diff --git a/notebooks/50_extra_bayesian_stuff.ipynb b/notebooks/50_extra_bayesian_stuff.ipynb
index f43f92c9..117a6931 100644
--- a/notebooks/50_extra_bayesian_stuff.ipynb
+++ b/notebooks/50_extra_bayesian_stuff.ipynb
@@ -16,6 +16,222 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "markdown",
+   "id": "ac11ba28-e886-4bd2-9e00-68eec3333cff",
+   "metadata": {},
+   "source": [
+    "## Beta-binomial demos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cb8193ab-420e-4c05-a340-651059f37420",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.special import binom, betaln\n",
+    "\n",
+    "def beta_binom(prior, y):\n",
+    "    \"\"\"\n",
+    "    Compute the marginal-log-likelihood for a beta-binomial model,\n",
+    "    analytically.\n",
+    "\n",
+    "    prior : tuple\n",
+    "        tuple of alpha and beta parameter for the prior (beta distribution)\n",
+    "    y : array\n",
+    "        array with \"1\" and \"0\" corresponding to the success and fails respectively\n",
+    "    \"\"\"\n",
+    "    α, β = prior\n",
+    "    success = np.sum(y)\n",
+    "    trials = len(y)\n",
+    "    return np.log(binom(trials, success)) + betaln(α + success, β+trials-success) - betaln(α, β)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af853f88-8400-47a3-812a-f42e34909186",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def posterior_grid(ngrid=10, α=1, β=1, heads=6, trials=9):\n",
+    "    grid = np.linspace(0, 1, ngrid)\n",
+    "    prior = stats.beta(α, β).pdf(grid)\n",
+    "    likelihood = stats.binom.pmf(heads, trials, grid)\n",
+    "    posterior = likelihood * prior\n",
+    "    posterior /= posterior.sum()\n",
+    "    return posterior"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "6a53d1fa-856f-4848-b1b4-0a37de6b5741",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normal_harmonic(sd_0, sd_1, y, s=10000):\n",
+    "    post_tau = 1/sd_0**2 + 1/sd_1**2\n",
+    "    posterior_samples = stats.norm(loc=(y/sd_1**2)/post_tau, scale=(1/post_tau)**0.5).rvs((s, len(x)))\n",
+    "    log_likelihood = stats.norm.logpdf(loc=x, scale=sd_1, x=posterior_samples).sum(1)\n",
+    "    return 1/np.mean(1/log_likelihood)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "021baa2e-e4d7-4cda-a68b-b53a959561e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1.2655121234846454"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "σ_0 = 1\n",
+    "σ_1 = 1\n",
+    "y = np.array([0])\n",
+    "stats.norm.logpdf(loc=0, scale=(σ_0**2+σ_1**2)**0.5, x=y).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b9b0fd66-8bc5-40c5-b391-f19562d6ab51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def posterior_ml_ic_normal(σ_0=1, σ_1=1, y=[1]):\n",
+    "    n = len(y)\n",
+    "    var_μ = 1/((1/σ_0**2) + (n/σ_1**2))\n",
+    "    μ = var_μ * np.sum(y)/σ_1**2\n",
+    "    σ_μ = var_μ**0.5\n",
+    "\n",
+    "    posterior = stats.norm(loc=μ, scale=σ_μ)\n",
+    "    samples = posterior.rvs(size=(2, 1000))\n",
+    "    log_likelihood = stats.norm(loc=samples[:, :, None], scale=σ_1).logpdf(y)\n",
+    "    idata = az.from_dict(log_likelihood={'o': log_likelihood})\n",
+    "\n",
+    "    log_ml = stats.norm.logpdf(loc=0, scale=(σ_0**2+σ_1**2)**0.5, x=y).sum()\n",
+    "    \n",
+    "    x = np.linspace(-5, 6, 300)\n",
+    "    density = posterior.pdf(x)\n",
+    "    \n",
+    "    return μ, σ_μ, x, density, log_ml, az.waic(idata).elpd_waic, az.loo(idata, reff=1).elpd_loo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0c5f177-506a-4ea5-98f5-db9e53fa2bea",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3609760c-ed1b-4116-b47e-2a76e3646cb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'μ')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 611,
+       "width": 1011
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import arviz as az\n",
+    "\n",
+    "y = np.array([ 0.65225338, -0.06122589,  0.27745188,  1.38026371, -0.72751008,\n",
+    "              -1.10323829,  2.07122286, -0.52652711,  0.51528113,  0.71297661])\n",
+    "\n",
+    "_, ax = plt.subplots(3, figsize=(10, 6), sharex=True, sharey=True,\n",
+    "                     constrained_layout=True)\n",
+    "\n",
+    "for i, σ_0 in enumerate((1, 10, 100)):\n",
+    "    μ_μ, σ_μ, x, density, log_ml, waic, loo = posterior_ml_ic_normal(σ_0, σ_1, y)\n",
+    "    ax[i].plot(x, stats.norm(loc=0, scale=(σ_0**2+σ_1**2)**0.5).pdf(x), lw=2)\n",
+    "    ax[i].plot(x, density, lw=2, color='C4')\n",
+    "    ax[i].plot(0, label=f'log_ml {log_ml:.1f}\\nwaic {waic:.1f}\\nloo {loo:.1f}\\n', alpha=0)\n",
+    "    ax[i].set_title(f'μ_μ={μ_μ:.2f} σ_μ={σ_μ:.2f}')\n",
+    "    ax[i].legend()\n",
+    "ax[2].set_yticks([])\n",
+    "\n",
+    "ax[2].set_xlabel(\"μ\")\n",
+    "\n",
+    "#plt.savefig(\"img/chp11/ml_waic_loo.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e3f2625c-5cca-4707-a8f2-8a989fbeffde",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1.2655121234846454"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "σ_0 = 1\n",
+    "σ_1 = 1\n",
+    "y = np.array([0])\n",
+    "stats.norm.logpdf(loc=0, scale=(σ_0**2 + σ_1**2)**0.5, x=y).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "400c0950-457b-4a00-9b3e-3aaba68672b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def posterior_grid(ngrid=10, α=1, β=1, heads=6, trials=9):\n",
+    "    grid = np.linspace(0, 1, ngrid)\n",
+    "    prior = stats.beta(α, β).pdf(grid)\n",
+    "    likelihood = stats.binom.pmf(heads, trials, grid)\n",
+    "    posterior = likelihood * prior\n",
+    "    posterior /= posterior.sum()\n",
+    "    return posterior"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1ad57ed2-8583-4d1f-a16c-ddd481ee3e5f",
@@ -239,7 +455,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.4"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/51_intro_to_Bayesian_stats.ipynb b/notebooks/51_intro_to_Bayesian_stats.ipynb
index 9a2216f5..cdf65415 100644
--- a/notebooks/51_intro_to_Bayesian_stats.ipynb
+++ b/notebooks/51_intro_to_Bayesian_stats.ipynb
@@ -88,365 +88,578 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "6b7ea783-c07c-4135-a358-29af4b4cdef9",
+   "id": "a58468ef-a3dc-4fd3-b39f-0610b96a009c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ministats.utils import savefigure"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "961abce3-4618-45e1-8d7c-5394d3809795",
+   "metadata": {},
+   "source": [
+    "## Definitions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17b6071b-d882-4c2f-8ca4-50bb1c0bf2c4",
+   "metadata": {},
+   "source": [
+    "## Bayesian inference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7900b8d-40af-4c26-b2ac-46ae1e9bb519",
+   "metadata": {},
+   "source": [
+    "### Bayesian updating of posterior probabilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2e004c40-572f-4b16-8974-290fc8b0094b",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to figures/bayesian/intro/panel_coin_posteriors.pdf\n",
+      "Saved figure to figures/bayesian/intro/panel_coin_posteriors.png\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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btm3RvXt3hIeH48SJE7h8+TIuX76c436yevPmDebPn29wba1nz57h2bNn2L59O6ZPn87fGBmjfZxVKhX/mWtkZmbCx8cHo0aNyrGiq/HixQuEhobi/v37uHHjBr+48qBBgwCoz8cRI0bg4cOHOq978OABHjx4gM2bN2P58uWoX79+ju/177//YvHixYiIiEDXrl3Rp08fKJVKXLx4EWfPnsWrV6+wc+dOTJ48GV26dNF7vZOTU443y8bI5XIsWbIEW7ZsgaOjIzp16oROnTpBLpfjyZMnOHr0KP89r1+/PubPn59jZc9Qaz7HccjMzERmZiacnZ1NTnO8Zs0aXLp0Cffv30d6ejr/uL+/PwBAJpPhl19+waFDh3Rep1Kp8OLFC7x48QI7d+7E119/jfHjx2d7k8YYw9q1a7F69WoolUp06NABLVu2BPD/G/+DBw/i4MGDqFKlCubNm5dtFtq7d+9i6tSpePHiBerXr8/fmERFReHs2bMICgpCUFAQKlasyDf0ZWXquZ3V7t27sXjxYtjZ2aFr165o164dMjIycPXqVZw9exbXr1/HX3/9hZUrV6JatWo6r926dStCQ0Px6NEjPHv2DCqVCsD/r2HHjh3DxIkT+c9ZKpXyQ+5z8x3LKjU1FYGBgThw4ACKFSuGzp07o2fPnkhNTcWDBw9w6tQpPH78GEFBQWjTpg1mz55tsLFt5cqVeP78OR4+fIjw8HC+nL179wYArF27ViejW2xsLFJSUkwu5+3bt7F+/XqEhYXpjSTRXIePHTuGn3/+GRkZGTrPx8bG4ty5czh37hw2b96MlStX6t2s3r17F0eOHMGTJ090znvN5xwfH48+ffogMjKSf432EF1te/bsweLFi5GUlIQ2bdpg8ODBcHBwwKtXr3D06FGcPHkSJ0+exIoVKzBr1iyTG1I07t+/j6+++kpvysWTJ0/w5MkTbN++Hb/88gsGDBhgdB8KhQIbN27EqlWrdL7jgHpdvcjISJw9exYtW7bEwoULc91AuGfPHsyYMUNn+QOpVIrExESEhobin3/+wYoVK1C3bl2d182YMQPbt2/P1XuZqk6dOjrfbVNpv0apVOpdD2QyGUqVKoVOnTrh+fPneSqbJe8dP2YCZiP94G/evEHr1q3530eMGIGRI0cCUH+ZExMT8fLlS5w/fx7Hjx+Hvb09evTogTFjxuSYrUWhUGDYsGG4efMmevbsiZ49e6JSpUrgOA4vXrzA9u3bdYZFNGjQAJs3bzYY2B8+fMi3/AFAy5YtjbZEaevQoQNevnyJL7/8Er/88ove84wxLFu2DH///Td8fHywfv16lCtXTmebly9fYtiwYXj37h0Aw6u3R0dHo0+fPoiOjsbQoUMxefJkvR6+y5cv47vvvkNqaqrO6uqxsbF49uwZ0tLSsGzZMjx58oR/TXarsC9atAhr165FvXr1MGTIENSpUweOjo54+/Ytjh49ik2bNvGVIw8PDxw7dgzFihXjX5/1s9d28uRJhIWF4ccff4RSqYSXlxcUCgWSkpL0tm3fvj2WLVtmcD85OXz4MMLDw5GcnIydO3fyQz8bNGiALVu26G3/7NkzHD9+HEqlEmfOnMHTp0/55zZt2oSGDRvyv6empmLz5s0A1A0E2j2Y8+bNg4eHByZPnowWLVqgffv2KFOmDBISEnDr1i3s2rVLpxLn4OCAf/75J9tKwOPHj/HLL78gKSkJf//9t16vEsdx2LJlC5YsWaJ3M6GplBhy+vRpTJ48GUWLFsWoUaPQpEkTFC1aFPHx8bh48SLWrl2rk+Fz48aNeuurRUZG4uDBg/znmp1y5crh1KlTePjwIRISEhAeHo7AwED+5tHf358/rhpffvklbty4obevMWPG4IsvvsCwYcPw7NkzuLi4wM3NDTExMfz+NFxcXHDixAm9hhZt8+fPx4YNG+Dl5cXfcGs7fvw4f84aIhAIsHjxYp21qEyVmJiIr7/+Grdv30b9+vWxbNkyne8ToK5MTps2DWfPngUAFC1aFOvWrTO48OfTp0/xxx9/5DgkZcGCBXyva04OHDiAt2/fQqFQYPv27UhMTASg/syWLFnCN5L17duXL9PTp0+xf/9+3Lp1S2dfAwcOxG+//WbwfWQyGSZOnIjg4GBUrFgRq1ev1qtMpqWlYf78+fxwIScnJ/z5558GW4YfP36MgQMHIi0tDVOnTsXQoUP1tjl06BCmTZsGhUKh833Jy7mtXcYff/wRZ8+eRfPmzbFkyRK9BqPg4GB8//33UCgUcHJywt69e3XOuzlz5iAmJgYxMTE6w5cmTZqEJk2aoH///nrDvjWfaW6+Y9revHmD0aNH4/nz52jfvj0CAwP1bqYjIyMxYcIEhISEAADKlCmDDRs26H1OP/zwA+RyOV6/fq1z/JYuXQpXV1eMHj1arwFw69atJjWIAOqGLE1yl6tXr+pcJ86cOYN9+/YhKCgIffv25a9t7969w7///osjR47oHLvSpUtj7969OjeZp0+fxpEjRyCVSnHjxg3+2lqpUiUcPHgQw4YN07s2NWzYEJs2beJ/Z4xh5syZ2L59O7y9vbF69Wq9BgG5XI6///4bK1euBKCe7zNr1iy+MppVlSpV+J/nzZuH2rVr44svvkBiYiLc3d3h6uqKmJgYg2u5zZgxw2BFKyEhAUOGDMGLFy8waNAgdO7cGeXLl0dmZiYeP36MTZs24dKlS/z23bp1wx9//GGwfNevX8fgwYP538+cOYPQ0FD88MMP4DgOxYsXh0AgQExMjF5DkFgsxu7du3V6vJ8/f46oqCjExMRg1qxZ/Ofg4+Ojk9wnq6z3IUFBQfjss890trl27RpkMhnOnTuHbdu28Y+PGzcO48eP19n22bNnOHToEE6ePKnTMG9I48aNsWHDBty5cwdpaWkICQnB8uXL+ecDAgIwf/58g6+1hnvHjxqzEREREUwikZj0r2PHjmzNmjXs8uXLLC0tLcd9p6WlMYlEwgYNGmR0m/Xr1+u8x6lTp4xuO3ToUH67unXrMrlcnu37x8TEsCpVqrC6deuyxMREg9ssWLCASSQSVrVqVfbgwQOj+7p06RL/3vXq1WMxMTE6z0+bNo1JJBLWsmVLplQqje4nKCiISSQSFhERYfD51atX6xwPY9sxxlhAQACrW7cuS01NNfj87du3WY0aNfh9zZ49W+d5pVLJoqOj2Zs3b9jRo0d13nf58uWsVq1abNmyZSw+Pp5/TUREBJsyZYreufH06VOj5TTVmDFj+P1ld85onDp1SqcM165dy3b7unXr8tsOHjyYNW/enIWEhBjcNj09nU2YMEFn/+3atWNSqdTg9rt372Y1atRgfn5+LCoqKttyhISEsHr16unsOzvz58/P9u+LjY1lbdu25ffVtWtXvW1CQ0PZ/v372fnz59nw4cP5bSdNmsQuX76s8y8sLEzv9e3bt8/2s0lMTGRRUVEsLCyMdezYkd/2p59+Ym3btmXDhw/XOdZSqZRt375d5zORSCRswYIFRo/Drl27+O0OHz5sdLslS5bo7HPbtm3832bs886JUqlkX375JZNIJKxZs2ZGv3OMMSaTyVjPnj3592/WrBmLi4vT2ebvv/9mNWrUYNWqVdMpa9WqVVm1atV0/j158iRPZZ49e7bOuduqVSu2atUqxnGcwe3379/PatWqpVOeK1euGNx28uTJTCKRMD8/PxYZGWm0DBzHsbFjx/L78/PzY8+fP9fbbsiQIUwikbABAwZk+zfNmzdP7/uS13NbqVSyfv36MYlEwpo3b55tTFu8eDG/3z59+hjdrlu3bvx2ixcvZi1btmRff/01O3HiBDt06BALCAhgEomEnTlzRu+1OX3HNNLS0linTp2YRCJhAQEBTKFQGN02KSmJtWzZkt9v9+7dWWZmpsFtOY5j9evX57ddu3Yt8/f3Zz/99BM7ffo02717N3+dMXSNMMX9+/d1zq9hw4axAQMGsNjYWIPbv3r1SueYaj5XYzZs2KBzHZw5cyZr1aoV27p1Kztz5gybOXMmk0gkbNy4cTqvW7p0KZNIJKxatWosNDQ0279B+3tVvXp1duPGDYPbZY2nzZo1Y9988w17+PAhv41SqWQnTpxg7dq109nez8+PvX37Vm+fjx8/ZhKJhE2ZMsXge3Icp1M+iURi9L7m2rVrOttt3bqV1a5dm/3xxx86MSw1NZVt3LiR+fn56WwfEBDAVCqVwX1rfw9btmxp9Fgypn8Pml0cj4mJ0dl22bJlettcvXqVHTx4kF28eJF17dpVJ7ZkvR6Eh4frvb5mzZr8a4wdZ8as797xY2OziS+6d++ODRs2YMOGDVi9ejWWLFmCn3/+Gf369UNmZiYWLlyIYcOG4bPPPsP333+PV69e5bhPzZAhQ4YPH47q1avzv2c3lOqLL77gf05LS8O1a9eyfd/jx4+DMYZBgwbBw8ND7/mwsDBs2LABgLpFQ7scWX3++ecoX748AHUPyZEjR/jnGGP88JU6depkO0dNM2zHmNymGe3Ro4fR4QB+fn4YOHAg//uVK1d0nrezs0Px4sXh4+OjN4zy77//xrp16zB+/HidVkNfX1/Mnz8f/fv319k+L0Pgssrt357bYRDaQ65CQ0Oxfv161KpVy+i2ixYt0mlRCw8Px/Hjx/W2vXPnDn777TcoFAqMGjUKJUqUyLYctWrV4udqmEoikei17ml4eXlh8uTJ/O+PHz/WG45Ss2ZN9OjRA82aNdMZblOmTBk0btxY5592C6xGTvOlPDw8UKJECVSpUkWnBXjfvn1o1KgR1q1bp3OsHRwc0L9/f6xdu1ZnrHzWc1RDpVLxC6Db29ujTZs2RssydOhQne+gnZ0d/7cZ+7xzsm7dOly/fh0A8NVXX2V77onFYp0eoKioKCxZskRnm6+++gr379/X6VUB1L2QDx8+1PlXuXLlPJVZu0cmPDwcvXr1wpgxY4wOAezRowdmzJih85ih0QJHjhzBgQMHAAADBgzg59QYIhAI8Ouvv/LzCNLT0zF79mydbRISEvhkSjktxmzo+pnXc3v79u18z9OAAQOyHfKsfR0NCQnBgwcPDG6nfQ1bv349WrVqhZUrV6J9+/bo2rUrgoKCUKpUKYOZzkydk7hgwQI8e/YMAPDdd99lu7alu7s7fvzxR/73R48e8TEvK4FAoHNeL126FGPHjsW8efPQpk0b9O7dG0FBQXBxcclzprasxzguLg7r1q0zOiqmbNmy2LBhg04MOnjwIN6+fWtwe+3j/+LFC1y4cAF79uzBwIED0apVK/z6668YMmSITlKU27dvY9WqVQCAdu3aGR2qrjFhwgS+PEqlEtOnT89xqP/KlSvRrVs3rFixQme4qZ2dHdq3b49du3bpXDfT09OxZ88eo/szdk8lEAgwefJkne+BqbF5wYIFWLRoEX788UedGObq6oohQ4Zg+/btOufHgwcPcPPmTYP7ys382twwZb8NGzZEt27d0KRJE50pBJUqVdK7HmTteQJMuw+xxnvHj43NVrK0A1OLFi3QqVMnDB48GLNmzUJwcDCmTJkCBwcHKBQKHD9+HD169DA4TAhQ36j+9ddfRoekaWgPH8kuC1bLli1RsmRJ/ndD81O0HTx4EGKxGF9++aXB57ds2cJfHI3dwGrTvkHT7v6Oj4/nu8azzhPKytPTE02aNDEapHIzqXHGjBk5rmCumSMBZH9ss365+/btm21q/nHjxukEd2NBLzdyO1E9t9tr/41ffPFFjkkiBAIBZs+erXNTmvWmGFAP29QMT+vevbtJZcnNnKAvvvgCgYGB2W6TdUJsTEyMyfs3RW7OS+3j7OHhgZ9//tnojX3dunV1vv/GzqMnT57w8+Y8PDyyvcnz8PDggxoAg0NccyMtLQ3//PMP/7spyV5q1aqlM4fvwIEDBZ7hT/v7UaJECYwePTrH1wQEBOhUdG7evKkzj4HjOKxYsYL/3ZRjUbJkSbRr147//cqVK7h37x7/u/Z8yJyun6VLl85TwoWsGGM6w/G0hxkbUqJECZ3YY2z4k/Z57ujoqFPBAdQ3rOfOnTOYkdeU79ibN2/4Yc8uLi4mHYsOHTro3DRv3LhRLyOvofL7+vpiyJAhOs+XLFkSt2/fNnhzaoqscebHH3/Mcb6hp6cnJk6cqPOYoeswoFt+hUKB7777DkWLFtXZZtq0aTpzzJYvX84Ph9M+T41xcXHRGSL48uVLo+XRqFu3Ln744Qejz7u7u+slETt58qTeduXLl8eqVasMDj/WEIvFOsPFTb3uDB8+PNt7tSpVquh9DobKCOTfen8FkfTBlPewxnvHj43NVrKyY2dnh+HDh2PmzJn8YxkZGfj2228NpsQWCARo3bp1ti1tAHSCV3apte3s7HQubidPnjQaLJ4+fYr79++jW7duRlvJzpw5w//8ySefZFtGADqtQ9oTZ7VP+ps3b+pNKs+6j/Xr1xstU24qDrVq1cq2FRmATnDNTdryrJNas/L29kapUqX43/MyKbWgaQdgU28SypUrp3OznHXu1PPnz/nWvHLlyuX4eeSFr69vjq2rjo6OOr215k5Rn9sKrcann36aY+pc7b/N2HmkfSNuyrIG2i2e2teXvDh79ixfUStevLjePCxjmjVrxv+sUCh05koUhKw3zDldhzW053+pVCqdCeF3797Fy5cvAaivx4Z6PQ3RPhYA+DlrgG6Dw5EjR/geQ0OqVq2K9evXm/Se2Xn27JnOfA3tSrkxxq7/xjRq1ChXN0SmfMeOHDnCz+GRSCQmZfYVCoU6lbGkpCTcvn07x9e1aNEi39dtM+W4A0DHjh11ep+ym8OqIRAIdBoZDYmOjtbpPc+uR0Jb1nmF2uezIdqjcIypWrWqzjy3ly9f6vWQicVitGrVKsd9aV/zsibH+JAy9urVS6dSbCxZRF7jRU7ya7+5fQ9rvHf82BTKSpZGQECAzsUgMTExV2shcBzHT078888/sz2xsurduzf/JUhKSjKamUnT2mdoAjWgHkqg3UKblJSEmJiYbNfjYlqTP7UzJbq6uvI3iowxTJ48GaNGjcLu3btNCsbm9u7dOwQHB2P16tU6kzhzw5QbSe2u+PxIQ21u2jcMptycaGif61lbm7QDtKGsZfkpOTkZly9fxqZNmzBjxgydYMqsJO+OKVmQtCuHxs4j7cCXlpbGTyQ2RpP9zN7e/oN7PrQ/45zS8GrL+r6aBAQFJa/ne9Yhe9rnvPaxKFKkiMlrUWXNwqZ9LCpVqsQfV4VCgeHDh+O7777DoUOH8q3377///tP5PSYmBnFxcdle/7UrqVmzlBmSH5PTC/JczI8lELJW2ky9aXZ1ddVJspBTqz+gvq7kNKRce81PACbfwNapU0dn6OPdu3ez3d7UkQvalTdD2fCMUSqVePjwIfbt24dFixbp3BeZGgtMKaOjo6POsDVzj5iwBYX53tGWFMoU7tq6deumk43q0qVLGDZsmNHtHz9+jHPnzuHq1asIDQ1Feno63NzcULVq1VwNnSpVqhQ+//xzXLx4EQCwd+9evfkZSqUShw4dQpMmTYzOZ9BO5wrAYObB7GRtnZ84cSJGjBgBjuPAGMOFCxf4C12pUqXQqFEjNG3aFM2aNcv1XKKcREdH4+zZs7hy5Qpu376N2NhYiMViSCQSk1pZDDEl+Gkfg7yu+2QLtG9mst6saqckNzTvz5i8HK/09HRcvHgRly5dws2bN/Hq1St+rakaNWpALBbrZS20NFNu7k05j7Ku23Ts2DGjQ2VjYmL43hZT14jLjva809wscJt1OGrWeXLWKmt2R+1rQV6Phbe3N9zd3fmFl7WPhVAoxIQJEzBt2jQA6uv3iRMncOLECQDq3o5GjRqhWbNmaNy4sVmGy2S9/htK9Z8dU4YUmZp2PzfyevwrVKig87sp52JelznIL9ldhw0x5fhnzT5n6rklEolQrlw5/vqfdXmavNIeHaJ5H2NCQkJw7tw5XL9+Hffv34dMJoOnpyeqV6+er5+ddi+Zqb3jhUlhune0ZYX+zMt6A2GsZfnGjRuYN28efzGqUaMGxo4dy08WFAgEOHr0aK6G0vTu3ZuvZF24cAGxsbE6F+Bz584hLi5OJz1pVppgrzFp0iSThgoIhUIUKVJEb7hZ48aNsXLlSsyePVtvXsm7d++wb98+7Nu3Dw4ODggICMCkSZM++Avz5s0bBAYG4uzZs+A4DiVLlkS3bt3QtGlT1KtXD2KxGLGxsTh8+PAHvY8xuWkdt2XagSRr67SpLY1Z5aanSSqVYvHixdi9ezcyMjLg7OyMDh06YOLEifjss8/4yl2HDh0+eP6RJWjfxBs7Lr6+vmjevDmf7nz16tVo166dXms7x3GYO3cuOI5D9erVMWXKlA8unyYNOqCeuGwqV1dX2Nvb88O7bGFILaD/vdY+5/N6LAB1OnvNdTfrsejVqxc4jsOiRYt03gNQ3wiHh4fzE+8HDRqEb775JsdhqNnJev1fv369SQ1LIpEIRYsWzfOcpA+V1+OfdV6SrZyL2rTPS3P1EmY911JSUkxulNE+puY6ntqjQxwdHfXWfgTU89EWLlzIV7gbNGiASZMmoXHjxvx92Zo1a3SWEzAn7cbE7JbbKKwKw71jYVDoK1lZL0SGAtSFCxcwduxYKJVKFCtWDAsXLjQ44Te3WrduDU9PTyQkJECpVOLAgQMYNWoU//yePXvwySef6M0D0JZ12EL58uU/uGytWrVC06ZNcfXqVVy5cgU3btxAWFiYznpAMpkMO3bsQHh4uM46HbkVFRWF/v37IzY2FnZ2dvj5558xcODAAm1Z0j6G1jI8LT9oD4nImuFHu+clNwt0GptLmJVSqcSoUaP45DI9e/bE1KlT8y17kyWYOu9j1qxZGDJkCL+uWv/+/fH111+jYcOGcHFxwf3797Fu3TrcuXMHbdu2xfz5883SopvbYWLaPDw8+KFNuc2eaSnaQ7E8PDx05l1pH4uMjAxIpVKTW/+1b84MHYs+ffqga9euuHjxIi5fvoybN2/i+fPnOteWtLQ0/P3334iJicG8efNy82dlq3bt2jbx+YhEIn7tw9yci1krWbbwt2alfV6aK9Na1h7JxMREkytZOZ3PeaE937RWrVp618bdu3fzPSfly5fHokWLsk2CkR+0R0v4+fkV6HtbA1u/dywsCn0lK+sNZdZubrlcjl9++YWfYzFr1iyzVLAA9YVRk2oeUA8Z1FSyoqOjceHChWwzmgH6lUTtifUfWrZmzZrxFby0tDTcvHkTZ8+exeHDh/kL1LVr13Dz5s1sM/hlJzAwkA86X3zxRba9drZC+4JiTZW2Fy9e8D937NhR5zntHlTtRaRzYupk5J07d/IVrGrVqiEwMDDfJ6Nbq5IlS2Lv3r0ICgrCnj17EBkZiVmzZvHPa7JqrV+/3iwZ6DS8vb35zzY+Ph4JCQkm34hpn9NZh2xZK+3J7K1bt9a5Ec06D+jp06c5JmXRMOVYODo6om3btmjbti0AdUXi+vXrOHPmDE6cOMH3Cu7fvx/ffPNNnns0ss45ff36td7is9bI29ubv3a8ePECHMeZ1AOXdeFvWzkXNTiO44f2OTs7G1zQOi+yzsF6+vRpjllnNfLju/3mzRv+56yZ/uLj4zF37lz+96VLl+qkgy8o2mU0JQlHYWPr946FRaFOfAHop1rO2rJ07tw5ftKyh4dHjll+citrClVNhre9e/fCxcUFAQEB2b5eIpHo/G4sDf2HcnV1RcuWLTFr1iwcOnRIJ7gbW2slJwkJCTrp63v16vXB5bQG2r1CmpspU7fPLzKZjB/KWqVKFb2kANrZBGNiYoxmW8pKM2coJzt27OB/DggIMHsFy5oqs6ZwdXXF119/je+//x5OTk74+eefsXXrVhw5cgS3bt3CmjVrzFrBAvSzR2VNmmAMY0wny6OtrHMSHBwMQN1iO2DAAJ3n8nosAN3hbaYeC09PT3Ts2BELFy7Etm3b+F4zxhgePXqU7WuzO7ezXv+NrfdjbbSPf1paGsLCwkx6XdZGUVPSTluT27dv8z13PXv2NFsa66zns/Y885zk5XzOieb75OzsrLccyKFDh/iermrVqlmkgqVSqfikKXXq1DE5G6M1MFess+V7x8Kk0FeyNIEYULcgZ63UaGfbKVeuXLbzd/KSma5SpUo6qbV37NgBpVKJnTt3omfPnjlOei1WrJhOy+XFixf1JjTmRpUqVXLMBlOmTBmdm5a8funv3bunU8HILrlF1hZMa6bdImvK3KKcbrLM4dChQ3wL0g8//KBXycnaeLB9+/Yc9ymXy/k5hdnJyMjQ6R3LKYlJXj5rW8gKqU2lUiEwMBCTJk1CYGAgBg8ejPr166Ny5cq5SgSQG1mHHWfNSGbM69ev+WGhpUqVsolKlnYDTocOHfR6qbL2IJh6LGQyGd/i6+zszPdUAeqW8SpVquR4/taqVUunJzmn62d253bjxo11euh27dplE9+FvJ6L2o0/fn5++ZI5MD9pGpucnZ3x9ddfm22/n3/+uc55cO3aNZNfq1kQWiAQoGvXrh9cloSEBJw7dw4AMHLkSL0hntr3VDnFgvw6l8+fP88n+ci6Bpy1M9cxseV7x8KkUFeyoqKidBZUGzt2rF63u3YrT3Y3zNHR0fjzzz/zVA7t3qxTp05h586diI6ORv/+/U16vfa6EAqFApMnT87TjaqmwmPKRGTt41SpUqVcvxeg3ypp7PgqFAr8+uuveXqP3DJHZU57jHtkZGS2az1FRETgr7/+yvN7mZICODExEYsXLwagbj01NESlRo0aOmu9bN++XWehVUM2btxo0vtnPZ+y+x6tWrUqTylfrTFRRnbn0s8//4xNmzbB29sbnTp1KpDyNGrUSOfcPHr0qElrdWmn2x49erRFE8UkJSWZ1Dv8xx9/ICUlBd7e3ny2P20VK1bUydh66dIlk9KsX79+nf9cBw4cqDOhX3PDYO7rZ3bntru7Ozp37sz//vz5cyxbtizH97e0rMM39+3bZ9INl/a5OHbs2HwpW16Ych28efMmn7zp119/NXmdOlO4urrqLC/w9OlThIaG5vi658+f8+d9hw4dchwuaEr2waVLl0Iul6N27do6c8w1TL2nevLkCTZu3Jjj+2WV0xw/hUKBJUuWAFAvjWNrw9WyJqz4ELZ671iY2EwlK7dDrpRKJSZPnszfZLRs2RJfffWV3nbaJ8Tr168NXrjCwsIwZMiQPF80O3bsyPdYyeVyzJs3Dw0bNjQ5bXlAQIDOSty3bt3C+PHj9TIOaVMoFNiyZYtOF7GmheT+/fs5vqemZ6JkyZJo2LChSeXMKmuF9ujRo3rbJCUlYezYsblKxvAhzJFdSXvogUKhwKpVqwxu9/r1a4wePVpnVfvcWrp0KVavXm20dSs6OhqDBw9GQkICevbsqTP3J6uffvqJ/1mpVOKrr74y2p0fHByMtWvXmjQ/0cPDQ+eG6tixY3rbqFQqLFq0CMeOHdNZtyU72j0+586dM3l+WEExVoGRSqX8MYiPj8fJkycLpKdWLBZj5MiR/O9JSUk5LoirUqmwbds2AOqFvU1t+MmvFsonT55gxIgRRrPAMsawYMEC7Nu3D2XLlsXmzZuNZg7T7klQKpVYunRpju+/ZcsWAOpRDd98843Oc5rKX26un7Vq1TJ4Y5ubc3vixIk6CQvWrFmDP//8M9tzKiEhAQsWLDB6w6b9+eXHZ1miRAmdxsVnz57h4MGD2b4mLS0NBw4cAKBOVZ/dfKb8Ln9WX331lU6DbVY3b97E119/DZFIhJkzZ+Y4DSAv5R87dqzOCIXFixfn+NrNmzcDUF+jp06dmm05AHXjRXYZlDdv3oydO3ciICAAGzduNJg5Uzvu37lzx+B3+fr16xgzZozOMHZjspbx22+/1VkiQJtcLsdPP/2Ely9fYurUqQb/ZmukfT04ceKE2XqzbPXesTCxmcQX2fUWZPXq1StMmTKFTw3apUsXzJs3z2ALbatWrfieBsYYxo4di2+//RY1atRAUlISTp06hevXr2Pu3Lk6rUe5ueFzdXVF+/btsX//fgDqkzjrHILsCIVCLF++HAMHDuS7e8+cOYM7d+6gR48eaNWqFXx8fGBnZ4eIiAhcvnwZe/fuRXJyMt+tD/y/hWTLli0ICAgwmlr45cuXOHDgAAQCAaZPn25wrZWsLRrJycl6k7v9/Pz47IqAOiikpqaiadOm4DgON27cwJ49ezBq1Cj4+/vrtPinp6cbvBnPeh7kNj2zKS2SOdFkhNO00q1btw5v375Fjx49UKJECcTHx+Ps2bM4ePAgfvvtN5QoUQKnTp3KU5k5jsPixYuxf/9+9OzZE3Xr1oWbmxvevXuHy5cvY8+ePZDL5Zg2bRqGDBmS7b5atmyJPn368Atyx8fHo1+/fujVqxfatm0LLy8vREdH49ixY7hz5w62bduGy5cv67Qua1Kza3NwcECTJk1w9uxZAMDZs2cxceJE9O7dG66urggLC8PmzZtRt25d7NmzB61ateK/P9mtl6WdQjY6OhpffPEF+vTpA29vb51kKtpMbUUFdM+lvFTyY2NjDaaoffr0KZ9ZjeM4fPvttxCLxShWrBgcHBz0hnKKRCK4u7ujSpUq6NOnzwfNYRg2bBh/bQDUPYc1a9Y0esO6ePFiPHnyBOXLl8eKFSuMJifI+jnlZ4X3+vXr6NixI7p164ZWrVqhdOnSSElJQVhYGLZt24bnz5+jWbNm+P333/WGKmnr1KkTTpw4gZMnTwJQ96b4+fmhb9++Brfftm0bzp8/Dy8vL/z9999657nmHNm4cWO28+lu3bqFixcvQiwWY/r06Qa3yc25XaJECSxbtgxjx46FVCoFYwx//fUXTp48iV69esHf3x8lS5aEXC7H06dPcebMGRw5cgQVK1Y0ujSA9ncjt9dQU79jP/74Iy5dusQPv5w9ezYqVapkMMscx3H47bffEBsbCz8/P8yZM8fofjmO06k85rb8eZGcnIyxY8fis88+Q9euXVGlShXY29vj9evXOHr0KIKDg+Hp6Ynly5ebdGOpfdxMvbepU6cOhg0bhn/++QeAegjm0qVLMWHCBIPbnzt3Djt37oSzszNWrlyJEiVK6G2T9XuclJSEESNGoEmTJujcuTMqV64MoVCIly9fYv/+/bhz5w7++OMPdOvWzWg5W7duzVeoMzMzMWTIEIwfPx4VK1ZEdHQ0jhw5gmfPnmH16tU4ePAgP5zeWCzI+vjDhw/RpUsXdOnSBa1bt4avry8yMzPx8OFDbN26FUqlEjt27DApm6H2uZOcnAzGmNH5xFk/p+x6nAzdH2WnXLly/ND8Bw8eYNiwYejWrRsf6728vPTWyNMuj7HvoTXeO35sBMwGBk0qFArMmzcPW7du5R+rWLEiWrZsidKlS8PJyQkpKSmIi4vDrVu3cPfuXTDG4OPjg4kTJ+a4gOPcuXMRFBRk8LkOHTpg1qxZcHd3x/bt2zFjxgwA6gwrv/32GypVqmTSQsU3btzAl19+CUC9ZsPZs2dzncY8Li4O06dPz7ZFTaNRo0aYOnWqTlrju3fvol+/fgDUE2AnT56MTz/9lL+oSKVSnDhxAr///juSkpLw888/69zERkRE4Pbt25DL5diwYYPO+PkmTZrwF97PP/+cb806efIkJkyYYLDVtWrVqli4cCEqV66M58+f61Syhg8fjpYtW6JixYpwcnLC6dOnwRjDtWvX+MoqoK7I9e/fHwKBQOd9tX355Zd8q4xIJML333+P4sWLo2TJknmeWH3+/HmMHz+ev5nOytXVFYsXL0bz5s1x5coVnQWwcypzq1atTB473aBBA/zyyy96i+AawxjDsmXLsGrVKqOtoPXq1cPKlStRtGhRbNy4UScFdb9+/dC3b1/4+Pjo3OC+evUK/fr1M9hCVrRoUcyZM4dfjPvzzz/nFxlt0qQJhg0bhjJlyuity5GcnIyAgACDx8LZ2RmLFy9Gy5YtcfnyZcTFxeHdu3dYtmwZf64JhUKMGzcOvr6+cHFxQZs2bfhtExMT8eeff/IB3NnZGRMnToSbmxvKli1rMOXvvn37dFpG27Rpg3bt2gFQ91ZrAo9MJkP//v11FoA2lb29PebOnas3mTw3kpKSMGrUKL5BSCQSYfDgwRg6dCh/o/X69WssX74chw4dMlphycjI4L93p0+f1pnfWq1aNf4mwN7eHm3bts3zorbLly/HihUrTNrW09OTr8CbklxFJpPhu+++4xsAAHUCntGjR/PDZ6Ojo/HPP/9g06ZNqFmzJhYvXmxwLtDRo0cxceJEAED79u3x3Xff6WR4S0tLw759+7BkyRIwxvDHH3/ozOnSZuq5re3evXuYNm1ajtlBRSIRevXqhe+//57PMKb9Wb58+RKrV6/mv/9FixbF4MGD4e3tDXt7ezRp0kTvmpSb75i2yMhIDBs2jO95cHJywtixY9GvXz9+aGtYWBjfg9KjRw/MnDlTL2FEXFwcLl++DI7jEBISojOn1NfXF/3794eHhwfEYvEHnYsab9680cuaZ4xQKERAQAB+/PHHbLN5Xr58GbGxscjIyMD69et1st/17NkTNWvWhLOzM8qVK2c05TjHcZgxYwZ27tzJP9aqVSt8++23fONMUlIStm/fjpUrV8LHxwdLliwxmvghIiICbdq0gZ+fHwQCAW7fvm20/H5+fvj9999RtmzZbI8HYwzjx4/XSXqlIRAIMHDgQEyZMgUODg5YuHAh1q5dCwBwc3PDzJkz4evri2rVqvE36Hv37sW0adPQuXNn3L5922gvN6CemjFt2rRsR0vcuXMHr1+/RlJSEpYuXapTiRs8eDBfOevevbvO9+bkyZM69161a9fGwIEDdeK4pnJ57do17Nu3j9/W09MT3377LZydnQ3Gl4iICPTs2dNgY1+xYsWwatUq1K5dG8HBwUhPT0dYWBhf2QbU2U4nTJiAokWLwsvLS2doKWD5e8ePmU1UsgYMGJDtlx9Qt6Z7eHjAy8sLdevWRZMmTdCkSROTKzIHDhzAzp07ERYWBqVSiYoVK2L06NE6N/7alSxtQUFBOd6sM8bQqlUrvH37Ft988w2+/fZbk8plSGhoKA4dOoRbt27h1atXkMlk/JfX398fXbp0MdiKExERgfnz5+PWrVt8y4erqyvc3d3BGENsbCwUCgX8/f0xYcIE1K1bV+f1WW8yjcl6PO7cuYO1a9fi9u3bSEtLQ/HixdG7d2+MHDmSvzHNWsnSGDduHAICAkwKeMY+B+1KlrYWLVpg9erVOe7XmOfPn2PNmjW4fv064uLiIBQKUbp0abRv3x4DBgzgV5w/ffo0xo0bZ3KZtStZI0eORNmyZXHixAk8f/4cCQkJKFq0KPz8/NCvXz+9i6mpHj16hG3btuH69et4+/YtRCIRSpUqhYYNG+Knn37ihy9krWRpO3PmjE7v5bt377Bq1SpcvHiR7+Vp1aoVJk6cqHPTpl3J0vD39+eHtmiLjIzEggULcPnyZUilUnh4eKBWrVoYN24cP6nX2OerzcfHB//++69J2wYEBGD+/Pl6j2d3/l+9elXnBkuhUGDXrl2YO3durocKOjs748yZMyanXzdEoVBg3bp12LRpk07F19vbGxzHISEhAbVr18aIESP4imJWubnRzHou5IZ2JcvHxwe///47du/ejZCQELx79w4ikQiVK1dGhw4d0Ldv31zfQDPGsGPHDvz999+IioriH/f09ISdnR3i4uJQuXJlDBkyBAEBAUbnpN2/fx9LlizBnTt3+B4ADw8PuLq6QqlUIjY2FhzHoUWLFvjxxx9znI9gyrmdFcdx+Pfff3HixAncu3cP7969g0qlgpubGypVqoTPP/8c3bt311uqJDefpaFrUm6+Y1mlp6dj+fLl2LVrF3/chEIhihcvDqlUipSUFDRq1AijR4822gt0/fp1k5f/+JBzUSPr8Vq8eDFevnyJ8+fP4/Xr18jIyEDp0qXRqFEjfPnllyalUzflGALGrz/aTpw4gaVLl+pkf/Xw8ICDgwPi4uJQqlQpDBo0CAMHDsw20U54eDgWLlyI33//HU5OTjh58iT27duH+/fvIzk5GU5OTpBIJOjduze6d+9u8nxNlUqFrVu34sCBA3jx4gUYY6hWrRq+++47neHz2pUsbdqf4ZEjR/D06VNMmDAB6enp2L17N06cOIFnz54hMzMT7u7ufC+fKfOvfvrpJ52GWmMeP35s8vdG853RrpgYY+zzffLkCebPn4/bt29DqVTC09MT9erVw8SJE/lGH1MaYI3FUsBy944fM5uoZBUW8+fPR1BQEP7991/+BtxSIiIi8OzZM0RGRiIlJQVOTk4oXrw4/Pz8TBonbcs4joNSqQTHcWZLsWtO2hfScePGYfz48RYuETGEMQalUgm5XA5nZ2ednpVLly5h9uzZkMvlmDlzJurVqwcHBwfY2dlBIBCAMQaO46BSqfDixQvMmTNHJz33nDlz0KdPnw8uo1KpxK1bt/D69WskJCRALBajdOnSqFOnjsWvQRpZK1mmtLbmBWMMISEhePbsGRISEiAUClGyZEnUrFlTrxc1O5q1kJ4/f47IyEh+CG3p0qVRt25dg73pRN2reP36dURGRiIpKQlOTk4oU6YMP6zcmmS9uTalIdUSHj16hLCwMMTFxYExhuLFi6NatWom3ewTkhcf871jXtjMnKzC4P79+2jRooVV3NyUKVPG5tLjmotQKDQ6ppgQUwkEAtjb2+uMO4+KisLUqVNx5coV+Pv7Y/ny5ToZ/7Rfa2dnBzs7O1StWhWrV69G+/bt+fk4pq5jlhORSISGDRvSBGSoj3mdOnV0ltTIC6FQiAoVKtjcQrmW5uDgoJfanXwYS61DRT5eH/O9Y17YTHZBW/f48WPcvHkzVwkvCCG2JTAwkE8UMn36dIMVLENcXFzQokUL/vfsEoIQQgghxPpRJauAbN26FWXLls02KxUhxHYlJSXxk71LlCgBiUSSq9drD5kylAmMEEIIIbaDKlkFIDU1FYcPH0a/fv1MyohFCLE94eHh/Hp+eRmbrr1YLg3vI4QQQmwbVbIKwLZt26BSqdCzZ09LF4UQkk+0F4zNzbp+gDpBxfXr1wEAEokE9erVM2vZCCGEEFKwqJKVz6Kjo7Fu3Tp06dLF6jIoEULMp0yZMnBzcwMAPHv2LNv1XLI6ePAgv/2kSZPypXyEEEIIKThUyTKjFy9eYO7cufyk9YiICIwdOxbp6ekYOXKkhUtHCMlPYrGYX7CRMYZ58+YZXfBZ28OHDzFnzhwAwKhRoygDGyGEEFIIUCXLjE6cOIGgoCA0adIErVu3Rrt27fDgwQMMHDiQ0v0Sk2kPNUtISLBgSUhujRs3DrVr1wYAnDx5Ej/99JPRoYNyuRxBQUEYMGAApFIpvvnmG/z4448FWVyroH18kpKS+HlthFhK1u8sXYcJIXlB62SZUXBwMAD1Cveale2bNGmCKVOmWLJYxAYEBwcjPT0djx8/RnJyMv/48ePHUalSJbi6usLLywuff/65BUtJcuLo6Ii1a9di/vz52LdvHw4cOIB///0Xbdq0QbVq1eDi4oL4+Hi8fPkSZ86cQXJyMvz8/DB58mTUrVvX0sUvMGFhYXj8+DEyMzNx7Ngx/vH09HTMnTsXNWvWhEAgQNu2beHs7GzBkpKPRUZGBk6fPg3GGE6ePKnz3Nq1a5Geng57e3tUqVIFVatWtVApCSG2RMBMGc9CcsQYw2+//cbfLH/yySfo1asXvvjiCwiF1GFIsteqVStERkZmu42/vz82b95cQCUiHyosLAwnT57ElStXEBUVxbeGu7u745NPPkG9evXQrl07VK9e3cIlLXjLly/HihUrctzuzJkz8PX1LYASkY/dmzdv0Lp16xy3GzduHMaPH18AJSKE2DqqZBFiBRQKBezs7CAQCCjNPyn0VCoVAEAoFNL5TqwCYwxKpZKuw4QQs6FKFiGEEEIIIYSYEY1jI4QQQgghhBAzokoWIYQQQgghhJgRVbIIIYQQQgghxIyokkUIIYQQQgghZkSVLEIIIYQQQggxI6pkEUIIIYQQQogZUSWLEEIIIYQQQsyIKlmEEEIIIYQQYkZUySKEEEIIIYQQM6JKFiGEEEIIIYSYEVWyCCGEEEIIIcSMqJJFCCGEEEIIIWZElSxCCCGEEEIIMSOqZBFCCCGEEEKIGVElixBCCCGEEELMiCpZhBBCCCGEEGJGVMkihBBCCCGEEDOiShYhhBBCCCGEmBFVsgghhBBCCCHEjKiSRQghhBBCCCFmRJUsQgghhBBCCDEjqmQRQgghhBBCiBlRJYsQQgghhBBCzIgqWYQQQgghhBBiRlTJIoQQQgghhBAzokoWIYQQQgghhJgRVbIIIYQQQgghxIyokkUIIYQQQgghZkSVLEIIIYQQQggxI6pkEUIIIYQQQogZUSWLEEIIIYQQQsyIKlmEEEIIIYQQYkZUySKEEEIIIYQQM6JKFiGEEEIIIYSYEVWyCCGEEEIIIcSMqJJFCCGEEEIIIWZElSxCCCGEEEIIMSORpQtQmDHG+H+EkI+TQCDg/xHysaO4SAj5WOIiVbLyAWMMmZmZkEqlUKlUli4OIcTCBAIBxGIxHB0dIRaLLV0cQgqcXC6HVCqFXC6nChYh5KOIiwJGVzuzYowhJSUFcrnc0kUhhFghV1dXODk5WboYhBSYzMxMpKWlWboYhBArVVjjotVXsu7cuQOO4yAQCCASWX/Hm+ZwOjk5QSQS8WUu7F2ihBDDGGPgOA5KpRJKpRJSqdSmhkkolUowxiAUCuHn52fp4lgNW4tNlqIZGujo6MjHRKFQaDPnPyHE/D40LtpKXLL6yMBxHAD1B6JQKCxcGtOIxWIIBALY2dnxlS4rr8sSQvKR5kac4zgIhUKbuZZp01yLiZotxiZLsbe3h1AohEgkgkAgoDlZhBCzxEVrj0tWX8nSXJAB9YXammkCh1gshlgshp2dnaWLlC1NSwIAalnMB4wxpKam8q0tRYoUsXSRChVbPX85juN7QKy9zIwxKJVKANQbn5UtxSZLyRoTTe3xs9Xvtq2g2JS/6PzNvdzERVuKS1ZfyRKJRFAoFBCJRKhVq5ali5MtjuMQHx8PAPD09LT6SpZSqURkZCQAoFSpUjTkxcyUSiX++ecfpKenw83NDb/88ouli1So2OL5q1KpkJCQAAAoVqwYhELrXkVDLpfj3r17AGATx7cg2VJsspS8xkRb/G7bEopN+YvO39zJbVy0pbhk3RHextDwB0KIqeh6QQo7OscJIblR2K4ZVMkihBBCCCGEEDOy7n42Qmyci4sLANCYd0IIIVaDYhMh+Y8qWYTkoz59+gAAfHx8LFwSQgghRI1iEyH5j4YLEkIIIYQQQogZUSWLFBqFbcIkIYQQklcUEwmxLBouSGzarVu38PLlSzx48AAXLlzApk2bUKZMGUsXixBCCClwFBPNJyEhAWKxGK6urpYuCrFRVMkiNu3atWuIjY3Fjh07AFjfwnTnzp2DVCqFp6cnPwbemuzfvx979uyBXC5HfHw8SpUqhS+++AKdOnWydNEIIYTkkqkx0dpjkyXdv38f27Ztw5EjR7BixQo0a9bM0kUiNooqWcSmjRs3DiqVig8o1iY8PBzp6emIjY21dFH0zJkzB7du3cLq1atRokQJKBQKLFq0CBMmTMCLFy8wbty4XO0vKioKJUuWzKfSEkIIyYmpMdGaY5MlvHv3DidPnsShQ4fg4OCA27dvf/A+Y2JiKLHIR47mZBGbZ2dnZ+ki2JwjR45g8+bNmDNnDkqUKAEAsLe3x+TJk1GhQgWsWLECjx49MmlfFy5cQEBAABYtWpSfRSaEEGICiom5t2zZMrx48QJ//PEHtm/f/kH7unfvHn744QfMnz/fTKUjtooqWYR8hNasWYPy5cvj008/1XlcKBSiS5cuYIzhwIEDJu3rxo0bePjwITw9PfOhpIQQQkj+mjdvHmbNmoWKFSt+8L4ePnyIe/fuwd3d3QwlI7aMKlmEfGRev36Nx48fQyKRGHy+Tp06AIDQ0FCT9icSqUcdOzo6mqV8hBBCiK3SxEQHBwcLl4RYGs3JIlbp4cOHCAoKwuPHjyEQCBATEwNfX19069YNffv25S9ihqSkpGDlypU4efIkEhIS4OPjg4CAAAwfPlzndRzH4cCBA9i/fz9kMhkyMzMhFovRoEEDDBs2jB9GpxEaGopNmzYhOjoaMTEx4DgOLVu2xNixY/lenMTERDx79gz37t3Dv//+C2dnZ3589759+9C+fXt07NgRs2bNQkREBADAyckJFSpUQGBgIKpWrQqVSoU+ffrg9evXSE1NhYeHB7777jsMHDgwV2Ux5smTJwAALy8vg89rxpC/fv062/1oCIXqtprshqjk9vN88+YNVq9ejQcPHkAgEEAul8PHxwddu3ZF586d+e0yMzOxZ88e3L17FwKBAJmZmShatCiaNm2KoUOHQiwW89syxnDy5Ens378fKSkpePfuHdzd3dG9e3d8+eWXsLe357dNSEjAmjVrcOPGDQiFQkilUhQvXhytW7fGF198YdJxIYQQczF3TCxSpAjKli2rs11+x8SzZ89i5MiRqFq1KmbNmoWrV69aRUw0N01M1PxvyKtXr7Bx40aEhITAzs4OUVFR8Pb2RocOHTBo0CA4OzvrbG9qTJLL5di2bRtOnDgBjuOQmZkJFxcXNGrUCMOGDYObm5vOfq9cuYJt27YhISEBUVFRcHBwQIcOHTBq1CidMqSnp2PDhg04f/48AGQba4kWZuVCQkLYrVu32N27dy1dlBwplUoWExPDYmJimFKptHRxcqRQKFh4eDgLDw9nCoXC0sXhrV+/nvn5+bGDBw/yjyUnJ7OffvqJSSQS1r9/f5aamqrzGolEwiQSCXv8+DHr1asXq1OnDmvevDmrWrUq/9zXX3+t85rp06ezRo0asadPn/KPPXr0iLVu3Zrt3buXf0wmk7GpU6eygIAAFhERwT9+4cIFVqNGDda0aVMWHR3NGGNs165dbNq0aax27dpMIpGwkSNHsvr167MqVaowiUTCJk2axBhTH/tOnToxiUTCZsyYYfA4bNmyhdWpU4e9e/cuT2UxZuvWrUwikbCFCxcafD41NZVJJBJWq1atbPfz5MkTNm/ePNasWTMmkUhYy5YtWZ8+fVifPn3YvHnz+O1y+3k+e/aM1a9fn82ZM4c/L6VSKVu8eDFr2bKlTjk7dOjAevbsyWJjYxljjHEcx44dO8Zq1qypc3zevXvH+vbtyyZPnszS09MZY4ypVCq2YsUKJpFI2LBhw/jvbGxsLGvRogX79ttvWUZGBmNM/d3esmULk0gk2R6TnNjaNUImk7Fbt26xW7dusZCQEEsXx6rYUmyylLye79Yamywlv2Jis2bN2OzZs/nXFERMPH36NOvWrRurVq2a1cREQzTH6Pz58ya/5t27d2zRokWsQ4cOTCKRsIYNG7LevXuzPn36sIkTJ/LbHT58mNWpU4f9888/jOM4xhhjmZmZbPHixUwikbAOHTqwqKgofntTYxLHcWz48OGsQ4cOOsfo6tWrzN/fn127do1/LDk5mX311Vds+PDhLD4+nn98z549TCKRsC5durC0tDS+bN26dWMDBw5kiYmJ/HsZirV5kdvrhC3FJapkmZGt3UBZYyA7fvw4k0gk7O+//zb4fL9+/ZhEImETJkzQeVxzQezfvz87fPgwU6lUjDH1RW/kyJH888HBwYwxxt68ecMkEgmbM2eO3nsEBQXpBJR58+YxiUTC7t27p7ftr7/+yiQSCZs5c6bO4wMHDmQSiYT5+/uzIUOGsFmzZrE9e/bobKe5mLVo0YIvr7ZRo0axxYsX6zyWl7Jk9ffffzOJRMKWLl1q8HmZTMYkEgmrVq1atvvRmDJlCpNIJDrHTCMvn6fmxiE5OVlnW47jWEBAAP+75vht3bpV7/wdN24cf+HXvK5Ro0YsMzNTrwwdO3ZkEomEHT9+nDHG2PLly5lEImEPHz7U21b7/fPC1q4RthTMCpotxSZLoUrWh8uPmDhixAj++dGjRzPGCi4mdunShV28eJGpVCqriYmG5KWSpbF06VImkUjYrFmz9M7f27dvs+rVq7Pp06cbfO3EiROZRCJh/fr14x8zNSbduHGDSSQStmHDBr3t5s2bp1PJGjduHKtevTqLiYnR21Zzfqxbt44xxtj+/fv5CnJW2rE2rwpzJYvmZBGrsnjxYgBAmzZtDD4/cuRIAMDRo0cRGRmp9/yXX36JLl268N30JUuWxLJly1C6dGkAQHBwMAAgNTUVAHDnzh3IZDKdfXTq1AnNmzcHAGRkZGDHjh0Qi8WoXr263vtVqVIFAPDff//pPK55/9KlS6N48eIQCATo1asXfv31V36brl27wsvLC2/fvsWFCxd0Xv/u3TuEhIRg+PDh/GN5LUtWCoUCAKBSqQw+rxn25+TklO1+TJGXz1Pz2Vy7dk1nW4FAgHnz5vG/a7YLCQnR2+8PP/yA4sWLAwCuX7+OBw8eoEqVKgbnjWnmpmmOm7H3B6Dz/oQQkt/yIyYuWbKEHwqmGaJXUDGxZcuWaNKkCYRCodXExIK0bNkyKJVKo5/nqFGjAKg/h5s3bwIwPSalpKQAAG7evAmO43S2Gzx4MH+MIiIicPr0afj4+MDb21tvn1mPm2a/ht5fO9YSfTQni1iNiIgIvHr1CgCMrrfUuHFjCAQCMMYQEhKitwZFw4YN9V7j5OSEjh07Yv369YiJiQEAVK5cGZUrV8a9e/fQqlUrNG7cGHXr1oW/v79OdqHQ0FBkZmbCzs4OvXv31hv3npmZibJly6JChQoGy5vdWHCxWIwBAwZg+fLl2LZtG1q0aME/t2vXLgwYMEAnO9GHlkX7fQEgKSnJ4POaAFukSJFs95OTvH6enTp1wunTp/Htt9/Cz88P/v7+qFu3LurXr89f/AGgWbNmWLJkCQ4cOIDQ0FA0atQI9erVQ4MGDVC+fHl+uxs3bgBQp9Xt3bu33jj55ORkVKhQgZ9v0L59ewQFBWH+/Pk4fPgw/P39Ub9+fb33J4SQ/JSfMbFcuXJ49OgRMjIyABRcTKxRo4bRv9dSMbGgyOVyPh4Z+zyrVq2KYsWKIT4+Hnfv3kWDBg1MjkkNGjSAt7c3goOD0aZNGz4m+vv7w9fXl9/uxo0bYIwhOjoavXr10ptPnZqaivLly/OvadGiBZYsWYLNmzfj8uXLRmMt0UeVLGI14uLi+J81kzWzcnZ2hpubG5KTk/keGVOUKVMGAFC0aFEA6t6adevWYdmyZTh37hwOHTqEQ4cOAVAnfpgwYQK6du3Kl8nBwQH79u3L9d9Urlw5lClTxmiSiYEDB2LNmjW4ePEiIiIiUKZMGSgUChw7dgw7d+7U2fZDy6JRrFgxAP9vncoqLS0NAPQmRedWXj/PTp06QSaTYfv27QgJCeEXhbS3t0fLli0xe/ZseHh4oGzZspg3bx527NiB0NBQbN26FVu3bgUA1KpVCzNnzkT16tX5ctSrVw+rV6/Osdx169bFqlWrsH79ety9excPHjzAhg0bIBQK0bBhQ8yePVsnYBFCSH7Iz5hYoUIFPHr0CKVKlQJQcDExpxESloiJBSUpKQlKpRKA+vM0pkSJEoiPj+c/T1NjkpubGzZu3IgVK1bg8uXL2LNnD/bs2QMAqFSpEn7++Wc0btyYP25lypTB3r17cyx32bJlsX79eqxevRo3btwwGmuJPhouSKyGdgvVy5cvjW6n6YnJTSVA093etGlT/rGSJUsiMDAQV65cwenTp/H777+jVatWiIyMxE8//YTY2Fi+NycjI4OvfORG9erV0apVK3Ts2NHg856enujatSs4jsO2bdsAACdPnkTr1q3h4eGhs+2HlkVD0yoZFRVl8HnN4x960fyQzzMgIAC7du3CrVu3sHnzZowfPx5ubm44deoUli1bxm9Xo0YNzJ49G9euXcP+/fsxffp0VK1aFaGhoZg8eTKA/x83TS+mKVq0aIHNmzfjv//+w86dOzFp0iT4+PjgypUrmD17tsn7IYSQvMrPmKjpSenbt6/OY/kdE3NiiZhYUEz9PDWp37U/T1NjUqVKlbB06VJcv34dR48exZw5c9CgQQM8e/YM33//PRQKBX/cYmNjTS573bp1sXr1aty8edNorCX6qJJFrEb58uX5XpaLFy8a3EYmkyExMRG+vr6oXbu2yft++vQpypcvjy5dugAA4uPjsXnzZv75smXLonv37li1ahU6deoEpVKJt2/fomrVqnxX+uXLl/P6p2Vr6NChAIDdu3cjIyMDmzZtwuDBg/W2M1dZPv30U7i5ueHRo0eQy+V6z9+/fx8A+GNlKsaYzu95/Ty3bNnCt7Q5OzvD398f48aN4z8vTWr569ev486dOwDU65JUr14dgwYNwu7du+Hj48NvpxmeEhYWhvj4+Bz/jmPHjvFp7sViMerUqYORI0diz549cHJyMjm1PSGEfIj8jInh4eH88Gzg446JBcXBwYGPR8Y+TwB4+/YtXFxc+OGSpsakp0+f4tixYwDUc+AqVaqEPn36ICgoCH5+fkhOTkZycjJfhqSkJD7eZ+fatWu4evUqgOxjLdFHlSxiNYRCIQYMGAAA2LlzJ5KTk/W2OXXqFFQqFaZOnWpwDQpDyRyio6Nx5coVBAYG8hfktLQ0nD592mA5XFxcUKRIEVSuXBklSpRA27ZtAQB//fVXroZjmKpy5cr4/PPPkZqaiqlTp8LX19fgeG1zlUUkEqFv376QSqV8IhBtwcHBaNiwIT799FOT9icQCACo10PRltfP8+7duwZb+VxdXQGoh/0B6vkKDx480NtOLBZDJBLx27Vp0wYlS5YEx3FYsWJFjn/P48eP8fDhQ4Pvzxjj9wuo1/NasWIF+vbti7CwsBz3TQghpsqvmBgTE4Pbt2/jxx9/pJhogLGkUKbSxERDn9egQYMAqHvnNPPttN2+fRvR0dH4/vvv+ZhnakyKjY01WHkTCoVwcnJC+fLl4eXlhdq1a6NmzZoAoDMyxJiIiAiDyUOyxlpAvZ7X2rVrMXjwYPz777857ruwo0oWsSpjxoxB06ZNkZSUhNGjR+tchC5evIhFixZh9uzZOpl5tHtjpk2bpvOaqKgo7N+/H/v27dO5EMTHx+PWrVs4c+aMzvufP38ex44dw9y5c/nsS7NmzUK1atUQFhaGUaNG6ew/NTUVa9as0cuEpBm6IJVKTfq7hwwZAgA4ceKEwRY7jbyUxZCxY8eicuXKmDdvHh49esQ/vmfPHjx9+hS///67SeUGwCeMWL9+PbZs2YJDhw7h3LlzAPL2eSYkJOCvv/7ih3gC6mA1a9Ys+Pn5YdiwYfx2WTNqKZVK/PXXX0hPT+ezVonFYqxYsQIeHh7Ytm0bAgMDdZJ+REREYObMmfzQiYSEBKxfv15nKEVmZiZmz56NkiVLYuLEifzjBw8exPLly+Hg4ICqVauafMwIIcQU+RETDxw4gJUrV+okoSiomJjdXCRtBR0TtWlf+xMSEnL1WuD/MfHUqVNYt24djhw5gqNHjwIAevbsid69e0OhUGD06NE6lad79+7hp59+wvjx43X+ZlNjUnx8PE6cOKGTcZcxhj179iAkJASBgYH840uXLoWPjw/Onz+PH374AdHR0fxzcXFxWLhwId9wGB8fj507dyI8PJzfxlCsBYALFy5g4cKFiI6ORrNmzXJ97AobAcs6xsfKhIaGQqFQQCQS5aor3BJUKhX/hfT09NTL2GJtlEolf4Pq4+OT7YrxBUmpVGL79u3Yv38/Xr16hXLlysHd3R1Vq1ZF//79Ua5cOZ3t4+PjMXr0aDRq1AgXLlxAZGQkvLy8UKtWLaPzoV6+fIk1a9bg8ePHUKlUcHJygkKhgK+vL0aNGqXXi6NZRf3YsWMIDw9HiRIlULZsWXh4eKBjx45o0qQJAHU602fPnvEXJ3t7e7i4uKBMmTL8BFRDGGNo27Yt3N3dc5yIampZcpKamorly5cjODgYbm5ucHJyQpUqVfDNN98YTOtqTFpaGn744QdcvnwZ9vb2+OyzzzB79mx+H7n9PIOCgnD+/Hm8ffsWbm5uYIxBKBSidevWGDJkCD//4OzZs9iyZQsiIiLg6uoKe3t7KJVK+Pn54auvvtL7G+Li4vDPP//g3LlziIuLQ4UKFVC8eHGUKlUKffr0QaVKlQAAhw4dwrFjx/Dq1Su4urpCKBSC4zg0btwYo0aN4lsXAXW63QsXLmDXrl0mXZ9s7Rohl8tx7949AOpzuVatWhYukfWwpdhkKXk93601NlmKuWNi27Zt8fvvvyM9PR3u7u6YPHlygcVEFxcXlCtXDo0bN8akSZOM/s2WiInffPMNoqKi8ObNG74hTiwWo1KlSnB2duaTPeQkMzMTkyZNwvnz5yEQCFCrVi3MmjVLJ8vhwYMHsXPnTjx58gSlSpWCt7c3ypQpg/79+6NatWo6+zM1JoWEhCAoKAhPnjyBvb09xGIxFAoFJBIJxowZo3eepKWlYdOmTTh9+jQiIyNRpkwZ+Pj4wMvLCz169OCvaxcuXMCePXvw/PlzODo6QiQSGY21M2bMwPbt27FkyRJ+KGpOcnudsKW4RJUsM7K1GygKZPlLqVRi7ty5SE9Ph5ubG3755RdLF6lQsfT5GxMTgxYtWqBTp05YuHChSa+xtWuELQWzgmZLsclSqJJlnSg25a+P9fyVy+Vo0qQJKlWqhK1bt/LDJnNSmCtZNFyQEELy4J9//kGZMmUwY8YMSxeFEEIIsaidO3dCKBRi8eLFJlewCjubql4byoRmTTiOg0qlgkAggFKp1Mu2Zm20J3d+6ERPoi/rMdWsj0HMw5Ln78uXL7F7925s2rQJjo6OJn+2KpUKHMeBMQaFQmH137v8mNReGFl7bLKUvMZEik35i2JT/voYz9/ExESsXLkS8+bNg5eXV67OqdzGRVuKSzZTyVIqlXz3oLVzdXVFZmamTdXkja2ZRMxDpVLpJGgg5lXQ529ERASWL1+OIkWK5OpzZYxBJpMhLS2NzodCwpZik6V8SEyk2JS/KDblr4/l/I2MjMTcuXNRoUKFXJ9PhTku2kwlixBCrIVmQWdCCCHkY+fj42PpIlglm6lkiUQivYwr1objOCQlJUEgEMDDw8Po5D3GGMITM5GhsGw3Mqfi+EVfvby8ILTL3RQ9Z3s7lC/qZFM9dgVJu8vbzs6OLkJmplKp+FbCkiVLWn0SCUBd5qSkJLi7u8PDw8PgujbWRKFQ0PpfObCF2GQppsZEQDcufmhssiRbiIsUm/KXLcYmS8ptXLSluGQzlSwAfOpma6VSqfgvk0gkMvjF4jiG7htu4OijmIIuXg6e5OlVnasVx8Fh/hAKrTegWIuPJcOQJdjZ2dnE8RUIBHwAsbe3p+BbSFh7bLIUU2IikFNczFtssiRbi4u2cO20VbYSmyypMMdF22keKiTi0uVWWMHKu6OPYhCXTpO+CSGE5A3FRUJIYUSVrALm5SJG52rFLV0Ms+lcrTi8XGyjFffNmzc4ffo0VqxYgcGDB2PlypWWLhIhhHz0KC5aRkZGBl6/fk0xkZB8Qn2YBUwoFODQcH+EJ1h+TpZSpURMtLr1sHiJ4hDZ5e50cLa3wyfFnPOjaPni6dOnuH//Pg4ePIh3796hQYMG+f6eLVq0gFKpRIkSJfK8j9evX+PJkycICwvD9evXMWDAAJNXUieEEGuXNS5+aGyyJFuJiy1atMCdO3cQHR2NPXv2FFhMNAeKicRW2M6VqxARCARWcRFWKpXwUKYAAHxKFLGJccNPnjzBypUrERkZCZlMBplMhvbt22P06NEoUqRItq9t2bIlWrZsibdv3+LQoUMFUt7y5csD+LDMO/fu3cP9+/exfft2ZGZmIiAgwEylI4QQ66AdF20xNllKXmNi+fLlUb58efj4+GDq1KkFFhPNgWIisRV05SI24/bt2xg1ahQWLFiANm3aAABu3LiBMWPG4Ny5c9i5cyecnXOuvNrapMrOnTujc+fOuHPnDu7cuWPp4hBCCLECFBMpJhLrRnOyiE1IS0vDN998gx49evDBBAD8/f3x1Vdf4cmTJ/jzzz8tWML85+joaOkiEEKyeJsiQ+V5Z9Bq1RUM2X4H0449wqor4TjyMBohb5ORkCEHY8zSxSSFDMVEionE+lFPFrEJe/bsQUJCAjp37qz3XLdu3bB48WIcPHgQkydPtqpWuZiYGHAcBwAoV66chUtDCDE3BuB5fAaex2cY3cZFbAdfd0eU8XCCr4cTyng4oYyH+vcy7urfizhSOCam+9CYqB2bCCH5g67qxCacOnUKACCRSPSeK1WqFIoXL46YmBi8efPGqiozx44dQ3p6Otzc3PDLL79YujiEEAtIl6vwODYdj2PTjW7j7ih6X/lygq+HI3zds1TGPJzgZG89DUjEsj40JmrHJkJI/qBKFrEJT58+hZOTE1xdXQ0+7+Pjg5iYGLx69SpXlSylUomVK1fiyJEjiIqKgre3Nzp16oQxY8YYfK/MzExs27YNFy5cQHp6OqKjo/HJJ5/gyy+/RNu2bXW2TU5OxqNHjxAVFQXGGA4fPowSJUpgwIABGDBggNEyxcfHIygoCI8ePcLr16+RkZGBOnXq4M2bN0ZfExYWhrVr1+Lly5cA1CuiV6hQAX369EGTJk1MPh6EkNxxEgnRpVpxxKfLkZChQGKmAklSBeSq3A0RTJYqkRyVivtRqUa3KeZsr1UR0+8N83F3hFhEswA+BrYaE/ft24f//vsPqampCAsLo5hICjWqZBGrJ5VKkZKSAm9vb6PbeHl5AVCPUzdVZGQkJk2ahKZNm2Lp0qV4+/YtlixZgrVr1+LatWvYtWsXvwo5ANy8eRM//PADRo8ejY0bN0IgECA1NRWTJ0/GuHHjMH78eIwbN47fvm/fvvDx8YG/vz/c3Nwwbtw4/PDDD5gxYwaioqIwYcIEnfIolUr8888/uHPnDmbMmMGnfX/06BFmzJiBiIgIg3/H9evXMWLECEycOBELFy7kyxUYGIjVq1dTQCEkHzmIhBhav5TOY4wxZChUiEmVIypNhpg0GeLS5YhPVyAhU10RS5YqoeRyVxGLz1AgPkOBu29TDD4vEAAlXB34HjBfrQqY7/sKWakiDhDZUUXMltlqTOzZsyfGjh2LFStWAACSkpIoJpJCjSpZxOqlpKhvKLJL4+vg4ABA3VplqosXL2Lfvn38hbtGjRqoVasWWrdujXv37iE0NBR16tQBAERHR+Orr76Cv78/Bg0axO+jSJEimD17Nv7991+sWrUK/fr14wNf1apV4eXlhczMTACAh4cHAgMD0bx5c2zatAnjxo2Dvb09v6/vvvsO//33H06dOqUzhKNatWoICgpCz5498ezZM72/Y+XKlXBycsLw4cN1yvXzzz9j7NixJh8PQoh5CAQCuIhF+KSYyOhyHYwxpMiUiEmVISpNhrg0BWLT5YjPUPeIJUnVFbHc1MMYA6JSZYhKleGm4ftP2AkFKFXEQWdoouZndYXMEcVdHSAUCvLwl5OCYKsxsVq1ajrp1ikmksKOKlnE6imVSp3/DdG0rpmSrlajRYsWeosElyhRAp988gmePHmCt2/f8gFl586dSE9PR+3atfX24+XlhWLFiiE+Ph4hISF8pqc///wTc+fO1du/l5cXYmNjkZCQwL//1atXERwcjH79+hkcI+/g4ABvb2+DASU1NRWpqam4f/8+Pv30U/5xV1dXmgdGiJUSCARwd7SHu6M9KnsbHvLFMYakDAWi02SITpMjNk2GuHSFuiKWqUBSphKpMiVy0x+m4hjeJEvxJlmKq68SDW4jthPCx92RH47oq1UB0wxTLOYshkBAFTFLsNWYqOnByrp/iomksKJKFrF6YrEYwP9b7wyRyWQAYHR8uiGalr6sNBd0qVTKP3bjxg0AwPbt23H27Fmd7RljcHR0RJUqVeDi4qL3XHJyMqKiojBnzhw8e/aM/zu0WxhPnz4NAKhUqZLJ5dfo2LEjHj58iP79+6NBgwaoV68e6tevDz8/P1SpUiXX+yOEWAehQABPFzE8XcSoVsLwNiqOIT5DjuhUGWI0FbEMdUUsMVOJpEwF0uSqXL2vXMXhZUIGXiYYz5joZC/USc7BZ010/3/PmKuYhiXmB1uPiY8fP0ZISAiePn1KMZEUalTJIlbP3d0ddnZ2kMlkkEqlBtfGSE9XZ+0yR2ZB7THnGrGxsQCAIUOGYMSIESbtZ+vWrThw4AA4jkOFChXQqlUrfP/99wgICMDr1691tn316hWA7Nf9MJZud+TIkRCLxdi3bx+uXr2KK1eu8Pvq0qULfvvtNz4oE0IKFzuhAMVdHVDc1fANMgAoVBzi0uWITtNUxOTqOWKaiphUgUxF7tJ5Zyo4PI1Lx9M44xkTvZ1F6FOlCJp+UgxycSaKOjmgqLMIRZ3FKOpkD08nezhQxsRcs9WYuGXLFqxevRocx6Fr164UE0mhR5UsYvXs7e1RtmxZvHz5ElFRUShfvrzeNu/evYOHhwdKlSqlvwMz0LTkxcTEmLT9oUOHEBgYCE9PTzRq1Aienp5o3Lix0e01gSQ6OtroNnK53ODjQqEQQ4cOxdChQ5GSkoLQ0FBcv34dW7ZswZ49e1ChQgWTgyAhpPCxtxOilJsjSrkZv2GVKlSIS5cjKlWdqCNWMyyRz5iohEyZu4pYqkyFdykyvE2R4lmaDEqmP7zQWWwHTyexTuXL3cEOqnQl3MQClFAxZDP16KNkqzFx9uzZqFGjBrZs2ZLjMEaKiaQwoEsXsQmNGzfGy5cvERISohdQ0tLS8OrVK/Tv398scwQY05/hUL16dYSGhvItYjnZuXMnAKBKlSrZTk7WkEgkCA4Oxvnz5zF+/Hi955VKJV68eGHwtcuWLcP48eMhEAjg5uaGJk2aoEmTJmjQoAFGjRql10JICCFZOdrbwff9/Ctj0uUqxKbJ+B6xuHT1P01FLFmqhCKXGRMz5CpkyDPxJtnIBqEPUcRRBE8ne74SVtTJHp7Omt9F8HCyh52B3pbCzFZj4uDBg02aJ0YxkRQGVMkiNqF///7Yvn07Dh8+jO7du+s8d/bsWYhEInzxxRdmea+MDP15CAMGDMCePXvw5MkTHD9+HB07dsx2H8nJ6juGrEMSXr9+jfj4eL3tu3fvjrVr1+LevXvYvXs3+vTpo/P85s2b+X1mdfXqVfTu3RulS5fWeVwzFr5evXrZlpUQQkzhIraDi6czynsaz5iYJlchOlWK6FQ5EjLk8HVUopizPRI5IdIUHDIVqlxlTASAVKkSqVIlXiVmGnxeIADcHdWVr6LvK1+eTiIUdRKjqLM9PJ3EcHMUFaqMibYaE4sWLarzOMVEUphRJYvYBIlEgq+++gqrVq3C+vXrMWzYMAiFQkRERGDp0qX49ddfUaFCBZP2pUmprvk/K81Ydu3AUrVqVfz222+YOXMmfvrpJ6SmpqJHjx58Jer27ds4fPgwfvvtNwBAgwYN8PTpUwiFQowYMQK+vr7477//cPbsWX58u/Z48vLly2Py5MkIDAzEr7/+iidPnqBTp06ws7PDmTNnULRoUdSqVQuhoaH8hGaNhIQELFmyBHPmzOEnLkdHR+OPP/5Au3bt0LVrV5OOCyGEfAiBQIAiDiIUcXBFJS9AwDi4MCmKuYhRw9EVAoEQDECmXIUUmTozYqpMiTSZCmlyFTIUSmTIOUiVKhjoPDGKMSApU4GkTAVeJhjeRigU8L1gRZ3f94a9r4SpK2L2cHUQ2UzGxA+NiQMHDgQAlC5dGtOmTQNQMDHxyJEjaNq0KYRCIcVEUugJmKF+YCsSGhoKhUIBkUhkMFWoNVGpVEhIUF/hPT09YWdn3RN6lUolIiMjAahXhzdlWJulHTx4EEFBQUhOTkbx4sXh4uKC4cOHo1GjRjm+duvWrdi/fz8ePXoEpVIJe3t7VK1aFYMGDUKPHj347Zo0aYLY2Fi4ubnhk08+wbx581CxYkUAwMOHDxEUFITr169DKpWiatWqcHd3R9WqVdG3b194enoCUA/XmDt3Ls6cOQNXV1e0atUKAQEBqFGjBj7//HPExcWhffv26NKlC9q1a8e/94ULF7Bhwwbcv38fKpUKn3zyCWbMmIGaNWuib9++CAkJgaenJz7//HMMHz4c1atXx9KlS3H79m2+zBzHwcHBAd26dUPv3r0NTlouDGzx/LW1a4RcLse9e/cAqOeB1KpVy8Ilsh6a2CTjBIgSG0n995HTrmThfSXLFCoVh3dxCUhXMHD2jkiXc0iTK5EuVyFdrkKmQgVpLueHmUJkp6mIid8PR7TX/d3JHs5iO6uqiOUlJmqunYcOHcK5c+cQFhZWIDExMDAQZ8+ehZubG5o3b47u3bsXyphoi7HJknIbF20pLlEly4xs7QaKLgT5i45v/rLF42tr1whbCmYFjSpZOctrJYvjOCQlJQFQL1hr6KZYxTGky5VIkanUwwnlSqTJ1BWxDIUKGXIOcpX5K2IOIuH/K1/OYvVcsfe9Y57v54w5WnnGRFu8dtoSOr65U5grWfTJE0IIIcSm2AkFcHO0h5ujPeBueBslx5AmUyJFqkSKTIk0uXpoYoZchXSFukdMocpdO7NMySEqRYaoFJnRbZzFdrpDE7VS1qsraGKIRYVzhAEh5P+okkVIPrp79y7kcjm8vb3RokULSxeHEEI+GiKhAB5O9vBwsje6jULFIVWmQopUiVS5AmlS9fywdLkSGe8TdSjzlDFRhchkqdFtXB1E/FywolqZEzXDFD2c7CHKx2FtFJsIyX9UySIkH929exfp6elwc3OjQEYIIVbG3k4IT2chPJ3tARhOXy9TckiVqXvDUqXqZB2a+WEZChUylRy4XFbE0mTq4Y0R2WRMdHN8nyFRO1mHVmXM3dE+zxkTKTYRkv+okkUIIYQQYoSDSAgHkRheLmKj22QqVOqK2PtKWNr7Slj6+16tzDxkTEzOVCI5U4lwI9sIhQK4O4reJ+X4f2VMs6aYp7M9XMWFK3U9IbbEZipZChXDfxFJli5G9hgHSDPhKrZDuiATAivP6qbiVIhJV6p/TsqEndC6J+vaGhWngma4v4oxvErQX2uE5J0tnr+M4yBLlyNNrsLrzGTAxEQAlqJQKgCOwZ5u0gjJlpO9HZzs7VDc1cHg8xx7XxGTKt6nr1chTaYelpiuUPeISRW5S9TBcQyJGQokZigAGI4vIqEAHu/XCivqJIKHsxgeDnZQvo9NDOr1zawpYyIhhYXNVLJi0uXosu2ipYuRLUc7ATqWd0KTTzzxLC0WSmZDF60HaZYuQaFURMEgBJAqVSHwzFNLF6fwspHzVyRgqOTKcOllAo6HZ0Kay0n3lnCkewmUcLb+Ciwh1kwoeL+Ys9gOJY1swzEgXTMsUabdI6Z8n7qegyyXqeuVHENcmhxxaXKdxzWxKTlTiW/339daL0wMDyeR1rBEMYo6i+BkbzO3i4RYDfrWEEIIIYRYmFAAFHEUoYij8VszFceQJtcMS1QhVabghyZmvJ8jltuMiXIVh+hUGaJTjWdMdLS308qOqEnU8f/KWFFneziIqDGGEG1UySKEEEIIsQF2QgHcHdVJL4xRqDikyVTve8QUSJWpK2FpmkQdclWu31eqUOGtQoW3KcYzJrqI7fi1wzy054Y5iVDUWQwPJ3vY21n3EGlCzMlmKlnujiIs6VrN0sXIlhAM3iIFfNwdUcve2ernW3AcQ2pKCgCgiJsbTY41M45jOH9fALkCcLIXomPtUpYuUqFik+cv4yBWZKBUEQe0qG4PDtZdZqlcCWdRqqWLQQjJBXs7IYo6qxdNNpQxkeM4HLwngEwBONgLUKOchzrboaY3TK5CRh4yJqoTfWTiTZLhjImAJmOiehiihyZlvVbPmIeTPeysfD47IaaymUqWnUCAMh6G06taCwFjcGHqhQidHeytPvEFx3EQyNRl9HASQWjl5bU1HMdBM5dYKBCgaDZrtZDcs8Xzl3EcILCDp7M9xAInwMonm2fKFBByVMkiecO0GxEYYOVtCh8lkUCI2qXcDD4nVXBIkSmQIlUv4pwm/3/WRHWiDhVyWQ9TLwwtVeJVNqnr3R2zVr50f3dzoIyJhVVhS8BiM5UsW6C51qgYgx3jAFj/TR8hpABx6oVNrT/dBSHmwaBOviDiVIANNISQ/3O0F8LR3sFoxkQG9cLLmjXE1Gt/qStj6vlhHKQKVa6ud4wBSZkKJGUqjG5j936Rac2wxKJO77MnOv9/zlgRB1Ghu2EvrBQK9WctEAgK3WdGlSxzEgigYkIoVAx2KgVgR4f3Y+dW1AtyZ1e4uBaxdFGIhTHGAJUcciUHFeysvheLkA8mEEDF7CBXchCJ5GB2dONrLcwRmwTQyphYxHjq+gy5VsZEqboSpllDLFOZ+9T1Ko4hPl2O+HS50W1EdurRI0Wd1OuF8Qk7NL872cNZbEfno4UxxiCVquf5icXiQvd5UC3AzJQCO2TKlXCwU6hvouzsAQisc5gE48Cvjsi4XC2USEzAOPg1bw8A8PBwB2O5CyQkB7Zw/mrKxKkAlRxKhRKZShUUAsM3JIQUNgqBCJlKGcQKJUTIALMTA5o17YzFRVv4btuyAoxN6oqYEC5iMUoVMbyYM8cxpMs12RLfD0uUve8NU6qQKecgV+WyjBxDYroMiekyvDCyiYNICA8ne/U/ZzE8HUX875o5YnnJmKhSqdSNau9/LmwVB3NRKBSQSqV8T5ajo6OFS2R+VMkyMwVEsGMckqUKOKs4iEXGW1osjjGI2fvyydKpZd3c6PjmLxs6vkqOQa7kkKlUQQZ7qASU6ph8HFQCO8iYPZJlCjipOIhFSohymk9jQ99tm2Rlx1cIoAiAIg4AHADA7v2//+MYg1TBQapUD0GUqtRrhsmV6gqYXMWgyu0EMagApoIqQ4r4DCDewBZiOyGcxXZwsbeDs4Odes69/fvfxXZwEYtgl+V85jgOMpk6HX5SUpJNzBe2NFdXV4jFhivhtowqWeYmEEAKMVRMCYVMCaFMaekSGcVxHJIS1ZPaPTg7uhCYGR3f/GVLx5cBUMEOCoEDVbDIR0chsAfHhFAqlLBTKHIc2GFL321bZOvH10EkhINI+L5C9n9yFafuDZMq/j9HTCtRR6ZChdx2iJnC1cFOJ0mHh6MdWGYq3MRCVBY5wcvVUa8iRtRzsMRiMRwdHQtlBQugSlb+EAiggD0U7w+vwEqnuavAISZTfcURFXWEnZWnnLc1dHzzl60cXz7DGrXGk4+YSmCnbmB4P4wqu7hoK99tW1Voj68IcBQBji6At4GnGQPSFSrEpMkQmy5HbKoccRlyJGQokJipQJJUgRSpOjmR+cRBIACKu4rh6+4EX3dH+Hg4wsdN/bOvhyN83Z1QoojDR1UR0yS5KOxDKamSlZ/enzzMKidkAQyMT7/KIAArLBdaK8HAsGflfGSkpcDTuzjGzFhs6SIVKnT+EmKDTIiL9N3OXx9tbBIALg5CfOJgj0+KGd6EMYYUmRIxqTJEpckQmyZHXLoccRkKJL6vjKXIlLlOXf8qSYZXSTKjz9sJBSjt5oAyHk4o4+EEX3cnlPFw5H8v4+EEbxcxpa63MVTJIiQfRUe8QFpSAjJSEi1dFEIIIQQAxSZjBAIB3B3t4e5oj8rerga3UXEMSZkKxKTJEJ0qQ0y6HHHpCiRkyJHwPv18ikyVq/dVcQwRSVJEJEkBGP5MxHZC+Lg78pUvXw8nlMlSGfN0ti/0vUO2hCpZhBBCCCGEmMBOKEAxFzGKuYhRrYR+CnyVUoXXEW+QLFNB4OqJ2Ewl4tJkiM14XxHLUCBJqk5jnxtyFYeXCRl4mZBhdBtnezv4aipd7k7//5n/5wg3R/tc/80kb6iSRQghhBBCiJnYCQXwdBLBp2QR2BlJAy9XcYhLl6t7w9LkiE2TIe59RSwxU4kkqQKZuVxDLEOhwpPYdDyJTTe6jZujCL7uxnvDyng4wllM1QNzoKNICCGEEEJIARLbCVHazRGl3YyvDyVVqBCXLkdUqux9wg4F4jPkiH8/Pyw5UwlZLlMmpkiVeChNw8PoNKPbeDrbG+kNU//s4+6YpzXEPjZUySKEEEIIIcTKONrbwfd9j5Mx6XKV7vwwvkdMPT8sSarMdcbEhPevD3mbYnSbEkUcsvSI6VbGSrs5QmT3cSetoUoWIYQQQgghNshFbIdPPJ3xiaezwecZY0iVqRCTJkV0qvx9og454tPViToSM5VIkSqgymXGxOhUdcXuvzfJBp8XCoBSbpr5YY7qipiH7tDEEq4OhTpjIlWyCCGEEEIIKYQEAgHcHEVwc3RFJS/D23BMkzFRjug0GWJT1b1h8VqJOlKkylyt+soxIDJZishkKa4Z2cbeTgAft/9Xunzc9YcmermIbTZjos1UshgYMmUKSxejUOFU/190TypXQJgfS6F/xDjV/zMHMQY6f82Mzt/8J5MrbShKWAaDOpsYMR+V1rVT+2diHlmPKZ2/5mWr56+72A7unk6o7Gl4aKKKY0jIUPeEqYclKhCnNT8sSapEWi4zJipUDOGJmQhPzDS6jaNInbre9/2/0kXs0bUEByeR9Q9FFDDGzLm0tdndvn0bjDFwjCFTadVFJUSPNDUJjOMgEArhWMTD0sUhJNecRAIIBQIIBALUrVvX0sWxGtqxKZf3FYRYXHpKIh+bXNyKWro4pJBgwPvrIsBB3ZvFMQbG1M9x7///UF6OQtgJrT8uWX0bpaYOKBQI4GJvm92F5OPl4ulp6SIQYhZW3h5X4LRjk6PVR1JCdDlSbCL5puDu1a09Lll9aBAKheA4DgKBACKR1ReXEEIKFaVSCcYYhELrH5pRkCg2EUKIZdhKXLL64YKEEEIIIYQQYkusuwpICCGEEEIIITaGKlmEEEIIIYQQYkZUySKEEEIIIYQQM6JKFiGEEEIIIYSYEVWyCCGEEEIIIcSMqJJFCCGEEEIIIWZElSxCCCGEEEIIMSOqZBFCCCGEEEKIGVElixBCCCGEEELMiCpZhBBCCCGEEGJGIksXIL8xxvh/hJCPk0Ag4P8RYg0oNhFCKDYVboW2kiWXyyGVSiGXyymIEUIgEAggFovh6OgIsVhs6eKQjxTFJkKINopNhZfVV7Lu3LkDjuMgEAggEplWXE3roEgkgkgkglBIoyIJ+dhxHAelUgmlUkkth7mgVCrBGINQKISfn5+li2M1KDYRQsyBYlPu2UpcsvpKFsdxANTBSaFQmPw6JycnPpDRSUvIx01zcysUCiEUCpGZmWnpItkczbWYqFFsIoR8KIpNH8ba45LVV7IEAgE/pMLe3j7H7TmOg0gkglgshoODAwWwbDDG+BNUKBTSsTIzxhhSU1P5C2iRIkUsXaRCJS/nr729PWQyGd9ySD0J2WOMQalUAgBdH7Kg2JR/KDblL4pN+YtiU/6ypbhk9ZUskUgEhUIBkUiEWrVqZbstYwzx8fFgjKFIkSJwdHQsoFLaJqVSicjISABAqVKlTB7yQkyjVCrxzz//ID09HW5ubvjll18sXaRCJa/nr1QqRWpqKgQCAYoVK2b1F2lLksvluHfvHgDQ9SELik35h2JT/qLYlL8oNuUvW4pLhaqqrJ2pyZSWRULIx0dzbaDMbqSgUGwihOSEYlPhU+gqWYQQYiq6ZpCCQOcZISQ36JpROBSqShYhhBBCCCGEWJp1D2YkxMa5uLgAAE0sJoQQYjUoNhGS/6iSRUg+6tOnDwDAx8fHwiUhhBBC1Cg2EZL/aLggMSsaR0wIIcSaUFwihFhCvvdk/ffff1izZg0SExORkpICkUiEbt26YejQoRCLxfn99qQA3Lp1Cy9fvsSDBw9w4cIFbNq0CWXKlLF0sQghZhQfH4+ePXsiMTER/v7+GDNmDD755BO97aRSKVavXo2LFy/yqctr1qyJ0aNHo2bNmhYouT5zxCWVSpXPpSQfguISIYWftcelfK1knThxAr/++ivWrFmDOnXqAACOHTuGH3/8EdeuXcPatWthZ2eX7T4okFm/a9euITY2Fjt27ABg/YvDWQuO47B7927s378fL1++hFQqRcmSJdG8eXOMGTMGnp6eli4iIbxixYph3759ePv2LUaPHo1evXph586dqFy5Mr+NVCrF4MGDUaFCBWzZsgWOjo5ISkrC+PHj0b9/f6xbtw6NGjWy4F9hnrgUHh6O+Ph4FC1atABKTPKC4lLevXjxAmvXrsW1a9cQFxcHLy8vVK9eHV9//TVq1Khh6eIRwrP2uJRvwwVfv36NKVOmYMyYMXwgA4BOnTqhd+/euHz5MrZu3ZrtPjiOQ2JiYn4VkZjJuHHj8Ouvv1q6GFbp3LlzOHHiBPbv36/33OjRo/Hrr7/i/v37cHV1hVgsRnh4ODZt2oRu3brhzZs3uX6/qKgocxSbEKNKly6N2bNnIz09HZMnT9Z5LjAwEK9evcKsWbP4BXc9PDzw+++/gzGGH3/8EXK53BLFBmC+uPT111/TeldWjuJS9ozFplu3bqFnz57Yt28fFAoFXF1d8fbtWwQHB6NPnz4IDg7O9Xu9e/fOXMUmxCBrjUv5VskKCgqCVCpF586d9Z7r1q0bABi88dR25swZKJXKfCkfMa+cWn4/VuHh4Xj+/DkeP36s83hGRgYuXryIH374ATdv3sSZM2dw48YNrFixAs7OzoiNjcWcOXNMfp8LFy4gICAAixYtMvefQIgePz8/lClTBg8fPkRYWBgAIC4uDvv27UO7du30htyVKlUKDRo0QFxcHC5evGiJIgMwX1x6/vw5nJyc8qWMxHwoLhlnLDbt2rULderUwcGDB3Hp0iVcvXoVBw4cQMWKFaFSqfDHH3+Y/B6hoaEYNGgQfvjhB3MXnxA91hiX8q2Sdfr0aXh4eKBEiRJ6z3366aews7PD48ePs609njp1Kr+KR4hFJSQkoFatWhg9ejR/syYQCNC2bVtMmjQJAHD+/HmTW1du3LiBhw8f0hBDUmBq164NALh37x4A4OzZs1AoFJBIJAa31/QchYaGFkj5DDFnXBKJKDkvKXwePnyIv/76C1WrVuUfq1atGn7//XcAQExMjMn7evDgAW7evElxiRQYa4tL+VLJSk1NRVRUFLy8vAw+7+joiGLFikGlUiEiIsLofp48eZIfxSPE4mJiYtCzZ0+Dz3Xo0AGAelhSWlqaSfvT3PBpusIJyW++vr4AgFevXgH4//Xa2HVfkyr69evXBVA6feaOS0IhJeclhYtcLkdgYCCcnZ31nitXrhwAoF69eibvj+ISKWjWFpfypSlO09Lh6upqdBsvLy/ExMQgPT3d6DaxsbFmLxsx3cOHDxEUFITHjx9DIBAgJiYGvr6+6NatG/r27ZttS25KSgpWrlyJkydPIiEhAT4+PggICMDw4cN1XsdxHA4cOID9+/dDJpMhMzMTYrEYDRo0wLBhw/RanENDQ7Fp0yZER0cjJiYGHMehZcuWGDt2LN9alpiYiGfPnuHevXs4e/YsRo4ciapVq2LWrFm4evUq2rdvj44dO2LWrFn8zZSTkxMqVKiAwMBAVK1aFSqVCn369MHr16+RmpoKDw8PfPfddxg4cGCuymJM3bp1Ubdu3Wy3KVKkiMktgJobvuyGx+T283zz5g1Wr16NBw8eQCAQQC6Xw8fHB127dtUZbpWeno4NGzbg/PnzAIDMzEwULVoUTZs21cvWxhjDyZMnsX//fqSkpODdu3dwd3dH9+7d8eWXX+rMc0lISMCaNWtw48YNCIVCSKVSFC9eHK1bt8YXX3xh0nEh+adYsWIAwDcEaK7Xxq77WbcvaOaOS5RIwTIoLuVfXBKLxahVq5bB586fPw83Nzf89NNP2X9AWkyJS69evcLGjRsREhICOzs7REVFwdvbGx06dMCgQYP0KnymxgW5XI5t27bhxIkT4DgOmZmZcHFxQaNGjTBs2DC4ubnp7PfKlSvYtm0bEhISEBUVBQcHB3To0AGjRo3SKUNu4h0peNYWl/KlkpWSkqLeeTYXOwcHBwDIdliGZj+k4P3zzz9YsWIFZsyYgfnz5wNQfx7z5s3DzJkzcfjwYaxdu9bgiZuRkYHvv/8ez58/h7u7OxQKBV68eIFFixYhJCQEK1eu5LedMWMGgoODERQUhEqVKgEAwsLCMG7cOEgkEr63Ry6XY8aMGQgLC8OyZcv41oqLFy9i7NixOHnyJPbs2YPixYsjODgYd+/exdGjR5GZmYkhQ4Zg9OjRePr0KVQqFVQqFZo1a4YTJ06ge/fuePbsGQICAvDbb7/x5bKzs8O+ffuwdetWLFy4EAcPHkTJkiVzVZa8DpF49OgRAKBr1645bvv06VPs3bsXx48fBwAcOHAAly5dAqCuyGkCYm4/z+fPn6N///7o0aMHdu3aBZFIBJlMhr/++guLFi3iK1lSqRQDBw6Eq6sr1q5dCw8PDzDGcOLECUyZMgWdOnXij09UVBS+++47lC9fHkuWLIGzszM4jsOqVauwYMECXLp0ic/sFhcXhz59+qBWrVrYunUrnJycoFKpsGPHDsyaNYsqWVZAcw1XKBQA1D1FgPHrftbtC5q54xJVsgoexaUPj0vFixfP1TFXqVS4ePEitmzZgs2bN/PHIztRUVHYtm0bTp8+DQC4fPky+vbtCwAoU6YMP3f4yJEjmD59Or799lv8+uuvEAgEkEqlWLVqFRYtWoT9+/dj48aNfKXW1LjAGMPYsWPx9u1bbNiwgT9G165dw3fffYeGDRvis88+A6A+fyZPngyFQoE//viDj9t79+7FtGnTEBwcjB07dsDFxSVX8Y5YhrXFpXwZ76BJVpFd+nVNC4ehbums+yEF68SJE1iwYAG++uorfjI4ALi5uWHevHnw8/PD7du3jWZu+u233zB06FD8999/OHfuHM6ePYtmzZoBAIKDg3HmzBkAQGRkJHbu3InOnTvrXLirVq2KIUOG6Oxz8eLF2Lt3L2bNmqVzEWvatCl69eqF6Oho/P333wDUK9nPnTuXTzX7559/YtKkSbh//z4CAwP5ACwSiTB8+HAA6kxLHMfp/S3nz5/H4MGD+Yt0bsuSF9u2bYObmxvGjBmT47aVK1fGTz/9xKcfHTduHHbt2oVdu3bxFay8fJ7r1q1DSkoKxo8fz1+cHBwc8P3338PDw4Pf7sSJEwgLC8OwYcP4xwUCATp27IjmzZvz2zHG8PXXXyMiIgIzZ87kv/dCoRDffPMNKlasiMuXL/NBeceOHXj79i3GjBnDz1mzs7PDF198QSmErYTmGq75fDRByth1X9OabamEEeaOS7TAbcGiuPR/BRGXDhw4gBYtWqBly5YYM2YMnj9/joULF+Lff//N8bUlS5bExIkT0alTJwBAv379+LikqWDduXMHU6ZMQdeuXTFs2DC+0cLR0RETJkxAly5d8OLFC3z33Xf8fk2NC7du3cKlS5fQr18/nWPUsGFDBAQE6JT1559/xsWLFzF//nydhtFevXqhadOmePLkCb8MgKnxjliOtcWlfKlkabpLk5KSjG4jk8kAqIdE5bQfUrAWL14MAGjTpo3B50eOHAkAOHr0KCIjI/We//LLL9GlSxf+ZC9ZsiSWLVuG0qVLAwCfAlbTwnDnzh3+fNDo1KkTf9HKyMjAjh07IBaLUb16db33q1KlCgD1AqPaNO/fsmVLNGnSBEKhEL169dIJwl27doWXlxfevn2LCxcu6Lz+3bt3CAkJ4QPeh5TFVMHBwTh37hwWLlxocHJ+XuTl89R8NteuXdPZViAQYN68efzvmlb9rNsBwA8//MC3ml6/fh0PHjxAlSpVDI7P10xK1Rw3Y+8PQOf9ieVoens013DN9To5Odng9qZc8/OTueMSVbIKFsUltYKKS9WqVcO+fftw4cIF3LlzB8OGDeN7xXbt2mXyfoxZtmwZlEql0c9z1KhRANSfw82bNwGYHhc0cenmzZt6ldTBgwfzxygiIgKnT5+Gj48PvL299faZ9biZGu+I5VhbXMqX4YKa1oDshvulpaXB3t4epUqVMrqNZqwkKTgRERH8hEHtFiBtjRs3hkAgAGMMISEh/MRBjYYNG+q9xsnJCR07dsT69ev5uRGVK1dG5cqVce/ePbRq1QqNGzdG3bp14e/vj4oVK/KvDQ0NRWZmJuzs7NC7d2+9bt/MzEyULVsWFSpUMFje7Ho+xGIxBgwYgOXLl2Pbtm1o0aIF/9yuXbswYMAAuLu7m60s2QkLC8PUqVMxd+5cs7WK5fXz7NSpE06fPo1vv/0Wfn5+8Pf3R926dVG/fn0+8ABAixYtsGTJEmzevBmXL19Go0aNUK9ePTRo0ADly5fnt7tx4wYAdcaf3r176yUNSE5ORoUKFfiKZfv27REUFIT58+fj8OHD8Pf3R/369fXen1iOZgy7ZkK85nptLJhpti9btmwBlE6fOePS27dvDfYwkPxBcakF/1xBxSXt66yTkxO+/vprPH/+HEeOHMHff//ND//LC7lczscEY59n1apVUaxYMcTHx+Pu3bto0KCByXGhQYMG8Pb2RnBwMNq0acPHJX9/f51evhs3boAxhujoaPTq1Utv7lhqairKly/Pv8bUeEcsx9riUr5Usnx8fODk5ISkpCTIZDJ+zKO2qKgoSCSSbMfH5+VGlXyYuLg4/mfNRNGsnJ2d4ebmhuTk5FyNYy1TpgwAoGjRogDU3bTr1q3DsmXLcO7cORw6dAiHDh0CoD6HJkyYgK5du/JlcnBwwL59+3L9N+XUDTxw4ECsWbMGFy9eREREBMqUKQOFQoFjx45h586dOtvmpiy5Ge4aERGBr776Cj///DN69Ohh8utyktfPs1OnTpDJZNi+fTtCQkJw+/ZtAIC9vT1atmyJ2bNnw8PDA2XLlsX69euxevVq3LhxA1u3buUXc61VqxZmzpyJ6tWr8+WoV68eVq9enWO569ati1WrVmH9+vW4e/cuHjx4gA0bNkAoFKJhw4aYPXu20eBMCoZmgdFq1aoBAH8DGh0dne32hlraC4I545KmkkXzsgoGxSXzxaUP0axZMxw5cgRRUVFgjOX5/E9KSuLjY2ZmptHtSpQogfj4eP7zNCUu+Pr6ws3NDRs3bsSKFStw+fJl7NmzB3v27AEAVKpUCT///DMaN27MH7cyZcpg7969OZbb1HhHLMfa4lK+DBcUCoX47LPPoFKp+Fz12p4/fw6pVIouXbpku5/GjRvnR/FINrRbx16+fGl0O00XbG5q/5qu/qZNm/KPlSxZEoGBgbhy5QpOnz6N33//Ha1atUJkZCR++uknxMbG8t24GRkZ+ZIBxtPTE127dgXHcdi2bRsA4OTJk2jdurXO/CMAuS6LRCJBtWrV+LUbDHnz5g1GjBiBqVOnmrWCBXzY5xkQEIBdu3bh1q1b2Lx5M8aPHw83NzecOnUKy5Yt47erW7cuVq9ejZs3b2L//v2YPn06qlatitDQUH7ldc1xy80aKy1atMDmzZvx33//YefOnZg0aRJ8fHxw5coVzJ492+T9kPzx4MEDSCQSvgVZMy8wJCTE6PYeHh5o0qRJgZVRm7njkqUSeHyMKC6ZNy4BhmNTdvMVAfDvW758+Q9qYDD189Q0hGh/nqbGhUqVKmHp0qW4fv06jh49ijlz5qBBgwZ49uwZvv/+eygUCv645SaTtSnxjliOtcWlfFvoQ5NS9PDhw3rPnT59GsWKFeNvKDmOw6+//oovvvgCz58/57fr0aMHtRQWsPLly/Pdq8ZWwJbJZEhMTISvr2+2lYesnj59ivLly/M3MfHx8di8eTP/fNmyZdG9e3esWrUKnTp1glKpxNu3b1G1alW+G//y5ct5/dOyNXToUADA7t27kZGRgU2bNmHw4MF62+W2LI0bN0arVq3QsWNHg8+/efMGI0eOxLRp0/j1sT5E1nkief08t2zZwrfyOTs7w9/fH+PGjeM/L82aEteuXcPVq1cBqCdsV69eHYMGDcLu3bvh4+PDb6cZGhMWFob4+Pgc/45jx47x61uIxWLUqVMHI0eOxJ49e+Dk5GSxtZaI2tu3b/Hw4UOMGDGCf6xmzZqoXbs2bty4oddqmJ6ejitXrmDw4MEWnWtrrrjk7OycbQs8MS+KS+aNS4Dh2DR16tRsr62aClFus7tmjUsODg58TDD2eQLq64yLiws/XNLUuPD06VMcO3YMgLpxpVKlSujTpw+CgoLg5+eH5ORkJCcn82VISkrC/fv3c/w7TI13xDKsMS7lWyWrefPm6NatG3bv3o0jR47wj9+/fx9BQUE6qTIfPnyInTt34tatW3yLDaAeS5l1LQOSv4RCIQYMGAAA2Llzp8FxrKdOnYJKpcLUqVMNLshpqDUsOjoaV65cQWBgIB8M0tLS+GxyWbm4uKBIkSKoXLkySpQogbZt2wIA/vrrr3xpQa5cuTI+//xzpKamYurUqfD19TU4HM2cZdFUsH755RedMffaskslrU3TGJGYmKjzeF4/z7t37xpsYdRkwNIsSBkREWFwMrVYLIZIJOK3a9OmDUqWLAmO47BixYoc/57Hjx/j4cOHBt+fMaazIGZUVBT++usv9O3bF2FhYTnum+SN5kYpPT0dM2bMQLdu3fR6XmfOnAlHR0f88MMPSEhIAKDu8QkMDET16tVNypiZn8wVl6ZNm0bZbwsQxaWCiUsNGzbEwoULDT6nVCqxd+9eNG/eHP369TNpf8biEgAMGjQIgLp3TjPfTtvt27cRHR2N77//no87psaF2NhYg5U3oVAIJycnlC9fHl5eXqhduzZq1qwJADqjM4wxNd4B6orbrl27MGzYMJMyMpK8sfq4xPKRSqVimzZtYl26dGHt2rVjAwcOZN988w27f/++znaZmZmsf//+7LPPPmPXrl3TeS4kJITdunWL3b17N8f3UyqVLCYmhsXExDClUmnWv6UwUigULDw8nIWHhzOFQsE/LpfL2YgRI5hEImF9+/Zl4eHh/HMXLlxgzZs3Z7t27dLZl0wmYxKJhEkkEjZy5Eid17x7946tWrWKRUVF6bzmv//+Y9X+x959x0dVpY8f/0xvyWTSE0LohF4FFBQVsYEVFfuKvf1su9ZdXbu46qr7tYMdC0UFFRQUEARRegk1DQghPZM6vdz7+2OSgZAOGdLO+/XKC71z5+bMncl95jn3nOcMGiSvWLGi1vbVq1fLo0aNkpctWxbcVl5eLl922WVySkqKPGPGjFrHr6yslGfNmiX//vvvtY5z+eWXyykpKbWO05jVq1cHX8PWrVsb3K+5bWno/MqyLB86dEieNGmSPGTIEHnChAl1fsaOHSsPGTJETklJaVbb33zzTTklJUU+7bTT5C+++EL+4Ycf5FWrVsmyfHzv5y233CLffPPNcmVlZa3Xfe+998rXXHON7HQ6ZVmW5ffff18+44wz5AMHDgT383q98rvvvitPmDBB3r9/f3B7amqqPG7cODklJUV+6aWX5LKyslrn49lnn5WLiopkWZblp556Sr744ouD/y/LsuxwOOSnn35aPv/882Wr1Ro8vy+++KKckpIi33jjjc06V+I60Xxut1vevHmzvHnzZnnFihXyNddcI994443y119/LUuSVO9zsrKy5Pvvv18+88wz5auuukq+7rrr5A8++EB2u90nufX1a424JMuyvHHjRhGbQqS+a6eIS60Tl2S54difk5Mjp6SkyM8//7xstVqD2ysqKuRHH31U/ve//x289jfHggUL5JSUFHnEiBHyrFmz5MWLF8tLliwJPv6vf/1LTklJkc8//3x59+7dwe2pqanyeeedJ7/99tu1jtecuCDLsvzjjz/KI0eOrPW3KUmS/M0338ijRo2SN2/eXOs1T5o0SU5JSZH/8Y9/1Po8FBcXy6+99pq8d+9eWZabH++8Xq/80UcfySkpKfJ5551XJ/43RFwnmqcjxSWFLLfvOrSpqal4vV7UanWTQwD8fn8wS42Kimp0lXEh0DNVU+o2KSmp1mRvn8/H3LlzWbRoEdnZ2fTs2ZOIiAgGDhzItddeG6zcUsNqtXLnnXcyfvx41qxZQ25uLjExMQwfPrzB4XIHDhxg9uzZpKWl4ff7MRgMeL1eunfvzh133MHQoUNr7V+zgvvPP//MwYMHiY+Pp0ePHlgsFqZMmRIcU/vwww+TmZkZvKthMpno2bMnEyZM4NFHH23wfMiyzHnnnUdERESTk2Cb05bGzu8ll1wSHPbQlF27dqHRaBrdx2az8fDDD7Nu3To0Gg2nnnoqL7zwQrAsbUvfzzlz5vD777+Tl5eH2WxGlmWUSiWTJ09mxowZwVvra9as4dtvvyUrKwu9Xo9arcbn8zFq1CjuuuuuOmVxS0pK+OSTT1i9ejUlJSX06dOHuLg4EhMTmT59enBdmh9//JGff/6Z7OxswsLCUCqVSJLEhAkTuOOOOwgLCwue3yeffJJNmzaxYMGCZg0TEteJ5vN4PMH5SxqNhuHDh7dxi9oPEZtCp6Frp4hLJx6XGju/AG+//TYrV64kPz+fbt26kZycTGJiIpdcckmd194Ur9fLv//97+DQveHDh/P888/XKmr2ww8/MH/+fNLT00lMTCQ2Npbk5GSuvfbaYPGCGs2JCxCYfzNnzhzS09PRaDRotVq8Xi8pKSncfffddT4nNpuNzz//nOXLl5Obm0tycjJJSUnExMRw+eWXB/++mxvvfD4fjz/+OEuWLOH1119vcp5nDXGdaJ6OFJdEktWFNXahFU6cz+fj1VdfxW63ExER0eJJsZIk4fV6662CJgTOb2pqKjfccANTp04NLnLZFHGdaL6OFMxONhGbQkfEptA60dgkNM7hcHDmmWfSs2dP5s2b12QnaQ1xnWiejhSXQjYnSxCEQE+e1+tt9tyqoymVSpFgNeHbb78lMTGx1kKegiAIQuNOJDYJjfvmm29QKpX885//FMXburgO1T3U1MVAkiT8fj8KhQKfz1enoo1Q29ETgZsq3Sq03LHnVEyUb12ZmZksXbqU119/Hb1e3+zz6/f7kSQJWZbxer3is98IUaa8eURsal0iNoWWiE2hU1ZWxvvvv89jjz1GbGxsiz6/IjY1T0eKSx0myfL5fPWubVKfsLAwnE6n6EFogYKCgrZuQqfm9/uDw1+E1pGXl8e7775LUlJSiz6/sizjdrux2WziPRFOmIhNoSViU2iJ2NS6cnNzeemll4Lzi0Vs6to6TJIlCIJwtJqV3AVBEAShPUhKSmrrJgjtSIdJstRqdZ1KM8eSJIny8nIUCgUWi6XRSYOyLHOwzInD27Fvxxo1KnpFGo6rZ9Tv9wd7WRISEsQky1Z29K1+lUolLr6t7Hg/v36/n/LyciIiIrBYLPWuqSMEeL1esfZYE0Rsqp+ITe2XiE2hJWJTaHWkuNRhkiygyRWZ/X5/8MOsVqsb/GBLksxln27kp71Frd7GtnDRoDh+uGUcSuXxD0FRqVSiglOIifMbOi35/CoUimDw0mg04guccMJEbKqfiE0dgzi/oSNiU9fWJdPkErun0wQxgJ/2FlFiFxWCBEEQOjIRmwRBEDqPLtl9EWPSctGguE4TzC4aFEeMqfGe1PaiuLiYnTt3kpmZyebNm4mPj+eFF15o62YJgiC0ORGb2sbhw4fZu3cvaWlpbNy4kVNPPZX/9//+X1s3SxCEDq5LJllKpYIfbx3HwdLOMe69d7SxzX6/JEnBFdKbIzs7mx07drBy5UoyMjKYNm1aiFvYOgoLC9m7dy/p6els3LiR008/nVtuuaWtmyUIQiciYlPbyMjIYNeuXfzwww/k5+czduzYtm5Ssxw6dIj09HT27dvHhg0buO6665g6dWpbN0sQhGpdMsmCwNjXjhIA2qPS0lIWLlzI119/zYgRI3jzzTeb9bwxY8YwZswYFAoFGRkZIW5l60lLS2Pz5s0sXLgQq9XKiBEjmvW8s88+G5/PR3x8fIhbKAhCZyBiU8ulpqby6aefsm3bNkpKSoiIiGDEiBHMmDGDU089tcnnT5o0iUmTJpGXl8ePP/54ElrcOnbu3MmuXbuYO3cuTqezRZ2WIjYJQuh12SRLaDm3282aNWv46aef2LdvH06nk4KCgmYnHEfraBM6zzzzTM4880zy8/NZsmRJs5/Xq1cvQJR1FQRBCIUff/yRJ554Ar/fT1xcHDExMeTn57Ny5UpWrlzJM888w/XXX9+sY3W0uHTRRRdx0UUXsW3bNrZt29ai54rYJAih1yULXwjHZ9OmTXz88cdMnTqVn3/+mfHjx7d1k046vV7f1k0QBEEQqq1YsYIxY8bw66+/snbtWlavXs2KFSsYPXo0ADNnzqSoqHPMcWuIiEuC0D6JJEtotjPOOIN58+Zx/vnni/UbBEEQhDZXVlbGM888Q8+ePYPbkpOTeeeddzCZTHi9Xn7//fc2bKEgCF2V+KYsCCFUVFREQUEBubm5bd0UQRCETichIYG+ffvW2R4dHc2YMWMAqKioONnNavdEbBKE0BNzsoR2Yc6cOcybN4/c3FzMZjOTJ0/m/vvvJzo6us6+Xq+XRYsW8csvv2C328nLyyMxMZHp06dz5ZVXolAcWfjS6XSyePFi/vjjD+x2O3v27MFsNjNt2jRuv/32BhcJtNlsfPXVV2zfvp3Dhw9TVlbG4MGDKS4ubvA1HD58mFmzZrF7924UCgVutxuHw0FCQgKDBw/mqaeeOvETJQiCIAS99tprDT5WEwtq5h+1hM/n491332XJkiUUFBQQGxvL1KlTufvuuwkLC6uzv9Pp5Ouvv2bNmjXY7XYKCwvp3bs3f/vb3zjvvPNq7VtRUcHChQvZsmULVVVV7Nu3j/j4eK677jquu+66BttktVqZM2cOe/fu5dChQzgcDkaOHMnhw4cbfM6+ffv48MMPOXDgABCIn71798bj8WA2mzGbzSI2CUKIiDtZQpsqKyvjiSeeQKlU8sorr/D222+TlJTE3LlzmT59Ojabrdb+6enpXHrppRw6dIgPPviAefPmsWLFCgYMGMCTTz7Jv/71r1r733XXXeTn5/PWW2/x8ccfs3LlSkaNGsWbb77JE088UW+bvvnmG+677z7OP/983n//fRYvXszXX3+NQqFgz5499T4nKyuLadOmodfrWbBgAd999x3z58/HYrE0+BxBEAQhNGRZZt++fVgsFiZOnNii5+bm5vLoo4+SnJzM//73P9544w30ej0ffvghN998M5Ik1dp/06ZNXHDBBeh0Oj777DO+/fZbfv75Z0wmE/fddx/vvPNOrf2vuOIKwsPDeeedd/j888/55ZdfiI2N5dlnn623Uq/P52P27Nk89dRTXH/99cyePZtly5Yxa9YsCgsLycnJqfd1bNiwgauuuoohQ4bw3XffBSsCG41Gdu/e3aJz0p7JsoxPknB7/Tg8PqpcXsqdXsocHkodHqx2N1a7mzKHh3KHlwqnF5vbh9Prw+uTkCS5rV+C0EmJO1lCm1q/fj3ffPMNKSkpwW1jxoxh8uTJ5Obm8vvvv3PRRRcBgZ7CO+64g4iICB5++OFgL6VWq+Wpp55i6dKlLFy4kOuvv55hw4YBkJiYWGs9K6PRyHPPPcfy5ctZvHgxjz76aK0Sti+99BJz585l6dKltdb+6tGjB++//z633XYbf/75Z53X8dFHH1FZWcn9998fvDum0+kYPnx4o72MgiAIQutbuXIlBQUFPP300+h0uhY9d+3atSxcuDAYG4YMGcLw4cOZPHkyO3fuJDU1lZEjRwKBNRTvuusuxo0bx4033hg8Rnh4OC+88AK//fYb77//Ptdccw2xsbEADBo0qFa5dYvFwsyZMznrrLP4/PPPue+++9BoNMHHH3zwQbZs2cKvv/6K2WwObh80aBBz5szhiiuuIDMzs87rePfddzEYDNx666212vXPf/6Tiy++uEXnJFRkWcbh8WPz+LC5A//aPYFkye7x4/D6cXklnNX/uv0Sbp8fl0/C45Pw+mW8koR8gnmSWqVAq1KiVSnRqZXo1Up0ahUGjRKjVoVRo8KoUROmUxGmC/xr1mmI0KsxalW1RtAIQg2RZAltatSoUbUSLAgkQiNGjGDVqlW1xov//PPPFBQUMHHixDoXNK1WS69evUhNTWXLli3BJOuVV16p8zt1Oh39+vVj+/bt5ObmBgNpdnY2X375JRMmTKh3cWWlUkm3bt3qfR1VVVVAIGk8//zzg9sVCkWwypUgCIIQepWVlcycOZNLLrmEG264ocXPP/vss+usHxUfH0/v3r1JT08nLy8vmGTNnz8fu91e71ImMTExREdHY7Va2bFjB+eeey5AnTtbNcePiYmhuLiY0tLS4O//66+/WLFiBddcc02tBKuGTqcjNja23iSrqqqKqqoqdu3axdChQ4PbTSZTcL5aqMiyjM3to9QZuKtU81Ph8lHh9FLl9lHh8lHl9uFvB3eSfH4Zn9+Pg5YvAq5WKYjQa7AY1EQZtEToVSg9biw6JSqzm7hwJVq1GDjWFYkkS2hTDfUw1gQTt9sd3LZx40Yg0EOZlpZWK9GSZZmysjL69+9PZGRkneMdOHCAbdu2kZ6eTkZGBllZWUBgfHqNFStWIEkS/fr1a/HrmDp1KsuXL+eBBx5g1KhRjBs3jpEjR+L1eusNjIIgCELr83q9PPjgg/Tr14+XX375uI7RVFxyuVzBbTVxae7cuaxatarW/rIso9frGTBgACaTqc5jaWlp7Nixg4yMDDIzM6msrAy+hhrLly8HOK64NGXKFPbs2cO1117L2LFjOeWUUxgzZgzDhg3DYrFgt9tbfMwakiRT6fJR4vBQYg/8WKuH55XavZQ6Pfj8bZ88nQw+v4zV7sFq95CFo/aD6YHk12LQkBCuIy5cR3yYlkSznkSznkiDRtwF68REkiW0S/WViC8pKQECgePpp59u1nGWLl3KG2+8QVlZGVOnTmXSpEncc889PPjgg/z111+19s3OzgYaX3Pk2LH4NaZOnYrb7Wbu3Lns2LGDrVu3Bl9HfHx8i+cECIIgCC0jSRKPP/44Op2Ot956q9aQu9ZQX1yqKYY0Y8YMbrvttmYd58svv2TWrFlIksQll1zCOeecw0MPPcS0adM4dOhQrX1PJC7dfvvtaLVaFi5cyF9//RUc6q7X60lKSmLQoEGNtlOWZcqdXgptboqq3BTaPBTb3BTbPBTb3XhDlEQpFaCpHrqnVSlQV/+rUdb8f+C/1SoFaqUCtVKJSgkqhQKVUoFSoUCpCIwkOTZ9kQkkiBIykgSSLOOXZLySjE+S8foD87s8fhmvP/Cvxx8YmujxB36O53XX3MnbV1R7nrleo6KbWUd3i4Fks5ZYtRezXnw17yzEOyl0GOHh4QDNXlhy8+bN/P3vfychIYFff/2VqKioRvevCWKFhYUN7uPxeBp8bNq0aUybNg2Hw8GuXbtYv349H3/8Mfn5+ezYsaNZbRYEQRBaTpIknnzySSRJ4u233271BKshNXe3mhuXfvzxR1544QWGDBnCl19+idFobHT/E4lLSqWSm2++mZtvvpnKykpSU1PZsGEDX375JVlZWej1eiIjI/H6JYqq3ORXucmvdFFoc1NQFUis3L76E7iWUisV6NVK9BoVBk1grpNJo8RQPd/JoKme96RVoVW176F1kgxOr//IXLLq+WNH/zg8Pppz6lxeP/utDvZbHagVMv3CZNYdLOWQU82wpAjGdrdwWs9IhiaEo27n50WoSyRZQrsk1zOLdfDgwSxdupSNGzfi8/kaLL9eY8GCBciyzPTp0xtMsGRZxu3z4/ZJdOvZB4A1a/9gb14ZfoUKr18K9nD5/BJbd+0FIKPYxuLdBfhlGUmGdT8vZMhpZ2GKiESWQVZ3I2rCNCbuP8jypT9RXlnJV1sPo6S6d01Brd42lQJUCiUqVaA3Tn1UL52mutdOUz0xV1M9MVerUqBVK9GrVaiVCjHkQBCELqkmwZJlmddffx2VShWS39NQXEpNTa23IFJ95s+fD8BNN93UZIIFkJKSwooVK/j999+5//776zzu8/nYv39/vc996623uP/++1EoFJjNZsZPmECfYWMw9xjIf5/6B3a7nSq3jwe+33VCFfYUEEiStCpMWhVhWhXhRxWHCNer0XeiOUlKBZiqX2ss2jqPS5JEeVk5HkkGrZEKj58yp5cKp49Kd2AeWmN3w2QZdhVUsTm3kk83BipHmrQqxiVbOL13FGf2iWZ8z0hMOvEVvr0T75Bw3Pz+lk8QbS6n01ln27Rp0/jggw+oqKjgs88+4/bbb0eWZZxePza3H/tRvUkOr5/0w4GexbRKmVl/HsTp8+P0SlRUlLF9X2Cc9Jtr9mPJMQDg9fdArTdhLSnmwedepfe519b6/cW7N5B3MDCXa1+RDfeeIz2Le7Zs5bAiCkvvwbWeo1YG/sQsUbGsybK20tmpS1ndS6hTKzGoVeg1yurewkCPYU0voUmrwqhVV/8bCIZhWjVatVIkaYIgdDg1CZZSqeTFF1+s9zomSRKyLJ9w8uVwOOpsu+666/j2229JT09n6dKlTJkypdFj1CyMfOzc4UOHDmG11o0Rl112GR9++CE7d+7km2++Yfr06bUe/+KLL+pdbLnS5WXlmj+IGz0JpzaCwxUuCipd+CSZiuzAkLWoqCj8Es1KsJRKBSatinCtCrNOjVmvJsKgIdKgxqzToFKK+FGLArQqBZZwHQn1DDO1eXyU2r0U26vnsTm9ONw+AgMa67J7/KzKsrIqywpkoFYqGJNsYVK/aCb3i2VCr0j0mtB0LgjHTyRZwnGrGYteWlra4ufWJFH1BS2/JFFeGajWl11Uxtr9VqrcPipdPs6/8wkWv/si/339dZbsyiPu1AtRqAOTlG0Fh8j962f6X3IbSrUGd0xf4C82/P4bzl7jUarV2AoPUbhtDXL1EnHyUWPZNQYTA6bdxZ4F/0f26oW4yktIPOUcVDo9pRk7kHxeYgaPo2TPRiSft1abvbZKsld/R1hiL9T6QO+k12lj37YtREVF0TdlwLHTYVuVJAXK4Do8fsrwNv2EY2hUCsK0asL0asK1asJ1KsL1mmAwNevVROgD5WpNWjVKEVAFQWhjNQnWwoULiYqK4owzzqjzuMPhwOVy8fLLL3PFFVc0eryauFRfJx8QLBRxdNwaOHAgzzzzDM899xxPPPEEVVVVXH755Wi1gTscW7duZfHixTzzzDMAjB07loyMDJYsWcLEiRNRKpVs2bKFVatWBed8HT3HqlevXjz22GPMnDmTp59+mvT0dKZOnYpKpWLlypVYLJEMGjKUvbt3sWF/EYfX7ien3Emly0d2XhHvvfV/pFx+FypNoD3uylKyln1JQlJ3unfvXucrvV6jwqxTEaFXY9FriDRqiDIE7kYpRUdcqwnTqgnTqukRaQhukyQJe2U5siRThY60Eie5lS7qy4F9ksz67DLWZ5fx8spM9GolE/tEceHAOC4cEMfAuDDRcdoOKOT67n+3I6mpqXi9XtRqdb0lUo/m9/uDX/ijoqJCNmSgs/D5fMES6UlJSU0Ov4PAQr0LFiygsrKSgwcPBrf36tULs9nM3//+dyZMmNDg85cu+4VZs2eTlZmBx+1GoVSS0LMffcZMpO/Zl1NeXdp1ywdPUZmTjlKjxRTfgz7nX09kn0AJWqe1gJw/l1CasQOfy4EpLhmtyYwhJpFuY89Db4kBQPL7OLB8LoU7/kChUBI9cDSxQ8cT2WcoWz54kqrDGVj6DCN+xOkknnJOsI2VORlkr1lEZXYafp8HQ2Qc/ab8jbgBo9g5900KUv9EpdUTP+gUep56LvEpI8j64ycK927FWVaExhAGyKBQkDjoFPqePgWNTodCHQhyMoFhJ7IMMjX/BibgSnJgvLcsyYGhiDSvl/FkUimPlKu16DXVvZkaoowaLNUBOcKgQV1P711rOp7PL4jrREt4PB527twJgEajYfjw4W3covZDxKbQae7f9qeffsp//vOfZh3zhRde4Oqrr673sa+++opFixaxd+9efD4fGo2GgQMHcuONN3L55ZcH9zvjjDMoLi7GbDbTu3dvXn75Zfr27QvAnj17mDNnDhs2bMDlcjFw4EAiIiIYOHAgV199dXDIus1mY+bMmaxatQqz2cxZZ53FZZddxpAhQzj99NMpKSnhggsu4OKLL661HMiaNWv45JNP2blrJz6/n6iEZMZdcw9OS3f+fPufVB3OQGMyE9lvOMmnX0J4t94cWDGPiuw0PLZy1HojsiyjVGuIHzGR3qecSZhORVSYnliziSijlmiTBr1afDZPlCRJlJeXA4E10OormFIfWZbAZcNq92BX6JEVStw+icwSO7sLq9hXZCfTasfhbXqyVw+LgYsGx3PpkHjO7huNrhO9rx0pLokkqws73i+pjXH7/Fjt3kA5V7uHEoeH0mBp10ACFWoKjiwsqAkuMKhAUz2fSXvUooOaWo9V/6sM/BuoWqQ47mEQx3uhrY+/pvJR9dwwr3Sk8pHPX1P1KFAFyeuT8Ehy9UKN1VWSJAmPT8YjSSelrK5CARa9hiiThmijlmiTlmijlpjqf6NMJ56EiSQr9DpSMDvZRGwKndaMTbIs4/V6UalUHeq8S5JMoc1NdpmT7DIH2WUOcspcePwtK0QRXMNJrybaqAlcgw1qXPbAaJETjU1CXa2ZZNU5tixzuNzJ9rwqdhZUkVZsazLpCtOquHBgHNOGJXLRoDjM+pNTFCZUOlJcEsMFhRZzeHwUVXkoPKqUa7E98N+VrtZPopRKBfqaVdg1yiMVitSBuUZ6tTJQnUgb2KbTqOqUbe3oVNXJXmBg5Il9UZBk8PgknL7A8EKX14+jer6ayyvh9Ppx+SRcPj8uX6AwSEu7YmQZypxeypzeuuuGEEjCooxaYsO0xJq0xJp0xIVpiQvTERumEws3CoLQKhQKRXDoXnslyzJlTi8HSx0cKHWQXeoku9yJy9uyec9GjSqwIG51MhUXpsOiV9cZNiZJEq4GjiG0b0qFgh6RRnpEGrl0SDySLHOg1MHmnAp25FeSZXVwbD+qzePn29R8vk3NR6dWcl7/WK4e2Y3LhiQQLsrFh5Q4u0K9fJJEic1DfpWLwioPhVVuCm0uiqo8rXY3SqEAg7qmdGuglKtJo8KkCxRjMGlVhOk6V1Wi9kCpIJCsapREGpru0ZIBt1eqLlXrx+b24fAECo04vBJ2jx+nz4+rGUMYgseUCS7euK+exyON1Qs3humID9eRUP0TadCK+WCCIHRoDo+P7DInB0odwcSqpR2U4ToVFoOGmOrOqvhwHUZR+KDLUSoU9I020TfaxDUju+H0+tmeV8nGQ+XsyK+k0l07UXf7JJbsLWTJ3kL0aiUXD47n2lFJXDQorlMNKWwvRJLVxfklGatLIv9wBYU2DwWV7kBiZfOc8FwgtVJRq6RrmFZN+FGFFExaVaefSHtwbyp+r4cwcwSDRp/a1s05LgpqkjItMaaG95NksLsDJWorXD6q3F6q3DXrhviwe6Vmf6bKHF7KHF72FtZeuFGrUpJg1pEYriMhXIvG4yXWoKRb+x71LAhCFyVJMnmVLg6UOthf6uCA1U5BlbtFowPCtCqijFpiTBriwnQkhGnRnWBC1Rlik1CXQaNifM9IxveMRJJlMkvs/JldxqacwHe8o7l8UvAOV5RRwzUjk7jplO6M62ERRTNaiUiyughJkil1eDhc4SK30kVuuZO8SheFVe7qyjVVx3Vcg6ZmTQwV4Xo1EToNEQY1kXoNBq3oFcnel4rbYcdgCu/0gUypgHC9mnC9mqSIuo/LgMPjp9zppcLlo8LlpdLlo8rjw+b2N2vRS49f4lCZk0Nltat/aXfvJdGsp1uEnqQIPd0jDCRF6Dv82HNBEDqWKpc3kEyVOjhgdXCgzIG7BXf5DRpV9XA/DfFhgTv4hhDcoepKsamrUioUpMSGkRIbxs1jkjlU5mDtgTLWHyonv8pda99Sh5f3/zzI+38eZFBcGLed2oO/ndKd2DBdG7W+cxBJVifk9UnkVrjIqXByuNxJToWT3Ap3i8d319CqFIEvzzo1Fr0ai0FDlEGLxaBGI1YgF5pJwZEFHOtLwjx+iTKnl/LquVzlzkDZ/iq3D18Td8A8frl6gnjt5MusV9M9wkB3SyDx6hFpIMYgLnuCIJw4vySRV+Eiy+pgv9XO/lIHxcfcLWiMRqUg0qAhtnr+VDezjjCxwKwQIj0ijdwQaeSG0UlklzlYlVXKnwfLKHXWXvZlb5GNRxbv4Z8/7+XyIQncNb4Xk/pFi7tbx0H8NXdwbq+fnAoX2WUODpU5ySl3kl/lPq6hfkZN4G6URa8OluSOMWrFHSnhpNCqlMSH6Yivp+fM7vFjtXsodXooc/godwXuhjV196vS5WOPq4o9hUfu1BrVCk6JUVHq9GK2OBjV3cLg+HDRYSAIQqPsbh/7Sx1kWe3sL3FwsMzRrDvwEJiDHKFTE23SEh+mJcGsJ8aoEV9chTbRM9LIzWOMzDglib1FNlakl7DxcAWuoz7PXr/MN6n5fJOaz4BYE3dP6MWMMclYmjGXWwgQSVYH4vVLHC53crB6uNTBUgf5Va4WV37TqZXVCw2qMSj8WLRKesVHoteKj4PQPgXugBnqLNxYaC2j3CXhUmoprb77Ve70Nnrny+OXKLb7+eNAKUsPHsbll9GplYzsZmZMsoUx3S2M62FhQGyYKLIhCF1UTQn1rBI7WaWBO1UFle6mn1hNr1YSZdQQF6YlIVxPolmHVnTkCO2MQqFgcHw4g+PDcfsk/souY3l6MWkltasCpxXb+fsPu3lq6T5mjEnmgYm9SYkNa6NWdxziW3U7JcsyxTZP9WRZOwdLA0P/mho2dTSFAsJ11XelDDUlXbXB4QhHr+UgSmYLHZFOpSDepMJiCa+1FkmVy0eR3U2x3YPVHhh+aPc0PFzW7ZPYcKicDYfKg9vMenUw4Tq1RySn9YwkPlyMTxeEzsjl9QeLU+wvCQz9czRyzThaYF1ANTGmIwlVfaXTBaE906mVnN03mrP7RlNQ5WbZviJW7y/FdtTfgd3j570/D/Lenwe5aFAc/zirL2f3FUMJGyKSrHbC7fVzsMxBljUwWXZ/qQNbC0qlK5UKLDo1kcbA+O7YMC1xJi1q0XMmdEE1BTj6Rh8ph+jxSxTbPJTYXGi8diL0Khq7UVXp8vFbZgm/ZZYEt/WKNDC+VxTje0YyoVckwxPN4m9MEDoYWZax2j1kWauH/lkdHK5wNntUiE6tJNqoITZMS7fqpEoMNxY6k4RwHTePTeaG0Un8ebCMn9OKyLLWnvP8094iftpbxJjuETw6qR9XDEtEJUZ/1CKSrDZS7vQGhiFY7WSW2MmpcDV7HpVSqSBCF1hwMM4UWB8jxqQVH25BaIRWpSQpQk+3cC24lcSF6Th/uI79pU72FdnILLFzoMxJkc1DQ3+JB8ucHCzLZe62XCAwjHFcsoXTe0dxRu8oTusZKSoaCkI74/VJHCp3BhOqLKu92etSKQjc1Y4xaYkP15JkNhBpEHephBBohyuRaFRKzuobzVl9o9lvtfP97kI2HKrAf1SPxObDFVzzxRb6RBv55zn9+dsp3cXoqGqdKslqrxe9mqF/GSU2MkscZJbYKbI1f2x3mFZFdPVQv4QwHfFhWlSi10wQTphWpWRgXBgD446MLXd6/WQU29lbbCOj2M7+UieVDdxVtnv8rMqysirLCgTK2I/sFsEZfaKY2DuKM/tEixK4QruNTZ1VmcNzpOKf1UFOC4baa1QKogyBeJto1tEtXHfCa1IJQkvJtL9rRp9oE/84sw/lTi8/7S3i14ySWsPw91sd3PHNDp77NY3Hz+nHbeN6oO/ifzudLslSKBTIsozX60Wlaps3V5Zlimxu0ortZBTbSC+2U35MicyGqFUKoqpLuiaG6+gWoQ/JGhmC0GVJfnyS3GCnoUGjYng3M8O7mYPbrHYPuwur2FtkI6PEweEKV71f2iQZtuZWsDW3grfWHgBgUFwYZ1aPcz+7b4yY19UFtZfY1BnV3KXaXxpIqPZbHc2OtxDoxIwxaYkP09EtIjAqRCmSYqEtNBGb2guLQcMNo5O4angiKzKK+XFPEVbHkb+5wxUu7l+0i5dXZvKvyf257dRkdOquec3rdEmWVqvF7XbjcrnQ6XQnrQexxO4mrcgW+GlBUmXUqoitLunazawnNkxc4DsTc2QMHmMYprDwtm6KQKADBL8Hj0/CjyowY70Zok1azuwTzZl9ooFApc9Mq4Od+ZXsK7aTZXU0WFhjb5GNvUU2Zv2VDQSSrrP7xXBOv0DSFW3Sts6LE9qttoxNnUnNXKr9pYFk6kBp4C6Vv5l3qVTK6nWpwrQkhgU6MU1ddIkSEZval+ONTW1Jp1Zy0aB4LhwQxx8HSvl2Z0GtRY7zKl3ct2gn//ktg6fOS+GWscldbu5ip0qyAPR6PW63G6/XS0VFBXq9Ho2m9edI2Nw+0kts7CsK3K2y2msvQKhu4O8jXK8mtroCUTeztp75G3Lgj+1kkCWCM31lqcWl4IUmyBKjzroAAIslAllu3noqQjM19/Nbs13yg9+Dz+vD6fPjVRz/HSWNSsmguDAGVQ8zlGWZ3AoXOwuq2F1gI73EXmeBxxo1Sdf7fx5EoYCR3cyc0y+Wc1NimNg7CqNYSqFTOlmxqTPw+/3IsozLJ7OnoJLDlW6yS50cLHdgO2YulYKG461BqyLGECgElRAeWPD32GUZuuR1WcSm0GrD2NQWVEoFZ/WNZmKfKDYeKmf+jnxyKlzBxw9XuLj721T+uzqLFy8cyFXDE7vM8igK+aR9oz8+qampeL1e1Go1I0aMaNZznE4nNputVdvhl2SKbW7yqtwUVLoodTR9p0oBGDRKwvUaLHo1FoMGXTuaDCjLMg5HYC0Eo9EoelZbmTi/odXS8+uTZDw+CafPjxsNXkVov+Ba7R6251Wys6CKtGIbxfamrxlalZLTe0UyOSWW81NiGZ0U0abByOPxsHPnTgA0Gg3Dhw9vs7a0N+0lNnUWfkmmzOnFavdQYvdQXOXC5m3+l3+lQoFRqyJMqyJCryZCr0GvaT/xtj0RsSm02ntsCjVZltl4qJyvt+eRW8/acqOTInjl4kFM7h97XMfvSHGpUyZZEHgTXC4XHo/nuO8MFVS62FVYxZ6CKtKL7c2aOBuhV1eP7dbTI0KPoR0PRZAkifKycgAskZZa6wwJJ06c39Bq6fmVAT8qvAo1fsXJ/7ssdXjYnlvJjuohhtZmdNREGzWcW51wXTgwjkSz/iS09IiOFMxOtraMTR2dX5LJr3IF7k6VOckudZBb6cTfghsqJq2K6OrFfhPD9cSH60SF3WYSsSm0OlpsChVJllmfXcbX2/MoqPLUefyiQXG8dslgBsa1bMhqR4pLnXZcilarRavVIsty8KcpNreP1Vkl/JpezIr0EnLKXU0+J8qoJiXGxJD4MEZ1iyDSeKQHQgLsJ/IiQsyPRJEzENXUkXpUCnGhbU3i/IZWS85vsFJTG/bYRhm1nNM/hnP6xwBQbHOzJbeCHXlV7Cu2UeWuO6fL6vAyf3se87fnATCim5kLBsQxZWAsE3pFdbnx7Z3B8cSmjsznl9hbZGN7XiXbcivYnlvBzvwqXL7mZ1QGtZLuETp6RRlJiTEyKC68VqwFaDpaCzVEbAqtjhabQkWpUDChVxSn9ohkVZaV+TvyKHMeGe77094ilu0r5s7xPXnughRiTB1rmGRzdNokq0ZNVaf6yLJMerGdn/YWsnRfEWv2W/H6Gw94Ro2SlBgTwxLCOaV7BN0thtrHbLWWh56MTM3NORkFsrjQtioZmW/f/Q8OWyVRsXHc/ewbbd2kTqWjf35jw3RcOCCOCwfEIcsyh8qdbD5cwY78KjJK7PVei3bkVbIjr5JXV2Vi1qk5LyWWqYPimDooXlQt7GAai00dld3tY2dBFdtyK44kVAVVuFuQUKmUCrqFa4nTQc9wNaf270bvaFOduwEdKda2NyI2hVZHj02tTaVUcG7/GM7sE8VPe4tYuKsAZ/VQYL8s8/6fB5m7LZcXLhzAXaf1RN2JOg87fZJ1LI9PYs1+K0v2FvLTnkKyrI5G91cqoJfFwJCEME5JsjAw3oRa3FoXmqkwZz+28lIclWVt3RShHVMoFPSMNNIz0siVwxLx+iX2FNrYfLiC1PzKese1V7p9fLczn+925gMwNtnC1EFxXDI4nlFJEZ3uC7zQfsiyzOEKV3XSH7gztSOvgvQSe4sKKCkUEG/S0sNioG+0kYFxYfSPMaFCJjc3sOB3UqRRDGcLARGbhJNNq1IybWgC5/aPYd72PJZnlAST0XKnl/sX7WLWX9m8dflQzu4X07aNbSVdIskqdXj4eW8Ri3cX8Et6cZMrvVv0agbFmRjVLYIxyRH1VAAUBEEIHY1KyYhuZkZUr9VV5vSyOaecLYcr2F1kC/YCHm1TTjmbcsp57td0kiL0XDQonksGxzO5f0yXXxBSOH5Wu4ddBZXsLrCxq6CSXQVV7MyvpKKJOHoshQLiTFqSI/T0jjIyINbEgNiweuct+331L4cgCELHF65Tc8epPbh4UByfbDrMtrzK4GO7Cqo454O/uGF0Ev+9ZEiHH6HRaZOsg6UOfthdwA+7Clh7oLTRdTSUCugVaWBEoplxyRH0jRY9Z4IgtB+RBg3npcRyXkosfkkmo9jO+kNlbM8LlLc+Vm6Fi9nrs5m9PhuTVsWFA+K4dGg8Fw2KJ8oo1uUSapNlmbxKF/uKbOwtDCwxsLd68e3Cqrqfr6aolIrqO1R6ekUZ6R9jIiXG1K4LQQmCcHIlmvU8Obkf23Mr+GhjDgW2I8Uxvtqay+I9hcycMoi7xvfssEVtOk2SJcsyuwqqWLSzgO935bP9qMy4PiaNikFxJk7pHsG4HhYixN0qQRA6AJVSwcD4MAbGB9bosjo8bDgUuIu1r7juXC67xx8cVqhSKji7bzSXDUng8qEJdeaUCp2XLMuUOrxkltjJtNpJLw6s8ZhRYiet2IatnsIrzWHSqEg060iO0NMrykD/aBO9oo1oO9G8CkEQQmdkUgT/u8zMT3sKWZBagLu6zGily8d9i3YyZ3MOH149gmGJ5jZuact16CRLlmU25ZTzXWo+i3YVkFnSeC2/OJOW4YnhjEu2MLxbuJhbJQhChxdt1DJ1YBxTB8bh9kmk5lWy/lA52+sZ0uWXZFZmlLAyo4QHvt/FqT0sTBuWyBXDEukXY2qjVyC0FofHR3aZk+wyJwdLHewvdQT+tTrIstpbPMTvaGqlgjiTlm5mHT0sBnpFGegXbSKugw/nEQSh7amVCi4bmsAZfaL4eGMOG3Mqgo9tzCnnlDfX8Nikfjx1bn860jf3DpdkSZLMX9llfJuax8Kd+Y2WWVcAPSMNjO5mZkIvC72ixJcIQRA6L51aydgeFsb2sCDLMllWB+sOlrH5cAX59Qz72nConA2Hynnip72MSDRz5YhErhqe2OJ1S4TQkqoX6i2ocpNX6SKvwkVepYvDFS4Olzs5XOEkp9xFib3uWjQtpVEqiDVpSTTrSDLrSbbo6RNlpLtFj0p0TAqCEELRRi2Pnd2X7bkVzNqQQ3H1Nc0nycxcmcE3O/KYdeUQOkqE6jBJlscv8+D3u/guNZ+8yoYTK7VSQUqMkVOSIji9d2SnrLsvCILQFIVCQb8YE/1iTMwY052CKjfrDpay4VA5B0qddUpg78gPLJT89LI0hiaEM31ENy4f1DkqPLUXXr+E3eOnyu2jwumlwuWjwuWl3Oml1OGlzOnF6vBQYvNQYvdQbHdTWOWhyObG18i84uMRaVATY9QSHx5YzLdHpJ6eFiPx4VqRTAmC0KZGJkXwv0vDmb89jyX7ioJVCDNK7EyetZGVVyYQrm3/16kOk2QV2z28/UdOvY9pVQoGxZoY1z2CU3tYCNcdeVmiSlHD/H5/vf8ttI5jz6n4LLYu8fltmViDmssHxXH5oDjKnV7+OlTO+pxA2e1jv7/vKqhiV0Eaz/ySxrJp8UTrRcGChpQ4vJzz3jokWcYryfj8Ml5Jwu2TcPkkXF4Jl8+P3ePH08Q6jK1Jo1QQaVATbdQQY9ISb9KSEK6je4SebmYdOnUD76kk45fa9u9J/G2HlohNoSU+v61DDdwwMpHTe1p4b/0hsqtHrsmAwycT3gFqOCnkdr7c/NatW5FlGb8kU+I6UrZYAaiVoFIoUCtBQcesPCJ0bs6qMmRJQqFUYgiPbOvmCEIdMjI+KTAcwy/XXeQ1Rq9EpQwsnDt69Og2aWN71FBsOhkUNT+KI/8qFYEoqFQEfhTBPQWhLhGbhI5FxiMFRrXJdJy41O7vZNXkgCqlgnij6E0VOhZjVFRbN0EQmtC8L+LtvD/upBOxSejIRGwSOhYFxnq2tve41O6TLKVSiSRJKBQK1Op231xBEIROxefzIcuyWDvwGCI2CYIgtI2OEpfa/XBBQRAEQRAEQRCEjqR9p4CCIAiCIAiCIAgdjEiyBEEQBEEQBEEQWpFIsgRBEARBEARBEFqRSLIEQRAEQRAEQRBakUiyBEEQBEEQBEEQWpFIsgRBEARBEARBEFqRSLIEQRAEQRAEQRBakUiyBEEQBEEQBEEQWpFIsgRBEARBEARBEFqRuq0bEGqyLAd/BEHomhQKRfBHENoDEZsEQRCxqXPrtEmWx+PB5XLh8XhEEBMEAYVCgVarRa/Xo9Vq27o5QhclYpMgCEcTsanzUsid8CrvdDqx2Wxt3QxBENqpsLAwDAZDWzdD6GJEbBIEoTEiNnUu7T7J2rZtG5IkoVAoUKubvvFWM/xCr9ejVqtRq9UolUpxK1YQujBZlpEkCZ/Ph8/nw+VyiSEazeTz+ZBlGaVSyahRo9q6Oe2GiE2CIJwoEZuOT0eJS+1+uKAkSUDgg+j1epv1HI1Gg1KpRK1Wo1AoxLh3QRCCX4YlSUKpVDb7eiIE1FyLhQARmwRBaA0iNh2/9h6X2n2SVROIIBCgGlMTsLRaLVqttlm9i11ZTQ8KIHpUQ0CWZaqqqoK9LeHh4W3dpE7lRD6/kiQF70KIz33DZFnG5/MBiPN0DBGbQkfEptASsSm0RGwKrY4Ul9r9lV6tVuP1elGr1QwfPrzRfSVJwmq1AhAVFYVKpToZTeywfD4fubm5ACQmJorA38p8Ph+ffPIJdrsds9nMU0891dZN6lSO9/Pr9/spLS0FIDo6GqVSrGTREI/Hw86dOwHE9eEYIjaFjohNoSViU2iJ2BRaHSkudap3UAy7EAShJcQ1QzgZxOdMEISWENeMzqFTJVmCIAiCIAiCIAhtTSRZgiAIgiAIgiAIrah9D2YUhA7OZDIBiInFgiAIQrshYpMghJ5IsgQhhKZPnw5AUlJSG7dEEARBEAJEbBKE0At5krVlyxZmz55NWVkZlZWVqNVqLr30Um6++Wa0Wm2of71wksmy3O5LagqC0HJWq5UrrriCsrIyxo0bx913303v3r3r7OdyuZg1axZr165FlmWsVivDhg3jzjvvZNiwYW3Q8rpaIy75/f4Qt1JoLSIuCULn1N7jUkjnZC1btox77rmHe+65hwULFrBs2TLuvfde/ve//3H33Xc3K0iJQNb+bd68mW+++YZnn32WyZMnk5OT09ZN6tA8Ho84h0K7Ex0dzcKFC/nqq6/YvXs3V155JRkZGbX2cblc3HTTTeTn5/Pll1/y3Xff8f3331NeXs61117LX3/91UatP6I14tLBgweDJdmF9knEpeMjSRI2mw2Xy4XX6xVV7oR2rb3HpZAlWYcOHeLxxx/n7rvvZuTIkcHtU6dO5aqrrmLdunV89dVXjR5DkiTKyspC1UShlaxfv55du3Yxd+5ccnNzRY/hCVi8eDETJ07k3XffbeumCEK9unXrxgsvvIDdbuexxx6r9djMmTPJzs7m+eefR6/XA2CxWHj11VeRZZlHHnkEj8fTFs0GWi8u3XvvvU0uQCy0LRGXjs/mzZs55ZRTGDFiBEOHDmXgwIEMGDCg1s+qVavaupmCUEt7jUshS7LmzJmDy+XioosuqvPYpZdeCsCiRYsaPcbKlSuDqzoL7dd9993H008/3dbNaJdWr17NsmXLmvysA+Tn5/PMM89QXl5+3L+voKDguJ8rCM01atQokpOT2bNnD/v27QOgpKSEhQsXcv7559cZcpeYmMjYsWMpKSlh7dq1bdFkoPXiUlZWFgaDISRtFFqHiEuNayg21QytjI+Pb/Cn5otqc+Xn57dm0wWhXu0xLoUsyVq+fDkWi4X4+Pg6jw0dOhSVSkVaWlqj2eOvv/4aquYJrUylUrV1E9qlgwcPkpWVRVpaWpP7Pvvss8c9NnjNmjVMmzaN119//bieLwgtNWLECAB27twJwKpVq/B6vaSkpNS7f82do9TU1JPSvvq0ZlxSq0XdqPZOxKWGNRabYmJiWLNmTYM/48ePb9bvSE1N5cYbb+Thhx9u7eYLQr3aW1wKSZJVVVVFQUEBMTEx9T6u1+uJjo7G7/c3Ok46PT09FM0ThHbn+++/p0+fPowZM+a4nr9x40b27NlDVFRUK7dMEOrXvXt3ALKzs4Ej1+uGrvs1VcwOHTp0ElpXV2vHJaVSLDMpdE6RkZGtcpzdu3ezadMmEZeEk6a9xaWQRImioiIAwsLCGtyn5gXb7fYG9ykuLm7dhglCO1RaWsq8efN46KGHjvsYNb3qLR3GIQjHKzo6GgCbzQYcuV43dN0/dv+TrbXjkpjjI3RWRqOxVY4j4pJwsrW3uBSS8Q6VlZWBgzcynEKn0wE0Oiyj5jhC29izZw9z5swhLS0NhUJBUVER3bt359JLL+Xqq69u9P2trKzk3Xff5ZdffqG0tJSkpCSmTZvGrbfeWut5kiTx/fffs2jRItxuN06nE61Wy9ixY7nlllvqDOtJTU3l888/p7CwkKKiIiRJYtKkSdxzzz3B3rKysjIyMzPZuXMnq1at4vbbb2fgwIE8//zz/PXXX1xwwQVMmTKF559/PthjbTAY6NOnDzNnzmTgwIH4/X6mT5/OoUOHqKqqwmKx8OCDD3L99de3qC3N8eqrr/KPf/wj+DdxPGp61RsbHtPS9/Pw4cPMmjWL3bt3o1Ao8Hg8JCUlcckll9Sa02K32/n000/5/fffAXA6nURGRjJx4sQ6JbFlWeaXX35h0aJFVFZWkp+fT0REBJdddhl/+9vfahUTKC0tZfbs2WzcuBGlUonL5SIuLo7Jkydzww03HPe5ElpHzefV6/UCgTtF0PB1/9j9T7bWjksiyWobIi6FPi6p1WqKior47LPP2Lx5Mz6fj169enHppZdy9tlnN/etalZcys7O5rPPPmPHjh2oVCoKCgqIjY3lwgsv5MYbb6yT8DU3Lng8Hr7++muWLVuGJEk4nU5MJhPjx4/nlltuwWw21zrun3/+yddff01paSkFBQXodDouvPBC7rjjjlptaEm8E06+9haXQpJk1RSraKwUbs0fX2M9JqLoRdv55JNPeOedd3j22Wf5z3/+AwQC1Msvv8xzzz3H4sWL+fDDD+vtHXA4HDz00ENkZWURERGB1+tl//79vP766+zYsaNW5bxnn32WFStWMGfOHPr16wfAvn37uO+++0hJSeGKK64AAhfMZ599ln379vHWW28FbwmvXbuWe+65h19++YVvv/2WuLg4VqxYwfbt2/npp59wOp3MmDGDO++8k4yMDPx+P36/nzPPPJNly5Zx2WWXkZmZybRp03jmmWeC7VKpVMGyoP/973/54YcfSEhIaFFbmhPQ/vrrLzQaDePGjTuet4mMjAy+++47li5dCgSGHf7xxx8AjB49mieeeAJo+fuZlZXFtddey+WXX86CBQtQq9W43W7ee+89Xn/99WCS5XK5uP766wkLC+PDDz/EYrEgyzLLli3j8ccfZ+rUqcHzU1BQwIMPPkivXr148803MRqNSJLE+++/zyuvvMIff/zBhx9+iEqloqSkhOnTpzN8+HC++uorDAYDfr+fefPm8fzzz4skqx2ouYbXFICoCVINXfdrvmi1VcGI1o5LorT1ySfi0onHpbi4uCbPc3Z2NhdffDEVFRXBbbt37+ann37ikksu4bXXXmu0k6GgoICvv/6a5cuXA7Bu3TquvvpqAJKTk4Nzh5csWcK///1vHnjgAZ5++mkUCgUul4v333+f119/nUWLFvHZZ58Fk9rmxgVZlrnnnnvIy8vj008/DZ6j9evX8+CDD3Laaadx6qmnBj8/jz32GF6vl9deey0Yt7/77jv+9a9/sWLFCubNm4fJZGpRvBPaRnuLSyEZLliTyTdWJc3tdgMQHh7e5HGEk2vZsmW88sor3HXXXcGKWwBms5mXX36ZUaNGsXXr1gYrNz3zzDPcfPPNbNmyhdWrV7Nq1SrOPPNMAFasWMHKlSsByM3NZf78+Vx00UXBQAYwcOBAZsyYUeuYb7zxBt999x3PP/98rYvYxIkTufLKKyksLOSDDz4AAivZv/TSSwwZMgSA//u//+PRRx9l165dzJw5MxiA1Wo1t956KxCotCRJUp3X8vvvv3PTTTcFL9ItbUtjPB4P//d//8cjjzzS5L4N6d+/P0888URwIvJ9993HggULWLBgQTDBOp7386OPPqKyspL7778/2AOk0+l46KGHsFgswf2WLVvGvn37uOWWW4LbFQoFU6ZM4ayzzgruJ8sy9957Lzk5OTz33HPBL7FKpZL/9//+H3379mXdunXBoDxv3jzy8vK4++67gxc/lUrFDTfcEHxfhbZVc7en5hpec70++ovZ0ZpzzQ+l1o5LIsk6uURcOiKUcQkCCekjjzzC+vXr2bRpE5988kmw3YsXL25ymYOEhAT+8Y9/MHXqVACuueaaYFyqSbC2bdvG448/ziWXXMItt9wSTNr0ej1///vfufjii9m/fz8PPvhg8LjNjQubN2/mjz/+4Jprrql1jk477TSmTZtWq61PPvkka9eu5T//+U+tjtErr7ySiRMnkp6ezrx584Dmxzuh7bS3uBSSJKvmg9rYcD+bzYZGoyExMbHBfWrGSgon1xtvvAHAueeeW+/jt99+OwA//fQTubm5dR7/29/+xsUXXxzsUUhISOCtt96iW7duQCCgwZHbuNu2bQt+0GtMnTo1eNFyOBzMmzcPrVbL4MGD6/y+AQMGALBly5Za22t+/6RJkzjjjDNQKpVceeWVtYLwJZdcQkxMDHl5eaxZs6bW8/Pz89mxY0cw4J1IW+oze/Zspk+fTkRERJP7nojjeT9r3pv169fX2lehUPDyyy8H/7/mb/zY/QAefvjhYK/phg0b2L17NwMGDKh3fH5N5Z+a89bQ7wdq/X6h7dSMYe/Zsydw5HrdUDCr2b9Hjx4noXV1tXZcqu/LrxA6Ii4FhDou9e/fn6+//pqrr76ayMhIzGYzp59+OvPmzWP48OEALFiwoMnjNOWtt97C5/M1+H7ecccdQOB92LRpE9D8uFDzN75p06Y6f6c33XRT8Bzl5OSwfPlykpKSiI2NrXPMY89bc+Od0HbaW1wKyXDBpKQkDAYD5eXluN3ueueaFBQUkJKS0uj46T59+oSieUIjcnJyglVZju4BOtqECRNQKBTIssyOHTuC1VlqnHbaaXWeYzAYmDJlCh9//HFwAnr//v3p378/O3fu5JxzzmHChAmMHj2acePG0bdv3+BzU1NTcTqdqFQqrrrqqjqfGafTSY8ePRr8vDR250Or1XLdddfx9ttv8/XXX9cab75gwQKuu+66WknQibalRk5ODhs2bGDOnDmN7neijvf9nDp1KsuXL+eBBx5g1KhRjBs3jtGjRzNmzJhg4AE4++yzefPNN/niiy9Yt24d48eP55RTTmHs2LH06tUruN/GjRuBQFnVq666qk5ltoqKCvr06RMcFnLBBRcwZ84c/vOf/7B48WLGjRvHmDFj6vx+oe3UrH0zaNAggODfbGFhYaP71/cl8GRozbiUl5eHJEliXtZJIuLS2cHHQhmXINAZUd9Qd61Wy2233caDDz4YfC+Ol8fjCcaEht7PgQMHEh0djdVqZfv27YwdO7bZcWHs2LHExsayYsUKzj333GBcGjduXK27fBs3bkSWZQoLC7nyyivrzB2rqqqiV69ewec0N94Jbae9xaWQJFlKpZJTTz2V1atXs3PnzjplqbOysnC5XFx88cWNHmfChAmhaJ7QiJKSkuB/10wUPZbRaMRsNlNRUdGiyYLJycnAkfKwKpWKjz76iLfeeovVq1fz448/8uOPPwKBL0R///vfueSSS4Jt0ul0LFy4sMWvqamxttdffz2zZ89m7dq15OTkkJycjNfr5eeff2b+/Pm19m1JWxqbU/jaa6+RmZnJlClTam0vKysDAuv5bN++HYB33323VnBvieN9P6dOnYrb7Wbu3Lns2LGDrVu3AqDRaJg0aRIvvPACFouFHj168PHHHzNr1iw2btzIV199FRxKMnz4cJ577jkGDx4cbMcpp5zCrFmzmmz36NGjef/99/n444/Zvn07u3fv5tNPP0WpVHLaaafxwgsvNBichZNj9+7dpKSkBL/c1AxZ3bFjR4P7WywWzjjjjJPWxqO1Zlz6448/8Hq9Ykj7SSLiUuvFpRPRv39/4MSrD5aXlwfjo9PpbHC/+Ph4rFZr8P1sTlzo3r07ZrOZzz77jHfeeYd169bx7bff8u233wLQr18/nnzySSZMmBA8b8nJyXz33XdNtru58U5oO+0tLoVsoY+aajeLFy+u89jy5cuJjo7m8ssvBwLDLp5++mluuOEGsrKygvtdfvnloqfwJDu6d+zAgQMN7lfz5aIlt1hrbvVPnDgxuC0hIYGZM2fy559/snz5cl599VXOOecccnNzeeKJJyguLg6OlXU4HCEpsxkVFcUll1yCJEl8/fXXAPzyyy9Mnjy51vwjoMVtSUlJYdCgQcEF8iBwHn799VdKS0s5cOBArZ+a+SI2my24rbFKZ005kfdz2rRpLFiwgM2bN/PFF19w//33Yzab+fXXX3nrrbeC+40ePZpZs2axadMmFi1axL///W8GDhxIamoqjz32GHDkvNX0FjfH2WefzRdffMGWLVuYP38+jz76KElJSfz555+88MILzT6O0Pry8vLYs2cPt912W3DbsGHDGDFiBBs3bqzTa2i32/nzzz+56aab2jQxaa24ZDQaG/1yKLQuEZdaNy5B/bGpKTXJzvEWaqrR3Pez5m7z0e9nc+NCv379+N///seGDRv46aefePHFFxk7diyZmZk89NBDeL3e4HlryXJBzYl3Qttoj3EpZEnWWWedxaWXXso333zDkiVLgtt37drFnDlzalVx2bNnD/Pnz2fz5s3BiwkExlIeW2ZTCK1evXoFx7CuXbu23n3cbjdlZWV07969RRfojIwMevXqFewptlqtfPHFF8HHe/TowWWXXcb777/P1KlT8fl85OXlMXDgwOBt/HXr1h3vS2vUzTffDMA333yDw+Hg888/56abbqqzX0vbMmHCBM4555xad6zCw8PZt28faWlpdX7uu+8+IJDg1Gyrue3dHMdOxj/e9/PLL78M9vIZjUbGjRvHfffdF3y/ahbuW79+PX/99RcQmLA9ePBgbrzxRr755huSkpKC+9UMjdm3bx9Wq7XJ1/Hzzz8HFxHUarWMHDmS22+/nW+//RaDwdBmC9p2ZTWfLbvdzrPPPsull14aTEhqPPfcc+j1eh5++GFKS0uBwBezmTNnMnjwYO6+++6T3exaWisu/etf/xLVb08iEZdaNy5B/bHpq6++arT6ZkZGBkqlstaX2OY4Ni7pdLpgTGjo/YTAl2aTyRQcLtncuJCRkcHPP/8MBO5g9+vXj+nTpzNnzhxGjRpFRUUFFRUVwTaUl5eza9euJl9Hc+OdcPK097gU0iXrX3nlFZ544glmzZrFBRdcwA033MAHH3zAhx9+yOmnnx7cr1+/fowePZrIyEjOP//8Wsdoq3K/XZVSqeS6664DYP78+fVOFvz111/x+/3885//rDO3BuovlVlYWMiff/7JzJkzg8HAZrMFq8kdy2QyER4eTv/+/YmPj+e8884D4L333gvJegb9+/fn9NNPp6qqin/+859079693uFooW5LzSKojQW6+tTc8a0ZbljjeN/P7du319vDWFMB65RTTgECcyXqm0yt1WpRq9XB/c4991wSEhKQJIl33nmnydeTlpbGnj176v39siwHjwuBeTTvvfceV199Nfv27Wvy2MLxKSkp4dprr+Xuu+9m0qRJzJw5s84+gwYNCi5fMG3aNKZPn86MGTPo0aMHs2fPbnS9nJOlNeLS9OnTg8PLhNATcenkxCWXy8WXX35Z72OSJPHVV1/x0EMPBQtgNKWhuARw4403AoG7c/XN8dq6dSuFhYU89NBDwbjT3LhQXFxcb/KmVCoxGAz06tWLmJgYRowYwbBhwwBqjc5oSHPjHQQStwULFnDLLbfw22+/NXls4fi0+7gkt3M7duyQN2/eLG/fvr3JfX0+n1xUVCQXFRXJPp/vJLSuY/N6vfLBgwflgwcPyl6vN7jd4/HIt912m5ySkiJfffXV8sGDB4OPrVmzRj7rrLPkBQsW1DqW2+2WU1JS5JSUFPn222+v9Zz8/Hz5/ffflwsKCmo9Z8uWLfKgQYPkFStW1Nq+evVqedSoUfKyZcuC28rLy+XLLrtMTklJkWfMmFHr+JWVlfKsWbPk33//vdZxLr/8cjklJaXWcRqzevXq4GvYunVrg/s1ty0Nnd/GPPDAA3JKSop84403Nmv/Gm+++aackpIin3baafIXX3wh//DDD/KqVatkWT6+9/OWW26Rb775ZrmysrLW67733nvla665RnY6nbIsy/L7778vn3HGGfKBAweC+3m9Xvndd9+VJ0yYIO/fvz+4PTU1VR43bpyckpIiv/TSS3JZWVnwsUOHDsnPPvusXFRUJMuyLD/11FPyxRdfHPx/WZZlh8MhP/300/L5558vW63W4Pl98cUXW3TOxHWi+dxut7x582Z58+bN8o4dO9q6Oe2KiE2hU9+1U8Sl1olLstxw7K+qqpInTJggL1q0SHa73cHtRUVF8hNPPCF//PHHzWpzjQULFsgpKSnyiBEj5FmzZsmLFy+WlyxZEnz8X//6l5ySkiKff/758u7du4PbU1NT5fPOO09+++23ax2vOXFBlmX5xx9/lEeOHFnrb1OSJPmbb76RR40aJW/evDm4PScnR540aZKckpIi/+Mf/6j1eSguLpZfe+01ee/evbIsNz/eeb1e+aOPPpJTUlLk8847r9nxX1wnmqcjxSWFLLfvxT5SU1Pxer2o1eomhwD4/f7grcCoqKh20Wvanvl8vmCp26SkpFoViXw+H3PnzmXRokVkZ2fTs2dPIiIiGDhwINdee22wPGYNq9XKnXfeyfjx41mzZg25ubnExMQwfPjwOkMSahw4cIDZs2eTlpaG3+/HYDDg9Xrp3r07d9xxB0OHDq21f80K7j///DMHDx4kPj6eHj16YLFYmDJlSnDi4sMPP0xmZmbwrobJZKJnz55MmDCBRx99tMHzIcsy5513HhEREU1Ogm1OWxo7v0f7/vvv+frrr3E4HGRkZAS3JyUlERUVxcsvvxyccNwQm83Gww8/zLp169BoNJx66qm88MILwbK0LX0/58yZw++//05eXh5msxlZllEqlUyePJkZM2YExy+vWbOGb7/9lqysLPR6PWq1Gp/Px6hRo7jrrrvqlMUtKSnhk08+YfXq1ZSUlNCnTx/i4uJITExk+vTpwXVpfvzxR37++Weys7MJCwtDqVQiSRITJkzgjjvuICwsLHh+n3zySTZt2sSCBQuaNUxIXCeaz+PxsHPnTiBQ9KS5PdhdgYhNodPQtVPEpROPS42dXwgMTZwzZw5FRUX07duX6OhoevTowdVXX13n/DbF6/Xy73//Ozh0b/jw4Tz//PO1qhz+8MMPzJ8/n/T0dBITE4mNjSU5OZlrr722zlD55sQFCBQ5mDNnDunp6Wg0GrRaLV6vl5SUFO6+++46r8Nms/H555+zfPlycnNzSU5OJikpiZiYGC6//PLg33dz453P5+Pxxx9nyZIlvP76600W06khrhPN05HikkiyurDmJgHC8fH5fLz66qvY7XYiIiLEpNhW5vP5SE1N5YYbbmDq1KnBRS6bIq4TzdeRgtnJJmJT6IjYFFoiNoWWw+HgzDPPpGfPnsybNw+NRtOs54nrRPN0pLgU0jlZgtDVeb1evF7vCVUIFBr27bffkpiYWGshT0EQBKFxIjaFzjfffINSqeSf//ynqJDdxXWo7qGmLgaSJOH3+1EoFPh8vjoVbYTajp4I3NJCC0LTjj2nohpZ68rMzGTp0qW8/vrr6PX6Zp9fv9+PJEnIsozX6xWf/UaEYjJ/ZyRiU+sSsSm0RGwKnbKyMt5//30ee+wxYmNjW/T5FbGpeTpSXOowSZbP5wveHmxKWFgYTqdT9CC0QEFBQVs3oVPz+/3B4S9C68jLy+Pdd98lKSmpRZ9fWZZxu93YbDbxnggnTMSm0BKxKbREbGpdubm5vPTSS8H5xSI2dW0dJskSBEE4Wt++fdu6CYIgCIIQlJSU1NZNENqRDpNkqdXqJhdllSSJ8vJyFAoFFoul0UmDsixzsMyJw9uxb8caNSp6RRqOq2fU7/cHe1kSEhLEJMtWdvStfpVKJS6+rex4P79+v5/y8nIiIiKwWCz1rqkjBHi9XrH2WBNEbKqfiE3tl4hNoSViU2h1pLjUYZIsIFg2uiF+vz/4YVar1Q1+sCVJ5rJPN/LT3qJWb2NbuGhQHD/cMg6l8viHoKhUKlHBKcTE+Q2dlnx+FQpFMHhpNBrxBU44YSI21U/Epo5BnN/QEbGpa+uSaXKJ3dNpghjAT3uLKLGLCkGCIAgdmYhNgiAInUeX7L6IMWm5aFBcpwlmFw2KI8bUeE9qe1FcXMzOnTvJzMxk8+bNxMfH88ILL7R1swRBENqciE1tQ8QlQRBCoUsmWUqlgh9vHcfB0s4x7r13tLGtm9Fs2dnZ7Nixg5UrV5KRkcG0adPauknNUlhYyN69e0lPT2fjxo2cfvrp3HLLLW3drC5FkmQ8fgmvJOGXwOP1Uub2owD0Dg9qlR+VQolGpUCjUqJWKk5omJIgnGwiNrUNEZcEQQiFLplkQWDsa0cJAO2J2+3m888/Z9myZRw4cACNRkOPHj245ppruOKKK5ocQzxmzBjGjBmDQqEgIyPjJLX6xKWlpbF582YWLlyI1WplxIgRzXre2Wefjc/nIz4+PsQt7FhkWcbm9lHu9FLm9FHh8lLl9lHp8lHl9mH3+HB4/Di8fpxeCbfPj9ffyNpCO6rq3azTKDGqVRi0KkwaFeF6NRFaFbEaDza3j/AymZ5RJpItBrTqLjl6WmhnRGxqORGXWhaX4OTEJp9fwuH1Y/f4cXn9uH0Sbr+Exyfhk2T8koxPlqlZNk4BKBSgVipQKwMdZRqVEoNGiV6tRK9RYdKqMGhUYhkEoUPoskmW0HIej4frr7+eXbt2YTQaiYyMpKCggJ07d7Jz507Wrl3LW2+91axjdbQJnWeeeSZnnnkm+fn5LFmypNnP69WrF9A1y7q6fX6KbR6KbG6KbB5K7G6sdi9Whwerw4OvsaSptdrglXB7JcqcRxYvVCtk+oXJ/HGglKUHM3D5ZZQK6B1lZEBsGAPiwhgcH87IJDNDE8LRqTvWZ1UQuhIRl1oel+DEYpNfClxTrXYPZU4vpQ4vZU4vlS4flS5vsLPM7ZNafOzmUCoVhGlVhOvUWAwaIgwaLHo1UUYtMabAT6RRg1pU5xPamEiyhGbbsmULBQUFvPLKK1x88cWo1WpsNhuvvvoq8+fP55dffmHbtm2MGjWqrZsaMnq9vq2b0O64vH7yKl3kVrjIr3JTUOmioMqNtQNNeJdkyLI6yLI6+HnfkfkwaqWCQfFhjOsRyYSekYzvFcmA2DDRiyoI7YSIS6GJS7IsU+nykV8VuJ4XVLopsrkptnkocXiQpNB3kjVEkuTqhM5HboWr3n2USgWxJi3x4Triw3R0i9CTFKGnW7gejRi1IJwkIskSmm3dunX861//4qKLLgpuCwsL47nnnmP79u2kpaVRWFjYhi0UQkmWZcqdXg6VO8kpc3G4wklOhZMS24knUyqlAp1aiU6lRK9RBv9bp1aiVSvRKhWoVUo01cNIVEpABqfdBoApLJD4+OXqISiSjFeS8fgkPH4Zt8+P2y/h8vrx+Pxo1T6akyf5JJmd+VXszK/i4w2HAIg2apjUL4Zz+sUwuX8M/WJMIukShDYi4tKJ80kS+ZUuDpU5ySkPdJjlVjixe0I3L1CpVKAkMDzw6KunTKDTSzpqGOHxkCSZwio3hVXuOr83PkxLD4uBnlFGekUaxXBxIWREkiU027nnnsvQoUPrbFcoFCQnJ7N///4WjQnvCoqKipCkwJCJnj17tnFrWqbK5eVAqZODZQ6ySx1klzupcvmO61g6tRKTVkWYVk24LjDMI0ynwqzTEK5To9e0PMBJkkS57ATAYtY1e+FGWZbAZaNflIELh6vIq/SSW+nicIWL/MrA3bhiu4eGOmqtDi/fpubzbWo+AH2ijFw0OJ5LBsdzZp9oEawF4SQScallJEmmoMrNn+m5FNm9VGw+TCHh+I7zzpRCAXq1CqNGiUGjwhD8N/DfOrUKgzowp0qjUqJVK5o9jE8mMK/L45dx+/24vRJOnx+XV8LpDcz3qpm76/D4cfikJu+wSZJMfqWb/Eo3Gw6VA4HEq4fFQN8YE/2ijfSLMWHWa47rfAjC0USSJTTbyJEj691eVVXF1q1bue+++0hMTDyuY8+ZM4d58+aRm5uL2Wxm8uTJ3H///URHR9fZ1+v1smjRIn755Rfsdjt5eXkkJiYyffp0rrzyylp3FZxOJ4sXL+aPP/7AbrezZ88ezGYz06ZN4/bbb29wkUCbzcZXX33F9u3bOXz4MGVlZQwePJji4uIGX8Phw4eZNWsWu3fvRqFQ4Ha7cTgcJCQkMHjwYJ566qnjOjcng0+SOFzmIqvUzgGrgwOljhavb6NQQJhOTYROTYReTaRBQ5RRQ6RRi74dJh4KhYJoo5Yok56hieG1HvP4JbLLnGQU28m02sm0OsivdFNf+N5f6uDtPw7w9h8HMOvUXD40gatHduO8lFg0qvb3ugWhMxFxqfG4tD/7EG++/R57du/GI8k4XG50llh6RuhITohF0hjxjWy8mqJSqSC8eg5UhF6NWa/GotcQoVcTrlOHrIqrAtColGhUYEIFhsb3lwGHx19dUMlLudNLhctHhcuHzeNr8M6YJMkcLHVwsNTByupt3SL01XN0TQyIDcOoFV+XhZYTnxrhhOTm5vL8889z9913M2PGjBY/v6ysjCeeeIKhQ4fyyiuvUFZWxnvvvcfcuXNZs2YNP/74I2FhYcH909PTefDBB5k8eTIffPABGo0Gj8fDiy++yJNPPsmWLVt4+eWXg/vfddddnHLKKcGJzw6Hg+eff54333yTzMxM/vvf/9Zp0zfffMNPP/3EM888w1133QXAoUOHeOmll9izZ0+9ryMrK4trr72Wyy+/nAULFqBWq7Hb7fztb39jz549DB48uMXnJpQcHh/7rQ4yrXayShwcKLU3Xr3vGDq1EoteTaRRS4xRE5hsbNSg6iRJhValpH+Mif4xpuA2p9fP3iIb2/Mq2V1QxaFyV52kq9LtY86Ww8zZcpgoo4arhnfj1nHJjE22iCGFgnCSdOW45Pb6ybTaSS+2s2nnXn545RHiR51F3xnPoVSp8Hs9ZK9eyN5Nv5CccG6t36Eg0FFmMaiJMmiINmmJMWqJMKhRdoDrlwIwaQMVCJMias9T80sypQ4vxfbqQkyOQBLmb+DOV16Fi7wKF6syS1AqFfSJMjI4IZyhCeH0sBjE9VxoFpFkCS3mcrmYMmUKCoUCq9WKz+ejsLCQqqoqbrvtNgyGJrqbjrJ+/Xq++eYbUlJSgtvGjBnD5MmTyc3N5ffffw+OtXc6ndxxxx1ERETw8MMPBy9yWq2Wp556iqVLl7Jw4UKuv/56hg0bBkBiYmKtdUOMRiPPPfccy5cvZ/HixTz66KO1Sti+9NJLzJ07l6VLl5KcnBzc3qNHD95//31uu+02/vzzzzqv46OPPqKyspL7778/2Aup0+kYPnw4hw8fbvb5CJUqt5eMYjsZJYGfw+XOZo9316mVRBk1RBs1xJl0xIfrMOu73qXDoFExOimC0UkRANg9frblVrDhUDmpBVV15i+UOrzMXp/N7PXZDIkP55ZxycwYk0x0B1icVRA6mq4al95+913+NuNWtm/ewJ8HS9n74+5g4rBv8Xx8Lju9zpmOsrpyokqjpfe511CRuhoIFPcZmxxBfJiOuDBtp737rlIqiA3TEhumZXD1qZVkKHV4yKs4Mky8vnlokiSTWWIns8TOj7sKsBjUDEs0MzwxgkFxYaKQhtCgrvdNSThhXq+Xd955hyFDhiBJEhs2bOCf//wnb7/9Nn/++SdfffVVs3t5Ro0aVSuQQSDgjBgxglWrVpGbmxvc/vPPP1NQUMDEiRPrHF+r1dKrVy9SU1PZsmVLMJi98sordX6nTqejX79+bN++ndzc3GAwy87O5ssvv2TChAm1AlkNpVJJt27d6n0dVVWBdZrWr1/P+eefH9yuUCgYPXp0c05Fq7K7faQV20gvtpNWbCOvgQpMx1IqFUTq1cSYtMSF6ehm1hGhV4teu3qYtCrO6B3FGb2jkGSZ9CI7vx+wsjGngopj5q7tLqzikcV7eGrpPq4f3Z37z+jFiG4RbdRyQeh8ulJcKra52V1QxZ7CKtKK7RTIgTvuVrsH81F3ZnwuBwDl+3fRbfhpRBm0xIVpiQ/Tojv1tMDvVSuDHUddjVJBsOT78Optdo+fQ+XOYLVcRz1JV7nTx9r9pazdX4pOrWRYoplRSREMTQhHr+lYywAIoSWSLKHFwsPDGTJkCBBIPMaPH8+LL77IbbfdxpYtW9i4cSOnnnpqs46l0+nq3W42m4HAIpM1Nm7cCMDKlStJS0urFdBkWaasrIz+/fsTGRlZ53gHDhxg27ZtpKenk5GRQVZWFhAIzDVWrFiBJEn069evWW0/2tSpU1m+fDkPPPAAo0aNYty4cYwcORKv1xt8LaHk8vrJLLGzt8hGWrGt2XeqdCol0SYN8eE6upn1JIRpUXfSnsxQUioUDIwPY2B8GHecKrO30MbKzBI25lTgOmqtGJdP4pONh/hk4yHO7hvNY5P6ccGAWJHECsIJ6uxxKTKxB/O25bKnsKpOxbx6X4NKyYBxZ1KyZyN75r2BN3UYphGjMA4dQWLscCyRFtwOezPORtdi0qoYFBfGoLjAcNAKp5eDZU4OlTsptHnqDC90+yQ255SzOaccjUrB8EQzo7uZsfhlNCpxXe/qRJIltIoJEyag0Wjwer3k5+ef8PHqqxRXUlICwJQpU3j66aebdZylS5fyxhtvUFZWxtSpU5k0aRL33HMPDz74IH/99VetfbOzs4HG1xypqRR4rKlTp+J2u5k7dy47duxg69atwdcRHx/PxIkTm9Xe5vJJEgdLnewtrCKtyEZWqaNZ65YYNSpiw7QkhgfWDYkxasQX/FamVCgYkhDOkIRw3D6JdQdLWZ5RQkaJo9Z+q7OsrM6yMrKbmSfO6c+VwxNRhWgCuSB0RR05LlW6vKTmVbJ04y4AdhQ5qcosqf+AciAuhevUjE6KoIdFT1yYFsXYqxgWZ2DFjwvJ3Lub9N2pAKjUahISEhk+bCgGU/2HFAIiDBpGGDSM6GbGL8nkVLg4UOogp9yF01v7LpfXL7PlcAVbDlegVSkYGKnhLI2NwQkRISsOIrRvIskSms3v96NS1X8rXKlUYjabsVqt9OnTJyS/Pzw8UAGuqKioiT0DNm/ezN///ncSEhL49ddfiYqKanT/muSqsTVVPJ6GK+5NmzaNadOm4XA42LVrF+vXr+fjjz8mPz+fHTt2NKvNDZFlmSKbmz0FNvYWBYaIuLxNr2Fi1KiIC9OSaNaRbDEQaRBlaU8mnVrJOdVramWXOVi8p4h12WW1ioxsz6vk2i+3MCgujOcvHMAVwxJF4isIzdRZ4pIsy7ir73rP3XYYV44eWYay6pDjriytdRwFYDFo6GbWUW5UUgAMiDUxNrn20L+zzr+Is86/CJfTyf70vexN3cavP3xL7uEctBo1p44//fhffBejUiroFWmgV2Rgfl+RzU1GiYPsMgdV7trx2OOXSS3xkFqSjcWg4dQegcXsE82tv3C00H6JJEtolj///JPNmzfzwAMP1Pt4eXk5paWlDBs2jOHDh9e7T0vI9Yx1Gzx4MEuXLmXjxo34fL4Gy9zWWLBgAbIsM3369CYTLCA4Bn/dunUNHj89Pb3e53755ZdceOGFxMTEYDQaGTduHKNHj+bgwYP89NNPwTlbLWF3+9hXZGNPYRV7i2xYm1FSXadWBla3r06qoowiqWovekYaue/0Xtw8pjvL0or5aV9RrcC8t8jG9DlbGJVk5qUpg8QwQkFoQkePSxZLJJkldrbnVrAjr5L91sDd7rwKN5HVVeJN8T0AKMtMRaOQ6WYxkhyhp1eUEZM2kFz+mJtd7+/65ftvOPWsc7BERqM3GBg8YjSDR4zm1ImTePT267HbxXDBExEXpiMuTMfpvSKx2j2kF9vJKnXUKZ5R7vTyS1oRv6QV0SfayIReUYxNtoj5W12AmHwhNMvo0aNZsGBBg3d5FixYgNlsrlWm9kQ4nc4626ZNm4bJZKKiooLPPvusyWNUVFQAYLFYam0vKysLDg082vnnn4/ZbKaoqIh33nmnzuO//vprg0nW9u3bOXDgQJ3tGk0gyYmLi2uyvZIks99qZ/HuAl75LYOHF+9m9vps/jhQ2mCCpVYqSDTrGJ0UwbSh8cw4JYkpA2MZ0c0sEqx2Kkyn5qrhiXxwxTBuHdu9zvu0LbeSqR9t4ILZ69mVX9lGrRSE9q8jxqXy8nIAMqrg8Z/28NqqTJanF5NbVILTWndIY9/Rp6MzhuGpKsO0ZykXDohlSEJ4MMHauHYVOQey6v1dGXt3kZ9zqM52vTEwRrA5nY9C80SbtIzvFcmNo5O4bHAcfSLUaOuZk7Xf6uDLLYd5bMke5mzO4aDVUW/yLnQOIskSmkWv19O7d2/uuOMOUlNTg9tlWWbJkiUsX76cOXPm0L9//2YdryZYORyOeh+v6WE7+vHY2FjeeOMNDAYDb7zxBrNmzar1eFpaGk899VRwSN/YsWMB+OWXX4Lb0tPT+fDDD4PDS46eY2U2m3nxxRfRaDS8//77PPbYY2zcuJHdu3cza9Ys0tLSOO+884DaE58BSktLee+992rdsaqoqGDz5s1ERUUxaNCgel9nucPLugNWPlyfzcOLd/PKb5ks2VPIfquj3sIVCgVEGTUMiQ9jyoBYbh7TnUsHxzM2OYK4MJ2489GB6NRKpg6M471pQ7l9XHcsx5TFX5FRwsg3fuee71IptjU90V0QupqOEpf+9eSTbDlYwuebcig0Be5M/bV6JeW2QNVXW+EhDq35AYUyEJci9UrGJkdwzYhE/ja+P/c88i9UajXff/UZ7/3nOfbs2MqBjH18//XnHNqfydgzzgLAe8xw9sryMhZ++QkOmy24zVZVyefvvE50TMxxFXkSmhYXpuWUeD2X9DZxfv9oekYa6szJcvsk1h0o5eXfMnhpRQZr9ltx+5qeAiB0LAq5nafQqampeL1e1Go1I0aMaHRfv99PaWlg3HJUVFSD47SFAJ/PFyxFm5SU1OQwhz/++IMvvviCXbt2odPpSElJwWKxMGHCBKZMmRK8a9OY5cuXM3v2bNLT03G5XCiVSgYNGsSUKVO44447gvtde+21bNu2Db1eT0pKCg8//DCnnRYoOXvo0CE+/fRT/vjjDyorK0lJSSEyMpLevXtzzTXXBMuse71e3njjDZYsWYJSqeTss89mypQpnHbaaVx99dXs2LGD8ePHc/HFF3PVVVcFf/eOHTuYNWsWW7duxe120717dx599FHOPPNM/v73v/Pzzz9jNBo5++yzmT59OhMmTGDOnDn8/vvv5OXlYTabkWUZhULByJEjueyyy+jZsycmkwmfXyLTag+U3y2wcbiibs/osYxaFYlhOrpb9PSMNGAQQwyAQIJc0ytssVjqnZReH1mWwBUYfmlX6JEV7aOvyeOXWLqviIW7CusMN7EYNLx68SBuHdvjpE6g9ng87Ny5EwjclW2NIVedhYhNodOS2NRe41K//v1RGcJRRsSjHXIWirDAXSPJ7+PA8rkU7vgDhUJJ9MDRJAyfwJARp/Db/x4jN2sfQ0eNYcI5FzBpyiXB3525dxc/zJ1D2u5UPG43cQnduP7O+xg5bjxvvfgUf61egd5gZNSpE5g05VKGnTKOZYvms23DX5QU5WMyhQfiklLJKRMmMm7iOahUaiwWC9pGij0JLVdfbHL7JNJLbOwttFPm9Nb7vDCtksnJepItegb37CauEw3oSHFJJFldWEuTLKFlas5vudtPqSKMvUV29hXbcHvrr1BYQ129aGKSOZBUxYjFa+vV2ZKsGnaPj/k78vklrRj/MVfn03tF8sFVIxiSEH5S2tKRgtnJJmJT6HTU2OT1SewqrGTr4Qp25Fc2eq3XqZUkmXX0jjLSK9JwUpfOON5rp9A8TZ3fEruHnQVV7Lc68B1VFVitkOkXJvPHgVJcaiN3TejNRYPiRdXZY3SkuNQxrlyC0IF4q3usduZXknq4CqvLDzRe+MKsV5NkDvRgJUfoxVpVXZhJq+bWsclMGRDLJ5sOsy3vyLysdQfLGPXG7/xrcn+ePLc/GvE5EYQ25fVJ7CmqYktOOTvyqxqt+qrXKEmO0NMnykQPi16U9e6iYkxaJvWN5oxekWSUONhVUFXn7taqTCtL00roFWngvjN6c9u4HkSI6sAdjkiyBOEE1ZRX31VQxZ6CKtKKbbVKdNdHo1IQH6arrhJlwKwXF0+htkSznicn92NzTjmzN+RQWh2EfZLM88vT+XF3AZ9dO4rh3UK/2LUgCEd4/RJ7C21sOVzO9rzKRhMrg0ZFskVPvygjSRY9SjFvVqimUSkZHB/G4PgwCm1ududXovDWng94sMzJI4v38Owvadw8rgcPnNGbfjFicbOOQiRZgnAcXF4/acW2YGJV0kD1P13BXvB7QaXB2HsY3aqHACaZRS+m0Dxjki0MTQhn/o58ftpXRM3oku15lYz93xqeuWAAj0/qJ4aUCEII+SSJfYU2Nh8uZ3tuZZ2FaI9mrEmsYowkmfXtsiDRwb2p+L0ewswRDBp9als3p8uLD9MR1y8ar12Lw+PlrwIPBXZf8HGbx887fxzg3T8OcNnQBP5+Zh/O6B3VLj9bwhEiyRKEZpAkmdwKJ7sLbewpqCLTascvNX63SqdSYijah+x2oDeFc/nlF5yk1gqdjV6jYsaY7kzsHcX//XGA3MpAtUGvJPPU0n0sTyviyxtGkxRhaOOWCkLn4Zck9hXZ2HK4gm25FTg8Td+x6t+OE6ujZe9Lxe2wYzCFiySrHdGoFIzpbuGtpATW51Tw455CMq1H7m7JwPe7Cvh+VwFjkiN4+Ky+XDksUUwxaKdEkiUIDah0edlbGFgMeE9hFZUuX6P7KxQQZdCSZNbRM8pAvEnDj6kK3ED7DrdCR9En2sh/Lx7E/B35/LC7kJo0//f9pQz/7+98eu1ILh2S0KZtFISOzC9JpBfb2XK4nG25ldjcDV/39RoVPSICd6y6R7T/xEroOFRKmNArkgm9AgtWf7+7kE055bWKIW3OqeC6L7fSM9LAQ2f24bZxPQjTia/17Yl4NwShmtcXKK++p7CKvYU2csqbLq+u16hIDNeSHGGgV1Tt8upHr8ElCK1Fo1Jy4+gkxiVbeGPtfkrsgblaZU4vl3+6iYcm9uaViweLohiC0Ex+SSKj2M6W3Aq2Ha6gqtHEKlC8ol+0ie5ijpVwEvSLMfHIWX2wOjws2VPIikwrzqMqV2aXOfn7D7t57pc07p7QiwfO6E2CWZTlbw9EkiV0WZIkk1vpZG+hjb2FNjJKmi5YoVQqiDFqSIrQ0yvSQKxJK3ovhTaREmvijYsH88H6bP7MLg9u/9/aA2w+XME3N40hPlzXdg0UhHasVmKVW0FVIyMVdOpAYtU3xkSPCDGfVmgb0UYtM8Ykc82IbqzIKGHJ3iJKHEeqEpa7fPznt0ze+D2LG09J5pGz+zAw7uQs9yHUTyRZQpdSYnezr9DGvmIb+wptjfZY1gjXqQN3qywGekQa0Io7BEI7YdSq+PvE3oxOsjJ7Qw6e6k6CPw6UMvqN3/l2xhjG94pq41YKQvvgkyTSimxsPVzB9rzGhwLqVEqSqocC9rQYRGIltBt6jYqLB8czZWAcGw6Vs2hXAQfKjoy88fhlPtl4iE82HuKyIfE8cnY/Tu8t4kBbEEmW0KlVurykFdtIK7Kxr8hGsa3+KoBH06oUxIXp6F59t0qsTdHJtOvl11tOoVBwdt8Y+kab+M+qLAqrP+P5VW7Ofu9PZk0fwc1jk9u4lYLQNmrKrW/LrWB7XuPFK7QqBd0j9PSNNtIr0igSK+HkamFsHSdWuwAA1ShJREFUUikVwXlbewur+HZnATvya6/J+cPuQn7YXcj4npE8NqkflwyOF5/rk6hTJVli2JZgd/tIL7aRVmwnrdhGXoWryecolQqiDRoSzTp6WgwkmHVinH0XIXeikiTJFgOvXjSIt/44wJbcwALGXknm1vnb2VdUxcwpg0RwbSMiNp1cLq+fXQVVbM+tYGdB4wsE1yRWfaoTK7EUgtAetDQ2DYoP59/x4eRWuPhuZz7rDpbjl49kbX9llzHts02kxJp45Oy+3Di6O/qj5pALodHpkiyFQoEsy3i9XlQq8QHq7GxuHxnFNtJL7GQU2zlc4URuRm9QhF5NQnhgMeBkiwGtWgwB7DIkPz5J7mw3tAAwaVU8Makv3+0sYP6O/OBrfHVVFmlFNr68fjQmUX3qpBOxKfQqnF5S8yvZnlvBvmIbvkbm1+rUSpLMevrFGOhpEXeshHaiFWJTUoSeB87ozd9Ge/lxTyHLM0pw+Y4UyUgvtnPnN6k8tXQf95/Rm3sm9CLKqD3xtgv16lTRVqFQoNVqcbvduFwudDqd6EHsZMocHjJL7KSX2MkssTfrThUEvnzGh2lJitDTI9JImFZ8yemKZFkGvwePT8KPKlB3v5NRKBRcNTyRHhYD//vjQHCe1g+7CznrvT9ZesepxIaJghgnk4hNrU+WZfIr3ezIryA1r5IDpY5GO9j0GhXJETr6RIviFUL709qxKdKoYcaY7lw9IpFlacX8tLeI8qOKuxTZPPx7WRovr8zk1lN78NDE3vSJNp3oyxCO0amSLAC9Xo/b7cbr9VJRUYFer0ejEXNq6uP3+wN/2NX/3d6CviTJFNo87C+1s9/qYH+JHauj9pwqdQNNNmhUxJq0JJoDc6uOnVclyyehvLosYYmOwxvmwhgWfnJ+Z1ciSwS/VclSw1+warZLfvB78Hl9OH1+vIrOnWiM62HhxQsHMPO3TMqdgeC6NbeCCW//wa93jqd3tLGNW9i1iNjUfA3FJp9fItPqYFd+JXsKqyixH4kHKqizIKFRq6K72UCfaD3dzPqjvrjKweN3SSI2hVY7ik0GjYppQxO4ZHA8aw9Y+X5XYXAxewCH1887fxzgvXUHuHxoAv84qy/je0a2u++DHZVCbudXmtTUVLxeL2q1mhEjRjTrOU6nE5vNFuKWdXySJFFeXg6AxWJBqWzbIXNev4TV7qHI7qHE5qHE7sHjb97FX6tSEq5TEaHXEGnUYGoHd6pkWcbhCKzUbjQaxUWrlbX0/PokGY9Pwunz40aDV9E1vuCWOjy8tDKT7PIjd33jwrQsu+M0RiZFNPpcj8fDzp07AdBoNAwfPjykbe1IRGwKnaNjk8YYRoHNS16Fi/wqV6PDAAGMGiVRBi0xYVrM+k7Xj9wqRGwKrfYcm2RZZnteJQt3FbC3yF7vPmOTLTx0Zh+uGp7YLtdb7EhxqVNegQwGAyqVCpfLhcfj6do9Vo2QZTkY8CMiGv+y1dokSSa/ys1+q4ODZQ4OWB3kVbmaXV3HpFURZ9KSYNbRPcJApEEdvJC5vP5GJzqfLJIkUV4WqPRjkVRtnsR2Ni09vzLgR4VXocOvaPsk/GSJMmp58cIBvLIqi12Fgb/3IpuHs95bx5LbTmVin+g2bmHXIWJT07x+iX2FVWzKKmJ/pY8SZ+MdbQoFxJq09LDo6RttwlI9aqGm006oS8Sm0GrPsUmhUDAqKYJRSRHst9pZuKuQjTnlSEddijbllHPDV1t5bLGee0/vxR2n9SDG1LlHfoRKp0yyALRaLVqtFlmWgz9CbV6vl9zcXCBwJytUQ1ckSeZAqYMthyvYmlfBtsMVbM+twOFt3l0qhQISw7X0jTKSEmtiWEJ4nTkljlA0/AT5kSiq/oKgjtSjUohA1ppacn6DlZq6aI+tQaPiycn9eGvdQf6qXri4yu3nwg/X8+Ot45jcP7ZtG9iFiNhUm1+S2VlQyW8ZVlZnlfDnwTLcvsZjg0mrZECMiVFJZsYkWzAfVcyl/r554WgiNoVWR4lNfaJNPHJWH6x2Dz/uKeS3TCvOo/72citdPLl0H88vT+fakd34f6f3Zkyy5aS3syPrtElWjZqqTkJdfv+Ruz1KpbJVKl75/BL7imxsy6tgW24l2w5XsC2vgkpX04v+1jColfSwGOgfY2RQXBhDEsII0x0zp+qEWxp6MnKwd0hGgSwCWasS57dlNColf5/YG4v+MEvTigFweiUu+mgji24ew5RB8W3cwq6lq8YmSZLZVVDF6qwSVmdZ+T3LSpnT2+Tzksw6hiaEMy45giEJ4aiPujvQEeJBeyKunaHV0c5vtEnLLWOTuXZkN1ZmlPDTvmKKj7oL7PZJfL75MJ9vPsy4ZAt3ju/JtSO7YdR2+hTihIkzJBw3q93DzvxKduRXkppfSWpeJbsKqprshTyaQgEJYTp6RRpIiTExOCGM3pGGTjN8YeF7/8FhqyQqNo67n32jrZsjdHFKhYJbx3bHoFGycFchAB6/xOWfbmLBTWO4bGhCG7dQ6Gw8PoltuRWsPVDKHwes/HGglFJH00mVQa2gt1nD2J7RnNYrSlTEbGUiNgnHMmhUXDw4nqmD4th6uIIf9hTWmbe1MaecjTnlPPzjbm48pTu3n9qDEd1O7nSTjkQkWUKTyp1e9hRWVf/Y2F1Qyc78Kgqq3E0/+RjRRg3JEXr6RgeG/g2OC8fQDopUhEphzn5s5aU4KsvauimCAATuoFw/KgmtSsm8HflAYNHi6XM2s/DmsVw8WNzREo5fQaWLjTnl/JVdxp8HStmUU15rnZ6GqJUKekcaGBwfxsiEcMI8ZaiUSpKS4lCpO2+MaCsiNgkNUSoUjEm2MCbZQm6Fi8V7Cll7oAz3UYXIKlw+3l13kHfXHWR0UgS3jEvm+lFJRIo1t2oRSZYABMbF77faSS+2k15sY1+RjbQiG/uKbeRXtjyZAogxaugeoad3lJF+MUYGxJqwGMQfoCC0B1cNT0SrUjJna2Bepk+SufLzTSy+dRznD4hr49YJHUGpw8OWwxVsOVzO1sMVbDhURk5589YuVCogOULPwLgwhieEM6KbGb0mkEz5fX5ycytC2XRBEJohKULP3eN7MmNMd37LtPJrenGtEvAQWBpk66IKHv5xN1MHxXP9qCQuHhwf/HvuykSS1YU4PD4OlDo5UOpgv9VORlEVO7Kt5FT5yVuwHG8TpXEbolEpSAzTBRb6tRjoF2Okf6yRMG3XKJEtCB3VpUPiUSrhs82BRMvrl7ns0038fPupTOoX08atE9qLmk64XQVVbM+rJDWvgu15lWSXOZt9DLVSQQ+LnpQYE0PiwxmeGI5JJ76CCEJHYNCouGhQHFMHxpJebOenfUVsOlxR63ujxy/z/a4Cvt9VgFmn5tIh8VwxPJELBsRh6KIJl7jCdRKSJFNs95BT7jzqx8WhMgcHy5xklzkosp1YOVulAmJMWhLCdCRb9PS0GOgbbaS7RY+qk8yhEoSu5uJB8fj8Ml9uywMCk5wv/ngDy+8az5huYW3cOuFkcnn9ZFkd7CuqCo5m2F1Yxd5CW7OG/B0tXKeid6SR/jFGBseHMzDWhK6LftEShM5CoVAwIC6MAXFhOL1+1uwvZWVmCftLa3e4VLp9fLk1ly+35mLSqpgyMI6pg+K5cEAsCWZ9G7X+5BNJVjvn8vopsrkprPIE/rW5ya90UVDlpqDSTV6li9zqRRqP907UsXQqJTEmDQnhOrqZ9XSP0NE7ykh3iwFtO1yYThCEE3P50AQ8fokFqQVATdXBDay669Q2bpnQmvySTGGVm0PlgRENB0sd1SMbHGSW2MmpcHI8FeX1aiXdzIE4kRJjYnB8GIld6IuUIHRFBo2KCwbEcsGAWHIrnPyWaWXdwTJKjilsY/f4+TY1n29TA3OARydFMLl/DGf3jeaM3tGEd+JFwzvvK2tHPD6JKrePSpePCpeXCpeXcqePcqeXMqeXcqeXUqeXUrsHq8OD1e6hxO6h2O7B7gnNoro6lZIooxqzWiZar6JPfCTJkUZ6RuqJNmq7ZGlhQejKpg9PDAz32B2oOljh8jH1k018PzUalVJcD9orWZapdPmwOjwU2wJxI9Ax5ya/yk1+dSfc4XIXeZUufNKJdcaF61R0M+tJjtDTp3rtwh4WfaepCCsIQsslRRj42ynduXF0EhkldlZnlbL5cAWl9SzPsDW3gq25Fby2OguVUsGoboH17k7pbmFMcgQD48LQdZJiNx0myaoZE34sObgWQSDYyNXbZOTAvzJIcmDNgsC/Mn4J/LKMXwr8+CQZvyzj9Uv4pMC/Xr+Mxy8Ffnwybr8ft0/C7ZNw+SRcXj9Ob82/fhxePw6PH3v1j83jw+b2UeX24/G3bJhFa9CrlUTo1UQZNMSYNMSYtMSH6Ug0B+ZOWQza6snFgbkYooKTIHRtCoWCG0Z1w+b2sSLTCkBBlYdyt0S0QVwbGlITm46ORRCIR1J1LJKOjjnV/3r91bFGkvH4JNx+KRhjnNXxxOmrjiduH3a3nyq3r7qTzkuFy0dZdUed/wQTp/qE61TEmrQkhutIthjoFWmgT7SRKFE9TBCEBigUClJiw0iJDeMOWWa/1cHag6Vsy62sUzADAtfPzYcr2Hy4AsgGAlNTekcZSYkNo2+MiW5mHQnhehLNOsJ1atRIyJKMugN0/nWYJKvA5mHMy7+1dTPanEoBYVo14ToVZr2aCJ2aSIOGKKOGaKOGGKOWhHAtxibGvrvdXvySPxic3V4vqjZIBjszv3TkLqQsB8650HrE5zc0ZoxMpMLpZVNuJQA+sdJrozpybArXqYjSB+JHQnigI66bWUdPi56wBhYaPRnXMfG3HVoiNoWW+Pwe0T1cy3XDErhuWAJlTi+bcyvZkV9FhtWBrYGRWpIMWVYHWVZHg8ddclk88cb23/mnkOXjGYF98mzduhW5uuevxNW5Pqg1ObhCEfhvRfV/Bx5THNlez+NCx+CqKkeWJBRKJfpwS1s3RxCaRQZcPhm/DDF6JSqlAoVCwejRo9u6ae1Ge45NR8eLI/8qUCqO3SZ0VSI2CW1NksFfPcos8G/zn9tR4lK7v5NVkwOqlIoOkbUKwtFMUVFt3QRBOC5hx6zA0M774046EZuEjkzEJqEzaO9xqd0nWUqlEkmSUCgUqNXtvrmCIAidis/nQ5ZlUdjgGCI2CYIgtI2OEpfa/XBBQRAEQRAEQRCEjqR9p4CCIAiCIAiCIAgdjEiyBEEQBEEQBEEQWpFIsgRBEARBEARBEFqRSLIEQRAEQRAEQRBakUiyBEEQBEEQBEEQWpFIsgRBEARBEARBEFqRSLIEQRAEQRAEQRBakUiyBEEQBEEQBEEQWpFIsgRBEARBEARBEFqRuq0bEGqyLAd/BEHomhQKRfBHENoDEZsEQWgpEcs6lk6bZHk8HlwuFx6PRwQxQRBQKBRotVr0ej1arbatmyN0USI2CYJwIkQs6zjafZK1bds2JElCoVCgVjevuTW9g2q1GrVajVIpRkUKQlcnSRI+nw+fzyd6AlvA5/MhyzJKpZJRo0a1dXPaDRGbBEFoCyKWdZy41O6TLEmSgEBw8nq9zX6ewWAIBrKu+iEUBCGg5sutUqlEqVTidDrbukkdTs21WAgQsUkQhJNNxLLa2ntcavdJlkKhCA6p0Gg0Te4vSRJqtRqtVotOpxMBrBGyLAc/oEqlUpyrVibLMlVVVcELYnh4eFs3qVM5ns+vRqPB7XYHewLFnYTGybKMz+cDENeHY4jYFDoiNoWWiE2hdTI+v105lnWkuNTukyy1Wo3X60WtVjN8+PBG95VlGavViizLhIeHo9frT1IrOyafz0dubi4AiYmJzR7yIjSPz+fjk08+wW63Yzabeeqpp9q6SZ3K8X5+XS4XVVVVKBQKoqOj2/1Fui15PB527twJIK4PxxCxKXREbAotEZtC62R9frtqLOtIcalTpb5HV2pqTs+iIAhdT821QVR2E04WEZsEQWhtIpa1f50uyRIEQWgucc0QTgbxORMEIZTENaZ96lRJliAIgiAIgiAIQltr34MZBaGDM5lMAGJisSAIgtBuiNgkCKEnkixBCKHp06cDkJSU1MYtEQRBEIQAEZsEIfTEcEGhVYlxwYIgCEJ7IuKSIAhtIeR3srZs2cLs2bMpKyujsrIStVrNpZdeys0334xWqw31rxdOgs2bN3PgwAF2797NmjVr+Pzzz0lOTm7rZgmC0IqsVitXXHEFZWVljBs3jrvvvpvevXvX2c/lcjFr1izWrl0bLF0+bNgw7rzzToYNG9YGLa+rNeKS3+8PcSuFEyHiUuspLS1Fq9USFhZW7+N2ux2lUolKpUKtVnepNZuEttXe41JIk6xly5bx9NNPM3v2bEaOHAnAzz//zCOPPML69ev58MMPUalUjR5DBLL2b/369RQXFzNv3jyg/S8O1544nU4+/fRTfvvtN2RZxu12o1QqmTJlCrfccotYT0doN6Kjo1m4cCF5eXnceeedXHnllcyfP5/+/fsH93G5XNx000306dOHL7/8Er1eT3l5Offffz/XXnstH330EePHj2/DV9E6cengwYNYrVYiIyNPQouF4yHi0onbtWsXX3/9NUuWLOGdd97hzDPPrHe/0aNHN3qcW2+9lccffzwUTRS6uPYel0LW3XDo0CEef/xx7r777mAgA5g6dSpXXXUV69at46uvvmr0GJIkUVZWFqomCq3kvvvu4+mnn27rZrRLq1evZtmyZSxatKjOY+Xl5Vx99dVs27aN2bNn891337FkyRKeeuopPvroI+66664WD3MpKChoraYLQr26devGCy+8gN1u57HHHqv12MyZM8nOzub5558PdhBYLBZeffVVZFnmkUcewePxtEWzgdaLS/fee69Y76qdE3GpcQ3Fpvz8fD777DOuuOIKXnrpJb777jvcbneTx4uOjiY+Pr7en5YW1ygsLBRDPIUWaa9xKWRJ1pw5c3C5XFx00UV1Hrv00ksB6v3iebSVK1fi8/lC0j6hdTXV89tVHTx4kKysLNLS0uo89t///pfMzExee+01oqKigtvHjRvHHXfcwfr169m8eXOzfs+aNWuYNm0ar7/+equ1XRAaMmrUKJKTk9mzZw/79u0DoKSkhIULF3L++efXGXKXmJjI2LFjKSkpYe3atW3RZKD14lJWVhYGgyEkbRRaj4hLDWsoNr311lvs37+f1157jblz5zb7eF999RVr1qyp9+fee+9t1jFKS0v5xz/+wbnnnovX623R6xGE9hiXQpZkLV++HIvFQnx8fJ3Hhg4dikqlIi0trdHs8ddffw1V8wShzS1fvhyz2YzFYqnz2ODBg4HABaI5Nm7cyJ49e2ola4IQSiNGjABg586dAKxatQqv10tKSkq9+9fcOUpNTT0p7atPa8YltVoU5xU6n5dffpnnn3+evn374vQe6eQusrnJKrGTV+Gi0uXFL0m1nldfHGupsrIyfvrpJwwGg5izLxyX9haXQhIlqqqqKCgooF+/fvU+rtfriY6OpqioiJycHPr27Vvvfunp6Zx33nmhaKIgtDmfz4fNZmPfvn0MHDiw1mN5eXnodDrGjBnTrGPVfOETc7iEk6V79+4AZGdnA4HrNUBMTEy9+9eUij506NBJaF1drRmXAJRKpZgzLHQKsixTUOUmrcjGwTIHRTY3RVUeqtxHkqx52/JYbs+s9TyzXk33iMAd3T1WD4N1HiKNx58c1QzBFXFMOF7tLS6FJMkqKioCaLASDQRecFFREXa7vcF9iouLW71tQvPt2bOHOXPmkJaWhkKhoKioiO7du3PppZdy9dVXN9qTW1lZybvvvssvv/xCaWkpSUlJTJs2jVtvvbXW8yRJ4vvvv2fRokW43W6cTidarZaxY8dyyy231OlxTk1N5fPPP6ewsJCioiIkSWLSpEncc889wbs4ZWVlZGZmsnPnTlatWsXtt9/OwIEDef755/nrr7+44IILmDJlCs8//zw5OTkAGAwG+vTpw8yZMxk4cCB+v5/p06dz6NAhqqqqsFgsPPjgg1x//fUtaktjxo8fz/Lly7nnnnuYOXNmcOJlRUUFn3/+Oc888wyxsbHNeq9qqjk1Njympe/n4cOHmTVrFrt370ahUODxeEhKSuKSSy6pNdzKbrfz6aef8vvvvwOBYh6RkZFMnDixTrU2WZb55ZdfWLRoEZWVleTn5xMREcFll13G3/72t1rzXEpLS5k9ezYbN25EqVTicrmIi4tj8uTJ3HDDDc06L0LoREdHA2Cz2YAj1+uGrvvH7n+ytXZcEoUU2oaIS60blxweP48u2UOVq+VTMypdPva4qgD4dEM2eZs/xpa5DaXHTvfERC664FyuvPLKZt+VqvmbaiyOVVZW8uWXX7JmzRpkWaa4uBiNRsPEiRO59dZb6datW639PR4PX3/9NcuWLUOSJJxOJyaTifHjx3PLLbdgNpuD+65YsYK5c+dSVVWFy+VCqVQyfPhwZsyYUafTZf/+/XzyySfBIjgul4vx48dz7733kpCQENxPkiQWLlzY7M+ScGL+P3vnHR5F1fbhe2t6sukJKfQQOqFX6SqoCCKKDRSl+Nqwt/e1oKLoZ0cEsQKCIFWkg/QindBSgRDSe8/W+f5YdpMlhQTSOfd1cZGdOTNz9szs+c1zznOep6HpUq0YWbm5ueaTV9LZ2dnZAVTqlmE5j6Du+fnnn5k7dy7vvfcen3zyCWC+Hx9//DHvv/8+69evZ+HCheU+uIWFhcycOZPY2Fjc3NzQ6/VcuHCBzz//nFOnTvHdd99Zy7733nts376dRYsWWUeYIyIiePbZZwkJCeG+++4DzM/Je++9R0REBN988411tGLv3r08/fTTbNmyhZUrV+Lj48P27ds5efIkGzZsoKioiMmTJzNt2jSio6MxGo0YjUZuu+02Nm/ezL333ktMTAzjxo3j3XfftdZLoVCwevVqfv/9d/7v//6PdevWWTvOqtbleobWm2++yblz50hISODxxx/ntttuY+zYsaxevZr//ve/DBgw4Lr3KTo6mlWrVrFp0yYA1q5dy759+wBzxKc33njjhu5nbGwsEydOZOzYsaxYsQKlUolWq2XevHl8/vnnViOruLiYhx9+GGdnZxYuXIhGo0GSJDZv3szrr7/O6NGjre2TnJzMCy+8QIsWLfjyyy9xdHTEZDLx/fffM2fOHPbt22eN7Jaens6ECRPo0qULv//+Ow4ODhiNRv744w9mzZoljKwGgKUPt6ydyMszv2xV1O9fW76uqWldEkZW3SN06eZ1KU/uxL6L6eTrJWSAzijdkIF1Lcfmv0Vharz1c9KlWI4c3MfCXxebw+f7VTxgaDQaWbhwIQcOHADMBukDDzxg3f/TTz/h4uJCREQEM2bMoGfPnvz44484OzsjSRKbNm3irbfeYu3atXz77bfWAUtJknj66adJTEzkl19+sbbVoUOHeOGFF+jbty99+vQBYMGCBcybN4+ffvrJ6kESHx/PzJkz2bhxI88995z1nF999RUbN27k22+/tXqhnD59mqeeeorNmzfz+++/4+joCMCsWbP4559/rvssCWqGhqZLtbImyxKsojJXCsvIu+VBrOw8grpl8+bNzJkzh+nTp1sXgwO4urry8ccfExYWxvHjxyuM3PTuu+/y+OOPc+zYMXbt2sXOnTutoV+3b9/Ojh07AEhISGD58uXcddddNi48oaGhTJ482eacX3zxBatWrWLWrFlW8QAYNGgQ48ePJyUlhfnz5wPmTPYfffQRHTt2BODrr7/m1Vdf5cyZM8yePdsqwEqlkilTpgDmSEuma3zMAXbv3s2kSZNsRqaqU5fKCAgIYM2aNYwaNQowB6946aWXOHv2LGfPnqW4uPi652jbti1vvPGGVVSeffZZVqxYwYoVK6wG1o3czx9//JHc3Fyee+45a+dkZ2fHzJkzbXzvN2/eTEREBE888YR1u0wmY9SoUQwePNhaTpIk/vOf/xAfH8/7779v/d3L5XKeeeYZWrduzf79+9m2bRsAf/zxB4mJicyYMcMaYEChUPDII49Y76ugfrH04Zb7YxGpivp9y+h0fQWMqGldEtHP6hahSyVUV5fuu89cl2n//YRPd8Zw4FI25T2+Mhm4O6ho5eFAt2YuDGlVMlB4WysPJnbz575Ofoxq583gVh50beZCkJvZtc+zXRh9X/6OQe8spsd/PsG7c39ze16K5eFnXubXI5dJyStf0xQKBTNmzOCZZ54BoHPnzlYdW7FiBS4uLuTl5TFt2jQcHR2ZM2eOtb1kMhmjR4/mzTffJC8vj+eee46MjAzAnCtt3759PPjggzZt1bdvX8aNG2f9rNPpmDdvHgMGDLBx0Q8KCrLWycKSJUuYP38+r7zyio2bf+fOnXnyyScpKCjgyy+/BMxREv/8888qPUuCmqGh6VKtGFmWqeHs7OwKy1hCglYW2lMsfKwfvvjiCwBGjBhR7v6nnnoKgA0bNpCQkFBm/2OPPcbdd99tfdj9/Pz45ptvrNP427dvB0pGGE6cOFEmROzo0aOtL+mFhYX88ccfqNVqa0CI0rRr1w4wJxgtjeX6Q4cOZeDAgcjlcsaPH28jwvfccw9eXl4kJiayZ88em+OTkpI4deqUVfBupi4VsWbNGpKTk/nrr7/47rvvCAsLIysri88//5zx48fXiMvsjdxPy705dOiQTVmZTMbHH39s/WwZ1b+2HMDLL7+Mj48PAP/++y9nz56lXbt25frbWxalWtqtousDNtcX1B+W2R5LH27pr3NycsotX5U+vzapaV0SRlbdInTJTHV0yWSSOHApk2ijGwCXI8+UqadMBu28nRjexpNJ3QN4oKs/I0O86RPsTjufkhlBZ7UCN3sV3s5qgt0dCPVxpm+wO6Pb+/D2Z3N5/63XGdm9HS18NLgHtqbjgzMJ7D8agNSzh9l9No53t0Tyy+GKja3KWLJkCSkpKQwbNqxcd8L7778fd3d38vLyrFERLfp05MiRMsbqpEmTrG2l1WrRarVERESU6b969erF+PHjrZ9/+eUXAJsUEBYs9/z48eMAVrfj6z1LgpqjoelSrbgLWtykKnP3y8/PR6VS4e/vX2EZi6+koO6Ij4+3LhgsPfJTmv79+yOTyZAkiVOnTlkXDlro27dvmWMcHBwYNWoUP/30k3VtRNu2bWnbti2nT59m2LBh9O/fn+7du9O7d28b/+fw8HCKiopQKBTcf//9ZaZ9i4qKCA4OplWrVuXWt7KZD7VazUMPPcS3337L0qVLGTJkiHXfihUreOihh3Bzc6uxupTmyy+/ZN26daxevRoPDw/atWvHiBEj2LZtG//73/+IiYnhk08+uamw7Dd6P0ePHs22bdt4/vnnCQsLo3fv3nTv3p2ePXtahQRgyJAhfPnllyxevJj9+/fTr18/evToQa9evWjRooW13OHDhwGzS8X9999vfdGwkJOTQ6tWraz+6XfccQeLFi3ik08+Yf369fTu3ZuePXuWub6g/rD4sDdv3hwo6a8rEjNL+eDg4DqoXVlqUpcSExPLnWEQ1A5Cl4ZY91VVlwp1RjKLdOgMEiZ9MfYevjh6m9tEpZChkIMEOKoUDGl9c+9ancLMsz8dfJ3p4OuM0SQRm1GI66gHuHJwE0gSxVmpqJ1cORSXxb+XsxjQwoOxnf1wsatavjmLG3xF91+hUNC3b182bdrEqVOnALOB5O3tzfbt2xkxYoRVn3r37m0z2+fi4sLAgQPZu3cvw4cPp3///lYdCw0Ntb6Ax8fHWw346dOnl5kI0Gq1BAQEWHWsefPmtGnT5rrPkqDmaGi6VCtGVkBAAA4ODmRnZ6PVaq0+j6VJTk4mJCSkUv/4qryoCmqW0iHDLQtEr8XR0RFXV1dycnKq5ccaFBQEgLu7O2DuFH/88Ue++eYbdu3axV9//cVff/0FmJ+hF198kXvuucdaJzs7O1avXl3t73S9aeCHH36YH374gb179xIfH09QUBB6vZ6NGzeyfPlym7LVqUtl7q4XLlxg4cKFvPTSS2XWbo0cORJ3d3ceeeQRdu3aVek1rseN3s/Ro0ej1WpZtmwZp06dso7MqVQqhg4dygcffIBGoyE4OJiffvqJBQsWcPjwYX7//XdrMtcuXbrw/vvv06FDB2s9evTowYIFC65b7+7du/P999/z008/cfLkSc6ePcsvv/yCXC6nb9++fPDBBxWKraBuSEpKAqB9+/YA1peGlJSUSsuXN+pfF9SkLlmMLLEuq24QulR1Xfru16X8cSKBiNR8Aq45p6NKQTtvJzr5OrElXMb1UwzfGAq5jBBvJ0K8Q9nl5k5udiYquxIPBkmCfRczOZ6Qw70d/bitlSdyeeW/Jct3LCwsrLCMxbix3H9XV1d+/fVX5s6dy/79+1m5ciUrV64EoE2bNrz99tv07292a/z888+ZO3cu27ZtY8uWLWzZsgUwB8OZOnUqjz/+uM1z+PPPP1e47tpgMJCQkIBCobCu9arsWRLUHA1Nl2rFXVAul9OnTx+MRqM1Vn1pYmNjKS4u5u677670PJaHX1B3lB4du3jxYoXlLCM41bH+LW4YgwYNsm7z8/Nj9uzZHDhwgG3btvHpp58ybNgwEhISeOONN0hLS7OOIhUWFtZKBBgPDw/uueceTCYTS5cuBWDLli0MHz68TO6P6tYlJCSE9u3bW3M3WDh48CBGo5G2bduWe1zPnj3x8PC4aZfZm7mf48aNY8WKFRw9epTFixfz3HPP4erqytatW/nmm2+s5bp3786CBQs4cuQIa9as4X//+x+hoaGEh4dbM69b2s0yWlwVhgwZwuLFizl27BjLly/n1VdfJSAggAMHDvDBBx9U+TyC2uHs2bOEhIRYZxYt6wIto8jllddoNAwcOLDO6liamtYlkSy17hC6VHVdenf9SSJSbevjqFLQr7mGh8Oa0TtYg71KgX/zNgS0DiU4pH2N1700RqMBN3cPpozoSYi3E6XHJQp1RpadSOCjHdFczqrYeIKSPFyV3X/LwEnp+9+mTRu++uor/v33XzZs2MCHH35Ir169iImJYebMmdbfsZubG2+//Ta7du1i9+7dfP3114wdO5b09HQ+/vhjTp8+bROJsKpaVpVnSVBzNDRdqrVkxJaQouvXry+zb9u2bXh6ejJ27FjAHOLynXfe4ZFHHiE2NtZabuzYsWKksI5p0aKFdXq1ogzYWq2WrKwsAgMDyxgPlREdHU2LFi2sLzEZGRksXrzYuj84OJh7772X77//ntGjR2MwGEhMTCQ0NNTqg71///4b/WqV8vjjjwPw559/UlhYyG+//cakSZPKlKtuXfr378+wYcOswS0sWGa5KhqVM5lMFBcX06tXr+p8jTLrRG70fi5ZssQ6aufo6Ejv3r159tlnrffLklPi0KFDHDx4EDAv2O7QoQOPPvoof/75JwEBAdZyFteYiIgI66Lkyti4caM1v4VaraZbt2489dRTrFy5EgcHh3rLtSQwk5iYyLlz53jyySet2zp37kzXrl05fPhwmVHDgoICDhw4wKRJk+p1rW1N6ZKjoyNFRUV1UmeB0KXr6ZLSOwjZVRfs9OiSl0m1Qkb3AFcmdvOni78rilKzRSHd+9Kx72C69h96w/U7emAPacmJFe7PTE+lIC+Xex58FFcHNUNbezKxazOCNbazeFeyi/j4nxgOXMoEyl/v2L17dwAOHDhQYRCDxERzXSzRb6Ojo9m4cSNgHmRp06YNEyZMYNGiRYSFhZGTk2N1I/v666+t5/Hz8+POO+9kzpw5TJ06FcA6m2gxaC2RECsjKyvL6tkBFT9LgpqhIepSrRlZgwcPZsyYMfz555/8/fff1u1nzpxh0aJFfPbZZ9ap1nPnzrF8+XKOHj1qHbEBsy9l6ZEDQe0jl8t56KGHAFi+fHm5fqxbt27FaDTy5ptvlllbA+VHcUlJSeHAgQPMnj3bKkz5+fnWaHLX4uTkhIuLC23btsXX19ealHrevHm1MoLctm1bBgwYQF5eHm+++SaBgYHluqPVVF0so+EVff9//vkHg8HA008/XaXzWQYjsrKybLbf6P08efJkuSOGlohOPXr0AMzCU16QD7VajVKptJYbMWIEfn5+mEwm5s6de93vExkZyblz58q9viRJ1vOC2cVr3rx5PPDAA0RERFz33IIbw/LiU1BQwHvvvceYMWOsBomF999/H3t7e15++WUyM80vTHq9ntmzZ9OhQwdmzJhR19W2oaZ06a233hLRb+sQoUvl65LeYGLFqUQWnc3Dq0NvAC7tXIVkMtDWy5EHuzWjV5AGleLGXvVM10m27e7pzaLvv65w/5Y1f9Ktdz9G3TfRus3VXsmoUG9Gh3rjYl86N5nEobhsAFLTyw7EPfDAA6hUKmugqGvJz89n79693H777daw7GlpaeUa5XK5HAcHB1q0aIGXlxcGg6Hcc4L5niuVSrp27YparbaGlv/ll1+um2aosLDQGlClvPNaniUw969//PEHL7zwgjU6oeD6NHRdqjUjC2DOnDm88cYbLFiwgDvuuINHHnmE+fPns3DhQpscQG3atKF79+64u7tz++2325yjvsL93srMmDGDQYMGkZ2dzbRp06wLjsE8ivj555/zwQcf2ER5Kp1X5q233rI5Jjk5mTVr1rB69Wqbl+OMjAyOHj1qDZ1rYffu3WzcuJGPPvrIJtdE+/btiYiIYOrUqTbnz8vL44cffigThcniwlHVEWdLSNXNmzeXO1po4Ubqci1t27ZlxowZbNiwge+//96m/fbu3cvHH3/MZ599ZvUrvh4WX/SffvqJJUuW8Ndff1nXc93I/czMzGTevHlWVxowLxydNWsWYWFhPPHEE4D5Hi5fvpxLly5ZyxkMBubNm0dBQYE1YpZarWbu3LloNBqWLl3K7NmzbaK8WUK7W1wnMjMz+emnn2xcKYqKiqxrsV566SXr9u3bt/Pdd99hZ2dnE1JXULOkp6czceJEZsyYwdChQ5k9e3aZMu3bt7fmiBs3bhwTJkxg8uTJBAcH88MPP1SaZLSuqAldmjBhgnUNj6BuELpkq0uXswr5aEcUO6LMfWTIvdNw9m9BQXIcCX/+Hx0cinBUmX9vhfn5/PXHIk4ePlila1rIyiwxdvJyssvsb92uPfl5uSz/+XvyShm+Om0xf/2xiKzMDF56b065Rm+QxoEHu/jTPcDV6kKodjUPcMRdvMD0l9/k77//5rfffgOgZcuWzJo1C7lczvvvv8+mTZusL9jp6ek8++yz9OzZkzlz5livkZGRwebNm21cxSRJYuXKlZw6dcrah2VkZHDlyhWbWScwBxRZtGgRr732mjWQysyZMxkwYACpqalMmjTJZjCwuLiYP/74g3Xr1gHmSKbHjh2r0rOUnJzMu+++y/bt20XurGrQ0HVJJjXwOLTh4eHo9XrrSEJlGI1Gq5Xq4eHRIAS9IWNZnAnmRZilF3sbDAaWLVvGmjVriIuLo3nz5ri5uREaGsrEiROtkVssZGRkMG3aNPr168eePXtISEjAy8uLLl26lOsuB2bf6h9++IHIyEiMRiMODg7o9XoCAwOZOnUqnTp1silvydy+ceNGLl26hK+vL8HBwWg0GkaNGmX1qX355ZeJiYmxzmo4OTnRvHlz+vfvz6uvvlphe0iSxMiRI3Fzc2PVqlWVtl1V6lJZ+1rYuXMnS5Ys4eLFi/j4+KBSqejQoQOTJk0qEx2rMvLz83n55ZfZv38/KpWKPn368MEHH+DtbU4AWd37uWjRInbv3k1iYiKurq5IkoRcLmf48OFMnjzZOrW+Z88eVq5cSWxsLPb29iiVSgwGA2FhYUyfPt16fQvp6en8/PPP7Nq1i/T0dFq1aoWPjw/+/v5MmDDBmkvkr7/+YuPGjcTFxeHs7IxcLsdkMtG/f3+mTp2Ks7OztX3ffvttjhw5wooVK6rkJiT6iaqj0+ms65dUKhVdunSp5xo1HIQ21R4V9Z1Cl1YhSRL/xKSzKjwJo6nk9U0ul9HRy46s4zs4tGs7SQnxeHh64xsQiIurK30Hj6BLT/MMj8lksg5yaTSaMkbQ5+++TmZaKqlJCeTnmWdrVCo1Ac1bYO/gyLtfluSBjDh9kuW/LCD+YixePn54+/qh8fSi3+DhdOjWg6qQXqBjR0w62UUGrhzYyKWdf2LUFuPu6897b7/JHSNLDOcTJ06wcOFCjh8/jr29Pa1bt8bZ2Zl7772XYcOG2Zz31KlTLFq0iKioKFQqFWq1Gr1eT0hICDNmzLA+LxkZGXz99ddERERYA6sYDAY8PT15/PHHret6St+T1atXs3btWqKiotBoNLRq1Qo3NzeGDBnCsGHDSEpK4sqVK/z111/WhNOVPUsbN27kxRdf5KGHHuK9996rUrvdqv1KY9IlYWTdwlTFCBDcOAaDgU8//ZSCggLc3NysQSAENYPBYCA8PJxHHnmE0aNHVznUvegnqk5jErO6RmhT7SG0qXwKdQYWHb3CiQRbd0mNvZLBrTzwcy2bg7A8TCYTGxYvQFtUgIOTC3c9Nr02qlstjCaJI1eyOZWYZ7Pdy1nN0/1aEKhpPF5NN/L8Tp06lfDwcP7+++8yg5MVcav2K41Jl2rVXVAguNXR6/Xo9XobtxVBzbFy5Ur8/f1tEnkKBAJBUyMuq5CPdkTbGFgyINTbmfs6+1XZwLJgMOgx6M3/GgIKuYy+we6MCvXGTlnyapqer2POPzEcv5Jdf5WrZSIiIti/fz+ffvpplQ0sQeOgUQ0PXe9F1WQyYTQakclkGAyGciPUCEoovRC4omg9ghvn2jYVC+VrlpiYGDZt2sTnn3+Ovb19ldvXaDRiMpmQJAm9Xi+e/UoQYcqrhtCmmkVoky3/Xs5m6clEDMaS58ZOKWdgcw2tPM1reqqTHFsy2T5/DSmxdqCrHfd38mV7TAYp+ebflc5oYsHBOO5sV8jd7b0bfNTp6j6/H374IU888QQDBgyo1nvCrapljUmXGo27YHVwdnbGzs6uwf8QBY0bSZLQGqFAb6LAIFGgN1FskNCbQGeU0Jskorb+gaG4EKW9I+3umIhaLkMll6FWyLBXgpNKjpNKhrNSjkohntfqYFkHVp21a3D1vmm1tZLbpinT0N0y6hqhTYLaxihJ/BNfzJFk27TB7vYK+vrZ4ay+cWek3WuWoC0swM7RicHjHr3ZqtY4JgnC07VEZ9n+xtpqlNzb2gl1E9FLrVbLqVOn6NWrV7X7BaFlDV+XGtVMlkBQHxhNEhnFJlKLjKQVGsnSmsjSmsguNqE1Vj5G4WI0++TqjHAgUVtpWSeVDI2dHI2dAg97OT4OcrwdFbjbycVLWTlYMrkLBAJBU6PQILEmpoC4XNuZjVZuKrp5q21yXjVF5DLo5m2Hu52coylaLJNv0dkGFp3P58EQJ1xuwshsKNjZ2dG7d+/6roaglmg0RpZSqbxuOGtLxByZTIZGo6l0EaAkSVzKKqJQ37inVx1VClq4O9zQS7jRaCQ5ORkwJ9+7VRZNVoYkSaQV6LiYWcTFzCIuZRWSkKvFZKr9Cd8CvUSB3khCvu0zaaeUE6Sxp6WHAy3cHWnl4YirfaP56dYaN/r8Go1GsrOzcXNzKzeylqAEvV4vco9dB6FN5SO06cZJztOy5OBl0gtKDCylXEafIDc6+DibF2PdBKXdBeUyORqN5uZOWItoNBDgqWNLdAaFOvNvIrXQyKKIQv7TL7hBBsSoq+f3VtWyxqRLjepN7XoZmY1Go/VhViqVFT7YJpPEvb8cZsP51BqvY31wV3sf1j3RG/lNjGwpFIpbNoJTVqGO86n5RKbmE5GaT3bRjfv7KuUylAoZCpnMPNJ49ZbI5eYkjEaThOHqP2MVDTetwURMeiEx6YWAOW+Jv6sd7XycCfV2pp2PM47qW/PeWajO8yuTyaxipFKpbrkXOEHNI7SpfIQ2VZ/zKXksOBhHUSkj20mtYHgbT/yrGdyiIkzYrsFq6C/nPi72jO/sx6aINNILzOu0cooNfL7nEtP7NaeTv2s917BiavP5FVrW8Ll1eq5SpBfomoyIAWw4n0p6gQ4fF7v6rkqjwGSSuJRVSHhSLqeT8riSXbWkkGAWO1c7JS52SlztlLjaK3G2U+CkNv9TlBIrk8nEumMytDpwUCq4t1szm3MZTBKFOgMFOiN5WiM5xQZytXryig3kaA0U6ytejJyUqyUpV8uumAzkchltPZ3o5O9C12au+LrUjBALBIK6RWjTrc2e2HSWnUy08ZzwdlJzezsvnG/xgTRHlYJ7O/qyIzqdS1lmzdYZTXy3/yKP9QyifwuPeq6hQFCWW/JX6+Wk5q72Pk1GzO5q74OXU+UjqQ2FK1eucP78eSIjIzl8+DB9+vThmWeeqfXrmkwSsZkFHIvP4URCTpVmq1zslHg6qvBxVuPjbIe3sxq1ouZG/JRyGa72KlztVfiXs7/YYCK9QEtKvo7UPB3pBbpyXYhMJonItHwi0/JZFZ5EMzd7egRo6BHkVmMjnwKBoPYR2lQ/1JcuWTCZJFafTmJbVJrN9pYeDgxr7YmyBnWnMaOUy7i9nTeH4rIITzLn0zJJ8NuReLKL9IwK9RHrlwUNilvSyJLLZfw1pTeXMpuG33vLqyFc65OYmBjatGlz3XLR0dGcOXOGdevWkZSURK9evWq1Xok5xRyKy+Lfy1nXNaxc7ZX4OasJcHMgSGOPg6pk6j0l8QqnzsQSFxvN+VPHGXHPffQbMqKSs9089ko5gW4OBLqV+JwX6IxcyS4iPqeY5DwtBbqyz29iTjGJOcmsP5dMoJsDfZpr6B3sjsZBVav1FQgEN4fQpvqhrnWpNHqDiV+OXObYFdv8V12budArSIO8EqMhJfEKly/WrS7VNzKgX3N3XOyUHLiUhWXOb92ZZLKL9EzsFnBT7qkCQU1ySxpZYPZlbSwC0JBJTU1l+vTpnDt3jsjIyOuWHzp0KEOHDiUxMZG//vqrVupUqDPw7+VsDlzK5HJWxa6ASrkMX2c1QRoHWno44GpfsRESG3meC1Hn2b5+NdriYm67fXSV6tKh9yBMBgOubm7V/h7l4aRW0M7HvA4LIKdIz4XMQuKyikkt0HJtQoYrOUVcCS9i9ekkOvi6MKCFB12buYqRUYGggSK06cbYvXs3ixcvJi0tDYVCQW5uLmFhYcyYMeO6kUjrQpfKI0+r5/sDl4hNL7RuU8hlDGzhTujVPr4yblSXoOa1qa7p5OeCo1rBjpgMq3vl7tgM8rQGnuwdLDRO0CC4ZY0sQc3w3nvvce7cuWofV9MLNCVJ4kJGIXsvZnA0Pht9BaHVVQoZzVztaeXhSCsPhyp3xP2HjqT/0JFEnztN1NnTVa6Xd0BzgFqL3uTmoCIswI2wADd0RhMXMgqJySgkKVeLqZTFJUlwNjmPs8l5uNgr6dfcnUEtPcVaCYFA0Oj58ssv+eOPP/juu+/o2bMnAJmZmbz00ktMmDCB5cuX07Zt2+uepy4DB6Tla/lm70VS80tSe9gp5Yxo62njvVAZN6pLUPvaVBe08nDEIVTOpsg0q+Yfv5JDkf4iM/q1wF4lAkEI6hdhZAlumI0bN1JcXFyvddAZTByJz2JnTAbxFQSwkMtlNHOxo62XU7UMq/JQqxuuUaJWyAn1cSbUxxmd0URseiGR6QWk5mkpbXLmFRvYGpnG1sg0Ovm5MLSNFx18XYSLhUAgaHQcPXqU+fPn8+abb1oNLAAPDw8++eQThg4dys8//8zHH39cj7W05VJmIXP3XySvuCREu4udkjtDvPC4gTVsDVmXaht/V3vGdvTj7/Op1oiM51Py+WJPLM8PbIWznXjNFdQf4ukT3BBZWVn88ssvfPjhh4wZM6bOr59dqOef2HT2Xcgod10SgLuDirZejoR6O+OgvrVGtNQKOe19nWnv60y+zkhEijkwRv41bXUmOY8zyXn4OKsZ1sab/i3csROjfwKBoJGwfft2AJo3b15mn5+fHx4eHmRkZNR1tSrkTFIuPxyKQ2soiR7r5aTmznbeON1iOlVTeDiqGNvRl78jUq2Ga1xmEZ/tjGHmba1wd2z4wVcETRNhZAluiE8//ZSXXnoJJyenOr3ulewitkencfhydrl5plQKGc3dHejo44xfA4isl5uRhslkQtIV4+nX7PoH1ALOagU9g9zoEeRGYk4xZ5LzuJxdROnmS83X8cfJBP46l8xtrTwZ2sZLBMoQCAQNHoPB/FJ98OBBhg4darOvoKCAnJwc+vfvXx9VK8O+Cxn8fiLBJkR7oJs9t4d4oarjNUQNQZtqEld7JeM6+rLhfCoZheYgV8l5Wj7dGcuLt7USrvGCekEYWYJqc/DgQWQyGf369ePKlSs3fT6DwcB3333H33//TXJyMt7e3owePZoZM2bg7OyMJElEpxWwJTKVM8l5GHVaEg5vITPqJEZtEdrcTFx9Ahgw+j7G3XUndsoSscrPy2X3lr+JPHOKwoIC4mKi8PD2YcQ99zHynvsqrFNOViab16zgUkwUqUkJFBcV0qZ9J1KTkyo8Ju5CNH8tW0xSwmWQJPR6PRgNBAUG0Lxla+6d8uxNt9XNIAMC3OwJcLOnSG/kTHI+51PzbZJeFuqMbI5IZXtUGn2au3NHO2+Rd0sgEDRY+vXrx+LFi1m8eDGurq5MmzbNmhx67ty59OnTh0ceeaTa572eLl1LUVERS5cuZc+ePRQUFJCSkkLLli157LHHGDFiBH+fS+HvcykA6IvyST6+C2NSNGnoWBsbXWe6ZNAbaBbcHDu5hLubKw5OLvWuTTWFg0rBmI6+bIpIIznPvNYts1DHp7timDmoFYGaqq11EwhqCmFkCaqFVqvlm2++Yf78+TVyvoSEBF599VUGDRrEV199RWJiIl9++SULFy7k0KFD/PerH9kanWaNvpR98Rzn/vyG4NvG0vWJ/9HMzZ62rjI2LPiMdd/OQpWbxPjHnrSe/+2nH2fsI0/w0ntzAMjPzeHbj97h568/JTMtlQenzLCpj9Fo4O8VS4k+d5opL7yGh5c3AJdiovj5609JTUoo93ucO3mMj9+cyYNTZvDsW+8jk8nIz8vlo5dmEBUVRfOWlUe3qmscVAp6BbnRI9CN2IwCTiXlklFQEuLeYJLYfzGTA5cy6RGo4c523gS5i4hnAoGgYTFs2DDGjx/PqlWr+Pbbb1m1ahXTpk3j/PnzaDQa5s+fj0pVvVn56+nSihUrkJdKPH/kyBFefvllpk2bxq+//opMJiMvL4/XXnuNZ599ltvGT0Yedpe1/LF5b3Db2Ed44uX/AHWrS4X5+Sz6/kvOnThC/379qtUujQG1Qs5d7X3YGpVuXaedV2zgs12xPD+wJa296tb7RnBrI2JcCqrF/PnzmThxIm41FPZ17969vPHGG9x333107NiRkSNH8tNPP6NQKjl9+jSfrNhmNbC0uZmcXjIHl2YtuW30fYzv7M+YDr60D/ThqZfeAGDNkp/Jzizxv2/epi2DS4W1dXZ1Y/qrbyOTydi0+g+rq4mFr2a9zYaVS3n69XesQgbQok0I//38OwKatyz3e6xa8jN2dvbcPeERazJERydnwnr0qJF2qi3kMmjr5cT9nf0Z08GHII09pcNfSBIcjc/mw+3RzNt/kUuZhRWeSyAQCOoamUzG7NmzmTNnDiqVisTERN577z3+/PNP4uPjiY+Pr/Y5y9OlX375BZVKxenTpwkPD7eWTUlJYfr06XTo0IFHH33U2v+7uLjw33feM59vzRK0edmAORBT89ZtmTzxfus56lSXnJ157OmZ1W6TxoRSLuPOdt60LpUKoVhv5Ms9sZxLyavHmgluNYSRJagyly5dIjw8nHvvvbfGzjlkyBB8fX0Bc9b7Y/HZLDydg72n2Udcm51uLZtydDtGbRGDenVnZIgX3s4li1k17p64atwxGo1Enz9j3f7Se3OQXxOW18PLBzd3D7TFxeRmZ1m3nzlxlKP7d9N70FCcnF3K1FWttsPdw7Pc71GYn0dhQT4XoiJstqtUKrp06VLV5qhX/F3tGR3qwwNd/Wnr5ci1wQZPJeby8Y5ovtl7gQsZBfVTSYFAILiGs2fPsmzZMv773/+yadMmHnzwQRQKBRs3bmTMmDFs3ry5WucrrUsWfH19adnSbMwkJiZaty9fvpyCggK6du1qUz63WM9vZ3NQObkhmYzkxkehUsgY2caT9z75vF51ycHRibAePcs9pqkgl8Hwtl609y1x7dQbJebuu8jxK9n1VzHBLYVwFxRUmdmzZ/Paa6/V6Dnt7OyQJImTCTn8fS6VKznm6X2lvXlK36jXYaeUE+rjRGJqDAB7Nq3lzJEDNueRkFDb2RHcqg0ODrZubZIkcfliDDHnzhJ/KZaEuIsU5Oebz28ocZE7sm8XAIHNW1T7e/QbMoJLMVG88/xTtO8SRmjnboR07ILRaMTV1bXa56tPNA4qhrXxok+wkRMJOUSkFdgEGbHk2+rk78KYjn40F26EAoGgnjh79iyPPfYYH374IaNHm70WZs2axbRp03j77bc5dOgQb775Jn379q1yTig7u/KDJFj68tKpSw4fPgzAsmXL2LlzJwB6o4nkPC16owm5So2TX3McnZy4K9QH36sBGOpLl0I7d6N1aAc0Gg3awqY9WCYDbmvpgb1CzonEXACMJokfDsXxWA8jA1qWb5wKBDWFMLIEVWLXrl0cOHCAy5cv22wv7dZw5513AjBu3DimT59epfOmF+j4aEc08VnX5Li66trQwt2eh7s1Q62UsyI7E4BR903k7geqtpB5y9o/WbdsESaTkQHD76BHv0E8OGUGb86YTEqibdCO5ATzZ1UlOUdKJ/gtzd0PPIpSqWL31g2cPXGUM8ePAObklgEBAfTpN6BK9W1IOKkVDGzpQY9AN04m5nIuJR9DKWPrTFIeZ5Ly6NLMlTEdfQnSCGNLIBDULR9++CH+/v5WA8tCYGAgCxcuZNy4ccTExHDkyBFGjhx5U9cqvQ7LQlpaGgCTJ0/mySefJCa9gHn7L+JTKl2Gm72SO9t5WyO21rcuqe3sCAgIpGOH9tX49o2X3sEa7JRyDl3OBsxu8IuOXiFfa+SOUJ/6rZygSSOMLEGVWLVqFXq9nosXL1ZYxrIvPT29wjIWcorMI3Wnk3IJucbAslcpcLFTkgO09HBEfTVaoOPViE5ZGWlVqvO+HZv5de7ntAwJ5Z3Pv8feofLIQuqro5dZ6RWfX6/TlbtdLpcz+v6HGH3/QxTk5xEbcY4zJ46yadUyLl++jIenV5Xq3BBxUCno19ydsAA3TibmcDbZ1tgKT8wlPDGXnkEa7u7gi38DCJ0vEAiaPoWFhZw4ccI6wHctarWawYMHExMTY404WNNYZrdSU1M5Gp/NL0cuYzCW9I8+zuYcWA5X8w/Wty6dPXmMretWcvFCLI4O9nTuGlat79tY6drMFbVCzt6LmVjuzurTSeTrDNzX2d+6Zk0gqEnEmixBlfj222+JjIws82/Hjh3WMpZtb7/9doXnuZBRwJe7YzlbzuJTe5WcHgFuPNzNH2e7skkZW7ZpB8DpY0eqVOcdf68FYNS4B68rZADBVyMAnjx8sNz9RqOBxPi4cvf9+dtCpKujiU7OLnTp2YeJTz5NvwEDAcjPa/yLbe2VcvoGu/NIWABd/F1QXrNo62h8Nu9vjeS3I/GkF2jrqZYCgeBWwWg0IkkShYUVB+QpKCjAzs6Ozp073/T1pHJmjDp06ADAlp17WHgozsbAau7uwN3tfawGFtS/Lj301H94/r8fAua2uZVo7+vMyBAv5KW0a2tkGouOXsFoMlVypEBwYwgjS3BTlO6kjUZjheWuZBcxb/9F5vwTQ0Rqvs0+O6WcsABXHurWjJ5BbqgUcrTFRWXOMeKe+1AoFMRfiuXQ7h1l9l9Lfp7ZB9vZ1TYSYkriFXKvuh6WZuCIO1EolcRGnmPnpr/K7N+8ZgUFV895LWePHyEjNaXMdkvoYC/vpuOSYK+S06+5Ow+HNaOTn4uNYEkSHLiUyTubI1l24op1xlIgEAhqGhcXFzp37syRI0fIysoqs7+wsJB//vmHxx57DA8Pj5u+XnnG3IQHH0SuUJAUd4HUMyWGUEdfZ25vWzbJcEPQJXtHs2t3TbRJY6OlhyOj23nbDBIeuJTJ/INx6AzC0BLULMLIEtwUycnJ5f5tITVPy0//xvHh9ihOJZYIgUlvnulwU5iY2LUZvYM0qEuJUfFVMSsuKjG2mrduyxPPv4pCoeD7T2exY8NaDPqSl/ios+H8/M1n1s/tu5jdIA7s3Irp6ihV5JlT/LNhHTKZ+VqmUqNX/oHBPDr9OWQyGQu//ITf5n1J1LnTxEScZfnP3yOTyWkdah611F3jnpGTncXyX+aj05XM4GRlpHHq5An8/f1p3qJFpe3YGHFQKRjQwp2HuzWjvY+zTTRCo0liV0wG/90UwdozSRTqDBWfSCAQCG6Q999/H7lczrPPPktCQkm+qJSUFJ577jn69OnDSy+9VKVzFV3Vm6KisoN8UDKoaDG2CrQGtqXb0ebuJ5HJFUSs+o6ko9vp2cyJgS09kMtlDU6XMtPTWLZwLgGBQQQGBlapXZoaAW723NPBFztlyTtHeGIuX+6OpUArtEpQc8ik8ua/GxDh4eHo9XqUSmWZEKnXYjQaycw0jwR5eHigUJR1OROUYDAYrKIUEBCAUlm1JXrZ2dlMmzYNg8HAxYsXrYLj4uJCYGAgo0eP5oHHnmDD+RT2XczEVGr9TsK/W0g5sYv8pEuYjEaUKhXNW7XljrETGDRylLXc0w/eTXZGOk7OLvgHBjPjtf8RENwCgIvRkWxes5xzJ4+j02kJbtUGZxdXmrduy7C7xuLqpgGgsKCAxd9/xfFD+8w5q/oMYNDIO2nZNpQZE0aTk5VJ70FDGTDsdnoPGmq99snDB9mwcikXoyIwmYz4BzZnyguv0bpde/737JPERJzFVeNO5x69uev+h2nZth0rfllA5NlwsjPTcXJywSSZUKnUdOs9gD6Dh+Ph4YHavmmvVcrTGjgcn01seiHXdiqOagV3tPNhaBtP7JQ187u80edX9BNVR6fTcfr0aaBxpSOoC4Q21R7V/W0nJiYyf/589u/fj7OzM+7u7ri6ujJ+/HgGDx583ev9/vvvrFmzhvPnz2MwGFCpVISGhvLoo48yduxYa7mBAweSlpaGq6srgcHNaT5mBkWO5rxVeYkXSTy0kcLL55EM+oarS2o7Bgy7nc49+iKTydBoNE1emyoiq0jPhvOpFJQKUuLrYscLg1ri6VRxoJHrcaPaVF1u1X6lMemSMLJuYWqjIyjQGtgSlcY/0WnojbaPllwuo7WHA72CNLjYNf2YKyaTiezsbAA0Gk25kamaIllFev69nE3ctREjAVd7Jfd08KV/Sw+UN9kewsiqfRqTmNU1Qptqj7p6Sb1RIlLy+OFQnM3LuZNawci2XtYQ7Q2ZW1WbyqNAZ2TD+VSySrm2u9oreX5gS4JuMD2JMLJql8akSw2r5xI0WrQGIztjMtgSmUqhznZtlkwGzTUO9A5yw92xdiI8CRoO7g4q7mznTVqBjoNxWSTllriq5BYb+P14Aluj0hjT0Y+egRqbNV0CgUDQUJEkiX9i0lkZnmTjoeHpqOKOdt63xOBhU8NJrWBsJ182RaSRnGfWqtxiA5/timVq3+Z09m9ceS4FDQvRIwhuCoPRxL5LmWw4l0JucVlf5maudvQO1uDr3PBH9wQ1i7eTmjEdfEnIKebQ5WzSC0rWC6Tl6/jp38tsiUhjbGc/Ovm5iBC6AoGgwaI3mPj9xBUOXrINsNHc3YHhbTzLBLgQNB7UCjl3t/dhe0w6lzLNHhhag4nv9l9kYlgAQ1o33hQsgvpFGFmCG8Jkkjgcn836c8mk55fN0eHjrKZnoBtBmuuHqG3KXDofjlGvw9nVjfbd+9R3deqFADd7xnf242JmIf9ezianlDF+JaeIufsu0trLiXGd/Gjr7VyPNRUIBIKyZBRo+eHQZS5llkQXlMmgi78LvYM0yBvhAJHQJlsUchm3h3hzKC6L8CRzyhVJgmXHE0jJ03J/F38Ut7BbpeDGEEaWoFpIksSJhBzWn00hMbe4zH53ByU9At1o7elUD7VreMRFhKMtLMDByeWWF7KWHo40d3ckOj2fI/E5NusZYtML+L9dsXTwdWFsZz+a36AvvEAgENQkZ5Jy+fnwZZv+SqWQcVtLD9p4NV6dE9pUFhnQr7k7bvYq9pVKWvxPdDpJuVqm9Q3GUS1emwVVRzwtgiohSRJnk/P462xyuQENXOyUhDVzpZ2PU6Mc1RPUDXIZtPN2po2nE2dT8jiekIu2VG6Scyl5nEvJIyzAjXs6+BJwi8+ECgSC+sFkkthwPoUN51MoHR7M1V7JyLZeeDmJ9cVNlQ6+zjjbKdgWlY7h6tq78yl5fLwjhmcHtsDX5daMxiioPsLIElSKJElEpOaz/lwysellEzE6qhR08Xehk58LChHAQFBFFHIZXfxdae/jTHhSHqeScm2iUZ5IyOFkQg49gjTc3cEXf1chagKBoG7IKtTxy+F4ItPybbYHudkzrI0X9irhNtbUCdY4MK6TH5siUsm/OouZmq9l9o4YpvQOomszt+ucQSAQRpagAiRJIjItnw3nUom6RmgA7FVyOvm40KWZi1jwK7hhVAo5PQLd6OTnwvGEHM6m5GO8OnIoAUfjszl2JZveQRpGt/fFTxhbAoGgFjmVmMNvR+Jt3APlchnd/V0JC3QVnhq3EB6OKsZ39mdLVEnkwWK9kXn7L3FnqA9jOvqKdVqCShFGlsAGSZKITM3n7/MpRKcVlNlvp5DT3teJsGZuqJWicxHUDHZKOf2au9OtmSvHruRwPq3AGiJZkuDfy9kcjjcbW6Pai5ktgUBQs2j1RladTmJ3bIbNdkeVgiGtPW75IE63KvYqc+TB/ZeyOJ9aMuC8OSKVCxmFTO0bjKu9qh5rKGjICCNLAJiNqzNJuWyMSCnXLVClkNHe25mwAFfsVbdGwjtB3eOgUjCwpQdhAW4cvZJDVFo+lnQ0VmPrcjY9gjTc3tazfisrEAiaBNHp+fx2JJ60ayLlBrrZM6S1J05qoXm3Mgq5jNtaeeDrombvxSyrt0VUWj6ztkXxRK8gOvqJfFqCsggj6xZHkiSisvQsib7I5XICWqgUMtp5OxEW4IajMK4EdYSTWsHgVh70CHTjaHw20ekFJcYWZjfCo/HZhLir6O9vR0C91lYgEDRGdAYT684msyM6zSa4hUIuo2eAK12buYr8fQIr7byd8XRUszUqjTyt2Z00r9jAN3svMiLEm3Gd/FCK5ROCUjQpI0t0hlVHbzRxMC6LzefyySg2ltlvNq6c6R7gioMwrgT1hLNawZDWnvQM0nAsPoeo9JKZLYCoLD1RWXr2p17kzlBfOlYzqbHoMwR1gXjOGh5nk3NZeiKhTJ5HdwcVQ1p74ONsV081EzRkvJzU3N/Fn39iMmwiLW+PSiMiNZ8negXh51z37oOij2mYNDkjSyaTIUkSer0ehUIYB9dSoDWw52IGu2IyyC7Sl9lvp5QT6u1Et2bCLVDQcHBWKxjc2oOeQW4cS8ghstSaLYDo9EKi910k0M2Bke286BmoqXBEUa83P/eW/kIgqG2ENjUccor0/BmeyJHL2Tbb5XIZnX2d6RWkEZFyBZWiVsi5s50351LyORBX4j54JbuI2TuiGdXOm47OEopa1hehZQ2fJmdkqdVqtFotxcXF2NnZiQfvKil5xfwTnc6BS1nojKYy++1Vcjr4ONPV31UEtKhBXN290Dk64+TsUt9VaRI4qRXc1tKDXoFuHL+SQ0RaPqXSbHElp4hfDsezKjyJoW28uK2VJ852Jd2cJEkUF5uTaKvVatE/COoEoU31j95oYmdMOhvOp1Kst/Xe0DgoGdzS45aKXiq06ebp4OuMv6sd26LSybo6aG00Sfx9PpXDjgrubulQa67sQssaB03KyAKwt7dHq9Wi1+vJycnB3t4elerWjPxiMkmcS8ln78UMzqfkWbcrS/0WHVUyWrup6RbkierqzJUklTXCBDeAZCJs8B0AaDRuol1rEHuljH7BrrRxMhGbo+diroHiUtZWoVbPhrNJbI1IpkeghoGtPPB3UlFcXGwd/bO3v3VeqAT1j9CmqmM0GpGuLpIyGo039QIpSRLhSbmsO5NMeoHZNdCigUqFjE6+rnRr5oJcLrt1+mihTTWGxl7B+E4+HEvI4UxSHhb/iswiA4vP5RFbmMg9HX1xVNfc67Zerxda1khockaWWq3G2dmZ/Px89Hq99SG8lSjWG4nNKCQ6vYB8rQGANs62ZZztlDRzUeMsNyKTSSgNhWAUIyE1iiShlq76+2sLQIw01SyShJNcTxd36O3vQkqBnoScIor0pV8YJNIzMlmdnoGLnZLWnk6E+jrj4eaKWq2ut6oLbj2ENlUdk8mEVmvOS5SdnY38BnMRJecWcyoxl7QCHRoZaErpoMZeSVsvJxzUctCVTVfSpBHaVKPIgV7eSkJdXYhMzaewlAalpGeweH8WYQFutPZ0qvGmdnZ2FlrWgGlyRhaAg4MDCoWC4uJidDqddUSsKWOS4HxqHvsuZHIqMccmOIAFmcycsb6LvyvNXO2QJImkTPMMl8akuGEhE5SPyWQiO0u0b21h077uCuwUclq6OxCfXcyJxByS82wXtBtMEkmFRrJ1cHv7ZjzVJ5huAW71UXXBLcqtqE03giRJ5OebcxK5uVX/NxqdXsD6s8lElZPr0dVOQe9gDc1c7SjUGynUlw381NQR2lR7BGscOBqfzankPJuIlTui0/F3tee+zn509r+5cO8W92N7e3thYDVwmqSRBeZRQ7VajSRJ1n9NkYiUfJaeTGD5yQQSc7TllnGxk9O/uTt3tPPG92rEpELAaDSRWmQecVG626OQiY62JjEi2rc2Kbd9ZeDp4cAID3fis4vYFJnGwbhsig0m9CasrhwxBy4x78Aluvi7MKlnEA+HBdxS6zEE9cetok03g16vJyEhAQCNRlMlt0qjSWLj+RS+3nuRf68JagHgpJYzOtSHUaE+qBQyymaDvHUQ2lSLKKB9kB16nY51sflEZxtK7Szi5/AsBrRw553bQ+jfwqPap7cEuRBrsBoHTdbIstAUH8ZLmYWsOJXIipOJHE/IqbBcaw8HhrX2ZGgbL2swi9JyLiGVyj0kQxIdbY0iIbHyu08ozM/Fw9uHGe99Ud9ValJc7/kNdHdial8nHuluZEdMOtui0knMsx2ICE/K45X153h9w3mGt/HigW7NGNfJD3dHMTooqF2aojbVFEZjyeySXC6vNBpjdpGeJceuMHf/xXJnrhyUcoa38eS+zn642puNtVvdrBXaVLtISHjYK3i8gytJkjO/nkgkvaDEPXhHbCY7vj9E/xbuvDmsLaPb+4i+oInS5I2spkJ0Wj5rzySz5kwyh+KyKiznrFbQJ0jDHe28aOXpVIc1FJRHSvwF8rMzKcyt+J4JahdHtYJ7Ovhyd3sfotIK2BSZxr/x2eiNJa9aRpPE1qg0tkal8fSqcG4P8WZsJ3/u6eCLj4vIlyMQNCQkSeLw5Wx++DeOP04kXLMO04y9Us6QVh6M7+KHu4MYNLkWoU21j0wmo1eghh7B7myNSuPP8GTydSUDCAcuZXHPz4fp5OfCswNb8nBYgE00XEHjR9zNBoreaOLApUw2RaTx97lkzqXkV1hWKZfRwceZwa086NfcXYRgFwjKQSaT0c7HmXY+zhTpjey/mMU/selEpxfajGzrjRIbzqey4XwqMhkMaOHBXe19GRXqQ2f/6iU7FggENUdkaj5LTySw7EQCMenlB6tws1MytI0n97T3wc1BRG8U1D8qhZy72vsyrI0X686msCEi1WZg4ExyHjNWhvPa+nM81jOQJ3sH07WZq9CaJoAwshoIkiRxPiWfnbHp/BOdzvbodPK0hgrLy2TQ2sORPkFuDGnthbujEBOBoKo4qBSMCPFiRIgXaQU6dsWms/9SNldyim3KSRLsu5jJvouZvLnxPM1c7bi9nQ/D2ngxtI0nAW4O9fQNBIKmj0mSOByfzeaoTNafS+Z0Ul6FZYPc7BnW2pOR7bywV4pkz4KGh4NKwcRuzbi3oy+bI9JYfz6V3FLveblaA9/tv8R3+y/RwdeZh8ICmNgtgNZewiupsSKMrHpCZzBxIiGHg3FZHLyUyZ6LmaTklR+4woJcBq08HOkR4MrgVp7CjUkgqAG8ndRM6NKMCV2akZhTxO4LmRyOzyH+GoMLIDFXy69H4vn1SDwAbb2cGNjKg/7NzbPIoT7OyOVi9FEguBEkSeJCRiFbI5NZdyyLoylaMrVJFZa3U8joHuDGne286OArZpkFjQMHlYJxnf24u4MPu2Iz2BiRVkZvzqXk87/NkfxvcyQdfV24q4Mvd3fwoW+wO0qF8FZqLAgjqw4o1Bk4n5rPiYQcjl3J4cSVHE4l5aI1XD8BoINSToiXEz0C3ejXXCMW5AsEtUgzNwceCgvgobAA0gt0HLiUxZEr2USnF2IoJy9CdHoB0ekF/HLYbHS52SvpHuBG90AN3QPd6OrvSltvJ1RCFAWCMmQU6AhPyuXw5Wz+vZzFobgskq8z2KiQQTtvJwa0cGdQCw8cxRoWQSNFpZAzMsSbkSHeRKfls/58Gkeu2K4XBjibksfZlDw+3RmDs1rBgJYe3NbKk0GtPOge4FajiY4FNYu4MzWEwWgiIaf4ahLgfGLSC4lKy+dsSh4XMwupapReuQwCXe1p7+tMz0A3Ovu6oBRrrASCOsfLSc2Yjr6M6eiL1mAiPCmXI/HZnEvJJzlfV+4xOcUGdsZmsDM2w7pNpZDRztuZjn4utPFyoq2XE228nGjl4YiPs52Y+RI0aSzaGJNRQFSaeVAiIiWP08l5JJQzW1weaoWMdt5O9Ahwo38LdzzEYKOgidHW25mXvJ0p1hs5dDmbXbEZnEvNL5PzNF9nZEtkGlsi0wDzO2N7Hxe6B7rR2d+V9j7OhPo408LDEYXQlnpHGFkVIEkSxQYTucUGsov0pBfoyCjUkVGgIzlPS1KelpTcYhJzi7mcXURCTnG5CYCvh1IuI9DNntYeDnTycyGsmSvO9mJ9lUDQkLBTyukVpKFXkAaAnCI9J5NyCU/MJTqjkKRcbYVhofVGiTPJeZxJLruexE4pJ0jjQJCbPf6u9vi52uHvYo+PixpPRzVeTub/3RyUuNmrxIyYoN7RG03kaQ3kFRvILtaTWagns1BHZqGelHwtyblaUvO1Zm3MKiIxt/raKAO8HRS0dFPSt5UvvYLdsVeJdVaCpo+9SsGQ1p4Mae1JntbAkfgcDsdncyY5j+JyvJ9MUslMV2nUCjlBGnuauzsS7O5AoJs9fi72+Lqo8XWxw8NBjbujCncHlfht1SKNxsjKKTbw8rrTZWaEpKuvNpJkzn1hTu5o/tskmfPoGE0SJknCYJLQmyQMRgm90YTWaEJrMP8rNpgo0Bkp0Bkp1BnJ0xnKTNneLHIZ+DipCXKzp7m7A6FeTrTzckR9zQNuNNRNBvrSuUhK/y2oGa5t07q6r7cK9fn8OqvkDAzWMDBYA0CR3khUWgER6QVczCzick4xGYX6yk8CaA0mYtILKoyUdi32Sjkudkoc1QqcVAoc1XLslArslXLslHLUChkquRylQoZKIUchA4VchlwmQy4DuUyGDJDLzf+DOYgOQMmWkm1Go4lx/iac1cK4qwiLNtUUlXk9SNeY8qXLlta/ks9mHSythyZJwmiSMFq00WTRRhN6o1kjtQYTOoNZI4v0Rgr1Jop0Rgr0xiq5uVcXO4V5sLGVuwNtvZzo5ONIcY55NtjPzxWFTPSfNYnQptqlprTJUSFjcAsNg1toMJokotILCE/K41xaPhcyi9BV8o6qM5qIzSgkNuP6abfVChnOaiVOdgqc1QocVArslHKrrqjkZm1RKuQo5TIUclmF2mLVE5mtxpTWl/KoznJKo9HEWH8TLo1Al2RSA083f/z4caSropBeXPOde21gedDkYH34FJaHUMze3lIU5mYhmUzI5HIcXd3ruzqCOsTygmuUSr/gQuPoxUrwspejkJsT53bv3r2+q9NgaIzaVN/YauNVXZSDQghjnSO0qfFjHSwppS8N+oW+BmksutTgZ7IsNqBCLsPXUUxpChoXDh4e9V0FQb3SdF4eG/h4XJ0jtEnQmBHa1BRoOvpyozR0XWrwRpZcLsdkMiGTyVAqG3x1BQKBoElhMBiQJAm5vOG7ZtQlQpsEAoGgfmgsutTg3QUFAoFAIBAIBAKBoDHRsE1AgUAgEAgEAoFAIGhkCCNLIBAIBAKBQCAQCGoQYWQJBAKBQCAQCAQCQQ0ijCyBQCAQCAQCgUAgqEGEkSUQCAQCgUAgEAgENYgwsgQCgUAgEAgEAoGgBhFGlkAgEAgEAoFAIBDUIMLIEggEAoFAIBAIBIIaRBhZAoFAIBAIBAKBQFCDCCNLIBAIBAKBQCAQCGoQYWQJBAKBQCAQCAQCQQ0ijCyBQCAQCAQCgUAgqEGU9V2B63HixAlMJhMymQylssFXVyAQCJoUBoMBSZKQy+WEhYXVd3UaDEKbBAKBoH5oLLrU4JXBZDIBIEkSer2+nmsjEAgEtyaWvlhgRmiTQCAQ1C8NXZcavJElk8mQJAkAlUpVz7VpWkiShMFgAECpVCKTyeq5RrVPod5IVqEe6ZrtcszPmkwGSGCQri0Bdgo5nk4q5FVsp1uxfesS0b61T+k2Fu1ri9Cm2kP8tmuX8to3vUBHscH2hVUGeDiqcFAp6qGWjRfx/NYujUmXGryRpVQq0ev1KJVKunTpUt/VaVLodDpOnz4NQPv27VGr1fVco9rlm70XmLnurM02DwcVo0K9GRXqjb2yREjis4tYdzaF/XFZ6I0lBldLD0c2Te1DiLfzda93q7VvXSPat/Yp3cbCJc4WoU21h/ht1y7Xtm+RUUbPd7dgMJUdXJTLYNv0fgxt41XX1Wy0iOe3dmlMutSwayewIkmS9V9NUXqa1WQyYTQaa+zcDQlJkpi9I4ZP/onBXlEy6nFXqDf3d/FHbdkmlbRHsJsdz/UPZmJXP+bsiiUpVwdAUk4RQ+bu5e+n+tK1mWul171V2re+EO17fcyzs7IGP9onaDjUhtZUF/Hbrl2ubd+tEekoZaC8qoW3h3iyNSrDWmbOjihua+le5/VsrIjn9/rcKtokjKwGjk6no7i4GJ1OV+OiV/qHn52djULRNF0CdkSnc+pCIqNaOAAgk0HvQA3dAlwBLWV8B0vh5Ahf3d6crVFpJOZqrdvnbg/n7REhuNpX/BO6Vdq3vhDtWzVkMhlqtRp7e3sxoiqokNrUmuoiftu1y7XteyExxaqPTmoFj4R5EuapIDwpDwAHhZbMzMx6qWtjRDy/VeNW0CZhZDVgioqKyM/Pr7Xzy2QynJ2drX83RdLydSTnaRnY0gMwf8+2Xo74udhV6zyTewYRnV5Acl6JoXXwUiYjQ7yRy8tvu1uhfesT0b5VQ5IktFotWq0WZ2dnHBwc6rtKggZGbWtNdRG/7dqldPtKgLOd0qqRvi52eDnbMaClB672JWsN87UGnO3EK2NVEM9v1bgVtEn8YhooOp3OKnoqlQp7e/saX1xtMBgoKioCQKPRNHjf1uqSrzWw5HAMOUUAMmTAba088fNyrPa5ZEBIkDOXo9K5nG1us5h8PZJDMQ+HBZR7TFNv3/pGtG/V0Ov1FBcXo9fryc/PR6FQNNlRQ0H1qQutqS7it127lG7fLKMd57IlzCoHLfxcwd4BR6WJmOgSwztJr6SPv0d9VLfRIZ7fqnEraJO48w2U4uJiwCx6bm5utTIaIkmS9bwKhaJJTWmbTBK/Hk0go9CARTw6+TnTpgoBKypjcBsv1pxJJrvIHNlm38UsgtwdGdK67KLgpty+DQHRvlVDoVBgZ2dHTk6OVdSampAJbpy60JrqIn7btUvp9j2TWoBBMv+tUsgIdHdEJpNhr5LjaKcit9isdTEZxfRvKe5DVRDPb9W4FbRJXt8VEJRFkiR0OnOgBXt7+wYheo2NjedTOZeSZ/3s66ymb/DNL9xVK+Tc2c4bVakAGstPJpKYU3zT5xYIaguZTIa9vT1Ag1hzI2gYCK0RnE4u0Uk/F3sUpdzffZxLXngvZxXVab0EtwZNXZtqfSbr2LFj/PDDD2RlZZGbm4tSqWTMmDE8/vjj1bZYLWLQ1DGZTBgMBmvkFUs+gJqm9OLMphT95kpOERsiUqyfHVQKhrX2QIaEqZwQtdXFRa1gRGtPNkelI2GeNfv5cByvDWllk0OrqbZvQ0G0b/WQyWSYTCarH7xcfv0xttJJdjMyMrjvvvvIysqid+/ezJgxg5YtW5Y5pri4mAULFrB3714kSSIjI4POnTszbdo0OnfuXKPf6UapCV1qCs9c6SiC9e0iKKh7MoqMpOaXvFe1cLe32e/jbEdMeiEASbnFGEwmlFXoNwSC6mDpeyz9UXUGexq6LtWqkbV582beeecdfvjhB7p16wbAxo0beeWVVzh06BALFy687jSqRcgMBoM1Lv6tgrOzM0VFRXUyupicnFzr16gLJEli0fl8qzElk0FPHzWGonyya3AgzhkIcVcTmWUWqPjsYtYcu0Bff/tyyzeV9m2oiPa9PhbjKj8/n4SEhGof7+npyerVq0lMTGTatGmMHz+e5cuX07ZtW2uZ4uJiJk2aRKtWrViyZAn29vZkZ2fz3HPPMXHiRH788Uf69etXk1+r2tSELl26dImMjAzc3Rt3WOumNmosqB7R2SUDuDKZOQ9kaUrPZBlMEldyimjh7lRn9RPcelS3T2roulRrQxKXL1/m9ddfZ8aMGVYhAxg9ejT3338/+/fv5/fff6/0HCaTiaysrNqqoqAJcjJNR0J+yQhzS1cVfk614w/d0VONk6rEAN6TUEyWtvGPbgsEldGsWTM++OADCgoKeO2112z2zZ49m7i4OGbNmmV1AdFoNHz66adIksQrr7xSrx4JNaVL//nPf8TMj6DRcyGnZKba20mNg8pWK70c1ZQOnhuTVlBXVRMIqkVD1aVam8latGgRxcXF3HXXXWX2jRkzhuXLl7NmzRomTZpU4Tl27NhhtWqVSiXt27evreo2KEwmE9nZ2chkMjQaTa0tmjQajdYZAD8/v0a/ODNPa2D3iRjrZ0eVgoGtvbFT1p57w1CVA3+fTwPAYIKtV4y8OCgImUzW5Nq3oSHat3oYjUays7Nxc3NDo9FU2V0wIiKizPawsDCCgoI4d+4cERERhIaGkp6ezurVqxk3blwZlzt/f3969erFoUOH2Lt3L8OHD6+x71UdakqXYmNjm2S4YcGtgyRJpBSWDAqWl9ZEIZfh4agmvcD8AhqbUciIOquhQFA9GqIu1ZqRtW3bNjQaDb6+vmX2derUCYVCQWRkJDqdrkIf+K1btzJy5Ejr56YWdaQijEaj9YVRqVTWycujQqFo9GFG1x5PpFBfIhq9g9xwUNfudwpwc6CDrzPnUsyhbmMzCjl4OYfbrok22BTatyEj2vf6yGQyq2GlUqluul/p2rUr8fHxnD59mtDQUHbu3IleryckJKTc8t26dePQoUOEh4fXm5FVU7oE5r5ZuNsJGisFeokiQ8nz6+VU/vPu61xiZF2pSZ97gaAWaGi6VCtD/Hl5eSQnJ+PlVTasNZijGHl6emI0GomPj6/wPFFRUbVRPUETJDI1j0NxJa6lzVztaOdzc+Haq0qfYA1O6pIX1jVnkinU1U6wEoGgoRAYGAhAXFwcUNJfV9TvBwSY88ldvny5DmpXlprWparMBAoEDZW0IpPN54qMLG/nkhmutAIdxXrhEi9ouDQ0XaoVlUhNTQWwZrwuD8sXLiio2Mc3LS2tZismaJKYTBJ/hidZPyvlMga2uLkF6dUZoVYr5AxqWZKksVBn5O9zKZUcIRA0fjw9PQGsiWwt/XVF/f615euamtYlEe5ccLPU50xoWlGJsaSUy9DYl+8JUDr4hSTBxUyxLkvQcGloulQr/jW5ubnmk1fivmNnZx4dqWyxmeU8AkFlHL2STXypHB4dfJxxd6y+a2nE6ZMkXbnMhagITh4+wP/+bx4+/s2qdGxzdwcC3ey5cjVf1s7YDAa10FS7Do0Rk8lEYWGh1bVVqVSKF9BbAEsfbgnznpdnzrdTUb9/bfm6pqZ1STzjghvh6NGjXLx4kbNnz7Jnzx5+++03goKC6rwepY0sF7uK+2w3exUqhQy90WwQRqUV0N7XtU7qWBqhM4Kq0NB0qVaMLEtep8ryiFhcLRwdHSssU1v5oQRNB4PRxLozJeG7HVQKegS53dC5zpw4Sk5mBtv/XgOYQ9pWh37N3VkZnmTNnfVneDL3Nm/664SOHj3KY489VmmZ+fPnM3ToUJttNZlDT1D3WPpwSwAIi0hV1O9b1oDVV8CImtYlsR5LcCMcOnSItLQ0/vjjD6D+jPXSRpbGoWKdksvMLoOJVwcQL2XWz7qsutKZqKgovvvuOxISEtBqtWi1Wu644w6mTZuGi4tLjX4nQc3T0HSpVt4ALQ9udnZ2hWW0Wi1ApQ+teNESXI89FzKti3IBuvi7oFbcmBfs/ZOewmQ0Wo2s6uLhqKJ9qSAYZ1Py6aZxorlb0w71bEke6OPjU2EZS9hUCzWRq6g0SUlJ9TIafCtjme2x9OGW/jonJ6fc8lXp82uTmtQlrVYrjCzBDfHss89iNBqtRlZ9IEkS6aXWZLk7VK5Rvk5qq5F1Jad+jKy60Jnjx48zdepU5syZw4gR5jiKhw8fZsaMGezatYvly5dXOgBzLenp6TRrVjVvGEHN0NB0qVaMLA8P8/qUytz98vPzUalU+Pv7V1jG4ispEJRHkd7AhvMla59c7JV09ru5H4r8JiOu9Qx0Izq9wOpaseVyMU91avqzWV5eXuzZs6dKZS25il544YUyuYoOHTrE8uXL+f333ysNo20hISGB+fPnc/nyZXbt2nWDtRfcCBYf9ubNmwMl/XVFYmYpHxwcXAe1K0tN6lJiYiImk6nCMgJBZdR3uonMIj064/UjC1ooHfwit9hATpEet+sYZrVBbepMfn4+zzzzDGPHjrUaWAC9e/dm+vTpfPHFF3z99de8+eab1712ZmYms2fPZv/+/Rw+fFjk1KtDGpou1Urgi4CAABwcHMjOzrZaideSnJxMSEhIpf7xrVq1qo3qCZoI26LSydeWuJT2CHBFIa9fH20HlYIeASXuiulFRs6k11/y1brC3b3qgUaul6sIYM2aqs0mJiYm8u+//6LRaKp8fUHNkJRkDjZjyV/YunVrAFJSyg/6YinfoUOHOqhdWWpal4SRJWisJObaPv/e11nDXDr4BcCFjPoJflGbOrNy5UoyMzMrLb9u3bpK3Y0tZGdns2vXLuzt7YVHVh3T0HSpVowsuVxOnz59MBqNnD59usz+2NhYiouLufvuuys9T//+/WujeoImQG6xnu1RJdEnPZ3UhHg51WONSujk54JLqUhNuxOKMRibtmtRdVwoqpOr6HpYXoavdRMR1D5nz54lJCSEdu3aAdCvXz8ATp06VWF5jUbDwIED66yOpalpXaqvAB4Cwc2SlFts/VutkOFcQWRBC05qBXbKktdFS4CnuqY2dcaS/668fEr+/v74+PiQlZXFlStXrnttiy5ZgioI6o6Gpku15sf08MMPs2vXLtavX0/Pnj1t9m3btg1PT0/Gjh0LmEcE33vvPWJjY5k1a5bV8hw7dizHjx+vrSoKGjFbI9PQGkpGknsFulW4gPhidCSb1yzn8oUYZHI5Welp+Pg3Y+DwOxl2170oFBX/DAry81i9+Cf+3bOT3JxsvP38uG3kXdz9wMM2x5lMJvZu28juLRvQ63TkFRZSaJTj1qI9QQPuZs/FTG4PLensw8PD+e2330hJSSE1NRWTycTQoUN5+umnrW5NWVlZxMTEcPr0aXbu3MlTTz1FaGgos2bN4uDBg9xxxx2MGjWKWbNmWfP6ODg40KpVK2bPnk1oaChGo5EJEyZw+fJl8vLy0Gg0vPDCCzz88MPVqsv1UCqVpKam8uuvv3L06FEMBgMtWrRgzJgxDBkyxFrOkquoTZs25Z7HkqsoNTWV+Ph4a19QEZZ7Xpn7TWpqKr/99hv//vsvCoWC5ORkXFxcGDZsGE888USZ0dGCggJ++eUXdu/eDUBRURHu7u4MGjTIZrG0yWRi7dq1rFmzBq1WS1FREWq1ml69evHEE0+UEfeqtrNOp2Pp0qVs3rwZk8lEUVERTk5O9OvXjyeeeAJX17qP7HUtiYmJnDt3jo8++si6rXPnznTt2pXDhw+TkpJi8/0LCgo4cOAAkydPrteR3ZrSpblz51rvt0AAcO7cORYtWkRkZCQymYzU1FQCAwMZM2YMDzzwQKWzo7m5uXz33Xds2bKFzMxMAgICGDduHFOmTLE5rqb6HJdedwHmvtPOWMT58BPERp7j+MF93PPgYzRv3ZZfvv0/zp44Qu9Bw+g7eDj7Pp9DQYY5yNQ+tR2/tG3TpHQmOjoaBweHCkN9BwQEkJqaSlxcnNUVrSIswRcqy6WXm5vLkiVL2LNnD5IkkZaWhkqlYtCgQUyZMqXMWq7q6ML27dtZtmwZeXl5FBcXI5fL6dKlC5MnTy6jqRcuXODnn3/m0qVLZGRkUFxcTL9+/fjPf/5jzTkF1X/26oMGqUtSLfLKK69I7du3l9avX2/ddvr0aalfv37Svn37bLaFhIRIISEh0qxZs2zOcejQIeno0aPSyZMna7OqDQqDwSClpqZKqampksFgqLXr6PV66dKlS9KlS5ckvV5fa9epaXKKdNIzq05J01aclKatOCm9u+m8dOxyZrn/3v/8W6lrt27S178stW7bcy5OmvrcS1JISIh0z7j7pX0R8TbHWJ7FtbuPSKPuGSt16dpN6jdgkNQuNNS67+Enptoc8/SLr0k9e/eR1u09Jh27nCkdvZwpzVjwt9SlzyDpnje+kl5ce1oq1hkkrVYrvfnmm9K4ceOk+Ph463fas2eP1LFjR2nQoEFSSkqKJEmStGLFCumtt96SunbtKoWEhEjbtm2TxowZI7Vv314KCQmRXn31VUmSzPdx9OjRUkhIiPTee++V22ZLliyRunXrJiUlJVm3VaculXHo0CFpwIABUq9evaztU/rfyy+/LJlMJkmSJCkmJkYKCQmRHnjggQrPN3bsWCkkJEQ6depUhWVyc3Olr776Srr77rulkJAQqWfPntKECROkCRMmSI899pj1egcOHJB69+4tffLJJ5JWq7W216JFi6T27dtLAwcOlCIiIqznLSoqksaMGSM9/PDDUlZWliRJkmQymaSNGzdKnTt3tmmn//3vf1K/fv2k6Oho67bz589Lw4cPl1atWnVD7WwymaQpU6ZId955p829OnjwoNS7d2/p0KFDFd+I63Aj/YpWq5WOHj1q0wfn5+dLU6dOld54440y5c+dOyeFhYVJjzzyiJSRkSFJkiTpdDrprbfekiZPnlyr/VlVqQldWrFihbRly5ZGr011pTXVpbFp008//SSFhYVJ69ats27LycmR3njjDSkkJESaOHGilJeXZ3OM5dmKjIyUxo8fL3Xr1k0aPHiwFFpKZ/7zn//YHFNTfU633v2kyT/tlKatOClNmTVXmv7Cy1LnLl2kkJAQacGytdLIUXdJoaFmnXnyPy9Ixy5nSv+3I1LqNnCYFBISIt395Mxy26Gx6kxRUZEUEhIiDRgwoMLyzzzzjBQSEiJt2LChwjIGg0H6/vvvpUcffVQKCQmROnfuLN1///1WbcrNzZUkyXzPBg8eLL388svW58JkMkkbNmyQunbtKvXo0UM6cOCA9bzV0YX58+dLXbp0kY4cOWLddvnyZem+++6TvvnmG5tzfvHFF9KIESOk8+fPW7eHh4dLvXv3lsLCwqTIyEjr9qo+ezdCdfuhxqRLtZqyfs6cObzxxhssWLCAO+64g0ceeYT58+ezcOFCBgwYYC3Xpk0bunfvjru7O7fffrvNOeor3K+g4bIlMs0aWAKgR2D5Idv/3fMPvy/4ljETJzNw+J3W7U7OLsx49b+EdOxM5NlwFn75SbnH//TVHEaNn8hP67Yzd9k6vv19Ld16m6eej+7fzdED5gW4aSlJ7Niwlv7DbieweUvAPEY4sk83AvuPBqBAZ2RrVBpffPEFq1atYtasWTajRIMGDWL8+PGkpKQwf/58ACZMmMBHH31Ex44dAfj666959dVXOXPmDLNnz7aOuCmVSqZMmQLArl27yl0rsnv3biZNmoSfn591W3Xqcj0KCwutEZuOHDnCzz//bK33+vXr+f3334Gay1Xk4uLCM888Y/WVHzp0KCtWrGDFihUsWrQImUxGfHw8zzzzDJ06deL111+3jlQplUoee+wxnnrqKVJTU3n66aeta3Q2b95MREQETzzxhHWdl0wmY9SoUQwePNh6/YSEBJYvX85dd91lM1oaGhrK5MmTbepanXY+evQo+/bt48EHH7S5V3379mXcuHEVtkddkJ6ezsSJE5kxYwZDhw5l9uzZZcq0b9+elStX4uHhwbhx45gwYQKTJ08mODiYH374od4X/EPN6NKECROqtT5E0HTZvHkzc+bMYfr06db+CMDV1ZWPP/6YsLAwjh8/zjvvvFPu8e+++y6PP/44x44dY9euXezcuZPbbrsNMM9I7NixA6i5Pue+++6jMDuDuF2rAeg3fDTTXn6blm1DAVjx6wIenvosizfvZfor/8XByeyG7+lsR9CAewC4fPpwuWuTGqvOVKd8ZW7CCoWCGTNm8PTTTwPQrl07li1bZtUmFxcX8vLymDZtGo6OjsyZM8eq4zKZjNGjR/Pmm2+Sl5fHc889R0ZGBlB1XdDpdMybN48BAwbYzNQHBQXxzDPP2NR1yZIlzJ8/n1deeYXQ0FDr9s6dO/Pkk09SUFDA559/DlTv2atrGrou1WrYM7lczqRJk64bJcze3p5ly5bVZlUETYScIj27Y9Otn/1d7QhwK389zh8/zQOg18DB5e6/54HH+Pzd1zi4cxsPPfUfvH1tI4rdMe4B+g0piTLk6e3DzHc+5pUpE0lPTebo/t307H8bhQXm6DTRZ0+j02lRq82dcbCbPe16DSKr2CxGm87Es3/ZH6jV6nIXWVp8iI8dO2az3eJyMHToUKvf8Pjx4xk/fnzJd7nnHr744gsSExPZs2ePjetEUlISp06d4rPPPrNuKyws5I8/ql+X8mjbti1Lly616agHDBhAr169eOSRRwgPD2fFihU8+uijNZarqCosWLCAgoICm0hRpZkyZQo//vgjCQkJbNq0ibFjx1rF9tChQ2WOe/nll63hgy0JDk+cOIFWq7XxvR89erT17+q2s+X6R44cYdKkSTbuJpMmTarXPC3e3t5VCjvdqlUrvvnmmzqo0Y1RU7pkZ2cn1mUJ+OKLLwAq7GeeeuopnnnmGTZs2MDLL79MQECAzf7HHnvMps/w8/Pjm2++YfTo0SQmJrJ9+3aGDx9eY31Osxbml+ScuAjAvJ4ZSlyvu/cdSJeefQAYcufdDLnTvEbR3UGFb9eBXNi2lOLsdDZt38ndd5R858asM5byleVmrSldWrJkCSkpKYwZM6bcl/v777+fL7/8kqysLJYtW8azzz5bZV2w5PWKiIggJycHN7eSAehevXrZtN0vv/wCYBN50cK196Wqz1590NB1qenHlhY0KbZEXTOLFVD+LFZKYgLJCeYFqp7e5efV6NS9FzKZDEmSiDl/toyR1TGsZ5lj7Ozt6TtkOH+v+J2sdLOxF9S8FYEtWhEbeY7nHxlH5x69CenYhfadu+EUsxtFQQGS2oHLTi0pLi5CoVBw//33lxk1KyoqIjg4uMKompYRu/JQq9U89NBDfPvttyxdutTGyFqxYgUPPfSQTYcbHh5OUdGN16U0Hh4e5frUq9VqnnzySV544QXi4uKs2+DmcxVVhX379gHYjPyVRqPR0LFjR8LDwzl58iRjx45lyJAhfPnllyxevJj9+/fTr18/evToQa9evWjRooX12LZt29K2bVtOnz7NsGHD6N+/P927d6d37942Pu/VbedevXrh7e3N9u3bGTFihPX6vXv3thkFFggE9U98fLy1b6uon+nfv79VZ06dOlXGyOrbt2+ZYxwcHBg1ahQ//fQTqampQM31OZk5+dh7+OLoba6Ht6NtePGWbduV+z3cHdXIlSoCet/BpX9WsOT3pTZGVmPWGUv5ytI7WMpXtGarqlxPlxQKBX379mXTpk3WYA1V1QUXFxcGDhzI3r17GT58OP3797fqV2hoqFVT4+PjSUhIAGD69Oll1iNptVoCAgKsdazqsycoizCyBI2G7CI9e0rNYjWrZBYrJzvT+ndxcRH2DmVHn+wdHHB0dqEgLxeDoeoj0j7+ZnFyuepOJlcoeOPjr1j520KOH9rHvu2b2bd9MwCOTk60Dw2lWcsQdHnZgFkAVq9eXeXrWbie6+zDDz/MDz/8wN69e4mPjycoKAi9Xs/GjRtZvny5Tdn0qwainZ3dDdWlqrRt2xYoGf2rqVxFVcHyHYuKKk6eaVkEa5mRCA4O5qeffmLBggUcPnyY33//3eqC0qVLF95//306dOiAQqHgxx9/5JtvvmHXrl389ddf/PXXX4B5gfSLL77IPffcU+12dnV15ddff2Xu3Lns37+flStXsnLlSsDsvvb222+LqKsCQQPB8vsGrIEIrsXR0RFXV1dycnKqNfNpSa5ucUutqT5n/dlk/j5nDmdtp5Bhr7adTbGzL19nnNUKVAoZzfrczuU9azh55GCT0RlJklAoFGi1WoqLi8uNVltQYA5bf72gF9fD0iaFhYUVlrlWl6qjC59//jlz585l27ZtbNmyhS1btgDmHGNTp07l8ccft3luf/755+sGHqnqsycoS62uyRIIapKtkalVmsUCcHYpibSTFH+5wnKqqyM4vs2qPktQdNU9sGuvkhFIT28fpr/yNgtWbuKrRSv5zxvv0r3fQAoLCjh+/DjFxUUoHcwCXFRUZE2AV5N4eHhwzz33YDKZWLp0KQBbtmxh+PDhZfJIWUa0CgsLa6UuFiwi0bt3b6DmchVVBct3vnDhQoVlLG4PpRMRdu/enQULFnDkyBHWrFnD//73P0JDQwkPD+e1116zlvPz82P27NkcOHCAbdu28emnnzJs2DASEhJ44403SEtLu6F2btOmDV999RX//vsvGzZs4MMPP6RXr17ExMQwc+ZM4aImEDQQSs/aXLx4scJylpmC6iQ8tbhoDRo0yLqtJvqcxFLh113U1XsFdHdQoXZyxafrIKQmpDMqlcp6b5KTk8stn5SUhEajuenBP0sbVfa8lKdLVdUFNzc33n77bXbt2sXu3bv5+uuvGTt2LOnp6Xz88cecPn3aJhKhZab0elTl2ROURRhZgkZBTpGePRcyrJ+budrRrIJZLAC/gCBcNeYRwFNHDpVbRqfTkpedhY9fM9qEVuyKdy3xly7gFxDEgKHmxfA5WZlsXrPCut+3WSCDRozi5fc/JSg4GEmS0BUV4uzXHK76Um/5Z3eVr1cdHn/8cQD+/PNPCgsL+e2338pdexIaGmr1B9+/f/8NX+/333+v1O89OjoauVzOk08+CdRcrqJrkaSyeci6d+8OlLhnlEdiYiJyudzqV37o0CEOHjwImBdBd+jQgUcffZQ///yTgIAALl82G+wZGRksXrzYep7g4GDuvfdevv/+e0aPHo3BYCAxMbHa7RwdHc3GjRsBc1u1adOGCRMmsGjRIsLCwsjJyakwc71AIKhbWrRogaenJwB79+4tt4xWqyUrK4vAwEC6du1a5XNHR0fTokULa19YU31OQm5pI6v8tCcVoXEwuxYG9Tcn7G1KOmOZCSovn1J+fj5xcXHcfffdFaaKKY/KdOnAgQMVfqfExEQAa2Lk6ujC119/bT2Pn58fd955J3PmzGHq1KkA1tlHiwF84MCB636Pqj57grIII0vQKNhWxbVYFuRyOSPvuQ+AHX+vIT+vrNvAkb3mSHyPPv1CufksTOV0gJnpaZw+dpgZr/4X+VXxKCos4Mj+8o0mpUqFUqnE08Mde1cPvDuYR9q++nZurcxItG3blgEDBpCXl8ebb75JYGBgub7fvr6+jBw5EoB58+bdcF2Ki4tZsmRJuftMJhO///47M2fOpEuXLtbtltwp69evL3PMtbmKqkp5vvePPPIIYF6se+TIkTL7ExISCA8PZ9KkSVbXnPj4+HIXYavVapRKJT169ADMortt27Zy6+Lk5ISLiwtt27atdjunpaWV+7Iml8txcHCgRYsWeHl5AeYZ0UWLFjF9+nTriLJAIKg75HI5Dz30EADLly8vdwBk69atGI1G3nzzzXJ1prwX7ZSUFA4cOMDs2bOtRkpN9Dl6g4nU/JKZHTd12cALleF+1chy8g3Cs23XJqUzEydORC6Xl1t+586dKJVKq6ZcD4shVp674gMPPIBKpSI5Odnqclea/Px89u7dy+23306fPuYAJFXVBYPBUO45wfyMKJVKunbtilqt5oEHHgDMATAqc6u01Kkqzx6YDcs//viDF154gS+//LLS894KCCNL0ODJK9azO7ZkFsv/OrNYFsY+8gRde/UlPy+XT996ieSEeOu+U0cOsezHeTz14hv0GlASfdBQSgQW/N+HNsdkpKWyZ+sGZn//K+06lYxI5mZnERF+0hrS3cLJwweIj4sjLCwMtUpFGy8nQu6dhrN/C1IvX2DylCetC3XB7B7yww8/sGeP7XksbhaVrS0qjSWk6ubNmyuNoDZr1izat29PREQEU6dOrVJdruXBBx/khx9+YO3atTYh19PS0nj77be5/fbbmT59us0xgwcPZsyYMfz555/8/fff1u1nzpxh0aJFfPbZZ1VOTunt7Q2YR0m//PJLNmzYwJ9//glAnz59eP755wF4/vnnOXSoZEYzLi6OZ599lvHjx9u4AGZkZLB8+XIuXbpk3WYwGJg3bx4FBQXWMMwZGRkcPXrUGl7Zwu7du9m4cSMfffSRdX1Addo5IyODzZs324ymSpLEypUrOXXqlE142rNnz/LRRx9x4sQJ64inQCCoW2bMmMGgQYPIzs5m2rRpNr/vvXv38vnnn/PBBx/YRB8s3Ve+9dZbNsckJyezZs0aVq9ebR3UgZrpc5LzitEXFXJ5z1oyok6isSt5BSwqNK850hZXrDMWIwugWT/z7H9T0ZmQkBCmT5/O3r17+emnn6ypUOLj4/nqq6945513qhSgA7BGoY2Li+ODDz5gw4YN/PbbbwC0bNmSWbNmIZfLef/999m0aZN1xis9PZ1nn32Wnj17MmfOHOv5qqoLGRkZXLlyxbqO2EJ4eDiLFi3itddeswZemTlzJgMGDCA1NZVJkyZx7tw5a/ni4mL++OMP1q5daz1vVZ+95ORk3n33XbZv3859991XpfZqysik8uYzGxDh4eHo9XqrBX4rYDQaycw0B27w8PCotRj+BoPBGmEmICDgptfA1BarTyexJaLEb/ju9j4VBry4FqPRwLa/VrNn20aSr8TjFxiEk7MLzVu3ZcTd4/ALCLIpn5OVyadvv0ynsJ6cPHKQtOQk3Nw9aNO+Iz36DaLv4OFlrpF05TLrlv3G5QsxmEwm1Hb2GA0GvP38cXFQ42Rvh4OTC0Mens7yU0kY9QYSDm+hMPJfCtOT8PX1JTg4GI1Gw6hRo6xh2l9++WViYmKIiDCH2nVycqJ58+b079+fV199tcLvLEkSI0eOxM3NjVWrVlXaPpYs8hs3buTSpUsV1qUy/vzzTxYtWkRqaiqtW7fG09OT4OBgHnjggQoXCZtMJpYsWcKff/6JTqfDy8sLd3d3nn766UqjKJbG8vz++OOPrF+/HoPBQNu2bXn33XdtwtLu3r2b3377jTNnzqDRaAgKCsLLy4v777+fXr162Zxzz549rFy5ktjYWOzt7VEqlRgMBsLCwpg+fbrVqLt48SI//PADkZGRGI1GHBwc0Ov1BAYGMnXqVDp16nRD7Xzq1CkWLVpEVFQUKpUKtVqNXq8nJCSEGTNm2LTnwoUL+b//+z9eeeUVqytIZdxIv6LT6azuNiqVymak+FanKWhTXWlNdWks2mTBYDCwbNky1qxZQ1xcHM2bN8fNzY3Q0FAmTpxYph/MyMhg2rRp9OvXjz179pCQkICXlxddunRh2LBhjBo1qsw1aqLPcfbyJ1GrwKdTPzzadmVsaydW/PA1CZcvEhcbDYCDoyO+zQLp3KM3D0991uacucUGlp00u4VJkkTE9y/h7eHepHRm3bp1LFq0iJycHHx8fHBycmLKlCn069fvunW0YDAYmDt3LkuWLKGoqIigoCBef/11hg4dai1z4sQJFi5cyPHjx7G3t6d169Y4Oztz7733MmzYMJvzVVUXMjIy+Prrr4mIiLAGYjEYDHh6evL444+X+Q6SJLF69WrWrl1LVFQUGo2GVq1a4ebmxpAhQ7j99tuRy+XVevY2btzIiy++yEMPPcR7771Xpfaqbj/UmHRJGFkNEGFklZCvNfDWpvNo9eZRJT9nO+7t5FvPtaoaJpOJdb/MRVtYgIOTC/dOeZbdFzKJSDXPTMlk8O7t7fB3rZrBKLClMTy/tcldd92FwWBg3bp15UbDuhZhZNUspbWpffv29V2dG8JkMpGdnY1MJkOj0TQYI8toNFoDEPj5+TWYejV21p5NZluU2SvEUSXjrpZOuLm6IZNXbZ2RJMGvxxIwmMyvjWM7+jAyxLvW6ttYuZWf3xkzZnD69GnWrl1rHZS8HkajkezsbCRJQqPRlOtWWxq9Xm8dfG7ounRrvZUIGh3bo9OsBhZAWIBrJaUbPj0C3YhKL8BkkpAkWB2eyDMDq+aCIBBY+Oeff4iPj2f58uVVMrAEtYfBYCh3cX1jwtnZmaKiomot6q8rKor2Jqg+F1JLIvw5q8wvsjm51Quk46yWkV1sNrJiUrLp4KS7zhG3NrfS8xsbG8v+/fuZNWsWOp3OOgh6PSRJQqvVkp+fX+VjGgtiTZagwVKgNbAzpmQtlo+zmmD3ynNFNXSc1Qo6+JQkMwxPyuNSZkE91kjQ2NDpdMyZM4e33nqr0c6gCASCuietsGTA0rWa4dvLOy6zuOKIf4Jbj++//54JEyZYQ+kLxEyWoAGzPTqNYn1JJ979OhEFGyIdeg/CZDDgWiqfSliAKxGp+VaXi1Xhybw8RGRNF1SNvLw8PvjgAyFkDQThLljz3MruVrVFkd5Iri7b+tkS9KI67oIAvoV5XL46+5WjwxpIQVDCrfj8FhcX8/TTTzNo0KBqz4hb3AXd3Nyq7S7Y0BFGlqBBUqA18M81s1jNG+EslneAeUFq6SSNjioFnf1dOJFgDpsalZZPZGoe7Xxc6qOKgkaGp6enNTePoGFgSTbb2DAajdYXQKVS2SBfBhUKxS233rI2SMm2TcqrsTPfa5lcdt2X2tK4O5Y86wU6I1ojONmJ+1MRt8rz6+zsXCZgR1WRyUqeQZVK1SD7oRul6d/5Jo4kSVzKLKJQX/1pe4PRQGqGOTFhtjIPpaL+HgdHlYIWHg7WEZBt18xihTXCWazK6OrvytnkPHRGy2xWEm8Od26QayIEAoHgZrSmutSmNl2rNbcKiTklodnlMnCrZiJiC+4OtvfiSk6RGCAUCCpAGFmNGJNJ4t5fDrPhfOr1C1+XqBo4x81xV3sf1j3Rm0K9scxarBaNcBarMuyUcro2c+NIfDYAcVlFnE7KpUuzpmVMCgSCxk/Nak11qXltsmiNvBpuco2dhJxi69/Odsob/u6u9irkchmmq+7u8dnCyBIIKkIEvmjEpBfo6kn0aocN51NJL9CVWYvVoxHPYuVmpJGdlkJWatkIQ539nLFXlUyLrzqdZBUugUAgaCg0Va25lUjMLXEX1Njf+Pi6XGZ7fEKOtpLSAsGtjZjJasR4Oam5q71PkxG/u9r7YKeU8U9MunVbY48oeGLPFrSFBSBX0rZ7X65cukjE6ZN4eHkz9aU36R7gyoFLWQAk52o5GJfJgJZivY1AIGg4NEWt8XJqnOvobgRJkkgo5S7ofhNGFoC7g4rMQj0AybnF1yktENy6CCOrESOXy/hrSu+bW5OVYhZNH1+fel+T1dLTkdWnk2zyYjXmWazS5OflEXP+LEcP7OHKpQvcdvtoADr4OHM6KY88rQGAtWeS6RXkjlrZMCaZU1JSOH/+PFFRURw+fJgBAwbwxBNP1He1BAJBHXKzWlNdalObLFpzK5FTbKBAV3LfPJzUgA6j0ci5U8dJSbhCbOQ5Th4+yHd//HXd9Wrujiq46tGfnK9FkqRbbo2bQFAVhJHVyJHJZDcsGAaDAY3BHOEuwNel3iPgZBfp2RldMovlW4uzWDERZ9m4chlRZ0+Tk52Jk7MLbdt3YtR9D9KhW48y5RMuX2LDn79z9sQxsjLSsXd0pEWbEEbecx+9Bg657vW8fXy4d8oMZDIZVy5dsG5XyGX0CXZje7RZsXKLDWyNTOXujn419l1vhsjISI4ePcrq1avJyMiga9eu9V0lgUBQD9yM1lSXhqZNdUVxcTEmk8ka6bGmoqyVnsUC8HZUI2l16PU6zp08RnLiFQ7u3AZQJWPJq1SEwUKdkZwiAxpHVY3UtSpotVrCw8O5ePEiJ06cICEhgUWLFtXZ9QWCqnJr9FyCRsGG8ynojKVmsQJrZxZr347NfD/nA0wmI+6e3rhpPMhIS+HogT0cPbCHKc+/ysgx463lz5w4yv/97xW0xcW4atzReHqSkZrK6WOHOX3sMGMefIyHpj5TpWuXFyq3tacT4Ul5pOab1whsjUpjcGtPXOzrTrQq4rbbbuO2224jKSmJv//+u76rIxAIBI2G8PBwfvnlF06cOEF6ejpubm507dqVyZMn06dPnzLlp06dyuHDhys8X8eOHVm9erXNtgsXLrBw4UIiIyNRKpUUFBTQp08fpk+fjq+vLwCJpVz6lAoZrvYKcrRgb+/A/ZOnkpacaDWyqsK1rpaxGQX0cNRU+fibJTc3l/3793Ps2DEOHz4scnUJGiwNwydJcMuTmqdl38VM6+dmrnYEaWpnFuvo/j2EdunGl7/9ybzl65m7bB1fLVpFu45dAFj0/VdkZZTMqJ06fBAf/wA+nr+IBSs38c2SNXy3/C96DhgMwF/LF3Mh6uYS4/UNdrf+rTWYWHembKCM+sTe3r6+qyAQCASNhr/++ouJEyeyceNGjEYjXl5epKens2PHDiZNmsTSpUvLPc7FxQVfX99y/3l5edmU3blzJ+PHj6d9+/asXLmSFStW8McffxAfH8/48eOJjIwEILFUcAo3O2WZ2arq5MkCcFIrcCwVtOliZmG1jr9ZvL29mTlzJjNmzKjT6woE1UUYWYIGwbqzydbIejIZ9AnW1Nq1crOzmPLcK/gFBFm3+TYL4MX3PsHB0RGDXs+Jf/eXlM/J4uGpz9CiTYh1m8bdk+fenoWXj9mt78i+XTdVJ39XO1p4lBiV++OyxIJigUAgaKRs376dnj17snXrVvbu3cuuXbvYvn073bt3B2D27NmkppYNJPLqq6+yZ8+ecv/98MMP1nKJiYm89NJLjBo1ikmTJlkNJRcXFz799FPy8/N57bXXMBgMNu6CmhrykPAsNZsVn11UScnaw87Orl6uKxBUFWFkCeqduKxCjl7NFwXQwt0BH+fa6zw9fXwJaN6yzHY3dw/adeoGQEFennW7XK6gU/feZcqr1XaE9R1gLp+fV2Z/dekTpMEywGgySSw9kYAkiZDuAoFA0NjIysri3XffpXnz5tZtQUFBzJ07FycnJ/R6Pbt37y5znEajqdL5lyxZQmFhIXfddVeZfe7u7txxxx1ERERw8OBBG3dB9xpaO+VdyshKFAOCAkG5iDVZgnpnzekk699yuYzeQZpavd4zb7xX4T6LG4VfYMks1/RX3r5++VKzYtVh85rlbF+/hrSUJJycXQno3BvXvmNRO7sRmZrPyYQcwgI1AOj1etasWcOWLVsoKCggMTERf39/JkyYwPjx421cQIqKili/fj379u2joKCAc+fO4erqyrhx43jqqacqXEien5/P77//zsmTJ7ly5QpZWVl06NCBtLS0Cr/DlStXWLBgAWfPnkUmk6HT6QgICOCee+4p9wVAIBAImjp+fn60bt26zHZPT0969uzJ7t27ycnJKbPf0bFqwUWOHj0KUOF6JMu1t/6zG32He6zbayp0vZdTibGWW2wgT6vHxa7+1xELBA0JYWQJ6pVzKXmcT8m3fg7xdETjUD8dtSRJxF2IxtnFla69+lbpmEsxkSgUCvoOHl6ta+Xl5PD9p7NoFdKep19/l7ycbNb8/jNndm3A4dQRejz7GUo7B5adTKCDnwtxF2J54YUXGD58OPPnz0elUqHT6fjwww95++23OXbsGB9//LH1/NOnT6dHjx588803ABQWFjJr1iy+/PJLYmJi+L//+78ydfrzzz/ZsGED7777LtOnTwfg8uXLfPTRR5w7d67c7xEbG8vEiRMZO3YsK1asQKlUotVqmTdvHp9//rkwsgQCwS3JZ599VuE+y4BYixYtyuxTKpVs3bqVNWvWkJCQgJubG3369GHixIk2a7Kys7MBc6S98nBycgLgckIi9h1KtntXwci6GB3JkgXfEBcbjclooHW7Dtz70GQ6de9lLeNZKsJg7pUYZr74E9rcTFJTUzGZTAwdOpSnn34aDw8Pm3MfOXKE9evXk5qaysWLF8nNzaVfv368+OKLBAVVPFi5c+dONmzYwJUrV0hISMDLy4vg4OAKy+t0OpYuXcrmzZsxmUwUFRXh5OREv379eOKJJ3B1db1uOwgEN4swsgT1hsFkYsXJBOtnlUJGz1qexaqMYwf2kpmWyhPPvYJafX13xUsxUUSfO8NdEx7Gw8u7Wtc6e/IoH879maCWJSOdoZ278cJj95GblUpG5HF8uwwgp8jA6hNxzH9lKm5ubrz88stWgVar1fz3v/9l06ZNrF69mocffpjOnTsD4O/vb5PPytHRkffff59t27axfv16Xn31VWvkKYCPPvqIZcuWsWnTJhuhCw4O5vvvv+fJJ5/kwIEDZb7Hjz/+SG5uLs8995x1dszOzo6ZM2eyd+/earWJQCAQNHUkSSIiIgKNRsOgQYPK7P/oo4+Ijo622Xb48GEWL17M/PnzCQsLA8DNzRx999y5c7Rr167Mea5cuQJAZnYOza5us1fJcVQrMJlMZcpb2LxmOYnxl7nv0SnY2dtzeO9O/l7xO2dOHOWVDz6jRz9znV3tlagwcnr1D+Qnx/HMfz9iynDzerO9e/fy9NNPs2XLFlauXImPjw8Aa9eu5ccff+SPP/7A2dkZgG3btvHiiy9y4MAB/vrrL2tZC5GRkXzyySfce++9fPrpp8jlcoqKili6dClffvllhW389NNPk5iYyC+//IKfn3nt9KFDh3jhhRfo27dvudEdBYKaRqzJEtQbO2PSScotGYXr6OuMk7pm8oJUl4L8PH6b9wUDht/B7ffef93yBoOBH7/8hNDO3XhwytMVlht494MMm/AEox5+ymZ7SIfONgYWgL2DA23adwRAVZhl3b58zXqSk5Pp0qVLmahQarXaOhp67Ngx6/Y5c+aUGamzs7OjTZs2ACQklBi3cXFxLFmyhD59+pQ7kiiXy2nWrFmZ7QB5V9euHTp0yGa7TCazmVkTCAQCAezYsYPk5GSef/75cgM3NGvWjHXr1hEeHs7mzZt55plnUKvVZGdn8/zzz6PTmVN99O/fH4Bly5aVMZoKCwutKTfkDs7W7a521x9Xz83OZsrzr9KxWw/ahHbk4anPMuKe+wDYum6VTdn4HX+QfHwn7e6dRr5aY90+aNAgxo8fT0pKCvPnz7dud3JyYsqUKVYDC2DkyJGMGzeOrKysMuHpT506xf3330/v3r0ZO3asNbiHg4MDTz75JB9++GG53+Ho0aPs27ePBx980GpgAfTt25dx48Zdtw0EgppCzGQJ6oWcIj1/nyuJrORip6B7LeXFuh4Gg4GvZr1FYPNWzHj1f1U65scvP8YkmXhl1meVJspUqMyuj0q1rYuGqoKZMicnFwACnOUUAxKQGXsGMItzZGSkjaElSRJZWVm0bdsWd3f3MuezJGuMiooiOjqa2NhYwLy+y8L27dsxmUxWA6w6jB49mm3btvH8888TFhZG79696d69Oz179ix3dFUgEAhuVXJzc5k9ezb33HMPjzzySJn9zz33HD169LAmIW7ZsiXPP/88HTp04JlnniE1NZXdu3czcuRIJk+ezNq1azl16hQvvvgiM2fOJCAggOjoaL755hs0Go3Zdc++RFfdq+CKP/zuskZIp7CebPtrFempJeuni4uKiNm/GZlShbN/CxJzbINfWPr/0oN/I0eOLPeaHTuaBxdLD/4BfPrpp+h0OsaPH1/eYQQGBpa7PTfXnMj6yJEjNpEXASZNmoSLi0u5xwkENY0wsgT1wtozyRTrjdbPfYI0qKqZq6MmMJlMzJvzPmq1mpnvfFypwWRh6cK5XIm7yNuffotjqRG5mkAmNxtQjioFrf1cOJOchy7fvDi624ChfP9/s6t0nk2bNvHFF1+QlZXF6NGjrf7xL7zwAgcPHrQpGxcXB1SeC6si95LRo0ej1WpZtmwZp06d4vjx4wCoVCqGDh3KBx98UOVoWQKBQNBU0ev1vPDCC7Rp06bCWf7evctGsQUYMWIEbdu2JTo6mkuXLgHg4eHB0qVLef/999m6dSubN29GJpPRv39/3nnnHT755BOioqJQ+pZE0i0drKIiVOqya7Ycnc1Gia7U+q/YyHMYdFqQyzk2/y1kcgX3/+aA/OogYFFREcHBwbRq1arM+XJzczl27Bjnz58nKiqK8+fPW9vIQmZmJseOHcPNza2MC+H16NWrF97e3mzfvp0RI0bQr18/evToQe/evSs0zASC2kAYWYI650JGAQcu2SYebu3lVOf1MJlM/PD5bCSTiRffm1MlA2vlbwuJPn+Wtz/9FgfH2q1zryA3YjMKUdqbo02duRBPZqEOD8fKFy4fPXqUF198ET8/P7Zu3Vpm4fG1WIyrlJSUCstYXFTKY9y4cYwbN47CwkLOnDnD4cOHWbp0KVu3bsXb25t33nmn0usLBAJBU8ZkMvH6669jZ2fHN998g0pV/eBOFiOrdPTBgIAAfvjhB/Ly8sjIyMDb2xsnJyd0Oh0nTpzAzt4ez3bdS8q7OZR36usiv8ZNHSA7M8O8T6mi538+AWDmba1o71vxLFFCQgLvv/8++/fvp0+fPtx+++3MnDmTkydP8vrrr9uUvXz5MpIkVZoLq6IUJ66urvz666/MnTuX/fv3s3LlSlauXAlAmzZtePvtt63ulgJBbSKMLEGdYjSZWHaixCVALpcxoHlZN7faxmpgSSaee2sWcsX114Kt/G0h0efO8ObHX6K2q3jWpzSXzodj1OtwdnWjfffrL7QtrRlqhZzBrTyIbNaStDMHybxwlp8OXuKVYW3LrM0qzYoVK5AkiQkTJlzXwAIICTEnWd6/fz8Gg6FcYzMqKqrcY5csWcKdd96Jl5cXjo6O9O7dm969ezNq1ChGjx7N5cuXr3t9gUAgaKqYTCbefvttTCYT33777Q0ZWFAyy1NewAYXFxcbF7itW7eSnZ1N7zvGolCbtcpRraiSu2BVcbrqxWHSaTEUmwcDYzMKKjSyDAYDTz31FBcuXOCLL76wiTx76tSpMuUtg39ZWVnodDrU5cywVRRZEczG1FdffYXJZOLChQucOHGCdevWceTIEWbOnMn+/ftv+F4IBFVFBL4Q1ClbItO4nFWSHb69txMeNZS3o6pYDCy5XMaMV/9XroFlMpkwGUvcGVf+tpCYiLO88sFn5RpYkiRhNBrKbI+LCCf29DGiTh6pUt20xUU2n5u7O9B/xCgUdg4Yigr4Z91y9lzIqPQcltwr17rpZWVlWV0DS3P77bfj6upKamoqc+fOLbN/69atFRpZJ0+e5OLFi2W2WxY29+jRo9K6CgQCQVPFYmBJksTnn39e4Ut9XFxcuYmJSxMdHc3QoUOvu3Y2NzeXzz77jGbNmhE87AHrdp+b0Nny5ouCW7VFLjdrZ1ZsOICNtl/LkSNHuHDhAkFBQVVK7REcHIy9vT16vb6Mi7uFa6Mwlt6+ceNGwBy4qU2bNkyYMIFFixYRFhZGTk5OuTnKBIKaRsxkCeqMK9lF/H2uxCXNWa2gd7CmTutgNrA+YveWDbhq3Dn+gG1nL0kSxUWF6LRaZrz6XwbfcTd//voDq5f8jLOLK889MrZMeW1xMTptMfc9OoX7J0+t9PparXlx8LXGlIXiwkLz/6X2j+zamoiHX+T4ks+5sG0pn2Ki1f+eJ8jbPAMYGRnJ4sWLeeedd1Cr1fTq1Ytdu3axZcsWJkyYgFqtJioqirVr11oXVJdeY+Xq6sqHH37Iyy+/zPfff09iYiL3338/Tk5O7Nu3D51Ox8iRI9m2bVuZkcPMzEzmzZvHN998Yx1JzcnJYdasWYSFhdmEkRcIBIJbBYuBJZfL+fDDD8v1PjCZTEiShK+vLy+++CI9e/a05rcqzZ49e9Dr9cyaNavSa2q1Wl577TWKioqY98NPLIwu6ef9XK6flqTC85ajVx5e3vQaOIR/9+zg0s5VeIb2JOGa4BelqWjwD8yDddfi6OjI6NGjWb16NZ9++ik9evSwiUqYk5PD4sWLy71WWloae/fuZfTo0Tbb5XI5Dg4OtGjRwibnmEBQWwgjS1AnGIwmfj0Sj9FkHhOTyWBQSw/UirqdTN20+g92b9kAQG52VqVlDQYDh/fuZPWSnwHIz8uttHx5M1kWrsRf5r/PTiH+ojm63/nTJ3nrP4/Td/Bwxjz4mLVcUZHZyNr21yoiTp/koaeeoWO3Hjxw10gUGj/iD/zNlSM7uOuO9XTuEIqnhwctW7bkP//5j9WdYvLkyWRkZPD3338zcuRIhgwZwqhRo3jttdc4evQo8fHxLFiwgISEBO6/3xyu/o477sDPz48FCxawZ88etm3bRmBgIK+++iq33XYbL774IgC///47CQkJTJgwgf79+zNkyBB2797NAw88gKurK5IkIZfLGT58OJMnTy7XxUMgEAiaMhYDa/Xq1Xh4eDBw4MAy+wsLCykuLubjjz/mvvvuY8iQIbz88su8/vrrtGxpDlYhSRI7duxg2bJlLF68uNIAEPHx8bz66qtkZWWxePFiCp19IbrEcyFIU/F6rNKDZ9riInDT2Owvujr4py22NaKeevF14uLiSI6LIfy3j2k3dipaQwh2SgV5eXksW7aM0NBQbrvtNrp27YqdnR0RERFERUUREhJCQUEBS5cuJTk5GQBjKe8RgNdee43jx48TExPDAw88wDPPPEPr1q2Ji4tj165dTJkyhQ8++KDM4F9GRgabN29m4sSJdO3a1dqWq1at4tSpUyxcuLDCthAIahKZVNHKwQZCeHg4er0epVJp/bE0dYxGI5mZ5sAQHh4e1tmHmsZgMFhDpgYEBFQp8MONsv5sss0sVqi3E4Nbe9ba9W4GSZIw6PUoFIoqrdUCMOj1IJPZtKHJZGLdL3PRFhbg4OTCvVOeval67b6QSURqvvXzgJYeTOpZNq/VrUJdPr9NgRvpV3Q6HadPnwbMESO7dOlSq3VsTDQFbaorrakujf23/csvv/DJJ59UqewHH3zAAw88QGpqKh9++CFHjhzBxcWFli1botFo6NevH2PGjLEJQ25BkiROnz7NqlWr2LJlC48++ihPPPEETk5OrDiZwI7odAAcVAom9QiwHmcymf6/vTsPb6La+wD+TdJ9hwItFAREU6AiCEJtkU0BZZEL3hcBUfZNvKD3Ci8iL+pVRPTKIsUdBC4gKpuAIFABUfZVChRsKW0tlLZ0X9OkyXn/qBkamrRNO2nT5vt5Hh7tzJnt5GR++c2cOYPs7GyUlOiw8p03kJ2Rjow7pfG5WWALNG4WgLeWfSaVP/jjD1i9ovR4Wra5H527P4YXps8GAKTmFCBizX+RFnUcRRnJCGoeiAfvL933QYMGmSSYv/zyC5YvX45bt26hW7du6Nu3L5599lls374db7/9Nvz9/fH8889jzJgx8Pcv/X2Qm5uLzz//HAcOHEBqair8/f3x9NNP47XXXsOJEycwdWpp75EePXogNDQU//jHP3Dx4kX897//RUxMDJydneHi4gKdTge1Wo0ZM2agdevWVf4sq6O+t9/aZu15qD7FJSZZdqihJVkJmYX44PB1GP66i+Xt5oT/6RRY63exapvcSVaJQWDH5RRkFt4d5nZ895YIb2OfyaqtMZBZh0mWvBpCbGKSVXeEEND9dTGvuvW+bt06XLlyBY899hiefPJJk654iyJjkJRd2s2vdSN3PB3cVJpnTLKA0u575hK4qjIIYO2ZJJT8Fd+fCm6GZx9uXu31NQSO0H7l1JCTLH7yZFN5Gh2+OJEoJVgKBdCrTaMGn2DZgpNSgYHqJth2KQU6fWl9bjp3C6183dGqkUclSxMRkb1QKBQ17k49YcIEs9MLiktwM+fuc1TNa/A8VmWUCiDI1w2Jfw16EXU7x+GTLCIj/tIlm9EbDFh96k9kFt59x1KHZl4V9g2nivm6OeOJB+4+sFtiEPjkeCJyinQVLEVERI7iekaByetAgnzLj4ibcDUKcVFn8UcVR76tSJvGd2P67dxiZBRYHlqdyJEwySKb2XEpxeQZogAvF4TXwTuxGpo2jdzxSAsf6e+sQi1W/HYDhVrLA28QEZFjiLlTIP2/m7MS/h7lh4639vUiFWnt546ygyeeScqu8TqJGgImWWQTZ/7MQmTMHelvTxcVBqibQqW0/BJdqrpHW/mhZZmrk8k5GkQcjUdxib6CpYiIqKGLvXP34mZTT5cKX14vB3dnlckQ8Rdu8R1URACTLLKBq6l5WHc2SfpbpVTgiQf84eliHw9V1yafRk3g2yQAjZoGyLpepQIYqG6CZl53+/TfyCjEFycSUVLmHVhEROQ4CrUl+DO78uex5I5NbRvffS44MasIecXswk7EgS9IVjFp+fjkWDxK9Hc7hIe28kULn/J9wh3BI32fBmD+BYw15axSYlD7Zth5JRXZfz2TdSUlD58dS8C0x1rD1dnxkloiIkd2Pb3Q5HmslhaegZY7NrVp5I7jCaXvnhQCuHAzB73b8YW/5Nh4J4tkE5degFXH46WR7wDgoQAvdGruU8FSVBNuTkoM7dAMXq53E6rLKXlYeiQOeRpeSSQiciTHEjKl/3d1UqKJmeexbMHb1QlNPO/2rDh3k10GiZhkkSxi7+Rj5dF4FOvudlXr0MwLPds2rsO9cgyeLioM7dDMpDtmYlYRlhy6jrQ8jvJEROQIbudqcDH5bnLTtpG7zZ/HKqvsKIPX0wtQrOMzwuTYmGRRjf16IwPLf70BTZkTqrqJBx5vy5EEa4uvmzOefSgQjctctUwv0OL9Q7G4cCu77naMiIhqRWTMHamroFIBdGlRu71I2pZ5X2OJQSDqdm6tbp/I3vCZLDtU9sqTKNu52s6UGAz4/vdkHInLMJnezt8Dfe73h7IWr6DZqwu/7IO2WANPL2/0fmakTbfl4aLC30ICsP+PO0jOLb2DVajV4/PjiejZNg/PdW4BNz6n5bDKnktq8+o22a/6EmuoctlFOpxKzJL+vs/PHb7ulrsK2iI2NfZwho+bE3I1pa8TOZOUje738WIrVawhxybeybJDCoUCKlXpj2GNRlPHe2Neco4Gy47EmSRYCgCdAr3xRDt/KDlUOwAgNysdOempyLqTWivbc1EpMbh9M6ibeppMPxafiUU/x+Bqal6t7AfZH+O5RKVSNbhARtVTH2INVc2h6+koMdz9sdolqOK7WLaKTWW7DF5KyUNSVqGs66eGpyHHJt7JskMKhQJubm4oKChAUVERhBBwcXGBk5O8H5der5euIOj1+io1bl2JAftj0nAwNh16g4DTX4s4qRQIu68RHmzqCUDwqigACANUKpX0T4jaGVpdqQD63t8IrXxccTwxUxqIJKugGKt+i0OHAG/8LSQQLXzr94iP1Wm/jqikpARarVYKZG5ubqwrAlB7scZa/G5bR1NSguM30uGkKK2zQG9XNPN0thxzbBib2jfxwLWUXBgEACGw/nQi5j7xAJyUjnNNn+23ahwhNjHJslPu7u7Q6XRSA7TFVUaDwYDi4tJuZdnZ2VBWcBI0GAQSsgoRdTsP+cUlaHu36zVcnZTo0MwLPm4C0ORbXIfDEQItg4JQoi2Gi6tbrddNOy8gKNgHV1PzkVdcIk3XFeRi25lctG3sgeCmXvAvMyJUfWJN+6VSLi4ucHc3P6QzOabaiDXW4nfbOtGpeQhyLQH+eiVWSIBzxfHGhrHJTwH0DXK7+64uocHR6EQ83MJXtm3YO7Zf6zXU2MQky04pFAr4+PhAo9FAq9VCp9PJfndICIH8/NKTq6+v+RNgkU6Po/GZOBibLr2L6e5OAg/4eyD8vkbQ6Q3IKNDKun/1ncFgwLlz51BcVAh3Ty+07NKzTvajpa8rLt3W4vytHGjLDK//243SoX5bN3JHn3b+eCTIF+716JmtqrRfKj2XODs7w8XFpUFeKaSaqY1YYy1+t6vujzv5+OJEIopLSu9GNfZwRsdmnhXGY1vHJg9nJa6k5iGrsPQ3w/GETCzor0ZQPe89UVVsv1XjCLGJSZYdUygUcHd3h7u7O4QQ0j+56HQ63Lp1C0DpCwmdnUsfks3RlGDvtVTsvJSCn2PTpZN3WS18XDChaxBCmvvAAKBAtr1qOPQw4NjxE8jPyYJv4yboP3lu3eyIAnggyB2BTf2x/XIKfo7JgL5sO0oowucXMuGsUuDxto0xuEMABqqbom3j2h3+11qW2i/dpVAopH9Eltg61liL3+2q2XHpNqZ8fxVa/d0YPT3UH4XKiu8I2Dw2qYAOrQLx1oEYGB8TS9gVh/3Tw+Dr1vB/drL9Vs5RYlPDb+0NhC0ao16vh04vcKugBFeiUnDmVh5OJmbhYnKuyQO0ZTXxcMZT6qYY2qEZnJ2U4JNXlgkIFGu1KC4uRrFWC6Go2y4Dnq5KvNitFZ4KboZtl1JwND4LxWWCs0Yv8NMf6fjpj3QAQBNPF4Te54ce9zVCSKA3gpt64YEmHnB1so+7XXr93VcGKJVK6QF+Iqo+e/jhw+92xYpL9PjseCJe230FZXPhx9s0Qs/7m0BU8vnVRmxq6++Jp9s3w/bLpQNrnEvOg/qDw3hrYDBmhLWGs6rhdqFj+yUjJln1lBACBgEYhIBOb0CJQUCnF9DqDdDoDCjS6VFUokeepgQ5mhLkFuuQUaBDSl4x0vKKkZyrQWx6PhKziv660nSnwu3d5+uGwR2aou/9/nBqwCdHR9DMyxUvhbXGhEdb4pe4DPxUZsj3stILtNhzNQ17rqZJ05QKIMjXDUG+7mjh44pAbzf4e7qgkbszGrk7w9fdCZ4uTvB0UcHTRQVXJyVcVSq4OCngolLCSamAk1IJJ5UCKoUCKqUCSkXDG7aViKgmDAaBEoNAbrEO6QVapBdoEXunAHuvpmF/TBryi01f9Ds4uAnGP9rSrl6dMvLh5jidlIObOaXP+WUW6vDKD5cRcfQGhj/UHB0DvNExwAstfd3h4aKCh7MKzqq6T/KJ5FJvkqzk3GL0nL+3rndDNuKee0Blr0bde3dIiNLS4q+kStxT3lYCPF3QpYU3erb2g7qJZ+mJTwjoS/gW96ooezULgN3Vm4sCGPiAPwY+4I9bOUU4mZSDs7dyEZ9ZZPEOpUEASdkaJGXL/3C8QlH6GgClQgFjjFVI8xT3/P3X/vx1x1W5NcX8OsFgXTMCWwc3RVMPXom1pKHFJvsg7vluN6zvcdn4b4zlQvq7NLmy0JmkHIUCeO6hAIwICQAMAnpD5XGmtmKTEsAbfdvi0xNJuJx2d3CN6+mF+OiXOLPLKBSASnH34psCd8/3QH05pzfs9lv3BLYMbopm9SAuKURdP+FaifPnz0MIAb1BIF1TO0NgOyqlAlApSn/klv63rveo/tPkZUMYDFAolXDz9qvr3akSAcBgAPR/JfV6UT7xJ8fRxE0JlbL06nLXrl3renfsBmMT1TUlABeVAk7V6FxS27FJbwCKrUgeiSpSX+KS3d/JMuaAKqUCAfUgayUqy7Nx47reBSJZ2Pn1uFrH2ET1GWMTNQT2HpfsPslSKpUwGAxQKBR1/oJEIiJHU1JSAiEE3/VyD8YmIqK6UV/ikt13FyQiIiIiIqpP7DsFJCIiIiIiqmeYZBEREREREcmISRYREREREZGMmGQRERERERHJiEkWERERERGRjJhkERERERERyYhJFhERERERkYyYZBEREREREcmISRYREREREZGMmGQRERERERHJiEkWERERERGRjJhkERERERERyYhJFhERERERkYyYZBEREREREcmISRYREREREZGMmGQRERERERHJiEkWERERERGRjJhkERERERERyYhJFhERERERkYyYZBEREREREcnIqa53gOoXrVYLnU4HJycnqFQqODmxCVVEo9HAYDBI9aVSqep6lxoMjUYDhUIBZ2dnKJVVu17kyO1Xq9UiMzMTgYGBdb0rRLJy5O91dTE22Q5jU9U19LikEEIIW27g3Llz+PLLL5GVlYXc3Fw4OTlh2LBhmDBhAlxcXKq0juTkZERERCAmJgYGgwE5OTno27cvZsyYgWbNmpUrn52djVWrVuHChQsQQiArKws9evTAjBkz0LZtW7kPsU7VtH7j4uLw9ddf4+TJk0hLS4Onpyc6duyIMWPGYMCAAeXKz58/H9u3b7e4Pj8/P5w6dapGx2RPalq/L774Ik6fPm1xfkhISLn61Gg0+OKLL/Dbb79BCIGMjAx06tQJ06ZNQ6dOnWp8TPakJvX7+uuvY8eOHVXaztatW9GpUyeHa78AkJKSgu+++w7ff/89RowYgTlz5li1vLXn0/rQfhmXbItxyfYYm2yLscm2HCUu2TRV3rdvH9588018+eWX6NKlCwBg7969mDNnDk6ePImvvvqq0qsnCQkJGD16NGbPno3FixdDoVDgjz/+wJQpUxAZGYnt27ejadOmUvnMzEyMGjUKgwcPxnfffQcnJyckJydj2rRp+Pvf/47vvvsODz74oC0Pu9bUtH5PnDiBl156CUVFRWjcuDGaNm2KlJQUHDt2DMeOHcPUqVPLNXwhBDw8PODt7W12nb6+vrIdX12To/0CgLe3Nzw8PMzOa9KkicnfGo0G48aNw/3334+NGzfCzc0N2dnZmDVrFkaPHo3Vq1cjLCysxsdmD+SsX0vl8vLy8Nhjj0knUUdpv/n5+Th06BB+/PFH3L59G6mpqcjJybF6PdaeT+tD+2Vcsi3GJdtjbLItxibbcMi4JGwkMTFRPPzww2LNmjXl5i1cuFCo1Wqxfv36Cteh1+vFoEGDxPTp08vN2717t1Cr1eLll182mT5p0iQxdOjQcuXPnz8v1Gq1GD58uJVHYp/kqN8lS5aIoUOHiujoaGnanTt3xMyZM4VarRZqtVpcunTJZJl58+aJpUuXynMQdkyO+hVCiBdeeEF8++23Vd7uwoULRY8ePURxcbHJ9OTkZNGhQwcRHh5ebl59JEf9zps3T3zzzTcW52dmZooePXqI2NhYk2Ucof1+9913YvLkyeLYsWNCiNJ2qFarxX/+8x+r1mPt+dTe2y/jkm0xLtkeY5NtMTbZjiPGJZsNfPHf//4XGo0GQ4YMKTdv2LBhAFDp7dSDBw8iLi7O7DoGDhwINzc3HD58GFlZWQCA6OhoHD161Gz5Rx55BK1atUJ0dDSuXbtWnUOyK3LUb1ZWFubMmYMOHTpI05o0aYJly5ahRYsWAIDIyMhyy/n5+dVgz+sHOerXqKr1lZ6eju3bt2PgwIHluiM0b94c3bt3R3p6On777bcqrc+eyVW/arXa4rzly5fjmWeewQMPPGAy3RHa73PPPYfVq1cjPDy82uuw9nxaH9ov45JtMS7ZHmOTbTE22Y4jxiWbJVmRkZHw8/NDQEBAuXkPPfQQVCoV/vjjD2i1WovrOHDgAADzjdXFxQUdOnRASUkJrl69Wml5AOjcuTMA4NKlS9YdjB2So36VSqXZxu7q6oq+ffsCAHJzc8vNt9S9oCGRo36Nqlpfhw8fhk6ns9h+jd0WoqKiqrQ+eyZH/TZq1AitW7c2O+/SpUs4cOAA/vGPf5Sb5wjtVw7Wnk/rQ/tlXLItxiXbY2yyLcYm+1bf4pJNnsnKy8tDSkpKuSzdyM3NDf7+/khLS0NSUhLatWtntlxMTAwAmPRtLysoKAgXLlxAYmIiwsPDpfL39iU2atmyJQAgMTHRquOxN3LV7+LFiy1uQ6FQAIDZE4WTkxNOnjyJzZs3IyEhAV5eXujcuTPGjBmDVq1aVeOI7Itc9Wvk5OSEAwcOYMeOHbh16xZ8fX0RGhqK0aNHm7TVytpvUFAQAODPP/+szmHZDbnqd968eWanGwwG/Pvf/8aMGTPMXhls6O1XLtaeT+29/TIu2Rbjku0xNtkWY5P9q29xySZJVlpaGgDAy8vLYpkmTZogLS0NBQUFFsvcuXOnwvUYKy0/P79K5f39/U3K11dy1W9FoqOj4eTkhEGDBpWbt27dOsTGxppMO3v2LL755ht89NFH6N+/f7W2aS/krt/33nuvXH2dPn0aGzZswOeff45HHnkEANtvWTVpv5s3b0Zubi7Gjh1rdn5Db79ysbY92nv7ZVyyLcYl22Nssi3GJvtX3+KSTboLGm/lVzTOv6urKwBUeMu1svUY16HT6UzKOzs7V6l8fSVX/Vpy9epV/P777xg/frzZW+YeHh7YvHkzoqKicPDgQcyfPx9eXl4oKirC3LlzpUZdX8ldvy1atMDOnTsRFRWFffv24eWXX4aLiwuys7Mxe/ZsaR15eXkVbpftt3KpqalYtmwZ5s6da/E80NDbr1ysPZ/ae/tlXLItxiXbY2yyLcYm+1ff4pJNkqySkhIAgF6vt7zhv17QVlEfVON6jP+tbB1VLe/u7m5xm/WBXPVrjk6nw8KFC9G9e3f885//LDd/9OjRWL9+Pbp27QpXV1e0bNkSEyZMwPr166FSqVBYWIg9e/ZYtU17I2f9zpo1C5999hnat28PV1dXtG3bFrNnz8by5csBlF45O3LkCIC7X3JL2zUOBcv2a9m7776L4OBgs+/SARyj/crF2vOpvbdfxiXbYlyyPcYm22Jssn/1LS7ZJMkyjuCRnZ1tsUxxcTEAWHwnQNn1WBpH37gO423AysobrzxUtM36QK76NefNN9+EwWDAp59+avZKQZcuXcw2xoceegi9evUCUPoOmfpMzvrt0aOH2fdk9O/fX3qPg7G+qtre2X7NO3DgACIjIzF37lyLZRyh/crF2vOpvbdfxiXbYlyyPcYm22Jssn/1LS7ZJMlq3LgxAPMjABnl5+fD2dkZzZs3t1jG2FfS0nqMfWKND8Eay1uqTGOfS0ujvtQXctXvvf7zn//g+vXrWLduXbUanPHEXN9HyLFV/d7r3vqqavu97777qr1Ne2CL+s3Ly8M777yDAQMGSM8RWKuhtF+5WHs+tff2y7hkW4xLtsfYZFuMTfavvsUlmyRZQUFBcHd3R3Z2tpQl3islJQVqtbrCvq/333+/VNac27dvw8nJSRqa0Vg+NTXVYnkAJu/fqI/kqt+yIiIicPHiRaxduxY+Pj7V2i/jbdnQ0NBqLW8vbFG/5txbX8aRiiprvx07dqz2Nu2BLer3ww8/RHp6OmbPnl3t/Woo7Vcu1p5P7b39Mi7ZFuOS7TE22RZjk/2rb3HJJkmWUqlEaGgo9Hq92Xd/xMXFQaPRYOjQoRWux/iujIsXL5qdHx0djV69esHX17dK5a9cuQK1Wo3g4OAqH4s9kqt+jSIiIvD7779j9erVFkdgyc7Oxs6dOytcT2xsLNq3by/d2q6v5KjfxMREqT+7JbGxsejXr580XGxYWBiAituvn58fHn/88aoeil2Su/2eOnUKW7ZswVNPPWXxXRiO1H7lYu351N7bL+OSbTEu2R5jk20xNtm/+haXbPYy4ueffx4AsHv37nLzIiMj4e/vj+HDhwMofXfAm2++ibFjxyIuLk4qN3z4cHh4eGDPnj0QQpis4/z588jMzMSkSZOkaU888QSaN2+On3/+GUVFRSblk5OTER0djcmTJ8t1iHVKjvoF7l4p/Oyzz+Dm5lZuXUIIlJSUwM/PD5s3b7Z49TYmJgbXrl3DRx99JD14WJ/VtH4DAgLw8ccfWxzm9ddff4VOp8M777wjTevUqRM6d+6M06dPl7vqUlBQgOPHj2PcuHHl3lpeH8nVfouKirBgwQIAwMyZMy1uz9Ha770qepAbAFasWIHnnnsOZ8+elaZZez6tD+2Xccm2GJdsj7HJthibao9DxCVhQ3PmzBEdOnQQu3fvlqZdunRJhIWFiaNHj5pMU6vVQq1Wi3feecdkHd9//71Qq9Vi0aJFori4WAghREZGhhgxYoSIiIgot80jR46Ijh07itmzZ4v8/HwhhBD5+fli6tSp4vXXX7fFYdaZmtbvihUrhFqtFj169BDh4eEm/8LCwkSXLl1EcHCwWLlypRBCiK1bt4rnnntOXL582WQ/Tp8+LSZMmCCio6NtfMS1q6b1+/HHH4vp06eLGzduSNMMBoOIjIwUkyZNEjdv3iy3zejoaPHII4+IsWPHioyMDCGEEFqtVrzxxhti/PjxoqSkxBaHWifkOD+8++67Qq1Wi2nTplW6PUdrv0YGg0EMGDBAqNVqMX/+/HLzMzMzpfqdPn26yTxrz6f1of0yLtkW45LtMTbZFmOT7TlKXFIIcc+lOBkZDAZs3LgRW7ZsgVarRZMmTdCoUSO89NJLCAkJkcppNBpMnDgR8fHx+Pjjj8v1Oz1y5Ai++uorJCcnIyAgAK6urhg1apTZFxICpbcFV61ahdjYWAQGBsLZ2RmDBw/G6NGjpTfGNwQ1qd8DBw5g1qxZVdrOjBkz8M9//hNFRUV4++23cezYMTg7O+OBBx6Aj48PunbtipEjRzaIq1hl1bT9pqWlYdGiRThz5gy8vb3Rtm1b+Pn5ISwsDMOGDbN4VerGjRtYsWIFLl68iGbNmsHZ2Rl9+vTBxIkTG1Qd17R+o6KiMGrUKBgMBqxdu1bqRmCJo7XfTz75BIcPH0ZmZiZu3bolTX/wwQfh7u6OxYsX48EHH4QQArNnz8bx48fxzjvvYMiQISbrsfZ8au/tl3HJthiXbI+xybYYm2zH0eKSTZMsqv+0Wi0UCoXFF78R1RW9Xg+9Xg+DwWC2SxERNUyMS2TPGJvIiEkWERERERGRjBrWU3RERERERER1jEkWERERERGRjJhkERERERERyYhJFhERERERkYyYZBEREREREcmISRYREREREZGMmGQRERERERHJiEkWERERERGRjJhkERERERERyYhJFhERERERkYyYZBEREREREcmISRYREREREZGMmGQRERERERHJiEkWERERERGRjJhkERERERERyYhJFhERERERkYyYZBERERGRwyooKEBSUlJd7wY1MEyyiIiIiADEx8dDo9E02O3RXfv27cOKFSswefJk9OzZEz/88ENd75IsNBoN8vLy6no3CIBTXe8AERERUV3IyMjA/v37ERMTgwsXLuDatWs4ePAgWrZsWW+3l5KSgrNnz2Lfvn2IiIiAQqGQbd31gcFgwLJly/Ddd99BpVLh559/hpeXV7lyN2/eRHFxMc6fP4+ioqI62FN5aDQa7N+/HwkJCYiKisKZM2fw9ttv49lnn63rXXN4vJNFREREDik/Px937txBZmYmrl27Vm+2t3//fjz66KP417/+JU376aef0KNHD/Tp0wevvfYaIiMjzSZY5pZtKAwGA1577TVs27YNSqUSWVlZMBgMZstOmTIF8+bNQ3BwcC3vpby0Wi3i4+ORkJCAo0ePori4uK53if7CJIuIiIgcUuvWrfHKK6/UWsIh1/a2b9+OvLw87NmzB5mZmQCAQYMG4cSJE1iyZInVy5qj1WprtI914a233kKLFi1w/PhxnDp1Cp988gnc3NwqXMbZ2bmW9s42fHx88Oqrr+Ldd9+t612hezDJIiIiIofm5FS7T0/UdHvPPvssvL29MWTIEDRu3FiarlKpEBgYWK1ljTIzM/HJJ5/g8ccfR2FhYY32szZt3boV33//PcaNGyfdwevfvz9cXFzqeM9qh4eHR13vAt2Dz2QRERER1SNPPfUUnnrqKbPzlMqKr59XtCwA7N27FytXroS7u3u9+eGu1+vxySefAACaNGlSx3tTNyr73Kn2MckiIiJyIKdOncK4ceMAAD///DOysrKwadMmnDt3Dnfu3IFSqURwcDAmTpxY4Y9xapiMd37qS4IFAAkJCUhOTgZQejePyB4wySIiInIgV69eBVD6LMfOnTuxatUqCCFMyly4cAEXLlzA/PnzMWHChDrYS6oKIUS1Rw+0tKzxjogc3ez0ej20Wi2cnZ1t2iUzKyvLZusmqi4mWURERA7EOKpdYWEhIiIi0KZNG8ycOROhoaEoLi7Gr7/+iqVLl6KoqAgfffQRBg0ahICAgBptMzExEUePHsW+ffuwYcMGAMC6deuwefNmJCcnw9PTE8HBwXjxxRfRv3//cstnZ2fj+PHj2LZtG9566y0EBgbiww8/xO7du9G8eXOTdxzp9Xr8+OOP2LVrF27cuIHMzEx4eXmhc+fOGD16NHr37l3p/hoMBmzatAlbt27FjRs34OPjg27dumHmzJlo3759ufJCCBw5cgQ7duzAjRs38Oeff8LT0xOPPvqoxWWqu73bt2/j6tWrOHz4MCIjI3Hy5MlKj8eaZY2Jl7nuZ+PHjy+3jJeXF5YsWYIBAwYAAGbPno39+/dL81UqFbZt24YOHTpUun/V/ez0er3F6dbe2YqOjsbSpUsRHR0NnU6HTp06YfLkyXj88cctLqPVarF582bs2rULN2/ehMFggFqtxujRo/HMM8+YlK1pWwFK70CvX78esbGx0Ov1aNmyZaXtOj8/H2vXrsWBAweQnp4OvV6Pdu3aoXfv3hg1apTZ5/OohgQRERE5jGHDhgm1Wi3UarUYO3asKCgoKFdmy5YtUpl169ZVe1tbt24Vjz32mLSuhx56SBgMBrFw4UIREhIi+vXrJzp16iTNV6vVYvHixdLysbGxYsCAASI4OFian5SUJBYsWCD9/cQTT0jlMzIyxJgxY8TAgQPF6dOnhcFgEDqdThw6dEj07t1bqNVq8cYbbwi9Xm+yn0lJSdL6/vzzT/H666+L9u3biz59+ojOnTtL8x5++GFx9OjRcsc5adIkMWXKFJGUlCSEEKKwsFCsXbtWhISEiIcfflhERUXJsr0dO3aIgQMHipCQEKnMvU6ePGl2XmXLXrlyRcybN0/6vEJCQkR4eLgIDw8XYWFhYu/evUIIIXbt2iUtv2TJErOf+61bt0THjh3F4MGDRVZWltky96ruZ7dmzRoxdOhQaZ/CwsJEWFiY6Nq1q/jiiy8q3e4LL7wg1Gq1WLlypTh79qzo2rWrCA0NFaGhoSbtcuPGjWaXP3PmjHjiiSfEokWLRHp6uhBCiISEBDF58mShVqvF66+/blLe2rZSVkxMjBg/frxYv3699L2Ni4sTb7zxhsl3ZNu2bSbLpaWlif79+4uRI0eKGzduCCGEyMvLE6tXrxYhISFi5cqVldYTWY9JFhERkYMoLi6WfmSHhoaKzMxMs+VKSkqk5GfBggU12l5OTo7YsGGDlGS98cYbYunSpSI3N1cq8+2334ouXbpIPxJ/+eUXad6ff/4pfvjhB2net99+K/r27SteffVVERYWJkaMGCGV/Z//+R/RuXNncevWrXL7kpCQIB3TsmXLTOaVTXpmzpwp3n33XZGdnS2EEEKr1YqNGzdK9fb444+LoqIik+WHDh0qrl27Vm6by5cvF2q1WkyYMEHW7X366adWJ1mVLavRaERJSYlYuXKlUKvVYt68eWaXF+Juoj5t2jSz8+Pj400+x8rU5LMTwvSYjclLVRmTrBdeeEGMHTtWXL16VZr366+/ikcffVSo1WrRrVs3odPpTJa9ePGiCAkJEXPnzjV7TH369BFqtVr89NNP0nRr24rR7t27RUhIiNi+fbvZ+Vu3brWYZC1cuFCo1Wpx/fr1cst9+OGHTLJshEOREBEROYgbN25Ap9MBAKZPn45GjRqZLadSqdCiRQsAQFFRUbW35+LiAh8fH7Rr1w5Aabeq1q1b41//+he8vb2lMqNGjcKKFSuk5TZv3izNa9WqFbp16ybN+/HHH7F7924sX74cmzZtktbz7bffIioqCv3795f2vazWrVvjueeeAwCsWbMGKSkpZve5WbNm+L//+z/4+voCKH2P0tixY/G///u/AIC0tDQcPnzYZJlPP/3U7Ett+/XrBwCIioqyWEfV2V5NRtCztKyrq2uVu9aNHj0aAHDixAmzw7zv2bMH7du3R58+faq0Prk+u5pITEzEqlWrTLrr9erVC7NnzwYA5OXlISEhwWSZpUuXQqfTYfjw4eXW5+Ligl69egEAdu/eLU2vTltJTU3FggUL0LRpU7PbAoC///3vFo/t/PnzAGD2+965c2d2FbQRJllEREQOwjjohVKpxN/+9rcKyxoMBgCAn59fjbdb9jmfiRMnmi3Tp08fdO3aFQBw8eJFi+saM2YMvLy8AABt27bF+vXrAZS+JwkAOnXqZHHZp59+GgCg0+lw6NAhs2UmT55sdvro0aPh6ekJALh8+bLJvFatWpn8bTAYEBcXJx1Hfn6+xX2qzvZqMoKeHKPvPfPMM3B3d0dxcXG5BBAAdu3aZdWAKXJ9djXRu3dvs23d2CaB0mcDjTIzM3Hq1CkAQMuWLc2u05jQJiYmStOq01a2b98OjUaDHj16VGugE2M7eu+998oNEjJw4ECMHTvW6nVS5TjwBRERkYMwJllqtbrCq9darVa6W9C8efMab9f4w9DJyQnOzs4Wy/Xs2RPnz59HQUGBxTLGu2JlaTQaxMTEACgdNdGSLl26QKVSQa/X48aNG2bLWBq63MXFBZ07d8bx48eRm5tbbn5cXBz27t2LY8eOISUlBSEhIRZ/fMuxvbrk5eWFAQMGYNeuXdi9ezeGDBkizfv999+Rn59vMq0icn52NWFpNEXjnVLAdICN6OhoaVTOESNGwM3NrdyyWq0WjRs3LtcOrG0rp0+fBgA0bdq06gdUxsSJE/HKK6/gxx9/xL59+9CxY0d0794dYWFh6NGjB1xdXau1XqoYkywiIiIHYUyyWrduXWG5c+fOobi4GAAQFhZm8/0yMl75DwwMtFjGeFW+rJycHOkHb0V3jZycnODj44OsrCzpTp01jImpv7+/yfQPPvgAX3/9NXr37o3XXnsN3bp1g1KpxM2bN7Fu3Tqrt1PZ9uzBs88+i127duG3335DRkaGtI/bt2/H6NGjqzwEfG19dtVl6SW/Ze8Ibd26FW3btq3S+qrTVqrSPdLSCItA6V3AZcuWYe3atbhy5QqioqIQFRWFNWvWwMvLCy+//DImTZpUpf2nqmN3QSIiIgfxxx9/AKj8HUjbtm0DAAQFBaFjx4423y8j44/s0NBQq5Yre7fh1q1bFZZ1d3cHALPP/lTG+Dxbjx49pGl79+7F119/jUceeQRffPEFunfvbvGHuRzbsxePPfYYgoKCUFJSgl27dgEovSsVGRkpPbNVFbX12cnN2GUVKO06WBXVbSvGu78V1U9lz04OGTIEW7duxalTp/Dll19i6tSpaNOmDfLz8/HBBx/gzJkzVToGqjomWURERA4gOTkZOTk5AICbN29aLHft2jXs3bsXAPDSSy9V+2W31REbGwsAGDlypFXLeXh4ICgoCABw9uxZi+WEENJzNeHh4VbvX3x8PNRqtcndve3btwMABg0aJFtyVdH2aou45wXV91IoFNIgDMakfN++fejZs6dV3dpq67OT23333Sf9/6VLl6q0THXbijGpPHHiBLRardkyxgso5hi7GwKlXTL79OmDOXPmYO/evdKAGxcuXKjy/lDVMMkiIiJyAMaugkDpwBLGhKaszMxMvPrqq9Dr9QgJCcGIESPKlcnLy8POnTul7oRyKSoqwi+//IJBgwbh4Ycftnp540uML1++jLi4OLNlYmJiUFhYiK5du1p9hy4pKQkxMTGYNWuWSeKZlJQEAGa7sH3zzTdWbaMq26stln7MlzVixAgoFArExsbi/Pnz+Pbbb/HCCy9YvS1bf3Y1YakbXrt27fDggw8CKB1NsSqq21aMoxRmZWXhq6++Mlumom6pK1euhEajKTddpVJJ67bHLqn1HZMsIiIiB2BMsgIDA6FSqTBt2jQcOHAAqampuHnzJr7//nsMHz4c8fHx8PPzQ0REBJycTB/dnjlzJkJDQ/Hxxx+Xm1cVer1e+qF5r5UrVyI4OBiLFi2y/uBQOkqft7c3hBD48MMPzf6Q/frrr+Hl5YUlS5ZYXI+l/Vu1ahXefPNNDBw40GR6s2bNAABbtmxBeno6gNJh1+fNm1fhM0bV3Z6tGbumVTYACVA6Ul737t0BAO+//z4KCwvRpUsXq7cp12dnCxXVwauvvgqgdNj1DRs2mC0jhJCe36puWxk2bJi0bEREBFatWiV1D8zPz8e7774rzTdusyytVisN436vCxcuwM/PD08++aTF7VP1MMkiIiJyANeuXQNQ2tVqxowZSE5OxqxZs9C7d288+eSTWLhwIVJTUxEUFIQNGzZIXbiM9Ho9fv31V+j1ekyYMKFaQ4Hr9XqMHDkSW7ZskX683r59G4sXL4anp6f0Q/peZR/8T05ONrvugIAArFixAu7u7vjll18wa9YsxMfHAygdenvx4sU4d+4c1q9fX27gj7Lvepo0aRI2bNgg7d+dO3ewYcMGTJw40exQ18Yuc3Fxcejbty/69euHIUOGYODAgRg/frxU7uLFi9JdkZpsz1jGKDU11eK827dvW7UsAOkuUUpKCoYNG4a5c+di6tSp0giA9zLe7YyKisKoUaPMlqlMTT67e4+joq6w9xJCSMmOpcElyiZZ95bp378/5syZA5VKhUWLFmHRokVS0qzVanHkyBGMGzdOOpbqtBWg9Lm1jz76CF5eXhBCICIiAqGhoXjyyScRHh6OFi1aSO9VA0pHPiwpKZH+Tk5Oxr///W+cP39eSsBycnIQERGBgwcPYsWKFbK8qoFMKURlnW6JiIio3nvyySdx8+ZNvP7665g4cSJ++OEHfPPNN7h+/TqEEGjbti2efvppvPjii9IAA2VFRUVh5MiRaN68Ofbv32/VsM+nTp3CuHHjAJS+l+fChQvIycnBfffdh/bt22PGjBlS16uyduzYgSVLlqCgoEAaBEKhUMDT0xO9evUyeYGxUVJSEr744gscO3YMd+7cQWBgIJo3b47Bgwdj2LBhZkcnjIqKwscff4zw8HBERkYiPj4eBQUFaNOmDQYOHIjp06dXeLwbNmzAxo0bkZycjJCQELz33nto164d4uLiMHjwYKlcr169sHr16mpvb9OmTVi5cqXJiHxubm5wc3PDe++9hwULFiAvL0/6gW6ct3fvXuzbt8/isgcPHjRJbr/88kusWbMGBQUFCAoKwsyZMy2+V62goAA9e/YEAPz2228mA1lYy9rP7tChQ5g/f77JMRvbR3h4OCIiIixu6/nnn8fVq1dNEl4fHx/069cPH374oTTtyJEjmDZtGoDS7nXe3t749NNPTV6QfenSJXzzzTc4ffo00tLS4O/vj8DAQISFhWHkyJEmA3VY21bKSkhIwJdffoljx44hIyMDAQEBmDJlCsaMGYPi4mKTbrbOzs545ZVXMHXqVGzatAmnT59GTEyM9Pl7eHigZ8+emDJlisnzZSQfJllEREQNXH5+Ph599FEIIfD1119LP4qt8f7772PdunX44IMPpCvyVVU2yaroAX2qn2bPng1PT0+8//77db0rRHaD3QWJiIgauGvXrkl3L9RqtdXLFxYWYufOnQgLC7M6waKGLyUlpdpdBYkaKiZZREREDZxx0ItGjRpZNby20YYNG6DX66s9KAU1XFevXoVGo6nWgBdEDRmTLCIiogbOmGSZe+6pMgUFBdixYweWLl2Kli1byr1rVM9t3ryZd7GIzLB+/FUiIiKqV4wjCwYHB1u9rKenJ/bs2VOt0QSpYUtLS8PPP/9sMrIdEZXinSwiIqIGrKSkRHrxcHXuZAFggkWSnTt3QqvVIjs7G3PnzsXf/vY3s8PuEzk6ji5IRERENrV7927MmTMHABAZGckho+upzMxM9OzZE66urtBqtQgMDMSOHTvg6+tb17tGZHd4J4uIiIhs4tChQwgNDcW8efOkaYMHD0ZoaCguX75ch3tG1ZGUlAR/f38oFAr069cPGzduZIJFZAHvZBEREZFNGAwGCCHY3ZCIHA6TLCIiIiIiIhmxuyAREREREZGMmGQRERERERHJiEkWERERERGRjJhkERERERERyYhJFhERERERkYyYZBEREREREcmISRYREREREZGMmGQRERERERHJiEkWERERERGRjJhkERERERERyYhJFhERERERkYyYZBEREREREcmISRYREREREZGMmGQRERERERHJiEkWERERERGRjJhkERERERERyYhJFhERERERkYyYZBEREREREcmISRYREREREZGMmGQRERERERHJ6P8BBX3nb+TrMH4AAAAASUVORK5CYII=",
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",
       "text/plain": [
-       "<Figure size 500x300 with 10 Axes>"
+       "<Figure size 600x400 with 8 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 338,
-       "width": 428
+       "height": 392,
+       "width": 588
       }
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "# via https://nbviewer.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers\n",
-    "# /blob/master/Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb\n",
-    "from scipy.stats import beta\n",
+    "# FIGURES ONLY\n",
     "from scipy.stats import bernoulli\n",
+    "from scipy.stats import beta\n",
+    "from ministats import plot_pdf\n",
     "\n",
-    "n_trials = [0, 1, 2, 3, 4, 5, 8, 15, 50, 500]\n",
-    "data = bernoulli.rvs(0.5, size=n_trials[-1])\n",
-    "x = np.linspace(0, 1, 100)\n",
-    "\n",
-    "# For the already prepared, I'm using Binomial's conj. prior.\n",
-    "for k, N in enumerate(n_trials):\n",
-    "    sx = plt.subplot(len(n_trials)//2, 2, k+1)\n",
-    "    plt.xlabel(\"$p$, probability of heads\") \\\n",
-    "        if k in [0, len(n_trials)-1] else None\n",
-    "    plt.setp(sx.get_yticklabels(), visible=False)\n",
-    "    heads = data[:N].sum()\n",
-    "    y = beta.pdf(x, 1 + heads, 1 + N - heads)\n",
-    "    plt.plot(x, y, label=\"observe %d tosses,\\n %d heads\" % (N, heads))\n",
-    "    plt.fill_between(x, 0, y, color=\"#348ABD\", alpha=0.4)\n",
-    "    plt.vlines(0.5, 0, 4, color=\"k\", linestyles=\"--\", lw=1)\n",
-    "\n",
-    "    leg = plt.legend()\n",
-    "    leg.get_frame().set_alpha(0.4)\n",
-    "    plt.autoscale(tight=True)\n",
+    "n_rows = 8\n",
+    "ns =       [0, 1, 2, 5, 10, 20, 30, 50]\n",
+    "outcomes = [0, 1, 1, 3,  7, 13, 22, 34]\n",
+    "# outcomes were generated from coin with true p = 0.7\n",
+    "assert len(outcomes) == n_rows\n",
+    "assert len(ns) == n_rows\n",
+    "n_rows = int(len(outcomes))\n",
     "\n",
+    "with plt.rc_context({\"figure.figsize\":(6,4)}):\n",
+    "    fig, axs_matrix = plt.subplots(n_rows//2, 2, sharex=True, sharey=True)\n",
+    "    axs = [ax for row in axs_matrix for ax in row]\n",
+    "    for i, ax in enumerate(axs):\n",
+    "        heads, n = outcomes[i], ns[i]\n",
+    "        rvPpost = beta(a=1+heads, b=1+n-heads)\n",
+    "        plot_pdf(rvPpost, rv_name=\"P\", ax=ax)\n",
+    "        if i==0:\n",
+    "            ax.set_title(\"flat prior\")\n",
+    "        else:\n",
+    "            ax.set_title(f\"{heads} heads in {n} tosses\")\n",
     "\n",
-    "plt.suptitle(\"Bayesian updating of posterior probabilities\",\n",
-    "             y=1.02,\n",
-    "             fontsize=14);"
+    "    filename = os.path.join(DESTDIR, \"panel_coin_posteriors.pdf\")\n",
+    "    savefigure(fig, filename)"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "711d2840-3c20-45a0-b3c6-330612efdcc4",
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "141f38d1-0476-45d0-a230-6cfc6a438cbd",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to figures/bayesian/intro/ridgeplot_coin_posteriors.pdf\n",
+      "Saved figure to figures/bayesian/intro/ridgeplot_coin_posteriors.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 475,
+       "width": 631
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "## Example 1: estimating the probability of a biased coin"
+    "# FIGURES ONLY\n",
+    "# Inspired by  https://python-graph-gallery.com/ridgeline-graph-seaborn/\n",
+    "\n",
+    "n_rows = 10\n",
+    "ns =       [0, 1, 2, 3, 4, 5, 10, 20, 30,  50]\n",
+    "outcomes = [0, 1, 1, 2, 2, 3,  7, 13, 22,  34]\n",
+    "# outcomes were generated from coin with true p = 0.7\n",
+    "assert len(outcomes) == n_rows\n",
+    "assert len(ns) == n_rows\n",
+    "n_rows = int(len(outcomes))\n",
+    "\n",
+    "dists = pd.DataFrame({\n",
+    "    \"case\": range(n_rows),\n",
+    "    \"n\": ns,\n",
+    "    \"outcome\": outcomes,\n",
+    "})\n",
+    "\n",
+    "labels = []\n",
+    "for i in range(n_rows):\n",
+    "    heads, n = outcomes[i], ns[i]\n",
+    "    if i==0:\n",
+    "        label = \"flat prior\"\n",
+    "    else:\n",
+    "        label = f\"{heads} heads in {n} tosses\"\n",
+    "    labels.append(label)\n",
+    "\n",
+    "\n",
+    "def plot_case(heads, n, *args, **kwargs):\n",
+    "    rvPpost = beta(a=1+heads, b=1+n-heads)\n",
+    "    eps = 0.001\n",
+    "    pis = np.linspace(0-eps, 1.0+eps, 1000)\n",
+    "    rvPpdf = rvPpost.pdf(pis)\n",
+    "    ax = sns.lineplot(x=pis, y=rvPpdf, alpha=1)\n",
+    "    ax.fill_between(pis, 0, rvPpdf, alpha=0.5)\n",
+    "\n",
+    "with sns.axes_style(\"white\"), plt.rc_context({\"axes.facecolor\": (0, 0, 0, 0), \"xtick.bottom\":True}):\n",
+    "    g = sns.FacetGrid(dists, row='case', aspect=12, height=0.5)\n",
+    "    g.map(plot_case, \"outcome\", \"n\")\n",
+    "    \n",
+    "    # add labels on the left\n",
+    "    for i, ax in enumerate(g.axes.flat):\n",
+    "        ax.text(-0.5, 0.3, labels[i], fontsize=10, ha=\"left\", multialignment=\"right\")\n",
+    "\n",
+    "    # adjust spacing to get the axes to overlap\n",
+    "    g.fig.subplots_adjust(hspace=-0.4)\n",
+    "\n",
+    "    # other figure cleanup\n",
+    "    g.set(xlim=[-0.3,1.1])\n",
+    "    g.set(xticks=np.linspace(0,1,6))\n",
+    "    for i, ax in enumerate(g.axes.flat):\n",
+    "        if i != n_rows-1:\n",
+    "            # remove x-ticks (except for last)\n",
+    "            ax.tick_params(bottom=False)\n",
+    "    g.set(xlabel=\"               p\")\n",
+    "    g.set_titles(template=\"\")\n",
+    "    g.set(yticks=[])\n",
+    "    g.set(ylabel=None)\n",
+    "    g.despine(bottom=True, left=True)\n",
+    "\n",
+    "    # save as PDF and PNG\n",
+    "    filename = os.path.join(DESTDIR, \"ridgeplot_coin_posteriors.pdf\")\n",
+    "    fig = plt.gcf()\n",
+    "    fig.savefig(filename, dpi=300, bbox_inches=\"tight\", pad_inches=0)\n",
+    "    filename2 = filename.replace(\".pdf\", \".png\")\n",
+    "    fig.savefig(filename2, dpi=300, bbox_inches=\"tight\", pad_inches=0)\n",
+    "    print(\"Saved figure to\", filename)    \n",
+    "    print(\"Saved figure to\", filename2)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2790bdf7-ab66-462f-89f7-f9b43e95240d",
+   "id": "2022e4c4-5baa-4a0f-ba46-100e94457e77",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fb69d135-09ac-4c4a-91a2-f3c59e53e2d2",
+   "cell_type": "markdown",
+   "id": "eb9e6ed0-90cf-4fda-9d30-410c9bef2fe1",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## Bayesian inference results"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "23543fca-71ed-418e-af17-62d0d92c68e4",
+   "execution_count": 7,
+   "id": "09518689-8cd2-4c8f-957c-4e25da381541",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from scipy.special import binom, betaln\n",
+    "heads = 34\n",
+    "n = 50\n",
     "\n",
-    "def beta_binom(prior, y):\n",
-    "    \"\"\"\n",
-    "    Compute the marginal-log-likelihood for a beta-binomial model,\n",
-    "    analytically.\n",
-    "\n",
-    "    prior : tuple\n",
-    "        tuple of alpha and beta parameter for the prior (beta distribution)\n",
-    "    y : array\n",
-    "        array with \"1\" and \"0\" corresponding to the success and fails respectively\n",
-    "    \"\"\"\n",
-    "    α, β = prior\n",
-    "    success = np.sum(y)\n",
-    "    trials = len(y)\n",
-    "    return np.log(binom(trials, success)) + betaln(α + success, β+trials-success) - betaln(α, β)"
+    "# posterior\n",
+    "rvPpost = beta(a=1+heads, b=1+n-heads)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2681f3f4-2057-441d-84f1-47bc83569253",
+   "cell_type": "markdown",
+   "id": "13520a48-1bad-4f49-85c9-6ba5590fe758",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from scipy import stats\n",
-    "\n",
-    "\n",
-    "def beta_binom_harmonic(prior, y, s=10000):\n",
-    "    \"\"\"\n",
-    "    Compute the marginal-log-likelihood for a beta-binomial model,\n",
-    "    using the harmonic mean estimator.\n",
-    "\n",
-    "    prior : tuple\n",
-    "        tuple of alpha and beta parameter for the prior (beta distribution)\n",
-    "    y : array\n",
-    "        array with \"1\" and \"0\" corresponding to the success and fails respectively\n",
-    "    s : int\n",
-    "        number of samples from the posterior\n",
-    "    \"\"\"\n",
-    "    α, β = prior\n",
-    "    success = np.sum(y)\n",
-    "    trials = len(y)\n",
-    "    posterior_samples = stats.beta(α + success, β+trials-success).rvs(s)\n",
-    "    log_likelihood = stats.binom.logpmf(success, trials, posterior_samples)\n",
-    "    return 1/np.mean(1/log_likelihood)"
+    "### Point estimates"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "34d28c13-469e-4f40-8567-687a2ec3ba33",
+   "execution_count": 8,
+   "id": "c5da2a80-e453-4db1-a45e-52b966795177",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x12fc25130>"
+       "(0.6730769230769231, 0.675311350478963)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
-    },
+    }
+   ],
+   "source": [
+    "# posterior mean and median\n",
+    "rvPpost.mean(), rvPpost.median()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "eea1dd06-9df9-47ce-a845-598f51c22bf7",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 500x300 with 2 Axes>"
+       "0.6796796796796797"
       ]
      },
-     "metadata": {
-      "image/png": {
-       "height": 274,
-       "width": 391
-      }
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# posterior mode\n",
+    "pis = np.linspace(0,1,1000)\n",
+    "pis[np.argmax(rvPpost.pdf(pis))]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e454daa-50f6-4aee-bcf9-5428ec83390a",
+   "metadata": {},
+   "source": [
+    "### Bayesian credible intervals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "96fb4cec-5121-4449-a8fd-a9217784d2f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.57183024, 0.78017613])"
+      ]
      },
-     "output_type": "display_data"
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "data = [np.repeat([1, 0], rep)\n",
-    "        for rep in ((1, 0), (1, 1), (5, 5), (100, 100))]\n",
-    "priors = ((1, 1), (100, 100), (1, 2), (1, 200))\n",
-    "\n",
-    "x_names = [repr((sum(x), len(x)-sum(x))) for x in data]\n",
-    "y_names = [\"Beta\" + repr(x) for x in priors]\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "error_matrix = np.zeros((len(priors), len(data)))\n",
-    "for i, prior in enumerate(priors):\n",
-    "    for j, y in enumerate(data):\n",
-    "        error_matrix[i, j] = 100 * \\\n",
-    "            (1 - (beta_binom_harmonic(prior, y) / beta_binom(prior, y)))\n",
-    "im = ax.imshow(error_matrix, cmap='viridis')\n",
-    "ax.set_xticks(np.arange(len(x_names)))\n",
-    "ax.set_yticks(np.arange(len(y_names)))\n",
-    "ax.set_xticklabels(x_names)\n",
-    "ax.set_yticklabels(y_names)\n",
-    "fig.colorbar(im)\n",
-    "# plt.savefig(\"img/chp11/harmonic_mean_heatmap.png\")"
+    "from arviz.stats import hdi\n",
+    "hdi(rvPpost.rvs(10000), hdi_prob=0.9)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "b5db0294-3643-4621-840d-b7ff4c9f4708",
+   "execution_count": 11,
+   "id": "d54bb73d-15b2-41b9-aae5-544668285309",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.568062486957449, 0.7795066045702477]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ALT. using helper function\n",
+    "from ministats.hpdi import hpdi_from_rv\n",
+    "hpdi_from_rv(rvPpost, hdi_prob=0.9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37d87805-bb35-415e-ab7d-4a27b718b737",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "def normal_harmonic(sd_0, sd_1, y, s=10000):\n",
-    "    post_tau = 1/sd_0**2 + 1/sd_1**2\n",
-    "    posterior_samples = stats.norm(loc=(y/sd_1**2)/post_tau, scale=(1/post_tau)**0.5).rvs((s, len(x)))\n",
-    "    log_likelihood = stats.norm.logpdf(loc=x, scale=sd_1, x=posterior_samples).sum(1)\n",
-    "    return 1/np.mean(1/log_likelihood)"
+    "### Bayesian predictions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "e4c076a2-8635-4c22-bbb1-26b1fc4587e7",
+   "execution_count": 12,
+   "id": "cf6522df-0217-4f64-a8ff-ea1348469dc2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-1.2655121234846454"
+       "[1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "σ_0 = 1\n",
-    "σ_1 = 1\n",
-    "y = np.array([0])\n",
-    "stats.norm.logpdf(loc=0, scale=(σ_0**2+σ_1**2)**0.5, x=y).sum()"
+    "np.random.seed(43)\n",
+    "\n",
+    "pred_tosses = []\n",
+    "for i in range(20):\n",
+    "    p_post = rvPpost.rvs(1)\n",
+    "    pred_toss = bernoulli(p=p_post).rvs(1)[0]\n",
+    "    pred_tosses.append(pred_toss)\n",
+    "pred_tosses"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "f8b33f54-4cd5-4c2b-ac4d-f09a5beaa3f0",
+   "execution_count": 13,
+   "id": "502befdb-b135-49c6-b6a0-c8ae378b4c68",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def posterior_ml_ic_normal(σ_0=1, σ_1=1, y=[1]):\n",
-    "    n = len(y)\n",
-    "    var_μ = 1/((1/σ_0**2) + (n/σ_1**2))\n",
-    "    μ = var_μ * np.sum(y)/σ_1**2\n",
-    "    σ_μ = var_μ**0.5\n",
-    "\n",
-    "    posterior = stats.norm(loc=μ, scale=σ_μ)\n",
-    "    samples = posterior.rvs(size=(2, 1000))\n",
-    "    log_likelihood = stats.norm(loc=samples[:, :, None], scale=σ_1).logpdf(y)\n",
-    "    idata = az.from_dict(log_likelihood={'o': log_likelihood})\n",
-    "\n",
-    "    log_ml = stats.norm.logpdf(loc=0, scale=(σ_0**2+σ_1**2)**0.5, x=y).sum()\n",
-    "    \n",
-    "    x = np.linspace(-5, 6, 300)\n",
-    "    density = posterior.pdf(x)\n",
-    "    \n",
-    "    return μ, σ_μ, x, density, log_ml, az.waic(idata).elpd_waic, az.loo(idata, reff=1).elpd_loo"
+    "# # ALT. using scipy.stats vectorized params\n",
+    "# np.random.seed(42)\n",
+    "# m = 20\n",
+    "# pis_post = rvPpost.rvs(m)\n",
+    "# pred_tosses = bernoulli(p=pis_post).rvs(m)\n",
+    "# pred_tosses"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6b1323f6-af88-4dfb-a5c1-537929e87ffb",
+   "id": "c0f52b2a-058a-4831-9fcd-903421621e0a",
    "metadata": {},
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "markdown",
+   "id": "483106ee-31a5-4d25-9fe3-73a3e954a8e8",
+   "metadata": {},
+   "source": [
+    "## TMP stuff (CUT ME)"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "2dc63628-2535-41c6-a24f-49d11c2c7329",
+   "execution_count": 14,
+   "id": "ac4c17ec-669c-422c-bd3c-63db5d628719",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# manual numerical integral function to check posterior is well normalized\n",
+    "def myint(xs, ys):\n",
+    "    delta = xs[1] - xs[0]\n",
+    "    return np.sum(ys) * delta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "2790bdf7-ab66-462f-89f7-f9b43e95240d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0.5, 0, 'μ')"
+       "34"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# data gen 1\n",
+    "from scipy.stats import bernoulli\n",
+    "\n",
+    "n = 50\n",
+    "np.random.seed(43)\n",
+    "tosses = bernoulli(p=0.7).rvs(n)\n",
+    "sum(tosses)\n",
+    "# tosses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9bb83ffb-8b79-42e6-81ff-02bcbe471c63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(101.81769095067206, 15.80843728680536)"
+      ]
+     },
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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",
       "text/plain": [
-       "<Figure size 1000x600 with 3 Axes>"
+       "<Figure size 500x300 with 1 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 611,
-       "width": 1011
+       "height": 274,
+       "width": 465
       }
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "import arviz as az\n",
+    "# data gen 2\n",
+    "from scipy.stats import norm\n",
+    "\n",
+    "n = 30\n",
+    "np.random.seed(50)\n",
+    "iqs = norm(loc=100, scale=15).rvs(n)\n",
+    "iqs\n",
+    "sns.kdeplot(iqs, bw_adjust=0.4)\n",
+    "np.mean(iqs), np.std(iqs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78559d2e-8243-49b0-bc18-a542abab0032",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "711d2840-3c20-45a0-b3c6-330612efdcc4",
+   "metadata": {},
+   "source": [
+    "## Example 1: estimating the probability of a biased coin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "cc712b5e-a76b-4c37-a0e6-3ca9951a573d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#######################################################\n",
+    "from scipy.stats import uniform\n",
+    "from scipy.stats import bernoulli\n",
     "\n",
-    "y = np.array([ 0.65225338, -0.06122589,  0.27745188,  1.38026371, -0.72751008,\n",
-    "              -1.10323829,  2.07122286, -0.52652711,  0.51528113,  0.71297661])\n",
+    "# Sample of coin tosses observations (1=heads 0=tails)\n",
+    "# We assume they come from a Bernoulli distribution\n",
+    "tosses = [1,1,1,1,1,0,1,1,1,0,1,0,1,1,0,1,1,1,1,0,0,0,\n",
+    "          1,0,1,1,1,0,1,1,1,1,1,1,0,0,0,1,1,1,0,1,0,1,\n",
+    "          1,1,0,1,1,0]\n",
     "\n",
-    "_, ax = plt.subplots(3, figsize=(10, 6), sharex=True, sharey=True,\n",
-    "                     constrained_layout=True)\n",
+    "# 1. Define a grid of possible `pi` values\n",
+    "ngrid1 = 101             # number of points in the grid\n",
+    "pis = np.linspace(0, 1, ngrid1) # [0, 0.01, ..., 1.0]\n",
+    "dgrid1 = (1-0) / ngrid1   # grid spacing\n",
     "\n",
-    "for i, σ_0 in enumerate((1, 10, 100)):\n",
-    "    μ_μ, σ_μ, x, density, log_ml, waic, loo = posterior_ml_ic_normal(σ_0, σ_1, y)\n",
-    "    ax[i].plot(x, stats.norm(loc=0, scale=(σ_0**2+σ_1**2)**0.5).pdf(x), lw=2)\n",
-    "    ax[i].plot(x, density, lw=2, color='C4')\n",
-    "    ax[i].plot(0, label=f'log_ml {log_ml:.1f}\\nwaic {waic:.1f}\\nloo {loo:.1f}\\n', alpha=0)\n",
-    "    ax[i].set_title(f'μ_μ={μ_μ:.2f} σ_μ={σ_μ:.2f}')\n",
-    "    ax[i].legend()\n",
-    "ax[2].set_yticks([])\n",
+    "# 2. Define a flat prior distribution\n",
+    "prior1 = uniform(0,1).pdf(pis)\n",
     "\n",
-    "ax[2].set_xlabel(\"μ\")\n",
+    "# 3. Compute the likelihood of `tosses` for all `pis`\n",
+    "likelihood = np.ones(ngrid1)\n",
+    "for toss in tosses:\n",
+    "    likelihood_toss = bernoulli(p=pis).pmf(toss)\n",
+    "    likelihood = likelihood * likelihood_toss\n",
+    "# ALT.\n",
+    "# np.prod(bernoulli(p=pis[:,np.newaxis]).pmf(tosses), axis=1)\n",
     "\n",
-    "#plt.savefig(\"img/chp11/ml_waic_loo.png\")"
+    "# 4. Calculate the posterior distribution\n",
+    "numerator = likelihood * prior1\n",
+    "posterior1 = numerator / np.sum(numerator) / dgrid1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "a172a527-1572-4fde-8fc9-c63b345b545c",
+   "execution_count": 18,
+   "id": "3931a256-368f-4acf-a9ee-65944bd85efe",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-1.2655121234846454"
+       "(101.0, 100.99999999999997, 1.01, 1.0099999999999998)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "σ_0 = 1\n",
-    "σ_1 = 1\n",
-    "y = np.array([0])\n",
-    "stats.norm.logpdf(loc=0, scale=(σ_0**2 + σ_1**2)**0.5, x=y).sum()"
+    "# normalization checks (CUT ME)\n",
+    "np.sum(prior1), np.sum(posterior1), myint(pis, prior1), myint(pis, posterior1)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "0ee2d843-352b-4e21-bf76-2cdeee505ce8",
+   "cell_type": "markdown",
+   "id": "c636421c-a3c9-4d7f-a869-c70049b336d0",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "N = 10000\n",
-    "x, y = np.random.uniform(-1, 1, size=(2, N))\n",
-    "inside = (x**2 + y**2) <= 1\n",
-    "pi = inside.sum()*4/N\n",
-    "error = abs((pi - np.pi) / pi) * 100"
+    "### Visualize the posterior"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "537dfd77-f109-4d51-ae5e-59114710815e",
+   "execution_count": 19,
+   "id": "acf7d5fa-b8f6-4f90-aa67-ca38ad3a338d",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x300 with 1 Axes>"
       ]
@@ -454,54 +667,945 @@
      "metadata": {
       "image/png": {
        "height": 274,
-       "width": 434
+       "width": 425
       }
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "total = 100000\n",
-    "\n",
-    "dims = []\n",
-    "prop = []\n",
-    "for d in range(2, 15):\n",
-    "    x = np.random.random(size=(d, total))\n",
-    "    inside = ((x * x).sum(axis=0) < 1).sum()\n",
-    "    dims.append(d)\n",
-    "    prop.append(inside / total)\n",
-    "    \n",
-    "plt.plot(dims, prop);"
+    "sns.lineplot(x=pis, y=prior1, label='prior')\n",
+    "sns.lineplot(x=pis, y=posterior1, label='posterior');"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "e4d74660-5c01-4e92-bc97-4ecaf0b2f577",
+   "cell_type": "markdown",
+   "id": "6a58fc52-7ec2-44a1-abfc-6dc9cc44e4e7",
+   "metadata": {},
+   "source": [
+    "### Summarize the posterior"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ff7a8f6-0dc4-4943-8616-819b769f3268",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "def posterior_grid(ngrid=10, α=1, β=1, heads=6, trials=9):\n",
-    "    grid = np.linspace(0, 1, ngrid)\n",
-    "    prior = stats.beta(α, β).pdf(grid)\n",
-    "    likelihood = stats.binom.pmf(heads, trials, grid)\n",
-    "    posterior = likelihood * prior\n",
-    "    posterior /= posterior.sum()\n",
-    "    return posterior"
+    "#### Posterior mean"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "1d9b92f6-d666-4014-8a7a-56452a8cbc53",
+   "execution_count": 20,
+   "id": "dfd943cd-1942-4846-a239-e90dfe29522a",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.006664127951256662"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(pis * posterior1 * dgrid1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "801bc6e0-48d7-4eff-a6ea-7acfda16b118",
+   "metadata": {},
+   "source": [
+    "#### Posterior median"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0645c7bf-7a59-48c4-a044-9086505b42f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.68"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cumsum1 = np.cumsum(posterior1 * dgrid1) \n",
+    "pis[cumsum1.searchsorted(0.5)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e441a1f-2ed9-4b51-a221-e6bb6a1652e9",
+   "metadata": {},
+   "source": [
+    "#### Posterior mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "0d47811e-ad92-4b0d-8e0b-c7e7f2efa71a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.68"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pis[np.argmax(posterior1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20162e4b-9d5d-4321-8934-c95da1006cfc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "35c51b26-f3dd-47cc-8038-ba9d38150434",
+   "metadata": {},
+   "source": [
+    "### Predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "9c03eee6-5c22-4dfe-a191-b8bd1e20f6c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import bernoulli\n",
+    "\n",
+    "np.random.seed(43)\n",
+    "pred_tosses = []\n",
+    "for i in range(20):\n",
+    "    pi_post = np.random.choice(pis, p=posterior1*dgrid1)\n",
+    "    pred_toss = bernoulli(p=pi_post).rvs(1)[0]\n",
+    "    pred_tosses.append(pred_toss)\n",
+    "pred_tosses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00b785ec-8f83-4236-8968-43710fd35dd4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a04a12ca-9982-4a50-a92e-20acaf2f5d30",
+   "metadata": {},
+   "source": [
+    "## Example 2: estimating the IQ score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "7e8c07d9-b6b8-44b8-aa51-39d115c06cae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import norm\n",
+    "\n",
+    "#######################################################\n",
+    "\n",
+    "# Sample of IQ observations\n",
+    "iqs = [ 76.6,  99.5,  90.7,  78. , 121.2,  92.8, 88.3,\n",
+    "       116.1,  80.8, 80.1, 101.9, 112.9, 110.5,   95,\n",
+    "        85. , 124., 149.7, 114.8, 101.9, 111.1,  94.1,\n",
+    "       102.2,  93.8,  97.6, 102.1, 104.3,  95.8, 125.7,\n",
+    "        97.8, 110.4]\n",
+    "\n",
+    "sigma = 15  # known population standard deviation\n",
+    "\n",
+    "\n",
+    "# 1. Define a grid of possible means\n",
+    "ngrid2 = 1001               # number of points in the grid\n",
+    "mus = np.linspace(80, 120, ngrid2)\n",
+    "dgrid2 = (120-80) /  ngrid2  # grid spacing\n",
+    "\n",
+    "# 2. Define the prior = normal centered at 100 with std 30\n",
+    "mu0 = 100\n",
+    "sigma0 = 30\n",
+    "prior2 = norm(loc=mu0, scale=sigma0).pdf(mus)\n",
+    "\n",
+    "# 3. Compute the likelihood of the data for all `mus`\n",
+    "likelihood = np.ones(ngrid2)\n",
+    "for iq in iqs:\n",
+    "    likelihood_iq = norm(loc=mus, scale=sigma).pdf(iq)\n",
+    "    likelihood = likelihood * likelihood_iq\n",
+    "# ALT.\n",
+    "# likelihood = np.prod(norm(loc=mus[:,np.newaxis], scale=sigma).pdf(iqs), axis=1))\n",
+    "\n",
+    "# 4. Calculate the posterior distribution\n",
+    "numerator = likelihood * prior2\n",
+    "posterior2 = numerator / np.sum(numerator) / dgrid2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "27ae6624-d27b-4684-8dc9-9e9a69b87120",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12.386019811983466,\n",
+       " 25.024999999999995,\n",
+       " 0.4954407924794161,\n",
+       " 1.0010000000001562)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# normalization checks of prior and posterior\n",
+    "np.sum(prior2), np.sum(posterior2), myint(mus, prior2), myint(mus, posterior2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46fca11e-32e6-49ad-bd56-b79ccaa6b755",
+   "metadata": {},
+   "source": [
+    "### Visualize the posterior"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "5c7f4784-460a-47bd-8a79-79d07e3f7770",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 274,
+       "width": 440
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lineplot(x=mus, y=prior2, label='prior')\n",
+    "sns.lineplot(x=mus, y=posterior2, label='posterior');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "974da8a6-52b5-460b-9afe-612212531f77",
+   "metadata": {},
+   "source": [
+    "### Summarize the posterior"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd486bca-9430-4e37-9c22-58257c41647d",
+   "metadata": {},
+   "source": [
+    "#### Posterior mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "4f44e598-119d-408d-85a0-938259439d91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101.80826446258413"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(mus * posterior2 * dgrid2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3843787-0613-494a-8602-70c18867d8c8",
+   "metadata": {},
+   "source": [
+    "#### Posterior median"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a7725866-8c7f-4026-bc6e-bbd831c86a17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101.8"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cumsum2 = np.cumsum(posterior2 * dgrid2)\n",
+    "mus[cumsum2.searchsorted(0.5)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "862ee1f7-2748-4f9d-bfb6-31d342ae51a8",
+   "metadata": {},
+   "source": [
+    "#### Posterior mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "9b4e0d74-a8ae-4307-abbb-483b7e3600c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101.8"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mus[np.argmax(posterior2)]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "99be6181-82ab-4e72-8351-958044559833",
+   "metadata": {},
+   "source": [
+    "### Predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "05c6bc33-de09-43ed-af37-7e36702e4e54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[83.63670562899156,\n",
+       " 104.30779433005354,\n",
+       " 106.69141315621167,\n",
+       " 115.25148552252136,\n",
+       " 86.5861308561721,\n",
+       " 126.49101566601618,\n",
+       " 92.9679979691743,\n",
+       " 79.87256003503298,\n",
+       " 101.04324394721351,\n",
+       " 110.0720956811958]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.seed(43)\n",
+    "\n",
+    "sigma = 15  # known population standard deviation\n",
+    "\n",
+    "pred_iqs = []\n",
+    "for i in range(10):\n",
+    "    mu_post = np.random.choice(mus, p=posterior2*dgrid2)\n",
+    "    pred_iq = norm(loc=mu_post, scale=sigma).rvs(1)[0]\n",
+    "    pred_iqs.append(pred_iq)\n",
+    "pred_iqs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac1e8112-f717-4603-a280-80c919f044e7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4afe4ff-1a31-4403-8244-646e1d91be29",
+   "metadata": {},
+   "source": [
+    "## Explanations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e06388b8-bcd3-4727-b6e5-1c6e3a0c86e0",
+   "metadata": {},
+   "source": [
+    "### Grid approximation details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f42a44bb-2d95-4c89-960e-e364c92a27ab",
+   "metadata": {},
+   "source": [
+    "### Choosing priors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "fe3a5c75-4b66-47cc-9a86-efa0fe966ad5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ministats.hpdi import hpdi_from_grid\n",
+    "\n",
+    "def get_post_mean_stats(sample, sigma=15, prior_rv=None, ngrid=1001, xlims=[70,130],\n",
+    "                       stats=[\"mean\", \"median\", \"mode\", \"ci90\"]):\n",
+    "    mus = np.linspace(*xlims, ngrid)\n",
+    "    dgrid = (xlims[1]-xlims[0]) /  ngrid\n",
+    "    prior = prior_rv.pdf(mus)\n",
+    "    likelihoodsM = norm(loc=mus[:,np.newaxis], scale=sigma).pdf(sample)\n",
+    "    likelihood = np.prod(likelihoodsM, axis=1)\n",
+    "    numerator = likelihood * prior\n",
+    "    posterior = numerator / np.sum(numerator) / dgrid\n",
+    "\n",
+    "    # compute stats\n",
+    "    results = {}\n",
+    "    if \"mean\" in stats:\n",
+    "        post_mean = np.sum(mus * posterior * dgrid)\n",
+    "        results[\"mean\"] = post_mean\n",
+    "    if \"median\" in stats:\n",
+    "        cumsum = np.cumsum(posterior * dgrid)\n",
+    "        post_median = mus[cumsum.searchsorted(0.5)]\n",
+    "        results[\"median\"] = post_median\n",
+    "    if \"mode\" in stats:\n",
+    "        post_mode = mus[np.argmax(posterior)]\n",
+    "        results[\"mode\"] = post_mode\n",
+    "    if \"ci90\" in stats:\n",
+    "        ci90 = hpdi_from_grid(mus, posterior*dgrid, hdi_prob=0.9)\n",
+    "        results[\"ci90\"] = ci90\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ece7d317-0477-40af-9073-144a4f3909df",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "78a3f921-1bf6-43db-8e23-97588f63256d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(101.82333333333334, [96.83429390645762, 106.81237276020906])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# frequentist summary\n",
+    "from ministats import ci_mean\n",
+    "np.mean(iqs), ci_mean(iqs, alpha=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "0e1e289f-6658-4e14-9da7-c09fe1215e14",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mean': 101.81787969425056,\n",
+       " 'median': 101.8,\n",
+       " 'mode': 101.8,\n",
+       " 'ci90': [97.3, 106.3]}"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_post_mean_stats(iqs, prior_rv=norm(loc=100, scale=50))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "91b15b8f-fc7c-4438-9ce4-e3c3955b8eab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mean': 101.80826446280992,\n",
+       " 'median': 101.8,\n",
+       " 'mode': 101.8,\n",
+       " 'ci90': [97.48, 106.47999999999999]}"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_post_mean_stats(iqs, prior_rv=norm(loc=100, scale=30))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "60d287a4-13e1-46a9-add3-986b1c2a6e0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mean': 101.78977505112476,\n",
+       " 'median': 101.8,\n",
+       " 'mode': 101.8,\n",
+       " 'ci90': [97.18, 106.12]}"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_post_mean_stats(iqs, prior_rv=norm(loc=100, scale=20))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "9f8e975d-3270-4cdd-85b7-f9bccc5bc21c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mean': 101.69612403100776,\n",
+       " 'median': 101.68,\n",
+       " 'mode': 101.68,\n",
+       " 'ci90': [97.24, 105.94]}"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_post_mean_stats(iqs, prior_rv=norm(loc=100, scale=10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "9cf209c3-a456-48d4-8f1e-21067a2a1314",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mean': 100.21450980392156,\n",
+       " 'median': 100.24,\n",
+       " 'mode': 100.24,\n",
+       " 'ci90': [98.62, 101.74]}"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_post_mean_stats(iqs, prior_rv=norm(loc=100, scale=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a781139-594e-459f-a562-230aeeafee30",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c4c5cf8-4ad9-4883-8fe5-1218160c0898",
+   "metadata": {},
+   "source": [
+    "## Discussion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d042f673-d12f-4081-9c08-8ffc47a7229d",
+   "metadata": {},
+   "source": [
+    "### Bayesian decision theory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e0d6c2a-47a4-4d0f-a58f-f87d507ef738",
+   "metadata": {},
+   "source": [
+    "### Comparing Bayesian and frequentist approaches to statistical inference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a96ba84c-bfbf-45da-b4d4-23e8c75f9d3e",
+   "metadata": {},
+   "source": [
+    "### Strengths and weaknesses of Bayesian approach"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05fde3d6-4e2a-49be-8551-901f718d2a3b",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3695c738-dc26-4f85-ba56-16a1809a2f20",
+   "metadata": {},
+   "source": [
+    "### Exercise: Bayes rule for diagnostic test\n",
+    "\n",
+    "A group of researchers has designed a new\n",
+    "inexpensive and painless test for detecting lung cancer. The test is\n",
+    "intended to be an initial screening test for the population in\n",
+    "general. A positive result (presence of lung cancer) from the test\n",
+    "would be followed up immediately with medication, surgery or more\n",
+    "extensive and expensive test. The researchers know from their\n",
+    "studies the following facts:\n",
+    "\n",
+    "-   Test gives a positive result in $98\\%$ of the time when the test\n",
+    "    subject has lung cancer.\n",
+    "-   Test gives a negative result in $96\\%$ of the time when the\n",
+    "    test subject does not have lung cancer.\n",
+    "-   In general population approximately one person in 1000 has lung\n",
+    "    cancer.\n",
+    "\n",
+    "\n",
+    "**a)** The researchers are happy with these preliminary results (about\n",
+    "$97\\%$ success rate), and wish to get the test to market as soon as\n",
+    "possible. How would you advise them? Base your answer on Bayes' rule\n",
+    "computations.\n",
+    "\n",
+    "HINT: Relatively high false negative (cancer doesn't get\n",
+    "detected) or high false positive (unnecessarily administer\n",
+    "medication) rates are typically bad and undesirable in tests.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "Here are some probability values that can help you figure\n",
+    "out if you copied the right conditional probabilities from the\n",
+    "question.\n",
+    "\n",
+    "-   P(Test gives positive \\| Subject does not have lung cancer) =\n",
+    "    $4\\%$\n",
+    "-   P(Test gives positive **and** Subject has lung cancer) =\n",
+    "    $0.098\\%$ this is also referred to as the **joint probability**\n",
+    "    of *test being positive* and the *subject having lung cancer*.\n",
+    "\n",
+    "\n",
+    "* Is p(has cancer|test result is positive) computed using Bayes' formula\n",
+    "(or its complement p(does not have cancer|test result is positive))?\n",
+    "* Is the result p(has cancer|test result is positive)=**...** (or p(does not have cancer|test result\n",
+    "is positive)=**...**)\n",
+    "* Is the result motivated with something like\n",
+    "\n",
+    "\n",
+    "\n",
+    "See here for viz https://sophieehill.shinyapps.io/base-rate-viz/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "0329ccfa-ec5e-4a51-8719-b7913ee57259",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# In general population approximately one person in 1000 has lung cancer.\n",
+    "prev = 0.001\n",
+    "\n",
+    "# Test gives a positive result in 98% of the time when the test subject has lung cancer.\n",
+    "# Sensitivity: the ability of a test to correctly identify patients with a disease.\n",
+    "pPgC = sens = 0.98\n",
+    "\n",
+    "\n",
+    "# Test gives a negative result in 96% of the time when the test subject does not have lung cancer.\n",
+    "# Specificity: the ability of a test to correctly identify people without the disease.\n",
+    "pNgNC = spec = 0.96"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "2b5b965c-3306-4bb8-9f73-45d0a939c655",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.040000000000000036"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# P(Test gives positive | Subject does not have lung cancer) = 4%\n",
+    "pPgNC = (1-spec)\n",
+    "pPgNC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "72624a3a-1407-447c-b1b4-a0ec49bb9159",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.00098"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# P(Test gives positive and Subject has lung cancer) = 0.098%\n",
+    "pPC = sens*prev\n",
+    "pPC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "615723e7-cd9e-4278-9774-ab94496a968f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.04094000000000004"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pP = prev*sens + (1-prev)*(1-spec)\n",
+    "pP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "be0afdc0-7772-4f18-b19a-96ebc5aa1233",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.02393746946751341"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pr(C|P) = Pr(C and P) / Pr(P) \n",
+    "pCgP      = pPC         / pP\n",
+    "pCgP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "ad44303c-4e7e-4a3b-9c44-a5a25e759cba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.02393746946751341"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ALT\n",
+    "# Pr(C|P) = Pr(P|C)Pr(C) / Pr(P)\n",
+    "pCgP      = pPgC*prev / pP\n",
+    "pCgP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "dd58d2df-9856-4da2-a85f-e7b1a403afee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9760625305324866"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 - pCgP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b891869b-bc39-4605-92d5-7c905c96d599",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Given the high rate of false-positives 97.6%, I would advise the researchers not to proceed with the screening to the whole population. There will be way too many people scared for nothing.\n",
+    "The test can be used for patients who are suspected they might have lung cancer. In such a sub-population the prevalence would be higher and thus the false-positive rate would decrease.\n",
+    "I would possibly use a visualization tool to illustrate the [base rate fallacy](https://sophieehill.shinyapps.io/base-rate-viz/) problem.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8760cb9-79e3-4f30-85f3-ce2490728839",
+   "metadata": {},
+   "source": [
+    "### Exercise: algae\n",
+    "\n",
+    "see [assignment2](./explorations/BDA_course/assignment2/assignment2.qmd) from the BDA course"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27056303-da8a-4a25-8d6c-c062489b680a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7ff38e8-bed5-4201-9332-51d3135c5285",
+   "metadata": {},
+   "source": [
+    "## Links"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "68190559-a068-496c-adcb-c7aa8bae08d0",
+   "id": "84030a86-6636-4308-939f-bc3771714c93",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -523,7 +1627,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.4"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/52_Bayesian_inference_computations.ipynb b/notebooks/52_Bayesian_inference_computations.ipynb
index fae7c085..c88c276f 100644
--- a/notebooks/52_Bayesian_inference_computations.ipynb
+++ b/notebooks/52_Bayesian_inference_computations.ipynb
@@ -9,114 +9,2185 @@
    "source": [
     "# Section 5.2 — Bayesian inference computations\n",
     "\n",
-    "This notebook contains the code examples from [Section 5.2 Bayesian inference computations]() from the **No Bullshit Guide to Statistics**."
+    "This notebook contains the code examples from [Section 5.2 Bayesian inference computations]() from the **No Bullshit Guide to Statistics**.\n",
+    "\n",
+    "See also:\n",
+    "\n",
+    "- [Chp_04.ipynb](./explorations/SR2024/week02/Chp_04.ipynb)\n",
+    "- [Chp_05.ipynb](./explorations/SR2024/week02/Chp_05.ipynb)\n",
+    "- [homework_week02_ivan_savov.ipynb](./explorations/SR2024/week02/homework_week02_ivan_savov.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2d8dda2-58a9-424e-9fb3-32ad6e8777d8",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Notebook setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "aea0223e-aee9-4875-a714-897b6646baaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load Python modules\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "efd86c5a-c9d2-4eab-b67d-a65e39b23ef2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figures setup\n",
+    "plt.clf()  # needed otherwise `sns.set_theme` doesn\"t work\n",
+    "from plot_helpers import RCPARAMS\n",
+    "RCPARAMS.update({\"figure.figsize\": (5, 3)})   # good for screen\n",
+    "# RCPARAMS.update({\"figure.figsize\": (5, 1.6)})  # good for print\n",
+    "sns.set_theme(\n",
+    "    context=\"paper\",\n",
+    "    style=\"whitegrid\",\n",
+    "    palette=\"colorblind\",\n",
+    "    rc=RCPARAMS,\n",
+    ")\n",
+    "\n",
+    "# High-resolution please\n",
+    "%config InlineBackend.figure_format = \"retina\"\n",
+    "\n",
+    "# Where to store figures\n",
+    "DESTDIR = \"figures/bayesian/computations\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "df811a10-417d-4389-8bff-30e59b5f6aef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set random seed for repeatability\n",
+    "np.random.seed(42)\n",
+    "#######################################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b473f755-7ed8-414e-819b-609cd9220b7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1292b1c9-a986-4b91-8556-8fd1ac00da53",
+   "metadata": {},
+   "source": [
+    "## Example 1: estimating the probability of a biased coin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "953b1bb0-ff29-49d0-a597-c3ce7dbffed0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7333333333333333"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import bernoulli\n",
+    "\n",
+    "true_p = 0.7\n",
+    "\n",
+    "np.random.seed(46)\n",
+    "wins = bernoulli(true_p).rvs(30)\n",
+    "dfcoin = pd.DataFrame({\"win\": wins})\n",
+    "\n",
+    "# dfcoin\n",
+    "dfcoin[\"win\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "907be791-ecd0-4cba-9673-f128ba560a08",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "624ddc91-1ae9-4096-8680-ae3c539b1f74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "       Formula: win ~ 1\n",
+       "        Family: bernoulli\n",
+       "          Link: p = identity\n",
+       "  Observations: 30\n",
+       "        Priors: \n",
+       "    target = p\n",
+       "        Common-level effects\n",
+       "            Intercept ~ Beta(alpha: 1.0, beta: 1.0)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import bambi as bmb\n",
+    "\n",
+    "formula = \"win ~ 1\"\n",
+    "\n",
+    "priors1 = {\n",
+    "    \"Intercept\": bmb.Prior(\"Beta\", alpha=1, beta=1),\n",
+    "}\n",
+    "\n",
+    "#######################################################\n",
+    "mod1 = bmb.Model(formula, priors=priors1,\n",
+    "                 family=\"bernoulli\", link=\"identity\",\n",
+    "                 data=dfcoin)\n",
+    "mod1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "70928fdc-a255-4cab-8624-3a34a24957e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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+       "<g id=\"clust1\" class=\"cluster\">\n",
+       "<title>cluster__obs__ (30)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M20,-8C20,-8 106,-8 106,-8 112,-8 118,-14 118,-20 118,-20 118,-211.32 118,-211.32 118,-217.32 112,-223.32 106,-223.32 106,-223.32 20,-223.32 20,-223.32 14,-223.32 8,-217.32 8,-211.32 8,-211.32 8,-20 8,-20 8,-14 14,-8 20,-8\"/>\n",
+       "<text text-anchor=\"middle\" x=\"74\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">__obs__ (30)</text>\n",
+       "</g>\n",
+       "<!-- Intercept -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>Intercept</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"63\" cy=\"-291.98\" rx=\"45.25\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"63\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">Intercept</text>\n",
+       "<text text-anchor=\"middle\" x=\"63\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"63\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Beta</text>\n",
+       "</g>\n",
+       "<!-- p -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>p</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"108.12,-215.32 17.88,-215.32 17.88,-157.82 108.12,-157.82 108.12,-215.32\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"63\" y=\"-181.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"63\" y=\"-165.02\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- Intercept&#45;&gt;p -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>Intercept&#45;&gt;p</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M63,-251C63,-243.15 63,-234.91 63,-227.08\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"66.5,-227.11 63,-217.11 59.5,-227.11 66.5,-227.11\"/>\n",
+       "</g>\n",
+       "<!-- win -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>win</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"63\" cy=\"-81.16\" rx=\"47.38\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"63\" y=\"-92.61\" font-family=\"Times,serif\" font-size=\"14.00\">win</text>\n",
+       "<text text-anchor=\"middle\" x=\"63\" y=\"-76.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"63\" y=\"-59.61\" font-family=\"Times,serif\" font-size=\"14.00\">Bernoulli</text>\n",
+       "</g>\n",
+       "<!-- p&#45;&gt;win -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>p&#45;&gt;win</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M63,-157.58C63,-150.1 63,-141.77 63,-133.45\"/>\n",
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+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x1777bb590>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mod1.build()\n",
+    "mod1.graph()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "eda4bf9e-c1ea-4636-951d-89689d41ff3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Modeling the probability that win==1\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [Intercept]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc5744849b1547f1ba4274fc0328357e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 1 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "idata1 = mod1.fit(draws=2000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "75e16c56-f159-4513-9b60-0f242c022119",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  background-color: var(--xr-background-color) !important;\n",
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+       "\n",
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+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 80kB\n",
+       "Dimensions:    (chain: 4, draw: 2000)\n",
+       "Coordinates:\n",
+       "  * chain      (chain) int64 32B 0 1 2 3\n",
+       "  * draw       (draw) int64 16kB 0 1 2 3 4 5 6 ... 1994 1995 1996 1997 1998 1999\n",
+       "Data variables:\n",
+       "    Intercept  (chain, draw) float64 64kB 0.7717 0.7379 0.7331 ... 0.5501 0.6827\n",
+       "Attributes:\n",
+       "    created_at:                  2024-09-03T15:55:27.771520+00:00\n",
+       "    arviz_version:               0.19.0\n",
+       "    inference_library:           pymc\n",
+       "    inference_library_version:   5.16.2\n",
+       "    sampling_time:               1.0736181735992432\n",
+       "    tuning_steps:                1000\n",
+       "    modeling_interface:          bambi\n",
+       "    modeling_interface_version:  0.14.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-24ebe41c-187d-4536-b5d7-8f2e3d58fb30' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-24ebe41c-187d-4536-b5d7-8f2e3d58fb30' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-382cd64c-08db-4661-8111-3031b14a9bb8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-382cd64c-08db-4661-8111-3031b14a9bb8' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-6606130b-0e73-40d9-ae1d-b423767acfa8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6606130b-0e73-40d9-ae1d-b423767acfa8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c257ab1-1ba0-4cc9-a6ff-a55aeddedb24' class='xr-var-data-in' type='checkbox'><label for='data-8c257ab1-1ba0-4cc9-a6ff-a55aeddedb24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-61cbb750-3681-4142-b3a3-bb1e9ffd9b79' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61cbb750-3681-4142-b3a3-bb1e9ffd9b79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8278c362-c25f-47a7-9fdc-d1ec6f78fa3b' class='xr-var-data-in' type='checkbox'><label for='data-8278c362-c25f-47a7-9fdc-d1ec6f78fa3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1997, 1998, 1999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c9d7d1e-c28c-4367-8539-977fa8a3d944' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7c9d7d1e-c28c-4367-8539-977fa8a3d944' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7717 0.7379 ... 0.5501 0.6827</div><input id='attrs-ac6ba9a2-696e-409a-b834-fa79a9e75c5f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac6ba9a2-696e-409a-b834-fa79a9e75c5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f0f062f-43a8-4391-bb38-f5a5050e85e4' class='xr-var-data-in' type='checkbox'><label for='data-9f0f062f-43a8-4391-bb38-f5a5050e85e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.77165714, 0.73786802, 0.73308926, ..., 0.70218781, 0.70334672,\n",
+       "        0.70334672],\n",
+       "       [0.5923499 , 0.61064996, 0.81141934, ..., 0.78743512, 0.64551237,\n",
+       "        0.71161458],\n",
+       "       [0.69898198, 0.56370181, 0.68356585, ..., 0.80015116, 0.73277882,\n",
+       "        0.80013317],\n",
+       "       [0.71084234, 0.62778373, 0.50221795, ..., 0.68504275, 0.55006548,\n",
+       "        0.68269285]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9609b85d-0d27-42d9-94ef-3cee0afe14e0' class='xr-section-summary-in' type='checkbox'  ><label for='section-9609b85d-0d27-42d9-94ef-3cee0afe14e0' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-970f8aee-881a-4f70-8253-708a2f21a690' class='xr-index-data-in' type='checkbox'/><label for='index-970f8aee-881a-4f70-8253-708a2f21a690' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-47693a01-deb8-479f-8c34-51459713a78e' class='xr-index-data-in' type='checkbox'/><label for='index-47693a01-deb8-479f-8c34-51459713a78e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
+       "       ...\n",
+       "       1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=2000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3196abae-0805-433a-a147-f8e24a493e45' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3196abae-0805-433a-a147-f8e24a493e45' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-09-03T15:55:27.771520+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.19.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd><dt><span>sampling_time :</span></dt><dd>1.0736181735992432</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.14.0</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "            <li class = \"xr-section-item\">\n",
+       "                  <input id=\"idata_sample_statsc1ca1c06-b7a6-4c3b-bb80-509954a8c0ca\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_sample_statsc1ca1c06-b7a6-4c3b-bb80-509954a8c0ca\" class = \"xr-section-summary\">sample_stats</label>\n",
+       "                  <div class=\"xr-section-inline-details\"></div>\n",
+       "                  <div class=\"xr-section-details\">\n",
+       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 992kB\n",
+       "Dimensions:                (chain: 4, draw: 2000)\n",
+       "Coordinates:\n",
+       "  * chain                  (chain) int64 32B 0 1 2 3\n",
+       "  * draw                   (draw) int64 16kB 0 1 2 3 4 ... 1996 1997 1998 1999\n",
+       "Data variables: (12/17)\n",
+       "    acceptance_rate        (chain, draw) float64 64kB 1.0 1.0 ... 0.4992 0.9944\n",
+       "    diverging              (chain, draw) bool 8kB False False ... False False\n",
+       "    energy                 (chain, draw) float64 64kB 19.6 19.17 ... 21.05 20.24\n",
+       "    energy_error           (chain, draw) float64 64kB -0.1662 ... -0.9452\n",
+       "    index_in_trajectory    (chain, draw) int64 64kB 1 -1 1 -1 -2 ... 1 2 -1 1 -3\n",
+       "    largest_eigval         (chain, draw) float64 64kB nan nan nan ... nan nan\n",
+       "    ...                     ...\n",
+       "    process_time_diff      (chain, draw) float64 64kB 5.6e-05 ... 0.000107\n",
+       "    reached_max_treedepth  (chain, draw) bool 8kB False False ... False False\n",
+       "    smallest_eigval        (chain, draw) float64 64kB nan nan nan ... nan nan\n",
+       "    step_size              (chain, draw) float64 64kB 1.422 1.422 ... 1.82 1.82\n",
+       "    step_size_bar          (chain, draw) float64 64kB 1.372 1.372 ... 1.471\n",
+       "    tree_depth             (chain, draw) int64 64kB 1 2 1 2 2 1 ... 2 1 2 1 2 2\n",
+       "Attributes:\n",
+       "    created_at:                  2024-09-03T15:55:27.778529+00:00\n",
+       "    arviz_version:               0.19.0\n",
+       "    inference_library:           pymc\n",
+       "    inference_library_version:   5.16.2\n",
+       "    sampling_time:               1.0736181735992432\n",
+       "    tuning_steps:                1000\n",
+       "    modeling_interface:          bambi\n",
+       "    modeling_interface_version:  0.14.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7a5bfcf5-7e2b-4f9e-bbc2-8f48f3ac4bed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7a5bfcf5-7e2b-4f9e-bbc2-8f48f3ac4bed' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-eb061224-4705-41a3-b53a-5a252e6fc8e2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eb061224-4705-41a3-b53a-5a252e6fc8e2' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-43c9097f-f2f9-40eb-8085-36291be2ffb3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-43c9097f-f2f9-40eb-8085-36291be2ffb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f05647ad-774c-4a2d-a049-0c09a845db93' class='xr-var-data-in' type='checkbox'><label for='data-f05647ad-774c-4a2d-a049-0c09a845db93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-41233b86-a962-4f2f-943d-10d00cca3816' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41233b86-a962-4f2f-943d-10d00cca3816' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b62650e-76fc-4b12-a259-6a49eae3c97c' class='xr-var-data-in' type='checkbox'><label for='data-3b62650e-76fc-4b12-a259-6a49eae3c97c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1997, 1998, 1999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f8b8bab0-d881-443b-ac5d-27d5f6dfd79c' class='xr-section-summary-in' type='checkbox'  ><label for='section-f8b8bab0-d881-443b-ac5d-27d5f6dfd79c' class='xr-section-summary' >Data variables: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 ... 1.0 0.4992 0.9944</div><input id='attrs-10ffacb4-097a-466d-a90f-dcb311f6f396' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10ffacb4-097a-466d-a90f-dcb311f6f396' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fadcfd1-e62d-4792-bb82-ab732e84624d' class='xr-var-data-in' type='checkbox'><label for='data-2fadcfd1-e62d-4792-bb82-ab732e84624d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.        , 1.        , 1.        , ..., 0.81026452, 1.        ,\n",
+       "        0.27314162],\n",
+       "       [0.71295887, 1.        , 1.        , ..., 0.84859088, 1.        ,\n",
+       "        1.        ],\n",
+       "       [1.        , 0.39575021, 1.        , ..., 1.        , 1.        ,\n",
+       "        0.75989434],\n",
+       "       [0.99844097, 0.72889834, 0.26991799, ..., 1.        , 0.49915986,\n",
+       "        0.9944279 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-24f4d3ca-c0e3-4156-8a23-15915c02d506' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24f4d3ca-c0e3-4156-8a23-15915c02d506' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9a363cd-41c0-435f-bbde-27911e85fc75' class='xr-var-data-in' type='checkbox'><label for='data-b9a363cd-41c0-435f-bbde-27911e85fc75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>19.6 19.17 19.04 ... 21.05 20.24</div><input id='attrs-0324cb4c-c04f-4df6-a295-a1de9f7ad863' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0324cb4c-c04f-4df6-a295-a1de9f7ad863' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5c3f12e-662d-474c-9fe1-4437a36b8f94' class='xr-var-data-in' type='checkbox'><label for='data-c5c3f12e-662d-474c-9fe1-4437a36b8f94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[19.59762216, 19.17173285, 19.04272347, ..., 19.47121467,\n",
+       "        19.04027032, 20.34601466],\n",
+       "       [20.37165972, 20.39658392, 19.82235932, ..., 19.43424219,\n",
+       "        19.43896443, 19.21696144],\n",
+       "       [19.09770957, 20.71246639, 19.86190967, ..., 20.0253918 ,\n",
+       "        19.34687396, 19.62129035],\n",
+       "       [19.01722703, 19.61164875, 22.62959822, ..., 19.18557702,\n",
+       "        21.04680657, 20.23891252]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1662 -0.08671 ... 1.059 -0.9452</div><input id='attrs-6ba61dbb-68b7-4edc-a93e-d412a42e6b2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ba61dbb-68b7-4edc-a93e-d412a42e6b2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-641d906a-d2af-477c-9df2-ccc62171a210' class='xr-var-data-in' type='checkbox'><label for='data-641d906a-d2af-477c-9df2-ccc62171a210' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1.66213341e-01, -8.67070336e-02, -5.59917081e-03, ...,\n",
+       "        -1.32286346e-02, -1.29645313e-03,  0.00000000e+00],\n",
+       "       [ 3.38331552e-01, -1.54303302e-01, -4.62435906e-02, ...,\n",
+       "         1.64178092e-01, -3.72423879e-03, -1.85276331e-01],\n",
+       "       [-4.39476574e-02,  9.26972054e-01, -9.04442700e-01, ...,\n",
+       "        -1.05690460e-01, -2.74707070e-01,  2.74575887e-01],\n",
+       "       [ 2.32704221e-03,  3.16221013e-01,  1.52540939e+00, ...,\n",
+       "        -4.64117466e-02,  1.05908942e+00, -9.45205716e-01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>index_in_trajectory</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 -1 1 -1 -2 -1 1 ... 2 1 2 -1 1 -3</div><input id='attrs-e5975149-ad32-469d-a332-84b161e35b0b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5975149-ad32-469d-a332-84b161e35b0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bd2225c-379a-4a03-b207-0821c3e01462' class='xr-var-data-in' type='checkbox'><label for='data-4bd2225c-379a-4a03-b207-0821c3e01462' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 1, -1,  1, ..., -2, -1,  0],\n",
+       "       [-1, -1, -2, ...,  1,  2, -1],\n",
+       "       [-1, -1,  1, ...,  1,  1,  1],\n",
+       "       [-1, -1,  1, ..., -1,  1, -3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>largest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-aba04424-373d-4c70-8433-d3758ec0f879' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aba04424-373d-4c70-8433-d3758ec0f879' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c357fd7-fc30-4c3c-892a-9c99f411bdc6' class='xr-var-data-in' type='checkbox'><label for='data-7c357fd7-fc30-4c3c-892a-9c99f411bdc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-19.25 -19.04 ... -20.94 -19.11</div><input id='attrs-e5b12f1d-a13f-4b99-bcae-dd8e243d7287' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5b12f1d-a13f-4b99-bcae-dd8e243d7287' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75ac3881-2dba-4e45-9719-19912deed366' class='xr-var-data-in' type='checkbox'><label for='data-75ac3881-2dba-4e45-9719-19912deed366' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-19.25410686, -19.04194146, -19.02878849, ..., -19.03338067,\n",
+       "        -19.03054315, -19.03054315],\n",
+       "       [-20.12024337, -19.83381014, -19.82038196, ..., -19.43298111,\n",
+       "        -19.40108731, -19.01615006],\n",
+       "       [-19.04226364, -20.64915119, -19.10570358, ..., -19.61970227,\n",
+       "        -19.02806865, -19.61940928],\n",
+       "       [-19.01705504, -19.60239172, -22.11892171, ..., -19.09816806,\n",
+       "        -20.93539192, -19.11030067]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1662 -0.1034 ... 1.059 -0.9452</div><input id='attrs-284a35ff-ac43-4a15-91a3-04e0f70ad4a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-284a35ff-ac43-4a15-91a3-04e0f70ad4a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cd46658-72f9-4327-b94c-8ce4de1ff239' class='xr-var-data-in' type='checkbox'><label for='data-2cd46658-72f9-4327-b94c-8ce4de1ff239' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1.66213341e-01, -1.03414708e-01, -5.59917081e-03, ...,\n",
+       "         3.89246182e-01, -1.29645313e-03,  1.29776486e+00],\n",
+       "       [ 3.38331552e-01, -1.54303302e-01, -3.71136612e-01, ...,\n",
+       "         1.64178092e-01, -1.83515110e-01, -1.85276331e-01],\n",
+       "       [-4.39476574e-02,  9.26972054e-01, -9.04442700e-01, ...,\n",
+       "        -1.05690460e-01, -2.74707070e-01,  2.74575887e-01],\n",
+       "       [ 2.32704221e-03,  3.16221013e-01,  1.52540939e+00, ...,\n",
+       "        -4.64117466e-02,  1.05908942e+00, -9.45205716e-01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 3.0 1.0 3.0 ... 3.0 1.0 3.0 3.0</div><input id='attrs-07c6910d-c095-44b7-aa38-9f033042256c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07c6910d-c095-44b7-aa38-9f033042256c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b796f8d-1370-469c-aa7b-fadd0452ec4c' class='xr-var-data-in' type='checkbox'><label for='data-9b796f8d-1370-469c-aa7b-fadd0452ec4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1., 3., 1., ..., 3., 1., 1.],\n",
+       "       [1., 1., 3., ..., 1., 3., 1.],\n",
+       "       [1., 1., 1., ..., 1., 1., 1.],\n",
+       "       [3., 1., 3., ..., 1., 3., 3.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.612e-05 0.0001136 ... 0.0001078</div><input id='attrs-6339a4f6-9952-45cf-ab4a-3b30a1c2de7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6339a4f6-9952-45cf-ab4a-3b30a1c2de7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88595e01-6039-4ee3-b6fb-c75473598a11' class='xr-var-data-in' type='checkbox'><label for='data-88595e01-6039-4ee3-b6fb-c75473598a11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5.61249908e-05, 1.13582995e-04, 5.43330098e-05, ...,\n",
+       "        1.06915948e-04, 5.56249870e-05, 5.17080189e-05],\n",
+       "       [5.54170110e-05, 5.38330059e-05, 1.07417000e-04, ...,\n",
+       "        5.28329983e-05, 1.05332991e-04, 5.10840327e-05],\n",
+       "       [5.47079835e-05, 5.43330098e-05, 5.37080341e-05, ...,\n",
+       "        5.21250186e-05, 5.11670369e-05, 5.17079607e-05],\n",
+       "       [1.10000023e-04, 8.95829871e-05, 1.15333009e-04, ...,\n",
+       "        5.40410401e-05, 1.07125030e-04, 1.07791973e-04]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_start</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.87e+05 4.87e+05 ... 4.87e+05</div><input id='attrs-1bd18c8d-6180-4cd3-afb5-909d927bb2ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1bd18c8d-6180-4cd3-afb5-909d927bb2ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d9cf830-d79f-414d-96a3-a8abfa21f805' class='xr-var-data-in' type='checkbox'><label for='data-3d9cf830-d79f-414d-96a3-a8abfa21f805' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[487005.21240533, 487005.21248888, 487005.21263113, ...,\n",
+       "        487005.44504825, 487005.44518292, 487005.44526458],\n",
+       "       [487005.21904567, 487005.21912946, 487005.21920896, ...,\n",
+       "        487005.47473775, 487005.47481537, 487005.47494667],\n",
+       "       [487005.24287833, 487005.24296008, 487005.2430415 , ...,\n",
+       "        487005.53067329, 487005.53075117, 487005.53082846],\n",
+       "       [487005.30158504, 487005.3018585 , 487005.30198321, ...,\n",
+       "        487005.606713  , 487005.60679196, 487005.60692433]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>process_time_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.6e-05 0.000115 ... 0.000107</div><input id='attrs-379e6e71-01d6-4d92-a125-ba98a8d1ad60' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-379e6e71-01d6-4d92-a125-ba98a8d1ad60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-766d2beb-ecf2-43af-8248-722dff1f824f' class='xr-var-data-in' type='checkbox'><label for='data-766d2beb-ecf2-43af-8248-722dff1f824f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5.60e-05, 1.15e-04, 5.40e-05, ..., 1.08e-04, 5.50e-05, 5.10e-05],\n",
+       "       [5.60e-05, 5.40e-05, 1.07e-04, ..., 5.40e-05, 1.05e-04, 5.10e-05],\n",
+       "       [5.50e-05, 5.40e-05, 5.50e-05, ..., 5.10e-05, 5.10e-05, 5.10e-05],\n",
+       "       [1.11e-04, 8.50e-05, 1.16e-04, ..., 5.30e-05, 1.07e-04, 1.07e-04]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reached_max_treedepth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e31f0cd3-ca19-42ad-babc-fcb0b07e0185' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e31f0cd3-ca19-42ad-babc-fcb0b07e0185' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8fc10f9-5f98-483d-8619-fbddb54abe4a' class='xr-var-data-in' type='checkbox'><label for='data-c8fc10f9-5f98-483d-8619-fbddb54abe4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smallest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-9c18f5c3-87b3-4ac7-be88-68247e18010d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c18f5c3-87b3-4ac7-be88-68247e18010d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9d367a7-008a-4532-b70b-3e74d011ace6' class='xr-var-data-in' type='checkbox'><label for='data-f9d367a7-008a-4532-b70b-3e74d011ace6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
+       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.422 1.422 1.422 ... 1.82 1.82</div><input id='attrs-50570c26-22db-4355-8880-199c0352fa67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50570c26-22db-4355-8880-199c0352fa67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89e14320-c511-4480-a114-9405a3a74296' class='xr-var-data-in' type='checkbox'><label for='data-89e14320-c511-4480-a114-9405a3a74296' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.421742  , 1.421742  , 1.421742  , ..., 1.421742  , 1.421742  ,\n",
+       "        1.421742  ],\n",
+       "       [1.02431752, 1.02431752, 1.02431752, ..., 1.02431752, 1.02431752,\n",
+       "        1.02431752],\n",
+       "       [0.96288096, 0.96288096, 0.96288096, ..., 0.96288096, 0.96288096,\n",
+       "        0.96288096],\n",
+       "       [1.82031716, 1.82031716, 1.82031716, ..., 1.82031716, 1.82031716,\n",
+       "        1.82031716]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size_bar</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.372 1.372 1.372 ... 1.471 1.471</div><input id='attrs-9f83323b-cda4-4206-8cd7-71750978e684' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f83323b-cda4-4206-8cd7-71750978e684' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-878dc72e-db55-413a-b27d-1829f23c8be4' class='xr-var-data-in' type='checkbox'><label for='data-878dc72e-db55-413a-b27d-1829f23c8be4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.37196654, 1.37196654, 1.37196654, ..., 1.37196654, 1.37196654,\n",
+       "        1.37196654],\n",
+       "       [1.26445491, 1.26445491, 1.26445491, ..., 1.26445491, 1.26445491,\n",
+       "        1.26445491],\n",
+       "       [1.38229439, 1.38229439, 1.38229439, ..., 1.38229439, 1.38229439,\n",
+       "        1.38229439],\n",
+       "       [1.47145144, 1.47145144, 1.47145144, ..., 1.47145144, 1.47145144,\n",
+       "        1.47145144]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 1 2 2 1 2 1 ... 2 2 2 1 2 1 2 2</div><input id='attrs-dd4ade0d-2e6a-450a-8b0c-340e62e77224' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd4ade0d-2e6a-450a-8b0c-340e62e77224' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a418bdca-fa51-457b-ba90-b27ce3e61579' class='xr-var-data-in' type='checkbox'><label for='data-a418bdca-fa51-457b-ba90-b27ce3e61579' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1, 2, 1, ..., 2, 1, 1],\n",
+       "       [1, 1, 2, ..., 1, 2, 1],\n",
+       "       [1, 1, 1, ..., 1, 1, 1],\n",
+       "       [2, 1, 2, ..., 1, 2, 2]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-394ff5f7-ad93-4a78-b725-a896bb45a7d6' class='xr-section-summary-in' type='checkbox'  ><label for='section-394ff5f7-ad93-4a78-b725-a896bb45a7d6' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f0c96235-c7a1-4b7b-af8a-b2eca688a27e' class='xr-index-data-in' type='checkbox'/><label for='index-f0c96235-c7a1-4b7b-af8a-b2eca688a27e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-77499fa5-2eac-41f6-984a-5b147c4679a4' class='xr-index-data-in' type='checkbox'/><label for='index-77499fa5-2eac-41f6-984a-5b147c4679a4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
+       "       ...\n",
+       "       1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=2000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bfc7a1ce-b015-4682-a221-06c9d038555b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bfc7a1ce-b015-4682-a221-06c9d038555b' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-09-03T15:55:27.778529+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.19.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd><dt><span>sampling_time :</span></dt><dd>1.0736181735992432</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.14.0</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "            <li class = \"xr-section-item\">\n",
+       "                  <input id=\"idata_observed_datafac1ee30-4f14-4fbe-b7d8-ae299ea8d4d1\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_observed_datafac1ee30-4f14-4fbe-b7d8-ae299ea8d4d1\" class = \"xr-section-summary\">observed_data</label>\n",
+       "                  <div class=\"xr-section-inline-details\"></div>\n",
+       "                  <div class=\"xr-section-details\">\n",
+       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 480B\n",
+       "Dimensions:  (__obs__: 30)\n",
+       "Coordinates:\n",
+       "  * __obs__  (__obs__) int64 240B 0 1 2 3 4 5 6 7 8 ... 22 23 24 25 26 27 28 29\n",
+       "Data variables:\n",
+       "    win      (__obs__) int64 240B 0 1 1 0 1 0 1 1 0 1 1 ... 1 1 1 1 1 0 1 1 1 1\n",
+       "Attributes:\n",
+       "    created_at:                  2024-09-03T15:55:27.780267+00:00\n",
+       "    arviz_version:               0.19.0\n",
+       "    inference_library:           pymc\n",
+       "    inference_library_version:   5.16.2\n",
+       "    modeling_interface:          bambi\n",
+       "    modeling_interface_version:  0.14.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b6c2ed21-03e2-4deb-9646-98123dfdec4f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b6c2ed21-03e2-4deb-9646-98123dfdec4f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>__obs__</span>: 30</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-88d5bd58-b495-4bfb-95d7-75421b3f65fb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-88d5bd58-b495-4bfb-95d7-75421b3f65fb' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>__obs__</span></div><div class='xr-var-dims'>(__obs__)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 24 25 26 27 28 29</div><input id='attrs-77fbffc1-be6b-4728-b300-d6cda0b57825' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77fbffc1-be6b-4728-b300-d6cda0b57825' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a02e97e-567d-4038-a481-496d4272d771' class='xr-var-data-in' type='checkbox'><label for='data-5a02e97e-567d-4038-a481-496d4272d771' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c3a8bc2a-1977-4b27-afb9-7b90181a2df8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c3a8bc2a-1977-4b27-afb9-7b90181a2df8' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>win</span></div><div class='xr-var-dims'>(__obs__)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 1 0 1 0 1 1 ... 1 1 1 0 1 1 1 1</div><input id='attrs-56291815-2147-4381-8588-5b747fc7703f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56291815-2147-4381-8588-5b747fc7703f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa3fdef4-bd6f-4927-8052-2d35a613423e' class='xr-var-data-in' type='checkbox'><label for='data-aa3fdef4-bd6f-4927-8052-2d35a613423e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 0, 1, 1, 1, 1])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-334fc379-0131-4051-9ed1-f7b40fa16dfc' class='xr-section-summary-in' type='checkbox'  ><label for='section-334fc379-0131-4051-9ed1-f7b40fa16dfc' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>__obs__</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4bb63317-a864-4f80-96aa-e711bfa44531' class='xr-index-data-in' type='checkbox'/><label for='index-4bb63317-a864-4f80-96aa-e711bfa44531' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;__obs__&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8678eeec-8525-4797-91de-fb4d24f0a454' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8678eeec-8525-4797-91de-fb4d24f0a454' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-09-03T15:55:27.780267+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.19.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.14.0</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "              </ul>\n",
+       "            </div>\n",
+       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-sections.group-sections {\n",
+       "  grid-template-columns: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       ".xr-wrap{width:700px!important;} </style>"
+      ],
+      "text/plain": [
+       "Inference data with groups:\n",
+       "\t> posterior\n",
+       "\t> sample_stats\n",
+       "\t> observed_data"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "idata1"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "a2d8dda2-58a9-424e-9fb3-32ad6e8777d8",
-   "metadata": {
-    "tags": []
-   },
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ff859442-5a5f-4b60-b6f3-06e3c8e9c0a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Intercept</th>\n",
+       "      <td>0.72</td>\n",
+       "      <td>0.077</td>\n",
+       "      <td>0.576</td>\n",
+       "      <td>0.865</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>3450.0</td>\n",
+       "      <td>5146.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
+       "Intercept  0.72  0.077   0.576    0.865      0.001    0.001    3450.0   \n",
+       "\n",
+       "           ess_tail  r_hat  \n",
+       "Intercept    5146.0    1.0  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#### Notebook setup"
+    "import arviz as az\n",
+    "az.summary(idata1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "aea0223e-aee9-4875-a714-897b6646baaa",
+   "execution_count": 10,
+   "id": "044e2d0b-9e80-4c3b-8b60-25522c13716b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 289,
+       "width": 407
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# load Python modules\n",
-    "import os\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt"
+    "az.plot_posterior(idata1, var_names=\"Intercept\", point_estimate=\"mode\", hdi_prob=0.9);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "efd86c5a-c9d2-4eab-b67d-a65e39b23ef2",
+   "execution_count": 11,
+   "id": "4c97ca76-da06-4e3a-86ac-bc823fad04d0",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
+       "<Figure size 500x300 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "image/png": {
+       "height": 274,
+       "width": 447
+      }
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "# Figures setup\n",
-    "plt.clf()  # needed otherwise `sns.set_theme` doesn\"t work\n",
-    "from plot_helpers import RCPARAMS\n",
-    "RCPARAMS.update({\"figure.figsize\": (5, 3)})   # good for screen\n",
-    "# RCPARAMS.update({\"figure.figsize\": (5, 1.6)})  # good for print\n",
-    "sns.set_theme(\n",
-    "    context=\"paper\",\n",
-    "    style=\"whitegrid\",\n",
-    "    palette=\"colorblind\",\n",
-    "    rc=RCPARAMS,\n",
-    ")\n",
-    "\n",
-    "# High-resolution please\n",
-    "%config InlineBackend.figure_format = \"retina\"\n",
-    "\n",
-    "# Where to store figures\n",
-    "DESTDIR = \"figures/bayesian/computations\""
+    "# ALT. manual plot of posterior density\n",
+    "post = az.extract(idata1, group=\"posterior\", var_names=\"Intercept\").values\n",
+    "sns.kdeplot(x=post.flatten(), bw_adjust=1);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "df811a10-417d-4389-8bff-30e59b5f6aef",
+   "execution_count": 12,
+   "id": "62240476-5dfd-465f-8d5d-b561de0cf061",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [Intercept]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 289,
+       "width": 427
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# set random seed for repeatability\n",
-    "np.random.seed(42)\n",
-    "#######################################################"
+    "mod1.plot_priors();"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b473f755-7ed8-414e-819b-609cd9220b7d",
+   "id": "8c25710e-2c83-4792-8296-98366d5f9236",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "\n"
-   ]
+   "source": []
   },
   {
-   "cell_type": "markdown",
-   "id": "1292b1c9-a986-4b91-8556-8fd1ac00da53",
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "64abe9f5-4ba7-4d1c-ab55-2915e218cf39",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Example 1: estimating the probability of a biased coin"
+    "# mod1.predict(idata1, kind=\"response\")\n",
+    "# preds = az.extract(idata1, group=\"posterior_predictive\", var_names=\"win\").values\n",
+    "# sns.histplot(x=preds.sum(axis=0), stat=\"density\")\n",
+    "# sns.kdeplot(x=post.mean(axis=0)*30)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "953b1bb0-ff29-49d0-a597-c3ce7dbffed0",
+   "cell_type": "markdown",
+   "id": "f3a3df96-6458-4f60-9fc5-8f4eff69bd61",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from scipy.stats import bernoulli\n",
-    "tosses = bernoulli(0.7).rvs(20)"
+    "### ALT. Declare a model in PyMC"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
    "id": "bcbb2198-3b4b-4ede-9cde-2348a9cf00f3",
    "metadata": {},
    "outputs": [
@@ -124,39 +2195,21 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [p]\n",
-      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n",
-      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n",
-      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n",
-      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
+      "NUTS: [p]\n"
      ]
     },
     {
      "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
-       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f6d20d05dd94f319788e180fcb06d91",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "Output()"
       ]
      },
      "metadata": {},
@@ -165,15 +2218,21 @@
     {
      "data": {
       "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 00:04&lt;00:00 Sampling 4 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
       ],
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "\n"
       ]
      },
      "metadata": {},
@@ -183,29 +2242,25 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 18 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 1 seconds.\n"
      ]
     }
    ],
    "source": [
-    "# Declare a model in PyMC\n",
-    "\n",
     "import pymc as pm\n",
     "\n",
     "with pm.Model() as model:\n",
     "    # Specify the prior distribution of unknown parameter\n",
     "    p = pm.Beta(\"p\", alpha=1, beta=1)\n",
-    "\n",
     "    # Specify the likelihood distribution and condition on the observed data\n",
-    "    y_obs = pm.Binomial(\"y_obs\", n=1, p=p, observed=tosses)\n",
-    "\n",
+    "    y_obs = pm.Binomial(\"y_obs\", n=1, p=p, observed=dfcoin)\n",
     "    # Sample from the posterior distribution\n",
-    "    idata = pm.sample(1000)"
+    "    idata1_alt = pm.sample(draws=2000)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 15,
    "id": "2bafd751-38f0-45f1-898f-6ae6190600ea",
    "metadata": {},
    "outputs": [
@@ -244,14 +2299,14 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>p</th>\n",
-       "      <td>0.688</td>\n",
-       "      <td>0.092</td>\n",
-       "      <td>0.522</td>\n",
-       "      <td>0.861</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>1601.0</td>\n",
-       "      <td>2528.0</td>\n",
+       "      <td>0.719</td>\n",
+       "      <td>0.079</td>\n",
+       "      <td>0.572</td>\n",
+       "      <td>0.864</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>3407.0</td>\n",
+       "      <td>5448.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -260,25 +2315,24 @@
       ],
       "text/plain": [
        "    mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
-       "p  0.688  0.092   0.522    0.861      0.002    0.002    1601.0    2528.0   \n",
+       "p  0.719  0.079   0.572    0.864      0.001    0.001    3407.0    5448.0   \n",
        "\n",
        "   r_hat  \n",
        "p    1.0  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "import arviz as az\n",
-    "az.summary(idata)"
+    "az.summary(idata1_alt)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 16,
    "id": "1745b4ca-a337-4a19-bf71-176bcc4e9165",
    "metadata": {},
    "outputs": [
@@ -287,31 +2341,18 @@
      "output_type": "stream",
      "text": [
       "Sampling: [p, y_obs]\n",
-      "Sampling: [y_obs]\n"
+      "Sampling: [p, y_obs]\n"
      ]
     },
     {
      "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
-       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ceeead8cac54141b165b595d4658ad5",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "Output()"
       ]
      },
      "metadata": {},
@@ -320,15 +2361,21 @@
     {
      "data": {
       "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='4000' class='' max='4000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [4000/4000 00:00&lt;00:00]\n",
-       "    </div>\n",
-       "    "
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
       ],
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "\n"
       ]
      },
      "metadata": {},
@@ -337,18 +2384,18 @@
    ],
    "source": [
     "pred_dists = (pm.sample_prior_predictive(1000, model).prior_predictive[\"y_obs\"].values,\n",
-    "              pm.sample_posterior_predictive(idata, model).posterior_predictive[\"y_obs\"].values)"
+    "              pm.sample_posterior_predictive(idata1, model).posterior_predictive[\"y_obs\"].values)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 17,
    "id": "7f719710-de85-4a83-b9b8-c1c8181887e8",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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s1Nnt9kLfr8vt3btX27dv16uvvupy2k5v06lTJ6WlpSklJUWZmZlq0KCBLBaLevfuXeCUS55u3rx5uvfee732Co2iqF27tr7++mvVr19fp06d0qJFizRp0iSlpKSof//+ioyM9LpjY8fvgnXr1qlixYr6v//7P1WpUkUnTpzQRx99pE8//VSbN2/WuHHjDE5assLDw5WWlqbu3bsbHcWt3nrrLTVr1kwDBgxQcnKyvv76a0nSww8/rEOHDt3w/WhKo3Llyql58+batm2bpk+fnm9qxT179iguLk6SvGJ/f2X/5lp6fJcP3kHx885OEkotx5y8hd1cw/HHmTdfZo1L/vrrL40bN059+vRxzsPq7YKCgvTLL7847z4fGRmpoUOHasSIEdq5c6dGjx5tdMRiFxMTIx8fH1PMM1uQX3/91TlliXRp6pGxY8eqWrVqmjJliiIiIpSUlOR1o/jT09OVmZkpSXrnnXfy3Kz0vvvu0zfffKPnn39eY8aMUadOna46ktnTrVmzRmvXrtXy5cuNjlIioqOjtXTpUo0fP141a9bUzz//rKVLl2rWrFlXbZonJCRo7ty58vf313/+8588y5KSkvTWW2/p/Pnz+uyzz3TfffcpNzdX69ev17Bhw9SrVy917txZn376aZ7X+emnnzR69GgNHTpUY8eOVWBgoHbu3KkRI0aoV69ezsZsdHS0Zs6cqXXr1kmSduzYoYceekjSpRF4P/zwg5o3by6bzabx48drzpw5Gjx4sIYMGSIfHx+tWLFCAwYMUGJior7//nvnpfhvvfWW1q9fr6ysLGemv/76S4MGDXJOYWSz2TRp0iTNnTtXJ06ckM1mK3Cqn3nz5mnYsGHq2bOnZsyYoUqVKunMmTP673//qx9//FFz587VDz/84Pxj+uDBg+rbt6+OHz/ubMT6+vpq+PDhmjNnjqTCr7C80qlTpzRixAg1atRIo0aN0ttvv62YmBj97W9/0/Tp0/P8Eec4/rNarXrppZd06tQpZ6P773//e55jv8zMTL388stKS0vT6NGjdccddyg7O1uLFi3SyJEjlZiYqC+++ELSpbm9165dq1OnTkmSvvjiC+fv0Zo1a2revHnq3Lmz4uPjnXnuu+8+LVy4MM/3lZ+fn0aMGKHVq1c77xHRokWLQutfv3693nzzTZUvX14BAQHOz3DdunUaMmSI89L2s2fP6uzZs9q9e7eOHz/uclsHDx5USkqKNmzYILvdnudzSEpK0o8//qi1a9dKutTcf/75551N/g4dOuj222/XDz/8oJMnTxb6x/PatWs1YMAAPf7445o0aZJzTtywsDD98MMPmjdvnsaMGeM8Jps/f74+/fRT5368Xr16SkhI0Ouvv66zZ8/Kz88vzxUJBZ1UuJLFYtFHH32k+++/X++9916RnuPJbDabhg4dqnvvvVc+Pj7as2ePPvjgA/3yyy9avXq1FixY4HV/96SkpGju3LkKCwszOoph3n77bd17773OE2D169dX//79ddttt6lfv35KTEzU2rVrvW66PscVW48//nie0ex169bV999/r3bt2mnFihWKiopy/m71dgkJCRozZoy++OKLAqcs8xYnTpzQ5MmT9corr6hTp06aMWOGZsyYoQ0bNigqKkoffvihXn75ZaNjFrsBAwaoZ8+e+vnnn1W+fHl17dpVZcqU0Z9//qnJkycrJCRE586dK3SUuydw1b+5/BivIPT4SgZz8KNEOUYoOC5fd8Xxh0LFihVLIhIMkp6err59+6p9+/am+OPOoUaNGs6b0vj7++uJJ55wnv1evHix1917wmq1avTo0aa5QqMglzf3L9e7d2/5+PjIZrN55U2HLr8M09UfcXfddZdCQ0N18eJFl6OdvUlGRoazGWuG0apr167Vm2++qaFDh+qxxx7THXfcoTFjxmjBggVq2rRpnjn5pUt//Obm5spisWjdunXOG5yOGzfOOaerdGlkd+/evRUXF6dffvlF999/v3x8fOTv76+2bdsqLCxMZcqU0dy5c/Xtt986n3fmzBmNHTtWTzzxhLp37+48Hrnzzjs1ceLEPH9wN2/eXGPHjlW7du0kSb169VJUVJSioqK0adMm58jqL774Qt9//71GjRqlJ598UmXKlFFgYKCefvpp/fOf/5Qkff/99/rtt9+UmZmpfv366eWXX3aOYpKk8ePHq3Xr1s4mfKVKlfTGG29oxYoV+te//lXg+xsREaEhQ4bo+eef14ABA1SpUiVJl5raw4cPV+fOnXXu3Dn17t1bCQkJki7d1HPy5MkaNWqUczuOujp06KCQkBDndq4mOjpazzzzjJo0aaJ33nknz3QDLVu21A8//JDnPXW836mpqZozZ45Wr16d574zjmO/ChUqaNq0adq5c6cGDx6sO+64Q9KlkV+dO3fWgAED8uR46aWXNGPGDOf/58+f76xp3rx5kqRvvvlGs2bNck4LcP78eefn26hRI0mX3vePPvpIq1atKtINALdv367x48drxowZ2rx5szZu3Jjnqpy5c+dq5cqVkqT7779f/fv3d95c2ZW+fftqyJAhzj/8Lz8xddNNN2ngwIEaPny4pEsjgjdu3Oisc8iQIerUqZOWL1+ufv36FfgaO3bs0FtvvaX77rtPI0eOVPXq1SVdGk34zjvvqH///srIyNA777yj+Ph4SZdGnUdHRztPUqWnp2vgwIEaMmSIoqOjFRUVpTlz5jg/3z179jibe4X5+OOPZbPZ9N///tfrm13Spc/zvvvuc56Uuf322/XNN9/Iz89PR48e1bJlywxOWPzGjBmjN9980+tGp1+LFi1auJyG5rHHHnPue44ePVrCqdzPMQWJq+O+ChUqOKfkcnUjVm/16aefqnHjxl53MudKp06dUrdu3fTwww+rX79+qlOnjv79738rMjJSTz31lOx2u0aNGqVDhw4ZHbXY3XfffZo8ebIaNWqk7777Tg8++KAefPBBrVq1Sv/973+VlZUlX19fj76Ct6D+jWM/X9h9pS4/zoP7MIIfJcoxOrWwH/709HQFBATku0QY3iM7O1tvvvmmmjdvrk8++cToOIa7fP7xhIQE1a1b18A0xWvWrFnav3+/XnzxxTyPO0awJiQk6Mknn5Qk9enTR506dSrpiIYKCQlxjujwxhENlzfwLm9qXq5+/frav3+/199sdOzYscrOztbrr79udBS3s1qt+uSTT/TAAw/onnvuybOsSZMmmjJlilq1aiWr1eocGTxnzhz9/vvvunjxomrUqKH27dvrpZdecs7Z6TBz5kzt3LlTzzzzjGrXrp3vtevVq6fnn39eU6dO1eTJk9W1a1fVrFlT27dvl9VqdXlp9E033ZTnCqOgoKCr1vjXX39p6tSpqlGjhssTeJ9//rkWLVokq9WqQYMGadCgQapcubJ69+6thg0bKi4uTr6+vvr888913333yWazqWfPnnleOyQkxOVrZ2dna/jw4bLb7QVObfXuu+9q4cKFztHf//nPfxQYGKi6devmmd5l8eLFWrRokSpUqKAjR45o2LBhV609OztbAwcOlMVi0auvvupynUceeUTVq1d3joYPCQnRqVOnnHO0BgYG6qabbtLFixclyTmdT7169RQRESHJ9WXst99+u/OEhVS0kWA333yzbr75ZgUHBysjI0NTpkzR1KlTVb9+fWVnZ6tTp05Fet8vd/DgQS1cuDDPCRHHCYSAgABZLBbNnj1bjz32mHN5URrZrta5lqn7rvx5udwnn3yinJycAr9nXnvtNU2fPl3nzp3TmDFjNHHiRPn6+qpMmTLOq0jOnz+vCRMmqFmzZs7n3XnnnapXr54OHDggu92u3bt3q23btgXm+Oqrr3Tw4EH9+uuvph7M07BhQ9WpU0fHjx/X6dOnjY5TrPbt26d58+YpJiZGI0eOdLlOz5495e/vr1atWplyjv5GjRrpwIEDXn3sV9Bxn2Ogk7cf9zmsWLFCERERmjlzptFR3G7MmDHKzs5Wz5498zxepUoVjR49WsnJydq4caPWr1+vhg0bGhPSjVq2bKlFixbp3LlzzuPZwMBA7dmzR+np6WrRooXHDvIprH9TqVIl5xV9WVlZLo+jLz/Og/swgh8lqk6dOipbtqxSUlIKvMnYmTNnFBoa6rVzkZtddna2+vbtq9tuu03Dhw/3iJvAFQfHaFVXHCMffXx8DLmRpDvNmzdPmZmZOnLkSJ5/jj9mrVar87HCruzxZhaLRVWrVnX+weNNKlWq5GyAXd6Uc6UoTTVPtX37dk2fPl1vvvmmKUauHD16VCdPnnSOULxSpUqVnI09x++At956SxEREdqwYYPmzZun9957z2Wzcu7cuZKUp8F4JcdJQ4vFolWrVkn6X/N14cKFWr9+fb65wn/99ddrqPDSH+w2m00333yzy+V+fn7O0di9e/fW2rVrtXHjRr3yyivO0Y1169Z1nljw9fVVWFiY/va3vzm3UdBx0Lp165w35y3oD6Vq1arp3nvvdWYtSNeuXZ3fk/Xr1y/SzZ+XL1+us2fP6s477yxyc8qxfztz5ozzscvrO336tPz9/RUaGur8rEaPHp2v8XnXXXfd8FV/jz32mOrXry/pUgNq6dKleUbZFuX4s3PnzgVe7eBoWuzevfuGcl6Pgm5aGRcXpz179kgq+GenTJkyzsvto6KiXN4Ir0KFCi6ff3mj/vJ7L1xp3Lhx2rFjh3755Revn5JNKnxKUul/x3+O70dvsWDBgjzHd5f/czhx4oSOHDmSZ59gJo6ptFq2bGlwkuLnGKhU0HGf4/evt01L6cqFCxc0fPhwPf744155Q+UrRUVFqV69ei6v3PHx8XGe9Pb2K7eqVq2qunXrOt+HWbNmSVK+AW+e4mr9m4CAAOfxa0H79NOnTys4OJhBvG5Ggx8lytfXVy1btlRubq527dqVb/mhQ4eUlZWlp59+2oB0cDfHL4dmzZrpww8/NDpOiZkzZ44WLFhQ4HLHHzzt2rUrdPSdJ5o/f7727duX759jTtY6deo4H7tytIc3iIyMLHTqnYSEBKWmpur111+/6pzknsoxj/XBgwddLj9y5Ijz5lTeKCcnR0OHDlX16tXVpUsXo+OUCMccnI7R+a44TnpefpXH1Thuziqp0OZg8+bNnY3Ow4cPS5IeeOABNW3aVOnp6Xr99df18MMP691339WMGTMKnBe9MI5m6fbt29WyZUs99NBDef61atVKaWlpql27turVq6eaNWvK19dXNpvNOYXJ9TZ2duzYIenqUxk6rp44d+6cUlNTXa5zPSPoYmJiJOma5pF1THvjyH6luLg4PfLII6pUqZLzRvTR0dFq27atOnbsqM8++0wrV6502XS+VsUxarCwExuOP15L0+hUx/vu4+NT6PeN43vGarW6/Lko6Ofu8hMLBc3BO27cOG3fvl0//fSTKU50Hjt2TEOGDClweW5uro4fP66aNWvmudLDG3zwwQcuj/0uvwF1ZGSk9u3bp//+978GJnWPY8eOOe+ZUZADBw6obdu2uvXWW0soVclx/G47cOCAy+WOv3vMMP/+l19+qXPnzql///5GRykRl1+Z6YrjBPD9999fUpEMt2PHDs2dO1dt27bNcy8yT1HU/k1hx3np6ek6duyYnn76adMM7jSKd3YTUKp169ZNkrRo0aJ8yyIiIhQSEmKaaTquNrLHmzh+Odx999169913Xa6Tm5tb6Eh3T/Xggw9q/PjxBY5qmzlzpurUqVPoH4LexvFeePvPQPXq1Qu8PF2Spk6dqr///e9eebMpB8dc6vPnz8+3bO/evdq/f79eeeWVa2r0epKJEyfqwIEDeuONN0wzF3HDhg1VvXp1rV27Vjk5OfmWJyYmOpuf13Kgn5qa6hz5V1ij19/f39mIdPxOCQgI0KRJk/T8888rODhY586d09KlS/XJJ5/o8ccfV9euXa/pHijJycmSLk1PsnnzZud86I5/Gzdu1LZt27R69eo8c+n/+uuvzinKHHOgXytHs76wkdJS3qtiCtrXOkbLX4uCRmdd+RqX/79Tp04qV66clixZku/qidOnTyspKck53U/z5s01adIk5/zV+/bt09SpU9WvXz+1atVKo0aNyreNa3E9NReFo17Hpeml6aokx/eM3W4v9Gfnat8zBf28Xn7s5uqzcYzc/+GHH1xeum+32wu9OV9pVtDPVr169bRt2zbn/QyutHz5cmVmZmrUqFEe+7vhWo/hLv/e84bj/YLqr1Gjhr799tsC99Hr1q2TxWJx3lfDExX22Xfs2FHBwcFaunRpnpvKS5dO1K9YsUItWrTw+ibv5s2bNWfOHD3xxBMKDQ01Ok6xKeyzb9WqlQ4dOuRyjn2bzaZly5apffv2Hvt+XOs+79SpUxo4cKBCQ0P15ZdfuimV+1xL/6ZLly7y9fV12eNbvXq1/P391b17dzcnBg1+lLjWrVurY8eOmjNnjhYvXux8fPfu3QoLC9NXX31likv27Ha7zp07J0lKSkoyOI17ZWVlqW/fvtqwYYPCw8PzjXR84IEHdOedd+q2227Tn3/+aXTcYueYwuHNN9/Mc8BjsVg0ZcoUHT582DmXs1k4pl1ITk4ucLoub9CsWTOlpqZq7NixzoagdOlnYuLEiTp79qzGjx/vtaP3pUsjufr06aPly5fnmQbl5MmTev/999W+fXv17dvXuIButG/fPv3444+qXr26nnvuOaPjlJiAgAB99tlnSkxM1IABA/L8jjt8+LD69u17Xfu7y0ceO+Z2L4jjhNHl8/RXq1ZNn376qaKjo7Vo0SINGzZM7dq1U0BAgLZt23ZNJ1kdI5Cv5ff3pk2bNHbs2Bu+Usvx2qmpqYU2ax2N1LJlyxbrcZXj0vrLPwNXxzSO+fWlS43jwYMH6+DBgxoxYkSeEz/r1q1Tv379nFf7SJdGdk6dOlV//vmnfv31V7311ltq2rSpsrOz9fPPP+v3338vtnqKw+X1O5rpVzavrmx8l+SxX1F/di5vvjvm3b+ay2t3/P9y3377rb7//nvt2rVLbdu2zXely913362mTZvqhx9+KGo5pcbVjuXvuecevfnmm4qKisrzeFRUlH766Sf99NNPeW4i7kmu5++Yy08OevrUPIXVHxQUpDZt2mjAgAF5piWy2+1auXKl8z4g13uS12hX++yDg4M1atQonTt3ToMHD85zr5UhQ4aoUqVK+vrrr0s0c3G7WqP34sWLzmMKbzrGvdpn//7776tatWrq379/nit2UlJS9OGHH+qmm27SiBEjSixvcbrWfd7mzZv1/PPP65ZbblFYWJjHTUt3rf2b0NBQ9e7dW+vXr9fkyZOdJ3FPnDihb775Rh9//LFXTkdb2jDJOQwxatQoNWvWTBMmTNC4ceNUtWpVVa5cWZMmTfLoO4sXxfjx47V69WolJSU5/8iaN2+edu7cqbJly2rEiBEFzlvsqb7++mtt2LBBkvL8EeiKp47gupoBAwZo1qxZevHFF3XTTTepUaNGCg4OVps2bRQWFmaKy9UOHDigIUOGyGq1Oi/bzc7OVuvWrVW7dm316NHDK6/eeffdd/XNN98oPDxctWvXVu3atVW9enW1b99eb7zxhtHxSsR7772nxo0ba8qUKZoyZYpq1KihgIAAvf3222rXrp3R8dzCarXqww8/lMViUc+ePT12hOb1at26tRYsWKAJEyaoc+fOCg4OVsWKFVWtWjUNHjxYY8eOveYGT7ly5VSnTh2dPHlSW7ZsKXA9u93uvKeH45LhpKQkJSUl6dZbb5WPj49CQ0MVGhqqrl27KjY2Vl27dtX27dsL3N6VHHMM//XXX0pOTnZ5Q1gHm82m8PBwffHFF+rZs6diY2Ov+ruwMI6Rb3a7XVu3bs1zo/bLOU4quroJ8I1wnDSJi4vTV199pc2bN+c7ptm2bVu+aYGee+45Va9eXZMmTdKTTz7pnKronnvu0VtvveVcb9euXbr11ltVtmxZlStXTg8++KAefPBBvf322xo2bJjCw8MVGxvr8vfFjYzsvx6ujuk2btwo6dIVSgcOHFCjRo00bNiwfFN2jB8/XsuXL1dQUJAmTpyo4ODgQqc2cLieGi8/rtyyZYsaN27scj3H90zjxo2velLIVe2SnL/vRowYoSNHjjinYLnafXY86fivqMfy3bp1U2Zmpj744AP5+PiocePGCg4O1r333qtp06Z55A1Wr+fvmBdffFEXL17MM2Vh7969VbduXbVs2dKjrmAtav1dunTRZ599pm7duqlixYqqX7++goOD9eCDD2rSpEkeObDjWj77Nm3aaNasWRo3bpzatWunGjVqyNfXV48//ri++OILj56DvSiN3tGjR+vEiRNq06ZNgftbT1LUz75OnTpauHChJk6cqP79+ysgIEAhISEqW7asnnrqKY+couVavu9zcnK0adMmTZ06VUePHtXAgQP1j3/8w+Nqlq6vf/Puu++qfv36CgsLU3h4uKpXr67y5cvrs88+K/ZjURTADgClgM1ms2dlZdlzc3ONjgIAN+ynn36yh4aG2ps3b25PTU01Ok6p8+KLL9pDQ0PtL7/88jU97/PPP7eHhobaGzdubD948KDLdeLj4+2hoaH2Ll26OB/btGmT/csvvyxwuy1btrS3adMmz2ODBg2yh4aG2r/66qt86+/YscMeGhpqDw0NtU+bNs3lNpOTk+3z5s2zt2/f3t69e3f7li1b7Hb7/2r/7rvvCq31u+++s4eGhtrbtm2b5/Hz58/bb7/9dntoaKj93XffLfD5b731lj00NNQeFRWV5/ETJ044s584caLQDK6sWbPG+fwPP/zQ5TojRoxwrlNQnW3btnW5/P333y/ws129erU9NDTU/u233+Z53PFaBT3v8tebN29eYeU5P/cXX3wx3zLHZ/fTTz8V+Pxu3brZe/fune/x+fPnO3Pu3bs33/KZM2cW+p5FR0fbQ0ND7a1atSrwtefNm+fcxuUsFou9ZcuW9tDQUPtzzz1X4PNHjhxpDw0Ntc+ePdvldq/8Xryc43Wv9v66kp2dbc/Jybnm5wFASfn+++/t//rXv5y/Sxz/nnrqKXvnzp3t+/fvt9vtl44PmjRp4vL3L7xXTk6OfciQIfbhw4fbIyIiTPE7jf5N6eN5p44BeCUfHx+VKVPGI0e0AMCVXnrpJe3Zs0fR0dEed1luSXBcunut8zC/9tprqlixoux2u7788kuXz//5559VoUIFffHFF87HcnJytG3bNpfbPHbsmJKTk/PMlS/9byqaTZs25RtdfOeddzpvjPndd9/lm/bk9OnT+uijj3TgwAENHz5c06ZN07333ntNtRakSpUqzntbLF++3GVdhw8f1urVq9W1a1fnVQzF5aGHHlKTJk0kXRrFNmzYMOdo/ezsbP344486d+6cgoODJV37iPPCPqvY2FgFBASoY8eOeR53fFbr16+/pte6XgXdmHnt2rWqWLGiy7l2L7+571dffeUc0Z6VlaVJkyZp48aNhU6L46jx3LlziouLu6a8/v7+zqskduzYoT/++CPfOklJSVqwYIHatGmjzp07X9P2L+fqvhtXExgY6NEjegF4v379+mnu3LlatWpVnhtHL168WHPmzHGO4r799tu1Y8cO7dixo9h//6L0ckxP+dFHH+mxxx4zxe80+jelD58EAABAMQsMDJS/v7/KlCljdJRS58KFC86G+MmTJ3XhwoUiP7dGjRr65ptvVLZsWa1Zs0Zvv/22c47jlJQUjRgxQlu3btWUKVNUr1495/NOnTqlbdu2acyYMc4b/NpsNm3btk1vvfWWHn30UfXu3TvPa912222SLt0jqFOnTho0aJBee+015yX5I0eO1P3336+UlBQ9//zz+u2335xTrAQGBqp58+aKi4vT3Xff7dxmbm6uEhMTnZkK45jCxtW9St577z21bt1aubm56tOnjxYsWKDs7GzZ7XZFR0erV69eevHFF11OfXH51EhXy+CKv7+/vv76a+f80Y65WR999FE98MADOnfunL7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Zs2S3242OAgAeiwY/AAAAAAAoFna7XcuXL9e2bduMjgIPcfLkSW3evNnoGADgsWjwAwAAAACAYrFq1SqatbhmERERSklJMToGAHgkGvwAAAAAAOCGrV+/Xhs2bDA6BjyQzWbT3LlzmaoHAK4DDX4AAAAAAHBDoqOjtWrVKqNjwIOdPHmSqZ0A4DrQ4AcAAAAAANdt27ZtWr58udEx4AWWLVum9PR0o2MAgEehwQ8AAAAAAK7Lzp07tWjRIqNjwEtYrVbNnz/f6BgA4FFo8AMAAAAAgGu2d+9e/fbbb0bHgJc5cuSI4uLijI4BAB7DdA1+u92uQ4cOGR0DAAAAAACPdfDgQW6KCrf5/ffflZWVZXQMAPAIpmrw79u3T0888YR69epldBQAAAAAADzS0aNHNWvWLNlsNqOjwEvl5OTojz/+MDoGAHgE0zT4c3Nz9eGHH+rYsWNGRwEAAAAAwCOdOnVK4eHhslqtRkeBl9u1a5dOnjxpdAwAKPVM0+CfPHmygoODjY4BAAAAAIBHOnv2rKZNm6acnByjo8Ak5syZw5UiAHAVpmjwHzlyROvXr2dqHgAAAAAArkNKSoqmTp2qixcvGh0FJpKamqqNGzcaHQMASjVTNPg/++wzDRs2TL6+pigXAAAAAIBik56erqlTp+rChQtGR4EJrV69Wunp6UbHAIBSy9/oAO42d+5c3X333WrYsKHOnTtndJyr4lJHAMXBYrG4/BoAbgT7FgDuwL6ldMvKytL06dOVlJRkdBSYlM1m07x589S1a9dreh77FgDuUBr3J17d4E9KStKCBQv0yy+/GB2lSKxWq3bt2mV0DABeJj4+3ugIALwQ+xYA7sC+pXSx2Wz6888/dfbsWaOjwOSOHj2qVatWqVq1atf1fPYtALyZV89ZM2rUKL333nsKDAw0OgoAAAAAAB7Dbrdr9+7dNPdRamzfvp0b7gKAC147gj8mJkY+Pj667777jI5SZP7+/mratKnRMQB4AYvF4hyl0qRJEwUEBBicCIA3YN8CwB3Yt5ROMTExOn78uNExAKfs7GxlZGSoVatWRVqffQsAd7h831JaeGWD32q1avTo0Ro/frzRUa4ZVxsAKG4BAQHsWwAUO/YtANyBfUvpEB8fr8jISKNjAPls2LBBLVq0ULly5a7peexbAHgzr2zwz5o1S/v379eLL76Y5/GsrCxJUkJCgp588klJUp8+fdSpU6eSjggAAAAAQKlz6tQpzZ8/3+gYgEu5ublasmSJnnvuOaOjAECp4ZUN/nnz5ikzM1NHjhxxudxqtTqXpaSklGAyAAAAAABKp/T0dM2cOVMWi8XoKECB4uLilJCQoBo1ahgdBQBKBa9s8Bc02mDz5s3q0aOH6tSpo1WrVpVwKgAAAAAASqfc3FzNnTtXFy5cMDoKcFXz589Xnz595OPjY3QUADCcr9EBSlJGRoakSwcuAAAAAADgkpUrV+rYsWNGxwCKJDExUXFxcUbHAIBSwVQN/tOnT0uSkpOTlZ2dbXAaAAAAAACMt2vXLkVHRxsdA7gmS5YskdVqNToGABjOK6foudyBAwc0ZMgQWa1WHThwQJKUnZ2t1q1bq3bt2urRowc32QUAAAAAmNL58+e1aNEio2MA1+zixYvauHGj/v73vxsdBQAM5fUN/kaNGmn27NlGxwAAAAAAoFTJzc3VvHnzuKkuPNb69evVokULBQUFGR0FAAxjqil6AAAAAADAJZGRkc6pbAFPZLVaFRkZaXQMADAUDX4AAAAAAEzm4MGD2rRpk9ExgBu2detWXbhwwegYAGAYGvwAAAAAAJhIZmamfv/9d6NjAMXCbrfrjz/+MDoGABiGBj8AAAAAACaydOlSpaenGx0DKDbx8fE6d+6c0TEAwBA0+AEAAAAAMIm4uDjt3r3b6BhAsVu4cKHREQDAEDT4AQAAAAAwgYyMDC1ZssToGIBbnDhxQidOnDA6BgCUOBr8AAAAAACYwJIlS5SZmWl0DMBtFi1aZHQEAChxNPgBAAAAAPBy8fHx2rt3r9ExALc6e/asDh06ZHQMAChRNPgBAAAAAPBi2dnZWrp0qdExgBKxZMkS2e12o2MAQImhwQ8AAAAAgBdbs2aN0tLSjI4BlIjk5GTt37/f6BgAUGJo8AMAAAAA4KVOnz6tzZs3Gx0DKFF//PEHo/gBmAYNfgAAAAAAvJDdbtfixYtpdMJ00tLStG/fPqNjAECJoMEPAAAAAIAXio2N1alTp4yOARgiIiKCk1sATIEGPwAAAAAAXiYrK0uRkZFGxwAMk56ezgkuAKZAgx8AAAAAAC+zZs0aZWZmGh0DMFRcXByj+AF4PRr8AAAAAAB4kcTERMXExBgdAzBcdna2Tp48aXQMAHArGvwAAAAAAHgJu92uZcuWMWoZ+H/27t3LzwMAr0aDHwAAAAAAL7F3714dOXLE6BhAqZGdna34+HijYwCA29DgBwAAAADAC1gsFq1YscLoGECps2rVKkbxA/BaNPgBAAAAAPACGzduVGpqqtExgFInLS1NBw4cMDoGALgFDX4AAAAAADxcenq6oqKijI4BlFrLly83OgIAuAUNfgAAAAAAPNzq1atlsViMjgGUWklJSdyfAoBXosEPAAAAAIAHS0xMVGxsrNExgFJv6dKlRkcAgGJHgx8AAAAAAA+2cuVKbiAKFMHZs2f1119/GR0DAIoVDX4AAAAAADzU4cOHuXkocA0YxQ/A29DgBwAAAADAA9ntdkVERBgdA/Aop06d0tmzZ42OAQDFhgY/AAAAAAAeaOfOnTpz5ozRMQCPs3z5cqMjAECxocEPAAAAAICHsVgsWrVqldExAI906NAhXbhwwegYAFAsaPADAAAAAOBhoqOjlZaWZnQMwGOtXLnS6AgAUCxo8AMAAAAA4EHS09O1YcMGo2MAHm337t3Kzs42OgYA3DAa/AAAAAAAeJC1a9cqJyfH6BiAR7PZbFq3bp3RMQDghtHgBwAAAADAQ5w9e1Zbt241OgbgFf7880/l5uYaHQMAbggNfgAAAAAAPMTKlStlt9uNjgF4BYvFoi1bthgdAwBuCA1+AAAAAAA8wNGjR7V//36jYwBeZd26dZw0A+DRaPADAAAAAFDK2e12RUREGB0D8DqZmZnau3ev0TEA4LrR4AcAAAAAoJTbs2ePTp06ZXQMwCtFRkYaHQEArhsNfgAAAAAASjGr1UoDEnCjpKQknTx50ugYAHBdaPADAAAAAFCK/fnnn0pJSTE6BuDVVqxYYXQEALguNPgBAAAAACilLl68qHXr1hkdA/B6x48fV2pqqtExAOCa0eAHAAAAAKCUWr9+vbKysoyOAZjCypUrjY4AANeMBj8AAAAAAKVQSkqKYmJijI4BmEZcXBwn1AB4HBr8AAAAAACUQqtWrVJubq7RMQDTsNls2rhxo9ExAOCa0OAHAAAAAKCUOX36tHbt2mV0DMB0Nm/ezIk1AB6FBj8AAAAAAKWI3W5XRESE0TEAU8rJydGOHTuMjgEAReZvdAB3SktL0+TJk7Vy5UqdPHlSZcuWVYMGDfTyyy+rXbt2RscDAAAAACCf/fv368iRI0bHAExr7dq1uvvuu+Xj42N0FAC4Kq8dwZ+UlKR//etf+vHHH5WYmKhKlSopOTlZW7Zs0dtvv61Ro0YZHREAAAAAgDxyc3O1YsUKo2MAppaWlsZJNgAew2sb/KtWrZKPj49+/PFHxcTEaO3atdqwYYMee+wxSdIvv/yihIQEg1MCAAAAAPA/f/75p5KSkoyOAZge02QB8BRe2+DfuHGjvvzyS7Vt29Z5SVXVqlU1duxYVa5cWXa7XYmJiQanBAAAAADgkszMTK1du9boGAAknTlzRmfPnjU6BgBcldc2+Lt166bmzZvnezwwMFA1a9ZUcHCwbr311pIPBgAAAACAC2vWrFFWVpbRMQD8PytXrjQ6AgBcldfeZPe+++5z+fjJkyd16NAhff755ypbtmwJp7q6nJwcoyMA8AIWi8Xl1wBwI9i3AHAH9i2XnDt3Tlu2bDE6BoDLHDhwQMnJySpfvrzRUQCUEqXxWMXHbrfbjQ5RUvbv369PP/1UXbt2VYcOHYyO47Rz585S+c0BAAAAACgZMTExTCMLlEK33HKL7rjjDqNjACiFAgICdOeddxodw3tH8DscO3ZMPXr0kJ+fnxITE+Xj46Ps7GydPXtW3bt3l7+/178FAAAAAIBSLDExkeY+UEodP35cTZs2lZ+fn9FRAMAlr+9uBwUF6ZdfflGDBg1ktVoVGRmpoUOHasSIEdq5c6dGjx5tdEQnf39/NW3a1OgYALyAxWJRfHy8JKlJkyYKCAgwOBEAb8C+BYA7mH3fkpubq40bNxodA0ABbDabLBaLy/s8AjCfy49bSguvb/DXqFHD+bW/v7+eeOIJpaSk6OOPP9bixYv17rvvqm7dugYmzCswMNDoCAC8TEBAAPsWAMWOfQsAdzDjviUqKkpJSUlGxwBQiI0bN6pVq1by8fExOgoA5ONrdAAjtG7d2vl1QkKCgUkAAAAAAGaVlpamdevWGR0DwFVkZmaWuhG7AODgtQ1+m81W4LLg4GBJko+Pj2655ZaSCQQAAAAAwGUiIiKUk5NjdAwARbBq1SqjIwCAS17Z4J8zZ44WLFhQ4PIjR45Iktq1a6eqVauWVCwAAAAAACRJR48e1e7du42OAaCIzp07p1OnThkdAwDy8coG/4MPPqjx48crIyPD5fKZM2eqTp06GjJkSAknAwAAAACYnc1m09KlS42OAeAaRUZGGh0BAPLxygb/zTffLEl68803dejQIefjFotFU6ZM0eHDhzV16tQ8N+AFAAAAAKAk/Pnnn0pMTDQ6BoBrdPjwYaWkpBgdAwDy8Dc6gLsMGDBAs2bN0osvvqibbrpJjRo1UnBwsNq0aaOwsDDufA4AAAAAKHHp6elavXq10TEAXKd169apY8eORscAACevbfA/9dRTeuqpp4yOAQAAAACA0/Lly5WdnW10DADXaefOnWrXrp2CgoKMjgIAkrx0ih4AAAAAAEqbgwcPcmNdwMPl5uYqJibG6BgA4ESDHwAAAAAAN7NYLPrjjz+MjgGgGGzcuFG5ublGxwAASTT4AQAAAABwu3Xr1ik5OdnoGACKQXZ2NlfjACg1aPADAAAAAOBGZ8+e1caNG42OAaAYrVmzRna73egYAECDHwAAAAAAd7Hb7Vq8eLFsNpvRUQAUo5SUFB05csToGABAgx8AAAAAAHeJjY3V8ePHjY4BwA1WrVpldAQAoMEPAAAAAIA7ZGRkaOXKlUbHAOAmJ0+e1NmzZ42OAcDkaPADAAAAAOAGERERunjxotExALjR6tWrjY4AwORo8AMAAAAAUMwOHjyoHTt2GB0DgJvFx8crPT3d6BgATIwGPwAAAAAAxSgrK0uLFi0yOgaAEmC327VhwwajYwAwMRr8AAAAAAAUoxUrVigtLc3oGABKyNatW2WxWIyOAcCkaPADAAAAAFBMDh48qNjYWKNjAChBVqtVW7duNToGAJOiwQ8AAAAAQDFgah7AvNavXy+73W50DAAmRIMfAAAAAIBiwNQ8gHllZmZq7969RscAYEI0+AEAAAAAuEFMzQNg1apVRkcAYEI0+AEAAAAAuAFMzQNAks6fP6+//vrL6BgATIYGPwAAAAAAN4CpeQA4REREGB0BgMnQ4AcAAAAA4DoxNQ+Ayx0/flxJSUlGxwBgIjT4AQAAAAC4DkzNA8CV5cuXGx0BgInQ4AcAAAAA4DosX76cqXkA5LN//372DQBKDA1+AAAAAACuUVxcnLZv3250DAClFHPxAygpNPgBAAAAALgGaWlpTM0DoFB79uxRRkaG0TEAmAANfgAAAAAAishut+v3339XVlaW0VEAlGJ2u12rV682OgYAE6DBDwAAAABAEUVHR+vw4cNGxwDgAWJjY3Xx4kWjYwDwcjT4AQAAAAAogoSEBEVGRhodA4CHsNlsWr9+vdExAHg5GvwAAAAAAFyF1WrV/PnzlZuba3QUAB4kJiZGOTk5RscA4MVo8AMAAAAAcBWRkZFKTEw0OgYAD5Obm6uoqCijYwDwYjT4AQAAAAAoxKFDhxQdHW10DAAeatOmTbJarUbHAOClaPADAAAAAFCA9PR0LViwwOgYADyYxWLR5s2bjY4BwEvR4AcAAAAAwAWbzaYFCxYoIyPD6CgAPNz69eu5hwcAt6DBDwAAAACACxs2bNDhw4eNjgHAC2RnZ+vPP/80OgYAL0SDHwAAAACAKxw7dkxr1qwxOgYAL7JmzRpG8QModjT4AQAAAAC4TGZmpubNmye73W50FABehFH8ANyBBj8AAAAAAP+P3W7Xb7/9pgsXLhgdBYAXYhQ/gOJGgx8AAAAAgP9n/fr1OnDggNExAHgpRvEDKG40+AEAAAAAkHTw4EGtXr3a6BgAvByj+AEUJxr8AAAAAADTS05O1vz5842OAcAEsrOzFRMTY3QMAF6CBj8AAAAAwNQsFotmz56tixcvGh0FgEmsWbNGVqvV6BgAvAANfgAAAACAadntdi1ZskRnzpwxOgoAE8nJyWEUP4BiQYMfAAAAAGBaW7Zs0Y4dO4yOAcCE1q5dyyh+ADeMBj8AAAAAwJQOHTqkpUuXGh0DgEnl5OQoOjra6BgAPBwNfgAAAACA6Zw9e1Zz5syR3W43OgoAE1u/fj2j+AHcEBr8AAAAAABTycjI0IwZM5SdnW10FAAml5OTo40bNxodA4AH8zc6gDtFRUUpLCxMcXFxSk5OVpUqVXT//ffr9ddfV9OmTY2OBwAAAAAoYVarVbNnz1ZKSorRUQBA0qVR/A888IACAwONjgLAA3ntCP4JEybo1Vdf1dq1a+Xn56fg4GAlJCRo8eLF6ty5s1atWmV0RAAAAABACbLb7Vq0aJGOHz9udBQAcLJarVq/fr3RMQB4KK9t8K9YsULt27fXunXrtGbNGm3YsEG///67GjRoIKvVqiFDhnA5JgAAAACYyOrVq7Vz506jYwBAPps2bVJWVpbRMQB4IK9t8Kenp2v48OGqXr2687EmTZro22+/la+vr5KSkrR161YDEwIAAAAASkpMTAwjZAGUWrm5uVqzZo3RMQB4IK9s8Ofk5Oi+++7TTTfdlG9ZaGioGjRoIElKTU0t6WgAAAAAgBIWFxenpUuXGh0DAAr1559/KjMz0+gYADyMV95kNzAwUJ9//nmBy318fCRJt9xySwklKrqcnByjIwDwAhaLxeXXAHAj2LcAcAd371uOHz+u+fPnF/t2AaC42Ww2RUREqH379kZHAVCA0vh3kI/dbrcbHaIkZWZmqkWLFrrlllu0ePFio+NIknbu3FkqvzkAAAAAwJOlpaVp48aNslqtRkcBgCLx8fHR//f//X8KCgoyOgqAqwgICNCdd95pdAzvnKKnMHPmzJHFYtHAgQONjgIAAAAAcJOMjAxt3ryZ5j4Aj2K327V3716jYwDwIF45RU9B/vrrL40bN059+vRRmzZtjI6Tj7+/v5o2bWp0DABewGKxKD4+XtKlG4wHBAQYnAiAN2DfAsAd3LFvSUtL07Rp05SdnX3D2wKAknbq1Cl16tTJ5b0lARjr8uOW0sI0Df709HT17dtX7du313vvvWd0nAIFBgYaHQGAlwkICGDfAqDYsW8B4A7FsW/JyMjQzJkzlZqaWkypAKBk2e12RUZG6oUXXjA6CgAPYIoperKzs/Xmm2+qefPmGj58uNFxAAAAAABukJWVpWnTpun8+fNGRwGAGxIfH6+kpCSjYwDwAF7f4M/Ozlbfvn112223afjw4fLx8TE6EgAAAACgmOXk5GjGjBk6c+aM0VEAoFgsWrTI6AgAPIBXN/gdzf1mzZrpww8/NDoOAAAAAMANLBaLZs6cqRMnThgdBQCKzdGjR3X69GmjYwAo5by2we9o7t9999169913Xa6Tm5srm81WssEAAAAAAMXGarVq9uzZOnLkiNFRAKDYMYofwNV45U12s7Ky1K9fP23YsEHx8fEKDw/Pszw3N1eZmZnKzs5WWFiYWrZsaVBSAAAAAMD1stlsmjdvng4ePGh0FAD/P3v3HR5Vtf5//5NkAglSggkgYqUEkCoqQdQfqIg0IyKCgiAWjqjo0a94OCIHRRFFsaJHSWxEAtKV0CR0pVfpCYQqEAKkh/TZzx88M4eQmRBgJpPJfr+uK5dh9pq173tnZjtz77XXglucOHFCBw8e1M033+zpUACUUxVyBP/48eP1559/SpJOnz5d7CclJUW5ubmSzo32AAAAAAB4F6vVql9//VV79+71dCgA4FYxMTEyDMPTYQAopyrkCP6RI0dq5MiRJbYxDEN5eXny9/cvo6gAAAAAAK5gGIbmzZunHTt2eDoUAHC7lJQU7d69W82aNfN0KADKoQo5gr80fHx8VLlyZfn6mvYQAAAAAIDXMQxDCxcu1NatWz0dCgCUmUWLFrGOJACHqG4DAAAAALyCYRhasmSJNm7c6OlQAKBMZWZmasuWLZ4OA0A5RIEfAAAAAOAVVq5cqTVr1ng6DADwiCVLlrCWJIBiKPADAAAAAMq91atXa+XKlZ4OAwA8Jjc3l4ucAIqhwA8AAAAAKNc2bNigJUuWeDoMAPC4P/74Q7m5uZ4OA0A5QoEfAAAAAFBubd26VQsXLvR0GABQLhQUFGjp0qWeDgNAOUKBHwAAAABQLsXFxSkmJsbTYQBAubJp0yalp6d7OgwA5QQFfgAAAABAuXPkyBHNnDlThmF4OhQAKFcMw9D8+fM9HQaAcoICPwAAAACgXElKStLUqVNVUFDg6VAAoFyKj49XYmKip8MAUA5Q4AcAAAAAlBtpaWmKjo5WTk6Op0MBgHJt7ty5ng4BQDlAgR8AAAAAUC5kZ2dr8uTJzC0NAKVw4sQJ7d+/39NhAPAwCvwAAAAAAI8rLCzUjBkzdPr0aU+HAgBeIyYmhrVKAJOjwA8AAAAA8Cir1arNmzfr2LFjng4FALxKenq6Nm/e7OkwAHgQBX4AAAAAgMcYhqEdO3YoKSnJ06EAgFeKjY1VXl6ep8MA4CEU+AEAAAAAHrNq1SodPXrU02EAgNfKy8vTkiVLPB0GAA+hwA8AAAAA8Ij169drzZo1ng4DALzepk2blJyc7OkwAHgABX4AAAAAQJnbuXOnFi1a5OkwAKBCMAxDv/32m6fDAOABFPgBAAAAAGXqwIEDmjNnjqfDAIAK5ciRI9q3b5+nwwBQxijwAwAAAADKzIkTJzRt2jRZrVZPhwIAFc7cuXNVWFjo6TAAlCEK/AAAAACAMpGcnKzo6Gjl5eV5OhQAqJAyMzNZ2wQwGQr8AAAAAAC3y8zM1OTJk5WVleXpUACgQlu5cqUyMzM9HQaAMkKBHwAAAADgVrm5uZoyZYpSUlI8HQoAVHiFhYUsuAuYCAV+AAAAAIDbFBYWavr06Tpx4oSnQwEA09i/f7/279/v6TAAlAEK/AAAAAAAtzAMQ7/++qsOHDjg6VAAwHTmzJmj/Px8T4cBwM0o8AMAAAAAXM4wDP3+++/auXOnp0MBAFM6e/asli1b5ukwALgZBX4AAAAAgMutXr1a69ev93QYAGBq69ev16lTpzwdBgA3osAPAAAAAHCpLVu2aOnSpZ4OAwBMzzAMzZw5U4ZheDoUAG5CgR8AAAAA4DK7d+/WvHnzPB0GAOD/l5SUpE2bNnk6DABuQoEfAAAAAOASBw4c0OzZsxkpCgDlzO+//6709HRPhwHADSjwAwAAAACu2N9//61ffvlFhYWFng4FAHCBwsJCTZ8+nQuwQAVEgR8AAAAAcEWSkpI0ZcoU5efnezoUAIATx44d08aNGz0dBgAXo8APAAAAALhsSUlJmjRpkrKzsz0dCgDgIhYvXqzU1FRPhwHAhSjwAwAAAAAuy6lTpxQVFaWzZ896OhQAQCkwVQ9Q8VDgBwAAAABcstOnT2vSpEnKysrydCgAgEtw4sQJrV271tNhAHARCvwAAAAAgEtCcR8AvNvSpUt1+vRpT4cBwAUo8AMAAAAASs02535mZqanQwEAXCar1aro6GgVFBR4OhQAV4gCPwAAAACgVP7++2/9+OOPFPcBoAJITU3VvHnzPB0GgCtEgR8AAAAAcFEHDhxQVFSUcnJyPB0KAMBF/vrrL+3evdvTYQC4AhT4AQAAAAAl2rNnj6ZMmaL8/HxPhwIAcLE5c+YoNTXV02EAuEwU+AEAAAAATm3ZskUzZsxQYWGhp0MBALhBQUGBpk6dynke8FIU+AEAAAAAxRiGoaVLlyomJkaGYXg6HACAGyUlJen333/3dBgALgMFfgAAAABAEQUFBZo9e7b+/PNPT4cCACgjGzdu1M6dOz0dBoBLZKoCf15enhITEz0dBgAAAACUW2fPntXPP/9MkQcATGjOnDnUzgAvY4oCf2Jior744gvde++9mjx5sqfDAQAAAIByKSkpSd9//72OHDni6VAAAB5gtVo1efJknT171tOhACgli6cDcJfMzEwtW7ZM8+bN04kTJ3Ty5EmlpaV5OiwAAAAAKJd27dql3377Tfn5+Z4OBQDgQVlZWZo6daqefvpp+fqaYmww4NUq7Lt0wYIFmjt3rgYNGqSYmBg1btzY0yEBAAAAQLljtVq1ePFizZw5k+I+AECS9Pfff2vRokWeDgNAKVTYEfx9+vRRnz59PB0GAAAAAJRbWVlZmjVrlg4ePOjpUAAA5czGjRt1zTXXqE2bNp4OBUAJKmyBHwAAAADgXEJCgubMmaOsrCxPhwIAKKfmzZunmjVr6uabb/Z0KACcoMBfzuTl5Xk6BAAVwPm313OrPQBX4dwCVAyFhYVasWKFNmzY4OlQAADlnGEYmjJlip5++mmFhIR4OhzA48rj9yAK/OVIQUGBduzY4ekwAFQwe/fu9XQIACogzi2Ad8rMzNTWrVuVlpbm6VAAAF6ioKBAUVFR+n//7/+pcuXKng4HwAUq7CK7AAAAAIBzDMPQ0aNH9ccff1DcBwBcstzcXK1bt06FhYWeDgXABRjBX45YLBY1bdrU02EAqADy8/Pto2ubNGkif39/D0cEoCLg3AJ4p6ysLC1atEjx8fGeDgUA4MUyMjIUHx+v3r17y9eXMcMwp/O/E5UXFPjLmUqVKnk6BAAVjL+/P+cWAC7HuQXwDrt379b8+fN19uxZT4cCAKgAEhIStHjxYj300EPy8fHxdDgARIEfAAAAACqc7OxsLVy4kDW+AAAut3XrVlWvXl0dO3b0dCgARIEfAAAAACqU+Ph4xcTEKDMz09OhAAAqqJUrV6pq1aq6/fbbPR0KYHoU+AEAAACgAsjJydHvv/+ubdu2eToUAIAJzJ8/X1dddRXrSQIeZpoVMVjlGwAAAEBFFR8fr2+++YbiPgCgTM2cOVOHDx/2dBiAqZmiwG8Yhk6fPi1JSk5O9nA0AAAAAOAamZmZmjlzpqZOnar09HRPhwMAMBmr1arJkyfr5MmTng4FMK0KPUXP119/reXLlys5OVnHjh2TJM2aNUvbt29XYGCgxo4dq0aNGnk4SgAAAAC4NIZhaNu2bVq8eLFycnI8HQ4AwMQKCgr0448/avDgwQoODvZ0OIDpVOgC/0svvaSXXnrJ02EAAAAAgMskJydr3rx5OnjwoKdDAQBAkpSbm6sffvhBgwcPVlBQkKfDAUzFFFP0AAAAAIC3Kygo0MqVK/XNN99Q3AcAlDtnz57VDz/8oIyMDE+HApgKBX4AAAAAKOcSEhL0zTffaMWKFSooKPB0OAAAOJSRkaEffvhBWVlZng4FMA0K/AAAAABQTmVkZGjmzJmaPHmykpOTPR0OAAAXlZqaqp9++knZ2dmeDgUwBQr8AAAAAFDOWK1WrVu3Tl999ZV27drl6XAAALgkp0+f1qRJk5Sbm+vpUIAKjwI/AAAAAJQjR44cUWRkpH7//Xfl5eV5OhwAAC7LyZMnFRUVRZEfcDOLpwMAAAAAAEgpKSlasmSJdu/e7elQAABwiePHj2vSpEl66qmnVLlyZU+HA1RIFPgBAAAAwINyc3O1atUqrV+/XoWFhZ4OBwAAlzpx4oR++uknPfXUUwoICPB0OECFQ4EfAAAAADzAarVq69atWrZsmc6ePevpcAAAcJvExET9+OOPGjRokAIDAz0dDlChMAc/AAAAAJSxAwcOaOLEiZo3bx7FfQCAKSQlJenHH3/k/3uAizGCHwAAAADKyOnTpxUbG6v4+HhPhwIAQJk7deqUIiMjNWjQINWoUcPT4QAVAgV+AAAAAHCz7OxsrVy5Uhs3bpTVavV0OAAAeExqaqoiIyP11FNPqVatWp4OB/B6FPgBAAAAwE0KCwu1adMmrVixQjk5OZ4OBwCAciErK0vfffedBgwYoOuuu87T4QBejQI/AAAAALiYYRjat2+fFi9erDNnzng6HAAAyp28vDz99NNPevzxx9WwYUNPhwN4LQr8AAAAAOBCSUlJ+v3333XgwAFPhwIAQLlWWFioKVOm6MEHH1Tbtm3l4+Pj6ZAAr0OBHwAAAABcICsrS8uXL9eWLVtkGIanwwEAwCsYwaSUkwAAq6tJREFUhqFFixbp1KlT6tq1q/z8/DwdEuBVKPADAAAAwBXIz8/X2rVrtXr1auXl5Xk6HAAAvNLmzZt1+vRp9e3bV4GBgZ4OB/AaFPgBAAAA4DJYrVZt375dy5YtU0ZGhqfDAQDA6x0+fFgTJ05Uv379VLt2bU+HA3gFCvwAAAAAcIkSEhIUGxurkydPejoUAAAqlLS0NEVERCg8PFwtW7b0dDhAuUeBHwAAAABKKSkpSbGxsdq/f7+nQwEAoMIqLCzUnDlzdOTIEXXp0kUWCyVMwBneHQAAAABwERkZGVq+fLm2bdvGAroAAJSRzZs369ChQ+rdu7euueYaT4cDlEsU+AEAAADAiby8PK1Zs0Zr1qxRfn6+p8MBAMB0zpw5o8jISN1///2688475ePj4+mQgHKFAj8AAAAAXMBqtWrr1q1asWKFMjMzPR0OAACmZrVaFRsbq/j4eD3yyCOqUaOGp0MCyg0K/AAAAADw/zMMQ/v371dsbKxOnTrl6XAAAMB5Dh8+rK+//loPPPCAbr/9dkbzA6LADwAAAACSpBMnTig2NlYHDx70dCgAAMCJ/Px8LViwQNu3b9fDDz+skJAQT4cEeBQFfgAAAACmdubMGS1fvly7du3ydCgAAKCU/v77b33zzTfq0KGD7rrrLvn5+Xk6JMAjKPADAAAAMKW0tDStXLlS27Ztk2EYng4HAABcIqvVquXLl2vbtm3q1q2bGjZs6OmQgDJHgR8AAACAqWRlZemPP/7Qpk2bVFhY6OlwAADAFUpJSVF0dLRCQ0PVpUsX1axZ09MhAWWGAj8AAAAAU8jJydGaNWu0bt065efnezocAADgYvHx8UpISNBdd92lu+++W/7+/p4OCXA7CvwAAAAAKrSsrCytW7dOGzZsUF5enqfDAQAAblRYWKhVq1Zp69at6tq1q5o0aSIfHx9PhwW4DQV+AAAAABVSenq61qxZo82bN6ugoMDT4QAAgDKUkZGh6dOn66abblLnzp1Vt25dT4cEuAUFfgAAAAAVSkpKilavXq1t27Yxxz4AACZ36NAhRUREqGXLlrrvvvtUo0YNT4cEuBQFfgAAAABezzAMHTlyROvWrdPevXs9HQ4AAChntm/frl27dqldu3a6++67FRAQ4OmQAJegwA8AAADAaxUWFmr37t1au3atTpw44elwAABAOVZYWKjVq1dry5Yt6tixo2677Tb5+fl5OizgilDgBwAAAOB1MjIytHXrVm3atEkZGRmeDgcAAHiR7OxsLVy4UOvWrdO9996r5s2bsxAvvBYFfgAAAABewWq1KiEhQVu2bFFcXJwMw/B0SAAAwIulpKRo9uzZ+uOPP3TvvfeqSZMmFPrhdSjwAwAAACjX0tLStG3bNm3dulVpaWmeDgcAAFQwp06d0vTp01WnTh3dd999atSoEYV+eA0K/AAAAADKnezsbO3atUs7d+7U4cOHPR0OAAAwgZMnT2rq1KmqV6+e7r77bjVu3JhCP8o9CvwAAAAAyoW8vDzFx8drx44d2r9/v6xWq6dDAgAAJnTs2DFNmzZNV199tdq3b69WrVrJYqGMivKJVyYAAAAAjzl79qzi4+O1d+9eJSQkqKCgwNMhAQAASJKSk5M1b948LVu2THfccYduvfVW1ahRw9NhAUVQ4AcAAABQZqxWq06cOKEDBw4oISFBR44cYbFcAABQrp09e1YrV67UqlWr1KhRI7Vp00aNGjWSr6+vp0MDKPADAAAAcB+r1aqkpCQdOXJEhw8f1sGDB5Wdne3psAAAAC6ZYRiKj49XfHy8qlatqmbNmumWW27R9ddfz1z98BgK/AAAAABcwjAMpaWlKTExUYmJiTp69Kj+/vtv5eXleTo0AAAAl8rMzNT69eu1fv16Va1aVU2bNlWTJk10ww03MF8/yhSvNgAAAACXpLCwUKmpqTpz5oz95/Tp00pMTFRubq6nwwMAAChTmZmZ2rhxozZu3CiLxaKbbrpJN954o+rVq6drr71WlStX9nSIqMBMUeDfvHmzIiIilJKSovT0dFksFoWHh2vQoEGqVKmSp8MDAAAAPMZqtSovL095eXnKzc0t9pORkVHkJz09XVlZWcybDwAA4EBBQYH279+v/fv3S5J8fHxUq1YtXXvttQoJCVFwcLBCQkJUs2ZN+fn5eThaVAQVvsC/aNEijRo1ShEREWrdurUkacGCBRo2bJjWrVunyMhI3kwAAADwOlar1V6Ez8nJUU5OjsMCvaPC/fmPMX0OAACA+xiGoaSkJCUlJRV53MfHR9WrV1f16tVVrVo1+09gYKACAgLs/7X9WCwWWSwW5vpHMRW6wH/kyBENHz5c//znP+3FfUnq1q2b1q1bp2nTpik6OloDBw70XJAAAAAwPcMwlJ2draysLGVmZhb5r+3HVsQ/v5APAAAA72RbuygtLe2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",
       "text/plain": [
        "<Figure size 900x900 with 4 Axes>"
       ]
@@ -383,7 +2430,7 @@
     "axes[0].tick_params(axis='both', pad=7)\n",
     "axes[0].set_xlabel(\"p\")\n",
     "\n",
-    "az.plot_dist(idata.posterior[\"p\"],\n",
+    "az.plot_dist(idata1.posterior[\"Intercept\"],\n",
     "             plot_kwargs={\"color\":\"0.5\"},\n",
     "             fill_kwargs={'alpha':1},\n",
     "             ax=axes[2])\n",
@@ -396,13 +2443,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "id": "9bad4afd-a779-4ec2-a642-0459a750ffc5",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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ltSZOnJjx48fP9kyKmU2bNi377bdfXn755Zx22mn5xje+Ub3tscceW5hTBQAAAABYKBbNo7csUP/617+yzjrrZOutt06S/M///E+SZPLkyXnwwQdn+zX/+c9/kiTbb7999SD/6aefXr29R48e2WqrrXLUUUflyCOPTJK8/vrrs3yfmc80SJKll166enmk+++//yM8qnnX9niHDx8+x7DR9nh32GGHmszpw+jTp0/1jI+hQ4d+4P0feuihvPzyy1l11VXbBQ0AAAAAWFw1NjYusm9aZsHw011MzGmPg2HDhuWVV17JKaecUj0g3rt373zuc59LMn0j8PeHhyS5+eabs+yyy+YnP/lJ9XM33XRTpk6dOst92/bz2GSTTaqfa7t00ejRo2e5/+67754kGTJkSN544415enwfxRe/+MWsvvrqSWZsUv5+N998c1ZbbbXsueeeC30+H1avXr2y7bbbJkn+7//+r91+G7MzZsyYJMmyyy47y20f9UyN2e13AgAAAAD11NTUlF69eqVXr16zvRw8i4eiLj/VqUOZDaYW877ooouywgor5Pvf/371clJDhw7NkCFDcvnll8+yefRJJ52U3XffPQ888ECOOuqo/PKXv8wyyyyTSqWSCy+8MEOHDs25556bVVddNcn0PRTefvvtnHHGGfnZz35W/aXwyiuv5Oyzz85ee+3VLmqstNJKSaZv0j1gwID07t07EydOzM4775xdd901Tz75ZK655prstddeOeGEE7LpppsmmR5nbr311owYMSL77LNP9fuNHz8+ydz3xZgyZUr148mTJ1c/7tixYwYMGJB99tknN9xwQ1ZZZZUccMABWXrppTN16tScdtppGTlyZM4999xZNvSel3HnZOLEibP9uE3b95zdbXP6uuOOOy6vvfZannnmmfTv3z/HHntsNdiMGzcugwYNSp8+ffLlL385n/rUp7LUUkvl2WefzfPPP5/evXtnwoQJGThwYEaMGJFk1jjxQXNuM3bs2EybNm2WzcoBAAAAoM09L/63puO1ViqZMGH6McIuE0al8QP2/10Ytlh7hZqPuaQp6ojkJqt0/+A7LaFWXnnlXHbZZTn77LOz7rrrpmfPntlmm21y/PHHz/b+n/jEJzJkyJCce+65ufPOO7PNNttk3XXXTceOHfOFL3wh1113XXUPh2T6ZuHf//7388gjj2THHXdMt27d0tLSkq5du+ZnP/vZLHtpbLbZZtlvv/0ycODAXHDBBVlnnXVy9NFHJ5l+Fscf//jHbL311rnqqqty2GGHZemll84666yTbt26ZbPNNquezXHhhRfm5ptvzrPPPpskGTx4cB588MEsv/zyufjii6vj/fCHP8yrr75a/XP//v3Ts2fPXHbZZenUqVPWW2+96lyuvfbaDBo0KOutt146duyYrbbaKocddli7vUXmddw52X///dvNZ//998+KK66Yyy67LFdddVWuueaaPPPMM0mSgQMHZtiwYfnRj35UvURYm6eeeir9+vXLF7/4xRx22GHp3r17rrzyygwcODA33XRTdt555/Tq1SurrbZall122Wy33Xb54he/mCT5+Mc/njPOOCMDBgzIrrvumk022SRbbLFF9txzz3Tp0iV33XVXHnjggZx99tnZdddd8+tf/3qOc37/visTJ07MN7/5zWy00Ub505/+9IHrAQAAAMCSadibYzJ1WmtNxmppbck770zfx7dnzxXT1Fi7szU6dWh0/LpGGiqzu/bQh/TEE0+kubk5HTt2TN++fed63+bm5rz44otJkrXXXjsdO3ZcUNNYopx55pk566yzctBBB+Xggw9ud1tra2v1bIDOnTsv0deSsxbtLYj1qFQqaWlpKf5sjXldiyXhd9bUqVPz5JNPJkk22mijWWLWksZ6zGAt2rMeM1iL9qzHDNZiBmvRnvWYwVq0Zz1msBbtWY8ZrMUM1qK9RXk97nnxv/nnq6MzpaVGUaOlJSNHTr9CyYor1vYSVEs1NWazNZZzpsZszE83mBdL9pFd4ENraGgoPmgAAAAAAGURNQAAAAAAgCKIGgAAAAAAQBFEjcK17QUwceLEOs8EAAAAAAAWLlGjULfffnt23nnnDBw4MEly+eWXZ+edd875559f55kBAAAAAMDCYZffQm2zzTbZZptt0tDQUO+pAAAAAABATYgahRIzAAAAAABY0rj8FAAAAAAAUARRAwAAAAAAKIKoAQAAAAAAFEHUAAAAAAAAiiBqAAAAAAAARRA1AAAAAACAIogaAAAAAABAEUQNAAAAAACgCKIGAAAAAABQBFEDAAAAAAAoQod6T2BejXt5dL2n8JF1W2u5ek8BZvH666/nxRdfzLPPPpt//etf2WWXXbL99tvXe1oAAAAAALMoJmokyYThY1OZ1lrvacy3hg6N6bLyMvWexmJv5MiR2W233bLMMsvksssuS+fOnes9pSI8+eSTefrppzNo0KBMmjQp3/nOd2a5z7XXXpvjjjsu/fv3z4EHHrjQ5lKrcQAAAACAMhV1+anKtNZUWirl/a/AELOg/Oc//6nZWI888khef/31PPXUU3nxxRdrNm7pvvGNb+SII45Inz595nifm2++ORMmTMiNN964QMac0/NiQY8DAAAAACxeijpTg3K89tprOeGEE/L888/n7rvvrsmYX/ziF/OVr3wl3bt3z/rrr1+TMRcnSy+99Bxv23PPPfPuu+9mzz33/EhjPPHEE/nTn/6U1tbWDBw4cKGNAwAAAAAsnkQNForXXnst99xzT03jQteuXXPeeefVbLwlyeabb57NN9/8I3+ff//733nooYey7bbbLtRxAAAAAIDFU1GXn2L2KpVKxo4dW+9ptNOxY8ckc3/3/5Ji3LhxaW1dci9BNrMOHaZ3VM8LAAAAAODDcKZGoV577bU8/PDDefzxx3PfffelX79+Oeigg3LFFVfkoosuyvDhw9OpU6esscYa6devX37wgx/M9vvccccdufrqqzNy5Mg0Nzdn9OjR2XDDDbPrrrvmy1/+8iz3f+ihh3LhhRdm5MiRaWlpSWtra/r06ZPdd989ffv2rd6voaEhSdLU1DTHx/D222/nggsuyDPPPJMxY8ZkzJgx2WSTTfKjH/2our/DpEmT8tJLL+WZZ57JfffdlzXWWCMHHXRQTjnllFx33XVZZpllcttttyVJRo8enUceeSS33XZbjjjiiPTo0aPdeC0tLbnppptyyy23ZPz48Zk4cWLGjRuXTTbZJHvvvXc+/elPV+87P+POzv3335+nn346jzzySO6///7ceOON6dKlS0466aTce++9GTt2bJZddtnq4535jJa33norL7zwQvWxXH311Xnuuedywgkn5KWXXspvfvOb7LzzzkmS5ubmXHPNNbn11lszYcKEvPXWW/n4xz+enXfeOd/97nerP4eZvfvuu7nkkkvy73//O2+++WYmTZqUT3/603nzzTdn+1haW1vzxhtv5N57783o0aPz05/+dLb3e+211zJ48OC88sorefnll/Pee+9l+eWXz3777Zfvfve7SZLGxukddXbPizmN8/e//z3HHXdc3njjjSRJ586ds9Zaa+WEE05Inz590tLSkp133jmvv/56xo0bl2WXXTY//elPs+uuu1a/9xNPPJFLLrkkI0aMyMiRI9Pa2pott9wyBx54YJZddtk5/hwBAAAAgEWLqFGocePGpaGhIc8991yGDx+e8ePH56ijjsqqq66aU089tbrZ8pVXXpmnnnoqjz76aE455ZTq1zc3N+fwww/Pv//97wwYMCAbbbRRkuSNN97IoYcemv79+2ePPfbIb3/72+rX3HDDDTniiCNyyimnZPvtt0+SvPPOOznyyCMzcODA9O3bN+PGjcvFF1+ce+65J0ny/PPP53vf+16S6e/Ov+SSS9LQ0JDLLrss55xzTgYMGJAjjzwySfLqq69m//33z0477ZTzzz8/m222WZ566qn87W9/y3333Zc333wzBx54YI455phcc801aWlpqb7j/5BDDsnDDz+cd999N0ly8MEHt4sa48aNy4EHHpgJEybk9NNPz5prrpkkefrpp3PIIYdkl112yeGHH5599903SeZ53Dl577330qVLl/z9739Pc3NzHn/88dx6663Zfvvts9dee+Xll1/ORRddlFtuuSV33XVXzjrrrHzlK19Jklx++eV54403cuutt1Z/Jv3798+kSZPS0tJSDRXPP/98fvrTn2brrbfOOeeck44dO2bq1Kn5wx/+kCOPPDLDhg3LiSeeWJ3TtGnTcuGFF+bRRx/N0UcfnW7dulXXfeZoMLMnn3wyxx9/fJ566qk0Nzfnc5/73Cz3mThxYk499dR07Ngxhx56aHVtnn322fzyl7/MqFGj8vbbb2fgwIG5/fbbkyRDhw6tPi9WXXXV7L333nMc58tf/nJuueWWfOtb38qLL76Y73znOzn66KOrtzc1NWXIkCG5/PLLq9FppZVWSpJMnTo1xxxzTJ599tmcccYZWWWVVZIk//jHP3LggQfm1ltvzZVXXlldCwAAAABg0ebyU4XacMMN069fv2y66aZJkmuvvTY//OEPs//++2eDDTbI5z73uRx99NE5/vjjk0wPEjNv2H3iiSfmlltuycknn1wNGsn0A8znn39+unXrlr/85S8ZPHhw9bbTTz8966yzTjVoJEnPnj3z85//vPrnbt265eCDD85uu+2WJNlqq61y5ZVX5sorr8yll16ahoaG3HXXXfn973+fffbZJ5/97GerX7vGGmvk0EMPTXNzc3Xen/3sZ3Psscdmxx13TJJcf/31+eQnP5nHHnss1157bfXg9RlnnJE///nPc1yvX/ziF3n88cdz2mmnZfXVV69+fv31188555yTxsbG/OlPf8rf//73+Rp3Trbffvvssssu1bBy880359RTT83Xvva19OnTJ9tvv30GDRqULbfcMlOnTs2RRx6ZadOmVed6+umnV7/XgAEDcvXVV+exxx7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",
       "text/plain": [
        "<Figure size 1000x500 with 1 Axes>"
       ]
@@ -419,7 +2466,7 @@
    "source": [
     "from scipy.stats import binom\n",
     "\n",
-    "predictions = (binom(n=1, p=idata.posterior[\"p\"].mean()).rvs((4000, len(tosses))),\n",
+    "predictions = (binom(n=1, p=idata1.posterior[\"Intercept\"].mean()).rvs((4000, len(wins))),\n",
     "               pred_dists[1])\n",
     "\n",
     "for d, c, l in zip(predictions, (\"C0\", \"C4\"), (\"posterior mean\", \"posterior predictive\")):\n",
@@ -439,6 +2486,22 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "markdown",
+   "id": "b0aac629-a01b-44db-a15e-118f8570974d",
+   "metadata": {},
+   "source": [
+    "## Example 2: estimating the probability of a biased coin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eceb0746-f8f3-48d3-af88-41d767b597b8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "393d7af3-3e51-458b-ad18-1dd7e2961064",
@@ -459,7 +2522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 19,
    "id": "d523e69b-9248-4c09-a5d6-d880e14a913b",
    "metadata": {},
    "outputs": [],
@@ -487,7 +2550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 20,
    "id": "11152a46-4fb9-409c-8714-694b3e0e22a1",
    "metadata": {},
    "outputs": [
@@ -560,7 +2623,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.4"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,