q5ZEX1NnBUd1ifLuBpvVjgveKj05rXd9L2W8qVspzWaf3+YBovjCzCORfGpvdb/6AatqOzZ0mMkdWwrr/xMtp0Ze4X6hhjZN2nDDejxWO7XC9by6pLNgqTXHMwyYkxcjR0fVrr+fi0YZqn18YoyR7pQpkNrsKnTfKX2ehudipD0/vUgXPJN+rmouXuqse5JdMixXVkM0FmUv7UnWXStigh2J8kn6jNZ+CysSXA0bpn3Ttq55jmKboj04prOwU7RXJYfl7iXOdDKsxj0lH5mD5PnQvkRuFCQEq4tWy4s+z5g3BKmWusTXqht/YrrA/86LChtZ5Jobm76imyJC6/Mis4bSwu2K0ztR78ku73nEM4s894HIQQ99xsXCbXimuzlzPm4nnwUhdAi8WC1WrFG2+8AcDf/tt/m4ODA/b39/nGN77Bd77zHf78n//zfO973+PmzZvjFtjIK8XlTCApoPORncE0r3UBLQSNTQnyb+6WnDQWJcTWyLBzkcYlL5O7655lbcmNQg2FzY3WopXgtE4n9cIofIx8dNwA8PpOavE3/ZBA3jpWncUM69urznF9JwU/Hq17SqOIgzOxlND7jmmj8SHZCYSQTvQbYbLzabW7sZ7TQVAtRLpoWBe2d8QRthsyz5LV4J20atO2025luDLNH6j32X5/52j69P37k+yReWerznG87gkxFZSVTmZ7m5GXEOkCal3yrckuPe96KNKkEMSYvpYyCfVP655ZofjB3RUfnzQIkYrSRdtzdZ7ciq/ZJFQPMf3duvOsO83+NNsWpIVR1H0qhKssbXedDiG63qe/3800B5OMXEnurJLOstCKneqzF+ee1JZKOk5qS2tTB3KaazIlcSIyyw0fHq05WvX86KhmbR1vzAqKXHNn3XFn2fHaTkVmUqRI13tWffKsujPt+cJBxY9dmzFftmljTAo6H7iz6phdyrire8eyTZqe813Skc+Wl/pVPz095V/5V/4VmqZBSsnVq1f57/67/w4hBL/yK7/Cz/zMz/DjP/7jZFnGr/3ar40bYCOvFGWWtrI6G7ZOuq0N23Fv03mqXG231ZL4VXF1XqQA1CKNNPKm53DR0fRpnCQHt+veh5Tm3idvmHXnub3sWDaWQNLu3DhpeHO35PbScWvRIUVaYb950qKVSGu/8iyd/ajukUCIgVzp4eckP5l+uNMWRA4mObNBl7EZLx2vO1rnaV1aqc6UZN17ci2ZD5tjMUbq3uNj0ko97YLIh4gLYZtB5X18qODZ+Ti8hn7771dtKhjcdqvrXmG386kz0dqkz1r3joMs39owtH3KeTNDRtT5zbCm9xydC6u1zrNo+/T6KokfOiBv71W0LpAbSdd7TtpAoSXLznHa2O0W13oocO4uWz48rrk2y3l7v9pmWuU6xZ7UnaNzySPH+sBelUZDWknuLM+WTFrnqfzTf28eRYgR61OSeBJ3J13N3iTj9qLlzrLlj26t+Oi0QSAhwu26Q617PCnkt+49P/H6FKVU6rjOk7h7Nbxmu1XGG7sVIcRtwQds3/PzX29Yd47KvJz5dy87L3VF8Pbbb/Pbv/3b9/2769ev8xu/8Ruf8SMaGflsmRcGzuksN6LZCCmraBgXxTjEdAwX6m5YA4akAfLTQOcDoTBYH5mX2RBdEbdr7Z1LHQEfI0pITpvUZThcJsffzUVeSrDBMy8L3DDSIgbWncWHyKzKeK0qqft0cW1FCnENMXJgcvanOZlKeVlu0LDMizMhNKTQ1XevTrZZZxth+GljOal7wpArdXWWP1XRbep6nK0mm6FT9SByI2md336PURJE6ladDEaAgnvHd/PC8IO7a+rOkmnFSW25MivYn2TUvUuxDXtlSh6XUJzTmSzb1K2LEe4s2yF4VLFqknao95F113NllvPFK9O0Xu8DP359jtxsJgE3jmsWbU9rI1enhkynn9m5wK1Fy/7komxAD7liyzaFsUYBJ4OB3+bnbvi0ieafhExLhv0AprneeuV0LomLXYT9ac7Hpy0AhdHpc2tTKv0msZ0Ih0uLlEnns9M/hm3D5ad7Saz8HF6OEV7yAmhkZOQimZYcDI6x5y+ychDKnv++MktbQ7lRfHk6Z7fs6F0yezuYZhRGs2rTSCMLkkwJ3NDNOFyntv47+xMiG+O1NK7JtNwmXBslsdZxe5WCVLVOTrdXpjl3lj3zA0Mk0trA1Xkai0iRXHHdoO+xPrBoHbNCs2w8kci0SEZwkYjgTJu0aPrtaKG2nnmpHzluehIKkx57plL+m1byoV4rVZYCabNhJKiVZF5qWnfOkZiNO/DZ+5OEwwotQUiB9WfjvllhCDEJwK13qePiwtkq9WAnUGTJuFCSXIfvrFJXrukjkUBvU9bV67slZaZZdpa2S5YK1nnq3mGUYpIlp/FMa8pcsmyHyIhMIRDb57FXZSgpaV0avVVGbU0m56XhtEn2B9NcP5dNsJ3SkFfJSTsM24j58JqvWj90RTWv71as2h4tJbuVonFwuGqQQlJkiqPacX1eYEPgcNUzHzLNOpuOt53SbP2Ylp1FIO4Rxc83+XcxCbGf1RbhyMN57gXQ97//fX7v934PIQQ/8RM/wZ/6U3/qeT+kkZFXgk0x5IcRyUaPAme6g/MX72vzIl3UB1+Z1jo+PKrxEQoj2atyjuqeg0lOZRRKCcpMEmIqUvbKjExJZoWmd2m0E0LAF0lo3fvAurXEYVx1MMm2nYvTpt963AjuvSOuezfEOMC0SG7Ii8YOoZ+KdefItbwQVTJkTT6T1/XK7PGXJlKe1sUR2WX3aX2pIDBKUGYarQa3caOSGHloJcwLTWdTR67K0pr7xk9pXhhuL1tOa0vwkSAijfXMC4OLAecCV2YVe5OMfFiVPx5chmeFRktB7xQfHtdMjEYrwWvzilmhublsgci8SEnok0yhpESrVOwWRqEGu4bzuWhGyece5SCEuO97AbDfW47Wkv0qrfFL4Oai4ajucSFyMCkQIg6hxWk8qZRgrzJMC81umby3OndmUlhm6oEbYA96HCOfLc+tALpx4wb/xr/xb/C//C//y9bM8OTkhH/yn/wn+Zt/82+Oa+gjI0+B85tbs8Js9UD3W49N33t2Uj6uLZtUiNYmD5lZrml6z6Qw23yr3TIj10kPtDfN0DKJdfcGX5lbyxYhehrrmGSKL16ZMCsNnQs0feo0lDp1EzIlh3DUNFLb6JA28Qm7VYoN2KsyiGcOy5sQ1r1JdnZ3b5I+5njdf6qQ02dBNXQh7DCKvHwxFELw5m7J4apPa/XFxTGLEILcKGbn/o0biqpmGC06H5mWmrYP7JQZr++ULLtU1G40V7NC07hAa9NKe+sCr88LMq1oXeDusqPKFK/v5lyZFUwLTef89vOjpLxwkU/OygWt9Q80+HuR2Ai3l63nnYNJKm5soO4tSqb8NK1h1Xje2Ct4e39K23tO2xRqemVSUBp1wcn9YZYrIy8Wz60A+ta3voW1lt/7vd/jK1/5CgC///u/z7e+9S3+zX/z3xz1OyMjz4An8QVRUlzQbmQqdXuUTJtFsyLFWxRGcVT3Q7hkl0JjAxBhf5qlnCgh8MGwN8m2q9u5Tl5C1sszw7dcbS+ae5OM3oWtfgkYxl4Jo5PjbueSKeOqS07XSRCeISXbjtenCTl9XOKQBv+4THMND2mKTIdw0BC5r8dRYSSrzrJu/fbnbdbfT+sUZHvawrsHU1wIhMFm4OpQpFZ5Mjpctm772M2wUReJQ4aZwvq0xRZiGqGZodOmZdomvB8vS3fjaNXx0UnD3XWPknAwSY7sde+JQvDGfkXdW+aFocqSUedr85zDlQYxWB9ketuh24xvR14Onts79ff//t/nf/1f/9dt8QPwla98hb/21/4a/9Q/9U89r4c1MjIysFtm24iH0qSTf2t92ugaAj+VlDQ2ZZAZlbZqrIvsVIaTJsVcXJ3llFl5z89vhvXwzUaMUmmkcr5rsLnwFzZsc5E2LriTTCXBc4TjZcfvLTtKo5gUmi9d0eRKwTmfmk8acvooYtysf4dtd+ppaVzuV7CGkITomZIoIVKshUqbYnuloe4dUsDNRYt1Hong9Z0irX1nGllqdsqMZWtpbdoCW9SWvUmWfl8kZVUNI6y+DtiQIlKsj0nfpcRDDfleBnyIvH9Uc+u04aS2GCO5tejYKQxX5xm7MaPtHZ2QzArDvFAUJgUWv75bsuwsTe85mGRMcrXd5nuZX5PPG8+tAHrnnXew1t7z351zvPnmm8/hEY2MjJwn05I3diucDxzVPcd1So3Ph8DH3KS18w8Pa+6uW+ZFxrrzxBhYtqkLELnXvRpS8XPa9Cwby61Fy06ZkRm5dT++zPlcpE1RYP2wQi4EP+o8nQsUJumB1n0KVZVCUPepwzG9FEvwtEh+Sulx+5CMEjdeTE+b8y7bRqWQkcwkgXV0ASklO6XhxlGND4HKKDqX3r8vHEwIg5miHKIVZkXK9vIh4gOE4KmVxIeQNFcyCc3zQYvkQiAQt9qkl5lV5xCADZHGBhaNZacyHK5a7qxa3t6tuDbNyI0HBJ1LYv0q00M+Xfos+pgyuK5M823xE0KkdR456I5GXkyem0/2X/pLf4l/79/79/j+97+/nZl+//vf59//9/99/vJf/svP62GNjIxcorb+bJ2eJMi9OsspteT9o5pFa1l3aQNGK1BKYn2KXsjk/U8xrfMc15Z17wik7s9uZZDiopZi2VoWrd0GoZ7viOjB2M+HFAvR9kn3IkTqDkkpKIcRjVGSde/uER8/E57ir0jRIXart1p3Z9tj1gcQcLxOm2917+hcMlvMjUQhEFLQW0/deY5WHa0N7Fdp5b7M9LZgtD7ls9U2DKGwScw+KzRv7paURrHqXIopWfccr/un9ySfI5NBrLxTKmZlGvfVNvDxSct7d1csOkdjHVWm2CmT/qyzgUVrcT5uu5GRs4DSGJPf1bJN3kAbt/KRF4/n1gH6c3/uz1HXNf/EP/FPbA0KnXNorfnWt77Ft771re33Hh0dPa+HOTLyuedyQ3/j4dINTsxaSaos+fDsVRlSQD8YAC47S5Hdu/GyyREzSmFkEgKbc4Ladee4tWjxw6rySklemxcXzOI2Yaz/39sr6s4jZAro/OLBlJ0yrRkfrXrUJi098sAO06chuWon3yIpBJP86dzxbwoOYNtNuDxZK7Wi0KkT5xF8fNoyLzRv7U1ACG4et9xqO64QUSu4Pi+ZDd0pF5LRYjtsiFkfscFvL+T5OSPJ0ihciLQ2ieOPa4sU4rk4Oj8tpnkqAHeqFHchBfzRrTVaJ63Zuk/2DZmSSASzQie7Ai3JhMRIv/28pjHkWWfyfKHd2XDBq2vkxeG5FUB/9a/+1ef1q0dGRp6AaZ5iDnqfvGY2J/1N2ngyTEyi6KuzFFvR13bbsVl1ySm5Hdylkz9Q+qOV4J2ySqvEg3amc0kb1No0JpNSMsk0VabuCYRddylnKzOKIlPsTzKuzAqWbXKM9iGw7gO7ldkmej9thBBp++wBmU+fFDcUa86nLS0pktdOiKmTVWaKunPUNrBsLRCpck1rkyXAm7slbe+pnGJaaHxM+XG7w8/0MW4L003m2KaorHJ9YetMSoGWYuuALUQqyqbh+Xj6PA3UEDD68WlKNGt9pMzV0EUMNF2AKLg2yynz9Nmre48UDJEsaQ0+AiHAaWPJtSQbRpNbsf4j8rtGnh/PrQD62Z/92ef1q0dGRp4AIe6fRK+V5N0rFaeNRUnJfpUhpaDpk6uzURI5pLw3vefuqkOrFOVgXboAH68tu6W54Ni8uXsWpMiHIkudj875ex5DGGIpNiOizqe4h03nZJJrhPBkUm2jNZ4VTzvKoDAqZaANsRSFSdEf+0PwrPWBdZcKz5N1z7r3KCE5sZ6DaUaMcGWasRqcvKUQzEuzdX4mQpCpCJKZYlYYrimZdD+XVrnF8G+P6+TmPS2SHutlv7T3PrBXGVato7eO13YKIA6fH4mScLi27E3y4RjoU2cyJJuFdedRMnlsbaI1divDbpXR9B4hYTpuhb2wPNd3xnvP3/7bf3trhPjH//gf58/+2T87ZnaNjLwkFEanyIBzlJnmYJLGWBvzuVuLllWXtpOyYY1aINgpkwh32bmhY5QS00+jJUSBjymJfl6mDs6GECLHdU9tHVqmIsAoyVu7FXLoSq2G3z8rDAeT7KXLWiqMYpYnw8NNN+38CG8ziiyMYm+awbrfdtis9xiVXLBdBCVStyPTCj1s63180rJbGaa55rWdMq3ch8jhqsOFlOG2V529boVRfOnqZLs2v+kYvcwoKZiXGVdmnhBTNttOaTite6QU9C4wzZP+56TusT55VykhKCeKj08bMi3pXGC3NMN7FJnm6r7WBSMvFs+t0vjd3/1d/uyf/bPcvHlzuwr/B3/wB1y9epW/83f+Dj/5kz/5vB7ayMjIp2SSayZDwvViSJQXJE8h71PXwfrUpdFSsGgsLiSh826ZISNkWvClKxP6YX39/GbVune4EMm05tqsIDNpVXnjwTLJk4NxCEmf8bJeqKtMU+VnIvTzF1UlUwG57hxGJMF3M0SbhJhe73lp2J/mqQAUgluLlqZP1gPzMhU/Rsutn09t/dZQ0YVIbf2FpHKj5LYD9SowKwwxwjv7E3782hwB3Fq2THNN4zy3T1Og6Woo0I0UqfAUsKhT18coSWvTmHKq5IXImZEXm+dWAP1b/9a/xVe/+lW+//3vs7e3B8Dx8TF/7s/9OX7u536O/+1/+9+e10MbGRl5iiiRLhKZlsOKeM6VacZpo2h6h1Kp06Nl6gx9dNLgQ6RzkVwnfc2s0A90r94dLu6zwjA550r8orsQPw5SCg4mSVelzkVLbCiMQgzRE3mm6F0qlqSQZCaJv6VI/9cPXbPjdce687y9P6HK9RA98nIWiJ8WJdPn6zw7LhlQ/ui4pso0SgnuLDtemxecdMnhunGe3Ejmw2aYEFDopBMaOz8vD8+tAPoH/+AfXCh+APb29viLf/Ev8lM/9VPP62GNjIw8ZaosaVl8SBlSmU7eKa/tFPgQaa3b6lRiTOvz89LgQorfyJTYRnhsmGTJ9diFmEwa8xTpsOock0zjY0qhDxEmuXqp3Xk752kG8W0Sjl+8wArORlSFUWRKMsk1q0EHNSvSc9+YRk6LDK0cy7YnM4Kr07MVpcoouqELpGUKNP28URrF3VWH9YF5oSkyvbVbqHuHj2n9/fqswOjkwVRW2QsVtTLyeDy3s8JXvvIVbt26xVe/+tUL//327dt8+ctffk6PamRk5GkjhKDK1IU77c2Gk5KCSZ5ywe4sO+IQ+yAH4bUQsDe5Ny9CSsHBNN+Kde+u+u36du/SVlTvIkWWcsmySx5CLwvOh226fYiwaN09I6hMJ/uAVCQJpoMeal/ff1SlpMCFJDL3IW5HlJvYkGmuybX83F7MOxfYrTKqTA3dyCQaP1w52t7z5v4EKQQ2RPafYbTKyLPnuZ0RfumXfomf//mf52/9rb/FjRs3uHHjBn/rb/0t/oP/4D/gV37lV1gsFts/IyMjLy4xRvrBE+hB5FpdSHi/J/yTVPDsTdIIYVOrCNJ6cXjAz06OvHFb/AAcry11n7pHi8bhQvjU3oQhRE5ry9Gq23ZWPgsuP+0Q7/9M5oXh+rzg6ix/4KbbrDDMco1RyRyxMIreped1d9kNbtCpc/Z5ZPM53migMq14bafEaEFjA52LuAAfnzQ4Hyj057NAfJV4bh2gf+lf+pcA+Ff/1X91e6exuZv7M3/mz2y/FkLg/b3rryMjI8+fEJLrrQ8RAQ/UQKhBy9IPeVmXv+f8hd0oyTRLRoYhQms9YUh6vx+bxPtNAaYkTLTadk70IFT9pDgfuLloOaktQsDuIMY+Lw5+VmRakg9bRp/2d2Za8uZeifURJQTt8DMHTe+WCGlL73N0fY8xclwnx/GN3UKuFfPCoKWgt5FcS24tWvRgrNmPl6WXnudWAP3mb/7m8/rVIyMjT4lkNjhEAAB178geMnq5LOLdMMk1iyZlXGVKopS40LVxD+kuCSHYrzIaO4yAcsWq84MPERc0Lp+Exnqc38QcpIJs+oyCVe/HbpVCaYUQn9p0UCuJVnBtp+B4ncaG09xQGLkdgZVDjMjnid6HrcVArhVSpNfdDC7ny8GVez54VqXYluf8oEc+Nc+tAPqn/+l/mrZt+b/+r/+L27dvE8JFi/p/+V/+l5/TIxsZGXlc5KU2wSe9cBZD7MLGQDHGJMLdFD6bNe0HPg4ptmv3oDBKbbU/UgrCkGb+oALirHt0798rKTBaIHqGDStB/ojH87R52vqleWG269obvc9GaP4yaqU+LVIk80cf4vB+n8WA5Ebx5StT1r1j1bnt6zOGnL78PLcC6Lvf/S7f/OY3uXv37j1/N469RkZeDpKfT0pD11J8KtdbJQUywmlt6bwnU5LSqK2x4ZNwfsTW9H4bSHm/DtT5zK3pOf+iDaVR2NwMxZFgrzTkz+nit2hTMKoZUt8/Tafm8mv6eSx8NighcD6yHByd9y45n2stqUTaPGxdYFqoez4nIy8fz+0T/+/+u/8u3/jGN/j4448JIVz4MxY/IyMvD7PCcGWaogI+7eik7lOGV4xpGyfyeHfanUtxGJcjHACW3Zmot+n9BbF2jHFb/AD3FTgLkQwHr0xydkvz3LKvWpvW4WNMI5tV/9mJsV91WucxOpk87pSG1t4bmrtoLIFUXPcuPlT0/2kIIabcPPf0g3tHLvLcStjbt2/zC7/wC1y/fv15PYSRkZEXjMtbTg/aemr6lFpeDnlZm8JFS8H+JLuwwi0QxHOKosvly/ngygeVNr0LnNQ9cfievUn2THPF7sfl1yI+4PrYu6Rn2ZhPjjwace6dDzHSWEfWywsdw/tt5KmnnIZ2fqkAkofTy+xh9aLz3F7Zr3/96/y9v/f3+LEf+7Hn9RBGXjHe/fZ//0Tf/8P/7F98Ro9k5EkIQ+QCJDfd1gZCjEgh7nvyP60t7bCp01zq+rgQsT6SnVtRnpeaReOIRGa54fBcB2cT8rloLQKxNQ28TOv8tkiKpG7MZ10AFVpRy9TBEgKqPF2cW+u3BY8QcFKfdbx2K/NKOGI/a8pMpfGWdaw7zyRTLFpL78M2gqUyilvLFkiv67N4/3t/0U6itYH75BCPPCWeWwH01//6X+cb3/gGf//v/31+8id/EmMuGkr9/M///HN6ZCPPkictUkZefU4au93AaaXgyjTbOhHfz4yvOzciT6JVti0cwb1C5lwrrs4eXARsHJQfhr70M88Hs35WpFiM9NooIZBD+vjp1rcnxWWcp3NhLIAek53KUHkFot/+t7QSn65NrUveSangfjZj0Mufs8vv58jT5bkVQP/lf/lf8nf/7t+lLEv+3t/7exdb1kKMBdDIyOeE8wnnPkRi5KF310ZKen/mYbNXZqy6YSSWqWei0akyTYhgXcBo+cB1/meNEAKjzp7f+dcO4LLl4xjM+WQokXK9Nk1FMxS6IcRUeA5CeOvD1qfuaaJVErc3vUdKwWwUWj9Tntur+5/8J/8J/+l/+p/y7W9/G/kc7qZGRkZeDIyS2wu5luKRQuqd0rDuXcr5yhRKSXaqZ38OmeYa7k3leK6kQvGsIzYf0s1ThpocV7WfECmTp9R6yF6bDCNYKcUFW4Y0bnw23ZnH6UiOPB2eWwHU9z3/2r/2r43Fz8jI55zd0mw1QI8TvikHJ+mm99TWM3vAqOxVwoeYhN4xaX/MJS+ajQZo83XJeAH9pMTh/wtwoZ+2V2VP9DkdefF5btXHz/7sz/Jf/Vf/1fP69SMjIy8IUgqmuWaa68dao3c+JK8gF5LHT/Pqr4OfNsn/p3We47q/IPwujGJWmLFr8BRIkRg97WCrcHouF+1JP6cjLz7PrQPkvecv/aW/xN/9u3+Xf+wf+8fuEUH/lb/yV57TIxsZGXmR8TFeisl49f1S3DmtT4ypI6TVeBF+2mw0aBs+D5+tzzPPrQD6nd/5Hf7xf/wfB+B3f/d3L/zd47az27blX//X/3X+4T/8h1RVxWuvvcav/uqv8u6773L79m2++c1v8t5775HnOb/6q7/KT//0Tz/15zEyMvLscT6ldGdKYqRECrH1xXlZOx+9C0TiY21p5UbRDuMXo+Tn2rX5WaIuaX2UEHQuuZK/6mPWzyMvfRjqz/3cz/HP//P/PEII/vpf/+v83M/9HL/xG7/Bt7/9bb72ta/x3e9+l+9973t8/etf57333kPrUVU/MvIycX4UoQaR6m5pqHtPrgXFS2gUtxlpASjp2C3NQ4uandKQa0mMj85FG/nkuBCZ5mnjr7EO6yMntb2vwebIy8/Ld+Y4R1EU/Av/wr+w/fprX/saf/Wv/lUAfv3Xf50f/OAHAPzUT/0U169f57d+67f403/6T9/zc7quo+u67deLxeKZPu6RkZHHp+kv+v4sW7uNybBBkBv1Ul2YYozb4ue0TmZ7zgfmpXmo6+/L2ul6WTifCVdodcH52YVI70dPpVeNV+pW4j//z/9z/syf+TMcHh4SQuDq1avbv3v33Xf54IMP7vvvfvmXf5mdnZ3tn7fffvuzesgjIyOPYCM43Qh/W3fmduNDpDuXmeR8oLX+meU0PQ3E4DXjfKD3ASHSf1t3D89A9ENGlPOjLuVZUJ/LgWudR3D2eQoxbk0J75c3N/Jy8soUQL/0S7/EH/7hH/IX/+JfBO7VET3sQ/uLv/iLnJ6ebv98+OGHz/SxjoyMPD6llqxax0ltCSFSXLoLl8Ox3rvA0brntLEcrrsXqlBYd45bi5bbi5bWevaqjEwrjBLMi7QA8jADR+sDh6uO08ZytO7HoMxnwMX8OMi1ZN27bYCu85Hby5bby45Fax/8g0ZeGl7qEdiGv/yX/zL/9X/9X/M//o//I1VVUVUVAHfu3Nl2gd5//33eeeed+/77PM/J8xfM4WxkZASAde+ZnsvoyrQgInEhhaFuAj8v5HXF1CmavgBi4bDx8CH5yixay7VZwdVZzqzQrDqHFIL5A3LIIOmgLmSROT8GnT5ldivDsnXEGJkWmqb37JRnQVyHdb+Nqmh6T6HV+B685Lz0795f+St/hb/5N/8m/8P/8D+wu7u7/e/f+MY3+M53vgPA9773PW7evDlugY2MvAIIIditMq5McybnogI2GzvH657jdU94gcdgTe+5u+qoe89uadifZA8VQV/OHrucGTXy6TFKsj/JOJjm5FptR6/WB07qnkVjh2ywkVeFl7oDdOPGDf7D//A/5Etf+hL/zD/zzwCpm/O//+//O7/yK7/Cz/zMz/DjP/7jZFnGr/3ar40bYCMjLyHTQg/mf2ks8SAxcJUpbi1SUrpR8oI26HmyMdBbdQ5BchHejFA8kUXr2J88PPK7zBQ+RnoXMEo8VCw98nTY5HAtF5Zcp4y5ZWsxE0mV6bH78wrwUh9Fb7311gO1PdevX+c3fuM3PuNHNDIy8rQxSnJtVhBCfKgDb4xc6AiFF0isOsk1VbaJrYisz222Pe7jfBGzyF5lhEj6rNb6rTniwTRnv8owY/HzSvBSF0AjIyOfHx4VPyCloDhnGCiA28sW2PjoPN8V5o3INtOCTJ0l2k+esJuz6hx155BSsFOabS7YyLNhkuk0AmsdRkomuR4LoFeE8V0cGRl5ZdgZ9DS7pSGSukIxciHT6UVgtzLsVRkHk4wye/zCzPrAunNENp5Ir34O2vNmkmuMkkxzzazUrDuHfYE2DEc+OWMHaGRk5JXCKEkQEecDIYJRAsGLJxqORIR4snvQy9Oy0ZPm6eJDxIVwT/SFkuKC5md82V8NxgJoZGTkhSeESG2TOV2VPdz52fvAP7qz4odHNUoI3tgteXu//NSPwYfIrUVL6zw7heFgmgQ5i9YO4mTJvNCPdKUOIXJU9/gQEcBO9fjjuUxLCq22Rn3nNU8jn5wQUgr8nVWH8xGjJG/vlVR56vh0LnXeprmmzMb191eF8egZ+VS8++3//nk/hJHPAcd1vw2o7Fx46NbUnVXH7VVPpiQhRCIRJT/9Bev2ouVo3QNpjT3TEi3lNqrDB48aNr4eRu/D1qk6AnXnn0iftFMZpkEjeLQuauTxOGksh6ue24sWJQQ7VcbdVcdVkTRXSgqmhSbXit3q4Rt7Iy8PYwE0MjLyQhNC3BY/kHQwMcYHdlpsiGzqAikFITzcZflx6c/pPmJMj+PyY3icja7LD/uTFDFP4/mMJGKMWB+QEojgYiTEtHF4/u2U4kUcpI58GsY+3sjIyAuNlOLCBV9L8dAx00GVMc2TT0thJG/tlU9lU2pemO0F0KjU6SmN2hY0QkD5GIGluVZMc42SglzLrd/MyPNBCIGWgkmu2a0MmZbMCs28NBRGbT97Ap5IsD7y4jMeeSMjIy88+1XGuk8bT49aG69yzR9/fU7nUlyBekpr4nuTbFhf94MOJF0Mr0xybAhoKR+7MzPJ9ajfeYHYGz5f06tTCp2K2o0z98Eko/dP9v6OvByMR+DIyMgLj5SC2RAa+jhoJR8aLfFJmRSayaXTppSCXI6dgZeZh32+hBDP3UNq5NkwjsBGRkZGRkZGPneMBdDIyMjIyMjI545xBDZygXGtfWRkZGTk88DYARoZGRkZGRn53DEWQCMjIyMjIyOfO8YCaGRkZGRkZORzx1gAjYyMjIyMjHzuGEXQIyMjryy9C4OJnaB4DJfm8zgfaN0n+7cjny9a63Ehkmv5VFzHRz4bxgJoZGTklcT6wEndcz6d63ELGR8iR+uzf+tDHJ2bR+5L3TuWbXIprzvYn2TPxIRz5OkzHtGvOONa+8jnld6FC8VP58JjF0DWX/y3vQtM8qf68EZeEXp3LiQXsD4yGke/HLzUBdDP//zP83f+zt/h/fff53d+53f4E3/iTwBw+/ZtvvnNb/Lee++R5zm/+qu/yk//9E9/Zo/rWRYdP/zP/sVn9rNHRl4ltLqY22TU4+c4aZmSvzdF0OWfNTKyQStJd64IGj8rLw8vdQH09a9/nf/4P/6P7yluvv3tb/O1r32N7373u3zve9/j61//Ou+99x5aP97T9d4DcOPGDebzOV/7pf/pqT/2T8pb/5+/8bwfwivDjRs3APjwww8B+OCDD9jd3X2Oj2jkadM5T+cimRJYozji8d/v3nlaF9ESbKY5/Wwe8shT5rM4vhvrsB4KLbCLsf3zPFksFsDZdfyhxFeAL3zhC/F3fud3tl9PJpN4+/bt7dc/9VM/FX/zN3/zgf++bdt4enq6/fM//8//cyTd/I1/xj/jn/HP+Gf8M/55yf789m//9iNrh5e6A3Q/Dg8PCSFw9erV7X979913+eCDDx74b375l3+Zv/AX/sI9//2PfvBDMCUAs8I8tU2QGCON9cQIpVFI+fRbps4Hjtb99mslBQfTJxcxOB84PicGneQaJQXLxhKBKtdMX3Jx6I0bN/jqV7/Khx9+yHw+f94PZ+QZc/79ns1m3Fl2278TwNV5AcCitXS9R0nBTpWhnsFx+knwIdJajxSCMrv3nNRaz6Kx2693KrNNM48xclJbrA8oKdh9gZ7Xs+Jhx3drPcvGUltHiDDJ0rksN4qd8mI6fNN7QowURl14zXyInNY9PkQyo5jlmqN1T4jxgT9r5NmxWCx4++23+fKXv/zI7325r1wPQIiLB3SM8QHfmfjFX/xFfuEXfmH79eYFRJdMZ2cHzHw4MT4p3gc+OK5xPnBlWiAECBEQgJOCg0l2z2P+tDgfsOpiATQ/VwBZHzhtLCFGMiWZF+ZCIeZD5KTuWVlHzBSzIh3AmZK4EJmas9d0Ms0/k5PopnCEVDg+rddsc1Kcz+djAfQ54Pz7PZvNaMXFAmg+L2itx2AxwyEvtWJeGXoXtsfM5nhxPuBCREvx2Ns/p43lpO4RAl6flxj94H9X945155ECZrnmtHVIORx/mWJeXLq4tpZoztr/5bmblKb3FMJSAMd1x61GcHWWv9KF0MOO727ZpXNZa7m9alk1gnVnUSrwx18r2akyCqM4WnWso6MwCqsEO5N8+/6f1pZSnp1bpVFMzn8tBPPZqKD/rFHq0Q2LV64AOjg4AODOnTvbLtD777/PO++888B/k+c5eX7vBzRe+t8xxk900f2/by65u0on2VXr2Jvk2zs3HyI+xO3dQvaQE+GToJVkkmvqzoHgwkly0VqOVz3LzlFmimmuWfduW+RsHufmpL5qA5n25FphtMT1F2erzgcaG7EukGl5zx3S0+KktvQ+iQ1bG9ifZE/9d4x8vhBCMC8MyzZ1TOYPuFOPpK7L6dBZkSLduPgYtx1SAexW2SOP4dZ67ixalq1DSHAu8sWrk/ueW5wP2xXrEOH4XGcHLm4gbciUpOHsGNXnjsXWelatZd07jteWzEiUFGgp2akuPnfrA70LaCW2HaRXEecDTe85qR2Hy44QIpNS8/u3lry9X5FJwfuHNYvWsVsZ3tqvsGUgl0NXjYs32EoIhIC4rVHHlfgXlVeuAAL4xje+wXe+8x3+/J//83zve9/j5s2bn2gL7LzvxzTXn6j4sc6z7t3262XnLoyipBCsOrfdIsi0hAguxG1x8kmZ3mc8FWPctnIh3RFOMkW41CTbHNRaSealITeSaa4xStL3DucDUqVi57S1rFpH3XtKI5mVhoPJ0+0KxRi3xQ+kk3MI8ZmMD18knnSjcNwSfHLKTN0zSsq1JFOS3geEgCrTrLuz4zgMn0cXzi5/EWidf2QBFGPk7qrbHvMSgfWRTN/7Wd4clzFGVp3D+oCRimmROqBGSvzwTZvjbTOq730gG45RSMVP5zw+wJ1Fj5BgpGTVOfYqD5wVQPbS6HunfHwPpZeJWaG5tXBEYK/SNJ1l3XusCxyuO0KAZdtztLYoJVj3jtIo3tqrtj9jkmt63xMjGCWpckVuJPUwppzcZ0w58mLwUhdA/86/8+/w3/63/y03b97kn/1n/1mm0yl/9Ed/xK/8yq/wMz/zM/z4j/84WZbxa7/2a4+9AXaeSa6ZDsXKJ73QaiWpMk3vAn5wCn1jp6RxnggUSl64q7u77JgVqdhadw7zCe6+Qoj4mLo3l4s2IdJ6b6Yld1bpbme3MvccpJuD2vuIVoL9KmPZOn7vowW9D8wLwxevVEgpaa3HDsWJ9ZEYGTQGT+/AF0KgpcCdO9m/6sXPyPNDCMHeJMOHiBTp67pPn+vNcSWFYFPrNH0qLmI0zB5xsySFQCkBDoQAJS92ac6TippI3XlaG5gVGi0FIcK80AjitrtcnhuHFUbdU7B0LiCEYKcydC7j45OWW6ctZSZ5Y+fieP8eDyX7+B5KLwubm6pMS6zzGKk4aSxKSureMy81SkLrAjYEMm2wPrDqHaeN3XagjZJcneaEeFaEaiWYf8LOT4wRF2L6nIznuGfKS10Afec73+E73/nOPf/9+vXr/MZv/MZT+R2f9iIrhOBLVybcWkhA8PpuidaS2XDmTGO1s3Zp5KKG6RHypXtwPnBUp7sRKQT7k3tn+zuV4cZxwyzX5EaixcVCadU5mt6zaiy3Vz25FljrOVr3LFpLjBBC4LgxvDYvEYCWEus9Ronh66d/4O5W2babthErjow8S9Q5nU/nPJ0NNDHy+k657fQ0vefE9RglEUNHd3ZZl3PpZ769Vw2jrchOmd33PNM5z2ltAUEgsluarVZo0x2+vWiBdB65ddpSd57SKOZlOj5ciHTW07mA82nULoeRV2YUuYFpoXDDKP78Bfw8m699SJ0oIlS5eqljH9a9p+k9rU1dsWVneXO3YpZrpIKPjltaF8iVYF4olASiQAIfnTQ4H7g26EKFEDwN+58YI8eDSF2QRrKvWuH5IjFeRT4Dqlzzxauz+/6dEILdMmPZWoQQvLFTUPepO6SlIH9CTVA9bJdBatO31t9j4Z/rtJXgz/33zclv3TkWjcWHyPuHNbX1THNN3QeIkWXrCDFilEQKaGwap+VGMM0zikxTmPSYj9b9ECGgqJ5CwaKkuFfwOTLyGZDE94JpMXyOz13sciPZrc70aP7yPPkSWqXvT8eQeKDuqO3PujBVpnEhdVmFgMoo1p3jaJ2E1FIIOheYxkjrPKpLnYuNbinXEufTMX51lrNTZSDE9rFGYFODxZi6DzuFpvMRo8T2+D2p+20XtvOeq9P8qS9wfFZ4n56HQNC6QLHRQ2lBDIJZYbAuECK8vjNBiECIgllpOFx13Fo0/DE34/Wd8qlFX/Q+bLvpEah7PxZAz5CxAHoByLS8oAuqMo2PESVS21oKcUFXYH2g7jxCwjTTF+4eY4i4ENAyfb98wMmpNCrdyZEKC6MEx0OHZzWM3nyM2xaUIFJkilnQNJ1jb2KQw5hOCIFRSfic7pJTMODmbnLZOjIlx3yckZeWy8fR+YZNrhUr4bY3Ho9zwdqMqGKMdC7gg7hHOyTOfSkEFDptns1zTYipUzvJNcvW0njP3tBJisONihCpGFs2lvfXPbNCs1el88xOaYhE1q1HSraFzCYDLQxF0OUO8vniLsakUXpZjY+LTNI6T64FRgsmmRnG+Z47ywYlUuFndMa0kEihOVr1NH0qKmeF5rRxSNHy5jlN0KdB8ODP2cjTZyyAXkCkFEhSQbIR/k4GQXMIkeNhxAUMGp5099m51M5ddw6B4Nq8uK9PyObnaSWIMQk+rU/z8FxLWpu0NjulwWaKGOHKNOfaLE8ruENBdl6TA3Ba90yHDs2isexWZnt3+Iib4peaUaj86lNlCjccI5mSlOeKHCUFB5N8663zuGOh8+MOSEsL57u1s1wTA7gQqPuA1EnwfNJa5sP3bW6eNvWZ85HF0PFpbGBaKOreg0jHoPVJi7hbpYWGg8nFlf66d9sFiRAj9aXt0Nwo2sGKwij5UmtUcq04mKRzWGE0h+sOiHx0UtPY5CBeW89+laOlQivJtZ0C5zx7ldm+Lu4pntwyLYeOe/KfetgodeTTMxZALyjOhwtbT3XvmOY6bZ2cO96sP/ti3XkQgp0yFUSbUdSDOC+uFmLo9AjBTpkKlz92dcaqdyiRWv8xQiSdMHIjmWTppBGTR0AySGwtbe/xpK2TMtNkSj619f6RkefBRjz8IJQUDxX9Ox+oh8Jh07V1IW6LH0jjjvMF0Pnf6Rft2bZZZFtobbUihSHXkpPGbkXbIaatsdd3Cjqbzg1mWMqA+1tu3NvpOvt6PWh/ci3JtXrk+eVlQCuJVqnD9vFpIBCQAgqj2S0lUsK71yb0NqIkzIqMSaa5vWzp3GAm+YARpvOBde8R4t5O/cOYXCqER54d46v8jIkxsuwc/eCR8ygNy6YlTmQbxmh9GoOFkObxSp7N7vNzJ6HLh9fldurDMEpu/YC0lOyUJmkV9Jm2QQjYO+e9s+4cEkEUScjZnLYcrTua3rNbGTKtmGSaTKdVWy3Fp55nh5BeHyl5ab1JnmVY7sizI4Q0WrIhbUQ9yKJi0yHJdRJFbzo9dZ/GUs5H9icZdhBWbz7HD+um5FrRuvRzN4aL0xzWHVvvLUieMxtvoGmRLqQxRtado/eB3TK7b1d42dqUYi7T4+59IFeKavjeunfbkTkkEfbLqv25jPWBHx3V3Fl2hBjTTZ73BCWotKFQkkoL1NDZy3RabGmsxyhJfp9z2uY933TTNu/5yIvFWAA9Y+ph0wDStoiW4qGC4PNmf1oKOhuGO0PFUd2zX2XsVxmtSx4T5wuKNJO2+BiphsLjSTh/55EKMY8S93e37Zy/cEJc9clUcWfYWtByKNRi4KR2F+5eHzSWexQhRA7PWcxP8zjeKY18Zqx6ty1C1g8o6E9ru/2eTEn2JlkyMKw7epc+t70LKCG237e5YXjYuGOnMmS9JBIptBqc2tMFduMPtFtlVJlKBobDqG4yFCqbrcn7dSHqPnl4AVifuknnRd0A1l0c8zgfeVUOvaNVxw8Pa4DBmynw5l7FaZ2c9E8ah5bwxm5JYwOL1g1jzwc7+IfI9jwFqRs08uLxinyEX1zOHwTw8A2Ry2MvH5LwWJ1bQe19uvu8XxGllXxk3lfv0u94UDdmc6CeNhYXkgYg14pZoS+coC8/j94FOpd+rhXJu0hw1sU6/32ftACyIVx4Pe+34TYy8qxo+6SvU8Oxcz+Lik1RA2mjJwzHSTh3/Qshbr9v0/3ZjJ3Ps4l+CUNm4Pnjphs2xBZN6uzUvSPTabx1uXhZDx1orS5qSjbH+uVjOW2bXTKGNHL7mDc+Yq8CTe+5s+xYDkVNlSl2y5J39it+3/q0wGHS67oaNmBXnSOENBLbLe9fBCV37TON5Kvyer1qjFePZ0xp1Db4VAguiCcvIweTws3pSA1dFHsueeLTiA6tD5zU/T3dmBhj2ippLa1LbfkQoHee4zoJKpUsk6vpUHCc33yph85WptMd6mtlEl+bQWR9OKzDbwqpT4q69PpsNt02fNKokpGRR7GJhdgcy1Lc36Li/Hg6Zf5tIjIMjfUIBJNcAWJbzAtx5v11/jO8aN12nNb0nivTs4utUQJ/7oZJS8GdZcfeJFKaM3+eujsbXfWebde47h13Vx0+RGaF2R5XgvtvsRVGbcd3mZYvtf/PeerekRvFlVnGsrEIImWuaZxj1fb0LtL2ilwpooj84M76LGRaCCT3Rohs2Kuy9J4/4rw/8vwYC6BnjFaSK5McGwJGyocK4aRMosdN8OE011sRog8XT2yPw2q48zPDnZ/14Z5uTKYlR+t+G466U6b19rvrFh9SlyXE5CdSXNp8uTLJt1EBm7vh0mimRRJrH9eWdWtxMSCFHDRMn/zEuYnlaK1HSsE0OxvXbbZptBTsVfc3lhsZ+aRYHzBaslcld+hpfn9R616VsWodkTSe3RQs+5OcVecQpFF1JOXtAVtvoUVrafo02t6tDN25blIY3IGtT47QWqUk99qmm4/Nsdr0nrb37FYZi9ayaNJxsUkjv7PqKLTkw+MaI5M1Rec6vnRlggCMfnCOX67VKzP22qCkYJprIjnrNnXR6tZy46jHKMUsV2RGcn2nIIbA7WXL0apnWmgWTc+VWc75CJHzSCnGDvULzvjufAZIKbbBeY8inWQufu/OA7YMHkYztOshzfU3gYfn0eps7XXjGbIZK1WZxg+eQpv/fXnrQ0pBIZPmoO791kBNktrurfXcWnZYH3hnf0IxrBJv6qiHRXY8iPtZ/K/7sygOF+LWvHFk5GmxOXY2Xdn8ARtQSt5/WyzTkn19cTS1N8m2HZ/eha1WMMTIavDO2uSFCQHOBZbnjulJrnlrr2LdOU5qu/3MR2DR9viQRleNTfYYUojthpjzEec9lRD4ELi97Mi1ZJLHz9Xq9ea59j5wdV4kG4G2R0mxHTvOCsNuZfjwaJ2WU2Tqak9yzTv70+f8DEY+DeNV4hXifCiiCxdFdy5EqkyyWxlW7VlbfGN41vap09PZZL+eBH+evWFb5WCaPXDrapprWutZtJ7CyO3cOxkqplXdVecojMQMeibrw9bP6HLXZlMYfdJuUXzS/JCRkUeQaTl0ZcIjFxkeh42flxu6olV2aZwL7JaG9RBcXBl1QR8Iycl4muvk8uxSBI4cjiUtJX4wRN2rshTuquVW7Lw/ybhxXKdCKHoqLVkJwd1Vzzv7JfPy87GxpGTqpCkpaIe4HyUEmVbslgYb0g3aaW25u+yprWfZOJSETAl658fR+0vMWAC9Iqw6t+34THJNYRTNEKkhgGIoXnKtWIuzcZYgtc5r6ym0Zl5qJpmmzFKisfVx6+OzaC29SyOvpMdJegY1hDNuWvmnjSU3EucDpVHslBo1nIh9jBwvOxaNRaukR3DDanuZqW3+USSt5u9V94pDL1MZdcF9eswJG3kW3K87+0lZ9257o5DMCSXFsOouhvH35uZADaMqIcQFx+ncJGPE28uWjxcNh8uOCCw7x9u7FYumx0fYnRj2h42lEKGznp0yI1OSSOrWHjZu22letsn8sLVJD6gHi4xXhTAsk2zkBadNGtU3wzhRGMkbuymrbZ4pVq3lzrLjtLFIUqG6U+ZcmeV8vGiYFpppMWZ2vYy8Op/qzzEbn48N684xyXIOpvlWF/OgGAohBLOhYIK0GXK46tJBnWvyXOF84HjdsR7uHk8bS2UUVa7pfeBgYlh3Dh9ScnzdOSa5IdOSSa7I9fCzjOL2sh0MFSOr1m+9UjY1zrrzW51S8kl5dAq1lIKDaU4IcdT+jLy07FSGWUjaosuWDxt7icuO05sV+LpLgZ6RSNd7Pj5t2K0MpRLbzCuASZaO51XTY30ad2klWLSOxjpKM0TaWM9i0Ch1LpktTnK91SW9rB5cMUaO6n7bLd9oG9fWp27ZLPmfHUxSR/rusuX9wxoXAzHCNJcgSwotaPpAlWt8TM735x21R14OxgLoFUBc2o7aHIIPcqed5pqTJo2fCqMojeKkTifb08ZSaMmdVcq7uT4vOG0sR+ueu6uOeaFZd45sntbtN2uhUkDtAosm3aFuTp7eBd65koIfY4w4H5EinUxDSJEdZXam67l8/nhQltn9GE8+Iy8Lk0wPGWBpxLLZEtp8hjt30fKhGUIxLx/TgrNIijicAXyMrDsLsL3xKY1if5pzXNvt9tdJ03N72XF1krFXmeH3pc1Nf2mK7EK84HFUmPCJtInPm36IAtmweV17FzhtelqblixunjR4Ij+8UyMltDagJEij2C/11oH7YLAciAxygicwnx15/owF0CvCvDQsh6JjVuiHjo0yLbk2Ky7MrvcmGd0gllx16STX2YAULZlWhBCJMWJ96rKo4d9lSuIClFnSIqw7eP+wGbZLAsvWsT/LWXWptd9ZT+sCu5Xh9d3iHsHlrDCExmK933aRRkZeNaQUXHlI13KzieVCGtWoBySObowP3z4omRaaVWsH7ZxgUVt2JhlVrrAhYp3HhVQArDvL3UVHIOKcZ16mIqgaNk83x/KmVDBSbAXYkDonL2MBdNlKY6c0SXwek9njUd8hRMQHjQLyTKBJ/moxRuaF4eZpS5lpQkx+TuWgwxrDnl8+xgLoFeF+21GPYpP+3A+ZNtMiFVHL1m2DUTdoJdgtMyaFotSKWWmSDfwgrGx6x0lj0/2PAIgsmh49JMZLIVESqlyTm8gk0/fdNlFSDOGsgfopxWeMjLyoPKhrmelkG7FqXdq21JLOeXyIwzp9yuxL4uyM3SrjrV04XHW0NgVp3l12FMMWkwCUlGRK4n3q1FrvcR5OsLQ2MB/0dkali/n+JBtMU9Pywqo/0x89qDMbQhyE3Y+/2flZsrHSaPpkpTHLNSeNTa83kduLlroNFMHx2jyntIZprpIQPdN8vEhaoH2Zp7gflbSNUqRuklEPlhuMvHiMBdDnGB/iWZgpMC80pZGD2226izSDUHl/kmNdGFxRLzpRl0bxQWs5WnfMCs3VWc6t0xatJEoIjmubwhpN+jdKCvQD7mjDcIKHzTqvHQugkc8dvQvJF6w0HNeWm4uOnTLQWU+VayKRRWu5csn5PR+WCkJMgcVGSaRInVUXYkqCnxha63A+eQ2JweC0Min7a7NEsOocx3WPFILXd0p2y2yrNZzex9D0/GanFIL9SfZCpsVfvlksTLrhUkIQQ0TIiA8BEeErr01Zdo5FY/notEWKZELZeYeSOa0L3F40w+utEaRu+qtiFPmqMxZAn2N6Fy7Y+d9edlubfx8js0Ihh2DUB+FD5L07Kz44rHE+IEjJ8UpJ9qrUIYrAa/OcgMD5ZL640TzEmE7KavACGhfYR14l4qCRSz5aj9+l3dQN1qfjw6ik8WldoBpqnssxO5D0fVoKbi87qkyntHOZwlKXrR3ClSN5pjGdY5LnlLni2jQtTWweX+c8txYtdhAD+VDzpatTMv3g9fjkBXb22F70qJqm93QuBZrOC81xJnltt6RzgVXrUFqQG0XvI1qlSBKtJAezHEm6QfMhcuOkAQRv7KYIoM6FsQB6SXhxP50jz5zLd2c+pJV3oyQatifPh7EcVuNzndbe696hZWSnMKx7h/Xw+ixjd3JvRtm6c/zouAYBO0XGlVme1tjzJLQWpGDGkZGXlWXntgaHnTvb3noUm9XzRbTowa1YS8k0Pxd+nN//2DBKkp3rsLbOc1L3rDpH0znurnp6H5gVhkmuuToryM8lykNydrfnlNB2EA8/rKPzaRYYPmuSb5nFhcCidrgQyLRg2aYx3/40w6gzK5FCq23Xa6/KeGsv2Qw0Nm3ZtjZgXQqI1S9g12vk/owF0OeYTKfuzkYzYKTAhsi8NPTOY0Qqao7WPbNCb0/cIaR9EyUFcgj9mxaGdevorOcwBK7PFJNcIZFcmxb3/G4fIrcWLf1wkj1peqo86RWmuaYyknXvt1lIm5Pz5biA8U5r5HnQOb+NrJkV5oGFgb+0TpW6OcMFuEmbWrPCXAg6dT6w7jwIuDYr2J9k1F3SrFyd5ltvrwfph3oXOKlTmLGRgsZ6ykwxHWwrkru7osw0pVFcneZIKYgx+eNYF3AhbWuGmH7X4/gATXOdMgR9IDfyE4cefxZsPJiWjeO47rhxXGM97FWaaW6YZCnSx0gx6BEt16Y5SsH1aUGRKepegg0URpPpSDW8xo/T5bM+DJEp6XUblz2eD2MB9DnnfFs+xsi691RZpDQFR+sUnNo5z+LYslcl6/7TxiJkuhOaZJr9aYYgcmoUuRGctp6j9f+fvT+PtTTP7/rw13d7trPdrbbepqfHMDPYjAcBZhwDSgRxIhMcY0ycOBFGRKAEBZuxIWak2GBkNBBkjDByggjYQlEigZM4IZuTkITYhshRGJb8ZjDj2Xqrrqq7ne3Zvtvvj+9zTt2qruquqq7u6uW8pVbXPffcc56zPN/n8/183otlUmjGuaRxHtmJOxZRH+IQrhiwIWCdhJgI2VoK1r3futZ27rZp2cW4gGXrOBh9MBxrd3j3IAyS8E1p4+uew/HrO5yQzAo3Ds5CsC3YF83tv088t+SHtfGp2YySnA8cjvOH8t05WXeMck3rPGfrnpFJY5mm76jygb8nGIo3jRCpk3uy6rEuqTQnhR7OrYiWyTvoeJXiMu5Om99AiHvHgLwbkWtJ3aV15Ma85bVFO3S/GQwiUwEopcAIRZlrbsw7ZqXh5XmDkpJZoZmVBqOTQORhuIrntd2OMM+bnkvj/F1JGn+/Y1cA7bCFELeLFB/idoFeNslsLdeSLx+vmRRJKut95PJUYKRkUhguTSPHyw7vAj6k+IzJ0LKve3dHAWSUGBRkKceo0IoXTwV7owwj5dYYMYWxpiiA/B0iQz//J/+Hd+R5dnhvIsR4B1fNv0H0SpXpbUJ8/gZBo7cfmzt4eRc9a+64X0gbgEWbHNWnhaHKFOd1CkANEcpMUmVJ2PDqvMX5wLKVZDp1badlcohedY55Y7k+b1g0FqMlMSZri0JrfIyIeNufqHP+vgVZCBE7RHC8GwnQGxgl2R9lrHtL0zvOVqkbN28sbj+S6UQmvzQUtk3nWbU9IQRaF3h6r0RKiZaCy9PXd7jfDBf5W3H4zHf1zzuPXQG0wz2xkaN3Lmzn4+dNz7qzSDEEOFpHkSmkEERBWkiajnnrOBqlVOynZmV6vLvObiFSZMXRJKfKNC4EauuJ655cq61KbD34B61NUr/cHRewww7vNLRKcvJNZ+fNcsHuVSyMC7317RpfSI3fcIQ24b73KzQWreWs7tP5QbqA+iHiYZQrbi47Oiu4PM1hECbEGId0eMkoN9tR27KzWBfoB2PGvktmqFcoyLTcjqE3EPcx+/Mhcrru8SHQ2cD+KHtTT7InCaMkhdZcnpWs+8Cqt8zXlhjhlfOGZZv4VwJ4+bTm5qqlNArnYZLr1ynwHgZlprbd7FzvHKSfFJ7YFeRXfuVX+Mmf/Em+8IUvIITgYx/7GH/0j/5RPvrRjz6pQ3rfY0P8g0QufrOW7V6Vpewv4Gun660Z4uk6ydpNLobOkNruYCZFxijX7FUZ09yg1KZD9PqvmtGSXCt6l8JPvY+DCaNACIkUgnGuyLQCIqvOUuV627Z/ty6sO7y/sBn79i5seXObYFQpxCPxN6qBfwOv/x7vV3eamt4LG0k7DM7PvcXo9Di9i5RGpy6rUVRGIUU6/89rixi8v858oMw0zidDPyWTC/S0MIn0m6U0dKME540lhEiZqfu+3k23dt5YrI/0IRBj8a4ei+VapVT3wxHn645CC07WHUYKBJFMS06WXXKr15LjVc/Ts4LaOpad47mD6pGed1qYrc/aezVW5P2AJ1IA/ezP/iz/1r/1b/GbftNv4pu/+ZsB+L//7/+bb/iGb+C/+C/+C37f7/t9T+Kw3tfY7P62vIPmwfx1lBQsO8t5bSEyLIiJOFlmyWEWhkydTKM7j5IaiWBUaA7KDH2fBbPKNIejSFpqNM2gGst06g5tEuFjjJzXFh9jMmy7izS6ww5vJ+re07nUkelcYN37Bya7vhHuV8C3NtAMXRfrwzbI9CJKo2i0orE+GY2i6U3KzQsxyebHucaHtLkY52bbXZIImt5teUuZlhiVoYRgUiRhwbRMVhaQOl4P0u2Qg7HqRj2mhKB1nhnv3gJoUij2R4ZV55JtgMz52lnDso0cjDOaPpBpxcFIct5aZgFmVcasNG9Z7bUrfJ48nkgB9B/+h/8hn/nMZ/gzf+bP3HH7n/pTf4of+qEf2hVAbxPiXf++GIVxP7TW09mQxlQpbZFpqZkM3kAHo/QVqjuHFIJpqVi3Hq1SR+i07plV5nUnu/eB+SChPxpnVHnK17m5aFP2WN1zdZpTGJ34Q4XeSvIXbU+ui13beId3BHezcOIbcH4eB+o+dX/WnUvnn/NcnhR3OAyP8qTKrIxklTvMwC8SQnB1VtL0Sdo9r5OdxMZj60OHo22gqRCCpvdDynkyBLwyzbfZgg9ygb/I+SkzhfWKZSuQQiAFdNa9qXz+SaLzkf0qpzSKZev5ysmKw1FAjpNz9rLtiFGy7HqmuWZWGGaVoR3GhfPGbl2iH2Q9vYgQUudtk4e4wzuPJ1IAvfbaa/z+3//7X3f7v/Pv/Dv8hb/wF57AEb3/sXF73Tq55g82m1cyLWaTQm+NzZ4/qGhsSPLQ3uN8sr/PtaDpBT6kXey6deRjxbq7kzQ5bywvn9ac1j2TXHO6trxwVFFk6fjWvcNISWsDs1KS64yTdT90sRyI1DW6V2G1ww6PG6VJfI0QI1KIN+X8vFUoKWjtbRXkJlz4bsVjpiXTKsNdqMeUEEiSsilFUsjteHozrs603Ob63Vq1w5ogCHXkSog8fVBRGHXPSIeLxo5aSmqbfHM2DsjTMqPKNDcXHa8tG5SQrPslLxyN3jERw8NARDhb9yxay61FS2sdnU3dPhctvcsxKpHAR4Xm2f2SQBpBjgcfpkVjubno6L1nr8weiBR9dyp978K7elT4fsUTKYD+xX/xX+QXfuEX+Lqv+7o7bv/FX/xFfttv+21P4pA+EBjnt3kHD7ojK4zi2qzgZN0zKw1XJjlKSVZDls7NZYcUaUFYNJb55qQWmpN1T5VrsgsLaef8sLg7QojUvUerlCcmpEjBqkNkxrpLJmRGJd7F6boDkRaeOPz+YgEUY9yOKnbxGTs8LigpOBpndziWv52YFgbrwzYlXg9j4HshuRgbGuu3xc+LZw1SCjIpWHWOidXb87DuXeIyqdSx0UKwaByFTlwfH9Nj3s9f66I9xWnXo1VyP45AY/02R0wIyFQ6B32InDU9V035trxfbwliMH10iQ9llGC+Tio6ESXndeIdGq0YD3E+B6O0IdvgbN2zsXu6uewojdp2yO8Hf4HDBdB5D+/iUeH7FU+kAPr2b/92fuiHfoj/9//9f/nUpz4FJA7Q3/7bf5sf/dEf5b/77/67O+67w+PDo7SiZ1VGmSVfkQBJqzvAh4iQglGmOFunBRFB6uKoxDu4m8jZuzAYgdmtiZwQqfWfG0HbJ5lxrm8vxIVRHI0LlLy98Nz9Us5ru1XmdC68J9Oqd3h3Qoj759c9bkgpuDQpMCqpMAXcESmx7hw+Roqhm1MOZOV5Y5m3jnXncD4wLbO06Rl+X7eJ51JlbE0Y96qM6/OWCIwKTWbemNDtfJLBh2ENCBfWgo1XV0qbd8zrfuszpoZQ5BDjtqh7t6Awkkwreh9YdY4815Q6eR+drS0gGOfJ00kPBd6s1MxrlzzMvMfH2w7fNoQ3fc7Nmrepa82bOO7v8PbgiRRAf+SP/BEAfuqnfoqf+qmfuufvYEgr93dKMHd45+F84Lzut1yIKlNoKXAhouQ2/J3Lk4LOeb56soYYmeSGo3F+x2KX5MP+QkK1IFOCW6sudXaCYFomfsPhOCcMwY56kB13Li04s8ExeoONBHiDznrYFUA7vIexV2U4n5RmG77bsrXbDkzbew7H+XZT01qf/H0KzVltiTFyNMm5MoxkLpqIIiLOB3Kj+Ni1CXXnMVpyaVzcs3saQmTZOc5WPadNP3R9IlfGRSJXa8lo4LEsGsu40Kz6NDY/yhOvaDUUQDcWLbPCUOX6iWeFVVlSmTbWkSnJpXFO3jg67/Eh8sx+UrE5DyFA2ztu+cC6d7iQRBkhpqJ0nCdjxPFgMXDxc7sbQggOqox1P1h6vM1j1R3ujSfyrocHqJB3ePfAhTuN35yPHIwyjlcd1kdCDGglOBgZTtaR6WDtLqSg7t3rMoY2VvNH44zT2g7FS2QRLPujjFGW2s2tdVxfprT6TCWvjFyngNViCHrcQAru2FG9W0mXO+zwMEhO7AGjBFrJO/K5UrBpQMnbY20fUuFU5Tr5ywiZxmDDhmHR2sF4NDlPKwm5UhTjROItsnt3Ipato3WePqTUdEHEB2icZy/L7sjs2xhDXpuVxBjZH2WJu8eG2B0oTCB0bjtCe1IQQ6TOM3slgUihNSerlhuLnt6n/9swkJxD5LS2ZErQ+5giRoxCK8XzhylIdlomRVnv/MCLyu9rG5A6SbvOz5PEruzc4U2xMQPbLL3Z4BS7aOyg9hB0LlJkmgOgswE5tHjdXVlIQkDwgVurjohILfFSs+48VkT2yTDDgvHqebvd7YYQ2RvdJj3f7ZArhGC/yoYQVbENLtxhh/cqOue3kRsbknGmb5skXozWgBRNk/KlEvnZDd3Tuk/cnExLSqOI5e0NQpJ5y+HxUqinQBBiyrjawA2bVi0lWkV67zFKbQnboyyNtXqXApEXjSM3ksNRTjYYR7bOE0IalW0UZm+zoO5NEUIidXc+dcS0DByMchZNsgk4XvZ01jMpNOs+ZaqFmF7Dxtw1eUEpZlXiiS1by6pzgzklPLX/LuQ+7QA8wQLo7/7dv8tP/MRP3GGE+Mf+2B/jd/7O3/mkDmmH+0APtvHdkNNVGIUP8Y72biSipWBWZtR9WrgRafe6kYfGGHnlvOELry1oWsczByNGuaLtPTYEjJSEmCIDQkiEZudTgnbktpu0gC2Z+yKMun9O0Q47vNfQubDddGw6QeNco4QYOEB3xk0oeTuL65WzmnXvUncnU9vz0fpAZz3joWNzsTuRTE47Vq1LsTiFYq/MkumiDyAEo1yhlMD7xIXZFGAbYviqc+RasT8ShACjTCFEGmvrPhU+vQtD90ncIZB4Euh9Wp8k6b15bd6yV2UEIrmSHFQZNniOlz2TMoAwXBrnKUqjMkPxIxjlyT8pRL8tfgBWvSWEx2PZsSGwGyWf+Ojw/YIn8i7+lb/yV/j0pz/Nd33Xd/H93//9QCJBf9u3fRt/8S/+Rf6D/+A/eBKHtcMb4G5liJKCg1FOjF0aiY3N7cJIwLqzCCkotGTZWqZlcpV+8WTNeiBqfvnWkk99+IAGuDbLtouxDxEXAt6ncFYBXJvl7A8LU2n0bsS1wxND5xI/JBsIsW8XtEwd0kQevl30v5lnzLpzw3gs0juH1gKJIAwO0C6kzYqWktJIzpqe3gVihOvzFudSmvvZuod9KHMNIinSMp26OS5EFm3ywJkMJot3HrsECZvUjI0NxyhPfmI+pvfvnXRzjzFueVCb8FmA41XHsk3E8XmTSM9aSUSEUSG5tfTslZpscNXer3KqXN1TKZdrlWwTBj7WRir/VtFav3UH71xAiDePYNnhzfFE3sHPfvaz/MRP/MQdhc73fd/38S3f8i382T/7Z3cF0HsEszIFMArYSnVP1z2n656bq45IpO48mRJMy2yw40+LcCIsC/oQ2RulXebpusP7yDRXtC6yPzLkJj2ulpLl4GFETDs3JVKg4y4SY4d3Ck1/O05GCDgc5W9bMV5lmrN1TxiKhdYmB+o36yY4n0ZgB6OMEBInb9k5zhu79QSqssQROl53QOqw1r2nt5517zld+8HbS/JcljY2nUgdo43E/l7u0ONcc1p31J2nMAojZfpb55FCbP2FnsSF57y2FCJ9dq0dwlA7h1GSVZdCZMWgVrsyK4C0JkkRmbeW0kdGuaax7p5FqPWpiHxqr9y67r/R52V9UsNuNpebdXHTZb8Id9fI/+6fd3g0PJECaLFY8K/+q//q627/1m/9Vn7oh37oCRzRDo+Ki7ugDecgRKg7jwsBESWLxtI5z7rzaBlpbeIPXJ6krtC8SSZkkSR3f+msZb/K0EoyLRPnYZOeHGPkizdWW57QrHU8s1/uiqABD5tk/9U/97vepiN5f+JiMGiMydLh7XTxzfRtyXgkEYzlfcJIN8hN4ttIIVBKpLyvGJk3DiGSpL61js4JlkMkTmMdX7q1ptSS42WHkvDMfknrAqfrHikEs9Iwb5Jo4X4jGC3T6KvuHee15daypcpS5yeRtO//t283rA9sLAo3o690PIoYUw6aksnuoLPp+G8uu4G7FDkaCQ5HSdHW+3DH+HDZWhatZd16ciO5Ok1BsvdblzqXctmArc3BpnMH6bt18XuVa0nd3eZh5vchVu/wcHhiPkD/zX/z3/An/sSfuOP2//a//W/53b/7dz+JQ9rhMUCJRHyWIo3BKqOYlIrGBn71xooyU8yqnGdiZFoYFkPkRWk0561jv9JbP47cpJ3jaZ3a8yFG8rFK0lPntwXQ1pX2HfJo2eGDDa0E/QVnjrd7FJsbuR3bpLHVmz9fYRRSCFwI2zHdKDMcjG53DVqbxiidC9TW0/aeKldoKTkY5exXmr1Rjg+R0iTRw0b11LvA6K7mj/Ope+F8ZNn0vHbecnPZkRnBhw4qrA9MCkMj3BMrgOSFYmRjaJkbxaK1lEYxygqUkuTbgjNZa0iROl5Swum6Y1IYSiM5XnUIYFoa6t6zaNzWjqPQlsvT++enrVq37f4kU1d3h9L27sLaDDzMix2jHd46nsg38eMf/zh/9s/+Wf7P//P/vCMM9Zd+6Zf4wR/8Qf7yX/7L2/t+3/d935M4xB0eAVIKSq0oM83VvQLvEgmyNHqrRokRLo0LxrnifG3pbcA6S9t7TmLAhzRaK4zCeo8dMnc65zlb9+xVZrsIayUoZEqND3eRsnfY4e3AJkLG+0hu5Oskzk2fsru0knf4VD0qpoUhG+Is8jfoKNyNTEsy5B0/71WG1iZJfd0nHtPeRjkpUuEkhGCcKaQUGJVMTIVIMvgYU8H07F3+Ws6nLpGPSUl1a9lxvG5pbMBHycnK0rnkmVNlioNRhnwA478w2G88riJzf5Qhh9HS5rOZlYamd1ybFVjvaWxgXGhciJTWMMosJ6seKSKqE1uz1/Pmtnz/vE5q2E2XOilmUydcCYaA2LANo120llWb0uQLI5kUZujC3a6sjX79a94VPo8fT6QA+ut//a+zv7/P5z//eT7/+c9vb9/b2+Ov//W/vv1ZCLErgN5jCBFmZcY4N9S92+5ej9cdCsHhOGNcGIwSPLVfcbxsaZ2n1CkG43zdMy0MnfPcXLR8+XiFEpLDkdkSKsPANdqvDId7FScDT0LLJIXfFUI7vF0QQty3sOncbX7QJpLlcRRBDxvrsmgtnQ1bU8RNEWGkYB3CYOoXaXpHbf2g1tJ0LiAFXJ5U7I+SmjNXgnnrkKWgs+kiXt11PJ1LmYBJ/RQ5rzsa6/E+Uo3M9vit91gvuLHsuDZ7Y2l4a/2WR1No9VhyspRMnMG7UWWKL91acb62TErN5WnOvHEcjAzzRnGygtYF6Bxm1W3HfEalMf8oNzyzX27Xuyof3K+Hkd/psD4BTAtN03uKTBFiCpId54pRnviOnQtIxD1Vrjs8fjyRAugrX/nKk3jaHd5m1L1j2Vma3jMudJLsSsFXb61Z96nFOy40e1KQKUXEMio0oo3UIe0eK6M5ry2ff3XOl26ueelszSgzLKcZX//0lFfOHIvWkmnJpDQsOrtdYF1I5mQ7iegOTwJ3e155/84TVdvecV73KCGwQnDe9FSZRpB8uxqbMvyMlJysO0IMWBcJo5yDkWGUa/aqjNN1jwuR3iUPMDPke607x1dP1uRD5yIGmDc9L5/VTAozJNlLDkcFTe+QwNVZzlAP0vSpkzvJDUYL2j4gJEzuCmdeXRgJtc5T+Xurrt4qfIi8et7Su4APgdNlt5W2ayGp28QF6qJHN47SKKSEL91YULvAODe8cDSi7gyz0mCkoBt8mvqhe70pfuD26DFGqHI9cLJSUZbrFLrbOE9tHXtldl8TxR0eD97XV4rnn3+eoigoikR9+8xnPsN3f/d3P+Gjen+id4FlmxQVXiVy4eEoSyGNg6ph2VpciCxbx5nvmZSGurccr3vWracLnqWyHFQZSopkT68lTec4XcHLpy2dD7TWczDK05z8rp3Sjgu9w5NCriXri0TVN8nVetyIMXJ93nKy7tAyjec2RcPNZctr8wYpJGUmCSFSGM26D0kdNhzveAhW3aiMQozbxzlZdYiYxmdN7+kHw9NcS6pBKWZUisUwSqTiZ69kVmXcnLc0vScKiCjmTRqZSZG6JN4nx+gN7j6NH+dpfVF9FWMKIpVCUGSKG4sWserS2tR2qQuWSegia5vep3XvOGksk0zTWM8rZ00aK9rAunNMS7MlyF90yAZQSjAusq2k/WKHsHN+2zmMMRWBB3rna/Z24okUQD/wAz9wz9uFEBRFwdd93dfxr//r/zoHBwdv+bl+9md/lm/4hm94y4+zwxvj4i4n04PrbKbRQpIbybp3WB+5tWiRpLZ5piTLxnM+pMjbALNcs9KWvcJQW5tkqCRTtkXbp52kELTOM8oMVyYFq95j/ZBwvWsd7/CEoKSgyhJRv8r0fXfvrfVJRaTkQ4+33giLxrIcoiZC9IwyzWSakuV7FyEKFp2l7gRlrtirJMsWBKnjsWwdB1UanbXWD9wgweVpTmUUN3xKdfc+MCoM1nu6LlAYNQQep7+bVYa68yglOBqk5iGCHlysMy2wPknMU+ix4nwQOxgtmRaa6aA4CzEyuiv25q3gbvXVrDQcDVEduVZMckWmJKvGsmz75OcjIjc7y7TQVJnidGWRwPGqByJd7zmYZNSdZ970NJ3lzEiqYTRWGUOIEaXEttN1MHrzwuZRi77O+YfmjH1Q8UQKoM997nP8w3/4D/He89GPfjRJm7/4RZRSfOxjH+Onfuqn+MEf/EF+8Rd/kV/3637dkzjEHR4SmUqutMvGsu6TdXyZKS5NMs7qnsNR4hD4GKltWpxvLBuO1x3OB1ob0FImR2gh+OrpmteGjo8ecsA6l9rJ09IwzTXXZjmZURzsip4dnjBijJzVdhtToaS/ZwHUWj+Y7UHDxpTv8Xx/N4qlWWnwIXI0SWnw694xLhTeG5RMXYhn90epWxvhcJwxby03Fw299VyaFLTOI4TASHAucLLuaHpLaz1na0vZOWaFwYbIee04GGU8f1Ry3lhknV5Tbz1ndc8m+TEG0BJWjUdJvyViW506Sa3zIGDRpse7l8/QW8WmwwKpU9f7wFN7FePcMG97RrnmV16b0/mAt8mVft17ZrkhM5Kz2jIpkq2Aj5EYQSs4XnRUefJEe/m8YVoZ8jYgIlzdg6Nx/qafc64VZRZo+zSmnDxCnM/FsNyNW/WuCLo/nkgBtOnu/PRP/zTT6RRI3kD/7r/77/Jbf+tv5Q/9oT/E93zP9/DpT3+an//5n39Lz/Vv/9v/NiEEfstv+S189rOf5dKlS6+7T9d1dF23/XmxWLyl5/wgQkrBfpk4APsjk4wLW8fROOdgnG3dX61PWTvWBUQUtF1SghktGA1+JP/klQWrtue0dUxzxSzLmAxz8oNRxsE4Z5JrWheQMqlalp3D+zCEQO4Koh0eDs4HOhe249qHxUbps0FjPZPi9YTbi/fZ/HxnWHB85AtWmWkmRRpP5UZwNCooMsVelSVydkydn9IkE8Qrs5Krs4Lr84bzuifXGhsiZ3XPuDAcjjN8CLx83gwGpymY+KlZwSjXQ/Bq6m6McsXxquNLN1e0LmI0jDODipFMKTofWPd2K/e+NM6YlSa5GhPJL7i7353z9zhhpNwWnrDhNokUvuwCuXZMC8Np3ZPlCkHk2YOS1846Xls2fPHGiitjw6+9OqWUkjxTKCVxw3sQSTwfKQVGC2xMm7bOppHbzUXLuvMUmeSpWYmUScEKaQ2dFuZ1Y7OHwUUlmfXJ9dvsLELuiydSAP2Fv/AX+F//1/91W/wATKdT/vSf/tN867d+K9///d/Pj/zIj/Ct3/qtb+l5/q//6//iueeew1rLf/Qf/Ud87/d+L//j//g/vu5+n/3sZ/nRH/3Rt/RcO6QTuNBqm1Ld9oHCSIxMHIJcSazz+OCTx0aMZJmmXfX0znN5VGBjGBQgjs56VhEuTwWzSvP0/mh7Us8bS5F1dC6kAMbOcbLuQAh+3bUpo1xvHXR3O6Ad7oXeBU5W3VC83PaSCjE+dMyAFHcGBuv7yLwTJ+f2RWrTJepd4LzptwZ4oyx1bmJkGzR6ERtn5YvE4FGmsIXB+TRK8hfG0pVRPH9YcX2eVJcuDiGmuWa/yjgtLed1T+cSZy8bZNir1tP0lpuLlpvLFiLkWnBpnLN2Hh8C1qeIidZ5lJRo4ak7z6VRTpWZ5G4c4bzpqfvkEH3eWIpMD4an4o5xzeNQzt0P5UZ9NRgZaik4XnX01lP3icNY5Zp5mxRtSkhePFnz6nnLqvdIBD7CjWUHQXBpKrk6NhyN8sSDcpa9KjlCt9ZTTYrBi0kk37N1n4okl6IypqXZ8oFGuX7Lr13LC2G53M5P3OHeeCIU8/l8zs2bN193+61bt7bdl729Pfq+f0vP89xzzwFgjOGP/bE/xi/8wi/c836f+cxnmM/n2/9eeumlt/S8H1SIIZqCmKS4mUnp0loll9VAMl17dd7S20DTpwVUK8VemdM5T6UlmSYZkklBriTP7RU8c1hxNM5S6KpIrWs7tLNP1x3HqxbrI9YFfvXmilvLjvPaclZb4pOOnN7hXYl502+Vg+fN7bWms+EN/urekEMQaaZkkm3fQ24NaTS0V23UVmbbrbwYoNn0nlvLRBpurX/dd/i87jmv08V0tYmHIZ1/PkbGRXrcVeewLvC1kzX/7LUlX7y5JMbk/ZNrtR2VjHLNpUmO0SJ51wDWRQqVssI6F7m57Oj6kLo5NtKHQKGTsCFXEsRwwZXJXDDTitxoRrki15JpYTgcFVuTxkynXLGjccblSc6sNEwLzaw0Q5ago+7d23LubpRuVaZZtm4b7Hy6ttSdwyjB1UnOpXFGCJ5Fbamtx9uICx4XQCrB/tiQZYLOeppBtv/aqkkeP63lyjRnf5QxKdLn7UK4w+zQ+siqvf35rTu37QbdDTuIP+73+w1mpaHQicc0q8zOEuRN8MRGYH/wD/5BfvzHf5zf/Jt/M0IIfvmXf5k//sf/ON/xHd8BwC//8i/za3/tr33k51iv11hr2dvbA+C//C//S37Db/gN97xvnufk+eOfN38QURiFHIk7GHxhyB9SIrXfXQjMm57TVc+yTUTCEAUna4fRCqM0pUlkzMqkXLAb5y1fubXiV2+smVWaZ/Yrlo1jf5RTGsWicUBayDbRGQoxKD7idke7ww4bbK6tWqYCfTN+elRX8VyrBxq/pvvdfSx3XtgufmfDwDUR4vaoboO6d3d0De6uF86bbtthcAGuLxpmZSLgHgx2zkIIrs0KQojMiz6FqPrAV0/XqVMiBaUWtD1MSk1pFEfjYlCKCUaZRCk5GCtKOhv40EGF0ZIbixatJCEElm3Py6drQhB84tkZVyclr807zpqOUabJVAoh7Qfe37Qw9C6wV92bMFz3bltATEvzSKPLzdtlfaTMFL3zTIuMY9fx5eM1N+Ydy9ZiBDQxEPrB0FDI4X0xxMHkddUmZ/uzVc+0NHzd5TGRVNx2LgydPIHzqTM9LZP30uYgBPdWsV7kjUkhOBzd3+tMDYX4Dg+GJ1IA/dW/+lf59Kc/zb/5b/6bOJe+wFprvvd7v5ef+ImfAOBjH/sY/9l/9p898nPcuHGD3/t7fy/ee2KMvPDCC/zNv/k3H8vx7/DGMEpsTcCsD+RaoiSIod94vEyhp9YHhJDsVYob847CqESsdIGPXZ2w7BzL1iOV4Cu3ar52suSkthglCT5yUOVoKTgc59TWs2ode5UhU3d6huw2QTvcC8UQNaCV5Mo0J1MKpQSjtzHb636YFGY7AitM6ppsCh0ziABgiHOIMeXuhUTEPabD6KSAnBT6toGgUcSBg+J8SBdYRBrXiTuLLh8inffMG8e6d2glOKxytJRMq5wXLk2YNz3TIiPPJG4YGYYhY0wrSW4U1yY5mdF0LsnD541FiSSNv36eZPhRJHPAW8uGRZvUZq+cNRDh8iRHDeHJIYt3FHsXEQY7jQ0WjX2kAmiUK+Z1MoBERLRSnK47vnRjycmqY91ari86RrmhyjV7heHDRyMAbEzWAZ31LFvLsus5XvT44LFOsWgdLsJTsxJLIEbBr7k05qzusSGp8jajTkiu3/ca12/MZCEVw70PFHLHc3wceCIF0Hg85q/9tb/GT/zET/DlL3+ZGCMf+chHGI/H2/t88pOffEvP8cILL/C5z33uLR7pDo8CIQQHVcbJuhuMwEALwbVpQWc9vYsIkYIeJ4UmU4qztWVRd8wbmwjTITAuDaM80naef359wXJwrs20ZN4mN9VMS+rec21W0lee/VFGmWmW7e3Qxsclod3h/YVpYSiqDCF44hEDmZZcGufEmMZpMaboiEi8w9pBCIgIzuueRWsZZ4pV5xllilGuORznHI3zrfuzdYEQIuvOE2OgMGnJLzN1R8RE59JIy8fA2bpP3TDgw4cjOhe5NCk4mhRbzx+jJEoljsnxuqV3fjtSmhUa61PRlUZMqfhad4HZKMnyF63j+ryhygyroQgqtKRxnlJqvE+GgQ+SffZWkGvF0TjlcUkJv/TFW5zVlq8crxOZWYEWUBjBU7OS3gemhcZozbLtuTlvWPce5z0CQecdszJjNsjcR9ntNPjUMEtjRjlYeWQxfe4uxPu+VikF+FSwzpukNCyN2rnePwY8USPE8XjMJz7xiSd5CDu8TUgnprjDpflgnJKUM6149axhbS2tDWRGcTTOOFm11L1jVmSMiwzvk4z2lXnNjWVLaz2ZViAFWkGmE5Gzd4nQmBmFkkmOv1dlb0lRs8MHA+8mp10xdGY2/75XynznUmEwHiIu1p1llKexUQW4EO4Yw/kYuToruDSJA8k5pJTzRcvzRyN8iCiZOrbWp3GMEqSR9KpnnGsuTQqe2U8d2fSYgVxJbi5bXj5b8+JpQ4yRyij2Koc8qOh9QA2Oyi+fdFSFJtOwbi0CwaQ02BhZ9hat0tjmYGQQSParjEuTjCLT5ErSWv86TxspUyTJqnMIuKfi7kFhByL3snFoqVDCIYWkcz0ySLRSEOKglJOcN5bnDg0hJNd6IwVGpfywp/cqMpWMIK/tlexX2dZUcpQns8iLU8rOel45T+/rpjMnhbiDED0d5PCrNnW5jJK4EFn17i0pxnZ4Bwug7/zO7+RnfuZnmE6nfOd3fucb3ve//q//63foqHZ4O6GkIPjbAYFKCKQUXJrkSCF48WTNOGO7KDx3OGLZOBAwqwxfvdGwqh2LtscYBSKNJz58acxHjsasO0+lLS+eNqw6x6zSHI4LpnlKly9M4hHsV4bep67TTiK/w3sZmxpAK3HH99moFApsLqjPOpc2B61NuXqvLTomA9G4MClv78WTeusXFGLka8drbq47Cq24PEkjsMIMzs0hFR4CwXljeeW05v/3ypy690kCHiN5JgcrjGzg0xieOazItOL5wxG1tYgo6X1knGkignEmuLaX0/SRzEiuzYptLMT5wH1RMnFfLhZBozwZE6b35dE2Ohf5NTeXHS54iHAw1iBzjBT4ynC67uh94HCkk6AjpgyxG4uWdedACHIjuHKQU2WaWWEotGSU6+3YMopUXGoptkXRorH0w79755lVGZmStLYjk4JsCKidlQYtxR2k97eKpk/KNyUFk8I8ttDZ9xLesQJoNpttv6Sz2eydetodniBmpWHZWkJMux85hJWe1z2XBo8RARyve7pFz9E47ZZOVy1fu2U5bSy3Vi3H6x4jBONCMysLPnRYYpSm7j0vdjUvzVtuLRouTwpaG/in62RoVuUqmbpZvx2Dldnr7el32OG9glwrqizQ2GSul+tEPi5MCuDcjEROlx3L3mFkCh89XffYEDivk9qtytTQjY2crlMhoKRkWhpuLDrO+34b6nn9PCmboogQwBOZ5IovH6+5teronWfZSZ47qMhVcsKuO4eQAu8juVIUmWKcaZ7eKzivk2XFl2+tKTLF5XFOiIIPHZXbgi6EyM1lmzpbRrHuAp31jHPDtEyXrRTeKt5SF68fJONt76g7R2MDo1zivOHqtOSf31hyvG5Ztx6pUuDrcz4VX0eTFNlzY9nRu8B+lXFtBpMiY1yaLaG71IovHa9ZdZZRlsJWq0yhhKDuHP3A8WlsoLB+y/nJdc+VabG9blaZoh1iSqRI/KFHRRpDpsLPhQitvS/Z/P2Md6wA+umf/untv3/qp36KEAKjUSKTffWrX+Xnfu7n+PjHP86/8q/8K+/UIe3wNmMzirqIGOO243Nj0SFFxHl4dr8g14K2d4iQ8fe/fMK69az7jsU6dXOqUuOjhyhx3vPlGzXL3jHK0q7sZNWzbC2dC2iRMneG8CEOB1fZtve7AmiHJ4JFa7fj2rvDPx8U6y7Jtse5vqdXUYyRW8uOL7y2YNU4XPCse8dRlXM0ypk3PVJEIoJ53TMpkqHhxn2494EiU5iQCqtVZzlZ9WgpePG0JtOK/UrzynnNed0hSOeXjFDlhqNxzuHYcLJ2SeWkA6umx3hJiBEjJT4Gmi5lAFZ9kqQvGsvlcb69Is2b9F51G68mHzgYDcWAiDgft12Uca4fOQA5U5Klt7x41tA5zyg32N7zwpWKL95Y8eqi4da8Y9n1SAmlMQgEPgRcmNBbT64FVabRWmCdQxCZFJrTdYcPcLNvuLnsCCGk4rKxXJuVHFSGKlPbEdzRONvK3AuTRn4X1YBCJMFHGGT7bwXhLrng22k++TixIb+7kAxE32rw9ROTwX/nd34n/96/9+9xfn7Opz71KYwxHB8f8xf/4l/k3//3//0ncVg7vAPoXBiMwAJ7peG06ZOsVMM/fXXBV4/XnKw6ztcOHyO9A6mSzJYoKLXmpdMaJUn2/fOOS3sFmUxSWxsCoywpwbR2GHVnps4Hsc27wzuLi86+kHbbtxYdy85SZTrxbgaex8Og7pO/zIZfI8WdrtU+RI6XHS+fr1m3llfOm4HjA20fOJqki+eVaUWVK27MWyCN0iZDfMak0PgYUVKSS1i3jnVnOVv3vDxv0VISQoF1Yav27Fwk15JV6/jy8RopRxQm8V16FygHhdrNZctXjtep09J7jBSUmWTRWLQQLKaO3kdmlaEdlFUvna25teiYFIZl57nmS6TI7ygem8HQ8VGQFHdqCG8VnK5aGutovE6cqc7RDyGlGoHA8ep5TeeSQlUrgZGCiKBrHcs2dVZe/fIpyz7lh3kfGRcahKDuHXoYOXUu8Mx+gdEKKVIht2zt1kn6fp4/dxc/m65QoeUDCz4yJe8YxT2s8eeTwrJzKTKFZC+QTDQfndbwRF71P/yH/3Ard//Zn/1Zrly5wuc+9zn+q//qv+JHfuRHdgXQ+xgXC5BJaTBasmottxYtXztecWvZs2gsdd+nkzJGtI60NnK6bnnZSJ5GEAQsa8dkZDhf9RyOMkaFIkTFrMhQUvLcwYhn9kdb4qAQb40sucMOb4ZUpNxO+h7liSjbOI/1kUVrORxlr9uBPwisD5zVdrtbN/rOMNXzuqfzHh/SWMP5gIuwV2VIAUeTjKaPnK17JkXFpUk+mBem7sxeqfn6p/e4uWyZr3tWvd9K8M9ri+s9QQVuLjouTXLGeQ5REGMqyKz3NJ3FuwA6eepIIajyjM4FVm3yw7mxaImAkYJJpoeE9FQgZVIyzhWrznJr2XGytJwsO5xPXeNJoXlGlXeQid+q2/Gs1BRK0YWwLU4TuToZS7JRCYZIcFA7SyDZCzx3kEjPo0KSacOlScb1ecvJqksdNeewTrDuPeWQFbY3+PQIIovWoWXa4CkpmJUZUjoWQxDsWd2/YYdr3bktL6juUqf7QTZ5m0DWTTH9XlHK3l0UvlWfzCdSANV1zWQyAeB/+V/+F77zO78TKSWf+tSn+NrXvvYkDmmHdwiFSVb0WoqBuBn50q2eVWuxQVAaldKMhcbZkEwUI0gZ8T615JUosC75hwUXmZaavSrnYGzoXaQqFIdVzvNHYyaFZlpmu87PDm87YrzTm2bVOaoheqEY/GJCTF485SN41kghtl0VwZ1jDB8ix6seHyK5SqPnk1VPNhRJMUYUkoNKsuqS7PzDl8ashk5RpiWFlhRaEAL4mGICFp2j7T0uBPYmGd4H+hDRSlJby7OHI57aK3nlrOVglGIt+ghPjzLsEM4qBFyfdwNpuMMMKikpBEoJDqsscYzwQzdYshwu7FoJhBJ0PkVHlEYnBVxMJF4pHi00dAPnQ4q1IOB9ZN1atJH4wb/saFIghSCISNsFmt7S9JHz2iIR7I0cv/7pMVenFaUW9CHiwm3vorPaUxnJrCgo88R3KgZidCoaw1YSr/qkbJsWmvZCpteqc/ctgPq7w11duKd68F4Q4q11T54Eykxhm7C1ecjeYuH2RAqgr/u6r+Pnfu7n+D2/5/fw8z//83z6058G4ObNm3fkg+3w/kSVJf6Cri3Hy5ZxJvHecG2W8eJJSoofZWnnebqyzJsueaIIwarz3Fr3GKkQ0bNoOhqXcTQqhp2x4EP7FR+/NuNwnO98MnZ4xyDuygPbfPPKTNH0sD/K0EJyMH60grw0aVTSO4cLcL7uuTIpiBFemzd0w0VTKcVHLhW8cGnM8aql6R29g7r3nKw79kcZ6+Hfr5w2W6HCa+fNEKORRlalkRyves5bh1YKRcQLweVRzrQwHI2zZFgYA7nRQMSopHyKEdadZ6/UGK24PMqYrzsqI1kFT4hJEbpqHdZ7Ask7TJBeS2VkUpVpmVLSjSI3ikuTFOMRh02UkuKRFWAhRK4vGl48XnNz2dE7RyAwNgaRa7rOsV+awcoeTmJH5yRFBsqnrlCVaSotmRaKxgXmdY9G8Ox+xY1Fg8OjlMLFwLoHhORo6LzdWnS8tuiGeJRsq2jbfHe236M3eHlGyy2RW8Aju5i/V1AYhZapM/c4ch6fSAH0Iz/yI9u099/xO34H3/zN3wykbtD94ip2eP9h2VoWQx6T1qkw+rpLE9a949aqQ8RIrMB5TwieEAMuBE7WHQejnKnRCKkpc0nnPSEaPnqtYpQbxvkFA7LBM+N+wZI77PC4MC3NkLye/i1E2qU2eJQQHI6zLb+jsR4hUmHzIAu5HLyBzhuBktC7ZIyXyMouPW4MTArDwSiNnbQsOV715EamQmcVsS6iZOBXb6wIIbktNzYdX5Gp1JUYOrR7pSGGiB/nNH063ivTYpCl5xyNU6fnyrSk7j3r1nF1UnDapJwyJQVd3dP2Hg9UhUYSOes8Gnh1XtPYZJA4MprORqpcg5A8s19R945pmRLnlRCcNZbD1rK2Q9yNTIXTo2x0eh+QCM4bR4A0zkOSGU3vHNeXPS5GpBasW0euFUKkZIH9yvDUXk6lNULAyapjf5yzV2ZDyKvadv9uLDvOG8fVaU6Zp2DoGMCGiI+wbBMv6KlZAdzOVFy2KRtso3q7F8a5RpBGnoWRT9zQ852AVvKxFS5PpAD6ru/6Ln7rb/2tXL9+nW/8xm/c3v47fsfv4Pf8nt/zJA5phyeAdWdZtJ4YQQvFwShnlCdreKlIXIIIfRUpjGPepqBCJRQhChrv6Vwk0zl174d0b4v3ghvLlufMKPEXBvdUSN4ol8b5W9457LDDvbCRo2/gfNj6zESSEmyvyjir+y0B9Y3yru5GNgSLQiqIeh+2CfKudUgk41xTGEWZpUiKMJCaK6NwLjkeG5U6LD2R3nlO1z3jXDFvFIUWKCGZVIZSp86LHaJrciOZlRmt9QiZ+EWbzcW4MMRJpDSSRdsPPj9wsuyZFIoQIlpKJlVObjy/enPBV04apoUhk4I+eAqdTP423Jer04Jl55nXfeIq+cjXzhr2yhT06YdC8lFI0Eom7su4UJyf9Kyt5/I0Z7803Jg7Qggp46txrLuUOzgrIwjDYam5PCkxWmFDpG1SV876QOk801FFiMlQMlOJ+/P0fpW6Vgi6mDLF9gby+bQySehx4XtkfXr+lHMo7hv18VaVUB9kPLF37urVq1y9evWO277pm77pCR3NDu80OucpjOLyNKezIck+Kbm17NCF5Lm9tNi/lK/pb0SMTAoK5xn4QwwBhIG6c+yNDEoll9qm9zx3UNEN83DnL8zJY+JLvN9bxQ+K5//k//BQ9//qn/tdb9ORvD/h7yE39uG2hBu4b97VBiHEbREvSFl7QqSYhcIoepfcn3UlkQL2L4Rh5lpxZVqm0FCteGYvFRAhRqaFSanrSqXxk0jPlWtNbjTlwFUZl4a690wKBckKiEJLusFLJsb0Oted2ya/B+DGsh1UT2CdpMoV1sOVacaXbq5TwnoAGzyd81jrGeeadZs6RzEmgrMNgdq6bcK9FGC9om0D/RCbs/E1ehgYJZmVhhcOx8wbl3LgcsWkMJSZ4saq48YijRYDSU13UOWM8tQpCyJyVlt66zmalbx0VpMbifWGX31tRTF09gojGWeKGFLROu96RiaZOK7aiDHJ/fpi98aHFLAKtwvnR8k62+GNsSsdd3giEAiKTHNlUiTvEaM4GGXsDdLd3qedz6p3HEwsXS9pfUTEwP4oIyKojKB3FucjPiRpZ2cDPsKr84ZLk4IySzLXjXRywxvY4dGwK5geDveSG8sh7mBDYn6zsUVj/baDqaTkyrTYOj+XWeqsrHuHEGm0dmvVI7idkL5JCI8x0jrPU3slrU0E4g8fjZhVqaNzXlsKkzg1anB8tj4wypJ79Kw0nNWpEFs0lnljWTaWwshUNJhkgvjPbyxxYdN5gkuTgirTCCk4GuXIYXy3bHoia5rOo4bOT9MH5q3F+shHLlVkRjEqNM4HQkiFlxpGiJ33afzj4+BG/fCXs8IorkwLPnJpzNm642SVXtOVvZyn95Jv0ry2TCvDXmEYlYa9UnOy7Hl10bBqA62VHM1K9soMKWGv0qxax6VpjiAVpdeG8da6T++zDy2XphnPHpSMMn1H92eDzfdDivRZ7PD4sSuAdngiyLTcqhXGQrBXGYySTK6mhfq1eUsIqdVdaElApZa0DVTGIY2k92kRt8FzXvcoBM8clEDaObphAZlVhqyXRCKFfvid4g47PCo2cuPOpZHHptg5GGWpaIGHdvRV8k4PITn4yiQlWIfzyUCw855n90d3Hg+3x3QCGBWGtU0+N+NCEaOg6R0uWLyPCMmgzAysOsd+ZWis58Yi0tuUS9b2nq73VIUmRsHJquVk1RNJhd+0SE7spdRM8mQYOCtNyvWLgipX/NrLYyaFYdE6GuuIge1FXw98o/M68YqEhEujjMYls8ZF696aKZ5gUNXBpUnqnlWZoSpMKtxQeDyj0nBlXKC14OaiwzswCrSCw8owqzJuLhuWncf5ZMaaG0WIgeNVjxCRZeuJITIuNfNa0PaRUZ7UgkkCn15zaz1t71NhVyiuzcpHe207vCF2BdAOTwzTwrzOlTl5nSRZ/I1FS24kzx9UHK86bmaa3necrjumVU4IAjeYH/bW0/tAFwJH44JF63nxeAXAQZU9sDR0h8eLh+kYPWy36L3SjRLi9fwNJcUDO5JXWSpArA9oef8IBEEK11y0qSsqBByNiu13/yK5FpKnTmEUT81K9kvHoknmo2d1SqMXEqRIJN3eB8aZZjQoOGOMnNZ94hIRWXQCseoZFWlsVttAiEniXbceIVK46ldP1jw1K8g1XJ5m+Dii1JqDcUGlBa+cNdgAvfV87aTmyqzkyjTn5bOazgWOVx23VqCFpHGJG/NWpNDWB24tO6RIBZcQKWj5+ryFmHhT4zzxpS6NczKVRpaZBBcDRkrGuaF3nivTjNN1x61Vhxbw1ZOaDx2MmJUZtU12AyfLnqb3fOiwYtlYpqVhYgf7AB85GmU01nO86ikzlQjh8Jbl3jvcG7sCaId3FZZDW/1LN1e8dNagVOrmZJkmCmi6kOI0sDx/WNL6QNt7VtYCkVuLln92Y8GzhxXL1vHyaU3vAlenadT2VrODdtjhncamixRjRAxeQHYwsPODHBxSfMSqc7x0WqfMsDzleD1/NBo8YpKx4aVJioWxPgx8uGTEt+odvUvnV6YVe5Xh1qpLHKUYOa075m2PFIJ1l3g3nQ/Unecjl0ZYH2ltIISAEqmLEkIgzxSQCqDzuuMrxyvGmWbVO/arDIg0nWOcFYxyw3zeIEUcCoGW0iiaPlL3qXCLAU6WHcYopqW+nWT/CJjXlrPa0rkUGaJl4haOMo1RkqcPRowyTQgBSeSlZYdzgbPaUSiN0olE/uJZjY/pPV13KbrHNj3HK4X1nuNVh9EKoyVn66QKW3WOVeu5tpcMWq33Sf0qBOvO0fvEUbq7X+1DMmH8ICi+3m7sCqAd3lU4Wff86o0Fn/vaGfPOcWVS4GJgminqzrHokm/IJHheORfkBowW2CgI1pMpwdkqLbLP7ldMykT0vLlst6OvSXHvHKUddng3QwjBWd3x8mnLqrUYLXhmv0Irycio1KUZvtshBjobUpfmPCW0b4wYp4UhxEBjA71LERn5wMERG1M+KVJwaoisrCWEFF9TZIlnp0SSn5+sezCpaDBSIETE2sCi6WlXkVlhuDwNnNcOG1Jkg/URIzwhpOJBGY0LIGQahxklUTIVE9fnLVJIYogEHzlddczKnM4HqkJTGkUkiRo2BeLDwIZNGKqnNIrLY8Np7WmsQ8mIj4HjVUMIkZdOal5bdjTW03SeSWVQETIjhzgK0DId6zoEnPMsG8ezBxW9D4wAYnLA36sMkmSw2FiPUgIlCkJMifHjQrPuHEKkz2vzupreb0NMcy0fW4Bp3Tusi+RGfqDI1rurwA7vKixbS+siea7oa4vzgbJQ1IM3iZYgQsR5gbMBhNqqNzob0Fqw6Bz/+OU5XR8Y5ZppZVh2Dj2QRlsb+AAGH+/wPsCr5w3njWXRpHNjlGkOxjm9T3y3TElGueJ46SiMIB86DqfrPvn0dI5poXExUreeaaWpe8f+KGOvyqh7z5HJUkEVAsfWc2vZJ7M9Ac/lJV5KtBJ0Q8aVELBqHbWPzKqMW32HFIppnoi8ZWYQpBDiL9oVWgWKXDGShksXEu1diGgEo0xxvu5RWgKRuksk5GcPRyAFSkBuJIhUBKx6R9sHTtc9B6PsoYqgKtP0RUju8zEyrXLyLDk+v3oOlyeadWu5tWy4vmi5ue5YN5YIjDLBWeuZVJrM5KybnoNRjskVi9Zx3jpGmeC1ecvVWca00ExLQdNrpoVBK4GWkd6DdYFMCQJicA5XWz+ni9jEXgDbsehb7QRdjG9pXfJ6eq85RD8qdgXQDu8a9C7JWnMteXqvTL4jOslHT1cdpZFMMs1aCqIPBAJKKCaZYmwKjuueQgo8ycr++rzlI00yGVu1no5kfnZ1tvva7/DeRAxJqr5xCrZD4ZMbiZDQdI5CK7IhnNRIiUwxVoO6MtD6yLp1NNYPOVcqZXMpyayUFFqlTLE24HwKSFVS0vtkGDgrNUfjfGv4aENkb/AG6lwiPEcCLkgORknccLCfsWo816Y5r543eBcZV5JxLilM4kItWoeNIRURSnKy7rl+ftsF/vmDES6EbSRIax0vna1TFtggxR8NHkgPisnAsRnlmt4FtEoEba1KRnnLqnN87ZZn3jog4n16/zNjCCJ5HxVasu4dViqemkmOxgV1t+ZgpKk7zytnNY31ZMrwG57bRyogpniPk1VH3TnKTPHKouW5vQoXIVOCcf56jpgU6bO8/fNbF3RsvkMbOB/5oFgLfUBe5g7vdsQYOW96roxzTpYdIcLRsBBaF7kyLVi3ATeBqrd4D0oJcpOCHK/NSq7ul7x8WuNCkgsrLWhc4ggJEVFCoqVk+hayg3Z4+/CwpOZ3O9ad2zr0Pq4d9dP7Zdqt54oDnTHOk5/MRtm1kaJHoM6Tyeis1FQ+bp2TC61Sgvk6kmlBpgVPzcptxlZhFHUnsINyrcqH6BqZczBO3aGzJqWWt9bjIzw1LQgxcl73RJGsKoSAo0nOpXHOskv8ltoGpJLYkKwvpFJMCo0bZO0xGlat42joagmRFHOTLDm7P7c/4qWzmrOmp9SKzkeCD/QuhY9Oy4crgKRMBQ/cHi+te0+VwaVJntaW/ZKzxtF0HpSgtwbnAplKJpG5loyzjEmpqQpDHwOHY8Oq85wuLZlRHIxzGuu5uWx5/mhEbhS3lp6zuse6yLJz2/Hhtb2SlMT2ekxLMwSlJhfoR7H0uHtUmGu5zR4T8IHiSO6uBDu8KxBiMinMjOLyrEAIwdVZyXxtaaxj3VsOxhmlEWRmRG89x3XP2Gg6l5xun788ZpoZXjxrUBKIkbpP6ouNcqUwmlfPG67Nyl1O2A5vG1Zdcg+GJGk+GInHQlrdH+X8xucNde+GLKrE/Tmv+6HLk3g/87qnyFKw5sEoQ0nJ1VlB78JAMA4IIqfrnjLT28wtGFLlh27Ix65OqIeL43gohE5WHXXvE7F66NhGIpPc0FeBZecoS8nTsxIhUuaXdak7tOwsnUt8Ex8CMUQQAkGS1de9G8Ywkb1Ssew8RkocSQFndLIA6AaytpGCubXp+I1M3aJH4AJBIpE3vdt2VY7GOS9cGuMGbtUz+wVfurlk3Xj6GHAucN4mArVUMC01jfXMF5ZJoZLL835klOntaDLPJI1NXbLFhRgTHyJna8GkNNvCs3V+u26tOofzaTR2OM4f6bvTu8B502+L3E3hlwwbU+cn0x+MOI0NdgXQDu8KbJJ9F01P3fmtE2xuJCcrT5lpbIgQAqe1Y9X1rDtLDJHKaEJMdv7TUvHRbMS6h6oQKTKjc6w6y7Jx/LqnZpzVFiVTgbXDDm8H3F2qpMfB1dgg05JMJ25I03tO191AkO5JeivwwzEoKZiWGZmWhBCRUtBaz7K1nK37C/EzHqMlccimWrY2EXczxbMHo9d3VULkbNWx7ByTwgApZBWRuClt75nmmkvTglGmENPk47PuU8J78BAFOA9tZ7m56uldej4l0kaod57CaGwINJ3Ah8CyDRADUYD3kSpTKeIjBEKQNH3y4Fn3qfv1oN5AIaTulQsxxYesIkJEcqW5tey4teg4r3uuTHPyQ8XXTtacrDuMToRtSeT6ec0o1xgJG3PvZ/cq9irDqnMYLfHOcbIKLFs78HlSAnyIkb0qIwax3ZhtCrFV57au0P3wmV7s0sQYiZHXbeju7kAuB9duSEV5adT2cXKtPjBjr4v4AL7kHd6NaHoPRIhweZLTDsnK40xyeZZzY96gVfIXWbY9uYYzBxGP0ZpF5xGiQ49zYhTsjwavklWPVrc5EC+f1VybVVvS6A47vB3ItNxGXAju9HF51A7F3dgYA95cdPiQzA/bQQm5cYwe5Xp7kdsUP5tsMkhZWJtj2Vwcl62ldyGZEvaOUW7YH7y0epdk3rV1nLWW0iS5fa4Vt5YtnY3EEFh2npvLDhcjAoFWgo9cHrM/zsiUZN25IYg14qNAKYHrAnOXCMnPj3KObWC/MkkFRsDFyKJxXD+vCRH2KoOSgv1RUqNZn0Z8r8yTdB7ANj0xvvm57kLiD91ctsxri/UegUCKyPV5R6aT1H7RBq7ODKXRON8nGwEksyqFwiopublqKYxnnGmmZcavuTrhZNlxc9lxfWlpOs9eqbEBtIBRZbg8KcgzSSaTc7RWt72jfLjz+C/+vPFNizF9x/aqpBi7VwfyboQQH9t38b2KXQG0wxNH525LO5GCQqfcoM561r1NFvk+4F0gDvfpBk8fIjiXMpKEELjguXneY3QihR6NckQUZEogh0Jo2Vo+dm3yBF/xDu93pMiL5ISca4keCqB5Y1OQqIC9oTPzMAgh8WL8kOKuZQpE7Z3f7vKFkJQqXfj2hpiLxVD0XLzWFZnEDRsBJZNiTAhBrhXrLsVMlCZdInoXcCFdbBdtyvx64ajixqJj2Vhu2o5ZoVn1ljLbhKdGztc9vUsmgmWm+PjVGQi4ueiIMXB90fLV0yWZUviYui8CQd176t4NYzeB82KQzYehqyPoXaDue/ZKM2SZCapMcb62hJB8cjItCQ+w19Ey8QknRRpBndZ+MGNtOF715EZRDGo1LZNk/8NHI24sO7SCXCn2jeTWsiMGT91E5lnP0/sFdetp+jRSzJWECMvOUeWGznpyHylzxX6ZEUhxIKHz5Nptg223xbRIxfXmM120PUTBuNDJCNYlbtXpqmPdO0aZJjdqILOb7Qgsxsi8tdAmmf3baRRrfSDESHah2H63YFcA7fDE4S50YyZ5IkSe1z02BBZNssW/Oi64MW8YVzr5jHSWxqXFDxHpBxO2z786HxKYoch1yudRkSIzfPggKSwuTXLGH8R+7w7vKO4eG/VDhwYYRk32ofgc89puZcrTfDDIEyLFU/SSaanJjWRkkjRd60T4P1n3bM6wpncQxVDwKI7GGi3lELCaLk5P75fkWnDeOEqTOhK99yzXjkgyYTxtHUamTkU9jGg668m0oLEhSdSItA7yLMKQf9ZYT2NTdM3Zuk+cmj4QYyAzikwmUcM0d4OHUE5pFDcWLS+fNbgQGOeJKI2AMktFTu8D+1WG85EoGEZGnqNJ9kBEYSkF+1WW0uU7xyhT2IFbOCsyPJHeRX7NlRHjPEVWzBvLlRhRWnI0yri1St0mKQXz1uPOG6QUuACFVpytewqj0SqN+IhxSKPXzMoMFwKn9e0xFbAtgDbKt03xc2vZMW8sr81bjE7fgSvTNNJPbtkdq9ahZc/zhxVGZWiVQledCyyG7pD1ga8erzgcp1Ho406WX3duK903SrJfmXdVEbS7CuzwxJFrybpLa6YQgoPKsGjSArgacnWe2x8xLQ2rzvJa0fLPX51zVCrGVboQfOhoxKL2BB+JMrLsAuve8SUlUWrCr7uWTBFHw2LzbjoJd/hg4mG+g53z20DfGKG2nulg8nkwymEUAUFh9BBkmhRDUopt8eOG2AdB4td96LBCyxTWevFYCqN4er8i0y03lh3L1nFpkrNsk0t7bTeRHKmDAWIYZTn2dcZeacgniigiTecZGYUaglq/drImeQ/GwehPcG2v4HTd4V3EGOg9dCHwkcMRUkiWnaU0iYBd9x4tBc8dlfQ2Mi1SyKpRgoNRTjNwmXoXiERKo1g+4HtslOTaLIWTXj9vEtFawIcO00XbSHj2cMSqdWQmxWNkUlIVmsvjnINRzum6w9Sa3ARyo2h7P5g3ZmRSUBrB3ijHutTByYZ8uKZ3F0wOI0bI5IE2KPwubtg6F4YOoMfo1AW3Pm7Jza+eN4nQXqSOYa4Vehg7Hq+6YYTpeWqWFIWBtPauOrcNz31c2HCXIBVb1ifl4bsFuwJohyeK1npa6zFKDAoENYQ9KlZt5NoQYVFmmmmp8T7Q9gEXIUpN20eqHL52sqZ1gT4E+j4QgNGgkIkxGSAejnL2Rg+2I9xhh8eNTQBw06cuzqN0IZ0PXD9vWHWOa3slHzqsKIx+3X2kuE2mHeeaVecGnh1DQGfky8crjkYZNkZmRSJVKyUojSJXMgV4Ilj3ju68Ts7FRKSMjAvD0/slX2hScnvwIY37lGSUa67NcgKCRdOTq3QRP1332GFEXRUKLWFv4M4UWrCOkTLTSH+bz3QwyjhdO87qLknxVepexaHJtOw841xzOC3IBhJ358Id5N43KoAWA9/JyNRBEyKRkp87HNG7QG7SOK4ymmcPKqpcb0NnV53HBk/uFb1zICTP7o9Y1i6ZN+aaMjOs+oDWHinhapkTIhQmFXOrNo36TlYti9azrC1RsB0Znaw7qkzxzH5JYQxVprZrpRSghWA2MhyNc2ZVUnWVRlH3aSQqBNs8sXnbbw0PiZFF00O883uY+FKPb31UUhCGDr8Yfn43YVcA7fDEYH24QMgEpWA0LFxH4xwl4WzdY7vAP/jyCX3vubFoWLWOzkU661CZhihZtJ5509NZjw+BSa65NM55+mDE5WlOTGrbd90JuMMHC9PCMMn1Q3cgc60os8CXbyZOSqYlr81bpBC8cGl8x/da36U2G+UpMqLQMvncdI7OOXqXCLBn6x5BzWyUcW1aECOsYrqfURJB5PpZi5CSurM8PS3pbODmssUowawwjDNF6wPPHlREYG19cpouDNPKJJd2JfjyrYZbq5ZirZiUisNRQRSR56ZjOuuxITISGqMlUgpO1v1gehg4qxtCjHz0yiRFZ5CS2K33W2LwZuzYuUCm3jjWoen9tij0wSO7NI7fZAYaJdircqZFRIpUOC5by+mq559fX/LqvGZtPUdVznNHIzIlOF33CJXidvZHGZPCoA14DzKmDDMhoHEWLRRKwsmqJ4TAqk9crio3WOc4q3tEI7cdno9enbLqHPtVcoguzEZSL6iMZjTweK5MC4RIirFZYbYFkIi3vyOl0RyOcvKhMO1dYFaZ13133ipmW9+iRDK/1/obY+KzPQkH6l0BtMMTw93qhotcoI0z67r1XF8sePW8wUjBae04XjZoAavOp/FZDy445MBrUFKRDy3zSa55br/iYJIzLR8sfXuHHd5OPOr4dVoYDsYZ60HqDQwREgEl3/jCIQeSc64VdW/pbaTKJaerntN1jzGC3KjE1xg8YXyMNI0dpPTp3HE+UPvA83s5t5YdIFFSYLTm6XHO/sjwlVtrzuue43XHU3sliME9WafwVKUkAsF57ci12xZNk8IghyyyUWaSN5BPytAr04Jla7m5bHn1vGVaJiL0SGiUESxai1Zp9Ff3fiArv/H77O9Sh10c1yRTx+TQvOmkpaDTFJp6vO64sWwJPj3nr1Fj1r2jsY79KufStKBUyXupsYHSKF45a7m5bNmrDK+e9xQ6vefrxiFlSaEVTklKIzEyuUhrmUae/QV/owhvyNeR97H4OBqnqBM7jNaUlFyfN7iQuk1XdHHPx2utT/lvKsWUPMz3d6PSux9iTF5UbrgWVFkYbBXeGewKoB2eGJIq4Lb8tjB37j4igEiuzi4EVo3HOsekzFAisrZp4a9yxapNslofAkYr9quM/XHG4Thnf5zzwlH12Hc3O+zwTiLGxGmJQxp4mSlmlcHI13+vQ4gs2jSeyo1kWhhsiBxNco4mOb0LvHy2TmaEEUp1Wy3V9mms9OxBleI0lKC1iXcihRyIxxohLOM8mf9pKfjIpTHndUfvHL1LPjynqz6NnkuD98mTpuvTOGaSG3yIvHLe8NReSZlp6i7lnCkpuDYt6Vygc45V52ltQCDorKeWgukgg9/EWfgQaYYLPCROyxup7FLKvCfEpKwrtErxIAPMhb8VJKWYHMjcUUSUVDjvhzT4Nc/sVZQmkcpdDOxVGR++PObVsxYh0msPKMaF5tJIc2PZ4X0kiojzkf09w5FQVEYOGWsCF1NYqguBV88bnpqVd1gqXEQ/dFHu5zdltOKFS6PB8NHzynmdCkWVLBvuRcrv3YUu/TA9e5wCEjdkwG3Q2sDk3nXY24JdAbTDE4OUgsNRWoylfH37c5QrFo3i2rTk5dOGRjvGhUJEWLaede7ZHxkqo5CD4mTdWRBphzTOkvJhVhpC3I2+dnhvYz5EIDx/aUTnHFcmZZLb36PTserdVjrd9J5skIRvuhyZllyeFMzKjGVrkxop02QmjVzOG8deZdirMmal5qy2vDZvOBxnHIwyKqNoh3T5iZLMSj0EDStaGwlJ+EWuFdNS8/R+xdm65+PXpnzxxgofGwoj0FvVl8H5wKrz7FfZIIVPJotFJqn7FogcjXOKTOFDKgY3TsqzcvAXiu6O9yG8gQeQkoLDUZZCWKVADNybDdH66rQYoj4ixUAk3h9lrFrLhw/HKFLh4IJnnBvWvUu2A86TC8VTBxWFTrwlI1PhE4mMc8OiToGqNkSOpjnTyvDMfsUoS++5koLL04KvnaxTEG0QyWTyPkTiW8uUW2YGpdf9ukMb+4QNOX7dOWZDMvS9Omav79KH193nrUAJsc21g3eeorArgHZ4olBS3NeDIteK5w4qRllaJKVMhmqvntWsWsveWU7tHMfzFi0FHzooWXSOtrcoBa23GF1iXaCxyV1ay0Si3KnAdnivYVPQGCUxKkuE4ftcMO6+7oeYHNOnRUzkXZ/iGBobOBrnGC0ptWJ7eROpkJiVJp0zQvDUXgXc9o0ZF4azuqexiXA7r1Mh9dzBiJfP1smHa5JRmZQxpiT0LnJlmm8vrKVRfOio4qn9kuNVh2nF4HSs6L1n1aWfn9kvmZWJSyRkGgmNC40avJYOR1nikQAxRMTgLH+/bskGUgqyC++hQGwvwtaH1xUSRkkmZcZzByNciMzrnoPRKDnV+8i0UEiZirFJppACrs3KlPCuJWvrGBlNrlKkx7y1CCF4/qDiyqQkv8BZGuWGp/dG3Fq1tPZ24dH7cEdnq+4cNxYdrU1dtw3f5p7fC1InMUS2kvgYImUu7/k3mb6zS59rtX2ewrx1Xx8pBbPKUHceKZKf0cMimXYm+4DJQ/79rgDa4V0NKQUH44Krnd/uXn/90wec1C29P+cffGlBZx3TUvHKvMYIEEKihKA0iVPwhetLXrg04kxJIjArNEYnNUWV7U6BHd4bMEpuxztSpHHM/VBlim4YQ20CUGOMiT8UAqdrS2mG7miIPL1X0Vi/dQ+uMs20MCkYtXdcrKdWnaX3Po1viLTOU3cOrZLvkNGCMtO01qMGFdlr85beB24uWiaFYVIkV2kfA9fnLaNMUZrEDZrXFht7poVm2ThGhSGEwNG4IBKRJPXXpgjItGDV31a5bbyRsock1PYu3LYaYBihqZT0HoFRprE+0NhkBHlYGSQxefTEiPeBEBXBw6VJRpUbxrkh04Eb84ZF5yh0shhACLSWjGqDEJHMKM4bi+5citNQA7dKbVy641YSvynqNk7gIQTqzm3HdzfmcFhlNDbgQyAQOagS4VmQuj6NDUgB40IPHk6R41XP0Ti7gyqgpOCgyrYRHJsAXIDGSg7egN/zoEgxHI9Ofp4PJGsgWQs8iPPlgN3qv8O7HkqKgY+wIWRmVOeCv/v519BKItGc1x4jI+feM8kNtYvMSse6szjvubFQFJlinKddKyRy3iY8cYcd3u3YK80dF+M32n0bJTka5fgYtz4/1iceT3JNh85FxoXcqiNHmcL7iA0p4HTTmdUXOEYuBHob6H0yKV22KbvPx8ikNFydFnTWYyT4CLdWHcfrHkicvxAjWiUPIet7fITOBhaNZVQYPnQ04mTV0fYepSRnXY+LPdYqJmW62OY6xXysOocg7frP6ttq0vS2PHxn4u6CUgDnjd0Wnf3gvL1oHMfLlvPGMi2z5AyNYDLJsSEOoaXpPfMx/dv65EwtpeD6ouXatODWWjCrMgolyYzC+sDx0jItDbmWWz+jSWEojWJSJFfnTEvO656XzxogeUR1Lo3tXEjr2UvnDb0LrAYe2F7V8+GjiohgXBhyE2h6T2tT4ZrGYZZJoV/HldTqgpP5hffZ+oDz4YlzK+OF8jwCD7Oa7wqgHd4TMEqyP7pN0JtVGU/NRkgEL5+3eAGXR4ZVl/SxAkHvk5NrbtKooLcBVYqB+JceJ8T4ujn3Dju8GyGleCiFjJQCeaEQ2PAtGLxuUjK8GBRVSZ228ZK5iEzL7fjJhWS8t+kkuRCoberGBJ/IxEIIisxQZY7eB0IIIJIKzXrJKFNouekSJfUmQiJl6npszPjO6h696V4JthfbzgVmpbij+2BUMmMcXt6bKsDuBa0k41yzHhLhp6XhdCjeIPFhsmGMnjhOglwlIvo01+RZKmLWrUPLVFhueEoXyckxRspc85HBFfls3acYC+u3F+8IzOuecuhQayUpzO1ct7q/zXXKdTKaHOWaziY+pRKCRW1Z29R16r3nvLZbvo9REqsC2ks24lspxJtycPSFLuS7xVZknOutv1FhFMI++DHtCqAd3pPIjeJjT41BpMT4TEquznL+v+srSiNQQvHMQcGlcUmZq0EFE5iVmqb3W38QJd/8pN9hh/cDpBTsVRl1nxx/J/n9OUR3ozBp/OJD5OaiHYqMdFE+GAmkSEGcldFcmiheO28Z5ZqnywylBLeWLUpInt4ruTIrOBplvHzecFqTTAgrw5VJTmuHSItB6eZ8ZL8yrC9I1O/F4ZuVKXE9xDT+e9DXdTfulpdnKsVsQCqqikyzVxmkgHmTMsLGueap/TK5MRO5MikIRIxMHZ+NjcB5bbeF1UX37XGhOa/tUPzJC6O9O1VoF9epca45Xd/uxnz4aISUgmXrtnJ5oyXKpYIt1+k9GecaN5hW7pcZsYycNRaF4GiSvaFv0sX3OQ48o3cDl7LKNPkw4tVKsrDNA//trgC6B7xPJ9vLL7/MdDp9wkezw/1wSQTUzBEmaQe0sg2/6XKg6yNlFjgoBDo6ZC9wVjDJFWElyWJkvUq7rVIrXnnlZQBefPFF9vb2cIvjJ/vCdnhb8PLL6XN+6aWXgNuf9wcVDxoRcTecD4TWoVxgTJIxeyJhKXExo20Ve0BhPE29QkjJNZ3UaMZK7GLFrbVEh8g0eGQEVStOmtsX0xgjhQusOsfZOknS5+v0+1GuaM/vf+G9+3W9lc87DjJ0GGTyUtC7FLlTuIAOkugkr7VvPgbKbSrQeqd4dfn649dA2zlungWMllRGMbeeECKlkfSLO4sT1VraPlDmiuXpcnu8q97jXGAsBcKn9Pm6UUx9wfX17ePclE+bPtpZC2cP8d6sHuK+7yQWiwVw+zr+RhBxR4B4Hf6f/+f/4Zu+6Zue9GHssMMOO+ywww6PgF/+5V/mN//m3/yG99kVQPfA2dkZBwcHvPTSS7sO0LsQzoc7ZvObmfyGYBkjW7LnpDRv2tZ9+eWX+fqv//r7ft5+SKffmIZtEo19iJyt+6FVHwjxdtu6yvUucf5dijf7vHd4f+Gd+rzntaUbVGR3n/8nq27LNRQiRf086fGR9YHzuifGxCO6F//rvYjFYsGzzz7L6ekp+/v7b3jf3Qp9DyiVLpjT6XS3QL4L0VqPVamBm3KCHKPcbH8nADn8LhrNdPrG1qKbz3jzefuQ2t5SpPnysrVU8jYBWw88gXXnGA23bxxoq1wjReJMTKu3LhHd4fHj7s97h/c33onP24dIQ7cdJwlgOi3wIaKkoKG94/7jcf7YuYd1n0Zs5QMmup+te8bq9tqYPcBm8b2EzXX8jfDABdA/+Sf/5IGf+BOf+MQD33eHHR4EcXBodX7I+IlwWvdYF4gipStvCM1N77aKkBAj+1X2hpb4dz/P6dDVgRRCeL+15OIik2nJorFbM7ZLk5x5bVEyEStDTDLbJ73r22GHHR4fNqaCMhk13yHBPh66PsnP57Z6auPxE0Jk1acIn433z6Ni3tjb/jy953CUPTIR/IOEBy6APvnJT6YgtoFh/kZ4EPLRDjs8CDYFyaKxNDapuLSSIMA6z8mqT0VP57i2VzLdusMmVcUoT+ZlD1oAWR/vsM/vXeBwlNG7sC1uNuZkxeDdcTvJWLDuk8HYyaqjzBTLIaNsb5RRZimj7K0sdG+Ezdhvt/DtsMPbj94Fzps0QtIyXRvPa0s2GEzaQV/ufEBLuQ2j3XgEndc9t1Yd1qfR/UeOxnfkjz3ssWwQYiKmZ2+yDmzUZ2GI+ng/dX8eFA9cAH3lK1/Z/vtzn/scf/yP/3H+xJ/4E3zzN38zAP/gH/wDfvzHf5z/+D/+jx//Ue7wgcWNRcvxqh8CEiHEkEzgiHz++oK69xgFh+OCfOjClMMikmuJFOKhCo5NJtBFZlzvA5Nc40IyUDyrLZVJicqlSY62686x7ByF0TTW4fqAj7BqLK1PfiuFUZysOrSS20wdQTKMK4wi1/e2o9/A+YCPcQiRvXNxW7Qp4FAA0/dZK3uH9w6e/5P/wwPf96t/7ne9jUfyeLEcDAWlgDJLbszrzg1J7Z4XFy0+JE8xKQXWeyZ56sLMm8QNkkKm6I/DCq0Ei8ZysupSV1slx+XLbzKuvx8yJbcu1g/qg2SU5NIkf6CmxvsVD1wAfehDH9r++/f9vt/HX/7Lf5lv+7Zv2972iU98gmeffZYf/uEf5ju+4zse60Hu8MFEcjgN9M6zbi2nTU+mJYejgmXbc/2sZtl5eu95/jBQaJnCFEvDMwcl3kdmE/3A3R8YojeqjNp6epecTueNpbMBowRSpp3bYvDz8D6ilEgW/o3lrO4pjWavNJytOxZtcqs9puNgnFH3fusJAqmb1NjAfmVo+tTK3pjBzcrbpMTW+m0qs5bJBG6zaDkftjEAEVi2blcA7bDDY8K6c9S9Z9H2XD9vGWWaK7OCUaborOfFkzU3Vx1N6ygLzazIUALqPlDodB8xdIjmTc95Y5gWhvPGcnPZoYRgVhoa67Zu3dngGv2gmJYa1Ysh8+3hfJA+qMUPPCIJ+p/+03/Khz/84dfd/uEPf5jPf/7zb/mgdtgBBqdVLfExJRiHGGn6QJc75rVLu6cgOG96miGLSMvk9xPjsFN7hIwZrSRTJTlb93iRZvVN71jHSK4FJ6tua0mfXGkNvXOcDflKhb4953c+YEOy0L9+3lDlmrN1x9k6FTqbIieSSIyZUkOqtifXclvIbDKaAFyI9D5s83M+yAvYDju83fBDO/h4mcZdfhh1jTPFK6c1L57VSCHoXKBe9RAhoPm6aUFpFGd12qTcWLQDD0jw9F7B/shwtjb0zjPKFUrIrbo1jdTTiF1LwbQwb1jUiMHde4eHwyMNHD/+8Y/zYz/2Y7TtbWZ713X82I/9GB//+Mcf28Ht8MFGptPcPFMpp+jpvdTVefFkza1Vw8naUjuHFOB85NayZd5a5m2fftf7rUX6g6J3gbp3OB9QapjrN6n7dFb3/MqNFdZ7MpXGbYrUnbm16CiGgiXGQOcDB6OcWWk4GuVMSk1mFI11fOH6glfPa146rTlZdcQQUxBj5+ic35IZL2JDuPYhEkIKYLz4u0mhE//nQrTBDjvs8GiwPiWM132KktjkpwkhthuPG4uWk7pj1To66wkxkCnB6arleNmzatOI7GictGG19QMZ2nNz2RJCCkHNjWJcGIy+fU43veds3bPuXOo4N/Zeh/lY0FpP0/sPZCbiI62U/+l/+p/yu3/37+bZZ5/lG7/xGwH4x//4HyOE4L//7//7x3qAO3xw0dqUAL9qPa8sGojgfeClk2R1rmWg60HJyCjTxBA5XXYYIdivCi5Pc1rnKZx8oLTh1nqaetiBAXuVwXu5bUl3wQ+dKCiMw3oIIaKcBAl7o4x53bPqIuPCcFgZlrkeHKclTe9ZDe10rQSZEix6y/4ow8fEGTqte5yLXJ7mHFSGdZcW13ljOV53GCnZqzKcj1ycclWZ3iXb77DDY8DG32tTDqROrGSvNFw/bzivIwcjw0tnHa0LjHJNb30KGdWCsybyynnNunN86HDEb3huj72RYbxUrFtHiBtlqWCv1GiVMS0MVZaS2QGIaT1yg3eQjz2zwX/scWLZ2u1z1v2do/UPAh5pxfymb/omvvKVr/Cf/+f/Of/sn/0zYox893d/N9/zPd/DaDR63Me4wwcUde9ZNI5FmzJ0zuue87rHEfA+oJVGy0BmNJ2H1+YtmdGc146XT9dcnRYPxYXpXCCKtBik9nOkzBRaCZadp3UpaTnGtEOLgFIbno6gNILWSPYzzaw0uBDT/30qbjItUTYVTQxZQJo04osOVp0jUwIhkvJt3TtmueaLN1eAwIfAwSjj6qzgeNVxaZLvuD477PCYYX3YFj/zpmfdOlobkjcYUHcWQWTRWOa1ZZJrhNFMK81r5w3ntcO5SGs9ISYTVWLKL1x3Hh/g0qSgc5FZmQJMbQiMh82S85HRWPHqvIUhSDX4yMm6Z5zrhzrnN7lf9+MUNRe6zS4k9dhG5fpBwCNvGauq4g//4T/8OI9lhx22iDFyvOz48q0lv/LaksxI6s7T2oD1gWVrWbWOKlNkSiNkREaBc4HTVYuPnmcPK67ulQ/U/QFoekfvHL1LO6+jSYZCMik0be9ZNR2Ho1R0uHC7RV5lmlGukAi0TNk9de/pfdgqthZNj1HJsfqg1PQRrs1KpAQRYd071p1nLVKhVEpYtY7Xzmu+eHONFDArsyRZHdrmmU5BjdOHSAiHtMA31qOEoMrUB2rHt8MObwajJILUHfnSzdVWgGCUIAQ4XXfslRlKkUxYs5So/to8jeBjTNllo0yTKclpY4dzTWOmkiJTlJkmN4lL5GNMieadu+NcvuQD697T20DjkihjHuyWH/Rm6JxnXlsiaTR+MNh51BfS7o28HfYqSCnyHyQ8cgH0pS99ib/0l/4SX/jCFxBC8PGPf5zv//7v5yMf+cjjPL4dPqA4WfWc1R2ndUfTe5RIUk+ItJ2n6wO9g4Cn6RuuTgv2JwW3lh09oJXC+7D13HgQSCHItOCVs25wgVZA4FBnXNsrORwbzmrL6bpHS8HRKMeGCAQKrSkzyfGqp7GBkQ+0nefcW86bns4GDkqNB6IQHFYZB2PN07OKr57UhAj7VUpaLgbe06oNnKwdIUDnA71rGReKRet4qkinbmv9dtGse4d1MRVQ2b0XyLvb+z7Ghy6gdtjh/Qw1qCxfPF1zvOxZNpZlZ7HeUxiDlJEbq45xpsi0RilJ7z1apqLH2sDeOEMgaK3HDCO0de8RCI5yjRIwy82WyxdC5NYiSeJLk4QQszJDKcc8Jm+hDQ/Q+sib8Z1DiLx63lJ3DqMTiXrdJS+1hMiytcxKw3JId68y/YHzEHukAujnf/7n+fZv/3Y++clP8i3f8i3EGPn7f//v8/Vf//X8nb/zd/iX/+V/+XEf5w4fAMQYsT45p84by/XzBhdgNtJcGue8fNpwuupY24DznuAdymQQI/PaslcZXPCIGMmU4Oay55WzmqvTkuoBFBJCQAhJPRZDakWH4LkxDyDgqVnBwUjiQ+rYaCXZKxVCClob+NWbaxaNRQsgZkQiZ6uO+eAhEvEUSrHqPeeNY9k6Vk3iBQkhUDLtwJI0PrXPjYDDcYbzntxofu3lKVKKbQGjZSrwNqTJTR6ZGOI47oYL4Q63WnvBQG2HHXZIEEIkBZaCpXW0zuOCo+kCeyPNWW2ZZiME0HQeJQVCSko0uY48vTdCyUjdJZHGzUUyRh1lii/fWnMwytgrNTcGGXymJGWeVF/peZN/2bQwZEpuLTBgsxF8Y6wHh+kI9MM47m6PsY1n0aw0SVjxASt+4BELoD/5J/8kn/70p/lzf+7Pve72H/qhH9oVQDs8NGKMnNV2axff2lRg5ErhdST4NMfOtebV5ZoYPNGD8oG9wlDkkjBcy31MknPnI5enBevW89xhxexNsrn2qowzm4wQbUwjrkyCVKnQuD5v2RtlVEaxaCyvnNa0LpIpKDKNdQEtwWjBWdORa4UffDm8jsQAK+uZdxYz5NSsOktuJCdLy95IMy0Nmc7ItWKUaXItmdeOy7McKQT742zrt19oxbjQ+BB59bzmZN1TGkVuJFV229l1wwMwSmKkvMPo8VGdZ3f4YOBhjA3fT7DeMzLJTmOvNDRK0DjFKjoCyWG+Dx5FGotdnhQ0zjN3gRADr80bppXGucjJ6YqTZdoYFVlSfEHg1bOAVBFIdhkfuzrbPr8Pt0UOhVFpTRrWwAfxNYvAKFdEIs6n7s4411gft2tslaW1YxP9Yy4EPX9Q8EgF0Be+8AX+1t/6W6+7/Q/+wT/IX/pLf+mtHtMOH0D0w84H0ux61dqhw6JBBKSQnDc9N1Yd1qZ28/OXCqwXjHPFtNT4IMiUYu0dLsLNZUtrLfNWclb3b1oAGSW5lOd0LgwLUHJrrTJN5zzntUVIaGygNJK2F3jrWHaw7Cy5URilWDSeaSGZlqkAMlqSSUH0gaxQrAcyZSQQo8Q70o5Pyq23kPWpRV1oyeHUcDAyzIqcIkuKts2Iy/vAi2c11+cNy9aRG8XRON+21jfHDanDdTjKOagyWhe2Ya87fHDwQS1oHhZ179Fa8fRBxZdvLFmFQNd7xkYyKwwuBjobmSnF1VnBhw4rWhdYNz0vnrbcWnf0PvF2lBAURtJZx/HS09h0fp+tO8a5AQESwVndkxvFKE8bn7shBa8LOb1f56bQilXrGA8ZY/tVUnftV4bep2PSQ2fpdu5h4gZ+kNaER9r+Xbp0iX/0j/7R627/R//oH3H58uW3ekxb/I2/8Tf49b/+16O15q/8lb9yx+9OTk74ju/4Dj7xiU/w8Y9/nO/93u+laZI8+md+5mfY29vjk5/8JJ/85Cf5l/6lf+mxHdMObw8EqQu07iyvnjWURjHOFMernt5HhEg7l673xBDJtSIgORinOfq687S2x7q0c4NIoSXHyxQSqB8wDiMVMUmiPspvS8s769FaYqTAuoBRiYh8suq5OW84XvVDGnOgcx6lUhv92rTk6qzg0iijKjIOJzmXh/8OqmzgBKVFrOkdL96qeelkxY15g3WeTEu8i1gPQqZuzsZgEeCs6RN5unPcXHac1/22WwbQ9km51vSOunPUXTJgHOc72fwOO1xECKkoeeWs5nTVpc4LyXdLi7TpKAuNMYJpnoQPjXXUvWXRpvPreG0xKnn/THNNpgRaCzItORjl7I/SSKtzntxIbPA4n6J0Fo2ld55Sy22OGCRu33ltWbaOk3W3jdw4XnW8ct7w0mlN299Wc6VNT78NX96vbgejbnyMNuvhB6jZc0880gr4h/7QH+IP/+E/zJe//GX+hX/hX0AIwS/+4i/y5//8n+cHf/AHH9vB/cbf+Bv5W3/rb/HZz372db/7sR/7MV544QV+7ud+Du89v+t3/S5++qd/mj/yR/4IAL/zd/5OfvZnf/axHcsObx+a3rNoLa/NW45XHW3vmbeWddvx0klDNczBz9YWrSNZkLgYWHQ9deuIArwL5JlGq5S+XuaKUZak6AoojWTZWkZvQPSLMXJr2XFz0bHqHJNc8+xhiXURHxTL1nL9vEMpkDKl0i8Hh+Y8RqSQXJ2VjDNL7yK9S27OV/cK5o2jzDyna0euJJFIphWXx4p53XOy7DivO4ySHI4zJFAVhoNRjhgKnuNFw/VFx+E4o8o0lyc5Te+Hhduy7hxago8BFwIGiZRwa5kWzcYlY8gr04JZZR5YHbfDDh8EnNU9Xz1JPL5byxYt4SsnNS8er8mUYlwajiYZV8c588bROcu8dmgp2R+lyJtMSV5dtDwzK+hdRCmJHST0V2cFB6MspcdLwbq3tJ2n7gPjMo2nfuW1Jcs2FUdP75XsVRmdvc3Ti5EtT6jpb8fjuBB4alC8biw6xLYLHO4rihgNo3sXIrlOWWVvhBgj68E0scr06zpS7zU8UgH0wz/8w0wmE378x3+cz3zmMwA89dRT/Ok//af5vu/7vsd2cBuTRSnvvXtfLpeEEOj7nrqueeaZZx7bc+/wzmHZppO4G05EIeBXri+Y145V3xPnLaNc8vJpTZ4bCiPQCqKD097S9A4RI4fjgtlIYzLNpVHO3ijjqb2STEtcANenlvThOL/ncXQusBrIg6VRLFrLvNHkWnFeO24te9a95elZiUAyKQxPzUo654cZfRgywxRVJlBCsvaW1xYtB1VOHQKrtsf59BrLLMVunKxTl8rGwKJ2rDqHMZK9zlNqxdW9gtNVy7oPzNt038qkgqw0irp3lJnissw5HGf4eLuj1g1mkp31SXIvBD5GVq0jHz9YARQu+IN8kPgBO3ywcGvZcbLqWbaWznpeXrWcrlLI8Dp6pBacLHpOlz0vnjSUmaTKNLmRHC97JkUaNz0zyxjnmi/OlxRa07tAAI4mOaVWnLaWtvecr21al7znalEwyTUndY/zgUxLbi079qoMpQRcMIdXUmA99O5Ox/jOpXgcAazaJK/PtWKvur/KU0lx3/XwXjiv7VY239rA0fi9bZz4SAWQEIJPf/rTfPrTn2a5XAIwmUwe64G9GX74h3+Y3/t7fy9Xr16laRq+53u+h2//9m/f/v7v/b2/xyc/+UlGoxGf/vSn+a7v+q77PlbXdXRdt/15sVi8rce+w+sRY6Rzfis5XXQWpSKtDcNYCRofqeueg8rQeyA6YohEIqvWIpVEG8mhj7jgWdaO6pKkyG8vAC7EN0w/FiQCYWeTCeNr89Q9WXeOXAtePrMsW8vJ2vLULEcpgYlpvv/xp2eMiww/FHEvn9S0IVDptFB0Lvl+JA8hz35lyIa8n3GpEQLqrqHxnv2RYX9kaHzyJPrKaU2uBK2LzCO0WaBxgQ8djlj3DiNADuqvw3GG9YFX5y3rQVafyNQ9Te9oe4UpHmzRsj5sZfMbL5H3+q5vhx02cD5w3ljCcI4vmn5rQupD6ugYlQQWB6Xh1rJnXjcs+oCo4fnDirN1x2hP8uLpmrp3ECWHI08McLJu6V1kUhiaPjItFc+VGV+8uSQimBUaHxRSCGxM60Y+dGE255mRgnb4eTRweja+Qo3tk4LMqO3YOwI+gg+JTL3x9gkhJnVYhFGmHpgWcBEbniZAiGkUp9/DxolviQRw69YtfuVXfgUhBB/96Ec5Ojp6qL//bb/tt/GFL3zhnr/73Oc+x7PPPnvfv/3bf/tv84lPfIL/7X/736jrmm//9m/nZ37mZ/gDf+AP8K/9a/8a/8a/8W9QVRVf+MIX+NZv/VaeeeYZPvWpT93zsT772c/yoz/6ow917DvcH3XvBq8K+TopdnJFjhh525l0WhrO1mkHVfcmdUmc53RlsSHivUcAXe/wIfKK9VytNJkxtMKDh1FhKLVAkMjPMQTGZeC1Rcc3Pldtn98MxoT3QmEUM2V45aymc55xJgdzxEDvI8tm6KQIxWuLBknkaJQjRVJcXJ2VQ/hq5Es31rx0VrNoPVUmmZQG65I54um6o3We03XH03sVv+XDh9xad8zrjkIJjBa0Hpat59AobsxblrWjVkCUzEpDriSTTNFaR9d7ri9b1l0qqqZlxo15y6J1OBfYHxvGuSECmU4dIykF+ZuMBNNnmdrpTe+wIVn4P8yOcYcd3s1YdWlNAbDDGMiGgAI6D5mUPHdQcnPdE0PgeN3Qu1Qs9S6wlykypYYAVLDO0/TJIFUCJ3Ua6c9GGZmWjHLJ3kE2KFoVWgt0TLzDSW6YFRolkxXHU7OSunfbPEM/KLUgjdCuDSOy3qWO0WatDZFtyDKkTZ9WMG9ud28657k0zh+6e7MRaUAqyN7rm6FHKoDW6zV/9I/+Uf7m3/ybhEF7rJTi9//+389P/uRPUlXVmzxCwi/8wi88ytMD8JM/+ZP8jb/xN1BKMZlM+K7v+i7+j//j/+AP/IE/cEch9vGPf5xv+7Zv45d+6ZfuWwB95jOf4Qd+4Ae2Py8Wizcsvna4P9ZdGuFAMulL5oJy+/Oiue1MmqmUoKyk4GicEYhoKTleJcdl51PqOVFyc5X+DiEYKVDGkBtF6z25EeyPS7RMYaYS0FqiZUpVr4ZUeMHG3PDe6F1gZS2ItGiMC01uFJK0o7px1rA/dEBq67GkoqnKFftVxihLpmhaCvrgWA9FA0ISiETEYMwoyJXERVBK8A3PzLi17Kh7x/Fex6/cXNI3jipPjrGndYcNAYRgv1R84zN7KAmvnresWkfjkr3+rMoQEr50c8X+KNuGqp6ve7SQ7I9yCiMAwbJ1NANx8tpegZKSMEj2L+4MpUjFz6pL9111Lr0vO/7QDu8DXMz/HOWKVab40H5FY8PgSxbwRDKtWfU9q87R2UAmBSqTCCFZNamrMik11qfz67zu0FKQa0GMCjF0S5IbfORwlLFsemJIhcSlabl1oH7+cMRoMDo9G9LhN8dqfUDJ2+demanX8XsKI7edGiFu+wbZcCeXyIdIbV0SdWiZIj3epCCalWa7KarMe99F/pEKoB/4gR/g7/29v8ff+Tt/h2/5lm8B4Bd/8Rf5vu/7Pn7wB3+Q/+Q/+U8e60HeCy+88AL/0//0P/FN3/RNWGv5n//n/5nf/tt/OwCvvPIKTz/9NAA3btzgf//f/3e++7u/+76Plec5eb7b1T4OuAvqBUjkvGwQG66H3ZYLkZNVcj2tck2uBas2ycjXvaPpHXmmmBU5fQgQIz4mYktrPUIKrPX0UjDODbNZiY1gJDw1K0GkxSbXgtKki/XdJmD3wrpznDVpYdgYiI0zQZElf56nZwW/emvFzUXHWAie3ivQSiBFcny1PvD0XkXrPNMi5+m9knnrGGcaLQWzkeZ0ZSh0j1QKoySntWV/5KhyTRQRGyOXJgX7lWdcZEzyRGq0ORSZZFYZ9kvNeeMY5YbjZUPdJ3XXqvPkSnBtT213ijFGXBQg+P+z9+dBtq1lfTj+eac17amnM92ZCyJeQSYhylVR1ASuSgRLpJREiaAXoyBoDPyqwC8lAqWlIoUWMQpEKUFEhFAmghURxYSgcoHEkMjVO997ph72tKZ3+v3xrLV693ROnz59+nSfsz5Vp+p07+69V++91rue93k+A6xz0FbAWAv6C2nhfHQtQ6wk4oAKxqVO2HSFuqHE2lRDcIr1iJSgIFZ+fRqntdg/XKok//63f9e+H0MnlNBZCe+BhYQ8ts5PCuSG1qdACDDh8U9FFb2jJPKyQD8OkZYlMusQwKMXCPQDgQkM0ioD0AJIhEAm6dqZ7yhIwTHNDYzzSELVRNvUUnTGNnpzScFQbuL/XAxJIJtRfihFc52GQiCveEOCMxTGNpsgU1I0zsXWSbaLn7kaMFV+m7rEsd6e/pI/+qM/woc//GF867d+a/O9u+66C3Ec4yUvecm+FUDvf//78frXvx6rq6v42Mc+hre//e34+Mc/jqc//en49V//ddx999148pOfDOcc7rzzzoaA/Ru/8Rv42Mc+BqUUnHN47Wtfi+c973n7ckwtLoxA8uYiYwAkYzg3yrGSkkxccfLkybXFY8McgeQ40Q8RB4o8KjTJ2G0SojQOygIMDIGIAZ7BaIUkEoDzyLSF4uTEPBdKRELgxCCkFwbDzfMJnnnb/K4vWO9949BcZ3wt9SKc6EcwFUdgoROAgRaWxSREIAVl7mQljKXQwkGicMNcBG0tViYl4kCgVynSjvdDTI2BrMzUGDwVIAFxgZwD4D3S0qETedy21EUgBB5dy6CdRT+SWMsNBajCI1QSC0mI1amBsw7zSYiFToBTvRDee1jvIBlHpGTlLm2ghGgcpEtNCrxepNANJQYJHWcwI5s91g8RZLz5TNPS4PQwgwcZwPXjNkqjxdFEIDmOdde7zZHi6Ec0Wl6zNLLuSoGAkwP8IFIIFUcvpFH3WqbhPY3OblyIMSkdJGdEkJYCQSVSWOgEWExC9CNFvMZq3EZ+XJTJlwQSvVA1HfPSOKjKjsNaj1Dxi97grfNYTcuq27RR1TVIFIKSOr1xtdmchfN+89MdCcxOHci6YPfYUwGUpilOnDix5fvHjx9HmqZ7ecpt8bKXvQwve9nLtn3scY97HD7xiU9s+9hb3/pWvPWtb92342ixO9RzcckZoiqsc5RpPDbMsZqWEBzgnuP+81Nk2uHMKMcgFrDO46YB8Ghp8NhaSoZgUuB4L2oycnLrEHMBxEC36lRwSKTWopw6BExgyDS0cbhlsYOvOtHDk28eQFQp8nEgmhR3zhl64fbcl6VOgAeXpyi0w/F+hEBSd2c1LfDQSoZprnGiH2GhFyIUAh7AP56bwHuHtZwuxFsZjYAHiUK/WjBX0xJnhjlK69FTEmtZibUpHa+Dx1Inoha1p6KjnyjcspCAMYabF2N4BiyPC5QWODsuwQFwXnd4PG5dSCAEx1wicbwXY6ETIA4FzoxLCMbAvIM2FjcsduCcw9lJgTS3WM1LTDINxTnmuiFuRwcn+hGA9V1VpATS0mKUlxCoxn9Vp++xYYZIcWTVoh7POFC3aHEUwBjDI8MU08JQFI1kcJ6h1B73nhthmBkIePL80Q6RYIhUACVovO6cg2QMmbXINcXa1Envp+YjzMckSogCjtLQxmVaGJwe55hPQkSKoxeqar2hAmeY6WaEHSmBwQWUXLOYljOcJuuQlhsjMCLFMSkMhpmG4Ghc4XeKzjkIuGozyxnbUa5/IUyL9UIu1xaB230ht6cC6Bu/8Rvx8z//8/jd3/1dRBEtllmW4c1vfjO+8Ru/cS9P2eKIIyttQyicFqYJ9Ds9ypoL0jpAOwoHXJ4UKLTBsrNY7IU4Oy3gHPDYWo5xbhEpi05MRmMFLNKC/Db6icIoLVEaGsF0lIBxQGEteolCJ1BwzCHVGv/v9BiJkjjRDzEtNBgYhOAkKfXYsqgwRjLvE70YuSE5/Om1HAu9AF98aAjvPXLjoF2GU3MxMm3RjQS6ocR95yfgDFieFtDG4AnHevBg8PA4Pczx8HKKqSEydzeU6IQKSSAwLS0K4zAXexTG4UQvxJKIAO+RlxbnXYGlbtB4/gCk4GBENcA40xS14an968GQhAIOHuOCip7UeYSCIwklzk8KTHIDxoBJqSEYkcLHpUXP+Q1cn3phkZzBOI9YkaR3WLljA9SdW011ZWJpYMYepwbxtl2hC5HjW7S4WpgWBpPcoDSk/FxJCxTG4tykwPlJCeMcrPZYzXP04xBzHYXlaQnGAAFyVAYHzqyWcJ7BGA/jDeY7AUY5xd5wBnDtYZzGtLDox4q8ugTHjfMJArUeceG9b4ofoA48vjg/Zxbee1qPrd9QWExLssUAAFigH9FGUHJ+VQjN3nusVB0rgBIBBpfYUWaMwdcjxOrfbrGnAujXf/3X8fznPx833XQTnvrUp4Ixhi984QuIomjHrkyLaxumItiNq+BPukmKKhHZUHaOdUgkR24Mck2LQ6gErPVYGgQY5QZJJGGcQyg41iYa2jl4C2hHKghjHCQX6HVJjTDKStiKSKhKh+NdAWPJATnXOZz30I5m8L1Iolu1kM0MIbBGN5JY0R5JJBA5AcY8ZJWfxRlQVv5AxjpMCuL8FMaSDJQxcJDE/vxUw2OCJJRkOc+B3FqU2kEIIDcW3VCitB6CgYQE3mMxIf8QIThWp2WlWCN/koBzBJLI4+PcIFAMc3GAY/0I58YF5iOJxV6IUAqc6IX4P4+OcP/ylGz4A4HFTohQcTy6mmJSGvRChbx0iAIqIEMlMJ8ohAFvitgaaWkhOQPnRGjvhRLWE0G6GwkIzjDKTJM0TZb+fANR+kLk+BYtriY4o38r0xJnRgU4AOMpe0swSnovvIX35Di/win7r6cEpsqhEwr0owCSexjPMS1KdEKBXqjAwDDMSnRDiXG13pXaISsNepXBq7YOc3I9pkdb4jBq65rOTH2TH2YaRdVln3V4rtGtjA3XUg3nqEs8ysn9PZAcdhNHs+YJXSkY6zCuRvbdUG5VBVeu1jUKYwFcWgE0iBXGuSYieiRRZuXFf6nCngqgJz/5yfjKV76C97///fi///f/wnuPl770pfihH/ohxHG8l6dsccQRKSLr1j47gjM4D/RjCc6AQRygWxmFcV4lLbMupOS4daEDwYEzQ+pOUJYWEAgGYwGtPeZjCXAGxjiWehycc4zWcmhLYYVxQO7P8A6p9liekhV8WqnAbphLYGYutO06EIIxdEOG82PyCerHARa7ASIlsNCh/KxYSXQCgYWOwsOrGc4OqcgKBAM8LYyqUlVY58EFGp8OKQQCzhAqjkwbOMfQjxVCJaGUQC9WODmoiNOhgnUOo1RjNSuRlQ4hB8aFwclBjJO9CPcvT+E94EEt5H4kcawXYZwbeACxoiwyxujv6UUKcwk50WrrYeFRGof5ToDFRGGQBOhHipKtZwJTQ8mhBKNIEgC3H+/Cew/vaVEtjWvUKpwRr8JuakNfiBzfosXVRKRIxXn/+QkkZ1joKKxOSvQigaVOgDPOwIFj6h2mhYXkAaJA4tR8iNIw3DCf4OQgwjgzODMm9ZdnHoFkkIKS3gvtsDwp0IsCLHVpLemHEklE4/y8tFCCodAOpXVYS0uU2iEORdNtzbRtJOiTwqC0Dse64UbVZmVsyBlQWlqL89JiKgyFLKsZjia7ssUPAIxy0yjShpkmD6OZok2wjWvNbpLuNyOQfIM1R5nt/nf3TOeO4xivfOUr9/rrLa4xKMGx0AlQWmojF9Yhqi6wWJEM3YNO8JsXOnDO4yFkyAwVTbmmEVokKb5hnBucHIQorEUUCix0Isx3AiRKwnuLc1ONUVZCQCCJSC4+Fyt4eIynGiujEv04QBhwjDONsmdxYydGL1JgIM+Pca435GGtTAtMjALgUZQe/TkqBpTgeOatCxjnGqayoT87LnFmmGE11TDeIwkoekNUOWJxwHDDIIF1wCQ3ODGIkRYGieJgkuOmoINRWiI3FqcGMQZJgCQQKK1DKDmCjsLDaxmy0mB5UpDKzHgkiuHsOIeDw73npvDOgTGGWxc70JaiPDJtkRua/RfGgXMaY1m7TozMtMPNCwkWkgBzSdC0yZOAWu1zMXXoANpVhZI6X5wxeNDf5OEgBf3eiX6E1bREIGmB27yQbSbHX6pao0WLKwXGGBa6Ab76VB8Pr2Y4Pcywkha4bTHBLQsJ/vGcwNlhDsUZxpmpgkUB7TnmuwpkbOExlyisTHO64TvaXHzNyT5x9jiDcQ5rU92MtI/3I1orNY3bzo1zGv8wkNRecswlZKxKGw463mlhkJbkPp+WFif70RahRxxIlJnGMNNEiJYcPNMYxLSmZZUr/jjX6IZyT6aIu8HmjZDzHrM0Zc4ZFpIA09JSR/mAFWZ7frV/+Id/wF/8xV/g7NmzjRdQjTe96U2XfWAtjh4YozT2bkR+O0pQUvukMM1M21gP4xyiQFL3wDpkpcZKWiLTFufG5FbMOcMwpy7JiR7tXAZxgMUkwCOjDABDohS8Z9AOMLmG4g5nRwbGkxO0h8Pjuh1MtMWZYYGbFxJESmB1Wm6wc6/nx5PS4HzmMMoNwBjS0mC+yt3y3mOUW5RV0GAcCDDGYawHFwzekUwdDIikwjQ3OD8uccN8UkVI0E0/1RaBAwDiE40yA8kLMACch+hWs3rGGObjAEZTMv3ZUQ5jgMIYxJHE/37YQAgaW3kGlNbCeSriehGRrwtjYb3HIFJVir1BP1FwALgwVfo8tde70cZw1EByLHWJd5SXdAz14+cnxczMvsSxboh+rBAHJJGvi6BZxBVvqc4cagugFocJgaD1ZZIbPLwyQa49/vHcBMd6CY71yKPHM4ZYkaw8Nx4r4wLDVKMXBkhLGvEuTwqspBqh5FjohuAcWOoFWJnSGGqpFzad6ZW0JDVY1Y22vsrt8x7jnGgDi90AghNPL1aisdvQ1WYk8xbnJwWkYBu6OZEScNXGsu66FNoCsYLkDLmx5AVkiJe0dIXMTTuhaIwca1ftzZCCYxBfnfVgTwXQf/yP/xGvetWrsLS0hJMnT24gZzHG2gLoGoKrPHEuRJCrixvGNs6UBWeIlGyItQDNhJXgzYIxzQ0eWp4iLRwKR+oEz4HjUYBRVqITSSjJ0YkCnKrUSSe6ERmUOYqQqDwCcX5cYHmSg3OObqTQCQM4z9EPJbqhwMq0xMl+vK2dO/2tDmnhmwtWVs7SSjA8ukajrno/E0mBuURiXPDKy4MTyZoxDGKFSWWrX4cfxoFEHABuUuDsKMc4LzHMDE4MIlhLqe7HBvT3MVCh4LwDmIfitKCtGeoYdSDgOUNa0m5ScIqo0MZinFsYR8e8mASQ1eJZaIfSWEjBKRpDKCgh6P0VfNtk+LySyAPAuHAoDREUs9JWYz8aZzoPiKrAuxC3+UoRn331GdLorvUmanHpYIxhsRPgzDBDN1QYKYvVzCBfneJEP0CgFE70PM4gp4iaEsi0xvI4xyjT0C7CKC0bg8HCOpybFFieGtxxqgfrAe884lDg3KiAlByJIguNbqjAGIPgqIJGGYSgUTmluRMnph5vCc5wbkxO77l2JL6YlFjsBhuKoNokse4c1V0e5zcaQG7u0uwnkkAiEBzO41By/vZUAL3lLW/BL/7iL+Lf//t/v9/H0+IQoU5pN5ZC9hY6W0l3s3JNwSigry6CYkUE2VpFhOr7vYiMBeEZUk1Fz7Ty3eEM6AcKglEIbizIf0NykqM/vJajEwp0Q4VuINGNFIx1eHglQ2oMkoxu6DfORfiaU31oAyQhR1ZarEw1pqXd0c49FAKRAo71InjnoYTAXBJACg4hiNeSVu3nOKDnedxCD2OtUWiHXiSQa4+1yueoHylYDwjuEQqBTFPbeqETQEoOzjgCzgFBY7ZJbpAyRpEghUFpPLT1cN6jEwgkQYxHXEYFTxJgUlgMYoUT/QiPW+pSVpBxcJ4KKOr6VPwCyRFKhvPjAmDAXBzglsXkggVD/ZnVTtCFdpiWJPUtDKlLTvTDPatHaqLnZl7ApT5H7XsiKmLoUbfnb3F1wKqsO844RMVL8Z46vjfOdTEfSRzvRbDW4wuPDGEcbeBEzPDYWo5ACiacrAAA0SBJREFUMngPSM4bg1cGQHCBJ53oYaVXYnmioX0GZhnWUo25jsKxXgTrqMsqOW+uu1hxLHaCLeOpuSRAVhqsTDVK43DfuQmWeiGy0uCWxU7TZaHiKcC0IGl+PV4SnCGcWQOvtCLzSo3X9gN7KoBWV1fx/d///ft9LC0OGcYFFTfUEdFk5NcLNxRBdfFjnccjwxxSkFrhhrm4mUsvdAJkFYcFIK+KGwYRzs3nGOYFEWeFAPPkpzE1Gmu5x8l+DMdpZzQpyEgR8JjmHou9ECd7IRY7IR5aTXGvnYB5hsVOiEEscfvxPm4/1kWuLc6NCwhOwYPTwmCxE0AJ19i5P7pGf08gBY73FRG0BUM/ltT18R4neyGWJwUmBSWvB1VBlMQC4B7eGSgp0Ys4Um2gRIhQ0d/fCRRODCL8/SMjDLMSgnHEocB8h0Z7pSEOkbYUlpiWFtPSwFiPtWmBtLRk+ig4Ts6FyAqLkAs846vm0QkVbhzE6MQKK9MC1no40KhsXM3/jXMw2sM5hk6oqgLJbggypI7aelcHoJb11HucnRQwxiEUESYVbypUxEm4ULTIhWCsw0q1Y2agRf1iO8TSuKYjVStK0hnfE+s80kpd06LFpWBSUDSM8R5LXYWvnLXwziF3wLgsMc1HlVqSIeSAkoAtOboRqa66EcepQYyeEhhpi/kowMm5CINEobQWoVI4NYghOccjKwIr1cYxURydUMB7YhIZV8BpWxVj4Y7FQzdSODXn8cDyFKEScA5YyzTmMr2BEKwE8Yg2Yy6hqJyDIEIfZuypAPr+7/9+fPKTn8Tdd9+938fT4hCBJOzr4ysPkmHWI6ROKCE4o5avtpgWBoNYIi0NHjifIlQC/Yjau6EUmOQFydKtRyeUWOqGiJXAIAlwbpxjXBgwweGsR248HhlmuHk+IuKxA/JSI1ISScCxmmrMJwrDwqB0DvMxdY0EA7765ADH+yEk5+hGAmupgZIMvcpfYic797lEgVtyQnYVibFWLswnCpEkAnGqLR5ZzbDQCbCa6eoGvq5w+6rjPVhHhR5jwGISYpSV0MaiFwUY5xrj3OJpN/fRjQI4Rx5AdZEzzUukhcFDqxnOjjIMM41+pDDKyFvkhvkQsRToxwFO9mN0I3J5NtZjeVpgLSuRFxbaO2jrsdQLcdNcgtVUIwo8QilgHfnyFIYKJHhqsfNqFFBL3oXgCLmACjgK6xrJsBS0aG63QKcl+aoAFHS73Q4zN65pw3uQNcDFCqBhphu32lFGPIvNHSzejsBaXCJGaYl7z03Iid2TYvJ4L8BjzsMVZF8xyTKMC4NQSkjBUJQWSgqEQgLSoxMESEKBGxcSPGsupmw9R2Oowji4TJP3Vl4iDiUS66Crgv7e02MsdENkxiKSFGNzrBduW7jUCCTZTPQjhWVTkjmp8xhmJfqx2hXHrvXiuoQC6J3vfGfz/yc84Ql44xvfiM9+9rN4ylOeAqU27rjqSIoWRxv9mLwrrCNFEWc0sqpjFCaFwXysMC0NpiCuyrQwKK2H5RZnRjmMdVjohLCORjlUQPnmd2+YS7DQCdCLFJbHOZZTjbwk6XovpMDQrDIQ1B7w2mBSlOjFEg+c9xCSo9QOjDN0IwXJBQTzlJM1LRErjuP9qDFp7FZF23ZQUuCmQYKiknXXN2Rtif9iHJkvMjBMC4O5RGIhCWCcp4gMRiaE/Tio3j8FXvn4PLqWY3laUAq7FOjHCpn2AAypPWJVefxoLE8KnBsXmORl5QNCTsulpfDCpPIK6kaSulTV8RtLx2isx2PjHJEQyI2j7tqcx7FuUIXQcgSSNb49o4wImr2IsonWshKCc4SSQ1RRGJOCjCGP98NqcSfX580LrXPrHCqAPEiiyjup0NRZC5WA2Fy47OJ89DPEBQ/iMnQCAW1cM0qrO1K+upG1aHEhGOvw4GqKUaaxPC1QaIqc4JyDM4ZJqaEtdR8z7RBK6nrHkgKOp6XGUldhrktRMoudCE+5cR7W04h4lJWoHSC0cZAVBSDX5K11ZlQg1Q4rWYn5JEAvlAilBANrureK8y3Ug1AKzCXrasrCuGpMLpuNVIuLY9cF0K/92q9t+Lrb7eLTn/40Pv3pT2/4PmOsLYCuEYRS4LbFTmNkVduoZ4VtTO06gQRARNgzE1JF9KMAN85HKDSpnCIlEEnypKkjFALJ4QGcGsRIS4O8sJjkJQTo5hoGDIMogPEe/TAgR+iSgga58NBW4N7zU5zqx+hGAqWlLsyYGxiPalRDbeUkEJjvKJzsR4i3IfvOQlVKhbK6qQJVplkVBEpzdI8zE49z4xLzXUpvFpzBOAAeWEtLzFVclJqjkhsL7RxWxxq3L3Uwn6imUKB3hLo72jrMdQIU2mBYCEjjYC0gBSkq6i5UEgikhcW0sBgkCmumxGpaIistJGOVPxBwvEtjNiUFBrFCJAUcgFgKZDPBiMaSYsRah1AJSEFfR4r4OYOqQJuLt/LAZrEdnVJbh0fXMowrB+obBjHmOwGsX+dU+ercmEvUjoVLJ5QbMn/qQna+s75TLo3DWlbAe9p999txWIsLwDhPvj0AAAYHB1b5Yw1ihbTQeHA6xTjXyLRDN+CIgwCn+iG0Bc6nOYzniCXHUi/CDfMxBGcYTjXWshJZYTHXUQhkxYcEw/EqW/DMKEc/UvAAjCXrDyVKaOuw2AlQVHzK2a7sLEIpEEqBQDAMc1156hDncrfw3jfvwfW4Ydh1AXTfffddyeNocQhgK9nkrHU652xDZAQDqa08iP8xKYiTcW5UoBsoOOfBsD4qCRWHth5JQPyc0jhY65tk8fmOwrlxgQfOAx4MSz2JSIU4Pkfz8tVxiTDgeGRYQGuPfhyAC8AZB20tAIdjvQ7OjTJMMvIRKg1JzOeSAJ1AohPJ5vV2i1l30aQKKqWRC5l7PWGpA+up4yEqguEoJ2JirjMs9UIc70UIJZEaC+3QCwNILrDYi8AYuWYHFdtylNFCBObBGcj00QNKCQwYw8leiNIBknsMkgDakox1mJcorQUHw0JCs39tHb4uUsi0QSdSCAVvujuldZCCN8UPQEaHealxeqibxTcOBHg1KlzoyC38oJ0gOP3O8qSAcb5ayF0zEvOeHHfnEtoxW7eurCstjQG3U6QBVACFVeGsBEdhaOynxHqMwKQwzWgtK2mkcBjVJy0OB4IqIgZVJINkDJn3mOZkTJhpi0JTtzEQHLfN93DDYgRnPcalxWLSh+ceSShwahBhLqGO+PK0gLYemTGYrBjcOB9T1pcg8vF8ZRuxOi0r+xAKUK1jKh5dy3FqECEOiYeYm52vi25VRJWGvIN2+rk6HsN5NAKVlWkJV3WrFjrXn4Dg8OXat7gqcM43FwNATr2zZNKaYOq9x3ysgIovAlSybe8wLjQk55CCYhPmEwVtHETFyfMeKLXDuUkOJQRuHEQoLce5cYbVTMM4h04o0QklbhjEMNYjKwzuPTPB6bUM/TiA9Q6KEYH4mIyri90hDiSytIDTlNgcVV40x/sBkkBd9Ma9GXXXY1x5GKWlgRQcJwcxtbIFhxQkgV/qhsiNJRVXSR0NsLpNrbDYCaj7AeDUXIRI0kgrVLQYrmYax7ohhmkJgGGpG+DMKMV8R2EuCrCSksR1eUpqp9VpiUw7TIoJBAf6UYg4YOgGEh7UibphPsJCJ8A4M5U5I3XvLHcNb6cbCihBqdCUFE+jqH84O0asBCIlyctpLrmgvB2gYkM7V6XIUwpaIFijNJnFpexQt/xu0/K3WEt18/25RF3XZM4WewNj5Dh/dlRAcVKxltpDSYblicZDqxOsTA1ZXUgO4x0ef6yHgHOcGedItcMwKxFJCW1rFReNogtjkZUOSUBjd10lv0eKgzGBrz7Rx+lRhlI79GOF3FhwRhuFrKTgVes1OCMBx4UQVURoV23KtlvvRplpzEgLTZy7er13/voUEOy6AHrd616HX/iFX0Cn08HrXve6C/7sr/7qr172gbW4cmh2AhVJL5Ac2rnmYgBoptyb+Z2VaUGmf5zBAlBVNk2kSO5+wyBGWliUziGSpGT68mMjDOKgypay8PA4O84bR+ivnJtACeC+8xnykm7A8AzH+0SO/tLZIc6sZVhONaQQKK1FVhoc70W4ZTFBEih478EBPJwVSAuHXJdVZynAYjeE9eySi58a5LNBC8Y4M+hWi1AgydAwZsCNcwnikIiRtdFfIDkCIai9bD2O96jlnZUUUZFWyemKc0hOhUtWksEZOU8zdKMADAZgwPFeCMeAfhyAweMfz06QGYuVKcnrk2MSZ8cO55CjEwQYJAq8Cjm1Djg3zpGEAp1QQon1IkFw6pwsTyweXs2I66QpeFYwhoWEY5xbnNhmQXXOgzEaedd2CdZ5rKUahTYYZQZznQBxYCm6YxBhJS2hBMOJfgTGGKXNe4/S2GbsGAoOyd22XZvSuIqAzbcUVqUhq4ZuKLGWlc0IrO3+tLgQpqXFJKdRkwcVCZPMYKEbINUl4HnTbS09wzAr8MhqhpsWEnQjibU0RaQElnoh4IF/Oj/BYsV5JE6egwU5LufaNXFA84lCIDluWeg0xzLOddMpXeoGGGYGjAlIDpweFehGxI3c3OFx1Zi9Xr6d8xvGwjX0jGExceg2DqyvRwHBrguge+65B1rr5v874XqcIx41bNgJGIvFLimmaO9OmCXRpYXB2RGNvSJF45TFRGG1slpfmZaY7wR46k1zODcpMM41RpnGMNWY5pQq3o8UTs3HAGPwzmNaaPzT+QkUZzg7KtGNOG6aSxBI4I4bB/g/j6whLSwm2mJS3dAW+tQSftrNc5jrhGTGJzkeXkkpd51ROzgOZSMFDS/jBmhmDBO7kYS2DsvTEpIznBrETaEFEIH6lsUOzo9zTAqLTiiawE/OGY51Q5we0rgn1xqFtogDCecpUPQfz05hnEM3kJhkGrLK2OqEEjcuxAglx/KkxNlxAcZJBm5rJjADcV38uq+H4AyPrGSYlhaZpg7PQoc6Y9PSIJCi8VZaS0tEUmBtqsGYRyeg8ZT2ZMY4KTQEJ4IxYwzDlAijpXH0nNWCnGuLQls8OqQ4AOs9jvcjcACL3XCDPNc538jgA0mGcIHkODvJYYYex3rRBt7DMNXNOdsNaaSZYX2MV5+vgeQ43ouuGxL0ba//k6t9CEca1lJG1zjXeHA5xWpGwoly7BEL8ts6P8lRGod+LODAUFqH+85PYaxDEopms7OWlrBOoRNILHUCjAXFz8RS4PykxKkB5WSSkzNlhc2iFyk8bqnbdNqjgIQna2mJaUn2G8QT2uiobGdiMoCNhc4sQrk+YqOuEnW4S0th0Xu1tDjK2HUB9KlPfWrb/7c4eti8EzCOds/znQBpaYnHEdBF7TztkkRlZjjMiLeSGery1ERe6zxO9EN0tMS00Dg7LnDfuSkGXYnHsy4CyWCtw0Ki8OgwwyS3ZMPuHRwcpoXH8R7FL1jncXZSYlpqdEKB82DoxALdQODGfoSlboB+YwZWVrbwHDcOiBB9shcBnvhHlyP1jJRo1E9pYWDhyZ/HkGx9oaNobl5l23RDiVAkWE1plNirdnsAqkwuCkcUJfFkkioOhEwfGSQ41jINcI9iTKGIUUBy17k4wFqqIRiwlITE0eJAKDgCKREFHN2Ke0MkaY7laqRJOV4ew9QgV8S16YHm/7WUvB8rlM4iLSwGFacgkeRXRGoxautHimMtJcL1NDdYnpa4YRBRDIZzSEuDTiBgHGW6hYpt2LFmpUVpHaa5wWpakj1BqFBoB2McprmFFFSYTauWvHW+KX4AshegIgdNdlqkBNkxGFsZva2fv9cbr6HFJYAxTEsSeTAA3lpwyWGMwXKlCOsGCqWwWOrFiKVAmhXwTFRu7YDzGpNCotQWtywmxIEzDqFilS8asJAEmBVm7XRK1u7NxrqGkuCc31DwbO7cyE1mszuteb2I5PHW+WaTdCGpfY28CmGVfHv7kKOMa+uvabErSE6OwNPSIFYCvVACEk1EnfOVB06u4T2pE/qxxNpUwzkanQwz6mLU4IyBc44bBjH+4cwIo1xDCmB5VMKaEcAHlI5e3ZyiLsfqtMAw04AD5voKS90AHh5ffGgNy5Mc2jgEgcDNix3cshAhVhLaAfevZDhuPKa5wTivZKYMODPKwRAhWpDQzmEyIen74h5ybkpDHQzFGXLrwAXDJDU4N8oxLS1CweF9B7ctbvy9SUFjLsYYxoWB4AzLkwJnhgXOTnJ0Q4lTgwhS0EiqF0mcHRWYT0j1URoDrT2MI85UWGV1pdpivhOiFyk8NszBmMd8EuLm+RhpSYWH4Awn+xFlfTGGW+c9HljJYb0HvEMUCJwZFVibFpjvKCx0IxzvBjDOYWWqYQ1wohdVoagCJ/sxhrne8J6EgmNlWuL0Wg7O0Yw4c+0gGFn4x0rCgoro472o6eLU0Rpr0wJfOTeBNh7zicKpOVq0z41zTAtbkZoZxoVGR0ksdoIN3cm6VR8HAjFosU9Lg4dXM3jvkSgBX3lYUYac2tVC3+L6g6yc4GNFyewPrnoUXsM7j1I7nJyLcWohQV5aBIrO/WGmobjATYsxrPeQkuHWBYVhxjAtHY1gnQdjEpl2KAxtDqVg1eZy5/BR7z1WUxIjTAtDXWgGqOrHleCw1mNqTVPE1A7WuXaVbcbOm77ZxyaFQVpQ7uJcrLY9Jm3JC605Phx8YOmVxK7/khe/+MW7ftKPfOQjezqYFgcDbSmCgHJrgHFhoATDaqabLJtz2jYneqQojoJzhqUeKQWmBREDvXMonMO00PAgg8NuJJEoARsFYKxEohS0dnhM5zg1iOE9pRgf74VYGZPfTkdJSC6QWwNtPU70IgRKQIChVxGjHegmLBjDQyspOKv8OUoLDuCm+RhzSYjlNIcqqgwcRxc9Z4yk/JJf1JqdiMYFMSRBpn2hFCgthYBGUqAb0uIzKUxzc821xbDy1AmqEdbZcY7TwwIPrEyhOIdzGkudAI87ts6w6keSHKK5QBZIWKchHNCrYjgYA1DlXS1PSgSC3GOt9xgXlE49SKhTwitejvcesUrAhUChLTyjBT3XFBabaeLeTEqDfpUQXWiHU3PUpg8qZRX5nxAnaKkbonTU9gdDRfqkRVsJhjhQiCrjt24gEQUCinOcHeUomiR4huVpCWOJKH1mXCCSHLef6FWjWI3SOixPSnRChbEz0M7heC9EaUmt0ou2ciDOj2kkBwCPZTkYKH9tUmhkmjgc2/EiWlzfiOQ6qZ5zBiVQXdcWkjuMMw2lOAahhAfQTxS0tViZlDhhQ5yai8E5hxASccgQSYFeIJAZh0iRRchqSmNzJUiyHqmdi59z4wLDXCOqxlWB5FioEuHnYoVMW4wrK4i0tFjqkv8Ym1Hu7galoQILoPVunJttrw9j/aavtx+vHVXsugAaDAZX8jhaHBBoLOCrGytDVmo8uGywPJFNanmuPaaFAQeQhJLybEStXmAYZhSLMZcoTDKDLKNQvnOjHOfBUBQWc50ApcnBAoFbFhNklUoKfV/J7CWyUkMIILcWj40ySAEsdCJ0A4FCkDeHA3BqkCA3DqW1WOgESLXFuVFGDsZgNJ9PFPpRUAWXZtCWCMiLVfpyt3KBTktgqRNekBi9Mi1wbkImhIPKzFByBik44sqpFYxGSPefn+KJJ+h9GeX1zs01BUkgGMZ5iZVJASE4FpMAnK8vgL6SptexDsd6IRhj0JMSpbGIBEcnkHDO4/QoJ2k+PDwCCkF0ZUUA5iitr8JbOUrrwEDHH3TJyPDhPEUn4CgMo3yyOo+scpUdZroJq+1GJL/lnDp+nAFgVOSFkuOOG/oYZZr4D6DdJOcM02ok2lF03qxlJVamxPXR1WdYZ6JNSwdjPYwHHhvmONmPkAQSK9MC5yteRlIRmQXnWIq3X66c982Yy/v1Ma331JkCgGlooCS/pnavLS4fo9xgWmicGeXIjcViEuD8RDdrS2YsgoAhjjis9hhpi2GqURiHx1ZTnOxHGCQCklF4qWIMy1ODJKTA0kgIzMcKhXE4fXbcEJ/nO8EWs8JhpivZvaNNi1vvystqUzWaMRl1lYePEpc+4l03nyBk2kLmulkLaijBNnRfrzWl5a5Xg/e+971X8jhaHBAoloLM9FamBSaFRSAFQuswLshPJg4kBtVF2wmpDRoLyl6aVMqoRElIzqsWKamViFxtwMARK44n3zRArATOT0uE8Oj2Q3rOSGIxCfHQCkmyDYBSAwARrM9PC4xyav/OdxQeXk0hBEMiBUrmEEgyXiydx7FuiEhwdBOJQJDUtBdK3LeSIVYcubY4OymaAqjmjUR8+wu5cXxmxC+eFgY3zidIC4P5RCFWXSyPCyynJZa6NN55ZC3D8R4luZPLK5EKu4mkeA8AhXZgxiKVfMOClWkL46hz5kHdjE4gIXsMYIBnwOlhBusr52Ue4fwkxyijzhljDOmoQBxwDGJypV5Nc8wlATxofEndHerAnBuX4Jwh1+v+RWWlxlKcFHPGukbVxbEeojjJabw3rYiUx7ohWGWHsDwpsDIlA0LBGc6Mc/RLUsdZS462hXFIlEAnFIgzDusK9CoHXQaAwwOMCJ79SGEt1ZgW1CncSc2Va8o0CyVHPyYO1HyiIDmNKzhnTTSANg649Gloi2sYy5MCj6ySwjUSDKsleXFJIREIQEqOU/0YHgzn0gynV7Oqk8yQGo+VVOPG+aQacXGMSwM42hCEUkAJUmKm2lLHVKw/Nkg2ntPaEjdHW9esk5EicUqvihRSgqGoMhUZwxZH9d2CTBSJ21N7v6WlRVpazCesud6k4FiovLwEZ9dcfMaet0PGGPzFX/wF/vEf/xE/+IM/iF6vh0cffRT9fh/dbnc/j7HFPmMuCRApgU4gEAcljHVEhI0YnGdN0CSZFlYBlB4IlUBhHQJFuVBraYnC2EYZlGkL5j16Mae2cRRgoRciCRQCySqeR4FuJNGLFATPEVQO0eROTAXLYhJCcY5/Oj/B+TPkpsoFw6legEBJ3DBHhUcAAQ6gG0vc0E8QKI5RqiE5tba1dhDJxtZw3SHZCR7UoRokRMwNK/dnYx06UOiECp1QIh6rhni7UsVm2KqQiQPRhHsWxsF4hkEikZa2MSOsMSs9Dau/HwwoLYWwrroSvuIH1dLxcxMqNKTgZAPAGQrD0I8Uzo5yrGV19ypoHJZHuca4MkHrBgJLvQhCcCShQAKBrLTN6Gw1Mwg0xZ+Mcj3Dn6EFeKFD+WWdUDaeR53GTE5DawfnDKxxUAIYF2Ra2YsUejG9fzfMJVjqlpX3CX1GC92oKjwpsqQXSkxLiygQ2LzMW+fx2JDcpTmrfZsC3DCg7py25OTLOBCrdfuCFi1m8dDaFKdHGUZ5CQYGbS25uDNACA7PAOcZ7j83BhiDkkDIJZJQQHIB7xweWk0xSBR6MRmvTnLdeOr0qlgcbR1sQJYhrk4A3oRQcWQlXScDxprx1iwGscK0WkcSJfZs8QHQfcA6Whvq8XF9rLPXSu15di1iTwXQAw88gOc///l48MEHURQFvvM7vxO9Xg+/9Eu/hDzP8e53v3u/j7PFPiNSlGhuPBnrOQ90wwALiUKqKQZiLaXxRaEthoHGXBzCOBpDecmQa4OTczFCJcEBTNZodh0qiUAIunA4tYazKvsmkBydkCTlvVjh1qUOzo8LAB7dmNRH2jo4ByRKotAOXFBaemY9mHBYSw28Z9VrceLkRJLGLM4js2TIlyQKg0jhRC+sZOPE37mQKiiUApFyyDWgIo65agGLAyoIyQFb4XiPSM65MZiLqaCs8306oWwWEM4YbhyEODvK0Q1pN0VBoCTTjpSglnfl9HqsE1CnwqMKTSWiugMlw3tHsvFQckwKjUlBPju5tnhweYrSkurrzLBAKARODCJMcuJV1dHraWGRhhqDWDU5Q86RUo1ULR4MvOLbKPQiMnIrar4VY2CcAkypUwT0I4FeHGBSWuTawjiH81ODE/0Iix0Bzug9ChUVM52QOojnxjmkIOl6/bkkASkMrQeSQMI6YCUtN4wuR5lGWljkpcVqVmK+6vIkAY3ulKBW/iCSKCur/2tt99ri8lAYC+kpcmdtWmJSaHQChflYwYOhF0vMd0Lcf34C4xnCyp9LG1JmGWcx0Q4qJ7NRziqJu3G0XlQE4sctdgDGMMxKaOPRj9S2o9h+pBBUKq1YiW1tHBhj+zrGFZx4S7MF0PWUI7and/I1r3kNvv7rvx5f/OIXsbi4LoN50YtehFe84hX7dnAtriw4ZzjRizCo3D8DwZFWfiwBZ3i4GpsEkjc77VFhcG5S4sa5CL1qTGGsQxwK3L7UhbYe1nt0Q4HCGJyfkBy8J2hHHyoJ60jy3I8VTvRDTHLyDDoxFyMQHI+uZXh4JcVcdVNbTVmTV5UWBnOJQiI4TvRDnBhEONaLKxM+4s1YCyz2Qsx1Aix1QkRKYnVK3apepLDYDS9YBA1ihV4oq86KxdlxDngigweSCkfWpbbwMNXNbi5SpOyqb7Ter8uy52NZESt5ZSK4/voUNUKfgXMeC90Q/aorZ1yBx0Y5cm0rs0SLhU4I5wUWkgBKCkSKCqtzowJJuM6F6UWyKXSnpYF3DkvdENp6xIHCQrLus1MXeIWxUAKQgpErrvWQBSlXetWoyjpSCZZVl4WeT2AulpCcITMG48xCSSKWelDhG1ek6G4kyRhSW3Qjhby0mBS6Om+oMLJOI9emCZb1nvxOePVmW+8hBMNaVmKUUYeynygowZAEFPtRR3fwypPoevEGarE7eA9EocStSzEEB5YnGTpxgExbhJxhvqPQjch+Q3KPaeHQjwSWBpSvJ4UAA0cUSCyPC9q0BRILXfIoW03LyiXf40Q3wFyiIBiwiVe8ARcq0qnoomL+cjo/04K82Rgjt/s4EGBsvfNzPXVK91QAfeYzn8Ff//VfIwg2ssZvvfVWPPLII/tyYC32hmlBnhax2prUvR04Z5SFA2B1WqK0DtZ5GomAugF1xENpPELJMMxLaGPRjSTmO0HFyZHoxxzLkwKqIjlHUpBqIadOwFwSYFIYnJ8QYTbiDJJTUGccUuJ6IDhODiLAezy8lsN4IjJ7iypuQuP8WOOWeYrBuHm+gziQODvKqBhzZLp3ephikhsEnCPVBsNUN+qzQPKLyqLXOw0z2VLaoROux2pESoB1yKTPg+bxpbGVWRkDPDAtNMa5BRjDsU4AxxlK7fDAyhRzcdC0seuCjHMKb11LaTG6/VgH956ZYC0tUFqPlakGB8PjjvcwnwSY6wQQFal6qRdgLdONcqP23+Gc4ab5BGcERxJZoPLH2ez2PUgUspLs8XPtMMwNFjsK3te8BSIRP7yaYXlSoLRUUMVKYJwTMV5whn4gaZdcdReFYIgGEb2mdYiq8ysvLax3SEsqUGob/0xbCM4rFZdpVHx1hIavjN+0sZX7MxHyS0McitkFvFbmAbTbXWjl8C0qhJLjpvkE5ycFMm0wLCzOT6YUOtwNIZlAUTqkmjrQknskIcfXnOwjiRXOjDJIIdEPOR5ayREqgTiQSLVBWhDnB9KjqGJmJGeNxJ2B1J+B5BgXFqYqPnaKoqhH7c6TA/tCpRC9VBjrmkBh7z3GucZiN6TA6uuwQ7qnAsg5B2vtlu8//PDD6PV62/xGi4PAKNfIKoJqXtqLdjo2Q1fFz2pawjnfyKNDxbHoQ6xlGtNCQwgamZSGbpTzSYBYEYluruJ41LbvdUTEQkLz7ElhmnZrrASsMzg3LsDgcXpYYKET4NRcjOODGGupRqJieAAPr6UIHcMIgHW26vyEGGYa5yYF0sJW0lGD5WmOXqSQBES4fWglg3YOo0zj4dUUcXDxAqjGZrXEZoRSYCFhOD8tMNUGZ8dk5McZqdNcxZ0iEzLyQFpNS8gJjRatpY5PpCitHSDCY1g5NbNKgTbMLFTV/j7eD3HrQoL5TtCQmCUncnWoBM6PCnRigVGhq/ef3GQnucbylPhB/UihsLZKfF9f+KalaSS1RNAGgmqdLQy19M+Nc/Ij0dR1450AQaUOqW0A6gJ6sRuQI7bYWJSMK05Spg2McfCeVHOzOWG9SEGVxEWaHQkMMw3nPeJQoGM8blmImy7U5kW8vh6AqnN1jcl4W1w6JoUhjzHJEQiOsnQ4NyqwOqGOTd0p1s7BOgclGU4NElLEek8Sd+ehhESoOG6c72B5SnlgnDEwCMwlZAehBCePrGqErKvzb1qQEWjdpYyUgCmpQ7ydnJ2y+mgt8p5I1f09FECbV7MLr27XPvbU6/rO7/xOvOMd72i+ZoxhMpng53/+53HXXXft17HhPe95D57ylKdASol3vetdGx47c+YMXvziF+Prvu7r8KQnPWnD8QDAW97yFjz+8Y/H4x//eLzxjW/ct2M6zNAzc9yaS7NbFMZCO49JUcVYZBqr0xKScxzrRbh5nqIJuqFEEkrMdQL0YwXG6MZyvB9iqRfixCCmOIrqIo6UwLHuuhneODOVDxHJuocZFW1rmQFnrDJe1BhmJTJtkWoLKYCAM8iARj3HKhfgtHRYTTXWppX8vNTkX8M5kZk5rwIFibsyLW2V90OdpN2gH6mGr5gE2/OHinpcZamLtppS7g8lk9MS0wlpxLSWlkhLyjQbZ4YS5b0n3szM55Ub2jWeHeVY6oeYTxRCyXCsFyIJFSn5AuJZ1Y7SrIremO8GlT0/eRgNMyp8yqozJFn1u5w37rE1ZlUlSSBQC9YYoxtHzTvSlsJyl6qIC1HFatQZXaESSEKBxW6IJBTNQltZGlVFIRHGM+OwOi3xwHKK1WmJTkhE0uVJiUBxJGrj+14XMZGU6MeyivuwsJWB5yw2jwqux7yjFutISxr/lFUn5J/OTTAxGpzRrbC0DqXxsPA4Pczw0GpGY19PJoMGgANDbhxuXUxwQz9GP1JY6kYYJHRdnhiEWOjQyCsO6BpgbD082jmPaSU6cB5N/hdAo93tsHnZ2et5rMS6Oz7DtWVquBfs6a//tV/7NXzbt30b7rjjDuR5jh/8wR/EV77yFSwtLeEDH/jAvh3cM5/5THzoQx/C2972ti2Pve51r8NTnvIUfOQjH8FkMsFznvMc3HnnnXjWs56Fv/zLv8QHPvABfOlLX4KUEnfeeSe+6Zu+Cf/iX/yLfTu2wwZX8TLS0iAOqPOyWzJbnawtK55NbWyXlhaB4NCGodTAiUGIJx3vYlhojDLyVemEEvNxUMm4WWOudXIQIaq6ArM3oU4k4HMad0SKIwklVqtUb1eZ/ZXGQxvytCEPHo7HH+8hNw5ZoVE6jzDg6MUSo8xUfBWHYUbdgrkkRGnJr0ZwhhsGcRMLkQQSpbUojUXNu7kQag7N5r9jMya5xrQkvlDAOWwvxIleAME4LIAFFaAX0eIbSgsHVOZobMNiNi0MlqcFpoVFP5LgnCOUDE+9aQ4rqUY/kpjrKBzvR00LvA5sjZTYIosVjJGnSbWuhlKAM1Q71a1Zab1IYpSTyeWxXohIkmol0ySRTQKJfmRhHdkR9GNF511hwUEdHCrMyFgRIDVaXMl7a7VcmblmIR4KypMDqCieSxSSUCIJ6ThHORG26w5QMBOGGknRdIes81ieFjgx4z7dCyXgKe6lDUdtsbng55w60VJ4FJaUVYHkZJdRWSyISGKhGyOWEt1QIDcWC9XoN1bUBbplMYEUJBxIKjNQVdCmrl91doNqhDypRuTdUDY8QYA2GfE2YyjnaD3Mqu7sXBygcxm5XYOYSNi1aer1jD0VQDfccAO+8IUv4IMf/CD+7u/+Ds45/OiP/ih+6Id+CHEc79vBPfWpTwWADcZxNb74xS/iNa95DQCg2+3iuc99Ln7v934Pz3rWs/AHf/AH+JEf+RF0OpS0+2/+zb/BBz7wgR0LoKIoUBRF8/VoNNq3v+GgsJqWVdYUefMc78W7Hn/N7pp7kQK8x/JUo9AOI1aCsRCDRCGSAqPSIlQSi9VOQgqSxJtUo1c5QANo/GSs91BgMI5u+N2QPFqASpTEgBvmiCsTB6rpgmjn0IkUGCNZ+q2LFPlwZkTEvUiJRm2Ra4tBTBe0qBYhBb4hAqMbqYqjwqnzcQm934sRDkPJK/UHqcDCgMwLjWN43PEE2pHnzjgnkrStwhB7EYWhAmh8gCh7jLpqk8qhG2BY6oUYJAEVJTOL5DDVGOUavJKizyUBrPdNiz8OBLRzSEIHkzl0QoGFTlAlw/MtRXLt+7Hh7weDch4AxVQc64foBpRltpqWpIwLRRVIyzDKNHqRwsnFCILz5jysiw8l1uNWYiXwwPKk2fkWhro59UhtkmuMPI10k1CiG5JHVVp18+CBlbQg1VyVYl9zfWQ1XiCSeYsWVDDn1bnDANwwl2CYayx0I8iqQJmLQ5TO4JG1HIHkyEuHxQ7HsZ7CpHBk4lo4JEriRD+CkhzOYwN/ZztOTWkcOKNruRdLjHMDBoZbFzu0Ya0UmZsxKSm8Oq44fVHAL7twafPxCHsqgN7//vfjZS97GV7+8pfj5S9/+YbH/t2/+3f45V/+5X05uAvhWc96Fn7/938fX//1X4/z58/jE5/4BJ70pCcBAB588EE897nPbX72tttuw4c//OEdn+ttb3sb3vzmN1/xY75S8JUjKLCuIriUFindBNcVXxSMSS7AnNFNKVIRtKVRDSWc80oJRK+rAbjMNd8nbyEqcqalqXYcrPIgohthWMkv+5Gq3H4Z0sJgXBhYB2SaigHGgdXUIgkos6c0HuQc5HHzfIIoECi1Q6TIoRUMmItDMMYbTxkTrku5pbg8SbR1VXeqJi4zVhGZKxIjA/ox8VY45wg5cVEybTApDDqBxIlBuCEkFFgvRMk2gMaBcx2SxsrKEXp24fLe4/SIXK8BGnn2I0XJ8DOo5bWLnQCB4BB74A7U71dhHEKpGp+o2Try7Jgs/+tOnhI7L9RJIJEE9Dec6McoDZE7OwFxyJyn9yM3Dt1QYiXVJIXvhZhPqICrR4raEEE0UhyDirSdG4fudSTnbbE7BJIKfG2pyz3KDW4YJAgeJzDOSyxPDACPrhIYpgbG0PXIABzrRUhCR7E1DOjHCitp2XDcLkRiniXj15zI45Xr+07w1XXkNnWtdgh7b7EH7KkA+smf/EnMzc3hu7/7uzd8/7WvfS0++MEP7roA+uZv/mZ8+ctf3vaxe+65BzfffPOOv/srv/Ir+Nmf/Vk84xnPwMmTJ/G85z0P586dax6fPbH8DnPVGm94wxvwute9rvl6NBpd8LUvF+S0S/yQWdn0XkEOobzh/FyqTLJ+/dKSCosz2j17VFlb1YilNBaz16vDRhIZuUrTe71WxREAQK4dIkULTlbaDTvy2ZGEMQ7nJwVy7dCLBAIZISsdIkVclbXM4OGVFOPC4LbFDua7IZTgSAuLwhCh11axCMZ6cO5J0VEZFqZVDhjD9q3m3WB1WuL0KIP3wM3zMXpxAMbQJK4ngYSqnLbr5OTSWNx/ftKkm9d+NZtB74XHMNWwzuFkL8RCNwLboVWtLRG769GS5ORNVBoHJdiGsdHlnmNE6CY1TN01UmJjCnWhXZPaOMo03CCC2GTTXy/q9fiOMYbjvQiR5JXKTmC+yVajIjqvjN94VVBnmjpRWbWT70YSjHkwrI99BSMLg0lOY4NeJK8rf5MWO4OM/ej/eUl8tkhxCEYbhNyQl9YwKpAajxODCKf6McY5bc6McUhiBQ+P1amuzi2GB5dTJIFAEkh0Qrr+6+uvHgcD6270idj59luaKlS1se+idjmvnPxb7A/2VAB98IMfxEtf+lL85//8n/Et3/ItAICf+qmfwkc+8hF86lOf2vXz/NVf/dVeXh4AsLCwgPe85z3N13fffTfuuOMOAMAtt9yC+++/v3nsgQcewC233LLjc4VhiDA8OI/8tYxiBgBUeUobW5++kgRfSptyPqGxAEA39zoTqaxGIbM3w+2wuWWbhBJZxflY6oZYnhakVHAUpNoJKMm8zgUDaBRUl5rGOYxz8sApjcV8h4oetsO1a53H/350SEnLglyNj/VDBJKycc6MMpTG4pG1FNPSVaRAShOvwwDJnVrDOY9H4XH7Urfxg5GCY6kTQjtSTO32vaWEdlJzCM5w3/kJxhVpcZxrPPWmOTDOkARUyC52Q1jnsdBZj29YmZS4bzlFaSw4y/GE410c70dbXss5uokDFMNxZlyisIDgQFY6dCJaXGviYqYdAll/JlQg1HygwtAIbacd6aVgw+4VaHKM6hTqomrtT3ODdMY7avP5ZqzDSkqLuuT0u4wxzCWqea8iyTect72q08PZ+oih7m7OTsaToFLgMFQmiAznJ2V1PnqspRrHem0ORot15NrCgzZMzpPB6FKlLF2ZlOCSIwAV69o5pLnFQysphpnGDXMRtCOVaOwEtKUA5XHOYR11Lm+aT3DjfIJuKGkjsM51BgMaOfp2js5puW6/wRiIOnCBMVmLvWFPBdDzn/98vPvd78b3fu/34pOf/CTe85734GMf+xg+9alP4YlPfOJ+H+O2WF5eRr/fh1IKn//85/HRj34U99xzDwDg+7//+/GTP/mT+Imf+AlIKfGe97wHb3nLWw7kuHYDN9OR8tXXtcHbbOUfCI65ZGvhkmtLYaUVwU5wKgA6M4z+aWEaomhhHKalvSTG/+wohQIwqTiSgo6rTg6ezYlhwMwNjqMbMTCsj5tCydHdpvMB0JisJgNq65EbC8E4Ck0GeZ1AYHmSIw4VhLDItMdUU46N4NR+HqUaxlqMM0o8T8IMNwyShmfDOUO4QwbYdqARS60Ws+gEAmmxLqseZRpnxwVCJSA4EAl67k4oNnh0ZNo2aqqa+zILbSkUtHaSpSwyVpEeqZjItaMQ1Kp4iJQAPJrigTOgtyksdL8kroXZqC6s5b0AGjdrADg+iLA6LQHQeGDzQj0tbLOo10aISSA3nLubz1sVSty62MEoJ05aIDg6AUV3FJr+RUoQaTqQ5FxuHM5NyMqhPoaLdYFbXH8wVSdysVIyKkHdzNI6ZNpBCgGAyP/1mRwFROZfmZY43osw3wmbEbSSHKOMVLRScJwf5+hWFg6dQDTdz7DyR6s79oW2G/iKAJqNUA3B2TUXRHoYsGcN3Etf+lKsrq7im77pm3Ds2DF8+tOfxhOe8IT9PDa8//3vx+tf/3qsrq7iYx/7GN7+9rfj4x//OJ7+9Kfjc5/7HH7qp34KSin0ej186EMfwqlTpwAA3/qt34qXvOQleMpTntIc6/Of//x9PbbLQRLIRi0Vyo05K9NivfIvq1C82c6MdR6jTDc722Gmt5BW6ZFNX29zA6gzYBSnIMm60KoLLMoHk1u6JXJmrDF7AwQoab12K51VXMxX2Vj136AtFU2zY4lBHKAwRePYWxoDU/FtBnGA+cSgYyzWco1+qHCsGyIOZJ3wQKMu7xEqAacNBCjbaq/Kn82eMdYDC90Ay1VSfKQEVPM3AfPJ1vcKIGVVnWpOIYjrXZnC2MZIkYGKGwb6/MihlWHzR1cX0FHAERnq3DEGzMVB45HDGBpC+m5BihTXjPRqKMEw6xogd9iBdkOJqOoCbjdumpYaw8xAchpJbcdTc5v+2PrrfqSAqmlWh7UCJKevuRejXDc/b6zDMNdgIFXOdtdIi+sbgeBIgWbEP4hCSM4xyTXmEomHVlNYZ3E8DBAHHIWmbnKsBEQlFujHCt2QIzMegRAY5xmkoDGVceuydlbFygB0nY1mLihT8XxmNwzdSMKk5MsWbeNv1WJ/sOsCaJYjM4vjx4/j6U9/On7zN3+z+d6v/uqvXv6RAXjZy16Gl73sZds+9oIXvAD33nvvjr/7pje9CW9605v25Tj2G91QIpRE0N1c1V+Mu+z8Rms+67bf2cZVNpWtugmbOSdZaRuTOOsseEEjBrepwDo9yqtdOmbIuBuP2XuPtNqRy2qBiBTHpKCd06z8uJYq1/e5WtHVCSSWKoWTtg79UCC31OEptMViJ8ATT3aRlRaTgiTu9UhjvkPGgfOJwr3nJpjmFrfE1HrevLO6FKhN/JVQcjzxRA/nO9RdIF4K/Uwt2d8OS70IX+2JqBsriVNz6+OvYoZITGoqiZsDibQkRZMDFUnOEf+GM9bYC4RSYKHDGsKx4BSgaCrV2aW0yr33jUstAMTKwoNCRVX1mVtH3ah6Mc613VIs7eROSz9LJJ7aKbvhnlVFV82NyjXxjWpy/zjXlBhfvdebvVJ0FUkyzDV8dcOYFAaJkk2xvh3nqsX1Ce+pyyMYgxAM0zF1jL33WEgCTAuLbqiwkChMCoaFriLivRR4YC1DpgSkpPVgIVHoRAoy15UdQxeFtjDeI5bEZ9u8KWJsI3dObMPZFJxd1trVYnfY9apQj5c24/GPfzxGo1Hz+PXuK7Bb7ETI7EUKNi13rPwl30h4TnbwgxCcYbFDab/1iGwWW3fa69+vH6lJpPWxcr59EN+kcjUlYz9fpXtzGAssdNSGm09p3IaORu1ETDfvEKHkjVHgo2t5E8SpFMcNcwl4JXPOtW1I2/UNWEmBJ53sY5QbGEtJ7pezcwqlwCBeJ4fXz3VyQAWMtg6T3BARd+YGvR2O92Mc72+1iNjcTamNymrvEGCdE2ac28IBICn7+u9b5zcc0267X3VXrsbKVDdmltbR+HSQrH+Oa2nZjKokN5SgfYGCy3kPeFS5YKJRFBbaIa9Gn0kgKnNFCoRdzUpSGlbHV7t3B1XIbn0DsZ6MI5VgGJfkxC04R6TWeUjOe2zNlG9xvWFzoT/ODIk5GMNjwxy5dsi0QaEdbhjEGBYa3jOMM4OlpRB3nOphZWKgJK1DUSABtk6sPt4P4X2tcuSI1Pa32PkkwLSkKUCnLc6vGnb9zl8KubnF3nGxyp8xhvlEobREPL2QsoUxtmFcNYtIUeI2JX+vF1JScISSjOa8wwZb9s1yzBra+qaTUWgLgCEvLaJAVNEI694ym2+Sm7/OK7WEFBxZaeCcQz8JYCx1UBiIX7JTdk0d8LdfuFBGjprhQu0VSTXCq5VcO/1NggFiF/ylWYL9WlbiWPfCUtsatTGib77e+PhswWxd9XlXRHttidtQfy4AxVXUHcFBTB0669ezjOJAYFqYDWPSrKQIAsYYGCf/oxp6JkGyJl/X18A4N9DV/weJwiCmMNSarB5s43fU4vpEad2GQr+wBqhG5WQC6xBKgVFm6LxVHAwMcSixkpaY5BacAx1IhIo8trpRQIRqT2vCYufiEUScs30RKLS4PLSl5xEEY5dPiKs7RNulC88lAXVqYt+4AgM7S6kDySEZQ1E9LxkcsuZYZ8d0geToR4rIy2JrR0lyhtJWSeExmS9a62F5PZQjntTmY6k7GLIygzxK6FwCOf1imC1SvafO3g418AZwTh5NNbl+LlY0UvJbHWrJQRZVthF1GD0oaDaqRq+1Gk1XkQODWGGpQ7tjUYW/SkHnRhOTwdYJy8Y6mEqxB2BLJ2v2GkgCgWFGNzXBWaOqDASv1D1H63xoceWw2Sn9eDeibDDr0Y0cqKnJqvBmhby0KKrie5x7BBJwnjIN+5FCKMgWolaYtiqto4Vdr7wvfvGL8b73vQ/9fh8vfvGLL/izH/nIRy77wFpceXDOEOxwsdY3jUXBq7DNnQuLbighBhFGmQbnDNY5TArqEHQCsWVMFwdi28A/gLo7k9wg4wY3DGLKGrMOs92AzeRZbR0eWkkrtRXDbUud65Y0GCqOsyNKau8E8pKGPoHkCOR6R2upkv9uXtRrO/41lMgr7x5g/RPawrevvo4CgaVu2BS/vVCiVG6DVw+AmcRs4gz142DH8wVAM0K1zm+wlDhqhXCLKw8paAM2rTh2/VhhqRdiWlispmTBkBUGJwcRAknqwjNrOYa5hnMWxwZxU+SfnIuaDdylKkxbHA7sugAaDAbNDm0wGFyxA2qxEaYK2rxa1uWcM0S7uLDjQDZW7cC6oogzdkk7cFFFFyQhpabX8/RAMGS69oPZeNqOMt14IBnvcW6c4+aFzq5f81qCEhR/EXkintfmi3vBhRb1QHIc70XoBBJpSX5P9ecSKY5ME0+NMYrJqLG5+A2lQNhd/9rMjCiIu4Zti5+ahF7z2zZzoVq02AnbbcAcPAIpsNCpx6WsUXwt9kL0YkViBO8RKVIVtiOso49dF0Dvfe97t/1/iyuHYaabUUJ/JmH9sGI2ImKzPP5SoQTHsW64wRCyu9U7EMDWjpDYyW3xOkDNQ2i+vsKv1wnllhFezdEhBeLuhRHO+cbSoD5uuU0OYB2BUcvt57fxymrR4lIgOfluTSqfr7lEYL4TNOsvdUdJaVk7lW+Wrrc4emg5QAeEWdXSdvyV2hW0W2VWmUoC7qrH1tISNy8kh1bOOylM421EvjeXf5w1+ddUPJJCU9q9tg6DRGGpG0FwchKeFgbTkhRri72j5/nivSebfesRqe2jMnaDWJHJWmN/cJXaIsY6jCs1WicUF+Ws5do29gscDEpQ96e2Zhjnpkl0L/S6dYDe5JVlLJl+jnINU+W+He9FCNv2UIsd4L3H6WGO//XQEA4ety4mpD40DllpMEo1Skfk6FODEGlpmo7zUdiYttgZu15ln/70p+96l/X5z39+zwd0LWI2SiDDVu+UlWnZEI21dViaUe5MKmdcwVnln3L4FC3O+ab4AYBJbi67AKLFx4JxICsswIAz4wxrqSbDxFFROVLTe3XTQgJTKYEOele2Od9qL5gUpvFlqk0i90J055vsD6zzzblXF9cHgVk12jB1WOpemBw6LUxT1Dh4dML1eIxhphup/Dg3Wzp+s1+uphppaXB6mMFYj0ESQNsMty522gTsI47bXv8nl/Tz97/9u3b1c2fHOR5eSZFbIu+fXqPYn1AyFMZjmGsUxuKm+Rij3IBhvas5zvWWAqheD7azH2lxuLDru9T3fu/3XsHDuLax2VFYW496alBzGWpYR4ZcglMeFsnI1/kVZHp3YIe+KzC2cWxxuXYrzvlmxFG7pvYjhUI7jKsbK2fUip6fofpcDdKrtq7hKs3mW10qNic8bza4rE0Bd2NwOGt/sJpuLa4PAhvUaLj4KI7es1mp+/bPBZDqqzCkEovVenepvpbqNPpaYu987Tx+yC6cFocCdUcxFAylAYy36IQShabQ6jqXa5wbLHZFtdlZV7nOYnPe3XwStGOyfYZzHtaTevlyC8xdF0A///M/f8lP/oEPfAAvfOEL0elcn4TUGoHgyLCeITXrMMw3GRsCwFqqm/b/LQtJs4OnrsDh6v4AtAj0Y4oiqO9hK9PykkY53hP/g1cGd7PSaME4JtV4aFIYyMp/x11xhsvFke6Qb3WpCBVvuhybO4TGOixPSqCSny8kwa6KvZ2K64PYlSbhxriXi3Vf6mBd5zyScGNyeyQ5zk+LxkRTVh4qm8n19bUUVfEYjBEHaTbBvkWLzTjWC/DoWoYokBCC4wnHu4iVAGfAsOq6Ow94B+Slg5KMwnoFRy/eWFRPy/X1gNzxcyShQDeUrSpxHzAbHXQ5G84aV5RQ8uM//uP4Z//sn+H222+/ki9z6FHzE2qzu82jjflEIdMW3gGTQjcdI5uWWOyGkJzBVqnmh7WlWhsGjnONSW4wyUt473HDXLxBHbYdZgNgIynQj2UzvgGApW6AYa7RjyVO9CJ4AKf60Y4uqweKTR/HdvlWu0GkBApjMcoMQsmr7DZyvD49zCj+Q5GEN9UW/V0sprUXTn0+HeT50w0lAsHh4eEcjQpCKXZUBCrBd+xOlVUMyDQ34JwRx8n7bU0e6VriGEQDlNZSFtgBjv5aHB0UxlIWouB4xi1zGBfrhOdIicZtfBArWEeeV1HAwRnDJNfgHBhlBtaRJ5Z1Hta5Kr+POtfdUKIwDMbpA+u+XstIC9tse01lyHo5YpsregdpE5jXcSFHYVZldRnrMCnXF+q6AJCCHxm2unV04dfOvecnJW5euPDRT2YCYHNjEVmOhSRAbiw4o67XMNfwYOjGZKKoncfxeOfnne0oXUn0Qknjlk3RG5f6+kR6pwLZAxjlBgudoCLH03MU2kErhw7b/dkwl6jGXTtSB7sDDSTfQI7PSouFzu66V7OwzkPy9dBgW6nFapPGWdTXEgAkR+aqWcel8lxa7A2FsVhL60BSi0GstnXgP96LmnWodi63ziPTDoEU8Bw4PczQrYNOQVerdR6Krythd3LRb3FpuBD/by84eivENQwp+IYd+1FUF1CYKV3snNHY7mJy0c2PMBDPZXaU1A/JILETSpwcRFWg7Pbvz2ybNNqUvr7f4FUbdhaziqY4EJRkfhFsXh/r0VUdJmscqVIiuTWM9kJgjF3V80ib9dGuB/HfLpXbHVcBuZHiKI2v8t9ax90We8dstAqAHTsJs+vQbLyK4KyhMsxeu3VWIUDd1jrrMDqCa/lhRDeScBnx+yK1dZpyqWgLoEOGuUShqNKxL/fDvRqIlMDxPrn91oq1i92oupHEWqphnUNSBXgWhmbpoeQNx8h6T/LUSiW1E+owUIA6SrHZefRyJTCeef2stEiUuGjXg0ajvAkYrR1mQymQpiW6gUR3oLYNo90rDoIPFMj1gp5hI/9tt4gDAQ9fZXqRI/n16vTdYn+w9Tz0yEqLUHKUxgLb+JglgYTkHM579EKJTJM69UQ/oiww0Dlem9f2YwVtHeYSdSTX8sMIwdll5y/Ooi2ADhku10DwMOBYN8S0knTP+tB47xv/jCQQzc1XMPJ8YYzBWI9RViKrxjZKUJAq5xQCe3ZcIKgS4zlj2+ZobVYUHTT2+vJzSdCEegrOkJbkbVQbsO3F06f2yZGb2vGradnkwF1JpUonlOCMwTjaYe+FCJpr2+y8tQMWD6kXVoujg1AKzCXEP9SGxs/j3OD0MEdYcYCO9ULMJRtvtvVGKlICvZnQ5U4o4TypU89Piub7kbq4B9Z+wnsSYjhPndOW+3ZhtLT0FruGsQ6jXFecnZ3v8IxRyGk3lBturKsp/e6kMFht5u/AtDQN30lbh5Xp+mPaOpiqg6AddSzqOXDt0roZvUg26eZJcLDdH4DM0WprgEtVfyixrpiqfYEA4hRstlO4GIx1WJ2WmBYGw0w3XJxU20YibpzHtDQXeprLRhwI9CK1ZyVWoWfGaJ5GnJthnScCfmFavkWLXSGUApJzjAqDwlhMc9PwfArjMMz0rnmsolIgCk5rH3kFbY3tudIYZQbj6u9YmZYtD/ciuKKfzq233gql2ryUwlikBRkgHlU5pHO+8bcAAGv9JXFrfOXFUkNbt+MIRs4Gb2Kd+CaqouZCMQlAFaPRu3qKizon63IhOYdxdubrS9vNaes3NKJK49A5gkIUIRgwU6Nt3tV67zeYiZbaQkoOYz0Cyfd1bNji2kFaUrGgjWtGz4Kvb7BqI8O0NMi1q7zZ5EXHxtvFwxwUCru+XrjKO2wvY+frBVf0U/rf//t/X8mnPxJwzjeEXACwTm+rNjjsMJXypsaldiPqQNdG2TZjYpUEspGRKsGx2AkwLSh4sDPTRZKCo1+pwBijNPFrGb1IAgWdQ3Fw6eMjJTYWjKrqhCVKoKi6QJIzdA75SKkTCDKCtH7bkYLz2OB3NMwNkmDdemJ2/NeiRQ1t6JzpRhKsoFFzNxIojUckBU70I5TGrY9fiRq0K1HD1UIg1nmEjNGmscXO2NPKNz8/v20VzBhDFEV4whOegB/5kR/By1/+8ss+wKMO5zfuwjc7/B4VyGpnVN9odhorkf8FjbQ2GyEuJEEzbpm96QrOsNQNN3SEBsn2z38hO4FrDZwzDOK9L7ZScAwqUr2cUbNwzrC46f3eb+TaUko8Ixfzy+EYMcYumLxdqw3ra2vzSx3Va67FlUUgeSNx70cK850ASvAN18XsGBqgzvd+oM7KA6hjtF9j+kGsMC1p85go0SolL4I9FUBvetOb8Iu/+It4wQtegGc/+9nw3uNv/uZv8Kd/+qf4t//23+K+++7Dq171Khhj8MpXvnK/j/lI4VqQtgPrcu9M28pdd/tTZ1wYpAXZxzsANw5iJOH6jfdCN7LDavJ4lBHKnUmYm9/vrCRjuEDyyzpPjXWNe7kG4HO/hUy6n2CMYSEJkGoLBmAupggZj9pV++iNnFvsP2qPMoA2YHEgwBh1t4OZjMXZ6yKUfMPGb7/W79VUr0fUZOW2pp57Qc2/bLE77Omd+sxnPoO3vOUtuPvuuzd8/z/8h/+AT37yk/ijP/ojfN3XfR3e+c53XvcFUM19OTPKEQiOQZxc8dd0zkM7B8W3StCt803Y5qWSUmuC34Vg7SYjxGmBm2cUXy0OJ3Jtm5tDHcmx18XebiJemj12YLz3jSVEncVUE0u3jsHWZfIAwDmQlQ7d6Ghy7lrsP9bSEgkn+kFpKBev7iYXxuL8hOJWAI9AUnxFpAQWO0FjvSEqbx/O2GV1bWZHtptNPemeQTEu7bl7ZbGnd/cTn/gEvuM7vmPL97/9278dn/jEJwAAd911F/7pn/7p8o7uGkBaWpybFJUU2OP0ML+izHxtHc5PCqylGsvTckP7XxuHh1ZSPLqW4fy42FFFdTkg0771UYQSvB1BHAHoTZwuvTmd9QIgnoRuzqdA8A2E7WQPhZT3HqupxjDTWJ2WeGyYNyGndXepRlZaLE9LrKYl1lKNca5hXeVBZNyWMUaL6xOz61Cdi1djmGkU2uKRtRSPrWWkeK1UYLzikAnGsJKWdE6mJdLLUE/Obi5mTT1rQv9qWmJ5Wl6RNbrFOvbUAVpYWMDHP/5xvPa1r93w/Y9//ONYWFgAAEynU/R6vcs/wiMO5zeSh90OFv77hbRcz0px3iPXtlEknBnlVbQC3fCSQO47nyZSAicHEaaFaYwQWy+Kw49Qisajib7e3d5IW4e1tKzOuYp7EEgsdAIUZm+dRoC6RrNFWVbappCiHfM6T2NWxl9aB+E2nm/uCm44doM23uJwYLabMpuLVxPs1zKNrAozneQavTiA80Atoiqt21BE0Tm5t3FTP1LNNTbbzSyM29AxTUt73XAerwb29Om98Y1vxKte9Sp86lOfwrOf/WwwxvC5z30O/+W//Be8+93vBgD82Z/9GZ773Ofu68EeNLz3ze4xUnsLkkwCiU4oMC2IOzPfuXKmc8BWiTCfuchn09O19dhBRX7ZWOyESBQZg8WqHX8dBQSSY74yYlSC77q9r63bIrVPgss39Jy1PGCMoTvjp9IJN0qROWOwM0fRCQXGVb6c9x6uchA/aD+oFocL84mCqs6jeObcZNU4y/v14Grj6P+z6+lmG4pZG47CWNiKS7TbsdV23Lyt6/eunqrFHrGnAuiVr3wl7rjjDrzrXe/CRz7yEXjv8aQnPQmf/vSn8ZznPAcA8DM/8zP7eqBXA2upRsQqXoTme7LgFpzhtsUOcm0hOENwhV1BO4GAtcQBmiWzMkZyZ+fQ3AyuJFnuqJK9r2cEcveFT43N3Z29mh1uBucMg0QhLYhvUasEga28iEGsMMo0rPcNuTWUApm2mOQaaUmKtEGs2t30dYzZoNzNmE8ClMZivkMijUiJLQpMKTgGsUJWWhJ0VOtnVq7z5xijDeBeu95KcPQiOaOgPLyS+2sBe74D3nnnnbjzzjv381i24D3veQ9+7dd+DV/+8pfxjne8Az/5kz/ZPHbmzBm86lWvwr333ouyLHH33Xfjp3/6pwEA73vf+/DTP/3TuO222wCQbP9Tn/rUJb++tg61nV15AeO+i4ECKQ+Gmc8Y29GgcD4JmhtcJ7i4oVeLFheDErzJr1P88tRjm7FVwbb9+bpdPlDTZZ05x3cKvGzRQnCGY70ImabCYycjw+1sOGadyb2nDeblXAdJIPc8Wmtxadjzu2ytxUc/+lF8+ctfBmMMd9xxB174whdCiP1bYJ75zGfiQx/6EN72trdteex1r3sdnvKUp+AjH/kIJpMJnvOc5+DOO+/Es571LADAd3zHd+DDH/7wZb0+n1k8a1fQo4yLydBbtNgLLiS1v5rYvAu/VCftndByeq5NqBkp/KVg83nWch6PDvZUAN17772466678Mgjj+Crv/qr4b3HP/zDP+Dmm2/Gn/zJn+Dxj3/8vhzcU5/6VAAA34as8sUvfhGvec1rAADdbhfPfe5z8Xu/93tNAXQpKIoCRbEeYDcajQBUfJ2q2j+q3gqmCtc8jIZYaWlQGuKcXC3r+GsRznmMCwN4GkVer9yXSIkmO4wz1voBtbggfKUyrDe7pXFISwPOGbrBzmae3YqTZq1HqC59jNzi6mFPn9SrX/1qPP7xj8dDDz2Ez3/+87jnnnvw4IMP4nGPexxe/epX7/cxbotnPetZ+P3f/30453D27Fl84hOfwP333988/ulPfxpPe9rTcOedd160E/S2t70Ng8Gg+XfzzTcDoEp+ECsMYnUkq/phJYU/N7kykvfLQZ3wXRiHSWFaqfI+YpiRJD03FmtZeV2Hg9YFYKZJKl9zNVq0mIVzJD+fXS/X0hJFZaNwofOmNh8cJC3H7KhhT9vuT3/60/jsZz/bSN4BYHFxEW9/+9sviRf0zd/8zfjyl7+87WP33HNPU4hsh1/5lV/Bz/7sz+IZz3gGTp48iec973k4d+4cAOC7v/u78ZKXvARJkuDLX/4y/vk//+e46aab8A3f8A3bPtcb3vAGvO51r2u+Ho1GF3ztowBtXWNoB5CJ3GG6OLfznYmx9+Orux6+kmFfr7sw7z3W0hLaOYSS+ArWe/Ad+DPXA2rrB4AIq5t9qdqRVovc2EZ+TjJ4s0HduNnMszB2Axn6qNMjrlfsqQAKwxDj8XjL9yeTCYJg90qpv/qrv9rLywMgL6L3vOc9zdd333037rjjDgDA0tJS8/2v+ZqvwV133YW//uu/3rEACsMQYXj0AkovBM4Ot5wylII8N5qvL69gGWa6iRspTYmlbngox35XGuPCwHmgNB6lMQgV3zfuy1EFA8NsIt/1/W602A6b10slGWDXzRNnN4/Gug0B197jsjL7Wlw97Omu893f/d34sR/7MfzP//k/4T05an72s5/F3XffjRe+8IX7fYzbYnl5GVpTW/Lzn/88PvrRj+InfuInAACPPPJI83NnzpzBn//5n+PpT3/6gRzXYYHgFELJqqDI/iG7QANJtgK9iEzzLpdEO7tD89gax3C9wFqPbiTRq/4NInXd707rEXad5H09FsYtLoxa3cUYmST2QoWFJEA/IgrELAfUuI0B18bu3jW9xeHCnjpA73znO/HDP/zD+MZv/EYoRTdWrTX+5b/8l3jHO96xbwf3/ve/H69//euxurqKj33sY3j729+Oj3/843j605+Oz33uc/ipn/opKKXQ6/XwoQ99CKdOnQIA/MZv/AY+9rGPQSkF5xxe+9rX4nnPe96+HddRwWGXU+5VdbEdIsUbJ2PB2XXb9YiUQGnd+mJ+CNVZB41Acix1r60Ob4v9B3VxNm4Ut5OzB2JjQOphoha0uDQwfxnBVPfeey++/OUvw3uPO+64A094whP289iuGlZXV7GwsIC///u/R7/fv9qH02KXKAzZ2AdiawjshfDQQw/hOc95Dv7X//pfmJubu3IHeEAojYX1HoEQR5K8f6Wx+fP+hrf+t6t9SC2uAD77//t2AFfm+nbOo7QOvN1kHDqMRiN87dd+LVZWVjA/P3/Bn911ATRLEr4YfvVXf3XXP3sY8Td/8zd49rOffbUPo0WLFi1atGixB3zuc5+7qC3Orucj99xzz65+7lrgG9SdrIceeqjtAB1yrEzLDTP4/h7iDh5++GF87dd+bfN518nQNTqhbH2KriFs/rxb7D+s81ierHurMQDH+tHOv3AF0X7e1xdqFfduJlK7XtX3EiVxVFG7Wff7/faCOeTwUm+Q+893gkvmFdWfcf1589BgOiOdbjOkri1s/rxb7D+899C8aMjCgjP0rxIPq/28r0/sJpWi3da2OBLQ1jVFSTeUTSBmL5LwOTm4JoHcF1J1JxBw3kMbh3Cb7J+rgWlBrtlStHEmRxWTwkAbB3WFg4gPA+pMwmlhwUDXaYsWhw3tWdni0MN7j9W0RM1WM043qh7OGeaS3XtP7QaMMfQPUZGRa9uY+ZWWPEvakdzRQlbapoCvybOHWaG5HzisGXGXiks1yrz/7d91hY6kxX7j+rTLbXGk4DwwS9Xf7OR7rWOzC+3mr1scfhi30SvmejuHW7Q4jGgLoBaHHoIzBDOjrcMwkjpIRHI9yIKBPI9aHC1ESmz4DK+FzkiLFkcd13YPtsU1g7lEoTC0i77eCiApOBa7IbR1kJw1/KcWRwdKcCx0Ahjn28+wRYtDgrYAanEkwBi77gqfWQjOIPj1+/dfC5CCo238tGhxeNBuQ1q0aNGiRYsW1x3aAqhFixYtWrRocd2hLYBaHDl476Gtw2XE2O0J1vk2+blFi4ugvj5dq3RrccjRcoBaHCkY67BSeQJxxrDQCQ4k8HNamMaLJw7EofIJatHisMA5j5W0hHUejAHzyaU7s7docVBoz8wWRwqpto0nkPMe2Uxm15XEbDRGVtp2d9uixTbIjW08jrwH0vJgrs8WLfaCtgBqcaTAN4XtHkDzB8DGkF8G4BrI/G3RYt/BcHWuzxYt9oK2AGpxpNAJBCIpwBlDJAXiA5LGD2IFwRk4Y+jHakNB1KJFC0IcCMQBXZ+h5Ohc43EfLY422rOzxZFCHbJ40Agkb/LHWrRosTP6kQKiq30ULVpcHG0HqEWLFi1atGhx3eFAC6B7770Xn/jEJ5BlGQAcuIy5xbUFbR3W0hLDVF+VcEnvPUa5xuq0RNaSPVtcx/DeY1xdC2lpLv4LLVocAhxIAbS8vIzv+I7vwBOf+ETcddddeOyxxwAAr3jFK/AzP/MzB3EILa4xeO+xmpYojENuLNbS8sCPYVIYZKVFaR1GuUZpWo+gFtcnpqVFWl0L49wgPyB1ZosWl4MDKYBe+9rXQkqJBx98EEmSNN//gR/4Afzpn/7pQRxCi2sMzgOzDcSr0QHa/JpX4xhatDgMsLa9FlocPRwICfqTn/wkPvGJT+Cmm27a8P2v+qqvwgMPPHAQh9DiGoPgDEpw6MqZObwKQamREk1CPWNElG7R4npEqDhyQ10fxoCwvRZaHAEcSAE0nU43dH5qnD9/HmHYKmta7A3ziWoKkKuRFB8pkvta5xFIfiCO1C1aHEZESkBwBmPba6HF0cGBlOnf8i3fgt/93d9tvmaMwTmHX/7lX8a3fdu3HcQhtLgGwRhDpMRVKX5qBJIjDkS74Le47qFEey20OFo4kA7QL//yL+Nbv/Vb8bd/+7coyxI/93M/h7//+7/HysoK/vqv//qSnuu2225DFEWIIjKaeMMb3oAf+IEf2PJzv/M7v4O3v/3tcM7h27/92/Gbv/mbkLK1PWrRokWLFi1aHFAH6I477sCXvvQlPPvZz8Z3fud3Yjqd4sUvfjHuuecePP7xj7/k5/vwhz+ML3zhC/jCF76wbfFz33334Y1vfCM+85nP4N5778Xp06fxO7/zO/vxp7Ro0aJFixYtrgEcWEvk5MmTePOb33wgr/XhD38YL3rRi3DixAkAwN13341f+qVfwo//+I9v+/NFUaAoiubr0Wh0IMfZokWLFi1atLg6OJAO0Hvf+1784R/+4Zbv/+Ef/iH+03/6T5f8fD/0Qz+EpzzlKXjFK16Bc+fObXn8wQcfxK233tp8fdttt+HBBx/c8fne9ra3YTAYNP9uvvnmSz6mFocXpjJMXEtLGHtxr55cW6xOSwwz3aa+t2hxBWCdxzDVWEvL1j+rxVXDgRRAb3/727G0tLTl+8ePH8db3/rWS3quv/zLv8QXv/hFfP7zn8fi4iJ++Id/eNufmw2rvJjj9Bve8AYMh8Pm30MPPXRJx9TicGM11SiMQ2EcVlN9wZ811mGYaZTWIdcW47x1tW3RYr8xzDRyY1EY2py0G40WVwMHMgJ74IEH8LjHPW7L92+99dYLdma2wy233AIAUErhp3/6p/HEJz5x25+5//77N7x+/XvbIQzDVo5/DcF7D+t8o0ZxMwWw8x7e+x3T3M2mhdg42p065+G8hxStv0mLawdX67yurysA8KDrkqNVj7U4WBzIWX/8+HF86Utf2vL9L37xi1hcXNz180ynU6ytrTVff+ADH8DTn/70LT/3fd/3ffjjP/5jnDlzBt57vPvd78ZLX/rSPR17i6MF5zyWpyWWpyXOT0o4v9GULZR8x+IHAALBwWcejwOBXFucmxRYrsZiLVpcCyiNw/kpndcr0/JAsxlnrSuUaH2DWlwdHEgH6KUvfSle/epXo9fr4Vu+5VsAAJ/+9Kfxmte85pIKkzNnzuD7vu/7YK2F9x6333574y/0ile8Ai984Qvxwhe+ELfffjve/OY3484774RzDs973vPwoz/6o1fkb2txuJBq29jwO+8xLQ3mkqDJJrqYZxDnDIudAIVx4BwIpcC58TpBPtcWnUC0naAWRx5paZo4GW1pRHxQnlr9SCGorqGLbUpatLhSOJAC6C1veQseeOABfPu3f3vjxeOcw7/+1//6kjhAt99+O+65555tH/vt3/7tDV+/8pWvxCtf+cq9H3SLI4nNy2jdzbmUhZ1zhjhY/3nGQH365ut2sW5x9MGu8sjpahqYtmgBHFABFAQB/uAP/gC/8Au/gC9+8YuI4xhPecpTNii1WrTYDySBgLYOpXFQgqMTXP4i248URrmG8x7dULbt+hbXBLqRhM08jHNX3VG9RYurgQO1Rn7iE5+4LWm5RYv9AmMMc0mwr88ZSI6lbkuSb3FtQXCGhc7+XistWhwlHEgBZK3F+973Pvy3//bfcPbsWTi30ffhz//8zw/iMFpcB8i1RVpaCMbQiyT4Hrs1F1KKXSqc8xjnBtZ7JEG7025xdWCdxzjXcB7tediiBQ6oAHrNa16D973vffiu7/ouPPnJT245FC0uG6VxcN43BErnPIZZiQdXUjBGROZpYSAEA2PAIFYI5e4W/OVJgUlhEEmBxW4AKTi890hLC86Iu8AYQ2EsJrkBq4ottQMxepTrJrV+mDlIzloSdYsDx6jyt6L/uwuqr4x1MM7vm0KrMBbeX5jwPCkMpoUBY8BcHCCYUW8WxmJakJDhQtcaQBsO6/0Ff6ZFC+CACqAPfvCD+NCHPoS77rrrIF6uxTWOaWEwKUwj252LFc6MC/z9o0OMUo1jvRDT3GAuUVjohvAeGOcGYffiBdA40zg9zOEBTGDAOTCfBHh4NcOkMOAMWOqGWOoGGKaaPEycQ2ksTvSjbRf3zd5Cl+P55pyv+EjtLr7FpcFu8sNaS0sAQKgEuuH6raAwtjm3GQMWkmDHgt17D+M8BGM7dltHuUZWUvESCI75bcZuxjpMC1M9J/1OPXb23jfHAwBr1TW+HXJtMcroZ3d6rRYtahwYCfoJT3jCQbxUi2sYtZQ90xbGOQxTKgTWUo1cW+hq17qalpCCQ8mti/bFRlu5sfDVz4ExlMZhLaMYDcEZHBgmhUEvUvAg+XDtDSQFx0ISbLkRJIFoHKUlZ1Bi7zvqtpvUYrfQ1sHOdHFiJTCpiow6foIxBlMYSM6aYjrXrik2vAcK47Y9x5zzWElLWOfBGG0Utuu65FXxAwBldUyX0lXyfoMI84J+RdPCND9bWofC2B1/tkWLA1k5f+Znfga//uu/fqBGWy2uLQxTjWFG/6aFQV46pKVFWhhoa1Fai0hKdEKBOBBY6gZYTBQAksZ3Q4lRrnF2XODcuIDeIRMsUgJpabAyLTEpNDw8RplBVlqMcyp0BGOwzqMw1W7T0+9Z56mA8h7GuuZ8TwKJ+STAIFZY6ASXNQK2m9pHtr2mWmyDwlisVMady9MCxjp0wvXzcBCrDefh7HklOdtw/u5UrORm3XPLeyCtRlRpaXB2lOPsKEeu7YYNAVlKbD1npeCN9UR9vebaYpTT2C6aGV/HF1B28k3X1uavW7SYxYF0gD7zmc/gU5/6FP7rf/2v+Nqv/VoopTY8/pGPfOQgDqPFEYX3VFjUCCRHYTRKYxEqAedpdxsNBDItkWmL0nicHpW4aT5CN1LQ1jdteOeJlLydAsY5GnGVxiGQHM5RB2e+E2CYacSKoxMKnBvnmJYWa2mBpW7YjBCs9ThXFPCebiTzVUco2KYbtRfEm7pJQdv9abEN8nImamKmi1Ofh5wxFKZsxlx198d7j0Jb5MbBOodj3WjHMevm4oLxdcJ/jVGusZAEmBQG1nlo63B+Wm64Nmr0I4VuIMEYHW/dWc1gMYgVooCD4cLXUi+SGGYa1nt0ggtzhVq0OJACaG5uDi960YsO4qVaXINgjIEz1mR6Sc5xrOIHaOsRCI4b52MIzrCWljg3Jm6D9R7npxq9OMDGJvqF0QklOhXFQFsHxhjmOwEWOgGWuiGGWYlpVUz1owCFoRFAKAUc1je4puoIJcH+XWZJtahb51sH3RY7QggGzOT4bu7iBJJjoRPAOLp+6kKkMA7a+Q2coJ0QKVGFDFsoztHd7jz31N2Zq4qgmg9nnEeq7ZbXqY9jc4e2tA79aOPGeTtIwbHYWla02CUOpAB673vfexAv0+IaxnyiGv5CJ5TQ1mEQUweHgfKElOCIlYS2ObRxsN6DM9rVBpIjUpTrxRi2XeC99yiMweq0RBxI9COFxU5QEa7pdTd3c4RgmI8VjvciALTjncWVcNulv3Xfn7bFNYROIKpRrEeo+LZdHCk4NgsjN9fTF6uvB7HCtGDQ1jXFfjeUmBQGDEA/vnjRsh0CyZGWFsY6OA/0owO1rGtxnaA9q1ocCdS7yBpKcMADw0wjlOsBps5TuvWDq1PESuB4L8QoNw3voRdSi327zskjaxms8Mi0Ra4dTvTCLa8LAJ1Q4Ya5CGspvfZCteMsjAU8dYB41RG6EF+hRYsrBbJmuPTig85Zh7y0EPziz5GWptmYFMbBWA/OGfqRbOwiaiRKoDQO2pIEP7lAFV9z7NLCVoHEDnEr6GqxzziwAujDH/4wPvShD+HBBx9EWZYbHvv85z9/UIfR4hrCMNcY5RqMAdp5HOuGGGcUWRFLgVhJ5MYhK6kAAnBBY8SsNCiZpt9hDGfHBW6aF9v+zkInxHwS4NykwKNrGam7qkKMMWCQ7N53qEWLw4R+pHY1bgI2WjzoSsreqbqrjBFHrd5w8Mp5ujRkVjrODTqhQKotjPWIFEcSyMbmYpIbOE/ju9KuF04tWuwXDuRseuc734mXv/zlOH78OO655x48+9nPxuLiIv7pn/4JL3jBCw7iEFocAhTG4vykwPlJcdnyVGsdViYltPUojcfypMS5SY4HVzOcHxXQjowL3SVIbjljWK6UM5m2UIJv8fCZxUpa4uyoQFpaPDrMsDZdL+xrmfHVxqQwODcusDot4S7HgKjFRZFrOr+XJzurDK81hDPjYGPdhvHw2VGBcxP6V18P3nusZWTlkBuLR9YyZCVZWIxzg8LY5r2rr1tjHRhaRVeL/ceBFEC/+Zu/id/6rd/Cu971LgRBgJ/7uZ/Dn/3Zn+HVr341hsPhQRxCi0OAYaZhnYd1vlF47BUegJzx08m1hrYekSSlSyAZuiFJa3ebDdaPFBa6AbqhQC+UyIwBx1ZCZg1t1gsKJTj0TMTLYdipknuugfMepXUYF+biv9RiT6jPaevIGHB0mef31YKrlFq7tSwJpcBCJ0AvkjjWi5rznpza66IHzZjM+Y0q+M0bBet8U0R1I4mgksf3Y3XRjUyuLbJq09OixW5wICOwBx98EM95znMAAHEcYzweAwD+1b/6V/iGb/gGvOtd7zqIw2hxFeG937DweX95eVtSUEDpKNdwzsNDYGVSYC0vYZyH9wzdSCFUfNc0ZCk4bl/q4LG1HLk2ODsssDIusdSLMIgV5pKN3im9SGA1JU+gWEkc6yqEgUAgtied7hV7fZ8238NaH64rh83v7VH0ZzLWYSUt4aux06yp5ygns1HFOfpVxl59TtYCBGB9XBVJscG6oj57BWe0Wag2FYNENedpraQUnFSfxnkc7/FdbSbGuUZaKTMFpyicViHZ4mI4kALo5MmTWF5exq233opbb70Vn/3sZ/HUpz4V9913X7soXydgjCEJRLNIJYG47AVqoROgExJnYPncBPedm6AwDguJwsm5GL1IIRBiRyfbzehFErkHlOSwXqA0FpnxmBQGSjIkVmzg9XRChRsGHqmmPDAHhlzbDaZtl4s6RuBCTrs7IZScTO2cB8OFDeRaXB6k4Aglb1y6dyMjP2yYlrYpRqwjMUCnMiSsPbRWpiXuX54gkALzSYBjvXDDORmp9XgWlgNZacGrrLwa84lq3ifGgEJbCM4Rq3W+3aVuIHK93kkivyGPQLYFUIsL40Cu0uc973n4+Mc/jmc84xn40R/9Ubz2ta/Fhz/8Yfzt3/4tXvziFx/EIbQ4BOhFqlnY9mNExBjZ95+f5HhsLYPgDIW2mJasIWRKzjCIN57mznlMykraPlMUSMERC06Fj6bxl+TVcfqtHIRxrjEpDYYp7T5vmIsgOMekMFiQly9ZKY1rbjx1ntl25o07gTEinWpLPKj9CLVssTPmkoB8o4AjGU+y+fSYVVYC1CEaZhqT3EBJh1xbOOfQi4JtpfY7kanr6zaduXYAjxvnEkR8b0W65AylpeNk1dctWlwMB1IA/dZv/RZcxY+4++67sbCwgM985jP4nu/5Htx9990HcQgtDgmuBDeGM8r9Kq1FYR1y7XBmVODG+RhScBTaY3YdXst004IvqugKgHKElksaN1lHct65RDVS+M3HnmuL02s5xrlBpi1CyXC8H+/b3+UvwbxxJzDG2p3wAeIwcL/2ik4gYR3xxWYtHCIpkHELbQHrKG/PG4dpbsDBoJ1HaRyO9UgZudvObqGJ+FzzgM6NC9w4F19QqbkTBrHCpDRwziMJ5J6eo8X1hwMpgDjn4Hx9YXjJS16Cl7zkJQfx0i2uIWjrcGaUQ1uHhSTAoCI3z8UKJ/sxHlnzmE+AE10FIQUCQe34WXIyQDvZGt6v5yAV2gIQUIJjqRshDgRuXUh23M0XxpLhG6NiZVrsbLKYa9tEBPRjuSuJfCgFIklqmZ2eNystnPeIlGg7PC12jWlhMC0NBGMYxFTkU8G/tcNYy9et85CM4f7lFKW1CANePRd1KVcnJUrjMJcEuxphScE2XIuMEXdq96y9jce4W+l+ixY1rlgB9KUvfQlPfvKTwTnHl770pQv+7Nd93dddqcNoccjgnMe4MPCedmqXkpF1ZpRjLSV1TVpmiJRAqASSUOKOG/qYjyXOTQpEAeUB1XVLuOk1whmCppwZDXVCibQgpQoAzMfBBUcZ/VChG0qU1mEQx1jsBI0j9Cy8J1VQ3c8ZZhrHe7tr9Q8ShZ7bfkc7zIiYSu+HxVL38omfs59PHIjWy+gIwnvirdUu0JujWIx1jSrLeI/RLkarjDFIwXCsHxHfzTuU1iMtDKwnW4qp93DVz3p/cc5ZN5SY7wSY5KZxaj+I0dVtr/+TK/4aLY4GrlgB9LSnPQ2nT5/G8ePH8bSnPa26KLa29BljsPbyPGFaHB0MM0p3BoDSlFjqhju2q+tgReNc5Qa7fp7UAY9htdPU1kEIysZyzuOGQYS5hEjS9W50nJP/iBIcvVACrGrvr9Hrh0rg8f0ORrlGpAR6kcIo1yi0A69IyB6oDNro5nLjQoy8pMePDyLqCuWmcuKl3C7vNyWRXeJka6f3Z1ZCbJzD+UkJzujGs9f8sVH1HtXPv9jlbWfpkCPXZHfAGUM/Vsi0bcQGpXUQnG0oZDerxJ338J4KX2upm7hT8SI4w1IvRGHIKdo6j7OjAtpSAHGo1mXwOz1HrWpkjOHUIMZ5XiDTFnKb4n2YaZTGQQm2JcG+RYvLxRUrgO677z4cO3as+X+LFsBG51iPC7e8x7lpOjXkGitR6BKuyvZKqgV2WhiMMo1RtZMstEVaMvRi3xA5s3L9pmCdhQjltuGNSnAM4gBKkKJrkpvG32VSGOr4GAfOiWi9mISwsW86SefGRVXfeKylGsd6VODNKuA6+6QQCgRv3p9pYdCpuA/j3GyQJl8KNn8+xjmIPRJTW1x5bPTUov+LTUWCsR6zp1wg1xVr3nt0Q4VxYRrCfV007dSdFZxtKLBvWRCYFAbjXDcdU7UN76w0DmsZyewjKTBIFHJtYavrubAO03I9IDUtTbPpKQwZKHbDw5/wfikdpvvf/l1X8EhaXAxXrAC69dZbAQBaa/x//9//hze+8Y24/fbbr9TLtTgiiBRvCoGstFj2BQTnmEsUlOCVgRolnW/2UomVwCCWWB6XsJwWxKVuiLQweHQtw2paQhvqBPUcMN9xjSJr83O5bbqR2jqcnxZVlhdDXClVao5Qri3ODHMMc3KgvnEuQhIIdCvugXUbacuzHc9epBBX2UiCsyp41TXeJ9PCYFoYgAFzcdDcfIx1WxK7a/RjCVGybf8W6/yeAlMjRccC0I0uOOQ3m+sR3pPLufV+S7HjvEcSrI946fza+hkOYoWz43yDSWGt8nJVlt18EmBaWnCGprjeDpwzdENJ4auORqeRpI5t7fsDoAkVBoDcWESGN9dWDWtnCvCZhya5gYdvuq+9SDUd1hYt9oorToJWSuGP//iP8cY3vvFKv1SLCrrKzQkEP3Ry3F60XujUrXDnaWGTgjXFUSCowzPMaBxDjsYCZ8cFSuMhJMfpYY5QcKxlJcaFxsq0hLYOS90QcVVo1TvVupjxnmSy8TbVQVpa1F37eixQj39YlSp/fppjLTXN7nSpGyGQAoGkUdGsAdzmEUD9WXjvsZquK9ECYRoJLzyNoZa6IXJtm909Z2TuNnsTYow1u+VA8OZnL6dwoR02g3N04zwqI4dck4dNKLcWivuFw3JdzXZr4D2EWC8kkkA0hHhjfXNebkZaWjhP3R7nPZQk+wZXFf/akJKydls31mN+B56Qcx4rablezHhgeVo2hfkgJvuL7T6VSAlMZ67LKNjoKZSWFtaREGAQSQxz3XzOw8xjqQoibtFiLzgQFdiLXvQifPSjH8XrXve6y3qePM/x0pe+FP/n//wfJEmCkydP4t3vfjduu+22DT/3F3/xF7jrrrvwxCc+sfne//gf/wNxvH8S5cOKwtiGKMwAzHcuzTzvIEAp0UCm15dEDyCb4fhk2sLBgzNAgCFWvHGHXZmWyCvzwU4gMM4Npnm92xTkYMsY1tISkgOltujFEotJAOM9JN/+psAZKm8TunHISOHm+QTnJzTWykuLREmsOA3vfCXj1VW3hSMJJXqRROzo77POkzHiTLGVlgbjnMYF3VCCMYZcuw037WaXPPN+OE/PVVpHxo7VGEIJBinWCaTG+QsWLtMZcuxOSp2jRnye1N0zXDkX4NI4rKUlPOi6mkuCSyLw7yf0bHwEY+iGVWeRsfURlOA7dgB9dS4tT9az6072Q8wlAXJtEQiOVBvk2iGUHN1QblFSOuexPKUcPMbQFGSSk7JrtkDMSroGupHEWqob1aJ3wCOjDN57LMQKSbQx7kJwhqUueSvBAw50bbiqe9pGXrS4XBxIAfSEJzwBv/ALv4D//t//O575zGei0+lsePzVr371rp/rx37sx/CCF7wAjDG8613vwo/92I/hk5/85Jafu+OOO/C3f/u3l33sRw2zjqgeaEi/hw2bJd6B4FidljDOoRNKTHKD2IqKUExt9kGk0AkEzngH6xiMczi9luHRUU5GhZxBG1KiZKVBEko8tpZDCo5jvRALneCCuWAMdKPT1sEDze755CCuojw8Hh1myIzFtDSIA45H1nIsdkgtVlYL9SBRWEvLhkyca4u5JEBpyPfEVcUTg0U3kgiVgBSMHJ+BxjV3cydDW9885/lJAcFL9CqzuTgQkILjQrVLnbIN0AiCM+J5bCab75VAfbUwWyheKRfg3NhmvEnXlb1qBVAgOUy5PuIKhNh116vu1kxyjXGmG5VYTaBey0oU2sEYh0lucL5ygz7Rj7A8KSA5RzcUWK6CgOk5HdYyjYVOCG39lvNQiPXIjGO9sBkN/8PpMXRtQWEcnlhdm/WIzzVKUYH5boBxbhBWnapxbhArjsLYI1ewtzg8OJCV7rd/+7cxNzeHv/u7v8Pf/d3fbXiMMbbrAiiKItx1113N19/wDd+Ad7zjHft5qEceRN5d//owO6LWEm/A4/y0hBBopLW1Bb8H7fp6gYCHx2I3RBwKTHOL08MMq5mGsQ6lsZCMIw5l1S1xKHQJ6z0ixlAYi7QwFyyASuvRTaj4qnf8i1WLvVaXzcUKvUAg4AyxUuBYd8w11sM4B2sdVqcFMk1FU6AYeqFsRgScM/RiSYobSTtjwRm6gQRjaLoXvVA2PkWR4mBgyKu0bG0pW62WNO8m5sLYjTtm4xwC8C1kc8n5Vbu57wWyUiMBVMReCdWa4hwZ1gutq7mp6EUKkhNHLtph5KetA2db3b8L42Cdh+AcvVghkAyDOICsQoSPdcOGZxNbEhFwDmSaBAbGWWjnNnB3cu0QCQ4lKMOrXzm+59rSuV6NaaeFwWpKo7FYUkTNpNAY5xZKMNw8H0NwjrQ0M5sHh6VugFAKhF2BWAmsTEswRp8BdanaAqjF3nAgBdCVUoG9853vxPd8z/ds+9j/+3//D894xjMghMDLX/5y/MRP/MSOz1MUBYqiaL4ejUb7fqwHhSSQcNWNsfbWOKyo/XEK4/DwaoqgGt0kkUA3lCiqm7K1rvK5CeG8A6vCGk+PCljnsDwp0YskBjHxfFYmDCvTEs479EKFoDSUM8RMo+TaDlIwTAtafBmos1AYC8EYnKccJOs8pBCwDggkwDmNE5wH4oBuAstpidPDHNNcQzsPVknhF7sBGNbHYJ1QQIn1m1R9I3NVojhFcTBIzhrOUqY5stLi7CiDkhyFCTCXKADrXIhJxRGRnG1I0Q7VumqMAQ1PaDcE8Z0+v8PAEepH6y7AcXBlDCHjQMB5Sko/DNfVhQre2e5j3R2sUX9cQTXaAqORdF2kMMYQBQI8q4QAgYCxFsuTAnnpoCTHXKwgBCkbz08KEgp4D8eA4z0yEK2l9PU5kpYGy9MCo4w6kD6iourcuISHR1p63PPAKm6YjzHJNfpxACU5+WdVpOc4EOCgTR6rNjXGOhSaCih6zaPVvWxxdXFkz5a3vvWt+MpXvoJ3v/vdWx57xjOegYcffhiDwQAPP/ww7rrrLiwtLe3oPv22t70Nb37zm6/0IR8YrnYQY2EsrPNNsvN28N5Xyi1a4HJDqhYq4DzmEoVAcDywPMFqbjDKS3zpoTVqsXNAO4dHV1MwDnBwOO8xnyj84/kppgXlCxkHJIpDcto5BpJjZVrs+P70Qgk4AY8qSFRwnBvlmBYWK2mJYVpilBsUhsiigQzQiRR6gSL1mlvP79LW47FxAa0d+onCY2s5Cu0QSIZRXpE+GcMDK1M88US/eZ8KbfHYMMP/v70zj7arrO/+53n2fMY7ZU5IQAYBMaCAUlwLx+II1YoDOABWilirFltb36pVa6urgLW1ivraat/XCUWtdQD1LVagTlBALRgQGUISktzce8+45+d5/3j2Odyb3JAEyL0J2Z+1stY95+yc85x9ztn7t3/D95trsx9HAtc4bGeK0arLSOAQpznNwCVMc6I0w5YuqrDuiLN82A+T5Ka5vFkxU2qD3iszFv1QI69vS3Z0zfRbzbf32ECd5uqhXo5inHlnlHpoym1/BwsLpQI8W75AF1nGhXh/sxmU+3bX55VkpnF+UN7cOTvoOxZZ0Zs2UjHl050zSI4lWd4MsGXMtk5MO8rY1o6RMmLVSEDVs5ioB7iWJFeKqmuRqUIyQRQaXWk+bFgGTTvM6Memby/Xmo1TPaI4J81zRisOM/2U+6b7bJwJQWmWNn3qvk3FdVBaG1PgNDNl4FSRKkU3zhHFsUMIQdWzWTUSPGyWt6RkNgt2pnzggQf45je/yf3330+SJHMeu+KKK/bpuS677DK+9rWv8YMf/IBKpbLL441GY/j36tWrefWrX83111+/2wDoL/7iL+Y0aLfbbdasWbNPayoxhElOu6jBdUXGeNXb7RRKJ8qG4622NMKBQWFhERZXdRXPQSG4f0eP+ya7pAp6cUo3zOilGXXfYUndoxOlbGlHxGnOTKqoeTaBBQozDdOJEnLtIAW0+um8J20hBBM1b6ja3ApTtrZCpnoJvSRHaGhH5u+KawQWw8iU3tphStrVbJ7WWHIwvaIRlinPmL6GjFxJ+rFpMDWTZrCtE7Gs7iOlYFsnphub/odWP8GzJDXfiNs1iueRUhC4pndIFgFNL8mouDY7J292zuZ4toVnP6QWnOfmZFhxrWKses86jYMSCZjeGC+dmxHRWs+ZAsqUXvSg/LFEa9OIP9BMSnK1IAFYq58OM3iuJXeZytJa0wqzoeVKxbVoBruuq+bZe/w8TBDkmwuJXCFlTJop2mFCzbdZUnGNtlZsSmK+Y6O1oBNn2L2YdpTiWRItYNNUf/iYa0lyrWhHKSqHfpoR54owzvFdQWx8UZFAN8xZOVJBCNPvF6Wmedq2BVOtDMc2fYPtOGOs4gwNg+cL6kpK5mNBjkr/7//9P8466ywOP/xwNmzYwJOe9CTuvfdetNY85SlP2afnuuKKK/jiF7/ID37wA0ZGRubdZsuWLSxbtgwpJZ1Oh29961u84Q1v2O1zep6H55XjlI8FO6s1704RNssVYaFgKwTUfAuBoBWlQEqmFZ4liZKMdpTSTVKmwoRelDPVi6k4gqpTTPsIjdSSfpzj2zZpnqGFJsk0YapYVrNohSmBa9OouERZTkPb815BPzRNZSa8pDRX2mmu6EUpAqOJUvUsslwjpSk5RZmiFSY0AwedZWS5ou47uBYkmckeCCFohRnTvZTJXsxM12K84bOtHSOA5c2ArJi2kYVSrsb0ThhxR1PKsy1J3bfNRJdSIExAGaWK8aqDJWCmb66Yx+seo+x6RdyNs6HkwFQvoVGx8SzzOZkekUd+AhmMVs/+TjyeAqCsKFEOiNJ8QQKgQfADZh/v/DllSqO0puaZ0lSU5iypP/LjmhACxy4mC6VkexhjWQKrFQ8nyALHoh1lSMB1LBwpjalwJyFwLZJM0Y4yAscmcCSZUoxXzW+wk5qm5lxpmoFDkiu2t0MqnsVU36jED96dUmY/p7lRhXZsaRrAbQvCFNcy2U1HCg6AqmzJQcKCdPL9xV/8BZdeeim/+tWv8H2fq6++mo0bN3LGGWdwzjnn7PXzPPDAA1x66aXMzMzwrGc9ixNPPJGnPe1pAPzBH/wB3/zmNwG4+uqrOeGEE1i/fj1Pf/rTed7znscFF1ywX95byVwGuiG7uz28XwqiNMOSpi9hLHAZrboEjo0lTZ9Lp+jHSZKMrTMhdcfGsgQCyIVESjO6nucKhRnLjbOMqiuRGnIFaIWWUPddGoHRIJrdaLwzaa7oxRlJZsQHa57NSOCQ5Hkxbp3TDs0ETaZU0Xsg0UXA9MBU35yEAptlTZ9Vo1VOWNPkiCVVtNb0ktSIzSnFTJTQj3PCJOXubV22dyJGfMdo2QhY1vCYqBr7DSFNdm1rOyaKU8arLkcsqbGsEQyzL0prskK/J8xytBZMdVMmO/Eu73N2Q7TnyOFtWwqc3XxmA2q+PTzJ+La1SwnIlnO1vR158DRU7w2WmHuSXaj3NzvYMTILilZ/8H0yDcsCY3XhO0aSoZ/kcyxT4ixnshOxoxvPuR9M4NuJUnqFFxzAsobPkpprbF9G/GGJuhuaMm47TpGY75BrSVKl2TQT8mArZEcnwvwyzXEgV5qq51D3bBxpSs2OJRivuDQrDjXXJlEKiSyyjEY4tebZSGG+35OdiN9s6zITxqBhrOJw7IomE3XzWxkt/PCi4oJhPvulkpIBC3JZdscdd/DFL37RvKBtE4YhtVqN97///Zx99tm86U1v2qvnWb169W6/0P/7f//v4d9/9Ed/xB/90R89+oWX7DODK30zuWTtdlpGYzIpaWbKTb00p44ZmdWZptXP2NKOsIVpGrYti0SlTFQ96o4Zww3THFtpKrakFUbmCljlrGxUqFZcssyoq7mWach0LXOQrvnzf+2zwm2+1TdrqriSkcBGKc3akQqrR6pFI2dC03fJi2Zz23NMiUwItIDtnYgVTZ/xqkc/yUCbgKNZcWj1jWijLQWtKEfK0OidNDzCNKcZOIxVXaa6Ma5toTXYAsIctncjJjsJ2wKbVMERS2pz7DCEMAFMO9cIBKLY9b0kY4K5mQCNKX15ttkf5iQj9kr80LEkS+v+bpugLSloVhzCxEwBzWc5cjAjpWC04tKPcxAMG4j3N6MVd1gyrrgWM2E6LHnmSjNWNYFEL8lxbUHNM1mpwbQfwP07+oUFBlTdlFVjAZ5tDct6w7Jl8X31bIsjltZxbIstrT5prslyTeBYJEXpSmuN2xO04j4Nz6UbpWTaNP9LIXjSygZbWhGpMsG1JQRrx3wsAZ4jiFNNvbCYqTo2OQrbcuglGTP9hEbgMhOmZLlmJjRltLrnsqMXDWUgRisejaLcN7tUOBSMLCmZhwX55Var1eGU1cqVK7n77rs5/vjjAZicnFyIJZQsEAN1YqUffhzZsSWubRG4Jo1f8x2sQtStGyVM9xP6cUaY5kz3EuIsoxOm9LOMsZqH60q6YY6lYUs7QmnNkoaHlDYzYYKSgjwzDcjNwGG85jJR9x52fDnNNf08G/bAZDk4rmRJw6MbJ9w/FRE4FmFsfLZ6UcpMzzR7G+sAB8eyyJViac2nHabcu6OH5wgC22bNWIU01zhSYkkYDRxcKfEciV00OiepZjqMiTPFplZIL85Z1QxoRSnbZmJs22gdTfdi+iPB0A6jW/RddeKMatEbpLQJiio7lSBb/ZQ0f+hEt7zmPSJl44cLlEyv0WPfHDwQv1vsHg/HkjQr+7bPHu3aB4ElmMB79rVgVqiKe7bFiqY/VAUfaGyB0caKC6uZVj+lG0PFs2gEDrYU9GLzf2xL0EsyHFsUfWW6UE+3sYTCDyRL6j7dKC364zL6qaIVZmgt0AocIVBKATaNwKVT+PU90Ovzm1yxsuGbScccJuoe7TCjF5vG+qleiso1FU/SCjPCVNGPM2xLDi+AB1IXvmPK3JtnQiqFFlY8q1SYKT3cNyUlO7MgAdDTn/50brzxRo477jhe9KIXcemll/LLX/6Sr33tazz96U9fiCWUPAqU0qRKzVGa3R1ZrpjqG8NDx5KMVuZ3cPZsi5FCsdmyjKJz4FqEKfTinC0zIe04ph8ppnqxeSzLQQt6YcqOXkKc5khhEeU5I75NxXbIcoVwJOSaJDfjs+M1o3K7J+0W2xJIJaHQe7EshpNOdd+l7idGmK14nq2diEwppvsJjiUIM4UQGgtwXclMKyTOFa2+Yqbf464HOyxp+Ahh1HyrvsvqUd/o2OQmqNkyE7G1HZp9pjE9RlHCdC9Goai6Hr5rDeeZhRDDsWAwfRLCtVg3blztlTa9WFM9059kSTG8Oh6Urg6EUfa9YSAfAKanam/7irJcGcNNa/GsPWarVe88mv5IsAp5hEEvkufMnfTSGqOWDkSZIih6ylxLsqMXkypFxXXoxTm92FjAdIqyUz/JGK+6bG1H+I5F03dwbNN0rbSm4Tk0ikzlWM3jV5vaKJ0yUTRlT4U5liXIc/Nc/SRnpp+weSZiayskUYpWLzXfY4wtTMW1SJWxzbAtU9q2haQTpSxv+GhMv91Y1R2W+ma73EdpzrZOTNUzmlqD/SLE/tGFKnl8sCAB0BVXXEG32wXgr/7qr+h2u3z5y1/myCOP5CMf+chCLKHkETLb50cAjcLXZ3f04nx4ZZrmqrhKk0NRtEG6vRtn2HLQc2Lcqe/a3uHe7T22zkRsafVJMo1CG4uMXBflJE0/loRJhu9aWMUY8ojv0ElTokQhpMtUv8+Kpo9VlL32xoHdsSSrqwHbOhFKQT2wh+/DsQTjtYC6ZzHTN2aoaM3mqYhumLGy6ePaEmmZ0ftWmNKJMma6MZPdhG6csrTu4YVGcM5yLOq+NTyg58qcsKZ6cXHyUPSjnMCzuWnjFEkCI1UbISTrqi5Lah6y+HyUMhNAadFYHiYWSxseEzWPbZ2Ire2YTBnfpNWjwZwTpxDmZLMzWa7oFRM+TjF1ttjiiN0i+AHTGF5x9qyAPNtPzZKCsQUckQ6Th/bfIPgxpaaY5XbwiE7MUTE4YAlR9KbpXUbx09z0o7Ujo7FVdS3S3EhLaHTxvTEj/NP9hDTPAUHVtbHEoF9HMTmToAtJitFZFxAmO9knTMyk5mjFxhYQ5TlSG1uYfpojRYZlGbHQJNNsng7JUERJxv07uozXPaqOjUTQDGzQmjh9yL+vl+QsrftEmWKyE2PbFjXXYu1EhSQ3AW0rSs33YNYFgG9LHFuiFVQ9iwdbZQBUMj8LEgB94AMf4DWveQ1aayqVCh//+McX4mVLHgMG0yZQ+HUl+cPrnux0rFFa89vtPcI0RwLLRjxcy4yJt6OUOFU0LLhzW5+b752iHWa0w4QtMyF130FIhWNZaAFxkpkDKxa2LQlcmzgxUv1aKLa1MyqOYMtMTpRkeJbgsLEK90/18RwjrrinE6bvWixr+rT6qUnRC9NUXHFtkiznvh19cpVjW1bRLGrS71O9mFWjFUYCTZwJtrRTRipWYV+R41oWCniwHSEKOwGAsZqLJy2TrREmiKl5NpkQ4EOS58z0cgLX+D01fIdjl9WIlTFNne6njAU22zox21oRqVIsq3vmRGZL7traJSkMPNuhKTEMLD48y6LiWShtGslnZ5JmwtRIGoQpQlBk0ZwDSnV3b5I5s3tAcqWNXckCMNt2pKe1EdCUgnaYkSrjldXcw8XEzkz34uFvKXAky5oBq0d3lQEJCwX1pFB93t6JCVybHT1TVh5kn+JM4dsS1y08uhxdeMxZ9BJjCeM5Eo0JmC3LBJEz/ZTJbszWmT4bp3u4tsOSmoNrSxzLAmkulmb6Ke1+SpzmZLlGC0U/zEmUouJI0lQjPYGUAqWEkXWQKd0oY/lIwEjFoZdmdKOUJFM82I7M+0tyDpuoMlJ1hhmeyiwBTK2h5prG6YMlu1myOCxIALRjxw5e9KIXMT4+zqte9Spe+9rXcuKJJy7ES5c8QgYNrnKnA8h8AcSgLj/o/8lyRaZM6SjJTFaiG2XGjiLJGKt6CGHMRXOtmQ4Vm6f6xIm5up3qp2glEAjaocKxzIRLL8roxIpqAL5l0+knIAST3Zia55CkCq0FWabppzlT3YSb750mPwwyrTl8vMrShr/H996PM3pJNrS2CBybdphx/1TITJhiCUFgmyvUFKOkHGdmQmVLKzSlv8AjTC1GqmaqK04zwkwBgjjNcKQgTRXbWiGe7dCO0mKc2pi7Niouq0YCJnsJm72IXpIx2TXvIUeQ5BndyGgNbZ7p0QozWlHCtlbMdD9hWzdmWd03dgORCWKyTFFxLeq+w0jVoVlxiLOcqW5sGsotycqmb8TqlB56os02oBwEQAPhxUf6vXokNAKzn9DGDmJvnkdKwSwHi12+z/uLdFagJYXAdeQwIzQo3fXibK8DoDgtdLOKUlqrb3pgRivuLtlNSwi01iitaIdmRL3igiVgx7C5XuNZEssyiuq+FGzrRKxoBIxUHZRKAAvfMc3xIxWHum+0dra3Izr9mN9s67KtG+HImE1TgsOX1nFkTidKibOMyW6Ma0tyzPdlac2nl/To9TPGKxUagYUjjYJzL86o+Rax7xBnZsTelgLPEswkOZPdhO3dGCOWYeQhpDRlYjOJKFnW9LGkGGoLWUWzeknJ7liQAOib3/wmMzMzXHXVVXzhC1/g7//+7znmmGN4zWtew7nnnruLm/uBQpTmRB1z1THwtzkUGBxkBYVfl28X1gpyl4mXMDEHPI2ZAKt69tA/C6BVeP8MGhP7aU7SCklzTStM8D0LlWm6sQmSwiRDKVg64iKQtOOENDPTH6mCXCjaodH7cC2BLS2EMI+PBhZxronznBHf9EGk2ig0R0nOtnbMkrq3xxNnp+iL6IQpSa5YMSLop+YqNE6NNUamFEtrZkQdBY71kIBgP86pejl2qtjWTVnW8GgEAd04Jcs0032jZFvxbYQUaGHKGEKCbVnEqTlpzURGPXe05hBN50XWSHP75hlyI/+DbUmm+okpy2WKLe2QXJsr+WbgsKTu0k8yhNZ04oxt7aj4fwIaZq0z4aAp2thnLG8G+LY11HRyi2keR0rSXLGlFRImORXHZtVosFeBkNaamb7Zn4MT076WgHxn15H7Pb2mUppOmCKEYFnD26cyXjtKiZIcSwpG9nG9ri0f8rNKcjMm7khG5UPPs7fBWDtKi3H3hDRXw0ye71rM9NNdAqDAkUx2Fd0oHzqz51phCVMydCyBbVnGVV4L0iwn0aZE6jiSMDHCjhpBmpuMr9Qw3U/pxQn3T/W4e7LLplZIFOe4jkQiSLIcxzNq1JkyQwRKGVuZONfUPYkrJeM1D9cRxEqzsuYyVvXY0TdBeKV4L1rD/VMhtjQXGq2+sczwCw2uOMuY6eUoclaNVkHqoa3HYL/nqiibl5TshgWbTx0ZGeGiiy7ioosu4oEHHuCLX/wi//zP/8x73vMesuzA/JJ2wpSaEwDQDtPHfQCktaYdZUx2YnxXYktJO8xYUveM/kfRDG0jhwfxQfAz6OvxbUk3NoJljcCm5juMV126cYZrCcI4476pPo4t6SYZy22fimuhUNi2QApYWnOouA7T/RhHSqLUWGFkhbVCphTdNKOiLDxy47eFRkqHqlSAReBZVH2LpRWPQt8Pe1aJ5+FwpcC1BUmu2NGLhxMnNc9iJhS0o5RG1eHYlQ2WdlK6SUInVMR5DlozFSZorUi0pOZbKAWT3YgwzemEOVGWFcGiZEWjwuaWscloRyl5McLuuQkTVb9oCjWNqK0w49b7W2hh7Ct8x0zwhHFGK85wpWkmr3oWzcBF64HZqoUtTEN1qjTdJGNilkBeOksTaPB3s+LgOZKGbxzqKXRYtrcf8nNq5Slez4zE74mpXsJ0L8F1JJ5t0Y2zoUrx8Hsl5WPasBqliiRX1IvX2RdFmCjNh+WzTM21FNkbBg7r/SQjkYWopTaLsKQYuq/viTQ31iquLfEcm6qXMdOXhc8WbJrpE7gWo8XajNWEeY1cazNtKQVxqmgGDqMVB9eysCzzu5rux6S5JleKdt8MF7i2xWjFZeVIQOBY7OjF3DvZQ6OZ6aWEhfGwVuY7H+WKFU1zjFjR8FDKKKYrFElsbCyW1ly291K00mR5jmP5VG1BL1F4Ts5YxSVJFY5jptg6YUorzEhzhcAE84PR+3bf+L4lSpmgf6bPYeNVBKYxfLbqVVkCK3k4FlygI01TbrrpJn76059y7733smzZsoVewl6jd/r7QDF/3F/M9FP6iRk9j7Ocsao7bLSYPd0lgNGqaYrMckWr6OUBmOnFFH8y1ZOsGQtwbcmKhodnW9y+uY20hGnWjRVZTdOOU3Z002HGRSkzGdKLEtpxSidSkGukkEigFhgT0X6imOxGWILiYC3Z3EpxJYBDxbVZPVpheb1Cs2KzejTY4z4Ik3zY/6K0QkpBP8lNM3gvIopTKq6gahvX+PGazxqvSpik3LG1xWQ7RWpBnChmeqk5aGfmqh0orsTNlXA/ypiyYlphzJbpmCTPkTUzVi+0U4zwasIsZaqb0E0y+kmKwuz36X6MpoJrFyaRtrm6toVAY7RVekkOuWBHkpjXtZjjWTbIiAhhVH1nj8wPMi5RmrO1HaGU+ay0YjiSHBf75uGyQP04Y+NUn1aYYluS1aMPiTfm6iH9GQGMVNx9ytLEWY5SDPuaZrOzDYiaJwIaiAHu6TX31iB2NgPvtUEfmNHesZio7b068+yXbQYOvi0ZDxy2FkrMVdcmTEzGdmDSWvNstrYidnQTslzRiVPWjFXIZzQz/QQpjO6UIyUz/YROaP7vjl4MSALPqEk/MNWlE+U8MBMSJhkV16IVJoU9hkbYFp6lGK25jFc8GoFF1XcYreRsmu7R6SXECuR0SK6UCc5QRCn0o4zVK2q0wpxelCKFS923C4mHBFsKk72LcySaZsXFtSTTYYwljdlx1bepukbs0bOMi/zSukea62JwQVJ1LWb2+ZMrOVRYsADouuuu4wtf+AJXX301eZ7zspe9jH//93/n2c9+9kItYZ+pzEot17z5rRMeT6RFutwSpgnWtSSriqAhKsTTBmnlJDeS9mmu2NaO2dqOsC1BlivWjddwbcm2TsS9k21sy2as6uJaipWjPlO9BEtKRquS7Z2YB6e7/GZ7hC2NQ7TSotD9yQgLLaBGYCHRJBmoSKOFph+n2LZFksGOXoTlmKZhpQV1NKNVl1UTFVY0PXzH3uMkWJTmTKXhsPSRK03VsUDA1pY5UWycjqn6kkwJEpWzcrSKJQTTfc3SWoWZfptuDBM1mzjT5mShNUmiTJo/yah4Ns3AphvnTEdd2mHKTGQsQeLcxrJgy0zEg+2IKMsJi4bUNFdIjQnMCvfrrGhwlrakWXFwpMncycJ2w3MsOrEJfrphSjvMaFTMZ5spIxMwVnVJcsVY1Z1XtHCyGw/Hz1EmgFLClBoVDpPdeE7gkuZqKBJY82zaUYpVlF4GStvNwGGyE6PRxai+ySBO9xJGqi6VwlG8n+RIMf/vrxdlbJzum34zR7JuvDpHpiFwjKdcXqgkV3fWQwpTQgpLB9eaY2fh2UbrKc1N1nFvpgjnw7VM78+gIXp2oNVPMtLMNKXPNxavi9LxoJ/Itc1E44NxPrSFqHo2D0xnLKmZ/R8mxjalFxubialeSDvMCeyEyW6HXpwzUjVTW65j7GPaUYalwXJgNDDinZumNa6UZBgLmHu2dwGB7wm2tVMsaYIYxzbWF75tU3UstrYihCg+UyHROmc6TMnyHFGIU8V5BtJjWzuiHWZ0Qhu/l9D0bFaMBNi2JIxz04sXpji26aFybUndd7GlKLJCNiMVl0wZxWrftcmU3sUjraRkdyxIALR69Wp27NjBmWeeySc/+Ule8pKX4Pt7TpsvNjXPplpcrS2UlsSjcZkeCH4NTgKDRlZbPqTf0yvsJSxpehA0pgRhdENyY1IaZzR8U2ufCVM6sXFhlpiSV5prLCHY2o4LV3FhLCsy6KUZ1nSPuu8w3U9xhEBaaaHDIljZNNMdQggsARtnQvJcELiCXmxOdv04pd2PmQmNW7QUkk6YY9mQ5zm269OLE6JYI7IMKQWtUOFYFEKEkk5oLCD6UY4zZvZjK0wRGN2h+TRkojRnKjE9I504YyQwV6RK6aHvUpLnyFjTsjKqrmWMTgVF47cCbfqB+omm6lnkSjDZS5nqx7SjBJErYkuS5IqZKCPLFO04I0pzAtcmjHPGqx6TbaOZ0kkVUZQhbTOm7NmSemCzeqRqbD0QKK0JbNOHYZpDjdjhliQEDUIKam6h/1N8r7Z3Y3NiL8bbA2nNO1autTYBWj/BLabhDl8SFF5uJmDWwOaZ0GQgbEGmiu+eMj1TTqG/0whcssxk+O6f6puprMyMPRtl7AStBb0kRSJpJwmBbRG4JoAerXhzMj07+ma8P1eaLdMh3ShleTNgWcM0ckspGK+6w89u5wAqTnPcIikYJjn1WUGWEIKxqktW9CwJIYa/HdsSc7bdE74jkcIu3q95/3GmhkGlcTOf+3uPMyMA2g6N35trC1Y26/xqc4s4zVFKDb3cLIH5LGTGxpkI3xHkaHZ0YqJU4zmSmX7Mxqk+YZaxtSPZNNWj4lokSpBlirGKsajY3otwhaRSt2hULVp9I7OQK5BSMdXJaVQcnOI3L6SRcOglKfdPGd0r1xJM91IQZgpNocgy6CamXB0nGVOdaSqeZKIWMFEXJgCUMB2mtPsJSa4IHEngmUBY5RpLC/pZjnQsllQdKp5D03cYq3lDHaRS86dkX1iQAOg973kP55xzDqOjowvxco8p++MHpYo+jEFKfPZV63Q/HV7x7YvL9GyhtcHV83QvMT5SmNKCKq6yAR5sxUjM2Hc/yRmpOCbQKUoCliVJitR9s1CKzYpAyXfMFWucmYNwP8rY0Y3xHEndddjWiZAI0IrJvnEptwRUqh6uI1m3pIrSmgem+iSZYns3IclyPMsEMu0oY7Kb0DdVI7RSuA7IDGwL0jxDFh5iWQ621ogi6HBsRcU2ytJeUX6a6af4tqAnoeo6UCQz5guCXCmHpRJLSg5fYTy8hGyzcYdivOYh0KxsekNhQduSNCuu0TCxTZDi2Db9OMWxbDbNhEyFGVlmVGkrFfOhaK1o+hadOGekIqm5DmGS8JttbSbbETNhQi/R9AuhukbgUvUcPEsilAapaVZsNII4NRN1k50EoU0/zZZORCfKOHppjTg32aiabzHVM0aVVc9k6eq+EbYL05ykKFM1fHOC78bZUHguzxWeJah6jslIKfMBtcKiv8cyGRfzdRZIabIwT1hSY7zm0uqntNOUTjdH5QrfdQrF6oxWmBClCgtNJ1H4Lkx2TGaj6jnM9BKOWm4yW+NVFykfmlDsxQnTYYpjSya7RrOqETgIbab/BnYcO8crs2/vzh9u8NuM0oeyOKmJI6nvxW9TFOKhraLRXAhzESF2Oq7MNlcdWDlsLHzlqp5DlGomuzF3b+uwaSYkjHNGaw5JokiUKU+rYmQvTIw+TqyMBpBnW/x2skcnSpjsxgSuQ+xa3Bv38WwztSlkhXrFJctzqhUjBWFbglYvI81z07MXJ2ZCU0hWNz06rs3Suske9sOEX22awSrK4v3IZIlzrWk6Fjmm90doyNFESuFqywwX5DbNikPVs7lnW5dWaDTCMqWo+Q7dOKPiSHzPYqLq0ghsMmWOaRqzT72i/LcnsdOSktksSAB00UUXLcTLHLAoZcT8Bn0WraLXBcyVXtM3/j1ZUeYYHnSTvXOZ1loPgx9geGU4OKRqzFWmXRx0p3oxv93eRQjBSGBTcc3JT2sjje/kxqEZbQ4sYHolBuWAXGn6cYZjmatxkxkQZlS1HVP3JZPdiHacEdgWUmhqvsOShkcrzFDKXP1qDVGcoTBj5DVP4FjGxkFIiW0pVGammNMUEBBnkGXGrsJzBbYq+rKEKNRjTQPqUUuqrF1Sp+rZtMOEGTS5grqXsbThzyuPH7gW0nOxJLSjjNGqa5RwA4fAtWn6XdPwKxQTdZ/RisdY1cWzLVwpCOMUz5XkGp64vE4/zdg8HeK7DoI+nci4VvvSYiRwOXJJDdeycO0+qsgitUJNL87Z2jEnwYpjI+ycfqZoRQm9OMV1BL/aMkPgOhy+pIYloBvlaKFxhWRzJ6IXpaS5Mam8Z3ufpXWXkapHqjS21IxWHPpRTpSGRKnCnqWYGyZGeboRmAmyNFfYtjE4HauajGha9GrJQfYrV9y7o4csPgdLChq+O8wsjlZctrbNa+XF7yFVejgNppUmzzVbe6aF1REOjjRaNY7M8B3zukobLR9LmwxiP8mY6adDsctulLJ52jTZJ5liedNnWcMny41X1myagYssfmt7UpXepZ9o1tcnyUyztWOJeXWS6p7NVC/GLawl4kwNL1CkhLrn4BY9LElmSoRCmMblVpgYWxHHohMloE22RwP9RLGlEzFec2kUgYIJsDKmuzGtfkbFk/SinO2dyDR2pzmOFOS2+ax9VxAngm6YsHkaEq2Z6obM9GLGAuMt5lqSKEmY6SWoXKOVouJbHBPUcGzBbyd7JLkxZ03zlFaU4TuSsYpLqjSubREnGXlxbAocC0toLKER2gwd6Dyn1dcEnkRKh36SYga4BJnK6MaaZU2LZsVl3XiFbd2EVpiaDKyGVSOVRbdHKTn4eHy5FB6AaP2QkjJAmuk5GiFam1S+JUxTaStKqbomXT4w7Xy4Rk8wV5lCPNQwKTBaILOxCxXmDilTvbSYuIFtnZiRQKG1IlcCz7YRGHXgsZpDkpkyRSdKi54I06RYcS0agcXWdowuArtcJ+RKMdVTbJo28gFrx6osafism6jwwFTIppkQpWCi7g4beyueRcO3mA4zemlMP8nRSiEZJmuGejRSgwJsIxyLJS2kJUjSDAuFKFJDnmNT803jcyc2z9eOMtpRSsWzdzkZgrHnyLXEKjI6Y1WXimvjOxaebU6wSzsRSaZoBM5w+unBVsQDrT53be8a8UQErTBl1YgRqZvupWyd6aNyTdW1aVRtxorSaitJSZSZaU9SXUycmWk6hEU9sOlqRZhrelEC2ihnx1WXkQBmujFpbvqCEILpXkSSa9phhmMJtLZJpaIdS1aOVpjpJ0z1zMh8nCtGqi413+HeqR71XgJFhqLq2kSpohtnhInJ6vi2RaaMYWxcKCzb0qh492Kj+i0FBLYgygSBo0xpVCniXkK7n9GOzARP3bcR0pw0798Ro7QiyzWjgYNlmbFzP7axLUHFMVNO3chkeSzp0olMkFB1bUTdox/ndGPTLN6LUxDGE8qSgqpn05gnu+Paksas74FSereZIM+26IpsOATguw9dGEz3k+F2zWDX0rVrS5OtKHqdOpEREoyzfOgjZ3q9jLFoa2ByKiHOFZPdhGOW1YhzhZDQ8GwEpn/LdyQV14gVJllGP1b0U1PGtoSm1UuZ7EWkqcJzrEJsUBDmeVEGlwgUSil6aU6rl9BJctjWRwMTNd/43iU5GoFlS6QlcKRg3USNBzsRNdtG+IIoyUly07fXizW9SLF0xKXp23SExrJ9AssIMUapAmHG2uNMsb2fMlHxSFJFP1GmcVzlZElOpDRL6zaWNNltKaXJeA7UzBH003yvrVEOJNb9+bf3aft7P/Si/bSSQ5OD7xtzkJEXPQoDBge9gR+TBDQP9R3YUhjbCGH8oTpROszozE7/70wzcIY9BXXfxrON8nCSmSvTgbz8kprP1nZM1bXoRSaTsnwkMArPrqTVT43flS2Z7MYoZdzEs0yRWtJYLxTjtZumIzbNRERpaq7oc8VMMcGRZDlCSqbDhAdbIcvqHu3ISAm0w4xtrZBcm5OozjXKNmJoKI1tSWxbksVGQ0QBjg15DkKDtEEryKWm7lo4tiSVkOQmk2BKVAHHLm8yWnHQxGzv5MNUed235+2vitIc4ZjMAIAjreF+E0Lg25KqazPQVhtYDkz3E2yMmanvSkYC08S7oukhhEauFjzY6lP3TSZp1ajP8poRbWtFMVO9BDBiiZum+yRpTpQak8iRioMvLXLdp5+YkfQwTfFii4YPm1sRuVLUXIcdvZh+kjFa8xjxjXij60tGqsYP7cF2RJgWJZMwAQTNCmxrmYDGsU0A5AgIRs100dZ2SM015rZhmjNaNcFHPzZic0JgApSqTa48tACd62GflW1JPEvSSjJqvpnYyZRmWSPAkYKtnZjAkWxqxVRdh4pns6ThGQuEJCOdFVB24xTfthkpRvzTXKMxJSnXlmSZInBttnUjwsQES0KY78TDTXnN1igSgjm2DwMsKZioeiQ79dTtrCyd5GqX75YQRveoVwSTtiXZ1omN+7lXKHNbcrht3R+UkWHdeBUwU09WocfUjU0JuBE4eJbgvh0hM/2YVpThSVEEKJAjsCzIFAhLoDLT57S04RM4kn6U0ktzfMcGYSQOprtmitMWmlhR9JfZSCnNL1FrslQx3c95goCGb5OM+NTjzJTVpUuc52zvpPTSlCyzcVyJl1vYUlPzbI5aUqMTpvxmsovSmgc7Ebk2mTAtJHVXMBOCIzT9Yv+arJHpcxutOESpz9Z2hGtLltT3fqqupGQ2ZQC0nxlofuRqMMlhmbRyIo2wl22UfwcBkWfPFXvrRNkw6zNI/0slCJMcIQvJ9yK741Yl3TgjSsxr1Twbdjo2SCk4YqLG5pYZba16NlFiekYUml4xBr6k7jPZjY1kfa7Z0ooIXEkzcBFoOv2ETpRR84yq7GQ3JnAsRNVlWyeiG2UsaRijz06Y0YqMxH3Vs6m6Fu1Q4bkOSxs+3TBjsheC1ti2RcPXuMIj9XJ6iVGSFkpT8c3JTkpzFVr1LEaqHlVX0upLMw7r2ow3XPqF+vTyhm+aKLVRp27YkpG90F8B0DspxySFMvKAuCjndGPjhp3mChKBXZWMV31Gqh4jVZeRIGKmnzzUTyPAskzTr22BLtqX41zjSoEXeASBZqLusrwR0E8zemlKL9M4wnzmtYrDsoZLq5+RKJMl6CUZriNQGYxWHZbUAzQ5oxUbSwi6aUbDtYgth7zoLQkc8/l4jmUmshSM1d1hKSVXmlaYMdmLWN4ImAkT2v2ULa2Qmu/SDGxSpam5FhXPNvYJvmQscHFd01ittNHASXPN8qZP4FqM1xz+Z3ObfpybTFTgmIAv8GgGNpaQxJkJhtM8p5fkhWGmMmPSFtwz2WOqG5EpwUTNJdMalRu9mziJsdCmZymwh5pD8xEXJSwwWcVenDEyj4KwlAJfWkPlc2BYVh7gyPkDLceShZhiSjdKh88xsMhwpBheKHmOZKLqsq0TY0lJP87oJsaoNEpzxqoDM9XiYgmNU1w4YVtYxfBDYVfHWNWj4lhEScxMbNS8p3oJWW58uyq2ZcQ4yYrysyDKc1RuLsgsG8YqLq5jGX0v30YpxZ3bOzgIWlFO3bc4fFmNPM8AgVIm05trsBAcu6LOVDfFsQt5ACkJ2qZHTSvzW/IsSb1um+xymNDqZ+SFaWsjsPFdQcUzPXOrRgJqvjNn3L2kZF8pA6D9zCBzsGk6JtWK1U0zdjJ77LVZcfAzM9Fj7CIK7X6tQZgr3cEVqQRm+snwRKyUHh6s21E2VO+Ns5zx2q4lM63NdNKa0QqN4oq8HabEaYbn2ghhauqdKKUfpWyZMQehTpxTd80VaMWxsF05lKN3bcmo75pSR5rhCmNE2A2NvkvDd7CkxLVN6rviSqqey4PtyLiie5JW3+i0BI7R+JEiY0fPGKg2PIHWpvE6URqtBTXPYqzqMxrY1CsOUZKzo5eB1DR8lzjLmexGOLbxvKr7DpVBdmk3vQKeLVHFhNagHDHdS2gUzc5o0/ALps+o5tnUpEWeK6I0ZyRwjbS/bbGkbk64UkgOG68QZYpekrG98DNKc02WG62T8aqDQOM7AikqhWu5xpGSPNdkmclKpTlUHMFo3WPNSMV4oWU90tgilRljFY9GxcZ1LNaMBhw2VmHjVESWG0NajSYH8kzhWhbHLKtR9x129BI6UVoIWVp4thlXl8II0HWTnLpvgqat7ZhOlNJLFb0kZEdHsLTpM90zB5NeltNPBGGiWDdRxXcs0twoIVvSKIFXPEmrZ7SjekmKFIKa53D4RI3AMerG3ThhqmuEEy0JYZIVJp2ayV5EkmqiNEchQJhG5wqCHXHORNUjTU0D/0yU0gsz8po26td785vd2dCuIElzWlGG0hqvKGsNLlbiTBXSAop7JyMsCyaq/i7j7YPR/JpvdKSM0KVp4LUtiSpMbX3XJnAz2mGKBaCMRMR0N0FaJhO5pRWhlSLKYKqfkKY5vSjDtyxcGxoVB60k0ldFqUlg9zM2zcS0+glZngHGDiNwLNCaVWMBSabpxpbpmQscllRd1i2p8sCOiOkoYUk9IFdGCyjHyCE0hcOasQCVa9LMDGW4UtKs2qxoBqwbrzJWzdjejYcK0xXXiIQmec6ypsvK0QqHT1TY3k2oBjauHYM2pb6Ka7OsEVAtgp5cG2f4x7s2W8n+pQyAFoCtnZBWnOJKyaZWRMWzh2ObAwbNk65tgoxMaaLEGIj2U2NLMV7zoBhdHzBbxXd2Y6/G2FBkRZNwM3DI1EOjtwOjwGphXxGlRltkRTOgFxltECkF0/2IqV5mRPWAqBWyshkwXjd+Xv3EHMRXjvjcunGaKNXkGsbrLkKYKzMtTYPrqlGfsDAvvXNTm06UI4RgRy+hHrggbcI0w5MS6gBd2lFO1TWNsrlSBEi0htGKw5HLajQDh5Gqy3Q3pR4Yb7DxiplqEkIw2Ymp+ja2NOP6tiXYnR2mEILRqpnmGpAUZa6RiskqeYW/WaaMBkuuNIdPVI3OjGcm4zxH8sB0iMaUSmqezZqxgB3dmDAyZR2FmciqOA5rJ3yiWLO9F7O0mvNgNyZONCuaPhXfZrIb4ztGVLIdZhy1tMoRS2r8YlOHuu8SZSlhmrGk6lD1XZY3PFaN+EXwq/Edoy1U9RxGK7axHHAdDhuv0U8yVo4EzPQt3DCl6lj4tsBzHfJcE6YZo4FD3bHIirKTRND0beLiO9OPMxTGI6ruu1iWwJKSMEmJPWO5kOYa17JIspRuZIxwNaY5XgJHLKkWfWkmG7ZxqkemFCqCw0YrrGj69JMcSxSBSJgTFeKCFiZgi7VmvOoakc3M9ERFaW4yFZak4tm7ndzqFM7pI4FDdaf+MCPMmRgz0SSj5tqkxaTl7H9prrhnsj+cItQ6Ys1oZc4J2rYko4FLL05J05xt7QgpBDOWHP4mW/0U3zE6RIMAy1May3JgxEhQpLnpERypOky2E+qe+TySLCeME5LcohsrVo8FxCmkUUqcaSzLYjSw6UcplnCwZeFbpgS+Z2NZkqUVi6UoAteh4TkoIah5HivHJEvyogk+Mxk/KQWelRZCiikSOGysRj/LQRt9nomqx3jNYypMzTpz41NWdSxyDaurAUsbHitHAiqew3Jp4VgBKxtGZX604g7VrQcM/iyDn5JHQxkA7WeiNGeqa4wuI8xUV5KrXQKg2VQLQ9EozRGIwvjRpI611kUw8FC6fIDnWGTFNJjWpqQiC2PEVpjMUcLNitS70mYEeNVIQKcYXQ1cRapzJruKuufSiRSWACE0Qgu6UcrdWzssbfosrwe4jhkd922b0YpDluWkCsarLktq5oQ41U9YUjMHsq2tiAe7Ef1EIYQizXNsy2ZF02OyI3EsTaY0SxoVpBWDBtdSTIem0ztwJEsbHk9c3kRp6MQpSWb0TsaqPhXHKCILIQhcG8+SVH0bXUy17cmDaWfN38F+y5We40elCn2Z0apHmOZsnlYIxzQpW5YpyTUCSZjmTNQ847quYdNUj+kww5EWK0YqRGlOMyhMI7VmrB6wvROSaQDjwTXadOgnOc2KS7PiooRgWcMnznKWNytM9RLGKi6rRj0magG+I40C8HhAmBjxytGKQ8Wx2N5NCBPNxh091kxUhlNUI4FNYEk2tWOqqcmYOFIyVjVTikluZA+iJKefmUCg4tlkSiGlcQKf6kdIIXlwJmRHJ6Lm2/iOzVjFQ0htAhxhMmlhbIxBHddCY4QSc6XoxibQFLkwrud1jwdmQpJM0dd6WFIWmIC24VtUC7dzJ5AoobEwgXg/UWgFqTLly3aUomb9EAY6R3Xfoc5D/mqz6aembylMc1qFu3ktd3ZxczcBiKITz2rdn0f5I1NGrmDjdGRK1qni+FU2rTBjvGaCr5nQaCJRfCekMMHE4UuqaG1eq5/mxKmiHxuT29Gqx33bO9zyQM94fhW//Ym6S55rtBJ0ImMqaiQtjOp4piWONLIFFcc0PLu2ZNxxcGwzEDAdJvTijCPGK9iWZFkjYLqf8GAnZsVoUAxhCFwpcBzJmtEq3ShhJHBZOeqzcrRCohSbpkM8zLFkpOIVIpiwbjwYTvJN1Exj+IqRh5zuc2X6IXOlqRa9ZSUlj5YyANrPJEVPQpTmaEyGZ2+0KgbaK4ND9WCqSwjBWMUlykyj9OwDcM2zi6BGF0rA6fCxwfTK7Ocbr7qo4spqkP0w3jvQ7qd04g5CwEhgviYN36UdxrR6CoU50K0YqVDzbNLMZI9Mj5PR51ha83Aci4pjIzHicZtmumxvR/SznAdaIWOBYxyjlcaxLNYtsXEtwW+29YnSjIZvymbdyEyqpZmm5kpqvks3zXClRcW2yAJN1fVZMRLg24KKb8Z3pRSsaAZQjO9XXHuP+3+OCjAM7SF826KXGKd4V0pybbIhgWtxxBJTTjIq18ZHbFBJGXx2A3+pXEOlGH02TbP2MJM2MJdVRc/MZCc0wnO2RU1KGoGNJc3+qgU2biapOBYrmuYqGkyzshlbl6wbr9MOTc/JkobLbRvbuLaFAKbClGo3YbzmEWeKyXbI5nZMJ8zIckXdd6nVLCqeg1+IMG6eCWkHCV4u8G3T8yOL0tKqUZ9N05ooU2zvxAhhoutubAJAxzZBUpLljFY8Ajen5jn04nTYb1RxTY9cmpsvrFKKyX5CWEytVV0zDeRXHALHlGybgUsvyZgu+tICRzJe9wnTjDg1z9NPjB7VQLhxNntrcuFaYpilcSwxJyMRZ7nRxYrzYYC1u0BbCNjaNuWdwZRoNNRPgqpnIaXpRUozbZrNgXpgMVHzQZuSl2NJNkUhni1NcKCNdUqcmgDGmKCaC5MHWiGtMKEXpYxWPBzLKCtXbZtWkg6NUaMsxxKSpXWPimd85aquZZrRpUBIweFLaqwaCdDAlpbp9TEXG5KNUyE7ujGBa3HY2AhjNY9G0YN11NIGnb7pB6y7JhgPc1VMyflM1L3dTnINDGlLSh5LygBoP+NakpGqi+eYE+qSur9XCs9SCpoVh16cF6Jr9pzHBtNJOzNXTXaQRTITI4PeHorbUhpvrdk4lhkfrgc2S2seSWau0leNBPTT3EzeaI1tG8uFMDE6RuPNgAfbcaF+7FP1HcaqTnFANmq6jYqDa/ewLcnyekAvzAgci9Gah4Vg+aiPaxnhtrrnUPMlgWuzdTpiSztG6ZxWmDHRDBivuQSWZEnDN1ozUz0j+uZIljdN2j3XZn/U/H37mpupHXMStuRDJ7pmxaGXmGyJZ0um+wlLikyTEIKlDX844ZcrhWOZfqZh2UUPMneCqmMxWnWHKfy676C0RqmIaaUYrXpIAVJojpioExeu5qNVl6leQsNzGKma3qeabywEBLBxum/EGaVgouGCFqwcDWj4JluxvZ1Q82yS3DT+Dsb849Rk7UYqDq5tYVuasYpL1bORwkwT+o5F4Fgsaxi3+DDJsKVgpOaSZJosN6PpGnCkMC7zSqFyI/x5RKPCaMVjpm/WgIAwMSPYplHdZEcmal4xep2hlel/68U5fcxnUfdtGr7LaNXBtsxAwLZOjmtZxYnd4tgVAb0oZbo42VrSZDZWNB3SWYHLoAw80NGa77tSKQQ1fcdiacOnXpSKBoF0mhuxTQDfk1SxqPs2Vd98/ztxSla8x4bv4NkmkO3ED0lbNHwbhBxevKxoBnSilN+EXWwJVc8ly0ErTTcx6tqxyLEtwZqxKv0kY/NUaKQa6h5amPH41aMBFd9mLHNxhOn1MUrOFr6ULG24SCFpxwlJVmT9hKReMX1vyxsecaYZlZJqllP1HEYqLk4xrNEoJiaNN13K2nFhFKZThV9cOHjFNF7FtThsolr47JnSfKUo9dUKMc6SkoWkDID2M4OAJHAt3Fk1/b3Bs615hdX2lmbgGA8lGI7Oz+c5tDNSCFzb4oilteF48ETDpxtl5No0pKa5aW52LONqbVuS41c0mOwlPNjum5OEkChhXOHzovy0ohlQ9xzCVOHZsrhqNVNLh49X6SdG/G9502dJw6efKMarHutiI3i3rZOwrO6xrOFz9LIGrm3KbytGjKt8xbEZqbr7tJ/nQwjjCL8zA/0kMFk1pY0VwXCfVxyazH8gr3gmu5HlGiFNqbPuO4xWXLpFE/LyhoclpFHlVpplDY9VY1XGKrbRSYpN6alZ2ImICkzUvOHnu3KkQjdKZwVWNhXXHmafVo8G3L3dlEhWNHyOmKjSjjNqvkOz6M9S2lg2eEXwKIVgzDX3jxdBW641Fc/DlkZ12zjPB+RK0i3MN4NejC0l9aqRZZBS4jkWa8aqJjjKFRVX4buCJNWFq7nLSMXoL6E108V2I5nGlhC4NisaAc2qM/xtOFbGSOFL5UiBa0nGa65xnvds6p4pfxovKYfWTp9LzbPNBCPMKzEhpSisOByiVJEqNcwSAnN0vUYrJota9R0qhcr64PEwyfFsiWdbPGlVk99u75ryz0SVJXV/ONo/CLrjLMeyBUKLQnFckM4q3wWubYJoBI7lUFlm0ajYWFLQTY2lyhlHT3D/dIhrSXTTY3MrQgCuZbFqNGD1WKUQX3SRRYBZ9SyiTFP3LI5ZXufBVkw3TglT0yNV851dfl+2ZcbRtXZZOVJhumemHiuuZXoXB2ueVUIWgqGWVknJYlAGQAvA7L6RheaRWHk4ReOuFIKJmkfds+kXjcFVz6IfG4PPFQ3fuDQXGiuBZ7PUErTDlDBR9MkZq3qsGQ1ICz2k41eOsGUmIs5z1owFJJkiShSBJ1k1WiWwBVlRljt2eZ2wUMZt9RMmu0bHpupK6r7HiqZvRp4LQ9CF6AuYreE0UMLeWyquRaZcPMuMc9eKAMiSYtjbA+bkNN1PSJUampVaUjBRM07iudJ0owyNKenNPmlXXYs8N3IJri0JHIt2ZNztwQRw69eMmJ4txzLP61goZTRapvtGeXi06hSj62pOMy5As+oW4oimFBSnGilNIHHcyjpb25HJYHo227sxcW5KW4OAZRBQKKXY0TP9cUIYZenBuPrgO9XEjOMva0qe4FULh/W5+zxwLEaqLvUiOHAtSSNwaQQurdA8v22Jhx2F35vP0ZR5LJzcDCmooixkPNlMKa3imjHuimsCkeleMuc5BhP0Y1WP0eLznu0/Ngi609wIS45VXKb7KbYUjFU9PFuSZGYK1GRofOPTJcxIvgmeTJnvSSsbLGsGjNY87q+E9JKM1SMVfNcicEzWsBk45ErTS8yghXkfxketXjQ5r52w6cYZljBB+8M1Hg88/ibqHhM76fMIYbLanSjbayuRkrnsq3DivnKoCS2WAVDJvAymwwa4jhklD1NjWBrMcyICyIqptHZRavMdC8uSWLPivyOX1QCY7iVzhOTmEyisFcHYsobP0TRIs7wwfjQnIyEElQVsiGxWHLxUDj3R9oXBNN7DnYgBRqougWchEPMK+FlFeXS3r7HTYwNpBDAlJmeeYFFpTZTlJsDAZAEde/6M5c6ZydnnscC1WTdRe+i25xQNvSZg9Ga9H10YudZ9h0EiYOd17c3Fw6Avrl+UeyuzJSb2Yn/vC1FqZCM0DwmTDjR+4izfJVisejZpIVux8/t/uEBi8Dsy+8b0iw36Y8aqbiHIKOd8P2b6CUsaJnNq/q/ZfrzqU/dMX958GWXb4mGHMiz58MHjvuDZFl6t1OwpOTAoA6CSvcaS87uoz8a1zZTPoGFxMNE2X3bGsoQx+pr1/HvCsS2cR1EWfCxYiGzeoyl97owt5bAMI8T8+3ngBQcmkxEWLu6PFteWTFQ9lNa7fAcGvUoDI1BvnmBvb5F78d18LBgMM4AJGuNMmfK2LecNVl1bFhOAu77/h8O1H8oqwdxpT3s32U7bksTZQxcU9ixRxodTwi4pOVQpA6CSxxTHkowVWiwDn6jdTcPUi/6kwXh56eS8f2gGxijTiGDOX8LYOSja2Uvu0SClQO5GXHCs6hqLjyKreKCzc4/QboSfd/k/u3v/u8MqyoRxZgYI9iboHqghZ4UdRxn0lJQ8PGUANA8Dmfp2u73IKzk4cQtF6BxAW3S7ycNuL4EkgyRciNXtyuBzfjx/3oP+jjCD3e3mLDLj0LYtyXFoxwu4QKATLczrPJrPW2tNEhp1dM+xiLHZ37spZ99+G4v9ezrQ2PnzVnF/MZdzQHPY27+yT9v/6n1n7qeVwJPee+0+bT9Yy+Bznm1ZszuE3putDjEeeOAB1qxZs9jLKCkpKSkpKXkEbNy4kdWrVz/sNmUANA9KKTZv3ky9Xl90qfV2u82aNWvYuHEjjUZjUdfyaDlQ30ue5/zmN7/hyCOPxLIO/DLMvnKg7veFZrAf7r33XrZt27bbz/tg3F/lmnfPwfL7Lj/DxwatNZ1Oh5UrVyL3UKMuS2DzIKXcY+S40DQajQPmC/ZoORDfyymnnLLYS9jvHIj7fTEYHR1l7dq1e9zuYNxf5Zrn52D6fZef4aOn2Wzu1XZll1xJSUlJSUnJIUcZAJWUlJSUlJQccpQB0AGO53m8973vxfO8PW98gPN4ei8HE+V+N+ztfjgY91e55oOfg3F/HIxrnk3ZBF1SUlJSUlJyyFFmgEpKSkpKSkoOOcoAqKSkpKSkpOSQowyASkpKSkpKSg45ygCopKSkpKSk5JCjDIBKSkpKSkpKDjnKAKik5HHGV77ykKHh5OQkL3rRi2g2mzzzmc/k/vvvX8SVlZSUlBw4lGPwByBKKW644Ybhyeqwww7jGc94xh59TQ5EHk/v5WDhKU95Cv/93/8NwEUXXcTY2Bhve9vb+MIXvsD111/P17/+9UVeYUlJSRzHc/RzvvrVr3L99ddz0kkncf755y/ewh4GpRSf+cxn+OIXvzjnmP6qV72KN7zhDQe019p8lAHQAcaNN97Ieeedx/Lly1m7di1aa+677z62bt3K//2//5dnPOMZi73Evebx9F4OJk466SRuueUWANavX89///d/Dw9M69ev57bbblvM5S0ojyQA/9nPfsaNN97ISSedxDOf+cwFWum+8/3vf3/eE9Hv/u7vLvLK9o6DZT/vL2ZfqHziE5/gyiuv5NWvfjXf+ta3OPPMM3n3u9+9yCvclYsvvpgtW7Zw8cUXs27duuEx/corr2T58uV88pOfXOwl7hu65IDihBNO0D//+c93uf9nP/uZftKTnrQIK3rkPJ7ey8HEscceq2+//Xb9P//zP/rEE0+c89j69esXZ1GLwA033KDXrl2rn/a0p+lXvOIV+pxzztGnnnqqXrt2rb7++uuH2z372c8e/v3Vr35Vr1mzRv/hH/6hPuqoo/SVV165GEvfI+9+97v1ySefrD/+8Y/r73znO/rb3/62/vjHP65PPvlk/Zd/+ZeLvbx5ORj38/5k9m/z1FNP1Q888IDWWut2u33AHh+PPPLIee9XSuknPOEJC7yaR08ZAB1gHHXUUY/osQORx9N7OZhYu3atPvzww/W6dev0unXr9MaNG7XWWs/MzOiTTjppkVe3cOxtAD77RPSMZzxD33HHHVprrR988MEDNmA88sgjdRzHu9wfRdEBeyI6GPfz/mT2b/GpT33qnMd2vnA5UDjqqKP09u3bd7l/27Ztuw2ODmTsxc5AlczlCU94Au9///t585vfzPj4OAA7duzgYx/7GIcffvgir27feDy9l4OJe++9d977Hcfh6quvXtjFLCJRFHHyySfvcv8pp5xCHMfD20KI4d+9Xo8nPvGJACxbtuyA7VXTWqOU2uV+pRT6AO1qOBj38/5kw4YNnHrqqWitueuuu+h0OtTrdbTWpGm62Mubl3e+852sX7+el73sZaxduxYhBPfccw/f+MY3eN/73rfYy9tnygDoAONf//Vfeec73zmsrw4OGueccw7/5//8n0Ve3b6x83sBcxA8GN/L44FKpXJIBZ57G4Dfc889vOIVr0BrzaZNm4iiCN/3AUiSZFHWvifOP/98TjnlFM4///zhiejee+/lc5/7HBdccMFiL29eDsb9vD/5zne+M+f24Fi/detW3vSmNy3GkvbIG97wBp7znOfw1a9+ddh7tm7dOq6//vqD8thSNkEfwExNTQEwNja2yCt59AzeCzw+3k/Jgc/27dv58z//c6666qpdLiY+9KEPsXTpUgA+97nPzfl/L3nJSxgbG2Pz5s380z/9Ex/84AcXfO17w49+9COuuuqqOU3Q55xzDmecccYir2x+Dtb9XPL4pQyADjDuu+8+LrroIu655x7OOuss/vqv/3p4lXTaaafx4x//eJFXuPfcdtttXHjhhdi2zWc/+1ne8Y538MMf/pDx8XG+9a1v8eQnP3mxl1hyiPB4upgoefzzrW99ixe/+MWLvYx94mBc86FXeD3AedOb3sRZZ53FF7/4RbZv385znvMcOp0OYHoaDib++I//mPe85z1ccsklPP/5z+dVr3oVvV6Pj370o7zjHe9Y7OWVHEKMjY3NCX6OPvroOY/ffPPNvO1tb+P3fu/3OOecc3jve9/L1q1bF3qZ+8TBtuY4jrnyyiv51re+BZgS+QUXXMDll19+wPa8LBb/9m//tthL2GcOxjWXGaADjNnaEAB/8zd/wze+8Q2+//3v86xnPWvOYwc6s/VoDjvssDkqxCeeeCK33nrrIq2s5FDg9ttv3+1jz33uc9m8eTMAH/3oR/nnf/5nzjjjDK699lqe85znoLXm3/7t3/jKV77C6aefvlBL3msOxjVfcMEFPPjgg/T7fY477jg2bNjAy1/+cq655hpWrFhx8GnIlBz8LPjcWcnDcswxx+xy39/93d/ppz71qQfdmOHs0dbXve51cx578pOfvMCrKTnUEELMkQOY/c9xnOF2xx57rO52u1prrbdu3apf8IIXaK21vvnmm/XTnva0RVn7njgY13zcccdppZTu9/u60WgM158kiT7++OMXeXULz3333ad/93d/Vx911FH60ksv1WEYDh97+tOfvogr2z1XXXXV8O/t27frF77whbrRaOgzzjhD33fffYu4skdGWQI7wDj22GO55ppr5tz3jne8g3PPPZe77757kVb1yFi2bBntdhuY2wC5ZcuWYV9TScn+Yu3atdxwww3cc889u/xbtmzZcDvHcahWqwAsWbKELVu2ACYbOyg/H2gcjGu2bRshBL7v4/v+cP2O4xySY/AXX3zxQdfu8Ld/+7fDv9/1rndxwgknsGHDBs466yze+ta3LuLKHhnlGPwBxpe+9KV57/+TP/kTXvnKVy7wah4d11577bz312q1Q0qPpmRxOOuss/jtb3/LypUrd3ns7LPPHv595JFH8r73vY8XvehFfOELX+ApT3kKAFmWHbDj2Qfjmo844gje8Y530Ol0OP7443n729/Oeeedx3e/+915P6PHOw8++CBvfvObAXOB+Dd/8zc85znP4fvf//4czaQDCT2rY+anP/3p0GbnT/7kT3aZ8jsYOPTC7gMcz/PmGOTNZtWqVQu8mv1DvV5n9erVi72Mksc5H/3oR3frN/exj31s+PcnPvEJ7rrrLi644AJarRaXX345AO12m3/8x39ckLXuKwfjmj/zmc+glKJarXL11Vdz7LHHcsEFF3DzzTdz5ZVXLvbyFpx+vz/n9rve9S5e8YpXzMkEHWjEccwdd9zB7bffjpRyjvnpgRq0PRxlE3RJSUlJSckC89KXvpQ//MM/5PnPf/6c+6+44gre8Y53zKv0vdisW7cOKeUwE3T99dezevVqWq3WQTekA2UAVFJSUrJbbrvtNtavX7/Yy9gnyjUfHAzsWObL+G/atOmgyvj3+322bt160KlBlwFQSUlJyW7YWb7hYKBcc0nJ3lE2QZeUPM44//zzmZmZ4Rvf+MZiL+Wg4OMf//i892ut6fV6C7yavaNcc0nJo6fMAJWUPM5otVporRkZGVnspRwUOI7DeeedN28T51e/+tUDsiG1XHNJyaOnDIBKSkoOaZ785Cfzla98hWOOOWaXx9asWcPGjRsXYVUPT7nmkpJHTzkG/zjlmc98Jm95y1t429vexujoKMuWLeNTn/oUvV6PCy64gHq9zhOe8AS++93vAvDDH/4QIQTf/va3Wb9+Pb7v87SnPY1f/vKXw+f87Gc/y8jICN/4xjc4+uij8X2f5z3veeWBa5H46le/ygknnEAQBIyPj/Pc5z6XXq/H+eefz+/93u8Nt+t0Opx33nlUq1VWrFjBRz7yEZ75zGfytre9bbjNunXr+MAHPsC5555LrVZj5cqVu4xTCyH4xCc+wQte8AKCIODwww/nK1/5ygK92/3H29/+9t1q53zoQx9a4NXsHeWaS0oeAxZefLpkITjjjDN0vV7XH/jAB/Sdd96pP/CBD2gppX7BC16gP/WpT+k777xTv+lNb9Lj4+O61+vp6667TgP62GOP1d/73vf0L37xC/3iF79Yr1u3TidJorXW+l/+5V+04zj65JNP1v/1X/+lb7rpJn3qqafq3/md31nkd3vosXnzZm3btr7iiiv0Pffco3/xi1/of/qnf9KdTke//vWv12efffZw2z/4gz/Qa9eu1T/4wQ/0L3/5S/3Sl75U1+t1/da3vnW4zdq1a3W9Xtd/+7d/qzds2KD/4R/+QVuWpb/3ve8NtwH0+Pi4/vSnP603bNig//Iv/1JblqVvv/32BXzn+58HHnhgsZewz5RrPjS55557NKBvueWWxV7KQUkZAD1OOeOMM/QznvGM4e0sy3S1WtWvfe1rh/dt2bJFA/rHP/7xMAD60pe+NHx8x44dOggC/eUvf1lrbQIgQP/kJz8ZbnPHHXdoQP/0pz9dgHdVMuDmm2/WgL733nt3eWx2ANRut7XjOPorX/nK8PGZmRldqVR2CYCe//znz3meV77ylUOPKa1NAHTxxRfP2eZpT3uaftOb3vQYvKMDhyVLliz2EvaZcs2HJlmW6S1btug0TRd7KQclZQnsccyTn/zk4d+WZTE+Ps4JJ5wwvG/gh7Rt27bhfaeddtrw77GxMY455hjuuOOO4X22bXPyyScPbz/xiU9kZGRkzjYl+5/169fznOc8hxNOOIFzzjmHT3/600xPT++y3W9/+1vSNOXUU08d3tdsNuftw5j92Q9u7/y57s02Bzv6IGyLLNd86JEkCZZlsXz5cmy7HOh+JJQB0OMYx3Hm3BZCzLlvMI2xJ8XRnac25pviOBhl0A9mLMvi+9//Pt/97nc57rjj+Md//EeOOeYY7rnnnjnbDU4yO38+e3vy2ZvP9fH22R+M76dc84HNJz/5SVatWrXLsfass87i9a9/PXfffTdnn302y5Yto1arccopp/CDH/xgzrbr1q3jr//6rzn//PNpNpu88Y1v5N5770UIwa233gpAnue84Q1v4PDDDycIAo455hg++tGPznmeQY/gZZddxooVKxgfH+fNb34zaZoOt4njmD/7sz9jzZo1eJ7HUUcdxWc+85nh47fffjsvfOELqdVqLFu2jNe+9rVMTk4+xntt/1MGQCVz+MlPfjL8e3p6mjvvvJMnPvGJw/uyLOOmm24a3t6wYQMzMzNztilZGIQQnH766bzvfe/jlltuwXVdvv71r8/Z5glPeAKO4/Czn/1seF+73eauu+7a5flmf/aD2zt/rnuzTUlJyVzOOeccJicnue6664b3TU9Pc+2113LeeefR7XZ54QtfyA9+8ANuueUWzjzzTF7ykpfsIg75d3/3dzzpSU/i5ptv5t3vfvcur6OUYvXq1Vx11VXcfvvtvOc97+Fd73oXV1111ZztrrvuOu6++26uu+46Pve5z/HZz36Wz372s8PHX/e61/GlL32Jf/iHf+COO+7gyiuvpFarAbBlyxbOOOMMTjzxRG666SauueYatm7dyite8YrHcI8tEItagCvZb5xxxhlzejy0Nn0eH/nIR+bcB+ivf/3rwx6g448/ftgse9ZZZ+nDDjtMx3GstX6oCfrUU0/VP/nJT/TNN9+sTzvtNP30pz99gd5VyYCf/OQn+oMf/KD++c9/ru+77z591VVXadd19Xe+8515m6APP/xw/R//8R/6V7/6lf793/99Xa/X9dve9rbhNmvXrtWNRkN/+MMf1hs2bNAf+9jHtGVZ+pprrhluA+iJiQn9mc98Rm/YsEG/5z3v0VJK/T//8z8L+db3Owdjb0q55gO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      "text/plain": [
       "<Figure size 640x480 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(\n",
    "    full_posterior[[\"mpp\", \"sigpp\", \"log_likelihood\", \"variance\"]],\n",
    "    alpha=0.1,\n",
    ")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WT8d56hwOMuj"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "provenance": []
  },
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   "name": "python3"
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