{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2ebd953c",
   "metadata": {},
   "source": [
    "# Tutorial 12 — Explicit FoldForests and torsions\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uw-ipd/tmol/blob/master/docs/tutorial/12_explicit_foldforests_and_torsions.ipynb)\n",
    "\n",
    "Build coupled and per-residue-root FoldForests, perturb an inter-chain jump, and assign named backbone torsions. Complete [minimization and kinematics](05_minimization_constraints_kinematics.ipynb) first.\n",
    "\n",
    "A FoldForest controls which atoms move together. It does not add chemical bonds, repair geometry, or score conformations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08919bab",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "In Colab, select **T4 GPU**, then **Run all**. For local execution, follow the [installation guide](../installation.md). Setup installs TMol and downloads the fixtures on first use.\n",
    "\n",
    "The notebook builds short idealized sequences, so it needs no downloaded structures. The examples are intentionally small enough for CPU documentation execution while using the same public kinematics operations on CUDA.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e879615d",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import google.colab  # noqa: F401\n",
    "except ImportError:\n",
    "    IN_COLAB = False\n",
    "else:\n",
    "    IN_COLAB = True\n",
    "\n",
    "if IN_COLAB:\n",
    "    from urllib.request import urlopen\n",
    "\n",
    "    exec(\n",
    "        urlopen(\n",
    "            \"https://raw.githubusercontent.com/uw-ipd/tmol/\"\n",
    "            \"master/docs/tutorial/colab_setup.py\"\n",
    "        ).read(),\n",
    "        globals(),\n",
    "    )\n",
    "    setup_colab([])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da2d05e2",
   "metadata": {
    "tags": [
     "collapse-code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TMol 0.1.62; PyTorch 2.14.1+cpu; device=cpu\n"
     ]
    }
   ],
   "source": [
    "import attrs\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "from IPython.display import display\n",
    "\n",
    "import tmol\n",
    "from tmol.kinematics import (\n",
    "    EdgeType,\n",
    "    FoldForest,\n",
    "    JumpDOFTypes,\n",
    "    NodeType,\n",
    "    PoseStackKinematicsModule,\n",
    "    inverseKin,\n",
    ")\n",
    "\n",
    "SEED = 20260910\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)\n",
    "device = (\n",
    "    torch.device(\"cuda\", torch.cuda.current_device())\n",
    "    if torch.cuda.is_available()\n",
    "    else torch.device(\"cpu\")\n",
    ")\n",
    "\n",
    "\n",
    "def show_table(frame):\n",
    "    try:\n",
    "        from itables import show\n",
    "    except ImportError:\n",
    "        return display(frame)\n",
    "    return show(frame)\n",
    "\n",
    "\n",
    "print(f\"TMol {tmol.__version__}; PyTorch {torch.__version__}; device={device}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd14464",
   "metadata": {},
   "source": [
    "## Compare automatic, coupled, and dandelion topologies\n",
    "\n",
    "The sequence grammar uses `:` for a chain break. `reasonable_fold_forest()` roots the two chains independently. The custom coupled forest instead attaches chain B to chain A with ordinary jump 0, making six rigid-body degrees of freedom control their relative placement. The dandelion forest independently attaches every block to TMol's virtual root.\n",
    "\n",
    "Each stored edge is `(edge type, start block, end block, jump index)`. Root jumps always start at `-1`; root-jump and polymer edges use jump index `-1`; ordinary jumps use contiguous indices beginning at zero. Unused padded rows also contain `-1`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "016d2bd8",
   "metadata": {
    "tags": [
     "collapse-code"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Environment variable CCD_MIRROR_PATH not set. Will not be able to use function requiring this variable. To set it you may:\n",
      "  (1) add the line 'export VAR_NAME=path/to/variable' to your .bashrc or .zshrc file\n",
      "  (2) set it in your current shell with 'export VAR_NAME=path/to/variable'\n",
      "  (3) write it to a .env file in the root of the atomworks.io repository\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Environment variable PDB_MIRROR_PATH not set. Will not be able to use function requiring this variable. To set it you may:\n",
      "  (1) add the line 'export VAR_NAME=path/to/variable' to your .bashrc or .zshrc file\n",
      "  (2) set it in your current shell with 'export VAR_NAME=path/to/variable'\n",
      "  (3) write it to a .env file in the root of the atomworks.io repository\n"
     ]
    },
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       "      <th style=\"border:1px solid #ddd;padding:8px\"><sup><noscript><span title=\"JavaScript not allowed. Please trust this notebook to get interactive tables.\">🔒</span></noscript><a href=https://itables.org/fallbacks/static_preview.html title=\"ITables v2.9.1 static preview\">ⓘ</a></sup>forest</th>\n",
       "      <th style=\"border:1px solid #ddd;padding:8px\">edge</th>\n",
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       "<tr><td style=\"border:1px solid #ddd;padding:8px\">automatic</td><td style=\"border:1px solid #ddd;padding:8px\">2</td><td style=\"border:1px solid #ddd;padding:8px\">root_jump</td><td style=\"border:1px solid #ddd;padding:8px\">-1</td><td style=\"border:1px solid #ddd;padding:8px\">4</td><td style=\"border:1px solid #ddd;padding:8px\">-1</td></tr>\n",
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       "<tr><td style=\"border:1px solid #ddd;padding:8px\">coupled</td><td style=\"border:1px solid #ddd;padding:8px\">1</td><td style=\"border:1px solid #ddd;padding:8px\">polymer</td><td style=\"border:1px solid #ddd;padding:8px\">0</td><td style=\"border:1px solid #ddd;padding:8px\">3</td><td style=\"border:1px solid #ddd;padding:8px\">-1</td></tr>\n",
       "<tr><td style=\"border:1px solid #ddd;padding:8px\">coupled</td><td style=\"border:1px solid #ddd;padding:8px\">2</td><td style=\"border:1px solid #ddd;padding:8px\">jump</td><td style=\"border:1px solid #ddd;padding:8px\">0</td><td style=\"border:1px solid #ddd;padding:8px\">4</td><td style=\"border:1px solid #ddd;padding:8px\">0</td></tr>\n",
       "<tr><td style=\"border:1px solid #ddd;padding:8px\">coupled</td><td style=\"border:1px solid #ddd;padding:8px\">3</td><td style=\"border:1px solid #ddd;padding:8px\">polymer</td><td style=\"border:1px solid #ddd;padding:8px\">4</td><td style=\"border:1px solid #ddd;padding:8px\">7</td><td style=\"border:1px solid #ddd;padding:8px\">-1</td></tr>\n",
       "<tr><td style=\"border:1px solid #ddd;padding:8px\">dandelion</td><td style=\"border:1px solid #ddd;padding:8px\">0</td><td style=\"border:1px solid #ddd;padding:8px\">root_jump</td><td style=\"border:1px solid #ddd;padding:8px\">-1</td><td style=\"border:1px solid #ddd;padding:8px\">0</td><td style=\"border:1px solid #ddd;padding:8px\">-1</td></tr>\n",
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   "source": [
    "two_chain_pose = tmol.extended_pose_stack_from_sequences(\n",
    "    \"ACDE:FGHI\", device=device\n",
    ")\n",
    "n_blocks = two_chain_pose.max_n_blocks\n",
    "chain_b_start = 4\n",
    "\n",
    "automatic_forest = FoldForest.reasonable_fold_forest(two_chain_pose)\n",
    "\n",
    "coupled_edges = np.full((1, 4, 4), -1, dtype=np.int64)\n",
    "coupled_edges[0, 0] = [EdgeType.root_jump, -1, 0, -1]\n",
    "coupled_edges[0, 1] = [EdgeType.polymer, 0, chain_b_start - 1, -1]\n",
    "coupled_edges[0, 2] = [EdgeType.jump, 0, chain_b_start, 0]\n",
    "coupled_edges[0, 3] = [EdgeType.polymer, chain_b_start, n_blocks - 1, -1]\n",
    "coupled_forest = FoldForest.from_edges(coupled_edges)\n",
    "\n",
    "dandelion_edges = np.full((1, n_blocks, 4), -1, dtype=np.int64)\n",
    "dandelion_edges[0, :, 0] = EdgeType.root_jump\n",
    "dandelion_edges[0, :, 1] = -1\n",
    "dandelion_edges[0, :, 2] = np.arange(n_blocks)\n",
    "dandelion_edges[0, :, 3] = -1\n",
    "dandelion_forest = FoldForest.from_edges(dandelion_edges)\n",
    "\n",
    "\n",
    "def edge_rows(label, forest):\n",
    "    rows = []\n",
    "    for edge_index in range(int(forest.n_edges[0])):\n",
    "        edge_type, start, end, jump = [\n",
    "            int(value) for value in forest.edges[0, edge_index]\n",
    "        ]\n",
    "        rows.append(\n",
    "            {\n",
    "                \"forest\": label,\n",
    "                \"edge\": edge_index,\n",
    "                \"type\": EdgeType(edge_type).name,\n",
    "                \"start\": start,\n",
    "                \"end\": end,\n",
    "                \"jump_index\": jump,\n",
    "            }\n",
    "        )\n",
    "    return rows\n",
    "\n",
    "\n",
    "edge_frame = pd.DataFrame(\n",
    "    edge_rows(\"automatic\", automatic_forest)\n",
    "    + edge_rows(\"coupled\", coupled_forest)\n",
    "    + edge_rows(\"dandelion\", dandelion_forest)\n",
    ")\n",
    "show_table(edge_frame)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4f0f07ed",
   "metadata": {},
   "outputs": [
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Bn3/+We/Nsak0Gg2GDBmC119/HfPmzcOePXsAVLeWkCQJdnZ2tdavGWjWklqyLy3ddvjw4di0aVOdmbSunoHFHE5OTgCqu0M1x/r162v9fuzYMaSmpmLSpEkNbmNvb4+RI0di48aNdepbs2YNtFotxowZAzs7O0yePBl//vmnSa2JwsPDsX//ftjb22P06NE4ffp0ves1dAw1JCwsDBUVFbhw4UKt5S15z+3t7TFixAhs3Lixzmf5+++/11nXEn879TH3czfV1KlTYWdnV2emOXPXM5ckSZgwYQK2bdtW5z1cs2YNZFnG+PHjG9x2/Pjx2Lp1a5336drPylQ1fx9r166ttfzkyZNISEho9O+nxuzZs7Fr1y6TEw4qlQr9+vXDCy+8gPvuuw9Hjx5FaWkpgOYfB42tP2nSJOzZs6dOsmrNmjUAgIkTJ9Zafu13SHJyMk6cOFHrPWhumebEM2HCBOzatQslJSW11jP179ySnwcRETWMCR0iIoV89NFHKC0txaxZsxAVFYXi4mKkp6dj06ZNmDNnTr1T1QLApUuXMH36dPzxxx9IT09HeXk59u3bhz179mDChAkAqm+6Zs2ahZUrV2Lnzp0oKSnBjh078OSTT2LkyJFtZl9auu2bb76JS5cuYeHChUhJSUFmZiZefvnlOuOCNFfNuCgbNmyot3taQ06ePIlffvkFhYWFOHLkCO644w507dq1wVmQarzzzjvIzc3FrbfeioSEBGRnZ+P999/Hjz/+iBdeeMHYzeGDDz6AEALTp0/HwYMHUVJSgjNnzmDJkiXYv39/nXI7deqEPXv2IDAwEGPHjsXhw4cBmHYMNeTWW2+Fi4sLlixZgsuXL+Py5ct45pln6u3G0RxvvvkmUlNTce+99yI5ORmZmZl44YUX6m3d1JJjpjHmfu6mCggIwD/+8Q98/vnnePHFF5GYmIiysjKcP38ey5Ytw+23396s9VrizTffRFlZGW666SbExcUhNzcXn376Kb7++mssWbIEXbp0aXDb1157Dbm5ubjjjjtw8eJFXLlyBa+99lqdBLKpZs2ahQkTJuC5557Db7/9hsLCQhw6dAi33norgoOD8eSTTzZZxltvvQU3NzdMnz4dO3bsQGFhIbKysrBz507cdttt2LdvHyoqKjB27FisWbMGKSkpqKiowLFjx7Bx40YMHToUjo6OAIBevXoBAP78809UVlY2WXdj67/22muQJAnz58/HmTNnkJeXh6+++gqffPIJFi1ahB49etRaPyUlBd9++y3y8/Nx6tQp3HzzzfDw8DB2tTOnTHPieeWVV1BWVobbb78dCQkJyMnJwYcffmhyksuSnwcRETWi1YddJiJqZ0yZ5era2V927twpAIiNGzfWWp6SkiIWLVokOnfuLDQajQgMDBQzZ84UGzZsEAaDocEYNm3aJObMmSMCAgKEo6OjiIyMFC+++KIoLCw0rpObmyvuvfde4evrK5ydncXUqVPF+fPnxcyZM0WvXr2ajPmNN94QAOrMctLQTEDm7oup29Y3O1TNezFo0CBhZ2cnQkJCxNKlS42zHcXExDS5nw19Ns8884zw9/cXsiwLACIqKqrB+GvKzs7OFvfdd59wd3cXzs7OYv78+SI5ObnWug3tR1RUlLjuuuuEq6ursLOzE/369RNff/11nbri4+PFHXfcIfz8/ISdnZ3o06eP+OSTT4yzQ9W3n0VFRWLixInC2dlZbNu2zfi+NXUMNWTbtm2iX79+QqvVivDwcLFy5cpGZ7ky9T3fvHmz8bPs0qWL+PTTT42zJF09y5UQph0zDdXfmIY+9+bsS1P1btq0SUydOlV4eHgIe3t70b17d/Hoo4+KCxcumLXetSZNmiTc3Nya3NejR4+KmTNnCjc3N6HVakWvXr3EZ599Vuvvtb5ZroQQYteuXWLYsGHCzs5OBAUFiffff19s3LixwVmuVq1a1WgsJSUl4vnnnxchISFCo9EIX19fcffdd9eaJalmlqs33nij3jKuXLkilixZIsLCwoRWqxX+/v5i8uTJYvXq1caZ8Pbu3StuvvlmERwcLOzt7UVoaKh48skn6/w9vvPOOyIoKMh4HKxdu7bR+BtbPzY2Vlx//fXCw8NDaLVa0b17d/HPf/5T6PV64zo1s1zFxcWJJ598Unh7ewtHR0cxbdq0emfPMqXM+mafMnVbIYQ4cOCAGDVqlLCzsxOdOnUSb7/9tti6dasAILZs2dJkPZb8PIiIqH6SEI2MYkhERGSD/vGPf+CFF15AVlYWfHx8lA6HiKhRy5Ytw0MPPYSkpCSEhIQoHU6D/v3vf+Oee+5BTEwM+vTpo3Q4REQdHrtcERFRu/Prr7+iR48eTOYQEVnQmjVr4OPj02i3LiIish4mdIiIyKbdfPPNxnFU4uPjsWjRIhw+fBivvfaa0qEREdmse+65B7t27UJhYSEuXryIF154AevXr8ff//53qNVqpcMjIiIA/DYmIiKbtmDBAjz//POIjY1FZWUl+vXrh7Vr1+L6669XOjQiIpt111134a233sLx48dRUlKCnj174vvvv8fdd9+tdGhERPQXjqFDRERERERERGRj2OWKiIiIiIiIiMjGMKFDRERERERERGRjmNAhIiIiIiIiIrIxTOgQEREREREREdkYJnSIiIiIiIiIiGwMEzpERERERERERDaGCR0iIiIiIiIiIhvDhA4RERERERERkY1hQoeIiIiIiIiIyMYwoUNEREREREREZGOY0CEiIiIiIiIisjFM6BARERERERER2RgmdIiIiIiIiIiIbAwTOkRERERERERENoYJHaI24o477kBISIhN1vHCCy9AkiSLl6skW96nWbNmoXfv3hYt0xrHJxER2Z5XX30VkiRBp9NZrMxvvvkGkiQhPj6+0fWWLFkCe3t7i9WrhOuvvx7du3evtWzw4MGYNm2aQhG1nK3HT2RLmNAhm3bjjTciPDxc6TBMZmvxUuvjMUFEREREROZQKx0AEVX74Ycf2kUd1D7x2CEiIrKO6OhopUNoEVuPn8iWsIUOEREREREREZGNYUKHrOaZZ56BJEmQJAmyLMPDwwPTpk1DVFRUrfX69+9fb7/bZ555Bmr1/xqVDR48GGvWrEFCQoKx3Gv7cK9ZswbDhw+Ho6MjXFxcMGHCBOzYsaNWuTXjjaSlpWH27NlwdnZGSEgIVq5cCQBITk7GnDlz4OLiAn9/f7z//vtm7VtT8TY0RklUVBTmzJkDb29vODk5YcSIEfj999+Nr+/atQuSJOHVV19t5N1Hg3XU7H9WVhbmz58PFxcXeHt747HHHkNlZWWdMr7++mtERkbC3t4e/fv3x5YtWxqsLyoqCjNnzoSHhwfs7e3Rr1+/Oi09aupPSUnBrFmz4OzsDD8/Pzz++OMoLS1tUZlK71NT9ZtyDDcUQ2PHRA1T9t/Uv8uWHjsnT57EnDlz4O/vD2dnZwwYMACfffYZqqqqGt1XIqKO7ty5c1iwYAECAgLg4OCA/v3745tvvoHBYDCus2fPHkyePBmurq5wdHTEkCFDsHr16lrlNNTFd9myZZAkCRcvXjQuqxmbprCwEHfeeSfc3d3h7u6OO++8E1euXDE57ltvvRW+vr7QarWIjIzEBx98ACFErfXWr1+P/v37w97eHt26dTNefzVHU3F++eWXkCQJBw8erLPt8uXLIUkS9uzZ02gdprzHNe9bUVERFi5cCC8vL3Tt2hUAoNfr8dprryE4OBiOjo4YO3YsTp48WW9d9Y1BEx4ejhtvvBFnz57FxIkT4ejoiKCgILz99tt1tp81a5bx3K5SqeDr64ubbroJFy5cqLfMmJgYTJw4EU5OTnj00Ucxe/ZsBAQE1HuOnj9/Pry9veu9nmosfnt7eyxZsqTeWK8d968mrhMnTmDMmDFwdHRE7969sX37dgDAsWPHMHbsWDg6OqJr1651Poeryzh+/DhGjx4NBwcHdOnSBW+99Vatvx0imyeIFFBVVSXi4uLEggULhKurq7h48aLxtX79+onrrruuzjZPP/20UKlUtZbNnz9fhIWF1VvHl19+KQCIZ599Vly+fFlcvHhR3HPPPUKlUol169YZ15s5c6aIiIgQ8+fPF1FRUSI/P1+89dZbQpIksXXrVjF9+nSxf/9+UVBQIP7xj38IAGLTpk1m7Vtj8S5YsEB06dKl1rLffvtNqFQqceutt4ozZ86IoqIicfDgQTF37lxRXFwshBBi586dAoB45ZVXGoypsTpq9v+mm24Se/bsEYWFhWLVqlVCrVaLN954o9a6n3zyiZAkSbz++usiKytLxMfHi3nz5okZM2aIa79O1q9fL9RqtbjvvvtEQkKCyM/PF19//bXQarXi008/rVV/t27dxMyZM8WePXtEQUGB+O2334SHh4eYOXOm2WUqvU+m1t/YMVEfU46J5tR/tcaO3ZYcO0VFRcLHx0fceOONIikpSZSVlYmYmBjxxBNPiO3bt5u870REHU10dLRwcnISEyZMENHR0aKoqEicPHlSLFy4UMTExAghhNi8ebNQq9XitttuE4mJiSIjI0O89NJLAoD4+OOPjWU1dL5ZunSpACCSkpKMy5544glhZ2cnbr/9dvH777+LgoICsWfPHtGlSxfRt29fUV5eblz3lVdeEQBEVVWVcdnx48eFi4uLmD59uoiNjRVFRUVi7dq1wsPDQzzxxBPG9TZs2CBkWRaLFy8WqampIiUlRTz44INi7ty5AoC4cOFCo++PqXEWFxcLNzc3ceedd9YpY/DgwaJbt26N1mPqe1wTz4IFC8TatWtFbm6u+Pbbb4UQQjz88MPCzs5OfPvttyIvL0+cOHFCTJ06VYwaNUpERkbWqm/QoEF1roXDwsLEuHHjxLx588TJkydFfn6+8br0l19+aTD28vJycfz4cTFhwgQRFhYmioqKapU5duxYMX36dHH8+HGRnp4ufvnlF7Fx40YBQKxevbpWWampqUKtVoslS5Y0+n7VF7+dnV2tz77GzJkzRa9evers69ixY8W8efPEmTNnRE5Ojrj//vuFo6OjOHDggJgxY4Y4deqUyM3NFYsXLxYqlUokJCTUKWPMmDFi+vTp4uTJkyI3N1d8+eWXQqvViqeeeqrR+IlsCRM6pKjKykrh4OAg3n33XeMySyR0ysvLhaenp5g4cWKt5Xq9XvTo0UOEh4cbl82cOVMAEAcPHjQuMxgMIigoSDg5OYl9+/bVWt6lSxdx0003mbVvzUnoVFVViYCAADF06NAm6zJVQzflAMSBAwdqLZ83b54IDg42/l5eXi48PDzE3Llza61XXFwsvLy8aiU/dDqdCAoKEqNHjxYGg6HW+kuWLBHu7u6irKysVv07d+6std6yZcsEALFjxw6zy1R6n5qqX4jmJXRMPSaaU3996jt2W3LsHDx4UAAQmzdvbrJuIiL6n+HDh4vAwEBRWlra4Dp9+/YVoaGhtRIqQlR/R7u4uIjCwkIhRPMTOgDE119/XWvdbdu2CQDiq6++Mi6rL6EzZswYERISUifuzz77TMiybHxg0KdPH9G7d+9a51WDwSD69OljckLH1Dgfe+wxYW9vL7Kzs43LoqOjBQDxzjvvNFqPqe9xTTxffvllrfWSkpKELMvi+eefr7U8Pj5eqFQqkxM69vb24vLly7WW9+jRQ0yZMqXR+GvqAiB+++23WmWq1epaD3CEqP4MwsLCxPjx42str/msY2NjG63LEgkde3t7kZ6eblyWk5Mj1Gq18PLyEmlpacbl+fn5QqPRiJdffrlOGRqNRiQnJ9davmTJEqFSqURKSkqj+0BkK9jliqwmJycHTzzxBMLCwmBnZwdJkqDValFWVtbktJTNdfz4ceTm5uKGG26otVyWZdxwww2Ij4/HpUuXjMs9PDwwbNgw4++SJCEiIgIqlQqjRo2qtTwyMhKJiYmtvm/Hjx9Heno6br/9drO2bw4PDw+MGDGi1rLevXsjNTXV2KT22LFjyMvLw5w5c2qt5+TkhClTptRaduLECaSmpuLGG2+sM/X35MmTkZ+fX6uZsYuLC8aPH19rveuvvx4AjF3kmlum0vtkSv3N1ZxjwtT6W3rsmlJPWFgYnJyc8Nxzz2HNmjXIz883YW+JiDq23NxcHDx4EPPmzYODg0O962RnZyMmJgZz5syp1S0dqO5iVVRUhMOHD5sdw7Xnx0mTJsHZ2blO9/Wr5eXlYd++fZg1a1aduCdPngyDwYB9+/bhypUriI2NxezZs2udVyVJwty5cy0e58MPP4zy8nJ89913xmWff/45VCoV7r777gbLNuc9vjaenTt3wmAw1FkeFhaGPn36mLaTAAYMGICAgIBay3r37l3nujQxMRF33XUXgoKCoNFoIEmSsbvdtef2fv36oUuXLrWWSZKEhx56CLt27cLZs2cBADqdDl9//TWGDh1ap4tUaxgwYAD8/f2Nv3t6esLX1xehoaHo1KmTcbmbmxsCAgLqvAdA9TAOwcHBtZZdf/310Ov12L17d+sFT2RFTOiQVQghMG3aNPz222/48ssvkZWVBYPBACEE3N3dTRpHQ1zT57oxOTk5AFDrRFCjZll2drZx2bUnRwDGMXPqW371Dakl9q0+WVlZAIDAwECztm+O+vbf1dUVQggUFhYC+N976ufnV2fda5dlZGQAAJ566imo1WqoVCqoVCrIsoxZs2bVKq+hMn19fSHLsvFzam6ZSu+TKfU3V3OOCVPqt8Sxa0o93t7e2Lx5Mzw9PXHrrbfC09MTgwYNwscff9zkeEFERB1VzRgwjX3nN/d6pz4NXV/JsgwfH586y/38/BotMysrC0IIfPHFF3XOl927dzfG3ZxzcGNMjbN79+6YOHEili1bBiEE8vPz8Z///AczZsyo91xWo7nvsUqlqhO/pfa1oXPu1delhYWFGD16NM6ePYs1a9YgLy8PQgjjdcy15/aGjq97770XDg4OWLp0KQDg999/x+XLl3H//febHK8pGjr+Gro2b2h5fQ+LGnu/m/q7ILIVTOiQVZw9exbR0dF48cUXMXnyZLi5uUGSJOTn59f5AnZzc0NRUVGdMtLS0kyuz9PTEwCQmZlZ57WaZd7e3sZl17a4aGr51Zqzb81Rc3HSnP02lyn76eXlBaDx97RGzXv71VdfQafTQa/XQ6/XG5MFQgjMmDHDuH5NouJqNcmFmnqbW6bS+2RK/c3VnGPCWseuqfs5atQo7NixA/n5+di2bRv69++PJUuW4M033zRpeyKijsaU7/zmXO809/rKYDDUOwByVlaW8fxZn5rXnnvuuQbPl48//nizzsGNaU6cjzzyCBITE7F582YsX74cZWVlTSYomntNqVar65wbLbWvppxzt23bhvT0dHzwwQcYNmwYnJ2dAQBJSUn1rq/RaOpdXvMQZsWKFSgpKcEXX3wBR0dH3HrrrSbHe7XmHn8tuTav0dj73dgxTGRLmNAhq7Kzs6v1+4oVK+qsExYWhvPnz9d6glBaWoqdO3fWWdfJyQkVFRV1lg8cOBAeHh5Yu3ZtreVCCKxduxZhYWF1mpe2lCn71lC89Rk4cCACAgKwatUqi8TXUgMGDIC7uzvWr19fa3lpaSm2bdtWa9mgQYPQqVMn/PzzzyaVXVhYWKfpa82sTZMmTTKrTFO05j6Zqi0cE6Ycu5bi5OSEiRMn4ttvv0VYWFiTs4oQEXVUnp6eGDFiBNauXYuysrJ61/Hx8UGfPn2wfv166PX6Wq+tWbMGzs7OGDp0KIDq66vs7OxaN7lCCPz5558NxnDt+XHHjh0oKioynpvr4+3tbZx9sbEuxj4+Pujduzc2bNhQ57V169Y1uF1L4pw7dy6CgoLw+eefY9myZfDz88PMmTMbLbs573FDxo8fD0mS6sSZlJSE2NjYpnbPLJY4tz/yyCMoKCjA3//+d+zYsQM33XQTXF1dzYonLCwMp06dqrUsKSkJZ86cMas8U9R0l7/a77//DpVKhXHjxrVavUTWxIQOWUVkZCQiIyPx/vvv4/Tp0ygoKMAPP/yATZs2wdfXt9a6DzzwAHJycvB///d/yMvLw4ULF3DvvffWGuOmRu/evZGeno4jR47UmoLQzs4Ob731FrZt24a//e1vyMjIQEpKChYuXIizZ8/igw8+UGTfGoq3Pmq1GkuXLkV0dDRuv/12nD17FiUlJTh8+DDmzZuHkpISAM2btrwl7O3t8eqrr+L333/H22+/jezsbCQmJuLuu++ucyGjVqvx5ZdfYufOnbjnnntw5swZlJWVISkpCatWrcLEiRNrrd+tWzf885//xP79+1FUVIT169fjb3/7G6ZNm4YJEyaYVabS+2Sq1jgmTNWcY7cl1q9fj3vvvRd79+5FXl4eiouLsXr1aiQnJxs/XyIiquvTTz9Ffn4+Zs6ciaNHj6KkpASxsbFYtGiRMRHw3nvvISkpCffccw8uXryIrKwsvPLKK1i/fj1ef/11uLi4AADuuusuqNVqPP7448jKykJKSgoefvhhdOvWrd667ezssHPnTmzYsAFFRUXYt28fFi5ciN69e+POO+9sNO7PP/8caWlpmDt3LqKjo1FaWoq0tDSsX78eU6dONSaV3n77bZw6dQoPP/ww0tLSkJaWhkceecQ41bcpmhOnSqXCokWLsGHDBsTFxeHuu++uMy5OfUx9jxvStWtXLF68GP/617/w/fffo6CgADExMXj44YcxfPhwk/fVFGPGjIG7uzteeuklJCcn48qVK3j//feRm5vb7LIGDRqEoUOH4sMPPwSAFnW3euCBB3D48GEsXboURUVFOHbsGJ588skmk2EtMXz4cCxevBixsbHIz8/HN998gy+++AKPPvponbF1iGwVEzpkFSqVCuvXr0fnzp0xatQohIaGYuvWrfjpp5/qNJ0cMWIEvvrqK6xZswb+/v64/fbb8fjjjyMiIqJOuQ8//DBuuOEGTJ06FSqVCpIkGcfkeOihh7B69Wrs2LEDoaGh6N69O+Lj47Fp06ZmD7ZnqX1rLN76zJ07F7t370Z+fj5GjhwJf39/PPnkk7jnnnvg5ORksX0w1RNPPIEvvvgCy5cvR2BgIK6//nrcf//99Q7oN2vWLERFRaG4uBgTJkyAu7s7pkyZgo0bN+L999+vta5Wq8Xnn3+ON998E/7+/rjvvvtw22234b///a/ZZSq9T6ZS8phozrHbEtdddx0mTpyIl19+GWFhYQgMDMR7772Hjz76CC+++KLF6iEiam8GDRqEw4cPw8fHB9OmTYOPj4/xoUOvXr0AANOmTcPWrVuRlpaG3r17o0uXLvjjjz/w448/4sknnzSWFRQUhP/+9784deoUgoODMXXqVEydOrXRxPpnn32Gn376CUFBQZg5cyZGjBiBbdu2wd7evtG4BwwYgGPHjsHf3x/z5s2Du7s7Ro4cie+++w7PPfeccRyT2bNnY+3atdi/fz9CQ0Mxbtw4DBkyxDg2namaE+cDDzxg7GZ03333mVS+qe9xUzE+99xzeOmll+Dv74+HHnoIb7/9dq3uWpbg4+ODP//8E6WlpejZsyf69u2LrKwsfPLJJ2aV98gjjwAAIiIiMGbMGLPjuueee/Dyyy/j9ddfh6+vL5599lm8//77cHd3N7vMpvj6+uL111/H4sWLERAQgNdeew0vvviiMUFF1B5IojkjzRIRWdisWbNw8eLFOs1wiYiISBlLlizBsmXLUF5ernQoFldYWAh/f38MGjQIe/fuVTqcNu+3337DvHnz8O677+L55583aZvBgwfDx8cHGzdubOXoGhYeHo7+/fvXeThI1N6whQ4REREREXUI69evR1lZGR544AGlQ7EJq1evhlarbXRq92sVFRVx0GEiK2FCh4iIiIiI2r3MzEy8//77CA0NNXu2po4kKioKv/76K+677756p22/lhACUVFRSEhIwIgRI6wQIRE1PQoYERERERGRDRs9ejQOHTqEvn374pdffoFWq1U6pDYrPz8fHh4ecHR0xKxZs/Dee++ZtN2MGTNw7NgxLFy4EAsXLmzlKIkI4Bg6REREREREREQ2h12uiIiIiIiIiIhsDBM6REREREREREQ2hgkdIiIiIiIiIiIbw4QOEREREREREZGNYUKHiIiIiIiIiMjGcNpyIiIiImqXdDodUlNTUVxcDJ1OB7VaDUdHRwQFBXHaaiIisnlM6BARERFRu2AwGHDixAlER0cjLi4OqampMBgMddaTJAmdOnVCt27dMGDAAAwZMgRqNS+LiYjItkhCCKF0EERERERE5iosLMSOHTuwefNm5OTkQKVSQa/XN7mdLMswGAxwdXXF1KlTMXnyZHh6elohYiIiopZjQoeIiIiIbJIQArt378Z3332HiooKtOSyVpZlyLKMO+64A9OmTYMsc6hJIiJq25jQISIiIiKbk5ubi2XLluHEiRMWLzsiIgKPPvoo/P39LV42ERGRpTChQ0REREQ25eLFi3jjjTdQUlJS7xg5LSXLMjQaDf7v//4PPXr0sHj5RERElsCEDgGongUiOTkZmZmZqKyshBACWq0Wvr6+6Ny5M2eCICIiojYhKSkJr7zyCiorK1slmVNDkiSoVCr83//9H3r37t1q9RAREZmLCZ0O7OLFi9i5cyfOnj2LlJSUBgcPlGUZnTp1Qvfu3TF+/Hh069YNkiRZOVoiIiLq6DIyMvC3v/0NZWVlrZrMqSFJEjQaDV5//XWEhoa2en1ERETNwYROB6PX6xEVFYWNGzfiwoULxtkdTFGzbufOnTF9+nSMHTsWGo2mlSMmIiIiqp6S/KWXXkJiYqJVkjk1ZFmGr68v/vnPf7LFMhERtSlM6HQgqamp+Oyzz5CYmAhJksyeCaJm24CAADz22GMIDw+3cKREREREta1fvx4rV65UpG5JkjB79mzccccditRPRERUHyZ0OgCDwYB169Zh9erVEEJY7KmWLMsQQmDu3Lm46aab2FqHiIiIWkV6ejqefvpp6HQ6xWKQJAlvvvkmunXrplgMREREV5OVDoBaV1VVFT788EP89NNP0Ov1Fm2ibDAYIITA77//jrfeegvl5eUWK5uIiIioxrp166zazao+kiTh119/VTQGIiKiqzGh047pdDr885//xJEjR1q1HiEEzp07hzfeeINJHSIiIrKokpIS7Nmzx6oJHQcHByxduhRTp041LjMYDDh27Biys7OtFgcREVFjmNBpp4QQWLp0KU6cOGH2WDnNYTAYkJCQgH/+85+KP0EjIiKi9mP37t1W72olSRK8vLzg4OBQZ/m2bdusGgsREVFDmNBppw4cOIC9e/daJZlTw2AwICYmBps2bbJanURERNS+7dmzx6rXM40xGAzYvXu30mEQEREBANRKB0CWl5+fj6+//rpFM1m1xA8//IABAwYgICDA6nUTERFR+6HT6XDp0iXj70888QQcHBzw6aef4tZbb0WvXr0QHx+PL774ArIsY9q0aRg+fDg8PDxQWFiIY8eOYf369aisrDSW0dR6gYGBePnllwEA8+bNw7Rp0wAAUVFRWLFiBXJyclBUVAQXFxfrvhlERETXYEKnHfrmm29QXl6u2NMsg8GAzz//HG+88QYkSVIkBiIiIrJ9qamp0Ov1xt9dXFzg4uKCRx99FHv37sW2bdvQs2dPAMDTTz+NPn36YMWKFTh79ixCQkJw7733YsCAAXj11VeN3baaWi8jIwNvvPEGPvroI2zevBmbN28GAFRUVBjjSExMRL9+/az4ThAREdXFLlftzOXLl3H48GFFx7ExGAyIi4vD+fPnFYuBiIiIbF9iYmKdZV27dsWmTZtw4MABXLp0CRs3bsTgwYMxZMgQrFixAtu2bUNaWhr279+Pzz77DBEREZg8eTIAmLSeXq9HXl4eAKC0tBS5ubnIzc1FSUkJgOoWPvXFRUREZG1M6LQzW7ZsgSxb92O98847sWLFilrLZFk2PtEiIiIiMkdeXh5UKlWtZcXFxTh58mStZQMGDAAA7N27t9byEydOoLCwEP3792/Weo2RJMmY8CEiIlISEzrtSEVFBXbs2GH11jmyLNdJIhkMBkRFRaGgoMCqsRAREVH7UVVVVWdZbm5unWVubm4oKSmp1S3q6vXd3d2btV5Trh6Th4iISClM6LQjsbGxKC8vVzoMI4PBgOjoaKXDICIiIhtVX6vj+pI8paWlcHBwqNOaBwBcXV1RWlrarPWaUt/2RERE1sZBkduRhIQEyLIMg8GAyMhIvPrqq3jvvffg6emJWbNmwdfXFy+99BKSkpLQo0cPzJ8/H6GhoZBlGcnJyVi3bl2dBExT6z399NMYMmQIZFnGqlWrjNs99thjyMvLQ0JCAiZNmmTV94GIiIjaBzs7O5MmeTh37hzGjx+Pvn374vjx48blwcHBcHd3x44dO5q1Xk3SSK2u/1LZzs7O7H0iIiKyFLbQaUcSEhJqXfSoVCqMHz8efn5+ePPNN/H2229Dp9NhwIAB+Pvf/478/Hz87W9/w1NPPYX4+Hg899xzmDhxonF7U9b78MMPsWnTJlRWVmLBggXGf9nZ2dDr9YiPj7f6+0BERETtQ2BgoEldyffu3Yu0tDTce++96Ny5MwDAx8cHjzzyCAoLC7Fp06ZmrVdVVYXs7GyEhobWmbFTr9cjKCjIkrtJRERkFrbQaUeuTegAgJOTE3766ScAQE5ODgDgqaeeQkZGBj7//HPj+itWrEBISAgWLFiAvXv3oqqqCnfddZdJ69W8Vt8FV0pKCnQ6XYNPuIiIiIgaEhoaatJ6VVVVeO2113DvvfcaH2BptVrExsbiX//6FwoLC5u1HgB8//33eOCBB/D999+jtLQUUVFRxkkgTI2LiIioNfEuu50QQqC4uLjO8iNHjtT63dPTE4GBgfjtt9/qJH+ioqLQu3dvdO3aFdnZ2SatFxcX12hcer0eZWVlcHFxMXPPiIiIqKPy9PSEi4sLioqKAAAff/xxnRYzNfLz8/HRRx9BpVLB2dkZJSUl0Ol0Zq93+PBhHD58GC4uLtBoNMaBlFUqFVvoEBFRm8CETjthMBjq7WN+7UwQNYmV+qbbzM/PB1A9KGDNRUtT65mivsELiYiIiEzRp08fHDx4EAaDwZjYaYxerzdplk1T17u6TlmWERkZyZbHRETUJnAMnXaivlkggOqLlavVtOKpb1pONzc3ANUXLqauZwrOBEFERETmmjJliknj6FiDwWDA1KlTlQ6DiIgIABM67YYkSdBqtU2ul5OTg/T0dAwaNKjOa0OHDkVJSQkSExNNXg8AKioqGk3acCYIIiIiMlfPnj0REBCgdBgAqls6Dx06VOkwiIiIADCh066Y2p/7xx9/RFBQEB544AF4eHjAxcUFN998M/r374///Oc/xi5Spq6XkpIClUqFfv361anL09MT9vb2lttJIiIi6lAkScKMGTOUDgOSJOG6665jdysiImozeEZqR8LDw3Hp0qU63ayudfjwYbzzzjuYP38+Pv30U8iyjOTkZHz88cfYv39/s9c7cOAA+vbtiyeeeAKOjo4QQuCxxx5DTk4OunXr1mr7S0RERB3DpEmTsHXrVqSmpirS/UqSJHh6emLOnDlWr5uIiKghkqhvJF2ySTt27MCyZcuMv8uybPWLHkmSIEkSDAYDZFnGrbfeiuuvv96qMRAREVH7c+nSJTz//POKjafzyiuvoFevXorUTUREVB92uWpHevfuXet3JS54hBDGeg0GQ52YiIiIiMzRpUsX3HjjjVavt6arFZM5RETU1jCh0474+vqiX79+Dc54ZU2SJKFLly4ICwtTOhQiIiJqJ2644QaMHDkSkiRZpT5ZltG7d2/cddddVqmPiIioOZS/8yeLmjZtWpuY2lMIgenTp1vtgouIiIjaP1mW8eijj1plpilJktCjRw88++yz0Gg0rV4fERFRczGh084MGDAAPj4+iiZSJEmCo6MjRo0apVgMRERE1D6p1Wo8+eSTmDZtGgBY/JqnpryRI0fib3/7G2frJCKiNouDIrdDMTExePPNNxWN4fHHH8fo0aMVjYGIiIjat5iYGHz66acoLCyEJS5pZVmGg4MDFi9ejOHDh1sgQiIiotbDFjrtUN++fTF58mRFWunIsozBgwezdQ4RERG1uoiICAQHB8PFxQWyLJt97SNJEmRZxpgxY/Dxxx8zmUNERDZBrXQA1DruvPNOHD9+HHl5eVYbU6fmqdaiRYs4dg4RERG1ur1796K0tBTe3t6YMmUKnJ2dsXHjRmRlZQEAVCoV9Hp9ne2uXu7h4YHrrrsOEydOhLu7uzXDJyIiahF2uWrHLl++jBdffBFlZWWtntSRJAkajQavvvoqwsPDW7UuIiIiotLSUrz77ruorKyELMt46qmn4O3tDYPBgPT0dCQmJhr/FRUVoaqqCmq1Gk5OTggNDTX+CwoKahMzhBIRETUXEzrt3KVLl/D666+jpKSk1ZI6sixDo9HgxRdfRPfu3VulDiIiIqKrpaen4+OPPwYADB8+HNdff72yAREREVkZEzodQHp6Ot5//32kpaVZZMDAq0mSBG9vbzzzzDPo2rWrRcsmIiIiaogQAnv37kVxcTEmT54MrVardEhERERWxYROB6HT6bB8+XJs3boVkiS1OLFTU8aMGTNw2223wc7OzkKREhEREREREVFTOChyB6HT6ZCRkYHAwEAUFRWhsLAQsiw3uxtWzTbdunXDggUL0KNHj1aKmIiIiKiu7OxsrFixAvb29rjvvvtgb2+vdEhERESKYEKng9i1axdKSkpgZ2eHYcOGYdy4cVixYgWOHz9uTOrUNxPE1cu0Wi3Gjx+PqVOnonPnzlbfByIiIqL169cbZ7G6cuUKgoODFY6IiIhIGUzodAA6nQ779u0DUJ2gmTp1Kjw8PKDT6dClSxdotVpMmTIFiYmJyMjIQEVFBYQQsLOzg4+PD8LCwoyzQKhUKoX3hoiIiDqqxMREnD9/HgDg7u4Of39/hSMiIiJSDhM6HYBer4dWq4VOp8O4cePg6emJtLQ0FBUVQZIkhIWFYezYsRg7dqzSoRIRERE1KDo62vjzlClToNFoFIyGiIhIWUzodAB2dnZ46KGHkJubi4iICABAQkKC8XVONU5ERES2wM/PDwAQHh6OAQMGKBwNERGRspjQaecyMjIgyzJ8fX3h4+NjXO7u7g4AcHR0RK9evRSKjoiIiMh0Y8eOxcCBA+Ho6AhZlpUOh4iISFGctrwdO3PmDH744QdIkoTHH3/c+FSrRkpKClxcXIzJHSIiIqK2qKCgAKdOnUKfPn3g6uqqdDhERERtAlvotFM6nQ4bNmwwzmCl0+nqrMNZIYiIiKitq6qqwrJly5CXl4cLFy7gnnvuUTokIiKiNoFtVdupgwcPIjc3FwAQGhqKTp06GV87dOgQXn/9dezcuVOp8IiIiIhMEhUVhby8PACAvb29wtEQERG1HUzotFOXLl0CAEiShJkzZ0KSJADVM15t3LgRpaWlOHbsmJIhEhERETWqqqrK+ABKkiSMHz9e2YCIiIjaEHa5aqfGjBmDsrIy9OvXD4GBgcblycnJKC8vBwAEBAQoFR4RERFRk0pKSlBWVgYA6N+/P/z9/RWOiIiIqO1gQqed6ty5MxYuXFhneXx8vPHnyMhIa4ZERERE1Czu7u6YN28esrOzMXnyZKXDISIialOY0GlnduzYgZMnT2LWrFno1q1bndcdHR0BAE5OTujevbu1wyMiIiJqlmHDhikdAhERUZvEhE47kpiYiC1btgAAjhw5Um9CZ+TIkfD29oaXlxecnJysHSIRERFRk/Lz87F8+XJIkoSFCxfC2dlZ6ZCIiIjaHCZ02gmDwYA//vjD+HtERES960mSxK5WRERE1Kb9+eefyMzMBACkp6fX+5CKiIioo+MsV+1ESkoK0tLSAAD+/v4YOHBgnXUOHTqE999/H0ePHrV2eEREREQmSU1NRUxMDADA2dkZnTt3VjgiIiKitokJnXbCyckJWq0WGo0Gc+fOhSzX/mirqqqwYcMG5OTk4NChQwpFSURERNS4Y8eOGX+eOHEi7OzsFIyGiIio7WKXq3bC29sbzz33HPR6Pdzc3Oq8npiYiKqqKgCAr6+vtcMjIiIiMklAQAAAoEuXLhg6dKjC0RAREbVdTOjYOL1ej2PHjsHPz6/RJslxcXHGnzm7FREREbVVQ4YMQc+ePWFnZwe1mpeqREREDWGXKxu3bt06rFmzBt988w3Ky8sbXE+r1QKo7oseHh5urfCIiIiITFJUVIS9e/ciNzcXTk5OTOYQERE1gWdKG5aeno7Dhw8DqJ69SqPRNLjupEmT4O3tjaCgINjb21srRCIiIqIm6fV6fPXVV7hy5QpOnz6NBx98UOmQiIiI2jwmdGzYli1bIIQAUD1ooEqlanBdtVqNQYMGWSs0IiIiIpNFR0fjypUrAAAHBweFoyEiIrIN7HJlw2oufNzd3TFy5MgG1zt06BA+++yzWuPoEBEREbUFBoMB27ZtM/4+YcIEBaMhIiKyHWyhY8Pmz5+PmJgYjBo1qsHuVuXl5Vi3bh30ej0OHDiAiIgIK0dJRERE1LDy8nIUFxcDAHr16tXoJA9ERET0P0zo2CCDwQBJktC1a1d07dq10XUvXLgAvV4PAPD09LRGeEREREQmc3R0xC233IK0tDRMnDhR6XCIiIhsBhM6Nqa8vBzfffcdMjMzcf/99zf5FOv8+fPGnzldOREREbVF/fv3R//+/ZUOg4iIyKZwDB0bs2vXLiQnJ6OiogIXL15scn1Zrv6InZ2dm2zNQ0RERGQtRUVF+Pjjj/Hee+8hPz9f6XCIiIhsDlvo2JC8vDzs27cPAKBSqdC7d+8mt5kxYwZ8fHwQHh7e6LTmRERERNa0efNmpKenAwCSk5Ph7u6ubEBEREQ2hgkdG3Lq1CnodDoAwKhRo0waE8fe3h5jxoxp7dCIiIiITJaVlYWjR48CqL5WCQ8PVzgiIiIi28MuVzakS5cu0Gg0CAoKMmlKz8OHD+O7775DWlqaFaIjIiIiMs2JEycghAAAjBs3Do6OjgpHREREZHvYQseGdO7cGa+//jqEEMaxcRpSVFSEtWvXQggBJycn3HLLLVaKkoiIiKhxnTp1AgAEBgZi1KhRCkdDRERkm5jQsQE5OTnYv38/+vbti5CQEEiS1OQ258+fNz75cnNza+0QiYiIiEzWu3dvvPrqq1Cr1VCreTlKRERkDna5auN0Oh2+++47HDhwAL/88ovJ28XFxRl/5nTlRERE1BaUlJRg9+7dyMzMhL29PZM5RERELcCEThsXFRWFnJwcAIC3t7fJ29UMnuzq6org4OBWiY2IiIjIVAaDAd999x02btyIn376SelwiIiIbB4fi7RhBoMBO3bsAABIkoSpU6eavO3cuXPh7++PXr16QaVStVaIRERERCaJiYkxTtTAQZCJiIhajgmdNqyqqsrY0mbQoEEIDAw0eVs3N7dmJYCIiIiIWtPWrVuNP0+cOFHBSIiIiNoHJnTaMDs7OyxcuBAZGRkYPHiwydtFR0cjPj4eU6dOhaenZytGSERERNQ0vV6P/Px8AEB4eDi6deumbEBERETtgCRqpkKiNqWoqAiyLMPJyalZ2+Xm5uL999+HEAKjRo3C7NmzWylCIiIiItOdOXMGFy9exLhx45p9fUNERER1sYVOG3Tp0iV8++23AIDHH3+8WYMhXz1dOS+WiIiISGlCCEiShJ49e6Jnz55Kh0NERNRuMKHTxhgMBqxbtw6VlZUAgOLi4mYldM6dO2f8mdOVExERkZJKSkrw3XffobCwEA888AB8fX2VDomIiKjd4LTlbczJkyeNM0AEBASgc+fOzdq+oqICAODp6YmAgACLx0dERERkqh07diAtLQ1FRUW4dOmS0uEQERG1K2yh08Zc3cJm5syZkOXm5dzmzp2Lw4cPY9CgQZAkydLhEREREZkkLy8PBw8eBABoNBpERkYqHBEREVH7woROGzNo0CBcvnwZffr0QXh4eLO3DwgIwNy5c1shMiIiIiLTxcTEQK/XAwBGjRoFV1dXhSMiIiJqX5jQaWMiIiLw9NNPm7Xt8ePHkZqaikmTJsHR0dHCkRERERGZLjAwEJIkwc/PD+PGjVM6HCIionaH05a3EceOHcOJEycwefLkZo+bAwBZWVn46KOPIITA5MmTMXny5FaIkoiIiMh0VVVVkCQJajWfIRIREVkaB0VuAzIyMvDLL78gLi4Ou3fvNquMM2fOGKcrt7e3t2R4RERERCYrKyvDrl27kJqaCo1Gw2QOERFRK2FCR2FCCPzxxx/GZEyXLl3MKofTlRMREZHShBBYsWIFNm3ahJUrVyodDhERUbvGhI7CMjIycOHCBQCAu7s7RowYYVY5paWlAABfX194e3tbLD4iIiIiU509exZJSUkAwPH8iIiIWhnbwCpMpVJBpVLBYDBg1qxZ0Gg0ZpUzZ84cREdHY/To0RaOkIiIiMg027dvN/48adIkBSMhIiJq/5jQUZivry8ef/xx6HQ6BAYGml1OeHi4WdOcExEREVlKXl4eACA4OBi9evVSOBoiIqL2jbNcKUQIgfj4eHh4eLS4i9TJkyeRk5ODMWPGmN3Ch4iIiKil4uPjERcXhzFjxsDFxUXpcIiIiNo1ttBRyPbt27Ft2zY4OjriueeeM3tmqrS0NPznP/+BEAIODg5mj8FDREREZK6a54NsMUxERGQ9TOgoIC8vD7t27QIAVFRUQKVSmV3W6dOnjRdRnBaUiIiIrK28vBzfffcdsrKycP/99yM4OFjpkIiIiDoEznKlgK1bt0Kn0wEARo0a1aJuUufPnzf+HBkZ2eLYiIiIiJpjz549SE5ORnl5OS5evKh0OERERB0GEzoKSElJAVA9neeECRNaVFZBQQEAICgoCK6uri2OjYiIiMhURUVF2LdvH4DqmTt79uypcEREREQdB/voKGD69Ok4cuQIxo0bBwcHhxaVNWPGDMTExGDKlCkWio6IiIjINGfOnEFlZSUAYOjQofDy8lI4IiIioo6Ds1wRERERkVlSU1OxbNkyeHp6YtGiRXB2dlY6JCIiog6DCR0r0el0WL16NTIyMnDHHXfAz8+vxWWePn0aJSUlGDx4MGSZveeIiIjI+vR6PYQQnJyBiIjIynjmtZKoqCjExsYCAE6dOtXihE5ycjJWrlwJALC3t0ffvn1bHCMRERGRKcrLyxEVFYWQkBB07dpV6XCIiIg6JDbrsIKSkhJs374dACBJEnr06NHiMmNiYlpcBhEREZE5Vq1ahc2bN2P58uUwGAxKh0NERNQhMaFjBbGxsSgvLwcADBw4EJ06dWpxmefOnQMAyLKM8PDwFpdHREREZIqEhAScP38eAODi4sJu30RERAphlysr8Pb2hiRJcHNzw9SpU1tcnhACeXl5AIAuXbrA0dGxxWUSERERmaKm1TEATJw4UcFIiIiIOjYmdKwgPDwcL730ElQqFezt7VtcniRJmDJlCs6dO4fZs2dbIEIiIiIi0+Tn5wMA/Pz8MGDAAGWDISIi6sA4y1UrKi0tRXR0NCIiIuDv7690OEREREQtlpycjDNnzmDkyJFwdXVVOhwiIqIOiwmdVmIwGPDFF18gNTUVPj4+ePrppy1W9vnz56HX69GzZ0+LlUlERETUGIPBAEmSIEmS0qEQEREROChyqzlx4gRSU1MBwKJj3CQkJGD58uVYsWIFEhISLFYuERERUUOqqqrw9ddf4+WXX0Z8fLzS4RARERGY0GkVQghs3rzZ+PvkyZMtVvbJkyeNP+t0OouVS0RERNSQAwcOICkpCTqdDklJSUqHQ0RERGBCp1VUVVWhuLgYANC9e3d069bNIuUKIYzTlavVanTt2tUi5RIRERE1pKysDLt27QJQPTFDnz59lA2IiIiIAHCWq1ah1WqxYMECpKSkYNy4cRYrt6qqCoWFhQCAsLAwaLVai5VNREREVJ+4uDiUlZUBAAYMGMCJHoiIiNoIJnQsTKfTQZZl9OzZ0+KDFmu1WgwbNgyXLl3CtGnTLFo2ERERUX0CAgJgZ2cHZ2dnXHfddUqHQ0RERH/hLFcWlJWVhW+++QZ6vR6PPfYY3N3dlQ6JiIiIqMUMBgOEEFCpVEqHQkRERH/hGDoWIoTA+vXrUVhYiJKSEly5csXidSQmJiI5Odni5RIRERFdq7KyEjt37kRcXBxkWWYyh4iIqI1hQsdC4uLicOHCBQCAu7s7QkJCLF7+V199haVLlyIjI8OiZRMRERFd67///S82b96Mf//736ioqFA6HCIiIroGEzoWcvV04tOnT4dGo7Fo+ceOHQNQ3RKovLzcomUTERERXS01NRUxMTEAAAcHB4tf1xAREVHLMaFjIZGRkdBqtRgwYAD69u1r0bINBgPi4uIAAHZ2dggODrZo+URERERX2759u/HnSZMmQZZ5yUhERNTWcJYrC+nXr58xkSNJkkXLLioqQmlpKQAgIiKCfdiJiIioVRUWFgIAPD09MWTIEIWjISIiovowodNCCQkJOHHiBEaPHg0/P79WqcPZ2Rndu3dHVlYWJk2a1Cp1EBEREdW48cYbERMTg6FDh0Kt5uUiERFRW8Rpy1sgLy8PH3zwAXQ6HXr27Im77rpL6ZCIiIiIzMbpyYmIiGwHO0S3wObNm6HT6QCg1VrnANUDE7bGNOhERERENfR6PZYvX46XXnoJp06dUjocIiIiagITOmbKycnBiRMnAACOjo4YO3Zsq9Rz5swZfPbZZ/jkk09QUFDQKnUQERERHTlyBBcuXIAQAklJSUqHQ0RERE1gQsdMVVVVxsGPp06dCgcHh1ap58iRI8b6ysrKWqUOIiIi6tgqKytrzWzVr18/BaMhIiIiU3CUOzP5+/vj/vvvR1VVFXr06NEqdeh0OsTHxwOoHhjZ19e3VeohIiKiji0pKQlFRUUAgF69eqFz584KR0RERERNYULHDFeuXIGjoyPCw8NbtZ7s7GxUVVUBACIjIyHLbFBFRERElufn5wcXFxeoVCrMmDFD6XCIiIjIBJzlqpkOHz6MX3/9Fa6urnjqqadgb2/fanVVVFTgq6++Qn5+PhYtWtSqAy8TERFRxyaEgMFg4AxXRERENoItdJqhpKQEf/75JwAYmyW3Jjs7Ozz66KPGsXqIiIiILKmqqgp79+6Fr68vevfuzWQOERGRDWEfnmbYsWMHysvLAQADBw5s1dY5AJCVlYXi4uJWrYOIiIg6rt9//x1btmzBjz/+aJWHVURERGQ5bKHTDDUDFGs0GkydOrVV6zp16hR++OEHODk54bnnnoOdnV2r1kdEREQdS2ZmJo4ePQqgulVwaz+oIiIiIstiC51mGDNmDAICAnDzzTfDzc2tVes6ePAggOpuXpyunIiIiCxt586dqBlKcfz48dBoNApHRERERM3BFjrNMHjwYAwePLjV66moqEBSUhIAwNPTs9WTR0RERNTx1HSxcnV1xciRIxWOhoiIiJqLCZ0mGAwGbNq0CZcvX8YNN9wAT0/PVq/z8uXL0Ov1AKqnK+egyERERGRp8+bNw/HjxzFw4EBotVqlwyEiIqJmYperJpw4cQJ79uxBfHy8sZ95a/P29oa7uzvs7Oz4xIyIiIgsymAwQKfTwdvbG1OmTIGXl5fSIREREZEZ2EKnEZWVldi0aZPx99DQUKvU6+LigmeeeQayLEOWmXMjIiIiyzAYDFi5ciXOnj2L+fPnY8iQIUqHRERERGZitqARsbGxKCwsBAB0794dYWFhVqm3oKAAer2eyRwiIiKyqJMnT+Ls2bMAgIsXLyobDBEREbUIW+g0wsnJCQBgb2+PGTNmWKXOkydPYtWqVfD29sZTTz3FpA4RERFZhE6nw9atW42/DxgwQMFoiIiIqKWY0GlE9+7dsWTJEtjb28Pd3d0qde7btw8AkJ2djYqKCjg4OFilXiIiImrf0tLSkJubCwDo1q0bwsPDFY6IiIiIWoIJnXrodDqcOXMGnTt3hr+/v9XqLS4uRmpqKgDAz8+PyRwiIiKyGC8vL3h5eaGqqgqzZs1SOhwiIiJqISZ0UD1mTWpqKg4cOAAvLy/k5OQgISEB/v7+WLJkidXiSElJgRACQHXrICIiIiJz6PV6ANXjAbq4uCA0NBTOzs549tlnYTAY2KWbiIioHejwCZ2SkhLs2LEDhw4dAgAkJCQYX6tJrliLj48P7O3tIYTgrBNERERktsuXL+PHH39Efn4+AMDd3R0DBgzAddddx2QOERFROyEJa2ct2pj169dj//799b42bNgwzJs3z6rxlJaWQqVSwc7Ozqr1EhERUftw4cIFfP/998ZWOlebM2cORo4cqUBUREREZGkd/hFNZmZmg69ptVqkpaVh27ZtyMrKavVYysvLYWdnx2QOERERmS0mJqbeZA4ApKenQ6/XIzk5GeXl5VaOjIiIiCypXXa5EkLgSkEpEtLykJlXgiqdHoAEO40K/p7OCA/0gKdr9YDDlZWVDZazd+9e7N27FwBw+vRpPPHEE60W8/Hjx7F69Wp07doVixcvbrV6yDIKSiqQkJaL1CtFqNTpYTAIaNUqeLk5IDzQE/6eTpAkSekwyUaVVVQhMT0flzIKUFZRBZ1BQKuW4epoh7BADwT7uEKl6vD5eDJTlU6PS5kFSErPR1FpJar0BmhUMhztNQjxd0PXAHfYadrl5UGHUVVV1eBr0dHROHPmDEpKSuDj44Onn37aipERERGRJbWbKzad3oBDZ9KwNToR55JzUFJefTEjSYD81421QQjUdDBzdbJD7xAfVJSpIQA0desdEBDQesED2LVrFwDg4sWLHKywDRJC4PTFK9h0KAExiVnIK6p+qnn18SVE9TEGAA5aNboFe2LSwBCM7tMZWo1KsdjJNqRkFWLjoXhEn09Hek4xgOrvJUmWIKH28aVRyQjxd8OYfp0xeVBXuDiyVR81LrewDFuOJOLAqRQkZxVCb6g+lmRJgiQBAoDhr2WSBAR6u2BYj0BMGxYGf09nBSMnAKio0uF8cg7i0/KQkJaH+LRclJRXQac3QK2qTvZ2C/JEWKAHwgM9/nqQVT8hBEpKSgBUJ354zUF6g+Gv46r62IpLyUVBSTmqdNXHl71W/dex5YnwQA9EdvaCo51G6bDJRgghkHqlCHEp1d9hcSk5uFJQiiqdAZIE2GlU6OzrZvwO697ZGx4u9kqHTTYku6AU55JzEJ+WiwspuUjLLkJllR4CgFYtw9/TGRHBnggL9ERksBcCvNrXdY3Nj6FTVFqB9Qcu4M+D8SgoqYAsScabnqbIslTdsgKV8BBX4IlsyDBcs46M8ePHY+LEiVCrWyf/lZ+fj3fffRcA0LlzZzz88MOtUg81X5VOj63RSVi3Pw5p2UVQyZLxRqgpklR9E+5kr8G0oWGYMyrC2DKMCKi+yDl4Jg2/74vD6YtXjN9JppIkQCXLGNe/M+aNjkQXf/fWC5ZsUlxKDn7dew5Rp9IACDTj8DKeTwdF+OP6MZHoH+7fanFS/dKuFGHjoXhsOZKIskodJAmQUP91jkqWjA+uHDQSnCsvwwM50KD+1jre3t647bbbEBgY2Nq7QW1UQXE5tkQn4Y+oC8gpLIOE6mvj+q5zZAnAX8eeVq3CpEEhmDE8HCE871ADyit12HMyGesPxOFiRgEANHgdffWxJ0nAsB6BmDWiG/qG+bLFO9VLbzDg6Pl0bDhwAcfjq4dQaew+7erXunf2wqwR3TCydxA0att/6G7TCZ1DZ9LwyZojKC6rNDmJUz8BCECDSgTiEqaNGQhvb2+kpKRg2LBhCA4OtljM9anpbgUAU6ZMwaRJk1q1PjJN4uU8fLD6IFKyCgFUP8E2lyxJsNOq8OCcQZgwoAtPToScwjJ89usRRJ9Pb1Yiuj4qWYIQwC0Te+Km8T3axcmJWqa8UocVm2Ow/sCFZiWi61OTaBzXvzMWzx7IFmFWkJlXgqW/ReNoXEazE701qlv2CbghD/5Ig6ujFqGhobhw4QIGDBiAGTNmQKvVWj54avNKy6vw/aaT2HIksVbr9eaoOS57hnjjkesHo7Ofm+UDJZuk1xuwdt95/LzzDMoqdMYHnM1Rc94K8HLGg3MGYWAEHyjQ/+yLTcE3G44jp7DMrGvomm1cHLS487o+mDY0zKbvzWwyoVNcVoll645i94lks74kGiQEIAFzRkbg7mn9rNZNJiUlBcuWLYNGo8Hjjz8OT09Pq9RL9dPrDfjPjjP4eecZQIJZF9KNGRwZgMfnD4GHC1vrdFQ7j1/E0t+PoqJKb9HjSwIQ7OeKZ24Zga4B7hYrl2zLmYtX8MHqg7hSUGq58yOqL4CcHbR44sahGNqjk+UKJiMhBDYdTsA3f5yATm+wzPeDEFBJBjxy/SBMGRYJIYRNX7hSyxyLy8C//nsIBcUVLXwYWk3+q1vwHVP7YN7oSI7v1sElZxbgw58PIeFynkXKq7nPmzokFPfP6A9He3b168jyi8vxxW9HEXU6tfqhhYXK7RPqgyU3DoOvh5OFSrQum0vo5BWV46VvdiL1SpFFTkT1kSSgV4gPXr57jNX6COfl5UGj0cDZuX316bM1VTo93lsVhUNn0iz2JXEtWZbg5eqAtx+YwLEpOhghBP6z/TR+2n661eqQJQlqtYxX7xmLPqG+rVYPtU0HTqXivVUHao25ZEk1F9cPzR2EGcPDLV5+R1ZaUYW3f9iPk/ENz75pvurRAsf374LH5w9hK74OyGAQ+PbP41i3/4JlH4ZepVugB/5+z1i4O3P8k45o85EELP3taK0x2SxFliS4OdvhtXvHomuAh0XLJttwKikLb67Yh7IKncWvb2RZglol45lbhmNEryCLlm0NNpXQKSgux7PLtiMzr8TiXxTXkiUJ3YI88ebC8bDXtu7Y0QaDAZIk8YmZwnR6A95auQ9H49Jb5ULnarIswdXRDh88PNlms8HUfD9sicXqnWdavZ6asXVev28ckzodyIFTqXj3p/01vYhb3YNzBmLmiG5WqKn9KyqtwEvf7sLF9IJWe1gFVH839A/zw0t3jeFg/R2IXm/Ah78cwp6Tya1ajyxL8HV3wruLJsDLzbFV66K25dc957B848lWraNm+ILX7xuH7p29W7Uualuiz6fjrZX7oDcYWu0e7a9hwvDE/KGYNKhr61TSSmymXWSVTo+/f7fbKskcoPrJ5oXUHLz74wG0Zs7r6NGjeOmll/Dzzz+3Wh1kms9+PWKVZA5Q/eSisLQC//f1TpSUV7Z+haS4Pw/GWyWZA1Q/edUbDHj1+z24lFlglTpJWacvXsF7qw5YLZkDAMvWHcP+UylWqq39Kquowt+/242LGa2bzAGqvxtOJGTi3Z/2Q6c3NL0B2TyDQeCTX4+0ejKnpq6s/BL839c7UVBS0er1Uduw4cCFVk/mANX3ZhWVOrz87W4kpVumSxe1fbGJWdXJHH3rJXOA6msnIYB//fcw9sa0/velJdlMQuc/O84gKSPfKsmcGgYBHI1Lx6bDCa1SvhAC27Ztg8FgwNmzZ1ulDjJN1OlUbD920SrJnBoGg8CV/FJ8+8cJ61VKiki7UoSvNxy3ap1CVLc6+2D1Qeh549aulVfq8MHqgxBCWC2ZU+OTNUeQV1Ru5Vrbl8/XRiPxsvWub4Softr507ZTVqmPlLX+QBx2HLtotfoMBoGMvJK/un7aTCcAMtPppCv4cv0xq9VnEEBllR6vLN+D0vL6Z/Cj9iO3sAxvrthb3TLHivV+sPqgTT0QtYmEzoXUXPyy64xVb7av9s0fJ5CZV2LxcrOyspCXV51hDgqyvf567UVhSQU+/fUIlOjwZhACW6OTcCwuXYHayRr0BgM+/OVgqz95r4/BIJCUno81e85ZvW6ynn9vOonsgtJmTUluKeWVOnz+WzRv3Mx08Ewadp9Mtvr3gxDAf3efRVxKjlXrJeu6nF2Ef2+KsXq9BoNATEIWthxJtHrdZD3llTp8+PPBv6a0tx6DECgorsB3f56wbsVkVUIIfPrrEZRX6a2eAxAAPvr5kM08EG3zCR2DQeCjXw5BUuR2u5pOb8Bnvx6xeLmJif870UVGRlq8fDLNN38cR0lZldWfbNeQJOBfvxxGRZVOoQioNW08mIC4lFyrti681o/bTiHtSpFi9VPrOXcpGxui4hV74GEwCBw6k4YDp1KVCcCGFZVW4NM1R6DU8HkSJHz48yFUVumVCYBaVc31s17BZOvXG44jK9/yD0SpbVixOQZXFHqYYBACm48k4viFDOtXTlax8/glRJ9PV+T62WAQSLich1/3nrd63eZo8wmdYxcykJJV2KKnVw52aqhbMI2iwSBwIj4TlzLyzS6jPh4e1aO029vbo2/fvhYtm0yTU1iG3Sda9nTU0U4Dd2d7yGZelQsB5BWXY18Mx6Job/QGA9bsaVl3Snut2iI3fOsPxLW8EGpzft17DqoWPB5VyRLsWjg4riRVt/ag5vl511kUl1Uql4wTApezi1qtWzkpa19sCs4l5yj6MKFKb8DKzbGK1U+tJ+1KEdYfuKDY9xdQfe5Z+vtRthBth6p0enxj5aEK6vPTtlgUFLf9buWtO32TBfwRdQGyLJl1QhrXvwvumNIbbk52UKtknL2UjWXrjiElq7DZZcmyhD8PJeChuYOavW1DunfvjscffxyOjo5wc3OzWLlkui1mXsi6OtlhTN9gjO4TjMhgL2jUKjz04Z9INbMVhCQB6w9csLlR1alxx+MykF1Q1uztwgM9MH1YOIZ0D4CdVg2VJCExPR8/bI1FTEJWs8szGAS2HU3CXdP6wtFO0+ztqW3KKSjFwTNpzb6gttOoMGlQV0wdHIoAL2eoVBIqKvXYE5OM/2w/3ezBTIUA4tPyEJ+Wi/BAz+YF00GVV+qw+XCC2Q8TvN0cMLRHIJwdtEjPKcLhs5dRYUZLG4HqZO/skd0402Y7s/5AHGRJMusYs9eqMaxHJwT7uuJiRgH2xZr3wMlgENgbk4yFM/vDjVOZtysbD8WbfX/WLcgTYZ084GSvQUZuMY7GZaC8svmt1IUA0nOKEZOYhX5hfs3entqu/adSUVRm3qQxro5a9A3zg7+nM4rLKpFwOQ8XUnPNKkv/1/Xz/HE9zNreWtp0C53MvBKzm1qN7hOMZ24ZjvUHLuCW137Fgjd/Q1mFDm8tnAB3Z7tml1dzQ2TpAbg6deoEd3d3i5ZJteXn5yMtLa3Ocp3egD8Oxpt1sTOiZyCCfFyxckss1lqgOZ4QaNEXDimnvLwciYmJMBjq9rPdEHXBrJZbj90wBJ39XPHmyn245dVfcefbvyMlqxBv3Dcew3sGmhVnRZUeu45fMmtbUo7BYEBCQgIqKuomWTYfSTSrO3JIgDt83B3x4c8Hcdvra3Hj39fgH6sOYFy/znjj/vFmtQhTyRL+iIpv/oYd1L6YZJRVmNfNdly/zlj29AwMCPeDnUaFmyf0xKdPTIOfh5NZ5WXklpiVKKa2Kyk9v7p1jhnXN8/fPhJfPzsTQ3p0wi0Te2FEr5aN8WgQAluPJrWoDGpbyit12HIksdn3ZyH+bvh8yTS8uXA8IoI94eyoxc0TeuKrZ2aafW1Tfe65YNa21HZt+Csh3Vx3TOmN9x+ejKE9OsHBTo3eXX3w7qKJeHPheDjZN/+BphDV1/L6eq7x25I2ndA5fDbN7K4G90zri1OJWVi3Pw5CAGUVOny29gicHTS4Yax5WbbKKj1OJmSaF9A1jhw5gldffRWbN2+2SHlUv8LCQtxwww2YO3cuHn74YZw4ccL4WnxqrtnTam4+kogv1x3DmYvZFmvqKcsSok5zHApbIoTAokWLcPPNN+PWW281zloHVF/wHLuQYdYF9fZjF/H8lzsQl1Kd4CurqB54trC0AjeM7W5WrBLA48sGvffee7jlllswZ84crFq1CuXl/2v6uy82xazj63xyDv69KQbJV3VnjknIwsZDCega4I4ufu7NLlNvEDhwKpVN3030x8F4s65v/Dyc8MSNQ7HpcCLe+mE/Vm6JxdOfb4NOb8DTtww3KxZZlrDxEJNxtubSpUt4++23sWbNGlRW1n6SveVIgtldMXefuIT7/7EBH/58yBJhQojq1vb8brAtBQUF+PDDD/Htt98iPz+/1msHTqWgzIwWNf6ezsgrKseDH/yJT9Ycwb83xWDJZ1uQcDkXz9wyHB4uzW/FpTcIHDyTxtkWbYxOp8O3336Ljz/+GKmpta9NkzMLcD4l16zrmyPn0/HgB3/iw58PYeWWWPxz9UG8/u+96Bfmh5sn9DQr1uyCMpyMb9sPPdp0Qic+Nc+sJsAh/u7w83TGkfO1Zw7KL67AhdS8FmWBE9LyzNr2agaDAZs3b0Z5eXmtBANZ3rlz51BYWN3F7vDhw1i4cKExsXMhLVexwSjrIwyCLXRsTElJCc6cOQOgepDzF154wZjYSbycZ3bf8nX74+o8+TIYBApLKuDp6mBWmQJAXGouL6ptzOHDhwEAOTk5+OCDDzB37lysWrUKBUUlSL3S/O7DjbHTqmEwCBSVmpfoLq2oQlZ+qUVjao8qqnRIMPP74bqhodCoVVh71cx1lTo9NkRdQI8u3ggP9Gh2mQaDQGzSleYHQ4r67LPP8Ouvv+Kdd97BvHnz8MsvvxgTOzEJWdCbOXbOwTNpqNRZdqDs7IIy5BY2v/sxKefXX3/FTz/9hKVLl2Lu3Ln44osvjImdU0lXzEoYxqXm4pXlu2slX4QAth+9CDutGn3N7DYlBHCeM/bZlOjoaCxduhQrV67E/Pnz8dprrxkTO6cvmn8+Op+cU+fcejIhE1U6PQK9XcwqUyVLONOCmKyhTY+hcy7FvMHcuvi5AqiervFal7OL0L2zF+w0qmb3NzdY6IY7LS0NxcXFAICAgIAWl0fNc/jwYRw+fBhh4+6EZOev2OxW1xIALvx1w82xDGxXTWKnc/8pgE9/i5Xb2c8NQT6uOHLustlllJZX4Up+KXzN7JpByqtJ7Pz75z+g6T67RWXZa9Ww16rhYKdGvzA/TBsaip93nUFOC2684tNyze7601FcTC8wO9nbK8QHV/JL63xG5y5lV7/e1QfxZjx4KiypQG5hmdkJY7K+vLz/fc6ZmZn4xz/+ge+//x533nU3UtvgvUd8Wh683ByVDoNMlJv7v/udkpISfPfdd1i9ejVuueUWnC3rYlbCsKGknvtf4yuZO0W0SpYQn5Zr9gN7sr6rW33p9XqsX78ef/75J2bMmAH70HFQyZLZSelr9e7qA41ahdhE81rZWOr+vzW12YROZZUe6TnmDTDr5KAFUP208FolFVWQZQlO9ppmJ3QEgAtpLf9A4+P/17S5e3fzuk9QyxVUqqHRKh1FbSXlVcgpLIM3L3psXkGlDAeDHpLcshmEAECtkvHE/CEQQuDnXWdaVFbC5TwmdNqBUmEH1xYmf6cPC8O8MZFwdtBCo1Zh8+EErNl9rukNG6CSJSRezseo3sFml9ERxKflQgLMepjg4+6E3KK6N0U1CZ6WJNPi0/IwlAkdm5aZmYmPv1wB72F3Kh1KLdU33HkYxhtum1ZSUoLvvv83/CcugSRZppOHnUaFuaMjUFpRhRPx5g1rYQs33NS0msSOzwgvqJ19WlTWHVN6Q5Yl+Ho4oWcXb/ywNRbrD5g31tLVLdzb6gP3NpvQKSmvMvsJVk2XgvoGi6xpIWjuzBJFJeW4/fbbzQvsL05OTvDy8oJer8cnn3xS72CqZBmlpQ03/5c1bfPC9ZHHn4KqyrJdKah16PUNJ4VljQNggQseSQKW3DQUEcFe+O7PE8Zxdcz14b8+g6YkucVxkXWkp6fXu1xS2wPCAEjmJwzX7j2PtXvPQ5KAPl198eTNwxAR7IWnv9iKKp155yVzu2t1JGnZRZDNfPpop1Whqp7uMDVdZOw05l3WSRJw6MRZqMsyzNqerK+mpfe11E5tb6Y5g0Hg1IVLOOZl3qw1ZH1ZWfW3ZlA7uFksmQNUTwLh7+mMZeuOotjMWY0EgPiUbBw7dsxicVHrSkpqeKB0lYN7i8uv1BmgVkmAAJzstQj0doGdVmX2ZATFZZUoKa+Cs0Mbawnwlzab0Klq5EapKTUD3bo41n3TnR200OsNKC4zc7YqSUZcXJzZsdXw8PBAeXk5ysrYp1gplmg50RqSU1JRVciLapsnqyySyX903hCM69cF/9l+usUzqgkhkHklG6WpLf8OI2VJsuVO30IAMYlZWL7xJJ69dQTG9u2M7ccumlWWuYmgjsSc6cWN21bqoVHXPXdp/1pWUWXexapBr8eaX3/Dv5OizI6N2gZJ1rS5J8kGIRB99Di2fveK0qFQS1nw3LNwZn+M698F6w/EtXiWxLyCQixatMhCkZGiLHCM/bzzf63Zwzp54L0HJ0GWJPxz9UGzy6yo0sO5bbYFaLsJHbVsfvY3KT0fANDZzxWIrf1aZz83JGcVQmdmP00hDNBomj/t2bVqnqxYoixqmMFgaLAVhRBt88ZDJUsAjwubIISATtfADZRB3+KL6sVzBmLqkFD8vPMMftx2yuxyakiSBFnm944tqapq4OGDsOygpQCQnlN9XvJqQbcbtapNz7XQJrRkYPKsvBL4e9btVlXTTTczt8TsstvULAFkvjb4OUqSZJEWq6Q8S7XOuXNqH8wdHYk/D8bjq/XHW1xeW0pgUstY+rNMuJyHs5eyMbRHpxaVY864vtbSZhM6jmbMFV8jPacY8Wm5GNU7GKu2nzZ23QrycUFXf3f8sDW28QIa4WSvxYYo859gHTlyBNu3b8f48eMxfLh5U4yS6Q4fPoyHH3643teErgLQtr2xav778yr4ezorHQaZoLi4GOPHj6/3NYOusrrpg5knpvum98OsEd3w884zWLnF/O+sa73x6t8xpm9ni5VHreuGG25AcnLdLnIGXYXZx5Ykod4uzX1CfQEACX89FDFHS87dHUV9LWxMdfriFfTq6gMfN0dcKfhfl+IeXbwBVM8+Yw5ZljGgf190HR1udmxkXZs2baq3W4ww6Nrgza1A5+BAzO57l9KBkImOHDmCs2fP1lkuDOa1ArzarRN74uYJPbHpUAKW/n60xeUBgL2dFnfdxePLViQmJmLfvn31viYsNP7k1TRqucWDLGs1bbNnB9CGEzr2WjW83RyRXWDeFKhfrT+ON+8fj0fnDcGaPWfh4miHh+cOQuqVQqzbb353g64BzZ8StIZer8eGDRtQUVGBQ4cOMaGjMHVlASRHD7MGplSrZGM/Sntt9Z+Rq6Md3J2rYBDV00ubw06jgq87B6xtD+TKAkhmTOsJALdN6oV5Y7tja3Qi1h+4YJwBoppAfrH545R0DXA3e1tqOwyluWY/KV00eyDKK3U4FpeBrLwSODtoMaRHJ9w0rgeiTqfi6Pn6x+1pit4gEOLvZta2HYmXq4PZYwRuPpyA60dH4sbxPYw3QnYaFWaN7IYzF68g4XLzZ7gCAAEJc6ZPxoQBIeYFRlYXExNTb0JHX27ehCKtSSXLGD6wDx6YPVDpUMhEH3zwQb0JnZYeX/PGRGLBlD7YdDgBn/8W3aKyrtY10AePP9yyMU7JejZt2tRgQsdQUQKVg2uzy3Sy18Df07nOebBvmC8igr2wLbrhcXuaolHJcG7DD6zabEIHALp39sKBU2VmDWB89lI2XvhqB24e3wOv3zsOlToDjl/IwKrtp83uv66SJUQEmz/Y3MWLF1FRUX0j5ufnZ3Y51DIBAQG4//77UeXWDT9sPW1W8/fwQA/83x2jjL/nFZXhhQUjAQC5heVY8tkWs2IL6+QB2cwkALUNzs7OuP3229F3xFS89dMhs8oY1iMQeUVlGBwZgMGRAbVeq9QZsPC9DWaVa6dRoZOXi1nbUtugUqkwZ84c3LbgLjzx5QGzyvh+40lMHBiCG8d1RydvFxgMAmnZRfhsbTR2nbjYovjCA9vegKxtTXigp9kTM2Tll+KjXw5hyY1D4efhhOTMAgzpXt2MvCVjA9TERbZvYI8QXGrB9lMGd4Wfh5OxlU+IvxvumNIbABB1Os2spKHeIBDG46td6BbaBcJORnFF84ctGBjhj/tm9EdRaQUKSyqMx1WNYxcycOZidrPLrb4/82r2dtT2+Pr6IjTAFSkFQHMb1EiShAdmDYAsS7iUWYCKSh2CfV3RP9wfB06n4ps/zO/a1zXAHao23KW8TSd0wgM9cOBUqtnbX0jNxVs/7LdYPHqDaNEFz4UL/5sujdOVW0dISAhUKhX0er0xkTNz5kxoNBqcTMg0+6L6XHIO7np7nUVjbWnCkKzP0dER/v7+yMjIMCZybrvtNri4uKCk3PzZPMxNCDYllAlDmxMWFobk5GRjIue+++5DQEB1ks/PwwmZec0fM6WiSo+NhxKw8VCCRWPValQI9GbCsCnhgea39AWAfbEpOH3xCgZHBsDZQYt/b45B9Pl0s8cGBPjZ2SKttvbEH6NGjcIDDzyA3r1745531hmnsm8und6Ayr8GN7+2u6+510xAy497sq5rj6+IiAgsWrQI48aNw7s/HcDB06nNvuHOKypvtAu5uWOUVN+f8fiyJdeO5ejr64t7770Xc+fOxfqoBKzYHAs0sw9FcVklXvhqB0L83RAe6AlXJzskpedj2bpjxjECzWELCcM2ndAZGBGA7zfFKB2GkSxLxjEGzGFnZweg+iYwIiLCUmFRI3x9fbFy5UpkZWVh2LBhtb5AIoO9YK9Vo7yy5f2BLUFvEBgYEdD0itRmyLKM7777DjExMRg2bBhcXP53Q+Rkr0VEsCcupOaa3b3CkiQJdVr7UNv32muv4brrrkPv3r3h7+9f67WhPTrhj4PxbWKgPlmWMKCbPxOGJnB1smtRl3Kg+sZoawuaj19NkoBwJnttzk033YRLly4hIiIC999/P3r3/l9rh+5dvBF1KtWsBMzO4y1p31M/O40KgT5MGNqS6dOnY+fOnXBzc8Pdd9+NcePGGVttRQZ74eDp5j9wT0rPN05cY2ndgtr2DTfVNnToUPTp0wclJSW46aabMHfuXGMSMSLYq0XJ44sZBbiYUWCpUKE3CHQLatsP3Nt0QqdrgDsigj0Rn5rXog/WEmRZwqjeQfBwsW965QaMHTsWnp6e8Pf3h6Nj2xuMt72KiIioN4Fmr1Vj6uCu2NBGboj8PJzQL4xd8WyNr68vJk+eXO9rs0Z0w4c/m9ftytIkScLUIaFKh0HN5Ojo2ODxNX1YONYfuFDva9ZmMAjMGsEBdU01eVBX/LzzjOLXNkD1ANmTBnVVOgxqpgkTJmDChAn1vjZxQBfsj02xckT1k2UJEweGQNWC2WvJ+sLDw/Hrr7/W+9rYfp3x/aaTVo6ofpIEhPi7I4gJQ5vi4uKC5cuX1/tazxBveLrYI7eo3MpR1U+rUWF4z0Clw2hUm/92nT2iW5u44DEYBGYO79aiMlQqFfr168fxc9qQ6cPD20QyRwIwa2Q3PiFtZ0b1DoZTGxhETSVLGN0n+JrBlcnWBfu6oleID+Q2MKONv6cT+oby3Gaq64aGQpg1JL/l2WvVGNuPM9+1J4MiA+Dl6qB0GACqr5+nD2Oytz3xdnPE0B6BbeKaVQhgzshubXBmNzKXSpYxc0Q3cyfytChZljBpYEibn8GzzSd0Rv11E6LkhyrLErr4u6FniLfZZRw5cgSffPIJTp8+bcHIqKWCfFwxoJuf4iclO60KkwaGKBoDWZ72r9lnlD4p6Q0Cc0aym2d7NG9MZJt46HH96EjFv0dtibebI4a1gRsiWa5uuVczWyO1D23lhkiWqic44eyK7c+sEW3jgaiDnRqj+zIh3d5MGRzaJpJ0BoPAjOFtPyHd5hM6GrUKj88fougYFEIILLlxqNkHVmVlJX7//XdcvnwZBw6YNysJtZ6Hrx8MlcIX1YtmD4SLo52iMVDruHFcD/i6OynWikKSgOnDwhDZmf3L26OhPTphaI9OiiUGVLKEbkEemDYsTJH6bdltk3o1d8xHi9OoZFw/OlLZIKhVTB8WBldHO0WTOgYB3DGlj3IBUKvpF+aH7p29FE9K3zKhJxPS7ZCHiz1mKZyUliUJI3sHIcTfXbkgTNTmEzoAMKR7J0wcEKLIDZEE4ObxPVs0u1V8fDx0uuqBd728eFPV1vh7OuO+6f0VqbtmINHJHL+g3bLXqvHUzcMUaUUhSxK8XB1w7/R+Vq+brEOSJDw6bzDsNWoodd3z5E3DOT6GGUI7eeDmCT0V+9wA4IHZA+DjzjH92iNnB62iD0RlScK0oaHoF86umO2RJEl48qZhUCl0xy3LEsI6eTAh3Y7dObUPfBR6ICqhuvXXQ3MHWb1uc9jMFdgDswfA3dnOqplgWZbQ2c8Nt0zs2aJy4uLijD9zuvK2acbwcPTuat2xKGRJgr1WjcfnD2kTzQqp9fQM8cH8sdb/2xcQWHLTMDjYte2+v9QyHi4OePSGwYo09rhnWj8E+7oqUHP7cPOEHgj2dbX6U25ZltA/3A9TB3Og9PZsaI9AjO/fxeo3RLJU/YT93hn9rVovWVcnbxfco9ADIwnA07cMh0plM7ey1ExKPhAVAB69YYjNjD1pM38Fzg5avLlwAhy0aqucmGRZgrerA16/bxw0alULy6p+m52cnBAe3vb74XVEsizhxTtHW+3CWpYAtVrGG/eNg7cbn452BHde1wej+wRbtfnoE/OHcua0DmJM385Wb4k1e2Q3zB3NsZlaQqNW4fnbR8Jeo7LaTbcsS3B3tm9RV3KyHYvnDESAl7PVkoaSVD2Gzwu3j4QjHya0e7NGdMOwHp2s3jXmoesH8WFCB9ArxAd3TLV+t81pQ0Mxuk+w1es1lyREGxhNsRkuZuTjxa93obi8stUG45JlCb7ujnjrgQnwdXdqcXmVlZWIjo5GSEgIOnXqZIEIqbUUlFTg5W934VJGQatlhGVJglajwmv3jkXPEJ9WqYPaJp3egA9/Poi9Ma03nWzNNdVj84dgCp++dzi/7DqLFZtjWr2eWSO64YFZAxQfP6G9OJ+cgxe/2YkqnaFVn0bKsgQXBy3ee3ASOnlzmt+OIqewDM8t247sgtJWHchWkqqvcV6+awwGRQa0Wj3UtlTp9Hjt+z2IScyyShe/e6f3ww0KtHomZQghsHzjSazde94q9Y3t2xlP3TLMprqS21xCBwAycovx7o8HkHA5r1XK7xfmi2duHWEzzazIskrLq/DJmsPYfyoVEiw7ZqWE6iaqz98+krM+dFB6gwE/bTuNX3adgQTJojdvsizB0U6DJ28aiqE9Ai1WLtmWHceS8MVvR1GlN1j05k2WJUio7mY1d3QEW3dYWHxaLl7+djdKK6pa5aZbliR4ujrgnUUT4O/pbPHyqW3LKyrDS9/sQsqVwla56ZZlCWqVjFfuHoO+bBna4VTp9HhvVRQOnklrlfJlqfp66cE5AzFzRLdWqYPaLiEEVm0/jVXbT0OS0GqJw+uGhOKh6wfZVDIHsNGEDgDo9Qas3XceP2yJhQBafPEjyxI0KhkPzB6AqRacKi06OhrHjx/H1KlT0aVLF4uUSdaxPzYFn/56BGWVOoscX0II3DiuB26b1KvF3fjI9sWl5OCD1QeRnlvc4hNTzcltVO8gPDR3ENyYjO7wsvJL8PF/DyMmIctiiemwTh54+pbhbObeinIKy/DZr0cQfT7dYmXWfD9MHBiCB2YNgLOD1mJlk20pr9Rh5eYYrDtwwXiDbCk9unjjyZuGIcCLycKOymAQ+OPgBSzfeBJ6g7BYYlqSAB93Jzx10zD06sqW7R3ZkXOX8fF/D6OotNJi31+yLMFOo8KDcwZhwoAuNvmwymYTOjWSMwvw07ZTiDqdBkCgud8dEgC1Ssa4/l1w2+ReFuliVaO0tBRvvvkmDAYDevfujTvuuMNiZZN1FBSX4z87TmNrdBIqqvTNvjGquWAaHBmA2yb1QkQwZzmj/6ms0uO3feex/kAc8osrmn2BrZIl6A0C4YHVs+WM6BXUitGSrRFCYOfxi/jvrnNIuVIIWZaadYFds76vuyOuHxOJGcPCOQClFVR/bpewbN1RlFfozE7G1SRy3Jzs8MSNQzGkO7t8U7XTSVfw4c8HkZVf2qLEjiRVX0PfO70fZg7vxi6YBABIzynGR78cwtlL2c0+71ytZts5I7vhzuv6cnpyAgAUl1Xi6/XHseP4ReN1sDlqjq/BkQF47IYh8HR1sHCk1mPzCZ0auYVl2HIkEVuOJOJKQSmA6pvpq5tl1fxcc+Lq5O2M6cPCMXlQ11Z5YnXy5EmsWrUKADB69GjMmjXL4nWQdZRVVGHXiUv482A8LmYUAPhfX3EBAOKv4wv/ay3m7myHyYNCMW1YGPw8LJcopPZHrzfg0NnL2BB1AacvXjEeQyq55vgSxicGNScuB60ao/sGY8bwcIQHeioUOdkCIQTOXMrGH1EXcOjsZVRW6QFUH181r0Oq7k5Vc3ypVTL6h/th1ohuGNDNnzdqCiguq8SOYxex/kAcMnJLTL5wrVkvxN8Nc0ZFYEzfzrwRojqqdHocOJWKDVEXcC45x9iSuKm7gprjy8vVATNHdMOUwV05RAHVIYRATEIWNkRdwKGzacbx/Zr6Cqs5vhy0akwdEooZw8M53hfVKyk9D38eTMCOY0mo1BlMOkdW37cJyJKE0X2CMXNEN3Tv7GWTrXKu1m4SOlcrKC5HwuU8xKflISuvBBVVesiSBI1GRicvF4QFeiCsk0erNzv++eefcezYMQDAwoULOcNVO1FaUYWky/mIT8tFWnYRKqr0MBgEtBoVvF0dEBboifBAD5vO9JJyqnR6XMosQEJaHi5m5KOsUge9XkCjluHqZIewTh4ID/SEv6eTzZ+AyPr0BgMuZxcjIS0XCen5KCmrRJXOALVKhqOdGl0D3BEW6InOvq5sjdNG1NwYRcel40JKLuIv56KiUl9nPUd7DboFeiIi2BNDe3RCZLDtX6SSdSSl5+PAqVRcSMvFhZQcFJZW1llHq1ahayd3RAZ5ol+4PwZF+tvcOBOkjOyCUuw5mYy4lBycT8lBdkFZnXVkWUKwjysiO3uhV4g3RvYOZiKaTFJSXom9MSk4eykb51NycDm7qN7EtL+nEyKDvRAR7IWx/Tq3q0R0u0zotBWrVq3CyZMn4ezsjBdeeAFqNb+YiIiIyHwGg0BWfgm2zpmHyvIKuAQFYsL338LbzYEJHLKI3MIyFJZWoLJKD7VKhr1WDT9PJyZwyCKKyyqRU1CKSp0BsixBq1bB39OJ40uSRZRX6pCVV4JKnR5CAFqNCj5ujnC01ygdWqthhqEVzZo1Cz4+PujRoweTOURERNRisizB39MZ3kVZ0BUXw9nbET7ujkqHRe2Ip6sDWxlTq3F20HJwdmo19lo1Ovu5KR2GVTHL0IpcXFwwefJkpcMgIiIiIiIionaGbSdbydGjR/Hjjz/iypUrSodCRERE7YzauXp6aI0zp4kmIiLqqJjQaQWFhYVYs2YNYmNjsXfvXqXDISIionam59/+Bq/hwxH55JNKh0JEREQKYZerVnDu3DkYDAYAgDOfnBEREZGFBc6ahcBZs5QOg4iIiBTEFjqt4Pz588afu3fvrmAkRERE1B6Vpacj+eefUZGbq3QoREREpBC20GkFlZWVAAA3NzcEBQUpHA0RERG1N9GPPor8EycQsHcvBn/6qdLhEBERkQKY0GkFc+bMwcGDB9G/f3/IMhtBERERkWUVx8cDAIouXFA4EiIiIlIKEzqtwMfHB7Nnz1Y6DCIiIiIiIiJqp9h8xMJOnDiB3377DcXFxUqHQkRERERERETtFFvoWFBOTg5Wr14NIQTs7e0xbdo0pUMiIiIiIiIionaILXQs6OzZsxBCAADs7e0VjoaIiIiIiIiI2ismdCyI05UTERERERERkTUwoWNBpaWlAAAvLy/4+fkpHA0RERERERERtVccQ8eCZs+ejcOHD2PEiBGQJEnpcIiIiIiIiIionWJCx4JCQkIQEhKidBhERERERERE1M6xy5WFxMTEYOvWraisrFQ6FCIiIiIiIiJq59hCxwIyMzOxatUqCCFgZ2eHsWPHKh0SERERtWOOnTuj8MwZOHXponQoREREpBAmdCzg9OnTxunKNRqNwtEQERFRezfo44+RsXUrAmfPVjoUIiIiUggTOhZw7tw548+RkZEKRkJEREQdgXNoKMIXL1Y6DCIiIlIQx9CxgKKiIgCAn58fPD09FY6GiIiI2rus3bsR/dhjyDt+XOlQiIiISCGSqOkrRGaLjY3FsWPHMHHiRAQHBysdDhEREbVz28ePR2lKCjyHDsWoVauUDoeIiIgUwC5XFtCnTx/06dNH6TCIiIiog6jMy6v1PxEREXU87HLVQqdPn0ZUVBQMBoPSoRARERERERFRB8EWOi2QmpqKH374AUIIaDQaDB48WOmQiIiIiIiIiKgDYAudFjh16pRxunJZ5ltJRERERERERNbBLEQL1ExXLkkSIiIiFI6GiIiIiIiIiDoKJnRaIO+vgQiDgoLg7OyscDRERERERERE1FEwodMCU6dORWhoKObOnat0KERERERERETUgXBQ5BYYNWoURo0apXQYRERERERERNTBsIWOmeLi4hAbG2scFJmIiIiIiIiIyFqY0DHDpUuXsHz5cvz44484f/680uEQERERERERUQfDhI4ZTp48aWyZo9frFY6GiIiIOhqv4cMBAD4jRigcCRERESmFY+g0kxDCOF25LMsICwtTOCIiIiLqaAZ//jmKLlyAa/fuSodCRERECmELnWYyGAzG6cpDQkJgb2+vcERERETU0chqNVwjIyFJktKhEBERkUKY0GkmlUqFMWPGICAgADNnzlQ6HCIiIuqA4j79FJsGDsSl//xH6VCIiIhIIZLgNE1ERERENmXT4MGoysuDa8+eGLd+vdLhEBERkQLYQqeZEhMTkZiYqHQYRERE1IGJqioAgOGv/4mIiKjjYUKnGRISEvDVV1/hq6++QkpKitLhEBEREREREVEHxYROMxw7dsz4c0VFhYKREBEREREREVFHxoSOiQwGA+Li4gAAGo0GXbp0UTgiIiIiIiIiIuqomNAxUVlZGYqKigAA4eHh0Gg0CkdERERERERERB0VEzomcnBwQJ8+feDl5YUpU6YoHQ4RERERERERdWBqpQOwFbIsY8GCBUqHQURERERERETEFjqmSklJQWZmptJhEBERERERERGxhY4p4uLi8N1330GlUuGpp56Cl5eX0iERERERERERUQfGFjomiI6OBgDo9XqUlZUpHA0RERF1dLJWW+t/IiIi6niY0GmCXq83Tlfu4OCAgIAAhSMiIiKiji5s4UJovbwQevfdSodCRERECmGXqybk5eWhvLwcABAREQGVSqVwRERERNTRhS9ejLBFiyBJktKhEBERkULYQqcJbm5u6NKlC5ydnTF+/HilwyEiIiKCobIS+SdOQOj1SodCRERECpGEEELpINo6IQSfgBEREVGbcej++5G1axe63H47+r7xhtLhEBERkQLYQqcJmZmZKCwsVDoMIiIiIqPcvyZsyDlyROFIiIiISCkcQ6cRZ86cwYoVK2Bvb49nn30WTk5OSodERERERERERMQWOo05dOgQAKC8vBylpaUKR0NEREREREREVI0JnQZUVlYiISEBAODq6gpvb2+FIyIiIiIiIiIiqsaETgMyMzOh0+kAAJGRkRwUmYiIiIiIiIjaDCZ0GuDl5QVvb2/Y2dlh9OjRSodDRERERERERGTEQZEb4OjoiCeffBKSJEGWmfciIiIiIiIioraDmYoG5Ofno7KykskcIiIiIiIiImpz2EKnHqdOncIPP/wAd3d3PPPMM1Cr+TYRERERERERUdvB5if12L9/P4DqVjplZWUKR0NEREREREREVBsTOtcoKyvDpUuXAADe3t5wcXFROCIiIiKi2rSengAAu7/+JyIioo6HfYmukZqaCoPBAKB6unIiIiKitqbvm28ide1adL37bqVDISIiIoUwoXMNb29vODk5oaqqCsOGDVM6HCIiIqI6fEaNgs+oUUqHQURERAqShBBC6SDamvLycsiyDK1Wq3QoRERERHUUnj+P9M2bETxvHhyDg5UOh4iIiBTAhM41ysrKoNFoOLMVERERtVm7ZsxA0fnz8J0wAcO++UbpcIiIiEgBzFpc5eTJk1i1ahWCgoLwyCOPQJIkpUMiIiIiqqMsLQ0AUJqaqnAkREREpBTOcnWVPXv2AKgeGFmn0ykcDRERERERERFR/ZjQ+UthYSHS/nraFRAQAI1Go3BERERERERERET1Y0LnL5cuXTL+3L17dwUjISIiIiIiIiJqHMfQ+YuXlxc0Gg1kWcagQYOUDoeIiIiIiIiIqEFM6PylU6dOeOaZZ6BWq+Hk5KR0OEREREREREREDWJC5y96vR4uLi6QZfZCIyIiIiIiIqK2jdkLACdOnMBLL72EH3/8UelQiIiIiIiIiIiaxIQOgO3bt0MIgfPnz0MIoXQ4RERERERERESN6vAJnZycHFy5cgUAEBQUBEmSFI6IiIiIiIiIiKhxHT6hk5iYaPyZ05UTERGRLXCJiAAAuEZGKhwJERERKaXDD4rs4eEBSZKg1WrRt29fpcMhIiIiatKQpUtx5cAB+I0fr3QoREREpBBJcNAYZGZmws7ODu7u7kqHQkRERERERETUpA7XQqeyshLnz5/H9u3b4e7uju7du6NHjx5wc3NTOjQiIiIik6T+9hsu/vADuj36KFvpEBERdVAdroXO5s2bsXfvXuh0OuMyWZZx3333ITw8HAaDAZIkcXBkIiIiarO2jhyJ8sxMeAwYgNH//a/S4RAREZECOlQLnYSEBOzevRsGg6HWcoPBgEOHDkGn02H9+vXQarV48MEHYWdnp1CkRERERA3TlZQAAKqKixWOhIiIiJTSoRI6ly5dqpPMqREbG4vY2Fjj7xkZGejSpYu1QiMiIiIiIvp/9u47Lqorffz4594pgFQFVLDGLmJBxd57jbG3qEk0vff95re7STbJpuymrCmbronGkqLGmtiiUewFxYJdsYAFBZE25d7fHyOzjqLCMDCAz/v1yivMnXvPeWa4Dvc+c85zhBCiwO6oZcvDwsIKtF/dunWpUaNGMUcjhBBCCCGEEEII4Z47KqFTtWrV2+5TvXp1Jk6ciKreUW+NEEIIIYQQQgghypByN+VKu3QC7cwOtJTdaGd2ol86gW7PBRQCjL5MDjFxLDuQZHsop+zhZOj+BAYGEhUVhaIo9O7dW2rniJvSMy9gP70NLXk3WvIu9AsH0G05oGtgMKMGVUONjEGNaIYaEYMaVt/bIYsyRLdkop3e7vj8St6NlrIb3ZIJmg0MZpQKoajVYlCrNkONaI5apSmKavB22KKM0O1WtOR457mlJcejZ18CuxUMRhSfINSqzR2fX1WboUbGoJj8vB22ADSrlb1vvcXl/fud22xZWQDknD2LJT0dRVXZ9847+FWtSv0nnpDFHYQQQog7QLlY5Uq3W7AfWIZt2zdop7Y6NqpGx03QDRQ0VFTs6Doc1aoT0e8lQlsNkxsjkS9d19FObMC6fRrawWWO5I1qBE0D8qnJdM25p1Rpiqn1AxiihsiNkbgp7dx+bDu+w7Z7LthyQLk6QlC/2fllB3SUoEiMrR7A2HwMSoXQEo1ZlB1aRjK2nT9g2zEdslIBBVT16nl0HcUA6I5zzxyAMeZejDETUSvdVcJRi2udXb2aLQ8+eNPnqw8bRvapU6Ru2QJAt2XLCGzQoKTCE0IIIYSXlOmEjq7r2ONnYlnzNmRfctwE5XcDdAuarqAqOkpgBKZe/8DYeFAxRSvKIvuxtVh++z/0S8dANeR/A3QreeekOQBTx2cwtn0YRS13A+OEm7QLB7Esexnt5CbHjbReyPMLHOeYYsDQYhzm7v8PxSfQ84GKMknPvIBl5avY9y24uqFwfx8B53mp1u2Jud/bqMFSX84bsk6dYnWPHuj2239GGAMC6LV+PaZA+SwQQgghyrsym9DR0k9iWfws2ok4D7WoADqGRoMw93tHvu2+w+m5GVhW/QN7/Ey3EoU3o1Rtjs/dH8tUrDucrtmwbf4c69r3HOeWO4mc6ykq+FfGZ/B/MNzVpejtiTLNtn8xlmUvQm6Gh84vAxjNmHu9jqHFvTKdxwviX36Zkz//fNv9Gjz5JA2feab4AxJCCCGE15XJhI7twFIsC58Am8UzF6rXUgzgE4jPiGkYarbzbNuiTNDO7iVn7r2Qec5jiRwnxQCKirnfOxhbjPNs26JM0DMvkPvjRLTknZ5v/Gry0RD7EOZer6IoUtz9TqPbLViWPI99z8/kfVHhaepdXfEZ9jWKT4DH2xY3l3niBH/07n3LUTrGgAB6/vkn5uDgEoxMCCGEEN5S5q72bQk/YvllClhzPJ/MAUebuZfJnT0K+5HVnm9flGr209vJmXEPZJ73fDIHHOeXZsWy9Hmsmz/3fPuiVNMyksn5bjBayu7i6eDqOWvf+iWWhU+iF3aKoCjTdFsOuT9Nwr7nl7wtxdKPdnw9OTOHoWenFUv7In/+tWpRfejQW+5T5/77JZkjhBBC3EHK1Agd276FWBY8QnFdpLpSwGDEZ+xcDDXbl0B/wtu0s3vJ+X4I2LKLJ5mTD1PfdzC1mlQifQnv0rNSyfnubvS0E8WTjL6BgqHZaMwDP5DpMXcA3W4l95fJaEdWlcznl2JArdoMn/E/oZj9i78/AUDm8WNsGtmHgCALRjMoBh3NrmDLheycQDosXY9PSIi3wxRllK7r6BnJaGf3QFYqus2CYjCCOQA1vBFKaD1ZQEQUiZ59ybGCZ0YKui3HMZLY5IdSqR5q5UYoRl9vhyjKMN2ahXZ2L/qlY+jWHEBHMfqhBFdHrdq03NaZLDMJHe38AXK+6eVc3aVEKCqY/PF7ZD1KQOWS6VN4hZ6bQfYXXa6OzCnJUQ0KPhPmY6jRtgT7FCVN13VyZ49GO7GhhM8vMPV+A1PslBLtU5Q8y5p/YtvwCSX29xFAUTFEDcVnyCcl1+cdSM+8gG33HOyHVjhutK1Z/3tOB5d8rU8gakRzDA0GYGw6otxevArP0a1Z2PctxLZ/IdqZnZCTds2z103bNPigVmmCWqc7xpjxqIERJRytKGt0zY52ZDW2PT+jndqKnpF8850VA0poPQy1O2FsMR61cuOSC1SUSbquoyXHY4+fhT1pA/rFY9zqOkgJrolaow3GZqNRa3UsN194lomEjq7ZyJnWH/3c/hK/GUIxoNbric+I6eXmly5ulLv0Bey7ZpfYyBwnRUUJqobvQ2tQTBVKtm9RYmw7ZzoK1HqDwYzvg2tk2elyzH4mntzpAyjRZM41zCO/w1i/j1f6Ls/syfHYtnyFff/Cq8XTC/r36eq1itEHY9NRGGMno4bJEubClZZ2Etu2b7DF/wCWK4VbAOJqfTa1fl9MsVMw1OpQjJGKskjPSce243us27+FjJTCreSZt7pitdYYY6dgaDxYagIKF7rdin3Pz1i3foN+bm/hViK+en4pFWtjbD0ZY/NxKOayfQ9WJhI61g0fY13zT6/GYL77E4zRw70agyge9qNryJ0z1nsBKCrG1pMx9/6H92IQxUZLP0XOl13Amu2dABQDarWW+ExYIBdE5ZBuyyHn657ol0pqKt/1VPALcYxk9avohf7LH92ShXXNP7Ft+6ZwF6n5UQ2gg6nTcxg7PIliMHkuUFEm6bqGbes3WP9403FuFeVz4+r5aYgegbn3Gyh+IR6LU5Rd9kMryF3yLGRfKtoXpVeTjGr1WMyD/iNfTAnAUSIjd+ET6OcTi7gSsePLDyWoGua7Py7TiyGV+oSOnnmB7I9bgmb1YhSK44L1yZ0oRh8vxiE8Tdd1cj7veLWuSQmPznGh4PvIetRKdbwYgygOub8+jn3fr1662f4f8z2fY4wa4tUYhOdZt3yFdeWreGt0DgCKAWObBzH3fNV7MZQT9qRNjoLmGWc8/DdJQQlviM+QT1ErR3mwXVGWaJeOY1n4JNrpbZ5tWDGAXwg+gz7CUK+XZ9sWZYaek45l+V8dqywW6Ub7OqoBFAOmHn/D2HqyzJi4Q+maDVvcf7Cu//DqBg9dVysG0DWMrR/A1P2VMjljotQndKwbP8G65u0ifygYmo9DDamBPWkT2rG1brVhHvIZxia3XmFClC324+vJnTWy0Mepd3W9aSbXuv4jsOcWrkHFgDF2CuZerxU6FlF6ORLSMaDZ3G5Drd4GNTIGAHvSRnR3VshSVNRqrfCduNDtOETpo+saOZ+1R08/SWESOmqd7hhqtLnp8/ZDyx21NArDHIDf07tRTH6FO0442fbOw7LwKUAvni8YFAMYTPiM/A7DXV08374o1exn4smdPRqsmUUb9XUzV2/gTb3fxBQ72fPti1JNy0ghd9ZIRw2TYvwCy9BsDOYB/5bi3HcY3ZZD7ryH0A6vpNi+wFJU1IgW+IyZheJbtlaLLNXj73XNjm3bt0W+sFHrdMc84F+YOj6DoVZH9xpRVMfwZ1GuWLd968j8F5KhVkdMHZ9Bt1vQbbku/7n1QaPbscX/gH5NsUtR9tmKUpepQig+987HPORTlODqKH4hmPu8ianv24VvS9fQTm1FO7ffvVhEqaQdX4+enkShP3M06w2fW7otF0PDgZg6PgO+IYUPxnLFUetFuMW252csvz7huBEqrtGiuh3sFnLnjsd+7M/i6UOUSvYz8eT+MAwsxZTMAed5a13xV6xbviiePkSppF85R+73dxd7MgfAvnsulkVPont1VL0oSbrdQu5P9zlW8SzO0ci6hpa8i5yZw9FzM4qvn2Jg9HYAt6Id//PW1dALwjcE88D3se/5BWPTwo/EcNI1tNPb0S4clOKCZYiu68yZM4fk5GSGDBlC3bp1//fclfNoh34v0sWzLW6q5/54Wa5gT1yMsekoz7QnSsTatWuJi4ujV69exMbGOocC67qObft0984v1YjP6B/AmkXOF13A9r/6O2rV5u4Fqhqwxf+Auc+b7h0vvOLAgQP8/PPPxMTE0KdPH4zG//3Ztu2cUbhCk1dpx9ejHV/vutHoh6nto2jpp9COril8oIqKbft0jM1GF/7YO5z9yGosi56mRKbNXf08yv1xIr6TFqJWbVb8fQqv0i4dd4zMsVlKbOqvdeVrKBXCpPbkHUC3ZpEzaxT65TMldH7p2PfOx1IhDB+pPVnu6bqOZeFTaMf+pGT+RtrRzyeS++MEfMb/jKKW6lSJU6keoWNP2gxFfCPN/d6G7DSsGz72QEQK2snNHmhHlJQjR47w/vvvM2vWLMaMGcP//d//ceTIEQDsp7d5uW7OdVQjdjm/yhSr1crf/vY35s2bx2OPPcaDDz7Ili1b0HUdPSPZUQfDDYboERgimmP5/f9ckjkAWsou94LV7Nivv4kXpd57773H/Pnz+fvf/86oUaNYsmQJNptjCp/9xAaPXUAbGg9C8Q3CtmsW7o0y1NBSEtBtOR6J506hZ18id+GTjvXHS6xTDTQbuQseuzqqVJRXuq5hWfikY2ROCddxsyx7ES39VIn2KUqedc3b6KmHSvz8sm/9SkYa3gHse3/Bvv9XSrROoG5HO7kZ2+bPS67PIirVCR0tOb5IQ0MNUUMxNBqMZclznimqrBrQkt2oXyG85uLFi86fdV1nxYoVzsRO6v61RU4YGlvfj7HTcxhb3YcSWr9owWo2tNM7itaGKFG5ublkZf1vmlx8fLwzsXPgz1/cbtfYaBBa2kn09FMYmgzH2OlZjC0noQRXL1K8euphueEuYy5duuT8OSkpiVdffZVRo0axYsEPkH3xFkcWjrH5WHTNhn3XHPcb0e0yra+QLMv/CjnplHhRa92OfvEo1vXvl2y/okTZtn7jKIDsjaL8dguWxc9Sykt1iiKwJ23CtvUb73w5qqjkLnq6zE2NEQWnXzmL5bf/I281qpJmXfsu2vkDXum7sEptQkfXdUdCx82LHCWgKua+/8S29aur7XiAZsN+Rm64y7q8xM6+1XPQ7O4Xq9XSklDDG6GYKmBoPAS/h//E3P9fFOWDR089JN+YlgPx8fH8Mfdj7Lp754IS3gjQ8X14PcZmoxznWJNh+D6yAWPMRPcD0+1o5xLdP16UCklJSSz6+p8ea0+pWBtDzfZoR1YXcZqzIl96FIL98Crse+d5cQU8HduGT9BSErzUvyhOWtpJx9LkXgvAjnZiPfbdc70Xgyg2ui0Xy6KnHMWwvRKABpnnsPzxlnf6F8Uu97e/gDUbr63iqevkLnqqTCSlS+/EsJz0q99aucc86CP0nDSsa9/zYFBgv3CY+Ph4j7Ypik/e9Kr81A6yorqZe7Ht+Rnr2nddLsSNbR/B3PNVtLQT2DZ+4l7Dmp19m5ZjDajh3vGiRF07Oud6NQNtjmkUbpxjik8Aim8w1p0zsC57ybndPPRLTH3/if3kJvQLB90JmeM7V5Fxzq1DhRfk5uaf4K0ZaMOugcED19LGZmMBsO2cWbSGVKNj6L0oEGvcR55Z2le5WtjfncSQomLd/Dk+Qz4tWgyi1LFt+9q9Ue7KTRaK0DXcubGyxv0HQ7PRstR0OWM/sPTqCotepGvY439A7/wCin+Yd2MRHqWlHkY7+FvhD7zZ5xdQ6BUkdTt6ym60Exsw1HZzUaUSUmoTOvp1dSMKw9jqPgx1upIza+QN9SeKzG5hypTJeGv4l/AcnyKseJjfzbRt8+cYW0/G2HSU+wkd4M1X/x+H0s3uBydKBR+D5vbNtm7NRvENxrblS5ftti1fYGw8GEOjgdjWFz6ho+swc9rXLDo+273ARKnhY9DRgCIv3KqoGJqNRLt8GvuRVUVsTJcpfQWkndvvmApTRMbYKZh7vwFA1ru1wV7IEZ66Hfv+hei9XpcbonJEt2Zhi/+h8Ek+gw8VXj7uWEHouhsf29avsa56vfCxpB1HOxGHoXanQh8rSi/btm/cT0j7BGLq8BSGRoNRgiLQ005ii//B0abdUri2dA3b7jmY2j9R+DhEqWXb8X3hF30w+eH3fP7XxopqRMtIIefjmMIFohiwbp8mCR23FeEbK0OD/miXz2Co1fF/y5RfXU9erdEWU9eXsSX8jH7x5qM3bsbdER2i9FEVzw+h09NOFHnVEDnHyoei/B71tCQIrOpYNeLa7VcfK/6VixBX6R86Km7PU58Tat0eqIERWNe975k6CKWp0HwpZtvxnVsrlF1LCa2PqdsraBnJqIER7geja9h2zcbU4Un32xBekZqaSnBwsMvqdwD2fb86CiG7ybZhqmMUsieoBqzbvpWEThmUlpaGn58fPj4+Ltu1c/vQTm93r1G/ivjeOw896yK58x5EP7cXxT8cY+vJqDXaoh1fV7j2dA3btm8xtn0URS3yVxyiBF25cgWAgIAAl+26JQtb/KzC/320ZpP9zo0zHNS6PfEdPRP7od8LH6RuRzu4DC0jBTWwauGPLyGlNqGjGHxuv9NN2HbPRQmp6dqe7WrGV9ccNUrcvIiyY2DChCLUsBAlKjk5mZUrV+b7nMWu4Ol5mUrF2kWsQQF9B95NGzXcQxGJ4mSxWJg7N//6ABa7gqa7d+OtJW3AUKMNSlA1lyksSlA1APSMFLfiVRRo1bYjQZ1kqeKyYsGCBWRk3Fj00WJXPDJO1FEM2Y5tl2dGbSlGX4+0U57puo5t369Fq52jGDDf/TH2wyvBbkEtyvLQuoZt3wJJ6JQxM2fO5KOPPqJKlSrcf//93H333ZjNjtG9tv2LPDOdzxM0O9rh5ei2XBSj+9f2omRt3ryZp59+Gh8fH8aMGcO4ceMIDnZ8OW5LXOJ2Qtrc63UwB5A7fcDV+iiO4rfWNe7XhdMzktFSEjBEtnC7DVGyUlJSGD9+PFeuXGHgwIE88MADVK/uWPhDOxEHVvcT0tczthgHFGFaua5jP/g7aqtJHovJ00ptQge/EDCYCz/0DhxFBq+jVKyNqe3DaKe2Yov7yO2wjEFVefqJp90+XpSsLVu23DShcy7bQJifGxc7qgm1emu0pI0um43tn0ANqoZl1T/cCdVpwiMvoPiFFKkNUTKuXLly04TO+WwDdjcTOrYdMzDGPoSp7cNYlr5wdauCsd2j6LZc7PsWuB1z3+GTGHBXF7ePFyVr7dq1+SZ0zmcbMBa1fk6FUAz1eqEd/QP98ukiNgboGkop/gartNAzzkBOWpHaMHV6FjWkJtk/TsDc89Wix3ThgNxwlzFr164F4OzZs7zzzjtMmzaN+++/n8GDB6Od2Vk6kjl5NDva+f0YIlp4OxJRQHFxcdhsNmw2G9988w1z5sxxJnZ83D2/KoRiiLrHUT/M6smSGApayi5J6JQh8fHxpKc7auUuXLiQJUuWOBM7lZN3gWoo0krXTlevc+xn4tHP7nGvDdWAllK6F3wotQkdRTWiVG6MnrzL26H8j2JArdbS21EIDwgJCcFUoxZkbwGtsCtd6Zg6Po3S63W0s3vQLZmokTGoES2wbv0a2+bP3Y5LCYqUZE45YDabqdiwMya7GwXdcNzwWX59FPPdn+ATWg8teTdqtVao4Q2xLHwCPe2E27GpVZu6fawoPSo26gwsLFIbxugRKAazo9aGJ+hakaec3gm0Il7XqBEtMHZ4ypHszTzvoaDs6OcTUSKae6Y9UeyuX3klL7Hz6w9f8EVsWpHaNrZ6AGO7x0DX0VMPO+qbbJ+O+6OaHSvgSUKn7Lj+/MrMzLya2JnN/H6n8HXjXDBUb4NiMIElE/OI6Y5peAYT2rn92DZMxX5gqXvBloEbbnFrdrvdmdj5ZjDcpWqeGYXsiesczeaRmnfFqdQmdAAMka2wnd3rxg33jfTsS1jWvot2aktRWkGVP0ZlSt7w4zwhISFMmDCBkSNHYj68GMviDYVvVLORO3sMSmh91MgYFP8wbDt2o514uGjfcisqamQr948XJc5gMKAoivPCx2w2M2zYMCZNmkQoF8n5yr2EDoD90HKyP2uHoW5PlIBwbNunYz+6GrJS3W5TCYxA8avo9vGi5JlMJpfHPXv25MEHH6RunbvI/vdyKEIRYj3zPJY1b2M/tKKoYTpJQuf2tJQ9oBrdu7Yx+mK++2O0ExuwJ/zowagUtJQEVEnolHmVbGduv9NN6dj2zse29Su0c/tR/CphbDkBU583Uas2w7LkWfeaVQ3oKQlFiEuUFr72DHx190bX5I3gNPX4K7at35C95DlQVcxd/w+f4d+Qu+BR90Ygaza00zvdikmULna7nYqWZBRfz5TEMDYfg557Jd/ZO4Whpx4p1aNYS3VCR41sAdu/9UxjOelFmmoFOL59lIROmdKkSRNiY2NJTk5m6NChjBw5kgoVKgCgFXFopp56CLuHl+hVZbhomeLn58fo0aNZuXIlvXr1YtKkSYSHO+of6VolMPkVbVhx9kXse37yTLCKAbV6a8+0JUrM2LFj+fzzz2nRogVTpkyhXr16zufUqs3QTm0D3JtaUdQLnOspQdVQKlTyaJvlUhGSsqYef0MJjCB3zlgPBgR2HebPnsbqL+M82q4oPocPH853e4i5CFOt7BYsvz7mfKhnnHEUR/arhKnlRKzbvkU/W/jEjG63sWPDKr5b9Kj7sYkSlZSUlO/24KKcX1eXrtfPJ2Jd9Zpzs2XZS6i1OmLq+rLbU8rTU47y3KNyfpUVqak3/zsYWJRz7BpqZEvU8EaO2jnWrKI1ptshNwMkoVN4hgb9wOhbpG8gPSqgCmqNtt6OQhSCyWTiv//9b77PKaH1USpHoZ9PLDVzzQ1R93g7BFFIL7zwAi+88MIN2xXVgDF6hHuV+ouDbscYPdLbUYhCuueee7jnnnvyfc7YbBSWIo069SBFxdBsjLejKBN0uwX0wn/7qIQ1wNjqPqyr/oF+OdlRlNTxjON/qgqae4VwNV3nXPJJtu5PL/SxonQxGXS3C/LfjP3g75haTsRQvTU2NxI6igKZly+ydetWzwUlvMLH4P7ICT3rIgD2k9f93dLtaKe3Y4we5liVOMeNzyG7Rc6vckBBL3p9wKuMzR1ffLhdDPk6ui3HI9PAioOH3rLiofgEYmw6ylEYydsUFVPrB2RJvHJEURRMrSeXjmSOYkCt3xc1KNLbkQgPMracVDqSOQCBVVHr9vB2FMKDDFFDweTv7TCcjDHjvR1CmaCoRuc31YU6zjcYdA1Tj7/i9/Jx53+GJvcA4Pf8QXwmuldXSQFsWmm9VBWFYdMUjyZzgGvOV/du5nVdzq/ywlaES2bt3D7HD/l9/jlPMfc6kPOrfNBRsHtitpXRD0PUELSUBLQUz9TjVQzm2+/kJaV6hA6AsdUkbDu/93YYgIKx+ThvByE8zNDkHlj5KliueDcQ3Y6p1f3ejUF4nFqlCWpkS7TkeO8mDhUVU+vJkpAuZxRzBYwtxmLbNs27iUPFgNqgL2pghPdiKEt8Atw6TDu1lex3atyw3Xz3Jxijh5P9r3pgz3WrbaNB5aHHn+HhlvJ3qKx47LHH2L37xkKwWTbP39gaG/QHwH7SvREQimqgc4++rP/3Rx6MShSnqVOn8uOPN9bpyrK5PxZAv3AQ7dx+DDXaYr32CdWIWi0W7cJBx7QWN1SqUo316/90OzZRspYvX84//pH/qsC5NoUKpqJldQyNB6H4BGL10OgcAEwVPNeWh5X6hI5aOQq1QX+0Q8u9d8GqqBhbP4DiH+ad/kWxUUwVMHV8Gusfb3kxCMfqaWrtTt6LQRQbU9eXyZ092nsBKCr4hmBsca/3YhDFxtj2EcfqDR5dArawdMyd3CyWegdSwxt5ZLEHj9I1zJFNMfj6ejsSUUCqmv+N9bHLpny3F4Sxw9MoBhO2g7+hXzqO4h+GscV4DC3GYts5E/3cXjdb1jFFRGOS86vMMBjy/wIoOdOARVMxq+59SWVZ/v/wGTsHU583sW34BFQDpi4voQRFkvvTRPeCVVTUiOb4yvlVZly/aM21Tmb70cCUVaTpTcbm49Atmdg8VCtQCayK4uaXMSWhVE+5yuPT710w+4M3Zq4pBpSg6pi6/aXk+xYlwtj2EZQqTa+pR1DCVCPmwVNR3BiCL0o/w11dMLS415FY8QZdwzzoQxS/EO/0L4qVGlQNc6/8v+UqGQrGjs+gVon2Ygxli8dXAtPs6JoN95eUdlCrNvVMPMJrOnTowKsffQ9uTg2wbf8W3XIFc//38HtiO773LUOtHotlyfNYlr3ofmC6JivglQMNGjTgvX+/j08191fD05I2kjtrFGpYA3wfWoPvlNUoQRHkzhqJdmS1m60qGOT8KvMqV67Myy+/TKNuYxxTk92khNRCrdYK+94FnpmBoagokS2L3k4xKvUjdACUgHDM/d/DsuCRku9c1zDfPRWlFA+zEkWjqEZ87v6YnG96FfV62C2mHn9DrVi75DsWJcbc8+9kH14JmedKduqVYsAQdQ/G+n1Krk9R4gwtxqPu+xUtaWPJjmRVDCih9TB1fLrk+iwHlLD6jhtuu8Uj7VkWPw2Li/Y7UIKqOWr0iDKjRo0axMfHA45EzkMPPUR0tCOxmrM9yjHVt7ByM7Bt/hzb5s89F+hVkjAsW2rWrOn8uUGDBjz00EN07doVRVGwWFY6imO7OdJQO7mZ3FmjPBUq6HbUCEnolCXVq1d3/ly5cmXuv/9+hgwZgtlsxpYAlh3ur3Ktp50g+92at9+xwBQMEe4nMUtCmUjoABga343h+Hrs8R6cC1cApq4vYZCVrco9Nbwh5r5vF+0bqMJSVNT6vTG2lpoF5Z3iE4jPsC/J/WEE2K2USOZQNaCE1Mbc583i70t4laIomO+eSs60AZB5vmSSOooBTH74DP2iVBcKLI0U1YhaqyPasT9LR9F0xYBBCqaXOS+88ALNmzenQYMGREVFuTyn1u2BlrK7dCz6gIpSuRGKX0VvByIKYejQoQQGBhIcHEy7du1cRpEb7uqKbds3XozuOkZf1MhW3o5CFEJ0dDRTp04lMzOTrl27ukzBUmt1xDErxwvfsudHt6PW7uLtKG5J0XU31s70El2zY/n1Mez7F1ESv2Rj20cw9fi7TIW5g1g3fYZ19RvF35GiotbsgM/oGShGmfN7p7AfWUXuT/c5LrKL80JbMaAEReIzcSFqYNXi60eUKlrqEXK+v9ux5GtxJgoUAxjN+Iz7GUO10j0MubSyH1rhfr2IYuA7eSVqlSbeDkN4iHb5DDmfxpaShA6YB37gXEJYlH26Zif7k1Zw5ay3QwHVgLH5eMz93/V2JMKDcn66D+3wSu9/6aGoKJUb4zd5pXfjuI0yUUMnj6IaMA/5DIPzj0IxJFqu1rkwdnpOkjl3IFO7xzD1eQtQirXmiVqvtyRz7kCGuj3xGT3LMd2iuFacUlSUSnXwnbRYkjl3GDW0Lr73LUEJqFx8NcEUA/gE4HvvfEnmFIFatweUhn+fiooa2VKSOeWMGhSJWr+P92oDXsvsjyFqiLejEB6kqAZMrR/wXm3Aa2l2jK3u83YUwsNMre/3fjIHQNcwtZ7i7ShuqxT8SywcRTVgHvBvzIP+41g+zJN/rBQD+FXEZ+T3mLu8KMmcO5Sp9QP43PsLSmCEZ/9YKQYwmDH1fgOfEd9KMucOZbirM75TVqNGxHi24aufhcZW9+N7/2+Om3pxx1Er1sZ3ymoMTYY6NnjsM8zx91C9qyu+D65BLeXzyUs7RTVgin0Iryz2cC1dw9jmIe/GIIqFKfZB798QKSrGmIlSh7IcMjYfB0UoXOsRigG1RlvUyo29G4fwOLV2Z5SKd3k5aaiAbwiGqLu9GEPBlKkpV9fTMpKxLHkB7ehqx7fdmpt/uK4ea4gegbn3G7IajABAt2RhXfNPxzxhxeD+hdHVY9XqsZgH/Qe10l2eDVSUSbquYdv2rWOKn2Zzf2i8ooKuoQRVx3z3VAw123s2UFFm2Q4tx7LkOchKxfH9jTvn2NWEg9kfc9+3MESPlC87PES3W8j5pjd66hHv3HgrBtRaHfAZO1d+p+VU7q+PY9/3q5fOLxX8K+P38J8oPoEl378odtYtX2Fd+XfvBaAaHdNFwxt6LwZRbOxJm8idOQxv1tIx3/MFRknoFD9d19FObcG2ffo1tXWU2//xUg2gaWAwY2w6EmOrSbLsqsiXlnoY247vscXPAmum4xuJ21X2z8so6zpq/T6YWt/vyDaXhuGpolTRr5zHtmsW1u3THPPRC3J+oYCqgmZHjWyJMXYyhoYDUYw+JRKzKDt0Sxb2ffOxbv0a/Xzi1fPLzm0vkK6eh0pwTYyxkzE2HSVfdhQDLSWBnGn9vFPrxOSH70N/ogZXv/2+okzSsy+R/XknyE7DvYRu0fiMnYvhrtJdTFS4T9c1cr8fgnZmp1eShqbu/w9T+ydKvF9Rciwr/oZt27cl/zdSMaA26Ivv8FJU/PsWynxC51r6lfPY9i1AO7MT7fQ29PST+eyloFS6C7VaK9RqrTFG3YPiG1TisYqyR7dkYU9cjP3kZrTT29FTD+f7B0wJjECp1hJDRAyGJvegBlXzQrSirNE1O/Yjq9CO/Yl2Zgfa2X1gz71xR98Q1MgWqBEtMDYaJLUvRIHouo52ejv2Q7+jJcejnYkHy5UbdzT5oVZpihoZg6FuD9TanSQRXcys697Huu7fJd6vFKq9M9gOLcfy06QS7lXBEDMRn/7vlHC/oqRpF4+R83UPsFtK7qZbMaBUjcZ30mIUb0/7EsVKt2aR81VP9Msn3Z+JU1iKAXyD8HvoTxT/sJLps4jKVULnenrOZfTLZ9BtOY4at0Y/lOAaKGaZyyuKTrfloF86AbYcdF1DMfqiBFRBqVDJ26GJckDX7OhpJ8CSiW63OkbfVKiEElBVpkeIItN1Hf3yKchJR7dZUAwm8AlCCakhCZwSpusalkVPY9/zCyU1tNzY4WnM3f5SIn0J7yvRqTGKinpXN3xGTnd8rohyz358Pblzxl0dXVzMn2GKASW4Br6TFpWZm21RNFpaEjnTB0L2peIfCaaoYPTFd8IC1KpNi7cvDyrXCR0hhBBCiNJO12xYFj2Dfe8vxd6Xsf0TmLq9IonhO4x18+dYV71ezL0oqHW64zP8axSTXzH3JUoT+5HV5P58f9FqAt6OYkAJqYXPvT+jBkYUTx+iVNJSD5Mzc7ijJmBxJXUUA5j8HFNFy9gqnpLQEUIIIYTwMl3XsK59D9uGqVeLnXvwolUxgKJi6vl3TLGlfwlWUTxsCT9hWfYS2K0ePr8cxfkNMZMw93lDRubcoeyntpI77yHIPFcsSR21Vid8hn6OUiHU422L0k9LP0nuL1PQU3Z7vnFFQQm5C5/h36BWbuT59ouZJHSEEEIIIUoJ++kdWBY+gZ52HDx0iaZUbY7P3VNRwxp4pD1RdmnpJ7EsfhbtRJxnGry6mpXP4P9IAWSBnpuBZdU/sMfPdCb6ikQxgMGEufc/MLS4V0YW3uF0zYZt8xdY177rOLeKmphWDKBrGNs/jqnz8yhGX88EWsIkoSOEEEIIUYrothxsGz/FuvVryEkr/I2RYnBc6AZUwdTuMYytH5DiocJJ13Xsu2Zj3TDVUast73wpMBXQwOSPseUETJ2ek6XJhQv7sT+xrn0X7cwON84vHJ95ioqh8d2Yuv0FNbhG8QQqyiTt/AEsa95GO7QcFKXwicOr56RaqyOmbq+UuSlW15OEjhBCCCFEKaTbcrEfWIpt2zdop7c7NioqoLjeILksR6+g1u6MMXYyhro9UVSDFyIXZYGu62jH12PdPg3t8PKr55ACquFqgdurlKvn0NVzTqkchan1ZAxN7kExyUIj4ua0s3ux7fgO256fwZrt2OjyeYUzeZN3zimBkRhb3Yex+VgpfCxuSbt8GtvOH7Dt/N5RXwcc55euXZPkue4zzScQY/OxGGMmoobW9UrcniYJHSGEEEKIUk7PzUBLSUBL2Y12bh/kXnasUGb0Ad8Q1CrRqBHNUatEyU22KDTdloN2LhEtZRdaSgJkXUS35aAYzWDyR63cGLVqM9SqTVH8Kno7XFHG6JodPfWw4/xK3o2ekYJuy3YUOjb5oYTWc5xfEc1kNU9RaLquo6efuvr5tRv94nHH+aXrjvMruPrV86s5SsXa5W41T0noCCGEEEIIIYQQQpQx5Ss9JYQQQgghhBBCCHEHkISOEEIIIYQQQgghRBkjCR0hhBBCCCGEEEKIMkYSOkIIIYQQQgghhBBljCR0hBBCCCGEEEIIIcoYSegIIYQQQgghhBBClDGS0BFCCCGEEEIIIYQoYyShI4QQQgghhBBCCFHGSEJHCCGEEEIIIYQQooyRhI4QQgghhBBCCCFEGSMJHSGEEEIIIYQQQogyRhI6QgghhBBCCCGEEGWMJHSEEEIIIYQQQgghyhhJ6AghhBBCCCGEEEKUMZLQEUIIIYQQQgghhChjJKEjimz37t289tprnDt3rsz1v379el577TUsFsstt4my47PPPmPOnDneDqPM++CDD5g3b16B9i3u97wwsQghhBBCCHGnkISOKLLdu3fz+uuvezWh427/69ev5/XXX78hoXP9NuE9U6dO5ccffyzw/pLQ8QxJ6AghhBBCCFG6Gb0dgBClTadOnXj11Vcxm83eDkXgSOi0aNGCUaNGFWj/xx57jEqVKhVzVEIIIYQQQgjhXZLQEeI6nTp1olOnTt4OQ7jpscce83YIQgghhBBCCFHsJKEjCkXTNJYsWcLevXupVq0aw4cPz3e/+Ph4FixYAICiKAQEBNC0aVN69uyJwWBw7rdy5UpnzZqkpCR+/fVXLBYLffr0oWnTpm73n+fgwYOsWrWKS5cuUatWLQYPHkxQUNAtj1m/fj0rV67klVdeuWGUzoYNG9i8eTMWi4WoqCj69euHyWRy6/VkZWXx3nvvERUVVeDRJ57svyjv0bX9nDhxgsWLF5OTk8Pzzz/vfG1LlizhyJEj+Pv707VrV5o1a3ZDO7fb7x//+AcXL15k3759vPbaawBUrlz5lkmbzz77jEqVKjFmzBjnttdee41OnTrRq1cvl32///57cnJyeOihh5zbPvjgA2rXrs3QoUNZsWIF8fHx1KtXj3vuuQdVdcxSXbFiBTt27CAyMpJRo0bh4+Pj0u61bSxfvpz4+HiqVKnC0KFDCQ4OvuV7687xmqaxatUqdu3ahaZpxMbG0r1793zbHDZsGH/88Qdbt26lRYsW9OnT55ax6Lru1muAgp8HmqaxZs0a4uPjMRgMtGvXjrZt296y7bS0ND799FNCQkJ49NFHnb8bIYQQQggh7hRyBSwK7MqVK3Tr1o3x48dz8OBB1q1bR+/evTl//vwtj7Pb7SQmJjJu3Djatm1LVlaW87mVK1fy+uuvs3z5csaOHcuxY8dYunQpLVq0YPbs2W73r2kajz/+OE2aNGHFihVcvHiRqVOnUq9ePbZs2XLLePOroZOdnc2gQYPo2bMnO3fuJCkpiUceeYRmzZpx9OhRt15PVlYWr7/+eoHqwxRH/0V5j/L6WbRoEaNHj+bgwYPMnz8fgG3btlGvXj3+7//+jzNnzrBu3TpatmzJlClT0DTN2UZB9yus/Oq5vP7666xcufKGfb///nu+/PJLl2159VomT57MtGnTOHnyJJMnT2b48OFomsb48eP55ptvOH36NM888ww9evS4Id68NiZOnMjXX3/NmTNnePPNN2nUqBF79uy57WsozPHHjx8nJiaGCRMmcPDgQY4fP86YMWPo06ePy7+1vDbvu+8+PvnkE44cOcLatWtvG4u7r6Ggv9+jR48SExPDqFGj2LVrF8ePH+fZZ59lwoQJN2376NGjdOjQgZkzZ9KvXz9J5gghhBBCiDuTLkQBPfXUU7rJZNJ37drl3Hbo0CG9Vq1aOqAnJCTc8vjk5GS9YsWK+quvvurc9vLLL+uAPmXKFN1qteq6ruuapul9+vTRa9SoodtsNrf6f/vtt3VFUfSlS5c6t9ntdn3YsGF69erV9ZycHOd+gJ6RkeFy7PXbnn32WV1RFH39+vUur6datWp6bGysW68nMzNTf/XVV/W5c+fe8n0rrv4L+h7lJ6+fCRMmOPu5cOGCnpOTo9eoUUNv3ry5fvnyZef+s2fP1gH9o48+0nVdL/B+uq7rdevW1YcPH37b9yhPkyZN9CFDhrhsA/SXX375hn179uypt2rVymVbtWrV9LCwMP2nn35yblu2bJkO6KNGjdJnz57t3L58+XId0H/55Zcb2ggNDdW///5757a0tDS9cePGepMmTXRN0275Ggp6vN1u15s1a6bXr19fT01Nde578uRJPSQkRH/22WdvaHP69OnObRcuXChyDLp+43te0N+vzWbTmzRpotetW1dPTk526X/Tpk0usYwfP17XdV1fv369HhYWpvfo0UO/dOnSTeMXQgghhBCivJOEjigQu92uBwcH6yNHjrzhuUceeSTfhE56err+008/6e+8847+2muv6a+++qpeq1YtvW/fvs598hIDW7dudTn266+/1gH96NGjhe5f0zS9SpUqeu/evW/Yd/PmzTqgL168WNf1giV08voeMGDADe29++67OqDv2LGjUK+nMIqj/8K8R/nJ62fdunUu23/99Vcd0GfNmnXDMU2bNtWjoqIKtZ+ueyeh06hRI5dtmqbpvr6+ev369W/Y7u/vrz/33HM3tFGnTp0bEjd5v4cNGzbc8jUU9Pi8hNJ33313QxtPPvmkXrFiRWcb1apV02vXrn3bZJI7r+H697ygv9+lS5fqgEuS6WaxjB8/Xp85c6bu4+OjT548WbdYLAV6HUIIIYQQQpRXUkNHFEhycjLp6ek0adLkhueio6Nv2LZq1SpGjBhBtWrV6NatG5UqVUJVVVRV5cKFCzfsHxUV5fK4atWqAJw+fZq77rqrUP2npKRw9uxZMjMzefPNN9F1HXDUArly5QoAhw4dKuArd7SXnp6eb+2PvG379+8nJiamwK+nMIqjf0+9R9f/PhITE13iuj7WOXPmoOt6gfdTFOW2MRSHxo0buzxWFIWwsDAaNWp0w/bw8HBOnz59QxtRUVE3xJ93ru7bt4/27dvfMoaCHB8fHw/A5s2bSUpKQnck6QFHbaRLly6RmppKWFjYTdssagz5KejvN2/qVsuWLW8by++//84PP/zAs88+ywcffFDg1yCEEEIIIUR5JQkdUSB5N4n53QzmPXethx56iCZNmrB27VqXIsi//PJLvu1XqFDB5XHeMTabrdD959XnMJlMzuOv7efVV1+ldevW+caRn1v1fTO3ez2FURz9e+o9qlixoluxuvOaisJgMGC322/YnpmZme/+179/eW3cbHt+v9dbnasFed0FOT7v95hfDO3ataNdu3Yuhb2v/315Iob8FPY8KIhmzZqRkpLCnDlzmDx5cr7JXSGEEEIIIe4kktARBRIZGUlwcDB79+694bnrt+Xk5HD06FHGjRvnksy5dOkSR44cuWH0iKf7j4iIICwsjNDQUOfKSEURERFBcHAwCQkJNzyXt+36ER2eVBz9e/o9ypM3giUhIeGG33NCQgINGzZEUZQC7wd4pOBtZGQkKSkpLttsNhuHDh2idu3aRW4/P/v27bthW965ev1IH3ePz1u5rHv37gwdOtTtWIsSQ34K+vvNG8GzY8eOm67CliciIoIff/yR/v3706VLF5YuXXrblbCEEEIIIYQoz2RpEFEgqqoyceJE5s+f75JYOHr0KMuWLXPZ19fXl2rVqrF161aX7X/729/w9/cv9v5VVeXZZ59lwYIFLF++/Ia2tmzZQnp6eqH6vu+++1i6dCmbNm1ybj937hxTp06lVatWLtOdCiorK4vXXnvttqtcFUf/nn6P8vTr149q1arx3nvvOaduAfz444/s3r2bKVOmFGo/cEwXO3fuXKFjuVanTp34/fffXfr6z3/+U6wjhNLS0vjhhx+cjzMyMvjwww+Jioq67XSrgh7ft29foqOj+etf/8rFixddjrdYLAVaxao4XkNBf7+9e/cmKiqKN95444aE2+bNm29oNzQ0lNWrV9OiRQt69erFqlWrivT6hBBCCCGEKMtkhI4osLfeeovt27fTqVMnRo4cicFgICEhgaeeeornn3/eZd9//etfTJw4kZ49exITE8PGjRvp2rUrzZs3Jy0trdj7/8tf/sKFCxcYOHAgvXr1Ijo6mvT0dOLj4zEYDDckgW7nn//8JwcOHKBHjx6MHDmSwMBAFixYgL+//w1LZBdU3rLlw4cPZ9SoUSXev6ffIwAfHx/mzZvHkCFDaNmyJf379yclJYVffvmF++67j6effrpQ+wGMHTuWJ554ggceeICaNWtSuXJlHnvssULF9fe//51ly5bRpk0b+vfvT2JiIrGxsUU6H2+nX79+LF26lF9//ZWIiAiWLl1KRkYGy5cvL1AiqSDHGwwGFi9ezMiRI6lXrx6DBw+matWqnDhxgs2bN3PvvffStWvXEn8NBf39GgwGFi1axJAhQ4iOjmbQoEFUqlSJTZs2Ubdu3XxH4AQEBLB06VJGjx7NwIEDmTNnDvfcc4/br1EIIYQQQoiyStELU8RA3PHsdjtLlixh7969VKtWjWHDhnH06FHmzZvHY489RuXKlZ37HjhwgJUrV5Kbm0vnzp2JjY3l+++/Jycnh4ceegiAlStXsn79+hum/Rw+fJiZM2dy3333uUyJKUz/ACdOnGDVqlWkpKRQpUoVWrZs6TKaZf369axcuZJXXnnFWWskv2154uLi2LRpE1arlaioKPr374/JZHI+X5jXk5WVxXvvvUdUVNRtEzrF0X9B36P83KyfPJmZmSxZsoQjR45QoUIFunbtSosWLdzeb+3atWzdupXMzEzCw8NvmdCJjo6mXr16LFiwwGV7SkoK8+fPJyMjg86dO9O+ffsbzkeADz74gNq1azNs2DCX4z/66COqV6/OiBEjXLZPnTqVqlWruvwOq1evTrdu3ZgxYwa//fYbu3btokqVKgwdOpSQkJCbxl6U4+Pi4ti2bRs5OTnUqVOHrl27uvx7uNnrupm8/YcOHXrbGD777DMqVarEmDFjXLYX9PeraRqrVq0iPj4eX19f2rZtS5s2bW4Zu91u5+OPP+bKlSs8+eSTBAcHF+h1CSGEEEIIUV5IQkcIUa40atSI5s2bM3fuXK/FkJeQmTlzpleOF0IIIYQQQpR/UkNHCFFuWK1WkpOTqVWrlrdDEUIIIYQQQohiJQkdIUS58O233zJo0CDsdjsPPPCAt8MRQgghhBBCiGIlRZGFEOWCwWBg6NChfPXVV9SsWdOrsTz33HNFWg69qMcLIYQQQgghyj+poSOEEEIIIYQQQghRxsiUKyGEEEIIIYQQQogyRhI6QgghhBBCCCGEEGWMJHSEEEIIIYQQQgghyhhJ6AghhBBCCCGEEEKUMZLQEUIIIYQQQgghhChjJKEjhBBCCCGEEEIIUcZIQkcIIYQQQgghhBCijJGEjhBCCCGEEEIIIUQZIwkdIYQQQgghhBBCiDJGEjpCCCGEEEIIIYQQZYwkdIQQQgghhBBCCCHKGKO3AxDedfnyZQ4fPszRo0c5fPgwZ86cwWKxoOs6ZrOZqlWrUrduXerUqUO9evWoVKmSt0MWQgghhBBCCCHueIqu67q3gxAlS9M09uzZw2+//cb27dvRdR1VVdF1netPB0VRUFUVu90OQNOmTenfvz8tW7ZEVWWAlxBCCCGEEEII4Q2S0LnDbNu2jenTp3Pu3DlUVUXTtEIdn3dMxYoVGT9+PJ07d0ZRlGKKVgghhBBCCCGEEPmRhM4d4sqVK3z77besX78eRVFuGInjrpYtW/Lwww9TsWJFj7QnhBBCCCGEEEKI25OEzh3g4MGDvPfee1y5cqXQI3JuR1VVfHx8eOaZZ4iJifFo20IIIYQQQgghhMifJHTKud27d/Puu+9is9k8NirneoqioCgKTz31FB06dCiWPoQQQgghhBBCCPE/UtW2HEtMTOSdd94p1mQOgK7raJrGf/7zH7Zv315s/QghhBBCCCGEEMJBEjrlVFpaGu+88w52u71YkznX0nWd999/n+Tk5BLpTwghhBBCCCGEuFNJQqcc0nWdL7/8kpycnBJL5uTRNI1PP/3U47V6hBBCCCGEEEII8T+S0CmH4uLi2LZtm1eSKpqmcfDgQZYtW1bifQshhBBCCCGEEHcKSeiUM1arlWnTpqEoilfjmDVrFleuXPFqDEIIIYQQQgghRHklCZ1yZvPmzWRkZJToVKumTZsyZMgQl202m421a9eWWAxCCCGEEEIIIcSdRBI65cyyZctKfHROy5YtGTlypMs2XddZtmxZidfwEUIIIYQQQggh7gSS0ClHTp06xaFDh0pNEuXcuXPs27fP22EIIYQQQgghhBDljtHbAQjPuTZ5EhoaSteuXYmLi+PixYvExsZSuXJl1qxZQ1paGgANGzakTp06qKpKUlISe/bsyTcZdKv92rVrR7169VBVlWHDhjmPWbZsGbm5uezfv58mTZoU7wsXQgghhBBCCCHuMJLQKUeOHj2KwWDAbrcTFhbGmDFjuHz5Ml27duX06dNUrlyZ3bt3k5OTwwsvvECDBg3YsmULVquVoUOHcv78ed555x3S09MB8PX1ve1+BoMBVVVRFAWTyeSMRVEUdF3nyJEj3no7hBBCCCGEEEKIcksSOuXIoUOHsNvtLtsGDBjAW2+9RWpqKoqiUKFCBSZNmkRUVBT/7//9P44dOwbAvHnzePfdd3n88cf55z//CVCg/eLi4qhXrx61atVi7ty5+cYkhBBCCCGEEEIIz5IaOuVIcnLyDdu2b99Oamoq4ChUbLFY6Ny5M5s2bXImaQDOnz/PihUraNGiBeHh4ZhMpgLtdzuXL18mKyvLA69OCCGEEEIIIYQQeSShU05omobNZrth+8mTJ10eV65cGbPZzIkTJ27YNykpCYBq1aoVeL+CsFgsBdpPCCGEEEIIIYQQBSMJnXLiZitbXblyxeVx3pLm+e2vaZpzn4LuVxB5+wshhBBCCCGEEMIzpIZOOZFXnPh2yZNz585hs9moUaPGDc/VrFkTgDNnznDp0qUC7Qc3TyblMZvNBXoNQgghhBBCCCGEKBgZoVOOBAcH33Yfi8VCXFwc7du3d5kyVbFiRXr37s2ePXs4e/ZsgfcDSE9Px2w24+fnd0N/JpOJChUqeODVCSGEEEIIIYQQIo+M0ClH6tWrx7Zt2247Ymb69OlUqVKFN998k40bN2Kz2Wjbti1paWl8+umnhd5vy5YtjBo1iueff57ExEQ0TWPZsmVkZ2dTu3ZtVFXyhkIIIYQQQgghhCcp+u3u/kWZMW/ePH788Uc0TSM0NJRevXrx559/5rv6FUDTpk2pU6cOqqqSlJTEzp07852yVZD9IiIiaNGiBYGBgSiKwqJFi8jNzaVPnz7cf//9xfJ6hRBCCCGEEEKIO5UkdMqRXbt28dZbb3k7DBft27enR48eNG7cWGrpCCGEEEIIIYQQHiIJnXLEZrPxyCOPcPnyZW+HAjhWwapVqxaqqmI2m+nQoQN9+vSRKVhCCCGEEEIIIUQRyZ11OWI0GktNwkRVVWrWrOmMxWKxsGbNGuf0L6vVyuXLl29b70cIIYQQQgghhBA3kqLI5UyvXr345ZdfvB0GmqYxZcoUTCYTO3fuJDExkYoVKxIaGorNZuOTTz7h7NmzhIWFERMTQ2xsLEFBQd4OWwghhBBCCCGEKBNkylU59NVXX7Fq1ap8CxyXBEVR8PPzo1atWvTs2ZOOHTtiMBicz1utVl577TXsdrtzW2BgIM8991y+S58LIYQQQgghhBDClffn5giPu/feewkJCUFRFK/0rygKYWFh5ObmsnTpUj7++GOys7Odz5tMJh544AHuuusu57asrCysVisAcXFx/PTTT8THx2OxWEo8fiGEEEIIIYQQorSTETrlVEJCAm+88YZX+q5cuTIBAQEu24YPH05sbOwN+6alpXHo0CHCw8OpXbs2aWlpvPPOO87nzWYz3bt3p1u3bl5LUAkhhBBCCCGEEKWNjNApp5o2bco999xT4v12796dsWPHYjT+rzxTUFAQDRo0AGD//v3Ex8djs9kACAkJITY2ltq1awMQEBDgMnLHYrHw+++/k5OTA0BmZiapqalSTFkIIYQQQgghxB1NiiKXY2PHjiUzM5MVK1aUSH+NGjXiwQcfxGg0Uq9ePebNm8fp06cZOHAgwcHBJCUl8d133wEQFhbG4MGDadiwoUsbRqORBx98kGPHjhEfH8/hw4epXbs2Pj4+ZGRk8NFHH5GZmUl4eDgtWrSgbdu2N4wGEkIIIYQQQgghyjuZclXO6brOjBkzWLx4cbH2ExAQQHh4OIMGDaJz587O7Xa7HYPBgMVi4eOPP+b8+fMuxw0ePJiOHTsWqI8LFy7w/vvvu4zOCQ0N5cUXX/TMixBCCCGEEEIIIcoImXJVzimKwsSJE3n22Wfx9/dHVT33K1dVFbPZzNChQwkPD0dRFJYuXUpCQoJzn7zVrdLT029I5gAcOXKkwP2FhYUxceJElylZOTk5aJqG3W5n8eLFzJ49m127dkkxZSGEEEIIIYQQ5ZpMubpDtG/fnsOHD7Nq1SqysrJQVdXtZc3zjjWbzbRr144xY8YQFhbGqlWr0HWdxYsX06RJE5fkUXh4OD169OCPP/5wjrAJCwtjwIABAOzevZu9e/fSrFkzoqKibloAuXHjxjRu3Ji0tDSOHz9OtWrVUFWVI0eOsH79egB27dqF2Wymf//+tG/f3q3XKIQQQgghhBBClGaS0LlDHD16lL1791K1alUCAgIIDg4mLi4OTdNQFOW2RYavTbC0bt0aXde5cOECSUlJJCQk0KtXL65cucLmzZupWLFivgmZPn36UK9ePX766ScuXbpEs2bNCAsLA2DBggVkZWWxa9cuGjZsyN13301oaOhN4wkJCaFFixbOxxEREVSrVo3Tp08DjmLKy5cvdyZ0UlNTsdlsVK5cWVbLEkIIIYQQQghR5kkNnTuArut88sknzmTHiBEjaN26NZ9//jm7du1C0zQMBgMXLlzI9/iKFStiMBjQNI169erxwgsvkJiYyPTp0wGoVKkSzz33HEajkdTUVEJCQjAYDM76Odez2+1cvHjRmcz56aefOHDgAJmZmS59Pv/88y6rZd2OpmkcP36cnTt3kpSURLNmzejZsyenT5/ms88+w263O4spd+jQAT8/vwK3LYQQQgghhBBClCYyQucOkJ2d7UzmRERE0LJlS7KyskhKSiI4OJjw8HCef/55rly5wrlz57BYLOi6jtlsJjw8nKCgIL744guOHTvGhQsXSE1NpWHDhtSrV4/Dhw9z8eJFUlNTqVKlinNUTUJCAj/++CN169Zl3LhxmM1mZzwGg4Hw8HAAMjIy2LFjh/M5k8mE1WolOzu70PV+VFWlTp061KlTx2V7Tk4OdrsdgPPnz7NixQqOHDnCQw89VPg3UwghhBBCCCGEKAWkKPIdwM/Pjw4dOlCzZk1Gjx6NqqocOnTIWUOnQYMGgGOlqjp16tCoUSMaN25M3bp1CQoKAnBZXjwxMRFFURg2bBh33XUXMTExztE2eQ4fPozVaiUxMZHZs2fftF5PYGAgbdu2dT62Wq3UqlWLJ554whnnO++8w/Tp0zl37pxbr79u3bqMHz/epZhyXjzZ2dnMmjWLGTNmSDFlIYQQQgghhBBlhozQuQMoisLdd9/tsi0tLc35c+PGjW/bRqNGjfjtt98AuHTpEuCYavXwww/nu3+HDh2Ij48nNzeX/fv3s2rVKnr37p3vvkOHDqVOnTosWLCA7OxsAgMDnQmihIQE0tLSSEtL4+DBg3Tp0oW+ffsWug5O06ZNadq0KWlpaZw5c4aaNWs629+9ezcAe/fuxWw2M2zYMJf6PEIIIYQQQgghRGkjCZ1yLCsri/nz52M0Ghk+fLhLPZrmzZtz7NgxqlSpQt26dW/bVtWqVenbty9Hjx6lTZs2Ls9pmsavv/5Keno6w4YNIygoiCpVqnDvvfcybdo0NE3jwIEDN03o5MVTt25dTpw4Qf369QHYuHEj6enpBAQEcOXKFTRNY82aNdSoUYMmTZq49Z6EhIQQEhLifNygQQMqV67sHP1jsVhYt26dM6Fz8uRJDAYDERERUkxZCCGEEEIIIUSpIUWRy7FffvmFrVu3AjBlyhTq1atXLP2kpKTw0UcfAY7RPpMmTXI+d/DgQTZv3kz79u0L1b/VauXVV191rsJVo0YNTp8+jcFg4JFHHiEyMpLMzExUVS1ycWNN0zh27Bjx8fGkpKTQuXNnmjVrxu7du5k1axbgWGI9JiaGzp07u9QDEkIIIYQQQgghvEFG6JRTycnJbNu2DQAfHx8iIiKcz12+fJmEhASaNGniMlqlIDIzM4mPj6dRo0bOAsihoaEEBQVx+fJl9u/fz+HDh53JmwYNGjhr9Fy6dMnZ762WJAcwGo00adKEhIQEdF0nKSmJpk2bcs899+Dv709qaioff/wxFouFdu3a0bt3b7cTO6qqUrdu3RtGKtlsNufPFy5cYMWKFVy4cIHRo0e71Y8QQgghhBBCCOEpUhS5nIqLiyNv8FWPHj3w9/d3PjdnzhwWLVrkHH1SGL/88guLFi3iu+++c7ZvMpno27evc5/169fne+yCBQtYunQpn3zyyW0LHCuKwtixYxkwYIBz6fPU1FTn68jIyCAnJwdN09iwYQP//ve/OXnyZKFfz63ExMQwevRol2LKedPWLly4wBdffMF3331HfHy8FFMWQgghhBBCCFGiZIROOVWhQgUAqlSpQocOHZzbL1++zNGjRwFuuvLUreQdc+7cOZKTk4mMjAQcyY9t27Zx7NgxZ9/Xq1ixIuBYWWratGk89thjBAYG3rQvVVXp0qULjRo1Yt++fURHRwNw4sQJDh48SNeuXdmwYQNWq5XMzEzWrFnDhAkTCv2abkZRFGJiYoiJiSEtLY3U1FRnMeW81wqwf/9+fHx8GD9+vHM0khBCCCGEEEIIUZwkoVNO9enThyZNmlC1alVMJpNz+4EDB5w/u5N8aNCgAYmJic628hI6qqrywAMPcPr0aee26/Xv358TJ06QnJzMpUuXWL16NUOGDLltn5UrV6Zy5coA6LrO999/T2ZmJkajkS5dunDhwgVOnTpFy5YtAcfUrrziyteOTCqK64spx8TEsHv3bi5evAhAbm4u27Zto0GDBtjtdg4dOkRAQADVqlWTYspCCCGEEEIIITxOEjrlzOXLlzlx4gQNGzakVq1aNzyfmprq/LlRo0aFbr9Ro0YsXLgQcEw7upbJZKJ27drY7Xb27NlDRESES60cHx8f7r//fj7//HMuXrzods2b6tWrc+DAAWw2G6tXryY2NpaXXnrJ+fwXX3xBWloaFSpUoG/fvsTGxqKqnp1dWKVKFV544QWOHTvGzp07SU9Pp0uXLgCsWbOGFStWABAeHk7Lli3p0qWLc+qYEEIIIYQQQghRVJLQKUdycnL49NNPSU9Pp3379vmOfmnRogVHjhyhVq1a1KhRo9B9VKpUid69e7Nv3z5iY2Pz3ScvoeHn58ezzz5LUFCQ87mgoCCeffZZzp496xzJo+t6gUexKIrCvffey2+//UZcXBzgmPKUR9d1/Pz8SEtLcy7bfvDgQe69916Pj5S5WTHla5NH58+f5/fff8disbjUGRJCCCGEEEIIIYpCEjrlyJo1a0hPT7/lPlWrVuXxxx8vUj89e/akZ8+et90vOzub5cuXM2LECJftJpOJ6tWrA/Dbb7+xbds2evXqRbt27QrUv8lkYvDgwURFRbFjxw5nbZ2MjAwOHz7M6NGjWbNmDfHx8YAj4aNpWomNkOnatSuBgYHs2LHDWa8or67Q4cOHWbx4MSEhIcTExNC4cWNZBl0IIYQQQgghRKFJQqecuHz5snN1KYPBQOfOnW/YJyMjg0OHDhEVFYWvr2+R+rNarezZs4fatWs7ix3nadeuHevWrSMnJ4ft27fTuXNnqlSpckMbuq6zfft2rly5woIFC/D19aVFixYFjuH60THTp0/n9OnTVKhQgXvuuYc2bdqwY8cO7rrrLgwGA2lpaaxZs4bIyEhatWpVbAkeVVVp3bo1rVu3Jj09nStXrjhHI23ZsoWUlBRSUlJITEykQoUK3Hfffc5iy0IIIYQQQgghREFIQqecOHfuHDabDYCOHTtSqVKlG/aZMWMGSUlJREVFMXHixCL1t3jxYjZv3kxYWBjPPfecyzQjf39/evTowdKlS9F1neTk5HwTOoqi0KlTJ5YtWwbATz/9RHh4ONWqVXMrpryaPFlZWcyaNYvOnTu7jA5auXIl27ZtA2DDhg0MGTLEZUny4hAcHExwcLDzcadOnUhKSiItLc0Z6759+6hZsyY5OTkkJiYSFhYmxZSFEMLLbDYbR44c4fDhw2RmZpKTk4Ou694OS4hSz2Qy4efnR1hYGE2aNMn3mlQIIYRnSEKnnKhTpw5dunTBbrfTu3fvG56/cOECSUlJgKPWTlHltXHhwgVOnjx5QwHmjh07kp2dTU5ODk2aNLlpO126dCE1NZUtW7Zgt9vZv3+/2wmdsWPHsmDBAhISEgCIi4ujX79+zpE49evXdyZ0UlJS+Oqrr3j88cfd7s8dNWvW5KWXXuL48ePEx8eTk5PjXFZ+wYIFzmli4eHhxMbG0qlTJ48XdBZCCHFzJ0+eZNOmTezdu5ecnByCg4MJCgrCx8dHPo+FuA1d18nJyeHcuXNs376dJUuWUL16dZo1a0aHDh0wGuXWQwghPEk+VcsJVVUZMGDATZ8v6nLl16tfvz67du0CIDEx8YaEjsFgKFARYEVRGDJkCH5+fqSkpBATE+N2TP7+/owbN45du3axadMm6tevj8FgwG63k5ycTFRUFI8++igLFy7k9OnTaJpGVlaW2/25S1VV6tSpQ506dVy2XzsN7vz58yxduhQfHx/atm1b0iEKIcQd6fDhw0yfPp3AwEBiY2Np0qQJVapUkRGTQrghNzeXgwcPsnfvXn7//XeOHDnChAkTJKkjhBAeJJ+oZdzx48eZPXs2NWrUYNy4cTf99vDs2bPOn91Zrvx6DRs2zLft6+m6zvz589m/fz/Dhg2jcePGN+xjMBjo37+/83FycjJnz54lOjq60H/0FUWhRYsWLrV4fvrpJ+Lj46lUqRIjR47k8ccfJzExEV3XqV+/PpmZmcyePRtN0+jRowf16tUrVJ+eMnjwYCIjI9m5cyfHjh0DcE7X2rRpE2vWrKFq1aq0aNGCqKgoKaYshBAelJfMqV27NqNHj8ZkMnk7JCHKNB8fH5o2bUrTpk05fPgwc+bMYcaMGZLUEUIID5JP0zLMZrPx448/kp6eTnp6OhaL5abFjps3b87hw4dp1KhRvvVsCiswMJBevXqxbds2WrVqdct9d+zYgc1m4+eff+bFF1+8ZUHm3NxcPv/8c3Jzc2nYsCETJ04scvHizMxMAC5evMiXX35Jnz596N69u/P5EydOcPjwYQCOHj1K06ZNGTp0qHNlqpJiMBho06YNbdq04fLly9jtdmfB6W3btpGWlkZaWhqJiYkEBgby0EMPER4eXqIxCiFEeZSdnc13330nyRwhikm9evUYM2YMc+bMYeXKlfTr18/bIQkhRLkgk8HLsI0bN3Lx4kXAUUPnVomSunXr8tJLL3H33Xd7bOh4r169+Mtf/nLLGjmKojifz8zM5I8//rhlm0aj0Tny5MCBA/z6669FLkI5fPhw55QwXddZvny5y1Sr+vXruxRHTkhI4LfffitSn0UVFBTksnpYnz59XIorZ2RkcPz4ccCRqNq8eTOnTp2Sgp1CCOGGffv2YbVaGTx4sCRzhCgm9erVo3nz5uzatUuuV4QQwkMkoVOGbd26FXAkTQYNGnTT/a5cuUJiYqJzFSxP0jSNAwcOkJ6eftN9+vbt6xxamxfzzRgMBsaOHesclbNlyxaOHj1apBhDQkJ4+OGH6d+/PwEBATRo0MC5IlZubi4mk4kHH3yQ4cOH4+/v7zwGwG63k5ubW6T+PaFBgwa8/PLLPPjgg7Rr144OHTo4p5X98MMPzJ8/n08++YQPPviADRs2yIWSEEIUQkJCAjVq1HBJnAshPK9JkyZcunSJ06dPezsUIYQoF2TKVRkWGRnJuXPn6Nq1K5GRkfnuo+s606ZN4/Tp07Rt25ahQ4d6NIZVq1axatUqKlWqxHPPPZfvnOhKlSrRr18/Fi9eTGRkJLqu33KUUJ06dRg5ciRz58697b4FpaoqXbt2pUuXLoAjCbZ27VqWLVtGzZo1GTFiBLGxsTRv3py0tDTnVKbp06dz6NAhoqKiGDRokFeX3lRVlbp161K3bl2X7UFBQc4Lo/Pnz7Nw4UJCQ0Nd6hwJIYTIX05ODocOHcp3hUghhGfVqlWLChUqsHv3bqpXr+7tcIQQosyThE4ZNmrUKAYNGkRAQMBN90lOTnbe7GdkZHg8hitXrgCOaT9Hjx696QpanTp1IiYmBj8/vwIlaFq0aEFkZCQWi8Wjf/Cv7TtvylJSUhJTp05l8ODBtG3blsqVKwOO0UcnT54EHMPxDx48yJAhQ4iNjfVYPJ4wbtw4du7c6SymbDabnSOMFi5cSHx8PDVr1iQmJobGjRtLMWUhhLjGhQsXsNvt1KxZ09uhCFHuGQwGatSoccsFNYQQQhScJHTKoMTERM6dO0eHDh1umcwB1+XKi2P1prp167J582ZnX7daEt3f3x9N09iwYQP+/v40b978lm3nJVbsdjszZ84kMzOT4cOHe6SoM8CAAQO4cOEC58+fx2azsWDBAho2bOhMhqiqyrhx4/jpp5/IyMjAZrPx66+/0qpVq5uuJuYNJpPJWUw5MzMTVVWdU8ri4+PJysoiMTGRxMREKlWqxKOPPkpgYKCXoxZCiNIhr6Za3uemEKJ4+fn5OWtACiGEKJrSc1cqCuTo0aNMnz6dpUuXsmnTptvun5yc7PzZE8uVX69BgwbO5MaZM2duu39CQgILFy5k9uzZ7N69u0B9JCcns3//fpKSkvj222+5fPlykWLOEx4ezpNPPknHjh1RFIXg4OAbVrZq0KABzz//PJ07dyYgIIDmzZujqip2u53ExETS0tI8Eoun+Pv7u9yUDB061KUmxMWLF7lw4QIAJ0+eZN26dVJMWQhxR7NarQAyelGIEmI2m7FYLN4OQwghygUZoVOGaJrGkiVLnI+DgoJue0x0dDQHDx6kRYsWxVL/xdfXlx49erBu3brbjrgBXEaG/Pbbb0RFReVbd+dakZGRVK9enVOnTpGens60adN44oknirycOTguKgYPHkzv3r1RVRWz2UxiYiILFiygZs2aDB48mMDAQAYOHMjAgQNdYl+3bh0Gg4EuXbrQvXv3Unkz0LRpU5o0acKxY8fYt28fQUFB1K5dG03T+Prrr50Fn8PDw+natSutW7f2csRCCCGEEEIIIQpCRuiUIfv27XPWw4mIiCA6Ovq2xzRr1ozXXnuNe+65p9ji6tWrF6+//jrt2rW77b533XWXc+rXxYsX2bZt222PUVWVSZMmOZfxTk5Odhl55Am+vr7OhEx8fDxpaWns3r2bDz/8kD179tywf14tHrvdzh9//MF//vMfl6XQS5O8YsqDBw+ma9euKIqCqqqEhYU59zl//jw///wz586d82KkQghRuqWlpTFy5EgSEhJuuV9OTg4jR45ky5YtJRRZ4RX0tRREcb3enTt3MnLkSKZOnerRdoUQQojyQhI6Zci1S4MPHDjwtnVcsrKySEpKKrHpNKdOnXIWSb4ZRVEYOHCgMyFyq+XOrxUYGMgDDzxAnTp1aNq0KREREUWO92a6dOnirE2UlZXFrFmznNOU8vTp04du3bo5RwmlpqY6iyyXFQ899BDDhg2jTp06gGOpdn9/f+x2O9988w1//etfmT59Ort27XJOSRBCiDtZTk4Oixcv5vz587fcz2azsXjxYlJSUkoossIr6GspiOJ6vV999RXLli3jnXfecY4oFUIIIcT/yJSrMqRt27bYbDbCw8NvW+BY0zS++uorkpOT6dWrF7169SrW2DZt2sSCBQuoVKkSTz/9ND4+PjfdNyIigsmTJ5OUlETHjh0L3Ed4eDgPPfQQ4FiO/fjx45jN5psu2e6uyMhInnnmGebPn8/evXtRFOWG6V1Go5F+/frRqlUr1q5di8FgoG7dumiaRlxcHABt2rS55fvgbT4+Ps5iyrm5uRgMBoxGo3MJX8BZTLlq1ao89thjpXJamRBCCO/y8/Nj7ty5Hl0FMisri19++YWXX36Z9957j4ULFzJy5EiPtS+EEEKUB5LQKSOysrLw8/Oja9euBdo/KSnJOS2pJFYSyOvj4sWLHDp06LbTwerVq0e9evXQdZ3MzEwqVKhQoOXM8xw9epSvvvoKgJEjR9KqVSv3g89HQEAA9957L8nJyZhMJipWrMjZs2dZunQpVatWpUePHvj4+BAeHs6IESOcx+3bt89Z5+jPP/9kwIABtGjRolCvzRuuTTz5+voyfPhwVq5c6RxBlZKSQk5ODmazmb1795KSkkLDhg2pVq1aqX9tQgjhabm5uXzyySfs3r0bo9HIuHHj6NSp0033T0pK4vnnn+f99993WR79o48+wmq18uKLL7rsv3z5cpYsWUJqaioNGzbkkUceITw83Pn8v/71L06fPs1HH31021hXrlzJ4sWLuXTpEs2bN+fhhx/G39/f+bzVauXLL79kx44dmEwmhg4dSo8ePVzamDhxItnZ2SiKQpUqVWjTpg3jxo1zftlhtVqZMWMGVatWJSIigrS0NB588EH+8pe/sH379lu2fTPz588nOzubxx57jB07dvD9999LQkcIIYS4jky5KgNWrFjBP/7xD+bMmVPgY65drjxvSk1xqlu3rvPnxMTEAh+3ZMkS3njjDRYsWFCo/q5NIvzyyy8cPny4UMcXtI/IyEjnRfSmTZs4cOAAa9euZerUqflOsapSpYozOZKRkcHcuXOdI3bKktjYWF5++WUefPBBunXrxtixYwkKCiI9PZ2ZM2eyYsUKPvnkE95///0Cr1YmhBDlxeTJk9m+fTvdunXDbDbTr18/fv3115vun5GRweLFi8nIyHDZvn379hvqzjz//POMGDGCgIAA+vfvz44dO+jYsaPLlzNbtmzhjz/+uG2czzzzDKNGjSIgIIB+/fqRmprq8iUEwIMPPsjevXvp2rUrAQEBDBo0iGXLlrnsM2bMGCZMmMD48eNp1KgR7733HuPGjXM+f/2Uq7zpXEOHDr1t2zfz/fff07dvX0JDQxk3bhyrV6/m5MmTBTpWCCGEuFPICJ1SLjk5mdWrVwM4CyIXxKlTp5w/N2zY0ONxXa9OnTqYTCasVqtL37eT95o2b95Ms2bNXBJDt3LXXXfRvn17Nm7ciKZpzJkzh//7v//zyMpXNxMTE8O2bduwWq2kpqby1Vdf8fTTT1O5cmXnPqGhoTz11FMsXryY/fv3A3ikPoE35BVTvvZ34uvrS2hoqLOm0IULF5g1axaNGjXCbDaj6zqaphXr70EIIbytY8eOTJs2DcCZ2HjppZcYPHjwbevb3cr69ev57LPP+Oyzz7j//vsBGDt2LO3bt+f999/nrbfeAuDFF18kMzPzlm398ccffPHFF8ybN4/+/fs7t6emprrsN3bsWN59913nz/v27WPmzJkuxwwYMMDlmAEDBhAVFcXevXtp0qTJTWMoSNv5OX78OOvWreOHH34AcK44OWvWLF5++eVbHiuEEELcSWSETim3dOlSZ1HjwsxNb9SoEYqi0K5dO5elwouLyWRyrqDUuHHjAh937TLZixcvRtO0Ah2nKAqDBw8mKioKcKw2ZbPZChd0IdWsWZOnn36aWrVqOfvMb2Wr0NBQJk2axJQpUxgwYIDzwnXVqlV88cUXbNu2rcCvs7Tx8fHhiSeeYNiwYdx1110A1KpVC4PBQGZmJv/+97/529/+xvTp04mPj5diykKIcmno0KE3PE5KSiIpKalI7S5btgxfX1/uvfde5zZVVRk6dCi///67c1ubNm3o3r37Ldtavnw5lStXviF5Ehoa6vL4+qncdevWveELpDNnzvCvf/2Lhx56iFGjRvHSSy+hqqrLaOD8FKTt/MyYMYPg4GBnIsnX15ehQ4cyY8aM2x4rhBBC3ElkhE4plpub6yxOW7FiRTp06FDgYzt27Ei7du1KdKREr1696N69e6H6jImJIS4ujjNnzpCcnMylS5duuNi8GVVVGT9+PPv27XOZ6lScwsLCePjhh9m3bx+qqlKrVi2ysrJYu3YtFStWJDY21vn68+oEgSP5s3LlSnRd59ixY2zZsoXhw4dTpUqVYo/Z03x9fZ3FlO12u/P1pqenO7/5zSumXLt2bR555BFvhiuEEB4XEhLi8jg4OBhwjFqsXbu22+2mpaVhMBhcpjMBnDx5khMnThSqrcuXLxfo76mvr6/LY1VVXb50OHbsGJ06daJt27YMGDCASpUqYTQaWbZs2W1HCd2u7fzous7MmTMxmUwuia3Tp09z5MgR1q9ff8t6RUIIIcSdRBI6pZjJZCI6OpqkpCRGjBiByWQq0HG5ublcvnzZpYBiSTEYDKSmplKhQgX8/Pxuu7+qqgwfPpyZM2cSGhpKUFBQoftr2rQp4LiQnj9/PlWrVqV///4YjcVzequq6lL0efPmzaxduxZw1EMYNWrUDe+9wWCgU6dOrFu3DnAUyJw2bRovv/xymS4qfG3yLiIigkGDBrFu3TpnMeUzZ844R5ht2LCBzMxMoqKipJiyEKJMu75u25EjRwCcIxevl/f3MCcnx2X7hQsXCAgIcD6uXbs2fn5+TJgw4YY2CjuVq3bt2syePZusrCwqVKhQqGOvNW/ePPz9/fnll1+cn9snTpzAbre73eatrFmzhqSkJD7//HMqVqzo8txbb73FjBkzJKEjhBBCXCUJnVJMVVWXb6cKwmaz8d///peUlBSGDRtGmzZtiim6/O3du5eZM2cSHBzM008/XaCkTrVq1TwyJ37nzp0cOXKEI0eOkJ2dzciRI0skaVCrVi0URUHXdU6ePMnUqVN5+umnCQsLc9lv4MCBNGjQgIULF3L+/Hl8fX2d8V070qWsUhSFTp060aFDB44dO8bx48epW7cuiqJw9OhRFi1aBMDq1asJDw9n0KBBJVLfSQghPO3zzz9nzJgxVK5cmYyMDD788EP69+9/0xExNWrUICAggOXLlztXZdy4cSNxcXH07dvXud/YsWP517/+RUZGBuPHj3duT0pKcimeXJBVrvLq17zyyiu8//77GAwG7HY7P//8M6NHjy7wa/Xx8SEjI4NLly5RqVIlrFYrf/3rXwt8fGF9//33REdHM2nSpBueO3z4MG+//Tbvv/++SyJMCCGEuFNJDZ1SyGq1snTpUpYtW1boWitHjx51rjKRt2x5STp79iy6rpOWluYsClxQuq6zatUqFixYcMO3mAURHR3tHJWzY8cO1qxZU+g23FGnTh0effRRZwLnVoWh69evz9NPP83jjz/unIq0ZcsW/va3v/Hpp58Wuf5CaZBXTLlnz57OqQeVKlVyGX11/vx5fvrpJ+djq9WKxWIp6VCFEMIt0dHRtGnThv79+9O0aVMuX77Mhx9+eNP9TSYT//rXv3jnnXfo1q0bPXr04KmnnnIZ7QlQvXp15s+fz1tvvUV0dDR33303bdq0YeDAgZjNZud+BVnlqnr16vz8888sWrSIhg0bMmDAABo1asTBgwcL9VonTpxI7dq1adGihbN2XXp6eoFHDRfG5cuX+fXXXxk+fHi+zw8bNowrV64wf/58j/cthBBClEUyQqcUWrVqFX/++SfgqMNSv379Ah97bYHCmw39Lk7X1g44cOAALVu2LPCxKSkprFixAnCMNLp+adXbiYiIYPTo0cyaNQtd19mwYQPdunUrkVE6NWvW5KmnnmLTpk1YLBaaNGmCpmns2rULf39/6tev74zDaDRSo0YN57GHDx9G0zROnjzJZ599Rrt27RgyZEi5mpIUEhLCs88+S0JCAjt37uTkyZPOgtbnzp3j008/xWq10qBBA2JiYoiOji7zI5aEEOVPxYoVmTt3Lu3bt0dRFPbv34+qqrRp08YlweHn58fcuXNdFjO477776Nu3L/v37yc8PJzGjRuza9euG6YudezYkT179pCQkEBKSgrVq1enYcOGLtOIC7LKFUC3bt1ITExkx44dXLlyhejoaGfttrzXkjdt+do477nnHufjoKAg4uLi2L59O6mpqdSuXZtGjRqxZMkS57HXv96Ctn293Nxcpk+fTvv27fN9vnbt2vz6669emVIuhBBClEaKnlfgQpQKly5d4v3338dms2EwGHjppZecxRYL4tNPP+XkyZOoqsrf//73GwoSFje73c4bb7xBTk4OISEh/OUvfynwsTk5Obzzzjvk5OSgKApPPvkkkZGRhY5h27ZtrFixgtjYWHr16lXo4z1l+/btzlEojRs3ZtiwYfmuOJaamsqMGTOcI6sAJk+eXKhEXlm2b98+vv/+e5dtzZs3Z+zYsV6KSAhxJ9m7dy8zZszgxRdfxN/f39vhCFHuLV26lKSkJJ555hlvhyKEEGWeTLkqZdavX+9cfrtjx46FSubA/0bltG3btsSTOeAoktu5c2eXWArK19eXHj16AI7pV3mFhgurdevW/N///R+9evXCbrdz/Phxr0znufZ3t3//fj766CMuXbp0w36hoaE8+eSTDB48GH9/fypWrEjlypUBx6ilCxculFjM3tCoUSP69u3r8n7lvWabzcayZctYunQpp06dQvLPQgghhBBCCOEgU65Kmbwb1sDAQLp3717o4/v370/Xrl2LtKJFUfXo0YN27doVqCDy9Tp06MDWrVs5f/58oesH5WflypX88ccfBAcH8+ijj96w1GxxqlevHhMmTGDevHlkZmaSmZnJnj17nAmvaxkMBjp27EjHjh3RNA1VVTl+/DhffPEFuq7TokULBgwYUOhVwMoCVVXp3r07Xbt25fjx45w+fZrGjRsDjlpIeYm9P//8k/DwcIYPH16kZYGFEEIIIYQQojyQhE4p069fP2rXrk2tWrUKnRCx2Wzk5OR4feUHRVHw9/cnMzMTs9lcqMKJRqORRx55hEOHDtGgQYMix5I3Mic9PZ1p06bxyCOPuJVocleTJk2oVasWK1euJCMjg2bNmgGOgtVGozHfOgB5S9NarVZngi8+Pp59+/YxefJkatWqVWLxlyRVValTpw516tRxbqtZsyYVKlQgKysLcBRTXrp0KY899hgAV65cQVVVryYwhRBCCCGEEMIbJKFTSuTm5nL27FkiIyOdN/2FYbVa+eSTTzh//jz33nuvs+Cstxw/fpyvv/4af39/nnrqqULVJfD396dFixbOQsGhoaFu37B3796dxMREUlNTOXv2LMuWLWPYsGFuteWugIAAlyKQ1468adeuHQMGDHBZvSRP/fr1GT58OMuWLSMrKwuLxcLWrVvLbUInP1WrVuWFF15gz549xMfHc/bsWee/jwMHDjB9+nQURXEWU27atKkzISaEEEIIIYQQ5ZkkdEoBm83G559/TnJyMl26dGHAgAGFbuPAgQOcPXsWgFOnTnk9oXPmzBlsNhvp6ekkJCTQrl27Qrexfv16li5dSqVKlXjqqafcqgkUEBDA/fffz3//+18yMzNJT08vdBueZrPZnCNvNm3axKFDh3jooYfyrZcUGxtLVFQUq1evJjk52bnyx/Hjx0lKSqJZs2YlOo3MGypUqECbNm1o06aNy/YLFy6g6zq6rpOYmEhiYiKnTp1i4MCBXopUCCGEEEIIIUqOJHRKgY0bN5KcnAxQoGVI85OYmOj8uTSM4Lg2hsTERLcSOnnvxcWLF/njjz/o37+/W7GEhYXx5JNPsm/fPpo0aeJWG55Ur1497rnnHpYsWYLVaiU1NZXNmzfTp0+ffPf39/dn8ODBzsd2u52vvvoKu93OihUr6NGjB507d3ZZ0vZO0K5dO7Kysti2bZszUXf58mUAMjIy+P333/H19aVFixZUq1atXC0DL4QQpU1OTg7z58+ne/fuVK1atUht2Ww2fvrpJzp37kz16tWLHNucOXNciupXqVKFpk2byvLnQgghyrw76w6wFMrKymLVqlWAo/ZMhw4d3Grn+PHjAJhMJpcaJN4SGRlJYGAgGRkZztgKq23btsTFxWGz2Vi/fj3t27d3ezRKSEiI8709cOAAGzdupE2bNl4bydSuXTvq1avHokWLuHDhgrMIcHZ2NgaDId8pWNeqWrUqp0+fxmq18vvvv3PixAnuu+++Eoi89DAYDPTu3ZuePXty7NgxLl686EzYrV+/nm3btjl/rly5MmPGjCEyMtKbIQshRLmVlpbGAw88wJIlS4qc0MnJyeGBBx5g7ty5HknoTJkyhcaNGxMdHY2u6xw7doyEhATefvttHn744SK3L4QQQniLFJvwsuTkZHJycgBo2bIl1apVc6udvOPatGlTqCLExUVRFDp27Ajg9k10pUqVnG3Y7XZOnDjhkdhWrFhBYmIiM2fO5MCBAx5p0x1hYWHcf//9vPjii9SoUYOLFy/yr3/9i9dff50//vgDu92e73EGg4HJkyfToUMH56iTU6dOlWTopYqqqtStW5fY2FhnraWGDRu6JMXOnTvHn3/+CThWkjt//rzbo+GEEEIUL5PJxJgxYzySzMkzZMgQpk2bxvTp01m7di3PP/88zz//PCdPnvRYH0IIIURJkxE6XlarVi1atGiB1Wp1q3ZOnlGjRtG7d28qVarkweiKpmvXrjRr1ozAwEC32+jZsyeXL18mNzeXRo0aeSSuxo0bc+rUKTRN44cffuDRRx8lIiLCI20XxeXLl52rOf3+++/s37+fiRMn5rtqWYUKFbj77rtp3bo1e/bsoWHDhgCcOHGC1atXU6tWLTp37lwqknveUKdOHf7yl7+wZ88edu7cyeXLl4mJiQEgLi6OxYsXo6oqDRo0oGXLljRt2lSmZAkhCuzixYts2rSJnJwcmjdvTt26dZ3PXTv1KDc3l507dxISEkK3bt346aefsNlsgGPaT3R0NJUrV873WE3TSEhIwGQy0aZNm3z/Fpw4cYLt27cTEBBAhw4dSEhIwGq10qVLF5f9srOz2bx5M6mpqTRs2JDo6GiX59etW8fly5cLVIMsJyeHLVu2cPHiRZo1a5bvqOAzZ87cMvbbvQ8Gg4E+ffo4txX2fSmIPn368Oabb7Jv3z5q1KjhVhtCCCGEt0lCx8uMRiNjxowpUhu6rqNpGmFhYR6KyjMURaFSpUrO5bfduWE2m82MHj3ao3F1796d5ORk9uzZg8ViYePGjSW+8lV+ateuTc+ePVm9ejW6rpOUlMQff/zhUj/nepGRkS4joNasWcOBAwc4cOAAW7duZciQIR5LhJU1NyumnJaWBoCmac5iyoMGDaJTp05eiFIIUdbMmDGDp59+mujoaEJCQli3bh2jR4/mv//9L4qiOKce9e3bl/3799OyZUtiY2Pp1q0bq1evJicnB13XOXXqFPHx8bz33ns88MADwP+mLfXv35/ExESio6NJTEwkKyuLlStXUrt2bWccn3zyCa+88gpt2rTBx8eHp59+mqCgIKpVq+aS0Fm+fDlTpkwhJCSEBg0asHHjRvr168dXX33lXBVw6tSpHDx48LYJnd9++81ZxL9Ro0b87W9/Y+DAgbzzzjsucd0u9tu9D9dPuSrM+1JQR44cAXB7ZLQQQghRGkhCx0vOnTvHL7/8QrVq1Rg8eLDbowNyc3P57LPPuHTpEpMnTy4VBZGvde7cOT7//HNMJhNPPvmk29+kgWOq1MGDBxk8eDA1a9Z0ux1VVZ1JosOHDztHt5QGvXv3pkGDBvz888+kpqY6X2dBE2KtWrXiwIEDaJrGpUuX+P7773nqqaeKXM+gPOnbty9Go5GdO3c6iynnTXs8c+YMK1eupGLFisTExEgxZSGEi+PHj/PUU0/xyiuv8OKLLwKwefNmevbsSceOHZkwYYJz34sXL7Jz507nVFCA//73vy7tLVy4kEmTJjFgwACXz+ns7Gx27NiBr68vVquVdu3a8cEHHzB16lQAjh49yiuvvMKHH37I5MmTAUeyZejQoS4JiqSkJMaOHcvQoUP54osvMBgMJCUl0b59e2bPns348eMB6NKlC/Xr17/tax87diwPPvgg77zzDqqqous6ixcvdtnvdrEX5n24XkHavpmEhARmz56NruucOHGCL7/8kscee+yG0UpCCCFEWSIJHS/QNI25c+dy+vRpTpw4QY8ePdxOdOzdu9e5XPmJEydKXULn9OnTzmlEO3bsuGEYeEFpmsYff/yBpmnMmTOH5557rkirOplMJu699140TUNVVWw2GxcvXnQZ8u0ttWrV4rnnnsNiseDj40Nubi5fffUVV65coXfv3rRs2fKmSYbo6GiefvppFi5c6Pz2MW9fd0dJlTcmk4l+/frRp08fjh8/TmZmprM49qpVq9i3bx/gmJpVtWpVJkyYQGhoqDdDFkKUEvPmzcNkMvHMM884t7Vt25Y+ffowe/Zsl4TOQw895JLMyXP48GEOHTpEWloamqZhsVjYtWuXSyJj8uTJ+Pr6Ao7PrI4dO7qsZjl//nxCQkK4//77ndv69etHixYtXPqaO3cu2dnZvP322xgMBgBq1qzJ+PHj+frrr50JnSeffPK2r33OnDmoqso//vEP58geRVFuGEV6u9gL8z5cr6Bt5+fo0aMsX74ccCTbTCYTYWFh2O1253sjhBBClDWS0PGC+Ph4Tp8+DUBERAT+/v5ut3XthYwniwd6yrUxJSYmup3QySt8e+jQIS5evMiGDRvcbuv6dnVd58svvyQpKYmWLVsyYsQI58WqtyiKgo+PDwAXLlxwFj3+6aef2Lt3LyNGjMj3RgEc9QimTJnCqVOnMBgMVKlShQsXLvDNN99gNBrp3bu31IzB8bu/vvZDTEwMBw4ccNZ2SElJYfv27fTp0webzUZycjIVK1Ys0kgzIUTZlZSURI0aNW6oT1anTh2WLl3qsu362my5ubmMHTuWdevW0aZNGypVquT8YuL8+fMu+15fD89sNjtHEl4bx/V/q2rXrk1ubq7z8fHjxwkJCWHlypUu+126dMmZ9C+opKQkatWq5Uyo3MztYi/M+1DYtm9lyJAh/PWvf3U+PnjwIG3atMHPz88lQSeEEEKUJZLQ8YINGzY4fx44cGCRbqyPHTsGgK+vb6kbnQOOlZxCQ0NJTU3l+PHjRfomrH///hw+fBhd14mLi/NIQifP5cuXAccooqCgIPr16+extosqMjKStm3bsnnzZgD27dvH77//ztChQ296jKIoLkUek5KSuHTpEgCzZs2iXr16jB07tkjJxPIoOjqaV155hT179rBr1y5yc3Od33gvXLiQLVu2OIspx8bGOpdJF0LcGcLDw7l48eIN21NTU28Y4Xn93/ZZs2YRFxfH3r17nftevnyZOXPmoOt6oeIICwtzfqZf6+LFiy6f68HBwWia5hyZcq0+ffoUqs+KFSveNuFSEJ58H4qiQYMG1KtXj5UrV0pCRwghRJkly5Z7Qd6qTy1atKBevXpFaitvKkjbtm1L5ZBhRVFo27Yt4PhmrSgxRkZGOpcxDwoK8kh84Ijx7rvvdl58r1mzhoMHD3qs/aJSFIWhQ4dy7733Oi/UC5uIadasmUtx5MOHD/PHH394NM7yIq+Y8oMPPsgTTzzhvOHIzs4G/ldMecaMGezevduboQohSljXrl1JSUlx+fxMS0vj999/p2vXrrc89uzZs1SsWNEl8TN79my34ujcuTPHjh1jx44dzm2nTp1i06ZNLvsNHDiQrKws/v73vzNt2jSX//72t78591u3bh1Lliy5ZZ/9+/fnwoULLFiwwGV7cnJyoWL35PtQFJmZmZw+fbpUrHIphBBCuEtG6HjB2LFjOX/+vEcuIu6//36PtVVcOnfuTN26dalYsWKR2xo4cCAtW7b0+PLsUVFR3H333fz666+AY9h3gwYNPNpHUUVHR1O/fn0uXLhAZGQkmqbx66+/kpqaSteuXW9Z0NJoNDJp0iT27dvHkiVLSEtLc47gyc7O5sqVK4SFhd3x07BuZfjw4VSqVIn4+HhnMeU8e/fuZcOGDVStWlWKKQtRjnXq1ImJEycyduxYnnzySYKDg/n2228JCwvjhRdeuOWxgwYN4u2332bChAl0796d3bt388svv7g1xbdbt24MGTKEoUOH8sQTT2A2m/nqq68IDg52+ezp2LEjzz//PD169ODee++lTp06pKSksGLFCjp27Mgbb7wBFGyVq06dOvHss88yadIkHnjgAaKioti7dy+HDx++oTBySb0PhZFXFBkcI5nyfn7++eeLtV8hhBCiOElCpwSdPn2aCxcu0LRpU48tk2k2m0tl7ZxrKYpCtWrVPDKUWlEUIiMj0XWdPXv2EBAQ4NZypflp3749wcHBXLhwgdjYWI+06Wk+Pj7Oc+fSpUvOaViHDx+mffv29O/fH7PZnO+xiqLQpEkToqKinAWXNU1j6tSpXLp0idq1azNkyJBSnRz0Jl9fX/r370/fvn1JSkpC0zRnDZ6VK1eSnJzMkSNHiIuLo3r16kyaNMk5Gk8IUX58/vnnDBgwgD/++INTp07x4IMPMmHCBGdtLT8/P8aMGXNDcd/o6Gg2bNjADz/8wObNm6lduzYbNmzgzTff5K677rrlsa1atSIkJMRl28yZM5kxYwZbtmyhQoUKfPXVV7z99ts3fO68+uqrDBo0iEWLFrF582aqV6/O22+/7Rw9CwVb5Qrgn//8JwMGDGDx4sXs2rWL5s2b8+677xYq9oK8DyaTiTFjxjivbwrzvuRnzJgx2O12li9fjqIoBAUFMW7cOMaOHeuRL5uEEEIIb1H0kpywfAdLTk7mk08+wW63M2TIENq3b1+k9nJycvjyyy+5cuUKU6ZMKRWrM91Keno6X375JQCPPPJIkW90ExIS+OGHH1AUhSlTplC3bl1PhOli06ZNJCcn06NHD4KDgz3eflHpus7MmTPZu3evc1vbtm1vWVvnehaLhbfeestZRFNRFAYNGuSc2iYKZsuWLfz666/Y7XbnthEjRtC6dWuysrI4c+YMVatWlWLKQpRCe/fuZcaMGbz44otlqq7Y+fPnCQ8Pd3nctGlT/v73v/PYY495MTIhbm3p0qUkJSVJ7SIhhPAAGaFTAnRdZ+nSpc6bvaIst50nPj6eM2fOAI7lykt7QufMmTOkpqYCsHnzZnr16lWk9vLeQ13XWbx4MU8++aRHh2tnZGQ46wQcPXqURx999KarSnmLoiiMHz+ejRs38ttvv2G1Wp11XgrKbDYzadIkfvnlF1JTU9F1ndWrV0tCp5DatGlDdHQ0e/bsISEhAYPB4FwK/fvvv+f48ePOYsrt27enYcOGXo5YCFHWffDBByQnJ9O+fXsuX77Mt99+S+3atZk4caK3QxNCCCFECZGiyCXgyJEjHDp0CHCsEpG3ak5RXLtceWRkZJHbK27Xxnht7O5q2LChs83k5GSPF6cNCAhwDu0+f/4833//vXMp69JEVVU6duzIs88+y+jRoxk+fDgAW7duZf78+di9z6MAAI7SSURBVAUqVlmnTh2effZZ+vbtS0REhLOwZ1ZWFhs3bix0wcs7VV4x5cmTJ3Pfffc5E4B5idy8YsrTpk3jxIkT3gxVCFEOvPXWWwwaNIiDBw9y7tw5XnnlFdatWycjAYUQQog7iIzQKQEpKSnOn/v164fJZCpSe7quc+TIEcCxYlZZSOgEBwcTERFBcnIyp06dIicnB19fX7fbU1WVgQMH8tVXXwGu77EnKIrCxIkT+eyzz7hy5QrHjx/n8OHDLitFlSaVKlVyForOzc1l3rx56LrOtm3b6NWrF126dLnlCmNGo5Hu3bvTvXt357b58+eTkJCAoii0adOGPn36lKnpCKXFpEmTWLdunbOYsqqqzt/Fn3/+SUJCAjVr1pRiykKIQlFVlREjRjBixAhvhyKEEEIIL5GETgmIjY3l4sWLhIaG0qxZsyK3l1fQLzU1ldatW5eZG8DWrVuzaNEiKlSo4JFpZ3Xr1mXkyJEkJSXRqVMnD0ToqlKlStx3333O0TnXF2MsrUwmE3Xr1uXw4cPY7XZ+//13Ll++zJAhQwrVTl7dIF3X2bx5M4mJiTz11FOS1CmkgIAAZzHl06dPYzabqVKlCuAopmyxWDh58iRxcXHcddddTJo0qUjJTiGEEEIIIcSdQRI6xUzXdXx8fLj77rs92u4jjzzC2bNnnStClAUdOnQgIiKCSpUqeSShA44VLlq1agU43mtPJ7eqV6/OSy+9hKIoGAwGrFYrNpsNPz8/j/bjSaqqct9997Fy5UrWrl2LruucOnWq0O3079+foKAgZ9IhPT2d1NRUSei4SVVV51LxeXr06MGKFSuc07KOHTtGcnIyd911FxcvXiQlJYWaNWvKFAohhBBCCCHEDSShU4y2bNnC4sWLiY2NZfDgwR5tOzAwsMwtiawoinOZZ0/7448/WL16NT169HCZNuQJecmnK1eu8P7772O32xkzZoyz6G1pZDQa6devH02aNGHfvn3ExMQAcOrUKY4ePUrz5s1vu3KXwWCgS5cutGjRgo0bN+Ln50f16tXJycnht99+w8fHh86dO0uyoQi6detGmzZt2LNnD/v27SMkJISaNWtit9v573//S0ZGhrOYcufOnYtlNTchRMmz2WwYDAbnlxDXP86PpmkAHl0AwB0FiVUIIYQQJUOKIheTS5cusXDhQiwWi8uy0kWVnZ3NZ599xtSpU7l8+bLH2i0pOTk5fP7553z00UcejX/fvn1YrVaWL1/uXP3L09LS0sjOzsZisTB79mxOnjxZLP14Uo0aNejbty+VK1dG13W++eYbli5dyocffsiOHTvQdf22bQQFBdG3b1+6dOmCqqrs3r2bTZs2sXbtWv79738TFxfnvNEQhZdXTPm+++7jnnvuwWAwoKqq82Ypr5jyN998w6VLl7wcrRCiqK5cuUJgYCCLFi3K9/HNTJ482ev1cgoaqxBCCCFKhiR0isnvv//uXBXJE3Vz8mzdupWkpCTOnDlTJlfKSUlJ4fjx46SkpLB+/XqPtdu0aVPAMe1qyZIlBUpUFFa1atWcK5RZrVa+++47rFarx/spLrquEx4eDjgSaz/++CO///57odupW7eucwWnnJwcFi1axMaNGz0a651OURQef/xxunbt6hxJ5evri9FoRNM05s2bx4cffsiiRYs4depUsZzvQoiSkTel19sjb4QQQghR9sjVQzGw2Wzs2rULcHz77skpQNcu+V1WivReKzw83DnywBPLl+fp0KGDc5WnI0eOFMvoJUVRGDFihLNu0ZUrV0hLS/N4P8Ulr7ZO3vQrgJ07dxa6ndDQUJ599lln7SIAi8XikRjF/wQHB9O/f39efvllnnrqKZ555hkCAwPJyspiy5YtnD17lri4OD755BO+++47Zx0eIUTZ4u/vT1paGgMHDiz0sZqmFejfvs1mK5bEr91uz7ddXddd4iqu/m/WvowaFUIIcaeQhE4xUBSFmjVrYjAYuOeeezxWQNdqtXL8+HHAcVMdFhbmkXZLkr+/PzVr1gTg3LlzpKene6Rdo9HI0KFDMZvNVK5cudhWCTIajUyaNImePXsybNgw54iXsqJChQqMHj2ae++9lyZNmjhrO2VmZrJv3z5yc3ML1E5gYCAjR47k6aefZvz48XTt2hVd15k1axb//ve/+fPPPyXB4CGqqhIZGUlQUBDg+DfUtWtXl2XoExMTyczMBBx1knbt2sWVK1e8Eq8Q5Zmmabz66qvUr1+f4OBgmjVrxscff+zyeXf06FFGjhxJWFgYgYGBdOzYkeXLl9+0zfymMSUmJtK7d28CAwOpWrUqTz75ZL7/pkePHk3Lli1vGXNKSgqBgYG8//779OzZk8qVKxMREcELL7xwwyjTwsberVs3HnzwwRu233vvvfTq1cul/7feeotevXpRpUoVqlSpwjPPPENKSgoPPPAAVatWJTQ0lNGjR3Px4kWXtlJTU3n00UedBeJbtmzJ4sWLb3h9H374Id27dyckJIThw4ff8j0RQgghygspilwMDAYDjzzyCFarFbPZ7NF2TSYTubm5tGzZsswWJGzZsiUnTpzAaDR6bLUrgPr16/PXv/4Vk8lUrO+Nr68vvXv3BhxJtrVr1xIQEECbNm3KzJD56OhooqOjnY+/+eYbzpw5Q3BwMCNHjqRevXoFaiciIoKIiAjAkRTavXs3AEuXLmXbtm2MGjWK6tWre/4F3MEURaF///507dqVPXv2cOjQIapXr05QUBDp6el8/vnn2Gw2ZzHlHj16OJOoQoiimT59Ol9//TULFiwgJiaGEydO8OWXX5KYmEiTJk3Izc1l8ODB1KpVi+3btxMUFMQHH3zAsGHD2LBhQ4GmYOfk5DBkyBCaNWvGgQMHMJvNvPrqqyxevJj+/fu77GswGAr8d/Sdd97h22+/pVevXmzdupUJEybg6+vLm2++CeBW7I8++iiPP/447777LqGhoYAjwbJo0SK++OILl32//fZbvv/+e2JjY/ntt98YM2YMP/74I3/729+YOnUqZ8+eZeDAgfzjH//go48+csbUr18/rFYr8+bNIyoqih9//JFx48bx559/OqdBA3zwwQdMmzaNrl27evTaSwghhCjNysbdZxmhaRobNmwgLi4OwOMXFKqq8sQTTzBhwgSPr+RUkvIKwD7++OMeXwLbbDajKApbt25l7dq1xT5KJCEhgZUrV7JgwQKWLVtWrH0Vp7zRHunp6Xz99desWbOm0G34+/vTs2dPZzLt3LlzzJ0715NhimvkFVPOGyEFjhFkJpMJ+F8x5S+//FJGSwnhISdOnKBatWq0bt0ao9FI3bp1effdd2nSpAkAc+fOJSkpia+//ppatWpRsWJF3njjDaKionjvvfcK1MfcuXM5d+4cn3/+OZGRkYSFhfHhhx/mO816zpw5bN++vUDtPvTQQ9x9991UqFCBrl278tJLL/Hpp5+SnZ3tduzDhw8nMDCQGTNmOLdNnz6doKAghg0b5rLvSy+9RKdOnfDx8WHIkCE0aNCAmJgYHn30UQICAqhbty4jRoxwXkMB/Pzzz+zZs4cvv/yS1q1bU6FCBe677z4GDRrEu+++69L+M888Q+/evSWZI4QQ4o4iCR0PiouLY+HChSxatIjDhw8XSx/h4eE0adKkzIwEyY+iKDRq1Mg5ssPTkpOT+eWXX1i2bNkth4p7QmRkpPN3sW7dOpcL0bJk/PjxLktir1692q0aBL179+bxxx+nRo0aAFSpUgVw1FO4dOmS1DUoZv7+/jzxxBMuxZTzakvl5uby9ddf869//UuKKQvhpnHjxnH+/HnatGnDa6+9xooVK1ymqu7du5datWoRGRnpcly7du3Yt29fgfrYu3cv9erVc454AceXFbebWnU7bdq0ueFxTk4OR44ccTt2Hx8f7rvvPr7++mtn3ZxvvvmGiRMn4uPj47Jv/fr1XR4HBwfnu+3aKVdbt24lNDSUli1bYrPZnP+1adOG+Ph4l2OvHXUqhBBC3ClkypWHZGZmsmrVKsCRsAgJCfFo+1lZWcycOROTycTYsWOLrUZMSbHZbPz444+kpaUxfvx4582nJwQGBmI0GrHZbKxfv562bds6b2o9rWrVqtxzzz3MmzcPcKxu1qZNG+coibIiJCSEyZMns3HjRrZu3epMGmqaRmpqKqGhoQVOIlavXp3HHnuMy5cvExgYCMCyZcv4888/CQsLY/DgwTRs2LA4X84dLTQ0lP79+9O3b19SU1MJDAzEYDBw+vRpZ6I5Li6OuLg4YmJiGD16tJcjFqLsaNiwIXv37mXVqlXExcXxwgsvkJmZyaJFi2jcuDFAvolSTdMKPBVYUZQit5Gf69vMe3xtm+70O2XKFD744ANWr15NdnY2p0+fZsqUKTfs507suq6Tmpqa7zXV9QkjGZkjhBDiTlR2h3mUMnFxceTk5ACOGjGeLpa7YcMGjh49yoEDBzh9+rRH2/aG1NRUdu/eTVJSEqtXr/Zo2wEBAXTs2BFwrMDhzvShwmjTpg29e/dGURTCw8NditWWJaqq0rFjR+ewdYCffvqJ999/n48//piUlJQCt6UoCsHBwc4k0Llz5wC4cOEC06ZNY86cOTJap5ipqkp4eLgz+VutWjXatWvncn7+//buOz6qMnv8+OfOpPeEFkpIAUIJvTdFqoBIB8WOig37Nre5v/2q67q6a9sFFVFQFClKBxGk9xZKKAmBkJCQQnqfen9/xIyEnsnM3JTzfr18mSn3Pmcml8ncc5/nnLi4ONvPp0+f5tChQ1JMWYhb8PHx4d577+Wf//wnR44cwcfHhy+++AKALl26kJKSQmpqqu35qqqyb98+27KsW+ncuTOJiYlcvnzZdp/BYLhuV8Lb7XIFsG/fviq39+/fj7e3N1FRUTWKvXXr1owdO5bPPvuMefPmMWLECNs+a6p37940atSI/Px8iouLq/yXk5PjkDGEEEKIukwSOg5S2WHGw8ODUaNGOXz/V7b4rovdra4WFBRkO7E8c+aMw5d+DB06FD8/PwCXnKAOHz6cP/3pT8yePRudTofRaKwXy1kqvzCnp6fz8ccfc+DAAbv2M2HCBCIiImy3jx49Wi8Sk3VJZde9P//5z0yePJmePXsyffp0oKIuyMKFC1m+fDn/+Mc/WLBgQbUSeEI0FH/84x/5+OOPSUxMpKSkhN27d5OZmWmbdTh9+nQiIyN54oknSEhIIDMzkz/+8Y/Ex8fzhz/84bbGmD59OqGhocyaNYukpCTS0tKYPXs2mZmZ1zz3drpcVZo3bx5Lly4lLy+PjRs38q9//Yvnn3/e1omzJrE/88wzrFu3jk2bNl2365W97rvvPlq3bs1DDz3E8ePHKS4uJjExkblz5/L73//eYeMIIYQQdZUkdBzk7rvvZuzYsTzzzDMOXT4EFcmiyitmzZs3d/j+teDp6Wm7gldQUFDlSqQjeHl58eyzzzJmzBgmTJjg0H3fiL+/Pzqdjvj4eN544w3+97//1fnZDlOnTrXVwbFYLKxatQqj0Vjt/QQHB/P0009z33330bx5c9q2bUvTpk1RVZXTp09z4cKFepEAqwsqiylPnz6dLl26AODt7W1brlBZTPnKIqdCiAovvfQSqampTJo0iYiICF5++WX+8Ic/8MQTTwAVF3XWrl1LkyZNGDp0KDExMRw4cIA1a9bYZrkoioJer7fNYLz6tqenJ2vWrMFsNtOnTx8GDhxIYGAgkyZNumYGaHW6XP3tb39j4cKFdO3alaeffppHHnmE119/3fa4PbFXGjZsGFFRUbRo0YKxY8dWeexG27i5uV3zenQ6XZXX4+HhwcaNG4mMjGTGjBmEh4czZcoUzp07xyuvvHLT/QshhBANgaLKWVSNWCwWiouLbSfzzlBWVsYbb7yB1Wq1tSuuDw4fPsyyZctQFIXf/OY3Tpt5pKoqRUVF+Pj4OLRN+o389NNPtmVkrVu3ZtasWXWups6VzGYzmzZtYu/evYSHh9tOXCwWS42Xlx07dozFixcDFbUp7r333noxA60uunz5MocOHeLo0aMUFBTQsWNHHn30UfLy8pg/fz4mk4nOnTvTo0cPWrZsWaNaHkLUJidPnuTrr7/md7/7ncM7L9YGGRkZREZGsm7dOoYNG+aUMcrLy2nXrh2zZ8/mtddec8oYov5Yv349KSkpvPzyy1qHIoQQdZ4URa4Bq9XKV199RXx8PCNGjGDEiBFOGcfb25tnnnmG7Oxsunfv7pQxtNCzZ0/c3d3x8fFx6kn8/v37WblyJWFhYTz11FNOT67069ePQ4cOUVhYSEpKCmvWrLmmfWtd4ubmxpgxYxg1apTtJH7Hjh1s3LiRtm3bMnnyZLtnjV1Z3Ds+Pp7ExERmzZpVZXmWcI0mTZrYiikXFxfbTmyTk5PJzs4Gfi2mfOedd15zFV4I0fCoqorRaOSdd97BaDTy9NNPax2SEEII0aDI/NQaiI2NJT4+HuC6a9sdqXXr1vTs2bNeTSlWFIWuXbvStm1bp45TWZD34sWL7Nmzx6ljQUXb1ccee8y2hMVZLexd7cop7fHx8VgsFuLj43n//fc5ceKEXfts3759lS5nFoulSr0o4Xo6nY6AgADb7KuYmBh69epVZTZWQkICUJHU3r9/PwcOHLDVERNC1C7OXJK0fft2GjduzOLFi/nyyy8JDg52+BhCCCGEuDGZoWMno9HIxo0bbbf79evnlHFKS0tZunQpfn5+TJw40SVLhlzJarWybt06MjMzmTp1qsPbvUNFl4y9e/eiqipbt26ld+/eTp9W36JFC2bNmsWePXvq1ayqSmPGjOGrr76isLCQ8vJylixZQmRkpK0QdXV06dKF9u3bs3v3bnJychgwYACqqnL48GEyMjLo06ePrY6PcD13d3emTZvGPffcQ1xcHOnp6fTo0QOAI0eOsGLFCgBWrlxJdHQ09957L40aNdIyZCHEFZo1a+a0em533XUXRUVFTtm3EEIIIW6tfmUHXCg9PZ3CwkIAOnTo4LRZJtu2bbPNWBg4cCAtWrRwyjhaKS4uZvfu3QBs3LiR++67z+FjtGjRgl69enHo0CHKy8tJSkqic+fODh/namFhYbbXU1BQwKFDh+jQoQMtW7Z0+tjO1qpVK15++WVWr17N0aNH8fHxqbJ8qro8PDwYOnSo7XZxcTHLly8HYM+ePQwaNIgRI0bg6elZ49iFfSqLKV8pJCQEvV6PxWKxFVM2m808+eSTGkUphBBCCCFEwyEJHTuFhoYSFRWFwWBg/PjxThunMpmjKEq96G51NR8fHzw8PDAajSQkJGC1Wp0yLXz06NHk5ORgMpk0qc+yYcMGjh49ytatW5k1axbh4eEuj8HRfHx8uP/++xkzZgyenp64ublx5swZNmzYQGRkJKNHj7Y7yePl5UVYWBgXL17EarWyc+dOLl++zGOPPebYFyFqJCoqihdffJEjR47Yiim3atUKqKi988UXX+Dh4UHXrl3p0aOH7TEhhBBCCCFEzUlCx06enp489dRTTh0jLy/PVv+ldevW9bL7hpubG+3atePkyZOUlJSQlpZGWFiYw8fx8/PTtFhjZdFns9nMwoULefbZZ2nSpIlm8TjSlYnGgwcPkpmZSWZmJmfOnGHatGm0adOm2vt0c3PjqaeeYtu2bWzfvh2z2SzT+mupZs2a2Yopm0wmW+2o8+fPYzAYMBgMtmLK99xzD3fccYfGEQshhBBCCFE/1J8Kuy5SWFjI4sWL2bZtm9PHurKjfNeuXZ0+nlaufG1XvmZn2bdvH9988w05OTlOH6vSXXfdZVuWV1payqZNm1w2tisNHjzYtiwqPz+fBQsWUFBQYNe+3N3dGTlyJK+++iqTJk3ikUceASpanb///vusWbOGsrIyh8Uuakan0+Hp6WnrhDZgwAC6detWpZhycnIyAAaDgS1btrBv3z6n1fYQwpU+/vhj3nnnHa3DuKHaHt/t2LZtG/fff7/WYQghhBC1iszQqQZVVVm2bBlnz57l2LFj9OzZk4CAAKeNFxISwiOPPEJhYeE1tSvqk65du2I2m9Hr9bRu3dqpY1ksFlatWoWqqmRnZ/PCCy+4pHOYm5sbDz30EPPmzSMtLQ1/f3+nj6mFyMhIXnrpJZYvX8758+exWCw13mdISEiVouO7d++2zQI6evQo48ePp1u3bjUeRziWl5cXM2bMoLS0lLi4OHJzc+nfvz8AW7dutSXFV69eTfv27Zk0aZJTP0+FcKYjR45UO3ldUlLCY489xmuvvUavXr1ueb+r43OFLVu2MH/+fL755ptbPvfixYusXbvWBVEJIYQQdYckdKohISGBs2fPAhAUFOSSJVCdOnVy+hhaUxTFYV9ab0Wv19OiRQvS0tJIT0/nyJEj9O7d2yVje3l5MXv2bLKysmxdm5xVM0hLISEhPPnkk1y4cAEfHx8CAwPJyspi06ZNtGzZksGDB9eoW9tdd93F4sWLMZlMlJSUsGTJElq2bGlb1iZql+sVU27RogWKoqCqKlarldOnT+Pr68vUqVOBisSroij17t+GEFcymUysXbuWmTNn3tb99VFycjLr1q27recOGTKE7777zskRCSGEEHWLJHSq4cplVmPGjKmylMDRSkpKWLVqFY0bN2bEiBEN4sRm586dXLhwgXvvvdcp7csrjR07lnnz5gGwfft2lyV0oGJZSmhoKAA///wzO3bsYNCgQYwcOdK2VKU+0Ol0REVF2W7v2rWLEydOcOLECY4dO8Z9991nex+qq1OnTvzmN79h3bp1nDhxAi8vL9syL4PBgLu7e4P491KXde3alaZNmxIbG8vRo0cpLCy01Vo6fPgwy5Ytw8/Pj27dutGzZ8960RlO1B9Go5HPP/+cvXv34ufnZ0tEXs1isbBkyRK2bt1KeXk5Xbt25cknnyQ4OJjy8nJbwuadd97hyy+/BOCTTz6xdYm78v4FCxbYLiL99NNPrFu3jpycHNq3b88zzzxTpSbb7cb37rvvkpaWxgcffHDD15qfn8+sWbP4wx/+wJ49ezh8+DDt27fnT3/6E5cvX2b+/PmcPHkSHx8fRowYwdSpU6v8LbvZc7Zt28ann36KyWRi2rRpQMVnw1//+tfrxnL+/HkWLVrEuHHjAFi6dCl79uy5Jv7777+f2bNn2+p1/fjjj3z33Xf88Y9/ZMmSJZw7d46WLVvy3HPPERwczLx584iNjSU4OJjHH3+8yjLwym3/9re/sXDhQpKSkggLC2P27Nk0b978hu+bEEII4Spy1lMNlQmcqKgop9e02bx5M8ePH2fLli229uj1mclkYt26dZw8edLpU6rbtGlj+/05Myl3KydOnLDVEtm5c6dmcbhC586dbUmW9PR05syZQ35+vt37CwoK4sEHH+S1117jlVdewd/fnwsXLvDmm2/y5ptvsn//fqxWq4OiF84QGhrKmDFjeO2113jrrbfo0aMH8GudneLiYnbv3s3HH3/M0aNHNYxUiKoefPBB3n33Xfr160fPnj159dVX2bhx4zXPe+yxx/j9739Pp06dGDZsGD/88AMDBgwgPz8fd3d3pk+fDsDQoUN5+OGHefjhh/H19b3u/ZXFxn/zm98wdepU/Pz8GDNmDEeOHGHQoEHk5uZWO74DBw6wdevWm77W8vJy1q5dy8SJEzlz5gz33HMPw4YNIysri759+/Ljjz8yYsQI2rZty+zZs3nuueds297qOVFRUfTv3x+9Xm97nSNGjLhhLFcvuUpISLhu/GvXriU1NdV2Oykpie+//55JkyYRGBjIyJEj+emnnxg2bBijRo0iNTWV0aNHU1BQwF133UVKSso12w4dOhQvLy+GDx/O3r17GThwIJcvX77peyeEEEK4gszQqYYZM2Zw4cIFoqOjnTqbQlVVTp06BVQkHHx8fJw2Vm2h1+vx9fWlpKSEhIQEzGZzjZbl3Mp9991H9+7dNW2jPGTIEJYsWQLA+vXrCQ0NJTo6WrN4nCk6OprZs2ezZMkSsrKyMBqNFBYW1ngm1pXbp6enYzKZMJlMrFixggMHDjBz5kz8/PxqFrxwKkVRqnyejho1irKyMk6dOmWrwZSRkQFAbm4ue/fuJSQkhC5dusjvVrjc1q1bWbt2LXv37qV79+5AxazPmJiYa563fPlyNm3axODBgwGYOnUqHTt25F//+hf/+Mc/GDNmDAB9+/Zl7Nixtm1vdP+uXbuYM2cOc+bMsc3umTFjBgMGDODf//43b7311m3HB/C73/2OkpKS23rdEyZM4H//+5/t9osvvoi7uzsbN260zZDs2LEj06ZNY+bMmfTt25c333zzls/p1q0bOp2O8ePH31Yc9jKbzXz55Ze2emzh4eGMGjWK++67jzfeeAOAadOmsXnzZlasWMFLL71UZdu3336bGTNmABUzgGJiYvjXv/7Fu+++69S4hRBCiFuRhM5tyM/PJz8/n/Dw8Ot+KXK0zMxMW/HCqKgo25W5+kyn0xEdHU1sbCxGo5ELFy7YukI5g16vt9UnunjxIt7e3i6vwdKjRw9ycnLYvHkzAKdPn663CR2Ali1b8sILL3D06FHc3d0JCwujvLyc3bt306RJE7p06VKjRGmfPn1ISUkhNjYWgLS0NPbu3cvIkSMd9RKEC/j5+fHggw9SWlrKyZMnKS0tZcCAAUBF4jMuLg6ANWvW0KlTJyZPntwgkt6idti5cyeRkZG2ZAlUfLZVFvyutGvXLkJDQ23JHAB/f39GjRrFrl277Bp7w4YNeHl58dBDD9nu0+l0TJo0ieXLl/PWW2/ddnxAtZotXD1zZteuXYwbN86WqAEYN24c3t7e7N69m759+97Wc1zF29u7yngREREA3Hnnnbb73NzcCAsLIy0t7ZrtJ0yYYPvZw8ODe+65h927dzsvYCGEEOI2SULnFvLy8vj4448pLS1lypQp9OnTx+ljmkwm28+dO3d2+ni1RefOnW0n41e+B8505swZFixYgLu7O88++ywtWrRwybiVhg8fjqenJ+fPn7edtNZn7u7uVf4N7dy5k59//hmomMUzZcoUAgMD7dq3m5sb9913H3369GHDhg3k5+fTrl07ALKzsykoKCAiIkLTZXbi9vn4+FzzeRsVFWVL6FitVuLi4mjevDnDhw9HVVVKS0vx8vKS37FwmtzcXIKDg6+5PygoCKPRaLudk5Nz3ecFBweTk5Nj19j5+fno9XoeeOCBKvdfvHjRtlTxduOrrqs/l2/0+oKCgmyv73ae4yoeHh5VLhhU/nxlsqny/quX63p6el6TNA4MDCQ7O9tJ0QohhBC3TxI6t7Bx40ZKS0uBioKrrtCqVSsmTZqEwWBwSQKptujUqRMTJ07EZDLRvn17l4xZ+TutrOHz5JNPurQ4saIo3HHHHbbijenp6aSkpNC9e/drvmjWR1cWu01ISODDDz/k5ZdfrlH76qioKGbPnm27XV5ezocffojJZCI0NJQJEyYQGRlZo7iFNgYOHEhUVBSxsbEcO3YMo9FoS9r9+OOPbN++HX9/f7p27Urv3r2laKlwuPDwcJYtW3bNsuBz584RFhZmux0ZGck333yDwWCo8lmemJho+/y50d+aG90fERGBt7c3Dz/88DWPVdYou934aioyMtLW9bNSYWEhmZmZttd3O8+pyd9bLy8vysvLq9yXl5dnW6rpKAaDgYsXL1Z5/86dOyd/R4QQQtQKUhT5JlJTU23FOH18fFzWWltRFPr168edd97ZoLr1KIpC//79ueOOO1z2umNiYggJCQEqvqAlJCS4ZNzrsVgszJ8/nxUrVjBv3rwaXU2tKzp16sTMmTPx9/cHoLS01Hal2VEsFottxkZGRgaffvopO3bscOgYwnWuLKb8+uuv07p1awAuXboEQFFRka2YcmJiopahinpoypQplJaW8t///td237Jly2wzxypNnjwZq9VapcbKzp072bx5sy0hExAQgJeXF1lZWVW2vdH9M2bMwGAwUFRUxPjx423/de/e3ZbYuN34oKLL1csvv2zX+/Dwww+zatUq26xagDfeeIOAgABbPZzbeU6TJk0wGAy2ZebV0alTJ1JTUzl58qTtvn//+992vZ5beeutt1BVFYDY2FjWrFlTZembEEIIoRWZoXMTV55YjhgxAm9vb6ePWVxczI8//khYWJiteF9Dc+zYMeLj4xkxYoQt2eIsbm5ujB49mm+//RaACxcuuGx20NX0ej1+fn4UFxeTmprKt99+y8MPP1zvl4+0b9+eV155hZ07d2K1Wmnfvj2qqnLy5EkCAwNrfFXZ19eXJ598khUrVthqIxw6dKhK7QRR902ePJm1a9dy5swZLBYLVqvV1kktOTmZw4cP07x5cymmLGqkdevWfPDBB7z00kssWbIEDw8PcnNz6dmzZ5XnhYWF8cknn/D888+zcuVKAgMDOXz4MM888wz33XcfUHER49FHH+W1115j1apVuLm52dqTX+/+Vq1asWLFCmbNmsXbb79NVFQUGRkZlJWV8dZbb1UrPqjocmXvRYyZM2dy+PBhhg0bRu/evbl8+TKXL1/m888/t7VQv53nDBkyhKioKAYPHkyHDh1u2rb8amPGjOGee+5h6NCh9OnTh9TUVFuRZUfy8fEhMzOTrl27EhoayqFDh5gyZQoPPvigQ8cRQggh7KGolZccxDVKSkr44YcfCA4OZsyYMS45sV62bBmHDx8G4O9//3uDWHZzJVVVef31123Lrio7eTh7zJ9//pnU1FQmTpxY485LNZGRkcHcuXNtS8FmzJhBt27dNItHKwcPHuT7778HoHfv3owbNw4vL68a7dNqtXLw4EFOnz5N37596dSpE1lZWRw5coR27drRpk0bR4QuNFZaWsrp06dRVZWePXui0+n48MMPSU9PByqWpnTt2pXJkyc3iILz4tZOnjzJ119/ze9+9zt8fX1va5usrCyOHj2Kj48PvXv35tSpU5hMpmsuxBQXF3P48GEMBgOdO3e+bp22M2fOkJycjMFgYMyYMbi7u9/0fqvVyokTJ8jIyKBVq1a0b9/+mq6QtxPfgQMHKCkpYejQoTd8nQaDgY0bNzJgwABbEuZKFy9e5NSpU7ZZzNcrUH6r5xiNRo4cOUJ2djaNGjW6YT25hQsX8tJLL9kStZVOnjxJRkYGbdu2JTw8nLVr11bpYnnhwgVOnz5t6x4GFUtxf/rpJwYOHFilIcLOnTsJDg621S+cO3cuf//738nIyODcuXOcP3+esLAwOnTocMP3TNza+vXrSUlJsXuGmBBCiF9JQqcWsVqtvPXWW5SUlODp6clf//pXp7buro1UVeWdd96xFX98/fXXNU9q5ebmkpaWRocOHWxfqJ0pMTGRBQsWYDabeeSRR2zduBqSuLg4Fi1aZLsdHBzM7NmzHT6zYu7cubaZeF26dOGee+7RNKEnnGPjxo1s3bq1yn33338/3bt3x2w2U1hYSGBgYL2fDSeuz56EjnC9N998k+++++66y8ec5cqEjnAcSegIIYTjNKxswQ1U5rSSkpIIDg4mMzOTdevW0bNnz5teuXK01NRUSkpKAGjbtm2DS+ZAxRT09u3bs3//fiwWC4mJiS5pFV9p//797Ny5k2HDhtGzZ08sFgtz5syhuLiYESNGXNO61Rnatm3L7373O4qLi13edau2iImJYcqUKaxZswaj0UheXh5xcXHXbb1bE2FhYbaEzokTJ0hOTubll1+WNtj1zN13303Xrl05evQocXFx6PV6W9viRYsWcebMGfz8/OjWrRt9+/alWbNm2gYshLApLy9n+vTp7Nq1i3/9619ahyOEEELUKg0vY3AdFy5cYPXq1aSnp6MoCjqdDovFwp49e1ya0CkrK7P97MokRm0TExPD/v37Aa7pYOFse/fuJTs7m2XLlqGqKi1atKC4uBioSLi5SmBgIIGBgVitVhYtWsTly5eZPHmy7SS0vlMUhT59+tCmTRtbp7mOHTsCcPnyZby8vGyFlGti7NixNGvWjB9//JGSkhIKCwspLy+XhE491Lx5c5o3b15l2QVgaz1cXFzM7t27OXDgAM8//7wkdYSoJdzd3Xnuuef46KOPXP43cMyYMQ3m764QQoi6qcEndE6dOsXixYsxmUxAxWydypaXld1TXKVt27aMGDECRVHo3r27S8euTaKjoxk3bhxFRUV07drVZeNaLBb8/f3JyMhAVVWWLVtWZZaUq9rWX6mgoMDWwWPBggU8++yzDepEMyQkhBkzZthunz9/nnnz5qEoCkOHDmXYsGE1Wiaj0+no06cPMTExHDt2jMDAQEJCQsjNzWXFihX4+/u7pDi30M7DDz/MTz/9ZCumbDKZbInkY8eOcerUKSIiIqSYshAa0ev1jB49WpOxIyIiJKEjhBCiVmvwCZ2DBw/akjlXqzxxvrLtsTPp9XqXLOmpCwYPHuzyMVeuXMnZs2er3Gc2m20/Vyb6XCk4OJgOHTpw5swZysvL+fLLL3nllVc0ryuklbKyMlRVtRWyPnPmDE888USNZ9T4+PhUKcR5/Phx27Fw/Phx7rrrLoYNG+bw7ilCe82aNePhhx+mtLSUhIQEfHx8CA8PB2D58uWYTCaOHTvGmjVr6NmzJxMnTmyQy2GFEEIIIUTt0+DPTm62pOfIkSPMnTuXv/71r6xfv96pcRQXF7N27VpOnTrl1HHqkvPnz7N69WpycnJcMl5SUtINH1MUhR49ergkjqvNmDHDVksnPz+fCxcuaBJHbdCpUydGjhxpS6ykpaXZluc5UteuXW1LusxmM5s3b+bAgQMOH0fUHj4+PnTv3p3o6GjbfVd2mLNarRw6dIhLly4BFcnFzMxMTRK9QgghhBBCQD2boaOqKolpeSRczOFsWi7xKTlczi/FbLEC4O6mp3mIL9GtG9G2ZQgdwxtjNBpvuL/8/Hxbe8z4+HjGjh3rtNjXrFnDsWPH2Lt3L2+++SaKojhtrLpiyZIlFBQUkJGRwVNPPeXUsXJycmwFqa9nyJAhDBgwgEvZRZy6cJnEtDziL+aQll2EyWxBVcFNryPY34sOrRvTtmUw0WGNaB/WCJ2uZr9LT09PHnvsMVatWoWiKERFRdVof3WZoigMHz6c9u3bs2rVKvLz82nbti1Q0frWzc3NIbNoQkJC+M1vfsPPP//M7t27sVqttrbpVqsVRVGc8m80r6icuKQs2+fYhYwCDEYzFquKm17B38eTdq1CaNsqhHYtg+kc2RQPd+nM5CxTp05l0KBBHD16lNOnTxMUFESzZs1sxdIvX75sK6bcv3//67Z1rk3KDCZOnL/MubRcElJzSUzLo6TciNlixU2nw8vTjTYtgiuOsZYhdIlqgr9Pw5wNWF379+/HZDI5dHZpeXk5q1evZsiQIU5bauuMuG/H999/T69evTRfzpSdnc2WLVu499578fb2BireE6PRyB133KFpbLejLsUqhBDCOepFQqe03MTW2Aus2XOWtOwiFECnU7BYq3ZktxjNJGUUkJJVyMYD5wEI8W6CN2UEkIeO63dw9/HxYfz48U6L32w2c/r0aQC8vLxQVVUSOmBbVpSUlERJSYlT28k2atSIkJAQ0tLSrnlMBeLTy9j26RZOXrgMgP56x5fVQkZuCVl5pWw/loyqQtMgH+4Z0I4RvSIJ8LX/xCggIICHH37YdjspKQlVVYmMjGyQx0qrVq2YPXu27d9KTk4Oc+fOxWw2M2bMGPr27Vvj98XLy4t77rmHwYMHU1xcTMuWLTEajcydO5ecnBwGDhzIsGHD8PDwuO72KSkpKIpCWFjYTcdRVZW4pMus23uWvSfTsKrqdY8vo1klp7CM3NNpHDh9Cauq4uvlzui+bRjdrw2hIVLfxRmuV0zZYrHYkv2VxZQPHz7Mb3/721pZZycls4AN+xPZdCgJg8mCTqf8snTx1+eYLFZMpUaOJWZy4nzWL0lEHUO6tWZs/7ZEhzXS7gXUAZ988gkFBQUOTYzk5+fz6KOPsm7dOqcldJwR9+144okn+PjjjzVP6MTHx/Poo49y9uxZWrVqBVS8Jzk5OXUiSVKXYhVCCOEcdTqhY7FY+WHnGb7bcgqjyULl6ZsK15wMVdnuisfyyhVylXDS1ZY04xLB5ODl6YmnpyeFhYV07NiRyZMnO6Sjzo0kJSXZZgpFR0dLnY5fdOjQgaysLFRV5ezZs04vFN29e/cqCR0VKCSIdFpxKsGIwmXbYzc7vqxXnCVl5Zey4MdjfLXxOBPvaM8DwzvXeEZFWloan376KVDRjtmVndhqm8qkTXZ2tq0b2YoVKzh16hQzZsywzaqpicqOY1Bx8p6eng7Atm3biI2N5YEHHrDVXKl08eJF5syZg6IoPPjgg3Tu3Pm6+45PyeGj7w+QklWITqfYjp2bHV+qCuovyeeSchMrdsbz/Y4zDO4SxtPjexLkV/PXLG5Or9fz9NNPs3XrVlsxZbPZjMViQVVVtm3bxsWLF2nXrp2mxZSz8kr434pDHDmbUXF8/XJcWW92fPHr8We2WNl2NJmfj1wgulUIL07tS3izQFeELoRm+vXrR1FRkdZh3Ja6FKsQQgjnqLMJneTMAv6zdB/nL+Xb7rvxV9Qbqzz3tqInXWlNqVsT/t/jdxPeojElJSUEBgY6fQbElUt9OnXq5NSx6pJOnTqxY8cOAJd8YbnjjjsICQlhxYoV5BeXc4lWFCnBtoPEnuMLKja3qCo/7DjD3rhUXp3en/at7b/afeWMkI0bNxIUFKRZfZ/aIjo6mn79+tnq6cTHx7N9+3buvvtuh44TEhLCmDFj+Omnn7BYLBQUFLB69WpeeOGF6z5fVVVWrlxJVFRUlcLNRpOFbzfH8cOOM7bPl5udZN9MZRJoz8lUYs9mMHtSb+7o6toOfQ1Rq1atbMWUz507R0hICIGBgRQWFrJx40agoovimjVrGDBgAPfcc4/LkvWqqvLjgXN8vu6obcmxvcdXZXIn8VIeL320kQdHdmbyHR3Q6xv2hYe0tDQOHz6Mn58f/fr1u+5zVqxYgclkQlEUmjVrRkxMDI0aVf3st1qtHD58mMzMTCIiIoiJibnud47MzEzi4uJwd3enV69e152xajAYOHjwIDk5OURHR9OxY0e74r7alUu/jEajrSNg5ayQrKwsDh8+jKqq9OvX75rXCBAbG0tycjKtWrWid+/e1zxe2VmyU6dON0yAX+ncuXOcPHkSHx8f+vbtS0BAgO2xxMRE4uLimDhxIocPHyYlJYXu3bsTGRmJ0Whkz549FBYW3rCLZo8ePaosx9+9ezeKotC7d29OnDhBZmYmMTEx1yTyjx07Rnx8PAC+vr60adOGDh06VHlO5b569uzJgQMHyMrKonXr1mRmZnLvvfdWeW5paSlr165l4MCBttlDt4p169at13wnyMrKYtu2bYwfP952kePKOOLi4khPT6dr166213T8+HEuXLhAVFTUNb+Pym179OhBXFwcGRkZdO/e/ZazUYUQQjhHnUzobD50nv+uOGT3CfZ1/fIFqtDsxWvzd/OHBwbSp0MLR45wQx07dqRfv374+Pjc1heZhiIiIoIxY8aQlZVFz549XTJmTEwMBn0A//hmLyaLWpHFcVBCT1UhI7eE332ymcdGd2PynR1uvdF1NGnShNGjR/Pjjz8CFbUIOnbs6JDZKHWVoihMmjSJjh078v3331NcXExoaKhTxhoyZAidOnVi7dq1nD17lvbt2wNgMplIS0sjNDSUpk2b4ubmhtlspri4mM2bN9uWbWbll/D6/O1cyilCpeJExhGsVpXSchP/WryXg2cu8eKUvrg18JNuV/Dx8aFLly62276+vsTExHDy5Emg4oR99+7d3HXXXfj7+5Ofn09RUREtWrRwSvfEcqOZf36zm8MJGQ7db2VC6KuNJ9h7Mo2/PXYngTVYRlqXzZ8/n1dffZXu3bvj6elJamoqgYGBNG/evMrz1q9fT3l5OaqqkpqaysmTJ3nvvfd49NFHAUhPT2fs2LEYDAZiYmJISUkhMDCQxYsXV0mKzJ07lxMnThATE8OZM2cwmUxs2rSpSkLh559/5oknnsDX15d27dqxf/9+xo8fz9y5c22JxNuN+2qVS7/Gjh3L8ePH6d69O3379uWOO+7g9ddf58MPP6Rnz564u7sTGxvLnDlzmDZtGlCxpPyBBx5g69atDBgwgJSUFCIjI7FarVXGsFgsPProo/zhD3+46fcgi8XCrFmzWLVqFQMGDCArK4vk5GQ++eQTJk2aBMCmTZv4y1/+wvz588nKyqJt27aEhISg1+sZN24cJSUldO3alWPHjl33YsjVy5g++OADkpOTMZvNNG3aFEVR2L17N++88w7PPvusbbtTp07ZGmiUlJSwf/9+evfuzZIlS2x/nz/44AMuXLiA0WikWbNmNGvWjPvuu4/p06dz6NAhYmJibPv77rvv+M1vfkNiYuIN34+rY33jjTfo2rVrldd15swZHn30UZKSkmx/Fz/44APOnz9PcXEx7dq1o6ioiEOHDvHPf/6TjRs3kpOTQ2hoKNu2beOZZ57h7bfftu2v8jWUlpbSsmVLTCYThw8fvub9EEII4Rp1LqGzencC89bGOm3/KgpGk4U3v9rFb+/v75Ir3Z6enrYvIqKqIUOGuHS8o4kZvLloHxarip0XtG+qcjbFlxuOUVxm5OFRXeyaATZkyBDy8/PZt28fiqJUaa/ekHXo0IE//vGPlJSU4O/vT3l5OQsXLqS8vJy77777mqul9mrSpAkzZ87EYrHYTsoXL17MqVOnUBQFd3f3Kr+TymVal7KLeO2zLRSUGHBQHqeKyl1uO5pMUamRPz00CHc3KZrsSnq9nocffpj09HSOHj1KYmIiYWFh+Pr6UlRUxAcffEB5ebmtmPLgwYMJDg6+rX1nZmZy6tQpevToQVBQ0DWPlxpMvD5/O2dTndsZ8NylPP7wyc+8/dRQgv29nTpWbZOcnMxvfvMb3n77bZ577jkAVq1axf33339NYmTevHlVbq9YsYInnniC0aNH06xZMxYsWICqqhw/fhw3t4qvY3v27KGsrKzKdgUFBcTGxuLt7Y3RaKRfv3785z//4cMPPwQgNTWV++67j3vvvZd58+bh5uZGcnIyAwYM4K677mLGjBnVivtGMjMziY2NtS0h/OKLL3j33XdZtGgRU6ZMAWDhwoXMnj2boUOH0rhxYz777DO2bt3K3r17adu2LVarlVmzZmEymarsW6fTMXXq1Fte1Pr888/54Ycf2LFjh22GzWuvvcYzzzzDgAEDbAmL0tJS+vTpw+uvv27bdtq0aQQGBrJ37158fX3Jy8u77SXLZ86cYfPmzfTt2xeA999/nz//+c/MnDnTlqyZMWMGM2bMsG2Tn5/P4MGD+e9//8tvf/tb2/0nT57kxx9/5M4777Td17lzZ+bNm8cHH3xQ5bVOmTLlujOeHOHs2bNs27bNdsHsscce4/e//z2vv/46f/zjH4GKC0aPPPIIs2fPrjJLKC4ursrv/bPPPuO3v/0tI0eOtDUqEEII4Rp16vLtxoPnnJrMqaRSceL93nf7OHjmklPHKioqYvPmzVy8eNGp49RlWVlZbNq0yenty08nZ/N/C3ditlqdksy52rJtp1m27bRd2yqKwoQJE3jiiSd44YUXamUhVq3odDpbzauMjAySkpJIT09nwYIFLF++/Kad7arryhkWBoMBqJhxc/UY3bp1I6eglD/N20pBicHu5S+3S1XhcEI67363F8tVV8KFa1QWUn7hhReYOHEiOp0Oi8ViO5GtLKb8v//975rZCjeyePFiNm7cyH//+18uX75c5TGjycL/LdzJ2dRcp39+Wa0q6bnF/PnzbRSXOe7fU13w/fff4+fnx9NPP227b8KECTdcvpOUlMSmTZtYtmwZBoMBg8HAsWPHgIrC6wUFBZw7d872/Ostr5k1a5atA5OHhweDBw+2NVKAiuOitLSUd955x5YYCg8P58EHH7Qllaob9/U8/fTTVf7WzJ8/n8GDB9tO6gEeeeQR2yyjytimTZtmO8nX6XT86U9/umbfOp2Or7/+mqlTp940hm+++YaJEydWifvPf/4zJSUlrF27tspzX3nlFdvPRUVFrF27lhdffNG2XC04OJjZs2ff1msfMGCALZkDMGzYMMrKykhOTq7yvMp/1ytWrOCnn36idevWtqXAlXr37l0lmQMV7+3ixYttteAOHDhAbGwss2bNuq347DFgwIAqs58rC2NXJvwq77NarSQkJFTZtn379lV+708++STBwcEsX77cafEKIYS4vjqT0Dmbmsv/Vhxy6ZiqqvLPb/eQlXfjdtY1tWLFCjZv3szXX3/ttDHqusWLF/Pzzz+zZMkSp41RWGLgja92YrFYnTJz4ka+/ukER+xcGqEoCu3ataNp06YUFBTwySef8N1339kSCwJat25dZQr7oUOH+Pnnn50yVrdu3W742M6du3jzq53kFZU7PZlTSVVh78k0vt9+xiXjiVsLCgpi1qxZxMTE2JKBOp0OVVWxWCx8//33zJ8/n71799pO7K7UuHFjoOKk8fPPPycvL8/22JcbjnHqwuUqRdmdyWpVSbtcxPvL9jts2WBdkJycTHh4+DXL5aKioqrcNhgMTJs2jT59+vDee+/xww8/sG7dOqDiIgXAU089xd13382gQYPo1q0bzz33HDt37rxmzKtnaHh6elJeXm67feHCBYKDg9m2bRtLly61/VdYWGhLFt1u3DfTokXVZehJSUn4+vpWGXPZsmX4+/tXGTcyMrLKdpGRkXbXk0pOTr5mBoi/vz9NmzYlKSnJdl9AQECVZhYXLlwArn29bdq0ua1xQ0JCqtyunJVz5e/h22+/pW3btrz88st88803rFmzhtTUVNvvu9LV7yNUzO5RFMX2Pefzzz+nW7dut13nyB5XvyZPT0/0er2tCUDlfVD1dcK176NOpyM8PNz2PgshhHCdOrHkymS28O8l+1BQbN1dXEGlosvHB8sP8NaTdzm8OLLBYLAV0JPOVjdWecUxJSWF/Pz86y41qKlPVh+mpMzkkpk5V1IU+GD5fua+OgZfr+u3v74dJ0+etH2RKi0t5dFHH3VKfY66RqfT8dBDD3Hw4EHWrl2L0Wh02vty6tSpGz4Wn6OQqeQBrm8x/83mOPp1bEF4aJDLxxbXioiIICIigtLSUi5evEjTpk3R6/VcuHCBgwcPAhVLIdasWcOwYcMYMWKEbdvBgwcTFxcHVCzD2bZtG5MmTeLE+SzW7j3r8tdiVVUOnL7EjmMpDOkefusN6oHGjRuTm5t7zf25ublVChUvXryY7du3ExcXZ1sGVFhYyPLly20JMF9fXz755BM++ugjYmNjWbt2LaNHj+arr76qMvvhVgICAjCbzaxZs+aaxyqXFN1u3Ddz9XeggIAALl68eM24MTExtGvXzjbulYlHgLy8vNuelXa1xo0bXzNbV1VV8vPzadKkyQ1jrUyGXh2Lo2b+mkwmnn/+ef72t7/x0ksv2e6vrF1zpet9l/T19eWhhx5i3rx5TJ48meXLl/Puu+9WOw69Xn9NgvXqJXyOcL1jKS8vz/Y+CyGEcJ06kUX4bsspLuUUuezK45WsVpUT57P48cC5Wz+5mhITE7FYLAAOq+1RH0VHR9t+rkyAOdLek6nsPH5Rk+NLVaGg2MD8dUdrtJ9OnTrZpuQnJCRcM/W8IVMUhb59+/L73/+ep556ihEjRmC1WlmzZg1ff/01qampDhnn6qvQlQx4kkULtEjmVPr30v1YLLL0qjbx8fGhffv2tvo5zZs3rzLzwGq12rr8WSwWFi5cyKJFi6rsw9/fn3KjmfeX7Uen0eGlAHNWHiavqPyWz60P7rzzTi5cuMChQ7/OGE5JSWHfvn1Vnpeenk5ISEiV4uxLly6t8pzKpdYeHh7069ePN954gy5durBnz55qxXTPPfdQVlbGm2++yddff13lvzfeeKNacVfH2LFjCQoKYsGCBVXG/Pzzz5k+fbpt3FWrVlWpmXP1+wAVSZmlS5faEpY3cscdd7BmzZoqM0ZWr15NeXm5rTDw9TRv3pzo6OhrlgRdLxZ75OfnU1ZWVqVTaU5OTrVmhD7zzDMcP36cF154AXd3d+67775qx9GyZctrlkdt3ry52vu5lcOHD1dJVMXGxpKYmHjT34EQQgjnqPUzdHIKy1i+7bRLl8Fcz4INxxjaIwIvD8e9Zfn5+bafJaFzYx07drR9KbryPXMEi9XKZ2tiUbC/LXlNWVWVTYeSmDAo2u5ZFEFBQTzyyCN8/vnnWCwWDh48yLhx42SWzhX8/Pxs9R+ysrLYvXs3AKdPn2bo0KEMGzasRu9XWFgYTZo0uaa2SSbNUTVM5litKknp+Ww7mszwXtdPOgnteXp68uSTT9qKKV+4cMFWIHbr1q1VaqZUio2NJd3ciOyCUs3+RqpAucnMki0neWZCL22CcKEhQ4YwYcIEJk+ezEsvvYSnpydz586tsrwHYNy4cfzzn/9k5syZDB06lOPHj/Pdd99VmY07b948du/ezejRo2nevDmHDh3izJkzvPfee9WKafDgwbz00kvcddddPPbYY0RFRZGRkcFPP/1Enz59+Mc//nHbcVfH3/72N8aOHcvw4cOZPHkygYGBnD17llWrVjF37lyaNGnCa6+9xsqVKxk9ejQzZszgwoULfPPNN9fMSr7dLld/+ctfWL9+PSNGjOCRRx7h8uXLfPDBBzz11FP06nXz4+/dd99l8uTJWK1W+vfvz88//3xNfRt7NWnShDvuuINXXnmFF154AaPRyKefflqtmUht27Zl+PDhfP/999fUK7pdjz32GGPHjuXll1+mR48e7N+/nw0bNlR7P7cSGBjIPffcw7PPPovJZOKjjz5izJgx3H333Q4fSwghxM3V+oTOTzWYGeOm13FH19ZEtQjCaLIQezaDuKTLt97wOkoNZnYdT2FE79tfb34rXbt25fz58zRq1MjW+lhcq1WrVowaNYqUlBR69+7t0H3HJmSQXVBq17Y6RaFzZBN6d2iBl4eebzfHkV9sX/0anU5h/f5zPFuDE6LIyEgefPBBfvzxRzp27CjJnJto3LgxkZGRJCUlYbVa+fnnnzGbzYwZM8bufUZFRTF+/HgWLlxo63Blwp0iJQh7Zuc0CfKhV3RzWjTyo6jMSGJaLrFnM+2KTVFgzZ6zktCpA5o3b35N16EbzUzMzslh7/aTqKpbxS+5GnQ6hd7tmxPVIhhvDzey8kvYG5dKrh0zbaxWlc2Hk3hkdFd8PN2rvX1d8/XXX/PVV1+xb98+fH19+fTTT4mLi6Ok5Nd6e126dGHnzp0sWrSI7du3ExERwe7du/m///s/IiIiAPi///s/9u3bx/r16zl79ixhYWEcOHDANivV29ubqVOn0qxZsyrj9+jR45pEzBtvvMG4ceNYs2YN27dvp1WrVvy///f/GDRoULXivp4bxREYGMi2bdv44Ycf2LNnDwaDgejoaH788UdatmwJQGhoKHv27OGzzz5j7969tGrVij179vCXv/zF9j7A7Xe5aty4Mfv372fBggUcOHAAHx8f5s+fz/jx423PadeuHRMnTrxm21GjRrFt2za++eYb9u7dy4ABA/jjH//IO++8g4+Pj+15/fr1o6ioyHZ70KBB1/w99ff3Z+rUqVWWgP/www98/vnnHDp0CF9fX9555x3y8/OrtB2/3r6uNGXKFDZv3lytYshXLuG666672Lx5Mz/88AOHDx+mb9++PPvss/zrX/+yzeK9URwRERHXFKX28PBg6tSp19T9ueOOO3jhhRfYsGEDGRkZ/OlPf+Kxxx677ZiFEEI4jqLW4mqGZouVx95eTUFJ9U+SA3w8eGvWUNzd9Px08Dz+Ph6MG9COzYeT+HT1kWrvT1EgMjSID1+Uqw/1yd++2M7Rc5nVLlQ7MKYVT43vSWpWITqdQpeopjz7n/WkXi669cY34OGu5+s/T3DYCZHFYiEpKYnQ0FDpgnUdFouFLVu2sHXrVqxWK926davSctZexcXFZGdnU1paysbYdLaezK727IkXJvdheK8Ijp7N5Ni5TIL9vBjVN4qkS/m8tWi33Z2F/jN7JO1ahdz6iaLWKC0t5Y033rhu4eEiAkhRbq+o65VaNwvkzw8N4nx6PkmX8rCq0Kt9KO3DGvHF+mN21+N5bmIvxvSrey2LT548yddff83vfve7264nI4QzTJs2jcLCQjZu3Hhbz586dSoBAQF88cUXTo7sV9OmTcPNzc3Wycwe69evJyUlhZdfftlxgQkhRANVq2foHElItyuZAzDr3p6EBHjz7H82UPjLPs5ezOWPDw3iWGIm+06lVWt/qkrFl9/0PCKbB9sV05WKioo4duwYHTt2vKaDhbi+oqIijh49SkxMzDXdGeyRU1DKkbP2dZhKySrgpY82UlBi4KGRnekS1bTG8RhNFnYeT+HuPtU/QbueXbt2sWHDBry8vHj66aevuerf0On1ekaOHEmXLl1ISkqie/fuQMUyloyMDPr162fXcVa5tEtVVd5ekWDXUpjmjfx46+tdHDyTbrtvS2wy7z8/kkfv7sr/Vla/459ep/DTwfOS0KmDKlueXy2XxhV/nKo5O6ewpJzfzt1MUemvicHl20/z/2beyZP3dGfn8ZRq/+1VgA37EutkQkcIrW3YsIGDBw+yYcOG20rmpKWlsXLlSrZt28aHH37oggiFEELUVrW6KPKp5Gz0dlR69PP2YHCXMHYeS7ElcwD2nEzlcn4pY/vb94VTAU5dyLZr26stW7aMtWvXXlPkUtzY0qVLWbduHV9//bVD2uSeuWh/d4vUy0V2JxtvRK9TOJPsmI4bgG3tfnl5OV9++SUFBQUO23d9EhoayoABA/D29qa0tJQlS5awfft2PvjgAw4cOGD3sXY5v9TuJXj/WrynSjIH4EJGPpeyi+jWttkNtro5i1UlLinr1k8UtYqPjw8jR4685n4VKFP8qp3MAcgvNlRJ5lQ6nZyNXq8jJMD7OlvdnApcyCyg3Giu9rZCNHQ///wzaWlpLFu2rMoyuRvJyMggLi6Ojz/+mAcffNAFEf5q0KBBDBw40KVjCiGEuLFaPUPn7MVcLHb0kY4OC8FNr+Ns6rVtFc+m5tKjnX0nRDqdwrlLebd+4i2UlJRw9mzFlHZ7W3c2RJUn1unp6WRnZ1dpUWqPc2l56HWKXceYM1isKvEpjkvoDBo0iFOnTnHx4kUKCwtZuXIljz76qMP2Xx95enrSsmVL0tLSMBqN/PDDDxQUFFz3hPpWEtPs/6y4XiJIr1MI8vcit9D+FrRp2UUYTGY83Wv1R7+4Su/evfHz86vSoUdx98VidlydLDe9jt7tm5OZV8LFrEK79qGqkJSeT8dwaV0sRHVUtxB2r169blkE2llkmZQQQtQutfZbvaqqnE27NiFzOxoHVBS3yyu+trhjXlEZ3p7u+Pt4XPcK5c1YrCpnHHDCnZCQYEtOSDHk2xcdHW0rLnjmzJkaJ3QS7EwYOlNqdqHDTrg9PDx49NFHmTNnDrm5uZSW2lf8uSHR6/U89dRTrF27loMHDwJw7tw5OxM6uQ5NGE4Y3J4AH09W70649ZNvoPKEu0NrOeGuS/z8/OjVqxfh4eGUl5cTFBTE8Qv5nPy2eu2tr9anQ3P6dGiBt6c7MRGNOZuax3vf7cNsZ4t7Rak47iWhI4QQQgjhGrU2oVNcZqTMYN/UbXf3ipVkZvO1X0pNv3xR9XCz78pmRm6xXdtdKSfn16SQJHRuX4cOHVi/fj1Q9T20V+pl+65CO5OqQmZuCa2bBTpkf35+fjz//POcPHnS1jlF3JynpydTpkyhS5cunD59mr59+wIVU9xTUlLo3LlzlY4oN3Ippxirg2rOx0Q04aGRnbmQkc+KHdfveHS7LmUXSUKnDlIUpUoSOzMvDZ2i1OgYyysq5/ylfAJ8PWkc4E3nyCZ0jmpK5uEku/anUxQuZdf8b6SrubtXFKI3mUwaRyJEw2A0GvHw8NA6DCGEqBdqbULHYLq2AOTtKjdWbOvhfm3SxvOX++xd528yW1FVtUqbyOrq1q0biYmJtGjRgqgox7VBr++aNm3KiBEjiI+Pd0j7cmMNjjFnMpodG5ePjw99+vQBKmab/Pzzz/To0cN2n7i+6OhoWxLMarUyd+5cDAYDmzZtYurUqbdMxhpNZrsKIl+tTYtg/vLIYHKLyvm/BTtrfHzU5LNV1B4Go7mifE4NjrHEtDzb0sClW08xe1JvXpjcmwvp+XYvL3b055crVLZzLi0trdKGWgjhHGVlZXh5eWkdhhBC1Au1tihyTU6EMn+ZRdM48Nqr6I0DfSgqNVBSrt2VuCZNmvDMM88wfvz4GiWGGqIRI0Ywe/ZsWrVqVeN91a7FVr9y0KSO69q+fTvnz5/n+++/59Ch6ndKaqhUVbWd6BUVFfHll1+yY8eOW2xT83HDmwXyf48Pocxg4s/ztnK5oObL5px5fAnXccav8efDSeh1OrsLbwMOKVjvao0bN0av13Px4kWtQxGi3rNaraSmptKsmf2fM0IIIX5VaxM6Hm72h5ZwMZcyg4lOEVWXFbjpdXRo3Yjj5+zv9OKm19UoCVNcXMyhQ4coKSmxex8NncFg4PDhw+Tn59doP+41OMacqSbH/q106dLF9vMPP/zAuXPnnDZWfaLX63niiSeqLFvbv3//TbfxcNdTk3Rty8b+vPHEXRhMFv40byuZeY75zLjezEVR97jbuWz4Zrw9K5Ye1aRTlb3LmbXk7e1N27ZtOXnypNahCFHvJScnU1JSQteuXbUORQgh6oXaeUYL+Pl42P3F0Gi28OP+cwzqHEb4FbVI7h3YDl8vD1busr8GRePA6rdzvdK3337L8uXL+e6772q0n4Zs5cqVLFu2jC+++KJGXcJCQ/wcGJXjNA66dX0We/Xu3dvWbtRqtbJnT82KqjYkAQEBzJw5kylTptCuXTvuvvtuoKIt/Pnz5zGbq54ENw3yQaezL6XTLNiXt568C7PFyp/mbSEj13EJ4KZOPL6E6zQN8rG74HaPdqEE+FStX+Hj6c79w2IoNZjYdyrNrv1aVZWmwb52bau1Ll262DoCCiGc5+TJkwQFBTlkprUQQohaXENHr9MR1SLI7q5SX/10gmYhfvzrmeEcPHMJfx8POkc25ZPVh+3ep06n0D6skV3bAuTn53P+/HmgYpaJsI/RWNGdLCsri0uXLtn9paBdqxASLubYdVLUorEfEwe3t+0HYMbwzpSUGykuM/LVxhN2xdQ02BdfL+cVClQUhXHjxmG1Wjlx4kSVGTvi1hRFoU+fPlXqD3366aekp6fTtGlTpk+fbjse27QItvuE+08PDaJRoA9HEtKZfGeHKo+ZLSqfrTli92to0zLY7m1F7dG2ZYjd2wb4ePDOM8PJLyonM78EPy8PYiKbUFhq4O8LdpBbWGbXflW17h5fnTp1YuXKlaxdu5bp06fj5lZrvx4JUWclJSVx7NgxBg4cKCUHhBDCQWr1N5boViGcTbWvtbTZYuXtb3YT2TyIyOZBGE0W3l92gPzrtDK/Xaqq1uhLdHz8rzODpOOQ/dq1a2ebGh8fH293QqdtS/tPuMsNZs5fygfg/KV8NnL+18fsXK6gUxQ61CBheNvj6HRMnDiRCRMmoCgKZrOZjIwMWrRogU5Xayft1Uqqqtpm5mRlZTFnzhwmTpxI3759aVODz4oVO+Px8rj+x7O1Bm3Qmwb5ODVhKFyneSM/PN31dhW53n4shZ0nLtKuVQgtGvljVVW+23LSViC5Jtq0qJsJHR8fHx5++GG+/vprli5dKkkdIRwsKSmJb7/9loiICEaMGKF1OEIIUW/U6m8r0WGNWL3nbI32kZSeT1J6vkPiUVWIDrP/JC0r69faPR06dLjJM8XNXPneXfmeVldNZlvlFpXz4wHH1p9RUWt0fFVX5dWxr776ioSEBNq1a8ejjz4qJzHVoCgKDz/8MEuWLCEtLQ2r1cqPP/5Inz59aNnYH29PN8oM1U/wbTua7PBYdTqFjhHSrry+0OkUosNCiEu6bFeha6tVJT4lh3g7Z6xeT2iIL37edTdh2L59e1tS55NPPqFz587ExMRUaRcvhLh9JpOJs2fPcvLkSeLj44mMjOSRRx7B3d1d69CEEKLeqNVnbv06tcTLw61GBRodqWmwDx1a239C1K1bNxISEmjTpg0tW7Z0YGQNS1BQEMOGDePYsWP07NnT7v20aOxPu1YhnEvLw1oLOrMoisKd3cJdPm5paUXnpLNnz/L9998zffp0mQpdDU2bNuW5555jy5YtHDp0iB49evzy/qnc2aUVm44k12hWjaNYrSrDe0ZqHYZwoBG9ojhx/rLWYQCgKDCqT5TWYdRY+/btmTVrFnv37mXv3r1s27aNRo0a4efnh5eXl8xiFOIWKmeulpWVcfnyZYxGI82bN2f48OEMHjxYkjlCCOFgilrLe4zOW3OEtfsSNT8hUoDHx3Zn4h3tNY1DONa22Av8e+nNuxW5gk6nMKhzK34/Y6DLx05OTmbevHm2pUPTpk2jV69eLo+jvlmyZAn7Yk+RqHTSOhQAmoX48tlv7rG7ULOofYwmC4/8YxUl5SatQ0GnU1j4x/EE+XlpHYrDmEwmEhISOHfuHKWlpZSVldXJtuxCuJq7uzve3t40btyYzp0707ixzA4VQghnqdUzdADG9G9b42VXjqDX6xjeK8Lu7UtKSrhw4QLR0dFydcJBLBYL8fHxtGzZksDAwFtvcB2DuoTxyeojmp8QWa0q9wxop8nY4eHh3H///XzzzTeoqkpeXs3raAi4dOkSnhjwUYsoVfygRk3Ma0ZR4N4B7SSZU894uOsZ3bcNK3bGazrLUKdTGNwlrF4lc6DipDQmJoaYmBitQxFCCCGEuK5aP3e4VZMAhvWMQKfhEhAFmHJnB/x9PO3aXlVVvvrqK77++mt++OEHxwbXgG3cuJGvvvqKefPmYbFUvzAogLubnodHadvpSadT6NG2GZ3CtbuC1blzZ5555hnGjRvH4MGDNYujPpk6dSpBQUE0Ix00vKivU6BRgHe9WA4jrjVhcHu8PNw0TBdW/I28f5gkPYQQQgghXK3WJ3QAZo3rgb+PB1rkdHQ6hVZNA7hvmP3LJrKzs0lOrihyWlJS4qjQGrzi4mKg6vtrjzH92hIT0Viz2QsebnpenNpX87o14eHhDB48GC8vLw4cOMDixYtrVHS6oQsLC+Pll1/mzt4daaRcRqusjlWFV6b1w9tTZgbWR8H+Xjw3sZeWOUMeubsrYU0DNIxACCGEEKJhqhMJHT9vD16a2teuTh41paoqr07vh7ub3u59nDlzxvZz27ZtHRGWoOp7eWVL+OrS6RRentYPvUYJnafv7UHjQB9Nxr4ek8nEqlWrOHbsGPPmzZMlWDXg5eXF1KlT+fgvT9Is2M/lMw11Cozt14aubZq5dFzhWnd2a02/Ti1dnpTW6RTatQpmwuBol44rhBBCCCEq1ImEDkCfDi2YMsT1rb6fvrcnbVvWrJV0enq67WdpV+440dHRtlktV77H9ggN8eOVaf0cEVa1DOsZwfBetavzkLu7O1FRFctzioqK+PLLLykvL9c4qrrN39ebPz88CA93Ha6aqaPTKUS1COaxMd1cMp7QjqIovDC5D82CfV2W1NHpFPy9Pfj9jIHopfOTEEIIIYQm6tS3sEfv7sqYfm1cNt4jd3dxSKHaLl264OvrS9++fWnSpIkDIhMAfn5+3HXXXfj4+NCtW81PWu/o2prnJ/d2QGS3Z2DnVrw4uY/mS62u5/7777d1pcjKyiIuLk7jiOq+yObB/L+ZQ3DXOz+po1MUWjXx5/8eHyJLrRqIQF9P/jFrKI0CvJ2e1NHpFHw83XnrybsIDfFz6lhCCCGEEOLGan3b8qupqsrCH4/z/Y4zKAoOX4alUxSsqsqscT0YP0imkTdE22Iv8P6yA6BUdJ9yJIWKU/kRvSJ5flJv9Pram1PNyclh4cKFlJSU8PTTT9O0aVOtQ6oXEi7m8Nf52ygzmFCdVMo2OiyEv88cgp+3h1P2L2qvnMIy/vL5VtKyi5yyTFmnKAT6VSSPWjWRujlCCCGEEFqqcwmdSgdOp/HR9wcpKjU6rF2rokDTIF9emd6PmAjHzKQpLS0lMzOT8PBwdDIt3SlUVSUlJYWQkBD8/f0dss+k9Dz+vWQ/yZkFDtkfVFzV9nLX8+yEXgzpHl4rZ+ZczWq1oqoqer0eo9FIeXk5AQFyEldTuYVlfPzDQQ7F12yp4JV0OgVUuH94DNPu6ohbLU4WCucqN5r5+qcTrN6dYLtIUVOVF1CG9gjnqXt7SrJQCCGEEKIWqLMJHYDiMiOfrTnC1thkdDrF7tkUOkVBRWX8wGgeGtUFLw83h8RntVqZM2cOqampDBkyhDFjxjhkv6KqHTt2sH79eho1asTLL7+Mu7tjlpiYLVaWbTvNd1tOgqpi72QdvU7BYlXp26EFsyf1JiTA2yHxuVJ5eTnvvvsu5eXlTJgwgb59+2odUp2nqirbjyYzd/URSstNVMzdqn6Sr/KzLzw0kN9M70dk82CHxyrqppMXLvP+0v1k5pXYndipTOQE+nry4pS+9O3YwgmRCiGEEEIIezgmc6ERP28PXp3en6lDOrJh/zk2HT6PwWixnUDfTOVz/LzdGdOvLXf3bUOzYF+Hxnfp0iVSU1MByM3Ndei+xa/y8/OBiiVC586dc1jhaTe9jhnDY7i7TxSbDp1n3d5E8orLq3V8uet1DO0Rzpj+bWtcXFtL5eXllJSUALBixQoCAgKkwHcNKYrCXT0i6NepJduOJrN8axxZBYZf15HeZAZXZRIaoFd0KOMGtKN721CXdzkStVtMRBM+/c1YDpy5xJo9ZzlxPsvWae1myZ0rn9O2RTDjBkYzuEsYHu72d3sUQgghhBCOV6dn6FytzGBi36k0Ei7mcCYlhwsZBZgt1irP8XDX06ZFMNGtQugY3pi+HVvUqCX5zWzevJnNmzcDMHHiRPr37++UcRq6uLg4Fi1aBED//v2ZOHGiU8axWKwcTsjg1IXLJKTmkJiaR5nRXOU5ep1C62aBtA9rRLtWIQzs3KreLE1Yv349O3bsACo6Yf3hD3/Az08KojqKqqrsjk1g2YadZBVZKVN8sHDtbLPmjfxoH9aIti2DGRDTiqYOTkSL+iv1ciEHTl8iMS2X+JQcsvJLr3lOiL8X7Vs3om3LEHpGh9bpRLQQQgghRH1Xp2foXM3b052hPSIY2iMCqDgBLygxYDRbAPB01xPo6+Wyq9hpaWm2n2U2g/O0a9cOvV6PxWKp8p47ml6vo2/HFrYlB6qqkl9ccXxZrSoe7noCfDycliDU2ujRo8nPz+f48eOYTCZyc3MloeNAiqIwuGd7+ndtQ3x8PIGBgQSGNOFSxmV279lL67CWDOzXGx+v+pEgFK7XqklAlULGpQYTJWUmTGYLbm46fDzd600CWgghhBCiIahXCZ2r6fU6TeuVdOzYkfj4eHr16kVQUJBmcdR3np6e3HnnnWzbto1OnTq5bFxFUQj293LZeFrT6XRMnz6dsLAwPDw8CAsL0zqkesnNzY2YmBjb7WOH9pAQd4SEuMOcj49j2rRpBAdLnRxRcz6e7vhIW3shhBBCiDqrXi25qo2sVqt0t3IRea9dy2KxsGXLFnQ6HXfeeafDilGLqo4fP863335ru+3r68urr76Kr68stRJCCCGEEKIhq9czdLRkMBjIz8+nWbNmWofSYOh0Oi5fvoyvry8+Pj5ah1PvnT17lp9//hmAzMxM7r//fkmoOUHXrl3x8fFh+fLl5OfnU1JSQn5+viR0hBBCCCGEaOBkho4TWCwW/ve//3Hp0iXGjRvH4MGDtQ6pQTh69CjfffcdISEhvPTSS3h6emodUr1WUFDAe++9h8lkAmDIkCGMGTNG46jqr/Lycg4cOIC3tze9evXCZDJx4MABQkNDadeundbhCSGEEEIIIVxMLqc7QXJyMpcuXQIgKytL42gajszMTKCiRXx8fLzG0dR/gYGBPPDAAyi/tDjesWOHrbW5cDwvLy/uvPNO+vTpg06nY+vWraxbt4758+fz3XffUVp6bcciIYQQQgghRP0lCR0nOHPmjO3nyMhIDSNpWK58r6/8HQjn6dixI5MmTcLNzY0mTZrg5dVwikRrrUmTJrafjx49ykcffSRJHSGEEEIIIRoQqaHjBBcvXgQquiBFR0drHE3DERUVhYeHB0aj0fY7EM7Xt29funTpgqenJzqdDoPBgJubG3p9/WzfXlv07NkTnU7HqlWrKC8vJz8/n+TkZDp27Kh1aEIIIYQQQggXkISOE7Rp04akpCT69OkjhUtdyM3NjcGDB7NlyxbatGmjdTgNire3NwBJSUnMnz+fwMBAHn/8cRo1aqRxZPWXoij06NGDqKgotm7diqIotGnTBlVVSUxMJCgoqMosHiGEEEIIIUT9IkWRnaSsrAwvLy9bfRHhGqqqYjAY8PT0lPdeA7t372bNmjUANG7cmGeffVaSmi524MABfvjhBxRF4c4772TkyJG4uUnuXgghhBBCiPpGaug4mMlkorCwEG9vb0koaEBRFLy8vCgpKcFoNGodToPTq1cvmjZtCkB2djbLli3TOKKGp7J1vKqqbN++nf/+97+Ul5drHJUQQgghhBDC0SSh40Amk4k5c+bw9ttvc/z4ca3DabASExN5++23ef/99ykrK9M6nAbFy8uLmTNn4u/vD1R0fLNarRpH1bD07NmT0aNH22oYZWRkEBcXp3FUQgghhBBCCEeThI4DnTt3jvT0dFRVlaK8Grp06RIWi4W8vDxOnjypdTgNTnBwME899RT9+vVj+vTpthkjwjV0Oh133XUXL7zwAtHR0YSHh9OuXTsA8vPzZbaOEEIIIYQQ9YQUVnCgK1tlh4eHaxhJw3ble3/mzBl69+6tYTQNU5MmTZg0aRIAhYWF7Nu3j6ioKNq2batxZA1HaGgojz/+uO32uXPnmD9/Pnq9ntGjRzNgwABJtgkhhBBCCFGHybd5B0pOTgZAr9fbrogL1wsLC8PHxwf49XcitLN161a2bNnC/PnzZcaUhgoKCrBarZhMJtasWcP8+fOlzpQQQgghhBB1mCR0HKhly5YA9OnTB09PT42jabh0Oh0DBw4EoFWrVhpHI5o3bw5UFOldvHgxKSkpGkfUMHXv3t327wIqZuwcPHhQw4iEEEIIIYQQNSFtyx3IarWSn59PUFCQLGXQmNVqpbCwED8/P2nZrDFVVVm6dCmxsbEAREZG8vTTT2scVcOVmJjIypUrKSkp4fHHHycsLAyLxWIroiyEEEIIIYSoGySh4yBWqxWz2YyHh4fWoYgrGI1G3NzcJMGmMbPZzNdff018fDy9e/dm6tSpWofU4FUmcbKzs/nss8/Q6XSMGzeOzp07ax2aEEIIIYQQ4jZIQscBjEYjc+bMITs7m5kzZ9KmTRutQxJUdLv69NNP8fb25oUXXsDX11frkBo0VVXJzc0lJCQERVEwm80ye6oWOHHiBN98843tdo8ePZg8eTLu7u4aRiWEEEIIIYS4FZm24ABnzpwhIyMDs9nMhQsXtA5H/CI1NRWDwUB+fj7Hjh3TOpwGT1EUGjVqhKIo7Ny5k7/97W98//33WK1WrUNr0Dp16kSXLl1st2NjY9m9e7eGEQkhhBBCCCFuhyR0HODKduWtW7fWMBJxpSsLIl/5OxLai4+Px2KxcPDgQdatW6d1OA2aXq/ngQceYPr06Xh5eaEoCsHBwVqHJYQQQgghhLgFWe/gAOfPnwfAw8ODyMhIjaMRlZo3b05gYCAFBQUkJSWhqiqKomgdlgCGDBnC+fPnsVqt7N69m5YtW9KzZ0+tw2qwFEWhZ8+edO7cmeLiYkJCQigrK2Px4sWYTCZGjx5NeHi41mEKIYQQQgghriAzdBygadOmQEW7cqkJUnsoisKAAQMAaNKkiSRzapF27doxadIk2+34+HgNoxGVPDw8CAkJASqWLCYkJJCUlMQnn3zChg0bsFgsGkcohBBCCCGEqCRFkR3AZDKRlZVFaGiotP6tZaxWK5mZmQQHB+Pl5aV1OOIqsbGxnDp1imHDhtG8eXOtwxFXsFgsfP755yQlJdnuu/vuuxk6dKiGUQkhhBBCCCEqSULHAWQpT+0nv6PaLyMjg4SEBHr27Imfn5/W4QgqEqI7duxg06ZNWCwWhg8fzsiRI7UOSwghhBBCCIEkdGrEYDDw2WefkZ+fz5NPPikzDGqp3NxcPv30U9zd3XnmmWckWVBLvffee2RnZxMSEsJzzz0nv6daJDc3l8zMTNq3b4+qqmzatIni4mKGDBlCkyZNtA5PCCGEEEKIBklq6NTAiRMnSEtLo6SkpMqyBFG7pKamUlBQQHZ2NocOHdI6HHEDlZ2VcnNzWbhwIUajUeOIRKWQkBA6duyITqcjIyODbdu2cejQIT766CP27NkjreeFEEIIIYTQgCR0auDKVtgtW7bUMBJxM1f+bqR9ee01depUAgICALh48SIHDhzQOCJxPU2bNrXNRjSZTKxevZrt27drHJUQQgghhBANjyR0auDcuXMA+Pj4EBYWpnE04kYaNWpkWxaSnJyMyWTSOCJxPYGBgcycORNvb28A2/9F7eLu7s6zzz5r6yAHcPnyZQ0jEkIIIYQQomGSHts1EBgYSFlZGb1790ank9xYbdanTx/Wr1+Pn5+fdCKrxZo3b87vf/97CgoKaNasGSAFrWsjDw8PJkyYQPfu3UlKSqJPnz5AxTLU7Oxs+vTpIzWQhBBCCCGEcDIpilwDJSUlZGZmEh4eLkmCWk5VVZKTkwkJCbEt6xG1m6qqfPfddyQlJTFhwgRiYmK0DkncRFFREW+99RYAvr6+TJo0ic6dO2sclRBCCCGEEPWXTCupAV9fX6KioiSZUwcoikJERIQkc+oQg8HA8ePHKSwsZPHixVy4cEHrkMRNeHp60qhRI6Ai2b1o0SJ2796tcVRCCCGEEELUX5LQsYPBYGDu3Ln85z//IS8vT+twxG0qKSnhv//9Lx988AFFRUVahyNuwcvLi759+wJgNptZuHAhhYWFGkclbsTDw4Nnn32WTp062e47ffq0hhEJIYQQQghRv0lCxw6HDh0iOTmZrKwsmTVQh1y6dInU1FQyMjLYu3ev1uGI2zB+/HjatWsHQFlZma0Quaid/Pz8ePjhh5kxYwY9e/Zk9OjRQEXR5BMnTkgreiGEEEIIIRxIiiLbIT4+3vZzaGiohpGI6qgssgsV7ctHjRqlYTTiduj1eh588EHWrVtHeXk5HTt21DokcQuKotCtWze6desGgMVi4eOPP8ZoNNKoUSOmT59OeHi4xlEKIYQQQghR98kMnWqyWCycP38eqOhyJQmduiMgIICWLVsCFbN1SkpKNI5I3A4vLy+mTJnCgw8+iJeXF0lJScTFxWG1WrUOTdwGq9WKj48PADk5OXzyySccOnRI46iEEEIIIYSo+yShU006nQ4vLy8AevbsKe2U65hevXoB4O7ujpubTFCra3Jycvjss89YtGgRq1evRpr01X7u7u48+eSTtG7dGqjoXrZr1y6NoxJCCCGEEKLuk7bldsjNzSUjI4P27dtLh6s6RlVVEhISCA4OpmnTplqHI6qpuLiYt99+G4vFAsCYMWMYMmSIxlGJ22GxWNi1axdxcXEMGjSI7t27YzAYyMnJITQ0FJ1Ori8IIYQQQghRHZLQEULUKUeOHGHp0qW226+99hpBQUHaBSTs9sEHH5CRkUHr1q2ZPn06jRs31jokIYQQQggh6gy5JFoN5eXlfPbZZ8ybN0/qr9RhRqORhQsXMmfOHGlfXgf17NmTu+++G6hYAik56brJarVSWloKQEpKCh9++CEnTpzQOCohhBBCCCHqDknoVMPevXs5f/48586d4+LFi1qHI+yUlZXF6dOnSUlJYdu2bVqHI+wwdOhQZs+ezSuvvEJwcLDW4Qg76HQ6Hn74YRo1agSAyWRi7dq1GkclhBBCCCFE3SEJnWq4sl15kyZNNIxE1ESjRo1s9Tqu/J2KuiUsLIwmTZpQUlLC3Llz+eKLL2TGVR0TFhbGSy+9xIABA/Dy8rK1OldVFYPBoHF0QgghhBBC1G7S5uc2GQwGkpOTgYpkTuVVZVH3eHt7Ex4eTlJSEtnZ2eTm5hISEqJ1WMJOiYmJtn+bCxYs4KmnnsLT01PjqMTt8vDwYMKECYwfP97WNXDZsmXExsYSExPDxIkT8fPz0zhKIYQQQgghah+ZoXObdDqdraNV9+7dtQ1G1FiPHj0AUBRFOpXVcdHR0baiyGlpaSxZskTbgIRdKpM5AElJSaiqSlxcHO+//z5nz57VMDIhhBBCCCFqJ+lyVQ1paWlkZWXRtWtXSQLUcaqqcuLECQICAoiIiNA6HFFDmZmZzJ07l/LycgBef/11fHx8NI5K2CsxMZHFixfbis/7+vry5z//WVqbCyGEEEIIcQVJ6Agh6oXk5GTWrl1LeHg448aN0zocUUNFRUWsWLGCU6dOERUVxVNPPaV1SEIIIYQQQtQqktC5DWVlZSxZsgQvLy8mT56Mh4eH1iEJB7BYLKxevZqcnBymT59OQECA1iEJB7FYLJw7d47GjRtLfaQ6rqioCB8fH/R6Pbt372bHjh107tyZu+++Wz6LhRBCCCFEgybz12/Dzp07OXPmDEePHiU9PV3rcISD5Ofns3//fhITE9m8ebPW4QgHOnz4MF988QX//ve/OXfunNbhiBrw9/e3LXE9ePAgBQUF7N69m48++ojU1FSNoxNCCCGEEEI7ktC5DadPn7b9LFf76w9/f3/c3CoavZ05cwaZrFZ/VNZasVgsfP3112RmZmockXCEkSNH2v7NZmdns2DBAoxGo8ZRCSGEEEIIoQ1J6NxCUVGRbVZOy5Yt8ff31zgi4SgeHh60adMGgMLCQjIyMjSOSDhKjx496NChAwDl5eUsX75c44iEI8TExPDSSy8RFhZmu68ywSOEEEIIIURDI9+Eb4OiKKiqSteuXbUORThYt27diI+P1zoM4WB6vZ4ZM2bw2WefkZaWhsFgQFXVKq2xRd3UpEkTnnnmGS5evEijRo3Q6XScPXuWHTt2EB0dzaBBg6QblhBCCCGEaBCkKPJtSEhIIDc3l759+8qJQj2jqipHjhzB09OTzp07ax2OcDCTycSpU6cIDw8nKChI63CEk3z66ackJSUBEB4ezrRp02jcuLHGUQkhhBBCCOFcktARQjQIycnJrF+/ng4dOnDXXXfJbJ16JDY2lqVLl9rqYAUEBPDqq6/i5eWlcWRCCCGEEEI4jyy5uonS0lJWrlxJUFAQd999t63TiqhfVFVly5YtpKWlMWHCBAIDA7UOSTjB3r17SU5OJjk5GavVyvDhw7UOSThIjx49CA4OZtmyZeTk5FBUVITVatU6LCGEEEIIIZxKEjo3sWXLFo4fPw5Anz59aNKkicYRCWcoLy9n06ZNQEWh5Pvvv1/jiIQzdO/enaNHjwKwadMmGjduTLdu3bQNSjhMREQEL774IidOnKBx48b4+PiQk5PDnj17iIyMJCYmRmZlCSGEEEKIekUKwtzEmTNngIoWyH5+fhpHI5zFw8PDtjQjPj4ei8WicUTCGTp06MCYMWNst/fu3athNMIZPD096d27NxEREQBs3LiR3bt3s2jRIr755huKi4u1DVAIIYQQQggHkoTODWRnZ5OdnQ1UFNn09vbWOCLhLHq9nnbt2gFQVlbGxYsXNY5IOMudd97JyJEjCQwMpFevXlqHI5wsKirK9nNcXBxz5szBYDBoGJEQQgghhBCOI0uubuDKWRpdunTRMBLhCl26dOHEiRMAmM1mjaMRzqIoCsOHD7fVzzGZTFy8eJHWrVvj5iYfh/VN//798fX1ZcWKFZSWlpKbm0teXh6hoaFahyaEEEIIIUSNSZerG6hsZ11SUsLgwYOlXXk9p6oq+/fvR1VV+vfvL7U2Gohvv/2W48eP07x5c55++mnpilRPFRUVsXfvXvz8/Ojfvz8Wi4UjR47QokULwsLCtA5PCCGEEEIIu0hCRwjRYC1YsMBWK6tt27bMnDlTutk1AOvXr2fHjh0ADBw4kNGjR+Ph4aFxVEIIIYQQQlSPTDu5jtLSUpYvX86uXbuQfFfDcujQIZYsWUJ+fr7WoQgXuOeee/Dx8QEgMTGRXbt2aRyRcIWAgADbz3v27GHOnDkYjUYNIxJCCCGEEKL6JKFzHRs3buTQoUOsXbuW0tJSrcMRLmK1Wvnhhx+IjY1lzZo1WocjXKBJkyY88sgjtvo5hYWFGkckXGHgwIGMGzfO9nvPyMggOTlZ46iEEEIIIYSoHqkCehVVVTl9+jQAbm5uMg2/gfHx8aG4uJiEhARMJhPu7u5ahyScLCIighdeeIGkpCS6deumdTjCBXQ6HYMHDyY6Opqff/4ZNzc3wsPDUVWVlJQUgoODq8ziEUIIIYQQojaShM5V0tPTbVfp27RpIyf0DYhOp6N9+/YcPnwYk8lEUlIS0dHRWoclXKBZs2Y0a9YMgMOHD3PixAmGDh1KeHi4xpEJZ2ratCkzZsyw3d6/fz8rVqxAr9czatQo7rjjDimIL4QQQgghai35pnqV8vJy288xMTEaRiK0cOXv/MpjQTQMqqqydu1azpw5w/z587l06ZLWIQkXMplMAFgsFjZs2MC8efNs9wkhhBBCCFHbSELnKhEREYwePZqRI0fSu3dvrcMRLtaxY0fuuecehg8fTqdOnbQOR7iYoih07NgRAKPRyIIFC6SuTgMyYMAA7rzzThRFASApKcm2BFcIIYQQQojaRtqWCyHEFYxGI59//jkpKSkAjBkzhiFDhmgclXClpKQk1q9fj8Vi4dFHHyUwMJCSkhK8vLykrb0QQgghhKg1JKFzhZKSErZs2UJERARdunTROhyhoYSEBE6fPs2QIUMICgrSOhzhYsXFxSxatIjs7Gwee+wxWrVqpXVIQkPnzp3jyy+/xN3dnQkTJtCtWzfbLB4hhBBCCCG0IgmdKyxdupQjR46gKApvvvmmXIltwN544w1KSkqIjo7m8ccf1zocoQFVVVFVFZ1Oh9FopLCwkMaNG2sdltDA3r17WbVqle12165dmT59uq3tuRBCCCGEEFqQGjq/sFgsnDp1CgBPT0+NoxFaqzwGEhMTpThyA6UoCjqdDpPJxL///W/ee+89Nm/erHVYQgN9+vSp0tL++PHjxMbGahiREEIIIYQQktCxuXjxou3EPTo6WmbnNHDt27cHwGq1cvbsWY2jEVqyWCyUlJQAsHnzZg4cOKBxRMLV3NzcmDFjBg888AB+fn54enoSGhoKVMzkEkIIIYQQQgsyX/wXxcXFtp8ru9yIhqtTp07s3bsXwHYyLxomLy8vxowZw5o1awBYuXIlkZGRNGnSROPIhKt17dqVzp07YzKZ8PT0JDs7my+++AIPDw/uvfde2rRpo3WIQgghhBCiAZGEzi+io6MZNGgQ7u7uVabWi4apbdu2jB49mry8PDkeBIMGDSIvL49du3ZhtVrJzc2VhE4DpdPpbEsyk5OTyc3NBWDevHkMGjSIsWPHygxPIYQQQgjhElIUWQghboPVaiU2Nhaz2UyfPn3Q6WTFakNnMpn48ssvOX/+vO2+iRMn0r9/fw2jEkIIIYQQDYWckVCx3Oqnn34iKSlJ61BELXPp0iU2btxIXl6e1qEIjel0Onr16kW/fv1QFIVNmzaxdu1aysrKtA5NaMTd3Z0nn3yScePG2Tpe+fj4aByVEEIIIYRoKGSGDvDtt99y/PhxvLy8+H//7/9pHY6oRf79739z+fJlIiMjefrpp7UOR9QSycnJzJ07F4CoqCgef/xxaWHdwJWUlFBUVERoaChlZWUsX74cVVUZNWqUrYCyEEIIIYQQjtTgZ+iYTCZOnz4NVFxtlfyWuFJlLYwLFy5UKZwtGrbQ0FB8fX0BOH/+PMuXL9c4IqE1X19fW+ImKSmJkydPcurUKT7++GN27NiB1WrVOEIhhBBCCFHfNPiEzvnz5zGZTEBFYWRFUTSOSNQmHTp0ACpaE8fHx2scjagtPD09efTRR3F3dwfg6NGjXL58WeOoRG3Rtm1bW3LHYrGwfv169u/fr3FUQgghhBCivmnwCZ2CggLbz9KuXFztymOisLBQw0hEbdO6dWtmzJiBt7c3jRs3JjAwUOuQRC3h4eHB7NmzueOOO2wXCUpLSzWOSgghhBBC1DcNvoZOSUkJq1evxt/fn7Fjx0rnGnGNzZs3k56ezr333ktQUJDtflVVZUaXwGg04ubmhk6no7y8HJ1Oh4eHh9ZhiVoiPT2drKwsOnfuDMC2bdsoLy/njjvuICAgQOPohBBCCCFEXdYgEzplZWWcO3eOoKAgWrRoIUkcUW2nT59m2bJldO7cmcmTJ2sdjqgFLl26xP/+9z+8vb157LHHaNWqldYhiVomJSWFOXPmAODt7c2ECRPo1q2bJIaFEEIIIYRdGlxbltOnT7N161ZSUlKAiloYHTt2ZPLkyXJVXVzDbDZz9uxZjh8/TkJCAs2bN+f+++9nw4YNlJaWcujQISZOnChJQcHly5exWCwUFxezYMECnnvuOUJCQrQOS9QizZo1IyQkhNzcXMrKyvjuu+8wGAz069dP69CEEEIIIUQd1KDOQktLS9mzZ48tmQNgMBg4evQoe/bswWKxcOTIEU6ePKlhlKK2UFWVzz77jIULFxIbG0tJSQmJiYn84x//ICsrC4CAgABJ5ggAYmJiiIiIAKC4uJhvvvlG24BErePp6cnzzz9Pt27dbPelpqZqGJEQQgghhKjLGtQMnSNHjnD27NnrPrZz504OHTpEdnY2AC+++CItWrRwZXiiljl//vx1T7aubD/cvHlzV4YkajE3NzceeeQR5s6dy+XLl8nMzMRoNFaZ+VdWVsZPP/1ESEgId9xxh4bRCq34+PgwY8YMunfvzsWLFxkwYAAAZ86cIT8/n+7du+Pl5aVxlEIIIYQQoi5oUAmdm9UpKCkpoaSkBAC9Xo+/v7+rwhK1VH5+fpXkzfVER0e7KBpRF/j4+DBr1ix27dpFeHj4Ncs4jx49yt69ewEICwuzzegRDU/Hjh1tXfTy8/NZsGABANu3b2fatGlERUVpGJ0QQgghhKgLGtRakduZTaEoCjNmzJCEjuDy5cs3fMzLy4sHHnhAal+IawQEBDB27FhiYmIoLi5mw4YNHDlyBLPZXKV1dXx8vIZRitrE09PT9jcnLy+PefPmERsbq3FUQgghhBCitqu3M3RUQxHWjBOoBamo5jJAIdSqo7V7NpdMQZiveumVdVAmT55say8rGraOHTuyY8eO687SadW8CTGNrVhP/YDVVIaqWlHcvFD8Q9GFdkXxDtYgYlHb7N27l+3btwOwcuXKKsfSuXPnbridarWg5iRivXwGjMWoFjOKmwf4NELXrDOKf3PpjFSPeHt78+yzz7Js2TKSkpJQVZWjR4/So0cPp4ynqipqwUWsmXFQlo9qMaLo3cEzAF3TTighkShKg7reI4QQQghRJ9WbhI6qqliTd2M+vgTrxQOoBSnXfd5j3qB6Q44lgGRLKLHGtkQNmMjQYcMwm80EBga6OHJRW4WHhzN16lSWLl0KQFNdHj3cE4h0y6BxTgGGBeoNt1X8m6Nr1Qe3zlPRtRmGotO7KmxRS6iqaqvJBWA0Gqs8Xl5eXuW2tfAS5qPfYj2/BWvmKbAYrnhUAa443ryD0TXvjlvH8eg7jUdx93HCKxCuFBISwqxZs9i/fz+JiYkMGTIEgJycHHJzc4mKikKv//VzRFVVvv/+ey5cuMC0adMIDw+/6f5VQxHmE8uxJKzHmn4MDEVXPHrV8eXug65ZZ/TtRuLW9X4U38YOfKVCCCGEEMJRFFVVb3xWWgeoxlLMx77FfHA+av4FUPSgWm5rW4uqoFdUaNwRj36z0HeeWnGVUohfqKqVhLUfoD/xNS3I+PWYuR2Vx6J/KO69Hset5yMoXpIwbCji4uJYtGjRdR/z8PDgkUceoW3btliS92A6+DnWsxsrHlRvXrfJRtFVPNfDD7fuD+LW+wl0QWEOil7UBiaTiTfeeAOj0UjLli2ZPn06zZo1AyoShK+//joAjRo14pVXXsHN7dprNNbsBMwH52M+sRTMlUnC2/0M04GiQ99xPG59Z6Fv3t0Br0oIIYQQQjhKnZ5TbUnZS/lnQzBteh01P7nizttM5gC2E3M1Ox7julcp/2IU1kxpWS4qWHOTMHw1gbAT79JCqWhTftvJHPj1WCzKwLT9n5R9MhhL4mYnRCpqoyvr5VzNaDQS4qvHsPoFDN9MwZr4U0Vy5naTOfDrc43FmA9+TvmngzEd/By1OvsQtZrFYsHdveIiQ1paGh9//DEnT1b8jfLw8CAoKAiomMWzb9++KtuqFhOmnf+mfN4wzMe+AXM5FYmc6nyGWcFqxnJ6FYYvx2L86S+oxhsf10IIIYQQwrXq5Awd1VSKaes/MB+a/+tVakdQKqazuw9+FbdBL6Lo6s2KNFENqqpiPvg5pq1vViRlrLefJLypX45VfeepeIx6U2br1HN79uxh9erV132snVsqk/z24amWoTg4AaNr1QePez9CFxzh0P0KbaSlpbFkyRKysiqSyq1ataJNmzbk5eVx4sQJKv+EN23alFdffRUAa9YpDKtmo16Op1oJnFtRdCj+LfAY/zH61v0dt18hhBBCCGGXOpfQUcsLMHz3ANb0o45L5FxDQdduJJ6TPkVx83LSGKI2Uq1mjOt/i+X4EucNouhRQiLxenA5il8z540jNPfGG29QUlJS5b6+HqcZ7XUAqwo6Z9Q11unB3RfPB5bIEpl6wmQysXnzZk6dOkVQUBBnz5695jkdOnTgsccew5K0A8PSR8BqrtaM1dum6AAFj/Ef4xYzyfH7F0IIIYQQt61OLblSywspXzSloqCjU5cVqFgTN2NY9hiqxXjrp4t6QVWtGNe8iOX4UicPZEHNTaJ84XjU4hu3Rhd137hx4/D29rbd7u9xktFeBwAnJXOgYkaZsQTDoslY0o86aRDhSu7u7owZM4aJEydeN5kDEBoaiiVpO4YlD4LV5JxkDtiWBhpXzcYct9w5YwghhBBCiNtSZ2boqFYzhkVTsKYddt4X1Wso6DuOx2PiXGkR3AAYNr2O5eDnOHSJws0oepRGbfGauV66FNVjJpOJ48ePU7D/K/rnOzlZeCVFDx6+eD2+UZZf1ROffvopSUlJ132shVsuj/v+iKKaUVz2GabD875F6KOGumY8IYQQQghRRZ2ZoWPeNxdr6gEXJnMAVCynV2E5+YMLxxRasJzfhuXgPFyWzIGKmTo5ZzFt+6frxhQu5+7uTo+2zehfssa1A6sVM3WMq1+QQsn1hKen53Xv12Nhoud2sLowmQOgqhhWPY9alue6MYUQQgghhE2dSOhYL8dj2vEvjUZXMP74GmpxpkbjC2dTDUUY1r78S20IVw9uxXzwcywp+279XFEnqaqKce0rYNZg+aZqwZp2CPOhL1w/tnC4yMhIdLprP6eGeB6lka4QXXW68DmECuUFGH/6q4vHFUIIIYQQUAcSOqqqYljzImi2MkwFUxmGH1/TaHzhbMatb0HJZSfXZboJRcG45kWp11RPWY4vwZq8y8WzC6sybXkDa/5FzcYXjjFkyBC6detW5b5QXQ6DPOPQbFWwasFy8nssiT9rFIAQQgghRMNV6xM61uTdqBnHHXAypPzSltyOb72qBWvCj1hzztUwBlHbqCXZWI5+Y0cy55fj6Xr/VTsIK2rBRSxn1ld/W1GrqaqKafcHWocBVgvmQ/O1jkI4wKRJkxg3bhx9+/alTZs2jGqUYsfnzk0+v+z5DFN0mPZ8WP3thBBCCCFEjbhpHcCtmA59UdGG11qDhI6HH15PbkEXFIZpz8eYtv2j+vtQ9Jhjv8JjxN/tj0PUOuZji+2ameN+1x9xH/gCqtV8zWNl73cCQ1H1dqjoMB+aj1vMxGrHImov64VdqPnJdm+vi7gD9wHPo2vRE1Qr1ov7MG7/F2rWyertSLVgPvoN7kN+LwW46zgPDw8GDx4MVCSkyz76e7UveLgP/RNu/Z659gFFh6LoMKz/HZaji25/h6oVa+pBrFln0DXtUK1YhBBCCCGE/Wp1QsdalI717MYaL4XxGPUm6Gv4Um0nRH+QE6J6QrVaMB3+okbHV9k7EY5ZSqNasaYdwpp1Gl3TjjXfn6gVTIe/rJjxYMcx4tbjEdxH/D9MW97AsOJpMBajCx+Ee6/HMG74XfWDMRZjObUat273V39bUSuZj32LPYXcTVvfwrT1rWvu93xsA7rQzlgTN1c/GEWP+chCPEa/Xf1thRBCCCGEXWr1kitLwk81rp2jjx6NPmYypk0OKNpoLMF6YXfN9yNqBWvGCSjK0DqMXyl6zGfWah2FcBDVXI418Se7kjlKo7a4j3oD05Y3MB/+EsrzwWrGmrTdvmQOVMwCO73avm1FrWQ+tcphtb+UJh3Qt+iOJXEzarEdn4uqBfOplaia1bsTQgghhGh4anVCx5pxrGK5lb18GuEx5t2KlueZ1VyicD2KHmvG8ZrvR9QK1vRj2FVTyVlUK9b0o1pHIRzEmnXG7qWibj0frah7c/RbxwX0y/ElJ9z1g2o2oGbHO2x/bt0eAMAcW42lVlcrz0ctSndQREIIIYQQ4lZq95Kr1ENwnRolt8tj7Huo5fmYdv0bJaBFzQNSrVguxeJe8z2JWsCacfyX+kz2H2PeLx0H7yAoy8NyYSemHe+h5tpbPFvFeumo3bGI2sWaUZkwrH4CRd96ANbsBNx6PYZbz0dRglpXHGNnN1Z0ZSvLsy+osjzU4gwU/+b2bS9qDevl0zWrLXclnTtunadgLUzDen5rzeJKP4bOEX9vhRBCCCHELdXaGTqq2VCDE2PQd5uBvt0ojOt/Cw5rB63KDIp6xHrpiN3JHLU4E8O631A+fwRl77XFsOJpdE064DVzA0qT9vYHVZaLWpxp//ai1rBmnLB7hqHi3xxdsxjc+jyJcd0rFcfY6ufRt7sbrweXg96jZnGJOk/NiMNRMwz10Xej+IRgsbNIvI3OTY4vIYQQQggXqrUJHcoL7f5iqQS2wmPE3zEfWYj14n4Hx1Xg2P0JzailOXZvaz40H8uxbyuWF5jKsCbvxrD0YdC54T7kjzWMK7dG24taojS3RrO/FJ0bpk2vV3yGmcsr6uds+we6pp3Qdxxfg7jsP+5F7aGW5tRsSfIV3Lo9gGq1YD66uOY7K5PPLyGEEEIIV6m1CR3VYrB7W/ehf0E1FGHa/k5Fh5nK/wAU5def7WE1Sw2K+sJhM7cqqAWpWLNOo2/Vp2b7Mdt/7IvaQzWX27/tLyfFlqsS0taUfQDomnezc8+KHF/1hOqgzy/Fvzm6yDuxntuCWnSphkGpcnwJIYQQQrhQra2ho+jtr1SjeAWi+DXF++W4ax5z6/8cbv2ewbDsUaznttgTGIpSiwrpCvvpnHD4KzrsqZlSZRf6WvvPUlSDUpNlUVmn0DVqW5GArrLTX27bvSxGrdFnq6g9FAd9fum73o+i02M+WoNiyJUURY4vIYQQQggXqr1nju6+dm9q+G7GNfcpwRF4P7sX897/Ydr2jxrE5WX/tqJWUTx8a7Ts6pr9BbVG17QDlnPbarajGhz7ohbx8K2YDWhH23LLqdW4dRyPPqw/lvh1tvt1rQcCYE07ZH9ccnzVDx5+DmlZ7tZ1OtaidCyJmx0QFBXHvRBCCCGEcIlau+RK8fQDv2Zah3ENXZOOWocgHERp1vmXGTXV5O6N54Pfo283CiWgJXgFoosaiuf0RWA2YNr+tv1BuXlVdDQSdZ6uSQe7t7XEr8NyfjvuI/+OLmIweAagazsCj7tew3LxAJYz6269kxvF1VQ+w+oDXZMONU7o6MIHoQuOwHLsO4ckh7Ca5W+kEEIIIYQL1d4ZOoCuZS+sCT865oumqqJazTXu4KFr0aPmsYhaQd+8W8XxVV2mMkzb/4lb78dxH/kmim9j1JLLWJN2YtrzIWrBRbtj0jXthOKgQqdCW7rmXe2anVPJsHwm7oNexmPMeyh+TVGLszAf+w7Tzn/b/zmm90Rp1NbumETtoQvtUuN9uHWZhmoxYj76rQMiqqAL7eqwfQkhhBBCiJur1QkdfWhXrAkbHbIvNT+Zsn+G1WwnVnPFSZqoF3ShXe0+MbamHsSYetDBAUnCsD7RNavhCbe5DNP2t2s24+squmaSMKwvFO8glIAWqIX2FzI2rn0Z1r7ssJjQe6A0loShEEIIIYSr1NolVwC6yCE1usLteAq68EFaByEcRNeyN7jVoppIVjP6yCFaRyEcRPEJQWnaiVrzMavo0EUN1ToK4UD6NsNr1rXRkRQ9uvBBDivWLIQQQgghbq2WnGlcn655N5SmMfbVOXE0RY8u+m50/s21jkQ4iOLph1uXabXnhMg/FF2bYVpHIRzIvfcTgAOWjDqIW/cHtQ5BOJBbz0dqz0UP1YJ778e1jkIIIYQQokGpBZmSG1MUBfc+Tzimhk5NqRbce83UOgrhYG69HqsdJ0SKDvdej8tymHpGHzOxdnSV0unRtbsbXUALrSMRDqRr1rlimWYtuOih+DeXGWBCCCGEEC6m/bfAW9B3mgBegYCiXRCKDiU4qqLbjKhXdE07oQvrp/0sHZ0bbt1maBuDcDjF3adiFoXWJ9xWC+59ntQ2BuEUbn2frgUXPRTc+syShLQQQgghhIvV+oSO4u6Dx+h3AFW7IFQVj3H/QdH6pEw4hcfod0DRMGEIuA/7K4pvY01jEM7hPvgV8G2qXVJH0aOPmYI+fKA24wun0nccjy58MGiVTFH0KI2jcevzhDbjCyGEEEI0YHUiQ6HvOB5d9BhtZlEoOtz6PIk+rJ/rxxYuoWvSHvc7f6/N4IoeXas+uEntiXpL8fTH894PtJlFoejAOwiPUW+6fmzhEoqi4DHufdB7aBSBiueE/6JoNr4QQgghRMNVJxI6iqLgOeZf4BXg2qSOokcJbI37Xa+5bkyhCbf+z6KEdnPx8aUDvTse934ks7/qOX3kEPQ9HnX9TDDViuc976N4B7l2XOFSusBWeIx6S5Ox3Qe/iq5ZZ03GFkIIIYRo6OrMWaTi2xivGd+Bm6drli4oevAJwfOBpSjuPs4fT2hK0bnhdd8ilMBWLkrq6EDR4zl9EbrgCBeMJ7TmMeoNdJF3uXTplfvIN9C3G+my8YR23LrNwG3giy4cUUHfZRpug19x4ZhCCCGEEOJKdSahA6AL7YrnA0vB3ce5J92KHnwb4/XQD+iCwpw3jqhVFN/GeD68AiWotZOPLx3o3fCctgB9xCDnjSNqFUXvjueU+egih+CKIu/uw/8mhZAbGPchr+HWf7ZLxtLHTMbjHqktJ4QQQgihJUVVVQ2rDdvHmnUaw/dPoOZdwBnFkpXQbnhOmYcuUJI5DZFako1h5TNYk3c7fueKDnyb4jn5M/St+jh+/6LWUy1GjD+9jiV2YcXx4MjaOjo96D3wGPMv3DpPddx+RZ1iOjAP05Y3Ko4t1eK4HSt6UK24DXwR9yG/l2SOEEIIIYTG6mRCB0A1l2Pa8R7mfXN+OSmq4ZdWRQ+KDvchf8Ct3zPSfrWBU1UVy9FFGDe9DhaTA46vihN3fY+H8Rj2Ooqnn2MCFXWWJWk7hjUvQcllhyV1dOGD8Bj3AbrAVg7Zn6i7rNkJGFa/gJpx3DE7VBSUoAg8xv8XfcuejtmnEEIIIYSokTqb0KlkSTuCads/KmZTKPrqn3grOlBVdO1G4TH0T+gaRzsnUFEnWQsuYtr2NpbTa3652l3NE+9fjkldi564D/kD+sg7nROoqJNUQxGmXf/BfORrMJVQsQq2GsfYL8eXEhSO+6CX0He9H8XVhZdFraVazZgPfYFp3xwozqz+38jKGWReQbj3eRK3AbNR3LycF7AQQgghhKiWOp/QqWTNOYc59ivMxxaDoajiTp0bWC3YlmUpuor/rOaK2z6NcOvxCG49HkQX0FKTuEXdoJZkYz62GPPhBahFlyru1LldleRRKpa8VB5f7t64dZ6KW89H0TWL0SRuUTeoxlIsp1ZiOjQfNetUxZ2VdZyuPAG/8jNNp0fXdhTuvWaiixgsiRxxQ6rVguXcz5gPzsd6YScVfxOv+ryCX4451faZpmvZC7feT6BvPxbFzVOL0IUQQgghxE3Um4ROJVW1ouYmYc04jjXjOGpBKqqprGK6uJs3SkgEutCu6EK7oQS2kpMgUS2qqqIWZ2BNP441/Rhq7rmK40u1VBxf/qHomndDF9oVpVFbWbonqk0ty8OacaLiMyzrNJhKUM3GipkRPiHoQrugC+2GrmkHmS0hqk01lWLNPFnxGZYZB+X5qGYDipsHeAaga9rpl7+RXVA8/bUOVwghhBBC3ES9S+gIIYQQQgghhBBC1HfSokIIIYQQQgghhBCijpGEjhBCCCGEEEIIIUQdIwkdIYQQQgghhBBCiDpGEjpCCCGEEEIIIYQQdYwkdIQQQgghhBBCCCHqGEnoCCGEEEIIIYQQQtQxktARQgghhBBCCCGEqGMkoSOEEEIIIYQQQghRx0hCRwghhBBCCCGEEKKOkYSOEEIIIYQQQgghRB0jCR0hhBBCCCGEEEKIOkYSOkIIIYQQQgghhBB1jCR0hBBCCCGEEEIIIeoYSegIIYQQQgghhBBC1DGS0BFCCCGEEEIIIYSoYyShI4QQQgghhBBCCFHHSEJHCCGEEEIIIYQQoo75/9jm3noEAPwCAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1150x750 with 4 Axes>"
      ]
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   "source": [
    "def expanded_graph(forest):\n",
    "    graph = nx.DiGraph()\n",
    "    graph.add_node(-1)\n",
    "    for block in range(n_blocks):\n",
    "        graph.add_node(block)\n",
    "    for edge_index in range(int(forest.n_edges[0])):\n",
    "        edge_type, start, end, _ = [\n",
    "            int(value) for value in forest.edges[0, edge_index]\n",
    "        ]\n",
    "        if edge_type == int(EdgeType.polymer):\n",
    "            step = 1 if end >= start else -1\n",
    "            for parent in range(start, end, step):\n",
    "                graph.add_edge(parent, parent + step, kind=\"polymer\")\n",
    "        elif edge_type == int(EdgeType.root_jump):\n",
    "            graph.add_edge(-1, end, kind=\"root jump\")\n",
    "        else:\n",
    "            graph.add_edge(start, end, kind=\"ordinary jump\")\n",
    "    return graph\n",
    "\n",
    "\n",
    "positions = {-1: (0.5, 1.15)}\n",
    "positions.update({block: (block % 4, 0.55 - 0.55 * (block // 4)) for block in range(n_blocks)})\n",
    "forest_map = {\n",
    "    \"automatic: independent chains\": automatic_forest,\n",
    "    \"coupled by ordinary jump\": coupled_forest,\n",
    "    \"dandelion: one root jump per block\": dandelion_forest,\n",
    "}\n",
    "fig, axes_grid = plt.subplots(2, 2, figsize=(11.5, 7.5))\n",
    "axes = axes_grid.flatten()\n",
    "for axis, (title, forest) in zip(axes[:3], forest_map.items()):\n",
    "    graph = expanded_graph(forest)\n",
    "    node_colors = [\n",
    "        \"#555555\" if node == -1 else \"#4c78a8\" if node < chain_b_start else \"#f58518\"\n",
    "        for node in graph.nodes\n",
    "    ]\n",
    "    nx.draw_networkx_nodes(graph, positions, node_color=node_colors, node_size=700, ax=axis)\n",
    "    nx.draw_networkx_labels(\n",
    "        graph,\n",
    "        positions,\n",
    "        labels={node: \"root\" if node == -1 else str(node) for node in graph.nodes},\n",
    "        font_color=\"white\",\n",
    "        ax=axis,\n",
    "    )\n",
    "    for kind, style, color in [\n",
    "        (\"polymer\", \"solid\", \"#333333\"),\n",
    "        (\"root jump\", \"dotted\", \"#777777\"),\n",
    "        (\"ordinary jump\", \"dashed\", \"#b22222\"),\n",
    "    ]:\n",
    "        selected = [(u, v) for u, v, data in graph.edges(data=True) if data[\"kind\"] == kind]\n",
    "        nx.draw_networkx_edges(\n",
    "            graph,\n",
    "            positions,\n",
    "            edgelist=selected,\n",
    "            style=style,\n",
    "            edge_color=color,\n",
    "            width=2.0,\n",
    "            arrows=True,\n",
    "            arrowsize=16,\n",
    "            ax=axis,\n",
    "        )\n",
    "    axis.set_title(title)\n",
    "    axis.set_axis_off()\n",
    "axes[3].set_axis_off()\n",
    "axes[3].text(\n",
    "    0.02,\n",
    "    0.78,\n",
    "    \"blue: chain A\\norange: chain B\\nsolid: polymer\\ndotted: root jump\\ndashed red: ordinary jump\",\n",
    "    transform=axes[3].transAxes,\n",
    "    va=\"top\",\n",
    "    fontsize=11,\n",
    "    bbox={\"boxstyle\": \"round,pad=0.6\", \"fc\": \"#f7f7f7\", \"ec\": \"#777777\"},\n",
    ")\n",
    "fig.suptitle(\"The same eight blocks under three FoldForest topologies\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a7bdcce",
   "metadata": {},
   "source": [
    "The automatic forest has two root jumps; the coupled forest has one root and one ordinary jump from block 0 to 4. The dandelion forest has eight root jumps, preserving each block's internal geometry. It neither changes chemical connectivity nor idealizes lengths and angles.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b16ca6c1",
   "metadata": {},
   "source": [
    "## Perturb an inter-chain jump\n",
    "\n",
    "`inverseKin()` initializes DOFs from coordinates; `PoseStackKinematicsModule` maps them back. Translate the ordinary jump by 3 Å along its frame and tilt it by 12°.\n",
    "\n",
    "Checks require chain A to stay fixed and every block in chain B to move, catching accidental selection of the virtual-root jump.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be8ca689",
   "metadata": {},
   "outputs": [
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dA3AzkVgAN0mVKlXUtWtXLV26VD/++GOW/cnJyRo7dqzuuusuDRw40K7nHjx4sI4dO6bRo0fr4MGD6t27d64WNhYrVkypqammzp35W9alS5fm+dhWrVpJunZLxL/t2LEjy2/0Hn74YUnXnpbybydOnNCuXbus+zPbLVSokCZNmqT4+HhrAhESEqIDBw5o3rx58vf3V506dfIc83/ddddd6tixoxYuXCjDMGwSi/yM7xNPPKGff/45VxfUHh4eNtuZC6hvhXbt2um3337L1VOXcsuR/THr4Ycf1u7du7PMWH755ZfW/ZlatGih6tWra86cOVq4cKHS0tL0/PPP2xyXl++BJLm6uio4OFgjR47UiBEj9Ndff+X41CTp2v9XkrL8/a1evTpX58tJq1at9PPPP2e5oF61alWWunntoxk3q7838t//2y5evKioqCib70N2OnToIBcXlyz/32U+GOGZZ56xb6BAPpFYADfRzJkz1bhxYz322GOaPXu2Lly4oJSUFEVFRalVq1Y6deqUvvrqK5UsWdKu533iiSdUsWJFvfvuu3JxcclykZKTmjVr6scff9Tff/+d73PXqVNHr7zyit544w29/fbbOnnypJKTk7V//35Nnz5dQ4YMyfHY+++/X+3atdO4ceO0cuVKXbp0STt37tTEiRNVo0YNm7rNmjXT008/rYkTJ2rx4sVKSEjQnj171LFjR3l7e+v111+31vX29lbDhg21bt06BQUFKSAgQJLUtGlTFS9eXN99951at26d799wjxs3TuPHj9dvv/2mpKQknTt3TjNnzpSLi4sefPBBa738jO9rr72mKlWqqG3btlq7dq3i4uJ04cIFRUVF6fnnn7deqDz55JPaunWrFi1apCtXrmj37t3q2LFjlvu/b5ZXX31VVapU0VNPPaV169YpMTFRx44d09SpU7VgwYI8t+fo/pgVGhoqHx8fPfPMM9qzZ48SExO1ePFiTZgwQU8//bSaN29uU//555/X1q1b9e677+r+++/Pss4gt9+DVq1aafHixTp+/LhSU1O1f/9+rVixQtWqVbvurUPNmzdXUFCQxo8fr4MHD+rixYv68MMPbR7zmh9jxoxRkSJF1LlzZx04cEBxcXH6+OOPs731J7d9tIeb1d8buXTpksLDw3Xx4kUdPnxYXbp0kYuLi8aOHXvd4+69914NHDhQ06ZN0+rVq5WSkqLNmzdr/Pjx6tGjhxo2bHhT4wZyi8QCuIm8vLy0efNmTZgwQXPmzFHlypXl4+OjPn36qGHDhvrtt990//332/28hQoVss6CtGzZMssC0py888478vHxUZUqVXJ8j0VuvPXWW5o/f75Wr16tmjVrys/PT88++6wuXLig0NDQ6x4bGRmpHj16aMCAASpTpozGjx+vGTNmZFlULUkRERF65ZVXNH78ePn7+6t169aqUqWKoqOjrclDpn/PUmRyd3e39tHMBevw4cNVuHBh9erVS/7+/qpZs6b27Nmj7777zubvNz/j6+Pjo507d6p9+/bW93vce++9Gjt2rB588EHrLW5jxozRmDFjFBoaKn9/fw0aNEgTJ07M8d0l9ubr66vo6Gi1a9dOL7zwgvz9/dWmTRtdvnw5ywL73HB0f8wKCAhQdHS0qlatqtatW8vPz0/jx4/XK6+8ooiIiCz1e/XqJTc3tyyLtjPl9nvw9ttv67vvvtNDDz0kHx8ftW3bVo0aNdKWLVuuO2vp6uqqL7/8UqVKlVLdunVVs2ZNJSYm6pVXXjE1DpUqVdLWrVtVuHBh1a9fX9WrV9fx48c1fvz4fPfRHm5Wf29k9OjRunjxooKDg1WnTh3rAyT++xjZ7Hz44YcKDQ3V8OHD5e3treeff14vvviiZs+efVNjBvLCxcjto2sAOJUZM2Zo6NChWrRokbp16+bocADAITZu3KiQkBB9//33NjOIt9Ly5cvVqVMn/fzzz2rQoIFDYgBuBWYsgNvU0qVL5evry723AO5omQu0b7RAGoB5JBbAbcYwDH399deKiorSqFGjsr2FCADuBFeuXNGXX34pf39/Va1a1dHhALc9txtXAeAs9u3bp1q1asnLy0vPP/+8Ro8e7eiQAMAh9uzZo8aNGysoKEiLFy/mlyzALcAaCwAAAACmFZgZi9jYWG3dulWXL19WjRo11KhRo+vW37x5s3777TebMj8/P3Xv3v1mhgkAAAAgGwUisXj77bf16aefqlGjRnJzc9Mrr7yiRo0aacWKFVlekJQpMjJSmzZtUtu2ba1lZl/sBQAAACB/CkRi0bBhQ40cOVJubtfCOXbsmKpUqaLVq1df94k2derUUXh4eL7Pa7FYdObMGZUoUSLfL8YCAAAAbleGYejSpUsqV66cXF2v/9ynApFY/PclUT4+PjcMXJL+/vtvffrpp/L29lbjxo1VqVKlPJ33zJkzWV6iBQAAAMDWqVOnVKFChevWKRCJhSSdOHFCK1asUGJiolauXKm+ffuqffv21z3mn3/+UXR0tGJiYtSrVy9NmDDhum/NTE1NtbldKnPd+okTJ+Tl5WWXfgAAAAC3i8TERAUGBqpEiRI3rFtgEovk5GQdP35c58+fV2xsrNzd3WWxWFSoUKFs6w8YMEAzZ860zmwsXrxY3bt31wMPPKDGjRtne8yUKVMUFhaWpTwjI0Pp6en26wwAAABwG8jIyJCkXC0bKJCPm42JiVGNGjUUFham4cOH5/q4MmXKaNiwYXrttdey3f/fGYvExEQFBAQoLi6OGQsAAADgPxITE1WyZEklJCTc8Hq5wMxY/Fv58uVVt25d/fLLL3k6rnDhwkpISMhxv4eHR7ZPmXJ1dc3Vmg4AAADgTpKXa2SHX01fvXpVR48etSk7f/689u3bp3vuucdatnHjRi1atEjStSmZP//80+aYLVu26NSpU2revPnNDxoAAACADYfPWBiGoWeeeUY1a9ZUcHCw4uPjFRERoSpVqmjYsGHWehEREYqOjlb37t1lGIY6deqk4OBg1ahRQydPntSCBQvUt29ftWvXzoG9AQAAAO5MDk8sChcurF27dmnVqlXavXu3fH199dlnn6lNmzY2i0RCQkJUrVo1SZKbm5vNMdWrV9fWrVvVsGFDR3UDAAAAuKMVyMXbt0piYqK8vb1ztRgFAAAAuNPk5XrZ4WssAAAAADg/EgsAAAAAppFYAAAAADCNxAIAAACAaSQWAAAAAEwjsQAAAABgGokFAAAAANMc/oI8AAAA4L+SJpd1dAgFgudrfzs6hFxjxgIAAACAaSQWAAAAAEwjsQAAAABgGokFAAAAANNILAAAAACYRmIBAAAAwDQSCwAAAACmkVgAAAAAMI3EAgAAAIBpJBYAAAAATCOxAAAAAGAaiQUAAAAA00gsAAAAAJhGYgEAAADANBILAAAAAKaRWAAAAAAwjcQCAAAAgGkkFgAAAABMI7EAAAAAYBqJBQAAAADTSCwAAAAAmObm6AAAAABuB0mTyzo6hALD87W/HR0CHIAZCwAAAACmkVgAAAAAMI3EAgAAAIBpJBYAAAAATCswi7ctFosOHTqky5cvq2rVqipZsmSujvvzzz8VGxuroKAg+fn53eQoAQCOxgLZ/88eC2QZz2tYbAyYVyBmLJYvX67q1avr2Wef1QsvvKDy5ctr9OjR1z0mKSlJjz/+uBo0aKChQ4cqICBA06dPv0URAwAAAPi3AjFjcfnyZW3btk1lypSRJG3fvl3NmzfXww8/rEceeSTbY8aPH6/9+/frzz//1F133aU1a9aobdu2atasmZo1a3YrwwcAAADueAVixqJ3797WpEKSGjZsKDc3N8XGxuZ4zPz589WvXz/dddddkqTHH39cdevW1eeff36zwwUAAADwHwVixkKSLly4oN27dysxMVHz5s1To0aN1LFjx2zrxsTE6J9//lG9evVsyuvVq6c9e/bkeI7U1FSlpqZatxMTEyVdW99hsVjMdwIAcNNZCsbvxAoEe/zsYjyvYSzti/G0H0dfo+bl/AUmsTh27JimTp2q8+fP69SpU5o0aZI8PT2zrRsXFydJ8vX1tSkvVaqUdV92pkyZorCwsGzbS09PNxE9AOBWSfGs6ugQCoyUixfNt8F4SmIs7Y3xtB97jKUZly5dynXdApNYNGjQQBs3bpQk7dy5U61atVLx4sXVvXv3LHULFy4sSUpOTrYpT0pKsu7LTmhoqEaNGmXdTkxMVEBAgEqWLCkvLy97dAMAcJMlJR1xdAgFhud/fsGWH4znNYylfTGe9mOPsTTDzS336UKBSSz+rWnTpmrYsKE2bNiQbWIREBAgV1dXnT592qb89OnTqlSpUo7tenh4yMPDI0u5q6urXF2ZbgMAZ+Aqbl3NZI+fXYznNYylfTGe9uPoa9S8nN/hV9MZGRm6cuWKTVlqaqqOHTtmXZgtSQcOHFB0dLQkqWjRomrRooVWrlxp3X/p0iVt2rRJbdq0uSVxAwAAAPj/HD5jcfXqVTVs2FA9e/ZUcHCw4uPjNXv2bKWnp2vo0KHWeu+8846io6O1b98+SdLkyZP10EMPaeTIkWratKk++ugjlS9fXv369XNUVwAAAIA7lsNnLIoUKaIffvhBaWlpmjdvntavX6+nnnpKBw8eVMWKFa31goOD1bRpU+t2s2bNtH37diUkJGj+/Plq2rSpoqKiVKxYMUd0AwAAALijOXzGQpL8/f31xhtvXLfOvxddZ2rQoIHmzp17s8ICAAAAkEsOn7EAAAAA4PxILAAAAACYRmIBAAAAwDQSCwAAAACmkVgAAAAAMM3UU6EyMjL0+++/W9+AHRAQoFq1ajn8DYEAAAAAbq18JRb79+/X+++/r8jISCUkJNjs8/HxUZcuXTRs2DAFBwfbJUgAAAAABVuepxYGDRqkpk2bKiUlRZ9++qn++usvJSUl6cqVK/rrr780a9YsXblyRU2aNNHgwYNvRswAAAAACpg8z1h4eXnp+PHj8vX1zbKvcuXKqly5srp06aKLFy9q2rRpdgkSAAAAQMGW58Qit8mCr68viQUAAABwh7gpq6xTUlK0ePHim9E0AAAAgALIronFL7/8osGDB6ts2bLq1auXPZsGAAAAUICZTizOnz+v8PBw1a5dWw0bNtS5c+f0wQcf6Ny5c/aIDwAAAIATyHVicfDgQXXp0kWDBg3S+fPntWbNGnXs2FHly5fXrFmz1L17d0nS8uXL1aNHD5UsWfKmBQ0AAACgYMn14u2OHTvqjTfekCSVLVtWhQsXVqdOnbRx40a1aNFCkjRmzJibEyUAAACAAi3XicXZs2fVpEkTWSwWpaenq2HDhnr00UfVqFGjmxkfAAAAACeQ61uh3n//fbVs2VIdOnTQ0qVL1bhxYw0bNkxly5bV0KFDtXv37psZJwAAAIACLNeJRbdu3fTXX39p9+7d6ty5s9577z3FxMTos88+019//aWGDRtKkj766COdPn36pgUMAAAAoOAx9VQod3d3PfPMM/r222918uRJTZ48We+//74CAgKsiQYAAACA25/d3mNRrlw5hYaG6vDhw9q6datq1qxpr6YBAAAAFHB5Tiz++OOPG9Zp0aKF5s2bl6u6AAAAAJxfnhOLkJAQ9ejRQ9u3b5dhGFn2WywWff/99+rWrZtat25tlyABAAAAFGy5ftxspgMHDmjSpEl6/PHH5ebmpnr16ql06dIyDENnz57Vrl27JEkDBw7UwYMH7R4wAAAAgIInzzMWXl5emjZtmmJiYvTxxx8rODhYSUlJSklJUc2aNfXZZ58pJiZG06ZNk5eX182IGQAAAEABk+cZi0zFixdX586d1blzZ3vGAwAAAMAJ2e2pUAAAAADuXCQWAAAAAEwjsQAAAABgmqnEIioqKl/7AAAAANxeTCUWLVq0yNc+AAAAALeXm3IrVHx8vEqUKHEzmgYAAABQAOXrcbPdu3fP9rN07c3b+/btU5MmTcxFBgAAAMBp5CuxcHNzy/azJLm7u6tLly7q37+/ucgAAAAAOI18JRaff/65JMnPz0/Tp0+3ZzwAAAAAnFC+37wtye5JRUpKigoVKiR3d/cb1k1MTFRSUpJNmbu7u0qVKmXXmAAAAADcmKnEIjk5WR988IG2b9+uixcvZtmf20fOLl26VNOnT9eBAweUkZGhevXq6YMPPlD9+vVzPGb06NH6/PPP5ePjYy0LCgrS999/n9duAAAAADDJVGIxaNAgbdiwQR07dlS9evXy1UZGRoZWrFihWbNmqW7dukpLS9OQIUP02GOP6dChQypZsmSOx7Zr107Lly/Pb/gAAAAA7MRUYrFq1SpFRUWpRo0a+W6jUKFCioiIsG4XLVpU//vf/zR37lz99NNPeuSRR657fEJCgooVK5ZlETkAAACAW8fUeyw8PT0VEBBgr1isjh07JkkqXbr0deutXLlS5cuXV7FixfTAAw9oz549do8FAAAAwI2Z+jV/u3btNH/+fA0dOtRe8Sg5OVnDhg3Tgw8+qLp16+ZYr169etq5c6caNGiguLg4vfjiiwoJCdG+fftyTEhSU1OVmppq3U5MTJR07d0bFovFbn0AANw8lpvzblenZI+fXYznNYylfTGe9uPoa9S8nN9UYhEXF6dhw4Zp+fLlqlq1qlxcXGz2z549O0/tXb16VZ07d1ZCQoK+/fbb69YdMGCA9bOvr6/mzJkjf39/LV++XC+++GK2x0yZMkVhYWHZ9iM9PT1PsQIAHCPFs6qjQygwUrJ5cEqe22A8JTGW9sZ42o89xtKMS5cu5bquqcTC1dVVXbp0kSRduXLFTFPWpGL//v364YcfVLZs2Twd7+npqXLlyllvo8pOaGioRo0aZd1OTExUQECASpYsKS8vr3zHDgC4dZKSjjg6hALD09fXdBuM5zWMpX0xnvZjj7E0Iy/rmE0lFv9edG1Genq6nn32We3du1fff/99tus2EhMTdfXqVet7KgzDsJkhiY2N1YkTJ1S5cuUcz+Ph4SEPD48s5a6urnJ1ZboNAJyBq7h1NZM9fnYxntcwlvbFeNqPo69R83J+h19NWywWde3aVVFRUYqIiFDhwoV19uxZnT17VsnJydZ6o0aN0oMPPijp2lqJFi1a6Ouvv9bRo0e1ZcsWPfnkkypXrpy6d+/uqK4AAAAAdyxTiYXFYtGMGTN03333ydvb21o+ZswYnT59OldtxMfHa+vWrXJxcdGTTz6punXrWv+sWLHCWs/b21t+fn6Srs08hIeHa9GiRXr00Uc1ZswYNW/eXLt27bKJAwAAAMCtYepWqPDwcH3wwQcaPXq0zYLpoKAgTZgwQZ988skN2/D19dXZs2dvWO+dd96x2W7QoIEiIyPzHjQAAAAAuzM1YzFr1ixFRkZq8ODBNuUhISE2sw0AAAAAbm+mEosTJ06oVq1akmSzkNrT09P6jggAAAAAtz9TiUWlSpW0a9cuSbaJxZdffqmgoCBzkQEAAABwGqbWWIwaNUo9e/bU5MmTJUk//PCD1q1bp/Dw8FytrwAAAABwezCVWAwcOFBpaWkaMWKELBaLWrZsKT8/P7399tvq2bOnvWIEAAAAUMCZSiwkaejQoRoyZIhOnz4ti8WigIAAh7/IAwAAAMCtZTqxkK6tr8jubdkAAAAA7gymE4uoqCht375dcXFxWfZNnTrVbPMAAAAAnICpxGLChAkKCwtT3bp15ePjY6eQAAAAADgbU4nFhx9+qHXr1ql169b2igcAAACAEzK1yjolJUVNmjSxVywAAAAAnJSpxKJt27aKjIy0VywAAAAAnJSpW6Heffdd1axZU5GRkapSpYrN27clacaMGaaCAwAAAOAcTCUW48aN0+XLl5WSkqKYmBh7xQQAAADAyZhKLJYuXapNmzapefPm9ooHAAAAgBMytcaiWLFiqlOnjr1iAQAAAOCkTCUWLVu21IIFC+wVCwAAAAAnZepWqLS0NA0ZMkSRkZGqWrVqlsXbs2fPNhUcAAAAAOdgKrEoXLiwunTpIkm6cuWKXQICAAAA4HxMJRYRERH2igMAAACAEzO1xgIAAAAAJJOJhcVi0YwZM3TffffJ29vbWj5mzBidPn3adHAAAAAAnIOpxCI8PFzTp09X//79lZiYaC0PCgrShAkTTAcHAAAAwDmYSixmzZqlyMhIDR482KY8JCREK1asMBUYAAAAAOdhKrE4ceKEatWqJUk2j5r19PS0mcEAAAAAcHszlVhUqlRJu3btkmSbWHz55ZcKCgoyFxkAAAAAp2HqcbOjRo1Sz549NXnyZEnSDz/8oHXr1ik8PFyffPKJXQIEAAAAUPCZSiwGDhyotLQ0jRgxQhaLRS1btpSfn5/efvtt9ezZ014xAgAAACjgTCUWkjR06FANGTJEp0+flsViUUBAgFxdeT0GAAAAcCcxnVhI19ZXBAQE2KMpAAAAAE7I1NTC2rVr9fzzz2cp79Onj9atW2emaQAAAABOxFRi8eqrr2rkyJFZykeOHKnXXnvNTNMAAAAAnIipxOLQoUMKDAzMUh4YGKgDBw6YaRoAAACAEzGVWNx9991au3ZtlvI1a9Zkm3AAAAAAuD2ZWrw9fPhwDRgwQEePHtUDDzwgwzC0detWTZs2TW+//ba9YgQAAABQwJmasRg4cKDefPNNvffee2rRooUeeOABhYeHKywsTAMHDsx1O7///rv69Omj6tWrq0aNGho4cKDOnDlzw+MWLFig+vXrq0KFCnrsscf022+/mekOAAAAgHwy/cKJkSNHKjY2VsePH9eJEycUGxub7YLunGRkZKhHjx5q2bKlvvnmG33xxRf6888/9fDDDys5OTnH4yIjI9WvXz8NHTpUmzZtUoUKFfTQQw8pNjbWbJcAAAAA5JFd3mTn6uqqwMBAVaxYMc8vxytUqJB2796tXr166Z577lHdunX12Wef6eDBg4qOjs7xuEmTJqlPnz7q3bu3qlevrlmzZsnNzU0zZ8402x0AAAAAeWT6BXlJSUmKjo7WyZMnlZ6ebrOvX79+uWrDxcXFZjtzpqJIkSLZ1k9ISNBvv/2msWPHWssKFSqkVq1aadu2bXkJHwAAAIAdmEos9u7dq3bt2uny5cuKj49X6dKlrbciVaxYMdeJxb8ZhqExY8YoKChIDRo0yLZO5vqL0qVL25SXLl1ae/bsybHt1NRUpaamWrcTExMlSRaLRRaLJc+xAgBuPYt9JttvC/b42cV4XsNY2hfjaT+OvkbNy/lNJRajRo1Sjx49NHnyZLm4uOjs2bM6ceKEunfvrjZt2uS7ze3bt2vr1q1yd3fPto5hGJKuzVL8m5ub23U7P2XKFIWFhWUpj4uLyzLbAgAomFI8qzo6hAIj5eJF820wnpIYS3tjPO3HHmNpxqVLl3Jd11RisWvXLi1btkzStduZ0tLSFBgYqDlz5qh169YaN25cntp79dVXNXfuXG3YsEG1a9fOsZ6/v78k6Z9//rEp/+eff6z7shMaGqpRo0ZZtxMTExUQEKCSJUvKy8srT7ECABwjKemIo0MoMDx9fU23wXhew1jaF+NpP/YYSzPc3HKfLphKLBISEuT7f5319/dXTEyMKleurLJly+rcuXN5auu1117Txx9/rPXr16tRo0bXrevv76/KlSsrKipK7du3t5Zv3bpVTz/9dI7HeXh4yMPDI0u5q6trnhedAwAcw1XcuprJHj+7GM9rGEv7Yjztx9HXqHk5v90ibd68ucaNG6fo6GiNHj1aNWrUyPWx48aN00cffaT169ercePG2dYZOXKkWrRoYd0eNmyYZs+erR9//FHp6el66623dObMmTy9PwMAAACAfZiasXj99detn6dNm6ZOnTqpadOmCgwMVERERK7auHDhgiZOnCgPDw+1bdvWZl94eLi6d+8u6dr9XXFxcdZ9w4cP17lz59S6dWulpqaqfPny+uqrr3TPPfeY6RIAAACAfHAxMldC20lKSkqOj4nNjmEYunDhQrb7SpQoYb116fLly0pPT5ePj49NHYvFoitXrqhEiRJ5jjUxMVHe3t5KSEhgjQUAOImkyWUdHUKB4fna36bbYDyvYSzti/G0H3uMpRl5uV42/R6L/8pLUiFdW/Tt5+d3w3rFixfPttzV1TVfSQUAAAAA+8lzYvHss8/mum5ub4cCAAAA4NzynFjkNHMAAAAA4M6V58Ri9uzZNyMOAAAAAE7MLmss0tPTdfLkSUlSxYoV8/QiDQAAAADOz9R7LFJTU/Xqq6/Kx8dHVapUUZUqVeTj46PXXntNaWlp9ooRAAAAQAFnamph+PDhWrNmjWbMmKGGDRtKkn7++We98cYbio+P18yZM+0SJAAAAICCzVRisXjxYm3atMmaVEhSjRo1FBwcrJCQEBILAAAA4A5h6lYoT09PVa1aNUt5tWrV5OnpaaZpAAAAAE7EVGLx6KOP6q233tK/X95tGIbeeustPfroo6aDAwAAAOAcTN0KlZSUpKlTp2rZsmWqX7++DMPQr7/+qqNHj6pTp07q16+ftS6PqQUAAABuX6YSC1dXV3Xp0sW67eLiogYNGqhBgwaSpMuXL5uLDgAAAIBTMJVYRERE2CsOAAAAAE7M1BqLtLQ0bdmyxbodHR2t3r17a8KECbp69arp4AAAAAA4B1OJxcSJE7Vz505J1257ateunWJjY/X5559r7NixdgkQAAAAQMFnKrFYuHChevXqJUlav369qlSporVr1+qbb77RkiVL7BIgAAAAgILPVGJx7tw5eXt7S5K2bNmixx9/XJJUqVIlXbhwwXx0AAAAAJyCqcSiZs2aeu+99/Trr78qIiLC+u6KP/74QzVq1LBLgAAAAAAKPlOJxdtvv633339f9evXV9u2bdW4cWNJ0nvvvacRI0bYIz4AAAAATsDU42YfeOABnTt3TpcvX5aXl5e1fPz48apWrZrp4AAAAAA4B1OJhXTtJXn/Tiok6Z577jHbLAAAAAAnkufE4tlnn5V07eV4mZ9zwgv0AAAAgDtDnhOL4sWLZ/sZAAAAwJ0rz4nF7Nmzs/0MAAAA4M5l6qlQAAAAACDlY8aiffv2ua67cuXKvDYPAAAAwAnlecaiUqVK1j9eXl5atWqVjh49qpIlS6pkyZI6cuSIVq1aleVJUQAAAABuX3mesQgPD7d+7tq1q958802NHz/epk5YWJgOHz5sOjgAAAAAzsHUeyw2btyomTNnZikfPny4qlevbqZpAAAAAE7E1OLttLQ0/fHHH1nK9+/fr7S0NDNNAwAAAHAipmYsevXqpY4dO2rs2LFq2LChDMPQL7/8ookTJ6p37952ChEAAABAQWcqsXjnnXfk7++vcePG6eLFi5KkUqVKaeTIkRozZoxdAgQAAABQ8JlKLNzc3DR27FiNHTtWsbGxkqTSpUvbJTAAAAAAzsNUYvFvJBQAAADAnYs3bwMAAAAwzW4zFmbt3r1bn3zyiQ4ePKjw8HDVrVv3uvXfffddff311zZllStX1rx5825ilAAAAACyUyASizfffFMrV67U008/rU8++UTx8fE3PObw4cMyDENhYWHWsmLFit3EKAEAAADkpEAkFkOHDtWbb76p06dPZ3mL9/X4+/urZcuWNy8wAAAAALliOrGIiorS9u3bFRcXl2Xf1KlTc9VGqVKl8nXuXbt26fHHH5e3t7datGihAQMGyM2tQORKAAAAwB3F1FX4hAkTFBYWprp168rHx8dOIeVOkSJF9PTTT+vBBx9UTEyMpk6dqmXLlmnTpk1ydc1+TXpqaqpSU1Ot24mJiZIki8Uii8VyS+IGAJhj4bkjVvb42cV4XsNY2hfjaT+OvkbNy/lNJRYffvih1q1bp9atW5tpJl8mT54sT09P63arVq0UHBys5cuXq3PnztkeM2XKFJs1GZni4uKUnp5+02IFANhPimdVR4dQYKT838tpTbXBeEpiLO2N8bQfe4ylGZcuXcp1XVOJRUpKipo0aWKmiXz7d1IhSdWrV1elSpW0Z8+eHBOL0NBQjRo1yrqdmJiogIAAlSxZUl5eXjc1XgCAfSQlHXF0CAWGp6+v6TYYz2sYS/tiPO3HHmNpRl6WGZhKLNq2bavIyEg9//zzZpqxi4yMDF28eFFFihTJsY6Hh4c8PDyylLu6uuZ4+xQAoGBxFbeuZrLHzy7G8xrG0r4YT/tx9DVqXs5vKrF49913VbNmTUVGRqpKlSpycXGx2T9jxgwzzdt4++239ccff2jevHm6evWqZs6cqRdffFFubm6yWCwaN26cLl26pKefftpu5wQAAACQO6YSi3Hjxuny5ctKSUlRTExMvttZt26dpk6dal1YPWLECPn4+Kh3797q3bu3JOnQoUP6+eefJUmFChXS6dOnVa5cOQUGBiomJkbu7u768ssvVbNmTTNdAgAAAJAPphKLpUuXatOmTWrevLmpIOrWras333wzS3mlSpWsn1955RXrU5xcXV319ttvKywsTAcOHFDJkiUVGBioQoUKmYoDAAAAQP6YSiyKFSumOnXqmA6iTJkyKlOmzHXrVK9ePUuZp6en6tevb/r8AAAAAMwxtRqkZcuWWrBggb1iAQAAAOCkTM1YpKWlaciQIYqMjFTVqlWzLN6ePXu2qeAAAAAAOAdTiUXhwoXVpUsXSdKVK1fsEhAAAAAA52MqsYiIiLBXHAAAAACcGG+FAwAAAGCa6cRiz5496t69u+rXr6969eqpe/fu2rNnjx1CAwAAAOAsTCUWq1atUv369XX27Fk99thjatu2rc6ePav69evr66+/tleMAAAAAAo402/efvfddzV8+HCb8vfff19jx47Vk08+aSo4AAAAAM7B1IzFwYMH1bt37yzlvXv31qFDh8w0DQAAAMCJmEos7rrrLu3evTtL+a5du+Tv72+maQAAAABOxNStUP3791eXLl0UGhqqRo0aSZJ+/PFHTZkyRS+++KJdAgQAAABQ8JleY1G0aFFNnDhRFy5ckCSVKlVKo0eP1ssvv2yXAAEAAAAUfKZuhdqxY4dGjx6t8+fP6+zZs4qNjdX58+c1evRo7dixw14xAgAAACjgTM1YtGjRQoZhSJJKly6d4z4AAAAAt7eb8ubt+Ph4lShR4mY0DQAAAKAAyteMRffu3bP9LEkWi0X79u1TkyZNzEUGAAAAwGnkK7Fwc3PL9rMkubu7q0uXLurfv7+5yAAAAAA4jXwlFp9//rkkyc/PT9OnT7dnPAAAAACckKk1FiQVAAAAACSTT4WSpKSkJEVHR+vkyZNKT0+32devXz+zzQMAAABwAqYSi71796pdu3a6fPmy4uPjVbp0acXGxkqSKlasSGIBAAAA3CFM3Qo1atQo9ejRQ3FxcZKks2fP6vjx42revDlJBQAAAHAHMZVY7Nq1Sy+//LIkycXFRWlpaQoMDNScOXP02Wef2SVAAAAAAAWfqcQiISFBvr6+kiR/f3/FxMRIksqWLatz586Zjw4AAACAU7Dbm7ebN2+ucePGKTo6WqNHj1aNGjXs1TQAAACAAs7U4u3XX3/d+nnatGnq1KmTmjZtqsDAQEVERJgODgAAAIBzMJVYTJw40fq5atWq2r17t1JSUlSkSBHTgQEAAABwHna7FSoTSQUAAABw57F7YgEAAADgzkNiAQAAAMA0EgsAAAAAppFYAAAAADDN1FOhkpOT9cEHH2j79u26ePFilv1RUVFmmgcAAADgJEwlFoMGDdKGDRvUsWNH1atXz14xAQAAAHAyphKLVatWKSoqyi5v2U5ISNDChQt18OBBDR8+XNWqVbvhMX///bcWLVqk2NhY1apVS127dpW7u7vpWAAAAADkjak1Fp6engoICDAdxKeffqp7771XO3fu1EcffaSYmJgbHnP48GHVqlVLW7ZsUYkSJTRp0iS1adNGGRkZpuMBAAAAkDemEot27dpp/vz5poNo2rSp/vzzT02bNi3Xx4wZM0Y1a9bUt99+q/Hjx2vz5s3asWOHvvjiC9PxAAAAAMgbU7dCxcXFadiwYVq+fLmqVq0qFxcXm/2zZ8/OVTu1atWytpcbV69e1dq1a/XBBx9Yz1mhQgW1bNlSq1atUs+ePfPQCwAAAABmmUosXF1d1aVLF0nSlStX7BJQbpw8eVKpqamqXLmyTfndd9+t7du353hcamqqUlNTrduJiYmSJIvFIovFcnOCBQDYlYUnpVvZ42cX43kNY2lfjKf9OPoaNS/nN5VYREREmDk835KTkyVJJUqUsCn38vJSUlJSjsdNmTJFYWFhWcrj4uKUnp5u3yABADdFimdVR4dQYKRk86j3PLfBeEpiLO2N8bQfe4ylGZcuXcp1XVOJhaMUL15ckhQfH29THhcXJy8vrxyPCw0N1ahRo6zbiYmJCggIUMmSJa97HACg4EhKOuLoEAoMT19f020wntcwlvbFeNqPPcbSDDe33KcLphOLpKQkRUdH6+TJk1l+69+vXz+zzWerYsWKKl68uA4cOKBHH33UWn7gwAEFBwfneJyHh4c8PDyylLu6usrVlek2AHAGruLW1Uz2+NnFeF7DWNoX42k/jr5Gzcv5TSUWe/fuVbt27XT58mXFx8erdOnSio2NlXTt4t+eicXixYt1/Phxvfbaa3J1dVWnTp30+eef64UXXlDRokW1Z88e7dixQ6NHj7bbOQEAAADkjqkUaNSoUerRo4f1aU5nz57V8ePH1bx58zwlFT/++KOGDBmicePGSZLef/99DRkyRGvWrLHW2bx5sxYvXmzdnjp1qq5evar69eura9euatWqlXr37q0nnnjCTJcAAAAA5IOpGYtdu3Zp2bJlkiQXFxelpaUpMDBQc+bMUevWra2Jwo14e3srKChIklS/fn1ruZ+fn/Vzt27d9PDDD1u377rrLu3evVvfffedYmNjNWzYMDVp0sRMdwAAAADkk6nEIiEhQb7/t6DE399fMTExqly5ssqWLatz587lup2goCBrYpGThx56KEuZh4eHnnzyybwFDQAAAMDu7LYapHnz5ho3bpyio6M1evRo1ahRw15NAwAAACjgTM1YvP7669bP06ZNU6dOndS0aVMFBgY67B0XAAAAAG49U4nFxIkTrZ+rVq2q3bt3KyUlRUWKFDEdGAAAAADnYfcH45JUAAAAAHceU4mFxWLRjBkzdN9998nb29taPmbMGJ0+fdp0cAAAAACcg6nEIjw8XNOnT1f//v2VmJhoLQ8KCtKECRNMBwcAAADAOZhKLGbNmqXIyEgNHjzYpjwkJEQrVqwwFRgAAAAA52EqsThx4oRq1aol6doL8jJ5enrazGAAAAAAuL2ZSiwqVaqkXbt2SbJNLL788ssbvvAOAAAAwO3D1ONmR40apZ49e2ry5MmSpB9++EHr1q1TeHi4PvnkE7sECAAAAKDgM5VYDBw4UGlpaRoxYoQsFotatmwpPz8/vf322+rZs6e9YgQAAABQwJlKLCRp6NChGjJkiE6fPi2LxaKAgAC5utr99RgAAAAACjDTiYV0bX1FQECAPZoCAAAA4IRMJxZRUVHavn274uLisuybOnWq2eYBAAAAOAFTicWECRMUFhamunXrysfHx04hAQAAAHA2phKLDz/8UOvWrVPr1q3tFQ8AAAAAJ2RqlXVKSoqaNGlir1gAAAAAOClTiUXbtm0VGRlpr1gAAAAAOClTt0K9++67qlmzpiIjI1WlShWbt29L0owZM0wFBwAAAMA5mEosxo0bp8uXLyslJUUxMTH2igkAAACAkzGVWCxdulSbNm1S8+bN7RUPAAAAACdkao1FsWLFVKdOHXvFAgAAAMBJmUosWrZsqQULFtgrFgAAAABOytStUGlpaRoyZIgiIyNVtWrVLIu3Z8+ebSo4AAAAAM7BVGJRuHBhdenSRZJ05coVuwQEAAAAwPmYSiwiIiLsFQcAAAAAJ2ZqjQUAAAAASCQWAAAAAOyAxAIAAACAaSQWAAAAAEwjsQAAAABgGokFAAAAANNILAAAAACYRmIBAAAAwDRTL8izp7S0NG3atEmxsbGqVauW6tevf93627Zt0/79+23KfH191blz55sZJgAAAIBsFIjE4p9//lGrVq2Ulpam2rVra+TIkerSpYtmzZqV4zFffPGFNmzYoJCQEGtZ+fLlb0W4AAAAAP6jQCQWoaGhkqTdu3fL09NTu3btUsOGDfXEE0+obdu2OR533333XTf5AAAAAHBrOHyNhcViUWRkpPr06SNPT09JUv369dWsWTNFRERc99hz585pwYIFWrVqlc6cOXMrwgUAAACQDYfPWJw6dUqXLl1ScHCwTXlwcLB++eWX6x574sQJrVu3TjExMfrpp580bdo0DRs2LMf6qampSk1NtW4nJiZKupbcWCwWE70AANwqFsf/TqzAsMfPLsbzGsbSvhhP+3H0NWpezu/wxCLz4t7Hx8emvGTJktZ92enTp49mzJghN7drXZg3b5769eun+++/P8eF31OmTFFYWFiW8ri4OKWnp+ezBwCAWynFs6qjQygwUi5eNN8G4ymJsbQ3xtN+7DGWZly6dCnXdR2eWBQtWlRS1qATExOtt0Zlp3Hjxjbbffr00auvvqr169fnmFiEhoZq1KhRNucICAhQyZIl5eXlld8uAABuoaSkI44OocDw9PU13QbjeQ1jaV+Mp/3YYyzNyPwlfq7q3sQ4ciUwMFCFCxfWsWPHbMqPHTumqlXzlqkWKVJEcXFxOe738PCQh4dHlnJXV1e5ujLdBgDOwFXcuprJHj+7GM9rGEv7Yjztx9HXqHk5v8Ovpt3d3fXoo48qIiJChmFIks6cOaMtW7boySeftNb74YcfFBkZKUnKyMjQ8ePHbdqJiorSyZMn1axZs1sWOwAAAIBrHD5jIUnTpk1Ts2bN9NRTT6lJkyZasGCBGjdurO7du1vrLFy4UNHR0ercubMMw9ATTzyhhg0bqkaNGjp58qTmzJmjbt266amnnnJgTwAAAIA7k8NnLCQpKChIv/32m5o0aaLY2Fi9/PLL2rhxo809XS1btlSXLl0kXbvX69dff1Xr1q119uxZlS5dWmvXrtWiRYvk4uLiqG4AAAAAd6wCMWMhSRUqVNBrr72W4/5/z15I126h6tq1q7p27XqzQwMAAABwAwVixgIAAACAcyOxAAAAAGAaiQUAAAAA00gsAAAAAJhGYgEAAADANBILAAAAAKaRWAAAAAAwjcQCAAAAgGkkFgAAAABMI7EAAAAAYBqJBQAAAADTSCwAAAAAmEZiAQAAAMA0EgsAAAAAppFYAAAAADCNxAIAAACAaSQWAAAAAEwjsQAAAABgGokFAAAAANNILAAAAACYRmIBAAAAwDQSCwAAAACmkVgAAAAAMI3EAgAAAIBpJBYAAAAATCOxAAAAAGAaiQUAAAAA00gsAAAAAJhGYgEAAADANBILAAAAAKaRWAAAAAAwjcQCAAAAgGkkFgAAAABMI7EAAAAAYFqBSixOnTqlX375RYmJiTf1GAAAAAD2VSASi5SUFD3zzDOqXr26evTooTJlyujDDz+0+zEAAAAAbg43RwcgSWFhYfrpp5909OhRlS1bVitXrlSHDh3UqFEjNW7c2G7HAAAAALg5CsSMxbx589SvXz+VLVtWktS+fXvVrFlT8+bNs+sxAAAAAG4Oh89YnDlzRrGxsapfv75NeaNGjbR79267HSNJqampSk1NtW4nJCRIkuLj42WxWPLbBQDALZSU4ugICo60+HjTbTCe1zCW9sV42o89xtKMzHXMhmHcsK7DE4uLFy9KkkqVKmVTXqpUKes+exwjSVOmTFFYWFiW8sDAwDzFDABAgTChpKMjuH0wlvbFeNpPARnLS5cuydvb+7p1HJ5YuLu7S7q2GPvfkpOTVbhwYbsdI0mhoaEaNWqUddtisejixYsqVaqUXFxc8hX/7SAxMVEBAQE6deqUvLy8HB2OU2Ms7YvxtB/G0r4YT/thLO2L8bQfxvIawzB06dIllStX7oZ1HZ5YBAQEyNXVVTExMTblMTExqlixot2OkSQPDw95eHjYlPn4+OQv8NuQl5fXHf0Px54YS/tiPO2HsbQvxtN+GEv7Yjzth7HUDWcqMjl88banp6eaNWumr7/+2lp25coVbdy4USEhIdayI0eOWNdP5PYYAAAAALeGwxMLSZo4caJWrlyp0NBQff3112rfvr3uuusuDRgwwFpn6tSp6tGjR56OAQAAAHBrFIjE4sEHH9SWLVt04sQJvf/++6pRo4aioqJUvHhxa51q1aqpXr16eToGuePh4aHx48dnuU0MecdY2hfjaT+MpX0xnvbDWNoX42k/jGXeuRi5eXYUAAAAAFxHgZixAAAAAODcSCwAAAAAmEZiAQAAAMA0h7/HAo51/vx5HTt2TBUrVlTp0qUdHY7T+/3335WUlKTGjRs7OhSnZhiGjh8/rqSkJFWpUkVFihRxdEhOLS0tTQcPHpSnp6cqV66sQoUKOTokp5eWlqaffvpJJUuWVI0aNRwdjlOKjo5Wenq6TVlgYKACAgIcFNHt4fDhw8rIyFBQUNAd/fLf/IqLi9P+/fuz3VerVq1cv8/hTsXi7TvYq6++qvDwcFWpUkVHjx5V3759NWPGDP4jyod58+bpgw8+0IkTJ+Tq6qrz5887OiSntWDBAv3vf/+TxWJRkSJFdObMGU2aNEkvvviio0NzOunp6Ro/frw+/fRTVahQQefOnZO7u7tmz56t1q1bOzo8pzZq1Ci9//77CgkJ0bp16xwdjlPy8fFR2bJlVapUKWvZgAED1LNnTwdG5bx27typPn366NKlS9ZfFEZEROiee+5xcGTOZceOHRo9erRNWUxMjI4fP669e/eqdu3aDorMOTBjcYdaunSpwsPDtW3bNjVs2FD79u1TkyZNVKdOHd4Fkg+HDx/WnDlztHXrVk2cONHR4Ti1mJgYbdy4UZUqVZIkLVu2TF26dFHdunV1//33OzY4J5Oamip/f3+dPHlSRYsWlWEYGjp0qDp37qzz58/L1ZW7YfNjzZo1Wrdundq1a6fU1FRHh+PUJkyYoI4dOzo6DKd3/PhxPfLIIxo8eLAmT54sV1dX/fHHH4qJiSGxyKNmzZopKirKpuypp55SyZIlSSpygZ8qd6i5c+fqscceU8OGDSVJNWvWVIcOHTR37lwHR+acpkyZYvOeFeRfaGioNamQpE6dOqlUqVLavn2744JyUsWKFdOIESNUtGhRSZKLi4sefPBBxcfH68qVKw6OzjmdOXNG/fv31xdffGEdV+TfuXPn9Msvv+iff/5xdChO7a233pKfn58mTZpk/YVBcHCwHnroIQdH5vzOnj2rNWvWqH///o4OxSmQWNyhdu/erfr169uUNWrUSHv27BF3x6Eg+euvv3Tx4kVVrVrV0aE4rSNHjmjbtm364osvNHbsWIWGhqpEiRKODsvpWCwWde/eXcOHD9d9993n6HBuC6+//rr69u2rihUr6pFHHlFMTIyjQ3JKmzZt0uOPP6709HT9+uuvOnHiBD/L7WT+/PkqXLiwunbt6uhQnAKJxR3q4sWLNve1SlKpUqWUmpqqpKQkB0UF2Lp69ap69+6t2rVr64knnnB0OE7ryy+/1JgxY/TSSy+pRIkS6tSpk6NDckqTJk2SYRh6+eWXHR3KbWH69Ok6f/689u7dq2PHjun8+fNcvOXTmTNndOHCBd17773q27evGjRooPr16+vw4cOODs3pzZ07V507d2bRdi6RWNyh3N3dlZKSYlOWnJwsSSpcuLAjQgJsZGRkqGvXrjpx4oRWrlwpd3d3R4fktMaMGaMdO3YoJiZGDz/8sFq2bKkLFy44OiyncuDAAU2aNEkDBgzQjh07FBUVpfPnzys+Pl5RUVH8QiYf+vXrZ31CWZkyZRQWFqatW7fq77//dnBkzsfd3V2rV6/Wt99+q927d+vkyZPy9fVVr169HB2aU9u2bZsOHz7MbVB5QGJxhwoMDMwy5RwTE6MyZcpwAQeHy8jIULdu3fTjjz9qy5YtCgwMdHRIt4VChQpp1KhRSkhI0M6dOx0djlO5cuWKGjRooI8++kivvvqqXn31Ve3du1eHDh3Sq6++qtjYWEeH6PQyn2TE7VB5V6lSJT3wwAO69957JUlFixZVr1699NNPP/GAARPmzJmj4OBgNWvWzNGhOA2eCnWHCgkJ0bfffqu33nrLutDr66+/VkhIiIMjw50uIyND3bt3144dO/T999/r7rvvdnRITuvKlSsqVqyYTdmRI0ckKcutkLi+Bg0aZHlSzLPPPqv4+HgeN5sP2X03169fr8KFC6tatWoOisp5PfLII1q7dq1N2enTp+Xl5SUPDw8HReXcEhMTtWzZMk2aNMnRoTgVEos71OjRo7VkyRJ1795dzz33nFauXKnDhw/riy++cHRoTumPP/7QxYsXdezYMaWnp1svQOrVqydPT08HR+dc+vfvrxUrVujTTz/VmTNndObMGUlS+fLlVblyZQdH51zWrVtnvT+4fPnyOnTokKZNm6ZHH31UTZo0cXR4uIOtW7dOs2fP1rPPPqvSpUtr69ateueddzR+/HjuZc+HUaNGaeHChRowYICeeeYZ/fnnn5o6dapCQ0MdHZrTWrJkiTIyMtSjRw9Hh+JUeEHeHezIkSN66623dPToUQUGBuqll17iDbL5NGbMmGwfh7pw4UIuhvPoqaeeyvb+/86dO2vYsGEOiMi5bdu2TQsWLNCJEydUpkwZPfbYY+rcuTNv37aDN998U5cvX9b06dMdHYpT+uGHH7Rw4UKdOnVKlSpVUvfu3dWiRQtHh+W0YmJi9NZbb2n//v0qXbq0OnXqpPbt2zs6LKc1fPhwFS1aVFOnTnV0KE6FxAIAAACAaSzeBgAAAGAaiQUAAAAA00gsAAAAAJhGYgEAAADANBILAAAAAKaRWAAAAAAwjcQCAAAAgGkkFgBwhzlz5oyWLVt23Trnzp1TRESE7Pmqo9yc92bEEBcXp4iICKWnp5tqBwBwfSQWAHCH+fXXX9WjR4/r1vnjjz/03HPPKSMj45ae92bEcPToUT333HNKSUkx1Q4A4PpILAAABdJdd92lLl26yNWVH1UA4Az43xoA7lAWi0V79+7VqlWrdPz48Vwdc+TIEa1YsUI//PCD0tLSsq3zzz//aM2aNdq6datSU1Ov296BAwe0dOlSXb58Ocs+Pz8/tW/fXi4uLpJsb6Xat2+fVq9eraNHj2bb7q+//qrVq1frr7/+yvHcsbGx+uabb7RlyxYlJCRYy9PT0xUZGalDhw7Z1P/++++1efPm6/YHAO5kJBYAcAeyWCx65JFH1KdPH4WHhysoKEizZs267jFDhw5V3bp1NWvWLPXr10/33nuvjhw5YlNn2rRpCgwM1KRJkzR+/Hg1adJEp06dyra977//Xs2aNdP58+dVvHjxLPv/eyvUr7/+qm7duumxxx5Tv379NGPGDAUHB+vjjz+2Oa53795q2bKlPvroIz388MN64403srT91ltvqXr16vroo48UFhamKlWqaM2aNZIkNzc3RUdH69FHH1ViYqIkaefOnQoJCdHVq1evO0YAcEczAAB3lG+++caQZLz44ovWsnnz5hlFixY1YmJiDMMwjC1bthiSjKtXrxqGYRirV6823N3djd27dxuGYRhpaWlGmzZtjJCQEGsb3377reHq6mqsX7/eWvbHH38Yhw4dsp7Xw8PDMAzDWLFihVGiRAnjiy++yDHO/8aQGff06dOtdWbMmGF4e3sbFovFMAzDWLVqleHh4WEcOHDAMAzDSEpKMho2bGhIMi5dumQYhmGsX7/e8PX1NY4ePWptZ9GiRYafn5+1TkpKilGnTh2jW7duRkJCglG5cmVj+PDhuR1iALgjMWMBAHeoV155xfq5V69e8vb21jfffJNt3YiICD322GOqW7euJMnd3V2jR4/Whg0bdPHiRUnS/Pnz1aZNG4WEhFiPu/fee3XPPffYtDV37lz17NlTS5cuVdeuXfMUs4uLi1544QXrdsuWLZWQkKDY2FhJUmRkpJ544gkFBQVJkooWLaqhQ4fatDFv3jwFBwfr119/1bJlyxQZGSlJunDhgn7//XdJkoeHhxYvXqyvvvpKzZs3V/HixTVt2rQ8xQoAdxo3RwcAALj1XF1dVbFiReu2i4uLAgMDdeLEiWzrnzhxQvXr17cpq1KlinWfr6+vTp48qUaNGl33vFevXtWgQYM0atQoPfbYY3mOu3DhwipWrJh128PDQ5KsT3w6efKkGjdubHNM5cqVbbaPHz+uuLg4LV++3Ka8c+fOcnd3t24HBwfrueee09y5c7VmzRrruQAA2SOxAIA7kMViUUJCgnx8fKxlcXFx8vPzy7a+n5+fdWYiU+Z25jE+Pj66cOHCdc/r7u6upUuX6tlnn1VwcHCeHj+bG6VKlVJcXJxN2X+3vby8VKFCBUVERFy3rd27d2vRokUKDg7WpEmT1KZNGxUqVMiu8QLA7YRboQDgDrVy5Urr53379unPP//U/fffn23d5s2ba926dUpKSrKWLVu2TJUqVVKFChUkSW3atNG3335rk1ykp6dnubB/6qmntGTJEg0YMECLFi2yY4+uxbl27Vqbp1F99dVXNnUeffRRffvtt1kWlf/999/Wl/ElJyerW7du6t27tzZv3qw///xTkydPtmusAHC7YcYCAO5Arq6uGjt2rE6ePCkfHx9Nnz5dTz/9dJbbiDINHjxYc+bM0UMPPaQ+ffro0KFD+uijjxQZGWl9HOygQYO0fPlyNW7cWC+88ILc3d21ZMkSzZw5UyVLlrRpr3379lqyZImee+45ubi4qFu3bnbp1wsvvKCZM2eqdevW6t69u/Wxs/82aNAgff3112rSpIlefPFF+fn5ac+ePdq4caMOHjwoFxcXvfTSS8rIyNC7776rYsWKae7cuerQoYMeeeSRG97uBQB3KmYsAOAOU758eXXr1k3bt2+XxWLRr7/+qpdfflmLFy+21vnvy+mKFCminTt3qlOnTtq5c6cMw1BUVJTat29vPaZIkSLasmWLQkNDdeDAAZ06dUoff/yx6tWrZz1v586drfXbt2+vpUuXau3atdm+R+O/Mfz3eEkqUaKEunTpYl13UaxYMUVHR6tly5b66aefVL16dW3evFldunSxrp/w8PDQ+vXrNX36dJ08eVK7du1SnTp1tHfvXrm6uurUqVOKi4vT4sWLre22bdtWEydOzHFxOwBAcjEy530BAAAAIJ+YsQAAAABgGokFAAAAANNILAAAAACYRmIBAAAAwDQSCwAAAACmkVgAAAAAMI3EAgAAAIBpJBYAAAAATCOxAAAAAGAaiQUAAAAA00gsAAAAAJhGYgEAAADAtP8HS8A0C3RifkoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x420 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kinematics_module = PoseStackKinematicsModule(two_chain_pose, coupled_forest)\n",
    "kmd = kinematics_module.kmd\n",
    "flat_atom_ids = kmd.forest.id[1:].to(torch.int64)\n",
    "kincoords = torch.zeros(\n",
    "    (kmd.forest.id.shape[0], 3), dtype=torch.float64, device=device\n",
    ")\n",
    "kincoords[1:] = two_chain_pose.coords.reshape(-1, 3)[flat_atom_ids].double()\n",
    "dofs = inverseKin(kmd.forest, kincoords)\n",
    "\n",
    "# Resolve ordinary jump 0 through its destination atom, not node order.\n",
    "jump_block, jump_atom = kmd.pose_stack_atom_for_jump[0, 0].tolist()\n",
    "jump_coord_id = int(two_chain_pose.block_coord_offset64[0, jump_block]) + jump_atom\n",
    "inter_chain_jump_node = int(\n",
    "    torch.nonzero(kmd.forest.id == jump_coord_id, as_tuple=False).item()\n",
    ")\n",
    "assert kmd.forest.doftype[inter_chain_jump_node] == NodeType.jump\n",
    "moved_dofs = dofs.raw.clone()\n",
    "moved_dofs[inter_chain_jump_node, JumpDOFTypes.RBx] += 3.0\n",
    "moved_dofs[inter_chain_jump_node, JumpDOFTypes.RBdel_alpha] += np.deg2rad(12.0)\n",
    "\n",
    "moved_kincoords = kinematics_module(moved_dofs)\n",
    "moved_flat = two_chain_pose.coords.reshape(-1, 3).clone()\n",
    "moved_flat[flat_atom_ids] = moved_kincoords[1:].to(moved_flat.dtype)\n",
    "jump_moved_pose = attrs.evolve(\n",
    "    two_chain_pose, coords=moved_flat.view_as(two_chain_pose.coords)\n",
    ")\n",
    "\n",
    "\n",
    "def mean_block_displacement(reference, mobile, block):\n",
    "    start = int(reference.block_coord_offset64[0, block])\n",
    "    block_type_index = int(reference.block_type_ind64[0, block])\n",
    "    n_atoms = int(reference.packed_block_types.n_atoms[block_type_index])\n",
    "    delta = mobile.coords[0, start : start + n_atoms] - reference.coords[0, start : start + n_atoms]\n",
    "    return float(torch.linalg.vector_norm(delta, dim=-1).mean().detach().cpu())\n",
    "\n",
    "\n",
    "displacement_frame = pd.DataFrame(\n",
    "    [\n",
    "        {\n",
    "            \"block\": block,\n",
    "            \"chain\": \"A\" if block < chain_b_start else \"B\",\n",
    "            \"mean_displacement_A\": mean_block_displacement(two_chain_pose, jump_moved_pose, block),\n",
    "        }\n",
    "        for block in range(n_blocks)\n",
    "    ]\n",
    ")\n",
    "show_table(displacement_frame)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4.2))\n",
    "colors = displacement_frame[\"chain\"].map({\"A\": \"#4c78a8\", \"B\": \"#f58518\"})\n",
    "ax.bar(displacement_frame[\"block\"], displacement_frame[\"mean_displacement_A\"], color=colors)\n",
    "ax.set(\n",
    "    xlabel=\"block index\",\n",
    "    ylabel=\"mean atom displacement (Å)\",\n",
    "    title=\"Only the downstream chain moves under jump 0\",\n",
    ")\n",
    "ax.set_xticks(range(n_blocks))\n",
    "ax.grid(axis=\"y\", alpha=0.25)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "assert displacement_frame.loc[displacement_frame.chain == \"A\", \"mean_displacement_A\"].max() < 1e-3\n",
    "assert displacement_frame.loc[displacement_frame.chain == \"B\", \"mean_displacement_A\"].min() > 1.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bcfd32b8",
   "metadata": {},
   "outputs": [
    {
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       "$3Dmolpromise.then(function() {\n",
       "viewer_1791410923760042 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_1791410923760042\"),{backgroundColor:\"white\"});\n",
       "viewer_1791410923760042.zoomTo();\n",
       "\tviewer_1791410923760042.addModel(\"MODEL 1\\nATOM      1  N   ALA A   1       0.334  -0.000   0.000  1.00 30.00\\nATOM      2  CA  ALA A   1       1.792   0.000   0.000  1.00 30.00\\nATOM      3  C   ALA A   1       2.343   1.420   0.000  1.00 30.00\\nATOM      4  O   ALA A   1       1.874   2.278  -0.748  1.00 30.00\\nATOM      5  CB  ALA A   1       2.322  -0.773  -1.199  1.00 30.00\\nATOM      6  HA  ALA A   1       2.130  -0.490   0.913  1.00 30.00\\nATOM      7  HB1 ALA A   1       3.412  -0.764  -1.185  1.00 30.00\\nATOM      8  HB2 ALA A   1       1.967  -1.802  -1.154  1.00 30.00\\nATOM      9  HB3 ALA A   1       1.967  -0.307  -2.117  1.00 30.00\\nATOM     10  H1  ALA A   1       0.000  -0.943  -0.000  1.00 30.00\\nATOM     11  H2  ALA A   1       0.000   0.471   0.816  1.00 30.00\\nATOM     12  H3  ALA A   1       0.000   0.471  -0.816  1.00 30.00\\nATOM     13  N   CYS A   2       3.341   1.662   0.843  1.00 30.00\\nATOM     14  CA  CYS A   2       3.958   2.980   0.942  1.00 30.00\\nATOM     15  C   CYS A   2       5.477   2.875   0.976  1.00 30.00\\nATOM     16  O   CYS A   2       6.036   2.041   1.688  1.00 30.00\\nATOM     17  CB  CYS A   2       3.474   3.708   2.196  1.00 30.00\\nATOM     18  SG  CYS A   2       2.297   2.761   3.192  1.00 30.00\\nATOM     19  H   CYS A   2       3.681   0.914   1.431  1.00 30.00\\nATOM     20  HA  CYS A   2       3.668   3.563   0.068  1.00 30.00\\nATOM     21  HB2 CYS A   2       4.328   3.953   2.827  1.00 30.00\\nATOM     22  HB3 CYS A   2       2.997   4.645   1.911  1.00 30.00\\nATOM     23  HG  CYS A   2       2.133   3.687   4.131  1.00 30.00\\nATOM     24  N   ASP A   3       6.141   3.727   0.202  1.00 30.00\\nATOM     25  CA  ASP A   3       7.598   3.732   0.142  1.00 30.00\\nATOM     26  C   ASP A   3       8.147   5.151   0.211  1.00 30.00\\nATOM     27  O   ASP A   3       7.647   6.054  -0.459  1.00 30.00\\nATOM     28  CB  ASP A   3       8.084   3.052  -1.140  1.00 30.00\\nATOM     29  CG  ASP A   3       6.943   2.550  -2.015  1.00 30.00\\nATOM     30  OD1 ASP A   3       5.809   2.726  -1.636  1.00 30.00\\nATOM     31  OD2 ASP A   3       7.217   1.997  -3.053  1.00 30.00\\nATOM     32  H   ASP A   3       5.623   4.388  -0.359  1.00 30.00\\nATOM     33  HA  ASP A   3       7.981   3.176   0.998  1.00 30.00\\nATOM     34  HB2 ASP A   3       8.685   3.754  -1.719  1.00 30.00\\nATOM     35  HB3 ASP A   3       8.725   2.207  -0.884  1.00 30.00\\nATOM     36  N   GLU A   4       9.179   5.342   1.027  1.00 30.00\\nATOM     37  CA  GLU A   4       9.798   6.652   1.185  1.00 30.00\\nATOM     38  C   GLU A   4      11.318   6.551   1.150  1.00 30.00\\nATOM     39  CB  GLU A   4       9.351   7.298   2.498  1.00 30.00\\nATOM     40  CG  GLU A   4       8.388   6.453   3.319  1.00 30.00\\nATOM     41  CD  GLU A   4       8.065   5.136   2.669  1.00 30.00\\nATOM     42  OE1 GLU A   4       8.574   4.880   1.604  1.00 30.00\\nATOM     43  OE2 GLU A   4       7.308   4.385   3.237  1.00 30.00\\nATOM     44  H   GLU A   4       9.543   4.559   1.550  1.00 30.00\\nATOM     45  HA  GLU A   4       9.481   7.287   0.357  1.00 30.00\\nATOM     46  HB2 GLU A   4      10.223   7.507   3.117  1.00 30.00\\nATOM     47  HB3 GLU A   4       8.863   8.251   2.287  1.00 30.00\\nATOM     48  HG2 GLU A   4       8.830   6.263   4.297  1.00 30.00\\nATOM     49  HG3 GLU A   4       7.466   7.013   3.470  1.00 30.00\\nATOM     50  O   GLU A   4      11.889   6.462   0.098  1.00 30.00\\nATOM     51  OXT GLU A   4      11.943   6.560   2.174  1.00 30.00\\nATOM     52  N   PHE B   5      20.406  -0.000   0.000  1.00 30.00\\nATOM     53  CA  PHE B   5      21.864   0.000   0.000  1.00 30.00\\nATOM     54  C   PHE B   5      22.415   1.420   0.000  1.00 30.00\\nATOM     55  O   PHE B   5      21.946   2.278  -0.748  1.00 30.00\\nATOM     56  CB  PHE B   5      22.398  -0.760  -1.216  1.00 30.00\\nATOM     57  CG  PHE B   5      21.322  -1.315  -2.104  1.00 30.00\\nATOM     58  CD1 PHE B   5      19.983  -1.124  -1.798  1.00 30.00\\nATOM     59  CD2 PHE B   5      21.646  -2.030  -3.247  1.00 30.00\\nATOM     60  CE1 PHE B   5      18.992  -1.634  -2.615  1.00 30.00\\nATOM     61  CE2 PHE B   5      20.658  -2.540  -4.066  1.00 30.00\\nATOM     62  CZ  PHE B   5      19.329  -2.342  -3.749  1.00 30.00\\nATOM     63  HA  PHE B   5      22.210  -0.502   0.905  1.00 30.00\\nATOM     64  HB2 PHE B   5      23.024  -0.097  -1.812  1.00 30.00\\nATOM     65  HB3 PHE B   5      23.024  -1.586  -0.882  1.00 30.00\\nATOM     66  HD1 PHE B   5      19.716  -0.563  -0.902  1.00 30.00\\nATOM     67  HD2 PHE B   5      22.696  -2.187  -3.497  1.00 30.00\\nATOM     68  HE1 PHE B   5      17.943  -1.477  -2.363  1.00 30.00\\nATOM     69  HE2 PHE B   5      20.926  -3.100  -4.962  1.00 30.00\\nATOM     70  HZ  PHE B   5      18.549  -2.746  -4.393  1.00 30.00\\nATOM     71  H1  PHE B   5      20.072  -0.943  -0.000  1.00 30.00\\nATOM     72  H2  PHE B   5      20.072   0.471   0.816  1.00 30.00\\nATOM     73  H3  PHE B   5      20.072   0.471  -0.816  1.00 30.00\\nATOM     74  N   GLY B   6      23.413   1.662   0.843  1.00 30.00\\nATOM     75  CA  GLY B   6      24.030   2.980   0.942  1.00 30.00\\nATOM     76  C   GLY B   6      25.549   2.875   0.976  1.00 30.00\\nATOM     77  O   GLY B   6      26.108   2.041   1.688  1.00 30.00\\nATOM     78  H   GLY B   6      23.753   0.914   1.431  1.00 30.00\\nATOM     79  HA2 GLY B   6      23.722   3.590   0.093  1.00 30.00\\nATOM     80  HA3 GLY B   6      23.676   3.480   1.842  1.00 30.00\\nATOM     81  N   HIS B   7      26.213   3.727   0.202  1.00 30.00\\nATOM     82  CA  HIS B   7      27.670   3.732   0.142  1.00 30.00\\nATOM     83  C   HIS B   7      28.219   5.151   0.211  1.00 30.00\\nATOM     84  O   HIS B   7      27.719   6.054  -0.459  1.00 30.00\\nATOM     85  CB  HIS B   7      28.161   3.050  -1.139  1.00 30.00\\nATOM     86  CG  HIS B   7      27.056   2.547  -2.014  1.00 30.00\\nATOM     87  ND1 HIS B   7      25.724   2.690  -1.687  1.00 30.00\\nATOM     88  CD2 HIS B   7      27.085   1.901  -3.204  1.00 30.00\\nATOM     89  CE1 HIS B   7      24.981   2.154  -2.640  1.00 30.00\\nATOM     90  NE2 HIS B   7      25.782   1.669  -3.570  1.00 30.00\\nATOM     91  H   HIS B   7      25.695   4.388  -0.359  1.00 30.00\\nATOM     92  HA  HIS B   7      28.071   3.181   0.993  1.00 30.00\\nATOM     93  HB2 HIS B   7      28.763   3.752  -1.717  1.00 30.00\\nATOM     94  HB3 HIS B   7      28.802   2.207  -0.879  1.00 30.00\\nATOM     95  HE1 HIS B   7      23.891   2.119  -2.654  1.00 30.00\\nATOM     96  HD2 HIS B   7      27.975   1.618  -3.766  1.00 30.00\\nATOM     97  HE2 HIS B   7      25.489   1.202  -4.417  1.00 30.00\\nATOM     98  N   ILE B   8      29.251   5.342   1.027  1.00 30.00\\nATOM     99  CA  ILE B   8      29.870   6.652   1.185  1.00 30.00\\nATOM    100  C   ILE B   8      31.390   6.551   1.150  1.00 30.00\\nATOM    101  CB  ILE B   8      29.429   7.312   2.504  1.00 30.00\\nATOM    102  CG1 ILE B   8      28.467   6.396   3.264  1.00 30.00\\nATOM    103  CG2 ILE B   8      28.782   8.662   2.233  1.00 30.00\\nATOM    104  CD1 ILE B   8      28.182   5.089   2.559  1.00 30.00\\nATOM    105  H   ILE B   8      29.615   4.559   1.550  1.00 30.00\\nATOM    106  HA  ILE B   8      29.552   7.287   0.359  1.00 30.00\\nATOM    107  HB  ILE B   8      30.298   7.458   3.146  1.00 30.00\\nATOM    108 HG12 ILE B   8      28.880   6.170   4.247  1.00 30.00\\nATOM    109 HG13 ILE B   8      27.519   6.912   3.420  1.00 30.00\\nATOM    110 HG21 ILE B   8      28.476   9.114   3.176  1.00 30.00\\nATOM    111 HG22 ILE B   8      29.497   9.314   1.734  1.00 30.00\\nATOM    112 HG23 ILE B   8      27.908   8.525   1.596  1.00 30.00\\nATOM    113 HD11 ILE B   8      27.493   4.495   3.158  1.00 30.00\\nATOM    114 HD12 ILE B   8      27.735   5.292   1.585  1.00 30.00\\nATOM    115 HD13 ILE B   8      29.112   4.539   2.424  1.00 30.00\\nATOM    116  O   ILE B   8      31.961   6.462   0.098  1.00 30.00\\nATOM    117  OXT ILE B   8      32.016   6.560   2.174  1.00 30.00\\nTER\\nENDMDL\\n\",\"pdb\");\n",
       "\tviewer_1791410923760042.setBackgroundColor(\"white\");\n",
       "\tviewer_1791410923760042.setStyle({},{\"cartoon\": {\"color\": \"spectrum\"}});\n",
       "\tviewer_1791410923760042.addStyle({\"not\": {\"hetflag\": true}},{\"stick\": {\"radius\": 0.08, \"opacity\": 0.55}});\n",
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       "\tviewer_1791410923760042.zoomTo();\n",
       "viewer_1791410923760042.render();\n",
       "});\n",
       "</script>\n",
       "</section>\n",
       "<section id=\"tmol-switch-a1d04fbbf9134f588d2359e282c9fa54-panel-1\" class=\"tmol-switch-panel\"\n",
       "         style=\"position:absolute;inset:0;visibility:hidden\">\n",
       "  <div class=\"tmol-viewer-note\">Chain A is fixed; chain B follows ordinary jump 0</div>\n",
       "  <div id=\"3dmolviewer_17914109237712996\"  style=\"position: relative; width: 720px; height: 400px;\">\n",
       "        <p id=\"3dmolwarning_17914109237712996\" style=\"background-color:#ffcccc;color:black\">3Dmol.js failed to load for some reason.  Please check your browser console for error messages.<br></p>\n",
       "        </div>\n",
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       "\n",
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       "  firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n",
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       "\n",
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       "  $3Dmolpromise = loadScriptAsync('https://cdn.jsdelivr.net/npm/3dmol@2.5.5/build/3Dmol-min.js');\n",
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       "    warn.parentNode.removeChild(warn);\n",
       "}\n",
       "$3Dmolpromise.then(function() {\n",
       "viewer_17914109237712996 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_17914109237712996\"),{backgroundColor:\"white\"});\n",
       "viewer_17914109237712996.zoomTo();\n",
       "\tviewer_17914109237712996.addModel(\"MODEL 1\\nATOM      1  N   ALA A   1       0.334  -0.000   0.000  1.00 30.00\\nATOM      2  CA  ALA A   1       1.792   0.000   0.000  1.00 30.00\\nATOM      3  C   ALA A   1       2.343   1.420   0.000  1.00 30.00\\nATOM      4  O   ALA A   1       1.874   2.278  -0.748  1.00 30.00\\nATOM      5  CB  ALA A   1       2.322  -0.773  -1.199  1.00 30.00\\nATOM      6  HA  ALA A   1       2.130  -0.490   0.913  1.00 30.00\\nATOM      7  HB1 ALA A   1       3.412  -0.764  -1.185  1.00 30.00\\nATOM      8  HB2 ALA A   1       1.967  -1.802  -1.154  1.00 30.00\\nATOM      9  HB3 ALA A   1       1.967  -0.307  -2.117  1.00 30.00\\nATOM     10  H1  ALA A   1       0.000  -0.943  -0.000  1.00 30.00\\nATOM     11  H2  ALA A   1      -0.000   0.471   0.816  1.00 30.00\\nATOM     12  H3  ALA A   1      -0.000   0.471  -0.816  1.00 30.00\\nATOM     13  N   CYS A   2       3.341   1.662   0.843  1.00 30.00\\nATOM     14  CA  CYS A   2       3.958   2.980   0.942  1.00 30.00\\nATOM     15  C   CYS A   2       5.477   2.875   0.976  1.00 30.00\\nATOM     16  O   CYS A   2       6.036   2.041   1.688  1.00 30.00\\nATOM     17  CB  CYS A   2       3.474   3.708   2.196  1.00 30.00\\nATOM     18  SG  CYS A   2       2.297   2.761   3.192  1.00 30.00\\nATOM     19  H   CYS A   2       3.681   0.914   1.431  1.00 30.00\\nATOM     20  HA  CYS A   2       3.668   3.563   0.068  1.00 30.00\\nATOM     21  HB2 CYS A   2       4.328   3.953   2.827  1.00 30.00\\nATOM     22  HB3 CYS A   2       2.997   4.645   1.911  1.00 30.00\\nATOM     23  HG  CYS A   2       2.133   3.687   4.131  1.00 30.00\\nATOM     24  N   ASP A   3       6.141   3.727   0.202  1.00 30.00\\nATOM     25  CA  ASP A   3       7.598   3.732   0.142  1.00 30.00\\nATOM     26  C   ASP A   3       8.147   5.151   0.211  1.00 30.00\\nATOM     27  O   ASP A   3       7.647   6.054  -0.459  1.00 30.00\\nATOM     28  CB  ASP A   3       8.084   3.052  -1.140  1.00 30.00\\nATOM     29  CG  ASP A   3       6.943   2.550  -2.015  1.00 30.00\\nATOM     30  OD1 ASP A   3       5.809   2.726  -1.636  1.00 30.00\\nATOM     31  OD2 ASP A   3       7.217   1.997  -3.053  1.00 30.00\\nATOM     32  H   ASP A   3       5.623   4.388  -0.359  1.00 30.00\\nATOM     33  HA  ASP A   3       7.981   3.176   0.998  1.00 30.00\\nATOM     34  HB2 ASP A   3       8.685   3.754  -1.719  1.00 30.00\\nATOM     35  HB3 ASP A   3       8.725   2.207  -0.884  1.00 30.00\\nATOM     36  N   GLU A   4       9.179   5.342   1.027  1.00 30.00\\nATOM     37  CA  GLU A   4       9.798   6.652   1.185  1.00 30.00\\nATOM     38  C   GLU A   4      11.318   6.551   1.150  1.00 30.00\\nATOM     39  CB  GLU A   4       9.351   7.298   2.498  1.00 30.00\\nATOM     40  CG  GLU A   4       8.388   6.453   3.319  1.00 30.00\\nATOM     41  CD  GLU A   4       8.065   5.136   2.669  1.00 30.00\\nATOM     42  OE1 GLU A   4       8.574   4.880   1.604  1.00 30.00\\nATOM     43  OE2 GLU A   4       7.308   4.385   3.237  1.00 30.00\\nATOM     44  H   GLU A   4       9.543   4.559   1.550  1.00 30.00\\nATOM     45  HA  GLU A   4       9.481   7.287   0.357  1.00 30.00\\nATOM     46  HB2 GLU A   4      10.223   7.507   3.117  1.00 30.00\\nATOM     47  HB3 GLU A   4       8.863   8.251   2.287  1.00 30.00\\nATOM     48  HG2 GLU A   4       8.830   6.263   4.297  1.00 30.00\\nATOM     49  HG3 GLU A   4       7.466   7.013   3.470  1.00 30.00\\nATOM     50  O   GLU A   4      11.889   6.462   0.098  1.00 30.00\\nATOM     51  OXT GLU A   4      11.943   6.560   2.174  1.00 30.00\\nATOM     52  N   PHE B   5      17.406  -0.000   0.000  1.00 30.00\\nATOM     53  CA  PHE B   5      18.864  -0.000   0.000  1.00 30.00\\nATOM     54  C   PHE B   5      19.415   1.389  -0.295  1.00 30.00\\nATOM     55  O   PHE B   5      18.946   2.073  -1.205  1.00 30.00\\nATOM     56  CB  PHE B   5      19.398  -0.996  -1.031  1.00 30.00\\nATOM     57  CG  PHE B   5      18.322  -1.724  -1.785  1.00 30.00\\nATOM     58  CD1 PHE B   5      16.983  -1.473  -1.525  1.00 30.00\\nATOM     59  CD2 PHE B   5      18.646  -2.661  -2.754  1.00 30.00\\nATOM     60  CE1 PHE B   5      15.992  -2.142  -2.218  1.00 30.00\\nATOM     61  CE2 PHE B   5      17.658  -3.330  -3.449  1.00 30.00\\nATOM     62  CZ  PHE B   5      16.329  -3.071  -3.180  1.00 30.00\\nATOM     63  HA  PHE B   5      19.210  -0.302   0.989  1.00 30.00\\nATOM     64  HB2 PHE B   5      20.024  -0.472  -1.753  1.00 30.00\\nATOM     65  HB3 PHE B   5      20.024  -1.735  -0.532  1.00 30.00\\nATOM     66  HD1 PHE B   5      16.716  -0.738  -0.766  1.00 30.00\\nATOM     67  HD2 PHE B   5      19.696  -2.866  -2.966  1.00 30.00\\nATOM     68  HE1 PHE B   5      14.943  -1.936  -2.004  1.00 30.00\\nATOM     69  HE2 PHE B   5      17.926  -4.064  -4.209  1.00 30.00\\nATOM     70  HZ  PHE B   5      15.549  -3.599  -3.726  1.00 30.00\\nATOM     71  H1  PHE B   5      17.072  -0.922   0.196  1.00 30.00\\nATOM     72  H2  PHE B   5      17.072   0.631   0.701  1.00 30.00\\nATOM     73  H3  PHE B   5      17.072   0.291  -0.897  1.00 30.00\\nATOM     74  N   GLY B   6      20.413   1.801   0.479  1.00 30.00\\nATOM     75  CA  GLY B   6      21.030   3.110   0.302  1.00 30.00\\nATOM     76  C   GLY B   6      22.549   3.015   0.357  1.00 30.00\\nATOM     77  O   GLY B   6      23.108   2.347   1.227  1.00 30.00\\nATOM     78  H   GLY B   6      20.753   1.192   1.209  1.00 30.00\\nATOM     79  HA2 GLY B   6      20.722   3.531  -0.656  1.00 30.00\\nATOM     80  HA3 GLY B   6      20.676   3.787   1.078  1.00 30.00\\nATOM     81  N   HIS B   7      23.213   3.687  -0.577  1.00 30.00\\nATOM     82  CA  HIS B   7      24.670   3.680  -0.637  1.00 30.00\\nATOM     83  C   HIS B   7      25.219   5.082  -0.864  1.00 30.00\\nATOM     84  O   HIS B   7      24.719   5.826  -1.708  1.00 30.00\\nATOM     85  CB  HIS B   7      25.161   2.747  -1.748  1.00 30.00\\nATOM     86  CG  HIS B   7      24.056   2.072  -2.500  1.00 30.00\\nATOM     87  ND1 HIS B   7      22.724   2.280  -2.209  1.00 30.00\\nATOM     88  CD2 HIS B   7      24.085   1.194  -3.529  1.00 30.00\\nATOM     89  CE1 HIS B   7      21.981   1.558  -3.030  1.00 30.00\\nATOM     90  NE2 HIS B   7      22.782   0.890  -3.839  1.00 30.00\\nATOM     91  H   HIS B   7      22.695   4.217  -1.264  1.00 30.00\\nATOM     92  HA  HIS B   7      25.071   3.318   0.309  1.00 30.00\\nATOM     93  HB2 HIS B   7      25.763   3.313  -2.460  1.00 30.00\\nATOM     94  HB3 HIS B   7      25.802   1.976  -1.319  1.00 30.00\\nATOM     95  HE1 HIS B   7      20.891   1.521  -3.037  1.00 30.00\\nATOM     96  HD2 HIS B   7      24.975   0.800  -4.020  1.00 30.00\\nATOM     97  HE2 HIS B   7      22.489   0.257  -4.570  1.00 30.00\\nATOM     98  N   ILE B   8      26.251   5.438  -0.106  1.00 30.00\\nATOM     99  CA  ILE B   8      26.870   6.753  -0.224  1.00 30.00\\nATOM    100  C   ILE B   8      28.390   6.647  -0.237  1.00 30.00\\nATOM    101  CB  ILE B   8      26.429   7.673   0.929  1.00 30.00\\nATOM    102  CG1 ILE B   8      25.467   6.934   1.863  1.00 30.00\\nATOM    103  CG2 ILE B   8      25.782   8.937   0.384  1.00 30.00\\nATOM    104  CD1 ILE B   8      25.182   5.510   1.445  1.00 30.00\\nATOM    105  H   ILE B   8      26.615   4.781   0.568  1.00 30.00\\nATOM    106  HA  ILE B   8      26.552   7.202  -1.164  1.00 30.00\\nATOM    107  HB  ILE B   8      27.298   7.949   1.526  1.00 30.00\\nATOM    108 HG12 ILE B   8      25.880   6.918   2.871  1.00 30.00\\nATOM    109 HG13 ILE B   8      24.519   7.472   1.908  1.00 30.00\\nATOM    110 HG21 ILE B   8      25.476   9.575   1.212  1.00 30.00\\nATOM    111 HG22 ILE B   8      26.497   9.471  -0.241  1.00 30.00\\nATOM    112 HG23 ILE B   8      24.908   8.670  -0.211  1.00 30.00\\nATOM    113 HD11 ILE B   8      24.493   5.053   2.155  1.00 30.00\\nATOM    114 HD12 ILE B   8      24.735   5.506   0.450  1.00 30.00\\nATOM    115 HD13 ILE B   8      26.112   4.944   1.427  1.00 30.00\\nATOM    116  O   ILE B   8      28.961   6.341  -1.247  1.00 30.00\\nATOM    117  OXT ILE B   8      29.016   6.869   0.762  1.00 30.00\\nTER\\nENDMDL\\n\",\"pdb\");\n",
       "\tviewer_17914109237712996.setBackgroundColor(\"white\");\n",
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   "source": [
    "try:\n",
    "    display(\n",
    "        tmol.switchable_view(\n",
    "            {\n",
    "                \"input two-chain pose\": two_chain_pose,\n",
    "                \"jump-translated and tilted chain B\": jump_moved_pose,\n",
    "            },\n",
    "            notes={\n",
    "                \"input two-chain pose\": \"Chains A and B in their sequence-built placements\",\n",
    "                \"jump-translated and tilted chain B\": \"Chain A is fixed; chain B follows ordinary jump 0\",\n",
    "            },\n",
    "            width=720,\n",
    "            height=400,\n",
    "        )\n",
    "    )\n",
    "except ImportError as exc:\n",
    "    print(\"Interactive jump comparison unavailable:\", exc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "450890f6",
   "metadata": {},
   "source": [
    "Blocks 0–3 remain numerically fixed, while blocks 4–7 move together. In the switcher, the internal geometry of chain B is unchanged because the operation modifies one rigid-body jump. This is coordinate manipulation, not docking: no search or score chose the new placement.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def037b2",
   "metadata": {},
   "source": [
    "## Assign named torsions in one kinematic pass\n",
    "\n",
    "`set_named_torsions()` accepts parallel pose, block, name, and value lists. The example sends the five internal alanines of an extended peptide toward an α-helical region (`φ=-60°`, `ψ=-45°`) in one forward-fold. Terminal torsions are deliberately excluded because a named torsion reaching a missing neighboring residue is undefined.\n",
    "\n",
    "The before/after Ramachandran plot checks the requested internal coordinates directly. Arrows show assignments, not an optimization trajectory.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a9bfadcd",
   "metadata": {},
   "outputs": [
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       "      <th style=\"border:1px solid #ddd;padding:8px\"><sup><noscript><span title=\"JavaScript not allowed. Please trust this notebook to get interactive tables.\">🔒</span></noscript><a href=https://itables.org/fallbacks/static_preview.html title=\"ITables v2.9.1 static preview\">ⓘ</a></sup>block</th>\n",
       "      <th style=\"border:1px solid #ddd;padding:8px\">phi_before</th>\n",
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",
      "text/plain": [
       "<Figure size 620x580 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "torsion_pose = tmol.extended_pose_stack_from_sequences(\n",
    "    \"AAAAAAA\", device=device\n",
    ")\n",
    "internal_blocks = list(range(1, 6))\n",
    "before_torsions = pd.DataFrame(\n",
    "    [\n",
    "        {\n",
    "            \"block\": block,\n",
    "            \"phi\": tmol.get_named_torsions(torsion_pose, 0, block, \"phi\"),\n",
    "            \"psi\": tmol.get_named_torsions(torsion_pose, 0, block, \"psi\"),\n",
    "        }\n",
    "        for block in internal_blocks\n",
    "    ]\n",
    ")\n",
    "\n",
    "request_blocks = [block for block in internal_blocks for _ in (\"phi\", \"psi\")]\n",
    "request_names = [name for _ in internal_blocks for name in (\"phi\", \"psi\")]\n",
    "request_values = [value for _ in internal_blocks for value in (-60.0, -45.0)]\n",
    "helical_pose = tmol.set_named_torsions(\n",
    "    torsion_pose,\n",
    "    poses=[0] * len(request_blocks),\n",
    "    blocks=request_blocks,\n",
    "    names=request_names,\n",
    "    values=request_values,\n",
    ")\n",
    "after_torsions = pd.DataFrame(\n",
    "    [\n",
    "        {\n",
    "            \"block\": block,\n",
    "            \"phi\": tmol.get_named_torsions(helical_pose, 0, block, \"phi\"),\n",
    "            \"psi\": tmol.get_named_torsions(helical_pose, 0, block, \"psi\"),\n",
    "        }\n",
    "        for block in internal_blocks\n",
    "    ]\n",
    ")\n",
    "torsion_frame = before_torsions.merge(\n",
    "    after_torsions, on=\"block\", suffixes=(\"_before\", \"_after\")\n",
    ")\n",
    "show_table(torsion_frame)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6.2, 5.8))\n",
    "for row in torsion_frame.itertuples():\n",
    "    ax.annotate(\n",
    "        \"\",\n",
    "        xy=(row.phi_after, row.psi_after),\n",
    "        xytext=(row.phi_before, row.psi_before),\n",
    "        arrowprops={\"arrowstyle\": \"->\", \"color\": \"0.55\", \"lw\": 1.5},\n",
    "    )\n",
    "ax.scatter(torsion_frame.phi_before, torsion_frame.psi_before, label=\"before\", s=60, color=\"#4c78a8\")\n",
    "ax.scatter(torsion_frame.phi_after, torsion_frame.psi_after, label=\"assigned\", s=75, color=\"#e45756\", marker=\"D\")\n",
    "for row in torsion_frame.itertuples():\n",
    "    ax.text(row.phi_after + 3, row.psi_after + 3, str(row.block), fontsize=9)\n",
    "ax.set(\n",
    "    xlim=(-180, 180),\n",
    "    ylim=(-180, 180),\n",
    "    xlabel=\"φ (degrees)\",\n",
    "    ylabel=\"ψ (degrees)\",\n",
    "    title=\"Named-torsion assignment for five internal residues\",\n",
    ")\n",
    "ax.axhline(0, color=\"0.85\", lw=1)\n",
    "ax.axvline(0, color=\"0.85\", lw=1)\n",
    "ax.grid(alpha=0.2)\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "np.testing.assert_allclose(torsion_frame.phi_after, -60.0, atol=1e-3)\n",
    "np.testing.assert_allclose(torsion_frame.psi_after, -45.0, atol=1e-3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "47da15af",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <label>Structure: <select id=\"tmol-switch-12447147b50e49149e040beab412265b-select\"><option value=\"0\">extended backbone</option><option value=\"1\">assigned helical φ/ψ</option></select></label>\n",
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       "viewer_17914109240354283.zoomTo();\n",
       "\tviewer_17914109240354283.addModel(\"MODEL 1\\nATOM      1  N   ALA A   1       0.000  -0.000   0.000  1.00 30.00\\nATOM      2  CA  ALA A   1       1.458   0.000   0.000  1.00 30.00\\nATOM      3  C   ALA A   1       2.009   1.420   0.000  1.00 30.00\\nATOM      4  O   ALA A   1       1.540   2.278  -0.748  1.00 30.00\\nATOM      5  CB  ALA A   1       1.988  -0.773  -1.199  1.00 30.00\\nATOM      6  HA  ALA A   1       1.797  -0.490   0.913  1.00 30.00\\nATOM      7  HB1 ALA A   1       3.078  -0.764  -1.185  1.00 30.00\\nATOM      8  HB2 ALA A   1       1.633  -1.802  -1.154  1.00 30.00\\nATOM      9  HB3 ALA A   1       1.633  -0.307  -2.117  1.00 30.00\\nATOM     10  H1  ALA A   1      -0.334  -0.943  -0.000  1.00 30.00\\nATOM     11  H2  ALA A   1      -0.334   0.471   0.816  1.00 30.00\\nATOM     12  H3  ALA A   1      -0.334   0.471  -0.816  1.00 30.00\\nATOM     13  N   ALA A   2       3.007   1.662   0.843  1.00 30.00\\nATOM     14  CA  ALA A   2       3.624   2.980   0.942  1.00 30.00\\nATOM     15  C   ALA A   2       5.143   2.875   0.976  1.00 30.00\\nATOM     16  O   ALA A   2       5.702   2.041   1.688  1.00 30.00\\nATOM     17  CB  ALA A   2       3.116   3.711   2.176  1.00 30.00\\nATOM     18  H   ALA A   2       3.347   0.914   1.431  1.00 30.00\\nATOM     19  HA  ALA A   2       3.348   3.550   0.055  1.00 30.00\\nATOM     20  HB1 ALA A   2       3.586   4.693   2.236  1.00 30.00\\nATOM     21  HB2 ALA A   2       2.035   3.830   2.109  1.00 30.00\\nATOM     22  HB3 ALA A   2       3.364   3.136   3.067  1.00 30.00\\nATOM     23  N   ALA A   3       5.807   3.727   0.202  1.00 30.00\\nATOM     24  CA  ALA A   3       7.264   3.732   0.142  1.00 30.00\\nATOM     25  C   ALA A   3       7.813   5.151   0.211  1.00 30.00\\nATOM     26  O   ALA A   3       7.313   6.054  -0.459  1.00 30.00\\nATOM     27  CB  ALA A   3       7.745   3.040  -1.125  1.00 30.00\\nATOM     28  H   ALA A   3       5.289   4.388  -0.359  1.00 30.00\\nATOM     29  HA  ALA A   3       7.640   3.186   1.007  1.00 30.00\\nATOM     30  HB1 ALA A   3       8.834   3.052  -1.155  1.00 30.00\\nATOM     31  HB2 ALA A   3       7.393   2.008  -1.132  1.00 30.00\\nATOM     32  HB3 ALA A   3       7.352   3.563  -1.996  1.00 30.00\\nATOM     33  N   ALA A   4       8.845   5.342   1.027  1.00 30.00\\nATOM     34  CA  ALA A   4       9.464   6.652   1.185  1.00 30.00\\nATOM     35  C   ALA A   4      10.984   6.551   1.150  1.00 30.00\\nATOM     36  O   ALA A   4      11.572   5.674   1.783  1.00 30.00\\nATOM     37  CB  ALA A   4       9.007   7.301   2.483  1.00 30.00\\nATOM     38  H   ALA A   4       9.210   4.559   1.550  1.00 30.00\\nATOM     39  HA  ALA A   4       9.152   7.278   0.349  1.00 30.00\\nATOM     40  HB1 ALA A   4       9.478   8.278   2.587  1.00 30.00\\nATOM     41  HB2 ALA A   4       7.924   7.420   2.469  1.00 30.00\\nATOM     42  HB3 ALA A   4       9.292   6.670   3.324  1.00 30.00\\nATOM     43  N   ALA A   5      11.615   7.453   0.406  1.00 30.00\\nATOM     44  CA  ALA A   5      13.068   7.467   0.287  1.00 30.00\\nATOM     45  C   ALA A   5      13.618   8.881   0.425  1.00 30.00\\nATOM     46  O   ALA A   5      13.090   9.823  -0.165  1.00 30.00\\nATOM     47  CB  ALA A   5      13.496   6.860  -1.041  1.00 30.00\\nATOM     48  H   ALA A   5      11.074   8.147  -0.090  1.00 30.00\\nATOM     49  HA  ALA A   5      13.480   6.867   1.099  1.00 30.00\\nATOM     50  HB1 ALA A   5      14.584   6.878  -1.115  1.00 30.00\\nATOM     51  HB2 ALA A   5      13.145   5.830  -1.101  1.00 30.00\\nATOM     52  HB3 ALA A   5      13.068   7.437  -1.859  1.00 30.00\\nATOM     53  N   ALA A   6      14.683   9.022   1.208  1.00 30.00\\nATOM     54  CA  ALA A   6      15.307  10.321   1.425  1.00 30.00\\nATOM     55  C   ALA A   6      16.824  10.228   1.321  1.00 30.00\\nATOM     56  O   ALA A   6      17.438   9.314   1.872  1.00 30.00\\nATOM     57  CB  ALA A   6      14.903  10.883   2.781  1.00 30.00\\nATOM     58  H   ALA A   6      15.069   8.208   1.664  1.00 30.00\\nATOM     59  HA  ALA A   6      14.960  10.999   0.645  1.00 30.00\\nATOM     60  HB1 ALA A   6      15.377  11.854   2.928  1.00 30.00\\nATOM     61  HB2 ALA A   6      13.820  10.999   2.818  1.00 30.00\\nATOM     62  HB3 ALA A   6      15.222  10.201   3.567  1.00 30.00\\nATOM     63  N   ALA A   7      17.423  11.179   0.612  1.00 30.00\\nATOM     64  CA  ALA A   7      18.870  11.205   0.435  1.00 30.00\\nATOM     65  C   ALA A   7      19.424  12.609   0.641  1.00 30.00\\nATOM     66  CB  ALA A   7      19.244  10.687  -0.946  1.00 30.00\\nATOM     67  H   ALA A   7      16.861  11.901   0.185  1.00 30.00\\nATOM     68  HA  ALA A   7      19.315  10.556   1.188  1.00 30.00\\nATOM     69  HB1 ALA A   7      20.327  10.713  -1.063  1.00 30.00\\nATOM     70  HB2 ALA A   7      18.891   9.661  -1.058  1.00 30.00\\nATOM     71  HB3 ALA A   7      18.782  11.314  -1.707  1.00 30.00\\nATOM     72  O   ALA A   7      19.602  13.026   1.752  1.00 30.00\\nATOM     73  OXT ALA A   7      19.683  13.297  -0.307  1.00 30.00\\nTER\\nENDMDL\\n\",\"pdb\");\n",
       "\tviewer_17914109240354283.setBackgroundColor(\"white\");\n",
       "\tviewer_17914109240354283.setStyle({},{\"cartoon\": {\"color\": \"spectrum\"}});\n",
       "\tviewer_17914109240354283.addStyle({\"not\": {\"hetflag\": true}},{\"stick\": {\"radius\": 0.08, \"opacity\": 0.55}});\n",
       "\tviewer_17914109240354283.setStyle({\"hetflag\": true},{\"stick\": {\"colorscheme\": \"orangeCarbon\", \"radius\": 0.22}});\n",
       "\tviewer_17914109240354283.setHoverable({},true,\"function(atom,viewer) {\\n        if(!atom.label) {\\n            atom.label = viewer.addLabel(\\n                atom.chain + ':' + atom.resn + '(' + atom.resi + '):' +\\n                atom.atom + '(idx' + atom.serial + ')',\\n                {position: atom, backgroundColor:\\\"white\\\", fontColor:\\\"black\\\"}\\n            );\\n        }\\n    }\",\"function(atom,viewer) {\\n        if(atom.label) {\\n            viewer.removeLabel(atom.label);\\n            delete atom.label;\\n        }\\n    }\");\n",
       "\tviewer_17914109240354283.zoomTo();\n",
       "viewer_17914109240354283.render();\n",
       "});\n",
       "</script>\n",
       "</section>\n",
       "<section id=\"tmol-switch-12447147b50e49149e040beab412265b-panel-1\" class=\"tmol-switch-panel\"\n",
       "         style=\"position:absolute;inset:0;visibility:hidden\">\n",
       "  <div class=\"tmol-viewer-note\">Internal blocks 1–5 set to φ=-60°, ψ=-45°</div>\n",
       "  <div id=\"3dmolviewer_17914109240435462\"  style=\"position: relative; width: 720px; height: 400px;\">\n",
       "        <p id=\"3dmolwarning_17914109240435462\" style=\"background-color:#ffcccc;color:black\">3Dmol.js failed to load for some reason.  Please check your browser console for error messages.<br></p>\n",
       "        </div>\n",
       "<script>\n",
       "\n",
       "var loadScriptAsync = function(uri){\n",
       "  return new Promise((resolve, reject) => {\n",
       "    //this is to ignore the existence of requirejs amd\n",
       "    var savedexports, savedmodule;\n",
       "    if (typeof exports !== 'undefined') savedexports = exports;\n",
       "    else exports = {}\n",
       "    if (typeof module !== 'undefined') savedmodule = module;\n",
       "    else module = {}\n",
       "\n",
       "    var tag = document.createElement('script');\n",
       "    tag.src = uri;\n",
       "    tag.async = true;\n",
       "    tag.onload = () => {\n",
       "        exports = savedexports;\n",
       "        module = savedmodule;\n",
       "        resolve();\n",
       "    };\n",
       "  var firstScriptTag = document.getElementsByTagName('script')[0];\n",
       "  firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n",
       "});\n",
       "};\n",
       "\n",
       "if(typeof $3Dmolpromise === 'undefined') {\n",
       "$3Dmolpromise = null;\n",
       "  $3Dmolpromise = loadScriptAsync('https://cdn.jsdelivr.net/npm/3dmol@2.5.5/build/3Dmol-min.js');\n",
       "}\n",
       "\n",
       "var viewer_17914109240435462 = null;\n",
       "var warn = document.getElementById(\"3dmolwarning_17914109240435462\");\n",
       "if(warn) {\n",
       "    warn.parentNode.removeChild(warn);\n",
       "}\n",
       "$3Dmolpromise.then(function() {\n",
       "viewer_17914109240435462 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_17914109240435462\"),{backgroundColor:\"white\"});\n",
       "viewer_17914109240435462.zoomTo();\n",
       "\tviewer_17914109240435462.addModel(\"MODEL 1\\nATOM      1  N   ALA A   1       0.000  -0.000   0.000  1.00 30.00\\nATOM      2  CA  ALA A   1       1.458   0.000   0.000  1.00 30.00\\nATOM      3  C   ALA A   1       2.009   1.420   0.000  1.00 30.00\\nATOM      4  O   ALA A   1       1.540   2.278  -0.748  1.00 30.00\\nATOM      5  CB  ALA A   1       1.988  -0.773  -1.199  1.00 30.00\\nATOM      6  HA  ALA A   1       1.797  -0.490   0.913  1.00 30.00\\nATOM      7  HB1 ALA A   1       3.078  -0.764  -1.185  1.00 30.00\\nATOM      8  HB2 ALA A   1       1.633  -1.802  -1.154  1.00 30.00\\nATOM      9  HB3 ALA A   1       1.633  -0.307  -2.117  1.00 30.00\\nATOM     10  H1  ALA A   1      -0.334  -0.943  -0.000  1.00 30.00\\nATOM     11  H2  ALA A   1      -0.334   0.471   0.816  1.00 30.00\\nATOM     12  H3  ALA A   1      -0.334   0.471  -0.816  1.00 30.00\\nATOM     13  N   ALA A   2       3.007   1.662   0.843  1.00 30.00\\nATOM     14  CA  ALA A   2       3.624   2.980   0.942  1.00 30.00\\nATOM     15  C   ALA A   2       4.226   3.407  -0.390  1.00 30.00\\nATOM     16  O   ALA A   2       4.051   4.545  -0.825  1.00 30.00\\nATOM     17  CB  ALA A   2       4.688   2.986   2.030  1.00 30.00\\nATOM     18  H   ALA A   2       3.347   0.914   1.431  1.00 30.00\\nATOM     19  HA  ALA A   2       2.847   3.699   1.203  1.00 30.00\\nATOM     20  HB1 ALA A   2       5.139   3.976   2.091  1.00 30.00\\nATOM     21  HB2 ALA A   2       4.231   2.736   2.987  1.00 30.00\\nATOM     22  HB3 ALA A   2       5.456   2.252   1.792  1.00 30.00\\nATOM     23  N   ALA A   3       4.937   2.488  -1.035  1.00 30.00\\nATOM     24  CA  ALA A   3       5.567   2.768  -2.319  1.00 30.00\\nATOM     25  C   ALA A   3       4.532   3.157  -3.367  1.00 30.00\\nATOM     26  O   ALA A   3       4.723   4.117  -4.115  1.00 30.00\\nATOM     27  CB  ALA A   3       6.369   1.563  -2.790  1.00 30.00\\nATOM     28  H   ALA A   3       5.043   1.571  -0.624  1.00 30.00\\nATOM     29  HA  ALA A   3       6.243   3.612  -2.188  1.00 30.00\\nATOM     30  HB1 ALA A   3       6.833   1.788  -3.750  1.00 30.00\\nATOM     31  HB2 ALA A   3       7.143   1.332  -2.058  1.00 30.00\\nATOM     32  HB3 ALA A   3       5.706   0.706  -2.900  1.00 30.00\\nATOM     33  N   ALA A   4       3.437   2.407  -3.417  1.00 30.00\\nATOM     34  CA  ALA A   4       2.369   2.673  -4.374  1.00 30.00\\nATOM     35  C   ALA A   4       1.788   4.067  -4.177  1.00 30.00\\nATOM     36  O   ALA A   4       1.571   4.802  -5.141  1.00 30.00\\nATOM     37  CB  ALA A   4       1.275   1.622  -4.253  1.00 30.00\\nATOM     38  H   ALA A   4       3.341   1.633  -2.775  1.00 30.00\\nATOM     39  HA  ALA A   4       2.794   2.627  -5.377  1.00 30.00\\nATOM     40  HB1 ALA A   4       0.486   1.834  -4.974  1.00 30.00\\nATOM     41  HB2 ALA A   4       1.695   0.636  -4.454  1.00 30.00\\nATOM     42  HB3 ALA A   4       0.862   1.642  -3.246  1.00 30.00\\nATOM     43  N   ALA A   5       1.536   4.427  -2.923  1.00 30.00\\nATOM     44  CA  ALA A   5       0.979   5.734  -2.597  1.00 30.00\\nATOM     45  C   ALA A   5       1.895   6.856  -3.069  1.00 30.00\\nATOM     46  O   ALA A   5       1.439   7.839  -3.653  1.00 30.00\\nATOM     47  CB  ALA A   5       0.732   5.847  -1.100  1.00 30.00\\nATOM     48  H   ALA A   5       1.737   3.777  -2.176  1.00 30.00\\nATOM     49  HA  ALA A   5       0.029   5.837  -3.121  1.00 30.00\\nATOM     50  HB1 ALA A   5       0.316   6.828  -0.873  1.00 30.00\\nATOM     51  HB2 ALA A   5       0.030   5.074  -0.788  1.00 30.00\\nATOM     52  HB3 ALA A   5       1.672   5.719  -0.566  1.00 30.00\\nATOM     53  N   ALA A   6       3.190   6.704  -2.811  1.00 30.00\\nATOM     54  CA  ALA A   6       4.173   7.704  -3.209  1.00 30.00\\nATOM     55  C   ALA A   6       4.174   7.906  -4.719  1.00 30.00\\nATOM     56  O   ALA A   6       4.198   9.037  -5.204  1.00 30.00\\nATOM     57  CB  ALA A   6       5.560   7.305  -2.728  1.00 30.00\\nATOM     58  H   ALA A   6       3.501   5.873  -2.327  1.00 30.00\\nATOM     59  HA  ALA A   6       3.898   8.652  -2.746  1.00 30.00\\nATOM     60  HB1 ALA A   6       6.283   8.061  -3.032  1.00 30.00\\nATOM     61  HB2 ALA A   6       5.558   7.221  -1.641  1.00 30.00\\nATOM     62  HB3 ALA A   6       5.834   6.345  -3.164  1.00 30.00\\nATOM     63  N   ALA A   7       4.147   6.802  -5.458  1.00 30.00\\nATOM     64  CA  ALA A   7       4.145   6.856  -6.915  1.00 30.00\\nATOM     65  C   ALA A   7       3.118   5.895  -7.500  1.00 30.00\\nATOM     66  CB  ALA A   7       5.531   6.542  -7.459  1.00 30.00\\nATOM     67  H   ALA A   7       4.128   5.903  -4.999  1.00 30.00\\nATOM     68  HA  ALA A   7       3.868   7.866  -7.216  1.00 30.00\\nATOM     69  HB1 ALA A   7       5.514   6.586  -8.548  1.00 30.00\\nATOM     70  HB2 ALA A   7       6.245   7.273  -7.078  1.00 30.00\\nATOM     71  HB3 ALA A   7       5.830   5.545  -7.141  1.00 30.00\\nATOM     72  O   ALA A   7       1.959   6.205  -7.536  1.00 30.00\\nATOM     73  OXT ALA A   7       3.467   4.829  -7.925  1.00 30.00\\nTER\\nENDMDL\\n\",\"pdb\");\n",
       "\tviewer_17914109240435462.setBackgroundColor(\"white\");\n",
       "\tviewer_17914109240435462.setStyle({},{\"cartoon\": {\"color\": \"spectrum\"}});\n",
       "\tviewer_17914109240435462.addStyle({\"not\": {\"hetflag\": true}},{\"stick\": {\"radius\": 0.08, \"opacity\": 0.55}});\n",
       "\tviewer_17914109240435462.setStyle({\"hetflag\": true},{\"stick\": {\"colorscheme\": \"orangeCarbon\", \"radius\": 0.22}});\n",
       "\tviewer_17914109240435462.setHoverable({},true,\"function(atom,viewer) {\\n        if(!atom.label) {\\n            atom.label = viewer.addLabel(\\n                atom.chain + ':' + atom.resn + '(' + atom.resi + '):' +\\n                atom.atom + '(idx' + atom.serial + ')',\\n                {position: atom, backgroundColor:\\\"white\\\", fontColor:\\\"black\\\"}\\n            );\\n        }\\n    }\",\"function(atom,viewer) {\\n        if(atom.label) {\\n            viewer.removeLabel(atom.label);\\n            delete atom.label;\\n        }\\n    }\");\n",
       "\tviewer_17914109240435462.zoomTo();\n",
       "viewer_17914109240435462.render();\n",
       "});\n",
       "</script>\n",
       "</section>\n",
       "  </div>\n",
       "</div>\n",
       "<script>\n",
       "(() => {\n",
       "  const root = document.getElementById(\"tmol-switch-12447147b50e49149e040beab412265b\");\n",
       "  const select = document.getElementById(\"tmol-switch-12447147b50e49149e040beab412265b-select\");\n",
       "  const panels = root.querySelectorAll('.tmol-switch-panel');\n",
       "  select.addEventListener('change', () => {\n",
       "    panels.forEach((panel, index) => {\n",
       "      panel.style.visibility = index === Number(select.value) ? 'visible' : 'hidden';\n",
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       "    window.dispatchEvent(new Event('resize'));\n",
       "  });\n",
       "})();\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    display(\n",
    "        tmol.switchable_view(\n",
    "            {\n",
    "                \"extended backbone\": torsion_pose,\n",
    "                \"assigned helical φ/ψ\": helical_pose,\n",
    "            },\n",
    "            notes={\n",
    "                \"extended backbone\": \"Sequence-built extended torsions\",\n",
    "                \"assigned helical φ/ψ\": \"Internal blocks 1–5 set to φ=-60°, ψ=-45°\",\n",
    "            },\n",
    "            width=720,\n",
    "            height=400,\n",
    "        )\n",
    "    )\n",
    "except ImportError as exc:\n",
    "    print(\"Interactive torsion comparison unavailable:\", exc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb4244a6",
   "metadata": {},
   "source": [
    "All measured internal torsions reach the requested values within numerical tolerance, and the peptide becomes compact and helical-looking. No score was minimized, so clashes or unfavorable side-chain interactions may remain. `set_named_torsions()` preserves the internal lengths and angles carried by the input kinematics; it is not an idealization protocol.\n",
    "\n",
    "## Choosing a topology\n",
    "\n",
    "- Use `reasonable_fold_forest()` for ordinary connected chains and gap-aware defaults.\n",
    "- Use an ordinary jump when one component's placement must be controlled relative to another.\n",
    "- Use a dandelion forest when a method operates on independent residue frames.\n",
    "- Validate edge coverage and jump numbering before minimization or differentiation.\n",
    "- Treat a FoldForest as a coordinate dependency graph, never as a replacement for chemical connectivity.\n",
    "\n",
    "## Exercises\n",
    "\n",
    "1. Reverse the polymer edge for chain A and observe which end remains stationary when changing an internal torsion.\n",
    "2. Add a second ordinary jump and verify that jump indices are contiguous.\n",
    "3. Differentiate a coordinate-based objective through the inter-chain jump and inspect the six rigid-body gradients.\n",
    "\n",
    "## References\n",
    "\n",
    "- [Tutorial 05 — Minimization, Constraints, and Kinematics](05_minimization_constraints_kinematics.ipynb)\n",
    "- [Tutorial 06 — FastRelax](06_fast_relax.ipynb)\n",
    "- [Kinematics API](../api/kinematics.rst)\n",
    "- [Optimization API](../api/optimization.rst)\n"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}