From 426a63c046cc1a09f2dc60818c039d069625bab0 Mon Sep 17 00:00:00 2001
From: Jonhas Colina <159941307+JonhasSC@users.noreply.github.com>
Date: Mon, 3 Jun 2024 20:48:41 -0400
Subject: [PATCH] PR to allow user to specify the inclusion of nested resource
 estimates (#39)

* commit to allow switching between the inclusion of nested resource estimates or not

* modified notebooks to make sure that generated resource estimates look good. Modified some parameters to adhere to new function signatures

* Updated pylint badge

* removed trailing commas

* Updated pylint badge

---------

Co-authored-by: github-actions <41898282+github-actions[bot]@users.noreply.github.com>
---
 README.md                                     |   2 +-
 ...HighTemperatureSuperConductorExample.ipynb | 135 ++++++------
 notebooks/MagneticLattices.ipynb              | 200 +++++++++---------
 notebooks/PhotosynthesisExample.ipynb         |   1 +
 notebooks/RuClExample.ipynb                   |   4 +-
 src/qca/utils/algo_utils.py                   |  16 +-
 src/qca/utils/utils.py                        |  71 ++++++-
 7 files changed, 248 insertions(+), 181 deletions(-)

diff --git a/README.md b/README.md
index 44ceb06..618cf45 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-![pylint](https://img.shields.io/badge/PyLint-9.45-yellow?logo=python&logoColor=white)
+![pylint](https://img.shields.io/badge/PyLint-9.48-yellow?logo=python&logoColor=white)
 # Quantum Computing Application Specifications
 
 Documentation of Applications for Quantum Computers
diff --git a/notebooks/HighTemperatureSuperConductorExample.ipynb b/notebooks/HighTemperatureSuperConductorExample.ipynb
index 35c635a..274cf05 100644
--- a/notebooks/HighTemperatureSuperConductorExample.ipynb
+++ b/notebooks/HighTemperatureSuperConductorExample.ipynb
@@ -50,11 +50,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 1,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jonhas/anaconda3/envs/cmp/lib/python3.11/site-packages/attr/_make.py:918: RuntimeWarning: Running interpreter doesn't sufficiently support code object introspection.  Some features like bare super() or accessing __class__ will not work with slotted classes.\n",
+      "  set_closure_cell(cell, cls)\n"
+     ]
+    }
+   ],
    "source": [
     "import time\n",
     "\n",
@@ -72,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 2,
    "metadata": {
     "tags": []
    },
@@ -174,18 +183,18 @@
      "output_type": "stream",
      "text": [
       "Estimating one_band\n",
-      "Time to generate circuit for GSEE: 8.066699956543744e-05 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.HPowGate circuit: 0.00020158299594186246 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.HPowGate circuit to Clifford+T: 0.0004812920233234763 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.identity.IdentityGate circuit: 2.72089964710176e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.identity.IdentityGate circuit to Clifford+T: 9.29099041968584e-06 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit: 0.00012083398178219795 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit to Clifford+T: 0.00011850000009872019 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit: 0.049456957989605144 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit to Clifford+T: 0.36098670901264995 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.measurement_gate.MeasurementGate circuit: 0.00025645800633355975 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.measurement_gate.MeasurementGate circuit to Clifford+T: 7.162499241530895e-05 seconds\n",
-      "Time to estimate one_band: 0.6886332079884596\n"
+      "Time to generate circuit for GSEE: 8.7958003859967e-05 seconds\n",
+      "   Time to decompose high level HPowGate circuit: 0.00017079099779948592 seconds \n",
+      "   Time to transform decomposed HPowGate circuit to Clifford+T: 0.00045045799924992025 seconds\n",
+      "   Time to decompose high level IdentityGate circuit: 1.7041980754584074e-05 seconds \n",
+      "   Time to transform decomposed IdentityGate circuit to Clifford+T: 7.541995728388429e-06 seconds\n",
+      "   Time to decompose high level PhaseOffset circuit: 9.745798888616264e-05 seconds \n",
+      "   Time to transform decomposed PhaseOffset circuit to Clifford+T: 0.00010429200483486056 seconds\n",
+      "   Time to decompose high level Trotter_Unitary circuit: 0.047857666009804234 seconds \n",
+      "   Time to transform decomposed Trotter_Unitary circuit to Clifford+T: 0.3390574579825625 seconds\n",
+      "   Time to decompose high level MeasurementGate circuit: 0.00021316600032150745 seconds \n",
+      "   Time to transform decomposed MeasurementGate circuit to Clifford+T: 5.299999611452222e-05 seconds\n",
+      "Time to estimate one_band: 0.6516820410033688\n"
      ]
     }
    ],
@@ -521,31 +530,31 @@
      "output_type": "stream",
      "text": [
       "Estimating Current Limit\n",
-      "Time to generate circuit for GSEE: 7.179100066423416e-05 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.HPowGate circuit: 0.00017420900985598564 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.HPowGate circuit to Clifford+T: 0.00020945898722857237 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.identity.IdentityGate circuit: 2.4416978703811765e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.identity.IdentityGate circuit to Clifford+T: 5.54099096916616e-06 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit: 0.00010366700007580221 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit to Clifford+T: 0.00010866599041037261 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit: 0.24183270902722143 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit to Clifford+T: 1.0117513749864884 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.measurement_gate.MeasurementGate circuit: 0.0007868749962653965 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.measurement_gate.MeasurementGate circuit to Clifford+T: 7.495898171328008e-05 seconds\n",
-      "Time to estimate Current Limit: 2.188670125004137\n",
+      "Time to generate circuit for GSEE: 4.075001925230026e-05 seconds\n",
+      "   Time to decompose high level HPowGate circuit: 0.00010466598905622959 seconds \n",
+      "   Time to transform decomposed HPowGate circuit to Clifford+T: 0.00020041698007844388 seconds\n",
+      "   Time to decompose high level IdentityGate circuit: 1.5375000657513738e-05 seconds \n",
+      "   Time to transform decomposed IdentityGate circuit to Clifford+T: 5.125009920448065e-06 seconds\n",
+      "   Time to decompose high level PhaseOffset circuit: 0.0001000420015770942 seconds \n",
+      "   Time to transform decomposed PhaseOffset circuit to Clifford+T: 0.00010762500460259616 seconds\n",
+      "   Time to decompose high level Trotter_Unitary circuit: 0.24668795900652185 seconds \n",
+      "   Time to transform decomposed Trotter_Unitary circuit to Clifford+T: 0.932925041008275 seconds\n",
+      "   Time to decompose high level MeasurementGate circuit: 0.0007643330027349293 seconds \n",
+      "   Time to transform decomposed MeasurementGate circuit to Clifford+T: 4.629199975170195e-05 seconds\n",
+      "Time to estimate Current Limit: 2.092620125011308\n",
       "Estimating Ideal\n",
-      "Time to generate circuit for GSEE: 4.7291978262364864e-05 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.HPowGate circuit: 7.916599861346185e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.HPowGate circuit to Clifford+T: 9.008299093693495e-05 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.identity.IdentityGate circuit: 1.0749994544312358e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.identity.IdentityGate circuit to Clifford+T: 3.374996595084667e-06 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit: 7.287500193342566e-05 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit to Clifford+T: 4.095799522474408e-05 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit: 0.27323129199794494 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit to Clifford+T: 0.7680455000081565 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.measurement_gate.MeasurementGate circuit: 0.0010527079866733402 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.measurement_gate.MeasurementGate circuit to Clifford+T: 5.0291011575609446e-05 seconds\n",
-      "Time to estimate Ideal: 2.1309333750104997\n"
+      "Time to generate circuit for GSEE: 4.383301711641252e-05 seconds\n",
+      "   Time to decompose high level HPowGate circuit: 8.158298442140222e-05 seconds \n",
+      "   Time to transform decomposed HPowGate circuit to Clifford+T: 8.833300671540201e-05 seconds\n",
+      "   Time to decompose high level IdentityGate circuit: 1.2375006917864084e-05 seconds \n",
+      "   Time to transform decomposed IdentityGate circuit to Clifford+T: 3.916997229680419e-06 seconds\n",
+      "   Time to decompose high level PhaseOffset circuit: 7.408397505059838e-05 seconds \n",
+      "   Time to transform decomposed PhaseOffset circuit to Clifford+T: 4.7542009269818664e-05 seconds\n",
+      "   Time to decompose high level Trotter_Unitary circuit: 0.28098991699516773 seconds \n",
+      "   Time to transform decomposed Trotter_Unitary circuit to Clifford+T: 0.7134910000022501 seconds\n",
+      "   Time to decompose high level MeasurementGate circuit: 0.000858165993122384 seconds \n",
+      "   Time to transform decomposed MeasurementGate circuit to Clifford+T: 4.474999150261283e-05 seconds\n",
+      "Time to estimate Ideal: 2.046745167026529\n"
      ]
     }
    ],
@@ -1034,31 +1043,31 @@
      "output_type": "stream",
      "text": [
       "Estimating Current Limit\n",
-      "Time to generate circuit for GSEE: 4.704200546257198e-05 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.HPowGate circuit: 0.00011325001833029091 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.HPowGate circuit to Clifford+T: 0.00022524999803863466 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.identity.IdentityGate circuit: 1.3165990822017193e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.identity.IdentityGate circuit to Clifford+T: 3.4999975468963385e-06 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit: 8.550001075491309e-05 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit to Clifford+T: 9.033299284055829e-05 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit: 0.7486400830093771 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit to Clifford+T: 2.5677436669939198 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.measurement_gate.MeasurementGate circuit: 0.00246395799331367 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.measurement_gate.MeasurementGate circuit to Clifford+T: 8.699999307282269e-05 seconds\n",
-      "Time to estimate Current Limit: 5.534946375002619\n",
+      "Time to generate circuit for GSEE: 4.562499816529453e-05 seconds\n",
+      "   Time to decompose high level HPowGate circuit: 8.22089787106961e-05 seconds \n",
+      "   Time to transform decomposed HPowGate circuit to Clifford+T: 0.00020158401457592845 seconds\n",
+      "   Time to decompose high level IdentityGate circuit: 1.5083001926541328e-05 seconds \n",
+      "   Time to transform decomposed IdentityGate circuit to Clifford+T: 4.66697383671999e-06 seconds\n",
+      "   Time to decompose high level PhaseOffset circuit: 9.208300616592169e-05 seconds \n",
+      "   Time to transform decomposed PhaseOffset circuit to Clifford+T: 0.00010825000936165452 seconds\n",
+      "   Time to decompose high level Trotter_Unitary circuit: 0.6796650000032969 seconds \n",
+      "   Time to transform decomposed Trotter_Unitary circuit to Clifford+T: 2.49565304198768 seconds\n",
+      "   Time to decompose high level MeasurementGate circuit: 0.002015333011513576 seconds \n",
+      "   Time to transform decomposed MeasurementGate circuit to Clifford+T: 5.7375000324100256e-05 seconds\n",
+      "Time to estimate Current Limit: 5.365840749989729\n",
       "Estimating Ideal\n",
-      "Time to generate circuit for GSEE: 5.1207985961809754e-05 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.HPowGate circuit: 9.524999768473208e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.HPowGate circuit to Clifford+T: 0.00019350001821294427 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.identity.IdentityGate circuit: 1.2125005014240742e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.identity.IdentityGate circuit to Clifford+T: 3.583991201594472e-06 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit: 6.925000343471766e-05 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.PhaseOffset circuit to Clifford+T: 7.974999607540667e-05 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit: 0.6040110420144629 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.PhaseEstimation.pe_gates.Trotter_Unitary circuit to Clifford+T: 2.368579125002725 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.measurement_gate.MeasurementGate circuit: 0.002168875013012439 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.measurement_gate.MeasurementGate circuit to Clifford+T: 9.10409726202488e-05 seconds\n",
-      "Time to estimate Ideal: 5.23599783401005\n"
+      "Time to generate circuit for GSEE: 5.845801206305623e-05 seconds\n",
+      "   Time to decompose high level HPowGate circuit: 8.083297871053219e-05 seconds \n",
+      "   Time to transform decomposed HPowGate circuit to Clifford+T: 0.00018908301717601717 seconds\n",
+      "   Time to decompose high level IdentityGate circuit: 1.5124998753890395e-05 seconds \n",
+      "   Time to transform decomposed IdentityGate circuit to Clifford+T: 4.084024112671614e-06 seconds\n",
+      "   Time to decompose high level PhaseOffset circuit: 7.508401176892221e-05 seconds \n",
+      "   Time to transform decomposed PhaseOffset circuit to Clifford+T: 7.750000804662704e-05 seconds\n",
+      "   Time to decompose high level Trotter_Unitary circuit: 0.5615481659770012 seconds \n",
+      "   Time to transform decomposed Trotter_Unitary circuit to Clifford+T: 2.3108590000192635 seconds\n",
+      "   Time to decompose high level MeasurementGate circuit: 0.0022298340045381337 seconds \n",
+      "   Time to transform decomposed MeasurementGate circuit to Clifford+T: 5.750000127591193e-05 seconds\n",
+      "Time to estimate Ideal: 5.098492958000861\n"
      ]
     }
    ],
diff --git a/notebooks/MagneticLattices.ipynb b/notebooks/MagneticLattices.ipynb
index bf3f66d..a7787c6 100644
--- a/notebooks/MagneticLattices.ipynb
+++ b/notebooks/MagneticLattices.ipynb
@@ -52,7 +52,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonhas/anaconda3/envs/other_qca/lib/python3.11/site-packages/attr/_make.py:918: RuntimeWarning: Running interpreter doesn't sufficiently support code object introspection.  Some features like bare super() or accessing __class__ will not work with slotted classes.\n",
+      "/Users/jonhas/anaconda3/envs/cmp/lib/python3.11/site-packages/attr/_make.py:918: RuntimeWarning: Running interpreter doesn't sufficiently support code object introspection.  Some features like bare super() or accessing __class__ will not work with slotted classes.\n",
       "  set_closure_cell(cell, cls)\n"
      ]
     }
@@ -95,7 +95,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -115,7 +115,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -135,7 +135,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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t2zZu3ryJnp4eAwcOxNbWFisrK3WXJwjC/xGBUhByGUmSuHTpEq6urhw/fpx8+fIxaNAgRo0aRZUqVdRdnkYJDg7G3Nycw4cP06NHD3WXk+s9fvyYbdu2sWvXLt68eUOdOnWws7OjT58+FC5cWN3lCUKuJgKlIOQS79+/Z+fOnWzYsIGAgACqVKmCvb09AwYMoFChQuouTyM5OTmxYMEC3rx5Q/78+dVdjvB/kpKSOHXqFNu2bePMmTPo6urSs2dP7OzsaNq0abY93lMQsjMRKAUhh7t37x6urq7s3buXlJQUevTogb29PY0bNxYX3h+oU6cORkZGHDp0SN2lCN8QFRXFzp072bZtGyEhIZiZmWFra8vgwYMpW7asussThFxDBEpByIESEhI4dOgQrq6u3Lp1C319fUaMGIGdnR2lS5dWd3nZQnh4OMbGxuzfv5/evXuruxzhBxQKBdeuXWPbtm0cPnyYxMRE2rdvj62tLR07dszS4zLjElMIj44jKUWBjrYcI70CFNDVzrL+BUEdRKAUBBXK6gtLWFgYGzduxM3NjejoaKytrbG3t6dDhw5oa4sLWkasXLmSWbNm8fr1azElIJv58OED+/btw83Njdu3b1OyZEkGDRqEnZ0dlpaWKukz6FUse70juRzwmsiYeP55YZUBBsXy06JCSfrXM8C8lPh7EnIeESgFQcmy+sKSmprK2bNncXV15cyZMxQuXBhbW1tGjhyJubl5ptvPrRo0aEDp0qVxd3dXdylCJjx8+JBt27axe/duYmJiaNiwIba2tvTu3ZuCBQtmuv1nMfHMcPfDM/gtWnIZqYpvX1K//LyJWXEWd7NCv5iYlyvkHCJQCoKSZPWF5c2bN2zbto2NGzcSHh5OrVq1cHBwoHfv3mIBSSY9e/YMAwMD9uzZQ//+/dVdjqAEiYmJHD9+HDc3N/7880/y589P7969sbOzo0GDBj81n3i/byRzT/iTopC++3n/X1pyGdpyGfM7V6ZPHYMM9ysImkgESkFQgqy6sEiShLe3N66urhw4cACZTEbfvn2xt7enTp06mXkLwj+sXbuWyZMn8+bNG7EdTQ4UERGRtpAnIiICS0tLbG1tGTRoEKVKlUpXG+suB7HifGCma5lkbYFjC/EkQcj+RKAUhEzKigtLXFwc+/btw9XVlXv37mFiYsKoUaMYMmQIenp6me5b+LcmTZpQuHBhTp06pe5SBBVSKBRcunQJNzc33N3dSU1NpWPHjtjZ2dG2bdtvzjve7xvJtKN+SqvDqbsVvcVIpZDNiUApCJmg6gtLQEAAGzZsYMeOHXz8+JGOHTtib2+PtbV1rjxXOyv89ddflC9fnm3btmFjY6PucoQsEhMTwx9//IGbmxv379+nTJkyDB48GFtb23/NRX4WE0/r1VdJTFEorW9dbTkXxjcTcyqFbE0ESkH4Saq6sJwd04h7nhdwdXXl4sWLlChRgqFDhzJ8+HCMjIyU1pfwdevXr2fcuHG8evWKYsWKqbscQQ3u3r2Lm5sbe/fu5cOHDzRt2hQ7Ozt69OjByP2PuBEanaGpLT+iJZfR0ESP3Xb1lNamIGQ1ESgF4ScNdPNW+oVFhkTqX495tmsqjRo1wt7enh49eqCrq6u0PoTva9GiBbq6upw9e1bdpQhq9vnzZ9zd3XFzc+PSpUsUNrCkSL8VKuvvwvimmJUUWwoJ2ZMIlILwE4JexdJmzTWVtb+uXSk6Nq2tsvaFr3v9+jVlypRh06ZNDB06VN3lCBokNDSUUVsv8TS1JDK5ltLb15LLGFjPkHmdKyu9bUHICmISliD8hL3ekWjJVXNsoZZcxu33+VTStvB9x44dQyaT0bVrV3WXImgYExMTEoqaqiRMAqQqJC4HvlZJ24KQFUSgFISfcDngtVIfdf+TuLCoz+HDh2nevDnFixdXdymChvmUmEJkTLxK+4iMjicuMUWlfQiCqohAKQgZJC4sOVN0dDSXLl2iZ8+e6i5F0EAR0XGoen6YBIRHx6m4F0FQDREoBSGDxIUlZzp+/DgKhYJu3bqpuxRBAyUpcTcHTehHEJRNBEpByCBxYcmZDh8+TNOmTdN9UoqQu+hoZ83lMqv6EQRlE3+5gpBB4sKS87x7944LFy7Qo0cPdZciaCgjvQKoZhne/yf7v34EITsSVyxByCBxYcl5Tp48SXJyMt27d1d3KYKGKqCrjYGKT7Ix0MtPAd2vH/coCJpOBEpByCBxYcl5Dh8+TMOGDSlXrpy6SxE0WIsKJVHRbmFoyaCFRUnVNC4IWUAESkHIoLi4OH6Je4akSFVJ+5Iilc8ht7l27Rri3AHV+/jxI+fOnROru4Xvevz4MXcOrkVFu4WRKkHgGTdCQkJU04EgqJgIlIKQTp8+fcLJyQkjIyMubpynsg2OZXItPtw+RbNmzahevTpbt24lPl612xTlZqdOnSIpKUnMnxS+KiAggP79+1OlShUeep7DOF+C0g810JJBee1YLh7di4WFBf379+fRo0dK7UMQVE0ESkH4gdjYWJYuXYqRkRGzZ8+mZ8+ePPW5QhOz4sq/sMhlNDErzlOfK5w/fx5DQ0OGDx9O+fLlmTJlCuHh4UrtT4AjR45Qt25dDAwM1F2KoEGCg4MZNGgQlSpV4tq1a7i6uhIUFMQux3ZoK/lzr60lZ9/4zoSHh+Ps7IyXlxdWVlZ07doVHx8fpfYlCKoiAqUgfENsbCxLlizB2NiYOXPm8NtvvxEcHMyGDRswMDBgcTcr5V9Y5DIWd7NCJpPRpk0bTpw4QXBwMEOGDGHLli2YmprStWtXLl68KB6HK8GnT584ffq0eNwtpAkLC8POzg5LS0suXLiAs7MzwcHBjBw5Eh0dHfSL5We+ks/bXtC5MvrF8pMvXz4cHBwICgpi+/btPHnyhHr16tGmTRsuX74sPvOCRhOBUhD+x8ePH1m8eDFGRkbMmzeP3r17ExISgqur679GsVR5YfknExMTVq5cyfPnz3F1dSU4OJjWrVtTpUoVNmzYwKdPn5RaQ25y5swZEhISxONugYiICIYPH46FhQUeHh6sXLmSkJAQHBwc0NXV/ddr+9QxYJK1hVL6nWxdgd51/j06rqOjg42NDY8fP+bgwYO8ffuWli1b0qhRI06dOiWCpaCRZJL4yxQE4O8g6eLiwqpVq/j06RPDhg1j2rRplC9f/ru/t+5yECvOB2a6/8nWFXBoYfbD10mSxJUrV3BxceH48eMUKlSIIUOG4ODggJnZj39f+P969+5NcHAwd+7cUXcpgpo8f/6cxYsXs3XrVooUKcLUqVMZNWoU+fP/eCeH/b6RzD3hT4pCIjUDq3W05DK05TIWdK78nzD5NZIkcebMGX7//Xdu3LhB1apVmTFjBj179kRLSzVzuQUho0SgFHK9jx8/4uzszKpVq4iLi2P48OFMnTr1h0Hyn7LqwvK/IiIi2LBhA1u2bOHdu3e0a9eO0aNHY21tjVwuHkB8T3x8PCVLlmTmzJlMnz5d3eUIWezFixcsWbKETZs2UbBgQaZMmYKDgwMFCxbMUDvPYuKZ4e6HZ/Bb5DK+uwpcSy4jVSHRxKw4i7tZ/edpxI9IksS1a9dYvHgx58+fx9zcnGnTpjFgwAB0dHQy1JYgKJsIlEKu9eHDB5ydnVm9ejXx8fFpQfJn9yL854UFRSp8ZxV4Zi8s/+vz58/s27cPFxcX7t+/j4WFBQ4ODtjY2PDLL79kqu2cIC4xhfDoOJJSFOhoyzHSK8D50yfp3r07AQEBWFgo5/GloPlevXqFk5MTGzZsIG/evEyaNInRo0dn+nMS9CqWyZtOcPuvz+QpWoZ/Xlhl/L23bAuLkgyob4BZyUKZ6gvA19eXJUuW4O7ujr6+PpMnT8bOzi5dI6uCoAoiUAq5zvv379OC5OfPnxkxYgRTp06lbNmymW47ISGB0hbVaTh4KnGFjYiMjlf5heWfJEni+vXruLi4cOTIEfLly8fgwYNxdHTE0tJSqX1puqBXsez1juRywGsiY/7776CbHIviuR8ea6dhXkq5/w6C5nnz5g3Lly9n3bp15MmThwkTJjBu3DgKFy6stD569erFq1evOPPnpf/cwKjqoAJ/f3+WLl3Kvn37KFasGOPHj8fe3l6p70sQ0kMESiHXeP/+PWvXrmX16tUkJiYyYsQIpkyZopQg+cWpU6fo1KkTjx49onLlyl8dGcuqE3CioqLYuHEjmzdv5vXr11hbWzN69GjatWuXo+dd/XOk+MtI8LfIkJCQKW2kWNA80dHRrFy5EmdnZ2QyGePGjWPChAkULVpUqf1IkkT58uUZNGgQS5YsUWrb6REaGsqyZcvYvn07+fLlw9HRkXHjxlG8ePEsr0XInUSgFHK89+/fs2bNGtasWUNiYiIjR45kypQplClTRul92dnZ4enpSUBAADKZqk/8Tp/ExEQOHjyIi4sLvr6+mJiY4ODggK2tLUWKFFF3eUqV2bms8ztXps9PzGUVNM+7d+9YvXo1a9asQaFQMHr0aCZNmoSenp5K+gsPD8fY2JgTJ07QqVMnlfSRHn/99RcrV65k48aNAAwfPpxJkyaJY0UFlROBUsix3r17lxYkk5OT04Jk6dKlVdJfSkoKZcqUwc7OjqVLl6qkj8zy9vbGxcWFgwcPkidPHgYOHIijoyNVqlRRd2mZpqzV9pOsLXBsYa6EigR1+PDhA2vXrmXVqlUkJSXh4ODA5MmTKVlStedk7927lwEDBvD27VuVhdaMiI6OxtnZGWdnZ+Li4rCxsWHKlCliJwhBZUSgFHKcmJgY1qxZw9q1a0lOTmbUqFFMnjxZZUHyi6tXr9K8eXNu3bpFvXr1VNpXZr18+ZLNmzezceNGXrx4QfPmzRkzZgydOnVCWztrHskr037fSKYd9VNae07drX5q1b2gPrGxsbi4uLBixQo+f/7MqFGjVHoD+b/s7e25fPkyT548yZL+0uvjx49s3LiRVatW8ebNG/r06cP06dNzxE2koFnEviJCjhETE8OsWbMwMjJixYoVDBs2jLCwMFauXJklFxV3d3fKli1LnTp1VN5XZpUuXZo5c+YQHh7Ovn37SEpKonv37piamuLk5ER0dLS6S0y3ZzHxzD3hr9Q255zw51mMOD89O/j06RNOTk4YGxszf/58BgwYQEhICKtWrcqyMAlw48YNGjZsmGX9pdcvv/zClClTCAsLE8c6CiolAqWQ7UVHRzNz5kyMjIxYvXo1I0aMICwsjBUrVlCqVKksqUGSJNzd3enatWu22v9RR0eHPn36cP36de7cuUPLli2ZO3cu5cuXx87Ojvv376u7xB+a4e5HSgbmS6ZHikJihrvyRjwF5YuPj2flypWYmJgwe/bstKNRnZ2dlbrQLj0+fvyIn58fjRo1ytJ+M+LLsY7BwcHiWEdBJbLPlU8Q/sfbt2+ZMWMGRkZGrFmzhpEjRxIWFsby5cuzLEh+ce/ePSIjI+nWrVuW9qtMNWvWZPv27Tx79ow5c+Zw/vx5atSoQZMmTTh48CDJycnqLvE/gl7F4hn8NkMLcNIjVSHhGfyW4NexSm1XyLyEhATWrl2LiYkJ06ZNo2vXrgQFBeHq6oq+vr5aarp16xYKhUKjA+UXefLkEcc6CiohAqWQ7bx9+5bp06djbGyMs7MzDg4OhIeHs2zZMpVPvP8Wd3d3ihYtSrNmzdTSvzKVKFGC6dOnExYWxuHDh5HL5fTu3RtjY2MWLVrE69ev1V1imr3ekWjJVbOaXksuY8+tSJW0LWRcYmIi69evx9TUlIkTJ9K+fXsCAgLYvHkzhoaGaq3txo0b6OnpZasN8rW0tOjVqxd3797l9OnTyOVyOnXqRPXq1Tlw4ACpqanqLlHIZkSgFLKNN2/eMG3aNIyMjHBxccHR0ZHw8HCWLl1KiRIl1Fqbu7s7HTt2JE+ePGqtQ5m0tbXp0aMHV69e5cGDB7Rr147Fixejr6/PoEGD8PX1VXeJXA54rfTRyS9SFRKXAzUnPOdWSUlJbNq0CTMzM8aMGUOrVq148uQJ27Ztw8TERN3lAXD9+nUaNmyoMVuFZYRMJqNdu3Z4enpy9epVSpcuTZ8+fahYsSLbtm0jKSlJ3SUK2YQIlILGe/PmDVOnTsXY2Jj169czZswYwsPDWbJkiUZs2hsUFIS/v3+2ftz9I1WrVmXLli08f/6cRYsW4enpSd26dalfvz579+5Vy0XnU2IKkSpeOBMZHU9cYopK+xC+Ljk5GTc3NywsLBg1ahRNmjTB39+fXbt2YW6uOds6paSkcOvWrWzxuPt7ZDIZTZs25dy5c/j6+mJlZYWdnR2mpqY4OzsTHy8WqQnfJwKloLFev37NlClTMDIywtXVlbFjxxIeHs7ixYs1Ikh+4e7uTr58+fj111/VXYrKFStWjMmTJxMcHMyxY8coWLAgAwYMwMDAgLlz5/LixYssqyUiOg5Vz/aSgPDoOBX3IvxTSkoKO3fuxNLSkqFDh1K3bl38/Pz4448/NPL4UD8/Pz59+pTtA+U/1a5dmyNHjuDv70+LFi2YMGECRkZGLFmyhA8fPqi7PEFDiUApaJzXr18zefJkjI2N2bhxI+PHjyc8PJzff/9dIzYM/l/u7u78+uuv5M+fe47t09LSokuXLly4cAF/f3+6d+/OypUrMTAwoG/fvty4cUPlk/uTUhQqbT+r+8ntUlNT2bt3L5UqVcLGxoZq1arx4MEDDh48SOXKldVd3jfduHGDPHnyUKtWLXWXonSVKlVi165dBAYG0qNHD+bNm4ehoSGzZs3i7du36i5P0DAiUAoa49WrV0yaNAkjIyM2bdrEhAkTCA8PZ9GiRRoZJOHvY85u3bqVox93/0ilSpVwdXXl+fPnLF++HF9fXxo1akTt2rXZsWMHCQkJKulXRztrvr6yqp/cSqFQcODAAapUqcKAAQOwtLTkzp07HD16lKpVq6q7vB+6fv06tWrVIl++fOouRWVMTEzYsGEDYWFhDB06lDVr1mBoaMj48eOJiopSd3mChhDflILavXz5kokTJ2JsbMyWLVuYNGkS4eHhLFy4kGLFiqm7vO86fvw4WlpadOzYUd2lqF2RIkUYN24cgYGBeHh4ULJkSYYMGYK+vj4zZ87k+fPnSu3PSK8Aql4CIfu/fgTlUygUHD58mKpVq9KnTx9MTEzw8fHhxIkT1KxZU93lpdv169dz1OPu7ylbtiwrVqwgIiKCSZMmsWPHDoyNjRk+fDjBwcHqLk9QMxEoBbV5+fIlEyZMwNjYmK1btzJ58mTCw8NZsGCBxgfJL9zd3WnevHm2qTcryOVy2rdvz5kzZwgICKBfv364uLhgZGREr169uHbtmlIehxfQ1cagmGqnGZQupE0B3ex3FKUmkySJY8eOUaNGDXr16kW5cuW4efMmHh4e2eKUqX96/vw5kZGRuSZQfqGnp8f8+fOJiIhg0aJFnDhxggoVKtC/f38ePXqk7vIENRGBUshyL168YPz48RgbG7Nt2zamTp1KeHg48+fPp2jRououL93evXvH5cuXc/Xj7h+xsLBg7dq1REVFsXbtWh49ekSzZs2oXr06W7duzdTK0WfPnqH1+imSQjX75UmKVAIuH6Vly5YcPXqUlBSx2jszJEni1KlT1K5dm27dulG8eHE8PT05d+4c9evXV3d5P+XGjRsAGnnkYlYQxzoK/yQCpZBl/vrrL8aOHYuJiQnbt29n2rRphIeHM2/evGwVJL/w8PAgJSWFrl27qrsUjVeoUCEcHBx4/Pgx58+fx9DQkOHDh1O+fHmmTJlCeHh4utsKDAxk6NChmJqa8vDIemRyLZXULJNrMa9/K5KSkujRowfGxsYsXrxYozZ2zw4kSeLs2bPUq1ePTp06UaBAAS5fvszFixdp3LixusvLlOvXr2NqaprlJ3Npmn8e67hjxw6ePn0qjnXMhUSgFFQuKiqKMWPGYGJiwq5du5g+fTrh4eHMnTuXIkWKqLu8n+bu7k7dunUpV66cukvJNmQyGW3atOHEiRMEBwdja2vLli1bMDExoWvXrly8ePGbF5/79+/Tu3dvLC0tOX36NIsXLybswU2amBVX+mk5WnIZTcyKM8bmN7y8vLh79y6//vorCxcuRF9fn4EDB+Lt7S0ulN8hSRIXLlygUaNGtGvXDm1tbS5cuMDVq1dp3ry5ustTitw0fzI98uTJw+DBg/H39+fQoUNpxzo2bNhQHOuYC4hAKahMVFQUo0ePxtTUlN27dzNz5kzCw8OZM2dOtg6SAJ8/f+bs2bPicXcmmJiYsGLFCp4/f87GjRsJCQmhdevWVKlShQ0bNvDp0ycAPD09ad++PTVq1OD27dts2LCB0NBQJk2aRKFChVjczQptJQdKbbmMxd2s0v57jRo12Lp1K1FRUfz+++9cv36d+vXrU7duXXbu3KmylezZ1ZUrV2jWrBlt2rQhNTWVs2fPcv36dVq1apUtT5P5mri4OO7fv59rH3d/j5aWFj179kw71lFLS0sc65gLiEApKN3z589xdHTExMSEvXv3MmvWLMLDw5k9ezaFCxdWd3lKcf78eeLj40WgVIICBQowfPhwHj58yOXLl7G0tMTR0ZHSpUtTrlw5mjZtyrNnz9i7dy8BAQGMGDGCvHnzpv2+frH8zO+s3H0KF3SujP5XFvwUK1aMSZMmERQUxKlTpyhevDg2NjaUL1+e6dOnExERodQ6shtPT09atmxJixYtiIuL49SpU9y6dYtff/01xwTJL3x8fEhNTRUjlN/x5VhHLy8vrl69SpkyZcSxjjmYCJSC0jx79gwHBwdMTU3Zt28fc+bMITw8nFmzZuWYIPmFu7s7FStWpEKFCuouJceQyWQ0adIk7bF2XFwcr1+/RiaToa+vT7FixZDLv/6V1aeOAZOsLZRSx2TrCvSuY/Dd12hpadGhQ4e0lewDBw7E1dUVExMTunXrxoULF3LV472bN29ibW1N06ZNiYmJ4dixY9y+fZsOHTrkuCD5xfXr1ylSpAiVKlVSdynZQtOmTTl79qw41jEHE4FSyLRnz55hb2+PmZkZ+/fvZ+7cuYSFhTFz5kx++eUXdZendMnJyZw4cYLu3buru5QcIykpCTc3NypWrEjv3r0pV64cly9f5sOHD2zdupUXL17Qrl07LC0tcXZ25uPHj/9pw7GFOUu7W6GrLc/wnEotuQxdbTlO3a1waGGWod+1sLBg9erVREVF4erqSnBwMG3atKFSpUqsW7fuq7XmFL6+vrRv356GDRvy119/cfjwYe7evUuXLl1ybJD84saNGzRo0OCbNznC14ljHXMu8UkQflpkZCSjRo3C1NSUgwcPMn/+fMLDw5kxY0aODJJfXLt2jXfv3onH3UoQFxfHmjVrMDU1ZejQoVhZWeHr68v58+dp3rw5+fPnx9bWlrt37+Lp6UmNGjWYMGEC5cqVw9HRkadPn/6rvT51DLgwvhkNTf4+WelHwfLLzxua6HFhfLMfjkx+T8GCBRkxYgQPHz7kypUrVKlShXHjxqXV+uTJk59uW9Pcu3ePzp07U7duXcLDwzlw4AAPHz6kR48euSJgKRQKbt68KeZPZsKXYx2DgoLEsY45hSQIGRQeHi6NGDFCypMnj6SnpyctXbpU+vjxo7rLyjIODg6SgYGBpFAo1F1KthUTEyMtWLBA0tPTk7S0tKTBgwdLjx8/TtfvPn/+XJo1a5ZUsmRJCZDatGkjnThxQkpJSfnX6wJffpRmH3solRu5RTKcdlIynHYq7T9G005JTZdfkuYefyQFvVLd3+6zZ8/+VWurVq0kd3d3KTk5WWV9qtKDBw+kbt26SYBkbm4u7d279z//v+cGfn5+EiBdunRJ3aXkGFFRUdLEiROlAgUKSPnz55fGjRsnPXv2TN1lCRkgAqWQbuHh4dLw4cOlPHnySMWLF5ecnJyk2NhYdZeVpVJTU6Vy5cpJY8aMUXcp2dKLFy+kKVOmSAULFpTy5s0rOTo6SuHh4T/VVkJCgrRr1y6pTp06EiCZmJhIK1askGJiYtJe8/DhQwmQzl64LD2Kei/djYiRHkW9lz4lZG2gS0hIkPbs2SM1aNBAAiQDAwNpyZIl0uvXr7O0jp/16NEjqWfPnmn/P+/cuTPbhmJl2Lhxo6SlpSV9+vRJ3aXkOG/fvpXmzJkjFSlSRMqTJ480bNgwKSgoSN1lCekgAqXwQ2FhYdKwYcMkbW1tqXjx4tKyZctyXZD8wtvbWwKky5cvq7uUbCU0NFQaNWqUpKurK/3yyy/StGnTpJcvXyqt/Vu3bkn9+/eX8uTJI+XPn18aPny45OfnJ23dulWSy+Ua9fd6+/ZtaciQIZKurq6kq6srDR48WPL19VV3WV/15MkTqU+fPpJMJpOMjIwkNzc3KSkpSd1lqd2gQYOkWrVqqbuMHO3jx4/SsmXLpFKlSklyuVzq16+f5Ofnp+6yhO8QgVL4ptDQUGno0KGStra2VKJECWn58uW5/o582rRpkp6eXq4encmIR48eSQMGDJC0tLSkEiVKSL///rv07t07lfX34sULaf78+VKZMmUkQCpTpoxkYGCgkf9eb968kZycnCRDQ0MJkOrWrSvt2rVLSkhIUHdpUmBgoDRgwABJLpdL+vr60qZNm6TExER1l6UxTE1NxVOKLBIfHy+tX79eMjAwkACpS5cukre3t7rLEr5CBErhP0JCQiQ7OztJW1tbKlmypLRixYpcHyS/qFChgjRkyBB1l6Hxbt26JXXp0kUCJH19fcnZ2VmKi4vLsv4TExOlffv2SQUKFEh7xLx06VLp7du3WVZDeqWkpEjHjx+X2rRpIwFSiRIlpBkzZkiRkZFZXktISIhkY2MjaWlpSWXLlpXWr1+vEQFXk7x8+VICpAMHDqi7lFwlKSlJ2rFjh1ShQgUJkFq3bi1dunRJzGXXICJQCmlCQkIkW1tbSUtLSypZsqS0cuXKLA0Bmu7x48cSIJ04cULdpWgkhUIhXbhwQWrZsqUESBUqVJC2b9+utpGt+Ph4SUtLS5oxY4ZkY2Mj6erqSnnz5pVsbW2le/fuqaWmH3n69Kk0ZswYqVChQpJcLpe6d++eJRfN8PDwtKcRpUuXltauXSt9/vxZpX1mV0ePHpUAsWBETVJSUqRDhw5JNWrUkACpfv360smTJ0Ww1AAiUApSUFBQ2qhEqVKlpFWrVokg+RW///67VKBAAXGh/R+pqamSu7t72uKYmjVrSocPH1b76t/r169LgHT37l1JkiTp9evX0uLFi6Xy5ctLgNS4cWPpwIEDGjkn8OPHj5Krq6tUqVIlCZAqVaokubq6Kn03hcjISGnkyJFSnjx5pBIlSoibyHSYOHGiZGBgoO4ycj2FQiGdPn1aatSokQRIVatWlfbv36/2753cTATKXCwoKEgaPHiwpKWlJZUuXVpavXq1uJh8R+3ataWePXuquwyNkZSUJO3atSst9DRr1kw6d+6cxowUrF69WsqbN+9/AmNycrJ0+PBhqVmzZhIglStXTlq4cKH06tUrNVX6bQqFQrp06ZLUvXt3SS6XS4UKFZJGjx4tPX36NFPtRkVFSY6OjpKOjo6kp6cnOTk5iWkt6VS/fn2pb9++6i5D+IerV69Kv/76a9p2Vlu3bhVzftVABMpcKDAwUBo0aJCkpaUllSlTRlqzZo0UHx+v7rI0WmRkpARIe/fuVXcpavdlkryRkZEESB07dpSuX7+u7rL+o2/fvlKDBg2++5oHDx5Iw4YNk/Llyyfp6OhIAwcOlHx8fLKowoyJiIiQZsyYIRUvXjxt/83jx49naETmxYsX0rhx4yRdXV2paNGi0uLFi3PVHrKZFR8fL+XJk0dat26duksRvuL27dtS9+7dJUAqX768tHbtWjFIkoVEoMxFAgICpIEDB0pyuVwqU6aMtHbtWhEk08nZ2VnKkyeP9P79e3WXojYfPnyQnJyc0rbx6Nu3r/TgwQN1l/VNpqam0tixY9P12ujoaGn58uVpIblevXrSnj17smyU41NCcrr3yfz8+bO0a9cuqV69ehIgGRoaSk5OTt9dcPTq1Stp4sSJUr58+aTChQtLCxYsyNV/yz/L09PzX9MoBM3k7+8vDRw4MG13icWLF6vk7z0jn9vcQCZJkqTas3gEdQsICGDRokX88ccflC5dmmnTpjFs2DDy5s2r7tKyjZYtW6Kjo8PZs2fVXUqWe/PmDc7Ozqxbt474+HgGDx7MlClTMDPL2JnXWSkmJgY9PT327t1Lv3790v17qampeHh44OLiwoULFyhVqhQjRoxgxIgRlC1bVqk1Br2KZa93JJcDXhMZE88/v4hlgEGx/LSoUJL+9QwwL1Xoq234+vqyfv169u/fj0wmo2/fvjg6OlKzZk0A3r59y4oVK3BxcUFLS4vx48czfvx4ihQpotT3kls4OTmxaNEi3r17h7a2trrLEX4gLCyMZcuWsW3bNvLly4ejoyNjx46lRIkSP92mMj63OZUIlGoUl5hCeHQcSSkKdLTlGOkVoICu8r6knj59yqJFi9i3bx9lypRh2rRpDB06VATJDIqOjqZUqVKsX7+eESNGqLucLPPs2TNWrlzJ5s2bkcvljBgxIu0cbU137tw52rZtS1BQ0E8H3ydPnrBu3Tp27txJYmIiPXv2ZPTo0TRo0ACZ7PtnhH/Ps5h4Zrj74Rn8Fi25jFTFt7+Cv/y8iVlxFnezQr9Y/q++7s2bN7i5ueHq6sqzZ8+oXbs25cqV4+LFi0iSxNixY5k4cSLFihX76boF6Ny5M58/f+bPP/9UdylCBrx48YJVq1axYcMGJEli+PDhTJw4kfLly6e7DVV8bnMaESizWFbc3Tx9+pSFCxeyb98+ypYty/Tp07GzsxNB8ift2LEDW1tb/vrrL0qXLq3uclQuMDAQJycndu/eTcGCBRkzZgyjR49GT09P3aWl28KFC1m9ejXR0dGZCn8AHz58YPv27axfv57g4GBq1qzJ6NGj6dOnT4Y/U/t9I5l7wp8UhfTdC9L/0pLL0JbLmN+5Mn3qGHzzdW/fvsXR0ZHDhw+TmppK/vz5024EMnLxFP5LkiRKlCiBo6Mj8+bNU3c5wk+Ijo7GxcUFZ2dnPn36hI2NTbqetqj6c5tTiECZRbLi7ubJkycsXLiQ/fv3U65cubQgqaurq6y3kSt16dKFt2/fcv36dXWXolL37t1jyZIlHD58mNKlSzNx4kSGDx9OoULZ77FNp06dSEpK4ty5c0prU6FQcPbsWVxcXDh79izFixdn2LBhjBo1Cn19/R/+/rrLQaw4H5jpOiZZW+DYwvxf/9vHjx9xdnZm5cqVJCQkYG9vT9euXTlw4AA7d+7k8+fPdOvWDUdHR5o2bZrpkJ0bBQQEYGlpyblz57C2tlZ3OUImxMbGsnHjRlauXMmbN2/o06cP06dPp0qVKv95rSo/tzmNXN0F5Ab7fSNpvfoqN0KjAX54h/Pl5zdCo2m9+ir7fSO/+/rHjx/Tt29fKleujJeXF66urgQHB2Nvby/CZCbFxcVx/vx5unXrpu5SVMbT05N27dpRs2ZN7ty5w8aNGwkNDWXixInZMkxKkoSPjw9169ZVartyuZz27dtz5swZAgIC6NevH+vWrcPY2JiePXty9epVvnV/vt83UikXJYAV5wM58H/fCZ8+fWLp0qUYGxuzaNEiBg0aRGhoKCtXrqRJkyasW7eOqKgo1q5dy6NHj2jevDlVq1Zl06ZNfPr0SSn15BbXr19HLpdTv359dZciZFKhQoWYPHkyYWFhuLi4cP36daysrOjatSs+Pj5pr1PV5zanEiOUKqbKuxt/f38WLlzIwYMHKV++PDNnzsTGxkaESCU6cuQIPXv2JDg4GFNTU3WXozSSJHHmzBmWLFmCl5cXVapUYcaMGfTq1SvbLzaIiIjAyMiIEydO0KlTJ5X2FRsby65du1i3bh1Pnz6latWqODo60r9/f/Ln//vJwrOYeFqvvkpiikJp/epqy+ldMJANKxbx4cMHhg0bxvTp07/7WFuSJC5dusS6des4ceIEhQoVYsiQIdjb22NunrNHTpTBzs6OO3fucP/+fXWXIihZcnIyf/zxB0uWLCEgIIBWrVoxfPx0Zt9KUvrn9sL4Zjl2TqUYoVQhVd3dPHr0iN69e2NlZcXNmzfZuHEjwcHBjBgxQoRJJXN3d8fKyirHhMnU1FQOHDhAjRo16NChAykpKZw4cYIHDx7Qt2/fbB8mgbQRhjp16qi8r0KFCuHg4MDjx485f/48hoaGjBgxgvLlyzN58mTCw8OZ4e5HSgbmXaVHQlIym+/H0r17d4KDg1m/fv0P50jKZDJatWqFu7s7oaGhjBo1it27d2NhYUHbtm05deoUqampSq0zJ7lx4wYNGzZUdxmCCuTJk4fBgwfj7+/P4cOHiYmJYfTuGyQmJSu1nxSFxAx3P6W2qUnECKWKqGJUQkdLRsXQw5zYtx1DQ0NmzpzJ4MGD0dHRUVofwv+XlJREyZIlGTt2LPPnz1d3OZmSlJTE7t27cXJyIigoCGtra6ZPn06zZs1y3Hy6KVOmcODAASIiItTSf2hoKK6urri5uRGn/Qtlh7qqrK8L45tiVvLnpyUkJCRw4MAB1q1bx+3btzE2Nsbe3h5bW1uxIvwfoqOjKV68OHv27KF///7qLkdQscCXH7Fe66my9jP7udVUYoRSRVQxKpGYlMwDnUps2bKFwMBAhg0bJsKkCl25coUPHz7QvXt3dZfy0+Li4lizZg0mJiYMGzYMKysrfH19OXfuHM2bN89xYRJQyfzJjDAxMWHFihU8f/6czhOWgUI1o35achl7bmVuTlbevHkZPHgwvr6+eHt707hxY2bOnEm5cuUYOnQo9+7dU1K12duNGzcAaNSokZorEbLCHz7P0JKr5rtRGZ9bTSUCpQoEvYrFM/hthrYXSA+ZljaUqUjzzr1FkMwCR48exdjYmKpVq6q7lAx79+4dCxcuxNDQkMmTJ9O6dWv8/f05cuQItWvXVnd5KpOamsrt27fVGii/KFCgAG+0S4JcSyXtpyokLge+Vlp7devWZdeuXTx79ozZs2dz7tw5atasSePGjdm/fz9JSUlK6yu7uX79OmXLlsXQ0FDdpQhZ4HLAa6Vfv79Q9udWk4hAqQJ7vSPF3U02p1AoOH78ON26dctWo3gvXrxgypQpGBgYsHjxYvr27UtwcDA7duygYsWK6i5P5Z48eUJcXFyWzJ/8kU+JKUTGxKu0j8joeOISU5TaZsmSJZkxYwZhYWEcOXIEHR0d+vbti6GhIXPnzuWvv/5San/ZwZf5k9npu0D4Odn1c6sJRKBUAXF3k/3dunWLly9fZpvtgr4ssjA2NmbTpk2MHj2a8PBwXFxcctWoio+PDzKZjFq1aqm7FCKi41D1BHUJCI+OU0nb2tradO/enUuXLvHo0SO6devGypUrMTQ0pHfv3nh6en5zm6ScJCkpCV9fX/G4O5fI7p9bdRKBUsnE3U3O4O7uTsmSJWnQoIG6S/muR48eMWDAACwsLDhy5Ahz584lMjKSxYsXU6pUKXWXl+V8fHyoVKmSRuyfmaTEBXnq7qdy5cq4uroSFRXFypUruX//Pk2bNqV69eps2bKFuLicd3H84u7duyQkJIhAmUvkpM9tVhOBUsnE3U32J0kS7u7udOnSBS0t1cx/yyxvb2+6du2KlZUV165dY82aNYSHhzN9+nQKFy6s7vLUxtfXVyPmTwLoaGfN12tW9QNQuHBhxowZw5MnTzh37hxGRkZp2yRNnDiR4ODgLKslq9y4cYN8+fJRvXp1dZciZIGc+LnNKjnvHamZuLvJ/h49ekRISIjGPe6WJIkLFy7QqlUr6tevz9OnT9m+fTvBwcE4OjqmbaSdW33+/JmHDx9qTKA00iuAqmfcyf6vn6wml8uxtrbm+PHjhISEMHz4cHbs2IGFhQUdOnTgzJkzKBQ54zvq+vXr1K1blzx58qi7FCEL5OTPraqJQKlk4u4m+3N3d6dQoUK0bNlS3aUAfy8QOnbsGPXq1aNNmzZ8+PCBw4cP4+/vj42NjVjx/3/u379PSkqKxgTKArraGKj4RAwDvfwU0FXvZvTGxsY4OTnx/Plz3NzcePHiBe3bt8fCwoJVq1bx7t07tdaXGZIkcf36dfG4OxfJLZ9bVRCpRMnE3U325+7uTocOHdR+6lBycjK7d+/GysqKbt26kT9/fs6dO4evry89evTQ2Mfx6uLj44Ouri5WVlbqLiVNE7NiyFQ0CUZLLqOFRUmVtP0z8uXLx5AhQ7hz5w43btygXr16TJs2jXLlyjF8+HAePHig7hIzLDQ0lFevXolAmcu0qFBSpTu1aNLnVplEoFQycXeTvYWFhXH//n21Pu7+/Pkzrq6umJubM2jQIExMTLh+/TpXrlzB2tpabF3yDT4+PtSoUUMjHk3Gx8ezatUq3KbZIKnoFjNVIaEIvMKnT59U0v7PkslkNGjQgL179/Ls2TNmzJiBh4cH1atXp2nTphw8eJDkZOUeaacqXzY0r1+/vporEbJSp0pFVbpTy4D6BippW91EoFQBVd7dICmoUlQEClU5duwYurq6tGvXLsv7/vjxI05OThgbGzN69GgaNmzIgwcPOHnypDhDOB3UfUIOwKdPn1i+fDnGxsZMmTKF9o1rUqtcfqV/H8hlUCThJUumj8PAwIDZs2fz+rXmbSdWqlQpZs2aRXh4OIcOHUIul9O7d28MDQ1ZsGABL168UHeJ33X9+nUqVaokjqHMJRQKBdu3b6d9oxokRT5Q+tMFLbmMJmbFc+SxiyACpUr0r2egsrsbZHI2Tx1Ix44d8fb2Vk0fuZi7uzutW7fO0m1n3rx5w6xZszAwMGDOnDl06dKFgIAA/vjjj2x5So86xMTEEBwcrLZAGRsby9KlSzE2NmbGjBl06dKFoKAg3NzcWNOvHtpKDpR5tOScnN2XkJAQBg8ezOrVqzE0NGTUqFEaudI6T5489OzZkytXrvDw4UM6d+6Mk5MTBgYG9O3bl+vXr2vknpZi/mTucfv2bRo2bIitrS2tW7fm+Kw+6Ggrd1qRtlzG4m6aMyVH2USgVAHzUoVoYlZc6aMSWnIZjUz12LF2KaGhodSvX582bdpw7do1pfaTW71+/RovL68se9z97Nkzxo4di6GhIWvWrGHo0KGEhYWxadMmzMzMsqSGnOL27dsAWR4oP3z4wKJFizAyMmLu3Ln06tWL4OBgNm/ejLGxMQD6xfIzv3Nlpfa7oHNl9Ivlx8DAgNWrVxMZGcmsWbM4cuQIFSpU4Lfffkv7/0TTWFlZsXHjRqKioli+fDm3b9+mcePG1KxZEzc3N+LjVbuPb3q9f/8ef39/8XQgh3v79i3Dhw+nbt26fP78matXr7J3717qVDRR2ec2pxKBUkUWd7NS+qiEtlzG0u5V6d+/P48ePeLQoUO8efOGZs2a0bRpU/7880+NvMvPLk6cOIFMJqNz584q7ScwMBA7OztMTU3ZvXs3U6dOJSIighUrVlC2bFmV9p1T+fj4UKRIkSwL4u/evWP+/PkYGRmxaNEi+vXrR0hICK6url89mahPHQMmWVsope/J1hXoXeffc7CKFSvGzJkziYiIYP369dy9e5c6derQsmVLzp07p5HfC0WKFGHcuHEEBARw5swZypcvz7BhwyhfvjyTJ08mNDRUrfXdunULSZLECGUOlZKSwvr167GwsODQoUM4Oztz584dmjZtmvYaVX9ucxoRKFVElaMS8Pc+cD179uTevXucOHGChIQErK2tadCgAadOndLIC4imc3d3p3HjxpQoUUIl7d+7d4/ffvsNS0tLzpw5w5IlS4iIiGDu3Lno6emppM/cwsfHhzp16qh8wVJ0dDSzZ8/GyMiIpUuXYmNjQ2hoKC4uLpQvX/67v+vYwpyl3a3Q1ZZn+OmFllyGrrYcp+5WOLT4dmjOly8fI0eOJCAggEOHDhEbG0vbtm2pXr06e/fu1cjFMHK5nLZt23Ly5EmCg4Oxs7PDzc0NMzMzOnXqxLlz57JkT8u4xBT8//rAvch3+P/1gSteNylRooR4WpADeXl5Ubt2bUaPHk2PHj0IDAzE0dERbe3/LnbNis9tTiGTRPJQqXWXg1hxPjDT7Uy2rvDdP0hJkvjzzz9ZuHAhXl5eVK9enVmzZtGtWzfkcnHf8CMfP36kRIkSODk5MW7cOKW27enpyeLFizl79iwmJiZMnTqVwYMHq31bopxCkiTKlCnD0KFDWbRokUr6ePv2LatWrcLFxQWFQsGoUaOYNGkSpUuXznBbz2LimeHuh2fwWyRFKjL5t+dpacllpCokmpgVZ3E3qww/LpMkiStXrrBs2TLOnj2LgYEBEyZMYOjQoRQooLlbj8XHx7Nv3z5cXFx48OAB5ubmODg4MHjwYIoUKaK0foJexbLXO5LLAa+JjIn/9xIMSUIn+SP9mlWjfz0DzEvlzIUUuclff/3FlClT2Lt3L3Xr1mXdunXUqVMnXb/7z8/tl8/lt2T2c5tdiUCZBfb7RjL3hD8pCilDi3W05DK05TIWdK6c7qFySZK4du0aCxcu5OLFi1SqVImZM2fSu3dvsW/h/4lLTCE8Oo6kFAU62nKM9Apw6tgR+vTpQ1hYGEZGRpnuQ5Ikzpw5w+LFi7l+/TpWVlZMnz6dXr16ffUuWPh5kZGRGBoacvz4caVPV3j16hUrV67E1dUVAEdHRyZMmEDJkpnfR659XzvCtctTolpzIqP/HWZk/L09WAuLkgyob6CUVaEPHjxg+fLl7N+/n8KFC+Pg4ICjo6NS3ouqSJLEjRs3WLduHYcPH0ZHR4eBAwfi4OCQqf1GRTjIXZKSkli7di0LFiwgX758aU8XfmawJe0mJPA1EW/j4B9PRVTxuc1ORKDMIv/8ApPL4Hu5UllfYDdv3mTRokWcPn0ac3Nzpk+fzoABAzRin76s9r2RCBmgkxwLfz3i1OqpmRqJSE1N5fDhwyxZsoQHDx7QoEEDZsyYQYcOHcT+kSpy+PBhevXqxV9//UWZMmWU0uaLFy9Yvnw5GzduRFtbm9GjRzN+/HiKFy+ulPaTkpIoXrw4U6ZMYdasWV+9yVHVXrMRERGsXr2aLVu2oFAosLW1ZcKECZiamqqkP2V58eIFW7ZsYePGjbx48YJmzZrh6OhIly5dMvSdltkb/PmdK9Mnh8+Fy0nOnz/PmDFjCA4OxsHBgfnz5yttlLusgTE9Bo/EdthwlX9uswMRKLNY0KtYZmw/w42wD+gUK6vyUQmAu3fvsmjRItzd3TEyMmLatGnY2NjkikeuGRmJkCEhIfupIJ+YmMju3btxcnIiODgYa2trZsyYQdOmTUWQVLGpU6eyb98+IiMjM91WVFQUy5YtY/Pmzejq6jJ27FjGjh2r9H0IL126RKtWrbh79y41atRQatvpFR0djaurK87OzsTExNCzZ0+mTJlCrVq11FJPeiUnJ+Pu7s66devw9PSkXLlyjBw5kmHDhlGqVKnv/q6ypiBNsrbAsYV5ptsRVCc8PJwJEybg7u5Os2bNcHFxUeopWomJieTNmxc3NzdsbW2V1m52JgKlGtjY2ODn58e1G95ZNioB4Ofnx+LFizlw4ABly5ZlypQpDB06lPz5c+YjnKwYiYiLi2Pz5s2sXLmSv/76i+7duzN9+nSNvyjnJC1atEBPT4/Dhw//dBvPnj1j6dKlbN26lfz58zN+/HjGjBmj1Pl6/zRx4kT27dtHVFSU2m844uPj2blzJytWrCA0NJRWrVoxZcoU2rRpo/bafuTBgwesX7+ePXv2kJKSwm+//YaDgwP169f/T+37fSOZdtRPaX07dbfK8at2s6PPnz+zbNkyli5dSrFixVi5ciW9e/dW+t9ycHAw5ubmXLhwgVatWim17exKrNZQAy8vLxo3bkwBXW0qly1MDYOiVC5bWOVD5VZWVuzbt48nT57QunVrJkyYgLGxMcuWLSM2NlalfWe1dZeDmHbUj8QURYY3mU9VSCSmKJh21I91l4O++pqYmBgWLFiAoaFh2sX38ePHHD58WITJLJSamsrt27d/ev/J8PBwRo4ciampKfv372fu3LlEREQwZ84clYVJAA8PD9q3b68RgS1//vyMGjWKgIAADhw4wLt37/j111+pWbMm+/btIyUlRd0lflO1atXYvHkzUVFRLF26lJs3b9KwYUNq167N9u3b+fz5M/D3k4q5J/yV2vecE/48i9GMPTOFv+fbHj9+nEqVKvH777+nbUnVp08flXzOIiIiAL66TVhuJQJlFnvx4gUhISE0btxYbTVUqFCBHTt2EBgYSNeuXZk1a1bafnrv379XW13Kst83UimPtQBWnA/kgO//f5T64sULpkyZgqGhIUuWLKFfv34EBwezfft2LC0tldKnkH5Pnz7l06dP6V6p+UVoaChDhw7F3NycI0eOsHDhQsLDw5kxYwa//PKLiqr9W0hICAEBAXTo0EGl/WSUtrZ22oboFy5coGTJkvTr1w9zc3NcXFyIi4tTd4nfVLRoUSZMmEBQUBAeHh6UKlUKW1tbypcvz9SpUxn/hw8pSj69LEUhMcNdeSOews8LCAigXbt2dO3aFUtLSx49esSSJUsoWLCgyvr8Eij19fVV1kd2IwJlFvPy8gJQa6D8wsTEhE2bNhESEkL//v35/fffMTQ0ZPbs2URHR6u7vJ+iqpGI6/efMmrUKIyNjdm0aROjR48mIiICZ2dncYeqRj4+PshksnSPCgcFBTFkyBAsLCw4efIkS5cuJTw8nKlTp2bZcZunT58mT548tG7dOkv6yyiZTEarVq04d+4c9+7do0GDBowfPx5DQ0PmzZvH27dv1V3iN8nlctq3b8/p06cJCgpi8ODBbD10ittRcUo/DjdVIeEZ/Jbg1znr6U52Ehsby9SpU7GysiIwMJDjx49z+vRpLCyUsxn590RERFCmTJlcsRYhvUSgzGJeXl6YmpoqbTWqMujr6+Ps7ExYWBjDhw9n1apVaY9yX716pe7yMmSGu5/SRyISk1PosfQwR44cYe7cuURGRrJ48WKN3m4lt/Dx8aFixYo/HFUMCAhg0KBBWFpacvbsWVasWEFYWBgTJ07M8v0YPTw8aNasWZaeF/+zqlevzh9//EFQUBD9+vVj2bJlGBgY4OjoSFhYmLrL+y4zMzNWrVrFyBV7kaGapQJachl7bmV+MZiQMZIk8ccff2BpaYmzszOzZ8/G39+fzp07Z9k0koiICDGY8D9EoMxiX+ZPaqLSpUuzfPlyIiIiGDt2LBs3bsTIyIixY8fy/PlzdZf3Q0GvYvEMfqv0kQhkcvIa1+Cirz/Tp0+ncOHCym1f+Gm+vr7fnT/5+PFj+vXrR8WKFbl06RJr164lNDSUcePGqWUxWlxcHFeuXKF9+/ZZ3ndmGBsb4+zsTGRkJNOmTePAgQOYmZnRt29f7t27p+7yvssr5B0SqgkZqQqJy4GvVdK28HUPHz6kWbNm9O/fnwYNGvD06VNmz55Nvnz5srQOESj/SwTKLBQbG8v9+/c1NlB+Ubx4cX7//XciIiKYPn06u3fvxtTUlJEjR2r0qMRe78gMH42VXlpyGUceiAuHJklISODBgwdfDZR+fn789ttvVKlSBS8vL9avX09ISAiOjo5ZfuH5p4sXL5KYmKhx8yfTq3jx4syZMydtuoe3tzc1a9bE2tqaCxcuaNyRr58SU4hU8cKZyOh44hI1d+FSTvHu3TtGjx5NjRo1ePPmDefPn+fw4cNqC3UiUP6XCJRZ6ObNmygUCo0PlF8ULVo07eKxYMECjh49irm5OUOGDCEo6Ourn9XpcsBr5Y9O/h8xEqF57t+/T0pKyr8C5f379+nRowdVq1bF19eXTZs2ERwczKhRozRirpOHhwdmZmZZMsdLlfLnz4+DgwOBgYHs37+ft2/f0qZNG2rVqsX+/fs1ZmV4RHScih52/38SEB6tuQuWsjuFQsHWrVuxsLBg586dLFu2jAcPHtCmTRu11ZSamsqzZ89EoPwfIlBmIS8vL4oXL06FChXUXUqGFCpUiKlTpxIeHs6KFSs4d+4clpaW9OvXD39/5S6A+VliJCL38fHxQUdHBysrK+7cuUOXLl2oUaMG9+/fx83NjcDAQIYNG4aOjo66SwX+nvd1+vTpbDs6+TXa2tr07t2bO3fu8Oeff1K8eHH69u2LhYUF69evJz5evdvqJKUoclQ/uY2Pjw/169dn2LBhtGvXjoCAACZOnKj2z/SLFy9ISUkRgfJ/iECZhb7Mn9SEved+Rv78+Rk3bhyhoaGsW7eO69evU6VKFXr06KH2eVRiJCL38fHxwdzcnO7du1O7dm2ePHnCzp07CQgIwNbWVuOOGPXz8+P58+c5KlB+IZPJaN26NefPn+fOnTvUq1ePMWPGYGBgwPz589W2MlxHO2sucVnVT27x+vVr7OzsqFevHsnJyXh5ebFr1y6NWcz65VQuAwOxsf0/iU9BFklOTubWrVvZ5nH39+TNm5dRo0YRFBSEm5sbDx48oGbNmnTq1Alvb2+11CRGInKXmzdv4u7ujr+/PyEhIezZs4fHjx8zaNAgtLU18yxdDw8PChQoQNOmTdVdikp92RA9ODiYPn364OTkhKGhIWPGjCE8PDxLazHSK6Ci5Tj/n+z/+hEyLyUlBWdnZywsLHB3d8fV1ZXbt2/TqFEjdZf2L2JT868TgTKL3Lt3j8+fP+eIQPmFjo4Otra2PH36lD179hASEkL9+vWxtrbm2rVrWVuLGInIFTw9PWnTpg0NGzYkPj4eBwcHHj16RP/+/TU2SH7h4eFB69atNWIuZ1YwNjZm3bp1REREMHnyZPbu3YuZmRn9+vXj/v37WVJDAV1tDIqpdjW/gV5+lZ9ylhtcuXKFGjVqMG7cOPr06UNgYCCjRo1CS0tL3aX9R0REBEWKFFH5IQjZjbg6ZhFPT0/y5ctHjRo11F2K0mlra9O/f38ePXrEoUOHeP36Nc2aNaNp06b8+eefWbLyU4xE5FySJHHlyhVatGhB06ZNef36NbNnzwZgzJgxGnnB+V/R0dHcvHkzRz7u/pESJUowb948IiMjWb16NTdu3KBGjRr8+uuvXLx4UeXfDy0qlERLRV8OWjJoYSH2o82M58+f07dvX1q0aEGhQoXw9fVl48aNFC9eXN2lfZNY4f11IlBmES8vL+rVq6f2ycSqJJfL6dmzJ/fu3ePEiRMkJCRgbW1NgwYNOHXqlEovHGIkIueRJIkLFy7QrFkzWrRowYcPH3B3d+fevXvo6upSpEgRzMzM1F1mupw/fx6FQpHt9p9UpgIFCjB69GiCg4P5448/eP36Na1bt6ZOnTocPHhQJSvDExMTkYKukaqir55UCaKu7ufly5eq6SAHS0xMZOnSpVhaWnL58mV27tyJl5dXuk+9UicRKL9OBMosIEkSXl5eNGnSRN2lZAmZTJY2n/LcuXPkyZOHTp06UbNmTY4cOYJCoZp5iC0qlFTZPpQoUin6+S8+ffqkmvaFNJIkce7cORo3bkybNm34/PkzJ06c4M6dO3Tt2hW5XI6Pjw+1a9dGLs8eX2EeHh5Ur16dcuXKqbsUtdPW1qZv377cvXuX8+fPU6RIEXr37k2FChVwdXXl8+fPme4jJSWFbdu2YWFhwaIpjhRLfKX0UUotGZThPQe3OmNsbMz48eN58eKFcjvJoc6cOYOVlRWzZs1i+PDhaSdZZZfPswiUX5c9/vWyucDAQN6+fZuj5k+mh0wmS5tPefnyZfT09OjZsydVq1Zl3759pKamKrU/o9TnKtuHErkWZ11moq+vz4wZM8SFQwUkScLDw4P69evTtm1bUlNTOX36ND4+PnTq1CltdwRJkvD29v7uCTmaJDU1lbNnz+bq0cmvkclktGnThgsXLnD79m3q1KnD6NGjMTQ0ZOHChURHR2e4TYVCwYEDB6hcuTJ2dnbUr18ff39/js/qg7aWci932lpyDk7ulnYW/Pbt2zE2NmbMmDFERUUpta+cIjQ0lC5dutC+fXv09fV58OABq1atylanj0mSJALlN4hAmQW8vLyQy+XUr19f3aWohUwmo3nz5ly4cIEbN25gaGiYdhzejh07SE5OzlT7Dx8+5Ndff2VI97bovgtD2YOUWnIZTcyKE3j7Gra2tqxbtw5DQ0OGDBnCo0ePlNtZLiRJEsePH6d27dp07NgRbW1tzp07x82bN2nXrt1/ttl6/vw5r169yjaB0tvbm+jo6Fw5fzK9vmyIHhgYSK9evVi8eDEGBgaMGzcubUXt93y5GalZsyZ9+vTB3Nycu3fvcuDAASwtLdEvlp/5nSsrteYFnSujXyw/RYsWZd68eURERDBz5kz27NmDiYkJDg4OPHv2TKl9Zlfx8fHMmTOHSpUqce/ePQ4dOsSFCxeoXFm5/yZZISYmhri4OBEov0IEyizg6elJtWrVxIowoEGDBnh4eHD79m2qVKnCkCFDsLCwYNOmTSQmJmaorb/++gs7OzuqV69OWFgYR48e5c/fh5BH2SMRchmLu1lhYGDAypUrefbsGYsXL+bPP//EysqKdu3aaeSxc5pOoVBw9OhRatSoQdeuXSlYsCAXL17Ey8sLa2vrb+7X6uPjA5BtAqWHhwd6enrUq1dP3aVoPFNTU9avX09kZCSTJk1KO/Z1wIABPHjw4Ku/c+XKFRo1akTHjh0pXLgwXl5enDp16j8LIPvUMWCStXJOKJpsXYHedf69B2HhwoWZPXs24eHhzJ07l/3796cdWZueUJwTSZLEkSNHqFixIk5OTkyaNIknT57Qs2fPbLsfs9gy6NtEoMwCuWn+ZHrVqlWLo0eP8vDhQ+rXr8+oUaMwNTXF2dn5h6drxMbGMmfOHMzNzTl+/DjOzs74+/vTrVs3DPQKqGwk4ovChQszadIkQkND2b17Ny9fvqRNmzbUqFGDPXv2ZHrENadTKBQcOnSI6tWr06NHD/T09Lhy5QpXr16lZcuWP7zQ+Pr6Ur58eY3Z5PhHTp8+Tdu2bbPFanRNUaJECebPn09ERASrVq3C09OT6tWr07ZtWy5fvowkSfj6+mJtbU2LFi1ITk7m3LlzaeHyWxxbmLO0uxW62vIMz7fWksvQ1Zbj1N0KhxbfXgz2yy+/MGPGDMLDw1m4cCFHjhzB3Nyc4cOHExYWlqE+s7MnT55gbW2dNs3J39+fRYsWUaBA9t4pQwTKbxOBUsVevHhBSEhIrps/mV5WVlbs27ePJ0+e0Lp1ayZMmICxsTHLly//zwKYlJQUNm/ejLm5OcuWLWP06NGEhITg6Oj4r1NRVD0S8YWOjg4DBgzg7t27XLhwgTJlyjBw4MC0+j98+KCUGnKK1NRU9u3bh5WVFb/99htlypTB09OTixcv0qxZs3S34+Pjk21GJ6Oiorh//7543P2TChYsyJgxYwgODmbPnj28ePGCli1bUqxYMerWrcvz5885evQoPj4+3x3V/qc+dQy4ML4ZDU30AH4YLL/8vKGJHhfGN/vm98H/+nJkbVhYGIsXL+b48eNYWFhgZ2dHSEhIutrIjj5+/MikSZOoWrUqYWFhnDp1ipMnT2abHRl+JCIignz58lGiRAl1l6JxRKBUsevXrwNo3E7/mqZChQrs2LGDwMBAunTpwsyZMzE0NGTRokW8f/+e06dPU61aNUaMGEGbNm0ICAhg6dKl35zMnRUjEV/IZDJatWrFmTNn8PPzw9rampkz/17AM2HChFz7uOuLlJQU9uzZQ+XKlenXrx+GhobcvHkzbSV3RqSmpnL79u1sEyhPnz6NXC7n119/VXcp2VqePHmoX78+VlZWACQkJACQlJTEq1ev0v57eukXy89uu3r8Oa4p/evqk/r+BfzP4a0ywFAvPwPrGXJhfFN229X715OK9CpYsCCTJk0iLCyMZcuWcfr0aSpUqICNjQ1BQUEZbk9TSZLE7t27qVChAhs2bGD+/Pk8evQox91MRUZGYmBgkG0f2auUJKjUmDFjJBMTE3WXke1ERkZKjo6Oko6OjqStrS0BUqNGjaTbt29nrJ3oOGnA1luS4bRTkskMD8lw2qlv/ufLzwdsvSVFRsdlqv6//vpLmjFjhlS0aFFJS0tL6tu3b4Zrz+6SkpKk7du3S2ZmZhIgderUSfLx8clUm/7+/hIgXbx4UUlVqlaXLl2kRo0aqbuMbO358+fSyJEjJW1tbalMmTLShg0bpMTERMnHx0fq1auXJJfLpRIlSkgLFy6UoqOjM9z+l7+p0+cvSo+i3kt3I2KkR1HvpU8JySp4N5IUHx8vrV27VipTpowkl8ulAQMGSE+ePFFJX1nl7t27UqNGjSRA+u2336TIyEh1l6Qy3bt3l9q0aaPuMjSSCJQqVrNmTWnQoEHqLiPbiYyMlAYNGiQBUrFixSRdXV2pQIEC0uTJk6WXL19muL3Alx+lWe4PpXIjtkiG007+K0gaTTslNV1+SZp7/JEU9OqjUt9HbGys5OzsLBkbG0uA1Lx5c+nUqVNSamqqUvvRJImJidLWrVslExMTCZC6du0q3blz56fb+5SQnHahX7xhlyTXySe9f/9eiRWrRkJCglSgQAFp8eLF6i4lW3rz5o00ceJEKW/evFKxYsWk5cuXS3Fx/73RCwoKkkaOHJn2HTFu3DgpIiIi3f3s2LFDAqQPHz4os/wf+vz5s+Ti4iKVK1dOkslkUt++fSV/f/8srSGz3r59K40aNUqSy+VS5cqVpUuXLqm7JJWrVauWNHToUHWXoZFEoFShjx8/SnK5XNqyZYu6S8k2Pnz4IE2fPl3KmzevVKJECWnDhg1ScnKy9ObNG2nGjBlSoUKFpLx580pjxoyRnj9/nqG279y5IwHSpWvXs2Qk4p9SUlKkQ4cOSXXr1pUAqWLFitKWLVukz58/q7zvrJKQkCBt3LhRMjQ0lACpZ8+e0v3793+qrcCXH6W5xx9JTZddkoz+dzR56kmp6bK/bwACXyr3BkCZzp8/LwHSgwcP1F1KtvLhwwdp7ty5UqFChaRChQpJc+fOTVfYe/nypTRr1iypaNGikra2tjRw4EDp4cOHP/w9BwcHqUKFCsoo/ackJCRIrq6ukr6+viSTyaTffvtN8vPzU1s96ZGSkiJt3LhRKlasmPTLL79Iq1evlpKSktRdVpYoXry4tHDhQnWXoZFEoFShLxeU7P44IyskJSVJ69evl0qUKCHly5dPmjlz5lcvIjExMdL8+fOlokWLSjo6OtKIESOksLCwdPWxfv16KU+ePGoNcQqFQvL09JS6du0qyWQyqWTJktLChQult2/fqq2mzPr8+bO0fv36tAti7969f/qCqK4pCqowduxYqXz58pJCoVB3KdlCXFyctGzZMqlYsWJS3rx5pUmTJklv3rzJcDuxsbHSmjVrJH19fQmQ2rVrJ125cuWb/w5169aVBgwYkNnyMy0hIUHatGlT2g1Zjx49NPJm5MaNG1LNmjUlQLKxsfmpJ0bZ1adPnyRA2rVrl7pL0UhiUY4KeXl5oaenR4UKFdRdisaS/m9TaysrKxwdHenQoQOBgYEsWrToq/t2Fi1alDlz5hAREcGCBQs4evQo5ubm2Nra/nCCu7e3N9WrVydv3ryqejs/JJPJaNy4Me7u7jx9+pTu3bvz+++/o6+vj4ODA8HBwWqrLaM+f/6Ms7MzpqamjB49mqZNm+Lv78/+/fupUqVKhtvb7xtJ69VXuRH69wkpPzr16MvPb4RG03r1Vfb7Rmb8TaiQh4cH7du3F5P3fyApKYkNGzZgZmbGjBkz+O233wgODmb58uUUL148w+0VLFiQsWPHEhISwu7du3n+/DnNmzenfv36HDly5F8ndCUlJXH//n3q1KmjzLf0U3R1dRk+fDiBgYFs3bqVu3fvUq1aNbp168a9e/fUXR4vX77ExsaGhg0bIpPJuHnzJtu3b6dUqVLqLi3LREb+/R0jtgz6OhEoVcjT05PGjRuLC8o3+Pr60rx5c7p27Ur58uW5e/cu27dvp3z58j/83X9uybF8+XLOnj2LpaUl/fv3x9/f/6u/c+vWLY3aXNrCwoINGzYQGRnJtGnTOHToEBYWFvTo0YMbN26ou7xvio+PZ9WqVRgbGzNhwgRat27N48eP2bNnDxUrVvypNtddDmLaUT8SUxQZPj4zVSGRmKJg2lE/1l3WjFWzgYGBBAcH57gVrsqUmprKrl27sLS0xMHBgdatWxMQEMCGDRuUcuZ5njx50jZEP336NPnz56dnz55UrFiRzZs3k5CQgJ+fH0lJSRoRKL/Q0dHBzs6OgIAAtm/fjp+fHzVr1qRLly7cuXMny+tJTk5m9erVWFhYcOrUKTZt2oS3t3euPPlN7EH5fSJQqkhycjK3bt0SG5p/RXh4OP3796du3bpER0dz+vRp/vzzT6pXr57htgoUKMC4ceMIDQ1l3bp1eHl5UaVKFXr27Pmvu/qYmBgCAwM18kuwRIkSaaOumzZtwt/fn0aNGtGwYUOOHj2q9DPPf9anT59Yvnw5xsbGTJ06lQ4dOvD06VN27tyZqVH4/b6RrDgfqJQaV5wP5IAGjFSePn0aXV1dWrVqpe5SNI4kSRw9epSqVasyePBgqlevjp+fH7t27cLExETp/clkMtq1a8fly5fx9vamatWqjBw5EiMjIxYuXIiWltZPffeoWp48ebCxseHp06fs2rWLp0+fph1P+uW0KFW7ePEi1atXZ9KkSQwcOJDAwECGDx+eazfpj4iIQEtLSyk3PDmRCJQqcu/ePT5//iw2NP+H9+/fM3XqVCwtLbl06RJbtmzh/v37Xz2vOaPy5s3LqFGjCAoKws3Njfv371OzZk06deqEt7d32hewJo1Q/q98+fIxbNgwHj9+zIkTJ9DR0aFHjx5UqFCB9evXExcXp5a6YmNjWbp0KcbGxsycOZMuXboQGBiIm5tbpjcrfhYTz9wTXx9R/llzTvjzLOb7py2pmoeHB82bN8/2p4IokyRJnDt3jjp16tCjRw/Kly+Pj48PR48ezbIznevWrcvhw4cJCAiga9eunDp1CkmSmDVrlsaeu62trc3AgQPTngKEhIRQr1492rVrx82bN1XSZ2RkJL169aJ169YULVqUO3fusH79eooVK6aS/rKLiIgIypUrh7a2trpL0UgiUKqIl5cX+fLl+895srlRUlISzs7OmJmZsW7dOqZNm0ZQUBBDhw5V+gdTR0cHW1tbnj59mvblW79+fRwcHPjll18wNTVVan+qIJfL6dSpE1euXMHHx4fatWszZswYDAwMmDVrFi9fvsySOj58+MCiRYswMjJi7ty59OrVi6CgIDZv3oyxsbFS+pjh7kdKBh9x/0iKQmKGu59S28yI2NhYrl69Kh53/8P169dp3rw5bdu2RVdXl8uXL6eFS3UwNzdn48aNWFhYYGVlxbZt2zAxMWHw4ME8evRILTX9iJaWFv379+fRo0fs27ePyMhIGjZsiLW1ddoBGpmVkJDA77//jqWlJV5eXuzevTvt2Evh70ApHnd/mwiUKuLp6Um9evXQ0dFRdylqI0kSR44coXLlyowfP55u3boRFBTEvHnzKFiwoEr71tbWTvvyPXToEG/evOHjx480b96cCxcuIEnKDTGqUqdOHfbv309ISAiDBg1izZo1GBoaMnToUB4/fqySPt+9e8f8+fMxMjJi0aJF9OvXj5CQEFxdXZX6ZRr0KhbP4LcZnjP5I6kKCc/gtwS/jlVqu+l14cIFkpOTad++vVr61yT37t2jQ4cONG7cmA8fPnDq1Cm8vLxo3ry5uksjPj6ewMBA7O3tiYyMZNmyZVy6dAkrKys6duzItWvXNPJ7QktLiz59+uDn58fBgwd58eIFjRs3plWrVly7du2n2z116hSVK1dm3rx5ODg4EBAQwIABA8QagH8QgfL7RKBUAUmS8PLyytWPu2/dukXjxo3p2bMnZmZm3L9/ny1btlC2bNksrUMul9OjRw+0tLTo168f8fHxtGnThgYNGuDh4aGRF4yvMTIyYvXq1Tx79owFCxZw5swZKleuTIcOHbh8+bJS3kd0dDSzZ8/GyMiIpUuXYmNjQ2hoKC4uLulaKJVRe70jM3wsZnppyWXsuaWeuZQeHh5UqFAhW4yGq0pAQAC9e/emZs2aBAcHc+DAAe7evUuHDh00JqDcu3eP1NRU6tSpQ6FChRg/fjwhISHs3LmT8PBwmjVrRoMGDTRqHvM/yeVyevXqxYMHDzhy5AjR0dE0a9aM5s2bZ+g7ISgoiA4dOtCpUydMTU3x8/Nj+fLlX91lI7cTgfL7RKBUgcDAQN6+fZsrF+SEhobSu3dvGjRowKdPnzh//jxnzpxJO4NXHYKCgnj//j2DBw/Gx8eHs2fPoq2tTceOHalVqxZHjx5FoVCorb6MKFq0aNrq9p07d/Ls2TNatmxJ7dq1+eOPP0hOTs5wm2/fvmXGjBkYGRmxatUqhg0bRlhYGKtXr1bpDcDlgNdKH538IlUhcTnwtUra/h5Jkjh9+nSufdwdERGBra0tlSpV4ubNm7i5ueHv789vv/2GXK5ZlxtfX1/y5s37ry2udHR0GDRoEH5+fnh4eJA3b1569OhBpUqV2LJlS4bPDM8Kcrmc7t27c/fuXY4dO8bHjx9p2bIlzZo1++7TmLi4OGbOnEmVKlXw9/fn6NGjnDt3DktLyyx+B9lDcnIyf/31lwiU36FZn/AcwsvLC7lcrpErilUlJiaGCRMmpM292b59O3fv3qVNmzbqLo1bt24Bf0/Il8lk/Prrr3h6enL58mWKFStGjx49qFq1Kvv27dPIkYiv+XLhe/DgAefPn6d48eL0798fU1NTVq1axcePH3/YxuvXr5kyZQpGRkY4Ozvj4OBAWFgYK1asoHTp0iqt/1NiCpEqXjgTGR1PXGKKSvv4X/fv3+fFixe5LlC+fPmSMWPGYG5ujoeHB2vWrCEoKAhbW1uNXcBw+/ZtqlevTp48ef7zM5lMRvv27bly5Qq3bt2iSpUqjBgxAmNjY5YuXcr79++zvuAfkMvlaVsLnTx5ks+fP9OmTRsaN27MuXPn0oKlJEkcOHAAS0tLVq5cybRp03j8+DHdunXTmNFjTRQVFYVCocDAwEDdpWgsEShVwMvLi2rVquWKRwaJiYmsWrUKU1NTtmzZwpw5cwgKCsLGxkZjtpbw9vbG0tKSIkWKpP1vMpksbT7ljRs3MDQ0pF+/flSsWJEdO3b81EifOshkMtq0acO5c+d48OABLVu2ZNq0aejr6zN58uSvrlx98eIFEydOxMjIiI0bNzJ27FjCw8NZunQpJUuWzJK6I6LjUPVkAwkIj87alfEeHh4UKlQo10x3iYmJYfr06ZiamrJ7927mz59PaGgoo0ePRldXV93lfZevr2+6FgXVq1ePI0eO8PTpUzp16sTcuXPR19dn0qRJPH/+XGX1xSWm4P/XB+5FvsP/rw/pvjmSyWRpWwudPn2a1NRU2rZtS4MGDdiwYQMtW7akT58+1KpVi8ePHzN//nzy58+vsveRU4g9KH9MJmWXSWTZiJmZGe3bt8fZ2VndpaiMJEkcOnSIadOmERkZybBhw5g3b55GnppQq1Ytqlatyvbt27/7ujt37rBo0SKOHTuGkZER06ZNw8bGRuMvjP8rKioKFxcXNm7cSFxcHL1792bixImULFmSZcuWsXnzZnR1dRk7dixjx45Vy1Yg9yLf0W2D6jdvdx/VkBoGRVXezxcNGjSgbNmyHDlyJMv6VIdPnz6xdu1ali9fTkpKCuPGjWPSpEn/umnTZO/fv6do0aLs3LmTQYMGZeh3X758ibOzM66ursTHx9O/f38mT55MpUqVMl1X0KtY9npHcjngNZEx8f+66ZIBBsXy06JCSfrXM8C8VKF0tSlJEseOHcPBwYEXL16gq6vL9OnTmTNnjhiRzIBdu3YxePBg4uLiRAD/BjFCqWQvXrwgJCQkR49QeHl50aBBA3r37k3lypV5+PAhGzZs0MgwGR8fz8OHD9O1/2StWrVwd3dPe/2oUaMwNTXF2dmZz58/q6S+nx2F+J5y5cqxdOlSnj17xooVK7hy5Qo1a9ZEX1+fbdu2MX36dMLDw5k/f77a9pXT0c6ar56s6gfgzZs3eHt75+jH3QkJCaxZswYTExMWLFiAjY0NISEhLFq0KNuESSDtxJmf2baodOnSLF68mGfPnrFkyRL+/PNPKleuTKdOnfDy8vqpBXLPYuIZ6OZNmzXX2O0dQcT/hEn4e8Q9Iiae3d4RtFlzjYFu3j/cb1WhULBjxw5GjhzJx48fsbOzo1atWsybN4/atWtz/PjxbLMwUd0iIiIoUaKECJPfIQKlkn3ZDywnBsqgoCB69OhBkyZNSE5O5tKlS5w8eVIpd+aqcvfuXVJSUjI0n9XKyor9+/fz5MkTWrduzYQJEzA2Nmb58uV8+vQp0zUFvYpl3gl/mi2/TJV55+jg4kW3DTfo4OJFlXnnaLb8MvNO+BP0KnPb3kRHR/PkyRNev35NwYIFKVu2LJ8+feLgwYO4u7uTmJiY6ffys4z0CqDqsRHZ//WTVb7MU8uJ2wWlpKSwdetWzM3NmTRpEp07dyYoKIg1a9Zo5I3kj/j6+lKoUKFMnfBUqFAhJk6cSGhoKDt27CA0NJQmTZrQqFEjjh07lu6Ffqo6w/727ds0bNgQW1tbWrVqRUBAAFu3bsXLy4tLly5RqFAhunbtSo0aNbLVwkR1ESu8f0wESiXz8vLCxMQky7fHUaW3b98yZswYKlWqhK+vL7t378bX15cWLVqou7QfunXrFvnz5//XSs70qlChAjt27CAwMJDOnTszc+ZMDA0NWbRoER8+fMhwe6oahfhfoaGhDB06FHNzc44cOcLChQv566+/ePbsGVevXsXExARbW1uMjIxYvHgxMTExGX4vmVVAVxuDYqq90zfQy08B3axbEOLh4UGtWrVUvqApKykUCvbv30+lSpUYNmwYjRs35vHjx2zdujVbL07w9fWlVq1aSll5rqOjw+DBg/Hz8+PkyZNoa2vTrVs3KlWqhJub23dv3FRxhv3bt28ZPnw4devWJT4+nitXrvDHH3+kHRcok8lo0aIFV65c4cqVK+jp6dGjRw+qV6/OoUOHRLD8BhEof0wESiXz9PTMMaOTCQkJLFu2DFNTU3bu3MmiRYvSNrvVtC1AvsXb25vatWtnaqWpiYkJmzdvJiQkhH79+rFo0SIMDQ2ZPXs20dHR6WpDVaMQ/xQUFMSQIUOwsLDg5MmTLF26lPDwcKZOnUqhQoWQyWQ0bdqUEydO8OTJEzp37syCBQvQ19dn9OjRhIaGpuu9KEszi+LIVbQ0R0suo4VF1iwwgr9H8M6ePZtjHndLksTJkyepUaMGffv2pUKFCty7d499+/ZhYWGh7vIy7fbt20o/pUcul6dtiH7jxg0qVqzIsGHDMDY2xsnJ6T83oco+w/4P73DWr1+PhYUFhw4dYu3atdy9e5dmzZp98/eaNWvGxYsX8fT0pHTp0vz2229pT2iyy44XWUUEyh/LHqkgm4iNjeX+/fvZPlAqFAr27t1LhQoVmDlzJoMGDSI4OJipU6eSL18+dZeXIbdu3VLa9k36+vq4uLgQFhbG0KFDWbVqFYaGhkyZMoVXr1598/dUMQrxTwEBAQwaNAhLS0vOnj3LihUrCAsLY+LEid88S9rS0pJNmzYRGRnJ5MmT2b9/P+bm5vTq1Qtvb+8M1ZhRycnJbN++nb3zRqJQ0YPvVIXEZ7/zWba9y82bN3n//n2OeNx9+fJlGjZsSOfOnSlWrBg3btzg5MmTOeb4vdevXxMZGUnt2rVV1keDBg1wd3fn8ePHdOjQgTlz5qCvr8+UKVOIiopSyRn2M47cZ9ysRXTv3p2AgABGjx6d7hvpxo0bc/78eW7cuIGBgQF9+/alSpUq7N27VwRL/r4mRkZGikD5AyJQKtGtW7dQKBTZekPzK1euULduXQYMGEDNmjV59OgRLi4ulChRQt2lZVhUVBTPnz9P14KcjChTpgwrVqwgPDycsWPHsnHjRoyNjRk3bhxRUVH/eq2yRyEO/GOk8vHjx2lbHV26dIm1a9cSGhrKuHHj0j1xvGTJksybN4+IiAjWr1/PgwcPqF+/Po0bN+bYsWNKvZgkJyfj5uaGpaUltra2VDMuRfVSuko/LUdLBnpJr1m7cDr6+vpMnDjxq9snKZOHhwclSpRQ29nUyuDt7U3r1q1p2bIlqamp/Pnnn1y6dIkGDRqouzSl8vX1BX5uQU5GWVpasmXLFsLCwrC3t2fTpk0YGxvTedF+klOV/GhZJqfd7J1s3br1p7f/atCgAWfOnOHWrVuYmpoyYMAAKlWqxO7du0lJydo9XTXJ69evSUxMFIHyB0SgVCIvLy/09PQyNdFbXZ4+fUqXLl1o0aIFcrmca9eu4e7uni3fyxdfRtpUtcF8iRIl+P3334mIiGDatGns2rULExMTRo0aRXh4uEpGIeac8OfPm/fo3bs3VapUwcvLi/Xr1xMSEoKjo+NPjyDnz5+fkSNH8vTpU44dO4ZMJqNbt25UrFiRDRs2EB//85uQJyUlsXXrViwsLBg6dCjVq1fn/v37HD16FJdBDdFWcqDU1pJzbGZvwsPDGTNmDNu2bcPExCTtBBRVOH36NO3atcs2U0H+yc/Pj65du1K/fn1evnyJu7t7WrjMidvK+Pr6oqenh5GRUZb1WbZs2bSdFyYtWME73VIo/ZAouRYP3yQr5Qz7evXqcerUKXx9fbG0tGTQoEHZbo9eZRJ7UKZP9vv202Bfzu/OTl/Cr1+/xt7enipVqvDw4UP27dvHrVu3svUo6xfe3t6UL19e5QukihYtypw5c4iIiGDBggUcOXIEc3NzOi/aT4qSRyESk1MYsPYUPj4+bNq0ieDgYEaNGqW0vTK/nLbh6enJrVu3qF69Oo6OjhgYGDB37lxev07/cYZJSUls3rwZCwsLhg0bRu3atdPOHa5WrRoA+sXyM79zZaXU/sWCzpXRL5af0qVL8/vvvxMZGcny5cu5cuUKVatWpX379ko7/xwgMjISPz+/bDd/Mjg4mP79+1OtWjX8/PzYs2cPDx48oGvXrtnqOyyjvmxoro73+Msvv6BTqVW2OcP+y9ZCd+/excrKiiFDhmBpaYmbm1uuCpYiUKaPCJRKkpyczK1bt7LN/Mn4+HgWL16MmZkZ+/btw8nJiadPn9KnT59sOcryNcqcP5kehQoVSjtne9qStbzTLUWqskchZHLyGdfk9PW7DBs2DB0dHSV38P/Vq1ePgwcPEhQURL9+/Vi5ciUGBgYMHz6cp0+ffvP3EhMT2bhxI+bm5owcOZJ69erh5+fHoUOHqFq16n9e36eOAZOslbPQY7J1BXrX+ffq40KFCjFu3DhCQkLYvXs3UVFRtGzZkrp163Lo0KFMP9Y/ffo0WlpaWFtbZ6qdrPL8+XNGjBiBpaUlV65cYcOGDTx9+pT+/ftrzOlWqiJJkkoW5GREdjzD/svWQvfv36dmzZppu0hs3ryZpKQkpfenaSIjIylYsGC22mtVHXJGctAA9+7dIz4+XuMDpUKhYOfOnVhYWDBv3jzs7OwIDg5m4sSJ2e5EmO9JSUnh9u3bSp8/mR4FChRAy6IZWioaAdGSyzhw+y+VtP01JiYmODs7ExkZydy5czl58iQVK1akc+fOXL16NW2kLzExEVdXV8zMzLC3t6dhw4b4+flx4MCBH27b5NjCnKXdrdDVlmd49EZLLkNXW45TdyscWph983V58uRhwIAB3L9/n7Nnz/LLL7/w22+/YWFhkXbqyc/w8PCgUaNGGn+xefPmDRMnTsTMzIwjR47g5OREcHAwI0aM+Op51jnRs2fPeP36tdoCZXY/w75atWocOnQIPz8/6tWrx8iRIzEzM2PDhg1q3ddW1b6s8M7JI/fKIAKlknh5eZEvXz5q1qyp7lK+6eLFi9SqVQsbGxsaNGjA48ePWb16NXp6euouTekePXpEfHx8lo5Q/tPlgNekqugEClWNQvxIsWLF0k7Z2b59O6GhoTRv3pzatWszZMgQTExMcHR0pGnTpvj7+7Nv3z4qV07/4+w+dQy4ML4ZDU3+/nv8UbD88vOGJnpcGN/sPyOT3yKTyfj111+5ePFi2mjV6NGjMTQ0ZP78+bx9+zbdNX/+/JmLFy9q9OPuDx8+MGfOHExMTNiyZQszZswgNDSUiRMnZrtdGzLry4IcVa7w/p6ccoZ9lSpVOHDgAI8ePaJx48Y4ODhgamrKunXrSEhIUGnf6iC2DEofESiVxMvLi3r16qn0EeTPevToEe3bt6d169bky5eP69evc+jQIczMvj2ak915e3ujpaWlloCf3UchfkRXVxcbGxt8fX0ZOXIkjx49YseOHXz48IGpU6eyceNGKlas+FNt6xfLz267evw5rikD6xliqJf/PxsLyQBDvfwMrGfIhfFN2W1XD/2f3CS9Vq1a7N+/n6CgIPr06YOTkxMGBgaMHj2asLCwH/7+1atX+fz5s0YGyvj4eJYtW5Z2ytOoUaMICwtjzpw5/PLLL+ouTy18fX0pV64cZcqUUUv/SSlZs2l4VvVTqVIl/vjjDx4/fkyLFi0YO3asyo+rVQcRKNNHBEolkCQpbUGOJnnx4gXDhw+nWrVqBAQEcOjQIa5fv07Dhg3VXZrK3bp1i2rVqqnl3NWcMgrxLZ8/f2bt2rWYmpqyefNmevfuzbFjx+jWrRsrVqxAX1+fqVOn/mcLpYwwL1WIeZ0rc3VSCx7N+xWP0Y1xH9UQj9GNeTTvV65OasG8zpUxK1lIKe/JxMQEFxcXIiMjmTp1Kvv378fMzIw+ffqknfv8NR4eHhgaGmrU8aNJSUmsX78eU1NTZs6cSd++fQkJCWHZsmU58mlERnxZkKMuOfEMe/h7e6Tdu3fz9OlT2rRpw4QJEzAxMWH16tWZ2iFCU4hAmT4iUCpBYGAgb9680ZhAGRcXx4IFC9KO3lu1ahVPnjyhZ8+euWYOiLe3t1rmT0LOG4X4Ij4+ntWrV2NiYsLEiROxtrbm6dOn7Nq1iy5durB7927CwsIYPnw4GzduxMjIiMGDB/Pw4cNM9VtAV5vKZQtTw6AolcsWVulxisWLF2fu3LlERETg4uKCr68vtWvXplWrVmlndX8hSRIeHh506NBBIz5Xqamp7Ny5kwoVKjBmzBisra0JCAhg/fr1Oeoo2J+lUCjUviAnJ55h/0/m5ubs2LGDgIAA2rdvz5QpUzA2NmbFihXExannBjizPnz4wIcPH0SgTAcRKJXAy8sLuVyu9g2AU1NTcXNzw9zcnN9//51Ro0YRHBzM2LFjNfJRvKq8f/+eJ0+eqG3+ZE4bhYiLi2PlypWYmJgwefJk2rVrx9OnT9mxYwfm5ub/em358uVZtmwZz549w8nJicuXL1OtWjWsra05f/680rbqUbX8+fNjb29PYGAgBw8e5OPHj7Rt25bq1auzZ88ekpOTefr0KWFhYWo/HUehUHD48GGsrKywsbGhVq1a+Pn5sXPnTkxMTNRamyYJDg7m48ePag2UOfEM+68xNTXFzc2NwMBAunTpwvTp0zEyMsLJyYlPnz6ptbaMElsGpZ8IlErg5eVFtWrV1Dov6dy5c9SoUYOhQ4fSvHlznj59yvLlyylatKjaalIXHx8fALWNUOaUUYi4uDiWL1+OiYkJ06ZNo2PHjgQGBrJt27Yfzr/95ZdfmDBhAiEhIfzxxx9ER0fz66+/Uq1aNXbu3JltthrR0tKiV69e+Pj4cPnyZcqVK8fAgQMxNTVl2rRp5M2blxYtWqilNkmSOHv2LHXq1KFXr14YGBjg6+vL4cOHNeoRvKZQ94KcL1pUKImKtqHM8jPsf8TY2JjNmzcTHBxMz549mT17NkZGRixevJiPHz+qu7x0EYEy/USgVAJ1zp988OAB1tbWtG3bliJFiuDt7c0ff/yBsbGxWurRBN7e3hQtWvQ/o2dZJbuPQnz69AknJyeMjIyYMWMGnTt3JjAwkK1bt2Z4xCtPnjz07duX27dvc/nyZQwMDLCxscHY2BgnJyfevXunkvegbDKZjObNm3P69GkePHhA8+bNOXnyJCkpKfz++++8fPkyS+vx9PSkWbNmtGvXjnz58nHlyhXOnj2r9rCkyXx9fTE1NVXrTbavry/Xtv2u/FNy/k+qQmJA/fTtdpCVDA0N2bBhAyEhIfTu3Zv58+djZGTEwoUL+fDhg7rL+66IiAh0dHQoXbq0ukvReCJQZtLLly8JDg7O8kAZFRWFra0tNWrUICIiAnd3d65evUrdunWztA5NdOvWLerWravWDdpbVCipstMw5DJUMgoRGxvL0qVLMTIyYvbs2XTv3p2goCC2bNmS6RuUL4Hs1KlT+Pv70759e+bMmYO+vj7jxo0jPDxcOW8iC1StWhUXFxe0tLRo2rQpzs7OGBkZMXz4cAICAlTa9507d2jXrh1Nmzbl06dPnD59Oi1cCt+nzgU59+/fp3PnztStW5cXAfcwL5Si/DPs5TKamBVX2kI1VdDX12f9+vWEhoYyYMAAfv/9dwwNDZk3bx7v379Xd3lfFRERgb6+fo458EOVxP9DmeTl5QWQZYEyNjaW2bNnY25uzsmTJ3FxceHRo0c5/ri09JIkCW9vb7XNn/yifz0DlZ2GoZDgqtsipR0f+PHjRxYvXoyRkRFz5syhV69eBAcHs2nTJpWcd1ypUiW2bNlCREQE48ePZ/fu3ZiamtK7d++0x5Ka7s8//yQlJQU3N7e0Dd9PnDhBxYoV6d69Ozdv3lRqf0+ePKFXr17Url2bsLAwDh48yO3bt2nXrp343KdDSkoK9+7dy/JA6e/vT8+ePalRowZPnjxh9+7dPHr0iG0j2yj/DHu5jMXdrJTapqqUK1cOZ2dnwsLCGDJkCE5OThgaGjJnzhxiYmLUXd6/iBXe6ScCZSZ5eXlhYmKi8lWUKSkpbNq0CTMzM5YvX87YsWMJDg7GwcEh15xykR4hISFER0erbf7kF+alCtHErLhKRiHMCiYTE/6Yli1bUq9ePQ4fPvxTxwd+/PiRRYsWYWRkxPz58+nTpw8hISFs2LABAwPVPzYrXbo0CxcuJDIyEhcXF+7cuUPdunVp1qwZJ0+eRKHI2lXsGeHh4UGlSpUwMjKiaNGiaRu+b968GX9/fxo2bEiTJk0y/T7Cw8MZMmQIVapUwdfXl+3bt/Po0SN69eolRkwywN/fn8+fP2dZoAwICKBfv35YWVlx584d3NzcePLkCQMGDEBLS0ulZ9hnJ2XKlGH16tWEhYUxbNgwVqxYgZGRETNnzszQAQOqJAJl+olvpExS9fzJL1uTVK1alZEjR/Lrr78SGBjIkiVLKFy4sMr6za68vb0BNOLR/+JuVioZhdg+ypr79+9z5swZChYsSK9evbC0tGTTpk3pOqXiw4cPLFiwAENDQxYtWkT//v0JCQlh/fr16OvrK7Xe9ChQoAD29vYEBARw9OhRUlJS6Ny5M5UqVWLz5s0at0GyQqHgzJkz/9nMPG/evAwdOpQnT55w7NgxFAoFnTt3pnLlymzbti1DR9O9ePECR0dHLCwsOHPmDGvXriUgIAAbGxu0tdW7gjc7un37NnK5XOUHHYSEhGBjY0OlSpXw9PRkw4YNBAQEYGtr+59/N1WfYZ+dlC5dmhUrVhAeHs6oUaNYu3YtRkZGTJs2jTdv3qi1tsjIyCy5wc4JRKDMhNjYWO7du6eyQHn37l1atWpFx44dKV26NHfu3GHXrl3ij/s7bt26hYWFhUZs4KzKUQiZTEbbtm25dOkSPj4+VK9enVGjRqWtoPzaYpf3798zb948DA0NWbx4MQMHDiQkJAQXFxfKly+v1Dp/hpaWFt26deP69etcv36dypUrM3LkyLQjEdV9Yfni7t27vHr16pun48jlcrp06cL169fx8vKiQoUK2NnZpS1E+t5csZiYGKZNm4apqSl//PEHCxcuJCQkBEdHR3R1dVX0jnI+X19fKlWqRIECqtkZISIigmHDhmFpacm5c+dYs2YNQUFBjBgx4rtbtmXFGfbZScmSJXFyciI8PJwxY8awfv16jIyMmDx5Mq9evcryehISEnj58qUYoUwnESgz4datWygUCqUHysjISAYNGkStWrV4+fIlp06d4uLFixp9TrimUOeG5l+TFaMQderU4dChQwQEBNC1a1cWLFiAgYEBEydO5Pnz57x79465c+em7QNnY2NDaGgozs7OlCtXTim1KVvDhg05cuQIgYGB/Pbbb2lHIo4aNYrAwEC11ubh4UHhwoXTdeJUo0aNOHbsGE+ePKFDhw7MmTMHAwMDJk+ezPPnz9NeFxsby8KFCzE2NmbdunVMnDiR0NBQpk6dqrIQlJt82aBe2aKionBwcMDc3Jxjx46xdOlSQkJCGD16NHnz5k1XG/85w/4HufJnz7DPTooXL87ixYsJDw9nwoQJbN68GWNjYyZMmJClOyo8e/YMEFsGpZdMyi47DWuguXPnsn79et68eaOUifEfPnxg6dKlrF69msKFC7NgwQLs7OzEI650SkhI4JdffmHNmjXY29uru5x/2e8bydwT/qQopAwt1tGSy9CWy1jQuXK6LxwvX77ExcWF9evXExsbi5aWFnK5HHt7eyZPnqy2c4wzIzo6mo0bN+Li4sLr16/p3LkzkyZNolGjRlm+KKVu3boYGxtz4MCBDP/uixcvcHZ2ZsOGDcTFxdGnTx/KlCnD9u3b+fjxI/b29kyfPp2SJTVnL8HsLiEhgUKFCrF27VqlfS+8evWKpUuXsmHDBvLnz8+UKVNwdHSkYMGCmWo36FUsc3b/ybWgaHSKlf3XEa4y/t4urIVFSQbUN9Do1dzK9u7dO9asWcPatWtJTExk+PDhTJ06VeVrFy5cuECbNm0IDg7G1NRUpX3lBCKpZEBcYgrh0XEkpSjQ0ZZz7YY3jRs3zvQFLTk5mc2bNzNv3jzi4uKYPHkyU6ZMoVCh3POFoQz37t0jOTlZo0Yov+hTx4BGpsWZ4e6HZ/BbtOSy7wbLLz9vaKLH4m5WGZps/2WRVmpqKnK5HF1dXT59+kRQUBAhISHZMlDq6ekxc+ZMJk6cyN69e1m5ciVNmjShbt26TJo0iW7duqnkxut/P/P5U+Pw9fXFwcHhp9orU6YMS5YsYfLkyYwYMYJ9+/aRmpqKvr4+rq6uuep41Kzy4MED/h97Zx0WZfb34XsAW9fWXYMuBcQClbWwa9W1sF07UDFQMdYOTAxs18DCrlUwERQQAxUU6bILFRXJOe8f+4N3XQt0hiGe+7r2j3VmzvmMzvOcz3PON1JTUxWSkPPixQuWLl2Ks7MzGhoaTJs2jXHjximsqYVBxRKUi/GgmMdpbgYGffTb0y5bTOUdcFRF6dKlmTNnDuPHj2f16tU4OTmxceNGhgwZgoODg8JDdtKve5+QRxSsqEuZCrnvnqkKpB3KbxD29C27/WLxCHlGbFzCR0+MQghKqafwez1D+tTTxKBi1gygEIJjx44xZcoUwsLC+OOPP5g3b16OPYbM6Tg5OTFt2jTi4+NzdOZ72NO3bLkYwq4LNylQ5hf4V1+dH9mFePHiBcuXL8fZ2Rm5XI6trS329vaUKlWKvXv3smTJEoKCgrCysmLy5Mn89ttvuTZTWC6X4+7uzrJly/Dw8EBHR4dx48YxaNAghewSfemaB0HKqyf0sTZnaFPjLF/zcrkcV1dXZs6cmVHkuU6dOri4uHDnzh0sLCyYPHkyv//+O+rq6j/0PST+wdnZmQkTJvD27dvvjkN99eoVy5cvZ9WqVQDY2dkxceJEpRRJr1OnDmZmZmzfvl3hY+cV4uPjWbNmDStWrODdu3cMHjwYBweHH8ov+Np1LwM0yxTF2qjCd631+QXJUH6B+3EJWd5NaqRfLtO7SVevXsXe3p5Lly7RokULli1bhrm5uSK/Qr7DxsaGhw8fZtQGzcmcOXOG1q1bc/tuMOqlfv6hXYjnz59nGEkgw0iWL1/+o/fJ5XJOnTrF4sWLuXz5MsbGxkyaNIk+ffrk6oQPf39/li9fzr59+yhRogQjR45kzJgxWd6JVeY1L4Tg+PHjzJgxgzt37tCxY0fmzZtHjRo1Ml53d3dn6dKleHh4oKenh729PQMGDKBIkSJZ+h4SH/PHH39w9+7d76pxGh8fz8qVK1mxYgXJycmMHj2ayZMnU65cOSUo/adLValSpVi3bh3Dhg1Tyhx5ibdv37J27VqWLVtGfHw8AwcOzOgbnlmUvdbnJ3Ln9oSScb0WSwsnT3wiXwJ8M+Yt/XWfyJe0cPLE9VrsF98bHR1N7969qVevHq9evcLNzY0zZ85IZlIB5ISC5pnl5s2blChRAlNjA0wqlaSWZmlMKpXMkpl89uwZkydPRltbm7Vr1zJmzBiio6NZvHjxJ2YS/sk+7tChA5cuXcLb2zsj+1hXV5elS5fmmt66/6V27drs3r2byMhIBg0ahLOzM1paWgwcOJA7d+5kagxlXvPnz5+nfv36dO7cmQoVKuDr68uxY8cyzCT800mobdu2GVn7tWvXxtbWFi0tLebNm8fLly8z9T0kPuV7OuS8e/cOR0dHdHR0WLhwIQMHDiQyMpIlS5YozUzCPxsNaWlpmUr4koASJUrg4OBAdHQ0CxYs4MiRIxgYGDBkyBAiIyO/+XllXvf5EclQ/gdnjzAcDgeSlCrPcqeTNLkgKVWOw+FAnD3CPnrt1atXTJo0CSMjIy5evMiWLVu4desWbdq0kWKmFMCTJ0+IiYnJkfGTn8Pf359atWp915Hz06dPsbe3R0dHh/Xr12e0Lly0aFGmFzsrKyuOHj1KUFAQbdq0Yfr06WhqajJ16lQeP36cZU05AU1NTZYvX879+/dZuHAhZ8+exczMjLZt23Lu3LkvdhVS1jV/5coVmjdvTosWLZDJZJw7dy7DXH4NCwsL9u/fT2hoKN27d2fhwoVoampiZ2eXq1pU5gTevn3LvXv3Mp3h/eHDB1asWIGuri4zZ87MKPbv5OSULb2cfXx8KFmyJNWrV1f6XHmJ4sWLM2nSJKKioli8eDF///03hoaGDBw4kPDw8M9+RlnXfX5GMpT/wvVaLMvOKKYkybIzoey7FktycjKrVq1CX1+f9evXM336dMLCwhg8eLAUI6VA0gua55YdSn9//yyXgXry5AkTJkxAR0eHTZs2MWHCBGJiYliwYMF3192sVq0af/31F1FRUQwbNiyj7tuwYcNUXp7neylZsiT29vZERUWxa9cunjx5QsuWLalVqxa7du0iJSUl473KuOYDAgLo2LEjDRo04NmzZxw7dgxfX1+aN2+epfH09PRYu3YtsbGxTJo0id27d6Ovr0/v3r25efOmQjTndfz9/RFCfHOHMikpiTVr1qCrq8vkyZPp1KkTYWFhrF27Nltj2n18fGjQoEGujW1WNcWKFWPChAlERkayfPlyTp8+jZGREf379//ofqaM615CMpQZ3I9LYNbxuwodc/qRAKrVbciECRPo2rUrYWFhzJw5U6orpwSuXLnCL7/8kiMKdH+LN2/eEB4enmlD+fjxY8aNG4eOjg5//fUXkyZNIiYmhnnz5lGmTBmFaKpcuTJLliwhNjaWuXPncuLECYyNjenatWuGWc9tFChQgD59+uDv78+5c+eoVKkS/fr1Q0dHh6VLl3I35qnCr/mpB29Sp0lrgoKC2L17N7du3aJjx44/dApRvnx5Zs+eTUxMDCtXruTKlSvUrl2bli1bcvbsWYX0c8+rXLt2jaJFi1KtWrXPvp6cnJzR0nbcuHG0atWKkJAQNm/enO21B+VyOb6+vtJxtwIoWrQodnZ2REREsGrVKi5cuEC1atXo27cvHlcDFH7dzzx+l/txCQodMzciJeX8j35/+eET+TLLW99fQ6SlUvz9I/aNbISpqanCxpX4lGbNmlGqVCkOHz6sainfxNPTk6ZNm3Lnzh1MTL7cSefRo0csXryYTZs2UahQIcaNG4ednZ1SMkv/S2JiIrt27WLp0qWEhobSpEkTJk+eTNu2bXN1iMadO3dYsWIFu3fvpmy32RSqaoqQKe65WsjT0C2awplpHZVWaSA1NZVDhw6xZMkS/P39qVmzJpMmTaJHjx5Szdr/YGNjw6NHj7h06dJHf56amsrOnTuZO3cuMTEx9OzZk5kzZ2JsbKwipf/0Gzc1NeX8+fM0a9ZMZTryIomJiWzdupVFixaR/OtwimibgwKve3U1GVa6Zdk5OHeEXCkLaYeSf8oFXAp/oVAzCSBT1+D9T5oUriBV2VcmaWlpXLt2LVfFTxYpUgQjI6PPvv7w4UPGjBmDrq4uLi4uTJ06lejoaGbPnp0tZhL+vy91UFAQhw8fJjExkfbt21OjRg127tz50bFxbsLU1JStW7ficeMeBTVrKNRMAsjU1IlKLEzMq2/3VP9eNDQ0sLGx4fr165w/f56KFSvSp08f9PX1WbVqFe/evVPa3LmN69evf3TcnZaWxu7du6levTqDBg2iTp06BAQEsGfPHpWaSfjnuFtNTQ1LS0uV6siLFC5cmFGjRuHuc4siOrUUaibhn5jKS+EvCH/2VqHj5jYkQwns9ovNch/VzKKuJmPXFSm+QpkEBQXx7t27XBU/aW5u/slu0v3797G1tUVXV5fdu3czY8YMoqOjmTlzJqVKlVKJ1vT+2r6+vnh6eqKpqUn//v3R09Nj5cqVuda8nIn8kOuveZlMRrNmzXB3d+fWrVs0atSIiRMnoqmpyZ9//smzZ8+UriEn8/LlSyIjI7GwsEAul7N//37MzMzo27cvxsbG+Pv7c/DgwRxzeuTj44O5ufkP11GV+DIHbj7J9dd9TkYylIBHyDOF706mkyYXeITm7xu7srly5QpqamrUqVNH1VIyRXqGdzqxsbGMHDkSfX19XF1dmTVrFtHR0cyYMYOSJUuqUOn/I5PJaNy4MSdPniQgIICmTZsyadKkXGte8to1b25uzs6dO4mIiGDAgAE4OTmhqanJiBEjCAvLn1mo169fB/6psFGrVi1sbGzQ1NTEz8+P48ePf3QN5gS8vb2l+Eklk9eu+5xGvjeU75JSiVVyMG3sywTeJ6UqdY78jJ+fH2ZmZrniyf79+/cEBwdTu3ZtYmJiGDFiBPr6+hw4cIDZs2cTHR3NtGnTFNbKTRmYmZnh4uLykXnR0tJi1KhRREREqFreN8nL17yWlhZOTk7cv3+fmTNncuTIEYyMjHJ1ctX3IIRg9+7dqKurY2trS7ly5bh8+TLu7u458kj5+fPnhIWF8euvv6paSp4lL1/3OYV8byhjXr5H2VlJAoh++V7Js+Rfrly5kmviJwMCAjI61hgYGHDo0CHmzZtHdHQ0U6dOzVX92zU1NXFyciI2Npbp06dz8OBBDA0NsbGxwd/fX9Xyvkh+uOZLly7NtGnTiImJYePGjdy5c4f69evTpEkT/v77b+Ryucq0KRMhBOfOncPKyoqdO3dSokQJPDw8OH/+fI42a76+vgDSDqUSyQ/XvarJ94YyOTV7bqzZNU9+Iz4+nqCgoFwRPxkZGcnEiRMBuHTpEgsWLCAqKoopU6bkit3VL1GmTBlmzJhBTEwMzs7OXL9+nTp16uTYsjb56ZovXLgwQ4cOzUiuSk5O5rfffsvoFZ2cnKxqiQojvXpCy5YtkcvllClThhEjRtC0aVNVS/smPj4+VKpU6Yd6UUt8nfx03auKfG8oC2pkz19Bds2T37h27RpCiBxtKCMiIhg0aBCGhobcvHmTypUrEx0dzaRJk3K1kfwvRYoUYeTIkYSGhrJv3z7i4uJo1aoVderUwdXVldTUnHEUlB+v+fTkKh8fHy5duoSenh4DBw7MqMn55s0bVUv8bnx9fWnRogVNmzbl7du3nDhxgsOHDxMXF5cjj7c/R3r8ZG4uyZXTyY/XfXaTf7/5/9AuWwxlX8Ky/80joXiuXLlCyZIlv1iCR5WEh4fzxx9/YGRkxKlTp1i6dClGRka0adMmTxe3V1dXp0ePHly/fp1z585Rrlw5evXqhaGhIWvXriUhQbUFgPPzNS+TyWjYsCHHjx//pO3m5MmTefjwoaolZprr16/Trl07rKysePr0KYcOHeLGjRt06NAhIyEnsy0XVUlycjLXrl3L0UfyeYH8fN1nF/neUBYrpIFmmaJKnUOzbFGKFZIKDisDPz8/LC0tc1SrsrCwMAYMGICRkRGnT59m2bJlREZGMmrUKIKCgrLccjG3IpPJaN68OWfOnMHf35969eoxduxYtLS0mDt3Li9fvlSJLuma/4f0tpvR0dGMHDmSjRs3oqOjw6BBgwgKClK1vC9y+/ZtOnfujIWFBVFRUbi6unL79m26dOmSscN37do1KlasmCs6Z928eZOkpCQpflLJSNe98sk5q7AKsTaqoNTaVNaGFZQydn5HCJGjEnJCQkLo168fxsbGnDt3DicnJyIjIxk3bhxFixbl7t27pKSk5BtD+W9q1arF3r17CQ8Px8bGBkdHRzQ1NbGzsyMmJibb9TTSL4NMWSH68jQqpD7PNfGJlSpVwtHRkfv377No0SLOnDmDiYkJv/32G5cuXcoxMbBBQUF0796dmjVrcufOHVxcXLhz5w42NjafPFBeu3YNCwuLXHGE7OPjQ+HChalZs6aqpeR5rI0qoKSlXlrrkQwlAH3qaSq1NpU+j3PMTTkvER0dzfPnz1UeP3nv3j369OlD9erV8fDwYNWqVURERDB27FiKFCmS8T5/f3/U1NSoUaOGCtWqFh0dHZydnYmJicHe3p5du3ahp6dH3759CQgIUPr8aWlpbNu2jR1/DkUo6wBMTZ2jyyaiq6vLihUrck3x959++omJEycSGRnJ9u3biYqKonHjxjRo0IDDhw+TlpamEl2hoaH06dMHU1NTrl27xpYtW7h37x79+vVDXV39k/cLIT7pkJOT8fb2xsLCgoIFC6paSp4mIiKCa64rUNJST5pc0Ld+/k6qkgwlYFCxBI30yyl8l1INQYEX4fTr1JLGjRtz7tw5yVgqkCtXrgCoLPA+KCiIXr16YWJigpeXF2vWrCE8PJzRo0dTuHDhT97v7+9PtWrVKFpUuccuuYHy5cszZ84cYmNjWbFiBZcuXcLc3Jy2bdty8eJFhV8nQghOnjxJzZo1GTRoEFYmOtSpXFTh17y6moxG+uUIuHSGFi1aMGXKFDQ1NZk1a5bKjvizSsGCBRkwYACBgYGcPHmSwoUL07VrV6pVq8bGjRv58OFDtuiIiopi4MCBVKtWDU9PT9atW0doaCiDBw/+ap/0qKgo4uLicoWhFELg7e0txU8qkdevXzNp0iSqV69OwKUz6BVLUdp1r18h95R9UwaSofwfC383Q0PBP7ICGuqcdxzC33//TVJSEi1btqRhw4acOXNGMpYKwM/PDz09PcqXL5+t86Yfs5mamuLt7c26desIDw9n1KhRnzWS6fj7++fL4+6vUaxYMcaOHUt4eDi7du3i0aNHWFtbU69ePQ4dOqSQXbGrV69ibW1Nhw4dKFeuHFevXmXfvn2s7F1P4de8hpqMhb+bUa1aNbZv305ERAT9+vVj6dKlaGpqMn78eO7fv6/QOZWFTCajXbt2XLx4ET8/P8zNzRk5ciTa2tosWLCAuLg4pcx7//59hg8fjqGhIW5ubjg5OREeHs6IESMytYt37do1IHck5MTExPDkyRMpflIJpKamsnbtWvT19Vm3bh0zZswgJCSE7aNaKe26z+9IhvJ/VC1TlDkdTRQ65tyOJmiWLUb79u3x8/Pj1KlTpKWl0bp1a6ysrHBzc5OM5Q+Q3fGTgYGBdO/eHTMzM65cucL69esJCwtjxIgRFCpU6KufTU1N5fbt25Kh/AIFChSgT58+3Lp1Czc3N4oXL063bt2oVq0amzZtIjExMctjhoeH06NHD+rVq8fLly85efIkFy5cyNi5UtY1X/Vfgf+ampqsWrWKmJgYJk6cyPbt29HT02PQoEEEBwcrdG5lYmlpyYEDBwgNDaVr167Mnz8fTU1Nxo0bp7AY2EePHjF69Gj09fU5fPgwixYtIjIykrFjx371Qe2/XLt2DS0trWx/0PwevL29AWjQoIGKleQdhBCcOnWKGjVqMGbMGDp27EhYWBh//vknRYsWzZbrPr8iGcp/0dNCE/tWhgoZa1IrI2ws/j+eQiaT0bZtW3x9fXF3d894+q9fvz4nT56UjGUWSUpK4ubNm9kSPxkQEEC3bt2oUaMG169fZ9OmTYSFhTF8+PBvGsl0goODSUxMzHH9g3MaMpmMNm3acOHCBfz8/KhRowYjRoxAW1ubRYsW8fr162+O8ezZM0aPHk21atXw9fVl27Zt3Lp1i3bt2n2SpKHMa/7flC9fnrlz5xIbG8uiRYs4ffo01atXp2vXrhk7armB9N2emJgYJkyYwM6dO9HT08t4GPgenj17xoQJE9DT02PPnj3MmjWLyMhI7O3tvys8JD0hJzfg4+ODkZER5cqVU7WUPEFgYCCtW7emffv2VKxYkRs3brB161YqVar00fuy67rPb0iG8j+MtjbAsYsZhTTUshxnoa4mo5CGGou7mGFrrf/Z98hkMlq3bo23tzdnzpyhQIECdOjQAUtLS06cOCEZy0xy69YtkpOTlbpDeevWLbp06YK5uTn+/v5s2bKF0NBQhg4dmuUA+vRWhFImZ+axtLTk4MGDhISE0KlTJ+bMmUPVqlWxt7f/bL3Ed+/eMXfuXPT09Ni1axcLFiwgNDSUP/7447PJG+ko+5r/NyVKlMhIfNm0aROBgYFYWlrSokWLXBVjXaFChQyD7OTkhLe3N7Vq1aJ169aZ/h4vX77EwcEBHR0d/vrrLxwcHIiKimLatGnf3YI0LS2NGzdu5CpDKR13/zhPnz5l+PDh1KxZk+joaI4ePcqFCxe++gCfndd9fkEylJ+hp4Um58Y3wUq3LMA3f2zpr1vpluXc+CaZelqRyWS0bNmSS5cucf78eYoWLUrHjh2pW7cux44dyzULi6rw8/OjUKFCSjFoN2/epHPnztSqVYuAgAC2bt1KSEjIN5MBvoa/vz/6+vqULFlSwWrzPgYGBmzcuJHo6GjGjBnDli1b0NHRYeDAgQQFBZGSksKGDRvQ19dnwYIFDB8+nIiICCZPnvxRlv3X+O81L+Rfj938nmv+3xQqVIghQ4Zw79499u/fT1xcHC1btsTS0lJhsaPZQbFixRgzZgzh4eHs3buX58+f07Jly692R3r9+jUzZ87MyPgfN24cUVFRzJo164evj+DgYN6/f58rDOXbt28JCAiQDOUPkJiYiKOjIwYGBhw4cIAVK1Zw584dOnXqlKmSUf+97hFfb5v4o9d9XkcmJOfyVcKevmW3XyzHb0TwMkntox+pjH8KmVobVqBvfc0fzvC6ePEis2fPxtPTk5o1azJz5kw6deqUo4p2q4r3SalEv3xPcqqcghpqzJ00htjIMHx9fRU2x40bN5gzZw4nTpxAT0+PP//8kz59+qCh8eOFahs3bswvv/zCvn37FKA0f/P27Vs2bdqEk5MTDx8+pFixYrx//55+/foxb948tLS0fmj8rYfcsN9wFGPrLjx+m/pRtUpFX/PpCCE4e/Ysjo6OeHh4YGRkxOTJk+nbt2+uKicjhOD8+fMsXbqUM2fOoK2tzYQJExg0aBBpaWmsWrWK5cuXk5ycjK2tLZMnT/7hWMd/3xtOu51k+thhvHr+hJ9++klB30o5nDt3jpYtW3L37l2qV6+uajm5CiEE+/btw8HBgYcPH2Jra8vMmTMpU6bMd48Z9CCOpkP+5Jc6LYiXF8yW6z7PISQyxbx580S5nyuLOw9fC/+YOHHn4WvxLjFFKXNdvHhRNGvWTACiRo0a4sCBAyItLU0pc+VkQp/Ei1nH7ojGSy4IbYe/hda//5tyQlSfvF/MOnZHhD6J/6F5rl69Ktq3by8AYWBgIHbs2CFSUhT3b5uWliZKlCghHB0dFTZmfufy5cuifv36AhDFihUTgLCyshLHjh374Wtl7NixQlNTU8jlcvEuMSVbrvl/4+vrKzp37iwAUblyZbFixQrx9u1bpc+raG7evCl69+4t1NTURJEiRUSRIkVEoUKFxNixY8Xjx49/aOxv3RsaL7mgkHuDMpkzZ44oXbp0vry3/wi+vr4Z137Hjh1FSEiIQsb19vYWgLh27ZpKrvu8gLT1lUkiIyPR1ayMSaWS1NIsjUmlkkprsdSkSRPOnz/PpUuXqFChAt27d8fc3Jz9+/cjl399Sz4vcD8ugX5/+dFypRc7/WKIiUv4tKeJTMZ7taLs9Iuh5Uov+v3lx/24rPWIvnr1Ku3bt8fS0pLw8HB27txJUFAQ/fv3V8iuZDoRERG8fftWyvBWAPfu3aNz5840bNiQpKQkzp49S3x8PMePH0cmk9GpUydMTU3Ztm3bd3eqcXNzo23btshkMooV0siWa/7f1K9fnyNHjnD37l2aN2/O5MmT0dLSYvbs2bmmliWAkZERdevWpXTp0iQlJZGSkgJASkrKdxd7z+y9ISYu4YfuDdmBj48PDRo0kE6gMklMTAy9e/emQYMGfPjwgfPnz3Ps2DEMDRWTXOPl5UXx4sWpWbOmSq77vID0S84kkZGR6OrqZuucDRs25OzZs3h7e1OpUiVsbGwwMzPD1dU118RYZRXXa7G0cPLEJ/KfhfNbHYzSX/eJfEkLJ09cr8V+c44rV67Qtm1b6tWrR2RkJLt37+bu3bv07dtXoUYynfSEHCnD+/t59OgRw4YNw9TUlNu3b7N7926uX79OixYtUFNT47fffuPy5ctcvnwZAwMDBg0ahI6ODsuWLSM+Pj7T80RERBAWFkabNm2U+G0yR/Xq1dmxYwfh4eH06dOHJUuWZNSyfPDggarlfZGkpKSM+n+TJk2ic+fORERE8OTJE2bMmMHBgwcxMjKie/fuWcpwz457Q3Yhl8vx9fWV4iczQXx8PNOmTcPIyAgPDw/++usvbty4QbNmzRQ6j6enJ7/++qtS1oD8gmQoM4kqDGU6VlZWnD59Gl9fXzQ1NenVqxempqbs2bMnTxlLZ48wHA4HkpQqz3IrzDS5IClVjsPhQJw9wj77Hh8fH1q3bk2DBg2IjY1l79693Llzh969e381C/hH8ff3R1NTUyoN8h3Ex8czY8aMjNqEy5cvJzg4mN69e392Z+fXX3/l2LFjBAUF0bp1a6ZNm4ampiZTp07lyZMn35zP3d2dAgUK0Lx5c2V8ne9CS0uL1atXZ5Tq2b59O7q6ujmulmVKSgqbNm3CwMCAsWPH0rx5c4KDg9myZQva2tqULVuWGTNmEBMTw7p167h16xaWlpZYW1tz6tSpryYiKvvekN3cvXuX+Ph4yVB+hbS0tIzf08qVK5k8eTJhYWEMGjRI4ffr1NRUvL29ady4sULHzW9IhjITJCUl8eDBA5UZynTq16+Pm5sbfn5+6Orq0qdPH0xMTNi1a9dnsylzE67XYll2JlQhYy07E8q+f+1GXL58mZYtW/Lrr7/y8OFD9u3bR2BgID179lSqkUxH6pCTdZKTk1m9ejV6enosX76ccePGERERwbhx4zJV+7NatWps3bqVqKgohg4dytq1a9HS0mLYsGGEhn75d+bm5kbDhg2/u2yNMilfvjzz5s0jNjaWhQsX4u7uniNqWaamprJ9+3aMjIwYMWIEVlZW3L17FxcXF/T1Py2pUqRIEYYPH05wcDCHDh0iISGB9u3bU6NGDVxcXD4JVVDmvUFV+Pj4oK6urrK2sTmds2fPUqtWLYYPH06rVq0ICQlh7ty5FC9eXCnz3b59m7dv39KkSROljJ9fkAxlJoiJiUEIoXJDmY6lpSUnT57k2rVrGBoa0q9fP6pXr46Li0uuNJb34xKYdfyuQsecefwuh9wv0rx5cxo1asSTJ084cOAAAQEB9OjRI9viloQQkqHMAnK5HFdXV6pVq8b48ePp1KkT4eHhLFy48LtKylSuXJmlS5cSGxvLnDlzOH78OMbGxnTt2pWrV69+9N7ExEQuXLhA27ZtFfV1lEKJEiWwt7cnKiqKTZs2ERAQoJJalmlpaezZswcTExMGDhxIrVq1uH37Nq6urhgbG3/z8+rq6nTp0oUrV67g6emJtrY2AwYMQFdXl+XLlxMfH6+0e4OqYyp9fHz+idUrVkylOnIa9+7do3379rRq1YqSJUty9epVdu7cSdWqVZU6r5eXF4ULF84V7TpzMpKhzASRkZEA6OjoqFjJx9StW5fjx49z48YNqlevzoABAzA2Nmbbtm0ZAfC5gWlHAknN4jHWt0hKSWXU9ku8ePGCgwcPcvv2bbp165btAfCxsbHExcVJhjITXLhwAUtLS3r16oWJiQkBAQFs2bKFypUr//DYpUqVwsHBgejoaDZu3EhgYCD16tXD2to6owWql5cXHz58yBHxk5khvZZlcHDwJ7UsDx8+rLQEPrlczsGDB6lRowZ9+vTB0NAQf39/Dh06hJlZ1vsZy2QyGjduzIkTJ7hz5w4tW7Zk6tSpaGpq0m3JYVLTFPs9UuWCaUcCFTpmVpEKmn/MixcvGD16NGZmZty7d48DBw7g5eWVbfVEPT09qV+/fqY7n0l8HslQZoLIyEg0NDSoUqWKqqV8ltq1a3P06FFu3rxJjRo1GDRoEMbGxvz111853liGPX3LpfAXWY6L+iYyNYro1ObA6Ut07dpVZZmUUkLOtwkICKBt27Y0b94cDQ0NPD09OX78OCYmiu23C1C4cGGGDh3KvXv3Mo5b27Vrh7m5OStWrKBSpUqYmpoqfF5loq6uTvfu3blx4wanT5+mePHidO3alerVq/9Qtvt/EUJw7NgxateuTffu3alSpQpXrlzhxIkTCvt9m5iYsG3bNqKiorAZNo6nstKkKfjWkCYXXAp/Qfizt4odOJM8ffqU8PBwyVDyTzjZ8uXL0dfXZ+fOnTg6OnLv3j26deuWqcLkikAul3Pp0iUpflIBSIYyE0RFRaGlpZXjs79q1qzJ4cOHuXXrFrVq1WLIkCEYGhqyefNmhS0qima3X2yW215lFnU1GXuu3lfK2JnF39+fihUr8ssvv6hUR04kNjaWAQMGULNmTSIiIjhw4AC+vr7ZcmP/93HrxYsXqVq1KqdPnyY+Pp7Vq1fz/v17pWtQNDKZjFatWuHh4YGvry/GxsYMGjQIPT09Vq5c+d2leoQQuLm5YWlpSefOnSldujSXLl3i9OnTSmt9WrlyZX5p2B11JXkKdTUZu66oJpYyvRnDr7/+qpL5cwJCCA4fPoyJiQlTpkyhT58+hIeHY29vn+27hEFBQcTFxUnxkwpAMpSZQJUZ3t+Dubk5Bw8eJCAgAAsLC4YPH57Rvi6nGUuPkGeK3538H2lygUfoM6WMnVnS4yez62k7N/Dq1SsmT56MoaEh7u7uODs7c/fu3WzdlUhHJpPRpEkT1q5dC0CdOnWwt7dHU1OTmTNn8vz582zVoyjq16/P0aNHuXPnDs2aNcPe3h4tLS3mzJmT6VqW4n9db3799VfatWtHoUKFuHDhAh4eHjRs2FDJ3+B/9wYlhYOq8t7g4+NDlSpVlB4XmFO5fv06TZo0oWvXrhgaGhIQEMDatWt/uGPS9+Ll5YWGhgb169dXyfx5CclQZoLcZijTMTMzY//+/QQGBmJlZcXIkSPR19dn/fr1JCUlqVoe75JSiVVycHzsywTeJ6kuUUlKyPl/EhMTWbZsGXp6eqxbtw4HBwfCw8MZNWrUd/dIVxRubm5oaGhw7NgxIiIi6N+/PytWrEBTUxNbW9uMOOrchomJCTt27CAiIoI+ffqwePFitLS0mDBhwldrWV66dAlra2tatGhBamoq7u7uGX+WHeTle4O3t3e+PO5+8OABAwYMwMLCgri4ONzd3Tl16pTK206mx2oWLVpUpTryApKh/AZCiFxrKNMxMTFh79693L17l0aNGjF69Gj09PRwdnYmMTFRZbpiXr7/tMuFghFA9EvVHF8+fvyYJ0+e5HtDmZaWhouLC0ZGRjg4ONCzZ0/Cw8OZPXt2jinP4+bmhpWVFSVLlkRTUxMnJydiY2OZPn06Bw4cwMDAgJ49e2bExOY2/l3Lcvz48Wzbtg1dXV0GDx5MSEhIxvuuXLlCq1ataNy4MW/evOH48eP4+fnRunXrbN09zqv3hqSkJK5fv56vjrvfv3/PrFmzMDQ0xM3NjQ0bNnDr1i1at26tamkIIfD09JTiJxWEZCi/QVxcHPHx8bnaUKZTrVo1du/eTVBQENbW1tjZ2aGnp8fq1av58OFDtutJTs2eNpLZNc9/uXnzJkC+NZRCCNzd3aldu3bGzkRQUBDr1q3j559/VrW8DJKSkj5bLqhMmTIZhbidnZ25du0aderUoWXLltlankeRpNeyjImJYeHChbi5uVGtWjWaN29Ow4YNadCgAY8ePeLgwYPcuHGD3377TSXhGnn13uDv709ycnK+2KGUy+Vs374dAwMDHB0dGTt2LOHh4QwfPjzH5COEh4fz5MkTKX5SQUiG8hvk1JJBP4KRkRE7d+7k3r17tGjRggkTJqCrq8vKlSuz1VgW1Mien192zfNf/P39KV26NFpaWiqZX5XcuHGDFi1a0LZtW0qWLImvry8HDx5UWN9dRXL58mXev3//xfqTRYoUYeTIkYSEhLBv376M8jx169Zl3759Sq39+j4plbuP3nAz9hV3H71R2BHtTz/9hL29PcePH8fc3JwLFy7g7e2NqakpTk5OdOnSRaU9pvPqvcHHx4ciRYpgbm6erfNmNxcvXqRu3boMHDiQxo0bExwcjKOjIz/99JOqpX2El5cXampq+cLgZweSofwG6YYyL+xQ/hdDQ0N27NhBcHAwbdq0wd7eHh0dHVasWEFCgvIL/2qXLYay9z5k/5tHFeTHhJzIyEh69epF3bp1efLkCcePH8+o8ZZTcXNz45dffqFGjRpffZ+GhgY9evTg+vXrnD17lrJly9KzZ0+MjIxYt26dwh7Gwp6+ZfbxuzRZ6oHp7NO0X3OZ39f70H7NZUxnn6bJUg9mH79L2NPvL3tz7949bGxssLCwID4+nm3btrFnzx40NDRo1aoV9erVU2oty2+RV+8N3t7eWFpaqjxmWFmEhYXx+++/Y21tTYECBfD29sbV1TXHbsh4eXlRs2bN72qaIPEpkqH8BlFRUZQqVYrSpUurWorS0NfXZ9u2bYSEhNChQwemTJmCjo4Oy5YtU2r5lGKFNNAso9xAaM2yRSlWSDXHK/kpIef58+fY2dlhbGyMl5cXW7Zs4fbt2yo7Ms0Kbm5utGnTJtM6ZTIZLVq04MyZM9y4cQNLS0vGjBmDlpYW8+bNIy4u7rt03I9LoN9ffrRc6cVOvxhi4hI+iSMUQExcAjv9Ymi50ot+f/llqetLWFgY/fr1w9TUlCtXrrB582aCg4P5448/6NWrF/7+/ri7u1O0aFGl1LLMLHnx3iCEwMfHJ0/GT7569Yrx48dTvXp1bty4wZ49e/D19c3xO39S/KRikQzlN8jtCTlZQU9Pjy1bthAaGkqnTp2YOnUq2traLF68+Ltr2H0La6MKSqtDKUPQUFc1DwIvX74kJiYmzxvKhIQEFixYgJ6eHtu3b2fOnDmEhYUxePDgHBMn9TViY2MJCgr67naLtWvXZu/evYSFhdGjRw8WLlyIpqYm48aNIzY283UOXa/F0sLJE5/If0r6fKuUVvrrPpEvaeHkies3+lNHRUUxaNAgqlWrxoULF3B2diYsLIwhQ4Z8tFsmk8lo3bo1Fy9exMfHByMjo49qWWZXfc6UlBQqyl+APE0p46vJwNqwglLG/hJRUVE8ffo0x5usrJCSksLq1avR19dny5YtzJkzh5CQEHr16qXSkInMEBMTQ0xMjBQ/qUBy9r94DiA/Gcp0dHR02LRpE+Hh4XTt2pU///wTbW1tFi1axNu3iu0u0a1WRaXVoRTI2DS53w/tGn0veT0hJzU1lS1btqCvr8+cOXMYPHgwERERTJ06NVeV33B3d0ddXZ2WLVv+0Di6uro4OzsTGxvLxIkT2blzJ7q6uvTr14/AwK+3+XP2CMPhcCBJqfIsXwtpckFSqhyHw4E4e4R98vr9+/cZMWIEhoaGnDp1iuXLlxMREcHIkSMpWLDgV8du0KABx44d486dO1hbW2fU58xKLcuskpiYyPr16zEwMODo0omgpq6UeeQCbh5YRUBAgFLG/xw+Pj4AOTr8I7MIIThx4gSmpqaMGzeOLl26EBYWxrRp0yhSpIiq5WWKS5cuAWRLTdX8gmQov0F+NJTpaGlpsWHDBsLDw+nRowezZ89GW1ubBQsWEB8f/0Njp3dK6NjEksTom8iEYmO11NVk1K1cDJs2TVi4cCFaWlrY29vz8OFDhc7zJfz9/SlevDj6+vrZMl92IYTg+PHj1KhRg6FDh9K0aVOCg4NxcnKiXLlyqpaXZdzc3GjQoAGlSpVSyHjly5dnzpw5xMTEsGLFCry8vKhRowbt2rXD09Pzk8xw12uxLDsTqpC5l50JZd//diofP37M2LFj0dfX5+DBgyxcuJCIiAjs7OwoXLhwlsY1MTHBxcWFiIgIevfujaOjY6ZqWWaF9+/fs2LFCnR1dRk9ejRWVlb4XzxFI/1yCj/BUFeToV04kYDLZzE3N6dTp05cvXpVoXN8Dm9vb4yNjSlbtqzS51Imt27dokWLFnTs2JGqVaty8+ZNNm/enKMqN2QGLy8vTExMcuV9K6ciGcqvkJKSQmxsbL41lOloamqybt06IiIi6NWrF3PnzkVbW5t58+bx5s2bLI8XEBBA8+bN6dq1K9WqVcN1QicKFlDs8aiGmgyn3pY4OzsTHR3N2LFj2bJlC7q6ugwdOpTQUMUs4l/C39+fmjVr5vhjn6yQ3haxU6dOVKpUievXr7Nnz55ce30kJydz7ty57z7u/hrFixfPKJOya9cuHjx4QNOmTalfvz6HDx8mLS2N+3EJzDp+V6Hzzjx2h5H2M9DV1WXnzp3MnDmTqKgoJk2aRLFiP5aAoqWlxZo1a4iJiWHcuHEZtSyHDBnyUS3LrPDmzRsWLlyItrY2U6ZMoW3btgQHB7Nnzx5MTU1Z+LsZGgo2lBpqMnaOaUtoaCg7duwgJCSEevXq0bp1a7y8vBQ617/J7fGTjx8/ZsiQIdSuXZuHDx/y999/c/bs2VybsS7FTyqevLPaKYH79++TlpaWYzPUspsqVarg7OxMZGQkffv2ZcGCBWhpaTF79mxev379zc+/ePGCkSNHUqtWLR49esTJkyc5efIkTS3MmNPRRKFa53Y0oer/gvorVqzIggULiImJYd68eZw4cQJjY2N69OihtELVeSkhJyQkhK5du2JlZcXbt29xd3fn7Nmz1KlTR9XSfghvb2/evXtHmzZtlDZHgQIF6NOnD7dv38bNze2jZJf+zm6kKjjcIzEllWOPizFlyhSio6OZPn26wovHV6hQgfnz52fUsjx16hTVqlWjW7du3LhxI1NjvHjxgj///BMtLS3mzp1Ljx49CA8P56+//sLAwCDjfVXLFFXavaFAgQL079+fu3fvsm/fPh4/fkyTJk1o3LgxZ86cUWid0fj4+IyOZbmNDx8+MH/+fAwMDDhy5AirV68mMDCQ9u3b5/iEuy/x5MkTQkNDpfhJBSMZyq+Ql0sG/QiVK1dm9erVREZGMnDgQBYvXoy2tjazZs3i1atXn7w/JSWFVatWYWBgwN69e1m+fDmBgYG0a9cu4z09LTSxb6WYGoWTWhlhY6H5yZ+XLFmSyZMnEx0dzfr167lx4wZ16tTJSEJQ1AISHx9PWFhYrjeUT548YeTIkZiYmHD9+nVcXFzw9/fP9q4pysLd3Z2KFStSs2ZNpc8lk8lo06YNHh4e+Pn5oV+nIVEfCis8flimpk5BzRr0HTVR6aVQ0mtZRkZGZnQ/qVu3Li1btuTChQufvZ4eP36Mvb092trarFixgiFDhhAVFcXatWu/WK9V2fcGdXV1evTowa1btzh27BiJiYm0bt2aevXqcfz4cYXcF/z8/BBC5CpDKZfL2b17N0ZGRsydO5fhw4cTHh7O6NGjc33Zo/T4yUaNGqlYSd5CMpRfISoqCjU1NTQ1PzUnElCpUiWcnJyIiopi8ODBLF26FC0tLWbMmJERtO/u7k6NGjWYMGECNjY2hIWFMW7cuM/ekEZbG+DYxYxCGmpZjptSV5NRSEONxV3MsLX+etxi4cKFGT58OCEhIezdu5cnT55gbW2dkYTwo7X3bt++DeTehJy3b98ya9Ys9PX12bdvH4sXLyYkJIR+/frlqSP89HJB2f2dLC0tseg5ASUVN0BdTcauK5nPMP9RChcuzLBhwwgJCcHV1ZUXL17QvHlz6tevz5EjR5DL5cTGxjJ69Gh0dHTYvHkz48aNIyYmhmXLlvHLL798c47suDeoqanRsWNH/Pz8OH36NEWKFKFTp07UrFmT/fv3k5b2/Rnn3t7elClTBiMjo+8eIzvx9vamQYMG9O3bN6PD1fLly/NM+TwvLy/09fWpVKmSqqXkKfLO6qAEIiMjqVq16jezIfM7P//8M8uXLycqKorhw4fj5OSEpqYm+vr6tG3blp9//hl/f382bNhA+fLlvzpWTwtNzo1vgpXuP4Hr31o80l+30i3LufFNPrsz+SU0NDTo2bMnt27d4uTJkxQsWJDOnTtjZmaGi4sLKSkpmR7r3/j7+1O4cGGqVav2XZ9XFSkpKaxduxZ9fX0WL16Mra0tERERTJw4McuJHDmdBw8eEBgYqJT4yczgEfIMJRU3IE0u8Ah9ppzBv4K6ujo2Njb4+/vj5uZGkSJF6NKlC2XKlEFHR4e9e/fy559/EhMTw/z587OcDJFd9waZTEarVq3w9PTE09OTn3/+GRsbm4zkpO+5L/j4+GBlZZXjd/ajoqLo0aMHDRs2JDU1lYsXL3Lo0KE8l1zo5eUlxU8qAclQfoX8nOH9PVSsWJHp06fTv39/Pnz4QGRkJIULF8bS0jJLT4JVyxRl5+B6nB3XmH71tNAqW/STrhkyQKtsUfrV0+Lc+MbsHFwvI2Yyq8hkMtq1a4eXlxeXL19GV1eXAQMGoK+vz5o1a7LcNcjf358aNWrkijqM8E/m9oEDB6hevTpjxoyhXbt2hIWFsXjx4jyzI/Ff3N3dUVNT++FyQd/Du6RUYrNQjPx7iH2ZoLA2jVlFJpNRpUoVKlWqhEwmIzExEblcTtGiRSlevPgPHZdm972hcePGnD59Gj8/P4yMjBgwYACGhoZs3LiRpKSkTI2RlpbGlStXcvRx95s3b5gyZQrGxsZ4e3uzY8cOrl27lidjDOPi4ggMDJQMpRKQCUVGHucxLCwsMDc3Z8uWLaqWkuNJS0vjr7/+Yvr06Xz48IFp06bRv39/1q5di7OzM3K5HFtbW+zt7alQIesFhd8npRL98j3JqXIKaqihXbaYUrtcBAYGsnjxYlxdXSldujR2dnbY2tpmymCZmZnRsGFD1q9frzR9isLT05PJkydz9epV2rVrh6OjI2ZmZqqWpXS6du3K48ePM2oDZid3H72h/ZrLSp/n5JiGmFTK3pZy169fZ8GCBRw9ehRNTU0cHBwYOHAg4eHhLFmyhD179lCqVCnGjh3L6NGjKVOmzA/PmZ33hoCAABYuXMj+/fupVKkSkyZNYujQoV+tvXr79m1q1qzJxYsXc5xBS68nO3PmTN6/f8/kyZOxt7f/4YoAOZnjx4/TqVMnoqKi0NbWVrWcPIW0Q/kVpB3KzOHp6UmdOnUYPnw47dq1IzQ0lGnTplGlShUWLVpEdHQ048aNY8OGDejo6GBvb8/Tp0+zNEexQhqYVCpJLc3SmFQqqfSWaWZmZuzatYvQ0FB69OjB/Pnz0dTUZNKkSTx69OiLn0tISCAoKCjHx0/euXOHDh060LRpU+RyORcuXODkyZP5wkympKQorVxQZkhOzZ7+2Nk1D/wTc9e2bVssLCy4e/cuW7duJTw8nJEjR1K4cGFMTU1xcXEhPDycXr16sWjRIjQ1NZk4ceIP14bNzntDjRo1cHV1JSgoiBYtWjBx4sSMbmJfqs3r4+ODhoYGFhYWStP1Pbi7u2Nubs7IkSMz7tuzZs3K02YS/jnurlq16heTwCS+H8lQfoHXr18TFxcnlQz6CtHR0XTv3p2mTZtSuHBhrly5wo4dOz453i5btiwLFiwgOjqaiRMnsnnzZnR0dJgwYQKPHz9WkfrMoaury9q1a4mJiWH06NFs2rQJHR0dhg0bRnh4+CfvDwwMRC6X51hD+eDBAwYPHoy5uTn37t3D1dUVPz8/rK2tVS0t2/D19SU+Pl5lhrKgRvbcdpU9jxCCc+fOYW1tTcOGDXnw4AF79+7l3r17DBw48LNH29ra2h/Vsvzrr7/Q0dFhyJAhSq8Nq0iMjY3Zvn07YWFhdOnShZkzZ6Ktrc2cOXM+6crl4+NDrVq1ckwHqTt37tCmTRvatm1LuXLluH79Otu3b6dy5cqqlpYtpMdP5vR41tyIZCi/QFRUFCCVDPoc7969Y8aMGRgbG+Pj44OLiws+Pj7Uq1fvq58rU6YMc+fOJTo6msmTJ7N161Z0dXWxs7P76q5fTqBixYosWrSI2NhY5syZw7FjxzAyMsLGxiajzSL8Ez+poaGBqampCtV+yuvXr5k6dSoGBgYcP36clStXcu/ePWxsbPJU5nZmcHNzo3z58ioz/dpli30S96doZP+bRxmkt91r0KABLVu25N27dxw9epTbt2/Ts2dP1NW/3S4xvZZlbGwsCxYs4OTJkxgbG9O9e/dM17LMCejo6LBhwwYiIiLo379/Rgk1BwcHnj37JzEqPSFH1Tx79oyRI0dibm5OeHg4hw8f5uLFi7m+nmxWePv2Lf7+/lL8pJLIXytJFpBqUH6KXC5n165dGBkZsWzZMuzt7b+rnEzp0qWZPXs20dHRTJ06FRcXF3R1dRkzZozCWrkpi5IlS+Lg4EB0dDTOzs5cu3aN2rVr06ZNGzw9Pblx4wampqYUKlRI1VIBSEpKwsnJCT09PVavXo29vT0RERGMGTMm31YvcHNzo3Xr1ioz0sUKaaD5nUkimUWzbFGFH/2mpaVx4MABatWqRceOHSlQoADu7u5cvXqVTp06fdff508//cSkSZOIiopiw4YN3Lx5k7p169KqVasv1rLMiVSpUoWVK1cSFRXFqFGjWLt2Ldra2gwdOpTIyEiVGsrExEQWL16Mvr4+e/fuZenSpQQFBfH777/nu106Hx8f0tLSclwsa15BMpRfICoqiuLFi0t9Pv+Hn58fVlZW9OvXDysrK+7du8f8+fMpXrz4d49ZqlQpZs6cSXR0NDNmzGD37t3o6elha2vL/fv3Fahe8RQpUoSRI0cSGhrK7t27efToEU2bNsXV1ZWyZcv+cC3LHyW9KLGxsTGTJk2iW7duhIeHM2/ePH766SeValMljx494vbt2yo77k7H2qiCwntUp6OuJsPaMOuJb18iJSUFFxcXTExM6NGjBxUqVMDT05NLly4prMh9ei3L4OBgXF1defbs2Se1LHMDFStWxNHRkZiYGKZMmcKePXuAfxJB0k+9sgshBPv376datWpMnz6dP/74g/DwcCZMmJBvHya9vLyoUKEChoaKKZQv8TGSofwC6Qk5+e0J7r88evSIAQMGUL9+fRITE7l48SIHDhxQaGxpyZIlmTFjBtHR0cyaNQtXV1f09PQYOXIksbHZV6D5e9DQ0KB3797cvn2bI0eOkJCQwPnz56lRowY7d+787lqWP8LZs2epW7cuffv2pWbNmty5c4eNGzdmqoB0Xuf06dMZdQZVSZ96mgrvkpNOmlzQt/6PN2NISkpi48aNGeVyjIyM8PPz48yZM0o7MtTQ0MgII3Fzc6Nw4cJ06dIFExMTtm/frpLr6XsoU6YMs2bNYtCgQZQqVYozZ85gYGDAgAEDCA4OVvr8V69epWHDhtjY2GBqasqdO3dYvXp1vt8gkeInlYtkKL9Afs/wTkxMZOHChRgaGnLq1Ck2btzIjRs3lHpU8NNPPzFt2jSio6OZO3cuBw4cQF9fn+HDhxMdHa20eRWBTCZDS0sLIQTr1q1DW1ub/v37Y2BggLOzc5ZrWX4PN2/epFWrVrRq1YoiRYpw+fJljhw5grGxsdLnzi24ublhaWmp8oXVoGIJGumXU/gupbqajEb65dCv8P39uxMSEli1alXGQ52FhUVGW0JLS0sFqv0y6a0qPT098fb2xsDAgIEDB2aEbrx//z5bdPwoN27coE2bNkRHR7N8+XLOnTtH9erVsbGxISAgQOHzxcbG0qdPH+rVq8e7d+84e/YsJ06ckO4B/NOT/OrVq1L8pBKRDOUXyK+GUgjBoUOHqFatGrNmzWL48OGEhYUxbNiwTAXbK4ISJUpkxCnOnz+fI0eOYGBgwJAhQzJiW3Mi/v7+yGQy+vfvz99//83t27exsrLCzs4ObW1tFixYwOvXrxU+b3R0NH379qV27drExsZy5MgRLl++zK+//qrwuXIzqampnD17ljZt2qhaCgALfzdDQ8GGUkNNxsLfv6/0U3x8PI6OjmhrazNx4kRatGhBUFAQ+/btw9zcXKE6s4KVlRXHjx8nMDCQJk2aMGHCBLS0tJg3b94nGdU5icTERG7cuIGVlRVFixbFzs6OyMhI1q9fz9WrVzE3N6dTp05cvXr1h+d6+/YtM2bMwMjIiPPnz7N582b8/f1p0aKFAr5J3sDPz4/k5GQpflKJSIbyM6SlpREdHZ3vDOXt27dp1qwZ3bp1o3r16ty5c4fly5dTqlQplegpXrw4kydPJioqCkdHR06cOIGhoSGDBg0iIiJCJZq+xs2bNzE2Ns6o41ajRg327NlDWFgYXbt2Zd68eWhqajJ58mSFlEt6+fIlEyZMyFhENm7cyJ07d+jcubN0pPMZrly5wuvXr1UeP5lO1TJFmdPRRKFjzu1okuWuMHFxccyaNQstLS1mzZpF165dCQsLY/v27TlqZ8vU1JSdO3cSHh5Oz549WbhwIVpaWtjb2/9wLUtlcOPGDZKTkz96sCtUqBDDhw8nNDSUHTt2EBISQr169WjdujVeXl5ZniO9oYShoSHLly9n4sSJhIWFMWTIkGzbAMgteHl5UapUqRxXgSMvIRnKz/Dw4UNSUlLyTQ3K58+fM2LECGrXrs3jx485deoUJ0+exMjISNXSAChWrBgTJ04kKiqKpUuX4ubmhpGREX/88QdhYWGqlpeBv7//Z0vR6Orqsn79eqKjoxk1ahQbNmxAW1ub4cOHf5cx/vDhA4sXL0ZPT4/Nmzfz559/Eh4ezrBhw3JNu0dV4O7uTtmyZalbt66qpWTQ00IT+1aKSRCY1MooS73snz59ypQpU9DS0mLp0qX88ccfGTtoOfnep62tjbOzMzExMYwdO5YtW7bkyFqWPj4+FC1alBo1anzyWoECBejfvz93795l3759PHnyhCZNmtC4cWPOnDmTqez28+fPU7t2bYYMGUKzZs0ICQlh/vz5lCjx/eEOeRkvLy8aNWqU78qkZSfS3+xnyC8lg5KTk3FycsLAwABXV1eWL19OYGBgjtnB+S9FixZl/PjxREZGsnz5cs6cOYOxsTH9+/cnJCREpdrS0tK4devWV2sb/vzzzzg6OhIbG8vs2bM5evQohoaG9OrVi1u3bmVqjm3btmFoaMiMGTPo168fERERzJgxI893t1AE6eWCctrOzWhrAxy7mFFIQy3LMZXqajIKaaixuIsZttb6mfrM/fv3GTt2LNra2qxfv54xY8YQHR2Nk5NTripuXaFCBRYsWEBsbCzz58/PcbUsvb29qVev3lcf8tTV1enRowe3bt3i+PHjJCYm0rp1a+rVq8fx48c/ayxDQkLo2LEjLVq0oHjx4ly5coXdu3ejqfnjiVh5leTkZHx8fKT4SWUjJD5h69atAhAfPnxQtRSlcerUKWFkZCTU1NTEiBEjxLNnz1QtKct8+PBBrF69WlSqVEmoqamJ3r17i3v37qlEy927dwUgPDw8Mv2ZhIQEsXbtWqGtrS0A0bZtW+Hp6SnkcvlH75PL5eLvv/8WJiYmAhA9evQQYWFhCv4GeZvHjx8LQOzcuVPVUr5I7Mv3ou+WK0LL4W+hM/VvoeXw5f90p50UWg5/i75brojYl+8zNX54eLgYMmSIKFCggChTpoyYO3euiIuLU/K3yj4+fPggNmzYIHR1dQUgWrZsKS5cuPDJ9ZQdyOVyUb58eTFjxowsf+7MmTOicePGAhA1atQQ+/btE6mpqeLFixdizJgxQkNDQ2hra4t9+/ap5LvlRnx8fAQgrl69qmopeRrJUH6GGTNmiMqVK6tahlIIDg4W7dq1E4CwtrYWt2/fVrWkH+bDhw/C2dlZVKlSRchkMtGzZ09x9+7dbNWwc+dOAYhXr15l+bMpKSli165dwtTUVADCyspKnDhxQqSlpQk/Pz/RpEkTAYimTZtKN8TvZPv27QIQT58+VbWUbxL6JF60+3ObqDJii9D+j5HUdvhbNF56Qcw6dkeEPY3P1Hh3794Vffv2FWpqaqJixYpiyZIlIj4+c5/NjaSkpIi9e/cKc3NzAYh69eqJI0eOiLS0tGzTEBYWJgBx6tSp7x7D09NTtGrVSgCifPnyomjRoqJEiRLC0dExT292KANHR0dRrFgxkZKSomopeRrJUH6G3r17i0aNGqlahkJ59eqVGD9+vNDQ0BA6Ojri0KFDee7pNjExUaxfv15UrVpVyGQy0aNHDxEYGJgtc48fP17o6en90BhpaWnixIkTwsrKSgDip59+EoAwMTERJ0+ezHP/XtlJz549Rd26dVUtI9P89ttvokWLFuJdYoq48/C18I+JE3cevhbvEjO/IPr7+4uuXbsKmUwmqlSpItasWSMSEhKUqDpnIZfLxalTpzJ2+6pVqya2b98ukpOTlTLfv/+tHDfuErIChX9oB1gul4vDhw+LKlWqCEAAomrVqmLDhg0iMTFRgcrzPu3atROtWrVStYw8jxRD+RnyUsmgtLQ0Nm7ciIGBAZs2bWLu3LkEBQXRpUuXPJcJXKhQIUaMGEF4eDgbNmzAz88PMzMzunfvTmBgoMLne5+Uyt1Hb7gZ+wrf4AeY1/mxGn1qampYWlpSs2ZN1NXVSU5OBv7pnR4dHU1iYqIiZOc70tLSOHPmTI6NDf4vQgj8/PyoV68exQppYFKpJLU0S2NSqWSm2in6+vrSoUMHateuza1bt9i8eTMRERGMHj2aIkWKZMM3yBnIZDLatm2bUctSX1+fP/74I6OWpSJqw4Y9fcvs43dpstQD09mnab/mMr+v92F9VCk0Jxyg85ZbzD5+l7Cnb7M0rr+/P9bW1nTp0oXq1asTGBiYUYZs5MiR6OnpsWrVqmypb5vbSUtL4/Lly1L8ZHagakebE6lQoYKYM2eOqmX8MB4eHhnHPv379xcPHz5UtaRsJSkpSWzevDkjRrFLly7i1q1bPzRm6JN4MevYHdF4yYVPjiO1HE6Ixkv+OY4MfZK1I8W3b9+KOXPmiOLFi4uSJUuKxYsXi4SEBHHr1i3Rs2dPoaamJipUqCAWLlz4Xcfq+Zn0+CkfHx9VS8kUUVFRAhDHjx/P9Gfkcrm4cOGCaNasmQBE9erVxa5du6Qjvv8QEBAg+vbtK9TV1UW5cuW+O4703/Gu6fGsioh3ffDggRgwYICQyWSievXqws3N7ZP33Lt3TwwYMECoq6uL8uXLC0dHR/HmzZssf4f8wo0bNwQgLl26pGopeR5ph/I/vHv3jmfPnuXoshnfIioqim7dumFtbU2RIkXw8/Njx44dVKpUSdXSspWCBQtmlBL566+/uHXrFjVr1qRz587cvHkzS2Pdj0ug319+tFzpxU6/GGLiEvg0/1JGTFwCO/1iaLnSi35/+XE/7us7CCkpKWzYsAF9fX0WLFjA8OHDiYyMZPLkyRQpUgRzc3P27t1LaGgov//+O7Nnz0ZLSwsHBweePHmSpe+QX3Fzc6N06dLZ1uXlR/Hz8wOgXr1633yvEIJTp07x66+/0qxZM169esWhQ4cIDAykT58+Uhmp/2BmZsbOnTsJCwvDxsaGhQsXoqmpmaValq7XYmnh5IlP5EuAb7bQTH/dJ/IlLZw8cb32aTvZ9+/fM2fOHAwNDTl58iRr167l9u3bny3Cb2xszPbt2wkLC6NLly7MnDkTbW1t5syZk6MLvasKLy8vChUqhIWFhaql5HkkQ/kfoqKigNxZMujdu3fMmDGDatWq4evry65du/D29s41C6myKFCgAIMGDSI4OJht27Zx584dateuTceOHTNVXkQZC4gQgsOHD2NqasqoUaNo1aoVoaGhLFu2jDJlynzyfj09PTZs2EB0dDQjRozIaO84cuTIHN09KCfg5uZGq1atcly5oC/h5+eHtrY2FSpU+OJ75HI5hw4dok6dOrRv3x6AkydPcuPGDbp06SLV2vsGOjo6ODs7Ex0dnVHLUldXl6FDh361lqWzRxgOhwNJSpVnuRd7mlyQlCrH4XAgzh7/1M+Vy+W4uLhgZGTEwoULsbW1JTw8nJEjR37zYUBHR4cNGzYQERFB//79Wbx4ccbD5rNnz7KkLS/j5eVF/fr1KVSokKql5Hmku85/yI2GUi6Xs3PnToyMjFi+fDmTJk0iJCSEPn36SAvLvyhQoAB//PEHwcHBuLi4EBISQt26denQoQPXrl377GcUvYAAGW0Ru3btira2Nv7+/ri4uKClpfXNMX/55RcWL15MbGwsM2fO5NChQxgYGNC7d2+l9AbO7Tx79ozr16/nmvhJICN+8nOkpqaye/duzMzM6NatG6VLl+bChQt4e3vTrl27PBcXrWwqVqzIggULiImJYd68eRl9r3v06IG/v/9H73W9FsuyM4opnL7sTCjzdp/DwsKCAQMG0KBBA+7du8eSJUsoWbJklsaqUqUKK1euJCoqCltbW9auXYu2tjbjxo3jwYMHCtGbWxFC4OXlJcVPZhOS2/gPkZGRFC5cmJ9//lnVUjKFn58fVlZW9O/fn19//ZV79+4xb948ihcvrmppORYNDQ369etHUFAQu3btIjw8HEtLS9q1a5dx3AiKX0BWHvejc+fONGrUiMTERM6ePcvp06epWbNmlscrVaoU06ZNIzo6mtWrV+Pj44O5uTnt27fn8uXLCtGcFzhz5gxAjunf/S1SUlLw9/f/xFAmJyezZcsWjIyM6Nu3Lzo6Ovj4+HD+/Hmsra0lI/mDlCxZksmTJxMdHc369eu5ceMGderUoXXr1ly8eJHYl++Zdfyu4iYUgs034xFFy3Dp0iUOHDjww5sYFStWxNHRkZiYGKZMmcKOHTvQ09PLCKPJjwQFBfHy5Uupf3c2IRnK/5Ce4Z3Tb9APHz6kf//+1K9fn6SkJDw9Pdm/fz/a2tqqlpZrUFdXp0+fPty9e5c9e/YQHR1N/fr1adOmDcfOXVb4ArLC6yG3wh+we/durl+/TosWLX542KJFi2Jra0tYWBg7d+4kJiaGRo0a0bBhQ06ePJmpFm55GTc3N2rXrk3FihVVLSVTBAQEkJiYmGEoP3z4wJo1a9DT02PYsGHUrl0bf39//v77bxo0aKBitXmPwoULM3z4cEJCQti7dy9Pnz7F2tqaFtP+IiU1TXETyWRoFChI9T/m0bBhQ8WNC5QpU4ZZs2YRExPD3LlzOXLkCIaGhgwYMIDg4GCFzpXT8fLyQkNDg/r166taSr5AMpT/IaeXDPrw4QMLFizA0NAQd3d3Nm3axPXr16Ut/R9AXV2dXr16ERgYiKurKw8ePGDYFg+SUlIVN4lMhnqBgvxqt4bevXsrPBShQIEC9O3bl4CAAI4fP45cLqdDhw6Ym5uzZ88eUlMV+F1yCWlpaZw+fTrX7E7CPycOGhoa6Ovrs2TJEnR0dBg/fjzW1tbcvXuXAwcOUKtWLVXLzPNoaGjQs2dPbt68yV8HTpJcRg85it1kkCPjcvhLwp9lraRQZvnpp5+YMmUK0dHRLF++nPPnz1O9enVsbGzyTXiMl5cXdevWlVrTZhOSofwPOdVQCiE4dOgQ1atXZ/bs2YwcOZKwsDCGDh2aa5INcjrq6urY2Nhw6Jw3RXRqg0yxl4dAhndknNIWEPinluVvv/2Gt7c3Fy9epHLlyvTp0wdDQ0PWr1+fr2pZ3rhxg5cvX+aq+MlLly5RoUIFjI2NmTFjBh07diQkJAQXFxeqVaumann5DplMRmxBrSz3WM8s6moydl35NGlPkRQtWhQ7OzsiIiLYsGEDV69exdzcnE6dOnH16lWlzq1KpPjJ7EcylP9CLpcTFRWV40oG3b59G2tra7p164aJiQl37txh2bJlWQ7elsgce68+yNULCPyzEDZp0gQ3Nzf8/f2xtLRk9OjRaGtr4+joyJs3b5SuQdW4ublRqlSpXHHc9ezZM6ZOncr+/ft5+vQp/fr1IyIigk2bNqGnp6dqefkaj5BnWU7IyyxpcoFHaPZkZBcqVIhhw4YRGhrKjh07CAkJoV69erRu3RovL69s0ZCdREZG8ujRI8lQZiOSofwXT548ITExMcfsUD5//pwRI0ZQu3Ztnj59ipubG3///TdGRkaqlpanySsLSDq1atXC1dWVkJAQOnXqxKxZs9DU1GTq1Kk8ffo0W7VkJ25ubrRs2TJH12J8+PAh48aNQ1tbmzVr1iCXy3FycmLVqlVUrVpV1fLyPe+SUon9Ri3ZHyX2ZQLvk7IvJKVAgQL079+fu3fvsm/fPp48eUKTJk1o3LgxZ86cyTNx156enshkMoXHqEp8GclQ/ov0TDhVG8rk5GScnJwwMDBg3759ODk5ERAQkKtiwXIreXEBSUdfX5+NGzcSFRXF8OHDcXZ2RktLi1GjRmWUy8orvHjxgqtXr+bYayYqKooRI0agq6vLjh07mDx5Mjt27ACgVatWKlYnkU7My/efaWCgWAQQ/fK9kmf5FHV1dXr06MGtW7c4fvw4iYmJtG7dmnr16nH8+PFcbyy9vLyoWbOmdJKXjUiG8l+kL6qqPPI+deoUZmZm2Nvb07t3b8LCwhg7diwFChRQmab8RF5eQNKpVKkSS5YsITY2lhkzZnDgwAEMDAzo27evUnqeq4L0nZacZiiDg4MZMGAABgYGHD58mLlz5xITE8Ps2bMJCgqiVKlSGBgYqFqmxP9ITpXnqXk+h0wm47fffsPPz48zZ85QpEgROnXqRM2aNdm/fz9paQrMbs9GpPjJ7EcylP8iMjKSihUrqiQjLDg4mHbt2tG+fXsqV67MzZs3WbduHeXKlct2LfmZ/LCApFO6dGlmzJhBTEwMK1eu5NKlS9SoUYMOHTrg7e2tank/hLu7O+bm5jmm3ejt27exsbGhevXqnD9/nuXLlxMdHc2UKVP46aefgH8yvC0tLaVmBDmIghrZ82+RXfN8DZlMRsuWLfH09MTT05Off/4ZGxsbTExMcHFxISUlRdUSM839+/eJioqSDGU2o/pfcQ5CFRner1+/Zvz48ZiZmREcHMzhw4c5f/48NWrUyFYdEv+QnxaQdIoWLcro0aMJDw9nx44dREVF0bBhQxo1asSpU6dy3dGXXC7H3d09R2R3+/n50bFjR2rWrMm1a9cyWuXZ2dlRtGjRjPcJIb7aIUdCNWiXLabgYkGfIvvfPDmJxo0bc/r0afz8/DAyMmLAgAEYGhqyceNGkpKSVC3vm6QnGTVq1EjFSvIXOWdVywFkp6FMS0tj48aNGBgYsHnzZubNm0dQUBC///57ji+qnpfJrwsI/H+wfmBgIEePHiUlJYX27dtTs2ZN9u7dm2tqWfr7+/P8+XOVGUohBJ6enrRs2ZL69esTFhaGi4sLoaGhDBs27LM9haOionjx4oVkKHMYxQppULVM0W+/8QfQLFuUYoVyZuKYpaUlx44d4/bt29SrV4+RI0eip6fHqlWrSEhQbqz5j+Dl5UX16tUpX768qqXkKyRD+S+yy1B6eHhQu3ZtRowYQYcOHQgLC8PBwYHChQsrfW6Jr1OskAaa+XgBgX9qWXbq1AlfX188PDz45Zdf6N27N0ZGRmzYsCHH17J0c3Pjp59+yvZOMkII3N3dady4MU2bNuX58+fs37+fO3fu0K9fv69mm6e3/LS0tMwuuRLfIL1v+uMbZxFy5cQRqqvJsDasoJSxFUmNGjVwdXUlKCiIFi1aMHHixIwSZPHx8aqW9wlS/KRqkAzl//jw4QOPHj1SakJOVFQUXbt2pVmzZhQrVoyrV6+ybds2fvnlF6XNKZF1rI0qKK0OpZCnkRTpj4+PT44/SpbJZDRt2hR3d/eM3sajRo1CW1ubxYsX58iFBP6Jn2zRokW2JbLJ5XKOHj2KhYUFbdu2JSUlhRMnTnDz5k26d++eqcYDfn5+6OrqSjsqOYDExETWr1+PoaEhffv2pWpiNDI15TSPSJML+tbXVMrYysDY2Jjt27cTFhZGly5dmDVrFlpaWsyePZu4uDhVywP+qekaHBwsGUoVIBnK/xEdHQ0op2TQu3fvmD59OtWqVcPPz4/du3fj7e2NhYWFwueS+HH61NNUWh1KmZo6L/2O8Ouvv2JhYYGLi0uuiEmqXbs2+/fvJyQkhN9++40///wTTU1Npk+fzrNn2VtX82vExcVx5cqVbDnuTktLY+/evdSoUYPff/+dEiVKcO7cOXx9fenQoUOWQlek+EnV8+bNGxwdHdHW1mb06NFYWlpy8+ZNLhzdQyP9cgp/yFRXk9FIvxz6FUoodNzsQEdHJyMeeMCAASxZsgQtLS0cHBxUfj+4dOkSgGQoVYBkKP9HeskgRRpKuVyOi4sLhoaGrFixgsmTJxMSEkLv3r2lOMkcjEHFEkpdQEKueXHq1CnKly/PgAED0NTUZObMmTx69Eih8ymD9JjfqKgohgwZwqpVq9DS0mL06NEZD2Wq5OzZs8jlcqWWC0pOTmbr1q1Uq1aN3r17o6mpyeXLl/Hw8KB58+ZZvraTk5O5efOmZChVxNOnT5k2bRqamprMmjUro92lq6srNWvWBGDh72ZoKPh+oKEmY+HvZgodM7upUqUKK1euJCoqCltbW9auXYu2tjbjxo3jwYMHKtHk6emJnp4elStXVsn8+RnJUP6PyMhIChYsqLAyI1euXKFBgwYMGDCARo0aERwczNy5c6Um9bkEZS4gampqtG3bFjc3N4KDg+nRowdOTk5oaWnRu3dvrly5kuOPwytXrsyyZcuIjY1l2rRpuLq6oq+vT79+/bhz547KdLm5uWFqakqVKlUUPnZiYiLr1q3DwMCAwYMHY2pqyvXr1zl16hS//vrrd48bEBBAUlKSZCizmejoaGxtbTO6FA0bNoyoqCg2bdqEvr7+R++tWqYoczqaKHT+uR1NlJ7wk11UrFgRR0dHYmJimDJlCi4uLujp6TF8+PCMhiHZhRQ/qTryvaF8n5TK3UdvuB71Ak1zKxJTf2whf/jwIf369aNBgwakpKTg6enJvn370NLSUpBiiewguxYQIyMj1qxZw4MHD1i2bBlXr16lQYMG1KtXj127duX44/AyZcrw559/EhMTw4oVK/D09MTMzIyOHTvi4+OTrVqUVS7o3bt3LF++HB0dHcaMGUPDhg0JDAzk8OHD1KlT54fH9/Pzo0CBAhm7YRLKJT1JSl9fn3379jFt2jRiYmJYunTpVzcUelpoYt/KUCEaJrUywsYi98ROZpYyZcowa9YsoqOjmTt3LkePHsXQ0JABAwYQHByslDnT1/Cbsa+4EvyAwHuhkqFUETKR07dClEDY07fs9ovFI+QZsXEJH3VGkQGaZYpibVSBPvU0MaiYufiWDx8+sHz5chYtWkSxYsVYuHAhAwcOzFRAvkTOxdkjjGVnQn94nEmtjLC11v/m++RyOW5ubqxevZozZ85QsWJFhg8fzogRI3JF8lZKSgp79uxh8eLF3Lt3j8aNG+Pg4ECbNm0UHubxPimV6JfvSU6VU1BDjTcPwvm1Xl0uXLiAtbX1D4//+vVrnJ2dWblyJW/evGHAgAE4ODh8snv1o/Tv35979+5x7do1hY4r8TG+vr4sWrSIEydOULVqVezt7Rk8eHCWT41cr8Uy6/hdUuUiS7HW6moyNNRkzO1okifN5OdISEhgy5YtLFmyhEePHtG9e3emT5/+w3WWv7aGCyGoXLIgrUwqZ2kNl/hx8pWhvB+XwLQjgVwKf4G6muyrN4P01xvpl2Ph72ZfPJoQQnDo0CHs7e159OgRdnZ2zJgxQ+ofmodQ1QJy7949nJ2d2bFjB8nJyXTv3p2xY8fmiqNRuVzO8ePHWbRoEVevXqVmzZo4ODjQrVu3H3rI+tpCAoK010/5o7UF/RvofPdC8vz5c1auXImzszNJSUkMGTKEyZMno6mpHBNgZGREy5YtcXZ2Vsr4+RkhBKdPn8bR0RFPT0+MjY2ZMmUKvXv3pmDBgt897v24BGy3XybgeQpqMvjabSGza0leJikpiR07drBo0SKio6Pp2LEj06dPz3KZLGWs4RKKI98Yyh81BXM6mtDzP6bg1q1b2NnZ4eXlRYcOHVi+fDmGhoo5EpHIWfz7RibkaV8tI6LoG9nr16/Zvn07a9asITIyEktLS8aOHUv37t1/aFHMDoQQXLx4kUWLFnH27Fn09PSYPHky/fv3z1Ld1exYSB49esTy5cvZsGEDMpmMkSNHMmHCBKXuDL969YoyZcrg4uJCv379lDZPfiMtLY1Dhw7h6OjIzZs3sbCwYOrUqXTq1ElhrS3Hjx/PPjdPhizegWfYC2Jffua0q2xRrA0r0Le+Zq7M5lY0KSkp7N27l4ULFxISEkKrVq2YPn16po6olbGGSyiWfGEoFXVsad/KkNHWBjx//pwZM2awefNmjI2NcXJyonXr1gpQKpHTme20EWf3W1Rv3o37rxKzdQFJS0vLOA4/e/YsP//8MyNGjGD48OH8/PPPCp1LGdy4cQNHR0cOHTpExYoVmTBhAsOHD8/oZf0llL2QREdHs2TJEv766y+KFCnCmDFjsLOzo1y5cpme63s5ffo0bdq0ISQkRHoYVQBJSUns3LmTJUuWEBYWRosWLXBwcKBZs2YKDblIS0ujatWqdO/enVWrVgGfhmBoly2WoxsYqJJ0w79gwQICAgJo1KgRM2bMoGXLlp/9d1L0Gi6hHPK8oXS9FovD4UCFjde8+BMOLZ2ITCZjzpw5jBw5MtsKKEuonq5du/Ls2TMuXbqk0gUkKCgo4zg8JSUFGxsbxo4dmytqm4aEhLB06VJcXFwoVqwYtra2jB07lgoVPu0YosyFJDQ0lEWLFrFr1y5KlizJhAkTsLW1zdZwlblz57Jy5UpevnwplRL7Ad6+fcumTZtYsWIFjx8/5vfff8fBwUFp14OnpydNmzbFx8cn2zsy5SWEEPz999/MmzePa9euYWFhwYwZM/jtt98yrgdFr+GLu5jlmxjW7CZPG8r7cQm0cPIkKVWumAGFQJ6aTPNEH5bOdsiWHQyJnINcLqd8+fKMHj2aOXPmqFoO8M9x+NatW3F2diYqKor69eszduxYunbtmuOPwx8+fMiKFSvYuHEjcrmcwYMHZ7R0A+UtJIGBgSxcuJD9+/dTsWJFJk2axLBhw1RS0qt9+/akpaXh7u6e7XPnBV68eMHq1atxdnbm7du39O3blylTpmBsbKzUeUeNGsWpU6eIioqSHgQUgBCCc+fOMX/+fLy8vKhRo8Y/MZbWbWm9+rLi1nCgkIYa58Y3kWIqlUCeLhs07UggqYrseCKToVGwEKm1uktmMh8SEBBAXFycQjKIFUWpUqWYMGECYWFhHDt2jGLFitG7d2+0tbWZO3cuT58+VbXEL1K5cmWWL19OTEwMDg4O7N27F319ffr37895v1vMOn5XofPNOBpI2+79qFGjBr6+vqxdu5bIyEjGjx+vEjMphJA65Hwn9+/fZ9y4cWhpabF8+XL69+9PZGQk27ZtU7qZTE1N5cCBA9jY2EhmUkHIZDJatmyJp6cnnp6e/Pzzz9jY2GA9ZTMpqYrto54qF0w7orgHVYn/J8/uUIY9fUvLlV5KG//c+MZSkHU+w8nJiWnTpvHq1assJZRkN3fv3mXNmjW4uLiQlpaWcRxet25dVUv7Ku/fv2fLli0sW7aM5F+HUUS7JsgU98wr0lJRexHBrMZl6NOnj8pDVSIiItDX1+fkyZO0a9dOpVpyC8HBwSxZsoRdu3ZRvHhxxowZw5gxY7L1Af/MmTO0bt0af39/atWqlW3z5jcOn/NhwvlXShtfWsMVT57dodztF6vw1nnpqKvJ2HUlViljS+RcLly4gJWVVY42kwAmJiZs2LCBhw8fsnDhQi5duoSFhQVWVla4urqSkpKiaomfpVixYtjZ2XHa9zZFdGor1EwCyNQ1EBWNaNiuq8rNJPxT0BzIcumU/Mj169fp2rUr1atX5/Tp0yxatIiYmBjmzJmT7adFrq6uGBgYSIXolUxAQklpDc9l5FlD6RHyLEsZoVkhTS7wCH2mlLElciapqal4eXnRrFkzVUvJNKVLl2bixImEh4dz9OhRihQpQq9evdDW1mb+/Pk8e5Yzf8P7/R/ni4XEz88PPT09KXzmCwghOH/+PC1atMDCwoKAgAA2btxIZGQkEydOpESJ7N9dSkpK4vDhw/Ts2VM67lYy0hqe+8iThvJdUiqxcQlKnSP2ZQLvk1KVOodEzsHf35/4+PgcFT+ZWdTV1enUqRPnz58nICCADh06sHDhQqpWrcoff/zBjRs3VC3xI/LLQiLFT34euVzOkSNHqF+/Pi1atODly5fs27eP4OBghg4dSqFChVSm7fTp07x584aePXuqTEN+QFrDcyd50lDGvHyPsgNDBRD98r2SZ5HIKXh4eFCsWLFcUZbna5iZmbFx40YePHjA/Pnz8fDwoG7dujRs2JD9+/er/Dg8vywkSUlJ3Lx5UzKU/yIlJYXt27djYmJCly5dKFKkCG5ubvj7+9OjR48c0cbW1dUVMzMzqlevrmopeRppDc+d5ElDmazAEgM5YR4J1XPhwgUaNWqUI2LvFEGZMmWYNGkSERERHD58mAIFCmBjY4OOjg4LFizg+fPnKtGVXxaS27dvk5ycLBlK/knGWr16NXp6egwcOBADAwN8fHy4ePGiUnrAfy/v37/n2LFj0u5kNiCt4bmTPGkoC2pkz9fKrnkkVEtycjKXL1/Olcfd30JDQ4Pff/8dDw8Pbt++Tdu2bZk/fz5Vq1Zl4MCB3Lx5M1v15JeFxM/Pj4IFC+brxI5Xr14xb948tLW1mTBhAk2aNCEwMJDjx4/nyGLhJ0+eJCEhARsbG1VLyfNIa3juJE/+bWqXLYayn2ll/5tHIu9z9epVEhISclVCzvdQo0YNNm/ezIMHD5gzZw7nz5+ndu3aNGrUiAMHDmTLcXh+WUj8/PyoWbOmSuMBVcWjR4+YNGkSmpqaLFy4kB49ehAWFsbOnTsxNTVVtbwv4urqioWFBXp6eqqWkueR1vDcSZ40lMUKaaCp5Cr4mmWLSn1a8wkeHh6ULFky39ScK1u2LFOmTCEyMpJDhw6hrq5Ojx490NXVZdGiRbx48UJpc+eXhSQ/JuSEh4czbNgwdHR02LRpE2PGjCE6Opq1a9eio6Ojanlf5c2bN5w6dUo67s4mpDU8d5InDSWAtVEFpZUeUQOaGpZXytgSOY8LFy7QpEmTHJEUkJ1oaGjQpUsXLl68yK1bt2jdujVz586lSpUqDB48mFu3bil8zvywkLx8+ZLw8PB8Yyhv3bpFz549MTIy4tixY8yZM4fY2FgWLlxIxYoVVS0vUxw7doykpCR69Oihain5gtevX1MsPhohV2yXnHTU1WRYG1ZQytj5mTxrKPvU01Ra6RE5cHTpRA4fPoxcLgX15mU+fPiAr69vnoyfzArm5uZs2bKF+/fvM3v2bM6cOUOtWrVo0qQJhw4dIjVVMVnTKSkpVEh7DkpaSIQ8jbg7lzh8+DBpacqZ41tcvXoVIE8bSiEEXl5etGvXjlq1auHn54ezszPR0dE4ODhQsmRJVUvMEq6urjRq1IgqVaqoWkqeJi4ujpkzZ6KtrY3nXwuQqSnnIT5NLuhbX1MpY+dn8qyhNKhYgkb65RS+S6muJqN6GTXKFkiha9eu1KhRg71796pscZJQLr6+viQlJeX5+MnMUq5cORwcHIiKiuLAgQMIIejWrRu6urosXryYly9ffte4KSkpbNmyBUNDQ44tswclLSQyNXWKPfKna9eu6Ovrs3z5cl6/fq2Uub6En58fZcuWzZOxeEII/v77bxo2bEiTJk24f/8+u3btIiwsjJEjR1KkSBFVS8wyL1684OzZs9JxtxJ59uwZDg4OaGlpsWzZMgYNGkT4jUtKW8Mb6ZeT2i4qgTxrKAEW/m6GhoJ/jBpqMjYObsKFCxe4fPkympqa9O7dm2rVqrFt2zaV1/GTUCweHh6ULVs2RycLqAINDQ26deuGl5cX/v7+tGjRglmzZlGlShWGDBlCQEBApsZJTk7OMJJDhw7FwsIC/4unlLqQXDlzlOvXr9OoUSOmTp1KlSpVGD16NKGhoQqd70v4+flhaWmZY8rhKILU1FR2795NjRo1+O233wA4ceIEt2/fpk+fPmho5N5YtfSTqG7duqlaSp7j8ePHTJgwAW1tbdauXYutrS3R0dGsWLGCX375RWlr+MLfzRQ6psQ/5GlDWbVMUeZ0NFHomHM7mlD1fzFev/76K6dOneL69euYmpoyaNAgDAwMWL9+PYmJiQqdV0I1eHh4YG1tjZpanr5UfohatWqxdetW7t+/z59//om7uzvm5uY0bdqUw4cPf/Y4PN1IGhkZMWzYMCwtLQkMDGT//v2YmpoqfSGpU6cOLi4uxMTEMHHiRPbv34+RkRHt27fn7NmzCKGccBkhBFevXs0zx92JiYmsX78eQ0ND+vbtS9WqVfH09OTy5ct06NAhT1w3rq6uNG/enAoVpJg7RXH//n1Gjx6Njo4OW7duxd7enujoaBwdHT/6e1b2Gi6hWHL/1f4NelpoYt/KUCFjTWplhI3Fp3EXderU4fDhwwQGBmJlZcXo0aPR1dXFycmJ9++lSvy5lXfv3uHn55fv4yczS/ny5Zk2bRpRUVHs37+ftLQ0unbtip6eHkuWLCEuLo7k5GQ2b96MoaFhhpEMCAhg3759H+0CZ9dC8ssvv2QkiWzbto2HDx/SqlUrTE1N2bRpEwkJiu3aEx4eTlxcXK43lG/evMHR0RFtbW1Gjx6NpaUlN2/e5NSpUzRu3DjP7L4+fvyYixcvSsfdCiIqKorhw4ejp6fH3r17mT59OtHR0cydO5eyZct+9jPZsYZLKAaZUNajeA7D9Voss47fJVUuspSso64mQ0NNxtyOJpn+IYaGhrJo0SJ27txJmTJlmDBhAqNGjeKnn376XvkSKuD06dO0adOGe/fuYWxsrGo5uRJ/f3/WrFnDnj17EEJQqFAh3r17R48ePfjzzz+/GUrg7BHGsjM/fhQ9qZURttb633xfejLJypUrOXbsGKVLl2bYsGHY2toqJCFj165d9OvXj5cvX1KmTJkfHi+7efr0KatWrWLt2rUkJiYyYMAAJk+ejL7+t/9ucyNr1qxh4sSJPH36lNKlS6taTq4lLCyMhQsXZqyJEydOZNSoUZQokfk4xuxcwyW+jzy/Q5lOTwtNzo1vgpXuP09B34rPSn/dSrcs58Y3ydIP0dDQkG3bthEeHk7Xrl2ZNWsWWlpazJ49m7i4uO//EhLZyoULF/j5558xMjJStZRci6mpKVZWVlSoUIGUlJSM5LXnz58THh7+zWS20dYGOHYxo5CGWpZjKtXVZBTSUGNxF7NMmUkAmUxGkyZNOHLkCOHh4QwYMIB169ahra1Nz549uXLlSpY0/Bc/Pz8MDAxynZmMjo7G1tYWbW1t1qxZw7Bhw4iKimLTpk151kzCP8fdbdq0kczkd3Lv3j369u2LsbEx7u7uLF26lKioKKZMmZIlMwnZu4ZLfB/5Zofy34Q9fctuv1g8Qp8R+zLho97BMv6pU2dtWIG+9TUVkgn28OFDli1bxsaNG1FXV8fW1pYJEyZIMTk5HEtLSwwMDNi9e7eqpeQ6kpOT2b59OwsWLOD+/fsZO5KGhoYcPnyY1atX4+Pjg5aWFqNHj2bw4MFfXbTvxyUw7Uggl8JfoK4m++oORfrrjfTLsfB3sx+Ol3r79i3bt29n9erVhIeHY2lpybhx4+jWrVuWe7vXq1cPAwMDdu3a9UOasos7d+6wePFi9u7dS+nSpbGzs8PW1jZfGKyYmBi0tbXZvXs3vXv3VrWcXEVAQADz58/n4MGDVK5cGQcHBwYPHkzhwoUVMn52r+ESmUTkc94lpog7D18L/5g4cefha/EuMUVpcz19+lQ4ODiI4sWLiyJFigg7Ozvx4MEDpc0n8f28fv1aqKmpic2bN6taSq4iKSlJbNy4UWhqagqZTCZsbGzEnTt3Pvvea9euif79+4uCBQuKIkWKiGHDhonAwMCvjh/6JF7MOnZHNF56QWg7/C20/vWftsPfovHSC2LWsTsi7Gm8wr9bWlqaOHHihGjevLkARKVKlcSCBQvE8+fPM/X5xMREUbBgQbF69WqFa1M0Pj4+4rfffhOAqFq1qli1apV49+6dqmVlK0uWLBGFCxcW8fGK/y3lVa5fvy46deokAKGtrS02btwoEhMTlTpndq7hEl8n3xtKVfDy5Usxa9YsUapUKVGwYEExYsQIERUVpWpZEv/i+PHjAhARERGqlpIryIqR/C9PnjwRc+fOFb/88osARLNmzcTRo0dFamrqVz+nyoUkICBADBkyRBQuXFgULlxYDBky5Jtm+MqVKwIQfn5+2aQya8jlcuHu7i6aNGkiAGFsbCy2bdsmkpKSVC1NJdSuXVt069ZN1TJyBb6+vqJdu3YCEAYGBmLbtm0iOTlZ1bIkshnJUKqQN2/eiEWLFoly5coJdXV18ccff4iQkBBVy5IQQowbN05oamoKuVyuaik5mv8ayZ49e4q7d+9+91h79+4VDRo0yNjhWLZsmYiLi1OwasXx/PlzsWDBAlGpUiUBiObNm4sTJ06ItLS0T967atUqUbBgQaXv2GSV1NRUsW/fPlGrVi0BCAsLC3H48OHPfof8QkhIiADEwYMHVS0lR+Pp6SlatGghAFGtWjWxe/dukZIi7RDmVyRDmQN49+6dcHJyEr/88otQU1MTPXv2FAEBAaqWla8xNzcXf/zxh6pl5FiSkpLEhg0bFGIkP8fVq1dFv379RIECBUTRokXFiBEjFDq+oklOThZ79uwRlpaWAhD6+vpi9erVHx2X9u7dW9SrV0+FKj8mMTFRbN68WRgYGAhAtGjRQpw/f156iBJCzJ07VxQvXlwkJCSoWkqOQy6Xi7Nnz4rGjRsLQNSoUUMcOHAgXz+ASPyDZChzEB8+fBDr168XWlpaAhCdO3cW169fV7WsfMfz588FIHbs2KFqKTkOZRvJ//L48WMxZ84c8fPPP2eYnuPHj3/zOFyV+Pr6ChsbG6Guri5++uknMX78eBEZGSn09PTE2LFjVS1PxMfHi2XLlolKlSoJmUwmunTpIq5evapqWTkGuVwuqlWrJvr27atqKTkKuVwuTp06JerXry8AUadOHXH06FHJSEpkIBnKHEhycrLYunWr0NfXF4Bo27at8Pb2VrWsfMPBgwcFIGJjY1UtJceQ3Ubyc/Pv3r1b1KtXTwBCV1dXrFixQrx69SrbNGSV2NhY4eDgIMqUKSNkMpkAxIwZM1S2A/j8+XMxc+ZMUbp0aaGhoSEGDhwo7t27pxItOZmAgAABiL///lvVUnIEcrlcHD16VNStW1cAokGDBuLUqVPSTrbEJ0iGMgeTmpoq9uzZI0xMTAQgrK2tpSOpbGDUqFFCX19f1TJyBOlGsmrVqkImk4levXqp/Oj5ypUrok+fPqJAgQKiWLFiYuTIkSIoKEilmr7G+/fvxejRowUgAGFubi62bdsmPnz4kC3zx8bGCjs7O1G0aFFRtGhRYWdnJz0sfYVp06aJ0qVL59tkpHTS0tLE/v37RY0aNQQgmjRpIs6dOyetPxJfRDKUuYC0tDRx6NChjKB5Kysr6QlRiVSrVk0MHTpU1TJUSmJioli/fv1HRjKnmbZHjx6JWbNmiYoVKwpAtGzZ8osJMapm5syZoly5cuL06dOiffv2AhDly5cXM2fOFI8fP1bKnPfu3RMDBw4UBQoUEKVLlxYzZ87MdImj/IpcLhe6urpiyJAhqpaiMlJTU8Xu3btFtWrVMsJMPD09VS1LIhcgGcpchFwuFydPnsyIYaldu3a+z8ZUNI8fPxaA2Lt3r6qlqITcYCT/S2Jioti1a5ewsLAQgNDT0xNOTk7i9evXqpaWQevWrUW7du0y/j8kJETY2tqKYsWKiQIFCoh+/fopLF762rVrokuXLkImk4lKlSqJZcuWSbUUM8nVq1cFIM6dO6dqKdlOcnKy2LZtW0aSVrt27YSPj4+qZUnkIiRDmQuRy+Xi/PnzwtraWgDCxMRE7NmzJ0cnKuQW9uzZIwCl7RrlVHKjkfwcV65cEb179xYaGhqiWLFiwtbWVuVxgnK5XJQuXVrMmTPnk9devXolli1blpGI17BhQ3Hw4MEsl15Jvyekl3DR19cXmzdvznElinI6EyZMEBUrVsxX99LExESxceNGoa2tLSWDSvwQkqHM5Vy+fFm0bds2o6Ds1q1bpYKyP8DQoUNF9erVVS0j20hMTBTr1q0TVapUETKZTPTu3TtXGsn/8vDhQzFz5kxRoUIFAYjWrVuLkydPqmQ3P72mobu7+xffk5KSIg4dOpRRikVTU1MsXbr0mzU409LSxOHDhzPKFdWqVUvs27cvXxkiRZGWliYqV64sRo8erWop2cKHDx/EmjVrMq79Hj16iNu3b6talkQuRjKUeYTr16+Lzp07C0BoaWmJ9evXZ1vQf15CT09P2NraqlqG0smrRvK/JCYmChcXF1GnTp2MnbtVq1aJN2/eKHXef3fxWbxpt5AVKJzpAu3+/v5iwIABomDBgqJo0aJi1KhRIjg4+KP3pB9PGhsbZyRMuLu7S3HVP8ClS5cEIC5fvqxqKUrl/fv3YsWKFRl1j/v06ZMnr32J7EcylHmMgIAA0bNnz4z4KScnJ/H+/XtVy8oVxMTECEAcOnRI1VKUxueMpKqPhLMDuVwufHx8RM+ePYWGhoYoXry4GD169CdG7UfI6DO+5NM+41pTTojGS/7pMx76JHPxjE+ePBGzZ8/O2GVt27atOHbsmFi5cqWoWrWqAMRvv/0mxbkpCFtbW1G1atU8G5MeHx8vHB0dRfny5TPKRoWGhqpalkQeQiaEEEjkOUJCQnB0dGTnzp2UKVOGCRMmMGrUKH766SdVS8ux7Nixg4EDB/L8+XPKli2rajkKJSkpia1bt7Jw4UIePXpEr169mDFjBsbGxqqWlu08fPiQDRs2sHHjRp4/f06bNm0YO3YsrVu3Rk1NLcvj3Y9LYNqRQC6Fv0BdTUaa/Mu31PTXG+mXY+HvZlQtU/Sb4yclJfHXX38xf/58Hj9+DIClpSVr1qzB0tIyy3olPiU1NZXKlSvTv39/li5dqmo5CuX169esWbOGlStX8vbtWwYNGoSDgwPa2tqqliaRx5AMZR4nKiqKJUuWsHXrVooWLYqdnR1jx46lTJkyqpaW4/jjjz+4ffs2N2/eVLUUhZFuRhYtWpTvjeR/SUxMZP/+/axatQp/f38MDAwYM2YMAwYMyPSDl+u1WGYdv0uqXHzVSP4XdTUZGmoy5nQ0oaeF5hff9+jRI5ycnNiwYQOpqam0bduWhIQEzp49S8mSJRk2bBi2trZUrVo103NLfMq5c+do2bIl169fp06dOqqWoxDi4uJYuXIlq1evJjExkaFDhzJ58mTptyKhNCRDmU94+PAhS5cuZdOmTWhoaGBra8v48eOpUKGCqqXlCIQQaGlp0a1bN1asWKFqOT+MZCQzjxACX19fVq9ezcGDBylatCgDBw5k9OjRGBgYfPFzzh5hLDsT+sPz27cyZLT1x/OEh4ezZMkSduzYQeHChbG1tcXOzo6KFSsCEB0djbOzM1u2bOHdu3d07doVOzs7GjRogEwm+2FN+Y0hQ4bg6elJaGhorv/7e/bsGStWrGDt2rXI5XJGjBiBvb09v/zyi6qlSeRxJEOZz3j69ClOTk6sXbuWtLQ0hg8fjr29PZUrV1a1NJUSERGBvr4+J06coEOHDqqW891IRvLHePDgQcZx+IsXL2jXrh1jx46lZcuWHx2Hu16LxeFwoMLmXdzFDBsLTW7duoWjoyMHDhygfPnyjB8/nhEjRlCyZMnPfu7du3fs2LGDVatWERYWhoWFBXZ2dnTv3p2CBQsqTF9eJjk5mYoVK2Jra8v8+fNVLee7efz4MUuXLmXDhg2oq6szevRoJkyYQPny5VUtTSKfIBnKfEpcXByrV69m1apVJCQkMGjQIKZMmZJv42o2b97MiBEjiIuL++LinZNJN5ILFy7k8ePH9O7dmxkzZmBkZKRqabmSxMREOstGkgAAI0pJREFUXF1dWb16NTdv3sTIyIgxY8bQv39/Xqeo08LJk6RUucLmK6AGWnddOH9sPzo6OkyaNIk//viDIkWKZOrzcrkcNzc3Vq1axdmzZ/nll18YNWoUw4cPlwzFN/j777/57bffCAwMxNTUVNVyssz9+/dZvHgxW7ZsoXDhwowdO5Zx48ZJYU0S2Y5kKPM58fHxrFu3juXLl/P69Wv69u3L1KlTMTQ0VLW0bKV3795ERETg5+enailZIjExMWNHUjKSikcIgbe3N6tXr+bw4cMUK1YMw+GridMoR5oC75wiLRWNuCjmNatAjx490NDQ+O6x7t69y+rVq3FxcUEIQd++fbGzs8PMzExxgvMQffv25datW9y5c0fVUrJEVFQUjo6ObNu2jRIlSjB+/HhGjx5NqVKlVC1NIp8iGUoJAN6/f8+mTZtYunQpT58+xcbGhmnTpuXKJ/asIoSgUqVKDBgwAEdHR1XLyRSSkcx+7t+/j+O67ZykttLmODe+MfoVSihkrJcvX7J582acnZ15+PAhzZo1w87Ojvbt26Ourq6QOXI7CQkJVKxYkSlTpjBjxgxVy8kUYWFhLFy4MKOCh729PSNHjqRECcX8biQkvpes18iQyJMUK1aM8ePHExkZibOzM97e3piZmdGlSxdu3LihanlKJTg4mCdPntCsWTNVS/kmiYmJrF27Fn19fcaOHYu1tTVBQUHs3LlTMpNKpmrVqpRv0AV1JeVsqKvJ2HUlVmHjlS1bFgcHB6KionB1dSUhIYFOnTphZGTEqlWriI+PV9hcuZVTp07x7t07bGxsVC3lmwQFBdGnTx+MjY05ffo0y5YtIzo6msmTJ0tmUiJHIBlKiY8oXLgwI0eOJDw8nK1btxIYGEjdunVp164dPj4+qpanFDw8PChQoAC//vqrqqV8kcTERJydndHT02Ps2LE0a9aMe/fuSUYym/EIeabQo+5/kyYXeIQ+U/i4BQoUwMbGBl9fX65cuYKlpSX29vZUqVKFcePGERERofA5cwuurq7UqVPnq9n8qiYgIIAePXpgamrKpUuXWL16NZGRkYwbN46iRb9dx1RCIruQDKXEZylQoAADBw7k3r177Nmzh9jYWH799VeaNWvGhQsXyEuREhcuXKBevXoUK1ZM1VI+4d9G0s7OjubNm3Pv3j1cXFzyXZyrqnmXlEpsXIJS54h9mcD7pFSljV+vXj327NlDdHQ0Y8aMYdeuXRgYGNC5c2c8PDzy1HX9LeLj4zl58iQ9e/ZUtZTPcuPGDTp37oy5uTnXr19n48aNhIeHY2trS+HChVUtT0LiEyRDKfFVNDQ06NWrFwEBARw6dIjXr1/TvHlzGjZsiJubW65fgORyORcvXsTa2lrVUj5CMpI5j5iX71H2r10A0S/fK3kWqFy5MgsWLOD+/fts2rSJ8PBwmjVrRs2aNdm6dSuJiYlK16Bqjh8/TmJiIj169FC1lI/w9fWlXbt21K1bl6CgILZv305ISAhDhw6VSkFJ5GgkQymRKdTU1DLiKU+ePIlcLs+46R05cgS5XHElVLKTwMBAXr58mWMM5X+NZIsWLSQjmUNIVmCZoJwwD0CRIkUYMmQIgYGBnDt3Dk1NTQYPHoympiZ//vlnRqvHvIirqyu//vormppf7lSUnXh5edGyZUusrKyIiYlh9+7d3Lt3jwEDBlCgQAFVy5OQ+CaSoZTIEjKZLCOe8vz58/z000906dIFc3NzXF1dSUtLU7XELOHh4UGhQoVo0KCBSnUkJiayZs2aj4xkcHAwO3bskIxkDqGgRvbcLrNrnn8jk8lo3rw5J06cIDQ0lJ49e7Jy5Uq0tLTo27cv169fz3ZNyiQuLo7Tp0+r/LhbCMG5c+do0qQJTZo04fnz5xw4cIDAwEB69+4tZeNL5CokQynxXchkMpo1a4aHhweXLl2iSpUq9OrVi2rVqrF9+3ZSUlJULTFTXLhwASsrK5XFJP3bSI4bN+4jI5mTEwXyI9pli6Hspnyy/82jSgwMDFi9ejUPHjxg8eLFeHt7Y2FhQcOGDTlw4ACpqcqL8cwuDh8+jFwup1u3biqZXwjBqVOnsLKyomXLliQkJHDs2DFu3rxJt27dPurKJCGRW5B+tRI/THo85bVr1zAxMWHgwIEYGBiwYcMGkpKSVC3vi6SmpuLp6amSckGSkcx9FCukgWYZ5WbVapYtSrFC31/UXJGULFmS8ePHEx4ezpEjR9DQ0KBHjx7o6uqyZMkS4uLiVC3xu9m3bx/W1tb8/PPP2TqvEIJjx45hYWFB+/btkclknDp1iqtXr9KxY8dc30dcIn8jGUoJhZEeTxkQEECDBg0YNWoUurq6rFy5koQE5WbHfg83b94kPj4+W+Mn042krq4u48aNo2XLlpKRzEVYG1VAXU05i766mgxrwwpKGftHUFdXp3Pnzly8eJGbN2/SvHlz/vzzT6pWrcrIkSO5d++eqiVmiadPn3LhwoVsPe6Wy+UcOHCAmjVr0rlzZ4oXL865c+fw9vambdu2kpGUyBNIhlJC4ZiZmbF3717u3btHy5Ytsbe3R1tbm8WLF+eoYsoeHh4UK1YMCwsLpc/14cMHVq9enWEkW7VqRXBwMNu3b5eMZC6iTz1N0uTKyfVOkwv61s8ZCSJfombNmmzbto3Y2FimTJnCkSNHqF69Om3atMHd3T1XJOcdPHgwI8lQ2aSmprJ7925MTU3p0aMHFStWxMvLi4sXL9K8eXPJSErkKSRDKaE0jIyM2L59O2FhYXTp0oWZM2eira3NnDlzePXqlarlceHCBRo2bKjUUhzpRlJPT48JEyZIRjKXY1CxBI30yyl8l1JdTUYj/XIKa7uobCpWrMjMmTOJiYnBxcWF58+f07ZtW6pXr8769et5/175pY++F1dXV1q3bk2ZMmWUNkdKSgrbt2+nWrVq9O3bF11dXXx9fTlz5gyNGjVS2rwSEqpEMpQSSkdHR4cNGzYQERFB//79cXR0REtLi6lTp/LsmeI7g2SGlJQULl++rLT4SclI5l0W/m6GhoINpYaajIW/myl0zOygUKFC9OvXj+vXr3Pp0iVMTU0ZPXo0VapUYfLkycTGKq6VpCK4f/8+ly9fVtpxd1JSEps2bcLQ0JCBAwdiamrK9evX+fvvv6lfv75S5pSQyClIhlIi26hSpQorV64kOjqaUaNG4ezsjLa2NuPHj+fRo0fZquXatWu8f/9e4fGTHz58YNWqVejq6jJhwgRat26dYST19fUVOpeEaqhapihzOpoodMy5HU2oquSEH2Uik8lo2LAhBw8eJDIykiFDhrBp0yZ0dXXp3r073t7eOaIJwv79+ylcuDAdO3ZU6Ljp9WP19fUZMWIElpaW3L59myNHjlCnTh2FziUhkVORiZxwlUvkS+Li4li1ahWrV68mISGBwYMHM3nyZLS1tRU+1/ukVKJfvic5VU5BDTX2/7WWVcuX8OLFCzQ0fjyr9sOHD2zatAlHR0eeP39Ov379mD59umQi8zDOHmEsOxP6w+NMamWErXXe+528e/cOFxcXVq9eTUhICHXq1GHcuHH06NFDZR1fLCws0NTU5NChQwoZLyEhgY0bN7JkyRKePXtG7969mTZtGtWqVVPI+BISuQnJUEqonDdv3rBu3TpWrFjB69ev6devH1OnTv3ho+Gwp2/Z7ReLR8gzYuMSPm6bJwQFk+Pp3dScPvU0Maj4fbFrkpHM37hei2XW8bukykWWknXU1WRoqMmY29EEG4ucnYjzo8jlck6fPs3KlSs5c+YMP//8M6NGjWL48OFUqKCcrPb/PkBqly3G4/vRGBgYsH//frp37/5D4799+5Z169axfPlyXr16pbB7loREbkYylBI5hvfv37Np0yaWLl3K06dPsbGxYdq0aZiammZpnPtxCUw7Esil8Beoq8m+utCnv95IvxwLfzfL9LHjf41k//79mT59Onp6elnSKpH7uR+XwNDNHgS/BjUZfM1Xfu/vLa8QFBTE6tWrcXFxQS6X07t3b+zs7DA3N//hsb/2ACkDisuSeHbrAmfXz6KGVvnvmuP169esWbOGlStX8vbtWwYNGoSDg4NSTlUkJHIbkqGUyHEkJiaybds2HB0diY2N5ffff2f69OmZikX60R2jOR1N6PmVHaMPHz6wceNGFi9eLBlJiQx69erF7ehndJ+6iouhz4l9+amh0SxbFGvDCvStr5lrsrmVRVxcHJs3b8bZ2ZkHDx7QtGlTxo0bR4cOHbLcbjArD5AIOcjUsmzoX758ycqVK1m9ejVJSUkMGzaMyZMnU6VKlSxplZDIy0iGUiLHkpyczK5du1i0aBHh4eG0a9eOGTNmfLHvtqJi2uxbGTLa+uOjK8lISnyJ+Ph4KlasyOzZs5kyZQrw+SPXnNIBJyeRkpLCkSNHWLVqFT4+Pujq6jJmzBgGDRrETz/99M3PK/sB8tmzZ6xYsYK1a9cil8sZMWIE9vb2/PLLL5meS0IivyAZSokcT2pqKvv372fBggUEBQXRrFkzZsyYQdOmTTMKA7tei8XhcKDC5lzcxQwbC03JSEp8k+3btzNo0CBiYmKoWrWqquXkWq5evcqqVasyMrEHDRrEmDFjvhiPrMwHyMePH7N06VI2bNiAuro6o0ePZsKECZQv/31H5RIS+QHJUErkGuRyOUePHmX+/PncvHkTKysrZsyYgYllY1qu9CIpVXFdOgppqNGrZDgbli3gxYsXDBgwgGnTpklGUuITmjdvjhCCCxcuqFpKnuDRo0esW7eOjRs38vLlSzp06ICdnR3NmjVT+gPk/fv3Wbx4MVu2bKFw4cLY2dlhZ2en1CLoEhJ5BclQSuQ6hBC4ubkxb948rly5gv6QlaSV00OO4opNC3kaSTEBtC8WxfTp09HV1VXY2BJ5hwcPHqCpqclff/3FwIEDVS0nT/Hhwwf27NnDqlWrCAwMxNTUlHHjxtG47e90WO+n0AfIguoy6r08y76/1lKiRAnGjx/PmDFjKFmypMLmkJDI60iGUiLXIoRg14nz/OmbpLQ5zo1vnO8TKCS+zJIlS5g1axZPnz7NVMyfRNYRQuDh4cGqVas4ceIElfospEAVU4RCHyBTkT8KZoyZjJEjR1KihHTNS0hkFalTjkSuRSaTEcEvCu+rnI66moxdV3JW6ziJnMWuXbvo1KmTZCaViEwmo1mzZhw7doyzfoFoVDFTqJkEkKlpoF7FlC5/SGZSQuJ7kQylRK7GI+RZlrI7s0KaXOARqppe4xI5n9u3bxMYGEi/fv1ULSXfcOkx0gOkhEQORTKUErmWd0mpxMYlKHWO2JcJ/F979x4U9XmocfxZWC8Bo6N4QwMjKeCIIU4ZjYmXBFODYxiNGm8o1itm2UvTtPGcM2nn2ObMOG2mtdNZdkFRlKJ4wTtzNOqJ1GOaxmGqE+PlRIgiRBoc8ApecNk9f6Rp06ax0d3lxy7fz7/q+z6Ouj7vb3/v+7bc8wR1DoSmTZs2qV+/fsrIyDA6SqfBAhLouCiUCFmXmloU7BeAfZJqmlqCPAtCTVtbm0pLSzV37lx16dLF6DidAgtIoGOjUCJktQZwl2dHmAeh48iRI6qvr1d2drbRUToNFpBAx0ahRMjqam6fv77tNQ9Cx6ZNm5ScnKxRo0YZHaXTYAEJdGz8T4mQNSQmOsB7Pb/O9Jd5gC+1tLRo586dys7O/utB2wg+FpBAx8a/HISs6G5mxfeJCuoc8TFR3MGMv7N37161tLTwdXc7YwEJdGwUSoS0CUP7B+0YEZ+3TVHXL6q+vj4o4yM0lZSUaNy4cUpISDA6SqcS3c2s2J5dgzoHC0jg0VEoEdLmj44P2jEipohIfVD8S8XHx2vWrFmqqKgQF0t1bg0NDTp06BBPJ9uRz+fT0aNHNXPmTH1SsUs+b1tQ5omMMGlCcv+gjA10BhRKhLSkAY9rfGLfgD+ljIwwaXxiX9WdqdRvf/tbnTlzRi+++KKGDx+uvLw83bhxI6DzITRs2bJFZrNZs2fPNjpK2GtpadHatWs1YsQIpaen6+zZs3o9M02miMigzNfm9Sn72figjA10BhRKhLxV01NlDnChNEeYtGp6qnr27CmbzaYzZ86ooqJCTz31lN544w0NHjxYFotFp06dCui86NhKSkqUmZmp3r17Gx0lbF24cEFvvvmmnnjiCVksFj355JM6fPiwzpw5o/98PSeoC8jE/ly7CDwqCiVCXlyfKP186vCAjvn21OGK+8qGH5PJpPT0dG3fvl2XLl3SihUrVF5erhEjRmj8+PHasmWLWltbA5oBHcvZs2d14sQJrloMAp/Pp8OHD2vq1KlKTExUUVGRcnJydOHCBe3Zs0cTJ0786476YC4gATw6CiXCwtxR8XozIzkgY63IGKo5o775q69BgwZp5cqVqqmp0Y4dO9SlSxfNmzdPcXFx+ulPf6raWu4DDkebNm1S79699fLLLxsdJWzcunVLeXl5GjZsmDIyMnTp0iWtXbtWn332md555x0NGTLka7+mPRaQAB6eyccuA4SRrZW1WrnvjDxe30Nt1omMMMkcYdLbU4c/sEx+k3Pnzik/P1/FxcVqbm7WlClTZLPZ9L3vfU8REazbQp3X61VCQoImT56sgoICo+OEvPPnzysvL08bN27U7du3NWPGDNntdo0fP/5bn+2ZV1GlXx0673eWFRlDZZuQ6Pc4QGdHoUTYqbt6W2/t/ljHqhsVGWF6YLH88sfHJ/bVqumpfj+laG5u1ubNm+V2u3Xq1CklJSUpNzdXixYt4r27EHb06FGlp6fr/fff19ixY42OE5K8Xq8OHDggp9OpgwcPql+/flq+fLksFoueeOKJRxrTqAUkgK+jUCJsVTXc0ubjtao4f0W1Tbf/7h5gk744c25Ccn9lPxsf8JfxfT6fPvjgA7lcLu3YsUNms1lZWVmy2WxKS0sL6FwIvpycHL333nv69NNPuR3nIV2/fl1FRUVyuVy6cOGCRo4cKYfDodmzZ6t79+5+j2/kAhLA31Ao0Sm03POopqlFrR6vupojNCQmut0OMG5oaND69etVUFCguro6jR49WlarNWD/oSK47t69q4EDB+oHP/iB3n77baPjhIzTp08rLy9PJSUlun//vmbNmiWHw6HRo0cHpZQbuYAEQKEE2o3H49H+/fvlcrl06NAhxcTEaOnSpbJYLNy60oGVlZVp9uzZ+uSTT5ScHJiNX+HK4/GovLxcTqdTFRUVio2NlcVi0fLlyzVw4MB2y2HkAhLorCiUgAGqqqpUUFCgoqIi3bhxQ5MnT5bNZtOkSZMUGRmcg5vxaF555RV9/vnnOn78uNFROqzGxkatW7dO+fn5qq2t1ZgxY+RwODRjxgx17Rrc6xIBdAwUSsBAt2/f1tatW+VyuXTixAklJCTIYrFoyZIl6tu3r9HxOr3GxkbFxsZq9erVcjgcRsfpcE6ePCmn06ktW7bI5/MpKytLDoeD94SBTohCCXQAPp9PlZWVcrlc2rZtmyRpzpw5slqteuaZZ9gIYhC3263XX39d9fX16tevn9FxOoT79+9r165dcjqd+sMf/qC4uDhZrVYtW7aMRRDQiVEogQ6msbFRGzZsUH5+vi5evKi0tDRZrVZlZWUpKopdqe3pueeeU9++fVVeXm50FMM1NDRozZo1Kigo0J///Gelp6fL4XBo6tSpMpt5PxHo7CiUQAfl9Xr17rvvyu12a//+/erVq5cWL14si8XC5pB2UF1draSkJG3dulVz5swxOo5hjh8/LqfTqe3bt6tLly7Kzs6W3W5XaipXFQL4GwolEAIuXryogoICrV+/Xk1NTXrppZdks9mUmZnJ06Eg+dnPfqbVq1eroaFBjz32mNFx2tW9e/e0fft2OZ1OVVZW6sknn5TNZtPixYs5oB/AP0WhBELI3bt3VVZWJrfbrQ8//FBxcXF67bXXtGzZMg0YMMDoeGHD5/MpKSlJzz//vIqKioyO024uX76sgoICrV27VleuXFFGRoYcDocmT57M6QMAHohCCYSoEydOyO12q7S0VB6PRzNnzpTVatXYsWPZxOOnP/7xjxozZoyOHDmiCRMmGB0nqHw+n95//305nU7t2rVLjz32mBYtWiS73a6hQ4caHQ9AiKBQAiHu2rVrKi4ultvtVlVVlVJTU2W1WpWdna0ePXoYHS8kWa1WlZeX69KlS4qIiDA6TlDcuXNHpaWlcjqd+uijjzR06FDZ7XZ9//vfV8+ePY2OByDEUCiBMOH1evXee+/J7XZr3759io6O1sKFC5Wbm6uUlBSj44WM1tZWxcbGKicnR7/4xS+MjhNwNTU1ys/P17p163Tt2jVlZmbK4XBo4sSJYVueAQQfhRIIQ3V1dVqzZo0KCwt15coVTZgwQVarVa+88oq6dOlidLwObe/evZo2bZpOnz6t4cOHGx0nIHw+n44cOSKn06ny8nL17NlTS5YskdVq1Xe+8x2j4wEIAxRKIIy1trZq165dcrvdOnbsmGJjY7V8+XItX75cgwYNMjpehzRr1ixVV1fr5MmTRkfxW3Nzs0pKSpSXl6ezZ8/qqaeeksPh0Pz58xUdHW10PABhhEIJdBKnTp1Sfn6+SkpKdPfuXU2fPl1Wq1Xp6els4vmL69eva+DAgVq1apV+9KMfGR3nkVVXV8vlcmnDhg26deuWpk2bJofDoRdeeIE/awBBQaEEOpmbN2+qpKRELpdL586d07Bhw2S1WrVgwQL16tXL6HiGKiwslMVi0WeffabY2Fij4zwUr9ergwcPyul06sCBA4qJiVFOTo5yc3MVHx9vdDwAYY5CCXRSPp9PR48eldvt1u7du9WtWzdlZ2fLarXq6aefNjqeIV544QV1795dBw8eNDrKt3bjxg1t3LhRLpdLVVVVSktLk8Ph0Jw5czrdgewAjEOhBKD6+noVFhZq7dq1qq+v17hx42S1WvXqq6+qa9euRsdrFzU1NUpISNDvfvc7LViwwOg4/9K5c+eUl5en4uJi3bt3TzNnzpTD4dBzzz3H19oA2h1nRADQoEGDtHLlStXU1GjHjh3q0qWL5s2bp7i4OP3kJz9RbW2t0RGDrrS0VFFRUZo+fbrRUb5RW1ub9u7dq4kTJyolJUU7d+7Uj3/8Y126dElbtmzRmDFjKJMADMETSgD/1Llz55Sfn6/i4mI1NzdrypQpslqtYXleoc/nU0pKikaOHKmSkhKj43zN1atXtX79erndbtXU1OjZZ5+V3W7XrFmzOs0TZAAdG4USwAM1Nzdr8+bNcrvdOnXqlJKSkpSbm6tFixapd+/eRscLiD/96U8aOXKk3n33XU2aNMnoOH916tQpOZ1Obd68WW1tbZo7d64cDodGjhxpdDQA+DsUSgDfis/n0wcffCC3262ysjKZzWZlZWXJZrMpLS3N6Hh++eEPf6ht27aprq5OZrPZ0Cz379/Xnj175HQ6dezYMQ0ePFi5ubnKyclR//79Dc0GAN+EQgngoTU0NGj9+vUqKChQXV2dRo8eLavVqtmzZ6t79+5Gx3soHo9HgwcP1vz587V69WrDcly5ckWFhYXKz8/X5cuX9fzzz8tut2vatGncbgSgw6NQAnhkHo9H+/fvl8vl0qFDhxQTE6OlS5fKYrEoISHB6HjfyoEDB/Tyyy/rxIkT+u53v9vu81dWVsrpdGrbtm2KjIzU/PnzZbfbNWLEiHbPAgCPikIJICCqqqpUUFCgoqIi3bhxQ5MnT5bNZtOkSZMUGRlpdLxvNG/ePH300Uc6ffp0u+2Qbm1tVVlZmZxOp44fP64hQ4bIarVq6dKl6tOnT7tkAIBACq+tmgAMk5SUpF//+te6fPmy1q1bp88//1yZmZlKSkrSO++8o8bGRqMjfs2tW7e0Z88eLViwoF3KZH19vVauXKn4+HhlZ2fr8ccf1549e1RdXa0VK1ZQJgGELJ5QAggKn8+nyspKuVwubdu2TZI0e/Zs2Ww2PfPMMx3ivMTi4mItWrRItbW1iouLC8ocX25mcjqd2rlzp7p166aFCxfKbrdr2LBhQZkTANobhRJA0DU2NmrDhg3Kz8/XxYsXlZaWJqvVqqysLEVFRQV9/pZ7HtU0tajV41VXc4SGxEQruptZEydOVFtbmyoqKgI+5507d7R161Y5nU6dPHlSiYmJstvtWrRoUae/Mx1A+KFQAmg3Xq9XBw8elMvl0v79+9WrVy8tXrxYFotFycnJAZ2rquGWNh+vVcUnV1R79ba++kFnkjSoV1f933s79W8zntO/5y4M2Ly1tbXKz89XYWGhmpqaNHnyZDkcDk2aNCnsDoQHgC9RKAEY4uLFi1qzZo3WrVunpqYmvfTSS7LZbMrMzPTrLMi6q7f11u6Pday6UZERJrV5v/kjzudtkykiUuMT+2rV9FTF9Xm0p6U+n0+///3v5XQ6tXfvXvXo0UNLliyRzWZTYmLio/5WACBkUCgBGOru3bsqKyuT2+3Whx9+qLi4OL322mtatmyZBgwY8FBjba2s1cp9Z+Tx+h5YJP9RZIRJ5giTfj51uOaOiv/Wv66lpUWbNm1SXl6eTp8+rZSUFNntdi1YsEA9evR4qOwAEMoolAA6jBMnTsjtdqu0tFQej0evvvqqbDabxo4d+y838eRVVOlXh877neHNjGTZJyQ98OdcuHBBLpdLRUVFunnzpqZMmSKHw6EXX3yxQ2w2AoD2RqEE0OFcu3ZNxcXFcrvdqqqqUmpqqqxWq7Kzs//pk7+tlbX6j10fB2z+X85I1Zx/eFLp9Xp1+PBhOZ1O7d+/X71799ayZcuUm5urIUOGBGxuAAhFFEoAHZbX69WRI0fkcrm0b98+RUdHa+HChcrNzVVKSoqkL96ZnPibo7rn8QZs3m7mCP3PGy8ork+Ubt68qeLiYuXl5en8+fMaMWKEHA5Hu+1QB4BQQKEEEBLq6uq0du1aFRYWqqGhQenp6bLZbNp9/Ql9ePHqQ70z+a9ERpg0YmB3DT6/Wxs3btSdO3c0Y8YMORwOjRs3jq+1AeAfUCgBhJTW1lbt2rXri008Z2s0KCc/aHPd2fGWls+dKovFosGDBwdtHgAIdRRKACHLvuGo/vuTm/KZAn++Y4R8mv9MvP5r+tMBHxsAwg2n7AIIWR83eoNSJiXJK5P+99OmoIwNAOGGQgkgJDXf86j26u2gzlHbdFst9zxBnQMAwgGFEkBIutTUomC/r+OTVNPUEuRZACD0USgBhKTWAB4T1BHmAYBQRqEEEJK6mtvn46u95gGAUMYnJYCQNCQmWsE+DdL0l3kAAA9GoQQQkqK7mRXfJ7g31cTHRCm6mzmocwBAOKBQAghZE4b2V2REcJ5TRkaYNCG5f1DGBoBwQ6EEELLmj44P6JWLX9Xm9Sn72figjA0A4YZCCSBkJQ14XOMT+wb8KWVkhEnjE/sqsf/jAR0XAMIVhRJASFs1PVXmABdKc4RJq6anBnRMAAhnFEoAIS2uT5R+PnV4QMd8e+pwxQV5ww8AhBMKJYCQN3dUvN7MSA7IWCsyhmrOKN6dBICHYfL5fMG+vQwA2sXWylqt3HdGHq/voTbrREaYZI4w6e2pwymTAPAIKJQAwkrd1dt6a/fHOlbdqMgI0wOL5Zc/Pj6xr1ZNT+VrbgB4RBRKAGGpquGWNh+vVcX5K6ptuq2vftCZ9MWh5ROS+yv72Xh2cwOAnyiUAMJeyz2Pappa1Orxqqs5QkNiorkBBwACiEIJAAAAv7DLGwAAAH6hUAIAAMAvFEoAAAD4hUIJAAAAv1AoAQAA4BcKJQAAAPxCoQQAAIBfKJQAAADwC4USAAAAfqFQAgAAwC8USgAAAPiFQgkAAAC/UCgBAADgFwolAAAA/EKhBAAAgF8olAAAAPALhRIAAAB+oVACAADALxRKAAAA+IVCCQAAAL9QKAEAAOAXCiUAAAD8QqEEAACAXyiUAAAA8AuFEgAAAH6hUAIAAMAvFEoAAAD4hUIJAAAAv1AoAQAA4BcKJQAAAPxCoQQAAIBfKJQAAADwC4USAAAAfqFQAgAAwC8USgAAAPiFQgkAAAC/UCgBAADgl/8HkwnbeTEij50AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -266,26 +266,26 @@
      "output_type": "stream",
      "text": [
       "Estimating Square\n",
-      "Time to generate high level QSP circuit: 0.07991908299982242 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.pauli_gates._PauliX circuit: 7.725000000391447e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.pauli_gates._PauliX circuit to Clifford+T: 2.9624999797306373e-05 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.Rx circuit: 3.779099984058121e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.Rx circuit to Clifford+T: 0.00695362499982366 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.circuits.operators.hamiltonian_encodings.UnitaryBlockEncode circuit: 0.31647270799999205 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.circuits.operators.hamiltonian_encodings.UnitaryBlockEncode circuit to Clifford+T: 1.3497573749998537 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.Ry circuit: 0.0018921250000403234 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.Ry circuit to Clifford+T: 0.006489625000085653 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.raw_types._InverseCompositeGate circuit: 0.3717178330000479 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.raw_types._InverseCompositeGate circuit to Clifford+T: 1.5489357500000551 seconds\n"
+      "Time to generate high level QSP circuit: 0.07918062500539236 seconds\n",
+      "   Time to decompose high level _PauliX circuit: 5.9708982007578015e-05 seconds \n",
+      "   Time to transform decomposed _PauliX circuit to Clifford+T: 2.5749992346391082e-05 seconds\n",
+      "   Time to decompose high level Rx circuit: 3.591601853258908e-05 seconds \n",
+      "   Time to transform decomposed Rx circuit to Clifford+T: 0.006769333005649969 seconds\n",
+      "   Time to decompose high level UnitaryBlockEncode circuit: 0.2945386669889558 seconds \n",
+      "   Time to transform decomposed UnitaryBlockEncode circuit to Clifford+T: 1.3021555830200668 seconds\n",
+      "   Time to decompose high level Ry circuit: 0.0017955419898498803 seconds \n",
+      "   Time to transform decomposed Ry circuit to Clifford+T: 0.005802708008559421 seconds\n",
+      "   Time to decompose high level _InverseCompositeGate circuit: 0.3383026249939576 seconds \n",
+      "   Time to transform decomposed _InverseCompositeGate circuit to Clifford+T: 1.2789183330023661 seconds\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonhas/anaconda3/envs/other_qca/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: divide by zero encountered in det\n",
+      "/Users/jonhas/anaconda3/envs/cmp/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: divide by zero encountered in det\n",
       "  r = _umath_linalg.det(a, signature=signature)\n",
-      "/Users/jonhas/anaconda3/envs/other_qca/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: invalid value encountered in det\n",
+      "/Users/jonhas/anaconda3/envs/cmp/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: invalid value encountered in det\n",
       "  r = _umath_linalg.det(a, signature=signature)\n"
      ]
     },
@@ -293,31 +293,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "   Time to decompose high level <class 'pyLIQTR.circuits.operators.reflect.Reflect circuit: 0.016955749999851832 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.circuits.operators.reflect.Reflect circuit to Clifford+T: 0.021166375000120752 seconds\n",
+      "   Time to decompose high level Reflect circuit: 0.006262708018766716 seconds \n",
+      "   Time to transform decomposed Reflect circuit to Clifford+T: 0.014709832990774885 seconds\n",
       "\n",
       "\n",
       "Estimating Triangle\n",
-      "Time to generate high level QSP circuit: 12.810611167000161 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.pauli_gates._PauliX circuit: 5.987500003357127e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.pauli_gates._PauliX circuit to Clifford+T: 2.2416999854613096e-05 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.Rx circuit: 0.00014012499991622462 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.Rx circuit to Clifford+T: 0.009160041999848545 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.circuits.operators.hamiltonian_encodings.UnitaryBlockEncode circuit: 10.175483457999917 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.circuits.operators.hamiltonian_encodings.UnitaryBlockEncode circuit to Clifford+T: 13.092461999999841 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.Ry circuit: 0.02360637499987206 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.Ry circuit to Clifford+T: 0.0053349160000379925 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.raw_types._InverseCompositeGate circuit: 4.910350249999965 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.raw_types._InverseCompositeGate circuit to Clifford+T: 16.846228041999893 seconds\n"
+      "Time to generate high level QSP circuit: 4.653942917007953 seconds\n",
+      "   Time to decompose high level _PauliX circuit: 5.870801396667957e-05 seconds \n",
+      "   Time to transform decomposed _PauliX circuit to Clifford+T: 2.2499996703118086e-05 seconds\n",
+      "   Time to decompose high level Rx circuit: 2.2791005903854966e-05 seconds \n",
+      "   Time to transform decomposed Rx circuit to Clifford+T: 0.006087709014536813 seconds\n",
+      "   Time to decompose high level UnitaryBlockEncode circuit: 4.595991707989015 seconds \n",
+      "   Time to transform decomposed UnitaryBlockEncode circuit to Clifford+T: 12.676242874993477 seconds\n",
+      "   Time to decompose high level Ry circuit: 0.022674957988783717 seconds \n",
+      "   Time to transform decomposed Ry circuit to Clifford+T: 0.005431291996501386 seconds\n",
+      "   Time to decompose high level _InverseCompositeGate circuit: 4.796438624995062 seconds \n",
+      "   Time to transform decomposed _InverseCompositeGate circuit to Clifford+T: 13.11652499999036 seconds\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonhas/anaconda3/envs/other_qca/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: divide by zero encountered in det\n",
+      "/Users/jonhas/anaconda3/envs/cmp/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: divide by zero encountered in det\n",
       "  r = _umath_linalg.det(a, signature=signature)\n",
-      "/Users/jonhas/anaconda3/envs/other_qca/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: invalid value encountered in det\n",
+      "/Users/jonhas/anaconda3/envs/cmp/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: invalid value encountered in det\n",
       "  r = _umath_linalg.det(a, signature=signature)\n"
      ]
     },
@@ -325,31 +325,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "   Time to decompose high level <class 'pyLIQTR.circuits.operators.reflect.Reflect circuit: 0.03436250000004293 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.circuits.operators.reflect.Reflect circuit to Clifford+T: 0.019091457999820705 seconds\n",
+      "   Time to decompose high level Reflect circuit: 0.03333766697323881 seconds \n",
+      "   Time to transform decomposed Reflect circuit to Clifford+T: 0.01843629201175645 seconds\n",
       "\n",
       "\n",
       "Estimating Cube\n",
-      "Time to generate high level QSP circuit: 11.95945516699976 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.pauli_gates._PauliX circuit: 7.1040999955585e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.pauli_gates._PauliX circuit to Clifford+T: 2.129199992850772e-05 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.Rx circuit: 3.1833999855734874e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.Rx circuit to Clifford+T: 0.008196582999971724 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.circuits.operators.hamiltonian_encodings.UnitaryBlockEncode circuit: 6.035081916000308 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.circuits.operators.hamiltonian_encodings.UnitaryBlockEncode circuit to Clifford+T: 22.444922209000197 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.Ry circuit: 0.061185666999790556 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.Ry circuit to Clifford+T: 0.01180554199981998 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.raw_types._InverseCompositeGate circuit: 17.44971004199988 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.raw_types._InverseCompositeGate circuit to Clifford+T: 19.09016233400007 seconds\n"
+      "Time to generate high level QSP circuit: 11.612685999978567 seconds\n",
+      "   Time to decompose high level _PauliX circuit: 6.500000017695129e-05 seconds \n",
+      "   Time to transform decomposed _PauliX circuit to Clifford+T: 2.2082997020334005e-05 seconds\n",
+      "   Time to decompose high level Rx circuit: 2.3333006538450718e-05 seconds \n",
+      "   Time to transform decomposed Rx circuit to Clifford+T: 0.007967374986037612 seconds\n",
+      "   Time to decompose high level UnitaryBlockEncode circuit: 5.883486999984598 seconds \n",
+      "   Time to transform decomposed UnitaryBlockEncode circuit to Clifford+T: 19.14764658297645 seconds\n",
+      "   Time to decompose high level Ry circuit: 0.038008792005712166 seconds \n",
+      "   Time to transform decomposed Ry circuit to Clifford+T: 0.005712959013180807 seconds\n",
+      "   Time to decompose high level _InverseCompositeGate circuit: 8.636526791000506 seconds \n",
+      "   Time to transform decomposed _InverseCompositeGate circuit to Clifford+T: 18.399842542014085 seconds\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonhas/anaconda3/envs/other_qca/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: divide by zero encountered in det\n",
+      "/Users/jonhas/anaconda3/envs/cmp/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: divide by zero encountered in det\n",
       "  r = _umath_linalg.det(a, signature=signature)\n",
-      "/Users/jonhas/anaconda3/envs/other_qca/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: invalid value encountered in det\n",
+      "/Users/jonhas/anaconda3/envs/cmp/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: invalid value encountered in det\n",
       "  r = _umath_linalg.det(a, signature=signature)\n"
      ]
     },
@@ -357,8 +357,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "   Time to decompose high level <class 'pyLIQTR.circuits.operators.reflect.Reflect circuit: 0.06816550000030475 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.circuits.operators.reflect.Reflect circuit to Clifford+T: 0.0263092919999508 seconds\n",
+      "   Time to decompose high level Reflect circuit: 0.0511204169888515 seconds \n",
+      "   Time to transform decomposed Reflect circuit to Clifford+T: 0.019495375017868355 seconds\n",
       "Finished estimating\n"
      ]
     }
@@ -373,7 +373,7 @@
     "    outdir='QSP/square_circuits/',\n",
     "    hamiltonian_name='square_qsp',\n",
     "    metadata=square_lattice_metadata,\n",
-    "    write_circuits=True\n",
+    "    write_circuits=True,\n",
     ")\n",
     "print('\\n')\n",
     "\n",
@@ -487,26 +487,26 @@
      "output_type": "stream",
      "text": [
       "Estimating Kitaev\n",
-      "Time to generate high level QSP circuit: 9.279183332999764 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.pauli_gates._PauliX circuit: 7.554200010417844e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.pauli_gates._PauliX circuit to Clifford+T: 2.7750000299420208e-05 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.Rx circuit: 3.28749997606792e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.Rx circuit to Clifford+T: 0.007395625000299333 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.circuits.operators.hamiltonian_encodings.UnitaryBlockEncode circuit: 2.8251104579999264 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.circuits.operators.hamiltonian_encodings.UnitaryBlockEncode circuit to Clifford+T: 13.164839750000283 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.Ry circuit: 0.019442875000095228 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.Ry circuit to Clifford+T: 0.005623125000056461 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.raw_types._InverseCompositeGate circuit: 10.978083165999578 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.raw_types._InverseCompositeGate circuit to Clifford+T: 29.467311916999734 seconds\n"
+      "Time to generate high level QSP circuit: 8.993816832982702 seconds\n",
+      "   Time to decompose high level _PauliX circuit: 6.60410150885582e-05 seconds \n",
+      "   Time to transform decomposed _PauliX circuit to Clifford+T: 2.2958003683015704e-05 seconds\n",
+      "   Time to decompose high level Rx circuit: 2.924998989328742e-05 seconds \n",
+      "   Time to transform decomposed Rx circuit to Clifford+T: 0.00718558399239555 seconds\n",
+      "   Time to decompose high level UnitaryBlockEncode circuit: 2.778516165999463 seconds \n",
+      "   Time to transform decomposed UnitaryBlockEncode circuit to Clifford+T: 13.102216583996778 seconds\n",
+      "   Time to decompose high level Ry circuit: 0.01926341699436307 seconds \n",
+      "   Time to transform decomposed Ry circuit to Clifford+T: 0.0055061250168364495 seconds\n",
+      "   Time to decompose high level _InverseCompositeGate circuit: 5.391434124991065 seconds \n",
+      "   Time to transform decomposed _InverseCompositeGate circuit to Clifford+T: 13.8181521660008 seconds\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonhas/anaconda3/envs/other_qca/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: divide by zero encountered in det\n",
+      "/Users/jonhas/anaconda3/envs/cmp/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: divide by zero encountered in det\n",
       "  r = _umath_linalg.det(a, signature=signature)\n",
-      "/Users/jonhas/anaconda3/envs/other_qca/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: invalid value encountered in det\n",
+      "/Users/jonhas/anaconda3/envs/cmp/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: invalid value encountered in det\n",
       "  r = _umath_linalg.det(a, signature=signature)\n"
      ]
     },
@@ -514,8 +514,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "   Time to decompose high level <class 'pyLIQTR.circuits.operators.reflect.Reflect circuit: 0.029925207999895065 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.circuits.operators.reflect.Reflect circuit to Clifford+T: 0.019216874999983702 seconds\n",
+      "   Time to decompose high level Reflect circuit: 0.02965449998737313 seconds \n",
+      "   Time to transform decomposed Reflect circuit to Clifford+T: 0.017918875004397705 seconds\n",
       "Finished Estimating\n"
      ]
     }
@@ -606,26 +606,26 @@
      "output_type": "stream",
      "text": [
       "Estimating Directional Triangle\n",
-      "Time to generate high level QSP circuit: 4.2945522910004 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.pauli_gates._PauliX circuit: 5.8042000091518275e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.pauli_gates._PauliX circuit to Clifford+T: 2.1624999590130756e-05 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.Rx circuit: 3.0416999834415037e-05 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.Rx circuit to Clifford+T: 0.006921207999766921 seconds\n",
-      "   Time to decompose high level <class 'pyLIQTR.circuits.operators.hamiltonian_encodings.UnitaryBlockEncode circuit: 6.548166333000154 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.circuits.operators.hamiltonian_encodings.UnitaryBlockEncode circuit to Clifford+T: 31.83904591700002 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.common_gates.Ry circuit: 0.018346291999932873 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.common_gates.Ry circuit to Clifford+T: 0.005959374999747524 seconds\n",
-      "   Time to decompose high level <class 'cirq.ops.raw_types._InverseCompositeGate circuit: 6.8520493330001955 seconds \n",
-      "   Time to transform decomposed <class 'cirq.ops.raw_types._InverseCompositeGate circuit to Clifford+T: 15.737349957999868 seconds\n"
+      "Time to generate high level QSP circuit: 4.321817083022324 seconds\n",
+      "   Time to decompose high level _PauliX circuit: 6.641700747422874e-05 seconds \n",
+      "   Time to transform decomposed _PauliX circuit to Clifford+T: 2.433298504911363e-05 seconds\n",
+      "   Time to decompose high level Rx circuit: 3.875000402331352e-05 seconds \n",
+      "   Time to transform decomposed Rx circuit to Clifford+T: 0.00672500001383014 seconds\n",
+      "   Time to decompose high level UnitaryBlockEncode circuit: 6.637649499985855 seconds \n",
+      "   Time to transform decomposed UnitaryBlockEncode circuit to Clifford+T: 18.00479366601212 seconds\n",
+      "   Time to decompose high level Ry circuit: 0.01793533400632441 seconds \n",
+      "   Time to transform decomposed Ry circuit to Clifford+T: 0.005916791997151449 seconds\n",
+      "   Time to decompose high level _InverseCompositeGate circuit: 2.9935035000089556 seconds \n",
+      "   Time to transform decomposed _InverseCompositeGate circuit to Clifford+T: 13.838551207998535 seconds\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonhas/anaconda3/envs/other_qca/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: divide by zero encountered in det\n",
+      "/Users/jonhas/anaconda3/envs/cmp/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: divide by zero encountered in det\n",
       "  r = _umath_linalg.det(a, signature=signature)\n",
-      "/Users/jonhas/anaconda3/envs/other_qca/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: invalid value encountered in det\n",
+      "/Users/jonhas/anaconda3/envs/cmp/lib/python3.11/site-packages/numpy/linalg/linalg.py:2180: RuntimeWarning: invalid value encountered in det\n",
       "  r = _umath_linalg.det(a, signature=signature)\n"
      ]
     },
@@ -633,8 +633,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "   Time to decompose high level <class 'pyLIQTR.circuits.operators.reflect.Reflect circuit: 0.03833775000020978 seconds \n",
-      "   Time to transform decomposed <class 'pyLIQTR.circuits.operators.reflect.Reflect circuit to Clifford+T: 0.04854183400038892 seconds\n",
+      "   Time to decompose high level Reflect circuit: 0.027545499993721023 seconds \n",
+      "   Time to transform decomposed Reflect circuit to Clifford+T: 0.018254082999192178 seconds\n",
       "Finished Estimating\n"
      ]
     }
@@ -773,30 +773,30 @@
      "output_type": "stream",
      "text": [
       "Estimating Square\n",
-      "Time to estimate number of trotter steps required (5366564): 0.03970441599994956 seconds\n",
-      "Time to find term ordering: 0.003895917000136251 seconds\n",
-      "Time to generate trotter circuit from openfermion: 2.2090002858021762e-06 seconds\n",
-      "Time to generate a clifford + T circuit from trotter circuit: 4.127191333000155 seconds\n",
+      "Time to estimate number of trotter steps required (5366564): 0.018260582990478724 seconds\n",
+      "Time to find term ordering: 0.0016187919827643782 seconds\n",
+      "Time to generate trotter circuit from openfermion: 1.0830117389559746e-06 seconds\n",
+      "Time to generate a clifford + T circuit from trotter circuit: 2.0698464589950163 seconds\n",
       "Estimating Triangle\n",
-      "Time to estimate number of trotter steps required (21707142): 5.272101959000338 seconds\n",
-      "Time to find term ordering: 0.04675249999991138 seconds\n",
-      "Time to generate trotter circuit from openfermion: 1.45800004247576e-06 seconds\n",
-      "Time to generate a clifford + T circuit from trotter circuit: 63.730407584000204 seconds\n",
+      "Time to estimate number of trotter steps required (21707142): 2.359785832988564 seconds\n",
+      "Time to find term ordering: 0.021950374997686595 seconds\n",
+      "Time to generate trotter circuit from openfermion: 1.207983586937189e-06 seconds\n",
+      "Time to generate a clifford + T circuit from trotter circuit: 39.68508270799066 seconds\n",
       "Estimating Cube\n",
-      "Time to estimate number of trotter steps required (27573901): 6.80979625000009 seconds\n",
-      "Time to find term ordering: 0.03995520899979965 seconds\n",
-      "Time to generate trotter circuit from openfermion: 1.0829999155248515e-06 seconds\n",
-      "Time to generate a clifford + T circuit from trotter circuit: 103.05683649999992 seconds\n",
+      "Time to estimate number of trotter steps required (27573901): 6.3579343339952175 seconds\n",
+      "Time to find term ordering: 0.040887916984502226 seconds\n",
+      "Time to generate trotter circuit from openfermion: 1.5830155462026596e-06 seconds\n",
+      "Time to generate a clifford + T circuit from trotter circuit: 63.78720529199927 seconds\n",
       "Estimating Kitaev\n",
-      "Time to estimate number of trotter steps required (222782406): 4.039062333000402 seconds\n",
-      "Time to find term ordering: 0.038102417000118294 seconds\n",
-      "Time to generate trotter circuit from openfermion: 1.500000053056283e-06 seconds\n",
-      "Time to generate a clifford + T circuit from trotter circuit: 43.558435459000066 seconds\n",
+      "Time to estimate number of trotter steps required (222782406): 1.7442480419995263 seconds\n",
+      "Time to find term ordering: 0.017395000002579764 seconds\n",
+      "Time to generate trotter circuit from openfermion: 1.2909877113997936e-06 seconds\n",
+      "Time to generate a clifford + T circuit from trotter circuit: 32.606457999994745 seconds\n",
       "Estimating Directional Triangle\n",
-      "Time to estimate number of trotter steps required (433303589): 1.6545264169999427 seconds\n",
-      "Time to find term ordering: 0.024931375000051048 seconds\n",
-      "Time to generate trotter circuit from openfermion: 1.8330001694266684e-06 seconds\n",
-      "Time to generate a clifford + T circuit from trotter circuit: 40.33192295800018 seconds\n",
+      "Time to estimate number of trotter steps required (433303589): 1.5597340839740355 seconds\n",
+      "Time to find term ordering: 0.02271900000050664 seconds\n",
+      "Time to generate trotter circuit from openfermion: 1.2919772416353226e-06 seconds\n",
+      "Time to generate a clifford + T circuit from trotter circuit: 38.11545720900176 seconds\n",
       "Finished with estimates\n"
      ]
     }
diff --git a/notebooks/PhotosynthesisExample.ipynb b/notebooks/PhotosynthesisExample.ipynb
index 2a3480d..7f1ba40 100644
--- a/notebooks/PhotosynthesisExample.ipynb
+++ b/notebooks/PhotosynthesisExample.ipynb
@@ -717,6 +717,7 @@
     "        init_state=init_state,\n",
     "        precision_order=1,\n",
     "        bits_precision=bits_precision,\n",
+    "        phase_offset=phase_offset,\n",
     "        circuit_name=f'Co2O9H12_{idx}',\n",
     "        metadata=molecular_metadata,\n",
     "        write_circuits=True\n",
diff --git a/notebooks/RuClExample.ipynb b/notebooks/RuClExample.ipynb
index 383d9fb..d004eb6 100644
--- a/notebooks/RuClExample.ipynb
+++ b/notebooks/RuClExample.ipynb
@@ -1030,8 +1030,8 @@
     "        openfermion_hamiltonian=openfermion_hamiltonian_rucl,\n",
     "        evolution_time=evolution_time,\n",
     "        energy_precision=energy_precision,\n",
-    "        metadata=trotter_metadata,\n",
     "        outdir='Trotter/RuCl_circuits/',\n",
+    "        metadata=trotter_metadata,\n",
     "        hamiltonian_name=f'rucl_trotter_{i}',\n",
     "        write_circuits=True\n",
     "    )\n",
@@ -1140,9 +1140,9 @@
     "        evolution_time=evolution_time,\n",
     "        numsteps=1000,\n",
     "        energy_precision=energy_precision,\n",
-    "        metadata=qsp_metadata,\n",
     "        outdir='QSP/RuCl_circuits/',\n",
     "        hamiltonian_name=f'rucl_qsp_{i}',\n",
+    "        metadata=qsp_metadata,\n",
     "        write_circuits=True\n",
     "    )\n",
     "    t1 = time.perf_counter()\n",
diff --git a/src/qca/utils/algo_utils.py b/src/qca/utils/algo_utils.py
index 8011993..7941394 100644
--- a/src/qca/utils/algo_utils.py
+++ b/src/qca/utils/algo_utils.py
@@ -32,6 +32,7 @@ def estimate_qsp(
     hamiltonian_name:str='hamiltonian',
     metadata: EstimateMetaData = None,
     write_circuits:bool=False,
+    include_nested_resources:bool=True
 ) -> Circuit:
     timestep_of_interest=evolution_time/numsteps
     random.seed(0)
@@ -52,7 +53,9 @@ def estimate_qsp(
         circuit=qsp_circuit,
         outdir=outdir,
         numsteps=numsteps,
-        write_circuits=write_circuits
+        algo_name='QSP',
+        write_circuits=write_circuits,
+        include_nested_resources=include_nested_resources
     )
     if metadata:
         re_metadata = asdict(metadata)
@@ -104,7 +107,8 @@ def estimate_trotter(
     metadata: EstimateMetaData=None,
     hamiltonian_name:str='hamiltonian',
     write_circuits:bool=False,
-    nsteps:int=None
+    nsteps:int=None,
+    include_nested_resources:bool=True
 ) -> Circuit:
 
     if not os.path.exists(outdir):
@@ -155,8 +159,9 @@ def estimate_trotter(
         cpt_circuit=cpt_trotter,
         outdir=outdir,
         is_extrapolated=True,
-        circuit_name=hamiltonian_name,
-        trotter_steps=nsteps
+        algo_name='TrotterStep',
+        trotter_steps=nsteps,
+        include_nested_resources=include_nested_resources
     )
 
     outfile = f'{outdir}{hamiltonian_name}_re.json'
@@ -175,6 +180,7 @@ def gsee_resource_estimation(
         bits_precision:int,
         phase_offset:float,
         circuit_name:str='Hamiltonian',
+        include_nested_resources:bool=True,
         metadata:EstimateMetaData=None,
         include_classical_bits:bool=False,
         write_circuits:bool=False
@@ -199,6 +205,8 @@ def gsee_resource_estimation(
         circuit=pe_circuit,
         outdir=outdir,
         numsteps=numsteps,
+        algo_name='GSEE',
+        include_nested_resources=include_nested_resources,
         bits_precision=bits_precision,
         write_circuits=write_circuits
     )
diff --git a/src/qca/utils/utils.py b/src/qca/utils/utils.py
index fb50fb9..0ccbf50 100644
--- a/src/qca/utils/utils.py
+++ b/src/qca/utils/utils.py
@@ -105,6 +105,7 @@ def get_T_depth(cpt_circuit: cirq.AbstractCircuit):
 def gen_resource_estimate(
         cpt_circuit: cirq.AbstractCircuit,
         is_extrapolated: bool,
+        circ_occurences:int=-1,
         total_steps:int = -1,
         bits_precision:int=1
     ) -> dict:
@@ -146,6 +147,9 @@ def gen_resource_estimate(
         resource_estimate['circuit_depth'] = resource_estimate['circuit_depth'] * scaling_factor
         resource_estimate['clifford_count'] = resource_estimate['clifford_count'] * scaling_factor
 
+    if circ_occurences > 0:
+        resource_estimate['subcircuit_occurences'] = circ_occurences
+
     return resource_estimate
 
 
@@ -153,7 +157,8 @@ def estimate_cpt_resources(
         cpt_circuit: cirq.AbstractCircuit,
         outdir: str,
         is_extrapolated:bool,
-        circuit_name:str,
+        algo_name:str,
+        include_nested_resources:bool,
         magnus_steps:int=1,
         trotter_steps:int=1
     ):
@@ -161,17 +166,45 @@ def estimate_cpt_resources(
         os.makedirs(outdir)
 
     total_steps = trotter_steps * magnus_steps
-    logical_re = gen_resource_estimate(
-        cpt_circuit=cpt_circuit,
-        is_extrapolated=is_extrapolated,
-        total_steps=total_steps
-    )
+    logical_re = {
+        'Logical_Abstract':  gen_resource_estimate(
+            cpt_circuit=cpt_circuit,
+            is_extrapolated=is_extrapolated,
+            total_steps=total_steps
+        )
+    }
+    logical_re['Logical_Abstract']['subcircuit_occurences'] = 1
+    if include_nested_resources:
+        logical_re['Logical_Abstract']['subcircuit_info'] = grab_single_step_estimates(
+            len(cpt_circuit.all_qubits()),
+            logical_re['Logical_Abstract'],
+            algo_name,
+            total_steps
+        )
+    else:
+        logical_re['Logical_Abstract']['subcircuit_info'] = {}
     return logical_re
 
+def grab_single_step_estimates(num_qubits: int, main_estimates: dict, algo_name:str, total_steps: int) -> dict:
+    return {
+        f'{algo_name}': {
+            'num_qubits': num_qubits,
+            't_count': main_estimates['t_count']//total_steps,
+            'circuit_depth': main_estimates['circuit_depth']//total_steps,
+            'gate_count': main_estimates['gate_count']//total_steps,
+            't_depth': main_estimates['t_depth']//total_steps,
+            'clifford_count': main_estimates['clifford_count']//total_steps,
+            'subcircuit_occurences': total_steps,
+            'subcircuit_info': {}
+        }
+    }
+
 def circuit_estimate(
         circuit: cirq.AbstractCircuit,
         outdir: str,
         numsteps: int,
+        algo_name: str,
+        include_nested_resources:bool,
         bits_precision:int=1,
         write_circuits:bool = False
     ) -> dict:
@@ -182,7 +215,7 @@ def circuit_estimate(
     for moment in circuit:
         for operation in moment:
             gate_type = type(operation.gate)
-            gate_type_name = f'{str(gate_type).split("/")[-1][:-2]}'
+            gate_type_name = gate_type.__name__
             if gate_type in subcircuit_counts:
                 subcircuit_counts[gate_type][0] += 1
             else:
@@ -213,11 +246,13 @@ def circuit_estimate(
     total_clifford_count = 0
     subcircuit_re = []
     for gate in subcircuit_counts:
+        occurence = subcircuit_counts[gate][0]
         subcircuit = subcircuit_counts[gate][1]
         subcircuit_name = subcircuit_counts[gate][2]
         resource_estimate = gen_resource_estimate(
             subcircuit,
             is_extrapolated=False,
+            circ_occurences=occurence,
             bits_precision=bits_precision
         )
         subcircuit_info = {subcircuit_name:resource_estimate}
@@ -234,19 +269,33 @@ def circuit_estimate(
         total_T_depth += subcircuit_counts[gate][0] * t_depth * numsteps
         total_T_count += subcircuit_counts[gate][0] * t_count * numsteps
         total_clifford_count += subcircuit_counts[gate][0] * clifford_count * numsteps
-    
-    return {
+
+    main_estimates = {
         'Logical_Abstract': {
             'num_qubits': len(circuit.all_qubits()),
             't_count': total_T_count,
             'circuit_depth': total_gate_depth,
             'gate_count': total_gate_count,
             't_depth': total_T_depth,
-            'clifford_count': total_clifford_count
+            'clifford_count': total_clifford_count,
+            'subcircuit_occurences': 1,
+            'subcircuit_info': {}
         }
     }
+    if include_nested_resources and subcircuit_re:
+        main_estimates['Logical_Abstract']['subcircuit_info'] = grab_single_step_estimates(
+            len(circuit.all_qubits()),
+            main_estimates['Logical_Abstract'],
+            algo_name,
+            numsteps
+        )
+        for op in subcircuit_re:
+            for op_key in op.keys():
+                main_estimates['Logical_Abstract']['subcircuit_info'][algo_name]['subcircuit_info'][op_key] = op[op_key]
+    return main_estimates
+
 
-def re_as_json(main_estimate:dict, outdir:str) -> None:
+def re_as_json(main_estimate:dict, outdir:str, estimates:list[dict]=None) -> None:
     with open(outdir, 'w') as f:
         json.dump(main_estimate, f,
             indent=4,