Repository: kazewong/flowMC (language: python)

Issue:
158 put training loop into nf class
Move the training loop into the normalizing flow for a cleaner API


Discussion / hints:

Fix patch:
diff --git a/example/dualmoon.py b/example/dualmoon.py
index 9bef5b0..5c9eeee 100644
--- a/example/dualmoon.py
+++ b/example/dualmoon.py
@@ -4,8 +4,8 @@
 import numpy as np
 
 from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline
-from flowMC.sampler.MALA import MALA
-from flowMC.sampler.Sampler import Sampler
+from flowMC.proposal.MALA import MALA
+from flowMC.Sampler import Sampler
 
 import corner
 import matplotlib.pyplot as plt
diff --git a/example/non_jax_likelihood.py b/example/non_jax_likelihood.py
index 4272ec6..3ec034c 100644
--- a/example/non_jax_likelihood.py
+++ b/example/non_jax_likelihood.py
@@ -3,9 +3,9 @@
 import jax.numpy as jnp
 from scipy.stats import norm
 from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline
-from flowMC.sampler.Gaussian_random_walk import GaussianRandomWalk
+from flowMC.proposal.Gaussian_random_walk import GaussianRandomWalk
 
-from flowMC.sampler.Sampler import Sampler
+from flowMC.Sampler import Sampler
 from flowMC.utils.PRNG_keys import initialize_rng_keys
 from flowMC.utils.PythonFunctionWrap import wrap_python_log_prob_fn
 
diff --git a/example/notebook/dualmoon.ipynb b/example/notebook/dualmoon.ipynb
index f8e3ea1..ea3790c 100644
--- a/example/notebook/dualmoon.ipynb
+++ b/example/notebook/dualmoon.ipynb
@@ -136,7 +136,6 @@
     "hidden_size = [32, 32]\n",
     "num_bins = 8\n",
     "data = jnp.zeros(n_dim)\n",
-    "\n",
     "rng_key, subkey = jax.random.split(rng_key)\n",
     "model = MaskedCouplingRQSpline(\n",
     "    n_dim, n_layers, hidden_size, num_bins, subkey\n",
@@ -167,10 +166,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from flowMC.sampler.MALA import MALA\n",
+    "from flowMC.proposal.MALA import MALA\n",
     "\n",
-    "step_size = 1e-1\n",
-    "MALA_Sampler = MALA(target_dual_moon, True, {\"step_size\": 0.1})"
+    "MALA_Sampler = MALA(target_dual_moon, True, step_size=0.1)"
    ]
   },
   {
@@ -197,12 +195,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from flowMC.sampler.Sampler import Sampler\n",
+    "from flowMC.Sampler import Sampler\n",
     "\n",
     "n_loop_training = 20\n",
     "n_loop_production = 20\n",
-    "n_local_steps = 100\n",
-    "n_global_steps = 10\n",
+    "n_local_steps = 50\n",
+    "n_global_steps = 50\n",
     "num_epochs = 5\n",
     "\n",
     "learning_rate = 0.005\n",
@@ -214,7 +212,7 @@
     "nf_sampler = Sampler(\n",
     "    n_dim,\n",
     "    subkey,\n",
-    "    {'data': jnp.zeros(5)},\n",
+    "    {'data': data},\n",
     "    MALA_Sampler,\n",
     "    model,\n",
     "    n_loop_training=n_loop_training,\n",
@@ -244,7 +242,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nf_sampler.sample(initial_position, data=None)"
+    "nf_sampler.sample(initial_position, data={'data':data})"
    ]
   },
   {
@@ -288,7 +286,7 @@
     "local_accs = np.array(out_train[\"local_accs\"])\n",
     "loss_vals = np.array(out_train[\"loss_vals\"])\n",
     "rng_key, subkey = jax.random.split(rng_key)\n",
-    "nf_samples = np.array(nf_sampler.sample_flow(subkey, 1000))\n",
+    "nf_samples = np.array(nf_sampler.sample_flow(subkey, 3000))\n",
     "\n",
     "\n",
     "# Plot 2 chains in the plane of 2 coordinates for first visual check\n",
@@ -453,6 +451,13 @@
     "plt.ylabel(r\"$\\hat{R}$\")\n",
     "plt.legend()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/example/notebook/normalizingFlow.ipynb b/example/notebook/normalizingFlow.ipynb
index be4ce46..49dd80c 100644
--- a/example/notebook/normalizingFlow.ipynb
+++ b/example/notebook/normalizingFlow.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,6 +28,7 @@
     "import jax.numpy as jnp  # JAX NumPy\n",
     "import jax.random as random  # JAX random\n",
     "import optax  # Optimizers\n",
+    "import equinox as eqx  # Equinox\n",
     "\n",
     "\n",
     "from flowMC.nfmodel.realNVP import RealNVP\n",
@@ -43,30 +44,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fe21c754b50>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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oZUay3LWiGVyXf3xtgESuzJ6BNL2N4XOCjzf1xXn0tp5qsPSB6feez60nrDmChYrP5wNA0zRCodA1PprFi6ZpwNnv8+0gxI1AcAnUTq6V2JamiI9DIzks1+HQaJY7ljeT0QxeOTHF4bEcsiRhmA4/Opxgz5kUZ5IaqiJh2i4F3UKRwK9KHB0roBk2r51KYU27mgAOj+VAgrzhdc4tmQ6KZqIZNi5Q0C3+be8I8bCP08kiIOFTZGJhH8mCwWROpzHio6Db5MoWAGXLwdJMXMelNL2fhpCK40DIp7C2M8pzB8YI+xVeOTFFQJHRdAu/IlM0bIpli+54iKGUxlRBJ69bLGmJkNMM/nHXACBx14pmUprJXStbAFjbFcV2XDYvibOpL86OIwme3jPMo5u7eWxrH23RII9t6Z0RAP3o5p4ZIkbU0BEsBhRFIRaLVSv3hsNhJEm6xke1eHBdF03TmJiYIBaLoSjK296XEDcCwQWoDTDeeXySQ6OeJUMCciUTx3EwbJcTEwXqAyo/PjZJdzzE8tY6JnI6g2mNqaJBrmxi2i6m7VI2dfyKTEdDkN6mMGemimiGjem4+GQJy7aRZIlTk0V8Nb9tv+qJF59sY7ngOA5pzWAsW8YFKkNoXj9b+jyvWwRUGVX2LEUA1nSqeCVlvKCbgERPPMxYrswXf3yCcEDh6FieaEglXTRBAp8iMVkwMB2HhqAP05bwK54lKK9bgIRhOxway9EeDfLS8QmGUyUe3NDJwxu6zlpdKoP9rEF/70B6zmBsuHh1ZIFgodDe3g5wTmsCwaUTi8Wq3+PbRYgbgeACVCwGNzVHMG2HHccmODaepz6gElAVHLw5uinipyMW5NBYjnTR4JbuBiZyOmXDRgIM20WWQJUlXMkTJhN5nWzZRDedqjixHBcXwPaER41OIRb0UTIdXAlwwa8qmLb3XrynzsFyKv8nzdhCAuqDKhKeaDFsh0LZZNfJFI7rEvbL2I7LVN5AtzyXWNivkClZpAsmEb/KQKpESbc4PJrDtB3qQyqJbJlDozn6EwX8qkxdQPXcVcBkXmfHkQQjmRJrOupZP6sGUK2Ame16qnUXCheVYCEjSRIdHR20trZimua1PpxFh8/ne0cWmwpC3AiuC97uhFepOVMJioWZQcSDySIuLt8/MMZUQaevMUxAlSlbtmdulsC0HRLZEq+eTGLbLgYOB0ezJIsGln1WuIR8CqbjVONtHFwsx8Zxz1pdzhdGF1AlYhEfyYliRfdgOw6Gde47ZMnTE4oMrgPO9Gcpkrf/kCrj88n0xEIk8joTeS9WqFi2iYZ83vnkLII+Bb8qUbbAcV2K00INCQzLoWxY2C5Ijsux8Twu3mcpsoQsgV+RWNIUJq9b/O3OU9zcUc/hsXw1BX4opdFSH5hx3S7F5SRcVILFgKIoV2SSFrw9hLgRXBe83SJylZozpu1warLAspY6dh6fJKMZHBzJsrN/kmzJoqBbhHwK71vVSnw4wxsDaTTDRrccJAl0B8ayOkGfQjygMpz2gnjDAYX2aICpgoGqyJSLnpip2FKmPUNVAeSTvefsWZrFsl1SBWPG85p5rrCRAL8iexYdF2oMP9guyEDRdMB0yGj5Ge+1gbqgQsiWGTV1ypaNb9qVVa4RUbYLOd0iHvaT1QxkWaZsOVXx5LguK9vrCPtUbNclXTQwbQfNtKsFBKNBtabmz9AF6wnNvpbCRSUQCC6GEDeC64LZE96lrO4TuXK1Yu9gusSJiQLLmiM8tKGTwZTGj49NUjJtoiEVw3JY0VZHfVBlY0+M4ZTGQMprRikBIb+MT/FcOX5VIuBT0Aybom4zlC6hW55byqnRI7OliQuYztznZ7swVby4iVvCCxo+H7NfUaSZQmo4Xa4OCo470y1W+xmm5dDbXs+UX2Ysp1e3rwi2Y2MFIgGV229q4rGtfYQDI5yYKBAN+6ttHypC5ZUTU7x6Msmyljoe29J7zudVBNDL/ZOs746xbXWrsNgIBIILIsSN4LpgtkvjfKv7So2ahzd2cWAky9OvD/HobT08tqKFp3YPkiuZbFvTxqHRLNmSiapI/OyaNnTLoWTYfO+tMXyKTGtDkKF0yRMGrmctsRyXkukwkikT9HnZRQD6tNXDOZ/PaZraoN+3y+W+vSJIauN25pJQlW0ifomgqpLWTA6P5rBspyrI3Jpti4ZN0bDpT3jWoWXNEZa11IHrem0qNINY2M+mvjjrOqPsH8pwcDjDwZp6QXC28efy1jp+ejLJW8NZTk0WLqlqtEAguHER4kZw3TG7iWWlIWVbNFitUQPQHPGT0QyOjeXYfiTBUEpjzxl4fSCN5Tjolo3tyLw5lCEW9rF/MIvtukRDKh9c30F/okBaM3GATMlCng6cqUzs50Oa/pstRN6psJlPZDy3VdFwKZveOeumM6eYCqgyruug23AmWeSJf3uLgKpwa2+Mhzd2EQv7GUxpXjr4bT2s744ROzDGnoE0SBKpolF1C+4fzvLeVS1ousX7VrWQLhocGs3ylZdPCYEjEAjOixA3guuOHUcnqhYZXHdGTZWHN3YBVC03kYDKKyemGMmUaAj5iAZVXumfornOT13Qh2naHBnLY9lO1X2TL1vsPD6JWlEz08yOkzkf08aeBcGlHIfEubE7MFOcVSw7PhnqgwqpgoNPloiF/QynS7RHg+w8Polm2Dy2pZdn9o9wJlnkpeMTjKQ1jozmCPgU4iEfYb/CsUSe5w8nCCgyu0+nOJbI83PrO/ide1ZWa+FcSlsMgUBwYyLEjWBRM2fg8HTqcU4zOJrIk8iVeen4ZDUj6vc/eDPgddw+NVlgx5EEtuMyntOZKujYDqSKOn5VoacpxGimjG6d/cxIQGFFaz2npxLV5+YSCaoEcyQzLTou5RQq25gOTBY8x5btuESDKl2xEI7jcngsx2Re542BFOmiSb5s8pP+KYaSGpppc1NrHUjwo8MJwgGFom6xrKuBLUsbGcuWSGoG33lzhOaIn2Vr26tNOivXfnbmm7DqCAQ3LkLcCBY1c2XbVJovHhzNsfdMilTR4NWTSYbTpWoX7QrLWuowLYdv7RkE23MNSYDpuGgli0LZQq6x0CgSlAybnccnCagSunX+yf96EDbvlOOJwnTTTwPL8VLIDcuhrylMyfQqHp9JFvHLEluWNjKSLpHIl2kngF+RWdsZ5SO3LyFdNPjxsUl2nUoR8im8d5VX/bi283gl8w045zoLBIIbCyFuBIuaTX1x9g6mZ7gp2qJBYmE/o5kS4YCKm9cxbAdVkRhMaRwcyTKU0jg0kuWZ/aO4uJRr0qp9MsiShI2LIgO4yHhumIpLJlO25jgawWwc12sTUYknUhQJvyozVfAKHHpxPA6qDG8MpjgzpYELuu2iGTZnkkXaokHWdTWws38Sx3HJlU12nUoCcNfKlmpsVU9jmDUd9fQ1RUSauEBwgyPP58537tzJgw8+SGdnJ5Ik8Z3vfOei73nxxRfZtGkTgUCA5cuX8/Wvf/2cbb74xS+yZMkSgsEgW7duZffu3Vf+4AULgkSuzHMHxryGkHM8Bs/6cu/NbdUJLZErk9EM7l3bzm/cfRNLmyMEVYUTiQJPvz7I/3zuCH/+g8N847UBRjMlRtPlGZ9pOF5dl4qYMWyYFV4juERcZqam65ZLSjNJ5A2sms6/lgNvDmQZy+qULYd82cRyXI6N5fnySycZSZfYvKSR25c30RUL0VgXYP9wFly36n4aSmmkNZNoUGXvQHrGPSIQCG4s5tVyUywW2bBhA7/yK7/CL/zCL1x0+9OnT/PBD36Q3/zN3+Sb3/wm27dv5xOf+AQdHR3cd999APzzP/8zjz/+OF/60pfYunUrTz75JPfddx/Hjh2jtbV1Pk9HcA2o1KvJlExiIR+DKY3nD45z/7p2okGVg6M5Tk8VWdsZZdv0e3YcSfDNXYNs6G6gtymC60LA51UVLho2xxN50kUT42K52ZzNYAr4JCxD+JmuJLVfvyqD7daKIBtZgoJh87c7T2LZLrIMq9vraQh7Qcclw57Rn6oibjMl86KFAQUCwfWN5LruVRmxJUni29/+No888sh5t/lv/+2/8eyzz3Lw4MHqcx/+8IfJZDI8//zzAGzdupXbbruNv/mbvwG8Hj09PT38p//0n/j0pz99SceSy+VoaGggm80SjUbf/kkJ5p1q40rNYGf/FAFVZv9QhlUd9Rwby+NXZVrrA6Q1kwc3dNLbGObQaJZ/fn2IaFClMxbidLKIZXvtA0KqV2jPsJ1qg8k6v0LhAqnbgqtLRa7EwyqRgEpGMzFtB91ykab7c9UHfdy1opkty5qqwcOVe6WnMcwz+0aq3cVF7I1AcP1wqfP3goq5efXVV7nnnntmPHfffffxyU9+EgDDMHjjjTd44oknqq/Lssw999zDq6++et796rqOruvVx7lc7soeuGDeqBTnS+TKxMJ+Do1m2X06xZHRHK7r0lofYkNPjP97OMG33xymLqAiSxKu65LRTLJli/VdUQ6P5SnqFhF/gHjEz0T+rMvCchZwgZkbkEorilzZIle28SsSIZ9C2bKme2ZJBFQZpOmAYtet1s55/uA4j97WwyfuXDaj1pFAILixWFDiZnx8nLa2thnPtbW1kcvlKJVKpNNpbNuec5ujR4+ed7+f+9zn+MxnPjMvxyx455yvD9Ts5x9Y30GmZGI5DrmyzdLmCKoiU9AtkkWDQtlAlRWQoGTa4HqBwUfG8pRNG1mS0Awbw7JJa2cDgnWR1rTgcKm4BF1c16v8XMGvSMQjfrpiIXTLIVe22Nk/RTzsw7Addp9Kkit5sTcCgeDG5Ib49T/xxBM8/vjj1ce5XI6enp5reESCWs7XB2qu57etbmUkrbH7dIqlzWEMy+WV/kkymomLF6tRkSohn4xPkRjP6dXI+dwcWU5C2iwsfNP1gSrXpbY4ooxnrY0EFE5MFBhKl3jfyha64yHuWN5MJKDy6skk+4YyhAMqp6aK3LG8mVdOTPHwxi7WdTVci1MSCARXmQUlbtrb20kkEjOeSyQSRKNRQqFQtYX8XNu0t7efd7+BQIBAIDAvxyx4+9TGSDy0ofMcF8Jc/aHaokHWdjbw3IGxaoVhXBdVkTBNl4AqV7NzdNNBn26U5AABRUK/1DLCgmuG6XrZabOjAUM+Cb+iYNk2/eN5ioaFhESxbNJc71n8Ht7YxbKWOkbSGvuGMhwazXJ6qsiBkSxF3eLOFS30NIar/atEsLFAcH2yoMTN7bffzg9+8IMZz/3f//t/uf322wHw+/28613vYvv27dXAZMdx2L59O7/92799tQ9X8A6pWGYe2tA5Z9BnpWt0rWsqkSszmCySK1uUTAdZsvErMoosYcqegKkYZ2ZH0phC2CwaZieyyUDJdCmZFj5ZQpJtvBhwl6F0ibLlkC7qDKU0GiN++poi+BSZ5a11VctNQJX54o9PEI/4q60zRLCxQHB9Mq/iplAocOLEierj06dPs2/fPhobG+nt7eWJJ55gZGSEb3zjGwD85m/+Jn/zN3/Dpz71KX7lV36FHTt28PTTT/Pss89W9/H444/zsY99jM2bN7NlyxaefPJJisUiH//4x+fzVATzQK1lptaKU1lVT+Z1/vy5I4xmy9y+rInfef8K9g6kef5QAlny3E4BVcYFGoI+XNelaJ4/OFiEDS9eZl47F7smuc12YSKno8gwli2jKDLvX93K8tY6Ht7YRUt9AM2wGUwWyZZMUkWDW3oaeOXEFD2NYeGqEgiuQ+ZV3OzZs4f3ve991ceVuJePfexjfP3rX2dsbIzBwcHq60uXLuXZZ5/lv/yX/8Jf/dVf0d3dzVe+8pVqjRuAX/zFX2RycpI//MM/ZHx8nI0bN/L888+fE2QsWPjUWmYyJZOdxyfpjocYTpcA2DuYZv9wBteV+PGxCXBd7l3bTsgno5sOHQ1BepvCHBnNkSkZ1AdUlJDEaFa/yCcLFhMVF5UseULGma5b5DhnCwQ6gOOA4zo0BlTiYR/D6RJDKY2hlMZ3949y18oW7lzRzJ6BNANTRd44kwbX5Q7hqhIIrjuuWp2bhYSoc7NweGrXAE/vGeb+tW30NkWqk0xPY5jnD46x+3SKsF/h5GSRkF+htzHMrlNJ8rqNKks8eEsHR8ZznJnSqA+qmJYjWiPc4EjAzR31bOyN89iWXk5MeFWOty5r4v95Vw9ffeUUPz2RpK85zPtXt7FvKFMV1edzkQoEgoXBoqxzI7h+OF969zlUKsxKEpmSyaGDY4ykSyDB4dEcmmGxua+R96+J8HL/JP2JPKva6xlIFmkI+Xj5xBSmaWM5DsmCITKfBLjAyckCubJFPOzju/tHGUmXGMmUWdUeJeRT0KZLBQDc1BJhJF1iY09sRguPS7p/BQLBgkSIG8G8cL707kSuzI4jCZAk1nc1gOvy6G1eWv43XxtgMKVh2DaN4QDhgEJBt9k/kiXiV7x4CkkmHvFTH/QzPB1IKhCci4Ttuuw+k2KqoBMJqPgViZeOT7C8pY4N3Q1M5nWePzhOXUDhrZEsYb9SFTK19+/soHaBQLDwEeJGMC/MlcYN3qTx9J5hAE5NFqqugE19cV46NsFASqOlLsBNrXUcGc1TMkwKZYs6v0J0Omh4OFUiWzIxL6E3lODGxHJccCCjGcRCfm7pbuCtkSyv9CfpH8+zdVkTy1vrODlRoCMWZDhdorcpUn1/7f17PqEuEAgWLkLcCOaNjGaw4+hEtfcPeJPFo5u7QZLobAhWM1baokEaI34MyyapuUTzOobtYNjguC45wyKuSIT8CoWy5U1eAsF5sByX0VwZOQdNdX6iIRW/ImNYNkXD4kyyiCLLvDWSpbk+wK/ffRO4LolcmbZosFoRG84v1AUCwcJFiBvBvFBroYmFfNX+UDuOTgBepeG9A2lOTBR4Zt8IAI0RP2G/QkazODNVpC7go7nOj2E5RAIq45kyU0WvKp8098deNyiAaOX5znGAZMHgmTdHURUJWZJQJImwX6U7FuLMVJGSYbPrVJIDwxl+cGCM//bAmhnp4bVCRyAQLA6EuBHMC7UWmsqKd+9Amm++NkBR97KZ1nfHsGyH7UcnODKWYzRTIle2vL5CtkNRt7Ach3jEz3i2PGOyvx7tNjJn67kIYXPlcADddjFtF0WRiEX82I5LRyzEkqYwb41kwYWCbjFZMHhq9yB3LG8WMTYCwSJGiBvBvNAWDfLY1r7q44MjWV7un6S53k+mZJArWwylNCbyOlN5HVwXWT5rj9Ft0KcrtY3dIHVr5gqNViToaAgyVdApv4MGnxKgyJVmlDcmDuDYLppuYUVc3hxMM5HXwYXueIi1nVGOJnL86EiCA8MZ/uN7l58TDF/baVwEGQsECxchbgTzSmVCeOHQODuOTdBaHyBXshjLlHjk1i5u7oiSL1ts6I7REPIxlimjmzY3QqNuiXMtUOp008gKjgsThTLGZZTu8ckwu1CzW/OJc71+oyDhNU/ddSoJwJKmMK31AbIlk5XtUfYMpKerHXulCSoxOHA2gypTMjk1WeDERAGg6nIVYkcgWDgIcSOYVyoTQtAn0xj2UzJtirrFvuEMewfSdMVDcBqOJ/LVQJobQdgA9DaGGEiVqo/9ikSd30t/Nxy32sn8QsJGnS7fW/ud1Qd9+FWZybyOJJ211lQMY7OtNw1BhYJhY98AgkeRoWx6AemOC2eSGhIuLhI/PDTGvWvbyJZMNnbH+OZrAxwczvDY1r5qYcm7VjRzcDjD6WSRtZ0NM6w4IqNKIFg4CHEjuGLMXr0eHMnySv8kG3tivGd5MwdGsvzw4BhF3UbTLT77/cPEwj4ymsFotozE2UBhWfL+ric3ymxLTTSo0tkQZDRbRpG9wOtk0cB2qQobJJDdc11WQZ9ENOgjHvYzVdDJly1wXWwX0ppJwCfjUySa6gIksmUc16uX2B4NkNHMan2gtno/zfUBjo/nLxrnM5elabFhO6BbnoXMBizbnf5uXV47lWI4VSLoU5BliaJusX84SzgwUi1ZEAv7Gc2WWdvZwCfuXDYjC7D2X4FAcG0R4kZwxag128dCPl44PM6OoxN8cH0Hj9zaxe5TSY6M5Qn7FQZTJUzbIVU0sJ3K9HJ28nTccztDL1YqomD26Yxky2RLXvZXQJHJli0qzVAczvZUCvhkyjV+pLBfxnVBM2zKZpmSYWE6EFAlXMvFBQzTwQUymknQr6KbFgFVoSsWpCni58h4HseFRN4gkTcu6Tyuh8shAa7jYkugyJ7NxplWdWXT4fRUkVjYz5qOKFuXNYHrsr47Vu07VWG2++lyM6qEG0sgmF/ki28iEJwlkSvz3IExErnyOa9t6ovz0IZOcF2e3jPESLpENOijrynC3oE0u06nSBYNkgUd3XJwXbBtB5CqXRiuJ1TZ61xeEQWq5P3g/Cr4ZAnXcfHJMj4Fgj6FuqDK8tYIfsX7WTouxMI+7l3TRsSvEPHLtNT5aQz76YqFiPgVFFmiLugjFlKJBLw/nyyhyt57t61pYXNfjGjIR75ssW8oy8nJIi31/hnp9BGfTHc8SDysIksQVL19VLheLo+DZ7GpNOCUZ5+YBD2NIVa315MrmRwYyQKeq6lS/6anMcxXXj7FwenX3g6VhcDegfTb3odAIDg/wnIjuCwuFFtQWb0mcmVOTRV5czDNkqYwACcm8+TKJrIEbfUBxnI6lu1OTzKuF0h71c/mnaFw1l00l1XDdb2U9qBPwnG8YnL5so3rumiWTbpkeasLCYJ+hY/dvoT3LG/mq6+c4keHEzgu/PutvXzk9iXcflMTu0+neLl/irRmctvSOm5f5rWtGEgWOZMs0hYN4roQD/vYO5hhMq+TL1kcHs1hWA6K5LmmjGlB6VMkDNs78khApViyQIK6gEpRt5CmXYPt0QD5sk1ev/gVUqY7dy90vOwxCb0mWEmWwCfLjGVKvHA4wc7+SSZyZd4czPAX/24D67oaSOTKPPmj4+wbygDMqIdzOQg3lkAwvwhxI7gsLmVQbosGeXhjF6enigylNL63f5TRTImC7tn/HRfCfoVsyaqKgoUcRDw71sSvShiWi41n4bActxob5FO8InGVmBaf4vXCUmQJ23bwKRJBVUG3HGzHxa/KBH0yPgmiIR/ruhr49ANr6IqF2D+U4bYljUzmdQ6OZClZNpbrols2bw5ksG2XD72rm4FkkbtXtoLr8t23xljZWocEpDSDE5MFcmWTlvoAvU1hxjIlGiN+Hr61i2++NsBEzkCWJUqWg27ahPwKflWmqIMqy9zUGqE7Hka3bA4O5wj5vViegWSpGhuk1oikxSBswLue+qyALseFsuWg5w3eGkqztiNKumgwlNZ4Zt8ILfUBvvLyKUYzJcJ+hbWd5+9IfDFEYUCBYH4R4kZwWZxvUJ4dQ3BgOMNYtsyajiiRgMpk4WytmuFMGVVePDEcPgWMmmhbo0aJ2TXCRpHgtr44h0dzlC0H24WmiJ+13Q30jxfwKzKmY6EZNqrsWU6WNkfIaF6frIPDGRKrWwE4Op7n6HieV05MAfD9A2NEgz7evbSRExMFJgs6+4ezTBZ0xrJlPri+g/XdMYq6xZ6BNLbjEg2qLG+twzAdwn6VxrDXbHTr0iaa64LIkkzQL1Mf8HFrb4ygT+HMVJGjiRz1QZWAT2EoVaI/UWBFax3LWiMk8zr1IR91ARPddpBwiYb8+BWJofS5rspaaosULmRc4Nh4nlBA5ZauBizXZW1nlK+8fIo3BzMUdItC2eLQaI5Hbr3WRysQCOZCiBvBJXGxAEiv3cIQLxwaBwnSmsFUXse0bHyqTHPET7FsUDC8gNfFVGelVtjInA3wdYGGkI+CYeE4sKwlwkRBx3a9oF/DcimZDgNTRQq6hSJLLGkOM5wq4WDhui5t0QCyJBEP+xjNlqsxGJphsaEnxh3Lm3n9dIpV7fVsWdJIVyyEbjms724g5FOwHZdUcYLGiJ9t08IoVzLBdYmG/XQ2BPnhoXH6miKsbq/nO2+OoJk2I2mNkE8mHAhx/83tfORnltAWDfLll04ymi1RH1AxHZdEzhOlZ5JFZEmiZNgEfAoNIR+r4iGSBZ2OWIityxr58kunqtY5ONfiFfLLlAxnwQscWYKAT+GtoSySBJ0NQf71jWEm8jqt9QFa6wOsbKvn4Y1d1/pQBQLBeRDiRnBJ1MbaVDol1wqdTX1xntk3wnMHx3FcF58iYzsOU9OTvbrIQ9cloCniQzNtJLw07vqQSrFsE1QVSobFqckikuTSFg3xq3cs5XiiQH8iz4mJAm3RAIoss2VJI8tbDPYPZTg5VSDgU3hwQydHxnKs6Yiek5GzdyDNT056BecqdVViYX/1u39q1wDHEwW6YiGvKvSW3hnH/dyBMdKayZ0rwty9qpUfHhrn2QNjbFvVytKWOvYNZdBtp3odH7m1i2jIB65LyK/w588dJVU0iIf9TBYMLBcCwC3dDYT8CvGInzNTRb7z5gi6ZSNzNjNstmWuaCx0WePhuFDQbWTpbNG/zFgOx4GlzRE2dMcYyZb4Sf8kLfUBJvM6z+wb4eGNXW87BkcgEFxZhLgRXBI9jWG64yF6GsNzBhW3RYMUDRvDdgj6ZPyqjGV78Sm5srWg69X4Zc8ak9fPf5CqLOFXZQq6jd8ns76rgbqg6jUCdUFVZEzLpTESoDseorU+yP3rOvjOmyMsb61jy9JGDo3mqA+onJwssqKtHs2wubU7xlRB59VTSVrqA1WRUduRurZHV1s0OENcblvTRizsp6cxzHMHxs6xrNVeN4C+pgjxsJ/VHVHes7y5OimDZ53bcSQBksS2NW20RYMsb63nmX0jBFSZHxwcRwIeWNfOSKbE9iMTtEYDaLpNtmTguhI+2eV8X+NicUtVLE6V+KGibuHgIiHx1nCW8VyZw6N5dtWl6G2KsHfw7O9BiBuBYGEgxI3gkhhKaQynSzPqfcwOKv7Qpi4GkkX8ipf+nMjrmJaDLC3smjWyLKPVWBVmu1NkwHRckkWdNR0N4MJkXmdjTwyAE4kCJcPCsF3+n3d1Vy0sO45O8PzBce5f186h0RwnJgo0L2uiOx7ijuXN3Le2nU19cSbznuvnfG6OinipCJrZ4vKB9R08d2CMp/cMsXcwPaO4XO11W9fVwCO3dtHbGK6KoMpknMiV+crLp3j1ZBKfInupXpJXaOcTdy5jMu+l7z+8sYuW+gB/9MxBHBcMy+Gm1joOjmSxHJu6gIpbNrEc73usCAQJr+v7VNGrqRNQZ2YqLSRqj0rCu3e96s1eELhpObi41AUUXumf5LaljcD5r5+oaSMQXH2EuBFcErWC5nxBxSXTIRr0EQ4oDKdK2LaDYTteDZuFOY9NZzs5M8SXJEE0oOJTJbIlC3N6htYtzy3x1lCGyYI3ST+8oYun9wzR0RBkfXeMbatbq9WZf/DWKLmSySv9k9iuy9rOBqIhH/uGMmiGPcPq1VIfYO9Aeob1Bs66A7vjIYbTJTLTRf/uWtF8rgtrMM2JiQI7jk4QC/nY1Bc/R4ie79rtHfDeu6G7gd6mCAdHsuwZSKMZNgdHsqzramA4XeLASJaDI1neGs4SDarcubyZx7b28fyBMbYfnfCsHNOiJ+RTcFyXbMn0avD4ZaaK3ufZjou6CBp5NkZ8hP0Kw+kyLl77BiRoDPtwXHju0DiJvM6f/vz68woX0ZpBILj6CHEjuCC1q87zDcwvHZvgH14bIOSTGcuWaG8IEvLJ+KJBMgWdsu3gTKuHhaZxKp22g6qEOZ3eDXjZQqrM0uY6jozlKOg2qgyr2+qZyutMFgxSRYOexjAdDUH6miKs72qYdlO5HBzNcXQ8T3tDENt1WdIUYVlLHZ0NwRluogrnmwAroqSnMcxQSiOjGezsn+KhDZ3VybRyjR7e2FXdptaKc6EJtfLensYwj27uqVqGXjuVpLU+wJlkkf3DWdZ1x6p9lV47lUQ3be5a2cLv3LMS8ALIHdfl5s4oqelCjZ2xENmSScivYNkOPp9CfcBz/znO4nBRZTUTa1qguy5M5HVSRYOlzREAZEliYvo7nP091363D23oFDVtBIKriBA3ggsyO5C4GpMxbaEA+IfXBth5fJKQX0HTbQy7RCyokilbREM+itnyghM18rQ1qTLB6pY7w8LUXB9AQmJdZ5RsyWAir7OmvZ7VHVHyuoVuOTRG/BwYzrB/OMtPTyYZTBY5PJYH4P61bdy7th2fLGE6Lr2NYXYen6xaYCpuogrnc/XVWloqReQqAcUVKtfooQ2dM4oonpgozDnp1jL7vbXH0NMY5sBwBiSJ9V0NPLNvhNNJjc54mJDfYHVHlB1HJzgwnGHXqST5ssWajiiqItEZD3NTc4STU0UKhk1GM0kWTSJ+BVX2qlP7JImAKlMwLtbV6upS60a1XMiW7Wopd9f1Ch5uXdbEGwNpNMNiMKXxjVdPc2g0S1csxLY1bUzmdf7nc0cYyZR497Im/vM9K4VLSiC4ighxI7ggm/riZEomGc1gx5EET+8Z9l6oicm4c0UzxxN5TMuLR9ANi9GyhQ0UytaCEzZwbgyQT5FwXa9TdDzsY2VrHabjsrN/koGkV7StbLr88NA4r55MYjkOPzmR5NHbetjQ3cD+4Sy9TRHWdcfAddm2po0dRxL87c5TRAIq6zqjbOyJcXQ8x8bus92kL8UyVstcbqW5XE+fuHNZdb9zcSGrwmxBBV7W1YmJAms7o2ctRCWTp18fwrAdOmMh0prJmo4oYb/Cy/1TnJwoYDsu9SGVtqifM8kSed0i5JNpqQ/iV2U0w5ohbir68lp6Mi8UH+a6cFNLhJf7J70qzniuy12n0xwdLxAL+QA4NeVZvAD2D2erIlPE3wgEVwchbgQXpC0aJBby8d39o9y1soX717ZxNJFn9+kU/RMFXLz4A58s0Rj1oygwni1fsC3BtaTiFqkloMrcvqwRF3hjIIMEbD86AXg9n5rr/CxribCpJ85ItoRfldnYGWPLkka2rW5l2+rWGUKi2i9IkogEVDb0xNi2po2vvHyKn55M0lx3Nq7mSsRjzCV4LlYBdy6LzflI5MpkSib33txWzaJa19XAwen4m954iPesaOHASJZcyaRk2IT9CtGQjxOJAiPpEstaIjSEfET8CgFVZnlbPc11AXKawVQ+gTvd0Vxi7jicax2f49T8u2cggztd3sCvKtP9w1zq/AqSJHFwNMdtS+Js6InR0RDAJ8uE/QrPHRgjUzLZeXyy2lxWiByBYH4Q4kZwDgdHsjPqdtRaBvYOpPneW2NkSwbdjSFOTRYZSWlYrku2bJEvG9iOl2G0EJwNs3sdaTXCJuyTsV2mq+tqtNYH8Mle6wTXhZDfm7jyZYuhVImlzXVkNJO7VrbMyEgCqqvyr7x8ihMTBQC2rW6dMYFVsmlqs2pqLWOJXPmqTXQXa6NRa2HYO5Bm5/HJGXE+4GVipYoGdyxvZl1XA0MpjW++NkC2ZLC5r5HVHVGaIn4OjeVoqfdSxu9e1cJwusSZqSIj6RLxiJ+6oI9UwcAF4mGVkumgylI1zqmpLoDtuNUg7mtNxbJjT3dmD/sVQj7Vq0gdUtl5fAJNt6gLqDSEvKrQr5yYYjhd4q4VzTy0oZOMZoggY4FgHhHiRnAOT+0a4Jn9o/z46AT/bnMPj9zaxQPrO3jp2AT/tGuA5no/pu0wmCwxlTeQJa9Sr2k5VHorLgRhA2c7P1cmJJ8qIzsOQZ/Kh97VhWm7vHZqioFkifGsjmE5WI5XwG51ez2DySLZkklDCHqnU7grgmB2XZlKxtHy1ro5s8rWdTWcUwel1jIWC/sveaJ7p+6NS7XswPmF0OznN/V51or9QxnCAZV9QxnuXtXKwxu7GExpTOVHOTyWq7ZwMG2H4+NejFJFclqOVwCyoyHIULpEU8RHWzTI6cniZZ/jfFO5p6JBlXcva6J/ooBflcmVvQakD23orAaCV/6tXK+5YqcEAsGVQ4ibG5QLTY7xiB/bcRlKaTy1e5DexjAPrO/gH14bYPeZNFuWNnJzZ5QXj00SUGUv9dd2KC2wwFCYrpTrznzcGAnQN92tHNflo7cv4dm3xijqFsNpjbLlsmVpnN+9dzX/87kjpDSTle1RHtnUXf2unjswds7Ke3a6/KVQcfnMTu2+GPOdXrypL05GM6qp53N9xmyB1BYN8jvvX1GN5Zk9mU8V9GmXjEWmZHqNKk2bZS0RGsM+Jgtep/imOj+O62LaDhnNIFkwsJyzFreFVjcp5JcpmTbN9X5002ZVez2PbOzi7lVeO4yKoK2k+1+onIJAILgyCHFzg3K+yTGRK5MqGnQ2BGmqC/D+NW30NIZ5avcgjREf7dEgmmGyd1CjULawHRcHKFueC2ghIOHFOquShDUdJFx5PqRK1AVVBpIah8fyuLjcnm/i5zd18739I9huCNeF+9Z20FIf4I7lzTTXB1jdVj/jM+ayZrydCet8Lp+LcSnd2d8JbdEgsbDfsyiFfJd0XrMFcyW7q2Lh+sSdy2iuC/CjIwlSBZ2epjDj2TIhn8LG7hjPHxynbNpM5MsokoSMS9E4e/0UCYI+rz/VQmIoVeLUVAlF8u67+qCPFw4nWN0RnbNmEQhXlEAw3whxc4Nyvslx70CasWyZbWvaqnElzx0Y4+nXh8hoBgXdZDitAaDI0oxaJfYCWk3Hwz6CPoWOhiDjuTIj6TKSBEG/Cq6LYTsoMsTCAU5NFqgPqCxtigASazuj1SDhfcNZuuMh9g1niYZ8M/o6XYkJaq7rcCkup6ux8r9cAVWZvDOaUf2eZru3pgo6ruuytKWO2/rivHY6xfLWOprrAjTXB6gP+pjSDGzbK6xYa6HxYlycBRekXmkCa7ug4LnW9pxJ8YUfHae3KUI0qLJtTds536fInBII5g8hbm5Qzjc5znatJHJlMprB/evayZdNfnxsgoJu4zouAVVGlT2hsBAqzfpkb6JxgWzJq6uSLprctbKZ5kiAgZRGxK/w7puaKeoW/RMFehpD7DqV4sVjk/z7d/dyx4qWGT2coKaAXsmcs8VBLZc7Yc11HRbKCv9yBVTl+8qUzDnjdSoxSSG/yni2DJJULRwIXuzK7jMpdp1KAeC6LuM5HUkCvyJj2A5hv0zEp3oCaAHccxVkCXwKmBY0hlQKusWPj01g2m71PqiNsXnuwBgZzeCFw4kL3k8CgeDtIcSNYAazGzPuHUjzwuEEHQ1BJCBTNLCmZxXddjAXUH+ggKrgl1xs2wtKLeg2pm3zyokpPrC+nZs7o/Q1RXjP8mYOjGTZurSRkUyJl49PYVgOI+kSv3vf6ur+5iqgd2qycMHieFdCmMy3y2m+qHxfiVx5RpbY7JikwWSR7741xtGxHNGgytGxHC8cGkczbRK5Mu9b3cq6zigjmRLf3DWI4zhE/CqTBZ2+pggr2+r56ckkRd3CsGyudaiXX5FY21HPyakitm2TKVsUDZs6v0JAVVjRWgeSVLVqVQos3ru2neWtdZdUbFEgEFweQtwIzmHH0Qm++doAG3pi3HtzG5bjsv2IV/elZNpV95M9LWxkFkY9m6Jhe1WGgaBPIhbyYbkumm7x1nCWpc11rO9SeWbfCCcmCjy6uQfwMr1002Z1R/SC+59dHG8uK82VECaLPdh0tkCufDe14meqaPDqySRnkhpNdX5eP5MiGvRx/7r2aqPOg6M5bulu4NRkkWhQJVOyiPhV3hrKTKeOuzgLwHpj2C5vDucA8CsQ8ilEAiqO7ZAtm+wZSNEQ9nHXyhaAakbdXDWSBALBlUGIGwEws2LtgeEM2ZLJ/qEMuC7Hx/OkSwYBVcFxvNojtnNW0FxLYaMA7nT2jE+RMG0XF3Bcl46GIJMFnaUtEX7z7psIqAqZkjkjXRuoVlretqbtop9XKzzmypha7MKkwjuNB7mQBasiEpe11IHr0hkLUR9QQfJqALVFg3xhez/f2z9KazRAQ8jH5iVxOmIhfu6WDv5p9yAnp7y4r0objYWCYXv9pxojPgplm7LlMJwu8939o3xIkbljeTPLW+uq5wkiuFggmA+EuBEA3mT09J4hFFkioxn0NYW5c0UL+bJJXjcxLZdoQCJnei4fCacaSHmt5hYvzsEz1Vi2O114T8a2HXTLYSBZBCTWdgS5/aZmAHYcSXDv2vYZvbEe29J7WZ97IzREfKfutUsJnq11XWmGzXf3j1Z7bvXGQ/hkiXzJZMuaRla11ZPI6QRUhfevauXURGG6W7tLQbfPCWavxF9dCxwXpgpm9XHEr7C0KcyrJ5Nohk2qaMzoLSYCiwWCK48QNzcQtYMoMGNA3dQXZ+9gmkOjWWJhr85Nb2OYjGYQUGV006FgePENsuNc8zojPhkkSSKoKkSCKkXdIlOyMA0XnwItdV614ZBfwXbdakuEFw4nqi6B2VzqJHM5rQsWK1cy7md25eYH1necI55mf957VrTwyokpJvI667tjXrXn6YDcTX1xepsi9DSG+Un/JF/eeYqUdlZM9MaD6LZDInftKhoHFNBtL33dclySBRPbdYmHfDMKQcLMLLNc2WIwWeSxrX3nFHwUCASXzlURN1/84hf5i7/4C8bHx9mwYQN//dd/zZYtW+bc9r3vfS8vvfTSOc9/4AMf4NlnnwXgl3/5l/n7v//7Ga/fd999PP/881f+4K8jaicUYMbkUhtPUltVNVMy+fdb+vj+gTGG06Vz2hlcKyTZs9YUDItwUCXkUyhbDqblEPar/MZdy2itDxL2K7xyYoqexjAt9QFe6Z/k1ZNJlrXUzbDYzDUBn4/FGvB7ObxT99rse222K3CuZp+1n3dgJEtaM9m8pJH1XQ3nFeXruhp47tA4KS2LBAR8Mk11AQ6O5qr7koGrbcQJ+lQiAdBMG910GM9quMBzB8c4MVVgMFnkPStaGEpphP0K3fEQubLFt3YPktIMwgFViBuB4B0w7+Lmn//5n3n88cf50pe+xNatW3nyySe57777OHbsGK2t566e/+3f/g3DOLviSiaTbNiwgX/37/7djO3uv/9+/u7v/q76OBAIzN9JXCfUpjYfGM5w18qWcwJjKwGfB0ayvLxrgOOJPLIskSuZWNfQXCPhuaFkGSQXJFdCllziYT+27WI5Ll0NQUzHpVC2GE6X+NjPLOW5aVFWcQOs62rg8Fh+Ztlizm2dcCGul7ia+WQuAThXcPF5cT3357rOKEMpbU5RHvYr/J+XTxENeM1NDdOmYNocGMlWSxNcq2rG+bKFXNPsU7c99+2pKY2hdIljY3kGUhr7h7PEwz58isyyljo+vKWXwWRxRv8xgUBw+cy7uPnLv/xLfu3Xfo2Pf/zjAHzpS1/i2Wef5Wtf+xqf/vSnz9m+sbFxxuNvfetbhMPhc8RNIBCgvb19/g78OqQyoTx3YIyd/VPVqrjPHRibUb9lx9EJvrC9n4ymY7tgWu41j9l08RpZlgybSECdfkaitylEoWxzc2eULUub6GwI8sqJqerkMHuS3bambc6ePudrnSDiId4es8XL7CrYF/tO57pOYb/CDw+Ns7Enxqa+OL//7QO8diqFKkvIkkR7Q4BSSjvbR2w68P1a4MCMTC4XUKfFeXcsyC09MRojfnIlk3TRYOvSRnKaAZLEuu4YLfVisSYQvBPmVdwYhsEbb7zBE088UX1OlmXuueceXn311Uvax1e/+lU+/OEPE4lEZjz/4osv0traSjweZ9u2bfzJn/wJTU1Nc+5D13V0Xa8+zuVyc253ozB7wu9pDGPZDjuOJJjM62Q0g6l8+ZoFZNZScSn4VYmlzWFOTBSRJa9SrWW7HBnLs66rgdXt0WqQ8OqOKHsH0rTUB+bsf3QpfZIqLJSCetcTl/KdziWO/vTZw2w/OlEV5R95dx9l06Zs2pye0gj5FRRZ9qxyjrsg7t+wT8anSGTLXjEex4GU5lX53rq0kbtWtnhZiRJ86/UhLMeluS4wo+WFENgCweUzr+JmamoK27Zpa5uZYtvW1sbRo0cv+v7du3dz8OBBvvrVr854/v777+cXfuEXWLp0KSdPnuS///f/zgMPPMCrr76Koijn7Odzn/scn/nMZ97ZyVxHzJ44hlIaKc0kr1u8enKKTMmqmtOvdaatA9QHFAI+Bd1yCKoynfEQhZKJPp0hNZYp8739o0wV9Grc0JUSJDdCfM3V5u1+pxVrXOXfu1e1cveqVp7aNcDf/fQMqiKzuqOe4ZQ2o+ZR2bw2AfAyoCheU03PpSph2C7ZkoVuOWxb08a2NW3sHUiz63SSXNlifXeU+9Z2zBlwDEJgCwSXyoLOlvrqV7/K+vXrzwk+/vCHP1z97/Xr13PLLbdw00038eKLL/L+97//nP088cQTPP7449XHuVyOnp6e+TvwRUZPY5gVrXX0xEMMpTSSBQO/KuPiYlzjCsQSXhl+14VEVqdk2vhkmd983wrWdzXwk/5JjibylEy7GihcyYS6EoJExNdced7ud1qJmZrNtjVtbD86wf6hDHcsb6Y+oDKaKXFLd4yGkI8XDo2T1AwaI3403SavW1dF7DhAcfqzJLxifxVWtNbxlZdPccfyZgZTGq/0T1E2bWzbnVGmAITAFgjeDvMqbpqbm1EUhUQiMeP5RCJx0XiZYrHIt771Lf74j//4op+zbNkympubOXHixJziJhAIiIDjC3BgJMv+oQx+VSbkVwj4ZFygbFwbYeOTJcya2cdyoS8aAAmGUyXGsmUODGfYtrqV3qYIr51OYdrTDRVdVwiS65CDI1me2TfCwxu7zhE4bdEgn7xnJU/tGkAzbUqmQ2csxHi+xA8OjmPZDrGwnw9t6mb36RTHE3nyZeuqZP1VbuPaj3KBZw+MAw5Pvz4EgOHYtNQFKZk2X3n5FA9v7GIopV3RJq0CwY2EPJ879/v9vOtd72L79u3V5xzHYfv27dx+++0XfO+//Mu/oOs6/+E//IeLfs7w8DDJZJKODjEAXA6JXJnnDowxktbIl01GMiWOj+cpmw6a4Vz19NkKtusi42W6NIRVArLEaLaMZbus6qjHsGxePDbJjiMJNvXF6YyFmMzrbOxuuGiV4co5J3Lly3pNcG15Zt8I33lzhD9/7ghP7R4kkSvPuF7ruhq4Y0ULiVwZ3bKZyOscGM5jWF7395taI0zkyhwczVHUrWtbVVsC03bQLciWLXTLZnNvI599eB239sY5MVHgmX0jfHf/aLU+k0AguDzm3S31+OOP87GPfYzNmzezZcsWnnzySYrFYjV76qMf/ShdXV187nOfm/G+r371qzzyyCPnBAkXCgU+85nP8KEPfYj29nZOnjzJpz71KZYvX859990336dzXVAJUBxMaXxv/yhhv4JmOmiGjYRnQr8WVOJ7Kqtdvyzx7mVNHBrJYbsuS1si0y4zBUWWqARVaIaFZlj0NkUuGnB5ofgFEduwcHl4Yxenp4oMpjSefn2IWMgHcE4hwEohyqXNddyuSBwezdEY8aNbDnsHM5Smu2zK0rWpfwOgytJ0dWWPlvoAn390IwCj2TLLmiOs745VLTcCgeDymXdx84u/+ItMTk7yh3/4h4yPj7Nx40aef/75apDx4OAgsjzTgHTs2DFeeeUVXnjhhXP2pygKb731Fn//939PJpOhs7OTe++9l89+9rPC9XQeZlcm/srLpzg0mkWRJLIlE9N26IoHOT1RRJLBsa9N+XoXaK3zk9YMTAe640Fu7Y6xvKWOtGai6RZnpoosaY7QFPGz61SSXMmkP1FAlWWiwYvfzheKXxCxDQuXdV0N/OnPr2fHkQRIUvUaZUomGc0gkSvPWYjyP753OeAJ1xePTTCcLuG4Xl2ka2aZdM6WVgiqcjVAulJE8tHNPeeNLxIIBJeG5LrutY0YvQbkcjkaGhrIZrNEoxfuBH09UKljs7y1jmUtdfzrG8MMp4tYNrxrSZz3rmrlhwe92jfXuq3Cxp4GYmEfr51MUR9UiUf83NYXJ6WZbOyJMVXQaa4L8I+vDZAs6tx7cztblzVVG1+KVNkbi0rz0korjKpVMlnk+UMJHt3czfruGP+0a4DRbIkjozkvNbtko+kWds2+KoUiXffqWXRiQZX71rUxkimTLOioiszv3ruKu1d5QfEiDVwgmMmlzt8LOltKcPnMttLsODpBTjOIhX28ejLp9YmyvOZ9jgvposG21Z64AQgoXrrq1dI4qgwNIR/5solhw/4hr2JrPOJjIqeTK1v8zE1N1QaVbdEgiVyZo+M5Xj+TYk1HlMe29IpJ4AZltrWt4lpsjPgxbYeDozkOjGR59sAYsiRh2Q5hv8LqjnoGk0US+Zn9p65maxFZgpxu8Z19o1iOS8gnAxIvHBqvihvhKhUI3h5C3Fxn1A6GGc3g//fiCUDi5s4oRd3ixGQBw3JoiwZJFgyOj+f5xS+/iqZb4Homc/kq9o+yHUgWTeTpQB8Xr8hZLKx6x2A7GJZzTiG+X71jGc11Ad6zvPmc8xaTwI3D7Eyi2sKUYb/CiYkC717WxAfXd5AqGuwdTGPaLluXNvILm7r5/A+Pevcf4FdlytbVsdkEZNArrRksFwmwbKgPKsQjfp47MOb1dtOMOdukCAEvEFwYIW6uM2pXsjuOTmA7oFteRoblOIxny1i2Q0BVCKgy2ZJFpmxV33+13FL+WRYix/WsOJbjpYKHVIWibNEQ9tMY8VdjKioMpbRqVklLfYCexjDd8RA9jeGrcwKCBclssfPMvhHes7yZ37j7JhK5Mn/0zEFen85A2ra6lZeOT/DjIxPotnvVhA3gFaXUzzrFQj6Zn9vQwa29jYykNf7oe4dwHZdoUOUTdy6rtkkRAl4guDTmNRVccPWpDO5t0SDbVrfy3lUttDeECPoUdNMhV7bwqzJj2bInLK5RapSqzPxgWYJYyE887KOvKUw84qcjFqIhqPKTE8lzUmI39cVZ3lrHiYkCewfSDKW0aoNMgQA4555oiwZpivixbId00WDHkQRDqRJ1NYHoigQNlxCY/k7J6faMx47jsvt0ipG0xki6RKpgMFUwSOR1Do7mSOTKbOqLV92zIEoXCAQXQlhurjNmm67/8z0rq2nf/eN5/IrEitY6RjJlIn6FoE9mKu/F31zN7BHddIgGVTTdwpqu4NoY8RHyqxTKJn5FxrJdbu6McveKlnMymGozYzb1xZnM68JyIwDO/gZ6GsPctcKrAPzUrgG2rWnj3rXt9E8UGMmWSBYNLNvBp5xd43mVhK9+HlXZdjmTLPHll07R0xikpd5PfUBlWUsdo5kSewfSPLC+oxo0/dyBMTKawQuHE9WGt8JVJRCcRVhurgNqV3CV2JOKpaMtGqwKA58qk8jrjOd16gIqLdEg96/rYFlr5Kqnxdou5MsWdQGvF5jjwkRO58xUkURex7QdioZFSFWIhf1z7qPWSiUsN4IKld/AUEoDSeKp3YP8465B9g6k0QybsVyZn55M8tZwlp7GMBG/StAnE1RlWqMBnKucMigDvmlDpul4IidZMLhtSSOfeXgdj27umbvXlCTNsF4KBIKzCMvNdUBtMO2mvvg5tT/2DqR5/uA4JcPGp8hM5XVKhs1opsRoxjOBX00U2QskdoG8bhMJKHTHQuiWQ7KoYzsuLfUBOmIhuuKhS4ozEDVqBBVmxJ0dSRAJqGzobqg+v7k3zq7TSbYubeTuVS18c9cghbJJSjOpC6okiyZXs12sgyfuZUBVQJVlwn6lWpSy9r5P5MpkSiZ3rWhm2+pWtq1unZEdKRAIPIS4WYTMdj3VChqAWMhXFQSxsJ+wX6EhpHJqqkChbDEJRPwqDX4fw2mN2t6Y890FvCcWZG13A6cmCpycLGK70FIX4F19cV44nGBNR5TlLXU0Rvz85ESS+oA6I87gfIj+O4IKtffCtjVtxML+GRlGn/7Amurv5ysvn+J4Io9pVXqT4RW6AYKqRPkqNo6VJC9jyrC9Luav9E/wSv8k99zcxnC6VO03tfP4JA9t6Kyej7jvBYJzEeJmETI77bktGjwraKZbEmzsbuDgaI7RTInOhiB7BzMUdYuAKmNYDrbjYNgO0iw5M59DuYQnzNwRWN5ax2i2THE6sHLL0kYiAZU7ljejGTaD0+6laNgvBm/BvDCZ9wpCblvVyni+xNGxAkgQ9isoloN+FbOnYGb5hbLl8PKJFLIEJ6eKGJbD6akin7xn5TliX6SICwTnIsTNImQuF0y1HP10kKEiS2iGzdKmMCPZErbr0hBSWdsZZc9ABs10sAo6QZ+EqV+d1akLGA4Mp8uUDJs7ljdzYCRLqmjw/bfG+NOfX18VbnetbOHX71omzO2Cd8Rc9Y8qz3XHQwynSzy0oZOMZjCcOsFYpoxuOdiOWxUbQZ9EfcBHqmhc1SJ/ALiwvDmM36eiGTZDKe0cN1WlnYoILBa8E643kSzEzSJkLhdM5blErsypqSKHRrOs7WygOeLnBwfHKRs2rirzxkCG0nTTKMNyMa6m2R0v3iboU2iPBmlvCHJqqkjIp3BqssCf/+AIqzui1XiC6+EHdqW53gag+WZTX5yMZpApmdUYtIobN6cZLGupq2bbtTeEmCwYmLZXyNKvSli2S3t9kCXNEV4/k6JozI81Z7Y7uNLU0wFOTWl86SOb52ykuXcgzYmJAmG/Wg0sFpZOwdvheiuEKsTNdcZkXmcqr9MWDfLwxi4OjGQJ+mRKpuzFFdSUl5nv+JrZn9NS7yegKhQNi+FMicaJAhG/ypLmMPsGM+w6neLNoQy/cfdNYuI+D9fbADTftEWDxMJ+vrt/lFjIN8ON+8KhcZa31rFtdSsHhjOMZ8v4ZBlUaK7zc2tvjEROx7AdXj+TpjyPnWRdZnYpr/2kqYLOT/on6W2KnPO+2orMoou44J1wqUkZi2WBJcTNImd2L6knf3Sc104lCfsUtixpZH13jG2rWnl9IMXJCc9KElBkUpp51YRNJKAg4aWtttUHaQj7MC2He9e2oxk2Yb9C2KeQLBqcnipyYDgjLDfnQWSFXT5zfWc9jWEUWfLcOQNpkCQv1kaWyGomjgvHEwVkSSJXMvApErYjgeVin++D3iFzSSdVBr9P4Wgiz77hLMA5rUgqj0UXccE74VKTMhbLAkuIm0XOjF5SJZOhlEZ7NIgiS4xkSvzgwBijmRKG7eC6YNkuQfXqWWwCqkRZt5FkiZMTRUqGw6NLu4mF/azuiFbLyqc0k7tXtdLTWBDm9QsgssIun7m+s6GUNi2sVcJ+hUzJ5F19cd4YSKMoErbrosoSAykNkAiqMt3xAD5F4sh44aode3tDkIAic3qyyNKWCD2N4UWzchZcnyyWBZYQN4uc2jTwkXQJw3K4e2ULpuPycv8UR8fzWNPCxsHLwjDmMQtE5qxwqg8qBFWFqaKBhEtHLMijm7tBknh6zxAv90+yvjvG+q6GagbI+q4Gntk3cl1WGhaT0sJhU1+cvYNevMorJ6YYTpe4a2UL67sa+PLOUwwmNZrr/Dy8sYuRtMaBkSyaYSPL0Bj2Ydg2ZdNhPhOqZAk03UYNyfRP5slOL14OjGR5+vUhHr2th8e29M7fAQiuaw6OZHlm3wgPb+y6LKvfYllgCXGzyKnEDzy9ZwjLcYkEVEzH5fBojuF0aUbWB3jWlPlMcG2M+JgqmgAY1nSquTs9UBsWu0+neOTWLpa31vHTk0mOjOWJhXzeZDOQ9rJWpisNX29m9qtpzhVC6sLUtu+oxKvols333xqjPqAgyxIt9QH+9OfX89TuQaYKBvUhhbGMTtDnkiuZzGcsvl+RkCVIaSYFw6I3HuaO6VYSR8ZyFA2Lg8MZEsJ9K3ibPLNvpDoeXW9jLQhxc11QWYUeGs1x+01NXq0Y3WI8W8JxZ47A8+2OypUtZMCvysTDPtKaiSxLSBKcniwylCoxVdC5Y0ULD93SQXS6wFptCvilFO1bjFxJc+7FxMti8YtfS2bHq3zi71/n5f4peuIhuuMhbl/WTCJXJlcyuaW7gcNjOQaS2lVx6Ro1KxLDchlIFmmN+nnx2CSKBOGAyrFEni9s72ddZ5T13TGGUtqMwGIhegSzqR03Ht7YBcAdy5t57sDYdXfPCHFzHVDJjAK8DKnhDHsG0iSLXkNMRQJVltCvQpEOw3aJBlU6GrwfSUPYR7poABIdMe+5k1NFUprJb79veXVyqZ34r6cfWC1X0pw7u+XGbKGzWPziC4mPvLsPgJ+7pYOAqnjtG45O8A+vnUFC4qaWyFXLMJyN6XgBzk2RAGXTZiKn09cUZv9QhsOjOU5NFRlOl6q1e0CIWsG5i6DZi551XQ08d2DsulwICXGzCJlr1T6U0jg0muP0VJHV7fW4NUOw7YJ9FauPKRKE/F4tm/vXtbP7dIq+pgiP3NrFjiMJ/nHXICta62bUHlksftyFQm39lh1HEuzsnwLODk7i+7x87l7Vyt2rWqu/LwBcF8cB3bJoiwZY0hxiMFWa11ib8+FXZII+hdFMibJpc2tvjPeviTOYLFYre1csNz2N4TlX48JdeWMxV9/BjT0xMprBwZEsB0ay5DSDu1a2VBdC18s9IsTNImTvQJqn9wxVK5ICDKY00kWdo2NejZvfet8KfnpikucPjWPOV+7qNEGfhCpL1QBLzbA5Np5nNF2iMeInrZncuSJMWzRY7fWTKZnsPD5ZrT0iuDRqB55K/Zbr2ZV3LaidELataePASJaf9E/y3MEEhbJFyK/QGlLRdAvNdGa4kOYLGUjkdKYKBrIsEfarIMGb0+7o3qYIv3H3TQAXXI0Ld+WNRU9jmO54iJ7GMHsH0uw8Pkl3PMS+oQynporsOeOJ+F+/62xl68o9MpjSmCrolx1wvFAQ4mYRUZnYehrDLG+t48REgR1HEhwcybKzfxK/IrOpL869N7ehGTbRkB+/ouC6Nq7DvNXnMCwXw3UJ+WUsw0FRJBojfkzLoTHi584VZ1cFtZWUK4HEgvNTK2Ym8zp//P1DDKdKfOxnlvDIrZ4rsrLCSuTKYrX+Dqj9fT20obM6IfzS1j72DqYZTJer9lBFlmlrCOHi0p8ozquryidDfdBH0bAwLJeQLLOyvZ7Tk0WG0xqG7bL9SIJHbu26qFtSuCtvLIZSWjVBY3bBx57GMMta6sB152zl83L/JNuPTgCLM+BYiJtFREVRP7Shk4c3dvHU7kFeOj7Jm4MZyqbNqvZ6fu6WDr744xMMJDXWdNSzuqOeg8NZ9Hkcfp3pXWvTpenLhkPKNYhF/HTFw3PGhAi3yYWpTLQZzai6nPYOpnlrKIuDF2BaKxSfOzA2Y1uxWr98an9flY7hJyYKPLq5h3vWtDGaLVMf8GJxhpIlmA6Sn2+7jemAZpj4ZAVXtvEpEsuaw7w1nPMWLzgMJIv8+XNH2LKkkW1r2s77+xK/uxuL2bGMsws+Vv6dvTDa1BdnMKXx/tWt1XjOxYYQN4uI2ht170Ca/UMZxjKlamr3sfE8n3vuKMmCjuVAWjOQJK5KIDF4gZZ+RcJ2XEzb5abmCNtWt4rJ9W0wV/ZYT2OYqYIOLvzS1r4ZAuiFwwk6G4IzfOcVak3TgvNTu7L1mlHmWNsZZVNfnE19cXTL4dBoljNTGgNJjZBPxq/KXvVi253XEgtlC8BGAlTb4cXjU+iWzfquBprrAuwZTLPrVIp9gxkAHtvaN49HI1gsTOZ19g561sjKwvLgSJandg/SGw/xnhUtDKW06hhSCXXYO5Bm31CGhzZ0LkqrDQhxs2iY3WYhoxmsaKvDtB06GoIcGs2RLBoUDav6nqsRC1AhOD3IR0M+ZAlkSeJD7+qurgJAmMIvh9nf2d6BtNemwq/SGw/RUh+YIYAqbso7VvjOcT3VmqYX60B1NaisbJ87MMaJiQJrO6PVmLYdRxI01wVY2lzHgeEsLfUBdMsmVTRY2xllaXOEV08mGc/p83qMLtOFOAs60aCPD72rm+Wt9YR3DZDUDIZTJZCki+5HcP2TyJV58kfH2T+UQTNs7ljezKa+OE/tHuTbe0doqvMzmC5xeDTH/eva6YyFePVkkua6ANGQj7tWNC/qMVuIm0XCjDYLmlHNOPrZm9v40ZEEWc0AoKUuQEG3yOvzHEU8i3cva+S+dR0cHM7woyMTpDWDl45N8sit3cIU/jao/c4qwaGm7fD6mRTRoI+posHDG7u4a2ULuG41W2YwWaxmoM2OIVnMA9V8MjsmabYp/7kDYzy9ZxjTdrilu4H1XVFeH0hT1G0CigLAW0MZirp1oY+5YriulwGpGTb/uneEnniIsWyZe2/2gvXFdb4xmH3fzpX2rRkWS1si9CfyHBvPsXcwjmnZyLLEspYI8ZCPqYLOkbEca9rr2TeU4ZX+SVRF5tHNPReM0VvosXxC3CwSagfcHUcnKOoW/RMFkgWd4XQJVZWxTQcXiIV9V03cyIAiQ7Jo8IMDY3xoUxeHxnKkigYly17wP4CFTiJXZjBZpDHi57YlceqDKiPpErtOJTkylqOlPkBGM1neWsfh0RyHR3NEQz5yZYuX+ydxXJePvHuJEJcXYLbbdLYY39QX59HN3RwcyTKaLXPv2nZc4I2BNC31ASbzOom8TuEq/ebOBjVL7DmT4uCIwoq2OnJlqxpvI7j+mX3fzn5cmTNe6Z/kuYPj1AVVDo1mUWQZRYbGiJ+RbIlC2eTVk0kkCTZ0N3AmWWR1a915ywmc7/MXGkLcLBJqB9xtq1u9J12XN4fSvDkEN7VEmMwbZEoGvfEwQVWmPF2MYz4Ljzl4O+9PFLAcl4xmcPeKFrKaya3dsQX/A1jo7B1I8/yhBKbtEPYr+GSJg6M5gqqMPlWkzq/QGg3y3lWe73wsW+al45O8NZwlrRlsno4XEZyfS3GbxsJ+HtvaV80yyZVMNMPGr0oUdIuGkI+ibl/VAn9l08ZxwXVdhlMlnto9CEBvY3iGO1MsLK5Pau/bRK5MpqZeTe2isqcxTCKvk9EM1nY2sLYzSkCV6WoI8eLxSRwXsiWTt4YyfOKum6rZrRcbuxd6uIEQN4uI2ht22+pWdhyd4MyUhmU7jGZ0VrZFeHPQZDBVxKhZRF7pATceUkGSyGgmLlAfUPEpEmXLoWTYpEsmv37XMrataau+Z6H+ABYCF7JubeqLc//aNl45meTQaJZTk0U0w8Z1XdZ3NZDSDIbSGt949QxjmTK65TCeLdPXFEaRJe5Y0VLdp7Cizc3F3Ka1WVSVmJznD46T0QxyZYtc2cSdThmUmd/ebRVkvCxFvwprOxtoqQ8wlC4xmCyybyhT3U4sLK5fZruud/ZP8dCGzqortfba/+nPr6+6qJ/aNcBQSkPTLQq6ScAnUzJtbMfrmba+q4G9A2l0y64uqi72+QsRIW4WEXsH0vzDa2f4173D3Lakke/tHyVVNGiPBjFsl1zJwnZdbHt+B1jNdNjQ08ChkSwlw0ECUkUTeboy8WimxB3Lm6t+YMGFudAKqS0apLcpgno6xer2BrrjIb67f5RoUCWtGUzmdJAgXTTQpy11HbEg71/dSjTsP2vlu8jnCM5PbRbVcwfG6GkM857lTbzSP4VPlbBsB8d1sRwXXChdhfLFlU/QLTg+UcB2XVa21nHv2nY0w56zbong+iSRK5MpmTMCgHsaw8TDPgZTmleJeDjjLUiHM7x6KslYtsyZqQK67TU1loCxbJmvvXKK7UcSFA2v5MDx8QKqLFXvqcW0KJKv9QEILp1NfXHCfpX9QxkGkkU29MRojPhZ3RlFkb0MiXjYhzLPV1W3HMYzJaJBn1ewr85PNOxjXXcDv3rHUjpjoWpga2VCrZazF5zDpr54NeC3Um+iVhR6MR89PLyxi1t74ty2pIm0ZtI/USRbtiiULcIBleWtdazramBjT4znDyXAdWdYbWabrWd/jmAmle8IPDE4lNL47v7R6Q7iDqeTRQplm5Xt9TTVBbBt96oIm9lousWRsRxvTmfFVOKGBDcGlcrDsbB/RjueI2N5vrd/lD/+/iH+1w+P8dc7+smVLTpjIWRJQpG9icKZDlA3bZdk0eTIeJ60ZpDRDFxcxnNl/nbnKb7z5siiGjOE5WaRcduSRlrrA2xZ2sjrp1O8d1ULB0ezTBV0EvkyfkXGvArj62C6TMgnEwmo9DaFWdmmEPIpJHJ6NbC1tymy4P2y15rZrqKndg/y9OtD3L+und7GMGG/wisnprhjeTNP7R5kz5kUHbEgqixVU/1tFyayZUJ+lWhIZTxbJq0ZjGRKVUvDM/tGqgXpJvM6T/7oONp02YBaK45wXZ1lrgDNTMkkoxncsbyZom5RMm36EwUMy8adzsC+2s01XcCnyPgViV2nkmSmXQv/tGuA/cNZ/sPWXlH35jqjNhNyttWmYsnZ0NPAy/1TnE4WKeo22ZLJi8cT/OLmXpIFnZF0CVUGRZJQFRlZBt10UCW4qTlCybRZ3lrP6vZ6fnIiydGxHM8fHOfRzd2L4n4S4mYRUSmsdNfKFv71jWHeGEgT9Ck4rktpWtEY1tVLAXddaK33c2A4i+OC47qkNYOyabF1WdM5VTEF53KOq8j1psVK7IRpOxwYyXJ6qlgNGM6VTXyKjDSdHQdgOOBONxE7NVkgX7Z4YyDF998a49aeGEPpErplE/YrPLNvhP1DGTb0xM4RnRfrNn4jMVuYt0WDxEK+avzNn/3CLSRyZb7z5givnpqiNJShoFusbKsnX7IYTpeQZeatyWbIJxFQFDJli4Juc2pKYzw3Qu9AmttvamL/cNZLTxd1b647Kr/TShf4SqxN5bWdxycxbS/+zrS90AHHhTcHshTKpzEsB8cFVZZZ3VEPLpxJFokEVJa1RIgEVW5qqcN0XG5b0sjazgYGUxpnktqiuZ+EuFlEVAbZQ6NZ3hrOUjYdSqZDwHf1brbaValhO+RLFpphs6w5wsaeGP2TBU5NaXTFwzfkhHi5zJ5AK41FexrDHBjJMpLWaKsPcNvSRv5p9yATeR1VkuhoCExXgrZxXO+q+BUZ3bY5k9SQgBMTRXIlE4BIQOHkZJHvvDnCr965jKJu0dcUueDx3EgxOnNZrOYS5nMJnt7GMN/arWHaLstb6mipCzCcKhHyy7i4OKYXi3OlNU7JdOlo8AMu2bJNnV+hqc7Php4YD2/sYllzhFzZAtet1j4SXB9U7r+KZTfsV6pp25XXdMvmRCLHaMZrvqPKEvGIj5OTBTpjId63qoV9wxnGsyWKhoNmWCiSRH+iwPFEnmjQR75soRkWW5Y0AvDobT1sW926KCy8QtwsYGbfQJUCY9/cNUDZslEVCPvV6Y7c5pz7uNImcnfWg8Y6P0GfwvtWt7K2s4HbljYSDY7xkXcvfLPlQqB2Ap19vYdSGi8cytHZEOSl45MMTGkUdYuM5gVvVwpQN9f5wPUCA6cKXjpyc50fRQLbdRnPloiFvW1GsiUASqbNP742AFDtJj37eG4kl+KFhNzs61J5/eBIlmf2jXDH8mY+vKWXo+M50kWD3adTlGqsavPJmSmNhqCKIkPQr5DSTNobAqzrapjRHTwW9l/3AvVGoraa9nC6xAuHxjk8lq+6jDb1xfmj7x5kNGdgTmfyhf0Ky1sivHYqzWi6xGBKw7C81xQJ6oMqPfFQVYQnsjo+RcZ14ek9w8DZ7uHn6zq/kLgqAcVf/OIXWbJkCcFgkK1bt7J79+7zbvv1r38dSZJm/AWDM5Wh67r84R/+IR0dHYRCIe655x76+/vn+zSuOpUBd8fRCZ47MMbBkSxfefkUZdNmWXMdAVWhaNgsaY4QC86tU6/0AKvUGIl6G0N86r7VfOr+1XTFQjy9Z4hDozn+9OfXc/eq1vPvRDAnlWy43//2Ab7z5jAv908SC/nYM5DmxWOT5Mompu3iclbYAEwVvPpGEwWjOjBF/ArtsRABVaE+6MOyobsxhCpLPLNvhF2nkiTyZY6M5c57PJUBdKGuzK4ktUHds6kNiq8NxH5m3wjf3T/KKyem+I27b2J1Wz2vn0lTthwU+eoMrg6QLluAxEROJ62ZfOXl09Wgzwudl2DxcL4EgLBfwbQdTMdhqqCz+3SqKsZ3n05hTt+L4NVF6p8oYjoumulUhY0qM13rxuLIeJ4TiQJl06Yh5ImdobRGuqizoadhxoJnod9X8/77++d//mcef/xx/uiP/oi9e/eyYcMG7rvvPiYmJs77nmg0ytjYWPVvYGBgxuv/63/9L77whS/wpS99iV27dhGJRLjvvvsolxdHFHctc920lecqZfNxXb67f5Sndg3w6skkPfEw71/TSkPYh+O4jGRK+NX5H0plCTb0NLCkKUxbvZ/HtvSyuiMKwPruWLW/UUWMLZao+mtJ7fWvZMPtGUjzJ88e4bmD44QDKjd3RDFsm/IFrAG1cR0+BcZzZUzLYW1XlHjYR0G3kAHLccmWDFqjQZojAdZMX78bnQsJudqBvFboPLyxi3tWtxJQZb780km2H5vAtB1CPoUNPQ3UBeeuD3IlqfzqLaemcafrVoXYQncdCC6N82WdvnJiigMjWc4kNYqGxU9PJvnCj44T9iusaq+jLqgS9CkEVYnGiB/bPtc56npeU1y8cUS3XdIFA0mWOD6R542BDGdSpWr9pNoMwoV8X827W+ov//Iv+bVf+zU+/vGPA/ClL32JZ599lq997Wt8+tOfnvM9kiTR3t4+52uu6/Lkk0/yB3/wBzz88MMAfOMb36CtrY3vfOc7fPjDH56fE5kn5jKHzy4a9tL0oOlTZVxgsqBzeCzn+dOB8aw+ryZwVZZQFYmwT6avKcIfP7yMoZQ2Y7B/aENntZtsRjMWvMlyoVD5/jIlk1jIx0fe7XX7HkhqdMVCxEM+kkUd3bh0N4dlA5LLiYk8qiJjOC6y63ImqeFMaZycKNJc5+fmzijvWd48n6d3XXAhV104oPLC4QQZzcCnyLTUB3jP8iZOT2le5okMtjN/2VPKdNVAB0/oRIIKK9uj9DSG2XEkwdN7hhdNdovg/JzPRfzwxi6KukWqaBD0Keimw56BNIm8zsCUhmZYVPTM7KaufmW6T9kcwWDpkkVOt7Ccs6ENGc3kO3uH+bc3R2itD/DfHlhTbeC7EAX0vIobwzB44403eOKJJ6rPybLMPffcw6uvvnre9xUKBfr6+nAch02bNvFnf/ZnrF27FoDTp08zPj7OPffcU92+oaGBrVu38uqrr84pbnRdR9fPXthc7vym+KvNXDft7OdeOJzg9TMpVLmJjoYgE7kymm6h6dPl1+fx+HwydMQC2A5YtsvxRIEDI1liIR+TeX1GGmJlEkjkyqKB3yVS2+X96T1DLG+t43fvXcUrJ6Zojvh5es8QQ+kS1mVc5EpLDN0GfXrkUmVvYeBTZXriIdpjQYZSJQ4MZ6qdwsVK/+LMrgp7YqJAa30AiUrMk8FopszpySIu0FoXIJHXZ7gRryTWtHCSJa9SeDSkciKR5/kDY3Q1hr2NJElc20XO+bJO13U1cOeKFp7eM8T7VrXSGw9xNJHnraEMLuBcIIrddma6t4MqTK+Xq1YcWfLi91wXXMfh1VNJBpJFTieL/NOuAe5c0bJgF7LzKm6mpqawbZu2trYZz7e1tXH06NE537Nq1Sq+9rWvccstt5DNZvn85z/Pz/zMz3Do0CG6u7sZHx+v7mP2PiuvzeZzn/scn/nMZ67AGV155rppZweZJos6siQxni0TDni9hAaSmlcRdZ4xHRhOl3FcaAr7eOiWDnKawd/95DSyBI2RAB95d9+MAVOkf186le/q4EiW7UcneHMwjaZbxCN+3hzKkCtb1Xo2F+JigeOOw7SLq55IQKUpEiCR1cmVrWqWxY2UHXUlqK0G6xX2s/n+W2N0x0McHsvhAjndwq/K1VINV5rKNXdcMB2HRE7HtF1+fGyCFW31LGkOV8vpi2t7fVFb6+bRzT3V+/FzPzjCVMFAMy/c62z2sFKe1dReAdqiAQzLwXbBmW65YzvgUyRKpl0NnViIC9kFly11++23c/vtt1cf/8zP/Axr1qzhy1/+Mp/97Gff1j6feOIJHn/88erjXC5HT0/POz7W+aKShfHwxi4ODGd4ayiLbtoMZ0p0xUNEQz6Wt9bx5mDmqvSxqWiokmWz/dgEhuUwmdMxHIfueHhB3tiLjaGUhu24xMJ+XumfZCynV5siXkrG28Ve74oHeXhDF7ctbeSFwwniIR+P3tZDTjP4252neHRzd7UXmLiel0atiF/X1cBTuwdJFgy64yHao0GgTLFsoc+X2WYWmuEgAwFVwrQdXjg8TlMkwJalWXKaQWPEKzEgrDjXBzuOTvDN1wbY0BPjsS297DiS4OBojtdOJcmVrRljgl/2amFdDg5MixmHkE+lzq+wqr2eqaJOZyxERjMZSmkLVizPq7hpbm5GURQSicSM5xOJxHljambj8/m49dZbOXHiBED1fYlEgo6OmsqqiQQbN26ccx+BQIBAIPA2zuDaUMnCAFjWUoeiSAR8Crbj0j+e59BIFr8qoyoSpu0iSWcFyJXEr0i4rovpeFlSTRE/bw5mkCWJjoYgrfUBfu6WDjFQXgFqrQCf/NabGPaliZoL0RD0sulkWSJXtjgxWWDvUJq9AxnaogE+/cAaciWT9HQDyFq3YsWaI67ppZPTDMZzZUYPlqrp+i3RAKOZ8rz8PmcjS7CkKYxPkQn5FGIhP2s7oxwYzrB/OItfkRlKadU2EiCsOIuFOQWp61LULfYPZQj7FfacSWPaXr2a2bfb5QobAFXyapk5DkiyzempIqbt8pF3L+E9y5s5MJJlMFnkqd2DbFvduuAa9M6ruPH7/bzrXe9i+/btPPLIIwA4jsP27dv57d/+7Uvah23bHDhwgA984AMALF26lPb2drZv314VM7lcjl27dvEf/+N/nI/TuOo8vLELTbcIqDI5zeA/bO0D1+XNoQyvnkyimQ5Wzd3qztPA6bouigwhv8r969oI+1SemW7a6FdlNMPm/3tjmLRmihLvb5PagaCnMcxXXz6FLHvCsuKO8kqkg4MnZi+VbNmullcv6BavnphCM21MB8qWQ6Zkki+bzC4BWQ1y1oxq7JQQORcnGvZjOw4F3SIeVgGJTT1x8qVJstM2f58E1jzEyamyF/hfMm3qgz5UReKDt3SwrKWOFw6Ns6G7gfXdsTlj+wQLn7nciuu7Y/zMTU30NkV4z/JmljVHQJL4xk9PkxsvvON7zYZq8cmy6S22hjMar/RPEg35wHW9HnZALOQ7JyGm9livBfPulnr88cf52Mc+xubNm9myZQtPPvkkxWKxmj310Y9+lK6uLj73uc8B8Md//Me8+93vZvny5WQyGf7iL/6CgYEBPvGJTwBeJtUnP/lJ/uRP/oQVK1awdOlS/t//9/+ls7OzKqAWE7WTG1D1oYYDKi8em8SnyPz6XcvY1BfnaCJPJKBQMi2Mq9BlwXS8oLJIQOKmlnqOjucI+hSWtkSIh/3gwqGxHIblLJqS3AuNvQNpnt4zxN7pWJsfHkrg4NLbGGIwXcKwXBy3kup9+cOUKks01/nJlkxcJOqDPkJ+hXtvbmPn8UmClRICNQq5GuRcMhfEILVY2La6lVzJZCBZ5PBYjoMjOcbzZVqiAbJlCwnw+2TMt7OMvgiW46WDj2d1sppJX1OYO5Y301QX4NRkgYc3drGuq2HBrKoFl0dtkkklbKG5LkBKM7ljRZiW+kC1snklvst2vUXR5SQj1KIAyOBMzzWqDN3xMCcni3z9J6dZ2hLBp0hsWdp4wYSYa8W8i5tf/MVfZHJykj/8wz9kfHycjRs38vzzz1cDggcHB5HlszVa0uk0v/Zrv8b4+DjxeJx3vetd/PSnP+Xmm2+ubvOpT32KYrHIr//6r5PJZLjjjjt4/vnnzyn2txioVblAtV/IiYkCG3pirOuMEvYr/O6/7OfgcBbTcdnYHWPvUGbeetbU0hT20VQX8Abs0RxF3eL4eIG2aJBHb+thy9JGkCS2rRZF+94Om/ri7B1Mc2KiwNrOKCvb6zBth8aQj4GkBnjpvs7bFLNly2UoXa66uLpiQX7vvlW8NC1suuIhziQ1omF/9T0zst5Cvms+SC0W2qLBarXnzz9/lKGUhiJBQJUJ+mQs26E4D8KmFkUCw3I4OVnkiy+eoCsWoj9RYFmL1zH+fKtqIXoWNrXxXV95+RTf3T/K+1e3zqi/9PSeIRRZwp1OkXI4G64wXTHgsjBd8NW8KR720x0LsX84S2s0wKGRHLmyyYq2+nMq6S+ExZDkuvPl1Fi45HI5GhoayGazRKPXtohZ7aAymder5dxHM6WqaPjKy6f4p12DlE0bWYKmOi9+KFU0LimT5u2iSPDQxk7uvbmdnsZwtSv1zZ1RtixtmuFnFVw+iVyZHUcS1XpFA8kibwykSRcNsiUD3fayoHriQYavQNyGBIQDCi11AcazZQKqzNLmCEtbIvzqHcuqKeGCd0YiV2bH0QkODGd4uX+KtGZQF1BJFnTmKWmqKqI082zqf8SvUrZs/IrMPWva+PQH1lTHmIolp0KlnH6ltpZg4VKbcFK5hgdHsjz5o+Nkpu+1V05MXdHFrwSsaIuQLJhohsX9a9tZ3V7PYLpEbzzEvuHsVbt3LnX+XnDZUjcalX5RleJ3JyYKACxrjrDzuFd+/+GNXbzSP8XJyTyW4xVjUiU4J1jiChJUJVrqAjSEfGRKJj3Aus4o67oaqqJGBJ6+M7zVltezZfOSOGPZMg1hr1ldVzzMSKZMxO+1T4ArU+25qNuUDQ3X9eLfDo/lSGoG996szZjsxEr+7VPpynzXyhb6miJsP5Lg1GQRvyojWw76PAgc26UqbIDpIFC8hANc+icK1eq2w+kSQ6mZ13uhuBIEF6fSN6wy/uqWzd/sOEFGM1nXHeWt4ewVt+qrioT3P+iKh3jk1q5qi51Erkx0ep5YSA1ahbhZAFRMxRt7YiiyxKHRLMta6mbUD1jfHeXUVBHH9e5aq1Ive54wbZdMyeS5A+O8eGyS99zUREozeWhDZ/XmXSiBY4uVTX1x7l/bxmC6xB3Lm9nUG2cwWeS7b43RUuenuS5A2K+wfzj7jq02QVVGnx7xHNfTxX5VQZFhbUe0ep9VRE1GM9jZPwWIa3s5JHJlMprBXStbWN/VwIHhDF2xEMPpErolcRVC5YBpl4Tt0hMP01zvozceoafRi82Ac0XMQnElCC5M7aKjMv4OpzVOTRWRJUhky2SKczdRfico03XWbNdFkfyMZmcubGNhv9egtSaw+FojxM0CoLZKre24rO1sqMaweA3QkvzrGyPYrpee563Izr5flXnHSl2ZTievzKG2C5ph41NljJLX+qFWbCVy5RnViQWXT1s0SG9ThH3DWTTDrrba+P6BMY6M5ShbDqva6ynq1sV3dhHK0zeILHlxN4btokoSPlXm7lWt5wjWu1a2LNjiXAuZvQNpdvZP8dCGTg4MZ/jbnafwqzJblzXy05NJ8vrVkjeQ023qgg7JgonjeCngAHsHvaSF2hW2sNQtDmoXlJv64mQ0gzeH0uRLFp3xAN3xMKenipQtl4Ai4UyX8nin2I5LQbdQZOiIBTkwnGEs61mTH1jfsSAtf0LcLAAqrqkdRye49+Y21nfHvNVzyeSFQ+McGctVq0mqigy4mDVmmythgqxE1gdUmbBfwa/K6KZD0bBxHJesZlZXC5V/Xzg0zvLWOra984+/YakdFBK5Ml95+RSnJou01PlZ0xllMq/jXkETnePCWFYn4pe5/aZm7l7VWhXSFavDxp5Y9ZjERHd51F7PHUcniARULzGgq4GhlEa+bKLVBBW/01pGFyOj6dzc2UBzXYDBlMbL/ZNsP+o1La51Swkr7OKg9v6qWEz6E0XqgiqWA9uPTGJNTxZXsnikOW06dmwYSpUYy5TZ3BdfUGJmNkLcLBAqfvqHNnRWi2xV3FSyJBH0STQEfJQsm1x5/jIuAj4Fv6oQDiikihog4Z/Oqpm9aqhk+ewdSIsB8W3SFg3S0xjmC9v7KeoWI5kSkgSG7aIZNv2Jwgwr3TshoHgi1luF2aQ0o1qavyJYd/ZPEQ/7ODKWB9cVtYsuk1r3zvquBjZ0N9Db6LVAyJVMcmWTkxPF6mJlvoRNxQ2pmS6HR3PUBVVOTxW5uTPKzyxrIqDKM4qvnW/lLSw6C4vZ7sOexjA3d0ZJFnR2nU5R1M15FcsBVSJbMgn5FHqbItXSJbXJJqvbo0RDvmuecCLEzQKhdnCZzOt0x0PgutiOy/tWt9LbGOa7+0c4NJqft2OwXchqJmWfjWGr+FQFcGkMB+iKhc5ZNXzizmXsOJJYcIFkC5m5Jotn9o3w/bdGiQZ93LmimZJps38oy1BSq67CrgS6DfGQiqrIlE2Hgaki//O5I9iuy97BNA9v7OKhDZ0MpjTeGs5ycCQrrus7YCilcXgsz/7hLFMFnam8zmCqNKPopgzIV8CtPJvueJCJvEG+bCFLEj5ZQjcddp1KsXVpIz85kQSS1RiJ88XcCIvOwqQ2Nu70VIGJnI4iSTRGAkxOd/9Wp0MNruStZTsutu3S0RIE162WLtk/lCFdNNhzJsW+wQyxsP+ax98IcbNAqB1c9g6kGU6XCKgyiiyxsq2OZ98a40iNsJkvc7YDlEwHv+JgWQ665bCkSa26ymon5YUaSLaQmWuyuGN5M0fH82zoaqAlGuCp3YOASyKvX/HeYYosYdkurfV+xnI6h8fy3NwZ5dBoFoBP3OkVjJwq6MIq9w7Z1Bfn0c3dHBzJ8uZgmoxmUhdQKcsWBd3BL3tlHZJFgyv9ax7NljEsF58qeXuWJJKagW7alEybRzd3gyRd1K2wEGMpbnQSuTJf2N7PnjMpljZHGM2UGU6XiPgVNi9t4s2BNIm8ccWFDXhCXJLg5ESBLUsauWtFM7myxYMbOr1CoNPFXKMLoD6WEDcLkE19cQ6NZvnn14dIFw1eOTGFaTkzblRlHlZ7tWTLltf1u87P79676rz9aMTgd3nM9X0dHc9zeqqAIsP+3VkymklAlc+3i3dEsuiZrXXLxrBdHMclVzJZ1hKZIWY+ceeyGZWzBZdPWzTIY1v7SOTK/Nen9zGWy9MTD2FaKopk4FNlUpo+L9XGq3E9DtiSTbbkYtmeuC2bdrWG1sWsciKLauGxdyDN/qEMUwWDqYKOZbu4uOiWzasnktUEhCsxPcwu/uc6XhJCybBJFQ3WdjZUA+gX2n0yPyOo4G1RqVsAsG/65rVcKJtONeC3wpUWNlLNnyxBdyzA5r44f/DBNWjG+VvbVwY/4bq4NOb6vo6O5UjkdF49maJomCiyl900H80WK7t0XG+icxyHk5MFwj6FRzf3VK9v7aT23IExErkrU2fnRqQtGuTOFS201gW4Z00bD6zrwK/KpIve6lqe584ltgsF3fEyZ2yHg6M5nn59qFr3ppbKGCSu98JlU1+cf//uPm5bGiegKtzaG6O5zk/ZcsmWrbfdbuFSsByXLUvjbFnayJaljQs6Y1aImwVExWWxdyBNVyyIIkNA9XynALXhF1d6PHRr/hwXZFnmr39pEwFV4bv7R6ut7YWIufJ0xUPTWWoy8XAAZ57bakh4WXHSdB5WLOzj3rXtc17f2ntS8PZ55NYuPnX/aj5y+xJGsiWSBQPb9bJPrkaN+MrCyHGgIaTy6G09c05I4novfNqiQR7b0sut3TF0y+bgaJa2+iBBVarOFVcKh5lzTdAvk8jpjGfLvH7GS4KJhf0Lcl4QbqmrxIWyDiqv9TSGuWtlCxnNQLdcwn6VZS0RRjMlJvLGjDib+RgPFVmiLeqnZDjIuPz+tw/wkXf3iXon80xXPExXLERLfYCwT+b/5iarr72dnjAXw2Xa7Tj93yXdRpv2jcy+T4Xb8cpQW4m88l2rMgRVhcJV6IKrKhLdsRCSBL+9bQWP3NoNiOu9mImG/WiGTcl0KOo2TXUBbMclWzKrzTOvBJLkCXBZgtb6ACcnCoDEe1f6WNfVQEYzzkk8WAhZdkLcXCUulHVQWzjt4EiWl49PIssSm/piFHWbgm4R9isYpj1vJkcJiPgVOhqCpP//7P15mFznXeYPf85a+9r7LsmSrNWSFVuKE9kG2WM7qz3AGEwIy5UQZn4TApNhGMJAhkxgkjAME3jDTIAAA4EkGAZiQ2KT2Iq3xLYsy5K1by313tXdte9nff84XaXqVkuW7apWSzqf65JlVVdXVXedep7v813uu2hQ0gxeGk7SFfLwOz9yS2ue1AVw3KQPj2c4NJ5lrGLUA9egR0IzrKb6hzUGS15FZH13iO0D0foCtfg6dXsumseBkTRffek8hmnTF/Mym9foDKuUkuWWlCABFAEEydmd5goaAY/EK+dSrO0MMZYqXaRE7b7f1wZHJrIcHs/wrzZ2cWwqhyjCRLqCJApNDWwAAorjWRbwSOimTVkzEQQBBIj6FB7dP8bwXJGP3rmmbsvzleeH61ZCV+t6coObZeJyJ6IdQzEyZZ3D4xleHUkzk69SNSzSRQ2fKhH2KvRGvRyZyNIq/XYbyFUMjk7mCHuU+V4AgVhAfcPvdXnr1Mwzp7JlprNlVrcHyJScLJ1hOVIAzcQC/KqIKon0RLzcviru+JidniPqV92TewvZMRTjay+PcGQ6R3fEgyqLBD0KktC64MawQTQdv43SvGDSofEsMMKxqTwPbOl2M7PXII8dnODpEzPcu6GT/3jfzRydzPLVF0coNEHNfDGCKGDZUKqaxAMqguCMhKeK2pJ6Z3uPJ/jB2STb+iNX9bpyg5tl4nInoq6wl6hPYSpb4bZVcYIeiYPjWQpVA820EEWByUylJVMVNWpNjb0RH9v6o5R1k3NzRdIlV8OmFTTqVPz1y6OMJIsYlk170INPlTk4mqGkmYi12lETsSybd65vYyJd5rXR9AIfM/fk3nwaU/S713VwdrbITL5KSTMZz5RBEJBEx/ukiUk6wLl0zPlmOhub/i4f67qCDM8VGJ4r8MSRKX77oa1v+PleCWWGG51ErsI3X5tgJFlk5+o4papBSTd5dP8Y923qIuyVyVaaH9xIgoAggCgK9EZ9lDUTnyIRnz/4XjRZKQioksjW/uhVvVbchuIVwo6hGO9cHaekGZi2U4tf3R7gg9t66A576nX6VtEVdhzALctmLF0mHlC5bVWcyUzZbS5sAfXyjyCwrT9CxCvTFlDpjnhJFzVk0VGGbsW4f9WwOTCS5tR0jrOzRTTjwrXlTss0h8bfo+P+PsZXnh/m3Wvb2dQbplx1bE10w6I34sMjSfVpxZYhgGY4Qn6vjWbIlQ2OT+Z47ODEG36r22h89Tkwkubr+0b51uEp9p1L4ffIjKVKpEsa//DaRMum7jTTxq9KtAc9dIW8SKLIXFHnicNTfOX5YYAFwwh7NnTysbvW1G1drhZu5maF0BX2MlfU+O6xBJYN67uC3NwV5LlTc8wWqpgtmqARgJBXpifi5VSiyFSuPN/gaPOjO/odt2o3Zd10Gss/ezZ0MpOvcmgsQ7aks64rSFtQZSRZ4nyy1PTntqHeoG6Udf7+1XH6Y/76111F2rfPUlYlRydzAHz4nUOEPDLnkkVyFQOfIpIqCci2gGXYeGQBQWh+74Rtw2iqhICAZtgIAnSEPRimxa//w+v85K6hBX5TjbjlyqvPjqEYj+wcZCRZZKgtwLOnZilWTUZSRcqahUcWWzKAYFoWD27vZftAjN6oj8MTjuJ2QTM5Opm9SOhzpWR/3eBmmWlM7wL1KamxVInda9v53okEY+kKmaLGYwen6m7OrcAjQdCrkCnpnJjKE/IpCEjkKwY+VWbPxi43Bd0iagvAkYksjx2c4P23OCefZLHK0YkcZc2gqJkE55v4dLP5ujc2EAuo3Le5i1zZYCDupyPkIVPWl5yAcLlylrIq+fwTx3nyyBQeWeS+zd2OJ8/LI/zz65OUNBNVEvF7RDTTIiCJmKaF1sSPvw1g24jzc+E+ReL2VXG+d3KWdEkj4JEvGdyslA3rRqYr7OUX7r4JcPaRkWSR83NF/IqMZetU5oNhjyQ01TSzYtg8e2qWbNkAATIlHVEQ8Mgim3sX9tU0Tv6OpUrutNSNROOJDuDR/WNIooBp2Tx82wC713XwjX1jTOfKtKB8uoCqCdWiDjiWC3etj2IDxyZz7L6pzd3YloHHDk7w+KFJilUDQRA4MJJGN21kUaBqWCgiCIKIKkKlyZVJAaeevu9cmplchZJm8ol71hH1KY6lhl91N7S3SOPod22B9ysSmbLBk0em+N7JWX5q1yCDbQFEQUQSLDTDqvfcaIZBUBUxNettzxCI8x5DAD0RP20hhaOTOVa1+7l7fYejNlvS2L22/W0+k0srWXwwFgSBkFdmU2+YU9N5jk3lsXEGEZqJBCRyGv9yNIEqOeVyjyLSHfby4PY+wBH6rF3vNb+p8XQZcKelbhhqF+ZA3M/hiSw9ES/nk0U290bwqxLPnJxFW1SDapWPVCOSCBXdZLagcee6dt69rqN+wbpBTuuoLQ4eWeTRV8fJlnQCXplYQMGriGTLBrkWRbleRUCVBF4fSyOKIofGMvVTV3/Mx0Dc/8YP4nJJFo/VP7JriES+yulEnlylwkSmTLqoEQ8oaKZ50ch/UbOa8rmv7XWqCBG/gl+RwRYYT5X58rNnsW3wq3LL+/pc3h6LD8aTmTJ3re9gTXuAszMF/KpE1TCRRbDnl4xmJP4snP1HAHTTJuKTKGomI8ki3z8zV/ehg4X7Wy1zc7Vwg5tlppbefeLwFM+dmuWu9R3cua6DHUMxvvL8MFNZJ9oVGla1VgU2AtARUilUDCwbZvNVZvNV4oGOS3pJuTSXLX0ROkIe/vDp01R1E9O2USWRsXQZbFCl1vT8C8BQW4C5gkZRM7m5288HtvWSKWlkyjrj6TJjqRJb+iLupMxbZHGfSkfIwz0bOgl5ZU4lCqSKGueTRfweecGHvNY30ezPvW45JQW/R6Q9qDJX1BieLTIY9/PAlm63n2aFs1Tf046hGLP5Kn/2wjnKmokoOOryomg1dRjBr4iYlo1h2XhkkXzVQDNsXjg9i2nb9fJUY/nyUiXO5cINbq4Siy/UvSdm8MgiP7yhk4pmcna2wHi60rLARhFgU1+EDd0hTkzn6Yl4QYDxTJl0SXcbCJeRAyNpJjNlbu4OMZ4qsb47xFiqzGSmjNCieUafKnJLf4QfnEliWZAp67w26rjRf2Bb7wLtk8sJULpcmtpCX5ucqgnm3bWunfs2OT03f/b8MOeTKfrjPkaSJQzLdjK1VvODGxuYzlUYTZXwqyKiAJIkIokCg3G/G7iucBqvp9phYzZf5YtPnSJZ0JySlA1GMxu1gL6oh0zJoD2skq8a9Md93LY6DjYk8lU290bqAn4rCTe4uUo01uQzZZ1HXxkD4GN3rQHgqy+dRzdtpnPVpj2nLFBXOLYFmMiUOTNTwLRttvSGeWTXEAMxPw9u73MbCJeRxlTu118e4dB4ljvXtfP0cae5vNkIOCPB/3xoCr8q41FEknmNV86nCHsVwl7ZdX5vIo0K5B/c1ltP2XeEPNhApqSxrjNO1bBbMh3XSHX+OC+LAgGPwtb+CDsGHRHR2vj/3uMJchWDsE+5Iudwl+WlJi1wYDTNXKHKi2eTSKJA2CtT1o2m6qEpoiMdUdJMogEFRRRJFXU+uL2fXEnD75Hr+8VKww1uriL1RW9dOw/fPgC2Xe/FWd8Z4uBYpmnPJQkQ9MiYtk2+amJYkClqyLKIT5YYbAuwpS9y1VOJ1xtXUtJZnMo9OJ7l7EyB2bxWv08z+66cpkNnxHNLX4BNPWGOTefY3BNmfVeIrf3RBf1WbqD79lgcHNak6TNlHUGAtoAHUYCpTHlZXo8qCXSFvZR1ix2DMQbjfkdCf7bAmvYAj+4fJ1PSiPpVoj7Ffe9XGDVpgddG00ymy5R1E2wI+WQkUWiqEqRtO/2Afo9ExKtQkUw6w14m0iW+9foUqiziVyU6Qp4VF+C4wc1VZPG4aM2T4+hklrOzRUpNTC+aNmQqBpt7Q0xlKqRKOqYNsg1D7X7e7U5KtIQrKek0BkB7NnZxZCLLKyNp4n6FySZm7hZrYET9Mvds6CTsV0nkq+xc3VbvB3PLUM2jMTh84vAUZ2YKrO0Mgm2TKels7g3z2limPr5b02JriTmuAO0BlalsBVEQeG0sDbZNb9THmZkCazqCPHxbfz1z42brVh5dYS+717bzL0enSeSr9YbxXLn5gweiBO0hDz1RH7ZtEw+opIoaE+kyAY9MzK8ssF5YSbjBzVWgcTOrXRCJXIXPffs4L551TOxmGk7tzWQyXaFY1ZEEkCTQLZupTJnHDk7Uo2+3gbR5XElJpzHN/NE717ClP8qxqTz3bepi37kUhxo2vreDBYQ9EookUtZNbu4O8U+vT9Ef89EWUPGrEk8cnmIg7nf9hlrE4ush6lf5m5dHSBY0AoqIIArYtk2xyX0TNQQBZgpOwCyLIq+PZRlLlVnXGaQ36mPrfIN7bWrOXQdWJi+cmSNfMYj4FNIlnXmHjaajGY5JZ8SvsrUvzHiqjG3bbOgJ17WarvZU1KVwg5urwN7jCR7dP84Dm7sYbAvUe29eHUmTKukYpt2SC1WVBPJVHcNyTodtXhXNtCjrJt85MsWa9gCP7BpyG0ibyOVKOo2CV2s7gxydzPKV54fpj/mo6AaFqsGnP7CZn//LV5hqUgYnVzWJ+kUUWWQ6W0UzTLIVnfaAB79HZjxd5oPbet33vUUsvh52DMU4OpllOltxNiu/gqZbFFOt6b2pTdD4FDBMG68ssL4zyLGpXH0UfEtvmOdOz60IrRKXpWmUkPizF4Yp660TCzEsyJcNjk/lSRU11nYECXvletC7UlsZ3ODmaiA4iefRdJmD41nAaSZ9x1CM41M5TicKLQluRAF8imOuZgP5qoEgQEmbV7+df11uA+nyUMvYrO0M1herMzMFXjg9x8lEgdFUmWxZR5aaaxqTK+lYOGnstqDK+zd3EvTITGTK3NThuJIfmcgyliqtCKXR65FErsLeEzMcHs8wla2wtjPIK+fTrGkPUDFMzrcouKmhm47Y20S2wo/vDONTJZ4/PcehsQxb+iILGp/ddWDlUZOQ2Hs8QdCjUNadTL8kOBmcZur4SQJYtkW2VCXqU4n6Zf7m5VEAHtk11LwnajJucHMV2LOhk6hPWbB47D2e4PhUjkxZa7o3SI2KYVMxDGTRmZYQgKpuIYtw26pY3ejMbSBdHmqNgWdmCoylSnV33W+/PsnpRJ6qYfLt16cIe2W8iliXV3+71B7FBpIFjedPzzlj4WeTbO2LcHa2yPBckfF02T29t4gDI2n+5qURsmWdu9Z3kCxUHbdlVaI77EUWk1jzmiLlJgqWCDiHHGxnDQh5nOm4X7p3PVv7IiAIbO2L1Ke5Vuqp/EalFhTnShonEnmOTeYYbPORLmkYFiiSU9asNmliKuQR8XsUZnNVqhZ4DIvxTAVZEOqH4drrWmklTDe4uQosDh4+/8Rxzs0Wmc5WKGsmXlnAMO362HazMSyQBJuwX6WsmwRUmfs3da+Yi/J6p3Eh+Oida9h7YoZMyTl5vWdrDy+cmUORHY+hqmmTrRiYzTaWmscGzs0V8Sgi927s4r5NXZQ0sx54+1WJF87MuWrFTWbHUIxt/REOjWcZjPkoVZ0s2oauEKPpsjOhokoIApSb2H/XeKrvDqnc3B1i3/kUW/uj9VO421C+cqkFxVPZCqZtIwkCJU3Cp8oUqwY+RSLdxMbiim5jYRD0ylR0k7BXZnNPmFsHYwtcv1diK0OLJMJcrpSahf2J6Twxv4otOLoCktS65xQFkCWJmF9hz82d/NDNHWztj7buCV0WUFsIDoykARieLfCdY4n6vx/ZOciu1XE88+rEmmk3c7qzjogjyW/bMJYssaU3zIaeMOCo6e4YivHCmbl6ZsmluWzpi/Chdw7Vp9Vu7g6xoSdMzK/QHfFQ0a36YIFXbl5pstZ8Opuv8tJwin85Os0XnzpV17kZiPuJ+xVGU6X6bS4rgx1DMdZ1BSlpBvmyTlCV2NwbZmtvmJhPQW/yQhHxykiCwPb+MO0hD2XdYixdvkj0ccdQbMUNIbiZm2WmllasjV+eSuQJqDLxgMKmnjDjmRI2UG2BnVCtHCXNOw7bwIaeMC8NJ/n6vlG29IZdJ/BloLGnae/xBD84m2Rb/wV33S19EWYLVUrzZahWeYtZOFk8GzBtm33nUxyZzDHZoLdydDKLX5XdzE2T2Xtihv/7g/NE/M7nXjctzs2W+dPnh0mXdMqahWFdKEf5FImK8dYXBWkJ+RNjPo3T5lcpaUY9m/jYwQkOjmc5NpVnMO5fMSdxFyfrv3NVnG8dnsa0YTxbpqCZrG73UTUtdKu5/mDJko4iCRyfLpCraPhVZcFa1fi6Vtp14gY3y0hNx+bpEzMUyjqrOgKcnyvSHfZi2TavjqSxWtVwA0iiQMSnkq/q6KbtaFl4ZdZ2BnnxbJJjkznXCXoZWLAQCE7vkyAIzOarfPUH5zk0kSWRvXBitgHfvLfLYnPFt0vtchOBH5yZQxIdC5C69UJDT5Dbf9FEbJt0UWMkWeLEVJ6gR0Y3LaR81Wkq1g1yFSdbIwCZt1lqWOqyEXGyuGs6/HzgFqeB2NHZyrGtP8LW/uiKOom7OPRGfUR9MrN5DduCZFEjV9bQrQsaSc3Cxpmqy1d0bEQ2doec3qxrADe4WUYOjDgbhYBNrmrgVyQeurUPjyTyrcNTJItOM7EIIDS34x2ccle+qlPRLWzbOQ3u2djFHpyRwkNjGfxqC+thLhexZ0Mnw7MFzswUeOzgBP/w6jipkk7ELy84bZeb1Ey8GFFw/hgWVHSLsE9isEHfpNbk7G5yzWXPxi5yZZ0njk4zlamwqt2PbtrsWh0nXdLZdy6FTxGdrJ0NhRY4douigG3D8EyJp0/MMJEp8+LZJOu6gmztj7rWCyuUF87MkSpqC7RtastDszO8NeHPimEjYDM8V+SfXp8k6lfrEiYrqYm4ETe4WUZqG8TRySz//PoUd9zUzi/cfRPPnpzh+bOOdXzVMLGAoCJSaIGQV0V36hCiALcNResXZdWwODVT4IUzc9x9c+cbPIpLs+gKe+sBRK2J9/nTjpDjQMxP1TCbpnGzGBHHfV6UBGzboiPk49/cNkDYKy9oDnQzec1h8UTJL/zQWsJ+lUdfGUM3bY5P5ShUdN57Sw9eVaKimfhVmZKmN/21qCLIksDqjgCKJLLvXIpcRUeRRPyKxHOnZp2oShDIlXXCXtktWV9FGq+dzb3h+u21g7BHFgDBObg26TlrBx+rIXDKlnVEQSBT1tl7PMFzp+fIlHWi82rWK+n6WJaG4j/6oz9i1apVeL1edu3axb59+y553z/90z/lzjvvJBaLEYvFuPfeey+6/8/+7M8iCMKCPw888ECrf4y3Ta0c8cCWHv7Vpq665cF3jiU4PpmjWDXracVmWi+A028jAhGfTNDrTGGcTBTqDYO717aztS/CbteGYdmouUWDE0Rs6YvwmQe3cN/mLnTDZjpX5uaeED0RT0ue38IxUNUMG0EQ0A2TkWSRrf3RFdcceD2wuJEcnMzdx+5aw67VcSzb5vRMgT95dpjZfIWybiIIkK82/5CjW1AxLDpCHla1BQh6ZPyKRMQnc3Qqx00dARAEHn1ljG/sG+XR/eMLXrfL8tJ47bxyLlUPOCTRybpXdJtyEwMbcGLbkEcm6JGQBYh4JW5bFefOdR1O8CsIfHBbL9j2Rdf1SqDlmZu//du/5ZOf/CRf/vKX2bVrF1/84he5//77OXnyJJ2dF2cInnnmGR555BHe9a534fV6+cIXvsB9993H0aNH6evrq9/vgQce4C/+4i/q//Z4WrMBtIKxVInxdLk+gXI6kceniBRts64g2szlTJUEvIpIvmJS1S0sbCwbxlMlvvL8MB+9cw0lzUSRxLpKqUvrWWp8sivsZUNXiKePz2BacGqqQKbcGisOuFD6rBg245kKTxyZRhAEPnHPuhV1CrseWEocs3bg2TEUY9/5FK+OpCnrJpIgIAhQaMVkAU5vhiTA2USBqmkT9kpMZStMZsoUqiYV3eT3/s12gHrmxg12l5cjE1keOzjBg9v7GIj76Y/58KsSyaKGKgvopo0ii/gUqSVlaxsoaiYIjr/Uj+3o58PvWgXAYNy/wBOxVqZaSQi2bbdOtxnYtWsXt99+O1/60pcAsCyLgYEBfvEXf5Ff+7Vfe8PvN02TWCzGl770JX76p38acDI3mUyGb37zm2/pNeVyOSKRCNlslnA4/Mbf0CQa5fYPj2dAEDg8nuHbR6YwLahqBq2wlJGY11sSYDDmo6CZVOdPbZ0hDx9+5yoG4v76B8ltHF0eFpcpav9++VySR/ePUTUszCZfD4vNMxcjCaDIAu9c08b9m7rdUsQy8uzJGf7XU6coaQa6aTGVqVI1mnsab8SriGA77vAdIS+39Ec4OpFlrqjRE/Xxn+67+aKS5KWu2ZVWkrge+J1vHePxQ5NOFnUwxuOHJokHVJ47NctMvkLVsJEFUBSBstb8q0TEEQUUBBBFkXcMRnnvLb1XvRfrSvfvlpalNE3j1Vdf5d57773whKLIvffey4svvnhFj1EqldB1nXg8vuD2Z555hs7OTm6++Wb+3b/7dySTyUs+RrVaJZfLLfhzNaid1MdSpXrKNx5QifoUShUDURJa8oaYOGOfpgVhv8qOwRg3d4eI+lQ29zpjfYuzSbWSiatz0Tpqp/baQlG7Pg6OZagaVtMbyuHSgY2IM5Fl2Y5w14tnkvz1y6MrLtV8PVPSTAQEyrrFbEFHN1sX2ICjTq6ZFiGvwi39EXyqRMWweMdQjJ+/c82SJ/HFpbWlSm0uzeHB7X18cFtvPXMT9yskC1X8qkRAdYouggB6k9VehYb/qZo2AY/MOwajTGbK/MmzZ9l7PNHU52sVLS1Lzc3NYZomXV1dC27v6urixIkTV/QY//k//2d6e3sXBEgPPPAAP/IjP8Lq1as5e/Ysv/7rv8573vMeXnzxRaQl1O8+97nP8ZnPfObt/TBNoLZY+FWJv3l5hKLmpJwruuVojhj2RZuPLF4wu3u7eGSRmVyVZKFKxKdyx01t7F7bzleeH2b32nbuWtdOpqzXT2MrTXHyeqeWei5rBook0BbwMFeoopt2SwIdgLBHRBBF1nYGGJ4pUdEtRAFCPkfPYiDu54nDU+7JfBnYMRTj4dv62XcuxYGRFFO6idnk0X+fIlA1bLDBq4rYNpiWzXi6zAdu6aE96Kn7nNXWhZJm1t//xaU114eudWzpi9Sz6E8cnuLYVB7ddILPyUyZ10YzKJJAstjchvOaRYc9//9b+yL83LtX8/knjlM17QW2CyuZFT0t9fnPf55vfOMbPPPMM3i9FxbWn/iJn6j//9atW7nlllu46aabeOaZZ7jnnnsuepxPfepTfPKTn6z/O5fLMTAw0LLXfalUbW1x+I+PHuTgWIZNPWEOjWUoaQaxgEJmiYu0ibYy+FSJkFdmU0+YnWva2LOhk688P1wPYmqpz1rnO7iL1nJSy55t6YtgWjapkkYiW2lZYAOO+rFHhI6gl6BH4XQijyKJBD0y8YDK4fEMz81Pb7lBbmvpCnvZs7GLiUyZ49Myklh10q5NpKo7ByhRAI8sYVk2mmlR0pyx9N1r2+kIeerrwrm5Isq8UnYty7h4BNi9LlpPLfDNVQxOTOUYniviU0WSBa3p2T2PIqJKAv75NSDkkfmVvztEpqxdZLuwkmlpcNPe3o4kSSQSC9NYiUSC7u7uy37v7/3e7/H5z3+ep556iltuueWy912zZg3t7e2cOXNmyeDG4/Esa8Px5bIee48nGJ4tosoifTEfJ6ZyiIJARTPqWZtWKdL6FZGwT2FDT5itfREOjKTr01G717Yzma1w17p2d9G6StSlAiaynJzOY9g2siQSmG8eLLWgabA6b6b6zMkZfIqMbllUdQsEm9lClY/sXuNOTi0TiVyFP3zqFE8eTVDWDdqCHhLZKnoTo9vaFWTZYGPTFlAYSZUZTZWx7DKT6TKbe52pyXNzRd5/Sw8eWVrw/rtZ3eWlrmoPhL0yr49nmMyUm3rwbaSiWxgmSJLIWKrM8GyBsm4jCtAX8V0zGdyW9tyoqso73vEOnn766fptlmXx9NNPc8cdd1zy+373d3+Xz372szz55JPcdtttb/g84+PjJJNJenpWxgdtsc9GrX/l2ZMzfPvINKIA8YBKxKugWzaZskFJv7CAtSKw6QgqaKaTfn7yyDSPHZzg8UOTlDST//K+TZQ0k+dOzRL1q9fMxXu9MpEtUzZM4gEVn+K4QiuSSBPtherUrrWKYZOr6KiySHfYQ3vAg2HaPH08wcAiHxmX1nBgJM2h8SweWWQw7scjiQgt7LrJlgxHONR2DlR9ES87VsUYiPvr05MeWVrQFwYr00foembviRn+9zNn+N/PnCVXMeiN+mjlGJCNIxVQ1gyquknUrxLxytzSH+Ejd65p3RM3mZZPS/3t3/4tP/MzP8Mf//Efs3PnTr74xS/y6KOPcuLECbq6uvjpn/5p+vr6+NznPgfAF77wBT796U/zta99jXe/+931xwkGgwSDQQqFAp/5zGf40R/9Ubq7uzl79iy/+qu/Sj6f5/Dhw1eUoVnuaamay65uWhycVwFOFTUUQUBRROYKGrbdmqDGkfavBVMy8aCHXavjhDwyCEJdnAtwpx6uMrXrZHt/hLmixu617Xzz4ATfOZqgrJstLU/JAoR9CmGfTLbs9PxU5jNFH9jWy+617e610QISuYrToCkIbO2LcHg8w8lEnoOjGcq6yUiqRLXJ+iVwQfxNFOYHDXwy79/aw2S2Qk/Ey1BbgLBPYWtfhLFUyX3vW0RjCwNw0STa3hMzvDyc5MWzSTTTZEN3iFzZIJGrkCnrWBYLlIqbiYCTKbp1KEpvxMdP7hpaEZO0V7p/t7zn5sd//MeZnZ3l05/+NNPT02zfvp0nn3yy3mQ8OjqKKF5IIP2f//N/0DSNH/uxH1vwOP/1v/5Xfuu3fgtJknj99df5y7/8SzKZDL29vdx333189rOfXbFaN42NxKvbA2zuDfP/Xh3nyGSOVFFvaRQuADGfQtUwkUSF6WyZ0zMFJtJlon4FRRLrflLv2dpTzzK5i9nys2MoxmiyyGtjGbyK0xi/oSvEubkixyaztFKCSBAEYgGFsmZR1gziMR/v29pB1bAYjPl4/NAkmZJW17Nwr423R21Ty5Q0Ht0/DkDUp7BnYxd/+eJ5Tk0XUCSI+j3k0ZtekrRw1KkN21G3vXUwymBbgPFMmccPTdEVUvmV+zcwliq5JagW0ljiAxb8rveemOF/f+8MmmES9Cpkyzb7z2cwLRtVFjCt1gQ1NWygI+RhLq+RLRl1f7lrZfx/WRqKP/7xj/Pxj398ya8988wzC/59/vz5yz6Wz+fjX/7lX5r0ypaXtqCHHYMxdgzFuOOmdr55YJwnjk5zbDLXdEPEGjZgYlOqmszkqlhAuqRRrDpaGnet73Dr6SuErrCXuaLGc6fmsOZdum3b6ZVSJQnNbF10o1s22bJBV9hDZ9iDX5XZubqtHvAOtgXIlHX32mgStc/ZXes7ePi2fhCEeqOublgosogiCbSHHKPbVlBbcaqGzbHJHFOZCqZtU9YNKobjBN8Rcg6MbgmqNSw1uFH///lTr2HZSKJAR1ClUNHxKhKr2gOcTuSotEbjEVmEkEfhgS3d9MX8YNv1yclMSbsmhgxW9LTUtU79dFbWee7ULP0xH+PpMuBcwINtAf7Dvev5lb87xGyhNSq0NlDWTEJeGY8sMdTuZ+dQnJBPIexTLhJkcqekrh6JXIX2oIe7bm7nTCLPRKaCKIAkqugNan5eWaDSZG0LgGRBI18x2Lkqxntv6cWvSvzOt47x4PY+3rO1hyMTWYZnCwzE/U1/7huNxZ+zvSdm+OqL55lIl3nP1h4m0mV+cDbJ6UQBw3LE2lrwlteZyWvkygb/alMnPkXCI4v1k/pK3sCudRYPbtQOE1/fN0quYvBTuwbJVw3G5zPtHlni9Eyek4kCYsPoSbOHUAwLOsLOpBQ4Rq+NAfm10HPlBjctpH4xrGvng9t668rEmbLOV188z/87ME7AI6PKIrIoYDS5qUJkvh5r29g2qLLA2o4gZ+eK9YvTVRtdORwYSXNwLMPd6zq4dSDG08cTjCSLpAoaumWjiBD0yHhViULFoKybTZ+Y8EgCk5kyubLOt1+f5NB4lmLV4JfuXc9jByc4M1Oob3oub53GTe2Jw1P8zUsjjKZK2Njcs6GLgEemLaQyk6/W1cVbWoPA8ZrKVwy+8jO3s/d4oq55tdTa4K4VrePASJpHXxkD4GN3OQ28z5ycpVg18HskSppV16Cp0YpLI1c2+KfXpyhWDXIljbBf5a517VddofhKcYObFlKLbGtuzx0hD1G/yuOHJpkrVEnkqth2laE2HzsGIrw2lm362KfTzSRg4ZTF4gGVrX2RemDTWGZwS1JXl9r1kinrvDScZF1XiN6ojxfPJvGYFnG/wrlkiUzZoDviwe+RmMtpTZNCsYFC1USSdA6Mpjk7W0QSBcqayTdfm+DFs0m29UdW/IntWmPHUIxtA1FyFZ2AKlHRTb5/Zo6ARyLslfHIztDBcpAp63SFvfV1KlfWmStUaQ+oHBzPAu5a0WoG4n429YSIBVRGk0WmchUyJY1sRSeoy04TeIsGUBp5cHsvM7kq+0fTvDaWIZGr8vDtA/XAZqUHuG5w00Jqp7OvvzzCo/vHefi2/vpk0jMnZ3htNIOFTVkzOTKVa2pgA/M+QZKIMW8hmyxU+f6ZJA9s6eYrzw+zuTdMf8xXLzO4JamrS+16SeQqDM8WODNT4L7N3WzsDjGaLvPC6Zn62G62rDfdOR6cgFgzHHf6eEAlFlDJlHVGko6Y25a+iHtybzJdYS+P7BwkkatQ0gzWdgSZyJRJZCtUDROb+QZgFm5obzeZI+FoBMqicwiSZZGoT+Hr+0bpjXiJB1SeOp7g/FyR+zZ3LyhFuGtF6xhLlUiVdPweme+dnGIsVaJUnddBs2FVe4DJTLlpZpm1DFDjtaRKcGwyR3vIQ9ir1AccsO0FzfAruffGDW6WgVzFIFPSyFUMusJeBuJ+9o+kEAXoj/nY0BPmmRPN9euQ56P7ynzdIqCKPLi9j5BH5oXTs5yYztfVR2tlBle4b2XQFfayuTfMS8NJfIpItC3As6fnMCxQJOgMeZnJVVv2/KYF3RFv3dNmLFWq/904suqe3JvH4Yks09kK2/oj9MX9hLwKo8kSVcPGsMwly9Zv9ygkiU7Par20adqcnilw/tmz3DoY5bXRDJIA2waiPLJzcEEp0l0rWkdjxn80VWI0WcTvkdBNG1kUsO3mHmqWuo46w15GkyWOTeW5a307H9m9pv75v1Z6b9zgZhkI+xSifpWwTwHgay+PMJYuIwAbusOsbQ/yojxHxWjuNIwgOIuXPL+IhbwKg3E/5ulZNnSH2NAdoi/qW7EX543MP78+xcnpPH/2wjk+sns14+kSk5kKNjCTryAKQsvaMKqGxfOn5/jhmzs5PJ5hIlPmhTNz3Lepq56tcU/uTca265mxPRs6GZ4tMDyTJ18BUbBRZYmKZja1qXhx4k8zbXIVA1kQSBc1Ah6Zbf0Rfune9W52bpnJlDRGUyXSRQ3DsvEqEgGPhCDA+bnyJQ1wm4VfkZBEgWRJw69IC3yuGj/7K/m6cIObZWDPhk6iPqU+Slfz6zBMm/3nU/zg7ByFSnMDm9oiKOCUpnJVnRNTOcJembvXdzKSLHJ0Msfm3siKvkBvRBK5CgMxH+0hD4Zp8WcvnCNX0emPeZnJVx3jQ+wFDYXNRADOzxX5X0+dQkBgIl2iYli8Pp6hPeiMBr9na89FDekub509G7sWaAg9uL2PF07Pkipp2LbgOIYLjjZNq3otJMF576umSSyg8uO3D7oNxMtMzYLjudOzmPN1qJu7Q3RHvPhkiRPTOeZkAa0Fo3OiwAWhUAFiAZW+mJ/7Nndfk9pnLbVfuNGpCeLN5p0SwuGJLI8fmqQv5udX7t/Ang2dVAyLXMVsWSRuA2XdIu73sKEnzHOn55grVJnKVljbGXRP3iuQb742wbcOT+GTJfpifjyyxG1DcX77oa1s7LmgyNkK1VpZdNyiLRuS+SrrugL4VYnusIdS1SDiUy5yjj8wkm7yK7nxqJV5apvHWKpEwCNzU0eIBzZ3EfRImJYTgATU1oS1pu0o0j60vY+P7F6z4PW47/XyULPgAIGYX6FQNcmUdObyGol8hdFUuWXRrShA1C/jlQX6oj4290b48DuHeOHMHI/uH6u/99fKteBmblpI7SKo6dvURsJrEbBPEdl/PkWxara0810UoDvs4cWzc/PTEB7u29TFno1d11QkfqMwkixS0S1u7vLyy/euZyxVwq9KfPWlEWwbdq6O8dpoGr3Jmn411drBuI/TiSJT2QpPHZuhUDW4qSNIwCMR8MiOB5nrHP+2uFQmpHb7QNzPzd1h9p9PEfarbOmL8NJwioBHpqQ1R7ltqbKmVxGZyVdJFqoLXqP7XjePy2XBdgzF+NA7h8C22XcuxamZAqPJEpmSo0Fl2s4hRISmH4gNy9FE6wo79htnZgqcmytS0gw290auuWZyN7hpIYtHwQfifr5/erbev/DHz55lLFNp+vNK883EHsn5W5UFzifLZMtZLBtmclV2rW5zA5sVyk/uGiLgkXlwex9b+iJ0hDz84tcOcGgsQ2fIw22r4rRtVHnySKLpQbENSKI47xoNRc3k5u4QH9m9Go8sLWgsdptK3zqXasjee2KG//v9c3SGPGwbiKIZFi+eTWJaFl5ZpGyYtMAcvs50tsrwXInzySL/5h0DPHlkmge2dDMY919zZYmVyuWa8WuTcwBb+6OMZ8ocmchSqDqBDTQ/qGnEtGxm8hVeOD1Lf8zP4YkcQ21+HtzeV3/vr5XPvRvctJCusLfelzAQ9/PYwQmePDJNruKk9TXDavp4JzgBjSI6lvWCBeu7QkR9Cq+NZshVDIJeiUxJqwt0uawsGpv3wFkMx9NlTBuSJY2nTiQIeRU8soBh2k1tMhVs0E0LvypRNUwiPpm+qI+YX6WkmXSEPBcJ+Ln9GG+eS55+bZt0UWMyW6Y96OFdN7VxcDzDyekisihgNdGm5aKsjSwyEPdxKlFgNlfltVFn3PfEVI6DYxnAnYxrBku990t9hrb0RfjST+7gvz52hOdPzyGaJoYJojj/OW1Bul+VRSzLCXJBIF3SkEThmhTudIObFlOL0uMB1Ukx+2R6ol7ef0sPiWyF//faOKcSxfr9m3W9mhbINngUkfVdQU4nioiigCILRH0qz52eqxtmuqxcau6/923u4rXRDIokMJuvkixqLbFgMIEziSIdIZV4QGE6W+HZU7MYljPNA1zUSOyOhb95LnX63bOxi1zF4MRUDgQ4lyxRrJpgO0rjrbRgsGyLobYA+apBqqAxV3Cm8/pivnpZwg1k3z5Lvfe1z1CjOe1svsqfvTDMaKqET5Xo8qpMZqp1eY9mo4jglSVkScCvSGzsDlHSTDpDHkaTxWvuMOwGNy2mFp0fnciSLmrzqsFVDo5lGE+XCXsVeqNeJptcnrJwGokrusWzp+a4dTDK+q4gk5ky927qYjxddj2CrgH2npjh0VfG2NQbRhQEpnMVypqF1uyGmwYsIJHXkEXnJNcT8XHnunZOJQqMJot1rzS4MDUFK78Gfy3QFfbyC3ffxBOHp3h0/xglzWA6W8GyoYW+qYAjF5Gr6KQKGmXdYnjWaWpuxA1kW0OjOvlXX3KseQzT4oUzSbBtVMXJtrcorgHA75ExLNu5BhDY0BPmfbf08OLZJE8eTTDYFrim3nM3uGkxtSg9U9bpDHspVHWmc1W+d2KG2XyVgEdqmtLkYmQRAh6ZXFnnlXNpNvWEkCWR8XSZ8XT5mkw13kgkchVeHk7On5g8PLC5ixfPJTk4liUe9DDdQiE/cMZCo36VmF/lqeMznJsrEPGp/NSuwQXiXddKDf5aIZGrMJos0hv11cfwl2OsVbfg8HgO07IQBLBsx406XdTqAY0byLaGRnXyp48nODCaRpUERBHifg+mZVPSWhfdKiKsbg8wEPfx7IlZyrpJsqjx8T3rWNMRBNu+5t5zN7hZJmpaN/vOJXn80CReRcKyYa6gt2xSKqDK3DoQpaSZjKRKHJ3MIooiFcPgh9Z3XXMX643GgZE0p2cKGJbF6USB1HwvRqGik10UDzdb0E/EmZypVA3G0yUMy0aVRLb1R9wpuxZzYCTNk0cdxfKadYrQqEHSQkQBBFFAAVa1BdjcG+a+zd2UNNNtIm8xRyayPHZwgjvXtTORLpGrGGzvj4LgZP5lkZZlbmxgLFWmUDEwbJt40MNMtsxvfPMwq9sCfOTONdfcZ94NbpaBxjr1aKqEZcN4poRpWS0dAdctm7OzRX7qnUOMpkr842vjlDTHCiKgKjx0a9+Sr/Fau4ivV3YMxfipXYPkKgYvnJ5l/0gaw7Lrk0yNNDu4sYCSZqGJFpJmEvQq3DIQqZtovntt+0V2DO618/ap9Vg9sLmLsF/lr186Dzglo8U0Yxx48XVTNSwUSaQr6mX3unbOzhYpaaYb0CwDjx2c4PFDk6zvClGomvX3fK6g4VEkii3wkqthWpCv6BQ1A68isbEnxIGRDBPZCqcSedqXGCRY6bjBzTJwYCTNo/vHeP60k+7Dhopmtfwk1h/1osiOs/An7llHsljlxTNJYgFn8uXASLq+aLm19KvD5YLKrrCXR3YNAbChO0S+cop8RaegGSQLC+vvrVj2RMAji/hUmfff0oNuWPzz4Sn2j6R55XwKs+ECdq+d5lDrsXr49gH2bOjk2ZMznJ0pYJh23TS19lsXm5DNafx2WXBusWwBSRToi/nZ3BupK6u7wWtreXB7H2OpEsenHMkOnypxOlGgaphYS0W3TUSRBNR5k+V1nUG8ioRPkRiI+VjfFeLB7c5B+Fo6BLvBzTIwEPcjiQL7R9Koksium+KMpUrM5qrMFrTWZW8E8KsSJxN5EAQ+/sPreHBb30UmiODW0q8We48n6o7xtUCmRuNC4sjvC+QqBtmyseRJvtnUmtINS+PoZI62gMo717ThVyXGUiWifrXelN7oLu/yNqi9sbbNgZE0VcPi9lVxjk/nKVVNPLJAvmqATVMnp0QBZEnAsJxevdXtAXJlHWz7ogZyl+aTyFUYS5UoVA3G0xUUSUQSbcqa3XIfKXB8xXojHm4ditIX9fHYwUlUWeSD2/sYjPvpCDm2K9fSIdgNbpaBsVQJ07K5fSjGlv4o2DZ/+sI5chWjZYGNKkIiV+XcXInD41mG2vz88r3r6xfklr5I3R7CDWiuIoKw8O8GGheSgbifVe1+In6ZqmFyfq5EIltdsPC1wkjTssGryIwknZ6t+zd3s3N1G35F4lyyxGMHJ1jTHnAb1JvE1v4ow3NFtvZH6Qh5yJT1ev/FdLZMtqy3pO/CsqlLC1RNs65O61dlHr6tf0W7P1/L1A4wtQBy+0CUE9N5UkUNzNYK9jUiAAXNYOequBPUAu8YihH2yguCmWvpEOwGN8vA4gvim69NkCvrLdMrAKek0R/3sf98mqpp41Oliy7Ixs0T3NLC1aDWaL7UYtF43RwYSXNsMsdMrkrYp1CqXjw54ZHFpl9TkgCbekNEvCqvjWXYfz7Fy8MpfmRHH36PzJmZAms6gu7m1yTGUqUFgWLUp/B/v59gPF3Gsq2WaBtJghMU18pe2DCWLhL2qvhUid6ojw094WumHLHSqTUO717bzgtn5jgzU+C+TV31z1BHyMMfPHWaqmFS0lrblykL4FFFyppFrqzz7KlZbh2MEfEpBFSJrf3Ruu4OLJzqWumlSje4WQYaJwyeODzFPxwYp1Q1kOZr5pLQ3BQzQLZiIGWrOP7RkCnpzOarCy7EpaJwd4NaXi43fbJY4boz7GV4tkiu4kzYWVzI1sgtmhW2bJjJVnnHYJzDE9m6HtO+cyk+/YHNC6wYXN4etRHweOBCuW8g7kcUBAzTRBBERMHGtsGnikiCQH6JIPfNEvMrVA2TqmHjUST8qoSA08iaKet851iCE9N5vrFvlJ/YOcgv3H3T237OG5la4/DxqRzpks66ziAIQn3tfepYgkxJx8L5XNuW81lvRZAjSSDYTtZYM22eOTVDd8RLd8TLuWSRsVRpyfXpWihPucHNMuMsVs6F2hHyUNRM8pXmGOE1UqgY5CrGvNOzwHS2wtf3jfKJe9YtOIE1Xpgr9SK9HrnSxrzaIvLBbb385wc28Jl/Osqp6TxgU9YtNNNGwAmEZvLN9ymzcSwffnB2jqlsBdt2MkQjyRJ/9sIwO1fFm/6cNyqNI+BjqRIdIQ+PHZwg4JHYNhhjJltmIlNBlkUG4755ify3z1xRrwfJPgU29YSo6CbZsoYqSQzGfIwmi6RKGqPJ4hs9nMsbUGvO9cgi3z+TXGBGCzCZKTtVart1o981dANE1bEBAqgajmFnpqTRH/Nfso/uWihPucHNMtDo9Pv1faNohkVf1EdZMzHN1ly9tUeN+BVu7g4ymiyTLFT5/BPHOZ0o8KF3DtUN2lyWnys9+TQuIl1hL1/6yR3sPTHD4fEMe48nmM47DelTmUrL6vOZksGZmaLTfyMLdIU9pIoa3zma4NhkzrXxaBI7hmI8fFt//RS/93iCZ0/N0hnysLYzyMhcCUV2SggnppsbZNg403FFzWD/+TR+VcKvKty1rp2HdvQzm6/inzdzdXl71LzjErkKm3sj+FWJF87M4VclTkzlaAs6n6+ibmJbdkuNUi0c5euoX0E3LVRZQpVFUiWdqlHk8ER2yT66a0HvyA1uloHaRtYf8/HS2TnG0iUQBOwlTA8lHH+fZlHWDLyyxE0dAc7NFclXdAzTJlfSmvgsLm+WKz35LK5xD8T9RH0KP7lriGNTOWYLGoLQ2hOeKjlTd5YNazsCpEoaZc3EFpzgeSWf3q4lGkf/E7kKRyaylHWTc7MFDoymqWgWiizgV0SKDTteM/RumH8MywJBEBhqC5At6/hUidl8lbFUiY9eg0JuV4MrzcrWPttPHJ5iPF3mhTNz7D+fRjct1naFODqRo2y12HMD0E2bgEemf95DbPfadr7y/DAz+erSAkvXCG5wswzUFv+BuJ8T03nOzpWoVVDF+fRj7RJq1qVcSzNXDZtXzqfwKjK3r47hUxzthLBfbdIzubwV3uzJp6aVpJsWmZLOB27pQZVEZFFAEATMeSXbVixFtg3bB6IUqwb3burir148jwUogsCquJ+vPD/Mg9v73Empt8hSm+GBkTST2Qq3Dcb43skZSppTOhAQWNcd4sRUrt5c3Ky49kL/lsA9Gzo5kcjz+ngWQRh1pne4OMt4LemeLBeXyspe6nc1EPfPBxZh5vJVECDiUzg8nq1/nlupTtweVHnn6ji6ZdMe9NAW9PDerT0gCOzZ0NmaJ10G3OBmGWhsDL13YyevjiQpac5lG/UpGKZFrgmNgeCc4jpCKqIoMFeoYpigSCJVwyRV0LhnYxc7V8Wv6Yv2RmTHUIwDo2meOzWLZlicSORJZMvYgqMqK4vOqbsVwY0oChyeyJAqaUzlKmRLOpIoIIgC+86nKVadnrHG4Mbd9K6cpTbDRiPFV86nKFZN1nT46Qz72Lk6xmiyRMXQm/YaFEmgM+RhKltBlgQef32KdZ1BFElkMOZj99r2JTN010Jj6XKzVFY2kavwleeHOTNTqH+t9vkYS5U4M1Pg0FiG0zMFVElgqD2AND8koIhO5jRbaX4WRxLAp4g8fWLW0WI7n+L507MoksjDtw3QFfZes59lN7hZJmon77lCFUkQCXsFvLJIrmJg2TZBj0ih+vZDc0GAiuG4gdu2829ZFPAqEhOZMl/fN8ov3H3TNXWRujisaQ/gkUVSRY14QOX7uoU+f3o3LGessxWq11XDZiLjNK+OpcrOBIdtoxs26aLGLQNRdq9t5+v7RsG22bOxy9303gSXK1Fu7Ytw++o4Lw0n8akyM7kKjx2cJDuvRaJKApr59t90y7LpDnvQTQsBgWLVYGNPmF2r4+QqBpny0oHUtdBYutwslZU9MJLmzEyBtZ3BemDTaEZ6YDTNt16fJF8xELDxKjI9UR/DM0UMi5YENgLOejGVq4INfTEvpgWz+Sp3re9YYK1yLX6W3eBmGaj5xUR8CqPJIiGfQrpUpVwy6s1itmXjkaH6NgenTBskQUCVBARZQjNssmUdSRTpj/koaSb7ziXZs6HTDXCuIQ6MpHnu9Bz9MR/pks7Wvggf2b2af3htgtOJglOaECDikclXjJY1F+umhVeWMSWbiE8h6lN57xbHWPHRV8YAFuhiuJveG3OpzfDR/WOs7QyyoSvE/vNpzs0VMUwbc/7dlUWaEtiAs27UfOiwYSJbpnY6evLINOBkmRe/zmuhsXQlsHgwYMdQjExJqweND27vYzRV4sBImlzF6aPzKSKKJFBt0ntco1Z+7It5kAQRSRSo6BY/fHMnmZIOgvN6avvDtfpZdoObFpPIVfiDp05xaDzL+q4gYZ/Ke9a28fK5FAfHMtQKCboNNGkiPFXSnfJU2MPmXj+5isFAzM/ajiDfOjzF6URhga+Uy8qnVpffvbZ9gUPzQzv6+Zk/f5kT0wVMC1RZJORtTQrbKwv4VRlZEgh4ZR7ZOUCubNTVdB/Y0s3xqRyj89o37vV1ad4o1V87zZ+ZKeDpDSMKUNZNBBss28a0nZ4nAbtppchs2WDf+RTrOkMcm8xxcDTDT9w+UJ/gqnlMNdq3uAekK2OpIHB4rsjRySzDswXaAyrHJnOIgsBAPMBsvspIsjUK9rXHzBQN+mI+FEnkno1ttAc9PHNyloBHrotIXqslKXCDm5ZzYCTNofEsxarBhu4w923qZsdQjJBHZjRZIl/VKTW4vTZrWirglcCG8XSF9qCHB7f3sWMoRl/Mt0AwymXlk8hVeOzgBGdmCuwYvBA01Baef3v3Tfz/9p5mKlMhXdCaFSNfRHfYS6FqzpdEBJ4/NYduOk2Ig3E/YZ/CSLLESLLEYNzvBjeX4UpS/WvaA6zpCHJkPEMiV8UwbbyyhCKCXjXRLRuRt95nJQngV0Xy8+VwVRYYT5XJlnREQUCVRcJ+tS4Z8cThKR7dP4YkCnXTVPc9vjIWBwm1MpVflXltNM1cQSNZqOJXZeJ+lVSh9dOsJc2kM+Thvbf0smdDJ3uPJwh4ZLb1R675khS4wU3L2TEU46d2DS7oPN97PMFrYxkkUaA/6mM0VaJqOCcwu0kGQXG/ikeR2NQTZteatvqHarE5o8vKZ3G9HhwJ9y8+daoutjU4b856brbUmsYbYDxdRhTBtMDvEfGrEmdni3z78BSGZfPBW3p4+LZ+p0ejpJHIVa65095y8Uap/loZ8oPbenlk1xAHxzIMzxXZPhjBMG32n0+/bdVa0wavIpGvOpNYnUEPbSEP3REvc3mNqF+hN+Kty+zXsklHJ7Ns7o24B6Q3QS1IGE2VGE0WiQVU7tvcjU8R+dwTJ5jNVRFEiCsixyZzVA2zZbYL/nl165s6gvzn92ykI+ThwEiarf1R/v18SflaL0kBCLZ9DQ+yv0VyuRyRSIRsNks4HF7W5/76yyP8yXPDVA2LqmGhmxaGZaGbNrppN23kT5UE3re1h51r2upBVU1I0E0pX1sslRr+L//wOv/0+hR9USebMp2rYNt2092iF1OLvUWgO+qlrJmokoAoiqztDPLR3avrfjkP3zZwzZ32riaN7zOw4D3/5W+8xneOTXPfpm4+eucaPvqXrzCda45CMTjvpyI7PR4Pbuut+4b1Rrwcm8rzwOYuBtsC7vrxFqn5Sc3mq3zv5Axhr8IDW7o5ncjz7Kk5ACI+Gcu2ybWgpNxI1CeztT/Kj+7owyNLjKZK/NOhSdZ1Bdm5Ks7W/uiKfo+vdP92MzfLzXy6tyPswa9IvDKSRjMsVElEN+23Hdh45qcnvIqIT5UWyHrXhATH02Xg2ksz3qgsVa+PBVS8ssiuNW30hL38w2sTjCSLRPwKumFT1k3KTZY2FQFJFNAtGwuYzFTwyiJr+kKMJsu8PJxkKlMi6leJ+lX8qrTizfVWEotLAI3jwht7whwcy7Cxx1nMK7qzATbLCd7CkRQIehRKmsntqx1bDc+8adlouszB8Swf3NbrrhtvgZoh6vaBKAJOw/bRySwzuWpdW6iimy0xRgXwKgIiApZlY1oW+8+nmMtXiAU8GJbFVLbMbL7KwdEM77qpjVTJaXS+lt9rN7hZRhK5Ctg2t62KM5kpE/UraLqJYYFhvf2NyK+KCAhopkln2MtP7hri8HiGTFmnN+K9qCHV5dqlL+anL+bn5u4wj+wcZCRVYixdolAxMSybtoCCka82VbrdUbCdF5/EMd3TTYvRZIlM2cAwbRI5jZ6ID9OyeeHMnBtIvwkWlwAag513r21nrlBlQ3eILzxxnIpu4pWdg0wzc++6afOD4ST5qoFp2fRGvDx8+wBb+yL107zLm6dRyHWuUCWRrxL2yiQLGp1BlVhA4dxc63y7KrpjoSwCiiIii5AqarSHvEykypR1k7aAY70w2BZg9zr/Nf9eu8HNMlKro9+1rp3BuJ9vH5lqWnuECAg26KaJLF44cUX96oKMTUkz3Y3mGqWxbLFnQydR3wXrg/s3d3N6psBMrsJ4usxUk0wVL4UkgmE6WYN0SQcbvLJIyCuze10Hg3H/ghKGyxvTmKGryUfcta6dgbifxw5O8NpohpeGk+TKBgGPQn/cx1iyVD9lN4NVcT83dQbpm5ccODdXwO+R2bOhc0kF6mt5muZq8P0zc+w9nkAQBSbTFmOZMiIC2bJG8/Jwl8YGdMMm6lcQBYFtfRGqhslUropm2DywpYOHbu27Lt5LcTme5I/+6I9YtWoVXq+XXbt2sW/fvsve/+/+7u/YsGEDXq+XrVu38u1vf3vB123b5tOf/jQ9PT34fD7uvfdeTp8+3cof4YqpeQAlcpWLbh9NlYj7FXqjPl45n3LUKpt0LVtAUbfQLKdR8NxsiU/9w+v4VYkPbuvlwe19fHBbr7vRXMPUTvIHRtL1jbC2CJU0k1xZZzpbwZy382jlMqnPqyGL875Whg2qIrK+O8jxqRwDcT9b+iILXqPLlXNgxFGjjvrVuoJtsWpwZqZA1TC5fVUM27LR3qCOLbyJ2wUgW9bwe2S+fybJYNzP5t4IZ2Yc6YhLvc7aNemykMa9oKZb9NTxBIl8lfH5bEl32EvAI2FaYJj2Jd+vZiDgaOd0hz0EVAlJhHRZZ+dQnK6wh3fd1HZd+Ye1PHPzt3/7t3zyk5/ky1/+Mrt27eKLX/wi999/PydPnqSz82ILgB/84Ac88sgjfO5zn+P9738/X/va13jooYc4cOAAW7ZsAeB3f/d3+cM//EP+8i//ktWrV/Obv/mb3H///Rw7dgyv9+q+MZcandt7YoZv7BtFFOD1iSwCsLknzInpPLlKc4d32/wKkiQyPFfgO0en+Z0fuQXA9f65xrnc5MKOoRg+VXLGg1ukVLwU4vxqrIgC2PDKuTSWbeNXJX7p3vXuqf4tsvi9zpQ0nj01y2iqRKqocXQyS0W3qBiXbz691GVwqdv9HoWYT2EyU+bv9o/x8T1r2TEYu+Sh6Fqepmk1jXvBwPw0Y6pQxStLoDiZOtOyCXpkdNMJdlo13qNKAl5VoqKZRAMqu9e2s+9ciheHk6zvDHLX+k4e2Tl4XX1OW565+f3f/31+/ud/np/7uZ9j06ZNfPnLX8bv9/Pnf/7nS97/D/7gD3jggQf4T//pP7Fx40Y++9nPsmPHDr70pS8BTtbmi1/8Ir/xG7/Bgw8+yC233MJf/dVfMTk5yTe/+c1W/zhvyI6h2EUZkkSuwr7hJFXDRBAEJjJlOsNe7tnQiYCzQUS80pt+rsVRvgAEVZG2kIfbV8UIKDKxgGuQeb3QmK1ZnCHsCntZ3RZAlQTagyrxgIIqtfIc6OAYdzo2H6Lo/FuVRM7MFvjmgXEe3T/GV54fviiT6XLldIW9RP0qVcOal8i3mchUSBdrpYw3jyI6flICzibQFVJpD6ps7Q8T8imOG3myyB8/O0ymdGnNlcUZRJcL1PaCgbifr+8bZSxVYiDuR5acYPXwRI6jU3lGkkVMu3VZG0kA07KxLBuPLDKTq5IuapQ0k2zJ8S47NJZhLFVq0Su4OrQ0c6NpGq+++iqf+tSn6reJosi9997Liy++uOT3vPjii3zyk59ccNv9999fD1zOnTvH9PQ09957b/3rkUiEXbt28eKLL/ITP/ETFz1mtVqlWr3Qg5DL5d7Oj3VZLiWlfmgiS6Fq8o6hGGXd4sPvHGIyW6E74uVmn8JktvymVWUXB/myKGBYjh5JR8hLLKDywpk5JtKv8ZE719T1DNyT9LVP7VSYKetEfQpVw2QiW2Yw7mcqW6GomcuUvXE80oIeiVsHnezRRKbMudkiJ6J5JFHgtdE0f/DUKYbaAoS9Mns2drnX3xuw1ORUpqTxx88NY9k405CKhG5aFKpvXhNFt0CRHOkJWRLJlg1s2+a7RxOcai+wuS/EbE5DM0we3T8OXLDVcN+7K6O2F3x93yjPnZoFwLBsMiVjwVSsjZNqbbLLQh1hPpNb0U36o3400yRZ1PjAtl5eG0vz+liWdV3B6y771tLgZm5uDtM06erqWnB7V1cXJ06cWPJ7pqenl7z/9PR0/eu12y51n8V87nOf4zOf+cxb+hmawY6hGO0Blcl0mUSuSsAj88KZOR7c3scv37ue0WSRP9x7BgBVci7EKx0JVwQQJAHDsDEs548sSUS9MuBl//kUp6YLtIc87BiMXbNqky4LqW12R8YzTGYrTGcrnJ4pEPbK5JvkMH8lVAyL9oBCxK8wnavgVyXu2dDFaEeRkmZS0gy8isTzp+d4/swc7QEPUb/qXn9vwOJyT1fYC4JAoWLMN3NblAFZEq4osKlliBs30PlpcjwKVHRrftLS5uR0nvaghzvXtaPKImdmCkxkyjx32tFjcd+7K6PWazORdjIim3rD9ES8vD6eobEToWpYeGShZe3EhlWbbhSwbIts2eC10Qx339zJrf1RTk7l2dAVuu6C1htiWupTn/rUgmxQLpdjYGBgWZ67doHvXB1nNl9l5+o4qaLGD84m8UgiVdMiV9YaGgMFjDdx5DYB0YJYQEE3bQoVg2LV5NnTc/z0HUO0B1WwHSM0gP6Yj4G4v/k/qMuyUitVTGYr9ES82LbNTK5C2CcvOT3jlYWWaWjMFXVSRR0LCHslVEkkFlAZSztyBxu6Q8zmq0R8Cvds7LruToitYHEGOJGrsO9ckqppYVsgiI66MLbTYyUCIZ9MSTOWNN+1gYBHpqwZC+QBBECRJKq6hWHZ9AUVNvRESBU1zieL+FWZ0zMFBuN+dyChgSuZEqtl3+J+hfagB78q8fSJGUzLviiQsSwbWboQcDYbCyfISRZ1qoaFaelMpEv0xfxE/Sph//XXvtDS4Ka9vR1JkkgkEgtuTyQSdHd3L/k93d3dl71/7e9EIkFPT8+C+2zfvn3Jx/R4PHg8nrf6Y7wtahf4Xes7+NUHNrBjKMY3X5vgB2eTvHB2jnNzRWJ+tR7QvFmXX9sGw7bJV3SifpX2oI/Zgk7QI9EX9fHhO1ZxYCRdL0mNp8t1UzSXa5uBuJ+1nUHagx7OJ0vcOhRjIOZj7tUJKrqzw9U2slYFNjVq+2Wu4gTWYY/MzT0hxlIlZFGgM+TBtGEw7r/uTohvhysdpT4wkuZ0ooBtO2P4Eb+KbTsBjSyJaIblBCiX2RxLVWOBerUsgl+VCHgk4n6FuaLGhp4Id6/v4DvHEqztDLJ7bXs9y+yuGRe4Es+lWnY1VzHY0hchV9bZfz6NKAh4ZBGvLJKrGlg2TdWjWgoBRwG5L+JlMlshX9GZyJT58B2riPqUel8Qtn3dlI1b2lCsqirveMc7ePrpp+u3WZbF008/zR133LHk99xxxx0L7g/w3e9+t37/1atX093dveA+uVyOl19++ZKPeTWpNZVtXbQwCEDUpxD3q6xq89d1aQBifgX1CvqLBaAn6iXslRGAQsUR3lJEgU29EfZs7FowqrlUs7PLtUtN9TTsldncG8a0bNZ3hfj1923kno1d+FW5paOll8K2IV8xME2Lkmbyg7NJXh/PYliWmzVcxJWOUu8YivGhdw7xY+/oY9eaNjb3hiloOuPpMumihm5aVHTrsn0bpj2vhzX/bwHnND+Xr5IqavRGfOQqOggCD982wEfvXMPdN3fyX963yQ1sFnGptXRxo//wXJFnT80yPFdkQ0+Yu9Y7GlA2NqIoICzTVKMsCpQqBmdmiwgIhDwK2DCbd3pRD09kefSVMR7dP37djPW3vCz1yU9+kp/5mZ/htttuY+fOnXzxi1+kWCzycz/3cwD89E//NH19fXzuc58D4Jd+6Ze4++67+Z//83/yvve9j2984xvs37+fP/mTPwFAEAR++Zd/md/+7d9m3bp19VHw3t5eHnrooVb/OG+aWnq55qh7YDSNR3ICmS19EX789kEG4n6+tPc0L5yew6dKvP+WHr6xb4wL52GHWiqzNuEgSwKi4NRsdQuqpkW6ZLBzdZxfvnc9XWHvAmVMt5n42qfxpN/Yl7E1X+WxgxP0Rn31HpyybhLyShiWjW5aLUt5L0aa7+3IVgzuXNfOt49MU6wYnE4U+PrLI3xi/tp0BeDe3Ch11KfQE/Gx/3ya/piPmzqCnJwuYNg2lmHxRof/gEfipo4gY6kiVcNCM23a/AptQQ8z+SqWbbO5N1JXI3a5NEsNjsDCjA4w7/wtOZpmwMnpHMmihoDQVPHFN8KwbGwRJNtmTWeAdR1BJrMVHjs4wXi6zF3r2nn49gGw7evm8Nvy4ObHf/zHmZ2d5dOf/jTT09Ns376dJ598st4QPDo6iiheyFq8613v4mtf+xq/8Ru/wa//+q+zbt06vvnNb9Y1bgB+9Vd/lWKxyMc+9jEymQy7d+/mySefvOoaN5ej5qh7ZqZAb9RH1K/SF/Pznq09JHIV4gGVWEAl4lPojvhoD6mMpStLNpnZOCZ3Aa9MRTOd+8zfSRYF7pk3yqz5+tSCK7eZ+NpncTq89l7uPTHD/vNp5gpVXhvNUKgayJLAQNyPIokcHs8u22tc2xlAlSU+sns1d9zUzlDcz9MnZxiZK/Hc6Vm29EV4ZNfQFaX2r3cutUkupva7ivkVilWD8XSZmF+l5nt8JRkAURAo6wamBSAgYlPQDMKWQkfYw51r2+tl7Bv9fXmrDMT99Md8+FWJE1M5eqM+bl8V48mj07xyPkUiVyGRqxIPKGimtSwTjX5FrD/Puq4gn37/ZgAeOzixwI7nejtguK7gy+gKXjup1mTpB+J+Do9nODKZ49xcgVVtAYbaArw2mua10TSKJDJbqFKdL5R7ZRHDtBAFuKkzyFB7gIpucnwyj2lZ5KsG7xiKEfGpdbuFu9Z31GuqK9np1eWNSeQq7D0xc1FdPJGr8AdPneLVkTQ+RWImX6Uj5GE2XyVT1ihrzpl+OT7osgBrOgNs7onw0K19nJjOc2IqR1/Mx0SmzOlEgQ+9c4g9GzrZezwBgsCeDZ3uNfkGNK4d3z8zx2iyyO2r43zxu6cZz5QZijt2CZmyjioJ6ObC0WJl3vAUqPd7gDPtZpgWiiSye107v/OvtwK4781bpHaI7I85GTaA21bF+PtXx8mUnPdGM206QyoBj8xEprRkA3gzkHAGTvqjXu7d2MWx6Rw7V8W5fVWcr740Qkkz+PA7V11zAazrCr4CqZWJaql4R5J7HN20uGNe+vrASJq//MF5UiWdsFdGn1+hRIELkb4No6kyZd1iQ08I23bqt70RH3ev62CwLVAPZjIljccPTbpuvtcBNUn+D27rXbDhHBhJcz5ZRBRgMlvBI4u8d2sPI8kif/vK2LIENTUsG04lipyZKXJ4IkPVsEkWNLrCHj52903ct6mbgbifrzw/zJmZAg/fNuBunldAY4bn8HiGY1N5BuN+NvSGyFd1Btr85Ks5vIrELf1hDNPm0HgG3XRK2B0hlems40DtU0XWdYaQJXhtJIMkOKa752YL7D2e4JFdQ3VPuqhPcdeNN0GtpONXJebyVRBgc2+YJ45IZEo61fn1fCavQV6jLaigFfSWfEZNnBaGkmZybCrHeLrMeGqCp4/PkMhXLqs8fT3gBjfLTGPKd8dQjIdv6ydXMQh75fmTts767iCZczrFqoFggyoLqJJAVbcQbEeUyaeIZEs6Fd1xAF/XFWRDVwgEgUxJqzdubu2P1sW3XK5tLtWfUSt5PndqFlkUWNUe4N1r23no1j7OJQvsP59GEUVKrR7J4EKXmGVDIlflHati9ES8bOoNX3itI055dm3n9Scc1kpq2ZuaXctousypRIGqYTGVrjiGiD4FnyLxyoTT2ydiYwOSKNAWVMlXddqDHsqaid8jYQG2IAAC+arBRKbME4enGHBHv98SdeG+l0d46VyKsm4ykSkjCSIeWcSyHP+/WjAzV2ht341HFqkaFien84iigAhoisiOwVi9L/N6xQ1ulpnGDaor7OWRXUP1ZuOaBkLMr+JTJMoa2IKJYdq0Bz2Yqk2haqDKIlXTplQ1mM1V+ak7VrG1L8JjByd48WwSRRIZnivWN5DryQztRuZS/RldYS8fvXMN7UEPL5yepawZPHZwYt4stZ+ukI9/Obq0wGWr8CoiHtkxBAx4ZBRJrKu0Aty3qateWlvcWOw2Gl+g8XfRKCvxsbvW4FclDoykMEyb6XwFWRRZ1e7nwEiGQtXEIwu8Y1WM4dkCiVyFmN/D7UNxAh6Z4bki7YpKzK+SK+vEAgpeRSZVdDO9zSBXMciWncPnyak8N/eEwLaYKWhgte6Q4ZUEKvPZIQGIBxSyZQPNtNg1GOeONW2E/eoNUW50g5tlpnGDaqyjr+0McnQyy6q2AGXNJOqTGYj78Ckipg0/uXOQZ0/O8vK5FFv7IyRyFY5OZEmXHPn9mnPwus4gPlVi99p2ilWDH5xNsqY9wCO7hq7yT+7SSrrCXgbjfmRJJKo40xlfe3mE41N5uiIe5JrD5XJhQ9gnc2Q8gyyJdARV7lrfAbbNc6fnFpTWapv2aLLIXFGjPaBycL4B+kbfYBdnesEpebxwZo72oIdYQKU96CHgkRlPl0kVNYq6iU8RCfkUwj4FURAJeBRuHYzSFvRwbq7I4HzZWhQEbhuK8dE711DSzAX9gLWBhOt9E3yrXC4ID3tlQh4Zw+neZudQnH12irF0az3WKg2NVpIAmbKObV/IpL57XccNM9bvBjfLyOIPQ23h+uC2Xh7c3kexalDWTRL5Cqs7gmRKOgGPwi/fu54tfRHKusXpmQLtQQ8/uXOQ7xydZrAtwI6hGLP5al3Q7eBYhpJmsrU/yvGpvFPHcrnuaRz7H0uVeOZkgtMzebLlKpWGkpSAY3JptrBKVTEsJjOlusz8MydnuXXQeX13re+ov9ZErkKmrHPXunaOTOZ46niCezd2uSWReRZnet+ztYff+dYxHj80ybvWtLG5N1JXH3/s4AT9MR/Pn56jP+bjlfNpUgWNkFdm15o4tg2vnE9x+1CMR3YN8fV9oxway7B7XQeTmTIIAjtCHrb0RdzpyitgceBZO6geHs+QqxjcOhTl5eEUqiTy6miKY1PZNxzXbxa1KduqbtET8SKLAqOpEl97eYT//iO33BDZUTe4WUaWMsODCx+MQ+NZUkWNgbiP/qiPiXSJ4bkCjx2cYEtfhN6IF0FwGgr9qlTXC6k99ni6zJr2wIKNIepT3E3iBqG2+SVyFcZSJc4lS5Q1k0ROI+JTyFc0qqZzohNwGg5bSS2wEQXYNhABQeA7R6dZ2xlkz7xcQWOT9CM7B/GrkquG28BSpchaMNMe9PDScJLHDk6wpiPIeLrMjsEYX/mZ20nkKuhPn2b/+RSbesOUNJN951J4FYkt/VG29EX4xD3rODCSJlPS+JuXRylWjfokXi3gdNcOh8ZgAKgHMrW11hkOGUMSBabmNaaKVR1JFOiJeNl3Pt2yw0RnSCVV0Orq06IAsgRYIIgikijSGXEUwofaAvXXf70Hr25ws4wsZYZXu7B2DMXY1h/h+dNzTGbKzOaqiAL0RX3sXtsO4Dh8Z8oEPTJHJ3PsPTFTD14WP3btg1jb7NwU841DbeHaORTHI4lE/Qpj6TJnZ0yqpuMW3opcnizAUi4Plg0nEwV+6d4Iw7MFzswU6tdnbROtZZvc/rCladxct/RF2NLnlKbnClXOzBRoD6h1fZXaZ31Lb5hDYxlm81VOJwrkyzoRn0KupPHsyZm6rcKOoRhHJrIcGs+CIFxyKu9GZrE436P7xxb0Mw7E/UiiM8yxvivIy+dSJHIaiijgV8WWZkln8trCG2xQRAlZFhAFga6wU5Z+YEs3D93qBMZvRjzyWsUNbpaRywl2dYW9/NK969naF3GmIWyb0VSJyWyFkuacsR/c3se5uSKZksbm3jDYdl31+KN3rqk/9uKU8o0QpbtcoHHh+jDwi187wOGJLJZ1oSQl4LgEm4bdtAzOpeyrJNHJFv3ZC8NOP5lfqStm1zbRw+MZ/vrlUbb1R/ile9cDXPdp80txZCJbbwivecJlynq9Ibv2Ge4Ke3lwex+PHZwAQWA8XeaFM3NO8Dia5sHtfdwxV+ToZI6hNj+GZVPSTb7xyhgdYQ8np/OUqga/8yO38MiuIfyeCbb2RegIOT581/PG92a56PA4L8h6YCTNe7b2MJYqYVo2bUEPJ6Zy+BWRiE+mUDUYS7W2z2YxsgQRr4wkCaiSyD0buxiM+xd8lq5UPPJaxg1uVhC16akai1OhW/oi/PK96+sLH8DTJ2Y4Opmrf8jg4g/ijRClu1ygceH6+ssjnJ7JAwIRn4RPkUiXNMeg1bRb3gMgCuBXJIJehf3n03XNm7FUCb8qoZsWVcPkyESWXEXn0Hi27m1zowbkjx2cuNDLMRhzJqTWtfPBbb0LGn1r9z0zU2BNe4C71neQK2mUoj7OzBQYS5V4cHsfJc0k5lPYuTrOy+dSZMs6cb9K3K8yOF+mqPmU1Ux1b7Tf+RuxOBioaZItXlv/+LmznJguEPJIbO4L8+pIGhMnuAcu6/31dhFwLHnCPoWZQhXLhraASr6i35CHBDe4WcEsNVmVKev1RQjAtGw294YXBC6LP4hLRek3QkOZCyAIdIa8tAVtSlWTsmZg2jYxn0LFMClqrQ1vPLJAV9jLxp4QM/kqbUGV7QNRMmWd50/PcnjCmYoyLZu71razpS+y4Fq+EQPy2sGllrmBCw3FjVlZoC73UDPJ/c7RaXoiXu7b3F3vBTk2mQMcpVxFEhiM+3lk52Bddh8u2Aa4xqZvjhNTOb7y/DC717aTKWmU57PsRc3k9fEcxvzHq5VBTQ1VFgioMu1Bp/Ro2pAuanzr9Sn65i1/biSleje4WUFcLuCoa1zMn+AaJ6Qe3N73pi9Ut1R1Y7BnQydRn8JoqsTjhyYxbQujDOmyjvjG3/62UEXH82w0VSKRqyBLIqokUtWTnEoUuHt9Bx/c1ntJf5ulrssbISiv9dTUaPw9LJWFrf0uGv3r7lyn1G+rGSJu7Y8CTkBU0swFj7s4c+OykMVZ9JrCtiQKHBrL8NJwEs2wmMyUkSUBnyJSqJrL4h0FTtamathohk5JMwn5VYKqxOqOAHesbiNXMXh0/zibesOkik6PzvW+7rvBzQpiqdHCxoWrdvviCam3siC5paobi3evbWcw7mcmX+F3nzxJUTMRRVAFaFXyRrOgtrprpokqWVTmmy63eRXAmfg5MZ0n7HWWojcKXm70oHxxFnbx72BNR5A17Y79yh8/e5aRZJGf3DVUb0CufX0plevGv10WspTb99rOIJt7wyRyjsFxxKcwni5j2za6adcDm/nBpZbaoNiAVxHwqzI+WaQv5qeomdy/qdsZ+395BIDBmDOgciO8z25ws4JYPBreuIgvVVraMRQjU9LIlHUSucqbOsneCA1lLhcW5Vq27yvPD3NTR4C5ooZpWpg2ZEpaPX3eSgzLJupXKFQM5goV/uS5YTyygEeWiPpVAIbnirw2mub/HVDr+k6NuJuww1JBYGOD9liqxNf3jZIuaQQ8Mlv6IpedgnLXg8vTWL47PJHlvk1dbO2P8tjBCUJemc29EdqDHrJlx07h7Gyh/r2tllyAWm+bTMAjc9fadgbbAjx5ZLqucbZnY1fdhud6zXguxg1uVhCLR8Mb/74UNZsFWKhpc72n7l2ujMUB85mZArvWtLG5N8zfvzrOdK7MXEF7g0dpDpYNqYI2P9lTwbKhO+zh5u4Qtw5EQRA4M+N4JR0cy9T1nWDhZu5uwktnsBavGY/sHGQkWaz38LiB4VuntjY/cXiqrtV0MpHnH1+bIOiVWd8VIl/R6Yl42T+SWpbDQiMBVeKmziCpQpXRVInbV8e5bVWMrfOfn8WmzTfCvuAGNyuUxSepS53UaulRbLu+2GXKOo++MsbDtw/wyM7Bq/L6XVYGjYvaQNzPw7cNMBD384UnjnNoPIO2zKuwYTv+N15ZQLecxsvxdJkHt/cxEPczPFvgwe29HJ3M1TdlcMtRi3mjQKUr7OUX7r7pottu9N/d2+3Z2jEU44XTszx3apZMWSNXNsiVDR7dP4YoCBimTWW5Ixvgps4g77+lhy8+dZqJbBkbCHmVBS0LN9pnyA1urhGu5KRWSzvuPZ5wvslepm42lxVNTT21N+JlS1+E0VSJs7NFVEki5lMYTVeQRcCZDm9Zb4AsOI9fNSwifg/be0KcmylQMUxePpfkhTOOgOWOQcfraO+JGQ6PZ9izscvNOlwBN9rm9Va4kt9RIldh74mZehN243RRV9jLlv4oPzibRBYEQl6Zim6gGTZg1Ue+W4kIBDwSZd3EtsHvkVndHuCp4zPkKjqqJLJ9IMrm3qUnD2+Uz5Ab3FwjLHVhXqq5sLG+6nLjcKlTaW2K5sWzSQ6OZ/GrEpphEvOrvHNNnL0nZ2nzq8zkq+imRbqktyTAqZ1nBQGSBY3zcyW6Ij5OTOd5/NAkflXi7vWdDMT9fOX5YV48mwSc0utH71xzw6XVL8eVHHZcLqbxd9RoXNwYwBwYSfPoK2OAc+2Np8tAw/q6oZOTUzmePDqNKIIoClim7Zh9t3oEEedzlK+ayKJA2CezrivIsckcPVEvW/sidIe9PLClpy4AeaN+Xtzg5hrhzaSU3fTzjcmlTqVdYS8fvXMNazqCPHtyhpfPpajqJkWtzFPHZ5jNV8mWdFRZpKQZLXt9tekR2wbbthlPl9jQE+KOm9o4NpmlrJmUqgbfPz3Li2eTrOsK4p93OL/Rhf0WcyWHHZeLafwd1TSD+mO+BQFMbXw+N98cvHi6rCvsZTxTZjZfRZFEBEHAmL+4rWWsSPVEPPTHfZSqFumSRr5i0BfzMZYuc3g8Q9SvLlCwv9Eye25w4+JynXC5k3tX2MsjOwc5MpHFtGw6wx7Kmolt2XhkkZ6Ih/F0Bc2wsbngKtwq7Pn/DM8UiAZU0iVnI9k/kiZd0kjkKvzQzR18+I5V7D0xw2iqBLa9wFH8RsYNZN4+jRNQtcwNXPisfP3lER7dP87Dt/UDTjDkVyVeODPHtoEI5+eKdIY8VA2T10Zb6/jtlQU044KiuAAUqwaHx3PopkV32EuubHBqOk/YpzgO7w26R0upKV/vuMGNi8t1whtteIlchcGYjw9s6+W+TV189aURXhpOEvTIZMvGgkbI5ejWMmw4NVNEFYv0xvz0Rj2MpspkyjoVw+TQWIYP3+FMAdbKBB+7yzXWdGkOjZ+XxZIDiVyFI5NO4JCrGHzl+WGOTmaZyVUYz1S4qSNAslB1NG4EoeU2JpVFxm02oM9niyRBYHVHgLMzRTTDYlV7gK19kXrGtrE0dSMFxG5w4+JynVO37ihpHBzP8sFtvdx9cyfpksb5uSIdYZWRudKC72l15qYRzYLRZIlMSUNVJLYPRFEkkbOzRb764nlCHplV7X42dIUuOnXeCIrFLsvP3hMz7D+fIuJXODCa5sRUjpJmkq3oGKZNqqiRqxjLYquwGBGIBhRsy6ZiWnglkS09Ee5Y3cYLZ+YwLctVmmZZ2p9cXFyuJvVauyDUxfwAjk7mmC1UOTVdIFs2EHEWBEkEdZmPPRaQKRuUqiaZko5uWqRKVfadS/Hk0QTn50oMtgUuaUtS68lxWZpErsITh6dI5JbXoXolc9nfiW2Trxgcncjx0tkkqaJGqqShGTaSAD5ZRLxKu+fG3hD/amMXumUjiQKIAmfmCpyYzjOTr7Kq7UKP0I38+XAzNy4u1zlLWXckchU8ssjqtgC6ZZEsaPhVCb8qkSrqeGQB09KXVYzMI8HqNh9PHpnGq4iEPY6TdV/UR65ikJnvxVk8CQYscMt2MzgXc6M1k14Ji38nRyayPHZwgge397FnYxffPjLNK+dSqLLIHavbODKRZTJbwbZhOl9lefObFzidKDiyDfP9cqIAr5xL45FFVFlka3+0/hm40fpsGnGDGxeX65ylau0HRtI8c3KWmVyFoE9BEgVSJY1C1cCyoaRZy2b6V6NqwqmZPFXD0fH4yJ1rwLbJVQxGkkWmshUQhLoSd013pKYc627el+ZG3uQuxeLfyWMHJ+rX0EfvXMPute1IAswWqiDArUNRZo4kHKE+zcKvSujmcpgrLEQzbRCgL+pjNF2mK6SimTYdIQ/v3drDng2d9fveaH02jbjBjYvLDciOoRjbBqJOX4FP4XQij2XaCCL1dPvVOJdW5yfRV7X7GYz7+dL3zjCddbJMP7yhk1xZ5/9+/xwRv8LOVU5WxxX5e2Nu5E3uUiy2JKgpYj+4vY9vvjbB1/eNEvBInJwucHamSCygOIHFPAVt+QMbAFmEgZifbFnj3FyRfNnA75ExLZvBuP+SmcsbrT/NDW5cXG4gErmKo2AtCDyyc5AtfRH2nUtyeqaAIAoosohl2wjYLZ8AWYw0r2AMUNEtHjs4gSwKCAJUdZPBmA+AqWyFkWSJ87NFOsPeusifu3m7vFkaS1M7hmLsGIzREfLw2liayUwZvyIhCuCVRccXbZkRYcHnUJUF2gIqk5kyJc0EQaBkmGwdiPLeLd2XLc/eaKVJN7hxcbmBcKwYxgFnxDrqU0jkKqztDNIWUDk9k+fcXAlsx2l4qWkQRRSwLLvpbsceWaSkW8ginJzKc2IqT9gr4/fIeGURBIGRZJGOoEpbyMPOVXHSRY0zMwX2Hk8scD2+0U6pLm+Nxcayj+4f44Uzc0xlKtjY6JaFKAgUNGPZzTDByZxKXHAW1wyb2bxGxKcQ9Su8IxilL+LjI3euYUtf5LLl2Rstu+kGNy4uNxA7hmKOKNm8yBfAgdE0RydzDMT9PLClm9987Cj5inHJmpTeomacku7sHo2biKqIFDWDeMAPts1UtsKejV189M41C4KYTFlfsKjfaKdUlzfP4gC40aakrJnO9ZWtoF+FoKaGPf/HI4FuXsji3NIf4aaOEKPJIo/sGqqPfb+RkOeN9FlwgxsXlxuIrrCXR3YNLbht99p2XhpO8vKw4+X0K/et53efPElJMxFFkAWBqmlflCJvNR4JyprjoXM+WeS7xxMYls1YusRsvgqwQHk16lPqafmBuH/B2LuLSyOJXGVemC9XtycAJ3voVyXagyr7R9JXJVtzEQIMtgW4dTDK3hOz5Mo6umEzmizyz4enAPidH7kFuLiP6EbOWrrBjYvLDcyRiSyff+IEI6kig3E/RyezlKoG79vazXeOJShpJl5FQpHBxqKiWcsmXFY1odowjfL6RBbbsjk2lSPuV7lzXceC7Ezj1NQHt/XeUKdUlyujUdDyzEwBv+p4l33ztQleOZ/i2GSOQtWgM6SiSCJRv0hJ0+uN7suJKjl+bIYFk+kyW/oiqJJIyKewoScMQNyvMtgWWPB9btbSwQ1uXFxuYB47OMFYuoQsity7sYuJdJknjyXwSCJtAZWQ16JYNcGyqM5LwPsUkfIy5+pFAbBsVFlEFEXigQuu9wNxP19/eQQEga19kSUzNm4Pjgtc2PjvWt/Bw7cNMBD3c3giyz8eGOfYVI61nUFu8gSYzJTBttFNm8a23uWcIPQpMvmqgQDolsXRiSwAtw/FCPsUtvZFGIz769d6o8u5m7V0gxsXlxuaB7f3UdKcSaSHdvTz+W8fp1Q16O8OsaY9wHePJShUF7YOL3dgo4qORYNmg6ZZeBV44sgUHkXk5eEUsiRwdqaIX5W4a31HvR+nkaVOs27Ac32z1Pu7YyhGpqyDbdc3/2dPzvD6eBYbm6hPYXV7gFRRQzdtCppJQBWxRNCt5Qts2vyOltOLw3NOttSEiUyFiE+hqJl85+g02DZRv1r/Wb/y/DBnZgo8fNvADZ2xqeEGNy4uNzBb+iL8zr/eCjgLJALE/Co9YS9l3USVBHyKgGE6o+GWBYLAsgr8LS6DaYbFudkSf/zsWQpVCxHoCHloC6o8eWSa9qCHX7j7pgXfs1Sj5d7jibrr8+I+JJdrn9r0U2NPTc2G4LnTc/XA4KVzKSqGhSTAvnMpXjqXdBy456+7omYtq0+RAJjYfP9MEs20sYF4UCXslemJ+jAti8F4kH3nUhybynHbqjhbesOcmSmwtjN4w2dsarjBjYuLC+CYBZ5OFLipM8hYukyurCNLIoMhL7mKzlTWaeK1l1nZb3FwY9vgkQWM+YSSBQy1+4n7VU5NFzgxnVtw/0tmaARh4d8u1xW16aeaVMDwXJHXRjOIIvRFfAzE/XSEPOxaHef5U7NohkV5Xopg8TW+3H3FmZLT5OORIORTeN8tPSiSyO617ZQ0k9Fkkb9+eYSSZnFoLMOWvggP3zbgZiEbaFlAmkql+NCHPkQ4HCYajfKRj3yEQqFw2fv/4i/+IjfffDM+n4/BwUE+8YlPkM1mF9xPEISL/nzjG99o1Y/h4nLjML+itwdUfnh9B7etivHDN3fSGfJgXg3740tg40jQV4wLfRCmaXF2tgACRLxK3RTx2ZMz/Jd/PMxXXxq5yDxwz4ZOPnbXmgVy9S7XD11hLx+9cw33be7myESWo5NZilWDw+NZ9o+kOTyeYe+JGaI+mZBPRlVEZFFAFK+GY5SDVxYZiHmRBOe6RhDZPhjj5u4w42lHuO89W3sI+1UiPpW71rfzoXcOsWdDJ+/Z2uMGNg20LHPzoQ99iKmpKb773e+i6zo/93M/x8c+9jG+9rWvLXn/yclJJicn+b3f+z02bdrEyMgI//bf/lsmJyf5+7//+wX3/Yu/+AseeOCB+r+j0WirfgwXlxuGPRu7GJ4rOlMkHpnzcyWCHolTMwWifoVMxcAnC+Qq5lVb/Gs0lsVs4MhUDt2wsW14cTiJblqcSxbJVwwm0mVHfXZRuv5G0/24UWhsrD08nuHZ07O8PpZl5+o46ztDzOSrrOkIsO98ilfOp8iUDMqawUDMS9Tvmc/8XZ0rvD2oEg96EICgVyHglblzXTvPnpwhVXLMbcEJzBs91lwuRrDt5ieZjx8/zqZNm3jllVe47bbbAHjyySd573vfy/j4OL29vVf0OH/3d3/HT/3UT1EsFpFlJw4TBIF//Md/5KGHHnrLry+XyxGJRMhms4TD4bf8OC4u1xu1jWE0WeTJownevbaNdFFjIlPmtdEMJc3EsOyrHtzUEHCqSh1BlZm8hg3IAtzUGWQg7mcmV8XG5iO7V+ORJQbifsZSJXdTuE5pbKztiXh58WySVFGjrJu875Yedq5u40+fG6ZqmEiCgA30x7xMZsoEvQqJXJVkQVvW67tRPyqgShiWzUDcx9beCIl8lXxF58xMAUGAD+0a4r+8b9MyvrqVx5Xu3y0pS7344otEo9F6YANw7733IooiL7/88hU/Tu3F1wKbGv/+3/972tvb2blzJ3/+53/OG8Vn1WqVXC634I+Ly41OrXSTyFXqt9WyGQ/t6Odjd63hw3esYktfhLOzRaqmhWHZSItWDVW6ej0rNs6YeF/MV2+dMW0IeiRCXpneqBfbhlfOpXj80GTd+Xlxicpl5bPU9bqYAyPpemPtUFuAgEdmdUeA9qAHgK19EQIeielshUxZJ+yV0UyL6ZzGqek8hYq+7IG7KAqIOIF6wCPhUyVmc1VePpeipBlOpqk9wP2bu+vmni5vTEvKUtPT03R2Lqxjy7JMPB5nenr6ih5jbm6Oz372s3zsYx9bcPt/+2//jT179uD3+/nOd77D//f//X8UCgU+8YlPXPKxPve5z/GZz3zmzf8gLi7XMYtNAxubbhuVTnMVA78qEfcrZMo6PkUiVbywCWimjbjME1QLsGEyU6lXEmycsdlzyRKqJGDbkCz5uHtdB1v7o/XMzRvhjoqvLK5EnK72vvpViX85Os0P3dxByCMzmi5zbq7In70wTMgjE/BIFKoGx6bySOIFy49WWYtcChEIqo6nmm7adAQ9bB+M8dSxaaqGiW5Y5Cs677qpjf/147cu62u71nlTwc2v/dqv8YUvfOGy9zl+/PjbekHgpJ3e9773sWnTJn7rt35rwdd+8zd/s/7/t956K8Vikf/xP/7HZYObT33qU3zyk59c8PgDAwNv+3W6uFzLLDYNXLxx1AXP1rVz1/oOHj84gW7aqJKNJIAqi05Tr+2Y+10tpXobSBaqBL0yQY9EpqTTF/VS1ExGU45A4XiqDILA4fHMFU9HuUqvK4uBuJ/+mDPl1Eij032tsfZ3vnWM7xxL0BH0IIkCH9jWC7bNP78+Rb5qIAlO/7yN40Z/FQSIAfCqIgXNyYiKAmzuC/OJe9Zh2zaHxrP0x3yUNJMNXSE32H6TvKng5j/+x//Iz/7sz172PmvWrKG7u5uZmZkFtxuGQSqVoru7+7Lfn8/neeCBBwiFQvzjP/4jiqJc9v67du3is5/9LNVqFY/Hs+R9PB7PJb/m4nKj0thQu2MoRqakkSnrJHKVeuam9rU9wFyhyvdOzmKaFgGPTNgrU9RNBCBX0Wm6TfgVYtrzf6oGXWEPVcNmKlvlR3b0MZEuUzZMbh2IkSvrfGPfKAGPTNSnXFbML5FzyhZ3rWt/wyyPu+k0n6V+p2OpEuPpMmOpUt0oEi52un/P1h4e3N7Hiakcx6fzaIbF44cmUCQRw3L0axoTNNWrdN0CVDWr/rERgMPjWU5M5RiK+xEEgfs2dVHSzEseQFwuzZsKbjo6Oujo6HjD+91xxx1kMhleffVV3vGOdwCwd+9eLMti165dl/y+XC7H/fffj8fj4fHHH8frfeOF4uDBg8RiMTd4cXF5G3SFvUT9Ko8fmqxvEI3BTyJXYUN3mIl0melcBdOyyVZ0REFwHMRxTsBXc2LcsmEsXcK2bPI41hKbeyPMFTReODOHX5VQZJFt/RF2DMWcE/+JGQ6PZ5jKOn0c79naw5GJLF986hQlzeDD71z1hgGLu+k0n6VKppeyFdgxFOOBzV2cSOQZTRY5MpHl8HiG9pAHdbZISTM4PpnHBvyKeFWv05p9gzj/P4osEFIkdNPGr0okclW+8vww55MlwBHZfGTnYP3nbPzb5fK0pOdm48aNPPDAA/z8z/88X/7yl9F1nY9//OP8xE/8RH1SamJignvuuYe/+qu/YufOneRyOe677z5KpRJ//dd/vaDxt6OjA0mS+Kd/+icSiQTvfOc78Xq9fPe73+W///f/zq/8yq+04sdwcbmhuNziufd4gm/sG0WVRYbifobnihimRUlfKXNTDppu18tjY+kKU9kqHSGVuYJGxCuzsSfMYFuA2XyVxw5O8OLZJDbwrpva6j/3YwcnODSWYdtA9Io2kkaPqycOT7kZnCawVMm0ZoZaayxu/D2Ppsv84EySZ07O8vSJGbJlnVzFYCDuY0j0sf9cGt2Ckm45fViWfdV6xEQBZBHCXgUL8M8HNxt7Q/RGfKiyyNHJHLIs8vJwEnBGv13pgjdHy3Ru/uZv/oaPf/zj3HPPPYiiyI/+6I/yh3/4h/Wv67rOyZMnKZWcCPXAgQP1Saq1a9cueKxz586xatUqFEXhj/7oj/gP/+E/YNs2a9eu5fd///f5+Z//+Vb9GC4uNwyXXTwFgYBHZlt/hMG2ACcTeSorLLAJeWVs26ZUNesBjmHZ8+PgkK/oHJ7Icmwqx4mpHJmyzrb+CFv6IuzZ2FXfKGsTKbvXtl9Ruan2e6s5koObwXm7NF6Li3ttarYZD2zuAkHghdOzjKRKZCs6pmkzni5zU0fAcf5OFFjd7pR4wK4LQIpXacDPxun10UzIlHQkSSRT1FFkkVPTBe7f3AO2TW/UR9SvcHqmwEiytKCM6nJltCy4icfjlxTsA1i1atWCEe4f+qEfesOR7gceeGCBeJ+Li8vy0CgaVuOZUwnGU2XyFYNM+UJLpiQ6eh25yvI2M1iW5XgCLbq9VoKomqBXDGRJZCJb5l9v76tPTwEcmcjy9X2jDMZ8fPTONVdUbmoUjLvSHh2Xi7lc39LhiSz7z6dZ0xF0em3mG8JH02W+f2aOZFGjK+TBJ0vIHoH7NnfxY+8Y4DP/dJRDYxkOT+TQTbteErJZ/rKUIkJQlZEkAVkSyVU0TMvGsmxUWcAji0ii0+W8tT/KHXNFdq9td9zJBcG9pt4CrreUi4vLG1I7Sdc2oYdu7eOhW/v4w6dP8/SxaRRRQLfmNxDLMd/UjGrdIgEu9Bu0iqL2xvNaogC9YZVsSSdX1nns4ARHJ7O8cGaOkWSR18ezRHwKc0WNB7f3cde69gVN1oupBUD9MR/j6TIf3NbrlqTeAotNLhsbu/cNJ5krVMiVNBI55+9V7X5iPoUfvrmDMzOOH9o/HZykUDXIlnW+f2aOiXQJbYXYhkgiqIqIJIl0Bj1UDYuKbiKIcEt/hHs2dhH2yuzZ2MWBkXTdauGRXUNuw/pbxA1uXFxcLkvj4lrbzDMljeG5Ii+dnSNbMRAFu660agPJYhVxOa2Ul2CpYMqvSgS8CpOZMk8cnUYWBQpVg/NzMygi9Ea9rGoLcGamUM/oPPrKGNj2ks7hjf02V6qf0yyulU3vSl5no8nlgZF0PVO298QM+0fTgACCwB8+fZrvnZihpJuEPDJ3rWtHlkSyZZ2uiJepbIVEtsK/HE1Q0hZmDq+mFlPVcMpQlg2z82XSm7uDrO8MsaEnzIbuEP9ydJpcxeDda9sXNE67DetvDTe4cXFxuSyLJ1cAMmVHEr435qesW5Q0A72hNFWoXpxFWe59ZannqxoW46kyharBudkiqzsCTGUqWEDYJzOTq1DRTd6/tZeBuJ8XTs+im9YltXEae0Max5OXg2tl07uS11kzuawFQeAERYfHM/gViXcMxQj7FPafT5Gr6PPWCTaKLDJXqHJ0IkuqqKHIIq9PZCgtkcW7msFNrden9v8BVeSD2/oYjPt5/NAkr8z7XMX8KoNx/4Lfkzsl9dZwgxsXF5fL0ri4Npanoj6FgbifLzx5glfOpVAkAdO2kQSnRHXVFIsvQ9WwsTGxbajoBrppUTUtZFGgVDXJlp3+oUPjGarz5pt33NTGng2dF4nFNZZOrkYG5VrZ9N7q6zwwkmYqW2FTb5iyZnJyOkfEp2CYFuu6QvhUiYOjGc7NFbEsG8MGQ790adK4WiqT89iARxbxqxI/dHMH717bzvfPzOGVRbrDHoKeTjb2hF2D1ybhBjcuLi6XZanFtfG23Te1cXwyO5/cEJgtaHhlAVEUKGvWijHZrKEZzivSTQh7ZTZ0hSjrJqokIIkChmFh2jZHJ3Ns7o3w4PY+DoykyZQ0/vrlUYpVg4l0iXRRY7AtQNgr89zpOWBhZqLVQc+1suld6evcezzBX788yrb+CL9073p2DMUYTRb55sEJxtJlfIpEV9jLrjVtpIoaz52axbRsdNMm4pUp6doy/DRvjsWlUdO0GIqH2Lm6jcPjGZ48Mk2mpBH1q3zsrjUX/Z6uldLjSsQNblxcXN4WD+3o558PT3F8Oo8sCIgC+BSJsFcmL5ukSvrVfolLIgjO5jGd06gaJrIoYphOMHZqusD2wSi717bzZ88Ps38kxT0bu4j5FXTTYt/5FEfGc0T9Cj+8oXPJKalmlo2u502u9rPlKga5ss7zp+cYagsQ9im8cGaOiUyFoEdmdYcfjyxxZn48ulg18cgipmUxU1gZgY0sQMinkC3r2LYzJbWgQiYIZMo6f/PSCB/Y1ssDW7o5MZVjwxIZG7h2So8rETe4cXFxeVt0hb18ZPdqfvtbx8mWNGRRwLJhOl9FFKAtoFDUjBWji1M7TZs2jKbKmNa8aixOYGPZIEsC09kKX983yvOn56gaJgfHMuimzW1DMeJ+lfFUGVUS2H8+xZbe8EVBx6XKMZeye1gqeKndninrPHdqFrj2Nrk3CsxqG/j2/gir2vxMZMo8fSLB+bkiVd0i4pUpVE1OTxcpagaWbRFQFfyqBDjK2NbVNG5twLAh5lfmnb0rCIKATxHQTZuAKhILeEgXNTTDIux1tt/zyRIbesJL/o6uldLjSuQqzzO4uLhcD3hkiVVtfuJBD2GfQtgnY5g2Vd0mW9LRVkhgAwvLBPO+n/X/lwTnjyDARKbM0cksumkhi06vRLFqEA+ohHwKN3UEsIF0SWMiU+aJw1MkcpX6Y9fKMV1hb11Vt7bRP35okgMj6fp9l7qt8XZse0nrgWuBS/1sNXYMxbhrXTsnEnlm8lX6Yn6S+SrZkk5RM5EkAc20KGpOj5RhQqqkkyrpZMs6hgWiIKCskN1sJlclV9IIehXagyoRn4xHFtnaH+XWwSg20Bn2cGQiy0SmDMBosrjk76jxGnJ5c7iZGxcXlzfN4tO4M8obI18x0AyLm3tCFKomFd1EN60Vcap+Iy5kbURSRQ1zvpwQ8DibUyJXQRLg0FiG9HypbTDuJ1XSGU+X+d7JWQbiPuJ+lZ+cHxt/7OAED27v4/B4hkf3j/Pwbf3s2dgFLDyNL3VCbzTvbFRQvto0vvfAFY15N/7dKHw4lioxEPfzL8em+cGZJIoksro9wLquEBOZMpZlY5o2NjYV/eL+rZqMjWDbK6a3S7ds2oMqHkWiN+IlV9bpCMG/2tTFwbEM92zoxAaeOjHDPRs6+dhda66KlMD1jhvcuLi4vGkaewEG4v55k8owc4X/f3t3HiVXXSf8/32XurV2dfW+pJfsC9ljSAiyCRlWBWZ8hjHyQ/QgqAOPoj4K/h4dD3ocBvWoRx/mMA4uZ3wAR2dA/DEDyiKGSEgga2dPZ+l9r32vW/f7++N2V7o6nZCEdNLdfF/nlKSqblXfulXW/dT3+/l+PhkQcPvKGaxsLONAb5SeSIrtbeFJU1DtdISwl4uDvVw3Y+bJ5izyTo1oKkdzhYe8gHnVPjxOvdC1uT2YZGdHmM2tQ6TMPN3hFEOJLB2hJMcGE1w6s9z+A4pyygTtkTpCI4HC9rYQGw8NXPTCgGNXiY1+74Fxi++NFU5mee1AP9curOa1A/388i/HUBU7cExl8+zrsRtb5q08m48MEfAadgVfAR3h9LjPOdpk+mQ5VIWmcjf90Qz90TShZA6PQ+WV/f0c6otx6/J6blsxg6oSJ1fMrSSZzVNV4rzgpQSmOxncSJJ0xkb/6r51uV0L5kevHGJXh70ktzeSJpExWVhbQnsoxe6OMBUlTtbNqWB7W4hY5kRhNZ+hEc9e2BYN72Z07mdy1EqvrGn/O5kxcTo0UjmdcCrHgV67E3WZ12DDmiZ+v6uLQ70xjg8liaRyeA2djmCSS5vLuHFJLS2dYZbOKC06kY3NqwmncgTcdk7J6J5KZ2IiEo+3t4V4aks7kVSWPZ1hNqxtLi4y1x5ib3eEH79yiDKvQSiRZcPaZqpKnIVVZr95p9N+MiFo6YrQH0uTzOaZVemlI5gqdMqu8Tvpi2VI5/LMry3hyECCdM5CVewcCnMyRTGnEM/m2dERxuPQC8n0PkPjI8tLWVhbUghsVjWV0R1OjbvSTnrvJskspSRJU8HIr/aOYJKbltbREUySzJosbwxw12XNLG8M4HXqtIdSvHM8SFc4ze6OCOVeg1uX16MPf+NoCgQ8eiGpcjIafR7NmoKMKWgLpdnTFeHtY0HqS120DyV4oaWH/9rdg9/tYH5NCbWlbq5ZUMX/+EADf71yBg5N5UBfjE2HB3hpTy8/euUQfdF0IQ/ntf19RXk1CMHvd3WzqXWQzlCqUCn5TIzObxl5/j1dkZPygc7GquYy5lX7SOYs3mkLFd77Gr+rUHxvZoWXjYcHeWpLO8/u6OKxlw6ceF2Kwo2La7ik3k80bXJ8KEGFz0mJ04HLoXH5nHI8hopDU9EUBZ9TZ1G9n8c+upyPXdpYKL43FQKbETlTEM+YKIChqVwxv5Ibl9axqqmsEPSNHJupmks12U3ebxZJkiadsfkTq5rLCCezRNMm3ZE0G9Y0ccXcShrLPSSzJr2RNKYl2N0Z4RPrmrl0VhnvHA+Ry0NfNFPIa5nsRnbT0BR8Lh1LwM6OMKUenZmVXlY2Bth6dIh9PVGunFfJ566ZS43fxZ6uCLs6I7x1NIjHoTKjzE0ymy8kjv5+VzdXza8qnOBGko8DHgOPobGpdZDGcg97uiI8vaWN5govt6+ccdKozJ6uCM/v7OKKUaX7x/a9Aor6g53Jaq0RAnAO58OEh3s8gd0eASEo9xoAlDg14mmT1r4YW30G5R4H9aUujg7E6Q6nWFLvZ1alj65QGksIjg8lKHHqXw0dGwAAN5dJREFUOFSVVNakL5pGURWODcT5+//7DrGMOSXytcaqLDFw6To+l4bX0NlwaRMdweRJlb6n49L+yUIGN5IknbGx+SI1fhcBj1GYdgi4Hdy0tI49XREGYxkqfQYp06I9mORnm45R4nJQ6jYIJ7OcqpisoSmTNj/HsgSpbJ583mJfMoeiQIlTZyieIRjP4tQ1dnWG+Z9Pb+f+D83l7eNB3jk+hCWgptbPF/9qPt3hFOFUjvpSFw1lbupLXXRH7LyWkS7lI8HJyMjN9vYQ/9XSM255frATl0dOnP/7lkuAk/teNZZ7eGZrO3s6w3RH7OBkdA+n37zdwY1Lamkq9xQlC7cPJdh6LEjGtHAbGn/c18eergjJXJ53jofwux2saCil0uckls5hCYGqKhzqi2NoKij2cvnqEif1ATctXRGi6RyxZA7DUAknsySyeQT26IxlCjJmnsgF7ip/LjSGE9EBQ7WT0QNuB5+5Zg7VJS7eODzAqwf6+eO+PpbU+7lqflVRpW9p4sjgRpKk92RVcxk3Lq6hPZQq5Ic8v7OLvT1RZgTcfOLyBv5jWycKUF/mJmPmUYBUziSTE6gqqCikTQsFJm1gAxRK/KuKfVLTFIWMaRENpe2TsyU42BvDtCD4wj5AkM4JFAWGElm7maOi8Ju3O7ik3k8wkWVT6yCbjwwRTGTxGBqaqvA3qxoKIyKN5R4ayz0kMibNFV48hsaDv96BosA9V8xmyQy7ijLAFXMrebGl56QTaFWJkyffOMrmI0MArJtTUQh2uoJJNh0ZpD+a4UBPlJ0d4UJj1Nb+OPUBN5qq4Bxea11X6uKdthD90QwocEm9n6YKL0saAhwfjPOf27v48LI6yj0GOzrC7OmKEEvbfcf+z59aae2Lk8nnyWPnNbV0xwB7qrLSZzAQy3KqAT1dvfhtFMAu1lftd9IbyRT2tcrvJJQ0CSZzHOyNcffls2gs9+B16lR6DTYeHuSqeZVFydnw7qvNpHMjgxtJkt6TGr+LpgovOzsjdASThZPtscEEyaxJdYmLJ+++lO1tIf64r5e+aBqvoWNadqKl26nbPZ7ipz6pTTYjUyWmJQp5Q2nTIpsXODVAhYGYffI3dIUKn5PF9X4ayz2EO8MANJW5uWJuJRkzz58O9hNKZhmICXRN4c0jg7QPJfE6dZbMKCXgdvDxtc10BJP8YW8vf9zXixAQS5s8uH4+HcFkofHkeBVtt7fZHbeXNwZYUu9naUOA53d2sfnIEP3RNNG0abeiqPPTVO6hPZhk85EhljcG2LCmiSX1frYeD7KvO8ol9X5mVXnpjqTJ5y36omneOjrE9ZfU8OaRIaKpHEcGEpTPdLKpdZBMLk/AY6BrCgd6Y4VAZ6y8gL7Y6SsNT4bABsBtaPb0ZCQDgMehUlfqoi8aQVUUgokse7oihfcFoKnCSziVs7vMY49yArIC8QSRwY0kSe/Z2FycqhIn1y2sBkUpGkXYcnQIIcBjaAig1KUTSZtEU5OjfP65CCVzlHkcpIfPvIqioiuQt0RhNVgwnuWd40Ee/PUOLqkrwefUCCazvNDSg9ep0RlMgWL3uoqlc+zrjmJoCjnL4l9eP0JlicHKpjJa++MEPA6unFdFKJklmTV5fmdXIaemsdxDQ5kbj6HxYktP0ZTUHasbC9dbuiLs7Y4Q8DhYPbOM3kh6uHSzKPR0SmRMytwOOoJJrl1UQ1coxav7+zHzFpfNrkBTIZG2ONATZVljgIN9MfqjGSq8Bj3hFP+5rYNkNo8lIJjMEkpkJ9WS7fcimxcMxbKoin3YLAGprIWhqyDgUF+MH71yiPxwFHzT0rpCvhNCFP5/MUImFJ9/MriRJOk9G5tDsL0txMbDg0U1WvqiaY4MJshbgrRpsXRGKfu6o4QSGXRNxeVQSWbz5KZYBqldn8Uup5/MmpS6HcN9q+xf9QqgaQrBRI7BhD2q4dRVWroiRFImXqeGqoJDVan2OwkmcwzGs1T6DIaiGYQAXYNIKsv+niiaonD1gipuWlLLf2zrJJe3Cr2tRkZoDvRECSZzzK/2cXwoyR2rG1jaEOCxF/fTHU4xr6aEXF4QSWW5ZWkda2dV8NONR3kp0mc3A/UYBDwGXZEUfzkyxI1LatnVGSZvCRRVocxrcNnsCt44NEjWtOgZrkUTy+Rwaip7e2J2WwRx4iBNpdVOp6MCPqeGQ1Nx6SqGpqKqkMiY+FwOdEWhPuAmmTWZVXEiAXtkddmG4QKPI+SIzcSQwY0kSefd6GTWZ7a2F36t5i2LpQ2llHsM/G4dQ1fRVRUFhfm1JTg0aO1PoCkKlhBF0xQuXSWXt5iMKTmxdB5VVTAt6ItlcCgn7nM5wMyLwpSbXQlZ4DE0YmmTVCaPooLmUGjtTxRGN1K5PLpqP1FDuYc/HRhgKJFFUxReOdDPm0eG6AqnONQfx6GpXIt93N84PMCr+/swLUFjmbvQ6PO5nV3s7oigqopdf0UIfC4HKAoeQ8OhK5R77ByfVSVOoqkcr+zvI5LO8cbhAaKpHBU+A2EJ/nRwgBUNpdyyrJa/tA7hd+vUlDg5rKokMva00+j3ScHOqXFoCukpFuWoQG2pk+YKD4f7EwzG7dWBlcOFBtPCwuey6x4tnVHKzUvrConhI7WLAh5DBjEXmAxuJEk670ZGcl5s6SnkGNyxuoG7LpvJ3q4Iz7zdQTJrIgTMqfbi0OzApT2YJpjIoasKV86rYG93jGgqi99tYGgQSecLrQ8mEwt7JdWI0a20UrmRLewu0aqqoioQTuWwxHA9HQsy+XzRCrJEJo+mKgTcOi6HxtxqL/RD1swTT+WIJLLk8wLTtNh4aIDZlV77pBpKIbADlpWNASqGk1lTWRNdU6gPuOiPZVEUBZ/TbhGwqXWQAz0xXLpqFxpsCPC7HV0cG4pT4XUSTZl2T6QSJ5UlTg50x3hpbx8KgsFEju5wmq5winjWHDcvZiSeyU+xwAZAVRWWNQS4en4V/7b5OPG03eOqP2aPqgU8Ol6HSmNtCQ/dtKhQoHHJjFJ7Wb/bIaedLgIZ3EiSNGFWNZdxx6WNIEShP9Km1kESGZO8EPicOvOqS9jWFqQ/lqXEpaModguElq4ImZxdPC+Zy2PoDhLpdKGo21SUs8CpCBRFKVQ9Bnv5uzJ8zaGAOrwqSFiCVM5iZ3uYeMbEoam4DQ0laxHPm6BAMpujOyzoCqfYejzI28eHWFjr56YldSAEQ4kseUtQ43cRTZvMrvTh0FKkc3k6Qikef72VNTPLmV3lpSec5s+HBvj1Ox0c6ovZScupHHlL4NQUZlX72HY8xFAiixAnCh0KIJwcP1F4qjMtweYjQ6RzeaJpE1VV0FDImfYqOL/Lgc+lc8uy+pNaKMgl3xePDG4kSZowNX4XG9Y0Fd22YU0T7cEk3aEkl82uYMPaZhQFXj84AAJmVXjoj2UYitsnUE2BdDZPMG+dsjbOVCIAh65i6CrxtD2akhk+UeqqQrXfyWA8C4qFotjTU5qqkMnlyeTyxFEocenkhzuap03IWyYv7enBEoJcXqCrCq/s7+NwfxxVsafF6gMueqNpoukc82t8hBJZhhJ2Hk9rf5xlDaW0DSXZeGgQS1iF4CWRzRPP5nHqCj3RzKReqn8+lbl14pk8lhBkTIstx4JYloXb0MmZApdDZUbATt72ux1EU7lCbo108SlCiPfHJ3WUaDRKaWkpkUgEv99/sXdHkt53xlbE7Yum+af/3s/GQwM4HRqRVI5ENo+q2FMhI6MPlmDKjtyogMepYebzWELB0BUUFLxOjYFYlrwAl65g5kVR8q2KnasiEJR5DKJpk8ZyN0f6E4W8luHUHBTsaZQqn0FPJGMHUhpYeShx68QzJirgNHRUwGtoDCaymHmB21BJZa1CQbqZlV6S2Tx90fQ5B5WTpS7NmVA4MRKlASubA/hcOtuOh0ibdm0jj6GzvKGUIwMJVs8s4/YVM/jVW210BJN4DJ37rpotR2om2Jmev+XIjSRJF9x4w/WpnP0reX6Nj1jGZHdnmDq/i6oSJ4piL6fO5MWUDGzAnmqKFxqHisIISDyTx6Ep+BwqLod2Uq0XC8gMb+tzauiait+lU+YxCCXtoGj0MdEVhWQmXzhR5/L2idu0BJYFJpBN232PRtecSWRPRCE5y74vms6dUXAyOjAYfX2qBDYjnDpkTMgDuzoizK32oqsqQljomsLaWeWsbCrjhiV1hQ7peUuwurmMpnIPYTl6M2nI4EaSpItue1uIvd1RMnmL+oCbVC7P/u4YqqrS0hXDEgLrNIPMujL5lxqf6kQvhv+3xO0gmDh9vZ9jg3YH7aFEloU1JUQzuZOSdDOmRWacP+bSVZKjurILRqosg0OHbI6iFV29w0vZR559bABz8ms49fWpwu82yOcF8azdmX0glqWqxKDU4yBn5mkbShBN57jrspnU+F1F9Z1GCiiOtCCRLi4Z3EiSNGHerSHjiFXNZfw/lzXTNpTghsW1PLejiwqfQV2pk0jSzr2JZkwcqp2vksyOOXmf7sw7BWTz0Bl6967dI686nbPY1xMhd4btlwQwmMihjnNfXkB+nAVoAntKbHTS8HRmT9PZU3e6otpFGNP2KrG6UhdpTeHYYJIS14nVT6NHIEcHOmf6uZcmznifdUmSpPNi5NfsSBfsU6nxu/jM1XP4wvr5bGodpDOUpK7URSpnF8cLeAzKvQYrm8t46MaFeIzir668Zcc37yfZ/NkHHGc7S3Sq7afjsbawl9lHUzmSOYtQIofToTGvxstHltczp8qHqkJ9wA3Aiy099EXT9EXTvNjSA9gF+Wr8rjP+3EsTR47cSJI0Yca2ZTidvmiaJ984OtwWwLCrFyezlHkNUpkcNX4niUyegViG+TUlHOiJ2HVihs/w02El1VQxHUdxBNATyaAodkKxptlNUbtCadqDSeZV+aj0Odmwpqmohxec3B/qbD730sSQwY0kSRPmbOp8jLQOWFxvN9782aajvNk6RJnbwZqZ5TSUuXn94AAv7umlJ5KittRNJGVOyqJ+0uSncmJkyqnbo3+mZfc7q/AazKstwe3QEALeOR4ka1rcd5Xdhb2qxAlwyv5Qsr7NxSeDG0mSJoVVzWWEk1lQFKpKnDx80yL+93Mt7OoIc/ncSu5aN5OMafHa/j50VSWVteu+SNLZcuoqyxpK6Y+k6AilWTojwGWzK9jZEeavLqmhM5TithUzqCpx8tqBfhQFDvfFQbEn5MYGLzKQmXxkzo0kSZNCjd9FwGOw8dAA29tC1PhdPLh+PlfMq2QgluFAT5Rc3kJRFG5aUsPK5jLcho7PqRZyQOQXmnQm8pZFJmdR5nOiaQqpbJ4ZATf3XjmbNw4Psrc7SkcwaTeAPTTAwlo/6+ZUsHRMBWJp8pIjN5IkTRpjcxWWzCjFY+j8flcXB3tj9EXTRNMmdaUumso9KIBlgd+lEUnnT0qA1VUocepEUyZyjEdSFTtHy+7pJbhibhXdoRSDiSw/3XiUhjI3+3tjLKwtoX0oAYrCVfOrQAh2doTpCCZParEgTU4yuJEkadIYL1ehqcyNW9fImBZrZpUzlMjideocG0qgKJDMWbjEiTEbBXA7VNI5C01VSeQsGdi8jwRcOtV+Jx2hJOmcKCQ/V5cYVJU46Q6lqSgx+F/XL2BhnR8EvNDSQyKbZ261j4V1fip9Tl7a0wvAfVfNZlVzGQGPIROEpxAZ3EiSdNGcST2Q21c12L170iZ+l05XOMXzu7pZ3VSGoalsPDxIxrTQFPA6dfxunb5oGoE9/WDJVVTTlkM9eZVcOm8RTGTJmaJoVVc8Y1Jf6sZtaDQE3PxxXx8Au7oidIdTqIrdunR2pZdo2uTGxTX4ZUAzZU3YFHUwGOTOO+/E7/cTCAS45557iMfjp33MNddcg6IoRZfPfvazRdu0t7dzyy234PF4qK6u5itf+QqmOT270UrSdHcm9UBq/C42rG2mqdzDxsOD7OoIE03lQLGnrRrLXJR57MJqn1jXjKaoeB06fpeOoSsYuopjOClHV8/sS2861nGZjsar+pwzLcKpHEKhqB5S1hS0BRMEExm2Hgvy/+3q5ldvtdEdSg43CRW09sf5zTudvLSnl6YKLxvWNMm6NVPUhI3c3HnnnfT09PDyyy+Ty+X41Kc+xX333cfTTz992sfde++9fOtb3ypc93g8hX/n83luueUWamtrefPNN+np6eETn/gEDoeDf/zHf5yolyJJ0gQ5m3ogI9t4DI0/7usjkTF5/eAAPpfBB+eWsrQhQDSZJZU1SeRMFBSEANOyMDSFMqeOrip2i4N3KdQyHeu4TEej3ycVcDpUMjm7yeXIiqhoyuTIQAyHpuF3OUhlLRyayrLGAHdd1jw8gjNIfcDNp6+cTXc4RTRtFvWJknVrpp4J6Qq+f/9+LrnkEt5++21Wr14NwEsvvcTNN99MZ2cn9fX14z7ummuuYcWKFfzoRz8a9/4XX3yRD3/4w3R3d1NTUwPAE088wUMPPcTAwACGYZzR/smu4JI0dY0U+9vRHsJt6CyfUUoolaM7nOL6xbV0hZJsah0k4HEwFM9yoCeGrtmBjq4qxLMnMnAU7N5Kk70vlfTuVEDXQFNVanwGvbEsFT6DaxZUk8yY7OuJUhdwEU7mqPW7eODaeSyZUTru1OiLLT38flc3ty6vl8u8J5kzPX9PyLTU5s2bCQQChcAGYP369aiqypYtW0772KeeeorKykqWLFnC1772NZLJZNHzLl26tBDYANxwww1Eo1H27t17yufMZDJEo9GiiyRJU5PdZDNCxrTIWxahZJZdHWECHgfRZJau4V/eZl5Q4TWwhCCds7cVSnEU4zFUdK14EspnyAXlU5GF3ZIinbPoiWXJ5i0E0B1OsWZ2BVfNr2IgliWczNEXzdARtM8tI0nso3O+VjWXcevyejlSM4VNyLRUb28v1dXVxX9I1ykvL6e3t/eUj/v4xz9Oc3Mz9fX17N69m4ceeoiDBw/y7LPPFp53dGADFK6f7nkfffRRHnnkkXN9OZIkTSKrmsvY3m53EV9cb69s2dcTw+3QeGlvH33RNIOJDO2DCWpLnficOhkzj0PXqPM7CSdz9Mft7tuJsQ04gbRpoal2xVpp8nDpKmnTQsEecbOwc6h01b59hK7aieQVXoPrL6lhQa2faxdWc+3C6kKysN+lnxS4jB3BkSM2U9tZBTcPP/wwjz322Gm32b9//znvzH333Vf499KlS6mrq+O6667jyJEjzJkz55yf92tf+xpf+tKXCtej0SiNjY3n/HySJF08NX4Xn75yNtvbQjSWe2jpDHPHpY0snVFKS2eYP+ztZfBIBgvI5QUfWlhNdyRF21CSoUSWXP70c1B5S+bcTDYKYAwHNwJ7KlFXleEu3sXtN7yGTtaymFnppS+aYe0sR2FUZsPa5lP+jdH9omRgM/WdVXDz5S9/mU9+8pOn3Wb27NnU1tbS399fdLtpmgSDQWpra8/4761duxaA1tZW5syZQ21tLVu3bi3apq/PXs53uud1Op04nc4z/ruSJE1eo39hb28LsfHwILcur2fJjFKWzChlT3cUQw/RVOrib1c1gKKwryeKW1dJCUFtqYvOYIp4xiQvwKUrNJV7ODqYwLTAqSukRyXhqECJUyOdt8jI5JwLpsyt26NoioKiKiRH5UqZAsgLzHQOa/gtsQMgheuX1FDqNmgoc/PG4UEyZp4XW3pOKjcwdqRGJg1PL2cV3FRVVVFVVfWu261bt45wOMy2bdv4wAc+AMBrr72GZVmFgOVM7Ny5E4C6urrC837nO9+hv7+/MO318ssv4/f7ueSSS87mpUiSNEWN/oU93glpw5omPIbGbStmsGRGKc9sbSdrWmTzAreh86EF1ezqjLCnM0w2L7hiXiX/89p5PPSfu9jXHcPMCxROjN4oCkQzeTmacwEpQKnHwG1aLGssZW6Vj73dEfpjGVy6Rmt/jEg6j0tXSQ4XulEVuHlpHZ9YN4uOoJ1U3tIVASCZNfnP7Z18eFkde7uj3LZiBh3BZNFIjZyKml4mJOdm0aJF3Hjjjdx777088cQT5HI5HnjgAT72sY8VVkp1dXVx3XXX8W//9m+sWbOGI0eO8PTTT3PzzTdTUVHB7t27+eIXv8hVV13FsmXLALj++uu55JJLuOuuu/jud79Lb28vX//617n//vvlyIwkvU+MDmhGfnGP/AIH6Agm+fSVswu/0q9daP8Qiiaz+D0GCMHe7ijXLKhmMJ4hls7xl9ZBvE4dp65iWhbVPsOulWIJxknLkSaYpkA4kcUUgmMDCW5bPoPF9aX8flc3DWVu2oaSqEqeulInQ4kcqayJpqqEklme2dpOdzjFZbPKWb+wGoeu8tbRIQ702O07+mMZAD595WxAjtRMVxNW5+app57igQce4LrrrkNVVT760Y/y4x//uHB/Lpfj4MGDhdVQhmHwyiuv8KMf/YhEIkFjYyMf/ehH+frXv154jKZpvPDCC3zuc59j3bp1eL1e7r777qK6OJIkTW9jf2GPHskBTsqbqPG7uHZhdVEAFPAYtA8leHZHlOoSJ5uPDNLSGaHM4yCbF8yp9nF8MEkomWV0iePRIzrS2dHV8YvujccSkM7lUVQFTVVoDyZZWFtCmcdBNJXFQqAqCtF0nlTOorLERSiRo6UzSk84TWO5hw/Oq6IpmOSnG48ihB3EjB65kSM109uE1LmZ7GSdG0maPkbnTgBFeRQj94VTOTYeGig0QURRiKZyPLO1HaeukrcEXeEUs6u8JDN5FtSWsLszQjCRwRKgKgLTUvAaWqG9g2XZK3asMd+gYwMgGRCdnlNTyIxK8tYUUBSFUrfd8NTn1JlZ6WX1zDLeOR6iPZgkksyiaSrza3zk8ha3LKujN5zGoavs64kSSeb45OUzuXZRDa8d6AchuHZRTWE070zafkiT00WtcyNJknShjK5TMrZmSWFURwhuXV4PQvB/t7TzL38+AsBnrp7DB4abIq6eWU5jmQevU2dutQ+/W8e0BHlLoCoqINA1Bb/LgUvX7M7SY6KWkS/U0ZVzzjSwcahgaO/lSExNmTGr1xy6wro5FVy3qBq/x8HyxlLuuLSRxfV+fE6N+bVefC4dj6ExFM+QyOTpCaf5/Pr5rJ1VQSSZI5jIEk2bduuONU0sbQjw5BtH2TOcgyPbKUx/snGmJEnTUl80TTiZ5ar5VVy7sLowktPSFWFXZwS/S7dPfDNKeX5nF5FUllf393PprHJmBNz4nA58Tp1Yerh3nYChRI5IKofH0BDYSaz5Uat1UEAR5zZSM7YB5HRhr2KCzGlaAKqAQwdD07hpaS2fWDeL//PaYcy8RSiVY+lwYvjurgjXLqzm1uUNdIWSbD0WpG0oyRuHB1naEODahdWsbi4rvL8jnt/ZVZiuXDKjVK6Meh+QwY0kSdPS6GXiIyM5NX4XX1g//6QE5M5QiuODcSIpk2A8C4rC/BofRwbidv6HeaL4jRBQ5jbImhnyloUyPEzjMTSSmTx5wOdUiWWmabQyDkOzqwOPRwBOXUNV7DyZxKgNNQV0TaXEqYGiEE+b5ExBS1eEt48FiWdM9nVFeOylAyxvKKXcYzCj1A1CEEray8DLvQ57alAIavwuPj/m/QW4bcWMov+eSb6NnLqa2mRwI0nStLSquYxwKkc4mS00QBzvhLWquYxwMkuN30mFz8mKxkAhP6fca7CpdZDBWIb+mJ1noygKOSG4aUktKBBKZNnfE6XC56Q3kiaUzJHLCwyVwkorp2Z3J49lxo8AVOz8nanKtE6fMOxyaDg0iI8avllU58PndFBb6mRfd5SeSBrTEmw+Okip20FliROf20E4nuFQb5RKr8F9V80mmjb56cajGLpKY7kHQ1dYXF/KtYtqThmQjNRAOhuyqN/UJoMbSZKmpRq/i4Dbwe93dRPwGNy0tG7cE1aN3wWKwjvHQyxvDHDpzHI7ZyOV4651M/lfNyxkT1eEZ7a0kbMsjg0mWVzvZ0Gtn/pSl92hPJvnSH8cr1MjY1r4XBqhRI4yt0YimyeXF5iWHdgogENTyI7KNVHVokVZhe3OdnrL51RJZqwJC5Q0YLzwTBk1NTfePmfzFoqwk4QH41lKnDorG8s41Bdj85Eg8bSJpoLXqTEUz3JkMMGD6+fTWO7hmS1tbGwd5FB/nOsX28VavU6d5Q2lbFjbTEcwWQhmRhpehpNZAh7jPY26yKmrqU0GN5IkTXmn+sU+9gQ13gmrL5qmpTNMJJVjV0cYj6GxvyfG/p4YfreDgNvBquYyvvM3y3ixpYdYuptcXvDUW22YlsVQPMsV8yopdTs41BfD0FUiyRzZvMDKmIXVVJaw+yOVuHRi6eKWAWNHPAwVnA6d2GkSVVSleKWWqoDH0Ilnsmd9/M7UKWaecOgqC+tK6AqlGIxnC/lImgIlbgc50yKXz9Nc6eeDcypZWOdnYW0Jm1oHCSXtYMdj6Fw5r5KhRJa7Lmvm6gV2faLPr5/PkoYACFG0lH/kvR49IjNyfziVe8+jLnKp+NQmgxtJkqa8U00hjD1BjXfC2t4WoieS5sp5lSxtCLB0RimzK72F5eK/ebuDOy5tLFQ+jqVzhJMZesIpvE6NBbUlLKy1T9ZPvnGUnR2RQpsGBaguMfC7HUSSJnWlLvpjGXxOnVq/Tl4IOkPpotEOj6GgKhriFOMvHoeKMpzok7cEmeF+S5aA/tiZBzYq4HGqpLMWI10lDMVubXC6kR9dtR+btez/CkBTFW5aXEssbfLkpmPk8hZVPifpXB5dUTAMDU1zoKsq1y+u5aaldTyztR2HqjC/2sfa2RW8fTyEpip856+XFgWoIyueRht5D8cGtSPvb180XQhKpfcnGdxIkjTlvZcphLEVj/ui6cLIwGv77d51CEFfNM2v3mpjV2cEQ1dxOjSaK7xcMa+KnR1hBuMZUrk8mgKaCm6HxqJ6P+Ueg2ODCdZfUkOZx8Fv3+kk4DV44ENz6Yukef1QPy1dEZIZC0NXEAJSOROHfqJSx+jpHl1TcOoa4aQ9QuJyqKROs9RqJJ9n7JSRx6nhMXQyuSxeh0p1icHxYAqwezTNrPAA0NqfKBohMi0KnblH/r7PqYOicNflM3n1QD+H++PEMybx4eThpnI31SVOAh4HjeWewjH1uw1uXFKLf3h0pztiByujA9C+aHrcWjVw5kGt9P4jgxtJkqa893IyO1XF43DKnjq649LGQoXjcDJLQ8DF2tkVZE2Lnkgav9vBrcvraSz3cGwwAQo0V3hZ1lDKwlo/z27vJJTIUuZ2EExkMS07UHphdw95S5DKWliWHVA4dQ3TslCG15NXeB14DY3eaAYzb4/lZHIWsfSJXlcCqzBFpWBPB6mKHYQI7N5YDsVeaj4y0gKQFxaZnEneEmRzeQbjWbuAHvZ2sZRJIpvHoSon1aJRFbseDcLuzO0xtMLS6+sWVlNZ4qS53M0f9vZR5nFw52XNvHF4kGTWpKUrwl9aBznQE7UDG5fOH/f2Ulfq4vrFtYVgc3QBxt+83UE2b3F0MFHUWuNcg1q5Emr6k8GNJEnSKIW8jWS2aCn5quYytreX0dofZ+2sikJPK4+h8dyOLhQFPrysjlmVXip9TnZ2hPG7HayeWc6ujjChVK4w/eV16lw6s4wXdvdwZDBhtxDwGfhdOsFElkTWwrQsnLqKoWuMFJLXlBNF70YCGmHZt1uMjBjp5PIWAgtL2NtomoJbU/AYGg5NJWtaxNI5UsNJzpaA3KiVXKYp6I1mUBWYU+UlmxcMxu2eTOVeB5mcYFFdCWVeg9LhUZelDQG2t4XY2xNlYW0Js6t8DCVyrGgM8MbhQcLJLCubykAIfr21naFElljG5MH18wu1h5bOKC2Mnv34lUPs6ozwkWV13HFpIy2dYVr740UjO+ca1MqVUNOfDG4kSZJGKcrbGJ6eGrn901fOLvrFv6q5jP/9XAubDg9iCYHHofGdv1lGXzRNU7mHVc1lhVGfxnLPSSt78pagqdzN8cEkc2u8zK7w8cLuHsAOWExLkDPzrGwqo9xr0BFMcqgvhqaqeJwa+bxAVcHn1OkKpe2kZYc9aWRaCi4d0jmBgqDa72YwnqGqxGkvVx+eyRqZYhqZ2FIVqC1xEkmbCASzqnyFROFsziRr6gQ8DmaUeQgmslT6VPqimcJre+PwAH8+NMBgLMNV8yrZ0xVhV0eY5Y2BQrPKaNpk0+EBklmTjmCSpQ0B9vfEQFHoi6Z58o2jvNMWImta+D0GG9Y0ndQf7L2QK6GmPxncSJIkjWO8UYHxprCSWZOGMhfJrEVThbew3ehu5SOPGVnZs6crwqbDA1w2uwKAZ7a2E4znqCnJs3hGCd2hNBUlTjqDKWpK3dz/obkks3k8hsajL+6nfShJY7mHulIXLl1jZVOAn286Tn8sjcehUR/wcKgvRl4InA4VXVNJZ/IIS+A1NFwOjXg6h2lZVHhdhFNZMqY9PTa32sviulJSZp5jAwkUYF6ND6eusLszSjiZY8mMUjasaaKlM0w0bTK70lsI2pY2BNh8ZIjDw0u3N6xtxuPUC80q93RFGIxn+PSVs0lm8ydWQA0nAG9vC9HaH2d1cxlLZpQWurqfzzwamZMz/cngRpIk6RyNnJhHj8qM2N4W4ldvHec/t3fy4Pr5RUuWn9nSxgstPXx4aR2fXz8fgDcOD7CvJ0pqeIVRU7mHoXiWRDbPptZBOkMpbl1ez1+vbOCZre3MrvRyuC9OImPiMTRKXDqWcPLBeZU0V3hpDyaJpXL4vQ6ypsVAPIOiwtHBJD6nzuIZpfSE0+QtQanboNzrwGPoVHgN3jw6xOVzKmgs93B0MIHX0PG5HGgaeBwaH15Wx5IZpbR0hnlpTy93rG4o5K5cu7C60Jx07HLtvmiaH71yiJ0dYRIZkyvnVQHFwcbYBG9JOhcyuJEkSRp2tommo0/KYyvgNpZ7CCWy7O60CwB+52+WFe5rqvBS7jFoqvBS43fRVO7BElBd4sTn1OkMpVhYU8KaWRUgBEsbAoXgqbHcw4HeKKlsHr9boyeSIm8JljWU0lzh5faVMxiIZXh1fx+H++P4XToVJU6ODyapKjFwqCqpXJ7rFlQTS5tsOjJIjd/FvGofrx8cAC+sX1hNU7mHPx8eoKbEyRXzqqjxO2kbShBN59jbHeX2lfb0UjhpN6kcfUw2rG0e93iNJGVX+gyCiSy/eacDkCudpPNPBjeSJEnDzmeiaUtXhEjaxKGpJHP5QgsIgNtXzijk5ADDycr2dMzKJjuA+eC8qqKAaeTf29tC7GwPM5TI4nPpZEyL3Z1hakvdXDmvihq/i+1tIbxOnZuW1LK0IYDbofLC7h4W1pSwtyfKpdU+bl/VwJNvHOXoQILuUAoFO7jL5S1uWzGDVc1ltIdS7BpOjHbqGg1lbjxGSaFHk9/tIOCx6/iMZyRYHBnZaiz3sKDWz58P9XN0MMHV86tk3os0IWRwI0mSNOy8JpoKQZnHYE6lg3Ayd9pVPqOTlcOpHDs7wnQEk+P2Q1rVXMbH1jSx6fAAkZSJ36WTzFqYeQuPofFiSw+N5R7uWN1YGIH6f5/dzdZjQaLpHH+9YgbXLqphIJahPZik3GsgLItgIkuZ12B1c9mJ6aR6P/u6o0XVgUfXA0KIwlJ5OHnkayRYbChzF6bVltT72dkRZnlDadGybkk6n2RwI0mSNOx8Tolcu6imUOXY79JZ1Vx22mmvM62uW+N38Zmr53D7yhlsbwvRHkzy663thJK5otyc0a+jucKLy2G3hQh4DGr8Lp584yhvHhnC0BQW1flJ5yyayj1sWNt8InhRFG5cUlt4npH9e7Glh3Aqd1LX9bEjX6fKSXqvfZ8k6d3I4EaSpPedC1HEbaRx58ZDA4UA4Jmt7UXtHE61T2Mr9I63r6ODIYSgPZTiirmVRSuQRty+coY9dTRqBOa2FTM40BOlP5bhynlVDMYztPbHCyNG29tCbDw0QEOZm50d4ZOaj141r5Jbl9cXFd0Lp3JcNa+yaPn8yD52BJNFt0nSRJLBjSRJ7zsXqojbSdNcw8X4Cv89g30a7/axAU9ThZednRGS2fy4r2d0f6aRkZdVzWV8/44VRbVjRv/7VKMup1rNNBIMjR7JebfXJkkTRQY3kiS971yoIm5jRymuXVRTVBjwTPZp7O0jRe5a++NA8fTPeM87tjfT2EBj9P6datXS6NyfU428nGofxhvRkaSJJoMbSZLedy7W1Mi5/N3xCge29seZW+07afpnPNvbQvzmbXvJ9ejA6nwHGqfah9ON6EjSRJHBjSRJ0iRwplM3Z1vkblVzGXdc2ljIt7nQgZ1sdSBdDDK4kSRJmgTONAg4m+BkJDfn2oXVF23URCYQSxeDerF3QJIkSToRBJzPIGRkNGh7W+i8PackTQVy5EaSJGmKOdOl7HJKSHq/kiM3kiRJU8ypRmRGlnn3RdPAxIwGSdJUIEduJEmSpphTjcjIejKSZJPBjSRJ0hRztrVmJOn9RgY3kiRJ04RcmSRJNplzI0mSJEnStCKDG0mSJEmSphUZ3EiSJEmSNK3I4EaSJEmSpGlFBjeSJEmSJE0rMriRJEmSJGlambDgJhgMcuedd+L3+wkEAtxzzz3E4/FTbn/8+HEURRn38tvf/raw3Xj3//rXv56olyFJkiRJ0hQzYXVu7rzzTnp6enj55ZfJ5XJ86lOf4r777uPpp58ed/vGxkZ6enqKbvvpT3/K9773PW666aai23/xi19w4403Fq4HAoHzvv+SJEmSJE1NExLc7N+/n5deeom3336b1atXA/CTn/yEm2++me9///vU19ef9BhN06itrS267bnnnuOOO+7A5/MV3R4IBE7aVpIkSZIkCSZoWmrz5s0EAoFCYAOwfv16VFVly5YtZ/Qc27ZtY+fOndxzzz0n3Xf//fdTWVnJmjVr+PnPf44Q4rTPlclkiEajRRdJkiRJkqanCRm56e3tpbq6uvgP6Trl5eX09vae0XP87Gc/Y9GiRVx++eVFt3/rW9/i2muvxePx8Mc//pG///u/Jx6P8/nPf/6Uz/Xoo4/yyCOPnP0LkSRJkiRpyjmrkZuHH374lEm/I5cDBw68551KpVI8/fTT447afOMb3+CDH/wgK1eu5KGHHuKrX/0q3/ve9077fF/72teIRCKFS0dHx3veR0mSJEmSJqezGrn58pe/zCc/+cnTbjN79mxqa2vp7+8vut00TYLB4BnlyvzHf/wHyWSST3ziE++67dq1a/n2t79NJpPB6XSOu43T6TzlfZIkSZIkTS9nFdxUVVVRVVX1rtutW7eOcDjMtm3b+MAHPgDAa6+9hmVZrF279l0f/7Of/Yxbb731jP7Wzp07KSsrO6vgZSRHR+beSJIkSdLUMXLefrdcW8QEufHGG8XKlSvFli1bxKZNm8S8efPEhg0bCvd3dnaKBQsWiC1bthQ97vDhw0JRFPHiiy+e9Jy///3vxb/+67+KlpYWcfjwYfHP//zPwuPxiH/4h384q33r6OgQgLzIi7zIi7zIi7xMwUtHR8dpz/MTVufmqaee4oEHHuC6665DVVU++tGP8uMf/7hwfy6X4+DBgySTyaLH/fznP6ehoYHrr7/+pOd0OBw8/vjjfPGLX0QIwdy5c/nBD37Avffee1b7Vl9fT0dHByUlJSiKcm4v8AKKRqM0NjbS0dGB3++/2Ltz0cnjUUwejxPksSgmj8cJ8lgUm6rHQwhBLBYbt6TMaIoQ7za2I11s0WiU0tJSIpHIlPoQThR5PIrJ43GCPBbF5PE4QR6LYtP9eMjeUpIkSZIkTSsyuJEkSZIkaVqRwc0U4HQ6+eY3vymXsw+Tx6OYPB4nyGNRTB6PE+SxKDbdj4fMuZEkSZIkaVqRIzeSJEmSJE0rMriRJEmSJGlakcGNJEmSJEnTigxuJEmSJEmaVmRwI0mSJEnStCKDm0nqO9/5Dpdffjkej4dAIHBGjxFC8A//8A/U1dXhdrtZv349hw8fntgdvUCCwSB33nknfr+fQCDAPffcQzweP+1jrrnmGhRFKbp89rOfvUB7fP48/vjjzJw5E5fLxdq1a9m6detpt//tb3/LwoULcblcLF26lP/+7/++QHt6YZzN8fjlL3950mfA5XJdwL2dOBs3buQjH/kI9fX1KIrC7373u3d9zOuvv86qVatwOp3MnTuXX/7ylxO+nxfK2R6P119//aTPhqIo9Pb2XpgdnkCPPvool156KSUlJVRXV3P77bdz8ODBd33cdPrukMHNJJXNZvnbv/1bPve5z53xY7773e/y4x//mCeeeIItW7bg9Xq54YYbSKfTE7inF8add97J3r17efnll3nhhRfYuHEj991337s+7t5776Wnp6dw+e53v3sB9vb8+fd//3e+9KUv8c1vfpPt27ezfPlybrjhBvr7+8fd/s0332TDhg3cc8897Nixg9tvv53bb7+dPXv2XOA9nxhnezwA/H5/0Wegra3tAu7xxEkkEixfvpzHH3/8jLY/duwYt9xyCx/60IfYuXMnDz74IJ/+9Kf5wx/+MMF7emGc7fEYcfDgwaLPR3V19QTt4YXz5z//mfvvv5+33nqLl19+mVwux/XXX08ikTjlY6bdd8dZtdOWLrhf/OIXorS09F23syxL1NbWiu9973uF28LhsHA6neKZZ56ZwD2cePv27ROAePvttwu3vfjii0JRFNHV1XXKx1199dXiC1/4wgXYw4mzZs0acf/99xeu5/N5UV9fLx599NFxt7/jjjvELbfcUnTb2rVrxWc+85kJ3c8L5WyPx5n+/2eqA8Rzzz132m2++tWvisWLFxfd9nd/93fihhtumMA9uzjO5Hj86U9/EoAIhUIXZJ8upv7+fgGIP//5z6fcZrp9d8iRm2ni2LFj9Pb2sn79+sJtpaWlrF27ls2bN1/EPXvvNm/eTCAQYPXq1YXb1q9fj6qqbNmy5bSPfeqpp6isrGTJkiV87WtfO6kL/WSWzWbZtm1b0Xuqqirr168/5Xu6efPmou0Bbrjhhin/GYBzOx4A8Xic5uZmGhsbue2229i7d++F2N1JZzp/Nt6LFStWUFdXx1/91V/xl7/85WLvzoSIRCIAlJeXn3Kb6fb50C/2Dkjnx8g8cU1NTdHtNTU1U34Oube396ShYl3XKS8vP+1r+/jHP05zczP19fXs3r2bhx56iIMHD/Lss89O9C6fF4ODg+Tz+XHf0wMHDoz7mN7e3mn5GYBzOx4LFizg5z//OcuWLSMSifD973+fyy+/nL1799LQ0HAhdnvSONVnIxqNkkqlcLvdF2nPLo66ujqeeOIJVq9eTSaT4cknn+Saa65hy5YtrFq16mLv3nljWRYPPvggH/zgB1myZMkpt5tu3x0yuLmAHn74YR577LHTbrN//34WLlx4gfbo4jrT43GuRufkLF26lLq6Oq677jqOHDnCnDlzzvl5palj3bp1rFu3rnD98ssvZ9GiRfzLv/wL3/72ty/inkkX24IFC1iwYEHh+uWXX86RI0f44Q9/yK9+9auLuGfn1/3338+ePXvYtGnTxd6VC0oGNxfQl7/8ZT75yU+edpvZs2ef03PX1tYC0NfXR11dXeH2vr4+VqxYcU7POdHO9HjU1taelDBqmibBYLDwus/E2rVrAWhtbZ0SwU1lZSWaptHX11d0e19f3ylfd21t7VltP5Wcy/EYy+FwsHLlSlpbWydiFye1U302/H7/+27U5lTWrFkzrYKABx54oLAA491GKqfbd4fMubmAqqqqWLhw4WkvhmGc03PPmjWL2tpaXn311cJt0WiULVu2FP1ynUzO9HisW7eOcDjMtm3bCo997bXXsCyrELCciZ07dwIUBX+TmWEYfOADHyh6Ty3L4tVXXz3le7pu3bqi7QFefvnlSfsZOBvncjzGyufztLS0TJnPwPk0nT8b58vOnTunxWdDCMEDDzzAc889x2uvvcasWbPe9THT7vNxsTOapfG1tbWJHTt2iEceeUT4fD6xY8cOsWPHDhGLxQrbLFiwQDz77LOF6//0T/8kAoGAeP7558Xu3bvFbbfdJmbNmiVSqdTFeAnn1Y033ihWrlwptmzZIjZt2iTmzZsnNmzYULi/s7NTLFiwQGzZskUIIURra6v41re+Jd555x1x7Ngx8fzzz4vZs2eLq6666mK9hHPy61//WjidTvHLX/5S7Nu3T9x3330iEAiI3t5eIYQQd911l3j44YcL2//lL38Ruq6L73//+2L//v3im9/8pnA4HKKlpeVivYTz6myPxyOPPCL+8Ic/iCNHjoht27aJj33sY8Llcom9e/derJdw3sRiscL3AiB+8IMfiB07doi2tjYhhBAPP/ywuOuuuwrbHz16VHg8HvGVr3xF7N+/Xzz++ONC0zTx0ksvXayXcF6d7fH44Q9/KH73u9+Jw4cPi5aWFvGFL3xBqKoqXnnllYv1Es6bz33uc6K0tFS8/vrroqenp3BJJpOFbab7d4cMbiapu+++WwAnXf70pz8VtgHEL37xi8J1y7LEN77xDVFTUyOcTqe47rrrxMGDBy/8zk+AoaEhsWHDBuHz+YTf7xef+tSnigK9Y8eOFR2f9vZ2cdVVV4ny8nLhdDrF3LlzxVe+8hURiUQu0is4dz/5yU9EU1OTMAxDrFmzRrz11luF+66++mpx9913F23/m9/8RsyfP18YhiEWL14s/uu//usC7/HEOpvj8eCDDxa2rampETfffLPYvn37Rdjr829kKfPYy8jrv/vuu8XVV1990mNWrFghDMMQs2fPLvr+mOrO9ng89thjYs6cOcLlcony8nJxzTXXiNdee+3i7Px5Nt5xGHu+mO7fHYoQQlywYSJJkiRJkqQJJnNuJEmSJEmaVmRwI0mSJEnStCKDG0mSJEmSphUZ3EiSJEmSNK3I4EaSJEmSpGlFBjeSJEmSJE0rMriRJEmSJGlakcGNJEmSJEnTigxuJEmSJEmaVmRwI0mSJEnStCKDG0mSJEmSppX/H3u98axNfWHzAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.datasets import make_moons\n",
     "import matplotlib.pyplot as plt\n",
@@ -91,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,17 +101,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Training NF, current loss: 1.653: 100%|██████████| 100/100 [00:15<00:00,  6.45it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Optimization parameters\n",
     "num_epochs = 100\n",
@@ -140,37 +112,16 @@
     "momentum = 0.9\n",
     "\n",
     "optim = optax.adam(learning_rate)\n",
-    "state = optim.init(model.parameters())\n",
+    "state = optim.init(eqx.filter(model, eqx.is_array))\n",
     "key, subkey = jax.random.split(key)\n",
-    "key, model, loss = model.train(key, data, num_epochs, batch_size, verbose=True)"
+    "key, model, state, loss = model.train(key, data, optim, state, num_epochs, batch_size, verbose=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Loss')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.plot(loss)\n",
     "plt.xlabel(\"Epoch\")\n",
@@ -186,20 +137,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "key, subkey = jax.random.split(key, 2)\n",
     "nf_samples = model.sample(subkey, 10000)\n",
@@ -227,17 +167,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Training NF, current loss: 1.207: 100%|██████████| 100/100 [00:33<00:00,  2.94it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Model parameters\n",
     "n_feature = 2\n",
@@ -263,37 +195,16 @@
     "momentum = 0.9\n",
     "\n",
     "optim = optax.adam(learning_rate)\n",
-    "state = optim.init(model.parameters())\n",
+    "state = optim.init(eqx.filter(model, eqx.is_array))\n",
     "key, subkey = jax.random.split(key)\n",
     "key, model, state, loss = model.train(key, data, optim, state, num_epochs, batch_size, verbose=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Loss')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.plot(loss)\n",
     "plt.xlabel(\"Epoch\")\n",
@@ -302,28 +213,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "key, subkey = jax.random.split(key, 2)\n",
     "nf_samples = model.sample(subkey, 10000)\n",
@@ -353,7 +245,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.4"
   },
   "vscode": {
    "interpreter": {
diff --git a/src/flowMC/sampler/Sampler.py b/src/flowMC/Sampler.py
similarity index 54%
rename from src/flowMC/sampler/Sampler.py
rename to src/flowMC/Sampler.py
index 6b5bdd4..cfd6b68 100644
--- a/src/flowMC/sampler/Sampler.py
+++ b/src/flowMC/Sampler.py
@@ -2,12 +2,14 @@
 import jax
 import jax.numpy as jnp
 from jaxtyping import Array, Int, Float, PRNGKeyArray
-from flowMC.sampler.NF_proposal import NFProposal
-import optax
-from flowMC.sampler.Proposal_Base import ProposalBase
-from flowMC.nfmodel.base import NFModel
 from tqdm import tqdm
 import equinox as eqx
+import optax
+from flowMC.proposal.NF_proposal import NFProposal
+from flowMC.proposal.base import ProposalBase
+from flowMC.nfmodel.base import NFModel
+from flowMC.strategy.base import Strategy
+from flowMC.strategy.global_tuning import GlobalTuning, GlobalSampling
 
 
 class Sampler:
@@ -49,6 +51,7 @@ class Sampler:
     rng_key: PRNGKeyArray
     data: dict
     local_sampler: ProposalBase
+    strategies: list[Strategy]
 
     # Sampling hyperparameters
     n_chains: int = 20
@@ -61,7 +64,7 @@ class Sampler:
     local_autotune: bool = False
 
     # Normalizing flow hyperparameters
-    _global_sampler: NFProposal
+    global_sampler: NFProposal
     use_global: bool = True
     batch_size: int = 10000
     n_epochs: int = 30
@@ -78,7 +81,7 @@ class Sampler:
 
     @property
     def nf_model(self):
-        return self._global_sampler.model
+        return self.global_sampler.model
 
     def __init__(
         self,
@@ -103,8 +106,6 @@ def __init__(
                 if not key.startswith("__"):
                     setattr(self, key, value)
 
-        self.variables: dict[str, Float] = {"mean": jnp.nan, "var": jnp.nan}
-
         # Initialized local and global samplers
 
         self.local_sampler = local_sampler
@@ -116,7 +117,7 @@ def __init__(
                 data=data,
             )
 
-        self._global_sampler = NFProposal(
+        self.global_sampler = NFProposal(
             self.local_sampler.logpdf,
             jit=self.local_sampler.jit,
             model=nf_model,
@@ -125,26 +126,44 @@ def __init__(
 
         self.likelihood_vec = self.local_sampler.logpdf_vmap
 
-        self.optim = optax.chain(optax.clip(1.0), optax.adam(self.learning_rate, self.momentum))
+        self.optim = optax.chain(
+            optax.clip(1.0), optax.adam(self.learning_rate, self.momentum)
+        )
         self.optim_state = self.optim.init(eqx.filter(self.nf_model, eqx.is_array))
 
-        # Initialized result dictionary
-        training = {}
-        training["chains"] = jnp.empty((self.n_chains, 0, self.n_dim))
-        training["log_prob"] = jnp.empty((self.n_chains, 0))
-        training["local_accs"] = jnp.empty((self.n_chains, 0))
-        training["global_accs"] = jnp.empty((self.n_chains, 0))
-        training["loss_vals"] = jnp.empty((0, self.n_epochs))
+        self.strategies = [
+            GlobalTuning(
+                n_dim=self.n_dim,
+                n_chains=self.n_chains,
+                n_local_steps=self.n_local_steps,
+                n_global_steps=self.n_global_steps,
+                n_loop=self.n_loop_training,
+                output_thinning=self.output_thinning,
+                train_thinning=self.train_thinning,
+                optim=self.optim,
+                optim_state=self.optim_state,
+                n_epochs=self.n_epochs,
+                batch_size=self.batch_size,
+                n_max_examples=self.n_max_examples,
+                verbose=self.verbose,
+            ),
+            GlobalSampling(
+                n_dim=self.n_dim,
+                n_chains=self.n_chains,
+                n_local_steps=self.n_local_steps,
+                n_global_steps=self.n_global_steps,
+                n_loop=self.n_loop_production,
+                output_thinning=self.output_thinning,
+                verbose=self.verbose,
+            ),
+        ]
 
-        production = {}
-        production["chains"] = jnp.empty((self.n_chains, 0, self.n_dim))
-        production["log_prob"] = jnp.empty((self.n_chains, 0))
-        production["local_accs"] = jnp.empty((self.n_chains, 0))
-        production["global_accs"] = jnp.empty((self.n_chains, 0))
+        if kwargs.get("strategies") is not None:
+            kwargs_strategies = kwargs.get("strategies")
+            assert isinstance(kwargs_strategies, list)
+            self.strategies = kwargs_strategies
 
         self.summary = {}
-        self.summary["training"] = training
-        self.summary["production"] = production
 
     def sample(self, initial_position: Float[Array, "n_chains n_dim"], data: dict):
         """
@@ -164,163 +183,26 @@ def sample(self, initial_position: Float[Array, "n_chains n_dim"], data: dict):
         # Note that auto-tune function needs to have the same number of steps
         # as the actual sampling loop to avoid recompilation.
 
-
-        initial_position = jnp.atleast_2d(initial_position)
-        self.local_sampler_tuning(initial_position, data)
+        initial_position = jnp.atleast_2d(initial_position) # type: ignore
+        rng_key = self.rng_key
         last_step = initial_position
-        if self.use_global is True:
-            last_step = self.global_sampler_tuning(last_step, data)
-
-        last_step = self.production_run(last_step, data)
-
-    def sampling_loop(
-        self,
-        initial_position: Float[Array, "n_chains n_dim"],
-        data: dict,
-        training=False,
-    ) -> Float[Array, "n_chains n_dim"]:
-        """
-        One sampling loop that iterate through the local sampler
-        and potentially the global sampler.
-        If training is set to True, the loop will also train the normalizing flow model.
-
-        Args:
-            initial_position (jnp.array): Initial position. Shape (n_chains, n_dim)
-            training (bool, optional):
-            Whether to train the normalizing flow model. Defaults to False.
-
-        Returns:
-            chains (jnp.array):
-            Samples from the posterior. Shape (n_chains, n_local_steps + n_global_steps, n_dim)
-        """
-
-        if training is True:
-            summary_mode = "training"
-        else:
-            summary_mode = "production"
-
-        self.rng_key, rng_keys_mcmc = jax.random.split(self.rng_key)
-        rng_keys_mcmc = jax.random.split(rng_keys_mcmc, self.n_chains)
-        (
-            rng_keys_mcmc,
-            positions,
-            log_prob,
-            local_acceptance,
-        ) = self.local_sampler.sample(
-            rng_keys_mcmc,
-            self.n_local_steps,
-            initial_position,
-            data,
-            verbose=self.verbose,
-        )
-
-        self.summary[summary_mode]["chains"] = jnp.append(
-            self.summary[summary_mode]["chains"],
-            positions[:, :: self.output_thinning],
-            axis=1,
-        )
-        self.summary[summary_mode]["log_prob"] = jnp.append(
-            self.summary[summary_mode]["log_prob"],
-            log_prob[:, :: self.output_thinning],
-            axis=1,
-        )
-
-        self.summary[summary_mode]["local_accs"] = jnp.append(
-            self.summary[summary_mode]["local_accs"],
-            local_acceptance[:, 1 :: self.output_thinning],
-            axis=1,
-        )
-
-        if self.use_global is True:
-            self.rng_key, rng_keys_nf = jax.random.split(self.rng_key)
-            if training is True:
-                positions = self.summary["training"]["chains"][
-                    :, :: self.train_thinning
-                ]
-                chain_size = positions.shape[0] * positions.shape[1]
-                if chain_size > self.n_max_examples:
-                    flat_chain = positions[
-                        :, -int(self.n_max_examples / self.n_chains) :
-                    ].reshape(-1, self.n_dim)
-                else:
-                    flat_chain = positions.reshape(-1, self.n_dim)
-
-                if flat_chain.shape[0] < self.n_max_examples:
-                    # This is to pad the training data to avoid recompilation.
-                    flat_chain = jnp.repeat(
-                        flat_chain,
-                        (self.n_max_examples // flat_chain.shape[0]) + 1,
-                        axis=0,
-                    )
-                    flat_chain = flat_chain[: self.n_max_examples]
-
-                self.variables["mean"] = jnp.mean(flat_chain, axis=0)
-                self.variables["cov"] = jnp.cov(flat_chain.T)
-                self._global_sampler.model = eqx.tree_at(
-                    lambda m: m._data_mean, self.nf_model, self.variables["mean"]
-                )
-                self._global_sampler.model = eqx.tree_at(
-                    lambda m: m._data_cov, self.nf_model, self.variables["cov"]
-                )
-
-
-                (
-                    rng_keys_nf,
-                    self._global_sampler.model,
-                    self.optim_state,
-                    loss_values,
-                ) = self._global_sampler.model.train(
-                    rng_keys_nf,
-                    flat_chain,
-                    self.optim,
-                    self.optim_state,
-                    self.n_epochs,
-                    self.batch_size,
-                    self.verbose,
-                )
-                self.summary["training"]["loss_vals"] = jnp.append(
-                    self.summary["training"]["loss_vals"],
-                    loss_values.reshape(1, -1),
-                    axis=0,
-                )
-
+        for strategy in self.strategies:
             (
-                rng_keys_nf,
-                nf_chain,
-                log_prob,
-                global_acceptance,
-            ) = self._global_sampler.sample(
-                rng_keys_nf,
-                self.n_global_steps,
-                positions[:, -1],
-                data,
-                verbose=self.verbose,
-                mode=summary_mode,
-            )
-
-            self.summary[summary_mode]["chains"] = jnp.append(
-                self.summary[summary_mode]["chains"],
-                nf_chain[:, :: self.output_thinning],
-                axis=1,
+                rng_key,
+                last_step,
+                self.local_sampler,
+                self.global_sampler,
+                summary,
+            ) = strategy(
+                rng_key, self.local_sampler, self.global_sampler, last_step, data
             )
-            self.summary[summary_mode]["log_prob"] = jnp.append(
-                self.summary[summary_mode]["log_prob"],
-                log_prob[:, :: self.output_thinning],
-                axis=1,
-            )
-
-            self.summary[summary_mode]["global_accs"] = jnp.append(
-                self.summary[summary_mode]["global_accs"],
-                global_acceptance[:, 1 :: self.output_thinning],
-                axis=1,
-            )
-
-        last_step = self.summary[summary_mode]["chains"][:, -1]
-
-        return last_step
+            self.summary[strategy.__name__] = summary
 
     def local_sampler_tuning(
-        self, initial_position: Float[Array, "n_chain n_dim"], data: dict, max_iter: int = 100
+        self,
+        initial_position: Float[Array, "n_chain n_dim"],
+        data: dict,
+        max_iter: int = 100,
     ):
         """
         Tuning the local sampler. This runs a number of iterations of the local sampler,
@@ -348,51 +230,6 @@ def local_sampler_tuning(
         else:
             print("No autotune found, use input sampler_params")
 
-    def global_sampler_tuning(
-        self, initial_position: Float[Array, "n_chain n_dim"], data: dict
-    ) -> Float[Array, "n_chains n_dim"]:
-        """
-        Tuning the global sampler. This runs both the local sampler and the global sampler,
-        and train the normalizing flow on the run.
-        To adapt the normalizing flow, we need to keep certain amount of the data generated during the sampling.
-        The data is stored in the summary dictionary and can be accessed through the `get_sampler_state` method.
-        This tuning run is meant to be followed by a production run as defined below.
-
-        Args:
-            initial_position (Device Array): Initial position for the sampler, shape (n_chains, n_dim)
-
-        """
-        print("Training normalizing flow")
-        last_step = initial_position
-        for _ in tqdm(
-            range(self.n_loop_training),
-            desc="Tuning global sampler",
-        ):
-            last_step = self.sampling_loop(last_step, data, training=True)
-        return last_step
-
-    def production_run(
-        self, initial_position: Float[Array, "n_chain n_dim"], data: dict
-    ) -> Float[Array, "n_chains n_dim"]:
-        """
-        Sampling procedure that produce the final set of samples.
-        The main difference between this and the global tuning step is
-        we do not train the normalizing flow, omitting training allows to maintain detail balance.
-        The data is stored in the summary dictionary and can be accessed through the `get_sampler_state` method.
-
-        Args:
-            initial_position (Device Array): Initial position for the sampler, shape (n_chains, n_dim)
-
-        """
-        print("Starting Production run")
-        last_step = initial_position
-        for _ in tqdm(
-            range(self.n_loop_production),
-            desc="Production run",
-        ):
-            last_step = self.sampling_loop(last_step, data)
-        return last_step
-
     def get_sampler_state(self, training: bool = False) -> dict:
         """
         Get the sampler state. There are two sets of sampler outputs one can get,
@@ -407,11 +244,13 @@ def get_sampler_state(self, training: bool = False) -> dict:
 
         """
         if training is True:
-            return self.summary["training"]
+            return self.summary["GlobalTuning"]
         else:
-            return self.summary["production"]
+            return self.summary["GlobalSampling"]
 
-    def sample_flow(self, rng_key: PRNGKeyArray, n_samples: int) -> Float[Array, "n_samples n_dim"]:
+    def sample_flow(
+        self, rng_key: PRNGKeyArray, n_samples: int
+    ) -> Float[Array, "n_samples n_dim"]:
         """
         Sample from the normalizing flow.
 
@@ -425,7 +264,9 @@ def sample_flow(self, rng_key: PRNGKeyArray, n_samples: int) -> Float[Array, "n_
         samples = self.nf_model.sample(rng_key, n_samples)
         return samples
 
-    def evalulate_flow(self, samples: Float[Array, "n_samples n_dim"]) -> Float[Array, "n_samples"]:
+    def evalulate_flow(
+        self, samples: Float[Array, "n_samples n_dim"]
+    ) -> Float[Array, "n_samples"]:
         """
         Evaluate the log probability of the normalizing flow.
 
@@ -461,22 +302,7 @@ def reset(self):
         Reset the sampler state.
 
         """
-        training = {}
-        training["chains"] = jnp.empty((self.n_chains, 0, self.n_dim))
-        training["log_prob"] = jnp.empty((self.n_chains, 0))
-        training["local_accs"] = jnp.empty((self.n_chains, 0))
-        training["global_accs"] = jnp.empty((self.n_chains, 0))
-        training["loss_vals"] = jnp.empty((0, self.n_epochs))
-
-        production = {}
-        production["chains"] = jnp.empty((self.n_chains, 0, self.n_dim))
-        production["log_prob"] = jnp.empty((self.n_chains, 0))
-        production["local_accs"] = jnp.empty((self.n_chains, 0))
-        production["global_accs"] = jnp.empty((self.n_chains, 0))
-
         self.summary = {}
-        self.summary["training"] = training
-        self.summary["production"] = production
 
     def get_global_acceptance_distribution(
         self, n_bins: int = 10, training: bool = False
diff --git a/src/flowMC/nfmodel/base.py b/src/flowMC/nfmodel/base.py
index 06bb406..32ce884 100644
--- a/src/flowMC/nfmodel/base.py
+++ b/src/flowMC/nfmodel/base.py
@@ -1,4 +1,5 @@
 from abc import abstractmethod
+import copy
 import equinox as eqx
 from typing import overload, Optional
 from typing_extensions import Self
@@ -125,9 +126,9 @@ def train_epoch(
             perms = perms.reshape((steps_per_epoch, batch_size))
             for perm in perms:
                 batch = data[perm, ...]
-                value, model, state = self.train_step(batch, optim, state)
+                value, model, state = model.train_step(batch, optim, state)
         else:
-            value, model, state = self.train_step(data, optim, state)
+            value, model, state = model.train_step(data, optim, state)
 
         return value, model, state
 
@@ -161,13 +162,13 @@ def train(
             pbar = trange(num_epochs, desc="Training NF", miniters=int(num_epochs / 10))
         else:
             pbar = range(num_epochs)
-        best_model = self
+        best_model = model = self
         best_loss = 1e9
         for epoch in pbar:
             # Use a separate PRNG key to permute image data during shuffling
             rng, input_rng = jax.random.split(rng)
             # Run an optimization step over a training batch
-            value, model, state = self.train_epoch(input_rng, optim, state, data, batch_size)
+            value, model, state = model.train_epoch(input_rng, optim, state, data, batch_size)
             loss_values = loss_values.at[epoch].set(value)
             if loss_values[epoch] < best_loss:
                 best_model = model
diff --git a/src/flowMC/sampler/Gaussian_random_walk.py b/src/flowMC/proposal/Gaussian_random_walk.py
similarity index 98%
rename from src/flowMC/sampler/Gaussian_random_walk.py
rename to src/flowMC/proposal/Gaussian_random_walk.py
index 09dd861..d77c8bf 100644
--- a/src/flowMC/sampler/Gaussian_random_walk.py
+++ b/src/flowMC/proposal/Gaussian_random_walk.py
@@ -2,7 +2,7 @@
 import jax
 import jax.numpy as jnp
 from tqdm import tqdm
-from flowMC.sampler.Proposal_Base import ProposalBase
+from flowMC.proposal.base import ProposalBase
 from jaxtyping import PyTree, Array, Float, Int, PRNGKeyArray
 
 
diff --git a/src/flowMC/sampler/HMC.py b/src/flowMC/proposal/HMC.py
similarity index 99%
rename from src/flowMC/sampler/HMC.py
rename to src/flowMC/proposal/HMC.py
index 37edd68..9285a0d 100644
--- a/src/flowMC/sampler/HMC.py
+++ b/src/flowMC/proposal/HMC.py
@@ -2,7 +2,7 @@
 import jax
 import jax.numpy as jnp
 from tqdm import tqdm
-from flowMC.sampler.Proposal_Base import ProposalBase
+from flowMC.proposal.base import ProposalBase
 from jaxtyping import PyTree, Array, Float, Int, PRNGKeyArray
 
 
diff --git a/src/flowMC/sampler/MALA.py b/src/flowMC/proposal/MALA.py
similarity index 99%
rename from src/flowMC/sampler/MALA.py
rename to src/flowMC/proposal/MALA.py
index 251b585..351b3e7 100644
--- a/src/flowMC/sampler/MALA.py
+++ b/src/flowMC/proposal/MALA.py
@@ -3,7 +3,7 @@
 import jax.numpy as jnp
 from jax.scipy.stats import multivariate_normal
 from tqdm import tqdm
-from flowMC.sampler.Proposal_Base import ProposalBase
+from flowMC.proposal.base import ProposalBase
 from functools import partialmethod
 from jaxtyping import PyTree, Array, Float, Int, PRNGKeyArray, Bool
 
diff --git a/src/flowMC/sampler/NF_proposal.py b/src/flowMC/proposal/NF_proposal.py
similarity index 98%
rename from src/flowMC/sampler/NF_proposal.py
rename to src/flowMC/proposal/NF_proposal.py
index caa54bd..a4cbf4d 100644
--- a/src/flowMC/sampler/NF_proposal.py
+++ b/src/flowMC/proposal/NF_proposal.py
@@ -4,7 +4,7 @@
 from tqdm import tqdm
 from flowMC.nfmodel.base import NFModel
 from typing import Callable
-from flowMC.sampler.Proposal_Base import ProposalBase
+from flowMC.proposal.base import ProposalBase
 from jaxtyping import Array, Float, Int, PRNGKeyArray, PyTree
 from math import ceil
 
@@ -112,7 +112,6 @@ def sample(
         initial_position: Float[Array, "n_chains  n_dim"],
         data: PyTree,
         verbose: bool = False,
-        mode: str = "training",
     ) -> tuple[
         PRNGKeyArray,
         Float[Array, "n_chains n_steps  n_dim"],
diff --git a/src/flowMC/sampler/__init__.py b/src/flowMC/proposal/__init__.py
similarity index 100%
rename from src/flowMC/sampler/__init__.py
rename to src/flowMC/proposal/__init__.py
diff --git a/src/flowMC/sampler/Proposal_Base.py b/src/flowMC/proposal/base.py
similarity index 100%
rename from src/flowMC/sampler/Proposal_Base.py
rename to src/flowMC/proposal/base.py
diff --git a/src/flowMC/sampler/flowHMC.py b/src/flowMC/proposal/flowHMC.py
similarity index 98%
rename from src/flowMC/sampler/flowHMC.py
rename to src/flowMC/proposal/flowHMC.py
index 4c62742..2c9ef2b 100644
--- a/src/flowMC/sampler/flowHMC.py
+++ b/src/flowMC/proposal/flowHMC.py
@@ -3,8 +3,8 @@
 from flowMC.nfmodel.base import NFModel
 from jaxtyping import Array, PRNGKeyArray, PyTree
 from typing import Callable
-from flowMC.sampler.HMC import HMC
-from flowMC.sampler.NF_proposal import NFProposal
+from flowMC.proposal.HMC import HMC
+from flowMC.proposal.NF_proposal import NFProposal
 from jaxtyping import Array, Float, Int, PRNGKeyArray
 from math import ceil
 from jax import random
diff --git a/src/flowMC/strategy/__init__.py b/src/flowMC/strategy/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/flowMC/strategy/base.py b/src/flowMC/strategy/base.py
new file mode 100644
index 0000000..17936c3
--- /dev/null
+++ b/src/flowMC/strategy/base.py
@@ -0,0 +1,36 @@
+from abc import abstractmethod
+from flowMC.proposal.base import ProposalBase
+from flowMC.proposal.NF_proposal import NFProposal
+from jaxtyping import Array, Float, PRNGKeyArray, PyTree
+class Strategy:
+    """
+    Base class for strategies, which are basically wrapper blocks that modify the state of the sampler
+
+    This is an abstract template that should not be directly used.
+    
+    """
+
+    @abstractmethod
+    def __name__(self):
+        raise NotImplementedError
+
+    @abstractmethod
+    def __init__(self):
+        raise NotImplementedError
+    
+    @abstractmethod
+    def __call__(
+        self,
+        rng_key: PRNGKeyArray,
+        local_sampler: ProposalBase,
+        global_sampler: NFProposal,
+        initial_position: Float[Array, "n_chains n_dim"],
+        data: dict,
+    ) -> tuple[
+        PRNGKeyArray,
+        Float[Array, "n_chains n_dim"],
+        ProposalBase,
+        NFProposal,
+        PyTree,
+    ]:
+        raise NotImplementedError
\ No newline at end of file
diff --git a/src/flowMC/strategy/global_tuning.py b/src/flowMC/strategy/global_tuning.py
new file mode 100644
index 0000000..d0666dd
--- /dev/null
+++ b/src/flowMC/strategy/global_tuning.py
@@ -0,0 +1,309 @@
+from flowMC.proposal.base import ProposalBase
+from flowMC.proposal.NF_proposal import NFProposal
+from flowMC.strategy.base import Strategy
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, Float, PRNGKeyArray, PyTree
+import optax
+import equinox as eqx
+from tqdm import tqdm
+
+
+class GlobalTuning(Strategy):
+
+    optim: optax.GradientTransformation
+    optim_state: optax.OptState
+
+    n_dim: int
+    n_chains: int
+    n_local_steps: int
+    n_global_steps: int
+    n_loop: int
+    output_thinning: int
+    train_thinning: int
+
+    n_epochs: int
+    batch_size: int
+    n_max_examples: int
+    verbose: bool
+
+    @property
+    def __name__(self):
+        return "GlobalTuning"
+
+    def __init__(
+        self,
+        **kwargs,
+    ):
+        class_keys = list(self.__class__.__annotations__.keys())
+        for key, value in kwargs.items():
+            if key in class_keys:
+                if not key.startswith("__"):
+                    setattr(self, key, value)
+
+    def __call__(
+        self,
+        rng_key: PRNGKeyArray,
+        local_sampler: ProposalBase,
+        global_sampler: NFProposal,
+        initial_position: Float[Array, "n_chains n_dim"],
+        data: dict,
+    ) -> tuple[
+        PRNGKeyArray,
+        Float[Array, "n_chains n_dim"],
+        ProposalBase,
+        NFProposal,
+        PyTree,
+    ]:
+        """
+        One sampling loop that iterate through the local sampler
+        and potentially the global sampler.
+        If training is set to True, the loop will also train the normalizing flow model.
+
+        Args:
+            initial_position (jnp.array): Initial position. Shape (n_chains, n_dim)
+            training (bool, optional):
+            Whether to train the normalizing flow model. Defaults to False.
+
+        Returns:
+            chains (jnp.array):
+            Samples from the posterior. Shape (n_chains, n_local_steps + n_global_steps, n_dim)
+        """
+
+        summary = {}
+        summary["chains"] = jnp.empty((self.n_chains, 0, self.n_dim))
+        summary["log_prob"] = jnp.empty((self.n_chains, 0))
+        summary["local_accs"] = jnp.empty((self.n_chains, 0))
+        summary["global_accs"] = jnp.empty((self.n_chains, 0))
+        summary["loss_vals"] = jnp.empty((0, self.n_epochs))
+        for _ in tqdm(
+            range(self.n_loop),
+            desc="Global Tuning",
+        ):
+            rng_key, rng_keys_mcmc = jax.random.split(rng_key)
+            rng_keys_mcmc = jax.random.split(rng_keys_mcmc, self.n_chains)
+            (
+                rng_keys_mcmc,
+                positions,
+                log_prob,
+                local_acceptance,
+            ) = local_sampler.sample(
+                rng_keys_mcmc,
+                self.n_local_steps,
+                initial_position,
+                data,
+                verbose=self.verbose,
+            )
+
+            summary["chains"] = jnp.append(
+                summary["chains"],
+                positions[:, :: self.output_thinning],
+                axis=1,
+            )
+            summary["log_prob"] = jnp.append(
+                summary["log_prob"],
+                log_prob[:, :: self.output_thinning],
+                axis=1,
+            )
+
+            summary["local_accs"] = jnp.append(
+                summary["local_accs"],
+                local_acceptance[:, 1 :: self.output_thinning],
+                axis=1,
+            )
+
+            rng_key, rng_keys_nf = jax.random.split(rng_key)
+            positions = summary["chains"][:, :: self.train_thinning]
+            chain_size = positions.shape[0] * positions.shape[1]
+            if chain_size > self.n_max_examples:
+                flat_chain = positions[
+                    :, -int(self.n_max_examples / self.n_chains) :
+                ].reshape(-1, self.n_dim)
+            else:
+                flat_chain = positions.reshape(-1, self.n_dim)
+
+            if flat_chain.shape[0] < self.n_max_examples:
+                # This is to pad the training data to avoid recompilation.
+                flat_chain = jnp.repeat(
+                    flat_chain,
+                    (self.n_max_examples // flat_chain.shape[0]) + 1,
+                    axis=0,
+                )
+                flat_chain = flat_chain[: self.n_max_examples]
+
+            data_mean = jnp.mean(flat_chain, axis=0)
+            data_cov = jnp.cov(flat_chain.T)
+            global_sampler.model = eqx.tree_at(
+                lambda m: m._data_mean, global_sampler.model, data_mean
+            )
+            global_sampler.model = eqx.tree_at(
+                lambda m: m._data_cov, global_sampler.model, data_cov
+            )
+
+            (
+                rng_keys_nf,
+                global_sampler.model,
+                self.optim_state,
+                loss_values,
+            ) = global_sampler.model.train(
+                rng_keys_nf,
+                flat_chain,
+                self.optim,
+                self.optim_state,
+                self.n_epochs,
+                self.batch_size,
+                self.verbose,
+            )
+            summary["loss_vals"] = jnp.append(
+                summary["loss_vals"],
+                loss_values.reshape(1, -1),
+                axis=0,
+            )
+
+            (
+                rng_keys_nf,
+                nf_chain,
+                log_prob,
+                global_acceptance,
+            ) = global_sampler.sample(
+                rng_keys_nf,
+                self.n_global_steps,
+                positions[:, -1],
+                data,
+                verbose=self.verbose,
+            )
+
+            summary["chains"] = jnp.append(
+                summary["chains"],
+                nf_chain[:, :: self.output_thinning],
+                axis=1,
+            )
+            summary["log_prob"] = jnp.append(
+                summary["log_prob"],
+                log_prob[:, :: self.output_thinning],
+                axis=1,
+            )
+
+            summary["global_accs"] = jnp.append(
+                summary["global_accs"],
+                global_acceptance[:, 1 :: self.output_thinning],
+                axis=1,
+            )
+
+        return rng_key, summary['chains'][:, -1], local_sampler, global_sampler, summary
+
+
+class GlobalSampling(Strategy):
+
+    n_dim: int
+    n_chains: int
+    n_local_steps: int
+    n_global_steps: int
+    n_loop: int
+    output_thinning: int
+    verbose: bool
+
+    @property
+    def __name__(self):
+        return "GlobalSampling"
+
+    def __init__(
+        self,
+        **kwargs,
+    ):
+        class_keys = list(self.__class__.__annotations__.keys())
+        print(class_keys)
+        for key, value in kwargs.items():
+            if key in class_keys:
+                if not key.startswith("__"):
+                    setattr(self, key, value)
+
+    def __call__(
+        self,
+        rng_key: PRNGKeyArray,
+        local_sampler: ProposalBase,
+        global_sampler: NFProposal,
+        initial_position: Float[Array, "n_chains n_dim"],
+        data: dict,
+    ) -> tuple[
+        PRNGKeyArray,
+        Float[Array, "n_chains n_dim"],
+        ProposalBase,
+        NFProposal,
+        PyTree,
+    ]:
+
+        summary = {}
+        summary["chains"] = jnp.empty((self.n_chains, 0, self.n_dim))
+        summary["log_prob"] = jnp.empty((self.n_chains, 0))
+        summary["local_accs"] = jnp.empty((self.n_chains, 0))
+        summary["global_accs"] = jnp.empty((self.n_chains, 0))
+
+        for _ in tqdm(
+            range(self.n_loop),
+            desc="Global Sampling",
+        ):
+            rng_key, rng_keys_mcmc = jax.random.split(rng_key)
+            rng_keys_mcmc = jax.random.split(rng_keys_mcmc, self.n_chains)
+            (
+                rng_keys_mcmc,
+                positions,
+                log_prob,
+                local_acceptance,
+            ) = local_sampler.sample(
+                rng_keys_mcmc,
+                self.n_local_steps,
+                initial_position,
+                data,
+                verbose=self.verbose,
+            )
+
+            summary["chains"] = jnp.append(
+                summary["chains"],
+                positions[:, :: self.output_thinning],
+                axis=1,
+            )
+            summary["log_prob"] = jnp.append(
+                summary["log_prob"],
+                log_prob[:, :: self.output_thinning],
+                axis=1,
+            )
+
+            summary["local_accs"] = jnp.append(
+                summary["local_accs"],
+                local_acceptance[:, 1 :: self.output_thinning],
+                axis=1,
+            )
+
+            rng_key, rng_keys_nf = jax.random.split(rng_key)
+            (
+                rng_keys_nf,
+                nf_chain,
+                log_prob,
+                global_acceptance,
+            ) = global_sampler.sample(
+                rng_keys_nf,
+                self.n_global_steps,
+                positions[:, -1],
+                data,
+                verbose=self.verbose,
+            )
+
+            summary["chains"] = jnp.append(
+                summary["chains"],
+                nf_chain[:, :: self.output_thinning],
+                axis=1,
+            )
+            summary["log_prob"] = jnp.append(
+                summary["log_prob"],
+                log_prob[:, :: self.output_thinning],
+                axis=1,
+            )
+
+            summary["global_accs"] = jnp.append(
+                summary["global_accs"],
+                global_acceptance[:, 1 :: self.output_thinning],
+                axis=1,
+            )
+
+        return rng_key, summary['chains'][:, -1], local_sampler, global_sampler, summary
diff --git a/src/flowMC/strategy/importance_sampling.py b/src/flowMC/strategy/importance_sampling.py
new file mode 100644
index 0000000..e69de29


Test patch:
diff --git a/test/integration/test_HMC.py b/test/integration/test_HMC.py
index 98d2c09..a272942 100644
--- a/test/integration/test_HMC.py
+++ b/test/integration/test_HMC.py
@@ -1,4 +1,4 @@
-from flowMC.sampler.HMC import HMC
+from flowMC.proposal.HMC import HMC
 import jax
 import jax.numpy as jnp
 from jax.scipy.special import logsumexp
@@ -59,7 +59,7 @@ def dual_moon_pe(x: Float[Array, "n_dim"], data: dict):
 
 
 from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline
-from flowMC.sampler.Sampler import Sampler
+from flowMC.Sampler import Sampler
 
 n_dim = 5
 n_chains = 2
@@ -75,7 +75,7 @@ def dual_moon_pe(x: Float[Array, "n_dim"], data: dict):
 initial_position = jax.random.normal(subkey, shape=(n_chains, n_dim)) * 1
 
 rng_key, subkey = jax.random.split(rng_key)
-model = MaskedCouplingRQSpline(2, 4, [32, 32], 4, subkey)
+model = MaskedCouplingRQSpline(n_dim, 4, [32, 32], 4, subkey)
 
 print("Initializing sampler class")
 
diff --git a/test/integration/test_MALA.py b/test/integration/test_MALA.py
index b43dd94..88a4ac5 100644
--- a/test/integration/test_MALA.py
+++ b/test/integration/test_MALA.py
@@ -1,4 +1,4 @@
-from flowMC.sampler.MALA import MALA
+from flowMC.proposal.MALA import MALA
 import jax
 import jax.numpy as jnp
 from jax.scipy.special import logsumexp
@@ -62,7 +62,7 @@ def dual_moon_pe(x: Float[Array, "n_dim"], data: dict):
 
 
 from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline
-from flowMC.sampler.Sampler import Sampler
+from flowMC.Sampler import Sampler
 
 n_dim = 5
 n_chains = 2
@@ -78,7 +78,7 @@ def dual_moon_pe(x: Float[Array, "n_dim"], data: dict):
 initial_position = jax.random.normal(subkey, shape=(n_chains, n_dim)) * 1
 
 rng_key, subkey = jax.random.split(rng_key)
-model = MaskedCouplingRQSpline(2, 4, [32, 32], 4, subkey)
+model = MaskedCouplingRQSpline(n_dim, 4, [32, 32], 4, subkey)
 
 print("Initializing sampler class")
 
diff --git a/test/integration/test_RWMCMC.py b/test/integration/test_RWMCMC.py
index 62deb0b..f00159a 100644
--- a/test/integration/test_RWMCMC.py
+++ b/test/integration/test_RWMCMC.py
@@ -1,4 +1,4 @@
-from flowMC.sampler.Gaussian_random_walk import GaussianRandomWalk
+from flowMC.proposal.Gaussian_random_walk import GaussianRandomWalk
 import jax
 import jax.numpy as jnp
 from jax.scipy.special import logsumexp
@@ -63,7 +63,7 @@ def dual_moon_pe(x: Float[Array, "n_dim"], data: dict):
 
 
 from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline
-from flowMC.sampler.Sampler import Sampler
+from flowMC.Sampler import Sampler
 
 n_dim = 5
 n_chains = 2
@@ -79,7 +79,7 @@ def dual_moon_pe(x: Float[Array, "n_dim"], data: dict):
 initial_position = jax.random.normal(subkey, shape=(n_chains, n_dim)) * 1
 
 rng_key, subkey = jax.random.split(rng_key)
-model = MaskedCouplingRQSpline(2, 4, [32, 32], 4, subkey)
+model = MaskedCouplingRQSpline(n_dim, 4, [32, 32], 4, subkey)
 
 print("Initializing sampler class")
 
diff --git a/test/integration/test_flowHMC.py b/test/integration/test_flowHMC.py
index 370893f..75660ac 100644
--- a/test/integration/test_flowHMC.py
+++ b/test/integration/test_flowHMC.py
@@ -1,11 +1,11 @@
-from flowMC.sampler.MALA import MALA
-from flowMC.sampler.flowHMC import flowHMC
+from flowMC.proposal.MALA import MALA
+from flowMC.proposal.flowHMC import flowHMC
 import jax
 import jax.numpy as jnp
 from jaxtyping import Float, Array
 from jax.scipy.special import logsumexp
 from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline
-from flowMC.sampler.Sampler import Sampler
+from flowMC.Sampler import Sampler
 
 
 def log_posterior(x, data):
diff --git a/test/integration/test_quickstart.py b/test/integration/test_quickstart.py
index 8fbe7e2..5401686 100644
--- a/test/integration/test_quickstart.py
+++ b/test/integration/test_quickstart.py
@@ -1,8 +1,8 @@
 import jax
 import jax.numpy as jnp
 from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline
-from flowMC.sampler.MALA import MALA
-from flowMC.sampler.Sampler import Sampler
+from flowMC.proposal.MALA import MALA
+from flowMC.Sampler import Sampler
 
 
 def log_posterior(x, data: dict):
diff --git a/test/unit/test_kernels.py b/test/unit/test_kernels.py
index 87e1f8c..9b6a123 100644
--- a/test/unit/test_kernels.py
+++ b/test/unit/test_kernels.py
@@ -1,7 +1,7 @@
-from flowMC.sampler.HMC import HMC
-from flowMC.sampler.MALA import MALA
-from flowMC.sampler.Gaussian_random_walk import GaussianRandomWalk
-from flowMC.sampler.NF_proposal import NFProposal
+from flowMC.proposal.HMC import HMC
+from flowMC.proposal.MALA import MALA
+from flowMC.proposal.Gaussian_random_walk import GaussianRandomWalk
+from flowMC.proposal.NF_proposal import NFProposal
 import jax
 import jax.numpy as jnp
 from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline