Test_Expert_baseline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from l2rpn_baselines.ExpertAgent import ExpertAgent\n",
    "from grid2op.Parameters import Parameters\n",
    "from grid2op.Runner import Runner\n",
    "from grid2op import make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You might need to install the LightSimBackend (provisory name) to gain massive speed up\n"
     ]
    }
   ],
   "source": [
    "dataset = \"rte_case14_realistic\"\n",
    "\n",
    "#use lightsim2grid to go a lot faster if available\n",
    "try:\n",
    "    from lightsim2grid.LightSimBackend import LightSimBackend\n",
    "    backend = LightSimBackend()\n",
    "except:\n",
    "    from grid2op.Backend import PandaPowerBackend\n",
    "    backend = PandaPowerBackend()\n",
    "    print(\"You might need to install the LightSimBackend (provisory name) to gain massive speed up\")\n",
    "\n",
    "param = Parameters()\n",
    "#param.init_from_dict({\"NO_OVERFLOW_DISCONNECTION\": True})\n",
    "env=make(dataset,param=param,backend=backend)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from l2rpn_baselines.ExpertAgent import evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.1.0'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import grid2op\n",
    "grid2op.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run agent on first scenario\n",
    "See where it stops and the result dataframe each time the Expert System is called"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "text": [
      "running Expert Agent on line with id:4 at timestep:518\n",
      "Number of generators of the powergrid: 5\n",
      "Number of loads of the powergrid: 11\n",
      "Number of powerline of the powergrid: 20\n",
      "Number of elements connected to each substations in the powergrid: [3 6 4 6 5 6 3 2 5 3 3 3 4 3]\n",
      "Total number of elements: 56\n",
      "    idx_or  idx_ex  init_flows  swapped  new_flows  new_flows_swapped  \\\n",
      "0        0       1      48.669    False     33.816              False   \n",
      "1        0       4      44.012    False     58.114              False   \n",
      "2        1       2      28.352    False     36.311              False   \n",
      "3        1       3      46.411    False     63.175              False   \n",
      "4        1       4      38.832    False      0.000              False   \n",
      "5        2       3      17.580    False     26.204              False   \n",
      "6        4       3      33.525     True     10.209              False   \n",
      "7        5      10      11.841    False     11.009              False   \n",
      "8        5      11       9.085    False      9.002              False   \n",
      "9        5      12      21.035    False     20.644              False   \n",
      "10       8       9       3.090    False      4.232              False   \n",
      "11       8      13       8.151    False      8.631              False   \n",
      "12      10       9       7.413     True      6.575              False   \n",
      "13      11      12       2.258    False      2.174              False   \n",
      "14      12      13       9.075    False      8.499              False   \n",
      "15       3       6      28.325    False     29.050              False   \n",
      "16       3       8      16.116    False     16.512              False   \n",
      "17       4       5      39.061    False     37.855              False   \n",
      "18       7       6       0.000     True      0.000              False   \n",
      "19       6       8      28.325     True     29.050              False   \n",
      "\n",
      "    delta_flows  gray_edges  \n",
      "0       -14.853       False  \n",
      "1        14.102       False  \n",
      "2         7.960       False  \n",
      "3        16.764       False  \n",
      "4       -38.832       False  \n",
      "5         8.624       False  \n",
      "6       -23.316       False  \n",
      "7        -0.832        True  \n",
      "8        -0.083        True  \n",
      "9        -0.391        True  \n",
      "10        1.141        True  \n",
      "11        0.481        True  \n",
      "12       -0.838        True  \n",
      "13       -0.084        True  \n",
      "14       -0.577        True  \n",
      "15        0.726        True  \n",
      "16        0.396        True  \n",
      "17       -1.206        True  \n",
      "18       -0.000        True  \n",
      "19        0.726        True  \n",
      "doing simulations\n",
      "Constrained path created\n",
      "In get_hubs(): c = \n",
      "################################################################\n",
      "ConstrainedPath = [0, 1, 4, 3] \n",
      "Details: (amont: [(0, 1)], constrained_edge: (1, 4), aval: [(4, 3)])\n",
      "################################################################\n",
      "shortest path between 0 and 3 failed\n",
      "shortest path between 0 and 1 failed\n",
      "shortest path between 1 and 4 failed\n",
      "shortest path between 4 and 3 failed\n",
      "\n",
      "##############################################################################\n",
      "##########...........COMPUTE NEW NETWORK CHANGES..........####################\n",
      "##############################################################################\n",
      "########### Compute new network changes on node [1] with new topo [[1 2 1 1 2 1]] ###########\n",
      "########### Compute new network changes on node [1] with new topo [[1 2 2 1 2 1]] ###########\n",
      "########### Compute new network changes on node [1] with new topo [[1 1 2 1 2 1]] ###########\n",
      "########### Compute new network changes on node [1] with new topo [[2 1 1 2 1 2]] ###########\n",
      "########### Compute new network changes on node [1] with new topo [[1 2 1 1 2 2]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 2 1 2 1]] ###########\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/antoinemarot/dev/ExpertOp4Grid/venv_expert4grid/lib/python3.6/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning:\n",
      "\n",
      "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########### Compute new network changes on node [4] with new topo [[2 1 2 1 2]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 2 2 1 2]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[2 1 1 2 1]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 1 2 1 2]] ###########\n",
      "--------------------------------------------------------------------------------------------\n",
      "----------------------------------- END RESULT DATAFRAME -----------------------------------\n",
      "--------------------------------------------------------------------------------------------\n",
      "   overflow ID  Flows before  Flows after  Delta flows Worsened line  \\\n",
      "0        4.000        38.832      -26.000       64.832          [17]   \n",
      "1        4.000        38.832      -23.024       61.856           [3]   \n",
      "2        4.000        38.832       -5.570       44.401            []   \n",
      "3        4.000        38.832      -23.024       61.856           [3]   \n",
      "4        4.000        38.832       28.563       10.269            []   \n",
      "5        4.000        38.832       39.501       -0.670            []   \n",
      "6        4.000        38.832       39.501       -0.670            []   \n",
      "7        4.000        38.832        8.347       30.485            []   \n",
      "8        4.000        38.832        8.347       30.485            []   \n",
      "9        4.000        38.832       45.844       -7.012           [4]   \n",
      "\n",
      "   Prod redispatched  Load redispatched    Topology applied  Substation ID  \\\n",
      "0              3.914              0.000  [1, 2, 1, 1, 2, 1]          1.000   \n",
      "1              6.839              0.000  [1, 2, 2, 1, 2, 1]          1.000   \n",
      "2              3.324              0.000  [1, 1, 2, 1, 2, 1]          1.000   \n",
      "3              6.839              0.000  [2, 1, 1, 2, 1, 2]          1.000   \n",
      "4              0.338              0.000  [1, 2, 1, 1, 2, 2]          1.000   \n",
      "5              0.106              0.000     [1, 2, 1, 2, 1]          4.000   \n",
      "6              0.106              0.000     [2, 1, 2, 1, 2]          4.000   \n",
      "7              0.630              0.000     [1, 2, 2, 1, 2]          4.000   \n",
      "8              0.630              0.000     [2, 1, 1, 2, 1]          4.000   \n",
      "9              0.095              0.000     [1, 1, 2, 1, 2]          4.000   \n",
      "\n",
      "   Rank Substation ID  Topology score  Topology simulated score  Efficacity  \n",
      "0               1.000         130.970                     1.000     -70.954  \n",
      "1               1.000         123.010                     1.000     -98.407  \n",
      "2               1.000         123.010                     4.000     152.462  \n",
      "3               1.000         123.010                     1.000     -98.407  \n",
      "4               1.000         116.120                     4.000      13.515  \n",
      "5               1.000          45.720                     0.000      -0.651  \n",
      "6               1.000          45.720                     0.000      -0.651  \n",
      "7               1.000          38.630                     4.000     135.999  \n",
      "8               1.000          38.630                     4.000     135.999  \n",
      "9               1.000          37.420                     0.000      -5.908  \n",
      "[<grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1488dd208>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1488dd0b8>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1488b9e80>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1488ddda0>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1488ddf60>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1488f0d68>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1488f0ba8>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1488f0eb8>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1488dd160>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1488f6080>]\n",
      "index_best_action\n",
      "2\n",
      "action we take is:\n",
      "This action will:\n",
      "\t - NOT change anything to the injections\n",
      "\t - NOT perform any redispatching action\n",
      "\t - NOT force any line status\n",
      "\t - NOT switch any line status\n",
      "\t - NOT switch anything in the topology\n",
      "\t - Set the bus of the following element:\n",
      "\t \t - assign bus 1 to line (extremity) 0 [on substation 1]\n",
      "\t \t - assign bus 2 to line (origin) 2 [on substation 1]\n",
      "\t \t - assign bus 1 to line (origin) 3 [on substation 1]\n",
      "\t \t - assign bus 2 to line (origin) 4 [on substation 1]\n",
      "\t \t - assign bus 1 to generator 0 [on substation 1]\n",
      "\t \t - assign bus 1 to load 0 [on substation 1]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running Expert Agent on line with id:17 at timestep:798\n",
      "Number of generators of the powergrid: 5\n",
      "Number of loads of the powergrid: 11\n",
      "Number of powerline of the powergrid: 20\n",
      "Number of elements connected to each substations in the powergrid: [3 6 4 6 5 6 3 2 5 3 3 3 4 3]\n",
      "Total number of elements: 56\n",
      "    idx_or  idx_ex  init_flows  swapped  new_flows  new_flows_swapped  \\\n",
      "0        0       1      20.645    False     25.770              False   \n",
      "1        0       4      73.941    False     71.573              False   \n",
      "2        1       2       6.456    False      8.866              False   \n",
      "3        1       3      87.959    False     92.336              False   \n",
      "4        4       1       6.456     True      8.866              False   \n",
      "5        3       2       7.286     True      3.206              False   \n",
      "6        4       3      16.671     True     51.794              False   \n",
      "7       10       5       9.070    False    -15.844               True   \n",
      "8        5      11       8.359    False      5.490              False   \n",
      "9        5      12      18.964    False      6.554              False   \n",
      "10       8       9       5.100    False     31.299              False   \n",
      "11       8      13       9.153    False     25.570              False   \n",
      "12       9      10       5.008     True    -20.794               True   \n",
      "13      11      12       1.947    False     -0.909              False   \n",
      "14      13      12       7.348    False     -7.966               True   \n",
      "15       3       6      28.070    False     55.411              False   \n",
      "16       3       8      15.983    False     31.557              False   \n",
      "17       4       5      40.093    False      0.000              False   \n",
      "18       7       6       0.000     True      0.000              False   \n",
      "19       6       8      28.070     True     55.411              False   \n",
      "\n",
      "    delta_flows  gray_edges  \n",
      "0         5.125        True  \n",
      "1        -2.368        True  \n",
      "2         2.410        True  \n",
      "3         4.377        True  \n",
      "4         2.410        True  \n",
      "5        -4.080        True  \n",
      "6        35.124       False  \n",
      "7        24.914       False  \n",
      "8        -2.869        True  \n",
      "9       -12.410       False  \n",
      "10       26.198       False  \n",
      "11       16.417       False  \n",
      "12       25.802       False  \n",
      "13       -1.039        True  \n",
      "14       15.314       False  \n",
      "15       27.341       False  \n",
      "16       15.574       False  \n",
      "17      -40.093       False  \n",
      "18        0.000        True  \n",
      "19       27.341       False  \n",
      "doing simulations\n",
      "Constrained path created\n",
      "In get_hubs(): c = \n",
      "################################################################\n",
      "ConstrainedPath = [4, 5, 12] \n",
      "Details: (amont: [], constrained_edge: (4, 5), aval: [(5, 12)])\n",
      "################################################################\n",
      "shortest path between 5 and 12 failed\n",
      "\n",
      "##############################################################################\n",
      "##########...........COMPUTE NEW NETWORK CHANGES..........####################\n",
      "##############################################################################\n",
      "########### Compute new network changes on node [4] with new topo [[2 2 1 1 1]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 1 2 2 2]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[2 2 2 1 1]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 1 1 2 2]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 1 2 1 2]] ###########\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/antoinemarot/dev/ExpertOp4Grid/venv_expert4grid/lib/python3.6/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning:\n",
      "\n",
      "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########### Compute new network changes on node [5] with new topo [[2 1 1 1 1 2]] ###########\n",
      "########### Compute new network changes on node [5] with new topo [[1 1 1 2 1 2]] ###########\n",
      "########### Compute new network changes on node [5] with new topo [[1 2 2 1 2 1]] ###########\n",
      "########### Compute new network changes on node [5] with new topo [[2 1 1 2 1 2]] ###########\n",
      "########### Compute new network changes on node [5] with new topo [[2 2 1 1 1 2]] ###########\n",
      "--------------------------------------------------------------------------------------------\n",
      "----------------------------------- END RESULT DATAFRAME -----------------------------------\n",
      "--------------------------------------------------------------------------------------------\n",
      "   overflow ID  Flows before  Flows after  Delta flows       Worsened line  \\\n",
      "0       17.000        40.093        0.000       40.093                  []   \n",
      "1       17.000        40.093          nan          nan                  []   \n",
      "2       17.000        40.093       50.157      -10.064             [3, 17]   \n",
      "3       17.000        40.093       50.157      -10.064             [3, 17]   \n",
      "4       17.000        40.093       50.157      -10.064                  []   \n",
      "5       17.000        40.093          nan          nan                  []   \n",
      "6       17.000        40.093       24.462       15.631  [8, 9, 10, 11, 13]   \n",
      "7       17.000        40.093       24.462       15.631                  []   \n",
      "8       17.000        40.093          nan          nan                  []   \n",
      "9       17.000        40.093          nan          nan                  []   \n",
      "\n",
      "   Prod redispatched  Load redispatched    Topology applied  Substation ID  \\\n",
      "0                nan                nan     [2, 2, 1, 1, 1]          4.000   \n",
      "1                nan                nan     [1, 1, 2, 2, 2]          4.000   \n",
      "2              1.042              0.000     [2, 2, 2, 1, 1]          4.000   \n",
      "3              1.042              0.000     [1, 1, 1, 2, 2]          4.000   \n",
      "4                nan                nan     [1, 1, 2, 1, 2]          4.000   \n",
      "5                nan                nan  [2, 1, 1, 1, 1, 2]          5.000   \n",
      "6              2.307              0.000  [1, 1, 1, 2, 1, 2]          5.000   \n",
      "7                nan                nan  [1, 2, 2, 1, 2, 1]          5.000   \n",
      "8                nan                nan  [2, 1, 1, 2, 1, 2]          5.000   \n",
      "9                nan                nan  [2, 2, 1, 1, 1, 2]          5.000   \n",
      "\n",
      "   Rank Substation ID  Topology score  Topology simulated score  Efficacity  \n",
      "0               1.000          39.900                     0.000         nan  \n",
      "1               1.000          39.900                     0.000         nan  \n",
      "2               1.000          37.530                     0.000      -8.098  \n",
      "3               1.000          37.530                     0.000      -8.098  \n",
      "4               1.000          37.490                     0.000         nan  \n",
      "5               1.000          51.990                     0.000         nan  \n",
      "6               1.000          49.120                     1.000     -25.678  \n",
      "7               1.000          49.120                     0.000         nan  \n",
      "8               1.000          49.120                     0.000         nan  \n",
      "9               1.000          40.190                     0.000         nan  \n",
      "[<grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1487d8ba8>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x14875da90>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1488b9978>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x148826940>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x143f96390>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1487d8ef0>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x143f96c50>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x10918cb38>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x148719588>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x144a237f0>]\n",
      "index_best_action\n",
      "6\n",
      "action we take is:\n",
      "This action will:\n",
      "\t - NOT change anything to the injections\n",
      "\t - NOT perform any redispatching action\n",
      "\t - NOT force any line status\n",
      "\t - NOT switch any line status\n",
      "\t - NOT switch anything in the topology\n",
      "\t - Set the bus of the following element:\n",
      "\t \t - assign bus 1 to line (origin) 7 [on substation 5]\n",
      "\t \t - assign bus 2 to line (origin) 8 [on substation 5]\n",
      "\t \t - assign bus 1 to line (origin) 9 [on substation 5]\n",
      "\t \t - assign bus 2 to line (extremity) 17 [on substation 5]\n",
      "\t \t - assign bus 1 to generator 2 [on substation 5]\n",
      "\t \t - assign bus 1 to load 4 [on substation 5]\n",
      "running Expert Agent on line with id:10 at timestep:799\n",
      "Number of generators of the powergrid: 5\n",
      "Number of loads of the powergrid: 11\n",
      "Number of powerline of the powergrid: 20\n",
      "Number of elements connected to each substations in the powergrid: [3 6 4 6 5 6 3 2 5 3 3 3 4 3]\n",
      "Total number of elements: 56\n",
      "    idx_or  idx_ex  init_flows  swapped  new_flows  new_flows_swapped  \\\n",
      "0        0       1      23.310    False     24.399              False   \n",
      "1        0       4      72.490    False     76.220              False   \n",
      "2        1       2       6.442    False      6.561              False   \n",
      "3        1       3      88.900    False     91.580              False   \n",
      "4        4       1       6.442     True      6.561              False   \n",
      "5        3       2       3.703     True      5.387              False   \n",
      "6        4       3      30.765     True     24.601              False   \n",
      "7        5      10       3.215     True    -14.473               True   \n",
      "8        5      11      24.576    False     34.009              False   \n",
      "9       12       5       0.485     True     18.373              False   \n",
      "10       8       9      17.632    False      0.000              False   \n",
      "11       8      13      14.075    False     26.235              False   \n",
      "12      10       9       7.431    False    -10.300               True   \n",
      "13      11      12      17.610    False     26.429              False   \n",
      "14      13      12       2.400    False     -8.475               True   \n",
      "15       3       6      39.573    False     36.093              False   \n",
      "16       3       8      22.534    False     20.542              False   \n",
      "17       4       5      24.576    False     34.009              False   \n",
      "18       6       7       0.000     True     -0.000               True   \n",
      "19       6       8      39.573     True     36.093              False   \n",
      "\n",
      "    delta_flows  gray_edges  \n",
      "0         1.090        True  \n",
      "1         3.730       False  \n",
      "2         0.119        True  \n",
      "3         2.679        True  \n",
      "4         0.119        True  \n",
      "5         1.684        True  \n",
      "6        -6.165       False  \n",
      "7        17.687       False  \n",
      "8         9.433       False  \n",
      "9        17.887       False  \n",
      "10      -17.632       False  \n",
      "11       12.160       False  \n",
      "12       17.731       False  \n",
      "13        8.819       False  \n",
      "14       10.874       False  \n",
      "15       -3.480        True  \n",
      "16       -1.992        True  \n",
      "17        9.433       False  \n",
      "18        0.000        True  \n",
      "19       -3.480        True  \n",
      "doing simulations\n",
      "Constrained path created\n",
      "In get_hubs(): c = \n",
      "################################################################\n",
      "ConstrainedPath = [8, 9] \n",
      "Details: (amont: [], constrained_edge: (8, 9), aval: [])\n",
      "################################################################\n",
      "\n",
      "##############################################################################\n",
      "##########...........COMPUTE NEW NETWORK CHANGES..........####################\n",
      "##############################################################################\n",
      "########### Compute new network changes on node [9] with new topo [[2 2 1]] ###########\n",
      "########### Compute new network changes on node [9] with new topo [[2 1 2]] ###########\n",
      "########### Compute new network changes on node [8] with new topo [[2 2 1 1 1]] ###########\n",
      "########### Compute new network changes on node [8] with new topo [[1 1 2 2 2]] ###########\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/antoinemarot/dev/ExpertOp4Grid/venv_expert4grid/lib/python3.6/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning:\n",
      "\n",
      "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########### Compute new network changes on node [8] with new topo [[1 1 2 2 1]] ###########\n",
      "########### Compute new network changes on node [8] with new topo [[2 2 1 1 2]] ###########\n",
      "########### Compute new network changes on node [8] with new topo [[2 2 1 2 1]] ###########\n",
      "--------------------------------------------------------------------------------------------\n",
      "----------------------------------- END RESULT DATAFRAME -----------------------------------\n",
      "--------------------------------------------------------------------------------------------\n",
      "   overflow ID  Flows before  Flows after  Delta flows   Worsened line  \\\n",
      "0       10.000        17.632        0.000       17.632              []   \n",
      "1       10.000        17.632       10.146        7.486  [8, 9, 11, 13]   \n",
      "2       10.000        17.632       10.146        7.486              []   \n",
      "3       10.000        17.632          nan          nan              []   \n",
      "4       10.000        17.632        6.609       11.023  [8, 9, 11, 13]   \n",
      "5       10.000        17.632        4.228       13.403  [8, 9, 11, 13]   \n",
      "6       10.000        17.632       12.888        4.744            [11]   \n",
      "\n",
      "   Prod redispatched  Load redispatched Topology applied  Substation ID  \\\n",
      "0                nan                nan        [2, 2, 1]          9.000   \n",
      "1              0.884              0.000        [2, 1, 2]          9.000   \n",
      "2                nan                nan  [2, 2, 1, 1, 1]          8.000   \n",
      "3                nan                nan  [1, 1, 2, 2, 2]          8.000   \n",
      "4              1.288              0.000  [1, 1, 2, 2, 1]          8.000   \n",
      "5              2.460              0.000  [2, 2, 1, 1, 2]          8.000   \n",
      "6              0.064              0.000  [2, 2, 1, 2, 1]          8.000   \n",
      "\n",
      "   Rank Substation ID  Topology score  Topology simulated score  Efficacity  \n",
      "0               1.000          27.830                     0.000         nan  \n",
      "1               1.000          17.730                     2.000       5.815  \n",
      "2               1.000          17.630                     0.000         nan  \n",
      "3               1.000          17.630                     0.000         nan  \n",
      "4               1.000          15.640                     1.000     -12.029  \n",
      "5               1.000          15.640                     0.000      -7.407  \n",
      "6               1.000          14.150                     1.000      -2.977  \n",
      "[<grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1487d8780>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1487d8e48>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x143f96898>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x143f96940>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x143f967b8>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x143cab400>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x13ffcaa90>]\n",
      "index_best_action\n",
      "1\n",
      "action we take is:\n",
      "This action will:\n",
      "\t - NOT change anything to the injections\n",
      "\t - NOT perform any redispatching action\n",
      "\t - NOT force any line status\n",
      "\t - NOT switch any line status\n",
      "\t - NOT switch anything in the topology\n",
      "\t - Set the bus of the following element:\n",
      "\t \t - assign bus 2 to line (extremity) 10 [on substation 9]\n",
      "\t \t - assign bus 1 to line (origin) 12 [on substation 9]\n",
      "\t \t - assign bus 2 to load 6 [on substation 9]\n",
      "running Expert Agent on line with id:13 at timestep:800\n",
      "Number of generators of the powergrid: 5\n",
      "Number of loads of the powergrid: 11\n",
      "Number of powerline of the powergrid: 20\n",
      "Number of elements connected to each substations in the powergrid: [3 6 4 6 5 6 3 2 5 3 3 3 4 3]\n",
      "Total number of elements: 56\n",
      "    idx_or  idx_ex  init_flows  swapped  new_flows  new_flows_swapped  \\\n",
      "0        0       1      23.734    False     26.860              False   \n",
      "1        0       4      74.171    False     74.128              False   \n",
      "2        1       2       6.468    False      8.622              False   \n",
      "3        1       3      90.120    False     94.614              False   \n",
      "4        4       1       6.468     True      8.622              False   \n",
      "5        3       2       4.284     True      3.166              False   \n",
      "6        4       3      27.545     True     47.843              False   \n",
      "7        5      10       3.918    False      3.918              False   \n",
      "8        5      11      29.233    False      6.470              False   \n",
      "9       12       5       7.918     True      8.018              False   \n",
      "10       8       9      10.448    False     10.449              False   \n",
      "11       8      13      19.087    False     43.218              False   \n",
      "12       9      10       0.000    False      0.000              False   \n",
      "13      11      12      21.984    False      0.000              False   \n",
      "14      13      12       1.657     True     22.387              False   \n",
      "15       3       6      37.985    False     53.848              False   \n",
      "16       3       8      21.551    False     30.520              False   \n",
      "17       4       5      29.233    False      6.470              False   \n",
      "18       7       6       0.000     True      0.000              False   \n",
      "19       6       8      37.985     True     53.848              False   \n",
      "\n",
      "    delta_flows  gray_edges  \n",
      "0         3.126        True  \n",
      "1        -0.042        True  \n",
      "2         2.154        True  \n",
      "3         4.494        True  \n",
      "4         2.154        True  \n",
      "5        -1.118        True  \n",
      "6        20.298       False  \n",
      "7         0.000        True  \n",
      "8       -22.762       False  \n",
      "9         0.100        True  \n",
      "10        0.000        True  \n",
      "11       24.131       False  \n",
      "12        0.000        True  \n",
      "13      -21.984       False  \n",
      "14       20.730       False  \n",
      "15       15.863       False  \n",
      "16        8.969       False  \n",
      "17      -22.762       False  \n",
      "18       -0.000        True  \n",
      "19       15.863       False  \n",
      "doing simulations\n",
      "Constrained path created\n",
      "In get_hubs(): c = \n",
      "################################################################\n",
      "ConstrainedPath = [5, 11, 4, 12] \n",
      "Details: (amont: [(5, 11), (4, 5)], constrained_edge: (11, 12), aval: [])\n",
      "################################################################\n",
      "shortest path between 5 and 12 failed\n",
      "shortest path between 5 and 4 failed\n",
      "shortest path between 5 and 11 failed\n",
      "shortest path between 11 and 12 failed\n",
      "shortest path between 11 and 4 failed\n",
      "\n",
      "##############################################################################\n",
      "##########...........COMPUTE NEW NETWORK CHANGES..........####################\n",
      "##############################################################################\n",
      "########### Compute new network changes on node [4] with new topo [[2 2 1 1 1]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 1 2 2 2]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[2 2 2 1 1]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 1 1 2 2]] ###########\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/antoinemarot/dev/ExpertOp4Grid/venv_expert4grid/lib/python3.6/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning:\n",
      "\n",
      "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########### Compute new network changes on node [4] with new topo [[1 1 2 1 2]] ###########\n",
      "########### Compute new network changes on node [12] with new topo [[2 2 1 1]] ###########\n",
      "########### Compute new network changes on node [12] with new topo [[1 1 2 2]] ###########\n",
      "########### Compute new network changes on node [12] with new topo [[2 1 1 2]] ###########\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "episode:  10%|▉         | 800/8064 [00:41<06:12, 19.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########### Compute new network changes on node [12] with new topo [[1 2 2 1]] ###########\n",
      "########### Compute new network changes on node [12] with new topo [[2 1 2 1]] ###########\n",
      "--------------------------------------------------------------------------------------------\n",
      "----------------------------------- END RESULT DATAFRAME -----------------------------------\n",
      "--------------------------------------------------------------------------------------------\n",
      "   overflow ID  Flows before  Flows after  Delta flows Worsened line  \\\n",
      "0       13.000        21.984        0.000       21.984            []   \n",
      "1       13.000        21.984          nan          nan            []   \n",
      "2       13.000        21.984          nan          nan            []   \n",
      "3       13.000        21.984          nan          nan            []   \n",
      "4       13.000        21.984          nan          nan            []   \n",
      "5       13.000        21.984          nan          nan            []   \n",
      "6       13.000        21.984          nan          nan            []   \n",
      "7       13.000        21.984          nan          nan            []   \n",
      "8       13.000        21.984          nan          nan            []   \n",
      "9       13.000        21.984          nan          nan            []   \n",
      "\n",
      "   Prod redispatched  Load redispatched Topology applied  Substation ID  \\\n",
      "0                nan                nan  [2, 2, 1, 1, 1]          4.000   \n",
      "1                nan                nan  [1, 1, 2, 2, 2]          4.000   \n",
      "2                nan                nan  [2, 2, 2, 1, 1]          4.000   \n",
      "3                nan                nan  [1, 1, 1, 2, 2]          4.000   \n",
      "4                nan                nan  [1, 1, 2, 1, 2]          4.000   \n",
      "5                nan                nan     [2, 2, 1, 1]         12.000   \n",
      "6                nan                nan     [1, 1, 2, 2]         12.000   \n",
      "7                nan                nan     [2, 1, 1, 2]         12.000   \n",
      "8                nan                nan     [1, 2, 2, 1]         12.000   \n",
      "9                nan                nan     [2, 1, 2, 1]         12.000   \n",
      "\n",
      "   Rank Substation ID  Topology score  Topology simulated score  Efficacity  \n",
      "0               1.000          22.490                     0.000         nan  \n",
      "1               1.000          22.490                     0.000         nan  \n",
      "2               1.000          22.450                     0.000         nan  \n",
      "3               1.000          22.450                     0.000         nan  \n",
      "4               1.000          20.340                     0.000         nan  \n",
      "5               1.000          34.230                     0.000         nan  \n",
      "6               1.000          34.230                     0.000         nan  \n",
      "7               1.000          20.730                     0.000         nan  \n",
      "8               1.000          20.730                     0.000         nan  \n",
      "9               1.000          13.600                     0.000         nan  \n",
      "[<grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x148792518>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1488f02e8>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1440b9828>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1440d4860>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1440b98d0>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1440b9b00>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x143cab588>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x143f96048>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x14401b438>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x148792668>]\n",
      "index_best_action\n",
      "nan\n",
      "action we take is:\n",
      "This action will:\n",
      "\t - NOT change anything to the injections\n",
      "\t - NOT perform any redispatching action\n",
      "\t - NOT force any line status\n",
      "\t - NOT switch any line status\n",
      "\t - NOT switch anything in the topology\n",
      "\t - NOT force any particular bus configuration\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "episode: 100%|██████████| 1/1 [00:46<00:00, 46.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation summary:\n",
      "chronics at: 000\ttotal reward: 889269.187500\ttime steps: 800/8064\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "res = evaluate(env, nb_episode=1,logs_path=\"ExpertAgent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Investigate a time step more closely\n",
    "Run the Expert system before that timestep\n",
    "Simulate actions at that time step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timestep_of_interest=798\n",
    "maxIter=timestep_to_investigate-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running Expert Agent on line with id:17 at timestep:1\n",
      "Number of generators of the powergrid: 5\n",
      "Number of loads of the powergrid: 11\n",
      "Number of powerline of the powergrid: 20\n",
      "Number of elements connected to each substations in the powergrid: [3 6 4 6 5 6 3 2 5 3 3 3 4 3]\n",
      "Total number of elements: 56\n",
      "    idx_or  idx_ex  init_flows  swapped  new_flows  new_flows_swapped  \\\n",
      "0        0       1      20.645    False     25.770              False   \n",
      "1        0       4      73.941    False     71.573              False   \n",
      "2        1       2       6.456    False      8.866              False   \n",
      "3        1       3      87.959    False     92.336              False   \n",
      "4        4       1       6.456     True      8.866              False   \n",
      "5        3       2       7.286     True      3.206              False   \n",
      "6        4       3      16.671     True     51.794              False   \n",
      "7       10       5       9.070    False    -15.844               True   \n",
      "8        5      11       8.359    False      5.490              False   \n",
      "9        5      12      18.964    False      6.554              False   \n",
      "10       8       9       5.100    False     31.299              False   \n",
      "11       8      13       9.153    False     25.570              False   \n",
      "12       9      10       5.008     True    -20.794               True   \n",
      "13      11      12       1.947    False     -0.909              False   \n",
      "14      13      12       7.348    False     -7.966               True   \n",
      "15       3       6      28.070    False     55.411              False   \n",
      "16       3       8      15.983    False     31.557              False   \n",
      "17       4       5      40.093    False      0.000              False   \n",
      "18       7       6       0.000     True      0.000              False   \n",
      "19       6       8      28.070     True     55.411              False   \n",
      "\n",
      "    delta_flows  gray_edges  \n",
      "0         5.125        True  \n",
      "1        -2.368        True  \n",
      "2         2.410        True  \n",
      "3         4.377        True  \n",
      "4         2.410        True  \n",
      "5        -4.080        True  \n",
      "6        35.124       False  \n",
      "7        24.914       False  \n",
      "8        -2.869        True  \n",
      "9       -12.410       False  \n",
      "10       26.198       False  \n",
      "11       16.417       False  \n",
      "12       25.802       False  \n",
      "13       -1.039        True  \n",
      "14       15.314       False  \n",
      "15       27.341       False  \n",
      "16       15.574       False  \n",
      "17      -40.093       False  \n",
      "18        0.000        True  \n",
      "19       27.341       False  \n",
      "doing simulations\n",
      "Constrained path created\n",
      "In get_hubs(): c = \n",
      "################################################################\n",
      "ConstrainedPath = [4, 5, 12] \n",
      "Details: (amont: [], constrained_edge: (4, 5), aval: [(5, 12)])\n",
      "################################################################\n",
      "shortest path between 5 and 12 failed\n",
      "\n",
      "##############################################################################\n",
      "##########...........COMPUTE NEW NETWORK CHANGES..........####################\n",
      "##############################################################################\n",
      "########### Compute new network changes on node [4] with new topo [[2 2 1 1 1]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 1 2 2 2]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[2 2 2 1 1]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 1 1 2 2]] ###########\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/antoinemarot/dev/ExpertOp4Grid/venv_expert4grid/lib/python3.6/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning:\n",
      "\n",
      "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########### Compute new network changes on node [4] with new topo [[1 1 2 1 2]] ###########\n",
      "########### Compute new network changes on node [5] with new topo [[2 1 1 1 1 2]] ###########\n",
      "########### Compute new network changes on node [5] with new topo [[1 1 1 2 1 2]] ###########\n",
      "########### Compute new network changes on node [5] with new topo [[1 2 2 1 2 1]] ###########\n",
      "########### Compute new network changes on node [5] with new topo [[2 1 1 2 1 2]] ###########\n",
      "########### Compute new network changes on node [5] with new topo [[2 2 1 1 1 2]] ###########\n",
      "--------------------------------------------------------------------------------------------\n",
      "----------------------------------- END RESULT DATAFRAME -----------------------------------\n",
      "--------------------------------------------------------------------------------------------\n",
      "   overflow ID  Flows before  Flows after  Delta flows       Worsened line  \\\n",
      "0       17.000        40.093        0.000       40.093                  []   \n",
      "1       17.000        40.093          nan          nan                  []   \n",
      "2       17.000        40.093          nan          nan                  []   \n",
      "3       17.000        40.093       50.157      -10.064             [3, 17]   \n",
      "4       17.000        40.093       50.157      -10.064                  []   \n",
      "5       17.000        40.093          nan          nan                  []   \n",
      "6       17.000        40.093       24.462       15.631  [8, 9, 10, 11, 13]   \n",
      "7       17.000        40.093       24.462       15.631                  []   \n",
      "8       17.000        40.093          nan          nan                  []   \n",
      "9       17.000        40.093          nan          nan                  []   \n",
      "\n",
      "   Prod redispatched  Load redispatched    Topology applied  Substation ID  \\\n",
      "0                nan                nan     [2, 2, 1, 1, 1]          4.000   \n",
      "1                nan                nan     [1, 1, 2, 2, 2]          4.000   \n",
      "2                nan                nan     [2, 2, 2, 1, 1]          4.000   \n",
      "3              1.042              0.000     [1, 1, 1, 2, 2]          4.000   \n",
      "4                nan                nan     [1, 1, 2, 1, 2]          4.000   \n",
      "5                nan                nan  [2, 1, 1, 1, 1, 2]          5.000   \n",
      "6              2.307              0.000  [1, 1, 1, 2, 1, 2]          5.000   \n",
      "7                nan                nan  [1, 2, 2, 1, 2, 1]          5.000   \n",
      "8                nan                nan  [2, 1, 1, 2, 1, 2]          5.000   \n",
      "9                nan                nan  [2, 2, 1, 1, 1, 2]          5.000   \n",
      "\n",
      "   Rank Substation ID  Topology score  Topology simulated score  Efficacity  \n",
      "0               1.000          39.900                     0.000         nan  \n",
      "1               1.000          39.900                     0.000         nan  \n",
      "2               1.000          37.530                     0.000         nan  \n",
      "3               1.000          37.530                     0.000      -8.098  \n",
      "4               1.000          37.490                     0.000         nan  \n",
      "5               1.000          51.990                     0.000         nan  \n",
      "6               1.000          49.120                     1.000     -25.678  \n",
      "7               1.000          49.120                     0.000         nan  \n",
      "8               1.000          49.120                     0.000         nan  \n",
      "9               1.000          40.190                     0.000         nan  \n",
      "[<grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x144f2c320>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x144f2c080>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x149adef28>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x14027f9e8>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x144488dd8>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x149a72f98>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x149b02da0>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x149a4bac8>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x14027f780>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x149a30828>]\n",
      "index_best_action\n",
      "6\n",
      "action we take is:\n",
      "This action will:\n",
      "\t - NOT change anything to the injections\n",
      "\t - NOT perform any redispatching action\n",
      "\t - NOT force any line status\n",
      "\t - NOT switch any line status\n",
      "\t - NOT switch anything in the topology\n",
      "\t - Set the bus of the following element:\n",
      "\t \t - assign bus 1 to line (origin) 7 [on substation 5]\n",
      "\t \t - assign bus 2 to line (origin) 8 [on substation 5]\n",
      "\t \t - assign bus 1 to line (origin) 9 [on substation 5]\n",
      "\t \t - assign bus 2 to line (extremity) 17 [on substation 5]\n",
      "\t \t - assign bus 1 to generator 2 [on substation 5]\n",
      "\t \t - assign bus 1 to load 4 [on substation 5]\n",
      "running Expert Agent on line with id:10 at timestep:2\n",
      "Number of generators of the powergrid: 5\n",
      "Number of loads of the powergrid: 11\n",
      "Number of powerline of the powergrid: 20\n",
      "Number of elements connected to each substations in the powergrid: [3 6 4 6 5 6 3 2 5 3 3 3 4 3]\n",
      "Total number of elements: 56\n",
      "    idx_or  idx_ex  init_flows  swapped  new_flows  new_flows_swapped  \\\n",
      "0        0       1      41.796    False     44.051              False   \n",
      "1        0       4      37.428    False     38.900              False   \n",
      "2        1       2      22.759    False     23.674              False   \n",
      "3        1       3      42.089    False     42.049              False   \n",
      "4        1       4      32.798    False     33.838              False   \n",
      "5        2       3      18.819    False     17.814              False   \n",
      "6        4       3      40.137     True     35.830              False   \n",
      "7        5      10       0.792     True    -13.314               True   \n",
      "8        5      11      21.779    False     28.650              False   \n",
      "9       12       5       0.992    False    -13.114               True   \n",
      "10       8       9      14.024    False      0.000              False   \n",
      "11       8      13      11.976    False     21.073              False   \n",
      "12      10       9       4.463    False     -9.500               True   \n",
      "13      11      12      15.670    False     22.161              False   \n",
      "14      13      12       3.717    False     -4.800               True   \n",
      "15       3       6      34.892    False     31.501              False   \n",
      "16       3       8      19.908    False     17.972              False   \n",
      "17       4       5      21.779    False     28.650              False   \n",
      "18       6       7       0.000     True     -0.000               True   \n",
      "19       6       8      34.892     True     31.501              False   \n",
      "\n",
      "    delta_flows  gray_edges  \n",
      "0         2.255        True  \n",
      "1         1.472        True  \n",
      "2         0.915        True  \n",
      "3        -0.039        True  \n",
      "4         1.041        True  \n",
      "5        -1.005        True  \n",
      "6        -4.307       False  \n",
      "7        14.106       False  \n",
      "8         6.870       False  \n",
      "9        14.106       False  \n",
      "10      -14.024       False  \n",
      "11        9.096       False  \n",
      "12       13.963       False  \n",
      "13        6.491       False  \n",
      "14        8.517       False  \n",
      "15       -3.391       False  \n",
      "16       -1.936        True  \n",
      "17        6.870       False  \n",
      "18        0.000        True  \n",
      "19       -3.391       False  \n",
      "doing simulations\n",
      "Constrained path created\n",
      "In get_hubs(): c = \n",
      "################################################################\n",
      "ConstrainedPath = [6, 8, 3, 4, 9] \n",
      "Details: (amont: [(6, 8), (3, 6), (4, 3)], constrained_edge: (8, 9), aval: [])\n",
      "################################################################\n",
      "shortest path between 6 and 9 failed\n",
      "shortest path between 6 and 4 failed\n",
      "shortest path between 6 and 3 failed\n",
      "shortest path between 6 and 8 failed\n",
      "shortest path between 8 and 4 failed\n",
      "shortest path between 8 and 3 failed\n",
      "shortest path between 3 and 9 failed\n",
      "shortest path between 3 and 4 failed\n",
      "\n",
      "##############################################################################\n",
      "##########...........COMPUTE NEW NETWORK CHANGES..........####################\n",
      "##############################################################################\n",
      "########### Compute new network changes on node [9] with new topo [[2 2 1]] ###########\n",
      "########### Compute new network changes on node [9] with new topo [[2 1 2]] ###########\n",
      "########### Compute new network changes on node [8] with new topo [[2 2 1 1 1]] ###########\n",
      "########### Compute new network changes on node [8] with new topo [[1 1 2 2 2]] ###########\n",
      "########### Compute new network changes on node [8] with new topo [[1 1 2 2 1]] ###########\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/antoinemarot/dev/ExpertOp4Grid/alphaDeesp/core/grid2op/Grid2opSimulation.py:197: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in float_scalars\n",
      "\n",
      "/Users/antoinemarot/dev/ExpertOp4Grid/venv_expert4grid/lib/python3.6/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning:\n",
      "\n",
      "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########### Compute new network changes on node [8] with new topo [[2 2 1 1 2]] ###########\n",
      "########### Compute new network changes on node [8] with new topo [[2 2 1 2 1]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 2 2 2 1]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[2 1 1 1 2]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 2 2 1 1]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 2 1 2 1]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[2 1 1 2 2]] ###########\n",
      "########### Compute new network changes on node [3] with new topo [[2 1 2 2 1 1]] ###########\n",
      "########### Compute new network changes on node [3] with new topo [[2 1 2 1 1 1]] ###########\n",
      "########### Compute new network changes on node [3] with new topo [[1 2 1 1 2 2]] ###########\n",
      "########### Compute new network changes on node [3] with new topo [[1 2 1 2 1 2]] ###########\n",
      "########### Compute new network changes on node [3] with new topo [[1 2 1 1 1 2]] ###########\n",
      "########### Compute new network changes on node [6] with new topo [[2 2 1]] ###########\n",
      "########### Compute new network changes on node [6] with new topo [[2 1 2]] ###########\n",
      "--------------------------------------------------------------------------------------------\n",
      "----------------------------------- END RESULT DATAFRAME -----------------------------------\n",
      "--------------------------------------------------------------------------------------------\n",
      "    overflow ID  Flows before  Flows after  Delta flows   Worsened line  \\\n",
      "0        10.000        14.024        0.000       14.024  [8, 9, 11, 13]   \n",
      "1        10.000        14.024        9.540        4.484  [8, 9, 11, 13]   \n",
      "2        10.000        14.024        9.540        4.484              []   \n",
      "3        10.000        14.024          nan          nan              []   \n",
      "4        10.000        14.024        4.290        9.734  [8, 9, 11, 13]   \n",
      "5        10.000        14.024        1.982       12.042  [8, 9, 11, 13]   \n",
      "6        10.000        14.024        9.533        4.491            [11]   \n",
      "7        10.000        14.024        9.931        4.093      [8, 9, 13]   \n",
      "8        10.000        14.024        9.931        4.093      [8, 9, 13]   \n",
      "9        10.000        14.024       12.106        1.918      [4, 8, 13]   \n",
      "10       10.000        14.024       12.919        1.105         [8, 13]   \n",
      "11       10.000        14.024       12.106        1.918      [4, 8, 13]   \n",
      "12       10.000        14.024       12.106        1.918              []   \n",
      "13       10.000        14.024          nan          nan              []   \n",
      "14       10.000        14.024          nan          nan              []   \n",
      "15       10.000        14.024       12.148        1.876         [8, 13]   \n",
      "16       10.000        14.024       12.148        1.876              []   \n",
      "17       10.000        14.024          nan          nan              []   \n",
      "18       10.000        14.024          nan          nan              []   \n",
      "\n",
      "    Prod redispatched  Load redispatched    Topology applied  Substation ID  \\\n",
      "0               1.849              0.000           [2, 2, 1]          9.000   \n",
      "1               0.351              0.000           [2, 1, 2]          9.000   \n",
      "2                 nan                nan     [2, 2, 1, 1, 1]          8.000   \n",
      "3                 nan                nan     [1, 1, 2, 2, 2]          8.000   \n",
      "4               0.887              0.000     [1, 1, 2, 2, 1]          8.000   \n",
      "5               1.819              0.000     [2, 2, 1, 1, 2]          8.000   \n",
      "6               0.004              0.000     [2, 2, 1, 2, 1]          8.000   \n",
      "7               2.925              0.000     [1, 2, 2, 2, 1]          4.000   \n",
      "8               2.925              0.000     [2, 1, 1, 1, 2]          4.000   \n",
      "9               0.648              0.000     [1, 2, 2, 1, 1]          4.000   \n",
      "10              0.187              0.000     [1, 2, 1, 2, 1]          4.000   \n",
      "11              0.648              0.000     [2, 1, 1, 2, 2]          4.000   \n",
      "12                nan                nan  [2, 1, 2, 2, 1, 1]          3.000   \n",
      "13                nan                nan  [2, 1, 2, 1, 1, 1]          3.000   \n",
      "14                nan                nan  [1, 2, 1, 1, 2, 2]          3.000   \n",
      "15              1.359              0.000  [1, 2, 1, 2, 1, 2]          3.000   \n",
      "16                nan                nan  [1, 2, 1, 1, 1, 2]          3.000   \n",
      "17                nan                nan           [2, 2, 1]          6.000   \n",
      "18                nan                nan           [2, 1, 2]          6.000   \n",
      "\n",
      "    Rank Substation ID  Topology score  Topology simulated score  Efficacity  \n",
      "0                1.000          23.460                     1.000        -inf  \n",
      "1                1.000          13.960                     2.000       3.749  \n",
      "2                1.000          14.430                     0.000         nan  \n",
      "3                1.000          14.430                     0.000         nan  \n",
      "4                1.000          12.490                     1.000     -17.082  \n",
      "5                1.000          12.490                     0.000      -7.908  \n",
      "6                1.000          11.040                     2.000       3.705  \n",
      "7                1.000           6.870                     2.000       3.631  \n",
      "8                1.000           6.870                     2.000       3.631  \n",
      "9                1.000           6.870                     2.000       1.472  \n",
      "10               1.000           6.870                     0.000      -0.797  \n",
      "11               1.000           6.870                     2.000       1.472  \n",
      "12               1.000           5.360                     0.000         nan  \n",
      "13               1.000           5.360                     0.000         nan  \n",
      "14               1.000           5.360                     0.000         nan  \n",
      "15               1.000           5.320                     2.000       1.408  \n",
      "16               1.000           5.320                     0.000         nan  \n",
      "17               1.000           3.390                     0.000         nan  \n",
      "18               1.000           3.390                     0.000         nan  \n",
      "[<grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x144050fd0>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x144050828>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x144032ef0>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1440502e8>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x144e3bc88>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x144f372b0>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x14382c4e0>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x144050320>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x14708c048>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x14716a438>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1091356d8>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x14507cc88>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1437f2470>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x149b02198>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x144e47780>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1437fe320>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x149b02eb8>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x144f37048>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x149adecf8>]\n",
      "index_best_action\n",
      "1\n",
      "action we take is:\n",
      "This action will:\n",
      "\t - NOT change anything to the injections\n",
      "\t - NOT perform any redispatching action\n",
      "\t - NOT force any line status\n",
      "\t - NOT switch any line status\n",
      "\t - NOT switch anything in the topology\n",
      "\t - Set the bus of the following element:\n",
      "\t \t - assign bus 2 to line (extremity) 10 [on substation 9]\n",
      "\t \t - assign bus 1 to line (origin) 12 [on substation 9]\n",
      "\t \t - assign bus 2 to load 6 [on substation 9]\n",
      "running Expert Agent on line with id:13 at timestep:3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of generators of the powergrid: 5\n",
      "Number of loads of the powergrid: 11\n",
      "Number of powerline of the powergrid: 20\n",
      "Number of elements connected to each substations in the powergrid: [3 6 4 6 5 6 3 2 5 3 3 3 4 3]\n",
      "Total number of elements: 56\n",
      "    idx_or  idx_ex  init_flows  swapped  new_flows  new_flows_swapped  \\\n",
      "0        0       1      44.059    False     45.356              False   \n",
      "1        0       4      38.234    False     37.002              False   \n",
      "2        1       2      23.500    False     24.960              False   \n",
      "3        1       3      42.041    False     44.295              False   \n",
      "4        1       4      33.129    False     31.388              False   \n",
      "5        2       3      18.144    False     19.072              False   \n",
      "6        4       3      38.617     True     54.391              False   \n",
      "7        5      10       3.515    False      3.514              False   \n",
      "8        5      11      24.505    False      5.652              False   \n",
      "9       12       5       3.515     True      3.314              False   \n",
      "10       8       9       9.439    False      9.539              False   \n",
      "11       8      13      14.511    False     33.504              False   \n",
      "12       9      10       0.000    False      0.000              False   \n",
      "13      11      12      18.156    False      0.000              False   \n",
      "14      13      12       1.526    False    -15.626               True   \n",
      "15       3       6      33.813    False     45.536              False   \n",
      "16       3       8      19.237    False     25.907              False   \n",
      "17       4       5      24.505    False      5.652              False   \n",
      "18       7       6       0.000    False     -0.000               True   \n",
      "19       6       8      33.813     True     45.536              False   \n",
      "\n",
      "    delta_flows  gray_edges  \n",
      "0         1.297        True  \n",
      "1        -1.232        True  \n",
      "2         1.461        True  \n",
      "3         2.254        True  \n",
      "4        -1.741        True  \n",
      "5         0.928        True  \n",
      "6        15.775       False  \n",
      "7        -0.000        True  \n",
      "8       -18.854       False  \n",
      "9        -0.200        True  \n",
      "10        0.100        True  \n",
      "11       18.993       False  \n",
      "12        0.000        True  \n",
      "13      -18.156       False  \n",
      "14       17.152       False  \n",
      "15       11.723       False  \n",
      "16        6.670       False  \n",
      "17      -18.854       False  \n",
      "18        0.000        True  \n",
      "19       11.723       False  \n",
      "doing simulations\n",
      "Constrained path created\n",
      "In get_hubs(): c = \n",
      "################################################################\n",
      "ConstrainedPath = [5, 11, 4, 12] \n",
      "Details: (amont: [(5, 11), (4, 5)], constrained_edge: (11, 12), aval: [])\n",
      "################################################################\n",
      "shortest path between 5 and 12 failed\n",
      "shortest path between 5 and 4 failed\n",
      "shortest path between 5 and 11 failed\n",
      "shortest path between 11 and 12 failed\n",
      "shortest path between 11 and 4 failed\n",
      "\n",
      "##############################################################################\n",
      "##########...........COMPUTE NEW NETWORK CHANGES..........####################\n",
      "##############################################################################\n",
      "########### Compute new network changes on node [12] with new topo [[2 2 1 1]] ###########\n",
      "########### Compute new network changes on node [12] with new topo [[1 1 2 2]] ###########\n",
      "########### Compute new network changes on node [12] with new topo [[2 1 1 2]] ###########\n",
      "########### Compute new network changes on node [12] with new topo [[1 2 2 1]] ###########\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/antoinemarot/dev/ExpertOp4Grid/venv_expert4grid/lib/python3.6/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning:\n",
      "\n",
      "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########### Compute new network changes on node [12] with new topo [[2 1 2 1]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[2 2 1 1 1]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 1 2 2 2]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[2 2 2 1 1]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 1 1 2 2]] ###########\n",
      "########### Compute new network changes on node [4] with new topo [[1 1 2 1 2]] ###########\n",
      "--------------------------------------------------------------------------------------------\n",
      "----------------------------------- END RESULT DATAFRAME -----------------------------------\n",
      "--------------------------------------------------------------------------------------------\n",
      "   overflow ID  Flows before  Flows after  Delta flows Worsened line  \\\n",
      "0       13.000        18.156        0.000       18.156            []   \n",
      "1       13.000        18.156          nan          nan            []   \n",
      "2       13.000        18.156          nan          nan            []   \n",
      "3       13.000        18.156          nan          nan            []   \n",
      "4       13.000        18.156          nan          nan            []   \n",
      "5       13.000        18.156          nan          nan            []   \n",
      "6       13.000        18.156          nan          nan            []   \n",
      "7       13.000        18.156          nan          nan            []   \n",
      "8       13.000        18.156          nan          nan            []   \n",
      "9       13.000        18.156          nan          nan            []   \n",
      "\n",
      "   Prod redispatched  Load redispatched Topology applied  Substation ID  \\\n",
      "0                nan                nan     [2, 2, 1, 1]         12.000   \n",
      "1                nan                nan     [1, 1, 2, 2]         12.000   \n",
      "2                nan                nan     [2, 1, 1, 2]         12.000   \n",
      "3                nan                nan     [1, 2, 2, 1]         12.000   \n",
      "4                nan                nan     [2, 1, 2, 1]         12.000   \n",
      "5                nan                nan  [2, 2, 1, 1, 1]          4.000   \n",
      "6                nan                nan  [1, 1, 2, 2, 2]          4.000   \n",
      "7                nan                nan  [2, 2, 2, 1, 1]          4.000   \n",
      "8                nan                nan  [1, 1, 1, 2, 2]          4.000   \n",
      "9                nan                nan  [1, 1, 2, 1, 2]          4.000   \n",
      "\n",
      "   Rank Substation ID  Topology score  Topology simulated score  Efficacity  \n",
      "0               1.000          28.850                     0.000         nan  \n",
      "1               1.000          28.850                     0.000         nan  \n",
      "2               1.000          17.350                     0.000         nan  \n",
      "3               1.000          17.350                     0.000         nan  \n",
      "4               1.000          11.900                     0.000         nan  \n",
      "5               1.000          18.740                     0.000         nan  \n",
      "6               1.000          18.740                     0.000         nan  \n",
      "7               1.000          17.510                     0.000         nan  \n",
      "8               1.000          17.510                     0.000         nan  \n",
      "9               1.000          17.000                     0.000         nan  \n",
      "[<grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1460b20b8>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1460b2e10>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1450d2278>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x145040668>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x122115320>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1444db2e8>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x144488d30>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1450e3908>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x144f4aa90>, <grid2op.Space.GridObjects.TopologyAndDispatchAction_rte_case14_realistic object at 0x1450d2b70>]\n",
      "index_best_action\n",
      "nan\n",
      "action we take is:\n",
      "This action will:\n",
      "\t - NOT change anything to the injections\n",
      "\t - NOT perform any redispatching action\n",
      "\t - NOT force any line status\n",
      "\t - NOT switch any line status\n",
      "\t - NOT switch anything in the topology\n",
      "\t - NOT force any particular bus configuration\n"
     ]
    }
   ],
   "source": [
    "timestep=0\n",
    "chronic_scenario=0\n",
    "env.set_id(chronic_scenario)\n",
    "env.reset()\n",
    "\n",
    "\n",
    "agent = ExpertAgent(env.action_space, env.observation_space,\"agent_test\")\n",
    "\n",
    "done=False\n",
    "reward=0.0\n",
    "info=None\n",
    "\n",
    "maxIter=797\n",
    "iter=0\n",
    "while not done and iter<maxIter:\n",
    "    act=agent.act(obs,reward,done)\n",
    "    obs,reward,done,info=env.step(act)\n",
    "    iter+=1\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot and simulate actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt  # pip install matplotlib\n",
    "import imageio  # pip install imageio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is the timestep 797 at which the expert system will try to act\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this is the timestep \"+str(iter)+\" at which the expert system will try to act\")\n",
    "from grid2op.PlotGrid import PlotMatplot\n",
    "plot_helper = PlotMatplot(env.observation_space)\n",
    "fig_obs = plot_helper.plot_obs(obs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_id=12#4#12\n",
    "target_topology=[2, 1, 1, 2]     #[1, 2, 1, 2, 2]#[1, 2, 1, 2, 2] #[2, 2, 1, 1]#[1, 1, 1, 2, 2] failing\n",
    "action_def={\"set_bus\": {\"substations_id\": [(sub_id, target_topology)] } }\n",
    "action=env.action_space(action_def)\n",
    "osb_simu,_reward,_done,_info=obs.simulate(action,time_step=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#successful\n",
    "#sub_id=12#4#12\n",
    "#target_topology=[2, 1, 1, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'disc_lines': array([False, False, False, False, False, False, False, False, False,\n",
      "       False, False, False, False, False, False, False, False, False,\n",
      "       False, False]), 'is_illegal': False, 'is_ambiguous': False, 'is_dispatching_illegal': False, 'is_illegal_reco': False, 'opponent_attack_line': None, 'opponent_attack_sub': None, 'opponent_attack_duration': 0, 'exception': [], 'rewards': {}}\n"
     ]
    }
   ],
   "source": [
    "print(_info)# to see if divengent load flow or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aUaFChfTVq1f7Z55zOBxs27bNLyoqKhngt99+82vatOm5Dh06nLdarVStWtXRv3//k//9738vbHDx008/hfTo0ePCAJNGjRolr1+//qLptb/99ptfvXr1ss5aK3CbN2/279+//8mIiAiH1WqlXbt255s2bXp+zpw5eSb0JkyYUHrgwIHVpk6durdXr145briRlcViwTNBT10FTeipIiEivbJsbNEXOAi4gM8BjDFxwD7ca+TlyhhzHpgNPOI5dRB3YjBThOdcXr4D2gG3AAtzKF+MeyReK9yj8wBWA/cDFXGP4rtUrFQlVroSK72Ild6e5xGXiUWpghNjlvBwekugMhlJz3PgizVsev0wcc+dYePIoztW/F/qrMWn1gJ1eSitITHm86IOWSmllFJKqeKoX79+Jz766KOwtWvX+pw/f16effbZijabzfTs2fMMQMuWLc+vXbs28JdffvF3uVwcOXLE9tlnn5WtX79+EkBcXJzPyZMn7R07drywTt3jjz9+et++fT5jx44tm5qaKvPmzQv44osvyjzxxBMnMus4HA6Sk5PF4XAIQHJysiQnJ4vL5co11tTUVElOTpaMjAxxuVwkJydLSkrKhXV6mjRpcm7KlCllDh8+bHO5XCxcuNB/zZo1gdHR0bkmEv/5z3+WHT58eJWvvvpqd/fu3c9mL9+0aZP3vHnzAjz9snDhQv8xY8ZU6NKlS/wVfqmVh66hp4qEMWY6MD3rORFZiDuxNtezuUU1YFv2a0WkJnDAGOPwrGt3H+7NLQC+BlaIyGvAceAJ3Am/vGJJFZFngGRjjEsuXW9ssacdpzFmu+fcUuAVYKVnFJ9brJQDHsBifxRo5g7Y7v5NahzuBPpsr7W4HDOAL4kxx/OKTakCEWMOA+97HhfUEfkXcO8jH134uVZKKaWUUkpdhddff/34uXPnrB06dKidlJRkqV+/fvKPP/64Kzg42AXQv3//hKNHj9oHDhwYcfLkSS9vb29Xs2bNzs2aNesgwOzZs0vdfffdZ7y9vS8MWYuMjEz/6quvdj///POVX3311cqhoaGOESNGHO7du/eF0W/Dhw+v8P7775fPPPb3978VYPv27Ztq167953vVLO64447ItWvXBmS5pnSFChXSDx8+vAlg5syZ+4cMGVKpcePG9ZKTk61lypRxPP7448eGDRt2Yb2/mjVr1u/Ro8fpMWPGHAMYMWJEFavVarp161Yra1/ffPPNrvbt258/deqUdejQoVX++OMPbxGhXLly6T179jz5+uuv63viqyQ6vFHdKESkOvAZUAb3aL03jDFfe8o+xb3ZxQ8iMhD3lNwM3EnphcA/MjemEJEBwAueZpcATxhjLtnCW0SWAB8YY77Ldr4P0NUY09VzbMO9rt5PxpiHPOdqA9uBF40xY4gVAQaA5UMsdiuVu1mIiBHC7gabZ2dvZwocXwT7Pzf88a2LjHQXuIYAkzzrnil1XYlIW2A+UMUYc6io41FKKaWUUup6WLduXXWbzbYoMjLyrK+vb45Jr+utfv36dV966aUjjzzySGJRx1KcpaSkeO3cuTPI6XS2jo6O3lvU8RQmTegpda1ixQKWj8E8Tr3h0OBlsPnnfY0zCba8BVv+CfApmCeIMYW6sKlS2YmIL+5k9SBjzOTL1VdKKaWUUqokuNESeqmpqTJixIjwUaNGHfPz89MkzTW4mRJ6uoaeUtfM8h4iA2j5NTR+6/LJPHDXuWU03PEtiLUfWD4o/DiVupgxJgVYhnuHZ6WUUkoppVQR8PHxMe++++5RTeapK6EJPaWuRaxUA/M0TT6yUPm+S4qNMQwePJjbb7+dZs2a8e9///viCpW6QNMJFjCDiJXqlzSgVOFbANwjItaiDkQppZRSSimlVP5oQk+payIv4FvBUKNfjqUbNmxg06ZNrF69mlWrVjF+/HjOns224U/13uBXKQPkpesQsFLZLQBKA1FFHYhSSimllFJKqfzRhJ5SVytWyoL0o8ErNiz2HKtUqlQJLy8v0tPTSUpKwtvbGy8vr4srWezQYKQd6EOshBV+4EpdZBPuHaF12q1SSimllFJKFROa0FPq6kWAy0b5e3KtEBoaSq1atahZsyaRkZEMGjQIHx+fSyuGtwGM1d2mUtePce+MtABN6CmllFJKKaVUsaEJPaWuXgAA1tw3wViwYAEHDhxg79697Nu3j8mTJ3Pw4MFLK9oDM58FXlqoVKFbAPxFRPTnTymllFJKKaWKAU3oKXX1EgBIP51npZCQEGw2G76+vvj4+HDu3LlLK6WdynwWX7AhKpUv/wXswJ1FHYhSSimllFJKqcvThJ5SV28XYktk/+e5Vrjnnnvw9vbmr3/9K7fddhstWrSgfv36l1bcNxPEdhbYWXjhKpUzY8wxYAPQrqhjUUoppZRSShU/O3bs8BKR6M2bN3sDTJgwoXRERESDoo6rJLMVdQBKFVsxJolYGcv2D0ZR5zkr3qUvqWKxWPj000/zbic9Aba/n4Fxvk2MOV9I0Sp1OfOBrkUdhFJKKaWUUqr4e/LJJ+OffPJJnYFWiHSEnlLX5iNc6clsehWMufKrjYGNr4ErLQUYX9DBKXUFFgCRIhJRxHEopZRSSimlipHU1FQp6hhuRprQU+paxJizGOdT7Bxv+P1ZcDnzf63Lifn9OVw7xjHyK+fv8ghXkRFUqsCsBFKA3LdtVkoppZRSShWJZs2a1e7Vq1eVtm3b1vD394+qUqVKg/Hjx4dmls+ePTu4fv36dQMDAxtHREQ0GDlyZFhGRgYAAwYMqNS5c+dqmXV79OgRYbVao0+ePGkFWLRokb+vr29USkqKAOzbt8/euXPnauXKlWtUunTpWzp37lztyJEjtuyxdOrUqXpQUFDjfv36Vc4e77hx40LDwsIaZR537949olOnTtV79epVpVSpUo1DQ0NvGTp0aIWs18TFxfm0atWqZmho6C3lypVr1LNnzypnz57VvFUudMqtUtcqxswgVjLYMW4qp38VbvmnjbItwWLNub5xwYllsGGEU06tNtNXMGHUtzwCbBCRPsaYpdc1fqUAY0yqiCwF2gKfFHU8SimllFJKXW/JycmydetW7+vVX7169dL8/PzyPbDjyy+/LDN58uQ9P/30055vvvkmuGfPnjUiIyNTvb29zaOPPlpjwoQJ+3r37p2wevVqv+7du9e02Wxm5MiRJ9q1a3d2wIAB1VwuFxaLhWXLlgVVrlw5bc6cOUH9+vVLmDdvXlDTpk3P+/r6mpSUFGndunXkPffck7h79+7Nxhh69+5dtUePHtVWrVq1KzOWr776KnTq1Kl7v//++73JycmWrAm/3CxYsKDU+PHj902ePPngkiVL/Nu3b1+7TZs25zp37nzu6NGjttatW9ceNmzY0blz5+45c+aMtXv37tUHDhxYefbs2Qeu9mtckmlCT6mCEGNiiZXNxK+bxsJWjfEu5yDiETvhbcArxF0nPQGOL4T9nztIPW7H4rUVXL17TzDr+0yUt3AnURaLyPvACGNMahHekbo5LQBGiojVGJNR1MEopZRSSil1PW3dutW7adOmOexiWDjWrl27pUmTJvl+33fnnXcmxsTEJAI8+OCDidOnTz/z6aeflrHZbNx1112J/fv3TwBo2bJl8lNPPXVsypQpZUeOHHmiffv255KTk60rV6708/HxcdntdtO3b98T8+fPD+rXr1/CokWLgjp37pwA8OWXXwYnJSVZJ06c+IfF4h4c9/777/9RvXr1Rnv27LHXqFHDAdCmTZszDz74YCJAYGCgKz/x33rrrecHDhyY4Lk+qU6dOimrV6/279y587lJkyaFVq1aNe3VV189AeDr6+t87bXXDnfq1Km20+k8YLNp+io7/YooVVBizEYgilipR9qJh9n1UW92vH/x0GOL1yFc6dOBWTyUtiXztDHmmIj8DegL/BtoLyK9jDHrructqJveAuA9oAnwaxHHopRSSiml1HVVr169tLVr1265fM2C6+9K6letWjUt+/GGDRv8bDabqV279kWJwVq1aqUdO3bMCyAgIMBER0efnzt3bpC3t7e54447znbs2PHshAkTwhMSEiwbNmzwnzhx4gGAnTt3+sTHx9uDg4MbZ23Py8vL7N271yszoVe1atX0K73fsLAwR9ZjPz8/17lz5ywAu3bt8t6yZYtfYGDgRf2KCIcOHbJXq1btomuVJvSUKngxZivwCrEyEgjyPARIxJV+lpicd88wxhhgsogsBqYCq0VkFPCWMUZ/eanrYStwBPe0W03oKaWUUkqpm4qfn5+5khFz19vBgwe9sh17V6hQwWG3282+ffsumiq8e/du7/Dw8AtJt1atWp3973//G+Tl5WUee+yxk02aNEk1xvCvf/2rXEhIiDPzvsPDwx3ly5dPO3To0Oa8YrFYLAW6Bnx4eLgjOjr6/OrVq3cWZLslmS4uqFRhiTGGGJNIjDlEjDnoeX7ZX3rGmH1AK2A48BKwUkTqFHa4SnmSygtwJ/SUUkoppZRSN5ClS5cGz549O9jpdPKf//wnaMGCBaUee+yxU/379z+1ZMmS4KlTp5ZyOp2sXLnSd/z48eG9e/c+lXntvffemxgXFxfw+++/B3Tq1OkcwB133HF23Lhx4S1btjybWa9nz54JTqdThg4dWuH06dNWgMOHD9s++eSTkMK8tyeeeOL01q1b/caMGVP23LlzFpfLxe7du+0zZswoVZj9Fmea0FPqBmSMcRlj3gNuBaxAnIgMFRH9P6sK2wKguYgEFXUgSimllFJKqT/df//9pydPnlwmODg46plnnqnyzjvvHGjfvv351q1bJ02dOnXP22+/Xb5UqVJRDz/8cI0BAwacePnll49nXtu8efOUwMDAjFq1aqWUKVMmA6Bt27aJ58+ft95zzz0XEnohISGuVatWbT906JBXgwYN6gUEBEQ1b968zrJlywIL895q1aqVvnTp0u0LFy4Mql69eoPg4ODG7dq1i9y4caNvYfZbnIm5/IAhpVQREhE7MAJ4GVgG9DXG6C4/qlCISFngBNDVGPN9UcejlFJKKaVUQVu3bl11m822KDIy8qyvr+8VrwVXFJo1a1b79ttvPzdu3LgjRR3LjSwlJcVr586dQU6ns3V0dPTeoo6nMOloH6VucMYYhzHmNaA5UB7YJCJ9RESKNjJVEhljTgK/A+2KOhallFJKKaWUUjnThJ5SxYQxZi3uKbifAVOAb0WkXNFGpUqoBUBbQXwE8SnqYJRSSimllFJKXUx3uVWqGDHGpADPiMgPuHfC3SIiA40x3xZtZKokEMQCtGE3jQmlBpDiOX8S+BqINZjlRRmjUkoppZRSN6M1a9bsKOoY1I1FR+gpVQwZYxYDjYA5wDciMk1EdPcfddUEKQcsBOZTg/Zc/NNUFngCWCbIt4JumKGUUkoppZRSRUkTekoVU8aYRGNMP6Ar0B732nptijgsVQwJUhpYDtyVj+pdgf8K4l2oQSmllFJKKaWUypUm9JQq5jw7kTYA1gK/iMg4EfEr4rBU8fIsEHkF9ZsC/QspFqWUUkoppZRSl6EJPaVKAM/OpN2BXp5HnIjcVtD9iGAVwVcEuwi6y24J4Jk+O/iyFQ2Q5uV+GACGC6LrsCqllFJKKaVUEdA3Y0qVEMYYA8wQkSXAZGCViLwFvGGMSQdApDYQA7QBSgFpwGZgFvALxjgz2xOhOtARaOjlRWOXi7pAQJZyp48Pe9PTiTOGLcBSYIUxuAr/blUBqgtcvCbeuQD4qSOsi8aypRHWzbfgOBR+URV75WOVXYmBC+Usa4F1wI/GcO66Ra2UUkoppZRSNzFN6ClVwhhjDolIO+BJ4B2gYx+RYVPhRaBdDpdEAY8CR3+V24bczq8V7HZ6AdH+/jhvuQVuuQVb/foQFgbe3uB0QmIitu3bidy0iZrr13PfkSO8YbdzXISZwFRj2Hy97lkVAJfAj52Qz3sic7pAup3aDRw0ru9FgyeE6tXB19ddNSUF9u0LZ/MWc8f6OG7fsQO7MTisVua4XMwA5mhiVymllFJKKaUKjyb0lCqBjDEu4CMR+SUSZr8KS/Kqn46dCTxZ/nXbqK/87C5Xt+4WiYmBNm2w2e2X7c4CeG3eDLNmETZ9OstjpF8AACAASURBVEP++INnLRamGcMIYzhSMHd1ZURkP+4RiCmeU28ZY764XFm2NizAv3BvOuIETgMDjDG7RSQC2ANsynJJd2PMnhzaeQV4CDgHPGiMOeA5PxX4xBiz8hpu9VrtYFGrZNvz7/tlxDWieUsnvd630707lClzuX0vBMDr1Cn4+mu8Zs6ky8qVdLfZ2CjCUGPy/rlTSil1YxGRccDfgKpAlDFmfZYyb+Bd3B8OpgIbjDE9c2ijNTAG96h+A/wEvOD52wQR6YT7tdWK+zW0jzHmbA7tTAV6A7caY+I85wKBo8BuY0xjEZkC7DHGjPaU9wRmANWMMfs95+YBXxljPru2r45SShVf48aNC33zzTcrHj9+fGNRx+Ln5xf15Zdf7u7UqZPO7rlGuoaeUiWYMWbnNthfLY86S7mDBrYd/MP2HgOfC+LIUYtlxgykQwfIRzLvggYN4M034cAB7LGxSPnyPGK1sleEf4gU2e+aB40xjT2P7Am7vMoy/Q34K3CLMaYRsBD4Z5byc1naaJxLMi8I6Ak0Aj7Gs16diNwDJBdlMk+EslabmcLdi/xahtZnwwZh5VI7jz8OZcrkv50yZeDxx2H5cmwbN8Kdd1IfWGy3850IZQvtBpRSShW0/wAtgAM5lI3BnaCLNMY0BP6eSxsJwEPGmHpANPAX3Ov7IiIBwGdAV2NMLeAI8Eoe8awD+mU5fhDYluV4MRfv0N4K+DXznIjYPPezKI8+lFJKXYaIRH/33XeBV3LNuHHjQsPCwhplP5+cnBynybyCoQk9pUoykQYW6Jpb8TgGc7csok7bKmzfaWXMGAgOvrYuLRZ4+GHYvRv766/jbbUyxmrle5E/198rRgzgDfiIiOBea+6PK2wjA/coBDvgD6R7diF+BXihAGO9IiJE2e1sCA+n09y5sHC+jYYNr73dBg3gl1+w/vwzhIdzr93ORhFuvfaWlVJKFTZjzDJjzCWvcyLiDzwGjPCs2Ysx5lgubcQZY/Z6nqcC64EIT3EHIM4Ys91z/DHwcB4hfQN08owOBOiLe53gTIuB5iLi5TluAbzFn0m+psApY8y+PPpQSimliiVN6ClVst0Ll+5Ga4B/MJahjOON0Va+/9FKtbyG8V0FX18YMQKWLEECA2lvt7NchNIF28tlTReRTSLymYhkHymWV1mmObinKx/DPcXnbmBklnJ/EVkrIr+LyEgRsWZvwBiTBLwHrAa6AP8G3gDezWmK0fUgwl1WKyv/8hfKbdiArUMHkALes7h9e1i/Hvtf/0pZq5UVIrQu2B6UUkpdRzWAeOAlEflNRJaLyN2Xu0hEwoEewI+eU1W4ePTffqC8ZyRdTpKBX4CuIlIH9980F0boGWMO4X59vk1EqnhiXIB7VCC4R+zp6DylVL6IEF0UjyuJ8Z///GfZypUrN/D3948KDQ29pXv37hHu2C8eQbdjxw4vEYnevHnzRWvojBo1qlxYWFijUqVKNb7//vsjEhMTLQAul4thw4ZVCAsLa+Tv7x8VFhbW6Omnn64IULNmzfoADz30UC0/P7+oO+64oxbA5MmTQ+rXr183KCiocUhIyC2tW7euuX37di+AefPmBTz//PNVT548affz84vy8/OLmjBhQumcYp09e3Zw/fr16wYGBjaOiIhoMHLkyLCMjIws3xeJfvPNN8vdeuutdfz8/KIiIyPrzZ8/vzgOFilwmtBTqmTzyenkRJ7gHf7B9Onw0ksFn8zJqkULWLMGW+nSNLDZ+FKES5JeheQOzzTZW4FTwLR8lmXVBGgAVAQq4J5yO9FTdhSoaIxpinvX4JbAczk1Yoz52DMltz1QyfOYJyIfichXIjLkGu7ziohQw2bjh7/9De/587GGhhZeX6GhMH8+1q5d8bHZ+F6EmoXXm1JKqUJkw72u3lZjTBNgCPCFiITldoFnyYk5wNvGmN+uoe/JuEcHPgZMyaE8c9rtXcBSY0wKcEJEqnnOLb6GvpVS6oaxadMm71GjRlX65ptvdiclJcXt3bt3U//+/U/l9/rTp0/bN2zY4Ldr167NcXFxW7Zt2+b7+OOPVwb4/vvvg2bNmlVmxYoV25OSkuI2bty4pWvXrmcAdu/evQVg9uzZu5KTk+OWLVu2CyAoKCjj008/3R8fH79+69atWwAeeuih6gDt27c//8477xwoW7asIzk5OS45OTnuySefjM8e09KlS/0effTRGs8888yx+Pj49TNmzNg7ceLEsDfffLNc1nrTp08vM3369H2JiYlxLVq0ONe3b98CHo5SPGlCT6mSbXf2E6tozlDLh7z6Kjz66PUJolYt+OEHbLg/KR91Pfo0xhz0/OsAPsCdcLtsWTa9gEXGmDOexbyn4b4HjDFpxpgTnufxuN9w5NYOcGEtn38Bw3Cvq3fSGHM/cJ+IVL/KW803EfztdubUqYPv559j8b7cnhcFwMsLZsxA6tbFx25njgj+hd+rUkqpyxGRXiKy3vPoe5nqBwEX8Dm4p9UC+4AcF2vwbF4xD/jeGPNetnaqZjmOAI4aY5y5dWyMWY37Q7WHgNk5VFmM+7W5FX9uArYUaIt7HVwdoaeUKhHsdrsxxsj69et94+PjLcHBwa4OHTqcz+/1xhgmTZp0KCgoyFWtWjXHyJEjD3/99dehTqcTLy8vV3p6usTFxfmcP39ewsLCMtq0aZOUV3s9evQ427x58xSbzUb58uWdo0aNOrxhwwb/hISEfOeZJk2aVPauu+5K7N+/f4Ldbqdly5bJTz311LEpU6ZcNINq8ODBxxs0aJBmt9sZNGjQyaNHj3odOnTopt/kVRN6SpVsX5Flaksq3jxo+5p77oGRI/O4qhA0awYTJ2IBXhThzsLsS0T8RaRUllMPA3GXK8vBXqB1lrV5OgGbPe2UExG757k30C2PdjI9B8R61h3yxz37Gc+/hZ7oEmGMry+1fvgBm69vYff2J19fd0LX15eaIrx9/XpWSimVG2PM9CybOuU08i1r3VO4R6m3A/CMfqvGxRtU4CkLwJ3Mm5e5+2wW84BbPdNnAQaRc5Iuu6HA340xOS2ivhi4HbgTWOE5txT3a+5hY8zhfLSvlFI3vDp16qR/+umne6dPn16matWqjRo0aFB30qRJ+V7SKCgoyBkaGnphLmutWrXSnU6n/PHHH/aOHTuef/XVV/945513yoeHhzdu0qRJ7W+//TYor/Z++umngObNm0eWLVu2UUBAQFS7du3qABw5ciTfWysePnzYXq1atbSs52rVqpV27Ngxr6znKlWqlJ75PDAw0AWQmJh4vWZ+3bBu+oymUiWaMQ5EXgGmA3zGY5yUcnwy2YqlCNL5jz0G06cb5+ZVZ99DSj0DpAE7MOZMAXcVBnztWdNOcCfmeuWjDBH5FPjBGPMD8BFQF9ggIg7ca+k94anaAnhDRDJw/y5dBLyZW0AiUgP31J97PadmAt+KyP3ASmPMpmu96Vw6rgZUmMVDZcUS+8SYMWIr6PUS8yMiAt5+G9ugQTwuwhhjOHT9o1BKKZUXEZkEdATCgfkics4Yk7lcwhPAZyIyFvdovcczk2XZXjuHAs1wrzPbzXPtV8aYN40x50SkP/CdZ9T6ZqD35eIyxizMo+yoiBwEzhhjMkeqrMKdcPzsir4ASqmbmjGsK+oYLqdnz55nevbsecbhcDBt2rSQgQMHVm/evHmSn5+f6/z58xcSXAcPHrwkqXb27Fnb6dOnrZlJvT179njZbDZTqVIlB8CwYcNODxs27HRKSoqMHTu23EMPPVTz2LFj60NCQlySbY2m1NRUeeCBB2o9++yzR+bOnbs7JCTEtXLlSt8WLVrU8+ydhCUfbzgrVqzo2Ldv30Xzhnbv3u0dHh6ents16k+a0FOqpDNmBiJBqXi/P8b2in3AQAsVKhRdOK+/LrZWrYJvXU6LpS3dH6SnIzIfmI4x/8n1wlj5EAgBPiTG/JpXH57d9aKutMxT3j/L8zRgQC71vsG9+16+GGP24N7dL/M4HgpppKJIMPA4EAPcAvA/mlO+jIN+/bzyvLQw9ekDr72G69gx/gEMLrJAlFLqZhArgUAXxBaD2OqCKwDECSTiSvsFiAVWE2MyR4tjjHk8t+Y8r5+tcinL+tr5Jnl8wOVJ+v1wufCNMX1yOb8EaJztXJ1sx+dx7y6vlFIlxoYNG7y3b9/u0759+3PBwcGukJCQDACr1UqDBg2Spk2bFtqtW7fE06dPW19//fVL3vGJCIMGDar0ySefHIqPj7e+8cYbFe67777TNpuNxYsX+6WmplpatmyZ5OfnZwIDAzNEBJvNnTIqU6aMY9u2bT5du3Y9B5CWliZpaWmWkJCQjJCQENf+/fvtI0aMqJi1vwoVKjjOnDljO3bsmDU8PDwjezwA/fv3P9WuXbvaU6dOLdWzZ88zv/76q+/48ePDBw0adLzAv4AlkE65VepmYMxHPfjP8MMZYfxjeP52wJg9ezYtW7YkKCgIEcHp/HN5m5SUFO6//35q1aqFxWLh5Zdfzncod94JtzdxMOnCQDe8gM7AV4h8jUhuk0EzE1SriZU4YqU3sZLjph83NZGGwHpgLJ5kngMbk60DeHa4F/lZNy+v7/2vv/5K586dCQ8PJygoiIYNGzJlSp4ztS7w9obhw7FbrQwQoegyi0opVZLFSjVirV8g1lOIdTrhbdpS/4UIGo8pQ6NR4dQeXJuAGo8Dq7B4HSJWXiI2111mlVJK3SDS0tIsY8eOLV+pUqVGAQEBUS+88ELl8ePH76tfv37axx9/fPDkyZP2MmXKNL777rsjY2JiTme/PjQ01NGgQYOUmjVrNrjlllvq16pVK3XSpEmHAM6dO2d9/vnnK4eFhTUODg5uPGXKlLIzZ87cnTm99ZVXXjk8bty48MDAwMatWrWqGRwc7Hrvvff2v/vuu+X9/Pyi2rVrV6tbt24JWfvr3Lnz2TZt2pypU6dOw8DAwMYTJ068ZHpw69atk6ZOnbrn7bffLl+qVKmohx9+uMaAAQNOvPzyy5rQywcxf34op5QqwUR4r25dntq6NX+JlPnz5xMfH09KSgqPPfYYDofjwic0qampTJgwgcaNG/Piiy/Spk0bRo/OvkxO7t54Az4bfZQDjhyHCv6EMZ0uORsr6fz5absL93TZRNy7zk4kxhy45JqbjUglYANw0YvlrzTjdn5l2zaoUyfnS7PK63s/d+5cTpw4wb333kvZsmVZsmQJXbp0Yfr06XTt2vWybe/Y4Y7hIwaNGcSEH4FV6AuRUkoVjFj5G2L7nIBqPtR51kblHuBT5tJ6xkDCejgQCzvGuTCutRhnV2LMsesftFJKXX/r1q2rbrPZFkVGRp719fXV6Z0lSEpKitfOnTuDnE5n6+jo6L1FHU9h0k/jlLpJeHnRulWr/I+KateuHQBLliy5pMzHx4dnnnnmwvMr1aIFvOooz0EqU+XSpdQ6InInxizNo4nM0cWlgL8Dw4mVOcA4YFHW6UM3mRfJlswDWE5LygQ7qF07f7OP8vre33vvvRcdt2rVitatW7N48eJ8JfQiI6FsqXSSzvi/ALwAHERkJvBPjMlzJy2llFJ5iJUYkBlU6yk0+Viw5bH7kQiUjnI/qvWysLRzNMlHVhErdxBj/rh+QSullFLqaumUW6VuEk4n9aKjizoKtyZN3P9upFFuVf5xBc3ZcI/Wuxf4L7CTWHnKs3bQzUOkNPBYTkWbaMitTQTJ32zrK3L27Fl+/fVXoqJyXZbwIiIQ3dSa9XtfBXgJWItIlTyuExEK4Q6UUqoEiJUKYJlK5GALt03OO5mXXamG0PZ/NvwqVUZsHxVekEoppZQqSDpCT6mbgAhWwB6U58bj109AgPvfJPxzrmCjIbFSP9vZyyVzMn+f1QTGA28RK82vOsji5m7qspAcV8hLwp+gkIL/dZ+ens6DDz5InTp16NmzZ76vCwqx5vS9rwvMR6QBxly0aK4IQcBaYLcIjxuDjh5RSqmLPY93qBA1lsxPb1q2bMm2bdt4+umnee211y5UfOedd/j222+xWCx8/PHHNGrUCHzLw63v2Vh+39+IlUbEmI1FdB9KKaWUyidN6Cl1czAALldRh+GWGYcNZ84VylEZ2HyN3QQWQBvFxz3AwpyLrGTgzDBcPieaf8nJyXTr1o309HTmzJlzYY29/Mhwmty+93Vwb3wyI9v524BIoDqwQ4TngYnGcIP8RCulVBGKlTDEOoj6L9mw/rkMRmxsLAsXLmT//v0Xzu3cuZM5c+awYsUKdu7cyYABA1i+fLm7sNLfILiek7M7RgI9ru9NKKWUUupKaUJPqZuAMbjsds4eOcINMUbvmGfJ7dLE51whjenA29nOrif331ku3EsInARmAt8ACbnULZmCsGJjLk4qZi8K5TRxBx1QQBvLJiQk0LFjR0qXLs133313xesoHj3koGFu33sYzKUJvb8ADtybothwj8DsJUJfY9h2ZdErpVSJ0xbETs2BF52sXLnyJRUXL15M586dsVgs1KlTh/j4eNLS0vD29gaxQO1hNtY83pVYkZt4PVqllFKqWNCEnlI3j1UrVtBu2LD8DdPKyMjA4XCQnu7e9CktLQ2n04mXlxcWi4W0tDSMMbhcLjIyMkhNTcViseDldfmk0YoVYJUMmpq1ORWncpoXiDFHLzobKzm9schM8iwD/g38SIzJZdjfTWCQjMK96+9Fbmc1n254gpQU8M3Hskp5fe9PnDhB27ZtqVu3LjNnzsRuz99GG5lSU+G3OCsD+V+O5QYq5vAD2oKLX68EiAY2ijAKGGMMujuZUupmVQavUk5sfpf9hXz69GnKlSt34bhUqVLEx8dTvnx59wm/KoCxAsHAmcIJVymllFIFQTfFUOom4XSydMkSnPn9vH3GjBn4+vpe2PE0ICAAX19fli1bBkDt2rXx9fVl+fLljBkzBl9fX9q2bZuvtpcvhyjbJgK4ZFNTAzyDyZbMu1QGkAJMAuoRY1oRY767qZN5bpOBn7OfbMlyHE4La9bkr5G8vveTJk1i06ZN/Pjjj4SEhBAQEEBAQAAdOnTIV9tr1kC600pLludYvoPa5UX4XYTPRXhFhPuBv3LpfGGb5zESd2Lv9vzdnVJKlTgOXPl7+QsNDSUh4c8B7ImJiZQunWVzdNeFz0b0QxKllFLqBqcJPaVuHgtOn8a+MJd11rLr06cPxphLHnfddRcA+/fvv6RsyZIll203JQW+nu2greOn7EXxQHeMuWSEmUfmeml7gKFAODFmMDFGp1xmMsYBdMU9WvHC+nIR7KemfT8zZ+Rvybm8vvevvvoqxhiSkpI4f/78hcfPP1+SR8zRzBkuatgPUI19OZbvJ+IHYBFQCugDfAHkNa7QinsjlFUi/FuEgHwFopRSJccfOBLtJB28bMW77rqLn376CWMMu3btolSpUu7ptpkS1oPYzhFjkgsxXqWUUkoVAE3oKXWTMIbf7XYWv/ZabjtRXB+ffAJnzhjzJBMOATuAr3Evvl0RY77N49KWQBugFjHmI2LM2esQbvFjTDrGDAMqA88CKwT2DnG8e3LaVDhwoOhCO3gQpk4xDHeMzm3ed2J75vcxhr8bQ0djqAE8BZfd/MKKewTfYOAPEZ4ToYJIAe4CopRSN665iO0oW8dedLJ379688847TJ8+nTZt2gDu0fUdOnSgRYsW9O3bl/Hjx/95geM8bH/XiXGORyml1A2nWbNmtYcMGVKhsNqPjo6u/eyzzxZa+6rg6Rp6St1EHA5eXbmSZb/8Avfcc/37P3cO3nwTR5rL6+NK5o9hV3RxTM4L7qlcGHMEeN/zYIjgZbew7403KP/ZZ0WT6Br1hiFcTtCbaTkVpwIPY0z2NZua8OemJ/kRDPzL80gUYTuwDS78uw3YZ0zRJraVUqrAxJh0YmU0u//vQ+q/ZMHPvTfStGk5/q5l+PDhDB8+/NKCXRPAed4JvFeI0SqllFKqgOgIPaVuIsaw3Gbj+5gYnEeOXPe+6dULV3w854Ex17d3ZQzpDgfPTp6MzJ59/fv/4gv49DNhrPNZvHBkL14L3I4xOc3bvYucP3wycKEhB7AKeBNoB9QAWgMvAmuASrhH730P7ASSRNgswlcivCHCwyJEieB3TTeplFJFZzLIMZb+zUlarruI5+7IfNj4sguT8QEx5lTBh6eUUkqpgqYj9JS6yTid9E5MJK5bNyovW4YtH5vSFoixY+H778EYuhnDsevTq8rKGL6wWPhLnz48Va8e1kaNrk+/mzdDn94mo6n8tvBhM/soEAKkAVuAWRizM6frRCgNVPccujwPG5AErASW4N7h+DdjSMt2+V5gcbb2AoE6QF3Pow7wAO41+KyAEeEAl47o22YMp6/6C6CUUoUtxqQSK604s2kp85uG0vJrOyGNL3+dMbBjHMQ958KYWcArhR6rUkrdqGIlukj6jTHrrvSS33//3WfYsGGVN23a5Ofl5WXuvvvuxA8//PCP0NDQDIC33nqr7OTJk8sdPXrUy8fHx9WiRYuzEyZMOFS+fHkngMPhYPDgwZX+85//hGZkZBATE6Mf5hRDOkJPqZuMMSQ6HHT+7TecXbqQkXTJRrMFb9w4eOklg2dttCWF36PKjTH8PSODX+++G+eGDYXf34YN0Lo1zgyXrF1rmnbGmD4Y0wVjHsCY13NL5nn4Agmex7fAc8CtQLAxtDOGt4xhZQ7JvBwZwzljWGsM043hRWO4zxjqAH5AfdxrOX6Ke4OWO4EPgOXAKRFOirBMhEkiDBOhvQhVRfR1VCl1g4gxOzGOpiQdXMu8JsYs7uBi3+futfGyO7cbNo2CH2s7+P0Zg8l4DVy9dbd4pZS68SUkJFjat28fWbt27ZSDBw9uWrNmzbZdu3b5PPjggxGZdcqXL+/4+uuvdycmJsatWrVq2969e70HDhxYObP8lVdeCZ8zZ07I/Pnzdxw5cmSjzWYzGzZs8C+SG1JXTUfoKXUTMoYtIrT673+Z17w5/j/8gC0iouD7SU+HZ59x8dHHFl5h1MkGbP43fFnwHal8MwaHCPeeOcMPzZvz15kzsXbrVjh9ffst9OxJRno6/3M66WwM6VcY62ERqgBJxmAKJ0r3dGRgq+dxgSdZV4mLR/TVBe4DynqqJXvW6cs6om87sOtK71cppa5ZjPmDWLkTeOi3Vb/869Yj88MsVq8MCa6bgT3YgsthSD1hOL/bC7EnYByxwGRizO9FHbpSSqn8+fLLL0s5HA75+OOP/7Db7QQGBro++OCDgy1atKh38OBBW5UqVZx9+vS5sC51ZGRk+t///vdjQ4YMicg8N2vWrDKDBg06Hh0dnQrw7rvvHpkxY0bZHLpTNzBN6Cl1kzKG1SI02b6dH+rXJ3LkSKxDh4KPT8G0P28ePDcknQN7M/iC3jzAV2VxT28sghXcVFbGkChCW5eLf3fvzuMPPIBr7FgsBZXU3b8fXngB1xdfYBFhsjEMzu8ouhxizWFoyfVhDC7goOcxP2uZCKFcPH23LtAPiMC9426GCHu4dPrudmPQHZqVUoUnxjhFJA4ywppVp/+vo9JsJKyvBQThXnM0EViKcSzUEXlKKVX8HDp0yF6xYsV0u91+4Vy9evXSAPbs2eNVpUoV57Rp00p98MEH4QcOHPBOT0+3uFwuUlJSLE6nE5vNxvHjx72qV69+4e9zm81GhQoV9MPoYkYTekrdxIxhtwhNHA7+MWIEL374IZbRo7H16AEBAVfenssFK1bAP99wMn+hja6WeXzneo5a7M6s0gdN6N0QPAm2J0T4+dtv+eDbb6kybBiWJ5+EatWurs39+2HCBHj/fVzAIWCYy8V3BRf1jcOzpt5Kz+MCz8YakVw8oq8j8Azg5alzhCzr8/Fnwu9YYY5EVErdVEYAW9fsZQoxxlXUwSilVLFwFWvZFYXKlSs7/p+9+46PssoeP/45UxISSEIIEQhV0IAQmhFcLIsIArrK7oqCjmAXBQUR1LW7istPRSyIhZVFxa8jIqKrYkFRkJW1sRAIUpQiSG8JJW3K+f0xEwwkgaBJJuW8X6/nBfM897n3PEDI5My9527ZsiXK5/NRmNRbuXJlNECbNm0K1q5d67722mvbPP/88+uHDh26NzY2VqdPn17/qquuaqMaeqvZqFGjgvXr1x+qpu73+9m6dWslVVc35cVq/xhTy6mSo8rfAwHabN3KG9deS7BhQwKDBxN8++1QkiZ4lB8F9uyBL7+EO+6AlikF9OwJuxcs5wvO4Z3gn4sm8yC0GYKpQlT5t89HW5+PMU89RVbr1nBaDx+TJoXq3+XllX5vXl6ozbPPQrczfJx4Ijz5JNk+H2N9PlJVa2Yy72jCX09LVXlDlQdVGaRKR6AucDIwAHiG0Ky/04DxwDxgC7BXhP+K8LIId4pwkQgnieCM1PMYY6ofEUkFBgP/ULVknjHG1DSXXnpplsvl0pEjRzY7cOCAbNy40XXbbbc179WrV3aLFi38+/btcwaDQU444QR/bGysLl++PHrChAlNivYxePDg3c8991zjJUuW1MnNzZU77rgjJTs7295zVjM2Q88YA4AqvwBXijA2P59L3nmHoTNn0gOgTh0C7doRaNoUV506OPw+Zd9ePyt/ULbtDn2Q08r9C0N907mcN+jozyxtmB2V8zTmeIRrvT0jwvOc/+G1/2uw98Wl91xM4GAM4gjS8iQfJ7d2UjfGiQgczA3w47oAG350o0EHzrq5BP8yG+onjvB/dMG/rHZccar4gZ/Cx/uF50UQoDHFl++eBzQNNysQYQ3FZ/StViW3sp6huhKROoRmBrcHcgn9PzRcVX8KX/8GiA43dxHaIKWzqi4roa+hwO2EdkXeDlyjqhvD1+YS+rsMAvuBUaq6pIQ+LgAeD78cq6qfhM9fCzRW1fHl8dymVrsHWAu8GelAjDHGlL8GDRoEP/zwwzWjR49u3qxZs85RUVHBc889N/vZZ5/9BaBrMazYMwAAIABJREFU1655d9999+Zhw4a1GjJkiDM1NTV38ODBuzMzM2ML+xg3bty2vXv3Ovv06dM2GAzi8Xh2de7cuRK2SzTlSQqnXBpjzJFEqE/oh9vCIwmISiAr6hJmXdSOVY4OrKADK2jOJuTYXV6E6gcVGrT5XQRpD6ygwA1rUmFFB8hMg40tID8aVCA6H1r+DB1WhI7UNRBdANBR0VKzueb4iBDPr4m+ogm/NoQSSgps4PAafYV1+nZHIOQqKZzQOxf4SFVVRG4BLlHVc0poewnwoKp2LOFaO2A+0FVVt4rIEOByVf1T+Hp9Vc0K//6vwN9VtXMJ/XwPFG5FM1tVTxORRoSSjuepWk0z89uJSGtgDXC9qr4S4XCMMabKWrx4cWuXy/V5amrqvpiYGPswugbJzc2NWrNmTbzf7z83PT19XaTjqUg2Q88YUypVsiihThjUB7nhReDG4+husSXzqoX1wC6ifA1JWwFpK2BwmXYm3g3U6G+YlS28eca34eMQEaKAkzh8Rt85wE1AbLjNTorP6FsJbKptdfpUNQ/4sMiprwnNsivJdcC/SrmWBixT1a3h1x8C00UkSVV3FybzwhKg1D9nH6G/J4FDs1mfAv5myTxTDu4iVMP09UgHYowxxpiKZQk9Y8xvdRvQjFDB/2NZCfy5YsMx5UHRXEGeJFTb7Xg8qWhORcRkDhde0vxD+DhEBAfQnMNn9LUHBgINw80OirCa4pty/FSLlkrfCvz7yJMi0hzoCQwt5b4M4FQRSVXVNcAQQkm5loQS2ojIdKBXuP0FpfRzJ/Bq+Pe3iciFwHZV/baU9saUiYi0ILT51C2q6otwOMYYY4ypYJbQM8b8Nqq5iAwARgPDCc0YOlw8kMR7rMeDqtVkqD4mA1cQWmZdFiuAZysuHFMWqgSBn8PHx0WvidCQw5fttgOuB1qFmwRE+ImSl+/ur4z4K4OI3EPo/6reJVy+GvhAVXeVdK+q/igiNxGalecC5gBZgL9ImyvD41wFPEYJST1VXQicHm4XR2imX38RGQ2cSag23xhVrS0JVlN+7iT07+fVYzU0xhhjTPVnNfSMMeVDpBuhZX/xhArP/49XuRoX3YH2ePQo+6WaqkaQhsAbQJ9jNJ0HXKaUnAQxVZsIsUBbiif7UoGocLPNlLx8d3t1Wr4rIrcDlwF9jlgei4gIoU0EhhduUlGG/hoTqmHYQLX47FQRyQWaqWqp9QxF5FlCicEfgamq2ktEHgI2qOrLZXsyY0BEmhAqmXC7qk6OdDzGGFPVWQ29mstq6BljzPFS/Q747rBzXlkBrAbGcPxLOE0EKbpLkH6EZjJ5gL8SqguGQzkQFN4GvMBnigYjF6n5PVTJAZaEj0NEcAEncvjy3e7AVYSS9gBZIsVn9AHrVQlUygOUkYiMAS6nhGRe2LmE3hN9eox+moQ3xHASmoH3nKrmiEh9IFZVt4Tb/YXQMtw9R+nrD0CCqn4sIp34teZeEKh3fE9oDHcAeym9BqQxxhhjahhL6BljKo5HN+GV/wfcg1dexaObIx2SKbtwou5T4FNBrl3wKad13cu30UH+EHWZroh0fKbiqOInNGvsR+C9wvMiCNCYwzfkaAf0A1LCzfJFWEPxGX1rVMmtrGf4NWZpBkwktGnLF6HJeOSr6ulFml0HvKx6eHI6vMQ2RVUfCJ+aJiItgWhCM+vuCZ9PAN4SkRhCCbmdwIVayjIIEXETSgheAqCqy0RknYhkArsIJdCNKRMROYHQpjj3qWqlf40ZY4wxJjIsoWeMqWhPANcS+uF1SIRjMb+RospOsU0varnwEtut4ePzotdESCCU3Cu6fPdyoA3gAFSEDRSf0bdStfSZbL8/Zv2F0OYVR2vjKeX8i0e8Pr+Udj8TmsFY1ph8hDbgKHru+rLeb8wRxgAHgSmRDsQYY4wxlccSesaYiuXRXLxyOzALrzyPRxdFOiRjTPlTJRv4JnwcIkI0oY0ois7oOxcYAcSE2+yg+Iy+VcCm6lSnz5jKJiJJwM3AP9Q2nzLGGGNqFUvoGWMqw2zgC2ASXumOx2quGVNbqJJPaCfkw5Zpi+AAWnD4jL4OwECgYbjZwSJ1+oom+35SxVcpD2BM1XYr4AOei3QgxhhjjKlcltAzxlQ8jypeuRVYClyDFe02ptZTJUhol9gNwMdFr4nQkMNn9J0C3AC0DDfxi/ATFNuUY7Uq+yshfGMiLrwZyyjgSVW1f/fGGGNMLeOIdADGmFrCo8uBF4DxeCUh0uEYY6ouVXapslCVf6oyRpXzVWkF1AVOJbTb7iwgAAwg9CHBd8A+ETaJMFeEZ0QYLsI5IjQKb+hhTE0yklB9yEmRDsQYY4w5mkmTJiU1atSoU6TjqGlshp4xpjI9QKhI/gPA2AjHYoypZlTJAZaEj0NEcAGtOXxG3x8IzQiOCzfbW2T5btElvBtUCVTKAxhTTkQkDhgNPKuqWZGOxxhjTO3k9/sREZxOZ6WMl5eXJ3Xq1LH6ymGW0DPGVB6P7sEr9wPP4JWX8OiqSIdkjKn+VPEDa8LHvwvPh2flNaH48t3zw+cB8kRYQ/Hluz+qkltZz2DMcRoBRANPRzoQY4ypKeQhSY/EuPqgLi5r202bNrmuueaalt98801cQkJC4N577908YsSIE99///01F1544f7PP/+87l133dVszZo1MbGxsYFLL7109xNPPLHF7XYDICLpjzzyyKa33367wapVq2KaNWuW/+yzz27s16/fgcIxJk+enPTss8822rJlS1SjRo18d95555Zhw4btBfjggw/iLrrootQpU6asGz9+fNMtW7ZE/fTTT8s+++yzuIkTJzbetGlTtNPp1K5dux58/vnnN7Zr167g448/rnfHHXe09Pl8Ehsb2xVg4sSJPw8fPnzP//73vzqjR49uvnz58tioqCjt3bt39rPPPvtLUlJSAKB79+5t27Vrl7tnzx7Xl19+GX/hhRfu8Xq9G8v1L6AasyW3xpjK9k9CPyw/hVdsCZwxpsKooqpsUWWeKpNVuUWV3qqkAImEZvENJ1TDLwbwAF4gg9CGHGtF+ECECSJcJ8IZIiRG7IGMAUSkLqFZ7s+r6q5Ix2OMMabyDBo0qHUwGJR169YtX7JkyQ+zZs069L4kIyMj+sILL0y9/vrrd+zcuXPp/PnzV3/88cf177vvvsZF+5g+fXrD6dOnr8/Ozl5y1lln7b/mmmtOLLw2adKkpH/84x8pL7300oasrKylzz333M9jxoxp9cknn9Qr2sdbb73V4Ouvv16ZnZ29JCUlxR8fHx+YOnXqhj179iz94YcfVgBcdtllrQH69+9/YMKECT8nJyf7cnJyluTk5CwZPnz4nr179zr69++f2rZt29yNGzcu//bbb1f++OOPdQYPHtzqiLGShg4dunvv3r1Lp0yZ8ksF/LFWW5bQM8ZULo/6CRXx7g/8KcLRGGNqKVWyVPlGlVdU+ZsqA1RJBWKBjsBg4BVgH9AHeBb4CtgjwjYR5ovwggijRDhPhOZWp89UkmFAPWBipAMxxhhTedauXev++uuv4yZMmPBLcnJyIDk5OfDoo49uLrz+1FNPndC7d++sYcOG7XW73aSmphaMHj162xtvvNGwaD8jR47cnpaWlu92uxkxYsTOrVu3Rm3atMkFMHny5EZjxozZ+sc//jHH6XTSr1+/AxdeeOGeadOmJRXt44knnvilcePGgZiYGHW5XFxyySX7evToketyuWjSpIl/3LhxmzMyMuru3bu31JzTzJkz6/t8Pnn++ed/iYuLC7Zs2dL39NNPb5w3b179jRs3HlpN2qdPn6zBgwdnO51O4uLiguX3J1r92ZJbY0zl8+h8vDKL0Cy9T/FofqRDMsYYAFXygczwcYgIDqAFvy7fPQVIAy4FCt/kHihSp6/oEt61qvgq5QFMjSYidYA7gCmquj3S8RhjjKk8P//8cxTAySefXFB4LjU19dDv161bV+ebb76Ji4uLO7QBoapKMHh4DqxZs2aH7ilMkGVnZzubN2/u37hxY50HH3yw+cMPP9yssE0gEJBu3bodtpt627ZtC4q+njNnTr1HHnkk5aeffqqTm5t7qKDeli1b3ImJiSX+rLdp0yZ306ZNCwqXAwO0b98+H2Dt2rVRLVq08AO0bNmyoKT7jSX0jDGRczuhH3hvBR6PcCzGGHNUqgSBDeHjo6LXREjm8Bp9pwA9CSUAAfwi/ETxDTlWqXIAY8ruOqABMCHSgRhjTE1zPLXsIqEwsfXjjz9Gpaen5xX+vvD6CSec4BswYMCet956a8NvHSMpKcl39913b7nlllt2H62d0+k8tDFFXl6eDBo06OQxY8Zs+fDDD39KTEwMfvXVVzFnnXVWe9VQM4ej+ES95s2b+7Zs2RLl8/koTOqtXLkyGqBNmzaHkngOh8M2wSiFLbk1xkSGR38mlMi7H680OVZzY4ypqlTZqcqXqvxTldtU6a9KS0LLItOBq4G3gSDwF+BfwHfAfhE2ijBXhGdEuEmEniI0suW75kgiEgX8DfiXqm6JdDzGGGMqV5s2bXzdu3fff+eddzbdtWuXc9euXc677767aeH1kSNH7vjoo48Sp02blpiXlyd+v5/MzMzoWbNmxZd1jJtuumn7hAkTmixYsCA2EAiQm5srCxYsiF24cGFsaffk5+dLfn6+IzExMZCYmBjcsGGD+957721atE1KSoovKyvLtW3btkOz9y699NIsl8ulI0eObHbgwAHZuHGj67bbbmveq1ev7MLZeeboLKFnjImkx4C9wP+LdCDGGFPeVDmoyv9UeV2V+1S5RJUOQF1Cs/n+AjwPbCW0QcfjwHxgG7BbhK9EmCrC7SL8SYTWIjhLGc7UJCJxiAxFZBIiUxF5agI8UwcaE/reaYwxphaaOXPmelWVE088sWPnzp3bX3jhhVkAMTExwZ49e+a89957a/71r381bNKkSafExMQul1xySZv169dHHavfQvfff/+Ou+66a8uIESNa1q9fv0tKSkqnO+64o/mBAwdKzR0lJCQEn3zyyQ0TJ05sEhsb27Vfv34nX3zxxXuLtrnooov29enTJ6tdu3Yd4+Liurz44osNGjRoEPzwww/XZGZmxjRr1qzzaaed1v7EE0/Mf/PNN9f/9j+h2kUKp0AaY0xEeGUwMAP4Ax79JtLhmKPwSgdCdcXS8OiKSIdjTE0TnpWXQvHlu6cQSuQA5AGrObxG3ypgjSp5lR2zKWciDYCnCNVmjDnycg74YuFF4C5Ucyo7PGOMqSkWL17c2uVyfZ6amrovJiam2tZoW7RoUcyZZ57ZfsOGDctatmxp9XqB3NzcqDVr1sT7/f5z09PT10U6nopkNfSMMZE2ExgBPItX/oBHbeciY0ytpIoCm8PHZ0WviVCfw5N87YAhQGtCKy6CIqyn+IYcK1XJqqxnML+DSGtgHtCqtCax4AZGAmcj0hfVnZUUnTHGmCrgu+++q5Ofn+/o0aNHzoYNG9xjxoxpfvrpp++3ZF7tZAk9Y0xkeVTxyq3AYuBK4JXIBmSMMVVPOCn3dfg4RIQ6wMkcnuw7D7gFqBNus50iCT5+TfhtDicRTdXwFkdJ5h2hCzAV+HOFRWOMMabK2bt3r3PYsGEnbt++3R0bGxs8/fTT97/wwgubIh2XiQxL6BljIs+jS/HKP4FH8cpsPLov0iEZY0x1EF5muzx8HCKCA2jJ4TP6OgGDCe2SCqFNOVZxxIw+YJ0q9kl/BTpieXXi5XhP/zODTo1jP+1YRSs24Dh2rnUAIl1QXVrhARtjjKkS+vbte3DDhg2ZkY7DVA2W0DPGVBX3A5cB9wF3RjgWY4yp1lQJAuvDx4eF58OJpGSK1+jrCbQIN/OJ8BPFZ/StUuVgZT1DTSNCa+CyqCgucrlI8/upV3htpuNy3gh6DrWNdebR3rGKc32fcDlv0JmM0rY9vhoYXbGRG2OMMaYqsoSeMaZq8OguvPIAMBGvTMWjayIdkjHG1DThJbY7wseXRa+JUA9oy+GbclxMaEmvK9xmE4fP5itM+O205bvFhWdKetxuRgPpCQn4Bw7E1bUrdOgQOho2BIdDUIXsbFi5EjIz67BsWRdmvN2ex7f+jVPcP3KD73lG8DzRHFa7PTEyT2aMMcaYSLOEnjGmKnkBuBF4ErgwwrEYY0ytosoBQvVMFxc9L4Kb0OYbRZfvngFcB4dmme0RKb4hB/BzeLZgrSPCH91uJvn9dL74YvTKK+G883C53aW2p3596NEjdAA880wU//0vvP76ydzzryd4NjiGx/xjuIRZhTP2dlTO0xhjjDGmqrGEnjGm6vCoP7xBxmd45Xw8+lGkQzLGmNouXE9vdfh4t/B8ePluU4ov3/0T0DjcLE+E1RRfvrtGlfzKeobKJEKMw8Fk4NozzyTw9NPQuXNpK2aPzuGAM88MHXff7eSeu1IY5H2L85zzeD1wWTCZXS+Vc/jGGGOMqSYsoWeMqVo8Og+vvAM8jVfm4dGCY95jjDGm0oWX2P4SPj4rek2ERH5N9BX+eiVwIuAAgiKso/iMvlXhHX2rJRFOcLv5OCqKTq+8AgMH4pTflMorrnlzeO11JyNugcsG/pGOO1flb/cnOW2dszHGGFM7WULPGFMVjSX0g91IYGKEYzHGGHOcVNkL/Dd8HCJCHUI1+YrO6OtL6P/7OuE22yg+o28lsKUq1+kToaHbzdeNG9Ps009xtm1bMeP06AGLl7m54IIG7t1L+FqEHqr8UDGjGWOMMaaqckQ6AGOMKcaj64EngAfwSqNIh2NMTSYic0VkmYgsFZGFItK1lHYOEXlSRH4It/9CRE4qcv1CEVklIj+KyGwRiS+ln1dERIuOIyJxInJARJaGX78sIvcVuT4kfE+rIuc+FpHrfv+fgKlMquSpslyVmao8pMplqnQhVIuvDaH6qU8A64AuwEPAp4RmAWaL8K0Ir4pwlwh/FqGtSAQ+oBaJQeQCRK5CZOheSezpcunMhg1p/s03uCsqmVeoYUOYP19cXbsS63bzgQgJFTuiMcaY0uzcudOZkZERvXPnTmekYzle3bt3bztq1KiUSMdhfhtL6Bljqqr/B+wHxkc6EGNquEGq2klVuxDakOaVUtoNAM4EOqtqJ2Ae4a9PEakH/Av4i6qeDGwB7j/KmIuBa4u8HkxoBlahL4BzirzuBXxTeE5EXMBZwOfHfDpTLagSUGWdKnNUmajK9aqcCSQBjQj93d9JaMZfY2AEoXp+q4AcEVaIMEuER0S4QoRTRahb7oGKdETkNWA7MIfQ18v0x/jbfKcEe733Hq4mTcp91BLFxsI77+CKj6e508n/hXfUNcYYU0kyMzOj+/bt26Zx48ZdunTpkta4ceMu/fr1a5OZmRkd6dhM7WDf+I0xVZNHDxL64e0avHJapMMxpqZS1aL1yhKg1CWNCkQDdUREgHhCs6YAzgeWqOqq8OvngcuPMuxs4EIRKXzDew0wrcj1L4AeIhIVfn0WoST/OeHX3YBdqrr+KGOYGkAVVWWHKgtUeVGVW1Xpp0oLII7Qv4VrCSX3HMDFhJJsi4EDIvwswsciPCXCMBH+KELybwpGxAN8CwwJjw1AJh14jLt49jknp1Xyd6umTUNJvUCACwk9uzHGmEqQmZkZfcYZZ5zy+eefJwSDoc3cg8Eg8+bNSzjjjDNOsaSeqQyW0DPGVGVvAIuASXjLq6y4MeZIIjJdRDYB44ChpTR7H5gPbAO2Ar2BB8LXWgA/F2m7AWgSnklXkhxCyyj/IiLtAKHIDD1V3RQe43QRaQHsAeYCZ4Sb9MJm59V6qhxQ5XtV/k+Ve1W5WJX2QCyh2nwXA1OAHYSSwhOBBcAOEXaJsFCEl0QYI8L5IpxY6iw3kXOA1wjX+SvqEXmAtLYFXBehBeBnnw0XX0zQ7eYhm6VnjDGVY+zYsc1ycnIcgUDgsJ9RAoGA5OTkOMaOHdusvMfs3r1722uuuab5RRdddGJcXFyXxo0bd3rssccO+5BqxowZCR06dDglLi6uS6tWrdIeeOCBRoFA4Kj9ZmVlufr27dumbt26XVu0aJE2efLkpMJrkyZNSmrUqFGnou3HjBmTkp6efqi4xPjx45ObN2+eVrdu3a5JSUmdBw4c2Kp8ntgci22KYYypujyqeGUU8D1wBfB/EY7ImBpJVa8EEJGrgMeAC0podhqQBjQF9gGPAi8Smq30W0wDHgYygJdLuF647PZnYIGq5orIDhE5MXz+td84rqnhVPERWoq7Cnin8LwIAjTj1113C3fgvYjQsl6AXBFWc8SGHH4c45wEiyXLVtGWmXoJMx524ChDKm3GjBk899xzZGRksH//fnw+Hy5X6O34unXrGDp0KKtXr6agoIDk5GSuvvpq7r33XhzH6PyBB3DMnk17Qkvj3z12JMYYY0oiIum/t49AICBz586tX5a+VHXx8fQ9a9aspNdff33tu+++u/61116rf91117X505/+tC8tLS1/wYIFsUOHDm3zwgsvrL/qqqv2fv3117EDBw48yeVy6QMPPLCjtD5nzpzZcNq0aWvnzJmzdvbs2QlDhgxpk5qamte3b9+Dx4pn+fLl0ePGjWv25ZdfruzWrVtedna2Y9GiRbHH80zmt7NP8YwxVZtH/0eoNtdjeKVepMMxpjoTkSvDm18sFZFrjryuqq8CvUQkqYTbrwQ+V9UsVQ0CrxKaKQewEWhZpG0rYKuq+kuLRVW/BlKAy4AZJTT5Itx/L0IzAyE0u6ovoVp+NkPPHJfw8t1NqnyqyiRVhqvSS5XGQANC/65uAT4jtKT8KuDNLixZ7iR4Vkl9vsNfaZTkZ+DAssWQmJjIiBEjePrpp4tdS05OZtq0aWzfvp19+/bx6aef4vV6ee65547Zb+fOcNZZBEQoYyTGGGOqo/79+2cNGDBgv9Pp5Oqrr86Kj4/3//e//40FmDJlSvI555yTff311+91u92cffbZOTfffPO2l19++ailJnr27Jnt8Xiy3W43gwcPzu7bt2/W1KlTG5YlHrfbraoqS5cujdmzZ48jISEheP755x8oj2c1x2Yz9Iwx1cG9wKXAPeHDGPMbqOp0YHrhaxGpD8Sq6pbw678AuwktcT3SOuACEXlCVQsI7UaaGb72MfCciLQL19EbQclJuiPdCjRU1f1SfFX9F8BUQsnBkeFzC4DJwGZV3VyG/o0pE1X2EirxsKjoeRHq/IN7hwHPlHTfQsc5/LGXC2cZ9zXs168fAPPnzy92LS4ujrZFtscVERwOB6tXry5T37174/z2W3qXLRJjjDElKcuMuZ07dzobN27cpbB2XkkcDgfbtm1bmpycfPT1rsepSZMmBUVfx8bGBvfv3+8E2Lx5s7tt27Z5Ra+ffPLJ+du2bYviKFq2bJl/5OuMjIwyzbJr165dwdSpU9dNmTIlecyYMS1btmyZP3LkyO033nhjSe8lTTmzGXrGmKrPozuAh4CxeKVNpMMxpgZJAN4VkeUikkFodtKFqqoAIjJVRAaE2z4HrAcyRGQZoRp6wwFUdT9wfbivnwgtaxx3rMFVdZ6qvlnKta2EZv7tUNXCT3oXASdis/NMJVEl7wI++qGka0GERY4zObtn+b6dPvvss4mJiaF169bs27ePm2++uYz3QUEBTURoXq4BGWOMOUxycnKgT58+WU6ns8SNxJxOp/bp0yervJN5x9K0aVPf+vXrD9uM46effopu3LhxQWn3AGzcuDHqiNfRKSkpPoD4+PhAbm7uYd/otm7d6i76esiQIVkLFy78cc+ePUtHjx69bfjw4ScuW7bMNgWpBJbQM8ZUF5MJzRCaGOlAjKkpVPVnVe2uqh1VtbOq9lHVpUWuX6+q74V/n6+qN6jqKaraSVX7quq6Im3fU9V2qnqSqv5FVbNLGfNqVS223lBV56tqlyPOtVPVPxR5fUBV3ap6E15x45UUvNIOr7TGKw3K48/EmBL8h9AmLYfJJoFsfz2KTKorFwsXLuTAgQN89dVXDB06lBNOOKFM9xWJo0X5RmSMMeZIEydO/CU2NjZ4ZFLP6XRqbGxscOLEib9UdkzXX3/9rvnz5ye88sor9f1+P1999VXM5MmTG1911VW7jnbfggULEmbMmJHg9/uZNWtW/Ny5c+tfd911uwC6d++ek5OT43zppZcSA4EAH3zwQdycOXMSC+/NyMiIfvPNNxOys7MdbrebxMTEAICzrFPXze9iCT1jTPXgUR8wGvgzXjkv0uEYYyLAKy68ch5ex8uIcy+wmdCmBWuB3cyIXolX7sIrrSIZpqlhVPOACUeezic0+aBOsX1vfz+n08kZZ5xB/fr1GTZsWJnuiYk59NsKiMgYY0xRaWlp+YsWLVrZu3fv7MKNixwOB717985etGjRyrS0tPxjdFHuzj333IOvvPLK2scff7xJ/fr1u15++eVtbrjhhh333Xff9qPdd+mll+6eNm1aw4SEhK633XZbiwkTJvzcv3//AwDt27cvGD9+/Mb777+/eUJCQtcpU6Y0HDRo0KEEYX5+vuOxxx5r0qxZs0716tXretdddzWfPHny+g4dOlT689dGEl5VY4wx1YNX3gNOAjqHk3ymsnilA6GaaWl4dEWkwzG1jFcGIa7nUX8S9Tv7OPFKN0ndwB0PQR/kbYfN78PPb/jx7XMh7k9R39V4QvUBjfldRKKAN4G/FJ7KIoFEspg7F847zo+Z5s+fT69evQ7b5bYk//jHP3jjjTfIzMwstU2hX36B5qHFtmeqHl4L0BhjzOEWL17c2uVyfZ6amrovJibmqEtSj2Xnzp3OLVu2uFJSUvyVvczWFJebmxu1Zs2aeL/ff256evq6Y99RfdmmGMaY6mYMsIJQ0f0Si5QbY2oQrzhAngRupfU1yim3Q3yqu8S2Tf8E6ZNcbP0EFt96DjmbVuCVC/Dofys3aFPjqBYgcglwPzAWqBfPPhJcB1i1ql5Z2bCtAAAgAElEQVSZE3qBQACfz0dBQehnx/z8fPx+P1FRUcybN4+6deuSnp6O0+lk4cKFPPPMM1x99dVl6nvVqkO/3XRcz2aMMeZ3SU5ODlgiz0SCLbk1xlQvHv0JeAp4CK8cdQt2Y0xN4HgRcYzkjDfg9H8K8alHb+6MgmYXwQUZbpr0jUOcn+OVPxz9JmPKQDWA6t+BRsBlDtTbQTN3/+fL0nc5PNJrr71GTEzMod1u69WrR0xMDF9++SX79+/npptuIikpiaSkJG6++WZGjRrF+PHjy9T3f/4DUVFsUbWEnjHGGFMb2JJbY0z145U4YDXwAR4tW3Eh8/vZkltT2UKJuP9y5pvQclCxy6tXr+bGG28EQFVZtGgRO3bsIDExXKs5UABf9A2wc1EG6jsNj73pMeVLhLuTknho+3bcka7/ffbZBL76ijeCQYZGNhJjjKn6ynPJralaatOSW5uhZ4ypfjy6H7gLuB6vnBrpcIwxFURcf6dBup8Wl5Z4uW3btsyfP5/58+fz8MMP069fv1+TeRCardflMSfqOxXoWykxm9pm9u7duGbNimwQGRnwn//gVCXCkRhjjDGmslhCzxhTXf0f8A0wCa9IpIMxxpQzr3RF/f3o+LCLMnyJv/rqq1x55ZXFLzQ8HRqfF8Dh/nv5B2lqO1VWO53MevBBfMGyr7wtdw8/TNDt5gfgvchFYYwxxpjKZAk9Y0z15NEgMAo4E7gswtEYY8rf6bjq+kk5/5gNc3Jy+OyzzxgwYEDJDVoNcRL0n2bJf1MRAgEeXr0a99SpkRn/yy9h9mwcPh8PqmLLyo0xxphawhJ6xpjqy6PfAS8DE/BK3UiHY4wpV4m46wfLMjvvnXfe4YILLqBOnTolN4hOAtQF2P8Tptypkgk8evPNBL/7rnLH3rwZLv6r+l0u3gdmV+7oxhhjjIkkS+gZY6q7e4B4QjX1jDE1x378B8s0o+7VV1/lqquuKr2Bbz9AEMgpl8iMKe4+YP6AAfi3bKmcAQ8ehEv+4iNh3yb9p/+aO1SJ4KJfY4wxxlQ2S+gZY6o3j24DHgbuwCsnRjocY0y5WYUvy82exUdttHnzZjZu3EiPHj1Kb7T1Y8URtS68VN+YcqdKwO9n0O7d/Ny9O/5Vqyp2vJ07oXdPHz8uPcj7/v7ua3jl7xU7ojHGmFLt3OkkIyOanTsrdL/z7t27tx01alRKRY5hqhdL6BljaoJJwM/AE5EOxBhTbubhcC9j+cOBozVq2rQpq46WPTmwDta/BsGCf5R3gMYUpcpun48eO3aQkZ5O4K23QCugot2iRZDeycf2jG185T+d9qwEGIRI2/IfzRhjTKkyM6Pp27cNjRt3oUuXNBo37kK/fm3IzIyOdGjl4d///ndcjx49UuvXr99FRNIza8hz1SSW0DPGVH8eLQBuAy7GK+dGOhxjTDnwqBL0Pcjm95zs/o2FyVRh+d8Vh3Mz8Hq5xmdMCVTZ6fNxVm4uLw8aBL16EVi6tHz63rgRrrgswJlnwik7F/CdvyttWVN42QH8uXxGMsYYc0yZmdGcccYpfP55AoXbnAeDMG9eAmeccUpNSOrVq1cv6PF4dr/44ovrIx2LKZkl9IwxNYNHPwQ+BJ7BK65Ih2OMKRfvIe5FfNHPz97jzIqoQsa9sP41Iegbi0d9FROiMYdTJS8Y5Abg7EWLWHbqqTBoEPrBB1BQcHx9BYOwcCEMv0lpe3KAb97+hVkM5OPAeTRk95HNY8vpEYwxxhzL2LHNyMlxEAgcXu83EBBychyMHdusIobNyspy9e3bt03dunW7tmjRIm3y5MlJhdcmTZqU1KhRo05F248ZMyYlPT390Azu8ePHJzdv3jytbt26XZOSkjoPHDiwVWlj9e7d++DIkSN3d+3aNbcinsX8fvZDrzGmJrkNyARuAiZHOBZjzO/l0SBe6YfvwAd88oezSH/aSetrwRl19PsOboJvbwyw9WMBrsejMyslXmOKUOU/IpwGXPbuu9z61lt0j4/HP3Agri5doEOH0NGwIbhcoeRdVhasXAmZmbB8Obz3dgGbtkWR5l7No74XuIkXiabUrKDNoDDGmN9LJP139xEICHPn1i9TX6pHLxZ8hJkzZzacNm3a2jlz5qydPXt2wpAhQ9qkpqbm9e3b9+Cx7l2+fHn0uHHjmn355Zcru3Xrlpedne1YtGiRfRhUjVlCzxhTc3h0DV55BngYr7yBR4tNXzDGVDMePYBXzkMZx3fD/8aSO/20vMxFKw8kdQNnLGgA8nfB5vdg/Wt+dn7lQlw7QAfh0f9E+hFM7RXeedYLeEVotW8fg19/nQGvvUaa3098YTuRw+vt1XPm0N6xiiG+j7mcN+joyzzWUFuAGRXwCMYYY6qQnj17Zns8nmyAwYMHZ0+fPj1r6tSpDcuS0HO73aqqsnTp0pg2bdoUNGjQIHj++ecfqPioTUWxhJ4xpqYZBwwN/zoiwrEYY8pDaLnsXXjlBfz7L2P9q1ex9qVTQhcdQOHmtY4ChPdBX0d9H+HRvAhFbEwxqmwAHgMeE0GARkB7IFGVKMAH7AdWZQfqeR0BPeM4uh+Han55x2yMMbVOWWbM7dzppHHjLodq55XE4YBt25aSnHzUzb2OV8uWLfOPfJ2RkVGmWXbt2rUrmDp16ropU6YkjxkzpmXLli3zR44cuf3GG2/cU54xmspjCT1jTM3i0X145W5gKl6ZgkczIh2SMaacePRnwgkRvNIOaAPB+Bte4p6CAJtevSl4GZfrvghHacwxqaLAtvBRnOiFwL+Bs4/VFXAPqi+Wa4DGGGNKl5wcoE+fLObNSyhWQw/A6VR6984u72QewMaNG6OOeB2dkpLiA4iPjw/k5uYetk/C1q1b3UVfDxkyJGvIkCFZPp+PV199NXHYsGGte/TocbBTp072oVA1ZJtiGGNqoleBxYQ2yCj+TdYYU/15dBUenYNH35g6n2+nLyQajyXzTA2huhfoBdwCfFtCixxOYjX3U8DrzKnc4IwxxjBx4i/ExgZxOvWw806nEhsbZOLEXypi2AULFiTMmDEjwe/3M2vWrPi5c+fWv+6663YBdO/ePScnJ8f50ksvJQYCAT744IO4OXPmJBbem5GREf3mm28mZGdnO9xuN4mJiYFQyM4SxwoEAuTk5EheXp4A5OfnS05Ojvj9/op4NPMbWELPGFPzeDQIjAJ6ApdEOBpjTMVbD5wY6SCMKVeqAVSfQ/V0oA3wZ2Aw0A9oxEN0pB3fA2/hlbhIhmqMMbVOWlo+ixatpHfvbBzhtIrDAb17Z7No0UrS0ipkxtull166e9q0aQ0TEhK63nbbbS0mTJjwc//+/Q8AtG/fvmD8+PEb77///uYJCQldp0yZ0nDQoEG7Cu/Nz893PPbYY02aNWvWqV69el3vuuuu5pMnT17foUOHEmP96KOP4urWrXtqly5d0gBOO+20DnXr1j31+eefTyqpval8oqrHbmWMMdWRV6YTSuqdgkdzIh1OteeVDoR2EU7DoysiHY4xhUTkCkIzc+uoqn1sbGoPrzQDlgDzgMvx2Bt7Y4wpi8WLF7d2uVyfp6am7ouJiSl1+/Ay2bnTyZYtLlJS/BWxzNYcn9zc3Kg1a9bE+/3+c9PT09dFOp6KZDP0jDE12V1AEnBHpAMxxlSo9YATaBbpQIypVB79BfAAg7CNoIwxJjKSkwN07pxvyTxT2SyhZ4ypuTy6BXgE+BteaRHpcIwxFWZ9+FdbdmtqH49+CjwMPIVXukU6HGOMMcZUDkvoGWNquqeALcCESAdijKkw24A8LKFnaq9xwAJC9fQaRDoYY4wxxlQ8S+gZY2o2j+YDY4BBeKVnpMMxxpQ/DRUE3oAl9Ext5dEAcAXgBl7FK/Ye3xhjjKnh7Ju9MaY2eB+YCzyDV0rel90YU93ZTremdvPoDkK74J6P1Y41xhhjajxL6Bljar7Qrn+jgQ7ADRGOxhhTMSyhZ4xH/0NoQ6h/4JU/RjocY4wxxlQcS+gZY2oHj64EJhP6IcfqCxlT81hCz5iQicAHwAy80ijSwRhjjDGmYlhCzxhTmzwEBMK/GmNqlvVAExGJiXQgxkRUaFb61YQ2ivFaqQljjDGmZrKEnjGm9vBoFnAPMByvpEU6HGNMuVof/rVlRKMwpioIfb+7FDgLeDDC0RhjjDGmAlhCzxhT27wMZBDaIEMiHYwxptwUJvRs2a0xAB5dDNwK3IdX+kU6HGOMqal2stOZQUb0TnZW6Izo7t27tx01alRKRY5hqhdL6BljahePBoBRwLnAXyMcjTGmnKjqXiAbS+gZU9QU4A3gdbzSPNLBGGNMTZJJZnRf+rZpTOMuXeiS1pjGXfrRr00mmdGRjq08/P3vf2/UoUOHU+Li4ro0aNCgc69evU76/vvv60Q6LvMrS+gZY2ofj34FeIGJeK3eljE1iG2MYUxRoXp6NwI7gDfxijvCERljTI2QSWb0GZxxyud8nhAkCECQIPOYl3AGZ5xSE5J6eXl5MnHixE1bt27N2Lhx47KTTz457/zzz089cOCArXKqIiyhZ4yprf4GnACMjXQgxphyYwk9Y47k0QPAJUBn4LEIR2OMMTXCWMY2yyHHESBwWHIrQEByyHGMZWyzihg3KyvL1bdv3zZ169bt2qJFi7TJkycnFV6bNGlSUqNGjToVbT9mzJiU9PT0toWvx48fn9y8efO0unXrdk1KSuo8cODAVqWN9eijj27r37//gXr16mm9evV0/PjxW3ft2uXOyMiwWXpVhCvSARhjTER49Be8Mh64B6+8gkd/iXRIxpjfQESAnsDle+G8ulAXkRxgLTATeAPVnyIaozGR5tEf8MqNwGt45T94dHakQzLGmKpIkPTf20eAgMxlbv2y9KXo4uPpe+bMmQ2nTZu2ds6cOWtnz56dMGTIkDapqal5ffv2PXise5cvXx49bty4Zl9++eXKbt265WVnZzsWLVoUW9ax33333fiYmJhgx44d848nZlNxbIaeMaY2mwhsBx6PdCDGmN9AJAVYAHwBDKsP9dwgQAyQBjwMrEHkCUQqtFC1MVWeR/8P+CfwMl45KdLhGGOMOX49e/bM9ng82W63m8GDB2f37ds3a+rUqQ3Lcq/b7VZVlaVLl8bs2bPHkZCQEDz//PMPlOXeb775Jmbs2LEtH3nkkU3x8fHB3/cUprxYQs8YU3t5NI/QktvL8cpZkQ7HGHMcQsm8/wBnH6sloa/z1ys8JmOqvlsJzV59y2rIGmNMcYouPtaxgx1LHcdIpThwsIMdS4/V1/HG17Jly/wjX2/evDmqLPe2a9euYOrUqeumT5/esGXLlp3S0tJOmTJlSoNj3ffFF1/E9u/fP/Vvf/vb5jFjxuw63phNxbGEnjGmtnsXmAdMwmszeIypRu7n+OrlDUakf0UFY0y1EPog61JCXzvPRDgaY4yplpJJDvShT5YTp5Z03YlT+9AnK5nkQHmPvXHjxqgjXkenpKT4AOLj4wO5ubmH5Xi2bt162GZIQ4YMyVq4cOGPe/bsWTp69Ohtw4cPP3HZsmWlbuDx73//O27AgAGpDz300C/33HPPzvJ8FvP7WULPGFO7hXYAvBXoBFwb4WiMMWUh0pTf9vV6f3mHYky149G1wDXADXhlaKTDMcaY6mgiE3+JJTZ4ZFLPiVNjiQ1OZGKF1OdesGBBwowZMxL8fj+zZs2Knzt3bv3rrrtuF0D37t1zcnJynC+99FJiIBDggw8+iJszZ05i4b0ZGRnRb775ZkJ2drbD7XaTmJgYAHA6S57TMH369PpXXHHFSZMmTfp51KhRuyvieczvI6olJpWNMaZ28cok4HLgZDyaFelwqiSvdAAygTQ8uiLS4ZhaTOSvwKGi/vupxw+0J5M0VtCB7TSigCgcBIklhzaspQMraM8P6sUT/ZA+6Itg9MZUDV6ZCNwEdLf/040xtc3ixYtbu1yuz1NTU/fFxMQU/JY+MsmMHsvYZp/xWf0gQRw46EOfrIlM/CWNtHLfOKJ79+5t27Vrl7tt2zb3V199Fd+gQQPf7bffvvXWW289lGx7/PHHk5988skmBw4ccPbq1SurUaNGviVLltRdvHjx6m+//Tbmpptuavnjjz/WUVVp0qRJwW233bZ1xIgRe0oar2nTph23bdsWFR0dfVjNvIkTJ/48fPjwEu+pCnJzc6PWrFkT7/f7z01PT18X6XgqkiX0jDEGwCuJwI/Aa3j0tkiHUyVZQs9UFSID9xE36x3+itc5lHnBXgTUSUx0gFNO9tOslYuoOk6CAeXgvgBrVgVYvzm0msTt1r0+n7wOvAH8VxV7I2RqJ6+4CW0qkwh0w6NlKoxujDE1QXkk9ArtZKdzC1tcKaT4K2KZrTk+tSmh54p0AMYYUyV4dC9euQ94Fq/8E4+ujHRIxpjiRGiQwuaLdjsaEhQn5/dXpnucnH46tGrlPGLZiBB6q+PiwAFYuRI++UQSX32VG3/6iVvcbjaIcAfwtiX2TK3jUR9eGQwsAf6JV64Il6EwxhhzHJJJDlgiz0SC1dAzxphfvQSsAJ7GKxLpYIwxvxLBJcJol4sNuYkpVzz+VBTbdzr59wcuPB5o0wZKKQEDQL160K0b3HcfrFmDe8kSuPRSWgBvud38V4TTKu1hjKkqPLoJuAK4jNDyW2OMMcZUE5bQM8aYQh4NAKOAvsBFEY7GGBMmQiOXiy9cLiaOHUvc+vW4Ro2CxMRj31tKf3TpAq+/juPbb+G000gX4RsRRotgyXxTu3j0E+ARQh9mWWLbGGOMqSYsoWeMMUV59EtgJvAkXil1C3djTOUQIdXt5vsmTTj9u+9wPPooJCSUX//dusFXX+EaPx6HCE+J8LwIR5nrZ0yN9BCwEHgrXFPWGGOMMVWcJfSMMaa4O4AUwDbHMCaCRGjidrOwY0caL16Mu0uXChuHu+6CWbPA5WKYCM9UzEjGVFGhGepXANHAK1Z2whhTCwQBVVX7/66GCf+dKqG/4xrNEnrGGHMkj24EHgPuwyspkQ7HmNpIhCi3m9kpKTT47DNcyckVP+bFF8Nrr+FQ5WYRrqr4EY2pQjy6HRgM/Am4PcLRGGNMRdumqr6DBw/GRjoQU74OHjwYq6o+YFukY6looraZlTHGFOeVWGAlsACPXhnpcKoEr3QAMoE0PLoi0uGYms3h4MmoKEZ9/z3OtLTKHfv22+GppygIBummyrLKHd2YCPPKncB4oBceXRjpcIwxpqIsXrz4drfbPbpJkyb5devWzRERS45UY6oqBw8ejN26dWu0z+d7Oj09/YlIx1TRLKFnjDGl8cqlhOrpnYFH/xvpcCLOEnqmkojQWoQfX3wRx7BhlT++3w+nnYZ/xQo+9fm4oPIjMCaCvOIA3gXSga54dEeEIzLGmAqxePFiAcY6nc5hIuIG2xirmlNV9QUCgX8CE9PT02t8sssSesYYU5pQDaEvgLrA6Xi0xtdhOCpL6JlK4nDwz2bNuHrtWtxud2RieP99GDAAgFNVWRKZKIyJkNDGGP8D1gL9wjX2jDGmRlq8eHEdoDFWkqy6CwLb0tPT8yIdSGWxhJ4xxhyNVzoBS4Ab8Oi0SIcTUZbQM5VAhEYOB5tffBHnDTccu/2MGTN47rnnyMjIYP/+/fh8Plwu16Hry5Yt45ZbbmHx4sUkJCQwbNgwHnzwQeQYNf9VoXNn/D/8wGy/n8G/97mMqXa8chrwFfAoHn0w0uEYY4wx5nCWgTbGmKPx6DLgReD/4ZWESIdjTC1wDuC47LKyNU5MTGTEiBE8/fTTxa7t37+ffv36ceaZZ7Jr1y4++eQTpk6dWmLbI4nAVVfhEqGfiL1fMrWQR78ntNv7/Xilb6TDMcYYY8zh7A2qMcYc2wOAG7g/0oEYUwuc3bEj/ri4sjXu168fl19+Oa1bty52bfbs2QQCAcaNG0dMTAwdO3bkjjvuYPLkyWXq+6yzwO8nAWh7HPEbU5O8ALwJvI5XmkU6GGOMMcb8yhJ6xhhzLB7dTSiZdytesR/sjalA0dH8sWdPyqVy3tKlS+natethS3C7devGunXr2Ldv3zHvP/VUiIoiCPQoj3iMqXY8qsAwYDfwJl6JUFVLY4wxxhzJEnrGGFM2U4CVwFORDsSYGi4pJaV8Otq3bx/169c/7FxiYuKha8fidkPDRL/25rOzEEtkmFrKo/uBS4CuwKMRjsYYY4wxYZbQM8aYsvCoH7gVOB+v/CnS4RhTU6nidJTTu5P4+HiysrIOO7d3795D18oi2hlw9uGza4DtiDyHWC1NUwt5NBO4CRiDV/4a6XCMMcYYYwk9Y4wpO49+AbwNPIVXoo7nVhHZICKrRWRp+Bhc5Fq0iEwWkR9FZLmI/N9R+rku3G6tiLwk4VlDItKjSN8rRGSKiESX0seU8DifSzg5ISEfiUib43kuY8qbCAfLMHmuTLp06cKSJUvw+/2Hzn3//fe0bt26TAk9Vcg+4KAuBwESgRHA/xApXrDPmJrOo9OBqcAreO17hTHGGBNpltAzxlQaEekvIt+LyDIR+VpEOhe5doKIfBxOVmWKyB+P0s8d4TY/iMg7IlI/fL6uiHwjIhnh42MRaVVKH9eG718iIqcWOf+wiHiO8hi3A80JzdY7XoNVtUv4eLPI+UcBBVJVtWN4jJJiPhEYB5wNnAQ0IlTbCCAD6KaqXYCOwAmEkg9H9pEGnBweZz4wNHzpeuALVV37G57LmHJTUMD/vvuOQFnbBwIB8vLyKCgoACA/P5+8vDyCwSAXX3wxTqeTBx98kNzcXDIzM3niiSe4+eaby9T3hg2wZ5+bTiwrero1MAcRew9laqNRwDrgLbxSJ9LBGGOMMbWZvRk1xlQKEUkEXgeuUtVOwB3h1/+fvTuPs3re4zj+es9WSSq7LMXFjaKkBTcUhaxZ49jKGlkvJZTSte9lXyP8LLlku/ZC9q1SKQlpkRaURM2cmc/94/uLU52ZZmpmzkzzeT4e86jf7/f9fX+f0zJ1Puf7/XyWuQ74yMy2A3oAkdLUrJLUOb6+u5ntCHwOXB1f/hPoZGYtzKwF8BowuJiQ+gNtCIm5y+K5mwE7m1lU7AtJ2DTgRqA/kTYtxUsvkaS6wKnA5WZmAGb2UzHDjwJeMLOf4rH3AMfF9/xhZgXxuDygDiFJuKICoJZCMqIukC9ps3ieW9b09Ti3psx49733jMJSpvQeffRR6tSpw/777w/AuuuuS506dXj33XepV68er732Gu+++y4bbLABnTp14pRTTuHCCy8s1dyjR0OuCmjLJyteagp0S3OLc2u3hP0JHA38g+L/fXXOOedcJfCEnnOusvwD+NnMJgKY2Whgq5TVcccQElSY2afAj8DeaeZpAbxnZovi4/8RrzIzs6Jl5yUJWI/0SS2AQqA2fye1sggNL0qz8u56YCFwbSnGphoWb3V9UNJG8bl/AL8Al8WrF0dL2reY+7cCfkg5nhafA0BSE0njgPlxfHetOIGZfQ2MAr4grDR6jPC6e5tZcsXxzlUqafv3+FePxYuV/eGHpbule/fumNlKXx06dABg5513ZvTo0fzxxx/89NNPDBw4kPDtYdVeebmINtljqMOSdJfPLF2Ezq1lEjaV8MHaGUQ6IdPhOOecczWVJ/Scc5XlG2ADSXsASDoUqAc0kbQBkLvCyrRppCSrUnwOdJK0aZy0Ox6oJ2n9ZQMkvQn8RFhFUNzeuj7AG0BfYBBhe+rLZvZDMeP/lrDFhBWG3YnUdpXjg73ilYmtCAm3R+LzOUBj4Csza03YzvSUpE1KOe9fzGxavDJxU6AWcEQx4/rF236PBjoDM4BpkoZK+m9qfT/nKo20O/DJHnywy245n3LtfzKbX546FZ4eDj2Ttxc3ZOPKjMe5KiVhzwK3AfcSqVmmw3HOOedqIk/oOecqhZktJGwZvVbS58B+wFdAmd61m9ko4CbgJeAjYF58KZkyphOwGfAUcHkx8zxrZq3MbG9gMXAYcIekayQ9JenqdPeleAoYDQwhWnUtLTObHv9YQHgTtGd8aTpQRLz92MzGAN8T6uCtaDoh+bdMk/jcis/6HXiSkOwslqT1CPX6BgAXAO8QthFeIanOql6Tc+UmbK9/AqgvoH9yAP97PYfPPstcSNdcVcTW2TM4jifSXi8ka0HaC87VHJcAXxLq6a2b6WCcc865msYTes65SmNmo8xsbzPbFbgIaERYmfYzkJSWq0nXhDTJqnieu8ystZm1IzR2mGlmv60wpgi4n7+bPpRkMPBvoAPQyMy6AVtI6ljsHQkzwvbctkCJW47iZh0NUk4dB4yJ45wPvAXsH4/dGtgamJRmqv8Ch6asTuxJSNwhaduUjrd5wOGwfCX/NK4DBpnZH4StxxZ/5RLq8DlXWU4mJVndhVdol/MZp3cv4I8/Kj+Y11+Hh4eJ/skryCmmP0cv7mwl8ZREN4l6lRyic5mXsHxCuYyNCSv1SreX3TnnnHPlwhN6zrlKEzdfWKY/MNLMpsbHwwkJKiS1ATYnrBgrdh5J6xC2y94QH28aN99YphurSGrF20snmdl4/k5qQVg1V/KKg4SNISQNrydSSW/oNwFGxd19xxNqA56Ucr0n0Du+NgI408xmxfE9EG9Pxsy+I6ymex+YSlideG88xz7AmLiG3hhgDqEjbnGv+19AHTN7Iz51J2F78njg0XhFpXOV5ejUAwGPJ7sxbfISzjy9ECuuEmYF+P57OO7oArrxNCcxLO2YJNmzHuOEPsBGhNW18yVekjhVYqO0Nzm3NkrYDMKHWsfhdSWdc865SiWrzP8lO+dqNEn3E7aa5gAfAuea2YL42ibAo4TVafnAOfH2WiQNAn40s3vi4/GEDyTy4nv+Y2YmqS0hwZVNyAl8C1xoZt8XE09D4HlgPzNbEq9se4awOvB74KiUzrHpRdqIUB/wHhLWd3V+XaqNUCdpAtCcRGhu4ly5kN4H9ljx9CscwF2hgawAACAASURBVEG8zMArs+jfHyp6/c+cOdC5YwF88w0fJttQl7TLA+cC+2E2LoTOhsAhhFWx+xFWuI4GngVGmKVfaezcWiXSfwi1aXcnYV9kOhznnHOuJvCEnnPOralI5xNWCTaLu/+tnTyh5yqK9CRhRe1K7qYnvbiTM8+EwUOyyKugzeBffQUHdSks0o8/8lZyr6ytmbbikHzgReA8zH5MN0e89fYAQkOagwiNfz4DngOeM0u7ld656i9SNvA64UO5ViTMa0w655xzFcy33Drn3Jq7i7AF9pZMB+JcNTW4uAtncQ/DOZpHH1jK3v8qYOzY8n1wMgl33w277krhrNnZnyuZv+XWTOtCaL5zP3A7cBqwKWZHFZfMAzBjkRnDzTiOsB33IGAsoenMVxKTJK6RaC3h9cbc2iNhhUACqA0M9Xp6zjnnXMXzhJ5zzq2phBUQ3rAfQqT9Mx2Oc9WNsJkfstvc4q4fybN8VNiG5NgJtGplnNqjiNmz1+yZZvDqq9CsGQW9elG4ZAm3FhTQ/lv7x4+YvYpZb8zOwOw8zB7E7Ncyzr/UjP+ZcTqh6/bewGuE7tOfAj9IDJbYWyJ7zV6Nc1VAwuYAx577CIc1OI2fJZmklqlDJG0n6QNJUyR9KqlZuqkkNZH0tqSFklZK40s6VdI3kr6VdP+yplBpxr0tKV/SxinntpFUJGlEfDxK0gkp1/tJWiKpdsq5yZL2LfOviXPOOVeBPKHnnHPlIWFvEOrx3UaU/o2Fc255EtkS5wBfdeGVRXPZaExxY5szkY+Tu/KoncALj/3251ZbWdGhh1L01FOUqRPut9/CVVdB06YUdOkC333HK2bsaEZvM/LL4WWtxIxCM9414wJCjc7WwDCgM6FT908SD0gcJFGrImJwrlIk7N1mm3Pf+OtZr3YuP6UZcS9wn5ltD1wPPFzMTL8B/Qir/pYTd4P/D6Em77aExlNnlBDVlyzf8f4U4POU41GELvfLdCSsrN0tft5mhL+375fwDOecc67SeQ0955wrL5H+AXwF9CVht2Y6nHLnNfRcOZJoTtjS2hq4EfiPoXxCt+XuwC4r3FIEvAvcLewloFtuLickk3TMy6Noxx0pbNmSvGbNYMstoVYtKCyE33+HKVNgwgSKxo4l+cMP5OXksCCZJAKGmfFxpb3oNCSaEhpqHA60ARYB/yM01XjFjEUZDM+5souUBTy/5bl02X4zOr814a8GVxsTylOsb2ZJSQJmA+1TOt4vR1IH4DYza5lyrjfwDzPrGR8fCFxmZu3T3P828F/gdDPbWVIW8DWhVMbeZtZV0p7AUDPbNm6ONRm4GtjSzAZKOo7Qfb5DOfzqOOecc+UmJ9MBOOfcWiNh3xLpZmAgkR4nYcVuIXSuppKoDVwOXEJYBbOrGV+GqwYwBBiC1BTYFahDSHKNXla/Lv4ocigwVGKTpUs5ZMwYdp4wgZZA84ICGqY8rzAvjx/y8xlrxgTgg2SSkWaU3MG6kpgxGbgWuFZiS6ArIbn3BFAg8SYhufeCGfMzF6lzpZSwIiKdDMwdcATXEKl9XGNvS2C2mSUB4u7004GtCIm+0toK+CHleFp8rjgzgJ8ktQMaEhrVpG6h/xhoJGlLQlOPTwgrZx8CBhJW7I0sQ3zOOedcpfCEnnPOla9rgJPjH0/LcCzOVSkSewP3AZsDvYE7zChMO9hsMmGlTInMmAM8sMJzBOQBhUVFJNcw7EpjxgxCE47bJTYEDiF0zL0buF/iXf7umDsjc5E6twoJ+2XeyZpbrza7ErbOXpnhiB4CTiUk9JZ9DwLAzPIlvU/Ydrs18LaZfStpi7iOXof4Xuecc65K8Rp6zjlXnhL2O2Hl0SlE2jXT4ThXFUg0kLiPsOplKtDMjMHFJvPWkBkWN6WoNsm8FZkx34yhZhwCbAgcB8whbAWcLvGpxKXxll3nylekOkTamUjtibQnkVoQqe6KwySdJGls/NUj9drSJPlfzeImYACROhNWym0mKSe+V4SVddPLGN10oHHKcZNSzDEC2B9oAbyV5voowkq8joTvUwAfAUcTkn8Z3ZrvnHPOpeMJPeecK3+PE94IDCGSMh2Mc5kiIYmjgEmEraQJ4GCz5bbLuVUwY5EZT5txLLARcDAwDvg3MEniK4mrJXaNVyc6V3aRcol0AFHWMJQ9n/BnbDShduVYlD2fKDsi0sFEygMws2Fm1jL+GrrilN3v4WngKeBxe5xc4AtgWUfZI4GZxdXPK8F/gUMlbRonBXsCT5Z0g5ktAS4EzjOzojRDRgGdgMYWVgcDvAP0B943swppmOOcc86tCU/oOedceUuYAecBu5OmQ59zNYHEFoRVMcOBV4CmZjxhhnfjWgNmLDHjZTNOAzYjbAd8ndDF8zNgmsRtEntLZGcwVFedRNoX5c4AXqHhLseyy03rcOAE6DorfB00EXYdXJsNWh8NvIhyZhPpoHRTSbpX0kxgi2QRr2WfQFvgF+CpTRvQCzhT0hSgL9Aj5b4HJB0a/3ydeI7hwI6SZkq6FsDMvgMGELrOTgXmEbrnlsjMnjWzV4u5/ClhO+4nKefeAbbD6+c555yrorzLrXPOVZRIDwBdgH/GW3GrN+9y60pBIgs4i9DoYS5whpm/Ia5o8cq8VoSae4cDOxASHS8Qmmq8ZcbSzEXoqqxIFwI3s8Xhxi43ZFFv25LH/z4NvuxXxLTHs4ArgKviD7JKekZzQrLsThLWu1zids4552o4X6HnnHMV5zJgXeDSTAfiXGWQaAa8BwwG7gR28mRe5YjrBn5uxuVm7EhI6N1KqBn2MjBP4gmJYyTqZTRYV3VEugC4hZY3iD3/u+pkHsC6TWCPx7JofSegQaB+q7wnYRMIif6LiXTYmgXtnHPOOfCEnnPOVZyEzSV09ruISNtkOhznKopEbYlBwBggB9jVjEvN+DPDodVYZkw241oz2hAaCPQDNgWeICT3XpToEXfTdTVRpA1Q9rU07w879oY0JV/HjRvHAQccwD777EOPHj2Wv7j92dDyekD9idRolc9L2CPAg8Aj/m+ic845t+Y8oeeccxXrDmAacHOG43CuQkjsBYwlNGjoA+xuxrjMRuVSmTHdjCFmdCQk9c4GBNwDzJEYKXGuxJYZDdRVtgvIqZvDDul3wObn53PhhRfy5JNPMnLkSIYOXannBfzzXKi1gYDSbqM9F/gBGE6k2qsZt3POOefwhJ5zzlWshOUDFwBdidQp0+E4V14kGkjcRygc/x3QzIzbzCjMcGiuBGbMM+MhMw4mdMxNEGodXgNMl/hEoq/EPzMaqKtYkeqh7AvZoU8Ouel3YH/00Uest956dO/enb333pvnn39+5UHZtaHZ5TmQdTaRVr3aM2F/AkcRmk3cukavwTnnnKvhPKHnnHMVLWGvAi8Bg4mUm+lwnFsTEpI4CpgEdCUkhA4y44fMRubKyozfzHjKjGMJyb2DgfHARcBkiYkSV0m0iptuuLVHE6ywLlt0LXbArFmz+Pzzzxk6dCgjRoygb9++/PrrrysP3PJwoCiPkKRbtYR9A5wC9CSSd4J3zjnnVpMn9JxzrnL8m/Bm56xMB+Lc6pLYAhgBDAdeBXYw4wkzSu5w6ao8M5aY8bIZpwKbAR2BN4GTgM+B7yVuldhLIjuTsbpy0QCAvAbFDlh//fVp164dDRs2pGHDhrRo0YJvvvlm5YF/z1H8ZCtK2DPAEOA+Iu1Q6vucc8459xdP6DnnXGUIKxJuBa4k0kaZDse5spDIkugFfAU0AzqZ0cOMnzMcmqsAZiTNeNuM8wkNNdoAjwMHELZYz5a4X6KLRK1MxupW20IAChYWO2C33Xbj22+/JT8/n/z8fCZOnEiTJk1WHpi/YPk5S683MAF4hkh1y3ivc845V+N5Qs855yrPVcAS4D+ZDsS50pJoBrwHDAbuAnYy463MRuUqixlmxmdmXG7GDsCOwG3ALsD/CB1zI4mjJdbNaLCuLL5H2X8wc0SxA+rXr88ll1xCx44dad++Peeddx4bb7zxygNnjgBl5QNTyhRBqDF7DKFRyz1EadrsOuecc65YMvNdMs45V2kinQwMBVqRsLGZDqdMIjUjrKZoTsImZjocV7EkagOXAX2BccDpZlSvP7OuQkk0JtRRPBzYEygAXgeeA17wFZxVXKSrya3fh64zc8hdzVxs4RIY0TjJ0rl3kbDzVzOOLoTk8Jkk7L7VC8Q555yreXyFnnPOVa5HgU+BIb4awVVVEnsCYwnNES4BdvNknluRGT+YMdiMDoRVVr0I/7e8B5gjMVLinLj2oqsAkg6Q9JmkLyV9JKlFyrWhkqZIGifpfUltVrj9VpK/J5l0018nBgwYgCTGjv37r/vSpUs555xz2G677dhpp5044YQT/p5hyp2wdD7ADZIOlDQh/to/JY5TJF1W7ItI2CuELstDiNRqdX8tnHPOuZrGE3rOOVeZElYEnEdYzXJMhqNxbjkSDSTuBd4FvgeamXGrGYUZDs1VcWbMM+NBMw4mdMw9HpgHXAvMkPhYoq/E9hkNdC0iqSGhtuHJZrYzoSbd4ylDngN2NLMWhN+H4ctNkLD50+YW3siEK2HyrXzy8cd8+umnNG7ceLlhffv2RRJTpkxh/Pjx3HRTnAD85l4Y0xuwq0jYLGAQcGD8dXUc4ybAicANq3g5A4APgeFEKn1zDeecc64G84Sec85VtoR9DDwC3EikdTIdjnMSkjiS0PTicEIy5kAzpmU0sBpAUg9JJqlryrnLJH0tqSj1fJp7d5L0rqTJ8aqohyTVSbneUNLj8SqtiZKuK2aeUyR9JWmM9PcKKUmDJCXK+prM+M2Mp8zoRkjuHULYrn8x8LXERIn/SLSS8JXKq+8fwM9moQSCmY0Gtlr2e2hmL5hZMh77EbC5pJxlN0s6dOsLOGfAM8xb/OG/Oad7F+695fLlHrB48WIefPBBrr76ahQvKt+0XgF8eLLxaU/ArgQbFA8vANYB6gL58blbgUtS4kgvYUnguPj+ob6C3TnnnFs1T+g551xmXAo0JGxndC5jJDYnrOR5hlD/bAczIjO8yG4Fk9QEOJ2QbEn1JtCFsFKyJEuAc8ysKdCCkEhJ/Z7yEDDGzLY3s2aEZhbp9Cd0sj2fUDcRSc2Anc0sKu3rSceMJWa8ZMaphG25HYG3gO7A58D3ErdK7CmRvSbPqoG+ATaQtAeEBB1QD2iSZuz5wP/MLCmplqTBwPPA84OeY+s2/Xnh1H8tXLTlF+3hz9kw7QlYOIlvJ37M+g3rc82Ai2m9U2PaN1/P3rp6K5gWLQQOI2EDSfxVkLsP4cOqh4GLJR0MzDGzT0r1ahL2EyGpdyhw4Wr+mjjnnHM1hif0nHMuExI2m9Dttg+RGq9quHPlTSJL4mxgErAT0NmM7t7IoHJIygIeAM4FlqZeM7NPzOy7Vc1hZt+Y2ZfxzwsJ9TmbxPNvC7QGbkkZ/1MxUxUCtYlXVsWx3UpIApUbM5JmvG3GecBWQFsg4u/k5Y8S90l0kahVns9eG5nZQuAo4FpJnwP7EVbZLrcaTtIJhBIPZ0jaDvgAOBU40cx6AHtMmkXWmfsUbQAc9Mef+YuLvrppKS/vSPKNfflh+kx2XHI/n102a+nAQ39//cAbWbRb/+TOJOyFFeIZbWbtzKwdMJ6QXO4n6QJJwyXdISmvxBeVsLeBfsD1RCFR6Zxzzrn0PKHnnHOZMxiYCdy0qoHOlSeJHYHRwBDgLmAnM97MbFQ1zr+B983s8/KYTFJd4DTCqiuAHQnfX+6W9Lmk1yXtUsztfYA3CB2NBwFnAy+b2Q/lEVs6ZpgZn5pxmRlN43iHAK0IHU/nSkQSR0msZgvWtZ+ZjTKzvc1sV0ITm0aEpB4AkroR6tN1BvYFviD8/7+VmT0WD9sHaKXj+UbHc9e8RdRufXnRr4fezPkL/uAwoOiwXWmNFW7Y6ZqiA/KTfP3xt/xzFaFdQ6ijtylwmJkdDfxM2M6/KtcTVgs/TaSNSvlL4ZxzztU4ntBzzrlMSdhSwraio4jUMdPhuLWfRC2JKwkdbPOA1mb0NeOPDIdWo0hqDhwJXFVO8+UBTwGvm9lz8ekcwgq4J+Nkz63AS5JyV7zfzJ41s1ZmtjewGDgMuEPSNZKeknR1ecRZEjMmmXG1Ga0JqwyvADaPX9c8iecluktsUNGxVCeSNks57A+MNLOp8bVjCH/GDiEk9R4nbIfd3cymLLvJzC41s83NrImZNQFmGnR54XMbss/V9gLwVoPT2YSE/S5pa2Brwsre4mLaDahvZq8SVn0u25JbBKVIzobmUScRVo4+RiTfiu2cc86l4Qk955zLrJeBV4HBRH8XK3euvEnsCYwjNCa4BNjdjLGZjarG2pOQtPpG0jRgN+A+SWeVdaI4QfcUMJvlt8hOB2aZ2SgAM3uFkMRd1Rb/wYTVgx2ARmbWDdhCqrwPHcz4wYzBZuwNbEbYlpwD3AvMkXhLoldc/7FMJDrHTTkOLOewM2VQ3BRlKuH39tSUa48TauqNBU4GpgEDzWyJpJ6SBq00W3o9gd6SxgMjgDPNbFa6gfGfx+sJqwWJt4R/J2kCoX7iY+nuW0nCfgaOju+5fBWjnXPOuRpJZl7z2jnnMipSU0K9ofNJ2F2ZDqdYkZoROlU2JxG6KrqqT6IB4Q32GYTk8VnevbZqkfQ2cJuZjSjN+ZTrOYRk3gLgNEv5T51CS9LxQMLMvpTUlrCVdXMzW1rMfN0IjTAujxssHG5mPSQNBZ41sxfX9LWuCYn1gAOBI+If6wIfE5q6PGfGlBJuXzbHR0A7wmqx7mY8WnERVyCpIaEOYTYwD7MZy1+WCH/nbyNss01U5BbqChPpXEKSeT8S5mUBnHPOuRS+Qs855zItYZMJtaOuIpJvJ3PlRuIIQj2tIwi1qw70ZF7VJ6mfpJnA7sADkmZKoZaYpEGSesZDuxF+b1sDYySNlXQnQJzcOxm4X9KXwJ3AkSUk8xoCvQjNeiAkfzeI710/Ps4oM34z40kzjgE2JGwl/YpQA/BriQkSgyR2kdCK90u0IyTzIPwfeJgUVpJVFonGEmdIDMnL451atZifnU2+RFFWFgW5uSysVYtP4uYg50k0T7k5G+kEpP8Bcwkr7z4HpiONRboEaUNJ9QmJ3rsJW607VMtkXnAHoQN3RKQyr8h0zjnn1ma+Qs8556qCSPWBKcAzJKxXpsNJy1foVRvxVsQ7gK7AI8BF3r3Wra0kcgjbmI8ADifU3ptGWLn3LPChGYUSzxDqA65Y3uAGoK8ZFfKfYomGQCI3lxMLCmhXuzaFO+xAYcuW5DVrBhtuCHl5kEzCggUwaRKMHUvBhAlo0SJy8vKYvF7+vGc+YI/9tmNq25KelQ/z94P8d8JrPMHM3qiI11SpIq0HfAbMAfYhYQUZjsg555yrEjyh55xzVUWkU4D7gV1I2JeZDmclntCr8iSygDOB64D5QE8zqv8beudKKf470JqQ2DsC2J6wmu1N4DhYeeUeoWnDI8DpZiTLMZY84OycHAZlZ7NO167o+OPJ2n//kMBblaIieO89iB43hj9ZyOLfi7ig6BYu5Vrq81ux9/0JhW9C50Pi+olrhUg7E7ZX307C+mQ6HOecc64q8C23zjlXdTxMqHU0mEjp3nS6Ykh6XdKX8ZbD0ZJ2Sbl2gKTP4usfSWpRwjy9JU2Q9JWk5yQ1SDPmSkkmqWUxcxwYzzFB0v4p50+RdNmavtbiY2dH4F3gduAeYCdP5rmaxowiMz4x41KgKdCM8HeiE+mTecTnTwKel1inPOKQ2Dc3l69zcrj5oouoN2cO2U8+SdYhh5QumQeQlQV77QX33Ct+nJvDdTfnce+6F7FdzvcM48Ri76sD2Ycs3yCl+gsfcp0N9CbSoZkOxznnnKsKPKHnnHNVRcKKgPMI3SWPzGww1c4xZrazmbUEbiEkR5fVBXscONnMdgZ6x8crkdQZ6AHsbmY7EmpTXb3CmLZAG6CkelSDCAX7D1x2v6RNgBMJW/vKlUQtiYGEelq1gTZmXGLGH+X9LOeqEzPMjK8ICb11VzE8C9gPeCveIrtq0u5IA5BuQ7oB6eQFalBforfEG126sNU335B13XVQv/6avZZateCCC2DqtFyOOaMhJzOMs7ibgpV2D//lUKSma/bUKiZhQ4GhwCNE2jrT4TjnnHOZ5gk955yrShL2IfAYcBOR6mQ6nOrCzBakHNaHv2ph/QP42SxsETaz0cBWklqlmaYF8J6ZLYqP/wd/L4ORtA6hLt2ZqwinAFiH0IEzPz53K3CJmZXbdr4QE+0JibzeQF9gNzPGlOcznFsLnA7UKsW4HKAt8IlEo7QjJCGdi/Qd8AEwkLAarrfBw/24+mfJbrjhBjRiBFlNmpRL/H/ZYAO4407xzDPwSN7pdM16kT+pnTZSYMfyfXqVcA4wAxhOpLQv3DnnnKspPKHnnHNVzyWEDo69Mx1IdSJpmKQZhC6dyxJx3xA6de4RjzkUqAc0STPF50AnSZtKEqErbD1J68fXbwDuNrMZqwilD6Ee18PAxZIOBuaY2Ser/eJWIFFf4h5gNGG1YHMzbinP+l/OrQ0kcoGLgOxS3pIFbAtMkegjsV3KZLnAE4Su5CutELuZi7hbZ2U/+aS4+GKoyMIJRx4JI9/J5r06nThbdxfXzWPtax6RsD+Aowi1EW/JcDTOOedcRnlCzznnqpqE/UjYqtmXSFtlOpzqwsxOMrMtgX7A9fG5hYQ3f9dK+pywpe4rWDnxZaGA/E3AS8BHwLz4UjLejtvYzIaWIo7RZtbOzNoB4wkJ2n6SLpA0XNIdkkpZRWtlEkcAkwjbsk8Aupjx/erO59xa7lBgU8Kq3fz4K13+axGhM+4nhJV3Ywl/d6dIjJcYNJl/3g50S/eQt9iHS3QD112fxTHHlP+LSGe33eCxJ3J42LpzDz1XvLyY8H1s7ZOwKcCpwFlEOi7T4TjnnHOZ4l1unXOuKgpbiSYCn5KwYzMdDlCtutxK+hPYwsx+XuF8LeAnoI2ZTV3FHLsBw81sS0nXEormL1vxsgWhc+aZZvZiCXPcDrxMWCn4gJl1lHQlMK00ycHl52JzwpbfrsAw4CIz5pdlDudqGomNgQeBnwl/Z5d9zUn5+Tyzv7bHp96bA+wFHN6AX4/4nq0bNWDhSs9YyHpsmzONjoetx1PDsyt0ZV46AwbAtVclGVe0EzswednpmzG7uHIjqWSRhgCnAK1J2ORVDXfOOefWNr5CzznnqqKELQH+DXQj0l6ZDqcqk9RAUqOU466EN++/xMebpQzvD4wsLpm3bGxcL28QcRMLM7vUzDY3syZm1gSYCRy4imTebkB9M3uVUE9v2SdoRay6QH/KPGRJnEVYWdgC2M+Mkz2Z59yqmTHXjEPM6G5GHzNuMmOYGa+ZMcaMWemSefG9STNGmnHuPDYakC6ZB3AH55CfV49776/8ZB7AFVfAttsUcZWuWHZqAnBt5UdS6S4mfPD1DJHqZjoY55xzrrJ5Qs8556quF4A3gCFEKm39p5qoPjBC0nhJ4whF0w+2v5egD5I0WdJUoDFhqxYAkgZJSt2r9rqkicA44D3CirgyU6i1dT2hdhdm9iXwnaQJQEdC45NSzMOOwLuELp33EmrlvbE6MTnnVl8OheunO7+Idbklpw/nXphDw9L1xi132dlw+cA8nqQbn7HrW8BerLA6ea2UsHzgGKARcBdRJtKpzjnnXOb4llvnnKvKIu0IfAn0ImH3ZjiWarPltrqTqAVcClxGqMN3mnevdS6DpFMIW3eX8xA96JV3PzN/zGaDDco+7a+//krfvn156aWXWLhwIbvvvju33347TZs2LdM8ySRss3VRcsbMrMFmrN1bbVcU6SBC7dPTSdgDmQ7HOeecqyy+Qs8556qyhH1FWCV2NZEytP7DVSaJ9sAYQpfjS4F2nsxzLuOeARasePJd9mK3tkWrlcwD6N69Oz/88APjxo1j/vz5NGvWjM6dO7N48eIyzZOTAwcfkpWTl8e+qxdJNZawlwlbjO8gUstMh+Occ85VFk/oOedc1TeQUH9tYGbDcBVJor7E3cBoYAZhe+3NZit35HXOVTKz34DbVjz9Xt4+7Nkxd7WmXLx4MS+99BIDBw5kww03pHbt2lx33XXMnj2bESNGlHm+PfeEggJ2lqiJ9eSuIHT1fYZI9TMdjHPOOVcZPKHnnHNVXcIWAJcDveJtr24tI3E4oenFUcCJwAFmfJ/ZqJxzK7gaiJYd/E5dvs3fitatV39CMyO1/M2y4y+++KLMc7VpA2ZkATXv34mEJYHjCA2HHvJ6es4552oCT+g551z18CChlt5t/kZl7SHRSOJZ4FngLWAHMx4zwwvcOlfVmCUxOx7oBUxdHC+Eq7+a68Hq1q1Lp06duOKKK5gzZw6LFy+mT58+mBm//fZbmedLiaMmrtCDhM0GjgW6AudnOBrnnHOuwnlCzznnqoOEFRLeoHQCDstwNG4NSWRJ9AQmAS2B/c04yYz5GQ7NObcqZndhtt1ntO4MUFS0+lM99thjNGrUiF133ZVtt92Whg0b0rRpUzbccMMyz1VY+PdPVz+iai5hbwP9gRuJtHuGo3HOOecqlCf0nHOuukjYaOBJ4BYi1c50OG71SOwAvAPcCdwH7GTG65mNyjlXVgfz8miJop9+Wv05Nt54Yx555BFmzpzJ7NmzOeecc/j+++/Zd9+y97ZYFsdb7HMBUvvVj6rauw54HXiaSGXPjDrnnHPVhCf0nHOueukDbAr8O9OBuLKRqCUxABhL2BLXxozeZpStnaVzrkowY2luLhM++GD15/j666+ZO3cuAFOnTuX4449nn332oVOnTmWe64MPYN3sP9iLdw8HRiPdjGpgiYaEFQEnAUXAY0Ty9zvOOefWSv4PnHPOVScJmwFcC1xGpM0zHY4rHYl/AWOAS4DLgLZmlL3qvXOuSsnPZ+TIgQQTcgAAIABJREFUkeSv7v3vv/8+rVq1Yp111qFjx47ssssu/Pe//12tuUa/W8QefEDO3ztu/02azrw1QsJ+Bo4B9iF8z3XOOefWOkrtrOWcc64aiFSH0BH1fRJ2QiU+txkwAWhOwiZW2nOrMYn6hO1fPQlbwHp691rn1h4SBwEvjR8PzZtnLo4FC6Dx5kn6/XEpvbkp9VIS+Cdm32UotMyKdB5wK9CZhI3MdDjOOedcefIVes45V90k7E/gYuB4Iv0r0+G49CQOJyRejwZOBA7wZJ5za51XcnP5+qqrWIPWGGtuyBDQ0iWczv0rXsoBLspASFXF7cBzwBNEapTpYJxzzrny5Ak951y1IulASV9IGitpgqSTixm3rqTXJM2XtCDN9YMlTZb0jaRnJa1XzDwPSzJJu6Scqyfpd0lj4+OhkvqlXD8hvqdJyrlXJZ26+q98Jc8Co4AhRMoux3ndGpJoJPEs4ffoLaCpGY+Z4UvinVvLmFFUUMDAp58ma/z4zMTwyy9w640FnFd4Cw1YmG5IBtcOZljCDDgV+I2Q1MvJcETOOedcufGEnnOu2lAo7v0Y0N3MWgIHA/dKqpdmeAFwPbBSZXFJ6wIPAl3NbDvgR6B/CY/+HDgl5bgbMCnleBTQIeW4I/DxsnOScoD2QPlt9wlvUs4HWgI9ym1et9oksiR6Ev5stAT2N+MkM+ZnODTnXMUanpPDF0ccQXLRosp9cGEhJLolqbvkZy4ovlzeatf4WyskbCFwFNAWuCrD0TjnnHPlxhN6zrnqxoAG8c/XA34Glq40yGypmY0EVlqdB3QBxpjZ5Pj4LuC4Ep75LHCwpFrxcQ/goZTro4DdJeXFx+0JjSs6xMdtgPlmVr7bLRM2HrgbuIZIDVY13FUciabAO8CdwH3ATma8ntmonHOVwYzCggK6TpvGopNOoqgyy1MPGACjRhr/TR7G+vyadkwReqvyIqqiEjYOOAe4hEgHZzoc55xzrjx4Qs85V21Y6OLTDXhW0g/Ae8DJZlbW1QdbAT+kHE8DNotX0qXzB/AG0FVSU0CkrNAzsxnAbKCdpK2AXwgNEPaIh3SkPFfnLe8KIDv+0VUyiTyJK4BxQF1C99reZizOcGjOuUpkxoxkkiOefx5OPx1LJiv8edx4I1x9Ndxe1It2fJJ23M+sT0N+PVniTIl1KjaqKu8h4BFgGJGaZDYU55xzbs15Qs85V23ECbd+wBFm1hjYF3hU0oaV8PiHCHV4TgWGprm+bNttB+AdM/sTmCtp6/jcqAqJKmG/EH5NziXSDhXyDJeWxB7AGKAv4fegrRmfZzYq51ymmPG2GYcPHUpBly4U/vxzxTxn6VI48/Qi+vSBW7mAM1ZuhLFM4Tha9P2N+h8CQ4AZEtdKbFExkVVxoVTF2cAsYDjRX6vunXPOuWrJE3rOueqkJdDIzN4FMLNPgZnALiXetbLpQOOU4ybAbDMrdk2FmX0ENAKOBZ5MM2QUYSVeR+Dt+Nw7wH7Av6i4FXoQtnh+BdxKJFXgcxwgUV/iTsIK0VlAczNuNKOC1+Q456o6M14oKmLPd95h3g47kHz4YSgqx/63r74KLXYuSj7x8FJ7lsO5gMHFDZ0HHLyPjbzejFMIK9MHE0pGfC8RSbQtv8iqiYT9Qain1xS4OcPROOecc2tEVpmFPpxzbg1I2gSYCrQ1s0mStgU+AVqa2fRi7mkCjDWzBinn6gHfAnuZ2WRJdwBLzOziNPc/HN9/m6R9gQ3N7ClJHYDb4uYcSNosnvMnYGcz+13SfsAdAGa2/UpzX6lahHp+ewL1gRxCJ74pwHAbYHNK/YsTaW9CIvFQEvZiqe8ri0jNgAlAcxI2sUKeUcVJdCXUyasFXAjevdY5tzKJjST+A5zRvDmF11xDzv77Q25u2ecygw8/hIEDKXzjDbJzcnixXvKXi39hg60J9V/3AuoBfwKfARHwEmZL0sRVi1C64kLCh2QfArcBz9aoDyUidSN8OHcsCXsq0+E455xzq8MTes65akXSccBlQBFhlfG1ZhbF1wYBP5rZPfHxl8BGwCaETrajzOzE+NqhwA2EJNoEQi2+hWme9zBxQm+F8x1ISejF5yYDC8xst/h4XeBX4EEz6/nXuCu1h9Cp2VnZ3ZJFybrNNmqWv3HdjbNys3K1YMmCoi/nfpm1NLk0Kzsre2SyKDkMiGxA8asH/xLpaaAV0IyErdQoZI3V4ISeRCPgduAIQqflf5sxL7NROeeqOonmOTnckkzSuUEDkokEOUcdBS1bQsOGxd+3eDFMmADPPw/DhlEwaxa5ubmMKyjgArO/VoGvSVwiJAIvAA4jrHa/A3jAjF/WdP5qIdIdwMlAaxL2dabDcc4558rKE3rOOVdJdKVygauAPjttvFPBSS1Oyu3WrBtb1t9yuXGL8xfz4pQXeXz844WvfPNKVpayPigoKjjWBtjMEh8QqTEwGRhIwq4v9xdQAxN6ElnA6cD1hORsTzNey2xUzrnqRuIfwLF5eZycn892ABtuSMFOO5G1ySZk5+VBMgm//ELR+PEkZ80iDyA3l58KChgGPAGMq4gVwRLbAOcSasRmExpHDDFjcok3Vnehht5ooA7QLt6O65xzzlUbntBzzrlKoCtVK1vZT2dnZR9090F3Z/do2QOVotzd2J/GcuTTRxbMWDhjfkFRwd42wL4p8YZIA4GLgO1J2OxyCf7vuWtUQk+iKaE+4b+AW4EB3r3WObcm4pVxWwLNgObAjhINs7OpVVREQVERvxE+mJkYf001oxyr8JUY23qEGnvnAdsArxC2476x1pYWCN1uvwBeAHrEjTOcc865asETes45Vwl0pYask7vO2a+d8Fp2+63al+neX/78hX0f2Tc5cd7E2QVFBdvaAMsvdnCkdQhvBkeRsJPXLOqV5q4RCT2JPELn2ssJb6hP9+61zrmaQiIbOJiwHbcDoenSYELN0LVvFVukg4EXgdNI2IOZDsc555wrLe9y65xzFUxXavMsZZ11Y+cb0ybzJk6cSPv27dlzzz1p3749n3766XLX16+zPiOOHZFTZEVbACeW+LCwZehi4CQitSu/V1EzSOwBjCEk9PoBbT2Z55yrScwoNON5MzoSush/QqghOkPiGonNMxthOUvYS4SyCncQqUWmw6lJJNWWNELSFEnjJL0RNzxbdr2NpPfja2Ml7VPCXCdLGh+PGyPpwJRr20n6IH7Op5KaFTNHd0km6cIVzr8Tn28QP+fNlGtbxNe6p5zrK+nR1fxlcc65UvOEnnPOVbw+G62zkZ26y6lpL2600Ua89NJLjB49mvvuu4/zzz9/pTGNGzSme8vu5GblDoxr8ZVkOPAOcDuRyvP7/NblOFeVIrGexJ3Ae4QGKjuZcWON6vronHMrMGOsGT2ArYAhhDp70yQiibaZja5c9SMkLp8hUv1MB1PD3Af808xaAM8DDwAo1CV5DhgQXzsGeFhSnRUnkLQ+IencOW5Wdi7wcMqQe4H7zGx7QvL24RXnSDGG0Cxl2dzbEuosLjMK2F1SXnzcEfiYsJqVlHMjS3rRzjlXHjyh55xzFUhXKitLWT1779E7t1ZOrbRjNt54Yxo0aABArVq1yM7OTjvusj0vU0FRwRZApxIfGmoAnQ/sCpy0+tGniLQR8FR8tH65zFlFSBxG2FLWDegO7GfGtxkNyjnnqhAz5phxJSGxdxqwA/CxxAcSx0jkZDbCNZSwJHAssB7wIFEpity6NWZmS8zsf/Z3DaiPgCbxzzcANjKzN+OxU4AFQJc0U2UBAurFxw0InZuRtDHQmtChHuC/wJapKwFXMB2YJ6lNfHwKMDQl5unAT/BXQrsDIUm4e/y8XELt3VElv3rnnFtzntBzzrmK1bDIivJabLrqXTzJZJJevXrRr1+/tNe3abgNdXLqFAKNVjlZwsYRPvW+jkjrlS3ktK4Bln0afUE5zJdxEptJPAOMAN4GdjBj2Fpb/N0559aQGUvNeARoRUhkzAGeBL6T6CPRMJPxrZHQSOo44HBCYxBX+c4nrNLDzOYDsyUdA2H7LfBP/k74/SUe2xP4QtIPwEOED+ggNKGZbWbJeKwRknZblRDHUOAUSdmElYFPrHB9FGEVHkB74A1grqStgTbAXDObVtoX7Zxzq6t6f5rmnHNVXy5ATlbJ326Lioo48cQTOeyww9h///2LHZedlW3L5iyF/oQVB/2APqW8Z2WR2hC2WS1bsXAEkdqSsE9We84MksgirDC5AfgVOMCM1zIblXPOVR/xBx/vAO9IbEPY4tgPGCDxCDDEjMmZjHG1JGwkkQYANxHpYxL2UaZDqrLCKsaWwF6EFXE5wG/AFOBVEra0LNNJugzYFtg35fRhwPWSLiU0qXoPVi6FIak+IRnY1swmSToEeE7SDmV+XcGzhA8yDwc+NrMFWn7R5ihCwu8R4Bcz+13SO4Qk92b46jznXCXxFXrOOVexfgaYvnB6sQPMjNNOO40WLVpw1llnFTtu4ZKF/J7/ew4wv1RPTth84FrgIiINXK16euGee4DClLOFwD3lXJ+vUkg0JazGuxt4EGjuyTznnFt9ZnxnxoXAFsBlwP7AJIn/SewnUd22r14DvAk8TaQNMh1MlRNpayJdQVbeVOALcuvfRN2tL6Xedr2pvelVoBEo52eirIeItG/q9mVJJ8VNK8ZK6pFy/mLgCKCLmf3VSdnMxpnZAWa2i5mdQNihMDFNVJ2BBWY2Kb7vRcL26cbADGAzSTnxs0RYnVfsf8zMbAnwCuH/Cg+lGTKKsMX2AEJim/jHjnj9POdcJap2b8acc646sQFWkK3sl2758Jbk3yVilvfyyy8TRRGvvvoqHTp04Igjjkg77vZPbicnK2cx8FYZQhhF+F4/APiAqMyfVncndDlMXWKYTfhUvke6G6oiiTyJ/sA4Qo2ddmZcZMbiDIfmnHNrBTN+M2MwsD3QFVgHeA2YIHGGxDoZDbC0ElZE6Cgv4NHq+OFVhYgkIvWErK/JW78/2565DZ0/gKN+zeGw72pxyJRaHDE7j67ToeV1dWmw8wnAmyj7GSI1ADCzYWbWMv4aCiDp34Stzp3NbEHqIyVtlvLz04HFpE+WfQe0lLRpPHZ3wv9bZpjZXOAL4IR47JHATDObuopXfAuhNt5KzzOzHwmJwosJHxICfADsSUj0+Qo951ylUHFvMJ1zzpUPXanWwKcvJ17mwO0OXK05Fi1dxOa3bJ5clL/oOhtg/Ut9Y9guu2xrbCFgwCDgehKWv4p7GxD+k1xcTaQFwDYk7NdSx5MBErsD9wP/ICQ2bzWjILNROefc2k9iF8JWyOOA3wndRu80Y1ZGAyuNSLsTtnh+DSRI2NgMR5Q5YZXdbaDzaHY57HQFZJWi+sdPb8L7xyXJX/gdVtAhrlP4F0lbEBJj3wGL4tNLzaxdfH0AcDwhuToJ6GVmM+JrPYFGZnZFfHw+cCZQQNiW29fM3oiv/ZPQ2XYDwrbgHmY2fsVwJXUHuppZ1zTXDGi4LOko6R5COZKGZvZ7fO7D+Ljpqn9xnHNuzXlCzznnKkHuf3JfbVy/8b7vnfJezqbrblqme4usiNNeOM2GjRu2pNAKt7QB9nOpb47UjtA1LlUhMBXoXmJ9oEiDgbMpvt5qEriHhJ1b6ngqkcR6hK1TZxM+YT/Tu9c651zlk9iU0LTgLEKn9KeB28z4NKOBrUqkH/i7ecLzwIC46VTNEulylP0f9ohE42PKdu/iGfDm3gX8MfNbrKA5CStc9U3OOedKw5eQO+dcJUgWJU+fvnD6zD0e3KNg/JyVPhQu1qKli+g2vBuPjHmEwl8KTytTMi9I930+m1B4+gMi3UakdVcaEWlnQpHzkrp55AC9iLTqFr6VTOIw4CvCqpDuQGdP5jnnXGaY8ZMZAwk1zU4HmgGfSLwvcbRUZRv1jU75+UHAWCI9VxX/3aswkRqgnEtpfkWxybzatWvToUMHOnTowH333bf8xbpbQsdXcrHkP4FuFR+wc87VHL5CzznnKomu1Ga5WbkjJLXu1aZXVmKnBLtutisrdE4D4MdFP/LUhKe47ePbkj8u/PHP5GPJfKbyC3ComZW+c2CkfxG2DBUnCcwFTiVhr8b3KL6nLavuhp4EPgP2IJH5f1AkNgNuJ9TIeRy40Ix5mY3KOedcqrhRxt7ABcChwEzC9+4HzKg6ZRwiXQX0BvJSziYJ/zaOIKzY+zIToVWaSFeQs25/us7MIa9+2iFNmjRh2rRpJc/z/vFFzBj+Hf9n776jpCqyB45/b4cZogRFCZIRJYpEkSAgGBAjCDIIi2JADKvoqj9XSa6uYdeMAQyg7KCoCIgBREmCYECyShAUUUCyCMx0uL8/qgeaoSc1k7mfc/pAv6pX7/bAme6+r+pWOHCGzdIzxpjcYQk9Y4zJRzJS/MDdCZ6Em1PDqdVrla8VuPz0y/0nlToJn8fH3pS9fLHpi+D8n+f7POLZH9bwO4rezwgA3gfOAPqq6kfZumCydADmZdErjJvJl4yrdXQe8FYOX1o/kjQ5h+fkGhE8wPXA47jafoNV+aSg4jHGGJM9ItTFzQgfhHsvGgc8q8qPBRkXAMkyCFf3zxujNS2x9z4wolgm9pJFEN8uGt5XjjMfyrBbmTJlaNGiBRUqVODJJ5+kTp06R3fa8z182BDgQpLUdpc3xphcYAk9Y4wpADJSBDcDrm+iN/E84ATALyJ7AqHA6pCGkoGPdLgeOHSOSAncF4v+wH3AE5rVL/Fk6UTOdlsbjqs3NxlXhDqaF7dBxi5cHb40CvQkSedTAEQ4HRgDtAeeBobZ7rXGGFO0iFAOt3v67UBt4CPc7/RZqhTMF5Zk6ULWO8tHJ/YeJElX5Xlc+cWV5PiTc6dDtYsz7LZ9+3ZOOukkZsyYwSOPPMLcuXNjd3y7TJDQX7eQpGNidzDGGJMThbVehTHGFGs6XBVYHHlk7xzVg5Ed2JYBTwBNReQG1cNJvxiyqpWqkcdB4HXgDZJ0I3DyUT2TpRGwEuhQGL6wiJAA3As8gKuX10aVbwo2KmOMMfFQZQ/wtAjPAZfgluPOBFaJ8AwwQZXM3u/ywsZs9En7PnUFaTe4io8SAHgTM+100kknAXDBBRdw0003ZdzRm6iE/iqZa9EZY8xxzjbFMMaYIkSdJ4HuQA9gnohUy+SUrH7P/wjcAlQmSW+NJPMKPRHaAkuA+4EHgdaWzDPGHO9E5FoRURG5POpYaxFZJCLficj3InJPJue3EZFlIrJGRD5Pe38RkSYisjTqsVFEdmYwRncRWRl5XBB1/DoRuT+r16BKSJUpqnQCmuPqtD4PbBLhYREye8/LbZtwZSkykzZjfRYwKm/DyXe7AeXAlgw77Nu3j1DI/QiWL19+KLl3lFAKBPb4gO25H6YxxhyfLKFnjDFFkKrOwC3ZLQt8LSJtMuia/vd8EPflYz2wA2hNkr5Ekv6ZZ8HmAhFKiNBEhGtEmAosALYAjVV5XJVAAYdojDEFSkRq4XaQXZSuaQzwiKqeBbQD7haRhjHO9+A2E7pDVetzeMkrqrpCVZulPYDpkb6xjMLddOoOPBwZ+xRcuYjHc/KaVPlOlYG43XGfj7y+jSL8T4RWORkrLkkaALZm0WsV0JUk7UaSLsvzmPJTkgYR32zWvhAkgwofq1evpmXLlnTs2JFbb72VsWPHxh7rp9dBNQRksB7XGGNMTtmSW2OMKaJUdU0kkTcRmCsiN6rqG+m6RSf0fsftIvgabsbBWtwMt//L8CIiXqAz0JPSnMHpwAYepJ+8Cnwe+XCeKyK7Hp6E2/gj7dEQaAxU48iafjfidkO0QrDGmONeJBn3Cm5zif+ma1agfOTvpYFUINbsuhZAUFXT6q6+DPxLREqo6sGoa5UA+uHeG2IJAKVwv7NTI8eeAu5V1WBOXtehF6BsAUaI8ChwNXAn8JUIC3FJx/dViWvsbFgPVEkfEu71PQ3cRZJmNYuvyNq8M/hwNf2yC9vmwimdjmpv3bo13333XeaDhFJh5UMB0FdI0l/zJlJjjDn+WELPGGOKMFXdIyKXAI8A40XkTI780jQPGIFbnvoRSVEJuGQZATxGsrxKP7YAlSMtf6C6B5FrI+O6439FRoE+kcdWRP6J6qvxxi/C9UBboAlwOm5zEHCzCENAQvqXDIxRJYMpAMYYc1waCixQ1W9F0u9nxLXAVBH5F1AJuElVY62hrAH8nPZEVf8Ukb1AVeCnqH5XAj+p6tIMYrkHGB/5+50i0gPYqqpf5fRFpafKQWCcCOOBc3F19t7GLcd9DnhVlV3Hep101uHep7y496U/gWG4G06DgBeBNbl8zQInIuWBIcAd84d7w21P+Lt4z58nJJTL+WCrHoYDv3tAH8vtOI0x5nhmu9waY0wxISLX4GZozAH6qmrmX2qukRKUZh1z8LKGihxOnoWBX3Ff7rLjaVTvzHm8lMbV5/Fy9I66GdkJ1IkUTzfGmOOeiDQGxgIdVTUgInOAp1V1SqT9LWCaqiaLSB3ckscLVHV1unF6AjeqanTdu23A2ar6U9Sxz4D3VPWFbMRWFrd090Lcctl2uCWsQ1U1NbNzs0uEuriZiYNw7yXjgGdVcynJlizDgJFACm7J8H9I0r0kSyLwBZAInE0/UoFWwIm4WYprUN2QKzHkIxGpgkuU3oy7ifbCpNv56KqzfZMp36Q8Haf6KF09e4OFg7Dsfvj+CYBbSdLReRW3McYcjyyhZ4wxxYiItAbex82nu1RVf8ig4ynAe7gvV7nhUlQ/yOlJItyKWwacHQoMUuX1nF7HGGOKKxG5GTdjLCVyqDKwFxgOvANsVtXEqP7vADNVdWy6cVoBb6rqGZHnZXEbGJRLW3IrIrVxu51XU9Xd2YjtOeBDXImHV1S1s4iMBDaqaq7+LhehHG424u1A7ch1nwY+O6byDMlSEbfE+B2S0s1sTJba/MpS3uY3lnAibgZktK9wtQZfIpcSmHlFROoC/wAGAntwy6RfVFV3Ay1ZTsPj/whvyVo0Huaj5tVQKoP9SUIHYfOH8P3jQXZ8oxC+kSQdlx+vwxhjjieW0DPGmGJGRKriknpn4GbqfZSuwwnAt0C9XLzs16i2judEER4B7iPzWXohYBnQSjXLHQeNMea4FT1DT1wd1D+AXqr6uYichCue0EdVv0x3ngeXeLteVWeLyN242Xm9ovo8BNRW1WuyEcfZwBBVHSAiTSMxdRGR4cBOVc3uzZwcEcELXIKbZXYubtOK74AVwEEOl3QIcXijqPR/DwFbVMl8mbBIX4Rx6FHlIdL7GrgE1aw22Mh3ItIMuBfojVty/QQwTlUPHNU5WUqDPIp4r0VDpanUPsipl/lIrAQeHwT+hO1fKr+8Gyb0lwfxf4EGbiVJl+fvqzLGmOODJfSMMaYYihQtfxm3q+B9w+GJEXABkIT70J6YyemHBPGyjnr8Qg0OUgKAEhykBr9Qj3X4OGJPjHqors95rIeWSF1D5ruvt8nyy5UxxhznYiy57Qo8hqud7cfNlHsy0jYYqKqqwyLP2+LeO0oAvwH9VXVTpM2DS/gMiNo4I6MY/MAsXCLxj8ixV4CzcbP+rsiyLEQuEKE58CBweeRQOPKAwzeRJOoRLRWoohpzAxEQuQi3229m71vRVjdmRadVNK4PlMOVuUjFzab8XpUd2RznmIkrtNgBtynWhbhk56PApGxtXJIsJYDuiCcJvOehodIQ9iK+/YhnHeHUN4BJJOnmPHwZxhhz3LOEnjHGFFORD+x3locnPoUtLV1h80wF8DGLrrxDb75NOJsfgvVIDftj9k3wBDjdt54WqV/Sm0k0Y2mrKvr7N1EBVMNtntEc9+XwT2A28D6qfx7uRlncF4khHN45MFoQeFOV63Lw8o0xxhgARBjL4Rp72RHEbSrVNcPluiJLgLMyG2QTp/I2fZjp7c5KTxN+D6RfkXuY389OYGUgwMfAW6pszGas2RZJyl6MS+S1xdUAfBT4SI/1S2GyCEn2xdIYY/KTJfSMMaY4EymxG1aWh7qZdfuF6jzOPbzt68f2YAXaNE/l3K4JNGoEjRpBnTpQogSIwIEDsGEDrFoFK1fC3FkBFi/xk+AP700NeCY8yKjkUQwfBnQl9syFA8CbwF2CdgZeAErjvmAMBhpz5C7sf+E2wtiWCz8RY4wxx5nIMtxpwPkc+f6SkQDQSJW1GQzYBfgsVlMY4W368JLvVuYF21GudIAel3lpdpaHRo2gYUM48UTw+yEQgF27YPVq9566bBlMmUJw7158fj9fBQKMBiYca6mJyIzJPrjyFo1w9QUfVdUvjmVcY4wxBcsSesYYU5yJZLrpxJ+U4VHu40nP3VSuItwwJIGrr3YJvJz46Sd46y14+aVwcNvmoO/O8H+4j0c5gT8zPofae85mUbk/OHkicIcq20Q4GVdEvBruS1cYuEuVp3MWkTHGGHOYCGWABUBDMk/qhYCHVBkZY5C0pap3AFekb/6Cdgz1Pcs3obO48vIw/f/m5cILITFbRS6c1FSYORMmTEAnTQKfj5WBAH9XZCnQE7gaaIBbsrsL+ASYSLqaiJFwSwHXAXcDpwJvAY+rWk07Y4wpDiyhZ4wxxZUrhr4R9yH+KLPpRJJvEgcSK/DgSB+33pqzLx2xpKTA6NEwaliQEim7SQ72pgsZl1raQK35tXVDxyPDpi4uqVcRV6C9kSqBY4vMGGPM8U6EqsA3wMmAN4NufwLnqLIy6kQ/bhb5DcR4Tz1ACW6T53lVB9G5Q4Ann/XTrNmxx7tyJQwdSujTT/H29bwVGhse5C3N/oy6LwD6orpJRCrgylj8HTgBeBX4r6r+dOxRGWOMKSwsoWeMMcWVSGXg91hNLzKYW2U0V1ymvDTWy0kn5e6ld+yAwTeEmDxFeFZv4xZeyKhrGGiC6uojQ6cF8B9guCrzcjc6Y4wxxysRmgCLgJLErqm3BaiM2/Di6V2U/6Y8e6YD7WMqkJ1sAAAgAElEQVSNt5WTucT3MT8mNuXVcT569nTlKXLTBx/AwGsCnLp/DR8Fz6cav8XsF4TN7eDDr6AvribtaOAZLYS76xpjjDl22d2VyRhjTDHxOP9gCC8yYqSHdybnfjIPXH2gSe95GTnKw62M5t/cl1FXD+6LxxFU+VaVzpbMM8YYk5tUWYFbLpt+VkMQeBs3A+9K3My2WQs55xcySea18y1mR5XGfL3ER69euZ/MA7jkEvhmqZ+UGvU5x/cVv1ItZj8fVLsXBgAPAzVU9X5L5hljTPFlM/SMMaa4crvZrSFqQ4wP6c4lfMCTT3m44478CeOZZ+COO+ADetCDD2N1eQ7V2/MnGmOMMQZEuAEYE3XoL6CeKlvSDjwi9/e+j0ff9sTY6DaAj67e2Wyq0povv0nglFPyPuYdO6B9m1TK/bycucF2JJJ6VB+FsEBjVL/P+4iMMcYUJJuhZ4wxxZVqGPh32tO11KOf9y0G9Ie//z3/wrj9dhg4IMw13omspV6sLpvzLxpjjDEGVBkLPI6bqafAvdHJPID7+Xf7WMk8gH/wBF972/L+9PxJ5oGb/f7+9ARWJzTjNp6P2Ufc97vO+RORMcaYgmQz9Iwxpjhzhbw/Azpc4P2Uraefy5ff+ClZMn/DOHgQ2rYKcNL38/k0dF50036gNqrb8jciY4wxxzsRPMDLQAWgjyqhdB3eAvqkP+8rWtGGr3jjDejfP19CPcLkydCzp9vcqhNzY3W5A9Vn8jsuY4wx+csSesYYU9yJlJrGJZMvY9oFM2dCt24FE8asWe7aC2lLWxalHf4Pqv8omIiMMcaYTIi8ANyc/vAlng/Z2rQbi5f486RmXnZ07hDA8+UCPgvFnIzXDdVZ+R2TMcaY/GVLbo0xprhT3d/TN01btgiHunbN+ekjRozA6/VSpkyZQ4++fY/axyJL550HZ7cM8JB3RNqhCcD/5TwiY4wxJl+MS3/gO5oxPdydB0fFl8zbunUrSUlJnHLKKZQvX562bdsyd27MWXaZenCkn89DnVjAOembvrFknjHGHB8soWeMMcWcCCeEQlww9C6PN96ZBG3btmXfvn2HHhMnTownDu64288noW6spd7NqPZHNRhfRMYYY0weU/0KmBF9aBK9qVM9lR494htyyJAhbNq0iZUrV7Jjxw569epFjx492LlzZ47G6dwZmpyeyiR6Rx8OQsbbyhtjjCleLKFnjDHF3zmqSOdCUCK7UydQPNRn7c8FHYsxxhiTDX8DVqQ9+cLXiU7dEuJeartu3Tp69epFpUqV8Hq93HTTTezbt4+1a9fmaBwR6HR+Al/4D7257wYuR/Wz+CIzxhhT1FhCzxhjirnK/N69Vs1woHLl+Mf47rvvqFSpEjVr1iQpKYkNGzbENc4pp0CtWqQCHeKPxhhjjMknqluBtsDzByix96twSzocwzvYvffey5QpU/j9998JBAKMHj2aunXr0rRp0xyP1aEDLA02ZjNV/w00QfXD+CMzxhhT1FhCzxhjiiORaog8iMjqLnx+W5PG6o93qF69erF69Wq2bdvGwoULERG6du3Kvn374hqvSRP8QK144zHGGGPylepfqN7WhsWtU8N+GjSIf6h27dpRokQJqlatSsmSJfnvf//L+PHjKRnH9vMNGkBYPZzK5gmo/hp/VMYYY4oiS+gZY0xxI9IdWAmMAhocpAQlSnnjHq5x48bUrFkTEaFatWq89tprbN68mYULF8Y1XqlSiAg5/+ZijDHGFKAVNBWAOHJvAITDYbp06ULlypXZsWMHBw8eZOzYsXTv3p2lS5fmeLy0OJrxXaX4IjLGGFOUWULPGGOKE5HzgClA+bRDiaSQciCci5cQRARVjev8gwdRVQ7mWkDGGGNM/kgBSEmJ7+Rdu3bx008/cfvtt1OxYkV8Ph+XXXYZdevWZcaMGVkPkD6YSBxv0n8mIpMRaRZfZMYYY4oiS+gZY0zx8hhwxPLa6mxi7feBuAecNGkS27dvB2Dr1q1cf/31nHLKKZxzzjlxjbd6NQHAlgYZY4wparaKEFqzJr6TTzzxRBo0aMDo0aPZu3cv4XCY6dOns2rVKlq0aJHj8dasASFMNTYnAFcAixBJii86Y4wxRY0l9IwxprgQ6QYc9Y2gPV/w/fpEIjm5HJswYQINGjSgVKlSNG/enEAgwKxZsyhbtmyOx9qxA9auJQGYH180xhhjTMFQZb/fz7L5x/AONnXqVLZv3069evUoX7489957L8899xxdu3bN8Vjz50Nj/xoqsDvtUCLwJiKt44/QGGNMUeEr6ACMMcbkmsaxDrZjAQDz5sGVV+Z80GnTph1TUNHmzTv01wW5NqgxxhiTT1JTmT1rFk1U8Yvk/PzTTjuNKVOm5Eosc2el0j7wefrDHmA4cHGuXMQYY0yhZTP0jDEmNySLh2QpRbIU2O/VvzI4XpFddPbOY/Qz8S+7zS3PPkvI72eBKjsKOhZjjDEmDpPXr8c/e3bBBrFoEXy7PIEreD9Wc3dEaud3TMYYY/KXxFvU3BhjjmsucdcO6Iv4e6OBioC7Vy/+3WjwfdBkYDZJGsqLEEQkEWgDdAY6NYZzlkFCrIzibDrRhdnMnw/t2+dFNFlbsODQtc9T5agpBcYYY0xRkJDAnDZtaDd/fsGtdrr4giA7P/+OhcHWZDBRsB2q8W1Hb4wxpkiwhJ4xxuREsghwPR7/KMKBypQ9PUCtfn5OqA/eUhA6AH9tgI3JAXYv9yP+HWjg38BTJOkxbTUbSeC1JpLAA9oCJYBNwGxgzm7oUw4uSH+uAh19Cwmd1Yq5C3z4/el75K1AADp2JPTtt3wTCNBWFXvzMcYYUySJcC4wZ8oUuOyy/L/+rFnQrRt8xEVcxCcZdWuM6qr8jMsYY0z+soSeMcZkV7IkIt7xaLgP9W+BejdC+SYZ99/7I6x/Db7/jyKeT9FgT5J0X3YvJyIJHJnAOweXwPuVSAIv8tigab/MReoB84Aq6cdbRlPaehZz022JPPV0HIV/jsHQofDss6SGQrRRZWm+XtwYY4zJZV4v40uVot+SJXhPOy3/rvvzz9DizABd/pzK2+GrYs7OW8Npf57OmluByapk+3OHMcaYosUSesYYkx3JkoD4puNJ6ELH971UOT/75/7xJcy9OEhg33dooEtGSb1IAq8VLnnXGZfAKwls5sgE3k+a2S9vkVrA27hk4JEvg770I5nx42HAgOy/hGMxYQL07w9Af1Um5M9VjTHGmLwjQkm/n0V16tBw8WJ85crl/TX37YNz2wVIWb2eRcGWlMmgeu7NvLD4JW5uAaQC7wFvAIuAUkBpoEwWf6b9fakqr+TtqzLGGBMvS+gZY0x2JMtNiPcFui30cNJRebKs7VkNn7QKEdo/nCR9GA4l8FpyOIHXDpfA+40jE3jrM03gxSIiQHsgCbcEtwLug/33Xfg8ZY506vbCCyKDB+f8peTEyy/DzTejwJPhMHfn7dWMMcaY/CNCTZ+Pb04/nfIzZ+KrWjXvrrVtG/S4MMBPK/7iy2ArTmNdRl3/i+rdIpwE9AEGEOMGXzoKhIDo0iB+YCdwsirHVDLEGGNM3rCEnjHGZCVZEhD/BupeW4XWL2e4VnX79u2cdtppPPXUUwwcOPDoDsseILjysb9q3Bp84vfdtMMl8EoBv+MSd2lJvHU5TuDlgAgCPAiMvPFG9MknkdKlc/ca+/fD0KHoyy8jwEhgpNXNM8YYU9yIUNvvZ1a5ctR45x18nTrl/jUWLoQ+Vwbw7Pgj+Gmwk68+a2N12wQ8iOr4GDF2A6bgPnNkVwh4QJVH4wraGGNMnou1GaIxxpgj9YdwFRr+X6aF50aOHEmHDh0y7nDGnSC+0td34i5gB3AncDpQTVWTVHWsqq7Ny2QegCqqyiig16uvcqBuXYJvvAHhXLj/Hg7DG29AnToEX3mFg8BVqoywZJ4xxpjiSJUNgQAtdu3ik86doXdvwhs25M7YmzZB/34h2rWDRtvnMC140W31WdsaeByYCLwFPAV0BGrGSuZFYvwUaA78gUvUZcce4LljfhHGGGPyjCX0jDEma12o3FUpUyvDDqtXr2bfvn00b94841EST8RX6ypG9vZ9pap9VXWMqq7J6wReRlR5LxSi7rZtjBs4EG3WjOCYMbBjR87H2rkTxoyBs84iOHAgum0b40Ih6qjybu5HbowxxhQequwOhbgUuHzKFH6pX5/wzTfDggU5v1mmCosXw223Qf16Ib6Y9BuTuIqPQ+dzJssHo/o1qveimoRqX1SHojqfLD5LqPIjLvG3CwhmEUYYeEg1gyJ9xhhjCgVbcmuMMVmZ6P+UWv260nZchl2uuOIKnnnmGV577TVq1aoVe8ktwHf/gB+fW8HVB5vmSaxxEqGJx8NwQS8VUf8F3cKc28VHo0bQqBHUqAESmZ+o6mYNrFwJq1bBvNkhZswUQioBEfkgFGKkKssL9hUZY4wx+U+EBGBwQgK3p6ZSt2pVAr174z/zTPd+2rAhRJe52L8fvv/evZ8uWwZT3knlp00JnObfwE2B57mF0ZQgJa37AVRzsmw2VnwNgPlAecCbQbcQ8G/gdVV+OpbrGWOMyTu+gg7AGGMKPQ3tI3Qgw+ZPPvmE+vXrU6NGjazHCu4HCt8db1VWAL3mSqe2P1Nz4Tszr+apz1rxe6ASAF5PmER/GBE4mOohFHYTvCv7t9Mi9DWvhCdSi42dOuq8hQX4MowxxpgCpUoq8KwIzwFn/vYbfV98kUsCAeqFw/gBEvyqif6QpAaElIDLqSV4ApzuW8/lqR+SRDLNA0uIUefjYC7E970IHYEvgLIc/X1QcfV8bwQeEOEL3C6576iy+1ivfyxEpARumXFD4ACwDbhZVdel69cF+BS4S1WfzmCsd4FzgCpABVXdHdWmwEoOL0++TVXnxxjjOuBuIAUYpKpLIsdHAT+oavIxvFxjjMmSJfSMMSZL+hN/LAgQDvjx+I9q/fbbb1m8eDEXXngh69ato2TJktSuXZtzzz033TAK2+YGCafGrGZdGJzLvD8ABoTfhDDspAKraMQv4RqkpCSiCImkUJOfacQqKgZ2RZ++vWCiNsYYYwqXSO3YpZHHvSL4gDpAw1qBNTXvCvz3P4mk+E5gLw34nnrhdfhSsyxv91EuxbY6ktSbB5zA4Zl6CmwGukf+fj5ul9zngOdEmIpL7s1UJZAbscRhDPCxqqqI3Aq8AnRKaxSRcsCjZP2zegkYAmzNoL1DdJIvAw8CjYEWwP1ALxFpBDRV1WFZvRBjjDlWtuTWGJNrRORZ4FKgJnCWqi7NTlu6MTzAf4ALcTVedgA3pN19FZEekXYvsAIYqKp7Y4wzDvgb0FxVv4scK4vbUXadqjYTkdeB9ar6r0j7NcCbQG1V3Rg59kmjU5m98jF5hDaveqh7baY/gxEjRmS85PbXD2DepQDNSXIxFToiAizHfUDNiVVAk6xq+BhjjDEGEHkRGJyDM8JAI1R/yL0QaIJL6pXFfa5S4FpVxqfrVx64Cpfca4+bGZeMS+4tLaiNr0SkJfCuqtaKOvYm8A5wpYst9gy9qP5K7Bl6FbJK6InIOqAN0BroD1wDfIL73PpzXC/KGGNywDbFMMbkpndxH/RifYjJrC3apUA74ExVbQp8BjwCICJlgFeBy1X1NOA33N3RjHwLXBf1vA/wfdTz2UTd1QU6A4vTjomID2i/6lcmAW+wcmSAUGqmwY8YMSJ2Mk/DsGJYEPF9VGiTeUAkIfevOM78lyXzjDHGmGwbhlvWmV3/zM1kHhwqt3EusC9yaAPwvxj9dqsyVpUOQD3gBdzntSXAchH+IULV3Iwtm/4OTE17IiK9gLCqTsuFsT8TkWUi8qSIlM6gzz24pb33AaNwM/4+tGSeMSa/WELPGJNrVHWeqv6a07b0XYFEoIS42WInAGnnXQR8p4c/0L4A9M1krMlADxFJjDy/Fngtqn020FZEEiLP2+OKQHeKPG8FbFfVDaAPs//XEPOvDBHKYQmbcBC+/FuYXctBg0VhCcY7cOTd+Sy8AUzKo1iMMcaY4kf1D6ADZLkb/B6gP6qP5k0YLMcl9eYDt6hmvgOuKutVGYlL7LUHFgL/BDaJMEOEfiJklADLNSJyfySG/4s8rww8gEvyHauaqtoCV2OvEvBErE6qOllVm6vqubj6yJcBz4vIIyLytog8nAuxGGNMhiyhZ4wpbD7AFWPeglseex7uLjZADY6c4bcRqBKZSRfLftyd08tF5AxAiJqhp6qbItdoIyI1gJ3ATNwHOHAz9j4HIEnXoaEu/P7JAWa0CbJ3TfZezV+/wGedgmxMDkK4B0n6bfZOLECqYVzy814y38BjP+6D9MDIOcYYY4zJLtXdqF6FS0w9gEuO/QAsX06TZb15O6UX75yB6oS8DYNlqnRU5ZMcnKOqLFDlJqAycDWQirshuEWE10XoLJL73zdF5G7cktqLVHV/5HAL3AYXS0VkI9ALGBZPUk1Vf4n8+Rfu5nGHbJz2DDAUd1O4qqr2AU4Vkc45vb4xxmSXbYphjClsWuLqt1UD9uIKG7+Eq0sSj9dwyyCWAa/HaE9bdvszMFdVD4jINhGpHTn+5qGeSfolydKCPd+/y/QzmlCpfZDa/X1U7wmJFQ+PGNgLm6bAxgkhtnzmweP9BcI9SYpdN7BQcstnH0dkNG5ZTU8SqUd1zmQLn7KPccBU3IddY4wxxsRLdT3wcOQBwJlyaIVCfzKYIVZYqHIQN7v/HRFOxiX3BuBuim4S4U3gTVWOecmwiAzFrc7oGl3jTlU/BE6J6jeObNTQizF+BSBFVfdH6jr3ATItlSIifYDvVXWFiFwKh2oKhoEyObm+McbkhCX0jDFxEZEBuDuRAM+oaqxkWTwGAJ+nfUgTkfG4WXMAvwDdovrWAn5X1QyXh6jqIhGpCjQAGuLu4EabDVyPS+i9HTk2F7ezWzvcTLXDknQNydIS6MH2BUn8seBSvhrsI6FcEF9pJbhfCOzxoRpGPDMgPIFweCpJh+4gFy0uYTcRmEiyNMLV+7mTJF1VsIEZY4wxxZcqe0V4DbhNhKeyWgpbWKiyDXgWeFaERriE5N+A+0X4Clem421Vtud0bBE5Ffgv8BMw21VmIUVV22Tj3MG4mXPDIs8/BM6MNK8SkbWq2gk4A3g5sjGGD1cnMMNlvJEE4C24z43gNsW4XkSW42oSZnvWozHG5JTtcmuMyXWRpQ6Xx9rJNrO2SPtQoDvQXVVTReReoIuqXhDZpXY90FFVfxCR54GDqnp3jHHGEbkzKyLnASep6tsi0gl4WlWbRfpViYy5BWiqqvtE5HzgeQBVrZ/pi02WE4ALcMtNyuCWoW4DZpCkOzM9t6g5nNBrbAk9Y4wxJm+JUAdYB1ytWnRr1YrgxZUxGQD0BPzAh7jk3keqpBRgeMYYU2TZDD1jTK4RkZeBi3HJrRki8qeq1stG2yvAtMiuZKNxs+mWiUgAl2gbDKCqf4rI9cCUSN28lbi7vplS1c8yaftdRH4Bdqtq2i5vC4HauB11M5eke3HLTIwxxhhjco0qP4kwFbiTIrz5lCohYBYwS4QhuPp3A4D3gF0ivIUrcbJYFZttYowx2WQz9IwxxmSPzdAzxhhj8pUIHXGlQNqqsqig48lNIlQH+uFuzp4BrMXN2pugysYCDM0YY4oE2+XWGGOMMcYYYwqn+bhNGe4o6EBymyqbVHkUV+O4Fa7e3B3ABhHmiHCduM1BjDHGxGAJPWOMMcYYY4wphCJLUJ8CekVmtBU7qqgq36hyO1AVuAzYDrwIbBUhWYQLRaxclDHGRLOEnjHGGGOMMcYUXm8DfwC3FnQgeU2VVFWmqdILqIKrH1gL+BjYJMJ/RGhakDEaY0xhYQk9Y4wxxhhjjCmkVEnFbRp2owilCzqe/KLKTlVeUuUcoD4wFrdL7jIRloowVITKBRulMcYUHEvoGWOMMcYYY0zh9jJQEreBxHFHlbWqDAPqAucC3wAjgM0ifCTC1SKULMgYjTEmv1lCzxhjjDHGGGMKMVX+AN4E/i5y/H6HUyWsyjxVrgdOwe2Sq8AEYIsIr4jQ8Xj+GRljjh/2i84YY4wxxhhjCr9ncEtPLyroQAoDVQ6o8pYqFwOn4mbstQDmAutFGCXCaQUZozHG5CVL6BljjDHGGGNMIafKSuBT4I6CjqWwUWWLKk+pchZwJvAucAOwRoSFIgwWoWLBRmmMMbnLEnrGGGOMMcYYUzQ8DXQVoXFBB1JYqbJclX8A1YELgQ3Ak8DvIrwrwqUiJBRokMYYkwssoWeMMcYYY4wxRcMnwI/YLL0sqRJUZYYq/YDKwGDgRGAqbjONZ0VoKYIUaKDGGBMnS+gZY4wxxhhjTBGgShhXS+8aESoVdDxFhSp7VXldlc5AbdxMxwuAr4FVItwnQvUCDdIYY3LIEnrGGGOMMcYYU3S8AezHzTgzOaTKRlUeBs4AzgZmA/cAP4swS4QBIpQp0CCNMSYbLKFnjDHGGGOMMUWEKn8BY4AhIiQWdDxFlSqqymJVbgGqAD2BP4FXgK0ivCFCVxG8BRqoMcZkwFfQARhjjDHGGGOMyZHngbuBPrgZe+YYqJICvA+8L8JJuJ/rANyuwptFmAC8qcqqAgzzEBHKAY0ijzOAcoAfCAB7gO+BlcBqVfYWVJwmPiKUBhrg/n0bAhWBBCAI7AN+AFYBq1TZUVBxmoInqlrQMRhjjCkKkqUR7sNhY5K0UHygNcYYY45XIkwETgdaqGJf6vKACGcA/SOP6sC3wJvARFW25XMsVYA+fj/9AwGaAyQmEqpfn1ClSngSEpDUVHTbNsJr1+JLSXGr8fx+vg4EeBOYpMrW/IzZZJ8IFYCefj/XBIN0VEV8PsL16hGsUgVPYiISCKA7dxL+8Ue8+/e7maMJCfyQmsobuP+TGwv0RZh8Zwk9Y4wx2WMJPWOMMabQEKE1sBjopMrcgo6nOBPBA5yLm7XXCygJfIybHfmBKgfz8Nqn+3w8HgxySalShHv2xHPFFUizZlCzJnhiFNEKhWDjRli6FCZPRidPJpySgsfjYUooxD2qrMureE3OiFBVhIeBa3w+PBdfDL1742neHOrWBV+MNZWq8OuvsGIFTJsGEycS3LsXn9/PnECAu1RZku8vxBQIS+gZY4zJHkvoGWOMMYWKCAuBrapcUdCxHC8iyyEvxyX3uuLq7r2NS+4tzK3ZkiJUBEaIMKR+fXTUKHyXXAIlS+Z8rP37YepUGDaMwE8/4QmHeQZ4SJXduRFrVkRkJlAZCON+Xrer6neRtmeBS4GawFmqujSDMTrhkqg/Rh1uq6oHIu1NgOeAUyJt/1TVyTHGeRk4B/gDuEJV94iIAB8Bt6rq+mN8udkiQingbq+X+085Bc/IkfivugrKlcv5WIEAzJwJI0cS/PprvB4Pb4bD3K/K5lwP3BQqtimGMcYYY4wxxhRNTwGXiVC3oAM5Xqjylyr/U+UCoAbwMC5B9AWwVoThItTJciCR2oj8HyJfIfIrIpsQWYjIXd3lo+5+PysrVuTml17Cu3Ilvt6940vmAZQqBX37wurV+J97Dm+5ctzu97NChLPiGzHHeqtqU1VtBjwJjItqexdoD/ycjXF+VNVmUY+0ZF4pYCrwgKo2ABoD89OfLCKNgdNUtQkwB7eUGuB6YHY+JvPq+f18U7Ikwx56iMR16/Bff318yTwAvx8uvhgWL8b31ltIlSr09flYLUK33I28aBGR00RkoYisEZGvRaRRJn0HichaEVkvImNFxJ+dtnRjzBGRVBE5OepYHREJi8iUyPPZInJNVPsDInJQREpEHftBRM7Lzmu0TTGMMcYYY4wxpmh6H/gVuA24o4BjOe5EZkA9IcJ/gDNxs/aG4GbWfYGbtffOETPh3Bf3Z4EbYgx56hQuazvbcx7ndgjpW5O8cuKJuRev3w9DhkCvXviuuorKCxbwpQhXqvJR7l3laKoaPROwHByexaiq8wDcJLm4JQGLVPWLyJgh3Ay89AJAooh4gNLAFhGpAvQFzj+WALJLhLN9PmaccQalpk7FW7t2ro5Nnz7Qowf+QYPwTprEDBFuVOWV3LtK/pORciJuVmxNoCxwENgFzASW6fAMl52+DIxR1XEi0guXSG511PgitYGHgObAVlxy+EZgdGZtGVxzOS5R/N/I8+twtTfTzAY6ARMizzsDS4GzgTmR/4+1gAUZjH8Em6FnjDHGGGOMMUWQKkHcMsNBkZ1PTQFQRVVZqspQ4FTgYmAz7t9miwhvi3Dx63JtGdyy0VjJPGZwPj1lMgOu8/PxjNxN5kU7+WSYNQtfv34kiPCBCJ3z5kqHicgbIrIJlxzpn1X/DNQVkSWR2VZDoo43BFJEZLqILI1cq1L6k1X1R1xCZQlQB5dUeQr4h6oG44wp20Ro4vUyu1s3Sn/5Jb7cTOZFK10aJk7E88ADCDBWhAF5c6W8IyMlQUZKP/9D/o8F2ZrgTRhzWsXT7mlepfmQRpUaDa1YsuK/gO8SvAlrZaQMk5FS44jz3Sy5lhxOnL0HVBeRejEu1wuYpqpb1NWkewmX5M2qLZbxwN8iMXhwO2YnR7WnJfQQkQSgNjA27Vjkz0Wqmq26nJbQM8YYY4wxxpii6xVAcDNBTAFTJaDKR6pcjasbdytQBZi+moa/cfiL+xE2UIu+3ne4uo/y0hhPzM0QcpPfD+PGIZdfDj4f74lQPS+vp6oDVLU68ADwWBxDLAFOVdXmwBXAYBHpHWnz4eoZ3gSchUumvphBHA9EluteBXQDNgEbReR1EXlPRPrEEVuWRKjg9/NB69b4pk7FW7p0XlzliOsxahTcdRd4PIwVcTsjFwUyUmr7Pf5FXvG+2bV2125vXPGGd/s/tnvW3LYm8dsbv01YOWRlwvZ/bPcvuG4BNza/sW7FkhUf8Ir3BxkpSVHDVAd+T0vURpJxv+CWyadXgyOXfG+M6jEcUzUAACAASURBVJdZWyybcDM/2+BmfX6Dm02YZjFQVUSq42blfYVb/p2WVO8MfJ7J+EewhJ4xxhhjjDHGFFGq7MItJbtdBG8Bh2OiqLJblVdU6diYFQ0e5KGY/VLxc6VvGtXrl2Tsq16ObfVp9onA+PF4atWirN/PVJG8L8mlquOBziKSo/mHqrpXVfdE/v4rMBHoEGn+BVcDb3MkcTMBlyzJkIicANwNDMctV5+Lm001TETirFaY0bUQn4//VahAtcmT8fljVmDLG48+Cu3b4/X7mSZC+fy7cnxkpDTzeXxL6lWs12TFzSvk42s+9l7T9BrKJpY9sp8I51Q/h+e6P8dvQ3/zD2k1pATwPxkp/yyYyI/wGjAo8ngtukFVU3HLaTtFHnMitRtPjdTR64SbxZctltAzxhhjjDHGmKLtGVzdpcsKOA6TgRU0LXkCf5aN1TaOgazShrw3zU+pUvkbV9myMHUqvlCIZrhadLlKRMqLSNWo55cDO4CdORynSmQJIyJSFugBfBdpngS0iiTpALoDy7IY8lFglKrux9XT08jDDyTkJLZsOC8Y5KK338ZXuXIuj5wFnw/efRdviUStfC5zxiByPSL9EGmYv5FkTUZKRZ/45pxT/ZyyX93wla9BpQbZOi/Rl8izFz0ro7uPBviXjJRrcTPlqoiIDyCyk3ENXPI3vV9w9fnS1Irql1lbRqYAF+Dqan4Wo302biZeZ9zsPIBFwFVANdwsvmyxhJ4xxhhjjDHGFGGqrAWmA3cWdCwmQzGTRAF8POIbznWDhHqxqnvlg4YNoV9SmJq+zWOD4luNyDeIPIdI26zOFaGTCGdk0qUcMEVEVojIMtwS5B6RmXSIyMsi8iuu9uAMEVl3eGx5RUQujTztCaSNsQj4FHgdQFV/AR4BForIcqALMDjjmKUdUFJVP40cGg3cAqwA3kybCRg3kWaIPI7I4rB4VrfwLZ3StUtIO3U6plHjVqkS3HW3eJd6W1y1hxPG4mYwrkJkGSJDiSS9CoG/l/SXLP1B3w+8ZRLKHNV48OBB2rVrR6dOnWjZsiUTJ048on1IqyFc3/x69Xv8DzGCXbhl2mk7yvYEflXVdRztPeBSEakcSfwNBt7KRltMkfp3dwK3q2o4RpfZuCXiNVX1h8ixucCDwILILL5sEc1wQxBjjDEmSrI0AlYCjUnSVQUdjjHGGGMOE6ELbjZIK1W+Keh4TDoiJ+F2JE6MPjyBflzrfYO16zzUqhXf0F9++SX//Oc/+frrr/F6vTRs2JAvvvgCjyf783d+/BEaNgjzlvbhKt6NbloJ9EN1efpzRKgDrI88nQM8DUxXJRTfKyni3I6o/wMOJUIX0pZ2LGT+fGjfPmfDNWrUiJ9/Ply+LRwOc+DAASZPnswVV1yRo7F27YJapwa4f/8D3Mvj6Zu/AC5FdVeMU/OFjJTyPo/v12Edh5V+8NwHY/ZRVYLBIH6/nz179tC4cWM2bdp0RJ8NuzZQ77l6GtbwIEawEFeO4ERgL3Ctqq4AlyzGbXYxLfL8BuC+yDBzgMGqGsiq7YjXIDIHeFpVp6Q7PhC4XFUvjzz34erqfaiqV0eOnQ78APyfqj6anZ8Z2Aw9Y4wxxhhjjCkOZgPLcfXATGGjup3IjLJoM7iAc9uHjimZd9FFFzFw4EC2bt3K9u3beeqpp5AcFuI7/XQ4u2WQmZyfvqkxsACRDjFOi57Z1QG31PAXEe4V4aQcBVDUiTTGzRw8YlbjDC6gdrWUHCfzAFatWsW+ffsOPR599FFOPPFELrroohyPVaECXHqFl5ne7rGa2wOTcx5hrurn9/hL3Nbmtgw7iAj+SAHCffv20bRp06P61K5Qm35N+pHgTRiqqj+qaltVra+qLdOSeQCqen1aMi/yfKyq1o08BkUn7DJri6aqndIn8yLHx6Ul8yLPg6paNi2ZFzn2o6pKTpJ5YAk9Y4wxxhhjjCnyVFHcDKk+IlTNqr8pEI/g6scdMt/fhQ6d498l4Z577mHQoEEMGDCAUqVK4fP5aNOmTY4TegAduiQw339erKYywGuIZLbpSlpbVeBfwG8ijBOhZY4DKZpeBk5Of3C+tzMduuTOLhgvvvgigwYNokSJEnGd36Gjh0W0IRB775NOiHQ5pgCzSUQ8IlJSRCpEaiPWZjfN6lSoEy5fIvN9O/bs2UPHjh1p2rRphrMUW1VtJap6al7EXtgUlrXSxhhjjDHGGGOOzURcsf9bgMKw26OJproJkU7AVKDO71Tm50A12rWLb7j9+/ezcOFC2rZtS+vWrVm/fj21atXi/vvvp2fPnjker107eOyxOuykAhU5avVlPaAvrv5aVtLyDEnA30T4FpdsfkeVlBwHVtiJnAeck/5wGGGRtubq9sc+j+rzzz9nzZo1DB6cYWnALLVrB/tDJVhOU1qw5Kj2bTD8FJEAUCLqkZjueW60HZ3h/AEST0s86nB65cqVY968efzxxx+0bNmSq666inLlyh3RJ9GXSFjDub2xSaFkCT1jjDHGGGOMKQZUOSjCi8CtIjysyv6Cjsmko7oSkbOA6zZR/TqgSbVq8Q21c+dOwuEw48ePZ/r06Zx11llMmzaNq6++mrlz59K2bZZ7Whzh1Micpp1UjJXQ4w9O6nuyHNpZFo7c/TOWtMRNc+ANYLQIL0X+XmxspEb/mjE2Pj1ICQ6ESxDvv2+0F154gQsvvJDatWvHPUbav+8OTozZvhk6AvPSHU4FDkY9UtI9j37szqQts/NSqMuA3/b/dpOq+jOaXZqamorf70dEKF26NImJiTFnK27euxmvx7s7659I0WcJPWOMMcYYY4wpPl4E/g+3u+OYAo7FxKK6F3i6jTAXWOKL81t52bJlARg4cCCtWrUC4Morr6Rz585MmTIlxwm9tDgCMSZQAXxB++5AzCJsWUjL0JwA3BN5FBvT6cEtvHDU8bSfY7z/vml+++03pk6dypQpR5Vny5Gs/n1Ph6+BSzmcfEvJYJfWXCcjJbTlry23frj2Q3rU7xGzz48//sgtt9yCx+MhJSWFUaNGkZh45Ky+lGAKL3/7ciA1lFrQNQHzhSX0jDEmH4jIhbh6IgnAfuAmVV0WaWsNPMvhqemvq+pR209F+lYAngdaAQHgA1W9L9L2N+BuIAQo8E9V/SjGGN3h0PZWd6nqjMjx64DKqvpIrrxoY4wxxuQ7VbaKkAzcIcLYSG09Uzj9BfDnn/GdXK5cOerWrRtXvbxY9u51f5bKYGJnM5a+BjwZdagm8GEWwwZxeYe1wHjgYyhey247Mm8AMZKUaT/HeP9904wZM4bq1avHtRlGtKz+fUvBm6q65ZguEicdrkv8D/k/HjFnRLeLT7vYF+v/dJMmTZg3L/0EwiO9vvR1/tj/hwCP5VGohYol9IwxJo9FknD/Azqq6ipxu4T9D7drGLi758NUdZqIVAR+EJHpqro6xnCvAQtUtV9k7MqRPysCzwH1VXWLiKTtVnVUcV5gFIfvrk4GZojIKUB/oFsuvGRjjDGmSBKREsBbQEPgALANuFlV10XaW+FqgZXB3TwbqqqfxxinCfBm1KHywAmqWlFETgQ+i2orBdQBTlbVnenGaQm8grsh+Jiqjo8c7wL0UdWbMngpTwNLce/rM7P/EzD5bKPXo6lLlkhC8+bxDXDbbbfx73//m6uvvpqmTZsyffp05s6dy6hRo3I81pIlcIJ3H6eGfo3VnFqbjcNVOdQoQszdPiOCuJl57+JuXC8qtsllWfkwcANQIfqwnyD1/RtYsqQ2vXrFN3QwGGTs2LHcfvvteDzHVotvSaRsXiNWxWreAow9pgsco2A4OOLb37+96OVvX2Zwy5zXCtywawPD5wwPquorOlxj/icubmyXW2OMyXt1gR2qugpAVecDNUQk7aOb4j7oA5TG1arYmX4QEakHtCTqzmjUXTQP7kNT2cjz8kBGb2QB3JeHtGsBPAXcq6rBnL44Y4wxppgZA5yuqmfiNi94BUDclJH3geGRtt7AOBEpmX4A/X/27js8qqp54Ph3toTeBBQEpNsAUQHFAiogYgFFbERABBXsDQuC0sT+iliwgCCga8FXXxUVFLFg+6FSlCbdAkhvoWXL/P44Gwlh0zcbksznefLA3nPvubMgZjN3zhzV31T1xLQvYCruYR6qujnD2CvApxmTeVH3A7fhKvOHROMoAwwF7svsDagyH/gSuDMvfwAmAUTqKfJeG8+3/lnf5H1V4+23387tt99O586dqVSpEkOHDuXtt9/m1FNPzfVcs76OcIZ+h5eY8byAZpskCUd/3Yh7gFxble6q/FBsk3mQtoQ65gqXNsEvmDUzq7xn1j744AM2b95M37598zxHmlmz4JiklRzOxoxDu4DuqO7N903yQYfobGD4jR/fyKOzHiWSi9W+P6/9mdNePS20dc/W3xV9sOCiPLRYQs8YYwreMqCqiJwOICJdcIm3etHxa4ERIvInsBR4IJNy9+NxSboXReQXEflMXFNlVHUT0B+YIyJ/4Cr5emcSz724JQ+vAQNE5CJgvarOzu8bNcYYY4oyVd2rqp+oalry4Uf2f7+uClRX1RnRc5fimsBnuQ4uWvV3NfBqJqf0zWIs7SFcafYnS4YCo1U1u6bvzwCdRDgum/NMorl2K78AF7YNzZQvpocI5j3nw8CBA/nrr7/YuXMnc+bM4eKLL871HHv3wldfhGgT+SrW8PO4ti6ZSftv80fgcqCWKiNUKZTlm4VC9SngITgwG9qGWcz+xcPmzXmbtlu3buzdu5dq1arlMzyYPjVIm9QvMg7NBk5F9at83SBOdIgOAe4aNHNQpN3EduH3F7/P3lDsPKOqMv+f+dzz2T20Htc6snnP5u+CkWAbHaKbEht14ZH936uMMcYUFBE5B/cBvDzwA3AOMDC6zPYt4ENVDYhIA+Br4LyMS25F5FJgCtBBVb8UkfNxVQP1cB/2PweuUdXFItIZ90H+OFVNJRMiUgH4BOiEWypwBrAet4TowOsC0gRYADQlWWPW6htjjDHFiYhMBrao6u3R16uBe1X1nejy229x38+fzmKOZFzP2hYxxk7Htb+oHatKXkSOA17GVdWPAFbjeuRenn3seHAPCmeokvv1a6ZguATvSqAmwCrq0ViWM268l969Cy+sF1+EO24JsiJSn9qsAbcpwlRgHNF+yxmJUAH32fQP4HlVfktYwIcq92+6P3AJUGEn5ann+5ubB1YiD6ug42b6dOjUCabTcXZHPl+PW5IfQHVJ4UWVORkmp/k9/ieDkeDp5fzlIpcdf5nnxBonSoWkCuwN7WVdyjreWfhOcNmWZX6/x78uGAmOAp7WIRrOdvJixBJ6xhiTYCJSCtenohXuyf4aVS2VbnwK8Jmqjs1wXUvgPVU9Kt2xjcBpwInADaraMcPY6aq6LItYnsM1M14GjFPVc0RkGLBaVScccLIl9IwxxpQgIvIA0Blor6q7o8ea45qtHwEsxCVlPlDVZ7OY5wvgv6p60DaYIvIqri1Htrt+iogX1w+vJ3AWcBmwA/cQbmvsa7g1Gm8dVfJYI2TiSuQWXN/jf/VhPLPqXs3i5Un53hE1L1JToV49DZX/Z/nUpXr0K0AKMA/VfG7nUIK5pfEnAxVbMbvb4nIte//9t3grV87uwvhThdatCc2Zw1fBYNHqly3DpBZwZZI3KRmoG46Ey3nEk+oRz/Z94X0fAW8CP+iQxOzGe6ixTTGMMSYBRKSmqq6LvnwQmKmqy6MfzneJSDtVnSki1YBTOXAHsTS/ADtE5ARV/TW6O64AfwEVgRNFpEZ0U4zTcP+P/yuLmFoDlVR1moicAP/2NongKgmNMcaYEklEBgCX4qri/90SMrpDfad05y2G2B3mo+P1gdZAtxhj5XF9+FrlMKw7cNVQ23CfJU7AJffuINpfL4YJuMq+G4BHc3gfU7CuyXjgAUZy/J89GDEChg1LfEAPPgjr14uu08Z3oPpH4iMohlT3AN8B/Cx879vH5bfcQvnJk/HEaVPiHHvlFZg9Gx+Z/3/ikKVDdA3u56JMq6BLMuuhZ4wxiTFcRJaIyHKgLq5fDqoaxn2Yf1JE5gPfAM+o6g8AItJfRIZHz1Xch8CxIvIr8ALQTVX3qeocYCQwMzrP88AVmklzWxHx457Y3x2d+1dgpYgswC0Hfr1A/hSMMcaYQ5yI3AV0B87N2KdORGqm+/31uGbyB+1ym04f4P1M+t1dCczXHCx5iyYGz8Utv/XjHtop2TyEUyUF157jFhH82d3HJESVjAcasYLRehvDh8NHHyU2mClT4IknIBKhnyqWzCsAqmwPhbjqjTeQ559P7L1//BFuvpkI8LAq3yf27qag2ZJbY4wxOWNLbo0xxhRzIlIbV92+EkhbbrhPVU+Njg/BbXAhwGLgZlX9KzrWHzhSVR+Kvvbgeov1UtUvY9zre2DsQS0uYsf1Ia5335Lo66G4B4IpwJWquirza6kbfT89VQlk+4dgCpbIt7iexQdQoK+M579le/LFVz5atiz4UH74Adq1I7xvH69EItxU8Hcs2UQY7PUy/H//Qy66qODvt3QptGlDaMsWZoZCXKBKieovVxJYQs8YY0zOWELPGGOMKZJEmILbROsUVewHwMIk0hOYFGtoL6W42DuV7/xn8+Y7Pjp3Lrgw3nsPkpMJh8N8FgpxiSqZbqJm4kMEj8fDOKD3mDFIv34Fd69vv4XOnQnt2sVvwSDtVYnZZ9MUbbbk1hhjjDHGGGOKt1FAS+D0wg6kJBPhKD+pHZZwTMzx0uzjo/AFXLFvMl26wD33wM44b0uxfTvceSd06wbBIONCIbpYMi8xVIlEIvSNRHi4f3/o1Qtdvz6+99i7Fx59FM4+G925k+nBIG0tmVd8WULPGGOMMcYYY4q3H4CfcBtomAQToYoITwJLQ/jb3ssTd6pbBn2QJIK8qn14mRsYO2onjeqHdOxYCOdzsWQoBGPGQL16hJ5/np1An3CY/qqE8jezyQ1VVJWHgEveeouNDRoQfuwxl4jL57y8/TY0bkzwwQdJDYcZFA7TJdpH0xRTtuTWGGNMztiSW2OMMabIEqE7btOrhqqsLuRwSgQRygC3AA8AYeBh4EVV9iFSCbcDcTJQNcblv/5Ks/HN+bWhCDfXqEG4Z0/83btD8+aQk51SVWHOHHjzTXj9dYIbN+KJRHgWt0HClni9T5M3IpQDBni9DKxQAW9yMr7u3eH008GTw9Kr3393f7+TJhFctQq/x8PkSIQHVPm7QIM3hwRL6BljjMkZS+gZY4wxRVZ0l9tVwFuqDIjv3PIZUAO36+5O4DZVnRsdWw3sA/ZET39UVd/OZJ5mwHPAEdFDg1T1PRG5Frg93am1gW9U9dIYczwIXBWN40pV/SN6/DXcJiTf5eOt5ogIXqAnMByohlvy/IQq22Oc7MftYNwMKAVsB2ag+z9riXA0cK3fT69gkCNr1SK1RQt8TZviadIEqlSBpCRITYUtW2DhQvjtNyK//EJ43Tr8fj9/B4NMAsarsqKg37/JHRGOBK5NSuKa1FQaV6tGsEULPM2a4W3aFKpXd3+/oZBbMr14Mfz2Gzp3LsFVq0jy+9kcDPI6MEGV+YX9fkziWELPGGNMzlhCzxhjjCnSRLgfGAjUViVu3dlEpLKqbov+viswVFWbR1+vBi5R1XnZzFEW9zmjl6p+KyJe4DBV3Rjj3AXAEFX9b4bjFXFLi4/H7UZ8gqoOEJFzga6qWqA7uYogwPnAY0ATYDwwVJU1cZz/NKCjCE2TkjgxNZX6qvtbaYkQTkpiVWoqc1VZAEwHZttmKIe+6N9vM+DC6N/vScEgjSIR/OnOiSQl8VcwyLxIhAXAV8CXtoNtyeQr7ACMMcYYY4wxxiTEK8BDQG9cJVxcpCXzoipBnpJHycCPqvptdM4wECuZdypwOPBhjDnCgBfwA+WA1Gii8EHgojzElGMinAI8AZwFfABcqcrieN4jmpT7PvqVdl8fUAb3noOq7Nm71/riFUXRv99fo1/Av9WeZYAkIBT9+w0WUojmEGMJPWOMMcYYY4wpAVTZIsJE4HYRxsSzqkdEJgHnRF9ekGF4kogIMBu4P1bVHa6qbp+ITMUtqf0VuDvGuX2Byap6UFJDVXeJyNPAj8A/wDW4Za//UdUdeXxrWRKhEfAIcDku0XamKgW+rDdNdFOLOO+Faw4V0X+jtrGFicl2uTXGGGOMMcaYkmM00BC4MJ6TqmovVa0DDAYeTzfUVlVPAE4GNgETM5nCB3QA+gEnAWuAF9OfICLlcP3xXs0ijjGqeqKqdsIlBmsD00TkBRGZIiK35ekNZiDCESK8ACzGLZPsSoKTecaYks0SesYYY7IXEA8wIfrKvncYY4wxRZQqS4BPgTsLZn6dCJwjIlWjr/+M/hoEngHaZHLpn8CXqrpGXaP314HWGc65HFioqouyi0NEfMBTwB1AD2Cjql4OdBWRBrl/Z2nzUl6EIcByXBLvJqCZKv+zPnXGmESyH8qMMcbkRB+gVfT3B+0oZ4wxxpgi5RngbBFOzO9EIlJZRI5M9/oSYDOwRUTKiUjldKd3B+ZmMtU7QKvoxhbglu1m3LGzL1lU52VwNxBQ1X9w/fTSkm0afZ0rIvhFuAlYEZ37MaCxKmOjy16NMSahbJdbY4wxWQtIFWAlkPaBfCdQj2TdUnhBGWOMMSavortprsFtItFSlfV5n0vqAlNwjfsjuI0sBqjqvGgl3H9xG1UI7vPE7aq6OnrtOOBDVf0w+roncF90njXADar6V3TsGOBn4EhVzbJnnIg0BJ4HLlBVFZHDgPeBqsB3qtov5+8PAbrh+uTVA8YAI1UP3rDDGGMSyRJ6xhhjshaQ53H9bNI2UgoDr5CsNxVeUMYYY4zJDxHWAjVxfe0uUuX/CjmkQ44IZ+F2rj0FeBMYrMrKwo3KGGMcW3JrjDEmcwFpjusNk35XdC/Qn4Dke5mOMcYYYxIvWnVWKfqyKvCtCDcUYkiHFBGaiTAV+ArYAbRQJdmSecaYQ4kl9IwxxsQWEAFewlXkZRQGXoqeY4wxxpii5TCgbPT3gntw97IIr4pQuvDCKlwi1BFhAq53Xy3gPFXOVWVOIYdmjDEHsYSeMcaYzCTjdpfzxRjzAacCVyc0ImOMMcbEQ/1Mjl8D/CDCUYkMprCJUEWEx4GlwNlAT1xV3meFGpgxxmTBeugZY4w5WEAq4HZxq4Z7ch+L4naxa0iy7khUaMYYY4zJHxEux+0qG0sISAG6qTIzcVElXrQa8RbgAdznmoeBMarsK9TAjDEmB6xCzxhjTCwPAlXIPJlHdKwy8FBCIjLGGGNMvNQHUjMZ8wEVgRkiDIj22ytWRPCK0Av4HRiBazHSUJVRlswzxhQVltAzxhhzoIAcC9xF7KW2GfmAOwjI8QUblDHGGGPiqD5ZP7TzRMefAKaI/Ntvr0gTQUQ4H5gLTAA+Bxqp8oAq2wo3OmOMyR1L6BljjMmoLW4n25zyRq8xxhhjTNHQCPDn4DwFukHRT+iJ0BL4AvgEWAU0U+U6VdYUbmTGGJM3Oam+MMYYU7JMAJbFOF4PGA/0AVZnGPuuYEMyxhhjTBw1zmIsjHtYtwiYAnygyqaERFUARGgEjASuAH4A2qoyq3CjMsaY/LNNMYwxxuRMQJoAC4CmJOvCwg7HGGOMMbknggfYx/7ijjBuea3gKvIGA2+psqpwIowPEQ7H9QTuj9voayDwP1XsB2BjTLFgS26NMcYYY4wxpuSozv5kXipuCWpfoAGwCwgV5WSeCOVFeAiXxLsMt4ttU1Xet2SeMaY4sSW3xhhjsibiAVpzHKdyLjCHE7laFqMaKezQjDHGGJM7qqwX4Upcld7nquxOGxNhPHCrCKNUCRVakHkggh+4DhiC6/n3ODBKlV2FGpgxxhQQW3JrjDEmNpGawD3AlcCRGUbXAu8AT6K6NtGhGWOMMSb+RGgALL+JFwa+wC3/AKWA7cBMVDcWbnSxiSDApcAjuN17XwJGqHJIxmuMMfFiCT1jjDEHEzkDeB+3LCcrm4CuqH5b8EEZY4wxpsCIlAIG/E2t+2uzpnyG0RBuh9jRqH6amHAQ4DDgiOhXjQy/PxK3uUdt3I69bwGDVVmRiPiMMaawWULPGGPMgUQaAL8C5XJ4xW7gBFTtA7QxxhhTFInUwPXSOykHZz+O6v0FEwb3A8m4hF0VDm4RFcRt3OGNfqXpq8r4gojJGGMOVbYphjHGmIwGkfNkHrg+NYMKKBZjjDHGFCQRIefJPID7ELmzgKK5BGjGgRt3pOcHktifzIsAj1gyzxhTElmFnjHGmP1EqgLrcB+YcyME1ER1U/yDMsYYY0yBEekGvJvLqzYC9VDdne2ZuQqFhsB83MNCyeb0ELAAOEWVYDzjMMaYosB2uTXGGJNerRBe/ze05RdasJAmLPSfxJ/UYW+kFAClPfs4ir9oEpxLExbSgl9oyzc+H+FauJ56xhhjjCk6bstsYD2H8znnsoCmLPI0ZZHvBLZGKpGq/uoR8W0Ol2ZrOMyiUIhfgYXATFVW5TUQVVaI0BN4Lwenh4GrLJlnjCmprELPGGMMACKcWoN1N0V8pXptCB1GzWqpNGkqNGnup0EDKF0aRGDPHli1ChbOD7LgN2XdpiQO923BE9o3+R9qjlHlx8J+L8YYY4zJIZGVuN1hAUihHP+lGwFvT2ZE2uH1wDENgzQ50c/xTTxUrw5+P4RCsHUrLF4M8+aRunQpvn378Pj9/BQMMhl464CdZkUqAmfheuOlAotQ/TV2SPwHuJOsq/RuVOWl/P8BGGNM0WQJPWOMKeFEaOrzMSoUokPDBhrsdY34u3eHxo1zdv2yZfDmmzB5kgaXrxC/z8cXoRB3qvJbwUZujDHGmHwT+RVoFsLLePrwoO9Rtmplzu+kJPf0cdFFUC4HnXVTU+HzzyEQQN97j0gwSGo4zMjlNPyhIStvBi4A8+zddwAAIABJREFUSme4bDEwGRiF6t7ozrYdgceB5rgeeRn7voeAz4ELVbEfZo0xJZYl9IwxpoQSobwITwL9mjQhPHo0vnPOcVV4eaEKX30Ft99OaMECvMBYVQaosjOOYRtjjDEmnkQGf8fpI/r7xrE4cgw33SQ8+JBQvXrep0xJgVGj4NGREa0WXi+jQzfRlf9ldcn8ZN64702S7wXaAR8Bo3G9/SqyP6mnwFbgOFU25D1CY4wp+iyhZ4wxJZAIjfx+Pi5blgbPPIOvZ0/werO/LiciEZg8Ge64g9CuXawKBrlAleXxmd0YY4wx8VRVNt+9Qyo/1a6dMvp5H8ceG7+5166FgfdFmPS6h3t4gse4H08mRXULaMKJzPu/ML57VfkGQIRzgekcuPS2sypT4xelMcYUTZbQM8aYEkaEk30+Zhx3HBWmTsV31FEFc58//4QuXQgtXMjOUIhzVfmlYO5kjDHGmNyKLm99DLh32DAYPBg8GRe3xslrr0G/68NcHH6f1zWZpEz2sYggPTwaeSNDnEOBB3HVeeNU6V8wURpjTNFiCT1jjClBRDjS52P+mWdS5cMP8VaoULD3S0mBLl0Iz5rFtlCIE1RZW7B3NMYYY0xOiHCbCKMnTIBrrin4+82cCZ0vCNFn30s8x62ZnfYzqq0yxOnF7Xp7JHCWKrsLOFRjjCkSLKFnjDElhAhJPh/f1KnDyXPn4q9UKTH33b4dTjqJ4N9/My8Y5ExVUhNzZ2OMMcbEIkJbEWYOH4538ODE3fftt+Gqq2AivejF5FinbET18MRFZIwxRVcBFVUbY4w5BA31+Wj50UeJS+YBVKoEH32E3+vlZGBY4u5sjDHGmIxEqODz8d+LLoIHHkjsva+8Eu6+S+nnGcsKGsQ6ZVdiIzLGmKLLEnrGGFMCiFDd6+WuESPwNmmS+Ps3aQIjR+L1evXOl6XfKYg0RqRi4iMxxhhjSiiRWogcexR/POD3a5UJE/AWVM+8rDz2uFCrtvAoMbOJ7yY6HmOMKaosoWeMMSXDXRUq4L3xxsILoF8/OKxiqNQq6v8fsBTYgsh0RHojUrrwIjPGGGMKnwilRXhOhAdEiM+yU/cA7RFEVgJ/76Ls4t2+ivfffrt4q1aNyx1yzeeDB4YkMVGuYTV10w/tAZ4qnKiMMabosYSeMcYUZyL1UqR8+1Le0B0DBuArVy5/03Xt2hURYcaMGbm+tlw5uPMePy96byGEF8ALdAQmAD8iUj9/0RljjDFFWi3gFuBhYI0Ik0U4Jc+zidwA/AYMBOoDvMMV7JSK3HVX7qcbNmwYDRs2pFKlSlSrVo3zzjuPefPm5Sm0nj2hxuERXuGGtEMhoC+q6/M0oTHGlECW0DPGmOJGpDQidyAyG1i1lKNn7Av7SnfunL9pJ02axO7d+dtY7qKLYEe4PPNpnnGoOTALkSPydQNjjDGm6BPAB1wJ/J8Ic0XoJULOq9lF+gEvA6XSH/6as2h9SoTq1XMf1FVXXcXPP//M9u3bWbt2LR07duS8884jHA7nei6/Hzp1TuJrXweAZUA7VN/MfVTGGFNyWULPGGOKE5G6wGxgFNAKYBZtqFw+SNOmeZ/277//ZvDgwYwdOzZf4TVpAlUqBJlFm1jDtYAB+bqBMcYYU3z4o7+eALwGrBPhERGOyvIqkfLAyFhDs/ztaXOOP9ZQto455hiqVKkCgKri9XrZsGEDW7ZsydN8bdrAj+FWYUGbozorT5MYY0wJZgk9Y4wpLkTKAV8DzdIfnseJtGwp5LXxtarSp08fBg8ezFFHZf0zRHY8HmjZUpjLSZmdciMiCdyD1xhjjDnkeXBVe5WBe4DVInwgQnsRJMb51wEHdcjbSXlWButwSt4X8fLxxx9TuXJlSpcuzV133cWdd95J9byU+wGnnAIRFS9wfN4jMsaYkstX2AEYY4yJmxvhwO7SACmUp3JVb54nffHFF1FVbrjhhuxPzoHK1XykUD6z4XIv0v/cm4TFcbmZMcYYU3TUycE5aT+/XQh0AX4U4br0J6ylRtua/HPQhWnfeytXznuAF154Idu2bWPLli1MnDiR2rVr53mudHFk+qHAGGNM5iyhZ0yCiUhV4It0h8oCDYDDVXWLiJwCPIvreVIamKCqT2Qy16nAK0AZ4G+gp6quiY71xC1f9ALrgWtV9c8Yc1wApM1/t6pOjx7vA9RQ1Ufy+ZZN4sRcriooEc3bhCtWrGDEiBH8+OOP+YnrABpRPEQyHX+J/lPidjNjjDGmeEp7UtcaWJB+4GMu5DpePegCwX0Y0Dx+JkjvsMMO4/bbb6dKlSocffTRNG9+UG/cbKWLI/MPBcYYYzJlCT1jEkxVNwMnpr0WkQHAWaqa1oDkFeAhVf1QRA4DlojIVFVdlH4eEfEAbwDXq+qX0XmeAS4XkWOBJ4GTVHWdiPQAXsQ9zc1oOHBB9PfvAdPFbUzQEzg3Tm/bFKSACNOoD8TcUKIKW1m0JsT+Vjw5N2vWLDZv3kyLFi0OON6tWzeuvPJKXnnllVzPuWFtiKPZFnMsiG9TEH9HIDXXExtjjDFFWx3g0yzGI7jlt1uAADAF2JzxpONZ1Bl4NOPxiuxAiLB+fXy6LkUiEYLBIMuWLctTQm/Dhn9/G/tDgTHGmCxZQs+YwtcXGJjuteJ6pACUwyU2YnUbbgGEVPXL6OuXgYdFpDTQFPhVVddFxz4BJolI1WhCMb0grkpQ2J9EGQXcp6qhvL8tExcBKQ0cidswIrOvI2lPEpNjT3EKs5k09zr27YNSpWKfk5krrriCDh06HHCsTp06vPzyy3Ts2DF3kwGpqTD7Fw89+b+Y435Cjy3S4+fmemJjjDGmiBNhbyZDQdxTuR9wD28/UCWY+UQ/LMH10WuY/nBZ9tDM/zvff38cl1+e+/hGjx7NVVddxRFHHMHGjRsZNGgQSUlJnHHGGbmfDPjuO/B62RMOW5sNY4zJC0voGVOIROR0oAowNd3ha4EPRORhoDrQT1UPboQCRwF/pL1Q1Z0isgOX/JkPnCwiR6vqUqAHLmFXl4Of5N4LTIz+/k4RuQhYr6qz8/0GTeYCIkA10iflYifr0je1jgD/AGuiX/OAj4E1+FlDaR5mL60z3qoNs9ib6mXOHDjttNyFWbZsWcqWLXvQ8WrVqnHYYYflbjJgzhzYm+qlDTE3s/s/XCWpMcYYYyAMhHCf055X5bccXaUaRuQm4CMgKf1Qm+AXfDuzYcbDOfL555/zyCOPkJKSQsWKFWnVqhUzZsygZs2auZ4LYNYsVITvVbEHyMYYkweW0DOmcPUFJmWohLsfGKiqARFpAHwtIj9nXHKbFVVdJiL9cVV5PlzSZxsc/IFJVWcBpwKISAVcNV8nEbkDOAPXf+8uVbUlkDmV06q6Az9Np7A/UbcGl5Rdk+FrPclZVE1eLWHgy4yHG7OMOv51vP1WDU47LdZmeLmj+Wi+8/bbUMu3nqNDSzMOvQVcj+ru/MRmjHGi1dpv4XaP3ANsAG5U1eXR8cOBSbgKnn3ATar6TYx5zgMeT3focOAfVT1ZRI4EJgD1onMsA/qr6sYY81i/VmNy5w9cNd5rqnlYkqr6GSLTcBtn/Ks9XzDmt5tZuhSOPjp3U06dOjX7k3Joxw6YOpVwKMRncZvUGGNKGMnPD2bGmLwTkfLAOqCVqi6JHqsGrFHVUunOmwJ8pqpjM1zfCpisqsdGX1cANgGVVHVvhnNrAKuBwzSLhImIPIdL/i0DxqnqOSIyDFitqhPy+56LPFdVV5WsE3XZVdXF/krWnXGJUeRZ4NaMh0dzG/cnPc2qP7zUqBGXO+Xa+vVQ/6gww1Pv2zeA/wSBtbi+jW+i+mvhRGVM8RRN6LUDPlVVFZFbgMtU9ezo+HjgT1UdGv1+8j5QX1UzX8bnrpsKfKmq/4n2W22sqt9Gx54Eqqtq7xjX/QxcGn35nqq2jF7/FnCutXgwBkQQXA/jDcBnqvncLEJkI241wL9CeDnet4wzkusyYWJ8eunlxSOPwEMPsTscprYqWwstEGOMKcIsoWdMIRGRvridZ89Md8wLbMT90DUzmuCbA1ypqj9kuN6DS7xdl25TjNaqell0vGZ0QwwvMB7YpKp3ZxFPa1yFRi8ROQF4RlXbicgQYIuqPhfXP4BDTUBKkbOquvRd6DJW1cX6yrqqriCI3AY8htv9GIDdlKGB70+631KNUaMSGs2/7r4bnnuOLcEgdVSxSjxjEkhEWgLvqmq96OsUoFFaSwcRmQ08oKozspjjSGAFUFdVN8QYvwy4JS1pmGHsB1xLCQFeVdXTRSSA+15jLR6MKQgiu3B9kg8wkV709Uxg0WJPrqv04mHbNmhUL0SV7ateWaaN+yU+AmOMKR4soWdMIRGR74GxGSvfRKQDbnmTD9cAeZyqPh0d6w8cqaoPRV+fhtsMozSu2qmnqv4VHfsU1zOvFK7q7h5V3ZdJLH5gBi6RuDF6bBzQGlf111VVi+bT04Or6jJL2qV/gh3BLTXOrqpuR2LeRB64is1LgCuA+oD3Ue73DpJHGn/8sXD++YkNZ9o0uOACUOVGVV5K7N2NMSIyGfdw5nYRqQqszVAN/g4wTVXHZzHHA0ALVe0WY8wLfAF8oKoHPTYQkTbAU9GXdwKHAe1V9c78vC9jTBZEFuKW3R8ghJcWvvl4jjma72b7idEqt8BEInBJ5xA/fbaV30LHpVRj84XEWO5vjDEme5bQM8YUXXmrqttF9lV1/yS8qi4BRBCPh8nly0a6z5nn8TRsmP018bByJZx0EqGUFN6JROihin3jMSaBoom4zrgE2u68JPRERHBV4bep6icxxl7C9dfrpqpZLhNM368VuB7r12pMfAWkEtCdj7mfAHVjnbKCBrT0zuOiK8sy6XUvkv8WuzkyYgQMHxLiSz2bM/kOXEuYxtiye2OMyTXbFMMYc+hxVXWHkX2vuvRVdcqBvep+BT7lwETdWmAHySXzSYYqukEO/+28vV9cfV67Y/nsSz8NGhTsPVeuhPbtCe3Zw++RCNdbMs+YxIq2Y7gU6JDWQ1VVN4tISERqpNtFvR7wZxZTnYWrBp8eY+xZoA5wSXbJvKhHgJFADeDidP1ar8ZtsmGMyS332ekM4Dpcdb5wLv/lXbykUjvj6Q1ZSSB8BRcGPqZadeU/TwueAm6p9/zzMGSI8qzekZbMA/f/nl649jDGGGNywRJ6xpjECkgSOauqK53uqoxVdb9RQqrq4kqk0uFw/6ehczl/7eec1upY3nnPz1lnFcztvvkGunYltHMnvweDnGt984xJLBG5C+iOS+Zl3CVzCtAfSNsUoxbwdRbT9QVeU9Vwhns8CzTCJfOyra6L9mutpKrTov1a05L8EaB8Dt6WMSa9gByOS4hdBxwDzAfuAQL01q1cK7VxDzibZrz0fKbxOj3o/dwk/lwtvDbZS4UK8Q8xGIR7BiijnxVGMoibeSHjKRdjCT1jjMk1W3JrDiBCWeBYoAmu50YlXB+3ILADWAwsBBarsquw4jSHoMyr6jIm76qnu0rJSa+6ElxVF1ci9wBPAOygAr09k/iQLtx0kzBkqFC1ajbX59DmzTB8ODz/PCrCR+EwPVU5dPsNGlMMiUht4C9gJZC2i/Y+VT01On4EMBnXYzMVt5nFl9Gx4bgluS9FX1fCVTg3U9WV6e5xBvAtsARI69G6SlW7ZhJT8e3XakwiBcQLnItL4l0M7AYCwDhgzkGfmUTKAHcAPTi4p17KW1z5w7W+10+tXtNX5umn8XfrRtyW4H71Fdx1a5Ali8KMi/QhmTdjnfY5qh3jc0djjCk5LKFnEKEqcJnfT89gkNMB8fmINGxIqFo1JCkJSU1Ft2xBly/HFwziAdTv5/+CQSYDU1TZWLjvwhSovFXV7SZnveqCiXkTBpEJQO+0lwqM5Xoe9D1KaumKDBrip29fqFIlb9Nv2wavvgrDhhHas4edoRADgbGq5GQJnjHGGGOyEpC6uN2i++CWuX+LS+JNIVlzVgUv0gw4GkgCtgDfoLpHhFoeD09EIiS3bk3o0UfxtW1LnpbhqsL8+TDsoTD/+8jLud4veCZ8K8ezOLNLXka1f+7vZIwxJZsl9EowEep5vTwaiXC5349cfDFceimeE0+ERo3AF2NBdijkemLNmwfvv0/k/ffR1FTE4+H9cJj7VFmR+Hdi8sxV1VUh+1512VXVreXgZN12q6o7xIi8DNyQ8fAOKvAY9zPKczcRj4/zO0H3Hl7OOguOOCLzp/SqsH69W1r7xhtEPvkEIhHCkQj/AR61qjxjjDEmn9xD1S64aryOuGrWicCrJOuSeN9OhFP8fkYHg7Q+skYk3KOXx9utGzRrBmXKZH5dMAhLlsAHH0BgYiqLlydxnG8Z/wndRiemkUXBXwQ4HtXf4/1ejDGmuLOEXgkkQkVgoMfD3XXqwLBh+C+9lDz1zNi1C/73PxgyhOCqVXgiEZ4BHlYlY68ek2juA2BNsk/WWVVdSSHSDXg3s+FtVOI9LuVNbw9mRs4moh4OqxikyfFKg2OSKF3aJff27oWVv6eycKGweYcfEdTn46tgkNeB9+zfvzHGGJNPATkO17uyF24TsOm4aryPSC7Y3aBFEOCEK3lrwo/+Nif9EayFRyI0PCpIk+Y+qh3uxe93D/q3bY2waH6Qpav8BEMeqvm2ckUoQHfe5HS+x5P9XlgTUO1TkO/HGGOKK0volTAiNPP7+ahUKWoNH47v5pshKSn/8waD8PLLMGgQoT17+CcY5GJV5uR/ZnOQzKvqMi6JPTzdVQpsIPtknVXVFWcigttQpEl2p26iKvNpzkKasJAm/OFpwD5PaRQPpSO7qRtZSRMWMptTbnyDHu+qsqng34AxxhhTjAWkHHA5rhrvDNzO0+OBCSRrVrtQFwyRlyPIDUs4lgU0ZRHHs4gmbPVWI1VK4ddUKkS2cawujn5aWEhTFuAn9DccvLNuRu9Cyn1w8grVZQl4N8YYU+xYQq8EEeFcr5cPW7XC9957+GrWjP89NmyAyy4j/N13hCIRuqnycfzvUozlrKruSCD9ooc9ZJ+oW2dVdQYA18R+GvHZTXIDUA/VPXGYyxhjjCl53IPaFrgkXjJu5cQHuGq8GSQfuLN0QoncDDyfhytH4z5r9APOB0qlGwsBM36HKcfCbbjqww6q8V8+bIwxxZ0l9EoIEdqIMPOqq/C89hqeeFTlZSYYhBtuQCdORFXpqMoXBXe3IsJ9WKtM9stfY1XVxepPl/5rm1XVmdwIipy4HmbVzn9S715Un4xLUMYYY0xJEpAqwNW4RF5z3G7R44DJJOuGwgztX2533FXAEbm4KhVohOpf0TkqA61wnzl2A3PYv8t1FeAToCHQUVXnxS94Y4wp/iyhVwKIUMvnY36nTlT+4AO8edmtKrdU4fLLiXzwATtDIZqr8keuJghIF6AsMI1kPbT7cQXET8561eW2qu6fgu6RYkomEennhRdHwb23wrFAW9wH7coc+N9pVn4EOqC6q6DiNMYYY4oV94C3LXA90A338PZtXCLv+0PyAa1IH+DVXFzxGKoDcz69lMdVJJ4MXKCqP+QyQmOMKbEsoVfMieDz+/m+Th1OnDMHf6VKibt3Sgq0aqmhlBX/bFoaavBdGfaWAnYCXwHvorol5oUBKY17gie4na++B/4HTCU5gTtgZV1Vl75f3eHRWNPkpFedVdWZQiEiTYGfgFGq+kCGwVLAM0D/bKZ5C7ge1ZQCCdIYY4wpTgJSA7gGV43XCPgZl8R7i2TdXpih5YhL6r3AgRupZRQBRqA6NPfTS2lgCnAO0EVVZ+YlTGOMKWksoVfMiXC1CJPnz0eaNUv8/X//HZo2ifBS+Hr6Mj79UBCXFLgVjfFBJiAbgOrRVwqEAR+wGngfmAp8m+cKtpxV1R2JqxJMs5ec9aqzqjpzSBKRssBsXGK9rWomfRVFGgNXLaPRfTVZ5y/Prt3AOty/vQCqCxMVszHGGFMkBcQHnIdL4nXGfe99HXiV5CK4tFSkIdAb6I5bIgtAEN8mP6E3gImozs379OIHJgOXAJep6tT8BWyMMcWfJfSKMRG8fj9LLr+cBm+8QQIW2sZ2fd8IX0xaw++hBvgJZRxeArRHde0BRwPyDdAmkymDgB/YhWu4+wHwKcm6KVpVV4mc9apLX1W3keyTdVutqs4UZSLyEnAVcKKqrs76XDzANuB+VcYkIDxjjDGm6AtIfaAPcC3uM+dXuGq890guJptIiRz5MINOeoUbpm7hsGYpWn5BfKYVL/AK0Avooapvx2NeY4wpriyhV4yJcKkI/124EI47LvfXqypDhw5l7NixbN++nRYtWjBmzBiaNm2aq3lWroSjG0eYFOlBMm/GOuVDVC8+4EhAXgL64qrysgyT/Ym5Dbg+YDmpqku/0cQ6knVfbt6TMUWNiHQD3gWuUNUp2Z9PY2ApcJoqPxZ0fMYYY0yRFZBSuMqy64AOwHpgAjCeZF1WmKEVFBGaAAuApqrErXJfRDzA07gdcK9X1dz07zPGmBIlu2SJKdo6tWxJ8Ljj8Ofl4qeeeorx48czffp0GjVqxPDhwznvvPP4/fffKV8+55tjNmgAZ50ZZto3nTJL6HWhkbRhOFvYX0F3FOSoqlDYvyT3S+BzrKrOmAOISF1cdcDYnCTzolrg/l3NL7DAjDHGmKIsIE1xD6B74fouf4JL7H1CciZtLUyWVDUiInfiliiPE5Hyqjq6sOMyxphDkSX0iiuRMuVKpXY85xx/npJ5AGPGjGHAgAE0izbfGzFiBOPGjeP999+nZ8+euZrrzLP9TP6hnVssG8vxfJPu1T5gK9kn9EJAWmn+QyTrhlwFZUwJICI+4A1cD7w7cnHpycAiVYrH8iBjjDEmHgJSHrgSV43XGliFqyh7jWRdU5ihFRfqlpA9KCI7gWdEpAIwUm1pmTHGHKDQ+qqZAiDiQ+QyRN7bQpWtu/b56555Zt6m2r59O6tXr+aUU07595jP5+Okk05i7tzc97s980xYFazNPxwR+4S5TAWaA9WAMsBZWUwXjv46E2hGsva3ZJ4xmRoCtASuVNXdubiuBTCnYEIyxhhjipCACAE5lYCMxT0gG4PbqK0D0IhkHWnJvPhT1SeAm4ERwGMiItlcYowxJYpV6BUXIjVw/bHOANjCYQDUrp236Xbs2AFA5cqVDzhepUqVf8dyIy2OTVSjBusPPuFvvidZf/33dUBWAREOTjorsBy4nWSdnutAjClBROQcYBBwi6r+lvPrEFyF3v8KKjZjjDHmkBeQqkAPXDVeU2AhMBh4nWTdXJihlRSqOkZEUnA9CSuIyC2qGinsuIwx5lBgCb3iwJWhfw0cnXYoGG2b58vj33DFihUB2LZt2wHHt27dSq1atXI9X1ocwdjt/HYDBza8TdYgAfkLqBs9EgJ2AANxDYYP2i7XGLOfiFQDXscl5V7M5eX1cb2ArELPGGNMyRIQD3AOLol3Ka5hzJvR17OtN3PiqeokEdmF+3soLyJ9VDP8LBCQBriHkRVxPba3A/NI1uWJjtcYYxLFEnrFwy2kS+YBlMWtrNu5M28TVqpUiXr16vHTTz9x2mmnARAKhZg3b16u++cBpKQcGFcGL6Axl8wuwiX0gsBTwGMka+7LA40pYaJLUibglqdfl4eeMy1wFbLz4h2bMcYYc0gKSC2gN26Ti/rAj8BNwDskax4/UZt4UdX/ikgX4H2gnIgk6xtUAa7E4++J++wC3tKuNU94rxeAt0rNI5I6GXjblkUbY4obS+gVdSKlgLsyHq7N31T07mLu3HKcfnrepr7pppt46qmnaNeuHQ0bNuThhx/G7/fTtWvXXM81Zw6U8eylfmRVxqHXcFV3sTwMrASeJFn/yPVNjSm5bgMuAM5W1S15uL4FsESVXfENyxhjjDmEBMSP+355XfTXbcAk4FWSdUFhhmYOpqrTROQ8gamDLmGO4mks3lIe6nTzUC8ZjmgP3iSXyIsEYf2XsDrQnD+nNCO853ECcjfwnFVZGmOKC0voFX21cRtJHMBLhNP1O2Z93YGbb87b3icDBgxg586ddOjQgR07dtCyZUumTZtG+fLlcz3Xt7OUUz0/kRQJguuD9z3wIqpvZHpRsn4fPc8YkyYgVYBTgEqAH7cUfSnJ+juAiJwEPAEMU9VZebzLydhyW2OMMcVVQBrhKvF6AzWAL4Bk4AOSdW8hRmayoW/w7R+b5IujqnKJNBkITR4AX9mDT/T4oWZHqNlRaPWil8VPwm9DnwFpQUD6WvseY0xxILb7d9ZkmBwOnMT+fgw7gAU6RP8u1MAAEfHeD+0ehc9ijY/kAZ6tMoQ/1yVRqlSio9svFIK6dcLh5v9M//wTLnwT+Bq1ijtjciwgZYHOiPdqVM+HSPRhjEch4nZ88yQt3LEr9c0TB9Jn1Ub+AtqrajjTOTMR3RBjEzBClWfi9h6MMcaYwhSQMrieeNcBZwNrce0pxpOsKwsxsiJJhCbAAqCpKgsTduOA9xnEcytnTvFQ55LcXbv2U/jmkgiR0DiSw/0KJkBjjEkcS+jFIMOkItDV5/H1CEfC7RQ9aKdVv8f/QzASnAS8q0MKfperaE+s2rjKnLSvll4ov4L9O0ek9yd1aORZyfMv+rjhhoKOMHOTJ8M11xBR5WhVVhReJMYUQQHpgPjeQsOHccQ5Yer18FHrIihVFRAI74FNP8IfAQ2vehsJp8j67TxTszL35OXpswh1gdVAW1XyWuFnjDHGHBoC0hyXxOsBVACmAuOAaVallXeFktALSD2Q5bR60Uvjg/NxCxcupF+/fogIqsqoUaNo1arVgSetnAg/Xqugx5KsSxMStzHGFBBL6GUgw+QKr3jHA2U6NeqkVze72tuxYUcql66MiLBj3w6+Wv0Vb/z6RuTDpR8SioRCEY3cBryiQ+L3hykiVYCWHJjAq4FbrroImB39+mk3tC4Dz8eapx8v81mt3izCq6JdAAAgAElEQVRdlYQ/5gazBSschuMbB6m8et4v/6entML+gzMm5wJyM/AcdS5VWr7goUyNrM8P74PlY2HOXRHQmWjoYpI15k40mRHhUuBdoJIq1gTcGGNM0ROQikB3XCKvJbACl8SbSLKuK8zQiovCSeh5XqLMkX3ostKPN+mg4Q0bNpCUlETlypVZtGgR1113Hd9/n6F7TyQEHx0dZNcfb5IcviYhcRtjTAGxhF6UDBPBbcLwQL8W/XRku5FStWzVLK/ZuW8nj377KI99+xjAOEX765C8LHGT0kBzDkzepe1a+xf7k3ezgV9UM+y0JZIEzADaZJx7NXU5xrOMewb6efjh3EaWfw8/DMMeCrFAm3AMS8eiWoi1gsYUIQHpDUzghIddfxiRnF+7+Sf4okOI8J6v0GDH3DR/FmEk0E2VY3MdszHGGFNYAiLA6bgk3hWAF/eAahzwtW2EEF8JT+gFpCrIP7Qa46Nx/2xPX7FiBb1792bWrBiLDVyVXgS0tiV4jTFFmW2Ksd+Dggx8tcurXHvStTn6yblCqQo80v4RWtduTbd3uvWJaCQE3JjVNSLiAY7hwORdc1xz++24pN2U6K8/qebgm4xqKiKdgJdxywn+VY8/eD5yEzeMHEvLlnBJLltN5Menn8JDDymj9U6OYSnA9Yh8iOrUxEVhTBEUkNKI/wkaXQ9NBx00vHfvXtq3b4/f7yclJYW7776b7t277z+hais4e6qPGW07AB2Az3Nxd9sQwxhjTNERkOpAL1wi71jgV+A+4A2SdWthhmbiqi6ojxodsj0xFApx8803M3jw4Ngn1GgPrqVSfcASesaYIssq9AAZJkd4xfvnyHYjk+47875Mz9u0aRONGzdm1KhR9O7d+4CxifMm0vuD3gocr0N0Cfzb964WGfre4fp3pAJzObD6brmqRvL3ZqQ5bpeuy3A99wRYd658vuOHMu2P//Ir8WRsJVEQ5s2Dc9oEuWjXO0zSHqTLkM5G9dSCj8CYIiwgNyP+Z7l4tYeyRx40rKqEQiH8fj/bt2+nadOm/PXXXwfP80X7MBtm/YwGT8tJZUJ0Q4z1wOOq/CcO78QYY4yJv4B4cA+srgcuBvYCAVw13i9WjVfwCqFCry3wNV3XQpmamZ4WiUS4+uqradu2LTfemEmdReo2eLcKQCeSdXpBhGuMMYlgFXrO3ZVKV/LceuqtWZ40bNgw2rQ5aFUrAD1O6MHwr4eHV61e9ZyIfMn+BF5NXN+7xbik3bvRX39V1dQ4vgdHdT4wH/dk8l8zhFK+VKa2acM5U6bg7dw57nf+1/TpcPmlIVru+56X9XoylDuegsgRqK4vuAiMKeLEP4hGN8RM5gGICP5oU8yUlBROOOGE2PM0G+plRttTgdOA72OfdIDaQHXglzxEbYwxxhSsgBwFXBv9qgt8B9wATCFZdxVmaKbAuWrLfZsyTeipKtdddx3NmzfPPJkHsHdj2u+2xDdEY4xJrIy7t5Y4MkwqeMV7631n3Ocr6y+b6XmLFi0iJSWFk08+Oea41+Nl6NlDfVSiA5W5BQgDo4F2QGVVbaKq16rqGFX9uUCSeVlQZV8oxAXBIJO6dIF774WdcW53v2sXDB4M55+vdNnzDp+GO1KWPbFOLRPfOxtTjASkLBqsSc3zsjxt+/bttG3blhNOOIGuXbvGPunwNuApFcEt88+JtP/Bzc1puMYYY0yBCkgSAelGQD7F7cJ+C+4B+fEk65kk62uWzCsRliP+LayanOkJH3/8MYFAgGnTpnH22Wdz6aWXxj5x9WQQ305gScGEaowxiVHiE3pA7bCGS5/f6PwsTxo0aBDDhg3L8pxOjTqhonAHV6tqN1V9XFW/VNUd8Qw4r1QJRiL0Bfo9/TS76tcnNG6c24k2PyIRmDgRjq4fZPRjuxmttzFZr6YUMXOWKbglfcaY2MoD4C+f5UmVKlXim2++YcmSJYwYMYLt27fHPtFXNgxUzOG9WwDLVclkMmOMMSZBAnIMAXkCt0HcFFwbmSuAWiTrAJJ1caHGZxIrWfegwf9n787jrKzL/4+/3sOquJHmgoCa5bevW8nYgrlrCoo7Jt5uCJhlZVn9WqzUrK+WLZpZpoaAy63mvoIrGCpqau6l4gaCuBHiCsPM9fvjc08OMDPMwAz3mZn38/GYx3jOuec+71E8nHPd1+dznc4zZ9c26LBbzLBhw/jwww+ZMmUKU6ZM4Zprrln6oIXz4F+/ryUWnUEWbdzeYGa2crmgV3zQXa1n0x+eJ02axGabbcbAgQObPdHqvVZf7JyVKIKI4PzaWjaZO5fzvvpV6jbaiJoTT4QnnmjduZ5+Gn72M9hsk4WMOrqOYW+MY3rtJnyLc5ZcZtvQuUQ02rZnZgDMA2DBW00esHDhQur3P+3Tpw+9evWid+/eSx9YVws173Sn5UtKBuHltmZmVpZcq5LrSHL9ndQ9NQI4F9iELIaQxVVkK3eVi1WUvxC17/D4T2F5tkmMgMdPhroFHwB/bPN0ZmYrmffQK/ZjmPvBXDbpu0mjBzz88MM88MADDBkyhOnTp7PKKquwySabsNNOOy123Fvv//cDeMVP1IrgDeCbEn+cNYsxv/0tR5x+OutttBELq6vpseWWaIstYO21oWdPWLgQ5s5NRbwnnyQefpiaF1+kZ78eb3BIzcWMZixb8HTzzwlvCX675P0SawJfBm6J4P32+Y3NOogsFnJ5r8d56ZItGTi80YsuzzzzDN/4xjeoqqpiwYIFnHrqqfTq1WvpA2deBbEI4B8tfPZq4MzljW5mZrZccg0iTak9DFgVuB4YCtxOFiu4lsQ6jSzeJdfXmX5BjrpB9R9EVY+W/WzdIvjnD+DZswP4Jll4NYKZdXhdfsqtfq5ePap6zDr+C8ev/ds9lqo1LeWUU05h4403XmrKLcBv7/stP7rjRx/WRm3/ODmabq+pQBJVwJeAPauq2LJHD7ZZsIABsFizXfTqxSs1NTxaV8eTwG3zWT1W590rSRvpN+klNoqM/N5pbDc0gncbPO//AjeTxsZ/N8LFBDNyHQBcw16Pw1pbLd85og5u3qKGd567nkMXHbyswyX6AbOA3SO4c/me1MzMrIVyrQVkpELeNsAzpCm1F5HF62VGs2Vb6VNuG8o1HHW7hL7bdOMzp3VnvV2gqok+lbpaeP1uePyni3jzgYC6kWSRr9S8ZmbtpFUFPUlDgF8CPYH3gWMjTVVteMyuwO3A9yLirEbOsRXQcDfTtYA1IuJjxeOfAiYA6wBvAyMjYqm/JCSNBMYB342IMxvcfzewI9CXNMb+iIjYvXisP2kfjqMjYnxx34/ox/BeX+/12RknzOi2bp91W/zvo6H3a95nwJkDFs39YO5ZcXL8v+U6SYWR6EXaz6sHUAO8F8GHjRzYDzietCxioyUe/Tdw2Y7cfc9UdrwCmAHsHcEcif2AnPTnqTvwZATLWb0w60RyVVHV40nW2+1/2OnGqibfpDbn+XHwwCiArclimQvqJYYBNwJrR3jqm5mZtYNcAnYgFfEOBgL4G6mQdy9ZF+806EBKLegB5Nqcqh7jqav5HD0/toiNs+6s/2Xo2Tc9vnAevHYnvJTXsOCNHlT1fJS6hUeRxeONnU7S0cCFwAERcV1x3+eBs4FeQG9gXESc0cjPVt7nW9giIo5Y5r9HM+vQWvwpUVJf4FJgx4h4StIOxe0tGxyzJvAr4JamzhMRTwCfbfAz55D+Mq93HnB+RIyXNBwYD3yuidP9EziKYomYpE+y+ATVycCfJfUspsruAjwA7FycF2AX5nJBbdT+6qTJJ6117t7nIjWzA1wTfnPvb5j34bw6GllS2lFFsABY0IIDZwM/Qvox6QrrBkAdMIPiL6u/AxKDgUnANImbSFPKgo+6ALeU2KKUNwVmlSSLOnJ9nTm338Y9B3dnu0uq6N6n5T///IXwwJgAnUlW19LdMauBF13MMzOzNpdrPdJ79jHAp0j7tZ4AXOalj7Zcsnga+Dy5NmPh3BFMP/8onj3nE4sdU9XzJeoWXgRcxogFTU60lbQxcAxw/xIPnQ+cFBE3SPoY8G9JN0XEYvsMVeTnW7i8qd/XzDqP1gzF2BR4q/5qQkRMBQZKGtTgmHNIHXwtWm4qqTdpr4yxxe11gW2BS4pDrgYGFC9kjZkBvCGp/gVxFOmqBkXGGcAc4PPFXTsDvwYGF8/XA/gSH3LrorpF3zjv4fPihFtPYFHdopbEr38OTpt6GqfcfQp1UffDODm67gTXiCDiESJuJmIiS1x5imA6sAfpqtU3i7sbVk9rAF9JMgPI4m6idg9m3fQeE7epYdYtUFfT/M+8/TTcd0TwwGggTof4fiuesRoPxDAzs7aSqxu59iLXNcArwInAbcAgstiWLP7iYp6tsCyeJYtTqVv4SaAP0A/YEFiNuoWfIIuTyaK5Yl4VqUP0WyzdyBCkzy0U517IMgaNVczn21T4M7NOrjXruJ4D1pa0XUTcJ2lfYHVgY+CR4mpDXXEF48AWnvNA4IWIeLS4PQB4NSLt4h4RIWkGMBCY3sQ5xgGjJD1CGmW/LfDnBo9PJl2luAfYHvg28F1Jm5A6yV6PiJeAl/RzxR8f/OOEf8z+h87Y/YzugwcMpkqN1zwjgkdefYSTppxUO/G5iQK+EyeHpyU1Q+J/gJtIy3gb0wMYKXFiBHUrL5lZhcribnJtybsvXcTde+9Ej7UWsdGI7mw4DHp9PO0XUzMf3rwfXry4hvlP90A9XgeOIYsbW/lsg4A/tcNvYWZmXUmujUlFiKOB/sDdxT9fQxYefmbtIy3Xfr/4ao3vAvdGxMONrNI6Grhe0i9J+4UfGxFzlnG+Svp8a2adXIsLehHxdlG0O13SasA04GlgkaT1gZ+SrhC0xmiKqxcr4BrgNOAA4IGImLfEi/Fk0gviBGBuRLxb7EOwM+kF779XL+LkuEw/19P/mPWPS7Yft/2W/VbrV3P41of32GPTPfjYKh9DEvM+nMeUl6Zw0WMX1bw478UePap6zAziiDg57lnB36NTk9gbuIK0B0Vzf+7WI/23uWslxLIuQtLasNigh1WBTwDrRsRSV1ol/ZC03GEh8CFwfEQ8WDz2BdISjFVIHQdHRMSsRs5xCnAycGBEXFvcJ+AFoG9ErCXpZGBARIwpHt8emArsEhFTAHQYJ0LN1LiUUdTMG8ELFx7F9L9stviTdZ9PLLocyImaqWTRqoK4xHqkq9nu0DMzs9bL1Yu0t9cYYHfgddLyvwvJ4tkSk5k1SdKWwEGk/eka8yPgxxGRS/oEcLekh5ZccruEivp8a2adW6t2Wo+IyRQvEJJ6kdp9nyYt1doAeLR4sVkH2FfSxyPiJ42dq7iC8EXSi2i9mcAGkrpHxKLiw+9AUutxU5k+lDQROJc0lGFJk4ELgCGkK4QU348oMo9f7Hwnx2P6ubYGtp797uxDz7z/zCPPuO+MDRoe06Oqx1s1dTWXAnlNXc2DcbI38G2OxCHAZfU3l3F4DXAkLuhZG4qIt1h8b5PvAzs1Ucz7LHAcaTPhdyUdTtpO4PPFsoxLgWMiYnJxnrNIG3s35mFSl8K1xe3dgDdJmxpDen26sMHxDfdBmdLgvq+RxQukN3enketjwBqkrtb5xKK3yKLlewUsrX7rhEdW4BxmZtbV5NqCVMA4kvR320RSh9LNZLGMfSLMVi5JR5I68gD+QBp0sTHwXPEZdn3gfEkbAFeSBmSMAIiIFyTdT1rO2mhBr1I/35pZ59Wqgp6kDSLi1eLmz4C7ImI6qV14vQbHjQcebWzKbQOjgGsjYl79HRHxetFafDjphegg4JXiOZrze+B5GikCRcRsSTOB75MmsQLcR+qw+TipC2fxn0kFuseAx/RznVgctyapGPV2TV3NG3Fy1C4jk33kHVL7ex9Swa5HM8f2AL4icVxEq1vmzVpqNPDjJh4L0p/DPsC7pL1TXikeqwYWFRc3IG1y/EtJvSNi6QnQaSnEHpLWL5ZojCIV8E4vHr8f6Cepf0S8QirknQr8ANJrLulN37TFzprFXJaxh0srVQMzInizDc9pZmadUa7VSMsAx5D27XqZVBwZTxYzy4xm1pyIuAi4aIm7z63/B0lTgLMi4jpJ3YD3JO0aEXdJWgf4AulzZ1Mq9vOtmXVOrSroAacW0227kz5gjm7JD0n6GtAvIk4qblcBI0lX85Z0LDBe0onAfNLeBc2KiOdofrrs5CLrPcXx70maAyyINKG16XOfHHXAa8WXLYcIbpHoC+wA7Ev6i6w/sIjG/wz2Ji3buKyRx8xWiKTtSF0ENzX2eEQ8JulM4EVJc0kbJNcvxRhI+uBSf+w7kuaTNmB+oYmnvAQ4StJ5pIlmP6Uo6EXEQkn3AbtI+huwSUTcIunsYlPlXYBpTRQL25IHYpiZWdNyifR32BjgUKAncB1pa4k7W7vVg1mli4haSV8BfiOpO+li71kRMQ069udbM+s8FF4taiuZhIDN+ai4N4iPRrvXTyG5O6LVezKaLZOksaSJ3T9o4vFNgBw4qLgC+k1gRERsL+kg4KsRsWeD418HvhgRLyxxnlNI3X2/AW4nDZzoR1oi8WhErFUc9xPSfn4TgOMiYoSkS0j7rxwKzIyIX7Tdv4HGfmdeBi6I4Jft+TxmZtbBpC0eDicV8rYiLTW8ALiELNzV3YVJbAE8CWwZwVNl5zEz64pa26FntsIiCOCp4uv0YkP+vYH9gT1JV313ktg2gofKS2od1ZJ7pETEuOL++mVCn2vmxw8CnmhwdXMc8EdJPUn7nWzU4HlWJy3Hb/JKaETMkvQyqYthu0YOqb/COoOP9s27m9Sdtwvpam+7kViH1HnoDj0zM4NcVaQtIMaQ9sOrBS4ndRndX0wTNTMzs5K5oGeli+A10r5iF0qsQlpu+zPgDokDIjypyVqniT1SAA4BHouIfzfz4y8AR0taLSLeBYYBzxbLYx8GekjapdhH71jgxhYsif0ZMCgipkvaeInH/gGsCxxG6lqFVNC7ibSx8YPLOPeK8kAMMzODXP1IF5FGkzrHHwS+BVxBFvNLTGZmZmaNcEHPKkoEHwCXS1wHXAxMkhgZ4f30rE2MJi0VWoykU4HZEfEX0kTazwEPSVoAvAdkABFRV0y9Pa/Y4242aaJYsyLiIWi82zQiaiTdA3ymvtAYEc8W3X/3RLT7lMBqYFZRWDczs64kV3dgL1I33l6k/b0uBsaSxeNlRjMzM7PmeQ89q1gSVaQJT98mTf38bbFc18zaiMRVQI8I9is7i5mZrSS5NiVd5BpJ6ga/C/grcC1Zuw9isk7Ae+iZmZXPHXpWsSKoA74jMQP4HTBA4oQIakuOZtaZDALGlx3CzMzaWa7epD3xxpD2aH2VtE/shWTxfJnRzMzMrPVc0LOKF8HvJWaR9kTbUOLwYmmuma0AiY8Bm+D988zMOq9cW5OKeIcDawA3k/ZsnUgWi8qMZmZmZsvPBT3rECK4QmIOcB1wu8R+EbxVdi6zDq5+IIYn3JqZdSa5VgdGkAp5nycNfPoNMIEsmpzMbmZmZh2HC3rWYURwt8T2wETgXomhEbxYdi6zDmwQMCeCV8sOYmZmKyiXgMGkvfEOIb3Pvxr4EXA3WdSVmM7MzMzamAt61qFE8JTEYOAWYJrEXhFeLmi2nKpxd56ZWceWax3SxPUxwObAE8CPgUvJYm6Z0czMzKz9uKBnHU4EsyR2JF11vltieAS3lp3LrAMaBORlhzAzs1bKVQXsRiriHQAsIL2ejwQeIosoL5yZmZmtDC7oWYcUwdsSewFjgZsljolgXNm5zDoKiTWBT+KBGGZmHUeu/sDRpGW1GwH3AccCV5LFu2VGMzMzs5XLBT3rsCJYKHEk8ApwocQA4BcR+Kq02bJ5IIaZWUeQqwcwjNSNNwT4DzABGEsWT5cZzczMzMrjgp51aEXx7scSM4E/Av0ljotgUcnRzCrdIOANYFbZQczMrBG5NiN14o0EPg7cTppcewNZLCgxmZmZmVUAF/SsU4jgzxKzgMuAfhKHRPBe2bnMKlg18LA7Ws3MKkiuVYGDSN14O5JWIfwFGEcWL5WYzMzMzCqMC3rWaURwvcSuwE3AFIlhEbxWdi6zClUNXFl2CDMzA3JtQyriHQb0AW4A9gJuI4vaMqOZmZlZZXJBzzqVCO6X2A6YBNwnMTSCZ8vOZVZJJNYANsMDMczMypNrTSAjFfIGAc8BpwEXkcWcMqOZmZlZ5XNBzzqdCJ6VGEzq1Luv6NS7v+xcZhXks8V3D8QwM1uZcgnYnlTEOxgQqVv6BGAqWXgbBDMzM2sRF/SsU4rgNYldgCuAuyRGRHBD2bnMKsQg4C1gRtlBzMy6hFzrAUeSCnmbAf8EvgdcRhbzyoxmZmZmHZMLetZpRfCuxH7AucC1Et+M4Nyyc5lVgGrgEQ/EMDNrR7m6AXuQinj7Au8DlwKHkoW3PDAzM7MV4oKedWoRLJL4KjAT+LPEQOBEFzKsi6sGri87hJlZp5RrI2BU8dUfmAqMBq4ii/fLjGZmZmadhwt61ukVxbtTJWYCFwD9JUZHsLDkaGYrnUQf4NPAyWVnMTPrNHL1BPYjdeN9GXgDmACMJYtnyoxmZmZmnZMLetZlRDBO4lXgKmB9iYMimF92LrOV7LOkTdg9EMPMbEXl2pzUfXcksDYwCRgO3EQWvnBoZmZm7cYFPetSIpgksSNwCzBVYq8IZpWdy2wlGgTMA14sO4iZWYeUqw/wFVI33nakAUN/BMaRxcwyo5mZmVnX4YKedTkRPCIxGJgITJMYGsFTZecyW0k8EMPMrLVyCdiWVMQ7FOgNXAfsCdxJFrUlpjMzM7MuyAU965IieFHiS8ANwD0S+0dwd9m5zFaCalIx28zMliVXX+AwUiHvM8C/gFOAi8nijRKTmZmZWRfngp51WRG8JbE7cClwm8QREfyt7Fxm7UViVWBz4JdlZzEzq1i5qoCdSHvjDQdqgSuA44BpZOEOZzMzMyudC3rWpUXwgcTBwJnAFRIbRnBm2bnM2snWQBXwSNlBzMwqTq4NgJGkQt6mwD+A44HLycJDtMzMzKyiuKBnXV4EtRLfBmYCv5cYCHwvgrqSo5m1tWpgPvB82UHMzCpCru7AENKS2mHAO8DFwFiyeKzMaGZmZmbNcUHPDCgGBPxGYhYwHuhfLMH9sNxkZm1qEPBPF6vNrMvL9QlgFHA00A+YDBwJXEsWH5QZzczMzKwlXNAzayCCXOJV0uS624phGXPLzmXWRqqBO8sOYWZWily9gf1J3Xi7AXOAccCFZDG9zGhmZmZmreWCntkSIpgssT1pEug9EkMjeLnsXGYrQqI3sAVwRtlZzMxWqlxbkfbFOwJYC7gZ2A+4hSwWlRnNzMzMbHm5oGfWiAiekPgiqag3TWKvCB4tO5fZCtiK9JrvgRhm1vnlWh04hNSN9wXgReB3wASymFVmNDMzM7O24IKeWRMieEViB+BaYKrEgRHcXnYus+VUDbwLPFt2EDOzdpFLpOLdGGAE0AO4GjgRmEIW3j/UzMzMOg0X9MyaEcE8iSGkPXZukRgdwUVl5zJbDh6IYWadU651gMNJhbwtgCeBnwCXkMVbZUYzMzMzay8u6JktQwQLJA4HZgITJPoDpxeTcc06impgatkhzMzaRK4qYFdSEe8AYCFwGWmvvAfJwn9Hm5mZWafmgp5ZCxRdTT+UmAmcDQyQ+FYE3kzbKp5EL9IeemeVncXMbIXk6g+MJBXuNgamAV8H/kYW75YXzMzMzGzlckHPrBUiOEdiNnApsKHEoRG8V3Yus2XYgrSXlAdimFnHk6sHsDepG28oMA+4CBhLFk+WGc3MzMysLC7ombVSBNdI7A7cANwlMSyCN8rOZdaMauB94N9lBzEza7FcnyJ14o0E1gPuADLgOrJYUGIyMzMzs9K5oGe2HCK4V2I7YBJwn8SQCJ4vO5dZEwYBj0ZQW3YQM7Nm5VoFOIjUjbcTMAu4ALiQLF4sM5qZmZlZJXFBz2w5RfCMxGDgZmBa0an3YNm5zBpRDTxQdggzsybl+iypiHc4sBpwIzAMuJUsvF+tmZmZ2RJc0DNbARHMkdgZuBKYIvGVCG4qOZbZf0n0ALYG/lx2FjOzxeRaEziUVMirBqYDpwMTyGJOmdHMzMzMKp0LemYrKIJ3JPYBzgOul/h6BOeXncussDnQCw/EMLNKkEvAl0hFvK8AAq4Cvgf8nSyixHRmZmZmHYYLemZtIIIaidHADOA8iQHASRH4g4mVrRr4EHi67CBm1oXlWhc4glTI+zTwGPD/gJws/lNmNDMzM7OOyAU9szZSFO9OkXgF+AswQOKYCGpKjmZdWzXwWATeg8rMVq5c3YAvkybV7gd8AOSkffIecTeemZmZ2fKrKjuAWWcTwV+BfYGDgZslVi85knVtg/ByW7MVImmIpIckPS7pfkmfaeSYXSXVSvpOE+foI+kBSY8VX5Mkbdzg8askzZYUktZqJssoSU9L+qekQQ3uP1VStmK/aRvJNZBcpwAvAhOB9YFjgH5k8XWyeNjFPDMzM7MVo/D7KbN2IbEtaQLubGCvCF4tOZJ1MRLdgfnAtyIYW3Yes45IUl/SsIYdI+IpSTsA50bElg2OWRO4HXgNuDMizmrkPFVAn4h4p7h9ArBzROxX3N4deLw4R9+ImNdEnheBLUndt8dHxHBJWwD/FxH7t9kv3lq5egL7kJbU7gm8CUwAxpLFv0vLZWbtQmIL4ElgywieKjuPmVlX5CW3Zu0kgockBgOTgGkSQyP4V9m5rEv5NLAK8HDZQcw6sE2BtyLiKYCImCppoKRBEVHf/XoO8EvgwKZOEhF1QH0xT8Aa8NE+qxFxR/HYsvLUAr2BPsDColB4JqkDbuXL9WnSktqjgHWAW0kd6jeSxcJSMpmZmZl1AS7ombWjCF6Q2A64EbhXYt8I7ik7lzVP0m2kJWL1H2ojkVwAACAASURBVMCPj4h/Fo99itR1sg7wNjCy/oP+EucYDJxb3OwB3FOcZ4Gko4FvNzi8P/D3iFiqGCDpZ8CIIschEfFycf944IKIuLeZX6UaWIgHYpitiOeAtSVtFxH3SdoXWB3YGHhE0nCgLiJukNRkQa+epDuArYA3SJ1srfUDUjfgO8CxwHHAzfWvDStFrj7AcFI33vakgVB/AsaRxYyVlsPMzMysC3NBz6ydRfCmxG6kjcDvkDgsgqvLzmXN+kr9cjdJBwDjgfo9s84Dzo+I8cUH+fHA5xo5x2PA5yKipuiguZr0wfvMiBgHjKs/UNKTwKVLnkDSGqTN4zcHDgO+BXxf0peB95dRzINU0Hs8AnfJmC2niHi7+H/9dEmrAdNIRfJFktYHfgrs3Irz7V68Jvyk+DqulXmuAa4BkDSANGxiiKTTSN2E0yPiJ605Z4vkEmlPzjFARur+vR4YAtxBFrVt/pxmZmZm1iQX9MxWggjelzgI+CNwpcQJEfyh7FzWuCX2rlqTYlmcpHWBbYE9iseuBs6R9MmImL7EOd5vcLMn6cPvUpuWSvoCsC5wQyNRaoFupA6/+uV1qwI/A4a14FfxQAyzNhARk4HJAJJ6AXNIRb1qYAPg0WKp7DrAvpI+3lxRLSLqJF1A6v5rVUFvCX8AvksqKPaLiEMkTZC0S5F5xeXqSyrgjQE+C/wbOBW4mCxeb5PnMDMzM7NWc0HPbCWJoFbiG8DLwFkSA4AfRFBXcjRrhKSLgF2Km3sV3wcAr0bEIoCICEkzgIGkTfOXPMfGpA6WTUkDUv7cyFONBi6OiJolH4iI9yT9HrifVEA4ivRB+ncRMb/5/HQDtgEuavYXNbNlkrRBRNQPNvoZcFdRxJ8OrNfguPHAo00MxVgfWBAR/ynuOoQ0BGN5Mx0C/CsiniiWAddfMKgDVlve8wL13Xg7kop4w4tzXwF8E7jPE2rNzMzMyueCntlKFEEAv5aYBVwI9Jc4KoIFJUezJUTEkQCSjgJ+zUdFvdac4yXgM8UyvUtIG+ZfXv+4pD6k/fG+2Mw5/kxRCJRUTdpvb5KkP5E6+6ZGxNmN/OhmwKp4IIZZWzi1mG7bnbTkdnRLfkjS10idcyeRCv/nSeoGCHietKS+/tib+Whp/1OSnouInZs4b1/gG3zULTwJGCPpceDF4nbr5VqfdOFgNPAp4CHgO8DlZPH2cp3TzMzMzNqFwhdZzUpR7Kt3LWlJ5AER/GcZP2IlkfQBqZDWjdSR87GIWFRMqnwV2H7JJbeNnGMEcFhE7NPgvpHAsRExuAUZupM2wj8U2BsYEBGnSJoMjI6IFxY/nsNJRePVXTA2sybl6k4azjEG2Ic0bOMSYCxZPFpmNDOrXBJbAE8CW0aw1HAwMzNrf+7QMytJBHdK7ADcAkyVGBrBzLJzdXWS1gJWjYjZxe39gbeAucUS20dIXTXjgYOAVxor5kn6JPByMRSjJ3AASy+vGw2MbWG07wF5RMwpOvvqr8YEaX+9JVUDT7qYZ2aNyrUJMAo4GtgQmELqzruGLD4oMZmZmZmZtYALemYliuAxicHAROD+oqi33HsqWZtYE7hS0iqkvajeAIbFR+3MxwLjJZ0IzCd9GAZA0l+BGyLiBmBX4HhJtaTX2juBXzQ49n9IG8wvcymvpE1Jm97XH3sJcK2kg4F7I+KJRn5sEF5ua2YN5eoF7E/qxtsdeI10cWIsWTxXYjIzMzMzayUvuTWrABJ9SctvtwEOjODOkiNZByZRBcwDfhjBuWXnMbOS5dqCVMQ7AuhLuoj0V+BmsqUH8piZLYuX3JqZlc8demYVIIL/SOxJmkg6UeLoCC4tO5d1WJ8CVscdemZdV67VSJN0x5AG77wEnAWMJ4tXSkxmZmZmZm3ABT2zChHBAolDgTOASyQGAL8uJuOatcYgoBZobCmumXVWuQR8nlTEGwH0JHV//wy4iyzqSkxnZmZmZm2oquwAZvaRCOoi+D7wHeA04ByJbityTkm9JV0n6VlJj0m6vRjYsORxu0qqlfSdZs71heIcz0q6S9KGxf1bSXq0wddLkuY2cY69JD1ZfO3Z4P5Rxb50tuKqgaci8Mb2Zl1BrrXJ9W3S4J37gcGkIt6GZDGCLO5wMc+sMrTx+7K+ki4tzvWUpF81eOyI4vxPSrpT0sAmzuH3ZWZmHZQ79MwqUAR/kJhFGn7QTyJbweLM+cDEYkrrN0l7J+1c/6CkNYFfkSbuNkpSFXApcExETJb0fdLyrYOLoQyfbXDsOdBkZ+GpfDTc4RrgVknrkfZ2+vLy/Xq2BA/EMOvsclUBu5C68Q4EaoDLgWOAB8i8SbJZBVvh92WFC0nDsQ4rfm794vungd8A20TEq5IOB84F9m7kHH5fZmbWQblDz6xCRXAV6Y3UTsCdEuss33niw4i4pcGU1vuBjZc47Bzgl8BbzZyqGlgUEZOL2+cB+0jq3fCg4vZhwNgmzlMDrAr0ARYW950J/DAiFi37N7LmSIhU0Huk7Cxm1g5ybUiunwDTgTuATYBvABuQxRiyuN/FPLPK1Vbvy4quvm2B3zc495ziH7cEHo+IV4vbtwBDJa3dyKn8vszMrINyh55ZBYtgqsSXSBMJ75MYEsELK3jabwPX19+QNByoi4gbJB3YzM8NBF7+KFu8I2k+0A8Wy3Qg8EJEPNrEeX4ATCj++QRJw4DXIuLB1v8q1ohNgTVxh55Z55GrB6mDZkzxfR5wMTCWLLxXplnHtrzvyzYHXgHOlbQtqfj3w4j4J/AYMEjSZhHxLHA4IGAjli4S+n2ZmVkH5YKeWYWL4F8Sg0lXV6dJ7B3BQ8tzrmIvlE8CuxW31wd+SoNlHm1gNE135xERU4EvFM+/Oun3GlLsEfMl4DXguxGxsKlzWLMGAXWkN/Nm1pHl+iTpNXUksD5wJ6kD+jqy+LDEZGbWBlbwfVl30hCcEyPiWElDgZskbRwRz0n6GnCRpO7AzaQLAUt13Pl9mZlZx+WCnlkHEMGrEjsBVwFTJA6OYGJrzlHseXcgsHtEvF/cXQ1sADwqCWAdYF9JH4+InyxxihmkK7v151ud1Ak2u8F9mwBfBA5qYazTgP8jfVDdLyJ2kfRz0gfWca35/ey/qoF/RfD+Mo80s8qTqzfptXoMaY+82aSLJBeSxYp2aJtZhWij92Wz6rdCiYiJknqS3qtNj4irSO8b6wuFPyQt1W+O35eZmXUgLuiZdRARzJfYm7Rx8o0Sx0Y03QnXkKTvAoeS3jTO++iccTOwXoPjxgOPRsRZjZzmYaCHpF2KN4/HAjdGLNYlMgq4tuFzNJPpi8CaETFJ0tZ8NESjDlitJb+XNaoaL7c163hybU0q4h0OrAHcBOwDTCLzPlZmnUkbvi+bL2nriHhc0udJy2pnFj+7QTEQoxvwa+BPDQqHjWXy+zIzsw7GBT2zDiSCGomRpKuyf5UYAPw8osmJskjqD/yOtM/d5OKK74KI+MKynq9YrtEvIk6KiLpiStp5xeCL2aQJaPXHVpGWhR3ZgvP2IL25HJ5+r3hc0guSngTeBA5Y1jlsaQ0GYtxYdhYza4FcawAjSIW8zwHPA2cAE8j+u5m9mXUibfi+LCQdBVwgaRVgAXBQRCwoDr9Q0kZAL9KS2xObOa/fl5mZdUAKD0Iz65AkjgX+DIwHvhZBTbmJrGwSm5A+IOwQwT1l5zGzRuQSMJhUxDsE6EZaFvdX4O9kUVdiOjOzFpHYAngS2DKCp8rOY2bWFblDz6yDiuA8idnAFUC/Yl+9d8vOZaUaRFoi09SEYTMrS66Pk7qaxwD/CzxO2tPqUrL4T5nRzMzMzKzjcUHPrAOL4EaJnUlLKe4uJuDOKTmWlacaeMaFXbMKkasK2J1UxNsf+BDISVsTPEzmZRJmZmZmtnxc0DPr4CJ4UGIwMAmYJjEkgmfKzmWl8EAMs0qQawBwNGlQ0EbAvcBXgSvJ4r0yo5mZmZlZ5+CCnlknEMH0oqh3E3CfxD4R3Fd2Llt5GgzEOK3sLGZdUq6ewDBSN94Q4C1gAjCWLP5VZjQzMzMz63xc0DPrJCJ4Q2JX4HLgToksgmvLzmUrzQBgHeCRsoOYdSm5/gcYDRwFfBy4DfgKcANZLCwzmpmZmZl1Xi7omXUiEbwncQBwDnC1xLci+FPZuWylqC6+/7PUFGZdQa5VgeGkbrwdgFeAvwAXksXLZUYzMzMzs67BBT2zTiaCRRJfB2YA50gMBH4cQV3J0ax9DQKei2B+2UHMOq1cg0hFvMOAVYHrgb2A28iitsxoZmZmZta1uKBn1glFEMBpEq8AY4H+EqMiWFByNGs/Hohh1h5yrQVkpELeNsAzwC+Ai8nitTKjmZmZmVnX5YKeWScWwUUSc4CrgYkSB0Twdtm5rG0VAzGqgd+UncWsU8gl0lLaMcDBQABXAscD95JFlJjOzMzMzMwFPbPOLoLbJHYAJgJTJfaK4JWyc1mb6gesiwdimK2YXOuRhluMBjYj/T/1XeAysphXZjQzMzMzs4Zc0DPrAiJ4VOKLpKLeNImhETxZdi5rM/UDMVzQM2utXN2APUndePsA7wGXAoeShf+fMjMzM7OK5IKeWRcRwcsS2wPXAfcUy28nl53L2sQg4IUI3EFk1lK5NgZGAUcD/YG/F7evJov3ywtmZmZmZrZsLuiZdSERzJXYA7gYmCRxVASXl53LVpgHYpi1RK5ewH6kbrzdgTeA8cBYsni2xGRmZmZmZq3igp5ZFxPBhxKHAL8HLpPoD/yumIxrHdMg4OyyQ5hVrFybk4p4RwJ9gUnAQcBNZFFTZjQzMzMzs+Xhgp5ZFxRBHfAdiZdJhb2BEidEUFtyNGslifVJQzG815dZQ7lWI02oPQYYDLwM/AEYTxYzy4xmZmZmZraiXNAz68IiOFNiFmkJ7oYSh0fwQdm5rFU8EMOsXi4BnyN1440AegPXAicDd5JFXYnpzMzMzMzajAt6Zl1cBH+TeI00LON2if0ieKvsXNZig4CX/N/MurRcHwMOJxXytgKeJhXxLiaLN8uMZmZmZmbWHlzQMzMiuLuYgDsRuFdiaAQvlp3LWqQad+dZV5SrCtiJVMQ7CKgFLgeOBe4nC+8LamZmZmadlgt6ZgZABE9JDAZuAaZJ7BXhQlEHUA2cW3YIs5UmVz/gKGA0sCnwIPAt4AqymF9mNDMzMzOzlcUFPTP7rwhmSewAXAPcLTE8glvLzmWNk1gX6I879Kyzy9UdGErqxtsbmE/a+3MsWTxeZjQzMzMzszK4oGdmi4lgvsRewFjgZokxEYwvOZY1blDx/eFSU5i1l1ybAqOAo4ENgLuAI4BryeLDMqOZmZmZmZXJBT0zW0oECyWOBGYC4yQGAL+MwHtSVZZBwMwI3ig7iFmbydUbOIDUjbcr8CowDriQLJ4vM5qZmZmZWaVwQc/MGlUU706UmAmcAwyQOC6CRSVHs494IIZ1Hrm2IhXxjgDWAG4G9gUmkoVfd8zMzMzMGnBBz8yaFcG5ErOBy4B+EodE8F7ZuQxIBb2xZYcwW265VgdGkAp5nwdeAH4DTCCL2WVGMzMzMzOrZC7omdkyRXC9xK7ATcAUib0jeL3sXF2ZxNrARnj/POtocgn4IqmIdwjpvcjVwI+BKWRRV2I6MzMzM7MOwQU9M2uRCO6X2A6YCEyTGBLBc2Xn6sLqB2J4ya11DLnWIS2nHQNsDjxBKuJdShZzy4xmZmZmZtbRuKBnZi0WwbNFUe8m4D6JfSK4v+xcXVQ1MDuCOWUHMWtSripgN1IR7wBgAWn5/kjgIbLwoB0zMzMzs+Xggp6ZtUoEr0nsDPwNuEtiRAQ3lByrKxqEu/OsUuXqDxwNjAI2BqYBXwP+RhbvlpjMzMzMzKxTcEHPzFotgvck9gPOBa6V+GYE55adq4upBi4uO4TZf+XqAQwjdeMNAf4DXASMJYunyoxmZmZmZtbZuKBnZsslgkUSXwVmAH+WGAD8JAIvoWtnEn2BT+CBGFYJcm0GjAaOAtYDbgcOBa4niwVlRjMzMzMz66xc0DOz5VYU734h8QpwAdBfYkwEC0uO1tltU3z3klsrR65VgYNI3Xg7ArOA84FxZPFimdHMzMzMzLoCF/TMbIVFME5iNnA1sIHEQRHMLztXJ1YNvAbMLjuIdTG5tiEV8Q4D+gA3AnsDt5JFbZnRzMzMzMy6Ehf0zKxNRHCrxI7ALcBUiaERLji1k0HAw17ebCtFrjVJS2iPIf3Zew44DbiILDxl2czMzMysBC7omVmbieARicHAROB+iSERPF12rk6oGrii7BDWieUSsD2pG+9gQMCVwAnAVLJwMdnMzMzMrEQu6JlZm4rgRYkvATcA90rsF8Hfy87VWUisAXwKD8Sw9pBrPeBIUiFvM+BR4PtAThbzyoxmZmZmZmYfcUHPzNpcBG9J7A5cCtwucUQEfys7VyfhgRjWtnJ1A75MKuLtB7xP+n/3ULLwnzMzMzMzswrkgp6ZtYsIPpA4GDgTuEJiwwjOLDtXJ1ANvAnMLDuIdXC5NgKOBkYBA4CpwGjgKrJ4v8xoZmZmZmbWPBf0zKzdRFAr8W1gBvB7iYHA9yKoKzlaR+aBGLb8cvUE9iV14+1BKg6PBy4ki3+XmMzMzMzMzFrBBT0za1dF4em3ErOACcCGEkdG8GHJ0TqqauDaskNYB5Prf0ndd0cBawOTgOHATWSxsMxoZmZmZmbWei7omdlKEcFlEnNIxajbJPaPYG7ZuToSidWB/8EDMawlcvUhTagdA3yJ1Cn7R2A8WcwoM5qZmZmZma0YF/TMbKWJYLLEDsBE4B6JoRG8XHauDuQzgPBADGtKLpG6OMcAGdAbuA7YE7iTLGpLTGdmZmZmZm3EBT0zW6kieELii6Si3jSJvSJ4tOxcHUQ18B/gpZJzWKXJ1Rc4jFTI+wzwL+AU4GKyeKPEZGZmZmZm1g5c0DOzlS6CVyS2Jy2//bvEQRHcXnauDsADMewjqRtvJ1IRbzhQC1wBHAdMIwv/OTEzMzMz66Rc0DOzUkTwtsRQYBxwi8ToCC4qO1eFqwZuLjuElSzXBqThFqOBTwL/AI4HLieL+WVGMzMzMzOzlcMFPTMrTQQLJA4HZgITJPoDp7sDbWkSfYD/BU4tO4uVIFd3YAipG28Y8A5wMTCWLB4rM5qZmZmZma18LuiZWakiqAN+KDETOBsYIPGtCBaVHK3SbA1U4Qm3XUuuTwCjgKOBfsAUUnfeNWTxQYnJzMzMzMysRC7omVlFiOAciVlADmwocWgE75Wdq4JUA28DL5QdxNpZrt7A/qRuvN2AOcB44EKyeK7EZGZmZmZmViFc0DOzihHBtRK7ATcCd0kMi8ATOpNq4BEvR+7Ecm1JKuIdAawF3ALsB0wki5oyo5mZmZmZWWVxQc/MKkoE90lsB0wC7pMYEsHzZeeqAIPAk4A7nVyrA4eQCnlfAF4Efg+MJ4tZZUYzMzMzM7PK5YKemVWcCJ6RGEya6Dqt6NR7sOxcZZFYBdgC+FXZWawN5BKpeDcGGAH0AK4BfgJMJou6EtOZmZmZmVkH4IKemVWkCOZI7ARcCUyWOCSCm8rOVZKtgG54IEbHlmtt0nLaMaQC7ZOkIt4lZPFWmdHMzMzMzKxjcUHPzCpWBO9K7AucB1wv8fUIzi87VwmqgXeA6WUHsVbKVQXsSiriHQDUAJcBo4EHycJ7IpqZmZmZWau5oGdmFS2CGonRwAzgPIkBwEldbDhENfDPCLwUs6PI1R8YSSrcbQzcDxwH/I0s3ikvmJmZmZmZdQYu6JlZxSuKd6dIzCR16w2QOCaCrjL5cxAwpewQtgy5egB7AccAQ4F5wEXAWLJ4ssxoZmZmZmbWubigZ2YdRgRjJV4l7au3gcTwCDp1t5NEL2BL0uRTq0S5PgWMInXkrQ/cAWTAdWSxoMRkZmZmZmbWSbmgZ2YdSgS3FMMybgbultg7glfLztWOtiJNQfVAjEqSaxXgQNLeeDsDs4G/AuPI4oUSk5mZmZmZWRfggp6ZdTgRPCQxGJgETJMYGsG/ys7VTgYB7wHPlh3EgFyfIRXxDgdWB24EhgG3ksWiMqOZmZmZmVnX4YKemXVIEbwgsR2poHKvxL4R3FN2rnZQDTwaQW3ZQbqsXGsAh5IKeduSpg3/CphAFnPKjGZmZmZmZl2TC3pm1mFF8KbEbsClwB0Sh0Vwddm52tgg4L6yQ3Q5uQRsRyrifQWoAq4Cvg/8nSy60pRlMzMzMzOrMC7omVmHFsH7EsOBs4ErJb4Twdll52oLEj2BrYFzys7SZeRaFziCVMj7NPAY8AMgJ4v/lBnNzMzMzMysngt6ZtbhRVAr8U1gBvAHiYHADyKoKznaitoC6IkHYrSvXN2A3UlFvP2AD0ldn4cDj7gbz8zMzMzMKo0LembWKUQQwK8lZgEXAhtKjIxgQcnRVsQg4APg32UH6ZRyDQSOBkYBA4F7gGOAq8jivTKjmZmZmZmZNccFPTPrVCK4ROJV4BrgVon9I5hXdq7lVA08FoGnp7aVXD2BfUjdeHsCbwITgLFk4cKpmZmZmZl1CC7omVmnE8GdEjsAE4F7JIZGMLPsXE2ROB44gNSJN73BVzXwjxKjdR65Pg2MBo4C1gFuBQ4GbiSLhWVGMzMzq1QSe5AmvC9p3eL7MRKvN7j/hQgub/9kZmam8NZAZtZJFXvpTQTWAoZG8HjJkRolcSewK1BT3NWjwcPzgcdZvNj3RATPrtSQHVGuPsBwUjfe9sBMYCwwjixmlBnNzMys0kkIeBnoz0fvUf77MOn9Sg1Q/4GyO1AL9I3AW1eYmbUzF/TMrFOT6AtcC2wDHBDBXSVHWorEEaRln2rmsEVAHWlIxjxg3Yil3lxbLpH2HhwDZMCqwHXAX4E7yKK2xHRmZmYdSrGK4EygqgWHLwLOj+Ab7ZvKzMzABT0z6wIkepEKZgcCIyPIS460GInVgDeA3i04vJb0u4wpBoEYQK6+pALeGOCzwDOkIt5FZPF6cz9qZmZmjZPoDjwBfArotozD5wGbRjC33YOZmZkLembWNUhUAWcA3wN+BJxRSQUxiYuBETS/t+kiYCqwp7vzqO/G25H/3959R1dVpf8ffz8394aq0hEEBAWZEUVhRESJoFiw90JEQWXUcXRszPycsQDKjGMdx69YxgIIRhQ7KhYQFBQRFVQQlSqgIAoB6bnl+f1xbiCEm5CEVPJ5rZUVz9n77P3cuBbZec4uQRLvXIIlPy8QJPI+IlO/4ERERHaVGT2AyTup5sAV7jxZ9hGJiAgooSci1YwZ1xEsHXkU+Is7lWIJphnHA+8WUiUGLAC6urO2fKKqpLJsb4LDLS4nmDHwOUES7zkyvXr/bERERMqAGc8TrHRI9eIxTrDf72HuJMo1MBGRakwJPRGpdsw4FxhNcGBGpjubKjgkzEgDlgONUxTHgWyCgfIP5RpYZZFlYeBEgtl4pwHrCf4fPkWmz6zI0ERERHZ3ZrQA5lHw9iBHuDO9HEMSEan2irK5qYjIbsWdF4HjgB7ARDMaVXBIJGcKjmDHU+ScYHbeSdUymZdlbciyO4HFwBtAfaA/0IxMv0bJPBERkbLnzjJgMOwwAy8GDFcyT0Sk/GmGnohUW2b8nmCW3haChNnCCo7nIIKNp/NygtN5X6uAkCpGltUAziSYjXcc8DNBsvNpMv37CoxMRESk2koeMvYt0IptE0M2Am3c0QFUIiLlTAk9EanWzGgGvAU0B05x57MKjmc20CHPrRvcebCi4ilXWdaBYF+8Swhm4o0n2BvvTTJdh4CIiIhUMDN6E/x+zvUvd26pqHhERKozJfREpNozYw/gReAo4Dz37Qaq5R3LDcADycthwLWV6TTeUpdldYHzCWbjdSNYWvsUMIJMX1aBkYmIiEgKZowDTk1eHuLOVxUZj4hIdaWEnogIYEYEeALoC1zpzlMVFEcz4CdgNnBoZTmFt1RlmQFdgD8CFwLpwCsEs/HeJ9N1Qp6IiEglZUYb4H8E22Ic5M6cCg5JRKRaSnXsuIhIteNO1IxLgaXAk2a0BIZUwOy4vZLf/7jbJfOyrAFBwnQAcDDwDXAbMJpM/7UiQxMREZGicWeRGdcTvHwUEZEKooSeiEhSMnl3mxnLgEeAlmZc5b7DybNl6Q8EJ8bNKsc+y06WhYCeBEm8swk+2xjgCmA6mZomLiIiIiIiUlxK6ImI5OPO42b8CDwPNDfjPHfWl1P3nYE57mwup/7KRpY1B/oTHHKxHzAduAZ4nkxfV4GRiYiIiIiIVHlK6ImIpODOG2YcA7wBTDbjFHd+Lou+DGsC9ALqMfCeU5jX7ns4syy6KltZFgZOJpiNdwqwBhgFPEWmf12RoYmIiMguMqtJcBjG0T+xd4uXOZs/8PmZ2PRluK+t6PBERKobHYohIlIIM9oC44E04CR3viu1trHewA0Eyby0fMWLgdHAvx3fUFp9loksawtcRjAjrxkwkeCAi1fJ9Ko901BERKS6M6sN/Jvg9/weKWpsAV4GrsP9l3KMTESkWlNCT0RkJ8xoTDBTb3/gdHc+3uU2saHALUWo+g3Q2/Glu9pnqcqymgR74g0AjiE4mXc48DSZvrAiQxMREZFSYtYIeBfoVITaS4HjcP++bIMSERFQQk9EpEjMqAM8BxwPZLrzSonbwv4MPFyMR74FDnK84k+9zbKOBEm8vsCeBInOJ4G3yfRYRYYmIiIipczsZeCsYjzxNXAI+iNTRKTMKaEnIlJEZoQJEnFXANe6MyxP4RFAR6Amwd5x7+O+bIc2sJrAQoKlqcVxieOjShj6rsmyPYELCRJ5XYAFBEm8kWT68gqJSURERMqWWUdgFmDFfPIc3F8ug4hERCQPHYohIlJE7sTM+BOwBHi4vmW3WUXDjSH8YqB1/uqYTQH+m29Qew5OM37cB1Y2gZx0CCWg9kbYbyHU3lRQ938hOGCi+LLsWwhJtgAAIABJREFUQKApkEOwz01Ovv/Oey+HTE+QZQZ0I0jiXUCwx9+LwN+AD8n0RIliERERkariEvIk8+KEWExr1lCPHNKJEGUP1rEfC4mw3ST9fgR76omISBlSQk9EpBjcceBfGTZl/TD+/N8QBc5yNuBo4OhNVmtYXda/mSDtjEibpackVtUj/lvdFE8kSN9vGdEOX+JHTYELx0CrrVvntShRwFkWAaYC9YvxTO6HMoKlMzcDz5Lpq0sUg4iIiFQ5q6nf+B1OZDwnMTu9M3Nj7dicqLFDvYhFOSCyiA45s+jFBE5jXOPiLkMQEZHi05JbEZHiMjNgJnBIYdW+4wD+y3WMDffh11h9Dj2U6JFHEunQATp0gObNIT0dEglYvx6++w7mzIGvZyd4d2KcddkRwkd9QqzfU9Bv5DxPzzmgRPFm2SvAaex4km5hcoA+wCtk6heFiIhIdWFG91CIP0csel7M09KOPipBpy5hOnSAAw+Exo0hEoFYDLKzYe7cYPzy1cw470+CaBS3tLS3YzEeB15PvgwVEZFSpoSeiEhxmZ0LjC2oeBUNGMIgHrWrabNvgn4D0rnwQth//6J3kZMD77wDo7PivPQShBpmZ0dXNLoEeLPYA+MsawfMpWgJPQc2Axlk+ufF6kdESszMHgJOB/YFOrn7rOT9msAY4EBgE7AS+JO7z0/RRmuCPS6/znP7HHdfUFhZinZuI9g3cx1wgbv/kLw/AnjC3T/ahY8qIpWUGW3T0rg3HufMww4j1r8/4fPOgyZNit7Gb7/Bq6/CqFHEJ0wgLRLho2iU69zRmEJEpJQpoSciUlxmk4EeqYrGcAF/SnuC9L1qcuddES67DMK7uLnB/Pnw17964tVXLRSJ8F7v6OsDXueMI4HzCPbuCxH8kf868ALuv+zQSJb9H3AVhW+14EAUOI5Mn7JrUYtIcZjZ0QQH5kwFzsyX0DsWGO/ubmbXAOe6e88UbbQGZrl7veKU5au3JzCDIIF4EdDR3Qea2fHAWe5+dUk/o4hUTmaEgH+EQgxu0wZ/8EHCp5wCVtyjMPKZNg2uu47YjBmEQyEeTyS43p3NpRK0iIgQqugARESqoH3y30hg/I276cMYLrqyLvMWRbjiil1P5gG0bQuvvGKhyZOhaeN4r5nhLou/5qDngLOBzsChwAkEJ/D+hNlDmOXf5OZZ2OnMvgRwupJ5IuXP3T/0FCdju/tmd3/Lt72B/YQdD+EpTXGC2bwRoA6QY2a1gdsI9tMUkd2IGbXDYV5NS+OOu+8mbe5cwqeeuuvJPIBu3WD6dMKjR0PNmgyIRPjIjGLM9xMRkcIooSciUnzr8l44cJmN4D9pAxk5Eh4eZuy5Z+l32qMHzPwyLdSmSyPLSPuYz/hDqmph4FrgTc63BmTZJWTZh8A0YCMFJ/USwPlk+julH7mIlKLrgNcKKa9jZjPM7Aszu93M0opYBoC7bwAeIEgcngH8F7gDuN/dfyvFzyEiFcyM2pEIk2rX5qRJk7CBA4O98Uq5Dy66CL74grRmzTg4EuFTM5qXbi8iItWTEnoiIsX3XN6Le/gbo+jLa6+HuOSSsu24USN4Z2KELkfX4qzwOFbSuKCqvQjxI/AU8AtwMsFJuSsLqN+fTH+59CMWkdJiZv8A2gJ/L6DKcmAfd+8CHAdkADcVoWw77v6Iux/q7r0J/t1oAbxtZsPMbKyZ/aXUPpSIVAgzLBTiyVq16DxtGuGMjLLtr317mD6dSIsWNI9EeMWM9LLtUURk96eEnohI8T0C/ArwHsfxD7uLe+4NcfLJ5dN5rVowZmyYcNNGnJ/2ErGCzrp4lRizaEumn0OmjyfT1xMsmcs/S+/PZPqoMg5bRHaBmQ0kWGZ/krtvTFXH3be4+8rkf68GniZI3BVaVkifYeA+4HqgL/CLu58HnGVm+5XKBxORivIXd/o8/zzhAw8snw733hvGjSOSlsYfzHigfHoVEdl9KaEnIlJcwZK08zdRc0O/8LOce45z443lG0LDhvDKGxGmh7rxMNekrrSFutxLm3x3RxGceJub1LuZTH+k7CIVkV1lZjcCfYDj3X1NIfWamFkk+d81CBKAM3dWVoibgCx3X0Gwn17uvxuevBaRKsiMNmbcf+ed0Lt3+fbdoQM88wxp7vzZjGPKt3cRkd2LTrkVESmhQ+zLod9FDr5l4eIQzStoN5gbboAxD//CwlgraqU+OO5k3MdvdyfLTnBnnGP3hS5K3FIugYpIoczsceAUYG9gFbDO3duaWQtgKcEJuLn7d25x967J5+4AfnL3x8zsbIL97uIE+2m+Dwx09y2FlRUQz/4EB+2cnDxdtwHwCtAQ+Mjdryz9n4KIlBmzA4DuQN0uNuPcn/fp3G3BwlC4tPfMK6oePYh9/DHTolGOrpgIRESqPiX0RERKwIwakQg/XHEFTR9+uPjPjxkzhmHDhvHll1+ybt06otEo4RIcifvTT7Bf6zj3Ra/jGoalqtIe9+/z30wLxce6Wxcn9CAwwp0CZ/2IiIhIFWU2ALgKgpO0ltCStqGFDHsszB//WLymbr75Zt58801++OEH6tSpQ8+ePbnnnnto2bJlscOaPBmOCebnHe3OlGI3ICIiWnIrIlJCR0ejNL0p5ZbyO1e/fn2uvvpqHnzwwV0KonlzyLwoxLPh/juUvXMCG82xVM8lPK27E9qXYH+sFWY8bsbBuxSMiIiIVA5m6ZiNBZ4gmcwDeIHz2XMPp1+/kjRpjBgxgl9//ZW5c+diZpx22mklCq9HD+jYkRjB/pwiIlICmqEnIlICZtzRogX/b+nSXTulbfLkyRxzzDElnqEH8Oyz0P/iGGt9T2qzCYDNNeDIj2FmZ5YB7Rzfbj2uGcsJlvbligIR4CPgQeA1d6IlCkhEREQqltljwA5L488IvU7o1JN55bUCDtQqhlmzZtGpUydWr15N/fr1i/383/4GDz3Egs2babvLwYiIVEOaoSciUgKRCD2POYYK2nlmexkZEPMwn3I4ANn14PTXYWZnAFoAVxShmdzPcgQwFlhmxm1m2yX9REREpLIz2xe4LP/tBMZHoQwyeux6Mg/g3XffZd999y1RMg+C8cuWLexvRqNSCUhEpJpRQk9EpATMaN+xY+rlrOWtVSvYs3aUT+odwH03wSFfwnsnbFfl2mI0lzvKbwLcTpDYe86MbmaV4/OKiIhIoa6EHV86rqEeq2L1OOigXe9gwoQJDBkyhMcee6zEbRy8baMPzdATESmBkq3vEhGp5typWbt2RUexTfoezt//X224IUVhwlpYGh3y3S3Kv/+5dS5MfnUz23rKpoiIiFRCK2hyYFNW7nB/E7UA2NXxyxtvvEHfvn0ZPXo0vXv3LnE7eeKotWsRiYhUT0roiYiUgBk5OTkVHcU2W6IGNbekLlzZpCYwuxS6mVYKbYiIiEgZGst5KU++TycYuER3YYfcZ599lquvvpoXXniBE088seQNAXnGUQUMYEREpDBK6ImIlMzyhQtLvudLPB4nGo1uzQpu2bKFWCxGeno6oVDxdkPIzob12WFotjx1hZz0Z4B78t2dDAXGHyP4/fAFMAp4H4gXKygRERGpEHux9lBgdP779VhDzdAWFi6swTHHFL/dhx9+mNtuu4033niDjIyMXY5z4cKt/1nAAEZERAqjU25FRErAjIcOOogrv/66ZKfcjhgxgksvvXSH+5MmTaJnz57FauvNN+HUU4GVjaHxr/mLNwP7Ob7dYDnFKbcQJO2iwHDgEfdSmdUnIiIi5c1sInBs/ts9w1Noc9FRDB9R/G1xzYxwOEyNGjW2uz9+/PgSJfj+9S8YMoRfcnJo6o7+KBURKSYdiiEiUjJT5swhsnZtyR7u378/7r7DV3GTeQAffgjpv1uQKpkXBS7Jn8zLJ5b8vhi4EdjbnauVzBMREanSLgUW5L/ZPTaZDydGKcmcDncnGo2yfv367b5KOlvvg0kJ3zO66nMl80RESkYJPRGREphAr3U1Qzk8+WTFxrFpEzw1MkrOaS/lL/oSONrxsQU8uhpw4F3gRGB/dx5yp4QpShEREak03JcAhwPPEbzgA+B0XmfhsnQmTqywyACYPx8mvo/9x687EbO7MNPfpSIixaQltyIixWXWD3hyIPeGRze8joVLIrt8YlxJPfQQ3PDXWCIx94B32G/ROuB7YIzjcwp7zoy9gAbuLCqXQEVERKRimDUEzgUygLp/SJt5RM0uBzee+nFayIq/8rZUXH5pgg9GL+Xb2P6Eg216n8J9QMVEIyJSNSmhJyJSHGaHADMBW0FT2oR+4NY7anDLLeUfyqpV8PvfE/vlFx5x57ryj0BERESqGjMygA9ffx1OO638+589GzodmuDx+AAuY3jeosNxn1H+EYmIVE2a2iwiUjy3AwawNz8zJHEbt9+W4P33yzeIeBwuvJD4mjX8Cgwt395FRESkqnJnSihE1kUXEctz0my5WLMGzjo1h658ysWMyl98a/lGIyJStWmGnohIUZnVB1aRTOhBsAnd+fYik/c6nRkzI7RuXT6h3Hwz3HsvsUSCo9z5tHx6FRERkd2BGbUjET5t147206cTrlu37PuMx+HM02J89l42X8Q60owV+av8jPveZR+JiMjuQTP0RESKrgF5knkkL4Z7P5qtn0fGEVHmFLpz3a5LJIJk3t13QyLBFUrmiYiISHG5szEa5bTvv2d9jx7EfvmlbPvbtAnOPSvOe+8keDF2ZqpkHuQbY4mISOGU0BMRKbpVBJPytlOXDUyOdWffXz+n2+ExXn65bDrPzoYzziB+zz3EgX7u2288IyIiIlJU7iyKxej69desOPRQYjPKaPe6hQvhqMOjTH5rAxMSx3IUHxdUVfvniYgUgxJ6IiJF5b4GeClVUQOymRjvwYUbh3POOXDqSTHmzi2dbqNRGDYM2rQh9vbbrHHnGHeeKZ3WRUREpLpy5/tolM4rVzK1a1f8j3/EV6ScPFd869bBLbdAh9/HiX07jxnxznTno8Ie0Z7AIiLFoISeiEjx3JFIMUsPoAY5/I8reJfjWTLhew4+KMEfBzjTpkFJtitdvRqeeAIOPJDoX/5CfO1a/i8W4wB3puzqhxAREREBcOeXWIxe7lw6ciS/7Lcf8X/8gxK/mFy6FO65B9q1iTLs7nUMzfkbM2KdaMuCAp95HjYbaP88EZFi0KEYIiJFZGZ1gYcugktHQCJcyEuROCGe5jK/NXL36pXRBg1b7pOI9bkoFD78cOjQAdq2hXB4+2dWr4Y5c2D2bBj/Roy33zHMEx63yAvxOLe6M79sP6GIiIhUZ2bUAW6MRLg2GqXxIQfmcG6fdDp1CsYvrVpBKM/oxx2WLw/GL199Ba+9GGXKJxHCYda3ii0Y9zFHHtWUla0K6/M3eLQh1I3BxcBzwF/c/dcy/aAiIrsBJfRERIrAzA4DsoCGwGUOy4F/Az1Indj7BLjd8AnAIcfw/t0/prc5YV7Ovjgh0sNxmjSIUSMd4glYv8H4dW06AHukbeAoPqJPfDRn8NqGvXxtOZw9JyIiIhIwI20UfV/7kB6njI+czrJoUwDq1IzRsF6cSBhicVjzW4i1GyIANI5k0yv2Dr19/Oz+jDzMnS0EL0PPATKBXkBasotlwBhgNO5fBn3aKcDjQAS42t1TbnMiIiIBJfRERAphZiFgIPBP4EPgEnf/MU+F5sDZQEuC09lWAK/ivjBfQ/2B4Rupxbf8jjl0YCVN2ExNwsSozUbaMY8OzKEFy/Ie87YK90Zl+iFFRERE8jMbBlwNsIa9+IYD+YYDyaY+OaQTIcoerON3fEsH5tCErUflvof7CSnaiwB7ADm4r0/dpdUD7gcuA8YC17j7ytL/cCIiVZ8SeiIiBbAgWfcMwSy8W4F73T1RwsZaAvOB9GI+ORr3i0vUp4iIiEhJmZ0GvF6CJwfifv+udW29gSeAmsCfgbGuP1xFRLajQzFERFIws9OBr4B9gSPd/e4SJ/MA3JcCI4v5VIJgZqCIiIhI+XIfB8wq5lO/AI/uetf+NtABeBl4HnjRzJruarsiIrsTJfRERPIws1oWLDF5DRgHdHb3GaXU/G3AN8Wofyvu35ZS3yIiIiLFdSWwtoh1o8CVuG8sjY7d/Td3vxI4ATgM+MbM+piZ7eRREZFqQQk9EZEkMzsYmAH0Bfq4+6Xuvq7UOnD/GcgAXtlJzbXAxbjfVWp9i4iIiBSX+6fAUcDXO6m5DDgB952NcUoQgr8HHEQwUy8LeMXMmpV2PyIiVY320BORai/5pvfPwH3A58BF7r64jDttB1wInLwSDsiGOu3hTYIT397AfVOZ9i8iIiJSVMFY6SiC02qPzoEGC6FZA3ijSbDE9l3cY+UQxrHAk0A94DpgtPbWE5HqSgk9EanWzKwx8DRwMnAnMNTLYUCaL4abgBvdfZ/y7FdERESkJMysAzAbOMjd55Rz33WBu4BrgDeAq9z9x/KMQUSkMtCSWxGptszseIKDLzoCPdx9cHkn85JWA/UroF8RERGRKsXd17v7tUBP4PfAHDPrr731RKS6UUJPRKodM0s3s3uBd4EPgUPcfWoFhpQN1DKzGhUYg4iIiEiV4e4fAIcAwwlWW7xpZi0rNioRkfITrugARETKk5m1J9hQuT1wGTCiEuy9kp38Xh9YUZGBiIiIiFQV7r4BuMHMXiRI7M1ObmXyVIHjuyxrBlwAHAahvTBCeGINMAsYQ6YvLafwRUR2ifbQE5FqIbkM4zLgIWAukOnu31dsVAEz6wh8CXRw928qOh4RERGRwlTkHnoFMbPawB3AjcAEYIC7LwEgy0LARVjkcjx6NGm1EzQ52qjRMFixlpOdYOWHCWLrw1hkGh59GhhOpscr5tOIiOycEnoiUmXYEAsBGcAZQBPD6ji+AVgJvAZM8UGe2OE5s/rA48B5wL3Are6eU36RFy65PGQJ0N3dP6roeEREREQKUxkTernMrBvBEtwWwMB1T/Fi3VrhUXiiNy1Od1r3DdH8ZAjX2v7B+BZY/g4sfjbB0pcAm4rHLiDTtXpCRColJfREpNKzIdYauCYSilwcTUSbtGvQLtq+Yfu0Oul1QhtyNiS+W/VdfN7qeZFIKLIymoiOBh72Qb4IwMwygGcJthi4xN0nVNgHKYCZ1QHWA6e5+xsVHY+IiIhIYSpzQg/AzGoCg/fei79+MjS8uVXzRumW8WKYxkcVrYHVn8OUc6Js/OknPNqDTP+hTAMWESkBJfREpFKzIXZ+mqU9vXfdvdP7H9o/0uegPnRo0mGHenNWzuG52c8xYtaI6Ir1K3Li0fgAhnIgcAvwJnC5u/9S3vEXRXI58BaCGEdVdDwiIiIihansCb1ca56KzKpTf59DIidOgTrFPC9j088wsWeM9QvmkogeQqb+cBaRykWn3IpIpWVD7Frg+QGdB9Sed+28yNBjh6ZM5gF0aNKBoccOZd618yIDOg+oTZjn6Mrfgb8AZ1TWZB5ActPmbIJDMURERERkV2VZj3q1YodEuj+TMpmXkZFBo0aNGDx4cOrnazWFI7PCJKIHA6eWaawiIiWghJ6IVEo2xHoBD93V6y4eO/UxqxWptdNnAGpFavHYqY/ZXb3ugpMIM5jvKsEptkWRDTSo6CBEREREdgsWHkzj7jGaHJ2yOCsri/vuu6/wNhp0guanxAlFhpBlVvpBioiUnBJ6IlLp2BCzSCgy9Ng2x8Zv7n5zyjqjR4+mW7dudOvWjYkTJ+5QfnP3mzm2zbHxSChypw2pEgMwzdATERERKQ1Z1gqP9eTAv4cLqtKyZRGX4B54cxqJaCcg9TIREZEKooSeiFRGPaKJ6BGDewxOS1W4Zs0a7rnnHiZNmsS4ceO4/vrricfjO9Qb3GNwWjQRPQLoUdYBl4LVKKEnIiIiUhr2BmCv3+96S9va2HvXGxMRKT1K6IlIZTTg8H0Oj2fsm5GycPr06WRkZFCzZk0aNWpEy5YtWbBgwQ71MvbNoOs+XePAgDKOtzRohp6IiIhI6bDtvu1SS1v/ZNbfziJSqegfJRGpdCKhSItDmh6ScnYewKpVq6hff1vuq379+qxatSpl3Y5NO6ZFQpEWpR9lqVNCT0RERKR0BIehrd/xhW+xrdvaxspdb0xEpPQUuKeAiEhFMbO0kBX8vqFhw4ZkZ2dvvV6zZg0NGzZMWTfN0jCzqvBvnQ7FEBERESkdi7DI58y9/1D27pXyJXG/fv347LPP2LRpE1OnTmXChAmpW/r2/gShyHwS0a/KMmARkeKqCn/kikg1kxPPWT5/9fwEBcwi7tq1K3/729/YsmULGzZsYMmSJey///4p25qfPT8RjUd/Kst4S4lm6ImIiIiUhkx3sux2lo9/k9WfQ4M/7FBl5MiRO29n7bfww/Mh8EFkeqIMIhURKTEtuRWRymjMxEUTQ7NXzk5ZWK9ePW666SZ69uzJKaecwgMPPEBa2o4vX2evnM2EhRNCjo8p64BLgQ7FEBERESk94wlFvmTapTG2pN6apVDR3+CT/jFC4YXA2FKPTkRkFymhJyKV0auRUOTbOz+4s8A3oZdccgnTpk1j2rRpHH/88Snr3PnBnYlIKPIt8GpZBVqKsoEaZlarogMRERERqfIy3UlEz+W3b1fybrcYa78p+rPrFsB7GTFWf76WRPR0Mj2et9jMHjKzxWbmZnZovrJ2ZvaxmX1vZjPMrEOesnfN7Cszm2VmU8ysU6ruzay1mU02s7VmNitF+eVmNs/MFpjZE2YWKaCdyWaWY2ZN8tzbz8wSZvZq8nqSmfXNU36rmW02s5p57n1rZr12/oMTkfKkhJ6IVDo+yBPRRHTw2G/Ghp6f/XyJ2nh+9vOM/WZsKJqIDvZBVWKJRO6mgJqlJyIiIlIaMn0+Hj2C9Yu/ZHynODOuhpVTINXQ0B1+/QQ+uw7e6hhn7dzv8NgRZPqcFC2/CHQHfkhR9jjwP3c/ALgbGJGn7Hx37+juhwIP5CvL6zfgViAzf4GZtQHuBDKAtkBT4IoC2gH4Crg4z/VlwOd5ricBPfNcHwPMAo5I9tcMaA18VEgfIlIBlNATkcrqBccf6vNSH39g2gO4e5EecncemPYAfV7q444/BLxQtmGWmtyEng7GEBERESktmb4Uj3YjkfN3Fjw1jwlHwyv7RPnkUvj8RvjiJvjkcnitVZR3u8H8xxYR3zgYjx5Gps9P1aS7f+juy/LfT86EOwwYnbz1EtDSzNomn1uTp/peQMoBrruvdvepwIYUxecCr7v7Cg8GyI8BfQr5CYwE+iXjCwEXAFl5yrcm9MwsHWgDPMG2JF9P4BN331xIHyJSAZTQE9kNmFlvM/ssOYX/EzM7JE/Z8OSU/y/N7CMz61KE9obkX0JQ2PKBfM/2Tz57Q777HyTv1zOzfmY2IU9Zi2RZ/60PDOb/MZgGjt8y8N2Bie5Pd4+PmDWCtZvXpox57ea1jJg1gu7Du8cHvjsw4fgtwPU+qIiZwIqnGXoiIiIiZSHTo2T6vSRy2gOHsHnF/Sx6dhLzHpnF98O+YtGoyWxc9hDwBxI5+5PpQ8ksUQKrJbDc3WMAyYTbEqBVbgUze8bMlhLMsrs4ZSuFa8X2MwMX520/haXACjPrCpwAfMa2cSfAdKC5mbUkmJX3KTCZYKYeye/vlyBOESljOuVWpIozs/rAs8DR7j7HzDKS1wclq7wC/NHdY2Z2KsGmvq0Lae9woAs7LiHIXT4wwszOJVgiUFBycCbBm8D/JNtsC+TdG24S8IiZpbt7DsFAYTrBG8ARyTrHAGN8kA+3IfbBpz9+evO0ZdNOumLcFaHebXvTtkHbUJ1IHTZENzB/9fzE2/PfJpaIJUIWetvxu3yQf1zYz60SWp38roSeiIiISFnIdCdYgvpVRYXg7pcAmFk/giW5J5dDt08DlxOMM/8H7JMnnhwz+4hgHN4GmOzuC5Iv3Gsm719eDjGKSDEpoSdS9e0PrHIP9vdw9ylm1srMOrv7F+7+ep66nwD7mFk4981hXmZWG3gYOAeYkud+7vKBE5K3XgIeNrO27imXIiwB6phZF3efQbBXx3CSCUB3X2JmK4DDgakEA4W7gX8n+4sARwFXAiSTc6fbEKsfTUTPfmveW2eFQ+HmZlbX3dfHErHlcY+/DLwcuz2WTRXk7pvMbAtK6ImIiIhUGmZ2CXBj8vK/7j68kOpLgWa5Y20zM4LZc0vyV3T3kWb2mJk1dPfiHMO7hGD8n6t1qvbzeZVgrL0FmAhckq98EsHL9DbAn5L3PgHOI0j+TS9GfCJSTpTQE6n65gENzexId//YzE4H9iD45f5FvrrXAW+lSuYl3QM86u5Lg/HHVjssHzCz3OUDKfcWIUjgXWZmXwDnEyQEH8lTnjtwmEqwqfB1wI3JjX6bASvdfXHeBn2QZwNPJb92H1nWBDjuxpPY0mV/zibLagHLgAklXO4hIiIiUqpsiNUBjgeacTX7MwPoQG8bYst9kK/eyeNVlrs/AzxTxLork2PfvgSrTs4Blrn7fDOrB9R2958AzOxMYBXbVmkU1UvAVDMbDPwMXAWM2Ulcm5Pb4Wx090S+cT4E4/KrgJi7f5u89wFwG/BRckWNiFQySuiJVHHuvja5BPYuM6sLTAO+AbZL2iWPoz8fODpVO2Z2PLCvu19TSqG9DPwLOAuY7u5r8g0eJhEk/EYCq919vZl9QDBbr1myfPeVZXsAZ2PhvmDHYiH79yV7mIVrnEJi48nE1oWx8AayQmPBnwMmkunxig5bREREqg8bYmGgd8hCmWmWdlbCEzX3qLFHrFabWr55n82si627F7g7fEf47bjHnwVe80G+sYLDLnNm9jhwCrA38I6ZrXP3tsniK4ERZvYPgtNqL03e3wsYa2a1gATwC3Bqcp89zOxJgsMuXk+umvkeqAHsZWbLgFHu/nd3X2hmg9h26uxkgq1xCuXuLxdSPINglcibee59kGz36Z21LSIVw4reMvnQAAAMpklEQVR6cqSIVA1mVgNYAXTJXQ5rZhcAQ4Fe7p5ySr6Z3UUw/T6avNUCWEkwKJlOMBOvQZ7lA8uB7vmX3CYPtjjT3c80s0cJTuK60N0nmpkD9ZPJvebJNq8H9nP3m5NJxYsJEnoj3P3ZUvqxVC5ZdhQWGQuJpjTr7bTum0aL0yBcZ1udjT/CD8/D4lFRsmdFsPBkPJZJpi+vuMBFRESkurAh1joSioyNJqKHHb7P4bGLO14cPu/A82hat+nWOr9t+Y1Xv32V0V+Njk9cNDGUZmmLo4noOT7IZ1Zg6CIi1YISeiK7ATNr5h4kesxsKPB7dz8neX0+8E/gOHfPf9BFYW0uJkjMzUpeTyZIsuUeinGzux+W4rn+bEvotQPOAO5PLtPdmtBL1v0OMOAv7v62mdUBZgONgQNylyTsVrKsD9homp3oHDEijVpNd/7Mr5/C1POibFqejUd7kLl1KYSIiIhIqbMh1jkcCr+/X/396ow9b2y4Y9OOO31m6dqlXPTyRbGPln7kCU+c5YP8zZ0+JCIiJaaEnshuwMyeADIIltFPA67NkzSLEszYy7vZbi93X2VmVwHN3f32FG0uZvuEXnuCvUAaklw+4O5fp3iuf/K5M1OU5U/oPUbyxC13X5+8Ny15/bsS/Cgqtyw7HniH9tcbne8HCxX92ZxseP/EGNkzf8Nj7cjcffeqERERkYpjQ6xFmqV9k7FvRu03+ryRVie9zs4fSoon4gwYN8Cf+fKZRMIT3X2Qf1KGoYqIVGtK6ImIlIcsMyzyOXv36kjPt9LYcTNiRo8ezbBhwwAYOnQovXr12r5Czhp4tVWM2Lq7yNwxCSsiIiKyq2yI/V/TOk2vWnTdonCtSK0dyjMyMpg7dy7XXHMNgwcP3qE84QmOfOrI2OfLP/8gelv0uHIIWUSkWirG9BAREdkFJ+DRThw8JGUyb82aNdxzzz1MmjSJcePGcf311xOP5zsDI70e/P6vYSx8I1lWr5ziFhERkWrChlizkIWuvPXoW1Mm8wCysrK47777CmwjZCGG9BwSjiVivWyIdS2rWEVEqjsl9EREyoNF/srex8VpdHjK4unTp5ORkUHNmjVp1KgRLVu2ZMGCBTtWbH8thGrUAC4r24BFRESkGvpT/Zr17fJOlxdYoWXLljtt5IT9T6Dz3p1jaZY2sDSDExGRbZTQExEpDxZqR5OeaQUVr1q1ivr162+9rl+/PqtWrdqxYno9qHewA23KIkwRERGp1vbr1qJbqKDZeUVlZvRs3TMcDoXblVJcIiKSjxJ6IiLlwmsTrl1gacOGDcnOzt56vWbNGho2bJi6crhOCCj6DtUiIiIiRWBYnTrpdUrlb8Takdqg8YqISJlRQk9EpFyEVrP55wJLu3btytSpU9myZQurV69myZIl7L///qkrb14eZ/tTi0VERER2meOrflr3U6w02vp5w884/mtptCUiIjsKV3QAIiLVQmLzuywcsR8HDwmTVmOH4nr16nHTTTfRs2dPAB544AHS0lKs0F39Oaz9Jh2YXKbxioiISHU0eeqSqZfNWzWPdg1Tr5bt168fn332GZs2bWLq1KlMmDBhhzq/bfmNMbPHxHLiOe+VdcAiItWVuXtFxyAisvvLshYQWkSXYWHaXVXydj44Pc5Pb8/Bo4eSqX/ARUREpPTYEAtHQpH5mQdnthpx5ggraTt3TbmL2ybdtinu8RY+yFeXZowiIhLQklsRkfKQ6cvAn+TrwTE2/liyNpa/Cz+OS8OjtyuZJyIiIqXNB3ksmogOHvXVKKYtnVaiNhZmL+Tuj+6OxT3+oJJ5IiJlRwk9EZFy44PJWb2Y946KsnZu8R5d8iJMPjWBpT0PjCuT8ERERETg2ZCF3u71TK/4+Hnji/XgzOUz6fZUt9jG6Ma5wL1lE56IiICW3IqIlK8sa4pFxmOhjhw8OI02faF2i9R13SH7C/h+mLNwuEHoUUhcS6bHyzdoERERqU5siKWnWdqIuMf7/LnLn7nyD1dycNODC6y/KHsRI78cyT+n/DMBfBRLxE73Qb6m/CIWEal+lNATESlvWZYODMHC1+LxOjQ+Kkar88PU3gdCNSG2HtZ+A4ueibJhUYRQZBmJ6EAy/fmKDl1ERESqBxtiBlwWCUX+GU1Em7Zv2D7at2PfSPuG7amTXodN0U0sWbuE52Y/F5vx04xwOBT+LZaI3Qvc5YP08lFEpKwpoSciUlGyrCbQGwv1BTsZj9faWhaK/EIi+hyQBXyqPfNERESkItgQCwFHAX0iocgF0US0QW5ZOBRen/DE6wlPZAHv+SDPqbBARUSqGSX0REQqiyyLADWBDWR6Ive2mdUExgAHApuAlcCf3H1+snwysC+wNvnISHf/T6ouzOxU4D4gDfga6O/uv+2sLF8bI4B+QGd3n5m8twewHJjv7oea2XBggbsPTZb3BUYBbdx9cfLe28BYd3+q2D8rERER2YGZnQwMJdgrPQzc6+4jk2VdgAeBuoADN7r7+ztpbwhwO9DJ3WcBWC1rzxZG4jQgGHv0d/c5KZ7tDwxP9vOfPPc/AI4G6gNnABe7+3HJshbAUuBSdx+RvHcz0MHdLy7RD0VEZDelQzFERCqLTI+S6evyJvPy+B/Q3t0PAV4DnsxXfoO7H5r8KiiZVxd4CjjT3dsBPwG37aysAJ8Dl+W5vgDIe9LHJKBnnutjgOm598wsDHQHCv1DQkRERIrGzAwYTZBgOxQ4FXjczPZIlr0CDEqOJc4HRphZrULaOxzoAvywXcFmHsV5zN0PAO4GRhQS1kyCl4C5bbYF8vY5CehmZunJ6+3GC3nuabwgIpKPEnoiIpWcu29297d825TqT4DWJWjqJGCmu3+bvH4E6FOEslReBk41sxrJ60uBp/OU5x+gdwfuYtsAvQvwq7svKsHnEBERkdQcqJf87z2BVcAWoCHQ2N0nALj798Aagt//OzCz2sDDwJX57jcBDiNIHAK8BLRMJupSWQL8kpwdCMHLwOFbg3VfAqwADk/e6kmQJOyW7C9CsNx3UuEfW0Sk+lFCT0Sk6rmOYJZeXv82s6/N7Hkz26+A51qx/Vv2xUCz5Gy5wspS2Qi8B5xpZr8DjDwz9Nx9KcES3K5m1gpYDbwLHJmsorftIiIipSj54u8C4GUz+wGYCvRz9xx3/xVYbmbnw9blt+0p+AXhPcCjyd/nebUElrt7LE+fSwjGEQUZDlxmZmkEMwOfy1c+iWBcAMELwPeAlWbWhuAF4Mrc7TpERGQbJfRERKoQM/sH0Bb4e57bF7v774COwBTgjXIK52ng8uTX8BTluctuewIfuPsmtg3Qe6K37SIiIqUm+RLuVuBsd98X6AWMMrNGySpnECTWZhK8HJwKxFK0czywr7un+t1eEi8TzAQ8C5ju7mvylU8Ceua+AHT39cAHbBtDaLwgIpKCEnoiIpWMmV1iZrOSX5fmuT8QOBs4yd035t7PfXvugYeB/cysYYqmlxAcnpGrNdveshdWlpK7fwI0By4kOLQjv9w37scAk5P3PgBOIFg+oxl6IiIipedQoLm7fwjg7jOAZUCn5PWX7t7b3Tu5e1+C3+E7HGYBHAt0NrPFZrYYaAG8ZWanERxYsXUGf3JvvlYE44iU3H0zMB54lO2358g1iWCJbW+CcQLJ77ljCI0XRERSUEJPRKSScfdn8hxwMRzAzG4k2NPu+Lxvts0sbGZN81yfA/zs7qtSNP02wQD9d8nrq9mWiCusrDDXAQPdfV2KsknAEUAPglkAEAzQbwJ+dPcfi9C+iIiIFE1usu33sPUAiv2B75LXzXIrmtkfgQ2kSJa5+9/dfR93b+3urQmSgie7+zh3Xwl8AfRNVj8HWObu83cS2wMEe+Ol6u+nZOwD2fYC8GMggyDRpxl6IiIpFLQ3koiIVBJm1gK4H1gITApehrPF3bsCNYA3k4dTJIBfgdPzPHsH8JO7P+bu68xsAPBq8s36bJInzxVWVhh3n1hI2XIzWwKsSS6fgWCA3obgRF0REREpJe7+s5ldAbxgZgmCyRvXJA+eALjCzC5i2763Z+UeuGVmVxHM7ru9CF1dSXBC7j+A3wgOxtpZbPOA+wqpMolgC4+pyfobzGwFwXjnpyLEJCJS7di2QxNFRERERERERESkstOSWxERERERERERkSpECT0REREREREREZEqRAk9ERERERERERGRKkQJPRERERERERERkSpECT0REREREREREZEqRAk9ERERERERERGRKkQJPRERERERERERkSpECT0REREREREREZEqRAk9ERERERERERGRKkQJPRERERERERERkSrk/wP8Tjj0P4niSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1280x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_obs_simlu = plot_helper.plot_obs(osb_simu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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