From 043f3c2b35bda2bfa0593bf8d07add501ae2d43a Mon Sep 17 00:00:00 2001 From: Anirban Chaudhuri <75496534+anirban-chaudhuri@users.noreply.github.com> Date: Thu, 21 Mar 2024 10:32:22 -0400 Subject: [PATCH 1/5] update optimize documentation for definition of static_parameter_intervention --- pyciemss/interfaces.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/pyciemss/interfaces.py b/pyciemss/interfaces.py index 2d6a9e579..4eaac1e5f 100644 --- a/pyciemss/interfaces.py +++ b/pyciemss/interfaces.py @@ -785,11 +785,11 @@ def optimize( - A callable function defining the quantity of interest to optimize over. risk_bounds: float - The threshold on the risk constraint. - static_parameter_interventions: Dict[torch.Tensor, str] - - A dictionary of the form {intervention_time: parameter_name} - of static parameter interventions to optimize over. - - Each key is the time at which the intervention is applied. - - Each value is a string with the intervention parameter name. + static_parameter_interventions: Callable[[torch.Tensor], Dict[float, Dict[str, Intervention]]] + - A callable function of static parameter interventions to optimize over. + - The callable functions are created using the provided templates: + - param_value_objective(): creates a static parameter intervention when optimizing over (multiple) parameter values + - start_time_objective(): creates a static parameter intervention when optimizing over (multiple) start times for different parameter objfun: Callable - The objective function defined as a callable function definition. - E.g., to minimize the absolute value of intervention parameters use lambda x: np.sum(np.abs(x)) From 6782c7de680981f558d5569838b006070d3ca4f2 Mon Sep 17 00:00:00 2001 From: Anirban Chaudhuri <75496534+anirban-chaudhuri@users.noreply.github.com> Date: Thu, 21 Mar 2024 11:00:55 -0400 Subject: [PATCH 2/5] Added warnings for optimize interface --- pyciemss/interfaces.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/pyciemss/interfaces.py b/pyciemss/interfaces.py index 4eaac1e5f..b70aa9ca4 100644 --- a/pyciemss/interfaces.py +++ b/pyciemss/interfaces.py @@ -1,5 +1,6 @@ import contextlib import time +import warnings from math import ceil from typing import Any, Callable, Dict, List, Optional, Union @@ -30,6 +31,8 @@ from pyciemss.ouu.ouu import computeRisk, solveOUU from pyciemss.ouu.risk_measures import alpha_superquantile +warnings.simplefilter("always", UserWarning) + @pyciemss_logging_wrapper def ensemble_sample( @@ -933,4 +936,19 @@ def round_up(num, dec=roundup_decimal): "policy": torch.tensor(opt_results.x), "OptResults": opt_results, } + # Check optimize results and provide appropriate warnings + if opt_results["success"]: + warnings.warn( + "Optimal intervention policy satisfies constraints and is within set bounds. If better policy is desired, try (i) expanding the bounds_interventions, (ii) relaxing risk_bounds, and/or (iii) increasing maxiter and maxfeval or different initial_guess_interventions." + ) + else: + if np.any(opt_results.x - u_min < 0) or np.any(u_max - opt_results.x < 0): + warnings.warn( + "Optimal intervention policy is out of bounds. Try (i) expanding the bounds_interventions and/or (ii) different initial_guess_interventions." + ) + if opt_results["lowest_optimization_result"]["maxcv"] > 0: + warnings.warn( + "Optimal intervention policy does not satisfy constraints. Check if the risk_bounds value is appropriate for given problem. Otherwise, try (i) different initial_guess_interventions, (ii) increasing maxiter and maxfeval, and/or (iii) increase n_samples_ouu to improve accuracy of Monte Carlo risk estimation. " + ) + return ouu_results From baa37a4a087e069964556ef08211dcb5896ce5c3 Mon Sep 17 00:00:00 2001 From: Anirban Chaudhuri <75496534+anirban-chaudhuri@users.noreply.github.com> Date: Thu, 21 Mar 2024 11:30:49 -0400 Subject: [PATCH 3/5] Linting --- docs/source/optimize_interface.ipynb | 297 ++++++++++++--------------- pyciemss/interfaces.py | 19 +- 2 files changed, 141 insertions(+), 175 deletions(-) diff --git a/docs/source/optimize_interface.ipynb b/docs/source/optimize_interface.ipynb index 40415714e..0c35f065b 100644 --- a/docs/source/optimize_interface.ipynb +++ b/docs/source/optimize_interface.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -114,25 +114,25 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Time taken: 2.960969924926758\n", - "Risk associated with QoI: [486.3824609375]\n" + "Time taken: 10.823821306228638\n", + "Risk associated with QoI: [490.8698352050781]\n" ] }, { "data": { - "image/png": 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7n//iT/1U/mWvDfwW8DvAa/Ov99HAVwHvA3w3z/Zg4L2B7wZu5YrfBl4LEM/20cBbA+8N3MpzuhV4ECCezcDvAK/Ns10ELgEvDezybMeBW4GLwEN40SD+fY4DXw18NLDL/2Jf/MVfbIBP/uRPFldd9QDXXXfdddddd911D3/4wx++VbX1cPPwLdh6KXgp/gVVqtXUKtVq1wq1SrWHPuzg+TgQBxfEhQtw4d7k3gvBhcEMXHXVVf9m14nreD5Owsne7nkuJ+FkDz3PZctsbaJN/gtMME3SxANM9jTBxAMMYkiTPMAKr/hPcAAHT0FP4QH+OvhrHuApt935lMPDw0Oe6Z577rnnnnvuuYerrrrqqquuuuqq/yPe/FN+6IdwbPJMv/V17/suh4eHh7wINjc3N1/nI77zh7if8vDnv+hd3oV/2WsDvwX8DvDa/Os9GHg6V3w38NvAzwC7PK/fBl4LEC/cawEvDXw08GBAPJuB3wFemyteGvgr4LeBz+Z5fTXw0oB40SCuuuyLv/iLDfDJn/zJ4qqr/hVe+qVf+qW3tra2Hn7T9Q/fMlsPh4dfB9ddC9fyL5ijeQ99lWoP/dye83wciIML4sIFuHABLlyEiwfmgKuuuuq/XS/6k3CS53ISTvZ2zwP00J+EkzyXk+Zkhzr+k6SUAww8QNo5wMADrGDFAwwwJE7+A92D7rkH7uGZniKeciAOeKa/efyT/oZnOjg4OHjKU57yFK666qqrrrrqqqv+B3nzT/7hHwZt8Ey/9XXv+y6Hh4eHvAg2Nzc3X+cjvvOHeBYf/fwXv/M78y97beC3gN8BXpt/m7cGvhp4EM/218BXA9/Ds/028FqAeE6vDXwU8NrAcZ7tEnAMEM9m4HeA1+aK1wZ+i3/ZywB/zb8McdVlX/zFX2yAT/7kTxZXXfUf5Lrrrrvuuuuuu+6666677rrj29c93Dx8C7Yebh6+KTZ5PnpF30PfQ99DP7fnPB+DGC7AhXvEPRfgwkW4eGAOuOqqq/5P2BJbW7DFA5yEk73d8wAn4WQPPQ9wrXUt/wkmmCZp4pnSzgEGHmAQQ5rkmSYxTfbEf4B70D33wD080z3innvEPTzTU2678ymHh4eHAAcHBwdPecpTnsJVV1111VVXXXXVf7A3/+Qv/EJ42IvzTM/4hc//tL/7u7/7O14EL/ESL/ESD3qzT/8CnuWpf//zX/ypn8q/7LWB3wJ+B3ht/n1eG3hr4LWBl+KKzwE+myt+G3gtQDzbewPfBVwCvhu4Ffhr4LeB3wZeCxDPZuB3gNfmitcGfgv4HuC7ecH+GtjlX4a46rIv/uIvNsAnf/Ini6uu+i/y0i/90i/98Ic//OEPL3r4w+HhD4OH8Xz0ir7i2pt+Ls176MMOno97xD0XxIULcOEiXLxgLnDVVVf9v9aL/iSc5AF66E/aJ3mA6+A6HuCkOdmhjv9AAwwpJc80wJB28kwrWPFMKeXgHPgP8Nfor3mme8Q994h7AA6GdvDUpz71qTzTU57ylKccHBwccNVVV1111VVXXfUCvMW7vuu7+pa3emeeSas/+s2f++qv/mpeBG/x0R/90Z6/yuvyTLrtZ374537wB3+Qf9lrA78F/A7w2vzrPRh4EPA7PKeXBv4K+GvgZbjit4HXAsSzXQSOAyeAXZ6TuUI8m4HfAV6bKx4MPB34HeC1eV4vDRwDfocXDeKqy774i7/YAJ/8yZ8srrrqv9FLv/RLv/TDH/7whz+86OHXwXUvBS/F81Gl2kPfm34uzatdK1Sej3vEPfeIe+5N7r1H3MNVV1111b/Bltjagi2eqYf+pH2SB7gOruMBTpqTHer4DzDBNEkTzzTAkHbyTCtY8UyTmCZ74t/hHnTPPXAPz/TXwV/zTE+57c6nHB4eHgI85SlPecrBwcEBV1111VVXXXXV/wuv/Mqv/MqnX/tjPpUHeMYvfP6n/d3f/d3f8UK8xEu8xEs86M0+/Qt4gHO//VVf+Md//Md/zL/stYHfAn4HeG3+9b4a+CjgbYCf5tmOAxeBvwFemit+G3gt4ASwyxUGbgUewnN6a+CnuEI8m4HfAV6bZ/tr4KWAlwH+mmd7aeCvgGcAD+ZFg7jqsi/+4i82wCd/8ieLq676H+bhD3/4wx/+8Ic//OHHth/+cHj4S8FL8XwEih76Ocx76HvoK1QeYBDDPeKe2+C2e+HeA3PAVVddddV/kZPiZA89z3SdfR0PcB1cxzP10J+wTvAfYCWteKYBhrQTICEHGHimFV7x7/AU9JQDOAB4injKgTgAeMptdz7l8PDwEOApT3nKUw4ODg646qqrrrrqqqv+13qzT/nar5WvfTD3Ux4+4+e/8Av/7u/+7u94Pl7iJV7iJR705p/6qTg2eSbr3lt/4Ys+8iN50bw28FvA7wCvzb/eg4G/Bgx8NHArcBz4aOC1gY8Bvporvhr4KOCngd8Gvgb4beC1gK8GfporXhv4bOAScAwQz7YLHAO+GvgZ4LeB1wZ+C9gFvhr4beDBwGcDDwbeBvhpXjSIqy774i/+YgN88id/srjqqv8FHv7whz/8uuuuu+7hN13/8Jc2L/1w8/BNsclzCRRzMZ+j+Ya9UaHyABfEhXvEPbcnt98j7uGqq6666n+oLbG1BVs800k42ds9z3QdXMczbZmtTbTJv8ME0yRNAGnnAAPPNIghTQIMMCRO/g2egp5yAAcATxFPORAHAE+57c6nHB4eHgI85SlPecrBwcEBV1111VVXXXXV/xgPfehDH/rYd/yir+a5xPIPf+Npv/Ebv/H0pz/96QAPechDHvLQ13u918vFq74ez+VxP/opH/20pz3tabxoXhv4LeB3gNfm3+a1ga8GXopnuwR8NfDZPNtLAz8NPIgrBDwY+G7gtXi2ZwAfDbw08FnA2wA/zRXvDXwXV/wO8Npc8dLAdwMvxbM9A/ho4Kd50SGuuuyLv/iLDfDJn/zJ4qqr/pe67rrrrnv4wx/+8IffdP3DX9q89MPNwzfFJg9QpbphNuYwn0vzsINnGsRwj7jnNrjtXrj3wBxw1VVXXfV/wHXiOp7pJJzs7R6gh/4knOSZrrWu5d9hgCGlBBhgSDsBBjGkSYABhsTJv9IBHDwFPQXgHnHPPeIegKfcdudTDg8PDw8ODg6e8pSnPIWrrrrqqquuuuq/xFu867u+q295q3fm3yAe9wPf/rM/+7M/y3+P48BLc8Vv84IdB3Z5TseBlwZuBW7lX/Zg4Faev9cGbgVu5V8PcdVlX/zFX2yAT/7kTxZXXfV/yNbW1tarv/qrv/pLH9t+6Vc3r74pNnmAOZpvSBtze95DzwMciIPbxG33wD23m9u56qqrrvp/ZEtsbcEWQA/9Sfskz3QdXMczXWtdy7/RAENKCTDAkHYCDGJIkwArvOJf6R50zz1wD8BTxFMOxMHB0A6e+tSnPhXgnnvuueeee+65h6uuuuqqq6666t/lLd71Xd/Vt7zVO/OvEI/7gW//2Z/92Z/lqn8PxFWXffEXf7EBPvmTP1lcddX/YQ9/+MMf/uov/zKv/urm1R8GD+MBqlTnaL5hb8yledjBA9wW3HYP3HM73H5gDrjqqquuuuo5XCeu45mus6/jma6D63ima61r+TdIKQcYACZ7mmACWMEKIKUcnAP/Sk9BTzmAgwNx8BTxFICn3HbnUw4PDw/vueeee+655557uOqqq6666qqrXqCHPvShD33MO330R8vXPpgXwrr31sf/yFd/9dOe9rSn8e/30sAxXnS/w/8tiKsu++Iv/mIDfPInf7K46qr/J7a2trZe+qVf+qVf/cbrX/2l4aWvhWt5gDmaz2G+ARs99DzAgTi4Tdx2D9xzL9w7mIGrrrrqqqv+Va4T1/FM19nX8UzXwXU807XWtfwrTTBN0gSwslcACTnAADCJabIn/hX+Gv01wFPEUw7EwcHQDp761Kc+FeCv//qv/5qrrrrqqquu+n/ulV/5lV/5zEMf+lDf8uIvDg99KAA87Wm67e///uzTnva0P/7jP/5j/uP8NvBavOjE/y2Iqy774i/+YgN88id/srjqqv+nHv7whz/8pV/6pV/61Yte/aXgpXiAQLEhbczt+Ya0EXbwAPeIe24Tt90Otx+YA6666qqrrvoP1Yv+JJwEOAkne7sHuA6uA+ihP2Gd4F9pJa0A0s4BBoAVrAAmMU32xIvoHnTPPXDPgTh4ingKwFNuu/Mph4eHh/fcc88999xzzz1cddVVV1111VVX/fshrrrsi7/4iw3wyZ/8yeKqq65ia2tr66Vf+qVf+tVvvP7VXxpe+lq4lgfoFf2GvbEBGz30PMA94p7Hicfdbm7nqquuuuqq/3InxckeeoDr7OsAeuhPwkmAk+ZkhzpeRCnlAAPAAEPaOcnTZE0AK7ziRXQAB09BTwH46+CvAf7m8U/6G4CnPOUpTzk4ODjgqquuuuqqq6666oVDXHXZF3/xFxvgkz/5k8VVV131PK677rrrXv3VX/3VX7ropV8NXo0HCBQb0sbcnm9IG2EHwIE4eIp4ylPhqQfmgKuuuuqqq/7HuU5cB7AFW1v2FsB1cB3ASXOyQx0vogGGlDLtHGAAWMEKYBLTZE+8CJ6CnnIAB/eIe+4R99xz4dI99957770HBwcHT3nKU57CVVddddVVV131/x3iqsu++Iu/2ACf/MmfLK666qp/0au/+qu/+kvfeP1Lvzq8+rVwLc8UKLZgawd2KlSe6bbgtqfAU243t3PVVVddddX/Kr3oT8JJgOvs6wC2YGsLtnroT1gneBGllAMMaecAA8AKVgArvOJFcAAHT0FPAfjr4K8B/ubxT/obgKc85SlPOTg4OOCqq6666qqrrvq/DHHVZV/8xV9sgE/+5E8WV1111b/Kddddd92rv/qrv/obF73xw+BhPNMczbdgawu2eKYDcfAU8ZTHw+MHM3DVVVddddX/GSfFyR76LdjasrcAroPrAE6akx3qeBEMMKSUAwxp5yRPkzVNYprsiRfBU9BTDuDgKeIpB+LgKbfd+ZTDw8PDv/7rv/5rrrrqqquuuuqq/+0QV132xV/8xQb45E/+ZHHVVVf9mz384Q9/+Nu/3Mu8/aubV98UmwCBYgu2dmCnQuWZnhI85anw1HvMPVx11VVXXfX/wpbY2oKtHvqT9ske+pNwEuBa61peBCnlAEPaOcAAsIIVwAqveBH8NfrrA3HwFPGUg6EdPPWpT33qPffcc88999xzD1ddddVVV1111f90iKsu++Iv/mIDfPInf7K46qqr/t22tra2Xv3VX/3V3/7Y9ts/DB7GM83RfAd2NmCDZzoQB48Tj3sqPHUwA1ddddVVV/2/dp24DuAknOzt/iSc7KHfMlubaJMXwQBDSrmyVwArWAGs8Ip/wT3onnvgnnvEPfeIe55y251POTw8PHzKU57ylIODgwOuuuqqq6666qr/boirLvviL/5iA3zyJ3+yuOqqq/5DPfzhD3/427/cy7z9G8Eb8UxVqltmawu2KlSe6SnBU54KT73H3MNVV1111VVXPR9bYmsLtnroT9one+hPwkmAa61r+ReklAMMaecAwyRPkzVNYprsiX/BX6O/Bvjr4K8B/ubxT/qbg4ODg6c85SlP4aqrrrrqqquu+q+AuOqyL/7iLzbAJ3/yJ4urrrrqP8XW1tbWG7/xG7/x2xe9/bVwLc+0IW1sma0N2OCZLogLjxOPux1uH8zAVVddddVVV/0rXCeuA7jOvq6H/iSc7KE/YZ3gXzDBNEnTAEPaOYghTa7win/BU9BTDuDgr4O/PhjawVOf+tSn3nPPPffcc88993DVVVddddX/aa/0Sje90kMfOj70JV48XuIh8kMAnm49/e/+Pv/uaU/rnvYnf3LHn3DVfxTEVZd98Rd/sQE++ZM/WVx11VX/6V76pV/6pd/4UY944zeCN+KZqlR30M4WbIUdAIMYbhO3PR4ef8Fc4Kqrrrrqqqv+nXrRn4STW7C1ZW9twdYWbJ00JzvU8S9YSSuAlb1KyAGGSUyTPfFCPAU95R5xz1PEU+65cOmee++9996nPOUpTzk4ODjgqquuuuqq/7Ue+tBbHvpR7z9+1EPsh/BCPF16+td8e/c1T3vabU/jX+/BwIN44X6H/z8QV132xV/8xQb45E/+ZHHVVVf9l9na2tp64zd+4zd++6K3vxau5Zm2FFtbsDW35zzTBXHhceJxTzVP5aqrrrrqqqv+k5wUJ3voT8LJ3u6vg+sArrWu5V8wwJBSruzVJE+TNQ0wJE5eiL9Gf30gDp4innLPhUv33Hvvvff+9V//9V9z1VVXXXXV/2jv8i7Xvsu7vLjehX+FH/p7/9AP/dC9P8S/zmcDn8W/7LOBz+E/34OB7wJeh3+7twbeGnhv/vUQV132xV/8xQb45E/+ZHHVVVf9t3j1V3/1V3/jG69/41eDV+OZqlR30M4WbIUdAIMYniKe8jfwN4MZuOqqq6666qr/Ir3oT8LJHvqT9ske+pNw8qQ52aGOF2IlrdLOAYZJniZrWuEVL8QBHDwFPeUecc894p6/efyT/ubg4ODgKU95ylO46qqrrrrqv9W7vuu17/rOL6Z35t/g2/8kvv1nf/aun+VF99nAZwHfA3w3z+utgfcGjgHvA3w3/7l+G3gtQPzbGfgd4LX510NcddkXf/EXG+CTP/mTxVVXXfXf6rrrrrvujV/7Nd74jc0bXwvX8kxbiq0de6eHHmAQw+PE4/7G/A1XXXXVVVdd9T/AdeK6HvqT9skt2NqCrZPmZIc6XoCUcoBhsqcJpkEMaXKFV7wQT0FPuUfc8xTxlKfcdudT7r333nuf8pSnPIWrrrrqqqv+0z30obc89Kvfb/hqnstvnvVv/sZv8BtPe9rB0wAe+tCth77e6/F6r3tGr8tz+ejv6D/6aU+77Wm8aD4b+Czgc4DP5vl7beC3gFuBh/Cf67eB1wLEv52B3wFem389xFWXffEXf7EBPvmTP1lcddVV/2O8+qu/+qu//Y3Xv/1LwUvxTL2iPwkn5/Yc4EAc/IH4g3vMPVx11VVXXXXV/0C96E/CyS3Y2rK3TsLJHvprrWt5IVLKAYYBhrRzBatJTJM98QI8BT3lHnHPU8RTnnLbnU85PDw8/Ou//uu/5qqrrrrqqv8wX/OF13/NQ+yH8ExH4ugLfshf8Hd/d+/f8Xy8xEtc+xKf9i76tA2zwTM9XXr6R33q3R/Fi+azgc8CPgf4bF4wc4X4t3sr4KV5tp8B/ppney3gq4GXBl4buAT8Nc/20sBb8Wy7wPcAu1zxYOBBwG8Dfw18NPAM4Fae7aWB1wKOA7cCvwPcyrMhrrrsi7/4iw3wyZ/8yeKqq676H+e666677u1f6zXe/o3NG2+KTYA5mp+G0xUqwD3inj8Qf3BgDrjqqquuuuqq/yV60Z+EkyfhZG/318F1PfQnrBO8ECtpNdnTBNMKVpOYJnviBbgH3XMP3PPXwV/fc+HSPffee++9f/3Xf/3XXHXVVVdd9a/yyq984yt/6lu0T+UBPu2H/Wl/93f3/h0vxEu8xLUv8QXvrC/gAb7w58oX/vEf3/nH/Ms+G/gs4HOAz+b5ezDwdOAZwIP5t/kt4LW54m+Al+KK7wbehyvMc/od4LW54qeAt+aKZwAP4opd4HWAvwY+G/gsntPnAJ8NHAc+C/horrgEHAN2gY8BvpsrEFdd9sVf/MUG+ORP/mRx1VVX/Y/29m//9m//3qH33hSbADto57h0POwAeFzwuL+BvxnMwFVXXXXVVVf9L7YltrZg6ySc3LK3TsLJk+ZkhzpegJW0SjsHGAYxTGganAMvwD3onnvgnr8O/vqeC5fuuffee+/967/+67/mqquuuuqq5+td3/Xad33nF9M780y/eda/+dVffe9X8yL46I++9qNf94xel2f64X/wD//gD977g/zLPhv4LOBzgM/meb0V8NnASwMfA3w1/3rvDXwX8DnAZ3PFceC3gZcCXgb4a674beC1APFsnw18FvA9wHvzbJ8NfBbwM8Bb82wGfgd4bZ7ts4HPAr4H+GhgFzgO/DbwUsDLAH8NIK667Iu/+IsN8Mmf/Mniqquu+h9va2tr68Pf7E0+/I3gjQACxXHp+I69AzCI4a/FXz/ePJ6rrrrqqquu+j+mF/1JOHkSTvZ2fx1ct2W2NtEmL8AAwwTTAMMghglNg3PgBbgH3fMU8ZSniKc85bY7n/LUpz71qffcc889XHXVVVf9P/eFX3D9F744fnGe6dN+2J/2d39379/xIniJl7j2Jb7gnfUFPNPfo7//1E+7+1P5l3028Fn8yz4H+Gz+bT4b+CzgbYCf5tleGnhr4KuBXa74beC1APFsnw28NvDWwC7PycDvAK/Nsxn4HeC1ueLBwF8BzwBemuf0YODpwM8Abw0grrrsi7/4iw3wyZ/8yeKqq676X+PhD3/4wz/85V7mw18KXgqgV/Qn4eTcngMciIM/EH9wj7mHq6666qqrrvp/4Dpx3RZsbdlb18F1W2ZrE23yAgwwpJQrezXJ02RNK7ziBfhr9NdPEU+5Z2z3PPWpT33qX//1X/81V1111VX/j/zQF173Q5tmk2d6ly/cf5fDw8NDXgSbm5ubP/Sp2z/EMx2Kw3f51HvehX/ZZwOfBTwDuJVne2ngGPA9wEcDu/zbvTbwW8Au8NPATwO/A+zyvH4beC1AvHCvBbw08NXA7wCvzbMZ+B3gtbnitYHfAr4b+G6e13cDx4ETAOKqy774i7/YAJ/8yZ8srrrqqv91Xv3VX/3VP/zG6z/8WrgWYEPaOGlOVqgAtwW3/Rn82YE54Kqrrrrqqqv+H7pOXLcFW1v21kk4uQVbJ6wTvAADDBNMAwwrWE1imuyJ5+MedM9TxFOeIp7ylNvufMq9995771Oe8pSncNVVV131f9APf+F1P7xhNnimd/nC/Xc5PDw85EWwubm5+UOfuv1DPNOROHrnT73nnfmXfTbwWcDnAJ/Nsz0Y+GngpYD3Ab6bf5/3Br4aOMaz/TTwPcBP82y/DbwWIJ7TewHvDbw0cJwrLgHHgN8BXptnM/A7wGtzxUcDX8W/TADiqsu++Iu/2ACf/MmfLK666qr/td77nd/hvd8+eftNsQlwHB3fkXbCDoC/Dv768fD4wQxcddVVV1111VWcFCe3YOukffIknNyCrRPWCZ6PlHKAYYBhIIfJmlZ4xQvw1+iv7xH3PGVsT3nqU5/61Kc85SlPOTg4OOCqq6666n+xL/yC67/wxfGL80yf9sP+tL/7u3v/jhfBS7zEtS/xBe+sL+CZ/h79/ad+2t2fyr/ss4HPAj4H+Gye00sDvw0cA14H+G3+/d4aeG3gtYGX4oq3AX6aK34beC1APNtXAx8FPAP4aeBW4K+B3wYM/A7w2jybgd8BXpsr3hv4LuBrgJ/mBfttAHHVZV/8xV9sgE/+5E8WV1111f9qW1tbWx/+Zm/y4W8EbwQQKE7CyS3YAhjE8KfiT59qnspVV1111VVXXfV8XSeu24Ktk/bJk3DypDnZoY7nY4BhgmmAYQWrSUyTPfF83IPuuQfu+evgr59y251PeepTn/rUe+655x6uuuqqq/6XeNd3vfZd3/nF9M4802+e9W9+9Vff+9W8CD76o6/96Nc9o9flmX74H/zDP/iD9/4g/7LPBj4L+Bzgs3leHw18FXAr8DLALv96Lw0cA36H5/TWwE8BvwO8Nlf8NvBagHg2A88AXhrY5dmOAxeB3wFem2cz8DvAa3PFawO/BXwN8NE8r9fiit8BEFdd9sVf/MUG+ORP/mRx1VVX/Z/w0i/90i/93o96xHu/FLwUQK/oT8LJuT0HuCAu/Jn4s3vMPVx11VVXXXXVVf+iLbG1BVvX2dedhJNbsHXCOsHzkVIOMAwwDOQwWdMKr3g+DuDgKegpfx389VNuu/MpT33qU596zz333MNVV1111f9Ar/zKN77yp75F+1Qe4NN+2J/2d39379/xQrzES1z7El/wzvoCHuALf6584R//8Z1/zL/ss4HPAj4H+Gyev98GXgv4GuCj+df7buC9gNcBfptnezDwdOB3gNfmit8GXgsQVzwYeDrwO8Br85w+Gvgq4K+Bl+HZDPwO8NpccRz4a+AY8DLArTzbawO/BfwO8NoA4qrLvviLv9gAn/zJnyyuuuqq/1Pe+I3f+I3f+9j2e18L1wJsKbaO28crVICnBE/5G/ibA3PAVVddddVVV131r3ZSnDwJJ7fsrevgupPmZIc6no8BhgmmAYYVrAYYEifP5QAOnoKe8tfBXz/ltjuf8tSnPvWp99xzzz1cddVVV/0P8LVfcN3XPhgezDMdisMv/CF/4d/93b1/x/PxEi9x7Ut86rvoUzfNJs90K9z6kZ92z0fyovls4LOAzwE+m+fvpYG/4orXAX6bf52XBv4K2AU+GrgVOA58NvDSwNsAP80VPw28FfDVwN8A3w38NfBSwGcDv80Vbw18NHAJOAaIZzOwC3w18DvAbwNvDfwUsAt8NvDXwIOBrwaOAy8D/DWAuOqyL/7iLzbAJ3/yJ4urrrrq/5ytra2tt3/zN3n7t0/eflNsBood2NmRdsKOQQyPE497PDx+MANXXXXVVVddddW/Sy/6k3DyOvu6Ldg6CSdPWCd4PiaYBhgGGFawGmBInDyXAzh4CnrKXwd//ZTb7nzKU5/61Kfec88993DVVVdd9V/soQ+95aFf/X7DV/NcfuM+/8Zv/Aa/8fSnHzwd4CEP2XrI670er/d61+j1eC4f/R39Rz/tabc9jRfNZwOfBXwO8Nm8YJ8NfBZwK/AQ/vXeGvhq4EE82zOAjwZ+mmd7aeC3gWPALnACeGngu4GX4tl+B/hs4L2B9wIeAtzKFZ8NfBZXfA3w0Vzx2sB3Aw/i2Z4BfDTw01yBuOqyL/7iLzbAJ3/yJ4urrrrq/6zrrrvuuvd+rdd47zeCNwKoUj1ujm/BFsCBOPhr8ddPNU/lqquuuuqqq676D3dSnDwJJ0/aJ0/CyWuta3k+JpgGGAYYVrAaYEicPJcDOHgKespfB3/9lNvufMpTn/rUp95zzz33cNVVV131n+xd3/Xad33nF9M782/w7X8S3/6zP3vXz/I/13HgpYFd4K95wY4DuzynBwMPBm4FbuVfdhzY5XkdB14a+Gtgl+eEuOqyL/7iLzbAJ3/yJ4urrrrq/7yXfumXfukPf9QjPvxh8DCAOZqfhJM99AD3iHv+RvzNPeYerrrqqquuuuqq/1QnxcmTcPKkffIknLzWupbnY4JpgGGAYQWrAYbEyXM5gIOnoKf8dfDXT7ntzqc89alPfeo999xzD1ddddVV/8He9V2vfdd3fjG9M/8K3/4n8e0/+7N3/SxX/Xsgrrrsi7/4iw3wyZ/8yeKqq676f+ON3/iN3/jDd7Y/fFNsAmwptk7CybAD4CnBU/4M/mwwA1ddddVVV1111X+Zk+LkSTh50j55Ek5ea13L8zHBNMAwwLCC1QBD4uS53IPueYp4yl+P7a+f+tSnPvWv//qv/5qrrrrqqv8AD33oLQ/96PcbPvrB8GBeiFvh1q/+jv6rn/a0257Gf42XBo7xorkE/DX/eyCuuuyLv/iLDfDJn/zJ4qqrrvp/ZWtra+vt3/xN3v69zHsBBIod2DkOxwEGMfyK+JUL5gJXXXXVVVddddV/m5Pi5Ek4edI+eRJOXmtdy/MxwTTAMMCwgtUAQ+Lkufw1+uuniKf89e13/vVTn/rUp95zzz33cNVVV131b/TKr3zjKz/0odNDX/zF4sUfKj8U4GnW0/7+H/Lvn/a0+rQ//uM7/5j/Wr8NvBYvmt8BXpv/PRBXXfbFX/zFBvjkT/5kcdVVV/2/dN1111334a/1Gh/+avBqAFWqp9HpuT0H+IPgD55insJVV1111VVXXfU/xklx8iScPGmfPAknr7Wu5fkYYBhgWMmrgRgG58BzuQfd8xTxlKeIp/zN45/0N3/913/911x11VVXXfU/EeKqy774i7/YAJ/8yZ8srrrqqv/XXvqlX/qlP/lRj/jka+FagJOKkzv2DsBTgqf8gfkDrrrqqquuuuqq/7FOipMn4eRJ++RJOHmtdS3Px0parezVIIaVWSVOnstT0FP+Wvz1Uy5eesrf/M3f/M0999xzD1ddddVVV/13Q1x12Rd/8Rcb4JM/+ZPFVVdddRXw3u/8Du/9Xua9ALYUWyfhZNhxQVz4FfErgxm46qqrrrrqqqv+VzgpTl4H1520T15nrttEmzyXCaYVrAYYVtJqcA48lwM4+Gvpr58invI3j3/S3/z1X//1X3PVVVddddV/NcRVl33xF3+xAT75kz9ZXHXVVVc906u/+qu/+iffcP0nb4rNXtFfY19ToQ5i+BXxKxfMBa666qqrrrrqqv91etFfB9edtE9eB9dda13L87GSVit7NYhhZVaJk+fyFPSUvxZ//ZSLl57yN3/zN39zzz333MNVV1111VX/mRD/uY4DL8UVzwBu5QU7DrwUV1wC/poX7DjwUlxxCfhr/p2++Iu/2ACf/MmfLK666qqrHuDhD3/4wz/55V7mkx8GDwsU10jXzO05wB8Ef/AU8xSuuuqqq6666qr/9U6Kk9fBdSftk9eZ6zbRJs9lgmkFqwGGlbQanAPP5R50z1+Lv/7ri5f++qlPfepTn/KUpzyFq6666qqr/iMh/nMcBz4L+Gie01cDH8Pzem/gq4DjPNtvA28D7PKcXhr4KeDBPNtfA28D3Mq/0Rd/8Rcb4JM/+ZPFVVddddVz2dra2vrkN3uTT341eDWAk4qTO/YOwFOCp/yB+QOuuuqqq6666qr/U3rRXwfXnbRPXgfXXWtdy/OxklYrezWIYWVWiZMHOICDv5b++q/H9td/8zd/8zdPecpTnsJVV1111VX/Hoj/HF8NfBTwO8BnA7cC7w18FvDdwPvwbK8N/BbwN8BHA7cC7w18FvDbwOvwbA8G/goQ8N7ArcBrA18F/DXwMvwbffEXf7EBPvmTP1lcddVVV70A7/3O7/De72XeC2BLsXUSToYdF8SFXxG/MpiBq6666qqrrrrq/6yT4uR1cN1J++R15rpNtMlzGWBYSasVXg0wTPbEAxzAwV9Lf/3XY/vrv/mbv/mbpzzlKU/hqquuuuqqfw3Ef7yXBv4KeAbwYJ7TVwMfBbwM8Ndc8VvAawMPAW7l2b4beC/gZYC/5orvBt4LeBvgp3m2zwY+C3gf4Lv5N/jiL/5iA3zyJ3+yuOqqq656IV791V/91T/5hus/eVNs9or+GvuaCnUQw6+IX7lgLnDVVVddddVVV/2/0Iv+OrjupH3yOrjuWutanssE0wpWK3m1QqvJnnguf43++q+Dv/6bxz/pb/76r//6r7nqqquuuuqFQfzHe2/gu4CPAb6a5/TawG8BXwN8NFcY+BvgpXlObw38FPA1wEdzxUXgEvBgntODgacDPwO8Nf8GX/zFX2yAT/7kTxZXXXXVVf+Chz/84Q//5Jd7mU9+GDwsUFwjXTO35wB/EPzBU8xTuOqqq6666qqr/l+6Tlx3nX3ddXDdSXOyQx0PMMG0gtVKXq3QarInnstfo7/+6+Cv/+bxT/qbv/7rv/5rrrrqqv/xXumVXumVHvrQBz30JRwv8RD7IQBPl57+d8q/e9rTnvG0P/mTP/kTrvqPgviP99nAZwGvA/w2z+k4cBH4HeC1gdcGfgv4HOCzeV4Gfgd4beDBwNOB3wFem+dl4FbgIfwbfPEXf7EBPvmTP1lcddVVV70Itra2tj75zd7kk18NXg3gpOLkjr0D8JTgKX9g/oCrrrrqqquuuur/vZPi5HVw3XX2ddeZ6zrU8QAp5cperWC1klaDc+C5/DX666eIp/z17Xf+9d/8zd/8zcHBwQFXXXXV/wgPfehDH/pRr/jyH/UQ8xBeiKeLp3/Nn/751zztaU97Gv8+rwW8Ns/218DP8P8L4j/eewPfBXwM8NU8p9cGfgv4HeC1gdcGfgv4HOCzeV4Gfgd4beC1gd8Cvgd4b57XrcCDAPFv8MVf/MUG+ORP/mRx1VVXXfWv8N7v/A7v/V7mvQC2FFsn4WTYcUFc+BXxK4MZuOqqq6666qqrrnqmk+LkdXDddfZ1J83JTbTJA6SUK3u1gtVKWg3OgefyFPSUvxZ//QdPeNIf/PVf//Vfc9VVV/23eJd3ecd3eZfkXfhX+KHgh37oh370h/jXe2ngp4AH87xuBT4G+Gn+87008NbAZ/Nv917ACeCr+bdB/Md7MPB04FbgZYBdnu23gNcGfgd4beCjga8CPgf4bJ6XuULAawO/BXwO8Nk8r98GXgsQ/wZf/MVfbIBP/uRPFlddddVV/0qv/uqv/uqffMP1n7wpNntFf419TYU6iOFXxK9cMBe46qqrrrrqqquuej62xNZ1cN119nXXmes20SYPkFIOMKzs1QpWK7ziufw1+uvfn9rv/83f/M3fPOUpT3kKV1111X+6d33nd3jXd7bemX+Db1+uvv1nf/Znf5YX3XHg6cBx4HOA3wZ2gePAawOfBewCLwPcyn8uA78DvDb/Nq8N/BbwOcBn82+D+M/x2cBnAbvAV3PFWwMCXgr4HeC1gfcGvgv4HOCzeV4GLgHHgdcGfgv4HOCzeV6/DbwWIJ7LF3/xF5sX0Sd/8ieLq6666qp/g4c//OEP/+SXe5lPfhg8LFBcI10zt+cAfxD8wVPMU7jqqquuuuqqq676F2yJrevguuvs664z122iTZ7LSlod2UcraTU4Bx7gAA7+Wvrr37946ff/5m/+5m/uueeee7jqqqv+Qz30oQ996Fe/wst/Nc/lN9Fv/sbjHvcbT3va054G8NCHPvShr/fYx77e6+LX5bl89J/9+Uc/7WlPexovmo8Gvgr4HOCzeV4fDXwV8D3Ae/Ofy8DvAK/Nv81rA78FfA7w2fzbIP7zfDTw2cAxrvgZ4L2Bi8DvAK8NvDbwW8DnAJ/N8zLwO8BrAy8N/BXwPcB787yeDjwYEM/li7/4i82L6JM/+ZPFVVddddW/0dbW1tYnv9mbfPKrwasBnFSc3LF3AJ4SPOUPzB9w1VVXXXXVVVdd9a/Qi/46uO46+7rr4LoT1gkeIKVc2asj+WiFVpM98QD3oHv+Wvz1799+5+//zd/8zd8cHBwccNVVV/27fM07v+PXPMQ8hGc6Qkdf8LjHfcHf/d3f/R3Px0u8xEu8xKc99rGftoE3eKani6d/1A//6Efxovls4LOAtwF+mud1HPhp4K+Bj+bf7r2AB/Ns3wPcyhXHgZcCfhv4a+CjgWcAt/JsLw28Fc92K/AzwC5XPBh4K+Crge8Gvhv4G2CXZ3tp4K244lbgZ4BdnhPiP99xYJcrjgMXge8B3ht4aeCvgM8BPpvnZeB3gNfmCgO/A7w2z8vA3wAvzb/BF3/xFxvgkz/5k8VVV1111b/Te7/zO7z3e5n3AthSbJ2Ek2HHBXHhV8SvDGbgqquuuuqqq6666t+gF/11cN0t9i3Xmes20SYPMMG0gtWROFqZVeLkAZ6CnvLX4q//+vY7//oP/uAP/oCrrrrqX+WVX/mVX/lTH3TLp/IAn/a4x3/a3/3d3/0dL8RLvMRLvMQXPPYxX8ADfOEzbvvCP/7jP/5j/mXvDXwX8NfA2wC38h/rOPBbwEtzxTOAB3HF+wDfDbw28Fs8p88BPhs4DvwW8NJc8QzgQVyxC7wMcCvw2cBn8ZxeB/ht4DjwXcBbc8UzgAcBu8D7AD/NsyH+4x0H3gp4BvDbPKf3Br4LeB/gu7liF3g68DI8p7cGfgr4HOCzueJW4Bhwguf00sBfAd8DvDf/Bl/8xV9sgE/+5E8WV1111VX/AV791V/91T/5hus/eVNs9or+GvuaCnUQw6+IX7lgLnDVVVddddVVV13177Qltq6D626xb7nOXNehjgcYYFhJqyP7aIVXPJe/Rn/918Ff/8Gf/eUfPOUpT3kKV1111Qv1ru/8Du/6ztY780y/iX7zq3/kR76aF8FHv9M7ffTr4tflmX5Y/uEf/OEf+0FeNL8NvBZX/DXw08BvA7/Dv99HA18FfAzw1VzxYOC3gQcBJ4BdrjDwO8Br82xfDXwU8DnAZ3PFceCrgfcCvgd4b654beC3gM8BPptn+27gvYDPAT6bK14a+G7gQcDLALdyBeI/3nHgIvDbwOvwbMeBvwJOAA8Gdrniu4H3Al4G+Gue7beA1wYeAtzKFR8NfBXwNsBP82xfDXwU8DrAb/Nv8MVf/MUG+ORP/mRx1VVXXfUf5OEPf/jDP/nlXuaTHwYPCxTXSNfM7TnAHwR/8BTzFK666qqrrrrqqqv+A50UJ6+D626xb7nWupbnspJWR/bRSloNzoEHOICDv5b++q/H9td/8Ad/8Af33HPPPVx11VXP4Qvf+Z2/8MWdL84zfdrjHv9pf/d3f/d3vAhe4iVe4iW+4LGP+QKe6e8Vf/+pP/zDn8qL7rOB9wYexHP6buBrgL/m3+argY8CHgLcyrO9NvDSwHcDu1xh4HeA1+bZvhs4Drw1z+k4cBH4HeC1ueK1gd8CPgf4bK54MPB04GeAt+Y5vTTwV8D3AO/NFYj/HF8NfBTw3cB3A8eBzwZeGngf4Lt5tgcDfw0Y+GjgVuCjgbcGvgb4aJ7tOHArYOCzgb8G3ht4b+BvgJfm3+iLv/iLDfDJn/zJ4qqrrrrqP9DW1tbWJ7/Zm3zyq8GrAZxUnNyxdwCeEjzlD8wfcNVVV1111VVXXfWf5Dpx3S32LdfBdSesEzxASrmyVytYHYmjyZ54gHvQPX8t/vr3b7/z9//gD/7gD7jqqqv4oXd6px/axJs807v8/C+8y+Hh4SEvgs3Nzc0fevM3+yGe6RAdvsuP/Mi78K/30sBrA68NvBXP9j7Ad/Ov997AdwG7wHcDvw38DM+fgd8BXpsX7DjwUsBLA18N/A7w2lzx2sBvAZ8DfDZXvDfwXcB3A9/N8/pt4HeA1+YKxH+O48BXA+/Fsz0D+Gjgp3leLw38NPAgnu1rgI/meT0Y+GngpXi27wE+Gtjl3+iLv/iLDfDJn/zJ4qqrrrrqP8F7v/M7vPd7mfcC2FJsnYSTYccFceFXxK8MZuCqq6666qqrrrrqP1Ev+uvgulvsW64z122iTR5ggmkFq5W8OrKOEicP8PvS7//12P76D/7gD/7gnnvuuYerrvp/6Iff6Z1+eANv8Ezv8vO/8C6Hh4eHvAg2Nzc3f+jN3+yHeKYjdPTOP/Ij78y/z3Hgo4HPAnaBhwC7/Ot9NvDRwDGe7aeBrwF+m2cz8DvAa/Oc3gt4b+C1ebZLwDHgd4DX5orXBn4L+Bzgs7nis4HP4l8mrkD853tt4FbgVv5lx4GXBn6bf9lx4KWBvwZ2+Xf64i/+YgN88id/srjqqquu+k/y6q/+6q/+yTdc/8mbYrNX9NfY11Sogxh+S/zWPeYerrrqqquuuuqqq/6LbImt6+C6W+xbrjPXdajjAQYYjuDoSDoanAMPcA+65/fF7//17Xf+9R/8wR/8AVdd9f/EF77zO3/hiztfnGf6tMc9/tP+7u/+7u94EbzES7zES3zBYx/zBTzT3yv+/lN/+Ic/lX/ZTwHPAD6aF+y7gfcCXgf4bf5tjgOvDbw28NbAg7jidYDf5goDvwO8Ns/2XcB7A88Afhr4a+Cvgb8GDPwO8Npc8drAbwGfA3w2V3w28FnAxwB/zQv221yBuOqyL/7iLzbAJ3/yJ4urrrrqqv9ED3/4wx/+yS/3Mp/8MHhYoLhGumZuzwF+JfiVe8w9XHXVVVddddVVV/03OClOXgfX3WLfcq11LQ+QUh7ZRyt5dWQdJU4e4Pel3//9i5d+/2/+5m/+5p577rmHq676P+pd3/kd3vWdrXfmmX4T/eZX/8iPfDUvgo9+p3f66NfFr8sz/bD8wz/4wz/2g/zLdgEDDwF2ef6+Gvgo4HWA3+Zf56WBY8Dv8Jw+Gvgq4HuA9+YKA78DvDZXPBh4OvA3wEvznB4MPB34HeC1ueK1gd8CPgf4bK54a+CngM8BPpvn9VrAJeCvuQJx1WVf/MVfbIBP/uRPFlddddVV/8m2tra2PvnN3uSTXw1eDeA0Or0FW4MYfkX8ygVzgauuuuqqq6666qr/ZreIW66zr7vF3LKJNnmAAYYDOFhJq8E58ABPQU/5a/HXf/CEJ/3BX//1X/81V131f8grv/Irv/KnPuiWT+UBPu1xj/+0v/u7v/s7XoiXeImXeIkveOxjvoAH+MJn3PaFf/zHf/zH/Ms+G/gs4KeB9wF2eU4vDfwWcAl4MP96Pw28FfAQ4Fae7aWBvwK+B3hvrtgFng68DFe8NvBbwO8Ar81z+mzgs4DfAV6bK14b+C3gc4DP5orjwK2AgYcAuzzbawO/BXwP8N5cgbjqsi/+4i82wCd/8ieLq6666qr/Iu/9zu/w3u9l3gvgNDq9BVuDGH5F/MoFc4Grrrrqqquuuuqq/yG2xNYtcMt19nU3WzfzABNMK1gdiaOVWSVOnukADv5a+uvfv3jp9//gD/7gDw4ODg646qr/5b72nd7xax8MD+aZDtHhFz7ucV/4d3/3d3/H8/ESL/ESL/Gpj33sp27iTZ7pVrj1I3/kRz+SF81x4LeBlwJ2gZ8GbuWKlwbemiveB/hu/vXeGvgp4Fbgs4FbgePAZwMvDbwO8Ntc8dvAawFfDfwN8N3ALnAM+Gjgr7nivYH3Bi4BTwdehiseDDwd2AW+Gvge4Fbgo4GvAm4FPhu4FXht4KMBAa8N/DVXIK667Iu/+IsN8Mmf/Mniqquuuuq/0Bu/8Ru/8Scd2/4kgNPo9BZsDWL4FfErF8wFrrrqqquuuuqqq/6H6UV/HVx3i33Ldea6TbTJA6yk1ZF9tJJWg3PgAZ6CnvL7we//wZ/95R885SlPeQpXXfW/0EMf+tCHfvUrvPxX81x+A/3Gb/zDP/zG05/+9KcDPOQhD3nI673Yi73e6+HX47l89J/9+Uc/7WlPexovuuPARwPvDTyI5/Q7wGcDv82/3VsDXw08iGd7BvDRwE/zbG8NfDdwDPgd4LWBlwZ+GngQz/Y7wEcDnw28FXAC2OWK7wbeiys+B/hsrnhr4LuBYzzb7wAfDfw1z4a46rIv/uIvNsAnf/Ini6uuuuqq/2Jv//Zv//YfVvRhANcprpvb8wNx8HPi5wYzcNVVV1111VVXXfU/2Elx8jq47hb7lmuta3mACaYj6WiFV0f2EQ9wAAe/L/3+X1+89Nd/8Ad/8AcHBwcHXHXV/xLv+s7v8K7vbL0z/wbfvlx9+8/+7M/+LP92DwYezBV/DezyH+fBwIOBXeCvecGOA7s8pwcDDwb+GtjlX/Zg4Fae13HgpYG/BnZ5XoirLvviL/5iA3zyJ3+yuOqqq676b/DJ7/QOn/xG8EaB4jq4rof+grjwK+JXBjNw1VVXXXXVVVdd9b9AL/pb4Jbr7OtuMbd0qOMBjuBoBasjcTTZEw/w+9Lv//7FS7//N3/zN39zzz333MNVV/0P967v/A7v+s7WO/Ov8O3L1bf/7M/+7M9y1b8H4qrLvviLv9gAn/zJnyyuuuqqq/6bfPI7vcMnvxG8UaC4Dq7rob8gLvyK+JXBDFx11VVXXXXVVVf9L3NSnLzFvuUWuOWEdYIHmGA6ko6O7KMVXvEAT0FP+eWp/fIf/MEf/ME999xzD1dd9T/UQx/60Id+9Cu8/Ec/GB7MC3Er3PrVf/bnX/20pz3tafzney1edJeAv+Z/F8RVl33xF3+xAT75kz9ZXHXVVVf9N/rkd3qHT34jeKNAcR1c10N/QVz4Ofg5rrrqqquuuuqqq/4X60V/C9xyi33Ldea6DnU8U0p5ZB8diaMj+4gHuAfd8/vi93/lz//yV57ylKc8hauu+h/olV/5lV/5oQ+++aEvTnnxh9oPBXia9LS/p/390269/Wl//Md//Mf81zEvut8BXpv/XRBXXfbFX/zFBvjkT/5kcdVVV1313+zb3+kdvv1h8LBe0V8H14UdTwme8gfmD7jqqquuuuqqq676P+I6cd0t9i3XwXUnrBM8wBEcHclHR9ZR4uSZ7kH3/L74/b++/c6//oM/+IM/4Kqrrvr/CHHVZV/8xV9sgE/+5E8WV1111VX/zba2tra++s3e5KsfBg/rFf11cF3Y8ZTgKX9g/oCrrrrqqquuuuqq/2O2xNYtcMvD7YefsE7wACtpdWQfHYmjyZ54pgM4+H3p93//9jt//w/+4A/+gKuuuur/C8RVl33xF3+xAT75kz9ZXHXVVVf9D7C1tbX11W/2Jl/9MHhYr+ivg+vCjqcET/kD8wdcddVVV1111VVX/R+1Jbaug+tusW+52bqZBxhgOICDlbQanAMP8PvS7//+xUu//wd/8Ad/cHBwcMBVV131fxXiqsu++Iu/2ACf/MmfLK666qqr/ofY2tra+vY3e5NvvxaunaP5dXAdwJ8Ff/Y48ziuuuqqq6666qqr/o/rRX8L3HKLfct15roOdTzTBNORdHQAB4Nz4AH+Gv3170/t9//gD/7gD+655557uOqqq/4vQVx12Rd/8Rcb4JM/+ZPFVVddddX/IA9/+MMf/tUv+zJfvSk2txRbp+3TAH8Q/MFTzFO46qqrrrrqqquu+n/kFnHLLfYtt5hbOtTxTBNMK1gdiaMj+4gHeAp6yi9P7Zf/5m/+5m+e8pSnPIWrrrrqfzvEVZd98Rd/sQE++ZM/WVx11VVX/Q/z8Ic//OFf/bIv89WbYnNLsXXaPg3wB8EfPMU8hauuuuqqq6666qr/h06Kkw+3H36LuWUTbfJMKeWRfbSSV0fWUeLkme5B9/y++P1f+fO//JWnPOUpT+Gqq6763whx1WVf/MVfbIBP/uRPFlddddVV/wM9/OEPf/hXv+zLfPWm2NxSbJ22TwP8VvBbt5nbuOqqq6666qqrrvp/7KQ4eYt9yy1wywnrBA9wBEdH8tGRdZQ4eaZ70D2/HPzyr/zW7/7KPffccw9XXXXV/xaIqy774i/+YgN88id/srjqqquu+h/q1V/91V/98268/vMATqPTW7A1iOFXxK9cMBe46qqrrrrqqquuuootsXUL3PJw++EnrBM8wEpaHZAHR9ZR4uSZnoKe8stT++U/+IM/+IN77rnnHq666qr/yRBXXfbFX/zFBvjkT/5kcdVVV131P9gbv/Ebv/EnHdv+JIDT6PQWbA1i+BXxKxfMBa666qqrrrrqqquuepZe9LfALbfYt9xs3cwDHMHRkXx0ZB0lTp7pKegpvzy1X/6DP/iDP7jnnnvu4aqrrvqfBnHVZV/8xV9sgE/+5E8WV1111VX/w73xG7/xG3/Sse1PAjiNTm/B1iCGXxG/csFc4Kqrrrrqqquuuuqq59GL/ha45Rb7lputm3mAIzg6ko+OrKPEyTM9BT3ll6f2y3/wB3/wB/fcc889XHXVVf8TIK667Iu/+IsN8Mmf/Mniqquuuup/gTd+4zd+4086tv1JANegazZg40Ac/Jz4ucEMXHXVVVddddVVV131AvWivwVuucW+5WbrZh7gCI6O5KMj6yhx8kxPQU/55an98q/8yq/8ysHBwQFXXXXVfxfEVZd98Rd/sQE++ZM/WVx11VVX/S/xye/0Dp/8RvBGgeI6uK6H/oK48CviVwYzcNVVV1111VVXXXXVv6gX/S1wyy32LTdbN/MAR3B0IA6O7CMe4Pel3//9i5d+/w/+4A/+4ODg4ICrrrrqvxLiqsu++Iu/2ACf/MmfLK666qqr/hf55Hd6h09+I3ijQHEdXNdDf0Fc+BXxK4MZuOqqq6666qqrrrrqRbYltm6BWx5uP/yEdYJnSimP7KMjcXRkH/EAvy/9/u9fvPT7f/AHf/AHBwcHB1x11VX/2RBXXfbFX/zFBvjkT/5kcdVVV131v8wnv9M7fPIbwRsFiuvguh76C+LCr4hfGczAVVddddVVV1111VX/alti6xa45eH2w09YJ3imlPLIPjoSR0f2EQ/w+9Lv//7FS7//B3/wB39wcHBwwFVXXfWfAXHVZV/8xV9sgE/+5E8WV1111VX/C331O73DV78UvFSV6g3ohrDjKcFT/sD8AVddddVVV1111VVX/btsia1b4JaH2w8/YZ3gmVLKI/voSBwd2Uc8wC9Lv/z7t9/5+3/wB3/wB1x11VX/kRBXXfbFX/zFBvjkT/5kcdVVV131v9DW1tbWV7/Zm3z1w+BhvaK/Dq4LO54SPOUPzB9w1VVXXXXVVVddddV/iC2xdQvc8nD74SesEzxTSnlkHx2JoyP7iGc6gINfln75V/78L3/lKU95ylO46qqr/r0QV132xV/8xQb45E/+ZHHVVVdd9b/U1tbW1le/2Zt89cPgYb2ivw6uCzueEjzlD8wfcNVVV1111VVXXXXVf6gtsfVw++EPNw/fRJs80wTTkXR0AAeDc+CZ7kH3/PjUfvwP/uAP/uCee+65h6uuuurfAnHVZV/8xV9sgE/+5E8WV1111VX/i21tbW199Zu9yVc/DB7WK/ob7BsA/kb+m79Gf81VV1111VVXXXXVVf8pToqTD7cffou5ZRNt8kwDDAdwcAAHiZNn+mv017+8e+mX/+AP/uAPDg4ODrjqqqteVIirLvviL/5iA3zyJ3+yuOqqq676X+7hD3/4w7/6ZV/mqzfF5pZi67R9GuAPgj94inkKV1111VVXXXXVVVf9pzopTj7Wfuwt5pYOdTzTERwdyUcH5oAH+GXpl3//9jt//w/+4A/+gKuuuupfgrjqsi/+4i82wCd/8ieLq6666qr/Ax7+8Ic//Ktf9mW+elNsbim2TtunAf4g+IOnmKdw1VVXXXXVVVddddV/iVvELQ+3H36zdTPPlFIe2UcHcLDCK57pAA5+WfrlX/nzv/yVpzzlKU/hqquuen4QV132xV/8xQb45E/+ZHHVVVdd9X/Ewx/+8Id/9cu+zFdvis0txdZp+zTAbwW/dZu5jauuuuqqq6666qqr/sv0or8Fbnms/dgT1gmeaYLpSDraw3uTPfFM96B7fnxqP/4Hf/AHf3DPPffcw1VXXXU/xFWXffEXf7EBPvmTP1lcddVVV/0f8tIv/dIv/VWPesRXAZxUnNyxdwYx/IT4icEMXHXVVVddddVVV131X25LbD3cfvjDzcM30SbPNMBwAAcHcJA4eabfl37/9y9e+v0/+IM/+IODg4MDrrrq/zfEVZd98Rd/sQE++ZM/WVx11VVX/R/zxm/8xm/8Sce2PwngOsV1c3t+j7jnV+BXuOqqq6666qqrrrrqv9V14rqH2w+/xdzSoY5nOoKjI/nowBzwTAdw8PvS7//+7Xf+/h/8wR/8AVdd9f8T4qrLvviLv9gAn/zJnyyuuuqqq/4Peu93fof3fi/zXoHiJummsOPPgj97nHkcV1111VVXXXXVVVf9j/Bw8fBb7Ftutm7mmVLKI/voAA5WeMUz3YPu+X3x+z/x27/7E/fcc889XHXV/x+Iqy774i/+YgN88id/srjqqquu+j/qq9/pHb76peClNqSNa8w1AD8X/NwFc4Grrrrqqquuuuqqq/7H6EX/cHj4w+2Hn7BO8EwTTEfS0R7em+yJZ3oKesqP71768T/4gz/4g4ODgwOuuur/NsRVl33xF3+xAT75kz9ZXHXVVVf9H3Xddddd9+2v+Rrfvik2TypO7tg7F8SFXxG/MpiBq6666qqrrrrqqqv+x9kSW4+1H3uLuWUTbfJMAwx78t6RdZQ4AQ7g4Pel3/+JP//Ln3jKU57yFK666v8mxFWXffEXf7EBPvmTP1lcddVVV/0f9uqv/uqv/nk3Xv95ADegG3ronxI85Q/MH3DVVVddddVVV1111f9o14nrHm4//BZzS4c6nukADg7gYIVXPNM96J4fn9qP/8qv/MqvHBwcHHDVVf93IK667Iu/+IsN8Mmf/Mniqquuuur/uA9/p3f48LeDt+sV/XVwXdjxW8Fv3WZu46qrrrrqqquuuuqq/xUeLh7+cPvh11rX8kwTTHuwdySOJnvimX5Z+uXfv/3O3/+DP/iDP+Cqq/73Q1x12Rd/8Rcb4JM/+ZPFVVddddX/cVtbW1tf/WZv8tUPg4ftoJ2TcHIQw8+JnzswB1x11VVXXXXVVVdd9b/Glth6uP3wh5uHb6JNnukIjg7EwZF9xDPdg+755eCXf+W3fvdX7rnnnnu46qr/nRBXXfbFX/zFBvjkT/5kcdVVV131/8DDH/7wh3/by73MtwFcg67ZgI17xD2/Ar/CVVddddVVV1111VX/K90ibnm4/fCbrZt5pgmmI+loD+9N9sQz/b70+79/8dLv/8qv/MqvcNVV/7sgrrrsi7/4iw3wyZ/8yeKqq6666v+Jt3/7t3/7Dyv6sEBxk3RT2PE38t/8Nfprrrrqqquuuuqqq676X6sX/cPh4Y9NP3YTbfJMK2l1QB4cmAOe6QAOfln65Z/47d/9iXvuuecerrrqfz7EVZd98Rd/sQE++ZM/WVx11VVX/T/y1e/0Dl/9UvBSczS/Dq4D+Lng5y6YC1x11VVXXXXVVVdd9b/edeK6h9sPv8Xc0qEOIKU8gIMDOBicA8/0FPSUH9+99ON/8Ad/8AcHBwcHXHXV/0yIqy774i/+YgN88id/srjqqquu+n9ka2tr64ff9E1+eFNsnlSc3LF3DsTBz4mfG8zAVVddddVVV1111VX/J/SivwVueaz92BPWCZ5pgGFP3juyjhInwAEc/L70+z/x53/5E095ylOewlVX/c+CuOqyL/7iLzbAJ3/yJ4urrrrqqv9nXv3VX/3VP+/G6z8P4AZ0Qw/9U4Kn/IH5A6666qqrrrrqqquu+j/npDj5cPvhDzcP71AHkFIe2UcHcLDCK57pHnTPj0/tx3/lV37lVw4ODg74T/ZGb3T9G73xa/HGH/Opd38MV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrq/6EPf6d3+PC3g7erUr0B3RB2/FbwW7eZ27jqqquuuuqqq6666v+sh4uHP9x++LXWtTzTBNMe7B3AQeLkmX5Z+uWf+PO//ImnPOUpT+E/0HXXXXfd271dvN0b35hvvAVbAK/zqfe8Dldd9fwhrrrsi7/4iw3wyZ/8yeKqq6666v+pb3+nd/j2h8HDthRbp+3Tgxh+TvzcgTngqquuuuqqq6666qr/07bE1sPthz/cPHwTbfJMR3B0IA6O7COe6SnoKT++e+nH/+AP/uAPDg4ODvg3eqM3uv6N3vi1eOOXxi/Nc3mdT73ndbjqqucP8Z/rwcCDuOIS8Ne8YMeBl+KKS8Bf84IdB16KKy4Bf82/0xd/8Rcb4JM/+ZPFVVddddX/Uw9/+MMf/tUv+zJfvSk2r0HXbMDGBXHh5+DnuOqqq6666qqrrrrq/41bxC0Ptx9+s3UzzzTBdAAHe7CXOAEO4OCXpV/+id/+3Z+455577uFFcN111133dm8Xb/fGN+Ybb8EWQEixYTZ2YOcufBfA63zqPa/DVVc9f4j/PJ8FfDbP6aeB9wF2eU7vDXwVcJxn+23gbYBdntNLAz8FPJhn+2vgbYBb+Tf64i/+YgN88id/srjqqquu+n/s7d/+7d/+w4o+LFDcADdUqH8j/81fo7/mqquuuuqqq6666qr/V3rRPxwe/tj0YzfRJs90AAcHcLDCK57pr9Ff//LupV/+lV/5lV/h+XijN7r+jd74tXjjl8YvzTP1Ur9j72zARkAA3Aq3ArzOp97zOlx11fOH+M/x0cBXAT8DfDawC7w38FnATwNvw7O9NvBbwN8AHw3cCrw38FnAbwOvw7M9GPgrQMB7A7cCrw18FfDXwMvwb/TFX/zFBvjkT/5kcdVVV131/9znv9M7fP6rwavN0fw6uA7gV4Jfucfcw1VXXXXVVVddddVV/y9dJ657rP3Ym62beaYBhj1578g6SpwA96B7fjn45V/5rd/9FYC3e7t4uze+Md94C7YAQooNs7EDOz3ueS63wq0Ar/Op97wOV131/CH+c/wV8NLACWCXZ/tt4LWAhwC3csVvAa8NPAS4lWf7buC9gJcB/porvht4L+BtgJ/m2T4b+CzgfYDv5t/gi7/4iw3wyZ/8yeKqq6666v+5ra2trR9+0zf54U2xeRwdPw7HD8TBz4mfG8zAVVddddVVV1111VX/b22JrYfbD3+seWyHOoCU8sg+2hW7kz2d7KaTJ2M8ORy/MFzYPn/hYPPSQS/1O/bOBmwEBMBojU8JnvI4eNzb2W8HcCvcCvA6n3rP63DVVc8f4j+HgVuBh/CcPhv4LOB1gN/mCgN/A7w0z+mtgZ8Cvgb4aK64CFwCHsxzejDwdOBngLfm3+CLv/iLDfDJn/zJ4qqrrrrqKl76pV/6pb/qUY/4KoAb0A099LcFt/2W+S2uuuqqq6666qqrrroKeLh4+MPth19rXVtEKWqbUaae0ixlQxYQ6gY4fm65PHZuaTXfS9z7FPyUpwx+Cs/0Xj3vBXAr3ArwOp96z+tw1VXPH+I/x28DrwW8DPDXXHEc+CvgBPBgYBd4beC3gM8BPpvnZeB3gNcGHgw8Hfgd4LV5XgZuBR7Cv8EXf/EXG+CTP/mTxVVXXXXVVZd9+Du9w4e/HbxdleoN6Iaw4w+CP3iKeQpXXXXVVVddddVVV10FPHxj/+GP7sdH3zh0D/XQzwGBhCyioWiWbCxbPnjyfPmEvz55719fqLsXeID36nkvgFvhVoDX+dR7Xoerrnr+EP85Xhr4aeAY8N3ALvDWwEOAjwa+myteG/gt4HOAz+Z5Gfgd4LWB1wZ+C/ge4L15XrcCDwLEv8EXf/EXG+CTP/mTxVVXXXXVVc/y7e/0Dt/+MHjYlmLrtH16EMPPiZ87MAdcddVVV1111VVXXfX/0lY3bT12vv/Yh/Wrh/VkDyBJfaivQ7/ZlhsLWgjAUpNZTfL+pJx4pgv9cOFxx88/7qlbdzwV4L163gvgVrgV4HU+9Z7X4aqrnj/Ef47jwEcBn81z+m3gfYBbueKjga8CPgf4bJ6XuULAawO/BXwO8Nk8r98GXgsQ/wZf/MVfbIBP/uRPFlddddVVVz3Lddddd923v+ZrfPum2DyNTm/B1gVx4efg57jqqquuuuqqq6666v+Vh2/sP/xhs+XDrovxOp6pSGWmnBXoEALA5NG4OPRy4Tr1OzyTIIlcD7TBwgCDcnjK1t5TXuLaZ7xEq0O7FW4FeJ1Pved1uOqq5w/xn+OvgJcGPgf4bmAXeGvgqwEDLwPcCrw38F3A5wCfzfMycAk4Drw28FvA5wCfzfP6beC1APFcvviLv9i8iD75kz9ZXHXVVVdd9Rze+I3f+I0/6dj2JwWKG+CGCvXx8uP/FP0pV1111VVXXXXVVVf9n7bVTVuPne8/9mH96mE92QNIUid3MzxDFJ5plTq8kOXChSwXeKaiKGdWszPbY38SR8czSTlMkeuGG8CijtfVjb31bdu7T7u0feHS63zqPa/DVVc9f4j/eO8NfBfwPcB785zeGvgp4HuA9wZeG/gt4HOAz+Z5Gfgd4LWBlwb+Cvge4L15Xk8HHgyI5/LFX/zF5kX0yZ/8yeKqq6666qrn8fnv9A6f/2rwar2iv8G+AeBXgl+5x9zDVVddddVVV1111VX/51w3X173mPnhY24p61t4piKVmXJWoEMIAJMXs1w428rZAQ28EMem/tjJoT9ZW7fDMwk3K9elW59EQMZK3Thc+22/di1XXfX8If7jfTbwWcDbAD/N8zLwO8BrAy8N/BXwOcBn87wM/A7w2lxh4HeA1+Z5Gfgb4KX5N/jiL/5iA3zyJ3+yuOqqq6666nlsbW1tffubvcm3XwvXHkfHj8PxQQw/IX5iMANXXXXVVVddddVVV/2f8PCN/Ye/1PzwpbbUtnimPuhneIYoPNMqdXghy4ULWS7wr9S79GdW8zMbY3cSKQBCbYuSKbUjgOu++xdPcNVVzx/iP95HA18FvA3w0zwvA78DvDZX7AJPB16G5/TWwE8BnwN8NlfcChwDTvCcXhr4K+B7gPfm3+CLv/iLDfDJn/zJ4qqrrrrqqufrpV/6pV/6qx71iK8CuE5x3dye3xbc9lvmt7jqqquuuuqqq6666n+tvmT/2MXeYx8zWz6mJ3uACEWP+w7PEALA5MUsF862cnZAA/8BTg7zk6fH7oys0wCq4wHAdd/9iye46qrnD/Ef76WBvwJ+G3gdntNHA18FfA7w2Vzx3cB7AS8D/DXP9lvAawMPAW7lio8Gvgp4G+CnebavBj4KeB3gt/k3+OIv/mIDfPInf7K46qqrrrrqBXrvd36H934v815VqjegG8KOPwv+7HHmcVx11VVXXXXVVVdd9b/KyX598rGL/cc+rK4fxjMVqcyUsyJ6nmmVOryQ5cKFLBf4T/Kog51XA1AdDwCu++5fPMFVVz1/iP8cXw18FPDTwHcDu8BrA58NPAN4aWCXKx4M/DVg4KOBW4GPBt4a+Brgo3m248CtgIHPBv4aeG/gvYG/AV6af6Mv/uIvNsAnf/Ini6uuuuqqq16ob3+nd/j2h8HDNqSNa8w1gxh+RfzKBXOBq6666qqrrrrqqqv+x7tlcXDLY+ZHj7kuxut4pk50s2AmXHmmvSwXL7Ry4cA64D/Zow52Xg1AdTwAuO67f/EEV131/CH+83w28NHAMZ7tZ4D3BnZ5Ti8N/DTwIJ7ta4CP5nk9GPhp4KV4tu8BPhrY5d/oi7/4iw3wyZ/8yeKqq6666qoX6rrrrrvu21/zNb59U2yeRqe3YOuCuPBz8HNcddVVV1111VVXXfU/Ul+yv2V2eMtLzQ9fakttC0CSernv5RkQAJi8mOXC2VbODmjgv8ijDnZeDUB1PAC47rt/8QRXXfX8If7zPRg4Dvw1/7LjwEsDv82/7Djw0sBfA7v8O33xF3+xAT75kz9ZXHXVVVdd9S964zd+4zf+pGPbnxQoroPreugfLz/+T9GfctVVV1111VVXXXXV/xhb3bT12Pn+Yx/Wrx7Wkz1AhGJGzir0CAGkNd7Tyj2XXC410/gv9qiDnVcDUB0PAK777l88wVVXPX+Iqy774i/+YgN88id/srjqqquuuupF8snv9A6f/EbwRr2iv8G+AeC3gt+6zdzGVVddddVVV1111VX/ra6bL697+Ozw4Q+r64fxTDVUe7IvoueZVqnDs1nPXsq4xH+jRx3svBqA6ngAcN13/+IJrrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVS+Sra2trW9/szf59mvh2h20cxJODmL4CfETgxm46qqrrrrqqquuuuq/3MM39h/+sNnyYdfFeB3P1Af9DM8QhWfay3LxbKtnl2bJ/wCPOth5NQDV8QDguu/+xRNcddXzh7jqsi/+4i82wCd/8ieLq6666qqrXmQPf/jDH/5tL/cy3wZwneK6uT2/R9zzK/ArXHXVVVddddVVV131X6Iv2T92sffYh/Wrh22pbQFI0kyedXIPBAAmz7Vy9qzr2WYa/4M86mDn1QBUxwOA6777F09w1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquu+ld573d+h/d+L/NegeIm6aaw48+CP3uceRxXXXXVVVddddVVV/2n2eqmrZfe2H3pm+t4c0/2ABGKOTkv0CEEkNZ4Tyv3XMhygf+hHnWw82oAquMBwHXf/YsnuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV/2rffU7vcNXvxS81Ia0cY25BuDngp+7YC5w1VVXXXXVVVddddV/qOvmy+seMz98zC1lfQvPVEN1Ls+FK890mLF3ttWzB9YB/8M96mDn1QBUxwOA6777F09w1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquu+le77rrrrvv213yNb98UmycVJ3fsnQviws/Bz3HVVVddddVVV1111X+I6+bL615qsf9S18V4Hc/UB/1MngMBgMk9l0v3TOWeAQ38L/Gog51XA1AdDwCu++5fPMFVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrq3+TVX/3VX/3zbrz+8wLFDXBDhfpnwZ89zjyOq6666qqrrrrqqqv+za6bL697qcX+S10X43UAkjSTZx2eIQSQ1nihxYWzrmebafwv86iDnVcDUB0PAK777l88wVVXPX+Iqy774i/+YgN88id/srjqqquuuurf7PPf6R0+/9Xg1eZofh1cN4jhJ8RPDGbgqquuuuqqq6666qp/lYdv7D/8YbPlw66L8ToASZrJsw7PEAJYpQ4vZLlwIcsF/hd71MHOqwGojgcA1333L57gqqueP8RVl33xF3+xAT75kz9ZXHXVVVdd9W+2tbW19cNv+iY/vCk2r1NcN7fnT5Wf+vvo97nqqquuuuqqq6666kXy8I39h7/U/PClttS2ACRpJs86PEMIYJU6vKd19xxYB/wf8KiDnVcDUB0PAK777l88wVVXPX+Iqy774i/+YgN88id/srjqqquuuurf5e3f/u3f/sOKPqxK9SZzE8CvBL9yj7mHq6666qqrrrrqqqteoIdv7D/8peaHL7WltgUQoZiT8yJ6nmmVOryndfccWAf8H/Kog51XA1AdDwCu++5fPMFVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrq3+2H3+kdfvhauPY4On4cjt8j7vkV+BWuuuqqq6666qqrrnoeD9/Yf/hLzQ9fakttCyBCMSfnRfQ8016Wi2dbPbs0S/4PetTBzqsBqI4HANd99y+e4Kqrnj/EVZd98Rd/sQE++ZM/WVx11VVXXfXv9tIv/dIv/VWPesRXBYqbpJvCjj8I/uAp5ilcddVVV1111VVXXUVfsn/sYu+xD+tXD9tS2wKIUMzJeRE9z7SX5eI9U7lnQAP/hz3qYOfVAFTHA4DrvvsXT3DVVc8f4qrLvviLv9gAn/zJnyyuuuqqq676D/H57/QOn/9q8Gpbiq3T9ulBDD8hfmIwA1ddddVVV1111VX/T/Ul+8cu9h77mNnyMT3ZA9RQ7cm+iJ5n2sty8Z6p3DOggf8HHnWw82oAquMBwHXf/YsnuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV/2HuO666677odd6jR8CuE5x3dye/438N3+N/pqrrrrqqquuuuqq/2f6kv1jF3uPfcxs+Zie7AFqqM7luXDlmfayXLxnKvcMaOD/kUcd7LwagOp4AHDdd//iCa666vlDXHXZF3/xFxvgkz/5k8VVV1111VX/Yd77nd/hvd/LvFev6G+wbwD4ieAnDswBV1111VVXXXXVVf8P9CX7xy72HvuY2fIxPdkD1FCdy3PhCoDJc62cPet6tpnG/0OPOth5NQDV8QDguu/+xRNcddXzh7jqsi/+4i82wCd/8ieLq6666qqr/sNsbW1tffubvcm3XwvXnkant2DrHnHPr8CvcNVVV1111VVXXfV/2FY3bb30xu5LP6yuH8Yz1VCdy3PhCoDJc62cPet6tpnG/2OPOth5NQDV8QDguu/+xRNcddXzh7jqsi/+4i82wCd/8ieLq6666qqr/kO98Ru/8Rt/0rHtTwoUN0k3hR2/EvzKPeYerrrqqquuuuqqq/6P2eqmrZfe2H3ph9X1w3imPuhneIYoAJg818rZs65nm2lcxaMOdl4NQHU8ALjuu3/xBFdd9fwhrrrsi7/4iw3wyZ/8yeKqq6666qr/cF/9Tu/w1S8FL7WDdk7CyQNx8BPwE1x11VVXXXXVVVf9H7HVTVsvvbH70g+r64fxTH3Qz+Q5EABpjRdaXDjreraZxlXP8qiDnVcDUB0PAK777l88wVVXPX+Iqy774i/+YgN88id/srjqqquuuuo/3MMf/vCHf9vLvcy3AdyEbqpQ/0b+m79Gf81VV1111VVXXXXV/2J9yf6xi73HvtTs8KV4pj7oZ/IcCIC0xntauedClgtc9Xw96mDn1QBUxwOA6777F09w1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquu+k/x4e/0Dh/+dvB2czS/Dq4bxPAT4icGM3DVVVddddVVV131v0xfsn/sYu+xj5ktH9OTPUAf9DN5DgRAWuM9rdxzIcsFrnqhHnWw82oAquMBwHXf/YsnuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV/2n2Nra2vrhN32TH94Um9egazZg46nyU38f/T5XXXXVVVddddVV/4s8dvPSY19qfvRSPdkD1FBdkAtEAUhrvKeVey5kucBVL5JHHey8GoDqeABw3Xf/4gmuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVV/2ne/u3f/u0/rOjDqlRvMjcB/ErwK/eYe7jqqquuuuqqq676H+7hG/sPf6n54UttqW0B1FCdy3PhCpDWeE8r91zIcoGr/lUedbDzagCq4wHAdd/9iye46qrnD3HVZV/8xV9sgE/+5E8WV1111VVX/af69nd6h29/GDzsODp+HI7fI+75FfgVrrrqqquuuuqqq/6HumVxcMsrLA5eYUttCyBCsSFvCFcATJ5r5ew9We/hqn+TRx3svBqA6ngAcN13/+IJrrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVf+pXvqlX/qlv+pRj/iqQHED3FCh/kHwB08xT+Gqq6666qqrrrrqf5Dr5svrXmqx/1LXxXgdQIRiTs6L6AEwea6Vs2ddzzbTuOrf7FEHO68GoDoeAFz33b94gquuev4QV132xV/8xQb45E/+ZHHVVVddddV/us9/p3f4/FeDV9tSbJ22Tw9i+AnxE4MZuOqqq6666qqrrvpvdrJfn3yFzUuvcF2M1wFI0kK5KKLnmS62cu6erPc007jq3+1RBzuvBqA6HgBc992/eIKrrnr+EFdd9sVf/MUG+ORP/mRx1VVXXXXVf7rrrrvuum9/zdf49k2xeZ3iurk9/xv5b/4a/TVXXXXVVVddddVV/022umnrpTd2X/phdf0wAEmaybMOzxAC2Mty8Z6p3DOggav+wzzqYOfVAFTHA4DrvvsXT3DVVc8f4qrLvviLv9gAn/zJnyyuuuqqq676L/He7/wO7/1e5r16RX+DfQPATwQ/cWAOuOqqq6666qqrrvovtNVNWy+9sfvSD6vrhwFI0kyedXiGEMBhxt6dU71zQANX/Yd71MHOqwGojgcA1333L57gqqueP8RVl33xF3+xAT75kz9ZXHXVVVdd9V9ia2tr69vf7E2+/Vq49jQ6vQVb94h7fgV+hauuuuqqq6666qr/An3J/rGLvcc+ZrZ8TE/2AH3Qz+Q5EACr1OE9rbvnwDrgqv80jzrYeTUA1fEA4Lrv/sUTXHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq/7LvPqrv/qrf96N139eoLhJuins+JXgV+4x93DVVVddddVVV131n6Qv2T92sffYx8yWj+nJHqAP+pk8BwJglTq8p3X3HFgHXPWf7lEHO68GoDoeAFz33b94gquuev4QV132xV/8xQb45E/+ZHHVVVddddV/qa9+p3f46peCl9pBOyfh5IE4+An4Ca666qqrrrrqqqv+Ezx8Y//hr7A4eIWe7AFqqM7luXAFSGu8p5V7LmS5wFX/ZR51sPNqAKrjAcB13/2LJ7jqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVf9l3r4wx/+8G97uZf5NoCb0E0V6t/If/PX6K+56qqrrrrqqquu+g/y8I39h7/U/PClttS2AGqozuW5cAVIa7ynlXsuZLnAVf/lHnWw82oAquMBwHXf/YsnuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV/2X+/B3eocPfzt4uzmaXwfXDWL4CfETgxm46qqrrrrqqquu+ne4br687hU29l7hpKaTABGKBV6E3AFg8lwrZ8+6nm2mcdV/i0cd7LwagOp4AHDdd//iCa666vlDXHXZF3/xFxvgkz/5k8VVV1111VX/5ba2trZ++E3f5Ic3xeY16JoN2Hiq/NTfR7/PVVddddVVV1111b/BVjdtvfTG7ks/rK4fBhChmJPzInoATJ5r5exZ17PNNK76b/Wog51XA1AdDwCu++5fPMFVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqv8Xbv/3bv/2HFX1YlepN5iaAXwl+5R5zD1ddddVVV1111VUvor5k/9jF3mMfM1s+pid7SZrJsw7PEALYy3LxzlbvbKZx1f8IjzrYeTUA1fEA4Lrv/sUTXHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq/7bfPs7vcO3PwwedhwdPw7HL4gLPwc/x1VXXXXVVVddddWL4JbFwS2vsDh4hS21LYBOdPPwAgiAvSwX75nKPQMauOp/lEcd7LwagOp4AHDdd//iCa666vlDXHXZF3/xFxvgkz/5k8VVV1111VX/bV76pV/6pb/qUY/4qkBxA9xQof5B8AdPMU/hqquuuuqqq6666gU42a9PvsLmpVe4LsbrAIpUFuGFcAUYU6s7W3fngXXAVf8jPepg59UAVMcDgOu++xdPcNVVzx/iqsu++Iu/2ACf/MmfLK666qqrrvpv9fnv9A6f/2rwahvSxjXmmkEMPyF+YjADV1111VVXXXXVVQ/Ql+xfeuPSSz+mP3oMgCQtlIsiegBM3tXqnReyXOCq/9EedbDzagCq4wHAdd/9iye46qrnD3HVZV/8xV9sgE/+5E8WV1111VVX/be67rrrrvv213yNb98Um9cprpvb88fLj/9T9KdcddVVV1111VVXPdNjNy899qXmRy/Vkz3ALJj1eI4QwMVWzt2T9Z5mGlf9j/eog51XA1AdDwCu++5fPMFVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqv917v/M7vPd7mffqFf0N9g0APxH8xIE54Kqrrrrqqquu+n/tuvnyulfbuPRqW2pbADVUF8oNIABWqcPbpu62AQ1c9b/Gow52Xg1AdTwAuO67f/EEV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqv92W1tbW9/+Zm/y7dfCtScVJ3fsnXvEPb8Cv8JVV1111VVXXfX/0lY3bb3C5u4r3FLWtwBEKBZ4EXIHkNZ429TddmAdcNX/Oo862Hk1ANXxAOC67/7FE1x11fOHuOqyL/7iLzbAJ3/yJ4urrrrqqqv+R3j1V3/1V/+8G6//vEBxk3RT2PFbwW/dZm7jqquuuuqqq676f6Mv2T92sffYl5odvhSAJM3kWSfPATB5rpWz92S9h6v+13rUwc6rAaiOBwDXffcvnuCqq54/xFWXffEXf7EBPvmTP1lcddVVV131P8ZXv9M7fPVLwUvtoJ2TcPJAHPwE/ARXXXXVVVddddX/Cw/f2H/4KywOXqEne4A+6Gd4gRDAXpaLd7Z6ZzONq/5Xe9TBzqsBqI4HANd99y+e4Kqrnj/EVZd98Rd/sQE++ZM/WVx11VVXXfU/xsMf/vCHf9vLvcy3AdyAbuih/xv5b/4a/TVXXXXVVVddddX/WdfNl9e91GL/pa6L8TqAGqpzeS5cAVapwztbf+fSLLnq/4RHHey8GoDqeABw3Xf/4gmuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVV/6N8+Du9w4e/HbzdHM2vg+sGMfyc+LkDc8BVV1111VVXXfV/Sl+yf8WtC6/4sLp+GECEYk7Oi+gB0hrvaeWeC1kucNX/KY862Hk1ANXxAOC67/7FE1x11fOH+I/3YOBBvHCXgL/mOR0HXoorLgF/zQt2HHgprrgE/DX/Tl/8xV9sgE/+5E8WV1111VVX/Y+ytbW19cNv+iY/vCk2r0HXbMDGU+Wn/j76fa666qqrrrrqqv8zXnpr96UfM1s+pid7gHkw7/AMIYBzU7n3rOvZZhpX/Z/zqIOdVwNQHQ8ArvvuXzzBVVc9f4j/eJ8NfBYv3O8Ar82zvTfwVcBxnu23gbcBdnlOLw38FPBgnu2vgbcBbuXf6Iu/+IsN8Mmf/Mniqquuuuqq/3He+I3f+I0/6dj2J1Wp3oBuCDt+JfiVe8w9XHXVVVddddVV/6vdsji45RUWB6+wpbYF0IluHl4AAXCYsXfnVO8c0MBV/2c96mDn1QBUxwOA6777F09w1VXPH+I/3msDr83z99bASwFfA3w0V7w28FvA3wAfDdwKvDfwWcBvA6/Dsz0Y+CtAwHsDtwKvDXwV8NfAy/Bv9MVf/MUG+ORP/mRx1VVXXXXV/0jf/k7v8O0Pg4cdR8ePw/EL4sLPwc9x1VVXXXXVVVf9r7TVTVuvtnXh1a6L8TqACMWGvCFcAcbU6s7W3XlgHXDV/3mPOth5NQDV8QDguu/+xRNcddXzh/iv89LAbwHPAF4b2OWK3wJeG3gIcCvP9t3AewEvA/w1V3w38F7A2wA/zbN9NvBZwPsA382/wRd/8Rcb4JM/+ZPFVVddddVV/yO99Eu/9Et/1aMe8VWB4ga4oUL9g+APnmKewlVXXXXVVVdd9b/KS2/tvvRjZsvH9GQvSXPlvIoZACbvzXrP2VbOctX/G4862Hk1ANXxAOC67/7FE1x11fOH+K/zV8BLAw8BbuXZDPwN8NI8p7cGfgr4GuCjueIicAl4MM/pwcDTgZ8B3pp/gy/+4i82wCd/8ieLq6666qqr/sf65Hd6h09+I3ijDWnjGnPNIIafED8xmIGrrrrqqquuuup/vJP9+uSrbe2+2klNJwH6oJ/hBUIAF1s5d0/We5ppXPX/yqMOdl4NQHU8ALjuu3/xBFdd9fwh/mt8NvBZwOcAn82zvTbwW8DnAJ/N8zLwO8BrAw8Gng78DvDaPC8DtwIP4d/gi7/4iw3wyZ/8yeKqq6666qr/sa677rrrvv01X+PbN8XmdYrr5vb88fLj/xT9KVddddVVV1111f9Yfcn+pTcuvfRj+qPHAEQoNuQN4QqwSh3e2fo7l2bJVf8vPepg59UAVMcDgOu++xdPcNVVzx/iP99x4OmAgOM8p9cGfgv4HOCzeV4Gfgd4beC1gd8Cvgd4b57XrcCDAPFv8MVf/MUG+ORP/mRx1VVXXXXV/2jv/c7v8N7vZd6rV/Q32DcA/ETwEwfmgKuuuuqqq6666n+cWxYHt7zC4uAVttS2AObBvMMzhDB5V6t3Xshygav+X3vUwc6rAaiOBwDXffcvnuCqq54/xH++zwY+C/gc4LN5Th8NfBXwOcBn87zMFQJeG/gt4HOAz+Z5/TbwWoD4N/jiL/5iA3zyJ3+yuOqqq6666n+8H36nd/jha+Ha0+j0Fmw9VX7q76Pf56qrrrrqqquu+h+jL9m/2vaFV7ulrG8BKFLZUG4gCsBelot3tnpnM42r/t971MHOqwGojgcA1333L57gqqueP8R/vouAgAcDuzyn9wa+C/gc4LN5XgYuAceB1wZ+C/gc4LN5Xr8NvBYgnssXf/EXmxfRJ3/yJ4urrrrqqqv+x3v1V3/1V/+8G6//vCrVm8xNAD8R/MSBOeCqq6666qqrrvpv99jNS499qfnRS/VkL0lz5byKGUBa421Td9uBdcBVVz3Tow52Xg1AdTwAuO67f/EEV131/CH+c7018FPA9wDvzfN6beC3gM8BPpvnZeB3gNcGXhr4K+B7gPfmeT0deDAgnssXf/EXmxfRJ3/yJ4urrrrqqqv+V/jqd3qHr34peKnT6PQWbD1Vfurvo9/nqquuuuqqq676b7PVTVuvtnXh1a6L8TqATnTz8AIIgHNTufes69lmGldd9QCPOth5NQDV8QDguu/+xRNcddXzh/jP9d3AewGvA/w2z+ulgb8CPgf4bJ6Xgd8BXpsrDPwO8No8LwN/A7w0/wZf/MVfbIBP/uRPFlddddVVV/2v8NIv/dIv/VWPesRXVanegG4IO34i+IkDc8BVV1111VVXXfVf7qW3dl/6pWaHLwUgSRvyRsgdwJha3db625ZmyVVXPR+POth5NQDV8QDguu/+xRNcddXzh/jP9XTgBHCcF2wXeDrwMjyntwZ+Cvgc4LO54lbgGHCC5/TSwF8B3wO8N/8GX/zFX2yAT/7kTxZXXXXVVVf9r/HV7/QOX/1S8FLH0fHjcPyp8lN/H/0+V1111VVXXXXVf5nr5svrXm3j0qttqW0BzIJZj+cIYfLerPecbeUsV131QjzqYOfVAFTHA4DrvvsXT3DVVc8f4j/Pg4GnA78DvDYv2HcD7wW8DPDXPNtvAa8NPAS4lSs+Gvgq4G2An+bZvhr4KOB1gN/m3+CLv/iLDfDJn/zJ4qqrrrrqqv81XvqlX/qlv+pRj/iqQHGTdFPY8XPBz10wF7jqqquuuuqqq/5T9SX7l9649NKP6Y8eAxCh2JA3hCvAYcbenVO9c0ADV131L3jUwc6rAaiOBwDXffcvnuCqq54/xH+e1wZ+C/gc4LN5wR4M/DVg4KOBW4GPBt4a+Brgo3m248CtgIHPBv4aeG/gvYG/AV6af6Mv/uIvNsAnf/Ini6uuuuqqq/5X+ep3eoevfil4qePo+HE4fo+451fgV7jqqquuuuqqq/7T3LI4uOXVNvZfrSd7gHkw7+Q5ACZvb91tlzIucdVVL6JHHey8GoDqeABw3Xf/4gmuuur5Q/zneW/gu4DPAT6bF+6lgZ8GHsSzfQ3w0TyvBwM/DbwUz/Y9wEcDu/wbffEXf7EBPvmTP1lcddVVV131v8pLv/RLv/RXPeoRXxUobpJuCjt+JfiVe8w9XHXVVVddddVV/6G2umnrFTZ3X+GWsr4FoIbqQrkBBMDFVs7dk/WeZhpXXfWv8KiDnVcDUB0PAK777l88wVVXPX+I/1mOAy8N/Db/suPASwN/Dezy7/TFX/zFBvjkT/5kcdVVV1111f86X/1O7/DVLwUvdRwdPw7H7xH3/Ar8ClddddVVV1111X+Yx25eeuxLzY9eqid7SZor51XMANIab5u62w6sA6666t/gUQc7rwagOh4AXPfdv3iCq656/hBXXfbFX/zFBvjkT/5kcdVVV1111f86L/3SL/3SX/WoR3xVoLhJuins+JXgV+4x93DVVVddddVVV/27nOzXJ19ta/fVTmo6CdCJbi5vIARwbir33pP1Hq666t/hUQc7rwagOh4AXPfdv3iCq656/hBXXfbFX/zFBvjkT/5kcdVVV1111f9KX/1O7/DVLwUvdRwdPw7H7xH3/Ar8ClddddVVV1111b9JX7J/7GLvsS81O3wpgAjFAi9C7gBWqcPbpu62AQ1cddW/06MOdl4NQHU8ALjuu3/xBFdd9fwhrrrsi7/4iw3wyZ/8yeKqq6666qr/lV76pV/6pb/qUY/4qkBxk3RT2PErwa/cY+7hqquuuuqqq676V7luvrzu1TYuvdqW2hbALJj1eI4QJu/Nes/ZVs5y1VX/QR51sPNqAKrjAcB13/2LJ7jqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVf9r/XV7/QOX/1S8FLH0fHjcPwecc+vwK9w1VVXXXXVVVe9SPqS/UtvXHrpx/RHjwEoUtlQbiAKwGHG3m2tu62ZxlVX/Qd61MHOqwGojgcA1333L57gqqueP8RVl33xF3+xAT75kz9ZXHXVVVdd9b/WS7/0S7/0Vz3qEV8VKG6Sbgo7fiX4lXvMPVx11VVXXXXVVS/UdfPlda+2cenVttS2AObBvJPnAGmNd7Z656WMS1x11X+CRx3svBqA6ngAcN13/+IJrrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVf+rffU7vcNXvxS81HF0/Dgcv0fc8yvwK1x11VVXXXXVVS/QK25ffMXH9EePAShS2VBuIArAxVbO3ZP1nmYaV131n+RRBzuvBqA6HgBc992/eIKrrnr+EFdd9sVf/MUG+ORP/mRx1VVXXXXV/2ov/dIv/dJf9ahHfFWguEm6Kez4leBX7jH3cNVVV1111VVXPYeT/frkq23tvtpJTScB5sG8k+cAY2p1W+tvW5olV131n+xRBzuvBqA6HgBc992/eIKrrnr+EFdd9sVf/MUG+ORP/mRx1VVXXXXV/3pf/U7v8NUvBS91HB0/DsfvEff8CvwKV1111VVXXXXVs7z01u5Lv9Ts8KUAIhSb5CaiAFxs5dw9We9ppnHVVf8FHnWw82oAquMBwHXf/YsnuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV/2v99Iv/dIv/VWPesRXBYqbpJvCjl8JfuUecw9XXXXVVVdd9f/cVjdtvdrWhVe7LsbrAGbBrMdzhNIab5u62w6sA6666r/Qow52Xg1AdTwAuO67f/EEV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqv8Tvvqd3uGrXwpe6jg6fhyO3yPu+RX4Fa666qqrrrrq/7HHbl567EvNj16qJ/sIxYa8IVwB9rJcvLPVO5tpXHXVf7FHHey8GoDqeABw3Xf/4gmuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVV/ye89Eu/9Et/1aMe8VWB4ibpprDjV4Jfucfcw1VXXXXVVVf9P9OX7F9n59zrXBfjdQCd6ObyBkKYvL11t13KuMRVV/03edTBzqsBqI4HANd99y+e4Kqrnj/EVZd98Rd/sQE++ZM/WVx11VVXXfV/xle/0zt89UvBSx1Hx4/D8XvEPb8Cv8JVV1111VVX/T9yy+Lgllfb2H+1nuwlaUPeCLkDOMzYu611tzXTuOqq/0aPOth5NQDV8QDguu/+xRNcddXzh7jqsi/+4i82wCd/8ieLq6666qqr/s946Zd+6Zf+qkc94qsCxU3STWHHrwS/co+5h6uuuuqqq676P64v2b/a9oVXu6WsbwHoRDeXNxDC5F2t3nkhywWuuup/gEcd7LwagOp4AHDdd//iCa666vlDXHXZF3/xFxvgkz/5k8VVV1111VX/p3z1O73DV78UvNRxdPw4HL9H3PMr8CtcddVVV1111f9h182X173O5u7r9GQvSXPlvIoZwCp1eNvU3Taggauu+h/iUQc7rwagOh4AXPfdv3iCq656/hBXXfbFX/zFBvjkT/5kcdVVV1111f8pL/3SL/3SX/WoR3xVoLhJuins+JXgV+4x93DVVVddddVV/8f0JfuX3rj00o/pjx4DUEN1odwAAuDeVu8628pZrrrqf5hHHey8GoDqeABw3Xf/4gmuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVV/+d89Tu9w1e/FLzUcXT8OBy/R9zzK/ArXHXVVVddddX/IdfNl9e92salV9tS2wKYB/NOngOMqdVtrb9taZZcddX/QI862Hk1ANXxAOC67/7FE1x11fOHuOqyL/7iLzbAJ3/yJ4urrrrqqqv+z7nuuuuu+6HXeo0fChQ3STeFHb8S/Mo95h6uuuqqq6666v+Al97afemXmh2+FECRyoZyA1EAzk3l3nuy3sNVV/0P9qiDnVcDUB0PAK777l88wVVXPX+Iqy774i/+YgN88id/srjqqquuuur/pE9+p3f45DeCNzqOjh+H4/eIe34FfoWrrrrqqquu+l/sZL8++Wpbu692UtNJgHkw7+Q5QFrj06f+6Uuz5Kqr/od71MHOqwGojgcA1333L57gqqueP8RVl33xF3+xAT75kz9ZXHXVVVdd9X/Sddddd90PvdZr/BDATeimCvVXgl+5x9zDVVddddVVV/0v9NjNS499hfnBKwBEKDbkDeEKcLGVc/dkvaeZxlVX/S/wqIOdVwNQHQ8ArvvuXzzBVVc9f4irLvviL/5iA3zyJ3+yuOqqq6666v+sT36nd/jkN4I32lJsnbZP3yPu+RX4Fa666qqrrrrqf5Gtbtp6ta0Lr3ZdjNcBzIJZj+cIpTXeNnW3HVgHXHXV/yKPOth5NQDV8QDguu/+xRNcddXzh7jqsi/+4i82wCd/8ieLq6666qqr/s+67rrrrvuh13qNHwK4Cd1Uof5B8AdPMU/hqquuuuqqq/4XeOzmpce+1PzopXqyl6TN8KZwBTjM2Lutdbc107jqqv9lHnWw82oAquMBwHXf/YsnuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV/2f9snv9A6f/EbwRluKrdP26QNx8BPwE1x11VVXXXXV/2B9yf7Vti+82i1lfQtAJ7q5vIEQJm9v3W2XMi5x1VX/Sz3qYOfVAFTHA4DrvvsXT3DVVc8f4qrLvviLv9gAn/zJnyyuuuqqq676P+2666677ode6zV+COAmdFOF+gfBHzzFPIWrrrrqqquu+h/oZL8++TpbF19nS21LkjbkjZA7gMOMvdtad1szjauu+l/sUQc7rwagOh4AXPfdv3iCq656/hBXXfbFX/zFBvjkT/5kcdVVV1111f95n/xO7/DJbwRvtKXYOm2fPhAHPwE/wVVXXXXVVVf9D/PYzUuPfYX5wSsAFKlsRG4Cgcl7s95ztpWzXHXV/wGPOth5NQDV8QDguu/+xRNcddXzh7jqsi/+4i82wCd/8ieLq6666qqr/s+77rrrrvuh13qNHwK4Cd1Uof5B8AdPMU/hqquuuuqqq/4H6Ev2r7Nz7nWui/E6gFkw6+UFwJhaPX3qnj6ggauu+j/iUQc7rwagOh4AXPfdv3iCq656/hBXXfbFX/zFBvjkT/5kcdVVV1111f8Ln/xO7/DJbwRvtKXYOm2fPhAHPwE/wVVX/R/Uq/Qnu42TW+q2tqLbUvQnmqI/inodz8dGTvfwANFW9/IA97Sje3iAC+PRhcFt4KqrrvoPcd18ed3rbO6+Tk/2krQhb4TcAVxs5dydrd7JVVf9H/Oog51XA1AdDwCu++5fPMFVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrq/4Xrrrvuuh96rdf4IYCb0E0V6h8Ef/AU8xSuuup/oa0y29oq/dbJmJ/sFX2W+bUAR1Gv47/Y3NOFMAPP1OV02DwdANzTju65MB5dGNwGrrrqqufx0lu7L/1Ss8OXAqihulBuAIHJ21t326WMS1x11f9BjzrYeTUA1fEA4Lrv/sUTXHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq/7f+OR3eodPfiN4oy3F1mn79IE4+An4Ca666n+ok93GyV6lv65sXNcr+lX0J1L0K9WTvBBGMhRDWApQAJGiYMQDCYdpPAdPPEDYEw8gaMLmX2Hu6cKsTRcvteU9F3J14cJ4dIGrrvp/rC/Zv87Oude5LsbrAObBvJPnAGNq9fSpe/qABq666v+oRx3svBqA6ngAcN13/+IJrrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVf9vXHfdddf90Gu9xg8B3IRuqlD/IPiDp5incNVV/422ymzr4f3xh/eKfhX9iSG0NVG2eCEMYRSGsBQWRZYSKv/FJDdZ5lncAAssuwWeeC6Bh3m2C9FW997Tju65MB5dGNwGrrrq/4Hr5svrXmdz93V6spekzfCmcAU4N5V778l6D1dd9X/cow52Xg1AdTwAuO67f/EEV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqv9XPvmd3uGT3wjeaEuxddo+fSAOfgJ+gquu+m/w8NnJh2902w87inodz4chjMJSMQhULctW4V9QyMOwWyGXxQzhNvTOJXbjgaQyKBY8wBhliwcYVbZ4gEZs8q8UMIGb8BR2EyTPZe7pwqxNFy+15T0XcnXhwnh0gauu+j/mpbd2X/qlZocvBVBDdUFuIoTJW6f+6QfWAVdd9f/Aow52Xg1AdTwAuO67f/EEV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqv9Xrrvuuut+6LVe44cAbkI3Vah/EPzBU8xTuOqq/wJbZbb18P74w1fd4mETZQvASAmdpQIqCQEEL1wWclnsIcihOpeyW+9cYjf+C00Ri0SFZ2pRF4mLrTJG2Uo053ml5BZmkt0CTzyXwMM824Voq3vvaUf3XBiPLgxuA1dd9b9QX7J/nZ1zr3NdjNcBzIN5J88BVqnDp7f+6c00rrrq/4lHHey8GoDqeABw3Xf/4gmuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVV/+988ju9wye/EbzRlmLrtH36QBz8BPwEV131n+i6fvu6G+vOwy/V+cN4JkM0lXmKDiOei+wx5KFmLiW36lzKbn22A/6XGaJsjVG2GrEYVbaA4LkETOAmPIXdBMlzmXu6MGvTxUttec+FXF24MB5d4Kqr/oe7br687nU2d1+nJ3tJ2gxvCleAc1O5956s93DVVf/PPOpg59UAVMcDgOu++xdPcNVVzx/iqsu++Iu/2ACf/MmfLK666qqrrvp/57rrrrvuh17rNX4I4CZ0U4X6B8EfPMU8hauu+g/Uq/S39MduqbOdl5ooWzxTQ70VfULlmQp52LkddNkOAreaueT/sIzoB9WtCS3GKFuJ5jyvlNzCTLJb4InnEniYZ7sQbXXvPe3onnuG/Xu46qr/QV56a/elX2p2+FIANVQX5CZCmLx16p9+YB1w1VX/Dz3qYOfVAFTHA4DrvvsXT3DVVc8f4qrLvviLv9gAn/zJnyyuuuqqq676f+mT3+kdPvmN4I22FFun7dMH4uAn4Ce46qr/AFtltvWo2emX3q+zmxP1AIZoij5RDwRXZO92aTOHeyJz4P+5IcrWGGWrEYtRZQsInkvABG7CU9hNkDyXjZzuiba690JbX7hn3L9ncBu46qr/Yn3J/nV2zr3OdTFeBzAP5p08B1ilDp/e+qc307jqqv+nHnWw82oAquMBwHXf/YsnuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV/2/dN111133Q6/1Gj8EcBO6qUL9g+APnmKewlVX/Rs9fHby4Rvd9sOOol7HMyWqqegTep5J9rjh6Z55jpewG1c9XxnRD6pbE1qMUbYSzXleKbmFmYSnsBvPZe7pwkYb7r1nWt5zz7h/z+A2cNVV/4mumy+ve53N3dfpyV6SNsObwhXg3FTuvSfrPVx11f9zjzrYeTUA1fEA4Lrv/sUTXHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq/7f+uR3eodPfiN4oy3F1mn79IE4+An4Ca666l9hq8y2Ht4ff/iqWzxsomwBGCmha4o5EDxT73ZxluOFPtsBV/2bDFG2xihbjViMKltA8EDCwpNMC3sKPPFc5p4uzNp08VJb3nPPdHjPQVsfcNVV/0FecfviKz6mP3oMQA3VBbmJECZvnfqnH1gHXHXVVTzqYOfVAFTHA4DrvvsXT3DVVc8f4qrLvviLv9gAn/zJnyyuuuqqq676f+u666677ode6zV+COAmdFOF+gfBHzzFPIWrrvoXXNdvX3dj3Xn4pTp/GM9kiKYyT9FhBCB7nDFdWOR0ITIHrvoPlRH9oLo1KrZGYstSx3MJmMBT2FPgiedSaQeb03jvpba850KuLlwYjy5w1VX/SlvdtPU62+df56SmkwDzYN7Jc4BV6vDprX96M42rrrrqskcd7LwagOp4AHDdd//iCa666vlDXHXZF3/xFxvgkz/5k8VVV1111VX/r33yO73DJ78RvNGWYuu0ffpAHPwE/ARXXfV89Cr9Lf2xW+ps56UmyhbP1FBvRZ9QeaZCHs6zXZi34QJX/ZfJiH5ULEbK1hhlK9Gc5yK5yUzCU5hJ2DxA4GGzjfes2+ree9rhPRfGowtcddULccvi4JZX29h/tZ7sIxSb5CaiAJybyr33ZL2Hq6666jk86mDn1QBUxwOA6777F09w1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquu+n/tuuuuu+6HXus1fgjgJnRThfoHwR88xTyFq656pq0y23rU7PRL79fZzYl6AEM0RZ+oB4Irsne7tJnDPZE5cNV/P6mso2yNlK0xylaiOc9FcpOZhKewmyB5gMDDZhvvcQ4X72lH91wYjy4MbgNXXQW84vbFV3xMf/QYgE50c3kDobTG26butgPrgKuuuup5POpg59UAVMcDgOu++xdPcNVVzx/iqsu++Iu/2ACf/MmfLK666qqrrvp/78Pf6R0+/O3g7bYUW6ft0wfi4CfgJ7jq/71bZsdvOd4de8xR1Ot4pkQ1FX1CzzPJHjc83TPP8RJ246r/0YYoW2OUrVFlqxGbPK8MmOScAk+C5LlU2kGfPoi2uvdCW1848HBwYTy6wFX/b2x109brbJ9/nZOaTgIswosqZgCHGXu3te62ZhpXXXXV8/Wog51XA1AdDwCu++5fPMFVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrq/72tra2tH37TN/nhTbF5E7qpQv2D4A+eYp7CVf8v9Sr9Syyue8VLdf4wACMldE0xB4Jn6t0uznK80Gc74Kr/tYYoW2OUrUYsRpUtIHhOGTAJN9kt8MQLMPd0Ydami83TwT3t6J4L49GFwW3gqv9Tblkc3PJqG/uv1pN9hGKT3EQUgHtbvetsK2e56qqrXqhHHey8GoDqeABw3Xf/4gmuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVVVwHv/c7v8N7vZd5rQ9q4xlxzIA5+An6Cq/7fOdltnLxhfubVVqonjdSkeUo9RgCyxxnThUVOFyJz4Kr/c6aIxRjd1oi2RpUtIHgukhuQMk24hd0EyfMReJhnuxBtde9BjgcHHg/uGfbv4ar/lV5x++IrPqY/egxAJ7q5vIFQWuPTp/7pS7Pkqquu+hc96mDn1QBUxwOA6777F09w1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquuugrY2tra+uE3fZMf3hSb1ymum9vzPwj+4CnmKVz1/8Zj52ceO/Q7rwCQUmnShq0CUMjDebYL8zZc4Kr/V6aIxaS6GBVbk2KRaM4LEDCBmyBlN0ETNs/H3NOFLvPAOVy8px3dc9CGg4O2PuCq/5H6kv2rbV94tVvK+haARXhRxQzgMGPvttbd1kzjqquuepE86mDn1QBUxwOA6777F09w1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquuuuqZ3vud3+G938u81xzNr4PrDsTBT8BPcNX/eb1K/2IbN7zafulvAWiKWUMLgMCr7ba6rWYuueqqZ5oiFpPqool+VNlKq7fU8fxlQIIn4SbIsBvPR+Bhnu3CPIeLF9r6wj3T4T0HbX3AVf+tTvbrk6+2tftqJzWdlKTN8KZwBbi31bvOtnKWq6666l/lUQc7rwagOh4AXPfdv3iCq656/hD/+V4aOMYVv8MLdhx4Ka64BPw1L9hx4KW44hLw1/w7ffEXf7EBPvmTP1lcddVVV1111TNtbW1t/fCbvskPb4rNm9BNFeofBH/wFPMUrvo/62S3cfKaxTWvM1G2jDSFNmx1ALOczm3lcA9246qr/iVSGRSLFnUxoUWT+kZs8gIETECCM+xJ0ITNcwk8zLNdiLa69552dM+F8ejC4DZw1X+JWxYHt7zaxv6r9WRfpLIRuQkEJp86zZ6yNEuuuuqqf7VHHey8GoDqeABw3Xf/4gmuuur5Q/zneWngt4DjPNutwNsAf81zem/gq4DjPNtvA28D7PKcXhr4KeDBPNtfA28D3Mq/0Rd/8Rcb4JM/+ZPFVVddddVVVz3AJ7/TO3zyG8EbbSm2TtunD8TBT8BPcNX/SY+dn3ns0O+8AkBKZSI2gQByK9e3zdp0iauu+neaIhZN0U+KRSMWE7Gw1PH8CIdp4Em4hd0EyXOptIPNabx3mcOFC7m6cM+wfw9X/Yd76a3dl36p2eFLAfRBP8MLhMbU6imtf0ozjauuuurf5FEHO68GoDoeAFz33b94gquuev4Q/zleG/gt4G+AzwZ+G3hr4KsBAw8BdrnitYHfAv4G+GjgVuC9gc8Cfht4HZ7twcBfAQLeG7gVeG3gq4C/Bl6Gf6Mv/uIvNsAnf/Ini6uuuuqqq656gOuuu+66H3qt1/ghgJvQTRXqHwR/8BTzFK76P6NX6V9s44ZX2y/9LQBNMWtoARB4daytnh6ZA1dd9Z9oiLLVoi6a6aeIRSM2ef5ScpNpYU+BJ56PjZzumedw8Z5pec+Ftrxw0NYHXPVv0pfsX237wqvdUta3ACzCiypmAHtZLt421du46qqr/l0edbDzagCq4wHAdd/9iye46qrnD/Gf47uBtwZeGriVZ/ts4LOA9wG+myt+C3ht4CHArTzbdwPvBbwM8Ndc8d3AewFvA/w0z/bZwGcB7wN8N/8GX/zFX2yAT/7kTxZXXXXVVVdd9Vw++Z3e4ZPfCN5oS7F12j59j7jnV+BXuOr/hJPdxslrFte8zkTZMtIU2rDVAcxyOrfV1ndy1VX/TTKin1A/RtkaVbYasQCC5yK5yUyym0QLu/FcAg+bbbzHOVy8px3dc2E8ujC4DVz1Qp3s1ydfbWv31U5qOilJm+FN4Qpw11Rvv5DlAlddddW/26MOdl4NQHU8ALjuu3/xBFdd9fwh/uM9GHg68DXAR/OcjgMvDdwK3MoVBv4GeGme01sDPwV8DfDRXHERuAQ8mOf0YODpwM8Ab82/wRd/8Rcb4JM/+ZPFVVddddVVVz2X66677rofeq3X+KFAcZN0U9jxK8Gv3GPu4ar/1R47P/PYod95BYCUykRsAgHkVq5vm7XpEldd9T/MFLGYVBcTWoxRthLNeW7CYRp4Em5hJmHzXOaeLszadPFSW95zIVcXLoxHF7jqWW5ZHNzyahv7r9aTfZHKRuQmEJh86jR7ytIsueqqq/5DPOpg59UAVMcDgOu++xdPcNVVzx/iP95rA78FvA3w08CDgQcBzwBu5Tm9NvBbwOcAn83zMvA7wGsDDwaeDvwO8No8LwO3Ag/h3+CLv/iLDfDJn/zJ4qqrrrrqqquej89/p3f4/FeDVzuOjh+H4/eIe34FfoWr/lfqVfoX27jh1fZLfwtAU8waWgAEXh1rq6dH5sBVV/0vMUTZalEXI9qaiIWljueVAZNwk90CTzwfGzndE2117z3t6J57hv17+H/qpbd2X/qlZocvBdAH/QwvEBpTq6e0/inNNK666qr/MI862Hk1ANXxAOC67/7FE1x11fOH+I/32cBnAW8DvBfw1jzbbwPvA9zKFa8N/BbwOcBn87wM/A7w2sBrA78FfA/w3jyvW4EHAeLf4Iu/+IsN8Mmf/Mniqquuuuqqq56Pl37pl37pr3rUI74qUNwk3RR2/FzwcxfMBa76X+W6fvu64/NTrzZRtow0hTZsdQCznM5ttfWdXHXV/3IZ0Y+KxaRYjCpbjdjk+QiYwE12CzwJkueykdM98xwu3jMt77ln3L9ncBv4P6wv2b/a9oVXu6WsbwFYhBdVzAD2sly8baq3cdVVV/2He9TBzqsBqI4HANd99y+e4Kqrnj/Ef7zPBj4L2AUuAV8N3Aq8NfBewK3AywC7wEcDXwV8DvDZPC9zhYDXBn4L+Bzgs3levw28FiD+Db74i7/YAJ/8yZ8srrrqqquuuuoF+Op3eoevfil4qePo+HE4/lT5qb+Pfp+r/td46cW1L33Qbb0UQEplIjaBAHIr17fN2nSJq676P2qKWEyqi1GxNSkWieY8N2HhKcwkPIXdeC5zTxc22nDvhba+cM90eM9BWx/wf8TJfn3y1bZ2X+2kppOStBneFK4Ad0319gtZLnDVVVf9p3jUwc6rAaiOBwDXffcvnuCqq54/xH+8zwY+C7gEPBjY5dk+G/gs4GOArwbeG/gu4HOAz+Z5GbgEHAdeG/gt4HOAz+Z5/TbwWoB4Ll/8xV9sXkSf/MmfLK666qqrrrrqBXjpl37pl/6qRz3iq6pUbzI3AfxE8BMH5oCr/kfrVfpHb970OkdRrwOYFPNEc4DAq2Nt9fTIHLjqqv9PpDIoFmOUrUYsRpUtIHgg4TANPIU9BZ54LpV2sDmN915qy3vumQ7vOWjrA/4XumVxcMurbey/Wk/2RSobkZtAYPLWqX/6gXXAVVdd9Z/mUQc7rwagOh4AXPfdv3iCq656/hD/8T4b+Czgc4DP5jkdBy4CvwO8NvDawG8BnwN8Ns/LwO8Arw28NPBXwPcA783zejrwYEA8ly/+4i82L6JP/uRPFlddddVVV131QvzwO73DD18L155Gp7dg66nyU38f/T5X/Y91Xb993c78zOsk6o3UFJsJFWDu8d7NabiHq6666rKM6AfVrVGxNRJbljqeS8AEnoRbmEnYPEDgYbON96zb6t572uE9F8ajC/wP99Jbuy/9UrPDlwLoRDeXNxAaU6unT93TBzRw1VVX/ad61MHOqwGojgcA1333L57gqqueP8R/vPcGvgv4HOCzeV4Gfgd4beClgb8CPgf4bJ6Xgd8BXpsrDPwO8No8LwN/A7w0/wZf/MVfbIBP/uRPFlddddVVV131QrzxG7/xG3/Sse1PqlK9ydwE8BPBTxyYA676H+elF9e+9EG39VIAieoUsYkRkNtt9fQ+2wFXXXXVCyaVdZStkbI1RtlKNOe5SG4yk/AUZhI2DxB4mGe7EG117z3t6J57hv17+B+iL9m/4taFV3xYXT8MYB7MO3kOsJfl4p2t3tlM46qrrvpP96iDnVcDUB0PAK777l88wVVXPX+I/3gvDfwV8D3Ae/OcHgw8HfgZ4K25Yhd4OvAyPKe3Bn4K+Bzgs7niVuAYcILn9NLAXwHfA7w3/wZf/MVfbIBP/uRPFlddddVVV131L/jhd3qHH74Wrj2NTm/B1t/If/PX6K+56n+MXqV/9OZNr3MU9TqASTFPNAco5OHxafV07MZVV131ryOVdZStSbEYVbYascnzyoBJzinwJEiey0ZO90Rb3XuQ48GFXF24MB5d4L/YVjdtvc72+dc5qemkJG3IGyF3APe2etfZVs5y1VVX/Zd51MHOqwGojgcA1333L57gqqueP8R/jt8GXgp4CLDLs3028FnA+wDfzRXfDbwX8DLAX/NsvwW8NvAQ4Fau+Gjgq4C3AX6aZ/tq4KOA1wF+m3+DL/7iLzbAJ3/yJ4urrrrqqquu+he88Ru/8Rt/0rHtT5qj+XVw3SCGnxA/MZiBq/7bXddvX7czP/M6iXojNcVmQgWYe7x3cxru4aqrrvoPM0TZGqNsjSpbjVgAwXNKyS3MJDyF3Xg+5p4uzNp0sXk6uKcd3XPQhoODtj7gP8F18+V1r7O5+zo92RepbCg3EAWTt0790w+sA6666qr/Uo862Hk1ANXxAOC67/7FE1x11fOH+M/x2sBvAbcCnw3cCrw38N7A3wAvzbM9GPhrwMBHA7cCHw28NfA1wEfzbMeBWwEDnw38NfDewHsDfwO8NP9GX/zFX2yAT/7kTxZXXXXVVVdd9SL4+Xd8h5/fFJvXKa6b2/O/kf/mr9Ffc9V/q5deXPvSB93WSwEkqlPEJkZAbrfV0/tsB1x11VX/qaaIxRjd1oi2RpUtIHggYeFJpgk3QYbdeD4CD/NsF+Y5XDzI6eBCri7cM+zfw7/DYzcvPfYV5gevANCJbi5vIDSmVk+fuqcPaOCqq676L/eog51XA1AdDwCu++5fPMFVVz1/iP88Lw38NPAgnu1ngPcGdnlOLw38NPAgnu1rgI/meT0Y+GngpXi27wE+Gtjl3+iLv/iLDfDJn/zJ4qqrrrrqqqteBO/9zu/w3u9l3muO5tfBdYMYfgh+iKv+W/Qq/aM3b3qdo6jXAUyKeaI5QCEPj0+rp2M3rrrqqv9yU8RiUl2Miq2R2LLU8XwETEAKN9lN0ITN81FpB336INrq3gttfeHAw8GF8egCL0Rfsn/FrQuv+LC6fhjAPJh38hxgL8vFO1u9s5nGVVdd9d/iUQc7rwagOh4AXPfdv3iCq656/hD/+Y4DLw38NbDLC3cceGngt/mXHQdeGvhrYJd/py/+4i82wCd/8ieLq6666qqrrnoRbG1tbf3wm77JD2+KzesU183t+R8Ef/AU8xSu+i91sts4eXpx3Rsl6o3UFJsJFWDu8d7NabiHq6666n+MjOhHxWJSLEaVrbR6Sx3Pj3CYBp5kpyADT7wAGznd0+V02Dwd3NOO7rkwHl0Y3Iatbtp6ne3zr3NS00lJ2pA3Qu4A7m31rrOtnOWqq676b/Wog51XA1AdDwCu++5fPMFVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqqhfRe7/zO7z3e5n32lJsnbZPH4iDn4Cf4Kr/Mie7jZOnF9e9UaI+UZ0iNjECcrutnt5nO+Cqq676n08qg2LRoi6a6aeIRSM2eQEkNyBlWtiTcAqS5+NE7Okx3ZO3ew+tkK3T1CEKJm+d+qcfWAdcddVV/+0edbDzagCq4wHAdd/9iye46qrnD3HVZV/8xV9sgE/+5E8WV1111VVXXfUiuu666677odd6jR8CuAndVKH+QfAHTzFP4ar/dCe7jZOnF9e9UaK+ob4pNgAKeXh8Wj0du3HVVVf9r5YR/YT6McpWIxZN0Sea8wIETOAmSNntpnrP7JZyxzZAKKNnDGHssjpsi7t2c9pdMi656qqr/ts96mDn1QBUxwOA6777F09w1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquuuupf4ZPf6R0++Y3gjbYUW6ft0wfi4CfgJ7jqP1Wv0j94+0Fvl6hvqG+KDYANT3ctpvVZrrrqqv/TpojFpLpooh9VthqxAIIHeES9dX5NOVcxzDRSPQlgyXy8lDtrI/NMPdPBLMfDA4aDAw8HXHXVVf/lHnWw82oAquMBwHXf/YsnuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV131r3Dddddd90Ov9Ro/BHATuqlC/ZXgV+4x93DVf4pepX/k1k1vtFI9mVKZFFsYzT3euzkN93DVVVf9/ySVQbFQsPnQ7uk3bMfBzKL0nqJqAuCij7UDb0yClJ3CKTDPpWc6mOV4eMBwcODhgKuuuuo/3aMOdl4NQHU8ALjuu3/xBFdd9fwhrrrsi7/4iw3wyZ/8yeKqq6666qqr/pU++Z3e4ZPfCN5oB+2chJP3iHt+BX6Fq/7D9Sr9I7dueqOV6smUyqTYwqh3u7g9rW7jqquu+n9tFkP/kPnTHzzXeoFRjbEHlyS46GPjoTfClnguIlOQggycPJee6WCW4+EBw8Eyp2VTNq666qr/UI862Hk1ANXxAOC67/7FE1x11fOHuOqyL/7iLzbAJ3/yJ4urrrrqqquu+ld6+MMf/vBve7mX+bZAcZN0U9jxK8Gv3GPu4ar/UC+59aA3Oop6nSHGKNsY9W4Xt6fVbVx11VX/r22Wo8XDZk97WCgLODqmXjKTy3TQti9NlAmgEbWp9A31DXUmguciMgUpyMDJc6luy57xcPB4cJDDQVM2rrrqqn+XRx3svBqA6ngAcN13/+IJrrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVVf9G3z1O73DV78UvNRxdPw4HL9dvv030W9y1X+Yl9u48dUv1fnDjDSFtmyVQh4eH5dP4aqrrvp/7Ux3/uT13d03hLIIR2XqJTO5TJfy2AVb5gVIqTSXvkn9RPQmguciMgUpyMDJc6luy57xsOW0HNSGZU7LpmxcddVVL7JHHey8GoDqeABw3Xf/4gmuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVVV/0bvPRLv/RLf9WjHvFVgeIm6aaw4yeCnzgwB1z17/ZyGze++qU6f5iRptCWrRJ4dWJaPgW7cdVVV/2/dd3svtPX1XtuBCjKUpg6gJVnq4O2dYl/pZRKc+mb1E9EbyJ4LoETnIIMnLwAPdNB2K3ztBrchkFtWOa0bMrGVVdd9RwedbDzagCq4wHAdd/9iye46qrnD3HVZV/8xV9sgE/+5E8WV1111VVXXfVv9NXv9A5f/VLwUqfR6S3Yeqr81N9Hv89V/y6PnZ957NDvvALAGLFtqwRenZiWT8FuXHXVVf9vPWR+283Hyu5JgKLWFVoBOMzF4TI3DvgPkFJpLn2TaiP6JCrPJXCCU2DAwhaYF6JnOgi3sXMOS6ZlI9uBhwOuuur/qUcd7LwagOp4AHDdd//iCa666vlDXHXZF3/xFxvgkz/5k8VVV1111VVX/Ru98Ru/8Rt/0rHtT6pSvcncBPATwU8cmAOu+jd5+Ozkw5mdeDWASWUjoQfyRFs+MTIHrrrqqv+XOrXykPmtD96Iwy2MarQatAKw3zb31p4v+c8iNFL6RvSN6JOovACB0xhBgi3kwMkLIdw62jLcxs45LJmWjWwHHg646qr/wx51sPNqAKrjAcB13/2LJ7jqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVdd9e/ww+/0Dj98LVx7Gp3egq2nyk/9ffT7XPWv9vDZyYczO/FqAJPKRkIP5LG2fErNXHLVVVf9vzSLoX/I/OkPnmu9wKjG2AdWIu9N27sT3cB/JaGR0ifqkqgJkZSOf0HgRBjbYAs5cPJCCLeOtqyeVoPHg6XH5UAbuOqq/wMedbDzagCq4wHAdd/9iye46qrnD3HVZV/8xV9sgE/+5E8WV1111VVXXfXv8MZv/MZv/EnHtj+pSvUmc9Mghp8QPzGYgateZCe7jZMnF9e/BcCkWCSaAXmsLZ9SM5dcddVV/y9tlqPFw2ZPe1goCzg6TZ2wmiP3287FiTLxP4XQ5OhMlBQliZoQSel4ISRbxghjW5AAgZPno5BD5/EwPC0PPB4sGZdcddX/Qo862Hk1ANXxAOC67/7FE1x11fOHuOqyL/7iLzbAJ3/yJ4urrrrqqquu+nf64Xd6hx++Fq69Bl2zARt/I//NX6O/5qoXyclu4+TpxXVvlKhvqG+KDYCtXN86a9Mlrrrqqv+XznTnT17f3X1DKItwVKZeMpPLdCmPXbBl/pdIqaRVUqUax0T0ICVReSEkW8bgFKSwBeYBhFtHW85yPDxgODjwcMBVV/0v8KiDnVcDUB0PAK777l88wVVXPX+Iqy774i/+YgN88id/srjqqquuuuqqf6e3f/u3f/sPK/qwOZpfB9cNYvgJ8RODGbjqhTrZbZw8vbjujRL1DfVNsQGwmePt8zZc4Kqrrvp/6Ux3/uSN/Z03AxRlKUwdwMqz1WFu7tky/0ekVNIqqVKNYyJ6o2IieD4kW5CyUzgF5rlUt2XPeDh4PFh6XA60gauu+h/mUQc7rwagOh4AXPfdv3iCq656/hBXXfbFX/zFBvjkT/5kcdVVV1111VX/TltbW1s//KZv8sObYvM6xXVze/438t/8NfprrnqBepX+ods3v8VE2WpS14hNgM0cb5+34QJXXXXV/0sPmd9287GyexKgqHWFVgBWXhwdtI19/h9pRE2pJOomok9Kx3ORbGwLUpCBk+dSyKHzeBielgceD5aMS6666r/Zow52Xg1AdTwAuO67f/EEV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqqv+A7z3O7/De7+Xea85ml8H1x2Ig5+An+Cq56tX6R+5ddMbrVRPplQmxRZGs5zObbX1nVx11VX/73Rq5Zb5bTdvx/4xjGq0GrQCsN8299aeL7mKiehTpTbUN0VvSzyXwAlOQQpbYB5AuM08HXSeVgcMBwceDrjqqv9ijzrYeTUA1fEA4Lrv/sUTXHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq676D7C1tbX1w2/6Jj+8KTZvQjdVqH8Q/MFTzFO46jn0Kv0jt256o5XqyZTKpNjCqHe7uD2tbuOqq676f6dTKw9bPOVhc60XGNUY+8BK5L1pe3eiG7jq+WpETaJrUj8RvYnguUi2IGWncArMc+mZDqqnVctpOagNBx4OuOqq/0SPOth5NQDV8QDguu/+xRNcddXzh7jqsi/+4i82wCd/8ieLq6666qqrrvoP8uHv9A4f/nbwdluKrdP26QNx8BPwE1z1HF5m8+bX2S/9LYYYo2xj1Ltd3J5Wt3HVVVf9v7NZjhYPmz3tYaEs4Og0dcJqjtxvOxcnysRVLzqhkdI3om+oS0rHc5FsbAtSkIGT56O6LSs5dJ5WS6bl0uNyoA1cddV/gEcd7LwagOp4AHDdd//iCa666vlDXHXZF3/xFxvgkz/5k8VVV1111VVX/Qe57rrrrvuh13qNHwK4Cd1Uof5B8AdPMU/hqstebuPGV79U5w8z0hTaslUCr06MR0/kqquu+n/nZL147Kb+zptDWcDRMfWSmVymS3nsgi1z1b/bRPRN0SdRm6K3JZ5L4AQn2EIOnLwAPdNBuI3hablkWi5zWjZl46qr/hUedbDzagCq4wHAdd/9iye46qrnD3HVZV/8xV9sgE/+5E8WV1111VVXXfUf6JPf6R0++Y3gjbYUW6ft0/eIe34FfoWreLmNG1/9Up0/zEhTaMtWCbw6MS2fgt246qqr/l85050/eWN/580ARVkKUwew8mx1mJt7tsxV/ykaUZPomtRPRG8ieD4kW5DYFqSwBeb5EG4dbTnL8XBwGwa14cDDAVdd9QI86mDn1QBUxwOA6777F09w1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquuuuo/0HXXXXfdD73Wa/wQwC2KW8KOXwl+5R5zD/+PPXZ+5rFDv/MKAJPKVkINvDoxLZ+C3bjqqqv+X3nI/Labj5XdkwCFVotaBVh5cXTQNva56r+W0EjpE3VJ1IY6E8ELEDgRxrYgAycvQHVbVnLoPK2WTMulx+VAG7jq/71HHey8GoDqeABw3Xf/4gmuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVVV/0H+/x3eofPfzV4tePo+HE4fo+451fgV/h/6uGzkw9nduLVACaVjYQeyGNt+ZSaueSqq676f6NTK7fMb7t5O/aPAVS1LmgFYL9t7q09X3LV/xgT0ZsoTaoNdVZUW+L5kGwZgxNsIQdOXoCe6SDcxs45LJmWg9uwZFxy1f8bjzrYeTUA1fEA4Lrv/sUTXHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq676D/bSL/3SL/1Vj3rEVwWKm6Sbwo5fCX7lHnMP/888fHby4cxOvBrApLKR0AN5rC2fUjOXXHXVVf9vdGrlYYunPGyu9QKjGmMfWIl80LYuDe7XXPU/n9Dk6JqiNyoNlaR0vACSLUhsC1LYAvMCVLdlyG2W4+HgNgxqwzKnZVM2rvo/5VEHO68GoDoeAFz33b94gquuev4QV132xV/8xQb45E/+ZHHVVVddddVV/wm++p3e4atfCl7qODp+HI4/VX7q76Pf5/+Rk93GydOL694oUT8p5onmQB5ry6fUzCVXXXXV/xub5WjxsNnTHhbKAo6qqQusRN6bjl2YKBNX/a/WiJpSSdRNRG9UTAQvQOBEGNuCFLbAvADCraMtq6dVsdsBw8HgNgy0gav+V3rUwc6rAaiOBwDXffcvnuCqq54/xFWXffEXf7EBPvmTP1lcddVVV1111X+Cl37pl37pr3rUI76qSvUmcxPATwQ/cWAO+H/gZLdx8vTiujdK1DfUN8UGwGaOt8/bcIGrrrrq/42dsr/14NkzHhzKAo6OqZfM5DJdymMXbJmr/s+aiD5VakJpqLOi2hIvgGRjW5ACAw6cvBA900HYrfO0WjItB7dhybjkqv/RHnWw82oAquMBwHXf/YsnuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV131n+Tb3+kdvv1h8LDT6PQWbD1Vfurvo9/n/7iT3cbJ04vr3ihR31DfFBsAmznePm/DBa666qr/N85050/e2N95M0BRlsLUAQzZr/e9dcmWuer/H6HJ0aVKTSgNdUbFRPACSLaMEca2IIUtMC9AIYfObdl5Wh0wHCxzWjZl46r/ER51sPNqAKrjAcB13/2LJ7jqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVdd9Z/kjd/4jd/4k45tf1KV6k3mJoCfCH7iwBzwf1Sv0j9y66Y3WqmeTFQnxRbAZo63z9twgauuuur/jYfMb7v5WNk9CVBotahVgJVnq4O2dYmrrno+JqK3pERdEjVRSaLyQgROhLEtSIDAyfNRyKFzW3aeVgcMB8uclk3ZuOq/3KMOdl4NQHU8ALjuu3/xBFdd9fwhrrrsi7/4iw3wyZ/8yeKqq6666qqr/hP98Du9ww9fC9eeRqe3YOtv5L/5a/TX/B/Uq/SP3LrpjVaqJ1Mqk2ILo97t4va0uo2rrrrq/4VOrTxkfuuDN+JwC6MarQatAOy3zb2150uuuupfKaWSVmmK3qg0VKyotsQLINmCxLYgAyfPRyGHzm0ZHg6XTMsDDwdc9Z/uUQc7rwagOh4AXPfdv3iCq656/hBXXfbFX/zFBvjkT/5kcdVVV1111VX/id74jd/4jT/p2PYn9Yr+BvuGQQw/IX5iMAP/x7z49oPeYqV6MqUyKbYw6t0ubk+r27jqqqv+X9gsR4ubZ7ffPNd6gVGNsQ+sRN6btncnuoGrrvqPJDQ5ulSpxjERvVExETwfki1IbAsycPJ8VLdlZVqFp+WSaXng4YCr/kM96mDn1QBUxwOA6777F09w1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquuuuo/2c+/4zv8/KbYvE5x3dye/438N3+N/pr/Q156ce1LH3RbL2WkMWIHo97t4va0uo2rrrrq/4Wdsr/14NkzHhzKAo6OqZdMc+R+27k4USauuuq/UCNqEl2TakNdUjqeD8kWJLYFGTh5PqrbsjKtwtNyybQ88HDAVf9mjzrYeTUA1fEA4Lrv/sUTXHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq676T/be7/wO7/1e5r3maH4dXDeI4SfETwxm4P+A6/rt67bm17wRwKSylVADr05My6dgN6666qr/885050/e2N95M0BRlnDrJDO5TJfy2AVb5qqr/geYiD5VakLXiJpE5fmQbEHKTsCBk+ejui17xsOW03JQGw48HHDVi+RRBzuvBqA6HgBc992/eIKrrnr+EFdd9sVf/MUG+ORP/mRx1VVXXXXVVf/Jtra2tn74Td/khzfF5nWK6+b2/A+CP3iKeQr/y/Uq/YO3H/R2ifpJMU80lz2ebMsnYjeuuuqq//MeMr/t5mNl9yRAodWiVgFWnq0O2tYlrrrqf7iJ6FOlJnSNqElUno/AibDsBBw4eT6q27KSQ+dpdcBwMLgNA23gqufwqIOdVwNQHQ8ArvvuXzzBVVc9f4irLvviL/5iA3zyJ3+yuOqqq6666qr/Au/9zu/w3u9l3mtD2rjGXHMgDn4CfoL/5V5m8+bX2S/9LYnqpNgC2G6rp/bZDrjqqqv+T+vUykPmtz54Iw63MKrRatAKwGFu7i9zfsRVV/0vNRF9qtSEbiJ6E8HzETgRlp2AAyfPh3DraMvqadVyWg5qw4GHA/4fe9TBzqsBqI4HANd99y+e4Kqrnj/EVZd98Rd/sQE++ZM/WVx11VVXXXXVf4Gtra2tn3uzN/k5gJvQTRXqHwR/8BTzFP6Xeuz8zGOHfucVjDQqtoGYe7x3cxru4aqrrvo/bbMcLW6e3X7zXOsFRjXGPrAS+aBtXRrcr7nqqv9LhCZH1xR9ErWhzkTwfEi2ILEtSGELzPNR3ZaVHDpPqwOGg8FtGGgD/w886mDn1QBUxwOA6777F09w1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquuuuq/yCe/0zt88hvBG20ptk7bpw/EwU/AT/C/0Mlu4+TpxXVvlKgfIzZtdYFXJ8ajJ3LVVVf9n7ZT9rcePHvGg0NZwNEx9ZJpjtxvOxcnysRVV/1/IDQ5uqbok6iJShKVFyBwglNgwIGT50O4dbRl9bRqOS2XTMsl45L/Yx51sPNqAKrjAcB13/2LJ7jqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVdd9V/kuuuuu+6HXus1fgjgJnRThforwa/cY+7hf5FepX/k1k1vtFI92VDfFBtAnmjLJ0bmwFVXXfV/1pnu/Mkb+ztvBijKEm6dZCaX6VIeu2DLXHXV/3MT0adKTSgNdUnpeAECJ8LYFqSwBeb5qG7LyrTqnMMBw8HgNgy0gf+lHnWw82oAquMBwHXf/YsnuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV131X+iT3+kdPvmN4I120M5JOHmPuOdX4Ff4X+QVN65/xQt14zEplUmxhdFWrm+dtekSV1111f9ZD5nfdvOxsnsSoNBqUasAK89WB23rElddddULlFJpRE3UJVEb6kwEz4dkyxicYAs5cPJ8CLeOtqyeVrgNS6blgYcD/hd41MHOqwGojgcA1333L57gqqueP8RVl33xF3+xAT75kz9ZXHXVVVddddV/oYc//OEP/7aXe5lvCxQ3STeFHb8S/Mo95h7+F7hldvyWfnbqdQDGiG1bpXe7uD2tbuOqq676P6lTKw+Z3/rgjTjcwqhGq0ErAIe5ub/M+RFXXXXVv8lE9KlSE0pDXVI6XoDAiTC2BSlsgXk+Cjl0bsvO02rJtBzchiXjkv9BHnWw82oAquMBwHXf/YsnuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV131X+yr3+kdvvql4KWOo+PH4fg94p5fgV/hf7hepX/w9oPeLlE/KRaJZrLHk235ROzGVVdd9X/OZjla3Dy7/ea51guMaox9YCXyQdu6NLhfc9VVV/2HSqk0oibqkqgNdSaCFyBwglNgwIGTF6BnOgi3sXMOBwwHy5yWTdn4b/Cog51XA1AdDwCu++5fPMFVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqqv9iL/3SL/3SX/WoR3xVoLhJuins+IngJw7MAf+DveTWg97oKOp1ieqk2AI41pZPqplLrrrqqv9zdsr+1oNnz3hwKAs4Ok2dsJoj99vOxYkycdVVV/3XEJocXarUhNJQl5SOF0CysS1IQQpbYJ4P4dbRlrMcDwe3YVAbDjwc8J/sUQc7rwagOh4AXPfdv3iCq656/hBXXfbFX/zFBvjkT/5kcdVVV1111VX/Db76nd7hq18KXuqk4uSOvfNU+am/j36f/6EeOz/z2KHfeQUjjRE7GM093rs5Dfdw1VVX/Z9zpjt/8sb+zpsBgiyF1klmcpku5bELtsxVV1313y6lklZpij6JmqgkUXkBAifC2BaksAXm+ahuy0oOnafVkmk5uA1LxiX/QR51sPNqAKrjAcB13/2LJ7jqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVdd9d/g1V/91V/98268/vOqVG8yNwH8RPATB+aA/2FOdhsnTy6ufwuASWUroRby8Pi4fApXXXXV/zkPmd9287GyexIgaLWqVYCVZ6uDtnWJq6666n+8iehNlBRlInorqi3xfEi2jMEJtpADJy9AdVtWcug8rQ4YDga3YaAN/Cs96mDn1QBUxwOA6777F09w1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquuuuq/yQ+/0zv88LVw7Wl0egu2nio/9ffR7/M/SK/SP3T75reYKFtNMWtoAeSp6ehx2I2rrrrq/4xOrTxkfuuDN+JwC6CqdUErAIe5ub/M+RFXXXXV/15Ck6NLlZrQNVSS0vECSDa2BSlIYQvMC9AzHYTb2DmHA4aDwW0YaAMvwKMOdl4NQHU8ALjuu3/xBFdd9fwhrrrsi7/4iw3wyZ/8yeKqq6666qqr/pu88Ru/8Rt/0rHtT6pSvcncNIjhJ8RPDGbgf4iX27jx1S/V+cNSKhOxDbCV61tnbbrEVVdd9X/GZjla3Dy7/ea51guMaox9YCXyQdu6NLhfc9VVV/2f1IiaUknUJVEb6kwEL0DgRBjbghS2wDwfwq2jLaunFW7Dkmm5zGnZlO1RBzuvBqA6HgBc992/eIKrrnr+EP9zHAdeiisuAX/NC3YceCmuuAT8Nf9OX/zFX2yAT/7kTxZXXXXVVVdd9d/oh9/pHX74Wrj2GnTNBmz8jfw3f43+mv8Bbpkdv6WfnXodI02hLVtlltO5rba+k6uuuur/jJ2yv/Xg2TMeHMoCjk5TJ6zmyP22c3GiTFx11VX/70xEb6KkKBPRGxUTwQsQOBHGtiCFLTDPh3C7fm88AUAdl4K84bt+YYerrnr+EP95zAv2OsBv82zvDXwVcJxn+23gbYBdntNLAz8FPJhn+2vgbYBb+Tf64i/+YgN88id/srjqqquuuuqq/0Zv/MZv/MafdGz7k+Zofh1cN4jhJ8RPDGbgv9FWmW3dsHnjWyTqJ8Ui0Szw6sS0fAp246qrrvo/4Ux3/uSN/Z03AwRZCq2TzOQyXcpjF2yZq6666qr7CU2OLlVqQmmos6LaEi9A4ERYdgIOnAA3XBqOAUx9NoAHfefPdlx11fOH+M/xYODpwO8Av83z+m7gVq54beC3gL8BPhq4FXhv4LOA3wZeh2d7MPBXgID3Bm4FXhv4KuCvgZfh3+iLv/iLDfDJn/zJ4qqrrrrqqqv+G21tbW398Ju+yQ9vis3rFNfN7fnfyH/z1+iv+W/04tsPeouV6skmdY3YBPJYWz6lZi656qqr/k94yPy2m4+V3ZMAQatVrQKsPFsdtK1LXHXVVVe9qIQmR5cqNaFrqCSl4wWQ7JsvrRYynvpsAA/6zp/tuOqq5w/xn+OtgZ8C3gf4bl643wJeG3gIcCvP9t3AewEvA/w1V3w38F7A2wA/zbN9NvBZwPsA382/wRd/8Rcb4JM/+ZPFVVddddVVV/03e+93fof3fi/zXnM0vw6uOxAHPwE/wX+Tl15c+9IH3dZLGWKMso3Rhqe7FtP6LFddddX/ep1auWl25w3Hyu5JgKrWBa0AHObm/jLnR1x11VVX/QdIqaRVmqI3Kg2VpHQAt+wt5wCtywngQd/5sx1XXfX8If5zfDbwWcDLAH/NC2fgb4CX5jm9NfBTwNcAH80VF4FLwIN5Tg8Gng78DPDW/Bt88Rd/sQE++ZM/WVx11VVXXXXVf7Otra2tH37TN/nhTbF5A7qhh/4Pgj94inkK/8Wu67ev25pf80YAk8pWQu3c9nam1dO56qqr/tfr1MrDFk952FzrBUY1xj6wEvmgbV0a3K+56qqrrvpP1oh6ej9vBGhdTgAP+s6f7bjqqucP8Z/jt4HXAk4ArwW8NPDbwN8AuzzbawO/BXwO8Nk8LwO/A7w28GDg6cDvAK/N8zJwK/AQ/g2++Iu/2ACf/MmfLK666qqrrrrqf4APf6d3+PC3g7fbUmydtk8fiIOfgJ/gv1Cv0j90++a3mChbk2KeaA7kqenocdiNq6666n+1zXK0uHl2+81zrReAqsY+sBJ5bzp2YaJMXHXVVVf9Fzmxz4MAWpcTwIO+82c7rrrq+UP857jIsx3n2XaB9wF+miteG/gt4HOAz+Z5Gfgd4LWB1wZ+C/ge4L15XrcCDwLEv8EXf/EXG+CTP/mTxVVXXXXVVVf9D3Dddddd90Ov9Ro/BHATuqlC/YPgD55insJ/kZfZvPl19kt/S0plIrYBttvqqX22A6666qr/1TbL0eJhs6c9LJQFHB1TL5nJZbqUxy7YMlddddVV/4VO7PMggNblBPCg7/zZjquuev4Q//GOAxe54nOA7+aKtwY+GzDwMsCtwEcDXwV8DvDZPC9zhYDXBn4L+Bzgs3levw28FiD+Db74i7/YAJ/8yZ8srrrqqquuuup/iE9+p3f45DeCN9pSbJ22T98j7vkV+BX+Czx2fuaxQ7/zCkYaFdtAzD3euzkN93DVVVf9r3amO3/y+u7uG0JZgiyF1klmcB33c+eiLXPVVVdd9V/sxD4PAmhdTgAP+s6f7bjqqucP8R/vOPDSwHHgp3lOHw18FfAzwFsD7w18F/A5wGfzvAxcAo4Drw38FvA5wGfzvH4beC1APJcv/uIvNi+iT/7kTxZXXXXVVVdd9T/Eddddd90PvdZr/BDALYpbwo5fCX7lHnMP/4lOdhsnTy+ue6NE/RixaasLvDoxHj2Rq6666n+1M935kzf2d94MUJSlMHUAK89WB23rElddddVV/01O7PMggNblBPCg7/zZjquuev4Q//UM/DXwMsBrA78FfA7w2TwvA78DvDbw0sBfAd8DvDfP6+nAgwHxXL74i7/YvIg++ZM/WVx11VVXXXXV/yCf/E7v8MlvBG90HB0/DsfvEff8CvwK/4lefPtBb7FSPdlQ3xQbQJ5oyydG5sBVV131v9ZD5rfdfKzsngQoal2hFYDDXBwuc+OAq6666qr/Rif2eRBA63ICeNB3/mzHVVc9f4j/egaeATwYeGngr4DPAT6b52Xgd4DX5goDvwO8Ns/LwN8AL82/wRd/8Rcb4JM/+ZPFVVddddVVV/0P8tIv/dIv/VWPesRXBYqbpJvCjl8JfuUecw//CV5x4/pXvFA3HmOIMco2Rps53j5vwwWuuuqq/5U6tXLT7M4bjpXdkwBVrQtaAdhvm3trz5dcddVVV/03O7HPgwBalxPAg77zZzuuuur5Q/zHe2vgo4CfAb6a53QcuAj8DvDaXLELPB14GZ7TWwM/BXwO8NlccStwDDjBc3pp4K+A7wHem3+DL/7iLzbAJ3/yJ4urrrrqqquu+h/mq9/pHb76peCljqPjx+H4U+Wn/j76ff6D3TI7fks/O/U6AGPEtq3Sue3tTKunc9VVV/2v1KmVhy2e8rC51guMaox9YCXy3rS9O9ENXHXVVVf9D3BinwcBtC4ngAd95892XHXV84f4j/fSwF8Bfw28DM/pq4GPAt4H+G6u+G7gvYCXAf6aZ/st4LWBhwC3csVHA18FvA3w0zzbVwMfBbwO8Nv8G3zxF3+xAT75kz9ZXHXVVVddddX/MC/90i/90l/1qEd8VaC4BW4B+IngJw7MAf9BepX+wdsPertE/aRYJJrJHk+25ROxG1ddddX/OrMY+ofMn/7gudYLQFVjH1iJvDcduzBRJq666qqr/oc4sc+DAFqXE8CDvvNnO6666vlD/Of4buC9gN8GPpsrPhp4a+B3gNfm2R4M/DVg4KOBW4GPBt4a+Brgo3m248CtgIHPBv4aeG/gvYG/AV6af6Mv/uIvNsAnf/Ini6uuuuqqq676H+jb3+kdvv1h8LDT6PQWbD1Vfurvo9/nP8hLbj3ojY6iXpeoTootgGNt+aSaueSqq676X2ezHC0eNnvaw0JZwNEx9ZKZXKZLeeyCLXPVVVdd9T/IiX0eBNC6nAAe9J0/23HVVc8f4j/HceCjgc/i2S4B3w18NM/rpYGfBh7Es30N8NE8rwcDPw28FM/2PcBHA7v8G33xF3+xAT75kz9ZXHXVVVddddX/QG/8xm/8xp90bPuTqlRvMjcB/ETwEwfmgH+nx87PPHbod17BSGPEDkZzj/duTsM9XHXVVf/rnKwXj93U33lzKItwVKZeMpPLdCmPXbBlrrrqqqv+hzmxz4MAWpcTwIO+82c7rrrq+UP853tprvhr/mXHgZcGfpt/2XHgpYG/Bnb5d/riL/5iA3zyJ3+yuOqqq6666qr/oX74nd7hh6+Fa0+j01uw9Xj58X+K/pR/h5PdxsmTi+vfAmCM2LTVFfLw+Lh8ClddddX/Ome68ydv7O+8GaAoS2HqAFaerQ7a1iWuuuqqq/6HOrHPgwBalxPAg77zZzuuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVVV/0P9cZv/MZv/EnHtj+pV/Q32DcMYvgJ8RODGfg36FX6R27d9EYr1ZNNMWtoAeSJtnxiZA5cddVV/6vcMrvjhpP1whmAQqtFrQKsvDg6aBv7XHXVVVf9D3ZinwcBtC4ngAd95892XHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq676H+yH3+kdfvhauPY6xXVze/438t/8Nfpr/g1ebuPGV79U5w9LqUyKLYy2cn3rrE2XuOqqq/5Xecj8tpuPld2TAFWtC1oB2G+be2vPl1x11VVX/Q93Yp8HAbQuJ4AHfefPdlx11fOHuOqyL/7iLzbAJ3/yJ4urrrrqqquu+h/svd/5Hd77vcx7zdH8OrhuEMNPiJ8YzMC/wi2z47f0s1OvY6QptGWrzHI6t9XWd3LVVVf9r9GplYctnvKwudYLjKrGLuRI5IO2dWlwv+aqq6666n+BE/s8CKB1OQE86Dt/tuOqq54/xFWXffEXf7EBPvmTP1lcddVVV1111f9gW1tbWz/8pm/yw5ti8zrFdXN7/gfBHzzFPIUX0VaZbd2weeNbJOonxSLRLPDqxLR8Cnbjqquu+l9hFkP/kPnTHzzXeoFRjbEPrETem45dmCgTV1111VX/S5zY50EArcsJ4EHf+bMdV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqqv+h3vvd36H934v814b0sY15poDcfAT8BO8iF5y60FvdBT1uiZ1jdgEONaWT6qZS6666qr/FTbL0eJhs6c9LJQFHB1TL5nJZdrLnd10NK666qqr/hc5sc+DAFqXE8CDvvNnO6666vlDXHXZF3/xFxvgkz/5k8VVV1111VVX/Q+3tbW19XNv9iY/B3ATuqlC/YPgD55insK/4KUX1770Qbf1UkYaI3Yw2vB012Jan+Wqq676X2Gn7G89ePaMB4eyCEdl6iUzuUyX8tgFW+aqq6666n+ZE/s8CKB1OQE86Dt/tuOqq54/xFWXffEXf7EBPvmTP1lcddVVV1111f8Cn/xO7/DJbwRvtKXYOm2fPhAHPwE/wQuxVWZbN2ze+BaJ+kllK6F2bns70+rpXHXVVf8rnOnOn7yxv/NmgKIshakDWHm2Omhbl7jqqquu+l/qxD4PAmhdTgAP+s6f7bjqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVdd9b/Addddd90PvdZr/BDATeimCvW3gt+6zdzGC/CSWw96o6Oo1zXUN8UGkKemo8dhN6666qr/8W7s7772THf2OoCg1apWAVaerQ7a1iWuuuqqq/4XO7HPgwBalxPAg77zZzuuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVVV/0v8cnv9A6f/EbwRjto5yScvEfc8yvwKzwft8yO39LPTr2OkcaIHYw2c7x93oYLXHXVVf/jPWR+283Hyu5JgKrWBa0A7LfNvbXnS6666qqr/pc7sc+DAFqXE8CDvvNnO6666vlDXHXZF3/xFxvgkz/5k8VVV1111VVX/S9x3XXXXfdDr/UaPxQobpJuCjt+JfiVe8w9PECv0j90++a3mChbk2KRaFbIw+Pj8ilcddVV/6N1auUh81sfvBGHWxhVjV3IkciHbWN/7fmSq6666qr/A07s8yCA1uUE8KDv/NmOq656/hBXXfbFX/zFBvjkT/5kcdVVV1111VX/i3z1O73DV78UvNRxdPw4HL9H3PMr8Cs8wCtuXP+KF+rGYxLVSbEFcKItHx+ZA1ddddX/WJ1aedjiKQ+ba73AqMbYB1Yi703HLkyUiauuuuqq/yNO7PMggNblBPCg7/zZjquuev4QV132xV/8xQb45E/+ZHHVVVddddVV/4u89Eu/9Et/1aMe8VWB4ibpprDjJ4KfODAHACe7jZMnF9e/BcAYsW2rzD3euzkN93DVVVf9jzWLoX/I/OkPnmu9AEenqRPW5DIdtO1LE2Xiqquuuur/kBP7PAigdTkBPOg7f7bjqqueP8RVl33xF3+xAT75kz9ZXHXVVVddddX/Ml/9Tu/w1S8FL3VScXLH3nmq/NTfR78P8OLbD3qLlerJSTFPNJc9nmzLJ2I3rrrqqv+RNsvR4mGzpz0slAUcHVMvmcllupTHLtgyV1111VX/x5zY50EArcsJ4EHf+bMdV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqqv+l3npl37pl/6qRz3iq6pUbzI3AfxE8BO3zM7cMvQ7r2CIMco2Rttt9dQ+2wFXXXXV/0ib5WjxsNnTHhbKAo6OqZfMkP1631uXbJmrrrrqqv+DTuzzIIDW5QTwoO/82Y6rrnr+EFdd9sVf/MUG+ORP/mRx1VVXXXXVVf8L/fA7vcMPXwvXnkant2Dr9ojb79h+8LWJ+jFi01bXue3tTKunc9VVV/2PdLJePHZTf+fNoSxFWQpTB7DybHXQti5x1VVXXfV/2Il9HgTQupwAHvSdP9tx1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquuuup/oTd+4zd+4086tv1JVao3mZvG6E78xfbNu4OiNmITyFPT0eOwG1ddddX/OGe68ydv7O+8GaAoS2HqAFaerQ7a1iWuuuqqq/6PO7HPgwBalxPAg77zZzuuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVVV/0v9cPv9A4/fC1ce0vUW9bR3XB3t334jNkJAbHh6a7FtD7LVVdd9T/Ome78yRv7O28GKMpSmDqAw1wcLnPjgKuuuuqq/wdO7PMggNblBPCg7/zZjquuev4QV132xV/8xQb45E/+ZHHVVVddddVV/0u98Ru/8Rt/6vFjn7pZNx+78LQ5qeivt29YOn14fFw+hauuuup/nBv7u6890529DqCqdUErAPttc2/t+ZKrrrrqqv8nTuzzIIDW5QTwoO/82Y6rrnr+EFdd9sVf/MUG+ORP/mRx1VVXXXXVVf9LbW1tbf3eW7/9Hyyjv2WzDbMgu7tmx4aD6P++Zi656qqr/kd5yPy2m4+V3ZMAVa0LWgHYb5t7a8+XXHXVVVf9P3JinwcBtC4ngAd95892XHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq676X+rhD3/4wz/iDd/7517h4Lbrwsw2c9U1yuoZs+N/wVVXXfU/ykPmt918rOyexKhGq0Eribw3be9OdANXXXXVVf/PnNjnQQCtywngQd/5sx1XXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqqq/6XeotP+aqv2vDpl/ugu//opSrZH5tWschxeV+/ddv5snGBq6666r9dp1YeMr/1wRtxuIVRjbEPrETem45dmCgTV1111VX/D53Y50EArcsJ4EHf+bMdV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqqv+F3rjN37jN64v/d6fJBSvcfDUx7zy/m3bO2042MyhDorhKbNTj+eqq676b9WplYctnvKwudYLjGqMfWAl8t507MJEmbjqqquu+n/qxD4PAmhdTgAP+s6f7bjqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVdd9b/M1tbW1ut8xLf/kM2WpGtOTEfbH3zvH+0s2nhxw9NO4Li327z9fNm4wFVXXfXfolMrD1s85WFzrReAqsY+sCaXaS93dtPRuOqqq676f+zEPg8CaF1OAA/6zp/tuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV131v8xbfPInf7J56TcSmoOvA8b3PP8X9zz86NzNHe5nnjbWKsunzk4+iauuuuq/3GY5Wjx49owHdxp7cHRMvWQml+lSHrtgy1x11VVX/T93Yp8HAbQuJ4AHfefPdlx11fOHuOqyL/7iLzbAJ3/yJ4urrrrqqquu+l/kpV/6pV/6pjf+5K8SCsQN2BX4mxvbHU/5oLv+4e0ANj0eE+i2fuepBzE74Kqrrvovs1mOFg+bPe1hoSzg6Jh6yUwu06U8dsGWueqqq666ihP7PAigdTkBPOg7f7bjqqueP8RVl33xF3+xAT75kz9ZXHXVVVddddX/Im/+KT/0Q1jXIR2XfRxx0X7CzwK879n9V3/I6sLDenLeu82X0R08vT/+VK666qr/EpvlaPGw2dMeFsoSZCm0TjIrz1YHbesSV1111VVXPcuJfR4E0LqcAB70nT/bcdVVzx/iqsu++Iu/2ACf/MmfLK666qqrrrrqf4m3eO/3fm9f98bvJVTBNwGg3V+x77kH4KWX29e93bm/fyNJ2shhR6Db+p2nHsTsgKuuuuo/1Znu/Mkb+ztvBijKUpg6gJVnq4O2dYmrrrrqqquew4l9HgTQupwAHvSdP9tx1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquuuup/geuuu+66l3/vr/4hAInrMHPg8eYJf8oDfOS95974zHB4bU/Oe7f5pdJfvLM7dhtXXXXVf5oz3fmTN/Z33gxQlKUwdQArL44O2sY+V1111VVXPY8T+zwIoHU5ATzoO3+246qrnj/EVZd98Rd/sQE++ZM/WVx11VVXXXXV/wJv8Slf9VX29S8tsYU5DYziaT+eDAMP8NLL7eve7tzfv5EkbeZwDOCp8xOPX1MHrrrqqv9wZ7rzJ2/s77wZoNBqUasA+21zb+35kquuuuqqq56vE/s8CKB1OQE86Dt/tuOqq54/xFWXffEXf7EBPvmTP1lcddVVV1111f9wr/7qr/7qx1/9wz9PKCzfJBPW0W/h227j+fjIe8+98Znh8No5baM6+0ulv3hnd+w2rrrqqv9QD5nfdvOxsnsSoKp1QSsA+21zb+35kquuuuqqq16gE/s8CKB1OQE86Dt/tuOqq54/xFWXffEXf7EBPvmTP1lcddVVV1111f9gW1tbW6/zEd/+QzZbSKdlbyHutZ/wy7wAb7BXH/6al576aiFiI8cdgKfOTzx+TR246qqr/kM8ZH7bzcfK7kmAqtYFrSTyYdvYX3u+5KqrrrrqqhfqxD4PAmhdTgAP+s6f7bjqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVdd9T/YW374h394br362wnNwdcBKO74icyDA16IT77rrrffbOPmnLZRnf1umZ27q9u5k6uuuurfpVMrN83uvOFY2T2JUdXYhRyJvDcduzBRJq666qqrrvoXndjnQQCtywngQd/5sx1XXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqqq/6HeumXfumXvumNP/mrABA3yPTA35gn/DX/gjfYqw9/zUtPfbUQsZHjTkPtKYuTj2+OxlVXXfVv0qmVhy2e8rC51guMaox9YCXy3nTswkSZuOqqq6666kVyYp8HAbQuJ4AHfefPdlx11fOHuOqyL/7iLzbAJ3/yJ4urrrrqqquu+h/qLT75277NbD8c6bjs44ZDeMKP8yL65LvuevvNNm4uPG0VXM+X+b33dtv3cNVVV/2rdWrlYYunPGyu9QKjGmMfWJPLdNC2L02Uiauuuuqqq15kJ/Z5EEDrcgJ40Hf+bMdVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqqv+B3vzt3/7tefjbf5hQtXyDTKDdX7HvuYcX0dtdHF/6pQ/ueqkCdeFxq6H2lMXJxzdH46qrrnqRdWrlYYunPGyu9QIcHVMvmcllupTHLtgyV1111VVX/auc2OdBAK3LCeBB3/mzHVdd9fwhrrrsi7/4iw3wyZ/8yeKqq6666qqr/oe57rrrrnuF9/nqb7PZkrgOM0c81X7C7/OvcCz7/qPueurbd85u4Wmr4Hq+zO+9t9u+h6uuuupFslmOFg+bPe1hoSzg6Jh6yUwu06U8dsGWueqqq6666l/txD4PAmhdTgAP+s6f7bjqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVdd9T/Mm3/K530efsSrI23IvgYYxdN+PBkG/pXe7uL40i99cNdLFagLj1sNtacsTj6+ORpXXXXVC7VZjhYPmz3tYaEs4OiYeskM2a/3vXXJlrnqqquuuurf5MQ+DwJoXU4AD/rOn+246qrnD3HVZV/8xV9sgE/+5E8WV1111VVXXfU/yKu/+qu/+vFX//DPEwrLN8kEEX/mfNzj+Dc4ln3/UXc99e07Z7fwtFVwPV/m997bbd/DVVdd9QJtlqPFw2ZPe1goCzg6pl4yK89WB23rElddddVVV/27nNjnQQCtywngQd/5sx1XXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqqq/6H2Nra2nrtj/i2b8O6TuIkZgdxr/2EX+bf4e0uji/90gd3vVSBuvC41VB7yuLk45ujcdVVVz2PzXK0eNjsaQ8LZSnKUpg6gJVnq4O2dYmrrrrqqqv+3U7s8yCA1uUE8KDv/NmOq656/hBXXfbFX/zFBvjkT/5kcdVVV1111VX/Q7zlh3/4h+fWq78d0AtuAED3/Zx94QL/Dsey7z/qrqe+fefsFp62Cq7ny/zee7vte7jqqquew2Y5Wjxs9rSHhbIUZSlMHcDKs9VB27rEVVddddVV/yFO7PMggNblBPCg7/zZjquuev4QV132xV/8xQb45E/+ZHHVVVddddVV/wM8/OEPf/ij3/7zvw0AcYNMD/yNecJf8x/g7S6OL/3SB3e9VIG68LjVUHvK4uTjm6Nx1VVXXXamO3/y+u7uG0JZirIUpg5g5dnqoG1d4qqrrrrqqv8wJ/Z5EEDrcgJ40Hf+bMdVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqqv8B3uJTvuqr7OtfWtIO9knDYfC0n02Ggf8Ax7LvP+qup7595+wWnrYKrufL/N57u+17uOqqqzjTnT95Y3/nzQBFWQpTB3CYm/vLnB9x1VVXXXXVf6gT+zwIoHU5ATzoO3+246qrnj/EVZd98Rd/sQE++ZM/WVx11VVXXXXVf7M3f/u3f3se/vYfJlQt3yATaPdX7Hvu4T/Q210cX/qlD+56qQJ14XGrofaUxcnHN0fjqqv+HzvTnT95Y3/nzQBFWQpTB7DfNvfWni+56qqrrrrqP9yJfR4E0LqcAB70nT/bcdVVzx/iqsu++Iu/2ACf/MmfLK666qqrrrrqv9HW1tbW63zEt/+QzZaka7A3BLcnT/hN/oMdy77/qLue+vads1t42iq4ni/ze+/ttu/hqqv+nzrTnT95Y3/nzQBFrSu0ArDfNvfWni+56qqrrrrqP8WJfR4E0LqcAB70nT/bcdVVzx/iv85Lc8Vf8/wdB16KKy4Bf80Ldhx4Ka64BPw1/05f/MVfbIBP/uRPFlddddVVV1313+jNP+XzPg8/4tWF5uDrgFFxx89mHhzwn+DtLo4v/dIHd71UgbrwuNVQe8ri5OObo3HVVf/PnOnOn7yxv/NmgKrWBa0A7LfNvbXnS6666qqrrvpPc2KfBwG0LieAB33nz3ZcddXzh/iv8dLAXwG/A7w2z+u9ga8CjvNsvw28DbDLc3pp4KeAB/Nsfw28DXAr/0Zf/MVfbIBP/uRPFlddddVVV1313+SlX/qlX/qmN/7krxIKxA3YlYg/cz7ucfwnOZZ9/1F3PfXtO2e38LRVcD1f5vfe223fw1VX/T9yy+yOG07WC2cAqloXtAKw3zb31p4vueqqq6666j/ViX0eBNC6nAAe9J0/23HVVc8f4j/fceC3gJcGfgd4bZ7TawO/BfwN8NHArcB7A58F/DbwOjzbg4G/AgS8N3Ar8NrAVwF/DbwM/0Zf/MVfbIBP/uRPFlddddVVV1313+TNP+WHfgjrOqTjso8jLtpP+Fn+k73dxfGlX/rgrpcqUBcetxpqT1mcfHxzNK666v+Bh8xvu/lY2T0JUNW6oJVE3pu2dye6gauuuuqqq/7TndjnQQCtywngQd/5sx1XXfX8If7zfTXw3sAx4HeA1+Y5/Rbw2sBDgFt5tu8G3gt4GeCvueK7gfcC3gb4aZ7ts4HPAt4H+G7+Db74i7/YAJ/8yZ8srrrqqquuuuq/wVu893u/t6974/cCesENAOi+n7MvXOA/2bHs+4+666lv3zm7haetguv5Mr/33m77Hq666v+4h8xvu/lY2T0JUNW6oJVE3puOXZgoE1ddddVVV/2XOLHPgwBalxPAg77zZzuuuur5Q/znem3gt4CPAb4K+B3gtXlOBv4GeGme01sDPwV8DfDRXHERuAQ8mOf0YODpwM8Ab82/wRd/8Rcb4JM/+ZPFVVddddVVV/0Xu+666657+ff+6h8CkLgOMwceb57wp/wXebuL40u/9MFdL1WgLjxuNdSesjj5+OZoXHXV/1EPmd9287GyexKjqrELORJ5bzp2YaJMXHXVVVdd9V/mxD4PAmhdTgAP+s6f7bjqqucP8Z/nOPB04HeAtwYM/A7w2jzbawO/BXwO8Nk8LwO/A7w28GDg6cDvAK/N8zJwK/AQ/g2++Iu/2ACf/MmfLK666qqrrrrqv9ibf8rnfR5+xKtLbGFOA6N42o8nw8B/kWPZ9x9111PfvnN2C09bBdfzZX7vvd32PVx11f9BD5nfdvOxsnsSoxpjH1iJvDcduzBRJq666qqrrvovdWKfBwG0LieAB33nz3ZcddXzh/jP81PA6wAPBnYBA78DvDbP9trAbwGfA3w2z8vA7wCvDbw28FvA9wDvzfO6FXgQIP4NvviLv9gAn/zJnyyuuuqqq6666r/QS7/0S7/0TW/8yV8lFJZvkgkx/UHylKfwX+ztLo4v/dIHd71UgbrwuNVQe8ri5OObo3HVVf9HdGrllvltN2/H/jGMaox9YCXy3nTswkSZuOqqq6666r/ciX0eBNC6nAAe9J0/23HVVc8f4j/HewPfBbwN8NNcYeB3gNfm2T4a+Crgc4DP5nmZKwS8NvBbwOcAn83z+m3gtQDxb/DFX/zFBvjkT/5kcdVVV1111VX/hd78U37oh7CuQzou+zjiXvsJv8x/g2PZ9x9111PfvnN2C09bBdfzZX7vvd32PVx11f8BnVp52OIpD5trvcCoxtgH1uQyHbTtSxNl4qqrrrrqqv8WJ/Z5EEDrcgJ40Hf+bMdVVz1/iP94Dwb+CvgZ4L15NgO/A7w2z/bewHcBnwN8Ns/LwCXgOPDawG8BnwN8Ns/rt4HXAsRz+eIv/mLzIvrkT/5kcdVVV1111VX/Rd7ivd/7vX3dG7+XUAXfBIDu+zn7wgX+m7zdxfGlX/rgrpcqUBcetxpqT1mcfHxzNK666n+xTq08bPGUh821XmBUY+wDa3KZLuWxC7bMVVddddVV/21O7PMggNblBPCg7/zZjquuev4Q//H+CjgBvDSwy7MZ+B3gtXm21wZ+C/gc4LN5XgZ+B3ht4KWBvwK+B3hvntfTgQcD4rl88Rd/sXkRffInf7K46qqrrrrqqv8CW1tbW6/zEd/+QzZbEtdh5sDjzRP+lP9Gx7LvP+qup7595+wWnrYKrufL/N57u+17uOqq/6U6tfKwxVMeNtd6AY6qqQusyWW6lMcu2DJXXXXVVVf9tzqxz4MAWpcTwIO+82c7rrrq+UP8xzP/st8BXht4aeCvgM8BPpvnZeB3gNfmCgO/A7w2z8vA3wAvzb/BF3/xFxvgkz/5k8VVV1111VVX/Rd4i0/+5E82L/1GSBuyrwFG8bQfT4aB/2Zvd3F86Zc+uOulCtSFx62G2lMWJx/fHI2rrvpfplMrD1s85WFzrRfg6Jh6yUwu06U8dsGWueqqq6666r/diX0eBNC6nAAe9J0/23HVVc8f4j/eZ/P8fRbwDOC7gVuB7+aKXeDpwMvwnN4a+Cngc4DP5opbgWPACZ7TSwN/BXwP8N78G3zxF3+xAT75kz9ZXHXVVVddddV/spd+6Zd+6Zve+JO/CkDSTdiViD9zPu5x/A9wLPv+o+566tt3zm7haavger7M7723276Hq676X2SzHC1unt1+81zrBTg6pl4yk8t0KY9dsGWuuuqqq676H+HEPg8CaF1OAA/6zp/tuOqq5w/xX8fA7wCvzXP6buC9gJcB/ppn+y3gtYGHALdyxUcDXwW8DfDTPNtXAx8FvA7w2/wbfPEXf7EBPvmTP1lcddVVV1111X+yt/jkb/s2s/1wpOOyjyMu2k/4Wf4HefNLPPaV9p7xCgXqwuNWQ+0pi5OPb47GVVf9L7BZjhYPmz3tYaEs4OiYeskM2a/3vXXJlrnqqquuuup/jBP7PAigdTkBPOg7f7bjqqueP8R/HQO/A7w2z+nBwF8DBj4auBX4aOCtga8BPppnOw7cChj4bOCvgfcG3hv4G+Cl+Tf64i/+YgN88id/srjqqquuuuqq/0Rv/MZv/Mb1pd/7k4Sq5RtkAu3+in3PPfwP88l33fX2m23cXHjaKrieL/N77+227+Gqq/6H2yxHi4fNnvawUBZwdEy9ZFaerQ7a1iWuuuqqq676H+fEPg8CaF1OAA/6zp/tuOqq5w/xX8fA7wCvzfN6aeCngQfxbF8DfDTP68HATwMvxbN9D/DRwC7/Rl/8xV9sgE/+5E8WV1111VVXXfWfZGtra+t1PuLbf8hmS9I12BuIp9pP+H3+B3qDvfrw17z01FcLERs57jTUnrI4+fjmaFx11f9Qm+Vo8bDZ0x4WyiIclamXzMqz1UHbusRVV1111VX/I53Y50EArcsJ4EHf+bMdV131/CH+ZzkOvDTw2/zLjgMvDfw1sMu/0xd/8Rcb4JM/+ZPFVVddddVVV/0necsP//APz61XfzuhOfg6YFTc8bOZBwf8D/XJd9319ptt3JzTNqqzP1/m997bbd/DVVf9D7RZjhYPmz3tYaEsRVkKUwew8mx10LYucdVVV1111f9YJ/Z5EEDrcgJ40Hf+bMdVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqqv8ED3/4wx/+6Lf//G8DkHQTdgX+xjzhr/kf7A326sNf89JTXy1EbOS401B7yuLk45ujcdVV/4NslqPFw2ZPe1goS1GWwtQBrDxbHbStS1x11VVXXfU/2ol9HgTQupwAHvSdP9tx1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquuuuo/wVt8yld9lX39S0vawT5pOIQn/Dj/C3zyXXe9/WYbN+e0jersL5X+4p3dsdu46qr/ITbL0eJhs6c9LJSlKEth6gBWXhwdtI19rrrqqquu+h/vxD4PAmhdTgAP+s6f7bjqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVdd9R/s1V/91V/9+Kt/+OcJheWbZALt/op9zz38L/AGe/Xhr3npqa8WIjZy3AF46vzE49fUgauu+m92sl48dlN/582hLEVZClMHsN8299aeL7nqqquuuup/hRP7PAigdTkBPOg7f7bjqqueP8RVl33xF3+xAT75kz9ZXHXVVVddddV/oK2tra3X/ohv+zas65BOy95C3Gs/4Zf5X+ST77rr7TfbuDmnbVRnf6n0F+/sjt3GVVf9NzrTnT95Y3/nzQBFWQpTB7DfNvfWni+56qqrrrrqf40T+zwIoHU5ATzoO3+246qrnj/EVZd98Rd/sQE++ZM/WVx11VVXXXXVf6C3eO/3fm9f98bvJTQHXweguOMnMg8O+F/kDfbqw1/z0lNfLURs5LgD8NT5icevqQNXXfXf4Ex3/uSN/Z03AxRlKUwdwH7b3Ft7vuSqq6666qr/VU7s8yCA1uUE8KDv/NmOq656/hBXXfbFX/zFBvjkT/5kcdVVV1111VX/Qa677rrrXv69v/qHACSuw8yBvzFP+Gv+F/rku+56+802bs5pG9XZXyr9xTu7Y7dx1VX/xc5050/e2N95M0BRlsLUAey3zb2150uuuuqqq676X+fEPg8CaF1OAA/6zp/tuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV131H+QtPuWrvsq+/qUltjCnDYfB0342GQb+F3qDvfrw17z01FcLERs57gA8dX7i8WvqwFVX/Rc5050/eWN/580ARVkKUwew3zb31p4vueqqq6666n+lE/s8CKB1OQE86Dt/tuOqq54/xFWXffEXf7EBPvmTP1lcddVVV1111X+Al37pl37pm974k79KKCzfJBNi+oPkKU/hf7FPvuuut99s4+actlGd/aXSX7yzO3YbV131X+BMd/7kjf2dNwMUta7QCsB+29xbe77kqquuuuqq/7VO7PMggNblBPCg7/zZjquuev4QV132xV/8xQb45E/+ZHHVVVddddVV/wHe/FN+6IewrpM4idlB3Gs/4Zf5X+4N9urDX/PSU18tRGzkuAPw1PmJx6+pA1dd9Z/oTHf+5I39nTcDVLUuaAVgv23urT1fctVVV1111f9qJ/Z5EEDrcgJ40Hf+bMdVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqqn+nt3jv935vX/fG7yVUwTcBoPt+zr5wgf8DPvmuu95+s42bc9pGdfaXSn/xzu7YbVx11X+SM935kzf2d94MUNW6oBWA/ba5t/Z8yVVXXXXVVf/rndjnQQCtywngQd/5sx1XXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqqq/4drrvuuute4X2++ttstiSuw8yBx5sn/Cn/R7zBXn34a1566quFiI0cdwCeOj/x+DV14Kqr/oOd6c6fvLG/82aAqtYFrQDst829tedLrrrqqquu+j/hxD4PAmhdTgAP+s6f7bjqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVdd9e/wFp/8yZ9sXvqNkDZkXwOM4mk/ngwD/4d88l13vf1mGzfntI3q7C+V/uKd3bHbuOqq/0BnuvMnb+zvvBmgqnVBKwD7bXNv7fmSq6666qqr/s84sc+DAFqXE8CDvvNnO6666vlDXHXZF3/xFxvgkz/5k8VVV1111VVX/Ru99Eu/9Evf9Maf/FVCgbgBuxLxZ87HPY7/Y95grz78NS899dVCxEaOOwBPnZ94/Jo6cNVV/wHOdOdP3tjfeTNAVeuCVhJ5bzp2YaJMXHXVVVdd9X/KiX0eBNC6nAAe9J0/23HVVc8f4qrLvviLv9gAn/zJnyyuuuqqq6666t/oLT75277NbD8c6bjs44iL9hN+lv+jPvmuu95+s42bc9pGdfaXSn/xzu7YbVx11b/TQ+a33Xys7J4EqGpd0Eoi703HLkyUiauuuuqqq/7PObHPgwBalxPAg77zZzuuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVVV/0bvPnbv/3b8/C3/zChavkGmUC7v2Lfcw//R73BXn34a1566quFiI0cdwCeOj/x+DV14Kqr/o0eMr/t5mNl9yRAVeuCVhJ5bzp2YaJMXHXVVVdd9X/SiX0eBNC6nAAe9J0/23HVVc8f4qrLvviLv9gAn/zJnyyuuuqqq6666l9pa2tr63U+4tt/yGZL0jXYG4in2k/4ff6P++S77nr7zTZuzmkb1dlfKv3FO7tjt3HVVf8GD5nfdvOxsnsSoKp1QSuJvDcduzBRJq666qqrrvo/68Q+DwJoXU4AD/rOn+246qrnD3HVZV/8xV9sgE/+5E8WV1111VVXXfWv9JYf/uEfnluv/nZCc/B1wKi442czDw74P+4N9urDX/PSU18tRGzkuAPw1PmJx6+pA1dd9a/wkPltNx8ruycBqloXtJLIe9OxCxNl4qqrrrrqqv/TTuzzIIDW5QTwoO/82Y6rrnr+EFdd9sVf/MUG+ORP/mRx1VVXXXXVVf8KD3/4wx/+6Lf//G8DkHQTdgX+xjzhr/l/4pPvuuvtN9u4OadtVGd/qfQX7+yO3cZVV72IHjK/7eZjZfckRjVaDVpJ5L3p2IWJMnHVVVddddX/eSf2eRBA63ICeNB3/mzHVVc9f4irLvviL/5iA3zyJ3+yuOqqq6666qp/hbf4lK/6Kvv6l5a0g33ScAhP+HH+H3mDvfrw17z01FcLERs57gA8dX7i8WvqwFVX/QseMr/t5mNl9yRGNcY+sBJ5bzp2YaJMXHXVVVdd9f/CiX0eBNC6nAAe9J0/23HVVc8f4qrLvviLv9gAn/zJnyyuuuqqq6666kX0xm/8xm9cX/q9P0koLN8kE2j3V+x77uH/mU++666332zj5py2UZ39pdJfvLM7dhtXXfVCPGR+283Hyu5JjGqMfWAl8t507MJEmbjqqquuuur/jRP7PAigdTkBPOg7f7bjqqueP8RVl33xF3+xAT75kz9ZXHXVVVddddWLYGtra+u1P+Lbvg3rOqTTsrcEtydP+E3+H3qDvfrw17z01FcLERs57gA8dX7i8WvqwFVXPR8Pmd9287GyexKjGmMfWIm8Nx27MFEmrrrqqquu+n/lxD4PAmhdTgAP+s6f7bjqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVdd9SJ4i/d+7/f2dW/8XkJz8HUAijt+IvPggP+nPvmuu95+s42bc9pGdfaXSn/xzu7YbVx11XN5yPy2m4+V3ZMY1Rj7wErkvenYhYkycdVVV1111f87J/Z5EEDrcgJ40Hf+bMdVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqqn/Bddddd93Lv/dX/xCAxHWYOfA35gl/zf9jb7BXH/6al576aiFiI8cdgKfOTzx+TR246qpnesj8tpuPld2TGNUY+8CaXKaDtn1pokxcddVVV131/9KJfR4E0LqcAB70nT/bcdVVzx/iqsu++Iu/2ACf/MmfLK666qqrrrrqX/AWn/JVX2Vf/9ISW5jThsPgaT+bDAP/z33yXXe9/WYbN+e0jersL5X+4p3dsdu46v+9Tq3cMr/t5u3YP4ZRjbEPrMllupTHLtgyV1111VVX/b91Yp8HAbQuJ4AHfefPdlx11fOHuOqyL/7iLzbAJ3/yJ4urrrrqqquueiFe+qVf+qVveuNP/iqhsHyTTFhHv4Vvu42reIO9+vDXvPTUVwsRGznuADx1fuLxa+rAVf9vdWrlYYunPGyu9QKjGmMfWJPLdCmPXbBlrrrqqquu+n/txD4PAmhdTgAP+s6f7bjqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVdd9UK8+af80A9hXSdxErODuNd+wi9z1bN83N33vuXxaXViTtuozv5S6S/e2R27jav+X+rUysMWT3nYXOsFRjXGPrAml+lSHrtgy1x11VVXXfX/3ol9HgTQupwAHvSdP9tx1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquuuuoFeIv3fu/39nVv/F5CFXwTALrv5+wLF7jqWV56uX3d2537+zcKERs57gA8dX7i8WvqwFX/r3Rq5WGLpzxsrvUCoxpjH1iTy3Qpj12wZa666qqrrroKOLHPgwBalxPAg77zZzuuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVVVz0f11133XWv8D5f/W02WxLXYebA480T/pSrnsdH3nvujc8Mh9f25Lx3my+jO3h6f/ypXPX/RqdWHrZ4ysPmWi8wqjH2gTW5TJfy2AVb5qqrrrrqqque6cQ+DwJoXU4AD/rOn+246qrnD3HVZV/8xV9sgE/+5E8WV1111VVXXfV8vMUnf/Inm5d+I6QN2dcAo3jajyfDwFXP46WX29e93bm/fyNJ2shhR6Db+p2nHsTsgKv+z+vUysMWT3nYXOsFRjXGPrAml+lSHrtgy1x11VVXXXXVA5zY50EArcsJ4EHf+bMdV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqquey0u/9Eu/9E1v/MlfJRSIG7CrmP4gecpTuOoF+sh7z73xmeHw2o6czdwWg2J4yuzU47nq/7ROrTxs8ZSHzbVegKNj6iUzuUyX8tgFW+aqq6666qqrnsuJfR4E0LqcAB70nT/bcdVVzx/iqsu++Iu/2ACf/MmfLK666qqrrrrqubzFJ3/bt5nthyMdl30ccdF+ws9y1Qv1qOHkyXe/96/eAmDD007gOF/m997bbd/DVf8ndWrlYYunPGyu9QIcHVMvmcllupTHLtgyV1111VVXXfV8nNjnQQCtywngQd/5sx1XXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqqqx7gzd/+7d+eh7/9hwlV8E0AaPdX7Hvu4ap/0fue3X/1h6wuPKxAXXjcaqg9ZXHy8c3RuOr/lE6tPGzxlIfNtV6Ao2PqJTO5TJfy2AVb5qqrrrrqqqtegBP7PAigdTkBPOg7f7bjqqueP8RVl33xF3+xAT75kz9ZXHXVVVddddUzbW1tbb3OR3z7D9lsSboGewPxVPsJv89VL5Jj2fcfdddT375zdgtPWwXXS6W/eGd37Dau+j+jUysPWzzlYXOtF+DomHrJTC7TpTx2wZa56qqrrrrqqhfixD4PAmhdTgAP+s6f7bjqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVdd9Uxv8cmf/Mnmpd9IaA6+DhjF0348GQauepG9+SUe+0p7z3iFELGR4w7A0+fHn7SkW3LV/3qdWnnY4ikPm2u9AEfH1Etmcpku5bELtsxVV1111VVX/QtO7PMggNblBPCg7/zZjquuev4QV132xV/8xQb45E/+ZHHVVVddddVVwEu/9Eu/9E1v/MlfBSDpJuwK/I15wl9z1b/aJ99119tvtnGzJ+e923wZ3cHT++NP5ar/1Tq18rDFUx4213oBjo6pl8zkMl3KYxdsmauuuuqqq656EZzY50EArcsJ4EHf+bMdV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqquAt/jkb/s2s/1wpOOyjyMu2k/4Wa76N3np5fZ1b3fu799IkjZy2BHo3m7z9vNl4wJX/a/UqZWHLZ7ysLnWC3B0TL1khuzX+966ZMtcddVVV1111YvoxD4PAmhdTgAP+s6f7bjqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVf9v/cW7/3e7+3r3vi9hCr4JgC0+yv2Pfdw1b/ZR9577o3PDIfXdrifedoYFMPT5yee1ByNq/5X6dTKwxZPedhc6wU4OqZeMivPVgdt6xJXXXXVVVdd9a90Yp8HAbQuJ4AHfefPdlx11fOHuOqyL/7iLzbAJ3/yJ4urrrrqqqv+X7vuuuuue4X3+epvs9mSuA4zRzzVfsLvc9W/y01ta+uD7vqHtwNYeNouuJwv83vv7bbv4ar/NTq18rDFUx4213oBjo6pl8zKs9VB27rEVVddddVVV/0bnNjnQQCtywngQd/5sx1XXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqq/9fe/FM+7/PwI14daUP2NcAonvbjyTBw1b/b210cX/qlD+56qQJ14XGroXbr/PiT1tSBq/7H2yxHi5tnt98813oBjo6pl8zKs9VB27rEVVddddVVV/0bndjnQQCtywngQd/5sx1XXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqq/7de+qVf+qVveuNP/iqhQNyAXcX0B8lTnsJV/yGOZd9/1F1PffvO2c1pG9XZH0R/6bb+2K1c9T/aZjlaPGz2tIeFsoCjY+ols/JsddC2LnHVVVddddVV/w4n9nkQQOtyAnjQd/5sx1VXPX+Iqy774i/+YgN88id/srjqqquuuur/pa2tra3X/ohv+zas6yROYnYQ99pP+GWu+g/1Bnv14a956amvFiIWOW4LdFu/89SDmB1w1f9Im+Vo8bDZ0x4WygKOjqmXzMqz1UHbusRVV1111VVX/Tud2OdBAK3LCeBB3/mzHVdd9fwhrrrsi7/4iw3wyZ/8yeKqq6666qr/l97ivd/7vX3dG78X0AtuAED3/Zx94QJX/Yf7uLvvfcvj0+pET857t/laZfnU2ckncdX/OJvlaPGw2dMeFsoCjo6pl8zKs9VB27rEVVddddVVV/0HOLHPgwBalxPAg77zZzuuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVV/+9cd9111738e3/1DwFIXIeZA483T/hTrvpP8dLL7eve7tzfv5EkLXLcDhz3dpu3ny8bF7jqf4zNcrR42OxpDwtlAUfH1Etm5dnqoG1d4qqrrrrqqqv+g5zY50EArcsJ4EHf+bMdV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqv933uJTvuqr7OtfWtIO9knDYfC0n02Ggav+07zv2f1Xf8jqwsOK3C1y2myoPWVx8vHN0bjqv91mOVo8bPa0h4WygKNj6iWz8mx10LYucdVVV1111VX/gU7s8yCA1uUE8KDv/NmOq656/hD/uR4MPIgrngHcygt2HHgprrgE/DUv2HHgpbjiEvDX/Dt98Rd/sQE++ZM/WVx11VVXXfX/yhu/8Ru/cX3p9/4kobB8k0xYR7+Fb7uNq/5T3dS2tt737se/ZefsFp62Cq67ZXburm7nTq76b7VZjhYPmz3tYaEs4OiYesmsPFsdtK1LXHXVVVddddV/sBP7PAigdTkBPOg7f7bjqqueP8R/nu8C3pvn9NXAx/C83hv4KuA4z/bbwNsAuzynlwZ+Cngwz/bXwNsAt/Jv9MVf/MUG+ORP/mRx1VVXXXXV/xtbW1tbr/MR3/5DNltIp2VvIe61n/DLXPVf4u0uji/90gd3vVSBsvC4DfDU+YnHr6kDV/232CxHi4fNnvawUJaiLIWpA1h5tjpoW5e46qqrrrrqqv8EJ/Z5EEDrcgJ40Hf+bMdVVz1/iP8cXw18FPA9wFdzxUcD7wV8DfDRPNtrA78F/A3w0cCtwHsDnwX8NvA6PNuDgb8CBLw3cCvw2sBXAX8NvAz/Rl/8xV9sgE/+5E8WV1111VVX/b/xlh/+4R+eW6/+dkJz8HXAqLjjZzMPDrjqv8Sx7PsPuefWt9xs4+actlGd/TK6g6f3x5/KVf/lNsvR4mGzpz0slKUoS2HqAFaerQ7a1iWuuuqqq6666j/JiX0eBNC6nAAe9J0/23HVVc8f4j/eceAi8DvAa/Oc/hp4KUA8228Brw08BLiVZ/tu4L2AlwH+miu+G3gv4G2An+bZPhv4LOB9gO/m3+CLv/iLDfDJn/zJ4qqrrrrqqv8XHv7whz/80W//+d8GgLhBpgf+xjzhr7nqv9SrHsxveZOLT3wdSdrIYUeg2/qdpx7E7ICr/stslqPFw2ZPe1goS1GWwtQBrDxbHbStS1x11VVXXXXVf6IT+zwIoHU5ATzoO3+246qrnj/Ef7wHA+8N/DXw0zyn3wZeCzgB7HKFgb8BXprn9NbATwFfA3w0V1wELgEP5jk9GHg68DPAW/Nv8MVf/MUG+ORP/mRx1VVXXXXV/wtv8cnf9m1m++FIx2UfNxzCE36cq/5bfOS95974zHB4bUfOZm6LQTE8ZXbq8Vz1X2KzHC0eNnvaw0JZirIUpg5g5dnqoG1d4qqrrrrqqqv+k53Y50EArcsJ4EHf+bMdV131/CH+6xwHng5cAh7MFa8N/BbwOcBn87wM/A7w2sCDgacDvwO8Ns/LwK3AQ/g3+OIv/mIDfPInf7K46qqrrrrq/7w3f/u3f3se/vYfJlQt3yATaPdX7Hvu4ar/Fo8aTp5893v/6i0ANjztBI7zZX7vvd32PVz1n2qzHC0eNnvaw0JZirIUpg5g5dnqoG1d4qqrrrrqqqv+C5zY50EArcsJ4EHf+bMdV131/CH+870W8NbAWwMngNcG/porXhv4LeBzgM/meRn4HeC1gdcGfgv4HuC9eV63Ag8CxL/BF3/xFxvgkz/5k8VVV1111VX/p21tbW29zkd8+w/ZbEm6BntDcHvyhN/kqv9W73t2/9UfsrrwsAJ14XGrofaUxcnHN0fjqv8Um+Vo8bDZ0x4WylKUpTB1ACvPVgdt6xJXXXXVVVdd9V/kxD4PAmhdTgAP+s6f7bjqqucP8Z/PPNtPAx8D3MoVHw18FfA5wGfzvMwVAl4b+C3gc4DP5nn9NvBagPg3+OIv/mIDfPInf7K46qqrrrrq/7Q3/5TP+zz8iFdH2pB9DTCKp/14Mgxc9d/qWPb9R9311LfvnN3C01bB9VLpL97ZHbuNq/7DbZajxcNmT3tYKEtRlsLUARzm5v4y50dcddVVV1111X+hE/s8CKB1OQE86Dt/tuOqq54/xH++BwO7wFsDXw0YeBngVuC9ge8CPgf4bJ6XgUvAceC1gd8CPgf4bJ7XbwOvBYjn8sVf/MXmRfTJn/zJ4qqrrrrqqv+zXvqlX/qlb3rjT/4qoUDcgF2J+DPn4x7HVf8jvPklHvtKe894hRCxkeMOwNPnx5+0pFty1X+YnbK/9eDZMx4cylKUpTB1APttc2/t+ZKrrrrqqquu+i92Yp8HAbQuJ4AHfefPdlx11fOH+K/13sB3Ad8DvDfw2sBvAZ8DfDbPy8DvAK8NvDTwV8D3AO/N83o68GBAPJcv/uIvNi+iT/7kTxZXXXXVVVf9n/Xmn/JDP4R1HdJx2ccRF+0n/CxX/Y/yyXfd9fabbdzsyXnvNl9Gd/D0/vhTueo/xJnu/Mkb+ztvBijKUpg6gP22ubf2fMlVV1111VVX/Tc4sc+DAFqXE8CDvvNnO6666vlD/Nc6DlwEfgd4beClgb8CPgf4bJ6Xgd8BXpsrDPwO8No8LwN/A7w0/wZf/MVfbIBP/uRPFlddddVVV/2f9Bbv/d7v7eve+L2EKvgmALT7K/Y993DV/ygvvdy+7u3O/f0bSdJGDjsC3dtt3n6+bFzgqn+XM935kzf2d94MUJSlMHUA+21zb+35kquuuuqqq676b3JinwcBtC4ngAd95892XHXV84f4j/fWwHcBHwN8N8/ppYG/An4GeGuu2AWeDrwMz+mtgZ8CPgf4bK64FTgGnOA5vTTwV8D3AO/Nv8EXf/EXG+CTP/mTxVVXXXXVVf/nXHfddde9wvt89bfZbElch5kDjzdP+FOu+h/pI+8998ZnhsNrO9zPPG0MiuHp8xNPao7GVf8mZ7rzJ2/s77wZoChLYeoA9tvm3trzJVddddVVV1313+jEPg8CaF1OAA/6zp/tuOqq5w/xH++lgb8C/hp4GZ7TVwMfBXwM8NVc8d3AewEvA/w1z/ZbwGsDDwFu5YqPBr4KeBvgp3m2rwY+Cngd4Lf5N/jiL/5iA3zyJ3+yuOqqq6666v+ct/iUr/oq+/qXltjCnAZG8bQfT4aBq/5HuqltbX3QXf/wdgALT9sFl/Nlfu+93fY9XPWvdqY7f/LG/s6bAYqyFKYOYL9t7q09X3LVVVddddVV/81O7PMggNblBPCg7/zZjquuev4Q/zm+G3gv4LeBz+aKjwbeGvgb4KV5tgcDfw0Y+GjgVuCjgbcGvgb4aJ7tOHArYOCzgb8G3ht4b+BvgJfm3+iLv/iLDfDJn/zJ4qqrrrrqqv9TXv3VX/3Vj7/6h3+eUFi+SSbE9AfJU57CVf+jvd3F8aVf+uCulypQFx63Gmq3zo8/aU0duOpFdqY7f/LG/s6bAapaF7QCsN8299aeL7nqqquuuuqq/wFO7PMggNblBPCg7/zZjquuev4Q/3k+G/ho4BjP9jXAZwO7PKeXBn4aeBDP9jXAR/O8Hgz8NPBSPNv3AB8N7PJv9MVf/MUG+ORP/mRx1VVXXXXV/xlbW1tbr/0R3/ZtWNdJnMTsIO61n/DLXPU/3rHs+4+666lv3zm7OW2jOvuD6C/d1h+7lateJGe68ydv7O+8GaCqdUErAPttc2/t+ZKrrrrqqquu+h/ixD4PAmhdTgAP+s6f7bjqqucP8Z/vpbnir/mXHQdeGvht/mXHgZcG/hrY5d/pi7/4iw3wyZ/8yeKqq6666qr/M97ywz/8w3Pr1d9OaA6+DkBxx09kHhxw1f8Kb7BXH/6al576aiFikeO2QLf1O089iNkBV71QZ7rzJ2/s77wZoKp1QSsA+21zb+35kquuuuqqq676H+TEPg8CaF1OAA/6zp/tuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV/2f8PCHP/zhj377z/82AMQNMj3wN+YJf81V/6t83N33vuXxaXWiJ+e923ytsnzq7OSTuOoFOtOdP3ljf+fNAFWtC1oB2G+be2vPl1x11VVXXXXV/zAn9nkQQOtyAnjQd/5sx1VXPX+Iqy774i/+YgN88id/srjqqquuuur/hLf4lK/6Kvv6l5a0g33ScBg87WeTYeCq/1Veerl93dud+/s3kqRFjtuB495u8/bzZeMCVz2PM935kzf2d94MUNW6oJVEPmhblwb3a6666qqrrrrqf6AT+zwIoHU5ATzoO3+246qrnj/EVZd98Rd/sQE++ZM/WVx11VVXXfW/3hu/8Ru/cX3p9/4kobB8k0xYR7+Fb7uNq/5Xet+z+6/+kNWFhxW5W+S02VB7yuLk45ujcdWzPGR+283Hyu5JgKrWBa0k8t507MJEmbjqqquuuuqq/6FO7PMggNblBPCg7/zZjquuev4QV132xV/8xQb45E/+ZHHVVVddddX/altbW1uv8xHf/kM2W5Kuwd4Q3J484Te56n+tm9rW1vve/fi37JzdwtNWwXW3zM7d1e3cyVWXPWR+283Hyu5JgKrWBa0k8t507MJEmbjqqquuuuqq/8FO7PMggNblBPCg7/zZjquuev4QV132xV/8xQb45E/+ZHHVVVddddX/am/xyZ/8yeal30hoDr4OGBV3/GzmwQFX/a/2dhfHl37pg7teqkBZeNwGeOr8xOPX1IH/5x4yv+3mY2X3JEBV64JWEnlvOnZhokxcddVVV1111f9wJ/Z5EEDrcgJ40Hf+bMdVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqf62XfumXfumb3viTvwpA0k3YFfgb84S/5qr/Ez75rrvefrONm3PaRnX2y+gOnt4ffyr/jz1kftvNx8ruSYCq1gWtJPLedOzCRJm46qqrrrrqqv8FTuzzIIDW5QTwoO/82Y6rrnr+EFdd9sVf/MUG+ORP/mRx1VVXXXXV/1pv8cnf9m1m++FIx2UfR1y0n/CzXPV/xqsezG95k4tPfB1J2shhR6Db+p2nHsTsgP+HHjK/7eZjZfckRjXGPrASeW86dmGiTFx11VVXXXXV/xIn9nkQQOtyAnjQd/5sx1VXPX+Iqy774i/+YgN88id/srjqqquuuup/pbd47/d+b1/3xu8lVME3AaDdX7HvuYer/k/5yHvPvfGZ4fDajpzN3BaDYnjK7NTj+X/mIfPbbj5Wdk9iVGPsAyuR96ZjFybKxFVXXXXVVVf9L3JinwcBtC4ngAd95892XHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq/7Xue666657hff56m+z2ZK4DjNHPNV+wu9z1f85jxpOnnz3e//qLQA2PO0EjvNlfu+93fY9/D/xkPltNx8ruycxqjH2gZXIe9OxCxNl4qqrrrrqqqv+lzmxz4MAWpcTwIO+82c7rrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVf/rvPmnfN7n4Ue8OtKG7GuAUTztx5Nh4Kr/k97t/PIVH31032MK1IXHrYbaUxYnH98cjf/jHjK/7eZjZfckRjXGPrASeW86dmGiTFx11VVXXXXV/0In9nkQQOtyAnjQd/5sx1VXPX+Iqy774i/+YgN88id/srjqqquuuup/lZd+6Zd+6Zve+JO/SigQN2BXIv7M+bjHcdX/Wcey7z/qrqe+fefsFp62Cq6XSn/xzu7Ybfwf9pD5bTcfK7snMaox9oE1uUwHbfvSRJm46qqrrrrqqv+lTuzzIIDW5QTwoO/82Y6rrnr+EFdd9sVf/MUG+ORP/mRx1VVXXXXV/xpbW1tbr/0R3/ZtWNdJnMTsIO61n/DLXPV/3ptf4rGvtPeMVwgRGznuADx9fvxJS7ol/8d0auVhi6c8bK71AqMaYx9Yk8t0KY9dsGWuuuqqq6666n+xE/s8CKB1OQE86Dt/tuOqq54/xFWXffEXf7EBPvmTP1lcddVVV131v8ZbvPd7v7eve+P3AnrBDQDovp+zL1zgqv8XPvmuu95+s42bPTnv3ebL6A6e3h9/Kv+HdGrlYYunPGyu9QKjGmMfWJPLdCmPXbBlrrrqqquuuup/uRP7PAigdTkBPOg7f7bjqqueP8RVl33xF3+xAT75kz9ZXHXVVVdd9b/Cddddd93Lv/dX/xCAxHWYOfB484Q/5ar/N156uX3d2537+zeSpEWO24HjfJnfe2+3fQ//B3Rq5WGLpzxsrvUCoxpjH1iTy3Qpj12wZa666qqrrrrq/4AT+zwIoHU5ATzoO3+246qrnj/EVZd98Rd/sQE++ZM/WVx11VVXXfW/wlt8yld9lX39S0tsYU4bDoOn/WwyDFz1/8oH37v7ujcOl24uUBcetwCePj/+pCXdkv/FOrXysMVTHjbXeoFRjbEPrMllupTHLtgyV1111VVXXfV/xIl9HgTQupwAHvSdP9tx1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquu+h/v1V/91V/9+Kt/+OcJheWbZMI6+i18221c9f/Osez7j7rrqW/fObuZc9HRZoNiePr8xJOao/G/UKdWHrZ4ysPmWi/A0WnqhDW5TJfy2AVb5qqrrrrqqqv+Dzmxz4MAWpcTwIO+82c7rrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVf+jXXfddde9wvt89bfZbEm6BnsDca/9hF/mqv+3XvVgfsubXHzi6wAsPG0XXHbL7Nxd3c6d/C/TqZWHLZ7ysLnWC3B0TL1kJpfpUh67YMtcddVVV1111f8xJ/Z5EEDrcgJ40Hf+bMdVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqf7S3+JSv+ir7+pdG2pB9DTAq7vjZzIMDrvp/7d3OL1/x0Uf3PaZAWXjcBriz3771Uswv8b9Ep1YetnjKw+ZaL8DRMfWSmVymS3nsgi1z1VVXXXXVVf8HndjnQQCtywngQd/5sx1XXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqq/7He/O3f/u15+Nt/mFBYvkkmiPgz5+Mex1X/7x3Lvn//e29/4+PT6kRHzmZui4baUxYnH98cjf/hNsvR4mGzpz0slAUcHVMvmcllupTHLtgyV1111VVXXfV/1Il9HgTQupwAHvSdP9tx1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquu+h/p4Q9/+MMf8w6f/1U2W5Kuwd5A3Gs/4Ze56qpnetRw8uS73/tXbwGw8LRVcD2I/tJt/bFb+R9ssxwtHjZ72sNCWcDRMfWSGbJf73vrki1z1VVXXXXVVf+HndjnQQCtywngQd/5sx1XXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqq/5He4pO/7dvM9sMl7WCfBEbxtB9PhoGrrnqAt7s4vvRLH9z1UpK0kcOOQPd2G3edL5tn+R9osxwtHjZ72sNCWYIshdZJZuXZ6qBtXeKqq6666qqr/h84sc+DAFqXE8CDvvNnO6666vlDXHXZF3/xFxvgkz/5k8VVV1111VX/47zFe7/3e/u6N34voWr5Bpmwjn4L33YbV131fHzkvefe+MxweG2Ru0VOmw21W+fHn7SmDvwPslP2tx48e8aDQ1mKshSmDmDl2eqgbV3iqquuuuqqq/6fOLHPgwBalxPAg77zZzuuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVV/6M8/OEPf/ij3/7zvw1A4jrMXHB78oTf5KqrXoCb2tbW+979+LfsnN2ctlGd/Vpl+dTZySfxP8SZ7vzJG/s7bwYoylKYOoCVZ6uDtnWJq6666qqrrvp/5MQ+DwJoXU4AD/rOn+246qrnD3HVZV/8xV9sgE/+5E8WV1111VVX/Y+xtbW19dof8W3fhnUd0nHZxw2HwdN+NhkGrrrqhXiDvfrw17z01FeTpEWO24HjfJnfe2+3fQ//zc5050/e2N95M0BRlsLUARzm5v4y50dcddVVV1111f8zJ/Z5EEDrcgJ40Hf+bMdVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqf4y3/PAP//DcevW3A3rBDQBo91fse+7hqqteBB987+7r3jhcurlAXXjcAnj6/PiTlnRL/puc6c6fvLG/82aAQqtFrQLst829tedLrrrqqquuuur/oRP7PAigdTkBPOg7f7bjqqueP8RVl33xF3+xAT75kz9ZXHXVVVdd9T/CS7/0S7/0TW/8yV8FgLhBpgceb57wp1x11YvoWPb9R9311LfvnF1Pznu3+aAYnj4/8aTmaPwXu2523+nr6j03AlS1LmgFYL9t7q09X3LVVVddddVV/0+d2OdBAK3LCeBB3/mzHVdd9fwhrrrsi7/4iw3wyZ/8yeKqq6666qr/dltbW1uv8xHf/kM2WxInMTuIi/LTfjkZBq666l/hpZfb173dub9/I4CFp+2Cy16ZXbij27md/0IPmd9287GyexKgqnVBKwD7bXNv7fmSq6666qqrrvp/7MQ+DwJoXU4AD/rOn+246qrnD3HVZV/8xV9sgE/+5E8WV1111VVX/bd780/5vM/Dj3h1oTn4OgB038/ZFy5w1VX/Bu92fvmKjz667zEFytzjlkB39tu3Xor5Jf4LPGR+283Hyu5JgKrWBa0k8t60vTvRDVx11VVXXXXV/3Mn9nkQQOtyAnjQd/5sx1VXPX+Iqy774i/+YgN88id/srjqqquuuuq/1au/+qu/+vFX//DPEwrEDdgV+BvzhL/mqqv+HT7u7nvf8vi0OtGRs5nboqH2lMXJxzdH4z/RQ+a33Xys7J7EqGrsQo5E3puOXZgoE1ddddVVV111FSf2eRBA63ICeNB3/mzHVVc9f4irLvviL/5iA3zyJ3+yuOqqq6666r/Nddddd90rvM9Xf5vNFtJp2VuIi/YTfparrvp3etRw8uS73/tXbwGw8LRVcF1Gd/D0/vhT+U/QqZVb5rfdvB37xzCqMfaBlch707ELE2Xiqquuuuqqq6667MQ+DwJoXU4AD/rOn+246qrnD3HVZV/8xV9sgE/+5E8WV1111VVX/bd5i0/5qq+yr39ppA3Z1wAjuu+X7QsXuOqq/wBvfonHvtLeM15BkjZy2BHo3m7jrvNl8yz/gTq18rDFUx4213qBUY2xD6xE3puOXZgoE1ddddVVV1111bOc2OdBAK3LCeBB3/mzHVdd9fwhrrrsi7/4iw3wyZ/8yeKqq6666qr/Fm/+9m//9jz87T9MKCzfJBNE/JnzcY/jqqv+A33kvefe+MxweG2Ru0VOmw212+bHnrqkW/IfoFMrD1s85WFzrRcY1Rj7wJpcpkt57IItc9VVV1111VVXPYcT+zwIoHU5ATzoO3+246qrnj/EVZd98Rd/sQE++ZM/WVx11VVXXfVf7uEPf/jDH/MOn/9VNluSrsHeQNxrP+GXueqq/2A3ta2t97378W/ZObuZc9HRZmuV5VNnJ5/Ev1OnVh62eMrD5lovwNFp6oQ1uUyX8tgFW+aqq6666qqrrnoeJ/Z5EEDrcgJ40Hf+bMdVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqv9xbfPK3fZvZfrikHeyTwKi442czDw646qr/BK96ML/lTS4+8XUkaZHjduA4X+b33ttt38O/0WY5Wtw8u/3mudYLcHRMvWQml+lSHrtgy1x11VVXXXXVVc/XiX0eBNC6nAAe9J0/23HVVc8f4qrLvviLv9gAn/zJnyyuuuqqq676L/UW7/3e7+3r3vi9hKrlG2TCOvotfNttXHXVf6IPvnf3dW8cLt1coCw8bgPc1u889SBmB/wrbZajxcNmT3tYKAs4OqZeMoPruJ87F22Zq6666qqrrrrqBTqxz4MAWpcTwIO+82c7rrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVf9lHv7whz/80W//+d8GIHEdZi64PXnCb3LVVf/JjmXff8g9t77lZhs3e3Leu80HxfD0+YknNUfjRbRZjhYPmz3tYaEs4OiYesmsPFsdtK1LXHXVVVddddVV/6IT+zwIoHU5ATzoO3+246qrnj/EVZd98Rd/sQE++ZM/WVx11VVXXfVfYmtra+u1P+Lbvg3rOqTjso8Do3jajyfDwFVX/Rd46eX2dW937u/fCGDhabvgchD9pdv6Y7fyItgsR4uHzZ72sFCWoiyFqQNYebY6aFuXuOqqq6666qqrXiQn9nkQQOtyAnjQd/5sx1VXPX+Iqy774i/+YgN88id/srjqqquuuuq/xFt++Id/eG69+tsBveAGALT7K/Y993DVVf+F3u388hUffXTfY0LEIsdtge7st2+9FPNLvBBnuvMnr+/uviGUpShLYeoAVp6tDtrWJa666qqrrrrqqhfZiX0eBNC6nAAe9J0/23HVVc8f4qrLvviLv9gAn/zJnyyuuuqqq676T/fSL/3SL33TG3/yVwEgbpDpgcebJ/wpV1313+Dj7r73LY9PqxMdOZu5LRpqt86PP2lNHXg+znTnT97Y33kzQFGWwtQBrLw4Omgb+1x11VVXXXXVVf8qJ/Z5EEDrcgJ40Hf+bMdVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqP9XW1tbW63zEt/+QzZbEScyO4TB42s8mw8BVV/03eNRw8uQ73fc3b9w5u7nbZiW7ZXQHT++PP5XncqY7f/LG/s6bAYqyFKYOYL9t7q09X3LVVVddddVVV/2rndjnQQCtywngQd/5sx1XXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqq/1Rv/imf93n4Ea8uNAdfB4Du+zn7wgWuuuq/0Ztf4rGvtPeMV5CkjRx2BLq327jrfNk8yzOd6c6fvLG/82aAqtYFrQDst829tedLrrrqqquuuuqqf5MT+zwIoHU5ATzoO3+246qrnj/EVZd98Rd/sQE++ZM/WVx11VVXXfWf5tVf/dVf/firf/jnCQXiBuwK/I15wl9z1VX/A3zkvefe+MxweG2BuvC4BfD0+fEnLemWD5nfdvOxsnsSoKp1QSsA+21zb+35kquuuuqqq6666t/sxD4PAmhdTgAP+s6f7bjqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVf9p7juuuuue4X3+epvs9lCOi17C3HRfsLPctVV/0Mcy77/qLue+vads5s5Fx1ttlZZ5rGD5bGyexKgqnVBK4l80LYuDe7XXHXVVVddddVV/y4n9nkQQOtyAnjQd/5sx1VXPX+Iqy774i/+YgN88id/srjqqquuuuo/xVt8yld9lX39SyNtyL4GGNF9v2xfuMBVV/0P8qoH81ve5OITX0eSFrnensdqK+brlvPlUGPsAyuR96ZjFybKxFVXXXXVVVdd9e92Yp8HAbQuJ4AHfefPdlx11fOH+M/1YOBBXHEJ+GtesOPAS3HFJeCvecGOAy/FFZeAv+bf6Yu/+IsN8Mmf/Mniqquuuuqq/3Bv/vZv//Y8/O0/TCgs3yQTRPyZ83GP46qr/gd637P7r/7w4dwjd2L/ZG2eAWhnb4o6kMh707ELE2Xiqquuuuqqq676D3FinwcBtC4ngAd95892XHXV84f4z3Ec+CrgvXlOfw28D/DXPKf3Br4KOM6z/TbwNsAuz+mlgZ8CHsyz/TXwNsCt/Bt98Rd/sQE++ZM/WVx11VVXXfUf6uEPf/jDH/MOn/9VNluSrsHeQNxrP+GXueqq/6FevsS1733fn7xbmdSRpUSqIONje0eXcns3HY2rrrrqqquuuuo/zIl9HgTQupwAHvSdP9tx1VXPH+I/x3cB7w18DfDTwK3AWwOfDRh4CLDLFa8N/BbwN8BHA7cC7w18FvDbwOvwbA8G/goQ8N7ArcBrA18F/DXwMvwbffEXf7EBPvmTP1lcddVVV131H+otPvnbvs1sP1zSDvZJYFTc8bOZBwdcddX/QK9Qh+veaf5LrzM7qlvdPdeeCrJ46sIZXndxcLRou1x11VVXXXXVVf+hTuzzIIDW5QTwoO/82Y6rrnr+EP/xHgw8Hfgd4LV5Th8NfBXwOcBnc8VvAa8NPAS4lWf7buC9gJcB/porvht4L+BtgJ/m2T4b+CzgfYDv5t/gi7/4iw3wyZ/8yeKqq6666qr/MG/x3u/93r7ujd9LqFq+QSaso9/Ct93GVVf9D/Rm/X0Pf6P+914NoNPUdxe3j+fFU33LbmrjDAGrRdsbulxy1VVXXXXVVVf9hzmxz4MAWpcTwIO+82c7rrrq+UP8x3tt4KuBnwY+m+f02sBvAT8DvDVXGPgb4KV5Tm8N/BTwNcBHc8VF4BLwYJ7Tg4GnAz8DvDX/Bl/8xV9sgE/+5E8WV1111VVX/Yd4+MMf/vBHv/3nfxuAxHWYueD25Am/yVVX/Q/0HvMnvdQr1L97aYBO42zOsAA4uO1RkeuNVKqUVJfYh1vtvMONq6666qqrrrrqP8SJfR4E0LqcAB70nT/bcdVVzx/iv9ZbAz8FfA7w2cBrA78FfA7w2TwvA78DvDbwYODpwO8Ar83zMnAr8BD+Db74i7/YAJ/8yZ8srrrqqquu+nfb2traeu2P+LZvw7oO6bjs48AonvbjyTBw1VX/w3zExl++2iPi6Q8HmLPe6DT1AHfmNbcfLk8tz9w9e3ixIpr6sGIqHo42p4tcddVVV1111VX/IU7s8yCA1uUE8KDv/NmOq656/hD/dY4DfwU8GHgd4LeB1wZ+C/gc4LN5XgZ+B3ht4LWB3wK+B3hvntetwIMA8W/wxV/8xQb45E/+ZHHVVVddddW/21t++Id/eG69+tsBveAGALT7K/Y993DVVf+DnIi+/9DFr73OtTp3nbDmWm9UWjc58l6fvvN8Hr8AcGy3P3PiYncDoDLFTMDUe3k0n/a46qqrrrrqqqv+3U7s8yCA1uUE8KDv/NmOq656/hD/db4LeG/ga4CP5oqPBr4K+Bzgs3le5goBrw38FvA5wGfzvH4beC1A/Bt88Rd/sQE++ZM/WVx11VVXXfXv8tIv/dIvfdMbf/JXASBukOmBx5sn/ClXXfU/yIno+49a/OIbndSlk8JaaLVVyDI58tZ281OOmC15gGvvnj1ksao7WFGaegFDn0eredvnqquuuuqqq676dzmxz4MAWpcTwIO+82c7rrrq+UP85zsOfBXw3sD3AO/Ns7038F3A5wCfzfMycAk4Drw28FvA5wCfzfP6beC1APFcvviLv9i8iD75kz9ZXHXVVVdd9W+2tbW19Tof8e0/ZLMlcRKzYzgMnvazyTBw1VX/Q7xY7U6++/xnXmeT5VYhy0zrjUKWpfvVne36246YLXkuYZVr75g/fDbFXKkoqR5gtWh7Q5dLrrrqqquuuuqqf7MT+zwIoHU5ATzoO3+246qrnj/Ef67jwG8BLw18D/DePKfXBn4L+Bzgs3leBn4HeG3gpYG/Ar4HeG+e19OBBwPiuXzxF3+xeRF98id/srjqqquuuurf7M0/5fM+Dz/i1YXm4OsA0H0/Z1+4wFVX/Q/xYrU7+T7zH3+jnqkvZFlotSWspfvV0/ygp7RU4wWYr8vizN2zhxcrlCol1QGsFm1v6HLJVVddddVVV131b3JinwcBtC4ngAd95892XHXV84f4z/PSwHcBLw18DvDZPK+XBv4K+Bzgs3leBn4HeG2uMPA7wGvzvAz8DfDS/Bt88Rd/sQE++ZM/WVx11VVXXfVv8pYf/uEfnluv/nZCgbgBuwJ/Y57w11x11f8Qb9bf9/A36n/v1QA6Tf2MYSGs/dzYu40bb2upxr9gvi6LM3fPHl6sUFMtVk3s1Va7MIUnrrrqqquuuuqqf7UT+zwIoHU5ATzoO3+246qrnj/Ef46XBn4LOA68D/DdvGC7wNOBl+E5vTXwU8DnAJ/NFbcCx4ATPKeXBv4K+B7gvfk3+OIv/mIDfPInf7K46qqrrrrqX+2N3/iN37i+9Ht/EgDiBpkecdF+ws9y1VX/Q7xZf9/D36j/vVcD6DT1c9YbABe9ffH2dv1t/CtsHdRjp8/OHgxQmjpZJbFXW+3CFJ646qqrrrrqqqv+VU7s8yCA1uUE8KDv/NmOq656/hD/Of4KeGngfYDv5oX7buC9gJcB/ppn+y3gtYGHALdyxUcDXwW8DfDTPNtXAx8FvA7w2/wbfPEXf7EBPvmTP1lcddVVV131r/Lwhz/84Y9++8//NgCk07K3gFE87ceTYeCqq/4H+IiNv3y1R8TTHw4wZ73RaeoB7mmn77rPJ8/yb7Cz1508eb6/GaA0dbJKina4M563MVddddVVV1111YvsxD4PAmhdTgAP+s6f7bjqqucP8R/vvYHvAnaBv+b5+xvgo7niwcBfAwY+GrgV+GjgrYGvAT6aZzsO3AoY+Gzgr4H3Bt4b+Bvgpfk3+uIv/mIDfPInf7K46qqrrrrqRXbddddd9wrv89XfZrMlaQf7JDCi+37ZvnCBq676b3Yi+v7d53/8Co+Ipz8cYM56o9PUA9yZ19x+Po9f4N/h5IX+up1L3bWAyqReSC08HW1PF2zMVVddddVVV131Ijmxz4MAWpcTwIO+82c7rrrq+UP8x/tt4LV44X4HeG2e7aWBnwYexLN9DfDRPK8HAz8NvBTP9j3ARwO7/Bt98Rd/sQE++ZM/WVx11VVXXfUi2dra2nqdD/+qrzLbD0fakH0NgJj+IHnKU7jqqv9mJ6LvP2rxi290UpdOCmuh1VYhy+TIW9vNTzlituQ/wOn7ZrdsHdYTgMqkXkhT8XC0OV3kqquuuuqqq656kZzY50EArcsJ4EHf+bMdV131/CH+ZzkOvDTw2/zLjgMvDfw1sMu/0xd/8Rcb4JM/+ZPFVVddddVVL5K3+JSv+ir7+pcGesR1MgH8jXnCX3PVVf/NXqx2J99h9kuvdlKXTkqOBavNQpbJkbe2m59yxGzJf6Br7549ZLGqOyCVSTMBU+/l0Xza46qrrrrqqquu+hed2OdBAK3LCeBB3/mzHVdd9fwhrrrsi7/4iw3wyZ/8yeKqq6666qp/0Vt80id9kvUybywUiBuwK+Kp9hN+n6uu+m/2YrU7+T7zH3+jnqkvZFlotSWspfvV0/ygp7RU4z9YWOXaO+YPn00xx4rS1AsY+jxazds+V1111VVXXXXVC3VinwcBtC4ngAd95892XHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq16oN37jN37j+tLv/UkAiBtkesRF+wk/y1VX/Td77f7SLW/e//ar9Ux9VdY5q01hHXpxeKtvenpLNf6ThFWuvWP+8NkUc6WipHqA1aLtDV0uueqqq6666qqrXqAT+zwIoHU5ATzoO3+246qrnj/EVZd98Rd/sQE++ZM/WVx11VVXXfUCvfRLv/RL3/TGn/xVAEinZW8ZDoOn/WwyDFx11X+jN+vve/gb9b/3agCdpn7OegPgorcv3t6uv43/AvN1WZy5e/bwYoVSpaQ6gNWi7Q1dLrnqqquuuuqqq56vE/s8CKB1OQE86Dt/tuOqq54/xFWXffEXf7EBPvmTP1lcddVVV131fD384Q9/+GPe4fO/ymZL0g72SWBE9/2yfeECV1313+j95//wCi9Zn/BYgBnjvNcwB7jg4+fuaNfcyX+h+bosztw9e3ixQk21WDWxV1vtwhSeuOqqq6666qqrnseJfR4E0LqcAB70nT/bcdVVzx/iqsu++Iu/2ACf/MmfLK666qqrrnoeW1tbW6/z4V/1VWb74RJbmNMA1tFv4dtu46qr/puciL5/9/kfv8Ij4ukPB5iz3ug09QB35jW3n8/jF/hvsHVQj50+O3swQGnqZJXEXm21C1N44qqrrrrqqquueg4n9nkQQOtyAnjQd/5sx1VXPX+Iqy774i/+YgN88id/srjqqquuuup5vMWnfNVX2de/NNAjrpMJIv7M+bjHcdVV/01uqVtb7zv76dc5qUsnhTVnvVnV6uTIu33tbRdz+xL/jXb2upMnz/c3A5SmTlZJ0Q53xvM25qqrrrrqqquuepYT+zwIoHU5ATzoO3+246qrnj/EVZd98Rd/sQE++ZM/WVx11VVXXfUc3uKTPumTrJd5Y6GwfJNMIJ5qP+H3ueqq/yavUIfr3mn+S6/TM/WFLDOtNwpZJkfe2m5+yhGzJf8DnLzQX7dzqbsWUJnUC6mFp6Pt6YKNueqqq6666qqrLjuxz4MAWpcTwIO+82c7rrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVc/y5m//9m/Pw9/+w4TC8nUyPeKi/YSf5aqr/pu8/fy2x7xm/bNXBKhM3VzDhrCW7le35Y1PX7sb+B/k9H2zW7YO6wlAZVIvpKl4ONqcLnLVVVddddVVV112Yp8HAbQuJ4AHfefPdlx11fOHuOqyL/7iLzbAJ3/yJ4urrrrqqqsue/VXf/VXP/7qH/55AEinZW8ZDoOn/WwyDFx11X+xE9H37z7/41d4RDz94QAzxnmvYQ5w0dsX7/J1d7ZU43+ga++ePWSxqjtYUZp6AVPv5dF82uOqq6666qqrruLEPg8CaF1OAA/6zp/tuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV/Hwhz/84Y95h8//KpstpOOyjwMjuu+X7QsXuOqq/2K31K2t95399Ouc1KWTwpprvVFpHcA97fRd9/nkWf4HC6tce8f84bMp5lhRmnoBQ59Hq3nb56qrrrrqqqv+nzuxz4MAWpcTwIO+82c7rrrq+UNcddkXf/EXG+CTP/mTxVVXXXXV/3NbW1tbr/0R3/ZtWNdJbGFOA6DdX7HvuYerrvov9gp1uO6d5r/0Oj1TX8gy03qjkGVy5G15/dMPvHnA/wJhlWvvmD98NsWcpNSMDmC1aHtDl0uuuuqqq6666v+xE/s8CKB1OQE86Dt/tuOqq54/xFWXffEXf7EBPvmTP1lcddVVV/0/9xaf/G3fZrYfDvSI62RCTH+QPOUpXHXVf7G3n9/2mNesf/aKAJWpm2vYENbS/eq2vPHpa3cD/4vM12Vx5u7Zw4sVSpWS6gBWi7Y3dLnkqquuuuqqq/6fOrHPgwBalxPAg77zZzuuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddX/Y2/xSZ/0SdbLvLFQtXyDTCCeaj/h97nqqv9CJ6Lv333+x6/wiHj6wwFmjPNewxzgorcv3uXr7mypxv9C83VZnLl79vBihZpqsWpir7bahSk8cdVVV1111VX/D53Y50EArcsJ4EHf+bMdV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrq/6k3f/u3f3se/vYfJhSWr5PpEffaT/hlrrrqv9AtdWvrfWc//TondemksOZab1RaB3BPO33XfT55lv/ltg7qsdNnZw8GKE2drJLYq612YQpPXHXVVVddddX/Myf2eRBA63ICeNB3/mzHVVc9f4irLvviL/5iA3zyJ3+yuOqqq676f+jVX/3VX/34q3/45wFIugZ7A3FRftovJ8PAVVf9F3mFOlz3TvNfep2eqS9kmWm9UcgyOfK2vP7pB9484P+Inb3u5Mnz/c0A0dSHFSna4c543sZcddVVV1111f8jJ/Z5EEDrcgJ40Hf+bMdVVz1/iKsu++Iv/mIDfPInf7K46qqrrvp/5uEPf/jDH/MOn/9VNlsSJzE7wIju+2X7wgWuuuq/yNvPb3vMa9Y/e0WAytTNNWwIa+l+dVve+PS1u4H/Y05e6K/budRdC6hM6oXUwtPR9nTBxlx11VVXXXXV/xMn9nkQQOtyAnjQd/5sx1VXPX+Iqy774i/+YgN88id/srjqqquu+n9ka2tr63U+4tt/yGZLYgtzGgDt/op9zz1cddV/gRPR9+8+/+NXeEQ8/eEAM8Z5r2EOcNHbF+/ydXe2VOP/qNP3zW7ZOqwnAJVJvZCm4uFoc7rIVVddddVVV/0/cWKfBwG0LieAB33nz3ZcddXzh7jqsi/+4i82wCd/8ieLq6666qr/J7a2trZe58O/6qvM9sOBXnADgJj+IHnKU7jqqv8Ct9Strfed/fTrnNSlk8Kaa71RaR3APe30Xff55Fn+H7j27tlDFqu6gxWlqRcw9V4ezac9rrrqqquuuur/gRP7PAigdTkBPOg7f7bjqqueP8RVl33xF3+xAT75kz9ZXHXVVVf9P/EWn/RJn2S9zBsLVcs3yATiqfYTfp+rrvov8Ap1uO6d5r/0Oj1TX8gy03qjkGVy5G15/dMPvHnA/xNhlWvvmD98NsUcK0pTL2Do82g1b/tcddVVV1111f9xJ/Z5EEDrcgJ40Hf+bMdVVz1/iKsu++Iv/mIDfPInf7K46qqrrvp/4C3e+73f29e98XsJheXrZHrB7ckTfpOrrvov8Pbz2x7zmvXPXhGgMnVzDRvCWrpf3ZY3Pn3tbuD/mbDKtXfMHz6bYk5SakYHsO7zaD1v+1x11VVXXXXV/2En9nkQQOtyAnjQd/5sx1VXPX+Iqy774i/+YgN88id/srjqqquu+j/ujd/4jd+4vvR7fxKApGuwNxAX5af9cjIMXHXVf6IT0ffvPv/jV3hEPP3hADPGea9hDnDR2xfv8nV3tlTj/6n5uizO3D17eLFCqVJSHcDUe3k0n/a46qqrrrrqqv+jTuzzIIDW5QTwoO/82Y6rrnr+EFdd9sVf/MUG+ORP/mRx1VVXXfV/2MMf/vCHP+YdPv+rbLaQTsveAkbFHT+beXDAVVf9J7qlbm297+ynX+ekLp0U1lzrjUrrAO5pp++6zyfPchXzdVmcuXv28GKFUhGpXkALT0fb0wUbc9VVV1111VX/x5zY50EArcsJ4EHf+bMdV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrq/6itra2t1/mIb/8hmy2JLcxpAHTfz9kXLnDVVf+JXqEO173T/Jdep2fqC1lmWm8UskyOvC2vf/qBNw+46lnm67I4c/fs4cUKrChNvYAWntYb7dIUnrjqqquuuuqq/0NO7PMggNblBPCg7/zZjquuev4QV132xV/8xQb45E/+ZHHVVVdd9X/Q1tbW1ut8+Fd9ldl+uNAcfB2AmP4gecpTuOqq/0RvP7/tMa9Z/+wVASpTN9ewIayl+9VteePT1+4Grnoe3aT+9N3zh8ymmAMqk3ohJfZqq12YwhNXXXXVVVdd9X/EiX0eBNC6nAAe9J0/23HVVc8f4qrLvviLv9gAn/zJnyyuuuqqq/4PeotP+qRPsl7mjYEecZ1MAI83T/hTrrrqP8mJ6Pt3n//xKzwinv5wgBnjvNcwB7jo7Yt3+bo7W6px1QsUVrn27vlDZuvYBBRNXVgBsFq0vaHLJVddddVVV131f8CJfR4E0LqcAB70nT/bcdVVzx/iqsu++Iu/2ACf/MmfLK666qqr/g/Z2traep2P+LzPs69/aaGwfJ1ML7g9ecJvctVV/0lerHYn32H2S692UpdOCmuu9UaldQD3tNN33eeTZ7nqRXb6vtktW4f1BEBp6mQVgPW8Haz7POSqq6666qqr/pc7sc+DAFqXE8CDvvNnO6666vlDXHXZF3/xFxvgkz/5k8VVV1111f8RW1tbW6/z4V/1VWb74UJh+TqZHnFRftovJ8PAVVf9J3j7+W2Pec36Z68IUMgy03qjkGVy5G15/dMPvHnAVf9qJy/01+1c6q4FUKqUVAcw9V4ezac9rrrqqquuuup/sRP7PAigdTkBPOg7f7bjqqueP8RVl33xF3+xAT75kz9ZXHXVVVf9H7C1tbX1Oh/+VV9lth8uVC1fI9MjLspP++VkGLjqqv9gt9StrfeY/cKrXatz1wF0Gmczxrmwlu5Xt+WNT1+7G7jq32xnrzt58nx/MwBJKRmdgKl4WG5Nuzbmqquuuuqqq/4XOrHPgwBalxPAg77zZzuuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddX/ctddd911r/Den/d5ZvvhQI+4TiYQF+Wn/XIyDFx11X+w1+4v3fLm/W+/Ws/UC2uu9UaldQAXvX3xLl93Z0s1rvp3m6/L4szds4cXK7CiNPUCWng62mi7Djeuuuqqq6666n+ZE/s8CKB1OQE86Dt/tuOqq54/xFWXffEXf7EBPvmTP1lcddVVV/0v9vCHP/zhj3mHz/8qmy2gR1wnE4iL8tN+ORkGrrrqP9CJ6Pv3W/zOq92iu24BqEzdXMOGsCZH3u1rb7uY25e46j/UfF0WJ+7rb5lNMQepTPRCSuzVVrswhSeuuuqqq6666n+RE/s8CKB1OQE86Dt/tuOqq54/xFWXffEXf7EBPvmTP1lcddVVV/0v9fCHP/zhj3mHz/8qmy2gR1wnE4inyk/702QYuOqq/0CvUIfr3nb+66+2yXJLWDMN845pBnDoxeGtvunpLdW46j9FWOXaO+YPn00xB1SaOlmR2MMi94cul1x11VVXXXXV/xIn9nkQQOtyAnjQd/5sx1VXPX+Iqy774i/+YgN88id/srjqqquu+l/o4Q9/+MMf8w6f/1U2WxJbhpMygXiq/YTf56qr/oO9//wfXuEl6xMeC1DIMtd6M8gAuKedvus+nzzLVf/pwionz/Y3bh3WEwClqZNVAFaLtjd0ueSqq6666qqr/hc4sc+DAFqXE8CDvvNnO6666vlDXHXZF3/xFxvgkz/5k8VVV1111f8yb/zGb/zG3cu894fZbElsYU4DIJ5qP+H3ueqq/0AvVruT7zD7pVc7qUsnAWaM817DHGDpfnVnu/62I2ZLrvovders7Mbtg3oaQE21WBVg6r08mk97XHXVVVddddX/cCf2eRBA63ICeNB3/mzHVVc9f4irLvviL/5iA3zyJ3+yuOqqq676X+SN3/iN37i+9Ht/EoDEFuY0VzzePOFPueqq/0BvP7/tMa9c/+qle6ZecixYbRayAFzw8XN3tGvu5Kr/Njt73cmT5/ubAZQqJdUBjF2uVxvtko256qqrrrrqqv+hTuzzIIDW5QTwoO/82Y6rrnr+EFdd9sVf/MUG+ORP/mRx1VVXXfW/xBu/8Ru/cX3p9/4kAEk72CcBxPQHyVOewlVX/Qc5EX3/oYtfe51rde46gE7jbMY4F9bgOt6R19524M0Drvpvt1iWrdP3zh5SrMCK0tQLaOHpaHu6YGOuuuqqq6666n+gE/s8CKB1OQE86Dt/tuOqq54/xFWXffEXf7EBPvmTP1lcddVVV/0v8Jbv9V7vlde/yXsDIJ2WvQUgpj9InvIUrrrqP8hr95duefP+t1+tZ+qFNdd6o9I6gP3c2LuNG29rqcZV/2PM12Vx+p7ZQ2qqw4rS6ISU2KutdmEKT1x11VVXXXXV/zAn9nkQQOtyAnjQd/5sx1VXPX+Iqy774i/+YgN88id/srjqqquu+h/uLT7pkz7Jepk3BkA6LXsLQEx/kDzlKVx11X+AE9H37z7/41d4RDz94QCVqZtr2BDW5Mi7fe1tF3P7Elf9jxRWufaO+cNnU8wBlUm9kBJ7tZGXppprrrrqqquuuup/kBP7PAigdTkBPOg7f7bjqqueP8RVl33xF3+xAT75kz9ZXHXVVVf9D/YWn/RJn2S9zBsDIJ2WvQWMaPc37Xvu4aqr/gO8Qh2ue9v5r7/aJsstYc00zDumGcChF4d35HW3rd0NXPU/WljlzD39LYtV3QEoTZ2sArBatL2hyyVXXXXVVVdd9T/EiX0eBNC6nAAe9J0/23HVVc8f4qrLvviLv9gAn/zJnyyuuuqqq/6HeotP+qRPsl7mjYXC8nUyPTCi+37ZvnCBq676D/Ae8ye91CvUv3tpgEKWmdYbhSwA97TTd93nk2e56n+V0/fNbtk6rCcA1FSLVQHWfR6t522fq6666qqrrvof4MQ+DwJoXU4AD/rOn+246qrnD3HVZV/8xV9sgE/+5E8WV1111VX/w2xtbW29zod/1VeZ7YcLheXrZHpgRPf9sn3hAldd9e/0YrU7+Q6zX3q1k7p0EmDGOO81zAGW7ld3tutvO2K25Kr/lXb2upMnz/c3AyhVSqoDmHovl4tp38ZcddVVV1111X+jE/s8CKB1OQE86Dt/tuOqq54/xFWXffEXf7EBPvmTP1lcddVVV/0PsrW1tfU6H/5VX2W2Hy4Ulq+T6REX4b7fty9c4Kqr/p3efn7bY165/tVL90y95Jh7vVHVKsAFHz93R7vmTq76X2/roB47ca6/pVihVESqF9DC09H2dMHGXHXVVVddddV/kxP7PAigdTkBPOg7f7bjqqueP8RVl33xF3+xAT75kz9ZXHXVVVf9D7G1tbX1Oh/+VV9lth8O9IjTMj3iovy0X06Ggauu+nc4EX3/oYtfe51rde46gE5TP2NYCGty5G15/dMPvHnAVf9nzNdlcebu2cOLFVhRmjqBUrTV5rQ7hSeuuuqqq6666r/BiX0eBNC6nAAe9J0/23HVVc8f4qrLvviLv9gAn/zJnyyuuuqqq/4HePjDH/7wx7z9J32S2X440COukwnERflpv5wMA1dd9e/w2v2lW968/+1X65l6Yc213qi0DmA/N/Zu48bbWqpx1f853aT+9N3zh8ymmAMqk3ohAazn7WDd5yFXXXXVVVdd9V/sxD4PAmhdTgAP+s6f7bjqqucPcdVlX/zFX2yAT/7kTxZXXXXVVf/NHv7whz/8Me/w+V9lswX0iOtkAnFRftovJ8PAVVf9G52Ivn+7/q9e6iXrEx4LUJV1zmpTWJMj7/XpO8/n8Qtc9X9aWOXau+cPma1jE1A01bAKQAtPq0Xbb8UDV1111VVXXfWfRLI6xr6Pqa+MvXaPbQG0LieAB33nz3ZcddXzh/ivcRx4KeB3eMGOAy/FFZeAv+YFOw68FFdcAv6af6cv/uIvNsAnf/Ini6uuuuqq/0YPf/jDH/6Yd/j8r7LZAnrEdTKBeKr9hN/nqqv+Hd58ft/DXrv+0Sv2TL2wZhrmHdMM4NCLwzvyutvW7gau+n/j9H2zW7YO6wkApSKSTkgA6z6PhkU7sDFXXXXVVVdd9e8kWR1j38fUV8a+aqo8QLt4am5Edm0CeNB3/mzHVVc9f4j/Gj8FvDUgnr/3Br4KOM6z/TbwNsAuz+mlgZ8CHsyz/TXwNsCt/Bt98Rd/sQE++ZM/WVx11VVX/Td56Zd+6Ze++U0++fNstiS2MKcBEE+1n/D7XHXVv9Er1OG6N5v97iuc1KWTAFVZZ6w3ggyAs3ni3rvzzD1c9f/S1kE9dvx8f2NNdQBqqsWqACnacmPaa8UDV1111VVXXfWvIFkdY9/H1FfGvmqqPBejTEcmkVw8vg3gfmoAD/rOn+246qrnD/Gf77uA9+YK8bxeG/gt4G+AjwZuBd4b+Czgt4HX4dkeDPwVIOC9gVuB1wa+Cvhr4GX4N/riL/5iA3zyJ3+yuOqqq676b/DGb/zGb1xf+r0/CUBiC3MaAPFU+wm/z1VX/RuciL5/9/kfv8Ij4ukPB5Acc9aLSusAlu5Xd7brbztituSq/9fCKifO9ddtH9TTAFhRGp2QAKbey+Vi2rcxV1111VVXXfV8SFbH2Pcx9ZWxr5oqz8Uo05FJpFHyALp47FgjWNa6WrtvL/M937vNVVc9f4j/PC8NfBfw0jybeF6/Bbw28BDgVp7tu4H3Al4G+Guu+G7gvYC3AX6aZ/ts4LOA9wG+m3+DL/7iLzbAJ3/yJ4urrrrqqv9ib/zGb/zG9aXf+5MAJLYwp7nib8wT/pqrrvo3ePv5bY955fpXL90z9QAzxnmncSasyZEXfezs3XnmHq666gEWy7J16r7ZLTXVAaiphlUFJPZqIy9NNddcddVVV131/55kdYx9H1NfGfuqqfJcjDIdmUQaJc9lmbPVUW4eHLbNgxOH+eIG9sv8AOBVv/dbT3DVVc8f4j/HSwN/BVwCPht4a+C1APG8DPwN8NI8p7cGfgr4GuCjueIicAl4MM/pwcDTgZ8B3pp/gy/+4i82wCd/8ieLq6666qr/Qm/8xm/8xvWl3/uTACROYnYAxPQHyVOewlVX/Su9Qh2ue7PZ777CSV06CVCVdcZ6I8gA2M+Nvbt87Z1rdwNXXfV8hFVOnOuv2z6op7lMKo1OVgCMXa5XG+2Sjbnqqquuuur/lcrY92XqOsa+09jzXIwyHZlEGiXPZZmz1VFuHhy2zYP9tnMwujSe6aHr868GsF/mBwCv+r3feoKrrnr+EP85Xht4b+CzgVuB3wZeCxDP6bWB3wI+B/hsnpeB3wFeG3gw8HTgd4DX5nkZuBV4CP8GX/zFX2yAT/7kTxZXXXXVVf8Ftra2tl77Iz7pk/AjXh0A6bTsLQAx/UHylKdw1VX/CrfUra236n/3pR4RT384gOSYe71R1SrA0v3q7jxz54E3D7jqqhfBfF0WJ+7rb5lNMQdQUw2rCkjsYZF7Q5crrrrqqquu+j9JsjrGvkarHWPfaex5LkaZjkwijZLnsszZ6ig3Dw7b5sF+2zkYXRovwEPX518NYL/MDwBe9Xu/9QRXXfX8If5r/DbwWoB4Tq8N/BbwOcBn87wM/A7w2sBrA78FfA/w3jyvW4EHAeLf4Iu/+IsN8Mmf/Mniqquuuuo/2Uu/9Eu/9M1v8smfZ7MlFBYnZW8Bo5j+NHnKU7jqqn+F95g/6aVeqj7+sT1TL6yeadZrmANMjjyXJ++5zyfPctVV/wYnL/TX7VzqrgUAKRpdWAEwFQ/LRdtzuHHVVVddddX/aqEsM41d1dQXpq5qqjyXRDaRtjIdiTAPsMzZ6ig3Dw7b5sF+2zkYXRovooeuz78awH6ZHwC86vd+6wmuuur5Q/zX+G3gtQDxnD4a+Crgc4DP5nmZKwS8NvBbwOcAn83z+m3gtQDxb/DFX/zFBvjkT/5kcdVVV131n2Rra2vrtd/rvd6L7dd4ewChOeI0dgVGdN8v2xcucNVVL6JXqMN1bzv/9VfbZLkFUJm6mcZFkAFw0dsX7/J1d7ZU46qr/h3m67I4cb6/cbaOTQCSUjI6AYk9zn2w7tsRV1111VVX/a9RabWLsa+MfRetBq3wXIwyHWnkdCTCPMAyZ6uj3Dw4bJsH+23nYHRp/Bs9dH3+1QD2y/wA4FW/91tPcNVVzx/iv8ZvA68FiOf03sB3AZ8DfDbPy8Al4Djw2sBvAZ8DfDbP67eB1wLEc/niL/5i8yL65E/+ZHHVVVdd9Z/g4Q9/+MMf/Q6f93lY1wFInMTsACAuSnf8ZubBAVdd9SK4pW5tvePs117hFt11C4DkmHu9UdUqwNL96u48c+eBNw+46qr/QMd2+zM7u/W6YgWgaOrCCoCpeBgWbX8KT1x11VVXXfU/imQVT11fpq5j7DuNPc/FFoZMIk2kUfJcBnfjUW4eHLbNg93p+KXRpfEf5KHr868GsF/mBwCv+r3feoKrrnr+EP81fht4LUA8p9cGfgv4HOCzeV4Gfgd4beClgb8Cvgd4b57X04EHA+K5fPEXf7F5EX3yJ3+yuOqqq676D/aW7/Ve75XXv8l7c0WPOC3Tc8XfmCf8NVdd9SI4EX3/5v3fP+al6uMf2zP1wuqZZr2GOcDkyHN58p77fPIsV131n6Sb1J++b37LbB2bACSlZFSBANbzdrDu85Crrrrqqqv+24SyzDR2Ra2rjH3VVHkuRjaRRtkcCZjnsszZ6ig3Dw7b5sFhbi7X2Q/8J3no+vyrAeyX+QHAq37vt57gqqueP8R/jd8GXgsQz+mlgb8CPgf4bJ6Xgd8BXpsrDPwO8No8LwN/A7w0/wZf/MVfbIBP/uRPFlddddVV/0Guu+66617hvT/v88z2wwEk7WCfBDAcSvf9pn3hAldd9SJ47f7SLW/U/8ErbLLcAug09TOGhbAALvj4ubt95p6Walx11X+BY7v9mZ3del2xAlA01bAKQAtP6412aQpPXHXVVVdd9Z+u0moXY1/VaqexD1rhuRhlEraV6UiEeS6HbePwMDcODtr2wV7bPuA/2RSxSEo3RSxe/PCuhwPsl/kBwKt+77ee4Kqrnj/Ef43fBl4LEM9rF3g68DI8p7cGfgr4HOCzueJW4Bhwguf00sBfAd8DvDf/Bl/8xV9sgE/+5E8WV1111VX/Ad7i7d7u7XjkO7y3zZZQRT6NmXPF48XT/joZBq666l9wS93aeo/ZL7zatTp3HUAhS8+wqGoV4NCLw7vbNXceMVty1VX/xcIqZ+7pb1ms6g6AUhGpTiCA9bwdDLM8sjFXXXXVVVf9h5Cs4qnry9R1jH3R1AUWD2ALQ5rIJNIoeS6Du3HdZstDbx3st62Dw7ax5D/JEGXLiq4p+olYpOgbseABXuHgti2A/TI/AHjV7/3WE1x11fOH+K/x28BrAeJ5fTfwXsDLAH/Ns/0W8NrAQ4BbueKjga8C3gb4aZ7tq4GPAl4H+G3+Db74i7/YAJ/8yZ8srrrqqqv+Hba2trZe+yM+6ZPwI14dAGkDfFomgBHt/qZ9zz1cddW/4ET0/Zv3f/+YV6h/99IAwpppmHdMM4DJkff69J3n8/gFrrrqv9nWQT124lx/S7ECkJpKsSpAirbcmPZa8cBVV1111VX/apWx76LVqlY7jX3QCs/FyCYyjZNIUPJcljlbHeXmwSoXy722fbDOfuA/klQGxaJFmRuVUbGVqE/U84JlQIKnV9i//STIl+psCfCq3/utJ7jqqucP8V/jt4HXAsTzejDw14CBjwZuBT4aeGvga4CP5tmOA7cCBj4b+GvgvYH3Bv4GeGn+jb74i7/YAJ/8yZ8srrrqqqv+jV791V/91U+8xod/ks2WUCBOY28ACG6Hp/1+MgxcddW/4LX7S7e8Uf8Hr7DJcgug09TPGBbCArjg4+fu9pl7Wqpx1VX/Q4RVTp7tb9w6rCcAsKI0OiEBrPs8GhbtwMZcddVVV131fIWyzDR2Ra0rTLXT2PN8GGUStpVJJGCey2HbODzMjYNVLpb7bedgdGn8B8iIfkJ9izJvqG+KRZMWtgovgEQDp0wTboIMu/EAL3t453UAu2W+Cmiv/L3fepqrrnr+EP81fht4LUA8fy8N/DTwIJ7ta4CP5nk9GPhp4KV4tu8BPhrY5d/oi7/4iw3wyZ/8yeKqq6666l9pa2tr63U+7MM+zHqZNwYQmlu+RiaAUUx/mjzlKVx11b/gxWp38q1nv/4K1+rcdQBVWXvWi0IWgEMvDu/I625buxu46qr/oRbLsnXqvtktNdUBqKkWqwKkaMO8HQxdrrjqqquu+n9Osoqnri9T1zH2RVMXWDyXRIbINE4iQclzaY48ahsHh946OGqL5V7bPuDfaYpYJCpT1M1EfZP6idjihQiYgARn2BPCYTdegOI87LMdlFzf8xLLc69a8fgM/NTmbK/zIz/2Olx11fOH+J/lOPDSwG/zLzsOvDTw18Au/05f/MVfbIBP/uRPFlddddVV/wov/dIv/dI3vcknfRLWdUKBfByzA4C4V7rj9zMPDrjqqhfilrq19Vb9777UI+LpDwcQ1oxh0WnqAQbX8V6fufNibl/iqqv+FwirnDjXX7d9UE8DYEVJqqwASNGGeTsYulxx1VVXXfX/RGXsu2i1qtVOYx+0wnMxso1NZBJpywjzXJY5Wx3l5sEqF8ujXCwP28aSf6MhypYVXVP0E7FI0TdiwQsiHKaBmyBlN+EUJC/ALNvFcA4l1/cc5Hhw4PHgnmH/Hh7gvcx7AdyKbwV4nR/5sdfhqqueP8RVl33xF3+xAT75kz9ZXHXVVVe9iN7yvd7rvfL6N3lvrugRp2V6rvgb84S/5qqrXohb6tbWW/W/+1KPiKc/nGfqNM5mjHNhAZzNE/fex+mzLdW46qr/ZRbLsnXqvtktNdUBKFUiVQUCSNGGeTsYulxx1VVXXfV/SKXVLqZa1LrK2FdNlefDKNORRk4iAfNcBnfjus2Wh946OGqL5V7bPuBfKSP6CfUtyryhvikWTVrYKrxgGZDgSWDZTdCEzQswy3ax83RAjhcu5PrCQQ4HF8ajC7wI3su8F8Ct+FaA1/mRH3sdrrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVv+DhD3/4wx/z9p/0SWb74QBIx2UfB0BchPt+375wgauuegFeoQ7XveHsj17qWp27jmfqNPU94zzIADj04vCOvO62tbuBq676X+7khf66nUvdtTyTUiVSVSCAFG2cteUwyyMbc9VVV131v0il1S6mWtS6wlSLpi6weC6JDJFG2SyDkufSHLnK+fIwNw5WuVjut52D0aXxIpoiFknppohFor5J/URs8UIETJYt04SbjANPvBCLNt7b0w5aTgf3tKN7DtpwcNDWB/w7vJd5L4Bb8a0Ar/MjP/Y6XHXV84e46rIv/uIvNsAnf/Ini6uuuuqqF+It3u7t3o5HvsN722wJVeTTmDlXPN484U+56qoX4BXqcN0bzv7opa7VuesAhFU19T3TLMgAGFzHe33mzou5fYmrrvo/pJvUH7vQX7d1WE/wTEqVSKqQABJ7nOfhMMsjG3PVVVdd9T9MpdUuplrUusJUO409z4eRbWwik0ij5PlY5mx1lJsHq1wsj3KxPGwbS/4lUhkUCyu6puhHxVaiPlHPCyIcpoEb4LAn4RQkL4DwOG/ThYXHCwc5HVzI1YWDNhwctPUB/wney7wXwK34VoDX+ZEfex2uuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddXzcd111133Cu/zSZ9kX//SAJJ2jI/LhOFQ2v19+557uOqq5+MV6nDdG87+6KWu1bnrAITVM806jTNhASzdry74+NnzefwCV131f1g3qT92ob9u67Ce4JmUijBVVgAk9jjPw2GWRzbmqquuuuq/QWXsu2i1KEthqp3GnufDyDY2kUmkLSPMcxncjes2Wx566+CoLZZ7bfuAF2KIsgURU8QiUd+kfiK2eCEkmozBk+wUpKAJmxdglu1i5+mAHC8c5Hhw4PHgnmH/Hv6LvZd5L4Bb8a0Ar/MjP/Y6XHXV84e46rIv/uIvNsAnf/Ini6uuuuqq5/Lqr/7qr37iNT78k2y2hAJxGnsDQHA7PO33k2HgqqueyyvU4bo3nP3RS12rc9cBCKtnmnUaZ8ICOPTi8N48ec+BNw+46qr/R8Iqxy92Zzb36pliBYBSEabKCoDEHud5OFSvHG5cddVVV/0nqYx9F61WtVqYuqqp8nwkMsYmMom0ZYR5Ls2Rq5wvD3Pj4KBtHyxzYzm6NJ5LRvQT6luUeUN9UywS9Yl6XoiACUhwCjdBht14AYTHeZsu9LSDltPBPe3onsFtuDAeXeB/iPcy7wVwK74V4HV+5Mdeh6uuev4QV132xV/8xQb45E/+ZHHVVVdd9UxbW1tbr/0Rn/RJ+BGvDoC0AT4tE8BoHf0+vu02rrrqubz5/L6HvVr985feZLkFIKyeadZpnAkL4NCLw3vz5D0H3jzgqqv+HwurHL/Yndncq2eKFQBKRZgqK3imqfdy2bdDhxtXXXXVVf9GklU8dX2ZukKWwtRVTZXnI5EhMo1NpFHyfDRHrnK+PMyNg1Uuloe5uVxnP3A/qQyKhRVdU/QTsUjRN2LBCyHRZAyeZKcgBU3YvADFedhnOyi5vmdwDhdydeHCeHRhcBv4H+69zHsB3IpvBXidH/mx1+Gqq54/xFWXffEXf7EBPvmTP1lcddVVVwEv/dIv/dI3vcknfRLWdUKBfByzA4C4V7rj9zMPDrjqqgd48/l9D3u1+ucvvclyC0ByzDzMq1onLIBDLw7vzZP3HHjzgKuuuupZwirHL3ZnNvfqmWIFAFaUpMgqPNPUe7ns26HDjauuuuqqF6LSaqiVGq0WT10XrQat8HwkMkSmsYk0Sp6Pwd24brPl0vPlKhfLw9xcrrMfAIYoWxAxRSwS9U3qm7SwVXhBhMM0cAMc9iScguSFWLTx3kIO5HjhQq4vDG7DPcP+Pfwv9l7mvQBuxbcCvM6P/NjrcNVVzx/iqsu++Iu/2ACf/MmfLK666qr/16677rrrXuG93uu9rJd5Y67oJV2DXYGRiL92Pu5xXHXVA7z5/L6HvVr985feZLkFIDlmHuadpp5nuujtixdz58KBNw+46qqrXqCwyval7uT2pXqmpjoAkEqjyio809R7uezbocONq6666v81ySqeui5arWo1aKXT2PMCGGUSBmWzDEqej8HduG6z5dLz5UHbPljmxnKprk9UpqibCaUpFon6RD0vRMBk2TJNuMk48MQLUZyHfbaDkut7BudwIVcXLoxHFwa3gf+D3su8F8Ct+FaA1/mRH3sdrrrq+UNcddkXf/EXG+CTP/mTxVVXXfX/0nXXXXfdK7zXe72X9TJvDCAUFjuyjwMgLsJ9v29fuMBVVwEnou9frb/j5lerf/7Smyy3ACTHzMO809TzTBe9ffG+PH3P2t3AVVdd9a+ys9ed3LnYXVdTHQBIpVFlFZ5p7HI99HnUigeuuuqq//MqY99FhpSlY+yLpi6weD6MDDgdaeREBiXPxzJnq7UXyyHrsOvj46E3xyX9IqE0xSJRn6jnhZBoMgZPAstugiZsXgDhcd6mCz3toOV0cE87umdwGy6MRxf4f+a9zHsB3IpvBXidH/mx1+Gqq54/xFWXffEXf7EBPvmTP1lcddVV/69cd911173Ce73Xe1kv88bcTzoO3pEJrvgb84S/5qqrgBPR92/e//1jXqo+/rE9Uw8gOWYe5p2mnme66O2L9+Xpe9buBq666qp/l5297uTOxe66muoAQCqNilXEFVPxsJ63w1Y8cNVVV/2vF8oSbqUvU1c8dUVZqqbKC2CUSdjGJtIoeQGWOVsdeXM88nzc83ZczJ2xKRaJ+kQ9L4REkzF4Elh2E05B8kLMsl3sPB2Q44WDHA8OPB5cGI8uDG4DV132Xua9AG7FtwK8zo/82Otw1VXPH+Kqy774i7/YAJ/8yZ8srrrqqv8Xrrvuuute4b3e672sl3ljnkliC3QcuwIg7oXdv7bvuYer/t87EX3/5v3fP+al6uMf2zP1AFVZO4ZZpXU800VvX7wvT9+zdjdw1VVX/Yfa2etO7lzsrqupjiukphJWFVdMxcN63g5b8cBVV131P55kFU9dF60WZSlMtdPY8wIkMsYmMok0MmCej4nCUS6GS7nTlsy89KJc8I4S9bxwGZDgBjjsSTgFyQtRnId9toOFxwsHOR1cyNWFgzYcHLT1AVf9i97LvBfArfhWgNf5kR97Ha666vlDXHXZF3/xFxvgkz/5k8VVV131f9p111133Su813u9l/Uyb8wzSWyBjmNXAMS9sPvX9j33cNX/e7fUra3Xqn/5sJeqj39sz9QDVGXtPMyrWgWYHLnHzoWzeeLs2t3AVVdd9Z9qsSxbxy/2183WsckVUlMJq4orWniaei+HmmuHG1ddddV/K8kqnrouWi3KUphq0dQFFs+HLRCZjkRyWjZKnkuiYtBebo9rZl56pgs+HgfeYHIxL1gGJLgBDntCOOzGC1Gch322g4XHC4NzuKcd3TO4DRfGowtc9e/yXua9AG7FtwK8zo/82Otw1VXPH+Kqy774i7/YAJ/8yZ8srrrqqv+Trrvuuute4b3e672sl3ljnkliC3QcuwIgLsLun9r33MNV/++9Trd/88v1f//wW3TXLTxTVdbOw7yqVYDJkRd97Ox9nD7bUo2rrrrqv9RiWbaOX+yvm61jkyukphJWFc82FQ9Tn8uxz7WNueqqq/7ThLKEW+nL1BWyBK10GnteCKM04TQ2kbaMMECiAGQUIB2558gbbc9b5dCLXDPLg9xIng+JJmNwAxz2hHDYjReiOA/7bAcLjxcG53BPO7pncBsujEcXuOo/zXuZ9wK4Fd8K8Do/8mOvw1VXPX+Iqy774i/+YgN88id/srjqqqv+T7nuuuuue4X3eq/3sl7mjXkmiS3QcewKYDgMpr9OnvIUrvp/7Za6tfWG9U8e8+j6lIf3TD3PVJm6jmlW1SrA5MiLPnb2Pk6fbanGVVdd9d9qsSxbxy/2183Wscn9klKskFV4gLHL9dh7OZVcc9VVV/2bVVoNtVKj1eKpC1mdxp4XwBaITMIYJ5FGBtygCGGiADRUGoUDb3DgjVwzy8O2kbvebjyXgMmyZZrAsptwCpIXQnict+lCTztoOR3c047uGdyGC+PRBa76b/Fe5r0AbsW3ArzOj/zY63DVVc8f4qrLvviLv9gAn/zJnyyuuuqq/xOuu+66617hvd7rvayXeWOeSWILdBy7AhgOg+mvk6c8hav+3zoRff9q/R03v3z528ee1KWTPFMhS6exr7ReWACTIy/62Nn7OH22pRpXXXXV/yiLZdna3K8ntw7rCZ5NSoVMCSt4psTOntW6a6tWPHDVVVc9X5Wx7yJDylI8dUVZqqbKC2BkwEnYxiYyCQwyCpAaFBAJBYkjL/IgN3LtWe7ldlvSe+1ZAiAcpgEJTuEmY0ETNi9EcR722Q4WHi8MzuFCri8MbsM9w/49XPU/znuZ9wK4Fd8K8Do/8mOvw1VXPX+Iqy774i/+YgN88id/srjqqqv+V7vuuuuue4X3eq/3sl7mjXkmiS3QcewKYDgMpr9OnvIUrvp/6xXqcN0r93//sEfE0x/OMwmrqnUd46yQhWdaul9d8PGzuxy71FKNq6666n+0sMr2pe7kxn45OZtizrNIapQwRUg8U4o2drlufS6n8MRVV/0/U2kVMvoydYGjMNWiLEErvABGBpyONCIJNWSISBOWZAhL4pn2c6stPfPas9zL7bak99qzBDIgwQ1w2BNA4Il/wSzbxXAOJdf3DM7hQq4uDG7DhfHoAlf9r/Je5r0AbsW3ArzOj/zY63DVVc8f4qrLvviLv9gAn/zJnyyuuuqq/5Wuu+66617hvd7rvayXeWOeSWILdBy7AhgOg+mvk6c8hav+X7qlbm29Vv3Lhz22PuXhmyy3eKaqrNVj32nqeabJkftsXjrfTp49Yrbkqquu+l+pm9Rv73UnN/bryZrqeBYpGlVWCMQztfA09V4ONdcON6666v+IUJZwK120KlkdYy8cVVPlhUhkjBtFJpwoEjmJQChR8ACTC0sv2oE3cs0sD9tGrtS3IWcTkOCUnYIUTkHyQgiP8zZdKORAjhcOcjw48HhwYTy6MLgNXPV/xnuZ9wK4Fd8K8Do/8mOvw1VXPX+Iqy774i/+YgN88id/srjqqqv+V7nuuuuue4X3eq/3sl7mjXkmiS3QcewKYDgMpr9OnvIUrvp/6c3n9z3s0eWpt9yiu27hmSRH76mvmvogg2faz429PbYunc/jF7jqqqv+T5mvy2Jzr57cOCwnixXcz4qSFKwinm0qHqY+l2Ofaxtz1VX/w0lW8dR1kSFl6Rh74aiaKi9EWhhhyemIJJyKSMspFZ6PdfYe6PPAG7mmz6PcGA+9MU0uk8CyG8JhN/4FxXnYZztYeLwwOIcLub4wuA33DPv3cNX/G+9l3gvgVnwrwOv8yI+9Dldd9fwhrrrsi7/4iw3wyZ/8yeKqq676X+G666677hXe673ey3qZN+aZJLZAx7ErgOEwmP46ecpTuOr/nRer3cnX7f/kMQ+K22/pmXqeqdPUV099Vas80+A6XvL2hQs+fmHtbuCqq676P2/roB6bH5Vji6NyrFjB/ZJSrJBVeICxy/XYezmVXHPVVf+NJKt46rrIkLIUT13I6jT2vABGMsiEE2HERJGNmop4IdbZe6DPS7k9rpnl4G7czZ0x7Akg8MS/QHict+lCIQdyvDA4hwu5unDQhoODtj7gqquA9zLvBXArvhXgdX7kx16Hq656/hBXXfbFX/zFBvjkT/5kcdVVV/2PtrW1tfW6b/d2b5fXv8l780wSW6Dj2BXAcBhMf5085Slc9f/Kiej71+uf8rAXL096+EldOskzFbJ0jLOq1gmLZ7ro7YsXc+fCgTcPuOqqq/5fCqts7ddji8M4tljVHZ5NSoVMCSt4psTOntW6a6tWPHDVVf8JKq1CRhetSlbH2AN0Gnuej4QAMJIpTlASJCFbagrzQkwuHOViOmKjNZdpz1tTuo4HuZgCT7wIFm28F6Dk+p7BOVzI1YXBbbgwHl3gqqteBO9l3gvgVnwrwOv8yI+9Dldd9fwhrrrsi7/4iw3wyZ/8yeKqq676H+nVXu3VXu3kq7/6qxMv8+o2WwASW6Dj2BXAcBhMf5085Slc9f/K63T7N7949+RbHhFPfzjPJKyqqe+Y+kIWnmnpfnXBx8/ucuxSSzWuuuqqq54prLJ9qTu5sV9OzqaY8yySGiVMERLPlKKNXa6nmutWPXDVVS8iySqeui4ypCyFLEErRVmCVngAIxkLQEgAE0UAzUVGJCIlG/F8GZKSh160tfu2dt+W3hgml9zPrSUvglm2i+EcFh4vDM7hnnZ0D8A9w/49XHXVf4D3Mu8FcCu+FeB1fuTHXoerrnr+EFdd9sVf/MUG+ORP/mRx1VVX/Y/x8Ic//OGPfqM3eiN2Xv3Vsa7jmSS2QMexK4DhMJj+OnnKU7jq/41b6tbWG9Y/eczD6jNu2WS5xTNVpq7T1FdaxzNNjtxj58LZPHF27W7gqquuuupf0E3qt/e6kxv79WRNdTyLFI0qKwTiAabioXU5tOr1FJ646v+1SquQ0ZepA+gYe+GomirPZCRjAQgJIK1AcjqUBAYSYURKNuJ52AgMsJdbY6Pk0otx7X4aPBsv5fbAv2DRxnsBFh4vDM7hQq4vDG7DhfHowuA2cNVV/8ney7wXwK34VoDX+ZEfex2uuur5Q1x12Rd/8Rcb4JM/+ZPFVVdd9d/quuuuu+4VXu3VXs2PfPu3x7qOZxKqFluCLewKYDgMpr9OnvIUrvp/4XW6/ZsfVm677mH1GbdsstzimSTHjGFWyC7I4Jn2c2Nvl2MXLub2Ja666qqr/o3m67LY3KsnNw7LyWIF97MikiITQuIBErt1HrIwDDXXDjeu+j+l0ipkdNGqZBVPXcgqmrrAMpKxAIQEkFYAWCgp2MLIRiTCiJTMAxmEDSDJAEvPx1X2eeTFMNBP65y1Q29Mk0vyfAiP8zZdKORAjhcA7mlH9wDcM+zfw1VX/Q/wXua9AG7FtwK8zo/82Otw1VXPH+Kqy774i7/YAJ/8yZ8srrrqqv9yW1tbW6/2aq/2av3LvP3bm+2H80xCgdgy3pLpeSbDYTD9dfKUp3DV/2m31K2tl6xPu/bR5am33KK7buEBhFXVuuqpr2qVZxpcxwt5/Owlti+t3Q1cddVVV/0H2jqox+ZH5djiqBwrVvBsIoliBVYIxAOkaNl5mEoOY/HocOOq/7Ekq3jqQlaNVgE6xh6g09gnBICRgivSCgALCWgUAOywgUYA0BTmfgZhA0gyALYH9zm4m9bMpnV27ZCNqbnmoTemySV5LrNsF8M59LSDltPBQY4HBx4PDtpwcNDWB1x11f8C72XeC+BWfCvA6/zIj70OV131/CGuuuyLv/iLDfDJn/zJ4qqrrvov80Zv9EZv1L3sq786fsSr80xCgbwB2sDe4NlGxG2w+xT7nnu46v+sF6vdyVepf/2wm8rd153UpZM8QCFLpXVFU1fIwjNNjtxn89LF3Llw4M0Drrrqqqv+k4VVNg7L1vyoHJsvy1ZNdTwHSUmECawinlMLT1P10DoPreZgY676L1NpFTK6yJCyFLIErQC1aKwAQgIwko0AEOKZGgWAtADZiEQYkZIxCBtAkgGwzWVm39trgP22OTRVH+bGOLr4yBsjDyA8ztt0AaDk+h6AC7m+MLgNF8ajC4PbwFVX/R/wXua9AG7FtwK8zo/82Otw1VXPH+Kqy774i7/YAJ/8yZ8srrrqqv9Ur/Zqr/ZqJ1/91V+deJlXt9niftIGsAHekAmeSXA7TLclT3kKV/2fdCL6/tX6O25+SNxx3YPi9lt6pp5nElah1UrrirIGGTzAfm7s7bF1aZdjl1qqcdVVV13136Sb1C+O6tbiMI7167JVrOCBrFASgpAV4jm18DT1uZrCY6seuOrfLJQl3ApAX6YOoGOcW1JhqoEDwEg2AkCIBzBBIgDSYYBGAJCSbRAYQJIBsA0ARuA1fVt71o48n1qWPGRjaq55KbcHnkl4nLfpAkDJ9T0AF3J9YXAbDtpwcNDWB1x11f8T72XeC+BWfCvA6/zIj70OV131/CGuuuyLv/iLDfDJn/zJ4qqrrvoP9/CHP/zhj36jN3ojdl791bGu49l6iS3DlkxwP3FRnh4Ht92WDANX/Z/zYrU7+Zj61GsfVZ5+y7U6dx0PIDkqU1dptdI6HmBwHQ9ZHBx469LF3L7EVVddddX/UPN1WcyWZWu+1NZiVXd4LkoFJgJCVvBcxi7XWRhazWEKT1x1WaVVyOgiQ8qC6WpM1UYlplpsGclGAAjxfCSBEWkBshGJsLAtA0gygG0EBhA2z7Smb2vP2uC+rbNra83aOmdtzaytsm/C47xNFwBKru8BuJDrC4PbcNCGg4O2PuCqq656lvcy7wVwK74V4HV+5Mdeh6uuev4QV132xV/8xQb45E/+ZHHVVVf9h7juuuuue4VXe7VX8yPf/u2xruOZhKrFlmALu/JMhkNFPE7cdlvmwQFX/Z/zOt3+zQ8rt133sPqMWzZZbvEAVVkrY1fILsjgAZbuV5dy58KBNw+OmC256qqrrvpfaLEsW4tl2ZqtytZsHZs8J5FEWCETQuIBErt1HrJ4nMJjdh5tzP8hklU8dQB9mWaGEC5Frcqoaqy2BGAh8YKZIBEA6TBAIixshw1IMjaAAYTNc9nz9gCw3zYHgEM2puaah96YSuN8OIdCDuR4AeBCri8MbsNBGw4O2vqAq6666l/lvcx7AdyKbwV4nR/5sdfhqqueP8RVl33xF3+xAT75kz9ZXHXVVf9mW1tbW6/2aq/2av3LvP3bm+2H80xCgdgy3pLpeSbDoeA2dN9T7AsXuOr/lFvq1tZL1qdd++jy1Ftu0V238ADCqmpdZeoKWYXFM02O3Gfz0pEXB7scu9RSjauuuuqq/0PCKhuHZWu2LFv9KrZmU8x5TlIqwgRWCMRzSdFazSmLxyk8ZufRxvwPI1nFU0copOwqLZKIjrEHCGUpbgGAEP+CRgEgLUAGaJIBTCQIbABzmRGY57Kmb2vPWnP4KBdjU/VhboxYh8M0uwBQcn0PwEGOBwceDwDuGfbv4aqrrvpP8V7mvQBuxbcCvM6P/NjrcNVVzx/iqsu++Iu/2ACf/MmfLK666qp/leuuu+66l3qpl3qp7mVf/dXxI16dZxIK5A3QBvYGzzYibjNHt+HbbuOq/zNORN+/dDl/7cPKbdfdVO6+7qQuneQBClkqrSuaukIWHmDpfrVk42A3Ny8dePOAq6666qr/R7pJ/eKobvWr2Jovy1ZNdTwHSUnIhECygucjRWs1pywep/CYQXO48Z+gqnUWHUDH2KdUChmFFqmInjEgQ7xoTJAIAFs2kJKxDJAqxjbPJGxegEbxoTdGgP22OQAsvVjV9MUVszGmuHdwDoNzuJCrCwD3DPv3cNVVV/23ei/zXgC34lsBXudHfux1uOqq5w9x1WVf/MVfbIBP/uRPFlddddULdd111133Ui/1Ui/Vv/RLv7TjpV8a6zoeSNoANsAbMsEzCW6H6bbkKU/hqv8TXqx2Jx9Tn3rt9XHu5A1xz3WbLLd4AGEVWq20rihrkMED7OfG3qE3Di6xfWntbuCqq6666qrLukn9xkF3bL7UVr8uW8UKnpsVMpIJgWQFz0diZ2FsXQ6tMDV5crjxfKRi1mksAMizQguAqtYDyE29psK/ggkSgcHIBiTcHAawZFsGEDb/giMvpomazeGjXIw97fCwbe4BTJPvWnm+Osjx4MDjAcCF8ejC4DZw1VVX/Y/3Xua9AG7FtwK8zo/82Otw1VXPH+Kqy774i7/YAJ/8yZ8srrrqqudw3XXXXfdSL/VSL9W/9Eu/tOOlXxrrOh5AKCzmwnPDlkxwP3FRnh4Ht92WDANX/a91S93aepjuPvGwctt115ZzJ6/Vuet4PqqyFrcaaqXSOh5gcB0PWRwceOvSxdy+xFVXXXXVVS+S+bosZsuy1Q1adOtYzKaY8/xYgS1ZEqqAZIcQYAkLIANFmZxdMyVV60qo8a+RBFgGSMkAiWwLhNMBYGHzItrz9gCwytm6mP3maNn6+0a38VLbuXRPO7oHYHAbLoxHF7jqqqv+T3kv814At+JbAV7nR37sdbjqqucPcdVlX/zFX2yAT/7kTxZXXfX/3HXXXXfdS73US71U/9Iv/dKOl35prOt4AKGwmAvPDXOZngcwHCriceK22zIPDrjqf6VXqMN1D6r3nHhwufO6U7p4cpPlFs+lkCWUpbjVUJZCFp7L0v3qUu5cOPDmwRGzJVddddVVV/2rpUo/ufQAG6wXBIvZqvab62kRU8y7dYnZOjpAAIVJPJMssMACB7bE8yNDaXa1KQ1HZtYETBJpAIRN8q+05+0hnJmu+6O7sXNbHWZ3Ycoync88f27qzwHcM+zfw1VXXfX/1nuZ9wK4Fd8K8Do/8mOvw1VXPX+Iqy774i/+YgN88id/srjqqv9nrrvuuute6qVe6qX6l37pl3a89EtjXccDCIXFXHhumMv0PDdxL+YedN9t9oULXPW/yi11a+thuvvEw8pt111bzp28Vueu47kIq8iluNWg1aIswuK5HHpxuGa2XLpf7nLsUks1rrrqqquuumykLkwUAKMo8vZc6wJga7Gho55n2tRyk2fa0DI6N/Ev0DiDVS8PnRhmsJzLCEnGkAgAEKTAQha2EC9Y1mbCzmITaSu9rGVyxj7AYW4ddTlcmlSm3Wm+u5ervUu5cemw+fDCeHSBq6666qoXwXuZ9wK4Fd8K8Do/8mOvw1VXPX+Iqy774i/+YgN88id/srjqqv/jHv7whz/8MS/1Ui/lR770S4tHvLTNFg8gFBZz4blhLtPz3MS9mHvQ7j32Pfdw1f8qr1CH6x5U7znx4HLndad08eQmyy2eSyFLKEtxq6EshSw8l8F1XLtfHnrj4IjZ8sCbB1x11VVX/T8w0G/xTEYxURdbsewKLQaXeacsGxxVUDSXfif2C8+0oWV0buJfwRiEAdLhRAZI1bQxQHOkJct4ImxkgDp2EesuYl1LDF2UVV94PgQ45cw6YSlMs5UywhoEzeBMjnimQQwX4MKBODiAgwtwYUzGe8Q9XHXVVVf9K72XeS+AW/GtAK/zIz/2Olx11fOHuOqyL/7iLzbAJ3/yJ4urrvo/5uEPf/jDH/NSL/VSfuRLv7R4xEvbbPEAQhV5DvSGuUzPcxP3Yu5Bu/fY99zDVf8r3FK3tq7lwtaj663XHo+DrVO6cPKkLp3kuQiryKW41aDVoizC4rkcenG4ZrY89OLgyPPl2t3AVVddddX/QiN1YaLwTJPq3FYBOBYHs8HdBsBM6zJnHc11DrAZyyhM4pmOcVD4V7BsHsAom4Mr5EZJnqm5TDxTSm4O869QaUOhDRc4fmnKcnSo1e59PnHfJXNpHJbjxYNrLl635rqdo+XOsXV/bFNs8nwEih76KtVq1x76kGJuz3khBjFcgAsXxIUBhnuTe4dguGAucNVVV131fLyXeS+AW/GtAK/zIz/2Olx11fOHuOqyL/7iLzbAJ3/yJ4urrvpfamtra+vhD3/4w29+2MMe5uuuu047D384XP9wmy0eQKgiz43mgjl25bmJezH3oN177Hvu4ar/0V6sdiev0YXNm8rdJ68t509ucbh1UpdO8gIUsoRaLc5SlDXI4LkMruPa/fLQGwdHzJYH3jzgqquuuup/AKMy0i14gJFukwdYMGwi9zzTlg42cQTALAbNWAfPtKFldG7iX8GyeQDZToWbA2Gj8ORiYQOeXJNnmggbmX+FShsKbQBYar53T5Y7AS6xeenvpqO/Azh/fuv8X/3Vhb86Orrr6ClPufSUg4ODA/4Vrrvuuuse/vCHP/zhN13/8Iebh2/B1kvBS/FCBIoe+irVate5NA87euh5IQ7EwQEcXBAXBhgO4OAwORyC4YK5wFVXXfX/0nuZ9wK4Fd8K8Do/8mOvw1VXPX+Iqy774i/+YgN88id/srjqqv8FXvqlX/qlr7322mu7hz/84dp5+MPh+ofbbPF8CFXkudFcMMeuPKdRcI/hAtq9x77nHq76H+kV6nDdtXV38wS7W9eW8ye3ONw6qUsneQEkR8FR3KqUkl2qWuX5OPTi8MjzgxXz5ZHny7W7gauuuuqq/2AjdWGi8ExGMVEXPEDIOzOGwjNJOd/0quMBdmK/8ADHOCj8K1g2DyAwNgAj1SESwIjJxcIJMBHGYZ5ppCT/CpU2FNrAMx2pXbg3d+4FOGJ99JfT/C95pt/8zaf/5sGBDgDuuafdc88999zDf4Otra2thz/84Q+/7rrrrrvu+PZ1DzcPvw6uexg8jBeiSrWa2kMfEHNpXu1aofIiuEfcA3BBXBhgOICDw+RwCIYL5gJXXXXV/znvZd4L4FZ8K8Dr/MiPvQ5XXfX8Ia667Iu/+IsN8Mmf/Mniqqv+B3npl37pl7722muvnV133XW+4aVf2lx3HdZ1vABCc0QPrkBv6GWC5zQK7nHEPfiee+wLF7jqf5RXqMN119bdzRPsbt1S7r5uS4dbmyy3eAEkR8FR3KrIEI6qVnkBBtfxkMXBOmfLA28eHDFbctVVV131Agz0WzyAUUzUBQ/QUGnUxaaOaiUFkKYrotvkqPAAm7GMwiSeqdcQCw/iRWQA2TyAbPMAjZJI5pkml8YzpSGpyTNNhI3Mv8KM4YBnCrkNGpZ7nu9dyo1LR6yPzsV07rZh6zaA22576m1//dflr3mmpzzl0lMODg4O+D/guuuuu+6666677uEPf/jDt6q2Xtq89BZsPQwexgvRK/qwYw5zgB76kKLatULlRXSPuAfggrgwwHAAB4fJ4RAMF8wFrrrqqv9V3su8F8Ct+FaA1/mRH3sdrrrq+UNcddkXf/EXG+CTP/mTxVVX/Td4+MMf/vBrr7322lMPf/jD84aHP1y+7jqz/XBeAKE5cjWqwnNQxa48f6PgHkfcg++5x75wgav+W52Ivn94HJwEeHS99doZY39tOXdyS4dbmyy3eAEKWURGwSXUinAUsvACTI5cM1uumS2HrMMRs+VSG8uWalx11VX/J43UhYnCAySla0TPcxnotgDmGmLGUABGYgNgS8vo3MQzhZq2dBQ8QK8hFh7Ev4IxCPMAss0DWHJSkmey5UYkDzC5Jg8wUpJ/hU7TMpyNZ7LGZVM2gNvbydsv6ejSwDgsVZe3DVu33XbbU28DuOce3XPPPXEPz/TXf33nX3PV8/Xwhz/84VtbW1sv/ehHvPSW2Xo4PPw6uO5auJZ/QaDooQeYwxyghz6kqHatUHkR3SPuATgQBwdwAHAAB4fJIcBBcHBgDrjqqqv+272XeS+AW/GtAK/zIz/2Olx11fOHuOqyL/7iLzbAJ3/yJ4urrvpPct1111133XXXXXfttddeO7vuuuvyhoc/XL7uOrP9cF6wHqkCvaAHeuzKCyLuBQ4wB2j3HungIPPggKv+S71CHa4DeFC958SCVX9tOX9yxtBv6XBrk+UW/4KqrCIj7Ai1IhyFLLwAg+s40g1Hnh80l3bEbLnUxrKlGlddddX/OKnSTy49zyUpXSN6nstIXRgVgLmGmDEU45goc4CeZJOjwnPZif3Cc9nQMjo38a9g2TwX2ea5SHhyGIV5Jhs3SvIAE2Ec5plScnOYF1HIrfO45AGscdmUjWd6BrNnnM/pPM/0p+f0p5cunbsEcHDAwVOeUp7CMz3lKZeecnBwcMBV/2Ue/vCHP3xra2vrpR/9iJcGeLh5+BZsXQfXXQvX8iIIFD30AHOYA/TQhxTVrhUq/0oH4uAADgAuiAsDDAAX4MKYjAAXgguDGbjqqqv+w72XeS+AW/GtAK/zIz/2Olx11fOHuOqyL/7iLzbAJ3/yJ4urrvo32Nra2nr4wx/+cIBbXuqlXgrAN7z0S3PZ9Q+32eKFEKoWPdALeuMq0/OCiIsyB4YL1tEFcXBgX7jAVf/pXqx2Jzc47K+tu5sn2N06HgdbO9rf6hj7k7p0kheBsIpcAIpbBQhaDTmCDF6AwXUc6YYjzw8GumHtbjjw5gFXXXXVf5iRujBReD5Guk2ej5G6MCo801xDzBhKErMGhWfa0jI6N/FcNmMZhUk8lw0to3MT/wqWzfMh2zwXCQOkw6kwgIzBTBQbmQeYXJMHSMnNYf4VKm0otIFnCrkNGpY8wDmP527z/Dae6WwMZ//6yft/zQP89V+Xv+YB/vqv7/xrrvo/Y2tra+vhD3/4wwFe+tGPeGmAh5uHb8HWdXDdtXAtL4JA0UMPUKVa7QpQoVapAvTQhx38G1wQFwYYAC6ICwMMAAMMF5OLPNNBcHBgDrjqqqteqPcy7wVwK74V4HV+5Mdeh6uuev4QV132xV/8xQb45E/+ZHHVVc/Hwx/+8IdvbW1tXXvttdfOrrvuOl933XXEddfhrS2z/XBeBEIVqMjVqAJVuGLmvACGw4ALhgvW0QVxcGBfuMBV/+FeoQ7X8UyPrrdeCzBj7K8t504CXKtz1/EiqsoKIFoJI5EhHJJVyMK/YOl+NbkOK2bLFfPl6GgH3jzgqqv+Hxrot3gBktI1ouf5aKg06oIH6CK14aOapmuKjgfY0jI6N/F87MR+4fnoNcTCg/hXMgZhng/Z5vmQMEA6nAoDyDZAIholeS6Ta/JcJsJG5l9hxnDAc5lifcADNGdbalzewfyOo4wjgKNYHR1ydPjXT/Zf8wD33KN77rkn7uGZ7rmn3XPPPffcw1VX/StsbW1tPfzhD384wEs/+hEvDfBw8/At2LoOrrsWruVfqVf0YQdAD31AAPTQhxQAPfRhB/8OB+LgAA54pgNxcAAHPNMFuDAmI890IbgwmIGrrvo/7r3MewHcim8FeJ0f+bHX4aqrnj/EVZd98Rd/sQE++ZM/WVz1/8p111133XXXXXcdwObm5uaphz/84QC+4aVfGgCuf7jNFi8CoTmAxRxA0IPD0MsEL4ThUOIAc4+YDqyDA/uee7jq3+SWurV1LRe2AK6tu5sn2N0CmDH215ZzJwE6xv6kLp3kX6mQRUIiI+yQUrKLZBWy8CI69OIQYM1s2ay2Yr5cZz8cMVty1VX/Q6RKP7n0vBCT6txW4QVoqDTqgucy1xAzhpLErEHhuZzgoPAChJq2dBS8AMc4KPwbWTYvgGzzAkiYZ5ocRmEA2eaZJoqNzHOZCOMwz2UibGT+FTpNy3A2HiDkNmhY8lwOWB8AXHC9cM79OYBLcXBpYBiecreecnCQBzzAX/91+Wse4J572j333HPPPVx11f9QW1tbWw9/+MMfDvDwhz/84VtVWwAPNw/fgi2Ah5uHb4pN/g16RR92APTQBwRASNFDzzNVu1ao/Ae5R9zDAwxiuAAXeC73JvfyXO4R93DVVf9DvZd5L4Bb8a0Ar/MjP/Y6XHXV84f43+k48FJccQn4a/6dvviLv9gAn/zJnyyu+l/tpV/6pV+aZ7rlpV7qpXimvOHhDxdbWwDmuuuwruNfQagCFbkaVeEAekPI9LwIDIcSBzKD4QIRA75wwb7nHq56vk5E3z88Dk7yAI+ut17LM11bzp+cMfQAWzrc2mS5xb9RVVaeqbhVniloFaCqVV5ES/erpLSBOkyuw0A3rN0N6dqOmC256qrnY6Df4l9gFBN1wb+gOfqm0vMCbOqoVlIjscHz0ZNsclR4ITZjGYVJvADFTVtaBv9Ols0LIdu8ABLmARKRlASQbR5gotjIPB+Ta/J8pOTmMP9KlTYU2sBziWjDwDTwXAbaMDANABdcL5xzf25UG3d1aRfgwBw85Sl+Cs/lr/+6/DXP5SlPufSUg4ODA6666qrn8fCHP/zhW1tbWwAPf/jDH75VtQXwcPPwLdgCeLh5+KbY5N+hSrWayjP10AcEz9RDH1LwTHN7zn+ie8Q9PJd7xD08H/cm9/J8HAQHB+aAq676d3gv814At+JbAV7nR37sdbjqqucP8b/PSwM/BTyYZ/tr4G2AW/k3+uIv/mIDfPInf7K46n+El37pl35pHuDaa6+9dnbdddcB5NbWlnYe/nAAvLVlth/Ov5FQBSqARQA9gKAHh6GXCV4U4l4AzAVgsI4uiGGQDg4yDw74f+ZE9P3D4+AkD/Doeuu1PMC15fzJGUPPM23pcGuT5Rb/TpKj4ADAqYILgJSSXXimqlb5Vxpcx5FuGKjD5Do0l3bEbAlw4M0DrvpvN9Bv8a8wqc5tFV5EI3VhVHgRTC5bPB+dkoUOCy/ECQ4KL0CoaUtHwQvQa4iFB/HvZAzCvBACY/PCSJjnkg6nwjIG89wmio3M85GGpCYvwEhJ/o06TctwNp6PKdYHPB9LjcvmbDzTBdcL59yfO4rV0SFHhzzTPfu655578h6ey1OeEk85ONABz+UpT7n0lIODgwOuuuqq/5Fe+qVf+qV5ppd+9CNemme6zlx3HVzHM10H110L1/IfZI7mPECI6E3PA4QUPfQ8l7k957/YPeIeno8DcXAAB7wA9yb38kJcCC4MZuCq/zPey7wXwK34VoDX+ZEfex2uuur5Q/zv8mDgrwAB7w3cCrw28FXAXwMvw7/RF3/xFxvgkz/5k8VV/2bXXXfdddddd911PMDND3vYw7S1tcUz5dbWlnYe/nAewFx3HdZ1/AcQmnM/0RsCQNCDg8tUsSv/CoZDiQOZwXCBiAFfuABg33MP/8u9Qh2u47lsad3dVO4+yXOZMfbXlnMneS5bOtzaZLnFfyBhFbnwAMWt8kyhVjACCDmCDP6NDr045JnWzJbNagD73jgAWGpj2VKN/8UG+i3+jZLSNaLn36Ch0qgL/pUMpbks+A/QKVnosPAv6Ek2OSr8CzZjGYVJvAC9hlh4EC8iA8jmRSDbvAgkzAswOYzCss3zkYhGSV6IiTAO8wIkohHJv9OM4YAXIKINA9PA89GcbalxyQOsHKvbPL/tKFZHhxwd8gD37Ouee+7Je3g+/vqvy1/zfNxzT7vnnnvuuYerrrrqqn+F66677rrrrrvuOp7p4Q9/+MO3qrZ4poebh2/BFs/0UvBS/CeaoznPZQ5zno+5NOf5qHatUPkfZBDDBbjAv+AecQ8vggM4OEwOeRHcI+7hqn+19zLvBXArvhXgdX7kx16Hq656/hD/u3w38F7A2wA/zbN9NvBZwPsA382/wRd/8Rcb4JM/+ZPF/0FbW1tbD3/4wx/O87G5ubl56uEPfzjPh6+77jriuut4vq5/uM0W/0mE5jyQXI0qgHAAPfczc/7tRsQFAJnBcAHAOroghkE6OMg8OOC/0IvV7uQGhz0vwIPqPScWrHpegGvL+ZMzhp7n44R2T/ZMPf8FhFXkwgMUt8oDhFrBiGcKOYIMng+jMBIvgkbkMmdrgCTyyPMVz3TI1hHPdMmbS4CkdI3o+Q80UhdGhf8AjVjYKvw36ZQsdFj4N9jSMjo38SLajGUUJvEi6jXEwoN4AMvmX0FgbF5UEuZFZZsXoFGTF2KkJi9AGpKavAApuTnMv1PIrfO45IWwxmVTNl6A5mxLjUuey8qxus3z2y7FwaWBYeC5POVuPeXgIA94Pg4OOHjKU8pTeAH++q/v/Guuuuqqq/6PeumXfumX5gG2tra2Hn7T9Q/nAbbM1sPh4TyXl4KX4r/YHM15PqpUq115AebSnBei2rVC5f+Ie8Q9/CtdEBcGGPg3ugAXxmTk3+kecQ//Ad7LvBfArfhWgNf5kR97Ha666vlD/O9yEbgEPJjn9GDg6cDPAG/Nv8EXf/EXG+CTP/mTxb/CS7/0S780/wY3P+xhD9PW1hb/Al933XXEddfxLzDXXYd1Hf/NhCpQeSDRG4JnEg6g5zmoYlf+g22U9e7NuicACrlEeQRQ5aPwtOSFuLHcc2yDdeVfkFBPxd41C1aVF8G2DuY7HMz4T5BESVT4N6hMwfPRqQXPRZiqJp7JRkmUQqqQ/EcaCJYsGg9wKbcbz7SmzyUzA6yZ5Tp78y/olCx0WPhPstCguYfgP0ioaUtHwb9TcdNmHIl/B9nm30jC/FvY5j+AEUlJ/gWJaJTkXzARxmFegJTcHOZfYcZwwL8g5DZoWPIvGGjDwDTwAtzB/I6jjKOzceEsz8eBOXjKU/wUXoinPCWecnCgA16Iv/7rO/+aq6666qqr/kd76Zd+6Zfmubz0ox/x0jwfL21emufjOrjuWriW/0ECRQ89L0SI6E3Pi6BCrVLlRTC351z1b3YrvhXgdX7kx16Hq656/hD/ezwYeDrwO8Br87wM3Ao8hH+Ft/jkb/s2s/3wVz9+62sD/P7ug3+b5+Mx5Wlb/C/zsHrbFvezZKLyfFgoUeX5UtgUng+jahDPdF2c6xYM4j/QsbJXjrFf+H+m0yheiOKUlLwglZQwz0+lKTD/GxmDMP9JZJv/QBLmP4yN+W9jRFKSf4OJYiPzIkrkdDEvopTcHOYBZgwH/CtY47IpGy+ipcZlczZeBAdaHvAC/I31N7wQT7lbTzk4yANegL/+6/LXvAD33NPuueeee+7hqquuuuqqq/6bvfRLv/RL83xcd9111113fPs6XoCXNi/NC/Fw8/BNscn/EXM051+phz4g+DfqoQ8p+Hea23P+A92KbwV4nR/5sdfhqqueP8T/Hq8N/BbwPcB787xuBR4EiH+Fv/+8V7gI8PPd2x0HePPxJ3b5VzAK/g2MAsS/xFgg8S8wCCT+DxJO/o1CqULyb1FJQRP/AoErDbB4ERRSQfKfRcL8d7DN/xJGJCX5T9IIJ2H+g6Qhqcm/UyIq0x7/RiG3QcOSf4PmbEuNS/6VmrItWS/5D3BoDp+CnsKL4K+f7L/mX/CUp8RTDg50wAtwcKCDpzzljqdw1VVXXXXVVVf9t9ja2tp6+MMf/nBeiK2tra2H33T9w3kRXGeuuw6u40XwUvBSXPXv9jo/8mOvw1VXPX+I/z1eG/gt4HOAz+Z5/TbwWoD4V/j7z3uFiwA/373dcYA3G39yj+ejMgX/C5jIRAnQ0YL/QsEUIf5DBalCiv9mIzX5P2IijMP8D5GIRiQvgkobCm3gP0FEGwamgf9AS43L5mz8B1hqvWxk47/JoTl8CnoK/wZPuVtPOTjIA15E99yje+65J+7hRXTPPe2ee+655x6uuuqqq6666qqr/hd76Zd+6ZfmX+mlH/2Il+bf4eHm4Vuwxb/TS8FL8Z/gdX7kx16Hq656/hD/e7w28FvA5wCfzfP6beC1APFcvviLv9hcddVVV1111VVXXXXVVVddddVV/yv88A//8Mv89V//9V9z1VXPH+J/j5cG/gr4HuC9eV5PBx4MiOfyxV/8xeaqq6666qqrrrrqqquuuuqqq676X+GTP/mTxVVXvWCI/10M/A7w2jwvA38DvDRX/Z/1xV/8xQb45E/+ZHHV/1tf/MVfbIBP/uRPFlf9v/fFX/zFBvjkT/5kcdX/e1/8xV9sgE/+5E8WV/2/98Vf/MUG+ORP/mRx1f97X/zFX2yAT/7kTxZX/b/1xV/8xQb45E/+ZHHVVVf9b4b43+VW4Bhwguf00sBfAd8DvDdX/Z/1xV/8xQb45E/+ZHHV/1tf/MVfbIBP/uRPFlf9v/fFX/zFBvjkT/5kcdX/e1/8xV9sgE/+5E8WV/2/98Vf/MUG+ORP/mRx1f97X/zFX2yAT/7kTxZX/b/1xV/8xQb45E/+ZHHVVVf9b4b43+Wjga8C3gb4aZ7tq4GPAl4H+G2u+j/ri7/4iw3wyZ/8yeKq/7e++Iu/2ACf/MmfLK76f++Lv/iLDfDJn/zJ4qr/9774i7/YAJ/8yZ8srvp/74u/+IsN8Mmf/Mniqv/3vviLv9gAn/zJnyyu+n/ri7/4iw3wyZ/8yeKqq6763wzxv8tx4FbAwGcDfw28N/DewN8AL81V/6d98Rd/sQE++ZM/WVz1/9YXf/EXG+CTP/mTxVX/733xF3+xAT75kz9ZXPX/3hd/8Rcb4JM/+ZPFVf/vffEXf7EBPvmTP1lc9f/eF3/xFxvgkz/5k8VV/2998Rd/sQE++ZM/WVx11VX/myH+93kw8NPAS/Fs3wN8NLDLVf+nffEXf7EBPvmTP1lc9f/WF3/xFxvgkz/5k8VV/+998Rd/sQE++ZM/WVz1/94Xf/EXG+CTP/mTxVX/733xF3+xAT75kz9ZXPX/3hd/8Rcb4JM/+ZPFVf9vffEXf7EBPvmTP1lcddVV/5sh/vc6Drw08NfALlf9v/DFX/zFBvjkT/5kcdX/W1/8xV9sgE/+5E8WV/2/98Vf/MUG+ORP/mRx1f97X/zFX2yAT/7kTxZX/b/3xV/8xQb45E/+ZHHV/3tf/MVfbIBP/uRPFlf9v/XFX/zFBvjkT/5kcdVVV/1vhrjqqv9FvviLv9gAn/zJnyyu+n/ri7/4iw3wyZ/8yeKq//e++Iu/2ACf/MmfLK76f++Lv/iLDfDJn/zJ4qr/9774i7/YAJ/8yZ8srvp/74u/+IsN8Mmf/Mniqv+3vviLv9gAn/zJnyyuuuqq/80QV131v8gXf/EXG+CTP/mTxVX/b33xF3+xAT75kz9ZXPX/3hd/8Rcb4JM/+ZPFVf/vffEXf7EBPvmTP1lc9f/eF3/xFxvgkz/5k8VV/+998Rd/sQE++ZM/WVz1/9YXf/EXG+CTP/mTxVVXXfW/GeKqq/4X+eIv/mIDfPInf7K46v+tL/7iLzbAJ3/yJ4ur/t/74i/+YgN88id/srjq/70v/uIvNsAnf/Ini6v+3/viL/5iA3zyJ3+yuOr/vS/+4i82wCd/8ieLq/7f+uIv/mIDfPInf7K46qqr/jdDXHXVVVddddVVV1111VVXXXXVVVddddX/Hoirrrrqqquuuuqqq6666qqrrrrqqquu+t8DcdVVV1111VVXXXXVVVddddVVV1111VX/eyCuuuqqq6666qqrrrrqqquuuuqqq6666n8PxFVXXXXVVVddddVVV1111VVXXXXVVVf974G46qqrrvqf7zjwUsDfALs8f8eBl+KKS8Bfc9X/VQ8GjgN/zVX/Xx0HXopn+x1euNfi2X6Hq/6veWngGFdcAv6aq/6/Og68FFdcAv6aF+6lgWNc8Ttc9X/NSwPHuOIZwK1cddVV/5cgrrrqf76nAw/m+Xsd4Le56v+67wLeG3gd4Ld5Xq8N/BRwnGf7a+B1gF2u+r/kOPB04G+A1+Z5vTTwV7xg4qr/7T4L+GjgOM+2C7wP8NM8p5cGfgp4MM92K/A2wF9z1f92x4GfAl6b5/TbwPsAt/KczAv2OsBvc9X/Zm8NfBdwnGe7FXgb4K95TseBnwJem2fbBd4H+Gmu+t/uOPBVwHvznL4a+Bie19OBB/P8fQ7w2Vx11VX/EyGuuup/tuPAReB3gN/meX03cCtX/V/2VcBHc8XrAL/Nc3ow8FeAgPcG/hp4a+CrgL8GXoar/q94MPBTwEsDvwO8Ns/rvYHvAr4HuJXn9dlc9b/ZewPfBfwO8NnArcBLA18NPAh4HeC3ueI48HRAwHsDfw28NvDVgIGHALtc9b/ZXwEvDXwO8NNc8dbAZwF/DbwMz/bawG8BvwP8Ns/ru4Fbuep/q5cG/gr4G+Czgd8G3hr4asDAQ4Bdnu2vgJcGPgb4beDBwHcDBl4GuJWr/jf7LuC9ge8BvporPhp4L+BrgI/mORn4G+CneV6/Dfw2V1111f9EiKuu+p/ttYHfAj4G+Gqu+v/kwcB3Aa8NXAKOAa8D/DbP6buB9wJeB/htnu2rgY8C3gf4bq763+69gK8GjnPF7wCvzfP6bOCzgJcB/pqr/q95OnACeDCwy7O9NPBXwO8Ar80Vnw18FvA+wHfzbO8NfBfwMcBXc9X/Vm8N/BTwNcBH85y+G3gv4HWA3+aK9wa+C3gb4Ke56v+anwbeCngZ4K95ts8GPgv4GOCrueK9ge8Cvgb4aJ7ttYHfAr4HeG+u+t/qwcDTgd8BXpvndCtwDDjBs7028FvA5wCfzVVXXfW/CeKqq/5n+2zgs4DXAX6bq/4/MVd8Dld8FvA6wG/znC4Cl4AH85xeGvgr4HuA9+aq/80+Gvgq4BnAWwN/BfwO8No8r98GXgsQV/1fcxz4aa54bZ6XgV3gBFf8FfDSwAlgl2c7DlwE/hp4Ga763+q9gc8G3hv4bZ7TWwM/BXwO8Nlc8dXARwEPAW7lqv9rPho4Dnw2z+mtgZ8CPgf4bK74buC9gJcB/prndCtwDDjBVf9bPRh4b+CvgZ/mOf028FqAeLaPBr4KeB3gt7nqqqv+N0FcddX/bN8NvBcg4K2Alwb+GvgdYJer/i/7buCzgVuBzwY+C3gd4Ld5tpcG/gr4HeC1eV4G/hp4Ga763+y9gQcDn80VBn4HeG2e10Xgb4D3Bl4LeDDw28DvcNX/ZceBi8DvAK/NFQZ+B3htntdvA68FiKv+L/pq4KOA9wG+myt+G3hp4MHAawEvDfw28DfALlf9X/VTwFsDrwP8Nlc8HXgwIJ7XbwOvBbwM8Ndc9X/Jg4G/Ai4BD+bZvht4L+AE8FLAawN/DfwNcCtXXXXV/2SIq676n+2vgAcDu8CDebZd4H2An+aq/w8+G/gs4HWA3+bZXhv4LeBzgM/meZkrxFX/lxj4HeC1eU4PBp4O3AocB47zbH8NvA/w11z1f9FPAW8NfAzw1Vxh4HeA1+Z5/TbwWoC46v+a48DTAQEPBna5wsAuVxzn2W4F3gf4ba76v+LBwFsBbw28NvA9wHvzbAYuAcd5Xp8NfBbwOsBvc9X/Ba8FvDXw1sAJ4LWBv+bZfht4LeBW4ME82y7wMcB3c9VVV/1Phbjqqv/ZzBWfA3w3V7w18NnAMeBlgL/mqv/rPhv4LOB1gN/m2V4b+C3gc4DP5nn9NvBagLjq/xIDvwO8Ns/ptYHfAi4BHw38NPBg4L2BjwJuBV4G2OWq/0veG/gu4G+Al+aK1wZ+C/gd4LV5Xr8NvBbwOsBvc9X/FceB3wJeGngf4Lu54sHA04FLwGcDPw0cB94a+CxgF3gZ4Fau+r/gs4HP4opd4KOB7+HZDPwO8No8r88GPgt4HeC3uer/AvNsPw18DHArz2au+Bzgu7nitYGvBo4BrwP8NlddddX/RIirrvqf6zjw0sBx4Kd5Tm8N/BTwM8Bbc9X/dZ8NfBbwOsBv82yvDfwW8DnAZ/O8fht4LUBc9X+Jgd8BXpvn9GDgpYFbgb/mOX038F7AxwBfzVX/V3wW8NnA3wCvDexyxYOBpwO/A7w2z+u3gdcCHgLcylX/FxwHfgt4aeB7gPfm2Y4Dr80VP81z+mzgs4DvAd6bq/4veDBwK/DSwFcDrwV8DfDRXGHgd4DX5nl9NvBZwOsAv81V/xcc54q3Br4aMPAywK1c8drAceCneU5vDfwU8DPAW3PVVVf9T4S46qr/vQzsAie46v+6zwY+C3gd4Ld5ttcGfgv4HOCzeV7mCnHV/yUGfgd4bV50rw38FvA9wHtz1f8F3wW8N/A3wGsDuzwnA78DvDbP67eB1wLEVf8XvDTwU8CDgY8BvpoX3XHgIvA7wGtz1f81x4G/Bh4EiCv+GngQcILn9dnAZwGvA/w2V/1f897AdwHfA7w3/zJzhbjqqqv+J0JcddX/Xk8HHgyIq/6v+2zgs4DXAX6b52Tgd4DX5nkZ+Bvgpbnq/xIDvwO8Ni+61wZ+C/gZ4K256n+z48BPAa8N/Azw3sAuz8vA7wCvzfP6beC1AHHV/3YvDfwWcBx4H+C7+dcz8DfAS3PV/0W/DbwW8DrAbwO/DbwWIJ7XbwOvBTwEuJWr/q85DlwEfgd4bf5lfwW8NCCuuuqq/4kQV131P9drA58F/A7w2Tyn48BF4HeA1+aq/+s+G/gs4HWA3+Y57QIXgYfwnF4a+Cvga4CP5qr/Swz8DvDaPKfPBt4KeB/gr3lObw38FPA5wGdz1f9Wx4HfAl4a+B7gvXnBfhp4K+AEsMuzHQcuAr8DvDZX/W/20sBvAQJeG/hrnr/3Bt4L+B7gu3lOLw38FfA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"text/plain": [ "" ] }, - "execution_count": 5, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -164,25 +164,25 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Time taken: 4.55891227722168\n", - "Risk associated with QoI: [4628619.619999999]\n" + "Time taken: 15.391172647476196\n", + "Risk associated with QoI: [5158925.469999998]\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "" ] }, - "execution_count": 6, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -217,28 +217,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 20%|██ | 24/120 [00:32<01:42, 1.06s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 23%|██▎ | 28/120 [00:34<01:08, 1.34it/s]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 26%|██▌ | 31/120 [00:35<00:47, 1.86it/s]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 28%|██▊ | 34/120 [00:37<00:52, 1.63it/s]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 30%|███ | 36/120 [00:38<00:51, 1.62it/s]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 46%|████▌ | 55/120 [00:58<01:08, 1.06s/it]" + " 26%|██▌ | 31/120 [02:09<06:12, 4.18s/it]" ] }, { @@ -248,17 +234,17 @@ "Optimal policy: tensor([0.1940], dtype=torch.float64)\n", "{'policy': tensor([0.1940], dtype=torch.float64), 'OptResults': message: ['requested number of basinhopping iterations completed successfully']\n", " success: True\n", - " fun: 0.15601666165676767\n", + " fun: 0.15601666455392296\n", " x: [ 1.940e-01]\n", " nit: 3\n", - " minimization_failures: 1\n", - " nfev: 54\n", + " minimization_failures: 0\n", + " nfev: 31\n", " lowest_optimization_result: message: Optimization terminated successfully.\n", " success: True\n", " status: 1\n", - " fun: 0.15601666165676767\n", + " fun: 0.15601666455392296\n", " x: [ 1.940e-01]\n", - " nfev: 9\n", + " nfev: 8\n", " maxcv: 0.0}\n" ] }, @@ -319,25 +305,31 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Intervention: {1.0: {'p_cbeta': tensor(0.1940)}}\n", - "Risk associated with QoI: [200.28645385742186]\n" + "Intervention: {1.0: {'p_cbeta': tensor(0.1940)}}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Risk associated with QoI: [200.10247192382812]\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "" ] }, - "execution_count": 8, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -371,14 +363,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 36%|███▌ | 43/120 [00:54<01:36, 1.26s/it]" + " 38%|███▊ | 45/120 [02:35<04:19, 3.46s/it]" ] }, { @@ -391,8 +383,8 @@ " fun: -7.69576122033028\n", " x: [ 7.696e+00]\n", " nit: 3\n", - " minimization_failures: 0\n", - " nfev: 43\n", + " minimization_failures: 1\n", + " nfev: 45\n", " lowest_optimization_result: message: Optimization terminated successfully.\n", " success: True\n", " status: 1\n", @@ -452,24 +444,24 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Risk associated with QoI: [145.44342834472656]\n" + "Risk associated with QoI: [148.92850952148436]\n" ] }, { "data": { - "image/png": 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+6Ze+5aVe6qXyhoc/HB7+cKzreC5CFXkO9ECPmfP8iHsx91hHF0IXLmQeHPBfRDz6vQAMtwL8/Be/8+tw1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquuuuqqq6666qqrrrrqf6CHP/zhD3/MS73US/Hwhz8cPfzhZvvhPBehAHqLufDc0MsEz01cBC7I0z3WhQv2hQv8NxKPfi8Aw60AP//F7/w6XHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq6666qqrrrrqqquu+m/28Ic//OEPe9jDHtY9/OEP187DH25f/9I8H0JzRG/oBXPsyvMaBfcYLqDde+x77uF/GPHo9wIw3Arw81/8zq/DVVc9f4irLvviL/5iA3zyJ3+yuOqqq6666qqrrrrqqquuuuqq/0LXXXfddQ972MMedurhD3+4b3jpl4brH26zxfPqJXqgB3rMnOdH3Iu5YB3dE7pwIfPggP/hxKPfC8BwK8DPf/E7vw5XXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqqq6666qqrrrrqqqv+k2xtbW09/OEPf/gtL/VSL5U3PPzh4hEvbbPFcxGqFj3QC88NvUzw3MRF4AKKC/iee+wLF/hfSDz6vQAMtwL8/Be/8+tw1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquuuuqqq6666qqrrrrqP8hLv/RLv/TND3vYw3j4wx/ueOmXxrqO5yIUQG8xF/TGc5nguRgOAy4YLqDde+QLF5Jh4P8A8ej3AjDcCvDzX/zOr8NVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqqquuuuqqq6666qp/g4c//OEPf9jDHvaw/qVf+qXRwx9uth/O8yE0R/SGXjDHrjyvEXEBc491dCF8zz3JMPB/lHj0ewEYbgX4+S9+59fhqqueP8RVl33xF3+xAT75kz9ZXHXVVVddddVVV1111VVXXXXVv+DhD3/4wx/2sIc9rHv4wx+unYc/3L7+pXn+eoneaA7uZXqeH3Ev5oJ1dE/owoXMgwP+HxGPfi8Aw60AP//F7/w6XHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq6666qqrrrrqqquueoCHP/zhD3/Ywx72sO7hD3+4dh7+cPv6l+b56yV6oAd6zJznR1wELsjTPdaFC/aFC/w/Jx79XgCGWwF+/ovf+XW46qrnD3HVZV/8xV9sgE/+5E8WV1111VVXXXXVVVddddVVV/2/9fCHP/zhD3vYwx7WPfzhD9fOwx9uX//SPB9C1aIHeuG5oZcJnovhUOIeFBfwhQv2Pfdw1fMQj34vAMOtAD//xe/8Olx11fOHuOqyL/7iLzbAJ3/yJ4urrrrqqquuuuqqq6666qqr/l+47rrrrnupl3qpl+oe/vCHa+fhD7evf2meD6Fq0QO98NzQywTPxXAYcMFwAe3eI1+4kAwDV/2LxKPfC8BwK8DPf/E7vw5XXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqqq6666qqrrrrqqv9zrrvuuuse9rCHPezUwx/+cN/w0i8N1z/cZovnIlQteqAXnht6meC5GA4DLhguoN175AsXkmHgqn8T8ej3AjDcCvDzX/zOr8NVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqqquuuuqqq6666n+1hz/84Q9/2MMe9rDZdddd5xte+qXh+ofbbPFchKpFD/TCc0MvEzyvEXEBc491dCF04ULmwQFX/YcRj34vAMOtAD//xe/8Olx11fOHuOqyL/7iLzbAJ3/yJ4urrrrqqquuuuqqq6666qqr/td4+MMf/vCHPexhD+se/vCHa+fhD7evf2meD6GwmAO98NzQywTPa0RcwNxjHV0IXbiQeXDAVf+pxKPfC8BwK8DPf/E7vw5XXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqqq6666qqrrrrqqv+RXvqlX/qlb37Ywx7Gwx/+cOK66+zrX5rnQ6ha9EAvPDf0MsHzGhEXMPdYRxdCFy5kHhxw1X858ej3AjDcCvDzX/zOr8NVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqqquuuuqqq6666r/dS7/0S7/0zQ972MN4+MMfjh7+cLP9cJ6/HqkCvfDc0MsEz2tEXMDcYx1dCF24kHlwwFX/I4hHvxeA4VaAn//id34drrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVVddddVVV1111VX/Zba2trYe/vCHP/yWl3qpl8obHv5w+brrzPbDef56iR7ogR4z5/kbERcw91hHF0IXLmQeHHDV/1ji0e8FYLgV4Oe/+J1fh6uuev4QV132xV/8xQb45E/+ZHHVVVddddVVV1111VVXXXXVf4rrrrvuuoc97GEPO/Xwhz88b3j4w+HhD8e6judDaI5cgR7oMXOeD8NhwAXDBevoQujChcyDA676X0U8+r0ADLcC/PwXv/PrcNVVzx/iqsu++Iu/2ACf/MmfLK666qqrrrrqqquuuuqqq676d3vpl37pl7722muv7R7+8Idr5+EPt69/aV4AoTmiN/TgXqbn+TAcBlwwXEC798gXLiTDwFX/64lHvxeA4VaAn//id34drrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVVddddVVV1111VUvsq2tra2HP/zhD7/lpV7qpXzdddehhz/cbD+c50MogN5iLuiNq0zP8yMuAhdQXMAXLsgXLiTDwFX/J4lHvxeA4VaAn//id34drrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVVddddVVV1111VXP13XXXXfdwx72sIedevjDH+4bXvqlzXXXYV3H89dL9EZVeG7oZYLnR1wELqC4gC9csO+5h6v+XxGPfi8Aw60AP//F7/w6XHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq6666qqrrrrqqqt46Zd+6Ze+9tprr+0e/vCHa+fhD7evf2leAKE5ojf0whUz5/kbERcwF8R0wbpwwb5wgav+3xOPfi8Aw60AP//F7/w6XHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq6666qqrrrrqqv9Hrrvuuuse9rCHPezUwx/+8Lzh4Q+Xr7vObD+c50OoAtViLuiBHrvyfBgOAy4YLlhHF0IXLmQeHHDVVc+HePR7ARhuBfj5L37n1+Gqq54/xH+N48BLAb/DC3YceCmuuAT8NS/YceCluOIS8Nf8O33xF3+xAT75kz9ZXHXVVVddddVVV1111VVX/R+0tbW19fCHP/zhNz/sYQ/zddddp52HP9y+/qV5wXqJ3qgKzw29TPD8iHuBAxQX8IUL8oULyTBw1VUvIvHo9wIw3Arw81/8zq/DVVc9f4j/Gj8FvDUgnr/3Br4KOM6z/TbwNsAuz+mlgZ8CHsyz/TXwNsCt/Bt98Rd/sQE++ZM/WVx11VVXXXXVVVddddVVV/0v9/CHP/zh11577bWnHv7wh/uGl35pc911WNfxfAgF0FvMgQruZXqevxFxAXOPmA6sCxfsCxe46qp/J/Ho9wIw3Arw81/8zq/DVVc9f4j/fN8FvDdXiOf12sBvAX8DfDRwK/DewGcBvw28Ds/2YOCvAAHvDdwKvDbwVcBfAy/Dv9EXf/EXG+CTP/mTxVVXXXXVVVddddVVV1111f8SW1tbWw9/+MMffstLvdRL+brrrkMPf7jZfjgvgNAcuQI90Bt6meD5MBwGXDBcQLv3SAcHmQcHXHXVfwLx6PcCMNwK8PNf/M6vw1VXPX+I/zwvDXwX8NI8m3hevwW8NvAQ4Fae7buB9wJeBvhrrvhu4L2AtwF+mmf7bOCzgPcBvpt/gy/+4i82wCd/8ieLq6666qqrrrrqqquuuuqq/4Fe+qVf+qWvvfbaa7uHP/zh2nn4w+H6h9ts8XwIVYse6AU90GNXnr8RcQFzQUwXrIMD+557uOqq/0Li0e8FYLgV4Oe/+J1fh6uuev4Q/zleGvgr4BLw2cBbA68FiOdl4G+Al+Y5vTXwU8DXAB/NFReBS8CDeU4PBp4O/Azw1vwbfPEXf7EBPvmTP1lcddVVV1111VVXXXXVVVf9N3rpl37pl7722muvnV133XW+4aVf2lx3HdZ1PB9CAfSI3tALV8ycF0RcBC5gDtDuPdLBQebBAVdd9d/oRPT9Kep73VDP1lviroNjOujf5LN+5mauuur5Q/zneG3gvYHPBm4Ffht4LUA8p9cGfgv4HOCzeV4Gfgd4beDBwNOB3wFem+dl4FbgIfwbfPEXf7EBPvmTP1lcddVVV1111VVXXXXVVVf9F3j4wx/+8GuvvfbaUw9/+MN9w0u/tLnuOqzreMF6id6oCs9BFbvyfBgOJQ4w94jpwLpwwb5wgauu+m/2YrU7eY0ubN5U7j55S7n7ui0dbm2y3Nrz1nUAlWkF8OKf8WcnuOqq5w/xX+O3gdcCxHN6beC3gM8BPpvnZeB3gNcGXhv4LeB7gPfmed0KPAgQ/wZf/MVfbIBP/uRPFlddddVVV1111VVXXXXVVf+BHv7whz/82muvvfbUwx/+8Lzh4Q+Xr7vObD+cF6xHqkAv6I2rTM8LIu4FDlBcwBcuyBcuJMPAVVf9NzoRff/wODj5oHrPievj3Mkd7W9dq3PX8QIsmV0fTjXKEcCLf8afneCqq54/xH+N3wZeCxDP6aOBrwI+B/hsnpe5QsBrA78FfA7w2Tyv3wZeCxD/Bl/8xV9sgE/+5E8WV1111VVXXXXVVVddddVV/wbXXXfddQ972MMedurhD3943vDwh8vXXWe2H84L1iNVoBf0xlWm5wURF2UODBesowuhCxcyDw646qr/Zi9Wu5PX6MLmTeXuk7eUu6/b0uHWJsstng/JUXAUtxpqRTgKWdaebQHseusA4MU/489OcNVVzx/iv8ZvA68FiOf03sB3AZ8DfDbPy8Al4Djw2sBvAZ8DfDbP67eB1wLEc/niL/5i8yL65E/+ZHHVVVddddVVV1111VVXXfVCPPzhD3/4tddee+2phz/84XnDwx8utrbs61+aF0CoAtViDlRwL9PzAhgOJQ4w91hHF8TBgX3hAldd9d/sRPT9w+Pg5KPrrdcej4OtU7pw8qQuneQFqMoqWilkyC5VrfICrD3bAtj11gHAi3/Gn53gqqueP8R/jd8GXgsQz+m1gd8CPgf4bJ6Xgd8BXht4aeCvgO8B3pvn9XTgwYB4Ll/8xV9sXkSf/MmfLK666qqrrrrqqquuuuqqq4CXfumXfulrr7322tl1112XNzz84fJ115nth/MCCFWgWsyBKlwxc14Aw6HEAeYeMR1YBwf2Pfdw1VX/A7xCHa67tu5unmB365Zy93UntHuyZ+p5PiRHcSsFl1ArgUuQwfMxOXLNbHnk+cFANyxzsTxitnxUecarAex66wDgxT/jz05w1VXPH+K/xm8DrwWI5/TSwF8BnwN8Ns/LwO8Ar80VBn4HeG2el4G/AV6af4Mv/uIvNsAnf/Ini6uuuuqqq6666qqrrrrq/42tra2thz/84Q+/+WEPe5i2trZ8w0u/tLnuOqzreAGEKlAt5kAVrpg5L4DhUOIAc4+YDqyDA/uee7jqqv8BXqEO111bdzdPsLt1S7n7ui0dbm2y3OIFqMoqWilkyC5VrfICLN2vVsyWk+uw742DpTaWLdV4Ph5VnvFqALveOgB48c/4sxNcddXzh/iv8dvAawHiee0CTwdehuf01sBPAZ8DfDZX3AocA07wnF4a+Cvge4D35t/gi7/4iw3wyZ/8yeKqq6666qqrrrrqqquu+j/nuuuuu+6666677uaHPexhvu6667Tz8IfD9Q+32eIF65Eq0At64yrT84KNiAuYC0Qc4AsX5AsXkmHgqqv+m71Y7U5eowubN5W7T15bzp/c4nDrpC6d5AUoZBEZBZeg1ZAjyOD5GFzHkW448vxgxXy5zn44YrbkX+FR5RmvBrDrrQOAF/+MPzvBVVc9f4j/Gr8NvBYgntd3A+8FvAzw1zzbbwGvDTwEuJUrPhr4KuBtgJ/m2b4a+CjgdYDf5t/gi7/4iw3wyZ/8yeKqq6666qqrrrrqqquu+l/r4Q9/+MOvvfbaa089/OEP93XXXUdcd519/UvzQgjNkatRFZ4bQqbnBTAcShxg7iFiwBcuyBcuJMPAVVf9N3ux2p28Rhc2byp3n7y2nD+5xeHWSV06yQtQyCIyCi6hVoSjkIUX4NCLw4E6rHO2PGK2XGpj2VKNf6dHlWe8GsCutw4AXvwz/uwEV131/CH+a/w28FqAeF4PBv4aMPDRwK3ARwNvDXwN8NE823HgVsDAZwN/Dbw38N7A3wAvzb/RF3/xFxvgkz/5k8VVV1111VVXXXXVVVdd9T/addddd91111133c0Pe9jDtLW15Rte+qXNdddhXccLIBRAj+jBFehBFbvygoiLwIC5R0wH1sGBfc89XHXV/wC31K2ta7mw9eh667XH42DrlC6cPKlLJ3kBJEdxKwWXUCvCUcjCC7B0v5pchxWz5Yr5cp39cMRsyX+SR5VnvBrArrcOAF78M/7sBFdd9fwh/mv8NvBagHj+Xhr4aeBBPNvXAB/N83ow8NPAS/Fs3wN8NLDLv9EXf/EXG+CTP/mTxVVXXXXVVVddddVVV131P8JLv/RLv/Tm5ubmqYc//OF5w8MfLra27OtfmheuFwqLOVCFq6GXCV4QcVHmwHDBOrogDg7sCxe46qr/AV6sdiev0YXNm8rdJ68t509ucbh1UpdO8gJIjoKjuFWRIRxVrfICDK7jSDcceX4w0A3LXCyPmC35L/ao8oxXA9j11gHAi3/Gn53gqqueP8T/LMeBlwZ+m3/ZceClgb8Gdvl3+uIv/mIDfPInf7K46qqrrrrqqquuuuqqq/7LXHfdddddd911193yUi/1Urm1taWdhz8crn+4zRYvgFAAPXI1qsJzQ8j0vDDiXuAAc4B275EODjIPDrjqqv8BXqEO111bdzdPsLt1S7n7ui0dbm2y3OIFkBwFR3GrIkM4qlrlBRhcx5FuOPL8YKAb1u6GA28e8D/Eo8ozXg1g11sHAC/+GX92gquuev4QV132xV/8xQb45E/+ZHHVVVddddVVV1111VVX/Yfa2traevjDH/7wmx/2sIdpa2vLN7z0S+OtLbP9cF64HqkCPVCFK2bOC2E4lDjA3EPEgC9ckC9cSIaBq676b3Yi+v7hcXDyQfWeE8fZ37q2nDt5Qrsne6aeF6CQRULFrYoM4ahqlRdgcuSa2XLNbLl0v1y7Gw68ecD/cI8qz3g1gF1vHQC8+Gf82Qmuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVVV1111VVXXfWvtrW1tfXwhz/84ddee+21s+uuuy5vePjDxdaWff1L80IIVaAiekMIz0EVu/LCiHuBA8yBdXRBHBzYFy5w1VX/A7xY7U5ucNg/ut567fE42NrR/ta1OncdL0RVVpwquIRaEY5CFl6AyZFrZss1s+WQdThitlxqY9lSjf+FHlWe8WoAu946AHjxz/izE1x11fOHuOqyL/7iLzbAJ3/yJ4urrrrqqquuuuqqq6666vna2traevjDH/7wa6+99trZddddlzc8/OFia8u+/qV5IYQC6JGrURX04MDMeWHERWDA3COmA+vgQL5wIRkGrrrqv9mJ6PuHx8HJa+vu5gl2t24pd1/XMfYndekkL4CwilxEK2EUtBpyBBm8AIPrONINR54fDHTD2t2w1MaypRr/hzyqPOPVAHa9dQDw4p/xZye46qrnD3HVZV/8xV9sgE/+5E8WV1111VVXXXXVVVdd9f/Y1tbW1sMf/vCHX3vttdfOrrvuurzh4Q8XW1v29S/NCyEUQI9cjaqgBwdmzgs3Ii4AB5gDtHsPDIN94QJXXfU/wCvU4botrbubyt0nj8fB1o72t67Vuet4ISRHwVHcqpSSXYqyCIsXYOl+NbkOK2bLFfPl6GgH3jzg/4lHlWe8GsCutw4AXvwz/uwEV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqquuuuqqq/6Pe/jDH/7wra2trZsf9rCHaWtryze89EsD2Ne/NC+EUAA9cjWqgh4cmDn/EnGvzGC4IKYD6+DAvucerrrqf4AXq93JDQ77R9dbr50x9teWcydPaPdkz9TzQlRlFRlhR6gVjKpa5YU49OJwoA6T67DvjYN0bUfMlvw/96jyjFcD2PXWAcCLf8afneCqq54/xFWXffEXf7EBPvmTP1lcddVVV1111VVXXXXV/wEv/dIv/dKbm5ubpx7+8If7uuuuI667zlx3HdZ1vBBCAfTI1agKenBg5vxLxL0yg+GCmA6sgwP5woVkGLjqqv9mJ6LvHx4HJx9U7zmxYNXfUu6+rmPsT+rSSV6IQhaRUXARGcJRlEVYvACTI9fMlmtmyyHrcMRsOdIPa3cDVz1fjyrPeDWAXW8dALz4Z/zZCa666vlDXHXZF3/xFxvgkz/5k8VVV1111VVXXXXVVVf9L3Ddddddd911111388Me9jBtbW3lDQ9/uNjasq9/af4FQhWoiN4Qgh4cmDn/EnGvzGC4IKYD6+BAvnAhGQauuuq/2S11a+taLmw9qN5zYsGqv6XcfR3AtTp3HS+E5Cg4RCuFDNkl5AgyeCEOvThMq62YLVfMl6OjHXjzgKv+1R5VnvFqALveOgB48c/4sxNcddXzh7jqsi/+4i82wCd/8ieLq6666qqrrrrqqquu+h/ipV/6pV96c3Nz89TDH/7w3Nra0s7DH26uuw7rOv4FQnOLAHrhAHpQxa78S8S9MoPhgpgOrIMD+cKFZBi46qr/Zi9Wu5MbHPYPqvecWLDqbyl3X9cx9id16SQvhLCKXERG2BFqRTgKWXghlu5XSWlHnh80l3bEbLnUxrKlGlf9h3lUecarAex66wDgxT/jz05w1VXPH+Kqy774i7/YAJ/8yZ8srrrqqquuuuqqq6666r/IS7/0S780wC0v9VIvBeAbXvqlAezrX5p/gVAFKnI1qoIeHIZeJnghDIcSB8AB5sA6uiCGQb5wIRkGrrrqv9mL1e7kBof9o+ut184Y+2vLuZNbOtzaZLnFCyGsIheREXaEWsGoqlVeiMmRa2bLNbNls9q+Nw7StR0xW3LVf4lHlWe8GsCutw4AXvwz/uwEV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqquuuuqqq/6DvPRLv/RLA9zyUi/1UgC+4aVfGsC+/qV5EQjNASzmwgH0hpDp+ZeIi8CAuQcA7d4Dw2BfuMBVV/0P8Ap1uG5L6+6mcvfJGWN/bTl3ckuHW5sst3ghJEfBIVoJo6BVySpk4V9w6MVhWm3FbLlivhwd7cCbB1z13+5R5RmvBrDrrQOAF/+MPzvBVVc9f4irLvviL/5iA3zyJ3+yuOqqq6666qqrrrrqqhfRS7/0S780wC0v9VIvBeAbXvqlAezrX5oXgdAcwGIOIDwHwMz5l42IC8AB5kBMB9bBgXzhQjIMXHXVf7MXq93JDQ77B9V7TixY9beUu68DuFbnruNfUMgiIdFKGAWtSlYhCy/E5Mg1s+VAHSbXYcV8OTraUhvLlmpc9T/Wo8ozXg1g11sHAC/+GX92gquuev4QV132xV/8xQb45E/+ZHHVVVddddVVV1111VXAddddd91111133ebm5uaphz/84bm1taWdhz8cwL7+pXkRCM0BLOYAwnMAzJwXhbgIDJh7ANDuPTAM9oULXHXVf7Nb6tbWtVzYurbubp5gd+vacv7kjKE/od2TPVPPv6AqK0Bxq1JKdgk5ggxeiMmRa2bLgTpMrsOK+XJ0tANvHnDV/1qPKs94NYBdbx0AvPhn/NkJrrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVVddddVV/y+89Eu/9EsD3Pywhz1MW1tbecPDHy62tsx112Fdx4umFwpEbwjhOQBmzotCXAQGzAVgsI4uiGGQL1xIhoGrrvpvdCL6/uFxcHJL6+6mcvfJGWN/bTl3ckuHW5sst/gXFLJISLQSRqFWMKpqlX/B4DqOdMOa2bJZbd8bB+najpgtuer/pEeVZ7wawK63DgBe/DP+7ARXXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqqq6666qr/9a677rrrrrvuuus2Nzc3Tz384Q/Pra0t7Tz84QD29S/Ni0AogN4igB5AeA6AmfOiEBeBAXMBGKyjC2IY5AsXkmHgqqv+G91St7au5cLWtXV38wS7WzPG/tpy7mTH2J/UpZP8CyRHwSEywg6RIRwhR5DBv2DpfpWUduT5AcC+Nw7StR0xW3LV/zuPKs94NYBdbx0AvPhn/NkJrrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVVddddVV/6Ndd91111133XXXbW5ubp56+MMfDuAbXvqluez6h9ts8aLphQLRGwKowhVUsSsvCnERGDAXgME6uiCGQb5wIRkGrrrqv9GL1e7kBof9g+o9Jxas+mvL+ZMzhn5Lh1ubLLd4EVRlxamCi5SSXSSrkIV/weA6jnTDmtmyWW3FfDk62lIby5ZqXHXVAzyqPOPVAHa9dQDw4p/xZye46qrnD3HVZV/8xV9sgE/+5E8WV1111VVXXXXVVVf9t7nuuuuuu+66667b3NzcPPXwhz8cwDe89EsDwPUPt9niRSAUQG8RQA8gPAfAzHnRjIgLAJh7ANDuPQDyhQvJMHDVVf9NTkTfPzwOTgI8ut56LcAt5e7rAK7Vuet4EQiryAWnCi5SSnYBqGqVf8HkyDWzZVptxWw50A1rd8NIP6zdDVx11b/Co8ozXg1g11sHAC/+GX92gquuev4QV132xV/8xQb45E/+ZHHVVVddddVVV1111X+Khz/84Q/f2trauvbaa6+dXXfddbm1taWdhz8cwL7+pXkRCVWgAljMAQQ9OEAVu/KiEBeBATjAHIjpwDo4gGGwL1zgqqv+G71CHa4DeFC958SCVX88DrZ2tL/VMfYndekkL4JCFgmJjLBDZAhHyBFk8CI49OIQ4MjzA4B9bxykaztituSqq/4DPao849UAdr11APDin/FnJ7jqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVddddVVV/2rXHfdddddd9111wHc8lIv9VIAvu6664jrrsNbW2b74byIhCpQLQLoAYTnAIZeJnjRjIgLAJh7ANDuPQDyhQvJMHDVVf8NTkTfPzwOTgI8ut56LcC15fzJGUO/pcOtTZZbvIiqsgIUtwoQagWjoizC4l8wOXLNbJlWWzFbAux74wDgwJsHXHXVf6FHlWe8GsCutw4AXvwz/uwEV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqquuuuqqy7a2trYe/vCHPxzglpd6qZcC8HXXXUdcdx2Aff1L86/TC4VFAD2A8BzA0MsEL5oRcQEAcw+AdXRBDIN0cJB5cMBVV/03uKVubV3Lha0trbubyt0nAW4pd18HcEK7J3umnhdRVVYA0UoYiQzhAKhqlRfB4DqOdMNAHSbXYaAb1u6GdG1HzJZcddX/II8qz3g1gF1vHQC8+Gf82Qmuuur5Q1x12Rd/8Rcb4JM/+ZPFVVddddVVV1111f9x11133XXXXXfddQA3P+xhD9PW1lZubW1p5+EPB4DrH26zxYtIqAIVwGLOFVW4AmDmvIgMhxIHAJh7AKyjC2IYpIODzIMDrrrqv9iJ6PuHx8FJgAfVe04sWPXH42BrR/tbANfq3HW8iIRV5AJQ3CpAqBWMJKuQhRfRoReHAGtmy2a1FfPl6GhLbSxbqnHVVf+LPKo849UAdr11APDin/FnJ7jqqucPcdVlX/zFX2yAT/7kTxZXXXXVVVddddVV/0u99Eu/9EsDbG5ubp56+MMfDpA3PPzhYmsLwL7+pflXEAqgB0D0hhAOoAfAzPlXMBxKHMgMhgsAaPceAPnChWQYuOqq/0Inou8fHgcnAR5U7zmxYNXPGPtry7mTACe0e7Jn6nkRSY6CA6cKLgChVjAKOYIMXkSHXhwCDNRhch2aSztitgQ48OYBV131f8yjyjNeDWDXWwcAL/4Zf3aCq656/hBXXfbFX/zFBvjkT/5kcdVVV1111VVXXfU/yHXXXXfddddddx3ALS/1Ui8FkFtbW9p5+MMBzHXXYV3Hv4JQAD0AojeEcAA9gKGXCV50I+ICAOYCMIjpwDo4ALDvuYerrvovdCL6/uFxcBLgQfWeEwtW/Yyxv7acOwlwQrsne6aeF5GwilwARCthJKVkF4CiLMLiRXToxSHAmtmyWW2gG9buhnRtR8yWXHXV/0OPKs94NYBdbx0AvPhn/NkJrrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVVddddV/soc//OEP39ra2gK45aVe6qV4Jt/w0i8NgLe2zPbD+dfrhcIigJ4rqnAFwMz51xL3AmAuAAMRA75wAUC+cCEZBq666r/IK9ThOoAtrbubyt0nAa4t50/OGHqAa3XuOv4VhFXkAiBaCSMpJbsAFGURFi+iyZFrZkuAI88PAFbMl6OjjfTD2t3AVVdd9Xw9qjzj1QB2vXUA8OKf8WcnuOqq5w9x1WVf/MVfbIBP/uRPFlddddVVV1111VX/SltbW1sPf/jDHw6wubm5eerhD384gK+77jriuusAzHXXYV3Hv5JQBSoAojeEcAA9l6liV/51RsQFrjjAHACg3XsApIODzIMDrrrqv8CL1e7kBoc9wKPrrdcCzBj7a8u5kwAntHuyZ+r5VxBWkQuAyAg7AIJWAUKOIIMX0eTINbMlwJrZslmtubQjZkuApTaWLdW46qqr/s0eVZ7xagC73joAePHP+LMTXHXV84e46rIv/uIvNsAnf/Ini6uuuuqqq6666irgpV/6pV+aZ7rlpV7qpXimvOHhDxdbWwD29S/Nv00vFACI3hAAwnMAQ8j0/GuJi8AAgLkHQEwH1sEBgHzhQjIMXHXVf7JXqMN1AFtadzeVu08CzBj7a8u5kwAdY39Sl07yr1SVFQCnCi4AIkM4AIqyCIt/hUMvDgEG6jC5DgD73jgAGOmHtbuBq6666j/do8ozXg1g11sHAC/+GX92gquuev4QV132xV/8xQb45E/+ZHHVVVddddVVV/2fs7W1tfXwhz/84QCbm5ubpx7+8IcD5NbWlnYe/nCeyb7+pfk3EpoDWATQc0UVrlymil351xsRFwAwF4ABAO3eAyAdHGQeHHDVVf+JXqx2Jzc47AEeVO85sWDVA1xbzp+cMfQA1+rcdfwbSI6CA6C4VQApJbsAhBxBBv9Kh14cAgzUYXIdAPa9cQAw0g9rdwNXXXXV/xiPKs94NYBdbx0AvPhn/NkJrrrq+UNcddkXf/EXG+CTP/mTxVVXXXXVVVdd9T/eS7/0S780z3Tzwx72MG1tbQH4uuuuI667DsBcdx3WdfwbCc25n+gNASDowQGAmfNvYDiUOADAXAAGALR7D89k33MPV131n+TFandyg8Me4Nq6u3mC3S2A43GwtaP9LYAtHW5tstzi36AqK89U3CqAlJJdACSrkIV/paX7VVIawJHnBwAD3bB2NwAstbFsqcZVV131v86jyjNeDWDXWwcAL/4Zf3aCq656/hBXXfbFX/zFBvjkT/5kcdVVV1111VVX/Ze57rrrrrvuuuuu45lueamXeimeKW94+MPF1haAue46rOv4NxIKoAewCKDnmYTn3M/M+bcS93I/cw8AEQO+cAFAOjjIPDjgqqv+g52Ivn94HJzkmR5db72WZ7ql3H0dz3RCuyd7pp5/A8lRcACIVsIIINQKRgBFWYTFv9LgOo50A8Ca2bJZrbm0I2ZLgJF+WLsbuOqqq/5Pe1R5xqsB7HrrAODFP+PPTnDVVc8f4qrLvviLv9gAn/zJnyyuuuqqq6666qp/ta2tra2HP/zhD+eZrr322mtn1113Hc/kG176pXmW6x9us8W/g9Cc+8nVqHJFFa4AhpDp+bcSF4EBAHMBGACsowtiGADkCxeSYeCqq/6DnIi+f3gcnOSZHlTvObFg1QMcj4OtHe1vAXSM/UldOsm/USGLhABERtgBIDKEAyDkCDL4Nzj04pBnOvL8gGfa98YBQLq2I2ZLrrrqqque6VHlGa8GsOutA4AX/4w/O8FVVz1/iKsu++Iv/mIDfPInf7K46qqrrrrqqv/Hrrvuuuuuu+6663imW17qpV6KZ/J1111HXHcdz3L9w222+HcQCqDnmSzmPFsVrgCGkOn5txsRF7ifuYf7afcensm+5x6uuuo/yIvV7uQGhz3AtXV38wS7WzzTLeXu63imLR1ubbLc4t9IWEUuPFNxqzxT0CrPVJRFWPwbDK7jSDcADNRhch0AVsyXo6MBLLWxbKnGVVddddW/waPKM14NYNdbBwAv/hl/doKrrnr+EFdd9sVf/MUG+ORP/mRx1VVXXXXVVf/LPfzhD3/41tbWFs9088Me9jBtbW3xTL7hpV+a+3lry2w/nP8AQnPuJ1ejyjMJz3kmQy8T/HuIi8DAFQeYAwAiBnzhApcNg33hAldd9e9wIvr+4XFwkme6tu5unmB3i2c6HgdbO9rfAugY+5O6dJJ/p6qsPJNoJYwARIZwAEhWIQv/RoPrONINAAN1mFwHgObSjpgtAUb6Ye1u4Kqrrrrqv8CjyjNeDWDXWwcAL/4Zf3aCq656/hBXXfbFX/zFBvjkT/5kcdVVV1111VX/za677rrrrrvuuut4pmuvvfba2XXXXccz5Q0Pf7jY2uJZrn+4zRb/AYQqUHkmiznPJBxAzzMZepng32dEXOB+5gIwAIjpwDo4AIBhsC9c4Kqr/g1eoQ7X8QCPrrdeywPcUu6+jmfqGPuTunSS/wCFLBICwKmCC88UtMozhRxBBv8Oh14c8kxHnh/wTCvmy9HRAJbaWLZU46qrrrrqf6BHlWe8GsCutw4AXvwz/uwEV131/CGuuuyLv/iLDfDJn/zJ4qqrrrrqqqv+Ha677rrrrrvuuut4gFte6qVeigfIGx7+cLG1xTOZ667Duo7/OL1QcD/RG4JnEvTgADCETM9/BHEvz3aAOeB+2r2HZ5IODjIPDrjqqhfBK9ThOh7g2rq7eYLdLZ5pxthfW86d5AFOaPdkz9TzH0BYRS7cz6mCC88kMoSDZ6pqlX+nQy8OeaaBOkyuA0BzaUfMlgDp2o6YLbnqqquu+j/iUeUZrwaw660DgBf/jD87wVVXPX+Iqy774i/+YgN88id/srjqqquuuur/rZd+6Zd+aR5gc3Nz89TDH/5wHsA3vPRL8xyuf7jNFv+BhCpQuZ9cjSrPVoUrz2ToZYL/AIZDiQPuZy4AAwARA75wgWeSDg4yDw646qrn48Vqd3KDw54HeHS99Voe4Npy/uSMoeeZOsb+pC6d5D9YIYuEeKbiVnmAoFUeoKpV/gMs3a+S0gDSaitmS55pxXw5OhrASD+s3Q1cddVVV/0/9qjyjFcD2PXWAcCLf8afneCqq54/xFWXffEXf7EBPvmTP1lcddVVV131v8p111133XXXXXcdD3DLS73US/EAubW1pZ2HP5wHMNddh3Ud/8GEAuh5AIs5DyDowcGzqGJX/qOIi8DA/cwFYOCZrKMLYhh4Jvuee7jqKuCWurV1LRe2eIAH1XtOLFj1PMAt5e7reIAtHW5tstziP0Ehi4R4JpERdvAAQas8QFEWYfEfYHLkmtmSZ0qrrZgteaaBbli7G3imA28ecNVVV1111b/ao8ozXg1g11sHAC/+GX92gquuev4QV132xV/8xQb45E/+ZHHVVVddddV/qpd+6Zd+aZ7LzQ972MO0tbXFA+QND3+42NriAcx112Fdx38SoTkPYBFAzwMIenDwLKrYlf9YI+ICz3aAOeCZxHRgHRzwTNLBQebBAVf9v/QKdbiO5/Loeuu1PJdbyt3X8QAdY39Sl07yn6SQRUI8QHGrPIDIEA6eSbIKWfgPtnS/SkrjmdbMls1qPNO+Nw54gANvHnDVVVddddV/qUeVZ7wawK63DgBe/DP+7ARXXfX8Ia667Iu/+IsN8Mmf/Mniqquuuuqqyx7+8Ic/fGtra4vncstLvdRL8Vx83XXXEdddx3Oxr39p/pMJVaDyQKI3BM8kHEDPc1DFrvzHGxEXeCBzDw9gHV0Qw8AzSQcHmQcHXPV/3i11a+taLmzxAFtadzeVu0/yXG4pd1/Hc7lW567jP5Gwilx4ANFKGPEAQas8gGQVsvCfYOl+lZTGMw3UYXIdeIB9bxzwAEttLFuqcdVVV1111f8ajyrPeDWAXW8dALz4Z/zZCa666vlDXHXZF3/xFxvgkz/5k8VVV1111f8iL/3SL/3SPB+3vNRLvRTPR97w8IeLrS0eyFtbZvvh/BcRmvPcRG8IHkDQg4PnoIpd+c8i7uUBZAbDBR7AOroghoFnkg4OMg8OuOr/nFeow3U8ly2tu5vK3Sd5LreUu6/juZzQ7smeqec/WVVWnktxqzyXoFUeQLIKWfhPMriOI93AAxx5fsADDHTD2t3AM6VrO2K25Kqrrrrqqv93HlWe8WoAu946AHjxz/izE1x11fOHuOqyL/7iLzbAJ3/yJ4urrrrqqv8AW1tbWw9/+MMfzvNx7bXXXju77rrreC65tbWlnYc/nOfDvv6l+S8mVIHKc7GY87yqcOW5mTn/yQyHEgc8kLkADNwvYsAXLvAA0sFB5sEBV/2vdkvd2rqWC1s8l2vr7uYJdrd4LreUu6/juWzpcGuT5Rb/BSRHwcEDOVVw4bmEWsGIB6hqlf9EkyPXzJY8wJrZslmNB9j3xgEPkK7tiNmSq6666qqrrvo3elR5xqsB7HrrAODFP+PPTnDVVc8f4qrLvviLv9gAn/zJnyyuuuqq/7Me/vCHP3xra2uL5+Paa6+9dnbdddfxfPi6664jrruO5+v6h9ts8d9EaM7zI3pD8FwEPTh4AEPI9PxXEReBgQcyF4CBBxDTgXVwwANIBweZBwdc9b/GK9ThOp6PB9V7TixY9TyX43GwtaP9LZ7Llg63Nllu8V+okEVCPJBTBReeS9Aqz6Uoi7D4T3boxSHP5cjzA57LvjcOeC4H3jzgqquuuuqqq/4HeFR5xqsB7HrrAODFP+PPTnDVVc8f4qrLvviLv9gAn/zJnyyuuuqq/3Qv/dIv/dK8ELe81Eu9FC+Eb3jpl+YFsK9/af4HEAqg5/mRq1HluQgH0PP8mDn/xQyHEgc8N3MPz0VMB9bBAc/Fvucervof4cVqd3KDw54X4NH11mt5Aa4t50/OGHqej46xP6lLJ/lvIqwiF56LyAg7eC5BqzwXySpk4b/A4DqOdAPP5cjzA57LivlydDQe4MCbB1x11VVXXXXVf4ItHW7x77QZ63mhFf4Ntjl6OMCutw4AXvwz/uwEV131/CGuuuyLv/iLDfDJn/zJ4qqr/g+77rrrrrvuuuuu44W45aVe6qV4IXJra0s7D384L4R9/UvzP1MvFDw/cjWqPH9VuPJ8GHqZ4L+LuJfnx1wABp6LdXRBDAPPYRjsCxe46j/Vi9Xu5AaHPS/AtXV38wS7W7wAx+Nga0f7W7wAWzrc2mS5xf8wVVl5PkQrYcRzERnCwXMJOYIM/gst3a+S0niAtNqK2ZLnsu+NA57LUhvLlmpcddVVV131P8aWDrf4D7ATR5v8B9jUaot/JznLXOsF/8utPdsC2PXWAcCLf8afneCqq54/xFWXffEXf7EBPvmTP1lcddX/YNddd91111133XU80y0v9VIvxTP5uuuuI667jmeyr39p/ocSmvNCWMx5AYQD6HlBzJz/GUbEBZ6/A8wBz4927+H5sO+5h6v+TU5E3z88Dk7yQjyo3nNiwarnBZgx9teWcyd5Ia7Vuev4H0xyFBy8AMWt8gKIDOHg+ZCsQhb+m0yOXDNb8lwG6jC5DjyXfW8c8FzStR0xW3LVVVdd9b/Qlg63+A8yj7HrGXv+A3Sa+p6p5z9Az7AotMJV/6kaZeLfKVGzw/wbHDA/CTC6OwJ48c/4sxNcddXzh7jqsi/+4i82wCd/8ieLq676L7C1tbX18Ic//OE8080Pe9jDtLW1BeDrrruOuO46nsm+/qX5DyJUgcoLI3pD8AIIB9Dzwpg5/xOJi8DA83eAOeD5ENOBdXDA8yFfuJAMA1c9jxer3ckNDntegC2tu5vK3Sd5IY7HwdaO9rd4IU5o92TP1PM/XCGLhHgBREbYwQsgMoSDFyDkCDL4H2bpfpWUxnMZqMPkOvBcBrph7W7guYz0w9rdwFVXXfV/RgmXhY8W/Aeax9j1jD3/gTa12uI/UM+wKLTCVf8tGmXiP0CiZof5d2oqE/9ONm5E43+5PW9dB1CZVgAv/hl/doKrrnr+EP87HQdeiisuAX/Nv9MXf/EXG+CTP/mTxVVXvYiuu+6666677rrreKabH/awh2lra4tn8g0v/dLcz1tbZvvh/DsJBdBzP9EbgiuqcOV+Zs7/VOJeXhhzDy+Mdu/hBZAvXEiGgf+HXqEO1/EveFC958SCVc8LcW05f3LG0PNCnNDuyZ6p538BYRW58EKIVsKIF0BKyS68EFWt8j/Y4DqOdAMvwJHnB7wAA92wdjfwfKRrO2K25Kqr/o/a0uEW/wnmMXY9Y89/sILLXOsF/8Gqxr7z1HPV/ziNMvEfJK00kfwHSClNJP8BmtWMzFX/qe7J0/fy73TWpy4s3Q/8G9ybp18N4LyP377MefuyL/jUV+Cqq54/xP8+Lw38FPBgnu2vgbcBbuXf6Iu/+IsN8Mmf/Mniqv8Xrrvuuuuuu+6663iAzc3NzVMPf/jDeabc2trSzsMfzjOZ667Duo7/AEIB9NxP9IYAEA6g55kMvUzwH8BwKHHAC2MuAAMvSMSAL1zghZAvXEiGgf/DXqEO1/EveFC958SCVc8LMWPsry3nTvIvuFbnruN/maqsvDBOFVx4IUSGcPBCFGURFv/DLd2vktJ4AQbqMLkOvAAD3bB2N/ACjPTD2t3AVf9nzTT2HUPPf4KqjA2tFvwnKbjMtV7wn6RnWBRa4ar/ZeRGNP4DpZUmkv9ATWXiP1CzmpG56r/FPXn6Xv4DnPWpC0v3A/9OT5wefA//TkdsDv8wjRf4X048+r0ADLcC/PwXv/PrcNVVzx/if5cHA38FCHhv4FbgtYGvAv4aeBn+jb74i7/YAJ/8yZ8srvof56Vf+qVfmueyubm5eerhD384z8U3vPRL8zyuf7jNFv/BhCpQuZ/oDcEzCc95IDPn329EXOB+5gIwABAx4AsXeCb5woVkGPhf7MVqd3KDw55/wYPqPScWrHpeiBljf205d5J/wZYOtzZZbvG/UCGLhHghRCthxAsRagUjXoiiLMLif4HJkWtmS16INbNlsxovQHNpR8yWvBAH3jzgqv80Wzrc4t9pM9bzQiv8O2xqtcW/Uc+wKLTCVf+rNMrEfwKD06XxHyyFTWn8B2sobSVX/Y8yuI4XfPwC/0H2vHWw6+0D/gNczOMH9+XxA/4DPCW3LlzMYeCq/xEitrbsrS3+o/n4GwEYbgX4+S9+59fhqqueP8T/Lt8NvBfwNsBP82yfDXwW8D7Ad/Nv8MVf/MUG+ORP/mRx1fP10i/90i/Nv+CWl3qpl+IFyBse/nCxtcXz460ts/1w/gsJVaDyXCzmPIDwnGcyhEzPf4wRcYH7mQvAAEDEgC9c4JnkCxeSYeA/wYvV7uQGhz3/gmvr7uYJdrf4FxyPg60d7W/xL9jS4dYmyy3+F6vKyr9AtBJGvBBSSnbhX1DVKv/LHHpxyAuRVlsxW/JCDHTD2t3AC7HUxrKlGv8DbbBehKbCf4LNWM8LrfAfqOAy13rBfxA5y1zrBf9PGYWR+A+SlhMl/wmscDqS/yQGTy7JfxKrtDTmv5mlko7CVS+SNV07m6cO+A+0dt/u9pkD/g0Mtals8lyeOj34kP9AT8wHH3HVv4nskzY9V/2fZrgV4Oe/+J1fh6uuev4Q/7tcBC4BD+Y5PRh4OvAzwFvzb/DFX/zFBvjkT/5k8R9oa2tr6+EPf/jD+Te65aVe6qX4N8gbHv5wsbXFv8RbW2b74fwPJzTnuVgE0PNchOc8F0MvE/wHuTHuq3NWwXOp4UvAyDMFuSflyHPZ0LrcpHu2eBH1DPX6cnaTF9G2DuY7HMz4PyBkBRn8C4KMwAAYKymV5yJMVRP/giAVpPg/ak2fa/eNf6cjb0wNJf+BCo4NHVX+i2xoWStN/BslClniqv8Sg6r5d2ouTv59Jqr5N2quTsRV/7vc1m4Y+E+wovfdeWbkP9iaWd41nZn4D3aRY+1CHmtcddVVV0kTeOI/ic09AD//xe/8Olx11fOH+N/jwcDTgd8BXpvnZeBW4CH8K7zFp3zVVwG82rH1RwP8we6pbzfbD+dF8KC4e7GhZeH/gIfV27b4F9il41/QpI4XwKhixPMx01Cu0/k5/0lmWse1ca5y1fOoTJLMvyScCiX/ksAqJP+SQqqQXHXV/3Wjio34z9BcnPzHsoOmMP9BbDFRzf9fFjb/QS55q13y9sR/gjXzvLudHvhPsqbPu/Lagf8kd+a145FnyX8zowmYuOp/JUFiBq76n2wwTq76f+Hnv/idX4errnr+EP97vDbwW8D3AO/N87oVeBAg/hX+/vNe4SLAz3dvdxzgzcef2OU/kJEA8W9kIvg3MBZI/AsMAon/pYST/wZVTcL8q9kGEKZTiheRZMJT8CIqSsIp/h+TMP8LTdTkf4Dm4kT8VzPy5DD/RRrVBvNvZ2Fz1X+J2/P6A/6d7vXp5Sr7xr/DU9stB/wb3e4blgc5b1x11VVXXXXV/2JCh+bup/Cf7Oe/+KM/mquuev4Q/3u8NvBbwOcAn83z+m3gtQDxr/D3n/cKFwF+vnu74wBvNv7EJZB4EVSlcIr/4SQsY4BENCJ5Lh0t+O+kVCXFfxJhVZr4bzBRbGT+j5oI4zD/i4yU5Kqr/ouc88nl0rPGf4J7fXq5yr7xH2jFvN3ZrlnyH+TIi/aMvH7J/1/3wj33cNX/G+Kee7jnnnu46n8lHxwc3PGUpzyFq/7HespTnvKUg4ODA6666qr/7xD/e7w28FvA5wCfzfP6beC1APFcvviLv9hcddVVV1111VVXXXXVVVddddVV/yt88id/srjqqhcM8b/HSwN/BXwP8N48r6cDDwbEc/niL/5ic9VVV1111VVXXXXVVVddddVVV/2v8Mmf/MniqqteMMT/LgZ+B3htnpeBvwFemqv+z/riL/5iA3zyJ3+yuOr/rS/+4i82wCd/8ieLq/7f++Iv/mIDfPInf7K46v+9L/7iLzbAJ3/yJ4ur/t/74i/+YgN88id/srjq/70v/uIvNsAnf/Ini6v+3/riL/5iA3zyJ3+yuOqqq/43Q/zvcitwDDjBc3pp4K+A7wHem6v+z/riL/5iA3zyJ3+yuOr/rS/+4i82wCd/8ieLq/7f++Iv/mIDfPInf7K46v+9L/7iLzbAJ3/yJ4ur/t/74i/+YgN88id/srjq/70v/uIvNsAnf/Ini6v+3/riL/5iA3zyJ3+yuOqqq/43Q/zv8tHAVwFvA/w0z/bVwEcBrwP8Nlf9n/XFX/zFBvjkT/5kcdX/W1/8xV9sgE/+5E8WV/2/98Vf/MUG+ORP/mRx1f97X/zFX2yAT/7kTxZX/b/3xV/8xQb45E/+ZHHV/3tf/MVfbIBP/uRPFlf9v/XFX/zFBvjkT/5kcdVVV/1vhvjf5ThwK2Dgs4G/Bt4beG/gb4CX5qr/0774i7/YAJ/8yZ8srvp/64u/+IsN8Mmf/Mniqv/3vviLv9gAn/zJnyyu+n/vi7/4iw3wyZ/8yeKq//e++Iu/2ACf/MmfLK76f++Lv/iLDfDJn/zJ4qr/t774i7/YAJ/8yZ8srrrqqv/NEP/7PBj4aeCleLbvAT4a2OWq/9O++Iu/2ACf/MmfLK76f+uLv/iLDfDJn/zJ4qr/9774i7/YAJ/8yZ8srvp/74u/+IsN8Mmf/Mniqv/3vviLv9gAn/zJnyyu+n/vi7/4iw3wyZ/8yeKq/7e++Iu/2ACf/MmfLK666qr/zRD/ex0HXhr4a2CXq/5f+OIv/mIDfPInf7K46v+tL/7iLzbAJ3/yJ4ur/t/74i/+YgN88id/srjq/70v/uIvNsAnf/Ini6v+3/viL/5iA3zyJ3+yuOr/vS/+4i82wCd/8ieLq/7f+uIv/mIDfPInf7K46qqr/jdDXHXV/yJf/MVfbIBP/uRPFlf9v/XFX/zFBvjkT/5kcdX/e1/8xV9sgE/+5E8WV/2/98Vf/MUG+ORP/mRx1f97X/zFX2yAT/7kTxZX/b/3xV/8xQb45E/+ZHHV/1tf/MVfbIBP/uRPFlddddX/Zoirrvpf5Iu/+IsN8Mmf/Mniqv+3vviLv9gAn/zJnyyu+n/vi7/4iw3wyZ/8yeKq//e++Iu/2ACf/MmfLK76f++Lv/iLDfDJn/zJ4qr/9774i7/YAJ/8yZ8srvp/64u/+IsN8Mmf/Mniqquu+t8McdVV/4t88Rd/sQE++ZM/WVz1/9YXf/EXG+CTP/mTxVX/733xF3+xAT75kz9ZXPX/3hd/8Rcb4JM/+ZPFVf/vffEXf7EBPvmTP1lc9f/eF3/xFxvgkz/5k8VV/2998Rd/sQE++ZM/WVx11VX/myGuuuqqq6666qqrrrrqqquuuuqqq6666n8PxFVXXXXVVVddddVVV1111VVXXXXVVVf974G46qqrrrrqqquuuuqqq6666qqrrrrqqv89EFddddVVV1111VVXXXXVVVddddVVV131vwfiqquuuuqqq6666qqrrrrqqquuuuqqq/73QFx11VVX/c93HHgp4G+AXZ6/48BLccUl4K+56v+qBwPHgb/mqv+vjgMvxbP9Di/ca/Fsv8NV/9e8NHCMKy4Bf81V/18dB16KKy4Bf80L99LAMa74Ha76v+algWNc8QzgVq666qr/SxBXXfU/39OBB/P8vQ7w21z1f913Ae8NvA7w2zyv1wZ+CjjOs/018DrALlf9X3IceDrwN8Br87xeGvgrXjBx1f92nwV8NHCcZ9sF3gf4aZ7TSwM/BTyYZ7sVeBvgr7nqf7vjwE8Br81z+m3gfYBbeU7mBXsd4Le56n+ztwa+CzjOs90KvA3w1zyn48BPAa/Ns+0C7wP8NFf9b3cc+CrgvXlOXw18DM/r6cCDef4+B/hsrrrqqv+JEFdd9T/bceAi8DvAb/O8vhu4lav+L/sq4KO54nWA3+Y5PRj4K0DAewN/Dbw18FXAXwMvw1X/VzwY+CngpYHfAV6b5/XewHcB3wPcyvP6bK763+y9ge8Cfgf4bOBW4KWBrwYeBLwO8NtccRx4OiDgvYG/Bl4b+GrAwEOAXa763+yvgJcGPgf4aa54a+CzgL8GXoZne23gt4DfAX6b5/XdwK1c9b/VSwN/BfwN8NnAbwNvDXw1YOAhwC7P9lfASwMfA/w28GDguwEDLwPcylX/m30X8N7A9wBfzRUfDbwX8DXAR/OcDPwN8NM8r98GfpurrrrqfyLEVVf9z/bawG8BHwN8NVf9f/Jg4LuA1wYuAceA1wF+m+f03cB7Aa8D/DbP9tXARwHvA3w3V/1v917AVwPHueJ3gNfmeX028FnAywB/zVX/1zwdOAE8GNjl2V4a+Cvgd4DX5orPBj4LeB/gu3m29wa+C/gY4Ku56n+rtwZ+Cvga4KN5Tt8NvBfwOsBvc8V7A98FvA3w01z1f81PA28FvAzw1zzbZwOfBXwM8NVc8d7AdwFfA3w0z/bawG8B3wO8N1f9b/Vg4OnA7wCvzXO6FTgGnODZXhv4LeBzgM/mqquu+t8EcdVV/7N9NvBZwOsAv81V/5+YKz6HKz4LeB3gt3lOF4FLwIN5Ti8N/BXwPcB7c9X/Zh8NfBXwDOCtgb8Cfgd4bZ7XbwOvBYir/q85Dvw0V7w2z8vALnCCK/4KeGngBLDLsx0HLgJ/DbwMV/1v9d7AZwPvDfw2z+mtgZ8CPgf4bK74auCjgIcAt3LV/zUfDRwHPpvn9NbATwGfA3w2V3w38F7AywB/zXO6FTgGnOCq/60eDLw38NfAT/Ocfht4LUA820cDXwW8DvDbXHXVVf+bIK666n+27wbeCxDwVsBLA38N/A6wy1X/l3038NnArcBnA58FvA7w2zzbSwN/BfwO8No8LwN/DbwMV/1v9t7Ag4HP5goDvwO8Ns/rIvA3wHsDrwU8GPht4He46v+y48BF4HeA1+YKA78DvDbP67eB1wLEVf8XfTXwUcD7AN/NFb8NvDTwYOC1gJcGfhv4G2CXq/6v+ingrYHXAX6bK54OPBgQz+u3gdcCXgb4a676v+TBwF8Bl4AH82zfDbwXcAJ4KeC1gb8G/ga4lauuuup/MsRVV/3P9lfAg4Fd4ME82y7wPsBPc9X/B58NfBbwOsBv82yvDfwW8DnAZ/O8zBXiqv9LDPwO8No8pwcDTwduBY4Dx3m2vwbeB/hrrvq/6KeAtwY+BvhqrjDwO8Br87x+G3gtQFz1f81x4OmAgAcDu1xhYJcrjvNstwLvA/w2V/1f8WDgrYC3Bl4b+B7gvXk2A5eA4zyvzwY+C3gd4Le56v+C1wLeGnhr4ATw2sBf82y/DbwWcCvwYJ5tF/gY4Lu56qqr/qdCXHXV/2zmis8Bvpsr3hr4bOAY8DLAX3PV/3WfDXwW8DrAb/Nsrw38FvA5wGfzvH4beC1AXPV/iYHfAV6b5/TawG8Bl4CPBn4aeDDw3sBHAbcCLwPsctX/Je8NfBfwN8BLc8VrA78F/A7w2jyv3wZeC3gd4Le56v+K48BvAS8NvA/w3VzxYODpwCXgs4GfBo4Dbw18FrALvAxwK1f9X/DZwGdxxS7w0cD38GwGfgd4bZ7XZwOfBbwO8Ntc9X+BebafBj4GuJVnM1d8DvDdXPHawFcDx4DXAX6bq6666n8ixFVX/c91HHhp4Djw0zyntwZ+CvgZ4K256v+6zwY+C3gd4Ld5ttcGfgv4HOCzeV6/DbwWIK76v8TA7wCvzXN6MPDSwK3AX/Ocvht4L+BjgK/mqv8rPgv4bOBvgNcGdrniwcDTgd8BXpvn9dvAawEPAW7lqv8LjgO/Bbw08D3Ae/Nsx4HX5oqf5jl9NvBZwPcA781V/xc8GLgVeGngq4HXAr4G+GiuMPA7wGvzvD4b+CzgdYDf5qr/C45zxVsDXw0YeBngVq54beA48NM8p7cGfgr4GeCtueqqq/4nQlx11f9eBnaBE1z1f91nA58FvA7w2zzbawO/BXwO8Nk8L3OFuOr/EgO/A7w2L7rXBn4L+B7gvbnq/4LvAt4b+BvgtYFdnpOB3wFem+f128BrAeKq/wteGvgp4MHAxwBfzYvuOHAR+B3gtbnq/5rjwF8DDwLEFX8NPAg4wfP6bOCzgNcBfpur/q95b+C7gO8B3pt/mblCXHXVVf8TIa666n+vpwMPBsRV/9d9NvBZwOsAv81zMvA7wGvzvAz8DfDSXPV/iYHfAV6bF91rA78F/Azw1lz1v9lx4KeA1wZ+BnhvYJfnZeB3gNfmef028FqAuOp/u5cGfgs4DrwP8N386xn4G+Cluer/ot8GXgt4HeC3gd8GXgsQz+u3gdcCHgLcylX/1xwHLgK/A7w2/7K/Al4aEFddddX/RIirrvqf67WBzwJ+B/hsntNx4CLwO8Brc9X/dZ8NfBbwOsBv85x2gYvAQ3hOLw38FfA1wEdz1f8lBn4HeG2e02cDbwW8D/DXPKe3Bn4K+Bzgs7nqf6vjwG8BLw18D/DevGA/DbwVcALY5dmOAxeB3wFem6v+N3tp4LcAAa8N/DXP33sD7wV8D/DdPKeXBv4K+Bngrbnqf6unA7vAy/C8fht4LeAEsAt8N/BewMsAf81zejpwAjjOVf9bvTXwXcDHAN/Nc3pp4K+AnwHeGnht4LOAvwE+mudl4G+Al+aqq676nwhx1VX/cz0YeDpwK/AywC7P9tnAZwEfA3w1V/1f99nAZwGvA/w2z+mzgc8CXgf4bZ7tp4C3Bl4H+G2u+r/EwO8Ar81z+mjgq4DvAd6b5/RXwEsDDwFu5ar/rb4a+Cjge4D35oV7b+C7gI8Bvppn+2zgs4D3Ab6bq/63ejDwV4CA1wb+mhfstYHfAn4beB2e008Bbw28DfDTXPW/1V8DLwW8DPDXPNtLA38FPAN4MFc8GHg68N3A+/Bsbw38FPA9wHtz1f9WLw38FfDbwOvwnL4a+CjgY4CvBh4MPB24FXgZYJdn+2zgs4DPAT6bq6666n8ixFVX/c/21cBHAb8NfDWwC7w38N7A3wAvzVX/H3w28FnA6wC/zXM6DtwKGPhs4K+BjwbeGvgZ4K256v8aA78DvDbP66+BlwK+Gvhp4Djw2cBLA18DfDRX/W/1YODpXPHbvGCvw7P9NfBSwGcDvw28NvDZwN8AL81V/5t9N/BewC7w1zx/3wN8N1f8NPBWwE8DXw0cB94beGvge4D35qr/zV4a+CtgF/ho4FbgpYHPBo4DLwP8Nc/228BrAT8NfDXw0sBnAwJeGriVq/43+27gvYDfBj6bKz4aeGvgd4DX5tk+G/gs4K+BzwZ2gfcG3hv4G+C1gV2uuuqq/4kQV131P99nAx8NHOPZvgb4aK76/+Kzgc8CXgf4bZ7XSwPfDbwUz/Y9wEcDu1z1f42B3wFem+d1HPhq4L14tkvARwPfzVX/m3028Fn8y8SzHQd+Gngtnu13gLcGdrnqf7OLwHFeuM8BPpsrjgMfDXwWz3YJ+Grgs7nq/4LXBr4beBDP9jfAewN/zXM6Dnw18F482+8AHw38NVf9b3cc+Gjgs3hOXwN8NrDLc/ps4KOBYzzb1wCfDexy1VVX/U+FuOqq/z1emiv+mquuev6OAy8N/DWwy1X/3702cCtwK1ddBa8N/DWwy1X/3700sAvcylX/Fx0HXhr4a2CXf9lrA7cCt3LV/0UvzRV/zb/swcBx4K+56qqr/jdAXHXVVVddddVVV1111VVXXXXVVVddddX/Hoirrrrqqquuuuqqq6666qqrrrrqqquu+t+DfwTTXWj6TSDGEgAAAABJRU5ErkJggg==", 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"text/plain": [ "" ] }, - "execution_count": 10, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -506,18 +498,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 53%|█████▎ | 64/120 [03:23<02:34, 2.77s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 79%|███████▉ | 95/120 [04:53<01:20, 3.21s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - "124it [06:19, 3.06s/it] " + "124it [15:23, 7.45s/it]" ] }, { @@ -599,20 +587,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Intervention: {10.0: {'beta_c': tensor(0.3500)}, 15.0: {'gamma': tensor(0.4495)}}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Intervention: {10.0: {'beta_c': tensor(0.3500)}, 15.0: {'gamma': tensor(0.4495)}}\n", "Risk associated with QoI: [29969.813515624985]\n" ] }, @@ -623,7 +605,7 @@ "" ] }, - "execution_count": 12, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -661,123 +643,98 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 8%|▊ | 9/120 [00:32<05:52, 3.18s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. 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Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 54%|█████▍ | 65/120 [02:22<02:06, 2.30s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 62%|██████▏ | 74/120 [02:46<02:10, 2.84s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 64%|██████▍ | 77/120 [02:51<01:42, 2.38s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 67%|██████▋ | 80/120 [02:57<01:27, 2.18s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 68%|██████▊ | 82/120 [03:00<01:13, 1.93s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 70%|███████ | 84/120 [03:03<01:03, 1.77s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 72%|███████▏ | 86/120 [03:06<00:57, 1.69s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 75%|███████▌ | 90/120 [03:15<01:03, 2.13s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 78%|███████▊ | 94/120 [03:24<00:57, 2.22s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 81%|████████ | 97/120 [03:30<00:48, 2.12s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 83%|████████▎ | 100/120 [03:36<00:41, 2.08s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 86%|████████▌ | 103/120 [03:41<00:34, 2.05s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. 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Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 92%|█████████▎| 111/120 [03:53<00:14, 1.60s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 94%|█████████▍| 113/120 [03:57<00:11, 1.61s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n", - " 96%|█████████▌| 115/120 [04:00<00:07, 1.57s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", " warnings.warn(\n", - " 98%|█████████▊| 118/120 [04:05<00:03, 1.76s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", " warnings.warn(\n", - "100%|██████████| 120/120 [04:08<00:00, 1.68s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", " warnings.warn(\n", - "122it [04:11, 1.61s/it] C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", " warnings.warn(\n", - "124it [04:11, 2.03s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal policy: tensor([39.0000, 6.0327], dtype=torch.float64)\n", - "{'policy': tensor([39.0000, 6.0327], dtype=torch.float64), 'OptResults': message: ['requested number of basinhopping iterations completed successfully']\n", - " success: False\n", - " fun: -45.032607877335636\n", - " x: [ 3.900e+01 6.033e+00]\n", - " nit: 3\n", - " minimization_failures: 4\n", - " nfev: 120\n", - " lowest_optimization_result: message: Did not converge to a solution satisfying the constraints. See `maxcv` for magnitude of violation.\n", - " success: False\n", - " status: 4\n", - " fun: -45.032607877335636\n", - " x: [ 3.900e+01 6.033e+00]\n", - " nfev: 30\n", - " maxcv: 0.006562499991559889}\n", - "Intervention: {39.0: {'beta_c': tensor(0.1500)}, 6.0327: {'gamma': tensor(0.4000)}}\n" + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[29], line 17\u001b[0m\n\u001b[0;32m 14\u001b[0m objfun \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[38;5;241m-\u001b[39mnp\u001b[38;5;241m.\u001b[39msum(np\u001b[38;5;241m.\u001b[39mabs(x))\n\u001b[0;32m 16\u001b[0m \u001b[38;5;66;03m# Run optimize interface\u001b[39;00m\n\u001b[1;32m---> 17\u001b[0m opt_result4 \u001b[38;5;241m=\u001b[39m \u001b[43mpyciemss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 18\u001b[0m \u001b[43m 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\u001b[49m\u001b[43mbounds_interventions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbounds_interventions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 27\u001b[0m \u001b[43m \u001b[49m\u001b[43mstart_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_time\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_samples_ouu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_samples_ouu\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 29\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaxiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaxiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 30\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaxfeval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaxfeval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 31\u001b[0m \u001b[43m \u001b[49m\u001b[43msolver_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrk4\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 32\u001b[0m \u001b[43m \u001b[49m\u001b[43msolver_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstep_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1.\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 33\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m 34\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOptimal policy:\u001b[39m\u001b[38;5;124m'\u001b[39m, opt_result4[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpolicy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m 35\u001b[0m \u001b[38;5;28mprint\u001b[39m(opt_result4)\n", + "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\interfaces.py:916\u001b[0m, in \u001b[0;36moptimize\u001b[1;34m(model_path_or_json, end_time, logging_step_size, qoi, risk_bound, static_parameter_interventions, objfun, initial_guess_interventions, bounds_interventions, alpha, solver_method, solver_options, start_time, inferred_parameters, n_samples_ouu, maxiter, maxfeval, verbose, roundup_decimal)\u001b[0m\n\u001b[0;32m 907\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m objfun(x)\n\u001b[0;32m 909\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m 910\u001b[0m opt_results \u001b[38;5;241m=\u001b[39m solveOUU(\n\u001b[0;32m 911\u001b[0m x0\u001b[38;5;241m=\u001b[39minitial_guess_interventions,\n\u001b[0;32m 912\u001b[0m objfun\u001b[38;5;241m=\u001b[39mobjfun_penalty,\n\u001b[0;32m 913\u001b[0m constraints\u001b[38;5;241m=\u001b[39mconstraints,\n\u001b[0;32m 914\u001b[0m maxiter\u001b[38;5;241m=\u001b[39mmaxiter,\n\u001b[0;32m 915\u001b[0m maxfeval\u001b[38;5;241m=\u001b[39mmaxfeval,\n\u001b[1;32m--> 916\u001b[0m u_bounds\u001b[38;5;241m=\u001b[39mbounds_np,\n\u001b[0;32m 917\u001b[0m )\u001b[38;5;241m.\u001b[39msolve()\n\u001b[0;32m 919\u001b[0m \u001b[38;5;66;03m# Rounding up to given number of decimal places\u001b[39;00m\n\u001b[0;32m 920\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mround_up\u001b[39m(num, dec\u001b[38;5;241m=\u001b[39mroundup_decimal):\n", + "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:213\u001b[0m, in \u001b[0;36msolveOUU.solve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 209\u001b[0m take_step \u001b[38;5;241m=\u001b[39m RandomDisplacementBounds(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mu_bounds[\u001b[38;5;241m0\u001b[39m, :], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mu_bounds[\u001b[38;5;241m1\u001b[39m, :])\n\u001b[0;32m 210\u001b[0m \u001b[38;5;66;03m# result = basinhopping(self._vrate, u_init, stepsize=stepsize, T=1.5,\u001b[39;00m\n\u001b[0;32m 211\u001b[0m \u001b[38;5;66;03m# niter=self.maxiter, minimizer_kwargs=minimizer_kwargs, take_step=take_step, interval=2)\u001b[39;00m\n\u001b[1;32m--> 213\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mbasinhopping\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobjfun\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 215\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 216\u001b[0m \u001b[43m \u001b[49m\u001b[43mT\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 217\u001b[0m \u001b[43m \u001b[49m\u001b[43mniter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaxiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 218\u001b[0m \u001b[43m \u001b[49m\u001b[43mminimizer_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mminimizer_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 219\u001b[0m \u001b[43m \u001b[49m\u001b[43mtake_step\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtake_step\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 220\u001b[0m \u001b[43m \u001b[49m\u001b[43minterval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 221\u001b[0m \u001b[43m \u001b[49m\u001b[43mdisp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 222\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 224\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\scipy\\optimize\\_basinhopping.py:714\u001b[0m, in \u001b[0;36mbasinhopping\u001b[1;34m(func, x0, niter, T, stepsize, minimizer_kwargs, take_step, accept_test, callback, interval, disp, niter_success, seed, target_accept_rate, stepwise_factor)\u001b[0m\n\u001b[0;32m 711\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m niter_success \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 712\u001b[0m niter_success \u001b[38;5;241m=\u001b[39m niter \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m--> 714\u001b[0m bh \u001b[38;5;241m=\u001b[39m \u001b[43mBasinHoppingRunner\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwrapped_minimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtake_step_wrapped\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 715\u001b[0m \u001b[43m \u001b[49m\u001b[43maccept_tests\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdisp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 717\u001b[0m \u001b[38;5;66;03m# The wrapped minimizer is called once during construction of\u001b[39;00m\n\u001b[0;32m 718\u001b[0m \u001b[38;5;66;03m# BasinHoppingRunner, so run the callback\u001b[39;00m\n\u001b[0;32m 719\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(callback):\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\scipy\\optimize\\_basinhopping.py:78\u001b[0m, in \u001b[0;36mBasinHoppingRunner.__init__\u001b[1;34m(self, x0, minimizer, step_taking, accept_tests, disp)\u001b[0m\n\u001b[0;32m 75\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mres\u001b[38;5;241m.\u001b[39mminimization_failures \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 77\u001b[0m \u001b[38;5;66;03m# do initial minimization\u001b[39;00m\n\u001b[1;32m---> 78\u001b[0m minres \u001b[38;5;241m=\u001b[39m \u001b[43mminimizer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 79\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m minres\u001b[38;5;241m.\u001b[39msuccess:\n\u001b[0;32m 80\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mres\u001b[38;5;241m.\u001b[39mminimization_failures \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\scipy\\optimize\\_basinhopping.py:303\u001b[0m, in \u001b[0;36mMinimizerWrapper.__call__\u001b[1;34m(self, x0)\u001b[0m\n\u001b[0;32m 301\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mminimizer(x0, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n\u001b[0;32m 302\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 303\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mminimizer(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc, x0, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\scipy\\optimize\\_minimize.py:716\u001b[0m, in \u001b[0;36mminimize\u001b[1;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[0;32m 713\u001b[0m res \u001b[38;5;241m=\u001b[39m _minimize_tnc(fun, x0, args, jac, bounds, callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[0;32m 714\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n\u001b[0;32m 715\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m meth \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcobyla\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m--> 716\u001b[0m res \u001b[38;5;241m=\u001b[39m _minimize_cobyla(fun, x0, args, constraints, callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[0;32m 717\u001b[0m bounds\u001b[38;5;241m=\u001b[39mbounds, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n\u001b[0;32m 718\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m meth \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mslsqp\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m 719\u001b[0m res \u001b[38;5;241m=\u001b[39m _minimize_slsqp(fun, x0, args, jac, bounds,\n\u001b[0;32m 720\u001b[0m constraints, callback\u001b[38;5;241m=\u001b[39mcallback, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\scipy\\optimize\\_cobyla_py.py:35\u001b[0m, in \u001b[0;36msynchronized..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m 33\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 34\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _module_lock:\n\u001b[1;32m---> 35\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\scipy\\optimize\\_cobyla_py.py:293\u001b[0m, in \u001b[0;36m_minimize_cobyla\u001b[1;34m(fun, x0, args, constraints, rhobeg, tol, maxiter, disp, catol, callback, bounds, **unknown_options)\u001b[0m\n\u001b[0;32m 290\u001b[0m callback(np\u001b[38;5;241m.\u001b[39mcopy(x))\n\u001b[0;32m 292\u001b[0m info \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros(\u001b[38;5;241m4\u001b[39m, np\u001b[38;5;241m.\u001b[39mfloat64)\n\u001b[1;32m--> 293\u001b[0m xopt, info \u001b[38;5;241m=\u001b[39m \u001b[43mcobyla\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mminimize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcalcfc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx0\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrhobeg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrhobeg\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 294\u001b[0m \u001b[43m \u001b[49m\u001b[43mrhoend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrhoend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miprint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43miprint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmaxfun\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaxfun\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 295\u001b[0m \u001b[43m \u001b[49m\u001b[43mdinfo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwrapped_callback\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 297\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m info[\u001b[38;5;241m3\u001b[39m] \u001b[38;5;241m>\u001b[39m catol:\n\u001b[0;32m 298\u001b[0m \u001b[38;5;66;03m# Check constraint violation\u001b[39;00m\n\u001b[0;32m 299\u001b[0m info[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\scipy\\optimize\\_cobyla_py.py:284\u001b[0m, in \u001b[0;36m_minimize_cobyla..calcfc\u001b[1;34m(x, con)\u001b[0m\n\u001b[0;32m 282\u001b[0m i \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 283\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m size, c \u001b[38;5;129;01min\u001b[39;00m izip(cons_lengths, constraints):\n\u001b[1;32m--> 284\u001b[0m con[i: i \u001b[38;5;241m+\u001b[39m size] \u001b[38;5;241m=\u001b[39m \u001b[43mc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfun\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43margs\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 285\u001b[0m i \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m size\n\u001b[0;32m 286\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m f\n", + "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\interfaces.py:888\u001b[0m, in \u001b[0;36m\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 871\u001b[0m RISK \u001b[38;5;241m=\u001b[39m computeRisk(\n\u001b[0;32m 872\u001b[0m model\u001b[38;5;241m=\u001b[39mcontrol_model,\n\u001b[0;32m 873\u001b[0m interventions\u001b[38;5;241m=\u001b[39mstatic_parameter_interventions,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 884\u001b[0m risk_bound\u001b[38;5;241m=\u001b[39mrisk_bound,\n\u001b[0;32m 885\u001b[0m )\n\u001b[0;32m 886\u001b[0m \u001b[38;5;66;03m# Define constraints >= 0\u001b[39;00m\n\u001b[0;32m 887\u001b[0m constraints \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m--> 888\u001b[0m \u001b[38;5;66;03m# risk constraint\u001b[39;00m\n\u001b[0;32m 889\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mineq\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfun\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mlambda\u001b[39;00m x: risk_bound \u001b[38;5;241m-\u001b[39m RISK(x)},\n\u001b[0;32m 890\u001b[0m \u001b[38;5;66;03m# bounds on control\u001b[39;00m\n\u001b[0;32m 891\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mineq\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfun\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mlambda\u001b[39;00m x: x \u001b[38;5;241m-\u001b[39m u_min},\n\u001b[0;32m 892\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mineq\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfun\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mlambda\u001b[39;00m x: u_max \u001b[38;5;241m-\u001b[39m x},\n\u001b[0;32m 893\u001b[0m )\n\u001b[0;32m 894\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verbose:\n\u001b[0;32m 895\u001b[0m \u001b[38;5;28mprint\u001b[39m(\n\u001b[0;32m 896\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPerforming risk-based optimization under uncertainty (using alpha-superquantile)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 897\u001b[0m )\n", + "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:108\u001b[0m, in \u001b[0;36mcomputeRisk.__call__\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 103\u001b[0m risk_estimate \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(\n\u001b[0;32m 104\u001b[0m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrisk_bound, \u001b[38;5;241m10.0\u001b[39m\n\u001b[0;32m 105\u001b[0m ) \u001b[38;5;66;03m# used as a penalty and the model is not run\u001b[39;00m\n\u001b[0;32m 106\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 107\u001b[0m \u001b[38;5;66;03m# Apply intervention and perform forward uncertainty propagation\u001b[39;00m\n\u001b[1;32m--> 108\u001b[0m samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpropagate_uncertainty\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 109\u001b[0m \u001b[38;5;66;03m# Compute quanity of interest\u001b[39;00m\n\u001b[0;32m 110\u001b[0m sample_qoi \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqoi(samples)\n", + "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:146\u001b[0m, in \u001b[0;36mcomputeRisk.propagate_uncertainty\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 138\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel(\n\u001b[0;32m 139\u001b[0m torch\u001b[38;5;241m.\u001b[39mas_tensor(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstart_time),\n\u001b[0;32m 140\u001b[0m torch\u001b[38;5;241m.\u001b[39mas_tensor(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mend_time),\n\u001b[0;32m 141\u001b[0m logging_times\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlogging_times,\n\u001b[0;32m 142\u001b[0m is_traced\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 143\u001b[0m )\n\u001b[0;32m 145\u001b[0m \u001b[38;5;66;03m# Sample from intervened model\u001b[39;00m\n\u001b[1;32m--> 146\u001b[0m samples \u001b[38;5;241m=\u001b[39m \u001b[43mpyro\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPredictive\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[43mwrapped_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43mguide\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mguide\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 150\u001b[0m \u001b[43m \u001b[49m\u001b[43mparallel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 151\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m samples\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\infer\\predictive.py:273\u001b[0m, in \u001b[0;36mPredictive.forward\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 263\u001b[0m return_sites \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m return_sites \u001b[38;5;28;01melse\u001b[39;00m return_sites\n\u001b[0;32m 264\u001b[0m posterior_samples \u001b[38;5;241m=\u001b[39m _predictive(\n\u001b[0;32m 265\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mguide,\n\u001b[0;32m 266\u001b[0m posterior_samples,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 271\u001b[0m model_kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[0;32m 272\u001b[0m )\n\u001b[1;32m--> 273\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_predictive\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 274\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 275\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 276\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 277\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_sites\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_sites\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 278\u001b[0m \u001b[43m \u001b[49m\u001b[43mparallel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparallel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 279\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 280\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 281\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\infer\\predictive.py:137\u001b[0m, in \u001b[0;36m_predictive\u001b[1;34m(model, posterior_samples, num_samples, return_sites, return_trace, parallel, model_args, model_kwargs)\u001b[0m\n\u001b[0;32m 126\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m parallel:\n\u001b[0;32m 127\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _predictive_sequential(\n\u001b[0;32m 128\u001b[0m model,\n\u001b[0;32m 129\u001b[0m posterior_samples,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 134\u001b[0m return_trace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 135\u001b[0m )\n\u001b[1;32m--> 137\u001b[0m trace \u001b[38;5;241m=\u001b[39m poutine\u001b[38;5;241m.\u001b[39mtrace(\n\u001b[0;32m 138\u001b[0m poutine\u001b[38;5;241m.\u001b[39mcondition(vectorize(model), reshaped_samples)\n\u001b[0;32m 139\u001b[0m )\u001b[38;5;241m.\u001b[39mget_trace(\u001b[38;5;241m*\u001b[39mmodel_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_kwargs)\n\u001b[0;32m 140\u001b[0m predictions \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m 141\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m site, shape \u001b[38;5;129;01min\u001b[39;00m return_site_shapes\u001b[38;5;241m.\u001b[39mitems():\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\trace_messenger.py:198\u001b[0m, in \u001b[0;36mTraceHandler.get_trace\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 190\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_trace\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 191\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 192\u001b[0m \u001b[38;5;124;03m :returns: data structure\u001b[39;00m\n\u001b[0;32m 193\u001b[0m \u001b[38;5;124;03m :rtype: pyro.poutine.Trace\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 196\u001b[0m \u001b[38;5;124;03m Calls this poutine and returns its trace instead of the function's return value.\u001b[39;00m\n\u001b[0;32m 197\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 198\u001b[0m \u001b[38;5;28mself\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 199\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmsngr\u001b[38;5;241m.\u001b[39mget_trace()\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\trace_messenger.py:174\u001b[0m, in \u001b[0;36mTraceHandler.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 170\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmsngr\u001b[38;5;241m.\u001b[39mtrace\u001b[38;5;241m.\u001b[39madd_node(\n\u001b[0;32m 171\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_INPUT\u001b[39m\u001b[38;5;124m\"\u001b[39m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_INPUT\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margs\u001b[39m\u001b[38;5;124m\"\u001b[39m, args\u001b[38;5;241m=\u001b[39margs, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[0;32m 172\u001b[0m )\n\u001b[0;32m 173\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 174\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 175\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mRuntimeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 176\u001b[0m exc_type, exc_value, traceback \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mexc_info()\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\messenger.py:12\u001b[0m, in \u001b[0;36m_context_wrap\u001b[1;34m(context, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_context_wrap\u001b[39m(context, fn, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m context:\n\u001b[1;32m---> 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\messenger.py:12\u001b[0m, in \u001b[0;36m_context_wrap\u001b[1;34m(context, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_context_wrap\u001b[39m(context, fn, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m context:\n\u001b[1;32m---> 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\utils\\_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator..decorate_context\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m 113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 114\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[1;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\messenger.py:12\u001b[0m, in \u001b[0;36m_context_wrap\u001b[1;34m(context, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_context_wrap\u001b[39m(context, fn, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m context:\n\u001b[1;32m---> 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:138\u001b[0m, in \u001b[0;36mcomputeRisk.propagate_uncertainty..wrapped_model\u001b[1;34m()\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m handler \u001b[38;5;129;01min\u001b[39;00m static_parameter_intervention_handlers:\n\u001b[0;32m 137\u001b[0m stack\u001b[38;5;241m.\u001b[39menter_context(handler)\n\u001b[1;32m--> 138\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 139\u001b[0m \u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart_time\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 140\u001b[0m \u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mend_time\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 141\u001b[0m \u001b[43m \u001b[49m\u001b[43mlogging_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogging_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 142\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_traced\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 143\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\nn\\module.py:449\u001b[0m, in \u001b[0;36mPyroModule.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 447\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 448\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pyro_context:\n\u001b[1;32m--> 449\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 450\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m 451\u001b[0m pyro\u001b[38;5;241m.\u001b[39msettings\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalidate_poutine\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 452\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pyro_context\u001b[38;5;241m.\u001b[39mactive\n\u001b[0;32m 453\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m _is_module_local_param_enabled()\n\u001b[0;32m 454\u001b[0m ):\n\u001b[0;32m 455\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_module_local_param_usage()\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\compiled_dynamics.py:77\u001b[0m, in \u001b[0;36mCompiledDynamics.forward\u001b[1;34m(self, start_time, end_time, logging_times, is_traced)\u001b[0m\n\u001b[0;32m 75\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m logging_times \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 76\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m LogTrajectory(logging_times) \u001b[38;5;28;01mas\u001b[39;00m lt:\n\u001b[1;32m---> 77\u001b[0m \u001b[43msimulate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mderiv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minitial_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_time\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 78\u001b[0m state \u001b[38;5;241m=\u001b[39m lt\u001b[38;5;241m.\u001b[39mtrajectory\n\u001b[0;32m 79\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:281\u001b[0m, in \u001b[0;36meffectful.._fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 264\u001b[0m msg \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 265\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mtype\u001b[39m,\n\u001b[0;32m 266\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: name,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 278\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfer\u001b[39m\u001b[38;5;124m\"\u001b[39m: infer,\n\u001b[0;32m 279\u001b[0m }\n\u001b[0;32m 280\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[1;32m--> 281\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:212\u001b[0m, in \u001b[0;36mapply_stack\u001b[1;34m(initial_msg)\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mreversed\u001b[39m(stack):\n\u001b[0;32m 210\u001b[0m pointer \u001b[38;5;241m=\u001b[39m pointer \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 212\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\messenger.py:162\u001b[0m, in \u001b[0;36mMessenger._process_message\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 160\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m]), \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 162\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\internals\\solver.py:109\u001b[0m, in \u001b[0;36mSolver._pyro_simulate\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ph\u001b[38;5;241m.\u001b[39mpriority \u001b[38;5;241m>\u001b[39m start_time:\n\u001b[0;32m 107\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m--> 109\u001b[0m state, start_time, next_interruption \u001b[38;5;241m=\u001b[39m simulate_to_interruption(\n\u001b[0;32m 110\u001b[0m possible_interruptions,\n\u001b[0;32m 111\u001b[0m dynamics,\n\u001b[0;32m 112\u001b[0m state,\n\u001b[0;32m 113\u001b[0m start_time,\n\u001b[0;32m 114\u001b[0m end_time,\n\u001b[0;32m 115\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmsg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 116\u001b[0m )\n\u001b[0;32m 118\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m next_interruption \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 119\u001b[0m dynamics, state \u001b[38;5;241m=\u001b[39m next_interruption\u001b[38;5;241m.\u001b[39mcallback(dynamics, state)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:281\u001b[0m, in \u001b[0;36meffectful.._fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 264\u001b[0m msg \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 265\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mtype\u001b[39m,\n\u001b[0;32m 266\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: name,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 278\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfer\u001b[39m\u001b[38;5;124m\"\u001b[39m: infer,\n\u001b[0;32m 279\u001b[0m }\n\u001b[0;32m 280\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[1;32m--> 281\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:212\u001b[0m, in \u001b[0;36mapply_stack\u001b[1;34m(initial_msg)\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mreversed\u001b[39m(stack):\n\u001b[0;32m 210\u001b[0m pointer \u001b[38;5;241m=\u001b[39m pointer \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 212\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\messenger.py:162\u001b[0m, in \u001b[0;36mMessenger._process_message\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 160\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m]), \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 162\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\handlers\\solver.py:89\u001b[0m, in \u001b[0;36mTorchDiffEq._pyro_simulate_to_interruption\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 87\u001b[0m interruptions, dynamics, initial_state, start_time, end_time \u001b[38;5;241m=\u001b[39m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 88\u001b[0m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39modeint_kwargs)\n\u001b[1;32m---> 89\u001b[0m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m torchdiffeq_simulate_to_interruption(\n\u001b[0;32m 90\u001b[0m interruptions,\n\u001b[0;32m 91\u001b[0m dynamics,\n\u001b[0;32m 92\u001b[0m initial_state,\n\u001b[0;32m 93\u001b[0m start_time,\n\u001b[0;32m 94\u001b[0m end_time,\n\u001b[0;32m 95\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmsg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 96\u001b[0m )\n\u001b[0;32m 97\u001b[0m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdone\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\internals\\backends\\torchdiffeq.py:252\u001b[0m, in \u001b[0;36mtorchdiffeq_simulate_to_interruption\u001b[1;34m(interruptions, dynamics, initial_state, start_time, end_time, **kwargs)\u001b[0m\n\u001b[0;32m 246\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(interruptions) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshould have at least one interruption here\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 248\u001b[0m (next_interruption,), interruption_time \u001b[38;5;241m=\u001b[39m _torchdiffeq_get_next_interruptions(\n\u001b[0;32m 249\u001b[0m dynamics, initial_state, start_time, interruptions, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m 250\u001b[0m )\n\u001b[1;32m--> 252\u001b[0m value \u001b[38;5;241m=\u001b[39m simulate_point(\n\u001b[0;32m 253\u001b[0m dynamics, initial_state, start_time, interruption_time, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m 254\u001b[0m )\n\u001b[0;32m 255\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m value, interruption_time, next_interruption\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:281\u001b[0m, in \u001b[0;36meffectful.._fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 264\u001b[0m msg \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 265\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mtype\u001b[39m,\n\u001b[0;32m 266\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: name,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 278\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfer\u001b[39m\u001b[38;5;124m\"\u001b[39m: infer,\n\u001b[0;32m 279\u001b[0m }\n\u001b[0;32m 280\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[1;32m--> 281\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:212\u001b[0m, in \u001b[0;36mapply_stack\u001b[1;34m(initial_msg)\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mreversed\u001b[39m(stack):\n\u001b[0;32m 210\u001b[0m pointer \u001b[38;5;241m=\u001b[39m pointer \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 212\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\messenger.py:162\u001b[0m, in \u001b[0;36mMessenger._process_message\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 160\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m]), \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 162\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\handlers\\trajectory.py:97\u001b[0m, in \u001b[0;36mLogTrajectory._pyro_simulate_point\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 92\u001b[0m timespan \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mconcat(\n\u001b[0;32m 93\u001b[0m (start_time\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m), filtered_timespan, end_time\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m 94\u001b[0m )\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mpoutine\u001b[38;5;241m.\u001b[39mmessenger\u001b[38;5;241m.\u001b[39mblock_messengers(\u001b[38;5;28;01mlambda\u001b[39;00m m: m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m---> 97\u001b[0m trajectory: State[T] \u001b[38;5;241m=\u001b[39m simulate_trajectory(\n\u001b[0;32m 98\u001b[0m dynamics, initial_state, timespan, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmsg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 99\u001b[0m )\n\u001b[0;32m 101\u001b[0m \u001b[38;5;66;03m# TODO support dim != -1\u001b[39;00m\n\u001b[0;32m 102\u001b[0m idx_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__time\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:281\u001b[0m, in \u001b[0;36meffectful.._fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 264\u001b[0m msg \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 265\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mtype\u001b[39m,\n\u001b[0;32m 266\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: name,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 278\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfer\u001b[39m\u001b[38;5;124m\"\u001b[39m: infer,\n\u001b[0;32m 279\u001b[0m }\n\u001b[0;32m 280\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[1;32m--> 281\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:212\u001b[0m, in \u001b[0;36mapply_stack\u001b[1;34m(initial_msg)\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mreversed\u001b[39m(stack):\n\u001b[0;32m 210\u001b[0m pointer \u001b[38;5;241m=\u001b[39m pointer \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 212\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\messenger.py:162\u001b[0m, in \u001b[0;36mMessenger._process_message\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 160\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m]), \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 162\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\handlers\\solver.py:77\u001b[0m, in \u001b[0;36mTorchDiffEq._pyro_simulate_trajectory\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 74\u001b[0m dynamics, initial_state, timespan \u001b[38;5;241m=\u001b[39m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 75\u001b[0m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39modeint_kwargs)\n\u001b[1;32m---> 77\u001b[0m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m torchdiffeq_simulate_trajectory(\n\u001b[0;32m 78\u001b[0m dynamics, initial_state, timespan, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmsg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 79\u001b[0m )\n\u001b[0;32m 80\u001b[0m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdone\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\internals\\backends\\torchdiffeq.py:169\u001b[0m, in \u001b[0;36mtorchdiffeq_simulate_trajectory\u001b[1;34m(dynamics, initial_state, timespan, **kwargs)\u001b[0m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtorchdiffeq_simulate_trajectory\u001b[39m(\n\u001b[0;32m 164\u001b[0m dynamics: Dynamics[torch\u001b[38;5;241m.\u001b[39mTensor],\n\u001b[0;32m 165\u001b[0m initial_state: State[torch\u001b[38;5;241m.\u001b[39mTensor],\n\u001b[0;32m 166\u001b[0m timespan: torch\u001b[38;5;241m.\u001b[39mTensor,\n\u001b[0;32m 167\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m 168\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m State[torch\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[1;32m--> 169\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _torchdiffeq_ode_simulate_inner(dynamics, initial_state, timespan, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\internals\\backends\\torchdiffeq.py:71\u001b[0m, in \u001b[0;36m_torchdiffeq_ode_simulate_inner\u001b[1;34m(dynamics, initial_state, timespan, **odeint_kwargs)\u001b[0m\n\u001b[0;32m 68\u001b[0m time_dim \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 70\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39many(diff):\n\u001b[1;32m---> 71\u001b[0m solns \u001b[38;5;241m=\u001b[39m _batched_odeint( \u001b[38;5;66;03m# torchdiffeq.odeint(\u001b[39;00m\n\u001b[0;32m 72\u001b[0m functools\u001b[38;5;241m.\u001b[39mpartial(_deriv, dynamics, var_order),\n\u001b[0;32m 73\u001b[0m \u001b[38;5;28mtuple\u001b[39m(initial_state[v] \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m var_order),\n\u001b[0;32m 74\u001b[0m timespan_,\n\u001b[0;32m 75\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39modeint_kwargs,\n\u001b[0;32m 76\u001b[0m )\n\u001b[0;32m 77\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 78\u001b[0m solns \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(initial_state[v]\u001b[38;5;241m.\u001b[39munsqueeze(time_dim) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m var_order)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\internals\\backends\\torchdiffeq.py:127\u001b[0m, in \u001b[0;36m_batched_odeint\u001b[1;34m(func, y0, t, event_fn, **odeint_kwargs)\u001b[0m\n\u001b[0;32m 123\u001b[0m event_t, yt_raw \u001b[38;5;241m=\u001b[39m torchdiffeq\u001b[38;5;241m.\u001b[39modeint_event(\n\u001b[0;32m 124\u001b[0m func, y0_expanded, t, event_fn\u001b[38;5;241m=\u001b[39mevent_fn, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39modeint_kwargs\n\u001b[0;32m 125\u001b[0m )\n\u001b[0;32m 126\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 127\u001b[0m yt_raw \u001b[38;5;241m=\u001b[39m torchdiffeq\u001b[38;5;241m.\u001b[39modeint(func, y0_expanded, t, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39modeint_kwargs)\n\u001b[0;32m 129\u001b[0m yt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\n\u001b[0;32m 130\u001b[0m torch\u001b[38;5;241m.\u001b[39mtranspose(\n\u001b[0;32m 131\u001b[0m yt_[(\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m+\u001b[39m yt_\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;28mlen\u001b[39m(yt_\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m-\u001b[39m event_dim :]],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 135\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m yt_ \u001b[38;5;129;01min\u001b[39;00m yt_raw\n\u001b[0;32m 136\u001b[0m )\n\u001b[0;32m 137\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m yt \u001b[38;5;28;01mif\u001b[39;00m event_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m (event_t, yt)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torchdiffeq\\_impl\\odeint.py:77\u001b[0m, in \u001b[0;36modeint\u001b[1;34m(func, y0, t, rtol, atol, method, options, event_fn)\u001b[0m\n\u001b[0;32m 74\u001b[0m solver \u001b[38;5;241m=\u001b[39m SOLVERS[method](func\u001b[38;5;241m=\u001b[39mfunc, y0\u001b[38;5;241m=\u001b[39my0, rtol\u001b[38;5;241m=\u001b[39mrtol, atol\u001b[38;5;241m=\u001b[39matol, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n\u001b[0;32m 76\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m event_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 77\u001b[0m solution \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 78\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 79\u001b[0m event_t, solution \u001b[38;5;241m=\u001b[39m solver\u001b[38;5;241m.\u001b[39mintegrate_until_event(t[\u001b[38;5;241m0\u001b[39m], event_fn)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torchdiffeq\\_impl\\solvers.py:105\u001b[0m, in \u001b[0;36mFixedGridODESolver.integrate\u001b[1;34m(self, t)\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t0, t1 \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(time_grid[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], time_grid[\u001b[38;5;241m1\u001b[39m:]):\n\u001b[0;32m 104\u001b[0m dt \u001b[38;5;241m=\u001b[39m t1 \u001b[38;5;241m-\u001b[39m t0\n\u001b[1;32m--> 105\u001b[0m dy, f0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_step_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 106\u001b[0m y1 \u001b[38;5;241m=\u001b[39m y0 \u001b[38;5;241m+\u001b[39m dy\n\u001b[0;32m 108\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m j \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(t) \u001b[38;5;129;01mand\u001b[39;00m t1 \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m t[j]:\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torchdiffeq\\_impl\\fixed_grid.py:28\u001b[0m, in \u001b[0;36mRK4._step_func\u001b[1;34m(self, func, t0, dt, t1, y0)\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_step_func\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, t0, dt, t1, y0):\n\u001b[1;32m---> 28\u001b[0m f0 \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperturb\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mPerturb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mNEXT\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mperturb\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mPerturb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mNONE\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m rk4_alt_step_func(func, t0, dt, t1, y0, f0\u001b[38;5;241m=\u001b[39mf0, perturb\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mperturb), f0\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torchdiffeq\\_impl\\misc.py:189\u001b[0m, in \u001b[0;36m_PerturbFunc.forward\u001b[1;34m(self, t, y, perturb)\u001b[0m\n\u001b[0;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 187\u001b[0m \u001b[38;5;66;03m# Do nothing.\u001b[39;00m\n\u001b[0;32m 188\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m--> 189\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torchdiffeq\\_impl\\misc.py:138\u001b[0m, in \u001b[0;36m_TupleFunc.forward\u001b[1;34m(self, t, y)\u001b[0m\n\u001b[0;32m 137\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, t, y):\n\u001b[1;32m--> 138\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_flat_to_shape\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshapes\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcat([f_\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m f_ \u001b[38;5;129;01min\u001b[39;00m f])\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\internals\\backends\\torchdiffeq.py:43\u001b[0m, in \u001b[0;36m_deriv\u001b[1;34m(dynamics, var_order, time, state)\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mt\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m var_order, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvariable name t is reserved for time\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 42\u001b[0m env: State[torch\u001b[38;5;241m.\u001b[39mTensor] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(var_order \u001b[38;5;241m+\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mt\u001b[39m\u001b[38;5;124m\"\u001b[39m,), state \u001b[38;5;241m+\u001b[39m (time,)))\n\u001b[1;32m---> 43\u001b[0m ddt: State[torch\u001b[38;5;241m.\u001b[39mTensor] \u001b[38;5;241m=\u001b[39m \u001b[43mdynamics\u001b[49m\u001b[43m(\u001b[49m\u001b[43menv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(ddt\u001b[38;5;241m.\u001b[39mget(var, torch\u001b[38;5;241m.\u001b[39mtensor(\u001b[38;5;241m0.0\u001b[39m)) \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m var_order)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\nn\\module.py:686\u001b[0m, in \u001b[0;36mpyro_method..cached_fn\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 683\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(fn)\n\u001b[0;32m 684\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcached_fn\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 685\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pyro_context:\n\u001b[1;32m--> 686\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\compiled_dynamics.py:44\u001b[0m, in \u001b[0;36mCompiledDynamics.deriv\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[38;5;129m@pyro\u001b[39m\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mpyro_method\n\u001b[0;32m 43\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mderiv\u001b[39m(\u001b[38;5;28mself\u001b[39m, X: State[torch\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43meval_deriv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\functools.py:889\u001b[0m, in \u001b[0;36msingledispatch..wrapper\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m 885\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[0;32m 886\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfuncname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires at least \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 887\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1 positional argument\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m--> 889\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dispatch(args[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n", + "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\mira_integration\\compiled_dynamics.py:124\u001b[0m, in \u001b[0;36m_eval_deriv_mira\u001b[1;34m(src, param_module, X)\u001b[0m\n\u001b[0;32m 112\u001b[0m \u001b[38;5;129m@eval_deriv\u001b[39m\u001b[38;5;241m.\u001b[39mregister(mira\u001b[38;5;241m.\u001b[39mmodeling\u001b[38;5;241m.\u001b[39mModel)\n\u001b[0;32m 113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_eval_deriv_mira\u001b[39m(\n\u001b[0;32m 114\u001b[0m src: mira\u001b[38;5;241m.\u001b[39mmodeling\u001b[38;5;241m.\u001b[39mModel,\n\u001b[0;32m 115\u001b[0m param_module: pyro\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mPyroModule,\n\u001b[0;32m 116\u001b[0m X: State[torch\u001b[38;5;241m.\u001b[39mTensor],\n\u001b[0;32m 117\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m State[torch\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[0;32m 118\u001b[0m parameters \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 119\u001b[0m get_name(param_info): \u001b[38;5;28mgetattr\u001b[39m(param_module, get_name(param_info))\n\u001b[0;32m 120\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m param_info \u001b[38;5;129;01min\u001b[39;00m src\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mvalues()\n\u001b[0;32m 121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m param_info\u001b[38;5;241m.\u001b[39mplaceholder\n\u001b[0;32m 122\u001b[0m }\n\u001b[1;32m--> 124\u001b[0m numeric_deriv \u001b[38;5;241m=\u001b[39m param_module\u001b[38;5;241m.\u001b[39mnumeric_deriv_func(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mX, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparameters)\n\u001b[0;32m 126\u001b[0m dX: State[torch\u001b[38;5;241m.\u001b[39mTensor] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m()\n\u001b[0;32m 127\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, var \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(src\u001b[38;5;241m.\u001b[39mvariables\u001b[38;5;241m.\u001b[39mvalues()):\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\sympytorch\\sympy_module.py:266\u001b[0m, in \u001b[0;36mSymPyModule.forward\u001b[1;34m(self, **symbols)\u001b[0m\n\u001b[0;32m 264\u001b[0m out \u001b[38;5;241m=\u001b[39m [node(symbols) \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_nodes]\n\u001b[0;32m 265\u001b[0m out \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mbroadcast_tensors(\u001b[38;5;241m*\u001b[39mout)\n\u001b[1;32m--> 266\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -822,7 +779,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -871,7 +828,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -919,7 +876,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [ { diff --git a/pyciemss/interfaces.py b/pyciemss/interfaces.py index b70aa9ca4..602b5b0d9 100644 --- a/pyciemss/interfaces.py +++ b/pyciemss/interfaces.py @@ -791,8 +791,10 @@ def optimize( static_parameter_interventions: Callable[[torch.Tensor], Dict[float, Dict[str, Intervention]]] - A callable function of static parameter interventions to optimize over. - The callable functions are created using the provided templates: - - param_value_objective(): creates a static parameter intervention when optimizing over (multiple) parameter values - - start_time_objective(): creates a static parameter intervention when optimizing over (multiple) start times for different parameter + - param_value_objective(): creates a static parameter intervention when optimizing over + (multiple) parameter values + - start_time_objective(): creates a static parameter intervention when optimizing over + (multiple) start times for different parameter objfun: Callable - The objective function defined as a callable function definition. - E.g., to minimize the absolute value of intervention parameters use lambda x: np.sum(np.abs(x)) @@ -939,16 +941,23 @@ def round_up(num, dec=roundup_decimal): # Check optimize results and provide appropriate warnings if opt_results["success"]: warnings.warn( - "Optimal intervention policy satisfies constraints and is within set bounds. If better policy is desired, try (i) expanding the bounds_interventions, (ii) relaxing risk_bounds, and/or (iii) increasing maxiter and maxfeval or different initial_guess_interventions." + "Optimal intervention policy satisfies constraints and is within set bounds." + "If better policy is desired, try" + "(i) expanding the bounds_interventions, (ii) relaxing risk_bounds, and/or" + "(iii) increasing maxiter and maxfeval or different initial_guess_interventions." ) else: if np.any(opt_results.x - u_min < 0) or np.any(u_max - opt_results.x < 0): warnings.warn( - "Optimal intervention policy is out of bounds. Try (i) expanding the bounds_interventions and/or (ii) different initial_guess_interventions." + "Optimal intervention policy is out of bounds. Try (i) expanding the bounds_interventions and/or" + "(ii) different initial_guess_interventions." ) if opt_results["lowest_optimization_result"]["maxcv"] > 0: warnings.warn( - "Optimal intervention policy does not satisfy constraints. Check if the risk_bounds value is appropriate for given problem. Otherwise, try (i) different initial_guess_interventions, (ii) increasing maxiter and maxfeval, and/or (iii) increase n_samples_ouu to improve accuracy of Monte Carlo risk estimation. " + "Optimal intervention policy does not satisfy constraints." + "Check if the risk_bounds value is appropriate for given problem." + "Otherwise, try (i) different initial_guess_interventions, (ii) increasing maxiter/maxfeval," + "and/or (iii) increase n_samples_ouu to improve accuracy of Monte Carlo risk estimation. " ) return ouu_results From 8a69de47f6d31a24b048dceda0639be89c53d9a7 Mon Sep 17 00:00:00 2001 From: Anirban Chaudhuri <75496534+anirban-chaudhuri@users.noreply.github.com> Date: Thu, 21 Mar 2024 11:56:57 -0400 Subject: [PATCH 4/5] Update optimize_interface.ipynb --- docs/source/optimize_interface.ipynb | 313 ++++++++++++++++----------- 1 file changed, 187 insertions(+), 126 deletions(-) diff --git a/docs/source/optimize_interface.ipynb b/docs/source/optimize_interface.ipynb index 0c35f065b..dac1ad922 100644 --- a/docs/source/optimize_interface.ipynb +++ b/docs/source/optimize_interface.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -114,25 +114,25 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Time taken: 10.823821306228638\n", - "Risk associated with QoI: [490.8698352050781]\n" + "Time taken: 2.518198251724243\n", + "Risk associated with QoI: [488.77843139648434]\n" ] }, { "data": { - "image/png": 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", "text/plain": [ "" ] }, - "execution_count": 21, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -164,25 +164,25 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Time taken: 15.391172647476196\n", - "Risk associated with QoI: [5158925.469999998]\n" + "Time taken: 5.338383674621582\n", + "Risk associated with QoI: [4217777.989999998]\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "" ] }, - "execution_count": 22, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -217,14 +217,26 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 26%|██▌ | 31/120 [02:09<06:12, 4.18s/it]" + " 20%|██ | 24/120 [00:36<02:21, 1.47s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 23%|██▎ | 28/120 [00:41<02:00, 1.31s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 25%|██▌ | 30/120 [00:42<01:28, 1.01it/s]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 28%|██▊ | 33/120 [00:45<01:27, 1.01s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 30%|███ | 36/120 [00:46<01:02, 1.35it/s]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 63%|██████▎ | 76/120 [01:32<00:53, 1.21s/it]" ] }, { @@ -234,17 +246,17 @@ "Optimal policy: tensor([0.1940], dtype=torch.float64)\n", "{'policy': tensor([0.1940], dtype=torch.float64), 'OptResults': message: ['requested number of basinhopping iterations completed successfully']\n", " success: True\n", - " fun: 0.15601666455392296\n", + " fun: 0.15601673345676997\n", " x: [ 1.940e-01]\n", " nit: 3\n", - " minimization_failures: 0\n", - " nfev: 31\n", + " minimization_failures: 2\n", + " nfev: 74\n", " lowest_optimization_result: message: Optimization terminated successfully.\n", " success: True\n", " status: 1\n", - " fun: 0.15601666455392296\n", + " fun: 0.15601673345676997\n", " x: [ 1.940e-01]\n", - " nfev: 8\n", + " nfev: 7\n", " maxcv: 0.0}\n" ] }, @@ -252,7 +264,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n" + "\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\interfaces.py:943: UserWarning: Optimal intervention policy satisfies constraints and is within set bounds.If better policy is desired, try(i) expanding the bounds_interventions, (ii) relaxing risk_bounds, and/or(iii) increasing maxiter and maxfeval or different initial_guess_interventions.\n", + " warnings.warn(\n" ] } ], @@ -305,31 +319,25 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Intervention: {1.0: {'p_cbeta': tensor(0.1940)}}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Risk associated with QoI: [200.10247192382812]\n" + "Intervention: {1.0: {'p_cbeta': tensor(0.1940)}}\n", + "Risk associated with QoI: [197.5618798828125]\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "" ] }, - "execution_count": 24, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -363,14 +371,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 38%|███▊ | 45/120 [02:35<04:19, 3.46s/it]" + " 37%|███▋ | 44/120 [00:48<01:23, 1.10s/it]" ] }, { @@ -383,8 +391,8 @@ " fun: -7.69576122033028\n", " x: [ 7.696e+00]\n", " nit: 3\n", - " minimization_failures: 1\n", - " nfev: 45\n", + " minimization_failures: 0\n", + " nfev: 44\n", " lowest_optimization_result: message: Optimization terminated successfully.\n", " success: True\n", " status: 1\n", @@ -399,7 +407,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n" + "\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\interfaces.py:943: UserWarning: Optimal intervention policy satisfies constraints and is within set bounds.If better policy is desired, try(i) expanding the bounds_interventions, (ii) relaxing risk_bounds, and/or(iii) increasing maxiter and maxfeval or different initial_guess_interventions.\n", + " warnings.warn(\n" ] } ], @@ -444,24 +454,24 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Risk associated with QoI: [148.92850952148436]\n" + "Risk associated with QoI: [144.8216143798828]\n" ] }, { "data": { - "image/png": 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", "text/plain": [ "" ] }, - "execution_count": 26, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -498,14 +508,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "124it [15:23, 7.45s/it]" + "124it [06:19, 3.06s/it] " ] }, { @@ -587,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -605,7 +615,7 @@ "" ] }, - "execution_count": 28, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -643,13 +653,79 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ + " 8%|▊ | 9/120 [00:26<05:05, 2.75s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. 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Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 47%|████▋ | 56/120 [01:52<01:42, 1.61s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 48%|████▊ | 58/120 [01:55<01:35, 1.54s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 50%|█████ | 60/120 [01:57<01:29, 1.50s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. 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Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 61%|██████ | 73/120 [02:25<01:43, 2.19s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 62%|██████▎ | 75/120 [02:28<01:32, 2.06s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 64%|██████▍ | 77/120 [02:32<01:24, 1.96s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 67%|██████▋ | 80/120 [02:39<01:28, 2.22s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 68%|██████▊ | 82/120 [02:43<01:18, 2.07s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 70%|███████ | 84/120 [02:46<01:12, 2.00s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 72%|███████▏ | 86/120 [02:50<01:05, 1.93s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 73%|███████▎ | 88/120 [02:54<01:01, 1.91s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 75%|███████▌ | 90/120 [02:57<00:56, 1.88s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 78%|███████▊ | 94/120 [03:07<00:59, 2.28s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 80%|████████ | 96/120 [03:11<00:51, 2.13s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", " warnings.warn(\n", "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", @@ -659,82 +735,67 @@ "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", " warnings.warn(\n", "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", - " warnings.warn(\n" + " warnings.warn(\n", + " 87%|████████▋ | 104/120 [03:18<00:22, 1.40s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 89%|████████▉ | 107/120 [03:22<00:17, 1.34s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 92%|█████████▏| 110/120 [03:25<00:12, 1.29s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + " 96%|█████████▌| 115/120 [03:29<00:05, 1.03s/it]C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + "122it [03:36, 1.08s/it] C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:100: UserWarning: Selected interventions are out of bounds. Will use a penalty instead of estimating risk.\n", + " warnings.warn(\n", + "124it [03:36, 1.74s/it]" ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[29], line 17\u001b[0m\n\u001b[0;32m 14\u001b[0m objfun \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[38;5;241m-\u001b[39mnp\u001b[38;5;241m.\u001b[39msum(np\u001b[38;5;241m.\u001b[39mabs(x))\n\u001b[0;32m 16\u001b[0m \u001b[38;5;66;03m# Run optimize interface\u001b[39;00m\n\u001b[1;32m---> 17\u001b[0m opt_result4 \u001b[38;5;241m=\u001b[39m \u001b[43mpyciemss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_opt2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43mend_time_SEIRHD\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43mlogging_step_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[43mqoi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 22\u001b[0m \u001b[43m \u001b[49m\u001b[43mrisk_bound\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 23\u001b[0m \u001b[43m \u001b[49m\u001b[43mstatic_parameter_interventions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 24\u001b[0m \u001b[43m \u001b[49m\u001b[43mobjfun\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 25\u001b[0m \u001b[43m \u001b[49m\u001b[43minitial_guess_interventions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial_guess_interventions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 26\u001b[0m \u001b[43m \u001b[49m\u001b[43mbounds_interventions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbounds_interventions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 27\u001b[0m \u001b[43m \u001b[49m\u001b[43mstart_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_time\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_samples_ouu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_samples_ouu\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 29\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaxiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaxiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 30\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaxfeval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaxfeval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 31\u001b[0m \u001b[43m \u001b[49m\u001b[43msolver_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrk4\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 32\u001b[0m \u001b[43m \u001b[49m\u001b[43msolver_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstep_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1.\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 33\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m 34\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOptimal policy:\u001b[39m\u001b[38;5;124m'\u001b[39m, opt_result4[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpolicy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m 35\u001b[0m \u001b[38;5;28mprint\u001b[39m(opt_result4)\n", - "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\interfaces.py:916\u001b[0m, in \u001b[0;36moptimize\u001b[1;34m(model_path_or_json, end_time, logging_step_size, qoi, risk_bound, static_parameter_interventions, objfun, initial_guess_interventions, bounds_interventions, alpha, solver_method, solver_options, start_time, inferred_parameters, n_samples_ouu, maxiter, maxfeval, verbose, roundup_decimal)\u001b[0m\n\u001b[0;32m 907\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m objfun(x)\n\u001b[0;32m 909\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m 910\u001b[0m opt_results \u001b[38;5;241m=\u001b[39m solveOUU(\n\u001b[0;32m 911\u001b[0m x0\u001b[38;5;241m=\u001b[39minitial_guess_interventions,\n\u001b[0;32m 912\u001b[0m objfun\u001b[38;5;241m=\u001b[39mobjfun_penalty,\n\u001b[0;32m 913\u001b[0m constraints\u001b[38;5;241m=\u001b[39mconstraints,\n\u001b[0;32m 914\u001b[0m maxiter\u001b[38;5;241m=\u001b[39mmaxiter,\n\u001b[0;32m 915\u001b[0m maxfeval\u001b[38;5;241m=\u001b[39mmaxfeval,\n\u001b[1;32m--> 916\u001b[0m u_bounds\u001b[38;5;241m=\u001b[39mbounds_np,\n\u001b[0;32m 917\u001b[0m )\u001b[38;5;241m.\u001b[39msolve()\n\u001b[0;32m 919\u001b[0m \u001b[38;5;66;03m# Rounding up to given number of decimal places\u001b[39;00m\n\u001b[0;32m 920\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mround_up\u001b[39m(num, dec\u001b[38;5;241m=\u001b[39mroundup_decimal):\n", - "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:213\u001b[0m, in \u001b[0;36msolveOUU.solve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 209\u001b[0m take_step \u001b[38;5;241m=\u001b[39m RandomDisplacementBounds(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mu_bounds[\u001b[38;5;241m0\u001b[39m, :], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mu_bounds[\u001b[38;5;241m1\u001b[39m, :])\n\u001b[0;32m 210\u001b[0m \u001b[38;5;66;03m# result = basinhopping(self._vrate, u_init, stepsize=stepsize, T=1.5,\u001b[39;00m\n\u001b[0;32m 211\u001b[0m \u001b[38;5;66;03m# niter=self.maxiter, minimizer_kwargs=minimizer_kwargs, take_step=take_step, interval=2)\u001b[39;00m\n\u001b[1;32m--> 213\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mbasinhopping\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobjfun\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 215\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 216\u001b[0m \u001b[43m \u001b[49m\u001b[43mT\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 217\u001b[0m \u001b[43m \u001b[49m\u001b[43mniter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaxiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 218\u001b[0m \u001b[43m \u001b[49m\u001b[43mminimizer_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mminimizer_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 219\u001b[0m \u001b[43m \u001b[49m\u001b[43mtake_step\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtake_step\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 220\u001b[0m \u001b[43m \u001b[49m\u001b[43minterval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 221\u001b[0m \u001b[43m \u001b[49m\u001b[43mdisp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 222\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 224\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\scipy\\optimize\\_basinhopping.py:714\u001b[0m, in \u001b[0;36mbasinhopping\u001b[1;34m(func, x0, niter, T, stepsize, minimizer_kwargs, take_step, accept_test, callback, interval, disp, niter_success, seed, target_accept_rate, stepwise_factor)\u001b[0m\n\u001b[0;32m 711\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m niter_success \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 712\u001b[0m niter_success \u001b[38;5;241m=\u001b[39m niter \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m--> 714\u001b[0m bh \u001b[38;5;241m=\u001b[39m \u001b[43mBasinHoppingRunner\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwrapped_minimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtake_step_wrapped\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 715\u001b[0m \u001b[43m \u001b[49m\u001b[43maccept_tests\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdisp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 717\u001b[0m \u001b[38;5;66;03m# The wrapped minimizer is called once during construction of\u001b[39;00m\n\u001b[0;32m 718\u001b[0m \u001b[38;5;66;03m# BasinHoppingRunner, so run the callback\u001b[39;00m\n\u001b[0;32m 719\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(callback):\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\scipy\\optimize\\_basinhopping.py:78\u001b[0m, in \u001b[0;36mBasinHoppingRunner.__init__\u001b[1;34m(self, x0, minimizer, step_taking, accept_tests, disp)\u001b[0m\n\u001b[0;32m 75\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mres\u001b[38;5;241m.\u001b[39mminimization_failures \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 77\u001b[0m \u001b[38;5;66;03m# do initial minimization\u001b[39;00m\n\u001b[1;32m---> 78\u001b[0m minres \u001b[38;5;241m=\u001b[39m \u001b[43mminimizer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 79\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m minres\u001b[38;5;241m.\u001b[39msuccess:\n\u001b[0;32m 80\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mres\u001b[38;5;241m.\u001b[39mminimization_failures \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\scipy\\optimize\\_basinhopping.py:303\u001b[0m, in \u001b[0;36mMinimizerWrapper.__call__\u001b[1;34m(self, x0)\u001b[0m\n\u001b[0;32m 301\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mminimizer(x0, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n\u001b[0;32m 302\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 303\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mminimizer(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc, x0, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\scipy\\optimize\\_minimize.py:716\u001b[0m, in \u001b[0;36mminimize\u001b[1;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[0;32m 713\u001b[0m res \u001b[38;5;241m=\u001b[39m _minimize_tnc(fun, x0, args, jac, bounds, callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[0;32m 714\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n\u001b[0;32m 715\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m meth \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcobyla\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m--> 716\u001b[0m res \u001b[38;5;241m=\u001b[39m _minimize_cobyla(fun, x0, args, constraints, callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[0;32m 717\u001b[0m bounds\u001b[38;5;241m=\u001b[39mbounds, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n\u001b[0;32m 718\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m meth \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mslsqp\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m 719\u001b[0m res \u001b[38;5;241m=\u001b[39m _minimize_slsqp(fun, x0, args, jac, bounds,\n\u001b[0;32m 720\u001b[0m constraints, callback\u001b[38;5;241m=\u001b[39mcallback, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\scipy\\optimize\\_cobyla_py.py:35\u001b[0m, in \u001b[0;36msynchronized..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m 33\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 34\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _module_lock:\n\u001b[1;32m---> 35\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\scipy\\optimize\\_cobyla_py.py:293\u001b[0m, in \u001b[0;36m_minimize_cobyla\u001b[1;34m(fun, x0, args, constraints, rhobeg, tol, maxiter, disp, catol, callback, bounds, **unknown_options)\u001b[0m\n\u001b[0;32m 290\u001b[0m callback(np\u001b[38;5;241m.\u001b[39mcopy(x))\n\u001b[0;32m 292\u001b[0m info \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros(\u001b[38;5;241m4\u001b[39m, np\u001b[38;5;241m.\u001b[39mfloat64)\n\u001b[1;32m--> 293\u001b[0m xopt, info \u001b[38;5;241m=\u001b[39m \u001b[43mcobyla\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mminimize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcalcfc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx0\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrhobeg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrhobeg\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 294\u001b[0m \u001b[43m \u001b[49m\u001b[43mrhoend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrhoend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miprint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43miprint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmaxfun\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaxfun\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 295\u001b[0m \u001b[43m \u001b[49m\u001b[43mdinfo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwrapped_callback\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 297\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m info[\u001b[38;5;241m3\u001b[39m] \u001b[38;5;241m>\u001b[39m catol:\n\u001b[0;32m 298\u001b[0m \u001b[38;5;66;03m# Check constraint violation\u001b[39;00m\n\u001b[0;32m 299\u001b[0m info[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\scipy\\optimize\\_cobyla_py.py:284\u001b[0m, in \u001b[0;36m_minimize_cobyla..calcfc\u001b[1;34m(x, con)\u001b[0m\n\u001b[0;32m 282\u001b[0m i \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 283\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m size, c \u001b[38;5;129;01min\u001b[39;00m izip(cons_lengths, constraints):\n\u001b[1;32m--> 284\u001b[0m con[i: i \u001b[38;5;241m+\u001b[39m size] \u001b[38;5;241m=\u001b[39m \u001b[43mc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfun\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43margs\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 285\u001b[0m i \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m size\n\u001b[0;32m 286\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m f\n", - "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\interfaces.py:888\u001b[0m, in \u001b[0;36m\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 871\u001b[0m RISK \u001b[38;5;241m=\u001b[39m computeRisk(\n\u001b[0;32m 872\u001b[0m model\u001b[38;5;241m=\u001b[39mcontrol_model,\n\u001b[0;32m 873\u001b[0m interventions\u001b[38;5;241m=\u001b[39mstatic_parameter_interventions,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 884\u001b[0m risk_bound\u001b[38;5;241m=\u001b[39mrisk_bound,\n\u001b[0;32m 885\u001b[0m )\n\u001b[0;32m 886\u001b[0m \u001b[38;5;66;03m# Define constraints >= 0\u001b[39;00m\n\u001b[0;32m 887\u001b[0m constraints \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m--> 888\u001b[0m \u001b[38;5;66;03m# risk constraint\u001b[39;00m\n\u001b[0;32m 889\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mineq\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfun\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mlambda\u001b[39;00m x: risk_bound \u001b[38;5;241m-\u001b[39m RISK(x)},\n\u001b[0;32m 890\u001b[0m \u001b[38;5;66;03m# bounds on control\u001b[39;00m\n\u001b[0;32m 891\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mineq\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfun\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mlambda\u001b[39;00m x: x \u001b[38;5;241m-\u001b[39m u_min},\n\u001b[0;32m 892\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mineq\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfun\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mlambda\u001b[39;00m x: u_max \u001b[38;5;241m-\u001b[39m x},\n\u001b[0;32m 893\u001b[0m )\n\u001b[0;32m 894\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verbose:\n\u001b[0;32m 895\u001b[0m \u001b[38;5;28mprint\u001b[39m(\n\u001b[0;32m 896\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPerforming risk-based optimization under uncertainty (using alpha-superquantile)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 897\u001b[0m )\n", - "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:108\u001b[0m, in \u001b[0;36mcomputeRisk.__call__\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 103\u001b[0m risk_estimate \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(\n\u001b[0;32m 104\u001b[0m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrisk_bound, \u001b[38;5;241m10.0\u001b[39m\n\u001b[0;32m 105\u001b[0m ) \u001b[38;5;66;03m# used as a penalty and the model is not run\u001b[39;00m\n\u001b[0;32m 106\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 107\u001b[0m \u001b[38;5;66;03m# Apply intervention and perform forward uncertainty propagation\u001b[39;00m\n\u001b[1;32m--> 108\u001b[0m samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpropagate_uncertainty\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 109\u001b[0m \u001b[38;5;66;03m# Compute quanity of interest\u001b[39;00m\n\u001b[0;32m 110\u001b[0m sample_qoi \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqoi(samples)\n", - "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:146\u001b[0m, in \u001b[0;36mcomputeRisk.propagate_uncertainty\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 138\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel(\n\u001b[0;32m 139\u001b[0m torch\u001b[38;5;241m.\u001b[39mas_tensor(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstart_time),\n\u001b[0;32m 140\u001b[0m torch\u001b[38;5;241m.\u001b[39mas_tensor(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mend_time),\n\u001b[0;32m 141\u001b[0m logging_times\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlogging_times,\n\u001b[0;32m 142\u001b[0m is_traced\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 143\u001b[0m )\n\u001b[0;32m 145\u001b[0m \u001b[38;5;66;03m# Sample from intervened model\u001b[39;00m\n\u001b[1;32m--> 146\u001b[0m samples \u001b[38;5;241m=\u001b[39m \u001b[43mpyro\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPredictive\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[43mwrapped_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43mguide\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mguide\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 150\u001b[0m \u001b[43m \u001b[49m\u001b[43mparallel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 151\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m samples\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\infer\\predictive.py:273\u001b[0m, in \u001b[0;36mPredictive.forward\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 263\u001b[0m return_sites \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m return_sites \u001b[38;5;28;01melse\u001b[39;00m return_sites\n\u001b[0;32m 264\u001b[0m posterior_samples \u001b[38;5;241m=\u001b[39m _predictive(\n\u001b[0;32m 265\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mguide,\n\u001b[0;32m 266\u001b[0m posterior_samples,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 271\u001b[0m model_kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[0;32m 272\u001b[0m )\n\u001b[1;32m--> 273\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_predictive\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 274\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 275\u001b[0m \u001b[43m \u001b[49m\u001b[43mposterior_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 276\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 277\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_sites\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_sites\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 278\u001b[0m \u001b[43m \u001b[49m\u001b[43mparallel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparallel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 279\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 280\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 281\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\infer\\predictive.py:137\u001b[0m, in \u001b[0;36m_predictive\u001b[1;34m(model, posterior_samples, num_samples, return_sites, return_trace, parallel, model_args, model_kwargs)\u001b[0m\n\u001b[0;32m 126\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m parallel:\n\u001b[0;32m 127\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _predictive_sequential(\n\u001b[0;32m 128\u001b[0m model,\n\u001b[0;32m 129\u001b[0m posterior_samples,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 134\u001b[0m return_trace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 135\u001b[0m )\n\u001b[1;32m--> 137\u001b[0m trace \u001b[38;5;241m=\u001b[39m poutine\u001b[38;5;241m.\u001b[39mtrace(\n\u001b[0;32m 138\u001b[0m poutine\u001b[38;5;241m.\u001b[39mcondition(vectorize(model), reshaped_samples)\n\u001b[0;32m 139\u001b[0m )\u001b[38;5;241m.\u001b[39mget_trace(\u001b[38;5;241m*\u001b[39mmodel_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_kwargs)\n\u001b[0;32m 140\u001b[0m predictions \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m 141\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m site, shape \u001b[38;5;129;01min\u001b[39;00m return_site_shapes\u001b[38;5;241m.\u001b[39mitems():\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\trace_messenger.py:198\u001b[0m, in \u001b[0;36mTraceHandler.get_trace\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 190\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_trace\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 191\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 192\u001b[0m \u001b[38;5;124;03m :returns: data structure\u001b[39;00m\n\u001b[0;32m 193\u001b[0m \u001b[38;5;124;03m :rtype: pyro.poutine.Trace\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 196\u001b[0m \u001b[38;5;124;03m Calls this poutine and returns its trace instead of the function's return value.\u001b[39;00m\n\u001b[0;32m 197\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 198\u001b[0m \u001b[38;5;28mself\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 199\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmsngr\u001b[38;5;241m.\u001b[39mget_trace()\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\trace_messenger.py:174\u001b[0m, in \u001b[0;36mTraceHandler.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 170\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmsngr\u001b[38;5;241m.\u001b[39mtrace\u001b[38;5;241m.\u001b[39madd_node(\n\u001b[0;32m 171\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_INPUT\u001b[39m\u001b[38;5;124m\"\u001b[39m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_INPUT\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margs\u001b[39m\u001b[38;5;124m\"\u001b[39m, args\u001b[38;5;241m=\u001b[39margs, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[0;32m 172\u001b[0m )\n\u001b[0;32m 173\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 174\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 175\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mRuntimeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 176\u001b[0m exc_type, exc_value, traceback \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mexc_info()\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\messenger.py:12\u001b[0m, in \u001b[0;36m_context_wrap\u001b[1;34m(context, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_context_wrap\u001b[39m(context, fn, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m context:\n\u001b[1;32m---> 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\messenger.py:12\u001b[0m, in \u001b[0;36m_context_wrap\u001b[1;34m(context, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_context_wrap\u001b[39m(context, fn, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m context:\n\u001b[1;32m---> 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\utils\\_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator..decorate_context\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m 113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 114\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[1;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\messenger.py:12\u001b[0m, in \u001b[0;36m_context_wrap\u001b[1;34m(context, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_context_wrap\u001b[39m(context, fn, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m context:\n\u001b[1;32m---> 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\ouu\\ouu.py:138\u001b[0m, in \u001b[0;36mcomputeRisk.propagate_uncertainty..wrapped_model\u001b[1;34m()\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m handler \u001b[38;5;129;01min\u001b[39;00m static_parameter_intervention_handlers:\n\u001b[0;32m 137\u001b[0m stack\u001b[38;5;241m.\u001b[39menter_context(handler)\n\u001b[1;32m--> 138\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 139\u001b[0m \u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart_time\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 140\u001b[0m \u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mend_time\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 141\u001b[0m \u001b[43m \u001b[49m\u001b[43mlogging_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogging_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 142\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_traced\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 143\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\nn\\module.py:449\u001b[0m, in \u001b[0;36mPyroModule.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 447\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 448\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pyro_context:\n\u001b[1;32m--> 449\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 450\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m 451\u001b[0m pyro\u001b[38;5;241m.\u001b[39msettings\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalidate_poutine\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 452\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pyro_context\u001b[38;5;241m.\u001b[39mactive\n\u001b[0;32m 453\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m _is_module_local_param_enabled()\n\u001b[0;32m 454\u001b[0m ):\n\u001b[0;32m 455\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_module_local_param_usage()\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\compiled_dynamics.py:77\u001b[0m, in \u001b[0;36mCompiledDynamics.forward\u001b[1;34m(self, start_time, end_time, logging_times, is_traced)\u001b[0m\n\u001b[0;32m 75\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m logging_times \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 76\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m LogTrajectory(logging_times) \u001b[38;5;28;01mas\u001b[39;00m lt:\n\u001b[1;32m---> 77\u001b[0m \u001b[43msimulate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mderiv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minitial_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_time\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 78\u001b[0m state \u001b[38;5;241m=\u001b[39m lt\u001b[38;5;241m.\u001b[39mtrajectory\n\u001b[0;32m 79\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:281\u001b[0m, in \u001b[0;36meffectful.._fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 264\u001b[0m msg \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 265\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mtype\u001b[39m,\n\u001b[0;32m 266\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: name,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 278\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfer\u001b[39m\u001b[38;5;124m\"\u001b[39m: infer,\n\u001b[0;32m 279\u001b[0m }\n\u001b[0;32m 280\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[1;32m--> 281\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:212\u001b[0m, in \u001b[0;36mapply_stack\u001b[1;34m(initial_msg)\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mreversed\u001b[39m(stack):\n\u001b[0;32m 210\u001b[0m pointer \u001b[38;5;241m=\u001b[39m pointer \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 212\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\messenger.py:162\u001b[0m, in \u001b[0;36mMessenger._process_message\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 160\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m]), \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 162\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\internals\\solver.py:109\u001b[0m, in \u001b[0;36mSolver._pyro_simulate\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ph\u001b[38;5;241m.\u001b[39mpriority \u001b[38;5;241m>\u001b[39m start_time:\n\u001b[0;32m 107\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m--> 109\u001b[0m state, start_time, next_interruption \u001b[38;5;241m=\u001b[39m simulate_to_interruption(\n\u001b[0;32m 110\u001b[0m possible_interruptions,\n\u001b[0;32m 111\u001b[0m dynamics,\n\u001b[0;32m 112\u001b[0m state,\n\u001b[0;32m 113\u001b[0m start_time,\n\u001b[0;32m 114\u001b[0m end_time,\n\u001b[0;32m 115\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmsg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 116\u001b[0m )\n\u001b[0;32m 118\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m next_interruption \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 119\u001b[0m dynamics, state \u001b[38;5;241m=\u001b[39m next_interruption\u001b[38;5;241m.\u001b[39mcallback(dynamics, state)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:281\u001b[0m, in \u001b[0;36meffectful.._fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 264\u001b[0m msg \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 265\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mtype\u001b[39m,\n\u001b[0;32m 266\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: name,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 278\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfer\u001b[39m\u001b[38;5;124m\"\u001b[39m: infer,\n\u001b[0;32m 279\u001b[0m }\n\u001b[0;32m 280\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[1;32m--> 281\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:212\u001b[0m, in \u001b[0;36mapply_stack\u001b[1;34m(initial_msg)\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mreversed\u001b[39m(stack):\n\u001b[0;32m 210\u001b[0m pointer \u001b[38;5;241m=\u001b[39m pointer \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 212\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\messenger.py:162\u001b[0m, in \u001b[0;36mMessenger._process_message\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 160\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m]), \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 162\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\handlers\\solver.py:89\u001b[0m, in \u001b[0;36mTorchDiffEq._pyro_simulate_to_interruption\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 87\u001b[0m interruptions, dynamics, initial_state, start_time, end_time \u001b[38;5;241m=\u001b[39m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 88\u001b[0m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39modeint_kwargs)\n\u001b[1;32m---> 89\u001b[0m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m torchdiffeq_simulate_to_interruption(\n\u001b[0;32m 90\u001b[0m interruptions,\n\u001b[0;32m 91\u001b[0m dynamics,\n\u001b[0;32m 92\u001b[0m initial_state,\n\u001b[0;32m 93\u001b[0m start_time,\n\u001b[0;32m 94\u001b[0m end_time,\n\u001b[0;32m 95\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmsg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 96\u001b[0m )\n\u001b[0;32m 97\u001b[0m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdone\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\internals\\backends\\torchdiffeq.py:252\u001b[0m, in \u001b[0;36mtorchdiffeq_simulate_to_interruption\u001b[1;34m(interruptions, dynamics, initial_state, start_time, end_time, **kwargs)\u001b[0m\n\u001b[0;32m 246\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(interruptions) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshould have at least one interruption here\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 248\u001b[0m (next_interruption,), interruption_time \u001b[38;5;241m=\u001b[39m _torchdiffeq_get_next_interruptions(\n\u001b[0;32m 249\u001b[0m dynamics, initial_state, start_time, interruptions, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m 250\u001b[0m )\n\u001b[1;32m--> 252\u001b[0m value \u001b[38;5;241m=\u001b[39m simulate_point(\n\u001b[0;32m 253\u001b[0m dynamics, initial_state, start_time, interruption_time, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m 254\u001b[0m )\n\u001b[0;32m 255\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m value, interruption_time, next_interruption\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:281\u001b[0m, in \u001b[0;36meffectful.._fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 264\u001b[0m msg \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 265\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mtype\u001b[39m,\n\u001b[0;32m 266\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: name,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 278\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfer\u001b[39m\u001b[38;5;124m\"\u001b[39m: infer,\n\u001b[0;32m 279\u001b[0m }\n\u001b[0;32m 280\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[1;32m--> 281\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:212\u001b[0m, in \u001b[0;36mapply_stack\u001b[1;34m(initial_msg)\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mreversed\u001b[39m(stack):\n\u001b[0;32m 210\u001b[0m pointer \u001b[38;5;241m=\u001b[39m pointer \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 212\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\messenger.py:162\u001b[0m, in \u001b[0;36mMessenger._process_message\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 160\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m]), \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 162\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\handlers\\trajectory.py:97\u001b[0m, in \u001b[0;36mLogTrajectory._pyro_simulate_point\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 92\u001b[0m timespan \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mconcat(\n\u001b[0;32m 93\u001b[0m (start_time\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m), filtered_timespan, end_time\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m 94\u001b[0m )\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mpoutine\u001b[38;5;241m.\u001b[39mmessenger\u001b[38;5;241m.\u001b[39mblock_messengers(\u001b[38;5;28;01mlambda\u001b[39;00m m: m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m---> 97\u001b[0m trajectory: State[T] \u001b[38;5;241m=\u001b[39m simulate_trajectory(\n\u001b[0;32m 98\u001b[0m dynamics, initial_state, timespan, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmsg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 99\u001b[0m )\n\u001b[0;32m 101\u001b[0m \u001b[38;5;66;03m# TODO support dim != -1\u001b[39;00m\n\u001b[0;32m 102\u001b[0m idx_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__time\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:281\u001b[0m, in \u001b[0;36meffectful.._fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 264\u001b[0m msg \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 265\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mtype\u001b[39m,\n\u001b[0;32m 266\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: name,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 278\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfer\u001b[39m\u001b[38;5;124m\"\u001b[39m: infer,\n\u001b[0;32m 279\u001b[0m }\n\u001b[0;32m 280\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[1;32m--> 281\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\runtime.py:212\u001b[0m, in \u001b[0;36mapply_stack\u001b[1;34m(initial_msg)\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mreversed\u001b[39m(stack):\n\u001b[0;32m 210\u001b[0m pointer \u001b[38;5;241m=\u001b[39m pointer \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 212\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\poutine\\messenger.py:162\u001b[0m, in \u001b[0;36mMessenger._process_message\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 160\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m]), \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 162\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\handlers\\solver.py:77\u001b[0m, in \u001b[0;36mTorchDiffEq._pyro_simulate_trajectory\u001b[1;34m(self, msg)\u001b[0m\n\u001b[0;32m 74\u001b[0m dynamics, initial_state, timespan \u001b[38;5;241m=\u001b[39m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 75\u001b[0m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39modeint_kwargs)\n\u001b[1;32m---> 77\u001b[0m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m torchdiffeq_simulate_trajectory(\n\u001b[0;32m 78\u001b[0m dynamics, initial_state, timespan, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmsg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 79\u001b[0m )\n\u001b[0;32m 80\u001b[0m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdone\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\internals\\backends\\torchdiffeq.py:169\u001b[0m, in \u001b[0;36mtorchdiffeq_simulate_trajectory\u001b[1;34m(dynamics, initial_state, timespan, **kwargs)\u001b[0m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtorchdiffeq_simulate_trajectory\u001b[39m(\n\u001b[0;32m 164\u001b[0m dynamics: Dynamics[torch\u001b[38;5;241m.\u001b[39mTensor],\n\u001b[0;32m 165\u001b[0m initial_state: State[torch\u001b[38;5;241m.\u001b[39mTensor],\n\u001b[0;32m 166\u001b[0m timespan: torch\u001b[38;5;241m.\u001b[39mTensor,\n\u001b[0;32m 167\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m 168\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m State[torch\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[1;32m--> 169\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _torchdiffeq_ode_simulate_inner(dynamics, initial_state, timespan, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\internals\\backends\\torchdiffeq.py:71\u001b[0m, in \u001b[0;36m_torchdiffeq_ode_simulate_inner\u001b[1;34m(dynamics, initial_state, timespan, **odeint_kwargs)\u001b[0m\n\u001b[0;32m 68\u001b[0m time_dim \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 70\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39many(diff):\n\u001b[1;32m---> 71\u001b[0m solns \u001b[38;5;241m=\u001b[39m _batched_odeint( \u001b[38;5;66;03m# torchdiffeq.odeint(\u001b[39;00m\n\u001b[0;32m 72\u001b[0m functools\u001b[38;5;241m.\u001b[39mpartial(_deriv, dynamics, var_order),\n\u001b[0;32m 73\u001b[0m \u001b[38;5;28mtuple\u001b[39m(initial_state[v] \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m var_order),\n\u001b[0;32m 74\u001b[0m timespan_,\n\u001b[0;32m 75\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39modeint_kwargs,\n\u001b[0;32m 76\u001b[0m )\n\u001b[0;32m 77\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 78\u001b[0m solns \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(initial_state[v]\u001b[38;5;241m.\u001b[39munsqueeze(time_dim) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m var_order)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\internals\\backends\\torchdiffeq.py:127\u001b[0m, in \u001b[0;36m_batched_odeint\u001b[1;34m(func, y0, t, event_fn, **odeint_kwargs)\u001b[0m\n\u001b[0;32m 123\u001b[0m event_t, yt_raw \u001b[38;5;241m=\u001b[39m torchdiffeq\u001b[38;5;241m.\u001b[39modeint_event(\n\u001b[0;32m 124\u001b[0m func, y0_expanded, t, event_fn\u001b[38;5;241m=\u001b[39mevent_fn, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39modeint_kwargs\n\u001b[0;32m 125\u001b[0m )\n\u001b[0;32m 126\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 127\u001b[0m yt_raw \u001b[38;5;241m=\u001b[39m torchdiffeq\u001b[38;5;241m.\u001b[39modeint(func, y0_expanded, t, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39modeint_kwargs)\n\u001b[0;32m 129\u001b[0m yt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\n\u001b[0;32m 130\u001b[0m torch\u001b[38;5;241m.\u001b[39mtranspose(\n\u001b[0;32m 131\u001b[0m yt_[(\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m+\u001b[39m yt_\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;28mlen\u001b[39m(yt_\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m-\u001b[39m event_dim :]],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 135\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m yt_ \u001b[38;5;129;01min\u001b[39;00m yt_raw\n\u001b[0;32m 136\u001b[0m )\n\u001b[0;32m 137\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m yt \u001b[38;5;28;01mif\u001b[39;00m event_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m (event_t, yt)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torchdiffeq\\_impl\\odeint.py:77\u001b[0m, in \u001b[0;36modeint\u001b[1;34m(func, y0, t, rtol, atol, method, options, event_fn)\u001b[0m\n\u001b[0;32m 74\u001b[0m solver \u001b[38;5;241m=\u001b[39m SOLVERS[method](func\u001b[38;5;241m=\u001b[39mfunc, y0\u001b[38;5;241m=\u001b[39my0, rtol\u001b[38;5;241m=\u001b[39mrtol, atol\u001b[38;5;241m=\u001b[39matol, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n\u001b[0;32m 76\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m event_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 77\u001b[0m solution \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 78\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 79\u001b[0m event_t, solution \u001b[38;5;241m=\u001b[39m solver\u001b[38;5;241m.\u001b[39mintegrate_until_event(t[\u001b[38;5;241m0\u001b[39m], event_fn)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torchdiffeq\\_impl\\solvers.py:105\u001b[0m, in \u001b[0;36mFixedGridODESolver.integrate\u001b[1;34m(self, t)\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t0, t1 \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(time_grid[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], time_grid[\u001b[38;5;241m1\u001b[39m:]):\n\u001b[0;32m 104\u001b[0m dt \u001b[38;5;241m=\u001b[39m t1 \u001b[38;5;241m-\u001b[39m t0\n\u001b[1;32m--> 105\u001b[0m dy, f0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_step_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 106\u001b[0m y1 \u001b[38;5;241m=\u001b[39m y0 \u001b[38;5;241m+\u001b[39m dy\n\u001b[0;32m 108\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m j \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(t) \u001b[38;5;129;01mand\u001b[39;00m t1 \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m t[j]:\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torchdiffeq\\_impl\\fixed_grid.py:28\u001b[0m, in \u001b[0;36mRK4._step_func\u001b[1;34m(self, func, t0, dt, t1, y0)\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_step_func\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, t0, dt, t1, y0):\n\u001b[1;32m---> 28\u001b[0m f0 \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mperturb\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mPerturb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mNEXT\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mperturb\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mPerturb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mNONE\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m rk4_alt_step_func(func, t0, dt, t1, y0, f0\u001b[38;5;241m=\u001b[39mf0, perturb\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mperturb), f0\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torchdiffeq\\_impl\\misc.py:189\u001b[0m, in \u001b[0;36m_PerturbFunc.forward\u001b[1;34m(self, t, y, perturb)\u001b[0m\n\u001b[0;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 187\u001b[0m \u001b[38;5;66;03m# Do nothing.\u001b[39;00m\n\u001b[0;32m 188\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m--> 189\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torchdiffeq\\_impl\\misc.py:138\u001b[0m, in \u001b[0;36m_TupleFunc.forward\u001b[1;34m(self, t, y)\u001b[0m\n\u001b[0;32m 137\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, t, y):\n\u001b[1;32m--> 138\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_flat_to_shape\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshapes\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcat([f_\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m f_ \u001b[38;5;129;01min\u001b[39;00m f])\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\chirho\\dynamical\\internals\\backends\\torchdiffeq.py:43\u001b[0m, in \u001b[0;36m_deriv\u001b[1;34m(dynamics, var_order, time, state)\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mt\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m var_order, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvariable name t is reserved for time\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 42\u001b[0m env: State[torch\u001b[38;5;241m.\u001b[39mTensor] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(var_order \u001b[38;5;241m+\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mt\u001b[39m\u001b[38;5;124m\"\u001b[39m,), state \u001b[38;5;241m+\u001b[39m (time,)))\n\u001b[1;32m---> 43\u001b[0m ddt: State[torch\u001b[38;5;241m.\u001b[39mTensor] \u001b[38;5;241m=\u001b[39m \u001b[43mdynamics\u001b[49m\u001b[43m(\u001b[49m\u001b[43menv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(ddt\u001b[38;5;241m.\u001b[39mget(var, torch\u001b[38;5;241m.\u001b[39mtensor(\u001b[38;5;241m0.0\u001b[39m)) \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m var_order)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\pyro\\nn\\module.py:686\u001b[0m, in \u001b[0;36mpyro_method..cached_fn\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 683\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(fn)\n\u001b[0;32m 684\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcached_fn\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 685\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pyro_context:\n\u001b[1;32m--> 686\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\compiled_dynamics.py:44\u001b[0m, in \u001b[0;36mCompiledDynamics.deriv\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[38;5;129m@pyro\u001b[39m\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mpyro_method\n\u001b[0;32m 43\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mderiv\u001b[39m(\u001b[38;5;28mself\u001b[39m, X: State[torch\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43meval_deriv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\functools.py:889\u001b[0m, in \u001b[0;36msingledispatch..wrapper\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m 885\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[0;32m 886\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfuncname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires at least \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 887\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1 positional argument\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m--> 889\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dispatch(args[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n", - "File \u001b[1;32m~\\Documents\\GitHub\\pyciemss\\pyciemss\\mira_integration\\compiled_dynamics.py:124\u001b[0m, in \u001b[0;36m_eval_deriv_mira\u001b[1;34m(src, param_module, X)\u001b[0m\n\u001b[0;32m 112\u001b[0m \u001b[38;5;129m@eval_deriv\u001b[39m\u001b[38;5;241m.\u001b[39mregister(mira\u001b[38;5;241m.\u001b[39mmodeling\u001b[38;5;241m.\u001b[39mModel)\n\u001b[0;32m 113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_eval_deriv_mira\u001b[39m(\n\u001b[0;32m 114\u001b[0m src: mira\u001b[38;5;241m.\u001b[39mmodeling\u001b[38;5;241m.\u001b[39mModel,\n\u001b[0;32m 115\u001b[0m param_module: pyro\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mPyroModule,\n\u001b[0;32m 116\u001b[0m X: State[torch\u001b[38;5;241m.\u001b[39mTensor],\n\u001b[0;32m 117\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m State[torch\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[0;32m 118\u001b[0m parameters \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 119\u001b[0m get_name(param_info): \u001b[38;5;28mgetattr\u001b[39m(param_module, get_name(param_info))\n\u001b[0;32m 120\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m param_info \u001b[38;5;129;01min\u001b[39;00m src\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mvalues()\n\u001b[0;32m 121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m param_info\u001b[38;5;241m.\u001b[39mplaceholder\n\u001b[0;32m 122\u001b[0m }\n\u001b[1;32m--> 124\u001b[0m numeric_deriv \u001b[38;5;241m=\u001b[39m param_module\u001b[38;5;241m.\u001b[39mnumeric_deriv_func(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mX, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparameters)\n\u001b[0;32m 126\u001b[0m dX: State[torch\u001b[38;5;241m.\u001b[39mTensor] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m()\n\u001b[0;32m 127\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, var \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(src\u001b[38;5;241m.\u001b[39mvariables\u001b[38;5;241m.\u001b[39mvalues()):\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\Anirban\\anaconda3\\envs\\askem\\lib\\site-packages\\sympytorch\\sympy_module.py:266\u001b[0m, in \u001b[0;36mSymPyModule.forward\u001b[1;34m(self, **symbols)\u001b[0m\n\u001b[0;32m 264\u001b[0m out \u001b[38;5;241m=\u001b[39m [node(symbols) \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_nodes]\n\u001b[0;32m 265\u001b[0m out \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mbroadcast_tensors(\u001b[38;5;241m*\u001b[39mout)\n\u001b[1;32m--> 266\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal policy: tensor([39.0000, 6.0327], dtype=torch.float64)\n", + "{'policy': tensor([39.0000, 6.0327], dtype=torch.float64), 'OptResults': message: ['requested number of basinhopping iterations completed successfully']\n", + " success: False\n", + " fun: -45.032607877335636\n", + " x: [ 3.900e+01 6.033e+00]\n", + " nit: 3\n", + " minimization_failures: 4\n", + " nfev: 120\n", + " lowest_optimization_result: message: Did not converge to a solution satisfying the constraints. See `maxcv` for magnitude of violation.\n", + " success: False\n", + " status: 4\n", + " fun: -45.032607877335636\n", + " x: [ 3.900e+01 6.033e+00]\n", + " nfev: 30\n", + " maxcv: 0.006562499991559889}\n", + "Intervention: {39.0: {'beta_c': tensor(0.1500)}, 6.0327: {'gamma': tensor(0.4000)}}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "C:\\Users\\Anirban\\Documents\\GitHub\\pyciemss\\pyciemss\\interfaces.py:956: UserWarning: Optimal intervention policy does not satisfy constraints.Check if the risk_bounds value is appropriate for given problem.Otherwise, try (i) different initial_guess_interventions, (ii) increasing maxiter/maxfeval,and/or (iii) increase n_samples_ouu to improve accuracy of Monte Carlo risk estimation. \n", + " warnings.warn(\n" ] } ], @@ -779,7 +840,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -787,7 +848,7 @@ "output_type": "stream", "text": [ "Intervention: {39.0: {'beta_c': tensor(0.1500)}, 6.0327: {'gamma': tensor(0.4000)}}\n", - "Time taken: 7.2512922286987305\n", + "Time taken: 7.552547931671143\n", "Risk associated with QoI: [30000.24984374999]\n" ] }, @@ -828,7 +889,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -836,7 +897,7 @@ "output_type": "stream", "text": [ "Intervention: {39.0: {'beta_c': tensor(0.1500)}, 6.0327: {'gamma': tensor(0.4000)}}\n", - "Time taken: 11.807223081588745\n", + "Time taken: 12.033756732940674\n", "Risk associated with QoI: [28489.04578124999]\n" ] }, @@ -876,7 +937,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -884,7 +945,7 @@ "output_type": "stream", "text": [ "Intervention: {39.0: {'beta_c': tensor(0.1500)}, 6.0327: {'gamma': tensor(0.4000)}}\n", - "Time taken: 11.77100157737732\n", + "Time taken: 14.085338592529297\n", "Risk associated with QoI: [30001.24765624999]\n" ] }, From 3df403d621eb1016c137a735603b92fa33bd36d1 Mon Sep 17 00:00:00 2001 From: Anirban Chaudhuri <75496534+anirban-chaudhuri@users.noreply.github.com> Date: Thu, 21 Mar 2024 12:44:25 -0500 Subject: [PATCH 5/5] Updating from main (#548) * Add check if parameters have distributions (#543) * add check for whether parameters have distribution * add test without distributions * lint * add check to see if there are any params with distributions (#546) * Updated optimize documentation for static_parameter_intervention definition (#544) * Updated optimize documentation for static_parameter_intervention definition * lint --------- Co-authored-by: Sam Witty --------- Co-authored-by: Sam Witty --- pyciemss/compiled_dynamics.py | 9 ++++++++- pyciemss/interfaces.py | 6 ++++++ tests/fixtures.py | 10 ++++++++++ tests/test_compiled_dynamics.py | 22 +++++++++++++++++++++- tests/test_interfaces.py | 11 +++++++++++ 5 files changed, 56 insertions(+), 2 deletions(-) diff --git a/pyciemss/compiled_dynamics.py b/pyciemss/compiled_dynamics.py index 8e7f53e3d..ddcebd45b 100644 --- a/pyciemss/compiled_dynamics.py +++ b/pyciemss/compiled_dynamics.py @@ -1,7 +1,7 @@ from __future__ import annotations import functools -from typing import Callable, Dict, Optional, Tuple, TypeVar, Union +from typing import Callable, Dict, List, Optional, Tuple, TypeVar, Union import mira import mira.metamodel @@ -39,6 +39,13 @@ def __init__(self, src, **kwargs): self.instantiate_parameters() + def params_with_distributions(self) -> List: + return [ + get_name(k) + for k, v in _compile_param_values(self.src).items() + if isinstance(v, pyro.nn.PyroSample) + ] + @pyro.nn.pyro_method def deriv(self, X: State[torch.Tensor]) -> None: return eval_deriv(self.src, self, X) diff --git a/pyciemss/interfaces.py b/pyciemss/interfaces.py index 602b5b0d9..631b7650a 100644 --- a/pyciemss/interfaces.py +++ b/pyciemss/interfaces.py @@ -651,6 +651,12 @@ def calibrate( model = CompiledDynamics.load(model_path_or_json) + if len(model.params_with_distributions()) == 0: + raise ValueError( + "The model does not contain distributions representing uncertainty over any parameters." + "As there is no uncertainty, `calibrate` will not update any parameters." + ) + data_timepoints, data = load_data(data_path, data_mapping=data_mapping) # Check that num_iterations is a positive integer diff --git a/tests/fixtures.py b/tests/fixtures.py index 50ab66f0d..ac107dcdc 100644 --- a/tests/fixtures.py +++ b/tests/fixtures.py @@ -85,6 +85,16 @@ def __init__( ModelFixture(os.path.join(MODELS_PATH, "SEIRHD_stockflow.json"), "p_cbeta"), ] +MODELS_WITHOUT_DISTRIBUTIONS = [ + ModelFixture( + os.path.join(MODELS_PATH, "SEIR_stockflow_no_uncertainty"), + "p_cbeta", + os.path.join(DATA_PATH, "traditional.csv"), + {"Infected": "I"}, + False, + ), +] + optimize_kwargs_SIRstockflow_param = { "qoi": lambda x: obs_nday_average_qoi(x, ["I_state"], 1), "risk_bound": 300.0, diff --git a/tests/test_compiled_dynamics.py b/tests/test_compiled_dynamics.py index a465d9d96..d859e05df 100644 --- a/tests/test_compiled_dynamics.py +++ b/tests/test_compiled_dynamics.py @@ -8,7 +8,13 @@ from pyciemss.compiled_dynamics import CompiledDynamics -from .fixtures import END_TIMES, MODEL_URLS, START_TIMES, check_is_state +from .fixtures import ( + END_TIMES, + MODEL_URLS, + MODELS_WITHOUT_DISTRIBUTIONS, + START_TIMES, + check_is_state, +) @pytest.mark.parametrize("url", MODEL_URLS) @@ -55,3 +61,17 @@ def test_compiled_dynamics_load_json(url, start_time, end_time): with TorchDiffEq(): simulation = model(torch.as_tensor(start_time), torch.as_tensor(end_time)) check_is_state(simulation, torch.Tensor) + + +@pytest.mark.parametrize("url", MODEL_URLS) +def test_params_with_distributions(url): + model = CompiledDynamics.load(url) + assert isinstance(model, CompiledDynamics) + assert len(model.params_with_distributions()) > 0 + + +@pytest.mark.parametrize("models", MODELS_WITHOUT_DISTRIBUTIONS) +def test_params_without_distributions(models): + model = CompiledDynamics.load(models.url) + assert isinstance(model, CompiledDynamics) + assert len(model.params_with_distributions()) == 0 diff --git a/tests/test_interfaces.py b/tests/test_interfaces.py index 8bd270021..0a39b7a6b 100644 --- a/tests/test_interfaces.py +++ b/tests/test_interfaces.py @@ -21,6 +21,7 @@ MAPPING_FOR_DATA_TESTS, MODEL_URLS, MODELS, + MODELS_WITHOUT_DISTRIBUTIONS, NON_POS_INTS, NUM_SAMPLES, OPT_MODELS, @@ -697,3 +698,13 @@ def test_bad_euler_solver_optimize(model_fixture): logging_step_size, **optimize_kwargs, ) + + +@pytest.mark.parametrize("model_fixture", MODELS_WITHOUT_DISTRIBUTIONS) +def test_calibrate_no_distributions(model_fixture): + with pytest.raises(ValueError): + calibrate( + model_fixture.url, + model_fixture.data_path, + data_mapping=model_fixture.data_mapping, + )