code/simulation/demo_sim_solve_ik_augmented.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Mujoco Engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MuJoCo version:[2.3.4]\n"
     ]
    }
   ],
   "source": [
    "import mujoco\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append(\"../../\")\n",
    "from model.mujoco_parser import MuJoCoParserClass\n",
    "from model.util import sample_xyzs,rpy2r, get_interp_const_vel_traj\n",
    "np.set_printoptions(precision=2,suppress=True,linewidth=100)\n",
    "plt.rc('xtick',labelsize=6); plt.rc('ytick',labelsize=6)\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline\n",
    "print (\"MuJoCo version:[%s]\"%(mujoco.__version__))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dt:[0.0020] HZ:[500]\n",
      "n_body:[52]\n",
      "geom_names:['floor', None, None, None, None, None, 'ur_shoulder_link_collision', None, None, None, None, 'ur_upper_arm_link_1_collision', 'ur_upper_arm_link_2_collision', None, None, None, None, 'ur_forearm_link_1_collision', 'ur_forearm_link_2_collision', None, None, None, 'ur_wrist_1_link_collision', None, None, None, 'ur_wrist_2_link_1_collision', 'ur_wrist_2_link_2_collision', None, 'ur_wrist_3_link_collision', 'ur_tcp_link_collision', 'camera_mount_collision', 'camera_mounted_gear_collision', 'ur_rg2_gripper_base_link_collision', 'camera_center_collision', 'ur_rg2_gripper_finger1_finger_link_collision', 'ur_rg2_gripper_finger1_inner_knuckle_link_collision', 'ur_rg2_gripper_finger1_finger_tip_link_collision', 'ur_rg2_gripper_finger2_finger_link_collision', 'ur_rg2_gripper_finger2_inner_knuckle_link_collision', 'ur_rg2_gripper_finger2_finger_tip_link_collision', 'front_object_table', 'side_object_table', 'base_table', 'obj_cylinder_01', 'obj_cylinder_02', 'obj_cylinder_03', 'obj_cylinder_04', 'obj_cylinder_05', 'obj_cylinder_06', 'obj_cylinder_07', 'obj_cylinder_08']\n",
      "n_body:[29]\n",
      "body_names:['world', 'base', 'shoulder_link', 'upper_arm_link', 'forearm_link', 'wrist_1_link', 'ur_wrist_2_link_collision', 'wrist_3_link', 'tcp_link', 'camera_mount', 'rg2_gripper_base_link', 'camera_center', 'rg2_gripper_finger1_finger_link', 'rg2_gripper_finger1_inner_knuckle_link', 'rg2_gripper_finger1_finger_tip_link', 'rg2_gripper_finger2_finger_link', 'rg2_gripper_finger2_inner_knuckle_link', 'rg2_gripper_finger2_finger_tip_link', 'front_object_table', 'side_object_table', 'base_table', 'obj_cylinder_01', 'obj_cylinder_02', 'obj_cylinder_03', 'obj_cylinder_04', 'obj_cylinder_05', 'obj_cylinder_06', 'obj_cylinder_07', 'obj_cylinder_08']\n",
      "n_joint:[20]\n",
      "joint_names:['shoulder_pan_joint', 'shoulder_lift_joint', 'elbow_joint', 'wrist_1_joint', 'wrist_2_joint', 'wrist_3_joint', 'gripper_finger1_joint', 'gripper_finger1_inner_knuckle_joint', 'gripper_finger1_finger_tip_joint', 'gripper_finger2_joint', 'gripper_finger2_inner_knuckle_joint', 'gripper_finger2_finger_tip_joint', None, None, None, None, None, None, None, None]\n",
      "joint_types:[3 3 3 3 3 3 3 3 3 3 3 3 0 0 0 0 0 0 0 0]\n",
      "joint_ranges:\n",
      "[[-6.28  6.28]\n",
      " [-6.28  6.28]\n",
      " [-3.14  3.14]\n",
      " [-6.28  6.28]\n",
      " [-6.28  6.28]\n",
      " [-6.28  6.28]\n",
      " [-1.    0.  ]\n",
      " [-1.    0.  ]\n",
      " [-1.    1.  ]\n",
      " [ 0.    1.  ]\n",
      " [ 0.    1.  ]\n",
      " [-1.    1.  ]\n",
      " [-6.28  6.28]\n",
      " [-6.28  6.28]\n",
      " [-6.28  6.28]\n",
      " [-6.28  6.28]\n",
      " [-6.28  6.28]\n",
      " [-6.28  6.28]\n",
      " [-6.28  6.28]\n",
      " [-6.28  6.28]]\n",
      "n_rev_joint:[12]\n",
      "rev_joint_idxs:[ 0  1  2  3  4  5  6  7  8  9 10 11]\n",
      "rev_joint_names:['shoulder_pan_joint', 'shoulder_lift_joint', 'elbow_joint', 'wrist_1_joint', 'wrist_2_joint', 'wrist_3_joint', 'gripper_finger1_joint', 'gripper_finger1_inner_knuckle_joint', 'gripper_finger1_finger_tip_joint', 'gripper_finger2_joint', 'gripper_finger2_inner_knuckle_joint', 'gripper_finger2_finger_tip_joint']\n",
      "rev_joint_mins:[-6.28 -6.28 -3.14 -6.28 -6.28 -6.28 -1.   -1.   -1.    0.    0.   -1.  ]\n",
      "rev_joint_maxs:[6.28 6.28 3.14 6.28 6.28 6.28 0.   0.   1.   1.   1.   1.  ]\n",
      "rev_joint_ranges:[12.57 12.57  6.28 12.57 12.57 12.57  1.    1.    2.    1.    1.    2.  ]\n",
      "n_pri_joint:[0]\n",
      "pri_joint_idxs:[]\n",
      "pri_joint_names:[]\n",
      "pri_joint_mins:[]\n",
      "pri_joint_maxs:[]\n",
      "pri_joint_ranges:[]\n",
      "n_ctrl:[7]\n",
      "ctrl_names:['shoulder_pan', 'shoulder_lift', 'elbow', 'wrist_1', 'wrist_2', 'wrist_3', 'gripper']\n",
      "ctrl_joint_idxs:[0, 1, 2, 3, 4, 5, 9]\n",
      "ctrl_qvel_idxs:[0, 1, 2, 3, 4, 5, 9]\n",
      "ctrl_ranges:\n",
      "[[-6.28  6.28]\n",
      " [-6.28  6.28]\n",
      " [-3.14  3.14]\n",
      " [-6.28  6.28]\n",
      " [-6.28  6.28]\n",
      " [-6.28  6.28]\n",
      " [ 0.    1.  ]]\n",
      "n_sensor:[0]\n",
      "sensor_names:[]\n",
      "n_site:[1]\n",
      "site_names:['attachment_site']\n",
      "Ready.\n"
     ]
    }
   ],
   "source": [
    "xml_path = '../../asset/ur5e/scene_ur5e_rg2_obj.xml'\n",
    "env = MuJoCoParserClass(name='UR5e with RG2 gripper',rel_xml_path=xml_path,VERBOSE=True)\n",
    "obj_names = [body_name for body_name in env.body_names\n",
    "             if body_name is not None and (body_name.startswith(\"obj_\"))]\n",
    "n_obj = len(obj_names)\n",
    "# Place objects in a row\n",
    "xyzs = sample_xyzs(n_sample=n_obj,\n",
    "                   x_range=[0.45,1.65],y_range=[-0.38,0.38],z_range=[0.81,0.81],min_dist=0.2)\n",
    "colors = np.array([plt.cm.gist_rainbow(x) for x in np.linspace(0,1,n_obj)])\n",
    "for obj_idx,obj_name in enumerate(obj_names):\n",
    "    jntadr = env.model.body(obj_name).jntadr[0]\n",
    "    env.model.joint(jntadr).qpos0[:3] = xyzs[obj_idx,:]\n",
    "    geomadr = env.model.body(obj_name).geomadr[0]\n",
    "    env.model.geom(geomadr).rgba = colors[obj_idx] # color\n",
    "\n",
    "# Move tables and robot base\n",
    "env.model.body('base_table').pos = np.array([0,0,0])\n",
    "env.model.body('front_object_table').pos = np.array([1.05,0,0])\n",
    "env.model.body('side_object_table').pos = np.array([0,-0.85,0])\n",
    "env.model.body('base').pos = np.array([0,0,0.8])\n",
    "print (\"Ready.\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get gripper joint indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gripper joint idxs: [6, 7, 8, 9, 10, 11]\n"
     ]
    }
   ],
   "source": [
    "joint_names = env.rev_joint_names[:6]\n",
    "idxs_forward = [env.model.joint(joint_name).qposadr[0] for joint_name in env.joint_names[:6]]\n",
    "idxs_jacobian = [env.model.joint(joint_name).dofadr[0] for joint_name in env.joint_names[:6]]\n",
    "list1, list2 = env.ctrl_joint_idxs, idxs_forward\n",
    "idxs_step = []\n",
    "for i in range(len(list2)):\n",
    "    if list2[i] not in list1:\n",
    "        idxs_step.append(list1.index(list2[i]))\n",
    "        \n",
    "gripper_joint_idxs = []\n",
    "\n",
    "for gripper_idx in env.rev_joint_idxs:\n",
    "    if gripper_idx in env.idxs_forward:\n",
    "        gripper_joint_idxs.append(gripper_idx)\n",
    "print(f\"Gripper joint idxs: {gripper_joint_idxs}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_init = [-1.570521656666891, -2.311883111993307, 2.1441715399371546, 1.73920385419812, 0.7853929996490479, -1.5711215178119105]\n",
    "q_close_grasp = [-0.03, -0.0273, -0.03, 0.03, 0.0273, 0.03]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Augmented Jacobian Inverse Kinematics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100/1000] IK error:[0.0128]\n",
      "[200/1000] IK error:[0.0107]\n",
      "[300/1000] IK error:[0.0106]\n",
      "Pressed ESC\n",
      "Quitting.\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "PI = math.pi\n",
    "\n",
    "env.init_viewer(viewer_title='IK using UR',viewer_width=1200,viewer_height=800,viewer_hide_menus=True)\n",
    "env.update_viewer(azimuth=0.0,distance=3.5,elevation=-60,lookat=[0.4,0.05,0.36])\n",
    "env.update_viewer(VIS_TRANSPARENT=False) # transparent\n",
    "env.reset() # reset\n",
    "\n",
    "env.forward(q=q_init,joint_idxs=env.idxs_forward)\n",
    "\n",
    "# Set (multiple) IK targets\n",
    "ik_body_names = ['tcp_link','wrist_3_link']\n",
    "ik_p_trgts = [env.get_p_body(body_name='wrist_3_link')+np.array([0.35, 0.0, 0.25]),\n",
    "              env.get_p_body(body_name='wrist_3_link')+np.array([0.2, 0.2, 0.2])]\n",
    "ik_R_trgts = [rpy2r(np.array([0, 1.0*PI, -0.5 *PI])),\n",
    "              rpy2r(np.array([0, 1.0*PI, -0.5 *PI]))]\n",
    "IK_Ps = [True,True]\n",
    "IK_Rs = [False,False]\n",
    "\n",
    "# Loop\n",
    "q = env.get_q(joint_idxs=env.idxs_jacobian)\n",
    "\n",
    "imgs,img_ticks,max_tick = [],[],1000\n",
    "while (env.tick < max_tick) and env.is_viewer_alive():\n",
    "    # Numerical IK\n",
    "    J_aug,err_aug = [],[]\n",
    "    for ik_idx,ik_body_name in enumerate(ik_body_names):\n",
    "        p_trgt,R_trgt = ik_p_trgts[ik_idx],ik_R_trgts[ik_idx]\n",
    "        IK_P,IK_R = IK_Ps[ik_idx],IK_Rs[ik_idx]\n",
    "        J,err = env.get_ik_ingredients(\n",
    "            body_name=ik_body_name,p_trgt=p_trgt,R_trgt=R_trgt,IK_P=IK_P,IK_R=IK_R)\n",
    "        if (J is None) and (err is None): continue\n",
    "        if len(J_aug) == 0:\n",
    "            J_aug,err_aug = J,err\n",
    "        else:\n",
    "            J_aug   = np.concatenate((J_aug,J),axis=0)\n",
    "            err_aug = np.concatenate((err_aug,err),axis=0)\n",
    "    dq = env.damped_ls(J_aug,err_aug,stepsize=1,eps=1e-1,th=5*np.pi/180.0)\n",
    "\n",
    "    # Update q and FK\n",
    "    q = q + dq[env.idxs_jacobian]\n",
    "    env.forward(q=q,joint_idxs=env.idxs_forward)\n",
    "\n",
    "    # Render\n",
    "    for ik_idx,ik_body_name in enumerate(ik_body_names):\n",
    "        p_trgt,R_trgt = ik_p_trgts[ik_idx],ik_R_trgts[ik_idx]\n",
    "        IK_P,IK_R = IK_Ps[ik_idx],IK_Rs[ik_idx]\n",
    "        if (IK_P is None) and (IK_R is None): continue\n",
    "        env.plot_T(p=env.get_p_body(body_name=ik_body_name),R=env.get_R_body(body_name=ik_body_name),\n",
    "                   PLOT_AXIS=IK_R,axis_len=0.2,axis_width=0.01,\n",
    "                   PLOT_SPHERE=IK_P,sphere_r=0.05,sphere_rgba=[1,0,0,0.9])\n",
    "        env.plot_T(p=p_trgt,R=R_trgt,\n",
    "                   PLOT_AXIS=IK_R,axis_len=0.2,axis_width=0.01,\n",
    "                   PLOT_SPHERE=IK_P,sphere_r=0.05,sphere_rgba=[0,0,1,0.9])\n",
    "    env.plot_T(p=[0,0,0],R=np.eye(3,3),PLOT_AXIS=True,axis_len=1.0)\n",
    "    env.render()\n",
    "    # Print and save image \n",
    "    if (env.tick)%(max_tick//10)==0 or (env.tick==1):\n",
    "        print (\"[%d/%d] IK error:[%.4f]\"%(env.tick,max_tick,np.linalg.norm(err_aug)))\n",
    "        img = env.grab_image()\n",
    "        imgs.append(img)\n",
    "        img_ticks.append(env.tick)\n",
    "\n",
    "# Close viewers\n",
    "env.close_viewer()\n",
    "print (\"Done.\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using a function: `solve_augmented_ik`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-2.48, -3.09,  0.88,  2.21, -0.91, -0.  ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.init_viewer(viewer_title='IK using UR',viewer_width=1200,viewer_height=800,viewer_hide_menus=True)\n",
    "env.update_viewer(azimuth=0.0,distance=3.5,elevation=-60,lookat=[0.4,0.05,0.36])\n",
    "env.update_viewer(VIS_TRANSPARENT=False) # transparent\n",
    "env.reset() # reset\n",
    "\n",
    "q_aug_ik = env.solve_augmented_ik(ik_body_names = ['tcp_link','wrist_3_link'],\n",
    "                    ik_p_trgts=[np.array([0.88, 0.3, 1.2]), np.array([0.6,0.3,1.2])],\n",
    "                    ik_R_trgts=[rpy2r(np.array([0, 1 * np.pi, -0.5 *np.pi])), rpy2r(np.array([0, 0, 0]))],\n",
    "                    IK_Ps = [True,True], IK_Rs = [True,False],\n",
    "                    q_init=q_init, idxs_forward=env.idxs_forward, idxs_jacobian=env.idxs_jacobian,\n",
    "                    RESET=True,DO_RENDER=True,render_every=1,th=1*np.pi/180.0,err_th=1e-2)\n",
    "\n",
    "# Close viewers\n",
    "env.close_viewer()\n",
    "print (\"Done.\")\n",
    "q_aug_ik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Joint trajectory ready. duration:[4.86]sec\n"
     ]
    }
   ],
   "source": [
    "times,q_aug_traj = get_interp_const_vel_traj(\n",
    "    np.vstack((q_init,q_aug_ik)),vel=np.radians(20),HZ=env.HZ)\n",
    "print (\"Joint trajectory ready. duration:[%.2f]sec\"%(times[-1]))\n",
    "\n",
    "traj_aug_len = q_aug_traj.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pressed ESC\n",
      "Quitting.\n"
     ]
    },
    {
     "data": {
      "image/png": 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P//zPQW7ZzOPVnnfTgdo58oMf/GDcc+Sb3/zmyLq1ZZ/73OfqZS5veMMb+Na3vsXDDz/M0NAQq1evZuXKleRyOS666KIxvQBfzfFT4dhrNJr9QztiGo1GaU444QQA7rnnnoP+Do/Hw+rVq3nrW99KMpnkzW9+80gNyWRSS0tctGjRfsvIVyoVzj33XH7xi1+MOGFnn332RJqpAV7/+tcDcO+999bZkunNxo0bRyYfan3VDgVut5t3vOMd3HXXXQDk83kefvjhkfdfzfGr3XN6enp4+umnD4G1Go1muqIdMY1GozQf+tCHgKrIxQ9+8IOD/h63282vf/1rTj/9dFKpFG95y1v429/+dqjM3Cfbtm3jz3/+MwAXXXTRfqU/1SJhd9xxB5Zlcdttt+23E7Zly5a9pmKdf/75QLUOprZsPPVBVbn44osBeOGFF/Z53mUymQNO2ZsKPPDAA3s9R770pS+NrFtbdu21145ZtjeklFx22WVAtV7sYAR3yuXyuD3eang8npG/G8bOIdOrOX6nnHLKiHrkpZdeOi2PrUajOTRoR0yj0SjNKaecMuJ8fPzjH+fyyy+nq6tr5P2BgQH+93//d8Rh2xsul4u77rqLd7zjHaTTad72trdx//3377beAw88gBACIcQhq/35v//7P2zbxrKs/ZLEr9WE/eIXvxgR5thXOqLm0HHSSSdx4YUXAtV6u0svvXRMjVOhUODxxx/nc5/7HB0dHfT19Y35/JYtW0bOoVcrX59IJBgYGBh51Wr5CoXCmOU1BcDR3HLLLSN2HIrmyqPZunUrxx57LD/84Q/ZtGnTiGNm2zaPP/44b33rW/n1r38NVCXyly5desC/0dXVxeLFi/n617/OmjVrRmobodpyoCZ44/P5xkSZX83xM02T6667DiEEDz/8MG9605t4+OGHRxzCYrHIAw88wPvf/35eeumlA94mjUYzfdCqiRqNRnl+9KMfUSwWueuuu7jqqqu46qqrCAaDCCFGBp9HHnnkfn2X0+nkzjvv5Oyzz+auu+7i9NNP55577hkR9JgIbNvm5ptvBuBtb3sbbW1t+/zMI488wu233w5UxQM+8YlP8IlPfGKP63/ve9/Tjtoh5oYbbsA0Tf73f/+Xa6+9lmuvvRa/34/D4SCRSIyJ1EykwMM73/lOHnzwwd2W33777SPnCFSdj0PtbO2Lp556iqeeegqoTnQEAgFSqRSFQmFknQsvvJDvf//7B/0bmzZt4otf/CJf/OIXMU2TUCg0RjjE6XRyyy23EI1Gx3zu1Ry/t771rdxyyy1cfPHFPPzww7zxjW/E5XLh9/tJJBIjDuFnP/vZg94ujUYz9dGOmEajUR6v18uvfvUr7rvvPn70ox/xxBNPMDAwQCAQ4IgjjuDkk0/mvPPO2+/vczgc/OIXv+Dcc8/ll7/8JWeccQarV6/eoxT8q+XPf/4z27ZtA/ZfpGP0ILFUKo0RIhiPXC538AZqxsXpdHLTTTdx0UUXceONN/LQQw+xY8cOCoUCzc3NLFu2jBNPPJH3ve99u8mnq0BLSwv//d//zWOPPcazzz5Lf38/8Xgct9vN/PnzOeGEE7joootG6rUOhlmzZvGb3/yG+++/n8cee4yuri76+vqwLItFixZxyimn8KlPfWrc/mSv9vh98IMf5MQTT+R73/sef/zjH9m6dSu5XI6Ojg5WrlzJe9/7Xg477LCD3jaNRjP1EXJfSdgajUajmVQuuOACfvzjH3P++edPS9nyg+Hkk0/mwQcf5Etf+tKrTrXTwJe//GW+8pWv1CWKNVWZN28eW7du5eabb96v9F2NRqOZaHSNmEaj0Wg0Go1Go9FMMtoR02g0minKj3/84xEhhJmoOPiud71rZPvGq1HSvHoefPDBkX38rne9q97mTDqrVq0a2X7dt0uj0Uw1dI2YRqPRTDFCoRAtLS1jlgWDwTpZM3FEIpHdttPv99fJmpmF3+/fbd9GIpE6WVM/Ghsbd9sPoyXpNRqNpp7oGjGNRqPRaDQajUajmWR0aqJGo9FoNBqNRqPRTDLaEdNoNBqNRqPRaDSaSUY7YhqNRqPRaDQajUYzyWhHTKPRaDQajUaj0WgmGe2IaTQajUaj0Wg0Gs0kM60csUKhwOc//3na29vxeDwcd9xx/OlPf6q3WRqNRqPRaDQajUZzQEwrR+yCCy7gO9/5Dueddx7f+973ME2Tt73tbTz88MP1Nk2j0Wg0Go1Go9Fo9ptp00fsySef5LjjjuPqq6/ms5/9LAD5fJ7DDz+c5uZmHn300TpbqNFoNBqNRqPRaDT7h1VvA/aXO++8E9M0ufjii0eWud1uPvShD3HFFVfQ2dnJnDlz9uu7urq69vp+Pp9n3bp1tLS00NTUhGVNm92k0Wg0Go1Go9FoDiHlcpn+/n4AVq5cidvtPiTfO208jDVr1rBkyRKCweCY5cceeywAzz777H47Yvu7nkaj0Wg0Go1Go9HUePLJJznmmGMOyXdNmxqx7u5u2tradlteW7Zjx47JNkmj0Wg0Go1Go9FoDoppExHL5XK4XK7dltdCg7lcbr+/q7Ozc5/vn3DCCQB0nHwhlsd/AJYePE6nk6VLltDW1oZpWgghJuV364EA3G6LFfNa6Orq5O4/PcJQIo09PUoWDxrTMGlqbmLZsmV43G6EmDZzIQeFZRnMaYkQcEru/dNDvLx5O6Vyud5mTSgCgcfjYcWKFUQbohjCmNnXshD4PA6OWNjC3597kd89+CT5fJGZfSWDZVrMmTObRYsXY83w+zWAw2GyZE4ThWyCu//wENt7BqjYdr3NmlAMIQiGQixfvpxgMIgxw+/XhiFoDPtY0Brizw89zpPPrSNfKNXbrAnH4XCwZPESZs1qn/FjLwCv28Fh85rZvr2Le/70CINDqRk/9rJMk8amJpYsXYLX4z2oazkd7+fmL30QgKampkNn2yH7pgnG4/FQKBR2W57P50fe319mz5693+taHj8Ob3DfK75KTMMgGo0yd+FSfD4/QogZfTMwDUFrY4iW1gBrXtpIRbgxPQKz3oZNMC6nk9bZ84k2z8LhcMzoYwzgdlosWNBOJjlIUTgx3X6Y4YM3IQQev59oSzuhUAihgCMWDnhoaWvFvXE7Dk+QslGst1kTjmVauANRApFmJa5lh2USamjGigQJhhvoT5UoVyr1NmtCEQhMlw+XL0wg0ohpzuwnlBDgCXhpbGth8eIYL23px05nZ/ykimmYFGwDb6gRl8s1469lyzIwPBFWrWrkxc29ZF/eQnHGT5BCvgy24cIfbsI0zVd1nA+ldsS0md5pa2uju7t7t+W1Ze3t7ZNt0iHFtCwam5pGbgIz/kZgmjSEvAwNDbF1e++MvwlA9UbgcDgIhYKY5swenEP1oe6wTFwOk/hQkmQqi23P9Ed6dfDmdDpxOtW4lgXgMA0MQ1AolpgmQryHCHW2VSIp2zZOhwOn01FvcyYFiaRULlMsFpAKHGspoVgqU65ImhvCBHxehDGz718AUtqkUkmyuawS9y+7IhlIZnE4XCzumIXTOW1iMgeNBAqFIr09vZTKUyvKO232/qpVq7j//vtJJpNjBDueeOKJkfcnAkMYE56OIITA6/HS1NiIYRjY0p5Wz/cDvU0LIXA6TEJeJy9u7acvlkBKZvSAVSAwDAOPx4PPV011tWd4ZMgwBKYhsAxJ/+AQ+WKp+lCfRuf2gSIAwzRwu92YpoFtS4SY2cdZGKI6sYCkVCojhMAQYiYfZgQ771dSSqS0kXLm3r+gGsgulso4fU58XjemYWDb9ow/znalQiaTxa7Y2KLCgT/xphf5YomhdI5QMEgo6KMvNkRJzuzIJwgKhSLpVJqAPzBq+cw8u20gnc2TLlRYOG82zQ3rKRT79xnhFtP83JdIYrEYiaEEVqOFYexhbL+Hw16xJ+Y6mDaO2Pve9z6uueYabrzxxpE+YoVCgZtvvpnjjjtuwpQQ53TMwROIjv/mIbtGq18Uj8dJJJKH6kunLJZlsnB2C4ZsJleUtLS2E45OrRmKicIwBL29vTPa6azhsAyMWdXj7HB5mDu3g2Jp5kc+oXqOd3Vtr7cZk4JlGVBqZtmsENGGRubNm0epVJmhQ5ixWJZjnzXHMwXLMpCFNLOPWsrC+fNI5KqO94w/zqIaMdm2bVu9LZkUHJaJnU9x3MqFHLF8Gbbpplic+fdtIQS5XJatW7fW25RJwWGZlLIJjjqsg2NeczhuXyeF4swfhwlDkEwmSWfSex3DjxcZzSYGJ8SmaeOIHXfccZx55plcfvnl9PX1sWjRIn784x+zZcsWfvSjH03Y7y5bupRgtGXCvl9KSS6X47nn/sHWbduQMzxKghB4XE46mryYpokv1MBhh7lmfNG3lFAqlXjxxRfZsmXthM2sTBUE4HQ6CLkAsZxoUxvLV3ioVGb2cQYoV8pseOUV1q59iUpl5jskTstClDLIo5cxt6ODgvBQVuA423aFrdu2sW7t2hkvQAPVdPJMvJXjDp/PksWLKJk+SuUKMz2Ty7Yr9Pb28o9//INisTTjUxRNw6B3R4Tl81o48vDDwBUkr0DKsW1L4vE4a9asIZ/PzfjtFYZg6xY/7VEfx77mcBy+KJlcYcZvt21L0uk0zz33HMlk4oDKJUrZiQmUTBtHDOAnP/kJX/ziF/npT39KPB7niCOO4N577+XEE0+csN80DQvTnLjdVC6XiceHSKVSVMqVGX+TN4Qg6PeysGMWuZIkWywjhIFpTptyxYPCtivk83kSyQTlSnnG3+ygWoDaGA1iY5ArlKopvubMjgRKKaEMuVyeSqUy4ycYoJpi67BMhDAoV2wMw8Cc5iks+6KajiirqWu2nPFpxgAVJNl8nlKpjNPlxjRNZrq/LaVECHC7PQghhlMxZ/a9W9o2Q8k0Pf2DHN7UjNPpoFSxZ7TDLaVEGJJAwI/f7yeby878a9qGVDrHuo3bWLxwHtGgj2KpQmWG13EbhsTn89HU3EQylaRi7/8k2kRNoE+r0a/b7ebqq6+mu7ubfD7Pk08+yZvf/OZ6m3XQSCkpFov09fcpUwxsmSZtTVEaoxEGUzlKRQVSW6ge60wmTbEw82ecoJpL7vW4aWmMUrGhVKkosd1QjXzmcrkZPXAZjRDgdFiAoFie+RHA0aiQYlxDymr9UK5QwFJAbAhqx1fgcrtwOl0zvTwMqGZrlSsVegfiOEyB1+2c8cdaCFFVyDQtwuEQ5p5qh2YY5UqZTdt2MDgYozHsm/ET4lB9XpmmSXNTE16Pd0rUvc38vT5FkbI6i5pKpxgcHFRi5lwAbpeTpQvn4PX5GUxkKCuw3VJKyuUKsVicUmnm52BDtRYuGPASDgXJFcsUFakZklJSKpWGj7MKW1zF5XSAgHJ55l/PqiKBYrFELl/AVEBJr0ZV/dXC43FjzHCHpEa5UmFH3yD5fI6QT53tNk2TSCRaVbyttzGTQMW2GYwn2bi1i7DfRcDrYuYf6qpwmt/vp7Gx8ZDK0B8s2hGrI5VKmb7ePvK5vBLRAsMwCPq9LJjbTqZQJp0tKBE1qEogl0ilkko43DDsiPm9+H1ektmCErVhMOyIlUtUKmUlzm0AIQycDidQTd1SyP9UDtu2KRSKmGZVGXPGj9kAEAijqoSqwCgVqKbbDsaTDCVS+D1ODAUcbyEEwhD4fF68Xi9ihjfvhmqUO5MrsGHLdkqFPGG/V5looNPppKWlmnpb7zuZGnt8ClLtW5Gmr69vxgs31LBMkwVz22hpbqR/KKNEQX+tliSXzZFKpWa+GMswlmnS3tyAz+slk1UjHbOKpFQsUVag3rOGEOByVVMTbSmV2W5QKzURJLYtq6mJRrVvnCKeGIYwcLs9ykSGbCnJZHMMDg0R9LlwKdBnCqop9W63m1A4tGdp8xlGqVJhc1cvA7EYTREvDmtmNy2vYRjV6Ge0oaHuKZlqnGlTjFqq2sDgwHAtycwfuAjA43aysKMdKSyGUrmZXww7jG3bJBJDw4pbMx+BwDJNmhsjCNMknStgK3COQ9XpzuerQh2qYJomHpcLhFDkOCuKZPj8LmIZQpkBmxDDvT69VaVfFZBSks0V6B2II7BxOx2qBAMxTYtIOKzMsbZtm0Qyw4YtXXhdZjUCOsMPdm0CzbIsWltacTqddbVHO2J1QEpJoVCgr7eX8hTr8D1RmKZJczRMx6w2UrkSuUJJmdStSrlCPD6kjOMpBLicTpobImQLFfJFNdL0pKym9GSzGSqVmS9nDtUJFodp4HW7AA5ICnhmIJQZoEqqkZJ8oYgx7IipsekCIaqREstyKLLN1ebd3X0x7EoFr3vmD85rGIZBKBzG5XYpE/Eulcus39RJKpmkKeLHnOHqxlB1xgzDIBIJEw6H65qSqR2xSaYm0hGLxUgm1akZclgm8+e2EYmE6Y9nKJfViBhU+8Rlq2mJUo1jbRgGkZCPxmiYXKGkjAMK1ZTjQqGoROSzisA0TZxOBxKhlCMmhFBmoFZDSkm+UMCuVHBapjI1U0IInE4HjmF1UBWoVGwGYwlKxQJhv1udOjEhcLtchEOhassVBajYNj39cTp39NAQ9OJxOZW4tKsTLB6aW1rqKtqhxlk2pZAUiwV6eroplRSZNRcCl9PB4vmzwXQQS2aUGLBJKbGlTTqdoVDIK5O2ZQhBUzSMx+MhlVUlLbGKbVej3VKB87uGZZk4HI5qRFCRyQZVkVKSyxcpVypYiqQmAiDA4XBUI2IKDFChei33x5PEhhL43A5laqagpp4YUeYct6UkkcqwflMnBhVCfrcyk0ymadDU2ITP56+b461GBeaUoRoNGxoaYmgooUykwDQM2lsa6JjdRjyVo1hSo3cYgF2pEI/HKBbVSEGF4RtbQwgMk1Qmjypj82q0u0KhUEAV6UAhwDINnA4LOSzmoJDfjRCMu73espMFyaYxy7YEBkg7CpNk2cRgS0kml6dUKg038a63RZODQGA5HLjdwwNUBU7yaj1ggZ7+GLNnzcbpsKrP7hm+6VX1RINQKITb7R6u7Z7hGw2UyhU2besmlUrRFPHRG0tRnOFCclVn08DjcdPa2kI6naZYKk66HdoRm0SkhHK5TG9vH/lCXomLG6ppiQvmtOP2eNnSGZvxndurSKAqypJIJpVxuquiLC5am6JIjGGne+Yf75rgTqFQpFBQox0FAKKamuiwTEplW4lI91h290QMKfjs82/mnE3HjVn+p/aXuGfus/yp/aVpm91Wk7sulctYCjR/HY1lWnh9NVnzmX8/H93Y2cAm6HORzRWoKHBvM4SBx1OVsU+n00qMWWwp6RtMsGnrdo5atRKPy0GpXFHC8TZNk+bmZrq7uyknSpOexaPWnbSO1GrDMpkMAwP9yqiqGUIQCflZMn82pQqkM3klBmvVNC1JOp0mlVSnPkwYBn6vh4ZIiGK5QqE082/kNUYrJiqyyQiqqYmWw0GxVFEqDXUnO7fZW3byH2vewb9sPma3tf55x3Kueup9vHPbqmkbMJWy2p6hVKo6YvXuvzOZCCHwuN3KRAEByhWb7T39pNIZAl510tUAXC4X0WgUoUhKppSSQrHEuk3bKOZzNEb8yvQUE8LA7w/Q3NyCYUx+Oqoae3mKYNs2/f395BRp4AzVtMS2piitLY0MJnPkFamLA5C2TTKZoFxWJxXTEBDwuQkG/CTSeUqKTDhAtXF3oaDGRMNoLMPAskxKlYpy277rwLQ9E+K9m1+DKcd/tHorTj689g2TYdqEUapUKJVLOB2mEgIONWqF/So0+q1hS8lQMkMilSbkcysVBTUMg3A4jKWIjD1ApVKhc0cfPX39NIV8OB3qbLtlWTQ3N+NyuSZ9gkmdq6rOSCnJZNL09PQqJW3tdDhYtnAugUCQ/nhKmRQ9gFKpTCwWx57hedajMQ2DtuYGAgE/6ZxaohUMR8RUmWSpYVkmDstBuWIrM+GwJzaxkZ/x472u8w2+zHQNicnhdOtSsYTbqZaAgxDg8XiwLEuZOKC0JalMlsH4EB6XhctpKRERFAIMQxAIBAkEAuqoJ0rJ4FCSVzZ34nEK/F41IsA1KftgMEhjUyPGJE84qHF21ZnRkvXpdEqZ9B3DMAgFvCycN4t0vlxt7KuIH1ZNU8uRTqeVOd6C4X5xjREsy0EqW1Rm26Ea8VbNEavKejtxOEwqFVsJEYPR7DpIsbH5HavpYhtpUshRtUQ5svyNv7CetZNs5SFEVnsOFUslHJahTESsGvkUuFzO6oy5CqNTqo53Nlegu28QA4nToYoTWj3eDodFMBhS5jwHKBbLbNi6g2I+T1PYp1B6YjUq1tLcgtvlmtTf1mIdk4CUkmKpSE9PD8WSQup5hsG82a00NkTZHstQLivihSGR0iaVSpPL59QZmAuBw7JobYqSL9lkc4XpOvF/EEhKpTLZbFaZekCoPry8bieWZVGu5OttTh0Q7Kq8sZVN/BsXEsLPmVyEBw8SyR+4mxd4gRhDyN0+NT2oCk5VKBZLGEJgGmKPypEzjVpRv9vtJpEQqHJzK5dtevvjIMuE/B5iiYwaBxwwTYuGhgZ27NhBuZKrtzmTQsW22dbdz5bOHSxatIgtDotKRYXemALTNGhoiBIOh8nl85OWwaUdsQlGSomUkqF4VbJelVQtAXg9LhbPn4VhOokl48pER6Ss3szi8ThlhRxvQwhCQS9N0TC5QhlbqpWqVi6XKZVKSm1zVcDAhcOylExN3DUwUnPLypTIk+cX3IQBVLApUKJMmbJzOkcVJKVyhUKxhEH1mlcJYRi43cMRMUVOdlva9McSZLM5gl4XhiGUUBGsNXf2+/243e6q0rUCYxh7uGn7K1u6WLJoPtGQl3yxpMQxh2qGR2trKwMDAxSKkyNlr0bMsc6UhqNhpVJRCSlvqKYlNoSDzJvTTipXIpPLqzKJBkClXCaRGFJKvMAwBE2RED6fl3RWnTRUqEUKSpTKZWUGaFDtr+R2OjANk7JCwiw7Gc8RGbtM7vKn64jjJ9KgCUVSjYhlcnkQUpm0pRqGUZU1VylVTUrJYDxJ70AMn0e1ukCB1+slFAoqNelQKlXYsGU7gwMxmiMBZRpbAxiGSWNjE8FQaNLOdXWuqDpQjYbZJJMpBmODVBQamZqmyYK5bTQ3NdAXTyuTllg75qlUmnQ6o1SamiEMGiIhTMtBQpE2BTWklJRKJSrlMip5YgJwuZyYpjF8jauz7eNkJY4sHt9Bk9WJuOGZ9ulKqVwhm80DUimHRAiBIQQ+nw9TISU9KSWlcpm+gThel4XP7VRCwKGGaZlEItG6yJrXC9u26RscYktXN0GvA6/bocQxr0VBnS4nba2tOKzJSRrUjtgEU6lU6OvrJZvJKhHWhuGmvi4HSxfMQWIymMgok5YIYNuShGKy9QBul4P2lgaEYZIvlJSJ/lava0kul6dUUueYC6oPLpfTCYaopiaqM+8AMK7MsRheOvpVYyacG7Ztky0UEaCUIwbD57vLhWlayvRQk1Sd7x19AxSLRQI+dcRKoJp+G46E6yJrXi8kkMsXWb+5EyHLNIR8ykQEhRBYpkVjYxNer3dSznXtiE0QtdqwbDZHX1+fWhLmpkF7cwNzZrUSTxcolctKpSUWS0VigzEqCh1zQwi8HjdN0TDliiRfUuuY21KSzWaVus4BhCFwOS1sG0VTEw+MmTA5IYFCoYhA4rTUcUiqCBxOB9YkzZRPFSp2hZ6+OOnhxs4qDcqFMPC4PQSDAaUc0HLFZuO2brZ399Ic9iuVniiEwOf30dLSgjkJkVDtiE0gtm0zODhQHaApNCp1WBYL5rbh8/noj6cpV9TZdiklhXyBTCatjDALDN+4vG7CoQCJTIGSQo27odpvp1AoKJWOCTtTEysVSamsllhHNSI43vLdF0pmRjQMqPbLK5QQSFxOa3rKPx4kQjDcrkGNVK0ati1JpNKk0mnCfg8OhQblUJU1j0SiiqWk2qQyOTZs6cLrMggHPChUHohpmLS0tODxuCd8skmh3Tq5SKrNXbu7e6oF/IpgCEHQ72XJgjnYwiSRySnjhNaioKlUkmw2NyNmv/cXwxC0NkUIh4LDQh3qbDsM940rKCjfPpyaWLFtpSLANXafIa+lJY5XQCZH/pvOyGFVNdu2cTlMpRwSEDgdDrxeLyp5oFJKEuksfYNxnA6BU6HjXm1bYBCJhHE6HcocdQkUikXWbewkmUzSFPZPSnRoKlBr8Oz3B2hqasKc4AbP2hGbAKSUyGH58qGhoUnrRTAVMAyDtqYoc2e1MhDPki+opSJn2zbx+BBlhZxvqAp1tDREcLlcpLIF1PLDJLZdoZAvoNTJLqoOuMvpqEob72XThajgMLNjXoKZ57iJ3f4/lplwdlQHaCUqlcpwupIqQ9MqhmHi8XiUSlOTQL5QpLt3EEuAx+VUKiVVCAO/3z9pNUNTBbsi2d47SHdPPw1BLy6nWg64w+GgtbUNp3Ni6wPVSnSeRErlMt3dOyiVJqcPwVTBMk2WLZqL1+dn3fZuhZQiq0OscrlEPB7HVki1QACWZdLa1ECxLEll1ei3ArUoKBSLRQqK9JmpIYajA263i4ot91gT2B5+ieWz/srrFv5izPK/vXwBL3efSG9y8SRYO1GMJ9axN2bG+ZEvFCmVyjhMdSIENYQAj8ejmv+JbUt6B+LYlTLhgIf+oRQzcC5ljzidLqLRKLFYHKQaG24jSaWzrN24lWVLFhIJeMnmS8o85wzDIBqN0NAQZceOHRP3OxP2zQojpU0ykagOyBUKDRhCEAn5Wdgxi2yhQiZXUEawQUpGZOuz2YwyNyqozhwF/R5aGsPki2UqFXWc0CrVNOSSYiqZAC6HhXs4IjZeyl1TYBPnvO4yTl72f7gdmTGv0w7/H8553WWEPD11sPzQsKcasT3XiU3/M0RKSa5QJF8oYilUM1NDCIHbo45gRQ1p2/QOxBlKJgl41VEQrGGaBuFwRCnRCoCKbfPK5u0MxmI0R/yYiimlWpZFa2srTqdzwn5DO2KHGCkl5XKZnt5e8oXCjHjw7i+WadIxq4Wmhih98TRFRXqH1bBtm6GhIcplNWbLagghaAiHCAT8pPMlKgo5oVB1wvP5glLiLDAcEXM6RlITx9t6rzNBxNe9x+9o8G/DaWUmzshJxNnWiP+IvUX3qvvIt2IBrtnNk2XWhJDLF8nlCzhMQ6nheNX3Evi8XpxOp1LbbgOpTI7evhhBnxOnQx2HpHbcg6EgPp9frfREaTOYSLFxSxfRgIuAz61UeqJhmESjUUKhMIaYGJdJO2KHkBHJ+lyO/r5+bIXknAXgdjlZumA2LreHwURWqdo4gFKpRDyulmw9VOuEGiIBHE4XiVROqSgwVB2xQqGgVBQUAAFOh4VlWdUo6EFsfiYN07uccueIxLu0g+ipx42JFNT+VosXSiThU47Gt2LB5Jp5CJFUWxVUI2JCsRQ9MVw74sTlco8fEp2hSCkplsr0DsSwBPi9LoU2X4z0TAwEAkpFQ6WEXK7Auo2d5PN5GoJezAlySKYqLpeb1tYWzAmKhqq1NycB27bp6+0lnU4roxYIO9MSF8+bTTJXIpnJKZOWCMOy9YUiqZRasvUALqeTWS2NWJaDTL6onENi2xVyuRxSobrAGk6nA9M0KO9h0qW/F15ZN/5n00n46uega+sEGjjRjJuauHsj5xozQTURJNKupic6HKZyqVpQrR1RqcFvjWKpTGd3P/lCHp9HrcbOAJbloLGxEdNUS16hXKmwubOHgcEYLdEAlkMt18E0TZqbW4bVUg89au3NCUZKSS6Xo7u7R7nGroZpsGhuG81NDfTF00pFRaqOhySRGCKXy86Agdb+I4TA7XTQ1BhBCsgXSko54ACVSplMNqPcdguqETHTNCmXy+M64N3b4Sufhh/8J3Rtgq7N1T87N8F1X4M1j8Gae/5n0m1/tdRSlfZvILpznRnRaU1W05Xyw6mJqvWUgmq9kNvtViwaWJ1oHoglyOVyRAJeTIUaS1WbOwuCwWA1LVUhJ9SWkngyzfpNnfhcJkGvW5ntrx13j8dNc3PThPyGWm79BFJNS7SJxQaVi4YJwOt2sXTRXMrSZDCRUWr7oaooNTQ0pJxQhQC8HheNkRDJdJF8sVRvkyYZSblcoVRUR0lqBAEuhwMwKJbGdzEk0NcHD/0envoLWEZ1WakCuVLVoSkk9lxDNtUZbygytofYzjVmygSNBGy7qpxoGBKHWZW0Vun0NwwDn6/qiKiUgi+lZCiVIZlM09IWxrIMSgrVRAsh8Pt9BINBslm1Jl1L5TJrN27ldUetoDkaIJbMoNChxzBMmhonprZXnemMSaBYLNHd3UNRMcn6Wu+wOe2tDGUKFIplpR7KAIVigXg8rlx6miEEzY1houEQSSUbOUO5XKZUUs0BBRA4nRbCEBTK5XFH4lJCxR771ujUPUNUnbPpy1hXzOmyMJsbd3lPUiug85zxTziWLZw06yYKKSX5fAG7InFYpjKz41CbITfwen2YiqlG2lKSSGXY0TuIy2HgtNTpK1XDNE2i0QiGYuqBtrTp6YuxpXMHLREvXrc6NYK1qJjPp1MTpyy1aFgylSQejylXI+SwTBZ2tBEKBukdTFFWLCokpU0mnSaXyykXCRSGoKUxgsfjHu4fVm+LJp9yuUy5XGKm9Ig6EJwOBwhBqVwZPyJWc8TYZe+IajRMCDCn81No1EDEWy7R2ujHc82/wQfeApaJHP0661Ss97wJ4XTUz95DhC1tcoUiFdvGMb096YNCCIHT5cQw1HLEAMrlCj0DMRyGJKBQihrsdMKj0ehwjaA6SFlVzXx5UyfSrhAOeJSrkZwosQ6dmniIKJXLdO/YQaFQVCpcLYTA53GzbNE8bEziyaxSUREpJbYtiQ8NKRkVMQ2D1qYoZRuS6bxS537NvcjlspRKJaW2HMA0BB63ExB7vOZNTxBHoBUpe8Z4YqMjYtN5Ynn0QMSwywgpEUcsxj5sEdkLTx9530Zi+90YTjEjeuBKKUlnc5TLZSXFOoQAl8ulXEQMqlGx7t5Bsvk8oYCb7sFEvU2aVIQQeLxefH7/sEiTOnf+YqnC+s3bSSWTtDYE6B5MYpdmwh1t3wgBYoJiV9oRe9VUJevTqTQDAwNK5YtDNTWtvaWBjlmtDCZz5BV0RkrlErFYTLn6MEMIAj4PrU1RCiWbcsVWKiJWbeItyWZzyh17qKYkr5zfwmGtHlp9LeSL0XHW6uCu7rfy0l9vHomKjfa7hICGgIszT+iYHKMPIVJCsdjOO17bhpQ2D61bx4M3/oQcLkwMjFG1YjW1xLKwCbY2cOKC+ZzwqTPruwEHiaDWOzDIvDYfc6NuMh2BkUmYZzYOsqk3XV8jJ5yqlLnL5SSdEUoNxqWU9MUSDMYSBEMNGEIwfjx85uJyuohEIsQGB5VqUySlTd/gEBu3bufo1xyB1+UgWa4o8tyfuBlD7Yi9SqSESqVCb2+PcsWbUFVNW7pwDi63h57tvUpFw2BYtj5fIJVKKVcfhhBEQgHCwSDZfEm5lFSoOhaFQl6pgRhUH0mmadDWEKA54KA5sOd0uwf8rmGnddTnR6Um+twmR84bz4mbJixtAyA+1Mc9L28kOZQbccQEYkwPMW/Yhz3UwPxl83n7G4+cFPM2x+GRbTv/vbIFjmw91L+ys3Zia19GAUcMHA4nPp+fWCym1FNfIsnm8vQNxDiyrQ2v2zmcDaEOhmEQjUTZYlqUVXLEqGohvLh+C0csX0JzNEA6W6CiyPNvotJwtSP2KqjJlheKBXr7+pS6IAEMBJGgn0UdsyiUJZlsUakymZ2y9Qly2ZxKmw5UU8oiIT9ut5vtsRwVxaLBUBMtyKPUiT/MojnNrFoye5/r2RJGnxm1PSWoFilP59TE0QghCDU3Y8tBCql8dcNHvWdaJv5IcNJqagpl+NbDcNdL8FzvzuULo/DPC+CaN4PPOSmmzEiEAI/HTfVMVuf6lxIKxRI9/TGOsO2qI5bJq7QLMIyqjL3H46FYVKscpWLbbN3Ry46ePtra2tneN0Q2r14m1KFEO2KvEtu26e/rJ5VMKTcrbpoGc9qbaWtuoieWJl9Ur06mYtvE43HlUlIBHJbF7LYmnC4X6VxSufMfwK7Y5PNqzQZXEYR8HhpC/n2uaUuQw8qJkp0KUbWI2ExxxKDaTzHa1ki5oUw2nqKWzuJwO/CEfJNqy583wZfv3318vDEGm2IwkIWb3w3+Q+yMdTT7+PvmQcqVmX1VCCFwuz1Vx3pmb+pulMplOrv7KBYLBLwuemPqpGcKIZCyWiMYiYRJJtV69tlSEk+kWb95Gx1zZhP0uckp2D/0UKKe3NEhRVIoFOju7qaiWANnATgcFssWzsHn9zEwlFEuLRGgkK/J1qu17dVmvg5aGiOYpkE2X1BqMDISDS8UyOXUS008EGy7GhEbbw8JAVs2rufuX/18ss065Pi9HtqbGpjV3EjH7FYOW7mYw1Yu4rCVi1i0uINZzY3Mam6kvamB4ATJINeQUtLXP7jHS1ICd62FeO7Q//aKOWGcM1zAo9bQ2+v1YijU1LiGbUNsKEU+lyMa9GIpuA8MwyASiWJZ6sUzCsUS6zd1USjkaI4GlGrsPRGodwYdILVZ2/GQEhKJBKlUclgBbAZN7e4DwzBoCAdYtrCDZLZMKpuvSlLX27DJREIum6VYKGAIoZIfgiEEXo+LlsYo6VyZXLFUPf6K7AQhqrU/pWIR27YxhKFUeoohxH7f7yp2deAmYUStoyr4UH3lcxn6e7ZPoLWTwztPOYF3nnJCvc0AqnUcn//3b8Ibr97zA2xMkuihxZjxzwKBYYDf58XjcWNnyhzsPOR03E9CQCyRJpZIMmduBMsyKNu2MhNSIzL2kQg+n5dKsqzMttfo6hmgc3sPCxctYKPTws6XZvwzcKKG+NoR2wcLZzfQ0Lx7N20J2JUKXqOA31w2Y1LT9vcyskyT5QtnccSSdrbHiyyZ06RcjZBt24TdEo84jEplP3SjJuoeVYcnuSkM5rQ38pqls0gUzOrxr+zWLWqGMqyEJyXxoAs3KymXy9P+IXQgp5EQguXz9634UJBQaJ4FpomUo66RmjMGGAakBWzNZAmVS4RDoQM3fgow5fopdb8AL90Hy98GYpcZ63IBnvkpZN8DoUMrlCKEYPGcRvKlXZ8HU2z/HBDjX9v5QhivUSCdzkz76/9AEFRT0xvDPha2ekhnW0lli8o5I8VChLBbkkgMKdVDVAhwmBY+j5P5zR4qy+fQF8/MmHHwnhj0lCfke7Ujtg+GBnoY7yYsEHi8HlYuXYSxfMmrf8SIkf9NeYQAt9PB0tk+0qkMmzZtJZVRr5mvaZrMn93CYYs66j8Im+SfN4SgoyVIxAcvbtzKYN+QkqqR4UCQN514fDU96UCOwRS7Vg749BGC5uC+i4u2S4h96DKMu76PzPWP9zUYAv4mYc0/1vKuvh28551vP1BrNLsigFIJHrkR0nFwh2Dl6VDMwgu/h0w/rFsN9jsm4MclQ4O95Aozv4DfNEyOWrkMr89X/2fAJGMKQUdrgIBDIAtphvoHsQ/BM2A6jSOEgIUdbTQ0rMQ0drarmCns7VAYAsIBN363ScBRYmOsl0Lx1V7zk3/wD+QXhwZ6973SQaAdsX1w52//htO3+wytZVksWrSIRQtLmJY1KZffqz9FD81JbgiD1oYgHqfB7+9/nD8/soZ8oXhIvnu6IIQgGAxw7LHH4fV6JyxkPWUYc+oILMvAshxs2Brjpl/8jr4BtWYEoXoPWLZsKfPnL1AuR14IweK2IK/t2LdYh6SanigZHmSNiobNhKbOUxZhgF2BF34HpgdeebT678wAVLIgK0zEwKdSsfn5b/5CPJk95N891XBYFstXrKBjbgeGCifxqE00hGBHLMTyjgYefeZF7n/iOSpltTqKCSGIhCMcd9yxuFyumeaH7RWBwONyUCjPorcnxm/+/Ai9A/EJGgdMzI490G8tZiamebl2xPZBNl+gZOTHLBMIAgEnwVCYYtnGsCcmXDlVsUxJOOAhlc7w4vqtJFIZ5dISDSFobGoGYVIolpWbDXU4nLhdBhs3DjIYT5HJqacc6HKCMCyKpQqGodbWH+jpbstxZrpHqSaqdfVMEsIcPlCiuvPTMXY6XhLkxEWssvlCVcBnhmOIEv0DMZqaW7EsS6nngBAQS+YoliTRcIBKxSabLyj3HChXBukbGKSxsREhDKXOgVK5QmdvktmNUZqjYbZ29VKewWPB0gTd09Saxj1EGIaguaUZfyCg1EVXw+kwaQi52dK5g207+mZ8XvB4GIZJJBJR8vgLAX63CyElPf0xiiX12hYAmJaJ0+ma+dHQPWAdgDKeLce4AMBOwQ5jL4JImleBMBjVLGDUG9V+AsuXdOBx60ZirwaJJJ/PKfkMrPUTK9mStuaaEqh6F3KlUiEWiyupGm1LSW8shcPh5vAl83A6HQqeAa8e7YgdIFXZdietLS1Y5syW6B0PQwgaw34cQvLK5i4KCvYOE4Db4yYcjiAMoaAzJvB6nBSLRbb3DFAqqxURhmpU3DQMHA5HvU2pCwJwO/fz/icE0uGq9hEbfbMYjoQZpoHh1A7BoUUMR8SMcbzc6oE4/9x30NgQOeS/bJkG55x23CH/3qlKoVBQ0hEDKJYqJNJ5QqEAAb9HyQmVim0Tiw1SLKpVngHV+3k2XySZK7KgYxYtDWEFx0OvHu2IHSBCGDQ0NBAKhZULQ0P1IdvWEKC3f5C1G7ZRrqjVP62KwO/z4fG4EQrO/5iGoCHkJZ/P0R9LKDkTCGBZDpxOJyrOAgsheP2Shv1b17TwXvZd5Amnw/DkVW2PiblLcL33w3jfetYEWaoyBiOVeCOnaM0btqtdtifiVw2DlYtmTch3TzmkJJfPU6moNxkF1YjIUDpPKBhgVmujkj3VkJJ0Ok0ul1NKObNGuVxhe3+CcDjMonmzDihTQlNF14gdIA6ng7a2NhwOh3KzP0KAz+Mi5HOxZkMvg0NqdZSvUWvk6HS66m1KXbBMA5/HSaw/zlAyreTDB8Dj9agbERMMq4Ttz8ogFh2BveCb5F5+D4asIKimK1Ya52A2d5DMpPEoOpidCG646acMJdMgQoxxxmrN3OSeWmxrDgQJFAtFsrkcPp+fkUZ5iiClJJnJ43E3MKtl2BFTbHJWArlcnvhQnEgkjEQqNUFvS4gns4g5jSxf3MEzL6wnnkxPK/XLeqMdsQNAIAgGAjQ2Ng4rJKlzsUF1axvDPoQs8+IrW0hnc0o+yk3TIBKJqDn7R7U2yO2w6OmLkUhl1LzhCvB6vJh69m+vFIcS9D7+LMneHeSzCcxdfALZswVbbqFUETRIAfPa6mzxzGDd+i2USjY4x3tGTWxETDUqlTLZbHa4fYeh1AStlJDK5MkVq3ViLodFScGaYduuEBuMMa9jHgpWrJDJFemLZ1k4bzazWhpJJLNU0PeX/UU7YgeAaRq0trbhdrtRzQmD6va3RPwMDMbZ3NmjZEqaADweD8FgECGEUg9dqG5/0OvCaUl6BmIUS2rNftYQCNxut4J3gQOj0D9A58/vIpdJMdDbhTFcsiRgpGbMluANRJBeD8x7b71NnhkYoxQTxehJw+F7trRBD5QOCRLI5/L7XG+mUq7YZHJFWpuiREOBqoKuYrNzUkqGhuLkcln8fj9STsEG7xOILSXdg0lmLW1j2cK5vLy5i8puDd01e0LNKf2DQCDw+fy0tDQjVOgXsgtCQNDnIehz8MrmTgaHksr1jYLqzTUcDuNy1cQF1DoXhAFBv4disURvf2x6dd88hAgBHo+aEzIHg+Vw4hgvlVdU1Sd9/qCS9ZYThwkYuzhhw0gJVIb7iGleLdKW1fogRe+Ftm2TzOYJ+L00NdRSYdVCUhVtSSQSSp4HOyOjFQ5bPJfGSFApR/TVoh2x/cQwDZqbm/B4vAjUU8ozhKA56ieTyfDShq2USmrWcximSSQSVTYtUQhB0OtiaChFd19M2fow0zRxuz1KRkWBA9abdzhdNLXOIdzQisPprr5cboLhJprb5uH2ePH5vITDoQkyWDGEMSxfv4sjJiUgOWzJPC487x11Mm7mkcmklX0mSmAwkUWYDtqao1imms/GcrnC4OAgFcVq5GoUS2V2DKRoamygY1YLpqJjpINBpybuJ06Hk7a2duWaNtZwOS1aGwL07Oiic0c/FQXTEgHcbjeRSHh4AK7geeCwCAdcbNq0g2RG3Vlgp9M5HBEDFWeAzf0894UQGMPrGg4nDkeUQDA67rorly/nn0468ZDZOBlUJyIeBe7a5Z15wMcQkzzX+ZIN623YLGs9xMb5fWlTcDp4OBjmNAmeCTh9F85p5rD5bazd3H3ov3wKks/nKZWKuFzqCThJIFcogTCY09aEx+2ilC4rN0UnpSQ+NEShUMSyLFR7Lti2pD+eZkH7bA5fMp/n120mk6sodx4cDBPmiKXTaa6++mqeeOIJnnzySeLxODfffDMXXHDBbuuuXbuWSy+9lIcffhin08npp5/Od77zHZqamsasZ9s211xzDT/4wQ/o7u5myZIlXH755ZxzzjkTtRlANRrU1NREOKxmjwQhwO914XEI1m7YykA8qWQkRCDweX14PB5Uu8nCcDqe24llGPQPVh846p0F1SPvdLowTXXnsSxr/1p3LJs/l19990v79Z3TU4HyUeDdQP8uy51AH5IvIpi8HmkbbXjAhh1SDEfEYGx9WPWVkhUesOFEEzwTYMecligLZjUp4YhJJKVymUKhiN+vlmoiABLyhRKFUoWmhgg+r4tkOotqqpwSSSadJp1O4/V6EUKtc0ECyUyeWKrAkgVzaGoIk93eq+xk7YEwYSOJgYEBvvrVrzJ37lyOPPJIHnjggXHX6+rq4sQTTyQUCvGNb3yDdDrNNddcw/PPP8+TTz453KenypVXXslVV13Fhz/8YY455hhWr17NueeeixCCs88+e6I2BYfDSVtbK6aiIXchBG2NQYqFPBu2bFc29G4Ygmg0OuacVA2vy0mlXGZ7Tz+FYqne5tQJgdPpxFRRHmsYyzT3KzvR6bBonoCmwVMByZPAuezuhAEUgW8AAeBzk2lWlVpqYu0gjUjXM6ySouY9fKKolCuUSqreD6FYrhBL5omEQkRDQXr640qWDxeLJWKxQZqbm5BSvbT1im3TM5hiRUeUxfPa6ezux1Z0vHggTJhn0dbWRnd3N1u3buXqq6/e43rf+MY3yGQy/PWvf+WTn/wkV1xxBXfccQfPPfcct9xyy8h627dv59vf/jYf+9jHuPHGG/nwhz/MPffcwxvf+EYuu+yyCXMOhBBEImGi0aiSDZwB3E6L5rCPTVt3sKWrT0mRDqjWh0WjEYRQ0yE3hKAh5KFUKtAzMETFVlQVSYDL7cIw1JKqHk1z0I2loGjRWDYB2/byvg38bZJs2UluMEZiaxdjmzkPH6sJbuasKpVKhXQ6reyzUUpJIp0jHPIzp71pJB1ZNaS0GRyMUS6r6XxICYOJDCVbsHLpAsIBn0IxwYNnwkaULpeL1tbWfa73q1/9ijPOOIO5c+eOLDv11FNZsmQJd9xxx8iy1atXUyqVuOSSS0aWCSH46Ec/SldXF4899th+29bV1bXXV3f3znQKyzRpbW3FqWDuN1QnVBtCfkxhs35TJ3mF09G8Hi/BYEjZ+jDDEAS8brK5HPFESlnFREMY+H0+JXsJ1ljS5sflUDciCFAqwd70Gcpl+OkvJ8+e3r4+Hn70MYrFEplMjt2EOoBaDzHTrW5a7URg2zaZTAZp19I/1UJKSGRyCMOkrSmq5POxRiqdIpVKoeJ5ANU01f6hLLNam2ltjg4/JzV7o65T+9u3b6evr4+jjz56t/eOPfZY1qxZM/LvNWvW4PP5OOyww3Zbr/b+/jJnzpy9vmrfCeD2uGlpUVcBxjIN2puCDA0lWLdxG6WymspQNdn66VnHcmgwTQOvy0Fvf5zBeErRxwwYhoHP51M2Mqqp8peH4L//Dyp7CC49txaefm7y7GlqbOT4447BEQrS+s9vxHC59ihfv+xT50+eYQogpU0un0NKW9X5KTK5AtlChdltTfi9HiWnqCRQKpaID8WVjY5WbJvugSThSIgjl83HoXAt9f5S1z1Uizy1tbXt9l5bWxuxWIxCoYDL5aK7u5uWlpbdZlpqn92xY8eE2Njc1DwizqDiLI/b6SDsc/H8S5vo7o8pW3hpmhYNDVFMU910NL/Hhccl6OmPUSiWlHbEnE6XsueBopu9GxUbfvJr2NALjQ3w8bOrfs/9T8FDT8PLL8Pc8QUiJwTDMCCfRdqC9tPeQKVgsvnOR6kUBeVMAdPjwPK4aD52FXOPWDJ5hilCIV/AVlRNGKqqeZl8kYZIiIZokEQ6o+R4oWJXGBwYZM7sORhO9cpZpIR4KksmX2HpgrmEgj4KgwklBd72l7o6YrlcDmBcyVe32z2yjsvlGvlzb+vtL52dnXt9v7u7eyQq1tzSonDPKGiOBnCYNmtf2UomV1D2UnK5XIRCNdVMtW6sUD0XQn4P5VKZnv4YEnVrTEzTwOlyouJ5ACi72eNRqcAzL4LDAw8PR78qZSjmIJeEuZOsU7L1Zz8jf/I/IwzBnHe+kTnvOJHkhm56HnyR8PI5NB+/FLDxBgOTa5gC5PN5isWismJOFRtiyRzzmn00RUNs2taDiul5UkoSiQSFQkHZc6FUrtAzmGZeaxOLOtoZiCWpKOiU7y91dcSqkaZqR/JdyefzY9bxeDz7td7+MHv27P1e1+fzoWo0zDQMWqJ+evsGWb+5azj/XT0EAr/fj9frRdVRqBCCoM9NMpVhR+8gCut04HA4cSn6gNWMj2BniuLo8cZkjz2EMNhw3U10DqQAczg1sTqRONDzDwb+ei9Im3xHKx63k/K/XQyhiXHKLn7PSfz5yZcoFGd+OrsECoU8uVwWv98/vES1Z0VVsMNoCzOnrYlnX9xArqjmgyKbyxIfihMI+JES5caPUkJfPMW8thArlszj+Ze3kMpkFXTL94+6OmK1tMLR4hg1uru7iUajI1GwtrY27r//fqSUY07q2mfb29snxMZqYz41Cfk9RANOHn1pO7FEClvRy0gIiDZEcTrVrQ9zOkwagm56enYQS6hbiAzVSR9VZzprKDauGJfRYoSjGa0SP9k4Ii1UBtdg9/Rj50sIISgwWlSlKl2flUWk24WcwJrf9qYwhkJ1lLYtyeXzgFRSulxKyOZLGCbMamnE7XaRVzSF3a7YxGMxZs+erayCZDpbIJktMH9uO03RIOlsTslU1f2hrnfJWbNm0dTUxNNPP73be08++SSrVq0a+feqVavIZrOsXbt2zHpPPPHEyPsTgUC9GypUFfKao35yuTzPv7yZXL5Yb5PqhmGaRMIR5Wa1RuN2OnA6TAZjCXL53SPTKuHxeDAMdRUDDUMoK160T0arxMs6OWMOi4Dfh+UwiJQzzCmnCFXSADidFk2NYRwKTypNHJJ8Lqek41EjVyiSzJRoaojg9bjrbU4dkQwODh5QycxMo1SpsL0vSXNjA4ct7tDPjL1Q9z3z3ve+l3vvvXdM3dZf/vIX1q9fz5lnnjmy7J3vfCcOh4Prr79+ZJmUkhtuuIFZs2ZxwgknTKCV6g3AHaZBa4OfWGyIbTv6sBXNRROA3+cnGAyiaoqqEFVHDFuyvXeAXF7NFgY13G43QqgbFTKEwDQV3fjRiDF/7Iakfh0eGqNBZs1qpuR2kcLGEwrS0hRk7qxGGqNBZWfpJxIpIZPJYu9JRlMByhWbeCpPQzREe1MEoeDYCarXfj6fJ51Ko2r2iJTQN5SmIg2WL5qHx+1S9GzYNxOad3fdddcxNDQ0omh4zz330NXVBcAnPvEJQqEQV1xxBb/85S855ZRT+NSnPkU6nebqq69m5cqVXHjhhSPfNXv2bD796U9z9dVXUyqVOOaYY7j77rt56KGHuO222zDNiZmhVnHgDRD0e/C7HTy2cSv9sYSit5Lq8Q+FQsrPIDeEPEhZorsvRnmCmqdPC4TAo/RMb/WaMBWOCO7GXjyxye6bPLu1iWNWLB35t33kcqRtY5jmbu0WhCFwKtyO49AjSafTlMulCRuPTHWkhKF0jsWzgrS3NvL3lzagqq5TqVRiYKCfpqbG4Ykr9caSxWKZ/qEsHbNbmTe7hRfWb9HpieMwoY7YNddcw9atW0f+fdddd3HXXXcB8P73v59QKMScOXN48MEH+cxnPsMXvvAFnE4np59+Ot/+9rd3U0m86qqriEQi/PCHP+SWW25h8eLF3HrrrZx77rkTuRnKYQhobQiQy2Z4eWMnFUWjYVCVrW9sbMQ0TGWd8qpQh4d8rsBgLKn0jdSyrFHCQGqeDyGvg7mN+y+OpBS7XBr2JM9ZvP2k1/H2k143uT+qAaqHvlgsUiqVcLnUnawZSuUoVQzamxswTYuKXaq3SXXBtm1isTjlchnDMJXMoLClpHsgScviVg5bNJdXtmwnV1C3zGVPTKgjtmXLlv1ab8WKFfzhD3/Y53qGYXD55Zdz+eWXv0rLNHvD63bS3uBn46bNbNneq/TA2+FwEAqFlHXCAExD4Pc46d7eS19sqN7m1BWnw6G0eiaA22EQ8aktVjKCGPvXkTvlcH3YZDtimvpSLpcoldR0PGoUS2VyxTKz25qIBP30DcaVzKiRQDKZJJ1OE426UFFJU0qIp7MUymWWLZrLw0+9wI6+mO4ptgt1rxHTTC1q/aJMAS9v6iSVVrf4WACBQACfz1tvU+qKz+PE5zHoGYiTVbiXHIDT6VQ27UizO7sOqwSMeGOWafHh8z8xyRZNHdxOi7PffGy9zZhUyuUy+XwBVeuCACq2JJ3LEwz4iIb99TanrpRKReLxOFLKutWL1ptCscyO/jSzW1tY2NGG1uzYHb1LNGMwDMGclhC5XIZ1GzspKj27J2hobMCheB1F0OdBVmy6+waxVX2aDONwaEdMdYYkPFqB9btmbO/mlRkUlr+GfkUvGcs0WdbRWm8zJpVSqUQqlVI6i8S2bfqHcrjdXtqaohgKj7ylhP7+AcoT2CZiqiMl7BhMYjldHL50vq5LHQd1rxDNuLgcDiIBD1u6eujs7q+3OXXF4bCIRiKAugp5Agj63GSyeXb0Dio9wBCAy+XSjpji9Ei4owKPjXbEdrk/SKoaBavLsE3dElvlkFKSy2WVjX5AdeCdzuZxOC3mzmpWeuAtkaRSSbLZTL1NqSvZXJF4Ks+ijnbam6OKJWjuG+2IaUYQApqjARxGhZde2UK+oLZMud8fIDAsW69abncNh8OkMexhKJGkd3BIbUdMCPwBP6apZuG1Zhz2ppio7qWiNLlctamzyqRzRSpS0tbUgNMxoVIEU558vsBQIqH0s7Ni23T1JwmGwixdOAfLUvuc2BXtiGlGcFgmsxoD9PTFWLexE9tW98YhgGAwiMOylB50uxwWXpeDeCJFNptXenghDAOvx6u0cAugdKrRuOytkZhGKSSQy+WG+26qewLk80XiqSLNTVGiYb+i05hVyuUSA/0DSp8TUkIskQEhWLF4PgGfVt0djX6iakbweVyE/C42b9vBQCyptLKNZVk0NTUOz9yo+xhxOS1MIejuGySTy9fbnLpiCIHb7ULlCCmAQ6dmAvt3Bqh7B1WXXC5HPq/2vdKWkEjnaAgHmNXSWG9z6s7Q0BClUlHpKHk6V6B7MMv8ue3MbWvCUPgZuivaEdMA1bTE1gY/VEq8sH4L6Wyu3ibVFYfDSXAkLVFdIgEPBhW29wxQLKos3FKNBLlcLqUjpAAOSz829geVB10qUyoVyeXUfn4CxJNZTMtBe3OD0lkEEshk0gwNDY1aoh5Swo6BJH6/j8OXzsPUz5ER9J7QANXZ/pZIgMH4EJu29Sidz1xLS/T71ZbeFQLCAQ/FYpH+WELRx8dOHA6H0o1aa7gUr/nIbNlC4qUXcXWswDV3GWJUqubINWKYBI5+C4bLh426TpkwhHJTWVKifEQMIJkpUCjD7LYmvB5Xvc2pK7XmzlLayt4LAIZSWdK5MssWzqUpElLu3rAn1H6iakaIBn1E/A7WPLuNgbgedDc0RJUXZTCEIOh1MxAfoHcgXm9z6o7X68XpdKJ6lPSEpQ31NqGu5Ht7yfbFCJx4Ai2fuIXKpocpvrAaw9zZxDn4ps9ihuZiOF3cW4EjDVCxBfY/H7ecXx2+gCde2FRvUyYRSTabQ0p11XYBSuUK+WKRlsYIkaCfdFZd51RKSX9/P/Pnz8PjUbcvablcYftAirmNjSzsaKN3cIiKrWVltSOmwRDQ0hAglc7y4itbKFcq9TaprjidTqLRKKrXAnlcTgI+iw0b4iTTWeWdc7fbraXrAaep7jVRwy6VsPNFEC6sxW/CsfRNI3cKOayYaNuCSr5A1qogHWo68G6nA6dDrWtGSkk6k6ZSqSitDlcu2wylCoT8PsJBP109A8o+Q6oiLllyuZzSjpgtoS+WYumcMEcsXcDfX9yofO05aEdMA7hdDmY1+enc1smWrj6l0xIBfH6/8mmJAAGfGyEl3X2DVBR3zgHcbq30pKmy7ed34fzlbxnq307CDVLsjH5ICdIGaQvisxYT8Hmxr/53cKudnqUKEshmMpTLZaUdMVtK+oeytC1oZP6cFtZu3Ea5om70I5/P09/fTyQSVbpmbiidpW+owJKFc2lrirJx2w5lHfQaukZMQ8jvxWmarNu4jVgiqfRFIYBwKITT6VQ6rQQg4HWRyxfZ3jtAReFWBlA9L9xuXR+mAYRAlitkc3kcyQKRWAEzW6CS3/myCwUCDi8Ui7z7Dccq3dRWRYrFIuVyud5m1J1qY2eDWS1NyqutSimJxat1YqoKdgDYNnQPJolGQixbOEdpp7SGdsQURwiY3RykkM+wdsM2ymW1Ix+madLY1IRpqjuTCWCZBs1RL6l0mh29g9iKR0kty8Lv99XbDM0UwPIGsAIRhNNJ0uEgVYbyqMvD5XQTCTYTDTcjhMHCue2Ypn7UqkSpVKJUKimfXZLKFMjmbdpaGnC7VayS3IkEhuJx0ulMvU2pO32xFPkSrFw6n0goUG9z6o7ao00NHpeDprCXDRs2sXVHn8LzNFUcDgehYHA4GqbuTI1lmfg9TrYP9pHKaClmh8OhdG6/ZixCCFweD9E5c5CVCrbJyO3CMh1Ypo6AqUypVCKbzRIOh+ttSl0pVSoMpQq0NEZpaYgQT6rthBSLRYaG4gRHxhhqUiiW6RvK0N7azJzWRuKJlNKTvdoRU5ymSAADmxdf2UIuX6y3OXUnHI7o+jDA5TBxmhY9/VWhDtWxHA4sy1I+jcIyDeX3QXtzA+8/49T9Xn9ee+sEWqOZipTLZdLpVL3NmBIMpbPMaojQ1hxl3eaueptTV2zbpr+/n/b2WTgcFqpO9lZsyfa+BHNXtrNqxULWbtxGoaRuKq92xBTGYRrMbQkQiw+xbmOn8oIMAohEIghhoOoNskbY78Zh2XR195MvaAfd4XBoxUSqjpihuCM2p7WZj5719nqbMW345Nmn8uhzG5WSqa6q5OlMAoBYIotNE7NaG3GYJiWFxxkSSCQSFAqF4Ym9eltUPwYTGZKZCksXzCUSCtA7EFc2I0snriuM2+0g5PeytauH7r5BZS+CGi6Xi8ZGtXsk1QgHvJRLJXoH1b05jsbtcmMqrIBWoxoRq7cVmulEa4OaaVi5XE75GjGATL5EqWIzp62JgE8rz2YyGZLJZL3NqDvlik33YIr21kaWzJ9db3PqinbEFKa9MYjDKPPi+s1KN1us4fX68Pl8yqdeCSDo9xBPZOjp042chRD4/X4sHRFj1bwwQY+uf9Jo9kU2m6NUKtXbjLpTa+zcEAkRDGjBo3K5zMBAf73NmBLsGEhgY7Fy2XylnXTtiCmKaQhaG4L09sdZt6lL+ahHLS3R5dIS5W6Xg4jfQd9AnFhC1zkAeL0eDEPfLp2WgWGoPVGh0ewPuVyOQkFPcJbLFXpjOQKBAC0NYcWT/qvpibFYTDvpQCZbZCiTZ/6cdpoVPjf0yEJRIkEvEb+DdRu2EYvrMLlhGDQ2NurBNtX+YZYJPf0xSroXTlUhTzvoGo3mAKjYFfJ57YhJIJ7K4fe5mD+nFUO3ciCVSpFIJOptRt0pV2y29SRoaYpy5GELMRUdf+mih31QsSszTsTCMAQtDX7yuQIvbdhGqWIr74C43G4CgQC2QgXl4yGEwOt2Uira1bpBifLnhmmauFzOGXcfOBh0zYvmwBEYhoGh4KmTz+f1fQNIpDJIBG3NDTgsCynVnuCzpWRoKD4sDqZqHKhK90CC4vwmDlvUwV8fe5ZkOjtlM7QmaiykHbF98OILL+AObK+3GYcUt8vJEfMiSHwEIy2sWqVubm6NbDbL+lfW19uMumOZBk3eI5HzGghGmznyyFX1NqnulMtltm7dyuYtW+ptSt1Z0SzgiPZ6m6GZRjgcFq95zWuoVKbq8GriyOXyrHl2jfITGKYQtAVNli+Zz4mvP55YQu1+YgCVSoV//OMfVOzJddSnmtsnAGclzUnHHs7pp53Cy1u6maqXSyYxwCv3Hfrv1Y7YPujavh2Hd2bVycyf3UI06CFbkvhDUbzBSL1Nqiu2bbN27Vo6t3Uip+xczOTgdjkxORynyyQYbqBD6JS8wcFBnn/heT2zDSSSi+ptgmaaYZkmixfMJ1dU6/qRUtLZ2cm6l9dR1inerH2lnVXL57Fy+RK27IjV25y6k8lk2Lhpo/IKigJ42C5w+OI5HLtqKWXDQ6l8KO4Vh34slxx0HfLvBF0jphwCOGzRXBoiYbb3J5TuZl6jUCjQP9CvvBMG4HJatDRGKJYlmVyh3uZMCfL5nPIpqzVMoR8ZmgPD67I45XA1m1p7PB7df3CYzu5+Uuk8Yb+33qZMCbxeL40NjfU2o+5IoHdwiFc2dxL0ugj5DtXkr5ig16FHP1UVIxTwcfiS+RQrgr64Tg+QUpLL5chms/U2ZUrQGA7R3BghnixSKOpZXIB8XjukUK0tdTh0EoXmwBBCKFkHI4TA6XRqR2yYnv4Yg/EhGsNunA69T4QQRKNRJa+NXcnmCrzw8haoFJnVHKy3OZOOdsQUY1ZLA3PaWhhMZsjltXwqVFPPtLpVlbbmKOGQl0Qmp+ODgJS2bsw6jCEEDksPoOTwfy+yma/xY77Gj9lKz8hyjaaG0+nE6XTW24wpQTqbp39wCJ/bwuPSvQgBotEoXq+OEEpg/ZYudvQO0toQVO45ox0xhbBMg5VL5xMOB+jsTVLR6VZIKYnFBvVAm2rQvaUxgmk6iKdy9TZnSlAuV8hkdOQYwOV00BxRb7ZyNBLJz/gzh3M+b+az/Ac/4j/4ESfzSQ7nfP7IU/U2UTOFcDqd+H26iTFALl9k244+hKwq82rA7XYTCoXqbcaUYCiZ4cX1W/C5oCnir7c5k4p2xBTC7/WwbFEHRRv64jNLgORgyeVyxOND9TZjSuD1uJnT1kShLEikdYQQqoqJuZxOWwVoiQZ48/HL621GXbmbh/gIV/MSW9hO/8jyLfTwElt4P1/jr/y9jhZqphKmaeLVjhgApXKZbTv6kXaJpoiOAgFYlklzU5PyLWIAKrbNP9ZtIj6UYk5LENNQJ2VTH32FWNjRTsfsFnoG0hRLailY7YlEIkG+oJ0OgGDAS0tThEKpSEmfH0DVESuVdAqvpsoDPEuGPd8vBkjwBC9NokWaqY7Xo52OGv2xITKZLGG/V9dGAUIYRCJRHA6dqgnQ2TPAls5umsI+vJ6JUSicimhHTBFcDgcrFs3FcrjZ3q+2XGqNSqVCLBbTinjDhPxeQoEAyUyBckXvEykl5XKJkpae1mheFQpNbu+Gx+PRTscw/bEEvQND+D0uHJYefgIEAgECgUC9zZgSJFMZXli/GYchaYmqE0nWV4IiNEaDLF04l2yhSCKt638AisUiA4MDuj6MqiLenPZmfD4PvbGsrh8cJpfL6x5Amp1sykFxL9dGpgKdOsK+K0ctbGBWVM3IkM/nw6UFO4CqOl5XTz8BryDo89TbnCmBZVk0NmoZewBbSl56ZRuxWILZTWFl0hO1I6YAQsCijnbaWhrp6k9rWXJqsvVZLcQwjGkYzG5pxOWySGe1XHuNTCajHTHNTu7rh9u6oTyOM1ay4Yed8GB88u2a4jhMA1XLYFwuJ06XOmlWe8OWkh29g5RLFYL+Q9UvavrT2NCg0xOH6RmIs27jNiIBJ+GAGs66bgqjAA7L4ohlC3B73Ozo79YCy8MMDGjZ+hoOy6KtKUq5AindyBnY2WNOR0yr6PQqqjrLq3vBtmGpb2d/TwmsScFfY3BWHe3TTDkMw8TtdpNM6pIAKSVbunoZSmRoDHnZskNg2/r+GggE8fv9xON6EqdUrrDmpQ2sOnwJs5pDxJJZZvojWDtiCtDWFGXJ/NnEkgWSGe14QPWBoG96O4mG/LQ0RRlKl8jli/U2Z8qQz+s03hoOSz8uALCBvw7CIzEwhz2xioS8rDpkaIdVsxPDMHC7dPSnxmAiSTyRpKWtBafDIl9QWwyp1vg7GokwFI8rP1EupWRzZw+9fQO0t7ezftvAjD9HFE0WUAfDECyZP5tIOEhXf0LPPg2TyWaIx2P1NmPK0NwYoSHqJ5nJzfjZp/1FSklOR0xHcDq0I7ZX5PBL+aGUZjSGYRAIBDCEHm4BZLJ5evoHcTkcuPQ9Bai2OWhqasYw1WpkvCcG4gmef3kzfpdFY3DmpyfqO8MMJ+D1sHLpfCoY9MV0PRRUB9jJRJJCUUd+ajRHQzgcLuK6f9gIxWJBp66OwuXUNQzAPgJeUvthmjEIIfD5fBimHm4BZPMFtnT2YgoI6zoxoHqOhEIh3LqWEIByxealDdvIZDPMagnP+BwDfWeY4bQ1R5k/t52BoZwWYRjGtm0GBgaoVHSvLACPy8nc9mZKFUE8oZsX18gXChQK+pqp8cZVi+ttwtRj1xGCdsI0uyCEwO1266a9w9i2pLs/hrSLNIZnfrRjf/H5vEQi0XqbMWXY3NnD+k1dtEa9+H0z22HXd4YZjBCCw5fOpzEapLMvoSXJhymVSsSH4lqEYRif10N7ayOlSplCSSsE1sjn8rrH3CiOP2KBFuzYE3IPf9eMEPWrO9vvcrmwdNrZCL2DQ8TjaYI+3dh5J4KGhga9P4bJF4o8v24TdqVCc8Rfb3MmFO2IzWACPg8rFs8jXxb0x3VaYo1sNks6na63GVMGv89NNBwklStSKusoIVTTVwuFgnbWNfuPPlf2yj+tbKu3CXXD5XLhds/sWf0DYTCeZHvvICG/E49LpzzXaGiI4vHoKCFAxbZZu3EbQ/Eh5rWFcFgzdyJDO2IzmPmzW5g7q5m+eFpHOkYxODigU86GEQJmtzYSDPjojWUpjdcfSUGklGSzWe2Iacayr8nqEcGOKUq2ABt31NsK5XA6Hfh8vnqbMWUolkrs6BvEZUmCMzztbH8RQuDxeAn4A/U2ZcrQ3R/npQ3bCPvcBH0zN6KuJWtmKC6ng8OXzMPl8tC1cYceUA5TqVQYjGm1xBpCGMxpbcLrcZLSQh0j2LZNJpvR141m+lOuwLW/gpe2QjILG7bDUYurszD/eRG06rqUiac6yNZUqdiSzu4+8vkyAZ+LnsF6WzQ1cDqdNDY20tffp589QKFY4qVXtnLScStpbwoxOENr2LUjNkMJB/0sXzyPfLlMLDUzT96DIZPJ6P5ho7BMg9amKDYGKS3mMkK5XCaX0z3ENFV6ZPXVP3psNF50TEK3Da/YsHgq5JuUyvDvN8N3fll1yGo8t7H65wub4fZ/h/nqpg1OFl6fdsRq1Bo7p9IZ2hp8bN4+SLmiszEMw6ChoQHTNCmXdRYTwNqNnWzc1sPsWe1s6BwgNwN7ik2FR4VmAljc0c6c9iY6e9Mzvhne/iKlJJVKUSrq/VEj5PfR1hwlmS6TyWk5/xqVSoWSbm8wghBixksI7401FbilDGtrY8W97IxnbPjtVCm1HEjCdXePdcJG8+Q6OOfroCfrJhyvx4upBTtGSGVyxBJJgj7njK7/OVBCoRB+/8wWpzgQUpksa1/ZitsyiARn5mSGdsRmIA7L4vAl83C63OzoT9TbnCmDbdv09fdRKmtHrEZjNERLY5hkNqdVNYeRUlIqlyiW9HlSw2GaWn57X0iqgh1TKaPoCzdCdh8px89tqkbONBOK1+vF6XTW24wpQyqTY9v2XlwOJ27do3AEy7KIRnW6cI1iqcyatRvJZDIsaA9jGDNvSlA/WWcgrU0Rli6cQzxdIJnRdT81yuUyiaGhepsxpWiMBHC73SR0fdgY8vk8Je2IjWBZ5ox8AB5yppITBvy4tI08UyM8ZwhwO9SMfAghcDgcuHTD3hHyhSLbdvTjsiQNup/YCEIImhqbcFi6cqjG9p4BNm3bQUPQNyNVNrUjNsMwDIPF82bREI3Q2ZukovOuR0inUySSyXqbMWVwOR10zGrGxmAgoeuhRpPNZnWO/igsy8QQ+nEB7J9y4hRhI1meYu9ZEU+ToMDEPyfCPievX9Y04b8zVTFNE5dTO2Kj6emPk8vlCQdmZsrZwRIKBbWM/ShSmRzPr9uMy7JpawzW25xDjn6yzjC8bierDluAYRn0xlJTaUxQV6SUxGIxPbgehcflZE5bMxW7Qi6v66FGk8vmtGrVKE45eilzWiP1NqOu5AsFEuk9TOQMnyqG5cQZCE2eUfugjORaNvMwg2wnR3HY4Sphs50cjxLjm2wkPwmOmBCiqtSoKKZp4vXqwfVoegfi9A4kCPs9WKYejkL1OnF7PITD4XqbMqV48ZWtdHUPMKclhHOGRdZ17HOG0dbcwOL5s+mLF3Va4igqlQqDg1q2fjRej4vGaJhMvkyxNDXSl6YKubyOEI4m7PcoX8chpU1k2XFse2E1FcY/P7xtHcx603sn2bI9YwAZynyfTXgwOIkGojhJUOKvDJDBJqaHAZOCaZoEAgEMw8DW9bgADKXSdPcNcvSsZnwep06RH8YyLRqbmujs6tITgsP0xxOs39zJicc3EfC6GUxk6m3SIUNPQcwghBActmguTQ0hdvQPYdv6Aq6RyWRIJLRwyWjamqNEwn56Y1mKOlI4gpQ2eS1drxmHhsNfz8r3fw2Hb2x0UFgOQnMO57CzLp8yqYmJeJzEwCAG1WxKATxKjN/Sw0MMIJFUkHz4P67AH5o6UbyZihDg8XirkUENAOWKTVdPP0La+D06bbOGEIKGaFSnso4imyvw9xc2UCmV6GgLzajgup4Km0GEAj5WLplHtgh98XS9zZkySClJppIUCnq2rYYQMLu1iWDATWJbEj3ptpNisUQup88Vze4IAc2HnYTpdpOOb6kukGA5vLQfcTpiCgWWn3rkUR7+05+ZhTXiiI0eu0jABsJNjVpWfVIQuN1uDMOgUplCJ0odqVRsNm/rIZcvEw152TGQ0M+iYbxeH6FQiHyffhbV2NzVS1d3Py2tbTis3hmTyTMhEbGnnnqKj3/846xYsQKfz8fcuXP5l3/5F9avX7/bumvXruUtb3kLfr+faDTKBz7wAfr7+3dbz7Zt/uu//ov58+fjdrs54ogj+PnPfz4R5k9b5rQ2Mm92K72xlO4dNgrbtunv66ekoz4jGEa1kbMQDlI6hXUMhUKBvHbaNaPI9/VTeGUzYls3iRfXYuVDhL1HEvYcQdh7BH7HIpIvrCP10npyL28ks3lrvU3GACzE8H9jkex0xPS4d/Jwu91YWg1vBCklPYNxMpkMLVFdJzYawzBobGzQEdRRxBJJXnh5MyGPoCkSqLc5h4wJuSN861vf4pFHHuHMM8/kiCOOoKenh+uuu46jjjqKxx9/nMMPPxyArq4uTjzxREKhEN/4xjdIp9Ncc801PP/88zz55JNjem5ceeWVXHXVVXz4wx/mmGOOYfXq1Zx77rkIITj77LMnYjOmFQ7L5Ihl8/EHvPxjazcVnZY4QqVSYSgxVG8zphQBr4f2lijJbJlUplBvc6YUhUJB13DsgupDgcHHnuKVu/9Ab7yPWCbObiGmYa/GbbmgpYPOpQvhi5+qn8GAgcCE4dTE8Z2xCnISZDo0NVwuFx63h5xOfR4hmc7SNxhn4fwwpmFS0mckMJye2NCI0+mkUNDPaADblvxj3SZOPO4IZjcH6RlIzoj+pxPiiH3mM5/hZz/72RhH6qyzzmLlypVcddVV3HrrrQB84xvfIJPJ8MwzzzB37lwAjj32WP75n/+ZW265hYsvvhiA7du38+1vf5uPfexjXHfddQD8v//3/zjppJO47LLLOPPMM5VPrQj6vaxYMo9MQc6oIsZDQSKZIJVK1duMKUU0HGBWa5R0Lk9ZtzgYQz6f1wXSu2BZat9fazSFGrGxyRQzlCo7I+xCCAIeP43BqTODLQBz2AHbU0SsAtiTGBNrDrlxOQwKJTXvOZZl4fV6icW1cFSNVDrHpm09rDxsCSG/i3xMZ/NA9Z7iD/jx+XzaERtFZ88AGzZv58iVy/B5nDNClG5C4sAnnHDCbh3kFy9ezIoVK1i7du3Isl/96lecccYZI04YwKmnnsqSJUu44447RpatXr2aUqnEJZdcMrJMCMFHP/pRurq6eOyxxw7Ivq6urr2+uru7D3ST687CjnbmzW6hZyBFoahT8GpIKRmKx7Vs/S5EQgF8Hh+JtHY6RmPbNtlsVkfERiGE0OlUwxiGQVukldnRWdVXpPqaE51Fe7QNl2PqFNcLqlExsYszJpEjjphwODDd7kmzafnsEEGPc98rzmC0hP1YypUK3X0xDFEmHND7ZjRul4vGhoZ6mzGlSGWyPLt2Iw4T2hpnRnripD1dpZT09vayYsUKoBrl6uvr4+ijj95t3WOPPZbf/va3I/9es2YNPp+Pww47bLf1au+/4Q1v2G9b5syZczCbMGVxO52sXDIPw3LR1T/9nMiJpFQq0d8/UG8zphQOy6KjvRlhmPTHc7pGZBRVRyyjndNRCCFwKu6ILVswl3f+0wn7vf7s5vo3Lq5GxMZmUAp21oTZSI54/et46zn/Ug/zlMXn8yEQw+6wBmBH3yCJZJqQ3zPmHFUdIQwaGhoxNm7Uk4PD2Lbk5c2d9A/EmN0cYkPn4LRPT5y0p+ttt93G9u3b+epXvwowEnVqa2vbbd22tjZisRiFQgGXy0V3dzctLS27pXzUPrtjx44Jtn5q0xgNsnxxB8lcfkaEaQ8VUkpyuRyptE5LHI3LadExqxmEJJ3TKQ+jsW1b12/sgmUahAPeeptRV97wmsN5w2sOr7cZ+0RK+EkFErZkc+/gqBqxXRUTq25AJwbrhMmKulirJl6vD8thUSrpFLwaPf0xuvsStLS14nI5tNjYKMLhMD6fT5dXjKJ3YIi1Gzo5+fWvIRLwMDDNy3EmxRFbt24dH/vYxzj++OM5//zzAUYGOy7X7qkc7uFUiVwuh8vlGvlzb+sdCJ2dnXt9v7u7eyTaNtUxhGDp/Dm0NEV5aVuSok5LHEMikSCf187paNwuJy2NUbKFik5j3YVKpUKhWKy3GVOKSMDLmaceVW8zNPvJVhv6C0UeuPwKWscR6YCdiok5JEkdfphUPB4PDodDO2KjyBWK9PbHWLSgHa92xMbgdDqJhCPaERtFqVxmzYsbOHbVMtqbQgwms9M6i2XCHbGenh5OP/10QqEQd95554iohsdTzQUerwixNnCurePxePZrvf1l9uzZB7T+VMbpsFi5bB5Ol4PeWFKH9EchpWRgcEDXh+1CczRMYzRAXzxPoaT3TQ0pJZVKmWJBO2K7Mv5wXjNVEYBDyj1Ew6rYyEkV6tDsrLd0OV1ks9l6mzNlKJUqbO7q4fijV+D3uogl9b6pYZomTc1NbN+xXfefG0ZK2NTZTeeOPubMbmdDl0U2P32d9wlt2pBIJHjrW9/K0NAQv//972lvbx95r5ZWOJ4wRnd3N9FodCQK1tbWRk9Pz24eb+2zo79XNdpbGjhs4Rx6BvMk0zryM5pSqUQ8Hq+3GVOOWS0NRMI+Eukctm5zMIZ8Pk+ppB0xzfREIum7/69ArYfYWMGOGvbwqwS8aENR3wYmDcuycLnUFizZlXKlwubOHoqlMq0NPowpojw6FRBCEA6FdxPAU53BRIrn1m4k4HHSEJ7eqfMT5ojl83ne/va3s379eu69916WL18+5v1Zs2bR1NTE008/vdtnn3zySVatWjXy71WrVpHNZscoLgI88cQTI++riGEIlsybTSQcYnt/Ansah2YngmQyQTqVrrcZUwohBK3NUSzLSTKrHfddyWZzlHSUUDNdkZKBRx4Gquku4z3g5chL4Fi8iHWy6pBNFo3BqaMsWQ8sy8Ln89XbjClHLJEimUwRCbgxDO2Ijcbv9xMOheptxpSiXK7w0oZtpNMp5jSHmc6++4Q4YpVKhbPOOovHHnuMX/7ylxx//PHjrvfe976Xe++9d0zN1l/+8hfWr1/PmWeeObLsne98Jw6Hg+uvv35kmZSSG264gVmzZnHCCfuvZjWTCPq9rFq+gKJt0xvTDsdopJTE40NUbB3KH43P42JWSwOpXEVHUMchl8tpNTPNjGB36XoxcmZLwDYE877w+Um1SQjBKYe3vqrvkFJSKZd2e01GjciB/rZtV3ZbFymryonTeeQ4ASRSWbq6B/C5Xbgcaqu07oplWTQ2NtbbjCnHth19vLxpB20NPvze6TvBMyFn+7/927/xm9/8hre//e3EYrGRBs413v/+9wNwxRVX8Mtf/pJTTjmFT33qU6TTaa6++mpWrlzJhRdeOLL+7Nmz+fSnP83VV19NqVTimGOO4e677+ahhx7itttuU7aZ8+yWRubPbWfHQI6sLm4dQ7FYZGBgYFoXcE4E4YCPue1NZPJ5iiXtpO5KNqdrE3ZFz07vmaeffpre3t7dlp9yyil4vROXLrNjxw7WrFmz23IpJTteWY+nqxPXkiUYA0+MK18vATl/HowjggWwadOmkQyUcDjM61//+nF/2+PxcMopp0yIUyGlZNvaZygWdopxBSJN5LNpVl9/JfbohtoI/umcT9Kx/BhCjWOVmIf6d9DftXHMMm8gzKxFK0f+nRzsoXfbK2PWcfsCzFmyasyyrWuf2e/fLpeKPPCL63j+kfvGfEdj+3wWHX8GSJvakbErZbJ9W6hV8BkON97G8dvs5Aa7qBRr+0TgberAsBzjrgvD6sED27DLwynXwsAVbKSQ6NvjZ3bFHWnDcvv3e/2DIZ3NsaWrj+OPXkE44CaT1ynio2lsbMTldFEoaqXjGtl8kWdf2sDKZfNpbQiSzvRPy2nUCXHEnn32WQDuuece7rnnnt3erzlic+bM4cEHH+Qzn/kMX/jCF3A6nZx++ul8+9vf3k0l8aqrriISifDDH/6QW265hcWLF3Prrbdy7rnnTsQmTHkMQ7By6XwaowFeem4Hlcr07qNwKJFSks/ntWz9OIQCPgJBPz1DBV0ftgu1dgeasTgU7yE2HolEgksvvZQ//vGP9PX1jSj4VioVstksl156KVddddWE1XX87W9/49xzzx03spJOp1m/aQOv+bcrsMrXIp59AYrFsT3E5s/Dcc1/IcLhcb//7rvv5rLLLsPn8+F2uzn11FNH3tu6dSuPPfYYPp+P5uZm1q9fP2GToff96Otk4704nU6klMxefhylQp5Ceogrr7ySxYsXs337dr72ta9x741f4c0XfIHXnrozm+bRe25h7RN/onfLOvz+qiNRKBRwuP28/V+/wrwVx/Lk73/Ouif/TPemF8es0zhnCRd+9Scj31WplHn83h/v92+nhwZ46g8/Z86c2XzjG98A4Fe/+hW/+93v6Fz/HAve/FEMR/W8GVz3CLH1j+H1eimXyxi+Rub9087JaKjenzI9G+l+5h5MWcayLLLZLAve8jGcVnjc/SelJNX1Ej1//x0Os9qQPF8o0bLqzfT8/T78fv+Y86dQKFAcVo31+XwYhkGpVKLhNW8n0L7kYA/jftMzEKNUKhD0edjen5zw35tOeL0+/AE/hUHtiNWwbZu1G7YxMDjEvNYgW7tj03KCeUKesA888MB+r7tixQr+8Ic/7HM9wzC4/PLLufzyy1+FZTOHSNDP4Us7SGQlA0M6LXFXhobi5LQq1Rgs02RuewtOh4PewbiuKdyFYrFILqsdsV1xOiy0aOJYLrvsMm6++WZOOukkvvjFL45MCO7YsYP3vve9fO973yOdTnPiiSfu8Tui0Shve9vbAHj88cfZsGHDbuu89a1vJRqNcs8995BM7hyYPv7440QiEdavXz/G2ZNS8q//+q/8/Oc/54mv/wcL33MmVmsTzt/ci8lO2Xr7X95F4/x5e93GRYsW8dRTT3HUUUdx++238+53v5s1a9awefNmotEo69ev50Mf+hA/+9nPOOOMM4hEIkDVGf31r389omp8yimnMGvWrHF/Y/srzxPv3VmaMO/wY/GHx6ZgffWrX+WjH/0o2WyWpcsOI5lMcsH5H+TTn/409957L0uXLqWrq4sjjjiC7s0vUS4VsBzVidxta58m6Khw9qc+NdLD9M477+TjH/84v/r+51n5+rex5v5fM39eBxdccAHHHHMMQgjuueceHnv6H7zw8G8BEGZ17216/vH9/u0/3XoNtl3h5ptvprGxkeeee44vfvGLXHTRRZxxxhls+uMPdx43u8yyZcu48847ufXWW7n2hz9hVzI9G9j+xK/xup38z//cyNKlS/dY9lEj2fkCPc/cRzgU5Ec/+hFOp5O3v+NdAIRCIdavXz9Gdfob3/gGV111FaZp8uyzz9LS0sKvfvUr/v3G34753koxT7pn9Pkq8TTMwekL79WefdG5o5/+wRQNIS+WaVDWE8wjOBwOGhsbicViOtNnFL0DcZ5/eTOnnXQcIb+b/vj06ymmpzqnKXPampg3p42uwZSWIB+HwVhs2ndbP9RYlsm82S2YJrrx9zgUi0WKOu1jN0KKN3PelWeeeYbf/va3vPGNb+TnP/85Dz30EGeddRZQdRp+/OMfc84553DTTTdx++2377H/TzAYHEn5e/755+nq6tptnRNOOIFQKMSDDz6IYRgj6Y75fB6Hw4HP5+M//uM/eOGFFwB43/vex/e+9z0SiQS//e1vefr558b9beu677DS70W+9rX84/vf4XkBtZhWJBJh0aJFbN++nbPPPpvu7m5M0+S6667jC1/4Aps3byaVSnHeeefxzDPP8Oc//5kbb7yRQCAAVJ3Bv/71ryORlaOPPpqmpqaR3y6UbLb2p2mcNZ/00CAbnvkrwWCQ/v5+zr38ht0cMZfLxfr167niiivIpFME/D4+/vGPs3XrVj7wgQ+wdOlSnnrqKS677DI+9elLOeWsT4w4Q1CN8px44omcffbZzJ07l6uvvpqhoSE+85nP8Pe/3InP5+P222/n5z//OZdccsnI5xoaGnjsV9+nWCySSCZpbJ9/QL9dKRV5+9vfzsqVK7nmmmv41re+xZe+9CUuueQSzjjjDN7//veTSqW45JJLmD13Hueddx5Op3OPUdShzWtwOy3+67/+i/e85z1s2bJl3PVq2OUS8Veewutxc9lllyGlHON0CSHw+/18/etfH8lieuWVanqmbdt85CMf4YorrsDtdmOXC0gpR6JnpVyS7qdW09DQgGmaFAoFxJKTX7UjFkuk6B9MMn/+LFxOi3JOpyfWMAyDhmh1f+uWPDvJF0u8+MpWTn3ja2hvCk1LR2xC5es1E4PH5WTVYQsxLQdbuxPoyZGxFItFYoOxepsx5XA5HLS1NJAvSfJFXVO4K8VigbLu07IbH33PiVpOehQDAwP09vZy7rnn4nA4uPDCC/nd737H7373O44//nj+8pe/cOONN2KaJqeffjoAS5Ys4ZJLLuGSSy7hvPPOAyCZTPL73/+epqYmwuEwF110EZdccgmnnHIKPp+Pj370o6xatYr+/n6y2Sxf+cpX6OzspLOzc4xw1eOPPz7y+x/5yEdIp9PMnTt3jM1nnHHGyO8fe+yxlDNp1nzpCn7z9rey7U9/ZHZbG/Pnzyefz/Ozn/2M//zP/8ThcDB//nwcDgcnnHACXq+XN73pTZx22mmYpsm8efNoaWnh5JNP5uGHHyYejzN//nwWLFgwJpXxxRdfpKOjg40bNzJ//nyWLVlIQGR44re38dJjv+ctb3kLL7zwwl77gQ4MDPDHP/6RcrmMw+Fg+fLlfPrTn0ZKybp167j++us58sgjd/vcvBXHsnXbNs4880x++9vf8sMf/pCbbrppzLqnnXYaTqeT++4bW8v1H//xH3R2dvLggw+yZPFi+rs2HtBvA3R0dJBIJLjmmmuAasSpWCzS0dHBwoULueGGGyiVSlxzzTU8++yzJBKJPe4DqCpEDwwMcNVVV+11Pbtcoufv95Ef6uaTn/wkhmGwadOmcdd94oknRs6fDRs20NLSwkc+8hEef/zxkcmBvn/8BbmL8JUQgkcffZTOzk5uueWWvdqzv+QLRbq6+3A7DDyuPde9qYgQgnA4dMB9c1Vg/ebtbNzczZzmIF739DtvdERsGhIK+Fi+uIN0vqQjG+OQTCa06MI4RMMBmqMB+ocK5At6Rm001brCAraOou6GYRiHVIyhDGwDvgqMTm5aDlwOzGVndGaq4nQ6Oeecc/jqV79KLpfjuuuuY9WqVZx//vlcf/313HrrrVQqFW6//Xbmzp3LzTffTGtrVS2wXC7T3t7OtddeS6VS4etf/zqFQjUSa1kWbrebzZs309zcjNPpxLZt3vCGN2CaJmvWrOEzn/kMAwMD49oVCoWwRtX0CSE455xz+N73vkcymcSyLIQQvO997+PJJ5/kNUet4vvf/z4tLS0YhkE2m+WVV17hfe97H62trVx33XX86U9/4qSTTsLn8/GWt7yFoaEhnnnmGa699lpOO+007rvvPl73utdxxx13IKWkXC4TCAT45Cc/yS9+8QuCwSDf//73R9LgnE4nhUKBd77znbz44ovkcjm2b9+OlJI//vRq3N6dohCpwZ49HoPRUYE9Nbo9/PWnc/8v/ptrr72WBx54gHvuuQe/309f306hinPPPZf169dz7rnnsmDBAoQQfOITn+DKK6/kzjvv5Ic//CF33HHHGHXm/fntGtVG8RXe8O4P8+jqHyGlpFgscs4557Bp0yY++MEP8stf/pJf/epXfOELX9jj95huH0//Yy2PP/Ekl3323/b6m/0v/JVk54usWrUKl8vFV77ylZHUzF2pRVB37NjBxRdfTCaT4bnnnqNUGjVZJ3e/L0opOffcc7nyyitxOA7N4LdYKrNh2w7eZB9NJOhlYGj6RTcmEpfLTUO0YY9RdlVJprO8sH4LCxfMIhr0kc0P1dukA0JHxKYhyxfNZe6sRjbvSJLTaoljsG2bWCyuQ/fj0N4cpbkxyFAqp9M2d0FKSTab1Y7YBCOBHwNLgZ8Aj456/S+wDPgZTBvlq1deeYVVq1bxtre9DcN4PX7/pzjvvPN4+9vfzuc//3m8Xi/HHXcc1157LYsWLWLRokWcfvrpXHXVVSxevHjke1588UWOPPJIjjrqKILBIM8++yxLlizhzW9+M3Pnzh2ZBR8aGuKpp55i4cKFI5/9wAc+wBVXXMEVV1zBnXfeCcDf//53oNp/6IYbbuDuu+9myZIlHHXUUWzcuHEkohYKhTjqqKN43etex6JFi7jgggt4/PHHd6tB+eY3v0l/fz+f+9zn+NSnPrXbfvjBD37AK6+8wmte8xqWLl3Kvffeyw033DBGOfIPf/gDixYt4rjjjqO/v5/vfe97APzxj3/kta99Lfl8ntNOPI4Pvu903nbycXStf47yATZXn3/4cTicu0cMHnzwQe666y5WrFjBvHnz+Nd//dcx7y9evJif/vSnnH/++axfv5477rgDh8PBQw89xKWXXsrChQt3EyR53eteR3Nz8/4ZJgTR1p1RStu22bBhA+eccw4f//jHefLJJ/f5Fa2veRsL3nwJ7mj7PtctpuO89rWv5eabb+bhhx8eV4Qon89z9dVXs3r1ah544AHe8IY3jGz3Y489NtYR2wXL5aVh6Qms+ccL9PTs2Vk+UCq2zY7eQcqlIs0Rj5b43wUhBI2NjRiGHrqPplQu8/cXXyGdyjJ/Vhhzmu0fHRGbZrgcDpYv6sCw3PTFttfbnClHqVRiYFDL1u+KAJobwjidblKZ3eW2VUc7YpPD/2fvvePkKsv+//c5Z3rb2Z3tLdlNTwiJhNBBmlQFpChFUUFRAQULKqKP+KjoTwT1+ao8ggWl+EhRaSIEpEpLQgLpfXuf3mdO+f2xmZPdzbZAtiS536/XZqecmXOfk9k59+e+rutzPQl8BRgphpAHrgcqgQ9N1qDeJ01NTXR2Bnn44Zls2fICqroV6P9MZbNZHn30UT7/+c/z7rvvAv3RtKETzCeeeIJUKmXWBz3yyCPouj7s95iqqmZND8DJJ59MIpEw91mIdhWQJIlHH30UTdMIBoM8//zzfPSjHwX6HRY3b97Miy++SCqV4ktf+hK/+c1vRvz+HOlxSZJYsWIFoVB/Svijjz7KRRddNGibRx55pN9ev6ODV199lSOOOMJ87o477uBDH/oQqqpiGAYzZ86kpqbGdBscL7M/cCI2x95C7JFHHqGiooIzzzyTyy67DE3TuOOOO/j+978P9AvXHTv6Le4ffvhhvv/971NfX09jYyO33HILX/nKV0gkEqYhCcD27dvHH5kwDF74v/836KGioiI+9rGP8eMf/xifz8fSpUtxuVxUVlYyd95chkrQfRElFquFiy++mIcffpi+vj6WLl1KeXk5Ho+HJUsOpzOXJJPJcOutt2Jx+lDTMV588UWeffZZ6uvrqa+vZ8OGDSO/v8ND6aKTibWOvM17JRxN0BuM4CsqRpElVE1cywdSXFyMy+Uy/+YF/XT0hNi6s5Wlhy/E5bQSTx449d5CiB1gVJYVs3j+DILRFNGEcHgbiGlbHxNh+6E4HXZqq0pJZnUiopHzXui6LqzrJ4EoMNb0IQ4cCMbVhmGYtUc/+tEd/Otftej6q9TVLaOhoYG77roLgE996lP8/Oc/5/7772fLli0EAgG+8Y1vvKd9zp07l5/85Ce88847ptvw1VdfzSuvvIIkSeZk/bzzzuP5558f8/1WrlzJ8uXLufbaaykuLubll1/mT3/6014Ro4nmpZdeorOzk7/+9a+0tLTwta99jdtvv32vHqQF+07DMLj00kt59dVXqaio4JRTTiGXy7Fl5b+J9OxZoMxnMxi6TmlpKZ///Of59a9/TTAYpL6+nra2NnK53CATCtgjeIqKijj66KP517/+xdtvv22K0MK+r7/++kH71jWNV/52N1bbbkv6jmYM4wMEAgHOPPNMnnnmGT784Q9TVFSEYRiccsopnHvuuWYdIfSnAc+fP59jTj2Hy2+5Z9znL96+mfSAhVmvFW688UasVquZklj4fLz6ysvU1dUhSRKGYWAvKkNNx2hvbzePe/ny5cO6eE4GkViCprZujq+uwu20ERXXKxNJknA4HPh8PiHEhhBPpnln8y6OPXIBVQEf8WTvVA9p3AghdgChKDLzGmspKirinR1BNNEHai8ikTBpUR+2Fx63k5m1FaSyGZHOOgy6rpPJigv+sBxi2UGGYZDP5830n8Jk3e12m9uk02m+/e1v8+Mf/5h58+bxz3/+HwDLli3jiiuuMK3bAbOO6vrrryeTyXDuuee+ZyFWEAfJZHJQ25e6ujoeeOAB06TD4/GwYMECcxzz5s3jmWeewW63M3PmTPN1fr+fww47jLvuugtd10kmk9x4443vKfWpoaEBh8NBNptl3rx5435dIBCgt7eXJ554AklW8JfX8NRTT3H77bcP2s5is3PiR6/hpYd/zfe//31uvPFGHn74YWbOnMlRRx3F0Ucfza71q4h2bGPBggW8/vrr6Lpu1uM98MADLFq0yHy/v/zlL2QyGW6++WbeeOMNvvjFL7JhwwZuu+02mpqaaOvoYs3v7iWfjvOLX/yCfD7PDTfcMOK+DUNn1+rnBu37z3/+M5/5zGe4/PLLSafTfOITn2D9+vX89a9/Rdd1GhsbBx3jE088wXPPPcf/+90DOBaePe5zmOzZhRxpMvcdgb3+D2655RYOO+wwLr30UqLRKLfffjv/+te/+t0t59aYx71jxw5eX/0u+Slq5ZHLq7R3h3DYDHxuhxBiQ7BarZSVltHV1SUyOIawaUcLzW291FUU0dwZPmAcxYUQO4BwOex8YOFskKE7JKI+wxEMhUR/rGHwup0Ein3Ekjkh4IdB01TTMEGwB1mW9qtjorFhPYbTidQ4a/jnDQM2bQRZgfnz99t+xzU2wyCZTJLL5bBarVgsFlpaWnC73bjdbhRFIRKJMHfuXI4++mieeeYZrrrqKv7yl7/w+uuvA3DhhRfy4osvcu2112KxWFBVFVVVOeyww/jFL34BMCgCsq+sXLmSs846C4vFQlFRkfn4ZZddxoknnmjeL3yWM5kM9913Hz/84Q9ZtGgRHo+HSy65xKzzWrJkCU899RQPP/ww2WyW2bNn84c//GHYCV4+n+fTn/40NpuNv//974Oe+/3vf8+dd96Jx+MhlUpx2WWX8cc//pFsNms2SR6JE088kT/84Q/U1NRgcXg54rSL6Vizd29RXVVJxkKU1s7i8ccfR5Ik/vnPf5qpl1u2bAHg2GOP5cEHH6S6uppkMsn555/PRz7yET7ykY8Mer8vfvGL3HvvvbS2tvLNb36TG264gblz59Ld3c2FF15IV08vbl8JiUgfbrcbi8WCruvj3nfjEaew/tV/8oMf/IArr7ySZ555BsMwuOaaa8hms1x22WXcd999HHfccZx33nncfPPNJJNJgsEgrTs3481C2WGnoOxu+jwQTdPM9gAFBu7bVrmArG9Py4C+Ta8QCoWIxWK0tvb3bXv55Zf529/+Zhpt/OxnP+Pvf/877e3tOEpqUDNTE3ExgI7uPhLJND63Y1AjckF/VKykpBiLopATQmwQvaEoW3a0cfLxpXjddrIRIcQOChx2Kzb78H09Jps5M6pZOLuGrlCWnKpis053b7HJJZPJkEokcE6T/6/pgizLzK6vwuOys6snhkWRUeRDLMwxBkldR5HAIT47g7BZLVgs+/F7prUFujoxLBak+hl7P799G3zvO6z94KlcMGfOXgYJ+51oFOOVVzBKSojMn8/mzZsBcDj6J79ut5vy8nKsVivr1q1DURRcLhcf/ehHueWWWzj11FM544wzuPLKK4H++qKvf/3rOJ1O7rrrLm644Qa+/vWv86tf/YqqqiqgXwB89rOfJR6PYxgGK1asoKWlBegXO0899ZTpiphIJHj88ccHpSE5nU5++MMfcuutt/LUU08RDAZHPLx8Ps9XvvIVQqEQixcvNvf/xz/+EYDXXnuNOXPmcMcdd1BSUsIzzzzDL37xCwzDGLRvVVX5xCc+wde//nX8fj+5XI6nnnqKdDrNggUL+M1vfkM2m+Xcc8/F4/Fw55138sMf/hBV7V/geOKJJ8z6Meg3JylE67q6unj66afRNA2rBBZl+Gicrmukon0sPeFs1r/+DD/96U+5557+9L1UKmWmFnd3d/PPf/7TdDP84x//yEMPPbTX+xXOaS6X4w9/+AOPPPIIVqsVTdOIRCKceuqpuN1uDvvkx/joRz/Kj370IwA2bdrEli1bxtz3vCNOADXLs8+/wLPPPmsal4TD/UYan/zkJ4lEIpx++ulceOGF3HLLLZx99tnkcjm0bJrIrrXULjkV65DvJFmS+c1vfsPvf/97ABw2KxZFGbTvkrr5+Kr3mMFEd6ziBz/4Qb+jpiThDVTxxBNP0NDQsGebaJRZs2bxkY98xPxcmkgSTocNWd4zZTQYnNJptVr327W3JxghGk1SW+6luStM7gCJbEwWpYESystKR/3bPxSRkHhn0w5OPnYJs2tLSKRy6MM4fr5XrBM05xZCbAw+deGHKCmrGOHZkdZqhj7+/ie9ErCwsZxFdcV0xPP4XPVjvuZQI53OM6/KiaYeuL2gJkIeKRaZw2dVM7PEgSxJzKgSfUiGkkxUcfhMvzB5GYLNqlBXWTb2huNF1+GB+2Dtaoy582HpEf0/q96CdWth40aIRPjVn+/l5qs/i2uCe+ZkmptJfvrTFJWV0fbJT/Ls5s1c8NnPUl9S0r+B10tXVxeapiFJEi0tLQSDQb7yla9QVVXNz3/+C1asWMGTTz6J3W5H13VsNhu/+c1vuPjcc9mxZg0/uOMOPvGJT+BwOIjH47jdbl5++WWgf/J69dVXA/3W9Yqi8NnPftb8HLa1tfHxj398kIOdJEkUFxcjSRJXXXUVsEc4DuVzn/scDz/8MN/97nfNhssACxYs4JZbbuG6666js7OTK6+80hQKVVVVLF++nEcffZQLLriAhoYGzj33XF555RU++clPEov1V/BdfPHFzJ8/nyuvvJJvf/vb3H333fzlL39BkiTi8Th+v58rr7yS3/3ud1x00UXIssxll13GmjVr+Nvf/mYaXbzxxhu8++67KIqC3WZhbl0xLauG//+qLHHz/dv+m2DX52jZtp5nn3yc1a+9yq9+9Su+/OUvE43F2Lh5C1d/7hpki5Uvf+trdHW089Cf7wXg1ltvpaqqii1btnDnnXcC/WL7rrvu4qc//anZGPu8887j17/+tXnO7rrrLm6//XZsdge5bAa/38+PfvQjs87qlltuIRgMsnHTZnPfJy6fx1e/+gW2rX2dZCwCgG7ofOfGL2EYBqtWreK+++4D4Ec/+hG6rhONRrnkkktYsmQJ//W973HVJWfiLxnc4Dp02kJy6T3CvGzmQh69N8h/nn2Maz7/eaw2Gx896wQWLj3a3OY7r/6J4476IPX19fy/X/2K/7r9Lu795X9T5vfysY99jOuvvx5d17FYLFx33XX87ne/GySynHYb117xYazWPULLMHS++fzd5v0zTzyCY04ZI51ynF+vLqedWdV+vE4bHns9mbH6O07J1/bULWYaGCxtLCYcjh4YmeP7OMj3cxku8rioLbJQ6y+mothOfoQMoH0ZUmE8PZ1W7nzvQxsRIcTGYObMWsqqaqZ6GLgdVuZWuNjVFeHFVVvI5MQK0UBkWaK6uooZM2oPuZqWsbBZZGZUeunojfLS6u0kUiLnfiiVFeU0NNZN9TCmHaVeGyVF+08MzaquoSZQSntnJ0TC8O4aePBPkM9DJgPpDMgSWCyT8ndsP+wwbM3NRIJBHrj1VmrKytCamshfeinlFgvSbbfh1zSMww8nN38+brcbTdPo7e1j9uwP8eMfz6Ox0c8bb7zBWWedRTQa7RdKfj+ZBx/ki+3tvFJRQWzGDDOK9sQTT3DUUUcB/e6CX/ziF0mlUixatIhrrrkG6Lep/853voPP5+Oee+7h5ptv5q233jLTys4//3w+9rGPmccxUh8nWZY5//zzefrpp9m6davZY0yWZex2O4899hj/93//N2jf0WjUdBMEaG5u5phjjuHHP/4xuVyOm2++2YxmnXPOOaxZs8bc9qabbqKoqIhbbrmFCy64AEmSOP7443nttdcoKSnh5JNP5qSTTkJVVb773e8SiUSA/l5emUyGJ/75DLtaOolGo/zpT38yRV91dTVXXHEFW9v6ePWNd3cfdA05pch0H/z2t7/N0Wd9nKNO+bA5HpvdTm/mNUpmLCLUvIGf/OQnyLLcHy2TJLxl9WiJXtLpNJqm4fSXk08nePrppwfVWGWzWSxOHx+9+us8/dDvCXft4qtf/ar5fDqdpqiygWu/8zOcrv5awoxk5z+vvwu4wdr/mGEYlMxcyvqt77Dmu9/lhz/8Yf+2mQxltY0kIn3k83my2SwSMGNm7V4LwQ2z9o4kf+2/b+eG//rxoOOW5T2r91ablccee8zsCThv8WEEKqpRk2Hz/3LmgiPYsOFtzjvvPPL5PJdccgmzZs2ira2NxkXLmDW7AWVAjzrDMLBY99wvrygbdmzvBYssoaGQzmls2LyL7r6ISE8cgtfrobGhbsKiNAcqEhKJrEaxR6GpqZ3mzv1n2hEJ9oy90XtAMsQS8F60tbVRV9c/KbvxV8/gC4wUEZscJAlmVJWwfH4p//fYSzz14luoB3DUZyKw2WwsP/JIKioqp3oo045AkYszjp7FMy+8wX3/+DfZnDDrGIgEHHbYYYP6Ogn6+UBjCRcctX+j72ff8i3+tXUTuFxgtYAs90fK8nnIZCEaw5/K0P7s87hGiPTsb2KxGA8++CCNjY0sXbSI8Esv4cnn8RQVIXd04H74YaR586Cvj654nKdSGh1Lf80nv1lCc1c7ks+HO5elvrKcWCSM3+9HkSTUfJ4v3XADTz7xBPlslkB5OY8/8QRHHnkk0D+Zffnll7nrrrvMKNlAbr/9di6//HJOOOEEdu3aBYDL5WLNmjWDIlxDyeVyLFu2zExdKisrY9WqVXsJtmAwyHXXXTfsvm+99VZmzJjBt771Lbq7B7e8uOqqq/jzn/9s9mu85ppruPvuu/d6D4A//OEPvPDCC2b0x9x3NIGndhHx9i3oWv93ktXhoaTxCLo39o9Hyyb57+9/n6uvvppUKsUp538S/2FnmO/Ru/k/lOndvPPOO8yePZuuSJqGkz+FbLUP2peu5Yl3Nw0emCQhGRrtq54yHwrMWoarvAEtv3e9qNXpxVVcSS4VJR3Ze0LmLCrH5i7a6/HhSEe6yaViuF1uFi9ejMViwV9ezbN/up3ulv72B7Ks8LkfP4jbVzKu9xyN5s1ryWX6TawkWaZh0ZH88/e3sW3NK+Y2l3zlZ2RSSYIdTTz/4J2sWrWK8vJyHn/8cV7ckWXesg+a20Z6O3j69z9k16a3+Z9f/Jy6ujr++Ox6DjvuLPZLBpAE82aUc3iDl7vuf5qXV24Q2QpDcLtdHHXU0RT7i8fe+BCjvsrPcYfV8NfH/s0TL7y531Jb86kYW574BQCtra3U1tbul/cVEbEx0HR9ypvfWhSZmVVeQuEEb2/YRia7b00uDwWcFgsuj2fK/6+mIx6XDZvFoLWzj2RaRMOGIssyNrsdVdNFA9Eh6BNh7KLru3M9Bry3RP/sS5L6I2ITXRs2DI2NjcyePZtAZSXp44/n9ZUrCfX0UDdzJjP/538AKP/pTyl/9FGuAF6wfoW71/x/9FTXkHUV4QzFmPFuJzWlNuYmu3Bq/W6LX77hBsosFookie/+/vdmrzDoTzP84Ac/yAknnDCsQUYhgvXSSy+ZE1FJksasnbNaraxZs2bM1wQCAR544IFR933aaaftNQlWFIXvfe97g+7fcsstw45FURTOOOMMM/oDoBsGZ1x3J92hOGWLTmHPZ6H/M+CdcTgAO5/9Lbfeeiv//d//jWEYuKrm4RmQplYYtyRJfPrTn+bRv/2dVD6LRR46tZFxVzQyFMMwmH329XsekCQkaWTHSFXTkO0e3BXDG5CoY6XQ7cbqLcXqLcXpcFA99wOmocnHvv5LjN3nQgIkWdkv5kq1c5fs9dhZV32bMwf8v8qygiRJ2F0eNE3jyCOPRJIkdF3nohvvGDSOdDLBtnde59Of/jSLFy8mEonsnisN+bt+H4TjKeyOMqrKAqiaJoTYEOKJJL29vXi9PnHdGkJ3MEE8nWXJwln8+413SKWj++V9JyoAIoTYAYDHZSfgc/HGqnX0hg6EDjuTT0lxCQ775KyeH2h43Q5SqRS9ochUD2VaYrFYcAzTBFYwQRg66MYAMWZgrqIXBNl7sE9/P7jdbmbPnk1RUZFp3S5JEi6Xi2wux5bdzX5bTjwR++LFIMuULD2CiwJWHP/fV+iqrOH+U69iezpDsNrLO5TS2NpEQ7YLn9dDQpc49zOfxWazM9ycSVGUUcWVxbJvl2pJksb9mve676EW92NZ3g98XtN1pN1pcsOeEEnBMAxqjrkIQ9+zmq3Y9v47zeVybNu2jUsvvZQtW7fx1j7Mufr3P3WpXfl8nkwmbQoxeRIXIAamLg6krHYWn/7+nwY9VlK1d8qhJEl88pOfpLy8nI0bN+738cVTWVJZqKooxmG3kc4IV9uB6LpOX1+QmTMbJt7U6AAjk83T1BFnVnUpc2bW0BeKTuvUViHEDgCqSr04rPDu5l0kpqi3x3RGkiQCpQGxKjQMVotCoMhFV2+Ujp7Q2C84BLHZbDgcdvH5mSwKEbGhV8bCpFySYQTnvIlCURQaGhro6uqivb2drq4uFEXZy3q9VZbxNzb2i5NEDBIxfJ/7DBVlZdy08lUqWlrZvsPDBtVGurWPx+fPZaatjdCi43mo7EiyQTixCEqGL+kSDECSJBz+scsCtm7datZzSbLCrHO+NNFD22/ohkEqld6rqfRUYnO4qJm9eMztDMPgtNNOM+9fcN1t+3UcqXSOSCJFY10FxUUeIcSGIRwOk81mTaMdQT+6YdDRF+OwWQEOn9/AqnVbp7XzphBi0xy7VaG2rIjm1h627mqf6uFMS5wOB8XFIk96OBw2C6VFVjZ0xInGk1M9nGmJzWZDUcRX4aShav1RsYIYGyTIpCmJiEH/xL+8vJytW7fS2dk57DZer3evyE8sHqent5eueJzSObN59/4H+Kwk4cvrHL72JSqiPSxZdDQrDz+eNS0G9cfMprjcPWwgSLBv+GcuxVM1d88DEijWAye6bRgGqXRqqoexz5RUzeDzP3100GPe4v3orkr/ZDqazNJQXkxJkZeObmHVPpR0Jk0oHBJCbBjCsTQ94TRLFzRQWVZCS8fEGG3sD8TsY5rjdTspK7Lw7/Xt9Ib3T57rwYbH68XpcE6bFcXphMNmxapAe1fftF4RmkrsNrtI7RiB/f03ZRiAqg6oE2NPtw9JGpSaaNCfwVh4aDIopOkVbMmHYrfbh3lVv338zJkzSWYyLL/lu1iPPZa4qtPxy/+Bnj52bW7lQ9eex4Jsli2nfRgayzDKypC+/OX+N5gi8Xmgo9hdKPYDdxJaaCKuado+p59OJRarjdKahrE3fB8YBvSGM8yq9FFTXsLmHS2omqgBH4iu6wT7glRVVolr2BA0Xae9J8bSOWXMmVFFe1fftPUQOHD+8g9BZEmivrKIbE7lzbWbyQm3u72QJInSQGBE++ZDGYl+x0RNM9je3EFeOG0Oi8vlEhexEVD2szh414BQ3UzYtX13RGyAGAPTrCOl69y0bSfl8xdyjAxnTeJ/T11dHZs3b35P5gAlpWXMPvpoNE0jlkignHku94bdNB8TY63la2hb3mHhK4/QuKoZXyZD929/S30+j/KpT8HJJ/cf/3HHTYlZiWBqSCWTaJp6QAmxySIcS2FIBvMa63h19QbUtEhPHEowFCSTyeB2u6d6KNOOjr4EixoDLF8yn1XrthFNTM/os1iCm8ZYLDJVpW46u/to7exFF65Be6EoCiUlgTELxQ9FJFmirNiBmlfp7AkN64x2qFMwZBCfn+GxKPs3FJUDDv/U1chIe0TYMKmJuXSal/70RyLAZPt8vh9Rns1m6Wxv58Gnn+X/gmn+JfnZJTvIWr1s6Qqzq62Jc37xSyqffRb1738n8o1voN9yC//+z3/YcMEF6BdcQO7aa9G3bt2PRzT9kCWJi09dNtXDmBZkczmzDYBgMNmcSianU10ZGOQ2KthDKpkikUwIV8lhSKaztPWmmDOzitrK/Zs6uz8Rs49pTFmxm4DPzup1O+gTaYnD4nK5KCoaX++WQw1FlvC67fQGI0QToj5sOGRZxulyinqdYZCk9ydKRsU07BhsW74nNVECi4KWzZLY7Vg4WcRisfeckvm3Rx8lEo7QsvBIttk8tMlWJIuOLeBHdnrQs2kMvT8y7QsE+MBVV2G96iqO+9vfmLt9O9l33uG+RIJI9uBe+ZckiSMXzpzqYUwLctksuVxeTKSHIZtT6ejNUFHqpbJU1IEPRzaXpa+3b6qHMS1RNZ227iiBkiKWLGjY7xke+4vpOSoBsixRUeIlHouzZWfrfuklcjBS7C8esW7jUMdpt+F1Omhq6yaeEG6bwyFJ0u62B0KJDcWqyCyun4BFDsMYXCM2qJ/YALMOWSYfidD1r3/t/zGMQldXF06nE4fDgc1m26eUsYxqsFNykHc4dgtZCbQMoVefxti2Cj2bMoXYQBxuN9aSEpy1tVz9wAOULB7btU5wcJBXVdIZ8f08HJquE46nCBR7qa0snerhTFuCoSDaOHvYHWr0RpJEkipLFjRQXDR8/7+pRiQlT1PcDhuN1V62bG1me8vwDl6HOrIkUVoqvpxHwue243VJtHWFSAnr32Hp7yEm+s8NhyxJlHknaJFD14ZExIw9jhwFMZZIQHryJ6gOhwOv14umaXv9qKqKtru57HCpvonDz+Uvz21FN7ZgqFnUZIxMIoaey+Cyy8ysKKXY5530YxJMX1RVJSkyFkYkmsygKDaqy4tRZHnaGi5MJdFolHg8LtyjhyGXV2nvjTG7voqGukqCkfi0iz4LITZNKfV7sCkyazbuICUKVIfF4XTi9/unehjTFo/LTjabpScYmeqhTFvsNpuoPZhkJEnCYnOQG2hfP9Csoz+UhOrx9G8ziei6jsViwel0ous6mqah67p5e6go03Xd/G0YBmdWGGzLK2yOG4TSKnouh67mkVwu9Kpqltc5qaupntRjEkx/DkQL+8kinswQSeSYUVOOx+WYtoYLU4mqqoTDIfx+v3CPHoJhQGt3jHl1RRx52Bw2bG2edgvTIjVxGiJLEjOqvKRSaTZsbSIvCnmHxef14XQK2/rhsCoypX43vcEYbZ29Uz2caYvD4RSOm5OMzeuj8byL+r3phwqt3ULM4vYw82OXM9lr36lUilwuh91ux+Fw4HQ6cblc5m+3243b7cbj8eD1evF6vfh8PrxeL319fQSkDCe4YhyV3IArFwfDwOoPYOSz6B4f0jga5QoOPZLJ5LRbpZ8uZHMaiXSWhrpKirzCGXA4dF2nt7eXfF44aw9HNJGmJ5LjsHkzKCuZfp4CIiI2DfG47ZQXe1i9dgNdveGpHs60RJIkAoEAVpuYRA+HzWqhvNhGa0uQYDQ+1cOZtvQbdQghP5koisLCUz9Eb9N2gq279jyx26jDVVbOzGNPwhMIYJ/E2j3DMNA0bZCDZmFyXIh4Dfw9NFrW3t7OypUrqampIZ3Lo7sawRXAABwVNdTX1lJsF7b0gr1Jp9Ooqiqi88OgGwbBaJa5NR4Cfh+tXb17rd8I+tMTs9ksVqtVXNOGkMtrtHRFOXZRBQtn19PS0cN0+ggJITYNKfO7sSsaG7a2kEhPtnnzgYEsy5SUlCAJk4VhsdksOG0y7d1BsqL/3Ii4nM6pHsIhhyRJuIuLOfzyT9Oy8vX+nllSf1dnCZhx9AnIigXFYkWaxHoQwzDo7u7GMIz+Mbrd2Gy2Qc6RhmEQDodNAVYQZbqu4/F4CIfDdHR0oPur4LiTUSN95Po6OPzw+fzslDnUuMQlt0BtRQkLG6rZuKtjqocy5WQyGbLZrBBiw2AYBl3BJAtm+Jg9o4r125pFltAwJFMpwpEwHs/0NKSYarqCcTL5CpYubOA/b28kNo1SXMVVYZpht1porPbT2hFk/damqR7OtMXjduPz+cTKzwgUe51ISGxv6iCXExet4ZBlGZdLpLqMhCLv378tTddp3dlGQtVJxuNkoxEq6ucytHdAurUDdAOr04lDlvdOX9yP6LpOMplEkiQ6OzuJxWK43W6qqqqwWCx79ZgzDAOfz0c0GiWZTJLL5TAMA8MwcDqd/Y2cYzF0xYNHU7HbLARkJ/MtGWrdIno/kNryYhY0VgkhRn+NTzqdxusVRi7DkUhnMYDG+iqsVkUIsWHQdZ2+3j7qauumeijTklQmR2cwzsI5M6mpCAghJhgZj8tOoMjGSxva6IvEpno405bi4hKxejgCkgSlfgcY0NbVhypsbYfFolhwOoVj4kgoirxfXf11XWfzC6+y6eU3yeRytAV7yevGHoMO2O2kqCMZUBsIELJYWHzMwv03iAEYhsELL7xAJpPBarWSz+dxOp1UVFRgsViGTfGRJAmr1UppaSk+n4/u7m6yu3t+Fdw3ZVlG323RLxkGXpeT0445YkKOQXBwYBgGmUzGjMYKBpPN5YkkdCrLivC4nMLAbASCoSDZbAaHQ2R6DEXTDXZ1xKhfWsURC2ezo6WTXH56CHohxKYZ9VU+LBKsfGcrqZRISxwORVEoLS0VF6wRkCSJIreNcCRKJC5skUfCarOKHnSjYFH2r5eT1WLhiIVzeOrlN3HYbHgcLhK5LPkhCwWKouCy2HA5nKBOXFptKpUinU6Tz+fJZDLYbDY0TWPt2rVkMhmSySR1dXtWl4899lhcLpd532azUVFRQXNzM4ZhmBE0v99PayRFvcvgI6efwCVHz6NYmAwIRkHXdRKJhBBiI5DNq3QGE8ypLKamIiCcgEcgnU4TiUapsDvE52gYgtEk8bTGkoUNPPvq2/SGo1M9JEAIsWmFzWqhsthLS0cPLZ3Tq5hwOuFwOCgqmn7ON9MFh82Kz+1iy9Y2okKIjYjVasViEYXNI2G1KBNagVlZXEwmnyeTzzEw9GZVZNz2/uiSDnulLu4v2traiMfjJJNJiouLef7559m2bRuhUAiLxYIsy8iyTC6XY+vWrZx11llcccUVnHzyyWb0K51Om9EMRVFIpVI0NzfzjW98g9NPP5VZs2ZNyNgFBxeGYZBKJdF1fVAqrKAfw+i3sS8qClBR6p/q4UxbVFUlFAxRXlYurmvDkM7mae6Ks3hmFbNnVgshJtibYp+TimIb/3irmZ7Q9PiATEe8Hi8ul0t80YyA12Wj2KvQ2hkikZr8hrgHCna7HYtFfAWOxEkLSvd7nVhloIRTj146/hcYcNiSiUlN9Hq9zJo1i2QyySuvvMKqVasoLS2lvr5+t0i3mN8xjY2NxONxvvGNb1BRUcHvfvc7ampqSCaTWK1WMpkMdrud4447jkcffZRzzjlnUDRNIBiLVDqNpmniO2kEIvEM6RzUVASw26zChGoYDMOgt6+XWflZIttjBNp7YxzWWMqS+Q28s2nntOgpJv7ipwmKLFFTWkQsnuTdLbvQNdE9fiQCgYDo/TQKLocNLZ+juy8setOMgtPhxGIRduIjYZHl/b7YsXT+LJbOn/ookWEY7Nq1i1WrVtHR0cG6desIBAKmOFcUBUmSzOO3WCz4/X6WLFnC1q1bueqqq/jlL3+Jx+PBMAxisRh2u53KykoaGxuH3aeu68Tj/a0kJEkioclsuidF5foAAO4joOK6Pd4k0u5/HJb9//8gmH5ks1nTwl78f+9NPJUlkcoyt6EGj8shhNgIxONxUqmkEGIjEE1k6Ium+MDCRv754kpapkGfVSHEpgl2m5WZVW5aWjrY2dIl0hJHQFEUAoGAuFCNgKLIlPndhKJJmjt6RL+VEZAkabcjnhBihyKpVIre3l5mzJjBo48+is/nw2azIe8Wn4ZhoKqquZBhGAaKomAYBo2NjbS0tPDtb3+bU089FbvdTjgc7k8LCoVwOp289tprlJSUmK+VJIl0Os0f/vAHJElCURSWXnETruU1bDw2iKxYkGSQ3gVN05Ck3WYp2TRHlFlYXBfAaReLTwcz2WyWXC43qA5RsAdV04mmstRUBvC5XQQjoj/mcGSzWXr7+iguLpnqoUxLVE1jW1uUYxYEWLKgkfbuINoktkkZDiHEpgllJW48Thtvrt0yrWw1pxtejwefzzfVw5i2WBWZioCDSDhMT1Ckt46G0ymaOR+qSJKEpmk8+eSTeDwe7Ha7GQXTdZ18Po+maWi7jURkWR4UhVcUhbfeeovOzk4aGhrMuh5JkvD7/Tz++ON77U+W5UG1rWuevp/zvvX/4XA7sDn6X6/rYAAWBZAg1Jnjmc3bWLdlO1efe8LEnpQp4MpzjuVfr60nKVzwUPN50uk0fr9/qocyLdF0g+5ghooGLzUVAZo7e9B1sdI4HMG+IGqDKtJch8EwoC+cQDPKOXxBAy++8S7xKS7hEP9L0wBJgppSL6lkki272kSPjFEoLi4WXy6jYLNa8DoUNneHSU+D3OfpiiRJOEUz50MWp9NJLBajtbUVl8s1yJwDMOvDCgJqIPF4nB07diDLMjNnzsTr9Zrbjibshz6v2GWqGpxI7N0lwAJoQManoOsaG5vb2dHWxazayv1zAqYJ9VWB/e7OeaCi7e5pJxgewzDoDSdxzCtmbkMtb67biq6LudJwRKIRksmkMDUbgXgqQ3NXnMPn1zOjpoL125qmdDziG3Aa4HE5qKvwsGFrC63TIF91umJRLAQCAeEqNQo+twOrxcK2pnaRQz8KFotFCLGxOIiDhRs2bOCf//wnTqcTm82GoigoioLFYsHhcOBwOHC5XDidTux2u/njcDjI5/PEYjGKi4spLS3FZrNhs9lMgw+r1Wo+NvC1hW0KzzskC9teW028T8UhMejHJYEdyGc1Ij0d7Ni2mf+sXmdG6AQHJ6mUyIYZjXQ2R1aTqKkqwSrqe0ckl8sRiUREjfgIGAZ09CVwONwsnF2HzTq1i/tiRjvFSECp341N0nh3SzPpTG6qhzRtsTvsFBX5p3oY05pSvwOrRaKlo2faNCucjth3T5QFwyNLEvJBmrZpGAb/+7s/ElhyMnNO+xjzz7yUuuWnmtGvgphSFGWQuCrcLgi2OXPmDBJhA8VWYbtCzdnQ97ZYLMhqFrWvneqABacBDvb8FAGyBGo2T7irjXB3B4888xJRkbZ+UJNMJsTkeRQyuTxdoRw1FX4CflGiMBKqqtLX14c+xbVP05mecIJYKsPRS+fhn+I+jyLHa4qxWBTm1BbRF0rwzuadU140OJ3x+Xy43W5R1zMCkgQ+l51YNEE4KlJcRsPhcAjnzVGQ5NHT7A5UNE3n1e29VJ12JSX1s/CWOLHaZBQpj3reObz76IOEWltBV03XscJkpmDisXr1ahRFoa6uDkXZsyo/8Hzpuo6u6+akupD2aLVaB6U/ahmDTF4DiwXH7reSABcQMyCXzpKMBqlrbCQWidPe1UNJkXfiT5RgSuhvMJ7DvruPnmAweVUnHEuytDFAZalfZBCNQigcIpfP4VRE5sdw5PIq29tiLKwvYf6sOnrDsSlbBBERsSnG67JTVuRi/dZmguHYVA9nWhMoEbb1o2G3WijyOmnp7CUcS0z1cKY1DmHUMSqyJCHv5x5iU41uGLzw7g6eXrUV2eEGQ0LNy+i6hGKzUVRbzYe/+nVO/8xnCSw4AsPANPEoCClZllFV1XyskNJYMPooiCxVVU2zj4JrosViwWaz4XQ6cTqdyLJM19uvYyQTFEv9F+NCvVhLVGXD+k76OrvIJBOUVpSh6ToPPvncVJ5CwQSTzebIZkVt72hEk1lsTgelJUUH3XfU/iSRSBCNCsOukdB1g86+OA6nk8PnN0xpqqsQYlNMVakXuxXe3rCDRCoz1cOZthTqwwQj43LYKPNbae0ME0uIiNhouER92Ki4HRY8joMrYeLNdVt5+Lk3sdgdOD0+nD47DreE3QlIkMtDMAu1hy3m3E9+kuIFy5B3pycOZKDgMgzD/CncL9iQq6pqRtMGpihaLBZ8Ph8ejwdN1di5rZ1OCbJAjv7f727t4IV/PErn9rUYuk5LczueYh8Wi1iIOpjRdI1MRswDRiMUS5NI5pk7sxqn6JU1Irqu09fbK1JdRyGSSNPWk+SIwxqpKZ+6+aUQYlOIzapQW+5jV0sXO1u6pno40xqPx43H45nqYUxrnHYrsq7R1RsWKa6jIMsyLpdIcR2NKr+D6uKDR6zmNZ23W8J4y6qontVIxYxiKqoVqgNQUQQBl0HAooNh0JWBtN3GCZdchHvGYbhcbnRdR9M0M92wkKaoaRqqqpo/mUzG/CkIsaEpi7lcDsMwcLlcKIpM6xv/xqEbWA1IGpAyIJdNc8K5Z3HWBacDu+3z7fb+/OODCKuicPFpR071MKYNmqaRTqfF5HkU0pk8yUyO2TOqcTuFEBuNYDAoIqyjoGk6HX1JSkuKmTWjqr934xQghNgUUuRxUlFsZf3WNnrDIoQ8GiUiLXFUZFki4HcTS6bZ1dYtLuSjoCiKcEw8xNjVFaY1nKbh8A9QPqMEb8CKwyahKOCRQO3q4eV/r0aN5XAbKsEUpGQbyz96Pq4Zi5Ak2Yx0aVp/1CKbzZLP58nl+tPJstksqVSKVCpFPp9H1/VBKYsF8ZbL5QgGg+i6jtvtpretk7VrmwjnIa1CIgfHHjWbWQtnsrO1v79NJpki0t1HfUP9FJ/J/YuiyCyZWzfVw5g26LpOIpkU39+joOk6fbEcgWIXxUVicXY0Eskk8XhcfJ5Gob0nSjqX5/hlC/G6pmZecHDlnhxAyLJEfaWPTCbHqnVbhcPdKFgsFkpLhW39aFhkmaqAk0QyRWdvCPG9OzJWS7/DnYiIjQ/dMMjkdWKZHM+tb6W4ohoMnYqZHjJJGV2DfM4gk0yiJsKcsagWwzCwSAa2aWAxnVdVXt3UTKBuNg53Efks5HM6CQwwwGqBrvY0nU07UBwBjl1Wj9cNPT3gdVqZedKx9G5eRSLRP6EpRLny+f72EIUomaqqJBL9tZmFGrCBvcMKvwtirLe3F5/Ph5TupWfrZmYvmommSqSz0GVX0HSI7V6gszns5DJZc5+CgxPDMEglk+i6Lq53I6BpOh29SRrLS2isrWJbc4do7DwCuWyOUDhEIBAQ17sRSGfztPQkmTWzgvrqciJbdk36GIQQmyLsVgv15V5a27toahcRjNGw2/tt68UXychYLAp+l4WtO2Ki1nAMLFaLsK4fJ8GUSnNcZ1VXkuIqH0VLZmOzSdgVyEgg+UABDMBluMnmXDwdg0QkS4maocopUeWUqC6dusai67Y18872dhqW1JNNpckkNTLJBLl0Ck1TkSSJ8vISPvyxj9IVzaG4FUoVUIuhdVcWv7+IXGktqZZmFEVBVVUzClZIWYT+Rs+pVAq3u98KuRAJK0yoh0tRzGaz2Gw2Wla9Rt2RJ+L2e0jFdFKShGKRUPP9KcYNh89ny1vv0NkuUtgPdtLpNJqmYbGI6dlwGEAsmcZms9FQX4H8uoyui/56w6EbOn29fcxqnCWE/Qiomk5zV5RFM+pZsqCBDduaJ720Q/ylTxGlfhcBn50nn91OOCoc7kbD6/XicrmmehjTGq/LjsNuZXtzB5ms6EU3GnabXaS5jkFe03lmQychaxGGx0HF7GIcDnDIYJX6xZfMHhFmIKFLYHVIWO3g9DlJxeysC2dY252gZPsWzj1yDjbL5E4GDMMgmVVxFZWQTcXJpOJkk3HUXGa3kUa/OOrKJEhHnKQNK7lMJdUzvNgcEm6/ha72IBWLj6Rn3Wry2ZS5aJbL5UwzDl3XiUajZh8xYNDEZ6AIK7y+IMYkSQI9SV9LF1Z7A4lwHF3Xcbo9yLKCYrWx5a13dr94os+YYKpJZzKicfcYpDN5oimdyrIi3A676K83CtFohHQ6jdcr2l6MRCiaoiea44hFDTzz8tv0hCKTun8hkacAWZaoKi0iFouxaUebMFYYg9LS0kH9egR7U1LkwOVQ2NXWRTYn0pdGw+FwiM/TGDRFUmyKW9BcTuwuGasNFHmPV0TBZn0ghW8xWerfzuqQcfmcKMWltFDC0xt7WN80uREdVdW456+PoWkq2XSSVDREPlswQygcgUF3exs9wQTxUJiOHU00bQkTiWjYHFYyqTQ2dw3WQA3pdNqsL0yn06YxR19fH+l02rS1L/QLK6QnFmrFhkb1C2mK2UyalY/fR7AjSLS3i2hvF4lImHw2g93lweXzUlpXg1BiBz+FekPByKRzebpDSWbVllNaLBo7j0YmmyUUComsq1HI5VTaexM01lczs7YceZKzr4QQmwLcDhsNlS627OpPSxSMjNVqJVAi8pvHwue0kUqkCUUS4gt3DPrd6oQQGw5D11H1NG6/H5fH2y+orCDL/QJLZk+/KxnAgHRGJ5XRyeQgnTHIZAzUnIGmgWH09yNTbDbWd0R55K2drNzSMnmfUQksNgdaLks2lUTX9tTiStIeYeny7UmdzGfT9La10NMcJZcBq91OX1srruq5ZDIZKisrUVWVrVu3kk6nSafT9PX1Ybfbsdlsg2rChoqvoXb3BQMPNZ9HTvTSvWsruWyGSE87fR1NZNNJJEkim84QD4YwxKLdQY+qqqTSIsIzGrpuEEtlKSvzExBCbFQMw6C3t1dEWUfBAJo7o+iGwTFL5+N0TG7pgkhNnALK/B4cFomV724nlRb1PKPh8XiEbf0Y2CwKfp+L1u6gaAo+DlwulxD2w2AYBvl8Gl3OYbE7sDmt2OwSsmIgyyAZEjndwCr3XzjshoFqQCqmARKGHbJ5yGg6ilUGyUDXJLLpPKloBEPXyRkyf372DfKpOMd9YNEkHBQkwn24ylPYnR6kYcxDJCRs9j2pz4ZhkM9m6G1rRZKtSCgE25twub1kMhkzHTGVSpl1YgMbPheiYgOFWOHzVjD2KJh+FEw+dF0n2r6Tba+voGbx0cT6urBY7ShWG7KioOXzaPk8xy6ZP/HnbJIRf4mD6TfsEEJsLEKxDKoONRUBbBYLOVUYno1EJBImk8ngdou2LSMRT2XoDGc5bF4d5QE/u9omL0gihNgkoygyM6u9xBIZ1m9tJq+KVYqRkICS4hJRzzMGDruVyhI7b+wKEY6LesPRkCQJ5xRZ1B4IxKNdlM+YhcVqQ9d1LDaZ9u2tlFeXkGzZgdtiYVN3jvbt2+h49z/YJY1gMEhDQwOZ3U2MW9vaUDUdDQtzjjsLl78cFBvOohLS8QjZnMqbm5s58vCF2JQJnhRI4A2UI8kKuqEhM3YkNNjeREn1DNRclkQ4iCQr5DJpUuEeYrEY2WwWSZLIZDJEo1Ez9dBisQwSXgUxBuwVGdN13fwZ6MQY624Bu4t8NksqFsFdVIyq5sll09SUl3LikUsn6kxNGccvnc2JH5jDK2u2TfVQpg2JZMLsVycYnnAsTTqdZ9GcGTz/+jvkEkKIjUQqlSIajZpGQoK9yeU1mjoinPyBKg6bO5Om9p5Jy9wQQmyS8brsVAXcvPbWOnqCkakezrRGURQCgRLh9jMGDrsVm6TT2RMlnxfCfjQsFgtOhxBiw2Ngd7lxeotweJxIRpatb75NbMc77EpqNK95HZ/bSVFREYqiUFvsRdd1ij0eDE1DUVUkXaeurIx8Po9hGETWPEdLLEYoEqW8cSFJVcJf08iLTVs559glzJtZM6FHJMsyy2dXs+LtDTQsXo7FOjjlZGCKYIHS2gbzfGSS8X6BFOwmHQ0Sj8fNPnTRaJSenh4cDgeBQACbzbZXRExRFDMNdrSLeuF8hZs3k8zpWOxOQp0tODw+ZMVCOhbhU+eehM978E2knHYbDptYbBtIKpUSzoljkFM14lmV2qpiXA4bMWHYMSJ5VSUYDFJVVSXE/Sj0hJPE0zpLF8zkpbfWTdpnSvyVTyISUF7swa7ovLulmaRISxwVm92O318svjhGQZIkAj4X6WyOHS2dZtqUYHhsVis2u7CuH45oqIOSmmrUTJzN/36E7jUv47ZZkdGxWa1UlZcOalAMg8XF0M9e4Tmn00mxYZDs3EkoFKLp7VcobljImrffZu6M6gn9+1ZkmdNPOpZV29pIJ2LYXYNTc3KZNJlkjKLSygED3/1bgnw2QzadJNjRDFre7BNWmCDH43GsVuteJh35fJ5QKITVaqWsrMxciR6Ymji0XkzTNPLZDB3vvkFJ3RwiPZ04YyE+eNKJVJYdxkUfPmvCzpNgepFJZ8jn80KIjYKqarT3ZphT4aK6opSuvshUD2la09fXh6qp2GRx/RuJZCZHe1+Swxc0UFtRyqZEy6TYI03IX/mGDRu49dZbWb16NV1dXbhcLhYuXMhNN93ERz7ykUHbbtq0ia985Su8+uqr2Gw2zj33XO68807KysoGbafrOj/72c+466676OzsZO7cudx8881cdtllE3EIE4LNZqGh2kdLRx+bd7RO9XCmPUU+nwilj4EiS1SWOsnk8rR29qILo45RcTgcWC1i9X0ohmHgcHvY+OJjSOE2bHoWh8OBpEsouyeDQ1PtBoqwwu1CzRMwKPWugN1ux57N0rbuTX7xyxAf/+iHJ/zYZtdVUe002N7Virc4gGLdY6hhd7qwO4dvjWEAuqYS6mwl1NWKxVCx2+2meCouLiaXy5HJZJBlGU3T2LBhA/l8HlVVCYVCSJJERUUFHo8HwzCorKykrq4OTdPM86OqKoZhkM1myaSzlM45ASOgc8WXr+Fkr4szTj9NLEYdYuTy/T3mCg6dgr3RdIPecILls6qYWVPB2o07xPVvFBLJBPFYnEAgMNVDmbboukFTR5TZVdUsO2w2O1o6yeYn3oV6QoRYc3Mz8XicT33qU1RXV5NKpXj00Uc577zz+O1vf8s111wDQFtbGyeddBJFRUXcdtttJBIJfvazn7Fu3TreeuutQU1Xb7nlFn7yk5/wuc99juXLl/PYY49x+eWXI0kSl1566UQcxn7H53ZQWWTj6bWt9AlThTEpKysTE5AxUBSZEpeVnr4Q8WR6qocz7XE4neIzNQTDMIgG21n/3P041ThejweryzXIfn2gCBu4Sl8QWfndF6uhkZ7CaxRFwTAMM1VPV3Pks5OXEXDTdVdz80/+h86dW6iZuwhJGqZWTAJfiYdYsD/qZeg6yUiQrl1bSEVDPP7gvdTXVg96SSgU4rTTTiMUCrF27VqsViu1tbXY7XbTndNms6EoCu3t7Wzfvh2AqqoqZsyYQVlZmSnGdF3H0MFXNZcl/+9GjnbaOHOia+gE0xJVVclkM6JObAySmRyaYqW2qhiLRSGXF3ViI6GqKn19vZSUlIjP1CiEYinCSZVlixt49tU1dAfDE77PCRFi55xzDuecc86gx66//nqWLVvGnXfeaQqx2267jWQyyerVq6mvrwfgqKOO4kMf+hD33nuvuV17ezt33HEH1113Hb/61a8A+OxnP8sHP/hBbrrpJi655JJpb0ctAfUVPiQZVr27lbRoujsqNpuN4uKSqR7GtMflsOF22Xm9uYN0RvSeGQunwylqDodh9b/ux57qxu73m6YThfM0UIgNvYAXImND658GGlAUXm+z2TAMA6/XSy6X48vXfmHSjs/r8XDOSUfx/939IA63h+LKOpTdx7ln0KDmNTPFMpOI0dW0jVRvGx+/4CPMqK/FNSRCkclkCAaDSJLEEUccgcfjMYWY1WrFYrFgsViQZZmenh7i8Thvv/02PT09dHZ2Uu9dQs0CH4bUfw5zMpR8/FQUu33Szo1g+qFpGqmUqHkai1Q6R284y4yqUnwel1jgHgXDMOgLBpmZy2EX3y8jksnl2dkRZ/ncSuY2VB+4Qmw4FEWhrq6OlStXmo89+uijfPjDHzZFGMDpp5/O3Llzeeihh0wh9thjj5HP57n22mvN7SRJ4otf/CKXX345r7/+OieccMK4x9LW1jbq852dneN+r/Fis1qoLfOyo6mL5s5e0etpDLxer0hLHAfFXgdFHgs7WrpIZ4S4Hw1ZlnG5hXX9cNi0FE6n06x1Gk54Dbw/tLapIF5yucGfwYIIGxhFK0TFEvEYGzZs4LDDDpvowwPgrNNPpbu7h1VbdtHc103VrAU4PT6kAcI80hMi3NWGms/R1bSFIinHn355G9VVlSjDCPgXXniBQCDAzJkzKS0tHZRKNvR8FRcX4/F4OP3001FVlVWrVrFl81u0JpzMmTObXC6HbdESHHPqJv5kCKY1uqaTSqZERGwMsjmVcCzFrLpKin0eIcTGIBaLkUqlBvU7FAzGMKArGEenlCXzZ7J6/XZSE7zIPaFCLJlMkk6niUajPP744zz99NN8/OMfB/qjXD09PRx55JF7ve6oo47in//8p3l/zZo1uN1uFixYsNd2hef3RYjV1U3+ha6kyEV5sY0XXm0iFBFfFqMhAcX+YvFlMQ68Dhv5dI6+cFzkx4+BLMvCMXEY8pkkdqsVNNWM3hSiYQN7Y8FgcTGSOQf0i7OCCCsYfBTuG4aB3W7niSee4KWXXmLevHl87Wtfw+/3T+hxyrLMZz55OR8Ohrjlhz9l22vN2Isr8ZVW4C7qT9dJJ2J07dqKw2ahwm3hB1+/gbqa6hHf89lnn6W2thaHw2E+VjjegQw8N4VzURCgu3btYvPmzdTU1FBxxrlIh6BBwzUXfpCXVm8hJ9q5AGBgkEqlzB51guExgEgyx/yZRQSKvexokcR1cBQy6QyRSGTCv2sPdKKJDJ2hFMuXzOGJf6+kuaNnQvc3od/4X/va1/jtb38L9F8EL7zwQjO1sBB1qqqq2ut1VVVVhEIhstksdrudzs5OKioq9rq4FV7b0dExkYfxvlEUmZoyH5FIjHc3N6FqwtluNGRFobQ0IC5AY2BVZIp9Ltp7QvSGIlM9nGmPxWIZNGEW9NO85t9o6ShOj2dQY+KRomIw2JxD0/onz4U6p6EMdFpUFMWcXCqKQjKZ5JVXXmHnzp2ce+65XHbZZRO++FIWKOG3d/6Y/7z+Jm+tfRe7w8J9D91LKpOlrrKcy674BNXFbk45/ugx38tut5vHNrSJ88D7Q9M3NU1DlmXmzp1LcXEx7777LuFwGPfKVUTtLjSfm+jsGXD4wdfAeTjqKkqQZLHoNpBUOiUyZ8ZBTzhFPqszr6GWtZt2kslOvLnCgYpu6PT09jBjxgyxyD0Kqqqxoy3GiYeVcvj8Rtq7g6jaxC0STagQu/HGG7n44ovp6OjgoYceQtM0M3Ulne43FhguV7UwWUqn09jtdvP3aNvtC62tozsWdnZ2mtG2/YHTZqWhys2OXe2T2q37QMUhbOvHhd1mparMwZatHQTD8akezrTHoliw20WUdSjW3ZHnQsrgwAjWUAriYqDgKrgkFhhqy154XSHNaqhQURSFlStX8q9//YtQKMQXvvCFCbftliSJE447hhOOOwbDMDjr5ONRNRW3y0Vtzfh6m0UiETKZfsORgm39QFfJgQtJA9M4C7cLwtVqtTJz5ky2btlK6q2VKGmNqCwTPevkQ0aICfYmnU6Tz+exWoXL62jEU1myeY25DdXYbVYhxMYgEo6QyaRxuUTpx0gYQE8kTkYv48jFDbz81rtEJ7Cn2IRe7ebPn8/8+f0XkiuvvJIzzjiDj3zkI7z55ptmLn02u3fuZeHiVtjG6XSOa7vxUltbu0/bv1/KS9z4XFbeWLOZeFIU4I6Fz1ckIhfjwG614LFAR0+MTE7Uh42FzWYb5MQqgFw6Qbh1E3a7fS8BVrhtGWJqMdwq/cDI2MCasYHCa6CToizLpplHQfylUim++93vctZZZzF79uxJOHrMMc5qbBh7wyG8+uqrvPPOO5SXlw+KiA1nHFU4H0P7ruXzedPi3+rwknJ48dCfni04tMnl+i3sXa7h2ysI+smrOn1JjYpSDz6Pm2hczLFGI5NJE4lEcDpFvfRoJNM5dnXEWTy3nhk1Fby7ZdeE7WtSc78uvvhiVq5cydatW820wuGMMTo7OykpKTGjYFVVVXR1de01ASi8trp65Bz+qUaWJerKvSTiSTbvbCUvcuBHRZIkyspKRVriGEhAsc+Jqmlsb+pAE42cx8ThcGARPcQGkc+mCLVvN8XQSJb1MLxt/UBzjoFioyDGVFU1BdrQlEdFUbBarWZD5IJYG2r4MR3RNM3skTk0wjewxm6oKBt4bgr3oT86VllTxSmnfZBli+ZyxILZ1FaUTu5BCaYVwjlxfKiqRmtXnAq/jxnV5VM9nGmPpuv09vYNm0Yu2IOuG7T3xnG6XCyaU4fdOnFxq0mtCi6kEEajUebNm0dZWRmrVq3aa7u33nqLpUuXmveXLl3K7373OzZt2sTChQvNx998803z+emKx2mntsLD22s30dEdmurhTHvswrZ+XEiyREXAiapr7GrrQhN1h2PidAnr+qG0vfsK7t09w4ZzTBwoKoYymm19QXwNNOgY+D6FWrHC7YFmINPNWrkQuZIkiRdeeIH169cDsGLFCjwez26Bbxl0fMOlVhbSEof+5HI5rFYrP/nBDzjzzDMn7bgE0xvDMEilksI5cQx0wyCezOD1VVBTWYIk9TvfCYbHMAxC4RC5XA6HwyE+W6PQF0nRF01z/LL5PP/6OySiEzOHn5BZSU/P3g4j+XyeP//5zzidTlNMXXTRRTz55JODaraef/55tm7dyiWXXGI+dv7552O1WvnNb35jPmYYBv/7v/9LTU0Nxx133EQcxvtGAsr8HuyorNnULPo8jQPPbtt68eUwOoosU+KyEQqniCaSUz2cAwK3S6RiDCXSsW1QXVhBQAyXXlc4dwWRNVBwFaJYhmGYtU+qqpLL5fY0Kx6mRkxRFFRVJZlMmo9Ho1F6e3sn4/BHpa+vj0QiwSOPPMI555zDJz/5Se655x5effVV/vOf/+DxeHC73Wa/MEmSBtXzDHe+ChTOnaqq5PN5DMPg6KPHNgcRHFokEklh2DEOEpkcsYxBbXkxzmm2kDMdicdixBOirnwscnmV7W0JaiuKWTCrHnmC5g8TEhH7/Oc/TywW46STTqKmpoauri4eeOABNm/ezB133IHH4wHg29/+Ng8//DCnnHIKN9xwA4lEgttvv53Fixfzmc98xny/2tpabrzxRm6//Xby+TzLly/nH//4B6+88goPPPDAtG3mbLUqzK7z0ROOs35Lk7BVHQfFfj92u6jjGQuH3YrXZeftdTtJpYXAHwsJCadT1FoMRFPzOJ1OdD1pComB9WGwR5AVomQDJ4XDTRB1XTcFRz6fR1EUNE1DUZRBQmwg6XSarq4uMxq2bt06Xn75Za699tpJqelTVdUcVygUIp1O4/P5iEQivPDCC/z1r38lEAj013FZrWZj6oGideh5G3q7QCFiODAipmka8Xj8kJ5wSxI4bFayOXXsjQ8h0um06bApGJlkOkcwnGLuzGq8bueE93060MmrKqFgkLLSsqkeyrRGNww6+qIctaCEIxY18Mprb07Ifibkr/vjH/84sixz11138cUvfpE777yT2tpaHnvsMb761a+a29XV1fHSSy8xa9YsvvWtb/HTn/6Uc845hxUrVuyVnvKTn/yE2267jWeeeYbrrruOpqYm7r//fi6//PKJOIT9gs/toNLvZNW7O0WjwXEgyzKlpWVIkrjojIXPZSNQbGNrc4cQYuPAYlFwu4UQG0jruleIdu3ayy1x4KRvOIEBewQFDBZfAx0UC9GeQp3Y0FTGgdGxAt/+9rd57LHHcDqdbNy4kR07dkyIQMnn83R1dZFOp9m5cyednZ0Eg0F0XTct6Tdu3Mg3v/lNbDYbVqsVu90+6LbFYhmUUllgoKgd2gC7IMIKvwvRsuXLl0+7lMzJxOty8JXLPzTVw5h2pNPpA6JmcqpRNZ1IKkttdYAir3ADHA+9vb1omlj4GItYMsPOziRHLmqksmxiymYmJCJ26aWXcumll45r20WLFvHMM8+MuZ0sy9x8883cfPPN73d4k0Z1qQeHVWbNxp1ihWYcOOx2ioqKRPrYOPA4bRh5lb5wQhh1jAOL1YpVOCaaGLoOWg5FUbBYLGYKYTAYZMuWLYOMJ1wuF8uXLzcbrBes2guia2CEpyDICpPHQm1VIYoEeyJphb/zLVu2mLcdDgeGYbB161ZOPfVUgsEg7e3tLFy4kEAgsM/fDYUaLIvFQm9vL729vaa5UzabJZlMEggEUBRlkDud1WrlwQcfpKysjOLiYqxW616icag9/0B3yYFNr4fWzRWEaz6fN4Xqeeedd0i74/WnxU7PzJapJJ/Pkc/nACEuRsMwDHojWebX+KitLGVna9eE9n06GIjHE8TjCYqLi6d6KNMaVdPp7Eswd2nVhJnBTKpZx6GE3WahrsLH1l1t7GrrPqTTTsZLcXGxsK0fBxZFptjrors3QldveKqHc0DgsDuwCsdEk1w6zo43ngJDIxqN0tTURC6X28u6vlDH1dLSgizL2Gw2Ghsbcbvd1NfXm6mH0F8LNVRsFFKrCoJvqJ19Op0mFAphGAa1tbWoqorD4aC6upqamhp8Ph/pdJrW1lYSiQQ1NTXj6qsUjUZNZ8YdO3ZQWVmJLMtUVlaagtLj8QxKfRwq8uLxOG6324yQDaRQCzfcsQVmzqSioQFFlsEw0HSdvGGQzecJ9kZpX7MKI5Mxz1UikSCfF72PBHuTz+fJZDLCsGMc9EaSGFopi+bM4PU1m4QQG4NcLkc4HMLv94vP1hi098WJJgMce8Q8/jgB7y+E2ARR5HZQ5bfxj5XtBCMiLXEs+tMShW39eLBaFGrLnXR2dtAbjE71cA4IHE6H+GwNIRQK0tXZMahGyuPxDOonpqoqmUzG7PuVTqdZv349mqaxceNGJEli6dKlZt2vJEnk83kzFVHTNDPaNjA1sXB7x44dpkX3smXLiEaj+Hw+zjnnHJxOJzabjUwmg81mIxwOs3LlSkpKSpg7d64ZlcvlcthsNjZs2IDP56OoqMg8JkVRmDFjBk6n05xsDO2LNhzhcJhUKjWo/qtA4bgKJhtWpxt3WQUzj1jCjCOOoi7gJ+ctItiTJhZJIes6NruDqko3FYk0tUefRsvK11j3z4f2cpMUCAaiahqpVHqqh3FAkMmqJDSdGdV+7DYr6axI6RwNTdfo7eujrq5eNA0fg0w2T1N3mpnVB1Bq4qGOIkvMqC4ilUmzct1W0TtsHNhsNvx+ESIfDzarQpFdZm1vnOTupuaC0XE5hXX9QDa99W9aW1qw2awsWLDArBFbtGiR2RtLURTC4TC7du3C4XCQyWTYuHGjmX4YCoXQdZ1nnnmG+vp6qqurCQQCZqNmVVXNn0Jq3sCIWDKZZOPGjaZF/ic+8Ql++9vfUllZaaYoWywW3G43LpcLu92O1+ulpaUFh8NBSUkJmUyGYDBIcXExNTU12O12ZFnG4/HsJbj2ZdW3IByH1m0VRFgmkyGfz+Nwe1h22TU0HHsk5S5IxCVkN1TIUFXpIFPpIAOkgZgKoS4dbKWULz2d+miGXateoEqWWbBggciaEOyFruvCwn6c5FWV9t4slSU+ygN+InHhJjwW4XCYbDYrhNgYqJpOc2eYo2d6JuT9hRAbg0QiDlbnPr3G43JQV2ynsydBKJY2V4sFI+N2uTEMg3hcWKqOhiRJBLx2MAzae6M4nS4s1kO3yH889Nco2UkkEmIys5utq19k5swZzJo1i5KSEiwWCzabbZBhh6IolJeXU1FRgWEY5PN55s2bh6ZpbNq0iaamJmKxGJIk0dLSQnt7O/Pnz8fr9WK1Ws2oVDabJZ/Pm+mKBSG2du1aUxzX1NTQ3d2NpmksXrwYgEwmY04SdF2nvb2d4uJiGhoasFgsqKqK0+lk9uzZg1IpYd9E10gUhODQ5suZTIZEIkH98lOZe8p5zFw2C7dTIgP4fP0XVZV+p04dKMgrRQF/hZdESCOTyFAyZzGhrnaCbTu56+7fv+/xHuiENDder+ghOZS8qhKPx8V31xhIEjS12ll8TANzZtbSGRSZSGMhSxKRSEQ0dx4HuWyacocQYlPCmyvfwuYq2qfXHDZnBq4PLaAlp3D4kiNQRbPdMTBobmlh5cq3EGvCo6PIEu4TlmGRGiguq+HYY22ieeUoFCIzmzdv5tX/vCo6fQLZaDeKmqShoWFQ9GtgSmKhCXMhxbAgcOx2O5qmsWzZMmbNmsWOHTvYunUrqVQKVVVZt24dXq+XBQsWmK6JsViM4uJi07TCYrEQiUTMRRdJkrjqqqt4/vnnmTFjBgsWLMBisdDd3U08Hsfj8eD3+5k1a5ZpFjJwrBNFwVZ+oBDTdZ1kMkXJnCM44mNXU1RWhGSRUAGb1N87srC2XPik6bt/ADQV2F0fV9kwk762hSRfepL2jo4JO44DBX/9Yk465qSpHsakMd4IaDwe44033xjkSCoYns6WKk5ZWsNJxy5Ddpei6eL7fiw6Ozv4z39eFe2VxkCSJGIdNRPy3kKIjUE2k0WXx5/+ZVEUZlYH8Pnd9G4KYrXZEUHf0dE0jVAwRFqk2Y2JRVGoDHhR7FZymoHDsW/R2kORbDZLMpUkIz5fYOgoyS7KS4tMQVNIDSxMDHVdH3S/4DyYz+exWq3k83nS6TSGYTBr1iyqq6vZtm0bHR0d1NfX43K52LFjBxUVFeRyuUF1YoUIWVNTE3V1dbjdbubOnctHPvIRTjjhBCorK3G73VgsFgKBANXV1aZQLDAZkYGSkhI8Hg/RaNQ01ShEBd21c1n8kauw2NxkkwZOj8TAEUm7f3QgT390TAfUPOgaZOI5dF2jZeM2JFmhdOZ8yB/aq/ey1U7t0tOwCrOmvRiYDisYnfauPrY1t1Nf34C/OUQqI0xwRsMwDIqLSzAMQ3y+xsG6LTsn5H2FENvPFHldHLt0Pn3hDL0RkaM8HjKZDNFoZKqHcUDgdtqpDHiJpg0yWbFCOh40TSObFe0jADB0PNlObD6vGeUa2Fy4EGXSNG1QJKhQ5xWPx8nlcuRyOTKZDLlcjo6ODi6//HIWL17MwoULcTqdPProo+zcuZOXX36ZRCKBxWLBarVSU1PDggULOPnkkznqqKMIBAKUlpZitVqpr6/HZrMNSi+cinSsQgSwcB4KFvwAubzKvOPORbHZyaWzOEs9yDJIcv/FdCQDdo3dwVij//Po8EAi0o3LW4rL58c4xNOoJCQUqxBhw2G1WnHY7aTTwrRjLBKpNO29MZYukrHbrEKIjYEkSbhcrn53WiHExiQ6QXWHQojtRyRJYkZ1OQ31AbZ2JsnkxER5LAzDIBwOkRVNK8eF3+dh7owqOnvjpHPiIjMecrksOSHETAZGwgqRpoGOhqqqDjI2KdR2DbWm37lzJx0dHXz1q1/lxhtvNHuA5XI5zjzzTFpbW5k9ezYAS5YsobS0FK/Xi91ux+FwmEYYkx3tGsrA/meAKTp9Ph/Nzc3U1NTg8XjQdR1X3UJQLER6OvAUFyHJgNF/IbUCXsDJnkhY4VtN0/t/sikdA5AkG5Js4d2XnyITCYsuUYIRURQFt9tDOBKZ6qFMezRNZ1drD3ouj8/lIBJLiXKHMbBarQQCpfT09grDoDHIZCdmziWE2H7EalE4aslcXA4HTV096CI/eUx0XScYCqGLnh/jwudxUVlRRPPmEJqoPRwX2WyOvKivAMAI7gBDH9Y2vZCSWOjxVXisECkb2Ji4tbWVlpYWrrvuOm666aZB/f8KdvN1dXUsXrzYrEMrmIEUmCrRVWBo37OhfO5zn+PJJ58klUr1u4pJEg5JJhkJUTyvBqvdgWIFuw3Q+8WXJoEugZLPE43FiANxIKEBips3n3qMVx6+H8PQSYSDJMK92BSZBfPnmU2jB1KwyB+IRVGQFcVMlRyK1WodFNGE/lRJyzDvP10onXMkkiIaOo/Eodzse1/QDZ1tzR0kM1mqyzy09UTQhBHFqMiyTKC0v6m9qEOcGoQQ24+UFhexZF4tHeE8sYQI846HXC7X39B1qgdyAGBRFKrKitENmXBCRBDHSzKVFK5QBTIRJDtmDVhBaBXEVsHVsCDSBtZHFX6nUik2btxITU0NH/3oRweJMF3XsVqt1NbWAnunF06VECiYjhTSLEcjkUiQSCQoKSlh/vz5bNy4kWXLlqG4ioim8xTlsuQzGdScRioKubSBpuooFhmPT8Fmk9jx5lt89YzTzPc0gM/97G7uuekLOKwWPv7xjwPVvPnmm6xbt47utmZOPv2MQQ2m47Eoq1e+SWZISlpNbR1VNXWoap533l61l1CbM28Bfb09hENB8zGb3c7Jp50xbYWYy1+JJIn2EsPR39/PbfbyE4yMYUBfKEokmaGyyI0iS4j1yrHxF/nxuN1EoqIv6VQghNh+ZGZNOfMaq3llXS9ZkZY4JoZhmJMewdjYbBYWzK4jncoTiol6gfGS3t0wWIAptgoRk4IYGzjBK0z4hk7wC6YZW7ZsAWDBggUcffTRg7aRZdmMgE0VA2veCqJr6AS2cMy6rpPNZkmn0wSDQVRVJZvNmjWFp512Gk1NTSSTSSy6TOPy2ajZKLqeobtpO4beb1Cfz2eRJBlvcTGuIh/NLSl0TeOhhx7C7/ezYsUK/vTLH5PPZrjrV/dw8cUXE41GCQaDvPDCC9x0002DxpfLZnl75Vvkslmuv/56LrjgAjRN4ytf+Qo7d+6ku6sLAwOHw8HPfvYz5s2bR2trK7fccgvbt25G0zSOOeYYbrvtNgC+973vTfyJF0wYDocTRVGEEBsH6WyObbu6OWbpfJwOK7mEyLYZi/70xIAQYlOEEGL7CbfLwfLFc0hlDDqCQliMl1AoRE7Uh40Lu9XKnPoK0qpGeoJylQ82DEMnKYSYibI7pa0gxgbeLlAQa4XbsKeOSpIks6fR2Wefvdf7T5UAG3gMw0W8CrVr/fbzSRKJBMlkknQ6PaqRyxlnnMFTTz3Frl27qKydAWQ566JzeOR3D5JK5pAVC2oui6bmkeR+S32Xt4hkLIIsyxx//PG89dZbNDU10bF9Mx/4wAc47bTT+NWvfsV//dd/8dOf/pTzzjuPBx54gA+fczZuT3+1WCQU5t8r/sUNN9zAz372M/74xz8SjUb5z3/+w/XXX899992Hw+Hgd7/7HWeffTa/+93vCAQC7Ny5k7lz59LU1EQ0GmXdunV84QtfoLq6mlNO/xAP/fUhOoZY5Z9x5hksXryYJx5/nK1bt+11Dq6++iqKior47W/vJpkcXKzu8/n43Oc+29/E6X0QmDkfR1E561vCRFNjf7dV+B3MrvS9r30eSKRmeDmsyjZsKqpgMIqiUOF3UOm3c/z8cqITkJ0US+dY1xzZ7+87lZSVldHc3IKqiSDCZCOE2H4iUOTliEX1dEbSxJIiLXE86LpOX7BvqodxwFBS5KXEa6c7pqKKmrpxoWmacBsbgKqqGEp/pKggTgomGwVhBoMFWKE+TJZlduzYQTqdRlEUzj///Ck5hoHGIgNrqAamWuq6btrs53I54vE4iUTCrKvShvn7KZyPwsJQOBxm165d1NXVsX37dix2Jxtff5X5hy/mgx89mz/96GeouTy6PuS9JIl0LGLeffDBB3n0749hsTk4/vjjURSF3/zmN+i6zi9/+Us+8YlPcOyxx/LJKy6jpKS/oXFbWxvf+uZNXH755dx9993ceOONZLNZtmzZws0338zf/vY3HA4HF1xwAddcc40pzAzD4Ac/+AE33XQTiUSC73znO1x11VWkUil++5tfs3XrVpxOJ2VlZRiGwfbt21FzGebPmc3LL76AYRhUV1cD/W0fdu7cySsvvcjChQt5Z81qysvLKSoqMs9POhnnM5+6clDt3/shmcmztik86jbZVIJQ8785+5yv7Zd9HjjMmeoBHHAcNzcwIe/b1JM46ISYz+fD5XYRix3aDq5TgRBi+wFZlliyoJHK4iKeW9dLXhWT5PGQSqeIRcUf/XiQgPrqcgI+L+s2h0WT8HGSz6vCMXEAuq6jGqopVGRZJpvNDqoLK4iwQn1XQYwBpFIpNE3DYpmcS8fA9MhCpGugEYWu66Zw0nWdaDRqCq9sNouqqoNE19B0S13XSSQSdHd3o2kafX19hEKhQdv4/X7Ky8vpbG9Dcvn5x+/uIZ9JD0oTs7tcVM+eza533wXDQNMGRy6q5ywim+qPJmWzWTo6OrA73bS3t4+ZEdDZ2WlG7Zqamqivrx8kfJqamoB+k5Suri5OPPFE/uu//ou6ujouv/xyoF+wrlmzhs997nNcdtllfPCDH8QwDH71q19x3333ce211+J0Orn//vv56Ec/imEYBINBPv7xj/P444/z+OOPM3v2bB555BEOP/xwDMNgzZo1fO1r+1cMfWhpNe80h0ftu67rGolw737dr0CwL0zPSsv3jiRJOBxOinxFQohNAUKI7QfcTgdL5tWSRaYvkhz1IiLoxzAMwqEw2ZyYJI8HSZaorSzG6XEQS2bEZ2ycZDIZ8nmRamFidZOK9WC3280GzYUmyw6HA03TBhlsDBQuAwXaRDGwvgswI3cDxVculyO1O920vwdhf11DOp02I2QDx10QnYVtgsF+E4tIJEIoFBq2z5xhGGSzWTRNQ1EUGhoaSKfTqJkEyWgYi9WG1eEEwyCfzZDPZOjaOXKzz0wiRi67JzIrSRI//tEP+frXRxcy27dvp7a2FofDQSaTMdsBFM5NU1MTc+bM4ZVXXsHlclFTUzPq+3360582BZksy9x5551UV1ezevVq7rjjDo477jguuOACNmzYwO23387DDz/MsmXLaGtr45FHHsFisXD44YeTTqc55ZRT+OY3v7mX++ZEE+vrwC++AAVTSHXAxeEzinm3efTo7YGExWKhrKyMtvY2YWM/yQghth+oKith2aJZ7OpMkEgLYTEedF0nGAwOmyIk2BunzUZVwEc8i6gPGyeGYZDNZcVnbAByxQL0WBP5fB6LxWJGxTRNI5fLmdbnBRt72Ft47a+L9FCxNLTGK5PJmNtkMhlzpdYwDEKhkGm2URBrA8lkMmR2NyhNJBK0tbUB/eIrl8vttX1B7IVCIfP9YrEY+XweXdfJZDLU19eTyuRQMwkUix+b3UlV43x2rH0dXddJ7zYdGk6o5rJp1AGRL8MwePrpf456fmRZ5tprr+XPf/4zv/71r4lEIrjdbvO5aDTKxRdfzB//+EcOO+wwstkslZWVo76npmksW7aMG264gccff9w0AYH+GpFQKMTpp5/O6aefTjKZpKSkBJvNhiRJlJeX861vfYsNGzagKAr33nsvL7zwAlu3bp3U2sC3n/0LJ31gttluQSCYbKyKjMN6cLVbkCSJQCCAzWoTC+STjBBi7xOb1cL8xlrcbidtW8Kid9g4yeVyhCMHz2rSROP1uJg3s4auvgSpjBBi4yWdSqENreE5xEmn09itCoqiYLFYMAwDu91uNjKWJAm73T4oVXF/irGBlvmF+r3C7UIkqvB8PB43BVk2mzUjW0NrvbLZrOnAWhBq4XDY3HaofX0ulyOfz6OqKpFIxLxd2LeqqsyaNYuioiLKy8u58cYbzfGvWb+Bm2+7g0QkSNOG1YPORWlpgFmzZvL8M88MPl/6niidy+Vizpw5rFixggULFuBwONB1neeffx6v1wv0u5jdfffd3HDDDVx22WW43W4kSeLKK69k48aNnH322VRVVfHzn/+cs846C6vVitPp5Cc/+Qnbt28fURhdcMEFXHTRRVx44YXccccd9PX1ceWVV9Le3g7014nMmbOnFulf//rXXgYdy5cv54tf/CIAHo9nSgxaHn74Ea644gqOPPLISd+3QABQVmTfbY9/8Mz53G43fn8R3T09Uz2UQwohxN4nbqeDE5fPJ5rM0R0SbonjJZlMkoiL8zVePG4HdTUlbGxNCKOOcWIYBplMVlg+D0RSwD+DdLINRVGwWq2m+HI6nYNMMGRZRlGUQRPtfYkuDo14Feq1CoKrYI6haRrxeNx8TcHJsCCeBtZQxeNxM00xl8sRi8Xo6+szx1YQX4V9FsabSCR2p6nmyeVyZDIZMwpYEHclJSW4XC4z9bGyspJTTz2V8847b1DkpbqmlMZGH5s39iBJ/b2LCueory9IX9+e/l1DWbFiBV/+8pe5+uqr+cEPfsDnP/95ent7WbFiBXfddRd2ux3DMCgrK+N///d/sVgshEIhEokELpeLL3/5y3zjG99A13XKysqQJMmsaTvyyCM5//zzmT9/Pt/85jeH3X8qleLee+/lnnvu4cgjj+SZZ57hrLPO4ve//z0A//73v/nMZz5jfiZcLhepVMqsSbPZbLz11lu89dZbWCwW5syZw4UXXjhpYqy7aRN9bdvJZDLCbVcwpSyfXcoL67tJZQ+e1HdZliktLaWnt1ekJ04iQoi9T2bVVzG7ppTNnSkyonfYuOkL9pFXRWRnPCiKTEXAj0VWCCZyoj5snOi6TiqVFBeUAUiyjFIyg3yiE1VVSaVSOJ1O83mbzWamJWqahqqqg8TYwNqx1atXc+655+61j4JIKkStcrmcme5XEGHQL45UVTXT/wq1WplMxhTPhW00TSMajZoCrq2tzRRqhVTGQgStMIZC/dfACBiA3W43ReeSJUuw2WxmJKwQGUulUuzcuZPt27cTj8c5/vjjaWxsNI+xqNjJkcfU0dkeIxbTSaU1rDY72gjNonUtj2KxsGXLFv7yl79wzjnn8MUvfhGbzcadd97J9u3bAbjnnnuIx+PcdtttbNq0CVVVufTSSwGYM2cOd911Fw8//DAXXnghr732Gg0NDSxfvhyAo48+miuvvJKWlpYR//+feOIJent7efzxx5Ekia6uLnburm1buXIlt956K7t27WLz5s1UVlZy8cUXc+GFF9LX18ebb77J7bffjqqqZDIZTjzxRObMmTOpEbF4qJtkRDjtCgQTRSAQwG6zkREmV5OGEGLvA4sis3huHUV+L23rmtCEk924UFWV4CirxoLBWBWFBbPqyeV0+iKiJ9Z4MQxdWNcPg7WoiqStiHiiB4/bjWEYuFwuYE8Uy2q1mqJsYE+x6upqdu7cSSaT4Z577uGcc84ZNBFPJBJmQ+TC61KplCnMCmmGA23is9nsoPqvZHKPeO7u7iadTptRs6HGHQNrx3RdJxaLmfb1hWifpmlccsklLFy4kJdeeolkMonf7zcFnKqqWCwWszau4Ahpt9vNCNIzzzzD4sWLueGGG0yXSYtFoW5GMZl0jlRKo7c7gq/MjdPtoK8zTfO7e8758qWHc80XvsiXrv0CP/jBD7j//vuZN28eAM899xxut5tUKmUK0wIFV0eA+++/n7a2NsrLy7n++uv56U9/Sj6fN5//3ve+h6qqHHPMMZx99tmsX79+r//7W2+9lSuvvJL/+Z//Qdd1rrrqKl544QU8Hg8///nP8fv9XHvttUC/W+Mll1xCKBTCZrNxzTXXcM899/DTn/4UgJdeeolvfetbvPXWW/vNvl6SJGwWmWx+72upYRjks+LvWTA9UGSJ4+aV8dy7nVM9lP2GJEl4PF7cbo8QYpOIEGLvg4Dfx9GHzaGtN0M4Li4Q4yWZShKLC4vU8WKzWpkzo4KcrpNMi3Sc8aJpOumM6Ok3HI6ZRxFd+X9I7HEqtNvtQH9D1EJUy263Y7FYkGUZwzDM24AZqRpoZW+32+nq6jJTCAvbFdIBCxGxgmAqkEgkSKVSGIZBe3v7oChXQXgVapUK1uqFdMd0Om0Kr2OOOQbDMLBarVx//fWUlZWhKP31cJs2bcLpdJo1WQPTMnVdN7crRAELAs1utxOPx3n55ZdJpVK0RduQnXtSFQ1VY9HsRkqOKqGpuwNHxTw8zU289vS/zW1i0QhHLfsAzc3NXP/N76FkIuZzn/rUp7jgggu4/PLLueaaawCoqKjg8MMP54orrjC3q6mpYf78+WYk7ayzzjJ7fgFcdtllfOQjH+FDH/oQb7zxBk8//bT5nMvl4nOf+xyPP/441157LV/60peA/lrd0047je9+97t85jOf4Tvf+Q633nqreZ7z+Tzf/OY3Wbx4Mddccw0XX3yx+f+vaRq1tbX78rEbE6/DwsmLKnlmbcdez6n5LG889rv9uj+B4L0iSRKVxc6xNzzAsNvtlJSUEAyJxfLJQgix94gsSdRUBGicWcbq7RFyIi1xXBiGQXS3c5lgfBR5XZQXOehJaKiqiLqOl3w+v5ctuaAf2erEVn042b6tSFLaFFqFujCr1Yosy2QyGWRZxmq1mlEjm81GMplk9erVPPPMM4OiYqqqEg6HyWazhMNhM81QVVVTuBXSFDs7O80IWaHxckF0Fcw5CtGuQm1ZwVDDYrGg6zo1NTWccMIJ2Gw27HY7xx57LIqi4PP5sNvt5HI5LBYLVqt1ULPqgccryzKqqpqmJIXn8/k8iqKYx59Op1m5ciXZfBbVUCmuKiYVTPG1r3yN6spqysvL6Y2G2Zx0sNK2xjzXZWVlrFixgr+8tpETz6jkgm99m5IiL53rN3Ha4jl4bAoS8NRTT5kC1GazcdRRR3HmmWeO+H9YiFwNx1/+8hf+9Kc/0djYiCRJ+Hw+fv3rX3Pttdea6ZsF5syZQ2VlJU8++aRp7T+QI488EofDwezZs/f63h5o6rI/kCQJeaRUR4O9m2cLBFOIVZGxKjL5gygbSpZlyspK2bFzh6ivniSEEHuPOOw2TjxyEVoemrpiiCqU8dFvWx8yJxyCsampKKUy4OeNnXHywqhj3GSzWVTRQ2xYJFnB3bCcUKgVLRnd3dDTQSwWw2azYbPZBgkyVVWx2WwANDQ0EA6HiUQiPPjggxx55JFUVFQA/eLXbrezbds2M8UuGo2STCZ31+ylCIfDg0w/4vG4ecFPJpNmI+bCb1VVSSaT5rgkSWL+/PmUlJRQWVmJy+XC5XLhdvc3SPZ6veRyOVwuF06n06wHGygiBta86bpuukcWLNELgrSQrlgQoHa73Uy/DLeEkWWZ5l3NhINhtm3b1i9SQyH+PMAs4+c//znLli1jbVcTsstKtcOGjkTZEQt4oqmPmRaFOpvGscceu1//j0899VSzFqzA0qVLR9x+4cKFo77f0Ucfvb+G9p5o27wafUBd8bPPruDYY4+dEtdGgQCgscLD/Noi1h1E/cQA/P5iXC6XmfYsmFiEEHuPlBYXsXh2FR2xHPGUWHUfL/l8nlA4NNXDOGCQJKgq91PkdxNLBkV7hH0gJazrR0WSJDyzjiW+cYUphux2uxkVKjRHHhg9MgwDRVEIBAL09fXx5JNPcvnll3PmmWf21/fYbPh8PlRVZe3atQCmYUdBmBWMNAopkQOjYj6fD7/fTz6f5/DDD+fyyy83BZKiKNx5551s2rSJjo4OYrEYwWCQuro6PB4PRUVFpkhKpVKUlpaaRiFut9uMuA205QfM+4Zh7CXQABwOxyDTEkVRzEibJEm88sorWK1W870Mw2DGjBl0d3dzyimnYLFYCIfDHHH0cfxfdyvLPnQ61UuW4i0poW5mGdF0jo6eJN6MRolj//Umevnll/nABz4AYDbAPpDZ9e5/0AYIscce+wf/9V/fFUJMMGVIksTB+Omz2WyUlJQIITZJCCH2Hlk0p45Z9eU8vbqbnFh1HzeJRILEkNQYwcjYrVaqAj7SeYmUaOQ8bgzDOCgmnxONvbgaadGHiG3oF2OFps5OpxNFUcwIF/RHGO12O4qiUFpaSigUIpvN8rOf/YwFCxbgdrvNnlx+vx/A7E+Vz+fNGq9CXZckSWQyGS699FJKSkpQVZWTTjqJBQsWmOMbOsn+7//+b5544gn+/Oc/m+6Gqqri9/uJx+NUVFSYDZhzuRxut5t0Oj2oPq1QFzbSPhRFMYXnwPqxQnSwUDdms9nIZDJ4PJ5B6XkFC2iArVu3UlZWhtvtxue04VEknvvzn6ifv5JjPn419soAdrcNuc7KKz15nL1NnLp4Jpb3aX5xwQUXUF9fb953u90HdPNj4XwqmK4sqC1iQ2vkoOonJssyZaVltLe371PLEsF7Qwix94DH5WDpvBkkczKhWErYiY8TwzDo6+sTfbD2AZfTzvzGOrqCKWHUsQ8U3PcEY2MtqsK78EMUxTbT09Fs1nTJsozdbiefz+NwOJBlmXQ6jdVqRVEU7HY7yWSSVatWcccdd3DdddfhdDrp6elBkiQaGxvZuXMn2WyWSCRCPB6nrKyMqqoqjjnmGC666CIMw6CystKMKI1FRUUFV199Neeeey5//etfWblyJZ2dncTjcXp7e8nlcng8HnM11+v14vV6CQaDrFy50jyugtgaGAEbeLsQrSukJRbq1gAsFotZU2axWPB4PIMibbCnDUDhXCmKQnd3Nz6fj+qqKrRYhNf+fBfVhy9i8bkfJq/YUN1WdvSV0PXCWi46biFu13s3AjjzzDNHrS870OjcsZ6d77w61cMQCPZibrXvoGvsDOD3+3E4HOI6OgkIIfYeCPh9HHV4I619cWIiLXHcaJpGSDjx7BMup4PG2lLawjmyIvI6bnRdJ50RTqbjQZIk7MXVXP/lz9C1/mX+3//7f6ZRhaqqplFFQbzYbDasVis1NTW0traSSqW47777KC0t5ZRTTiGRSJgNoX0+H9Fo1OzPtWzZMu644473Pd6qqipuvPFGWltb+cY3vkFHRwd2ux1N0yguLiadTuP1es2ImcPhME0qCvVghVqwwu3Cexd+F8RYISWx4LRYEFYFkeZwOIA97pOAWcumaZppMb9u3TrC4TDFxcUsWrQIOZ9n9RNPoOoWZpx6Bppmx+UrorXTQ0swznynQ6Td7UbLZ1FzwgFVMD052P5MJUnC7Xbj9XiEEJsEDtxchSnCalH4wIJZuB0OdnYmRO+wfSCRSBCLibTE8SLLEmUlRThthUbOB9eK20QiHBP3HVmSuOmmm3j77bc5++yz8fv9Zr1VIbUvn8+TTqcJh8MkEgl8Ph+KopBKpXjggQf49a9/TXd3Nx0dHQSDQYqLi80UweXLl/Ptb397v465rq6Ou+++m5tvvpnZs2fT09NDR0cHmzdvpq2tjZaWFnp6eujq6iKbzZo2+kMdHAsRr4F/YwNdFBVFwWKxYLFYUBTFNO4YmPJXiLQNtPgvvE9BvHV0dLBt2zaeffZZWlpayOVybHtxBc//8hfEe9sBA19pBU++005778FlADBeij027NYB7QEMg2D7zikckUAwMjaLzBlLqsfe8ADDYrFQVlY+1cM4JBARsX3E5bSzfPFMUppBT1gUMo4XwzCIRqNksmJVc7woisL8xlo0TaIrJFJg94WB5hCCsZHoFxI2m43q6mruvfdeXn/9dR599FEeeughNE0jk8mYPcMKaX0Fa/RoNEpTUxOtra00Nzcze/ZsqqqqiEajOBwOvvCFL3DUUUcRCAT2+9i9Xi9nnXUWJ510Ei+++CL3338/XV1dZDIZioqKAHA6nWQyGbPWCxjklliIjhWs62GwEBvOyMNisZi1dANdYA3DMJsp2+12s1m2ruuDep+tXbsWRVEoLy+nrKyLdU/4WHLRp5EkiaQu8+qGJj5W6j+ga7veCwvr/Ly4oYuOUH9E29A13n3xb1M8KoFgeCRJwm7dfyY70wVJkgiUBsxUbMHEIYTYPtJYV8WS2XWsb4mTzoiJ3njRdZ2+vj5R+LkPWBWFuTMq0SSJeFII2PFS6EElLh7jZ6AAKXDsscdy5JFHcuutt/Liiy/yj3/8A0mSeOqpp0ilUoO2dzqdprPg/PnzkWWZSCTCj370IwKBAA7HxKfZuVwuzjnnHBYtWsTdd9/NK6+8Qi6XI5VKEQgEzBTFgvlGoS9aQZwVHi+IrqGCbKD4LNSHFUTdQIGay+Xo7Owc1JNs6PsM/Onp6aG3t5eW1lY8AQ/Vyy5AAla8sZaj5lTRWFs1oeftQCWZTLFu3TqWLFky1UMRHOIUe2x4nVbi6YNrTujd7UY7XH9Bwf5DCLF9wGpRmFNfgc/voXN9H5podjduhG39vuNxO6nwuwimNfKqELD7QjqTFkJsH5AkCUXeWyhZrVasVisf/vCH+fCHP4xhGLz66qum6yEMdrRzOByceOKJU1rbNGPGDH74wx+ycuVK/va3v/Huu+/S0dGBw+EgnU7j8XiwWCymoUYhKjbQLVFRFNO+fiRBZrfbsVqtey0uaZpmnpOB7zXwfBV+Cn3L+hvdh3ny1z/nA+fHaFj2QWSrHUk++Fba95VNrz9NJrV3SnsoFOIf/3hMCDHBlDOjzEOl33HQCTGLxUIgEDDbjQgmBiHE9oEij5tTjjmMtp4Uwaiwxt4X4vG4KPrcRypL/dRVBljXkSSvCtE/XgzDIJPOmBNpwdj4vS7mzagccztJkjjxxBMnYUTvD0mSOOqoo1i2bBk7duzgG9/4BtFo1IzcFX7bbLZBphwFYVaIjg1MRyz8Lty2Wq2DBBf0R/5feeUVYrEY0O88NjTaOLAWrXC78J7JRILVTz1IXpew2B289tZqZp5/1iFt2hHpbh3UyFkgEEwOkiRTGihl165dItV/Ajm0ks/fB7IkMaOmnBkVxbSGUmRzYrV9vBiGQTAo0hL3lbISH+WlPqLJlIi+7gOGYZBKp8QK3j5QXuJl2YIZUz2M/Y6iKMydO5df/OIXfOpTn8JqtRKPx4nH40SjUVKpFKlUilwuZ5p5FJpAa5pmRrcK9V0FUw+73T6opqwgtMLhMO+8846570KD7KHbFu4De6Uqxvs62fbWv1GzWR597MkpO3fTATWfI5sWC3iC6c/Jh1UelO6JRUVFuJzvvZWGYGyEEBsnVouFE46Yh9VmY0d7BF1M8sZNPp8XOcb7iNVqoSpQRE6XSGSE6N8XDMMgLZo5CwYwc+ZMPvWpT/HAAw9w/vnnY7PZiMVihMNhYrGY6QxZqC0sCDNVVcnn84N6iRXqw2BwWqYsy4ST/W6SAIFAwIyqDfwZTYwVCDVtJh7u5YPHHT2JZ2n6EWzfwda3Vkz1MASCMSnx2DnIdBjQn27uLy6e6mEc1IjUxHFSHihiyZxaWkN5Emlhiz1eCo114wnhMLkvOO02FsyqpyeYIZESjZz3BV3XSKdFD7GJJp1J0xvqHde2TruTskDZBI9odCRJwuPx8KUvfYkPfvCD/OhHPyIUCpkRMKfTic1mAzDTFAspigUzD4vFgtvtNnuEDSQajbLi6RXmviwWyyChNbTGbGDq7NDobSYVJ9y6jfO/fd0hmZbod9tM10SB4EBAkSV8LhuR5MF1vZZlmfKyclpaWkSWyQQhhNg4kCSJmTXlzG2s4pm3u0Ra4j4SjUXJZITr377gtNuYN7OcWDpLJitys/eFfF4Vn7cJxDAM1m5ay5tr3mT1+tXjek1ZSRlHHHbEsM/5fX5OPfZUkECWJidJ4/DDD+f//u//+Nvf/saLL77Ihg0byOfzZs1YoWdYQXwVfhduF6zwB9Z+rVq1itbmVqB/Fdnr9QIMqgEbeLsg9Iad3BgGNocTi80+wWdievKhw6vZ2Bodc+I3tMZOIJgqXHYLx88v56nVbVM9lP2O3+/H6XSSEpkmE4IQYuPA7bRzzNJ5xJIaXSGRr74vGIZBX6+oD9sXJEmixO/F47CyK5xF18Uq1L7Qn1ImPm8TgWEYvPXOW9z39/vI5safGdAb6uWZl58Z9jlFUVjxygqqyqv49MWfxu/z76fRjo4kSVx00UWcccYZrF+/nu9///vEYjHy+TwOhwOn02nWh2mahsViwW7fI4wKlvyZTIZgMMirr74K9Jt4VFZWDtrP0NuFaNhQ58mBjaVbNq0lEe6FgHdiT8Q0xTAMXvv7b0fd5t577+Wyyy5l3rx5kzQqgeDQw+F04Pf7hRCbIIQQGwfFRV6OWFBPRyQt+jntI/l8nrCwrd8nFFli7sxaZNlCZzAs0gH2kXQ6jaYLIbYvyOOMKOi6zmPPPbZPImwsNE0jFA0RioZYu2ktHzzqg5Ma4XB73HREOrji01fQvKOZp558Cp/PZ9aJOZ1OfD4fxcXFHHfccXi9XiRJwu/390cH167l6aefNtNhi4qKzBTH4Y5jaIRs4O/CbcMwCLXvIpOITfThT2uyydGPP5FICDc3wbShscJDscdGOHFwpSdaLVZKA6V0dXUJN+IJQAixMVBkmaMWz6GsyMOqtT2o4kO4T0SjUZJiFWWfUBSFuTMqUKwWIvE0QobtCwaplHBM3FcUZXz9qp595Vn6Qn0TNo6//+vvVJZWMn/W/Anbx1A6uztp6WhhR8sODN3AFXDR29lLoCSA3W43f7xeL0cddRRWq9U8X/F4nP/93//lP//5D9DfVLp4SGH7SKJyrM+oJCscdDZs+0Ckp5V8VtSJCQ4cKvxOPA7LQSfEoN98yGq1ks0Kj4T9jXBNHAOX08aSubVkDIVgNImY340fXdfp6+sTKyj7iMthp7LYRTSrkxdNifcJXTdEv7r3gKKMfSnQdX3C2wKkMil+/9Dv+ePDfySVmfgFHF3XeXnly6QzabK5LDk1R/28erPJstVqJZ/Pk8/nzcbBBUv7bDbLz3/+c1566SXzO664uHhQz7GCM+JAMTbUIXG4xzVNw1tahc3pnuhTMG3ZvvoFUjGRTSE4sLCO47v0QEOSJLxeLx63Z6qHclBy8H1i9jOVZSUcvbiRTS0RkpmDb5VjIulPSxSpdftKabGPmTUVNHclyeVFit2+oOs66YxYRd9XLPLYEbGeYA8rXp14K/FILMJrb7/GXfffRSQWmdB9tXe18+IbL7KrbdeeByVQZAVFUchmsxiGQUlJCV6vl1gshqZphMNhHn/8cR5++GF0XUeSJMrLy3E6naYAK1jXFwTWwD5kw4mxgY/ruk7DjDr8RUUTevwCgWD/8uEja6d6CBOCxWKhrGxqnW8PVoQQG4PZ9ZU4HE66I2lhmrAPGIZBOp0iFj+0axzeCyV+DzWVfiLxNKomoon7gqqq5ETqxD7ziXPG7lf19ItPT2rt3eYdm7n3kXsnNKJeWlLKlz71JcoD5YMez6k506RD0zQqKytRVZVoNEpTUxM333wz3/nOd9i6dSvQn7bj8/lQFMUUYKqqoqqqKcBGYqggK1jmn37ScVRXHJoTn0S4h20r/z3VwxAI9hlFPnin1YFAwOyhKNh/HLyfmP3EcUvm0B1N0x0SfbD2lXAkQkb0c9onrBaFihIfhiQTF7b1+0wulyObE5HrfaWxZvQJf0t7C5t2bJqk0exhZ+tOnn/teZ77z3M895/nWPnuyv36LaSOmQAAiz9JREFU/k6Hk0VzFnHF+VeYjykWhQVHLQC5XxQVxFQsFuPtt9/mq1/9Kn/84x/Ztas/ilZaWorf70dRFAzDMFMZR0pBLDDa8xUVFXzta1/br8d6IOF2OZg9o2pc277wwgsi60IwbXBYFepKXVM9jAnB5/Ph9Yj0xP2NkLZjUFfuZ0d3imxe1OrsC4ZhEOzrQxcXyH3CZrOycPYM+iJZYgkR2dkXDMMgl88JF7UJwGq1DuqZNVmkM2keeuoh877dZt8rPfJDJ3yI+up6836JvwSrxTrufRiGwVMvPGXelyQJt9+NNEuia3sXyVSSFStW8Nxzz/H6668TDAaBfvt6n8+Hz+czBVihJmys/Q2kkMqo7q4HLaQvTsX5ni6Ul5dz6qmn8vbbb4+57UMPPcz1118/CaMSCMbG7bAwt7qI1r6Dy6RMkiRsNhvFxcWEI5GpHs5BhRBiY+DxuVm1LYImUsT2iXw+RygkCq33FbvNysKGSuKprKhJfA9k0hlUIcT2O+u3ricYDk71MMjmsuxq3TXosbv/cveg+ycffTI+r8+8X1FawfLDl4/6vr3B3r0ec/qcNBzRwJZ3tvDm22/S1dplTkYqKiqYO3cuPT09BINBMpnMIFOOwm+bzWY2dh7IQDHmcrkoKyujq6uLUCiEYRimKBMIBILpgqIolJaW0dzcIlrE7EeEEBuD1r4M8ZSY2O0LhmEQiURJZ0TPtX1BAop9HorcNlriGpqoSdxnstkMmnDp3P8cQB/FF998cdB9u83OE88/Mey2F511EVt3bSWaiO71XEFMzV86nxlzZ/Dmc2+SiCRw2BwEAgH6+vrYvn07uVxurxqwgYKsEEGTZZny8v5atIF9xux2O36/H1mW0XWdSCRCfX39pPZSEwgE+4+jZpeyZmeQ0EFmYy9JEiUlJdjtNlKi7GS/IYTYGHSFkmRzh26KyHvBMAxCoSCaJlZM9gVJlphdX4XD7qB1R0iYw+wjhmGQTIoeYvsbwzDQjQNX3GZzWbp6u4Z97tf3/Xpc7+F0OTn5vJPZtGoTm9dspqury0wn1HUddkfDisqqcPn8GLpBLpMi0t1GLp+n0PekpaUFWZbx+XwUFRVhsVioqqpCVVVkWaakpARd1/nOd74z7t5uAoFgeuGyKwetaYfD4cDv9wshth8RQmwM2voSYPWNvaHAJJ/PEwwGxYR4H5Elmca6cpwOG+GYEBTvhZRoHr7PyJI0at/gWDzG0y8+PXkDmsYkE0kSiQSyLOP1esmqOt7SKqrnHIanuBS704XFZgfA0HUyqQTZVILO7Rvpa9tFItxrRr3i8Ther5d4PI7b7TbdFbPZrOi9KBAc4FT4HfTGDr6soEJkv7OrS8xR9hNCiI1BMp3DMf6670MewzBIpVPEYvGpHsoBh9Nuo9LvJqka5FQRTdxXdF0TQuw9oMgyEiMrsZyam5TmytOdeCROR3MHHo+HGTNmYji8+Gpm4y2txGp3DJtKaLU78BaXUlJZRyoeoW3zO7RuWkMmGUfTNGKxGKtXr2bhwoU4nc7+2jAJNL2/abRITxQIDjwkSeKkhRWsb4lM9VAmBL+/GIfdIXp27icOztjpfiQvJsT7TCQcIZM9+FaCJhq/z82s+iqau9Nkc6JYf1/J51XxuXsPyLLMKDqMvz75V7HyCeSzeYp9xTTMnouvfh4ZQ8Hm8owowgYiKwruohLmLD+JI864iKoZs3A4nABkMhneeecdwuEwADUNNax4a8Uhf87nzp2L2+0ec7t4PM6OHTsmYUQCgUCSJDweDz7f3iZEgvfGpAmxH/3oR0iSxGGHHbbXc6+99honnHACLpeLyspKvvzlL5NI7N23K5vN8s1vfpPq6mqcTidHH300K1as2Gu7/ckhfi3cZwzDoK+v75CfRLwXirwuGmoDhGIpVLEAsM/kcjnhNvcecNitI9Yz5NU8qbSIhvXbySsoTg9SSS2WonKKK2qxu9zjjlpJkoSiWCiurGPWUadSXt/I/PnzzUjYli1b6OvrA6k/Cnmo89GPXkBFRcWY27W1tfHkk0+NuZ1AMJkUe2zMryma6mFMCDablUAgICL2+4lJEWJt/397dx4nV1nni//znL32rq23dGcnkJBACEskRlBBDKCIS9yHUVFGxzGjcx25qKhXB+fOjeL2cxRmvKiowzCZ68xcHRe4A4psEUkkrEkgW3c6vXfXXnWW5/dHp4t0ujtd3V3nPLV8369XK33q5PS3qk6der7neZ7v09ODL3/5yzPe3dq7dy+uuOIK5HI53H777fjgBz+IO++8E9u3b5+27/ve9z7cfvvteM973oNvfOMbkGUZ11xzDX73u9958TRIBYrFYvnOLqmcIktIRsOQZRnpollPRepqRi6XowIxC/D2112E7vbYjI/9/qnf4+CRgx5HVJue23MAwSVr0L5yLULRBNqWr4ZmzLBw6xyNEyZJMIJhdJ61Aedu2oyLL74Yfr8fpmni2LFjyGUp8SWk3vk0BfGQLjoMVzDGkEy2NmxBEq95Mkfsk5/8JF7xilfAtu2JO36n+PSnP41oNIoHH3wQ4fBEUYzly5fjQx/6EH7961/jqquuAgDs3r0b99xzD3bu3IlPfvKTAIAbbrgB69evx6c+9Sk88sgjXjwVcgYTZetpWOJCqKqCtauXYjRtYjRFCznPF+cc+TwVOFkIWZIgzZI8TC4u3Oxsy0GwdRla2pdAUbWTd4JnSbhOe7lSwwMIRhOQJGniIcYAx8Gys8/CNe99N/79K1/G2rUZPPXUUygWi1A0mrpNSCMwNBmMNeLIKoZQKAR/IIBUKiU6mLrnejr729/+Frt27cLXv/71aY+lUincd999eO9731tOwoCJBCsYDOLee+8tb9u1axdkWcZNN91U3mYYBm688UY8+uijOHbsmKvPg8yNc47h4WEaHrYAmqpiw6pOZPMlpPOUiM3XROn6LCUNxBUn+rLwtXTC8FcyL2LqORiKJacN4TGCYdz0l3+GrlUrEV//Svh8PqxYsQL+gB9nbzq7ipETQkS5bF0bQg1a7U1VVSQTSdFhNARXb73Zto2Pfexj+OAHP4gNGzZMe3zfvn2wLAsXXXTRlO2apmHjxo3Ys2dPeduePXuwZs2aKQkbAFxyySUAJoY4dnd3VxRXT0/PGR/v6+ur6DhkKtM0MTo2So3hBYgE/IgFdYwWbRpetwCO46BQoAS2mhzHQSpNdzsLBQupjISW9hZIsjwtqZq83p26ffIKyE7bXv4vxhBhDD4DOO+KK3Fsz8PQxsagaRoYGC6/5HKaf0FInTtTx3m9Y4whHo/jyNEjdPN9kVxNxL773e/iyJEjuP/++2d8fDLh6ejomPZYR0cHHnrooSn7zrYfABw/frziuCpN2Mj8FAoFjI+Niw6j7jAGLOtuQzgUxL4X07BpIed5sywLBSqlW1X5Yh4/+6+fiQ5DuL5jw9D8S6Bo2pTt1bjhpIMh1h6FLxKfGAZ6MoX7ze7f4JrXXEPJWMU4lfsnNYcBWNfVgsf2D4oOxRWRlgh8Ph/SaVquaDFcG5o4PDyMz33uc7j11luRTM7cfZk/uTK3rk+f0GgYRvnxyX1n2+/UYxFxRkaGUSxRr8R8McawvCOBcNCH4fEsHErE5s2yLBSKdO4txGyN16ERMdVP/T4//L4ZimAIkkrbUDQdkjS1N8ws5mEWC2CMTe8lcxxwe/a7xC2RIDRVhQogEQ5iy3s/BFk3sGnrJsiK7NZTaVjf+c5353UzlhAvMMawtqsxKycyxuD3+dESaREdSt1zrUfss5/9LGKxGD72sY/Nuo/PN7GOSnGGBlShUCg/PrnvbPudeqxKzDWfrK+vrzzkkVTGcRwMDjbmXR+36aqKtmgAJQ4Ui9TFP1+cc5RKJZglU3QodUmWZk7E/vOB/4R1hmTCDaqi4k+u/xNomoaB4QH85vHfYGB4oPw459zT5DCbdaAYYSiqPq0a4owVE0/ijgPu2JDkmb9it1xyPhLxFpiYeP37Dh9HMV+AZlBJ6FNt374df/d3fzfnfiMjIzQ8itSkRv44K4qCRCKBYz1Uo2ExXEnEDhw4gDvvvBNf//rXp9ylKhQKME0Thw8fRjgcLg8rnGlOVl9fHzo7O8u/d3R0oLe3d8b9AEzZdy5dXV0V70sqk8/nMT5OwxIXIhT0Y83yJTg6UEDBpMbEQhSLRZRMWntpvhhjEws6nyaVSSGV8W5+WFuiDevXrMfVl1+NUCgEiU3E9IoLXjGlgT04Moif/b+Xh0uOZ8bRe2L690K15AsOOFPBZklWZyMrCgAFlmkC4FBUbcb9GAAbgD8UQTDZCU694WWMMbzmNa+pKBEjpFYtTQRw3rIonjrSmMv6JBIJ6JpOo6EWwZVErLe3F47jYMeOHdixY8e0x1esWIG//Mu/xP/4H/8DiqLgiSeewNvf/vby46VSCXv37p2ybePGjXjggQeQSqWmFOx4/PHHy48TMTjnGB8fR75AZesXIujTcdayJPYcSsE0qVDHQhSLRTiOIzqMusOAGROxg0cOur5+2LWvvRat8VYAwPIly9HZNv1mWtAfnPJ7S7gFn7jxE+Xfh0aGsP/w/in7/OcD/4nR8ZcbPY7jLLhnb3ywD2YxNOttbduyphXwOLV4B+fOGWtXWwAccHDuYOO178ChR38CYOJOMyGk/imyBFVp3PW2fD4fIpEwBmhE1IK5crVfv349fvrTn07b/tnPfhbpdBrf+MY3sGrVKkQiEVx55ZX40Y9+hFtvvRWh0ERp4LvvvhuZTGbKos5ve9vb8JWvfAV33nlneR2xYrGIu+66C5s3b6YCHAJxzieGhlBvzrzJkoRESxiGpiJVsOBQxckFyWQzVK1zAXyGhtVdiy9BrMgKOto6cOx45UNUNqzZgFXLVi3q7yZiCSRiiSnbNp27CQ5/OSnv7evFfz36XwCA/Yf3z78SJOfgsyT5jDHk0+Pwh1umbDeLBWiGD6p25gVdixzIlhzk0hl0LF+KQu/EKJF3vuGdNESRkAbRGfVhr8QashCXLMtIJlsxOCRmTnEjcCURSyQSuP7666dtn1xL7NTHbrvtNmzZsgWXX345brrpJvT09OCrX/0qrrrqKmzbtq283+bNm7F9+3bccsstGBgYwOrVq/GDH/wAhw8fxve+9z03ngapkGVZGB4ZLlf8IpVTFBnnrOpGOm9jmBZyXrB8Lic6hLoUCwfw2ovPmbLNsiw88OgD8zoOB4djV94jqSgKJNmdu8SGbkz5/awVZ+GsFWcBAL5x1zfwdPrpio5jmjZO9I7BaPGD27MsbM0YGJMw2t+LltZOcMeBbVnQjNnnLDPGoKkqHAAFAIW8hYEjBxHduKb8mmjlRaMJIfVu06o47n+qD/lS4414YYwhGo1C07QZ6ziQuQnvL920aRPuv/9++Hw+fOITn8Cdd96JG2+8Ebt27Zq27w9/+EN8/OMfx913340dO3bANE387Gc/w2WXXSYgcjIpl8vR/LAFUhUFG1YvQaFoYTxDlT8XwnEcZLNZ0WE0DIc7OHr86Lz+jW3b6O2vfK7W1gu3YvmS5fOMbPHOXnl2xQkOA6CoCmyzBA7nZK/Y1IWBJEmCLxRGS3KiJ4tJ0rQy96eLxyJ4/Ws2owRgxOEYHcyh54WnkB4ZBjAxX67ltB42QgipRYwxRCIR+P21U+m23ng6EP3BBx+ccfvWrVvx8MMPz/nvDcPAzp07sXPnzipHRhbq5WGJVLFuIYJ+A4mwjnHTgWk13t0yL1iWRXfi6kzJLGFkbAQAEAlHoMxSXbDarnzllSgUC3j4iYcxlh47476KKiPZHkFvbw62ZUHV9fKcsNM7/9kM8+xmEwr4wSUJQxwYzQIDR3tRzKVx4rkHIDOG8885Hx2t09fMJLPjnONrX/s6vv71r4kOhZBpZInhVWvb8Os/NuYSC7quIx6LY3S0MQuSuE14jxipb47jYGhoiAYlLlBXewLJWAsOn8jDtulVXIhisQiTbgRUjSzJWL9mvat/45EnH8EtO2/BLTtvwf0P3+/q3zqVoih40+vehM/t+BxWL1895/7xjm6ouoHWpauw5uJXYrj3CHKpMTBJxqk9Y/PxtjddiXEAgyVgqL+EA394GOsu3QxVtuHTfbjuddct6LiNqL29Deeff15F+x44cMDlaAhZGIkxJMJnni9azxhjaG1tpeHUC0SJGFmUfD6P8RQNS1wIxoAlbTHEW4IYHEvDpqp/C5LP5+m1W6CZvjdlWcaFGy50/W9Prgn2H/f9B37121/Btr3pEWaMIRQM4cPv/jD+/L1/js7WTsRb4jPuK6sarGIBqZFB9B94Gpw75UWcJVkGWOVfoYxJkGQFaUlCnw0MDpVw9Nnn0PP8UygVJuagtYRbyqX7ycRSM5s3bxYdBiGLpqsy9AaunhgOhxHwB0SHUZeoRi5ZMM45xlPjyOdobtNCaIqCtpYAuCQhTws5LwjnHPl8nkrXL5A8S6OfLbC3ZyFMy8SuX+zCyNhIudCGqqrYdtk2yLL8ckxVvtsaCUWwcd1GbFy3EcNjw3h87+PT9nnggUfwfCCI4d7DePU1V+P4kcPQDD+440CSZciyDM6liQWcwU8OWTylZ5sxsJMFPcAYSrksegsOAmNAz4HjePaR+yFJJg4/9Tt0d8i47h3vh6qqVX2ehBDxVrYFsaojhGePNeaNa13XEYvHkMlmRIdSdygRIwvGOcfI8AhMi4aFLYTP0HHOqm4cHSqgUKJEbCE458hms1Q2d4Fmq1y47qx1OHfNuXhm/zOexTJZYh6YSLp+98TvyglhS7gFb9321vLjfp9/xnXH5msyuUtEE7j2NddOe/ylp1/CRds68Idf/z/8+/e/B1lRy/9OYfrJNcSkk0MV+WlrhrGTidjEfxdzaeTSQ3j05w9g9aZX4YXHHsThfU9gyeoVGO/LgEn0dUxIo2r0YXuyLCMej6O3t9ez0Q2Ngq78ZMFs28bwyUpfZP78Ph1rV7bjpf4cipSILQjnHMVikRKxBZJnKTKhazouu/gyPH/wediO91+qnHMMj758bRkaHcLf3fF35d+jkSjOX3v+lH/z2ktfi2T85TXRGNiUHrWF+sN//hznXXEtnvjV/4WqGeDcgSRJaGnrgCwryIyNTCyKzSQwBjgOP3k+njwnOSYWdgbDK193JZYu68K+/S/g+d2/AZMl+MNBpI70Q9VWLjpWQkjtOn95DC/0phpyPTHGGOKxOHRdR46Wk5kXSsTm0NXWglgyVvXjVvYxrO6Htdof/Wwmi+7WKNqjNC54viRJwrLOVsSDOvpSDrraWmgdtgUwSybG2mPwKy8nC/QqVm55R2LWx9adtQ5dHV040nvEw4gqMzo+igcfe3DKtt1/3A1Zejnxaku04ZpXX1P+vSXSgu6O7nn/rSPPPIm1W69EMJZAamgAxVwWHavWoW352Yi2dSEUa4EvqMIISlAU4PAzB3DshUNIDQ0iNz4G7jiwLBO6PwiJSxjN5rH75/fAKuVxwZWvw6+++1WsPLsD4WAYkWBkwa9JsxsbG0NPTw+6urpEh0LIjFa3h7CsIwqrARMxAHBaIyimhzEyOtMN+ur0CM7vKNXthcylRvBiVY84gRKxObx2fRs6l8xjCEwNf76qHZptW9h2fluVj9ocGGOIBHzwqQrOafdjWdyP5u7UWdiT55zjqvVJWDb1KC6E35h9PpKhG7hiyxW4a9ddddHjmMtPvQubzqbxzR98s/x7IprAqmWrpuzzxiveiEgoAl3TZx06ZBULeO7hX+HSN16P3/7bf6CQTePIs0/CLObRtnwNou3diLZ1QjN0yLKMaNtaRNvXYuDIIYwPnEA+M46Dex5BMBrH7x97Av2H98OxLazf+noc2L0bYwMngLM7sGrpqooqOZKZvfDCC7jvvvvw/ve/X3QohMxIVSS88aIulMzGndN85bnxGqpiXN1E7ETfcfzn399c1WMClIjN6fP/373wh6OuHX/m5o2LjZ55Hnq23RVZxvr169Ha2rrokJqRqsh49To/njs6gK/88NfIzFLwpA7av1Ww8CcZCoawadMm6Mb8SgN7/7LW5hv55ouWnHHuwsXnX4zfPfE77D+038Oo3DE0OoSh0aEp2/Y8sweyJOPNr38z/L6XFyRNxpJYuXRlOQE98tSTCEYTeP373o+Hdv0flPITizAPHDmISGsnoq2d8IUiMIJh6L4AuGMjlx6Hmc8hNTKIUj6Hl/Y+BsssQZJlXHLNO9H/4tPY9//+vVk+5IQ0PYkx7Hv2AL696zeiQ1m02b41QuEwLty0Caqqzb2zSzG4ZXQw7cpxKRGbwwuHe6H63Xnx61koGEJbdwFFNGYFILeFAzpM28ELPSN49uBRZPIF0SHVpWQiiY7lGSgKvX4LUbLaz/i4Iit467a34vE/Po4HH3uw4apTlswSAOAn//GTKdujkSha/C341x/868QGznHgsd+ibelKvO76a/H//u8vYdsOzEIOQz2HMHriGFTdD0mWoKgauOPAtkpwLBulYgHcscG5A03XsPWt29H/Yi8e/dfvwbEtcM5hlkzoeuOuM7QY733ve3Hvvf+CsbEx0aEQsmhj6SyeffGY6DBcYxgGjHAC8fjsw97rVWo45cpxKREjCxIKheDz++fekcwoGvIhGjaw59mXULJoWN1CGYY+USiBLEwFnTErl67E8q7lOHj4II4eP+p+TDXg8KHD+N0vfofUSKpc8MMsFvDQPd/D6NbXYtNlr8HY6CBefOoZMADctlHKZ8EYQ4lNVrGffHEn/n/NhRcg0bkU+x/5HR79P3fDPmU47f49+9H2FzTMeyZr1qyBpmlz70hIHVixJIm2WBj9I+406kUrFosYGhpGNBqj7+YK0atE5o2BIRaLQVEoj1+oiE9FSAMO9fTDsqjU60IFAoGGLwtcCyRJwvVXXQ+/0fg3XzjnOH7oODJjGXDOp/QCcsfBMw8/gN/c+wP4ggbe9JH3I9railhrEqrhO/nvAXBA1Q20JBOIt3fgTX9+IxRVwd7/uh+P/duP4Th2edgj5xyyLONf7v0XqjZGSIO74OylWLGk8XqLJnHOMTw8TCXs54Fa0mTeZFlGa2uSGsALJMsSAj4N/WMFjKXzcGiOyIL5/ZSILco8Xrr1a9bjz979Z3hs72N49MlH3YtJMLNo4pnfPwNJksA5n3Z+ccfB8LEX8es7v41I+1JsfeOb0bFyGfqHHDiWCXbyLrCiaWgJWxg+dgJ3f/7zSPf3wCoVphQ+mUz0OOd4+OGH8dvf/hbbtm3z9PkSQkg1jY6Oolgs0OL0FaJEjMxbMBhEIBAUHUbd0hQZS9vCeP7QEaQydAd8oSQmIRCgpRMWijE2r8nOjDGsO2sdVi9bjVAghPsfvr/h5owBQC6bm3htGIMkSSgUZm5QWMU8Ro7ux8+++1Wohg/+cAy+UARLzr0Y/Qf+iOzoENKjw7BLeTi2PWPlyclET1VVSJKEEydOwHEcGtKzQN/85rdw/fXXIxp1r8AWIYv1l+96HXY/fWfD3oQtlooYHhlBMBiE9yU16g8lYmTeEok4DUtcBFWR0BmR8djjg8hkZ66WSOYmyzIVOFgEiQEL6UzUNA1v3fZWxFvieOTJR2pynbHFePaJZwFMDMecTMYsy5r17i53bJRyGZj5LMb7j+HEwaenPn5aY+v03x3Hga5PzHX81re+hXe+850wDKOKz6h5nDhxgoZEkZrXHg9P5CeNmYeBc47BgQF0d3VDkigRmwvddiPzoigKYrE4DQdbIAbA79NhmQ56R7IwqdGwYLqh09CHRZj4DC/scyxJEl675bX4y/f/JW56100I+BunZ3KyN2wyCdM0DZzzqq+lxjlHPp+H3++Hruvlv0cIaWyaoqC7NSY6DFeNjY3RnNcK0VWfzIthGIhEwpSILRCTGJYkQ8jlC3jxaH9dLJRbqwzDKFe0I/O3LBlANLC4anShQAgXn3cxPvj2D+IVF7wCiqxAYpV/rShy7fWsn5qESZJUTpIKhZfnd1Xj+jd5vMleXUmSUCwW8fOf/3zRx240NAKDNJJYJIB3vv4S0WG4KpfPY3x8nNo4FaCrG5mXcDgMP83LWTBJYlgSUZErFHCsb4AuUovg9/koEVuEZFhHwKjOV8D6s9djzco1ePu1b8fTLzyNJ/Y9AQDI5rN48ciL0/aPR+NYu2ot3vL6t+Df7vs3jKXGAEz0Ej174FnYjrie4skkjDGGUCgEVVVh2zZKpRJyuVxV5iVms1k4joNwOIxIJFJO/izLwpNPPom3vvWtVXgmjUGWZfzN33wJH/jAjaJDIYRUyDRNDA8PoaOjg27cz4ESMTIviXgcskSN34XSFBk+VcHxwTHkCkXR4dQ1n89PQ7lqiKZq0FQNl266FJduuhQAkM6m8dzB56bt25Zow7IlywAAf/LmPylvdxwHe5/dC+uUNbaeO/hcObGbVCi6t4A3w0RSFAgE4PP5ygkZYwzpdBrpdBp+v3/K2laVNjQcxyknYYFAAJFIBIqiTKnOWCqVXHle9YoxdnLSPyGN47JNa/DjXzyGnoFR0aG4ZmhoGJZl0TqAc6BEjFRMVRQkElS2fjHCQQPJqB//+ZuXUDJpIeeFkiQJhmHQuVjjQoEQLjm/8iE4kiRh0/pNU7ZtWr8J73zjO8u/O46Dn/76p8gXXi500z/Uj0PHDi0+YEw0/BVFKZ9fkiTB7/eXe6/T6TTGx8cRDAYhy3JFhTUsy0KxWEQmk4GmaQiHw+VET5IkOI5T7hVrxEqUhJCpVi5JIhL0NXQils6kkc6kEY/FRYdS0ygRIxULhULw+xt/QVc3hQ0FMb+Eg0f7YFqUiC2UJEnw+XyiwyAeUGRl2lyyd1/37im/j4yN4PjA8fLvf9j3B+x7YV/5d845UplURX9PkiSEw2EoilIeoqhpGvL5PDjniEQiME0T6XQajuOUJ6QbhjEtKUun07BPlq6fHIp4amGOU4vNMMagairOOf+cyl4YMo1lWfj7v/8OPve5W0WHQsicbrz+Vfir2/9ZdBiusSwLQ4ODiEVjdNP0DCgRIxWLxxM0aXoRZIkhYGgYzZQwmsrBcWh+2ELJslweNkZIrCWGWMvLVcjWrV43Zf6laZn4v//v/8KyLPSc6MH+Q/tnPRYHL6/rBZxM4lKpcnGNYDAI0zRhGAZyuRwsy4KiKEilUkilpiZ7k0ssqKoKv99frsI4Pj5eTuzC4TAkSYJt2zB0A+9523uq/Oo0D845nn9++lBYQmrRmqXtokNw3dDQMFasMGl44hlQq5pUZKJsfZTm5CyCIsvobovgwJETGB3PiA6nrqknh46RhWvkJPb065Qsy9h+zXYAwHh6HEOjQ+XHdv9xN55+4eW1vyQmweIWHMdBsVhEsViEdbL3enLu2KRUKlUubT9TQ2OyN02WZQSDwfJrPpmwFYtFjIyMwOfzQdM0pNPphn5fCCEvUxUJkaAP45nGXU80nU4hm81CVVW6ts2CEjFSEcMw0NISFR1GXVMUCd0xBQ88P4DxdFZ0OHVNVTW6w7ZIUpN+KUZCEURCkfLvK7tXAm94+fF1HevwhS98AV1dXXjxxRfL87eAlxOryZEBkiQhn89DUZRpowUYYzAMA5ZlldcKAyaKcZzaILEsCy0tLUin0+WEjxDS+LrbYnj3tlfgO7seEB2Ka/L5AsbGRtHS0iI6lJpFiRipSDgUpqFgi+TTVcAG+kZzKFKhjkUxfAb1zi4Sk+izDEzvGbzuuuvw93//9yiVSrj++uvBGENfXx8OHTqEw4cPT1lLzDAM6LoOSZJmLGsvy/KUQhyT1RFlWcaGDRuwZMkShMNhOI6DX/7yl/hv/+2/lRM28rLNmzdj27Zt+OUvfyk6FEKqZuK6IDoKdzncweDgIJYtWwbGqOL2TKglQyqSSCZozaZFYIyhIxGCZVl4/tBxOJwqoy1GIBCgmwKLwNC8PWJzCYVC+OhHP4pjx44hnU5DlmUcOXIE8XgcS5cunVZgY7Kgx2SvmKqq5f8+9fFT/41hGDBNE/v27YPjONi9ezeSySRe+9rX0g2GGUyU+g+LDoOQqnvl+asRDTV2EbSRkREUi7Qsx2zoik/mpKoq4nEqP7oYEgM6Iipsq4iXjvaBU6GORQnSouKL4tMVvPKcpOgwatab3vQmbNiwAQ8//DAymQxe//rX45JLLsEVV1wB4Mzz6+ZapH1oaAiXXnoptm7dinA4jAcffBDHjh3D6173Oqxevbqqz6MZjYyMYnBwUHQYhFTkgrOXIhxs7ArAhZNzYee6NjYrSsTInMLhMPy+xr5j4zb15ELOA+MFZPMF0OVo4aTTCiaQ+ZMYYKjUwz2bQCCAD3zgAygUCnjsscdw7Ngx2LYNWZYxMjKyqGOvWrUKZ599NgqFAgqFAgYHB7H0rKX4xF99okrRN7cnnngCv/3tb0WHQUjFFLmxm+K2bWNoeIgSsVk09rtPFo2BIR6LQ9PUuXcmswr4NHQkgnjyucO0kPMiybJMhTqI69785jfjs5/7LEbGRvDQQw/hkUceQalUwiWXVL5A9elM08SaNWvQ39+Phx56CIcPH0Y4HMYXbvkCgoFgFaMnhNSLz9z4hrl3qnOjIyMoFAqiw6hJVKyDnJGsyIhGo2CMcvbF8OsKWoMy9h86Tgs5L5Ku65SIEddJkoQ3Xf8m6LqOWz9zKw4dOoRMJoOVK1ciFotheHh43vMUJ9a5eh69vb3IZrNQFAW33347Xve617n0LAghtYwxBr/e+N9n6XQa2WyGir7NgBIxcka6riMWo1XRF0NiDEGfjkzBwlAqA8umQh2LoRsGFIXWJCHua0u04U/e8yeItcTwV3/1V+jt7cXY2Bj8fj8Mw6h4gXvHcWBZFvL5PEZHR5HL5XD11Vfjne98JyVhhDS5WCSAVV1JvNjTuHMbS6aJoaEhJBI0N/l0lIiRM4pEIrRw7iLJsoSu1jCO9A5haCQtOpy6Z+gGVfBcJMphKydJEt7whjdA1mR8/Vtfx/HDxzE6OgpZluH3+6EoCiRJgmG8vKSCbdsAgEKhAM45SqUScrkcLMtCLBbD9u3bceutt9Jcx3kIhyPlJQAIaSTdbTFsOW91QydiADA4OIizzlpT8Q2sZkGvBjmj1tZW6nlYJEWWsDSuYt/Tgxgeo0RssXx+HyVii0Sf6flhjOHqq67G6698PV588UX88Ic/xI9//GOMj4+XX8vJ0vWccziOg1Lp5XLNnHP4fD786Z/+KT7wgQ9g6dKlVKZ+nj7zmU/jnnvuQTpN11BC6lE6nUEmm0FLpEV0KDWFEjEyK13XEaXV0BdN1xSoYOgbL6BQorU0FoMxBkM3KJFYJHr95m9yIeY1a9bgi1/8It7xjncglUrhc5/7HKwzzPu84YYbcOGFF0JRFJx77rn02i/QfBLXr3/9G7jmmmvg8zV2WXDSOP7k2kvxfx/6I8bSOdGhuKZkljA8PIzIyd5tMoESMTKrcCgMv58Wzl0MBqAtFoQkAc8e7IHt0PywxZAkCYEALaWwWLSY8+JIkoQNGzaAc477779/zv3pGuqtY8eOwaFrLakjHYlIw5exdxwHI8PDWLZ0KRSFKnFPaux3nSwYA0M02kLV6RaJMYa2sAaZm3jhUA8cWsh5USRJgkFVlxYtGtBAr+DiMcYq+iGEkDNhYOhqi4kOw3Ujo6MoFIo01/MUlIiRGUmyhHg8QY2IRZIVCX5NxkimhHQ2TxefRVJkBX4abrRoW9e2QpLos00IIbVAliV8dPtrRIfhulw2h/HxcdFh1BRKxMiMDMNALBalRGyRfJqKzkQYe184hmLJFB1O3VNVBapKvbSEEEJIvXG4g/7+ftFh1BRKxMiMWiIt0DRddBh1z9BkdLQoeO6lHpRMSsQWy/D5IDf4OHpCyMxUleaVkMZ1VncbLjh7qegwXDcyOjKlqmyzoxYNmYYxhtbWJPWGLZLEGAKGhpJpY3A8A9OyRYdU94LBIBijyxYhzcbn8+ELX/i86DAIcU1bPIyl7Y0/Tyyfy2F0dJSmapxELRoyja7riNA6D4smSQxdrREcHxhD/xCNia6GgJ8qJhLSjBhjFZejz+fz+P73v+9uQIS4wKc3/tB707IwOjpCidhJlIiRacLhMILBAKhDbHFkScKyhIrj/cM4MTgqOpy6JzEJgUBAdBiEkBpn2zaefvoZ0WEQMm9/9Z6rEPA19rQQzjkGh4Zg0nQNAJSIkRm0RFpOFkSgTGwxNE2GLknoTxWQLxRFh1P3ZFmi5RSqgDHQTRZCCKlBsiw1RctrfGwc+XxedBg1gRIxMoUsy0gmaX5YNSRaAjA0GfsOHINFi4sumqbrVECmChhYU3zRE0JIvVEVCa88f7XoMFxXMksYGh4SHUZNoESMTKHrOlpaIpSILRIDkAyp8Ckczx44CsemRGyxNE2Dqqp0bi4SY6AuMUIIqUGqouCqS88VHYYnBgcGYNtUxIwSMTJFPBajXocqkGUJPlVBOm9hPJODQ5NSF83QDSiKIjqMutfW4kM8SJ9xQgipTc1xoyyVSiObzTZ90Q5KxEiZJEk0LLFKdFXBktYInjp4HPkirZdRDYahQ1Fk0WHUvWRYRzRIc+1I/YnFYmhpaalo3+HhIYyNjbkaDyFuuHzTGrxiw0rRYbguX8hjdJQKmVEiRsp0XUc4HBEdRkPQVBndUQXPvXgUxSJVBlosxhgMw0driBHSxC6//HK84hWbK9r3v/7rATzxxBMuR0RI9QV8elOUsbdtGyMjw3CafA49tWpIWSgYPLlgLvWILQZjgE9XwR2OgbEMShYlYovFGEMgSKXrCSGENL41S9uaoi02OEhl7CkRI2Ut0SiVB68CiTF0JsMYHM2gd2AMTT78uSokxuD3+Zrii4kQQkhz+9M3bIEiN34TPZfLIp1OiQ5DqMZ/l0lFJElCW2tz3IFxmyRJWJbQMTA8ip6+QdHhNARZluH3U49YNdAnnBBCSC2wHQcnTvQ3dcEOSsQIAMDn8yEUCokOoyFoqgy/LGE4U0ImVxAdTkNQFAWqqooOoyEwiVIxQgipZQGfjre/7mLRYXhidHQEpVLzFjVzJRF78MEHwRib8eexxx6bsu8jjzyCrVu3wu/3o729HTt27EAmk5l2zGKxiJtvvhmdnZ3w+XzYvHkz7rvvPjfCb0qJeJyGJVZJS8iHUEDDU/uPwaI1MqrC7/dDlqliYjVQrzchhNQ2TVVwVner6DA8kc5kkEqnmrZXzNVFeXbs2IGLL56a0a9e/fKK4Xv37sUVV1yBtWvX4vbbb0dPTw++8pWv4MCBA/jFL34x5d+9733vw65du/Dxj38cZ511Fr7//e/jmmuuwQMPPICtW7e6+TQaniRJiEZjkCTqIK2GWEBFxGD44/OHYNNCzlXh9wcogagSeh1JPYvF4hXvOzIyAs45nfOkLgV8OjRVQcm0RIfiqlKxiPGxMSTiCdGhCOFqIvaqV70Kb3vb22Z9/NOf/jSi0SgefPBBhMNhAMDy5cvxoQ99CL/+9a9x1VVXAQB2796Ne+65Bzt37sQnP/lJAMANN9yA9evX41Of+hQeeeQRN59GwzN0HbFYVHQYDUGWGPy6gkLJwVgqC7vJy7JWSyBAiVi10O0WUs8+97lbsWvXroqGMn3hC/8Db37zm2lYM6lL1249D/f8ejf+8NwR0aG4yuEc/f0DWL58BRTF1bSkJrn+nZxOp2FZ07P5VCqF++67D+9973vLSRgwkWAFg0Hce++95W27du2CLMu46aabytsMw8CNN96IRx99FMeOHZtXTD09PWf86evrW8AzrV+BQBDBYIgaulWgKDI6k2E8e7gf2ULzjnmuJsYY/AG/6DAagipLeOU5zTHchTSm+XxPNetQJ9IYGGNgTVJeaWxsFMViUXQYQriaer7//e9HJpOBLMt41atehZ07d+Kiiy4CAOzbtw+WZZV/n6RpGjZu3Ig9e/aUt+3Zswdr1qyZkrABwCWXXAJgYohjd3d3xXHNZ99mEIvHmvIuhBtUWcLyhI5dTxxFLk+FOqpBlmVoKs1frAbGgJCPPuuEEFIPtm1ZjyeeOyw6DNeVSiUMDw/D7/c3XaeAKz1imqbhrW99K77xjW/g3//93/E3f/M32LdvH171qleVE6zJXqeOjo5p/76jowPHjx8v/97X1zfrfgCm7EvmR5IktCZbm+7EdwMDYOgqFHAMjqVRLDX3IoXVoqkaDMMQHQYhhBDiqVdfeHZTtM8czjEw0A/Hab4CZ67cGt2yZQu2bNlS/v26667D2972Npx33nm45ZZb8Mtf/hL5fB4AoOv6tH9vGEb5cQDI5/Oz7jf5+HzMNZSxr6+v3NvW6AJ+P0KhoOgwGgJjDO3xMMYzRRzpG6NhMVWiaSo0TW2KLyNCCCFkEmMMssRg2Y3fnhgfTyGfLzTdnHDPxqisXr0ab3rTm/B//s//gW3b8Pl8ADDjmNBCoVB+HJhY42q2/SYfn4+urq557d/I4vE4VBr2VRWSxLAsqWMslcJLPX1o/MumN3Rdp3OUEEJI02mLh/GhN1+G7+x6UHQorstkJ8rYBwIB0aF4ytMCWt3d3SiVSshms+VhhTMVxujr60NnZ2f5946Ojln3AzBlX1I5WZIQi8VofaYqURQJfkXCWK6EVCYnOpyGoaoazWGsEsbQJFO/CZkoCvYv//IvosMgZMFkSYLfaI4bkZZlYWR4WHQYnvM0EXvppZdgGAaCwSDWr18PRVHwxBNPTNmnVCph79692LhxY3nbxo0bsX//fqRSqSn7Pv744+XHyfxpuo5oNCY6jIYRDhiIh/146sBxmFbzjXN2SyDQfJN33SJRJkbqnK7rFX/nF4tFPPXUU+4GRIjLVnW1IhxojnnS/QMDM1Zab2SuJGKDg4PTtv3xj3/Ef/zHf+Cqq66CJEmIRCK48sor8aMf/QjpdLq83913341MJoPt27eXt73tbW+Dbdu48847y9uKxSLuuusubN68maogLlDAH0AwGKRGbpVEfCoSQRl7nn0Rtk2JWDUwxppuvLir6HUkdS4YDOId73i76DAI8cxrLjoHHYkW0WF4IpvNnswJmmdyhyvjfd7xjnfA5/Nhy5YtaG1txbPPPos777wTfr8f//N//s/yfrfddhu2bNmCyy+/HDfddBN6enrw1a9+FVdddRW2bdtW3m/z5s3Yvn07brnlFgwMDGD16tX4wQ9+gMOHD+N73/ueG0+hKSSTSRqWWCUSY/BpMiybY2g0DcumhZyrgTEGv7+5xou7SaJEjBBCSI2yLAsDgwOIRqNNc9/QlR6x66+/HkNDQ7j99tvx53/+5/jnf/5nvOUtb8ETTzyBtWvXlvfbtGkT7r//fvh8PnziE5/AnXfeiRtvvBG7du2adswf/vCH+PjHP467774bO3bsgGma+NnPfobLLrvMjafQ8GRZRjKZEB1Gw5BlCUtaIzjYM4x0bn5VPMnsZEmC30+LOVdLwFCaZoFQQghpFH++/TWiQ/AE5xwjwyMwzeZZ/seVHrEdO3Zgx44dFe27detWPPzww3PuZxgGdu7ciZ07dy42PIKJ4R2BAJWtrxZFlrCy1cADj7yAdJYSsWpRFBWqqooOo2Fcvq4VikyJGCGE1JPlHXHRIXhmfHwcuVwOqtocy9Z4WqyD1I5oNApN05riJPeCrirQGTA0lkGh2Dx3ctzm9/to+GyV0WeeNJPh4WFks1nRYRCyKLqmIh5pjmH6+UIe4+NjosPwDCViTUiSJMSiUWrgVgljDG3xEPIlGy8eH4Xt0PywavH7/ZAkukwRQhbmpz/9Nzz77LOiwyBkUZZ1xPGW114oOgxPcM7R3z8AzpujYAe1cJqQrumIxxN0Z7xKJAZ0xXXk81nsP9QDNMnFwwtUMZEQcrq2tnaEw2HRYRBCXDIyOoJisSg6DE9QItaEAsFA061c7iZZlhBQJWSKJkbG001UdNVdjDEYho8SsSqiV5I0gm3bXo9zzz1XdBiEeOqKS9aiJdQcxauKxSJGR0eaoleMErEm1NraSsO9qijg05BsCeLpF/tRMmn9sGqRZRmGoYsOo7FQUksIIXVpw6ouBHzN8Z1oWRYGB4fgNMFUD2qNNxlFURCPxUSH0VCChoKOFhV/ePogLIcSsWpRFAW6YYgOo2EwRj1ihBBSzwyteaoIj46OwjRLosNwHSViTSYUCiEYDIkOo2EwBhiaAsaBE8NjsC1KxKpFUzUYukFDE6uJXkpCCKlLjAGfvfFa0WF4Znx8DJlMtuGHJ1Ii1mRaIhHouk6N2yqRJQmdiQiODIxjPJOn+WFVpKoqNE0THUbDUGQJqkyXfNJ8xsfHG74xRxofYwyq0jzVri3bxtDQkOgwXEffyk1EkiTE4wmaH1ZFsiRhdbuB5148ipHxjOhwGoqua1AUV9acb0orWoM4u5MqzZHm85nPfEZ0CIRURWssjDVL20SH4ZmBgQHYdmOPNKIWeRPRVBXxeIx6w6pIU2X4ZYbhsQzy+eYoteoVv99P52oVMdBizqQ52XbjT/gnzaG7LYZN5ywTHYZnMtkMMplMQ/doUyLWREKhMHy+5ih96gUGIBENwubAwd5RWE1Q3cdLgUBQdAiEkBp1zTVXiw6BEOKyYrGIkZFh0WG4ihKxJkJl66uMMSyJ6eBWCc8cONrQd2y8xhhDMEiLORNCZnbttW+g6wNpSje95TJEgj7RYXjCcRwMDw839PBEapU3CVVVEY1GRYfRUGSZwa9IKFgm+kfGKBGrIkmSqPeWEEIIOU08EoQkNc9NiKHhYRSLjTv1gxKxJhEKBhEK0VCvavJpKtpiITx7eBgl0xIdTkNRFRWq2jzrpXiBOg8IIaT+SYxhVVer6DA8k8/nMT4+JjoM11Ai1iQikQh0WpOpqnyajO64jj/sOwCL1g+rKp/PB1lunjK9nqDPPmlSQ0ND+PnP/1N0GIRUhaLIuPG6raLD8AznHAMDA+C8MefhUyLWBCQmIZmk+WHVNLGQswyFAT0DI7AaePyyCH4/JWLVJlEiRppUJpPBU089JToMQsgCjYyMolBozOGJtEjPHBhjdd+LpGoT88M45zSPqUoUWUJbLIITI1mMZQoAqDR4Nfn9E/PDHKpEWUX02SeEkEZw7qoluHDtcjz5/BHRoXgiX8hjPDUOXdeFxeC41CNHidgczlpzFvzhuOgwJiywHVUqFXH48OGqhtLsdE3FtouWYGAsh1iiHevURph/VxsNdc4Bx7HxwgsviA6lYUgSw2vWvVJ0GIRUTTAYwMaNG7Fnzx7RoRDiuWQ0hIvOX4cCC4gOxROMAZl0BsNDw8I6FPKpEVeOS4nYHFatWIVwvH5XMeec49lnn8H+A/upN6yKIsEADKcIrgaxpHspku00NLFaLMvC3r17cPTYMdGhNAxVkXHhqutFh0FI1cRiMbzxjW+gRIw0rZXLujFUMtAsTbvBwUE89tijMC0xxdHMXMqV41IiNod6H5pYKpUwOkal1autIxlFKODH0bQD2+F1fY7UGtu2kc/nRYdBCCGE1Kyrzu/EnkMjMK3mGMIfjUbh8/lgptOiQ6kqqt7QwDjnyGazSKca66QVjTGGVd3tiISDONSfoSS3ijjnsG0L+UJBdCiEkAYyMjLS0GsRkebDGNBMt4BlWUYimRQdRtVRItbgxsfHUChSo7aaZElCW9QHnypheDTbNMMCvFIqmShSIkYIqaLvf//7OHTosOgwCKkaWWI4e0lYdBieYYwhmUg2XEVlSsQaGOcc/QMD1GNTZYauYXlXO8ZMDpPK1ledaZq0HAAhhBByBoos4ezOiOgwPBUOhxHwN1aBEkrEGlipVMLY6KjoMBqO36dj4zkrcPB4FrbdHGOzvZTNZunmQZXRGmKEENJ4munSzhiD3+9HONxYvYCUiDUozvnJMfEl0aE0FMYYgoaOrpgfQ2MZWLTOVdVlMhnRITQcKiZDCCGNZ11XC1a2NcLyOZWRZRnJZLKhvtMoEWtQk4mYTUO8qkqSGJYvaYMJhlzJpvlhLshms6JDaDis2WZ1k6bwute9DkuWLBEdBiHCqIoEWWqeiztjDMlkAorSOEXfKRFrUKVSCSOjI+A1skhvo5CZhM0bV2MoVcJQiipwVZvDHeRyOdFhNJwGunlISNnatWvR0tIiOgxChOqKN9acqbn4fP6G+txTItaAOOfI5/NIjbuz+Fwz01QFy5Ih5AolZPOUiFWbWTJhWqboMBrOm1+9CUGfLjoMQoTKZmnYM2k8l5yVQBN1ikGWZbQmkw0zyIMSsQY1OjqKYokShWpLxiKIhYNIFTkV6nBBPp+HZVmiw2g4q7qT0NTGGcpByEL89/9+i+gQCCFVEI3FoOmNcXORErEGxDnHwMCA6DAa0tKOBFoTUbx4IkPzw6qMc45CoUDzGgkhrqCbPKQR+XUZr1jTeAsdz4YxhnCoccrYUyLWgIrFIsbGxkSH0XBkSUIi4kfEp6J/JA2HMrGqy+VylIgRQgghFZIlCdFgY/QOVcowdESjLaLDqApKxBpMuWw9DUusOk1VsLKrDWmbw6Rhia4oFotwaEkAQgghpGJ+XYYqN8qsqbkxJqGtrR0Sq/80pv6fAZliMhGjIRjVp2kqLtxwFl7qy8G0qNem2mzbRi5PFRPdwBpmWjMhU1133XWiQyBEuPOWRdEe9YkOw1PRaAsMwxAdxqJRItZgSqUSRkaGRYfRcBgAn65hWSKAgdEMLOoRqzrHcZDP5UWH0ZCofD1pVFdfvU10CIQI10gLHFdKVVUkEgnRYSwaldFqMMViEeMpKltfbUxiWNqRACQJOdOG49D8sGqzbRv5AiVi1cYwkeTSsgCkEVnzmFN6/PhxPPjgg9i6dauLEREixnnLojg21DyjSiRJRjweR09vT11PaaBErKFwDA0NwjSpwVVtEpNwyYbVSBcc9I8VRIfTkCzLQiFPiVjVMYbv/eh7uPcnfyc6EkKqbvjESMX75vJ5fOPb38E//O8fuBgRId7TAhEs3fxmQAuLDsVTsVgMhmEgl6vfBJQSsQbiOBz9VLbeFaoiY2lbGIWSifEMJWJuKJVK87q7TSrDAKihLqT6j8Cx6SYNaRzc4Xjx+Rcr3r+9oxOlkolSadzFqAjxEJOg6n60XnwF7CZLwhhjCAaDCAaClIiR2pDLZZFOpUWH0ZCikRBao2FkSpzmh7kkk82A05IArmhtX4a+od2UiJGGMtw3jGKu8grBwXBzNVRJg2MSlpx/BeKrNkGSVdHRCCHLMtraWjEwWL+dEFSso0FwzjE2No5CkXpr3NCRiGJpRyte7M9RsuACzjmy2azoMBoTA1rCQQQDjbH4JSHAxDXDnkf12lgiAV2v/wprhAAAk2S0r3sVkmddAlnRmrJYBzDRK5ZIJCHLsuhQFowSsQZBZevdIzGGeNhAIqyjd3CcEjFXcGQzGdFBNCQGhmQsir/44F+IDoWQqjGLJgZ6ByvaV5ZlJJKtdd1YI+RlDB0bXoP2c18FJlEzPhDwoyUSER3GgtE72CAsy8TwMJWtd4OiyFje1Ya8A5QsG5SHVZ/jcOSoUIc7GNAS8sPn80NRddHREFIVA8cGgQqvxT6/Hy3RmLsBEeIB1R9Gx/rL0brmkqbtBTudqmqIx+u3jD0lYg0il8thfJwmILtBkWVsPv9sHOovoGRSMQk3mKYJi6p9uoIBaAkZ6Fi2DktWbhQdDiGLls/kkRmvvAd96fKVLkZDiDeCrctwzlUfQtu6rWAS9e5OYowhHo9DVetznhwlYg2Ac46hoSHYDiUJbtA1FStaQ+gfSVEi5pJCoYASJWKuYEyCXwFs24GkhTCRmhFSv3KZPMxiZdeLeCIJfyBAvQekrgVbl2P5pW+FovvpXD4NYwzRaBSGUZ9zQCkRawCO42BwcEh0GA2JMYYlbXGoqoyc6cCmhZyrjnOOYrFA8xtdIrGJH3CO7nWvg6TU511DQgDAsR2kR1IV7StJEhKtNDeM1LdgchmWX/oWqAYVXJqNYRhoibSIDmNBqHx9nZusNpdOV/bFROZHYgyb1q2E6cjoG6WKlG7J5ykRcwuTJDDGJqbTMAm+YALZseOiwyJkQfLZPNJjlQ1LDASDiLREXY6IEHcoRhDLLrkO/ngnFM0nOpya197ejp6eHvBKJ4/WCOoRawDjqXEqdOASWZawvC0M07IxMl6/CwbWulKpCMeh9dncoCoy9JNj55mkovPsV4sNiJBFGOwdqrhIR2dXNw3jInVJVnUsf8X1CHesoiSsQi0tEehG/RWkokSsznHOMTI8AtumuUtuCAf9aE+0IG9xmPNYs4ZUznEcZLOU5LrF0DQEAgZKHOAc0P0tUPWg6LAImTezaMIqVdZzrmla3U7eJ81N9YWw4pXbEWxdLjqUuuLz+RCP1V91VErE6pxlWRgapvlhbkm0hLB6aScO9udp/TCXcM6Ry1Ei5hZdVxHwGcjaAMARaV2NQLRLdFiEzAvnHJmxDPKZykZ/RKIxBIIhl6MipIoYg6L7sWzzmxBqW0G9ufPAGIOiqIjFYnX3ulEiVuey2Swy6bToMBoSYwyRgIHOmB9HTozCoUTMFbZtUyLmIkNTEfD7kM69PHI+nFguMiRC5o07HH2HT1S0ryTJWLpsubsBEVJl7eu2Yt0bPkY9YYuQTCbrriecErE6NlG2fhA2za1xhSxJWLakFSUOlGxayNktjmMjX6A5jm7RNQ0Bn45MtlSeW9O26lIwRpd/Uj9s2654VEJ7ZydkhWqRkfrAmITWs1+BtrVbISta3fXo1ArGGEKhMAJ+v+hQ5sXVb+Inn3wS1113HWKxGPx+P9avX49vfvObU/Z55JFHsHXrVvj9frS3t2PHjh3IZKZXRCoWi7j55pvR2dkJn8+HzZs347777nMz/Jpn2xYGB4doyJxLZFnClgvW4uhQCfkizQ9zS6FQhENzHF1jnByamM4Vyj1ikqwinKRFbkn96D/SD8ee+6ajruuIxePUmCV1o3XtFnSefyUkmW4eLJYsy2htbRMdxry49q7/+te/xhvf+EZccMEFuPXWWxEMBvHiiy+ip6envM/evXtxxRVXYO3atbj99tvR09ODr3zlKzhw4AB+8YtfTDne+973PuzatQsf//jHcdZZZ+H73/8+rrnmGjzwwAPYunWrW0+jZk3Oq5kpaSXVoSoKVrWHcXwkhWKFE8TJ/OVyWRr26SJdZfDpEjL5Iia7xBTVQGzJBowPHBQbHCEVyKVzSI1UNgQ/HGmhuWGkLvhjnei64PXwRdvoxkEVJRIJHHzxYN0UsXMlEUulUrjhhhtw7bXXYteuXZCkmTvePv3pTyMajeLBBx9EOBwGACxfvhwf+tCH8Otf/xpXXXUVAGD37t245557sHPnTnzyk58EANxwww1Yv349PvWpT+GRRx5x42nUvFQqjVwuKzqMhsQY0JGMwjBU5EbyNPzTJZxzZDJZ6tV1CWMMCpOgMo580TztQTExETIfnHPYlg27gqq1jDF009wwUgcC8SVY8crtUH1006DaQqEQQqEQxsbGRIdSEVeGJv7kJz9Bf38/brvtNkiShGw2O22NoFQqhfvuuw/vfe97y0kYMJFgBYNB3HvvveVtu3btgizLuOmmm8rbDMPAjTfeiEcffRTHjh1z42nUtIn5YUOUILiEgWHDWcugqgaOjxZpfpiLslnq1XULK//PROn6U8/j5NILEWjpFBEWIfNy4kh/Rftpug4m0R0GUtuWLl+JC695HzRKwqqOMQbDMBCto4XcXUnE7r//foTDYfT29uLss89GMBhEOBzGRz7yERQKBQDAvn37YFkWLrrooin/VtM0bNy4EXv27Clv27NnD9asWTMlYQOASy65BMDEEMf56OnpOeNPX1/fAp61txzHxuDgoOgwGpYsSVjaFgbA0T9MiYJbHMdBPl8QHUbDYoxBkScu85xPvWmj6n5IiiYiLEIqlhpJo5gvVrRvx5IuKEp9VUwjzcOBBDl5Fj76Fx/DujXdE0NvSNVJkoREIjHraLxa48rQxAMHDsCyLLzpTW/CjTfeiL/927/Fgw8+iG9961sYGxvDP/3TP5WTnY6Ojmn/vqOjAw899FD5976+vln3A4Djx4/PK77u7u557V+LUqkU8nmqNOcWv0/HkmQURZujaNH8MLeYpomSWRIdRsNiAFpCwWm9YZNWbLweT93/da/DIqQitmVjbGAM3Jl7SEIgGEQi2epBVITMnw0Zg3InNLsFFpfQ6pchS0AFI27JPDHGkEgkoGlaufOnlrmSiGUyGeRyOXz4wx8uV0l8y1veglKphDvuuANf/OIXy0mEruvT/r1hGFOSjHw+P+t+k483k8lhiZZNCYJbwsEA1q1eihcHixU1AshCcJRKRZglSsRcwxiikSBMADMNYlZ1PxiTpvWWEVILivkiUiOpivZta++ALMsuR0TI/DlgGJQ7kZNCsAtFDKSK6OqIQtdUWHn6/nODrutoaWnBiROVrT0okiv9dj6fDwDwrne9a8r2d7/73QCARx99tLxPsTh9yEGhUCg/Pnm82fY79e9V6tixY2f82b1797yO5zXLsjAyMkIFDlzCwBDyaVjWGsJLvSOgPMwdnE98/kumOffOZEEYY4i3hJExMWNlSj0Qw5JzXiMgMkLOjHOOfKaym6w+vx8t0ZjLEREyfzZkDMhdyLHgxO+Og0f/eBBdCT8iARoa7hZJktDeVh/VKF3pEevs7MQzzzyDtraptfxbWyeGDYyOjmLVqlUAMON8rL6+PnR2vjyJvKOjA729vTPuN/n35qOrq2te+9cSzjny+RzGxyu7S0jmT5IYujoSsAGUrMoXESXzVywWYVIi5hrGgFgkhFyBw7Gnn8eMSVA0PyYGMdJ5TmoHdziGjg9XtG+ytR2qRo1aUjs4gDRrQUaKoCAFytsdh2Pf/sPwK4BflenK66KWlhboul7zwxNd6RG78MILAWBa8jQ5lyuZTGL9+vVQFAVPPPHElH1KpRL27t2LjRs3lrdt3LgR+/fvRyo1Nfl4/PHHy483k1QqjSyVrXeNJDG8ctM6HB+zkCnQ8E83FQrFaRVVSfUwALGWIDK5EuxZbih0rLkMvlDS28AImcPQ8SGUCnMP2/L5/WifYQ45IaJwACkphiG5Y0oSBkyMTBjP5jGYKaI9EYFEVT5dwRhDKBRCOFT7lSldScTe/va3AwC+973vTdn+j//4j1AUBa9+9asRiURw5ZVX4kc/+hHS6ZcXarz77ruRyWSwffv28ra3ve1tsG0bd955Z3lbsVjEXXfdhc2bNzdE8Y35GBwcpF4aF6mKgrOWtKB/JI1cBQ0BsjATi5LTDQU3McYQDfuRLZTgzDLGljEJih6Y8TFCROCcw6qwioGiKFR9jtQMDiAtRTEitc56XqazBfzxuSM4e0mAEjEXqaqGaB0MWXZlaOIFF1yAD3zgA/jf//t/w7IsXH755XjwwQfxL//yL7jlllvKQwlvu+02bNmyBZdffjluuukm9PT04Ktf/SquuuoqbNu2rXy8zZs3Y/v27bjlllswMDCA1atX4wc/+AEOHz48LdlrdJZlYXCIyta7KRmLIOTX0ZO2aJ02F3HOkc1SIuYmxhgiPgkD/cUzJGIMKy+4Hn+872seR0fIzIr5EoYrHJa4bMWqupgHQhpfETqyUhhjUuKMNweyuQIOHDmOi89dAUWWYVrUznBLe3s7Dh48UNNtOVcSMQD47ne/i6VLl+Kuu+7CT3/6Uyxbtgxf+9rX8PGPf7y8z6ZNm3D//ffj5ptvxic+8QmEQqFyufvT/fCHP8Stt96Ku+++G6OjozjvvPPws5/9DJdddplbT6HmcM4xPj5e8+Nd6xljwNkrlyAYDKLn8DComJx7HO4gm8uJDqOhMcYgAzAta8ZiHZMkWYWi+WCVmqsCLalN/UcrW8A5kWyFMc9iXYS4oQQd/Uo3LDb3XEXbcdA7nIXNgfZ4CIePj9A8MRdMDk/0+wNIZ9Jz/wNBXEvEVFXF5z//eXz+858/435bt27Fww8/POfxDMPAzp07sXPnzmqFWJeGh4dg0bpWrpGYhO5kCLoioWdwnC6OLnJsB4UmW3rCa9LJu7Kc44wzwn3hNrSu2IzjLzzoSVyEzIZzDrOCIeGyLCPR2kol64lQHJNJWBcsVLaYOOccTx84ilwuh2VJA4f7QBU7XKKqKpLJZE0nYvWx7DQBAFiWiZHRUZof5iJdU7EkGYPpAMUSJbxuKhQKsG3qcnSTrqmQGMNEHjb7dYMxBt3fAiZRo5aINTYwhkJu+nI1pwsEgwhHWtwPiJAzMMs9Yfq85iqeGBxBJldEQFWgyNQUdwtjDPF4vKZv2NC7Xyc45ygUChgbHRUdSkPz+3Scf85KHB4unXEoF1m8XC4Hh8Z+uoYBiIQCE19AnGOu07njrK1Q9aAnsREyE9uyMTpQ2c3GJd3LaG4YEYYDGJGSEz1hFQxHPF3JsvBCzwg6k2H4DVp6wS2MMbS0tMDv84sOZVaUiNWR8fEU8jQ/zFV+XcWqjggOHBumsuoumijUkaHeXTcxIBLyA7KEufsXJvgj7a6GRMiZ5NI5ZFNzzxuNJ5MI1kFZatKYbEgYlJdgTErAZPqCjuE4HA898Qy6Igr8Wu321jSCYDCAUA1fLygRqyMDAwPUcHWRxBiWtMXBJIaSZc3Zg0AWJ5vN0vnsKoZIKADOZBTMuV9nxiQsOee1HsRFyMwGe4cq2k9RFEgSNV+I9zgYhuV2ZKTIopZNcBwHPf3D4AACPo1WYHARYxJaW1tFhzErupLVCdM0MTxSWTlfsjCSJGHLpnUYzDgYz1e2hg1ZGM4dFAqV9tOQhWoJBcG5hFyREl5S22xmgCtzD41VFAVdS5e7HxAhp8mxAAbkTmRYZNHH4gDGMnnsPzaEc5bGy4WVSPUxxtDa2gpVrayYitcoEasDnHOMjY1R2XqXKbKENUuiGBrLIF3BZHGycKZpoVik19hNE3PE/OAACkWzon8TiHQg0rbG1bgIOR0HUJJisHxzD41t6+is6Yn3pPFwAGkWRr/chewie8JONTqewdMHjmJJRKaFnV2m6zpaIotPoN1AiVgd4JxjeGQYpllZY4osTDQSQkvQh5xpw7SoR8xNpVIJxRIlYm6LBDVw7iBfYSKmGkHo/qjLUREyFYeCnLoCWih+xv103UA0HqciHcRTGRbBoLwEnFX3BkCxZGJgNAtFkhDwLWyuGamMoihIJlvBUHvXDkrE6oBtWxgdGaH5NC5b2dWGZLwFvWMmvdYu4pzDNEsoleZeK4gsHGMMPhlwbLviHjEA6FzzKsgKNQqIdzhTwMHgj3cDZ2goRaJRBAJU2ZN4w4aMNItgWG6vWi/YqRzO8cyhE5AkoLuNzms3McYQi8WgarU3PNG1BZ1J9ZRKJYyMjIgOo6FJjKEzEULIr+Hw8VEq1OGyUsmkRMxljDGAMTgOh2VX3sPrCyVpPTHiqay6EnPdF5YkCd3LlnsSDyElaOhXumBifuuDzQfnHE/vPwyrWEBMlyExwKG2hysmy9j7DF/NtT2oR6wOjI6O1dyJ02hURcGS1hhsh6NQoiGgbisU8rQ8gMsYw4ImgDNJQfe5V7kQESHTlaQWmFJ0zsZuZ1c3zQ0jnjChol/phskM15KwSZlcHkOZEmKRAFSF+kbcpGkaYvGY6DCmoUSsxnHOceLECdBNEnfpmoIL16/G0TELNt2Scl0mmxUdQsNTZBk+XQMH5rU4OWOM5okRT3BMzA/jbO4GqKbrNDeMuIoDMKFNJGHwZpFly3bw6B9fxOqOEAxaT8x1He0dNTdLjBKxGlcoFDA2PiY6jIanqQrOXhLFwWMjlIi5jHOObIYSMbdpqoJwcKJq4nyH2ra0r0FL+9muxEXIyxhy6oo59wqFw4jFzlzIg5DFcCChyHw4oXSj5EFPWPnvOg72PHsQQRXQFUrE3MQYQzgcRiAQEB3KFJSI1TDOOVKpceTzedGhNDTGGNoSLVBkCfmSCYcSMVdxzpGlHjHXqaqCUNCP3AJOZ1nRIatG9YMi5BRFOQmbnXaezdD+bW1rh0zDtohLOIARqRXHlRUwmbeFihzOMZ4pIJU30ZEM03piLtN1HbFYbQ1PpESshnFwjI6OwaT5Ya6SGMOWC9YiVWIYy1HZerc5jo1CgW4uuG2yRyy9wJd6+flvBJOo8UvcY0otADulGSLJgDF1rR+f34+WaG01nEjjcMAwJHcgJYkbjn1iaBzPvXQc53QHveqIa1qyLCMeT0CSaif9qZ1IyDS2bWN4eIjmh7lMlhjWdMcxms5jJJ0THU7DKxQKsG0q1OE2TVEQCviQztlYyEVEUX2QZErEiDss5p9IxE7HpjZL2to7oKi1V3Ka1DeOiSRsRGpDmrV4NhRxJmOpDHr6hxGQGGSZMjE3McYQj8ehad7MAawEJWI1rFgoYpjK1rsuHAwgHvKjYFkwTeoRc1s2m4Pj0OvsNlVTEA76kM4WFnQzR1YNLDvv2qrHRQgHUJLjcCTflO1MkqH6wuXfA8Egkm3tHkdHmkGOhXBUWTPREya4G8q0bRzqT8Hv09AaDQmNpRmEQiEEa2ieGCViNYpzjpGREViWJTqUhrekLY6ujgR6x+x5VZcj88c5Rz6fo9fZA5rMENQlpHMFLKRLjDEGRfWBMfqaINXFIaOgLJm2XVI0GLGXt3cs6aqpIUSk/nEAWRbCoNwBh8nCk7DJmJ585kXokoNkiHp/3cYYQ1tbm+gwyugKV7M4BgYGwKnB6irGGNpjAbRG/DjYM0ILOXsgm83SGmIekBiDwjCRiC3wvE4s3YhwcmV1AyNNL68shVNBeXB/IOhBNKSZ5FgIA/ISOBUsmeAVzjle6jmBkmUhaKiQJfHJYaNrbW2tmXXbKBGrSRz5fAGp1LjoQBqeLEnoTMbgAMgWqCiK2xzHQaFQFB1Gw2N4uficZTsLnmc60RtGjQJSPRwMDlPn7IlItrZBr6F5HKS+2ZCROdkTxmuwl9+ybLxwbBgrOmNQqYy963w+P8LhyNw7eqD2zkYCzoFUKkWL3npAkSVs3ngOelMWFZDwgGVZKBYLosNofIxBqdKXebRzXVWOQwgA2CyAonzmeV+KoiCebIUkU4OULJ4JFSfkpRiQu2qqJ+xUlm3jt79/FkujChQq2OEqxlhNlbGnRKwGTZStH4VpmqJDaXiqImNtdwwHe0ZhUiLmMg7LslAoUCLmNgYgGp4Y1rXY4c2J7vNrYh4FqX8cQFZdMef5FAgGEY7Uxt1qUt8sKOiXu1CUfDV9HbNtjuMDIwDnCPq8XcusGTHGkEgkoNRAZWBKxGqQYzsYHBoUHUbDYwCSsQh0VUa+UKKFnF3GOWCaJgpFGprohVhLGA6w6HmPsmog2rG2KjE1Ok4/Z/wBAKeCBXO7lk0ka2c6DqvhRnUjEn3uLORnIgnrRum06py1iIOjfzSDvpEMzl4Wq+WcsSEwxhCLxaBp4oujiE8FyTTFYgHjY2Oiw2h4jDFcvGENSlAwkqfeMC+YpokSJWIeYIhHw8g7wGIXClBUA+de9j5IjCq4nklPzzE8+9xzVIjmDCzTgm2eebhheNl5KOkKzFmqJeqqihvesAWvOn8V7t19AhaNZHDdwYMH8eKLL4KX0+DaxjlHsVBAybRgM/EN7Ur19g/jSE8/OlpbIUkMtl0fr3e90jQN8XgCuZ5jQuOgRKzGcM4xNDREZes9IDGGs5cmkMmbGBih+XheKBQKVLreA4wB8UgI2SJgV2HJNsY0GMEIlRKfBeccqi8NLvvBGSUGM+Gcw3SKADvz51/zBcBledYmv8VkWFDR2toKX7gEixqrrgvH03CO9NZFFefJJKxoMaCOkjAAyOYLGEzl0d2twa9rSOfopqWbJsvY9/QcE3qLgb5VawznHIODQ9RY9UDAbyARCaBoWSjQfDxPZDIZ0SE0jUQ0hGzegl2FHhrHccBp6O4caCzRmXDO5zyHZFmeM9l3uINMLg+Ahid6JRgM1cVrzTlHoVBAsU5HXTgOxx+eO4pYSENrS+0Pp2wEkUgEfr9faAyUiNWQicVu80ilUqJDaQqt8Ras6m5HX9oGjSbyRpYqgXqCAYhG/MjlS1WrBloqlerijrgoddBOFco25+6aVVV1zgY/5xPflYxNVL0l7vP7fZBqsOT7JM45LNNELper66HvnHP84ZkXocFGQJPo1o7LGGMIBALCy9jX7ierSWUyaaQzadFhNDwGIB7xobs1jBeOjFAD0wMcnM5tjzAGtOgMmUKxKj1iAOBYdLfiTBhj1HCaheM4sK0zJ2KMsYp6XTjncDiHJEkI+o1qhUjOQJYV+P2120NjWxay2SysOh/ZwgGkMlmM50poj4dpKLgHFEVBPBEX2uNL73KNGRkZpflhHmASQ0ciCgBI56icuhcc26nbISN1hzEwMOQLJuwqDSl0HAeWSdemmUx8hzPqFpsB5xy2Zc95s0tWlIrWDeMAchZggyEUqK85QPVKkiQEAgHRYUzDOYd5siesUZRMG48/cwRnLwlBpvXEXMcYQzKZhCxwzUJKxGqI4zg40X9CdBhNQZIkXHrBWvRnHZg02dsT+XwedjUqR5C5cQaAw+EOFl2//hRUEXB2lIPNggNWae4EXtO0ig85ni7CNC1E/PSie0GSJASDQdFhTGNbFnLZbEONaDEtC3ufewkhlYbeeiUcCiMo8EYDvcs1JJfLUTEDj8hMwvrlSbzUO4ZSBXMXyOJRIuYdQ1chn/wSr2YTxSyalIyReamkkazOIwkDgPFMFqVSCUHqMfDExFya2knEHMdBNpNpqJ6wSY7jYHA8h4LFsSQZoeHOHpAkCW1tbeL+vrC/TKaYKFs/SMMSPRJrCcGvK8jkqzeHhsyOc45cLkeNeI+Egn6oinKysEH1jss5r25m11CoyXQ6zjnM0pnn7TDGoMjyvOZojKezKBRKtP6Oh3RDFzp8a5LjOMhls7Asq6F6wiZxAC/1DODE4AhWd/jpsuIBxhji8TgURcwVhRKxGuE4DoaHR6ih6gHGgAvWrYSkGhjOO1VtqJLZ5fN5OLT4qiciQT8UVYEbr3axQPP8ZsIYtZlO59jOnJ/5SueGnWo8k0OhWAJAQ0K9wBjgM3xQVbFz8hzHQS6Xa/iRFf2DoxgaTSGgSOWRDcRd4bC44Yn0DteAybUvxsbHRIfSFBgY1nQnYVoOegepip8XOHdQKBTAqTvFE6GgD5KiIO9Ce8VxnIa8E714DJSKvYxzXtF5Mp+5YZPy+SJKJwvHUCLmBQbDMKAJSsQ45ygWi8hls7CbYNSQads4PJRGMhpEJECVQd3GGIPP50c4IqaMPSViNSKTySCdpqTAC4auobUlgJJtI18oiQ6nKViWjXwhLzqMphEOBiAxBdli9RMmx3YqKr7QbBjlYdPMdZ6wBZbnnkzwZFkCq+H1rRqJruvznstXDZNJWKGJ5hg7nOM3u59Fwi8h4hM/HLQZMMbQ1tompIw9XcFqxNDwEA1L9Eg0HMTald0YyNCwRK/Yto1CnhIxLzAA4YAPkiQhV3AnYaKezelErkNTiyopWV/JAs4z4Sd/gj4dKg3d8oQsy/AZ3q4lxjlHqVhEsdBcS8xwzvHS0T6AcwQMjXp9PTA5T0zE8Fu6gtUAx3Ew0D8gOoymwAC0BA2s7orh2SPD1KD0iG1byOeb68tUpHBABWNAzqUeX8em4YlTUXfY6eaaGyZJ0sInx3OOsRJH0K9AVakZ45VQyLvKiZxzFAsFFJosCZtUKJl4oXcU5yxLQKJMzBO6riMRj3v+d+kKJhxHNptBLpcVHUhTYIyhNR6BxIDxdJ56xDxiWRZKJSry4AXGGHQJ4I6NnEuFNcySSYkYmVUlRTpUTVtwLyIHMDCSQ8AAVBq55QnGmCdriXHOYVkWcrkcisXm/c7IF0w88fQBdLUokCRKxLwgyzISiaTnt9QoEROMc2BwcAgl88wlfkl1MMbwyk3rMJx3UKSFnD2TzeXgUMPdU7bDUSi6N5fLtppjvkalqGriBM75nMMSJUlaVCl0DuDE4Cj8EqBQb4FnQqGw60NwbdueKE/f5G2ikmXh2IlRyIwh4PN+bl6zisWi0A1vC6RQIiaY49gYGxsFp/lhnpAYw3kr23D4RBoFKjjgmVyWeny9wyYaS5y7OvR2rvWhmg+jEn6YuNk1V5KuLHBu2KkGR8YggRoxXtJ1Dari3hwa27KQz+Wotx0AOMfRgTHkiyZWLYnSpcUDjDGEwxHPy9jTeohzWN4ZR6w16drxC4UCRtqiiAdnubjNej2q/oWqpi59bgTDgGDAh3jEjxNZhqXtsQVf8GvqtQJqMKCpSpkhyOZK0WHUvGq8jaqiYFV3BzRVwYqOhGs9kZIkwRfQIMnUQgCAkGbDyq6E2eR38h3bQbFQmnUlcUmW4fMtrugDYwwt4SAYgCWtAfiD+qKORypjlvzIXrAW6dTJCs9V+uhzzlEqlVAsFmvopjQT28PNAF1TUSwUcFZrEKls0oVreYXPUNALIer1V+yNOHLkKE7/Rs6lRvDiL6r/9xinWw/T9PT0oLu7GwDwxxcOo3NJl2t/iwEolQqohZa0+AhOcjEQTZERDRrIWxwF0/H+SbtxZamZN252tm02Telh0RgDIn4dmqpgLO/M2iCuBlmRaP7CSY7jwCrNnoA0A46J0ttneg0YGOQFlq0/5SBQZAkRv4aczWALuJQ3Jz7RE17FazkH4IDDnmNOYTNiDIiHfACTkCk6TTi8X8x3i2WZcGxrWi/k8d5eXLThHADAsWPH0NVVndyAesTmcNe/P4RwbJYescV+JhiwYvkydLRHF3kgUqlViQDAc/jezx9HLmfSlzeAajdhTj+aIsvYsP4s+H20MKUXNEXGpmV+9PSN48e/fByO495ZrmoKDL8Bmh0FRFuCWLO0E8oi5j7VO9vhKJjWGau8+VS1KlXgDFXCxqUafvHY8zhyYsy94WxVO7Vr7zMy34gYA5Z2JLCkNTHL8eZ3RM45csUiXJ8kUHsvfUUYgGVxCcuTPtzzq904MZwSHdKC1NvLr2oK1q1diYDfP2X7YH/Olb9Hidgcfvbgbqj+8MwPLvK6L8syLrtMQbSf1lfygqrIaNnUhVKuhB//x29gUbEBAO7fSQ4GghizfAh4PO66WQX9GtZ2JfD0S33Y9cvfuXoXlTGGYDhAc6MAtLe14+KLVWgCFr2tFblMDtzhkGbp8VJUFbquoRpNs5aAio1LQ3jmwDH86pGnmrC3wHsMwOrVq7Fhw3mLnuPHOUc+l2v6obxnwhhwKBHCB69YhYd+vw9PHThG8+c8IMsyzjs2hhUrpk6nSA33u/L3aJ7rHCZHWcz4g8X9hEIhGIZBHyyPGJqMZR0RPHNkYvHsxb5/jfLjNsPQF1UhjcyPLAEBAxgeS7v+/k7M7aCG1ASvPlG1iXM+cV3lfNbvtIl1w6qVtE/8jdZ4C60H6REOIJfLwbIW3oc1uUhzLpulJGwOnAO5YgmcAauWdkxsox/XfyzbxtDQ8KLO8/mgREygWCwGTdNdLwdLJmgyQ9Iv4eE/PAsXR2uR0/h8voUv3ErmTWKAXwaGR8fhRWJAZewnTCQgoqMQp1QsgTuzJ2GyLM/aU7YQDgdyHGhPRlF/g5/qVy6XX3ADdTIJy+cXfoxmky85ODps4jUXn0ttRQ8NjwyjWPSmfgMlYoLIsoxoNFbVLyYyOwYgHPCBcaB3YJh6IT1kGAZkmc5zrzBMXNiHx9KedNDYtk3JWJObKFRy5ob1YhZwnonlAOki0BYPURrmoXxhIoma73foZGXEQqHgUmSNqViycGxgDEtbg4svckMqlstmkUqlPbm5Ru+qIKqqIpFI0B0OrzBg7fI4Dp0Yx3i2KDqapsEYO1mqms5z70y81kOjKU8GbJ2pF6SZNPNrMDkscTaKolT9pqNtA6msg4Sfhj17qVgowjRL8/o3tm2jWCyikKf58PNlOxyZoo1gwIc1K5aIDqdpcAB9fX2e/C1KxAQJhULw+xe3lgqpHANDR0jCcy/1YHQ8IzqcpiFJEnw+P91w8NDkK53NeXfnuVQsNXUi0uzM4pnn+jDGqn4NsB0H6VwBMq2j7SkOjmw2W9m+nMO2beSyWRSpJ2zBekYKUHUD68/qoluaHhoZHfFkHiMlYoK0t7eDegm8Y+gqVFnGiZFx5ArUI+YVWZbhP60ELHGXLElgAGzu3bo8Dq0B1LSJqG3bsMwzDEtkDKoLlSRtx0GuMNkzQ9+lXkqn0xXt59g2spnMGXtLydyOD6VgmxY6ogEqfOWhfD6P0dFR16/tlIgJoCoqYtGY6DCaSmciCENT8FKf+x8q8jJZkk4OTSReCQWMiSGJHp7mHDQ8kaM5kzHLPPN8IU1VXfm7fOIFhyxJMPTmXTJAhLkSMc45bMtCLpdrys9EtTmOg5TFsW7VUgT9tB6nV8ySidHREbj9ZUqJmAChcIjWVPJY3C+Dm0U8se8gFTr2kKKoMAxddBhNJRwwUHQA28sTnQOlwvzmjTScJmxwTjS4Zy/UwhiDrCiuDU3mAHRNQSwcdOX4ZGbZbBZ8lh53zjlyuRwy1BNWPRx45kgK56/pRogSMc9wcAwODsI8U49/FbiWiL3vfe8rjwuf6ae3t7e87yOPPIKtW7fC7/ejvb0dO3bsQCYzfR5PsVjEzTffjM7OTvh8PmzevBn33XefW0/BNS2RCHSdytZ7RZYYgn4d+ZKF/uEx0eE0FX/ADxo25K1QwEA6a6LkcSXDM60f1Qya8Zk7tnPGRMyNIh2n01QV0RZKxLxkmiaKxelD/CcXabZofbCq4gBODKcQ0CXEInSue2lkZASForvzG11b3OfP/uzPcOWVV07ZxjnHhz/8YSxfvhxLlkxUf9m7dy+uuOIKrF27Frfffjt6enrwla98BQcOHMAvfvGLKf/+fe97H3bt2oWPf/zjOOuss/D9738f11xzDR544AFs3brVradSVbIkIZFIUhLmIVWRsbqrBS8c6IFNc1k8RT2/3gsHFKQyedfv4p3OMi3Ytk1rxjUJzjmK+TPPt3VjbtipCgBkTUU0EnL175CpLMtCLpeHz/fy/N/JJIwWaXZHwXSQLQGv3LQOTx042tQ3vbxk2TaGBocQCrp3jXHtG/PSSy/FpZdeOmXb7373O+RyObznPe8pb/v0pz+NaDSKBx98EOFwGACwfPlyfOhDH8Kvf/1rXHXVVQCA3bt345577sHOnTvxyU9+EgBwww03YP369fjUpz6FRx55xK2nUlWqqiIej1Mi5iFFAtoCMr735LN08fJYKEh377zEAIQNIJ3KoeRxIgYA3ObgMm/O61uTXVsmE+/ZaC4nYQCQKQKSJCMWpoJAXrJsG/l8HsDEIubOyfL0lIS5J503cXggg0vO6cQdAGjlRu/095/A8uXLXTu+p3PEfvKTn4Axhne/+90AgFQqhfvuuw/vfe97y0kYMJFgBYNB3HvvveVtu3btgizLuOmmm8rbDMPAjTfeiEcffRTHjh2rOI6enp4z/ri5dkCkpYXmzHgsFDAgATjc20+JmMeoR8x7PgakMmISsWITVyRtpivLXMNQOeeulKw/XSbvABwI6YwGQHvItmzk8zlwPjFMMZPJUBLmspJpYSyTRyhgIEJzIj2VSqdPzot05yrv2RgS0zRx7733YsuWLeXMct++fbAsCxdddNGUfTVNw8aNG7Fnz57ytj179mDNmjVTEjYAuOSSSwBMDHHs7u6uKJZK93NDW2srGKMaKV46Z2kCJ0ZzGM0Um6qxJJqiKNB1uungNQYglRWTiAE445yhRmaZ1sSPLOZ19xLn/MxJN2PggKuNcwZgbDwD04wAtg3LMuHQBd4TDEBqPIVCoYDSDHPFSPVxDgxmLazpjGLd6m787g/Pig6paUyWsY/43enl9ywR+9WvfoXh4eEpwxIne546Ojqm7d/R0YGHHnpoyr6z7QcAx48fr3bIVaepGlpaoqLDaCoMQEdYwotH+zBAhTo8pWs6FEVtzmFqgqUzOZgCEiLOOfLZvOd/txbktBxymRy4p+Uqa5NzchFft+XzOZhWG8ySefKOtet/kpw0MDiAZZllUF1anoBMd/D4GF67LomuZBgMzdULL5Jt2xgZGUHI1+rK8T1LxH7yk59AVVW8/e1vL2+bGGOMGe+aG4ZRfnxy39n2O/VYlZhrGGNfX1+5p62aQqEQgsEgqF3qHV1XoCsyToyOI5XJiQ6nqeiGDlWlwg1emry2FIrmGefvEBdQq8hz3OEAB/w+HbIkw6Jz3jPZbBa2bVMi5qFMrgjLcbBySStUVRE26qEZ9ff3o7s97sqxPWklZTIZ/Pu//zte//rXIx5/+YlMLvQ6UxnUQqEwZSFYn883636nHqsSXV1dFe9bTZGTZeupnLd3ki0BRIIGDp8Yp/lhHjP0iR4x4h1VlSHLk0Of6Xz3EqfXW5hYSwiqQomYl/K5HByHXm8vcQ70jNu4eMNZ8OkaJWIeyuWySM+wrFY1eDJZ6d/+7d+mVUsEXh5WOFNxjL6+PnR2dk7Zd7b9AEzZtxZJjKG1tZWGaXksakjQmYVH9+ynZpLHdN2gUuYeM3QNqiKDo+mK+JEmxAGYABLRMF1rPGZZFvIFd9dXIlNxDjx7aBAr20IwNLrJ6SWHcwwODLpybE8SsR//+McIBoO47rrrpmxfv349FEXBE088MWV7qVTC3r17sXHjxvK2jRs3Yv/+/UilUlP2ffzxx8uP1zJFVRGPxygR8xBjQNCvo2Q66DnhzgeIzIwxBp/PR8NwPebTVMiyjKxJWZjXJnrc6XX32ngJiLeEoCmy6FCaCsfEaCfirWyhBDAJa1eJKzrXrMbGRl05ruuJ2ODgIO6//368+c1vht8/da2PSCSCK6+8Ej/60Y+QTqfL2++++25kMhls3769vO1tb3sbbNvGnXfeWd5WLBZx1113YfPmzUIrIVYiHo9DVd1fV4W8TJYlrFkaw4snUjAtWsjZS4yxk6XrKRPzkk+fKI4yli5QSuA1Tr2QIoymbcSjYZqPKsCp7TbijVTBxmDWxmsuPodudHosk3OnAJHrV65//ud/hmVZ04YlTrrtttuwZcsWXH755bjpppvQ09ODr371q7jqqquwbdu28n6bN2/G9u3bccstt2BgYACrV6/GD37wAxw+fBjf+9733H4ai8IAtCaTkCQqW+8lGUBnSMHP73selkOJmJckSZp244W4z9AZJOZgLO1+xTpyOsrCRBhL5xBdkYQqU6vUa5l0urxmHPFGKlNA/3AaHbEAFFkWUh23WZWKJVeO63pm8OMf/xitra248sorZ3x806ZNuP/+++Hz+fCJT3wCd955J2688Ubs2rVr2r4//OEP8fGPfxx33303duzYAdM08bOf/QyXXXaZ209jUTRdRyTSIjqMpuP3aZAZcPBoLxxKxDwlMYZAgBIxr/kUgHEbYylKxLxGaZgYo+kcNAbqHRAgn89TdVaP2Q5HznLQFo+iqy0pOpymUrcLOj/66KNz7rN161Y8/PDDc+5nGAZ27tyJnTt3ViM0zwQCAYTDIbpr5LE13QmMZUsYSdGCk15TVBWaRkNxvSYD4LaDceoR8xxVZRUjk5soGCHR96vnTNNEoVBAMBgUHUpTOXgihw0Xd2JldxKHek+IDocsEo2V80BLpAWaNn0NNOKuzoiMnhNDONY/LDqUphPw+8EYXV68xjCx+CQlYqRZOBwAA0LUA+8507JQKDTnAu4iHesfgyIByYifbkA0AGopuYwxhvb2duoN85iqSDBUGQOj4xgZT839D0hV0R1ScSzbwViKqpl5jlOvmCgMQHsyKjqMpmOaJvL5Amhgrrcsx0GmxHHJeWdDkalaaL2jRMxluqahpSUiOoymE4v4kWwJ4MhAGo5DXxJe8/sDdPNBAMYYHO6gaJqiQ2k6tKCzGByADaA13iI4kuZj2zYKhQJVC/WY43A835vBhWd3QaVlG+oeJWIuiycSVLZegLAuIaIBv3vyeWoeCTBRup54SWIMAePktYZOeu/Ray6E6QAZE2hPUI+YCNlshopheYxzoGdgFEFDQSxCo0/qHSViLmKMIR6LQaauY88FDA2W4+ClozSR1WuKokDXdapi5jFJmljAnHPKCUSgHjExLMtBNu+gNd5CqxYKkM1maUiuAHmLw4SMSy84R3QoZJEoEXORruuIRmOiw2g6kgScszyBY8M5FE1LdDhNR9O0kxUTqVnkJUmSEPTrGCtwGipEmoZpOUjnimhtoZEnImQyWeoRE2BgLI+h8QI2ntVB37R1jhIxF/kMA5FImObKeIyBoSuiYPcf98OkRMxzuqZBVVXRYTQdiQFBn4bh8RyoT8x71CsghmnZyOSLoFs/YuQLeVgWfc96LVcwkSmUkIgEEQ5SxdB6RomYi+LxBBSFGqRe8+kqFMbw/Es9MGmxSc+93CNGvMQYEPQBI6NUJZQ0D8t2kC+WJrIwuunpOe44yGZpuQwRBnI2Vi3tRFc7LexczygRcwkD0NbWSr1hAqxaEkPRcjCcpoWcRdB1HYri+lrx5DQSAwIyMDyWov4wETinXjFBOAc0VUHAR+t1eo0DyGRpuQwRnjs8gnjIQCLiEx0KWQRKxFzi8/kQDlPZehE6IwoGh0fxUg8V6hCBSteLwiADGB5PgyaJeY9ecbF8uoZIiKq1eo1zjkyaEjERUpmJNdzOWtZBCzvXMUrEXBKPx2l4lgCyNLGQ8/BYGn2Do6LDaTqMMSpdL9gwDU0UgzIxYWxw6IaOSIhKeYuQyWaoN1gAm3McHbfx6ovWQZKoOV+v6J1zAWMMUSpbL0Q4qKEzGUbvcBY2VXLy3EQiRhOHRRoZp7vTIlD5enHS5sTc1AgVLRCiWCyiZJZEh9F0LMvB/qNDWNYagkw9YnWLEjEXaJqGeCxOw7MECGoSkn4Jv/3D86JDaUrUIyaOcvLGT8m0KCUQgV50YVIZB6qiIhKiREyEUqmEYoHmZHuNA8gUSlA1FevXLBMdDlkgSsRc4DN8iERofpgIfl0DB8fzL/WIDqUpqaoKVaUhuSKE/Drd/BGKinWIksoVoMhAyEdFgkQolUooFikRE2E4a6HkSLh4w0rRoZAFokTMBa2trTReV5BzlidxYqyAXNEUHUpTCgSoUIcooYABB9QxIwq97uKkswU4tgOZLj1CmCUTxWKRbkQI0D+cwXimgM5oEIpM7c56RO9alTEAySSt6SBKd1TBk88eQp6GSQgR8PspERMk5NeRK3EULZobKQI1QsXJFUpwHIeWERPE4Q7y+ZzoMJqS7XDkLQerlrYj3hISHQ5ZAErEqiwYDCIUog+DCIauQJckPP/iMRRL1CMmAvWIiRMOKMjkCnTuk6bjnEyCW8JByBJdf0RIp6lyoijPH89jzbJ2JKNh0aGQBaBErMpaWqLQdZqrIcKytig4kzCYLtAwIUH81CMmTMhgyOYKKNKwXDHooiNcIhqGLFG1YhEyGUrERDlyYhg+VUIiQss31CNKxKpIkiTE4lS2XpTOiIxMJo3nX+oVHUpTUhQFuq6LDqNp+RmQzeVRoB4xIah8vUAcMAG0xiKQaZ6MEJSIiVMyHeQsYOuF60C3QesPXbGqSFEUJOIJ6hEQQGIMhqpgPJ3FkeMDosNpSqqqUm+wYDQ0USxqiIrBAYwXgWQsQgULBDFNqpwoSslysP94Gpes7aJ5knWIrlhV5Pf5EA7TGF0RfIaKpe0tODGWh21TsQIRVFWFplGPmEiZXB7FEi2sKgIlYWKNZZyTQxOpWSMC5xzZbFZ0GE3Jsh30DY0j4NMQDVONgnpDV6wqam1ro94AQXwqQ2dYwW/+8AINEBJEO9kjRsRgALL5IoolS3QohHhuNJ1DLCCD8jAxHM6RyWREh9G00iaH4ffhog2rRYdC5okuWVUiSRKSCSpbL4pf1yAx4OkXDosOpWlpmg5FoQVVRVBkCbIiwbHtcgU54jF63YUaS+eoQSMQ5xyZLM0TE6VnMAfT4li9JE7zxOoMXbeqJBQMIhgMUo+YIGcvT2A4W0I6T2PURaHS9eIYugpDpSRYJGp+ipXKFgAAChXLEiaXzcFxaGqACOOZPIolC0vb4/DpmuhwyDxQIlYl4XAYPp9PdBhNa1lMw779PUif/DIm3gsEAqJDaFq6qkBVFZQoGxCHc1A6Jg7nHIwxRCM0R0aUYqkI06RiQaL0Zxycd/ZyRML0XVxPKBGrAsYYEokEJBqcLoSqyDBkhudfOoZMPi86nKbEAASDdPEXxdAUqIqM0awtOpSmxUGjE0VjjKEtERUdRtMqFAooUbEgYfa9NISlySDCfpqrXU8oc6gCRZaRSCRpWJYgS5JhaKqKwVSBGkKCMMbg91MiJoquMSgyx1iKJssLQ9ceoZyTP22xiOhQmlapWIRpUiImymgmBw5g7apu0aGQeaBErAqCoRANyxKoPazAMgvYt/+o6FCalqZp0DQaly6KLgMK4xgdp0RMFFrQWaySA+Qd6hETqWSaKBSKVLBDkJLFcSJt4YrN54oOhcwDJWJV0NraSsMSBWEM8GkKsrkC9h/uFR1O0/L7/dQjLJAMgDkORqlHjDQp0+TIFxy0xmktT5EyWboGiZIvWjh0fAxL4gFa2LyO0Du1SLIsIxFPiA6jaWmqguWdMQylCihZND9GFJ/fTzcjBGIAbErEhKJeALGKpo1cwUQsQFUTRcqk06JDaFoO58gWTURCAaxe2ik6HFIhajktUjBAZetF0mSGpS0qfrf3oOhQmpqfEjHhHIeGJopHyZgoJdNCtlACpWFipdO0lphIvaMmfD4/zl3dJToUUiFqOS1SKByC3+8XHUbTMjQFMgOefPZF0aE0NZ/PRzcjBJIkBsdxkMrkRIfS1Kj9KY5p2SgUS5AkCRJdi4TJ5WgtMZGODYzDsmy0R/30OagTlIgtAmMMrUmaHybSmmUJpEsWxrNUtl4URVbgMygREyngmyhXbFMDSDBOvQECOZzDZ+gI+A3RoTQty7aQy9ENIVFMy0bBcrBx7Sr4fVTGvh5QBrEIjDEkk0nRYTS15XEd+w+dwMh4VnQoTUtWZBgGXfBFYYwhSA3PmkA5mHgBn46Q3yc6jKblOA4lYoI9fyKH81d3wqdTJeN6QInYIsRiMRgGNYBEUWQJPpnhwJHjVKRAIEVR6HMgEGNAOGAgT7VqhKMS9mLZAAJ+A6EgJWKicIcjk6HvY5EOHh2CX5eRiIZEh0IqQInYIiQSCRqWKFBrNISg38BAqgjHoQaQKIqiQNcpEROFgSHoUzGSLlEaIBgNSxQrbQKGoSNIPWLCOHyiR4w+C+KUHI4iZ9h64TrRoZAKUBaxQKqqIhaN0bwYgZJhGapkY+/zR0SH0tQM3YCiKKLDaFqMAaEAw/BYmho/otHLL9R4xoHP0BEK0o0hkXL5HGybuuhFSectHB3I4eK1VDmxHlAitkA+nw/hcJgSMYF8ioxioYh9+w+LDqWpBYIB+hwIxAAEJWB0PE2JgHD0BoiUyhagSYBPpaaNSIVCAaZpig6jaRWKJobHs2gJGIiGg6LDIXOgq9UCRcJhKlsvkCJLWNWdwGi2hEKxJDqcphYMBESH0PRkgHrEagC9/GKNZ/LgnFPDRrBCoQDLskSH0dTGChztyTjOXkm9YrWOrlcL1NrWRr0AAikSw/KYhseePkQT5AULBOiOWy0YGU/TJ0EwSoTFSucm5gurCi3rLNJkjxh9HsQ5cHwchqZgSZwKdtQ6SsQWQFEUJOJx0WE0NV1ToEjAE/sO0l1owfwB6hmuBSPUI1YD6PUXaeL850hEI3SjVCDbtpHLUwl7kYbHsnBsB6u6W6HI1NSvZfTuLEAsGqUqcYKt6oqjaDsYydD6YSIZug5NpbVKRFJVGYwBKVrUXDjKg2tDaywCSaJETKRMOi06hKbXl3WwZePZ0DVVdCjkDCgRW4B4PA5ZpqEPIq1I6jhyfAT9w3SxF8nw+WgJB8GCPh2MMeoNqwn0HohmAUjGI5CoR0yoNK0lJhQH8MyhYSxvDUFV6Du6ltG7M0+qqiIWi1PjUyBZYvDJEg73nMDA8JjocJqaz+ejmxKChfzGROlEIhwlw2JxAOMm0BZvoaGJgqWpR0y4sUwOkCRsPGel6FDIGVA2MU+GbiASiYgOo6lFwwFEw34MpkswLVqrRCQf9YgJF/RrsByGkiM6EkJ5mFicT6wllogGaWiiYMVikUrYC5YpcaSLwJZNa0SHQs6AWlDzFI1GYRg0P0ykeFBGSAf+8Nxh0aE0Pb/PT4mYYKGAglzRRL5AjR7S7DjG0nlEDRmMuomFsm0buRzN4RZpNJVH33Aa3bEgVRKtYdSCmqfW1lYa8iCYociwTQtPPnNQdChNTZZlGIZBnwfBgjpQKBSRp/X0hKOhiWJxDoxl8jRUtwZMJGJUQEgky3aQL1roSMbQ0UqVvmsVJWLzoGka4lS2XiiJMazuTiCVN5HNFUSH09RkWYbP5xMdRtPzMyBXKCJfoERMNFrTULx0tgCGiSVOiDgTiRiVsBftpeESOltjWNZBbddaRYnYPESjUWgaleoWSWLAyoSOP7zQA8uhSTEiUSJWGxiAfL6IfLEoOhRCPWLC2bYDSWJIRGkut0icc+RyWTj0PS3Ui73DUCSOtpaA6FDILCgRqxBjDLFYDKpK6zGIpKkKNBn4/b79sG26wIskSRIlYjWCesRqA+VhtUFiElrjlIiJlsvlYdtUUEukkmkjbwOv2HgOLexco+hdqZCiKIjH4jQfRrBlnVE4YBgapzVKRKPS9TWCMeTyRRQoESMEDgBIDK0xSsREy+dzlIgJ5nDgwIkcLj6nCzIV1qpJ9K5USNM0RKNR0WE0vRVJHf3DKfQMjIsOpekF/DTUQTRFlqApMoqmiZJliQ6n6XFOvfSiFR2gyBkSlIgJl8tTj5honHMcPj4Cv64gEQ2LDofMgBKxCiXiCRqWKJjEJhZyPt4/hJ4Tg6LDaXrBICViommqAkOj61KtoKGJ4hVLHMUS0BoNiQ6l6RUKBRRLNHdVtJzpALKKLZvWig6FzIASsQq1traKDqHphQMGktEQhjMlFIq0ZpJogQAlYqKpigxdU0D9MIRMKJQsFEsmwjpNI6gF2SytJSbaUKqEsZyFdcupHVuLqL7rDKxThvhY+QwMw4DCS0gN99McMYHCLIDCmB//9cgfYOZSosNpaowxOKUcfSYE0+wAhof8ONRzgj4TNSAzNoiUT6XPhEDFtIxDhxiGB/rpM1EDBnoOI+KjatMipYaBF9ptKFYOBishnaWlfxbCyr9cm8Cq4lQAxmkFyml+//vf45JLLhEdBiGEEEIIIaSG7N69GxdffHFVjkVDE2fQ398vOgRCCCGEEEJIA6OhiTM455xzyv/9yCOPoLu7W2A0RJS+vr5yz+ju3bvR0dEhOCLiNToHCJ0DhM4BQucAsSwLg4MTheI2bNhQteNSIjYDwzDK/93d3Y2uri6B0ZBa0NHRQedBk6NzgNA5QOgcIHQONK/ly5dX/Zg0NJEQQgghhBBCPEaJGCGEEEIIIYR4jBIxQgghhBBCCPEYJWKEEEIIIYQQ4jFKxAghhBBCCCHEY5SIEUIIIYQQQojHKBEjhBBCCCGEEI8xzjkXHQQhhBBCCCGENBPqESOEEEIIIYQQj1EiRgghhBBCCCEeo0SMEEIIIYQQQjxGiRghhBBCCCGEeIwSMUIIIYQQQgjxGCVihBBCCCGEEOIxSsQIIYQQQgghxGOUiBFCCCGEEEKIxygRI4QQQgghhBCPUSJGCCGEEEIIIR6jROw0xWIRN998Mzo7O+Hz+bB582bcd999osMii/T73/8ef/EXf4Fzzz0XgUAAS5cuxdvf/nbs379/2r7PPfcctm3bhmAwiFgshj/5kz/B4ODgtP0cx8H/+l//CytWrIBhGDjvvPPwT//0T148HVIlt912GxhjWL9+/bTHHnnkEWzduhV+vx/t7e3YsWMHMpnMtP3omlF/nnzySVx33XWIxWLw+/1Yv349vvnNb07Zh97/xnXgwAG8853vRFdXF/x+P8455xx88YtfRC6Xm7IfnQONIZPJ4POf/zy2bduGWCwGxhi+//3vz7ivG9//lR6TNClOpnjnO9/JFUXhn/zkJ/kdd9zBL730Uq4oCn/ooYdEh0YW4a1vfStvb2/nH/vYx/g//MM/8C996Uu8ra2NBwIBvm/fvvJ+x44d44lEgq9atYp/4xvf4LfddhuPRqP8/PPP58Viccox//t//+8cAP/Qhz7E77zzTn7ttddyAPyf/umfvH56ZAGOHTvG/X4/DwQC/Nxzz53y2J49e7hhGPyCCy7g3/nOd/hnPvMZrus637Zt27Tj0DWjvvzqV7/imqbxzZs389tvv53feeed/Oabb+Z//dd/Xd6H3v/GdfToUd7S0sKXLVvG//Zv/5bfcccd/H3vex8HwK+77rryfnQONI5Dhw5xAHzp0qX81a9+NQfA77rrrmn7ufH9P59jkuZEidgpHn/8cQ6A79y5s7wtn8/zVatW8UsvvVRgZGSxHn744WkXvf3793Nd1/l73vOe8raPfOQj3Ofz8SNHjpS33XfffRwAv+OOO8rbenp6uKqq/KMf/Wh5m+M4/FWvehXv6urilmW5+GxINbzjHe/gr33ta/nll18+LRG7+uqreUdHBx8fHy9v+4d/+AcOgP/qV78qb6NrRn0ZHx/nbW1t/M1vfjO3bXvW/ej9b1y33XYbB8CffvrpKdtvuOEGDoCPjIxwzukcaCSFQoH39fVxzjn//e9/P2si5sb3f6XHJM2LErFT/PVf/zWXZXnKhZdzzr/85S9zAPzo0aOCIiNu2bRpE9+0aVP599bWVr59+/Zp+61Zs4ZfccUV5d+//e1vcwD8mWeembLfT37yEw6A7oTWuN/85jdclmX+1FNPTUvExsfHuaIoU3pIOOe8WCzyYDDIb7zxxvI2umbUl+985zscAH/22Wc555xnMplpCRm9/43t5ptv5gD44ODgtO2SJPFMJkPnQAM7UyLmxvd/pcckzYvmiJ1iz549WLNmDcLh8JTtl1xyCQBg7969AqIibuGco7+/H4lEAgDQ29uLgYEBXHTRRdP2veSSS7Bnz57y73v27EEgEMDatWun7Tf5OKlNtm3jYx/7GD74wQ9iw4YN0x7ft28fLMuadh5omoaNGzdOOw/omlE/7r//foTDYfT29uLss89GMBhEOBzGRz7yERQKBQD0/je6V7/61QCAG2+8EXv37sWxY8fwz//8z/jOd76DHTt2IBAI0DnQhNz4/p/PMUnzokTsFH19fejo6Ji2fXLb8ePHvQ6JuOjHP/4xent78Y53vAPAxPsPYNZzYGRkBMVisbxvW1sbGGPT9gPoXKll3/3ud3HkyBF86UtfmvHxuc6DU99bumbUlwMHDsCyLLzpTW/C61//evzrv/4rPvCBD+C73/0u3v/+9wOg97/Rbdu2DV/60pdw33334YILLsDSpUvxzne+Ex/72Mfwta99DQCdA83Ije//+RyTNC9FdAC1JJ/PQ9f1adsNwyg/ThrD888/j49+9KO49NJL8ad/+qcAXn5/5zoHdF2nc6VODQ8P43Of+xxuvfVWJJPJGfeZ6zw49b2l86C+ZDIZ5HI5fPjDHy5XSXzLW96CUqmEO+64A1/84hfp/W8Cy5cvx2WXXYa3vvWtiMfj+PnPf44vf/nLaG9vx1/8xV/QOdCE3Pj+n88xSfOiROwUPp9vxrsTk0NWfD6f1yERF5w4cQLXXnstIpEIdu3aBVmWAbz8/lZyDtC5Up8++9nPIhaL4WMf+9is+8x1Hpz63tJ5UF8m3493vetdU7a/+93vxh133IFHH30Ufr8fAL3/jeqee+7BTTfdhP3796OrqwvARDLuOA5uvvlmvOtd76JrQBNy4/t/PsckzYuGJp6io6Oj3JV8qsltnZ2dXodEqmx8fBxXX301xsbG8Mtf/nLKezo5fGC2cyAWi5XvXHV0dODEiRPgnE/bD6BzpRYdOHAAd955J3bs2IHjx4/j8OHDOHz4MAqFAkzTxOHDhzEyMjLneXD6OUPXjPox+X60tbVN2d7a2goAGB0dpfe/wf393/89LrjggnISNum6665DLpfDnj176BxoQm58/8/nmKR5USJ2io0bN2L//v1IpVJTtj/++OPlx0n9KhQKeOMb34j9+/fjZz/7GdatWzfl8SVLliCZTOKJJ56Y9m9379495f3fuHEjcrkcnnvuuSn70blSu3p7e+E4Dnbs2IEVK1aUfx5//HHs378fK1aswBe/+EWsX78eiqJMOw9KpRL27t077Tyga0b9uPDCCwFMnAunmpzTkUwm6f1vcP39/bBte9p20zQBAJZl0TnQhNz4/p/PMUkTE1y1saY89thj09YDKRQKfPXq1Xzz5s0CIyOLZVkWv+6667iiKPznP//5rPt9+MMf5j6fb0rJ4fvvv58D4N/5znfK244dOzbrOiJLliyhdcRq0ODgIP/pT3867efcc8/lS5cu5T/96U/5U089xTnnfNu2bbyjo4OnUqnyv//Hf/xHDoD/4he/KG+ja0Z9efLJJzkA/u53v3vK9ne9611cURTe29vLOaf3v5G94Q1v4Jqm8RdeeGHK9uuvv55LkkTnQIM7U/l6N77/Kz0maV6UiJ1m+/bt5fVD7rjjDr5lyxauKAr/zW9+Izo0sgh/+Zd/yQHwN77xjfzuu++e9jPp6NGjPB6P81WrVvFvfvOb/Mtf/jKPRqN8w4YNvFAoTDnmX//1X3MA/KabbuL/8A//wK+99loOgP/4xz/2+umRRZhpQec//OEPXNd1fsEFF/DvfOc7/DOf+Qw3DINfddVV0/49XTPqywc+8AEOgL/97W/n3/72t/n27ds5AH7LLbeU96H3v3FNriHY2trKv/jFL/Jvf/vb/Oqrr+YA+Ac/+MHyfnQONJZvfetb/Etf+hL/yEc+wgHwt7zlLfxLX/oS/9KXvsTHxsY45+58/8/nmKQ5USJ2mnw+zz/5yU/y9vZ2rus6v/jii/kvf/lL0WGRRbr88ss5gFl/TvX000/zq666ivv9ft7S0sLf85738BMnTkw7pm3b/Mtf/jJftmwZ1zSNn3vuufxHP/qRV0+JVMlMiRjnnD/00EN8y5Yt3DAMnkwm+Uc/+tEpd8cn0TWjvpRKJf6FL3yBL1u2jKuqylevXs2/9rWvTduP3v/G9fjjj/Orr76at7e3c1VV+Zo1a/htt93GTdOcsh+dA41j2bJls37/Hzp0qLyfG9//lR6TNCfG+WmzDQkhhBBCCCGEuIqKdRBCCCGEEEKIxygRI4QQQgghhBCPUSJGCCGEEEIIIR6jRIwQQgghhBBCPEaJGCGEEEIIIYR4jBIxQgghhBBCCPEYJWKEEEIIIYQQ4jFKxAghhBBCCCHEY5SIEUIIIYQQQojHKBEjhBBCCCGEEI9RIkYIIYQQQgghHqNEjBBCCCGEEEI8RokYIYQQQgghhHiMEjFCCCGEEEII8RglYoQQQgghhBDiMUrECCGEEEIIIcRjlIgRQgghhBBCiMcoESOEEEIIIYQQj1EiRgghhBBCCCEeo0SMEEIIIYQQQjxGiRghhBBCCCGEeIwSMUIIIYQQQgjx2P8PZcQgc9eAD0oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 314,
       "width": 433
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    }
   ],
   "source": [
    "env.init_viewer(viewer_title='UR5e with RG2 gripper',viewer_width=1200,viewer_height=800,\n",
    "                viewer_hide_menus=True)\n",
    "env.update_viewer(azimuth=0.0,distance=3.5,elevation=-60,lookat=[0.4,0.05,0.36],\n",
    "                  VIS_TRANSPARENT=False,VIS_CONTACTPOINT=True,\n",
    "                  contactwidth=0.05,contactheight=0.05,contactrgba=np.array([1,0,0,1]),\n",
    "                  VIS_JOINT=True,jointlength=0.5,jointwidth=0.1,jointrgba=[0.2,0.6,0.8,0.6])\n",
    "env.reset()\n",
    "env.forward(q=q_init,joint_idxs=env.idxs_forward)\n",
    "\n",
    "tick = 0\n",
    "while (env.get_sim_time() < 100.0) and env.is_viewer_alive():\n",
    "    tick = tick + 1\n",
    "    \n",
    "    if tick >= traj_aug_len-1:\n",
    "        q = q_aug_traj[-1,:]\n",
    "    else:\n",
    "        q = q_aug_traj[tick,:]\n",
    "        \n",
    "    env.step(ctrl=q, ctrl_idxs=env.idxs_forward)\n",
    "    # Render\n",
    "    p_tcp,R_tcp = env.get_pR_body(body_name='tcp_link')\n",
    "    p_cam,R_cam = env.get_pR_body(body_name='camera_center')\n",
    "    p_base,R_base = env.get_pR_body(body_name='base')\n",
    "    # env.plot_T(p=np.zeros(3),R=np.eye(3,3),PLOT_AXIS=True,axis_len=1.0,axis_width=0.01)\n",
    "    env.plot_T(p=p_tcp,R=R_tcp,PLOT_AXIS=True,axis_len=0.1,axis_width=0.0025,\n",
    "               label=f'TCP Position: {p_tcp}')\n",
    "    env.plot_T(p=p_cam,R=R_cam,PLOT_AXIS=True,axis_len=0.1,axis_width=0.0025,\n",
    "               label=f'Cam Position: {p_cam}')\n",
    "    # env.plot_T(p=p_base,R=R_base,PLOT_AXIS=True,axis_len=1.0,axis_width=0.01)\n",
    "    env.plot_T(p=p_base+np.array([0,0,0.5]),R=np.eye(3,3),\n",
    "                PLOT_AXIS=False,label='[%.2f]sec'%(env.get_sim_time()))\n",
    "    env.render()\n",
    "    if env.loop_every(HZ=1) or (tick == 1):\n",
    "        scene_img = env.grab_image()\n",
    "# Plot an image\n",
    "plt.figure(figsize=(5,4)); plt.imshow(scene_img)\n",
    "plt.title(\"Tick:[%d] Time:[%.2f]sec\"%(env.tick,env.get_sim_time()),fontsize=9)\n",
    "plt.show()\n",
    "# Close viewer\n",
    "env.close_viewer()\n",
    "print (\"Done.\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Repelling IK: `TO DO`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collision detected!\n",
      "Index: 0, geom1: ur_rg2_gripper_finger1_finger_tip_link_collision, geom2: ur_rg2_gripper_finger2_finger_tip_link_collision\n",
      "ur_rg2_gripper_finger1_finger_tip_link_collision is not in all_body_names\n",
      "new geom1 name: ur_rg2_gripper_finger1_finger_tip_link\n",
      "ur_rg2_gripper_finger2_finger_tip_link_collision is not in all_body_names\n",
      "new geom2 name: ur_rg2_gripper_finger2_finger_tip_link\n",
      "ur_rg2_gripper_finger1_finger_tip_link\n",
      "contact body: ur_rg2_gripper_finger1_finger_tip_link\n",
      "['ur_rg2_gripper_finger1_finger_tip_link_collision']\n",
      "ur_rg2_gripper_finger1_finger_tip_link_collision\n",
      "[ 0.34 -0.01  1.13]\n",
      "******************************\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "\"Invalid name 'ur_rg2_gripper_finger1_finger_tip_link'. Valid names: ['base', 'base_table', 'camera_center', 'camera_mount', 'forearm_link', 'front_object_table', 'obj_cylinder_01', 'obj_cylinder_02', 'obj_cylinder_03', 'obj_cylinder_04', 'obj_cylinder_05', 'obj_cylinder_06', 'obj_cylinder_07', 'obj_cylinder_08', 'rg2_gripper_base_link', 'rg2_gripper_finger1_finger_link', 'rg2_gripper_finger1_finger_tip_link', 'rg2_gripper_finger1_inner_knuckle_link', 'rg2_gripper_finger2_finger_link', 'rg2_gripper_finger2_finger_tip_link', 'rg2_gripper_finger2_inner_knuckle_link', 'shoulder_link', 'side_object_table', 'tcp_link', 'upper_arm_link', 'ur_wrist_2_link_collision', 'world', 'wrist_1_link', 'wrist_3_link']\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 102\u001b[0m\n\u001b[1;32m     99\u001b[0m repel_scale \u001b[39m=\u001b[39m \u001b[39m1.0\u001b[39m\n\u001b[1;32m    100\u001b[0m p_repel_trgt \u001b[39m=\u001b[39m p_contact \u001b[39m+\u001b[39m repel_direction \u001b[39m*\u001b[39m repel_scale\n\u001b[0;32m--> 102\u001b[0m J_contact, err_contact \u001b[39m=\u001b[39m env\u001b[39m.\u001b[39;49mget_ik_ingredients(\n\u001b[1;32m    103\u001b[0m body_name\u001b[39m=\u001b[39;49mcontact_body,p_trgt\u001b[39m=\u001b[39;49mp_repel_trgt,R_trgt\u001b[39m=\u001b[39;49mnp\u001b[39m.\u001b[39;49meye(\u001b[39m3\u001b[39;49m),IK_P\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,IK_R\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)\n\u001b[1;32m    104\u001b[0m dq \u001b[39m=\u001b[39m env\u001b[39m.\u001b[39mdamped_ls(J_contact, err_contact,stepsize\u001b[39m=\u001b[39mrepel_step_size,eps\u001b[39m=\u001b[39m\u001b[39m1e-1\u001b[39m,th\u001b[39m=\u001b[39m\u001b[39m5\u001b[39m\u001b[39m*\u001b[39mnp\u001b[39m.\u001b[39mpi\u001b[39m/\u001b[39m\u001b[39m180.0\u001b[39m)\n\u001b[1;32m    105\u001b[0m q \u001b[39m=\u001b[39m q \u001b[39m+\u001b[39m dq[env\u001b[39m.\u001b[39midxs_jacobian]\n",
      "File \u001b[0;32m~/course/xai615-realworld/code/simulation/../../model/mujoco_parser.py:360\u001b[0m, in \u001b[0;36mMuJoCoParserClass.get_ik_ingredients\u001b[0;34m(self, body_name, p_trgt, R_trgt, IK_P, IK_R, w_weight)\u001b[0m\n\u001b[1;32m    356\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget_ik_ingredients\u001b[39m(\u001b[39mself\u001b[39m,body_name,p_trgt\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,R_trgt\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,IK_P\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,IK_R\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, w_weight\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m):\n\u001b[1;32m    357\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    358\u001b[0m \u001b[39m        Get IK ingredients\u001b[39;00m\n\u001b[1;32m    359\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 360\u001b[0m     J_p,J_R,J_full \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_J_body(body_name\u001b[39m=\u001b[39;49mbody_name)\n\u001b[1;32m    361\u001b[0m     p_curr,R_curr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_pR_body(body_name\u001b[39m=\u001b[39mbody_name)\n\u001b[1;32m    362\u001b[0m     \u001b[39mif\u001b[39;00m (IK_P \u001b[39mand\u001b[39;00m IK_R):\n",
      "File \u001b[0;32m~/course/xai615-realworld/code/simulation/../../model/mujoco_parser.py:352\u001b[0m, in \u001b[0;36mMuJoCoParserClass.get_J_body\u001b[0;34m(self, body_name)\u001b[0m\n\u001b[1;32m    350\u001b[0m J_p \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros((\u001b[39m3\u001b[39m,\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\u001b[39m.\u001b[39mnv)) \u001b[39m# nv: nDoF\u001b[39;00m\n\u001b[1;32m    351\u001b[0m J_R \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros((\u001b[39m3\u001b[39m,\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\u001b[39m.\u001b[39mnv))\n\u001b[0;32m--> 352\u001b[0m mujoco\u001b[39m.\u001b[39mmj_jacBody(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel,\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata,J_p,J_R,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdata\u001b[39m.\u001b[39;49mbody(body_name)\u001b[39m.\u001b[39mid)\n\u001b[1;32m    353\u001b[0m J_full \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(np\u001b[39m.\u001b[39mvstack([J_p,J_R]))\n\u001b[1;32m    354\u001b[0m \u001b[39mreturn\u001b[39;00m J_p,J_R,J_full\n",
      "\u001b[0;31mKeyError\u001b[0m: \"Invalid name 'ur_rg2_gripper_finger1_finger_tip_link'. Valid names: ['base', 'base_table', 'camera_center', 'camera_mount', 'forearm_link', 'front_object_table', 'obj_cylinder_01', 'obj_cylinder_02', 'obj_cylinder_03', 'obj_cylinder_04', 'obj_cylinder_05', 'obj_cylinder_06', 'obj_cylinder_07', 'obj_cylinder_08', 'rg2_gripper_base_link', 'rg2_gripper_finger1_finger_link', 'rg2_gripper_finger1_finger_tip_link', 'rg2_gripper_finger1_inner_knuckle_link', 'rg2_gripper_finger2_finger_link', 'rg2_gripper_finger2_finger_tip_link', 'rg2_gripper_finger2_inner_knuckle_link', 'shoulder_link', 'side_object_table', 'tcp_link', 'upper_arm_link', 'ur_wrist_2_link_collision', 'world', 'wrist_1_link', 'wrist_3_link']\""
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "import math\n",
    "PI = math.pi\n",
    "\n",
    "env.init_viewer(viewer_title='IK using UR',viewer_width=1200,viewer_height=800,viewer_hide_menus=True)\n",
    "env.update_viewer(azimuth=0.0,distance=3.5,elevation=-60,lookat=[0.4,0.05,0.36])\n",
    "env.update_viewer(VIS_TRANSPARENT=False) # transparent\n",
    "env.reset() # reset\n",
    "\n",
    "# Set to initial joint values\n",
    "env.forward(q=q_init, joint_idxs=env.idxs_forward)\n",
    "\n",
    "tcp_target = np.array([0.9, -0.2, 0.9])\n",
    "\n",
    "delta_p =  (env.get_p_body(\"tcp_link\")-env.get_p_body('wrist_3_link'))\n",
    "R_ = env.get_R_body(\"wrist_3_link\")\n",
    "p_offset = R_.T @ delta_p\n",
    "wrist3_target = tcp_target - p_offset[[1,0,2]]\n",
    "\n",
    "# Set (multiple) IK targets\n",
    "ik_body_names = ['tcp_link','wrist_3_link']\n",
    "ik_p_trgts = [tcp_target,\n",
    "              wrist3_target]\n",
    "ik_R_trgts = [rpy2r(np.array([0, 1*PI, -0.5 *PI])),\n",
    "              rpy2r(np.array([0, 0, 0]))]\n",
    "IK_Ps = [True,True]\n",
    "IK_Rs = [True,False]\n",
    "all_body_names = env.get_body_names(prefix='')\n",
    "\n",
    "new_geom1_name = None\n",
    "\n",
    "# Loop\n",
    "q = env.get_q(joint_idxs=env.idxs_jacobian)\n",
    "\n",
    "# q = env.data.qpos[16:22]\n",
    "imgs,img_ticks,max_tick = [],[],1000\n",
    "while (env.tick < max_tick) and env.is_viewer_alive():\n",
    "    # Numerical IK\n",
    "    J_aug,err_aug = [],[]\n",
    "    for ik_idx,ik_body_name in enumerate(ik_body_names):\n",
    "        p_trgt,R_trgt = ik_p_trgts[ik_idx],ik_R_trgts[ik_idx]\n",
    "        IK_P,IK_R = IK_Ps[ik_idx],IK_Rs[ik_idx]\n",
    "        J,err = env.get_ik_ingredients(\n",
    "            body_name=ik_body_name,p_trgt=p_trgt,R_trgt=R_trgt,IK_P=IK_P,IK_R=IK_R)\n",
    "        if (J is None) and (err is None): continue\n",
    "        if len(J_aug) == 0:\n",
    "            J_aug,err_aug = J,err\n",
    "        else:\n",
    "            J_aug   = np.concatenate((J_aug,J),axis=0)\n",
    "            err_aug = np.concatenate((err_aug,err),axis=0)\n",
    "    dq = env.damped_ls(J_aug,err_aug,stepsize=1,eps=1e-1,th=5*np.pi/180.0)\n",
    "\n",
    "    # Update q and FK\n",
    "    q = q + dq[env.idxs_jacobian]\n",
    "    env.forward(q=q,joint_idxs=env.idxs_forward)\n",
    "\n",
    "    p_contacts,f_contacts,geom1s,geom2s = env.get_contact_info(must_include_prefix=None,must_exclude_prefix='rg2_')\n",
    "    while len(geom1s) > 0 and len(geom2s) > 0:\n",
    "    # while len(geom1s_exclude_eef) > 0 and len(geom2s_exclude_eef) > 0:\n",
    "        print(\"Collision detected!\")\n",
    "        # revert to previous state. (Before collision.)\n",
    "        q = q - dq[env.idxs_jacobian]\n",
    "\n",
    "        for contact_geom_idx, (geom1, geom2) in enumerate(zip(geom1s, geom2s)):\n",
    "            print(f\"Index: {contact_geom_idx}, geom1: {geom1}, geom2: {geom2}\")\n",
    "            # Detect Self-collision.\n",
    "            if geom1 not in all_body_names: # if the name of the robot's geom is not in the list of all body names\n",
    "                print(f\"{geom1} is not in all_body_names\")\n",
    "                cut_geom1_name = geom1.split('_')[0:-1] # slice all elements except the last one\n",
    "                new_geom1_name = '_'.join(cut_geom1_name)     # join the sliced elements back into a string\n",
    "                print(f\"new geom1 name: {new_geom1_name}\")\n",
    "\n",
    "            # Detect with the Table.\n",
    "            if geom2 not in all_body_names: # if the name of the robot's geom is not in the list of all body names\n",
    "                print(f\"{geom2} is not in all_body_names\")\n",
    "                cut_geom2_name = geom2.split('_')[0:-1] # slice all elements except the last one\n",
    "                new_geom2_name = '_'.join(cut_geom2_name)     # join the sliced elements back into a string\n",
    "                print(f\"new geom2 name: {new_geom2_name}\")\n",
    "\n",
    "        if new_geom1_name is not None:\n",
    "            print(new_geom1_name)\n",
    "            contact_body = new_geom1_name\n",
    "        else:\n",
    "            contact_body = geom1s[0]    # heuristically choose the first one\n",
    "        print(f\"contact body: {contact_body}\")\n",
    "        print(geom1s)\n",
    "        print(geom1s[contact_geom_idx])\n",
    "        print(p_contacts[contact_geom_idx])\n",
    "        print(\"***\"*10)\n",
    "\n",
    "        p_contact = p_contacts[contact_geom_idx]\n",
    "        f_contact = f_contacts[contact_geom_idx]\n",
    "        f_norm = np.linalg.norm(f_contact) # normalize the force\n",
    "        repel_direction = f_contact / (f_norm+1e-8) # numerical prevention\n",
    "        env.plot_arrow(p=p_contact,uv=repel_direction,r_stem=0.01,len_arrow=0.05,rgba=[1,0,0,1], label='')\n",
    "        env.plot_sphere(p=p_contact,r=0.02,rgba=[1,0.2,0.2,1],label='')\n",
    "        for p in p_contacts: env.plot_sphere(p=p,r=0.05,rgba=[1,0.2,0.2,1],label='contact points')\n",
    "\n",
    "        repel_step_size = 1\n",
    "        repel_scale = 1.0\n",
    "        p_repel_trgt = p_contact + repel_direction * repel_scale\n",
    "\n",
    "        J_contact, err_contact = env.get_ik_ingredients(\n",
    "        body_name=contact_body,p_trgt=p_repel_trgt,R_trgt=np.eye(3),IK_P=True,IK_R=False)\n",
    "        dq = env.damped_ls(J_contact, err_contact,stepsize=repel_step_size,eps=1e-1,th=5*np.pi/180.0)\n",
    "        q = q + dq[env.idxs_jacobian]\n",
    "        env.forward(q=q,joint_idxs=env.idxs_forward)\n",
    "\n",
    "        env.plot_T(p=p_contact,R=np.eye(3),PLOT_AXIS=True,axis_len=0.1,axis_width=0.005, label='p_contact')\n",
    "        env.plot_T(p=p_repel_trgt,R=np.eye(3),PLOT_AXIS=True,axis_len=0.2,axis_width=0.005, label='p_repel_trgt')\n",
    "        env.render()\n",
    "\n",
    "        p_contacts,f_contacts,geom1s,geom2s = env.get_contact_info(must_include_prefix=None,must_exclude_prefix='rg2_')\n",
    "\n",
    "\n",
    "    # Render\n",
    "    for ik_idx,ik_body_name in enumerate(ik_body_names):\n",
    "        p_trgt,R_trgt = ik_p_trgts[ik_idx],ik_R_trgts[ik_idx]\n",
    "        IK_P,IK_R = IK_Ps[ik_idx],IK_Rs[ik_idx]\n",
    "        if (IK_P is None) and (IK_R is None): continue\n",
    "        env.plot_T(p=env.get_p_body(body_name=ik_body_name),R=env.get_R_body(body_name=ik_body_name),\n",
    "                   PLOT_AXIS=IK_R,axis_len=0.2,axis_width=0.01,\n",
    "                   PLOT_SPHERE=IK_P,sphere_r=0.05,sphere_rgba=[1,0,0,0.9])\n",
    "        env.plot_T(p=p_trgt,R=R_trgt,\n",
    "                   PLOT_AXIS=IK_R,axis_len=0.2,axis_width=0.01,\n",
    "                   PLOT_SPHERE=IK_P,sphere_r=0.05,sphere_rgba=[0,0,1,0.9])\n",
    "    env.plot_T(p=[0,0,0],R=np.eye(3,3),PLOT_AXIS=True,axis_len=1.0)\n",
    "    env.render()\n",
    "    # Print and save image \n",
    "    if (env.tick)%(max_tick//10)==0 or (env.tick==1):\n",
    "        print (\"[%d/%d] IK error:[%.4f]\"%(env.tick,max_tick,np.linalg.norm(err_aug)))\n",
    "        img = env.grab_image()\n",
    "        imgs.append(img)\n",
    "        img_ticks.append(env.tick)\n",
    "# Close viewers\n",
    "env.close_viewer()\n",
    "print (\"Done.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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