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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"from typing import NamedTuple, Callable, Tuple\n", | |
"\n", | |
"import jax\n", | |
"import jax.numpy as jnp\n", | |
"import optax\n", | |
"import chex\n", | |
"\n", | |
"import lovelyplots\n", | |
"import matplotlib.pyplot as plt\n", | |
"import matplotlib.cm as cm\n", | |
"\n", | |
"plt.style.use(\"ipynb\")" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"class ScheduleFreeState(NamedTuple):\n", | |
" x: optax.Params\n", | |
" z: optax.Params\n", | |
" t: float\n", | |
" base_optimizer_state: optax.OptState\n", | |
"\n", | |
"\n", | |
"def schedule_free(\n", | |
" base_optimizer: optax.GradientTransformation,\n", | |
" beta: float,\n", | |
") -> optax.GradientTransformation:\n", | |
" \"\"\"Wraps an optimizer to make it schedule-free.\"\"\"\n", | |
"\n", | |
" def init_fn(params: optax.Params) -> ScheduleFreeState:\n", | |
" return ScheduleFreeState(\n", | |
" x=params,\n", | |
" z=params,\n", | |
" t=1.0,\n", | |
" base_optimizer_state=base_optimizer.init(params),\n", | |
" )\n", | |
"\n", | |
" def update_fn(\n", | |
" updates: optax.Updates, opt_state: ScheduleFreeState, params: optax.Params\n", | |
" ) -> Tuple[optax.Updates, ScheduleFreeState]:\n", | |
" x_curr = opt_state.x\n", | |
" z_curr = opt_state.z\n", | |
" t = opt_state.t\n", | |
" base_optimizer_state = opt_state.base_optimizer_state\n", | |
"\n", | |
" z_updates, base_optimizer_state = base_optimizer.update(\n", | |
" updates, base_optimizer_state, params\n", | |
" )\n", | |
" z_next = optax.apply_updates(z_curr, z_updates)\n", | |
" x_next = jax.tree.map(lambda x, z: x * (1 - 1 / t) + z * (1 / t), x_curr, z_next)\n", | |
" y_next = jax.tree.map(lambda x, z: x * beta + z * (1 - beta), x_next, z_next)\n", | |
" updates = jax.tree.map(lambda y_dash, y: y_dash - y, y_next, params)\n", | |
"\n", | |
" opt_state = opt_state._replace(\n", | |
" x=x_next,\n", | |
" z=z_next,\n", | |
" t=t + 1,\n", | |
" base_optimizer_state=base_optimizer_state,\n", | |
" )\n", | |
" return (updates, opt_state)\n", | |
"\n", | |
" return optax.GradientTransformation(init_fn, update_fn)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def f(x):\n", | |
" \"\"\"The function to minimize.\"\"\"\n", | |
" return 0.1 * (x**4) - 0.5 * (x**2) + 4 * x" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def optimize_with(\n", | |
" f: Callable[[optax.Params], float],\n", | |
" tx: optax.GradientTransformation,\n", | |
" initial_params: optax.Params,\n", | |
" n_steps: int,\n", | |
"):\n", | |
" \"\"\"Minimizes f() using the given optimizer, returning the loss history.\"\"\"\n", | |
"\n", | |
" opt_state = tx.init(initial_params)\n", | |
" params = initial_params\n", | |
"\n", | |
" @jax.jit\n", | |
" def train_step(params, opt_state):\n", | |
" grads = jax.grad(f)(params)\n", | |
" updates, opt_state = tx.update(grads, opt_state, params)\n", | |
" params = optax.apply_updates(params, updates)\n", | |
" return params, opt_state\n", | |
"\n", | |
" loss = jnp.zeros(n_steps)\n", | |
" for t in range(n_steps):\n", | |
" if isinstance(opt_state, ScheduleFreeState):\n", | |
" eval_params = opt_state.x\n", | |
" else:\n", | |
" eval_params = params\n", | |
" loss = loss.at[t].set(f(eval_params))\n", | |
"\n", | |
" params, opt_state = train_step(params, opt_state)\n", | |
" return loss" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 12, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"ename": "AttributeError", | |
"evalue": "module 'jax' has no attribute 'tree'", | |
"output_type": "error", | |
"traceback": [ | |
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", | |
"Cell \u001b[0;32mIn[12], line 10\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m learning_rate \u001b[38;5;129;01min\u001b[39;00m sgd_learning_rates:\n\u001b[1;32m 9\u001b[0m trajectories[(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSGD\u001b[39m\u001b[38;5;124m\"\u001b[39m, learning_rate)] \u001b[38;5;241m=\u001b[39m optimize_with_fn(optax\u001b[38;5;241m.\u001b[39msgd(learning_rate))\n\u001b[0;32m---> 10\u001b[0m trajectories[(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSGD ScheduleFree\u001b[39m\u001b[38;5;124m\"\u001b[39m, lr_factor \u001b[38;5;241m*\u001b[39m learning_rate)] \u001b[38;5;241m=\u001b[39m \u001b[43moptimize_with_fn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mschedule_free\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msgd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlr_factor\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbeta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.95\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 14\u001b[0m adam_learning_rates \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0.05\u001b[39m, \u001b[38;5;241m0.1\u001b[39m]\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m learning_rate \u001b[38;5;129;01min\u001b[39;00m adam_learning_rates:\n", | |
"Cell \u001b[0;32mIn[12], line 1\u001b[0m, in \u001b[0;36m<lambda>\u001b[0;34m(tx)\u001b[0m\n\u001b[0;32m----> 1\u001b[0m optimize_with_fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m tx: \u001b[43moptimize_with\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\n\u001b[1;32m 3\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m trajectories \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 6\u001b[0m lr_factor \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n", | |
"Cell \u001b[0;32mIn[11], line 27\u001b[0m, in \u001b[0;36moptimize_with\u001b[0;34m(f, tx, initial_params, n_steps)\u001b[0m\n\u001b[1;32m 24\u001b[0m eval_params \u001b[38;5;241m=\u001b[39m params\n\u001b[1;32m 25\u001b[0m loss \u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mat[t]\u001b[38;5;241m.\u001b[39mset(f(eval_params))\n\u001b[0;32m---> 27\u001b[0m params, opt_state \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopt_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n", | |
" \u001b[0;31m[... skipping hidden 12 frame]\u001b[0m\n", | |
"Cell \u001b[0;32mIn[11], line 15\u001b[0m, in \u001b[0;36moptimize_with.<locals>.train_step\u001b[0;34m(params, opt_state)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;129m@jax\u001b[39m\u001b[38;5;241m.\u001b[39mjit\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtrain_step\u001b[39m(params, opt_state):\n\u001b[1;32m 14\u001b[0m grads \u001b[38;5;241m=\u001b[39m jax\u001b[38;5;241m.\u001b[39mgrad(f)(params)\n\u001b[0;32m---> 15\u001b[0m updates, opt_state \u001b[38;5;241m=\u001b[39m \u001b[43mtx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopt_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m params \u001b[38;5;241m=\u001b[39m optax\u001b[38;5;241m.\u001b[39mapply_updates(params, updates)\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m params, opt_state\n", | |
"Cell \u001b[0;32mIn[9], line 34\u001b[0m, in \u001b[0;36mschedule_free.<locals>.update_fn\u001b[0;34m(updates, opt_state, params)\u001b[0m\n\u001b[1;32m 30\u001b[0m z_updates, base_optimizer_state \u001b[38;5;241m=\u001b[39m base_optimizer\u001b[38;5;241m.\u001b[39mupdate(\n\u001b[1;32m 31\u001b[0m updates, base_optimizer_state, params\n\u001b[1;32m 32\u001b[0m )\n\u001b[1;32m 33\u001b[0m z_next \u001b[38;5;241m=\u001b[39m optax\u001b[38;5;241m.\u001b[39mapply_updates(z_curr, z_updates)\n\u001b[0;32m---> 34\u001b[0m x_next \u001b[38;5;241m=\u001b[39m \u001b[43mjax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtree\u001b[49m\u001b[38;5;241m.\u001b[39mmap(\u001b[38;5;28;01mlambda\u001b[39;00m x, z: x \u001b[38;5;241m*\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m/\u001b[39m t) \u001b[38;5;241m+\u001b[39m z \u001b[38;5;241m*\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m/\u001b[39m t), x_curr, z_next)\n\u001b[1;32m 35\u001b[0m y_next \u001b[38;5;241m=\u001b[39m jax\u001b[38;5;241m.\u001b[39mtree\u001b[38;5;241m.\u001b[39mmap(\u001b[38;5;28;01mlambda\u001b[39;00m x, z: x \u001b[38;5;241m*\u001b[39m beta \u001b[38;5;241m+\u001b[39m z \u001b[38;5;241m*\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m beta), x_next, z_next)\n\u001b[1;32m 36\u001b[0m updates \u001b[38;5;241m=\u001b[39m jax\u001b[38;5;241m.\u001b[39mtree\u001b[38;5;241m.\u001b[39mmap(\u001b[38;5;28;01mlambda\u001b[39;00m y_dash, y: y_dash \u001b[38;5;241m-\u001b[39m y, y_next, params)\n", | |
"File \u001b[0;32m~/Documents/spherical-harmonic-net/.venv/lib/python3.10/site-packages/jax/_src/deprecations.py:53\u001b[0m, in \u001b[0;36mdeprecation_getattr.<locals>.getattr\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 51\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(message, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m, stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn\n\u001b[0;32m---> 53\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodule\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", | |
"\u001b[0;31mAttributeError\u001b[0m: module 'jax' has no attribute 'tree'" | |
] | |
} | |
], | |
"source": [ | |
"optimize_with_fn = lambda tx: optimize_with(\n", | |
" f, tx, initial_params=jnp.asarray(1.0), n_steps=100\n", | |
")\n", | |
"\n", | |
"trajectories = {}\n", | |
"lr_factor = 2\n", | |
"sgd_learning_rates = [0.05, 0.1]\n", | |
"for learning_rate in sgd_learning_rates:\n", | |
" trajectories[(\"SGD\", learning_rate)] = optimize_with_fn(optax.sgd(learning_rate))\n", | |
" trajectories[(\"SGD ScheduleFree\", lr_factor * learning_rate)] = optimize_with_fn(\n", | |
" schedule_free(optax.sgd(lr_factor * learning_rate), beta=0.95)\n", | |
" )\n", | |
"\n", | |
"adam_learning_rates = [0.05, 0.1]\n", | |
"for learning_rate in adam_learning_rates:\n", | |
" trajectories[(\"Adam\", learning_rate)] = optimize_with_fn(optax.adam(learning_rate))\n", | |
" trajectories[(\"Adam ScheduleFree\", lr_factor * learning_rate)] = optimize_with_fn(\n", | |
" schedule_free(optax.adam(lr_factor * learning_rate), beta=0.95)\n", | |
" )" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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3DS5cgDXHHI1ocyJoP1qCxEnZdihOKyW85SuQjr/sTDx2GQndxr/7BMrbTwJ+X+6F8xBy1HPfpIZNR0THXMofs0K4ph430untgjLvCShB/7BfMUhMREREREREREREREREREREREREVmOYmIiIiIhGDTMCxeEli3WHKr+67GbMa1WyrmWFj5d14oUv1tk9DN2B4tU/PxiBjz8ytO6Smfuj/l//hlxUlOswc1Z74UVofPZ5W8O1rvHjMf3d9zDur9cCTqeh20ZXrMDqo2fB/978URUkTtoYDKvKfiUp4S0hBHpChRHS7QnFbJ9YgOyjN1CM7xYkunLnKo8hR637Jm25K+Std7d8DDRymB3CtTxQHOqDeP0hKCsXDeyjGSQmIiIiIiIiIiIiIiIiIiIiIiKifGCYmIiIiIhGlVwDxZHly9F0ykkZQ5W1DzyCW//Xle0wLXX7vGUIRuJ2D0PXcyFCIUPrLD34ENTfcSdktyfX4ZnGzu7IqWOoPP4ENNz/IOSyMkO3VTo70XL2WaMuSJyUCIYdA3iyCJ8PCW/1ReKIK4UR0I0pAoERsB8g+1gWiqyeCGyy3cb/tyHkmO6+MUg89lgVwrU8UKzEIT5/C8rbT0BpWsogMREREREREREREREREREREREREeUFw8RERERENOrkFCiOxxFZsUL118lQ5dx1cfgC0RxGaR1fIIo3vl1v9zAAmNMtOql81lGYeOttkFwuE0Y2OhXvsQca5zwL1+TJpq2zkIPESZK3BNLeRxu7UZrwVqGFc/sKbLxkPnNDkVIisLvPsXBstw+kLXe1NeSYet8YJB57rO7ma3mgGAB8bRALX836PohYxLqx6TQSxkBERERERERERERERERERERERET6MExMRERERKOSmSHWpGSo0lFTg6c/azJtvVZ4+tMmCDEyOqia8VxUnHgSxt9wIySHw8SRjU7uGTPQ+MxzKNpxJ1PWVehB4iS5vBaYtIm+hSUJ0p6zhoW3CqUrcVKhjZesIXlLIO11VHbduZOKyyDvcwzkrXeHJCc+SpK33h3yzBNs7ZYqeUsgzzyBQeIxxuogcVJeAsVaNO6DsmgBlLefggj12TCwBBHqg/L2U1AWLbBtDERERERERERERERERERERERERKQfw8RERERENGqZGShODVV+09yDxet6TRihdb5f14tvmnvsHsaA5HPhbGgwfNvK007DuKuvGQiwUWaO6mpM+s9/UHb4EVmvYzQFiZOkKVvpW1AIiBVfDftxoWVzC228ZA2hxIFvPwLCwaxuLzVsCnn/kyHVThr+O6c71+HlbCSMgfInX0HiJN2BYknKudYgpZWaQWLx/SdAbyeU956zJVCc+jyI7z9hoJiIiIiIiIiIiIiIiIiIiIiIiKgA8Gp8IiIiIhrVzAgUDw1Vvv3dBrOGZ6mRNk5HZSXkImOdMat/+SvUXfFHSGaHdMYA2e3BhL/fjJqLf5PV7b3bbQ9HTY3Jo7KPCPVBfPOh/hss/h+U9uZBP5IL7GVYaOMl84lICMoHL0Cs/t74jR0uSDsdAGnXQyC5veYPjsigfAeJk3QFioUA3EWAw2VO0b4eiB++gBgyCcBAkDjJhkBxuueBgWIiIiIiIiIiIiIiIiIiIiIiIqKRj2FiIiIiIhr1cgkUp+vO+u0I6virZaSNs+s/DyOydKnu5avOORe1l1zKIHEOJElCza/Pw8R/3A7J4zF0257nnsWGa66CUBSLRpc/ugJo6W73wQtQgv6B/3cUWDq30MZL5hJ+H5R3nwbamozfWHZA2nMW5Klbcx9MI4KIRWwJEifpChRHgkBRCbDJdoDsyK2gUCB++BzK6w9B+f5TiFh0eJA4KY+BYq3jKQPFREREREREREREREREREREREREIxvDxEREREQ0JiQDxUY6raYLEiuKwPfreq0YoukWr+uFogi7hwEAiDY1of32fxi6Td/b8xBrb7dmQGNM2SE/x8S7/gk4jIWbup94Ahuu+nNBB4qzDRIDAOIxiLefGAhoFbtzDIflWUmBjZfMI9pboLzzNNDbld0KlDjE5/Py2u2USIvkdENq2FR9AQuDxANj0BEoliZvDsd2+0A++HRI07YBcg3jxyIQiz6G8tqDECu+UV8uD4FiPcdT0bQUIhaxbAxERERERERERERERERERERERESUPYaJiYiIiGjMcNbWou6PV+paVvJ4hwWJAWBNZwB94bgVwzOdPxzHms6g3cOAEALr//gHIBQydLvI8uVomn0yA8UmiLW3o/3GG4C48ddu99NPYf2VfyzIQHFOQeKkUADK/GcgQn0ocTsKptuvU5YKLvxM5lDWLoHy/nOJLqm5yGO3UyI95K13h7TlrsN/kYcgcZJWoFjaclfIW++e+L6oFPKO+0M+8FSgfnruhSNBIBwAJI2Pci3cZnUdT5PPg9Nten0iIiIiIiIiIiIiIiIiIiIiIiLKHcPERERERDSmuDfR6GqXQoRDiPv9w36+qiNg9pAstarD/hBY95ynEVjwcVa3ZaA4d7H2djTNPhmR5cuzXkfPs89g/R+ugMgijGwXU4LESX5fYl3hAMq9ztzXlwflXiekXDtiUkERQkD5/lOIT14HFJO2VQaKaYQZFijOY5A4KV2gODVIPIjLDfT6zCsuMkzsYcE2ayhInMfngYiIiIiIiIiIiIiIiIiIiIiIiIxhmJiIiIiIxoxYeztaf3OR7uWbTjh+WIg1GCmcMCUAhKL2dpMNr1yJDVdfldM6GCjOnhlB4qSe559D6+9/VxCBYn3BpyqgvEb/SvsDWjWOSO4DzIOKosIIPZM5hBKH+N9bEIuymLihbjJQWqX+ewaKaYQZCBTbGGBNDRSrBYlNndTCCBO3WQaJiYiIiIiIiIiIiIiIiIiIiIiIRg+GiYmIiIhoTMgmVBnv7MDaU04aFGKNxYUVw7NMNGZfmDjW3o61xx4N6AyfOqqrVX/HQLFxel7zUomx4E/vSy+i9fLfQsRiuQ7PMvqDT8dA3vlngJHuvb2daPzudbhjwdwHarHx5R67hzAmiJj94XIl0AvlgxcgVn9n+LbSjB9D3nMW5H2OGdRldRgGimmEkbfeHfLME2wNsEreEsgzT8g+SGxl93gTtlkGiYmIiIiIiIiIiIiIiIiIiIiIiEYXhomJiIiIaNTLpTtrdMWKQSFWp8PC4IcFXE57Tvlj7e1Yc+zRUHp6dC3v3mQTND7/ItwzZqguw0Cxfnpe8+4ZMzD1tTdQevDBhtbd+8rLaL3s0hEZKDYafJKqxkHabEdDNRz+Luyy9u0RHSgu9zrZmTgPlEULoLz9lK0BW6WjFeKNh4C2JsO3lbbdC9J2+0KS5UFdVlUxUEwjjOR02z2EtGPQeyySDjkT2H5fQLLoXC2HbZZBYiIiIiIiIiIiIiIiIiIiIiIiotGHYWIiIiIiGtVyCRInpYZYi9wOE0dnPa8r/6f8sfZ2NJ1yMmLNzbpvM/4v18JVX4+GRx9noDhHeoPEDY8+DtfEiZh4y20o+/mhhmr0vvYq1l1yMUQ0mutwTZNt8EnaajegtNJQraJQN3ZcPW/EBoobq72QrOx4SVAWLYD4/hNbA7bKuhUQ858GFIMd6GUH5D0Og7zpDoNeJwwUE+XOyLFILiqFY8a2kA4+HfAUWzOgLLZZBomJiIiIiIiIiIiIiIiIiIiIiIhGJ4aJiYiIiGjUMiNInJQMsU6RRmZ4UM3UmvyGPAYe8xX6H/OK409A0U47AQCctbUMFOfASJDYWVsLAJCcTkz4+80oO/xwQ7X8b7yBdRdfBBGJ5DRmM+QSfJIcTsg77m+4ZmmkZ0QGil0OCRMrvHYPY1QbCBIn2RCwja/4GuKjlwEhjN3QUwR5n+Mg1affxzJQTJS9bI9FcnEZ5ANOBsqqrBmYgW2WQWIiIiIiIiIiIiIiIiIiIiIiIqLRi2FiIiIiIhqV9IQq5cpKQ+uMLF8OXHQ2Jir+HEeXH6UeBxqri/JWL5vwtqO2FrWX/XbQzxgozk42QeIkyenEhJv+jvIjZxmq6X9rLlouutDWQLEZwSeprgHStG0M1x6JgeLNx5fAIbMrsVWGBYmT8hSwFUIg/u2HwBfvGL9xWTXkmSdCqh6vuRgDxUTG5XosSmx3x2hvd7no7YTy3rOa2yyDxERERERERERERERERERERERERKMbw8RERERENOroDlU+/Ajgchlad3T5cvx53q2oCPbkOkzLbTGxDHKeQoXZdoGu+8OVcFRUDPs5A8XG5BIkTpIcDoy/4UaUH32Modp9b89DywXnQYmEDd3ODMngk9Leqr6QzuCT9OM9gSzCUSMpUFzhdWJSZeauxEII+MMxbOgNY113CM2+ENZ1h7ChNwx/OAZhtNvtGCFiEYimpeoLWBywFUocyqdvAIv/Z/zGtQ2Q9zseUnG5rsUZKCbSz6wQrq7tLhe9XVDmPQElMHxSHAaJiYiIiIiIiIiIiIiIiIiIiIiIRj+GiYmIiIhoVDESqvRutRXKjzjCcI3ajmZc/c7IDxRvM0lfaCxX2QaJi/fcC2U//7nq7xko1seMIHGS5HBg/HXXo/zY4wyNoe/dd9Hy619DCecvUJwMPsVamtB09zPonJemW6yB4JPk8kDeYb+sxjJSAsW94Ri6g7FhPxdCwBeIYkmrH5+s9GHe4g58uKwLX6zpwVdNvfimuRdfNfXiizU9+HBZF+Yt7sAnK31Y0uqHLxBluLif5HTbFrAVkVAi6Ld2ifEbN24Bea+jILk8hm7GQDFRZmaHcC0PFIf6IN54EMrKRQP79mzvg4hFrBmjASNhDERERERERERERERERERERERERIWCYWIiIiIiGjWyCVVWn3k2IBnv3tvQs27EB4pnbjXO8hrZBoklrxfjr74GUobHnoFibWYGiZMkWcb4v16LihNONDSWwPvvoeXXv4QSChm6XTZSg8Qt97+AaFsXut75bHCgOIsOilL9DEgNm2Y1ppEQKFYE8PmabvhDiUBxXBFo6gpiwQofFq70YWVHEF2BKOKKdjg4rgh0BaJY2RHEwpU+LFjhQ1NXMOPtxgI7ArairxvK208BHS3Gb7zlrpB3/hkkObuPfxgoJlJnVTdfywPFigLx+VtQ5j0OpWlpVvdBWbQAyttP2brdi1AflLefgrJogW1jICIiIiIiIiIiIiIiIiIiIiIiKiQMExMRERHRqJBtqNI9YwZKDzgwq5ojOVC8VX0ZfmRxZ+Jsg8QAUHPBhXBNnqxrWQaK07MiSJwkyTLGXX0NKk462dDtAh98gJZfngslaF2gNl2QOGkgUJxFeCtJ2m4fwGAH16SRECiOxgU+XeXD8rY+zP+hA9+2+NETGt6t2IieUAzftvgx/4cOrO0KjvlOxfkM2IqOdVDefhLo8xm+rbTjAXBsvXvGSRsyroeBYqJhrAoSJ1keKAaA7naIha9mFSQW339i63af+viL7z9hoJiIiIiIiIiIiIiIiIiIiIiIiEgHhomJiIiIqODlGqqsOvucrGuP1EDxcTs35Bwg05JLkNi9+RaoOu10Q7dhoHgwK4PESZIsY9xVV6Ny9qmGbhf4+CM0n3sOlEAgq7paRCyiGiRO6nrnM3Qt2pBVeAtIBLikbffKeozJQLFDiWa9jlxF4gJLNwQQjZsb+o3GBRa1+PH5mm4Eo3FT111o8hGwFU0/QJn/DBAx2O1bkiD99EjI07bOqm7aVTJQTDTA6iBxUl4CxVq0gsRJNmz36R5/BoqJiIiIiIiIiIiIiIiIiIiIiIgyY5iYiIiIiAqaGaHKom23RdFuu2c9hpEWKK4sduGgbcZbtv5cgsSQJIy/9lpILpfhmzJQnJCPIHGSJEmou/JPqDztNEO3Cy5cgOZzzoLSZ264SHK6ES8dpxokTvK/9VZOtaUpWwHjGrO+/fryKYjLxl/jhaLdH8VHy7rQ2h22eyi2sipgK4SAsvgzKAtfA4RibFCyA9K+J0CeMNXY7XQwdH9jEdPrE40E+QoSJ+kOFJs9gUxxGaS9jtIOEiflMVCs9fgzUExERERERERERERERERERERERKSNYWIiIiIiKlhmhiqrzz03p7GMpEDxRftvgiK3w5J1K3192QeJAVScdDKKtt0u6/pGAsVmB1lHAj2Pv1lB4iRJklB3xR9RdeZZhm4X/PRTNJ99FhS/35RxAIltvuXa2zSDxMn7L5fk0A1SkiDvOBNwOA3fNr75LuiZtmPWtQtFTBH4sqkHazqDdg/FVmYHioUSh/h8HsS3HxkfjMMJ6YBTIFdbN5mEnvsrNWwKyem2bAxEdsl3kDhJ135GCMBTBJi17QV6IRa+BtG6GkIIiFgEommp+vJ5CBTrefxF01JOZkBERERERERERERERERERERERKSCYWIiIiIiKkhmd2ct3uMn8Gy9dU5jGgmB4p9sUoNZO0y0bP1ySQlKDz4kq9s6xo1D7SWX5jwGPYHi0oMPySlMOlJlevzNDhInSZKE2st/h6qzzzF0u+D/PkPTWWcg7u/NeQz57MgMAFJJBaSt9zB2oy13hftHe2DHxgpsU18Kl8PcTpEuh4RNxhXDNYLeyX+3zs9AsUmBYqeIQ/nwRYhVi4wPwuWB9LNfQC6rMn5bg7Tur7TlrpC33t3yMRDlm4hFbAkSJ+naz4SDgLcE2GzHrCbDGKajBcqHL0B55ylgQxOkvY4yvRO7XoaC3JzMgIiIiIiIiIiIiIiIiIiIiIiIKK0RdAkyEREREZE+VoQKJUlC9Tk6uxNL6gFBOwPFpR4Hrjp8C0ga4zND7YUXofr8Cwzfbtyf/gxHWZkpY9AKFFeffwFqL7zIlDojkdrjb1WQOEmSJNRe9ltU/+rXhm4X+uILNJ9xOuK92QeK8x0kTpI23Q6o0t/lVSqtTHyVJDRUFWGfzWqwTX0pyr25hbrKvU5sU1+KfTarwSZ1JdhpaiWcsrXbuRHfrfOjtTts9zBslWuguEhE8ZPYOmDDWuPFPUWQDpwNudic/ase6e4vg8Q0mklON6SGTdUXsDBIPDAGPZ3BJ28Gx4/3hHzw6ZBmbAtIJnz027UeyscvQ3z0EqTNdsx7oNiujtBERERERERERERERERERERERESjDcPERERERFRQlL4+y0KFpQf+DK4pUzIvKAQc1epBimSg2BsNGaqfC0kCrj5iS4yv8OalXu2FF6Fk5v66ly/Zbz+UHvgzU8eQLlA82oPESUMDxVYHiZMkSULNxb9B9XnnG7pd6Msv0Xz6aYj3GA/Z2xUkBgBJkiHvdIDuMJb46j2IUGDg/x1yIlS8+/RK7DatEtNqilBd7MoYBHbKEqqLXZhWU4TdplVi9+mVaKgqgqP/dhVFLuzQWI6REycGvm3pRTAat3sYtso2UFyphPDTWAvKEDVYUAKKSiEfcArkotIsR5291PvLIDGNBfLWu0Pactfhv8hjkFVvZ3DJWwJ5+30h/+xUYNIm5hT3tUF8/hYgRKIDshoTA8UMEhMREREREREREREREREREREREZmHYWIiIiIiKihySQlKDz5E9fe5hAolhwNVZ56ta1klFodr+nTV33/cuCNCrvwEewHgD4dsjgO21t9BNVfxzk4EP/9c17JScTHG/flqSzompwaKx0qQOCkZKM5XkDhJkiTUXnQxagw+1qGvv0LTaaci7vPpvo2dQeIkqaIW0hY761s4EoL4av7wdUgSKotd2HxCKXaZVomZW9Rgz02qsENjObZtKMOPJpVh24Yy7NBYjj03qcLMLWqwy7RKbD6hFJXFrrTbTlWxC2VeR473zjwxRWBRSy+EEHYPxVZGAsVuEccEpQ+7x1vhgWKskKcY8r7HQ/7ZqbYG6CRvCeSZJzBITGPGsECxDUFWQ53BnS6gRyOImw1/FxDqA2SNj5VNCBQzSExERERERERERERERERERERERGQuhomJiIiIqOAM7cqaZEaosHzWLDjq6jIuJ3q6UX74kYO64ibN2eZQPPOjw7Ieg1FXHLIZjtulIW/1AKDtxhug+Lp0LVt70cVw1ddbNhZnbS0an31+TAWJk2ovvAiNzz6ftyBxqprzL0DNby4xdJvwt98mAsVdmV87IyFInCRtsbN2ODSFWPsDRMsK7fVJEko8Towr82BihReTKr2YWOHFuDIPSjxOXcH7Jl8IPaGR1Qm43R9Fsy9/HdlHKr2B4r1jTdgpvgEOGAxgl1dD3u94SNUTIDnduQ3WBCNhDET5NBAotjHIqqczuK4wbi6UDJMg5BAoZpCYiIiIiIiIiIiIiIiIiIiIiIjIfAwTExEREVFBGhooNitUKHs8qDrtdF3Lds95CpMefHhQoNh3/Bl4facjcxqDUU5Hfk/rAwsWoOfFF3Qt69l6a1TOPtXiESU6Vo9Vdt73ml/9GrWX/dbQbcLffYemX5yKeKd6QGgkBYkBQHI4Ie+4v+7llf97ByIatmw8cUXgh/XZd3u00pL1fYgrY7s7MaAvUGy4GzEAjGuEvO/xkEoqchgdEeVK3np3yDNPGLGdwXWFcaU8nD9mEShmkJiIiIiIiIiIiIiIiIiIiIiIiMgaDBMTERERUcFKBorNDhVWnHgi5LKyjMvFWloQ/GQhGh59HO4ZM1B9/gXY5a9/wHPn7YY9NtHXxdQMd8xbBl8gmpdaSjiM9Vf9Sd/Csozxf70OktNp7aDIVtXnnIva3/3e0G3Ci7/H2l/MRqyzY9jvRlqQOEmqrYc0Y1t9Cwf9EN98ZNlY1nWHEI2PzMBuNC6wrpvdiQGdHYqNrG/aNpB/egQkl8eU9RFRbkZCV+50Y9AbxpUOOQPSbj8HHBafpxkIFDNITEREREREREREREREREREREREZB2GiYmIiIiooNVeeBEan33e1FCho7QMlSedrGvZzvvuhaO6Go3PPo/aCy8CAEyo8OLuU7bDVYdvicpil2njUtMdjOHOt9WDl2bq/NfdiK5apWvZylNPhXebbawdEI0I1Weehbor/mDoNpElS9A0+xTE2tsHfjZSg8RJ0jY/AYozTzQAAGLF1xBtzaaPQQiBNZ0jO6y7pjMEIUZm2DnfzAoUSz/6KaQdZkKSHSaNjIhGIyNhXLmoFHLDppAPPh0oLrd2YDoCxQwSExERERERERERERERERERERERWYthYiIiIiIqeHKJ+YGCyl+cBsmdueNcZOlS9L03f9gYJEnCUTvW443f/ARXH7EltpyoL4CYrec+b8ai5h5La4SXLUXnfffqWtY5cSJqL/qNpeOhkaXq9DNQd6XOrtX9IkuXomn2yYi1tY34IDEASC435B1m6l5e+XweRDxm6hi6gzH0hMxdp9l6QjF0B0f2GPNJ8pZA2mzH7G4sOyDv9nPIm+8ESZLMHRgRjSrZhnElbwnk/Y43rYu6Ko1AcTZjF7GIVSPVbSSMgYiIiIiIiIiIiIiIiIiIiIiISC+GiYmIiIiI0nDW1qL8mGN0Ldt5zz2qvytyOzBrh3o8ee7OePTsnXDaTxqx09RKlHq0u0uWehzYaWoljtx+Ihw68mNCANe/ugSKYk03UKEo2PCnPwHRqK7lx/35KktC3jSyVZ36C4z781WGbhNZvhxrTjwea088YUQHiZOkCVMhNW6hb2F/F8T3n5haf31PeNjPHIq+7TKf0o1zLBJCQFm0AOLzt4zf2FMMee9jIDVsav7AiGhUybWrr1ld1DPq7YQy/5lBgeJsxq4sWgDl7ac0Ox1bTYT6oLz9FJRFC2wbAxERERERERERERERERERERERkRFOuwdARERERDRSVZ1xFrqfegpQFM3lQl98juD//oeinXZSXUaSJPy4oQI/bqgAACiKwJrOIFZ19CEUVRCNKXA5ZXhdMqbWlKCxugiynEgRVxa78PBHazKO99vmHrz4fy04asdJBu6lPj3PPoPg5//TtWzpgT9D6cz9TR8DFYbKU2YDTic2/Fl/l+LYGu3X90gJEidJ2+4N0boaiAQzLiuWfA7RsBmkyjpTavuGdPx1x4LYcfU8rC+fghV1PzalhhnYmTjRsVL59E2gRT0kr6qkEvJesyCVVJg/MCIaVXINEiclA8UZ15Urvw/K3Ech7X0MJE9RVkHi5EQdynvPZbxfVkh9zMX3n0ABIG+9e17HQEREREREREREREREREREREREZBQ7ExMRERERqXA3NqLskJ/rWrbzXvXuxOnIsoSptcXYZ/M6HLTNeBy23UQctM147LN5HabWFg8EiQHgnL2noa7Mo2u9t89bju6AuV1KY+3taPvbTbqWlUtKUHel/hApjU6VJ5yI8ddeB0g62mpnMNKCxAAgeYqA2np9CwsFyudvQWSYlEDXqoRAT2hjSDcZJC6N9GBG+zeY3vZ1zjXM0hOKQQhrOqUXAuHvhvLO09kFiYHEtuPg/G9EpM2sIHFS3joUR0IQbz0G5bUHsw4SA0h0On7vubx2KE73mIvvP2GHYiIiIiIiIiIiIiIiIiIiIiIiGvEYJiYiIiIi0lB11tm6luub/y7CixdbMoYSjxOXHbSp6u+90dDA975AFP98J8vwmoq266+D0tOja9maSy6Fa8IEU+tTYao47niMv/6GnALFIzFIDCTCTIZCol0bIJb+X851+yJxxJVEQDc1SJw0kgLFMUUgEInbPQxbiA1robzzJNDTkf1K/F15D8gRUWExO0iclLdAMQAoGseJTEHipDwGirUecwaKiYiIiIiIiIiIiIiIiIiIiIhopGOYmIiIiIhIg3errVC85166lu28/17LxvGzrcdhl2lVw35+7Dev4Ia5N6IimAgU7jKtCsfv0mBa3b4P3kfvf1/Rtaxj3HhUnnSyabWp8FUcfQwm3Pi3rALFclkZ6u+9f0QGidOGmTIQ3y2A8Ptyqp0M56YLEieNpEBxn0qYWAgBfziGDb1hrOsOodkXwrruEDb0huEPF25HYyEElGVfQvngeSASynyDTGzouElEhcGqIHFSXgPF6ZRVDRq7iEUgmpaqL5+H/aWex1w0LYWIRSwbAxERERERERERERERERERERERUS4YJiYiIiIiyqD63HN1Ldf76quIrl1ryRgkScLvD9kMTnljKPPYb17Bcd/+Fw096/DX+bfh5gPqce8vtseMcaWm1FSCQWy46irdy8c3rEfHP+8ypTaNHuWzZmHC328GZGNvP5XeXrScezbCyzTCQ3mWMcykJR6D8vnbOQVl44rQDBInje9ZDYcSzbqOWZJdlIUQ8AWiWNLqxycrfZi3uAMfLuvCF2t68FVTL75p7sVXTb34Yk0PPlzWhXmLO/DJSh+WtPrhC0Q1H7ORENoSsQhEPAbx+TyIL+cDBp9jzaUZKCaiIawOEifpDhS7vYDsyLpOWqEAxA9fQPi7E2NxujOPxcL9paHH3Ok2vT4REdFIYvd7MBGLjIgxEBEREREREREREREREREVIoaJiYiIiIgyKNp5F3i32y7zgvE4Oh98wLJxzBhXipN2mwxgY5A4aaKvBZtedzHiHR2m1eu4605Em4yFozvvuhPtd9xu2hhodCg//AhMuPlWw4HiyLJlWHP0Ueh54QWLRmaMrjCTlra1EKsWZT+AUCBjkNjvLsfnU/ZHXHZlX8cksbhAU1cQC1b4sHClDys7gugKRAdCxmriikBXIIqVHUEsXOnDghU+NHUFh91OWbQAyttP2Rq0FaE+KPOegPLGw1k9t3448ZFjInqh8XwxUExE/fIVJE7SFSiOhIDiMmDGtoDTpGNPNAzxw+dQ3ngI8Q9fhFi3EvAU2RIozvdjTkRElImdQdbkezDF77Ov/rwnoLz1BJRFC+wZg9+XeAxsqs8wNRERERERERERERERERHlgmFiIiIiIqIMJElC9Tn6uhP3PPsMYiYGeof65T7TcNqS1wYFiZMiy5ejafbJiLW351wnvGQJuh56MKvbMlBM6RTvthucdXWGbyeCQbT+7rdo/cMVUEIhC0ZmjO5OjSrE1+9DBI2HnESoD9VfvKIrSBxxFmU1NrMtXt+Hb1v86AnFclpPTyiGb1v8mP9DB9Z2BSGEgLJoAcT3n9gatBWhPijvPA34fUDQb/j2bfDiQ+ck+GQvFjgLM1Cs9Nk/npEwBqJ8sCvUquu45/cBG9ZC2u8ESFvuCrhM7M7bugrKRy8lJm1Y/T2k3X+et0Axg8RERDSU3SHKRJj3SVveF6S+BxNzH0H8q/ftqe/3AX0+iO8/yXugN/7V+xBzH0k8BjbUHwlhahHqszVMTURERERERERERERERES5YZiYiIiIiEiHkv1mwj1jRsblRDgM36OPWDaOolgYB7Z9pfp7MwLFQlGw/k9/BGLZBwD9r7/GgBcNiLW3J16X69dnvY6eZ5/BmmOPRmTFikE/t+N1NjRYpYQNXFAejUD58l1D9ZJhJmdfl+oyIy1IDCBjB2KjonGBRS1+NC+Yn7iIPMmGoG2yIzEC6uFuTTO2wyfOCYhJiY9lIpIDC5wT895xMxftd9yONcccZcoEFtmKrFmDNcccZesEFjzWUT6IWMTWUKuuQHFvJ8SCVyFtviPkg8+AtPXugNtr3iD6eiC++RBi3hNAeQ1QXK45llz3lwwSExHRUMmuvHadj28M83ZBmftoXscxUHvgBwqw9Iu8BYqH1e+Xz0Bv/Kv3gaVfJO67DfVHQphahPqgzH/WtjD1wDjYGZmIiIiIiIiIiIiIiIgoawwTExERERHpIMkyqs7W153Y99ijUPzGu1TqIZeUYPOnn9IMNucaKO5+8gmEvvwyyxEC7hkz0PDo45BLGKygjUHiyPLlOa8rsmQJVh91JHpeeRmAvWHGZLAqJtxouvsZdM4bfmGzquZlEM3LdC2qJ8w0EoPEVpne9jUmtqSZUCGPQVsl6Icy91Eg21o77AfH9vsAkjToxxHJoSusl3o/7Qqytt9xOzrvuhOR5cux9uSTbNkGN9x4A1Yd/DNEli9H51132hIojrW32x5mprFBcrohNWyqvkAeQq16AsVSw6aJsbq9kLfcFdK+JwAek49NShxoXpqYzEF2qC+Xw3FBV5C4tMr2IDGDNEQ0Ftm170vtyqu892zeA8XDwrSRUN4CxWpBXgB5CRRr1kd+Ar0DQWKb6o+EMLUI9SXeB/s3TnJmS3dmv8/2zsg8ByMiIiIiIiIiIiIiIqJCxjAxEREREZFO5YceCufEiRmXU3p64Hv6KcvG4aytRcOjj1sSKI6tX4/2W27OemzJILGztjbrddDoYWaQOEkEAmi99BKsnnXkQJgx127c2Yr7g1j3wIuItnWh653PDAWKlf97ByIS0lyGQeLBHEoU43tWqy+Qh0Cx0tMJ8cZ/gAzPXVqSDOmns+CY/mP1RXR2/1Teew7tt95sS5g+GSROiq5cgVU/Pziv49hw4w3wPfgAEI0O/CzfgeJYezvWnnyirWFmgJ2RxxJ5690hbbnr8F/ksTuu1j5K2nJXyFvvPvD/ItQH8fHLQDho3YCUuPbvszgu6AoSu70AhO51WkGE+mwP0hAR5ZtdnYGHBTnz3BlYNUybh0CxiEUgmpZqL2RhoFjEIhBrl2Rebs0SywKeWkHigfpNSy2rPxLC1ANB4jTvg/PdHVrMfcTWzsg8ByMiIiIiIiIiIiIiIqJCxzAxEREREZFOktuNqjPO1LVs10MPQYmELRtLroFiRRFY1d6H+Uva8Po3rXjly3V4/ZtWLPr9lVl3VWaQmFLpCRK7pk9H2ZGzslp/eNG3A9/bESgeuH8rVgz8zFCgOBSA+PoD1V8zSDxcXHbh8yn7w+8uV1/IwkCx0t4M8dZjQDyaeeGhHE5I+58IecKUjIvqCRR3vvA6Ov/977y/9ocGiZOUrq68BYoHgsRp5CvUG2tvx6qfH4zoypV5rz10HOyMPLYMCxTnMUiclG4flS5InDGQmy8Gjgu6g8SREOD3WT6BhZrUcdoVpCGisWusdQa2M8gL6AjTWjwOyemGtNvBgJzhz8nLv4Ti95k/gFhU3/wdUv+yJlP8PmD5l9oLyTKk3Q6G5HSbXn8khKm1gsQDy1gYpk4aCHUrysa6eT4PEqE+KPOftf0cjJ2RiYiIiIiIiIiIiIiIKBcMExMRERERGVBx7HGQKyszLhffsB69L71k6ViMBIqjbW34em03bpu7FGc+9Dl+euN7OOLOhbjoia/x+2cX4coXvsOc259E0UfvZjUWBokplZ4gsXvGDEx+7AlM/NvfMeHW24Di4pxq5jNUqXX/jASKxapFEBvWDv85g8SqIs4iWwLFyurvIeY/Awhl8M/DOi7idXkg/ewXkCvqdNfTChR3zvsEXe98NvD/+XrtK3198L/+mvrv8xAo1goSJ/lff83Sbr3JILHS1TXsd/kMFKfuh+zsjEz5NxAotiFInJS6j8oqSFxWBWmnA4G6yXkYLXQdFwwFiQ2s12zpxslAMRHly1jrDJyxK28+AsV6wrSRkGUBaxHqg1j4+qAAZ1qKAvHRy6aOYeCY1+fLvLAFk3yIUB/ERy/ru+8LX7fmdWBzmFpPkBgllYlzUgvC1Ela3aHzdR408Fj4N74PtOMcTPH7bO+MzDAzERERERERERERERFRYWOYmIiIiIjIALm4GJWnzNa1bOf990HE45aOR2+g+JPDjsH5d76Dhz9ag/+t8qEvPHhc3mgIZ/3vyazGwCAxpdIbJE59zZQfehimPv8iXBqvYz3yEarUc//83yzTFzIFoHw+DyLlol8GiTPLZ6BYCIH4tx9DfPbmsN91zvsETXc/g1hvQH0F3mJIB86GXFxmuHa6QPHQIHFSPl77ckkJGh59HK6pU1WXsTJQHFmzBr5HH9FeyOXChNvvgFxiTbhSK0icZHWYOTmOofshuwLFVt9XSk/eenfIM0+wJUicJHlLIM88IYsgcTXkvY+BPHUrOPY+GtK+xydCulbTOC5kFSTWsV6zaY2TgWIistrgzsD5m0jBzs7AktOdOB8vrVRfyMJxGArT9nZZEqbNeHwcNAbzXhuGa9td34LtYiSEqZX5z2oHiYFEkNlCWkHiJKs7I2uFqvN5Dhb/6n2IuY/Y2hlZhPpsDzMTERERERERERERERFRbhgmJiIiIiIyqOqU2ZCKMof4oitXwj/vLcvHoydQPK6zGVe/cysqgj1pf3/cN6+gLmDgIs1+DBJTqmyCxAM/nz4dU55/EaWHHpbTGKwMVeq6f9Ono/7i0yF7dHbl6euG+G4hAP0hrCWbHTRmg8RJuQSKhRDwh2PY0BvGuu4Qmn0hrOsOYUNvGP5wDEIkWj+JeAzKgv8Ciz8dtupkqDfa1oWW+19IHyguLoO8/8mQi0qzvp+pgWIlHIH/m2Wqy+atO7fQbo2ldHVh7cknmTqOWHs7Ws49G4hm6LYVjaL1ogst2/4zBYldU6ag4dHHLQszJ8ehth/Kd6A41t6ONcccxa7INrGy+1s2Y9AfJB7cTVmumQj5wNlAWZWVQ03o7YQy73Eovb6BH+kad2mVduA5D8E6PeO0OkhDRCODHdv58M7A+QkUj4jOwIAtnYELLkxr4hiyrm13fbsffyvq+9Xf+wywIMicpPh9wPIvtReSZUi7HWzZubGe7sz5OAcbCFWndMrOd6B4IGBuY5gZYGdkIiIiIiIiIiIiIiKiXDFMTERERERkkKO6GhXHHa9r2c577x0IpllJT6C4oWdd2kDx1M41+PkPbxuuKU+bziAxDcglSJwkFxWh/tbbUPenPwNS9u1trAhV6r5/jz0B92G/SASPdBI/fAGldZXuEFb9+DwErgqAkUBxd1c3lrT68clKH+Yt7sCHy7rwxZoefNXUi2+ae/FVUy++WNODD5d1Yd7iDnyxpBmBN58AWtIENod0B04bKC6pgLyfOV1Dk4FiuXYC6s+aBVed+vOfjzB9dPXqjMtGV64wbRx6tr1UVm3/a08+UTNIDCCn/ZbecWTsjJ6HzshDx2JXV2QaObINEicl9nPHDOrEbplQAOLNhxH/bC6UtiZ9497nGMj7HKs9PguDdYYe3xEQMici6yiLFkB5+6m8dQVO1kzbGTgPgeKx2hm4IMK0ssafl3MYg67aJZXarwmr61t03w2NYaTVt6gzs/jo5UHh2bQUBWLh69btAzIEiVFSafk5mFZ35nyFegcei5SAuR2BYnZGJiIiIiIiIiIiIiIiyh3DxEREREREWag6/QzA6cy4XPibrxFcuDAPI0oEipdd+Q80V0xUXWZooFhWFJz72WOQDQaem8on4oJdzsM76+M5jZlGBzOCxKmqZp+KhsceB1yurMcUWb4cTaeY0x3V6P2TvCWQ9zkG0N2RVkB8/IruENbECi9cDmtDi4VCb6AYHzyH5vWd6ApEEVe093el/vXYYtHL8Kbp1j40SJw0KFBcVgV53+NMCRInJQPFzvoGjD/xIMCh/nGOXWF6K8aRTV2zag8dQ3TlyozLRletsrczev9+yMrOyGpjsStQnI/gNGnLNUiclNqJPS9Wfwfx3rO6x61rfBYFacx4fImo8A2EevPUFRjQ0Rk4X2MZQ52BCyJMW1YNaeaJph8TdR/z9j3Wkkk+7LzvhsZg9/NvQd2cx2HVOdj8Z7WDxABg8UczWkHiJKs7I2uFqvMZKGZnZCIiIiIiIiIiIiIiInMwTExERERElAVXfT3KDztc17Kd9/7b4tEkPP1pEy57qwVX7XsJmsr1BYoPWvouNunM3GUyVVP5RFy93yVokUtx+TPfYs6nTbkOnQqY2UHipOKdd8HUN+ZCLivLemyRFSuw9sTjLQ8zprt/krcE8swTAafOQLSiEcwfEhJyyBI2G8/AEABIwYCuQHFppAc7rp4Hdyyoub5JXcuw0+p58CjhYb9TwhH4v1mmettoWxdaHnoZyjb7WhLokrwlULbZB+vnvAXEtbszWRGmNRrozXUcuupqTDhgV5jZtjC3y4UJt99haD9r9ljyHSiOtbdjzTFHsSuyjcwOuuY9UKwlzbjzHSge6UFiBilorLLjtT+sO3CeQrwDnYHt7sw+RjoDF0qYVt77aMgVdaa+Nowe88w+Jtt53w2Pwc7n38Igs6FxWFR72BhSuvCq8vss2wcqfh+w/EvthWQZ0m4HW9YZWU93ZqvDzIPGwc7IREREREREREREREREOWOYmIiIiIgoS1Vnn61rucBHHyH07beWjmXOp024/tUlEALoLirH1ftlDhT/dd7fccJXLxqqkwwSdxclQntCANe9uoSB4jHKqiDxwG0nT8bUN9+CY9y4rMcYXb0aa46aZVmYUev+Sd4SSPsca7juICohoYZKL2pLs+/cPBpUPXU/Jl1+Jhy+zpwDxZJQsHnrZ9iq9RPIKq3PZI8b9WfNgquuSrVGtLUdzWedbVln2uazzka0Vd+68xWmdU2ZAtfUqaaOQ++21/jCi3DPmGFqbSNjsKJu1uOIRtF60YWWvPaMjMX/+mt56RacOha7uiKPdVYFXXUHiiWL28C53BBrf4AI9A4um6dAsd5Aj21BYgYpaIxSFi2A8vZTeekKnFpzUJA4KV+BYm8JpN0OBmSNPyfa1ZndgnHYFagspDBt8rhj1hiyPaews77dj79t9S0KMhsah8X7Irv2PenGIT56GVC0JxKDokAsfN26CR0yBIkHzkctCjNnGgc7IxMRERERERERERERERnHMDERERERUZY8m2yKkpn761q28757LBvH3EXrcf1rSwb9TE+geKJ/A4ri+i9AGhokTnX9a0swd9F6/YOmgmd1kDjJWVuLKS++DNfkyVmvI9a6DqsOOQjRdev038ak+ydXjgN+vKeh8Q7QCGFJkoSt68vglC0OU41QVU/dj6qnH4C7aRUm/um8nALFrlgYO6x5B41dP2Ss6ywrzhgotq0zrclj0bsNTH7yaUx+4inTQr1Gtj3vZpuj4dHHTQ8U2xWizmYcZtfMZSzJ50UusTbYmG4sdgWK8xGcHoms7pirKyAjBOAtAarGG16/Lp2tEF+9B+W1BxB/52koP3wO0detf3xWB2kAwKZTgNTx2RmkoLHLrhDNQKi3txPKe8/mJVAsYhGIpqXqC+QhUCxCfRALX88caMt3Z3YLxmFXoLIQw7RmjSHXcwo769v9+NtV36rzIL3jkGaeaP85mMl1cx6HVRM6zH9WO0gMWH4+ys7Igyl+n+0T+jDMTERERERERERERERU+BgmJiIiIiLKQfW55+pazv/GG4isWmV6/dbuEK556XuINI009QSK9Woqn6AaJAYSeY5rXvoerd0ZLjKjUSFfQeIkZ20tJj/9DFzTpme9DsXnw6qfHYDQosxdws2+f/KmOwA1BrdDhxPSrgdphrCKXA5sU19mbL2jQDJInJRLoLg01IVdVr2B6oD+yRCcZcUYf+JBgEP9IxWtUKcQAv5wDHJxFeTSGshldYmvxVXwh2MQQ3bougKlWY5FjdFtwFlba0qoN5ttz6zaRsdgdog6m3GYXTOnsbhcmHD7Habs87MdS74DxbH2dqw55qgx1xXZ6iBxkq6gSqgPiEUh7XkUpOk/AhyurOtp6lwH8fUHUF5/CPG3n4Sy+DMgFrUvSJPk9+WlK2mqdONjoJjyyY7OwMm6g7oD93ZBmfuo9V2Bne68dENXY0egbax1Bi7kMG2uYzDrnMLO+nY//nbVt7Mzs1xRZ985mAX7nqzGYVHtYWNICc6qsvB8lJ2RB4t/9T7E3EdsndBHhPpsDzMTEREREREREREREVHuGCYmIiIiIspB0Xbbo2jnnTMvKAS67r/P1NpCCFzz8vfwh+Oqy5gRKG4trcPV+12qGiRO8ofjuObl74cF4Wh0yXeQOMlZW4vJjz+hGdzLRIRCWHP0Ueh55WXVZYzePz1dKSVJgrzzzwDJwFvweAzi/ee1O5EBmFDhwZYVY+et/dAgcVI2geJdV76BnVe+ieKo39AYYr0BNM15F4hrd2dLhjqjbW3wBaJY0urHJyt9mLe4Ax8u64Krfku4JmwO1/hNE1/rt8SHy7owb3EHPlnpw5JWPzrXrsvcIXdCLSbfcq0tnYFTt/FcQ7257FvsCjObHWQ2Mg641EOTZgaKdYeao1G0XnShJV2RjYzF//preekWnDoWu7oi2yFfQeIkvUEV8eV8SFvtBvnQsyBttw9Qpt49Pmdd6yG+/QjKGw9D+eAFoH4aUFKhOT5TgzQ5rD9XWuNjoJjyYXBn4PwF6YcFiZMiofwEir0lkHY7GJA1zvmt6kqZ50DbWOsMPBrCtNmOwexzCjvr2/3421XfzkC9bWFmk/c9WY3D4mPBSDkfZWfkweJfvQ8s/QJQNn4Ole/z74HnxMYwM8DOyEREREREREREREREZhg7VxwTEREREVmk+pxf6lqu54XnEduwwbS6L3zRgo+XZb64Kxkobi4bb7hGt6cUV+5/ecYgcdLHyzrxwhfrDNehwqD09dkSJE7SE9zLSFHQeukl2PDXv0DEYoN+ZTRI2H7H7VhzzFG6wnNSaSWw+U7GxhoJQVn4KpTP5kJEw2kXibW3Q5x9Ejb97yPG1m0SZ1933mpJwQBKPnpb9fdGA8XeWABOqE/GkE7UH8LK/7wBtLbqWj6yfDmWnnASPvu/FVjZEURXIIq4oj3hQlwR6ApEsWZ5M1p+MTvz63HO8yg65DjdodZoWxv84Rg29IaxrjuEZl8I67pD2NAbRndza07beLbhWjMmKbArzGxXZ+TGF160rCuykbGYXTOXsSSfH7kk9xCr0bHYFSjOR3A6Kd9B4iQjYRHEY5A32Q7ygadC3utoYNImpo0jre52YMnnQF93zsGOrEN7OtefKz3jy0eQYlA9G4ME+dz21OrbOYZ4d1fe66cGepVwBEp7a14CxSIWSTu5jxLuf/3lIVAsQn0QC18fFCBKy4qulHkMtI21zsCFGqYVQoGIxyCiYYhwMLGuQC+E3wdEwpC23xco1vj8qLczMSHHGw9r1/YWA1O3gmheDmX5V4l/y76CsuzLxL+l/wdl6RdQfkj++xxi9eLEsd9TpF3/9YegvP5QhvolwPRtIFpXQ1n9PZQ1i6Gs/SFxrGteBtGyAmLdSojWVRDrVwM9nZC22QMoLtOu/e4cKB2tULo2QJn/TME9/7nWHSqXcdi1DzA7yGx0HNLME+0NM1tQV3Uc7IwMICVInEa+Qr0Dj0XKc2JHoFjx+2zvjMwwMxER5Zvdxx4Ri9g6BtZnfdZn/bFaPzkGIiIiIiKrSIJtw4iIBkjS8GmsuZskq7300kvDfnbEEUfYMBIiylbc78faE09AZMnijMtWnX0O6n57ec41g5E4DrrtI/gCUQCANxpCyOVNv7AQ2H3t5zjzsydQEdF/cVVUduCCQ/+KjpIaQ2OrLHbhjd/8BEVuh+oyiiKwpjOAVR0BBCNxxOICToeEIrcDU2uK0VhdDFm2uL0EZaX9jtvRededaX9nZZA4ldGAmxrvdtuh/s5/wjl+fFZB4uTjoOd+Jy6GfBbo1XExZDol5ZB3PghSbf3Aj2Lt7Wg65SREVqxIjOPsX2PZoaciliGsapbpD1wHx6cL0PzXuxEY15iXmg5fJyb+6Ty4m1apLhNpmIp1f/0n4pXVcMeC2HH1PJRGenKuHQjEsfbB/8LZstbwbVPHpIee+5nudafndaw2lmxrpmNkewJg6iQF2YSCzQgz27EOKzvF57KfNftYoLdTc+MLL8K72eam1Mx2LNXnX4DaCy+ydAxDx1J68CGW1xSxCJS3n8p7kHjQGPQGPWaeMHAxf6J71jOA32fJmAxTeYx03beSykTnNa37YtFzkG58SjgC2ZMSmrD4+R8q3tkG5cOX4JixNeStd89LzaT2O26H//XXUH/PfXA35uf8Z2j93v++AgiBssMOz9s+J6ntT7+F/533gKJSlB1+RF7qpwaJO+d9Av9XSyEgULbtZqiedbDlr72h+5JIRzdaH/kvSn+0Car33zWxkNsL+cDZGcdh9DNAte0PwOBtMFWO22OmfZISjgBl1XDsfUQi5GzCscFomE/p6xuYPCSXMKKZgV4j6wJguK4QAhAKEIsC8TgQjwLxWOJfbOP3SqgPWPwpEAqor1vqD4ILjYC6JAEub2IZoSTC7IqxiZgoB5KUCEU7XIngvuxI+Tf4/yXHxp8JRQFalmuHH52uxNdYVH2ZsipIex0NuahU13Cz2ZbM2v7s2gfYtf+QvCWWTPIzUs5Hsw40m3wuOtCFN1OgubQS8j7HWnYeohUkHjDkPZDZMoWqpS13zcv5ePyr94HlXw5MrpKvuqkUvw/io5chNWxquLZZfwcWsYil4XUionTs3vckg1R2jcHO+sqiBRBNSyH95HDIpZX21F+7BBCA1Lh5/o+9rM/6BVw/1/O/Qr//rJ+75PtSafJmttQHxvYxeCTUT46B7wGJiAoHsyBjUyFnzxgmJiJKUcg7dCpcPIEkKmzJi8krTv0F2q76c8bl5ZISTHvvAzjK9XX6VfP85y245uXvAQAVwR5c/c6t+LhxRzzzo8MGLVcd6MJZ/3sSOzd/ZbjG3376S3w2efusxnf1EVti1g4bQ49CCHzT1IO3v9+Ab5t78P26XvSF1S9ELfE4sOXEMmwzqRwztxqHH00qT3uczhbDzLlJFyjOV5A4yaxAsVxZiXFXX4POO+/IKkic7vdD5dTdZRAJ0hY7Q9pqV8Q7u7D2uKMQbWoZtET5r89H06zT0O7XuDDYBNMfuA74738BAK66KsR/+1ss23RfS2smGQ4URwPYZdWbKIppXFCfQVfUg/X3PgNX8xrVZYTTBUnjgmy9gWI99885fQYaH0v/essmUKynpmvGDEzOFJoXAiIchhLoQ3TtWrRe8htE12qEr90eAAKIqM+s7KiuRsVJJyeOm7IDktMx7KvkcAIOeeCr0teH9ltuRqylRXW9csNkeK+5AYrLjfCfLodYrX7frQhRD11Xtre1IlCsN7yLqPrr3axjgpF9vdXHId1do599Pi/dkdeefCKiK1cCyE+IOTXMBwwJs+UpSDr0eDo00Jp6Qbl5x970hoVp9cohSANsDJ+phgmtCFIMGV/nvE/g/2YZJp76c7hqKvMeJG6/9Wb0PjcHQomjbNvNUHPBhXm7mGbQOaDLhcrZp2Lc76/IS+1h9fvlcxKDtj/9Fl1Pv5DX+qmTGXTO+wRd73w26PdV++1seaBYhPqgvPsM0OdD+2sfonvB10Bc2Vg/GSguq4K89zGa4zDyGaDq9pcapk7WHirL7TLTPimx/S8fCJPXnHNWzoE2o6G4yJo1aDn37EGTaeQzyJjsyp3uWK9rAimHM/E1HlNfRnYARWWJEG88tjE43P93ilhvH5xl+dnnqsn6OEgFREq8XpP/nP1f5Y0/k5wbvxeKAFqWaQeZi8sg7TATcLggPn/LtFCqXWFmO9aRnEzBrjCzEghA+fAlyBF/znWzGYfmhBomnZNqjSNtfYvOhRW/D2LuIwPh2bT1ZRnSzBMhV9SZWjtpaJBY9f5bGGYGNoaqh9bPZ6A4/tX7UL77FFAUyB634dpm/B1Y8fsQf+dZWyZUAnghPdnL7ufe7tc/w6xjM0w26LNYWQZmbAfHtnvZU79fPo+9rM/6hV4/l/O/0XD/WT83Q9+L2TKZ0hg+Bo+E+kBuE1qZYSyfA7O+/fWTY+BnAFRomAUZmwo5e8YwMRFRikLeoVPh4gkkUeEa1Jl0+gwowSBi69SDS0m1l1yK6l/+Kuu6QgiccM9nWLyudyBI3NCzDgAwZ5tD8cyPDoMkFMxc/iFmf/kciqMaF/KpiMhO/PrwG9BdlF3oecuJZXjy3J0Riip4/Zv1ePqzJixe15vVupLrO27nBhz8o/GaHY/VMMxsfn2jnXnNrg8MD3bJZeVQeo13oBVINFdRkylInG65gXVbEGaKOUrQcscjiDavS/v76vMvQGj2OViyvg/R+PBzWUUIdPSG0eaPIBJTEFcEHLIEt1NGXakbNWUeyGle7w4JiIvBQeIkI4HibOunKl6xCBMuPzfRlUtFpGEqev/4V8wIr0ZNoDXjuNQ0owY9dz8Md9NqzVrrL7sW42++MmPIufkvd2GDoyTt/Z8Y9+NHf7s04zo2XPdPbLXVVEyo8Az7vRACa5c3offc0+Faq76e3gmNeP/X10EKhrDn/VehrE39+BUvr0Rgj31RWeREcTQEJRiA0tcHpS8A0dcHJdCX+P9AQPM5KURyWTnKDjsMzvHj4Sgvh1xRAUdZOeTyMjjKKyCXlyf+eRLPhZ7AqZgyDUv/eBt6iyoQjQsU+buwxQ2XwNOs/hrT2s8aCRRLVdVYvN6PZRv8CITjiMYFXA4JxR4HNhlXik0cIaw7bXbGdU24/Q60XnSh4RBzPK5o1t9ifCkcDln3/dJTM5WR+qnMCm1nW3/oWFb9/GAoXYODSnoCfbnWT15EMSjMtsu2qL3pTl0Xz5tx/5PH1ciqlYM6gxoNEsckB2ShQIbxz3yGhWkNCjiL8VbdPogpwP4dH6AipnGOnCb40v67C+D/9Gv1MKFKoMHo468WZBwIczpkVOy+Lepuu1tXgMGM57/91pvR+e9/D/pZ1X476woU51pf7Ryw8owzdQWKraoP5Gf7Txckzlf95Ou+6/X30/5eT6A42/qp20H7ax+i+8Mv09cfCBRrb3+vvf8FIkJCXEhwSAJuSeCQvXYwvv2lqz1Uylj03H85GswYJB5av/r8C3QGiquwdPODsMQXH1S/0hHF3uvfhyfo07jtxvux4cYb4Hv0kYEJTVJfe3r2/b2ucrxTtydicZF5/1tcBmmr3RMdYmMRIBpBx2NPw//BAghFQdluO6D60P0GfodYOPFVKyRsgmSgvWL3H6P2kJ9aWktN2u7ceZSxOzfrF3592QFU1gFuLySnK9FN2ZH4qkTjgNMFubQMcLr6f++GiMcgvpwP9HWrrtbQPkBlfz50QgF9QVxz9oHJCS3r77kP7sZG3SFg/PQoLO5W0h4DNq90YNMlb2hPgtA/ho5770fvf18BFAWlW09F9Z4/1jVuM45BAND5/lfwf75oYEKZtPufNM+bkXMArcdUc0IPjUBxNucghiYUyRBmzukcKOXi9bT1Syoh76vdmTnXc9BkkFjt/me6qN6M9yDxr95H57/+Nay+ngv6jZ4Dao0h8ul7aH34ZZT+aBPdEyqZcf+BxGcB8WXfAAJwbPoj3Reym1UfAGIbWiAWvmYoTG1m/Xh3Yj/pqKjStTzrm/z6W74I0m6HwDmuPuPyltS38fVvZ/3k54BKOALIMuStdtEdZjW9PmBoModCr58aZBt0/r3pDrqeA8vqQ1+gjfVZn/WzP/8bLfef9XP8G1j/e7HUCQXHyjGQ9fvXk+WEVqPhHJT1WR/gZwCsb2/95BiM1E9iFmRsKuTsGcPEREQpCnmHToWLJ5BEhSndxdyO2jrE29sy3tZRU4Np774H2evNqvbXTd2Yfd//hgWJk17bdF80djdjmw0/ZLX+pKbyibh6v0uyDhSfuecUPPd5C3wB8zqkVha7cNH+MzBrh3pd4d5gJM4ws4X1kxcyZgpPWXn/kwGv0oMPQdVpp2P9Fb+H/625Go+GMcqUaZjxxBNw1dVB6evDmmOO0h0msyRI3BtAy/0vINqmfqFnsjOmKCrGuu4QVncE8W1LDxY19WBtZwDNXUGEY4rq7T1OGZOqijC5uhjbNFRg6/oyTKkpghBA8NorhgWJk9QCxUIkwuPZ1p9cXTTo+fe2N2PyH89FfEOH6jpSx1R/1iw4y4ozLjuUIoD/iUaU/ftu3V2Q9XT4ba6YiKv2Hb5vVdunq9UCgK0mlqKxumjg98FoHItaetHuj8LR0YaJf/o13OuaVNeXKUhP+klu90DQWCryIrJyJURAvRv2+pIa3L7HWehzFeO3H/4LDT3qgXc9IVU9YdcN1fW4er9L0eYqTfv7imAPrnn3VkzqVn8Npo5FT03XjBlYd82deL05hsXrerG6I5Bx+59SU4ztSmOY9Z+r4NQIxOsZo6IoWLC8C298u95Q/S0mluHgH43HbtOroHR26urU3PjCi/ButvmgH5tRX5Y3/lFDLUicNDTQZ3Z9wFhnUivqA8CG6/4C32OPD0xeUHX8LNT99e8A9AU6VoQ8OGflNADAQ9NXYLJHvTv6UMPDtNmFuRQBRIUEj6zxmVOaLqfp3oOkDROWVQN7HoWFzeGsHv/DtijHjmveHhQsSRckBICK/XbDuH/cMzxsY/Lzny5InPoYDL2g3sz6WkFeIH2gOJ/1AWu3f60gcT7q67n/QwPFZtSXIsGMQeJB9VNCPfna/obVHqINJbiybQt83R7TrD/BE8e901eiwRlM+3ut+noDxcl9b2fMlbidM4p7p63EdG9Y9TbDgsQPPpC2fu2FF0EIBaK3C+KjlzXDhFFFgiQJOA2eBBsOc1tg6Ouw4qfb5T1QrNmdOw90d+dmfdZXEROJJt8ureuGikohbbs3pOIywOkGXG7A6ULHv/6dCNMKgbLDDjc0mUGu+8BBx0KXC5WzT8W431+hq/bKsAdnr9hYO8nIGDruvX/4OejBe2kGis06BgFA5wdfD5tURHX/U1aFzyfPxCtLegwdg3ec4Mb5ri9RFB5+DNF1DOh/rIS7KKdzkF3rPcAHzxubUCS1e7VZ50DznwX8Xdr1j/wZ5H0Gh4nNPAdMDRKr3v8hnZHNfg+SDBKr1R96QbcV74HjX72P9tvvGHb8TRcotqK+smgBOu68Y9D+Vy3MbNVnAG1/vAT+d9+HEDbV/9Nv4X/nPQgBlM3ce+DzB9a3vv6g158kULbv3qi77lZ76tvw+rezftoJBbfdDNW/+lXaMGve6qtM5jCa6qcG2dKe/6YJFOe1PoYH2lif9Vmf9VnfnONPapA43YSCo/0YyPoJRia0Gm3noKzP+gA/A2B9e+sDQLR5DZpOPA6le++pWl8NsyBjUyFnzxgmJiJKUcg7dCpcPIEkKjyaFzM7HLq6Mo67+hpUnnRyVvVvm7sUL8z7NmPozAy5Boqtsscm1bjq8C0xoSJ9IFsIgRe+aMHt85aP6TBzPuorfX0DHVnsqD90DEII+B75D9r+dtNAx6pstRVX44oDf4/66Q0D9V09Xbq6U066/z5I37wLya8e+l0R8mBBbylOqO2AQ8fF7LqCxNOno+GxJ+Csrd34+H+6Fotb/ZkLqEg+/ttXAcqpszRDvKmB4khMwZdrfFiwrB0tPuMd0pMmVRVhtxk12K6xEuW+dbqDxKljMhoo9sclXLO4Goe+cq+hcC8AXYHioftWPUHi8KSpaL22v5aiwNHjg6N9PabFe1Dp70D36iZ0rWqCo209HB0b4OxsgzTKugSPRb6SKjSdeSn2O3g3lE+aoDkRiZ5wr9pxXc9rMF2oOZeaavSMBS6X5j7eOX0GPjz/RsxZHsTaTvWL0jPZ0hvB79/4O4rXrcm4bOrj4w/H8PSnTXjhi5ac6k+uLsKsHepx/C4N8Pb6NIPEyTE0Pvs8Ak6P6fVLPU7dnVGtuP+lHicAaIbJLAuz9VMN01oQ5loR8uDqrq2w33ZTdD3+6cIUa6NenL5s6rDgSCbpHhOtICEwOFBsxfMf+uc/VIPESVUH74XaW/6Nvrhsav1jt6lC50nHZeySngwUm33/j92mCh3HH43oqlWay7umTMGUF182ffs/dotSdBx5CKJt2pPjuBobMeWlV8yvv00VOo47CtHVqzWXd9ZUYPL5xwM14zHHuyue/Kozp/rb1DpxR8MyVMZ7MwaJgcT5ZsOvjx3oVJHP7Q/QDnQO3e/pqZlKT/3kPjg2/1nIGd7/JCdzyLTv7ZGL4dl+bxS5Hdhw17/R/dyLqstWHbAbqvfdSXOMucg2zG0mtddhPgPFurpzW8juQDfrs/6w+ofsg+pZByUCx5IE0dYEKaq+X2sKu/CHtZPRF3fg5ilrME1jH+hzlMEz81iUlperngMmzz2sCjMrpVVw7nNM2iBxUqZAca7HICB9kHigvsprIFNdI+MwcgzwOcpwYdMm+LY9uy711c4oHtpkFSa7Nn6Gpre+Ulpl+jmQrvr9k7n0SSa/B926DEXzH0fn3AXq9Wfugpobb4dcUWfJe5CixR+nDRIP1E/e/5knmP4eJPkecCBIrHL8TV5Qa9V78ORFxOn2v6kX81r5GUDbHy9B1zMv21c/zcRKqROasb519VVff8cePihQPFpf/3bWL1722UCIJu35V0qg2Jb6KWGaUVd/h3Eo+vBZoLdT+/y3P1BsSf0P5gB+n3b9kkrIB5xk/vGP9Vmf9Vl/rNbfpQElIjw4SJxuQsH++pLTPfqOgaw/8B5Qz4RWo/EclPVZH+BnAKxvb31gY5A42tqetn4mzIKMTYWcPWOYmIgoRSHv0Klw8QSSqLDo6Uyqh6thMqbOfQuS05l54SEuuOttzHr4asuDxEkjNVBc6nHgqiO2xIFbjx/089buEK55+Xt8vMy8brBDjfQwM+sLvDHnbVTc9EfU+vWHTtNpKp+AtzbZC+9N2w2uykpctP8MHNboRvOpp2juBxzjatBw5hGqAdbUCxu3K+7DdZPXYKJb/WJDPUFiZ00FJl1yJlyHnooXv+s2/fEvL3LitIYI9vvXHzTH4aqrwhu/uhGPrHYgEDEvzLo5fLj+7b9BMRAkTh2TkUDxmo4oVt//Euq71bvFpgsSA4nX39dfL8cut12u2eE1uW8FkDE42eMuweIJm2OqK4IqfyecHW2QYuY9t1Q45LIyOGrr4KyrhbO2Do66Ojhra+GoGwdnXS2Ew4Flv/sDitc3q64jmzB7c8VErLnqDsw+ZNthM3SaGSjWG2qecPsdaL3oQlNDzNmMZSjXjBl4/7wbcc9X3egzcf83Me7Hza9dA7e/R732lCmY9PiTeGRJAA++v8rU+iVuB67peg9TXn5Uc7mq887HSzscaUn9M/aaisM+fALdDz2oXj/LMIOuEEOmMK3JYa7VITf+11eKLwPFWBwpw682LMSUVzI8/mnCFGYEKZRwBE13P6N57AcSgeK3TrgSd3/UaurzP90ZwM2vXYN4h09zOWdjI+b/7p+4/9P1pr/+zp8usNNffwXENIIhTic++euD+NfiiOnb/23v3AhHe5vmcq6pU/H+hX+3ZP9z0/xbULShRbt+bRU++f0duOObsKn1t4AP1711I5R27defq6YS9eccBWdZseHX/VCp20Gkoxtr//H4QBe4tBwyGs47Hp4JNYN+bNr2d9ccRDO9/uuqMfnXxwyEmYfKdv+nhCNY/c9nMj7+zilTMP/yu/Dc5034R8Myzf1pUJEgAfBqdWZPkU2Y20y5hrnNkOkxyEeg2FB3bgvYHehmfdbPZ/3euIx3usvRK9zYftFCeN94TXXZytNOR83vfo8nP1iK3ZvfxVSP+v7XyIQOK0IeXNy0CS5u/zjzewALz8Fb3vkSwXkfatfPMVCcbZBYrX625yFmTChi5jmQkfryz/bHqUsa0RTUavltbBz3T1+J8g/ez1z/4L3w5qzLTX8PsklxFHevfATd73yqXf+sM/DS7idZ8h747u16MfG5/2gffw/eC68e9Qfc+2GT6fXv2CGIKS89or3/O/8CPNRVb9lnAEe8fge6nn057TJ5qf/mXeia82L6+sfPQs01N+HBj9awvgX1M77+jj0cNX+92b76Fr/+7Kxf6wHmbLkKypvztOv/8pd4qHeKJfWf2XIF4m++o13/iAPxaPGepu//7K5f4nbgkt2rsPczN6Hr7fTHoKr9dkb1gbvjmaqZuOPjNlPrNxQpeHSzVYjPzXD/j/wZnvTuZvrxl/VZn/VZfyzXf2Sz1ahU/NoTCh53KLDXMXjo83Yeg0dZ/eR7wEmvPJFhQquD8Kj3J6PyPSDrj+36/AyA9e2uf8ZPGhFf1zQoSJxaX2+gmFmQsamQs2cMExMRpSjkHToVLp5AEhUePaEZPSbcehvKDz3M0G0iG9rw0c+P0gyoZSMqO+FS1C+OH6mBYkkC/nDI5jhulwYAwNxF63HNS9/DH7a+G+dIDTOz/sb6peE+nPfJw9ip+euca4YdLnzUuDPmbro3xu+6A/70kzpEzjtTcz+gFmBNd3FfiRzHlZOa8LPK4UExPUFiYGOIoq2kFrOXNGZ98aCa5EWFk3wtaPrn09qhitpqXLbPZVjtqlFfxoAp0Q7cPP9moF3jdeWQNcekN1Cs5/EO1k/BhuvuHhYk9gUieO5/Tfih1a8riBh0eiALBZ44g8FkLgFAq+H5huJq/OMnZ6HbU4Yr3r9b83Waeg6wzaRyXHfUVphaO7gjfay9HStOOglYtULXetLRs820VU9C/SOPYtpmjZZ0RTYyFrNrao3lmrdvxqTe9doLTp6Cmw/9PT7xmXMBdypvNISb3rwO9b0bNJdrr5yA3+x/BUIu9Q7a2Zr9xbM4fMlbmstohcly6YqmO0xrUZhLCUew6q45EJnChP2dWYfefzOCFOHWjszHfocM/Pp0nOvb3rRzkGpnFPfWfQPHf55CrKNbc9muqvH47d6Xmf5+pSLYgzvevwnFne0Zl20un4Cr9rvU3O3/nVswqUd9cpNUVux/DO0L7Tj/S5F6rmdGkEfvOfjQ2qly3f5ivQE03/ucrtd/1emzMKNWfbKybLpSLm+PofW+5zChVzvMHq2uQctJJ6OivAg1zigmeyJwaJ2I6JRLmNsMusPcKvtfM+h9DBovPgmumkrT6wMMdLM+69tVX+85aNlPt4PnwL0RVCRUOePwaEzWEIjLEABKHOr7lOTxIhCM63oP0Fk1HtXnHocZ5ep1szoG9UhY/8+nMD7DMUhrH5zze4AsjwF5nVBkSH2zwsxG668SZTkFmYeOY6rUq7t+7OxTcW7z5qa+B7ln0hK47ns0Y33UVOHCA67EOqnUlNpJ9a4wHlj+EPwf/Z/mcl01E3Hhvr83/T14kRzHo02PQLy3UHM5576746T62QgqDlPrA8AtK59A48L3NJdx7LsHTq4/xZL6N614EtM/ma+5zPq99sf5k441vfZYr18kx/HI2keB9xdoLte+13741aTjLalv5+vf7voAcM53z+OAr97UXKZ8v11wesMpaIl6TK1d7Yzi3tWPQbyf+f6f2XCyNX8Ds7n+Pasfz/j6r9pvZ/h+upcpx97U2vdOW4nKD3VM5sH6rM/6rM/6ptef7g3rmlAw/LMD8IsfpozKY+BYrl/vCuOhtY+h513tCa2+2OMw3DDlUFNrA/afg7L+2K4P8DMA1re3PgDsXxXGec/9BbH16a8H0BsoZhZkbCrk7Jn5V5cREREREY1yztpaNDz6ONwzZuS0nq577zX0xiHW3o5Vp5xsapA4Lsm4c9fT8KvDb0BT+UTV5Rp61uHqd25FRVC9I54dhACue3UJ5nzahKc/bcLlz3yblyAxAPjDcVz+zLeY82nTwM/mLlqPo/+50NIgLQB8vKwTR/9zIeYuGhwsYv3B9f2eEty056/xyHZHIy7l9vbXE49iv5Uf48a5N+Dwf16KO//yIJZfdqPmfiDa1oWW+19ArDcw8DO1i/r6FAeuWDsFf1k7CfGU3YKREEO0w4eW+19AXV877p22EtVO8wKqyT9kNUa7sP7JN7QvZgeA9k7cPP9mTInm1hka0BckcdVVoeG84+Gqq1JdJt3zMZSex7upfCIu/cmF+L/ewa+pr9f6cOubP+CHVj8AoNdTgrt3nY32IvUxFcXCDBKTJTLld8YFOnH9W3/DP//7J82Q2PqSGvxj9zPQ60mEh79t7sHx//4Ucz4b3Pn4+ZVhnLfzeVmfS+gJrDWVT8QVe12ME+Ysx5zPmnWdj2Vz/qJnLIpT/Y/EZp4zJceSMUgMAGtX44TH/2LJuZonFkkk1DOIxJXEsiYb19uGg5e+q72QQ8b4Ew/KKkQAAJ0xF85ZOQ0rQsMvgpQ9bow/8aBEWFZD94KvM19sngUlEoNDxxMgQYISGT450HRvOON5gVaQItYb0HfsjytwPfU87qn71pRzkGpnFPfUfQvno3MyBikBoKprPf7y7i2mbgMVwR5c++7NuoLEADCpp9X87V9nkBiwZv9jaFKFPJ//DZV6rqfndT9UtkHiobVT5bL9Jceg9/Xf8sCLWN6uPkFY6liqnVHcP22FZoirtzsE8cicjEFiAHB1dmCTpx/FtqINU73mBIljvQG0PvJfXfue9U++oXmOny0lEoPIYf+bKyOPwbpHXrXkMYh0dKN7QYbJuTSOwblSwhH4v1qacbner36AEjb/HIT1Wd/O+nrPQXsXfI3i7g5McMc0g8QAUOxQNIPEcQEUy3HcPXUV7p70A+p0HEeVuIKbV9didVh9HzD0GKSnM/JlyyYiLjIfUGJCVt0Hax2HM41DicQQ1fEeKN0xwMh5iNo4sj0G5XoOlG39bOpqjcNI/SlSn2mfgybHMEXq01XfBYE76pea/hns7eVfwb/wK+0FHTKqjjkAxUXmX0RbGQ9CfLM443Kxr75HZTxoev2p6MH0pRnuP4DW//0AETb/c9XKeB+mL/0y43Ljv/0fKuN9rG8yEY6i7ctlmesv+hxTYf5nYHa//u2uP1H4ccBa7RALAAS+WoLbLdj/3TNpCZyLMt9/fP0d7pm0ZFTWdy36PuOyvV/9gKlSr+nHv6lSr+7zX9ZnfdZnfdY3r/50b1jXZ3DdC77GON86S64DGQnHwLFc//b6pQh+/UPGZXdY/AEmCr9ptZPsPgdl/bFdn58BsL6d9YHE36TPnXO1apAYAPzvfYB4t76/GRMVEoaJiYiIiIiyYEagOLz4ewQ+eF/Xssnue1od/4zyu4pw8SHX4P3pu6O7qBxX73dJQQaKgUSg+PpXlyDfkzqNpDAz66vUlyS8suWBuOmnv0Isx0Bx0ozONTj940dQc+FstEzaDK7Jk1WXTQ0W6AkzveirxklLN0EgLhkOMaTWa4x2mf6HrMZol7HxmBAo0Rskrj9rFjwTalB/1ixDgWIhgLCSuDhVb5D46v0uQZuzFE8sWIMFyzogBQNYMn8h1jz5DGZ9/jwu/fAe3Prq1Xj8mQtw/Vt/Q22QHyhS4Rrf14Gb37wOT845D/e8eDluePN6XPD2XVh/9Z/x+uXXofv55/HU3c/iP4+/i17hyOpcQm+QONltMxRVcP1/F+Pf81fCWVuLt395vWnnL3rHctn+V6BvYqMpNXMZixV11cZR79fuSAYA9b0bLKl/xfv/hEvJENJSCZMZ6cylFig2EqY1O8xlJEyYnFQkXf1sgxTZBCpdjz6dc6A4GSR2Pfq0ofOg+u5W0wLFFcEe/OXdWzChW0eYP4Vd2/9IqZ+v8z812QaKcwkSp6udKpvtL5sx1He3Yv0DL2Bth3qgbbo3jLlbLMZbWyzGVK/6crHeALoefA5SlmHuXJkV5jZjDLnuf3Otb/djYFegWwggqgDhcByKjjCXAgm+oAJfTB40OVfaZUXiX6b6gP2BctYf2/XtOAd1SMAEdwzTY52ofuIJuLoy74Nqe9pwxbx/YFLEp7ncdG8Yr26+BP/dfIlmkHhV2I3ffVeDi+feqes9CDq7sPq+F1Xvf7rjcKYgcaw3gNX3vQh0+jKWVzsG5GtCkXT1cw0zZ1s/l0BxunMxI/XN+Bx06Oefeuub8f4ndQz31H0L19PP65vM6flXTKudOoY76pfCKekIU0uwJEz994nLoWdGszpXHONh/oWkFUrIkmVZX5/x6NM1mYUMCX+fuHzUvf5HRv0M+x/YP5kD69s/mQfrsz7rsz7rm1ff6ISCVlwHUkjHwLFcf3Sfg7L+WK3PzwBY3876uq5JnFCLhifnwFGhfi0iUaGSRKH0UCYiyoNCbjVPheull14a9rMjjjjChpEQUTaSId/I8uVZ3b5ol10w+bEnLK2RzobiavzhwCvQXVQ+6OdGAz00Mhy0zTi88a2OC+zGaP2cAhF6OZ1ATOPi0dpqXLbPZVjtqtG1uqnRDtzy3s1Q2rLr8pwM2La4K3HGium6QlTpZB0kTmXwvicZCRI7y4oHfqbnwntXXRUqzzgaF7dviTURj67A0vqyOjyy7VGoDPViUk8rJvWsw6SeVtQGGBYmSgo6PfB5ylAV6oE3rh5SSp5LAMjpvGPX6dX4ZEWnKecvRtdREezBTR/+AzXtzbqWNyLX45ZZ52rZjsPO+qnHBSNB4lS5hgqddVWYNOTYlI1sA43pjo1JQx8TM4PEQ8cQnX08zm3bJqvHP5sgcaqWign4876XZv0aTAaJ67s1ugI7ZM2Le6zc/p01FZAgaXbCtru+led/qK6ED15Udqo/P0b2BUa3+ajs1JzgQG0b1Lv95bLtadXXq5Dr51o71zHYXT85holnzYJLYwyR/smU3BodTNt9IbTc/yJKdHZmB4COynGoP+tINFSrdyddGfLgVyunQQHw7wydSZe3x9DywIva++IULRUTUH/mkZhR61RdJrkdAsjYGXV5ewzrHngRE3XWb62YgMcO+RX+sFk3JnvUz4N74zIkAKUa3WE7Yw683uTCj579D0o6M3cHBwCltgbS7GPRWOPSfG71Mvo6NOv1z/rZ1TdzDIW6D5Rqq9F49pGq9fVu/wNBYoOTmug9/ieZfR6Qrr6RcyAz6xt9H2bGpC7J+mtcVTm9B8z2889c3v+kjiGb90Fm1M5lDHbXz/Z9hxrDExuxvq317X79sX5u+97U2tnsg1nf3s9AWZ/1WZ/1Wd++v0GNlGMQ69tX3+5zQNZnfX4GwPpjpX4ySOyapN5oIBWzIGNTIWfPGCYmIkpRyDt0Klw8gSQqfLmGfSc/PQdF2+9gybrTaS6bgKtmql9cz0AxjSZ5CRLrpHe7MSvEkfxDRqe3HCct28TaC+kyBGqMfqiXbZA4Sc8f3pJBIwCZA0MS9EyGSQbF+7uFO4T6aycuyQg4vZAhIAsFDqFAFgqcSn66oJN14pIMRZLg0ngujZxv5HL+ku1trThn0rPOltJxgJToBGxW3WzGocXO+nZeyJ3UVTUOoVOOxx4TBOThHzVlZGWYry3qxO/WTMaaiCenIHFUdmhuv9k8D7of9+pKuCRongdlGyjWEyR21VVh/IkHYf2Tb2iO04rtP/ncAsj4HFlZvynsQvyRZ7T/wGvB+V9ynb6YS/dYtS7sMnrhVlP5RNy6x9m45OP7dNdOtSLkwW9XN6LSGcNfJzeh3j14Bnc9YzDyPsBooKsQ6md63zHSw9TJjrhqx4ZYbwBN97+AuEb9mOzQPBfWM6kEoB6msyrQnY8JLeyuj9pqTLExzKin/gUrp8ItK/jH1NWYohJ6jvUGsPb+F41PblZbjbLTj0JNpReOLM5/UusXYpjV7vpmjKGQJ5TQUz8mEh/tuDT2wWvuexHC6LaXob6dE4oYCTObWV/vRfVmBImH1jd6QbcpEynC3gmV7H4PbHd9sy4mNXwRK+uPiPp2v/5Y3/7JHFjf/sk8WJ/1WZ/1WT+/9VPHYPcxiPXH3oRWrM/6dte3+z0o64+d+o5xNWh8+hndQWKAWZCxqpCzZwwTExGlKOQdOhUunkASjQ65hH5LZu6PSf/6t6nrVGNmmJGBYhrp9LyOBRI50XwxozOmkRBJctlQcSlmL5uB1RGvrnEa+QNGU/lE3L/Xabh64T2mBEpyDRIn6fkDXNDpgSwUeOJR1WXGMgEg4PQi5PIi6PIi5PQg5PQi5PIg6Nz4s2DKz0NOLyQRx4lfv4y6gPpzaEZnWkkokIWAQ4lD7g8ZO4QCWVFQGfTh0o/uxUS/eievPlcRJCFQHAupLhORXejyVqAoHkJJJKAZfCZzCQA+bxk6i6vQ5a2Ar6gCnUWV6CqqgM9bgc6ixM983nIosgNAducvuZ7zmHnOZGRdQG5dnXMdh5WBZl1B4gxhspHQmTZToEdNvsJ8ikgfZjMSqLxswb0ZQ7d6/6Cs9w/HLRUTcOsBF+LmxjW6ljXyOtAbJK478ygUlRfpfqzM2v5Tn9OIIkHu67OlfvICmbJgj+7wrxnnf0PXZfQx0wryGH0ujdbWQ88YsnkfMNrq6wnyWxlmFtWVUCDB0Wm8fqYgqdWBdj1hNqu6c9sVZhsp9dONId/dyfNVP9P5T288MaFUmUpnZj2Bei0jPcxqSn2LJlUohAkl7K4v11RAQILIov7asBuXrG5EIO7AndNWZbXt6XkPZuX+32h9I52Rza6v94JuI59/tpSOg0MSGN+r/jmTlRMq6ZlMxK4w80ion+vFpFlfxMr6I6K+3a8/1rd/MgfWt38yD9ZnfdZnfdY3v77ez+DsPgax/tib0Ir1Wd/u+na/B2X9MVC/thp/3P9y/P3CgzG1tkT3upkFGZsKOXvGMDERUYpC3qFT4eIJJNHokUv4d8prr8OzyaaD1rX2xOMRXb3atPFZ0ZmKgWIaqfS+fm/a89fYbe3nOO7b/2r+McZMuXTGHHpRoKGL/kuLccWaRrzdU6E5PqNB4uR9ySYEMpRZQWIgcR4fWdeO1sdeRczn11x2rAo53BCShKJY+o5YQH6OHWYfbywLZHrL4I2FURwNojQSQHEkgJJoACWRIEoGvu//Fw2iLNSDGZ1r4M7TvmWsUiChx1uGLm8Fuooq0Ocqwrbrv0d5WH27NzuQa8ZreCRsO0bXCZgfaNZ7LLSiK6wVY1A7ZuUS5LU7zDc0UKknfJvpD8pGgsTJcHA2t9Fi5L78rnML/K1xbVYhVLXaRkOxl6+ZjL81rjV8vpZr/aEXxuTr/E9tHdkGigEYunCru6oOXx99OkoqvKhxxlDljKEq1A08Mkcz8KZ3GzSy7WfzPmC01c+ldlSRIAC45cGf+RvZloHMx7+h9TMFuawOtNvZndvOMNlIqA/oDzOP1vpJZgTqc6kPAGFFgoTh+wC99e0Os1o1qUIhnIOO9fpdVePw3M9/iZAi47jX/oVxvvWG6mttf1YeA808/hitn+mC7mw+/wRgyvuf1DHofU9z8+7nmDqZk9ExtJbVQQJsC1O3ldWizhUHNCZ0yfZiUl3vR6orE187faxvU/22mAt1Pfa8/ux+/Y+E+gokWydzsHsyiUKpPxLe/7A+67M+67O+OfWNfgZn9zGI9UffhFYj4RyU9cdufX4GwPq21k9Z9zaTyvHoWTtClmVd62cWZGwq5OwZw8RERCkKeYdOhYsnkESjS7aB4vJZR2HCTX8DAEQ3bMCaWUcg3qb+oYxRVoYpGCimkSa7QFY3rp97I8ZpdFI1U1P5BFy936WGAmFqHX6MXkz/bEcVbl5Xj4gY/mFXtkHipFzCIDOi7fjbe7dAaTMWJFaiMUQ7uhFt60K0rQuR9v6vbT6IyNjoOKxAgq+oAu3FVegorkKvuxi7NH+FylCv6m2sCAPmcsww63hTSIHMgMsLSQjNMDdZJyI7ISRJszO5VSF2M1//dnVGtmL7MRqMMrMrrJVjUAv01LsiuGXKGtS5EhMOFEKYT66rRueJJ2GVqxqrI26sDnvQ7gvjV2/ehYlZXtSeSyjYrEBxNqHoXLraDq2dbSAj1/M2o/XVLojJ5fzPjDCy0cfQH5chAShxKDlvQ2Zsg7msYyzVrzjjaPQVlaE77kBP3IG+niDGP/EEvJ3tumurBamy2YbN2HZy3YbtnNDBrNrZjgGwL8yVrG93mK1Q6lsRqM+mfuoYgOzClL6qcfh41pkokuPY9fkHUWxg/5NJPidVyLV2trdhffvrh/on1ChKCdNbfQxMd1F9rufRdh+Defy1r36lM5rze4ehjLwfAcD6Ntb3xVxj+vU/EuoXyvnnWK9v92QerM/6rM/6rG9efaMTCtp9DGL90TWh1Ug5B2X9sVufnwGwvp31U9d54f4zcOaeU3XVYBZkbCrk7BnDxEREKQp5h06FiyeQRKNPVoFipxPT5r2DWGsrmk47FSIUMm08uYZ9GSimQmJ1IMtMPm8Z/jzzMgRcxbrG/I8DL8AtW3UM694DGL+QcEXQg9+vbcSysHfg92YEUgDjH8BJEDjRsxrHvvpvxLTGX1uJ6oP2gNIXRCQZHG7zIdbVA4zyc3afpwwdJVXoKK4eCAx3FFWhoyTx/11FlYjLDgD2hQFHwjoKMZAJZP4j5PqSWjz148Phiie6IFYGu1EZ6kn8C/agMtTNQLIFFAAdxVXo8Zahx5P41+spQbenDL2e0v6flya+95Shz12E8pB/TG5/+aqf7oJ4MwPFVo5B7WICI8fekRDmU1tHqCeI5vtf0Dz2p/uDshlh4FzXkcsF+fkMQqhdjJRrEMKsC2Gy+QNstn+0dUCg3BFHlTOGSmcMlY44JkS6cMCL96O0S3uWdCuCQIUSxh0p9RUBCAAOSWcX0ooJ+Mu+l6CjqGLY73J5DZvx3sfuztx2T6hgV3f4c9u2AQBb63fGXKY8/0n53IeaVX8khPkKJcyYuv/0xxOTupU6lIGfZbsvHovHENbPf31HXRWKTjsGkeIyBBUZQUVGQJER7g1g3JNPwNOhL1S/NuzG5WsmI6TIuHXKGkzL44Q8VpwD5BrqzeU4ameYeSTUt/v4x/r2n3+M5df/SKhfKOd/Y73+SDj/ZX3WZ33WZ31z6hsdg93HINYfXRNasT7r213f7vegrD+26yeVuB2Ye9lPUepxatYAmAUZqwo5e8YwMRFRikLeoVPh4gkk0eiUTaDYs+VWCC/+3tRQnFkhXwaKqRDkK8xkJgEgKjvhVmKqy6SOeWj3nlRGLySMKMCdrRPxREcNqpwxU4LESXo/iPvPwefh5Jp2FD3+lGZNSNKoDQwLAMPfhWw0tIu1lrHaGdWsMdi1DjPqeqOhlIBxImw83r8B+65YgJJoUHW9ZJ64JKPXXQK/uxi1gS544xHVZVtKx+Ev+/0GMdlp+7aT623zVd9fXYetzz0s7YXwy9tjaHngRc0/ZJox/paKCag/80jMqB3+h6JYbwBr738RSpvxP0DpOWY666pQe+YslJSPzCCCkfW4fnEM/tS1BVoibtxZt8iUIJidfwS3u6tB6hjsmlE/yewZpR111fjh2NlwlxejMhkedsRR4YynXd7IaxmAaR0FjdYu9K6GqaKKBF/cgS5fGHh0DhztHZrrsiIIaldnbLs7g9vZHf3yNZPxt8a1OdfOdgyrQ25AAqZ4IrbUT30MzHj/nM3rAIBp799zeR3aHai3M8yXrJ/NsRyA4e332pkXI15SihJZQYkjjtL+rzWhbhz6yj2oMDCZRtJYCcOyfuHWj9TUYvlxsxEoLkNIkRP/hDTwvdTnx0Ev3Ydq34aM9a3af2azDzJjQo6RsP+zs77dxx/W5/F/rNe3ezIH1s/P+Sfr213/N+gpKoeEjX8/rAz24Mp3bkVDj/b2n6y/KuTGRaumAgBun7oKU7363kO3VEzAzftfhJ7++5+sXxbswWXzbs+4/xl47x5249LVjQCAW6as0f0ePlm/d8jjb7T+qrAbv1k1BQBw25TVuu9/c/kEXD/zN+guKkfyr9ICQHmwB1e+fZvux7+wX3+sP9LqGx2D3ccg1h9dE1qxPuvbXd/u96CsP7brJ110wAyc8dOp6rfvxyzI2FTI2TOGiYmIUhTyDp0KF08giUavrDoUm8jscC8DxTSS5TvMmC9BpwcLG3bAuvJxA11pY+VluG4rH2aUDg9SZHMh3+d9xZjoimJcuDvjbbur6vDBrLMQLymBUxKJf8DA967kzySBsmA3NpnzKOIa65PcLkAREDH1MPVotaG4GjfsfQF6PSW4+p3bcn7tFnIQNtdxjIQws523zfZ+BJxeSBDsamyjTJMJdHtK8dqm+6KruAoBdxGCTi8CrqKB74MuL0JOT2LCBdi/DVtd/4QpQZw7fvCF4smLMuK9wbxsf46yorSTenTFHCgL9lo+o/lt6yZghjeM7Ur6sF1xAJUpoUq7w4xG1ycArMuwrK9qHB48+Dw0uarQHXegN+6AUNlq9P4xen1ZHW7Z/Sw4hMBvFtyPcX71bmrOylLE9tsbd/oa0ReT4FAUyGLjv+T/l0sRnFLThlo5gngojJ4F3yDepz6RQ0R2ARCak8lIXjeKNpkM2emAPybjU38JYgogicQjIAll0PceScGPigLwxiMIN62HiKqvOy4luiI6hKJe3+mAa1wVIpILyyNexESaz0z7nwsJAg4p0THYK2KY6FsHaJ3byf2XQioa9d0ueBsnQHIlwvuSJCX2dZIESKn/D0CSBv5ficYQ/GE1lJD6hA6S05EYfyx9KBkA5JIiVOy2DWSvF5KcWLckS4As99eXE/dDkiDJcqJ2OILOeZ8g3u1XXa+zqgx1R+4LSBLaXngXsa4e1WVdtZWY+ItD4SgvTdy/ZL00n18D5oepozU1WH78bHR4KtEVcyZCwylfu2JO+BUZgGT4YhAAps1mb0dHiaETPOWrq2m6CxGT8tlZNaJIcMsb/2aSz4sx07GjfupjkO/6qWFqO+oPfR3adSEmYP+FcLmsw8zu3kbG8Ou2bVAkKbi9bhGkR+ZkCHLWYMOJJ6O4vAglsoJSR3zgq8fgPmA0TqjB+oVXXy4thgAg/JlvE6iuxZdHnw5/cRkiioywkDZ+FRLCioyIkODs68Mpr/0TNRkCzVZMqGL3/sfu+oV8ISrr517f7tcf6xfeZDiFVH9dxQTcdeAF6CsqhywJyMCgr2XBXpz5xl0Y51ufsb6zrBj+uAwJQIlD0XXc7K6qw/uzzkK0uBQyAKm/tgTAIQl4An7s8vyDKO3UN5lNWEl8huGRhc7JRGrQeuLJECWlkFLuO/q/d/T1oerxJ+Ds0J7MLFk/1n/a6pT0nTegthru046BXFoyUFdKeRwUfx8CDz+nOaFkav3k5YaSzvp2T+bC+ubWN/r8O+qqUHXG0ZBLE/UFACGkxFckXn89Dz2v+/UX76/v0Flf1FYjOvt4oLQESkrd5Djg70PR409lnEwwWT/av/27dG7/4ZparDr+FMSKyxL3d8hjIPf5sdmcR1DUqf55fmr9UH99r876PVV1eG/WWQgVlQ2qnfze3efHgS/fh8quzJMZJfe/AFCqc/+7oXI8HjrofPhT6isp9YWQUBzswa/fvBMTujMfA5pclbho1VQoAO6YuhqTC+AYyPojd0Ir1md9u+vb/R6U9cd2fQCYXF2Ely/YDbIsqy4DMAsyVhVy9oxhYiKiFIW8Q6fCxRNIotHNrkCxVaFeBoppJLIrzGi74iK4K0vhrBj8T3I50fH6x4h1dqveNNcL+YQQUEIRKIEQ4oEg4oFQ4vu+0MbvA0HEevoQbm4D4uphjdEkIjvRWlqH2kAXimMh1eXMDvPZFQYEMnc1zPaYoHcct+5xNi75+D5bw8zZ3saMurmuB8j8HHYUVeLNTfZGcSyI6oAP1UEfqoPdqA764GUQ2XaKJCHgLELQlQgaRxxONPS0aobE24qr8e+dT0HY5cF5Cx/GRL/6xV9Wbj+A8X3IuePWDwSKh3Zqzde2OzRE1hVzoKo/1GtlZ9qh91mCwFRPGDv0B4u3L+nTNTGI0c6sE39xKOQiLxBXIOJxCCXxNfH/yX/xgd/H+oLofHMB4j19quuVXE5AQHMyEcnjgrdxIiRJGqgp4gpicYF4XEE8LqDEFSj9Y4CiQIrH4VFiQDSqul6igidvDDBLcn+oWZYhyYkL65S+kGZQW0+YGx43Phu/DXqcRVAkGXFZhiIl/sUlx8DP4pIMrwM4osaHarcCEY2h55NvEPerh+kDTi8AaJ4rO6vKUXfUfnCWFmFt1IM/NTeiU/H0j0FCXHKg3BXHrVPXYlpxFJAkxAMhrHvoZcv3v8DwIC9g/f4/aWiQ1I4xpJPPMDPr21/fjEBzLkHmzpgr5+7c2XYVMquzhhlB5mzHAOQ+oYNLUgYFjMeFfTj19bszXlQ9of8ctNXEyWwA8yfUYH3Wt6L+xLMSEyplev1Hamqw4vjZCBeXIdofYo4KCTEhJf6//2tUSHD2+XHAS/dl7E4uTj0Ov+/cAjEh4W8138ORYTIBPWFmwPi+DDAvTA1kd0GoWReRsr799e1+/Y3W+usqJuDa/X4Df3EZZCTCo+m+loe6cd6bd2K8jjBTqLgUAOAN+HUEWcfh3SPPRKykdCA860jz1RPoxfbPPYwSHYFWpaQEACD39WWsH6+tQejkEyCVFcOBRIB0aH0ZgNTnR+ThZyE0XsvJ+o7SYtOCpEmFNJkH67M+67M+69s3oauRMTjrqlB75lGJSZhSQszKkO/j/j70PfwchI5Qu9Q/oRN0TOgUq6lB18knIV5SMijInQy2J7+X+/yof+pxeDoyh8qT63LqOAfwV9fh06POQLi4dOC+DoTak2NBIlS+94v3Z3wPVn/WLIT7z4E8Os6B2ivH48lDfgV/cdlAgH5obQgJRcFenP3GnajNMKFV8j2gIiTcVPM95AzvAVsrJuAv+/0GvqLywfUBpE6LPVrPgVm/MOrb/R6U9cd2fQD41+ztsMcm2ssxCzI2FXL2jGFiIqIUhbxDp8LFE0ii0S/W3o7VRxyGeJv6B4pmsjrMqzd0ccWBv0fI5bVkDERJVgbc9axbLvJAUQQQVu9+NlI5ayoSF9JJwLr7XkS0w6e6rOR2wT2uCko4mggKB0OAMnbPk+UiD9zjquGqq4K7rgquuiq0VozHb5un4aJ5/8xrZ1q7wvQtpeMACajvVf9jVa7HIz3jiMpOuDQ6O1odZjY7VG1HsDzrmkKgOBpCdbArJWTsG/i+rq8dk7vXaXa+pJEtLsnwecsQdnoQlV2IOlyIOpyIyi5EHM7E/8v9Xx0uRBwuxGQnJCWOfVcuQGW4V3Xdfa4iAEBJVD1wtq60DrftcRZ6vOUQkgRFkiEgYXZdO/au6MVv1kxFZ9wNRZIgJAkCEsrCvbhy/h1o6FH/Q6axbXcCrtv7AvR6yyAJ0d+RVqDKEcHtjatQJsdQIccAIRKfHwmBuD+A9XPeQqxTveOonvvvKCtG5U+2g+RyQiiJsKyIK+gMy/hvZzmiMcAh4nAqccj9X0sRxTilD1Oal8IR0Qj7y1JyOnv1ZYiIRiGl/+InGer7v2RnbsnpHOgKLUkyQnAAsoQihxjUlTrxewlKXEFg2VrN94ZhRyKU54lrTHpQUoyqXbeG7PX0d6NGfx154/dSSqfqlDGs9yvofOczVAd8qqsPuLwQAEqi6oHu9pIa1B6xDxqqHIO6ggPJmhj4WeJvKhLW+OLYMOctjOtV/+yrpWw8btrzVwAkXP7B3ZjUqx4+WFc+HhNPOwzTapwDPxv4+4008J+B71d0xNDy4EuaFzMZOQdoqZiA+jOPxIxap+oyqWK9Aay+70XNC1OM1EdtNaacfaShMJtd9YUQiPkDWHf/ixkCvRNw7T4XAQCunH+75vnaQHf2/o5MEICAwKqQB1etrcc1k5sx1RNOXtWIuD+A1sdeQ7Tdp7rOlrJxuPkn5wIALvvwHtT71d/LOmsqMP74A+EoLkreS6wJufHHpgb0xJwABCodMfy1YS0meyKI+4PY8Ow8zfO/1pJa/GuX2QCAX3/6KMb3qV/46qwqR+0Re8NR7EVz2I0bWiaiJ+4cuL9S/zcVjih+N3EdJrkjiAVC6HjlfcR8Wufgic9ptbZ/Z2Upqg/+CRxFXqwLO/GP1gnojTsH1QUAKeU8sswRw4Xlq+B49wPEu/2q6w46PQCgOfGRo6IUzn13x8fRGkiQ4JXi8MoCXlmBFwq8DgUeScArJToke2UFDgDxYBhd73yqPaGNO7EPFhH1fbCjvARV++6U2AfrJQSUUBhd8z83p/4+OxqrD5hbv//+p/ubuZp4KKLv8ZcAEc5Qf/9d4SxW+ZuCypjiwTA631qo+fqTPP33X6O+s6IU1QfvAUfRkPrp6qb8TAmG0PHaR5rbn+xxJ5bVOE9wVpah9rC94ChJrS8N+jL0Z/FACO0vzUesS722s6ocdbP2BQC0vfAuYl3q+yq5qhzFR+4PpagYipAQg4S4kBCXJMQFEt9DTnwPCUogiIr/vgqnz6dev7oClcccAECC79m5mhNg9lVU48MDTkCoqLQ/MCBDQX8tJOoPfJUkVId6cPS7j0B0qtdHdSXu3fssKAB++d4DQKf6sQo1Vbh1z1+i2V2BuNh4xqhA2tgZUNp4YX991IerPrwL6NBe5x/3vAgCwPUf3J5x2Sv2vBir3YMvTh185rrxxdAY7cRNH9yW8cLY3+6VOP7//f1bc7iIVqB/WqKBf1OiHbhx/i0Z1/mnfS+BBOAv72au/7f9L0aLuwqyJAa6kibrJbukJr8vDfXgrNfvytgdvOKMowEA3Q8+l7Ej6+uHn4NosiMqMLwzLDYGTD3BXuzx/AMoydAhsebMoyABaH/gec360ZSOrA5JDARYkx1hh47H4e+D+7GnIGUItE7sn0xh3RidzIH1WZ/1WZ/1WX801R8JY2D9sVc/Lja+B4r5A2h74HnEdEyoBWQ+B0VtNXDqcRClG4PkqWHq5PdAIuQNfx+KH38KcoYu6ePOPAoAsCHDOXikpgZrTzgFsZLSwfWGhaoT9R39XdK9Ot4DAEBHhvp91bX45KgzEekPsifD7EBKd/iUx8Id8GPvF+5HaYZAe1X/e6AuHe+BXjnsXAT7u8IPvO/t//3Q+kWBXhzz6r8zTiio9z1YR+U4/OeQ8+D3lg+/z0DiDXjK/08Id+GCuXdqTuqD2mrcduCFEELCJW/drus9aJO7auBHg+77kPoNkS788e3M78Gv2e83EACufifzslfuewnWuqs31hWDP4NKHc+USCeu1/Ee/PJ9LoMA8HcTg7SAsXAuAFODvCOh/uHbTcRfZ22luQyzIGNTIWfPGCYmIkpRyDt0Klw8gSQaGyItzVg1cz/Lu3PmqytwpuDRnG0OxTM/OszSMRDlo1O2nhrrysfj9U32xp6rP8WmHauyqkMjU0xy4Lu6TbGmahJ8lXU4ZQuBqZNK4SgtGr6sgYu3zepMa3eYXotZx6NcxpGP7d+KULUdwXKzX0t6g+CSEHCKsdG5nEaW5Cc9+i+PJyIiotFAzzmAgv5zVUmCWxLDM2Tp/o4EIBSX4I5HNcPq+upLCDvdKJIVfecqQkAACMcluJQoZK1FddQX/WNwJJM7IvU3qf9PREREY8qwEwjJ3EnKZGnQeZaeSQ2EEEDcpIkMnY6Nk/XoJIQAouoTXeomAXA5Ne+zlOYMTggBEY3mfn4mJSZ9MDKRxED9iEn1XVnWN+n+az7+Kus37flHpvrpByCEAGImfbaefP0brW/W9ueQVeurXrMnhHmTHsvGtn3T62vuezTuv1m74OSEYUaYWR9Z1jfTWK5v9/Nv8Ni7sf4o2f4z1NfcN+ZhH6zGknMwo/XzcQzMR/0M54Cq9fNxDmJ1fR3nwJr1R8E5qO317X4PwPojtr7a6oUQgEn1keX9N6W+wSAxAGw+oRRzfrWr5jLMgoxNhZw9Y5iYiChFIe/QqXDxBJJo7Gi/43Z03nVnTutwTZ2K8ddeh56PFqDnX3cN+l2+gsRJakEhBokpH/IRJM6mVnXQhwOXvYefrvoU3njhdSseiwS0L6ROfR1VO6O4d9pKTPcO7qKjZwZYszvT3rrH2bjk4/tsD9NbUdeMcYyEMHMu47AjWG7WftXsrs59riKsqmxAWaQPlcFulEfUOx0RERERERERERERERERERERERENkkWQGAC8LhkfX7E3HA716VyZBRmbCjl7xjAxEVGKQt6hU+HiCSTR2KH4/Vi+108h/H7jN3Y4UHXGmai54ELIXi8A4MMrrsW45x4GkP8gcdLQwBCDxJQP+QwSZ1uzOBLEXqsW4sCl72FyliFEslZbcRVu+ekv0VlUiT+/+w/dz+3QQLGZQeIkvV1dXYr6jK92hWmtOh4ZGcdICDObMQ47guW57l+zub3R2zjjUVSGelAV7O7/50NVqBtVwZ7E98FuVAa7URnu1byvREREREREREREREREREREREQ0ymUZJE566tydsWW9+jVXzIKMTYWcPWOYmIgoRSHv0Klw8QSSaGzZcP118D38kKHbeLbcEuOvvwHerbcZ9HMhBB45+w+Y/OWHtgSJk5IhoI8bd2SQmCxnR5A4p9pCYMemr3DhJw+hOBoydTw0XFyS0VZcjYpwL4piYdXlcg0zJgPFjdEu04PESbl0xLUiTHvN2zdjUu96zeVaysbhzzN/a9nxaHJXM26ae71miDoqO3HxIVdhQ9k4U2vbFaq2I1ie7X42l/2z2fv2xPpuQUNPq+oycUmGJARk8D0/EREREREREREREREREREREdGok2OQGAD+MmtLHLFdvervmQUZmwo5e6beZ5uIiIiIiEwVa29H3/x3Dd2m+pe/QuOzzw8LEgOJNyI/u+UqXHvYH20LEgNAd1E5rjjw9wwSk+W80ZBtQWIg8Vq/er9L0FQ+UXWZhp51uPqdW+HtDw5XhHpxytcvMkhsspjbA0/DeJRuvzmw30/xr73PxsWHXI3zDrsOMYfLUJAYMPbcVgR70Blz4dLvarD6vhctCRLrHZPZNbXGctXMy9DjLtVe0MLPwrzREC75+D7N0CwAvLbZvqYHiYGNz0dLaeZ1N5eNN+050PM6MLtDtdHtAcg9DJxNTTUbx6IeJG4qn4hzj7gJ5xz5NzSVT9BcX5+rCP83YSssqZ2OdaXj4HcVay5PREREREREREREREREREREREQ2czpyDhIDQDASN2lARCMDOxMTEaUo5NkhqHBxNhqisSHW3o6m2Scjsny5odu5Z8xAw6OPw1lbq7rM3EXrcfkz34KHLBoLjv3mFRz37X/T/s7KIHGqTKG5Odscimd+dJiucF1ckiALgeFnoQQAkGUsrp2OFZWNaC6fMPDP5y3HueM34ICKbpyzcho6Yy5TOpvqXcete5yNSz6+Ly/BdiMdca3eBiqCPbj1tWtQHvFrjuGKA3+PkMtren2t7R8AXtriADy2/TGm102qCPbgL2//HfW9G1SX6faU4tKDr7KlM/pQub4e7Ngect2Os7l9NrdxKHGUhf0oD/tRHupFebh34/+He1Ee9qMq4MMmnaszBuCJiIiIiIiIiIiIiIiIiIiIiMhkTgcuO/KvOYeJL/3Zpjh1j0bV3zMLMjYVcvaMYWIiohSFvEOnwsUTSKLRL9sgcZKeQPGcT5tw3atLsh0iUUFJFyjMV5A4SS18ZiRInBwzIHDN27dgUu961WUVALJJYy84tdWqMwQWyXEEFYcpQeIkPeuKyk7Tu8GaMabfHfgHrK2aZEpNrbFoBYqT24BV1ALFVgeJvdEQbph7o+ZzYFWQOMmOYLkd20O223Mu+wEz9yF61xd0eiAJAW88orpMXJIRlZ2ayxDRCCDLkGQJkGVAAkQkBs2ZniQJkAAo6stILifcE2shO52A3L+8JEHCxu8hSYnPMiUASPy/iMURXNkMEVbfb0hOBwBAxNRnT5a9HhRvMRWyyznwMwGV8ab8WInGEFyyCkpIY7/lSNRHXGP2ZrcbX9dtgqDkhiQEZAhIQkBKfhUCshAABNxQsIU3AK+kQMRiiLR2aN63uCQDEHBkeI4ktwuAgFAEonFAEgrk/rEQEREREREREREREREREVEB0Lj2UK8rfr4ZTthlsurvmQUZmwo5e8YwMRFRikLeoVPh4gkk0eimK0icPP5oHHP0Boqvf20JOxTTmJAaKMx3kDhpaFgsmyCxkc6YzWXjcd9OJ8ITj6I20IWagX+dqA10oTrQBbfNHTAjDhd63SUIub2YFGgHohrjqarElXtdiJDTg5vfuwVo71RfVuNDPbNDgHrXaVYtvay4n7mMJV2g2OogcVLq9h+VnXhts30tDRKnqzuU1UHipMldzbhp7vUjLsxu9liMvt7z2Zk80/0xNpkEdC37l30vQsTpRlE0hOJIEMWxEIqiQRRHgiiKhVAcDaI4Gkz8Ppr4/7JQL2Z0roFbiaqum8guSiL9qhnKlNwuiMn1WKxUQEgyHE4ZTocEl0OC27nxn9cpwesEvE7A5ZQhYjH0LPwWcX9Afd0edyLLqxG4dVZXYPyJP4OztHhQWHjgq5Ty//1ivQG03P8Com1dqut11VWh/qxZAKB7WWdZseoyqcyu76yrQtFpxyBQXAZ/3IHeuAN+RU7/fdyB4mAPznz9nxA6zukA4Ob5N2d9/pdU7Yzi3mkrMd0b1nX/jex/Ux//FSEPzlk5DZ0xFyAEahwR/HvqCkxzhwBFSXx+rAjEewJoffxVRDu6Vdfb5yoCAJREg6rLOCpKUTVzFzg8bkAICEUBFIFoXMAJBSKuDPq5UBL/39kbQ+8X36Ms3Ke67ojshARonkuEHW54JtTA68TgGooCoYhB9zkWF+iOSpAUBQ5FQVEsxMA1EREREREREREREREREY0cOQaK/zJrSxyxXb3q75kFGZsKOXvGMDERUYpC3qFT4eIJJNHopSdInAwJA9C9rFageO6i9bjmpe/hD2t0eSIaJY795hXsseZzW4LEScnQ2MeNO2YdJB66rqyDbEKgPOxHTaALU8JtOK9oGcq72tD7f0s0u9NpkmW4x1UhUlKGT+K16HCXoddTil53SeJr//d+Tyl6PKWION2YEu0wHA7J5jaAtQHbbAKUVod59d7fKw78PUIuryVjSB3LNW/fPNBRO19B4qTk9n/DXr/GhrJxea07NFCcryCxnYFyO7YHvff31j3OxiUf35e3zuRa67JqMgkrJkTQE2br9pRh/rTdICQZTiUGdzwKVzwKVzwGl5L46o5HB7539X/viUVQHuqFg2GynMUgQXE4EZMciMuJf0KWUeVW4HL0h1kdiX+JYKsMIQQire2J7rgqIrITkCS44+phc0dZMar22RGOkv4wrVOG5HBAkmW0xL24YX0DOoUXMdmBmOxAqQu4oW4pXM+/gmi7T3W9LRUT8Od9LwUA/OXdW1Df3aq6rKuuCtHZx+Pctm0SIc4MxoV8uOqdWzGue73mOsefNQsSgFYTw7xGgrzJ9WVzGwCICyAiJESFhLAiJ772BhD/zzOQNM6lkuuKlZQAAJx9fbrDt5n2P/k8/0vKNkhsZP+bLlAMYKBuKivDzHosb4+h5YEXNbcpI/VbKiag/swjMaPWqbpMRJFw0rIZWBEu0j050o17nQeHUHD5B3ejvneD6rLrS2px304nIeAuQqUcwV/r16BUjg8KNCcD1fO6yvCOrxwl4T4c9d3rqA6ph7nDDjcAAY/G/s/vKsbn9T9CzOHs74KtQBYKvFIcuxT3okSKAQJYFXRhbdgFWUl0rXbFI5jetRaeuPp7r3j/3x+0OmPHJQlBZ+I4LSOxbkkog7t0s0s2ERERERERERERERERkX45BIqfOndnbFmv/jdzZkHGpkLOnjFMTESUopB36FS4eAJJNDoZCRInw8HZ3Cad1u4Qrnn5e3y8TOOibKJRwhsNWR6a1DuGkdIZMxmsaIx2ZQw0hJweCABFsbDqMskgwxpX1caObCpyCYUYvW0+gpVGApT57AqsNaZ8hnqHhunzza7tPzVQ3Fw2HlfNvMzy590bDeGGuTfa2pnaju1BT82o7DS1U3O2+xZbJ5PIcl2Avs7IZj9+60rrcPeup8KpxPHLTx/D+L521WXbiqvxn+2PRtBVDKk/QCULAQlKyvdiIOwlCYGSSABHf/caqoPqYbKQww0A8GoEvjqLKvDcVj+H31MMRZIhJAkCEg6o6sF2JQHcuWEieuJOKJIMRZIgJBnFkQBO+2IOxgXUj2WtpbW4a7fTISDh/IUPYaK/TXXZdI9/tTOKB6avwBTP8LHnM8yY2qm12hnFPXXfwvXo05q1k0Hi1DCnWYFio+sCoGvM6yom4IaZF6OvuAxOScABwCmJgX8OABWhHpz5xl2o82mHmJOPZSAuAwCKHYqu52xdxQT8ff+L0OapRFRIiGPw55dGQrHJ8zkAus8XM+0H8nn+l5RrkDjJaKB4dcgNSBi2/eUjzKzFSJDYSH09geIVIQ8u/a4GF8+909LJNKqdUTyz6VJUOQdPnHbP+nG4Z8P4vB3/kq+9t7orcM+G8QM/t+X4O+g4qKAy2I0r598xMNlPOi2l4/CPPc6EBODij+/XPAa1ltTi37ucgj5PaSLAnAwyD/uKRNgZQGnYj9O+eAZ1GsfBgDNx/l4cC6ku01FUiWe2+TmC/ZOfABiohZS6GPh/QIJAUTSAI7+bqxkoDzk9AACvxnvgLm85Xt18PwRdRZCG/H0ozV+QBr7zRkM45Id3URXqUa+v4xyky1uO1zfdFyGXF2J4QdVRFEVDOOSHd7TrO90ApIz3/+UtDkDQVQQx5G9mLlkgosgp4xp4VlAUDWLWd29oPv7B/sdf6zOIjqJKzNnmUATciX1fcgwHVHRj25IAHthQh464e+B+J8dQFA3gpK9fQm1AfV+s5/XXVlyNh3c4Fn3uEggAh1V14fAqX3+ZRK1eRcafmiajJ+5E8tVYEunDWZ8/hXF9Harr1jOhT2tJLf6166nwe0pxTHUHjq3phJRSGymP/UtdlfhP+ziUhf04f+HDmKCxTa8rrcPte5wFgczbf0vpONz6k7PhKPHi5ilr0OCODqqLlG+7Yw5c/H0dznnvAc1JIprLxuNve/4KgITLP7hbc1/VXDYe/9rvXFy7eSemesOQhtQcKu4PYM3DrwId6s99c9kEXLvPBRCShCvn34GGHvVjJmqq0XjGoXCWlagvkyLmD2DdQy9nOA+YgGv3uQgAcOX82zXru+qqMPH0w+EsHXz8XxH24A9rGnB9YxOmezZuQ/rrX9hfX/v+u+qqMPG0w+BIqb8y5Mb5q6aiq/98vMoZxV1TV2GaJ4y4P4B1D2tPKNRcNh7X730+AOAP792l+fy7aisxYfbP4SgtxqqQG79ZPQVdcSekIX+ur3JGcGvjGkyO+dD62GvaExqVjcPNP/klAOCyj/6t+Vp11VRg/IkHoclZgd+tbYQvljgPSj0eJF+GFc4obqxeAucL/0WsQ33ft6GsFrftdhYA4DcL78e4XvX3wM7qcsQOPwR/7twU3fF0tQc/EBWOGK6uWgrnK28g1qW+/w/073+KNfY/zqpyxA7eH9f7ZqA75hy4n4NqisE/K3fE8PvK5XC++TZiXb0a9fv3v1H1/a+zsgyx/ffBLd3T0Bt39t/vxHsuSRKQAchIfHVIiXMPGQLjor048asXgG71+pKn/7ihNeFmeSne2eEQ9LhLEuc3/Xv4ga/SxudCAiBLiZ+XRfqw++dzIXr86vXdiW1HRNQntJHKSvDljjPR5045/wFSTzWGPf+SAEqiffjRF/Mhevtyrv/1tnvDnzz+przuBCRIQkAMHg4EgJJIEDt+/R6EP7f6KC3GR1vvg15ncaKGwEC91H8QEkT/KLySwMFF6yF98gXi/oDqqsPOxPPviak//47SYsR32QFP9U5EnyIj7QFniBI5hhNK18Hx2ReI+9W3LX31ixDfaQc85Z+IgOIY9vuhzz0AFMtxHF/aCsf/TKq/4/aY0zcRff2fWWgpcSg4rroDVUoAPQu/0ayv5/zXUVqE8t1+jC5HMZ7uqE77GKQqluM4vqYTVfEAehZ+bU793X/8/+zdd5hU9fk34GcbvVcpAqKiIBIQESsqqLHEmlgSe9dYU94Yo/lhYkk0zZbYY40NS6yQiI2IJkbBhiU2ikjvIGXZ3fcPg0FlZ2Z3p+3MfV9Xrvf9eb57Ps+cHWaeOTvPObGotEXcu6DjBvPX78tbllbFER0XRIeqz2LJS+nIbxFtdxwUC0tbxD215a/LLquK73ZcEJ3K10b16jWx5MU3Ej7/U8//RpQ2axLzK8vj7gUdY8XXavj8ALQorYojO83/PH/Vmljy4uuJ81P4/FXWqkW03WnwF/l/WdAxVlR9OX/dZ4KWpVVx1Pr5E19LmJ/K54+y1i2i3X/z51VWxF3zN/T4Y738BdG5ojKqV62JxRNfi6placjfech/88vjzvmdEj7+YzrNi84Vnz/+xS9MTnv+HfM7J3z8GcnfZZv/5c/rlDi/8/z/5f9jkvxC+P0nef5/kV9WFUd3mp/+/Fz++1//9W9tedw1/8u//6+ek4r4/Dgcue49KMlrYMqvwTt+IxaWt4i/zO+U0nvwkZ3mR4e1n6XwHvDf96AkPVjbHb8RC8ta1PL+82UtS6vie+sef7L8urwHl33+Hlzb8++L/P++D3+en6734MQ9wFe1KK2K737Rgza0B24eVcM+70GTHft1WpZWZa0H3pAW6/LT1QNvu03ct3yjOuWntQfPdf7QIfHgio1iZfXnnwFKvnr68Yv//+eff1uUVscBLedE2SuTE+av+W9+kyT5NdsOiXErO8fK6tL1zkF8pYaSmi/9323Wfha7vPdCxIran/9R8d+/aVXW/h2SaNki/rnlzrG8ovkGHu/6ar7031pXroxt352YlvzJ/Xf84nzNl7K/8pi/9GOVq+Ibb78YNQnyS/6bX5Mgv6Rli3hrqx2+OF+T/BPw//IHTHkpLfnvDNz+S38D+mLbBn/i82PSvHJlbPnWvxqcX5+B4mYVpfHi+btGWVntn5nNghSnxjx7ZpgYYD2N+QWdxksDCYWnIUPB6RoorqmpiYcnzYqrxn8Qiz9L8Af6OmrXoiK+PbRH3PKPqWnbJxSCXA2C1fbl9roMhkSkPkyUaKC4oXeXq8s+Ltr+1Dhpwm1ZGaxMdZjxvL1+FjPa92hQVkNryvbdgSPyY6A/F3JxZ/QN3RV5nWwNs2+8aGZc/vfL0jq8m0x97orc0Frq+jqcLxeTyPWdkXOdX9f9RdR/mKt5aVWs/MofdrPx+NcfoPyqXAwzfrSqafxk+sZxeYd36zxIvE46Borru4/6DkF/Nbuhd7fN1jDsV/u4+vSNX83PZv+3bh/pGiRep6H/DrI5zLwh9Rkkrkt+soHitcs+i2k3/TXh7y9dF9PoUF4ZN23yUWzS7PMvoNRnkDgd7z9ffQ9obO9/+VBDRG4uaCK/8edvqAfLRv6pXebEqV0/Hz78eFWTOPnjvl/qSTKZv372+u6Y1ymunN0tY//+Z7bZKHqclPiCEovWlsUJb3TN2AUlrtzrrLhywPzo3az2Lx7W530opc+YnTpE75MPSnpBj2z34GuqS6JJ6f/+bp3N/HT1sfXtn9f/N9eQPrYh/fP68uGCSnWtISLSlp0P+RF1+ywREQ3+7CI/f/Jz/fwr5vxc/g2q2PPrez6kLvm1vffKly9fvvzc5X+1jmJ8D5T/vxqKtQeVLz+iuD8Dy899/hYbtYr7Tx+ecI1ZkOLUmGfPDBMDrKcxv6DTeGkgobCkYxg4XQPFEREr11TFuLfmxH0vfxLvzKr9yuzJ9O/WOg7frmfsPbBrNKsoje/e8O8G7Q8KSSbukFuffebyi3TpGCSp077KSiOqqmvdnIthxmwNc9ZWUy4GiYtdLgapNzRQnC93xc5kLfUZKM7Gnck/adMtfr/jyfHDF2/K+cUkcn1n5Fzn13c/ucyu689euddZ8bsBC9I2SFyX/ETDjCuXrox5tzxU72HcdTXU9w/KDf0yfkMGihs6CNHQodh0DEI0pH/Mev/XqUNcPOoH8X9bLE7bIPE69f13kOth5pVLV8bk6x6Prktqv7tfOvJrGyjO1uPf0EUV1t0ZONfvP7nOb8w1FNIFTeQXR/6pXebEnm2XfO39NBv56w8Ur6kuiXsXdKz3IHFd6l7eoXNsder+G+wB6ztIXJf8T9tuFBufdGD06bjhLxOuXfZZzLj5r1E9LzMXtEg2UJzrC4rkIn/aqiYRJRG9m67Jav76fXQ6LojT0D66oRclSsdAca6HmXOdH1G/zyO5+AwjP/35uX7+FXN+ri/mUMz52TyHlOtzWPLly5cvP7/eg+TnPn9dDcXag8qXH1Hcn4Hl5z4/IuKAwd3i4oMHJFxjFqQ4NebZM8PEAOtpzC/oNF4aSCgc6RwCTue+Ij5/P3tz5tJ4+u258dbMpfHurGWxfHVVretbNS2LLbu1joE92sSoAV1i6x5tvvQ++dCrn8YvHn0naS4UukwO19Vl32Wtm+fsi3TpPgEXkeJJvVrkcpgxVwPFL/YaapC4iKw/UJyt51yzylXxq7//OqfP/7oMFKerllQyK0vL03qn5lwNY+R6EKmh+8hl/dl6/LUNM+RymDGV7GSDxOvXUNc/KOfyj+Dp/hJQJu9qkIkr6rcrr8xJ/1feuX30OOngiIi0DRKvU9ffaa6HmbOd/9WB4mznr7+vdXcmzfX7T67zC6GGXNcvX35dfzYddyav78+uG2Y+e2rv+LSyadaO34YGihs6SFyX/NoGahsySFyX/HzswYs1/6NVTeMn0zeOK3rNaND5z/rkr99PN3SQeP38+n6WyfUwc67zIxr2hdB0nMuWn7v8XD//ijk/1xdzKOb8hg6y1Sd//XNZESFfvnz58nN0MaWI4n4PlB9f1FCsPah8+RHF/RlYfu7z17nu6MGx42aJ15gFKU6NefbMMDHAehrzCzqNlwYSCkO6h38ztc91qqtrYvrClTF1wYpYVVkdlWuro6K8NJpVlEafji2jV4fmUVr69ffFdVauqYq9/zAxFn9WWadcKCTZGC6tyxf7ezddk/Uv0v3yvXbx86f/kNZBknXqM1CcD8OMn7TpFufv9dOs3bE2F3fHJfcOffOx2HH6q1kdXt/QXZHXydZQ88aLZsblf78srcO7ydRliDldtdTl/SUi+UBhvt3VLZ356aohl9l12VeuhwnrOkiZ6iDx+jWk+gflH0/vFT986uq0XM06om5/DP/tDqckvSN5KoPE62fX5UtJn7bqElES0X3Z3KT5yQaJv1pDKl+Imd26c2xUsTZiYe1rMtn/lXdsGyVREpULFte6JpOvvxWd20fX7+4dc+4Zl7Nh5ojcDFOn83NHffLX32eu339ynV9INRTbMKp8+Q3ZRzovplCXGtYfKP54VZP44dudGjxIXJf8DfXADR0kbmh+MV1QJJ/y11SXROmKFTnJn17RPn4yfeO4vMO7DR4kXj+/rl+ozfUwc67zIwrji6jy67ePXD//ijk/HUM06+fXdZgmIoo2f92FNBo6yFaf/PLWLWLaqiYRJRG9m66RL1++fPlZvphSrt+D5Oc+32dA+cWeH1Hcn4Hl5z5/nV4dW8QjZw6P0tLS2vcRZkGKVWOePTNMDLCexvyCTuOlgYTGL5NDv5ncd0M99OrM+MWj72Y1E/JFNgaJ65KVzqGKugxRzL5nXKxNcNK4voMk69RloDhbw4zJjs/9A7/lLsFkRS4GyTc0UJwv//YyWUt9BoqzcjGJFAYKMznMFZG+Qeb65Od6mCwTz8nGMEyY6iBlXQeJ168h2R+Uyzu2jbU1JRELFyesM9VB4nVS/aN4ZWlZVFRXJcxOdZB4/ey6DBQnUtdB4q/WkMoXYxLKYv+3Idl4/Y2y0oiq6rTXkO2+vz75qQyTZ/piGr/f8eSkA/3e/xpXDY1xoFu+/GLrQZd36Bytjvt2/Hh6r7QNEtclf/0eOF2DxPXNz1UPLj/3+ZU1JTHvlodydkGl8xZumdNh5ohIy5eI65u/cG1FWu8oU599yc9dfrq+xL5OY3v+5zL/vIVbpu2u8Ovnp/raO7OiXURJRI81i4syv7ply2hSWlPUFxORL1++/GLNXzfQXKzvgcWenw8XtIoo3h5Yfu7znQOQn+v89Z2z56Zxws59EmZEmAUpVo159swwMcB6GvMLOo2XBhIat2wM++brQHFNTU18/67X4sUP6vclb2iscjHQVp9BtobUk44hioYOkqyT0km9stK4aJ/zYkrrPg3KSlVtx8cgMcVg/YHibA0SN6tcFb/6+69zMki8Tl1eh7N5MYlM1JGrQea65Od6mCwT2XWpIdfDhMl6gPoOEq9fQ7I/KCdSn0HidVL9gnKi7LoOEq+f3dCB4voOEn+1hnoPFGez/9uAfHj9zcowc5HnV5aWR0X12rTXUMzvf/lQQ2Ma6JYvP1f5uf73P6tV56gpKcnZa2AqF5Qp5B5cfv7nZ/KCShWd20eHI/aJhfeOzckwc/XRh0WURJTecX/Ohqkvntkjfv70H9LyJdJ16vJl0ohI25dY5dct/+JRP4if95iZti+xr9NYnv+5zu984iFRUVJT1BeTkC9fvnz58nORX966RVqHmetTQ0Rx/w5ynZ/LC1rlugeVX9z5zgHIz3X++vts2aQs/v7jnaNV0/KkGWZBilNjnj0zTAywnsb8gk7jpYGExiubQ775OlA8e8mq+PYf/xnLV9d+Zy4oJLkYJK5LdjrradAgQZoGSdZJ5xUH0+Wrx8cgMcXk0Dcfix2nv5qVQeL1M796V+R1sjXUvPGimXH53y/LyABTbbL92t/Q3Gzm53KYLFPZdakhkVzmN3SQeP0a6jNQ3JBB4nXqO1DckEHi9bPrO1Dc0EHir9ZQ54HiXPR/68mH199cDzMXe346amgMr7/58B5UzAPd8uXnMj8f/v0nIl9+Medn5YJKGbqoUyrZuR6mLu/YNiJKYm2C/Pp+Hknpc0eHdp//vwkuZiA/c/llHdtGaY6ff7l+/ud7fq4v5iBfvnz58uUXWn5F5/bR9bt7x5x7xqV1kLUuNeT6GMjP/x5QvnznAOQXZP5X9n32HpvGibv0SWn/ZkGKU2OePTNMDLCexvyCTuOlgYTGKRfDvfk6UPz3KXPiJ2PeCm+ZFLp8uTNmXQZrcvJFugwN9ebzQPGLvYYaJKboNKtcFasqmmU1c0MDxdkaJG5MF5PI9TBXrvPTUUMusxtaQy7z0zVIvH4Ndel70jFIvE5dB4rTMUi8fvb6A8Uzb3ww1i5Ykji/Y7vofsohDR4k/moNvSoXpZQfHdrHj0f+v8z0f89ckfgPxhExp3XnuGDUT3L6+pvrYeZiz09nDY3x9beQamjsw2Ty5etB5csvvvxcX1ApHTU0JDsf8ht6TriuFzKSn1/5uX7+FXt+Mfdf8uXLly9ffkbzk1xMqCiOgfxa5boHlC/fOQD5BZ3/34zWfTaJO08aGqWlpSnt2yxIcWrMs2epPbMBAIAv5Gqot7xTp+h551+iyaab1rpmzYcfxidHHxlr589PW24ye23VNX627xZZy4NcWVXRLF7sNbTW7dkYaCtr3Ty6n3jQf6/EmFg66lnSvE1cNPKH8UmbbimtL+/cPi4e9YOMDPNOq+gYP97txxGdOtS+aP7C+O1zv412VSvSnr8hS5q3ifP3+qlBYopStgeJIyLGbL1/3D/wW1/839kaJG5WuSpng8QR/3st/rRVl6RrZ7bumrY66voeEJHe41Cf/HTVkMvshtSQifyZbTZKaX26B4nX1fB/u/8oZrfunHRtRcd2sfbow9IySBwRsXBtRZw6b2BUHn7I51/aSaSsNLp+d++0DBKvyz7l403io1VNo7RJeZTE1/8A9VU1URPTalqmZZB4/Rqm17RIKX92ZXnMiZYNzv2qOdEy5lQmfzxdKyqjpElZ2vOXNG8Tv9/x5KgsLU+4rrK0PH414vtpf/6n+tr/aesuaX8Pypf8VF8DC/H1t7G9B6Wrhvpmy5dfzPm5/vcvX36uPoNkKr8uj392265p+xy07vPPp21Tf/wR6fksVt/sfMhPx8UlUzrvLD8v83P9/Cv2/GLuv+TLly9fvvyM52dwkDjlGuTnZX6ue0D58p0DkF/w+f/97uGvdmqT8iAxNEae3QAAUAfVK1bk9O7AdRkorl6RnWG6iIjDtusZF+y3RWzgQksZVVISccF+W8QF+xlmJju+Osi2TjYG2pqXVsWNm3wcvZuuSTrUkZMv0pWVxkbf3Tv+b4vF0by0qsG5G5LKSb2PNh0Ui8vSP8xSm1wMVEIxW/c6nK1B4oj8uJhERESyeb4lTVvF6FE/NkyWoy+SZ+J5kA/H/7IRZ0Z1aeIhzcrSsvjtDqdk5N/A6vImKYyyRqyNiLM+7ZeWQdp1Fq6tiFOXDIkYNiTxwqrqmHPPuPhwaUnah3mn1rSOVt/YPOn6tVsNiFNnbpH2x3/KzC2jbOvkn7X+vcm2GemJVlU0i+d7D0+67qPNB2ek/2tWuSp++OJNUVG9NuG6J/vtHnNbJ/93Wlery5skfe2PiIia/64tsHzD3PnxHtQY3oPlyy/m/ELtQeUXd/7okT+KFe2SfykwU/m/3uOciA7tkq5d2a5DXLh7+j+D/9/uP4o5bbumtD7Xw8zpvKhUvb5MnIYvka5Try+zys9pfq6ff4WYPzvF156I3F9MYmabjQouvy79Z67zi7n/li9fvvxc5meqhly/BxZ7fmO6oFUh9sDyG09+rj+Dyi+y/PkLo+r0Y6Ny5vQG50G+MkwMAAB1UNqyZbTaZ99at2dykHidVAaKW+2zb5S2zN4wXcTnA8VXHDowWjVN/x2hNqRV07K44tCBcdh2PYt+mHnvgen/4rr82n11oHhR555ZGSJbWV0Wz39aFp/e/HBULlhc67qcfZH8v4M0z39aFiurM/c6kOik3kfDd4vz+n43Y9kbUmzPf/lftku/zL3fp2J433pcrbMA8sdsvX+cv9dPY8ut+mQ1M1cXk1h3Z+Tuy+bWumZJ01bxo31GZ2yYs5iHyX414oyUsn+/48kFefzntu4cj28+MuGaiuqq+OGLN0WzylVpzy9pWhFdh2yWdN2jPXaIWSWt0p6/cG1FnLfl0dFqp8QDxc0Hbh7nfLp52od5z57aO1qN3D7ajxxW67o2u28X5/U9PK3Z69dw7MZHR7Pdah/obT9yWBxw0ICMXUymttffdTLZ/yW7mERExCNb7hl3DflOxvJf6L1d0nUv9NkuY8Pcucwv9mHuurwHpfv9b/0ain2gW778XOUXew8qX/6SqhTOLWbqPbi0eaxI4dzmorXlGclf07JldD3imxFlib/OVV1aFhfvcmb6Lyg16tyUhqmjQ7u4bNS5GRmmntc6+fm28o7t4sLdf5iWL5Gu88V55xQff7q+xCr/f/kX7v7DKOvYNunaeW06p+1L7Ovkw/M/1/kX7v7j+KxdCud7O7SPX+9xTs4uJrGiXccYPTL9v/9c56faf+Y6v1D7b/ny5cvPZX6uLyiW6/fAYs/P9QWtct2Dyi/ufOcA5Oc8P8FAceXs+fHJdw8zUEzBMkwMAAB11Onsc6LDmWd97b9nY5B4nUQDxR3OPCs6nX1OxmvYkL226hoPnrF97LhZZoeLdtysQzx4xvax11b/u0p2MQ8zX37o1vKznD9m6/1jzMBvxaruvWPoIw/Emd8ZlvH8ZpWrou/9d0blvEW1rsnkUFsqf8yqnLco+t5/Z0aGidY3raJj/L/dfhxlXf53ojDbg8TF/PzfYH5246Mk8iP/2iO/Eafv3jcn+afv3jduPHZI0eYfv9eArOd/daCtyaabxsf/d1UszfDFJJINsy3N4CDxuvxiHiab27pzjN1894RrKqrXZmyYNtfHPyLizm2+E49usWfCNS/2GpqR/JXVZTF+yD4Jh2kfG7RP3LHVQWnPjohoXloVV/SaEV332yna7jx4g2vajxwWYwfvG59WNk17/qeVTePeBR2iwx7DN3gM2o8cFp333C6u6DUjY8O8iQaK248cFh32GB5PLWmb0YvJ1DZQnI3+L9Ew86MZHCROJT8i4v6B34oxW+9fkPnFPswdkdp7UKbe/yJy/x6cD8Ns8uXnKr/Ye1D58nOd/0TPHXKWX7O6MpaPGRtRVZ1w3WP9Rmbk/TfVYeoV1WWxpLR52vNLmpRF54rkn22WVpXGtJrWac9fUpr677Qua+WnZlpN61iewsUEOpdXRkmT9H8GzfXzP9f5q8ubxOKqxBcz+Ty/NGP5qVxMYsnaspxezCKT+an0n7nOL9T+W758+fJzlZ8PFxTMh/fAYs7P9QWtct2Dyi/ufOcA5Ocyvy4DxVVLav+uJjRWhokBAKAevjpQnM1B4nU2NFCcy0HidTZq2yz+dNTgGH1A/2jXIr13x2rXoiJGH9A//nTU4Nio7ddPEhTrMLP83OQPu/j8GPjEo1HeqVNW8pN9mX5Nzz7xm33SeyXSr1o3UDyzbe0DxZkaJlpfq6ZlccaRu0av+8ZExUadov3hB0fpTy8tqudf3uUfNjBaZim/ZdOyuOKw/Mk/bbdN4mff2jKaVWTnNFuzitL42be2jNN220R+DvIf2+bAmH3IcV/0ficesE1W8msb5ipt3z6m/+HOWN2mXU7y13lo0P45HSZ7ov9eGR3oy+UwbURuj/86d27znXhiwF45yb9hbtcYs/X+GxymnXfQUXH/4EMylr2yuiyeWvL5XZE67bvz5wPF690hrP3IYTF2yH5x5ezU7hxQH1fO7h53zOv4xUBxRcd2Ud6x7ReDvBGR8WHehWsr4pRNjo3Wo7b/Wv4t87vGDXO7Jt9JA617/a3o3TvKO3eI9ocfHEvP/kVWXv/HbL1/PDRoved4RUW0O+HE6PXzC3OT/1+zDzkuHtvmwILOT/T6N3ffw+OB7Q7LWX5E7l9/M30Mcv0enA/DZPLl5yo/Qg8qX36x5q+qaBZPdv/6hXzWl+n331SGqZ/ouUNGXn8Wl7WMjzb/RtJ1j/fcMYP5g5Ou+2jzwbG4rKX8NFtV0Swe67ljTvNz+fzPh/xU+p9c5xdq/ydfvnz58oszPyK1C4r9fYvMXEwoIvfHQH5uL2iVDz2o/OLNdw5Afi7zI1IbKG616y5R1rZ9RvIhlwwTAwBAPa0bKM7FIPE66w8U58Mg8TolJSVxyNDuMe4HO8VFB/aP/t0adnW4/t1ax0UH9o9xP9gpDhnaPUoS3IKzWIeZ5ecmv7Tl/05WZSN/zNb7x0sjDv3af2+y6aax5f33xq0//mbGH/9WW/eJLe67Z4N3Rm9y8ukx8+BjM5q//vGv6NErej02Njpf/JuifP7lW/5DZ2wfO26a4fxNO8RDeZh/2LAecd9p28XAHpm9Q+3AHm3ivtO2i8OG9ZCfw/wRv74wej3w0Be9X7by39n7yCg94bQv/u+KTfpGnyfGxiF7bJ2T/HU6nHlWHHH9JRnNH7P1/vHcDt/e4LZ2J5wYB9/ym4w//sn7nxCl3z1mg9tKTzgt3tn7yIzm5/L4R3z+/D/45t9EuxNOjKj4X4+drfyJFZtH2Ymn/2+YtnOH6HDaabHTFRdl/Pl/w9yu8fDKjSPi84Hijc89Mio6t/98mPb002PEtw/O+OMfX7ZlLO25dXTYY3j0PPOw2PjMw78YJF7Se5uYWLF5RvMH9mgT15+8U2z0hxui509P/SK/pP/w2Os7B2T19bf3Xx+N3k/+LTpf/Jusvv4fcf0lX3z+7zP2b9Hlp+fnJL+iT5+o6N07Opx5Voz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| |
"text/plain": [ | |
"<Figure size 4500x2700 with 1 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"plt.figure(figsize=(10, 6))\n", | |
"\n", | |
"for index, learning_rate in enumerate(sgd_learning_rates):\n", | |
" plt.plot(\n", | |
" trajectories[\"SGD\", learning_rate],\n", | |
" label=f\"SGD lr={learning_rate}\",\n", | |
" c=cm.Blues(0.3 + 0.4 * index),\n", | |
" linestyle=\"--\",\n", | |
" marker=\"o\",\n", | |
" )\n", | |
"\n", | |
"for index, learning_rate in enumerate(sgd_learning_rates):\n", | |
" plt.plot(\n", | |
" trajectories[\"SGD ScheduleFree\", lr_factor * learning_rate],\n", | |
" label=f\"SGD ScheduleFree lr={lr_factor * learning_rate}\",\n", | |
" c=cm.Reds(0.3 + 0.4 * index),\n", | |
" marker=\"x\",\n", | |
" )\n", | |
"\n", | |
"plt.grid()\n", | |
"plt.xlabel(\"Iteration\")\n", | |
"plt.ylabel(\"Loss\")\n", | |
"plt.title(\"Optimization Trajectories\")\n", | |
"plt.legend()\n", | |
"\n", | |
"plt.show();" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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