{ "cells": [ { "cell_type": "markdown", "id": "55a568ac", "metadata": {}, "source": [ "# Temperature Autoregression — a leak-free evaluation walkthrough\n", "\n", "This notebook is a self-contained walkthrough of the **`swedish-temperatures:ar`**\n", "problem in [emflow](https://github.com/rebase-energy/emflow): a least-squares\n", "**autoregressive (AR)** model that forecasts hourly air temperature **one hour\n", "ahead** for the SMHI station *Stockholm-Observatoriekullen A*.\n", "\n", "The model is trained on all data **before 2026** and evaluated on **2026**.\n", "\n", "> **The point of this notebook:** to make it *unmistakably clear that the\n", "> evaluation has no data leakage*. Every claim below is backed by a runnable\n", "> `assert` — if the evaluation leaked, a cell would fail.\n", "\n", "All data is bundled inside the `emflow` package, so this notebook runs\n", "top-to-bottom **offline** — no network access required." ] }, { "cell_type": "markdown", "id": "c0358eec", "metadata": {}, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/rebase-energy/emflow/blob/main/docs/notebooks/swedish_temperatures.ipynb)" ] }, { "cell_type": "code", "execution_count": null, "id": "1733ef33", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:29.335923Z", "iopub.status.busy": "2026-06-02T11:55:29.335593Z", "iopub.status.idle": "2026-06-02T11:55:29.340229Z", "shell.execute_reply": "2026-06-02T11:55:29.339712Z" } }, "outputs": [], "source": [ "# On Colab, uncomment to install the package (the dataset ships with it):\n", "# !pip install emflow" ] }, { "cell_type": "code", "execution_count": null, "id": "66456302", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:29.343653Z", "iopub.status.busy": "2026-06-02T11:55:29.343277Z", "iopub.status.idle": "2026-06-02T11:55:30.756787Z", "shell.execute_reply": "2026-06-02T11:55:30.756527Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "import emflow as ef\n", "from emflow.examples.swedish_temperatures.predictor import ARPredictor\n", "from emflow.problems.objective import MeanSquaredError" ] }, { "cell_type": "markdown", "id": "dc0588d3", "metadata": {}, "source": [ "## 1. Load the problem\n", "\n", "`emflow` problems are loaded by name. Each returns a `(dataset, environment,\n", "objective)` triple. The dataset (a single Parquet file) is bundled in the\n", "package and read via `importlib.resources` — nothing is downloaded." ] }, { "cell_type": "code", "execution_count": null, "id": "99f14acc", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:30.758209Z", "iopub.status.busy": "2026-06-02T11:55:30.757979Z", "iopub.status.idle": "2026-06-02T11:55:30.941768Z", "shell.execute_reply": "2026-06-02T11:55:30.941570Z" } }, "outputs": [ { "data": { "text/plain": [ "['gefcom2014-solar:simple',\n", " 'heftcom2024:baseline',\n", " 'solar-battery',\n", " 'swedish-temperatures:ar']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ef.list_problems()" ] }, { "cell_type": "code", "execution_count": null, "id": "6b03e1ea", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:30.942800Z", "iopub.status.busy": "2026-06-02T11:55:30.942742Z", "iopub.status.idle": "2026-06-02T11:55:30.944952Z", "shell.execute_reply": "2026-06-02T11:55:30.944788Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hourly air temperature (°C) from SMHI station Stockholm-Observatoriekullen A (id 98230). Benchmark for 1-step-ahead autoregressive forecasting: train < 2026, evaluate on 2026.\n", "\n", "Station : ('98230', 'Stockholm-Observatoriekullen A')\n", "Full range: 2016-06-02 08:00:00+00:00 -> 2026-06-02 08:00:00+00:00\n", "Objective : MeanAbsoluteError\n" ] } ], "source": [ "dataset, env, obj = ef.load_problem(\"swedish-temperatures:ar\")\n", "\n", "print(dataset.description)\n", "print()\n", "temps = dataset.data[\"temperatures\"]\n", "series = temps.iloc[:, 0] # the Stockholm temperature series\n", "print(\"Station :\", temps.columns[0])\n", "print(\"Full range:\", series.index.min(), \"->\", series.index.max())\n", "print(\"Objective :\", obj.name)" ] }, { "cell_type": "markdown", "id": "ceb70d5f", "metadata": {}, "source": [ "## 2. Explore the data\n", "\n", "Ten years of hourly temperatures. The red line marks `TEST_START` (2026-01-01):\n", "everything to its left is available for training, everything to its right is the\n", "evaluation period.\n", "\n", "Note the honest caveat: SMHI's *corrected-archive* (quality-controlled) data ends\n", "~3 months before \"now\", so the **actual** observations stop on 2026-03-01. The\n", "later 2026 rows exist but are `NaN` and are simply skipped when scoring." ] }, { "cell_type": "code", "execution_count": null, "id": "5559db28", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:30.945938Z", "iopub.status.busy": "2026-06-02T11:55:30.945879Z", "iopub.status.idle": "2026-06-02T11:55:31.253421Z", "shell.execute_reply": "2026-06-02T11:55:31.253244Z" } }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABEEAAAEiCAYAAAAWOcyHAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAA9lJJREFUeJzsnQW4G8XXxifVW3d3L1WgVLFSCsXd3d0d/uiHuzuF4u4ubYFChbq7u/dWr7X5nnd2ZzM7mZW47PnxhOYmm81mZnbkzDnvCYXD4TAjCIIgCIIgCIIgCILIc8pl+gIIgiAIgiAIgiAIgiDSARlBCIIgCIIgCIIgCIIIBGQEIQiCIAiCIAiCIAgiEJARhCAIgiAIgiAIgiCIQEBGEIIgCIIgCIIgCIIgAgEZQQiCIAiCIAiCIAiCCARkBCEIgiAIgiAIgiAIIhCQEYQgCIIgCIIgCIIgiEBARhCCIAiCIAiCIAiCIAIBGUEIgiDyhGHDhrFQKMSWLFmS1u+9//77+fcSRL6C9n3NNde4HvPnn3/y47744ovA3Fuiz5kwYUKmLyVwffYFF1zAqlevnrRrGThwIH8IcE24NlwjQRBEvkFGEIIgiCRN+DGB7NatG5VnHLzyyis02dawc+dOvhDGAjtIrF+/nl1//fWsc+fOrEqVKqxhw4asT58+7Pbbb2fbt2+3jvvoo4/Yc889l9FrzTeWLVvGrrjiCta6dWtWuXJlXvYnnHAC+/fff1kQeeSRR9g333yT6csILLfddhsfd08//fRMXwpBEHkEGUEIgiCIhLj77rvZrl27EjoHGUGcjSAPPPBAoIwgmzZtYvvttx9777332NFHH81eeOEFdtNNN7H27duzV199lW3YsME6lowgyQWGju7du7OPP/6YnXzyyfy+hDFq5syZ7MADD2QvvvgiCxqpNIKce+65vO9s1apVSs6f64TDYd4WYZD7/vvv2bZt2zJ9SQRB5AkVMn0BBEEQRGLs2LGDVatWLWPFWKFCBf5wY8+ePaykpIQVFBSwIFNWVsbLolKlSnQdDgwdOpR7I2BBPmDAANt7W7duzXjZ5SubN29mp5xyCve8Qdm3a9fOeg9GqCFDhrAbbriB9erVK6pesmGxXFRUxK89l/rs8uXL8wehB8bfFStWsBEjRvD299VXX7Hzzz+fiosgiIQhTxCCIIgUL3offPBBvqCAazl2tO666y5WXFxsOw7uvgh7UMHxiP1WQ3L++usvdtVVV3FX9ebNm2u/G5PF+vXrs9LS0qj3Dj/8cNapUyfXax81ahQ79dRTWcuWLfm1t2jRgt14441RXh863QKhofDhhx+yrl278s//8ssv2u/Bb8ROM34TPoeHHJu+ZcsWvvjC9+M88Ah4/PHHuTFBjV9/6qmn2Msvv8zatm3Lqlatyn/n8uXL+SIJ9YCywkLp+OOP5x4H6nUcc8wx7LfffmN77703N9h06dKFT7xVYr0mhGyINjBr1ixuELr33nv5grJWrVp8QYSd9pEjR9o+36BBA/4c3iCibEQ7UWP4BWgv+C1+rgPMmTOHL37r1q3LfzO8ML777juWKRYuXMgXhv369Yt6r2bNmpYhDb/9xx9/ZEuXLrXKRv7d69atYxdffDFr1KgR/0zPnj3Zu+++G3VO1Nnzzz/PPSBwHMr8iCOO8NS5eOihh1i5cuWivCNwvocffpi3NZzv0EMPZQsWLIj6/Oeff87rH+0R9+k555zDVq5c6Vk+4t7C59E+8fn+/fuz6dOn8/dff/113h7x3Sgjv3oT+NyaNWvYk08+aTOAAHwHyg7f/X//939aj6XLL7+c1atXj9fReeedx40qMihPLGTxW3G+Nm3asIsuuiiq7NBG0Wfg+lF3OK96LnGv/vrrr7y94ny4foQjHnLIIVHXh/M2a9aMt3MB7gcYc3DN+DzqQtVzwe+FwUL8djzk/njy5MnsyCOP5L8Z+hyo67Fjx9rO4dZnO2mC/Pzzz7w/QL9Qo0YN7hGFPtKLKVOm8PaLekfYmJOmTCJaJH76C3F+GNNgQMM14beceOKJPNTNLxg/0MZRp4MHD+Z/EwRBJAPyBCEIgoiRwsJCm0u+QGdsuOSSS/gEGpPGm2++mY0bN449+uijbPbs2ezrr7+Ou+wxmcbEEgtpTNKdXK0RUoCFAhYMAix0sLN23333uX4HFllY3Fx55ZV8ofDff//xBR925vCeF/iOzz77jC/YsPCRF6gyWPRce+21fBHxv//9j7+GxQ/A9x988MF8cYjFEAwyo0ePZnfeeSdbvXp1lB4EJskwMOB8MHI88cQT7LTTTmODBg3iu4rQlMCCFL/jlltuYW+//bbt8/Pnz+ex59BEgBHpnXfe4YYgGHAOO+ywuK4J58Au9WWXXcaND1g8wKPhrbfeYmeeeSa79NJLuZs3PCCwSEQ5wwiD+kX4B8ofi4eTTjqJn69Hjx4sHnTXgYXV/vvvzxeId9xxB1+ooM6gAfHll1/y7003CA3YvXs3e//99113fdFWcC+iPT777LP8NSEUCUMdFoKoa7Q/LLjRZrGAhQELIR4CGEqwaMNiFvcrDJcwAGIxiwWeUwgYwiSw8Eb9yTz22GPcOIL2hetDGzz77LP5vS/A91144YWsd+/evD9Yu3YtN8Rg0YiFde3atV3LCNeHhefVV1/N/8Y5cI9DPwEhLOgfYDjAd8PQgHvRC4QbYFGL+0UHyvCAAw7g50L5yl4XKGNcMxbdc+fO5e0WxikhFguDFAySaNNoZzgWC3DVwIj7SZTNddddxxYvXsxeeuklXiYom4oVK1rH4ntw/+AzqAMYdXHv4hrQxzVu3Ng69p9//mGrVq1iZ5xxhvUayvu4447jdYM+45NPPuH3+g8//MCNDgBtEG0CejS4b4AwEIkQIRhAUO64NrQHtDsYPPr27Rtzny2+E+0efQEMq+hvUJ4oe5SDUz86fvx4/hm02W+//TYlXjGx9hfoh+vUqcPHGtQ3+ka0lU8//dTzu7BRgHNi3ASoa7QLtW4JgiDiIkwQBEH44p133gmj23R7dO3a1Tp+ypQp/LVLLrnEdp5bbrmFvz5ixAjrNfx93333RX1nq1atwueff37UNRxwwAHhsrIy7fUtXryY/7179+5w8+bNw6effrrtuGeeeSYcCoXCixYtcv29O3fujHrt0Ucf5Z9dunSp9RquWx1O8He5cuXCM2fODPsB5XbwwQdHvf7ggw+Gq1WrFp43b57t9TvuuCNcvnz58LJly/jf+M34zgYNGoS3bNliHXfnnXfy13v27BkuLS21Xj/zzDPDlSpVChcVFdnKGsd++eWX1muFhYXhJk2ahPfZZ5+4r6lmzZrhdevW2Y5F3RUXF9te27x5c7hRo0bhiy66yHpt/fr1jm0D5aUrM7QX/BaB23Uceuih4e7du9vKYc+ePeEBAwaEO3ToEM4Ea9as4fWIa+7cuXP4iiuuCH/00Ue2ehUcffTRtt8qeO655/jnP/jgA+u1kpKScP/+/cPVq1cPb926lb+GexDHXXfddVHnQDkIcMzVV1/Nn9988828bQ8bNsx2/MiRI/lxe+21l61un3/+ef769OnTreto2LBhuFu3buFdu3ZZx/3www/8uHvvvdfz3qpcubJ1n4PXX3+dv964cWPrt8ntXz7Widq1a/P7xA2UE843bdo0W5/Tq1cv/rsETzzxBH/922+/5X9//fXX/O/x48c7nnvUqFH8mA8//ND2+i+//BL1urhX8Z7M3Llz+esvvvii7fWrrrqK17vcp6n9G64fdTJo0CDb67jX5T5YcMIJJ/A+ZOHChdZrq1atCteoUSN80EEHxdVnb9u2jdfDpZdeGnVP1KpVy/Y6rgnXBv755x9+f+N+kO9lXfvRfa+uPxH9Bo6Ntb8Q5x88eLDtPrrxxht5H6m7l1W++OILfo758+fzv9GuCwoKws8++6znZwmCILygcBiCIIgYQbjF77//HvVQd+h/+ukn/i/cgWXEzhZc+eMFO59eseTYjcYuJ3aMZUE5eEvADRw7u27IO4nYuYT3Cz6HdRh2JL2AtwRcmRMBu/fYbcVuIr5fPOAaDW+Bv//+23Y8dnIRXiIQu7EINZB1S/A6dn/V8IOmTZvadjOFaz9+L3Yg47kmCEyKsBYB6k5oW8BVH14r8EDALu6kSZNYKlCvA9+JXX3s/KN9iN+xceNGvqMMrxg/4RnJBl5AU6dO5d448GZ47bXX2FlnncXDCBDSZNgB3MG9h91i7B4LsFMP7wKECWCnHmCnGZ4KOq8oNYwA34tdbHgQfPDBB45eKtitlnVL0FbAokWLrLAQeEbAM0DWyIH3AbLh+OkXEHYhewSIdo46RviE+rr4bjfQBuTP6hDvw5NJBl4SspcGvJdwv4k+UHi2wMtC5zEn7ivcu/C4ku8rhKnAw0cOFQPov9BOZTp27Mi9qGRPA9yTCHM59thjbX2a/BztDF47qCs/9x/OibA5eEAg9E7QpEkT3lbheaKWkZ8+G+MIPJXQbuUywOdQl2oZALyGckCbgGcNvLxSQTz9BdqFfB+hfFF28BLyAuMU+kOEdgERFkQhMQRBJAMKhyEIgogRuEbr3OTFoliAiR4MEWISJ8DiDIsCPxNBJ7wMGAIs4OFSjdAbPIcL+cSJE/nC0guIU8J1G0YUNSYfC4ZkXaMbmFhPmzYtyoggwGJSBqEpMsIgAu0O3evq70JdqYtfLKwA3LlRd7Fek1M5IEzq6aef5jH28sIwGeWmQz0vQkWwsL/nnnv4w+m3wPVdB2L7saCJB5Sd24IQi0mEACC0A+WNkC60Y7RHvIcQBTdwb3Xo0IHffzJ77bWX9b7QH4HhC6FBXiC0DAYUXJdsXFFR2yD6Bbmtie/WafLACIIFtBfxtnNcv5xiGHUg2jEWmV7ZN8T7qrEEZS0DowXqSWhOwCAKAw20bRC6hJARGBBgMBCLdtQz+hUYuxK5rxASA90lLMjRdhGSg8+qKVZhkIGuC3Q0ZI0mnYaGru0jTEVXh2hjMGxCiwjaJl7XK4MyAAjf0wGjrAzC22AYgKEIYSleAtWJEE9/4XUvOAFDEAxoMDrKejoIxYHhct68eVa/TBAEEQ9kBCEIgkgxfibVTjgtMv3Ge8MTAxNk7FzDCIJ/sUvtFPcvfy92ZLH7Bx0NLM4Q/42FBXQVZAFQJ5IRk47vwXUg5l6HOhF2Wlg7ve7HqyDRa9KVA+oB5YiF4K233soXfrhGaDtgYe63Xemu32+bEXUI7Qp1R12gGvBkoGcRryEPWg9O2gbqb0R54oHFHhbb2An2MoKkAizAsGCGRgXuHyfDSTLbmhPxtnOIgcIQIeuvCEMFFu/weIJBwMmbAMY/eHyoRg8/9QhvDOisQHsERi1olcAIiNdgNEF7xH3gtNOvGh2d+hcYO6DPA88SiBfDOABjEMRuZU0V6IEcdNBB3NAGgw1+F3RzkHY5FfjpD8U9CV0Qne6FauRAPR111FFcAwS6RbL2k9vYE4/xMp7+It57AXWHdoj2gYcK2ojcjgmCIGKFjCAEQRApAgsMTByxuyd2oAFEELHThfflHTK8JoNwDQhtJgqMHwjJwbkwwcdiUuzIOYFME9htg7cCPi+7a6cCp8k6RAixc41Qk3Qgdjvl60E5ALFoT8Y1YUEIN3q4r8vfpYZluBnQUIe6MAe/hgnhxo/FXzy/BQsRNVOQX+IRNsT14jfL94RT+eDewoId95/sDQKvG/G+qEssyGHs8/IGwQIPQqPwYsCCevjw4Z7hI07XBuCVpe744zW5X0g2uJchsKlbmGMBPWbMGL4ARfiYCowlMB6gragLevRxclYW3B+oJyzQZZDxBw9kz0FfhHA9CJLCqIW6+OOPP7ixKREDKjwu4K2HkBh4EuAeg7FRNuzAmwChSKh7+XUYQVR0bQwGGWSfQn2poI2hzaleOX4QoqswBvm5J3FtuA+R7QqhgMgqI2eMEv08xhZZbDce42Wi/UUs4Dch048uTA3is2g7ZAQhCCIRSBOEIAgiRYgFgJot5JlnnuH/igwEYvKrakm88cYbcYcbyMB1H5NlZMTAolm3wFERO3jyjh2eQw8hFcDLRDUCAey4Y2GGxYoKjoeORjJBBgk5aw/i+hEGAZ0BsXBPxjXpyhfZQ3BeGSy0xHlV0Gaw4JJTTkJLA1k0/ICFFhZMWFTojG1eqSyxWMViKJ6HrIWhgnLQZc9A1hzoD8ghCGg3utAs3HvQcJG1IVAvyAoErwOEZwCEaKAOdAsq3W41dH/gpo/sTtCYiMcIhFA6lD1C0uQwDCxgcV65X0g2WMjK9YA6FCDLCq4LnkmqcQ1hF9A6QZkgJEkFfZUc0oWQIZQ3Mu6I8Ae1PHFPAVEGuK/Q30H3RQXn0t0DTsAbBB4myP6EEEU1FAb3H/pEuX+Fkeebb77x1Tfh88h2Aw8MOc0sDNxYoMPQpIau+AEeFvgcMg/ptFN09yQ8+2DogWcW2iTuE9WoIo8tIuVvrCTaX/gFYUS4XrQHZFVTH2iHMFbL2ZYIgiBihTxBCIIgUkTPnj25eCIWCJhEY+GFCSomoNiZlHdOsRMKIUgsyhBqgcUsFtlILZso2LXEzjV2eLEb6GeRhfAXTKDh+owQGEzMsXvqFcsdLwjZwcIJMfrYcceEG7vkWJBBkwS71AgfwXGYxMNTBd4UWIAko4wECLtAylSkm4RAJxZRWNjIO8TJuCZ8FgsXiLCiPhAegkUxwpdkzQbsiOM1LOZxbfBWwA4pHggngEENCydcM+LxcQ7oEKiijG4iv1iwde/enQs3YpGM3wtjDFLPoh2mG4QCYCcYZYOyxSIPxgHUBYwn0HsQ4H2UDTydsAiEgQMLQQgyYrGG+oEGDrx4UDcwEMEoKTw4cA8ilfQLL7zAvRlwn8B7BB4PeA+eBCrwZMDiF4YWLMqwcJZFQb3AsdA3wWIOfQKMlCJFLq7zxhtvZJkAabBRRmiP++67L++T0PZgTELaWiw8cY0QR1aB1xqEObFwhXcEQkzQrhByAtDn4TXUKfoVaIu8+eabvF8RxmKUBQwxCAlD2BGMDCgr1Av6Lnw3ytsPuA70XXjgnlE9F/Abce+gvqFLgnsH9wL6HngQyaCNwUMFx0M/Bp4mEClFXwXPOPxOiNwiVAVtDkYdeAzFA8oD/SDaJOoAKX3Rf0OfCYK5MFohHEsF/QQ0TtBnwvAE4V/0EShD6HKgf0C/BeMN7iNxzlhJR38BIxIMZqLtqKC9oKzRR6hpiAmCIHzjmT+GIAiCsKX9c0rziPSCcopcgLSsDzzwQLhNmzbhihUrhlu0aMHTVsopBkU629tvvz1cv379cNWqVcNDhgwJL1iwwDFFru4adGkPBZ999hl/77LLLvNdm7NmzeIpDpFaEteF9IxTp06NSpvolMZTpBT1A1JAIr0j0kvis3KqRqSNRJm1b9+ep6TEtSAl41NPPWWl5RTpHJ988klt2tLPP//c9rquHFHWuIZff/013KNHD56GFCla1c8mek0AaSMfeeQR/p34HqTgRYpUNb0tGD16NE9Biu9R0+UiBWzbtm35e3vvvTe/dqcUubrrAEjxed555/H0qmijzZo1Cx9zzDE8RWUmQPrVW2+9NbzvvvuG69atG65QoQJPU3zqqaeGJ02aZDt2+/bt4bPOOounFcVvlH/32rVrwxdeeCGvG5QPUnvK7VaAtKUoG9Q1jkN63iOPPDI8ceJE1/aM9K+4NqSgxv3r1NZ0qUbBp59+yusd9Y/fefbZZ4dXrFhhO8bvvRVr+3cD58K93rJlS94eUH7HHXccT2GrIu6jv/76i/ctderU4f0FfsvGjRut41BvSEuNc+L3IkUw2tiECROizvnGG2/w9l6lShXeH6DebrvtNp5+Vr1X3dh///21KcoFQ4cO5WldxX2O36Ir7zlz5vCUt7gevCf3x/hd6Kvxm9FvH3LIIfx+1ZVRLH026g3nRVpcpIVt165d+IILLrCVl5wiV7Bhw4Zwly5d+L0sUsuiHfft25e3bZQ/UqTHmyLXb3/h9JtFe8S/TqC+cZ1uDBw4kLchOe05QRBELITwP/8mE4IgCCIXwc41vE/gZixSdhJ2sAuP3VPsqBIEQRAEQRD5CWmCEARBBAC4nsN1WRZFJAiCIAiCIIigQZogBEEQeQwyLyDGHfHkiKlPJF0vQRAEQRAEQeQ6ZAQhCILIYyC6CLFICONBvI8gCIIgCIIgggxpghAEQRAEQRAEQRAEEQhIE4QgCIIgCIIgCIIgiEBARhCCIAiCIAiCIAiCIAIBaYIo7Nmzh61atYrVqFGDBAQJgiAIgiAIgiAIIgcIh8Ns27ZtrGnTpqxcOWd/DzKCKMAA0qJFi1TXD0EQBEEQBEEQBEEQSWb58uWsefPmju+TEUQBHiCi4GrWrJns+iAIgiAIgiAIgiCI1LFjB2NNmxrPV61irFq1QJT21q1buUODWNM7QUYQhVAoxP+FAYSMIARBEARBEARBEEROUb585Dk29gNiBFHX9E6QEYQgCIIgCIIgCIIg8oUqVRibMSPynLBBRhCCIAiCIAiCIAiCyBcgCtq1a6avImuhFLkEQRAEQRAEQRAEQQQC8gQhCIIgCIIgCIIgiHyhpISxRx4xnt91F2OVKmX6irKKUBjJdAmbomytWrVYYWEhCaMSBEEQBEEQBEEQuZcdpnp14/n27YERRt3qcy1P4TAEQRAEQRAEQRAEQQQCMoIQBEEQBEEQBEEQBBEIyAhCEARBEARB5Dxv/L0w05dAEARB5ABkBCEIgiAIgiByns8mrMj0JRAEQRA5ABlBCIIgCIIgCIIgCIIIBGQEIQiCIAiCIAiCIAgiEFTI9AUQBEEQBEHkA+FwmIVCoUxfBkEQBBF0CgoY+++/yHPCBnmCEAQRKHaWlGX6EgiCyFPOfHNspi+BIAiCIBgrX56x3r2NB54TNsgIQhBEYFhduIt1uffXTF8GQRB5yobtJZm+BIIgCIIgPCAjCEEQec/VH07i/27eUZrpSyEIgiAIgiCI1FJSwtiTTxoPPCdskBGEIIi858fpqzN9CQRBEARBEASRHkpLGbvtNuOB54QNMoIQBEEQBEEQec3U5VsyfQkEQRBElkBGEIIgAoNI2vDDtFWZvhSCIAgijdz8+VQqb4IgCIJDRhCCIALBqi272NqtRfz5C8PnZ/pyAsemHSVsV8nuTF8GQRABT2FMEARBEGQEIQgirxk1fz3/99yh49gdX07P9OUEltf/Xsj+XbAh05dBEESOs3jDDvb6Xwtj/pzpCEgQBEEQuWMEefXVV1mPHj1YzZo1+aN///7s559/tt4vKipiV199NatXrx6rXr06O/nkk9natWszes0EQWSe1YWG98fC9TtYmBm7gLQZSOSTzsG8tdsyfRkEkTa27ipl01YUxvVZ6vsJgiCInDKCNG/enD322GNs4sSJbMKECWzQoEHs+OOPZzNnzuTv33jjjez7779nn3/+Ofvrr7/YqlWr2EknnZTpyyYIIosI0V5gRiFH9OQzdtFGEnwkAkc8Gb+EJhRBEARBVMiVIjj22GNtfz/88MPcO2Ts2LHcQDJ06FD20UcfceMIeOedd9hee+3F3+/Xr1+GrpogiGxk/rrtrHBnKatVtWKmLyUwkAEqdYYlMi5lB0Wlu9mCddszfRmEC3SvEAQRGAoKGBs5MvKcyE0jiMzu3bu5x8eOHTt4WAy8Q0pLS9ngwYOtYzp37sxatmzJxowZ42oEKS4u5g/B1q1bU379BEFkhjWmMCpYvHEHmzGtkJ3TrxVVB5GzPPbzHP7vafu1yPSlBJ6yPbTETgfbi8vi+hwZYgmCCBTlyzM2cGCmryJryZlwGDB9+nSu91G5cmV2xRVXsK+//pp16dKFrVmzhlWqVInVrl3bdnyjRo34e248+uijrFatWtajRQuaSBJELoOdWOzI+skS8N6YJWm5JoLIFmat2spKd+8hHZE0MHNVfLoVhDtXfTgp7iKi7DAEQRBEzhlBOnXqxKZMmcLGjRvHrrzySnb++eezWbNmJXTOO++8kxUWFlqP5cuXJ+16CYJIHVd+MFH7+jO/z2UrNu/0/Dzt2aYXisfPDgPIUS+MYhu3l7AHf0hs7CSiUSUnrv9kChVTFkF9EJHLlO3ek+lLIHKN0lLGXn7ZeOA5kbtGEHh7tG/fnvXq1Yt7cPTs2ZM9//zzrHHjxqykpIRt2bLFdjyyw+A9N+BVIjLOiAdBELmh6wHeGrUo7gwAlCkgfazfFgk7JDIbRkCLQSJoiHGCjN9ErrbfU14bk+nLIEx0886spKSEsWuuMR54TuSuEURlz549XM8DRpGKFSuy4cOHW+/NnTuXLVu2jGuGEASRX5SU7bEECD8Z7+69NXnZZkcDiDCkEKllZ0kZ+2LiCnJFzyLIAJgY9347I+o1Mi6lh3jK2WucIHyW43/LuEcZkV7QfjGOEplnV8lu9tCPszN9GUSQjCAIW/n777/ZkiVLuDYI/v7zzz/Z2WefzbU8Lr74YnbTTTexkSNHcqHUCy+8kBtAgp4Z5tspKzN9CQSRdDbtcLZo/zTdrgP08X9Ok1/aE0wXpWVU1tkE1pD/LNiQ6cvIWS4eNp79OXd9pi8jsDgZ8JZt3KnNzvPlxBX89TlrtpHxL0Hmrd3ONmwnr75MhMKQ7nJ6QObAGz6ZbHutuCyiM1e6h8KS8oWcyQ6zbt06dt5557HVq1dzo0ePHj3Yr7/+yg477DD+/rPPPsvKlSvHTj75ZO4dMmTIEPbKK6+woPPyyAXs+L2bZfoyCCLroJ1wgiDiYficdZ5hR9Ur58z0Ku+FaAt3RWLhw2T8TgjydsoMSzZ665wRyaFszx62akskkyA44eXR7OfrD6QizjNyxhNk6NCh3AsEBg4YRP744w/LAAIKCgrYyy+/zDZt2sRT53711VeeeiBBgBZ6qeNf2knNWvz4HZBvQnLaPhZ8U5bb9ZiiiMN9nUghIW+PKsI/t30x1Tbe/jl3HRs5Z53WK4FIHNmo4Qfq6xNjtNTfD/1ncYJnI4jcAxnV4rlfiOwmZ4wgBJFt3PfdzExfApEAFw0bT+WXhLaPTDxv/p0jImEBR+zi/jHL8GSYs4Zi+5PBpGVbbIttGEPWbLXvJBLZAW0Mxc7939NchyB0ezqfjl9G90sOQ0aQgPPUr3MzfQkEEXOWkXdGL07YyWBbkSEyNnohWe0TIRRDDdCObHbw4/RVxhOqkJRBzk9EPumAYCf84/+iF3wEkW+EfMZ8QXSfPKNyGzKC5Dlu9/L1n0xmv82yi0gSRLaztaiUjV240fo70XXc4g07Er4mgshXwxXhn7DpZoD/3/HVdCq6NKV8duKdfxezSQ7ZwYjY2L0nzMYv2cSfk+00Newh5dOcMpIs3biTGwi/nryCZS2VKzP2ww/GA88JG2QECbDr57dTVvEbmCByCXX5Rsu5zLGmsIj9MmONb2Ms1VV2QYuZxNms0VURxhAi9f2P26bPis27uH7Ie2OWUFUQWc8pr41O+XeUlO3xNB4S/gSUb/h0Cv937dYszpZUoQJjRx9tPPCcsEFGkDzhoR9mZfoSAgOVdX6VOe2Kx8/qwl1sxFz3TBkybtOKB6kPSxuU4SF57PPg75YAKpk+so+QuWMrIPtU/Exfoc++QySHrUVlbNoKD5HxBBkxZx179c8FKf0OgsgVyAiSJ6BjI9LD3/PXU1FnUZnrFh5iogsX3mQrfxOM7QmHbWXrtmuCsvWz8EBs7aotu6h40+jOe9Nnxk4W4c1P01e7vr9hezEtsLMQXdfzwPcz2fJNlHI01rFxPmU7ShllfJwMs+Ne+pelGjIExk7OZvoqLWVs2DDjgeeEDTKC5AmLNuygeEIikKJVTiEWI+euY3d/4y8u/+RXR9P9EwOL1u9g7/t0MT/xFf+TutmrKVtJOhD3TFa78WYZz/0xL+q11/5aqF0s0iIjU3gH3MFgu3LzLlZChm9fdL//1yhPPQr3Sj6v/LmQLVxP+mTZQF6F7ZaUMHbhhcYDzwkbZATJI8jFmchXZO0aiLNFUlE6uxhMWrqZzfepebOrZDfdPzFSZnqCTF2+ha0zF9P/LTaE82SKSvf4NpgQRC7x2M9zrOfoiv739XTf8eREZhY0qCeqHf+g/wYfjqPMMKkkVm9UhM0Ule7Wvnf4s38l6aoIP5DRO3chI0ieDVZkoSfynbu/nuF5jFiETFi62bfxkAay2CkuMyZuO0qMydhpr4/RHjeXBJgT5suJsSnQQxDS6fO0CEzNvfDDNCNkhvqS1KELmcMCcmdJWVR719VDOB93e4mc56tJK2M6/oXhC6KEgQVuCQ9os9Qfou9YsG4b3+hxo7hMb4wish8yguQRl38wka126BTjTRGqcwEmiEwzTRJoS8agThOD+Lho2Hj+r5fxVX6fUhLHx6tS6IUfej7wG3tr1CLPz89YSWKHyeCQp/5MynkIdxaulxd4Rr/y7uglrMu9RtiG3N6XbNyh1TMiiGxjZQJ6WD9MW8Xmr93m+/jX/17EvV8Jb6YsL2RjF220vabOd577Yz4VZY5CRpA8YveePZ47fLrFyuXvT4hbDI4g0o5k9EBERjLmtMgQQ1NjZ5yMHCIkxu1z6mfd+htan6RWMFvcOn/PiwgNLyOByKRD7ZilNZuXMGzIxmwIGeoE46luEofGyuxi0tItWiPKLZ9P1R4PUXOkyiVS077dPHGI7IKMIAFi1qqt7P7vZmb6MvKamasK2UQfIRhE/MhT4BeGz09YsZ7cor05882xcZXt6W+M5WJvcopKIv526uZxo3tvug8vD1oUJh9aJKaOWL32dqo73lQ5RB7dC7/NXMPWb48WuF6/rZh9IYVATlq2ma8BCG+e/m2uLZSauoz8hYwgeYTXZBZq6NuKyyz3OSL5wLUcgpxEasr2u6n2dvv88MTdEP2IrAadTTv0quIhn58Tom85m2YuS4i1iRabooau56QpXkrKnsisd4jg6o8m2f6mcJgkQPdC1vDzjDXsr7nroqpE1QuZuGQzm7EqYhSnft+Z7eY6KZHmjw1RIvshI0hAJwtyKko31y1y63LGaUEXy+Dy6E+z2YrNtEvuhw/GLo1JvyAUw+JGTIx/nbnG9/mJ2BeQT/461/04KlRHyoXsIVtXfzjJ1l51i3Ra8CUPeJzBjZzILH769Tf/XuR4X+BPMmglhnwfYLHtJRxJpAa0Y4hybi0q044JROa8g9dujdZnRP2kncqVGfvsM+OB54QNMoLkEerAvmF7sW3STDvdqWejw4456kIHwgRECjrCnU/GL/cdxxrLJBdGKxFSc/n7E6kaMggEyEio09n1We7D567dZjMq6Zq8agQhAeD4WLfNmNAOfGqkr+Odxtqh/yyO8woIHR+MXaZt16sKd3nWTYhuhri55L2IrtOs1YXsR9KOyxg/TTeNHuHoMSGKMIX/JgM/80vdMaiftFOhAmOnnmo88JywQUaQPOaMN5zj+GknJDU88cvcmOsCLFq/nW12MKAEGcSxqiR77iruBdrj9S6jVNcF6nteJiYKOYKbI4Ju4e3HcYHGAm82bDP65uWb4s/gAD4dbyzaieTw7ZTY0oqKe+KP2WupCjTImnG6sTceJi/bzPaQB5UjyOC1PA5xaq+xV/f+/d+TJqAfwh7t2dc5aEKZE5ARJI/wE4bx5aQVthh9InmUxVimv89aywp3lVpeDpOXk5aIym1fTLP9DU0Qt/jveKCxKgll6FGIqkdCPIuXoGOEwyieHTGIoBr9U/S9Q+0/+TiVKU2Mk4Cm+xdeHSEf5UxaCHaQKeqdfw0PpX8WbHAce/2i6nbd+dV0ttuhQoI+DiDFPPptMQ90oqg0Op0tivT/fpgVOde743mItpvHd5RIMBEzt385LbcGzbIyxj7/3HjgOWGDjCAB5c1R5JabbCA863eiixztz/w+j41R8o8T8Ql0JoRZYbRAiW1XCeUlDFJeu1KqEeTFEQvcqoLQUK6ce/mMmh9ZwOhY4pChh8IkU8twyesA/dfOEpqIxsvLI+39RlzdhfkhygpmsK2ojG3e6b4Id6Lbfb9GvYaMbX5xGgeCAtKTG1m/3I9z0nZaLYmfikOQjQ2ohhUxRr8u6eUQLGnzkKwdR4uLGTvtNOOB54QNMoLkEbp7MJaB/g5YOCXicdEj/PHfkk02cVqQrX1ophm90H1x54jPWI2ww675ODJQ+W6bXu+LiZnXsXQLOCN7QOnuCbdyFX07ySCkCakuJi/bYtOMGr2ADN/x8vXklWzsok2RYlYMGvJ7TlBkRnKzaNzzzUxWttu953bt8wM88dleVMZmrNqaEu8kp9nPRMpemBBorrr60gkwE9kPGUHyEHgZxIOcFxuMnLvO9vdnE5YndF35TmmZ0e3t0rguqnw23l6WtCvl3JbPenNczHWBxZ7OhdRdEyQybBWV7WGP/DQ75u8NEqmYuFGWAfc2LXYE7/12Zkz1ofbtts/RbC3p3CZtKFD4RXKRPQ3U3e63zbAON6g+ktMJCH2blVt2aT0Vpq8o5GlCRQYTHUGf96zZWuQrbbxTCLBr+QW9cFMERPR1ayH1DrhUEg8mshcyguQhx7/8D/933dYi2yTBbZj7ZcZqX2nnFqwj0UKn+cKH/y3l/z73x3zH+Nf5puijTk0d56XytYMJlorf3ew3fLp9Bnknyi8L12/X7qiKuoDq+f++nh73+Sn1qDd+3KaJ7NtkoH49cZ75bW6MZe58o9A9lJ6Qani7Tlm+hY8Nz/4+3yXjVZIuKIdxKwOnOaHfeZDTZhCVu0t9eJTpG38vjnseGWtfRqQWMoLkEWHF3RO5w9dti8SAPWrubOsWNDNWbo2ySOsUva/4IAN5rrMMp8HHqw+EW/TZb43TlmtYU75weffrzZAPIDUqDG1e6MS9ej7wW9wK9OJTNClwRi7a7yXhu5BGdDmezSi4BRMecGFUgyUbdvD+WqR29p+2T9f30CpEMHKO3ftRR+me2ASwUbpXfUjjpq+yCoe1/fgvM800oEng3KGxexYS8dWl8GBw62Oo94kug88lTwPMCeMJYxRlv0gKRSXiQwybkc2asFaTy2u+jhr5eUby+jIiccgIEiBEXP7O4t1agSaV+7+PKE8T3vhZhMModdrrY3x5Lbz650K2eWdw0ubCa2n55p1xf/b9sYYnTrq1dPId1Utj7daIGBuRPkSbXF24i5W5GFJVxi/ZZBm47/gy2luHjH8RHvYRArfVI5ODrnwhPunl7QR9hVgzjOUbyCzy4bj4+nG/yPpERGRTZ02hPTQDHk9OYSw6thaVRs2BxLmd+ph5a71DQYKAapyWx9gdxWXs84krojxkkeY57FEfbgz3YfANKnJ9+J0LXjhsPM+ohPtIB16XNy2I7ICMIHmGH5csnVV5yUb7xOAXjbWyQ6PqiV1cwBHlvkWZRDspf4u0f4Q/4vWambEqOrUojDFqPQWVaSsiwo5R2WGkNprs1MWEznU8zFZujg4RsypEw4Qlm20LbRXSvomtjzj99bExNU3sgiOLg84DU+bJX+bwEIIgU1y2hxWVRhuCvKY11IaTH3qKv3+bGcls5Ab6pds+n8aNWEs37mDbikrN7GERL0+Vh8z0rhSOqitPu5fkPd/MsP7+fdZanrQAx6ji+jKXvz/Rtc5UzT/CG9FWN2zXb05CA+eKDyb6Du0mMg8ZQfIJh4lC6zt+ZD9PX23tMul2EdXli9CukOnfth7/F+ciWEw7sRu2F7Ny5oJRDTsS4R1OnSeRWr6atDLqtQ/GLmVLHVKKEsx1US3ARE2384HFYJQYHNlPXJE9BG7+fGrcbuWlmiwOThO6IIKd7xFzohd+2OETxGqbFpkzvDJocCg2IIrL3zcEBuFloPO2hIeNOqf5YZr3HIX2GJLXHHEc6uCGT6fwPmbmqq3cq0qUsU6Y+c9561kQQRtWvcLcnMS2KWNsrIKbIhQpalON+hob0FCcoBihUdbYpHx39JIYytudtG+uVarE2DvvGA88J2yQESQPwQJjmbKAe/aPeVwjRNYGkdHtvjid+5nf5yXpSvML3W4VXOC+mbySbdlZyg0hbmhd3AM0UCVjHVyaJHdy8mqQykKZPIm/neK8hWFk2oro3T/HCV+A2nk8nPTqaEMYlTFH45yTR5mMVx8UdBC28qZG9E6mcGdsk9ih/xjnO+qFUQldW1AR4StIj7toQ3QoS5lGowWpiInkCoi7gb4H4RmxjKPCEB60rv/7aavYO0oWo1R6w2BDZ7Hmvgm6FhTKXA47woYNPJlk4HUDrTNk8fGDUyiMzHpJpzEtVKzI2AUXGA88J2yQESSPkDs11fVK7mPHLY52uYWCt4gDffjHWbZdksd+nsMXNsHuMr3RDSrnSCJs306xd7A61kqdqFj0EP6Zszr+7EW6eQi8eASySyoRXV6iz8BiBUx0ScsK7v5muu1fwp8YcKzCzI//MoeKNomoO7Ox8n9mGIAK9fWiHMIxGbjv06SLJvyDDC6JesZEjQWOxwV7owfAUwYPOXNjnJruvvj4v+Xs2d/nsY2KATxo5a6CMj9v6H/adin/JXvtTDbvFSd2lOx2DVEisg8yggSETT53RhZt2M5dF+U+YdKyzay0LDIBIXGfaBdqPPy44OoIJXGCHWTCKXBxvvOryAJ97KKNLIiEHP5G/L7b8W8ru10qH4xdxu+bsYs2UTiMDzBxdpu4BnxOmzPoRMjBe2NSKwiay5Ts3sPukPpiGd2mDhFHGTv05374cNwyX4PHRcPGx/0d+cbcNdvS1pfDw0ENSQq6EUQ2uELU1A//5yNZRCL3UUooK2Psxx+NB54TNsgIkke4dWp+3UPdbmAK29fvZNzy+TQ2Yva6aJ0D0+VT7LTEOik2xK+CNVIl8nPlz8YTzvLbrGgxYIi8EXpeGD7fM3WxF5e8O4Gt3rJL2+88/4f9/EFnuiku6LzLmtbLyUt0ZYt0xERmSYU+E90vUlkwxq5OYhpn0RUJHTRxH8FDWe3XgxqW4bZh46a1lSz8hE8GhVVbdtn6/h8dNjTFGBwPGdMgKi5m7JhjjAeeEzbICEL4At3lPws2BL60dOMGhK5e+2uhY9lAJMyNaz+ebC9r80t08c+EM6Pmr/fMaCK46bCOUa8Nn22opd8iCU/qhCSDRjyD960O4p0qo+Zv4C6kBz4xIur7EDtNKIZXl+b43+KN7LPxy6nIEgB6N+qi7JIYhQiJBMpf81qqNgKoZ7ezO4nl/M/8DVH1Ke4jp3795s/8jRn5iGqQ0AkAJ8p/iscUtf/ozbJU2oXI5pSdkBEkj5Dv350lZUk/8a8+06XlMx/+t4zntv/Iyf0zCex17y+B7TgTsZaPXhgJV3l/rLtreffmtbTpzYCT507AqiIhpnoYodyEmYPW5v0Q8pE6e9KyLWwFpeHLOXZIu77U9KPDeIUwarJ3tIPmZRkPu1zmkTrPV8FLIxf4/g5RDbGOGfnEM7/N0xqkthaVprzfefufxVHZaoJoDEl1Cdz46ZQUfwOR10aQRx99lPXu3ZvVqFGDNWzYkJ1wwgls7ty5tmOKiorY1VdfzerVq8eqV6/OTj75ZLZ2bXAW7vKgfvG7ydu9ws7Yk7/ayzqo7BQDh6x3EEru5NVvpp68A+WYYEFS2sPUE+zpUmbxdB0Ph1lR6W42T5PinPBRvmF33ZVkIYvVHv/yv6n/whxCLn8/i7N4Q2UCvu6zMWfNNpuhdc4aw3v19i+nJ2083rKzhG8gEWbZSAWhGjtEu481E1U8m0YQav43gF7e8noJ7dTJmJqsTIGbd5b4MiAS6SVnjCB//fUXN3CMHTuW/f7776y0tJQdfvjhbMeOyC7BjTfeyL7//nv2+eef8+NXrVrFTjrppIxed64RSX8ZAX2DLv0Z4c7VH03KyGeDjO8BSzPeiR1HuKKqi01MCoM6cEW7jIZTaoi6KcBu0YlkXgCYO3e+5xeutRLbd9CKkJdtmqyoSL1IRHPrF9PSWCzBa/NO7Q6vy23/mo/sIbrJYEfxbquvUUOAgw6MUDJlphGEuuXUonb3uk2G/6QMgcnige8po1W2kDNGkF9++YVdcMEFrGvXrqxnz55s2LBhbNmyZWzixIn8/cLCQjZ06FD2zDPPsEGDBrFevXqxd955h40ePZobToJAqoZ0WEipM3YmFIf6txfis0Eu90wtzKDFMn2FXQBrexGpahOZQ2Tkwi3htk4Xu1lOYstOIK0xxGnltI0CNbVi0NjsU1ScSD6ptEkJTxCEAwQFt3TZoRTXn+ib8P+5pqcJ2LC9mM1aFaxNBiy23dp2KsOtBQPa1Uv5d+QKMAD6zaAZC7S5kP3kjBFEBUYPULduXf4vjCHwDhk8eLB1TOfOnVnLli3ZmDFjMnaduYa4aeX+OcDr8Iwj3FKDiFMK1qRMmj2Om6oYQYIMhRhlFrX/dRPrjbevLt29h6eAfvZ3e2w66PXQHyzI+M2sRuQWwsb+0X+pX3AGtZ3LXoRO4/npr49lR70wigWP+M1OUz0yDvrBp9523hJWauL6T6awCUs2J/c7pC/ZRhtpWUlOGkH27NnDbrjhBrb//vuzbt268dfWrFnDKlWqxGrXrm07tlGjRvw9J4qLi9nWrVttj1wFN1yim+dhs0OQT4PwgN17AqpTkWEX6hdGUJrQ2OrCO9Vzh4bVWaXy8XV967cFe1ccUMacTJa9ez+8prAorvOK2P+gG7ywG7gug/f4D5QRyUYqm6OsAfDlxBXsp+n6tJhEclnkIXQbBBLVmXj0Z2ePHr8ENTWxDjHuISzJbe4YK6KEv5+awUx3lSox9tJLxgPPidw3gkAbZMaMGeyTTz5JiuBqrVq1rEeLFi1YkDHcrUNRu+KbUyjQlEuEHdzcVOPTyiRlaQhyOEwyY2xlTtsv/nu8LIDGQLUtu7lUJ5OAr8ct5L7mxFdGux7797xImmgiPhZv2JG0/jtWPv6PUhynC3lshWjhFprjZAT5XvMy8hLJQxYGDmLYhrzWkZc8J786OonfYZTri8pm5igzhXRaqFgRi2bjgedEbhtBrrnmGvbDDz+wkSNHsubNm1uvN27cmJWUlLAtW+xuYsgOg/ecuPPOO3lojXgsX567kxCn1J6xMC3AacoScaVTlaXPf/u/tF9TfqQpsyt2p4JLD2qbgPGL5SUIhXDi8vcN3SW/JKOMeJaOxE+TdxSXGeKCqXJpz9f2HSsXvhN//x2rl1kQFyBupKs05DE7XYK4ucCdX6VTnNZOrFpG+czEpZtYaSo3XcKG51uQCWnDttzH2Fignj37yRkjCCYKMIB8/fXXbMSIEaxNmza29yGEWrFiRTZ8+HDrNaTQhXhq//79Hc9buXJlVrNmTdsjyDzw/SwSglNAykkVdd6qS7en+xzhn3jWBhB41FGrit0C3qBGZaoKyQX/5izLyPLp+OWBEsrLFoaNXpLpS8h9fKynkQITu97jFm3kWalGzKFsMXK/L8bOtO6YEpxfZ67NaN1/NiF3NyJjwcvu9vgvc1MqyI5tBnGfBXWxLn53qmygXnPYz9PR1nfvZuzPP40HnhO5aQRBCMwHH3zAPvroI1ajRg2u84HHrl2GKx1CWS6++GJ20003cS8RCKVeeOGF3ADSr1+/TF9+TrFma5HvBegzv81l+c5xL/3junu3aEP0Yg0LuBWbM+NSHWTe+HuR9vV3L+rD7jyys/V3x0Y1+L+t6lX1JUImxkjdbfGMRkwyl/jkv+XcJTlZivTJmFC99tdCS48o6KjpylPF/74xdEGIxNirsdG3eIXcvDt6CdtlLkK+m5LBmPEsA4szpHkGqdTpUL03CQN4Byxavz3rxvB8w2vdDSHZD8YuTdn3y5nG8kWnJZb1iPA1nbh0M5uWIiF8L3/W19PR1ouKGDvkEOOB50RuGkFeffVVHq4ycOBA1qRJE+vx6aefWsc8++yz7JhjjmEnn3wyO+igg3gYzFdffZXR685Vnv3D38LupxnOorP5gm4dtmRjxG1zxsqt5NKcJZR3MenHa+1fsnGHoxYM+Hn66pxerG8tKk2u1kcSFhfi/hKLRCL1mUqWbzKMtjd9OsXxmD/nrqOq8KB8OX8dzV+Sfkvu9h6Jo7a3tT7Ffds1qJbUbooC8CJkajjbWVKWNx6AbnMCP20NoemfT1zB0mEEfPCHWSwfiHU9Mnv1Vm6Qlpm3Nnnt7yYXD9vvpq7Km7aey+SMEYTHiGseF1xwgXVMQUEBe/nll9mmTZvYjh07uAHETQ+E0EPRsW7tMLbXCf/ASJFoOfpdgMSbdUl3ffPXbWdD/1nMcpUl5iSgcBeJH2cj6e5b3PRhHvlpNgsCW3fF74buV19CDvVwquP3x+RXeBLc79Uw0Rmr7Luwk31qmyWq42HTBGHBxO1eTwaxVlG2hWUmwhlvjk2o/aaj3080S002sXD99riMComWgFto9XQXDxO/xl4iteSMEYTIzgkzWTINFm/MD3fCXG/LFWI0gpTzMUvDrs32YncDAXkspAY0AaSsm7fWOdsPkVxWSZOzGSsLA2X8kENC04FYCDllxXh3TOrc4TPBl5NWJC3sLh6m3ne49Zz2LRgbv3hTRurBafGNDYV8YcvOEm0K8007jBTcmTY/zFy11Wak0l1vLjE/Bg8OjGvJWis1rV0lLo8f8jzLDsgIkmck48baUeLtgt7x7p8T/p58Ipm5xeWwC9FRw7Xyqg9jy9KRa8gxquDzicuT5gmC8tRNvPxMREbN28B1M/IVeUfKLVPF6IX+RAqTvbjYsL2YPfRjsBbh2TJZuv6TyfzfEXMoDMYvoTjbeFDwas1yF/TbLGeRzv1a1dG+3qim885slYrlredXfTCJBQn07RDkTWfYyz8kbGvj2ykr2cg52ZHGHJojV34wMW1eQakmlv2vG1xCPmOlRuUKrn0Z5u1lu6NvtByOoM4ryAiSZ6TLEyTZi/5cIV2Z9JAxQN0hgeVesC5NO5WZZP222BcGTi6mwmXx2kHtba/7uV2+mrzS0mLI97CnqS7um2//s9jXLq6TsN2zp/eM+Xo27yhJ2z1HOLOrZDfbZmrHBIV0pIsXRsegTIj9uN/7Nfo9dnKPhBZKqrdPvvftk5ZtidI/8Kv9Fi9bHEIsSxw8n4JCtoSh5JMndyzhccmaP7esW5UVSIZVFfQp8FBZpNx34j0i85ARhEhKPnMiNcxabRg+hjz3N1sqibH2eSSSCjqfFh2yxTyeQcLJk6F5nap8Md3MxXXRjeyYsqQGee6g7hTKbC8u85WtwSkr0rE9msZ8bbmeeSdR3DxzZKq77EbFw/y129iU5Vt4pgggsgcFaeJ2+uvuMf1OxFJEiepa5EObVoUIU9nGnMqb61Gx/Matb08VTq1bNcbkI07teOKyzSxb2Cql4M31vj0U5+9OhGsGtWevnL2v6zHrHbz8KDtVdkBGkDwjE/3Y7V8GJ7Virg8U2cyjP89h24qlQTmO1hzr4OZ3kRkUVA8vtXwSCc2IZ8EX9Pp5acQCNmmpt0fCuxf1Tur3IjXjA9/PZJt3RnZyg1YTser86Nyij+3pz/AXlHbu1AVAE0Us0hMtirVbnT0IQxpPs6AQQHtbVtaBGGOzrT6+nryS5TKZKM+DOjRglSqUc+3Xt0hjqExxujLfVazI2BNPGA88J2yQESTP2Jzj4kbp5MXh81kuI1Kb5iupWBeopwzG0sM/5wwdZ/t77CK7l9emHf7a3LJNEa+lRBCbl/m6SCwu281e+2uh4/sIw0LaSC/aN6wR1/e/cW4v7evlyoW0IRrZNnHPJhrXKuD/ViofmVY9elJ318+MM+PwEaoQZJBd6/upq1L6HU1qFUS136WbduZVSEC2Qf2FHmwmZEtIjB/9nXxETCkSaaMNzTDrmgWxe2K+MGIB/3eZ5OGdEipVYuzWW40HnhM2yAiSZ8S6VqhYPrs64nTyzZTctnznJVL7Tfay9+CODVjfNnWtv2vEMHBNWLrZdcDM5TV6yMFlGoYH1WVzthme5UVRaXJivr9L8cIo05TuDrPHfp7jesxnE7xFeeOZyC185CjWom5V7Xvv/LuETVVSlf67YENOt/NUEzbLVN4Z9KqWuWvcsx4FZYGOe+CWz430qKlqYojfV73RrF15pU5a3/EjyzcyMdPLtoV+pqH+M3X4yfSX9O80RYYuOqCNZ6Y1Jy56d3zSr4vwDxlB8o7YphC1qsTvHpXrcZ2q+CiReX70oTkRLx0a1WBtG1S3pdOluExnznxzLLs4iQO0U+aeIKMrEewMjV6wwQoT+HVmanboUB+xTMpHUaYHbRmqf/dvV8/6m3bC7aC0nNpcmRUOkxozyFOneggzm9/7+t/OnlkEkWjIr5wphkjdYPrJfxER94+l54Fj927Gxo83HnhO2CAjSJ4R+/wh/oWJ2MXKV1d1HZme1M5bu409+av7znG+kOpmhV3BADXdmLU6NmwvsXl0ZLKsgiQguWD9NvbXvPWWEcQPwSmd7KFapfJsUOeG1t8X7t+a/3vFwe1874QHrftRb+Mnfokey0Q/89YofZapeNF5Pa3YbLii3//9LOu1rybR4pRIPelYmPdvGzHIBo23/11sPX/293lsdaFdsH3uWncvvFhIRHMY3n4pXUMVFTHWp4/xwHPCBhlB8oxYb8ZE1hY/z1id9xO5v+etty1EMrHYkEMUrvpwEvvdIXZzz54wf+QLiYhwJror6UQQjCbZaNTMxmtKFfLC2a87eUVJhyKVBKcWvGlSu4pNU+Xsvq1iHl/d2vWkLMoikap+XadDIN4fs9DQS9FxSq/mSbmWV/4kr49UMnxOsHQmZOa7hLLhtk+mYf/ADvVZ0HErzXXbitnl70+0zeWv/nBS8r48wflJJjI3EQZkBMkzTnt9TNq+69spRrz+ojwOK3lp5AK2w8xYMmLOWrZEETEqS0O++5NeHW0TUhSMnLPOdtznE5fzR97gMS7cfFjHJHiCxDb4/GQa/sA70k5Dqg026eS/xZsCpUeQSRw1ZmI8T0HF8nF9fz6120yQ6EJGV/oiY4mauUSkK85l/Bj1/KwHkrXok69GzoSUj3wxcUVGNI/i9Xgl/PP+xX1ZZU2WkgA5UHr2xdNWFGozfsVbRu0bRkKrE7VhnJrGdRthh4wgAQe6CIQ7Yp38z/yNbNkmu8Fn9urUD9ayQOHyTbvYvLXbLaFCmV0lu1lJnJOObMRLryPRAR5NP9bBa8TsiOHpw3H5GWfqNHEdZxpHUrUgIc2QyGQtWz1faLSIkCojIbR4dLuD6dzgyIZ+vzQNu6PyGPLM7/NYPiM2reJlzoNHsHRx+LN/s3wG/btTX9qibpWYztXBXIy3qV8t2EYQH8cs3ZCaTCyJbiYU7spvA2w2Q0aQgFO9ciRDRh8pcwbBtAOKujYpySKhIVxagMa8hHdh8fFEhVF/nGaGhGXnmtUXoVQLDjLGLnFQT5c5ed9mgcowANdc9bfhtWf/mJe1k1i4eMveaPmOuL+9dgIfP1mfCter/tzezlfRZq+fJd73E9p56YHe/YobXn0LdEl6PvAbywcS3e+CtxmE9A9oT6EXqbwHGlQ30q76pW/b1M7bJy/bbGnnZDNqX6vbzHlvzBLr+cotdo2QWHnkxEifH6+99tx+rfLemz7bISNInrN/e//CSMf1bJrSa8lFQh4aEsVJSgUaD+q1vPrnwoykCcvcZNnfyNO6nj4NaMMaBeyM3i1iuqayPXvY2q0RcannzAVrLi9X1CYT665Go5oFnsfcfUwX7+vIU2OHEye+8m/UayeboW/pWv/G01/sLA6OEeT54f68A5zabiLCqPLEev22Yt8iuVkNNhJ8Zp3z0w/1bl03ZoHIu47qzPyyvbgsb3Zpk6FB0aBGZfbBJX2Tcj1Bhrdsh+p40semgsxDJ+gNsG60axDtNeIEdHtmrtrKcg2n7JVqPxrPvKNhjcq2TeN4Ddbvj10a1+eI5EFGkIBzo6SrUKMg4hUiqJQmwb1sRnRvujnEPd/OYJnicyXGF+JPuWoDGbcoWgTPa1jxO+5UcGjDjWpWZiftG5vA3urCInbbF9Oixc9yeNcWWWBiJRVRdMf2bMpqSJ5p+Q4y76gLvSJNzHIq6dy4RtLOddfX01ku8b8ErvewLo18eT959cd/zjWyAMnMMbOuiXAYLMR7P/wHWyrpUen6y1wgJGkOeRFPl1pf2UUX3jpOBhWvDBHVKvnrj7I1fE0mGV32jYMT0+FSqVopPi2jXAeLZqf6qFu1ku/zvHL2vik3iOXKlFI2ZuwsMXT8/BhH4gll2adlbfs5wslZK81atTVvjK65Aq1wA85R3Zs4vtesdhVWzmcLydVJmRfQQXCb4CzMMje2XBmwVP73TbQxKdXzyl6t6rKqlWObhDld05cBS6uYCo+j/VrXYS+ctY/2vRxYYySVcBbvDj+uSWvKYljcZgvjl3hfr9BfEtw6pBP/94Hjutr6Wt1E+ri9E/OsFLuL8zULdV1/mQsUl+1hI+asYyVl3l4t8d3zYW74UBfXsleN287v9BWFcekUtbnzJ5btJKPLPrqH83wxHvrmeQj2Rw66YWiPyfDMkefv8vn2a1XH9R46uGODmL4n18bfNYX+U8HG4839+rn72f4+p28rNv5/g6PKX8ehUlp1FYjtL1yfZK2pihUZu+8+44HnhA0yguQ5h3RqGCWi1Mu8QXs0r2Wb2KmdMqyd5X121L/OzN9UaFCN37KzxMgmksTlyYMndLN2qs7vH51eMR5y1RMkE5krrhzYjofEJINE40tzjSFdGyf0+T4Obuzdmxl9ktoO8qld3/jpFGuXX013rYptek0+Yag+p19LV8G8ZLMxD7KUxLtzr2ufqIPj93bSs3HWEfFrBBEhUrmCCBHUcbdpvHl55ALP86wudO5Txa5rO6U8hbHjHpfwO7e+ZNziyGbOqi278ip1ZSKL7ncu7M1SwU2HGXNPHYU7S9kfs9amJQNfqnDyjnPrf+Jtca3qRsJ+h17QOyp8Q8bt/lAxmk1u3QfCS/rF4fNtr+t+xR1fJe7B2LJeVVarakVf4THdNGNI5HMp2MisVImx++83HnhO2CAjSJ6j2436/PL+/F+IXIGrBrbTfrZjoxrWMV5kk0BoKthWVJb0zqm2z7INKp6aID7Okc7wCj9CfrmAn/Xh+QNaJ/QdOg8zCO9VdHEpRfne9OkUlut8PXkl21ZUamWCkRFNSNTBpxPcU173bl2HndJLr2uTKsNRvrRz1csjVkQpVKlUXpuiGHWIhafsTdClSU3f5xdrP1Hccn1ms03wp+nOYrKxGKHUdPQyR3QzjLDtGtiNIDBaoIxFkYtyaiktEP0yav56Vrpnj2O7z7X7IFX9wQ/XHuDrOJ3wvttGx++z17IN24vZ7lxzQ+D9eiSjn2M4jKY+mtdxzgzz+rm9XM954f6t2eC9jDA9dd4ejtFD7uP/ltk8p7K9CjAvkA2YY03P9F9mrsmI2PSJ++iN4l58OWlFXun65QJkBMlzsNMt7z6hCyhnzhAqVyhv2yFQb73rDu3AOvuctGV7J+kFYgg9dxziSKnqBk71qhnT2dXFOuzGhe/858v9MpfYWlTK4+C98DNUTH9gSFIXDGskUVSVM94wUlsGAadMUn4zTFWpWF67G6Uzuu4w20Lbu35i01bajQaCP+euY8s3Zb+CvYrTpAyLAz8LxbrVKrO9W9jjk1Odgtjtqj7IIqE3ZDWY4dBe4h2vxPwUHz/ChzcUDq9YPlLuL5ypD/fSoS6y5WvO8eGWfTd1Fd/pT7ZXA+4nlLEIeRHldP2hHXydd2tRZNy5/cvp7Ilf5vLnMFoe9/I/1j15/3cz2d0Z1AOLBXFPJnL3O7n3Y27ptrPN5PavabRu8ymUNRaEuTi33OLRtp1+E8ZFp41H4X0pZ3SU6du2HjuoY33Hz/vdEHph+Hy2cvMua+zFWLTNHIOzVcgT84IXR0S8yyYv26JtX7G0pSa14vMS/v3Gg1j35tFjctv61fjD6xKSbgSBIXfmTOMhGXUJAzKC5Al+UlCqKeV0QqgqfufNOThO2Xjq13ns34XuuiaGRdz9l9atFpu7WYdGNXhe+KNdtFncGKkI601VdpZzkR73/8Zu/3IaGzY6ks5MR9hn+//uGn87VX7ZVbJb6/K9aWf2hwlAeCuV+B2+a1et5HtXHC70m8wQDDVcRBaYXLIxu/R53BDdiFt3gowgiSJrIwy/6eC4PBJU3PpAOQVhqtuaFxOXOhtBksEtZhipG5jPis2GWJmxKvf7cjcvjy27kt9fWk1TzXjlc4KCBaCOE18ZzWas3GqdB2NTJjPDxcK75jiKsN54qVGgX1if5JDWXEYsRAs0IqhuO/Niwy4XU0V7rWOdwh7CHsZqaKiIEDBd5kekXB1756HW3yLUHTSs6S/17qj5G9hLIxewN/9exMfeLyausMTgRVvKR0FanWdNrCx57Gg+p9dx+cFt2e83HcyuPkTvee+luRU3u3Yx1q2b8cBzwgYZQfIEt073/47raj0/vbc+ftxpF9evVTIHxylLy+HBH2Z5Hme4NsNVcHPSrwGK9sLls2mc1ucgIi9wdNkahp6/n+WuHis1XQyERWW72b8LNjhqBGQzN3w62fOYaz6aFPf5/W5iwMusfzv39N1Hmi7vmDDmq4uo0wTfj3Ck6k5+0f5tHI0gHRtVZ63rV+MpoWFwPXW/5glcs8v1SO9d+3H87SiZwEsoWVl3dKKa8viq45ETY09hCd4b473r+qvi7p1rJDuririfQinSxpCvtlIFmj5fYIZF3n30XlF6RFZmHrNODunUgC1+9Ch7eXpU/6fjlyXV+zbVfDlxRZTOUzLbPTwcikqMvuy0/Vpo27aY76AO9mlpePAc1KFBzO3+P1M4eu1WuzE+lzRa1F8cS7H7Tb/t+1rM0Egno/hcMyvYP+bckkgP1IvnCW4d3ID29aNeG37zwez2Izpbf+/VpCZrVLMganDzv/jIoZFK2dVfsdnbjZ6LM/o4n9PgdlxPQ5ulk2QlFsdiUVLRFEl4+KTYJ8zrXEI08hnkr3dr//tr2r0T6u5L58Y1fS2GKkiu7mhL+YDsEu6XyuaCwC3zgow4ql2DauzOIyP9kIxws+Zx4T5mwn6/OxuA1xDCHZyMIH7bkixcrd4Chlin8aIQ7nzs5B6exicv/O7MJiP7QTJ47a+FUeF1o5I40dSNr20bVGP1a1TmZQAXdVAtielARck+9asRrpFt+PFi4p6VSU7pbBlBzLb3jiIQaXxv/GyX+kZZNyEWwdt0g6JYsM49FXC8VHVIIYzvFGWPGvnxugO4ToLaJ7gaA8IIx9idUylDMVZthoi+RytDO41nxlyyew/buCM2D0HM7Z8+raevY2evjnjvYUqkEyc+9fUxLNvwO17GUuZqW+1pJpLw9T2adu21lloqebImwwuU8AcZQfKEWAd2CIo1lrwOvrl6gO39awa15//6TZGbq54g6BaTuXhyKoZmpuBVd6kj7WHGDV5yYFvLeh9LjnjBujzoMJ3CHPyS6HorarIcYuzDS/rqv0t6LoxXoLgs+40g4j69L8nx7C1M4UFdf6HTThD1hYmG+KzKJWbo3qL1O9j8FE3iM8WdX01nSzfttO1yrtsWuzFzsOQBpXpRX31Ie6uc8VyQaFc9wcUbLkvsHrZJrM6TaKKP9Lj6c/rbyR1x80CeNQYIsd+rpDrId3o//Iev/nBtDKks/cwzGpsbOeJeQN+C2H6EnCajjX4wTu+dk0XNXgv60HQx44EhvL8R/TrqrGvTWjwEMhbu/34mm7V6K7v6w+zwKPMDb1um17Ab8Xq3wLvDr+6W09zejSOfH2U9r1C+nPY6hUZXNuGUnU+th1hCq+SPQstDiM7GAzwyvaQFLnt/ovX8zDeDoy+XacgIkifodt1iSZHo5KLl1xMk11PJ4We6pi3zMbCJ43TAdVHeLdq3ZW3Wpn61qOOqVU7ebmEu8+M07wwDXsQy2ZU9OvhnpcWLCtddCdldRo3vy/apcAQv/Zt40fUXiIVVkQ2PiHPWuZbL5yrdrb+xNm4v5lkcchW5vxgj1Uk4DpfdM/tEhzo6tchEWuquJIWWpANejklKO/jvHYP4eZBiHiGMfhHtuHU9o7+/71j/6SmdCEsi0tnapr00+LCrHWsGEq/98+fPMIRn5W6oWuUKtr65Vb2q7MUYBGrl+Y1TRphs7vpFlqJk4hYuCuFOp5TRKm61udPc3Z+y3D3TSjbiVdpYjJcqIY8wmu7vw0tPZKJqUss5k4xMvJpEW3eVZp1xD54p48zML/Hi91aQj8P8EH/v09KfCLnKIZ0b2uYz8aSvR4hsssMHCTKC5A2qlREK0af3tscM3htDbvBYLae5agPBz1uxxU84DGMvj1zo43z6ghAGj/8dtRerX12/I4LXxUQ5Vn72kZYwn5Gb/8U+RYJlmte2eyNgvMJk+UqH9NG6cTRJyTdSygpT9T3ZwNX/2J5Nubq9iohL1nlGfXRpX1avemXWul6k/BvWMBaYXsX58sgF/Pe8ODyiCp8zhI2+Qs5oE6vuSZ2qFW1trq2SLjTbF2ephm/Kopzh7eejHFY57CYKuGdHCLvaNbUpcb1C7Y7u0cTmAaijnk9hbfFz1Hj9bMLLYHHSK/+yHT7d2MX4qRteIUYY+c5oI6sahjSocyM2sFMDFguLNxieFNDuEuGzsYqgZ5JkdwNq+OgZfVry++O//0VEOQXdmtV0NKAEdU2H3622/TP7tGAPHN/N9XMfXtrXCoH08gj56NJ+ynfGVtjJyiaWTKCbcXoMWfii+v0YikDnIR7LGC3rxcF46rSp5pdHf57NnvtDL9xMxA95guQJ6r2JARphFkxKEacTj/Q8r8/hM1ctlLhqWfVdGBRGzlmXlN/3xCk9rOcQJ6xTrZLj4AJ3UbggxoPskRA01BShYtLlt+3CQ6eltAgXn8U9Jevm6DjGXNjE8n2ZBLv4iFtOBuf3b2U9//LKAeyyA9tayvVeiHsAKbyd+jKvCce3U1byY9D2vTIJZRvGwjxkE0FTe5hX/nQ3ujYwNSdk1B3udLRJuW/MtmHgrq+nG5kYlHIarfGEuucbHyFicfw+tbtfuH57wuE2uYDXT3Dy7nIjFnf2HmboKfomlXg9I9CWhOFJZKwKIvLcUnh/9GxRizXQeEipi78nT43oU8jtXBjQZd20XMPqbz2al+7+VsvU9p65sYPx0m/TxfiQSJsXOl/ZRKKZgmJRYtEVF166x+dmsvz5c/q1Ykd1j4QFh+MMGX/eIXsVET/Z18qJuCjnMRm+8bCOjrH3OkRfI3ZsBS0dzvHV5JVsc45PCsQggfjCNYrYqN8BRO2j5fJ65exevs5xTj/nDD66AQrfmavhSMjakOi1q0alWMfJQzs31L8RdhblE80hUet+JpiepDTK+O2iTGC8g96NrD0RC8+dvo9tIllQsZznZE8NUctmrwdVuE3XRu3GhDCbt3ab7zJTM+oI+rVNrsK9QOyGIxuCHL+s/qxsWMwjVS7CpobPjggpT1garWvip/2gjV9ygPNiRQfGDjkUUizOQXScuXERZ781Tnuu0TmUOSCcxLAqUTcHdtB7cKjCpAd3bMBeP9cYb3UbC/FuchvtOfNtOlaS3Tce2CFaDNhpfqN2AUNMjSj0TfJbZ/U15j2PnRxfNqVswRDRdy/wryattJ47aY/JlFfCdeMB8xw8Kvo818xVW7NuTI01MQ02OBMFeiAYa0/u1Zz3K/FUBdZn6hoCovCxzBvizpBXsSJjt9xiPPCcsJF7M3hCS9829ljCLk3cs1t4IbQpbhjcwaZdUbuq/SaSO9R9Hvw9bzVBYu00Bf3axp6Jwc9lqMapshw1gjz921z297z4NB3EmAAjCB7ClVyUn98x486j9oqaSL9xXq+onRS5XsYuMjxvQkncpchm9msVHdLStHYVq+wE8c6ZujSN9FeoN2TncTM8Tl2+hc1ftz1n2v0JL/8b0/HhGMtMoJbZpQe1ZcfvbWSmEpy2X3Puvp4IZeYuPjxwxL2gW2hm8paQi2LVliL2o2fIoHfrrVFQMcprLFbkzFPqYlxcM9q2U3pulfmmseyrSSsSFphOJuK6ksX0+w9n15qC7SpCUFyM4fC4dNNMiNdDCgZ7pzadDQa/dOG0IFP7H2T+ciqVQzo1tG3M3WWOJbm4saDiNffYphEXdWo+CA89IIZMd05grMZ46TfMJZ65a6pJdMMsnlsUc0GMtehP6lav5Cv0HyGP/yeFNqlFfv6A1r42VuV5g1/jVRSVKjH25JPGA88JG7nf2xCc/VrbFymJWHChpC5Sn+FfudO8bUhn2w2dK4sQJx75abb2dbX4Hv95jue5IPCI0vjkMnsspoqXO52fEt1biitHXTsJtmU7yZg3YoIADRyxk4TUlPEiPKiw2FEHKdUoopKbNeAP3c450mgfpOwIJmPnyBTYd2W56YmwZWfE+yzLNq1sQEtAJuzjXtCFbHgRklK0Gn+HtEJ5sWhayKjhYW79PxZK2WIY5G7QLpdywyeT07rr+fYF+2kXk2JsjSVF4tUfGdkzJi/bYmUYGjEn4vWSboQB54oPItkOEgXtGH0yHjow5t46pJPj+1Hni7Ou1eaOLE/w2Fq5eVdW6KLp6h2/9abPpibVaAND4FdXeWcdObtfK+0gj9CAgzs1sDIo8etMdMc7R/GqhrF3HmrzgELffflBsXmjCcr27LGMTDUqO4vbAoTNygZuMceUrzfd/Yw6nlz38eSoY3Sv2Y0ooQTCYfx9Fmsmt5To5+K+8MFcyZCcD8bBbIRKNU9I5rjhdqMf0KG+zb00S+a4CaeO87IwL9oQvcOmesWgDrgLrkddHNWtCXvmtL0d3/dTlc+cvretDnLVGBVruxXeGrJh7rpDO3BRSPEKXBb5ueO4Hl3GHoEqprdlZ/ZlZvBFHAUjhwIJDZZy5aJdPEXfkWpNNfE9tlafxXNndYc+VbvGojreOs9YZCcLITZcXcleNXv1VmuCXLjLfj9gMaMaf9KJkyaeruyRijOVzeeo7hHtICdx1JkPDPFcAIp2L3uKzFsbPTY98pO30T6XELH8yCKF/l4FfRFC8dSwmKRfR9iuKgB9l99nreUil9ngCaLWOzKroH2o92ai1CyoyPbVCF77NWijnavFFUmbznIW6zfE8BnRopz0KtC2ZSqUK8cGxOkZgnoToUxNakfrccn8OdfuoXvKa6Ojjrlo2ASWTtR5OvptFd1rgt9m+TfaoC+RxU2ToSkiIwRuvRAZwIQQLvqZwljmnkjTtWSJ8fBK2RVAyAiSJ6iGi2SmRMNNJ6f1C2VAfyBViGKavHwLV552Qje/0WVyaY1FtHKsmg4Lgxo/zoE7jnQX41SZvrIwZz1BYh1YZGNFKuZK3ZrV8p2+jAuMhqLbCAaopRsN41q24lV2hz79p+3vQZJuSpNazpOncuaI4lSjcI9+7Rx3bRzed8WxoMiCNUja7wMV0e+LTDGVK5ZjdcxQASf+vGWg53lFCmPhDaVe6f3fz+TeC7KxB20hHXXy4A+zPI/xug6/KdDj5abDOtr+Fl8Vq6aN3zaSiXXkLzNWc+0VpzJPZC4gn0sty3iIt67VRZgR8mWmz83C/mfaCuf0sum4N9FvXLR/G20/5eQllg+eILHMv0W7Ed7XfojX4NaoZgE7o3d8oZBi00f305Zs2JGWdN2izUB/EPdiPOF/EFT3AzwmDU+lyA82RLb9fpOHsPs1+/s6y4PfG+PbM7/P4/9u3FHCLns/BuPTrl2MtWljPPCcyF0jyN9//82OPfZY1rRpU97JfPPNN1Edw7333suaNGnCqlSpwgYPHszmzw+Gmm4yx41HTrQLU6Gs2zWobqWEi+W7bvxsCstmQpLGwGO/zIlpoAlrDFH4DDrPqpIrXKzDlZzX3c+CHBkQdvpMNZhtxGus693aLqpmniwp51b546aDHNMhysbHUlO1a8LSTezDcctYtlEmVMXcIwM4C00PKUFnSWMI+er9tF0dlx/cjh2hiHcmi1wzgsgLKujiyNfv9VtO2qeZr+9Avy0bsHX4CY0R/dAR3eweDQLshgvELlW6wmH+8qEpxOUszWvRitJmKLuT2kV5XUFx6R5Hd29+LvO3ZeJWuOKDSa7Zjm7+PL65ALJ9Jfv3ePVTTqiGDngkiteSYcRMlFAMY8FVHxphVKkELvwnaPoqnV6QGK91w/Zl76XX48AvawqL2MSlkZCRh36cHZNBFemCYzVo8D2CWC9U81n5a/2IdC7asIMbHXSXC22oacsL0zZmQlQ5FmFlmRdHLIj7+7HZGE/qYF19+Q1JFW1pW1GZpcdVIQlCuUQcRpARI0awLl26sK1bo92NCgsLWdeuXdmoUaNYqtixYwfr2bMne/nll7XvP/HEE+yFF15gr732Ghs3bhyrVq0aGzJkCCsqsmf6yEeSeUvo8o8f1LEBm3TPYeZ3Rb6tqcuuMFhbmDtlLzp33SCm68R6S2KRiK989vS9WeOaBaxW1Yq22HlkuvDDexf14f/K3/2u+ZqcjlTHLzPXsFyEz9vjGNEfPSlaQX7vFnbXxVg5ed/m2tfbN6zBjSA6IUoZ2eV4rZJdKNMsWr+dXf9J/AZJeZIkvAL8MPqOQTF/1ym9mnt+Rzo2C4vLdtt2tpOJupjCLprNCOLx+f87ISK65oWXMdBPWZ60b3NbXWLijgVAo5qGZ8hqqZ9Hdq2IEYSlpZ680pXKxhj1kjCx55P7NC5iRZ3IdW6k5XavDKRIFJl5XM/PMk+y7F9G6ETmDAzQSJPbkXwpPORLo5WQKfxeAu7XxRt2JC66GCe6cBi3xTgW4NkINBtOfnVM3J9/6MTu2jpzM0pgft6iTnzizBhXdWv4RDeMDONKOK0bB5no4/ZqUpN19ZgHCuQijXUzAGOamE+u2LwrKqNissPbgkxMRpDnnnuOXXrppaxmzehGUKtWLXb55ZezZ555hqWKI488kj300EPsxBNPjHoPgySu7+6772bHH38869GjB3vvvffYqlWrojxG8pFkhr94nUruRP+67RBPFexsiJVNNMWZ7jcM7hJJb4j4ysa1Ciy9FLmMvr7Kn9sbsm2AkO61UIj93/FdWVDBbqBAxH2rzXRAu8QU1K8c2I7/G/aZgYlfg3ZCAZfLVSybwCCMgVUQ6z3ZtWnEwFSjwNtt95y+rWztNxaQzvujS/u5igqHNL9D1ehJlKnLC9lTv81lqQCx+jLjF9szrLjVT8dG1Vl1D1G7WICo8HOSxpATal2eM3QcW7u1WNt/QqATvzEdniDLN+1iLwyf7z4mung/LdtkLLB2laYvXjrSfr37E1nnYs6abVwTAExYos/Kkz0kp+79CCWnErmOYGyT2/SUZVtcPYwyjdNU7sAnRnK3egHmLk7IXq3JAiGVCNXT3bPZYMDzi9O1xqQJomk4bvN5pBiOV/vmlsM7WZlmxFe8fNa+LBmko/2ncjyBmPjgvRry8dXNANWrlb+087YaDMc+pr08coElkC7aCIwwM1YVJiX1LxGHEWTq1KnsiCOOcHz/8MMPZxMnJk8RPBYWL17M1qxZw0NgZMNM37592Zgx8Vtqc4VQhs7lR7E40X7ry4krWKrQDTa6uD+vn/DUqT1tf8uaH7GqOsvCs/LOyeFdUhNGkFE85B/uPtpIm6c7JkqUU6mzZItz+j1fNk6G1UWqGu4SC17T1ON6NrXEaePF6Z5R9R/kNpDsCVI6Q9PfHbPU1seEY+hrEgVuuSJE6YHjDEPrkK4RA6+Oj/9zDveC9trf84ywiHCW6LDJu5ROBqZ0Lr6EpxMMftb3h/Rt7ulTe9p2hv8zjR9rdN5mGV5BIlxHoBZzvLfnwyd2Z1XjzGaUDOSdWBj2sBNruagXl7EvzPlJtmRCktFd0YfjlkY0rRSO6REd7lYhBSrXtx3RmXVsZIhtVypfjtWvHsm8lkuSIE7X6ndTkgvtaipJiJcmG4yrqhfa0Zo6z1ZERC9wKmJ5sycWsBHw1vm9WSqIp2eAgVVwwTvj+b9Hd2/MtQhP9BkOS3gT0+ps7dq1rGJF5922ChUqsPXrveNzUwEMIKBRI/vkDX+L93QUFxfz8B75wYKeHcbTfdr9fcSHz1gZiQ9MNEPAK3/GH8MXD7hcGB2WbJTcjjU/QXYhVdX+kdJs35bRGQD8ctdRdnFUpwmyihCNe/6P+TbRqmwm7GGdx06UWJx5Lc5hKRcaBvF6R312eX/9+TXnwyvH793UppeRhXNhtmTDTqs9p+P6kvUVLevq3X5FVSA1pfWdSfxdsqt4rOwoLmPrpAWqX68b+bgVm5xDHnSZRRIF5Ynd2dP2a8H/VlXxVd4ctdhR/BeLwTf+Xsifp0MszwmbIwg8Qczi/XH6aut1aEGJLAgi7XI6qFa5At/N7dS4hi3zkk4UsmeL2myoZnIu9/HZwm1fTrOey0YBuLHLGW1iFavWbQxkgmWbdrKnf5tny+ywygwFy8JuX+sh9c1kZ2HI8we01rbVVIJ0u8KL7+y+LVndatGp6LPRwKSOPzKxTD10IYP3HZs9Xr+68A91Tsn18NJwLdC/8+NFEc9YLsbVTy6LzP+O7N6Y1XPQg/OilZQ4IZ7mi3TGglUixLRciGeCzAfx4GwhppGlWbNmbMaMGY7vT5s2jYuS5hKPPvoo9xgRjxYtjElgoMNhlL/vkPQtdO9ffrA9Z/n0FVv4ZEHglX7Wi2R2rn4WJBhwUZyXSmJc8qfgLodFtleKuM+vGMDSrZ2ByQR+44/TI+EYl2SpqJgYPKFX4fh+KMSO7t7ENqA4H5uc1KNOIqh+WF24y1r4YccwHUApXTY66oCauFBST1SnoXkd9xAXnD5Zm4cX7h89KTcwvuDub2akZKIs3/tu3PzZ1KjXxi3eyN76Z3HU65OXbWbz1rpkoJKeT01zVi3ch7WrVmJVTNd3uSgPUVJDC0p3h9l+ki6STHMzZv1fSSgzk8htfuLSzbYJ8ntjjJ1xZLfJBHL2DF3GMf66JpuYbFRwEyvOFOEsX8Q+f0YkBAwbFl9eGRmvMeY4AQFsnTBhNvxGNVvGhu3RWjnjl0Tav05wXOXls5MTLuGEXJIX7t9GO/5mY/sGD5iZO6JFlv3jNmdZ+MhRLNUIQXdZ+0bmq6ui57FqdhJ4Fc1xSU2bCvwGTh3ylD3TnY75Dx9pPZfb3/F7N2N14pwPQkBViKjGo5eiWzfhN+N1N++sXJMfyCkjyFFHHcXuuecerdDorl272H333ceOOeYYlgkaN25seavI4G/xno4777yTi7qKx/Lly1N+rbmGrH3BCdk9ISqKvJjmIhBsLzaUjGPB6TOLEnDdVznzzbG2v3VdyUfjlvEF9UJpce4Wt/nkKT203xWPirQAiuqyBRrfXkXjEjxQWaDc++1Mdtizf1uLGOxKZ3OniCL8Y/bapMSDy6WdLkP55xNX2PQ/UOeiuL3EGpMpzvbJ+Piy0cRjoLz20A4sXTjdQ7rfm4lWPlWTgjJaT8j497W/FlpxvjoyeZ/KBkTVyOVmYNfVD86C3SpxXvSnqcKt/crfy/vBJMbxJwNdLP6tQzrFdS517MyGjUI5bXsWXE4U8m7q82fsw3pJBj23hT90WWRPEEG6jN7poo9pENH91kwzadlmNmtV5r22xRx4Z0kZ+3qyERalduPoe5zmtzjWTTcrkTmkF2GlD5Xn8TJCg0j9nMzMVYVWWFguEmvIul/EBoLT0O4W9oRNhk5myJgwsKI5oL7KOwgZo61dNMwInbGoUIGxq64yHnhO2Iip5iE6umnTJtaxY0eeieXbb7/lj8cff5x16tSJv/e///2PZYI2bdpwY8fw4cOt1xDagiwx/fvr3dtB5cqVudCr/Mh15MwksYIJMFyu3BDvtjF3p+Tjkb4J3PZFxC1W1wHAWIIdbJkTXv435QsDsTAVO+e68R3ZDYx0t9J1SO9/c/X+ts8l0wtH0LBGgWWB3rtFbXbZQW151pnKUtYMhH7oJijyblDX+35NSP8hHXhVsfoLLz4gsnN6uGSgc1KcTwbyd6qx/dg1PKdfS2vCIOL9hbUeceSDfOxGxItcPrMddmPktaJ8T8kpLZNJzSoV2RMn642DseDk9ilCGGQSrft/5m/gj2Qgl7F8Wd9PdRbMTUcmFT+eZliIy5fiFn/sVD9yOM/b/0Z7xSSL0Qs3uC5WBTd86p0Zya0f12VMSxSEwhhfLL4/+hi/mcWyEb8aN27EaxTyg7ypINo+9Cn8oFucvvPvEpZPNDAzP2WChjXs3y3mlYJpy7fYUtNmevG8vaiMfTZ+hbYvueajSY7zWxHuFa/QabKoX72S47zfz+wWHoRFLlocT/82N+nePMK7QqRkTybN4hB0d8046fC+W1gLDGtLTdHuEtNbB8cjTMbJEySs23yrXJkxZFTFA88JGzGNsNDX+Pfff1m3bt24BwWytOBx11138df++eefKE2OZLJ9+3Y2ZcoU/hBiqHi+bBl27kPshhtu4NljvvvuOzZ9+nR23nnnsaZNm7ITTjghZdeUb/hRAr/qkPZ8kBI3uHw/wkopa4D0aF5L6yYKsdO/5rnrx2DxCPE3hJ4kG5HG0Wniq3onyD+haiW7NTWFxnprt0EMtnKnecPgDjwrjUDewc3CzRstGBgnSO7pOvBb5JSpZ/ZpqfUSwXGpckkW36kCoxQG42pmm8A1iN0hEVLx+YTlKU3xJ//iI58fZbVtJ36bFWnbqWom1SpVYKf1Tjy0UDdJcDKMJmowXbBuG1us6Fwk2pyu/nCS7Vxuho5ktd0PLu4b82fcDADH9oxo3qjIm4SYSIt6EO29RkHFlHoB7CqJbff96BdGsQWSh5/8s9368TP7pC5M1q18stiJzxub4ZVlHYfu1TDqNT+Zr8DWorKsDIdJKmn6ObquR+33t+yyL+wwd8ROeaaxwqJCanZBf32rWMiHHFLFpwNc3seX9nPs/6KSBGjEXKtVqqDti4WXyd/zN8QtWOrFmEUbk37OAe2iMwHGy6Gdo/sZgVtYCzTcikyh6V0le6y6gEHQyUMo05m0cpGYtxlat27NfvrpJ7ZhwwbuZTF27Fj+HK/BGyOVTJgwge2zzz78AW666Sb+/N577+V/33bbbezaa69ll112Gevduzc3mvzyyy+soMA5/Vc+kuoF8D4tarND92qkXZhjIiAPThgAdJMD3evqZX83ZRWbuXJrSl0CnfhhWkQ8D6heKzKpFilCWYkFnvxVSJWGetC59OXKfGyoRjtBBV45SOOJ3RK3QYOXk/n8rfNSo/Kt0qB6ZdavbT1rooNBX9TDTNNd98MUhgPogCaPG6l2W02m95auun+ZYRe6FrHMYYf29eSvc3x9lxrLHfL5W6EB4zTBQ6iS3/JIlifIAXFkFkA5v3nefvz5oM4NuUChH+TQn4M6RGuHZHqHUwX35I5iOVW0z8VKCvtT8b1eMe5q6GO8YW26rCCpAJlTEiWVIWJyfVuLUU0V+B3ec2XM1aHTNxNlkomfJW96CGOqYOP2YjZy7nqetSddbdkJNVREm/Y6zjaT7CxgTqDfwb3gNI/Fe40kryCkVg5rNk/lflVw8quj+b+Vy5djX09eydbqMlrFSSrvtyeTVPYioYFTP+blda+GVKGO5Dmmr3k1vhsJS/CIo9De9jFHD4wRBFogjz32GLvjjjv4cxga+vTpw+rUcReITBYDBw40rZD2x7Bhw6yb9f/+7/94Nhhc3x9//MFDdwj/XDWwvb/FQoixr682BJPk+/i+72babni8p7vtjI7B/Xu41RPxb0k2gsDl/Q9zNzyUhAldqo1OTWsVWANUKEUZLdLJ/d/NjPuz316zv+1vtQ0JbxgozqcD7Bye1781E2MSDHsikwE8ooa76J0kE1vols9x7u9562NquzqV+FTfE7rzqG634vfKGgRySt2XRxqZSrzAeaJ3vaLFTe3XF+I6H8kQAPVa8EG4cbBk9Ewm+B3insGCo56ZsrJxTfcNBLm8RPjJ21JYQKoXhuE4FsvxtM1UZOTxtUiSnl87yFmLJ5bff8Ybdl2sdBCPMCA4QGNYSyU6Q5hOi0uHrv9JFfguZDZKBjULKrCCCuV9GdtSxdWHtLP9/f01B9j+lmvlyg8mWWF2+z30B8skcoZAdX4cC5kwNIkyF9fqZgRuIIUnbdlZGiXAW7VyBbZLo4kjdFDWbSti749ZytZtzazRKhMc3qWx42aAGs5+1UD7fSAQZVvOY02EdjRtRaE9hf3OnYw1bGg88DxGPpbPFXQjyMUXX8zmz5/P6tWrxwYPHpy6qyISIpH1BwQ5fS0WeChA+ajOc5QSU891GjShgLqUWurfOOvuJBtB5q3dzkbNX2+5RGdzqqnHT+7O/3329L0tfZB4rvfaQXbD1ncuugTpIBEdCtHmgFEU9lbzzgXp8QARWAKQ5l2He0NMmvH84ndTn5kn3hYsZ3DyQyZ2OnW742Z4rEVEgT0xwsr3cUOtclZZ68hS1g/7XKh7fD8839wE1iDc+Nb5hrdGuti/vbtXidwfiVjkmabeEg+ZlH41Fm5umijxEEub1ItbS8/Nf3XeZunwaNF27Q6/r1szu0EyHiHydBJv3wE9rHRen64KGpmGwAePd09bmk5NH2gEOKULjVWg9dYjOtvuUzGG+s2+kSj929ZjJ+5jD/0oqOQsximHW/P3MuiCI/d/VhmqHs5ZOscUZS5+g9s0++6ju7ieq6qDoVDUTfWCCnx8cNBejYuwkt0mOwmx3m3qWtnSVNS1jZoZCRugMjxFLrJTORlBzPJGmSR7rM1XYmqSI0eO5CEot956KzeGrFu3LnVXRmQtEA06QJocqwtzeXG1dOMO9oOUrlWAj6zYbF+E6W5rDHjJNlTAUjp5WWK7KLIuSKoGuV6t6kadH0/lCbmfb1aPeWH4fJYrYJCo7CAO+Nzp+9h2KDIx4RBu6GJMEumVM0ksU8JYJrp+Y95TPSVVr2PpRqMfSXQuHFbq7tU/F7LPJriHDuG7VeOJyuadhnHgd0mLRYebNs7/Hd+NZYIHT3Bf+MlzMREfv8rMEPbIid15nQiNGizcUKbJRHy/lw6OHzJ13x7ZvbGjIKSs89REmhCrY+Kgp//i/ya7fDNJKrTA4mkD8JQA+0rZY1TBWqTYTaUAsA6n/k64zsfi4ec3LCwVPKDp29QdctnQgZAKGE5iCQVT55rJ4tT9WrhmBOPvhbI7hEpcnts826tFVKtcQVvOGB9/mLaKdWxUg4unpiK0/dqPJ7NsZUjXRjyU2wk1HKad0uc9f6Yh/SDA0Wjucl3NX7uN/TrTCBEOS+WeS/P8nDGCHHzwwez5559nTz31FGvZsiVrCPcaInDAbfrI7k2sv9XwtF9nrrXlqR+7KFrFG53lc3/MZ3d8ad9ZZarQZQrCYeS+Pl433S+ucM44lEowQREChF6IyQG8FSYsidQBXBmTrdSdKuCGfu8xxi7ELYfbQ9uQnahJreSoeCe6IBcTRxR5Ot2iwfglm0wtnj0xTaziuUo1faoTSZtHa84zZuFG7a64PFF2yxritXMowiz/mL3O81xu14kdWdxrqUwRm2q8jGTygklk1hBibiJd9E5TMA/tU431T5TWZoYyUVdubNxuGKOcDeDGb3ksCVmN4jGo49sb1iywGblvOyKSHUVOpelUK6onZqzEu6vuGco1KbvTZ4qrx2JNReziyguPp0/d23aMaoxPNcm2U+hSfWfS00Kd8xmZ34zrgBaIMLTqPEN0RKUNTRKykK4Y9iHUmQW2jZhx06fwEq8XCRUufMdezsbcczNPv4s5ZzI3NDPpARSLkUz17pAR0VQiFa6XUDOM3Es27rCV44otu9iLIwyDh1wk85WQJUJPTDOSoUOHcmHUtWvX2lLREtlFune0nDo20Unp3LJ2miJKXp0rJs7JylMvFqfy6XRiTn4Qug/8fCx9cI0Vn32/WBRvKypjT/w6Nyr9Vi6AuhJlfUS3iOENdGlak113qHOcfFrdqEMRw5POBiLyxaeCp36bxxeaSNPHr8nvFCzGSQQO95NC8sbBHVmrusbiNFF09xYE1nSLSPnX3PtttO6MUK8/7+3/tLuH+H3Ytfp0/HLb4v6sN8e5XqNq9MKCD5O/rbuSl7ovE/M9tFkv9+URcyLGB1WsDZPqLaYXDICqvd8UpImUC4xPOkOkLosAJugCUeXIygBDBFKQ+zX6JVcgNYLa56Va/6nNnT+l5HPLTG+tWEJf0jGP2adlbdtc5bVzevnyQDtK8t7JtrDaWD058LMQJuy2WMv0bzhp3+bsnKGRflieE+7J4H6OuIyXRsxn/y02Npp+mr6Gt6dTzewubn13ppfx6OeE8UO2gajhf+u3uWt5uOnmIHQDXoLwBExkP3OJ0udluuySgSj7Id0aa8ea0jL7r1xVWMQ9oWwemOVCbMbKrbyOfppuT+hAeBPTjKRq1ao8He6TTz7JWrVqFctHiTS6j2aLgRQDqw4sQNTsK04DYTI1QZ7+fW5U+SQjrV065z9Gphj73zqwW3LvtzP480UbtlsDNOjTum5a45cTIVvashNhZRL8zO9ztUaIdJR3RCTS3/G4Jrn53HlkZ4/jw5Y2jRuYQMH1NV0TerGw9rqXT3zlX744xm65U1aB7cW7ub5CLLf0rNVb2aL1kQka+jYYIL0U3LNNF0HlnQv72DR4vKhYwV5qWKic9VZk4YIysVJKmh5piaKr80d/ms3GLvaXNvFfFy8fpCBvXa9aXCmHE6F21YrsiK6N2Yku+ly6FK1+wG6sW6azWPFbh37uY4RPCZJtLHPirqP28jZmmC/J93OmdR7WFhYnTQemaW0jzOqOIzrbFlZidz8beO2vhWytJKopzwn9eILEytO/zfX1mtP9KI+tC6WU3NlMIi3azcOvfHlkgjQEVRO5b5BpLd8Q/c3uPXvY3UfvxbPouI1v6BffG7PUNm8TGYpWbdnF7vxKrxNEOJOekYbIa9xCEnR9XtmePdwqrKJz38IAlywjiMgSIXcsQvQs2enlUoWRbSdy/U4lAzVpkaL13wX2BQHKE3UQL9jlSHfIR7YiykG0UWMXKPq4v+atT+l14DtjdQ+N1QD4v6P3Yp9fnt4wsFjufPycb6esZCPmRGtviLIRv/hsaXFufRe+zKFM1PYu/z164UaehQaMND0jkqnVVq1y9ixGQOfGNbSTX3XhqnqRoM+XF5JXfDAx4WsZOTc6DCZZXRNSkNerXokbJdKBGOY+vaw/jyM/p5+/NMWx3CvYRUSKy2Rx1YfOdSiL9/kZwoXrd9v61Xioo2qgSAXtG1TnGZDCmvnKCXs3tR3rJsBYnObw0oOeHBklbH3PN8amR6xpY8X8TR4P+raty248rKPlzZBtFEpedrt3e9/wsdpJflbSsDu95nzeiJYd2g0eyTD6JhuM6ToDoHq7etku3Iwg6AeEV2QiXt2qgTLbN8j8IPpFeElibOzfLqJ1o5ujiU0E2ZiUApmVQEFGkDwikuoqvd97dA+9y+7pb4zRdlTiNbgbu4GfAU8Qv7m0BYU7S22DpAos0vJENxGw6y3HaqcaLLblSb5c1wd2sGdyUEX2sHCDFgs6Uj9iYk7Aq+Qpl12RpJLlA51u8uzHuPDp+GUxq/j7uQ71uRtoA7HcWQM7NYz5XkwUt75MFhzr0qQm+2rSCramsIjrEDll8RHGEKcJqbGDF4q6x097fYzttbZ3/aRdbDz802z+76YdxTF75rgRr3ZRqhnY0Qjz6tumri1EUNYTaWWm3i01J3rglT8XsI3bi2MWcVR54pe5CZVVyOH5Ud2MceHbKatSJqoYdS0iS0OM95gwHqht/P0xkVTFAnFMOsB3IRwKhslY5iQ49uWz9+X9TaqBZxtShdYsqGh9N/oS8NwZhiihuHS0Xx04PhWCjzJYRHvdK3PNFNWzVxubH4nw8And+fwBmS3qm+myMw36bPTvIsTX73iL9OWLkhw+hnA0pJgX91pIm249YgTRpY/NBg6R7rHlUj+nlqiuiMVGAESp3TwGy5crZ40Baj+wPIYMdfm42BeGnQv3b8P6tq0XNfdw6q51/anwDIvSDKxQgbHzzzceeB4j87PQeJdMyAiSh2SLhVQ2Nug4vEsj6/kMM62iDeTE3h22xIP8cvJro9mpr412fH+OOVmIZcKcLSG/319zgG3Ql4UL3zzPnj5T/WUYrA54fKThCSJN6LRl78KnE5Z7arkEBTEJkncp5IFrUOeG7IIBraM+98XEFVx7YvqKwqReh/rcDbSjx3+ZY/2NNHY6sDObKdzuuxZ1q7DuzWrx51cMbMe+UVLMPmIaJMDKzbuMUDLX7wqx6Q73Aryq1DACt2KGOHSyhNugUeEnDCmT9TNu8aYoDRSxOygMroYwasgyXmzeWcr6PZoabbF40nvK/epNh0cESYXQayrp2Ci+TCh1qlbkITuy4KuRwj7E3h+7NOr4RIzf8YC0mAgPi0UzA/dhOlISC87t39oSNkW5qYtqsTBR01UKOjWuEVcowVlvjvV97FHPj2LP/j7P9Zj/JPHzeJCbBgSHYdQ8bb8WrEVdfXrPTCAEURvWrOw7HAbC1rG2e6++e+G67dy4gqN6NK8VZQXhGcNCEeNZyON7LjuoLcs0sq6Hej26DZsFZpgPDBlymKPOYCTuL7VYD3xipO/ri+pDsmSdkwgIG37j3F6sca0CVt3MsCNSVOvaofBG03lii1CYVuZ4YFG5MmPDhhkPPCdskBGESAnvXdTH8T1xX2MxLnZkb/h0ivZYTEhi3WXBORdKMfpu/KJxccz2TlOXfg1FVKCIUzlNPPG67Np7o1L2eM8tneec1dvYakmdPZW4Da7ZgG4clgeuoefvZ6VYVCfWM1cVJq0cw3Hu1E41jTBYdJzVR+9+/7AUq58p2jaINsR8eeUAdv9xXXkfUclsJ/JON0Q7hZEEO/rYIfVjl1BT5QLs5EE/Qv6417rOaec4Vg7v0tjaqc4GnMqwXQP7wlWUjzgeehSqToq8o5soP8chCmdPP66v0HR4T4h06JFr8beZYRj27Ade/dEkx7YpFoNOehKxGB/g5QHdL7cwEVXDyg+9W6cvvFRFLTeUh3gJmXt0XrAXH9Amru9CGJ0OhE6q9YPdWLdyTgbJ0EdLNWJslQ3zqQjN9dKuEG9DgBtGahVcppi2ot7k83191YCo42FsyjQIQXO6Hl24S0guf5cqQGIEMYdPpKayZSMy2VRW5u2VpbJWm7bov3XtU7wnZ36ksHVvyAiSRwiXtG7mxD+z11Iu4cEWuzI8HCaG3k+Ewfi1/E9L0k58ujAmldGaIEIcyXaswzmwOBw2OtpVWrb6w13dzTCRDEX2N/42NFr85J/PVkSMq1zWqJ7/O76r8XooFEmfK7VJfM6picJjB1lKYkKah6yTBOTckK9n2IW9HSd+apxqOoEBAIQc+rtererw/kQsrp9UsiAJhAaOW98jvgM7rks12SzgPRWSJoxevdLJrzp7o+Uydk2ikNZgeeuQTtY7Nk+QFApePm3ulKOeho1eHPPncW23S+KQiXppxMKjJ3WPNoL4HGfVMoV2AbwwdU1d3AfxiKOq/TXEt4FbWJ9Y+rgN+X3a2A1Aj56U3hTF0alY7eXrNv14+ax9kz7fGvbvYm39JMMYd7yicyKT/SaQiGFQ3vARxYLwOp2hKFHbDjw+sGGBdv6eJsRM53nGDenma6qhdx9JQy6byvyGwR0d30N/rrYdkRkMnjhiTIDmUKweNn49JtV1wOs+5o8iDEcbJpIFeHksyvOVA9rXt9qLvL4KS/NGtT/mn8djxw7jkQOGznRDRpA8AjujyBCzf3u7NkQmqGWKyem8OMSN7XU/Tlq6mafDnGS6+cqIzCcqujjoZJJON11nYVT79UDFXVfOUd6DYbubshPY3Qh5DEaJCKsKEA6Sy8AVvZ9pIJCzoaB9yy7EYvDeLqUlxktYHOra9rqtRezPubEJqc5YVWilgBW6FF7Ik+rmdbLH5VlnBHPbmcPPkPUoNkqaIOJjfoQLxbE7Snbbjhep+eQJNtx7vaYTYlKSqF5ANu+Aydc2QDKWXX1Ie6vOhLs6PGNEPcnHujEhDhf/sYs28nAkP4Q09SXz0ln7WN5E2Qj6/k8uM8SK5fa4tUgfiioMUm5ChvxcmsH5swkrlGOMf902KfAWvBi27HIeb0I+Nk7SRUhZeHx91f4puUfdFn5wINPpwsghrDLFZbtZ6zt+lK7F+WKapVG/LBUIz0Vddpj7vpup1Xryu+wTwrK6eSjOix3276SQS7fz4j25CkNZIKrvhQhZ0Rl/axRUjPJGfPTnOVafIpqzV9aqsA+PSV2fP3nZ5qiwJ7U/8hJnrl21kvb96wa1Z5niyO6No43sUmORf7K89pD7b9W7sqgsYgThZbZzJ2PVqxsPPCdsZH7UIZKG18QmnYgbU9f5C3ctNz2Oryev4DGu0O/QTejGOLiSpip1XbYYUDHhlCdpcNHFbrjc2UfQl4VYgAvCPstWgO+KN7YcE7ZsK9N4qSkJc8qgaGQVdF3VwBV6884SnvovERdy3CcAngtrHHZh5DK3XWeOZPiRXdJ1IOWzHM+8rQi74GHez+Bzh3ZuaLn66sr11s+nugq0CWMRn+zZPHn8erQlRtVK2eUN9cQpPa3nchF0bFRDayRev62Y10cZzw4Tcg0FULkjjpR/SzRePE6ow4X69zE9mmYoHaq/PuCpU3ta474siIlxQnfZot92M8whuxLSMOr6DhgC1b7fzQiC99A3uWkfiY/rwk3SDa7liVMinigoWz+136FhJGOSG99MXsn/HfqPs6cS+mVdFg2n/kY1juBegw6GLtTSdeGeAwPyk79GNKzUcQxhRDoPmlKfmXvGLNL3SWHFOwsLcjVd6zdTVto/E/aXujfTm2oyXh7XTvN1tD9UwT3HdPFMCaxrY2jXsveCrs8/8ZXR7ONxy6KuyAtZPF3m4RO78X/7ta3HjnJI7JAOereu65pdE2B+L7LEhXyU6fJNkfs+GR7bHbKojaaC7Fk1E0kh07uG4oataIZn6AbuF0c4h1oIHvkperALkmKxG2rH5pRBR20LSzYaO9pVK9o9R3RNRk2/JwP333h3R3s/9IfVJtZ7pPKTXRmzjWdO62kKEDJNOEzYZpS60IwZV8vZyZCkpkH2k/bZbWFz/Ev/al/3cq8WC9ZM8+ll/VwnjLcM6WRLIwuRMSxc0YYhvtmlaU1eHz2b19KW6+cTV7Ahz/3teR08ttt8ju5NN8eVSyzene1OjfwtqDLF3i1qW8+HS+mIUeaoK5FNQi4LNDWRIheL7FTsSMejaRCPgGo6MDRBvH+P7Fpv+7xpGGUOfY7bqXcU7+YGWnDCy/aQrndHL7EW8uJeQsy/F26/RN7JzPSEG21YLdMqihFSJxR9nEuYiczLI425z7pt9rFvqTk2i3YMg9Yfii5XLDbrX2esYQvXReuiudV7thrFG0kiqPL9esXB7WxtGmEnKzTeXH61VJzuN0PfI2SGSoX5ghzeIfLx6liOe0MOUc7OkrXjx2Fx8F7RGZvQXuVx1a285SL+zPQEXl1YxE56ZbTnxpqTd5sb9RwyG4nr4NeexZWDcn3nQkMotW396uyNc+3JDwCKbd+WkTG5oGKk3fkxxAUdMoLkGbcOiY5pTidCJErEh+v6NTc9Cnn3EOzTsjY72EzDmI1GIF0MebrBoKO6xAG1KC5+dzz/9/wBrdnR3ZvwQXyAQ3YGpBl1GnSghr5fa3sct1/gLilS/amxslhcyRNM3W/KFoxwiLCzJ4g0oxCq3ypO3gdqXLobch07NX0nY4fXAPn5FdECbplATGSQhcEPdSS3VwiawhiBnSa4Ujv95J0luyV9HX1J3vPtTFsdqZM2hM0InQRxDIh1GvLT9QeyXMEm0mzWVW0zFFLui9HXGOEwIe5pJoee4D144ySKCAeLBXVHN1tMIqEkiAgipEtd2FlpdM2z4331GMPbkGnTrWLRIPoN8bEJS51DlkQb8PLkEsLHfu/xdPLQCd1sBlidsTnWtYbq8n/wk39yTylRRzDgTjMzVYn6GTVfHyIZy0LHaaEPMdqqWaq/JQvPyn3K5WYGE3l80/Xdfm07ENNH+fwzf4Py+YguXbQam2E0jCLM2Fl99ULj2Yrq8aauJWCA0oWVokzQrOCB6eRRIl6Rq+KfBUY5vzJygdGveFSU/HbYnKO68fq5vVhrh400K7WxTwHqTCLCkLDRU6datGcLyk5oIQJ4wCLtt3iPcCd7VxlEXBwmpZ1NN6ft15wvyM/s0zJpC9imtaqwvRKYGGGh+etM/xlg3ETesqG8dYJ545fo09U6aYIgLn/ppp2szZ0/sVWFRZZRQuWur6ZzF1MVsSOSCg7vaghhgsF7Za4te+FmqEDZaLVwlPnDWo2AqaiLeMpXnUM41avgL43uiJwGMlsWhCBsihA6Ie8Qyu1TVEOJ6TIuTwow6ZIXzpgEwmDiZDSS6xTfIWdwwFvIHgOjS+R8xr/PeKS2lMFiK1ENkXSg88qJZIMJR9UJinS36eavNu0z3xzLJi+P1sYBGzy8xWzfzxKnQxpEUP3Qs0XtqBj9WLCyMYTdPUFe+XMhO+6lf9lEM+W5VYbhMO8/VM8MXqfSLipAhhivhZVXbybCP15yucczhXo/hhM8F+pAzI9mrYoYmf42jRz/Ld7EjYQh6f4Q3iPacALVC8H8U2ekf/3vRdrruvTAtqydJgNXNiAvquWFckUlBTeQtaEEsSwEcT+cM3Rc1OdFE9CdyinsSL5uXb2dtE8zlk2IyxV9uzq3xfvjNXodGDPlX6cbvsT7cjEI4x3GW9wXTvUk5uRyO7/XQb9F5vAujdj/jjZCdFSO6NaE/05ostTNsvTz8rgpP+/fLqL1KBcVyk0ej1FMBWaSjGR4d4VZfkNGECJpCONHnzZ1fLnSy/dnyMEVFkYVXaiHX5CH/MuJ3gJKgjccJgnZAjIBfHGFIYQnQLn9detA367emHA+ZopaofOEIUQHrMtyui3rvKHYXHNjERq948jI7oP8PJvo2rRmlBHk9N4tWUNz0cIHISXtmW7HThevevzL//rODKGiTrRwLqATizN+Ry3XbDy4Dtkokg0ILwMV2WVa1kZCvDKKBZMBeDDJJQSPhL3u/cX2e1GvCOnTtb3rBnWwPo8u6fBn/44SqZTJflNG/Fy0vxniZRP/cxOv5fuFtrZ9zSGGIB0ymYhdcBW85xcsuOPVKhIM6pwdhlekqFRTDseCuAfUhYVqBFm0fgebvrLQ8vrDy09AdyEUsvoPgehHLC8S83XdGBHLPYA2kekw3nh4REod7jd8EfMjzGkamfonUyTj3yjTAwEaRhgbRJnIO966daLa5PdqUjPm1MTYfFDTNGejEUreXBOGs91J8gQBwhho/7wRboywDdnw7Va+PFQ2ZMzX+N8O3ofZhDDaiL5dh66Pr1FQgc89RJ+j8wSBh6TR90dKQhgAYQSRdebUeczrfxlz8glS3cieDzJypiw3LScYmDH+XDmwHQ+fzVZEeSF7mDFfDLEzetvTF6O4ROgVMsgA0Z8nOh4GATKCEEkDsbS4QU/Yu5lvTxA1PZ6K2D2UEZ3k/d/NtOIK3TppfHzdNvfUXbkCOnY1bex9x3bVppL9RfGACWtSWeqIuEpjAqI/JpbdbRWnkNG3LzBiH0fddkhW74YbMcL2wRoTAGH4QDx909pVonbW5AEJxzvt9Mru6LEgezAs3rDD0fjBPMI+BPh52eYiLfQm3GJ/Uc5iAoQFWtg0QMGtVJ5fyXoWArQ7TIRloVXBs39E2jw8qWRQ9Orkz6+g5gUDWrNcQ7h623atROiDSButqNxf89FkNnz2uqjFA0JSVm2JvX/W7a4mI2tVPiDqRS0ha6FhvrPF1P4oLo2UG7REQg5lLd8z1340mf87SwmZkbnhkymuqegRApkZ4dnEiSfcAZtD0MYRu8/46aPNsIB3/l1i9UHot577Y37U53XDgrrQsTyyWH4gzwWEEQKGCfG6/Pt1C/BYdsPXaDaEMF8R55XHVLcwJNxf+IQwDPj1IMkkoTjbtShet2xRyHj38aX9bOWAOYpsZBLzFz/9gZPXyBl97AYCN07Yp1nWiY6rdG5c09pgBrWqVOAi76KIrvloEi8L8beou5Gml++iDTuitIUIO2QEIZKCcMdCB4aHWGjLgj1RYCCTOjxd38d3Z5XFmtg9QYzcDintKD+lZuhHfylP8vKNAzp4p0QWGiC6QYq7M0qDingKa/tXk+yq58nAycVdDH5ILyuyeWQbIp0Z2mSjWgVc3VsgJmVTl2/hRhJ1MJcna5jk1nPIW2942sQ+hZXP7xUKA7y+I5sm0aIk3zovWhhM5e1/FluT1W3FZfyz8DTgfZL0o8Si45x+kUmf6Grcygbn12Uh8LuY66sYfr1U+bMaXZ8tPRcCyuiXMclFtq+IJ0HE2IqFoYpXSJguvTSFQLuXh1holJaFYxJmFV3L2EWb2P3fzXJMKawyZ42zgURGJ/iXTXj1O37bHdostINkYfGz3rKHX6CfcjLmzTB1QmTUvkr8qeujvPqibETXOyKFvDDiy8YInV3fr2YK5rDwolE90Izw1ujjxfihz9hhjAfCI1pXb9mmeeZnHNKVrzAyyTobKkhZjvmRXBUi3GbVll18PvTSiGijn9P5smlukmya1Cpgx5tCy2qoTvuGNdjlpiCw8GZF8atzDyGOCn2bqz+dytgppxiP8sbmDgyvqt5TUMmuu5DIG0R2GLddIhELKNCNVcKtUDfowz1MdUHXEcuueiIeDtmMnGlFHchgcJC9CMLSQvqHaaujzpUOsaWrBkY6+mxDxPu2rFuV7yao5YpsI9iJVTNgqJMxZC4R2ZSc4u7dgIVfzpJkq0Mfnz9Uo/SerSkTxZW09mEcQ2ptcEgnQ1AZkwYUDSbNaLuqN0nzOpF7A/0RDLeym7rKR/8t43XnhdOU8rpDO9j+vv3ITiwXkdOHQr9H9TCTDdiiaXJBYWXncKBZT2pWAS9XXt37utey1aCaDtS+Gt6TwCmdttwHFZXusfoX+TzQvUlm34Bz+bmvM0kyrw9hFULPQtdHlC9XzhZiJJf1dZ8Y3jcyuswkEB2/4VPDC8eN7OnhYwPjJvqWL68cwHZLBiOdIdptvoKwYFkHThhmt0rhFrJnoV9wFtkTRJcxJV5x+VRRq2pF1qKue9YuraeNWbxu3TX6jJBSF2IDdObKrWzmqq3szVFG2uhF67e7ZphB+LfTdzl5nOUS6GvUOYITG3eU8DId1LkBn2+K2hGaIGi7xRUqMfb558ajoMCaI3l5Cgt4veVxWA0ZQYjUNCxz8nuoS4w1dyH3cMnnbo8ObuaYqM1evc2H4SK702AlG93uDrfCm2JRwvAkFoPYnRVK3U/9OtfKzOO0g5KMsvRKW3fSvkaWoWwDvx2TUiMcxv7eYyf3sJ6HyjE2zExtJlAHEniMqHWFgUmNnXVCpDwWPPPbXOs5pUaLiORhIvrSyAVc/wClemzPJrZyk7sg3Bv43LdT9Gk/UT9Oi/PrPo5eoOhQ201lc8Ki0rlxDSvGNxspkLRXKlUwUkg6ISa/x/Vsau3gimIIOegxec27ooWfI5lLbMex4OFk5BDeGyu3GOFckU0Io9zExBhlK3t6qPoFfscAZANTufRAu+ZAPo7ND/84S5v9C54GH/+3zHFRMXnZZlaxXEgRWJYFhsO+PUF8kaNlL/pgeAND90ng5JXhNPcYs3CDdS58dsWmXVEn4um9Fd2bP2a7hxigL+KaICIcRnNMNkb8PnPa3o7v4XJ1Xbxof+Lf32au1SYYEJ9FyNHd30znaeaB6gWI8paz0KhC5Sfu28yxkbttXmTTpo4XXpc6dXkhW2amQMfv2q9VXfbP7YdY73c0k0k4ibwb3iP+roWLOedQ2cUKGUGIpHCqmRpXTZcrOkZZUEogFpNebm/q5FoYRbBQRHYHxBu6gUEuKKmiju3ZlHegSC0sA+EkNWZTDkvZuL2Erd1axFZJcapOIUTJMArf/bW3unc2IjyPDN0Oe0HIoTHAKRzmjb8XWnogUKPXqfuPmLOOzV0DrQRnl3NVqR0CtyJFtRAOe07SsVDxuiXwtk4bI1dQw48QH4s+R7gxawWaUa/SC4M13jJynwVNAycQihMr5/VvZT2HZ0X/dvVYtiLcm+HJgc1YdVIPI7Vov2GzK8E9I4xTeI5UfkJfRY3HtybXDh2OqmkTzvMdK4EcSuEGxILlPmqTFB4BbRYQdnDNl0sW90zPB36zvZ/IeKr2i/lQY2pxYFcbouwqO0vKeCiHKD91zoN7APc9vHBkRFPXGRp1VeF3geNX0DWjaH6MLPArG/x02mjyOFBctpud9voY6++wOt6Z47ts9EBdxdLcsSNv6IYhHCa3lljqHEa9b4XRVM5G8u5oYzwVxfzGqEUOoq/GeIE6WL2lyOoHIIgvEP233L+oG2bxaqYJrhtkCHLnMiPmrGXTVm6JaNaUM2QIxK0i5oFOguOxUKF84oLj2Uxu3aFE1iLHqQmuOLitNXgUayzDquip33AY1ZrpNSHDcfl7C9u5cP/WrFPjGrbFFJBd1e0hSEbJrNy8i93+5TTbIkLejZIR5X3vtzM8PTqciPdzmQbut2Ii6tamxKJPAK8QYZV/5Kc5lreNrpwxMcUkGEaOC975z/E71Iky6k5kqHniFyP7j05czzreywgSZuzrqwawbAQ6Nl4pRtVdkHP7tdLWmWjPQvBW7k8u1Cjl47zi++NJke228Divf0QkFSEmlx/UlmUrIq1hoxoFXJCtoZnxQmQ1we8U7VyUKXZGJ2G3u3yI1a9WmTWpVYUVmqFFqg6CmHg59e/qqzhMnazxzAUO11/NRyhlNqLznhQx4DKNaxbYxtSh/yyKTl1rvq8aO+Xv0E2AE5kTD5HSoBvaIyznweIZ7clrt1mk5HZaVCCFLYSZ5dTd8M4RAol+dBv4nMlHRhtxbLYy58EjjMW2eZFyVgxRfsg6IjZUTtq3GfcMUZF3sVV9OHV+qetr1M06Ly4/uC0fu3EuNyN5LoJQLSBE30/brzn7TQhvhsOWvhbK7M6vpts+q3q4Yu7fs3ktm9evqCt5/iRvBGHzSB2j/YK2hOu+fnBHluvI2Z8QDqZ6y4ssOZirVCkpiqTg2mH3HvYD+hwyghBEHPBdVfNOvfbjydZkV3RIO0p22yZbqhgbwM0nBv4vzFS34n5HPKearlWXHQbnePSn2YGoQ+Q9x0JYnSzJC3e7903IUp02PGaYZxoyUUf/LY7OGZ/vYBf7iVN6GG6uLuNwxQqq+Gx5x4FbXayLtaDYgUJ569xLdecR1Y4Y23gW43KzgSiX2LXPNM8qbrqeRrRwmLWtX83yFvjpugN5JiqdpwBe+7/ju1opcuFdffsRRopcXZYi1MvFB0SniPVLLAvIbCl/HcIbCv8d1LEBz74DXju3F/9Xbu5hSX8F/QYWB5ce1JaXrxB2jjLqmSdwcsWV3eCN74gOh/nntkGOC9NEUq9nFM3vaVu/Omtt6j6JNj91RaFjFyV+uigbtyapK//PJ9qzssXCPsrCMCe8ETyA7sxdR+/F+2BdGIzgMtOo+fgvkdBFGYzBC9fv4OKI+7ePeIG1MRedIqOGjFo9buUphDoF2ewhi2xrIUmPRe6LRZtE92g9d/AQUPt8uc9GemjrdYfywPndNEF078HDE/1Zq7rGtev6oFzUwxbD0eumkPFxPSOaaGGzDkJmmU1cap8f4nW5GFCd7RpW59pqTtmrwOiFG63nr5/bi48766RNJOBnswDXjM9mc+ZBv/BwbPM5+hz1J4lNTzmsSOaF4c6bYyoUDkMQcVKvWiV2dI8mVkemTqYQG4tOc+H67VbYCzpBeaCHVoXotF7/ayH/134au+K0DHYc+fEsHLFWx4Hs+pcLQHBLqJxjMgXkTlItrpP3bc6a1K7CRs3f4Cv2T8wpeKaHsH9BWRjCBAhNyFW6NavF26rbZFNNvYY27CREpU6QxHmf/X2emd65mF387vioz4kdd3mypzMCOuFV1dkkKNm9ea2YDAlYAP44fbU1ge7StCZfHGLCcL0iOibbU1BPC9dttybDujTCMFJhksuPD4W4UFss6CbE4n61H8eyGuMecL5OeUGxrcgwqK4xw+1KTQMGilf07z/PMESYMR7wz+/BvVbT8fyqq+/arcWWAVFQs0oFdk4/u1dc5JpiD1fKNCiPp0/bmz14fFfb6zzNpLmqkkMC5DqQ+wZxrIgdV8tYXijoEpW8PGJB3L8Bxif5nsn2du53LoDQBxjmznvb2XuveZ0qVigqvG/U/losAlH+HRoadQN+1AiUC9RxyM17VtRrbSkEIds5sIMhnAwjtmrYwAJaeBbpQlTBgHb12I3m7r8IU5GxhJoZY4M0Gnbc4ziO68bXCKPBRcMmRL+fg2pFqseB7GEsUt3yetjD2Ly126O9F0zDfqm0ufm46bVq9/6Lzsjntglyy5DcFBd3wstAtmF7iTWP0An3ijCsZIQz7yguYxsUo1M+kb3bTETOU7tqJXb83pKlWBqgujY1Jv24dw99+i8zDZlhyVdVzcX9jfcRmywPYm7hMA/9YKTyc8g2Z0PV0JC5NsdiCKEj0LGRMYESZeXmRovdIXVX1Q25vEUq1p+nr2Yj5xpx5ipLN+7ghq3vp67yJWCVC8Tqxo2F3VUfTtK+hzaPFHoidECcFxlIRL3p6kfsDgrCPnZVD5TSKbvtAkKUM9eBYUFu9Zgo8BA8xbAhlwPKG/pFYpKt2+WTBSPj2VXy2278pDnOJBcd0Jo1qB7tdSbQeZUJXQoR+oLPir7qlsONiezl70/k/8IgW69aZUdXXF1aXbVNo/4uGBAJMXICgtG5wA/XHsgX4qf0ioQGWIu7ciF2TI8mfLwSbvhOba2+mZ5bLDBEvyHGZblZ6wzjJTGMF27AazGHbCDsrqM6W2WkG0fRJt1cxytXLG+lv5S1EOT2e9H+bXhYjN++JcoTxKVAxb0qvLayHblrkfsZ0SYRTgfxaPSVE5Zu0nr64b5AWJwoX7cFpuopA7B58eJIf0Y/IYTKr10KH9GRi54gqvecrHmCooeRBIfoPDUNTxB4t4bZ3/PWG+cK20WXhVes3I+L8UGcg68TVO8eJZ0sMrzlS5k7gSLq0qQmnxuKaqlTtZIVDoOQXV14mMBviMuqLUW2LIT5BhlBiLQRlib2z5+xD8+MIVS2l23ayV340fnp9EPEAIPJtLzQw80vpzNTdwbFgCl2YJwY2NE5XWiretW0qva5hJgEqGBAgjVfLEr8jBFy37l8807u9ohO8sJ3or0VhDfPX5KBRDfRyDViXfs6tWkxGH09eSWbuaowahIrBIV1E9tmtQvYoM4NbcYvEV/rR+9A7MbrdqdyaXfWibpVK0YZTL2MIMLz472xS63PuKEaABCC44Vf9/+2iqdPNoroQQvBKaxELlchwixeEoZpsXuLSdtDP9pDFvE6Jtl+w2HAT9Ojd8z96Chkk9eTH9SfNKRrI75YQxgXPF+EZ5G88YAMSYKDzfFO1IM4TNwbcplpQwT87Cy4IM4ou9PnAvAUwNxFx5INO9kvM1bzuYwOZAPDGCnKmBtYQ9HeSWIh7XeMCWv6FycvA9HOcyUk4AhJP0Zuk+LeN8IXw1wcFWFE2nAYyTMGc0w1japXb4z5JdK2+uFCxeDqFs2YGzVgD+VC2KOMPJfjmRxNgc6TXx3t6Dkoug7d7xfrA7nPGWYKrwqMVPf2z8G7ZNI9h1l/y/MiJz2iXAf3OcoJc0Rxb/Rta4TQoR6a1q7COkgGJBWnUBmVfNFtcoKMIETKgVCUQJ4gIB5QLMREp4aFoF5ZOmJ1ljtA3PzD56yzND9k1zoRUwuXsL5t3LMsuM2TvXbVshn8Lrgei3S46m/Aziwyxwg1dHXBACGqqHANm3J3mE1Ystn9Glhiat7ZCEJRnj3dOZ2cLr7ZbTATmTaQ0WHu2m3WexB+c4pVRsrXFpJx7/dZa7kYoh8QJvXbzDVRr39z9f7WNeU6aHPi9wARq6wuAOQdEbGgF145XhPVeBYTftePIpQwmzE0m/Tvyf2E6g4u2rPY1dO1N7yOSbYqMizQ7Tb+oAkb8LP7hxDCXEL9TTeZXjQt6lblfQ1CTZ3GLBjz1bSWRliTXvtAt7vuJJrtBkIw1M0I/nW5OLBqQIjn7V/axSBlgVPRXiF8CriTQDjay0wYYv0Y74A6Prulv4RgNPj+2gP4v8f0MLxSshXRroHcz0T1H2F3YdOvJq1kC9Zt4x6XuvfdQNiv39A51cPVzRMkF9wSHj85IqSLRbXIOiK/drBpGBHeaFt2lkRpdsiaf3KfozLgsRH83/dGG5sQ4ryRc0DTo5w226Qb4p66WWpPuQ7KBW0TqeXVvqJWlYpcSNgtO4ya9tkJ4cGTr5ARhEg5h5pxlriPPhpnTM7E3wIYKmoWVGDXfTzFsgY3NfUsBNvNgUhetAj3OV3edrHwPLJbYyvV5IMndLNlX5AXHPj+fENY38tbFnvsEkXAgIIBQriWqxoITmJj8sTOq3vEoJfNAmzxgMHYzbChIlLvgesUTQoRp4x2vbN4N3vg+5n295XJ3a+m8QI756p4rViUQ3RYhzCS8IHN5Tqz3Wjl5dnFTIOHXO4iHEZu4xA4tQsrO3tjyKFE71/ch/+rekH4MYqEzTSKssbA7NVbcy57CcoTky0nTRR1/oWyETHkog9H+eE4uDKL/per/5sizRVcPEEQV+5nYucmaigQKatzBd8LZM1rp/duab0eyYxklHdFs/3K/Y3ObVoVsfUDziz3mR9d0peHwzSXhBFzGZ1+0KINEQ8CUYpCp8sJ0Yf47YKjPEF8pHQVfUs82a0yBe5jK3wrHCnz76chzDY6jEIGAqhrCvW6BuIT8KTavEPvVSwz9HxDFFTnqSDflnzepTQJodEAg+BxPbPfyI2+QhXeF2MW/oXu36FmGnn02agPp7mH4YkpZYDBPw7dGLLryMhrAd195gbm+bjufEM0daQcDikreehCHduzqWuYu585+S8z1vAxOsungwlBRhAi9Y1MKNGzsKOwGDpIGCqwE7LRjBt/z1xoqDsp8s0rjCrqRFfuNOU88hDJEtx+RCfbzn6erdM5oljkgUP+mXBTH7NoI+/sgCh7WStFTV0Za0d69zcz8jrFlh9kgSrEgiOlqLqbjrLcVlwWteukCl/d8eU06/k3UyI6K0BdG6mhR8LVGrHpbtZ9VQAtm2jboBqrLok/OqGLGzbiZ43f9vwZe/P+QOyAy8eLn49FvmxAEqmnhbu5Wk5yjLTjdYXD7N87BnHjgUhlp7LksaOzOjOMAKk7L9CkEZb7hk6mS+5nl/dn+7Sow2pIdSfaPbInbTXbvQiRwWQZi3KnPuY6SWgZhm4wS2NM8iNuDW8/xFfnCigjP7o9urITqY2N943XwmY/I/oH2RtTZ4SKJ5oC95j4GP4d0L4+98A5bT+7vkk+oRv3GpmGaHkR+OKZ+0T12djh9YNcPYd0asDrcr0ZfqYDxkY/hsFsA23u8ZN72F8rF2L/LtjI1pmhz25educMHaddzKH8xI65EHA2XrcffVzPpp5ZpeT+v3W9aqy80offdJgh0ArjX3tJ+DaXeOeC3vzfE/dpZmtHaOpuxll4HPMNMZEBxmX+od43BabRTmiCxAIMNbcfmVseIBD8dQphF4SlsBa53Efddgj/F+VUumcP21OuHNs26HDGjjoKu6HWcW+NWux5HU//NtfREzlfyP5ZFpHziI4SC+2pKyLugrb+Ert+iusgXHtlxG3oZ0EtH/LcH/OtDlf+SnWR4XWjh3NQJBU/Cdds7S5pfiLqQUx6/5q33vbeoXs14uUtXHh15/d1HSzYyB4JqIsWdSJtW4i1OZURN4JIf0Mw1Ql1Ue4U+vHNVfuzP2brhWzB06f1ZFnt3eSjQan3M7oXvCa8EeT+RWSOQfEhbl4IjMk0rFmZZ1AyrkFci/0Yp0W4jLisaw/tYIXryGkCcwXDmyjs+TsbmwbpTo1rsJN7NWcPn9Rd8TSzixUaqdUZm7BkE68rXTgG6Nm8FjvVdM9WxwoVUb5uWTHk0Klsx28Uie4YtHUxHoo4flSj0GABEC4U6BaW8di0nbzP8gXd/AHGPRXdQrFZnSqsnSl0HXvq5sj3IgU16nbEnOi+HUZfMOHuiHZCLoFyE3NJVajxrLfGuc7hXBfOMIKYDRr6ZU5tXIQSudWO/D0wKKrj8XGmKG4urylFX3v1Ie21Yb1Ohn28jD4d5QrPa8NTRl+aE5ZuZkukDIK/XH8Q/5ensY9x1ZqLmhboh6GZ6IZo6/B+lJu3qB/8bmxAFleoxJa+9xljP/7IWEFkc9hJuyje+VauQkYQIvWNzLxBxy/ZzGMzBfKNVVS2mw/+cho0tYO0dq+U0UmXQssQB5O8UMyPyJZr1a3OKz0sjpaz3WQ72P2EQjSuWRiY5F94rpQ60snIgTI6b+h/7PYvp7HWd/xonEM6iZdBSp5sB5mqphHknm9msJWbd9kE0zCIYY/UqYjgASXfCvAggAukQG7G6mRPNSyKyZ6sYq9DTn2XjYjwNjfUcAyUseF1Ex1zj75GtGXsxF60f3RGkVuHdI54CwgjiFLe/U1hMrkfiyZs7SyKeshHTynL8GwWUUjZUTXeM8odk767j97LcO01++/rP5nCd3mdigYGFYj1IU7da2P7bXP3UtxjwnNExuueyCYMo6l3m1F3WzEmoM0Kw8b/vp5hjbPoG4QRpGGNyGTZzRPQCbHgdrv+fOeWwztq+1ErMDVsfy7KXp736HbL10mereKzoKCiOcY7NAtx/lxq5zIYykR36+TZpc4zvp2y0q4p4SIwCTBfiswZw1rRay9vBxkRKiLIFUHaWPhi4gr+L4oLv+8YhzAf9PVP/TbPECdXynD4zQdHHb9FCvUVbRZj+AHt7eKsXiDkVaQCzyXeOt8Ys5wQoZ+qJ4jOeJHI9DvkkHo6X8jN3pDIKbaYO9eG23lksJaBwKZqlHDK5y4WcgK41CKeUz0WoQU3mPnhdTexn4XeexfZQ3JyjTP6GGEXYtEtD+z7tqrtOahjUINVXpTvSyPm295Hubr1j0KButDFewF8clk/ls+IWHiEZqlZWV7/a5G5KIgOxwAQGcPgv2xjxHJ/7Iv/WMaV+47tKn3OXpfyn3Cfh/YCjChei5BsHvMg+HX/cZHf7ETUAiJk9B0R4UHzOCwQKoS4senDS4x2KH/06O6RSV1kQa8XL0RoGUCqUicGdopWrn/OY9GYiwijsuiu5bISxSvSon98aT+ucXPNR5PZqsJddkOrS2OE+v1Tp/bUZsPo3qxW1GtiMo1T5pIeggruczVeX4eu5EQIUuRkxkLw+T/msUoVojVB5LjyRyUvnlj54JK+1nM/157r7NuqjqMXUzR646zOOCpr4cxfu83a0e3ZvLbWo1AIxOe6MQr3uCgbp8uWm/WjP89mD/4w28oKxt93+IwIQZWFs8WxCF3xW1bXK3pfIvRJYF1/DpW7FyLbDsZb/vs0hYw5h/jtKzbvtA4R/7ppV6jaLrJ+k59MgxgjciUldCw8+etc67muPcn9yHdTV7LFpmfNzpKymPTGCipCNzB/TQV5+ctefvll1rp1a1ZQUMD69u3L/vvvv0xfUqDZvLPEGmz4jofDDSVbyRHHp+6yiokZBioshAQzNYrfGPPgdfL88Pm2ibQtHMa8DtnKLHjylB6sW7OaUSnBcomQaQXnz80OER4fIaWT1A0Qz57eky185KionYvvp662TZC5EcRlR/KajyZFpaDUpWbVhR/kqyaI+ns/nbCcvTdmqeOO1XtjlvAXLno3koJYhC8hhSAyQWCiJtx1ZeTzIH2mX5f0bDaCXHJgJNuUimxgVRfP+O1cGLV85H4XivVwW8fOijzBesYMCbrogDaWKr5q9EBIhvyS2AEXxx9qpur74OLIAlAnqAvjVK5hZPZwfl+s1YQGiC3kRRobEEIkyh3GUjnUAwaRFT7cdnUbrJdoUpqfZ3q/oc/aKwd3B2VE3+6G3FcLLyVV7JpHw4QZe3PUYp4tzPhc5H1508FPv4DxVGeAkvs8P9eeq8BQBHd/zC90Bjz1FWQEq1qpguXRKs971A0fVRMK2cMuftfIvCQkRlTDy+UHtdNeJzwbED6TK9SsUtHTeCAbjbC5IFJzC5yMUgVm2NKMlVutPl5UncisJ16vVtl5ASnC7e47tov2/TyyfdgwdCOMzTYnrwHRrO/6egZbU2hkvXOaHzl5Qan1n4thpMlCFsXXe4IY/1YpKWI3ndiLNW/RgIW3b2djFhrelfceo2+jgnXbinjYGfrqo7rnT2rhvDeCfPrpp+ymm25i9913H5s0aRLr2bMnGzJkCFu3zjn+nUgtQkleDFCvnLOv+Y69k5M9QcRgIrIoyOnmdu+OCLiBqUred/nc+M7/O76r1qVaWJHnPXQk/1fuR07atzl7+lT77myuWe/lnyw6SSE+KL8myuECKcc93oMBRDWC4CMoS1EvlcqXt6UBLFJSJ4rY5CxeUycV3WAOMMm1XKBDjB3cqUHUgCaXkfy8pMyuCSLOAzbtLGFLNu7g9eXladurdR12jrkIdIrbBa+cvS8XH81FDu/S2HEXNRJ6EdmNE4ZPlIfwWhLIbV+478oizyJFoIxIFyjurQtN0VCvuvGb7SPbcLuvsbgDHczFnZo5QRiwZf0JpIcO27KW2OP0Yyk/J/0jgKYhp9/MB3QhAme/aWglAExkQ+Z9IC8wcA+IhfTU5cZYulsKgZEXNXPXeGvewACi3hcirEYND8gnTtinmRV28vJZ+/IFobzDHQl9kT3+QuyaQ9qzvZrUtFKuq6lgTzB1JIR3mdAl4udS7kCcb6cyBouNHPV+eOksMQ/LfuBJ0KtVHS5g77b77+W2/+KI+TZxfKGDJpfjkK7GGCJeG2iO1TNWFbLLD27L2tR3FlkWY8bnE4wQEZXKppF+kJkxMZ8w9IXKOY4JshcMQuMBqgvGP2xeCE0cQdj0WrCfw37Ou47aiwUdiMTrxj/ZK7hKaRGrWLSLXfHBRFPA1tAEGWd6ruqYtHSLpeWYi0LKgTWCPPPMM+zSSy9lF154IevSpQt77bXXWNWqVdnbb7+d6UsLLMiGAcSuiNglUscrZCYRt1ozc4cC2WLk98V5vBYN6rnFxKNVvYjl2E2ADIMZhPzyBfFTEfMaNifMbplExGRCF6Jk7JwbXUfVyuXZiyMWWO+/9tdC2/FiHeqYuu7+w/m/fuLbc4G7j9EPysKoJ1I567xvVm3ZZWU9kYsLiwd1ARmW6sfIHuMsVigyJeE7IZoVUtq+KsB1VPcmMaX/zRY+v6K/7W+1yYkwABEbL8oUx2FyqhpBbOcy26dVbpbGkPIdDn2KV+vOxSkGDNFuWXqQOUbWa5L77K+vGqD9DIyBCJ985c8F7ObDOvK2qUt7q6Ir9t9mGRmvcs3TKV7kxfXVh7SzQukEiMcPW/dB9MJM7u9lDwSRlQ1M03hd6q5DNzzDcH7nkfm7aGlk6qh8NWmF1d7nrNlmvS8WEk79tHhfvk94+J7ZX+lC7GxyLeZ5L3xnvDatd6fG9sV7l6a5kw3pAlOjCeOS7FWqIpetrrywCdRa+fzvs9fa+gNoE/Fz7WFs+aad7LdZxpg9b8023n7dDNqYNyJsVSeQDWOX2Ay548jOLN/gniCaeYjYcNGNjVZ4aShk6RA1rxMxoL72lz1Dklu4L8vBxAXxIs/VkcFR1yZ1+jPQozOEyEM8tfO/C52NIKJs/WS8y2Xy6teVlJSwiRMnssGDB1uvlStXjv89ZswY7WeKi4vZ1q1bbQ8iuRxhitCJnVkxqKsTUQw4U5Zvsbmi69wYubCSx9aqOs9QhVExOcdkQw4hUNWucx25hMTECmnbxi3axFNCitdGzl0flS1HTK7UchYZNsTrqnupU2ynk/BjDdMYkC+LkrP6tOS7ViqyUSFk/q3u3A79ZzGvF60wqlSbwvUUoA4xUYb7rc7VF+19VaEipKdes5SuN5dRF+RRniCmMKoY1Hn7N0OYZ63ayicFIEpKRFKXFx4hfna75H/lMJt8AUaOB3xoswivsZDmvnfaYfp91lpWq2pFXqeva1KFRrX1mHeqwtZixC1jTC4hl2VYY3wW7tNoo2JDAl6Swt3fOEd0Py7fR376aXg1qBotSC+dz7uJcv8z2lxYIBzmls+n2o6BLpMoQmzw6EpELqY5q7ex1YXGRtAR3aIX9XJ16DYaRN1hrMnVlKzg7L4REfe7j3Z245fLwCnEUJ06qlF9Ym6De2Tt1iJLD63ENAbq9Ici5w6xpQ7he7rQpnwCxk+UnRrGIry21XJvVa+a5ZlqJKgK28YGngJdOVcDqa8C6FPk/iufBTxlbJtkuw2jhopuibRw/Q6+qWCFDYedy+v6T4wU9KfuZ4T25it5ZQTZsGED2717N2vUyO5qhr/XrNHvCj366KOsVq1a1qNFi/zNWZ8pxA0qQiPEbtP1g+0iUnDPEruxto5tT5id9voYPmHDw9gdib7Dt7rkeMclqF4NVSuWZy9IgoReKanO6x+dMSKbkQ0Yoo9cv72YPyAmqHaSFaUXxCRZ6Kbs27K2tJB0XmhiIR8P+TI/Rlv3+i1yiluhlwCuHdSBDdCI2EFDZVtxKW/TeOwq3W25iWJBj6o6uGNDLhoWD/k0cZB/SZQmiCmQJ/oOnnLRnAeIkD0d8iS5R3O7F5s6IY6kzjWNIUp2oHxr/37SeZ66nzGm6l127X8LL0GxCO8maUsM+zfSt5zy2mj7dfgsP+x+Y2dS1N8VB7dj5+dYv+6XM/u0tH6nCFMU4oW6lPFgn5Z1ovp1nYaTGw+f2D1n23MitJS8TIGQPhNljX8x7qpzk7CLyObw2WvZBDN0QIccLmCIlCvnzp+u3Rbe49S+5LHMaQdb9NnCkCruh0tNDSEx50GogDB8gK27jLIOlXOfc8neN07Xlo/g52E8UH8mjCBvnLdfVEjps3/MszZ9RFp0/lzSu5FPhblSP1PXSIBj5fLGd8PQmO/I7V+IsauI8VaEpQp2FJdxfR2wx6VJijlRvhuv88oIEg933nknKywstB7Lly/P9CXlPcIYocYNO91qeyT17pAZrwzhVJXHfp5jPQ9r1KtVN0h02LBGW9/vca/n2m7uN1fvbz0Xiwq40Qn3crVzQzynQEymhCFFlB0W2rIUqrrwxgI9HvJ8fmDjS9NdGjSUBiiEvejCC+AJAsG25Zt2cQMgJgdC20XUj1Pbxe7vDYM7sI+kzAxNzPCYfCfKEGruxonJGHcEwQSYhW314JYibtBehoFd3AFR4TBq9gLJ3dcNI/WffvKcL+iKoK4klommPNt0Ixc1JwtTfzBumfVc1jbi5/YRUITdcMTi33VUZ8swA248zMgglm+oWWAMEd8QX8Q5Lcgg6Ank++G/xZus52HHTATuaaHzfB6tRSymhRHJEI6MLBLhHeNmuIaoOw5183p951/Dew0g1GKRmQFC8OAJXfOyz3cSe525KuLJ7SXQuWGHESomEpogiwh/35wGQbdCzlooDLJuTVkY2O+Uwl2EV7McVpZvQKgd5Y2yUkOb8RpCmMQYiLAgFUOc2TQWSq/LZY1Q3ijPZEXfCM+gG5Pv+BnvRJ97vZkhU4C5/LOn7c36ta0byH45r40g9evXZ+XLl2dr1xoxfAL83bixXt22cuXKrGbNmrYHkVrksAsvIJYkT7xguECHe5bkHgnO7NPCMXYceczv/Xamj2/Lrx5BDsHAAhts2F5ihRypO7NytYjxWudxw8U9zedIa5kM8skI4rYYUHn0pB6OO0eLHz2K/zt5mVFfmFyo4UZiguY2mMGVeIC0wBZpn5FZJp/KXi2DqF2OEGOXvjeBt3v8dhSd8ATZ19wB154X/1N2qWQeOqE7e8xMHYpr6NKkpmQMCXmKV4pF561D8kuoU0VnCDpPEmPGuCBcxpHOz1g8yqEYzg1V9G9+uGD/NlaIZj5hF9w0/pV1bkTYG9qmXFzy50Ss+b+SGO3yzRH3fuyeyx6Vn11u6PC8L2U/Ms5TLo9HVneER4EoAiur3R7TC0dq0yIEzwnMaYTRVocYy8HgvRpG7ZSLehB9fr5wspl9C8j95qt/LuTljHYKI9Nppiu/bJRQtVfgJTVi9jqr7YvXy3bv4Q/MgeA9dpGpS+Jm0EbmQxi/5PYv5mGqJ3I+8c2UVabekL1vAZHwU/383xCLj/YEUdGFwasZefmtlr/FHBNq4gO5HKtVrmBkx2NEXhlBKlWqxHr16sWGDx9uvbZnzx7+d//+dtE8Indu5Cd+iXh4YELwv69n2AaUTo1q8Ph0DFjycUKU9bKD7Ck1dZoNIJ+tosLrRnZzVsdk2bVdTNzEoIN6QDkT3mBR7MY5/VpaaVRhMHHasRKTLTkEo1SZYTSrU4XrgbgJBaseTOK8wsDltDDPNbADdM8xe7FTezXnv0mnCSLr2hheIMZ9L4er2Jfe5m6T9Bzg1DB2ANShWHyg39Htlos/T95XH1+LibIuxXGu8665+JKzfDmlNMZCQ6RxBQ/+MMtmyHJbfOjeU28rp7LPF9QykPsEw3Bt7JoaKXJlg0mI1SioYHkD7teqji1kT9ZfwQJeXogIg69sKPRzbfkKFtvdmxv3sZpqdeg/i7ixTvytEwO/cqDdwwFGEr9FV7FCOUuwXO3z8rn81d/c5+E/2I/TV7Mqlcpb3hv7ta6ryRAVtsJc3vpnkeW5IxbtMMjCewN/Q1C/Yc0Cx8xqwsiCMRXXU1QW8YjN35KPAA8+Hg6j9C3yPMPSkdPMd2SvNaRN1wmjGgkRor9bfql36zrssgPbsnP75WeIYyyINVDFCuXZzA77sCXderM9poacWD+FfZzHbfMhH8grIwhAetw333yTvfvuu2z27NnsyiuvZDt27ODZYojs5sR9jRRzMriPsZu12hR33F4cCQOY//CRVueIuMNSabImBroDOjSIyhUPrj802gXabbBqXa9qTmbMEDSpVYUbf2RldTFJO9cUp5J3nCwRW9FZhpE6rpE1KdalUHVbZOR5P+oLUWZ7JD0C8O8CZ4Xu+tUrsYamEQOD16btkQkCwMQN+jnxTLRE3fYwJ+25DiarUJi/7YjO3KihTrZkvQ7UgVxmYhGowxBGFfHjxmuWpoL5t2wsNLJjhLQZmNRFTr6DfgcgtaSOyhUiAsEila6ffkOIFQpqSvWnitIGteztGaWMBt+2gdEm5XJFWV56YFurT2gkhU4gU4Ms6Ih9BifPBDltfVC5+IA27JjuRkiRWFQLY8cfs9fxXXLRL0WFzplhK1FZrXx6EJzRu6VNQDQoqB4WPMugS5GpxiloKnCtKPM8aMdHdmvMXyvZHY4yeujaOU6FtMgYgyCM+9P01dZ7tUz9hXwVRoXOTSS81O6B2a2ZocFkvG8vd4EVlmq+Lub6QNZkkc/hxKF7NeKbCRDVzmt8dAkoKpR9+WrV2N03vMg+eHQYK65Y2fJucjN03PX1dBYU8m7UOv3009lTTz3F7r33Xrb33nuzKVOmsF9++SVKLJXIPnpqFmMHdWhgW5SLyS8mYsJijxsYA5guHObZ03qyryatdIx91nWwiJ1WyZe87lhQC9V0MZ50blKDnbRPM2shV6VieWvAtiYMLMx+MAd2vPP6uftFnbtmFeeF5H3fuYcjuX02XxA6NrtK9LopxRpxTrRJTJ4FA5/60xbKInZQVE8QJ28nGVl0Mp9AW0V/oBpB7G7QRpnhfkA2i9qSNoWamQrGK5E5KpLm0ljaRLxLImFJ4ntDAdkF9MPmHRHPApVic9d06Pm9o8Ja0FfBA00cIyN7KMgLk+dON8Sug+7sK3493P1FCOOb5+3H2z1CXEYv3KA1essTYkyi7dlhwuyQzg21Xggw2OaLV1m8wNVf9AURYdTI+7IBSvTZspEDY7DqwYZQOz92EHxWdX0PAnJ4BYTz+Wtm4YvSkIdHUZYL10cMqZjvyAtsIzQPXjvG616ONKhTMafcuKOYLVwX0WYRoaj57knLx7sQjH12OYKnzfFU3UBw0gSJ+Xvz2MvJiYMcQqjVcjm4UwNu8JBDibCpjPtjZ+luq+zOe/s/22dlHah8J++MIOCaa65hS5cu5elvx40bx/r2tcerEunn9xsP0r7uNWm6+xgjHVpEzJNFTRzgyYAbfc4aQxTraHMnBnil0pUR4mFqVglw77HOadlyCfuutlE2Q8wFtSgrpL0UoUXCYoyPCBFDp7EKLu8QW4oVLNib17Er6+cj55v6B19PjjbKgYd/itZX8Wq+Qo09nnmALA6ZV5gChGo6PYFhODLKDAaQ05RyUD1jIDgoRPMsV+o9ImuMGTZmia2G2KeXGaGXeId2xw2+n7bKsbog+Cv6GjUTCcaHTo1r8NAYlVHzI4t4WVAYu4FBBwYgIVJ9wsv/miFawgjI2IsjFrCRZrY2IAx33ZvXYvdLaY/D5nswjLuNhfC8clvCBHCdYs1ZVGNsWPFekD1r1FAlEeYHDx4/xDLfyRcQViqy1wlgDDL6Z4MezWrxbEngswkr2Kotu9gMSZTWCI+MzEdRP0J/S/Wc0iHXGcJhVA8GZPo5w/z+fEO0OCPbmr39VatUwRpPRds0tEMix8B7SYScxtp8EZIq91dBwS1NtJpqHoZstGWRah5GcYy1U5dvYdvMjJqy903QyEsjCJF9uLmnXTuovWcKscigEtIK3SGWWUyma/Pvio6FFXGbdx4VEcmSaWTGfMagsZdT4HeLuE2dO64YpNBBCk8QMVlD3YjXnOrpov3bsCO7RXvYeJGfTqLROAn3guP3bqp9fe3W6FAuGeF5EM9uSL7Ol9FMEbr21vm9ba/PX7dNMhxFe8/4IaIJEuYGlMjrkWPqSJmrkHIU4s5BXATKyGXlJiqoEzgVqY1Vpq6IiEKeuE8klBKTPrhh67z9gsSEpUZqVfTbfEeb6T0Qzu7b0urTsUNYs8AYq28/ojMPA8Wi7ttr9mct61blnxW77LLWEIzY55hhlTqCuDivb5ZPlL6QOQb/7+i9jNekssFzNWwilr4jiLvi6OvF3M0phAieHSLkERnT1m4tssTfBfInhEeOX+cE2YuhRZ2qrJqSEj1fM1A5hR92bWr09x9f2s96TR5vYdQTBidsDskelLGkuEVfJMKNggT0bvxSoWgn+/h/x7KJL5zFqpQU8dS3mNcvePhIW3i8zBIly1Q+Q0YQIqMId0PglUHMyPCiH5XknRVoLehCWsQOoVdoSyzZPXIJ/G7Za2CflrW54JfwfBGD1KDODfnuCoBxCR4emJdhEsykGhCCs4if5Z8vZ7iNCuuyX4IybVMzJgggfPrIie5iqiqyB0K8ocbxGAFygXrVK3FvJnXhhckvwKvCE8TJK42n0tUs3MRLmLDBzReaFiAcwHKOhRfP3MfxPRG6xY2viieIlX7R9LwBwksNma50iz88RWYIIVwbFF45e1/HMVYW6xX/GuksGXv4xO7W2MszJpnvH7pXQ3bHkXtxnR0ci3F175a1rfviJalOsRDp3bqupRulEsR7QBiT5qw2jK+2BXPIKF/VEwTlLAx+D5qhHagbNVRAFQMNOnIqW6BLNS+Hz6H/nmt6DgN5c0h4kkBj7rW/Fvqan8hjMLTtBnaKDhnLV0S5od2KOY7OY9tJLFycA308/hXzTBVduIxOXDgIqEY/N+D5UWtHIau3y2jvO0vKuFEQjw/HRtLOy8iG2HzX86OelMg85l0Gl1pY0A/u2MDr0OgJtNTZIp94dRehQy8uNFOh5SNIkwj3foA0Wdi1UGOUoY8gOlkRI45H/3b1bIPRXUcZO1k7FI2LE18Z7etaYIABQZkfO8VrY0KGhUUsiza5zuTJHRhg1pMX+VrumIjp3MfXmC6fKC4v75n929XT1ocod9Xw5DVRCLo+hR+4C7rGogeXXhhqxTu6cEUZ4d+W53O3KDpKmgMdG9dgTU1RWtHOI0aQEOvTui6/T0SrlMU6RVvdsrOUG6agx4L+6fTeLdi9x3SxxlqdYOetQyJelteYOjpAzdAWJOSwC5HSMySNt7KxVXggYOMhsjkU3Vfdcnh+p9OOld17jCwuEW+n6GPsXUuI7d2iNnvilB7sukM7RIWUQigYHnxz1myzZQcTIOug/dySKH/A+h7Rfg0jiPH8ggFtoo+TChihqnIZhaSQJKeygzFWJd8X6MlALaO/5q23+vC5a7d5fz7PWzMZQYiMYoj3NOSDPkIC6lavxAclt+N1qGrHslX/h2sPiO2a8njBcsI+zWxpKcuJnacQBjPsCnazHc8nZeYC5ChTa0XtVP+et972txCvFcYSp8U/RPrAPi3qsCAg3MhVsNOH5npGn9g1Og7r0ogNaG83enx4SV/f7Vyk3w0CVribkulFR9+29bSGFLEA14VnuO100c6tM2GHvv1QSYBTXmRMXR5ZVOoQQob5ntrPjeqVy/PMLmB7URkbvSCinwKQalv2TBPtWa4C7BgitAhCehhPMRYg9CCSCcn9GqpWLm8JCws9hiAi2i6yp2GDBu2yV6s6WtFOYQSBu7supejjJ+s9BrFgD+ICRiDSfopfK7rnwp2llnaQvSzCrEvTWqw7xMHNxTd/mO9WrliePffHfOtotalDR6pprQL2zoVGyKU8HGCYD1LfI9ppu4bVrVBQ4UmsOw58dGnfqD4bnsnYoHMSkJU/I0JgglPKzniJ4KOdy8B7Va6LGSsLo7KtBQkyghBpoV61ytpUlG+f35t7BGDCho4QNygmCDJyijJ1oSG0FBC2IbO/qcgda4xh0Dhsr4ZcvIrv/2GiqxSwkWXDGNQtdW/N0CMyn8iIicFVAyM7gvKiW3iCPH5KDxYEnOLi4ckhl7vc/mWDFQyF4l9RA63rV4sSleXp5nxcjx/Bt3xCuEhjkbF+W3Fcxk5RXmqxITPGqNsOsR8rPcfkMOiZM5zAGKBDeJ4B2dvpBweRVZQvBDxRq7IoYhBBdgpkgAFrthaxd/5dYmvvuAfkHe6IESSSCQOvwFACd2rZ60PsInrpT4jvc6rfoLB1Vyl7acR8tneLWrwPQlHDA0HrCRISWhSR8RZ/izLEPEc3jjx6UjDGUCcQRoeNsKH/LLb6C7Tfp3+fxz3JgDzW4bnQhQprPDjkEhYGEplLDmzL/rj5YHaIGfYiGz34eQLU+aAMrxrYjmdQa12vmuPi3B6yaC9QtGm0YWRru2WI3stJ/owIHw6SsSleVA0/zLtlz/mFUpakIEJGECItiIFbjWVrWa+qVhFdRs6woI7/1wzqoM0VL1uiEfsWywIkX8MEdJzbvzUXrUXxYTBTXZxRb5hQIF6zhhnjrNOgmHjPYVGvoU7vP7aLTRQMIU9B8LiJpV3Bu0DdJcHuKXCOj03O9bjdd/mGMC5hUQE3Z6+wCh0Qg5z1f0PYMVIGKtHHBCHDUSrAGCBoYXovqGKncp8TUrJ5ycCYLrSJAtS0o9i0s4RrqQiMkAr7MciQ8eaoRVHeUZaHgunFByE9WwYTyxPEvf8WXRq8H4LMCyMWsKd+m2f9LTL1iOKTy5Z7ZobD3BvWCocJh617BGVqGMGVjDNBbuxm+4aBaOnGnbb+QoyjahnheKELZb0sNWe5bYccxgERVmx8n2QE4SEdwakP4S0s2ifCjEAqSyCyIRds/KxV1HBpNRT4+k+muH4+37sWMoIQaUWnV2CFZPgwgjgtnJOpPi+r3gcF9IkoQtUTBOW6cUeJbVB3y3IiOsyLho3nz+E+rXs/iLgtGuRBCd5Q4tgB7SIeTSu3GNmPDPzoIrgTtB0rYQgVfU2p0o4fP9nfbiomvzo9BJVgmfiSwzsX9onq05FZRjZYiXtF7fPlscWeIDN4IK0qxGHVTDGCaSsK2ftjl1oZ1cQ9IRbooH71yty4993UVdzFX2BpjHhcQxDFUP0gvDyslMWKlw3q4vi9m7HbvpgWtYiBQUl4UD59ak/rdafpUyxZJHIZZJWSN8KEwQP3QE3T+1guI9nbRsxthAcZfx6KLeNO2/qRTbagtXo4aos2inveKfwZ/J8p9psook6CNH/R0apuVfaYy7ylY6PqUWuroJeZChlBiLTx7kV92P8db9eciFiSnT8nh8M4DeqqJ0giCyV14R4EsGgQcd+2162UoJHXhMupjsnLjMn2sk07uQuwfeGu7LAEbLaA347UnX4QRXP/cV2j7gOnXW6v2FAVTFaGXRQcV3Xc15ce2MaaFKiheWoYXrKgOUdsTL3vcH6fiL4HseIyTv0Gxpb5Zmxz0D1B4AnZo7labs4dLnYHEUoUlrLwIIxU9DnqRBpeOqIvv9tM9apiCbGy4IIUwypGcphIqcyXxAl1G0KykQQZeI7t0ZSfo4uZhpSf06GX0YlJ5iNqNi9RhA+d0I1rVYBPxy+3stkh84sw+Il+Qs0wJYAB8AMXnS0IaB/TM+IZGLS+B/1AJLtUJFtdKu97t9DsoABdlCUbd7rOW9Bu94RCbGrjDmxp2y78uRvLN+3kD5l8b8tkBCHShlPWF3gfuLlzVpZ0ERB3mGpPkCAi4l514TBAVM99x3ZxPY9szIIbsMjIoZ6PfycLFjWrVGCNa0bc/QWi7cvl0aFRtEFD7LCs2LyL7Sgpiz6P8vfRPewhGyqY9AVlkiw4ukdT1rlJDW7srFc9PR5fQWvnyZjcQURYdBVC1FN4qa3eUuQ5OXPLMhAU3IR629avFqWp8N7FfXhopJXeUtrVbaT0E19PXmkdB30E/fdTy2+sCdkSWTAEwnAna4LI7GOGF6jhk7biDXhjF5ogtjLm6dIrW8Y6EV4HjTJkkxHHhK0UuZHzyUaqch7jpEYimwUJHjIt5jDwCjGfJ9IkveYuok6dxOaDADzW1X5chac3r16NHX/+s+zJB99nxRUr29Zah0ri4+Did8ezC4eNZ0EiuC2IyBq8tAlkTxAnj48gd4bJC4eBN4j9dQi5YZATr6/bVux6nu+n2kUL1Vq1GUECNknGzuyVA9tFvf6GmSVHLqtXzu4VdZxcdqsV45LXfUMYIF65Sa0qUYM/kV1goibaOyZyP05bzfuoX2euYdPNlKOql5lM0PSG/CB3t+p4i3W3yBaDfhlu1Dje0v/QjLte3bf4SMC6ec/NmdELN3JhZgHCvdRwGJH5SzdOGvoX9td04TBCmyEIqGUSyTgl6d2Y71esEGKluw1NEC6MankuhNhzZ+xtfU6ghgj76W/yffdcRjbcyZ4gOu79dqYVTq3b+Bxkjssvn7Wv63diun/5QW1ZXY0gf5DwE5bb1BTUF8X97pil1nsVNXNEZIo5v38rFhRolkxkHNyIZ/Vp5csTxMlYsq2o1PU7/N7UQbj5dUV499Fd2MGdGthiW3U7V0s27PD1HUs37tDW10tn7WM9D+LcWJcqFfH7YOG67XyH1YmtRRHvDzne36k8g7z48OLOo/Qu/Mnky6sG8H8DNB9OGmKBAtqYu134+7spq1gnH9m+kHGqvSbFcZCQxcGRzlNeqKltEv20vNjjIRtcvNO5E1G9/FSo+2HsiK6NtWUjh4UeI+16Q4MF+haqiLiqkVatcgWlPqN7GbcQjnwD7VcuUzHvkI1KYtGN+WZEEwQZYSLpobua6URjCdtVF/PYnX/qtIheS74jxHz5cx+pyXXhR7px2W19jzmTnAGS8Ea3drr7mEh5D3rqTzZvrQgndR4r8g0yghAZBxb8s/q29LWj7SS2tq04OjxAzgiDLCh+8HtcLqMrQkyqahZUtMUZC9B3xhpuhJ0W8VkZkWHmoI4NArlI79bMnrNdZlVhERcs9BMu0FBjBFHxs4NFpA7cTzLdXeqesKPLZsKd/XxMsoUHm59dsnwG/YSgekEFW3mKLBq2lLkxzAbP69/Kt3ZW0Dz+ZIZIRpBfZq6xnsPTRlc+COkQKV2h0aLj1F7N+UNGviXEXKpAY3DPV9RMX+gjwqaHsDoXwUZE6e49pqEvssqTW6ntuR9PEJuGSMhKxx4ERAbBiCdI2FddefXjNwyOZBVUaVWvGp9DBh0nPSYbO3eyf169iD1047GsoLRIOx8Hi6QNziCJWgenlyRyAl3HKGeHwWJdhwiTGX7zwSm8uvwgHldNKyNAkvrGgzrUD/TkWEdR6W7XLEk2118fRRekgSybef1cI7Tp2dMNV2vCG9UzQW7Pot/QhXvFkgo935FdnaGZ4NYbqGm6/XQdnuEwATdCcaQi2LCtxFMbC/OYDduN45yMTMJDxym8SWRJgldJUEDfcJSUttx0puGaNmWmaqfw+MB8EveD8HC1Si4U/9gZ5JZuCKNGvGlE2cv1IfPYz3PMY0MxZ5Ik7POKgZ18hPWGw6z51nWs3obVLBR2NpTLyF13vqffDk4vSeQEuo5RThMKHjwhOsOMyBojQgsIZy47qG3s9ZLkAnUyZgWZUfMNAUiVTo1qRO/a+pik0SIkO6A+KXYMocKI4fWA9obRdNaqrdak7LcbD0pyTeUv2OVz6zKwkxuL59jhXRpbYUpO4Gx3HtmZBRknbRp5niP35YW7ImG9TuEwkXNHyPN1iidNahWwmw6LeA5YQp083EW8FklBb4TDiOwwQpjcpoxqAe0JN4Je9jDcfT5xRZQniFwfTnXmBu3hZG5esU0Kvc53yAhCZD1IQSZzbr9o3Y6uTcjV3C+n7tcipvKHwJq1IEmCOQS7tWf0ju0a8gmn0C+kpNQtLE7dr3lcmS9oI5bIVdQMGtjRhffT4g07rLYvZ4Mg4uPMPkY/DLFC2WjqtbA7oEN9LjDsxiGdGzruBgeFWD1qZEOUUziM9b5kJJE1QYK4KH/voj62vy97f2LUMaJc4EFWamaHMTQsjN1uebyUn/uZLwXZq3XTjoiHkyw0G2udEdnDxGWb+b9BEJAnIwiR9fiZ7CK13793DErL9QSNR0/qbgl69mjubmw6p5+ztosgFPBJwyMndte+PuzCPqyZqeQto3p94C+vuFtAaaOJXEbuIxDWL3Zy3YT1CNcSjXqlpukOPXzOOrswqnRMhzhDjJrXqcpa1K0a6CqpW7VSTLUii8BX9fCWRPkK+ret75lBL5/x0xcIQ1FBRUMY1coOozlHLH2LTpQ2SCzfHNEXQtNT9Vmc8FvGXh5RRPIpZ1ZNnzZ18751kxGEyCpa14veCT+4YwNWz0cqLN0CkkgceCg8clL3KH0WHX5cqvO9U00EXfGpc1pMHnxIh5AmCJEXiDn1lOVb+L/TVhj/UgroZPQtkRfJaJp8/IQkyl35vi3rsNb1qvo2Ph1tZpaRvUIgxuoVqhQkkPJT1RRCqlaUfMhmVI18JhYzEg+rYcGlWqWIsS65humQr7AaIvnMM7PEBGHCTkYQIqt46/z9ol4zFn2x34lPnRqcNGWpBOUvRPZCSZj0HdiBUpvFglqmxsTNIw0dGUGIPEJu75t3lvI+pEGNylHHUZ/vjK5n7t26jrafeVoaOw/sQFkYkokq3it35RhrhaCpLGzrhM4QiHokg1Y06tQEGwmGZojQBElAGDXAVpAaBanVd8MmKJH8EEg/AqmhALRrMoIQOYFIcxarlgWRXLws/aoniG6xft+xXala4ihzeOQYL/gzzgcoOQCRx8AA7iQarEJ9fmx9916S3pbcd/eUxs57j+3iu66I2BeN6gbPWX2MkNJWpkeIGwFYo8SE7A0cUoxOqmHDEF928AQJJa7xFRRaK15Hn13ePynnFXVQx4cXOOFRkF26sBVN27AwL1N74z6yW2O2f3t9Jp5kaABmOzRNJnKCEjPNGZHdUEaSFJSpOQ5ZkzX+h5/P5f8ARuQ/ftJGE3pkzxhdbyCLm5aj2WBKER4HX1wxwNUIctEBbfi/TWtXYceY4S5OBF1zRUWUHbwHwhojYOOaBYr4snxX2FNEt6xb1VMDDVy4f5tA6xM1r1PFFq5eN0lGC1Gi9atHe/wRMVC1KmMzZ7LbH/iIFVUsiDLw1a1WiZV36fzzXfOGhj0iJ4B6PZF5Yh3raSGeOG3rV4+aLOsGJrVuujWjjElE7uNXaI+IRhbJ9Oq7g7DrlynaNojslquhKm7N+6Wz9nU9740OegmHdWnEgojQ0B+8V3RWC7T/58/Y2/o7kh1GpNOVjmVGSFE8YdhBo2qlCmziPYdl+jIID2ybaArlA9z1kxGEyBmO69k005cQeEIx7toODuhkLJn0b2e4KnpNx9QdE6Sn7NgodbnkCSIdlJEnSMKeeZcd1NbTCEILvtQgUszr1tOz/m8I30lPhKa1It4NgtuP6MyCiDAwPf7L3Kj38JbalRiaIIytKiyKmtvgftlDe28ZgxIdJBcxN9cKZJdzHhzy3Q5IRhAiZ/AjFEaknsF7RQwblx/c1vbesYqhalAA8oynkwPa1zcmblzUzf7eh5f0tf0txzsTRK7SvgEZ8uJl35a1uXgmdrtlTw9d9pBSCjlNCV9fNcBcgIe1u+iJhlI8cQoJwMuLuUM7N2Tbi8uiygmaN7JGmREOExkjF67fETlPyDBekWEwucRi8GtYsyBKRJiIg507GevalT31wNmsoLQoyjs7FEJYZLQhVbyX71NIWlUSOUOlCgH22coWQiGbiNKAdvZML12b1lQPJ/wXrc19XebGwR35EgbHvHneflrjhjqZxt/H9CDvKSK3cXL5J7xpWMNYSPD+QuoedN1MNSnVJZE80A/jv1QZpA+gbGuRsmYhNqC9fU4y1Mw4CAOJ8ARBZil4eXAPHXOZt7MkYjhBhp4nT+lJmwhJRs46RaQJdDyzZrEWqxezUJix7UXRBsIDA5wBjIwgRM6gSwdHpBcjPatz9hfVW4fizP3TsEZl9tq5vbTvXT+4g/Ucgm2iLryQP0cQRPAQPXTIxWCKsDnKwpA6grCjmg0Ynh1GSa/bWsz/bVXP8HqCwUPoC103qD2vD3U+I+PkvUPET6ypm2kPLfl8NXmlUsYhx2NrVqmY8hTImYZWlUTOAOs8kXl3xlgU6WMc8wINFiaVK5T3PC5s/hdkRXqCILwRXURUpgzl70dP6k7FmUKwAF+8IRJuQaQGuV3/OW+dq1HDEEY1NEHUz/K/QyG6LzKcPZBMUEku/xBjB3X07/Vxaq/mee9NnN8mHiKvIE2QzDOwU0NfgxQs/hBiouwwyUMua8O9mqYIBEE4Y+x0h00NIedMMb1a1aVizKIdcCI+oEkG48ZDP86Omnv0a1uPtagT2cDhOjmybpZmU2G/1nRfJBOaD2aWx0/pwaZvsYfD7N++HmsqpUqXCcJGG22tEzlDpQrUXHMN0Yc6aV0QMZSl+a8QRaWkGQRBuPe/oYgIpPT6TaSzklbICJIeqlWuwGoUVOTP1RnHoXs1sgltRsTFaTMhXaC4q1YqH9PxRPKoWVCBNVCyCB7RrQlrWc+/d3e+QatKImeoTEaQrAO7Kf3bRoRSnSbhSNdKJAaKUpRnAAz0BEEkiKx5IPcZmPi2qJtYalbCP7QJkH50GWJksIkgpy6mITX1oA86omvjNHwToWOflnXYJQfaMzqq9G5dhwUJMoIQOUPF8jRMZRtlu8OsZ4vaUa+rNUU7YYnz+RUD+L+GoJv7vfCleSxB5COx7CYGHZH9Qu0zlm/alaErCh4VaO6SdeHTliaIxkhIpAYexkuFm17QsFu1YntatmLVTS8p18NZsG4E0gSJg927d7PS0tLk1wbhSLMa5Vn1CmFWVFREpZRN7C5htSra6wV1hZ2vsj2R12tVCvPXk1l/FStWZOXLB2cxVKtKRSvG32ucqlXVe7AjiFzloRNIyNMPQvMAfQZFJGYOkm9KP608XPyt7DDmspzqKL3Ze4g0UbUqY0uW+Pd4CLFAkTNGkIcffpj9+OOPbMqUKaxSpUpsy5YtUccsW7aMXXnllWzkyJGsevXq7Pzzz2ePPvooq1AhOT8TN++aNWu0302klvsPachqV93FFi9eTEWdRdQP72a16u2x1QvqSky+xeuHtyjH9m/UMOn1V7t2bda4ceNACDiBAzvUZ9UqVSCBMSLQ0ILeH6Jf/GP2Ota9ebTHHpE+AzaRPdkEh89Zx5rUKrAJo5JoZ3ogE0iWE2aBImeMICUlJezUU09l/fv3Z0OHDtV6Zxx99NF8QTR69Gi2evVqdt555/Hd4kceeSQp1yAMIA0bNmRVq1YNzMIrGyipspU1qlnAaletlOlLISSKS3ez3eEwq1qpgq2uBG0a1+T/VttaxF3Y/bjj+TVI7ty5k61bZ6TBa9IkGJojIp6Teh4iyNDY6x+Mm/8s2MCWbdppe500QdIHhYOmHzePgz/nruf/PnpSj0iKXBpU00IsxRyw9XhWUL3AmMvLIsL5TM4YQR544AH+77Bhw7Tv//bbb2zWrFnsjz/+YI0aNWJ77703e/DBB9ntt9/O7r//fu49kggwsggDSL16zkKQRGoIVShilQsKWEEBGUGyiYICfV1F3jcOqFAUZgUFFVhBkowgoEoVQ9gPhhDcl0EKjaEJGxFkaL3in/cv7sPa3PkT26OkkyqoEJz+MtNQf53+8t6zx9+xwlbSuKZmMkMkFTVVN5EGdu1i7KCDjOd//42Js2P2zZKyPezKge3YiDnr2IPHdwtE9eSNMOqYMWNY9+7duQFEMGTIELZ161Y2c+ZMx88VFxfzY+SHDqEBAg8QIjNQ15nbpMKqL+5H0ughiPyBdgBTkyaXyAwDOzWkok8jJ+3T3Hd7F5ogA9rT5ma2zeNpzp8EYA2cMMF4uFgGW9atyvZvX88yCvZvF4z7IW+MIAhVkQ0gQPyN95yAZkitWrWsR4sWLVy/h6yYmYPKPneBUGoqYm6D2iaC+auJILfvHs1rRd6nGyBm1CUhmUTSx2n7uc8rieRy7aD2PAWuE81qS7vhpAmStVAflV5CAZxZZtQIcscdd/BFjNtjzpw5Kb2GO++8kxUWFlqP5cuXp/T7iPghMbzcoK5Gt6VhzQJWrXLORN9lPegb92sVrHzuRHB44pQeUa9dc0h79tElfTNyPfmAujN+QPv6GbuWINIhIDH22YCR+tavJ0jkM0RqadugGrtiYDvfx1MflX5CAbsNMroqufnmm9kFF1zgekzbtoYQoBcQRP3vv/9sr61du9Z6z4nKlSvzB5H9ZPu9CcHeTz/9lOvTBJkqlcozZtfgi4vXXnuNZ4T6/vvvk3FZeXcv3Htsl0xfBkGkhH1a2g18EGk7vGvjQO9YJUolJVvG/cd1zdi1EETKNUH8GkHM46hHST3YCOvYqIbv46mPSh/VK1dgL5y5D9tWZEg/BIWMeoI0aNCAde7c2fXhV9AUWWOmT59uZYsAv//+O6tZsybr0iW4iwUvTxuIxi5ZssTx/bFjx1rCsI899hivEwhS1q1bl/Xt25e99dZbvr/Hi6+//pr169ePhyXVqFGDde3ald1www38vYtPPYbVqFLJ8fwDBw60znP55ZdzkczPP/886jtwHeIzOAbhT5dddhnbtGkT+/PPPz1/B47RUVRUxO655x523333Wa+9+eab7MADD2R16tThj8GDB0cZ6jAA33vvvTy7CcoVx8yfP996H3Vz8cUXszZt2vD327Vrx78D2ZLU8zz11FOsY8eO3KjXrFkznlbaC5QR6hQCptDU+emnn2zvf/XVV+zwww/nYsD4/UhR7YfCzZvZnddeyu8/pLHFb9i+fbutvGAAxXcihfUJJ5wQdY6LLrqITZo0iY0aNcrXdwYNCvEngkKnxjW0cfyEP/q3rcfO7tuKiosIkBHE+f2LDmhjPafsMATB2JdXDmB1q1UKnEdUzvinL1u2jC9U8S8W5GIx1r59e1a9enW+UIOx49xzz2VPPPEE1wG5++672dVXXx1oTw+kChbASwEL7rlz51qvoew2bNjAnyOzDgwPMiITDrLzvP766+yll15i++23HxeQnTBhAtu8ebPv73Fj+PDh7PTTT+cL9+OOO44vuJHtB4Ys8Mwb77NmtSryVKwIWerTp4/teoWxDGlTP/nkE3bbbbext99+m6dVVsFn8Fm0o9mzZ/PFNkKh3n//fdvvuP766/nvfOedd6zXYPzR8cUXX/AF//7772+9BoPJmWeeyQYMGMCNDI8//jhvpxDqhZECoK2+8MIL7N133+WGDhhSIOiL347PIBxsz549vOzR1mfMmMEuvfRStmPHDm70kK8VHih4DYYF3Ct4uIFU0rg+6OIcc8wx7KOPPuLGCBgeunUzlKHxPQcccAA77bTT+Pf65c7rLmUb1q3l9QfR0gsvvJAbm/AdAGUPo851113HvvzyS+05UKdnnXUWLx8YkwiJEMXLEsHh5bP2tf1NniCxAaNRubxRgCMId7CQc/MEgQCkSlD1xQgi0Gm8wznC+eefjx4t6jFy5EjrmCVLloSPPPLIcJUqVcL169cP33zzzeHS0tKYvqewsJCfF//K7Nq1Kzxr1iz+b67yzjvvhGvVqhX1+uLFi/lvnjx5suNne/bsGb7//vsT+h43rr/++vDAgQMd35+6fHN4Z3Gp5/UOGzYs3K9fv/CWLVvCVatWDS9btsz2/n333cd/i8xNN90UrlOnjrbNHX/88b6u/+ijjw7fcsstrseUlZWFa9SoEX733Xf533v27Ak3btw4/OSTT1rH4LorV64c/vjjjx3P88QTT4TbtGlj/Y12WaFChfCcOXPCsXDaaafx65bp27dv+PLLL4+rjQj+HT+FH/vRDyOs137++edwKBQKr1y5MqZy/uuvv8KVKlUK79y5U/t+PtyX8XD/dzPC01dsyfRlEETaaXX7D+EtO0uo5GPgtNdGh9dtLaIyyyCHPfMnlX+aWL1lV/jsN8c6vj9zZSHvR8C1H02ynhNE3rF9ezhcv77xwHMPlm3ckRf3g9NaXiVn9gaGDRvGXf7VhxwG0apVK+7OD2+A9evX811xuNqnnB07nB9FRf6PRT5nP8dmAOiqjBgxgpdrqs4PDwl4Ojjhx1IPXY5zzjmHh9QceeSRvN24gXCTX3/91XfYlRP//PMP95BxA+0SXhHCm2Tx4sXcYwkhMAJcN8KMkPLZCXityB4p0MyAds4PP/zAvUlat27NLrnkEk9PEHyH/N0AXihu3+2H8f+NZTVq1WJde+5jvYbvKVeuHBs3blxM50KZlpWVxfy5fOfeY7qwbs0i2TIIIkjUqlIx05eQU2A2SBvdmaVH89oZvoLggE1tN08QOZxut1vcDEHkOtWqMYZ1Gx54TtjIGSNIVoNQD6fHySfbj23Y0PnYI4+0H9u6tf64FIGwDYStyA/BM888ww0gMFb06NGDXXHFFeznn39O2ndfe+21rHfv3jyUA4v4M844g4ezFBcXW8d4TeKgpQENE4TVABhDEMqiqoRDOwa/DeEYMBrA+HL77bfHfe1btmzhhommTZu6HofvwDHC8CBSN+tSOzuldV6wYAF78cUXue6JYNGiRWzp0qVc3+O9997jhp+JEyeyU045Ja600m4ppf2wbdMGVrdeA9trMEbCcBPruatWrcoNQ/h9RARy3SUIwi8YA4MW651tPHVqz0xfQqDGR1cjiPTWPwuMcHCCIIIHGUEIm5YHtFbkhwB6K/DSgJEBGhoQoD322GO5x0EyqFatGs8EgkU+tFxgpED2IGh/wIPCTxw4jCbwZKhf30j9d9RRR3HjBDxYZDp16sR/2/jx47lhAp+BESZedpkePNDwcAKistAqgfir23FurFy5kh1xxBFc50TW54BmCIxFMIBAOwPeUfCIGTlyJNdlgY6ObNh65JFHWLKAMUw1mpVLcmwhjFWiDRAEQRCxgd3uwMZ8EwH1BHF+XzaQ7NuSPHQIQgBbeb+2eu3DfCRnhFGzGinrRRTly9v/lrLXRKEqly1ZwtIJMqVAfNMJhDPAWwMPZG354IMPuBDt//73P+5RkQyQ/QQPGFdwXmQ7gXGm12EnunqCQGgT4qLwNJBDoP6/vTsPjqJq1wD+JiQhCSRB/BK2sCqreFmiCC5hCZE/cGNRClRCKbJfEAu5IpQsankREbXY5IviVSEqqBiUTdlFoIrNTyDsoGhQYxRIEIhJzq3nYA89k4UE0z1MzvOrGhJmenp63unp9Hn7nPfgfiRHkpKSPPdh6Iv1PpGc6Nmzpy78+vzzz1/VNlszp1hFYn1hWBZeB8VY0YvGYk3djKmcMTuMBf9v27at1zoyMzOla9euurfOggULvB7Dc/GeEStLy5Yt9U8kQPA8e0LLGkqD17emkba/dmlTSvuaNm2ajBs3zus+PP/37Cypd12E5z4MacHwnPKs24LnYSYpIqLm5ZhikS4nQUKYBCFDoNeTbw9gu8b/ujwsIK+g0KWtIvIDXKS1Rhmg937E5fPykoSF+LRbKzEmQSpCecZZObWsH1hTD2MGESdgWAyGQ5Rl/agFk5OTI7t379ZT31rQewUzk2DICqZqLQ56nnTr1k2GDx9+xSEtxUFSBbHAjC6Y/cUOs79gxhvUHfGtGYLEEZICmBnHSnpgNhrUv8C22HuAIJGRkJCgh/cgGWWHGWmQZDh69KhOIMGhQ4c8dXKQICkuuYVppfHa1jTEgNlccH9ZxcXF6ZvvenPOnJE9u3ZJ0l2X1oXeOOixgnon5YH3hOl027W7XF+EiMy1emyivzch4OSzJwgZlwQp+fGo8FCpXvVS8ycvn0kQqsQKC0U2brz8+xUEGTZskkkQ8sjOzi5SswGJAwzfQH0JNLbREwENdxT1nDBhgu590KJFi38cxSlTpughDxjCgoY7khaYGhWFRJOTkwWVQUr7amL4B3p0tGnjPe4WyYmxY8fKokWL9HTJxUGjHT00MEwEUwBfDQypQXFUe0IBU+JiqmBMC4uEjhVba+gIDjZY/oUXXpCmTZt6pshFIgZT1VoJEAxvQUzQo8RemNbqVYEaI+3bt9fDlF577TWdbMB7RdzsvUN8YVrdzp07y8yZM3XsMFwH0x7be5pY01KjJwpY0x7jtUvq1YFeKHd0SZKx/z1CUhe8qT/DUaNG6Tov9iQTkkZ5eXn6NZDAsnqr2HvBbN68WRd9tZI7RERU/p4grAlCpggKLr0wKuRezNc/8wpYGJXIrrReVJUNa4KQBxrTGFphvy1btszTyMcsJKgDgoZ1SkqKTn6sWbOmQmbgQWMcBT4HDhyo14uZXZA0wPpRw0PvrCVkKDGEA/VE+vgWof17CE+vXr10kqQ0SJSkpqbKyZMnr2r7H3/8cd0bBTVILPPmzdONfCSQ7DFFMsMyfvx4XY9kyJAhephRbm6urFq1ylM3BD0zUCcFPTbi4+O91mN/j/hsUAslMTFRJzSQiEBSozRIaCFBg6QHkkdLly7Vn3fr1q09y6Snp+teGFgnIJGB/8+fP7/Udb/0xr+labNmehgSElt33nlnkWE8uB/rwrZv2LBB/+7b4yMtLc2r/gkREZUPGoQcDkOmwJlimSd9UUr2T+vh8BYRBYYgMUsQ5sn190ZcSzAcAbNRoDEbHR3tuR9d8tH7AVfrr7awJV29gz/nSPPa1/ZYcBQsRY8M9JAx3X9+PC11a0TIv6pXvep1YNYeDFPC0B58J4vD7yURUen+75sTknJ7I4aJjHDuYr4M+Pc2+WzUnSUu0+iZL+TE//aU+2Z/LR8N7SThoebUQSCDoJyANdMn6ldeocxC5unz8j8f/0fee7x8Q9cDpS3viz1BiCrIjBkzvKYVpn/m1KlTesabkhIgRER0ZUyAkEnQaxh1cErzaMeG+ueVhs0QmSTIsK4gTIKQK3ynUrXf8NiVxESEyrUOdT/+yVS7VHR4FoZhEREREZXVvsyzpT7+/AOXhv0iB2Jaw4+ILmFhVHJFcVOpWkrrqmSpHcMhSIHkhlgUfvX3VhARERERGSoy0t9bcM1iEoRcUdxUqlR5Vft7+jkiIiIit4SFBMvopKZlWpYXa6hSQw0Q1AUpB2XQCDEOhyEiIiIiooBXJThInkpuVqZl5z2cIGFV2BQigriocJne97/EFLxcW06FhYXOfBJEVG78PhIREdHVqF+TQwWI7AnEejUixBRMgpRRWFiYBAcHS2ZmpsTGxur/B7EfHZFfYGbvvLw8ycrK0t9LfB+JiIiIiEhELlwQ6dPnUig+/lgknPUV7ZgEKSM0tBo3bqyn7UQihIj8LzIyUho0aKC/n0REREREJCIFBSIrVlz+nbwwCVIOuNqMBld+fr4UcGci8qsqVapISEgIe2QREREREVGZMQlSThgCExoaqm9EREREREREFDjYh5yIiIiIiIiIjMAkCBEREREREREZgUkQIiIiIiIiIjICa4IUM/UmnD171h+fBxEREREREdHVO3fu8u9o1xoyqcfZv9vwVpu+JEyC+MjJydE/69ev79RnQ0REREREROS8unWNbNPHxMSU+HiQulKaxDCFhYWSmZkpUVFRXlNvIquExMjJkyclOjrar9toEsadsTcN93nG3TTc5xl303CfZ9xNwv2dsXcTUhtIgNStW1eCg0uu/MGeID4QrPj4+BIDhgQIkyDuY9z9h7Fn3E3C/Z2xNw33ecbeNNznGXfTmLbPx5TSA8TCwqhEREREREREZAQmQYiIiIiIiIjICEyClFHVqlVl8uTJ+ie5h3H3H8aecTcJ93fG3jTc5xl703CfZ9xNw32+ZCyMSkRERERERERGYE8QIiIiIiIiIjICkyBEREREREREZAQmQYiIiIiIiIjICEyCEBEREREREZERjEmCvPTSS3LrrbdKVFSUxMXFyQMPPCAHDx70WubChQsycuRIuf7666V69erSp08f+eWXX7yWGT16tCQkJOhqu23bti32tZRS8sorr0izZs30cvXq1ZMXX3xRTOVW7KdMmSJBQUFFbtWqVRMTubnPr169Wjp27KhfKzY2Vq/nxIkTYiI34/7RRx/pxyIjI6Vhw4YyY8YMMVlFxP7bb7+V/v37S/369SUiIkJatmwpr7/+epHX2rBhg7Rv315/PjfeeKO88847Yiq34n7q1CkZMGCA/tsaHBwsTz75pJjOrdh/8sknkpycrI/v0dHR0qlTJ33cN5Vbcf/666/ljjvu0OvAMi1atJBZs2aJqdw8xlu2bNkiISEhJf4dNoVbscff1uLO5X/++WcxkZv7/MWLF2XixIn6fBLnNo0aNZK3335bKitjkiAbN27UO8i2bdvkyy+/lL/++kvuvvtuOXfunGeZsWPHyvLly2XJkiV6+czMTOndu3eRdT322GPSr1+/El9rzJgxkpqaqhMhBw4ckPT0dOnQoYOYyq3Yjxs3Tp8k22+tWrWSBx98UEzkVtyPHz8u999/v3Tr1k327NmjT4x/++23YtdjArfivnLlSnn44Ydl2LBhsnfvXpk7d64+OZ49e7aYqiJiv3PnTn2i8f7778u+ffv0CcGECRO84op9vmfPntK1a1e9z6MxPnjwYGMbhW7FHSdoaIRPmjRJ2rRp4/r7NDn2mzZt0kmQFStW6OWx7997772ye/duMZFbccdFnFGjRun4Z2Rk6H0ftwULFoiJ3Iq75fTp0zJw4EBJSkoS07kdezT07efzeJ6J3Iz7Qw89JGvXrpW33npLxz8tLU2aN28ulZYy1K+//qrw9jdu3Kj/f/r0aRUaGqqWLFniWSYjI0Mvs3Xr1iLPnzx5smrTpk2R+/fv369CQkLUgQMHHH4Hgcup2Pvas2ePXsemTZsq+B0EJqfijudjny8oKPDcl56eroKCglReXp4ynVNx79+/v+rbt6/XfW+88YaKj49XhYWFjrwX02JvGTFihOratavn/+PHj1c33XST1zL9+vVTPXr0cOR9BBqn4m7XuXNnNWbMGAe2PrC5EXtLq1at1NSpUytw6wOXm3Hv1auXeuSRRypw6wOX03HHcX3SpEllPu80iVOxX79+vX7OH3/84fA7CExOxX3lypUqJiZGZWdnK1MY0xPE15kzZ/TPmjVrerJkyK51797dswy6HTZo0EC2bt1a5vUiE9ekSRP5/PPPpXHjxrorEa4Q/v777w68i8DkVOx9oTcOuk3fddddFbDVgc+puGPIBrqmL1y4UAoKCvTrvPfee3q9oaGhYjqn4o6r4uHh4V73oZvjjz/+KN9//32FbX8gq6jYYz3WOgDL2tcBPXr0+EfHq8rEqbjTtRP7wsJCycnJ4efjctzR8+abb76Rzp07l7iMSZyMO85pjh07JpMnT3Zs+wOZ0/s8hh/VqVNH90DDkCRyNu7p6elyyy23yMsvv6zLOKD9hB7258+fr7ShDxED4Y83ui9jnGXr1q31fRhrFhYWJjVq1PBatlatWuUah4YDJhog6JL07rvv6kYhuin17dtX1q1bJ6ZzMva+4+MWLVokzzzzTIVsd6BzMu5I9q1Zs0Z3oxs6dKje5zFeHN2mTedk3NHoxrFl0KBBumv6kSNHZObMmfoxdB1FAtZkFRV7NDg+/PBD+eKLLzz3YVk8x3cdZ8+e1ScMSEaZysm407UTewz3zc3N1cd907kR9/j4eMnKypL8/Hxd/wwX10znZNwPHz6szx83b96s64GQe7FH4mP+/Pm6QY6LPbig2aVLF9m+fbuuw2UyJ+N+7NgxXYMIF9c+/fRTPax9xIgRkp2drROClZGR32yMrcIYenzYTuyg+NIiAYIsGmBsFa6WY3xVpR5b5efY2+ELjKtUKSkpjr5OoHAy7jjIPvHEEzrWKLyEuD/33HM68YfxiyhoZSon446YHz16VO655x59FQDFClGPCCfI6JljuoqIPZ6Peje4EogxuMS4X8vc2ucXL14sU6dOlc8++8zYcfpuxx2NcSSdUBcAjXMUY8bfW5M5FXdcyEEBZuzj1nk8uRN7QDvJ3la6/fbb9bkOap6hl7HJnIx7YWGhPl/HBeSYmBh936uvvqrP5VFzrjJe3DHuTBkFpjBUZf369Tqzbqldu7bk5eXpIkh2qK6Lx8oKGUxkje0HTlThhR9++EFM5nTs7ZA5RuPQ92qtiZyO+5w5c/QBE13o2rVrJ4mJibr4EoorIXNvKqfjjj9W06dP1yfG6H2GZJRVgBlD8kxWEbHfv3+/LoY3ZMgQXYjQDsv6zuaD/yMRVRlPFK6VuJP/Y//BBx/oXgiYmcp3SJiJ3Io7elzefPPNOvmNHoBIdpvMybjjQs6OHTv0a+B8Hrdp06bpGTbwu+m9uv1xnMe5DXq7mszpuNepU0cPg7ESIFb7FTOeYph1paQMgUKBI0eOVHXr1lWHDh0q8rhVWGbp0qWe+1DctLzFClevXq2fc+TIkSIFOg8ePKhM5FbsLceOHdNFOZcvX65M5lbcn3rqKdWhQwev+zIzM/V6tmzZokzj9v5u9+ijj6pOnTopU1VU7Pfu3avi4uLU008/XezroDBq69atixSqNbUwqltxt2NhVPdjv3jxYhUeHq6WLVumTOePfd6CYrQNGzZUJnIj7ijy/t1333ndhg8frpo3b65/z83NVSby5z7fvXt3XRDYRG7F/c0331QREREqJyfHcx+O9cHBwerPP/9UlZExSRAcwFD1dsOGDerUqVOem/2DHTZsmGrQoIFat26d2rFjh25M+DYoDh8+rHbv3q2GDh2qmjVrpn/H7eLFi56DZ/v27VViYqLatWuXXs9tt92mkpOTlancir0FlbxxsMjPz1cmcyvua9eu1UknnJjhAL1z507dGMRJWmU9cF4Lcc/KylLz5s3TVcBx/+jRo3UDZfv27cpUFRF7nOTGxsbq2Rfs60BFdnuiNTIyUp9MIP5z5sxRVapUUatWrVImcivuYH0PEhIS1IABA/Tv+/btU6ZyK/aLFi3Ss4BhX7cvgxNwE7kV99mzZ+vZ1vC3FbfU1FQVFRWlJk6cqEzk5rHGjrPDuBf7WbNm6cY3zoGwPGYBQ0P8q6++UiZyK+45OTl6dkHMOoi/qZh9pmnTpmrw4MGqsjImCYKMWHG3hQsXepY5f/68njLouuuu0ye4yDpiJ/G9+lTceo4fP+5Z5qefflK9e/dW1atXV7Vq1VKDBg0yasohf8YeSSh8iZ999lllOjfjnpaWptq1a6eqVaumD7T33XefbhyayK24IwnSsWNHHXOsIykpSW3btk2ZrCJij5Pd4tbhe+UV0/i1bdtWhYWFqSZNmni9hmncjHtZljGJW7Ev6XiUkpKiTORW3DHtOabjxvOjo6P139m5c+d6TUlvEjePNXZMgrgX++nTp6sbbrhBX9SpWbOm6tKli27cm8rNfT4jI0P3ukGPELSl0NO7Ml/MDMI//h6SQ0RERERERETkNOMKoxIRERERERGRmZgEISIiIiIiIiIjMAlCREREREREREZgEoSIiIiIiIiIjMAkCBEREREREREZgUkQIiIiIiIiIjICkyBEREREREREZAQmQYiIiIiIiIjICEyCEBEREREREZERmAQhIiIiIiIiIiMwCUJERERERERERmAShIiIiIiIiIjEBP8PMjx3cjvg6kcAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Last real observation: 2026-03-01 06:00:00+00:00\n" ] } ], "source": [ "fig, ax = plt.subplots(figsize=(11, 3))\n", "ax.plot(series.index, series.values, lw=0.3)\n", "ax.axvline(env.test_start, color=\"red\", ls=\"--\", lw=1.5, label=\"TEST_START (2026-01-01)\")\n", "ax.set_title(\"Hourly air temperature — Stockholm-Observatoriekullen A\")\n", "ax.set_ylabel(\"°C\"); ax.legend(loc=\"lower left\")\n", "plt.tight_layout(); plt.show()\n", "\n", "print(\"Last real observation:\", series.last_valid_index())" ] }, { "cell_type": "markdown", "id": "26bcece6", "metadata": {}, "source": [ "## 3. The evaluation contract — what the model is (and isn't) allowed to see\n", "\n", "The environment enforces the train/test boundary:\n", "\n", "- **`env.reset()`** returns the **training** data (`initial_data[\"target\"]`,\n", " timestamps *before* `TEST_START`) plus a forecast `input`.\n", "- **`env.step()`** returns the **test actuals** — and only *after* we've made our\n", " predictions.\n", "\n", "So the targets we are scored against are handed back *last*. The model never sees\n", "them while fitting or predicting." ] }, { "cell_type": "code", "execution_count": null, "id": "ef46d5e2", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:31.254461Z", "iopub.status.busy": "2026-06-02T11:55:31.254385Z", "iopub.status.idle": "2026-06-02T11:55:31.257039Z", "shell.execute_reply": "2026-06-02T11:55:31.256746Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train rows : 83,992\n", "Last training hour: 2025-12-31 23:00:00+00:00\n", "TEST_START : 2026-01-01 00:00:00+00:00\n", "\n", "assert train.index.max() < TEST_START -> OK\n" ] } ], "source": [ "initial_data, forecast_input = env.reset()\n", "train = initial_data[\"target\"]\n", "\n", "print(\"Train rows :\", f\"{len(train):,}\")\n", "print(\"Last training hour:\", train.index.max())\n", "print(\"TEST_START :\", env.test_start)\n", "\n", "# The model is fit strictly on the past:\n", "assert train.index.max() < env.test_start\n", "print(\"\\nassert train.index.max() < TEST_START -> OK\")" ] }, { "cell_type": "markdown", "id": "f74414a0", "metadata": {}, "source": [ "## 4. The autoregressive model\n", "\n", "`ARPredictor` fits an ordinary-least-squares AR model\n", "\n", "$$\\hat{y}_t = c + \\sum_i w_i\\, y_{t-\\ell_i}, \\qquad \\ell \\in \\{1,\\dots,24\\}\\cup\\{168\\}$$\n", "\n", "i.e. the previous 24 hours plus the same hour one week earlier, with an intercept.\n", "It is plain `numpy.linalg.lstsq` — **no scaler or normalizer** is fit on the data,\n", "so no global/test statistic can sneak in." ] }, { "cell_type": "code", "execution_count": null, "id": "d4e226e8", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:31.258029Z", "iopub.status.busy": "2026-06-02T11:55:31.257947Z", "iopub.status.idle": "2026-06-02T11:55:31.293496Z", "shell.execute_reply": "2026-06-02T11:55:31.293283Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lags : [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 168]\n", "n parameters : 26 (intercept + lags)\n", "intercept : 0.0115\n", "lag-1 weight : 1.2349\n", "largest |w| at: lag 1 (temperature is highly persistent)\n" ] } ], "source": [ "model = ARPredictor() # lags = [1..24, 168]\n", "model.train(train) # <-- fit on TRAIN ONLY\n", "\n", "col = train.columns[0]\n", "intercept, weights = model.coefs[col]\n", "print(\"lags :\", model.lags)\n", "print(\"n parameters :\", len(weights) + 1, \"(intercept + lags)\")\n", "print(\"intercept :\", round(intercept, 4))\n", "print(\"lag-1 weight :\", round(float(weights[model.lags.index(1)]), 4))\n", "print(\"largest |w| at: lag\", model.lags[int(np.abs(weights).argmax())],\n", " \"(temperature is highly persistent)\")" ] }, { "cell_type": "markdown", "id": "2fe8cf43", "metadata": {}, "source": [ "## 5. Forecast and evaluate on 2026\n", "\n", "We predict, *then* call `env.step()` to reveal the actuals, then score. We compare\n", "the AR model against two naive baselines on the **same** test hours:\n", "\n", "- **Persistence:** $\\hat{y}_t = y_{t-1}$\n", "- **Seasonal-naive (24h):** $\\hat{y}_t = y_{t-24}$" ] }, { "cell_type": "code", "execution_count": null, "id": "80c88ed0", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:31.294598Z", "iopub.status.busy": "2026-06-02T11:55:31.294528Z", "iopub.status.idle": "2026-06-02T11:55:31.313259Z", "shell.execute_reply": "2026-06-02T11:55:31.313055Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Evaluated on 1,423 non-missing test hours (2026)\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MAE (°C)RMSE (°C)
AR (lags 1-24+168)0.2890.399
Persistence (t-1)0.3130.455
Seasonal naive (t-24)2.2612.937
\n", "
" ], "text/plain": [ " MAE (°C) RMSE (°C)\n", "AR (lags 1-24+168) 0.289 0.399\n", "Persistence (t-1) 0.313 0.455\n", "Seasonal naive (t-24) 2.261 2.937" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "preds = model.predict(forecast_input) # 1-step-ahead forecasts\n", "_, test_target, done = env.step() # actuals revealed now\n", "\n", "y = test_target.iloc[:, 0]\n", "yhat = preds.iloc[:, 0].reindex(y.index)\n", "persistence = series.shift(1).reindex(y.index)\n", "seasonal = series.shift(24).reindex(y.index)\n", "\n", "mse = MeanSquaredError()\n", "def score(p):\n", " return obj.calculate(y, p), mse.calculate(y, p, squared=False) # MAE, RMSE\n", "\n", "results = pd.DataFrame(\n", " {\"AR (lags 1-24+168)\": score(yhat),\n", " \"Persistence (t-1)\": score(persistence),\n", " \"Seasonal naive (t-24)\": score(seasonal)},\n", " index=[\"MAE (°C)\", \"RMSE (°C)\"],\n", ").T.round(3)\n", "\n", "n_eval = int(y.reindex(yhat.index).notna().sum())\n", "print(f\"Evaluated on {n_eval:,} non-missing test hours (2026)\")\n", "results" ] }, { "cell_type": "code", "execution_count": null, "id": "a3d0c786", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:31.314189Z", "iopub.status.busy": "2026-06-02T11:55:31.314121Z", "iopub.status.idle": "2026-06-02T11:55:31.356066Z", "shell.execute_reply": "2026-06-02T11:55:31.355859Z" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "win = slice(\"2026-01-01\", \"2026-01-14\")\n", "fig, ax = plt.subplots(figsize=(11, 3))\n", "ax.plot(y.loc[win].index, y.loc[win].values, label=\"actual\", lw=1.2)\n", "ax.plot(yhat.loc[win].index, yhat.loc[win].values, label=\"AR forecast\", lw=1.0)\n", "ax.set_title(\"1-step-ahead forecast vs actual — first two weeks of 2026\")\n", "ax.set_ylabel(\"°C\"); ax.legend()\n", "plt.tight_layout(); plt.show()" ] }, { "cell_type": "markdown", "id": "e63924ce", "metadata": {}, "source": [ "## 6. No data leakage — demonstrated\n", "\n", "It can *look* suspicious that `env.reset()` hands the predictor the **full** series\n", "(including the test period). Below we prove that this is purely a vectorization\n", "convenience and **cannot leak**: every forecast $\\hat{y}_t$ depends only on\n", "observations strictly *before* $t$.\n", "\n", "### (a) The train and test windows are disjoint" ] }, { "cell_type": "code", "execution_count": null, "id": "7606ba2d", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:31.357053Z", "iopub.status.busy": "2026-06-02T11:55:31.356988Z", "iopub.status.idle": "2026-06-02T11:55:31.390661Z", "shell.execute_reply": "2026-06-02T11:55:31.390471Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "train: 2016-06-02 08:00:00+00:00 -> 2025-12-31 23:00:00+00:00\n", "test : 2026-01-01 00:00:00+00:00 -> 2026-06-02 08:00:00+00:00\n", "overlap: 0 hours\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_idx, test_idx = train.index, y.index\n", "assert train_idx.max() < env.test_start <= test_idx.min()\n", "assert len(train_idx.intersection(test_idx)) == 0\n", "print(\"train:\", train_idx.min(), \"->\", train_idx.max())\n", "print(\"test :\", test_idx.min(), \"->\", test_idx.max())\n", "print(\"overlap:\", len(train_idx.intersection(test_idx)), \"hours\")\n", "\n", "fig, ax = plt.subplots(figsize=(11, 1.5))\n", "ax.hlines(1, train_idx.min(), train_idx.max(), color=\"C0\", lw=10, label=\"train (used to fit)\")\n", "ax.hlines(1, test_idx.min(), test_idx.max(), color=\"C1\", lw=10, label=\"test (held out)\")\n", "ax.axvline(env.test_start, color=\"red\", ls=\"--\")\n", "ax.set_yticks([]); ax.legend(loc=\"upper center\", ncol=2); ax.set_title(\"Disjoint train / test windows\")\n", "plt.tight_layout(); plt.show()" ] }, { "cell_type": "markdown", "id": "444567c3", "metadata": {}, "source": [ "### (b) The model was fit on the past only\n", "\n", "`model.train(train)` received data ending at the last training hour below — months\n", "before any test timestamp." ] }, { "cell_type": "code", "execution_count": null, "id": "d3ad1558", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:31.391630Z", "iopub.status.busy": "2026-06-02T11:55:31.391560Z", "iopub.status.idle": "2026-06-02T11:55:31.393233Z", "shell.execute_reply": "2026-06-02T11:55:31.393060Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "model.train() input ended at: 2025-12-31 23:00:00+00:00\n", "first test timestamp : 2026-01-01 00:00:00+00:00\n", "OK — fit data strictly precedes the evaluation period.\n" ] } ], "source": [ "print(\"model.train() input ended at:\", train.index.max())\n", "print(\"first test timestamp :\", test_idx.min())\n", "assert train.index.max() < test_idx.min()\n", "print(\"OK — fit data strictly precedes the evaluation period.\")" ] }, { "cell_type": "markdown", "id": "c772815b", "metadata": {}, "source": [ "### (c) Each forecast uses strictly past lags\n", "\n", "To predict the very first test hour, the model only references earlier timestamps\n", "(the previous 24 hours and 168h earlier) — all of which fall in the **training**\n", "period." ] }, { "cell_type": "code", "execution_count": null, "id": "6cdaad1b", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:31.394115Z", "iopub.status.busy": "2026-06-02T11:55:31.394055Z", "iopub.status.idle": "2026-06-02T11:55:31.395898Z", "shell.execute_reply": "2026-06-02T11:55:31.395723Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "To predict 2026-01-01 00:00:00+00:00 the model uses (all strictly earlier):\n", " ['2025-12-31 23:00:00+00:00', '2025-12-31 22:00:00+00:00', '...', '2025-12-25 00:00:00+00:00']\n", " all < t and all in the training period: True\n" ] } ], "source": [ "t0 = test_idx.min()\n", "lag_times = [t0 - pd.Timedelta(hours=L) for L in model.lags]\n", "assert all(lt < t0 for lt in lag_times)\n", "print(\"To predict\", t0, \"the model uses (all strictly earlier):\")\n", "print(\" \", [str(lag_times[0]), str(lag_times[1]), \"...\", str(lag_times[-1])])\n", "print(\" all < t and all in the training period:\", all(lt < env.test_start for lt in lag_times))" ] }, { "cell_type": "markdown", "id": "612af853", "metadata": {}, "source": [ "### (d) The whole supervised dataset in one picture\n", "\n", "Here is the most direct way to *see* there is no leakage. Each 1-step-ahead\n", "prediction is one **sample**: its **inputs** are the lag timestamps\n", "$\\{t-1,\\dots,t-24,\\,t-168\\}$ and its **output** is the target time $t$. We loop over\n", "target hours straddling the train/test boundary, collect each sample, and plot the\n", "times: x-axis = sample, y-axis = time, with one colour for inputs and one for outputs.\n", "\n", "Read the chart two ways:\n", "\n", "- For **every** sample (every x), all input points lie **below** the output point —\n", " the model only ever looks **back** in time. (Leakage would be an input *above* its\n", " output; that never happens.)\n", "- The **green** region holds samples whose target is *before* `TEST_START` — these\n", " (input → output) pairs are exactly the rows `model.train()` was fit on. The **red**\n", " region holds samples *predicted but never trained on* (the 2026 test set)." ] }, { "cell_type": "code", "execution_count": null, "id": "24943ddc", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:31.396792Z", "iopub.status.busy": "2026-06-02T11:55:31.396738Z", "iopub.status.idle": "2026-06-02T11:55:31.461109Z", "shell.execute_reply": "2026-06-02T11:55:31.460916Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "201 samples (100 train, 101 test) — for EVERY one, all inputs precede the output. ✓" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# Build one (output_time, input_times) sample per prediction, around the boundary.\n", "window = pd.Timedelta(hours=100)\n", "targets = series.index[(series.index >= env.test_start - window) &\n", " (series.index <= env.test_start + window)]\n", "\n", "samples = [] # (output_time, input_times)\n", "for t in targets:\n", " input_times = pd.DatetimeIndex([t - pd.Timedelta(hours=L) for L in model.lags])\n", " if series.reindex(input_times).notna().all() and pd.notna(series.loc[t]):\n", " samples.append((t, input_times)) # a genuine design-matrix row\n", "\n", "# The ridiculously-easy leakage check: every input is strictly before its output.\n", "assert all(input_times.max() < t for t, input_times in samples)\n", "n_train = sum(t < env.test_start for t, _ in samples)\n", "print(f\"{len(samples)} samples ({n_train} train, {len(samples) - n_train} test) — \"\n", " \"for EVERY one, all inputs precede the output. ✓\")" ] }, { "cell_type": "code", "execution_count": null, "id": "d81d5f0b", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:31.462085Z", "iopub.status.busy": "2026-06-02T11:55:31.462029Z", "iopub.status.idle": "2026-06-02T11:55:32.123658Z", "shell.execute_reply": "2026-06-02T11:55:32.123440Z" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "naive = lambda ts: pd.DatetimeIndex(ts).tz_convert(None) # drop tz for plotting only\n", "\n", "fig, ax = plt.subplots(figsize=(12, 6))\n", "for x, (t, input_times) in enumerate(samples):\n", " ax.scatter([x] * len(input_times), naive(input_times), s=8, color=\"tab:blue\")\n", " ax.scatter([x], naive([t]), s=22, color=\"tab:orange\", zorder=3)\n", "\n", "# Train vs test shown via background regions (keeps just two point colours).\n", "ax.axvspan(-0.5, n_train - 0.5, color=\"tab:green\", alpha=0.07)\n", "ax.axvspan(n_train - 0.5, len(samples) - 0.5, color=\"tab:red\", alpha=0.07)\n", "ax.axvline(n_train - 0.5, color=\"black\", ls=\":\", lw=1)\n", "ax.axhline(pd.Timestamp(env.test_start).tz_convert(None), color=\"red\", ls=\"--\", lw=1)\n", "\n", "ymax = ax.get_ylim()[1]\n", "ax.text(n_train * 0.5, ymax, \"TRAIN\\n(model.train fit on these)\",\n", " ha=\"center\", va=\"top\", color=\"green\", fontsize=9)\n", "ax.text((n_train + len(samples)) * 0.5, ymax, \"TEST\\n(predicted, never trained on)\",\n", " ha=\"center\", va=\"top\", color=\"firebrick\", fontsize=9)\n", "\n", "from matplotlib.lines import Line2D\n", "ax.legend(handles=[\n", " Line2D([0], [0], marker=\"o\", ls=\"\", color=\"tab:blue\", label=\"input (lag) times\"),\n", " Line2D([0], [0], marker=\"o\", ls=\"\", color=\"tab:orange\", label=\"output (target) time\"),\n", "], loc=\"lower right\")\n", "ax.set_xlabel(\"sample index (ordered by target time)\")\n", "ax.set_ylabel(\"time\")\n", "ax.set_title(\"Every prediction's inputs lie strictly below (before) its output\\n\"\n", " \"red dashed line = TEST_START\")\n", "plt.tight_layout(); plt.show()" ] }, { "cell_type": "markdown", "id": "0eb2030c", "metadata": {}, "source": [ "Notice that the **test** samples (red region) have inputs dipping *below* the\n", "`TEST_START` line: they legitimately use already-observed history as lags. That is\n", "allowed — what matters is that no input ever sits *above* its own output." ] }, { "cell_type": "markdown", "id": "cd136a52", "metadata": {}, "source": [ "### (e) Causality proof: hide the present and future, predictions don't change\n", "\n", "For a set of test timestamps $t^\\*$, we recompute the forecast from a copy of the\n", "series in which **every value at $t \\ge t^\\*$ is deleted** (set to `NaN`). If a\n", "prediction secretly used present/future information, it would change. It does not:\n", "the maximum difference is exactly **0**." ] }, { "cell_type": "code", "execution_count": null, "id": "192c7cb8", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:32.124750Z", "iopub.status.busy": "2026-06-02T11:55:32.124690Z", "iopub.status.idle": "2026-06-02T11:55:32.608415Z", "shell.execute_reply": "2026-06-02T11:55:32.608218Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "checked 51 test hours\n", "max |Δ| between full-series and future-hidden forecasts: 0.0\n", "✓ Every forecast depends only on strictly past observations — no leakage.\n" ] } ], "source": [ "full_pred = model.predict(forecast_input).iloc[:, 0]\n", "\n", "def predict_with_future_hidden(cutoff):\n", " masked = forecast_input.copy()\n", " masked.loc[masked.index >= cutoff] = np.nan # hide present + future\n", " return model.predict(masked).iloc[:, 0]\n", "\n", "# Sample test hours that actually have a forecast (most of Mar-Jun 2026 is NaN),\n", "# always including the very first test hour. Sample by position to keep tz info.\n", "rng = np.random.default_rng(0)\n", "predictable = full_pred.dropna().index\n", "predictable = predictable[predictable >= env.test_start]\n", "pos = rng.choice(len(predictable), size=min(50, len(predictable)), replace=False)\n", "sample = [test_idx.min()] + [predictable[p] for p in pos]\n", "\n", "max_diff, n_checked = 0.0, 0\n", "for t in sample:\n", " a = full_pred.loc[t]\n", " b = predict_with_future_hidden(t).loc[t]\n", " if np.isnan(a) and np.isnan(b):\n", " continue # lag genuinely missing in both\n", " max_diff = max(max_diff, abs(float(a) - float(b)))\n", " n_checked += 1\n", "\n", "print(f\"checked {n_checked} test hours\")\n", "print(\"max |Δ| between full-series and future-hidden forecasts:\", max_diff)\n", "assert max_diff == 0.0\n", "print(\"✓ Every forecast depends only on strictly past observations — no leakage.\")" ] }, { "cell_type": "markdown", "id": "6aa228e7", "metadata": {}, "source": [ "### (f) Corrupting *every* test actual leaves early forecasts untouched\n", "\n", "As a final illustration: add large random noise to **all** test-period values and\n", "re-predict the first test hour. Because its lags lie entirely in the (untouched)\n", "training period, the forecast is byte-for-byte identical." ] }, { "cell_type": "code", "execution_count": null, "id": "a316876d", "metadata": { "execution": { "iopub.execute_input": "2026-06-02T11:55:32.609500Z", "iopub.status.busy": "2026-06-02T11:55:32.609427Z", "iopub.status.idle": "2026-06-02T11:55:32.633090Z", "shell.execute_reply": "2026-06-02T11:55:32.632893Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "first test-hour forecast — original : -4.449448\n", "first test-hour forecast — corrupted: -4.449448\n", "✓ Corrupting all test actuals does not change the first test forecast.\n" ] } ], "source": [ "noise = pd.DataFrame(0.0, index=forecast_input.index, columns=forecast_input.columns)\n", "m = forecast_input.index >= env.test_start\n", "noise.loc[m] = rng.normal(0, 10, size=(int(m.sum()), forecast_input.shape[1]))\n", "corrupted = forecast_input + noise\n", "\n", "orig = model.predict(forecast_input).iloc[:, 0].loc[t0]\n", "corr = model.predict(corrupted).iloc[:, 0].loc[t0]\n", "print(\"first test-hour forecast — original :\", round(float(orig), 6))\n", "print(\"first test-hour forecast — corrupted:\", round(float(corr), 6))\n", "assert orig == corr\n", "print(\"✓ Corrupting all test actuals does not change the first test forecast.\")" ] }, { "cell_type": "markdown", "id": "006e7981", "metadata": {}, "source": [ "## 7. Recap & next steps\n", "\n", "- The split is **disjoint**, the model is **fit on the past only**, there is **no\n", " preprocessing leak**, and each 1-step-ahead forecast provably uses **strictly past\n", " lags** — verified above with hard `assert`s.\n", "- The AR model comfortably beats persistence and seasonal-naive baselines.\n", "\n", "Ideas to extend this:\n", "- **All 15 stations** — `ARPredictor` already loops over columns; drop the\n", " single-station selection in the problem to forecast the whole network.\n", "- **Multi-step horizons** — roll the AR forward recursively and watch error grow.\n", "- **Fresher 2026 data** — top up past the 2026-03-01 archive cutoff with SMHI's\n", " `latest-months` period (note: that tail is provisional, not yet quality-corrected)." ] } ], "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": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 5 }