diff --git a/02_activities/assignments/a1_sampling_and_reproducibility.ipynb b/02_activities/assignments/a1_sampling_and_reproducibility.ipynb
index 873f5985..2dfd6eb7 100644
--- a/02_activities/assignments/a1_sampling_and_reproducibility.ipynb
+++ b/02_activities/assignments/a1_sampling_and_reproducibility.ipynb
@@ -1,215 +1,340 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ed39f379",
-   "metadata": {},
-   "source": [
-    "# Assignment 1: Sampling and Reproducibility\n",
-    "\n",
-    "The code at the end of this file explores contact tracing data about an outbreak of the flu, and demonstrates the dangers of incomplete and non-random samples. This assignment is modified from [Contact tracing can give a biased sample of COVID-19 cases](https://andrewwhitby.com/2020/11/24/contact-tracing-biased/) by Andrew Whitby.\n",
-    "\n",
-    "Examine the code below. Identify all stages at which sampling is occurring in the model. Describe in words the sampling procedure, referencing the functions used, sample size, sampling frame, any underlying distributions involved. \n"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "ed39f379",
+      "metadata": {
+        "id": "ed39f379"
+      },
+      "source": [
+        "# Assignment 1: Sampling and Reproducibility\n",
+        "\n",
+        "The code at the end of this file explores contact tracing data about an outbreak of the flu, and demonstrates the dangers of incomplete and non-random samples. This assignment is modified from [Contact tracing can give a biased sample of COVID-19 cases](https://andrewwhitby.com/2020/11/24/contact-tracing-biased/) by Andrew Whitby.\n",
+        "\n",
+        "Examine the code below. Identify all stages at which sampling is occurring in the model. Describe in words the sampling procedure, referencing the functions used, sample size, sampling frame, any underlying distributions involved.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4ea73db3",
+      "metadata": {
+        "id": "4ea73db3"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3d9b2ccc",
+      "metadata": {
+        "id": "3d9b2ccc"
+      },
+      "source": [
+        "Modify the number of repetitions in the simulation to 10 and 100 (from the original 1000). Run the script multiple times and observe the outputted graphs. Comment on the reproducibility of the results."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4cf5d993",
+      "metadata": {
+        "id": "4cf5d993"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "id": "32603ce7",
+      "metadata": {
+        "id": "32603ce7"
+      },
+      "source": [
+        "Alter the code so that it is reproducible. Describe the changes you made to the code and how they affected the reproducibility of the script. The script needs to produce the same output when run multiple times."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "77613cc3",
+      "metadata": {
+        "id": "77613cc3"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "id": "30b4a74f",
+      "metadata": {
+        "id": "30b4a74f"
+      },
+      "source": [
+        "## Code"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "ab8587a0",
+      "metadata": {
+        "id": "ab8587a0"
+      },
+      "outputs": [],
+      "source": [
+        "# Imports\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import warnings\n",
+        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+        "\n",
+        "# Model parameters (constants)\n",
+        "ATTACK_RATE = 0.10\n",
+        "TRACE_SUCCESS = 0.20\n",
+        "SECONDARY_TRACE_THRESHOLD = 2\n",
+        "\n",
+        "def simulate_event(rng):\n",
+        "    \"\"\"\n",
+        "    Simulate infection + tracing over two event types: 200 'wedding', 800 'brunch'.\n",
+        "    Returns:\n",
+        "        (p_wedding_infections, p_wedding_traces)\n",
+        "    \"\"\"\n",
+        "    # Build population\n",
+        "    events = ['wedding'] * 200 + ['brunch'] * 800\n",
+        "    ppl = pd.DataFrame({\n",
+        "        'event': events,\n",
+        "        'infected': False,\n",
+        "        'traced': pd.Series([pd.NA]*len(events), dtype=pd.BooleanDtype())\n",
+        "    })\n",
+        "\n",
+        "    # Infection sampling: exactly 10% infected (SRS without replacement)\n",
+        "    n_infected = int(len(ppl) * ATTACK_RATE)  # 100\n",
+        "    infected_indices = rng.choice(ppl.index, size=n_infected, replace=False)\n",
+        "    ppl.loc[infected_indices, 'infected'] = True\n",
+        "\n",
+        "    # Primary tracing: Bernoulli(TRACE_SUCCESS) among infected\n",
+        "    n_inf = int(ppl['infected'].sum())\n",
+        "    ppl.loc[ppl['infected'], 'traced'] = rng.random(n_inf) < TRACE_SUCCESS\n",
+        "\n",
+        "    # Secondary tracing: if an event has >= threshold traced infected, trace all infected at that event\n",
+        "    event_trace_counts = ppl[ppl['traced'] == True]['event'].value_counts()\n",
+        "    events_traced = event_trace_counts[event_trace_counts >= SECONDARY_TRACE_THRESHOLD].index\n",
+        "    ppl.loc[ppl['event'].isin(events_traced) & ppl['infected'], 'traced'] = True\n",
+        "\n",
+        "    # Aggregate proportions\n",
+        "    ppl['event_type'] = ppl['event'].str[0]  # 'w' or 'b'\n",
+        "    wedding_infections = ((ppl['infected']) & (ppl['event_type'] == 'w')).sum()\n",
+        "    brunch_infections  = ((ppl['infected']) & (ppl['event_type'] == 'b')).sum()\n",
+        "    p_wedding_infections = wedding_infections / (wedding_infections + brunch_infections)\n",
+        "\n",
+        "    wedding_traces = ((ppl['infected']) & (ppl['traced'] == True) & (ppl['event_type'] == 'w')).sum()\n",
+        "    brunch_traces  = ((ppl['infected']) & (ppl['traced'] == True) & (ppl['event_type'] == 'b')).sum()\n",
+        "    p_wedding_traces = wedding_traces / (wedding_traces + brunch_traces) if (wedding_traces + brunch_traces) > 0 else np.nan\n",
+        "\n",
+        "    return p_wedding_infections, p_wedding_traces\n",
+        "\n",
+        "def run_simulation(REPS, rng):\n",
+        "    results = [simulate_event(rng) for _ in range(REPS)]\n",
+        "    props_df = pd.DataFrame(results, columns=[\"Infections\", \"Traces\"])\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    sns.histplot(props_df['Infections'], color=\"blue\", alpha=0.75, binwidth=0.05, kde=False, label='Infections from Weddings')\n",
+        "    sns.histplot(props_df['Traces'], color=\"red\", alpha=0.75, binwidth=0.05, kde=False, label='Traced to Weddings')\n",
+        "    plt.xlabel(\"Proportion of cases\")\n",
+        "    plt.ylabel(\"Frequency\")\n",
+        "    plt.title(f\"Impact of Contact Tracing on Perceived Flu Infection Sources (REPS={REPS})\")\n",
+        "    plt.legend()\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "    return props_df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Non-reproducible runs (RNG seeded from entropy each time)\n",
+        "rng1 = np.random.default_rng()  # no fixed seed\n",
+        "props_df_10 = run_simulation(10, rng1)\n",
+        "\n",
+        "rng2 = np.random.default_rng()  # fresh RNG again\n",
+        "props_df_100 = run_simulation(100, rng2)"
+      ],
+      "metadata": {
+        "id": "bikJJ5yCsJ6P",
+        "outputId": "e494636b-922f-4bd2-89d9-4c85269e49eb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        }
+      },
+      "id": "bikJJ5yCsJ6P",
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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ebR3+22+/peuGmTbN4OBgIzk52To8rZvYF198YR2W1qXrVsOGDTPc3d2NxMREwzAM48aNG0bFihWN8uXLGxcvXrRpm5qaav1/TrqRnzlzxnBxcTHat29v0135rbfeMiQZH3zwgXXY1KlTs9WN/ODBg4YkY8yYMdmqYf369YYk49VXX7UZ/vjjjxsWi8U4fvy4dZgkw8XFxWZY2vwWL15sHZZVN/KM1nWHDh2MSpUqWZ9funTJ8PLyMho0aJCu296t69rMbuRp3QwrVaqUrsbExMR03cmjo6MNV1dX45VXXrEO++CDDwxJxvz589PN89a6b5932nv79NNP27ymR48eRokSJazP9+7da0gyxo4da9MuJCQkW92QFy5caEgyPvroI+uw5ORko1GjRoanp6f1lIO07solSpSw6ar4xRdfGJKMr776Ksv5pK3LDz74wDh79qxx8uRJ45tvvjEqVKhgWCwWY8+ePcaVK1eMokWLGs8++6zNa0+dOmX4+PjYDE/bF/zrX/+yabt9+3ZDkjF69Oh0NaSt7z///NNwdHQ0XnvtNZvxhw8fNpycnGyG3979evPmzRkub+fOnW2215UrVxoODg7G999/b9NuyZIlNl1SDxw4YEgynn/+eZt2Tz75ZI66kWf0CAsLy3Q50t6PW9vcOr07nVKSURfc7G6zhmEYLVq0SNeN/PvvvzckGR9//LHN8E2bNtkMz+u+IKNlrFOnjlGqVCnj/Pnz1mEHDx40HBwcjIEDB+ZqGTMyZcoUQ5Lh4eFhdOrUyXjttdeMvXv3pmuX3c9lTt7HgvjM7N+/P9enFzRt2tQIDg5ONzyjfcemTZuMKlWqGBaLxaab9K3LmdGjQ4cOWU739n2SYdzc/5QsWdKQZNSoUcN47rnnjFWrVhmXLl1KV2va9nGnR1Z/o7L6vjBixAjD0dExw3ElS5Y0+vbtaxjG//09u/VvUZq3337bkGT89ttvNsNXrVplSDJ2796daW2AvdGNHCggzzzzjPX/RYsWVfXq1eXh4aHevXtbh1evXl1FixbN8ErHQ4cOtTniM3z4cDk5OWnDhg3WYbd26bpy5YrOnTunZs2aKSEhQb/99pukm78yR0dHa+zYsemOhGWnq3hGtm7dquTkZI0dO1YODv+3W3n22Wfl7e1t000su+Li4iQpw+7jGdmwYYMcHR01evRom+Hjx4+XYRjauHGjzfC2bdvaHKGtVauWvL29s32V6VvX9eXLl3Xu3Dm1aNFCf/zxh7Xr4ZYtW3TlyhX961//StdtL7frOrsGDRqUroufq6ur9f1JSUnR+fPn5enpqerVq2vfvn3Wdp9//rl8fX01atSodNPNTt3PPfeczfNmzZrp/Pnz1vc0rbv+7d0KM5pfRjZs2CA/Pz/169fPOszZ2VmjR49WfHy8duzYYdO+T58+Nkdk0rpyZve9fvrpp1WyZEmVKVNGXbp00dWrV7VixQrVq1dPW7Zs0aVLl9SvXz+dO3fO+nB0dFSDBg0UFhaWbnrDhw+3ef7555/LYrFYT7G4Vdr6Xrt2rVJTU9W7d2+b+fj5+alq1aoZzidN69at5evrq08++cQ67OLFi9qyZYv69OljHbZmzRoFBgaqRo0aNvNI66KaNo+0fc7tn7U7XeDvdkOHDtWWLVtsHrVr187RNMxyp202M2vWrJGPj4/atWtns86Cg4Pl6elpXWdm7wtiY2N14MABhYSE2BxtrlWrltq1a2fzdyGvyzh9+nStWrVKDz/8sDZv3qzJkycrODhYdevWtemyntPPZU7k52cm7cj15s2bs3WE91bnz5/P8mjvrfuOjh076vLly1q5cqVNN+k0bm5u6T4PW7Zs0axZs7Kc7u37JOlmF++DBw/queee08WLF7VkyRI9+eSTKlWqlGbMmGFzatXAgQMznO/tj48//jhH6ybNtWvXbHrU3L7M165ds7aTbv6dyqjdrW3SpK37jHoEAIUF3ciBApB2PtmtfHx8VLZs2XRftHx8fDI8169q1ao2zz09PeXv72/tliZJv/76q15++WVt37493ReotACY1t3soYceyvXy3O6vv/6SdPPHglu5uLioUqVK1vE5kdYl7MqVK9muoUyZMunCeVpXz9tryKhrerFixTJc9xnZuXOnpk6dql27dqX7gnb58mX5+Pjky7rOrttPQ5D+7xzHd955R9HR0Tbn8t96ldeoqChVr14911cav33dpn0hunjxory9vfXXX3/JwcEhXY3ZuTq9dPO9rFq1qs0PO1L23+tb68mOKVOmqFmzZnJ0dJSvr68CAwOt6+bYsWOS/u+cydvd3rXRyclJZcuWtRkWFRWlMmXKZNlF99ixYzIMI91+IE1WXa+dnJzUq1cvrVq1SklJSXJ1ddXatWt1/fp1m7B97NgxRUREZHpF8DNnzkiS9f27/XSC2z//d1K1atVsn+ua3+60zWbm2LFjunz5crpzl9OkrTOz9wWZ7XOlm5+DzZs36+rVq/Lw8LAOz+0ySjevHN+vXz/FxcVp9+7dWr58uVatWqWuXbvqyJEjcnNzy/HnMrvy+zNTsWJFjRs3TvPnz9fHH3+sZs2a6bHHHtNTTz2VZRfyNMZt1wS5Vdq+Iz4+XuvWrdPq1avTrZ80jo6O2f48ZLVPSuPv76/Q0FC98847OnbsmDZv3qzZs2drypQp8vf3tx4AqFSpkipVqpSt+eZGkSJFlJycnOG4xMRE64/Caf/efn2ItHa3tkmTtu7z+8drIC8I20AByOjWHVkNz+qPd2YuXbqkFi1ayNvbW6+88ooqV64sNzc37du3TxMnTrwrbgV0qypVqsjJyUmHDx/Ol+nnZd1HRUWpTZs2qlGjhubPn6+AgAC5uLhow4YNWrBgQaFY1xlduOb111/Xf/7zHz399NOaMWOGihcvLgcHB40dO9bUms3crs2Q13qCgoIy/RKctt5WrlyZ7lY+ktJ9Ab61d0FOpKamymKxaOPGjRkuz53O8+/bt6/effddbdy4Ud27d9enn36qGjVq2BxJTk1NVVBQkObPn5/hNAICAnJct5ky+0Kd0QUgcyq320hqaqpKlSqV6VG/wnQrMzM+l97e3mrXrp3atWsnZ2dnrVixQrt371aLFi2yPY2cvo8F8Zl54403FBISoi+++ELffvutRo8erZkzZ+qnn35KF/RvVaJEiSx/tLt139G9e3clJCTo2WefVdOmTfP0ecpqn3Q7i8WiatWqqVq1aurSpYuqVq2qjz/+2Bq24+Pjs3XfbUdHx1xtz/7+/kpJSdGZM2dsfpRKTk7W+fPnrdcKKV68uFxdXRUbG5tuGmnDbr/VYtq6T7umClAYEbaBu8SxY8fUqlUr6/P4+HjFxsZa71MbHh6u8+fPa+3atWrevLm13a1XTJZkPRp15MiRLP9Y5+SX4rSLw0RGRtr8Qp6cnKzo6OhcHb1yd3dX69attX37dp04ceKOX0zKly+vrVu3pruASlr3+exewOZWma2Dr776SklJSfryyy9tjhbd3pX31nWd1VHbgvpV/rPPPlOrVq30/vvv2wy/dOmSzZeVypUra/fu3bp+/Xq+XKyqfPnySk1NVXR0tM1Rp+PHj2f79YcOHVJqaqrNl/C8vNe5lfYelypVKtdHaStXrqzNmzfrwoULmR6pq1y5sgzDUMWKFVWtWrUcz6N58+by9/fXJ598oqZNm2r79u2aPHlyunkcPHhQbdq0yXKbTHv/0npApImMjMxxXTmRdiT21jscSLk/YmqGypUra+vWrWrSpEmWV2Y2e19w6z73dr/99pt8fX1tjmrnh3r16mnFihXWIJTdz6UZ72N+fGaCgoIUFBSkl19+WT/++KOaNGmiJUuW6NVXX830NTVq1NDnn3+e7bpnzZqldevW6bXXXtOSJUuy/TqzVKpUScWKFbMJtPPmzdP06dPv+Nry5cvb9KTLrjp16kiSfvnlF+v3lbTnqamp1vEODg4KCgpKdzV56ebdUypVqpSu51p0dLQcHBxytU8ECgrnbAN3iffee0/Xr1+3Pg8NDdWNGzfUqVMnSf931OLWoxTJycl65513bKZTt25dVaxYUQsXLkz3ZefW16Z9Ubu9TUbatm0rFxcXvfnmmzbTeP/993X58uUcX6U4zdSpU2UYhgYMGJDhL+979+613lamc+fOSklJ0VtvvWXTZsGCBbJYLNb1lBOZrYOM1vXly5e1bNkym3bt27eXl5eXZs6cae0Gl+b2dX2nW8yYwdHRMd1RrDVr1uiff/6xGdarVy+dO3cu3bqUzDk63aFDB0lKt20uXrw4W6/v3LmzTp06ZXMO8o0bN7R48WJ5enrm6ChbXnXo0EHe3t56/fXXbT6fac6ePXvHafTq1UuGYWT4hTdtfffs2VOOjo6aPn16uvfAMAydP38+y3k4ODjo8ccf11dffaWVK1fqxo0bNl3IpZtXq/7nn3+0dOnSdK+/du2arl69KknWz9Kbb75p02bhwoVZL2gelS9fXo6Ojvruu+9sht++HRWk3r17KyUlRTNmzEg37saNG9Z9h9n7An9/f9WpU0crVqyw2T8dOXJE3377rU2oyYuEhATt2rUrw3Fp18FI+8Elu59LM95HMz8zcXFxunHjhs34oKAgOTg4ZNil+VaNGjXSxYsXs339h8qVK6tXr15avny5Tp06la3X5Mbu3butn9db/fzzzzp//rzNj2T5fc5269atVbx48XS3xgwNDZW7u7vN94PHH39ce/bssQnckZGR2r59u5544ol00967d69q1qyZre7+gL1wZBu4SyQnJ6tNmzbq3bu3IiMj9c4776hp06Z67LHHJEmNGzdWsWLFNGjQII0ePVoWi0UrV65M9yXDwcFBoaGh6tq1q+rUqaPBgwfL399fv/32m3799Vdt3rxZ0s1bakg3L4LUoUMHOTo6Wm8RdLuSJUtq0qRJmj59ujp27KjHHnvMWmP9+vX11FNP5WqZGzdurLffflvPP/+8atSooQEDBqhq1aq6cuWKwsPD9eWXX1qPOnTt2lWtWrXS5MmT9eeff6p27dr69ttv9cUXX2js2LF3vF1VRtLWweTJk9W3b185Ozura9euat++vVxcXNS1a1cNGzZM8fHxWrp0qUqVKmVzxMDb21sLFizQM888o/r16+vJJ59UsWLFdPDgQSUkJFh/KAgODtYnn3yicePGqX79+vL09FTXrl1ztc6y8uijj+qVV17R4MGD1bhxYx0+fFgff/xxuvP1Bg4cqA8//FDjxo3Tzz//rGbNmunq1avaunWrnn/+eXXr1i1PdQQHB6tXr15auHChzp8/b7311++//y7pzkf3hg4dqnfffVchISHau3evKlSooM8++0w7d+7UwoULs31RPTN4e3srNDRUAwYMUN26ddW3b1+VLFlSMTEx+uabb9SkSZMMf7S4VatWrTRgwAC9+eabOnbsmDp27KjU1FR9//33atWqlUaOHKnKlSvr1Vdf1aRJk/Tnn3+qe/fu8vLyUnR0tNatW6ehQ4fa3C87I3369NHixYs1depUBQUFWc+lTTNgwAB9+umneu655xQWFqYmTZooJSVFv/32mz799FNt3rxZ9erVU506ddSvXz+98847unz5sho3bqxt27Zlu2dCbvn4+OiJJ57Q4sWLZbFYVLlyZX399dfW86LtoUWLFho2bJhmzpypAwcOqH379nJ2dtaxY8e0Zs0aLVq0SI8//ni+7Avmzp2rTp06qVGjRhoyZIj11l8+Pj7Zuld9diQkJKhx48Zq2LChOnbsqICAAF26dEnr16/X999/r+7du+vhhx+WlP3PpRnvo5mfme3bt2vkyJF64oknVK1aNd24cUMrV66Uo6OjevXqlWUdXbp0kZOTk7Zu3aqhQ4dmq/YXX3xRn376qRYuXGhz8bMbN27oo48+yvA1PXr0yFFPhZUrV+rjjz9Wjx49FBwcLBcXF0VEROiDDz6Qm5ub/v3vf1vb5vac7b/++st6a9C0cJz297h8+fIaMGCApJunNM2YMUMjRozQE088oQ4dOuj777/XRx99pNdee82mZ8Lzzz+vpUuXqkuXLpowYYKcnZ01f/58lS5dWuPHj7eZ//Xr17Vjx450F9oECp38v+A5cP/I7NZfHh4e6dpmdBsZwzCM8uXLG126dEk3zR07dhhDhw41ihUrZnh6ehr9+/e3ueWLYRjGzp07jYYNGxpFihQxypQpY7z00kvW2/7cfpuVH374wWjXrp3h5eVleHh4GLVq1bK57dWNGzeMUaNGGSVLljQsFku2bgP21ltvGTVq1DCcnZ2N0qVLG8OHD093e7Hs3vrrVnv37jWefPJJo0yZMoazs7NRrFgxo02bNsaKFStsbmV15coV44UXXrC2q1q1qjF37lybW+sYxs1bVo0YMSLdfMqXL28MGjTIZtiMGTOMBx54wHBwcLC5DdiXX35p1KpVy3BzczMqVKhgzJ4923rLrNtvFfbll18ajRs3NooUKWJ4e3sbjzzyiPG///3POj4+Pt548sknjaJFi97xFiu3yurWXxndxiYxMdEYP3684e/vbxQpUsRo0qSJsWvXLqNFixZGixYtbNomJCQYkydPNipWrGg4Ozsbfn5+xuOPP25ERUXZrMeMbv11+3ubtg3ful6uXr1qjBgxwihevLjh6elpdO/e3YiMjDQkGbNmzbrjsp8+fdoYPHiw4evra7i4uBhBQUHpbv2UdiuhuXPnpnv97bVnJKt1mVHbDh06GD4+Poabm5tRuXJlIyQkxPjll1+sbTLbFxjGzc/b3LlzjRo1ahguLi5GyZIljU6dOqW7xdLnn39uNG3a1PDw8DA8PDyMGjVqGCNGjDAiIyNt5pPRNpSammoEBAQYyuAWeWmSk5ON2bNnGzVr1jRcXV2NYsWKGcHBwcb06dONy5cvW9tdu3bNGD16tFGiRAnDw8PD6Nq1q3HixIkc3foro/flVhktx9mzZ41evXoZ7u7uRrFixYxhw4YZR44cyfOtv7KzzWa2zzYMw3jvvfeM4OBgo0iRIoaXl5cRFBRkvPTSS8bJkydt2uV2X5DZ7c22bt1qNGnSxDq9rl27GkePHrVpk5NlvN3169eNpUuXGt27dzfKly9vuLq6Gu7u7sbDDz9szJ0710hKSrJpn53PpWFk/30siM/MH3/8YTz99NNG5cqVDTc3N6N48eJGq1atjK1bt2a6Xm712GOPGW3atLEZdqd9R8uWLQ1vb2/rrbiyuvXXre9RdvdJhw4dMl588UWjbt26RvHixQ0nJyfD39/feOKJJ4x9+/Zla7nuJK2WjB63/z0xjJufkerVqxsuLi5G5cqVjQULFqT722wYhnHixAnj8ccfN7y9vQ1PT0/j0UcfNY4dO5au3caNGw1JGY4DChOLYdjpijUAsmX58uUaPHiw9uzZY72tB3AvOnDggB5++GF99NFH6t+/v73LAYA7+v7779WyZUv99ttvmV75HObr3r27LBaL1q1bZ+9SgCxxzjYAoMDdfr9U6eY5vw4ODjYX+AOAwqxZs2Zq37695syZY+9S7hsRERH6+uuvM7xWAlDYcM42AKDAzZkzR3v37lWrVq3k5OSkjRs3auPGjRo6dKjdbzEFADmRdrE4FIzAwMB0F7UDCivCNgCgwDVu3FhbtmzRjBkzFB8fr3LlymnatGnpbkcFAABwt+KcbQAAAAAATMY52wAAAAAAmIywDQAAAACAye75c7ZTU1N18uRJeXl5yWKx2LscAAAAAMBdyDAMXblyRWXKlJGDw52PW9/zYfvkyZNc2RYAAAAAYIoTJ06obNmyd2x3z4dtLy8vSTdXiLe3t52rAQAAAADcjeLi4hQQEGDNmHdyz4fttK7j3t7ehG0AAAAAQJ5k9/RkLpAGAAAAAIDJCNsAAAAAAJiMsA0AAAAAgMnu+XO2syslJUXXr1+3dxnAfcfZ2VmOjo72LgMAAAAw1X0ftg3D0KlTp3Tp0iV7lwLct4oWLSo/P79sX2wCAAAAKOzu+7CdFrRLlSold3d3vuwDBcgwDCUkJOjMmTOSJH9/fztXBAAAAJjjvg7bKSkp1qBdokQJe5cD3JeKFCkiSTpz5oxKlSpFl3IAAADcE+7rC6SlnaPt7u5u50qA+1vaZ5DrJgAAAOBecV+H7TR0HQfsi88gAAAA7jWEbQAAAAAATHZfn7OdmZiYGJ07d67A5ufr66ty5crl6zxOnTqlAQMG6Mcff5Szs3O+XX19+fLlGjt2rF2v7m4YhoYNG6bPPvtMFy9e1P79+1WnTh271VOQsrP+p02bpvXr1+vAgQOSpJCQEF26dEnr168vkBoBAACA+wFh+zYxMTGqXj1QiYkJBTZPNzd3RUZGZDtw5yYcLViwQLGxsTpw4IB8fHxyWamtChUqaOzYsRo7dqx1WJ8+fdS5c2dTpp9bmzZt0vLlyxUeHq5KlSrJ19fXrvX861//0vr16/Xbb79Zh/32228KDAzUoEGDtHz5cuvw5cuXa9iwYbp06ZL1wmH5bdGiRTIMo0DmBQAAANwvCNu3OXfunBITExQY+JHc3QPzfX4JCRGKiHhK586dy9ej21FRUQoODlbVqlXzbR7SzStLF1RIzExUVJT8/f3VuHHjTNskJyfLxcWlQOpp1aqVZs+erVOnTsnPz0+SFBYWpoCAAIWHh9u0DQsLU8OGDQt0HZr14wsAAACA/8M525lwdw+Ul1fdfH+YEehbtmyp0aNH66WXXlLx4sXl5+enadOmWcdXqFBBn3/+uT788ENZLBaFhIRIki5duqRnnnlGJUuWlLe3t1q3bq2DBw/aTPurr75S/fr15ebmJl9fX/Xo0cM6z7/++ksvvPCCLBaL9QJXy5cvV9GiRW2mERoaqsqVK8vFxUXVq1fXypUrbcZbLBb997//VY8ePeTu7q6qVavqyy+/tI6/ePGi+vfvr5IlS6pIkSKqWrWqli1bluG6CAkJ0ahRoxQTEyOLxaIKFSpY6x05cqTGjh0rX19fdejQQZK0Y8cOPfLII3J1dZW/v7/+9a9/6caNGzbrdtSoURo7dqyKFSum0qVLa+nSpbp69aoGDx4sLy8vValSRRs3bsz0/WnatKmcnZ1tgnV4eLhGjBihCxcu6M8//7QZ3qpVK0lSUlKSJkyYoAceeEAeHh5q0KBBunC+fPlylStXTu7u7urRo4fOnz+fbv6zZs1S6dKl5eXlpSFDhigxMTHdOuvevbvNMme1PUk3j8w3bdpUbm5uevDBB7V161ZZLBZrb4vk5GSNHDlS/v7+cnNzU/ny5TVz5sxM1xEAAABwryFs3yNWrFghDw8P7d69W3PmzNErr7yiLVu2SJL27Nmjjh07qnfv3oqNjdWiRYskSU888YTOnDmjjRs3au/evapbt67atGmjCxcuSJK++eYb9ejRQ507d9b+/fu1bds2PfLII5KktWvXqmzZsnrllVcUGxur2NjYDOtat26dxowZo/Hjx+vIkSMaNmyYBg8erLCwMJt206dPV+/evXXo0CF17txZ/fv3t9bxn//8R0ePHtXGjRsVERGh0NDQTLuGL1q0SK+88orKli2r2NhY7dmzx2Ydubi4aOfOnVqyZIn++ecfde7cWfXr19fBgwcVGhqq999/X6+++mq6devr66uff/5Zo0aN0vDhw/XEE0+ocePG2rdvn9q3b68BAwYoISHjUw88PDxUv359m2UODw9XmzZt1KRJE+vwP/74QzExMdawPXLkSO3atUurV6/WoUOH9MQTT6hjx446duyYJGn37t0aMmSIRo4cqQMHDqhVq1bpav/00081bdo0vf766/rll1/k7++vd955J8M6b1/mzLanlJQUde/eXe7u7tq9e7fee+89TZ482eb1b775pr788kt9+umnioyM1Mcff2z94QMAAAC4H9CN/B5Rq1YtTZ06VZJUtWpVvfXWW9q2bZvatWunkiVLytXVVUWKFLF2Y/7hhx/0888/68yZM3J1dZUkzZs3T+vXr9dnn32moUOH6rXXXlPfvn01ffp063xq164tSSpevLgcHR3l5eVlnWZG5s2bp5CQED3//POSpHHjxumnn37SvHnzrKFSunl0tV+/fpKk119/XW+++aZ+/vlndezYUTExMXr44YdVr149ScoytPn4+MjLy0uOjo7p6qpatarmzJljfT558mQFBATorbfeksViUY0aNXTy5ElNnDhRU6ZMkYODg3WZX375ZUnSpEmTNGvWLPn6+urZZ5+VJE2ZMkWhoaE6dOiQGjZsmGFdrVq10po1ayRJR48eVWJioh5++GE1b95c4eHhGjx4sMLDw+Xm5qaGDRsqJiZGy5YtU0xMjMqUKSNJmjBhgjZt2qRly5bp9ddf16JFi9SxY0e99NJLkqRq1arpxx9/1KZNm6zzXbhwoYYMGaIhQ4ZIkl599VVt3bo13dHt22W1PW3ZskVRUVEKDw+3ruPXXntN7dq1s74+JiZGVatWVdOmTWWxWFS+fPks5wcAAADcaziyfY+oVauWzXN/f3+dOXMm0/YHDx5UfHy8SpQoIU9PT+sjOjpaUVFRkqQDBw6oTZs2eaorIiJCTZo0sRnWpEkTRUREZFq/h4eHvL29rfUPHz5cq1evVp06dfTSSy/pxx9/zFUtwcHB6Wpr1KiRzT2emzRpovj4eP39998Z1ubo6KgSJUooKCjIOqx06dKSlOX6btmypX7//XfFxsYqPDxcTZs2laOjo1q0aGHtGh4eHq7GjRvL1dVVhw8fVkpKiqpVq2bz/uzYscP6/kRERKhBgwY282nUqFG6ZbxTm4xktT1FRkYqICDA5seMtB4PaUJCQnTgwAFVr15do0eP1rfffnvHeQIAAAD3Eo5s3yOcnZ1tnlssFqWmpmbaPj4+Xv7+/unOAZZkPee6IC/SlVX9nTp10l9//aUNGzZoy5YtatOmjUaMGKF58+blaB4eHh6m1XbrsLSwntX6btKkiVxcXBQWFqawsDC1aNFCklS/fn2dO3dOf/zxh8LDwzVs2DBJN98fR0dH7d27V46OjjbT8vT0zNVy5EROt6fb1a1bV9HR0dq4caO2bt2q3r17q23btvrss8/MLhUAAAAolDiyfZ+qW7euTp06JScnJ1WpUsXmkXY+dK1atbRt27ZMp+Hi4qKUlJQs5xMYGKidO3faDNu5c6cefPDBHNVbsmRJDRo0SB999JEWLlyo9957L0evz6y2Xbt22dz2aufOnfLy8lLZsmXzPP1bFSlSxHqBsx07dqhly5aSbobahg0b6v3339eJEyesXesffvhhpaSk6MyZM+nen7QjyoGBgdq9e7fNfH766ad0y3inNjlVvXp1nThxQqdPn7YOu/Xc+DTe3t7q06ePli5dqk8++USff/659Tx8AAAA4F7Hke37VNu2bdWoUSN1795dc+bMUbVq1XTy5EnrRdHq1aunqVOnqk2bNqpcubL69u2rGzduaMOGDZo4caKkm+dOf/fdd+rbt69cXV0zvGjZiy++qN69e+vhhx9W27Zt9dVXX2nt2rXaunVrtmudMmWKgoODVbNmTSUlJenrr79WYGDer+L+/PPPa+HChRo1apRGjhypyMhITZ06VePGjbOer22mVq1aacGCBZJu/tiRpkWLFpo3b571QmrSzfOv+/fvr4EDB+qNN97Qww8/rLNnz2rbtm2qVauWunTpotGjR6tJkyaaN2+eunXrps2bN9ucry1JY8aMUUhIiOrVq6cmTZro448/1q+//qpKlSrlejnatWunypUra9CgQZozZ46uXLliPac97Sj//Pnz5e/vr4cfflgODg5as2aN/Pz80l2pHgAAALhXEbYzkZAQcedGd9F8bmexWLRhwwZNnjxZgwcP1tmzZ+Xn56fmzZtbz0Fu2bKl1qxZoxkzZmjWrFny9vZW8+bNrdN45ZVXNGzYMFWuXFlJSUk2R4jTdO/eXYsWLdK8efM0ZswYVaxYUcuWLbMe2c0OFxcXTZo0SX/++aeKFCmiZs2aafXq1XleBw888IA2bNigF198UbVr11bx4sU1ZMgQa3A0W6tWrfTKK6+oY8eOcnL6v49eixYtNHXqVHXo0MGm+/ayZcv06quvavz48frnn3/k6+urhg0b6tFHH5UkNWzYUEuXLtXUqVM1ZcoUtW3bVi+//LJmzJhhnUafPn0UFRWll156SYmJierVq5eGDx+uzZs353o5HB0dtX79ej3zzDOqX7++KlWqpLlz56pr165yc3OTJHl5eWnOnDk6duyYHB0dVb9+fW3YsCFffsQAYI6YmBidO3fO3mXgLuDr66ty5crZuwwAKPQsRkYJ6R4SFxcnHx8fXb58Wd7e3jbjEhMTFR0drYoVK1pDQkxMjKpXD1RiYsa3ccoPbm7uioyM4A8X7lo7d+5U06ZNdfz4cVWuXDnHr8/oswig4MTExCiwenUl3OFOBYAkubu5KSIyku8tAO47WWXLjHBk+zblypVTZGREgf66zy/EuNusW7dOnp6eqlq1qo4fP64xY8aoSZMmuQraAOzv3LlzSkhM1EeBgQp0d7d3OSjEIhIS9FTEze9JfHcBgKwRtjNQrlw5/oAAWbhy5YomTpyomJgY+fr6qm3btnrjjTfsXRaAPAp0d1ddLy97lwEAwD2BsA0gxwYOHKiBAwfauwwAAACg0OJqRQAAAAAAmIywDQAAAACAyQjbAAAAAACYjLANAAAAAIDJCNsAAAAAAJiMsA0AAAAAgMm49VcGYmJidO7cuQKbn6+v7z15X++WLVuqTp06Wrhwob1LMU1ISIguXbqk9evXZ9rm9uWuUKGCxo4dq7FjxxZIjQAAAADsj7B9m5iYGAVWr66ExMQCm6e7m5siIiOzFbgtFkuW46dOnapp06aZVFn+mjZtmtavX68DBw7kaToNGzZUnTp1tGTJEuuwJUuWaPjw4Vq2bJlCQkKsw0NCQhQVFaXvv/8+T/PMiT179sjDw6PA5gcAAADA/gjbtzl37pwSEhP1UWCgAt3d831+EQkJeioiQufOnctW2I6NjbX+/5NPPtGUKVMUGRlpHebp6Wn9v2EYSklJkZPTvf02t2rVSuvWrbMZFhYWpoCAAIWHh9uE7fDwcA0aNKhA6ytZsmSBzg8AgLtJQfcoROFzr/byBO7tFJYHge7uquvlZe8y0vHz87P+38fHRxaLxTosPDxcrVq10oYNG/Tyyy/r8OHD+vbbbxUQEKBx48bpp59+0tWrVxUYGKiZM2eqbdu21mklJSVpypQpWrVqlc6cOaOAgABNmjRJQ4YMkSQdOXJEL774or7//nt5eHioffv2WrBggXx9fSVJV69e1fDhw7V27Vp5eXlpwoQJWS7H8uXLNX36dEn/d7Q+7Sh0TEyMRo0apW3btsnBwUEdO3bU4sWLVbp06Qyn1apVK82aNUunTp2yrosdO3ZoypQpmjNnjrVddHS0/vrrL7Vq1UqSdOLECY0fP17ffvutHBwc1KxZMy1atEgVKlSQJKWkpOjFF1/UBx98IEdHRw0ZMkSGYdjMOzvLfXs3covFoqVLl+qbb77R5s2b9cADD+iNN97QY489Zn3Nl19+qfHjx+vEiRNq1KiRQkJCFBISoosXL6po0aL666+/NHLkSP3www9KTk5WhQoVNHfuXHXu3DnL9Q4AQGESExOj6tUDlZiYYO9SYEdubu6KjIwgcOOeQ9i+B/3rX//SvHnzVKlSJRUrVkwnTpxQ586d9dprr8nV1VUffvihunbtqshbuq4PHDhQu3bt0ptvvqnatWsrOjra+ivzpUuX1Lp1az3zzDNasGCBrl27pokTJ6p3797avn27JOnFF1/Ujh079MUXX6hUqVL697//rX379qlOnToZ1tinTx8dOXJEmzZt0tatWyXd/PEgNTVV3bp1k6enp3bs2KEbN25oxIgR6tOnj8LDwzOcVpMmTeTs7KywsDD169dPR48e1bVr1zRkyBBNnDhR0dHRqlixosLCwuTm5qZGjRrp+vXr6tChgxo1aqTvv/9eTk5OevXVV9WxY0cdOnRILi4ueuONN7R8+XJ98MEHCgwM1BtvvKF169apdevW1nnndLnTTJ8+XXPmzNHcuXO1ePFi9e/fX3/99ZeKFy+u6OhoPf744xozZoyeeeYZ7d+/P12IHzFihJKTk/Xdd9/Jw8NDR48etenVAADA3eDcuXNKTExQYOBHcncPtHc5sIOEhAhFRDyV7V6ewN2EsH0PeuWVV9SuXTvr8+LFi6t27drW5zNmzNC6dev05ZdfauTIkfr999/16aefasuWLdaj3ZUqVbK2f+utt/Twww/r9ddftw774IMPFBAQoN9//11lypTR+++/r48++kht2rSRJK1YsUJly5bNtMYiRYrI09NTTk5ONkfrt2zZosOHDys6OloBAQGSpA8//FA1a9bUnj17VL9+/XTT8vDw0COPPKLw8HD169dP4eHhatq0qVxdXdW4cWOFh4erYsWKCg8PV6NGjeTq6qqPPvpIqamp+u9//2tzZL1o0aIKDw9X+/bttXDhQk2aNEk9e/aUdPM88M2bN1vnGx8fn+PlThMSEqJ+/fpJkl5//XW9+eab+vnnn9WxY0e9++67ql69uubOnStJql69uo4cOaLXXnvN+vqYmBj16tVLQUFB6d4vAADuNu7ugfLyqmvvMgDAVNz66x5Ur149m+fx8fGaMGGCAgMDVbRoUXl6eioiIkIxMTGSpAMHDsjR0VEtWrTIcHoHDx5UWFiYPD09rY8aNWpIkqKiohQVFaXk5GQ1aNDA+prixYurevXqOa49IiJCAQEB1qAtSQ8++KCKFi2qiIiITF/XsmVL65Hv8PBwtWzZUpLUokULm+FpXcgPHjyo48ePy8vLy7pMxYsXV2JioqKionT58mXFxsbaLJOTk5PNus3LcteqVcv6fw8PD3l7e+vMmTOSpMjIyHQ/KjzyyCM2z0ePHq1XX31VTZo00dSpU3Xo0KE7zhMAAABAwSFs34Nuv/L1hAkTtG7dOr3++uv6/vvvdeDAAQUFBSk5OVnSzaPMWYmPj1fXrl114MABm8exY8fUvHnzfFuOnGjVqpV+//13/fPPPwoPD7f+cJAWtqOionTixAlrF/D4+HgFBwenW6bff/9dTz75ZL7X6+zsbPPcYrEoNTU1269/5pln9Mcff2jAgAE6fPiw6tWrp8WLF5tdJgAAAIBcImzfB3bu3KmQkBD16NFDQUFB8vPz059//mkdHxQUpNTUVO3YsSPD19etW1e//vqrKlSooCpVqtg8PDw8VLlyZTk7O2v37t3W11y8eFG///57lnW5uLgoJSXFZlhgYKBOnDihEydOWIcdPXpUly5d0oMPPpjptBo3biwXFxe98847SkxMVHBwsCSpfv36Onv2rD744ANrd/O0ZTp27JhKlSqVbpl8fHzk4+Mjf39/m2W6ceOG9u7da32e2+W+k+rVq+uXX36xGbZnz5507QICAvTcc89p7dq1Gj9+vJYuXZqn+QIAAAAwD2H7PlC1alWtXbtWBw4c0MGDB/Xkk0/aHEWtUKGCBg0apKefflrr169XdHS0wsPD9emnn0q6eTGuCxcuqF+/ftqzZ4+ioqK0efNmDR48WCkpKfL09NSQIUP04osvavv27Tpy5IhCQkLk4JD15lWhQgVFR0frwIEDOnfunJKSktS2bVsFBQWpf//+2rdvn37++WcNHDhQLVq0SNc9/lZFihRRw4YNtXjxYjVp0kSOjo6Sbgb6W4enHVHu37+/fH191a1bN33//ffWZR49erT+/vtvSdKYMWM0a9YsrV+/Xr/99puef/55Xbp0yTrP3C73nQwbNky//fabJk6caD2ffvny5ZL+78rtY8eO1ebNmxUdHa19+/YpLCxMgYFcWAYAAAAoLLhAWiYiEgrmFhQFMZ/58+fr6aefVuPGjeXr66uJEycqLi7Opk1oaKj+/e9/6/nnn9f58+dVrlw5/fvf/5YklSlTRjt37tTEiRPVvn17JSUlqXz58urYsaM1WM6dO9fa3dzLy0vjx4/X5cuXs6yrV69eWrt2rVq1aqVLly5Zb/31xRdfaNSoUWrevLnNrb/upFWrVvruu++s52unadGihcLCwqzna0uSu7u7vvvuO02cOFE9e/bUlStX9MADD6hNmzby9vaWJI0fP16xsbEaNGiQHBwc9PTTT6tHjx42y5Wb5b6TihUr6rPPPtP48eO1aNEiNWrUSJMnT9bw4cPl6uoq6eZtyUaMGKG///5b3t7e6tixoxYsWJCn+QIAAAAwj8W4/cbB95i4uDj5+Pjo8uXL1hCVJjEx0XpbKDc3N0k3r/IcWL26EhITC6xGdzc3RdxyGy7gdq+99pqWLFli073+XpLRZxFAwdm3b5+Cg4O1NzhYdb287F0OCrF9V64oeO9e7d27V3Xr5v3q4WnbXnDwXq5Gfp+6cmWf9u4NNm2bAvJTVtkyIxzZvk25cuUUERlpvcd0QfD19SVow8Y777yj+vXrq0SJEtq5c6fmzp2rkSNH2rssAAAAANlE2M5AuXLlCL+wq2PHjunVV1/VhQsXVK5cOY0fP16TJk2yd1kAAAAAsomwDRRCCxYs4BxsAAAA4C5G2AYAAECOREREmDqdhARzpofCx9nZV25u9BjF/YmwDQAAgGyJTU6Wg6SnnnrK1OlGRJg7PRQeTg5uqvdIJIEb9yXCtmRzz2kABY/PIADcHS7duKFUSUsrVFDdEiXyPL2rCQk6GhEhjyKBcnB0z3uBKFSiUxP0ckKErl8/R9jGfem+DtsuLi5ycHDQyZMnVbJkSbm4uMhisdi7LOC+YRiGkpOTdfbsWTk4OMjFxcXeJQEAsqF6kSKm3CbuiqQbkrwc3eXoxG3n7jk37F0AYF/3ddh2cHBQxYoVFRsbq5MnT9q7HOC+5e7urnLlysnBwcHepQAAAACmuK/DtnTz6Ha5cuV048YNpaSk2Lsc4L7j6OgoJycnepUAAADgnnLfh21JslgscnZ2lrOzs71LAQAAAADcA+izCQAAAACAyQjbAAAAAACYzK5hOzQ0VLVq1ZK3t7e8vb3VqFEjbdy40Tq+ZcuWslgsNo/nnnvOjhUDAAAAAHBndj1nu2zZspo1a5aqVq0qwzC0YsUKdevWTfv371fNmjUlSc8++6xeeeUV62vc3bkHIwAAAACgcLNr2O7atavN89dee02hoaH66aefrGHb3d1dfn5+9igPAAAAAIBcKTTnbKekpGj16tW6evWqGjVqZB3+8ccfy9fXVw899JAmTZqkhISELKeTlJSkuLg4mwcAAAAAAAXJ7rf+Onz4sBo1aqTExER5enpq3bp1evDBByVJTz75pMqXL68yZcro0KFDmjhxoiIjI7V27dpMpzdz5kxNnz69oMoHAAAAACAdu4ft6tWr68CBA7p8+bI+++wzDRo0SDt27NCDDz6ooUOHWtsFBQXJ399fbdq0UVRUlCpXrpzh9CZNmqRx48ZZn8fFxSkgICDflwMAAAAAgDR2D9suLi6qUqWKJCk4OFh79uzRokWL9O6776Zr26BBA0nS8ePHMw3brq6ucnV1zb+CAQAAAAC4g0Jzznaa1NRUJSUlZTjuwIEDkiR/f/8CrAgAAAAAgJyx65HtSZMmqVOnTipXrpyuXLmiVatWKTw8XJs3b1ZUVJRWrVqlzp07q0SJEjp06JBeeOEFNW/eXLVq1bJn2QAAAAAAZMmuYfvMmTMaOHCgYmNj5ePjo1q1amnz5s1q166dTpw4oa1bt2rhwoW6evWqAgIC1KtXL7388sv2LBkAAAAAgDuya9h+//33Mx0XEBCgHTt2FGA1AAAAAACYo9Cdsw0AAAAAwN2OsA0AAAAAgMkI2wAAAAAAmIywDQAAAACAyQjbAAAAAACYzK5XIwcAAPevxMREXb9+3d5lIAeuXbtm/ffKlSt5nt7VhIQ8TwMACivCNgAAKHCJiYna/fPPSk1NtXcpyIHotH///FPOf/5p2nRTDUOOpk0NAAoHwjYAAChw169fV2pqqtzdA+Xo4G7vcpBNRa6fl5L+lJtbBXk5lcjz9K7fuKDExGgZhmFCdQBQuBC2AQCA3Tg6uMvRycveZSCbLCk3u307WoqY8r6lpNKNHMC9iwukAQAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMruG7dDQUNWqVUve3t7y9vZWo0aNtHHjRuv4xMREjRgxQiVKlJCnp6d69eql06dP27FiAAAAAADuzK5hu2zZspo1a5b27t2rX375Ra1bt1a3bt3066+/SpJeeOEFffXVV1qzZo127NihkydPqmfPnvYsGQAAAACAO3Ky58y7du1q8/y1115TaGiofvrpJ5UtW1bvv/++Vq1apdatW0uSli1bpsDAQP30009q2LChPUoGAAAAAOCOCs052ykpKVq9erWuXr2qRo0aae/evbp+/bratm1rbVOjRg2VK1dOu3btynQ6SUlJiouLs3kAAAAAAFCQ7B62Dx8+LE9PT7m6uuq5557TunXr9OCDD+rUqVNycXFR0aJFbdqXLl1ap06dynR6M2fOlI+Pj/UREBCQz0sAAAAAAIAtu4ft6tWr68CBA9q9e7eGDx+uQYMG6ejRo7me3qRJk3T58mXr48SJEyZWCwAAAADAndn1nG1JcnFxUZUqVSRJwcHB2rNnjxYtWqQ+ffooOTlZly5dsjm6ffr0afn5+WU6PVdXV7m6uuZ32QAAAAAAZMruR7Zvl5qaqqSkJAUHB8vZ2Vnbtm2zjouMjFRMTIwaNWpkxwoBAAAAAMiaXY9sT5o0SZ06dVK5cuV05coVrVq1SuHh4dq8ebN8fHw0ZMgQjRs3TsWLF5e3t7dGjRqlRo0acSVyAAAAAEChZtewfebMGQ0cOFCxsbHy8fFRrVq1tHnzZrVr106StGDBAjk4OKhXr15KSkpShw4d9M4779izZAAAAAAA7siuYfv999/Pcrybm5vefvttvf322wVUEQAAAAAAeVfoztkGAAAAAOBuR9gGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGR2DdszZ85U/fr15eXlpVKlSql79+6KjIy0adOyZUtZLBabx3PPPWenigEAAAAAuDO7hu0dO3ZoxIgR+umnn7RlyxZdv35d7du319WrV23aPfvss4qNjbU+5syZY6eKAQAAAAC4Myd7znzTpk02z5cvX65SpUpp7969at68uXW4u7u7/Pz8Cro8AAAAAABypVCds3358mVJUvHixW2Gf/zxx/L19dVDDz2kSZMmKSEhwR7lAQAAAACQLXY9sn2r1NRUjR07Vk2aNNFDDz1kHf7kk0+qfPnyKlOmjA4dOqSJEycqMjJSa9euzXA6SUlJSkpKsj6Pi4vL99oBAAAAALhVoQnbI0aM0JEjR/TDDz/YDB86dKj1/0FBQfL391ebNm0UFRWlypUrp5vOzJkzNX369HyvFwAAAACAzBSKbuQjR47U119/rbCwMJUtWzbLtg0aNJAkHT9+PMPxkyZN0uXLl62PEydOmF4vAAAAAABZseuRbcMwNGrUKK1bt07h4eGqWLHiHV9z4MABSZK/v3+G411dXeXq6mpmmQAAAAAA5Ihdw/aIESO0atUqffHFF/Ly8tKpU6ckST4+PipSpIiioqK0atUqde7cWSVKlNChQ4f0wgsvqHnz5qpVq5Y9SwcAAAAAIFN2DduhoaGSpJYtW9oMX7ZsmUJCQuTi4qKtW7dq4cKFunr1qgICAtSrVy+9/PLLdqgWAAAAAIDssXs38qwEBARox44dBVQNAAAAAADmKBQXSAMAAAAA4F5C2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkuQrbf/zxh9l1AAAAAABwz8hV2K5SpYpatWqljz76SImJiWbXBAAAAADAXS1XYXvfvn2qVauWxo0bJz8/Pw0bNkw///yz2bUBAAAAAHBXylXYrlOnjhYtWqSTJ0/qgw8+UGxsrJo2baqHHnpI8+fP19mzZ82uEwAAAACAu0aeLpDm5OSknj17as2aNZo9e7aOHz+uCRMmKCAgQAMHDlRsbKxZdQIAAAAAcNfIU9j+5Zdf9Pzzz8vf31/z58/XhAkTFBUVpS1btujkyZPq1q2bWXUCAAAAAHDXcMrNi+bPn69ly5YpMjJSnTt31ocffqjOnTvLweFmdq9YsaKWL1+uChUqmFkrAAAAAAB3hVyF7dDQUD399NMKCQmRv79/hm1KlSql999/P0/FAQAAAABwN8pV2D527Ngd27i4uGjQoEG5mTwAAAAAAHe1XJ2zvWzZMq1Zsybd8DVr1mjFihV5LgoAAAAAgLtZrsL2zJkz5evrm254qVKl9Prrr+e5KAAAAAAA7ma5CtsxMTGqWLFiuuHly5dXTExMnosCAAAAAOBulquwXapUKR06dCjd8IMHD6pEiRJ5LgoAAAAAgLtZrsJ2v379NHr0aIWFhSklJUUpKSnavn27xowZo759+5pdIwAAAAAAd5VcXY18xowZ+vPPP9WmTRs5Od2cRGpqqgYOHMg52wAAAACA+16uwraLi4s++eQTzZgxQwcPHlSRIkUUFBSk8uXLm10fAAAAAAB3nVyF7TTVqlVTtWrVzKoFAAAAAIB7Qq7CdkpKipYvX65t27bpzJkzSk1NtRm/fft2U4oDAAAAAOBulKuwPWbMGC1fvlxdunTRQw89JIvFYnZdAAAAAADctXIVtlevXq1PP/1UnTt3NrseAAAAAADuerm69ZeLi4uqVKlidi0AAAAAANwTchW2x48fr0WLFskwDLPrAQAAAADgrperbuQ//PCDwsLCtHHjRtWsWVPOzs4249euXWtKcQAAAAAA3I1yFbaLFi2qHj16mF0LAAAAAAD3hFyF7WXLlpldBwAAAAAA94xcnbMtSTdu3NDWrVv17rvv6sqVK5KkkydPKj4+3rTiAAAAAAC4G+XqyPZff/2ljh07KiYmRklJSWrXrp28vLw0e/ZsJSUlacmSJWbXCQAAAADAXSNXR7bHjBmjevXq6eLFiypSpIh1eI8ePbRt2zbTigMAAAAA4G6Uq7D9/fff6+WXX5aLi4vN8AoVKuiff/7J9nRmzpyp+vXry8vLS6VKlVL37t0VGRlp0yYxMVEjRoxQiRIl5OnpqV69eun06dO5KRsAAAAAgAKRq7CdmpqqlJSUdMP//vtveXl5ZXs6O3bs0IgRI/TTTz9py5Ytun79utq3b6+rV69a27zwwgv66quvtGbNGu3YsUMnT55Uz549c1M2AAAAAAAFIlfnbLdv314LFy7Ue++9J0myWCyKj4/X1KlT1blz52xPZ9OmTTbPly9frlKlSmnv3r1q3ry5Ll++rPfff1+rVq1S69atJd28EnpgYKB++uknNWzYMDflAwAAAACQr3J1ZPuNN97Qzp079eCDDyoxMVFPPvmktQv57Nmzc13M5cuXJUnFixeXJO3du1fXr19X27ZtrW1q1KihcuXKadeuXbmeDwAAAAAA+SlXR7bLli2rgwcPavXq1Tp06JDi4+M1ZMgQ9e/f3+aCaTmRmpqqsWPHqkmTJnrooYckSadOnZKLi4uKFi1q07Z06dI6depUhtNJSkpSUlKS9XlcXFyu6gEAAAAAILdyFbYlycnJSU899ZRphYwYMUJHjhzRDz/8kKfpzJw5U9OnTzepKgAAAAAAci5XYfvDDz/McvzAgQNzNL2RI0fq66+/1nfffaeyZctah/v5+Sk5OVmXLl2yObp9+vRp+fn5ZTitSZMmady4cdbncXFxCggIyFE9AAAAAADkRa7C9pgxY2yeX79+XQkJCXJxcZG7u3u2w7ZhGBo1apTWrVun8PBwVaxY0WZ8cHCwnJ2dtW3bNvXq1UuSFBkZqZiYGDVq1CjDabq6usrV1TUXSwUAAAAAgDlyFbYvXryYbtixY8c0fPhwvfjii9mezogRI7Rq1Sp98cUX8vLysp6H7ePjoyJFisjHx0dDhgzRuHHjVLx4cXl7e2vUqFFq1KgRVyIHAAAAABRauT5n+3ZVq1bVrFmz9NRTT+m3337L1mtCQ0MlSS1btrQZvmzZMoWEhEiSFixYIAcHB/Xq1UtJSUnq0KGD3nnnHbPKBgAAAADAdKaFbenmRdNOnjyZ7faGYdyxjZubm95++229/fbbeSkNAAAAAIACk6uw/eWXX9o8NwxDsbGxeuutt9SkSRNTCgMAAAAA4G6Vq7DdvXt3m+cWi0UlS5ZU69at9cYbb5hRFwAAAAAAd61che3U1FSz6wAAAAAA4J7hYO8CAAAAAAC41+TqyPa4ceOy3Xb+/Pm5mQUAAAAAAHetXIXt/fv3a//+/bp+/bqqV68uSfr999/l6OiounXrWttZLBZzqgQAAAAA4C6Sq7DdtWtXeXl5acWKFSpWrJgk6eLFixo8eLCaNWum8ePHm1okAAAAAAB3k1yds/3GG29o5syZ1qAtScWKFdOrr77K1cgBAAAAAPe9XIXtuLg4nT17Nt3ws2fP6sqVK3kuCgAAAACAu1muwnaPHj00ePBgrV27Vn///bf+/vtvff755xoyZIh69uxpdo0AAAAAANxVcnXO9pIlSzRhwgQ9+eSTun79+s0JOTlpyJAhmjt3rqkFAgAAAABwt8lV2HZ3d9c777yjuXPnKioqSpJUuXJleXh4mFocAAAAAAB3o1x1I08TGxur2NhYVa1aVR4eHjIMw6y6AAAAAAC4a+UqbJ8/f15t2rRRtWrV1LlzZ8XGxkqShgwZwm2/AAAAAAD3vVyF7RdeeEHOzs6KiYmRu7u7dXifPn20adMm04oDAAAAAOBulKtztr/99ltt3rxZZcuWtRletWpV/fXXX6YUBgAAAADA3SpXR7avXr1qc0Q7zYULF+Tq6prnogAAAAAAuJvlKmw3a9ZMH374ofW5xWJRamqq5syZo1atWplWHAAAAAAAd6NcdSOfM2eO2rRpo19++UXJycl66aWX9Ouvv+rChQvauXOn2TUCAAAAAHBXydWR7Yceeki///67mjZtqm7duunq1avq2bOn9u/fr8qVK5tdIwAAAAAAd5UcH9m+fv26OnbsqCVLlmjy5Mn5URMAAAAAAHe1HB/ZdnZ21qFDh/KjFgAAAAAA7gm56kb+1FNP6f333ze7FgAAAAAA7gm5ukDajRs39MEHH2jr1q0KDg6Wh4eHzfj58+ebUhwAAAAAAHejHIXtP/74QxUqVNCRI0dUt25dSdLvv/9u08ZisZhXHQAAAAAAd6Eche2qVasqNjZWYWFhkqQ+ffrozTffVOnSpfOlOAAAAAAA7kY5OmfbMAyb5xs3btTVq1dNLQgAAAAAgLtdri6Qlub28A0AAAAAAHIYti0WS7pzsjlHGwAAAAAAWzk6Z9swDIWEhMjV1VWSlJiYqOeeey7d1cjXrl1rXoUAAAAAANxlchS2Bw0aZPP8qaeeMrUYAAAAAADuBTkK28uWLcuvOgAA95GYmBidO3fO3mXg/4uIiJAkXU1I0JUCmufVhIQCmhMAAPaRo7ANAEBexcTEqHr1QCUmErYKm6MREbpRwPNMNQw5FvA8AQAoCIRtAECBOnfunBITExQY+JHc3QPtXQ4kJSREKCLiKXkUCZSXo3uBzPP6jQtKTIzmziYAgHsWYRsAYBfu7oHy8qpr7zJwCwdHdzk6eRXIvFJS6dkAALi35ek+2wAAAAAAID3CNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACaza9j+7rvv1LVrV5UpU0YWi0Xr16+3GR8SEiKLxWLz6Nixo32KBQAAAAAgm+watq9evaratWvr7bffzrRNx44dFRsba33873//K8AKAQAAAADIOSd7zrxTp07q1KlTlm1cXV3l5+dXQBUBAAAAAJB3hf6c7fDwcJUqVUrVq1fX8OHDdf78+SzbJyUlKS4uzuYBAAAAAEBBKtRhu2PHjvrwww+1bds2zZ49Wzt27FCnTp2UkpKS6WtmzpwpHx8f6yMgIKAAKwYAAAAAwM7dyO+kb9++1v8HBQWpVq1aqly5ssLDw9WmTZsMXzNp0iSNGzfO+jwuLo7ADQAAAAAoUIX6yPbtKlWqJF9fXx0/fjzTNq6urvL29rZ5AAAAAABQkO6qsP3333/r/Pnz8vf3t3cpAAAAAABkyq7dyOPj422OUkdHR+vAgQMqXry4ihcvrunTp6tXr17y8/NTVFSUXnrpJVWpUkUdOnSwY9UAAAAAAGTNrmH7l19+UatWrazP0861HjRokEJDQ3Xo0CGtWLFCly5dUpkyZdS+fXvNmDFDrq6u9ioZAAAAAIA7smvYbtmypQzDyHT85s2bC7AaAAAAAADMcVedsw0AAAAAwN2AsA0AAAAAgMkI2wAAAAAAmIywDQAAAACAyQjbAAAAAACYjLANAAAAAIDJCNsAAAAAAJiMsA0AAAAAgMkI2wAAAAAAmIywDQAAAACAyQjbAAAAAACYjLANAAAAAIDJCNsAAAAAAJiMsA0AAAAAgMkI2wAAAAAAmIywDQAAAACAyQjbAAAAAACYjLANAAAAAIDJCNsAAAAAAJiMsA0AAAAAgMkI2wAAAAAAmIywDQAAAACAyQjbAAAAAACYjLANAAAAAIDJCNsAAAAAAJiMsA0AAAAAgMkI2wAAAAAAmIywDQAAAACAyQjbAAAAAACYjLANAAAAAIDJnOxdAAAAAIB7V0JCxB3HbdiwQRERmbfDva9SpUpq1KiRvcswFWEbAAAAgOnOpSbLQVJExFN3bPuf//wn/wtCoeYg6Ycff7ynAjdhGwAAAIDprhg3lCrpFdcKquxcIsM212+cV2Lin3J1KS8nR/eCLRCFRlTKZU1LPqk//viDsA0AAAAA2VHRoYgCnbwyHJecmqAESe5OxeXi4lOwhaHwSJKkk/auwnRcIA0AAAAAAJMRtgEAAAAAMBlhGwAAAAAAkxG2AQAAAAAwGWEbAAAAAACTEbYBAAAAADAZYRsAAAAAAJMRtgEAAAAAMBlhGwAAAAAAkxG2AQAAAAAwGWEbAAAAAACTEbYBAAAAADAZYRsAAAAAAJMRtgEAAAAAMBlhGwAAAAAAkxG2AQAAAAAwGWEbAAAAAACTEbYBAAAAADCZXcP2d999p65du6pMmTKyWCxav369zXjDMDRlyhT5+/urSJEiatu2rY4dO2afYgEAAAAAyCa7hu2rV6+qdu3aevvttzMcP2fOHL355ptasmSJdu/eLQ8PD3Xo0EGJiYkFXCkAAAAAANnnZM+Zd+rUSZ06dcpwnGEYWrhwoV5++WV169ZNkvThhx+qdOnSWr9+vfr27VuQpQIAAAAAkG2F9pzt6OhonTp1Sm3btrUO8/HxUYMGDbRr165MX5eUlKS4uDibBwAAAAAABanQhu1Tp05JkkqXLm0zvHTp0tZxGZk5c6Z8fHysj4CAgHytEwAAAACA2xXasJ1bkyZN0uXLl62PEydO2LskAAAAAMB9ptCGbT8/P0nS6dOnbYafPn3aOi4jrq6u8vb2tnkAAAAAAFCQCm3Yrlixovz8/LRt2zbrsLi4OO3evVuNGjWyY2UAAAAAAGTNrlcjj4+P1/Hjx63Po6OjdeDAARUvXlzlypXT2LFj9eqrr6pq1aqqWLGi/vOf/6hMmTLq3r27/YoGAAAAAOAO7Bq2f/nlF7Vq1cr6fNy4cZKkQYMGafny5XrppZd09epVDR06VJcuXVLTpk21adMmubm52atkAAAAAADuyK5hu2XLljIMI9PxFotFr7zyil555ZUCrAoAAAAAgLwptOdsAwAAAABwtyJsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACaz69XIAQD5KyYmRufOnbN3GTYiIiIkSQkJEXauBGl4LwAAMB9hGwDuUTExMQqsXl0JiYn2LiVDERFP2bsE3MYwku1dAgAA9wzCNgDco86dO6eExER9FBioQHd3e5djdTUhQUcjIuRRJFAOjoWnrvvZzhvn9U7inzJSb9i7FAAA7hmEbQC4xwW6u6uul5e9y7C6IumGJC9Hdzk6FZ667mfRKQn2LgEAgHsOF0gDAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhG0AAAAAAExG2AYAAAAAwGSEbQAAAAAATEbYBgAAAADAZIRtAAAAAABMRtgGAAAAAMBkhTpsT5s2TRaLxeZRo0YNe5cFAAAAAECWnOxdwJ3UrFlTW7dutT53cir0JQMAAAAA7nOFPrk6OTnJz8/P3mUAAAAAAJBthbobuSQdO3ZMZcqUUaVKldS/f3/FxMTYuyQAAAAAALJUqI9sN2jQQMuXL1f16tUVGxur6dOnq1mzZjpy5Ii8vLwyfE1SUpKSkpKsz+Pi4gqq3DyLiYnRuXPn7F0G7gK+vr4qV66cvcsAAAAAkIlCHbY7depk/X+tWrXUoEEDlS9fXp9++qmGDBmS4Wtmzpyp6dOnF1SJpomJiVFg9epKSEy0dym4C7i7uSkiMpLADQAAABRShTps365o0aKqVq2ajh8/nmmbSZMmady4cdbncXFxCggIKIjy8uTcuXNKSEzUR4GBCnR3t3c5KMQiEhL0VESEzp07R9gGAAAACqm7KmzHx8crKipKAwYMyLSNq6urXF1dC7AqcwW6u6tuJl3kAQAAAAB3h0J9gbQJEyZox44d+vPPP/Xjjz+qR48ecnR0VL9+/exdGgAAAAAAmSrUR7b//vtv9evXT+fPn1fJkiXVtGlT/fTTTypZsqS9SwMAAAAAIFOFOmyvXr3a3iUAAAAAAJBjhbobOQAAAAAAdyPCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmI2wDAAAAAGAywjYAAAAAACYjbAMAAAAAYDLCNgAAAAAAJiNsAwAAAABgMsI2AAAAAAAmuyvC9ttvv60KFSrIzc1NDRo00M8//2zvkgAAAAAAyFShD9uffPKJxo0bp6lTp2rfvn2qXbu2OnTooDNnzti7NAAAAAAAMlTow/b8+fP17LPPavDgwXrwwQe1ZMkSubu764MPPrB3aQAAAAAAZKhQh+3k5GTt3btXbdu2tQ5zcHBQ27ZttWvXLjtWBgAAAABA5pzsXUBWzp07p5SUFJUuXdpmeOnSpfXbb79l+JqkpCQlJSVZn1++fFmSFBcXl3+FmiA+Pl6StPfKFcWnpNi5GhRmkQkJkqS9e/datxsgI5GRkZIK334lISFBv0lyu35Wjjeu2LscSPrjxs2/lb/euKhrRsFsKzdS4pQkyfX6OTmlXi2QeSLvzN5W2A7ubdnZXtgGIEl/pNzMagkJCYU6t6XVZhhGttpbjOy2tIOTJ0/qgQce0I8//qhGjRpZh7/00kvasWOHdu/ene4106ZN0/Tp0wuyTAAAAADAfeLEiRMqW7bsHdsV6iPbvr6+cnR01OnTp22Gnz59Wn5+fhm+ZtKkSRo3bpz1eWpqqi5cuKASJUrIYrHka715ERcXp4CAAJ04cULe3t72Lgd3GbYf5AXbD/KC7Qd5wfaDvGD7QV7kZvsxDENXrlxRmTJlstW+UIdtFxcXBQcHa9u2berevbukm+F527ZtGjlyZIavcXV1laurq82wokWL5nOl5vH29mZngVxj+0FesP0gL9h+kBdsP8gLth/kRU63Hx8fn2y3LdRhW5LGjRunQYMGqV69enrkkUe0cOFCXb16VYMHD7Z3aQAAAAAAZKjQh+0+ffro7NmzmjJlik6dOqU6depo06ZN6S6aBgAAAABAYVHow7YkjRw5MtNu4/cKV1dXTZ06NV0XeCA72H6QF2w/yAu2H+QF2w/ygu0HeVEQ20+hvho5AAAAAAB3Iwd7FwAAAAAAwL2GsA0AAAAAgMkI2wAAAAAAmIywnU/efvttVahQQW5ubmrQoIF+/vnnLNuvWbNGNWrUkJubm4KCgrRhwwab8YZhaMqUKfL391eRIkXUtm1bHTt2LD8XAXZk9vYTEhIii8Vi8+jYsWN+LgLsKCfbz6+//qpevXqpQoUKslgsWrhwYZ6nibub2dvPtGnT0u1/atSokY9LAHvKyfazdOlSNWvWTMWKFVOxYsXUtm3bdO35/nN/MXv74fvP/SUn28/atWtVr149FS1aVB4eHqpTp45Wrlxp08aM/Q9hOx988sknGjdunKZOnap9+/apdu3a6tChg86cOZNh+x9//FH9+vXTkCFDtH//fnXv3l3du3fXkSNHrG3mzJmjN998U0uWLNHu3bvl4eGhDh06KDExsaAWCwUkP7YfSerYsaNiY2Otj//9738FsTgoYDndfhISElSpUiXNmjVLfn5+pkwTd6/82H4kqWbNmjb7nx9++CG/FgF2lNPtJzw8XP369VNYWJh27dqlgIAAtW/fXv/884+1Dd9/7h/5sf1IfP+5X+R0+ylevLgmT56sXbt26dChQxo8eLAGDx6szZs3W9uYsv8xYLpHHnnEGDFihPV5SkqKUaZMGWPmzJkZtu/du7fRpUsXm2ENGjQwhg0bZhiGYaSmphp+fn7G3LlzreMvXbpkuLq6Gv/73//yYQlgT2ZvP4ZhGIMGDTK6deuWL/WicMnp9nOr8uXLGwsWLDB1mri75Mf2M3XqVKN27domVonCKq/7ihs3bhheXl7GihUrDMPg+8/9xuztxzD4/nM/MeO7ysMPP2y8/PLLhmGYt//hyLbJkpOTtXfvXrVt29Y6zMHBQW3bttWuXbsyfM2uXbts2ktShw4drO2jo6N16tQpmzY+Pj5q0KBBptPE3Sk/tp804eHhKlWqlKpXr67hw4fr/Pnz5i8A7Co32489ponCKT/f62PHjqlMmTKqVKmS+vfvr5iYmLyWi0LGjO0nISFB169fV/HixSXx/ed+kh/bTxq+/9z78rr9GIahbdu2KTIyUs2bN5dk3v6HsG2yc+fOKSUlRaVLl7YZXrp0aZ06dSrD15w6dSrL9mn/5mSauDvlx/Yj3exC9eGHH2rbtm2aPXu2duzYoU6dOiklJcX8hYDd5Gb7scc0UTjl13vdoEEDLV++XJs2bVJoaKiio6PVrFkzXblyJa8loxAxY/uZOHGiypQpY/1yy/ef+0d+bD8S33/uF7ndfi5fvixPT0+5uLioS5cuWrx4sdq1ayfJvP2PU7ZbArhr9e3b1/r/oKAg1apVS5UrV1Z4eLjatGljx8oA3Os6depk/X+tWrXUoEEDlS9fXp9++qmGDBlix8pQmMyaNUurV69WeHi43Nzc7F0O7jKZbT98/0FWvLy8dODAAcXHx2vbtm0aN26cKlWqpJYtW5o2D45sm8zX11eOjo46ffq0zfDTp09nevEYPz+/LNun/ZuTaeLulB/bT0YqVaokX19fHT9+PO9Fo9DIzfZjj2micCqo97po0aKqVq0a+597TF62n3nz5mnWrFn69ttvVatWLetwvv/cP/Jj+8kI33/uTbndfhwcHFSlShXVqVNH48eP1+OPP66ZM2dKMm//Q9g2mYuLi4KDg7Vt2zbrsNTUVG3btk2NGjXK8DWNGjWyaS9JW7ZssbavWLGi/Pz8bNrExcVp9+7dmU4Td6f82H4y8vfff+v8+fPy9/c3p3AUCrnZfuwxTRROBfVex8fHKyoqiv3PPSa328+cOXM0Y8YMbdq0SfXq1bMZx/ef+0d+bD8Z4fvPvcmsv1+pqalKSkqSZOL+J9uXUkO2rV692nB1dTWWL19uHD161Bg6dKhRtGhR49SpU4ZhGMaAAQOMf/3rX9b2O3fuNJycnIx58+YZERERxtSpUw1nZ2fj8OHD1jazZs0yihYtanzxxRfGoUOHjG7duhkVK1Y0rl27VuDLh/xl9vZz5coVY8KECcauXbuM6OhoY+vWrUbdunWNqlWrGomJiXZZRuSfnG4/SUlJxv79+439+/cb/v7+xoQJE4z9+/cbx44dy/Y0ce/Ij+1n/PjxRnh4uBEdHW3s3LnTaNu2reHr62ucOXOmwJcP+Sun28+sWbMMFxcX47PPPjNiY2OtjytXrti04fvP/cHs7YfvP/eXnG4/r7/+uvHtt98aUVFRxtGjR4158+YZTk5OxtKlS61tzNj/ELbzyeLFi41y5coZLi4uxiOPPGL89NNP1nEtWrQwBg0aZNP+008/NapVq2a4uLgYNWvWNL755hub8ampqcZ//vMfo3Tp0oarq6vRpk0bIzIysiAWBXZg5vaTkJBgtG/f3ihZsqTh7OxslC9f3nj22WcJSvewnGw/0dHRhqR0jxYtWmR7mri3mL399OnTx/D39zdcXFyMBx54wOjTp49x/PjxAlwiFKScbD/ly5fPcPuZOnWqtQ3ff+4vZm4/fP+5/+Rk+5k8ebJRpUoVw83NzShWrJjRqFEjY/Xq1TbTM2P/YzEMw8j+cXAAAAAAAHAnnLMNAAAAAIDJCNsAAAAAAJiMsA0AAAAAgMkI2wAAAAAAmIywDQAAAACAyQjbAAAAAACYjLANAAAAAIDJCNsAAAAAAJiMsA0AwD3EYrFo/fr19i5Dp06dUrt27eTh4aGiRYvauxwAAAocYRsAcN8LCQmRxWKRxWKRi4uLqlSpoldeeUU3btywd2mZmjZtmurUqZNueGxsrDp16lTwBd1mwYIFio2N1YEDB/T777/buxwAAAqck70LAACgMOjYsaOWLVumpKQkbdiwQSNGjJCzs7MmTZqUrm1ycrJcXFzsUKVkGIZSUlIyHe/n51eA1WQuKipKwcHBqlq1qr1LAQDALjiyDQCAJFdXV/n5+al8+fIaPny42rZtqy+//FLSzSPf3bt312uvvaYyZcqoevXqkqTDhw+rdevWKlKkiEqUKKGhQ4cqPj7eOs20102fPl0lS5aUt7e3nnvuOSUnJ1vbJCUlafTo0SpVqpTc3NzUtGlT7dmzxzo+PDxcFotFGzduVHBwsFxdXfXRRx9p+vTpOnjwoPWI/PLlyyWl70ae3RrnzZsnf39/lShRQiNGjND169ezXF+hoaGqXLmyXFxcVL16da1cudI6rkKFCvr888/14YcfymKxKCQkJNPpfPDBB6pZs6ZcXV3l7++vkSNHWsfNnz9fQUFB8vDwUEBAgJ5//nmb2v/66y917dpVxYoVk4eHh2rWrKkNGzZYxx85ckSdOnWSp6enSpcurQEDBujcuXPW8Z999pmCgoKs66Zt27a6evVqlssNAEB2EbYBAMhAkSJFbELxtm3bFBkZqS1btujrr7/W1atX1aFDBxUrVkx79uzRmjVrtHXrVpuwmPa6iIgIhYeH63//+5/Wrl2r6dOnW8e/9NJL+vzzz7VixQrt27dPVapUUYcOHXThwgWb6fzrX//SrFmzFBERoXbt2mn8+PGqWbOmYmNjFRsbqz59+qRbhuzWGBYWpqioKIWFhWnFihVavny5NbxnZN26dRozZozGjx+vI0eOaNiwYRo8eLDCwsIkSXv27FHHjh3Vu3dvxcbGatGiRRlOJzQ0VCNGjNDQoUN1+PBhffnll6pSpYp1vIODg9588039+uuvWrFihbZv366XXnrJOn7EiBFKSkrSd999p8OHD2v27Nny9PSUJF26dEmtW7fWww8/rF9++UWbNm3S6dOn1bt3b0k3u9v369dPTz/9tPX96dmzpwzDyHS5AQDIEQMAgPvcoEGDjG7duhmGYRipqanGli1bDFdXV2PChAnW8aVLlzaSkpKsr3nvvfeMYsWKGfHx8dZh33zzjeHg4GCcOnXK+rrixYsbV69etbYJDQ01PD09jZSUFCM+Pt5wdnY2Pv74Y+v45ORko0yZMsacOXMMwzCMsLAwQ5Kxfv16m5qnTp1q1K5dO92ySDLWrVuXoxrLly9v3Lhxw9rmiSeeMPr06ZPp+mrcuLHx7LPP2gx74oknjM6dO1ufd+vWzRg0aFCm0zAMwyhTpowxefLkLNvcas2aNUaJEiWsz4OCgoxp06Zl2HbGjBlG+/btbYadOHHCkGRERkYae/fuNSQZf/75Z7bnDwBATnBkGwAASV9//bU8PT3l5uamTp06qU+fPpo2bZp1fFBQkM152hEREapdu7Y8PDysw5o0aaLU1FRFRkZah9WuXVvu7u7W540aNVJ8fLxOnDihqKgoXb9+XU2aNLGOd3Z21iOPPKKIiAib+urVq5fjZcpujTVr1pSjo6P1ub+/v86cOZPldG+tOW26t9eclTNnzujkyZNq06ZNpm22bt2qNm3a6IEHHpCXl5cGDBig8+fPKyEhQZI0evRovfrqq2rSpImmTp2qQ4cOWV978OBBhYWFydPT0/qoUaOGpJvnk9euXVtt2rRRUFCQnnjiCS1dulQXL17Mdv0AANwJYRsAAEmtWrXSgQMHdOzYMV27dk0rVqywCam3/t8e8nP+zs7ONs8tFotSU1PzbX7SzW76Wfnzzz/16KOPqlatWvr888+1d+9evf3225Jk7d7/zDPP6I8//tCAAQN0+PBh1atXT4sXL5YkxcfHq2vXrjpw4IDN49ixY2revLkcHR21ZcsWbdy4UQ8++KAWL16s6tWrKzo6Ol+XGwBw/yBsAwCgm2G2SpUqKleunJyc7nyzjsDAQB08eNDmglo7d+6Ug4OD9QJq0s0jrNeuXbM+/+mnn+Tp6amAgADrBcZ27txpHX/9+nXt2bNHDz74YJbzd3FxyfKq5DmpMacCAwNtak6b7p1qvpWXl5cqVKigbdu2ZTh+7969Sk1N1RtvvKGGDRuqWrVqOnnyZLp2AQEBeu6557R27VqNHz9eS5culSTVrVtXv/76qypUqKAqVarYPNJ+uLBYLGrSpImmT5+u/fv3y8XFRevWrcv2MgAAkBXCNgAAudC/f3+5ublp0KBBOnLkiMLCwjRq1CgNGDBApUuXtrZLTk7WkCFDdPToUW3YsEFTp07VyJEj5eDgIA8PDw0fPlwvvviiNm3apKNHj+rZZ59VQkKChgwZkuX8K1SooOjoaB04cEDnzp1TUlJSrmvMqRdffFHLly9XaGiojh07pvnz52vt2rWaMGFCjqYzbdo0vfHGG3rzzTd17Ngx7du3z3pkukqVKrp+/boWL16sP/74QytXrtSSJUtsXj927Fht3rxZ0dHR2rdvn8LCwhQYGCjp5sXTLly4oH79+mnPnj2KiorS5s2bNXjwYKWkpGj37t16/fXX9csvvygmJkZr167V2bNnra8HACCvCNsAAOSCu7u7Nm/erAsXLqh+/fp6/PHH1aZNG7311ls27dq0aaOqVauqefPm6tOnjx577DGbc8FnzZqlXr16acCAAapbt66OHz+uzZs3q1ixYlnOv1evXurYsaNatWqlkiVL6n//+1+ua8yp7t27a9GiRZo3b55q1qypd999V8uWLVPLli1zNJ1BgwZp4cKFeuedd1SzZk09+uijOnbsmKSb57rPnz9fs2fP1kMPPaSPP/5YM2fOtHl9SkqKRowYocDAQHXs2FHVqlXTO++8I0kqU6aMdu7cqZSUFLVv315BQUEaO3asihYtKgcHB3l7e+u7775T586dVa1aNb388st644031KlTpzytGwAA0lgMg3tcAACQH0JCQnTp0iWb+14DAID7A0e2AQAAAAAwGWEbAAAAAACT0Y0cAAAAAACTcWQbAAAAAACTEbYBAAAAADAZYRsAAAAAAJMRtgEAAAAAMBlhGwAAAAAAkxG2AQAAAAAwGWEbAAAAAACTEbYBAAAAADAZYRsAAAAAAJP9P5NP463NSWAvAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Repetitions: I ran the simulation with REPS = 10 and REPS = 100 using a fresh RNG each time.\n",
+        "Observation: Each time I re-ran the notebook, the histograms changed because the random infection assignment and primary tracing step produce different samples on each run. With REPS = 10, the results varied widely. With REPS = 100, the distributions looked more stable, but still not identical run-to-run due to randomness.\n",
+        "Conclusion: Without controlling the random number generator, the results are not reproducible."
+      ],
+      "metadata": {
+        "id": "QrvhhG7ZsOar"
+      },
+      "id": "QrvhhG7ZsOar"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Change for reproducibility: I introduced a fixed random seed and used the modern numpy.random.Generator API throughout. This ensures the infection sampling and tracing decisions are generated from the same seeded sequence each time, producing identical results when the notebook/script is re-run from top to bottom.\n",
+        "Concretely:\n",
+        "\n",
+        "Replaced np.random.choice / np.random.rand with rng.choice / rng.random.\n",
+        "Created rng = np.random.default_rng(42) before running simulations.\n",
+        "Ensured we do not re-seed within the loop.\n",
+        "Effect: Re-running the notebook yields the exact same figures and numbers.\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "qv6bDlX2sYIA"
+      },
+      "id": "qv6bDlX2sYIA"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Reproducible runs (fixed seed)\n",
+        "RNG_SEED = 42\n",
+        "rng_fixed_10  = np.random.default_rng(RNG_SEED)\n",
+        "props_df_10_r = run_simulation(10, rng_fixed_10)\n",
+        "\n",
+        "rng_fixed_100 = np.random.default_rng(RNG_SEED)\n",
+        "props_df_100_r = run_simulation(100, rng_fixed_100)"
+      ],
+      "metadata": {
+        "id": "sF0ABvSIse_r",
+        "outputId": "b027f4dd-e760-418f-a567-1fd8a8ccac5d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        }
+      },
+      "id": "sF0ABvSIse_r",
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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OP3jwYKbumRnzDAkJMVJTU63DM7qPffPNN9ZhGV29rjVgwADD3d3dSE5ONgzDMK5cuWKUKlXKKFGihHHmzBmbadPT063/z0338hMnThguLi5Gq1atbLoxv/fee4Yk4+OPP7YOGzt2bI66l+/Zs8eQZAwbNixHNaxatcqQZLz11ls2wx9//HHDYrEYhw8ftg6TZLi4uNgMy1jenDlzrMOy616e1bZu3bq1Ubp0aevzs2fPGl5eXkbt2rUzdee7dlvnZ/fyjO6HpUuXzlRjcnJypm7msbGxhqurq/Hmm29ah3388ceGJGP69OmZlnlt3dcvO6Ntn376aZvXdO7c2ShcuLD1+Y4dOwxJxvDhw22mCw0NzVH35JkzZxqSjE8//dQ6LDU11ahbt67h6elpPRUhoxtz4cKFbbowfvPNN4Yk47vvvst2ORnb8uOPPzZOnjxpHD161Pjhhx+MkiVLGhaLxdi+fbtx4cIFw9fX13j22WdtXnvs2DHDx8fHZnjGZ8Grr75qM+2mTZsMScbQoUMz1ZCxvf/++2/D0dHRePvtt23G79u3z3BycrIZfn237HXr1mW5vu3atbN5vy5ZssRwcHAwfv75Z5vpFixYYNNVdffu3YYkY9CgQTbTPfnkk7nqXp7VIzw8/IbrkdEe105z7fxudqpJVl1zc/qeNQzDaNy4cabu5T///LMhyfjss89shq9du9Zm+K1+FmS1jtWqVTOKFi1qnDp1yjpsz549hoODg9GnT588rWNW3njjDUOS4eHhYbRt29Z4++23jR07dmSaLqf7ZW7a8XbsM7t27crzaQcNGjQwQkJCMg3P6rNj7dq1RtmyZQ2LxWLTffra9czq0bp162zne/1nkmFc/fwpUqSIIcmoWLGi8fzzzxuff/65cfbs2Uy1Zrw/bvbI7jsqu78XBg8ebDg6OmY5rkiRIkaPHj0Mw/i/77Nrv4syzJ0715BkHDx40Gb4559/bkgytm3bdsPaAHuiezlgB88884z1/76+vqpQoYI8PDzUrVs36/AKFSrI19c3yysjP/fcczZHgAYOHCgnJyetXr3aOuzarl4XLlxQQkKCGjZsqKSkJB08eFDS1V+dY2NjNXz48ExHxnLShTwrGzZsUGpqqoYPHy4Hh//7iHn22Wfl7e1t030sp86fPy9JWXYrz8rq1avl6OiooUOH2gwfOXKkDMPQmjVrbIa3aNHC5ohtlSpV5O3tneOrUl+7rc+dO6eEhAQ1btxYf/31l7VL4vr163XhwgW9+uqrmbrz5XVb51Tfvn0zdf1zdXW1tk9aWppOnTolT09PVahQQTt37rRO9/XXX8vPz08vvPBCpvnmpO7nn3/e5nnDhg116tQpa5tmdOO/vrthVsvLyurVqxUQEKCePXtahzk7O2vo0KFKTEzU5s2bbabv3r27zRGajC6eOW3rp59+WkWKFFGxYsXUvn17Xbx4UYsXL1bNmjW1fv16nT17Vj179lRCQoL14ejoqNq1ays8PDzT/AYOHGjz/Ouvv5bFYrGeenGtjO29YsUKpaenq1u3bjbLCQgIULly5bJcToZmzZrJz89PX375pXXYmTNntH79enXv3t06bPny5QoODlbFihVtlpHRdTVjGRmfOdfvaze7EOD1nnvuOa1fv97mUbVq1VzNI7/c7D17I8uXL5ePj49atmxps81CQkLk6elp3Wb5/VkQHx+v3bt3KzQ01Oboc5UqVdSyZUub74VbXcfx48fr888/V/Xq1bVu3TqNGTNGISEhqlGjhk1X9tzul7lh5j6TcSR73bp1OTrie61Tp05le/T32s+ONm3a6Ny5c1qyZIlN9+kMbm5umfaH9evXa9KkSdnO9/rPJOlq1+89e/bo+eef15kzZ7RgwQI9+eSTKlq0qCZMmGBzylWfPn2yXO71j88++yxX2ybDpUuXbHrYXL/Oly5dsk4nXf2eymq6a6fJkLHts+ohANwJ6F4O3GYZ55tdy8fHRw8++GCmP7h8fHyyPBewXLlyNs89PT0VGBho7a4mSX/++adee+01bdq0KdMfUhlBMKMb2sMPP5zn9bneP//8I+nqjwbXcnFxUenSpa3jcyOjq9iFCxdyXEOxYsUyhfSMLqDX15BVl/WCBQtmue2zsmXLFo0dO1Zbt27N9IfauXPn5OPjY8q2zqnrT0+Q/u8cyHnz5ik2NtbmXP9rrwobExOjChUq5PnK5Ndv24w/jM6cOSNvb2/9888/cnBwyFRjTq5mL11ty3Llytn8wCPlvK2vrScn3njjDTVs2FCOjo7y8/NTcHCwddtER0dL+r9zKq93fZdHJycnPfjggzbDYmJiVKxYsWy77kZHR8swjEyfAxmy65Lt5OSkrl276vPPP1dKSopcXV21YsUKXb582SZ0R0dHKzIy8oZXED9x4oQkWdvv+tMMrt//b6ZcuXI5PhfWbDd7z95IdHS0zp07l+nc5gwZ2yy/Pwtu9JkrXd0P1q1bp4sXL8rDw8M6PK/rKF290nzPnj11/vx5bdu2TWFhYfr888/VoUMH7d+/X25ubrneL3PK7H2mVKlSGjFihKZPn67PPvtMDRs21GOPPaannnoq267lGYzrrhlyrYzPjsTERK1cuVJLly7NtH0yODo65nh/yO4zKUNgYKDmz5+vefPmKTo6WuvWrdPkyZP1xhtvKDAw0HogoHTp0ipdunSOlpsXBQoUUGpqapbjkpOTrT8OZ/x7/fUjMqa7dpoMGdve7B+xgbwidAO3WVa3/MhueHZf4jdy9uxZNW7cWN7e3nrzzTdVpkwZubm5aefOnXrllVfuilsIXats2bJycnLSvn37TJn/rWz7mJgYNW/eXBUrVtT06dMVFBQkFxcXrV69WjNmzLgjtnVWF7h555139Prrr+vpp5/WhAkTVKhQITk4OGj48OH5WnN+vq/zw63WU7ly5Rv+MZyx3ZYsWZLpFkCSMv0hfG1vg9xIT0+XxWLRmjVrslyfm10HoEePHnr//fe1Zs0aderUScuWLVPFihVtjiynp6ercuXKmj59epbzCAoKynXd+elGf1hndaHI3MrreyQ9PV1Fixa94VHAO+kWaPmxX3p7e6tly5Zq2bKlnJ2dtXjxYm3btk2NGzfO8Txy2463Y5959913FRoaqm+++UY//vijhg4dqokTJ+q3337LFPivVbhw4Wx/vLv2s6NTp05KSkrSs88+qwYNGtzS/pTdZ9L1LBaLypcvr/Lly6t9+/YqV66cPvvsM2voTkxMzNF9ux0dHfP0fg4MDFRaWppOnDhh8+NUamqqTp06Zb2WSKFCheTq6qr4+PhM88gYdv0tGjO2fcY1V4A7DaEbuAtFR0eradOm1ueJiYmKj4+33uc2IiJCp06d0ooVK9SoUSPrdNdeYVmS9ejU/v37s/3Szs0vxxkXkYmKirL5xTw1NVWxsbF5Oprl7u6uZs2aadOmTTpy5MhN/0ApUaKENmzYkOlCKxnd6nN6oZtr3WgbfPfdd0pJSdG3335rc/To+i6+127r7I7i3q5f6b/66is1bdpUH330kc3ws2fP2vzRUqZMGW3btk2XL1825aJWJUqUUHp6umJjY22OQh0+fDjHr9+7d6/S09Nt/hi/lbbOq4w2Llq0aJ6P2pYpU0br1q3T6dOnb3jkrkyZMjIMQ6VKlVL58uVzvYxGjRopMDBQX375pRo0aKBNmzZpzJgxmZaxZ88eNW/ePNv3ZEb7ZfSIyBAVFZXrunIj48jstXdEkPJ+BDU/lClTRhs2bFD9+vWzvZJzfn8WXPuZe72DBw/Kz8/P5ii3GWrWrKnFixdbA1FO98v8aEcz9pnKlSurcuXKeu211/Trr7+qfv36WrBggd56660bvqZixYr6+uuvc1z3pEmTtHLlSr399ttasGBBjl+XX0qXLq2CBQvaBNtp06Zp/PjxN31tiRIlbHrW5VS1atUkSX/88Yf175WM5+np6dbxDg4Oqly5cqarz0tX77ZSunTpTD3ZYmNj5eDgkKfPROB24Jxu4C70wQcf6PLly9bn8+fP15UrV9S2bVtJ/3cU49qjFqmpqZo3b57NfGrUqKFSpUpp5syZmf7oufa1GX+wXT9NVlq0aCEXFxfNnj3bZh4fffSRzp07l+urGmcYO3asDMNQ7969s/wlfseOHdbb0bRr105paWl67733bKaZMWOGLBaLdTvlxo22QVbb+ty5c1q0aJHNdK1atZKXl5cmTpxo7R6X4fptfbNb0+QHR0fHTEe1li9frv/++89mWNeuXZWQkJBpW0r5c7S6devWkpTpvTlnzpwcvb5du3Y6duyYzTnKV65c0Zw5c+Tp6Zmro263qnXr1vL29tY777xjs39mOHny5E3n0bVrVxmGkeUfvhnbu0uXLnJ0dNT48eMztYFhGDp16lS2y3BwcNDjjz+u7777TkuWLNGVK1dsupZLV69u/d9//2nhwoWZXn/p0iVdvHhRkqz70uzZs22mmTlzZvYreotKlCghR0dH/fTTTzbDr38f3U7dunVTWlqaJkyYkGnclStXrJ8d+f1ZEBgYqGrVqmnx4sU2n0/79+/Xjz/+aBNubkVSUpK2bt2a5biM62Rk/PCS0/0yP9oxP/eZ8+fP68qVKzbjK1euLAcHhyy7Ol+rbt26OnPmTI6vD1GmTBl17dpVYWFhOnbsWI5ekxfbtm2z7q/X+v3333Xq1CmbH8vMPqe7WbNmKlSoUKZbas6fP1/u7u42fx88/vjj2r59u03wjoqK0qZNm/TEE09kmveOHTtUqVKlHJ0GANgDR7qBu1BqaqqaN2+ubt26KSoqSvPmzVODBg302GOPSZLq1aunggULqm/fvho6dKgsFouWLFmS6Y8NBwcHzZ8/Xx06dFC1atXUr18/BQYG6uDBg/rzzz+1bt06SVdvxSFdvVhS69at5ejoaL210PWKFCmi0aNHa/z48WrTpo0ee+wxa421atXSU089lad1rlevnubOnatBgwapYsWK6t27t8qVK6cLFy4oIiJC3377rfUoRIcOHdS0aVONGTNGf//9t6pWraoff/xR33zzjYYPH37T21xlJWMbjBkzRj169JCzs7M6dOigVq1aycXFRR06dNCAAQOUmJiohQsXqmjRojZHELy9vTVjxgw988wzqlWrlp588kkVLFhQe/bsUVJSkvUHg5CQEH355ZcaMWKEatWqJU9PT3Xo0CFP2yw7jz76qN58803169dP9erV0759+/TZZ59lOp+vT58++uSTTzRixAj9/vvvatiwoS5evKgNGzZo0KBB6tix4y3VERISoq5du2rmzJk6deqU9ZZhhw4dknTzo33PPfec3n//fYWGhmrHjh0qWbKkvvrqK23ZskUzZ87M8cX38oO3t7fmz5+v3r17q0aNGurRo4eKFCmiuLg4/fDDD6pfv36WP15cq2nTpurdu7dmz56t6OhotWnTRunp6fr555/VtGlTDRkyRGXKlNFbb72l0aNH6++//1anTp3k5eWl2NhYrVy5Us8995zN/baz0r17d82ZM0djx45V5cqVrefaZujdu7eWLVum559/XuHh4apfv77S0tJ08OBBLVu2TOvWrVPNmjVVrVo19ezZU/PmzdO5c+dUr149bdy4Mcc9FfLKx8dHTzzxhObMmSOLxaIyZcro+++/t543bQ+NGzfWgAEDNHHiRO3evVutWrWSs7OzoqOjtXz5cs2aNUuPP/64KZ8FU6dOVdu2bVW3bl3179/fesswHx+fHN3rPieSkpJUr1491alTR23atFFQUJDOnj2rVatW6eeff1anTp1UvXp1STnfL/OjHfNzn9m0aZOGDBmiJ554QuXLl9eVK1e0ZMkSOTo6qmvXrtnW0b59ezk5OWnDhg167rnnclT7Sy+9pGXLlmnmzJk2F0m7cuWKPv300yxf07lz51z1XFiyZIk+++wzde7cWSEhIXJxcVFkZKQ+/vhjubm56X//+5912rye0/3PP/9YbymaEZIzvo9LlCih3r17S7p6qtOECRM0ePBgPfHEE2rdurV+/vlnffrpp3r77bdteioMGjRICxcuVPv27TVq1Cg5Oztr+vTp8vf318iRI22Wf/nyZW3evDnTBTmBO4r5F0gH7k83umWYh4dHpmmzuv2MYRhGiRIljPbt22ea5+bNm43nnnvOKFiwoOHp6Wn06tXL5lYxhmEYW7ZsMerUqWMUKFDAKFasmPHyyy9bbxd0/e1ZfvnlF6Nly5aGl5eX4eHhYVSpUsXmdllXrlwxXnjhBaNIkSKGxWLJ0e3D3nvvPaNixYqGs7Oz4e/vbwwcODDTbclyesuwa+3YscN48sknjWLFihnOzs5GwYIFjebNmxuLFy+2uQXWhQsXjBdffNE6Xbly5YypU6fa3JLHMK7e6mrw4MGZllOiRAmjb9++NsMmTJhgPPDAA4aDg4PN7cO+/fZbo0qVKoabm5tRsmRJY/LkydZbbV1/i7Fvv/3WqFevnlGgQAHD29vbeOSRR4wvvvjCOj4xMdF48sknDV9f35vemuVa2d0yLKvb3yQnJxsjR440AgMDjQIFChj169c3tm7dajRu3Nho3LixzbRJSUnGmDFjjFKlShnOzs5GQECA8fjjjxsxMTE22zGrW4Zd37YZ7+Frt8vFixeNwYMHG4UKFTI8PT2NTp06GVFRUYYkY9KkSTdd9+PHjxv9+vUz/Pz8DBcXF6Ny5cqZbhmVcQuiqVOnZnr99bVnJbttmdW0rVu3Nnx8fAw3NzejTJkyRmhoqPHHH39Yp7nRZ4FhXN3fpk6dalSsWNFwcXExihQpYrRt2zbTrZm+/vpro0GDBoaHh4fh4eFhVKxY0Rg8eLARFRVls5ys3kPp6elGUFCQoSxurZchNTXVmDx5slGpUiXD1dXVKFiwoBESEmKMHz/eOHfunHW6S5cuGUOHDjUKFy5seHh4GB06dDCOHDmSq1uGZdUu18pqPU6ePGl07drVcHd3NwoWLGgMGDDA2L9//y3fMiwn79kbfWYbhmF88MEHRkhIiFGgQAHDy8vLqFy5svHyyy8bR48etZkur58FN7ot2oYNG4z69etb59ehQwfjwIEDNtPkZh2vd/nyZWPhwoVGp06djBIlShiurq6Gu7u7Ub16dWPq1KlGSkqKzfQ52S8NI+fteDv2mb/++st4+umnjTJlyhhubm5GoUKFjKZNmxobNmy44Xa51mOPPWY0b97cZtjNPjuaNGlieHt7W2/hld0tw65to5x+Ju3du9d46aWXjBo1ahiFChUynJycjMDAQOOJJ54wdu7cmaP1upmMWrJ6XP99YhhX95EKFSoYLi4uRpkyZYwZM2Zk+m42DMM4cuSI8fjjjxve3t6Gp6en8eijjxrR0dGZpluzZo0hKctxwJ3CYhh2upoNgFwLCwtTv379tH37duvtQIB70e7du1W9enV9+umn6tWrl73LAYCb+vnnn9WkSRMdPHjwhldKR/7r1KmTLBaLVq5cae9SgBvinG4AgF1df79V6eo5wQ4ODjYXAgSAO1nDhg3VqlUrTZkyxd6l3DciIyP1/fffZ3ktBeBOwjndAAC7mjJlinbs2KGmTZvKyclJa9as0Zo1a/Tcc8/Z/dZUAJAbGReVw+0RHByc6eJ3wJ2I0A0AsKt69epp/fr1mjBhghITE1W8eHGNGzcu022sAAAA7kac0w0AAAAAgEk4pxsAAAAAAJMQugEAAAAAMMk9f053enq6jh49Ki8vL1ksFnuXAwAAAAC4BxiGoQsXLqhYsWJycLjx8ex7PnQfPXqUq98CAAAAAExx5MgRPfjggzccf8+Hbi8vL0lXN4S3t7edqwEAAAAA3AvOnz+voKAga+a8kXs+dGd0Kff29iZ0AwAAAADy1c1OY+ZCagAAAAAAmITQDQAAAACASQjdAAAAAACY5J4/pxsAAAC4m6Wlpeny5cv2LgO47zg7O8vR0fGW50PoBgAAAO5AhmHo2LFjOnv2rL1LAe5bvr6+CggIuOnF0rJD6AYAAADuQBmBu2jRonJ3d7+lP/oB5I5hGEpKStKJEyckSYGBgXmeF6EbAAAAuMOkpaVZA3fhwoXtXQ5wXypQoIAk6cSJEypatGieu5pzITUAAADgDpNxDre7u7udKwHubxn74K1cV4HQDQAAANyh6FIO2Fd+7IOEbgAAAAAATMI53QAAAMBdJC4uTgkJCbdteX5+fipevLipyzh27Jh69+6tX3/9Vc7OzqZdsT0sLEzDhw+36xXhDcPQgAED9NVXX+nMmTPatWuXqlWrZrd6bqecbP9x48Zp1apV2r17tyQpNDRUZ8+e1apVq25LjWYgdAMAAAB3ibi4OFWoEKzk5KTbtkw3N3dFRUXmOHjnJSTNmDFD8fHx2r17t3x8fPJYqa2SJUtq+PDhGj58uHVY9+7d1a5du3yZf16tXbtWYWFhioiIUOnSpeXn52fXel599VWtWrVKBw8etA47ePCggoOD1bdvX4WFhVmHh4WFacCAATp79qz1ImNmmzVrlgzDuC3LMguhGwAAALhLJCQkKDk5ScHBn8rdPdj05SUlRSoy8iklJCSYerQ7JiZGISEhKleunGnLkK5ejfp2hcUbiYmJUWBgoOrVq3fDaVJTU+Xi4nJb6mnatKkmT56sY8eOKSAgQJIUHh6uoKAgRURE2EwbHh6uOnXq3NZtmF8/wtgT53QDAAAAdxl392B5edUw/ZEfwb5JkyYaOnSoXn75ZRUqVEgBAQEaN26cdXzJkiX19ddf65NPPpHFYlFoaKgk6ezZs3rmmWdUpEgReXt7q1mzZtqzZ4/NvL/77jvVqlVLbm5u8vPzU+fOna3L/Oeff/Tiiy/KYrFYL4YVFhYmX19fm3nMnz9fZcqUkYuLiypUqKAlS5bYjLdYLPrwww/VuXNnubu7q1y5cvr222+t48+cOaNevXqpSJEiKlCggMqVK6dFixZluS1CQ0P1wgsvKC4uThaLRSVLlrTWO2TIEA0fPlx+fn5q3bq1JGnz5s165JFH5OrqqsDAQL366qu6cuWKzbZ94YUXNHz4cBUsWFD+/v5auHChLl68qH79+snLy0tly5bVmjVrbtg+DRo0kLOzs03AjoiI0ODBg3X69Gn9/fffNsObNm0qSUpJSdGoUaP0wAMPyMPDQ7Vr184U0sPCwlS8eHG5u7urc+fOOnXqVKblT5o0Sf7+/vLy8lL//v2VnJycaZt16tTJZp2zez9JV4/UN2jQQG5ubnrooYe0YcMGWSwWa++L1NRUDRkyRIGBgXJzc1OJEiU0ceLEG26jW0XoBgAAAGCqxYsXy8PDQ9u2bdOUKVP05ptvav369ZKk7du3q02bNurWrZvi4+M1a9YsSdITTzyhEydOaM2aNdqxY4dq1Kih5s2b6/Tp05KkH374QZ07d1a7du20a9cubdy4UY888ogkacWKFXrwwQf15ptvKj4+XvHx8VnWtXLlSg0bNkwjR47U/v37NWDAAPXr10/h4eE2040fP17dunXT3r171a5dO/Xq1ctax+uvv64DBw5ozZo1ioyM1Pz582/YZXzWrFl688039eCDDyo+Pl7bt2+32UYuLi7asmWLFixYoP/++0/t2rVTrVq1tGfPHs2fP18fffSR3nrrrUzb1s/PT7///rteeOEFDRw4UE888YTq1aunnTt3qlWrVurdu7eSkrI+JcHDw0O1atWyWeeIiAg1b95c9evXtw7/66+/FBcXZw3dQ4YM0datW7V06VLt3btXTzzxhNq0aaPo6GhJ0rZt29S/f38NGTJEu3fvVtOmTTPVvmzZMo0bN07vvPOO/vjjDwUGBmrevHlZ1nn9Ot/o/ZSWlqZOnTrJ3d1d27Zt0wcffKAxY8bYvH727Nn69ttvtWzZMkVFRemzzz6z/gBiBrqXAwAAADBVlSpVNHbsWElSuXLl9N5772njxo1q2bKlihQpIldXVxUoUMDavfmXX37R77//rhMnTsjV1VWSNG3aNK1atUpfffWVnnvuOb399tvq0aOHxo8fb11O1apVJUmFChWSo6OjvLy8rPPMyrRp0xQaGqpBgwZJkkaMGKHffvtN06ZNs4ZL6erR1p49e0qS3nnnHc2ePVu///672rRpo7i4OFWvXl01a9aUpGzDm4+Pj7y8vOTo6JiprnLlymnKlCnW52PGjFFQUJDee+89WSwWVaxYUUePHtUrr7yiN954Qw4ODtZ1fu211yRJo0eP1qRJk+Tn56dnn31WkvTGG29o/vz52rt3r+rUqZNlXU2bNtXy5cslSQcOHFBycrKqV6+uRo0aKSIiQv369VNERITc3NxUp04dxcXFadGiRYqLi1OxYsUkSaNGjdLatWu1aNEivfPOO5o1a5batGmjl19+WZJUvnx5/frrr1q7dq11uTNnzlT//v3Vv39/SdJbb72lDRs2ZDrafb3s3k/r169XTEyMIiIirNv47bffVsuWLa2vj4uLU7ly5dSgQQNZLBaVKFEi2+XdKo50AwAAADBVlSpVbJ4HBgbqxIkTN5x+z549SkxMVOHCheXp6Wl9xMbGKiYmRpK0e/duNW/e/JbqioyMVP369W2G1a9fX5GRkTes38PDQ97e3tb6Bw4cqKVLl6patWp6+eWX9euvv+aplpCQkEy11a1b1+Y+0fXr11diYqL+/fffLGtzdHRU4cKFVblyZeswf39/Scp2ezdp0kSHDh1SfHy8IiIi1KBBAzk6Oqpx48bWLuMRERGqV6+eXF1dtW/fPqWlpal8+fI27bN582Zr+0RGRqp27do2y6lbt26mdbzZNFnJ7v0UFRWloKAgmx81MnpAZAgNDdXu3btVoUIFDR06VD/++ONNl3krONINAAAAwFTOzs42zy0Wi9LT0284fWJiogIDAzOdIyzJek727byYV3b1t23bVv/8849Wr16t9evXq3nz5ho8eLCmTZuWq2V4eHjkW23XDssI7dlt7/r168vFxUXh4eEKDw9X48aNJUm1atVSQkKC/vrrL0VERGjAgAGSrraPo6OjduzYIUdHR5t5eXp65mk9ciO376fr1ahRQ7GxsVqzZo02bNigbt26qUWLFvrqq6/yu1RJHOkGAAAAcIepUaOGjh07JicnJ5UtW9bmkXG+dJUqVbRx48YbzsPFxUVpaWnZLic4OFhbtmyxGbZlyxY99NBDuaq3SJEi6tu3rz799FPNnDlTH3zwQa5ef6Patm7danO7rC1btsjLy0sPPvjgLc//WgUKFLBeCG3z5s1q0qSJpKvhtk6dOvroo4905MgRa5f76tWrKy0tTSdOnMjUPhlHmIODg7Vt2zab5fz222+Z1vFm0+RWhQoVdOTIER0/ftw67Npz5zN4e3ure/fuWrhwob788kt9/fXX1vP08xtHugEAAADcUVq0aKG6deuqU6dOmjJlisqXL6+jR49aL55Ws2ZNjR07Vs2bN1eZMmXUo0cPXblyRatXr9Yrr7wi6eq51T/99JN69OghV1fXLC9u9tJLL6lbt26qXr26WrRooe+++04rVqzQhg0bclzrG2+8oZCQEFWqVEkpKSn6/vvvFRx861d9HzRokGbOnKkXXnhBQ4YMUVRUlMaOHasRI0ZYz+fOT02bNtWMGTMkXf3RI0Pjxo01bdo06wXXpKvnZ/fq1Ut9+vTRu+++q+rVq+vkyZPauHGjqlSpovbt22vo0KGqX7++pk2bpo4dO2rdunU253NL0rBhwxQaGqqaNWuqfv36+uyzz/Tnn3+qdOnSeV6Pli1bqkyZMurbt6+mTJmiCxcuWM95zzjqP336dAUGBqp69epycHDQ8uXLFRAQkOnK9vmF0A0AAEwTFxenhIQEe5eBO4ifn5+p93u+XyQlRd58ortoOdezWCxavXq1xowZo379+unkyZMKCAhQo0aNrOcoN2nSRMuXL9eECRM0adIkeXt7q1GjRtZ5vPnmmxowYIDKlCmjlJQUmyPGGTp16qRZs2Zp2rRpGjZsmEqVKqVFixZZj/TmhIuLi0aPHq2///5bBQoUUMOGDbV06dJb3gYPPPCAVq9erZdeeklVq1ZVoUKF1L9/f2uAzG9NmzbVm2++qTZt2sjJ6f9iYuPGjTV27Fi1bt3aplv3okWL9NZbb2nkyJH677//5Ofnpzp16ujRRx+VJNWpU0cLFy7U2LFj9cYbb6hFixZ67bXXNGHCBOs8unfvrpiYGL388stKTk5W165dNXDgQK1bty7P6+Ho6KhVq1bpmWeeUa1atVS6dGlNnTpVHTp0kJubmyTJy8tLU6ZMUXR0tBwdHVWrVi2tXr3alB8zJMliZPXuu4ecP39ePj4+OnfunLy9ve1dDgAA9424uDhVqBCs5OSsb1OD+5Obm7uioiIJ3jeRnJys2NhYlSpVyhoUJPvsV7QZ7nZbtmxRgwYNdPjwYZUpUyZXr73RvijlPGtypBsAAJgiISFByclJCg7+VO7ut97VEne/pKRIRUY+pYSEBAJcHhUvXlxRUZG3tQcJvRNwt1m5cqU8PT1Vrlw5HT58WMOGDVP9+vVzHbjzC6EbAACYyt09WF5eNW4+IYAcKV68OCEYyMaFCxf0yiuvKC4uTn5+fmrRooXeffddu9VD6AYAAAAA3DP69OmjPn362LsMK24ZBgAAAACASewauseNGyeLxWLzqFixonV8cnKyBg8erMKFC8vT01Ndu3a1ud8aAAAAAAB3Mrsf6a5UqZLi4+Otj19++cU67sUXX9R3332n5cuXa/PmzTp69Ki6dOlix2oBAAAAAMg5u5/T7eTkpICAgEzDz507p48++kiff/65mjVrJunqveCCg4P122+/qU6dOre7VAAAAAAAcsXuR7qjo6NVrFgxlS5dWr169VJcXJwkaceOHbp8+bJatGhhnbZixYoqXry4tm7daq9yAQAAAADIMbse6a5du7bCwsJUoUIFxcfHa/z48WrYsKH279+vY8eOycXFRb6+vjav8ff317Fjx244z5SUFKWkpFifnz9/3qzyAQAAAADIll1Dd9u2ba3/r1KlimrXrq0SJUpo2bJlKlCgQJ7mOXHiRI0fPz6/SgQAAADuKHFxcUpISLhty/Pz87sn7wvepEkTVatWTTNnzrR3KfkmNDRUZ8+e1apVq244zfXrXbJkSQ0fPlzDhw+/LTXej+x+Tve1fH19Vb58eR0+fFgtW7ZUamqqzp49a3O0+/jx41meA55h9OjRGjFihPX5+fPnFRQUZGbZAAAAwG0RFxen4AoVlJScfNuW6e7mpsioqBwFb4vFku34sWPHaty4cflUmbnGjRunVatWaffu3bc0nzp16qhatWpasGCBddiCBQs0cOBALVq0SKGhodbhoaGhiomJ0c8//3xLy8yN7du3y8PD47Yt7350R4XuxMRExcTEqHfv3goJCZGzs7M2btyorl27SpKioqIUFxenunXr3nAerq6ucnV1vV0lAwAAALdNQkKCkpKT9WlwsILd3U1fXmRSkp6KjFRCQkKOQnd8fLz1/19++aXeeOMNRUVFWYd5enpa/28YhtLS0uTkdEdFknzXtGlTrVy50mZYeHi4goKCFBERYRO6IyIi1Ldv39taX5EiRW7r8u5Hdr2Q2qhRo7R582b9/fff+vXXX9W5c2c5OjqqZ8+e8vHxUf/+/TVixAiFh4drx44d6tevn+rWrcuVywEAAHBfC3Z3Vw0vL9MfuQ32AQEB1oePj48sFov1+cGDB+Xl5aU1a9YoJCRErq6u+uWXXxQTE6OOHTvK399fnp6eqlWrljZs2GAz35SUFL3yyisKCgqSq6urypYtq48++sg6fv/+/Wrbtq08PT3l7++v3r1723TBv3jxovr06SNPT08FBgbq3XffzXY9wsLCNH78eO3Zs0cWi0UWi0VhYWGSrvY26Nixozw9PeXt7a1u3brp+PHjN5xX06ZNFRUVZXNdqs2bN+vVV19VRESEdVhsbKz++ecfNW3aVJJ05MgRdevWTb6+vipUqJA6duyov//+2zp9WlqaRowYIV9fXxUuXFgvv/yyDMOwWXZO1rtkyZI2XewtFos+/PBDde7cWe7u7ipXrpy+/fZbm9d8++23KleunNzc3NS0aVMtXrxYFotFZ8+elST9888/6tChgwoWLCgPDw9VqlRJq1evzm6T39PsGrr//fdf9ezZUxUqVFC3bt1UuHBh/fbbb9ZfW2bMmKFHH31UXbt2VaNGjRQQEKAVK1bYs2QAAAAAt+DVV1/VpEmTFBkZqSpVqigxMVHt2rXTxo0btWvXLrVp00YdOnSw3tVIkvr06aMvvvhCs2fPVmRkpN5//33rUfOzZ8+qWbNmql69uv744w+tXbtWx48fV7du3ayvf+mll7R582Z98803+vHHHxUREaGdO3fesMbu3btr5MiRqlSpkuLj4xUfH6/u3bsrPT1dHTt21OnTp7V582atX79ef/31l7p3737DedWvX1/Ozs4KDw+XJB04cECXLl1S//79derUKcXGxkq6evTbzc1NdevW1eXLl9W6dWt5eXnp559/1pYtW+Tp6ak2bdooNTVVkvTuu+8qLCxMH3/8sX755RedPn060xH13K53hvHjx6tbt27au3ev2rVrp169eun06dOSrv448Pjjj6tTp07as2ePBgwYoDFjxti8fvDgwUpJSdFPP/2kffv2afLkyTa9HO43du3LsXTp0mzHu7m5ae7cuZo7d+5tqggAAACAmd588021bNnS+rxQoUKqWrWq9fmECRO0cuVKffvttxoyZIgOHTqkZcuWaf369dbbCZcuXdo6/Xvvvafq1avrnXfesQ77+OOPFRQUpEOHDqlYsWL66KOP9Omnn6p58+aSpMWLF+vBBx+8YY0FChSQp6ennJycbK4ntX79eu3bt0+xsbHW60Z98sknqlSpkrZv365atWplmpeHh4ceeeQRRUREqGfPnoqIiFCDBg3k6uqqevXqKSIiQqVKlVJERITq1q0rV1dXffrpp0pPT9eHH35oPU9+0aJF8vX1VUREhFq1aqWZM2dq9OjR6tKli6Sr54mvW7fOutzExMRcr3eG0NBQ9ezZU5L0zjvvaPbs2fr999/Vpk0bvf/++6pQoYKmTp0qSapQoYL279+vt99+2/r6uLg4de3aVZUrV87UXvcju9+nGwAAAMD9o2bNmjbPExMTNWrUKAUHB8vX11eenp6KjIy0HunevXu3HB0d1bhx4yznt2fPHoWHh8vT09P6qFixoiQpJiZGMTExSk1NVe3ata2vKVSokCpUqJDr2iMjIxUUFGRzoeaHHnpIvr6+ioyMvOHrmjRpYu1KHhERoSZNmkiSGjdubDM8o2v5nj17dPjwYXl5eVnXqVChQkpOTlZMTIzOnTun+Ph4m3VycnKy2ba3st5VqlSx/t/Dw0Pe3t46ceKEpKvX2br+x4VHHnnE5vnQoUP11ltvqX79+ho7dqz27t1702XeywjdAAAAAG6b66+UPWrUKK1cuVLvvPOOfv75Z+3evVuVK1e2dqO+2a2EExMT1aFDB+3evdvmER0drUaNGpm2HrnRtGlTHTp0SP/9958iIiKsPyBkhO6YmBgdOXJEzZo1k3R1nUJCQjKt06FDh/Tkk0+aXq+zs7PNc4vFovT09By//plnntFff/2l3r17a9++fapZs6bmzJmT32XeNQjdAAAAAOxmy5YtCg0NVefOnVW5cmUFBATYXDCscuXKSk9P1+bNm7N8fY0aNfTnn3+qZMmSKlu2rM3Dw8NDZcqUkbOzs7Zt22Z9zZkzZ3To0KFs63JxcVFaWprNsODgYB05ckRHjhyxDjtw4IDOnj2rhx566IbzqlevnlxcXDRv3jwlJycrJCREklSrVi2dPHlSH3/8sbUbesY6RUdHq2jRopnWycfHRz4+PgoMDLRZpytXrmjHjh3W53ld75upUKGC/vjjD5th27dvzzRdUFCQnn/+ea1YsUIjR47UwoULb2m5dzNCNwAAAAC7KVeunFasWKHdu3drz549evLJJ22OqpYsWVJ9+/bV008/rVWrVik2NlYRERFatmyZpKsX7Tp9+rR69uyp7du3KyYmRuvWrVO/fv2UlpYmT09P9e/fXy+99JI2bdqk/fv3KzQ0VA4O2UehkiVLKjY2Vrt371ZCQoJSUlLUokULVa5cWb169dLOnTv1+++/q0+fPmrcuHGmbvPXKlCggOrUqaM5c+aofv36cnR0lHQ12F87POMIc69eveTn56eOHTvq559/tq7z0KFD9e+//0qShg0bpkmTJmnVqlU6ePCgBg0aZL16uKQ8r/fNDBgwQAcPHtQrr7xiPd8+48ruGeefDx8+XOvWrVNsbKx27typ8PBwBQcH39Jy72b39k3xAAAAgHtQZFLSPbOc6dOn6+mnn1a9evXk5+enV155RefPn7eZZv78+frf//6nQYMG6dSpUypevLj+97//SZKKFSumLVu26JVXXlGrVq2UkpKiEiVKqE2bNtaAOXXqVGs3dC8vL40cOVLnzp3Ltq6uXbtqxYoVatq0qc6ePatFixYpNDRU33zzjV544QU1atRIDg4OatOmTY66Tjdt2lQ//fST9XzuDI0bN1Z4eLj1fG5Jcnd3108//aRXXnlFXbp00YULF/TAAw+oefPm8vb2liSNHDlS8fHx6tu3rxwcHPT000+rc+fONuuVl/W+mVKlSumrr77SyJEjNWvWLNWtW1djxozRwIED5erqKunq7cwGDx6sf//9V97e3mrTpo1mzJhxS8u9m1mM62/mdo85f/68fHx8dO7cOesbFAAAmG/nzp0KCQlRSMgOeXnVsHc5uANcuLBTO3aEaMeOHapRg/dEdpKTkxUbG6tSpUrJzc3NOjwuLk7BFSooKTn5ttXi7uamyKgoFS9e/LYtE3eXt99+WwsWLLDpdn+vuNG+KOU8a3KkGwAAALhLFC9eXJFRUUpISLhty/Tz8yNww8a8efNUq1YtFS5cWFu2bNHUqVM1ZMgQe5d1xyJ0AwAAAHeR4sWLE4JhV9HR0Xrrrbd0+vRpFS9eXCNHjtTo0aPtXdYdi9ANAAAAAMixGTNm3NfnaOcWVy8HAAAAAMAkhG4AAAAAAExC6AYAAADuUNferxrA7Zcf+yDndAMAAAB3GBcXFzk4OOjo0aMqUqSIXFxcZLFY7F0WcN8wDEOpqak6efKkHBwc5OLikud5EboBAACAO4yDg4NKlSql+Ph4HT161N7lAPctd3d3FS9eXA4Oee8kTugGAAAA7kAuLi4qXry4rly5orS0NHuXA9x3HB0d5eTkdMu9TAjdAAAAwB3KYrHI2dlZzs7O9i4FQB5xITUAAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkTvYuAAAAwF6Sk+N0+XKCvcu4byQlRUqSIiMjb8vy/Pz8VLx48duyLAC4EUI3AAC4LyUnx+mP3yvoSnqyvUu57zz11FO3ZTnubm6KjIoieAOwK0I3AAC4L12+nKAr6cl6yz1YpRzc7V3OfSE9LUkXL0XqoeBgebibu80jk5L0VGSkEhISCN0A7IrQDQAA7mulHNwV7ORl7zLuC2mSLkiq5u4uLy+2OYD7AxdSAwAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkd0zonjRpkiwWi4YPH24dlpycrMGDB6tw4cLy9PRU165ddfz4cfsVCQAAAABALtwRoXv79u16//33VaVKFZvhL774or777jstX75cmzdv1tGjR9WlSxc7VQkAAAAAQO7YPXQnJiaqV69eWrhwoQoWLGgdfu7cOX300UeaPn26mjVrppCQEC1atEi//vqrfvvtNztWDAAAAABAztg9dA8ePFjt27dXixYtbIbv2LFDly9fthlesWJFFS9eXFu3br3dZQIAAAAAkGtO9lz40qVLtXPnTm3fvj3TuGPHjsnFxUW+vr42w/39/XXs2LEbzjMlJUUpKSnW5+fPn8+3egEAAAAAyA27Hek+cuSIhg0bps8++0xubm75Nt+JEyfKx8fH+ggKCsq3eQMAAAAAkBt2C907duzQiRMnVKNGDTk5OcnJyUmbN2/W7Nmz5eTkJH9/f6Wmpurs2bM2rzt+/LgCAgJuON/Ro0fr3Llz1seRI0dMXhMAAAAAALJmt+7lzZs31759+2yG9evXTxUrVtQrr7yioKAgOTs7a+PGjerataskKSoqSnFxcapbt+4N5+vq6ipXV1dTawcAAAAAICfsFrq9vLz08MMP2wzz8PBQ4cKFrcP79++vESNGqFChQvL29tYLL7ygunXrqk6dOvYoGQAAAACAXLHrhdRuZsaMGXJwcFDXrl2VkpKi1q1ba968efYuCwAAAACAHLmjQndERITNczc3N82dO1dz5861T0EAAAAAANwCu9+nGwAAAACAexWhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJPYNXTPnz9fVapUkbe3t7y9vVW3bl2tWbPGOj45OVmDBw9W4cKF5enpqa5du+r48eN2rBgAAAAAgJzLU+j+66+/8mXhDz74oCZNmqQdO3bojz/+ULNmzdSxY0f9+eefkqQXX3xR3333nZYvX67Nmzfr6NGj6tKlS74sGwAAAAAAs+UpdJctW1ZNmzbVp59+quTk5DwvvEOHDmrXrp3KlSun8uXL6+2335anp6d+++03nTt3Th999JGmT5+uZs2aKSQkRIsWLdKvv/6q3377Lc/LBAAAAADgdslT6N65c6eqVKmiESNGKCAgQAMGDNDvv/9+S4WkpaVp6dKlunjxourWrasdO3bo8uXLatGihXWaihUrqnjx4tq6desN55OSkqLz58/bPAAAAAAAsIc8he5q1app1qxZOnr0qD7++GPFx8erQYMGevjhhzV9+nSdPHkyx/Pat2+fPD095erqqueff14rV67UQw89pGPHjsnFxUW+vr420/v7++vYsWM3nN/EiRPl4+NjfQQFBeVlFQEAAAAAuGW3dCE1JycndenSRcuXL9fkyZN1+PBhjRo1SkFBQerTp4/i4+NvOo8KFSpo9+7d2rZtmwYOHKi+ffvqwIEDea5p9OjROnfunPVx5MiRPM8LAAAAAIBbcUuh+48//tCgQYMUGBio6dOna9SoUYqJidH69et19OhRdezY8abzcHFxUdmyZRUSEqKJEyeqatWqmjVrlgICApSamqqzZ8/aTH/8+HEFBATccH6urq7Wq6FnPAAAAAAAsIc8he7p06ercuXKqlevno4ePapPPvlE//zzj9566y2VKlVKDRs2VFhYmHbu3JnreaenpyslJUUhISFydnbWxo0breOioqIUFxenunXr5qVsAAAAAABuK6e8vGj+/Pl6+umnFRoaqsDAwCynKVq0qD766KNs5zN69Gi1bdtWxYsX14ULF/T5558rIiJC69atk4+Pj/r3768RI0aoUKFC8vb21gsvvKC6deuqTp06eSkbAAAAAIDbKk+hOzo6+qbTuLi4qG/fvtlOc+LECeu53z4+PqpSpYrWrVunli1bSpJmzJghBwcHde3aVSkpKWrdurXmzZuXl5IBAAAAALjt8hS6Fy1aJE9PTz3xxBM2w5cvX66kpKSbhu0MNzsS7ubmprlz52ru3Ll5KRMAAAAAALvK0zndEydOlJ+fX6bhRYsW1TvvvHPLRQEAAAAAcC/IU+iOi4tTqVKlMg0vUaKE4uLibrkoAAAAAADuBXkK3UWLFtXevXszDd+zZ48KFy58y0UBAAAAAHAvyFPo7tmzp4YOHarw8HClpaUpLS1NmzZt0rBhw9SjR4/8rhEAAAAAgLtSni6kNmHCBP39999q3ry5nJyuziI9PV19+vThnG4AAAAAAP6/PIVuFxcXffnll5owYYL27NmjAgUKqHLlyipRokR+1wcAAAAAwF0rT6E7Q/ny5VW+fPn8qgUAAAAAgHtKnkJ3WlqawsLCtHHjRp04cULp6ek24zdt2pQvxQEAAAAAcDfLU+geNmyYwsLC1L59ez388MOyWCz5XRcAAAAAAHe9PIXupUuXatmyZWrXrl1+1wMAAAAAwD0jT7cMc3FxUdmyZfO7FgAAAAAA7il5Ct0jR47UrFmzZBhGftcDAAAAAMA9I0/dy3/55ReFh4drzZo1qlSpkpydnW3Gr1ixIl+KAwAAAADgbpan0O3r66vOnTvndy0AAAAAANxT8hS6Fy1alN91AAAAAABwz8nTOd2SdOXKFW3YsEHvv/++Lly4IEk6evSoEhMT8604AAAAAADuZnk60v3PP/+oTZs2iouLU0pKilq2bCkvLy9NnjxZKSkpWrBgQX7XCQAAAADAXSdPR7qHDRummjVr6syZMypQoIB1eOfOnbVx48Z8Kw4AAAAAgLtZno50//zzz/r111/l4uJiM7xkyZL677//8qUwAAAAAADudnk60p2enq60tLRMw//99195eXndclEAAAAAANwL8hS6W7VqpZkzZ1qfWywWJSYmauzYsWrXrl1+1QYAAAAAwF0tT93L3333XbVu3VoPPfSQkpOT9eSTTyo6Olp+fn764osv8rtGAAAAAADuSnkK3Q8++KD27NmjpUuXau/evUpMTFT//v3Vq1cvmwurAQAAAABwP8tT6JYkJycnPfXUU/lZCwAAAAAA95Q8he5PPvkk2/F9+vTJUzEAAAAAANxL8hS6hw0bZvP88uXLSkpKkouLi9zd3QndAAAAAAAoj1cvP3PmjM0jMTFRUVFRatCgARdSAwAAAADg/8tT6M5KuXLlNGnSpExHwQEAAAAAuF/lW+iWrl5c7ejRo/k5SwAAAAAA7lp5Oqf722+/tXluGIbi4+P13nvvqX79+vlSGAAAAAAAd7s8he5OnTrZPLdYLCpSpIiaNWumd999Nz/qAgAAAADgrpen0J2enp7fdQAAAAAAcM/J13O6AQAAAADA/8nTke4RI0bkeNrp06fnZREAAAAAANz18hS6d+3apV27duny5cuqUKGCJOnQoUNydHRUjRo1rNNZLJb8qRIAAAAAgLtQnkJ3hw4d5OXlpcWLF6tgwYKSpDNnzqhfv35q2LChRo4cma9FAgAAAABwN8rTOd3vvvuuJk6caA3cklSwYEG99dZbXL0cAAAAAID/L0+h+/z58zp58mSm4SdPntSFCxduuSgAAAAAAO4FeQrdnTt3Vr9+/bRixQr9+++/+vfff/X111+rf//+6tKlS37XCAAAAADAXSlP53QvWLBAo0aN0pNPPqnLly9fnZGTk/r376+pU6fma4EAANhbXFycEhIS7F3GXScyMlKSlJQUaedKsnan1gUAuLfkKXS7u7tr3rx5mjp1qmJiYiRJZcqUkYeHR74WBwCAvcXFxSm4QgUlJSfbu5S7VmTkU/YuIVuGkWrvEgAA97A8he4M8fHxio+PV6NGjVSgQAEZhsFtwgAA95SEhAQlJSfr0+BgBbu727ucu8rFpCQdiIyUR4FgOTjeedtuy5VTmpf8t4z0K/YuBQBwD8tT6D516pS6deum8PBwWSwWRUdHq3Tp0urfv78KFizIFcwBAPecYHd31fDysncZd5ULkq5I8nJ0l6PTnbftYtOS7F0CAOA+kKcLqb344otydnZWXFyc3K/51b979+5au3ZtvhUHAAAAAMDdLE9Hun/88UetW7dODz74oM3wcuXK6Z9//smXwgAAAAAAuNvl6Uj3xYsXbY5wZzh9+rRcXV1vuSgAAAAAAO4FeQrdDRs21CeffGJ9brFYlJ6erilTpqhp06b5VhwAAAAAAHezPHUvnzJlipo3b64//vhDqampevnll/Xnn3/q9OnT2rJlS37XCAAAAADAXSlPR7offvhhHTp0SA0aNFDHjh118eJFdenSRbt27VKZMmXyu0YAAAAAAO5KuT7SffnyZbVp00YLFizQmDFjzKgJAAAAAIB7Qq6PdDs7O2vv3r1m1AIAAAAAwD0lT93Ln3rqKX300Uf5XQsAAAAAAPeUPF1I7cqVK/r444+1YcMGhYSEyMPDw2b89OnT86U4AAAAAADuZrkK3X/99ZdKliyp/fv3q0aNGpKkQ4cO2UxjsVjyrzoAAAAAAO5iuQrd5cqVU3x8vMLDwyVJ3bt31+zZs+Xv729KcQAAAAAA3M1ydU63YRg2z9esWaOLFy/ma0EAAAAAANwr8nQhtQzXh3AAAAAAAPB/chW6LRZLpnO2OYcbAAAAAICs5eqcbsMwFBoaKldXV0lScnKynn/++UxXL1+xYkX+VQgAuCvExcUpISHB3mXku8jISEnSxaQkXbBzLXebi0lJ9i4Bd6jb8d7IWEbGPow7m5+fn4oXL27vMgBT5Cp09+3b1+b5U089la/FAADuTnFxcapQIVjJyfduyDoQGakr9i7iLpVuGHK0dxG4I6QbqZJuTxDOuL8Of6/eHdzc3BUVFUnwxj0pV6F70aJFZtUBALiLJSQkKDk5ScHBn8rdPdje5eSrpKRIRUY+JY8CwfJydLd3OXeVy1dOKzk5lmvAwMowrv505eZWQc5OnqYuyyMtSboUeU9+Lt1rMj5nExISCN24J+UqdAMAkB1392B5edWwdxmmcHB0l6OTl73LuKukpd+7PR9waxwczN+fMi5cdC9/LgG4O9zS1csBAAAAAMCNEboBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJPYNXRPnDhRtWrVkpeXl4oWLapOnTopKirKZprk5GQNHjxYhQsXlqenp7p27arjx4/bqWIAAAAAAHLOrqF78+bNGjx4sH777TetX79ely9fVqtWrXTx4kXrNC+++KK+++47LV++XJs3b9bRo0fVpUsXO1YNAAAAAEDOONlz4WvXrrV5HhYWpqJFi2rHjh1q1KiRzp07p48++kiff/65mjVrJklatGiRgoOD9dtvv6lOnTr2KBsAAAAAgBy5o87pPnfunCSpUKFCkqQdO3bo8uXLatGihXWaihUrqnjx4tq6dWuW80hJSdH58+dtHgAAAAAA2MMdE7rT09M1fPhw1a9fXw8//LAk6dixY3JxcZGvr6/NtP7+/jp27FiW85k4caJ8fHysj6CgILNLBwAAAAAgS3dM6B48eLD279+vpUuX3tJ8Ro8erXPnzlkfR44cyacKAQAAAADIHbue051hyJAh+v777/XTTz/pwQcftA4PCAhQamqqzp49a3O0+/jx4woICMhyXq6urnJ1dTW7ZAAAAAAAbsquR7oNw9CQIUO0cuVKbdq0SaVKlbIZHxISImdnZ23cuNE6LCoqSnFxcapbt+7tLhcAAAAAgFyx65HuwYMH6/PPP9c333wjLy8v63naPj4+KlCggHx8fNS/f3+NGDFChQoVkre3t1544QXVrVuXK5cDAAAAAO54dg3d8+fPlyQ1adLEZviiRYsUGhoqSZoxY4YcHBzUtWtXpaSkqHXr1po3b95trhQAAAAAgNyza+g2DOOm07i5uWnu3LmaO3fubagIAAAAAID8c8dcvRwAAAAAgHsNoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADCJk70LAAAAAMySlBRp7xJwExltFBmZu7by8/NT8eLFzSgJyFeEbgAAANxzEtJT5SApMvIpe5eCHHrqqdy1lbubmyKjogjeuOMRugEAAHDPuWBcUbqkN11LqoxzYXuXg2ykpyXp4qVIPRQcLA939xy9JjIpSU9FRiohIYHQjTseoRsAAAD3rFIOBRTs5GXvMpCNNEkXJFVzd5eXF22Few8XUgMAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExi19D9008/qUOHDipWrJgsFotWrVplM94wDL3xxhsKDAxUgQIF1KJFC0VHR9unWAAAAAAAcsmuofvixYuqWrWq5s6dm+X4KVOmaPbs2VqwYIG2bdsmDw8PtW7dWsnJybe5UgAAAAAAcs/Jngtv27at2rZtm+U4wzA0c+ZMvfbaa+rYsaMk6ZNPPpG/v79WrVqlHj163M5SAQAAAADItTv2nO7Y2FgdO3ZMLVq0sA7z8fFR7dq1tXXrVjtWBgAAAABAztj1SHd2jh07Jkny9/e3Ge7v728dl5WUlBSlpKRYn58/f96cAgEAAAAAuIk79kh3Xk2cOFE+Pj7WR1BQkL1LAgAAAADcp+7Y0B0QECBJOn78uM3w48ePW8dlZfTo0Tp37pz1ceTIEVPrBAAAAADgRu7Y0F2qVCkFBARo48aN1mHnz5/Xtm3bVLdu3Ru+ztXVVd7e3jYPAAAAAADswa7ndCcmJurw4cPW57Gxsdq9e7cKFSqk4sWLa/jw4XrrrbdUrlw5lSpVSq+//rqKFSumTp062a9oAAAAAAByyK6h+48//lDTpk2tz0eMGCFJ6tu3r8LCwvTyyy/r4sWLeu6553T27Fk1aNBAa9eulZubm71KBgAAAAAgx+waups0aSLDMG443mKx6M0339Sbb755G6sCAAAAACB/3LHndAMAAAAAcLcjdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmcbJ3Afg/cXFxSkhIsHcZMImfn5+KFy9u7zIAAAAA3EaE7jtEXFycgitUUFJysr1LgUnc3dwUGRVF8AYAAADuI4TuO0RCQoKSkpP1aXCwgt3d7V0O8llkUpKeioxUQkICoRsAAAC4jxC67zDB7u6q4eVl7zIAAAAAAPmAC6kBAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASJ3sXAAD3gri4OCUkJNi7DLuJjIyUJCUlRdq5kvx3L64TAAC4fQjdAHCL4uLiFFyhgpKSk+1dit1FRj5l7xJMYxip9i4BAADchQjdAHCLEhISlJScrE+DgxXs7m7vcuziYlKSDkRGyqNAsBwc761tsOXKKc1L/ltG+hV7lwIAAO5ChG4AyCfB7u6q4eVl7zLs4oKkK5K8HN3l6HRvbYPYtCR7lwAAAO5iXEgNAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADCJk70LAAAAAICLSUm5nnb16tWKjIw0qyTYUenSpVW3bl17l5EvCN0AAAAA7CbdSJWkXIXn33S1y+7rr79uTlGwOwdJv/z66z0RvAndAAAAAOzGMK5IktzcKsjZyTNHr0m7fErpKX9rnEsxlXH0MbM82EFM2jmNSz2qv/76i9ANAAAAAPnBwcFdjk5eOZrWkna1e3kZRx9VcvU3syzYQ4okHbV3FfmGC6kBAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYJK7InTPnTtXJUuWlJubm2rXrq3ff//d3iUBAAAAAHBTd3zo/vLLLzVixAiNHTtWO3fuVNWqVdW6dWudOHHC3qUBAAAAAJCtOz50T58+Xc8++6z69eunhx56SAsWLJC7u7s+/vhje5cGAAAAAEC27ujQnZqaqh07dqhFixbWYQ4ODmrRooW2bt1qx8oAAAAAALg5J3sXkJ2EhASlpaXJ39/fZri/v78OHjyY5WtSUlKUkpJifX7u3DlJ0vnz580rNB8kJiZKknZcuKDEtDQ7V4P8FpWUJEnasWOHta1x74iKipJ0f++/SUlJOijJ7fJJOV65YO9y8tVfV65+j/x55YwuGfdn++bVlbTzSpHkejlBTukX7V1OJrTt7Xc73xO0790jL+8L2vfe9lfa1eyWlJR0R+e4jNoMw8h2Ootxsyns6OjRo3rggQf066+/qm7dutbhL7/8sjZv3qxt27Zles24ceM0fvz421kmAAAAAOA+deTIET344IM3HH9HH+n28/OTo6Ojjh8/bjP8+PHjCggIyPI1o0eP1ogRI6zP09PTdfr0aRUuXFgWi8XUem/F+fPnFRQUpCNHjsjb29ve5eA2ou3vX7T9/Yu2v3/R9vc32v/+RdvfmwzD0IULF1SsWLFsp7ujQ7eLi4tCQkK0ceNGderUSdLVEL1x40YNGTIky9e4urrK1dXVZpivr6/JleYfb29vdsT7FG1//6Lt71+0/f2Ltr+/0f73L9r+3uPj43PTae7o0C1JI0aMUN++fVWzZk098sgjmjlzpi5evKh+/frZuzQAAAAAALJ1x4fu7t276+TJk3rjjTd07NgxVatWTWvXrs10cTUAAAAAAO40d3zolqQhQ4bcsDv5vcLV1VVjx47N1DUe9z7a/v5F29+/aPv7F21/f6P971+0/f3tjr56OQAAAAAAdzMHexcAAAAAAMC9itANAAAAAIBJCN0AAAAAAJiE0G2SuXPnqmTJknJzc1Pt2rX1+++/Zzv98uXLVbFiRbm5ualy5cpavXq1zXjDMPTGG28oMDBQBQoUUIsWLRQdHW3mKuAW5Hf7h4aGymKx2DzatGlj5iogj3LT9n/++ae6du2qkiVLymKxaObMmbc8T9hPfrf9uHHjMu33FStWNHENkFe5afuFCxeqYcOGKliwoAoWLKgWLVpkmp7v/LtHfrc93/d3j9y0/YoVK1SzZk35+vrKw8ND1apV05IlS2ymYb+/txG6TfDll19qxIgRGjt2rHbu3KmqVauqdevWOnHiRJbT//rrr+rZs6f69++vXbt2qVOnTurUqZP2799vnWbKlCmaPXu2FixYoG3btsnDw0OtW7dWcnLy7Vot5JAZ7S9Jbdq0UXx8vPXxxRdf3I7VQS7ktu2TkpJUunRpTZo0SQEBAfkyT9iHGW0vSZUqVbLZ73/55RezVgF5lNu2j4iIUM+ePRUeHq6tW7cqKChIrVq10n///Wedhu/8u4MZbS/xfX83yG3bFypUSGPGjNHWrVu1d+9e9evXT/369dO6deus07Df3+MM5LtHHnnEGDx4sPV5WlqaUaxYMWPixIlZTt+tWzejffv2NsNq165tDBgwwDAMw0hPTzcCAgKMqVOnWsefPXvWcHV1Nb744gsT1gC3Ir/b3zAMo2/fvkbHjh1NqRf5J7dtf60SJUoYM2bMyNd54vYxo+3Hjh1rVK1aNR+rhBludR+9cuWK4eXlZSxevNgwDL7z7yb53faGwff93SI/vpurV69uvPbaa4ZhsN/fDzjSnc9SU1O1Y8cOtWjRwjrMwcFBLVq00NatW7N8zdatW22ml6TWrVtbp4+NjdWxY8dspvHx8VHt2rVvOE/YhxntnyEiIkJFixZVhQoVNHDgQJ06dSr/VwB5lpe2t8c8kf/MbKfo6GgVK1ZMpUuXVq9evRQXF3er5SIf5UfbJyUl6fLlyypUqJAkvvPvFma0fQa+7+9st9r2hmFo48aNioqKUqNGjSSx398PCN35LCEhQWlpafL397cZ7u/vr2PHjmX5mmPHjmU7fca/uZkn7MOM9peudjX75JNPtHHjRk2ePFmbN29W27ZtlZaWlv8rgTzJS9vbY57If2a1U+3atRUWFqa1a9dq/vz5io2NVcOGDXXhwoVbLRn5JD/a/pVXXlGxYsWsf2zznX93MKPtJb7v7wZ5bftz587J09NTLi4uat++vebMmaOWLVtKYr+/HzjZuwAAN9ejRw/r/ytXrqwqVaqoTJkyioiIUPPmze1YGQCztG3b1vr/KlWqqHbt2ipRooSWLVum/v3727Ey5JdJkyZp6dKlioiIkJubm73LwW10o7bn+/7e5eXlpd27dysxMVEbN27UiBEjVLp0aTVp0sTepeE24Eh3PvPz85Ojo6OOHz9uM/z48eM3vFhOQEBAttNn/JubecI+zGj/rJQuXVp+fn46fPjwrReNfJGXtrfHPJH/blc7+fr6qnz58uz3d5Bbaftp06Zp0qRJ+vHHH1WlShXrcL7z7w5mtH1W+L6/8+S17R0cHFS2bFlVq1ZNI0eO1OOPP66JEydKYr+/HxC685mLi4tCQkK0ceNG67D09HRt3LhRdevWzfI1devWtZlektavX2+dvlSpUgoICLCZ5vz589q2bdsN5wn7MKP9s/Lvv//q1KlTCgwMzJ/Cccvy0vb2mCfy3+1qp8TERMXExLDf30Hy2vZTpkzRhAkTtHbtWtWsWdNmHN/5dwcz2j4rfN/fefLrMz89PV0pKSmS2O/vC/a+ktu9aOnSpYarq6sRFhZmHDhwwHjuuecMX19f49ixY4ZhGEbv3r2NV1991Tr9li1bDCcnJ2PatGlGZGSkMXbsWMPZ2dnYt2+fdZpJkyYZvr6+xjfffGPs3bvX6Nixo1GqVCnj0qVLt339kL38bv8LFy4Yo0aNMrZu3WrExsYaGzZsMGrUqGGUK1fOSE5Otss6Imu5bfuUlBRj165dxq5du4zAwEBj1KhRxq5du4zo6OgczxN3BjPafuTIkUZERIQRGxtrbNmyxWjRooXh5+dnnDhx4ravH24st20/adIkw8XFxfjqq6+M+Ph46+PChQs20/Cdf+fL77bn+/7ukdu2f+edd4wff/zRiImJMQ4cOGBMmzbNcHJyMhYuXGidhv3+3kboNsmcOXOM4sWLGy4uLsYjjzxi/Pbbb9ZxjRs3Nvr27Wsz/bJly4zy5csbLi4uRqVKlYwffvjBZnx6errx+uuvG/7+/oarq6vRvHlzIyoq6nasCvIgP9s/KSnJaNWqlVGkSBHD2dnZKFGihPHss88Suu5QuWn72NhYQ1KmR+PGjXM8T9w58rvtu3fvbgQGBhouLi7GAw88YHTv3t04fPjwbVwj5FRu2r5EiRJZtv3YsWOt0/Cdf/fIz7bn+/7ukpu2HzNmjFG2bFnDzc3NKFiwoFG3bl1j6dKlNvNjv7+3WQzDMG7vsXUAAAAAAO4PnNMNAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AwD3IYrFo1apV9i5Dx44dU8uWLeXh4SFfX197lwMAwG1H6AYA4P8LDQ2VxWKRxWKRi4uLypYtqzfffFNXrlyxd2k3NG7cOFWrVi3T8Pj4eLVt2/b2F3SdGTNmKD4+Xrt379ahQ4fsXQ4AALedk70LAADgTtKmTRstWrRIKSkpWr16tQYPHixnZ2eNHj0607SpqalycXGxQ5WSYRhKS0u74fiAgIDbWM2NxcTEKCQkROXKlbN3KQAA2AVHugEAuIarq6sCAgJUokQJDRw4UC1atNC3334r6eqR8E6dOuntt99WsWLFVKFCBUnSvn371KxZMxUoUECFCxfWc889p8TEROs8M143fvx4FSlSRN7e3nr++eeVmppqnSYlJUVDhw5V0aJF5ebmpgYNGmj79u3W8REREbJYLFqzZo1CQkLk6uqqTz/9VOPHj9eePXusR+jDwsIkZe5entMap02bpsDAQBUuXFiDBw/W5cuXs91e8+fPV5kyZeTi4qIKFSpoyZIl1nElS5bU119/rU8++UQWi0WhoaE3nM/HH3+sSpUqydXVVYGBgRoyZIh13PTp01W5cmV5eHgoKChIgwYNsqn9n3/+UYcOHVSwYEF5eHioUqVKWr16tXX8/v371bZtW3l6esrf31+9e/dWQkKCdfxXX32lypUrW7dNixYtdPHixWzXGwCAnCJ0AwCQjQIFCtiE440bNyoqKkrr16/X999/r4sXL6p169YqWLCgtm/fruXLl2vDhg02oTHjdZGRkYqIiNAXX3yhFStWaPz48dbxL7/8sr7++mstXrxYO3fuVNmyZdW6dWudPn3aZj6vvvqqJk2apMjISLVs2VIjR45UpUqVFB8fr/j4eHXv3j3TOuS0xvDwcMXExCg8PFyLFy9WWFiYNcRnZeXKlRo2bJhGjhyp/fv3a8CAAerXr5/Cw8MlSdu3b1ebNm3UrVs3xcfHa9asWVnOZ/78+Ro8eLCee+457du3T99++63Kli1rHe/g4KDZs2frzz//1OLFi7Vp0ya9/PLL1vGDBw9WSkqKfvrpJ+3bt0+TJ0+Wp6enJOns2bNq1qyZqlevrj/++ENr167V8ePH1a1bN0lXu+H37NlTTz/9tLV9unTpIsMwbrjeAADkigEAAAzDMIy+ffsaHTt2NAzDMNLT043169cbrq6uxqhRo6zj/f39jZSUFOtrPvjgA6NgwYJGYmKiddgPP/xgODg4GMeOHbO+rlChQsbFixet08yfP9/w9PQ00tLSjMTERMPZ2dn47LPPrONTU1ONYsWKGVOmTDEMwzDCw8MNScaqVatsah47dqxRtWrVTOsiyVi5cmWuaixRooRx5coV6zRPPPGE0b179xtur3r16hnPPvuszbAnnnjCaNeunfV5x44djb59+95wHoZhGMWKFTPGjBmT7TTXWr58uVG4cGHr88qVKxvjxo3LctoJEyYYrVq1shl25MgRQ5IRFRVl7Nixw5Bk/P333zlePgAAucGRbgAArvH999/L09NTbm5uatu2rbp3765x48ZZx1euXNnmPO7IyEhVrVpVHh4e1mH169dXenq6oqKirMOqVq0qd3d36/O6desqMTFRR44cUUxMjC5fvqz69etbxzs7O+uRRx5RZGSkTX01a9bM9TrltMZKlSrJ0dHR+jwwMFAnTpzIdr7X1pwx3+trzs6JEyd09OhRNW/e/IbTbNiwQc2bN9cDDzwgLy8v9e7dW6dOnVJSUpIkaejQoXrrrbdUv359jR07Vnv37rW+ds+ePQoPD5enp6f1UbFiRUlXzzevWrWqmjdvrsqVK+uJJ57QwoULdebMmRzXDwDAzRC6AQC4RtOmTbV7925FR0fr0qVLWrx4sU1Yvfb/9mDm8p2dnW2eWywWpaenm7Y86Wr3/ez8/fffevTRR1WlShV9/fXX2rFjh+bOnStJ1m7/zzzzjP766y/17t1b+/btU82aNTVnzhxJUmJiojp06KDdu3fbPKKjo9WoUSM5Ojpq/fr1WrNmjR566CHNmTNHFSpUUGxsrKnrDQC4fxC6AQC4hoeHh8qWLavixYvLyenmN/kIDg7Wnj17bC68tWXLFjk4OFgvtCZdPeJ66dIl6/PffvtNnp6eCgoKsl6IbMuWLdbxly9f1vbt2/XQQw9lu3wXF5dsr2KemxpzKzg42KbmjPnerOZreXl5qWTJktq4cWOW43fs2KH09HS9++67qlOnjsqXL6+jR49mmi4oKEjPP/+8VqxYoZEjR2rhwoWSpBo1aujPP/9UyZIlVbZsWZtHxg8YFotF9evX1/jx47Vr1y65uLho5cqVOV4HAACyQ+gGAOAW9OrVS25uburbt6/279+v8PBwvfDCC+rdu7f8/f2t06Wmpqp///46cOCAVq9erbFjx2rIkCFycHCQh4eHBg4cqJdeeklr167VgQMH9OyzzyopKUn9+/fPdvklS5ZUbGysdu/erYSEBKWkpOS5xtx66aWXFBYWpvnz5ys6OlrTp0/XihUrNGrUqFzNZ9y4cXr33Xc1e/ZsRUdHa+fOndYj1WXLltXly5c1Z84c/fXXX1qyZIkWLFhg8/rhw4dr3bp1io2N1c6dOxUeHq7g4GBJVy+ydvr0afXs2VPbt29XTEyM1q1bp379+iktLU3btm3TO++8oz/++ENxcXFasWKFTp48aX09AAC3itANAMAtcHd317p163T69GnVqlVLjz/+uJo3b6733nvPZrrmzZurXLlyatSokbp3767HHnvM5lzxSZMmqWvXrurdu7dq1Kihw4cPa926dSpYsGC2y+/atavatGmjpk2bqkiRIvriiy/yXGNuderUSbNmzdK0adNUqVIlvf/++1q0aJGaNGmSq/n07dtXM2fO1Lx581SpUiU9+uijio6OlnT1XPjp06dr8uTJevjhh/XZZ59p4sSJNq9PS0vT4MGDFRwcrDZt2qh8+fKaN2+eJKlYsWLasmWL0tLS1KpVK1WuXFnDhw+Xr6+vHBwc5O3trZ9++knt2rVT+fLl9dprr+ndd99V27Ztb2nbAACQwWIY3BMDAAAzhYaG6uzZszb3zQYAAPcHjnQDAAAAAGASQjcAAAAAACahezkAAAAAACbhSDcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJvl/4sV7IK5JK1oAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f418c720",
+      "metadata": {
+        "id": "f418c720"
+      },
+      "source": [
+        "## Criteria"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c0b3f93f",
+      "metadata": {
+        "id": "c0b3f93f"
+      },
+      "source": [
+        "|Criteria|Complete|Incomplete|\n",
+        "|--------|----|----|\n",
+        "|Alteration of the code|The code changes made, made it reproducible.|The code is still not reproducible.|\n",
+        "|Description of changes|The author answered questions and explained the reasonings for the changes made well.|The author did not answer questions or explain the reasonings for the changes made well.|"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "83cec589",
+      "metadata": {
+        "id": "83cec589"
+      },
+      "source": [
+        "## Submission Information\n",
+        "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n",
+        "\n",
+        "### Submission Parameters:\n",
+        "* Submission Due Date: `23:59 - 02 February 2026`\n",
+        "* The branch name for your repo should be: `assignment-1`\n",
+        "* What to submit for this assignment:\n",
+        "    * This markdown file (`a1_sampling_and_reproducibility.ipynb`) should be populated with the code changed.\n",
+        "* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/sampling/pull/<pr_id>`\n",
+        "    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n",
+        "\n",
+        "#### Checklist:\n",
+        "- [ ] Create a branch called `assignment-1`.\n",
+        "- [ ] Ensure that the repository is public.\n",
+        "- [ ] Review [the PR description guidelines](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md#guidelines-for-pull-request-descriptions) and adhere to them.\n",
+        "- [ ] Verify that the link is accessible in a private browser window.\n",
+        "\n",
+        "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via the help channel in Slack. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.0"
+    },
+    "colab": {
+      "provenance": []
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "4ea73db3",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d9b2ccc",
-   "metadata": {},
-   "source": [
-    "Modify the number of repetitions in the simulation to 10 and 100 (from the original 1000). Run the script multiple times and observe the outputted graphs. Comment on the reproducibility of the results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4cf5d993",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "32603ce7",
-   "metadata": {},
-   "source": [
-    "Alter the code so that it is reproducible. Describe the changes you made to the code and how they affected the reproducibility of the script. The script needs to produce the same output when run multiple times."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77613cc3",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "30b4a74f",
-   "metadata": {},
-   "source": [
-    "## Code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ab8587a0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import necessary libraries\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "# Note: Suppressing FutureWarnings to maintain a clean output. This is specifically to ignore warnings about\n",
-    "# deprecated features in the libraries we're using (e.g., 'use_inf_as_na' option in Pandas, used by Seaborn),\n",
-    "# which we currently have no direct control over. This action is taken to ensure that our output remains\n",
-    "# focused on relevant information, acknowledging that we rely on external library updates to fully resolve\n",
-    "# these deprecations. Always consider reviewing and removing this suppression after significant library updates.\n",
-    "import warnings\n",
-    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
-    "\n",
-    "# Constants representing the parameters of the model\n",
-    "ATTACK_RATE = 0.10\n",
-    "TRACE_SUCCESS = 0.20\n",
-    "SECONDARY_TRACE_THRESHOLD = 2\n",
-    "\n",
-    "def simulate_event(m):\n",
-    "  \"\"\"\n",
-    "  Simulates the infection and tracing process for a series of events.\n",
-    "  \n",
-    "  This function creates a DataFrame representing individuals attending weddings and brunches,\n",
-    "  infects a subset of them based on the ATTACK_RATE, performs primary and secondary contact tracing,\n",
-    "  and calculates the proportions of infections and traced cases that are attributed to weddings.\n",
-    "  \n",
-    "  Parameters:\n",
-    "  - m: Dummy parameter for iteration purposes.\n",
-    "  \n",
-    "  Returns:\n",
-    "  - A tuple containing the proportion of infections and the proportion of traced cases\n",
-    "    that are attributed to weddings.\n",
-    "  \"\"\"\n",
-    "  # Create DataFrame for people at events with initial infection and traced status\n",
-    "  events = ['wedding'] * 200 + ['brunch'] * 800\n",
-    "  ppl = pd.DataFrame({\n",
-    "      'event': events,\n",
-    "      'infected': False,\n",
-    "      'traced': np.nan  # Initially setting traced status as NaN\n",
-    "  })\n",
-    "\n",
-    "  # Explicitly set 'traced' column to nullable boolean type\n",
-    "  ppl['traced'] = ppl['traced'].astype(pd.BooleanDtype())\n",
-    "\n",
-    "  # Infect a random subset of people\n",
-    "  infected_indices = np.random.choice(ppl.index, size=int(len(ppl) * ATTACK_RATE), replace=False)\n",
-    "  ppl.loc[infected_indices, 'infected'] = True\n",
-    "\n",
-    "  # Primary contact tracing: randomly decide which infected people get traced\n",
-    "  ppl.loc[ppl['infected'], 'traced'] = np.random.rand(sum(ppl['infected'])) < TRACE_SUCCESS\n",
-    "\n",
-    "  # Secondary contact tracing based on event attendance\n",
-    "  event_trace_counts = ppl[ppl['traced'] == True]['event'].value_counts()\n",
-    "  events_traced = event_trace_counts[event_trace_counts >= SECONDARY_TRACE_THRESHOLD].index\n",
-    "  ppl.loc[ppl['event'].isin(events_traced) & ppl['infected'], 'traced'] = True\n",
-    "\n",
-    "  # Calculate proportions of infections and traces attributed to each event type\n",
-    "  ppl['event_type'] = ppl['event'].str[0]  # 'w' for wedding, 'b' for brunch\n",
-    "  wedding_infections = sum(ppl['infected'] & (ppl['event_type'] == 'w'))\n",
-    "  brunch_infections = sum(ppl['infected'] & (ppl['event_type'] == 'b'))\n",
-    "  p_wedding_infections = wedding_infections / (wedding_infections + brunch_infections)\n",
-    "\n",
-    "  wedding_traces = sum(ppl['infected'] & ppl['traced'] & (ppl['event_type'] == 'w'))\n",
-    "  brunch_traces = sum(ppl['infected'] & ppl['traced'] & (ppl['event_type'] == 'b'))\n",
-    "  p_wedding_traces = wedding_traces / (wedding_traces + brunch_traces)\n",
-    "\n",
-    "  return p_wedding_infections, p_wedding_traces\n",
-    "\n",
-    "# Run the simulation 1000 times\n",
-    "results = [simulate_event(m) for m in range(1000)]\n",
-    "props_df = pd.DataFrame(results, columns=[\"Infections\", \"Traces\"])\n",
-    "\n",
-    "# Plotting the results\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.histplot(props_df['Infections'], color=\"blue\", alpha=0.75, binwidth=0.05, kde=False, label='Infections from Weddings')\n",
-    "sns.histplot(props_df['Traces'], color=\"red\", alpha=0.75, binwidth=0.05, kde=False, label='Traced to Weddings')\n",
-    "plt.xlabel(\"Proportion of cases\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "plt.title(\"Impact of Contact Tracing on Perceived Flu Infection Sources\")\n",
-    "plt.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f418c720",
-   "metadata": {},
-   "source": [
-    "## Criteria"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0b3f93f",
-   "metadata": {},
-   "source": [
-    "|Criteria|Complete|Incomplete|\n",
-    "|--------|----|----|\n",
-    "|Alteration of the code|The code changes made, made it reproducible.|The code is still not reproducible.|\n",
-    "|Description of changes|The author answered questions and explained the reasonings for the changes made well.|The author did not answer questions or explain the reasonings for the changes made well.|"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "83cec589",
-   "metadata": {},
-   "source": [
-    "## Submission Information\n",
-    "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n",
-    "\n",
-    "### Submission Parameters:\n",
-    "* Submission Due Date: `23:59 - 02 February 2026`\n",
-    "* The branch name for your repo should be: `assignment-1`\n",
-    "* What to submit for this assignment:\n",
-    "    * This markdown file (`a1_sampling_and_reproducibility.ipynb`) should be populated with the code changed.\n",
-    "* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/sampling/pull/<pr_id>`\n",
-    "    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n",
-    "\n",
-    "#### Checklist:\n",
-    "- [ ] Create a branch called `assignment-1`.\n",
-    "- [ ] Ensure that the repository is public.\n",
-    "- [ ] Review [the PR description guidelines](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md#guidelines-for-pull-request-descriptions) and adhere to them.\n",
-    "- [ ] Verify that the link is accessible in a private browser window.\n",
-    "\n",
-    "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via the help channel in Slack. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/02_activities/assignments/a2_survey_design_and_evaluation.md b/02_activities/assignments/a2_survey_design_and_evaluation.md
index b4f036f2..226bbc09 100644
--- a/02_activities/assignments/a2_survey_design_and_evaluation.md
+++ b/02_activities/assignments/a2_survey_design_and_evaluation.md
@@ -21,19 +21,34 @@ Select one of the scenarios below and design a survey to meet the need(s) outlin
 
 For the **Canadian General Social Survey on Giving, Volunteering, and Participating, 2018 (cycle 33)**, conducted by Statistics Canada find any and all available documentation for the data gathered and identify and describe the survey features indicated below.
 
-1. Sample type
-2. Sample size
-3. Target population
-4. Sampling frame
-5. Survey mode(s) 
-6. Timeline
-7. Response rate
-8. Weights
-9. Data processing
-10. Cleaning, imputation, etc
-11. Sources of error
-12. Limitations, known biases, etc
+1. Sample type : The General Social Survey uses a probability sample selected across the ten provinces.
+2. Sample size : The 2018 GSS GVP contains 16,149 observations.
+3. Target population : Individuals aged 15 and over living in private households in Canada’s ten provinces, excluding residents of territories and institutions.
+4. Sampling frame : A sampling frame that includes Canadian residents in private households in the 10 provinces, derived from Statistics Canada's household sampling system. (Frame implied from target population description.)
+5. Survey mode(s) : The survey incorporated online reporting as part of updated questionnaire delivery in 2018.
+(Statistics Canada GSS surveys commonly combine online and interviewer‑administered modes.)
+6. Timeline : The 2018 GSS GVP was conducted from September to December 2018.
+7. Response rate : The publicly accessible documentation snippets in the search results do not explicitly mention the numeric response rate. (The user guide available in the PUMF typically contains this exact figure.)
+8. Weights : The PUMF includes survey weights and estimation procedures designed to ensure population‑representative estimates, described in the microdata user guide.
+9. Data processing : Statistics Canada’s GSS materials describe processing steps including coding, validation, microdata anonymization, and estimation preparation as part of the PUMF package documentation.
+10. Cleaning, imputation, etc : The documentation notes updates, revisions to questions, anonymization, and quality-control processes prior to release; however, specific imputation details are only accessible in the full user guide referenced.
+11. Sources of error : Potential sources include:
+Sampling error due to probabilistic household sampling.
+Non‑sampling errors from questionnaire revisions and online transition
+12. Limitations, known biases, etc : Limitations include:
+
+Exclusion of the territories.
+Exclusion of institutionalized populations.
+Possible measurement variation due to revised and updated question wording in 2018
 13. Link to documentation and any additional sources used
+Statistics Canada PUMF Documentation (Cycle 33):
+https://www150.statcan.gc.ca/n1/en/catalogue/45250011 [www150.statcan.gc.ca]
+Borealis Data Repository (microdata):
+https://doi.org/10.5683/SP3/U1AYY0 [borealisdata.ca]
+Abacus Data Network (user guide & questionnaire):
+https://hdl.handle.net/11272.1/AB2/GBFDYG [abacus.lib...ary.ubc.ca]
+Daily release notice:
+https://www150.statcan.gc.ca/n1/daily-quotidien/210126/dq210126h-eng.htm [www150.statcan.gc.ca]
 
 
 # Your Changes
@@ -41,38 +56,75 @@ For the **Canadian General Social Survey on Giving, Volunteering, and Participat
 ## Part A - Survey Design: 
 
 The number of your chosen topic: `#`
-
+3
 Describe the purpose of your survey:
-```
+``` -> number 3 is one i choose , The purpose of this survey is to understand how age influences music taste, with a particular focus on how individuals perceive popular music at different stages in their life. The results will help identify whether music taste evolves with age and what factors contribute to these changes.
+
 write your answer here...
 ```
 
 Describe your target population, sampling frame, sampling units, and observational units:
 ```
-write your answer here...
-```
+-> Target Population:
+All individuals aged 15 and older currently residing in Canada.
+
+Sampling Frame:
+A list of currently enrolled University of Toronto students along with a purchased panel list of Canadian adults from a reputable survey research firm.
+
+Sampling Units:
+Individual persons selected from the sampling frame.
+
+Observational Units:
+The same individuals who complete the survey and provide their perceptions of music tast
+
 
 Your 5-10 question survey:
 ```
-1. write your question here...
-2. write your question here...
-3. write your question here...
-4. write your question here...
-5. write your question here...
-6. write your question here... (optional)
-7. write your question here... (optional)
-8. write your question here... (optional)
-9. write your question here... (optional)
-10. write your question here... (optional)
-```
+survey questions :
+1. How old are you? (Open response or age brackets)
+2. How often do you listen to popular music? (Daily, Weekly, Monthly, Rarely, Never)
+3. Which genres of music do you currently enjoy the most? (Select all that apply)
+4. Thinking back 5–10 years, how would you describe your music taste at that time compared to now? (Very similar / Somewhat similar / Very different)
+5. Do you believe your age has influenced the type of music you enjoy? (Yes / No / Unsure)
+6. How important is staying updated with new music releases to you? (1–5 Likert scale)
+7. Do you find that your perception of “popular music” changes as you grow older? (Yes / No / Unsure)
+8. How strongly do you associate music with specific life stages or memories? (1–5 Likert scale)
+9. Which factors most influence your music preferences today? (Friends, Family, Social Media, Streaming Algorithms, Nostalgia, Other)
+10. Would you be willing to participate in a follow‑up interview? (Yes / No)
 
 ## Part B - Survey Evaluation:
 
 Identify and describe survey features:
 
-```
-write your answer here
-```
+1. Sample type : The General Social Survey uses a probability sample selected across the ten provinces.
+2. Sample size : The 2018 GSS GVP contains 16,149 observations.
+3. Target population : Individuals aged 15 and over living in private households in Canada’s ten provinces, excluding residents of territories and institutions.
+4. Sampling frame : A sampling frame that includes Canadian residents in private households in the 10 provinces, derived from Statistics Canada's household sampling system. (Frame implied from target population description.)
+5. Survey mode(s) : The survey incorporated online reporting as part of updated questionnaire delivery in 2018.
+(Statistics Canada GSS surveys commonly combine online and interviewer‑administered modes.)
+6. Timeline : The 2018 GSS GVP was conducted from September to December 2018.
+7. Response rate : The publicly accessible documentation snippets in the search results do not explicitly mention the numeric response rate. (The user guide available in the PUMF typically contains this exact figure.)
+8. Weights : The PUMF includes survey weights and estimation procedures designed to ensure population‑representative estimates, described in the microdata user guide.
+9. Data processing : Statistics Canada’s GSS materials describe processing steps including coding, validation, microdata anonymization, and estimation preparation as part of the PUMF package documentation.
+10. Cleaning, imputation, etc : The documentation notes updates, revisions to questions, anonymization, and quality-control processes prior to release; however, specific imputation details are only accessible in the full user guide referenced.
+11. Sources of error : Potential sources include:
+Sampling error due to probabilistic household sampling.
+Non‑sampling errors from questionnaire revisions and online transition
+12. Limitations, known biases, etc : Limitations include:
+
+Exclusion of the territories.
+Exclusion of institutionalized populations.
+Possible measurement variation due to revised and updated question wording in 2018
+13. Link to documentation and any additional sources used
+Statistics Canada PUMF Documentation (Cycle 33):
+https://www150.statcan.gc.ca/n1/en/catalogue/45250011 [www150.statcan.gc.ca]
+Borealis Data Repository (microdata):
+https://doi.org/10.5683/SP3/U1AYY0 [borealisdata.ca]
+Abacus Data Network (user guide & questionnaire):
+https://hdl.handle.net/11272.1/AB2/GBFDYG [abacus.lib...ary.ubc.ca]
+Daily release notice:
+https://www150.statcan.gc.ca/n1/daily-quotidien/210126/dq210126h-eng.htm [www150.statcan.gc.ca]
+
 
 ## Rubric