Repository: jhuggins/viabel (language: python)

Issue:
VariationalFamily abstract class
Objects representing variational families should inherit from the VariationalFamily abstract class:

```
class VariationalFamily:
    def var_param_dim()
    def init_param()
    def log_density(var_param)
    def sample(var_param, n_samples)
    def entropy(var_param)
    property supports_entropy
    def kl(var_param0, var_param1)
    property supports_kl
    def mean(var_param)
    def cov(var_param)
    def pth_moment(var_param, p)
    property supported_moments
```


Discussion / hints:

Fix patch:
diff --git a/.gitignore b/.gitignore
index 407076ca..685cde16 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,6 +4,7 @@ __pycache__/
 
 # pickles
 *.pkl
+*.pck
 
 # C extensions
 *.so
diff --git a/docs/source/api/api.rst b/docs/source/api/api.rst
index 73388b98..43ad3ff2 100644
--- a/docs/source/api/api.rst
+++ b/docs/source/api/api.rst
@@ -66,4 +66,4 @@ Optimization
    StochasticGradientOptimizer
    RMSProp
    AdaGrad
-   SASA
\ No newline at end of file
+   RAABBVI
\ No newline at end of file
diff --git a/docs/source/quickstart.ipynb b/docs/source/quickstart.ipynb
index 772c9bb9..edd41748 100644
--- a/docs/source/quickstart.ipynb
+++ b/docs/source/quickstart.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,31 +40,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "average loss = 0.57177 | learning rate = 0.0125 | (-0.034842, 0.029293):  40%|████      | 4000/10000 [00:09<00:14, 401.59it/s]  "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Stopping rule reached at iteration 4000\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from viabel import bbvi, MFStudentT\n",
     "results = bbvi(D, log_density=log_density)"
@@ -79,20 +57,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
@@ -131,19 +98,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pareto k is estimated to be khat = 0.77\n",
-      "WARNING: khat > 0.7 means importance sampling is not feasible.\n",
-      "WARNING: not running further diagnostics\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from viabel import vi_diagnostics\n",
     "import warnings\n",
@@ -163,20 +120,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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YmU5SKBRBEcuamqlDQYfCPMe45MBW3aC4QXVzcEuEnsncmybaGzFN3opgi5WOOV+g2NHgYzSZR3VIVHsVVFlkd1PwbRUu1jJnpqMT6z7uP0pSeCsct2uV7G/niKK1nMMz3VFm0nmyuTzNTnj11GXOSBYRl8jhdve1D0oyqK5rm5TsRHAsdAoe6vKtS6pfa6LXneDwm78B2ZaNadm4FImIqizZ9N/s+cwXMHYEr825ikana2UiLxbKz6YB+XT5AagIOFIiuVSRCaNEzhDXRbQ3sv5uxZq93jHnCxSJXIl6vxO/UybokUkW3v4SJOuZM+vBP0pSeCtMCteT7G/3tH1Y+Rxs0wAtC4UsB70ZLudynNFKuGQJBZOuKpVt9QHweEEtS5dzturVcCNS/VpI9XaMBV+M+eeezOs0hVw8vr0Gj+q4rrnyZkEQhFmzkRM8QWCWKIzSNa2vmAO9SCGf45FqkeaAxkjKID9RwHbVgMsLqmdV5/2NrL/lvrO5xkNBtyga5i1rfzufgJ++EAXgyZ11t4VwsZ45sx7c8aRwIzbG28GkcCfZum3bLkuMhUz5USpU3lMBWRLJ2TJRy0VOFZEbGnDeYPz8WqXg24FU7WVMYm8mkmm5c3+7SUwQBJDV8sNbJiPbNNhfPUsQWo4uZXY+pKfLD0HEdnrB5QOXb4lGeCPrb7nvTKY0hhIFqrzKDV2ntYxjPgF//EBz5fXbfV/mcCvmzB1PCjdqfnm7TQq3wu9wM2FbFmiZssmgkClHslQggNMDTi84PRRsjUfrpZtyLdcqBd8IqdqWCaWyw5uShq0XZ80l1/IebFMvvzaNeYlws4lullnJKVg56U1YmLU8F/o6GxqLJIEklzfKubwCuexI3+ec3XwNm+1VzvLr2xCC5AC3v/xg9roWc1DIljVIvVj2Scz6JWzZWSYItx8UF4Ig3ND6SxUMwm4ZRRKZShc5PpTgQGv4TQkEb/c+8Gax3HqZv1/cCO5YUlixhWFrkPftun4Lw5utgt/Ixn27+R1syywTQD5Vfp4vCUsyRdnD6ZjN3s31OJVr0t+Btmu+g7dKglpMqlnNQDIKyGkNS8thF7Kg5aA0Z/YolDerm4z5uoIw959K6Yy1fW/F/wlC2f+iusv+GKcHYY6MXV5wehFk5W3Ls1gw511+cM2ShFEqE0QhM0sSs+HG6WmQZGy3n321gbJZURDWXEcs4HIQz+s8sCnC5lrfTdGy7wTT4noxf7/w38D371hSWCwppmZbGDYF3poWhotxIxv37eB3sG0L8hnIJ5cSgeICd6As5TkU+ibSnIxO4/Pn2F7vq5hPtJLB6eEke1uCyLPZwnPv2bZN0TA5M5LirqbAsu9fG8vK/4Oy9iIYRcRCBlHLkhhJ8oBVoNXWGE0VKZy0sYMrN1+xAduhYssKtkPFcijYkow99yw5sGcT3mzRgS1KIErlZ0HEXinD+dpgmZ/BLMzLWhYsE8Eql9oQLBPBNGYfOqVSibNRjX1BE9XWy+doFBFM45otf945zIclyViKG9PpwVI92E4PptOH5fQuMNvMJ46516v9by3PPRNpjg/E8CgCXfX+a++JDgRvaNbcZJfJOT+rOZg6ZGLlxyxB4AmB4qx8f/FaWrwOTgyWu9ONxPNIgnDbRpStBzfDGrDcfqGnZ9Z9nDuWFOa3MDw9lCRV0Lm3PcyxwQQXJ259C8M53MqN+0b9DrZtr/rAtkHXkApppGK2vHnNwpRUdMVFyeEqb4yWzU9Pj5DIG+SKJjU+mecvjHLkqkTI7eDRLRF6JrMc7U/isEt01nqXjKdnMsuJgSSqYC77/rKwTEQti5RPIRVSSIVMmQzmZTXfByBCUYeM7mBPlQPD6cNS3diKC0txYytOLNmJrTjL4a8rSNXLSdvCouebhVnqKL+2bXoms5y0EzhDIbbNuz62aSDoRURdQygVEEsFhFIesVh+LZbyCKY+e31SS37HUlyYLv+1hzuArbhWvAZrxQtXYwvmwwvd4xzrnazMh8UQBAFEN4LHjcPSkfUCjlKufC9nCcKSZJ4esZjWHeQNqA+ovNwzycnBGAG3zLY6D2dGUjiVcqRTU9DF49tq6Kjx3rDZ53YqMHkzrAHL7Rdta11v83DHkgKUbYz7W4K0Vnl4rS+GIPCWO2xvtsNY001ODyW4qyVQLuthm6TzRUZMk3TBwDRKGEUNXVtl018Bgm2i6AVUPYdkXSsJYIgyJfkaEVRglRfaA+0heqdznB/P4lIcyJLBtlov0XSR75+bJqebNEfcnBzNcXlaI+JReGxbNc/3TJPIG6TyJUwkjo/muRrTiXhlHt9ee21c2JBPI2RiCJk4ZOKQSy0gqwocCngC4AkguP3g9tGfEjgzXCC0rW7ZBXVt8fuXLP61bgy3ujLp1voI5yc1BpLpNQsUtm1jlwpQyJKOx/nO2Sk+0Wzg0zOQz8wSRwE5NXntOw6lLMH7whRcIc6kZfZ21K6owS33/EhnLVcmM5wby+BxKqiaxfY6H1uq3RVyXVYDBEqCg5LiA9mLZJVQ9AKKUUA0dd5VY3M5lufEtIXs8mIZNm01KluqXQzEcsRSWUqaRqZo8OCmEK1+AauYp8krIAgOstksgiBUHqIorvg33PhGfDPnws0UKpf1UzrXLwDc0aQQcDlIF03SeR2/U8ajOt7yFoZrq+djY1kWlmVh2zaFos7p0RR7GnzIklB537ZtLk2kea0/CXqezlov8VSGuxvdNAadjCU1Utk8xcD1b9v8xSCZJZRiBkcpX96AAVuQymYGlx8UJw5Anved+WYCQRCI6w7sKZ2MIWE7VMJBPwe2eDk/muRH56NsrvdQsDT2NofYVu9DdUgc3uWkJ5rhxxcmmMqUqAmIHGqN0FnrQcnFsROT2MlJSM0sqGtUgduP4AuDL4zgmzVHzDoqYXZBTc4uqIhvxQW12uJf68ZwK/w6b1agEAQBYTbk99VBnZfjKerbN/H+A43lQIF8GjubgEy8/JyOI+gaJCchOclgWuJsTCY4JLOjtQpCtQihOnD5KBrWihufFwjkbQyhQKokYIoyoYCPmqqF12U+kSx+Pf9v3bIQilkchQyORIJiqUQynsQqgddy4JZ9FE14cFOIxoDKWFJD000s68acwi9cjZHMG+R1ixq/yk8vjnOsb4oqr8rj22sq5LGYVOZwM+fCzRYqF/sph0dG132MO5IUFrPr8aE4fpdMU8jF/tbQW6ZKzm3mmXyJB9sDNAadDMVyxFIZ0l6hQgSL0TOZ5UR/EhWjYk6Zr5I3BlWOD6e5OJkn4lU40BZBEAQifs+KUtByk5d8quzcmxdCitML3jCC24csrL3AoGbYS5ziTlliMqXRN53FrYh4VHkBIb58ZZozwwmO9MXYHHJwuX8GeWqQCTXNE1WLnL5OD0KgGvxVCP5ZInAoC+9NQEWdd37XW1CrSWHAdSU027Z55sI4sVSeZE5D00q8eCbDmUsiEafE4Y5FqrkgXOuPIInlhL25KCNZKWcYzz/lmxAa/fs/uUw0qZHWdDZVeZf0FRC8Qahrr5wPxTzPnBogFk+SLWZo9mR4I1ri3PQ4EWWUJ2p0UF302tWcTLjwi23saK9d8rtrCZJYznexIlwuCFajFaZ5xJ+jWS4wEstTyKXxpXUO1QXKc0Nx0Vy3unl0vpC13P/ma76KCKJtsSmssKXaTT6fX3Z4oijy4tUYiYJJXjep87t4ada8VeVTeffOhhty+N/s8PjFfkq/lVn3Me5IUli8GbRXexZIqDeC5djftm1M06xs7vNfz00wgC3h8m+m8wVe753hw7tqMIyFFRvnT6pcyaSt2sfp8QK9cZ2IV+Xdu1u4MpXlzEiKqpCbolC4oXOyLatcMTM1fU36FqVZk0GkstGuF4sn2zPdUb55dJjL0TQPdEQwLRutZPJizzRdDQFsvcTDgRwXYsMoOY0OtUhEEKixTJxYFJ1BnFW1CKFaCNQgON0r/vZKktn1FtQS0hBttgdstrlzkMtwOZfg9EQep1REypTYpmboFNOYx/KztZqKPGxBj+7hqVwVU7ZKjVDkfs8MnXIW69TKprplITnKYaaqsxxR5HSR13wccrtpVj2MSE7yQ2lsuaZsInN5yoXyVsH/dbCDZy9O8vzlyev2FRCEcijxobu3V66LO+hEicbZ4dfZKiV49mqMWN4kZ07SrFq89tNBzrhdVFVHOLx/MwSqEURxXVE76xG4DnRUA9XYtk1XXa7scyikIZeEXLKc/xCoRlA9iOL6qybPrdmZkoQ5USRVEjCQ8Lrd+L3uBeQxX6izLIt7WwMVMhFtA9vQZ81bKolEuUqtJEmIorjgMfe/JULbLG63sNg7khRuJrs+3T3BTKZIplCizqvw04vjHL0aJeCUeGRL+Lrfn3/zT/QlOTKUpjns5f13NS6R5t9917UyDkGvm6wBu+dt/Kqskdctoilt3edkW1Z5AaVnyrH1ULa/+6vBE0S4gQW0GuY23KJp0hR0M5rM81Cdi04xgXnqGZ7tiRMrCqSyIpItcTqjIqpOLLefTCRITUfDdTeTtdhb5y+o4ViOfCaDPR7HTs2gpGaQRhNkJ0zGSmmymonknEFWcgBIJQ85LcK4aJC3HDicMdTZ9+bwcqmGC0aAHrzUKCY9phdF8DKplngiVFjotLXta/kMllnOczB0iiWD0zmVvUoK1SwnfEEMgL1z3x2E7bMvrROzL0QJ/GX7v+APQyCCEKiCYBUEqhBkhYBbQRRgNKEhIKypr8D89RNNFckJKkpDE+6Gu3hkh8HlwXFOXxnDJWRwTKfpUjJ0Wkmsk31ljaeqGaGmBcINCNL15+eNmFsEQUBzuDid8bK3oRa1EIdsohziqmVBcWEHasrJceuQ0Oc+WzLhsa7aBRux2728YDKfIKYKoNsF4ppF0RRQZRmn4qiQh2mamObyEXBzGv4cSUiShCRJ7GsJVsZ1O4TF3pGkAOtn1zkV0jCMyo0zTZPd1TK9lDif0RFtEdG22FzlZku1B2DBTVz8umRYnB5J8WLPOFPpEmlNZ2ttgB92T/NyX3JJa8DrkdmNSAy2ZUE2VtYM5hLMFFeZDNzXwgRvtqO0ci75IuPJKbKxGaRoEsVfHsPBiEAPVRwbduJyKliSg7DXiSYJ1IV9fOPoMJ11Pmr9zhUdaquZh+ySBjPj7E2OQzyKHYuyLTEJWn5BekCh6OeQYNLs0BhxudBUP1RHwBugWPTziNtFc7WfUV2hIKiI7ZHZGk3l5LFHLEidHsOI5djR4Kd7PE1zxMOj+xqRHNKaruuV4QQ/OD6MZ38De2qcUNQquRO2loNCDvLluH47m4ZcqvzQ8pCcgeTMtWilylGFMmGEa+mdrKNFVvlkZxVph2dJX4HlxrjSXHMqDhRvgLynxKSznoKrhFIv4ZTi2DOjFDMZTncPsXesD1V2IFQ3I9S1Q7iBomlztD+OINjc2xHhpZ7pN+VEXUgmDWUSmAtnLRVgeqhMDsHacs7GOshhPZqOIAiVDdxA4omd9QuuWzAYXGCiWs2ysBJpiKKIYQtcmMhyoC2CS5UrQuWbwStXp9f9nTuWFK53U+cIQNd1TNPEMIxlI3NUh4jskNBMiGsWui3h93mpqQqtqO7NoX8mw6nhBI9tq2U4nl+TCr/axr+eiWrbdllySk2WJVIok8EKC+RmOsdsy4TpEXLdVzmYnabZZTLiEdEQoboBobqFV2YULkzkSYkpanwq0bSGDRi2TTpfYiKlcaA1xKHO6hV/p0I8BZ2JzCjZmTjS+DSOwhBWcoX4a8UJwWqEWWl6fyCM4I+AL0TAG1hg1z8w72srXRGnBLuaAszkSiTzOj6nzK6mQGVzXUu/6d6pLDnN5B/OT3Opxju7KbYC1+nfphchk4B0AjsVg3QMOzkDyWlIx3h2UiQ2kSRoZeiUivQ8ewGPAyIhH1YxArXNCHWt9OXkJWNcLRNWEFgyR8XWTbD1bgb6Rjl9ZhC/naLLjGFHB7CjAxRFle9naugueHG4XFR51Rt2ov7o3Dinh5NEvMoCMvG7HIQ9Knubt6BqybIgVCrA1CCoHuxQXdnxfgsxd9003SRVMCpZw/N9fcthPmnMF0rnk8fVRaHbc2TkcDgqj/WYzJ7pjjKdWX/C5h1LCouRyhX5m+PDfGR3LU4HS2z6cI3xDRsuTOTY3xbBrcrIGYF37/ZR43Py7KUoBd1e9eIvNmucHU3RN5VlMJZHEldX4W9GBqWtZSE+Uc4ShfJGGKhdVpW+mSFvdi6JPdaLPdEHepF9AuAXINzAjvr2sllhNmHqULVJUhujZFnsrA9wYSLFaDxPSbc4ko/jkiXOj6Up6NaSsdilIoz3Y4/3k+sZ52BqgmYhy4jhpGBLoKbLppVIHUJ1I0TqEcK1EK4Dj/9NS1eLsRyRr+W66qbFSCLPSCJPW8TDYCyHKov43WtbdoKsls8pXLeEPGzT5JHYFJf7Rjk9MIPLiOOIZeiyonQWxrDP2TxbqCJmyeQkN01VQV6NVnOmpppIXS1P7qhf8ntzBPdoZ3Vlbs49zz/f1k0dHElrnBcNIqU4dnKKHwzo9OZTNKhxgm6FP5ucJlwVor3ah2bY6zLzNgZdvNEfI1XQ2VJzjUywbV7ti82SWzW2N1wOX05Pl8tsRPuwPUEI1i2pt3SzsV4haz5pOBwL7//T3RNMpzUymk5btY+TozkuRnMEZ/M+5u9lcyQhyzKyLK861w9urUbIx9d9bnc0Kdi2TalUQtd1fnxunBcuTeGXLZ7cXpY+JUlCluXKhZxTxy6Opzg/kaPK76arQals1BfHU8RzOnua5FXNAstJQCGPzK883M7elhB/d3KUeKF088/XKEFiolLCGEmGUB24AytOjutJa9czf9i2BdOjWCOXIRG99oY3iNCwBaGuDUFZmkXulKVrEnahhN8p89DmqnLEUkZDkURGEwW6Gvx01nmwo8PYw5exR65CdLCSI7EPQBIgXMuOuhaobUGobS5vlGuovHojKEfpaGWzTiHLPkvDTmswo7FNL9dFKpZ0epI6p+PgdAs48jbbwwKdugPr2CVwyLzLoVKyDHpyGjmHjlmy2RJxLsjRWAuWu0eCJOGsqUcx3OS1EJNOB4VmA6Xdj0tMYk8Oc3BimJ7haU6nNVzTgziMKNvH03T6LMzhTQjNnQht23h2SLsuwc2fRw5JYDSR5wN7GpjMeIk5a4kYMxSm4iQyGtmMQcgR5wNVk1hGI/6Wdloaa1c1iWq6yVde7ifoltF0i8aAi6MDcSaSGtF0gVimhN8lLzs+2xeG1BSkY2WHdD5dNjX5I9d11K8XtyJZ9dDWmiW+xr3NIbbWepAoWzwMwyCnlTg3XA5lVxxlDWCOHBRFWSLIzmna68UdSQqGYVAsFikWi/zpS0NMZkpkNYP2KhdPX47zxlCWxpCbf/ee7Qu+t9INHU7kaQm5F/z/qQtRRuL5ZSWB5XwDH9hTdpxeHE/hVBwcaL2+k3qtKJuK4uVN2bbKzk1/TTlE7zrq5PX8GCtJPLZpYk/0Yg91l+vYQLkDV107QsOW2QW3+oRbTsJ2ySLjKQ3ZYRMpJZCOdyPHu7HmfgPK51fbgtC4CaGhA+rbVo1OWg/sQg5motixSUjMzJpjYpBJYmdSkElCNr2oAOBSyIAoRsg6Whi3i2QFFdEYRrZiFZ+GDOiOLTRItTRZKUQxgH5uEunrwxi+IHgDCL4ABMJlc1cwAqFqhKpaCNUgyGVpd6V7pOkmxwfj3NMaIl002O1R0GwQWrYitGzFDShjSQrnhohqSfIzMzjMAdTiBPSew+49hw08FGrgSngbZ6QGnFXuZc088+fR+XSS/pkc0WSBx3fU0RPNMJ7SIGeTL+UJOExqnBpuNLrMARgYgEwjHU1dnC4oy5a67p/OYtk2PtWBaeuYFnTW+3hkazWSKDAwnaNgmMsKNoIoQai+rDkkouVopWS0HK0UabypJqVbUd14pTXqmq0vpijliMGRTJKemRJVfpPNERnTNNF1HV3XyefzyLKM0+nE4XDM8yWuP5LpjiKFvFYinU4vUKd+6cFWXulP8NLVOBG/l2Qxw8GtNRV7/nwpa6Ub+r7ddQzM5Dk9kuByNM1wPF+2iW6pXlESWLzhvXRl7X2Z1+P0tfUixEav1b9x+yFUv67Q0uuZP+oCzorjt8Yr84Q/iT10kWKhwOmUg711Xpxt2xDqNyPIa//dxaYy29A5efQ0h6av0jRziZGCOGsOyoIvhNC2HaF5KzRtRlBvvIaVbVkQm8QeG4ToCPbkKPbkCExPQG6NcduyCm4vuMrF6ARFLTug5dm8A8lBUXPziGTTrBiMFCU0sxFBzZejv3Qd9CJtWYVteoZmPcWIlkIzjXKl1thkeYxzY178+4LAs/5dxP115DwRmqsDvBqPcqa6mqpIuTdw/3SWbNFkMl0uIf1oZzV7FheXM2we3dNWuff5kokYtLBHrmAPXYbRq6iJcaTJFFktwvhFlWywAYevE6Vp4bFeujKNppu4FQcPdoQ5PpQgUzTJlQzSmkHILVPnD7OvJYAgiBS9FoI9UTY3zoxxtT/KyVwIv7aVrq5NCIKwYB52VHk5M5JkNFHOz2kMuQm4ZLoaAnhVBy/0TK9qhhJkFWpasQsZiI+XzavRPmx/NQRrborWcKvK7q/ma5x/jdqrfJwazXFlukjYI/PIlnDFWjL3kCQJp9OJoijsbFy/ifqOIoXLozNUhYMAqKqK0+kkLEn4xgvkShaDsWzFni9LIm/0xXApC5tZL3dD/S4FWSr/XeVVSWk6iizid8mki/qyksCb6cu8FntkxZGcGC+HOkqOcgige/03eSWn4tx40/kSo4k8/lKSD0TGsSeLFE34XjzEtKuW4Kat7FihP8L1zU82jA9gXz6OffUse+dVKu1qakbYtAuhvatsDpofKdUXWxtpWhZMT2APXcEevII9fBXGBqFYoIjEaamWvWYUdU5+l1WoqkOI1JSl8mCkLKEHImWp3TdbQkO+vk367nmvg7PjPrboWtwz7zOhueuhFSCbgmwKO52AVBw7GStHGsWmKlrMwVQ3PZkJzki1qL0ZHIKPbeYko956vh5u5LSjAckf4JTgojrg5S9fHWBfa3iBILKSD0sIVsGuB8oNk8b6KJ7s5tD4VZqNMUbi4+SfPo11RkHYdqD88IX4hQfbK3OmKejGqebpavBTKBk8sCmyYEO79rutPF1qIDYyQjYVpVlO8dqRk5y50ENV2yYO7duyYN3Y2Lx7Zx0Ht1YzmdYqm+N6IvMElw+7fks5CCM9U/Y5FNLYVS0IivO69/V6WM9YVlof8/+vlUxODyf41N3N+Jzykj1hNe1EdUioqoplWRSLRTRNwzRNcrkchUJhxfDY1XBHkcK58SwJR5y6kIcnd1wzz6Q1g0/tb2J/W5iTg3HSmsHXjwxydCBBS9jFnqZQRXJP5EvL3tD5N/qly1O83h9blySwFglirfZI2zIhNlbOSIZyF6xQ/U21oTtlieODcY4NxBCKecxUiiNREzEhYioRpFA15ws299WFeb0vzrnR5WvyrGjayGQ49dox7po8gZqeuvaFmmaELXchbN5djr1fBquRpm2aMNKLffUCdl83dt/F5aV/f4j+6h2cUVoJdgTYsbUVahrAH7plpabXQvaCIJS1D5cbqusRWH7jsE0Dd2wS5fIg+Z5pork4uVQGKxnHl4tTm50kJ41iAympnna3hlBVy3ZfPdva96x5zILkgJZODrR0lv1HE0P4e89iXz1TDoU98hPso09D63bUnffjkBuWzPE9m6qApRE5czjU1UhP2M/poTrcRgwGRzHzGe5NHMM6O8rFfCPpksSwlSNXMuio8hDxqkS81/pJrDdAQxDFsknJ7YeZUYoFjdMnzrN3eztq8Pqmz9WEnfWMZaU5Mf///VNZXroyTZ1P5f13NS45xlr2FlEUcblcOJ1OSqUShUIBy7LI5XJLjnc93FGk4HG7uKu1aon0/bP3tVZeK7KEUDQpGTa7Z5tZlwybkmHxM/e2LMgQnn+T5t/o+pCbn7vPu+4Mw+tJEGuxR9olrRx/bZTKJZrDDeUyxLcAO4Iwpk0xntZw2iCqMheFMC7VQyFpUe1T6Z3OMp4s8PCWKt63u443ZiX4lWLQw+Q5mDrLd0+NMm3K+Jw5ukL+axJneGUn60qkGRaKPKH3o104xamr4+zND16T/KFsk2/rRGjdAq1beTbnJ27KZIsGDR6FvxlO0DntpbZU4vAO4abnbLxZ5+NyG4cgOaCmkWLBzbs2i5U51T2WZCoao1mPURqbIRuPk81IJLJJPNlexN6f4viHP8GoaUDYvg+haz/CtrvWZPYTBBEa2hEa2rEf+gAMX8G+dBy7/wIMXsQavEheauTQtrto2XEPo1lzwRy/bua5bhN11jAadGKnY4wUJ2F4ktMjSWqrg2zbv4OhWJ6rU1n2tIRI5Ut85+RoRYK+EQiqB7t+C32X+jg2kWYw1cNH9jah1jSv6o97syHca/Ffnh5O8NT5CQzTpiHgXFKiZD7Wqp0IgoCqqiiKQj6fXzEhbzUI9mplNW8TjI6O8thjj/Fzn/8qm9ta+Oj+phUX83yziGXZvNo7w5YaHx5F4vGu2rc9Y/DieIoXeqbxO8vF++aH/9mFDEwPl53JshOqW8p20psM29Cx+89iD1/iTFLku1NuEkoY1esj7FPZXuunZzLNTFZnOlOuSvnP7muls97PCz3TPNpZvcBc1hR0Mzo8xp5EN6PDY/ToHs6XfNxb68DR0I6zpp6UZvIrhzpWrVJq21SO2Uie0Us97ImeoXPsNCoWF8UILzlaOOTNs2NbK8LmnbB5B1awakHiUKGk0xPNcHY0jWXZvDGY4HBnmMe2RnCI5dpTrw2keKDVT3NI5cJElh01Kko6CbEZSjMxLk4V2GXEULMpyOcoFTS6Sy525cdRjSLoernfAVAUJPrkMOdd9dTZBSaUADutOJvlErJLxXa6sH0+8PqxfX7sYAjCEV7OuUlYDvKGTY1HYSav41EcFVvx/MJs8+vu1PqcnBlJ4XeXI+s2R1yEcjOEZ4YpDPWxd+RI2UQ1B9VZJojd9yHsvhfBvb5yynY+UyaHC0fKET5Q9rPsfADhrod5diB3zT/ldxJNa3hVxwJSPDmU4PRwAkkQGE0UmM5o5dyhXJJ4KkdcF6hRBba31VBTV4NPlemfznB5Msun9jctK0GvBfM3Z6OQ42jfFDurZHY1+Dh8/04ESV7x8yudy3ysZh5asD6SefY2h2ivclf8lyGXwguXp5jMFLirOcxgLMtj22p5cmfdDZPgYsztnc8//zxNTU1r+s4dpSm0htycH0+zpzm74uY+X9VK5Eo0hdw8sb0G95tsZn2zsJjxU3mdN/pi7A1bqOnZkE93ACJNN700BYAdn8C6+PpsqQUBu7aN+zqauBrTMEyLZF7H6RDpic6WIQYibgffPjlKyK3QGHRVpJ58SUdLJBg79yrZWJwTggmii15XM6GWWgZcbrSCSU08j9+t0De18L7Zts3VyTRv9E5yZSLO+1sc6K++QfrSOEJ6kqyg4jCGeVlpJ1rVTjLUgK+uln9A5RkRAnGRe8enYHxqyXk+dznNhckSomAjiQIn+qZ4/UoUUdNoNNJEMjP83SsC/kKSGV2iavCnbMuOAzDiqeNyaBuRxGW25cr3ZMBTR3doG8HEDNtyUYqCg3O+ZvZkhnHbJrKnSCEUYMYooDls1MQVPLnoknHNx6OCgyvBJs7VbsPpkhD8tTRHFJpaqpkUmrA8XkqmTc9MkY6QgmnqTMRLqEUHbstgk1OhLaggSyWEkIIQ2Yp3+zb6hffhmhrFPdSDq/8CyuQI9pnXyw/JgbFlN9b+gwi77kFSnQvCtZeD4PYh7H8X2q6HOX3sLHsmjqJG+7FPPo999mUe3v4APU13c2Zq+eggKGviOxr89EQzCCLsbwvx/VOj5GU/dY1+XDMxClqJ/OQY3+2NU1sTRpTEZYv8XQ+Lg0v+6vVBLoylcMoSkXCQrJFjMlXgmdfOcfi+HQv8DOuNLlpvXa75/ktJ0Ms1w3SLkURuTSVK3grcUaQwlS1xf0f9AvX84NZqjvbHEASBezvCqA5pzc2s344mG4vtkd1jSV48N4i/2qSrWoFADQRqyqWLB1Z3tq4rismysPvPYA9eKP/DF0bcfj8H/BFODiXYVBesXK8zw0n+1RNb0XSTF69M41MdpArl4nr7WkJMZjS2+y1Sp44yOjzGE64ZJr0+Mm17mazdSuz0JG5dIJVKIYngEp20egV+eLyX5xzgV8oVZOM5nXzJQIzPcHI8xWR2AI9t8Ig5QqOjRE/jbobq3kdDZwf5HEzMlPC5HAg5g/ZweUMElhQgE0WRHfVQb02RmJhBiU8zmcjzweGjiCWNS95GLnrq6Pa2sDln8FDyEsf9HXy3+UEEp4sm1aLJJXHcuZvviV5sWabZ56DZp3LC/CCXVQd5W6RoiwSb3GwLK+hTBQ6K0OS0GU0WKWglRGcRLZfn3LTGHiuOnElBMgHJBFo8zjnNiauQRstqJJIFzOkcoROXaZ4lEysU4fKmfYxUdVHfFsDf3IohOEjpAiVbQJUdqLK0pISCDmjBWhLBWthzEEcmiW/oIv7+btzjA8iXT8HlUxhON8nO/UxuPcB5w8/OOg8uVV6QIDWXSSvLMldnCpzQ/Pgf+AxdYhLr5PMwcBHl/EtI586TDR9gon0zuZK9rB9u/kYZE4rU+J3EcyUMoez4ttIZfHKKR0I5nBTpsauvWyFgOSzeqO9pDzOSyBPPlVAlCTUQYXejTbvb5MjJbvbu7sTp8S4Z42o+xfXW5VrJfzmZ0thS4+UTdzdX/KFvN+4oUnA4WMLefVNZfnp5Chuo8ip0NQTW7Ai6GaUfbpRYnumOMpMtkk0maJYLvDZmcTbvpipiczgorGlsax2/rWWxzr9c7luAgNC+i1LTDk6PptnnNpdcr7ljXRxP4XfK+J0OpjNFbGA6lSNz9TJi8iRhy+Qc1Qw07uNqoINpzWbo8gTFQhGtYKNIIl5JoFiwKRVsjJJBo7+8mQslnee7rzAxmcGjF6iyBfqkMHeFBK5sup+qB3fR7FBJR3M0NQcxE0WGi0lEt4xXNtmyuYadjcFKpqht21gDfejHXsc4exLf+XP8g6MNS/XhNPKEHS7cWoaT9bs4Vb0d2+2mWvGQcDZxue4JdNXFJw80IwgCF8ZTVAXdaMk8n6gPIAg2F8bTRIJuTl+cJOJV8KoOtoTcnE1r9GUdRGqqOTi7GczvED42nuJKzzSN88yEc9e2t2eaGtXmsJCnKTfF0OA4udEGpJGzPJd0kLAVcuMF2gZf4MI5P3HZQ7VX4eMdTqJb96OHmti5o2VJ3Z355RPKrxsxt2wjb36IXCqOcuEY0rmjdCdM9p59jcnzF4mG76J6ezN1XVuXdGY7MponpVnl7HOXxN+9Ns0zqoOIdz+HH7mLwKXX0YbjHJp8jabSKcZ2HaagL+3ABgs3xKcvRBlPFDBsm5FEDqcc4vG7t+McPc/zvWmysWkGjSJZ0bsmCbpSHsOzsDxGIl/ivrYwV6azFW1Yrm5hNDHBqWgGv3KFrh2d5f7Xi8bYO5Xl+GCcTTXeBet7LRrF/HXVUe3l9HCSdKFEybDY2Vhu+PSv52k+N2oiu9m4o0ihULQq7H18MM53TowQz5VQHCIC8Gc/7SXiUXl4a9WqDr434xhcTAI3SiwHt1Zz+eogZ6bzuJwSij/AjvZ6RhN5vnl0eNWxrWf8dmoa6+wLUNJAdSPufBghVMvl4QTfPzOKWxbZ07LUkW1ZFslsnn31KjUukXxKh9Q077l4hO/PePmRGUH2B5gJ1fNjw4kzXaJoWNi2xf5GN9Gcwc46DyCS0UF3KChui7qgi+y5Y+w//0MsTWVG3saAWoMrFCbvDVN3fwfv3V2P6pC4OJ6iL5Wlpc6BpIo8uduzQOoSTBPjxBH0115Cf+MVrOh4Zfz9njou12/i3ojII1trGG/cit7Yyn6nh8a0xmRGI54tcWIogSccxKNIeNTycpgvJXpUacH/6oNOWsJuUpq+ZDOYPzdWcsQvTpSMpjW61RDR+loOP/5EZfzvLepcOt/LqXP9+GIjpEanebzvGC0jE5wdaWDPD7+LItikt+9Evv9hlEeewNG+adW5CkB9PdqmbXyv+VFePzfIkeQQvoleNiUHuXokxpHuCXbt3sIjj92DYVkYhsGj3iKXJjI8fzVJnU/EIVi0+EXaggJTtszUtkOEa8ao6zuBMzeD+sbf8Jqnk97778cTCqOqaiWEfP5G+fEDzZwcSuCSRWp8Ks9enMSUnex812HOFd7g454R9gcnOGlUkcqvXCNrDg2z5TGSms6WRffmwliajppy8Mi3jw/zD+cmCHsUmqv8vDaa4ezUOSL1DTx5V+uCMaoOkWzRXGL6XG++wtw+EcuUc0puZrOmm407ihTu3xRmf1djxRa/pynAMxcnyRYNsKHKp/Lkjjp2Na1+sedY/vW+aV7pnaajyk1XQ3hNWYlzN7d7PIVHcdxwxIlaSCDnk+R0i6gQJocDWRJ4bHvtdSWQtdo9rclB7O7Xytm54XrEXQd59kqC2JXhSpG2H5yd4PxYEq9sc3+zB03TKBQKlEol5tyR+YLBR/KniExdAuBjNRInmvbyYsJNsmhSKzs5tLMGSXJwbChNXjfJZtO0NtRTH3LjkkWa3ALDT/+Yi399mklTxmM4OVe3m0K4i5m8gkd14FMcdI+nODWUxMamNeKpXNs5Z9/2Gg9bBs9SeuEZUq/8FDtzLRxVCIV5ft8HuVC1merWJg621jGZLvLcIkfhxfEU4ykNy4adjYElPqflVP75/7s4nl52M7g4nqoICCsnStYzMJO77r1zqjJqQwNaRia+fQeGZhDa9DmSg/1cOtlHaOgYm0+/gNl9DrP7HNpX/xyxtR3lkSdQDr8Pqa1jWYHlme4o50dTnB9Lsa+jkUSumsG6baSyExhDV9ByOVpe/ha+Kz9E/OA/Qdh1L4Ig0JMeI27mSYp+/GGJjrYQm6uclcSpUjDIRMMm3ANnmb54iZ5ojupnv4e0cw/T1deiA+eybufCJ3fVe5BlmbMjSc6MJumq9yMIIp95/4PYiUmscy/yfn0aBB27UIvgWuokny8kNQWcHBmMM5EsIAoC97SVTcrzN/rP3Nd27d4Ea1AKJl1Bk23BLLauIcjON2UeWm5sJ4fK5UFODSWo9qoLckoObq2+bXpFwx1GCptrfAuSOy6OpygZVrkSoA2yQ8StSGvqtetSRLrH0lyayJAvGhzcWrPq9xZPktFEnpFcnniuSHuVZ13p7nY+DYkJCobFI7taaGmqX9DNTJYEUnmdoZkZgh55iQSyFinFGr2CffkIAELjFoTOexFEEd0wGZxO0zeVps4jcnEkRToh0RFWmFQWhq+pqorb0Kg58wyO1DS2IML+d3Hcu5sL41nGCylqfB6G0kXeGMoxFMshiQKtETcPdEQ4PpSgs2gQiV5l6I1ny81bBIWWhjB/7nmE/rxI2FZ4aHOA0UQBtyIxOJPjVw9trphs5jbO7S6d9jd+ROo//S329DXHsrRpC/Khx5EfOIjU2UXHaIrTp0ZJKQ62Kg5kR2nJfVnN52QVNIojI5R6xqkeH6c0MYERi6PH44zF49iZLJYUpCGfozY9SdQZ5BuRTfh0DU11UWUV+DtPGLdDoOT2YLh9pFwyBV+AXLMPYXMdtjNCKmcyNJNf9v4uN85vHx/mH7qniHgidDzayJn0PVz9+OcITgxwqPcV9Jd/ijU0gPZXX+apf3iFVHsX2q4DNO/btUBLqfc7yRYNqnwKI/E8k2mNWK4IkRYyW+q4V4xx4orFpakEka9+DaHhVeL7H6cn76g0UyoaFm8MptjbvlB6f6Y7Six4L5kd22gcPMGxGQvP8R48dVm67ruLom5Usm5jyTQ9M0XSRYu8AdGcTbJk892Tw3RPpKnyqhzeUYd493uxzvwUckmsEz9B3H+43Jt7HuYTsGmLbKvzc2hrNW5ZWnajXrDGYnmCLg+yy0QVDJgcxK7fvG7z0EpS/9xxsiUdbEgVSmyq8SKI9gIT+M1s9Tq3x+1u9K3as30l3FGkUCotLDJX0C12NQb4wO56bGAodv2cgv7pLH/8XA+6aSOLZed031SW33vqMjsaAwuiG1YrkeFSJeoDToYS4rqS3Gy9SDE6xOlogb2dLTgjZalj/mQo6BabqjxcmcqyKeJZ9pxWk1Ks0R7sy0cBEDbvw2zYSiaZJJvN0iim6TPTZHMFYqZIPK9zb4OTR7ZGCHg9FQlOVVWEkSsUnvpbTmQU9tZU43rPZxBqmrkvX+LEUIrNNR72NIW4MJGiKeTi4a1V/ODsOAXDYmdjEFcpy/aX/5qtA0cAgZ7Gu/hW/bsYC1eRi6YJukwUSSSe05nOFnlgUxVuVVpgshnr6SNx/DiFU09hZcYAEJvbUJ58H8q7DiO1dgCz9uQfXybiUZZIi3c1BTk1dE0S298aQisUefnV85QGBtg23E3P1Stoff2UxsbKGeSroH3e62ZGqR28zHCwgavV7Yi5OLZHo2F6gKJDwV0qUJuLMemJ0O9QkGcG6Klqh0A1Wus23AEHI631NOzbhHvHDuT6a5ndK0q3s5vUjuYqtt3Xjm2/i3Mf+3V2xfsQXnyGB55/ht6eNOfGx7Cf/y4ceJTt732Y9+3uYGAmz3iqQLIAJcPApUj8/K5WmiMeXrwyjeiqQt28ja3T3eTe+Cl3Db/KwMRFtK5309zxIGOZEg/PNoZajMoaMU28D72b5IVuukZfpzN9EvXCDMJ7fh7doVIoFDg3HGcgXcQyTDJFk+GUToPPweXRGfLZFDsaAszUOvD5fCh3v7tsAk1OYZ18GnH/kwuIYb6Q5Hc6MC25Uh5jJVTW2HSWTVUeCl4nRTHG6eEke8Vh1Pr2ZQUv26aSq7MWqX5ubCXDJlfUyRZNEvkiHlXm+GCci+Ppm1pcD8p73ImBGOh5wsLy7UVXwx1FCoVCgUKhgNPpRBAE9reGFiycu9uXd27BQkn/o3ubePXqDP2xHMm8TtirVOKD52Ox+r14kngVB09sr11zklu54ugwffESp2ICfs3JjlXGuaXGx0RaI1M0iWWLCybKSlKKNdFfIYRCYxeTpovc5cuV90XAFkRaQi5cqsJUMY/gDbN965YF47C6j2K98B36im5Oe7cQPPQudtTUADCWLFAyLWwgWSgxmSpXPU1r89T3q/0IQ1fYV+wBj5+zD/4c7r33kn9tkIlYjniuREvExXCsgGZYhD0Kj2+vwTNrxjEH+7nnB9+k7uSLjDpDaA4X8qNPwAc/yfnwZva1hZDmLcr59uSQW1kgLV6dyjKZKiCPDNDYfZz0K69xvmeU55r2gwD5q6/TniwTjuBwoDQ3oTQ2oDSUH3JVBEc4jCMcQvL7EV0uRJcTUVUrYcPeqRzTvQmckoU/V2JzGLaQw0gmMeIJGiamODutcTq5l0QyT07Tqe27wIg7xMyFHnr/9gfcM3oepaEB30MP4H/wfvwPP4xSX77nq2mHF8dTnBrLEOjcQde/fwD3P/83RL//FNrrF4lGh8kcP0bhhb/B8Z53IR3+NGnNoD6g4hBFttZ4KyY+v1PGo5bzZ6Y69jPk7qBq7FUcp18nd+kc46N95A48jjxrklmMxR3d8tVtKNuaUE98GyYGsb/zJ7zY9UnitpNsEfZsamIkkSM1mSFtpKiXFBBMmv0OdoVhfLzsI3I6nQQbdxO2zyKkpsvEcPf7FhRJXE/piQVrrPraGjurgZYw8CsJujxeCrq85Jg34kOcG1ssW6Krzk/AoxDxKKTyOm7Vsabw17UEtMydVyqvUe2GY4MprHxiTWOcjzsqee273/0u9fX1OBwOvF7vuhpOLE4meerCBH3TWRoCLjJFY0GCzDPdUaIpjSuTGfa1hIjlSnhVB4l8iUcWTZL5m/P18PSxy8RiCbKmQF1jE5OZ0pLEmJWSXtbSq9lKTmGdfAbBtoh6G4m5y+q9IAh4vV58Ph9er5f//cYIhZKF6hCRRIFoWmNbnb8yDuvcqzzzk1fKtfgbt1O3aw+T6RJDsRwCAi0RN/mSQUbTqfWpDMzk2NEQQLdttLxG/ORRDo68ggsTq+sA7sMf4cWRArU+lc01Xi5PpPi70+NglwNdPrq3kW0NfkwL9rpLFP7XH1N6+oflk3J7UD/ySZwf/1nEmrpK8t9c0t/8BW6aJkcG4/idMqIgsLPGjTA8yEx3D+4zJ5g2JaLeCFO+CE5Dx+mUkYJBrECYqoiXQzsbee/BHYhrqHu0GHMO05Xmxty4H9xULrFw8mqUmkKSoYEJtkz2UXf5FMb5c5ip9ILjunftJPjkEwQPP8FZVx1DsRyHd9QxmdZ4sWeakFtZNtEq4lVxOgTqR3vo/7vvkzl3jrsyw5yt3U7gQx9h08c+zGi6REG32NHg51vHh7mrKchgPE/vVJZ0QedAa5hoWiM2Okq4+zU+kTjKiBKm9MSnOfC+w2u+DvuqJKwf/x+IDlF0Bbhy/2c4kxIr8zuRLRHLl2gLuxlJFNhe5+HwlgCZTIZMJlNpdSnYJh2ZIZxauhxSfeDJJclna8H8NVbrc/Lt4yN01vkIuGQavQLR8SheRSTS0MiTe1qA9SW1rScicbVk1uU+t9L7AFrJ4MzQNKcG49QHVKayBu0+k3/+cx995yavud1uBEHAMAzS6TRut7tSVvZ6WCxppQsl3t1Vx4f2Ni6JDz64tZqnzk8wnirQUSj7CzbXeCjorkpo2nptf3apwKFIicvInCl4y819CkuL7d1oFcZsfBr5zPM4bIuYq4qYuxqv10soFMLv9yPN66X7iw91LCAelypVxmGdexX7pe9x0ClyZcd7OONqx63IyA6dD93VUAnZ3FEVYDSZJ+CUKegWAZfM0MQM3jeewpfO4lcEJh7+WeIN28iOFyuRNpcmMvREs3TV+2kOuRhJFHBIInsaAxS//TVSX/nzcjauLKN+4udw/twvIgaC5UU5sDQqy+9y0NXgX2BPvk8pUHjqh0y9+gZCLsdg2366UgnUhjbevymAsmM7x32N5EUVBPCqDp7cUcuupiDiDTr6djT4OT2cZJNDXDA3Fvuijg8mSBd0RhIF9rfWgbuKpo++q1xF1rJIXrjI6z89Seuplym++ir58xfIn7/A+B/8EdO77mXoXR9hLHCQ3duaVi3CWJkvjfcSvvdejMvdJP/8jyn1TtLwld/HfvqbbP1P/x3Hjt1cHE+RLZrIDpEnd9TRFll4zPc8vJvODx1A/u6X6XrjWfjRFzBzA4gf/+ySBMuVNFjxQ7+K9cO/QB3rQ3rxO+Ta38OEUJ7nzWE3T+6sW0Ak4XCIcDhMoajz2pUJ6lWdEwMxzlg1fEDR8GXiFM+8hLL3XdcVDhdv0ovLgBdNC6/qwKVKuD1uFLeHroDBtnAB27YRBGFdSW3r0SZSBYOwW64Q/WINZ/H8ealnmu+eGmNva5D37boW/GwYBsVCDtvQyZdMkhoYgoQivcN9CrIsEwiUJQjTNMlmsyiKgtvtXpPWMF/F3N8aoqBb1AddC+KD525C33QGt+Lg6EAc24bxeIGAR6nkQqwHtm1DYgLVISD7guRyrLrhr0cVLpVKTIyPExg+jcvSySk+zPY9dEaqUNXlS2SsRDzKSA/Wy98vf+bRj6BEdpCbV654fsjmTy9PMpMtVQoOHusZwXf2Zeqz4+wIODn9wOf4yCM7Kyn98xdSe7UHv9NR6XTXIJXI/qtfxThRdozLjzyB69f/P0gN1ySb1RZl31SWnGbgGOhFe+0YU2/8hPbkGFXAiQc/SGz7faQ7P0KwsY7NnWUT2OvHR5hOFEAARRJxK443Ffmx0kaw3LibQi4e66phU7V3wf0VRJGJqmYG9zhp/+SH2RtWSb/2Oj98+iRjF66S03TC3/82f/vCT3lpVxcd732M9m1taxIgHNt2MPnv/weXnzlK4KffYGv/KX7wH/+/ZB/7ANpd9ywg2nxJr3RLS+V1Lo6n2FbfhPRP/iVWx3asb30R+8UfYGk5xM/8JoJ0/esmKCriB34Z68d/iXY1ysHR52l94JcZzdsLHP2L19ZALE/PTAkt5KK34ETTitQ0tPOofhU5Mc7UuSMEOvficq1can25ezO/DPjBzREuTGQoGRZV3gyNQTeyXEC1SuWmPd7QqsLaHOnE80XSeWNdPoKAy0E8rzOZ1pbdVxbPn5SmoxkmTYHy+dq2TaFQQNPKXRiLJhze3UR7tY+ReJ7hkdHr3psl9+pOMh/NqUC2bVdCJ6FsHpnTGt5sBcw51fIHZ8doCrq4MJZGEgUUh8i97ZHr1kJZfKzTw0n21jhQ48MULfjrYSeqIvPeXfUVyWB/a+iGkuASiQRjY2MEclNEkqOczDrZf/jduH3+6353sZqfj02z97X/DXoR4Z7DiPc+uawpAKjElb96dYbz4ynuq5YY+f53uCvTR2djiMFP/hteHM7z6Gz/5ZXU44vjKZ574Qz3fPdP6Yz2IATDuP/Df0V56JFlx7ySqv3it58m/Vf/h8D5k0x6Ihj+APZjhzEeOEi/6aTer2LY5fuhOgT2NvromUiRLxq0h2QmUkXqfA62V6vL9s6d33h98ePERIlU0UIzIOwSSWg2Llkg6JS4p9GJKIoMJnVOT+r4FImcYXNvi5fOGk+lGbzD4eD1oUw5QcyEhpCbmZyOT1UqIYuXx5O88fwx3K+9xFDfGFumB2hNR5n54Cdp+cWfZ9OOjhVNmotNHxOJLOrRl3C+/jyNxSTdOw7S8ZlPM1YU2NscoqCXHbaVisEDcT6xvwlNt9jXGkTuPYf1pf8GpSLCgYOI/+zfLNAYVpvLdqmI9Xd/BjPj0LQZ/b2/xOnRzJLPzg/ljKYLzGRKOGUR1SER9sr4jQyPKlHeVWvSG96Gp7qBcU1hX1uocpzVTD7zN9umoJs3+mfY1RDg4dmS3fl0kv2udLm7YcNWBFHk5FACSYBYrkSVR8Gwy5rRYtPgWky/1xvb/Ot3cTzFl1/ux7YhVdC5tz2MQxJxOsDrsDm0uXy/VVXF5XItEJDf8bWP5iAIAi6XC0VRyOVy5VZ1uRyapuF2u5FvwCY8hzmJIOxRcUgiLRH3islKc1hpEcxJKP5cia4A9Ok+RpJZbIpLJIP1OrASiQQjIyOIlkltLkpPTuKs3EYkY9O1hiZQ8zeO7XVerFf+skwIW+5CuOfwks8sN6atdT7GZjKM/+j75ApFTtbs4fLD7yc3plUkpVi2SJVP5cN7G5c0+En19FDz91/jmFjNpXt20/DJT/Hk/Z0rjnmxBpWemObKv/+XyM+/RLy6nZpIkK5PfAj78XcRM+ClvhRFI0Y6azGVNWhyGdS6baZSkM8JXEoJELBp9NjoWTg3sfD3dAuGMgJtPhvHCoporQ2WIRDPCjh1yBWhymtT7bAZGSl/ZjAjUCtCxLaJ6QKXr05RjC6UxUwLMnmBoaxAQoV4ETr8Ap6QzGtRlagmMSy7CLznA5iJDO5TLyD/8DvUf/evMX70HbL/9rfo+MVf5PRYZklns8XSpqLK3PWZT7Dpwa1c+KM/ITM2weBXvkrxg59GbguzpznCM93RStOo+9sj/OjcOMPxAhfHQ3zmvt2c/th/Yvf3fh/1xMtY1Q1IH/gnld9bbS4Lior4/l/E+vYXYLSX3ldf4ZTYuqKGlS3pbK7x8PLVabyqTNBdjiw6vKOdHTMmUnKcmlyUV9NwOSUScF+LOjq4tZrzoyl+eG6cppBriXltvuQf9qhsrfNVSnbb9X6Y6C036sknwRuuEMDlaIZHO6uZTBQWJJpeMw3mkWZNYytpbtfTfOdfv4Ju8fMPtFXKztjYWEaJtoiLLdUeRFHE6/Uu6f18o7gjSWEOkiTh8/nI5Aoc6Z1kd4MP08ygKAoul2uBHX09WLz5rJSsNIfFi2C+FNDsd/CXJ0cYSRvocoqWsGdB9rXX5aAl5CaZL1EsmbzUM31dlTOdTjMyu+P0jSc5OyWRU3y0dDXeUFibfeZlmBop911+1yfWrG3lSyYHrz5F08wRRqo2k/vEJ3B7vQvt0bvqK4twfoOfCz/5Kcd++Hc4SxrOu+9n789/ku3LNPKxbZtisUgmkyFsZcnFc1wcyVF65jmUv/z/kSoUGK1t58yj7yOzu5nGgIQ+PMqJGZHRHEiCgO6yKZggueFqViZvSei2RGtEYrQIPXGBziqVh9u8leJwkiQxkNTJX5pCnB6neuwq2sgouViCXstD7UQvI44gLdP9WL4a8g1dYOsUvUGUwjg+1URtqMfZ3Eh7Rwfurm0I6jVNZO4x13/XMAwK8SKDBZ2saVG0TLAtjKJGqqgRzQg0zBGLU6D3vvvgQCeBv/8HPMdPMvrffpeXvv88pz/wGSZ3N7GrKYTX68Xr9ZYFpWVMH96HD+HwNvLwH/8OVZeO85Kpk2z4v6AhUNm0vnl0mJF4nmhaY3djgJJh8+cv9DISF/F97LfY9o3/jv3U32A1dfCcsolYtrTiXJ4vPL3Y/j5mjr1E7ng3zYeWztvFoZz5kolpgWXZOGUJj+LA1XUPT//9j5kp5hhSswQ9Kk+fGeTsSKhynMlUgf6ZHB5VwqMuzAlZzUwrCAK2v6rc9TAT4+mhIvGsTjJfQtNNnu6O0hPNsKPBj88lX9c0uBjLmaOuF6J6YTSJS7KRTJ1M0cAhugn4PJVozJuFO5IUFkvmo2mdqwmDqoBFW0CkVCpRKpWWVafWgsUSctnm6V8yeVbKepzv/HSZBTojCvtbw1zNOSjo5oLs6621XgZm8vz4wgRTqSI1AZUHNi/tGTEHy7IqhFAdCbN5qpsen8XZQBPuFZK1VoOt5bGPPQuA+OjH19WZat/0Oazu58HpZtev/0uE2iYujqeua+OWRwbQv/QF8t52Ju9/gv7Oe7hXEJGwicfjJJNJkskkmUyGWDJNb9y4Jq3bNv4f/YTAU09zrGkXM5s6Sd33AKbHy7ik0B0v1+0vWSL1tU48qkxz2I0oOdjXFmFnY6AiodX4nDx1fgKf38HBfc3saQlh5HJ8+29fZPDCFSavDqKO9PKcJ4xaKuAtpAlmY/Q072JKCBH31CDHpyiVDDb3niCUnibhrybhUPBP9i04Z1GWqTqwn7pDD9Nw+HEie+9aspA9QwnumTXXDcdyZAs6O+vdlEolHpjtST73KNuRI2i/8Wscea0H48hpSrpN7d9+k5cST/JaTRifA3aGLSRJYtJ0U+P3ssUfIud3ks6XO+Ad2LsZ64/+B8d/698ST2vIX/0C9h//IU65nGFfH3SSL5kYlsVwPE9BNyutat/IlTh39y8ROvUsT3zjTzn4n/9fetLOFedy/3SWYwMxBmNZ3nf/HvpjvZy6OoGr9zTypvtWTDKMZUuoDomIR6GzzlfJRxLcIQ7tauVyTz8Fo4TgcJHKZdmyrZ7JnME3jw5zOZrmwY5wxXz40pXpinBy3eQzTwASE1DSONRWT0/c4KkLE0ymi2QKOtO5Ijsb/Avm+z1t4WX9I8tZExaT0nSmyEgijyDaC7SHzjovhUKB6WSKe5u9NAadTOUtEOVVfSk3iltOCq+//jpf+tKX6OnpQdd1duzYwWc/+1kOHjx4w8dcvtSEh5MjWS5NivhkeKjdX1lATqcTp9O5bnKYw0qT57rOz6JBNJWhZNp4fT7MTH5J9vWR/jjnx1L0RNPUeJ30RNMokshkWltW0k+n05imidPppFbQsNFx+CLkJPcix+AaSeHcq6AXoXkrQtv2VT87f2Irpo71vb8EQPzYLyPUlu2V13OS21qB7P/9r9FMm32ba/lhUxfZWIy/f3GCPu/SRJuxnMBwwUFtxE1XvR/5/3wN/amnQRR59y9/lMx7PsJT3ZNMpYrESzptDV4KukG+ZCIgkNJNWh0KP/9gW2UxypLAmeEkiZzOcDzHwa3VPP/CKX5w8gSlo6/S0XsSMdyEWbMJtyLj6GjDUVeHv6aKftGP16kwaUnUBFyMWwL7mv34VImgaNPlMSGbRpucIjs8TLq3n/jZc8TPnmPqjSNMvXGEc7/3P/Bt6qDt4x+l6ROf4IrgZ19rcME8m9/+1OPxLLz+2xeaKQ8+8i7OXB7hhT/7Gq7TRwm98iqdH3sMf3sjPZM5GpQiVWIG0hn609dsZE/3eejTPBgOF3z4czR/+6u8MSlw8Y//mvr3vJuIV+WJrlryRYOCbuIQBBRZXNiq9rFDbE0ehyuDvPit79Hddv+SufyT7mil3pMqCTx1Psqx/jidkV0YwgzjoxPkImnktvCC5LD51+Phrdeyp+fnIzk7duC40k8xk8b2RCgYOvlMhse6ysl+RdOs2Pfnku7W6r8rGjanZ0T2Bgxe6h7lQkLgR+cmEAXIagYNIRffPT1Ga8TDpmovH93XuKJmsJxJbfG+cnE8xfOXpxAAVZLIFQ0sQyeZTHF2LM2eBh9uZzmwpuUmmYqWwy0lhe9+97v8h//wH1AUhfvuuw/Lsjh69Cif/exn+fznP8+nPvWpdR3vlavTMGatWmpiZ2OQbfU+HALk83l0XUfTNIrFYqUo142Sw2KsFpFQ0C0e2RSkuZhnJCNzpigum319cGs1iVwJ3SzHi3ePp2kKujjUuXwBsGQyCUA4HMYeOgVAMdzMow3VCxyDq/WcmINtmthnXwVAvPvx657v/Im9rfu5ck/hls0I918r5Laa9GWaJvE/+0Mcg31Mtd3H9wJd9PbF2Oa3mSzZ9MUktteoPN5ZRXdKomjLOIIKh8MeJtNFTr18FONYH/cqCpu+9Occb9nD+cvTlU2ooFskckVSmoFbcVAyDEIehXvbFyZbvdgzTX3AiUdxIBQ1Tj39CkTHaZgZ4P6rJwnv28vMwx+isW0LjZvbyJTMihPx2GAMbIFTw3G2NocQBNjdFsHG5rXeGDXNtXTtXEquxWSSyVdeI/rSywz/4Idk+vo5//t/wLP/+29J/dyvof7T91O0xEqzodV8VEtq9ysO/FVh/J/8NJrHReHCFaQv/wX1X/0S/aJIZ0eQaqfNkatRmpw6uUyKVCpFLpcjYuWYSJV9Gey7G8cbR+l47lvsefcB/K07gXJQwqfvbl62Va0iibg+8Vnyv/svUS4coXbTfvQ6/4K5/J5ddXzz2Eilp4EsiQzF84CbHbWNfChzhFGjj4Lesm7fmuD2o3kiHCJG/WaZVydtppOp6yf7reE3Lk+k+f6VLK42OFSvksTFY9ur0Q2b1/tmqPKq1AedHN5et2JjnMU5T9crcBlxK6Q0nYJWwtZ1ktkcmiZyYSJHXdjPnprrB5G8WdwyUpicnOS3f/u38fl8fOMb32Dr1q0AnDt3jl/4hV/gd37nd3jkkUeorV25PeNi3NMeISv511xqwufzoes6hUIBwzAq5DBXxmG95DAnYexo8NE9Xo6YWEky3t8aws7GISbS1RxiR3XLgmPNl3Z2NQWYyZVI5nV8TpldTYEVJZi5RB7VIUGyXANo/55Onr2SWOAYXJNvYWKg3GwnWA0NHSue92Iz2au9M5x6uYeIo40n3/szqzYDMk2TyclJxsbGmBro554ffY8/bXmCC1vuZqbkoMojM2U5GM0JOFWZx9tb2LqjidxwEo8qcmE8jVtxQHoSzzf/Es2Epj/5Y0LveZKDurmEUFVJRBCLRDxyJWPXmOfT1XSTHQ0B3IrI333vZVJvnMaybXy2zuPvOsCTf/l5Rl0Rjhwd5oH2MIe21VTuq8sxZ+MuzZYrKDGe1EgVDPxOedUwRDUYpOUD76PlA+/jwO/9Dt/52xe58soxxs5fwv+Dv+XP+0aw7jvIQ7uauX9T1YJNa6WS0EuKs3XV0nT/v+CL//oPeUV20vO//oZdv/5LHBtKzzpADX7l4CbuaQhgWRapVIp4PM7J/kn6+xL0uyLkWnaz68Iw43/0u1z4pd+gsbGRXfX1WILE6eEkVT6Vn7uvtVLVNK0ZCB1t9HcdovtqjNaRy/ha9y6Yy36Xwj3tYV7vm2FgptwzuC6gEnQrKOF2jpzpgcFh4rUpckVr3SUf9u9ox748hV2M0xEKV3yJi9fmi7M+juuFjM7N996pLDlD4Ae9WS7GdQqeaqp9LgZnslgImJaFURJWLeu9XM7TigUuhxNsqXExNG2wvUZlIgVXpgoMJYvsb49wbDDFxYncmy6DcT3cMlL4+te/TqlU4ld/9VcrhACwe/duPvvZz/JHf/RHfOtb3+Jf/It/seZjqg6Rori+UhOyLCPL8gJymIvrXa/msFz521XtksXZlojK8na/OZIRsNeUl6DpJmfGc7R6bIRMvNy20xdGkNV1d4wCsAcuAiB07FzVUbUkeiUdpytxhU63gbDjwLLfKRaL9Pf309/fT7FYtl83nzmGo1Tk0+5pXn/0Lp7tSTKdLZEu6gSdMntbQjx/eZruiQyabvL4tprKvZ546lncDi/9Dz3B3XsfpI6ypraYUGt9aiURqncqy7nRJE/MK1/SP53lwngKXnmOvmNX8RVzhDa3c+hnP8Sk6eBHMw6yxRj3t0eYSGv88OxEZRGeHErwaGc1EykNSRDY2xoi7FboiWYoGCt3HVsM0eHgQx97lJ4HD/AXf3+UsVdeIabZqM89x6vmo5wcSi7ocJfWSmiGuWxJ6DnMn4e/8du/xI8/8vNczic58/JJpqqaqPaW58jijTAUCpF0hNnRJTCVSPN90UX/QC1dPWfovXqFaDSKJEmYvjqGSj6e3N1YMXXE8zq5sRQD0zky1fto7vk7jl+ZIFi/m6agq5ILBGDZ8OE9DbwxEGc0nmcqU6Q94mH39ja2DhuQH+OKq8gZU1nzdZyDEKrDBkhOQvDadVi8NldL9psP3bQYSeQZSeRpi3gYHMvgEEskUkl+7oFNuBzljnAdNV5KurliY5yVcp5M02JPc7DyOdUhYhk6sXSWYqFsNlJkB+/eXc3zV2IcG8mSzuvruiZvBreMFF555RUAHn98qVni8ccf54/+6I94+eWX10UKUGb/BzdFiOVK7J6NFV4p8WU+liMHTdMq5LCaQ3ru5h4biJEs6JwYtKn1ufjLVwfY1RQkVdCX7T+MUSrbJccK7POYS96fI5n5sfvXawrUPV2kf6pIk1jABQj+ssZxI5nQ9ky5toywipaw3LGzU1M4sHDu3FduMD8PhUKBnp4eBgcHK1pNIBCgqamJ2leexgbqP/Fp4ppAqmgQ8cgk8yVM2+L8SBJFLi+Ktoibvz01xq7GALF4mtTAEJdbdhLcs4cfnJ3g4kQGv8vBTLbIg5sjC6I95q7h/Fr4o4lCRdsJZ6b5+6M9eIo57v/gYzz0sSfRDJv3zraLPD2SwMLmxGCcf/ZgK/taw8C1rOUqj4JTcVDnd9LVEEBxiLwwL8lvLRnoc9d007Z2ahprOfHicepOvYR+9BUaP/0J9rWFeKlnirBHxed0rFgSejE03eR8TqLq536WwvdfJnjpPMEn21FnaxultXIWfXuVp2K7j2WLlWvz+H27GOrbyfcFCVdeYcodZGwqTXF0Aq80wb/vHqaxKoTXpXJwa801E64YoFUSaM+NsW+Tn+1tC6sO728NUTIsan1lTWEolmc0UUCRRJzN7dhXTuPIzJAr1a7rOgLg9oPkQNCLiLaJqnrWNI9X+o3HtteiGxY90QxpTWc6b7IjJOPxKsgOkV98+Np6mRPsFocBwzVhqnc6w/6WICXTptanUhe8lng2twclszkeaAvQHHYzU7B5fSBJf8KkdyqLS5Y4MhAHGwzTXkAotwK3hBRs26a3txdRFOnoWLrhtLW1IYoivb29lTTytWJxrPCedWYXzycHTdPQdb3ikJ7THBaHss7d3L6ZDIm8jm7alfK3fqeDRKG0pAkHAKZBX0LnVCJHIHjt/fU2+Zn/eb/HzUuXkhSSJe5SZCJOif2zE3I5U9aqTrW5JuzB6zcwmX/swcvPUcCBUF+uk6/pJqeHEvj0Gfqu9GCaJgB1dXVs2bKFqqoqAKauXuGsv51IzRYun0/x/p31dDX4+crL/cQLJZpDbgqGScmw2Vrno1OAu9siXHj6FU7YAmZ1HaLipH8mw3RWo7PWC4KIIokLSqovd31Vh0hBN1FlkbFvf5uqZJQHH9jJRz/38QXXZW7T6B6b5lI0zQ/OjLOvNYymm/zODy8ykdJojXi4qzlYuW/JQmlh3+2CsWIlzfkmyOODcQ51VmMYFkXLZnjkKoV4gpmeXmYie6gPuGgNu0lqOppuXbckNFwTNCLb9rJ39IvUDx4h8Yuf4choZsFGOJrIr9j7Qaqrx+y7zOOFMfwf/hSn+iZ4/kw/8USCUqmElZrAq0RQHDXXTLiySNTfSC6VRk5Mom6uXzK2i+NJjg7EaQw6+di+pnKS6JlxRophHge0RJIHdncRz5XY5VXW3FNdEARQPZBPIVs6wWBwTfN4Ja3cKUsYlk1TyMV4Is903uR41GJvq8FLVxaG2a7mA1mc85TXDbbW+dhW56tYK+Zyh/e1hHC5XMiyTJMgsK0xTE80w9Wpspm6ZFrU+pzUB9YeHXijuCWkkEqlKJVKhMPhZWsTORwOQqEQsViMXC6H17u0ccZyWOxofjOlZufIYc6cNJ8cZLkc6jWXDPLylWnOj6boncwhSwJDsQLfPz2KLInsbw0v2CDmxvJMd5SZ0QRZTae5uWpJX+n1mHrubQ/z+z/pIVUo4VYkwm6ZbKlIt+YgeqVAsCNLR7WXrGaQK5YJa37W8IpONW022se1vGQ1HwtUcXMGzZrhSCnAfsPk0mic771ylmZHhkaPTUNDA11dXSguD6eHk/hDFgo233Zu5WSgg03jOq0RD+fGUpwbSxHyKlT5VFIFHcOwyGgG58eSbKrxIUsCD2qjRBPj9PkeKvfazRYJuhXC7nKo7+Jrv9z13dMcrDiDxyZTaLLKpo9/eMmm/ZWX+0kWdCzLpj3i5uUr03zsi6+jOES8qkRr2M3ATIbBmbKm8un9jXTWhsvd/2yDzVUuLk9meaU3tuw1n2+CzM46ak0Lfub+DlKXqvjO379OQ2yID+9974IcmeuVhJ4jwrk4+oTiJN22k+mZcfYLOj93bwvNYTffOj7Cd0+OoRnmAsfn/NIWY0oI3VPDcCzLAVni0nSRkZIb26VSZWeZzuYYG4pzdSpNdU0N926u4YnttTRc0hiODVNIZ5eMLZrS6J3K8cCmKi5OpLkwVu6F8jP3trB1ehpGYZ9X4xzwzMUoP3t3y7qKTVqSAxGQbItAYGVBca2teqezRfwumZA7QL3L5uRwklcGUtSGTD736JY1dUiERcLUTJZ4OkfSaVbedzgclb1mvnA8RygRr4osiRR0i84631vSre2WkMJc+YnVYmidzjLjrYcUFjuarxWqs5ZV39YCh6Ncs900zYojeq4RyFyXqINbqkjkSpQsC7dDwu+S2V7nJeBSuBTNVMbSEnZzfjxJRtPL2ZS5CX50JU+TKCI7rBWzKZerozInZWq6yY/PT1RMMbmSSbIkkimAywkPNiq81hvjqQtRzo8laQ17qPKqC8wlK05acfZ6WeYKV2cFyAp9YpBv9Rd56aluZiZGcKPTX5SR61rx+qrx+/0VQro4nsStyOgOhR2ZEa7O5Kj2u9ANi7ZqD/taQ5wYjHNoazUuReLFnimKul3pJdFNiPP120iaAmHAtG0s2+LMSBLLhpJp8jP3tFZIdb6ZYDieo2cyw96mIKYND26KkFYsagcvEpucYXx8nFQqRTabJZfL8YArx/fHSvSnBVTBIFcUKaZNHIKJgMVgn0XCciJiUSdm+eblnxKRynVn+vUA05YHBQPDFvnh8yo1qk5QtZFlmZLoZExTUWSJZywHEY+D01dH2dcSpCHioyGdYFf/MVyRD6E4RBqDLr59fIT37alnW51/VX/THBE+dWGC8WQB24b74iO0j1/hwNZaHH4/mm6ypcZLwOngqe7oAsdnQTfpHktimBaybeIt5TgqNXL16DD5ksl7d9YxmSmiSNW80TtFs5hAMTQuRCfI17roamjFtIp0WTHEeveSsT11foLJjIbP6cCwLFyKRJVXKc97weaZQhXxMZm+zESlI+DFicyaBD7LsigVNZxAMBR6UxUN5vALD7ZXkvcyBYOiaVPvLhd/fO7iJNvqfddykVYR7Pa3hiqm6ka3TaO7vO/N7S2LyWAOml5OZltsGn0rcEtIYT1RPespvdQ9nqa1ybdgM51MlZ2+1ytUd73YZEmS8HjKTWbm7Hxz5CBJEttq3czkividMg6HyAObyyaXqWypMpbzo0le64/RFCgX2YvmLPqSBq7RJF6Pa03ZlPPV0dFEodI6sTHoRBZFMppOyOvEMNIoIlwaT3NOs1AkkSpvWdr+s5/24nVKyKJIe7Vn5UmrOMvRR1oO3GtzXj3THWU6V0VWauIhT56fDI8Qy5o0hVzs2d7BPR3VS6So0USeeC7PuL+OzfEhjEIBb40PAZWSYTGTKRL2qMzkSqSmdSZSRe5tDzORLpIpmlgeH75ilj1TvXS+ez+v98cIexRys6VHIqqypAHK3PVN5YpcHJrmuRMX2O3TOD+a4Kq3hu3WeUZ++19z4sPvh0XzVSwG0Y1qSgiABZi4KZFDIWk7cQgWVZKGKjkYEqopiRodapaQCJMFiawlkceBbknUlJKE7DS2BiXLiWR4MBFIWR7UVA4BuDyeJZ6ZJPX1bzETrKdnOk/muz8hJXh5Y0hne62bu9sidDUE0HRzWdPUnDZ7eSKDLAlcHU+Qr9/NAYeL+2fzHP76yBBH+mIUdIOAS+aNvjixXJEr0Qy//cEd3NUcpCeawTiSoDofZcq5m801XhpDTvwumbGUhluRcKoKMbsGj5ChzZPk/JUB/o+sEo6phKUGDigunPPmy3xn65nRJKIgEHBKPLKttjzvtRy6LTCqSYyYs87dWK7sB3Fff4saHx8nbJadvaGqmjXN4+thTrCoDzop5DKEnCL7mr2kbJWmkIvHu2pnc5GW90/Ytl0hA13XK8dVFKVCBquhfzpb0STnm0bfCtwSUnC7y5LCXNTJcpir6jf32bWgeyxFSQnwaGc1VyezTKWLHB9KcGAZE8JirDX+WRRF3G43TqeTYrGIpmnl+Pp0lv0NLjbXBpnMGQt693731Bh901kyms7OhiBffqWfr746iF4s8L52FVMGrWTyYs/K2ZTPdEcXbKT/+9VBSoZJrmgQ9DgYTRYoGRbb6/38+qObefnoOY5dHKJRKtHgEZFUJ06HBAJU+1S21nrLLRjjBVSHtLxTLVJHMZXgzPkB9j9YfV1Nq1xQTmRrWx3ne7pxjF3F8nURcCvctW0zRdNets+0S5WoCzgRqvxEswHqczM8sX0ng7Gy+erJnXV8+/gwo4k8EymNkNdB/0yOgm6yuzHA3Q8f4NUfvYodT5AcjSKJCookEIl4FjTm6ZvK8MrFEcYGr3J5aIqRmSSJTAGPUOKM4efHgoCIRUtTmN7p3TjzGVznJ7nrow/hDwZxu914vV6cwzqdiGiGyEiqSO+MRm3Yxc7GICnNIJ4tUjLL7RTfs7Oe0USedMEgOpRAmU4zFddQHSIObIrhZnIBJweaPERUeLU/QaZQJDutYYthgo4SdQWD9LN9nGq7h2xdPVcFP6dOZdEpIGDzhz84yl/+5CgNQSdPbKtiQHPjVTvY1XRtDh3cWs2rV6fRLRPdhLrUNHnZhXbfQ/w/z16lJeRGNywagk6evzzFpioPsiTilkVmMsWKT0yWBDKDw1iKn1xdC69cmUJyiDQGXBUhRnUIfOfEKHc1VxPwCmQnEtjRKzTGB3jd0UJIqa40j1rN2Tq3Fsxz4zzmimE0hOiZtMmVDEzLZmuNl8e3rxyyrukmL14YotpOU2cWsQHJ8+bi+OcLjwXd4omuWnJhg3+4qBPXLEKBayHjywl2tm1XglnmfGvAiv7KxVivv/FW4JaQwly9lUQigWEYS1jRMAwSiQSqquL3r/0mNgRcJPJ6pZTE412111XfbvQii6JYaU1ZLBbZ2yyWTThWiQa3sCDXoTHo4tmLkzx/eRKv00FdwMn2Oj8uM8cmt86YbrO3s3bVULLFdvDOei8hl0JPNE3vdI6JpMbmGi8f2tNAxKvSXBdhc+oKtT6T72hwYjKH5FCZyhRxqxKWVSaHjKZjmBZOh7BE/RSqG+nrGeRU7yTBLddPdiuHc6Zp8TWRE2SIpym64H0HNlMX8VLlUSp9pm3b5tJEGtuyyesWXsXB43vbiLz6V0y4U8AjfPxAc+XYn7mvjZ+cn+DC+DA+w4FLsckWDZJ5nRPjOeq3tDJ1/hLTr7zOtg+9l4Pb6srdq7IF3ujp58rwBGNTMeRSlhO2goSJYYtIIhjuEJ0+H41hL7bsYmvDfYzsHmPoq19l84s/xDN2gb2/+3nqDh1CEAQOHIAvvtBLoWRSFxFpihRIFQzSmsFYskB+tive5YkMsihWTAnZko7TIWBaNtVeJ5IELoeDT93Xyq6mIBfG0nyysYFYtsSWsRS9k2k6ek+S/vbf0jl2hcI9j2D84j8jkCxwdjxDKm8QdhSZLjiY1EyMySTfi03hEUr87tnTNFSH2dJSx2cO7cLn8/HRfU185eV+rozG8I2O0pyNc/973kvXgx1889gIFyfSaLqFKAhcjqaxEaj1qVT7nJVaXHVWlvuvvMyAr5F+T4QrvTMc7qojkdf5xtFhBMoVg52yxKnhFPUBDz2pLEJyipJYT3PExeuDac5N5CtrbDln69xcs00TokOogoXhCdIUsmgOuXCIAoZlryqonBuY5LXL4zxQYyMAuLxLouHmsNYs5vnC45zgdmJ8kE9t89G8ZTOjOXtBLtIctteXKyikUqlrTYEEYd05UTcSWn6zcUtIQRAENm/ezLlz5xgcHGTz5s0L3h8YGMCyrAX5C2uBUxGxl5SSWD287M1e5Pk3tlQqVTSHudagqqridTpxzoY/Dsay5UQnWcK0ZaJZjZypXTe8bnG4XMmwuRzNcG4shUMQ6Kj2oDoEnuqOMpkp8sT2Vqypo2AWqXNa7K4S2dUWwe31cbQ/TqZosKMhsGL53me6o8xkaskWQzSnBnn16sqF+BYT64khDdUToTEzwD8NxDiTMYlpKR7vqq1Eg/VOZXE6JHY2BiqS/J5dT5D6xhfwXnoDd8/r0P6BBcfvncoiSyK5kkk8X2J3U4DWSDki6Z4PP8bJ7nN0vPEP3N1ioW/+MBdeu8DVq1cpGhYFy4lhegh7nAT91dyzqYbqqipeGtKYSBeJhFzsbg0xlCigCQ6SwVrkn/0Fit/Ikjp/lOc/8kmq7t7Pjn/1mzQ+8Ri/+FB7Zd48Wl/LaDLPjvoAV6IphpMaO+sDld7Uc6aEkmHjd5eT5nIlA59TJuRRKr0a9reGyk7XaIzpIydQn/sJpy2FXP0O9t73EAd/45f47tUM29uCTJYcaDNZ/IEIuWSBOr/Egw0yI5MJZuJJrEIWfaqf6MxF/vj08+RDmyl46hFFJ9VXz5MWRPp33MMFtZr7ZpPHRhJ5ZqaybKr24FYk8iUTUSzPvblaXO1/+p95SQ1R2Hc/bpeTTTVSRWuTRYEav5OBmRxht4JuFakLuKnvrKbm2DEkTDydu0guWmPzJereqSzHB+OVZlWMXCkHPIRqaW+pY8dm6br5Os90RxmLpRmJTlPnkeidyRMvqFTVhXlyhfV1PUvBSsJj2C1xuFoEVPB76Qos3Nwty6pYFOZM4aIoXutzvo7Iyjniciniupts3UzcsjyFhx9+mHPnzvHcc88tIYXnnnsOgEOHDq3rmFNpDdHnXVBKYi3hZTfSyWwxBEFAVVUURVlgK5yLWJpK5/jY3nru7ajm5GC5ntEHdzfQrI0ykjbJF5dPcIGVk9iOD8TZ0eBnMlPugTyaKPCeneWqo4IoIVQ1oI0PEZJt2lq9yGKWWp+HwI5aXrwyc12yvOxXOdXtxVWcRklE6dq/k/Yq9xKb9WJiba/24G5047hsc65vhstikvs6yhFWc9JkS8TNgdYwZ0ZSlTrxghrC9dl/Tv53/jOFP/+fyPvuRqy5Fi10dSrD49trGE0UiKY0dtT52dUU4IWeaaadMupHPor0B2dI/K//xdWTx+jbvxcEgY7WFhzVbYwaARqqg6Q1g7xD4MRUCVWROLyzlivRDM9enkSRJLrqfWXz05Y25P/6P4m+8RrZH/8DHH+Vl372nyKqKg2PPUr2nkPM1HYiWvXkdBuPKrG3NUxSmyZZKOF3LjUlxLIlnJJE2KfQEfHw8tUZYjNJxrpPMPXGEUpvHGOiN8pkTQfB7AzsuJ+aBx6kbv9WBjQR1SGytzlI71SWmYxGyC2TK+pUeV3kHF4mBJG8L0jXZhf7wyXMmSH6+/tR4n0URwfIDGTZOjWKUVuP/9C76awtb8yWDfe1hQl7FAzTon86h0+VKRgm05kiskPEcfQVxCOv8IA3QPQj72F6KEeyQKVcyAf3NDCeKNA7lcXrksmWTHY2+HlcytH/0wF+qrQzWtVBftGcmy9Rz88d6WoIYHWXGysJnfs40BaufG41rXV7lczpS9MItk1VyE8uG6PLZ7B9d9uSz67YuawlyPt2X+tctpLw2Om3IRkH1Y0gXCOE+YEpc5AkCafTecN9XeaIq9anrrnJ1q3ALSOFj370o3z1q1/lK1/5Cg899BA7d5brqJw/f56vfvWrOJ1OfvZnf3Zdxzy8ow7TFVpWfVttEq2nk9n1IAjCgnBWTdMolUp8eGc5Ft8h6hzuquZ9e8qtK+2JOF2KBtUrX+qVktjmMkfHUxqyIs6Gp11bbEJdB31XR7mYmmFfbTOymWJycpKJkpNHttZQ63dWShEshlOWUCSRfE0HE4Npsv1XcBzYyWiisESiWo5Yz1PPZucEac3EnBzjqs816/PwcU97hAvjqWU1M+XdH6D01A8wTh0j++9/E98X/w9IChfHU0xnyklUc41E0kWTH5wZJ+KVabaiXJzuIb9/P23PjrLl2Ek2OWQ6//xPCNaVs433zmsIlCoYuBWJ0yMJwm6VprDJB/Y00l7lZmAmjyBea4TyxD/9CJt/4+MMfe2v6fva18kMDDL6458wdPIqilHCKCSRduzlSFsH7sZ62oIBWuuDxFQ/o8eGCYcVGgUBPZNBjSeIJOLkxyY4PzTNcN6FdOEVhmIj6JKDkZpNqJLIzF33U9y8BT0YJmnZ/L8vDdASdrGjIcDpkRQhj8JH9jaxpznIi1emmU5rXIqmqfGp3LcpQt9Ujr6Sm3272vnwhz/Mpad+wsXf+yIX/K3YDgfnWjuoGh7l8Z1lm/z+1hAngY4aL81hN09fiDI4k+ORzmpsYODcZRJf+9+0AqFf/5fEgwHSPSnqA2qlXIgkipX4/TkTj66XcH7/S2g4aK9xcd+eBmIaq7aVrPc7+cbRYba6DWq6ozzhkxG2372m9ZdIJDh5eYDhtI5TdVIsmuRzeRw+BbV2aROZxZt9crZzWWNoYWTkShFramm2Z7arPH/nr/k5rBRWulYsJq5oWuPsSIrRROEt8yPMxy0jhaamJv7dv/t3fP7zn+fTn/409957LwBHjx7FMAx+//d/n0gkcp2jLIRHddB0A174tZLHeuFwOPB6vViWVZEaDMMgm81ekxrcAShpkEuBe+Fvr8XfsRKhPdMdZSZrks16aJYLXBiaRnJ7sfIp7m0Cv5UhmoJ4XmePc/nbXNAtHn14L42FY3wnmuQffnqCSFtHZSwnBhOVbO0l4xixUdpaKPTGmIlnsCemaWut44HNVSjSyuqvIIp4/tsfkPnsz2D2XCT3f/9rhj7325wfT/NoZzX1QRcvXplGEEB2CDzQ4uLsy0/xwkyMlOmm9eE9eB+7m8J//z14/Qgjn/hZxN/77+x/8IEl93i5Mt5+l4Islf/un8ny2tUZbNtm272tdP3Gr9H1G79GdnCIydfeoOnVNzh+aQzx3Ot4j78Mx1/GABKzjzk8N/s8t+nnVS8Fp4ei4sKfG2WgeSexe95FsnUbjW31tO3YzJOt1bxydYbjQ3FyxXJHuJRW7hvQN5XlZ+5tqZhI/bPlO9xqDq9abmNaMi32NofYGnGS+MpfYP3hF7C99bg6Qszs3Y2ZLpKcGOF/fm2c+w7spbO1YcEGM9+fY1y5zJYv/0fsZBz1o59G/eDHKQwlKrkNizu6zZWR753McPb7P6A4M8VWf4ixzZtxyyK14aWm2fmbc7JQYjxVYN9UNwedMYTdjyB4r1O80bb52zeuMBiNldvohv1YkooeH0EVbYqRlmX9CXOb/ZnhJKeGEqQ1nfvawhwfSHBpPLPsWssVDYZiea5MptldkylfI9VDIZ3GMK4JWGuNJLoebgc/wnzc0iqpn/nMZ2hoaOCrX/0qp06dQlEU9u3bx6/92q9x//3338qffkuxUsRSLpejYAv4ASGfwjZKCI5ryXxrmQzXK9t9aqYeV7ofR2yMfTsfoslbx/eO9JDszWIIEttbG1Z0rs8du3Dfk2z54Y/ZEz/GxcY6XEo5fn1+tvbcZzXdJFUwKCFzRA8zGfDTmBqjMNqPHnbzem+MezsiPNpZvaBo2oLrFQzh/f0/4wf//r8T7yuQ/59f4r6f+SSTOZ2BWJ7JlEZT0EXfyATdvcfwm2nw1xGp20GgvZFN+5uw7r+f3l/6VbTeXno+/mncT76bmV/659x3f9d1w37n/t89liKaLvBs9yT3tl8L+Txvetn3qU9QfPQ9uHum2dvsoTEdZbq7h1NDMdpSo5jT05QSCWzLBtvGtizGw41kI5u5y1XCFQkxGGxmU0cDr8XBQiCf0qjaFCFti1yayHA1mmZwOodTLjeTT+fLXbuCLnlZE+lcBdwyyRnkX3uNq3/6/5AZHOFqdTv3v2s/H/uvv82VpM6L5weZHLhEfzRJ77EX2OO/H9uuXSDJarrJ8Z+8zNY/+U8o+QyOex/E9Zv/dtV5N///8skXyQz00idX8f9n77/D7Drv+170s8pea/c6e0/d0xsGHSCIQhSCXRQlUS2SI1mWJccpN7mJo9hJjn1OnufYuT5Ozrlx4jh23K6tyGqWTVkURbETBAEQIMqgzmB6b7v3str9Y2OGGAIg1UhR1HyeBw8ecmbW2ljzrvf3/uo3sekAlii+pS742akEpydTCAI4CwmuJEuUnO3UubbeMRcAtbj9/Pw8HY4yYsDGYtVOfzTC3Pwy26sJ+toEHLu33fHnS5rJ5w+0U7qhp4Ao3vZdu3ncx11tQRZXYnwtXiTgsrPXU37j33IjX/Cjini9mZ9UiPsnxTuup3D06FGOHj36Tt/mPcHNFUvVapVSqYRpgiY7UPQSenIJqa5lrRLhx1kMqz9bdARZzCxTLJeRl6eoa93NJw9t5tjgKFeXiqQTK6B4GWgK3lFCdMLXyRVfL9HUBPkr53ghtZ1kUacl4LylW7sl4OD8TIq2iJd0MU6wu4kDS4tcnV2m/eo8H7v/V7E31TaO1aFpt/NUpM5uHv7d3+LC//5/cnFpFut//j7Shz9DS2sbh3vqOHvxGomrp5nX3bhDW7l7az+yKPHMtSUWMmW2Nvt44PmnWfrDP2bxD/+Iq6cvM7j436lubuDAL30c1/Ztd9S+fn5omfM3vCCPvTaq+De+dZGQS+VAd4hXRuJsb/ERcKm0BpycWSjhVkMU2u+m0mISuCnMB294fFpF59ANvd3FG7KMsYxIolDBqUhEfOq6UtuWoGOtbt9tt2FYFo9uqWdTo++2IdLGgJN/GFDxnD3J5f/1d4yPT9IXnyS+dS+JT/8qxiN34/C4sFIpFkoCfdvupmIfx5i9yLGXXiCdjPPQI49yYTbDzhYvQ//rq5x+4Ryy4GLrAwdx/ebvIMhv3/j17NUl4pcGyb12lqiV55Vdn2C6pFEnq7edYLDKQJOfkEtlaWEZ2/x15lHYfPAAm7bcWTtY13Wmp6cpFAqoskhLcxNL8yUW00XyC9PIHrB3bkFQ71zavvoMry1k8NhtuBSJbFm/5V27eVqphI5cLTIQsdHSEv6RKol+GH6SIe4fl59J5bX3OjcnpTVNo4qJLVdCKmXIJlVsDvdazfKPsxhKmsnR/ggt/XamX3uF0swIVl8bfm8d7W2tDC6OspQpU9RLbGt0osr+tZ+diOX5xusznByP18Y+797D2ecLeHNZdmWvYW05wEvX49zVHsAmC7XSVtNkOlGkNeDk6qRKqgKFao7s3g/gyT/Fltg55N//DZ559NdJOkJvWwbs7OrG97//NuX/8b9YWpgi961vsn1fP8m7D/L8uSGiksWH9/XzUszBmak0Ybcdy4RMscpcssh/fzVH69GPc6nlANLrp4iceInjg1XOnv1dIpEAH/rIPSzvO8r5ueK6HMk/OdzJ9wKLPHlpAVkU0U2TUrUWZ35+aAW/w8ZcusRMqkQ66CSer+C2WcimTkAx+caxBWwYOCWD3fU2BMOinDOYzYLht5OqCDhVG5vCTh7d3srZmQwvj6xgmLWk7VQ8T66kIUsi0YCL6WQBWRKQBIGpZImP7o7ecjAojY0T+erXiX/zW6wkEtQDcwP3cOGL/wy276Ld5+Crp2foa/AwHqtVfm1t8bOvay/DY2FGTj7DxYsXKSgBXpuHly+dxLUwRUs1y4UHPsvkkSP4huMEXepblm1alsWhqZcZOvF9BqV6XPuOklUCNIvL7O9968ax/V0hro5MM3ftdRyWQbCpESXata7p8Ob7lkolpqam0DQNWZZpb29nKFbhaJ+b5tQIM54sZdmB0Dbwg78vb/GuKZKArlWIZ/IYeY2yZiDKTjzBOpQfspLoh+WdCnH/KGwYhXcQQRBQFAVbIIRpFBGLGZzlDHlBplKpoCgK25s9azHJH3YxvLGQfGze3IM1O4x5+Tji3R9Et0Q+fqAfsZRkZCHJ9Ow8DU6B71zP43cqlDWTwz1hLs9nmIoXaPY7EDsGOLT0Ilvz0zw/aidTbuTpy4t01bs50lM7La2GusJ1QdKxBQbUIg/0h0g2f5ri35Zh4hUO/v3vMvqhf86gPfq240gqTi8P/5t/QvjJr/CNY0M8eWYS59kx1Po25lq2UM148DsEZlMlFjJpylWDOo9KwKWQrxSxTIuqYsd934P0Pnov158/QfOL32FxJs+f/+l3KX3lJaKdUZ4d2Ma5gV7qG0I8tLkBjypTrBqUqlUKFR3Dsrg4k2AuVSLqtMiVylQqVQarabSqhre8ginZUI0iFcmJu5rArWW4QG1jycs+kvYmZKuKLijYtRxlm4fvfWUJw9NAxdVAndeO1+PmYFOA+bJAFWjw20Gw+MCWRhRZYHA2w/hKnk2NXkpXr5F+4SVWnnmewYUcvbFJbKaOo7+PyC/9Ips/9lFGMwYXZlNki1UuzqWp6gYdYTctfucblV+hRup3P8hLx06R+c5L7F6eYNjewFSoi9KebdRv6mFzkx8si1fH189sWqe2Z5mY3/oTbMe/hyzVUdx2kIW2LSxeukqvG3q6Ot5yvVr5DMUXn+CIlCTa1cL83iOUNPO25aKpVIq5uTksy8LhcNDW1oaiKOxuc2LF5zEXhxjwCYi7D96xN+HO78v6d+3mSqJsocQ9bR4GlCxzGQcldwjV/s4PoXsvIVg/zJyJnxJzc3Pcf//9vPDCC7S03NnVfC9jGTrMXwfLpOKqoyi+EXv9casXatc3MF//HuRTEGhA3Hk/glhrIovFYiwtLTGRqnJhxWBbRwO5qkmL33kj0anjUmSuLWbpd+m0z5zhetmO4K9j3NFCNOikNeTiSG+Yl67H8NplsmWdBjOOkFnAH2liworwD3Y2In/zjzh/YQi7pfFa9/347tpPVoO2gIPpVGldhdWbKVy6yJnf/c8MaQ78WoGXu+6haaCflDvAbKKEYRmYFqSLWm2zc6moNglZFJhJFQk4bYRcKv94fxTn6eM88f1zZGfmacjFiLmC9MQm6a93E9x/N8+072VQ8CFLJicnkhQ0japee/YCFlig6hnCpTnKNh9u2cDhcGDKDtyKSK+7QosLTETmywoWJoKpM5y1oWsGJd3EVVpmSbeTkwN4qknaCtcoSy5ycoAVZyui6qTRo1CQ3LSFfezxC4QWppgZn0McG8Udm+fuuctM+psZ7NzF0f4I+z/zYVw36Tv/2fEJXp9MggAiAqIAyWKV7S1+Gvx2dkYD9AVVKk89wfm//hZDthDhao6XNj+Mb/sWeqN1XJxNEw048TpsNNwIf62WEbcEHLx0Pca9QYO+7/0hzI6DbGPwsX+Fc8tOqqlFjp8fQnW5+UcffeCO69dKrWD+/Z9ALgXhZsSP/TOeG02vxfBX7+tSRcRKkQH/jbEVgQDNzc1rIRsrl8I8+30wNITOHYidd84lvB23qySyyTLOQgyxWgSnFyHc9iNf/73Aj7J3bngK7xKCJGMFmyAxh1pMYqvvpGLU5qnruk4ul0OSpLXxuT+scRAkCXH7UczXn4bUEta1U7D5HgRBYDBmMp+2M7ecJ2IXePXqDFlTod7nYDZVpNnnxOOROdAZYj5V5FX/DmJzC3SkUtRXTdTwJtyKzMvXY+vc71TORSy3xEvXlhg1dBo8Kh0Pf4FB42UiV45zeOw5xuITLG97kNez3rcdR+Lcuo0Xdx1kbKZCb3aZhZKFcOIUO3wWxfY9eBvruaenjldH41R1ky3NPi7OpQm7VZyqhCKL7G71c24xz/Z7jpJ19FLM5DgzPU/j3Bhyehp96BorQ9fYyV+yWZQZjrRSbd3KsjvAcF0fqk1AVu0oiorT7qbO28uh3gZW8lUqmkmuovGBzQ00BpxrY9xfuvFcAJ67tkxb0MlrE0kUWaQuW6InpFIq5plc3oqRz+NIxZCKEu5UjPrxKbI2PzHVw3hsEltsEu2GAUs2d/DakX+F1dvPvp0DTJVM4ppM6Nry2rPb3Ogl5FKYTxdZzlaZSeQpaQYWUMgWMZ8/QfmJv8BKxLC7Gljxd3OifSuZhk4eqw/wid0t7Ij6GXmTUFD2Rif8VCJPdPEarz75Ohd0hZB/B4/86ue4q72PSqXCs+dGaXZZHDiw9c4GYWkG88k/q83Yqm9F/NCvICj2WwotREx8ZoEGj4kgiDQ1NREMBteua5ULmIMv1AxCfRtCx9Yf8i2scfNk5FVWK4mkYhqqxdqwyGDTnS/yPmbDKLybuPxQykIxi5icw1HfdUvF0rpy1h+yCUZwuNG23Mu5l15kpzmBXRBg0/4bL58dExFFy2MmMmz267REbDy+o5vJeJGSXhM4d6gS26J+LrhUMhNjKIUsyuhZtnYeYtPdbwgJDTT5+L3vLzK05GMllUOSM/zBS2O1IWLeZvbc/ykuvf483swi9x3/Uwa7j2BvOYRNFu9YbrewHOOVhBPNG6D9nnsIj0xgzhaw5sZpWMlQ8oZIL7TiaurF43aQLWtMJYoUqwaCUGvO+v6VJcZjBbrr3Ay0+Lg4q5Oxh/Ac7mD48F5mj38L2+VLNKUz1GdzeMoVAsUyFaGK3W9Sl4lTtjnwaEUKbj8WFZ4fm6RkUwna4KGAzujKJMdMG1+XZKJOgVjF4v977jqYJg2SzpJhMF+yEKpVNN3AKi6gZnO05EtkKwYL7jCHcitci3STttfjLefpjg9xpm0H43VNmA4HrZ95lF/8hY8wmapyYTaF0+XAphVveXb7u+u4tpDhO4MLLGbLWKbF3oCB+tpLpCcmeLFa5DPJFFcGjnB576NMpQuU0kl210vrlOVWmwRXCx4OdIUQ5ic5/+KLqMkJbIKHnoFOygc/RLWlCZWatK6maUQikdvK6lqWhXXlFNbxvwdDh9Z+xEc/h2Creck3F1pMLCeZW04SalRx+l20tbWtm4tmFXOYF56DShH8EYSBgz/wu7Gq97G12YOpVdaVld48k8gqFyC1VPtCqBlB+vGnrf4ssmEU3kUEQcAKNtf6FqplSMwi1LWuVSytTmddK2ctlXA4HD+UcZgoKwzae/AWRxhYHMcyNNQth7BJAmUDbA4fjUETVaxweSZBUIGBpgZeGU2skzi9Z3MbI14H1ZFLZPIlpBe/hk18EGvzvrXP8k8Od/JM0M7XXrlKQNaJm1U6wyHuG6gnWaiy5QtfpPfsk4wdP0FhcoyF2VHyfXuQWw7eksh89uoSxy5PogsSLR6JyaxGOdREJNrKHq2e7LFjOCcu0nItxYt1W5C7uqnbuRM6ooQCLk5Pphicy5CvGPgdNq4uZRleyZEtaXQHbRy7PEa1UsEj9bB7m0nTAw/QuW8f0nSKlrExTo0u05ZcZFIEM5MkqXgJJpeY8dSjlgrY9CTB2CSl2Dg5V5CtiRkEYDLUSqSUIV3XDkB9fIrhcCc74jMESmlOte3igt1FNB1n+/xVljxh5uui6J4oQZ+XbT4//pYG5m1ubLMpbPFrmMlJ/u7UNOcvn+LuRz7JUMm7NkPqzRUzz15d4vx0iqhq0K/N8fpcjvmrSXJakY/HrrCpp4W5X/q/ue5s5d6uOtyvn+GFwThBuYov7FozMlfms2te4Mz4LMW//yvU6+cpyK0sOespbj1IbHM/07ES4WAen5VndnYWSZLYvn37LevTqpaxXvwbrNHB2trfsh/h8EcR3lTGWawabA0KKHqFiKgiKQ56enrW1f5bhTTm+edq8rbeEOL2o7dc505YlsX1hRSnRpdAK9aEmaiVld4sxWvpVYjNABZ4QgjOn26y96fJhlF4lxEkGSvSBovjUMxCahEr0IggCOsG8JXLZUzTXDMObzdLZV0jXHMDp1YkLi2MEFpZ5MHqcxR929emy8bsLmYzFq0ejZMTCXLDcTqawjy+s21dVUZnpJOWo/3MPP8UpbFlrJe+hTVxFfHejyF4g/icCk7FhurykcwlSFdKFMplMkWNQsVAUe04P/krVLsOcu8rf0945AwvXC6THn8Z8/5DCIc+gGB3romwZAplDEtgrqJQXcnT6HNQ1OGrvu3c/+sPcLg8ReU7f8uHX30Jls8xfPEYYt1mPE1BbOEtZN0hkmULu01C0010waKiGVyezWIICl6xzL5tPdy1+UN4Ai6amhtoam6GA1s4QK1k8blry9S5FZ48N0OhUEYqarSrGn1WkfnWOsa1rbiNMvWlFqxylUm1nqqpIcouECX03iiNsoMdahO6x8cR1cO06KanwUM0Wo/NUsnGSkQDDmZTJVbyFWaNWg6jr7WRyJY2pEKc6xfPkpq7yF898Qz+zm08vv0oPqfyhpreZJytuVnufv0U7tcvM5ixsBBw+Ltpskrku/u4/PEPM9tQX1sTXjtnp1IMZ23EDCcL6RJy6I0S6N1tAazFGcxv/gV9Z14C0+C80sK9+wYY7zpAPPXGROKXh5aYmxzDjshH9vfzjcEYn9pjXxOvtyavYb78t5BPg01FuO+TiL07b1mzlUoFn5ZAEUpgEzmwqZVwOLxujVvxecwrx0Gvgr8eccfRdb0+dxp0Z1kWT1+aZylVIFfWaPTaeH0my0iiSoPfxcNb3hipYRk6rEyBqYPdDYFbVeN+ntgwCj8FBJsdK9wKK9OQS9Til/6a+/3mAXy1XgeTYrG45jnczji8OT6ruL1svnsPvUtnIb3CztIJxO1H6Qw30L6U48KsQr3bRmFigc0RgXZ/hVI2xabG8C3X3vL4JzCvd2C9/HcwPYT51/8ZYd8jCNsPki3rfHZ/B66Ki6+fnsYopAi7WtnW/Eat/V27+mFXP1defY3kcyfZmhjEfOIv4JlvIhx5jMMHPsB1r5+LYxIOUadigsshcX9/HbOpMvmKTkvIhS16ENu+gzxzepT5c4MMjc6xffI855ccROZexSWr5INtWC4PYbuXWLGMoYOJjCRJNDW3YXq8lA040veGBOnNBrWzzs3/fGWM0eUcAZfC9q56nDaRJQsaPHacqkRv2E1doBba+IRNJOKx82evjtMecvHBbU3MJos8c2WR4aU8XofM/s46EoUqhiiTKlb51J4o0aCT8ZU83x6cZ3Ojl0Sxujbf6rF7drB9Uw9nLmxl8fVzZCcH+f2/TPDQ9s00JBewJi9zYr4My5foLywhuBoo1m8jH+2mLdrBpu3dvDyWwhOuu0UIJqBYDNhiHGxpoqsvTLFqYI1fw3z+77AunrqxQEWEu+9jz4c/hxAM06MZa2vLbhNYmJsholTZ1hpiRnNxbGSeBo/KB7vdmMe/DWOXqFgiF5w97P7IR7GHbw0tZTIZZmdnMU0Tm81Ga2srLtcbCoCWZWFNX8UauwBYEI4ibjl0S6XRmyuXLMtaG1y5s9HOmGxweUHH53JSQWdHa2Bd+M0yDViZBK0CNhXqou9o6enPAhtG4aeE4PBghaM1lzWzggXgi6wtyDf3OqzOZ181Dqvu7+r3364RTvGGcXR8EPPSy5CJYZ59GqV7F7LURKaoMR0v4vf4CAdlbEKBpaUl8vk80Wj0FvUqsW83VksP1ivfxhq7iPXqd7CGzvAL+x+F9gEsqxGxnOXSTJIXzg3zSw/exfYWP3DTpmtGaPvop3htZDeXRs8RWh7nwae/jvzM3yD2H8GgH69QoSJVyRkmx0YS+JzyLWMJjuzq5Ps2B0nnIoWH78d2fYr7li8wMrGAkILmxRRX3M1k3FHajCo5pxdJEFiMF3CLFmaT57ZNS//5mevohkm6pNHf6CFfMlhI17QSHtnSSLZUZaDJuyb+s5osrzVFKfQ3eLkyn2VXm5+2kIvvX11CFkU2NXrXddCu3luRRcIee22irsnafCvZ1LnPnqPBVaUu6ME+PEo5mSA1cplxm5e6ao6WapqzHXcz0tZKrqWLD9y7k7FEmbGVPKcmMyiSxGSixHKuymyqiCTU1kaDWsWSS9Q7ZPpHj2Oe+D7G3GTtQcg2hP0PIj7wMYRwY+0UfqN/YHVtnR0aJ5krMK4pTEhOClOLdAbtPPHyRZ7/ziKNYol/E1KY6H2AQSFKQLNzcxdBqaLx/OA4TUqlNnbE6yUaja7rDraqZcyhUxCbrb0LndsROrat26zfPCLm1bE45ybjuG1wpLtWeupQZNxOJ5ZcJVEyKFTfJISz6iFUyyArUN/xA5e3vp/ZeALvAndycQWnDyvUAok5yKyAoWMFm9Yt/rVeB5ttnXFYFf6+2TjcrjlHUB2Iux/Cuv461vwI1shZimaEzkAvQ7qJbljg8LBSsVFnZcnn84yMjNDa2orHsz4ZLLi8CB/4HNbUEOaxv4PEEuZ3/wIa23m+/jBLUjNjhTzFSp6vv3yRgZ526tzqOi/GqcgoLa1s3b+dvuIcwrG/x7r0GqXhqwxIMXoEieO6wfZN9cg+L7OpIqOxAltbfAw0eZlLFXni/DxjK3kcNoljMwViuhf1wCd55Ith9sxPYb76ffKDM+xKTwACX2k5hLuQpCjb6ZwYoevFcbJ/raBH27kU7GJ3VwTBGWaLX+RK0sQuSzR4HUxWi3SHPTT67bhsMmMreTLF6toGv6owly5WKZQ0/ujlcYYWM5SqJgGXgiyJrOTK/OkrE6g2EWOfyfaov9YRnMyRT6Zp0vKcuJAmnyuwvzTLXQvXWYknCGXnGKzbQc7uY0mpx6cX8VazTDR24+zZT3BnDzlTRbJJ/NKOZmySyLPXx6gaJhXDoj3kZCpRwKVKbGry8PjOZmZiWV45PcTjqRH6vnUO09CoIHHB1c2uA7sQD3+QC2mBXQE/KutP4SXNpNtVIRFfweMXaevrpWzZePHMMO7lGWxFiQOOBHK4iW90309BUGn12tdrk3f5een8CGems9zV7ODAplbq6ureFC6aw7x2srZRSzbEzfcgRFpveadu7j6WRROjWqG90UVP2LVudLWZSXO0L3JLw5qlV2F5shaWkmw3DMLPZ2L5zWwYhXeBt5rlLrgDWKIE8RnIJ2uGoa4FQVyfSHs74+BwONjV6l97wW6+jyBKCJv2YYWaeOblMySKGQrzF3AHmjiTqilvlTSTXz7Qiluv6RVPTk4SDoepr6+/pa1faN+EGP23WJdPYr3+PCxOcWhhhuuBfqZcnSzkc1RjaXzuZer7WqnqBtcWMmRKGpLwxjgPe98W6NuClY6z9cTzFE4M4swu4rB0ms9f4pynl2iome5wK4UbYwlWVd2eH1rCIddmBu3rDFHWdEbjJVzuMN+fWySnlvncFz/Ld6btMJ/FrecJlAuk3UFmyivklvI0Tp/hbCCL+p1h8pID3dOBzdMArghTsyK6rKCspDEVmSuCG8Vm4+T8DJYoUvFZ/EJUYCQv8D/nRKaLUDShTzWZ0iEXL6BgEkSnnSL79QWiX/sOmT8cZ1syyZjg4ZInilDJoHiiOGQ70aVz9BbmQRAYad/BtZa7Eb1+sk4/aVXiwtWrqJUCCcHHykyZXClPNFQLQw0tZbEsC68qY5hvqJcdbvfxiLSA8o3v0Hv5NH3F/Bu/x77tTG5+gIs0ExxogAqcn4mtaWqni1XKN+YFSXqZpekxtgTgIwe20Jxd4Mljg+QTPqZEiYLNg2PPDh48vOOWeV6bmjyMzMb479+bpKgZtPjtzGtunp8oEVqpldda1TLW2HmshbHahwvU1wyC3X3bQ5Uqi1iGRiKbp1KSKFR0bLJEwOdZV5hxu4Y1q1KE2HStIspmh0j7uvEeP6ggz/uVDaPwDvKDqr4JTi9WpANiU7WS1aVxrHDbWunezdzJOLxdzgFAiLRy76NB/vLJ41xZLJJcWaSESr7opiPs5yuvzbKzLYBNV+jzVInFYuTzeVpbW28ZdCZIMsKOw1gDd2NdOMYrr17mynSBCW2aqF3kasHF+MUFzi+WKe3v4vJClgMdQY70R24ZMSD465jc+QGuOXdTuPY6w1MJ9hpLZItl9uVe4cjYLLPeZgojm1F2D2C6uwi4FPwOG5PxIp1hF7plYVgm1vJ1crkccsceXtNacIcM7osEmUuVcCoSZc2gHHmYwcUEC9UsHfk430hsJVExiRkyjfkYvdk53HqJs75ObKkJDiaukqvbRlMlTVWQiFRzRKoZnpcdPB3exrizgYKoUJDtXMxUsSyBvKyiGhoKBu2ZOfrmj9FfWMIEVEDyB7hS389Fu5MF1c9hv8nTew7wXbcHm8dLxYRCSWcslqfD76JsWYSaWllemCeViLG1LULQaZHIV/i97w8znSgQcNaazQq5HJXMIs3ZFbTBIWzVYSxAAJZlJwuN3STv+zxJHDV1u2yZ//5ibTNu9jsIuhRWsgXOTidQbTLdQZVIeZZt7jL75Qp1z/8l5XKFi5l2PhIusvvgPp5YcZCyq7eEMfMljcTyMp2OClJAZqag0hFtZCFbrukVNLgw50awxs+DVq3lM7p3IrQOrK3hmw9Vmxq9a+s+lStwoN1HNOgkXrLQke44kG8VK5+CxDy17kQXRNpuOYD9oNK971c2jMI7yA8zElewu7AaumsnGK0Ci2M1j+EOpXFvZRxWPYfblbLa3W72HtjL3KvDaCsxbHoFLVelp95ACDWxuclHf2MLeqXMzMwMpVKJ0dFRWlpa8Pv9t34OxY6w92HuHThA+ulXGRlaYblkUdV1REsnvrTMf322Qn3AjaYZ5CoGXodM0KVS0Q2OXY/dZDhdfMfRypAMRZeD/+3+zVRGruEZz9KVnuXC1SqlS08zKnfh8HbT3eimqjQyvyxSlRQQBJKvXwDg6N4djGgmXqeNBo/KfLpMc8CJU5Vo8jtYrIrk1UYmhH6q8QJ+QaBOEljOlkkUSiiWQY9sYpOaOWkeJJc3cNgKaFWDTjFNWbc4YKaJVTzkDRVJgElLwMBObYuRaHNDi91GQ+cWhMfvxtNeR9XlZbBkQ3A4+VVFZngxw99eWGDOaydfNXhkcz2xXIUrixl0y8Jtl7GwqBgG7kCY4sxVtFiF3shuzs/m8NhlJhYT6MUSC9k82mKOe8rT/JPqeZZFN2XBRqVjMxdb9zKfXGA4nuahBz+I3ROkRxW5spBlZ1ugZjGAXa0Bjo2skMhXWciUccgC8aVF9tkSdEkx6uxxAMZ9PYSat7Bp7xZKooCajnFXR62iZzWMWecQeH1okkSmQn29k6bGBpYWyyznKhQqBnIhie3CCaxsonbzYCNi390IrtqaX61KG1nOsbXZy1dOTdEeUAi5ZI72hNgZ9a+FiVreJjlsWWatByF3417uIAQb1wnnvBf0kX9Q3klvZsMovIP8sFNQBZuK1dAF8bmaxxCbwXIHIdCIcIfJjHcyDqulrE6n85YOaROBfZvbGAn5WZibZ2QpTyqWwJVOIjkzKI07UV0uent7mZ2dJZvNMjMzQz6fp6mp6bZTIu0eD9sOHmDZv0h2YZbyVBK/mUe0BPK5MvvsBmqgnU2RRgRJWZuxs2o4v3ZmmtlkEUG20+u3yOUM/nSkxCN3fwDpC19g6uIw33pplNfy83hS8+zOjnAu30BcTOIxNdx2CZfbzbWcSj64h8GEjY4mheeHY7hUCVWS8DlslDSDwbkMzT4HpyeTuFWJXFkDAQIOlZJmYIgyol2lrSXIeCxPRTfYvznIJw90MJsscm4qzonhBfaGNFIFneUpDd0EA5GyIeBUbMimieB3MFw1MEIust0tyJsbGFnIcGE2hl3O4lRsvDi0QlkzGV7OocgSz1xdpifiplAxMEwLzTCp6DWN60e2NzI+nScXnyN/7PvkcgqDJQeJG4bIhsEyKqftUezNUX79SCvCpp0Mpiy++vIQ4tIkzU4FtS7K2ckMbQFHreAgUaSsGSTyFbIljaHFLIViBadexlfMkDJkrldk9nhFnvPtJtnYT0H1k86W+cOXx4Gah7EmYem2sTMssTS7RNQtYK/z09bWxpWlIkf7vLTYykxfHqR4JQ5+A1QnYu8eiLSuW6eHe8N8//Ii86kiMgazyQIDkRAHO4M4nc4fWO6yXChw4coYO8MCqiRCsAnBE7zl+95rugZvxTvpzWzMPnqHOTedwnGTItjNYiV3wrKs2okmtQRYIKtQ1/KW44Fv/tmbS1mhJhO4ahze/LkS+SrpVBpvaopgcYWyCbvqpJr7Hu0DWSGRSLC4uIhlWaiqSltbG/bbDAlbvWaxovOXJ6dYiqcw0kksXWO/mmLBsBO1VfDW1dHQFmVJqcPjtFOo6jV9gKrB2EqeLrfGqcFriDYnHzi8B59TJV/RCboVrsxliOVKtApFwoUVBjLjsDjNVd1Ds5VjWvDit8qkBTsLtgBX1CY+Gqkw0Byk5AnhDEd4PS0yka6gygItASeFssaTl5bY2eon7FHJl3SuLmbpb/Qyupwj5LKxy5XkpVMXmFiIEdNsGMFOhGqh9swNHamYxLA5wOZAdgcQnD48dpWB1jocikzQaccmC7SFXDR47cyliuQrOtcWM3TWeZhO5okGnCykShxqdeGs5jk5noBSEUoFDlanqEvPg2Wy01jiqhhGxOKCGObPnbuoYMftsGG3y9y/Lcr/62gPp8YTJPJVxmM5zlwZJZXN0xwJ0dLUxJHeMGenk2ujO/wOG4lMns82FpmfmOKpJZm0qWATDEwkPjXg4/FH9oHbv7Zx1nvsnJ+pyQ3taguwnC2zrcmLU0tRLRUBCIVCNDY2IooiVi6JNXkZa2W6tmAkG0LbQG2tvWlk97NXl1jJlnj+6hIruTJV3USVRSI+Bw9ubqTOrb7t6d2yLMgnuTYyxcszJe7t8DKwqfst36PVsSWr873ealbXT4ObvZk3z6m63fPYmH30HuRHGYkrCAJ467DsLojP1sJJS+NYnrpai794Z3fx5lLWSqWy5jnkcjlkWcbpdCLL8psMUxjowcrEMEfPQ3oZa2IQa/oKQksvoegmXN3dzMzMUKlUGB0dpbm5mUAgsO6ktnrNc9Mp7u2L0HagneGlLOcvD5HLGzwmxYiXTErxeezpcWTLRX9rkFKkHV9HF0VR5fpyjmtpgbzkJaQlmVtYIBeqJ5Gv0lFXj2oT8btUwnVBiloTnr4HsUyL0oUJrs1cZ3L4OqoSJCW6kQyd+lKM8cki1vgwIUq4zRwztn5WbEHSooMhRSbskPhYQMbIFaiUVOIFARcSwzMx5pbjXFqZ4/mVMUxHEMsWxPK4cEgCcqAOBQ2vmcXu95PO5ojnsmg2FSyBZNXOiYtJPG4H//yeNnZGQ1ydTyAVNZZms/TJFebjGssLFYpli6yepqDLuM9dpcks8Dl0omaWWdFLGRm7YPCcvZu5nM7mzmY2P/o4kykPvUNJZlMlTMsCWWRLow+P3YZmmMyliowsJLHyCcBOQ7gOQbAYXsqSKFQo5QoEjRxLUzlCRoYnZkQapTJZM4AgiRh2B03NTcg9TeD2c3oiyWwif6NoQKCimwhAPFchlinw3NISDU6R7U0uOttvVLClljGmr0BiobZQRBGhpR+hfQuCcuvhwjAMdjc7uUaFBo9Mg8fL9ZUC21oDCAI3QpxvfXq3tDLPnh4mkSuRr5pEwz5OZB1cGoy/ZTjovaRrcDveDW9mwyi8hxEURy3PkL4RC83FoZjBCjUhOLxv/bM3NcGtjs/QdZ1sNouiKDgcjluUowRfGHH3Q5Baxpy6DMlFrOlrWDNDqJFWupv7mM9VSGcyzM3Nkc/naW5uvuU6u9sCawZiT0eI7S0+vvzsWYYVD35FJF9VWMwuUUxnkRdG2RsfhGtw3tnN/7u+lXKohb+Vw8yMpZidHGd7bxuqLDKbLDK6nOOxbY08MNCw7qU9uruLXLPEK2dP4Gzbxkc++DAXxpfpkCvML2ewrCSlYorvZkwWyjJeLUdaFDA0mC1I+FdiVAWRT1eH2I/MtOjhGbmTTYDps9PtasAr6kwpLi4LEeqpMIOCF4Gw6GGHbHEoYDCieTlpCGT0ChMoKLJAREuy9OIoL1sac6KXRjPHuBShZGRYknzkUWi0ctjMCvutHOfUNvJugzqfi82dXhaNBhJqgLzDR/z6Jb47KHC3dxv3W810tqhsy5l8aHsjEa+dwdnMmtLd/ZvqqeoGz71+DdESCQQCdIfs6OkEnvkE6eU0Eb1MvVThQtVHt7MKgQhFIUCfJtDitzOwbRuvz+RoCTqZiOV59uoio7E8v3BXlAc2N/DMlSUsy2JXvcz34zlOLBZZ8Kjs6Avjzq9gXjtWm9wLIMoIzT0IbZsR7Lee1ldlbcvlMgIgiwINfhdzmSrJskG5atBa537LEKxlmZCJQSbGkUaRYbudwZwdZzCIkr51dtSbeS/pGtyOd0OlbcMovMcRxFoM1HIFIDlXq99emcayu2uJMttbz3pfHZ9xs3GoVqtUq9Vb5r+8kbwKo+56ECsbrxmFlWms5WlYnqbZHSAYaGamIpJOpymVSncMJ93s6t6zo59Tg0OcT5QIe+Azn/0HLMVSlGbGoTAGsyPsKo7B5BjXrrvoqIZpd8N0RWdm8CyPPHSEkikymShgmOCx2255aUfyDtx6hk5rHqdNZq5gEWiuw7IFGKOdx3c20yUInJlIYJWLlCcTNClVxhNlXIqXOqPM6YqH6WSBiarCsuAlL6q0mjlO21o4qM1SrZoExBxNZoakVEeLkabLytJhJjDNAlvQScitJLDjFivUmwWarTyTuEiZNtxCkQVvlKzl5qrRhCKJBFWJvCtKSrQR7ArT6nOCLHHPzmYku40jN3UUW1oFEYPOgMKRvvDaiIpVjvS90T2syiILkxOEKwl6HBUMM0bx9XE+4IwRlcs8L4T4Hk3IATcFw0Oqs4FkbIUWKcPhJoXDhw8zmzNZuhLnyyenAChUNJYyJf7m/BznZtLc0x0kHovzf19MkamY+Ow2hHKJP/72SYKyzqGQzoPNdoRoH0JL3209A8uy1rza1Wi2oiicXyxjIGGTRI72hqnoJuWqwcvXY7f87i3LgmKGSnyBC/N5djbYUf1BbKqTQi75npC5/EnxTnszG0bhZwRBveE15OKQXoFyHhZGsTxB8NW/bSfmqo60qqqUSqW1UQCVSmXNaLw5eSV46xC2HsYqF7DmrmPNj0I+hSOfoleUyNv9JCo+RkcqNLe03BJOutnV9ThUers76Y5N4KfAxXOnOXjwIEp3C3AES9dgfhxrZoTy9VmOZJaJymVmbXZeWdG48LUvU3T4uavOx9JEkK+mEoQawjy8tXntfvX19cTVJuIJG9ZUkrBLYXS5Nko66FI4O1UTbp9NFmny28nbHCSdPpr9Eg0RNzvbAvzWnz3J1aSOZbPT6POgi06UhlbqVJGOzn20GwZDK0WKZQ1/torikNjU6WGpoFNUZNJVk/s6/URDLo7NFDg1m8fe7Kd6bYSXv/Y/KJWruPZ8jO6+FszlPB63gqLaKAngVWXCfheTiSICMJssMtDkW3c6nIll0QUb9ZG6WzY3q5iD2DzWyhzW8gzW8ix3p+BwwKg9S91OWbEz0NmCEO3ByoZoSQu01rvZ6rCxNDuFU8pgijLVcD/fvZYiX9E51BvmOxfmmU8Xa13Boki2qGETDGZm59lTL1HXqHN2oUShUMEC6hSLB9sdbNvah9jcedsBdpZloWkaxWJxLf91c4jzi4e61tZPi9/JXLrIzmjglpO+Vbkx3bRSZDxW5XzMxNsYYXOogfJ06j0dDvpReKe9mQ2j8DNELdcQrnkN6eVas1suCfk0lrcOvHVvmW+AWtLZ7Xaj6zrFYhFd13nq4izpkoFmibQG3LeU4gl2F0L3LqzO7VixWaz5UYTkIp5iAg8JNFEmm5tjOdxKuGvTWjjpza5uxRTYv3snsZELpNNpTpw4wcGDB2vVUbIN2voR2vrZcwisUh4WJom/+gIPJxbJWA4uFgTslWVk3c4mNUvfcAHjUhgh1AihBrzBBvZ4ylzJlAjbLfZ3NXBmKsFSukxbyIkgWrT4HTzQHyGeryCJAjtbAxTLOt88N8eV0SmuX3gNIv0owWbygohgWZQ0A4/dRl60s7M9wKZumE0VuTKXoaybOBvDKOkim29sWKubdVM1xWejtQ1pd1uAAz1h/vc//lsSZRDmYog2B+miRq5s4HfK7Ir6gZospMduW/d7CLlVjvTUcf7rV2jQRYKygDl0BpLLWPEliM9DMbd+vQCbbAJx2YVn78NsaeqA+laeG47XPDhRZ097Len92rVJQmaW3fUSBw/eg2R38+SlBQTRwme3oUgClgWabmJTRJL5EssLBQaCVbYpWYYqIicrCjFNRlBdqOEA7h09OJr9t12HN68/eOPQcnOl3FuFSmojOJbZ6a2ganmenSySKENechJta+DkbJFLKzPr8gfvxXDQe5ENo/AziCDJEGrG8oQgtVjzGjIrkItjecO10b9vYxxkWcbj8aBpGge7YGQ5x+WFPBhVJNG6bexVECWE+naob8cq5rCWJrAWJ7CVcoRKcZiJo89fxoy0ITd2gL/+tq7uoUOHeOWVV0ilUrz66qtrhmFd7bXDDV1baWrs4o//+I+Zz+m4W/qQfSrFVBpZXEAtF2qbYnIZRsEC/kOnzJWKixcHX2V+0se1jJuUIZOMJ+lrcPOpPa0MtHgZWsrz5KVFQMBrt/H4jmZ+58vfQ2/Yir+uHpvTRqGi47bbsEkiD22uJ1fW2NLsZXwlz2sTScZX8nRF3Os2rJv1hm8+0c2lSlTUEHcfuIdjx14hJUCouRVVlhho9NLX4GF/Zx1OVUawYjTbLeZX4mxCo8/IoS6kKC3P8Z97bfgUCc5/lVvKBm0q1DUiRKIkJCfffPkkaWS+8IUvIjW+MfnzZg/OrogMT86QTGfpjQgcOHCAYDDItYUMI8t5BECVJIIeOzImSykDP0UWqxpdosYHg2UEBMruOrb21/GhjlZEUWQqUaSs31rYaJompVKJSqVSW1M3hTdvV156u/VjVcuMj05xfiSON+pgIKJyZHOU4ZLK4FwWpyJjk6vv2XLS9zobRuFnGEGxQ31HTRwkvQyVG39nYjWD4Q295TyX1R6HSCjAfE6nqGVZSBXJVXR0zY0i3flkVbE5uWC1sPPuzajFNNr8KObyNDajCotjmItjINnYWddExdfE4JSLXd31a6fovQfu4ctPHUdPpDh16hQHDhxgIla4pfba6XTy6KOP8t+//hQsjXDgkX/I6/NlCmE3YqsXEotYiSVILmEllikvTlMui7jNMqcXVSarKkGhQEgrMpqw8S8uT3O3mqXTZXLQIXFlycukqRKWqrRpK8jOeuwOlaKuE3JI+OwigqExMhcnXjT4tckEliBS0nWWMiXKuk5VM7ArEplilaH5NN8+N4ND0Omvs3NhJsnOegeH/RWGy1nKrT6sFhdXTZMt1gp9ATebzBUaFwvsWklwLamTz7lZEHXypoxkT6AoBSzADtgVCQMBKdSAEKyH4I2/w03gDSIIIpqm8c0/+ROSZu3U39i4fhT06gl8cCbFixcnWEkX6PGKmHUd/NfjS1gs0hZyEXIqZHI5tFiW7nKKVKlA2CbS7DCZs4k0B5zY+7ci1LezR3Ww56Z73NURWnfP2+UNVFXF4XDctu/lds1Zm4Iyz56f4GvX8rWKIq/MibjIRd1JnUekJSC/ownYnxc2jML7AMHuwqrvqMkdZlZqxiEbg2wcy+WvGQfFceefFwQs0cYj26KEnQITyxmyhRLpdBqn03nbzuj1+Yc6FF8dRu8elsaGkJLzeCpZ7EYZa3masdE5ziVsuKccbO5sRAg2sFCyYwbaSWcmGR5Jcmz2FepbWm/pJD3cGyZlC3NwoI3R60N866nn8PTuZbtdroWc6lsR6t8YmKal0/zOb/xf2Ow2An1b2KQZZMoKK6YDQdCImhW22XKUqyJes4xSruJDpsVWpFxfx71inkuVJc7pfuyVMuFyhZCkUUxZHFSzPFkIM6c7MBCol6pksjJaPM+kZsd3JUXRlCiYKt8ZPsnzosGs4cTtnmFAKSBVXWTKYZzeRh6SND6kjuPKGZQtiV1qFoBy1ct+Z5akzUvIZafiGkCIesEd4E+++XcMLazwhX/xa2zfseOOv88XX3yRZDJJXV0dhw4dWve11c0Wy+SeiMFEKYvslGhva8PlcuKzV+l1VBmZm2CTlWEup7PTp9MXNriiyliqk7pIkHywC11xIrbeuedm9V5bm9wY1Zp4FNS8VJfLdUvV2u3Wl9cuMeAXarm0SpEj9RbDssJgRsJRH0bJVtncHLhFKOj9kj/4abBhFN4nCIIADjc43LXEWyZe64oupKCQwlJd4AnWxMiFW09mN4c6Ql4XhUJhrTO6UqmsvcRvNwqgqW8L8XgD44uLnJ3JoJc1LK1C1Jnl1HyRr49OAlO0OU06fS4WBDuipZFIZ9DMadp3DqyrvR5fqW0OYmgLk2KG/FyBvcF5TkjSLSMIyprBUMJgT0eIVydS5LIafn+AlVIRxSah2qFkkzkR2MRyqkCXX0YydPaFLdLH/45Y1kO8ZQDD46CuBHZBIWPJtKh5sMChSHRpZaqWSNxQmDHsFE2JCGU2KXnOVf2kTRsKBuO48MsmfWqJU2KUS4JIWrHRERAoFySSw9f4rlXl0Y98lM1NXs7kZXZ2hLnL5+daUmNkJL6ucWp4ZIynFgya7V62bL2zNvHk5CRnzpzBFCRad9+PgbDuJa9ttkmiUhYpPUNYASvsJxdbIjFdQrZ0gj6NQlliUbYoCgq618+yz4Ez4sFfF75jV/ubGVvO8droMlQL9NW7b5s3eDOr6ytXrhC165w4P8RFGUIOkYe63KjBEDbVRqGQYimnrfMI3uvlpD8rbBiF9yGC6oKIC0ur1Pob8qma91ApgChjuQPgDtx24B7UTnJer5dcocRrY8tsa/Kg6zp2u53DPXVcX87fsXlGEATC4TAOhwODSSYSZcZSTlraN6EkMnzEUYRckitLBRxaFrWU54NBHRN4Ia7x4vEzNERCnM1nuTrnoaBBa8DJXKpIpGMTc1euc/3aZba4/dzd0b4uZjwRy/ON12cgsoP87EWMTArV5cFhq01TLWkGGCZDSzlcikRzUx0PbGqgpJn8we//R6atEP/gyEcYSelsb3eSKlapc6vEKxq5ioG72Uu5pKMkC/irJj5MMkWNhBSiJRrAjOdxWdARdDKZLNIYcLB3oIHlXJm8ZuK1SazoJgNeld88dpqSLhJfdNIjuLGJItdGyziVxG0N7usvnyCr1HF4R/sdT9iFQoFvf/vbAHRu38dUQWR8Jc9Ak49nri6SSOXIZ7P4CjFGU3nCiklJA70aZ7oo4ZVNOjwC56oBvD4Pjc0h6iWNyUIJu8NBU1MToVDotve+mWeuLrKcKpIulGjw2DgzfUPxLODi4c3+O/6cZVkcbnUwPJliMJbC4ZFQBIuBejf9HY3gCyKIEuXs+6+i6L3EhlF4HyPY1FqPg7++ZhjyyVp3dDYG2VjNe3D7wem77ajuuazGSFIn6NHpDCiUy2VEsQqW8baxW7fbzeb+PpYvjJBfSnNlYgHZ5cHdWZNcKSlLLFtlipkssr9IKZOiz2kwVqzQkp8llRLo8hlcLbtQQy7sup29TR4q2TDL82VeH7zCkc1RVFlaNzhtf1eIq/MSZUcIWz7O/qZuqjYXU/EiI8s5BASyJY2IR2UyVuKiu7bpVioVbOlh+ho9LJRy2BWJ2EqFkFsh6FLY2uzEMC0kQcQ0TfZ2hFjOlUnlq5ydTuFx2PA5FFRJpK/BQ1kzKWkm8Xxt+NuBrhCiIHBmKsELwyvovhZM3aRQ1Xnq0iK9ETcORabJ77jRve3CJgtkyxr5YpGXhxawG3nExgG+dnpmbajgaszdsiyeeuopLqUkqu4+QnKQKBlePTXHBaOE18izSanyelomZQh4ZIuiDm67iuBx4m90cykNiaAHTbc4tD2CvZJEMC3afB7a2tpwOt9+zIphGOxqdDBMlcslAZddwWPJ7GgL3THpa2mV2vospFENDVu1SkEzWaqoFBQ3tvpm7AH/2vdveATvLBtG4ecAQZRqYzM8IajWTuoUM294D8kFLIcXXD6we3huaGUtRNQWdHFhvsjQchGPzeJwV4BkJsfeqIuepiCT8SKvTyXpirhvMQyKolAXaeSoTcFpFVnKV5icWyKmKdzdWUe2rLOtt5VXJxL4mmXiK3EUfYZTGYmULrFU1QjYqswuVriel9jum+MLEZOmVhdX0zqLr73A1moPhzxuvp+qspjK0xl24VRt9Db4WRmf4erwdX79cx/m5eEV8lWdgMPGyLJJ2K2yudmz5uX8tmUhmDqGYdEScBIN1HIwdS6FgFuh3mPntYkEH9rexNYWH+MreRYyZUwEtjT7eGBThOlEbd7Pw1sa8DuXALi3L8JzQ0tUNAOvw8ZsskS2qKErbiyjiGVaeOwymmkh6iYlzaDJb18zuAe6Qrz6yiuYepWe1ha8Pg+GZRJx2zgzuoi9kKBUyBOtLLBTTNGwyc8zcZH05CjdAR25JDLg0+nzG4wWFUYKMrNlkbawj184sgnR7uDCbArTtNCzcZyKjChrLC3O0+G34XK5aGtrQ5bvvFWUNYML0yk2ReyYehUJkCURHYlMVaCkWbccHCxdq63BQhqqpTcuJtkoOZzce1c7rWHvhifwU2DDKPwcIQhCbYa86sIym2ovZT5dMwzFTO2PIHI45GZYkRhcZi1EtC0aoK/BjaVrbL/RL2ZWimDq5Cv6WpjizdzVEcJqDxKPx3EuLjKRyjEXNwk4ZebSFQ50hRho9OJSZS7NZjEcUUZWFmlxmMxJQRwNIQSzgioXsHmqDEQKUC5wT0QEyjx7ZphEVWS8IOKoiJw+vUhSl9kaVPjCDhejmWXSo5dpkT1sDtmw2Wwk8jZkWaCiv7FZ1dXVkUqlcAtlPr2ni4jHzvcuLzCbLCEVNRZTZc7PpNkR9XFXe5CSZnKgK0SyUCXkVjBM+MRd0bV/92Pbm7gwk2Y+VSRZ0ChWMsTyVRRJ4OHNEY69EAd3mHjJRBQFyrpJo99BT8TFtgYXUbfA7EqG3OI01sIIm1qa8NbVc/zMMBVdY8Ktsd2n8+SYzGxJwmcz2enzUzCg2W5yKuvgclkm5FHY29/G3y2keGFqHtOCI1tbSeh2nhlNU+cpUdFNUoUqLQEHO8Mi5WKVimYSDNb/QPmD64tpTo4uguajr96NqqrYVIsHNnvXl5LqVShmbxxIijctTBGcvprXqrq4q+WNfMOGJ/Dus2EUfk4RRKk2U94drL2shRtGoVpCrWSxZaoUYiUWi04KpowseLHbZLDJKIrCkxemid+Yi9/od/DqWOyOs+cFQeDCisF82s7J60nA4vLiGK2RAIPTSQRRpD3opNFvp1i14fcHcZhJ5nN5ltM2OluauLstylK2zDdtMkGfSHN+guuDrxNwOuht6WZsSmOHq4KmadSrFertJvuCEe4HsKY4n5LolyFZhW0+EZ8ooGYVvvFdhV/bZuN/+8hhJmc72aVN0F6WuLZgMjZZYHC5TJ1TptGr4rcZvHBphlI2Q8hlo8VnZ3gqw71dPrY3uLEyccACy+QvTy7y/GiKzRE7u5uczKVKiGWdXFHHwTJ7owHCDgPJUUC0LISywZ6SRtNchY60zoWMzE6/jiqC1NlEZllhIqvjlqpIAryesnG1oFI2JbY5s2RKFc5IfuzhFjoifiKxPLIksK89yHQ+jy0zy/aggO6P0tvWzFy6yOZGL5fmMtzTHaI96OTstQmS2Qz9YTvNzc24vH5OT6buOLP/mauLLCbzZAoVmrwKr8/mGEvp1PucNTU1ywKtzCZXpWYM5uduXhTg8NSMgcNLxbBulKA6NspIf8psGIUNEGQFfGHwhWvx3WKWUnyRe6MQ9UnMZjRKcxNYUgocHkS7m0e2Rrk8l+T1iTg20UKvVOhp8bIleuucelhtmLJT0SGRSnB8MsdiPIlNdbA7GmApW0ZVJHx2mY4GP3pFwa8vYislyaYUHPUd65LaitTM1ZlFToyMIOZdhPr3ozoVKsUKfe1uutwWLw/PET/7LEG7xP5dO3BIAlRLVEolLqREFtIVJlJwbDTHYy0hNjeG+NNJk8WhETKahCqaaHmJ8wkJy4JOl4GimrySWMRvq/13i2pyYkFkUAKHaHIyaWOTx2CxJGIvSwxPCRhxnaolsM+voVQl0jGLe7oiRBSDTZ4UUafJbFGkbMJmt8ETyw5ihorbL/HqtTnm8wa+lg52tYQ4MVckVbbQVAtfwEE6lmAklsbtcvHI3u0cH01xbSlLtqyxrz3I+GKc5blpPLLIns2dTJSda6Gp5UyZsm4imAYr89NEVI3Ghlq4yO12c20hc8eZ/ZqmsaNexUmVy+UqXqeDCgZbm730+0WsxByUcjXJy7WFJtYq5Jw+cHjW5bEmlu58rw3eXTaMwgbrEGwq+MLctTtci/uWsgx4s7UeiGqp9iezgiqIOEsSpq6TyEOxaqBVymjlEorkuiXksNowZQgiksOH01ZGkSxyxSKiqdFd72ZnNEBJM/DaZaJBJ2eHAjxz5irTC0vIsojdE1wXm/7whz/Mn/7pnzISi9PUNMXj9xxdC1VMGCJXtCATch/x177HpbKdL33pSwD8h78Z5Mp8BrcqMtBp5zupEk9cyTE3NUqbT+Hhu7fy3FSJeMXEZjfxCxYrZZirSuSAf9QDHW64krZwOEEWYSAIE1mBy0XIAAgQ8cKlJCi6is8uovtVHgjbmRs6z6VLIyhbd7LlkaNgU/HZVJ4dz/A/lkpcMbLsaw/yZ1evcu3qIqFwA53OJmbzOokyeB0qhYrOQiJLPpNFtansGtiGIMh84WAHpRu6yvlCgYW5WVqdFvfu6MHwNdNmExldzrOSrfD6dIqtjS6ePDOKKlnUuVX+4ZE+jo2lSOSTt62CenCgfk0X3CaCIksYpkUylSGTLTCUWaS/3w3SjRCQJNc8AocX7O5bxKJ+ltTOfl7YMAob3BFBtoEnBJ4QlmnWxmmU87UToF6llC9yNCwS9cFMziRfSCMWIF8pYvf4Ud6kl7s6siBRqLK5ycv0YoyhxQzX52I0h/3Y2oNsj4bWmp4ObO2kUNaYHLtOqDJHpNGzLunocDh4/PHH+fKXv8zcldPEtnczV3KubTKqJJBwtDDv30JyaIXgc+ewe8O0BpwoksS56SQzeYvprMGWhjAzx55hvpikcdt+spLEbLlAsQpV3cAmiXjcdsqGwV8t2nh8ezOaA5btMs9cWuB4RUYUBPZsqgnPxPMVRKAl6OLRXU0MNPrQLdjSFuDf/s7vMD09zS/80i9SCbZwYSZNslAiUYRC1aTOpTK8mOb06CJOJD7z8H6mKg7GlnMUqwbtdTYES2clHickldm9qYu6gIeOsJsdrQGuLWSQTI2l+RnKuklbawvbt25GEATKmkEsV+FIX5izEytkkjFETLY0+zm8vQdVVe44s7877CSbTiNoJexGFcXSkFIZ7gsJRH0yx6bh1HyVbS0yA211NWNgs7+lOtrPktrZzwsbRmGDHwhBFMHprf0BLL3K7uANI1EusFm5ESaoZKACViGGblORHB5QnaA6b1GcO+t3siOaQa5kWMprzCwss6nRu65b+rH9A4xHVC5evEh2doiBZh/wxnVaW1s5fPgwL7z8Cn/wzWf49S9+mifjBa7MZ7DbJBr8LoSOHhauFzh27FX+1T/+HIOzBvmKjmbU5C4jXpW+xgBDjRFmhxdZmF9gX383H9zWwFOXlxhayGJXJDrDLkwLsCxi+QqPbWuiM+Kmt97D8ZEY4/E8blWmWNWp99rxOW0E7Ao2SWJ7a4BMscp/+vbrTM4v4vd62bFjByM3mvMOdIVoCTiZz5QwijAyOYOol9jTEaSltZUem0iTT2UmVaKq62QSKzQKWRoa6vnIoe1savSueVDxdBZPbobmoI4SbCLa3rW2MQ8tZvnelSU2h23MLydw2QQsWSXa3IzLrqw912uLGTJFDcnMUcgXIV1BxcJh6ty8xe9uVHl2RudiTiAveNm3rYWT6SqXSmVCbpOHNt+5kx7eHX2ADX44NozCBj8SgqzUOqQ9wRsJxUqtoqRSwCoXEAwNSSuDVl77GUuygeoAxQE2O7tb3CAFSKfTuObmODWb5A+/P4ji8rwplOCir6+P69evc+bMGQ4ePEhdXd3adQ8ePMhrw3Ncmc3zlSee5oGHHmY2VSRZqKJKEr09XZiTp6lMD/Ld41vxRlpYzJQZaPTwibtayJV0hpdytHX3Mj09ycLUde7Z2c9CukKj14EswPByjvPTKfwuhcM9YUZXCjw/tMLWXIWHNjcQy5a5OJ+pid4XqtS5FT68rZnrSznGVvI8e3WJqmbw9IVp9Lpeuu7azd+cW1gLm6yO9b62kKHNLZCbH0e2LBoGjrCULZEt1j7jI5sbOH35OrliFUMJsL93E4okrm2ixWKR/NRlIkqVhoYG9u27G1EU18I0Y7EciXSeZxaL2GSRw70R9va1UNIMrGoJqmXG55JcHllif6PEkXoHs3adUqGC5LXXhvApDrC7bhh7F0eahLXTvk2WmEsV10p3fxDe62pnP29sGIUNfmwEQQDFXvvjCSIAeqVMJZdC1MpIRhXZ1BAMDYparRJlFVHCb1ORAy5GV0qIgsZEOoUadL0psVxPtVplcnKSkydPcvjwYfx+/9pm5+zcSV38OGdnc0w/c5am5ha6I250wyRd1Ljv/gf42veO8cy56/zapzv5h3s389zQEqoscWRHPY3TKfZH7+a1//WfmM/O0/r5j3NXe4i/H5wn4FboiniI5SrMp0vMJAvUeWq60cvZMs9eXeK1ySQtfjtbm7zMJIuMx2reStUw0AyL715coFDVycyPoDfvRG/cxtnpJFtbfGthkxa/g/v7w3znr/8MZzWF2L6brKXS7HPQ4heoGAZiKU1sZYl+pcSXfvGDSK7g2iZaqVR46dhxri5X2NUWZO/evWu5Hc0wmU0VGJlPErGbxDQT2RKolssMCCsgVHn2ZIFEySRfNdnXILKYN3hyrITfpXK0N4zu9iG7PLeMSbFLrJ32L2fTTMQLLKVL3NV++6KDN7PRjPbeYsMobPCOIKt2JKWBYrFIqVIBy0IVwSELCKseRLUMpgGVIjOxKovZKq0eGdXQWZiaJG9KyH4dxauBrLBjoA9DqzI+M89ffu8En3/syE0xaYND+/fy3RdPIi9eYeu2VrrbGoh4VP7r86NcKQcoBntRUlO8dHqQyVSF2WSR7S1+YHVjCrBv9w5ee+01ps88R/OjnyTkVtdE3HdG/ZybTZMqVDFMUG0i21r89Dd61sXGwx6VkmbiUCRCqsK+jhCzySLfePUauWQMNdDM4/v66Kz3cGYytRY2ubs9yLGzV/jenA3L086etlYyJY0/eWUCANEyyE1fxi4YPLR/G1t7Otaet67rnDx5kol4kSXdQbijH1ErY5WqoFW4r65CcSnD8VyJSgkKFYv9IQmrUuZrFzVCDpEjHV6GMzC4oiH77OhmhdZmLwNNfuw+z1v2KxwbiVHWDJyKzD2dQV6fTpGrGBsJ459BNozCBu8YgiDgcrmw2Wy1wXqWhWaIuDxhbDYblmXx7JUFEpkC+ZJFNKzw+kwGr2TS7JHZ45cpZdPgeiMEtbveiU2IMjdR4OrFS+ztb0EuGRSyebxOB60dXRjzl5k88ywP7vhV/vrsAouZMk5For+zhdeuGbw4kWWnbYWP3dVxS7XLL//yL/Paa6/xV3/1V2w69MF1YY3BmTT72oOMrOTRTZN0SV8X/149LaeLGi0BBw9siuBSZUqaiV0WGZuaxXCFaGntwKXaEAVx3fUT2SLHjr2MohmEOntx2lUkSSDsUWkNOpi7dIqiuURdZw+93T1YxSwYGugaT5wZJ1UQEFwh7m20cfrSOFeHxdoguQ4ndgDTYlukpg9xLa4j2x04fG4G2kP0t4SwKzakuRSJ+XkqGZ2iZuFyOgj6vW+ZLAb45Xs61qmk2dW310Pe4L3JhlHY4B1HURQkSSKfz2MYBrlcbk0a9Eh/w9pm4vQ76RTdbG104yonUKwqIZuE4fIhmTrPXk+SyFfIV016QipXkybTZ+dJlw3ujTqI+kx2OUOUBg6zXKjy9WdfRxNVtgcFTsxmSBUNVG8Ar5HHll/BI0fJYrAp4qG/yYdlmhzYv59t27Zx6dIlRk5+n1/+5V8GamGNgSYf56ZTdEbct41/3y42vhoO+Z2TFylceZ46Pck/+qef5vxMmt8YiKCKgGWwKWTja2cuUnY14vB6KVoqU8spvKqIzS3TF8zx4UNduBybkUQRiEMsvnbvD3V7GE5UGVypIosC83mTx/o8bGnykKqUyZc17E6Fj9/TjiEqWJcWkWWJgmBDsTuwKzY0TSOeznKgvdaJnKgI6Jb4tgYBbp8wNi2L89PpOza/bfDeZMMobPCuIEkSXq+XYrFIpVJZk2J0uVy3bCYOVaE72s3k5CQrpRIpPUdTSyv2iIueFpGr82lkOywtjNEfEPhAV4j2xjAYGgMODcvQqOrq2iZ5LV5hIV3Gp4rc1eRCN5wUdIuXBqdo9spYaoYLi2ZN7F0S+PJ//Hc1cRlNx5gbroVNBAEQ2KXW/iaTYJMkgATWchqAXXYAC9IJNokWKBbWQq3L+Vf6RH7l9/4pPq+HiUyGlFFi/NIlBsIK3xsr8O2RArGSCL4WtIqJVq5imQZ+WUbUTXTTybU07LQLSIIIsg0kG5l8kYWVWuhGC7RRsOtcqVhMlLMsSAECxQrFooYsyzx4dy92u51z0yk+tSe6ZryKVYNSqUSpVGJ7kwebrTbzqOUHGI99M282itcWsiznKrdtSLudiM4G7w02jMIG7xqr4SRZlikUClSr1ZpmQ1m75YQtyzKdnZ1MTU1RKBQ4fnGE8aKDroiHgmaRkGRc4WbiqQkujE1Tkh0MDNQmsGJZXL98hT96fhBNctDQ1smWZpV6t42o18aro8sc8JU41OpEDQQZipVYKRh4VY2BOgXdhJG8jZ0NHkRDA+Ot/10V3eLCcnnNqNwOv8/Pnw5m8NnLlHWLqEfixHyFi3GdbBWcko4HjaQu4HU5iLhstPjtPNQbYOHaGV5+PUfa0463dS+bb+RB5ufnOX3xAgA7du/hT84mCbpUnKrMvnY/L1yaZsQjEnIpfObePtQbfSO72wJrm/LOVh9auWYQAOx2Ow6H4wfyDt7MasL4dg1pZ6dSpEsa//hIJ6osvUmkaSO5/F5iwyhs8K6jquq6cFJ3QMLjqRmLmzcISZIYKzm5PpsmnS8RcWu8NlEh4LLT4newe3uU+SU7ualLDA8P4/F4iEajVHSTa2UvGTWCPT1FQ6rAzrse4vtXV2ht9LGjz8Hgs9/g716TuXvnFvo2baE1bOdktsxFXaJY0ZiYTvAb//E/0+zQ+bM/+VNcLidYq5rDVk0Q+oZK8vhSjjOZBFOmwke3N6LaRCqayYX5HDujPn7z3/87zk/G6Dr6cT54ZC9mycARdDJxbYWQqpDOLnPq0jXySh2yO4StqqPaHTTIdr52/Arx5TQOT4AP7dzMyYkk52bSLKWydFUnEYDNmzdTtnlp9JVoDTpJFSvIlTwNToG+iJP7dvWvGYRVJmJ5zk0nEY0yPXWONYOtKAo/LrdrSPPYZdLFKv/r1DQuRd7oYH4Ps2EUNvipsCrkk8/n0XWdbDaLy+W6ZfM60heh3qvy/OAEsmDgtooc6Wlka2uoJnrf5GPMC5cuXeLcuXOcmS8zlTW5OJumNdrCYiXN6cU8ymtXiba20VvvZiZZZOuBhxh95mXOXBqmpbUdR52L8ViOoEvFY5fZv7WPM6/t5PzEKL/+P/6GP/qtf7ruc5U1gz95ZYKAw0ZZN1HsDp4ZSbJQgK0tPloCDs4vlvjWsUGeHC3g8kX5wKG7uTiX48pClq6wiya/gyafwv/zwhg5JYzHHWBrRx2KLKLIIslkHGNlgnpFpGvHNlx2G7ayhl0ymZ6ZxeE2kbwRymk7heUEnXVuzk4nODu+QpNboNlvp601ittpX/vcq6f4TLFCnd3itYkUVxZyNIe8PLL1ztKaPww35xdeHF4mnq/SGnSwvSXAXKrIbKFIslBZ04zYSEi/t9gwChv81BBFEY/Hs5ZnWJUAvTl8YbdJKLKE3e0jV8hQqGosLcyztdkHN2LRXV1d5HI5njw3TWFlBKWuuVZhs1SgojSh5RepW56jtb+Jv3x1krlMmRa/nc62Zs4vaXzj1SEuL5UZaPbSVecmXdZwKBL3Hb2Xr1w8zvG/+Z+cun8H+/fvX/vsE7E8lmUxmSiwkqvgsEnUeRSuLWSYThZQJImtYYlnn38e0+Gnd/dBfC4HqVKSiEflIzubGFrI8s2XBilXTfwOA5fXiUORcNtlHtsU5PiTL6DLZTYfeIhFQeXF4WVWsmXEYoJ6WWNed9Nf10SurKPaRFRZYG4lid8O93X7GOhqQ7fW5wUO94a5MpvgzHgO1aWiKjK7OyNs+gmHcFbzCxGPyqujcS4vZHAoEg5VotFnZzolbnQwv0f54TJJG2zwE0YQBJxO55qqV7lcplAo1Lqkb1DSTO7rj/CF+7dysCtQC+9MTKBp2to1tm/fzsGeEIWqxvmxeZYzJUQRTEGmqb6OBdPNd05erY21cCvMJkukXa1Yoo1iLkUik6ZUNdFNay3p7QtF+NQnPw6Gzr/7d/+ORCLBs1eX+NrpGU6M1U7mpmUSy5WZiOUxTOgMuzjcE6Yn4uTrX/kylVKera11RJpb+c7FeTIljW3NXi7OZBiZTzA3O4VLS/GB3b0E3SqyKNLgs/Pq8WPo1TJ9fX20dvZwtC/Mv36glwFPmXK5gtOhYvNH2NLsY29nkMGZFF8/OUqqqNEbspMRPFxbKpLIV9aeo2VZWHoVrVKmWDVIlEwMoWZ0f9Kb8u62AANNPuo8dnobPARd6poR0E2LBzfV8/jOZo72hTc6mN9jbHgKG/zUEQQBu92OKIrk83mq1SqmaeJ218Teb+54ffCuTUxMTFAsFpmYmKCrqwtZlhFFkYP79zKy/DxTE1UsrYglOLFJAq0tTcjlDKlcnuWVZfZv6aJYNcmVNeqCQZKxCqXZITJNYYaXoMFn5/GdzYyt5Bm0HWXXhZOcP3uG3/qt3+K//Nc/WKdRLYkih7vDFDUDC4t0ScewTL7+0gXmVjK4fSE++vFPsq0tvDa9FFEkXahw5downtIKjd2bkZx+NikGj21rZGToCqcWl+nyuXnsscfWDOa14REWY0lciojubWQqXmYpXabB7+ChdpVUTuP8okBdXRi7alsXlrEsa80jK+sm9w000N3gZy5Vesc35bcq1d1IMr/32PAUNnjPoCgKXm+tUWo1z2Ca6zesqmGxZHoR5Jqu8uTkJIZhrP38ps1bCdtFiqUq8WwRRRbJlw36+vtxSBbkE4zMLNPgVdnW4md3dxOKw0m+VOXiyBRbmrz84v42PHYbqixS1Cx+5dd+E6/Xy/Hjx/nWN7++roQ26FKp99n54LZG/um93Xx2byvPnh1lamEFqiX+/ec/zObWMDtaA/gcNpYyZYYWsrw2NEM+NkPJ1UDfwFa2NHn51J4o6VSKqQuvUi8V+dCHPrRmEOaXVvj/vXyNwYSI7AkRcLs40BnizHSS00MzzCVzeO02WhvCeJ3KurCMZVnk83kqlZrXcE9fE7s6IngdCgNNvlsGFf6olDWDU+MJKvr6cq1Vr8Fjt/1E77fBO8OGp7DBe4qbE9CGYZDNZvF4PEhSLbwxEctzcT7Lwa4wSmGFUqnE1NQUHR0diKJIBRuR+gjp6goCVTyqh2SxwraWCB/av5Vzrx3Hn0shNN9PX6OfZFFjvLGRyXKZyvIix6/NcHY6hYBAa8hJa8DJ9UyZ+3/1/+AbX/lL/j9/+jd8NK7xC5/4KG11LsZX8lycS9MVcfP7z49yfmic85eHEXMxons/yKmEk8mhZXa3BShpJr90oJ1kJsdvnz5GWXRx/84BPrizlf5GDxIWx5/8OqZpctddd9Hd3Q3Ak+eneerUFaayInd3BFHcfoaXc7QGnfgknXyxyNEOD5orTJfHue5EbprmWjJfEAQ8Hs9b6i3/OGyUmb4/2DAKG7znkCQJj8dDLpdbMwyvzRVJF421UsYzUxnskkI1l2dPU61mv6WlhcO9Yeq9dr78UpWp5RTZTJr+tmb2dgTZFu3Elp7iteFZipNjfGLPA2RLOo0+O89YCa4Pz3Pmygi/+4UPYFdsXLmRHLXJAp+4fx9CZp6vvnyZrx+/yvL18/w//9fv1DyRisHwQpryhSc5//IljMgmetpbaO3soN6n8isHazOKVk/I/+cfPEFFM7H76xG8Ea4tZOhv9HD8+HFWVlYIBoPcf//9ADxzZZHTl69TqGiEPQ6qNjez8TwC4BA1hlcyCAgInjru6Wlce4YDTT5M01x7hoIg4PV614zrT5INoZz3FxtGYYP3JKIo4vV6iacynJ9OsbneTbwicmkhu7ZRb4sGafPVMzs9SSqVwmaz0dDQUAubuPw0+spUy0VCZgLNbOW5a8uUmnaxMlJATi7y3546z8GtnVR1g00DWxifWyRZ0nj51Fk+9uDBtRDR4EyaRL5KdOdRDstNvDiS4PnMMrt/9feIBpyoZokvl01SsSUsbyMdvZvo6WplPlVCTRZZzpYJuVXKmsHv/t1pnhtKIEsinz26nasxjScuzLO4kqR48QRQU5Rb7Reo02PUCQWWFQVHqI5C1cBhkwg5RLLpLLIg0Bj2M7hYZiw5s7YRm6a5Fn5brfJ6JwwCbAjlvN/YMAobvGcRBIGVElxdKuJVJRAEckWNReGNUka/143Y1sbU1BQrKysoikJJE/js/jbCzg7+9vvHKJSzuMsrbOnt4fqSnaWVTQxevMj0zBKHN7dQqIJuCdy1YxuvnjjBNwdlio5ROhuDPL6zma3NPn7/+RGShSqlqoPGcIS0aCK5nEyVSqhTFzGdfiK2Cg99/DGWdBf9EQ+yIKLK4trJOV+ucu7CedxaikDnXmbzUNQM7ukKcu7s62iVIJFoB5HGJgCWlpaYnhhDFkXcwTCNASeyJBL12dAycVRZwev1sKm1nrn0GwPobjYIq17XW004/XHZEMp5f7FhFDZ4T3JzSKK7wc+5uSyxXIk6t8JjW1tZzFbWqma8Xi/Nzc3Mz88zNzdHb0cHHk/tlPr4vXdx7NWTfOfUEJ/1B3l9Ks9k0UbB2YSYy/LUiUs8cnA3Zc0k1NnK8PgUsYzM2NVB/o+Pfh67TWZHa4C72oM8fXmRbFnD41BQGqM0e0Qwqmx+5C6KmsXH9vfjsNt57toyzUEnbqeNFr+T54eWCLpUpkaG0JILWP4WXKF6lrJlDNNiYWaSajFPyOOgvb2D8ZU8QTv83rfPsicIzW3t3NfVQTToZGI5w8jENFvq7SQMB0tp1m3ENlF4Vw3CKhtCOe8fNozCBu9Jbg5JOBUZt9PO7lYvHQEVq1qkJ1wb3LZKKBSiWq0Si8WYmZmhp6cHRVFoaGjAEWljammW7x57nf7tuwi5GplrdHP65AmSmSr20goX4wrXlnJorihqdo6htMDn//A5dvZ38G8f6Wd3W5DFdInnh5exSQLFqkFbJMSn90TpjLj5xuuzPDecJORW6axzc3EuxUyyRJ07R6PPgarnuH7tMnbLpHfrVo5saSHiVfm70xOcGkkg4KGttYfuiJdXx+KcuDjCSNKkOeDjSwe3IwgCpmniqCTpC9lwu91URT9H66W1jbhQ1n4qBgE2hHLeT2wYhQ3ek9wSkqgaeN0u3E6ZarVKPp+/pZKmoaGBcrlMLpdjamqKyaqHZEEjh4+2UIor8TyxwevUN0cRZRtbNw9w5uIVXj/9Gg9+8CO0hVxcns8QdTZy7voMmZEzfOoTOwHY3xXi2+fnCDgU7moPki5ohFwK21trm+Fn97Wti6tbwKNbGjjUG2YukeN3/vDv0QWZ7m130R1tpK3OxeBMEmf8Cveoyzjad4LLzfcuLzK2mESvavT4JK4W3fzrb1ykwa/yCwMuyuUyiqLQ1tZG5005gv4Gz0/NIGzw/mJj1WzwnmU1JLHa+VrWrTXRHsuy1iprVhEEgWg0iqIolMtlOhxlNjV5UOSaTnOqKqKWU9TJZY72hfmnH7yLezr9lKo62etn6KhzUtZNXP4gfp8Hs1rmW1//Cs9cXeRrp2ewKxKP72ii3munPezCZX/DIL3ZiAVdKr0NHlyqzNMvHKMcX6DPq/MffulRjvaFGV/Jc210kmwqSXPIy0eO3k3IrdJfp2A3i8gCdLU2YrdJ3NMd4pMDPtLpNKIo0t7evi5pvPosNgzCBj8JNlbOBu9Zbtf0JAgCbrcbWZZvaxhkWaa9vR1RFCkXcuQzaQoVg9F4mbLNS6YqIMbGafPJeB0KX/zYw4RVk7+9lODy6Ayf2t3Cbz46wD97ZCei3c3FixdREuMMNHnpCLvorvfitss8sKmeLxzsWPd532zESprJiYujPHNuBNnS+Ddf/DRnZ7NcnM0wG0uRnL7OguHF6jzAyYkUh3tCdLBIq8sC2U6sbJGvGJhahXy6JqgTjUax298YcHfzM1itMtowCBv8OGysng1+5lhtwpIkaa0W/+bOZ7vdTmtrKwDPXZ4jVyjhVGTu29zMiuXm+ILJnzz1GpZl4fP58PfvY9b0MT1ymfv7gjT6HXz6QA//9hP3APDXX/5LErEVMkWNE+NxskXtttU1NxuxuVSJS9Mxfv8bT6NoOcJbDnEuZUczTDY1uhkZuopoGTQ3NxMIBvnsvjbMxAx6KU/ArfLPH9rEbz46wMd2NDC9GAOgvr4en68Wry9rBqfG4iTT2XWNaW82CHfqMt5ggzuxYRQ2+JnkzYYhn8+vG6Ln9XoJh8N8qN/LJp+OUxHpqfeyp7+NDp+NZjHDv//aKX7t64NcyqhEfTauFVz84h8+x+99f5iyZqC2bGHvgXuoVCr81V9/lSaPhMcu01nnetvqmkPdIS6/8jTFXJbGcIi9e3Yz0OTlgYF6Rq4Ps5LKoStunOEoI8t5Lo3PMz4+jiAIfOnxfTy8pYmpeIF+T5WHu1243W4ikcja9cdXcpweX2FkKfOWjWmrXcbjK/mf3MPf4H3NhlHY4GcWURRxu91rs5LePF21oaEBv8eFZRosLCdYzJSomAJbBvqRRdimrHBX1IUii2zp60ESwJGZ5uE2iSfOz3FmOsmhRz+B1LqT+bzF3z/3Ct1hN4vZCiNLOZ69unTHz/b0U08yOT6KZHex7+gjlA2wSQLVUpGTp88iWQZ9/ZuwKzYCDpknTg5xakUk5YwSCASYiOU5dm2W64sZZFkmGo0iCMLalNZjw8vUuyXOTGf57lCKF4Zj6+7/7NUlvnxyiq+enqHRa+fEWIKvnZ55y8+8wQawYRQ2+BlnNbEKUK1W12QloeZNtLa2opkCW+pE7mlROdoXxuUP1mLzosn8zBT5is5y0cDlD5E0HPzeE2f4/tUlFFHk5ESahm0HQXGxODfDtUuDIFgYlsn+rtBtP9Ply5f59re/jSna+Bcfv4/PHOxdyzE899xzhKwMH94c5AN3byIacuDRMxhahb6Ik0hDI187PcOLVxfwUObSUpnTcZmXRhJArVS3J2wHU8duk3A5VLZFgxzpC6/7DId7w3jsMguZEulSda3L+M3ft8EGb2bDKGzwM48sy7jdbqCmx7A6DRRqk1Mf3t1DZ0Ahn4rR7rexuy3Atm3bUFWVlXSBe1vt/OajA+ztb0FX3GSKZYxCiol4gVMTCRYLFo/eux9dsPHcydd54fQljl1fYeI2IZlXX32V//Jf/guWZfFLHzzEhw7tWkuUeyoxrl69iizLPPaBh7FJAvFUjqHpRYqGAIEoh3sj9NW7yGfTqLJIwOdlV0dkbTOXBWtNDyFVtqiYwi35jWevLvHE+XmuLmRwKjKnJ5OcHEtwdiqx0WW8wduy0aewwfsCRVFwuVwUCgUKhQKSJK31MHi9Xurq6ojH48zOztLT04Oqqmzfvp1K5Qy28ix+tY/feGSAl+tl/vA7JxhfTLFSUbAEkUe3NZAu6oS6tjM/OUpmahY1VeZbHoVrizlCboWjvSH++q//mhdeeAGAI0eO8Pjjj699vnK5zPe+9z0Ajh49it/vZyyVIFhZpKnOJG1vYiKtM5cqkorHKFQNVMWG4nCsbfqruZOybnJvf4TepuBt9RBWG//GYjl2t/qpGhb1HpUGv+Pd+WVs8DPNhlHY4H2Dqqrouk6lUiGfz+P1eteqcRoaGshms1QqFZaXl2lsbKQu0kBSCuGtJLh48SL79u2jtaUJf10jVmwRrZhE8kXIlgw6wi7qvb2MRzy8OjgCqTmeejnNZZ9CWC7zVHKEZDKJzWbjc5/7HPfee++6z/bCCy+Qy+VoaWnh7rvvBsBdiTGXLFGVHETDdez3O3n+yhzTi3ECDpnPHd7EckGnpJlrmgimabKrNbCmOzHgUG55Dqs9E0GXiiyJFDWd3gbPRqfxBj8QG0Zhg/cVTqcTXdcxDGOt61kQBERRJBqNMj4+TiwWw+v1Mp3RyTvCVAsZ5IUFFhcXKWl2Dm7v4eSJZWYLBZyuEguZEgvpErva/Bza0slKRSI2NYphiMwl06jZi5SpEgwG+Zf/8l/S2dm57jPNzc1x/vx5RFHkscceW1OYGx4eps9vUt/bx1RewCZZFLIZDrQ6OTDQTsjnInRjHy8UCmulp6vJ9bdiYxbRBj8qG0Zhg/cVq6WqmUwGXdcpFou4XC4AXC4X4XCYJy9M88LkFVz+EF0RP1cK9UwuLzLy/Hn+zS88zOYmLw1ygf/5/fP48jNs3holWuel0WdnLlWiM+LjQPdB/v78NHlvmHs/cDdHOr10dHTgcKwP0ZimyVNPPQXA/v37CYfDWJbFxYsXMU2TrvZWnHUhriZWGE4mKWomHreLhkjd2jUqlcpansTtdv9AI7A3ZhFt8KOykWje4H3Hamcv1DZUTdPWvlZfX8+Bdj91DoET1xfRDIOy4KSrzkGno8L169ex2yS6OzuIhv1Yus7Q9ev01rvZ11VHR9hNoWpwcjxOvgptdQHOxiX+eLDCv/320C1NYqdOnWJlZQW/38+hQ4cAWFhYYHl5GZvNxpYtWyhpJrsa7exrlLi7xYk3ULfmCRiGQaFQAGpe0M1DADfY4J1gwyhs8L5EluW1U/tqLB5qBqOro43RRIWRlQLfvTTHRKKIt6ENWYTR0VES6Sxnp1N85oHdbHLlURNjXLw6DEAiX2Gg0YPPqRDyqCDAUrYElkl7yLWuSWx+fp6XX34ZgA984APYbDZ0XefSpUsAdPf2c2G+QH+9C6+RxWkTuWdzO/u6a01qlmWtGQSbzYaqqu/Ks9vg55t3JHxULpf5i7/4C55++mlmZmYQBIHOzk4++tGP8pnPfGZjNssG7wp2ux1N09Ya29xuN//pmesspcucmy7jkmFkMYPH6eCr5yocbKyjmk1SPDVI3t5EXTDMo488zJ8/8TyT546RvKt3rbLn2kIWzTCZSRYwEUAQ6Il41gR1fHaRyVf/HtM02bt375re8sjICKVSCZ/Ph+kOc34kTjmbJCQZuFwuAoE3wj6lUmktj+Byud42j7DBBj8JfuJGoVAo8LnPfY4rV67g9/vZs2cPmqZx8eJFfud3fodTp07xB3/wB++YNOAGG6yyuplms1k0TaNcLvNPDnfy3LVlCpUqVrXIaKLCjkYXbQ0+DN1gKpXmtYkkd20OcWIsQbassuTuQimM8O1vf5vPf/7z2CSBbElja5MXj9NGVTNJFrR1UpRTF0+QTCaJRCLcd999QO3dGBkZ4UpSpMHXwMR4kohL5PjIAk6bxKZOP103Nv7Vzwu1PMLGQWqDd4ufuFH44z/+Y65cucL+/fv5b//tv+H1eoFaBcYXv/hFXnjhBf7mb/6GT3/60z/pW2/wPqWsGVyYSbOrzf9DN19JkoTL5SKfz1MqlfB6vdhlkZJuoRsyFb1MPJOlMeThcG89bQ6NFwbHOXVtGslXT9hj51OHtvLdF1K8MFWk8O3jHNi9jX/1YO9aZc+1hSzLucqaAtrK0iKXLpxHkiQef/zxtX6JS5cuYZom929pwd3czIWZFMVsClkU2NUR5p4tzQBr/QhQ83Y28ggbvJv8xI8fTzzxBAC//du/vWYQAFpaWvj1X/91gLVqjA02+EH4cYe6KYqyFo8vFotkShqf2t3C5w928dhAkDa/jc1+C8OC9tYo2FRUqlAtoNhEQl4X2zYPEBKLpK6dQM3Nrxvp3RF2r43M3hKAZ154EYBDhw5RX18P1PSWFxcXkWWZXdu3YpMEVpIZFtMlqpZAJFy3ZvBKpRKWZa3Li2ywwbvFT9RTKBQKtLe309jYSDQaveXrHR21+fMrKys/ydtu8D7lZp3m1oBzLV4fcis8tLnhh7qWw+GgWq2i6zqPbQlzbaXMrjY/R3tDXL9+Hcuq0lWnMByv8vH9/cwMXeB6JkUq52YxU8LpC7Jz+zbmrpzmiSeeoFqtsmvXLuCN8s/p6Wlee+Zv8WlVuru7OXDgAFA7+V++fBmA/v5+HA4H+YU8/T6DiMuF5I1QMWqD/DRNWys/dTqdG3mEDd51fqJGweVy8ZWvfOWOX199MRoafrgXeoOfT27Wab45Xt/f6PmhryWK4loY6dpcgnMLZXwOmc6wm5mynQa5yMLCAru6uxGEAKdySywWl9gaqvLwzuYbDWAh+urdvPDCCzz11FMkk0l27qzJdc7NzfG9730PXdfZsmULH/7wh9fyZpOTk+RyOVwu11rCuUmtYPfb8Hg8dHQ0Aeurjex2+zqp0Q02eLd411ZdtVrlj/7ojwB46KGH3q3bbvAzzC06zRXjtuI2PygvjyaZT2TJFqvUeVS+enoGr11mPl3iwaiATSqRTqcJBAJs2bKFpaUlrMwCZmXTGw1gbQdQFIWnn36aU6dOcerUqXX32LVrF48++ujaCV/TNIaHa+WsW7ZsqSnClcskk0kAGhsb1362VCphmiaiKG6EjTb4qfG2RuFLX/oSV69efdsLPfjgg3zpS1+67dcsy+Lf//t/z9TUFF1dXXzyk5/84T/pBj+X/CTHNRzuDTO8qHDy+iJnppIMLZfob/Byb1+E8ViKoZUcocVxPv/gLjweD52dnYyPj3Px4kUOHTq0ttHfdddd+Hw+pqenuX79+tr1t27duu77AIaGhljJVXgl7uCwtzZqe3FxEYBgMLgmranr+lq10Ub56QY/Td7WKCwsLDA5Ofm2F4rFYrf9/4Zh8Fu/9Vt897vfxefz8Qd/8Acoyq1DvDbY4Hb8JMc12G0SZ6fTnJ/Po+kGQYfMSr7C4Gyaqm5yT6ObJqdFPB4nEonQ39/P7Ows8XicmZkZ2tra1q7V09NDT08PDzzwwB3vl06nGRsb42xMZKxg41tn5/jsngZyuRyiKK6FUS3Lolgs1j7jRrXRBj9l3tYofO1rX/uRL14sFvnX//pf89JLL+H3+/nzP/9zurq6fuTrbbDBj8vmRi8hl42ZeI54rsSVpQIORSLkUWiIuLGV4qysrBAMBlFVlW3btnH27FmuXLlCY2PjD3ygsSyLL/7FKWJ5GVNS6Kr38Lfn5/j2uSkCdvj9T25dyxmsNtgJgrDmOWywwU+Ld6wjJh6P89nPfpaXXnqJhoYGvvKVr/D/b+/uY6Mq9zyAf+e9My0zben7i32DabaUthQqeol6fYFsoqgXr6nKBa2r7NXdyEaihK5koWZNSIgBu6KxeBNSSm2yW6/3avC6JlrK1WXrW6HjlTeh0tKWUs5Maed9ztk/xjnb15kptkwPfD+J/5xzOvPQ1PM9z3me5/eUlpbO1dcRReX2RSkozjBDrdYCEmBNMeHu4hSs/rt0qPVGJCQkQBRFeYZcbm4uUlJS4PF48P3330f9Pd3d3Xggy4XSFA0STEYkGHVQQURFug4v/CoDqanBTXPG9hKMRmPYRWpuXwBfnh2aVF+JaDbNSSj09vaiuroaNpsNVqsVLS0tWLx48Vx8FdGMuXwi7i1Jxz/dXYSHy9Lg83pQkmXB8rwk+ZXO0NAQfD4fVCoVysvLAQRnEQ0PD0f8fJ/PB5vNBoseyM1Mh8svontoBFddXui1aljzMuWbv9vtlgeXI9U2+qXrNYiiMeuhYLfbUVNTg56eHlRVVeHQoUOcgkrzyvK8JJRkWZCWZEZxegKWpJvkSqomkwlmsxmSJMm9BYvFgoKCAkiShOPHj0OSgmsKLlwZwTMH/hd9dte4zz958iQ8Hg+SkpIg6Ux4fEUudvx9IVYXxcPpDw4wA8HxttCe0uEGlz+x9aP52E9oOzkIty+AtlODaD72Ez6x9c/J74dubrMeCjt37kR3dzdKSkrQ0NAglzAmmm/GTv0cHR2Vb/ahh5grV67A6/UCAEpKSqDT6XDp0iX5NdIf2s+j47wd77SdlT+zt7cXp06dAgCUlZWh9oES/OOvi5AgjuC3Syz41wdK5Zt/KBD0en3YweU7rakoyTLD4fahR3DB4fKhJMss79tMNJtmdZ3C6dOncfjwYQDBJ5/t27dPeV1ycjJqa2tn86uJrklcXBw8Hg9EUYTX64XBYEBcXBwsFgscDgcGBweRnZ0Ng8GAqqoqfPnll3j+v87C88ce+EUVMs0G/Pn4Rfz33y4hyajGP+TZAQBLlizBwoXBKaiCIMifnZiYCCDYSwgFTqQ1CUdODeJEjwM/9F1F2gIDfui7Cp1ajYFh94xXdhNFMquhcPToUflpq6OjY9rrsrOzGQo0L4Rm/DidTrjdbuj1eqhUKqSnp8PhcODKlStITU2FXq9HRkYGKioq8DvXt/j0oogfXUYkxOkguLy4Nc+CSn0/RFFEQUEBrFYrgOBA8sDAAAAgLS1tUi/BYDBErBh8pzUVwqgXXlFEaaYFXX0O5CQZ2VOgOTGroVBTU4OamprZ/EiiOWcwGOB2uxEIBODz+aDX66fsLQDB+l0rr15FW9+PGHF78GO/Hx4REAb7YcrxIDU1FeXl5fLNXxAEXBBc+MM3dry+fhGSML6XEM0U1DidBktzLLg86oXd5YU5ToelOZZrXtlNFA6LtNNNb+z6gFCFUgByhdOhoSH5Jg4Ey1WIBhNuS9dgY5EHyxJ9sLt8iI+Px6233irPLBJFEQMDA/jTDw78bdCLhiM/yt8BBMcSot1XJLSy++Fl2bi7OPUXrewmCocVt4gwvrcwdmwhMTERdrsdAwMDcuVftVqNQ/+8BoIgwOl0IlS0JS0tbdyA8UNvtGFg2A1vAMhMNP089jCAJKMGBzaUzai+0Wyu7CYKhz0FIkTuLQiCIJe0DklKSkJ2drb839hAEEURL65KRnmGHjqdBglxOmg0KtyaZ8GuB61RjSUQxQJDgehnBoMBarVanokUOhbaN3m6+l5TEQQBCw0qxBv08AYk9NqdcLn96Lxgh17DKqg0fzEUiH42trcQqlgKQC5JIQiCvMgtHEmS5ADxQId1Fdl4e/1yFKQacXHYi49+uMI9l2ne4l8m0RgGgwEqlQqBQAB+vx8A5JlIY2/24djtdni9Xuj1euzbuBJ9wx7825+6cNUVQEWOGX8+PoCH/+Ov+H3j13P9zyGaMYYC0RgqlUquQTRVb2HsKuepjC2ml5qaCpVKhbqHSrCqKAlajQoLjDrodGrcZU3Bv/+GBSJp/mEoEE0QCgWv1wtRDE79NJlMsFgsEEURPT098kD0RAMDA/B4POPGIlIXxEEDCS6viF67B06vCKNOg4UJ4QvgEcUCQ4FoAo1GA51Oh8ERL2pbv8PQSHDWUVZWFjQaDUZGRiAIwqSfczqd8uul3NxcedzA5wuuY/hNRTr2PFaBx1fkQnBO39sgiiWuUyCaQlxcHD7sOoe/nrmCvI6f8Nzdi6HT6ZCVlYULFy6gr68PZrNZ3ihHkiT09PQAAFJSUmAymeTPcrvd+Jdf58NoNMJoNGJxujkm/yaiaDAUiCb4fePX6He4MOzyIicxDv/5dQ/+YruEDEsc3vpdJQRBwMjICPr6+uQFbUNDQ3C73dDpdONKxfv9fu6qRorCUCCaoO6hEjT9z0/4i60PCQYNhpw+3GldiCd/VQCVSoXs7GycOnUKgiBAEARotVp5plJWVta46aahwerQrCai+Y5jCkQTpJmNMOo0cPlF9NjdcHkCMGhU8sCwwWDALbfcArM5+BooFAhms1k+BmDSIjgiJWBPgWgKgtOLx1fcglWFZnxi68flEfe48xaLRd7POUSr1Y7rDYQCQavVsqQFKQZDgWgK2+4vARAsc5294P8Hk8fe9DUaTdibfahWEnsJpCR8fUQUxtgbf7hFaxP5/X4EAgGoVCro9fq5ah7RrGMoEEUQetKfWCU1nFCAhHZyI1IKhgJRBKEn/dDTfySSJPHVESkWQ4EoArVaLQdDNL0Fn88HSZIijjkQzUcMBaIojH2FNF3do5CxvQS+OiKlYSgQRUGr1UKtVkOSpLADzoFAQN5zgQPMpEQMBaIoTNyAZ7rewtgVzNxIh5SIf7VEUZpqA56xRFGUXx2xzhEpFUOBKEpjN+CZasA5dEyn03GAmRSLoUA0A6EewNgNeABOQ6UbB0OBaAbUajV0Oh2A8b0Fn88HURTHnSdSIoYC0QyFegtjp6dyGirdKFgQj2iGQtNTRVGctC0nXx2R0rGnQDRDKpUKRqNx0nFOQ6UbAXsKRNfAYDCwV0A3JD7WEBGRjKFAREQyhgIREckYCkREJGMoEBGRjKFAREQyhgIREckYCkREJGMoEBGRjKFAREQyhgIREckUUfsoEAgAAPr7+2PcEiIi5QjdM0P30GgoIhQGBwcBAOvXr49xS4iIlGdwcBB5eXlRXauSQruEzGNutxtdXV1ITU3l3rdERFEKBAIYHBxEaWmpvDlUJIoIBSIiuj440ExERDKGAhERyRgKREQkYygQEZGMoUBERDKGAhERyRgKREQkU1QouN1u7Nu3D2vXrkV5eTkqKiqwbt06NDY2QhTFWDdP8err61FcXMxyIlH64osvsHHjRqxcuRKVlZXYsGEDjhw5EutmKV5rayuKi4vx1VdfxbopihQIBHDw4EE88sgjWLZsGcrKynD//ffjzTffhMfjifjzilm8Njo6io0bN6KrqwuJiYlYunQpfD4fOjs74XK5cO+996K+vp4rnq/Rp59+is2bN8Pv96OtrQ0ZGRmxbtK81traim3btkGv1+O2226DKIo4duwYfD4f6urqUF1dHesmKtK3336Lp59+Gk6nE01NTVixYkWsm6QogUAAzz//PD7//HOYTCaUl5dDq9Wis7MTw8PDKC8vx4EDB2A0Gqf/EEkhdu/eLVmtVunJJ5+UHA6HfPzChQvSmjVrJKvVKjU3N8ewhcp18OBBacmSJZLVapWsVqvU19cX6ybNa/39/VJpaam0fPly6eTJk/Lxzs5OqbKyUlq6dKnU398fwxYq08cffywtW7ZM/jvs6OiIdZMUp7m5WbJardLatWvH/Q0ODQ1J1dXVktVqlXbv3h32MxTz+uj9998HALz66qswm83y8ZycHLz00ksAgI8++igmbVOqs2fPYtOmTairq0NCQgLi4+Nj3SRFaGpqgtfrxVNPPQWr1SofLysrw7PPPguPx4OWlpYYtlBZ+vv78fLLL+OFF16AKIpISUmJdZMUK3SfrK2tRXp6unw8OTkZO3bsABD5PqmIUBgdHUV+fj7KysqQm5s76XxBQQEA4NKlS9e7aYq2Y8cOtLW1YdWqVWhtbUViYmKsm6QI7e3tAID77rtv0rnQMY4tRG/Pnj344IMPUFpaipaWFhQWFsa6SYqVlJSEwsJClJWVTTqXn58PIPJ9UhGls+Pj43Hw4MFpz584cQIA+B58hkpLS1FTU4N77rkn1k1RDEmScObMGajV6ilvXvn5+VCr1Thz5gwkSYJKpYpBK5WlsLAQu3btwoMPPgi1WhHPqfPW22+/Pe25aO+TigiFcLxeL9566y0AwJo1a2LcGmXZunVrrJugOA6HA16vF8nJydDr9ZPOa7VaJCUlYWhoCKOjo0hISIhBK5Vl06ZNsW7CDU+SJOzduxdA5PtkzEJhy5YtsNlsEa9bvXo1tmzZMuU5SZKwbds2nD9/HkVFRXj00Udnu5mKMRu/T4rM5XIBQNjZG6G69QwFmi9ef/11dHR0ICUlBc8880zYa2MWChcvXsS5c+ciXhfadW2iQCCAV155BR9++CEsFgvq6+unfHK7WfzS3ydFZyavNyRlzPamG9zevXvxzjvvQK/XY8+ePUhOTg57fcxCobm5+Zp/1ul04sUXX8Rnn32GxMREvPvuuygqKprF1inPL/l9UvRMJhMAhF0E5Ha7x11LFAt+vx91dXVoaWmBwWBAfX09qqqqIv6c4sYULl++jE2bNsFmsyEjIwP79+/H4sWLY90sukkkJCTAZDJBEAT4/X5oteP/F/L7/RAEAQaDYdzUaaLraXR0FJs3b0Z7ezvMZjP27dsXVSAACpmSGtLb24vq6mrYbDZYrVa0tLQwEOi6UqlUWLRoEQKBAM6fPz/p/Llz5yCK4rj1C0TXk8PhwIYNG9De3o7MzEw0NTVFHQiAgkLBbrejpqYGPT09qKqqwqFDhzgFlWLijjvuABAsDTJR6Nhdd911XdtEBARnY4bepCxatAjvvffejB9QFBMKO3fuRHd3N0pKStDQ0IAFCxbEukl0k1q3bh0MBgMaGhrQ1dUlHz9x4gT279+PuLg4PPHEEzFsId2s3njjDXz33XfIzMxEY2PjNT04K2JM4fTp0zh8+DCA4EK27du3T3ldcnIyamtrr2fT6CaUk5ODrVu3oq6uDo899hhWrlwJADh27Bj8fj927dqFhQsXxriVdLMRBAGNjY0AgvfC1157bdprd+/ePe05RYTC0aNH5el9HR0d016XnZ3NUKDrYv369cjKysL+/fvxzTffQK/Xo7KyEs899xxuv/32WDePbkIdHR3yzDebzRZ23VK4UFBM6WwiIpp7ihlTICKiucdQICIiGUOBiIhkDAUiIpIxFIiISMZQICIiGUOBiIhkDAUiIpIxFIiISMZQICIi2f8BIP5vGlRMsncAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "weights = np.exp(diagnostics['smoothed_log_weights'])\n",
     "all_samples = diagnostics['samples']\n",
@@ -188,13 +134,27 @@
     "plot_approx_and_exact_contours(log_density, results['objective'].approx, results['var_param'], \n",
     "                               xlim=[-2, 2], ylim=[-3.25, 2])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "virtual-env",
-   "language": "python",
-   "name": "virtual-env"
+   "display_name": "Python 3 (Spyder)",
+   "language": "python3",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -206,7 +166,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/robust-regression.ipynb b/docs/source/robust-regression.ipynb
index 7977fec9..e89bcfe4 100644
--- a/docs/source/robust-regression.ipynb
+++ b/docs/source/robust-regression.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,24 +162,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "average loss = 21.352 | learning rate = 0.025 | (-0.082572, 0.093071):  30%|███       | 3000/10000 [00:09<00:22, 309.39it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Stopping rule reached at iteration 3000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "mf_results = bbvi(2, fit=fit, num_mc_samples=50)"
    ]
@@ -193,19 +178,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pareto k is estimated to be khat = 0.91\n",
-      "WARNING: khat > 0.7 means importance sampling is not feasible.\n",
-      "WARNING: not running further diagnostics\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "mf_objective = mf_results['objective']\n",
     "with warnings.catch_warnings():\n",
@@ -223,20 +198,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_approx_and_exact_contours(mf_objective.model, mf_objective.approx, \n",
     "                               mf_results['var_param'])"
@@ -251,20 +215,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean error             = 0.0142\n",
-      "stdev error            = 0.718\n",
-      "||cov error||_2^{1/2}  = 0.905\n",
-      "||true cov||_2^{1/2}   = 0.916\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "check_accuracy(true_mean, true_cov, mf_results['var_param'], mf_objective.approx)"
    ]
@@ -280,24 +233,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Approximation does not support KL. Using base stochastic optimization algorithm instead.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "average loss = 23.05: 100%|██████████| 2500/2500 [00:13<00:00, 182.72it/s] \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "t_results = bbvi(2, n_iters=2500, fit=fit, approx=MultivariateT(2, 100), num_mc_samples=100)"
    ]
@@ -311,21 +249,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pareto k is estimated to be khat = -0.20\n",
-      "\n",
-      "The 2-divergence is estimated to be d2 = 0.011\n",
-      "\n",
-      "All diagnostics pass.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "t_objective = t_results['objective']\n",
     "with warnings.catch_warnings():\n",
@@ -343,50 +269,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_approx_and_exact_contours(t_objective.model, t_objective.approx, t_results['var_param'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean error             = 0.00852\n",
-      "stdev error            = 0.0327\n",
-      "||cov error||_2^{1/2}  = 0.242\n",
-      "||true cov||_2^{1/2}   = 0.916\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "check_accuracy(true_mean, true_cov, t_results['var_param'], t_objective.approx)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "virtual-env",
-   "language": "python",
-   "name": "virtual-env"
+   "display_name": "Python 3 (Spyder)",
+   "language": "python3",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -398,7 +309,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/viabel/_mc_diagnostics.py b/viabel/_mc_diagnostics.py
new file mode 100755
index 00000000..5b763fe6
--- /dev/null
+++ b/viabel/_mc_diagnostics.py
@@ -0,0 +1,181 @@
+import autograd.numpy as np
+from scipy.fftpack import next_fast_len
+import  warnings
+
+
+
+def autocov(samples, axis=-1):
+        """Compute autocovariance estimates for every lag for the input array.
+        Parameters
+        ----------
+        samples : `numpy.ndarray(n_chains, n_iters)`
+            An array containing samples
+        Returns
+        -------
+        acov: `numpy.ndarray`
+            Autocovariance of samples that has same size as the input array
+        """
+        axis = axis if axis > 0 else len(samples.shape) + axis
+        n = samples.shape[axis]
+        m = next_fast_len(2 * n)
+
+        samples = samples - samples.mean(axis, keepdims=True)
+
+        # added to silence tuple warning for a submodule
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+
+            ifft_samp = np.fft.rfft(samples, n=m, axis=axis)
+            ifft_samp *= np.conjugate(ifft_samp)
+
+            shape = tuple(
+                slice(None) if dim_len != axis else slice(0, n) for dim_len, _ in enumerate(samples.shape)
+            )
+            cov = np.fft.irfft(ifft_samp, n=m, axis=axis)[shape]
+            cov /= n
+            return cov
+
+def ess(samples):
+    """
+    Computing Effective Sample Size
+
+    Parameters
+    ----------
+     samples : `numpy.ndarray(n_chains, n_iters)`
+        An array containing samples
+
+    Returns
+    -------
+    ess : `real`
+       effective sample size of the given sample
+
+    """
+    n_chain, n_draw = samples.shape
+    if (n_chain > 1):
+        ValueError("Number of chains must be 1")
+    acov = autocov(samples, axis=1)
+    #chain_mean = samples.mean(axis=1)
+    mean_var = np.mean(acov[:, 0]) * n_draw / (n_draw - 1.0)
+    var_plus = mean_var * (n_draw - 1.0) / n_draw
+    #if n_chain > 1:
+     #   var_plus += _numba_var(svar, np.var, chain_mean, axis=None, ddof=1)
+
+    rho_hat_t = np.zeros(n_draw)
+    rho_hat_even = 1.0
+    rho_hat_t[0] = rho_hat_even
+    rho_hat_odd = 1.0 - (mean_var - np.mean(acov[:, 1])) / var_plus
+    rho_hat_t[1] = rho_hat_odd
+
+    # Geyer's initial positive sequence
+    t = 1
+    while t < (n_draw - 3) and (rho_hat_even + rho_hat_odd) > 0.0:
+        rho_hat_even = 1.0 - (mean_var - np.mean(acov[:, t + 1])) / var_plus
+        rho_hat_odd = 1.0 - (mean_var - np.mean(acov[:, t + 2])) / var_plus
+        if (rho_hat_even + rho_hat_odd) >= 0:
+            rho_hat_t[t + 1] = rho_hat_even
+            rho_hat_t[t + 2] = rho_hat_odd
+        t += 2
+
+    max_t = t - 2
+    # improve estimation
+    if rho_hat_even > 0:
+        rho_hat_t[max_t + 1] = rho_hat_even
+    # Geyer's initial monotone sequence
+    t = 1
+    while t <= max_t - 2:
+        if (rho_hat_t[t + 1] + rho_hat_t[t + 2]) > (rho_hat_t[t - 1] + rho_hat_t[t]):
+            rho_hat_t[t + 1] = (rho_hat_t[t - 1] + rho_hat_t[t]) / 2.0
+            rho_hat_t[t + 2] = rho_hat_t[t + 1]
+        t += 2
+
+    ess = n_chain * n_draw
+    tau_hat = -1.0 + 2.0 * np.sum(rho_hat_t[: max_t + 1]) + np.sum(rho_hat_t[max_t + 1 : max_t + 2])
+    tau_hat = max(tau_hat, 1 / np.log10(ess))
+    ess = ess / tau_hat
+    if np.isnan(rho_hat_t).any():
+        ess = np.nan
+    return ess
+
+def MCSE(sample):
+    """
+    Computing the Monte Carlo standard error(MCSE)
+
+    Parameters
+    ----------
+    samples : `numpy.ndarray(n_iters, 2*dim)`
+        An array containing variational samples
+
+
+    Returns
+    -------
+    mcse : `numpy.ndarray(2*dim)`
+        MCSE for each variational parameter
+
+    """
+    n_iters, d = sample.shape
+    sd_dev = np.sqrt(np.var(sample,ddof=1,axis=0))
+    eff_samp = [ess(sample[:,i].reshape(1,n_iters)) for i in range(d)]
+    mcse = sd_dev/np.sqrt(eff_samp)
+    return mcse
+
+def compute_R_hat(chains, warmup=0, jitter = 1e-8):
+    """
+    Computing R hat values using split R hat approach
+
+    Parameters
+    ----------
+    chains : `numpy_ndarray(n_iters, dimensions)`
+        Sample of parameter estimates that has one chain
+    warmup : `int`, optional
+        Number of iterations needed for warmup. The default is 0.
+    jitter : `float`, optional
+        Smoothing term that avoids division by zero. The default is 1e-8.
+
+    Returns
+    -------
+    R_hat : `numpy_ndarray(dimenstions,)`
+        Computed R hat values for each parameter
+
+    """
+    n_chains = 1
+    chains = chains[warmup:,:]
+    n_iters, d = chains.shape
+    if n_iters%2 ==1:
+        n_iters = int(n_iters-1)
+        chains = chains[:n_iters,:]
+    n_iters = n_iters // 2
+    n_chains2 = n_chains *2
+    psi = np.reshape(chains,(n_chains2,n_iters,d))
+    psi_dot_j = np.mean(psi,axis=1)
+    psi_dot_dot = np.mean(psi_dot_j,axis=0)
+    s_j_2 = np.sum((psi - np.expand_dims(psi_dot_j, axis=1)) ** 2, axis=1) / (n_iters - 1)
+    B = n_iters * np.sum((psi_dot_j - psi_dot_dot) ** 2, axis=0) / (n_chains2 - 1)
+    W = np.nanmean(s_j_2, axis=0)
+    W = W + jitter
+    var_hat = (n_iters - 1) / n_iters + (B / (n_iters*W))
+    R_hat = np.sqrt(var_hat)
+    return R_hat
+
+def R_hat_convergence_check(samples, windows):
+    """
+    Convergence check of samples using R_hat
+
+    Parameters
+    ----------
+    samples : `list`
+        Samples to check the convergence
+    windows : `numpy_array`
+        Windows that should be used to do the check
+
+    Returns
+    -------
+    W : `int`
+        Best window size, or None if none met convergence criterion
+
+    """
+    R_hat_array = [np.max(compute_R_hat(np.array(samples[-window:]),0)) for window in windows]
+    best_R_hat_ind = np.argmin(R_hat_array)
+    if R_hat_array[best_R_hat_ind] <= 1.1:
+        return windows[best_R_hat_ind]
+    else:
+        return None
\ No newline at end of file
diff --git a/viabel/_utils.py b/viabel/_utils.py
index ee0b75c2..be492c13 100644
--- a/viabel/_utils.py
+++ b/viabel/_utils.py
@@ -1,3 +1,8 @@
+import os
+import shutil
+import pickle
+import pystan
+from hashlib import md5
 import autograd.numpy as np
 
 
@@ -14,3 +19,48 @@ def ensure_2d(a):
     while a.ndim < 2:
         a = a[:,np.newaxis]
     return a
+
+
+def _data_file_path(filename):
+    """Returns the path to an internal file"""
+    return os.path.abspath(os.path.join(__file__, '../data', filename))
+
+def _stan_model_cache_dir():
+    return _data_file_path('cached-stan-models')
+
+
+def clear_stan_model_cache():
+    stan_model_dir =_stan_model_cache_dir()
+    if os.path.exists(stan_model_dir):
+        shutil.rmtree(stan_model_dir)
+        
+        
+def StanModel_cache(model_code=None, model_name=None, **kwargs):
+    """Use just as you would `StanModel`"""
+    if model_code is None:
+        if model_name is None:
+            raise ValueError('Either model_code or model_name must be provided')
+        model_file = _data_file_path(model_name + '.stan')
+        if not os.path.isfile(model_file):
+            raise ValueError('invalid model "{}"'.format(model_name))
+        with open(model_file) as f:
+            model_code = f.read()
+    stan_model_dir =_stan_model_cache_dir()
+    os.makedirs(stan_model_dir, exist_ok=True)
+    code_hash = md5(model_code.encode('ascii')).hexdigest()
+    if model_name is None:
+        cache_fn = 'cached-model-{}.pck'.format(code_hash)
+    else:
+        cache_fn = 'cached-{}-{}.pck'.format(model_name, code_hash)
+    cache_file = os.path.join(stan_model_dir, cache_fn)
+    if os.path.exists(cache_file):
+        print('Using cached StanModel{}'.format('' if model_name is None
+                                                else ' for ' + model_name))
+        with open(cache_file, 'rb') as f:
+            sm = pickle.load(f)
+    else:
+        sm = pystan.StanModel(model_code=model_code, model_name=model_name)
+        with open(cache_file, 'wb') as f:
+            pickle.dump(sm, f)
+
+    return sm
diff --git a/viabel/convenience.py b/viabel/convenience.py
index aa679da3..ffc00b63 100644
--- a/viabel/convenience.py
+++ b/viabel/convenience.py
@@ -1,10 +1,9 @@
 import numpy as np
-
 from viabel.approximations import MFGaussian
 from viabel.diagnostics import all_diagnostics
 from viabel.models import Model, StanModel
 from viabel.objectives import ExclusiveKL
-from viabel.optimization import SASA, RMSProp
+from viabel.optimization import RAABBVI, RMSProp
 from viabel._psis import psislw
 
 all = [
@@ -15,11 +14,11 @@
 
 def bbvi(dimension, *, n_iters=10000, num_mc_samples=10, log_density=None,
          approx=None, objective=None, fit=None, adaptive=True,
-         init_var_param=None, learning_rate=0.1,
-         RMS_kwargs=dict(), SASA_kwargs=dict()):
+         init_var_param=None, learning_rate=0.1, 
+         RMS_kwargs=dict(), RAABBVI_kwargs=dict()):
     """Fit a model using black-box variational inference.
 
-    Currently the objective is optimized using ``viabel.optimization.SASA``.
+    Currently the objective is optimized using ``viabel.optimization.RAABBVI``.
 
     Parameters
     ----------
@@ -44,13 +43,13 @@ def bbvi(dimension, *, n_iters=10000, num_mc_samples=10, log_density=None,
     init_var_param, optional
         Initial variational parameter.
     adaptive : `bool`, optional
-        If ``True``, use ``SASA`` with ``RMSProp``. Otherwise use ``RMSProp``.
+        If ``True``, use ``RAABBVI`` with ``RMSProp``. Otherwise use ``RMSProp``.
     learning_rate : `float`
         Tuning parameter that determines the step size.
     RMS_kwargs : `dict`, optional
         Dictionary of keyword arguments to pass to ``RMSProp``.
-    SASA_kwargs : `dict`, optional
-         Dictionary of keyword arguments to pass to ``SASA``.
+    RAABBVI_kwargs : `dict`, optional
+         Dictionary of keyword arguments to pass to ``RAABBVI``.
 
     Returns
     -------
@@ -79,7 +78,7 @@ def bbvi(dimension, *, n_iters=10000, num_mc_samples=10, log_density=None,
         init_var_param = approx.init_param()
     base_opt = RMSProp(learning_rate, **RMS_kwargs)
     if adaptive:
-        opt = SASA(base_opt, dimension, **SASA_kwargs)
+        opt = RAABBVI(base_opt, dimension, **RAABBVI_kwargs)
     else:
         opt = base_opt
     opt_results = opt.optimize(n_iters, objective, init_var_param)
diff --git a/viabel/data/weighted_lin_regression.stan b/viabel/data/weighted_lin_regression.stan
new file mode 100755
index 00000000..34b10059
--- /dev/null
+++ b/viabel/data/weighted_lin_regression.stan
@@ -0,0 +1,26 @@
+data {
+   int<lower=0> N;
+   vector[N] y; // log(SKL)
+   vector[N] x; // log(\gamma) 
+   real<lower=0> rho;
+   vector[N] w; //weights
+}
+
+parameters {
+    real<lower=0,upper=1>  kappa; //power
+    real log_c; // log(c)
+    real<lower=0> sigma; // \sigma
+}
+
+model {
+    real mu;
+        
+    kappa ~ uniform(0,1);
+    log_c ~ cauchy(0,10);
+    sigma ~ cauchy(0,10);
+
+    for (n in 1:N) {
+        mu = log_c + 2*log((1/rho^kappa)-1) + 2*kappa*x[n];
+        target += normal_lpdf(y[n] | mu, sigma) * w[n];  
+    }     
+}
\ No newline at end of file
diff --git a/viabel/optimization.py b/viabel/optimization.py
index 6cd54661..17fa3a85 100644
--- a/viabel/optimization.py
+++ b/viabel/optimization.py
@@ -1,15 +1,17 @@
 from abc import ABC, abstractmethod
 import tqdm
 import autograd.numpy as np
-from scipy.stats import t as tdist
 from viabel.approximations import MFGaussian
+from viabel._mc_diagnostics import MCSE, R_hat_convergence_check
+from viabel._utils import StanModel_cache
+
 
 __all__ = [
     'Optimizer',
     'StochasticGradientOptimizer',
     'RMSProp',
     'AdaGrad',
-    'SASA'
+    'RAABBVI'
 ]
 
 
@@ -125,7 +127,6 @@ def descent_direction(self, grad, history):
         descent_dir = grad / np.sqrt(self._jitter+history)
         return (descent_dir, history)
 
-
 class AdaGrad(StochasticGradientOptimizer):
     """Adagrad optimization method
     """
@@ -141,67 +142,68 @@ def descent_direction(self, grad, history):
         return (descent_dir, history)
 
 
-class SASA(Optimizer):
-    """A class of Statistical Adaptive Stochastic Gradient Optimizer
+class RAABBVI(Optimizer):
+    """A robust, automated, and accurate BBVI optimizer
 
     Parameters
     ----------
     sgo : `class`
         A subclass of StochasticGradientOptimizer
     dim : `int`
-        dimension of the underlyin parameter space
-    theta : `float`, optional
-        Fraction of the samples to use for testing. The default is 1/8
+        dimension of the underlying parameter space
     rho : `float`, optional
         Learning rate reducing factor. The default is 0.5
-    W0 : `int`, optional
-        Minimum number of samples for tesing. The default is 1000.
-    t_check : `int`, optional
-        Period to perform statistical test. The default is 100.
-    delta : `float, optional
-        Significance level to compute the confidence interval. The default is 0.05.
     eps : `float`, optional
-        Threshold to determine the stopping iterations. The default is 1e-3.
+        Threshold to determine the stopping iterations. The default is 0.1.
+    tol :`float` optional
+        Tolerance level to determine MCSE of variational estimates. The default
+        is same as eps (0.1).
+    W_min : `int`, optional
+        Minimum window size for checking convergence. The default is 200.
+    k_check : `int`, optional
+        Frequency with which to check convergence. The default is `W_min`.
     """
-    def __init__(self, sgo, dim, theta=1/8, rho=0.5, W0 = 1000, t_check = 100, delta = 0.05, eps = 1e-3):
+    def __init__(self, sgo, dim, rho=0.5, eps=0.1, tol=None, W_min=200, k_check=None):
         if not isinstance(sgo, StochasticGradientOptimizer):
             raise ValueError('sgo must be a subclass of StochasticGradientOptimizer')
         self._sgo = sgo
         self._dim = dim
-        self._theta = theta
         self._rho = rho
-        self._W0 = W0
-        self._t_check = t_check
-        self._delta = delta
         self._eps = eps
+        self._tol = eps if tol is None else tol
+        self._W_min = W_min
+        self._k_check = W_min if k_check is None else k_check
 
-    def convergence_check(self, W, Delta_history):
+    def weighted_linear_regression(self, model, y, x, s=4, a=0.25):
         """
         Parameters
         ----------
-        W : `int`
-            Window size to use for the convergence check
-        Delta_history : `numpy.ndarray`
-            Computed Delta values
+        model : `pystan model`
+            Pystan model to conduct the sampling
+        y : `numpy_ndarray`
+            Response variable
+        x : `numpy_ndarray`
+            Predictor variable
 
         Returns
         -------
-        bool
-            Indicates whether the convergence reached or not
+        kappa : `float`
+            Power
+        c : `float`
+            Constant 
         """
-        m = b = np.floor(np.sqrt(W)).astype(int)
-        Delta_reshaped = np.reshape(Delta_history[-m*b:],(m,b))
-        mu_n = np.mean(Delta_reshaped)
-        Delta_batch_means = np.mean(Delta_reshaped,axis=1)
-        sigma_n = np.sqrt((m/(b-1))* np.sum((Delta_batch_means - mu_n)**2))
-        sd_error = tdist.ppf(1-self._delta/2, df=b-1) * (sigma_n/np.sqrt(m*b))
-        lower = mu_n - sd_error
-        upper = mu_n + sd_error
-
-        if lower < 0 and upper > 0:
-            return True, lower, upper
-        else:
-            return False, lower, upper
+        def initfun(kappa, log_c, sigma, chain_id=1):
+            return dict(kappa=kappa, log_c=log_c, sigma=sigma)
+        N = len(y)
+        w = 1/(1 + np.arange(N)[::-1]**2/s**2)**a
+        n_chains = 4
+        data = dict(N=N, y=y, x=x, rho=self._rho, w=np.array(w))
+        init = [initfun(0.5, 100, 5, chain_id=i) for i in range(n_chains) ]
+        fit = model.sampling(data=data, init=init, iter=1000, chains=n_chains,
+                             control=dict(adapt_delta=0.95))
+        kappa = np.mean(fit['kappa'])
+        c = np.exp(np.mean(fit['log_c']))
+        return kappa, c
 
 
     def optimize(self, n_iters, objective, init_param):
@@ -231,53 +233,94 @@ def optimize(self, n_iters, objective, init_param):
             print('Approximation does not support KL. Using base stochastic'
                   ' optimization algorithm instead.', flush=True)
             return self._sgo.optimize(n_iters, objective, init_param)
-        t0 = 0
-        history = None
+
+        k0 = 0
+        k_conv = None # Iteration number when reached convergence
+        history = None # Information needed to compute descent direction in optimization algorithm
+        success = False
         learning_rate = self._sgo._learning_rate
+        model = StanModel_cache(model_name='weighted_lin_regression')
         variational_param = init_param.copy()
         variational_param_mean = init_param.copy()
-        value_history = []
-        Delta_history = []
         variational_param_history = []
-        lower = upper = np.nan
+        value_history = []
+        SKL_history = []
+        learn_rate_hist = []
         stopped = False
         with tqdm.trange(n_iters) as progress:
             try:
-                for t in progress:
+                for k in progress:
                     object_val, object_grad = objective(variational_param)
                     value_history.append(object_val)
                     descent_dir, history = self._sgo.descent_direction(object_grad, history)
-                    # must be computed before updated variational parameter
-                    Delta = np.dot(variational_param, descent_dir) - 0.5*learning_rate*np.sum(descent_dir**2)
-                    Delta_history.append(Delta)
                     variational_param -= learning_rate * descent_dir
                     variational_param_history.append(variational_param.copy())
-                    W = np.max([np.min([t-t0, self._W0]), np.ceil(self._theta*(t-t0)).astype(int)])
-                    if (W >= self._W0) and (t % self._t_check == 0):
-                        convg, lower, upper = self.convergence_check(W, Delta_history)
-                        if convg == True:
-                            m = b = np.floor(np.sqrt(W)).astype(int)
+
+                    # when convergence has not been reached check if the
+                    # recheck for convergence using R_hat approach
+                    if k_conv is None and k % self._k_check == 0:
+                        W_upper = int(0.95*k)
+                        if W_upper > self._W_min:
+                            windows = np.linspace(self._W_min, W_upper, num=5, dtype=int)
+                            W = R_hat_convergence_check(variational_param_history, windows)
+                            if W is not None:
+                                k_conv = k-W-k0
+                                W_check = W  # immediately check MCSE
+
+                    # Once convergence has been reached compute the MCS
+                    if k_conv is not None and k - k_conv - k0 == W_check:
+                        W = W_check
+                        if isinstance(objective.approx, MFGaussian):
+                            # For MF Gaussian, use MCSE(mu/sigma,log_sigma)
+                            var_param_log_sd = np.array(variational_param_history)[-W:,-self._dim:]
+                            var_param_mu = np.array(variational_param_history)[-W:,self._dim:]
+                            var_param_mu_stand = var_param_mu/np.exp(var_param_log_sd)
+                            var_param_new = np.concatenate((var_param_mu_stand,var_param_log_sd),axis=1)
+                            mcse = MCSE(var_param_new)
+                        else:
+                            var_param = np.array(variational_param_history)[-W:,:]
+                            mcse = MCSE(var_param)
+                        if np.mean(mcse) < self._tol:
+                            success = True
                             learning_rate = self._rho * learning_rate
+                            learn_rate_hist.append(learning_rate)
                             variational_param_mean_prev = variational_param_mean
-                            variational_param_mean = np.mean(np.array(variational_param_history[-m*b:]), axis=0)
-                            t0 = t
-                            SKL = objective.approx.kl(variational_param_mean_prev, variational_param_mean) + objective.approx.kl(variational_param_mean, variational_param_mean_prev)
-                            if (SKL/self._rho < self._eps):
-                                stopped = True
-                                break
-                    if t % 10 == 0:
-                        avg_loss = np.mean(value_history[max(0, t - 1000):t + 1])
+                            variational_param_mean = np.mean(np.array(variational_param_history[-W:]), axis=0)
+                            variational_param = variational_param_mean
+                            if len(learn_rate_hist) > 1:
+                                SKL = (objective.approx.kl(variational_param_mean_prev, variational_param_mean) +
+                                       objective.approx.kl(variational_param_mean, variational_param_mean_prev))
+                                SKL_history.append(SKL)
+                            k_conv = None
+                            k0 = k
+                            # Conduct weighted linear regression to estimate parameters
+                            # for the stopping rule
+                            if len(SKL_history) > 0:
+                                y = np.log(SKL_history)
+                                x = np.log(learn_rate_hist[:-1])
+                                kappa, c = self.weighted_linear_regression(model, y, x)
+                                if (c * learn_rate_hist[-2]**(2*kappa) < self._eps**2):
+                                    stopped = True
+                                    break
+                        else:
+                            W_check = int(1.25*W_check)
+
+
+                    if k % 10 == 0:
+                        avg_loss = np.mean(value_history[max(0, k - 1000):k + 1])
                         progress.set_description(
-                            'average loss = {:,.5g} | learning rate = {:,.5g} | ({:,.5g}, {:,.5g})'.format(avg_loss, learning_rate, lower, upper))
+                            'average loss = {:,.5g} | learning rate = {:,.5g} |'.format(avg_loss, learning_rate))
             except (KeyboardInterrupt, StopIteration) as e:  # pragma: no cover
                 # do not print log on the same line
                 progress.close()
             finally:
                 progress.close()
+        if not success:
+            UserWarning('Failed to converge')
         if stopped:
-            print('Stopping rule reached at iteration', t)
-        if t - t0 > self._W0:
-            variational_param_mean = np.mean(np.array(variational_param_history[-self._W0:]), axis = 0)
+            print('Stopping rule reached at iteration', k)
+        if k - k0 > self._W_min:
+            variational_param_mean = np.mean(np.array(variational_param_history[-self._W_min:]), axis = 0)
         return dict(smoothed_opt_param = variational_param_mean,
                     variational_param_history = variational_param_history,
                     value_history = np.array(value_history))


Test patch:
diff --git a/viabel/tests/test_convenience.py b/viabel/tests/test_convenience.py
index 75520dc4..dbde2aef 100644
--- a/viabel/tests/test_convenience.py
+++ b/viabel/tests/test_convenience.py
@@ -14,7 +14,7 @@ def test_bbvi():
     stdev = np.array([2.,5.])[np.newaxis,:]
     log_p = lambda x: anp.sum(norm.logpdf(x, loc=mean, scale=stdev), axis=1)
     # large number of MC samples and smaller epsilon and learning rate to ensure accuracy
-    results = convenience.bbvi(2, log_density=log_p, num_mc_samples=100)
+    results = convenience.bbvi(2, log_density=log_p, num_mc_samples=100, RAABBVI_kwargs=dict(eps=0.01))
     # iterate averaging introduces some bias, so use last iterate
     est_mean, est_cov = results['objective'].approx.mean_and_cov(results['var_param']) #results['var_param_history'][-1])
     est_stdev = np.sqrt(np.diag(est_cov))
diff --git a/viabel/tests/test_objectives.py b/viabel/tests/test_objectives.py
index 07a2b8ed..433296a4 100644
--- a/viabel/tests/test_objectives.py
+++ b/viabel/tests/test_objectives.py
@@ -1,5 +1,5 @@
 from viabel.approximations import MFStudentT
-from viabel.optimization import SASA, RMSProp
+from viabel.optimization import RAABBVI, RMSProp
 from viabel.objectives import ExclusiveKL, AlphaDivergence
 
 import autograd.numpy as anp
diff --git a/viabel/tests/test_optimization.py b/viabel/tests/test_optimization.py
index 6c92379b..0f70246f 100644
--- a/viabel/tests/test_optimization.py
+++ b/viabel/tests/test_optimization.py
@@ -1,4 +1,4 @@
-from viabel.optimization import SASA, RMSProp, AdaGrad
+from viabel.optimization import RAABBVI, RMSProp, AdaGrad
 from viabel.objectives import VariationalObjective
 
 import autograd.numpy as anp
@@ -51,10 +51,10 @@ def test_adagrad_optimize():
         _test_optimizer(sgd, objective, true_value, 20000)
 
 
-def test_sasa_rmsprop_optimize():
+def test_raabbvi_rmsprop_optimize():
     for scales in [np.ones(2), np.ones(4), np.geomspace(.1, 1, 4)]:
         objective = DummyObjective(noise=.2, scales=scales)
         true_value = np.zeros_like(scales)
         dim = int(true_value.size/2)
-        sasa = SASA(RMSProp(0.1), dim)
+        sasa = RAABBVI(RMSProp(0.1), dim)
         _test_optimizer(sasa, objective, true_value, 10000)