diff --git a/QCBasics/CH3-QuantumProtocols/Bell_Inequality_with_Qiskit_Student.ipynb b/QCBasics/CH3-QuantumProtocols/Bell_Inequality_with_Qiskit_Student.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this Qiskit in Classrooms module, students must have a working python environment with the following packages installed:\n",
+    "- qiskit v $\\geq$ 1.3\n",
+    "- qiskit_ibm_runtime v $\\geq$ 0.29.0\n",
+    "- qiskit-aer v $\\geq$ 15.0\n",
+    "- qiskit.visualization\n",
+    "\n",
+    "To set up and install the packages above, see the guidance on IBM Quantum's [Documentation page](https://docs.quantum.ibm.com/guides/install-qiskit). \n",
+    "In order to run jobs on real quantum computers, students will need to set up an account with IBM Quantum. This can be done by following the steps outlined on [Documentation here](https://docs.quantum.ibm.com/guides/setup-channel)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Nature of Quantum States: Hidden Variables vs Bell's Inequality\n",
+    "\n",
+    "## The Problem\n",
+    "\n",
+    "In many calculations throughout quantum mechanics, you start with a known state of a system, and that state is typically known through a measurement. Today we want to answer the question, “What can you say about a particle's state prior to any measurement?” An obvious corollary is, “How can we know, if we're not allowed to measure?”\n",
+    "\n",
+    "This question dates back to the early days of quantum mechanics. Pioneers in the field fell into factions with Einstein and many others saying that a particle is simply in some unknown state prior to measurement.  Others, notably Max Born, and later Niels Bohr made a more radical claim, saying the state of a particle was truly undetermined by nature prior to measurement, not merely unknown to humans. Measurement then probabilistically collapses the particle into a definite state. Einstein, dissatisfied with this explanation, famously quipped at this, \"Gott würfelt nicht,\" which roughly translates into \"God does not play dice.\"\n",
+    "\n",
+    "For decades after this disagreement emerged, many thought it might never be answered, or that it was a matter of perspective. Then, in 1964, John Bell, a physicist from Northern Ireland, wrote a paper in which he explored the statistics of certain experiments that could answer this question definitively. He showed that in a particular test, one arrives at one set of statistics from defined (but unknown) quantum states, and a different set of statistics from quantum states undetermined by nature.\n",
+    "\n",
+    "At that time of Bell's paper, experimental tests of the statistics involved were inaccessible to all but researchers at the very forefront of physics. But today, IBM Quantum has made it possible for students all over the world to use real quantum devices, remotely over the cloud, and for free, to explore the nature of quantum states. This is what you will do today."
+   ]
+  },
+  {
+   "attachments": {
+    "EPR_pic-2.png": {
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+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup of the Thought Experiment: Entanglement of Spin\n",
+    "\n",
+    "There are processes in which a particle with no spin decays into two particles that each have spin. Since spin is a kind of angular momentum, the law of conservation of angular momentum would suggest that the two outcoming particles must have spins exactly anti-aligned. Indeed this is experimentally observed.\n",
+    "\n",
+    "An example: a neutral pi meson sometimes decays into a positron and an electron:\n",
+    "$$\\pi^0\\rightarrow e^+ + e^-$$\n",
+    "Don’t worry if you don’t know what those particles are, and don't worry if you know them so well that you know this decay type is relatively unlikely. Just know that if one of the outcoming particles is spin up, the other must be spin down, and vice versa. Of course, there is nothing special about \"up\" and \"down\"; the same antialignment is observed if measurements are made along what we often call $x$ or $y$. This decay is a compelling context for us to consider, because we can sidestep questions about what measurements took place in the past; the positron and electron didn't even exist until the moment of decay. \n",
+    "\n",
+    "We can let $\\pi^0$ mesons decay and watch the deflection of the outcoming particles under the influence of an inhomogeneous magnetic field. An inhomogeneous field used to deflect spins is often called a Stern Gerlach device, after the researchers who first used it to (accidentally) gather evidence of the existence of quantum mechanical spin. Note that the story here is more complicated than the original experiment since the electron and positron are also charged (unlike the silver atoms in the Stern Gerlach experiment). But we know how charged particles move in a magnetic field, and we can subtract out that effect. In what follows, we will assume the deflections used in our calculations are due to the spin of the particles and not the charge. Consequently, for our purposes it doesn't matter which observer gets the positron and which gets the electron. The experimental setup is something like this:\n",
+    "\n",
+    "![EPR_pic-2.png](attachment:EPR_pic-2.png)\n",
+    "\n",
+    "As the meson decays, an electron is kicked out in one direction, and a positron in the other. Each of these two particles will travel through an inhomogeneous magnetic field, causing it to be deflected either in the direction of the magnetic field, or opposite the magnetic field.\n",
+    "\n",
+    "If we have a source of many mesons, we can collect statistics on this. If an observer on the left and one on the right (call them Lucas and Rihanna, respectively) always measure along the same axis, these statistics will not be very interesting: every time one measures up, the other measures down; every time one measures into the page, the other will measure out of the page, and so on. However, if the players are free to measure the spin along any direction they like, we may find something more interesting.\n",
+    "\n",
+    "The experiment described above, in which particles fly off with spin angular momentum that is measured by two observers was initially proposed by Einstein, Podolsky, and Rosen (EPR) in [this paper](https://journals.aps.org/pr/pdf/10.1103/PhysRev.47.777)., and this is sometimes referred to as an \"EPR experiment\"."
+   ]
+  },
+  {
+   "attachments": {
+    "Bell_Hidden_variables_instructions_meas.png": {
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feOONnXO/jXnzzTc7Y16MfTEG5o4VAADQHoozAgBQOpvqbKobt6LJtUVxvU11cZ7bVGdTHQAAAABAznbbbZdmz56dbr755vTqq692n6q2I4899tj/bfD67Gc/a4PXkBRnbLa4DuJ6iOsiro+4TtqUGAdvuummzrgY42PuGAEAAAAAwHStu+666TOf+Uz65je/mR5//PHuU+p25Pnnn0/33HNPuuyyy9KJJ56Y9txzzzRjxozscWK0iibXFqMV10BcC//4j//YuTbiGolrpU2JsTDGxBgbY4zMHScAAKC5FGcEAGDibKqzqW7SiibXFqNnU51NdQAAAABAe62xxhrpwAMPTBdffHH62c9+1n1y2uz8z//8z7s2eNnMUZzijO0T10tcN3H9tG0zWIyTMV7GuBnjZ+74AAAAAADAMHbdddd0xhlnpDvuuCOtWLGi+yS6ufnzn/+cHnnkkXTdddelL3zhC+lTn/pU2mKLLbLHhskomlxbTMaWW27ZuXbiGoprKa6pv/zlL91vprmJMTLGyhgzY+zMHRsAAKBZFGcEAGDsbKqzqa5sRZNri8nIbaqL66oNsakOAAAAAGi67bffPs2ZMyctWrQovfbaa92no82LDV6ToTgjPXF9tWkzWIyfMY5+/vOf96OAAAAAAAAMtN5666VDDz00XXnllelXv/pV92lzM/P000+nxYsXpwsvvDAdc8wxnWJq9mdUT9Hk2qI8cW3FNRbXWlxzce3FNdjkxBj6rW99K332s5/tjK254wIAANSb4owAAIyFTXX540I5iibXFuXKbaqL67CpsakOAAAAAGiC97znPZ1nu5deemlatmxZ9wlos2KDV3kUZ6SfNm0Gi/E1xtkYb2PczR0PAAAAAADaZffdd0/z5s1Ld999d/dpcrPypz/9KS1dujRdddVV6Z/+6Z/SzJkz01//9V9njwXVUzS5tqieuBbjmoxrM67RuFbjmm1i7rrrrs5etxhzc8cCAACoH8UZAQAYCZvqqLKiybVF9dhUBwAAAABQPTvuuGNnk8UPfvCDtHz58u5TzubkwQcfTN/4xjfSkUcembbeeuvsMWAyFGekiLhu4/qN6ziu56Ylxt0Yf2McjvE4dwwAAAAAAGieDTfcMB1++OGdQmhPPfVU96lxcxJ7RRYuXJjmzp2b9thjj+wxoD6KJtcW9RHXblzDcS03cf9XjL0xBsdYHGNy7hgAAADVpzgjAACF2VRHXRRNri3qw6Y6AAAAAIDJ+vCHP5zOPffc9Oijj3afZDYjL7/8crrjjjs6n+2AAw5I6623XvbzUw7FGRmFuK7j+o7rPK73uO6blBiX47PFOJ37/AAAAAAA1NdGG22UTjjhhM7+gqblpz/9abriiivSMccck7bbbrvs56e+iibXFvUV13Zc43GtxzXftMTYfPzxx3fG6tznBwAAqklxRgAApsSmOuqoaHJtUV8rb6q7/fbbbaoDAAAAABiBnXbaKZ199tnpoYce6j6trH9+/etfp+uvvz7NmTMnffSjH81+bqpDcUbGJa7/GAdiPIhxoSmJ8TrG7Ri/c58bAAAAAIDqW3/99dOxxx6bbrnllu7T3/rnT3/6U7rrrrvSeeedlz71qU+lDTfcMPvZaY6iybVFc8S1H2NAjAV33313Z2xoSmLMjrE7xvDcZwcAAKpDcUYAAAayqY66K5pcWzSLTXUAAAAAAFP3oQ99KM2bNy/9+Mc/7j6VrHfimeo3vvGNdNRRR6Wtt946+5mpLsUZmZQYH2KciPGiKWvnMY7HeB7jeu4zAwAAAABQHeuuu246+uij04033phWrFjRfdJb3zz99NNp4cKFae7cuWmPPfbIfmaarWhybdFsMUbEWBFjRowddc9f/vKXzlgeY/o666yT/cwAAEC5FGcEACDLpjqapGhybdFsNtUBAAAAAORtu+226Ywzzkj33Xdf9+ljPfPKK6+kO+64I82fPz8dcMABaf31189+XupDcUbKEuNHjCMxnsS4EuNLnRPje4zzMd7nPi8AAAAAAJO31lprpc997nOdgmRvvPFG94luPfPTn/40XXHFFemYY45J2223Xfbz0i5Fk2uLdokxJMaSGFNibKlzYmyPMT7G+hjzc58XAACYPMUZAQD4PzbV0VRFk2uLdrGpDgAAAABos6222irNnTs3/fCHP+w+ZaxfnnrqqXT99denU089NX30ox/Nfk7qTXFGqiTGmRhvYtyJ8aeuiXE/xv+YB3KfEwAAAACA8VljjTXSYYcd1nnW/Nprr3Wf3NYrf/rTn9Jdd92VzjvvvPSpT30qbbjhhtnPSrsVTa4t2i3GmBhrYsy5++67O2NQHRNj/ve+973OHBBzQe6zAgAAk6E4IwBAy9lURxsUTa4tsKkOAAAAAGiyzTbbLM2ZMyfdc8893aeJ9cqvf/3r9O1vfzsdddRRaZtttsl+RppFcUaqLMahGI9iXIrxqY6J+SDmhZgfcp8RAAAAAIDROOSQQ9J3v/vd9Morr3Sf0NYnr7/+err11ls7exT22GOP7OeDVRVNri1YVYxFMSbF2LR8+fLu2VOfxFwQc0LMDbnPBwAAjJfijAAALWRTHW1TNLm2YFU21QEAAAAAdbfJJpuk2bNnpzvuuKP71LBeufPOO9OZZ56Zdt111+zno9kUZ6ROYpyK8SrGrTom5omYL2LeyH0+AAAAAACmZtasWemaa65JL730UvdJbH3y85//PF1yySXpoIMOSmuuuWb280E/RZNrC/qJMSrGqksvvTQtW7aseybVJzFHxFwRc0bu8wEAAKOnOCMAQEvYVEebFU2uLRjEpjoAAAAAoA7e9773pRNPPDEtXry4+3SwPnniiSfSFVdckQ455JA0Y8aM7OejPRRnpK5i/IpxLMazJ598sjvC1Scxf8Q8EvNJ7vMBAAAAAJAXBcKuuuqq9Ic//KH7xLUe+dOf/pRuvvnmdMopp6QPfvCD2c8GU1E0ubZgKmIM+/znP59uueWWzthWp8TcEXNIzCW5zwYAAIyG4owAAA1mUx28pWhybcFUrLypLsa1usWmOgAAAABolg022CAdd9xxadGiRd2ngPXIX/7yl3T77ben008/Pe2yi8J2vJPijDRFjG///M//3BnvYtyrU2Jeifkl5pncZwMAAAAAaLtPfvKT6Vvf+lZ69tlnu09W65FHH300ff3rX+/0P/e5YDqKJtcWTMcBBxzQGetizKtTYk6JucUYDQAAo6c4IwBAw9hUB+9WNLm2YDpsqgMAAAAAynD44Yenm266qfu0rx755S9/mS6//PL0d3/3d+m9731v9nNBUJyRJlpnnXU641+MgzEe1ikx38S8k/tcAAAAAABtsueee3ae8/7ud7/rPkGtfl5++eV04403ppNPPjltu+222c8Fo1I0ubZgVGLsizEwxsIYE+uSmGtizom5J/e5AACAqVGcEQCgIWyqg9UrmlxbMCox7tlUBwAAAACMy0477ZS++tWvpmeeeab7ZK/aeeONN9Jtt92W5s6d2+l77jNBjuKMtEGMizE+xjj55ptvdkfOaifmn5iHjOkAAAAAQJust9566ZRTTklLly7tPi2tfh5++OF04YUXpv322y/7mWBciibXFoxLjI0xRsZYWZfEHBRzUcxJuc8EAAAMpjgjAECN2VQHwymaXFswLitvqovxsg6xqQ4AAAAAqueYY45Jd9xxR/cpXrXzi1/8Ii1YsCAdfPDBaa211sp+HhhEcUbaJsbLWbNmpcsuu6wzjtYhMS/F/JT7PAAAAAAATbDPPvukq6++Oi1fvrz7ZLS6efHFF9PChQvTiSeemLbaaqvs54FJKJpcWzAJMWbG2BljaIylVU/MSTE3xRyV+zwAAMDqKc4IAFBDNtXB1BRNri2YBJvqAAAAAICp2H333dMll1ySnn/++e4Tu2rm9ddfT7feems69dRT0w477JD9LDBVijPSdjGexrga42uMs1VOzFMxX8W8lfssAAAAAAB1svHGG6fTTjstPfzww92noNXNT37yk3TBBRekfffdN/tZoAxFk2sLyjBz5sz0L//yL+nBBx/snp3VTcxVMWfF3JX7LAAAwDspzggAUBM21UFxRZNrC8pgUx0AAAAAsKr4kZcTTjghLVmypPt0rpp59tln0xVXXJEOOuigtOaaa2Y/C0yH4ozwthhnY7yNcTfG3yon5q+Yx/zIIwAAAABQN5/85CfTtdde233aWd3E/oPjjz8+bbHFFtnPAWUrmlxbULYYa2PMjbG36ok5LOay3OcAAADeojgjAECF2VQHo1E0ubagbCtvqvv973/fPVurGZvqAAAAAGD09txzz/TNb34zvfzyy90ncdVL/IjLt7/97c6zzNxngFFSnBFWL8bhGI+r/COQMZ/FvBbzW+4zAAAAAABUwWabbZbmzZuXli1b1n26Wc0sXrw4nXjiiWnjjTfOfg6okqLJtQVVEmNwjMUxJlc5MafF3BZzXO5zAABAmynOCABQQTbVwWgVTa4tqBqb6gAAAACg+WbMmJFOOeWUtHTp0u5Tt+rlxRdfTNdcc02aNWtW9jPAuCjOCMOJ8TnG6Zdeeqk7clcv999/f5o9e3Zn3st9BgAAAACASYtnqwsXLuw+xaxm7rjjjs6z1U022ST7GaCqiibXFlRVjM0xRsdYXeXEXOe/9wAAgLcpzggAUBE21cH4FE2uLaiy3qa6GK+rGpvqAAAAAGB4e++9d7rqqqvS66+/3n3CVq288sor6bvf/W465JBDsv2HSVCcEabuM5/5TGf8jnG8iol5L+a/mAdz/QcAAAAAGKdtttkmnXPOOemJJ57oPrWsXu655540Z86ctNlmm2U/A9RB0eTagjqIMTvG7hjDq5qY+2IOjLkw9xkAAKAtFGcEACiZTXUwfkWTawvqwqY6AAAAAKin973vfWnu3LnpoYce6j5Nq1Zee+21dP3116fDDjssrbHGGtnPAJOkOCMUF+N4jOff+973OuN7FRPzYcyLMT/mPgMAAAAAwKgceuih6ZZbbuk+naxefvjDH3ael2655ZbZ/kPdFE2uLaibGMtjTI+xvaqJOTHmxlz/AQCg6RRnBAAogU11MFlFk2sL6samOgAAAACoh/3337/zgysrVqzoPjmrTt588820cOHC9LnPfS6ttdZa2f5DWRRnhNGI8T3G+RjvY9yvWmJ+jHky5stc/wEAAAAAithhhx3SBRdckJ5++unu08hq5b777kv//M//nLbZZpts/6HOiibXFtRZjPEx1seYX8XEHBlzZcyZuf4DAEATKc4IADBBNtVBOYom1xbUmU11AAAAAFAtm266afrCF76Qfv7zn3efklUn8bzupptuSsccc0xad911s/2HKlCcEUYvxv2jjz463XjjjZX87xti3oz5M+bRXP8BAAAAAAY56qij0n/+5392nzpWKw888ECaN29e+tCHPpTtOzRF0eTagqaIsT/WwWIuqGJi7ow5NNd3AABoEsUZAQDGzKY6KF/R5NqCprCpDgAAAADKs9dee6Xrrruu+zSsWlm0aFH6h3/4h7T++utn+w5VozgjjFfMBzEvxPxQxcR8GvNqru8AAAAAACvbfPPN0/nnn5+ee+657hPG6uShhx5KZ599dtpxxx2zfYcmKppcW9BEMSfE3BBzRNUSc2nMqTG35voOAAB1pzgjAMCY2FQH1VE0ubagiWyqAwAAAIDJOOSQQ9Idd9zRffJVndx2223phBNOSBtttFG231BlijPC5MQ8EfNFzBtVS8yvMc/m+g0AAAAAtNvuu++errrqqu7TxOrkkUceSeecc07aZRdrB7RT0eTagqaLuSLmjJg7qpaYY2OuzfUbAADqSnFGAIARs6kOqqdocm1B09lUBwAAAACjd/LJJ1fuP5C/++67O/16//vfn+0z1IXijFCOmD9iHon5pEqJ+fakk07K9hkAAAAAaJcDDzww3XLLLd2nh9XI448/nr7yla+k3XbbLdtnaJOiybUFbRJzSMwlTzzxRPeqqEZizo25N9dnAACoG8UZAQBGxKY6qK6iybUFbVLlTXXRr1yfAQAAAKAq/vqv/zp9+ctfTs8880z3yVb5efbZZ9NFF12UPvzhD2f7DHWkOCOUL+aVmF9inqlKYv6NeTjm41yfAQAAAIDm+vu///u0dOnS7tPCauT73/9+Ovjgg7P9hbYqmlxb0FYxtyxcuLB7dVQjMQfHXJzrLwAA1IXijAAA02BTHdRD0eTagrayqQ4AAAAAhrPTTjulb3zjG+nNN9/sPskqP4sXL05HHnlktr9Qd4ozQrXEfBPzTlUS83HMyzE/5/oLAAAAADTDjBkz0hlnnJGefPLJ7tPB8vPzn/88feELX0ibbrppts/QdkWTawvaLuaaefPmpWXLlnWvlPITc3LMzTFH5/oMAABVpjgjAEABNtVBvRRNri3ApjoAAAAAyNl3333TwoULu0+tys9vfvObdP7556ftt98+219oCsUZoZpi/ol5KOajqiTm6Zivc/0FAAAAAOppm222SV/72tfSK6+80n0SWH6++93vpv333z/bX+BtRZNrC3hbzEHXXntt94opPzFHx1wdc3auvwAAUEWKMwIATIFNdVBPRZNrC3hbVTfVzZw5M9tfAAAAABiHI444It17773dJ1Tl5+abb06f/exns32FJlKcEaov5qWYn6qSmLdj/s71FQAAAACohz333DN95zvf6T71Kz8PPfRQmjt3bnrf+96X7S/wbkWTawt4t5iTTjvttPTwww93r57yE3N3zOG5/gIAQJUozggAMASb6qDeiibXFpBnUx0AAAAAbbLmmmumOXPmpGXLlnWfSJWbJ554In35y19OW2+9dba/0GSKM0J9xDwV81XMW1VIzOMxn8e8nusvAAAAAFA9n/70p9PixYu7T/nKzeuvv56uuuqqtPfee2f7CvRXNLm2gP722WefdPXVV6fly5d3r6RyE3N5zOm5vgIAQBUozggAsBo21UFzFE2uLaC/qm2q++///m+b6gAAAAAYmU033TR95StfSc8//3z3CVS5WbhwYTr44IOzfYW2UJwR6inmr5jHqpCY188777zOPJ/rKwAAAABQvhNPPDE99NBD3ad65eb+++9Ps2fPTjNmzMj2FRhO0eTaAoYTc9cpp5ySli5d2r2iyk3M7THH5/oKAABlUpwRAGAVNtVB8xRNri1geDbVAQAAANAUu+22W7ryyiu7T5vKTfyw2Lx58zzrgi7FGaHeYj6Lea0qP5wZ833M+7m+AgAAAACTtdFGG6Wzzjor/fa3v+0+wSsvf/zjH9Pll1+e9thjj2xfgakrmlxbwNTtueee6Zvf/GZ6+eWXu1dXeYm5Pub8mPtzfQUAgElTnBEAoMumOmiuosm1BUydTXUAAAAA1NUnP/nJdNNNN3WfLJWba6+9ttOfXD+hzRRnhOaIeS7muyok5n/zLgAAAACUY4cddkgLFixIy5cv7z6xKy/33ntvOv7449N73vOebF+B4oom1xZQXMxxJ5xwQlqyZEn3KisvMffHPUDcC+T6CgAAk6I4IwDQejbVQfMVTa4tYHpsqgMAAACgDo4++uh03333dZ8klZeHH344nXbaaWnjjTfO9hNQnBGaKOa9mP9iHiw7cT8Q9wW5fgIAAAAAo7X33nun66+/vvt0rrz8z//8T7r44ovT7rvvnu0nMBpFk2sLGI2Y+y655JLOXFh24p4g7g1y/QQAgHFTnBEAaC2b6qA9iibXFjAaVdtUd8wxx2T7CQAAAEC7zJ49Oz3++OPdJ0flZPny5enqq69O++yzT7aPwDspzgjNFvNhzIsxP5aZuD+I+4RcHwEAAACA6TnwwAPTXXfd1X0aV15uv/12/105TFDR5NoCRi/mxJgby07cI8S9Qq6PAAAwLoozAgCtY1MdtE/R5NoCRs+mOgAAAADKdsIJJ6Rf/OIX3SdF5eTJJ59MZ5xxRlpvvfWyfQTyFGeEdoj5MebJmC/LTNwvxH1Dro8AAAAAwNTst99+6T//8z+7T9/KyZ///Od0+eWXp5133jnbR2B8iibXFjA+MUfGXBlzZpmJe4a4d8j1EQAARk1xRgCgNWyqg/YqmlxbwPjYVAcAAADApP393/99evTRR7tPhsrJ0qVL07HHHpvtHzCY4ozQPjFvxvxZZuL+Ie4jcv0DAAAAAPrbe++906JFi7pP28rJ73//+3TOOeek97///dk+AuNXNLm2gPGLOTPmzphDy0zcQ8S9RK6PAAAwKoozAgCNZ1MdUDS5toDJsKkOAAAAgHH63Oc+lx588MHuk6ByEv+x+EEHHZTtHzA8xRmhvWIeLXsDd9xPxH1Frn8AAAAAwDvtueee6YYbbug+XSsnjzzySDr55JOz/QMmq2hybQGTFXNpzKllJu4p4t4i1z8AAJguxRkBgMayqQ7oKZpcW8Bk2VQHAAAAwCh99rOfTffff3/3yU85ufrqq9NHPvKRbP+AqVOcEYh59aqrrurOtOUk7i/iPiPXPwAAAABou3iGd/3113efppWTO++80zM8qJiiybUFlCPm1phjy0zcY/jvcAAAGDXFGQGAxrGpDlhV0eTaAsphUx0AAAAA0zFr1qy0ZMmS7pOeyeeFF15IF1xwQdpiiy2y/QOKU5wR6Il5NubbmHfLStxvxH1Hrn8AAAAA0Da77LJL+s53vtN9elZOrrvuurTXXntl+weUq2hybQHlirk25twyE/ccce+R6x8AAEyV4owAQGPYVAesTtHk2gLKZVMdAAAAAFNxwAEHlPoL/Y899liaO3duWnvttbP9A6ZPcUZgVTHvxvwb83BZifuPuA/J9Q8AAAAAmm777bcv9Yf5X3vttXTJJZekD33oQ9n+AdVQNLm2gGqIuTfm4JiLy0rcg8S9SK5/AAAwLMUZAYDas6kOGKRocm0B1WBTHQAAAAD9zJw5M912223dJzmTz49+9KN05JFHZvsGjJbijEA/MR//8Ic/7M7Qk0/cj8R9Sa5vAAAAANA02267bbriiiu6T8cmn6eeeirNmzcvbbDBBtn+AdVSNLm2gGqJuTjm5Jiby0rck8S9Sa5/AAAwiOKMAEBt2VQHDKtocm0B1WNTHQAAAAA9e+21V7r55pu7T24mnxtuuCHtt99+2b4B46E4IzCMmJ9jni4rcX8S9ym5vgEAAABA3W2xxRZpwYIF3adhk89PfvKTdPzxx2f7BlRX0eTaAqor5uiYq8tK3KPEvUqubwAAsDqKMwIAtWNTHTBVRZNrC6gum+oAAAAA2utv/uZv0sKFC7tPaiabFStWdH5t/8Mf/nC2b8B4Kc4ITMUuu+zSmbdj/i4jcb8S9y25vgEAAABA3WyyySbpoosuKu152w9+8IN08MEHZ/sGVF/R5NoCqi/m7Ji7y0jcq8Q9S9y75PoGAACrUpwRAKgNm+qAooom1xZQfTbVAQAAALTHbrvtlq677rruk5nJ5rnnnkvnnnuu/3AbSqY4I1BEzN8xj8d8Xkbi/iXuY3J9AwAAAICq22ijjdIFF1yQli9f3n3iNdlcc801/nttaICiybUF1MfHPvaxzlxeRuLeJe5h4l4m1zcAAOhRnBEAqDyb6oDpKppcW0B92FQHAAAA0Fw77bRTaf+h9s9+9rN0yimnZPsFTJ7ijMB0xbwe83sZifuZuK/J9QsAAAAAqmbGjBmd/z77lVde6T7hmlxeeuml9NWvfjVttdVW2b4B9VM0ubaA+ok5Peb2mOMnnbiXiXuauLfJ9Q0AABRnBAAqy6Y6YFSKJtcWUE821QEAAAA0wwc/+MF05ZVXdp+8TDZ33313Ouyww7L9AsqjOCMwKjHPx3xfRuL+Ju5zcv0CAAAAgLKttdZa6ayzzkovvPBC94nW5PL444+n008/Pb33ve/N9g2or6LJtQXUV8zxMdfHnD/pxL1N3OPEvU6ubwAAtJfijABA5dhUB4xa0eTaAurNpjoAAACAetpkk03SN77xje6TlsnmnnvuSQcddFC2X0D5FGcERi3m/Zj/y0jc77z//e/P9gsAAAAAynDmmWem5557rvsEa3L51a9+1fmB/lyfgGYomlxbQDPE3B/3AJNO3OucccYZ2T4BANBOijMCAJVhUx0wLkWTawtohrI31cV9T65fAAAAALxb/MfPr776avfpyuTyX//1X+kzn/lMtk9AdSjOCIxL3AfE/cCk88orr9j8BQAAAEDpDj300PSzn/2s+9Rqcvntb3+b5s6dm+0T0CxFk2sLaJa4F4h7gkkn7n3iHijXJwAA2kVxRgCgEmyqA8apaHJtAc1S1qa6uO+xqQ4AAACgv7I2fP34xz9Ohx9+eLZPQPUozgiMW9wXxP3BpGPzFwAAAABl+MhHPpIWLVrUfUo1uTz33HPpzDPPzPYJaKaiybUFNFPcG8Q9wqQT90JxT5TrEwAA7aA4IwBQKpvqgEkomlxbQDPZVAcAAABQHWVt+PrpT3+ajj766GyfgOpSnBGYlLhPiPuFSeeWW25Ju+++e7ZPAAAAADAqG220UVqwYEH3qdTk8uKLL6azzz47rb322tl+Ac1VNLm2gOaKe4S4V4h7hkkn7o3iHinXLwAAmk1xRgCgFDbVAZNUNLm2gGYra1Nd3BfZVAcAAAC03YYbbpguvfTS7hOTyWXZsmXpuOOOy/YJqD7FGYFJi/uGuH+YdOI+Ke6Xcn0CAAAAgOmYO3dueuGFF7pPoiaTV199Nc2fPz/NmDEj2yeg+Yom1xbQfHHPcO6553buISaZuEf6p3/6p2yfAABoLsUZAYCJsqkOKEPR5NoC2sGmOgAAAIDJiv+IedIbvh5//PF00kknZfsD1IfijEBZ4j4i7icmGZu/AAAAABilT3/60+mhhx7qPn2aTJYvX54uuOCCtNFGG2X7BLRH0eTaAtoj7iHiXiLuKSaZuGeKe6dcnwAAaB7FGQGAibGpDihL0eTaAtrFpjoAAACA8Spjw9dTTz2V5syZk+0PUD+KMwJli/uKuL+YZB588ME0a9asbH8AAAAAYJBddtkl3XDDDd2nTZPJihUr0kUXXZQ22WSTbJ+A9imaXFtA+8Q9RdxbxD3GJBP3UHEvlesTAADNoTgjADB2NtUBZSuaXFtAO9lUBwAAADBaO++8c/qP//iP7pOQyeSZZ55Jp59+erY/QH0pzghURdxnxP3GJBP3U3FflesPAAAAAKxqxowZnSJGk86CBQvSFltske0T0F5Fk2sLaK+4x4h7jUkn7qni3irXJwAA6k9xRgBgbGyqA6qiaHJtAe1mUx0AAADA9Ky77rrpa1/7WvfJx2Ty/PPPp3nz5qU11lgj2yeg3hRnBKok7jfiviPuPyaZCy+8sHOflesTAAAAAIRTTjklPfvss90nSpPJFVdckbbddttsfwCKJtcWQNxzxL3HJBP3VnGPlesPAAD1pjgjADByNtUBVVM0ubYAbKoDAAAAKGb27Nnp97//ffdpx/jzxz/+MX35y19O66yzTrY/QDMozghUUdx/xH1I3I9MKnGfdfLJJ2f7AwAAAEB7HXjggWnp0qXdp0iTydVXX5122GGHbH8Aeoom1xZAz/bbb9+5F5lk4l4r7rly/QEA4P9n7/7/f5/r+4H/KRsn0RZq4QhL8iVRvltLaIlovsScS5fmqJ0W+RLb8iVfNuYYygchXwrzJWsyOtpoIi1ZWEMMyZfh+fncT88+hfs55/1+PJ/P1/v55Xq7XK4/7v54/dS535973++GyXFGAKBVluqAPipNVgvg1yzVAQAAAMzN3nvvXX33u9+tv3B0n+eff7760pe+VP3+7/9++nuAcXGcEeiz6EeiL4n+ZFaJviv6r+z3AAAAADAdcZjoiiuuqL8azSaXXnppte22voMDc1OarBbA60VPcskll9T/yzGbRO8VPVj2ewAAGBbHGQGAVliqA/qsNFktgNezVAcAAACQ23LLLavLL7+8/qLRfV5++eXqy1/+cvW2t70t/T3AODnOCAxB9CfRp0S/Mqt87WtfW9mPZb8HAAAAgPFatGhRdeqpp9ZfiWaTOES0ww47pL8HYFVKk9UCWJXoUWZ9sDp6sejJst8DAMAwOM4IADRiqQ4YgtJktQBWxVIdAAAAwK+svfbaK/9jFrPM3/7t31Ybb7xx+nuAcXOcERiS6Feib5lloi+L/iz7PQAAAACMy6c+9anqkUceqb8MdZ9rrrmm2nnnndPfArAmpclqAaxJ9CzRu8wq0ZNFb5b9FgAA+s9xRgCgiKU6YEhKk9UCWBNLdQAAAMCULVmypHr44Yfrrxbd58orr6y22mqr9LcA0+A4IzBE0b9EHzOrRH8WfVr2WwAAAAAYvg996EPV7bffXn8N6j533XVXte+++6a/BWCuSpPVApir6GGil5lVokeLXi37LQAA9JfjjADAvFmqA4amNFktgLmyVAcAAABMyQc/+MHqtttuq79UdJ/4I+mPfOQj6W8BpsVxRmDIZr38Ff1a9G3ZbwEAAABgeDbffPPqkksuqb/+dJ/HH3+8Wrp0afpbAOarNFktgPmKniZ6m1klerbo3bLfAgBA/zjOCADMmaU6YKhKk9UCmC9LdQAAAMCYrbvuutV5551Xf5noPvFH0UcffXT6W4BpcpwRGIPob2a5/BX9W/Rx2W8BAAAAYBiOOeaY6uWXX66/+HSfM888s3rLW96S/haAEqXJagGUiN4mepxZJXq36OGy3wIAQL84zggArJGlOmDoSpPVAihlqQ4AAAAYm4MPPrh6+OGH668R3eess86y8AW8geOMwFhEnxP9zqwSfVz0c9lvAQAAAKC/dtppp+o73/lO/ZWn+1x77bXVdtttl/4WgCZKk9UCaCJ6neh5ZpXo5aKny34LAAD94DgjALBaluqAMShNVgugCUt1AAAAwBhssskm1eWXX15/geg+8cfP733ve9PfAuA4IzA20ffMcvkr+rqNN944/S0AAAAA9Mspp5xSf9XpPvfcc0/1sY99LP0dAG0oTVYLoA3R+0QPNKtEb5f9DgAAFp7jjABAylIdMCalyWoBtMFSHQAAADBUf/7nf14988wz9VeHbvP973+/2n///dPfAfBrjjMCYxV9UPRDs0j0d9HnZb8DAAAAgIW3zz77VPfee2/9Nafb/M///E+1bNmy9HcAtKk0WS2ANkUvFD3RLBI9XvR62e8AAGDhOM4IALyBpTpgbEqT1QJok6U6AAAAYCi22Wab6sYbb6y/NHSbp59+uvrc5z6X/g6A13OcERi76IuiP5pFot+Lvi/7HQAAAADM3nrrrVedd9559deb7nPOOedUG2ywQfpbANpWmqwWQNuiJ4reaFZZvnz5yt4v+y0AAMye44wAwP9nqQ4Yq9JktQC6YKkOAAAA6LPjjz++/rLQff7+7/++2nDDDdPfAZBxnBGYguiPzj333Lpj6j7HHXdc+jsAAAAAmJ2DDz64evjhh+svNt3m+uuvr3bcccf0dwB0pTRZLYCuRI90ww031P8L1G2i94seMPsdAADMluOMAMBKluqAMStNVgugK5bqAAAAgL7ZbbfdqjvvvLP+mtBt/vEf/7F6//vfn/4OgNVxnBGYkuiXom+aRaIPjH4w+x0AAAAAdGeTTTapLr/88vorTbe57777qoMOOij9HQBdK01WC6Br0TNF7zSLRC8YPWH2OwAAmA3HGQFg4izVAVNQmqwWQNcs1QEAAAB98OUvf7n+gtBt7r///uoTn/hE+hsA5sJxRmCKon+KPmoWOf3009PfAAAAAED7jjrqqOrZZ5+tv8x0l+eee6469thj098AMCulyWoBzEr0UNFLdZ3oCaM3zH4DAADdc5wRACbMUh0wFaXJagHMiqU6AAAAYCH8yZ/8yUy+STz//PPVcccdl/4GgPlwnBGYsuinoq/qOtEfRp+Y/QYAAAAAmttmm22qG2+8sf4a023OP//86h3veEf6OwBmqTRZLYBZil4qeqpZ5Kabbqq23dbfGQAAzJrjjAAwQZbqgKkpTVYLYNYs1QEAAACz8Pu///vVBRdcUH8p6DbxzkYbbZT+DoD5cpwRmLroq2bZx0XfmP0OAAAAAMocf/zx9deXbnPzzTdXu+yyS/obABZCabJaAAsheqtbbrml/l+nbhM9Y/YbAADohuOMADAhluqAqSpNVgtgIViqAwAAALr0yU9+svqv//qv+utAd7n11lur3XbbLf0NAKUcZwT4lV133bX61re+VXde3SX6xsMOOyz9DQAAAADM3e67715997vfrb+6dJcf/ehH1aGHHpr+BoCFVJqsFsBCil7rP/7jP+r/leou0TtGD5n9BgAA2uU4IwBMhKU6YMpKk9UCWEiW6gAAAIA2vfOd76y+/vWv118EusuDDz648v9Xlf0GgKYcZwR4rei7ov/qOtFHRj+Z/QYAAAAAVu+MM86ov7J0l5deeqk68cQT0/cB+qA0WS2APojeK3qwrhO9ZPY+AADtcZwRAEbOUh2A/2cdMD6W6gAAAICmPvOZz1TPPfdc/RWgu/hjYKBrjjMC5M4888y6I+su0U8effTR6fsAAAAAvNFHP/rR6oc//GH9daW7XH311dXmm2+e/gaAvihNVgugL7bYYovqmmuuqf8Xq7s88MADK3vL7DcAANCc44wAMGKzWqqLP+bO3gfoi9JktQD6xFIdAAAAMF/vfe97q1tuuaWe/LvLihUrqj322CP9DQBtcpwRYNWiH4u+rOtEfxl9ZvYbAAAAAPid6q1vfWt14YUX1l9Tustjjz1WHXHEEelvAOib0mS1APomerLozbpO9JjRa2a/AQCAco4zAsAIWaoDeK3SZLUA+sZSHQAAADBXX/ziF+tJv9uccMIJ6fsAXXCcEWDNoj+bRaLfzN4HAAAAmLLDDz+8+u///u/6C0p3ueiii6r1118//Q0AfVSarBZAH0VvFj1a14leM3rO7DcAAFDGcUYAGBlLdQBvVJqsFkBfWaoDAAAAViX+4w533XVXPd13l5tvvrnabrvt0t8A0BXHGQHmJvq06Ne6TvSd/mOvAAAAAL9TLV68uLr66qvrrybd5YEHHqj222+/9DcA9FlpsloAfRa9WvRsXSd6z+hBs98AAMD8OM4IACNhqQ5g1UqT1QLoM0t1AAAAwOsdf/zx9UTfXX7xi19US5cuTd8H6JrjjADzE31b9G9dJ/rQ7H0AAACAKTjkkEOqJ598sv5S0l3OOOOM9H2AIShNVgtgCM4888z6f8m6S/Sg0Ytm7wMAMHeOMwLACFiqA1i90mS1AIbAUh0AAACw+eabVzfeeGM9xXeXK6+80n9xHVhQjjMCzN+mm266so/rOtGPRl+a/QYAAACAsVq+fHn9daS7rFixwn9oHRi80mS1AIYierjo5bpO9KTZ+wAAzI3jjAAwYLNcqos/ys5+A8AQlCarBTAUluoAAABgug4//PDqmWeeqaf3bvLoo49Whx56aPo+wCw5zghQLvq56Ou6TPSl0Z9m7wMAAACMSRzauffee+uvIt3lhBNOSN8HGJrSZLUAhiZ6uq4TvamD3gAAZRxnBICBslQHMHelyWoBDI2lOgAAAJiORYsWVRdccEE9sXeX888/v3rLW96S/gaAWXOcEaCZ3/u936v+4R/+oe70ukv0qdGvZr8BAAAAYOiOP/74+itId7npppuqbbf1jRkYj9JktQCGKHq76PG6TvSq2fsAAKya44wAMDCzWqqLP7q2VAeMRWmyWgBDFH1dHE3oOpbqAAAAYOF88IMfrO6///56Su8m9913X/WRj3wkfR9goTjOCNCO6POi3+sy0a9G35q9DwAAADBEm2++eXXjjTfWXz+6ybPPPlsdddRR6fsAQ1aarBbAkEWv94tf/KL+X7luEj1r9K7Z+wAAvJHjjAAwIJbqAMqUJqsFMGSW6gAAAGCcTjrppHoy7y6nnXZa+jbAQnOcEaBd0fd1nehfs7cBAAAAhuTwww+vnnnmmfqLRze54oorqsWLF6fvAwxdabJaAEMXPV/0fl0metfoYbP3AQB4LccZAWAgLNUBlCtNVgtgDCzVAQAAwDhsueWW1S233FJP493kjjvuqHbZZZf0fYA+cJwRoH277rrryj6wy0QfG/1s9j4AAABAny1atKi68MIL668c3eSRRx6pDjnkkPR9gLEoTVYLYCyiB4xesMtELxs9bfY+AAC/4jgjAPTcrJbq4o+qs/cBxqA0WS2AsbBUBwAAAMN25JFHVs8991w9hXeTL3zhC+nbAH3iOCNAd6If7DLRz0Zfm70NAAAA0Ed//Md/XP3whz+sv250k+XLl1frrbde+j7AmJQmqwUwJtELRk/YZaKnjd42ex8AAMcZAaDXLNUBtKM0WS2AsbFUBwAAAMPy5je/ufrqV79aT97d5IYbbqje8573pO8D9I3jjADd2nrrrat//Md/rDvFbhL9bfS52fsAAAAAfXHSSSfVXzO6yb333lvts88+6dsAY1SarBbAGEVvGD1il4keN3sbAGDqHGcEgB6axVJd/NG0pTpgKkqT1QIYo+gL4+hCl7FUBwAAAM3ttdde1Y9+9KN62m4/Tz/9dPXpT386fRugrxxnBJiN6BOjX+wq0edGv5u9DQAAALCQttxyy+pb3/pW/RWjm5x66qnp2wBjVpqsFsCYRa/YZaLXjZ43exsAYKocZwSAnrFUB9C+0mS1AMbMUh0AAAD011//9V/XE3Y3ueyyy6qNNtoofRugzxxnBJidjTfeeGXf2GWi783eBgAAAFgIS5YsqX75y1/WXy7az+23317tvPPO6dsAY1earBbA2EXPGL1jV4meN3rf7G0AgClynBEAesRSHUA3SpPVAhi76Bct1QEAAEB/vOc976m+/e1v11N1+3nyySerQw45JH0bYAgcZwSYvegfo4/sKtH/Rh+cvQ0AAAAwC+uuu2518cUX118ruskJJ5yQvg0wFaXJagFMRfSQXSZ64OiFs7cBAKbEcUYA6AFLdQDdKk1WC2AqLNUBAADAwvvUpz5VvfDCC/U03X6uu+66avHixenbAEPhOCPAwog+MvrJrhJ9cPTD2dsAAAAAXdp7772rH//4x/VXivZz7733VnvssUf6NsCUlCarBTAl0UtGT9lVoheOnjh7GwBgKhxnBIAFZqkOoHulyWoBTImlOgAAAFgYb3nLW6pLLrmknqC7ybHHHpu+DTA0jjMCLKzoK7tM9MXRH2dvAwAAALTtS1/6Uv1VopssX748fRdgikqT1QKYmt/93d9d2Vt2meiNs7cBAKbAcUYAWCCW6gBmpzRZLYApslQHAAAAs7PvvvtWP/nJT+qpuf3cfffd1S677JK+DTBEjjMCLLzoL6PP7CrRH0efnL0NAAAA0Iatt966+ud//uf6a0T7efLJJ6tDDjkkfRtgqkqT1QKYqkMPPXRlr9lVokeOXjl7GwBgzBxnBIAFYKkOYLZKk9UCmCpLdQAAANC9U045pZ6Uu8k555yTvgswZI4zAvRH9JtdJvrl7F0AAACAJj796U9XL730Uv0Fov1cd9111aabbpq+DTBlpclqAUxZ9JrRc3aV6JWjZ87eBgAYK8cZAWDGLNUBzF5psloAU2epDgAAANq35ZZbVt/5znfq6bj9PP7449UnPvGJ9G2AoXOcEaBfou+M/rOrRN8c/XP2NgAAAMB8LFq0qLrsssvqrw7d5JhjjknfBsC+F0Dbjj322Pp/KbtJ9M7RQ2dvAwCMjeOMADAjluoAFk5psloAWKoDAACANu2///7Vk08+WU/F7efaa6+tNtpoo/RtgDFwnBGgf6L/jD60q0T/HH109jYAAADAXGy//fbVPffcU39taD933313tcsuu6RvA/ArpclqAfAr0YNGL9pVooeOXjp7GwBgTBxnBIAZsFQHsLBKk9UC4Fcs1QEAAEBzJ554Yj0Jd5PPfe5z6bsAY+I4I0B/RT/aZaKfzt4FAAAAWJ3DDjusevHFF+svDO3nnHPOSd8F4LVKk9UC4LWiJ+0q0UtHT529CwAwFo4zAkDHLNUBLLzSZLUAeC1LdQAAADB/66yzTnX55ZfX02/7+d73vld94AMfSN8GGBvHGQH6LfrS6E+7SvTV0V9nbwMAAAC83mmnnVZ/VWg/jz32WHXQQQel7wLwRqXJagHwRtGbRo/aVaK3zt4FABgDxxkBoCOW6gD6ozRZLQDeyFIdAAAAzN0OO+xQ/fu//3s99bafs88+O30XYKwcZwQYhuhTu0r01zvuuGP6LgAAAEDYcMMNq+uvv77+mtB+rrnmmmqjjTZK3wYgV5qsFgC56FGjV+0q0WNHr529DQAwZI4zAkAHLNUB9EtpsloArJqlOgAAAFi9ww8/vHrppZfqabfd/Nd//Vd1wAEHpO8CjJnjjADDEf1q9K1dJPrs6LezdwEAAIBp23PPPasHH3yw/orQfpYtW5a+C8DqlSarBcDqfe5zn6v/V7T9RK8dPXf2LgDAUDnOCAAts1QH0D+lyWoBsHqW6gAAACB3+umn1xNu+7nqqquqt7/97em7AGPnOCPAsETfGv1rV/nyl7+cvgsAAABM09KlS+uvBu1nxYoV1fvf//70XQDWrDRZLQDWLHrX6GG7SvTe2bsAAEPkOCMAtMhSHUA/lSarBcCaWaoDAACA34g5+YYbbqin2vbz2c9+Nn0XYCocZwQYpuhju0r03/7OCgAAAFi+fHn9taD9nHXWWembAMxdabJaAMxd9LJd5bzzzkvfBAAYGscZAaAFluoA+q00WS0A5s5SHQAAAFP3R3/0R9VDDz1UT7Pt5s4776ze9773pe8CTInjjADDFf1s9LVdJPrw6MezdwEAAIBx22KLLarbbrut/krQbh599NFq//33T98FYH5Kk9UCYH6ip43etotELx49efYuAMBQOM4IAA1ZqgPov9JktQCYH0t1AAAATNXRRx9dT7Dt54wzzkjfBJgixxkBhi/6264SfXn2JgAAADBO++23X/XEE0/UXwbazZVXXlm97W1vS98FYP5Kk9UCYP6it40et4tET/7Rj340fRcAYAgcZwSABizVAQxDabJaAJSxVAcAAMCULF++vJ5a283DDz+8cqEsexNgqhxnBBiH6HOj3+0i559/fvomAAAAMC7HHXdc/TWg3bz66qv+VhWgA6XJagFQ7jOf+czKnreLRI+evQkA0HeOMwJAIUt1AMNRmqwWAOUs1QEAADB2W2yxRfWd73ynnlbbzRVXXFGtv/766bsAU+Y4I8B4RL8bfW8XiT79D//wD9N3AQAAgGFbtGhRddlll9VfAdrNnXfeWW2//fbpuwA0U5qsFgDNRM8bvW8XiV49evbsXQCAvnKcEQDmyVIdwPCUJqsFQDOW6gAAABir+I8S/PznP6+n1HZz4oknpm8C4DgjwBhF/9tFol/3H80FAACAcYkjMvfcc089/bebCy+8MH0TgHaUJqsFQDuiB+4id999t6PnAMCgOM4IAPNgqQ5gmEqT1QKgHZbqAAAAGJPjjz++nkzbzVNPPVXtv//+6ZsA/IrjjADjFH1w9MNdJPr37E0AAABgWA477LDqhRdeqCf+drNs2bL0TQDaU5qsFgDtiV64i0TvHj189iYAQN84zggAc2SpDmC4SpPVAqA9luoAAAAYukWLFlWXXXZZPY22m3/5l3+pttxyy/RdAH7DcUaA8Yp+OPriLhJ9fPTz2bsAAABA/5122mn1lN9uHn744WqvvfZK3wSgXaXJagHQruiJozfuItHLZ28CAPSJ44wAsAaW6gCGrzRZLQDaZakOAACAodp+++2re+65p55C280FF1yQvgnAGznOCDB+0R93kejno6/P3gQAAAD6acMNN6yuu+66erpvNzfffHO10UYbpe8C0L7SZLUAaN/GG2+8skfuItHTR2+fvQsA0AeOMwLAaliqAxiH0mS1AOiGpToAAACG5LDDDqtefPHFevpsN5/97GfTNwHIOc4IMA3RJ3eR6Oujv8/eBAAAAPplzz33rB588MF6qm83Z511VvomAN0pTVYLgO5Er9xForePHj97EwBgoTnOCACrYKkOYDxKk9UCoDuW6gAAABiCU089tZ44281Pf/rT6kMf+lD6JgCr5jgjwHREvxx9cxc57bTT0jcBAACAfjjqqKPqKb7dvPLKK9WRRx6ZvglAt0qT1QKgW9EzR+/cRaLXz94EAFhIjjMCQMJSHcC4lCarBUC3LNUBAADQV29605uqb3zjG/WU2W5uvPHG6h3veEf6LgCr5zgjwLRE3xz9cxeJfj/6/uxdAAAAYOGcc8459fTebu67775qp512St8EoHulyWoB0L3onaOH7iLR82dvAgAsFMcZAeC3WKoDGKfSZLUA6J6lOgAAAPpm8803r+666656umw3Z555ZvomAHPjOCPANEUf3UWi74/+P3sTAAAAmK211lqruuqqq+qpvd18/etfr9Zbb730XQBmozRZLQBmI3ro6KW7SPT+MQNk7wIAzJrjjABQs1QHMF6lyWoBMDuW6gAAAOiD3XbbrXr44YfrqbK9vPLKK9Wf/dmfpW8CMHeOMwJMV/TT0Ve3nej/d9111/RNAAAAYDYWL15c3XHHHfW03m5OOumk9E0AZqs0WS0AZuvkk0+u/1e53cQMsOmmm6ZvAgDMkuOMAPD/WKoDGLfSZLUAmC1LdQAAACykgw46qPrf//3feppsLz/4wQ+qD3zgA+mbAMyP44wA0xZ9dfTXbSfmgJgHsjcBAACAbu20007VT37yk3pKby9PP/10deCBB6ZvAjB7pclqATB70VtHj912HnrooZUzQfYmAMCsOM4IwORZqgMYv9JktQCYPUt1AAAALIS/+Iu/qCfIdnPllVdW6667bvomAPPnOCMA0V9Hn91FYi7I3gQAAAC6ccABB1TPP/98PZm3lzvvvLPaaqut0jcBWBilyWoBsDDe8573rOy1207MBDEbZG8CAMyC44wATJqlOoBpKE1WC4CFYakOAACAWTr99NPrqbHdnHTSSel7AJRznBGAX4t+u4vEfJC9BwAAALRr6dKl9TTebi666KL0PQAWVmmyWgAsrOi5u0jMCNl7AABdc5wRgMmyVAcwHaXJagGwsCzVAQAA0LVLL720nhbby9NPP119/OMfT98DoBnHGQH4bdF3R//ddmJOyN4DAAAA2nHKKafUU3i7WbZsWfoeAAuvNFktABZe9N5dJGaF7D0AgC45zgjAJFmqA5iW0mS1AFh4luoAAADowtve9rbq1ltvrafE9nLnnXdW7373u9M3AWjOcUYAXm+rrbZa2Ye3nZgXNtxww/RNAAAAoNxXv/rVevpuL4888ki11157pe8B0A+lyWoB0A8f/vCHV/bibefiiy9O3wMA6IrjjABMSpdLdfFHvdmbACy80mS1AOgHS3UAAAC0abvttqvuv//+ejpsLxdeeGH6HgDtcZwRgFW56KKL6s68vcTcEPND9h4AAAAwP29961urm2++uZ6620vU3HjjjdM3AeiP0mS1AOiP6MW76vPXX3/99E0AgLY5zgjAZHS1VBd/xJu9B0B/lCarBUC/WKoDAACgqX322ad66qmn6qmwvSxbtix9D4B2Oc4IwOpEX952Yn7Ye++90/cAAACAudl6662re++9t56228tZZ52VvgdA/5QmqwVA/0Rv3nZihohZInsPAKBNjjMCMAmW6gCmrTRZLQD6x1IdAAAApY488sh6EmwvjzzySLXXXnul7wHQPscZAViTD3/4wyv79LYT80T2HgAAALB68f9Le+KJJ+oJu528+uqr1ZIlS9L3AOin0mS1AOin6NGjV28zP//5z/19HgDQOccZARi9rpbq4o92s/cA6J/SZLUA6CdLdQAAAMzXiSeeWE9/7eWOO+6oFi9enL4HQDccZwRgLqJPj3697ZxwwgnpewAAAEDu8MMPr6fq9vLwww9Xu+22W/oeAP1VmqwWAP0VvXr07G3niCOOSN8DAGiD44wAjJqlOgBCabJaAPSXpToAAADm6rzzzqunvvZy9dVXV2uttVb6HgDdcZwRgLmKfj369rYT80X2HgAAAPBaxx13XD1Nt5e77rqr2mKLLdL3AOi30mS1AOi3zTfffGXv3nZixsjeAwBoynFGAEbLUh0Av1aarBYA/WapDgAAgNV505veVH3jG9+op732cs4556TvAdA9xxkBmK/o39vOtddeu3LeyN4DAAAAfqc699xz6ym6vcT/32+dddZJ3wOg/0qT1QKg/6J37+Jv92LWyN4DAGjCcUYARsdSHQCvV5qsFgDDYKkOAACA14v/+vaKFSvqKa+9fOELX0jfA2A2HGcEoMRxxx1Xd/TtJeaNzTbbLH0PAAAApmrttdfu5D+6vXz58vQ9AIajNFktAIYjevm2c80116ycPbL3AABKOM4IwKhYqgMgU5qsFgDDYakOAACAX9t1112rn/70p/V0114OP/zw9D0AZsdxRgBKRT/fdmLuiPkjew8AAACmZtNNN63uvPPOempuLyeeeGL6HgDDUpqsFgDDEj1924nZI2aQ7D0AgPlynBGA0bBUB8CqlCarBcCwWKoDAADgoIMOql566aV6qmsnTzzxRPWhD30ofQ+A2XKcEYAm9tprr5X9fZuJ+ePAAw9M3wMAAICp2Hnnnav//M//rKfl9rJkyZL0PQCGpzRZLQCG58gjj6z/l729xAwSs0j2HgDAfDjOCMAoWKoDYHVKk9UCYHgs1QEAAEzXZz/72XqSay/33ntvtfXWW6fvATB7jjMC0FT099Hnt52YR7L3AAAAYOwOOOCA6sUXX6wn5Hby1FNPVfvss0/6HgDDVJqsFgDDFD1+9PptJmaRj3/84+l7AABz5TgjAINnqQ6ANSlNVguAYbJUBwAAMD2nnXZaPb21l5tvvrl661vfmr4HwMJwnBGANkSfH/1+24m5JHsPAAAAxuroo4+up+L2cv/991fbbbdd+h4Aw1WarBYAwxW9fvT8bSdmk+w9AIC5cJwRgEGzVAfAXJQmqwXAcFmqAwAAmI6LLrqontray1e/+tX0LQAWluOMALTp4osvrieA9hLzSfYWAAAAjM3JJ59cT8Pt5dZbb6023HDD9D0Ahq00WS0Ahi16/uj9207MKNl7AABr4jgjAINlqQ6AuSpNVguA4bNUBwAAMG5f+9rX6mmtvZxyyinpWwAsPMcZAWhb9P9tJ+aU7C0AAAAYi9NPP72egtvLpZdemr4FwDiUJqsFwDhccskl9f/at5eYVbK3AABWx3FGAAbJUh0A81GarBYA42CpDgAAYJyuueaaekprL0uXLk3fAqAfHGcEoAtHH310PRG0l5hXsrcAAABg6P7u7/6unn7biwMqAONXmqwWAOPRxeH3mFmytwAAVsVxRgAGx1IdAPNVmqwWAONhqQ4AAGA81l577erGG2+sp7N28sILL1QHHHBA+h4A/eE4IwBdiXkg5oI2E3NLzC/ZewAAADBEF1xwQT31tpfPfvaz6VsAjEtpsloAjEvMBG0nZpfsLQCAjOOMAAyGpToASpUmqwXAuFiqAwAAGL7f+73fq7797W/XU1k7eeihh6qdd945fQ+AfnGcEYAuxVwQ80Gbifkl5pjsPQAAABiSSy+9tJ5228lLL71UHXTQQelbAIxPabJaAIzPgQceuHJGaDOXXHJJ+hYAwOs5zgjAIFiqA6CJ0mS1ABgfS3UAAADD9fa3v726884762msnUS9TTfdNH0PgP5xnBGArsV80Pbc8d3vfnflPJO9BwAAAENw1VVX1VNuO3n44YerXXfdNX0LgHEqTVYLgHGKGSFmhTYTs0z2FgDAb3OcEYDes1QHQFOlyWoBME6W6gAAAIbnne98Z3XPPffUU1g7ufrqq6u11147fQ+AfnKcEYBZiDkh5oU2E/NMzDXZewAAANBXv/u7v1vdcMMN9XTbTlasWFFtttlm6XsAjFdpsloAjFfMCjEztJnrr79+5WyTvQcAEBxnBKDXLNUB0IbSZLUAGC9LdQAAAMPx7ne/u/rhD39YT1/t5Nxzz03fAqDfHGcEYJZibmgzMdfEfJO9BQAAAH2z7rrrVrfeems91baTa6+9tlq0aFH6HgDjVpqsFgDjFjNDzA5tJmabmHGy9wAAHGcEoLcs1QHQltJktQAYP0t1AAAA/fbe9763+s///M966monxx13XPoWAP3nOCMAsxbzQ5uJ+SbmnOwtAAAA6IsNN9ywuuOOO+pptp2cd9556VsATENpsloATEPMEG0mZpyYdbK3AIBpc5wRgF6yVAdAm0qT1QJgGizVAQAA9NPOO+9c/fd//3c9bbWTI444In0LgGFwnBGAhRBzRJuJOSfmnewtAAAAWGibbLJJ9W//9m/1FNtOTjjhhPQtAKajNFktAKYjZok2E7NOzDzZWwDAdDnOCEDvWKoDoG2lyWoBMB2W6gAAAPrlj/7oj6qnn366nrKa55lnnqn22muv9C0AhsNxRgAWSswTMVe0lZh3Yu7J3gIAAICF8q53vau6//776+m1nfzZn/1Z+hYA01KarBYA0xIzRZuJmSdmn+wtAGCaHGcEoFcs1QHQhdJktQCYFkt1AAAA/bDvvvtWL7zwQj1dNc/jjz9e7bLLLulbAAyL44wALKSYK2K+aCsx98T8k70FAAAAs7bddttVP/nJT+qptZ184hOfSN8CYHpKk9UCYHr+9E//tP6XoZ3E7BMzUPYWADA9jjMC0BuW6gDoSmmyWgBMj6U6AACAhXXAAQfUE1U7+elPf1q9733vS98CYHgcZwRgocV8EXNGm4k5KHsLAAAAZuUDH/hA9bOf/ayeVJvnlVdeqfbbb7/0LQCmqTRZLQCmKWaMmDXaSsxAMQtlbwEA0+I4IwC9YKkOgC6VJqsFwDRZqgMAAFgYhxxySD1FtZMf/ehH1Xve8570LQCGyXFGAPog5oyYN9rMoYcemr4FAAAAXdtzzz2rp556qp5Qm+e5556r9tprr/QtAKarNFktAKbrwx/+8MqZo63ELBQzUfYWADAdjjMCsOAs1QHQtdJktQCYLkt1AAAAs7VkyZJ6emon3//+96vNNtssfQuA4XKcEYC+iHkj5o42E3NR9hYAAAB0Ze+9966ef/75ejJtnp///OfV7rvvnr4FwLSVJqsFwLTFzPHkk0/W/1I0T8xEMRtlbwEA0+A4IwALylIdALNQmqwWANNmqQ4AAGA2li5dWk9N7WTFihXVO97xjvQtAIbNcUYA+iTmjpg/2kzMR9lbAAAA0Lb999+/evXVV+uJtHkeffTRascdd0zfAoDSZLUAIGaPmEHaSsxGMSNlbwEA4+c4IwALxlIdALNSmqwWAFiqAwAA6NbnP//5elpqJ7fddlv1+7//++lbAAyf44wA9E3MHzGHtJmYk7K3AAAAoC0HH3xwPYW2kx//+MfV1ltvnb4FAKE0WS0ACNtss83KWaTNxKyUvQUAjJvjjAAsCEt1AMxSabJaABAs1QEAAHTjxBNPrKekdnLTTTdVb3rTm9K3ABgHxxkB6KOYQ2IeaTMxL2VvAQAAQFNHHnlkPX22kx/84AfVFltskb4FAL9WmqwWAPxazCIxk7SZmJmytwCA8XKcEYCZs1QHwKyVJqsFAL9mqQ4AAKBdp5xySj0dtZNvfOMb6TsAjIvjjAD0WcwlbSbmpuwdAAAAKHXUUUfVU2c7+d73vldttNFG6VsA8NtKk9UCgN+28cYbr5xN2kzMTtlbAMA4Oc4IwExZqgNgIZQmqwUAr2epDgAAoLmzzjqrnorayRVXXJG+A8D4OM4IQN9dfvnl9aTSTmJ+yt4BAACA+frLv/zLetpsJ7fffnu1/vrrp28BwOuVJqsFAK8Xs0nMKG0mZqjsLQBgfBxnBGBmLNUBsFBKk9UCgIylOgAAgHJnn312PQ21k69+9avpOwCMk+OMAAxBzCltJuao7B0AAACYq7YPM95yyy3VOuusk74FAJnSZLUAIPPmN7955azSZhxoBIBpcJwRgJmwVAfAQipNVgsAVsVSHQAAwPydcsop9RTUTpYvX56+A8B4Oc4IwFDEvNJmYp7K3gEAAIA1Wbp0aT1dtpNvfvOb6TsAsDqlyWoBwOpcd9119b8i7eSoo45K3wEAxsNxRgA6Z6kOgIVWmqwWAKyOpToAAIC5++IXv1hPP+3krLPOSt8BYNwcZwRgSGJuaTMnnnhi+g4AAACsypFHHllPle3kyiuvTN8BgDUpTVYLANYkZpc2E7NV9g4AMA6OMwLQqfjjzzZjqQ6AEqXJagHAmliqAwAAWLPPf/7z9dTTThy3B5guxxkBGJq2/2PHMV9l7wAAAMDrHXLIIfU02U4uvvji9B0AmIvSZLUAYC5ihmkzBx98cPoOADB8jjMC0BlLdQD0RWmyWgAwF5bqAAAAVm3p0qX1tNNOvvjFL6bvADANjjMCMERt/0ePY87K3gEAAIBf23///espsp2cf/756TsAMFelyWoBwFzFLNNmYtbK3gEAhs1xRgA6YakOgD4pTVYLAObKUh0AAMAbLVmypJ5y2olj9gA4zgjAULX9Hz8+8sgj03cAAABg7733rl599dV6gmyes88+O30HAOajNFktAJiPmGnaSsxaMXNl7wAAw+U4IwCtiz/ybDOW6gBoqjRZLQCYD0t1AAAAv3HIIYfU0007Ofroo9N3AJgWxxkBGLKYa9rMwQcfnL4DAADAdO25557V888/X0+OzXPqqaem7wDAfJUmqwUA8xWzTVuJmStmr+wdAGCYHGcEoFWW6gDoo9JktQBgvizVAQAA/E61//7711NNO1myZEn6DgDT4zgjAEMX802bifkrewcAAIDp+cAHPlA99dRT9cTYPF/84hfTdwCgRGmyWgBQImacthKzV8xg2TsAwPA4zghAayzVAdBXpclqAUAJS3UAAMCU7bPPPvU0004OPfTQ9B0ApslxRgDGIOacNrP33nun7wAAADAd2223XfWzn/2snhSb5/Of/3z6DgCUKk1WCwBKHXPMMfW/MM0TM1jMYtk7AMCwOM4IQCvijznbjKU6ANpUmqwWAJSyVAcAAEzRnnvuWT3//PP1JNM8H//4x9N3AJguxxkBGIuYd9pKzGExj2XvAAAAMH7vete7qp/85Cf1lNg8Rx99dPoOADRRmqwWADQRM09biVksZrLsHQBgOBxnBKAxS3UA9F1psloA0ISlOgAAYEo+8IEPVE899VQ9xTTP/vvvn74DwLQ5zgjAmBxwwAH1BNQ8MY/FXJa9AwAAwHgtXry4uv/+++vpsHkcZgSgK6XJagFAU20eaIyZbJNNNknfAQCGwXFGABppe6ku/rg0ewcAmihNVgsAmrJUBwAATMG2225b/exnP6unl+Y55JBD0ncAwHFGAMYm5p+2EnNZzGfZOwAAAIzPhhtuWP3bv/1bPRU2zzHHHJO+AwBtKE1WCwDa8PnPf77+16Z5YjaLGS17BwDoP8cZAShmqQ6AoShNVgsA2mCpDgAAGLN3vetd1U9+8pN6ammeJUuWpO8AQHCcEYAxijmorcR8FnNa9g4AAADjse6661Z33HFHPQ02zxe/+MX0HQBoS2myWgDQlpiF2krMaDGrZe8AAP3mOCMARSzVATAkpclqAUBbLNUBAABjtMkmm1T33XdfPa00z9KlS9N3AODXHGcEYKyOPvroejJqnpjTYl7L3gEAAGD4fvd3f7e69dZb6ymweU499dT0HQBoU2myWgDQppiJ2krMajGzZe8AAP3lOCMA89b2Ul38EWn2DgC0pTRZLQBok6U6AABgTDbYYIPq3/7t3+oppXk+//nPp+8AwG9znBGAMYu5qK3867/+68q5LXsHAACAYbv++uvr6a95zj777PQNAGhbabJaANC2mI3ayg033JC+AQD0l+OMAMyLpToAhqg0WS0AaJulOgAAYAzWXXfd6l/+5V/q6aR5vvjFL6bvAMDrOc4IwNjFfNRWYm6L+S17BwAAgGG66qqr6qmvec4///z0DQDoQmmyWgDQhZiR2krMbtkbAEA/Oc4IwJxZqgNgqEqT1QKALliqAwAAhu5b3/pWPZU0zymnnJK+AQAZxxkBmIJTTz21npiaJ+a37A0AAACG55JLLqmnvea5+OKL0zcAoCulyWoBQFdiVmorl156afoGANA/jjMCMGdtLtXFH4tmbwBAF0qT1QKArliqAwAAhur666+vp5HmOfvss9M3AGBVHGcEYCpiXmor1113XfoGAAAAw3HBBRfUU17zXHnllekbANCl0mS1AKBLMTO1lZjlsjcAgH5xnBGAObFUB8CQlSarBQBdslQHAAAMzde//vV6Cmme888/P30DAFbHcUYApiTmprYS81z2BgAAAP33d3/3d/V01zz+1hCAhVKarBYAdO2b3/xm/S9R88RMl70BAPSH44wArJGlOgCGrjRZLQDomqU6AABgKP7P//k/9fTRPBdffHH6BgCsieOMAExNm7NY1MreAAAAoL9OP/30eqprnltuuSV9AwBmoTRZLQCYhZih2krMdtkbAEA/OM4IwGr5Q04AxqA0WS0AmAWzGAAA0HdtHpa/8sor0zcAYC4cZwRgimKOaiv+g8sAAADDcfLJJ9fTXPPcfvvt1TrrrJO+AwCzUJqsFgDMQsxQMUu1lZjxsncAgIXnOCMAq2SpDoCxKE1WCwBmxVIdAADQV6eccko9bTTPddddl74BAHPlOCMAUxXzVFuJOS97AwAAgP74zGc+U09xzfO9732vWn/99dN3AGBWSpPVAoBZiVkqZqq2cvTRR6fvAAALy3FGAFKW6gAYk9JktQBglizVAQAAfRN/CNhWbrnllvQNAJgPxxkBmLJvfetb9YTVPBa/AAAA+uvjH/94Pb01zw9+8INqo402St8BgFkqTVYLAGYpZqqYrdrKAQcckL4DACwcxxkBeIM2l+rijz+zNwBglkqT1QKAWbNUBwAA9EX8AWBbuf3226s3v/nN6TsAMB+OMwIwZeuuu+7K+aqtWPwCAADon5133rl68cUX68mtWX784x9XW2yxRfoOAMxaabJaADBrMVvFjNVGYuaL2S97BwBYGI4zAvAaluoAGKPSZLUAYNZirrJUBwAALLSddtqpev755+vJoln+9V//tdpggw3SdwBgvhxnBGDqYr6KOauNxNwX81/2DgAAALP3zne+s/rP//zPemprlkcffbTaZptt0ncAYCGUJqsFAAth6623XjlrtZGY/TbddNP0HQBg9hxnBOD/s1QHwFiVJqsFAAvBUh0AALCQFi9eXD300EP1VNEsP/jBD6qNN944fQcASjjOCAC/U22yySbVfffdV09ezRLzX8yB2TsAAADMztprr13deeed9bTWLE8++WS14447pu8AwEIpTVYLABZKzFo///nP63+lmiVmwJgFs3cAgNlynBGAldpcqos/8rRUB0CflCarBQALJeYsS3UAAMCsrbXWWtUdd9xRTxPN8uCDD1Z/+Id/mL4DAKUcZwSAX3nXu961cu5qIzEHxjyYvQMAAMBsXHPNNfWU1izPPfdctfvuu6dvAMBCKk1WCwAWUsxcMXu1kauvvjp9AwCYLccZAbBUB8DolSarBQALKeYtS3UAAMAsXXXVVfUU0Sz/9V//VW27rWNUALTPcUYA+I2Yu2L+aiMxD2ZvAAAA0L1zzz23ns6a5ZVXXqn22muv9A0AWGilyWoBwEKL2StmsDYSM2H2BgAwO44zAmCpDoDRK01WCwAWmqU6AABgVv7u7/6unh6a5amnnqre//73p28AQFOOMwLAa33gAx9YOYe1kZgLszcAAADoznHHHVdPZc2z3377pW8AQB+UJqsFAH0QM1hbidkwewMAmA3HGQEmzlIdAFNQmqwWAPRBzF+W6gAAgC594QtfqKeGZvnlL39Z7bHHHukbANAGxxkB4I323HPPlfNYGzn22GPTNwAAAGjfEUccUU9jzfOJT3wifQMA+qI0WS0A6IuYxdrK4Ycfnr4BAHTPcUaACbNUB8BUlCarBQB9EXOYpToAAKALn/zkJ+tpoXn22Wef9A0AaIvjjACQi3msrcScmL0BAABAe/baa696CmueP/uzP0vfAIA+KU1WCwD6JGaythKzYvYGANAtxxkBJspSHQBTUpqsFgD0iaU6AACgbX/8x39cTwnNc+SRR6ZvAECbHGcEgFWLuaytxLyYvQEAAEBzW2+9dfXzn/+8nsCa5YQTTkjfAIC+KU1WCwD6JmazNvLEE0+snBmzNwCA7jjOCDBBluoAmJrSZLUAoG8s1QEAAG3Zaqutqscee6yeEJrlxBNPTN8AgLY5zggAqxfzWRuJeTHmxuwNAAAAyq2//vrVvffeW09fzXLeeeelbwBAH5UmqwUAfRQzWhuJmfGtb31r+gYA0A3HGQEmxlIdAFNUmqwWAPSRpToAAKCp3/u936u+//3v19NBsyxfvjx9AwC64DgjAKxZzGltJObGmB+zNwAAAChz880311NXs1x77bVpfQDoq9JktQCgr2JWayMxO2b1AYBuOM4IMCGW6gCYqtJktQCgryzVAQAATdx44431VNAs3/zmN9P6ANAVxxkBYG6+8Y1v1JNbs8T8mNUHAABg/i6++OJ62mqWFStWVIsWLUrfAIC+Kk1WCwD6Kma1mNnayFe/+tX0DQCgfY4zAkyIpToApqo0WS0A6DNLdQAAQImLLrqongaa5a677qrWWWed9A0A6IrjjAAwNzGvxdzWRmKOzN4AAABg7k455ZR6ymqWhx9+uNpss83SNwCgz0qT1QKAPouZLWa3NhKzZPYGANAuxxkBJsJSHQBTVpqsFgD0maU6AABgvv76r/+6ngKa5ZFHHqm22GKL9A0A6JLjjAAwdzG3xfzWRmKezN4AAABgzZYuXVpPV83y0ksvVbvuumv6BgD0XWmyWgDQdzG7xQzXRmKmzN4AANrjOCPABFiqA2DqSpPVAoC+s1QHAADM1Z//+Z/X3X+zvPzyy9Xuu++evgEAXXOcEQDmJ+a3mOPaSMyV2RsAAACs2gEHHFBPVc1z4IEHpm8AwBCUJqsFAENw0EEH1f+aNU/MltkbAEA7HGcEGDlLdQDg/1kHwPRYqgMAANZkv/32q7v+5vnTP/3T9A0AmAXHGQFg/j7xiU/UE13zxHyZvQEAAMAb7bTTTtXzzz9fT1TN8tnPfjZ9AwCGojRZLQAYipjl2kjMljFjZm8AAM05zggwYpbqAOBXSpPVAoChsFQHAACsyo477lj94he/qDv+Zlm2bFn6BgDMiuOMAFAm5rk2EvNlzJnZGwAAAPzGpptuWj300EP1NNUsp59+evoGAAxJabJaADAkMdO1kZ/85CfV4sWL0zcAgGYcZwQYKUt1APAbpclqAcCQWKoDAABeb6ONNqr+4z/+o+72m+WMM85I3wCAWXKcEQDKxVzXRmLOjHkzewMAAIDfqdZaa63qjjvuqKeoZrnkkkvSNwBgaEqT1QKAobn00kvrf9maJWbNmDmzNwCAco4zAoyQpToAeK3SZLUAYGgs1QEAAL/tO9/5Tt3lN8vXvva1tD4AzJrjjADQTMx3bSTmzaw+AAAAv1NdddVV9fTULLfeemtaHwCGqDRZLQAYopjx2kjMnFl9AKCc44wAI2SpDgBeqzRZLQAYIkt1AABAuOKKK+ruvln++Z//Oa0PAAvBcUYAaO7b3/52PfE1S8ydWX0AAIApO+ecc+qpqVnuv//+asMNN0zfAIAhKk1WCwCGKGa8mPXaSMye2RsAQBnHGQFGxlIdALxRabJaADBUluoAAGDa/uqv/qru6pvlgQceqP7gD/4gfQMAFoLjjADQXMx5Me+1kZg/szcAAACm6KijjqqnpWZ56qmnqu222y59AwCGqjRZLQAYqpj1YuZrIzGDZm8AAPPnOCPAiFiqA4BcabJaADBUluoAAGC6DjrooLqbb5Znnnmmet/73pe+AQALxXFGAGjH9ttvv3LuayMxh2ZvAAAATMmee+5ZT0nNs88++6RvAMCQlSarBQBDFjNfW9ljjz3SNwCA+XGcEWAkLNUBwKqVJqsFAENmqQ4AAKZn6623rp599tm6k2+WP/mTP0nfAICF5DgjALTnIx/5SD0BNkvMoTGPZm8AAABMwYYbblg9+OCD9ZTULEceeWT6BgAMXWmyWgAwdEuWLKn/pWuWmEU32GCD9A0AYO4cZwQYAUt1ALB6pclqAcDQWaoDAIBpWbFiRd3FN8unPvWptD4ALDTHGQGgXTH/tZGYR7P6AAAAU3DdddfV01GznHjiiWl9ABiD0mS1AGAMYgZsIzGTZvUBgLlznBFgBCzVAcDqlSarBQBjYKkOAACm4Stf+UrdvTfLySefnNYHgD5wnBEA2hdzYBuJuTSrDwAAMGannXZaPRU1y/Lly9P6ADAWpclqAcBYxCzYRk499dS0PgAwN44zAgycpToAWLPSZLUAYCws1QEAwLh97nOfq7v2ZrngggvS+gDQF44zAkA3Yh5sIzGfZvUBAADG6LDDDqunoWb5xje+kdYHgDEpTVYLAMYkZsI2EjNqVh8AWDPHGQEGzFIdAMxNabJaADAmluoAAGCcPvzhD9fderPccMMNaX0A6BPHGQGgOzEXtpGYU7P6AAAAY7L99ttXL7zwQj0Jleeuu+6q1llnnfQNABiT0mS1AGBMYiaM2bBpYkaNWTV7AwBYPccZAQbKUh0AzF1psloAMDaW6gAAYFw22WST6tFHH6079fLcfffd1XrrrZe+AQB94jgjAHQn5sKYD5sm5tSYV7M3AAAAxmDRokWtzE8PP/xwtfnmm6dvAMDYlCarBQBjs8UWW6ycEZsmZtW11147fQMAWDXHGQEGyFIdAMxPabJaADA2luoAAGBcbrnllrpLL89jjz1Wbbnllml9AOgbxxkBoFsxH8ac2DQxr2b1AQAAxuCyyy6rp5/yvPrqq9Vuu+2W1geAMSpNVgsAxihmxJgVmyZm1qw+ALBqjjMCDJClOgCYn9JktQBgjCzVAQDAOJx99tl1d94sf/Inf5LWB4A+cpwRALoXc2Ibibk1qw8AADBkxx13XD31NMuSJUvS+gAwVqXJagHAWMWs2EZids3qAwA5xxkBBsZSHQDMX2myWgAwVpbqAABg2Nr6A7wvfOELaX0A6CvHGQFgNmJebCOOjQAAAGPy0Y9+tJ52muWss85K6wPAmJUmqwUAYxYzYxuJGTarDwC8keOMAANiqQ4AypQmqwUAY2apDgAAhmmnnXaqu/FmufTSS9P6ANBnjjMCwOzE3NhGYo7N6gMAAAzJFltsUT3xxBP1pFOem2++Oa0PAGNXmqwWAIxdzI5NEzPs5ptvntYHAF7LcUaAgbBUBwDlSpPVAoCxs1QHAADDst5661X3339/3YmX5+67767WWmut9A0A6DPHGQFgdmJujPmxaWKOjXk2ewMAAGAobrvttnrKKc8jjzxSbbzxxml9ABi70mS1AGDsYnaMGbJpYpbN6gMAr+U4I8AAWKoDgGZKk9UCgLGzVAcAAMNy1VVX1V14eZ5//vnqve99b1ofAPrOcUYAmK2YH2OObJqYZ7P6AAAAQ3DeeefV002z7LXXXml9AJiC0mS1AGAKYoZsIzHTZvUBgN9wnBFgACzVAUAzpclqAcAUWKoDAIBhOPnkk+vuu1kOPfTQtD4ADIHjjAAwezFHtpGYa7P6AAAAfbZ06dJ6qmmWZcuWpfUBYCpKk9UCgKmIWbKNxGyb1QcAfsVxRoCes1QHAM2VJqsFAFNhqQ4AAPrtwAMPrLvuZjn11FPT+gAwFI4zAsDCiHmyjcR8m9UHAADooz333LOeZprloosuSusDwJSUJqsFAFMSM2UbiRk3qw8AOM4I0GuW6gCgHaXJagHAlFiqAwCAftpqq62qZ555pu64y3Pdddel9QFgSBxnBICFE3Nl08R8G3NuVh8AAKBPNtxww+rBBx+sp5ny3HnnnWl9AJia0mS1AGBqYrZsmphxY9bN6gPA1DnOCNBTluoAoD2lyWoBwNRYqgMAgP757ne/W3fb5Yk/qttggw3S+gAwJI4zAsDCibmyjcMkMedm9QEAAPrk+uuvr6eY8jz99NP+lg4AaqXJagHA1MRsGTNm08Ssm9UHgKlznBGgpyzVAUB7SpPVAoCpsVQHAAD9ctFFF9VddrPsscceaX0AGBrHGQFgYcV82UZi3s3qAwAA9MFpp51WTy/NcuCBB6b1AWCKSpPVAoApihmzjcTMm9UHgClznBGghyzVAUC7SpPVAoApslQHAAD9sGzZsrq7bpajjjoqrQ8AQ+Q4IwAsvJgz20jMvVl9AACAhXTYYYfVU0uznHTSSWl9AJiq0mS1AGCqYtZsIzH7ZvUBYKocZwToGUt1ANC+0mS1AGCqLNUBAMDC2muvvequulnOO++8tD4ADJXjjADQDzFvtpGYf7P6AAAAC2H77bevXnzxxXpiKc/Xv/71tD4ATFlpsloAMGUxczZNzL4xA2f1AWCKHGcE6BFLdQDQjdJktQBgyizVAQDAwthoo42qRx55pO6oy3Pbbbel9QFgyBxnBID+iLmzaWL+jTk4qw8AADBLixYtqu655556WinPfffdV6233nrpGwAwZaXJagHAlMXMGbNn08QMHLNw9gYATI3jjAA9YakOALpTmqwWAEydpToAAJi9m2++ue6my/P4449Xm2++eVofAIbMcUYA6I8tttiieuKJJ+pJtDwxB2f1AQAAZumyyy6rp5TyvPLKK9VOO+2U1geAqStNVgsApi5mz5hBmyZm4aw+AEyN44wAPWGpDgC6U5qsFgBMnaU6AACYrRNPPLHuopvlox/9aFofAIbOcUYA6JeYP9tIzMNZfQAAgFn49Kc/XU8nzXLkkUem9QEA+14A0LaYQdtIzMRZfQCYEscZAXrAUh0AdKs0WS0AwFIdAADMyp577ll3z81y3HHHpfUBYAwcZwSA/ok5tI3EXJzVBwAA6NI222xTvfTSS/VkUp6zzjorrQ8A/EppsloAwK/ELNo0MRNvvfXWaX0AmArHGQEWmKU6AOheabJaAMCvWKoDAIBurbXWWtUPfvCDunMuz2WXXZbWB4CxcJwRAPop5tGmibk45uOsPgAAQFf++Z//uZ5KynPzzTentQGA3yhNVgsA+I2YSZsmZuOsNgBMheOMAAvIUh0AzEZpsloAwG9YqgMAgO6cf/75dddcnnvuuadatGhRWh8AxsJxRgDop5hHYy5tmpiPs/oAAABd+NKXvlRPI+V5+OGHq4022iitDwD8RmmyWgDAb8RMGrNp0/zN3/xNWh8ApsBxRoAFZKkOAGajNFktAOA3LNUBAEA3DjvssLpbLs8LL7xQve9970vrA8CYOM4IAP0Vc2nMp01z6KGHpvUBAADatPfee9dTSLPstddeaX0A4LVKk9UCAF4rZtM28uEPfzitDwBj5zgjwAKxVAcAs1OarBYA8FqW6gAAoF3vfOc7q6eeeqrulMsT/7+orD4AjI3jjADQb238rWTMyTEvZ/UBAADasO6661Y//vGP6ymkPMuWLUvrAwBvVJqsFgDwRjGjNk3MyjEzZ/UBYMwcZwRYAJbqAGC2SpPVAgDeyFIdAAC054Ybbqi75PKcdtppaW0AGCPHGQGg/2JObZrrr78+rQ0AANCGiy++uJ4+ynPhhRemtQGAXGmyWgBALmbVpomZOasNAGPmOCPAArBUBwCzVZqsFgCQs1QHAADNfeELX6i74/LoqwGYGscZAWAYYl5tmmOPPTatDQAA0MSSJUvqqaM8d955Z1obAFi10mS1AIBVi5m1aWJ2zmoDwFg5zggwY5bqAGD2SpPVAgBWzVIdAACU23XXXeuuuDxPPPFEtckmm6T1AWCsHGcEgGGIeTXm1qaJ+TmrDwAAUOLd73539ctf/rKeOMry6quvVttvv31aHwBYtdJktQCAVYuZNWbXJonZecstt0zrA8AYOc4IMEOW6gBgYZQmqwUArJqlOgAAKHf33XfXHXF5Dj744LQ2AIyZ44wAMBwxtzZNzM9ZbQAAgBLf+ta36mmjPJ/5zGfS2gDA6pUmqwUArN7RRx9d/0tanpihs9oAMEaOMwLMkKU6AFgYpclqAQCrZ6kOAADm79xzz6274fL8/d//fVobAMbOcUYAGJaYX5vmnHPOSWsDAADMx8knn1xPGeW58sor09oAwJqVJqsFAKxZzLBNc9JJJ6W1AWBsHGcEmBFLdQCwcEqT1QIA1sxSHQAAzN0nPvGJugsuz/e///20NgBMgeOMADA8Mcc2TczTWW0AAIC5+OM//uN6uijPo48+Wr3tbW9L6wMAa1aarBYAsGYxw8Ys2zQf/OAH0/oAMCaOMwLMgKU6AFhYpclqAQBzY6kOAADWbKONNqoee+yxugMuz+67757WB4ApcJwRAIYn5timiXk65uqsPgAAwOosWrSo+uEPf1hPF+XZf//90/oAwNyUJqsFAMxNzLJNc//996+crbP6ADAWjjMCdMxSHQAsvNJktQCAubFUBwAAa3bttdfW3W95jjvuuLQ2AEyF44wAMEwxzzbNNddck9YGAABYnQsvvLCeKspz1llnpbUBgLkrTVYLAJi7mGmb5oILLkhrA8BYOM4I0DFLdQCw8EqT1QIA5s5SHQAArNrnPve5uustzz/+4z+mtQFgShxnBIDhirm2aWK+zmoDAABkDj/88HqaKM+KFSvS2gDA/JQmqwUAzE/Mtk0TM3ZWGwDGwHFGgA5ZqgOAfihNVgsAmB9LdQAA8Ebvf//76263PE8//XS12WabpfUBYEocZwSA4Yq5Nubbpok5O6sPAADw2zbffPPqmWeeqSeJ8phBAKAdpclqAQDz08bfscaMHbN2Vh8Ahs5xRoCOWKoDgP4oTVYLAJgfS3UAAPBGd911V93plueTn/xkWhsApsZxRgAYtphvm2bFihVpbQAAgN9244031lNEefyHhgGgPaXJagEA87ds2bL6X9fyxKyd1QaAoXOcEaAjluoAoD9Kk9UCAObPUh0AAPzGWWedVXe55bngggvS2gAwRY4zAsDwxZzbNDFvZ7UBAADC8ccfX08P5bnmmmvS2gBAmdJktQCAMjHrNk3M3FltABgyxxkBOmCpDgD6pTRZLQCgjKU6AAD4nWr//fevu9vy3H///dXaa6+d1geAKXKcEQCGL+bcmHebJuburD4AADBte+yxRz01lOexxx6rNtpoo7Q+AFCmNFktAKBMzLox8zbN7rvvntYHgKFynBGgZZbqAKB/SpPVAgDKWKoDAGDqNtxww+rRRx+tO9vyfPCDH0zrA8BUOc4IAOMQ827TPPLIIyvn76w+AAAwXffee289NZTnoIMOSmsDAOVKk9UCAMrFzNs0//7v/57WBoChcpwRoEWW6gCgn0qT1QIAylmqAwBgyq688sq6qy3PSSedlNYGgClznBEAxiPm3qaJ+TurDQAATNPy5cvraaE855xzTlobAGimNFktAKCZmH2b5rzzzktrA8AQOc4I0CJLdQDQT6XJagEAzViqAwBgio4++ui6my3Pt771rbQ2AEyd44wAMC4x/zZNzOFZbQAAYFoOOeSQekooz913353WBgCaK01WCwBoLmbgpolZPKsNAEPjOCNASyzVAUB/lSarBQA0Z6kOAIApee9731u98sordSdbll/+8pfVlltumdYHgKlznBEAxiXm35iDmyTm8JjHs/oAAMA0LF68uHryySfrKaE8u+yyS1ofAGiuNFktAKC5mIGbJmbxmMmz+gAwJI4zArTAUh0A9FtpsloAQHOW6gAAmJJvf/vbdRdbniVLlqS1AQDHGQFgjGIObpp/+qd/SmsDAADTcPXVV9fTQXmOPfbYtDYA0I7SZLUAgHYcc8wx9b+45bnqqqvS2gAwJI4zArQg/oivaSzVAUB3SpPVAgDaYakOAIApWLZsWd29lufiiy9OawMAv+I4IwCMU8zDTRNzeVYbAAAYt8MPP7yeCspz3XXXpbUBgPaUJqsFALQnZuKmidk8qw0AQ+E4I0BDluoAoP9Kk9UCANpjqQ4AgDHbYostqhdffLHuXMvy4x//uFp33XXT+gDArzjOCADjFPNwzMVNEnN5zOdZfQAAYJze+ta3Vv/93/9dTwVlefLJJ6tNN900rQ8AtKc0WS0AoD0xE8ds3CQ/+9nPVs7oWX0AGALHGQEasFQHAMNQmqwWANAeS3UAAIzZNddcU3et5dl7773T2gDAbzjOCADjFXNx08R8ntUGAADG6cILL6yngfIccsghaW0AoF2lyWoBAO2K2bhpYkbPagPAEDjOCNCApToAGIbSZLUAgHZZqgMAYIyOOOKIulstz5e+9KW0NgDwWo4zAsC4xXzcNDGnZ7UBAIBx+ehHP1pPAeVZvnx5WhsAaF9psloAQPtiRm6amNWz2gDQd44zAhSyVAcAw1GarBYA0D5LdQAAjMn6669fPfbYY3WnWpbbbrstrQ0AvJHjjAAwfjEnN0nM6TGvZ7UBAIDxeOCBB+opoCz33ntvWhcA6EZpsloAQDdiVm6SH/7wh2ldAOg7xxkBCliqA4BhKU1WCwDohqU6AADG4qKLLqq71LL87//+b7XNNtuktQGAN3KcEQDGL+bkmJebJOb1rDYAADAOZ5xxRt39l2ePPfZIawMA3ShNVgsA6EbMyk3z5S9/Oa0NAH3mOCNAAUt1ADAspclqAQDdsFQHAMAY7LfffnV3Wp4///M/T2sDADnHGQFgGmJebpqY27PaAADAsO2+++5111+eE044Ia0NAHSnNFktAKA7MTM3TczuWW0A6CvHGQHmyVIdAAxPabJaAEB3LNUBADB0DzzwQN2ZluWb3/xmWhcAWDXHGQFgOmJubpKY27O6AADAsH33u9+tu/6y3H777WldAKBbpclqAQDditm5SWJ2z+oCQF85zggwT5bqAGB4SpPVAgC6ZakOAIChOuOMM+qutCwvv/xyteWWW6a1AYBVc5wRAKYj5uaYn5sk5vesNgAAMEzHH3983e2XZ+edd05rAwDdKk1WCwDoVszOTRMzfFYbAPrIcUaAebBUBwDDVJqsFgDQLUt1AAAM0R577FF3o+X5/Oc/n9YGAFbPcUYAmJaYn5tm9913T2sDAADDsu2229ZdfnlOPfXUtDYA0L3SZLUAgO7FDN0022yzTVobAPrGcUaAOYo/xmsaS3UAsDBKk9UCALpnqQ4AgKH57ne/W3eiZfnOd76T1gUA1sxxRgCYnpijmyTm+KwuAAAwLDfddFPd5Zfl3nvvTesCALNRmqwWADAbMUs3yY033pjWBYC+cZwRYI4s1QHAcJUmqwUAzIalOgAAhuKEE06ou9Dy7LTTTmltAGDNHGcEgOmJObppjj/++LQ2AAAwDEcddVTd3Zdnn332SWsDALNRmqwWADAb++67b/0vcnlips9qA0CfOM4IMAeW6gBg2EqT1QIAZsNSHQAAQ7DtttvW3Wd5TjnllLQ2ADA3jjMCwDTFPN00MddntQEAgH7bZJNNqmeffbbu7MuyfPnytDYAMDulyWoBALMTM3WTPPPMMytn+6w2APSF44wAa2CpDgCGrzRZLQBgdizVAQDQdzfddFPdeZbl3nvvTesCAHPnOCMATFfM1U1y4403pnUBAIB+u/zyy+uuviyPPPJItd5666W1AYDZKU1WCwCYnbe85S0rZ+smidk+qw0AfeE4I8AaWKoDgOErTVYLAJgtS3UAAPTVUUcdVXed5dlnn33S2gDA3DnOCADTFXN108R8n9UGAAD66eCDD667+fIccsghaW0AYLZKk9UCAGYrZuumiRk/qw0AfeA4I8BqWKoDgHEoTVYLAJgtS3UAAPTRJptsUj3zzDN1x1mW5cuXp7UBgPlxnBEApi3m6yaJ+T7m/Kw2AADQL+utt1718MMP1918Wa644oq0NgAwe6XJagEAsxczdpPEjB+zflYbABaa44wAq2CpDgDGozRZLQBg9s4777z6X+eyWKoDAKBtl19+ed1tlsUflAFAexxnBIBpi/n6kUceqSfussScn9UGAAD6pelx9meffbZavHhxWhsAmL3SZLUAgNmLGTtm7SaJnbGsNgAsNMcZAVbBUh0AjEdpsloAwOy18V88t1QHAEBbDj744LrLLM8hhxyS1gYA5s9xRgAg5uymiXk/qw0AAPTDPvvsU3fv5TnqqKPS2gDAwihNVgsAWBhLly6t/4UuT8z8WW0AWEiOMwIkLNUBwLiUJqsFACyMNmZ1S3UAADS17rrrNj4cfsUVV6S1AYAyjjMCACHm7SaJeT/m/qw2AACw8O699966ey/LTTfdlNYFABZOabJaAMDCiZm7SWLmz+oCwEJynBHgdSzVAcD4lCarBQAsnMsvv7z+V7osluoAAGjqvPPOq7vLsjz77LPV4sWL09oAQBnHGQGAEPP2L37xi3oCL0vM/VltAABgYZ1yyil1116ebbf1XQ4A+qY0WS0AYOHEzN00MftntQFgoTjOCPA6luoAYHxKk9UCABbOJptssnLubhJLdQAAlNp7773rrrI8S5cuTWsDAOUcZwQAfi3m7qaJ+T+rDQAALIyddtqp7tbLc8IJJ6S1AYCFVZqsFgCwsGL2bpr4BpDVBoCF4DgjwG+xVAcA41SarBYAsLCOOuqo+l/q8liqAwCgxL//+7/XHWVZbrrpprQuANCM44wAwG+L+btJYv7P6gIAAAvjO9/5Tt2tl2XFihVpXQBg4ZUmqwUALLyYwZskvgFkdQFgITjOCPBbLNUBwDiVJqsFACw8S3UAAMzal770pbqbLM+22zrEBABdcJwRAPhtMX83TXwHyGoDAACzdcwxx9Rdenn22GOPtDYAsPBKk9UCABZezOBNE98CstoAMGuOMwLULNUBwHiVJqsFACw8S3UAAMzSDjvsUHeR5TnhhBPS2gBAc44zAgCvF3N408T3gKw2AAAwG5tvvnn18ssv1x16Wc4444y0NgDQD6XJagEA/RCzeJPEt4D4JpDVBoBZcpwR4P+xVAcA41aarBYA0A+W6gAAmJXrrruu7iDLsmLFirQuANAOxxkBgEzM400S3wOyugAAwGxccskldXdelgceeCCtCwD0R2myWgBAf8RM3iTxTSCrCwCz5DgjwP9jqQ4Axq00WS0AoD8s1QEA0LUDDzyw7h7Ls8cee6S1AYB2OM4IAGRiHm+a+C6Q1QYAALr1oQ99qO7Ky7PffvultQGA/ihNVgsA6I+YyZsmvg1ktQFgVhxnBCbPUh0AjF9psloAQH9YqgMAoGv33ntv3TmW5cwzz0zrAgDtcZwRAFiVmMubJL4LZHUBAIBu3X777XVXXpaLLroorQsA9EtpsloAQL/EbN4k8W0gqwsAs+I4IzB5luoAYPxKk9UCAPrFUh0AAF055phj6q6xLD/60Y/SugBAuxxnBABWJ+bzJonvA1ldAACgG5/61Kfqbrwsjz32WLX++uuntQGAfilNVgsA6JeYzWNGb5L4RpDVBoBZcJwRmDRLdQAwDaXJagEA/WOpDgCAtr397W+vnn322bpjLMvHPvaxtDYA0C7HGQGA1Yn5vEni+0B8J8hqAwAA7Vq0aFH1yCOP1N14WY444oi0NgDQP6XJagEA/RMzepPEN4L4VpDVBoCuOc4ITJalOgCYjtJktQCA/rFUBwBA284777y6WyzLV77ylbQuANA+xxkBgDWJOb1Jli9fntYFAADadeqpp9ZdeFmuvvrqtC4A0E+lyWoBAP0Us3qTxLeCrC4AdM1xRmCyLNUBwHSUJqsFAPSTpToAANqy00471V1iWZ544olqgw02SGsDAO1znBEAWJOY02Neb5L4XpDVBgAA2vHud7+77r7L8tJLL1Wbb755WhsA6KfSZLUAgH6KWT1m9iaJbwZZbQDokuOMwCRZqgOAaSlNVgsA6CdLdQAAtOXGG2+sO8SyHH300WldAKAbjjMCAHMR83qT3HTTTWldAACgHVdccUXdfZflxBNPTOsCAP1VmqwWANBfMbM3SXwzyOoCQJccZwQmyVIdAExLabJaAEB/WaoDAKCpgw8+uO4Oy/K9730vrQsAdMdxRgBgrmJub5L4bpDVBQAAmtl3333rrrssP/rRj9K6AEC/lSarBQD023/8x3/U/5KXZZ999knrAkBXHGcEJsdSHQBMT2myWgBAv1mqAwCgiR/+8Id1Z1iWj3zkI2ldAKA7jjMCAHMVc3uTPPDAA2ldAACgmRUrVtRdd1kOPfTQtC4A0G+lyWoBAP0Ws3uTxLeDrC4AdMVxRmByLNUBwPSUJqsFAPSbpToAAEodf/zxdVdYlq9//etpXQCgW44zAgDzEfN7k8T3g6wuAABQZunSpXW3XZabb745rQsA9F9psloAQP/FDN8k8Q0hqwsAXXCcEZgUS3UAME2lyWoBAP1nqQ4AgPnaeOONq+eff77uCMvynve8J60NAHTLcUYAYD5ifm+S+H4Q3xGy2gAAwPyst9561WOPPVZ322XZZZdd0toAQP+VJqsFAPRfzPBNEt8Q4ltCVhsA2uY4IzAZluoAYLpKk9UCAPrPUh0AAPN14YUX1t1gWU4//fS0LgDQPccZAYD5ijm+SeI7QlYXAACYnzPPPLPussty/vnnp3UBgGEoTVYLABiGmOWbJL4lZHUBoG2OMwKTYakOAKarNFktAGAYLNUBADBXu+22W90FluVnP/tZtc4666S1AYDuOc4IAMxXzPExzzdJfE/IagMAAHOz7bbb1t11WZ577rnqHe94R1obABiG0mS1AIBhiFk+ZvomiW8KWW0AaJPjjMAkWKoDgGkrTVYLABgGS3UAAMzVrbfeWneAZfn0pz+d1gUAZsNxRgCgRMzzTRLfE7K6AADA3FxzzTV1d12WY489Nq0LAAxHabJaAMBwxEzfJPFNIasLAG1ynBGYBEt1ADBtpclqAQDDYakOAIA1+eQnP1l3f2W5884707oAwOw4zggAlIq5vkniu0JWFwAAWL2PfexjdVddlvvuuy+tCwAMS2myWgDAsMRs3yT77bdfWhcA2uI4IzB6luoAgNJktQCAYbFUBwDA6jz44IN151eWD3/4w2ldAGB2HGcEAErFXN8k8V0hqwsAAKze3XffXXfVZTnooIPSugDAsJQmqwUADEvM9k0S3xayugDQFscZgdGzVAcAlCarBQAMi6U6AABW5aSTTqq7vrJ87WtfS+sCALPlOCMA0ETM900S3xeyugAAQG7ZsmV1N12WG264Ia0LAAxPabJaAMDwxIzfJPGNIasLAG1wnBEYNUt1AEAoTVYLABgeS3UAALzeO9/5zurll1+uO76yvOtd70prAwCz5TgjANBEzPdNEt8XNttss7Q2AADwWuuvv3711FNP1d10WXbccce0NgAwPKXJagEAwxMzfpPEN4b41pDVBoCmHGcERstSHQDwa6XJagEAw2OpDgCA17v44ovrbq8sX/rSl9K6AMDsOc4IADQVc36TxHeGrC4AAPBa55xzTt1FlyX+77O6AMAwlSarBQAM07nnnlv/C18W3woA6IrjjMBoWaoDAH6tNFktAGCYLNUBAPBrf/RHf1R3eWX56U9/Wq211lppbQBg9hxnBACaijk/5v0m+eAHP5jWBgAAfmWHHXaou+ey/M///E+1wQYbpLUBgGEqTVYLABimmPVj5m+S+OaQ1QaAJhxnBEbJUh0A8NtKk9UCAIbJUh0AAL9222231R1eWZYsWZLWBQAWhuOMAEAbYt5vkvjekNUFAAB+5frrr6+757IsW7YsrQsADFdpsloAwHB97nOfq/+VL0t8c8jqAkATjjMCo2SpDgD4baXJagEAw2WpDgCAI488su7uyqInBID+cZwRAGiLvz0FAIBuHHjggXXXXJZ77rknrQsADFtpsloAwLDF7N8k8e0hqwsApRxnBEbHUh0A8HqlyWoBAMNmqQ4AYLrWWmut6qc//Wnd2ZXlgx/8YFobAFg4jjMCAG2Jub9J4rtDfH/IagMAwJT94Ac/qLvmsnzsYx9L6wIAw1aarBYAMGwx+zdJfHvI6gJAKccZgVGxVAcAZEqT1QIAhs1SHQDAdP3N3/xN3dWV5eKLL07rAgALy3FGAKBNMf83SXx/yOoCAMBUHXvssXW3XJZrr702rQsADF9psloAwPDFN4AmiW8QWV0AKOE4IzAqluoAgExpsloAwPBZqgMAmJ4NN9ywev755+uObv55+eWXq8022yytDQAsLMcZAYA2xfwf3wFKE98f4jtEVhsAAKZm0aJF1eOPP153y2XZbrvt0toAwPCVJqsFAAxffANokvgGEd8istoAMF+OMwKjYakOAFiV0mS1AIDhs1QHADA9X/7yl+turiwnnXRSWhcAWHiOMwIAbYvvAE1y+umnp3UBAGBqTjjhhLpLLsuZZ56Z1gUAxqE0WS0AYBzOOuus+l/8ssS3iKwuAMyX44zAaMQfszWJpToAGK/SZLUAgHGwVAcAMB0bbbRRo+PcDz74YFoXAOgHxxkBgC7E94DSxHeId7zjHWldAACYivXWW6966qmn6i55/nn88cert7zlLWltAGAcSpPVAgDGIb4FPPHEE/W/+vNPfIuIbxJZbQCYD8cZgVGIP2KzVAcArEppsloAwHhYqgMAmIazzz677uLK8slPfjKtCwD0g+OMAEAX4ntAk8T3iKwuAABMxV/91V/V3XFZli5dmtYFAMajNFktAGA8jj766Ppf/bLEN4msLgDMh+OMwChYqgMAVqc0WS0AYDws1QEAjN873/nOunsry6233prWBQD6w3FGAKAr8V2gSTbddNO0LgAAjN36669f/eIXv6g74/nnrrvuSusCAONSmqwWADAu3/ve9+p/+eef+CYR3yayugAwV44zAoMXf7zWJJbqAGD8SpPVAgDGxVIdAMC4/f3f/33duZVlt912S+sCAP3hOCMA0JX4LtAk8V0iqwsAAGN36qmn1l1xWfbdd9+0LgAwLqXJagEA4/KRj3yk/pe/LPFtIqsLAHPlOCMweJbqAIA1KU1WCwAYF0t1AADj9Yd/+Id111aWiy++OK0LAPSL44wAQJfi+0CTxPeJrC4AAIzVH/zBH1Qvvvhi3RHPP9dcc01aFwAYn9JktQCA8bn22mvrf/3nn/g2Ed8osroAMBeOMwKDZqkOAJiL0mS1AIDxsVQHADBOF1xwQd2xlWXrrbdO6wIA/eI4IwDQpfg+0CT/8A//kNYFAICxOvPMM+tuuCw777xzWhcAGJ/SZLUAgPGJbwRNEt8osroAMBeOMwKDFn+01iSW6gBgGkqT1QIAxsdSHQDA+LznPe+pu7WyXHjhhWldAKB/HGcEALoW3wmaJL5TZHUBAGBsFi9eXL366qt1Jzz/XHHFFWldAGCcSpPVAgDG6corr6w7gPknvlHEt4qsLgCsieOMwGBZqgMA5qo0WS0AYJws1QEAjMtXv/rVulMry5ZbbpnWBQD6x3FGAKBr8Z2gSeI7RVYXAADG5pxzzqm74LLssMMOaV0AYJxKk9UCAMYpvhU0SXyryOoCwJo4zggMlqU6AGCuSpPVAgDGyVIdAMB4vPe97627tLKcd955aV0AoJ8cZwQAZiG+FzTJdtttl9YFAICx2GKLLerutyyXXHJJWhcAGK/SZLUAgPG69NJL6y6gLPHNIqsLAKvjOCMwSPFHak1iqQ4ApqU0WS0AYLws1QEAjMNll11Wd2hl2WyzzdK6AEA/Oc4IAMxCfC9okvhekdUFAICxOP/88+vutyzbbuu7GQBMTWmyWgDAeDW9LxPfLLK6ALA6jjMCg2SpDgCYj9JktQCA8bJUBwAwfDvuuGPdnZXlb//2b9O6AEB/Oc4IAMxKfDdokvhukdUFAIChe/e73113vWX5yle+ktYFAMatNFktAGDc4ttBk8S3i6wuAKyK44zA4FiqAwDmqzRZLQBg3CzVAQAM29e//vW6M5t/XnnllWrjjTdO6wIA/eU4IwAwK/HdIL4flObKK69M6wIAwNBddNFFdddbFgcSAGCaSpPVAgDGbauttqo7gbLEt4usLgCsiuOMwOBYqgMA5qs0WS0AYNws1QEADNcuu+xSd2VlOeOMM9K6AEC/Oc4IAMxSfD9okvh+kdUFAICh2nbbbetutyznn39+WhcAGL/SZLUAgPGLbwhNEt8wsroAkHGcERgUS3UAQInSZLUAgPGzVAcAMEzXXntt3ZHNPy+88EL19re/Pa0LAPSb44wAwCzF94P4jlCa+H6R1QUAgKG65JJL6m63LFtssUVaFwAYv9JktQCA8YtvCE0S3zCyugCQcZwRGBRLdQBAidJktQCA8bNUBwAwPHvuuWfdjZXllFNOSesCAP3nOCMAMGvxHaFJ9thjj7QuAAAMzQ477FB3uWU555xz0roAwDSUJqsFAEzDueeeW3cEZYlvGVldAHg9xxmBwbBUBwCUKk1WCwCYBkt1AADDcsMNN9Sd2Pzz7LPPVm9961vTugBA/znOCADMWnxHiO8JpYnvGFldAAAYmiuuuKLucuefV199tVq8eHFaFwCYhtJktQCAaYhvCU0S3zKyugDweo4zAoNhqQ4AKFWarBYAMA2W6gAAhuNDH/pQ3YWV5eSTT07rAgDD4DgjALAQ4ntCk8T3jKwuAAAMxc4771x3t2U588wz07oAwHSUJqsFAEzHWWedVXcFZYlvGlldAPhtjjMCg2CpDgBoojRZLQBgOizVAQAMwy233FJ3YPPPU089Va277rppXQBgGBxnBAAWQnxPiO8Kpbn55pvTugAAMBTXXHNN3d3OPy+++GL1B3/wB2ldAGA6SpPVAgCmI74pvPTSS3VnMP/EN42sLgD8NscZgUGwVAcANFGarBYAMB2W6gAA+m/fffetu6+ynHDCCWldAGA4HGcEABZKfFdokviukdUFAIC+22OPPequtiynnnpqWhcAmJbSZLUAgGk57bTT6s6gLPFtI6sLAL/mOCPQe5bqAICmSpPVAgCmxVIdAEC/ffvb3647r/nnscceqxYtWpTWBQCGw3FGAGChxHeF+L5QmviukdUFAIC+u/766+uudv557rnnqvXXXz+tCwBMS2myWgDAtMS3hfjGUJr4tpHVBYBfc5wR6D1LdQBAU6XJagEA02KpDgCgvz72sY/VXVdZjjnmmLQuADAsjjMCAAspvi80SXzfyOoCAEBffehDH6q72bL81V/9VVoXAJie0mS1AIDp+eu//uu6OyhLfOPI6gJAcJwR6DVLdQBAG0qT1QIApsdSHQBAP/3Lv/xL3XHNP4888khaEwAYHscZAYCFFt8ZShPfN7KaAADQVzfffHPdzc4///M//1Ott956aV0AYHpKk9UCAKYnvjHEt4bSxDeOrC4ABMcZgV6zVAcAtKE0WS0AYJos1QEA9MuBBx5Yd1tl+Yu/+Iu0LgAwPI4zAgALLb4zNEl858jqAgBA3+yzzz51F1uWE088Ma0LAExTabJaAMA0xbeGJolvHVldAHCcEegtS3UAQFtKk9UCAKbJUh0AQL/cdddddac1/zz00ENpTQBgmBxnBAD6IL43lCa+c2Q1AQCgb/7pn/6p7mLnn8cff7x605velNYFAKapNFktAGCa4ltDfHMoTXzryOoCgOOMQG9ZqgMA2lKarBYAMF2W6gAA+mHfffetu6yyLF26NK0LAAyT44wAQB/E94Ymie8dWV0AAOiLPfbYo+5ey3LsscemdQGA6SpNVgsAmK745tAk8c0jqwvAtDnOCPSSpToAoE2lyWoBANNlqQ4AoB+uv/76usOaf370ox+lNQGA4XKcEQDoi/juUJr43pHVBACAvrjiiivq7nX+efTRR9OaAMC0lSarBQBMW3x7KE1888hqAjBtjjMCvWSpDgBoU2myWgDAtFmqAwBYWDvssEPdXZXlU5/6VFoXABguxxkBgL6I7w5NEt89sroAALDQttpqq7prLcuyZcvSugDAtJUmqwUATFt8e2iS+PaR1QVguhxnBHrHUh0A0LbSZLUAgGmzVAcAsLC+8pWv1J3V/HPfffelNQGAYXOcEQDok/j+UJr47pHVBACAhXbOOefUXev889BDD6U1AQBKk9UCAIhvEKWJbx9ZTQCmy3FGoHcs1QEAbStNVgsAwFIdAMDCWLx4cd1VleWII45I6wIAw+Y4IwDQJ/H9oUni+0dWFwAAFsr6669fvfjii3XHOv8sXbo0rQsAUJqsFgBAfIMoTXz7iG8gWV0ApslxRqBXLNUBAF0oTVYLAMBSHQDAwjj11FPrjmr++f73v5/WBACGz3FGAKBv4jtEaeL7R1YTAAAWygknnFB3q/PPj3/847QmAEAoTVYLACDEt4jSxDeQrCYA0+Q4I9ArluoAgC6UJqsFABAs1QEAzNaiRYuqp556qu6o5p9Pf/rTaV0AYPgcZwQA+ia+Q5Qmvn/Ed5CsLgAALIRHH3207lbnn7/8y79MawIAhNJktQAAQnyLKE18A8lqAjBNjjMCvWGpDgDoSmmyWgAAwVIdAMBs/cVf/EXdTc0/P/vZz9KaAMA4OM4IAPRRfI8oTXwHyWoCAMCsfepTn6q71Pnn6aefrtZZZ520LgBAKE1WCwAgvPnNb175TaI08S0kqwvA9DjOCPSGpToAoCulyWoBAPyapTrg/7L37/++l/O++P+niOwUkexSKizREpZsLTVRDq0toZWEFJZWlkMHaq5iIdxIPnKohHCjKKXM7NSmcNNekaKFaIccSmp8v89cYzcbPccY7+t6H8brcH/cbvcf9mqORzXMOd/Xfl7zegbA4txwww3lJFWfd7/73WknADAMljMCAF0U84jWxBwk6wQAgEW79tpryym1Pv/xH/+RdgIALGtN1gUAsCxmEq2JWUjWCcD4WM4IdIZHdQDAvLQm6wIAWOZRHQDAYhx++OHlFFWfv/71r0s77bRT2gsADIPljABAF8U8IuYSrYl5SNYLAACL8k//9E/ldNqWJzzhCWkvAMCy1mRdAADL9thjj3JqaEvMRLJeAMbFckagEzyqAwDmqTVZFwDAMo/qAAAW48orrywnqPp89KMfTTsBgOGwnBEA6KqYS7Qm5iFZJwAALMrFF19cTqf1+fSnP512AgBsrTVZFwDA1mI20ZqYiWSdAIyL5YxAJ3hUBwDMU2uyLgCArXlUBwAwX89//vPL6aktT33qU9NeAGA4LGcEALoq5hLTJOYiWS8AAMzbc57znHIqbcs//MM/pL0AAFtrTdYFALC1Zz/72eXk0JaYjWS9AIyH5YzAhvOoDgCYt9ZkXQAAW/OoDgBgvr74xS+Wk1N9LrjggrQTABgWyxkBgC6L+URrYi6SdQIAwLydc8455VRan6997WtpJwDASq3JugAAVooZRWtiNpJ1AjAeljMCG86jOgBg3lqTdQEArORRHQDAfDztaU8rp6a2bNq0Ke0FAIbFckYAoMtiPjFNYj6S9QIAwLzsueee5TTalpe+9KVpLwDASq3JugAAVooZxTSJGUnWC8A4WM4IbCiP6gCARWhN1gUAsJJHdQAA83HmmWeWE1N9tmzZknYCAMNjOSMA0HUxp2hNzEeyTgAAmJf3v//95TRan+9+97tpJwBApjVZFwBAJmYVrYkZSdYJwDhYzghsKI/qAIBFaE3WBQCQ8agOAGC2HvvYxy7dc8895cRUn8MPPzztBQCGx3JGAKDrYk7RmpiPxJwk6wUAgFnbfvvtl/7whz+U02h9Xv/616e9AACZ1mRdAACZmFW0JmYkMSvJegEYPssZgQ3jUR0AsCityboAADIe1QEAzNYpp5xSTkv1+c///M+0EwAYJssZAYA+iHlFa2JOknUCAMCsvf3tby+n0Pr87Gc/SzsBAFbTmqwLAGA1P//5z8spoj4xK8k6ARg+yxmBDeNRHQCwKK3JugAAVuNRHQDA7PzqV78qJ6X6HHfccWknADBMljMCAH0Q84rWxJwk6wQAgFn76U9/Wk6h9TnhhBPSTgCA1bQm6wIAWE3MLFoTs5KsE4Dhs5wR2DAe1QEAi9KarAsAYDUe1QEAzMYb3/jGckqqz+233770sIc9LO0FAIbJckYAoA9iXhFzi9bEvCTrBQCAWXnNa15TTp/1+fOf/7z0qEc9Ku0FAFhNa7IuAIDVxMwiZhetiZlJ1gvAsFnOCGwIj+oAgEVqTdYFALAaj+oAAGbjBz/4QTkh1ef0009POwGA4bKcEQDoi5hbtCbmJVknAADMytVXX11On/X50Ic+lHYCAKylNVkXAMBaYnbRmpiZZJ0ADJvljMCG8KgOAFik1mRdAABr8agOAGA6hx56aDkdteXxj3982gsADJfljABAX8TcYprE3CTrBQCAab3oRS8qp862PPnJT057AQDW0pqsCwBgLTG7mCYxO8l6ARguyxmBhfOoDgBYtNZkXQAAa/GoDgBgOpdeemk5GdXn7LPPTjsBgGGznBEA6JOYX7Qm5iZZJwAATOurX/1qOXXW57Of/WzaCQCwntZkXQAA6zn//PPLaaI+MTvJOgEYLssZgYXzqA4AWLTWZF0AAOvxqA4AoM1zn/vccipqyzOf+cy0FwAYNssZAYA+ifnFNIn5SdYLAACtnvGMZ5TTZlv233//tBcAYD2tyboAANYTM4xpEjOUrBeAYbKcEVgoj+oAgI3QmqwLAGA9HtUBALT57Gc/W05E9bnwwgvTTgBg+CxnBAD6JuYYrYn5SdYJAACtpvkP0V522WVpJwDAJFqTdQEATCJmGa2JGUrWCcAwWc4ILJRHdQDARmhN1gUAMAmP6gAA6jzxiU8sp6G2vOhFL0p7AYDhs5wRAOibmGNMk5ijZL0AAFDr8Y9/fDlltuXlL3952gsAMInWZF0AAJOIWcY0iVlK1gvA8FjOCCyMR3UAwEZpTdYFADAJj+oAAOp88IMfLCeh+lx99dVpJwAwDpYzAgB9FPOM1sQcJesEAIBap59+ejll1ucHP/hB2gkAMKnWZF0AAJP64Q9/WE4V9YlZStYJwPBYzggsjEd1AMBGaU3WBQAwKY/qAAAms+222y7dcccd5SRUn6OOOirtBQDGwXJGAKCPYp7RmpijxDwl6wUAgBq//vWvyymzPm984xvTTgCASbUm6wIAmFTMNFoTs5SsE4DhsZwRWAiP6gCAjdSarAsAYFIe1QEATOboo48up6D63HTTTWknADAeljMCAH0Vc43WxDwl6wQAgEkdccQR5XRZn1/96ldpJwBAjdZkXQAANWK20ZqYqWSdAAyL5YzAQnhUBwBspNZkXQAANTyqAwBY35YtW8oJqD7veMc70k4AYDwsZwQA+irmGq2JeUrWCQAAk7rkkkvK6bI+p5xyStoJAFCjNVkXAECNmG20JmYqWScAw2I5I7AQHtUBABupNVkXAEANj+oAANa27777ltNPff74xz8ubb/99mkvADAeljMCAH0Vc42Yb7Qm5ipZLwAArOcpT3lKOVXW569//evSzjvvnPYCANRoTdYFAFAjZhv33HNPOV3UJ2YrWS8Aw2E5IzB3HtUBAButNVkXAEANj+oAANb2oQ99qJx86nPGGWeknQDAuFjOCAD0Wcw3WhNzlawTAADW8573vKecKutz5plnpp0AALVak3UBANT62Mc+Vk4X9YnZStYJwHBYzgjMnUd1AMBGa03WBQBQy6M6AIDV3XbbbeXkU5+99tor7QQAxsVyRgCgz2K+0ZqYq2SdAACwnltuuaWcKuvztKc9Le0EAKjVmqwLAKBWzDhaE7OVrBOA4bCcEZg7j+oAgI3WmqwLAKCWR3UAALlXv/rV5dRTn69+9atpJwAwPpYzAgB9F3OO1sR8JesEAIDVHHbYYeU0WZ9vfOMbaScAQIvWZF0AAC0uvfTScsKoT8xYsk4AhsFyRmCuPKoDALqgNVkXAEALj+oAAB4sHm+1xh9oAgCWWc4IAPSd5TgAACzSNH+O6cgjj0w7AQBatCbrAgBoEbOO1tiJAzBsljMCc+VRHQDQBa3JugAAWnhUBwDwQHvvvXc57dTnlltuSTsBgHGynBEAGIKYd7Qm5ixZJwAArLTnnnuWU2R9fvOb36SdAACtWpN1AQC0uu2228opoz4xa8k6Aeg/yxmBufGoDgDoitZkXQAArTyqAwC433vf+95y0qnPe97znrQTABgnyxkBgCGIeUdrYs6SdQIAwEqnnHJKOUXW50Mf+lDaCQDQqjVZFwBAq5h5tCZmLVknAP1nOSMwNx7VAQBd0ZqsCwCglUd1AAD3+6//+q9y0qnPU57ylLQTABgnyxkBgCGIeUdrYs6SdQIAwEo33nhjOUXW5xnPeEbaCQDQqjVZFwBAq5h5tCZmLVknAP1nOSMwNx7VAQBd0ZqsCwCglUd1AAB/84pXvKKccupzySWXpJ0AwHhZzggADEXMPVoT85asEwAAlh1yyCHl9Fifb33rW2knAMA0WpN1AQBMY8uWLeWkUZ+YuWSdAPSb5YzAXHhUBwB0SWuyLgCAaXhUBwDwkKULL7ywnHDqc8QRR6SdAMB4Wc4IAAxFzD1aE/OWrBMAAJZdcMEF5fRYn6OPPjrtBACYRmuyLgCAacTsozUxc8k6Aeg3yxmBufCoDgDoktZkXQAA0/CoDgAYu7322qucburz61//Ou0EAMbNckYAYEhi/tGamLtknQAAsOuuu5ZTY31+//vfLz3sYQ9LewEAptGarAsAYBox+7jjjjvKaaM+MXvJegHoL8sZgZnzqA4A6JrWZF0AANPyqA4AGLNTTz21nGzqc8YZZ6SdAMC4Wc4IAAxJzD9aE3OXrBMAAE444YRyaqzPxz72sbQTAGBarcm6AACmFTOQ1sTsJesEoL8sZwRmzqM6AKBrWpN1AQBMy6M6AGDMfvrTn5aTTX2e/vSnp50AwLhZzggADEnMP1oTc5esEwAArr/++nJqrM9znvOctBMAYFqtyboAAKYVM5DWxOwl6wSgvyxnBGbOozoAoGtak3UBAEzLozoAYKz+6Z/+qZxq6nPFFVeknQAAljMCAEMTc5DWxPwl6wQAYLwOOuigclqsz9VXX512AgDMQmuyLgCAWYhZSGtiBpN1AtBPljMCM+VRHQDQRa3JugAAZsGjOgBgjL74xS+WE019Xve616WdAACWMwIAQxNzkNbE/CXrBABgvM4777xyWqzPm9/85rQTAGAWWpN1AQDMQsxCWhMzmKwTgH6ynBGYKY/qAIAuak3WBQAwCx7VAQBjs+uuu5bTTH1+//vfLz30oQ9NewEALGcEAIYm5iAxD2lNzGGyXgAAxmennXYqp8T63HnnnUvbb7992gsAMAutyboAAGYhZiF33XVXOXXU5zGPeUzaC0D/WM4IzIxHdQBAV7Um6wIAmAWP6gCAsTnhhBPKSaY+H/3oR9NOAIBgOSMAMEQxD2lNzGGyTgAAxuetb31rOSXW55Of/GTaCQAwK63JugAAZiVmIq2JWUzWCUD/WM4IzIxHdQBAV7Um6wIAmBWP6gCAMfk//+f/lJNMfZ797GennQAAwXJGAGCIYh7SmpjDZJ0AAIzPddddV06J9Xne856XdgIAzEprsi4AgFmJmUhrYhaTdQLQP5YzAjPjUR0A0FWtyboAAGbFozoAYCwOOuigcoqpz1VXXZV2AgAss5wRABiq73znO2VCUp+Yx2SdAACMxwEHHFBOh/WxSAAAWITWZF0AALM0zX/wImYyWScA/WI5IzATHtUBAF3WmqwLAGCWPKoDAMbgvPPOKyeY+rzxjW9MOwEAllnOCAAMVcxFWhPzmKwTAIDx+OQnP1lOh/U5/vjj004AgFlqTdYFADBLMRtpTcxksk4A+sVyRmAmPKoDALqsNVkXAMAseVQHAAzdTjvtVE4v9bnzzjuXHvGIR6S9AADLLGcEAIYq5iIxH2lNzGWyXgAAhm+HHXZYuuuuu8rJsD6Pecxj0l4AgFlqTdYFADBLMRtpTcxktt9++7QXgP6wnBGYmkd1AEDXtSbrAgCYJY/qAIChe+tb31pOLvX5xCc+kXYCAGzNckYAYMjOPvvsMimpT8xlsk4AAIbvzW9+czkV1sd/MBYAWJTWZF0AALMWM5LWxGwm6wSgPyxnBKbmUR0A0HWtyboAAGbNozoAYMiuu+66cnKpzz/+4z+mnQAAW7OcEQAYspiPtCbmMlknAADDd/XVV5dTYX1e+MIXpp0AALPWmqwLAGDWYkbSmpjNZJ0A9IfljMDUPKoDALquNVkXAMCseVQHAAzVAQccUE4t9bn22mvTTgCAlSxnBACGLuYkrYn5TNYJAMBwPec5zymnwfpcf/31aScAwDy0JusCAJiHmJW0JmY0WScA/WA5IzAVj+oAgD5oTdYFADAPHtUBAEN09tlnlxNLfY4//vi0EwBgJcsZAYChizlJa2I+k3UCADBcZ555ZjkN1ued73xn2gkAMA+tyboAAOYhZiWtiRlN1glAP1jOCEzFozoAoA9ak3UBAMyDR3UAwBDddttt5cRSl3vvvXfp0Y9+dNoJALCS5YwAwNDFnCTmJS2J+UzWCQDAcP3iF78op8H67LLLLmknAMA8tCbrAgCYh5iVtCZmNFknAP1gOSMwFY/qAIA+aE3WBQAwDx7VAQBDc8ghh5TTSn3OPffctBMAIGM5IwAwBjEvaU3MabJOAACG56CDDiqnwPp84QtfSDsBAOalNVkXAMC8xMykNTGryToB6D7LGYFmHtUBAH3RmqwLAGBePKoDAIbk05/+dDmp1OeFL3xh2gkAkLGcEQAYg5iXtCbmNFknAADD8/GPf7ycAuvz0pe+NO0EAJiX1mRdAADzEjOT1sSsJusEoPssZwSaeVQHAPRFa7IuAIB58agOABiS3/3ud+WkUpcf/ehHaR8AwGosZwQAxiLmJi2JOU3WBwDA8Nx6663lFFiXn/zkJ2kfAMA8tSbrAgCYp5idtCRmNVkfAN1nOSPQzKM6AKAvWpN1AQDMk0d1AMAQvOxlLyunlPqcfPLJaScAwGosZwQAxiLmJq2JeU3WCQDAcLz4xS8up7/6/Pu//3vaCQAwT63JugAA5ilmJ62JmU3WCUC3Wc4INPGoDgDok9ZkXQAA8+RRHQAwBOeee245odRn7733TjsBAFZjOSMAMBYxN2lNzGuyTgAAhuPss88up7/67LvvvmknAMA8tSbrAgCYp5idtCZmNlknAN1mOSPQxKM6AKBPWpN1AQDMk0d1AEDfbbPNNkt/+MMfygmlLtdcc03aCQCwFssZAYAxiflJS2JeE3ObrBMAgGH4v//3/5bTX11+8IMfpH0AAPPWmqwLAGDeYobSkpjZZH0AdJvljEA1j+oAgL5pTdYFADBvHtUBAH122GGHldNJfU444YS0EwBgLZYzAgBjEvOT1sTcJusEAKD/DjnkkHLqq88pp5ySdgIAzFtrsi4AgHmLGUprYnaTdQLQXZYzAtU8qgMA+qY1WRcAwLx5VAcA9Nn5559fTib1efKTn5x2AgCsxXJGAGBMYn7SmpjbZJ0AAPTfpz/96XLqq88++5hXAQAbozVZFwDAvMUMpTUxu8k6AeguyxmBah7VAQB905qsCwBg3jyqAwD6atttt13685//XE4mdbnqqqvSTgCA9VjOCACMTcxRWhJzm5jfZJ0AAPTb7373u3Lqq8v3vve9tA8AYBFak3UBACzCtddeW04kdYnZTdYHQHdZzghU8agOAOij1mRdAACL4FEdANBHr3rVq8qppD5vf/vb004AgPVYzggAjE3MUVoT85usEwCA/jr00EPLaa8+J510UtoJALAIrcm6AAAW4eSTTy4nkvrEDCfrBKCbLGcEqnhUBwD0UWuyLgCARfCoDgDooy984QvlRFKfvfbaK+0EAFiP5YwAwNjEHKU1Mb/JOgEA6K9zzz23nPbqs/fee6edAACL0JqsCwBgEWKW0pqY4WSdAHST5YxAFY/qAIA+ak3WBQCwCB7VAQB9s9122y395S9/KSeSunz7299OOwEAJmE5IwAwRldeeWWZrNQl5jcxx8k6AQDon2222WbpD3/4Qznt1eWaa65JOwEAFqU1WRcAwKLETKUlMcOJWU7WCUD3WM4ITMyjOgCgr1qTdQEALIpHdQBAnxxxxBHlNFKft771rWknAMAkLGcEAMYo5imtiTlO1gkAQP+84hWvKKe8+rzzne9MOwEAFqU1WRcAwKKccMIJ5VRSn5jlZJ0AdI/ljMDEPKoDAPqqNVkXAMCieFQHAPTJl770pXISqc8TnvCEtBMAYBKWMwIAYxTzlNbEHCfrBACgf84///xyyqvPk570pLQTAGBRWpN1AQAsypOf/ORyKqlPzHKyTgC6x3JGYGIe1QEAfdWarAsAYFE8qgMA+mKHHXZYuueee8pJpC7f+ta30k4AgElZzggAjNUVV1xRJix1iTlOzHOyTgAA+mPbbbdd+vOf/1xOeXW56qqr0k4AgEVqTdYFALBIMVtpScxyYqaTdQLQLZYzAhPxqA4A6LPWZF0AAIvkUR0A0Aevec1ryimkPscdd1zaCQAwKcsZAYCxirlKa2Kek3UCANAfhx9+eDnd1edtb3tb2gkAsEityboAABYpZiutiZlO1glAt1jOCEzEozoAoM9ak3UBACySR3UAQB985StfKSeQ+jz+8Y9POwEAJmU5IwAwVjFXaU3Mc7JOAAD644ILLiinu/rsueeeaScAwCK1JusCAFikmK20JmY6WScA3WI5IzARj+oAgD5rTdYFALBIHtUBAF234447ltNHfb75zW+mnQAANSxnBADG7LLLLiuTlvrEXCfrBACg+7bbbrulv/zlL+VkV5crr7wy7QQAWLTWZF0AAIsWM5aWxEwnZjtZJwDdYTkjsC6P6gCAvmtN1gUAsGge1QEAXfa6172unDzq8+Y3vzntBACoYTkjADBmMV9pTcx1sk4AALrv1a9+dTnV1ef4449POwEAFq01WRcAwKLFjKU1MdvJOgHoDssZgXV5VAcA9F1rsi4AgEXzqA4A6LKLLrqonDzq89//+39POwEAaljOCACMWcxXWhNznawTAIDu+/KXv1xOdfXZfffd004AgEVrTdYFALBoMWNpTcx2sk4AusNyRmBdHtUBAH3XmqwLAGDRPKoDALpqp512KqeO+nzjG99IOwEAalnOCACM3SWXXFImLvWJ+U7WCQBAdz3ykY9cuueee8qJri6XX3552gkAsBFak3UBAGyEK664opxQ6hKznZjxZJ0AdIPljMCaPKoDAIagNVkXAMBG8KgOAOiiN7zhDeXEUZ9jjz027QQAqGU5IwAwdjFnaU3Md7JOAAC666ijjiqnufq85S1vSTsBADZCa7IuAICNELOW1sSMJ+sEoBssZwTW5FEdADAErcm6AAA2gkd1AEAXXXzxxeXEUZ/HPvaxaScAQC3LGQGAsYs5S2tivpN1AgDQXV/96lfLaa4+u+66a9oJALARWpN1AQBshJi1tCZmPFknAN1gOSOwJo/qAIAhaE3WBQCwETyqAwC65nGPe1w5bdTn61//etoJANDCckYAgIcsfe1rXyuTl/rEnCfrBACgex796EeXU1x9Lr300rQTAGCjtCbrAgDYKJdddlk5pdQnZj1ZJwAbz3JGYFUe1QEAQ9GarAsAYKN4VAcAdMkb3/jGctKoz9FHH512AgC0sJwRAOAh981bWhNznqwTAIDuef3rX19OcfV505velHYCAGyU1mRdAAAbJWYurYlZT9YJwMaznBFYlUd1AMBQtCbrAgDYKB7VAQBdcumll5aTRn38V14BgFmynBEA4CH3zVtaE3OerBMAgO7xH3cFAIakNVkXAMBGiZlLa2LWk3UCsPEsZwRW5VEdADAUrcm6AAA2ikd1AEBX7LzzzuWUUZ8LL7ww7QQAaGU5IwDA33z1q18tE5j6PPaxj007AQDojh133LGc3upz8cUXp50AABupNVkXAMBGuuSSS8pJpT6PetSj0k4ANpbljEDKozoAYEhak3UBAGykaR7Vxbwn6wQAqPXqV7+6nDDq89rXvjbtBABoZTkjAMDfxNylNTHvyToBAOiOV77yleX0Vp83vOENaScAwEZqTdYFALCRjjnmmHJSqU/MfLJOADaW5YxAyqM6AGBIWpN1AQBsJI/qAIAuOOecc8oJoy733nvv0iMf+ci0EwCgleWMAAB/E3OXmL+0JOY9WScAAN1x9tlnl9NbfXbaaae0EwBgI7Um6wIA2Egxe2lNzHyyTgA2luWMQMqjOgBgSFqTdQEAbCSP6gCALvjlL39ZThh1+fKXv5z2AQBMw3JGAID7xfylJTHvyfoAAOiOn/3sZ+X0VpeLLroo7QMA2GityboAADZazGBaEjOfrA+AjWU5I5DyqA4AGJLWZF0AABvNozoAYCPtt99+5XRRnyOPPDLtBACYhuWMAAD3i/lLa2Luk3UCALDxnvGMZ5RTW31e//rXp50AAButNVkXAMBGixlMa2L2k3UCsHEsZwQexKM6AGBoWpN1AQBsNI/qAICN9K53vaucLOrzuMc9Lu0EAJiG5YwAAPeL+UtrYu6TdQIAsPHe+c53llNbfXbbbbe0EwBgo7Um6wIA2Ggxg2lNzH6yTgA2juWMwINM86hu5513TjsBADZSa7IuAICNFvOX1nhUBwBM68orrywni7p85zvfSfsAAKZlOSMAwAPFHKYlMffJ+gAA2Hjf/OY3y6mtLt/97nfTPgCALmhN1gUA0AXf+973yomlLjH7yfoA2DiWMwIP4lEdADA0rcm6AAC6wKM6AGAjPOYxjymnivps3rw57QQAmJbljAAADxRzmNY8+tGPTjsBANg4O+ywQzmt1ec973lP2gkA0AWtyboAALrgve99bzmx1CdmQFknABvDckbgATyqAwCGqDVZFwBAF0zzqC7mP1knAMB6Dj/88HKiqM/++++fdgIATMtyRgCAB4o5TGti/pN1AgCwcV7+8peX01p9DjzwwLQTAKALWpN1AQB0waZNm8qJpT4xA8o6AdgYljMCD+BRHQAwRK3JugAAusCjOgBgI3zqU58qJ4q6/OY3v0n7AABmwXJGAIAHi3lMS2L+k/UBALBxPv7xj5fTWl1++9vfpn0AAF3RmqwLAKArfve735VTS11iBpT1AbAxLGcEHsCjOgBgiFqTdQEAdIVHdQDAot1yyy3lRFGX888/P+0DAJgFyxkBAB4s5jEtiflP1gcAwMa56aabymmtLhdccEHaBwDQFa3JugAAuuKLX/xiObXUJWZAWR8AG8NyRuABPKoDAIaoNVkXAEBXeFQHACzSs571rHKaqM9rX/vatBMAYBYsZwQAeLCYx7Tmmc98ZtoJAMDiPf3pTy+ntPq84Q1vSDsBALqiNVkXAEBXxEymNX//93+fdgKweJYzAv+PR3UAwFC1JusCAOiKaR7VxRwo6wQAWM2JJ55YThL12XXXXdNOAIBZsJwRAODBYh7TmpgDZZ0AACze29/+9nJKq88TnvCEtBMAoCtak3UBAHRFzGRa87a3vS3tBGDxLGcE/p9pHtXtsssuaScAQBe0JusCAOiKmMe0xqM6AKDWFVdcUU4Sdfnf//t/p30AALNiOSMAQC7mMi2JOVDWBwDA4l166aXllFaX6667Lu0DAOiS1mRdAABd8v3vf7+cXOoSs6CsD4DFs5wR+H88qgMAhqo1WRcAQJd4VAcALMKjHvWocoqoz+mnn552AgDMiuWMAAC5mMu05pGPfGTaCQDA4jziEY9Yuvfee8sJrS7ve9/70k4AgC5pTdYFANAlMZtpScyCtttuu7QTgMWynBG4j0d1AMCQtSbrAgDokmke1cU8KOsEAFjpFa94RTlB1OeAAw5IOwEAZsVyRgCAXMxlWhPzoKwTAIDFednLXlZOZ/V5wQtekHYCAHRJa7IuAIAuidlMa2ImlHUCsFiWMwL38agOABiy1mRdAABd4lEdALAIn/jEJ8oJoi6333572gcAMEuWMwIArC7mMy2JeVDWBwDA4nzsYx8rp7O63HHHHWkfAEDXtCbrAgDompjRtCRmQlkfAItlOSNwH4/qAIAha03WBQDQNR7VAQDzdvPNN5cTRF2+8IUvpH0AALNkOSMAwOpiPtOSmAdlfQAALM5PfvKTcjqry5e//OW0DwCga1qTdQEAdE3MaFoSM6GsD4DFspwRuI9HdQDAkLUm6wIA6BqP6gCAedp3333L6aE+Rx99dNoJADBLljMCAKwu5jOtefrTn552AgAwf0972tPKqaw+b3zjG9NOAICuaU3WBQDQNTGjaU3MhrJOABbHckbAozoAYPBak3UBAHTNNI/qYi6UdQIALHvHO95RTg712X333dNOAIBZspwRAGB1MZ9pTcyFsk4AAObvrW99azmV1WevvfZKOwEAuqY1WRcAQNfEjKY1MRvKOgFYHMsZgake1e22225pJwBAl7Qm6wIA6JqYz7TGozoAYD2XXXZZOTnU5dprr037AABmzXJGAIC1xZymJTEXyvoAAJi/iy++uJzK6vLDH/4w7QMA6KLWZF0AAF0Us5qWxGwo6wNgcSxnBDyqAwAGrzVZFwBAF3lUBwDMw/bbb19ODfX5j//4j7QTAGDWLGcEAFhbzGla84hHPCLtBABgfh7+8Icv3X333eVEVpczzjgj7QQA6KLWZF0AAF0Us5qWxGwoZkRZJwCLYTkjjJxHdQDAGLQm6wIA6KJpHtXFfCjrBAA49NBDy4mhPs9//vPTTgCAWbOcEQBgbTGnaU3Mh7JOAADm55BDDimnsfocfPDBaScAQBe1JusCAOiimNW0JmZEWScAi2E5I4ycR3UAwBi0JusCAOgij+oAgHk466yzyomhLr///e/TPgCAebCcEQBgfTGvaUnMh7I+AADm5yMf+Ug5jdXlT3/6U9oHANBVrcm6AAC6KmY2LYkZUdYHwGJYzggj51EdADAGrcm6AAC6yqM6AGDWbrzxxnJiqMuXvvSltA8AYB4sZwQAWF/Ma1oS86GsDwCA+bnhhhvKaawuX/3qV9M+AICuak3WBQDQVTGzaUnMiLI+ABbDckYYOY/qAIAxaE3WBQDQVR7VAQCztM8++5TTQn2OPfbYtBMAYB4sZwQAWF/Ma1rztKc9Le0EAGD2nvKUp5RTWH3e/OY3p50AAF3VmqwLAKCrYmbTmpgVZZ0AzJ/ljDBiHtUBAGPRmqwLAKCrpnlUF3OirBMAGK9/+7d/KyeF+uyxxx5pJwDAPFjOCACwvpjXtCbmRFknAACzd9xxx5VTWH2e9KQnpZ0AAF3VmqwLAKCrYmbTmpgVZZ0AzJ/ljDBiHtUBAGPRmqwLAKCrPKoDAGbpkksuKSeFuvzgBz9I+wAA5sVyRgCAycTcpiUxJ8r6AACYva997WvlFFaX66+/Pu0DAOiy1mRdAABdFrOblsSsKOsDYP4sZ4QR86gOABiL1mRdAABd5lEdADAL/+2//belv/71r+WkUJcPfOADaScAwLxYzggAMJmY27Qk5kQPf/jD004AAGbnoQ996NJdd91VTmF1+dCHPpR2AgB0WWuyLgCALovZTUtiVhQzo6wTgPmynBFGyqM6AGBMWpN1AQB02TSP6mJelHUCAOPzwhe+sJwS6nPQQQelnQAA82I5IwDAZGJu05qYF2WdAADMzoEHHlhOX/V58YtfnHYCAHRZa7IuAIAui9lNa2JmlHUCMF+WM8JIeVQHAIxJa7IuAIAu86gOAJiFd7/73eWEUJc//vGPaR8AwDxZzggAMLmY37Qk5kVZHwAAs3PiiSeW01dd7rzzzqWHPvShaScAQJe1JusCAOiymN3cdddd5TRTl5NOOintBGC+LGeEkfKoDgAYk9ZkXQAAXedRHQAwrYsvvricEOryla98Je0DAJgnyxkBACYX85uWxLwo6wMAYHYuuuiicvqqS3xd1gcA0HWtyboAALrO7AegXyxnhJHyqA4AGJPWZF0AAF3nUR0AMK3f/va35YRQl7e85S1pHwDAPFnOCAAwuZjftCTmRVkfAACzc9ttt5XTV12OP/74tA8AoOtak3UBAHRdzHBaEjOjrA+A+bKcEUbKozoAYExak3UBAHSdR3UAwDSe8YxnlNNBfZ75zGemnQAA82Q5IwDA5GJ+05qYG2WdAABMb5999imnrvrst99+aScAQNe1JusCAOi6mOG05u///u/TTgDmx3JGGCGP6gCAsWlN1gUA0HUe1QEA03jzm99cTgZ1+f3vf5/2AQDMm+WMAAB17rjjjjLRqUvMjbI+AACmd+yxx5ZTV13+9Kc/pX0AAH3QmqwLAKAPYpbTkpgdZX0AzI/ljDBCHtUBAGPTmqwLAKAPPKoDAFqdd9555WRQl2984xtpHwDAvFnOCABQJ+Y4LYm5UdYHAMD0PvOZz5RTV12++c1vpn0AAH3QmqwLAKAPYpbTkpgdZX0AzI/ljDBCHtUBAGPTmqwLAKAPPKoDAFrdeOON5WRQl1NPPTXtAwCYN8sZAQDqxBynJTE3yvoAAJjeDTfcUE5ddTnttNPSPgCAPmhN1gUA0Acxy2lJzI6yPgDmx3JGGCGP6gCAsWlN1gUA0Ace1QEALXbfffdyKqjPQQcdlHYCAMyb5YwAAHVijtOamB9lnQAAtNt1113Laas+L3nJS9JOAIA+aE3WBQDQBzHLaU3MkLJOAObDckYYGY/qAIAxak3WBQDQBx7VAQAtXvWqV5UTQX122GGHtBMAYN4sZwQAqBNznNbE/CjrBACg3WGHHVZOW/XZcccd004AgD5oTdYFANAHMctpTcyQsk4A5sNyRhgZj+oAgDFqTdYFANAHHtUBAC0+/OEPlxNBXb73ve+lfQAAi2A5IwBAvZjntCTmR1kfAADtzjjjjHLaqsv3v//9tA8AoC9ak3UBAPRFzHRaEjOkrA+A+bCcEUbGozoAYIxak3UBAPSFR3UAQK1rrrmmnAjq8tGPfjTtAwBYBMsZAQDqxTynJTE/yvoAAGj3ne98p5y26nLWWWelfQAAfdGarAsAoC9iptOSmCFlfQDMh+WMMDIe1QEAY9SarAsAoC88qgMAajziEY8op4H6/PM//3PaCQCwCJYzAgDUi3lOa2KOlHUCAFDv4Q9/+NK9995bTlp1OfLII9NOAIC+aE3WBQDQFzHTaUnMkGKWlHUCMHuWM8KIeFQHAIxVa7IuAIC+8KgOAKjxghe8oJwE6rPHHnuknQAAi2A5IwBAvZjntCbmSFknAAD1DjzwwHLKqs8Tn/jEtBMAoC9ak3UBAPRFzHRaE7OkrBOA2bOcEUbEozoAYKxak3UBAPSFR3UAQI13vetd5SRQl5tvvjntAwBYFMsZAQDaxFynJTFHyvoAAKh34oknllNWXW655Za0DwCgT1qTdQEA9EnMdlpy0kknpX0AzJ7ljDAiHtUBAGPVmqwLAKBPPKoDACZ18cUXl5NAXT73uc+lfQAAi2I5IwBAm5jrtOTrX/962gcAQL2LLrqonLLqcsEFF6R9AAB90pqsCwCgT2K205KYJWV9AMye5YwwIvGHoVriUR0A0HetyboAAPrEozoAYFK//e1vy0mgLm95y1vSPgCARbGcEQCgzXHHHVcmPHWJOVLWBwBAvdtuu62csupy/PHHp30AAH3SmqwLAKBPYrbTkpglZX0AzJ7ljDAiHtUBAGPVmqwLAKBPPKoDACax7777llNAfZ71rGelnQAAi2I5IwBAm5jrtCbmSVknAACT22effcrpqj777bdf2gkA0CetyboAAPokZjutiZlS1gnAbFnOCCPhUR0AMGatyboAAPrEozoAYBJvetObygmgLnfccUfaBwCwSJYzAgC0i/lOS2KelPUBADC5Y445ppyu6vKnP/0p7QMA6JvWZF0AAH0TM56WHHvssWkfALNlOSOMhEd1AMCYtSbrAgDoG4/qAID1nHfeeeUEUJdvfOMbaR8AwCJZzggA0O7SSy8tk566xDwp6wMAYHKf+cxnyumqLt/85jfTPgCAvmlN1gUA0Dcx42lJzJSyPgBmy3JGGAmP6gCAMWtN1gUA0Dce1QEA67nxxhvLCaAup556atoHALBIljMCALTbvHlzmfTUJeZJWR8AAJO74YYbyumqLqeddlraBwDQN63JugAA+iZmPC358Y9/nPYBMFuWM8JIeFQHAIxZa7IuAIC+8agOAFjLbrvtVj7963PwwQennQAAi2Q5IwBAu5jvtCbmSlknAADr22WXXcqpqj4veclL0k4AgL5pTdYFANA3MeNpTcyWsk4AZsdyRhgBj+oAgLFrTdYFANA3HtUBAGt55StfWT7567PDDjuknQAAi2Q5IwBAu0c+8pFl0lOfmCtlnQAArO+www4rp6r67LjjjmknAEDftCbrAgDom5jxtCZmS1knALNjOSOMwDSP6uIPXWWdAAB90pqsCwCgbzyqAwDW8uEPf7h88tfle9/7XtoHALBoljMCAEzn2muvLROfusRcKesDAGB9Z5xxRjlV1eX73/9+2gcA0EetyboAAPooZj0tidlS1gfA7FjOCCPgUR0AMHatyboAAPrIozoAYDXXXHNN+eSvy0c/+tG0DwBg0SxnBACYTsx5WhJzpawPAID1fec73ymnqrqcddZZaR8AQB+1JusCAOijmPW0JGZLWR8As2M5I4yAR3UAwNi1JusCAOgjj+oAgMx2221XPvXrc8QRR6SdAACLZjkjAMB0Ys7TmpgvZZ0AAKzu4Q9/+NK9995bTlR1OfLII9NOAIA+ak3WBQDQRzHraUnMlmLGlHUCMBuWM8LAeVQHAOCyDgDAozoAIPOCF7ygfOLXZ4899kg7AQAWzXJGAIDp7LnnnmXiU5/nP//5aScAAKs78MADy2mqPk984hPTTgCAPmpN1gUA0Ecx62lNzJiyTgBmw3JGGLj4Q0+tiT9slXUCAPRNa7IuAIA+8qgOAMi8613vKp/4dbn55pvTPgCAjWA5IwDA9GLe05KYL2V9AACs7sQTTyynqbrccsstaR8AQF+1JusCAOirmPm05KSTTkr7AJgNyxlh4DyqAwBwWQcAEDyqAwBWuvjii8snfl0+97nPpX0AABvBckYAgOnFvKclX//619M+AABWd9FFF5XTVF0uuOCCtA8AoK9ak3UBAPRVzHxaEjOmrA+A2bCcEQYu/tBTSzyqAwCGpDVZFwBAX3lUBwCsdOutt5ZP/Locd9xxaR8AwEawnBEAYHox72nJr371q7QPAIDV/eIXvyinqbocf/zxaR8AQF+1JusCAOirmPm0JGZMWR8As2E5Iwxc/KGnlnhUBwAMSWuyLgCAvvKoDgDY2q677lo+7evzrGc9K+0EANgIljMCAEzvH/7hH8rkpz677LJL2gkAwIM97nGPK6eo+uy3335pJwBAX7Um6wIA6KuY+bQmZk1ZJwDTs5wRBiz+sFNr4g9ZZZ0AAH3UmqwLAKCvpnlUF8ubsk4AoL8OPvjg8klflzvuuCPtAwDYKJYzAgDMRsx9WnLQQQelfQAAPNgLX/jCcoqqy5/+9Ke0DwCgz1qTdQEA9FnMfloSs6asD4DpWc4IA+ZRHQDA37Qm6wIA6LPWR3UxZ8r6AID+etvb3lY+6ety1VVXpX0AABvFckYAgNmIuU9LYs6U9QEA8GBvfetbyymqLldffXXaBwDQZ63JugAA+ixmPy2JWVPWB8D0LGeEAfOoDgDgb1qTdQEA9JlHdQDAsnPOOad80tfl4x//eNoHALBRLGcEAJiNmPu05DOf+UzaBwDAg33qU58qp6i6nH322WkfAECftSbrAgDos5j9tCRmTVkfANOznBEGLP6wU0s8qgMAhqY1WRcAQJ95VAcALLv22mvLJ31d/uVf/iXtAwDYKJYzAgDMRsx9WvK9730v7QMApnPzzTeXT9vu5Ic//GH6z8rkvvvd75bvZl3+9V//Ne0DAOiz1mRdAAB9FrOflsSsKesDYHqWM8KAxR92aolHdQDA0LQm6wIA6DOP6gCAZXfddVf5pK/LAQcckPYBAGwUyxkBAGYj5j4tiTlT1gcATMdyxmH685//XL6bddm0aVPaBwDQZ63JugAA+ixmPy2JWVPWB8D0LGeEAfOoDgDgb1qTdQEA9JlHdQBAeNrTnlY+5evz6Ec/Ou0EANgoljMCAMxGzH1a89SnPjXtBADaWc44PHvvvXf5TtbnsY99bNoJANBnrcm6AAD6LGY/rYmZU9YJwHQsZ4SB8qgOAOB+rcm6AAD6bJpHdTFvyjoBgP45/PDDyyd8XX72s5+lfQAAG8lyRgCA2Yn5T0ti3pT1AQDtLGccnle+8pXlO1mXW265Je0DAOi71mRdAAB9FzOglsTMKesDYDqWM8JAeVQHAHC/1mRdAAB951EdAPCe97ynfMLX5aKLLkr7AAA2kuWMAACzE/Oflpx++ulpHwDQznLG4TnttNPKd7IuF198cdoHANB3rcm6AAD6LmZALYmZU9YHwHQsZ4SBij/k1BKP6gCAIWpN1gUA0Hetj+piiVPWBwD0z4UXXlg+4evy3ve+N+0DANhIljMCAMxOzH9a8tWvfjXtAwCms80223RK9s/I5L7yla+U01Nd3ve+96V9AAB915qsCwCg72IG1JKYOWV9AEzHckYYKI/qAADu15qsCwCg71of1cW8KesDAPrnpptuKp/wdTniiCPSPgCAjWQ5IwDA7MT8pyUxb8r6AAC4309/+tNyeqrLkUcemfYBAPRda7IuAIC+ixlQS2LmlPUBMB3LGWGgPKoDALhfa7IuAIC+86gOAMZtxx13LJ/u9dlnH4uEAIDusZwRAGB2Yv7Tmpg7ZZ0AADxk6ZGPfGQ5NdXn6U9/etoJANB3rcm6AAD6LmZArYnZU9YJQDvLGWGAPKoDAHig1mRdAAB951EdAIzbP/7jP5ZP9rrcfffdaR8AwEaznBEAYLZiDtSSmDtlfQAAPGRp//33L6emuvz1r39d2mabbdJOAIC+a03WBQDQdzEDillQS2L2lHUC0M5yRhggj+oAAB6oNVkXAMAQeFQHAOP15je/uXyy1+W6665L+wAANprljAAAsxVzoJa86U1vSvsAAHjI0hvf+MZyaqrLD37wg7QPAGAIWpN1AQAMQcyCWhKzp6wPgHaWM8IAxR9uaolHdQDAULUm6wIAGILWR3WxzCnrAwD646yzziqf7HU599xz0z4AgI1mOSMAwGzFHKglMXfK+gAAeMjSmWeeWU5NdfnsZz+b9gEADEFrsi4AgCGIWVBLYvaU9QHQznJGGCCP6gAAHqg1WRcAwBB4VAcA4/Xtb3+7fLLX5R3veEfaBwCw0SxnBACYrZgDtSTmTlkfAAzBpk2b7rN58+YHWP6/Z18DW7vyyivLqaku73znO9M+AIAhaE3WBQAwBDELaknMnrI+ANpZzggD5FEdAMADtSbrAgAYAo/qAGC8fve735VP9rq86EUvSvsAADaa5YwAALMVc6CW/Pa3v037AKCPYuHili1b7jNplhc2Zn1w++23l1vrhDAAAP/0SURBVJ8pdXnJS16S9gEADEFrsi4AgCGIWVBLYvaU9QHQznJGGKD4w00t8agOABiq1mRdAABD0PqoLpY5ZX0AQD/stdde5VO9Po9//OPTTgCAjWY5IwDAbMUcqDV77rln2gkAfRALGWO54rSxoJGV9thjj/Kzoz677bZb2gkAMAStyboAAIYgZkGtiRlU1glAG8sZYWDiDzW1xqM6AGCoWpN1AQAMwTSP6mKpU9YJAHTf//yf/7N8otfl17/+ddoHANAFljMCAMxezINaEvOnrA8Aum4WSxm3zpYtW9K/D+N0yCGHlJ8ZdbntttvSPgCAoWhN1gUAMBQxE2pJzKCyPgDaWM4IA+NRHQDAg7Um6wIAGAqP6gBgfE4++eTyiV6Xyy+/PO0DAOgCyxkBAGYv5kEtiflT1gcAXbVp06byKZYnliyG+HFh+WsmWeYYP2bl349xOumkk8rPirpcccUVaR8AwFC0JusCABiKmAm1JGZQWR8AbSxnhIHxqA4A4MFak3UBAAyFR3UAMD5f+MIXyid6XT784Q+nfQAAXWA5IwDA7MU8qCWf//zn0z4A6KL1Fiyut1xxvcWOkeWFjozb5z73ufIzoi4f+chH0j4AgKFoTdYFADAUMRNqScygsj4A2ljOCAMTf6ipJR7VAQBD1pqsCwBgKFof1cVSp6wPAOi+66+/vnyi1+Xoo49O+wAAusByRgCA2Yt5UEti/pT1AUDXbNmypXx6PTjx1yZdqrjegkbLGQk/+tGPys+IurzhDW9I+wAAhqI1WRcAwFDETKglMYPK+gBoYzkjDIxHdQAAD9aarAsAYCg8qgOAcXn4wx9ePs3rs99++6WdAABdYDkjAMDsxTyoNdtuu23aCQBdsdZixs2bN6dfs5a1En+v7GsYj4c97GHlZ0N9nvOc56SdAABD0ZqsCwBgKGIm1JqYRWWdANSznBEGxKM6AIBca7IuAIChmOZRXcyhsk4AoLue9axnlU/y+my33XZpJwBAF1jOCAAwezEPak3MobJOAOiCWS9mDGt1Ws7IM5/5zPKzoT7bb7992gkAMBStyboAAIYiZkKtiVlU1glAPcsZYUA8qgMAyLUm6wIAGAqP6gBgXF772teWT/K63HDDDWkfAEBXWM4IADAfMRdqScyhsj4A2GixfHG1TLNE0XJG1nLUUUeVnw11+fGPf5z2AQAMSWuyLgCAIYnZUEtiFpX1AVDPckYYEI/qAAByrcm6AACGxKM6ABiPD37wg+WTvC5f/OIX0z4AgK6wnBEAYD5iLtSSmENlfQCwkdZazBjJvmZSljOyljPOOKP8bKjLl770pbQPAGBIWpN1AQAMScyGWhKzqKwPgHqWM8KAeFQHAJBrTdYFADAkHtUBwHhceuml5ZO8LqecckraBwDQFZYzAgDMR8yFWhJzqKwPADbKeosZ469nXzeptZYzTttN/33jG98oPxvqcuqpp6Z9AABD0pqsCwBgSGI21JKYRWV9ANSznBEGxKM6AIBca7IuAIAh8agOAMbjl7/8Zfkkr8vLX/7ytA8AoCssZwQAmI+YC7Uk5lBZHwBslLUyi+WJa8VyRn7xi1+Unw11Oeyww9I+AIAhaU3WBQAwJDEbaknMorI+AOpZzggD4lEdAECuNVkXAMCQeFQHAOPwuMc9rnyK1+dJT3pS2gkA0BWWMwIAzEfMhVoT86isEwAWLZYjrpXsa2ps2rSpNOWJv559HeOw8847l58J9Xnyk5+cdgIADElrsi4AgCGJ2VBrYiaVdQJQx3JGGAiP6gAAVtearAsAYEg8qgOAcdhvv/3KJ3hd7rjjjrQPAKBLLGcEAJifmA+1JOZRWR8ALNpaicWN2dfUmPfyR/qt9Y7uD3/4Q9oHADA0rcm6AACGJmZELXFPBzAbljPCQHhUBwCwutZkXQAAQ+NRHQAM3+GHH14+wevyne98J+0DAOgSyxkBAOYn5kMtiXlU1gcAizTvxYnr9c9i+SP99qpXvar8bKjL1VdfnfYBAAxNa7IuAIChiRlRS2ImlfUBUMdyRhgIj+oAAFbXmqwLAGBoPKoDgOE74YQTyid4Xc4///y0DwCgSyxnBACYn5gPtSTmUVkfACzSWpl2ceKmTZtKU54tW7akX8e4vPOd7yw/I+ryuc99Lu0DABia1mRdAABDEzOilsRMKusDoI7ljDAQHtUBAKyuNVkXAMDQeFQHAMP38Y9/vHyC1+U973lP2gcA0CWWMwIAzE/Mh1oS86isDwAWJZYvrpVpljOu1x3Jvo7xOeuss8rPiLq8973vTfsAAIamNVkXAMDQxIyoJTGTyvoAqGM5IwyER3UAAKtrTdYFADA0HtUBwPBdcskl5RO8Lsccc0zaBwDQJZYzAgDMT8yHWhLzqKwPABZly5Yt5VMpT/Y169m0adO6vZH4cdnXMz4XX3xx+VlRl2OPPTbtAwAYmtZkXQAAQxMzopbETCrrA6CO5YwwEB7VAQCsrjVZFwDA0HhUBwDDd8MNN5RP8LocdNBBaR8AQJdYzggAMD8xH2pJzKOyPgBYlLWyefPm9GsyywsZJ1nKGD/GYka29p//+Z/lZ0dd3NEBAGPRmqwLAGBoDj744HL6qUvMpLI+AOpYzggD4VEdAMDqWpN1AQAMjUd1ADB8d955Z/kEr8uTnvSktA8AoEssZwQAmJ+YD7Uk5lFZHwAsQixfXCsrlzPGQsVl8dcmXca4dWoWPjIef/7zn8vPkLo8+clPTvsAAIamNVkXAMDQxIyoJTGTyvoAqGM5IwyER3UAAKtrTdYFADA0HtUBwLA9/vGPL5/e9XnYwx6WdgIAdInljAAA8xPzodbEXCrrBIB5W2854ywTSxxjqWP2z8G47brrruVnSX223XbbtBMAYGhak3UBAAxNzIhaE7OprBOAyVnOCAPgUR0AwNpak3UBAAyNR3UAMGz7779/+eSuy89//vO0DwCgayxnBACYr5gTtSTmUlkfAMzbvGMhI5N47nOfW37G1OWWW25J+wAAhqg1WRcAwBDFrKglMZvK+gCYnOWMMAAe1QEArK01WRcAwBB5VAcAw/XqV7+6fHLX5corr0z7AAC6xnJGAID5ijlRS2IulfUBwDzF0sRZJ5YxWshIrSOOOKL8DKrLt7/97bQPAGCIWpN1AQAMUcyKWhKzqawPgMlZzggD4FEdAMDaWpN1AQAMkUd1ADBcJ598cvnkrsu5556b9gEAdI3ljAAA8xVzopbEXCrrA4B52rx5c/kkyrO8aDETXxtiCeOy7O8Bk3BHBwCwvtZkXQAAQ3TeeeeVE1Bd3NMBTM9yRhgAF3YAAGtrTdYFADBEHtUBwHB98pOfLJ/cdYnHh1kfAEDXWM4IADBf6y25Wi0xl8r6AGCe1vvcsnCRRTn77LPLz7q6/Pu//3vaBwAwRK3JugAAhihmRS2J2VTWB8DkLGeEAfCoDgBgba3JugAAhsijOgAYrm9+85vlk7sur3vd69I+AICusZwRAGC+Yk7UkphLZX0AME/r/fmH7GtgHi677LLys64ur3/969M+AIAhak3WBQAwRDEraknMprI+ACZnOSMMgEd1AABra03WBQAwRB7VAcBw3XjjjeWTuy6bNm1K+wAAusZyRgCA+Yo5UUtiLpX1AcA8bdmypXwS5cm+BubhJz/5SflZV5fnP//5aR8AwBC1JusCABiimBW1JGZTWR8Ak7OcEQbAozoAgLW1JusCABgij+oAYLjuvvvu8sldlz333DPtAwDoGssZAQDmK+ZELYm5VNYHAPNkOSNd8Ze//KX8rKuLOzoAYExak3UBAAzRXnvtVU5AdYnZVNYHwOQsZ4QB8KgOAGBtrcm6AACGyKM6ABimJzzhCeVTuy733HNP2gcA0EWWMwIAzF/Mi1oS86msDwDmZa3ljPHXsq+BWdt9993Lz7q6uKMDAMamNVkXAMAQbbPNNkv33ntvOQXVJWZUWScAk7GcEXrOozoAgPW1JusCABgqj+oAYHgOPPDA8oldl5tuuintAwDoIssZAQDmL+ZFLYn5VNYHAPNiOSNdcMABB5SfdXVxRwcAjE1rsi4AgKFqvaeLGVXWB8BkLGeEnvOoDgBgfa3JugAAhsqjOgAYnte+9rXlE7sul19+edoHANBFljMCAMxfzItaEvOprA8A5sVyRrrgqKOOKj/r6nLFFVekfQAAQ9WarAsAYKhiZtSSmFFlfQBMxnJG6DmP6gAA1tearAsAYKg8qgOA4Tn11FPLJ3ZdPvWpT6V9AABdZDkjAMD8xbyoJTGfyvoAYF7WWs4Yyb5mFjZv3pz+3xmnU045pfyMq8unP/3ptA8AYKhak3UBAAxVzIxaEjOqrA+AyVjOCD3nUR0AwPpak3UBAAyVR3UAMDyf+cxnyid2Xd797nenfQAAXWQ5IwDA/MW8qCUxn8r6AGBeNmI54/Lf04JGlrU+mHdHBwCMTWuyLgCAofIfAgHYGJYzQs95VAcAsL7WZF0AAEPlUR0ADM+3vvWt8oldl9e85jVpHwBAF1nOCAAwfzEvaknMp7I+AJiXTZs2lU+hPPHXs69rkf29ZtlPf7mjAwCYTGuyLgCAoTrqqKPKKagu7ukApmM5I/ScCzsAgPW1JusCABgqj+oAYHh+9rOflU/sujzvec9L+wAAushyRgCA+Yt5UUtiPpX1AcC8rLecccuWLenX1Vrt7zOrfvrt5ptvLj8j6uKODgAYm9ZkXQAAQ9V6TxczqqwPgMlYzgg951EdAMD6WpN1AQAMlUd1ADAsD33oQ8undX122223tBMAoIssZwQAmL+YF7Um5lRZJwDMSyxIXC2zWJ64efPm0vbAWMxI2GabbZbuvffe8rOiLrvvvnvaCQAwVK3JugAAhipmRi2JGVXMqrJOANZnOSP0mEd1AACTaU3WBQAwVB7VAcCwPPGJTyyf1HW566670j4AgK6ynBEAYDFibtSSmFNlfQAwL6stT1xO/PXs69azadOmVRc/WszIsr322qv8rKiLOzoAYIxak3UBAAzZX/7yl3ISqkvMqrI+ANZnOSP0mEd1AACTaU3WBQAwZB7VAcBwvOAFLyif1HX58Y9/nPYBAHSV5YwAAIsRc6OWxJwq6wOAeVovNQsa11rKGLGYka09//nPLz8z6uKODgAYo9ZkXQAAQ/aTn/yknITqErOqrA+A9VnOCD3mUR0AwGRak3UBAAyZR3UAMBxHH310+aSuy6WXXpr2AQB0leWMAACLEXOjlsScKusDgHmK5YuTJH7csljCuCz+32stZFxO/Ljs7894vf71ry8/O+rijg4AGKPWZF0AAEN22WWXlZNQXWJWlfUBsD7LGaHHPKoDAJhMa7IuAIAh86gOAIbj9NNPL5/UdfnEJz6R9gEAdJXljAAAixFzo5bEnCrrA4B5m2S54jSxmJHMaaedVn6G1MUdHQAwRq3JugAAhqz1ni5mVVkfAOuznBF6zKM6AIDJtCbrAgAYMo/qAGA4PvvZz5ZP6rqceOKJaR8AQFdZzggAsBgxN2pJzKmyPgBYhHksaIzOTZs2pX8/OO+888rPlLq4owMAxqg1WRcAwJCddNJJ5SRUl5hVZX0ArM9yRugxj+oAACbTmqwLAGDIPKoDgOH4X//rf5VP6rr88z//c9oHANBVljMCACxGzI1aEnOqrA8AFmXz5s3lU2m6WMrIJL797W+XnzF1cUcHAIxRa7IuAIAhO+KII8pJqC4xq8r6AFif5YzQYx7VAQBMpjVZFwDAkHlUBwDD8V//9V/lk7ou/+N//I+0DwCgqyxnBABYjJgbtSTmVFkfACxSLGhsXdJoKSM1brnllvIzpy7u6ACAMWpN1gUAMGTPfe5zy0moLjGryvoAWJ/ljNBjHtUBAEymNVkXAMCQeVQHAMNx9913l0/quuyyyy5pHwBAV1nOCACwGDE3aknMqbI+ANgoy4saY+niysT/LcRft5CRFn/5y1/Kz6a6uKMDAMaoNVkXAMCQ7brrruUkVJeYVWV9AKzPckboMY/qAAAm05qsCwBgyDyqA4Bh2GmnncqndF18pgMAfWQ5IwDA4rT+2eWYV2V9AABD4o4OAKBOa7IuAIChc08HsFiWM0JPubADAJhca7IuAIChc1kHAP331Kc+tXxC1+UXv/hF2gcA0GWWMwIALE7Mj1oS86qsDwBgSNzRAQDUaU3WBQAwdL/85S/Laagu7ukA2ljOCD3lwg4AYHKtyboAAIbOozoA6L9NmzaVT+i6XHfddWkfAECXWc4IALA4MT9qScyrsj4AgCE58MADy+mnLu7oAICxak3WBQAwdN///vfLaaguMbPK+gBYm+WM0FMe1QEATK41WRcAwNB5VAcA/Xf44YeXT+i6XHLJJWkfAECXWc4IALA4MT9qScyrsj4AgCF51ateVU4/dfnGN76R9gEADF1rsi4AgKGLGVJLYmaV9QGwNssZoac8qgMAmFxrsi4AgKHzqA4A+u9f//Vfyyd0Xc4555y0DwCgyyxnBABYnJgftSTmVVkfAMCQHHfcceX0U5dzzz037QMAGLrWZF0AAEMXM6SWxMwq6wNgbZYzQk95VAcAMLnWZF0AAEPnUR0A9N/pp59ePqHr8v73vz/tAwDoMssZAQAWJ+ZHLYl5VdYHADAkrXd0H/jAB9I+AICha03WBQAwdDFDaol7OoA2ljNCT3lUBwAwudZkXQAAQ+dRHQD039lnn10+oevy9re/Pe0DAOgyyxkBABYn5kctiXlV1gcAMCTu6AAA6rQm6wIAGLp3vOMd5TRUF/d0AG0sZ4SecmEHADC51mRdAABD51EdAPTfhRdeWD6h6/Ka17wm7QMA6DLLGQEAFifmRy2JeVXWBwAwJK13dEcddVTaBwAwdK3JugAAhi5mSC1xTwfQxnJG6CmP6gAAJtearAsAYOg8qgOA/rvmmmvKJ3RdDj744LQPAKDLLGcEAFicmB+1JOZVWR8AwJC03tG96EUvSvsAAIauNVkXAMDQxQypJVdffXXaB8DaLGeEnvKoDgBgcq3JugAAhs6jOgDov5tvvrl8Qtdl3333TfsAALrMckYAgMWJ+VFLYl6V9QEADMlNN91UTj91ecYznpH2AQAMXWuyLgCAoYsZUktiZpX1AbA2yxmhpzyqAwCYXGuyLgCAofOoDgD6749//GP5hK7LLrvskvYBAHSZ5YwAAIsT86OWxLwq6wMAGJLWO7pdd9017QMAGLrWZF0AAEMXM6SWuKcDaGM5I/SUR3UAAJNrTdYFADB0HtUBQL898pGPLJ/O9XnoQx+adgIAdJnljAAAixPzo9bE3CrrBAAYAnd0AAD1WpN1AQAMnXs6gMWynBF6yIUdAECd1mRdAABD57IOAPrtSU96UvlkrstvfvObtA8AoOssZwQAWKyYI7Uk5lZZHwDAELTe0d12221pHwDAGLQm6wIAGIOYJbXEPR1APcsZoYc8qgMAqNOarAsAYAw8qgOA/tp///3LJ3NdfvSjH6V9AABdZzkjAMBixRypJTG3yvoAAIag9Y7u+uuvT/sAAMagNVkXAMAYxCypJe7pAOpZzgg95FEdAECd1mRdAABj4FEdAPTXIYccUj6Z63L55ZenfQAAXWc5IwDAYsUcqSUxt8r6AACGoPWO7oorrkj7AADGoDVZFwDAGMQsqSXu6QDqWc4IPeRRHQBAndZkXQAAY+BRHQD012te85ryyVyXL37xi2kfAEDXWc4IALBYMUdqScytsj4AYOPtvvPeSy99zpvSv8ZkjjzyyHLqqcuXvvSltA8AYAxak3UBAIxBzJJaErOrrA+A1VnOCD3kUR0AQJ3WZF0AAGPgUR0A9Ndxxx1XPpnrcvbZZ6d9AABdZzkjAMBixRypJTG3yvoAgI0VixnPP/nWpVe/cHP615nMW97ylnLqqcsnP/nJtA8AYAxak3UBAIxBzJJaErOrrI/cP//dPy7tsv2j078GjIfljNBDHtUBANRpTdYFADAGHtUBQH+9+93vLp/MdfnABz6Q9gEAdJ3ljAAAixVzpJbE3CrrAwA2ztP32rT0vmO2LF38H0uWM06p9Y7ujDPOSPsAAMagNVkXAMAYxCypJe9617vSPnKb93vV0g1HfsSCRhg5yxmhhzyqAwCo05qsCwBgDDyqA4D++uAHP1g+mety8sknp30AAF1nOSMAwGLFHKklMbfK+gCAjbH1YkbLGafnjg4AoF5rsi4AgDFwT7cYb33GIff9Oa9vvfzflw7c7WnpjwGGz3JG6CEXdgAAdVqTdQEAjIHLOgDor0996lPlk7ku//Iv/5L2AQB0neWMAACLFXOklsTcKusDABZv5WJGyxmn544OAKBea7IuAIAxcE+3GMvLGcN9Cxofb0EjjJHljNBDLuwAAOq0JusCABgDl3UA0F9f/vKXyydzXV796lenfQAAXWc5IwDAYsUcqSUxt8r6AIDFyhYzWs44PXd0AAD1WpN1AQCMgXu6xdh6OWOwoBHGyXJG6CEXdgAAdVqTdQEAjIHLOgDor8svv7x8MtflpS99adoHANB1ljMCACxWzJFaEnOrrA8AWJzVFjNazjg9d3QAAPVak3UBAIyBe7rFWLmcMVjQCONjOSP0kAs7AIA6rcm6AADGwGUdAPTXddddVz6Z67L//vunfQAAXWc5IwDAYj33uc8tE6W6XHvttWkfALAYay1mDJYzTscdHQBAvdZkXQAAYxCzpJa4p6uTLWcM33r5ZgsaYUQsZ4QecmEHAFCnNVkXAMAYeFQHAP110003lU/mujz1qU9N+wAAus5yRgCAxYo5UktibpX1AQDzt95ixmA543Tc0QEA1GtN1gUAMAat93Q//elP0z5yqy1nDBY0wnhYzgg95MIOAKBOa7IuAIAx8KgOAPrr9ttvL5/Mddl1113TPgCArrOcEQBgsWKO1JKYW2V9AMB8TbKYMVjOOB13dAAA9VqTdQEAjIF7usVYazljsKARxsFyRughF3YAAHVak3UBAIyByzoA6K977rmnfDLX5RGPeETaBwDQdZYzAgAsVsyRWhJzq6wPAJifSRczBssZp+OODgCgXmuyLgCAMXBPtxjrLWcMV7x889IBFjTCoFnOCD3kwg4AoE5rsi4AgDFwWQcA/bTjjjuWT+W63HXXXWkfAEAfWM4IALB4MU9qScyvsj4AYPZqFjMGyxnbuaMDAGjTmqwLAGAsWu/pHvWoR6V9PNgkyxnDFYda0AhDZjkj9IwLOwCAeq3JugAAxsKjOgDonz322KN8Itfl17/+ddoHANAHljMCACzerbfeWiZLdYn5VdYHAMxW7WLGYDljO3d0AABtWpN1AQCMRcyUWvKEJzwh7ePBJl3OGP62oPGpaQ/Qb5YzQs+4sAMAqNearAsAYCw8qgOA/tl3333LJ3JdbrjhhrQPAKAPLGcEAFi8mCe1JOZXWR8AMDstixmD5Yzt3NEBALRpTdYFADAWrfd0T3/609M+HqxmOWOwoBGGyXJG6BkXdgAA9VqTdQEAjIVHdQDQP8973vPKJ3JdrrnmmrQPAKAPLGcEAFi8mCe1JOZXWR8AMButixmD5Yzt3NEBALRpTdYFADAW7unmr3Y5Y7CgEYbHckboGRd2AAD1WpN1AQCMhcs6AOifgw8+uHwi1+Xyyy9P+wAA+sByRgCAxYt5UktifpX1AQDTm2YxY7CcsZ07OgCANq3JugAAxsI93fy1LGcMFjTCsFjOCD3jwg4AoF5rsi4AgLFwWQcA/XPooYeWT+S6XHTRRWkfAEAfWM4IALB4X/va18pkqS4xv8r6AIDpTLuYMVjO2K71ji7OVFkfAMBYtCbrAgAYC/d089e6nDFcfuipS8+zoBEGwXJG6BmP6gAA6rUm6wIAGAuXdQDQP0cccUT5RK7LBRdckPYBAPSB5YwAAIsX86SWxPwq6wMA2s1iMWOwnLGdOzoAgDatyboAAMbCPd38TbOcMVjQCMNgOSP0jAs7AIB6rcm6AADGwmUdAPTPG97whvKJXJfPfOYzaR8AQB9YzggAsHjnnHNOmSzVJeZXWR8A0GZWixmD5YztWu/o4kyV9QEAjEVrsi4AgLFwTzd/0y5nDBY0Qv9Zzgg941EdAEC91mRdAABj4bIOAPrnuOOOK5/IdTnrrLPSPgCAPrCcEQBg8WKe1JKYX2V9AEC9WS5mDJYztnNHBwDQpjVZFwDAWLinm79ZLGcM9y1o3NWCRugryxmhZ1zYAQDUa03WBQAwFi7rAKB/TjjhhPKJXJczzjgj7QMA6APLGQEAFu+DH/xgmSzVJeZXWR8AUGfWixmD5YztWu/o4kyV9QEAjEVrsi4AgLFwTzd/s1rOGCxohP6ynBF6xqM6AIB6rcm6AADGwmUdAPTP5s2byydyXU477bS0DwCgDyxnBABYvJgntSTmV1kfADC53Xfee+n8k29NFyxO499eec59Sx+p96F/P2fp9p8vVfvw6eelfQAAY5GdkSZJdk4GABgL93TzN8vljOFvCxr3Tv9eQHdZzgg941EdAEC91mRdAABj4bIOAPrnAx/4QPlErsuJJ56Y9gEA9IHljAAAi3fSSSeVyVJdYn6V9QEAk4kFNu87Zku6XBEAAIZg6cbb1hX3bQAAY/XJd743PSOt5wunfzTt48HOeeFb0j/3NY0bjvzI0i7bPzqd+wLdZDkj9IxHdQAA9VqTdQEAjIVHdQDQP2eeeWb5RK7L8ccfn/YBAPRB/IHY7A+0dlX882b/HgAAfRLzpJbE/CrrAwDWZzEjAABjkN2vAQDAEHzz0FOX/nHXvdP5L9A9ljNCz3hUBwBQrzVZFwDAWHhUBwD98+lPf7p8ItflmGOOSfsAAPrAckYAgMU79thjy2SpLjG/yvrovy1btixt3rw5/WsATM9iRgAAxiK7XwMAgKGYx4JG93QwH5YzQs94VAcAUK81WRcAwFh4VMdKLusAuu/zn/98+USuy5FHHpn2AQD0geWMAACLF/OklsT8Kuuj/+IeaTnukwBmy2JGAADGJLtfAwCAIZn1gkb3dDAfljNCz3hUBwBQrzVZFwDAWHhUx0ou6wC678ILLyy/U9fl5S9/edoHANAHljMCACzeYYcdViZLdYn5VdZH/219j7Qc90kA07OYEQCAscnu1wAAYGi+eegpM1vQ6J4O5sNyRugZj+oAAOq1JusCABgLj+pYyWUdQPdddtll5Xfourz4xS9O+wAA+sByRgCAxYt5UktifpX10X/ZPdJy3CcBtLGYEQCAMcru1wAAYIi++bLZLGh0TwfzYTkj9IxHdQAA9VqTdQEAjIVHdazksg6g+6666qryO3NdDjjggLQPAKAPLGcEAFi8mCe1JOZXWR/9t9Y90nLcJwFMzmJGAADGKrtfAwCAoZrFgkb3dDAfljNCz3hUBwBQrzVZFwDAWHhUx0ou6wC677rrriu/I9fl2c9+dtoHANAHljMCACxezJNaEvOrrI/+m+QeaTnukwDWZjEjAABjlt2vAQDAkF32slOW9p9iQaN7OpgPyxmhZzyqAwCo15qsCwBgLDyqYyWXdQDd98Mf/rD8TlyXfffdN+0DAOgDyxkBABYv5kktiflV1kf/1dwjLcd9EsCDWcwIAMDYZfdrAAAwdNMsaHRPB/NhOSP0jEd1AAD1WpN1AQCMhUd1rOSyDqD7rr/++vI7cF322ceCIACgvyxnBABYvJgntSTmV1kf/ddyj7Qc90mT2bRp033f563F927W37/4+0TnvP8+wINZzAgAAEvp/RoAAIzB3xY0PiWdH68l7nJa4/5nMu7pxslyRugZj+oAAOq1JusCABgLj+pYKS50WuMSaDJbX9YtZ/kiLfvxrbJLweW/T/y17GuAfrjhhhvK7x512Xvvtv/KJgBAF1jOCACweDFPaknMr7I++i/uGaaN+6Tc8r3OWpnF925Rfx8gZzEjAAD8TXa/BgAAY9GyoNE93fzE92W9LOqeznuvxbOcEXrGozoAgHqtyboAAMbCozpWclk3P5Nc1kWmvUjzqA6G78Ybbyy/muvyd3/3d2kfAEAfWM4IALB4MU9qScyvsj76bxb3SMtxV3G/mu9r/NisYxKT3lVlXwtMz2JGAAC4X3a/BgAAY1K7oNE93Xy4p8NyRugZj+oAAOq1JusCABgLj+pYyWXdfNR+X1sXNE56Wee/pAb9dtNNN5VfzXV54hOfmPYBAPSB5YwAAIsX86SWxPwq66P/ZnmPtJyx3ye1fE9bvmeT3iFFsq8HpmMxIwAAPFB2vwYAAGNz34LGXSZb0OiebvZavqct77EmvaeLf57s65kvyxmhZzyqAwCo15qsCwBgLDyqYyWXdbPX8j1tuVDzqA7G4+c//3n51VyXPfbYI+0DAOgDyxkBABYv5kktiflV1kf/zeMeaTljvE+a5vuZ9a2m5g5p7Pd6MA8WMwIAwINl92sAADBGl77s3UvPnWBBo3u62Wr9fta+93JP132WM0LPeFQHAFCvNVkXAMBYeFTHSi7rZqvmEm1lav5rajV/n9qLQKB7fvGLX5Rf0XXZbbfd0j4AgD6wnBEAYPFintSSmF9lffTfPO+RljOW+6Tsbie+v/F/n+TeZ9J7pPhxNRnL9x8WxWJGAADIZfdrAAAwVpMsaHRPNzvZ93Ie93STdG0d93Qbw3JG6BmP6gAA6rUm6wIAGAuP6ljJZd3sZJdo8f2NS7j4a+t9r+OvZ70reVQH43PrrbeWX9F12WWXXdI+AIA+sJwRAGDxYp7UkphfZX303yLukZYz5PuM7A5p5SOu9e5/Jr1Hqs2kj8mA9VnMCAAAq8vu1wAAYMzWW9Donm42Vt7Txfd15f1Ydpe3dSa5p6t96xXJepg/yxmhZzyqAwCo15qsCwBgLDyqYyWXdbMxyaO6sNb3e5LLulCbIX/fYSxuu+228iu6LjvvvHPaBwDQB31bzrh0Y9uZTUREREREHpih3WtkD7FWW4i43r1d9jVbW+3r43saf8/sPivrAepZzAgAAGtL79cAAGDszvpfS0s33/63S5sOZGj3dDV3Y9O+91ot7um6x3JG6BmP6gAA6omIiIiISD8ztMu6sDKr/TtmD/C2zmqP8Zat9ahutR+z9dcD/XT77W1/4GKnnXZK+wAA+sByRhERERGRcWco90kr723W+vea5h4perOs/Jqt/3kmeUgGrM9iRgAAWF96vwYAAHRuQWNkKPd0K9Ole7qhfI/7yHJG6BmP6gAA6omIiIiISL8zlIuklZdo6/17rZVZXNbF/zsu7JZt/deAfvr9739ffsXXZccdd0z7AAD6wHJGERERERGJDOE+afmOZ5J/l7Wy2tdPeocEzJ7FjAAAMJn0fg0AAPibDi5ojPT9nm75fdWk/y5rZbV7N/d0/WM5I/SMR3UAAPVERERERGQY6ftlXVi+TJvk32Xr/9LZyqy2TNFlHYzbH//4x/Krvi477LBD2gcA0AeWM4qIiIiIyNbp+33SpP/8tfdIcVeUxR0SzJ/FjAAAMLn0fg0AALhfLGj8WfcWNEbc0+Xfg/i/ZXFP122WM0LPeFQHAFBPRERERESGlb5f1k16ebbaI7nIassZs/T9+wVM7s477yy/8uuy3XbbpX0AAH1gOaOIiIiIiGQZ+v1I7T1S9kjMHRLMn8WMAABQJ71fAwAAHqjDCxojQ7+Din+/1ZLd02WxmLH7LGeEnvGoDgCgnoiIiIiIDDNjflQXWfnjs8s9j+pgXO6+++7yq78u2267bdoHANAHljOKiIiIiMhaGepdSc09kjsk2Dhn/usP0oUzAABALr1fAwAAHuysqzq9oDHins49XZ9Zzgg941EdAEA9EREREREZdoZ8KbVWtv6vpLmsA8K9995bfgeoyzbbbJP2AQD0geWMIiIiIiIySYZ4b7Jly5byb/fgLN8juUOCjfX0vTYtve+YLenSGQAA4MHS+zUAACD3kSv/dvnT8QzxbmqtLP8Y93T9Zjkj9IxHdQAA9UREREREZBwZ4gWVR3VADfdIAMAYWc4oIiIiIiLrJe5btv6PXg3FJPdIKxNfs7IHmC8LGgEAYHLp/RoAAPBgH/zW0tJ/3vq3C6AOZ6z3dGFlvPXqF8sZoWc8qgMAqCciIiIiIsPPUC/rssWLy1m+lFsZj+pgvO6+++7yO0Fdtt1227QPAGAMRERERERkuBnq/dGy7FHXcuLfPbtnynqA+bOgEQAAJpMunQEAAB6oB4sZh35Pt957r/j33zreevWP5YzQMx7VAQDUExERERGR4cajOo/qgPvdeeed5XeCumy33XZpHwBAH8Ryg+zhVlfd/vNyCBMRERERkbll6PdHy9a6R8oyhu8JdJkFjQAAsL508QwAAHC/WMz4f7q7mNE9XZ6sg26znBF6xqM6AIB6IiIiIiIyvIzlsi7UJJY1Zh3AOPzxj38svxvUZYcddkj7AAD6wHJGERERERFZzpjuj5ZNGndI0A0WNAIAwNrS5TMAAMDfdHgx49ju6WqWM7qn6yfLGaFnPKoDAKgnIiIiIiLDyRgf1cW/8yRxWQf8/ve/L78j1GXHHXdM+wAA+sByRhERERERGeP90bJJ7pHcIUG3WNAIAACrSxfQAAAAnV3M6J5u7bin6y/LGaFnPKoDAKgnIiIiIiL9j8u6teOyDgi33357+V2hLjvttFPaBwDQB5YzioiIiIiMN2O+P1oWd0RrJb5H2dcBG8uCRgAAyKVLaAAAYOw6uJjRPd3677289eo3yxmhZzyqAwCoJyIiIiIi/Y3Luofc9++/VjyqA5bddttt5XeGuuy8885pHwBAH1jOKCIiIiIyvrg/ut9690jZ1wDdYEEjAAA8WLqIBgAAxqxjixnd091vvf+IWvY19IfljNAzHtUBANQTEREREZH+xWXd/dZ7VOf7BCy79da2P3Sxyy67pH0AAH3Qt+WM8c+b/XsAAPRJzJNaEvOrrI/+i3udRcT90YO5R4J+s6ARAAAeKF1GAwAAI/W/X/W+pZfs8cx0vrzMPd3GWe+eLvsa+sNyRugZj+oAAOrF/8e2RdYFADAWHtWxksu6jbVWfL+AZb/4xS/K7wx12W233dI+AIA+sJwRAGDxYp7UkphfZX3037zvkdwfrW2t+L5B91nQCAAA98sW0gAAwBhNspgxuKfbWGvF963fLGeEnvGoDgAAAIBF8KiOlVzWbay1vv+bN29OvwYYn5///Ofld4a67LHHHmkfAEAfWM4IALB4MU9qScyvsj76b173SO6PJuMeCfrPgkYAAPibbCkNAACMzXdf9b6ll+7xrHSevJJ7uo211vff96/fLGeEnvGoDgAAAIBF8KiOlVzWbay1vv/x17KvAcbnpptuKr8z1OWJT3xi2gcA0AeWMwIALF7Mk1oS86usj/6b9T2S+6PJxfLFteIeCfrDgkYAAPj//39kk8U0AAAwJjWLGYN7uo3jnm7YLGeEnvGoDgAAAIBF8KiOlVzWbZz1Lusi2dcB43PjjTeW3xXq8nd/93dpHwBAH1jOCACweDFPaknMr7I++m9W90juj+rE92qSZF8LdJMFjQAAjF22nAYAAMbivsWMe06+mDG4p9sYk7z1iu9p9rX0g+WM0DMe1QEAAACwCB7VsZLLuo0x6aM631Mg3HDDDeV3hbrsvffeaR8AQB9YzggAsHgxT2pJzK+yPvpv2nsk90dtJo3vLfSLBY0AAIxZtqAGAADGoGUxY3BPt3iTvvWKZF9PP1jOCD3jUR0AAAAAi+BRHSu5rNsYk8b3FgjXX399+V2hLvvss0/aBwDQB5YzAgAsXsyTWhLzq6yP/mu9R3J/1K7me+57DP1jQSMAAGOVLakBAICha13MGNzTLV5NfI/7y3JG6BmP6gAAAABYBI/qWMll3eJt3ry5fBfXT3yfsw5gXH74wx+W3xXqsu+++6Z9AAB9YDkjAMDixTypJTG/yvrov9p7JPdH08nukNa6V3KPBP1kQSMAAGOULaoBAIAh++6r3t+8mDG4p1uslXdy8f1c63+D+PFZD91nOSP0jEd1AAAAACyCR3Ws5LJusbIHdPH9XC3x/c56gHG57rrryu8KdXn2s5+d9gEA9IHljAAAixfzpJbE/Crro/8mvUdyfzS91RYzukeCYbKgEQCAscmW1QAAwFB971XvXzpkisWMwT3d4qz21ss93TBZzgg941EdAAAAAIvgUR0ruaxbnNUe1cVfWysre4Dxueqqq8rvCHU54IAD0j4AgD6wnBEAYPFintSSmF9lffTfevdI7o9mY607pLBWtu4B+sWCRgAAxiRbWAMAAEM0i8WMwT3dYqx1T7fWcsbIyi76wXJG6BmP6gAAAABYBI/qWMll3WJkF3JbP6pb638H33/gsssuK78j1OXFL35x2gcA0AeWMwIALF7Mk1oS86usj/5b7f7C/dHsZHdI8f3d+se4R4LhsqARAICxyJbWAADA0MxqMWNwTzd/2T3d1m+9wlrxv0M/Wc4IPeNRHQAAAACL4FEdK7msW4yVie/v1n99tf8dIisv9oDxufDCC8vvCHU57LDD0j4AgD6wnBEAYPFe/vKXl8lSXWJ+lfXRfyvvL9wfzV52R7Tye7zWPVL8ta1/LNA/FjQCADAG2eIaAAAYku8dHosZ/yGdA7dwTzd/WVb+mLXu6fzv0U+WM0LPeFQHAAAAwCJ4VMdKLuvmL7uIW/k9jgWMqyW+fusfC4zP5z//+fI7Ql2OPPLItA8AoA8sZwQAWLyYJ7Uk5ldZH/23fMfh/mg+sjuk7D/aFd/71eIeCYbBgkYAAIYuW14DAABDMevFjME93XxNek+X/bjluKfrJ8sZoWc8qgMAAABgETyqYyWXdfOVLV2sfVQXWfnjgXH59Kc/XX43qMuxxx6b9gEA9IHljAAAi3fMMceUyVJdYn6V9dF/cafh/mg+Jr1DCuvdI03yv1H8GP9bQrftvvPeS+effGs6d5jGv73ynPvmFtT77P93ydLtP1+qdvK/fjDtAwAYs2yBDQAADME8FjMG93TzM+lixrDePV32NSv537FbLGeEnvGoDgAAAIBF8KiOlVzWzU/No7qwVib53yi61+oH+uvMM88svxvU5fjjj0/7AAD6IB5pZQsFuir+ebN/DwCAPol5UktifpX1AbnsDikegWU/dtlaWe8eaeu/n7sk6LaYL7zvmC3p7KHVq1/o130rd3QAALOzabd9AABYw5nHn7q0dONt1b7y/rPTPh7snBe+JV2uOI1bj/7U0t6P2S09A9NN2T3dWvdn6y1ndE/XP5YzQs+4sAMAAABgETyqg9mIy7Nl2V/PLusi2Y9dlv2X15az3oM8l3UwbB/4wAfKr/C6nHTSSWkfAEAfWM4IALB4J554Ypks1SXmV1kfjNXWWXmXtNod0noPt6a5R1oZd0nQbbNe0Gg5Yzt3dAAAAAAsSsyUWuKebnJvfcYh6YLFVtce/v6lf9rzH9K/Fxsj7tu2zsr7t9Xu27b+MZnWe7qV/zyR7MexWJYzQs+4sAMAAABgETyqg9nYOot4VBfJvmbZynhUB8Oy2u8r6+W0005L+wAA+sByRgCAxYt5UkvMpOF+K+e5cf8Td0RhtbugSX4NrTcnXu0eauXf069X6IdZLmi0nLHder/3rhZ3dAAAAADUck83f7NczmgxYzetnOnO6p5uta9dTvY1wT1dN1nOCD3jwg4AAACARXBZB9OLS7mViV8j017WZb1bJ/569nUr58t+vcLwnHDCCeVXeF0++MEPpn0AAH1gOSMAwOKdccYZZbJUl5hfZX0wRivvbdbLpPc6690jxR3Vyq9ZeW+V/Rigu2a1oNFyxnbu6AAAAABYlJgptcQ93eRmtZzx2sM/YDFjR3Xlni57X+atV3dYzgg948IOAAAAgEXwqA6mN6/LurBeVi5o9KgOxuG4444rv8rrctZZZ6V9AAB9YDkjAMDixTypJTG/yvpgjFbe3ayV2kdYk3RH52o/LusEum0WCxotZ2znjg4AAACARXFPN3+zWM5oMWO31aT2nm6SrHZP561Xt1jOCD3jwg4AAACARXBZB9OLy7JJU3uBNsmjuvgxq/24rBPovze84Q3lV3ldzjnnnLQPAKAPLGcEAFi8z3zmM2WyVJeYX2V9MEaTpuURVs0d1cqs/A+AAf0x7YJGyxnbuaMDAAAAYFFiptQS93STm3Y5o8WM3Tdpahczhknee62WrI+NYzkj9IwLOwAAAAAWwaM6mN6kaXlUFw/jWuNRHQzXEUccUX6l1+WCCy5I+wAA+sByRgCAxYt5UktifpX1wdhMes/Tcoe0rCXukKD/plnQaDljO3d0AAAAACyKe7r5m2Y5432LGfeymLHLJv2PnLXe07W+93JP1z2WM0LPuLADAAAAYBFc1sH0Jsk0j+pa/mtqLutg2A499NDyq70uX/va19I+AIA+sJwRAGDxLrroojJZqkvMr7I+GJtJHn1Nc4cUah5+xd/LHRIMR+uCRssZ27mjAwAAAGBRYqbUEvd0k2tdznjd4R9Y+p97PTvtpDsmuaeLH5N97aQmXQAZcU/XXZYzQs+4sAMAAABgETyqg+l4VAdshIMPPrj8qq/L5ZdfnvYBAPSB5YwAAIsX86SWxPwq64OxWe8eadoHX8smua+a1d8L6JaWBY2WM7ZzRwcAAADAorinm7+W5YwWM/ZHvK9aK+7pWGY5I/SMCzsAAAAAFsFlHUxnvUu0WV6gLfLvBXTb8573vPIrvy7/P/b+w2ueurwf//8UC4oNbAELoMYWW6KxoqJGk1gi9q7EQmxgI4IfFY2xfcSGJbZjBWvQKCSi5ug3REWD3ViwYeH+/S6d/XB7c+1r9/Xamd2Z3cfznMc5n3DPXDs7M/fufK77/bo877zz0noAAFNgOCMAwPpFP6kl0b/K6sGuif9Brfj7zUFD/Q9tzerHYrMw5GsB41E7oNFwxnb+RgcAAADAuvg73fBqhzMazDgt/k7HsgxnhInxBzsAAAAA1sEf62B1B/9gF//3UH9AW+drAeN129vetvtGrssFF1yQ1gMAmALDGQEA1i/6SS2J/lVWDwAYRs2ARsMZ2/kbHQAAAADr0vp3utvc5jZpPS6vZjijwYywvQxnhInxBzsAAAAA1sGiOgCYnqOOOqr7Rq7L9773vbQeAMAUGM4IALB+3//+97vOUl2if5XVAwCGs+yARsMZ27X+jS6eqbJ6AAAAADBP69/pbnzjG6f1uLxlhzMazAjbzXBGmBiL6gAAAABYB4vqAGB6DjvssO4buS6XXHJJWg8AYAoMZwQAWL/oJ7Uk+ldZPQBgWMsMaDScsZ2/0QEAAACwLq1/p7vWta6V1uPylhnO+IXjTzeYEbac4YwwMf5gBwAAAMA6WFQHANP0u9/9rvtWrsvVrna1tB4AwNgZzggAsF7RR2pJ9K2yegDAeiwa0Gg442r8jQ4AAACAofk73XosGs74hYcZzAi7wHBGmCB/sAMAAABgSP5YBwDT9aMf/aj7Zq7LkUcemdYDABg7wxkBANYr+kgtib5VVg8AWJ/SgEbDGVfjb3QAAAAADM3f6dajNJzxiw87fe9vDWaEnWA4I0yQP9gBAAAAMCR/rAOA6brwwgu7b+a63PKWt0zrAQCMneGMAADrFX2klkTfKqsHAKzXvAGNhjOu5utf/3r31FMXf6MDAAAAYFn+Trce84YzGswIu8VwRpggi+oAAAAAGJI/1gHAdJ1//vndN3Nd7nznO6f1AADGznBGAID1ustd7tJ1lOryhS98Ia0HAKxfNqDRcMbVtP6NLp6tsnoAAAAAcFDr3+mid5XVI5cNZzSYEXaP4YwwQRbVAQAAADAki+oAYLo++clPdt/Mdbn//e+f1gMAGDvDGQEA1iv6SC2JvlVWDwDYjIMDGg1nXI2/0QEAAAAwNH+nW4+Dwxn/MJjxjum2wPYynBEmyB/sAAAAABiSP9YBwHS9733v676Z6/KIRzwirQcAMHaGMwIArFf0kVoSfausHgCwOfsHNBrOuBp/owMAAABgaP5Otx77hzMazAi7y3BGmCB/sAMAAABgSP5YBwDT9aY3van7Zq7L3//936f1AADGznBGAID1ij5SS6JvldUDADbrRte7xd47nvc9wxlX5G90AAAAAAzN3+nWYzac0WBG2G2GM8IE+YMdAAAAAEPyxzoAmK5XvOIV3TdzXZ73vOel9QAAxs5wRgCA9Yo+Ukuib5XVAwA2LwY03v9OT0l/xnL8jQ4AAACAofk73XqccseH7n3v8W/au8W1b5j+HNgNhjPCBPmDHQAAAABD8sc6AJiuF7zgBd03c11e/vKXp/UAAMbOcEYAgPWKPlJLom+V1QMA2AbPf/7zu6eeupx++ulpPQAAAAA4KHpJLYneVVaP3FP+7D4GMwKGM8IUWVQHAAAAwJAsqgOA6Xra057WfTPX5YwzzkjrAQCMneGMAADrFX2klkTfKqsHALANnvrUp3ZPPXV54xvfmNYDAAAAgIOil9SS6F1l9QCYz3BGmCCL6gAAAAAYkkV1ADBdj3rUo7pv5rq85z3vSesBAIyd4YwAAOsVfaSWRN8qqwcAsA0e+chHdk89dXnve9+b1gMAAACAg6KX1JLoXWX1AJjPcEaYIIvqAAAAABiSRXUAMF0PeMADum/munzyk59M6wEAjJ3hjAAA6xV9pJZE3yqrBwCwDVr/RvepT30qrQcAAAAAB0UvqSX+TgdQz3BGmCCL6gAAAAAYkkV1ADBdd7nLXbpv5rp8+ctfTusBAIyd4YwAAOsVfaSWRN8qqwcAsA1a/0b3la98Ja0HAAAAAAdFL6kl/k4HUM9wRpggi+oAAAAAGJJFdQAwXTe96U27b+a6/OAHP0jrAQCMneGMAADrFX2klkTfKqsHALANWv9G98Mf/jCtBwAAAAAHRS+pJf5OB1DPcEaYIIvqAAAAABiSRXUAMF3XvOY1u2/m+lzpSldKawIAjJnhjAAA6xP9o9ZE3yqrCQCwDfyNDgAAAIAh+TsdwHoZzggT5A92AAAAAAzFH+sAYPp+/vOfd9/OdTniiCPSegAAY2Y4IwDA+kT/qCXRr8rqAQBsk9a/0R155JFpPQAAAACYiR5SS/ydDqCN4YwwURbVAQAAADAEi+oAYPq+8Y1vdN/QdbntbW+b1gMAGDPDGQEA1if6Ry2JflVWDwBgm1x44YXd009dbne726X1AAAAAGAmekgtiZ5VVg+AMsMZYaIsqgMAAABgCBbVAcD0nXfeed03dF3uc5/7pPUAAMbMcEYAgPWJ/lFLol+V1QMA2Cbnnntu9/RTl/ve975pPQAAAACYiR5SS/ydDqCN4YwwURbVAQAAADAEi+oAYPo++MEPdt/QdXnUox6V1gMAGDPDGQEA1if6Ry2JflVWDwBgm/gbHQAAAABDefSjH911k+ri73QAbQxnhInyBzsAAAAAhmBRHQBM3xlnnNF9Q9flWc96VloPAGDMDGcEAFif6B+1JPpVWT0AgG3ib3QAAAAADOXZz352102qi7/TAbQxnBEmyh/sAAAAABiCRXUAMH2nnnpq9w1dl5e97GVpPQCAMTOcEQBgfaJ/1JLoV2X1AAC2Sevf6F7+8pen9QAAAABgJnpILfF3OoA2hjPCRFlUBwAAAMAQLKoDgOl7+tOf3n1D1+Wtb31rWg8AYMwMZwQAWJ/oH7Uk+lVZPQCAbfK0pz2te/qpy5lnnpnWAwAAAICZ6CG1JHpWWT0AygxnhImyqA4AAACAIVhUBwDTd/zxx3ff0HU5++yz03oAAGNmOCMAwPpE/6gl0a/K6gEAbJOHPvSh3dNPXfyNDgAAAIBFPvrRj3bdpLpEzyqrB0CZ4YwwURbVAQAAADAEi+oAYPqOPfbY7hu6Ll/4whfSegAAY2Y4IwDA+kT/qCXRr8rqAQBsk3vc4x7d009d/I0OAAAAgEW++MUvdt2kukTPKqsHQJnhjDBRFtUBAAAAMASL6gBg+m55y1t239B1+fa3v53WAwAYM8MZAQDWJ/pHLYl+VVYPAGCb+BsdAAAAAEP5zne+03WT6uLvdABtDGeEifIHOwAAAACGYFEdAEzfda5zne4bui6/+c1v0noAAGNmOCMAwPpE/6gl0a/K6gEAbBN/owMAAABgKP5OB7BehjPCRPmDHQAAAABD8Mc6ANgOrd/pRxxxRFoPAGCsDGcEAFiP6Bu1xL9dBgB2ya9//evuKagu/kYHAAAAwDxHHnlk10WqS/SqsnoALGY4I0yYRXUAAAAA9MmiOgDYHt/61re6b+q6/OVf/mVaDwBgrAxnBABYj+gbtST6VFk9AIBtdNFFF3VPQXXxNzoAAAAA5rnzne/cdZHqEr2qrB4AixnOCBNmUR0AAAAAfbKoDgC2x7/9279139R1edjDHpbWAwAYK8MZAQDWI/pGLYk+VVYPAGAbfeYzn+megurib3QAAAAAzPPwhz+86yLVJXpVWT0AFjOcESbMojoAAAAA+mRRHQBsj7e//e3dN3VdTjrppLQeAMBYGc4IALAe0TdqSfSpsnoAANvobW97W/cUVBd/owMAAABgnpNPPrnrItUlelVZPQAWM5wRJsyiOgAAAAD6ZFEdAGyPU089tfumrssb3vCGtB4AwFgZzggAsB7RN2pJ9KmyegAA2+jFL35x9xRUF3+jAwAAAGCe1r/TRa8qqwfAYoYzwoRZVAcAAABAnyyqA4Dt8fjHP777pq7Lxz72sbQeAMBYGc4IALAe0TdqSfSpsnoAANvocY97XPcUVBd/owMAAABgno9//ONdF6ku0avK6gGwmOGMMGEW1QEAAADQJ4vqAGB73Ote9+q+qevy3//932k9AICxMpwRAGA9om/UkuhTZfUAALbRPe95z+4pqC7+RgcAAADAPF/96le7LlJdoleV1QNgMcMZYcIsqgMAAACgTxbVAcD2uMlNbtJ9U9flkksuSesBAIyV4YwAAOsRfaOWRJ8qqwcAsI2OOeaY7imoLv5GBwAAAMA8v/71r7suUl2iV5XVA2AxwxlhwiyqAwAAAKBPFtUBwPa40pWu1H1T1+eGN7xhWhMAYIwMZwQAGF70i1oTfaqsJgDANrriFa+4d+mll3ZPQnW50Y1ulNYEAAAAYHdFz6gl0aOKXlVWE4DFDGeECbOoDgAAAIC+WFQHANvnm9/8ZvdtXZe73e1uaT0AgDEynBEAYHjRL2pJ9KeyegAA2+wb3/hG9zRUF3+jAwAAAOCg1r/TRY8qqwfAcgxnhImzqA4AAACAPlhUBwDb51//9V+7b+y6POpRj0rrAQCMkeGMAADDi35RS6I/ldUDANhm/kYHAAAAQF8e/ehHd92juvg7HcBqDGeEifMHOwAAAAD6YFEdAGyft7zlLd03dl1e8IIXpPUAAMbIcEYAgOFFv6gl0Z/K6gEAbLM3v/nN3dNQXfyNDgAAAICDXvjCF3bdo7pEjyqrB8ByDGeEibOoDgAAAIA+WFQHANvnRS96UfeNXZc3velNaT0AgDEynBEAYHjRL2pJ9KeyegAA28yCeQAAAAD60vo/BBI9qqweAMsxnBEmzqI6AAAAAPpgUR0AbJ/HPOYx3Td2XT75yU+m9QAAxshwRgCA4UW/qCXRn8rqAQBss0c/+tHd01BdPvWpT6X1AAAAANhd0TNqSfSosnoALMdwRpg4i+oAAAAA6INFdQCwfe5xj3t039h1ufDCC9N6AABjZDgjAMDwol/UkuhPZfUAALbZ3e9+9+5pqC7+RgcAAADAQa1/p4seVVYPgOUYzggTZ1EdAAAAAH2wqA4Ats+Nb3zj7hu7Lr/73e/SegAAY2Q4IwDA8KJf1JLoT2X1AAC22Y1udKPuaagu/kYHAAAAwH5XvOIV9y699NKue1SX6FFlNQFYjuGMMHEW1QEAAADQB4vqAGA7/eY3v+m+tety9NFHp/UAAMbGcEYAgGFFn6gl0ZfK6gEA7IJf//rX3VNRXfyNDgAAAICZY445pusa1SV6U1k9AJZnOCNsAYvqAAAAAFiFRXUAsL2+9rWvdd/cdTn2WEODAIBpMJwRAGBY0SdqSfSlsnoAALvgq1/9avdUVJd73vOeaT0AAAAAdk/0iloSvamsHgDLM5wRtoBFdQAAAACswqI6ANhen/jEJ7pv7ro89rGPTesBAIyN4YwAAMOKPlFLoi+V1QMA2AUf//jHu6eiujzucY9L6wEAAACwe6JX1JLoTWX1AFie4YywBSyqAwAAAGAVFtUBwPZ64xvf2H1z1+WUU05J6wEAjI3hjAAAw4o+UUuiL5XVAwDYBWeccUb3VFSXf/zHf0zrAQAAALB7olfUkuhNZfUAWJ7hjLAFLKoDAAAAYBUW1QHA9nre857XfXPX5cwzz0zrAQCMjeGMAADDij5RS6IvldUDANgF/kYHAAAAwKre9ra3dV2juvg7HcDqDGeELeAPdgAAAACswqI6ANhej3jEI7pv7rp8+tOfTusBAIyN4YwAAMOKPlFLoi+V1QMA2AUPf/jDu6eiunzmM59J6wEAAACwe6JX1JLoTWX1AFie4YywBSyqAwAAAGAVFtUBwPa6y13u0n1z1+V//ud/0noAAGNjOCMAwLCiT9SS6Etl9QAAdsGd73zn7qmoLhdddFFaDwAAAIDdE72ilkRvKqsHwPIMZ4QtYFEdAAAAAKuwqA4AttcNbnCD7pu7Ple+8pXTmgAAY2I4IwDAcKI/1JroS2U1AQB2wZFHHtk9FdXnkEMOSWsCAAAAsDuiR9Sa6E1lNQFYnuGMsAUsqgMAAACglUV1ALD9fvWrX3Xf3nW56U1vmtYDABgTwxkBAIYT/aGWRD8qqwcAsEt++ctfdk9HdbnZzW6W1gMAAABgd0SPqCXRk8rqAVDHcEbYEhbVAQAAANDCojoA2H4XXHBB9w1el+OOOy6tBwAwJoYzAgAMJ/pDLYl+VFYPAGCX/Nd//Vf3dFQXf6MDAAAA4D73uU/XLapL9KSyegDUMZwRtoRFdQAAAAC0sKgOALbf2Wef3X2D1+WJT3xiWg8AYEwMZwQAGE70h1oS/aisHgDALjnrrLO6p6O6POlJT0rrAQAAALA7okfUkuhJZfUAqGM4I2wJi+oAAAAAaGFRHQBsv9e//vXdN3hdTjvttLQeAMCYGM4IADCc6A+1JPpRWT0AgF3yute9rns6qstLXvKStB4AAAAAuyN6RC2JnlRWD4A6hjPClrCoDgAAAIAWFtUBwPZ77nOf232D1+Ud73hHWg8AYEwMZwQAGE70h1oS/aisHgDALnnOc57TPR3V5Z3vfGdaDwAAAIDdET2ilkRPKqsHQB3DGWFLWFQHAAAAQAuL6gBg+x1//PHdN3hdPve5z6X1AADGxHBGAIDhRH+oJdGPyuoBAOyShz70od3TUV3OPffctB4AAAAAuyN6RC2JnlRWD4A6hjPClrCoDgAAAIAWFtUBwPa74x3v2H2D1+Xiiy9O6wEAjInhjAAAw4n+UEuiH5XVAwDYJa1/o/vZz36W1gMAAABgd0SPqCX+TgfQD8MZYUtYVAcAAABAC4vqAGD7Xf/61+++wetz05veNK0JADAWhjMCAAwj+kKtiX5UVhMAYJdc73rX656O6nOzm90srQkAAADA9oveUGuiJ5XVBKCO4YywJSyqAwAAAKCWRXUAsDu+853vdN/idXnQgx6U1gMAGAvDGQEAhhF9oZZEHyqrBwCwi7797W93T0l1efCDH5zWAwAAAGD7RW+oJdGLyuoBUM9wRtgiFtUBAAAAUMOiOgDYHR/72Me6b/K6vPCFL0zrAQCMheGMAADDiL5QS6IPldUDANhFH/3oR7unpLq86EUvSusBAAAAsP2iN9SS6EVl9QCoZzgjbBGL6gAAAACoYVEdAOyOV7ziFd03eV3e8573pPUAAMbCcEYAgGFEX6gl0YfK6gEA7KLTTz+9e0qqy3vf+960HgAAAADbL3pDLYleVFYPgHqGM8IWsagOAAAAgBoW1QHA7njMYx7TfZPX5YILLkjrAQCMheGMAADDiL5QS6IPldUDANhFj370o7unpLr893//d1oPAAAAgO0XvaGWRC8qqwdAPcMZYYtYVAcAAABADYvqAGB33OEOd+i+yetz6KGHpjUBAMbAcEYAgP5FP6g10YfKagIA7KLb3/723VNSfa5+9aunNQEAAADYXtETak30orKaANQznBG2iEV1AAAAACzLojoA2C1XucpVum/y+tzxjndMawIAjIHhjAAA/Yt+UGsOOeSQtCYAwC668pWv3D0l1edOd7pTWhMAAACA7RU9odZELyqrCUA9wxlhi1hUBwAAAMCyVllUF32orCYAMG5f+cpXum/zujz+8Y9P6wEAjIHhjAAA/Yt+UEui/5TVAwDYZV/+8pe7p6W6POEJT0jrAQAAALC9nvjEJ3bdobpEDyqrB0Abwxlhy1hUBwAAAMAyLKoDgN3zrne9q/tGr8s//dM/pfUAAMbAcEYAgP5FP6gl//Iv/5LWAwDYZe985zu7p6W6/PM//3NaDwAAAIDtFT2hlkQPKqsHQBvDGWHLWFQHAAAAwDIsqgOA3fO85z2v+0avyyc/+cm0HgDAGBjOCADQv+gHtST6T1k9AIBddvLJJ3dPS3X51Kc+ldYDAAAAYHtFT6gl0YPK6gHQxnBG2DIW1QEAAACwDIvqAGD3/M3f/E33jV6X73//+2k9AIAxMJwRAKB/0Q9qSfSfsnoAALvsAQ94QPe0VJcf/vCHaT0AAAAAtlf0hFoSPaisHgBtDGeELWNRHQAAAADLsKgOAHbPMccc032j1+cGN7hBWhMAYNMMZwQA6Ff0gVpz9NFHpzUBAHbZUUcd1T0t1eeGN7xhWhMAAACA7XOjG92o6wrVJ3pQWU0A2hjOCFvGojoAAAAAFrGoDgB2109+8pPuW70u973vfdN6AACbZjgjAEC/og/Ukh//+MdpPQAArrD3ox/9qHtqqstf/dVfpfUAAAAA2D7RC2pJ9J6yegC0M5wRtpBFdQAAAACUWFQHALvrM5/5TPfNXpdnP/vZaT0AgE0znBEAoF/RB2pJ9J2yegAAXGHv05/+dPfUVJfnPOc5aT0AAAAAtk/0gloSvaesHgDtDGeELWRRHQAAAAAlFtUBwO563ete132z1+XMM89M6wEAbJrhjAAA/Yo+UEui75TVAwDgCnuvec1ruqemurz97W9P6wEAAACwfaIX1JLoPWX1AGhnOCNsIYvqAAAAACixqA4AdtcJJ5zQfbPX5Qtf+EJaDwBg0wxnBADoV/SBWvKUpzwlrQcAwBX2nvzkJ3dPTXX50pe+lNYDAAAAYPtEL6gl0XvK6gHQznBG2EIW1QEAAABQYlEdAOyuu971rt03e11+85vfpPUAADbNcEYAgH5FH6gl0XfK6gEAcIW9u9zlLt1TU11+97vf7V3xildMawIAAACwPaIHFL2glkTvKasJQDvDGWELWVQHAAAAQIlFdQCwuw477LDum70+t771rdOaAACbZDgjAEB/ov/Tmug7ZTUBALjC3jWvec3uqak+t7nNbdKaAAAAAGyP6AG1JnpPWU0A2hnOCFvIojoAAAAA5rGoDgC48MILu2/3ujz84Q9P6wEAbJLhjAAA/Yn+T0ui35TVAwDgMl//+te7p6e6PPKRj0zrAQAAALA9ogfUkug5ZfUAWI3hjLClLKoDAAAAIGNRHQDwwQ9+sPuGr8tLXvKStB4AwCYZzggA0J/o/7Qk+k1ZPQAALvP+97+/e3qqy0tf+tK0HgAAAADbI3pALYmeU1YPgNUYzghbyqI6AAAAADIW1QEAp512WvcNX5cPfehDaT0AgE0ynBEAoD/R/2nJqaeemtYDAOAyL37xi7unp7qcddZZaT0AAAAAtkf0gFoSPaesHuN20kkn7Z1zzjl/5FWvelW6LbAZhjPClrKoDgAAAICMRXUAwPHHH999w9flm9/8ZloPAGCTDGcEAOhP9H9aEv2mrB4AAJf5u7/7u+7pqS4XXXRRWg8AAACA7RE9oJZEzymrx7jFIMaDiQGN2bbAZhjOCFvKojoAAAAAMhbVAQC3utWtum/4+hx++OFpTQCATTGcEQCgH9H3ac0tb3nLtCYAAJe5xS1u0T091ee6171uWhMAAACA6YveT2ui55TVZNwMZ4TxM5wRtpRFdQAAAAActMqiuug3ZTUBgGm65JJLum/5utz97ndP6wEAbIrhjAAA/Yi+T0uiz5TVAwDg8n75y192T1F1OfZYPSUAAACAbRW9n5ZErymrx/gZzgjjZzgjbDGL6gAAAADYz6I6AGDm/PPP777p6/L3f//3aT0AgE0xnBEAoB/R92nJ5z//+bQeAACX9x//8R/dU1Rdnv70p6f1AAAAAJi+6P20JHpNWT3Gz3BGGD/DGWGLxT92aolFdQAAAADbyaI6AGDmrW99a/dNX5fXv/71aT0AgE0xnBEAoB/R92nJW97ylrQeAACX96Y3val7iqrLGWeckdYDAAAAYPqi99OS6DVl9Rg/wxlh/AxnhC0W/9ipJRbVAQAAAGwni+oAgJlnPvOZ3Td9XT772c+m9QAANsVwRgCAfkTfpyXRZ8rqAQBweSeeeGL3FFWXc889N60HAAAAwPRF76cl0WvK6jF+hjPC+BnOCFvMojoAAAAA9rOoDgCYuc997tN909fl4osvTusBAGyK4YwAAP2Ivk9LjjvuuLQeAACXd+9737t7iqrLL37xi7QeAAAAANMXvZ+WRK8pq8f4Gc4I42c4I2wxi+oAAAAA2K91UV30mbJ6AMB0HXnkkd03fX3ucIc7pDUBADbBcEYAgNX9+Z//edf5qc8RRxyR1gQA4PKuf/3rd09R9bnjHe+Y1gQAAABguqLn05roNWU1GT/DGWH8DGeELbbKorr4R1ZZTaDsGc94RvdbtHyyOus21eMG+OAHP9h9Ki2Xr371q2mddZvqcQMA02ZRHQBw0Pe+973u274uT3va09J6AACbYDgjAMDqot/Tku9+97tpPQAA5vv2t7/dPU3VJdZ9ZPUAAAAAmK6WWR+R6DFl9ZgGwxlh/AxnhC0X/+ipJRbVQRvDGQHWy3BGAIDlWVQHABx01llndd/4dXnnO9+Z1gMA2ATDGQEAVhf9npZ85CMfSesBADDfhz70oe5pqi7vfve703oAAAAATFf0fFoSPaasHtNgOCOMn+GMsOXiHz21xKI6aBeDDmPoVgzSWiZZjU2Y6nEDxGfXssMOxzLkMD5zZ5+7y8RwRgCgDxbVAQAHPf/5z+++8evyjW98I60HALAJhjMCAKwu+j0tif5SVg8AgPlOPvnk7mmqLhdddFFaDwAAAIDpip5PS0466aS0HtNgOCOMn+GMsOUsqtt+yw51KiVqZLVLNvG6McSqlPh5tt8mLTrmSLbfpk31uIE/Vhq2uq3D/hYNmB3r+57qcY9N6Tw6h0AtnylsI4vqAICD7nWve3Xf+PU56qij0poAAOtmOCMAwGqOPvroruNTn3ve855pTQAA5rvHPe7RPU3V5yY3uUlaEwAAAIDpiV5Pa6LHlNVkGgxnhPEznBG2XPyjp9bEP7bKajIufQxJbBlquInXXTQwsOV9rMOic5XtMwZTPW7gMrs6VKqUMb/vUgwBW86u3vPAMHymsG0sqgMAMoceemj3jV+fhz/84WlNAIB1M5wRAGA10edpTfSXspoAAMx3latcZe/SSy/tnqjq8shHPjKtCQAAAMD0RK+nJdFbih5TVpNpMJwRxs9wRthyFtXthhiiVxockiW272OYYe1rr/K6Ux3OuOi4s33GYKrHDVym9PkcP8v22QZTfd+lobjbfL36tKv3PDAMnylsG4vqAIB5/v3f/7371q/Lq1/96rQeAMC6Gc4IALCa6PO05LzzzkvrAQCw2Oc+97nuqaour3vd69J6AAAAAExP9HpaEr2lrB7TYTgjjJ/hjLAD4h8/tcSiuulZNExvliEGiZQGl8yy6usazrhehjPC9O3qUKmpDjk0nHF1u3rPA8PwmcK2sagOAJjnn/7pn7pv/rp8/vOfT+sBAKyb4YwAAKuJPk9Loq+U1QMAYLHTTz+9e6qqyxe/+MW0HgAAAADTE72elkRvKavHdBjOCONnOCPsgNZFdeeff35aj3ErDXaaZYghhosG+UVWfd1FrzHE++pLKdn2Y1FKtj0wLrs6VGqqQw5L33OGgC1nV+95YBg+U9g2FtUBAPM89KEP7b7563ONa1wjrQkAsE6GMwIAtLvmNa/ZdXrq83d/93dpTQAAFnvwgx/cPVXV57DDDktrAgAAADAd0eNpTfSWsppMh+GMMH6GM8IOiH/81Jr4R1dZTcZtUbJ9+rAo2T41DGdcv1Ky7YFx2dWhUoYz7q5dveeBYfhMYZtYVAcAlNzwhjfsvvnrc5/73CetCQCwToYzAgC0i/5Oa6KvlNUEAGCxI444onuqqs9f/dVfpTUBAAAAmI7o8bQmektZTdbnsY997N4pp5zS7Etf+lJ3NS/LN7/5zXTbZZ144onpsQJtDGeEHWBR3e5ZlGyfPpSGl0SyfWoYzrh+pWTbA+Oyq0Olpjrk0HDG1e3qPT8l84anxn/Ptof91n3/+Exhm1hUBwAs8rWvfa379q/Li170orQeAMA6Gc4IANAu+jstiX5SVg8AgOX993//d/d0VZcXv/jFaT0AAAAApiN6PC254IIL0nqs1znnnNNdkfHkJz/5SXqsQBvDGWFHtC6qi8nIWT3GrZQhh4iUhpdEsn1qxNCqeI15sn3GopRs+7EoJdseGJfss3JmzANtV2U44+7af48ftM33/JQYzsgqNjGccR6fKUyNRXUAwCJve9vbuieAunz0ox9N6wEArJPhjAAA7aK/05LoJ2X1AABY3lve8pbu6aoun/jEJ9J6AAAAAExH9HhaEj2lrB7rZTgjbD/DGWFHtC6q+9jHPpbWY9xKiUEi2T59iNrzMuTrTkEp2fZjUUq2PcAYGM4I4zXvedFwRpbh/oF2FtUBAIuccMIJ3RNAXS6++OK0HgDAOhnOCADQLvo7LXnKU56S1gMAYHlPetKTuqeruvziF79I6wEAAAAwHdHjackTn/jEtB7rZTgjbD/DGWFHxD+CaolFddM0b2hJZMjhTpt63SkoJdt+LErJtgcYA8MZYbzmPS8arscy3D/QzqI6AGCR2972tt0TQH3ucIc7pDUBANbFcEYAgDbR12lN9JOymgAALO/Wt75193RVnzve8Y5pTQAAAADGL3o7rYmeUlaT9TKcEbaf4YywIyyq2y3zhpZEhhzutKnXnYJSsu3HopRse4AxMJwRxmteDNdjGfPi/oEyi+oAgGX9+Mc/7p4C6vLUpz41rQcAsC6GMwIAtIm+Tkuij5TVAwCg3g9/+MPuKasu8e+Os3oAAAAAjF9pTXkp0UvK6rF+j33sY/dOOeWUZl/60pe6q3pZvvnNb6bbLuvEE09MjxVoYzgj7JDWRXVPe9rT0nqM16aGJG7qdaeglGz7sSgl2x5gDAxnhPGaF8P1WMa8uH+gzKI6AGBZZ511VvckUJd3vvOdaT0AgHUxnBEAoE30dVrykY98JK0HAEC9D33oQ91TVl3e/e53p/UAAAAAGL/o7bQkeklZPabnVa96VXdVL8s555yTbgtshuGMsEPiH0O1xKK66dnUkMRNve4UlJJtPxalZNsDjIHhjDBOMUBvXgzXYxH3D7SzqA4AWNYLXvCC7kmgLt/4xjfSegAA62I4IwBAm+jrtOT5z39+Wg8AgHonn3xy95RVl4suuiitBwAAAMD4RW+nJSeddFJaj+kxnBHGz3BG2CHxj6FaYlHd9GxqSOKmXncKSsm2H4tSsu0BxsBwRhin0rOi4Xos4v6BdhbVAQDLute97tU9CdTnqKOOSmsCAKyD4YwAAPWin9Oa6CNlNQEAqHePe9yje8qqz01ucpO0JgAAAADjFT2d1kQvKavJ9BjOCONnOCPsEIvqdkdpcMmQw5029bpTUEq2/ViUkm0PMAaGM8I4lWK4HouU4v6B+SyqAwBqXO1qV+ueBOrzsIc9LK0JALAOhjMCANSLfk5roo+U1QQAoN5VrnKVvUsvvbR70qrLIx/5yLQmAAAAAOMVPZ2WRA8peklZTabHcEYYP8MZYYdYVLc7NjUkcVOvuy4x9CXEe5n3XuO/xzYxYGv/vqXs324IUzzuOI7ZcWeZvZf4ebb/GMzO+cHMO9f0a3b/zO6VLGO+FvPun1mmcP/s/z0uXYPZdchqtIrXnpd4vWyf/WbHnmWI451Z9bhXscy1ip9P4d4bq3n3VZzbIe+rzOxYStc8Mju22fGF1nug9DqRdb7/GovOkd+Jy85R6TzNft56H5VqR8Z6/6xidq5K53Z2TqdwH87ey8Gs+z3E6+w/t/MSP5uZbT/V33eL6gCAWv/+7//ePQ3U5dWvfnVaDwBgHQxnBACoF/2clpx33nlpPQAA2n3uc5/rnrbq8rrXvS6tBwAAAMB4RU+nJdFDyuoxTYYzwvgZzgg7Jv5RVEssqpuWRUMmsn360Pfr1iar0YfS+1omMcSjlOw1+zDF4170mvMS7zUGpmQ1l1GbrEaI468577Htpoa8xPlaNVndg1Z5nTg/Wc1MnMd4rdb7fpVrUZusRqi5f7JzU3uuV/mdmSdqrnLN+zqmOJfzUrqvao99lfsm03rcq1j2njuYOFd9vvcWtder5v7qs/aQx1mr9lhKqbknW++zRTl4rmqzf9/91v15fHD/RWoy1P0Udfu4rlFj0TGu6/6pvW5DntvWxL59fDbXJqsRan6XIrHtUN8tq5zXg4njzF5jrCyqAwBq/dM//VP3RFCXz3/+82k9AIB1MJwRAKBe9HNaEv2jrB4AAO1OP/307mmrLl/84hfTegAAAACMV/R0WhI9pKwe02Q4I4yf4YywYyyq2w2lARhDDpLo+3Vrk9VYRen99JnstVcxxeOuHdwyL1GjZaBLbbIaqxz/UENoSvoYUJPVPWiV14lzmtXcL+r3ec9P5f7Jzk3tuY7tD9ZoEeesj/tplmWu+yJxTPOS1S9tv0z6OOZQe9yr6Oua9XUftRjynu+j9ir31Viv98HEe8xeL8TPhnrdWQ6e99rs33cmzn1NsmtV+74P7r9ITbJ7cxVxXWvP0TLJXmfd90/t6/V9bvs8r1Gr9Pu5SG2yGqu8n1WO/aCh7qM+j3FoFtUBALUe+tCHdk8E9bnGNa6R1gQAGJrhjAAAdaKP05roH2U1AQBo9+AHP7h72qrPYYcdltYEAAAAYHyil9Oa6CFlNXfVscce+3sx0DDMS/zslFNO+f22WZ1NMZwRxs9wRtgxFtXthtIgjPhZtk8f+n7d2qEsWY0W8bqLhonEz+PYYtuZ+L9bhn9kx9Biqse96LWz4130PmO77LXmmdVdJvHaB/dddDzLpPaYV1Xznvdndg9lNTOtr7PoNZY557HNTE1qr8XsPS57TAf3bcnBOjPLHkek5jpmlj32OJ6ZZTPv/S2rdGwHa7fcn1lWPeZQc9yt4jVK1yJ+FucktptZdI76OrYWQ97zNbXjPB3cd9X0dV4XvYc41oPXe9n3fvB9z/Tx/pdJvM7+150d/zLHfvD8xr4tmXedlj2OSLZ/SU3tg+doFaXXjJ/Fa83uo9m1WDb7X6dmv1WSnZv4b+s+t3Guljm3Yf+5DYsS22avuUjN9YvjO7jvsuewlNZj32/Rcew/p7P3HJY5/j6Obx0sqgMAWtzoRjfqngjqc9xxx6U1AQCGZjgjAECd6OO0JvpHWU0AANodeeSR3dNWff7qr/4qrQkAAADA+EQvpzXRQ8pq7prZQMbWxKDGrO66Gc4I42c4I+wYi+p2Q2mYxMHhGX0a8nVj+MWiZPvVWvQ6Mawj22+/qLHMQI9Zshq1pnrcpdeL48n2mVn0nhftP8+iuvvv5XnbxjYzy2Z/3XVb9jhXPcZF5zay6DUWHWvpXo/Xj58vyirvs4/7Z5ksc4yrnKuSZd5jbJPtG+J1Fx1bpFRjkdIx7j93844j/vt+yya23X8ctZY97lar1i+diz6Ob1Wl44u03vNhUe399+u8beO/zyybVX4PQulYlqldumci82os81nbR0rXdNGxxzlYdttS9tfJLFM7228ZUXvR/bTKfb9f6XXm3QcztedgDPdPWMe5XXRuljm3i45zUY1FFh3j/t+BedvGNjPLZn/dFvNeK/77Mudk0fte9byui0V1AECrr33ta91TQV1e9KIXpfUAAIZmOCMAQJ3o47Qk+kZZPQAAVnfBBRd0T111efGLX5zWAwAAAGB8opfTkugdZfV2xaoDGQ9mDAMaDWeE8TOcEXaQRXXbrzT0YtUhFyVDv+6iQS3ZPjX6HsCxqN4s2b41pnrcpftl2WMuHesq91zpXpvVPfja8d/nDelZ9pwu+76HULoes2T71Vrl93hR+rhvZlnlWvRx/8TPS9dkVmeRUubdryWLrt+yxxVK7y9SU+ug0jWe1T34+vF/l677ovc+yyr3zjLH3aqv2gfP2/6s8t77UkrLPb9fKbP3fvD8xGvOOy+lcznLKte9dM9m289Tundqr3npmFa9PpnS683O7cH3F/99/7HEz0vXaplrVNo/ku2zrNL1ifRxXucd/zLvfWbRcWb7HLTu+yeUsuprls5JzbkNi+6xeK1sv2WVzv3sWA++n/jv887RovthltbjLh1vtv08peNc9Zyui0V1AECrt73tbd2TQV0++tGPpvUAAIZmOCMAQJ3o47Qk+kZZPQAAVveWt7yle+qqyyc+8Ym0HgAAAADjE72clkTvKKu3C2KQ4qLEUMPYbmaZxMDH7PXWxXBGGD/DGWEHWVS3/UrDOWoHfdQY+nUXDfHI9qlROv7W4RulmrNk+9WY4nH3OeikNHyldWjOonvt4M+Xvb8XndfW4+3Dovccyfartcq1L2XRvgcter+rXItS7bgHDv68dOzz7pk+7rmW91j6fVv2mPYrHV8k22cZi67BwdctXYP9SnVnaTkPM4uOO9tnWfPOdUvdUrLt12ne+4ys+hm7qPbBny9zXy1zT2X7LVKqu8xxHTSvXm2t0mfIqtcnUzoPcb0O/rz0fuZd//jv2fb7lY4jku1TY9G9me2zrFLtbPuS0vXPtj9o3fdPmMq5DaV6kdrf1/0W3cMHfx7HktU5aNExt5zj0rG2nIN59VpqbYJFdQBAqxNOOKF7MqjLT3/607QeAMDQDGcEAKgTfZyWRN8oqwcAwOqe9KQndU9ddfnFL36R1gMAAABgfKKX05LoHWX1tlkMT4xhhfMSPysNWFxmSGO237oYzgjjZzgj7KDWRXUXX3xxWo/xKQ25WHZQRot1vG4p2fbLKh37KoNXSsNBZsn2W9YUj7tUu/WYS8m2X8ayqR3MsijZPuuyKNk+teZd/2U+I+ZliPsmku2zrGWzzP2TZdnP1Dg389Jy3kr1su0XKdWLtF7bZT7DIi3fTcvUHuK4W451pvRdkW2/SOm6tb73vgx5bKXa+1N7rRbVbTnuUs1s+2Vk99Eyn2H7lY6r5X0uY9kM/XlcSrZ9jaHOa+kzqaVuqV62/UGbuH+Ges3S5/IqdUup/Ww6aNnUfi4sSrZPSemaZdsvo4/Pv02xqA4AaHW7292uezKoz+1vf/u0JgDAkAxnBABYXvRvWhN9o6wmAACr+7M/+7Puqas+d7zjHdOaAAAAAIxH9HBac+tb3zqtua1i6GIpMXgx2++gRQMaS8Mdh2Y4I4yf4Yywgyyq236lYR+rDuUoWcfrlpJtv4zSoJg+jrs0ICSS7bOMqR536T7Jtl9GqWbrgJtl0jKUZdF53eSgl6Gu+X7zXmOZ9z0v2bbLKP0ORbJ9lrVMlr3W2Tlb9ne8dE3jZ9k+JfPqtdSaGeL3d9G1jazyOVk6r5HW2qXjHqLmUOc322ddStdmlfs0LLrukdbrVEpLzXlZ5b4PB7Ps59jMkNdnnmWy7PvIjn/Zc1pKtn2Noc7rvKxyH8071mzbg4Z6nyVDvOYQn/UzpeON1P7O7rdMWur3fczzsunPv02wqA4AWNWPf/zj7umgLk996lPTegAAQzKcEQBgedG/aUn0i7J6AAD054c//GH39FWXKfx7JgAAAIBdFz2clkTPKKu3rRYNVFx2MONMKZschmg4I4yf4Yywoyyq224xeGJsWXUYxkwp2fbLKJ2vPga9LBo4ku2zjCked+n/w7jKMZeOtfXeW5TW4130/2ne5D+MWMexZddq2WuUZZX7Zsj3uyi1x30wy56z0u9Gy7mbVy/bdlmlz7LW399F1zay6v1cOu5Its8ipeNuPRel48y2X1Ypq57bVfR9z+9Xqj1Ltt8y+v49mJfW+2jm4HHWXushr888i1L7ugez7DktJdu+xhDndahrNe9zLtv2oKGOqWSI1yz9vvfxPkpZ5TNgUVqPvfTdF6n9nJmXTX/+bYJFdQDAqs4666zuCaEu73//+9N6AABDMpwRAGB50b9pSfSLsnoAAPTnQx/6UPf0VZfYL6sHAAAAwHjo/Sy2aDDjscfW/7urGHg4L4YzAiWGM8KOsqhuu5UGfmwqqw7DmCkl234ZpWTb11o0cCTbZxmlZNvXGuK4SwNuVh1wUkq2/SKlrHo/l9LHUJ5VlK5RH8eWfT5t8j2Xsso9WUpfn4fLGPp69qF0jK3natHnVx/vvXTckZbXKB1367mYl1Xvw9L77+P8thryuEq1I6t8Ziyqne1TMi/r/PzJDHl95illneejlGz7GkOc11Ky7ddhE/fPFM9t6ZgjrZ9Vpaz6u1RK7Xmel01//m2CRXUAwKpe8IIXdE8Idfn5z3+e1gMAGJLhjAAAy4v+TUuiX5TVAwCgPyeffHL39FWXX/3qV3tXutKV0poAAAAAbF70bi655JKum1OXk046Ka25bRYNZoyfZ/stYjgj0MpwRthRFtVttxg8Mbb0NQyjlGz7RUrDS/o65kVDwrJ9FpnqcZeSbV+jdN+3DM8ppaXefn0fa5+GuO77HUxf92ur0rVYZdBaKeu8xqXPik3fazOle671/hj6Pp4ppeXY+z4XQ17/dZ3jWkO+51LtSLbPsvo+n6Vk26/LkNdnnlKGes1MKdn2Nfo+r6V6m/ze7vt9LmOK53bR50nruSpl1fMf731eamuXkm2/zSyqAwBWde9737t7QqjPcccdl9YEABiK4YwAAMuJvk1rol+U1QQAoD/3uMc9uqev+tzvfvdLawIAAACwedG7aU30jLKa2+TYY4/t3m2e1sGMoTT00XBGoMRwRthRFtVtt9Jwi0gMuBhC6XX7GjZSSrb9In0OAikpJdt+kSked2lQTB/H3Pf9V0q2fY14v/PS5/VrVUpcx2yfZWTXaNPvt3TfrPJeS1mlbq2x32uh9NnQ8rsbhqiZKd0/kWyfkr6Pu3R82fY1SscaWed9vt+Q9/zQv0+lZNuXlNLn70Ctoc9hppR13qelZNvX6Pu8lj47hrpOy9jE/TPVc1tK6+uUkm1fo8/zXMomP//WzaI6AKAPV73qVfd++9vfdk8JdXn5y1+e1gQAGIrhjAAAy4m+TUuiT3SVq1wlrQkAQH+udKUr7V1yySXdU1hdXvnKV6Y1AQAAANi86N20JHpF0TPKam6TGEo4L6sMZgxjHc74hCc8Ye+tb33rHznppJPSbYHNMJwRdpRFddutNPRjyIEU63jdUrLtFykl275VKdn2i5SSbd+qlGz7kj6HrmRK9Vvuv1Ky7WsMfS5WNdTxZcm2W6fSe11laFcp2fZDGfu9NjMvrd8dfQ85nKf0vRepvYf6Pu5Ssu1rlbLOoXf7DXnPD/37VEq2fcmie7PP34MaQ5/DTCnZ9kMpJdu+Rt/ntZRs+3XZxP2zznPb5+fmos+AbJ9FSsm2r9HneR7r59+6rbKoLvpFWU0AYDedffbZ3ZNCXb70pS+l9QAAhmI4IwDAcqJv05LoE2X1AADo34c//OHuKawuX/7yl9N6AAAAAGzeV77yla6LU5foFWX1tklpeGIk26fGkIMfge1mOCPsMIvqtldpIMWQwyjW8bqlZNuXlIaARLJ9WpWSbV8y1eMupY9hNH2fl1Ky7Wv0OYBmCKUBcZFsn0Wy9zyG4Tila7HKfVlKtv1Qxn6vzcxL6z1Suof7vO9K5zdSe477PO7Ssa3jO3lT91fpfa96TEPWDqVk25eUjnWWTXwGD30OM6Vk2w+llGz7Gn2e10X3TrbPumzi/pnquV30Wi3POKVk29dY53mObOLzb90sqgMA+vIP//AP3ZNCfY466qi0JgDAEAxnBABYLPo1rYk+UVYTAID+Pe1pT+uewupz05veNK0JAAAAwOZEz6Y10SvKam6TUvoYnliK4YxAieGMsMMsqtteMWxiXoYcRLGO1y0l276kNLij7/NUSrZ9yVSPu5RVhuDNlIaaRbJ9SkrJtq/R5wCaoZTScr2yz4dsu3UrXYtV7stSsu2HMoV7LcxL62da6fOgz8/JRZ87tee4z+MuXfu+zkH2ez3Lpu6vIe/5IWuHUrLtF1k267xWQ5/DTCnZ9kMpJdu+Rp/ntVSrz8/PFpu4f9Z1biPZPq0WvVbLM04p2fY1+jzPYdm01J4Ci+oAgD7d+ta37p4U6vOkJz0prQkAMATDGQEAFot+TWtudatbpTUBAOjfzW9+8+4prD4nnHBCWhMAAACAzYmeTWuiV5TV3BYxHLGUbJ8aQ9cHtpvhjLDDLKrbXqVhSUMOVVnH65aSbV+yzvNUSrZ9yRSPe9EAs2yfWoteo3YITSnZ9jX6HkAzhD6PMbs2fd+rrUrvs2Vw0Uwp2fZDmcK9Fual9T4pfR6s83Oyj9+VWWqPO7afl76ufek1+j7Pyxrynh+ydigl236R0v2Upa/7omToc5gpJdt+KKVk29fo87yO8fd6ZhP3z1TP7aLf/5bzVUq2fY2+r+0YP//WaZVFddEnymoCALvta1/7Wve0UJf3vve9aT0AgCEYzggAsFj0a1oS/aGsHgAAw7ngggu6p7G6vP/970/rAQAAALA5H/jAB7ruTV2iR5TV2xaLBifGz7P9apTSR31guxnOCDvOorrttKmhKut43VKy7UvWeZ5KybYvmeJx1w5IGSJxDNmxzVNKtn2NvgfQDGHRNcv2mSe7Z2uvx1BK12KVYywl234oU7jXwry0fqaV7t91fk7Wvlafx5393q0zfZ/nZQ15zw/9+1RKtv0yFn2WZxny2g19DjOlZNsPpZRs+xp9ntfSZ8emfq9nNnH/TPXcLvrdbzlfpWTb1xji2o7t82+dLKoDAPr2ute9rntiqMtPf/rTtB4AwBAMZwQAWCz6NS2J/lBWDwCA4fzzP/9z9zRWl1/84hdpPQAAAAA2J3o2LYkeUVZvWywazpjtU2NR/WOP9W+4gDLDGWHHWVS3nTY1VGUdr1tKtn1JKX2fp1Ky7UtKGetxlwaurCsxoCU7tnlKybavMcQAmiGUUnM+D2bIz6GD4jjjnIZ43ZllUnvP7FdKtv1QxnCvxXnMrsMyie2ymovE681La815Sql9rT6Pe9Pp+zwva8h7fsjaoZRs+2WV7qtS4hrGvlnNVkOfw0wp2fZDKSXbvkaf57WUoa7RsjZx/6zr3Pb9mbno977lfJWSbV9jqGs7ps+/dbKoDgDo2wMf+MDuiaE+97znPdOaAAB9M5wRAKAs+jStif5QVhMAgOE84AEP6J7G6nPcccelNQEAAABYv/vc5z5d16Y+0SPKam6LUmKwYrbPshYNZly1PrAbDGeEHWdR3XaKYRLzMuSwpHW8binZ9iWl9H2eSsm2LyllrMddGriyrtQOWCkl277GUANo+tbHcWY1hnyPUTt+D0qfR8tmlaE8pWTbD6WPa1grztvsOqya1s+00jCmdX5O1r5Wn8e96fR9npc15D0/9O9TKdn2NeLeimvSkj6v5dDnMFNKtv1QSsm2r9HneS1lqGu0rE3cP+s6t31/Zpa+TyIt56uUbPsaQ17bsXz+rYtFdQDAEK5+9at3Twz1+T//5/+kNQEA+mY4IwBAWfRpWnO1q10trQkAwHCucpWr7P3mN7/pnsjqcvrpp6c1AQAAAFi/6NW0JHpD0SPKam6DRcMTjz227d9XxX7nnHNOVyWPwYzAsgxnhB1nUd12Kg2fGHLAxDpet5Rs+5JS+j5PpWTbl5Qy1uMuDVyJxM+HFoNZsmObp5Rs+xpxPPMSP8v22YRFg32yfQ46+LnQ9z0a4pwdfJ0+UnvP7FdKtv1Q1nWvxbkqvVZrWu+X0r27zs/J2tfq87hLiVpxvYaWHdfQ4nXnZdVjGrJ2KCXbvkUcZ1z/lvTxHoc+h5lSsu2HUkq2fY0+z2spQ12jZW3i/lnXue37uymU0nK+Ssm2r7GOaxt1Nvn5ty6rLKqL/lBWEwAgfPzjH++eGupy/vnnp/UAAPpmOCMAQFn0aVoSfaGsHgAAwzvrrLO6p7K6/Od//mdaDwAAAID1i15NS6I3lNXbFqXEcMVsn5JlhjJGDGYEahjOCFhUt4VKQyeGGPwxs47XLSXbvqSUvs9TKdn2JaWM9bhLA1eGvCdXUUq2fY11DKDpSymLhhdmg+b6fH+l83gwcZ+F2Ge/+G/zsuj9lZSSbT+U0jmKn2X71IhzVDqHB5NdgzAvsX32uov0OeRwkVJqX6vP4y5llXt77Er3U/ws22dZQ9YOpWTbryKON+6p2qz6+zP0OcyUkm0/lFKy7Wv0eV5LGeoaLWsT98+6zm3f302hlJbzVUq2fY11Xtuot4nPv3WxqA4AGMqzn/3s7smhPje60Y3SmgAAfTKcEQBgvujPtCb6QllNAACGd+KJJ3ZPZfU55phj0poAAAAArE/0aFoTvaGs5jaIQYqlLBqgGPuH2G6ZgYyzGMwI1DKcEbCobguVhk0MOVRiHa9bSrZ9SSl9n6dSsu1LShnrcZcGrgx5T66ilGz7GuscQLOqVY41+0zItqu1zEDA+Pky57L0/lYZYFdKtv1QVrl+i5RqzxLbLHMe56X186HPIYeLlFL7Wn0edymr3Ntjt6l7ftXaoZRs+z7Ecce9VZvWe2joc5gpJdt+KKVk29fo87yWMtQ1WtYm7p91ndu+v5tCKS3nq5Rs+xqburbr/PxbB4vqAIAh3fa2t+2eHOrz+Mc/Pq0JANAnwxkBAOaL/kxrbnOb26Q1AQAY3q1udavuqaw+T37yk9OaAAAAAKxP9GhaE72hrOY2iCGJpcTP94sBjDMtif2y4wBYxHBGwKK6LVQaMjHE4I+ZdbxuKdn2JaX0fZ5KybYvKWWsx10auBLJ9tm0UrLta2xiAE2r0rC4SLbPzMH0cX+Wzl3Urz1/pXqrDN8pJdt+KEPca3Fe4lzPS9StPXfz0nrPlO7bdX5O1p7jPo+7lNZrPwVD3PMzQ9YOpWTb92nR7/XBtP4eDX0OM6Vk2w+llGz7Gn2e11L6/vystYn7Z6rntvR9Emk5X6Vk29fYxLWdWdfn3zqssqgu+kJZTQCA/b7xjW90Tw91ede73pXWAwDok+GMAADzRX+mJdEPyuoBALA+Nf+2aX/e9773pfUAAAAAWJ/o0bRkzGvY+tA6ZLEm8Rox2PHYY/07LaCd4YzA71lUt11Kf4Ad8kF8Ha9bSrZ9yaJk+7QqJdu+ZFGyfVqVkm0/T2ngSiTbZ9NKybavsckBNC1KiYE22T7Ze8y2qzHEZ0zpWsx7b8soJdt+KH3fa4uGPbWes3lpva6l4+zr+2imlNpz3Odxl9Jy7aei73t+vyFrh1Ky7YdS+qzdn5bfpaHPYaaUbPuhlJJtX6PP81q6/n1/ftbaxP2zrnMbyfZpNcT3dSnZ9jU2cW0zQ37+rYNFdQDA0N7whjd0TxB1+dGPfpTWAwDok+GMAADzRX+mJdEPyuoBALA+r33ta7uns7pcfPHFaT0AAAAA1id6NC2JnlBWb1sMlRjIGAxkBPpiOCPwexbVbZdNDVVZx+uWkm1fUjreSLZPq1Ky7UumeNyLBsRk+2xaKdn2NcYygGZZLcd78D5d9TNgqGF7pffWMrhoppRs+6H0fa+VPn+GOF+t13ao+yVTSu056fO4S9eq5dpPRd/3/H5D1g6lZNsPqXQv7k+2b8nQ5zBTSrb9UErJtq/R53ktfXZEsn3WZRP3z1TPbem4I9k+i5SSbV9jE9d2nqE+/9bBojoAYGgPechDuieI+tz97ndPawIA9MVwRgCAXPRlWhP9oKwmAADr87d/+7fd01l97nWve6U1AQAAABhe9GZaEz2hrOY2iMGJq2Y2hHE2iNEwRmAohjMCv2dR3XYpDf6oHe5UYx2vW0q2fcmioSW1Q7VKSsm2L5nqcZfS5zH3pZRs+xpjGkCzrFIObpu9v1Wv8bzPl1U/W0rXYpVjLiXbfih93mtxPuZl1es7L63Xt3SsfX0fhdLrRGrPS5/HXbr2fZ6Dsenznj9oyNqhlGz7dYh7pZTa9z30OcyUkm0/lFKy7Wv0eV5LtSLZPuuyiftnned21e/S/Ra9VrbPIqVk29fo8zz3pe/Pv6FZVAcArMO1rnWt7gmiPqeeempaEwCgL4YzAgDkoi/Tmmte85ppTQAA1udqV7va3qWXXto9odXlpS99aVoTAAAAgOFFb6Yl0Qs69NBD05rb4JRTTuneaZ74+Wzg4kFZPYAhGc4I/J5FddulNEhiyIFM63jdUrLtS9Y5IKWUbPuSqR53KWMbbhJKybavUbqGYzwXoXTMB++5g58Fq/7+D3m+at5XjVKy7YfS57krJdu+xry03jtx7ealr++jUHqdSLZPSZ/HXbr2kWyfbdDnPX/QkLVDKdn261JKn/dlH+cwU0q2/VBKWeX7JvR5Xku1Iqse6yo2cf9M9dzG7+a8tH4PlpJtX2MT13YZpfT5PNGHVRbVRT8oqwkAkPnUpz7VPUXU5d///d/TegAAfTGcEQAgF32ZlkQfKKsHAMD6fexjH+ue0uryhS98Ia0HAAAAwPC++MUvdl2aukQvKKu3LRYNZzSEERgTwxmB/8eiuu0xxDCOZazjdUvJti9ZNFSrr0Egi14n26dkqse9qfuyVSnZ9jXGOoCmpHQ/HDzmg1n1PZXunWz7GqXa8Z6zfZZRSrb9UPq612quf61S7dbPhiFqZkrnt+V1+jzuUq1I/Dzbb+r6uuczQ9YOpWTbr0uf9/nQ5zBTSrb9UEpZ9fexz/O66LNjqOu0jE3cP1M9t/G7OS+tr1NKtn2NPs9zn0rH1efzRB8sqgMA1uWkk07qniTqc+SRR6Y1AQD6YDgjAMDlRT+mNdEHymoCALB+z3rWs7qntPrc+MY3TmsCAAAAMJzoybTmmc98ZlpzWywazpjtA7AphjMC/49FddujNIxjyCES63jdUrLtSxYNSIlk+9UqDfWIZPuUTPW4+643tFKy7WuUzkX8LNtnDEqZbZO9t/01WpQ+W7Lta5RqrzIsq5Rs+6H0da+VPndWvWdLtVu/O4aomSndPy3npe/jLmXV6zZWfd3zmSFrh1Ky7delz/uydA77/N3cr5Rs+6GUsur9U0pL7VL6uNdbbeL+Kb1m3+c2ku3TopRs+2WUkm1fo+/z3Jd1PU+syqI6AGCd7nCHO3RPEvV5zGMek9YEAOiD4YwAAJcX/ZjW3P72t09rAgCwfre5zW26p7T6POEJT0hrAgAAADCc6Mm05s/+7M/SmtvinHPO6d5pnmwfgE0xnBH4fyyq2x4xKGJehhwisY7XLSXbfpFFiYEc2X41Suclku2zyKJM8bg3OXglU0q2fY2xDqBZpHTcs3vuYPp4P6Vk2y+rNHAnssrvUSnZ9kPp617rq05miO+ORdc226dFKS33T+m4W85F6bpFsn2mbsh7dcjaoZRs+3kOJtum1rzU3pebuCdLybYfSumzbpX7Z9HnXUvtRddple/HVWzi/im95ljPbek1Wr9XQynZ9jX6Os8Hk21Ta15WOZd9W2VRXfSBspoAACUXXXRR9zRRl3e84x1pPQCAPhjOCABwedGPaUn0f7J6AABszoUXXtg9rdXl3e9+d1oPAAAAgOG85z3v6bozdYkeUFZvmxjOCEyJ4YzAH7GobjuUMuQQidLQmb5et5Rs+0UWDUipGQaSWVQ/ku23yFSPu3SPRLJ9WsVwm1UG3JSSbV+jdH5XvXZDKg2AiuPO3ldWp1Yp2fbLWnQ/jvX+qdHXvdZXnYNK91Sk9btjUd1sn1ql1xjiuFtqLjoPq1y7TN/1Wgx1r4Yha4dSsu3nOZghj6229jp+Nw8qJdt+KKXvnNbPjFCqG2m5/uv67IjXCdnPMpu4f+K9zstYz23pnlilfinZ9jX6Os8H08f5nJc+avfFojoAYN3e9KY3dU8UdfnBD36Q1gMA6IPhjAAAlxf9mJZE/yerBwDA5vzf//t/u6e1uvz4xz9O6wEAAAAwnJ/85Cddd6Yur3/969N628RwRmBKDGcE/ohFdduhlFWGvyyyaDhMtk+tUrLtF1k0ICVSMzRmv2VqR7J9F5nqcZcGr0T6uj/3v4fW81BKtn2NvgbQbEIpBz8D+rqepQx5n69yLUppPeYWfd1rpTqrXOdF3xuRbL9FFl3fPn7PShnivmw9z4vS1/04u5Z9/d636uuezwxZO5SSbT/Pwax6TUr3Ze37XvS72df9uF8pQ7zePKX7J5Lts8gyn6Fj/ezYfy9kP89s4v4Z4vd+0XXL9llW6Ryt+llQSrZ9jb7O88GM6fNvSBbVAQDrdvzxx3dPFPW5y13uktYEAFiV4YwAAH8s+jCtif5PVhMAgM158IMf3D2t1ece97hHWhMAAACA/h177LFdV6Y+D3rQg9Ka28RwRmBKDGcE/ohFdduhlFUHVJQsSrZPrVJaB7IsGpDScs4WDY/ZH8f9x2k57v2y4S4tx1pK63ufKQ2gWfX9D6107AeT7d+idM+0DMZZ9j5fZehOKavePzX6utcWnbNsn0UWfRbMku27yKLjXfX3rFR/ldqLfr+yfRZZ5n5f5Z6MfQ9ey1XP7yr6uuczQ9YOpdRcoyyrfJ6V7qGWe2dRsn0yy752KS3H32rR73ftPXTw925eWu/N0nWPrHLPHzwXNbUWJdsns+y1H+L3ftG5XeX3tXRfrHq/l7Jq7b7Oc5ZVzmfpWq36nvtiUR0AsAnXvva1uyeK+pxyyilpTQCAVRnOCADwx6IP05rDDz88rQkAwOZc4xrX6J7W6nPaaaelNQEAAADo30te8pKuK1Of6AFlNbeJ4YzAlBjOCPwRi+qmb9Gwj0i2Xx8WJdunRmloR6R1QMYy56xmKMjB44z6pWN33JdPy7CbqDtvIE3tsQ51r83MO85Zsn3GYpnrF2m5hvP0eT0O1irVjp9lNRZZdI5WvX9qlFJ7jUqpqRXvf//vQPy/9//fB5PVWGSZ+7T1+oZSVrm+i+71bJ9llM7vLC3HPe94a+6HvpWy6nGVsmrtRfdszf06L9m2y5h3/7S+50X3+TJ1Z8e0aNtF53WV39cWi7LM8cQ2+6/J7BzMyzLnc579r5Mlfl5zDg8e+yw1x7jO+yeUUnPcB82OYV5a7s3SuVnlWMOi895yvPstOh/ZPpl5ybZdxrzjWvV89mmVRXXR/8lqAgAs49Of/nT3VFGXz33uc2k9AIBVGc4IAPDHog/Tkuj7ZPUAANi8T3ziE91TW13+4z/+I60HAAAAQP8+//nPd12ZukTvJ6u3bTYxnDHW4MXrZj8DKDGcEbgci+qmbdHgjEhsk+27ihjIsSirvu6ioR2r1F/mvEVKg0eixsFjnG1fOvZdPO5F13KWRa8Rx1k6B/E62X6LLDq+Vd57WJTS9RqDZdLne4hai7Lo9eLn8+7zUg7WWUbpnoy03pe1ljlv2X7zLPq9WOZ9HTw3s31K56zl922Z9x5ZdN9kSuehpd5+i85xa/1lz0e8/qLXiOsx5DlYxTLvM9tvGUPWDn1+bsxLTY2Z0vtuvdbLnMt592J2/5WOo8/z2oeDx55l3jHF+zz4fvZvW8r+OjWWuVaROK5F12Hee6+9BsscU9TMjqf2/lnmtbL9ljXvnMxSOraDSsdae44zi441zm2237IWZdlzMS8t56B0TmuuzdAsqgMANuX5z39+92RRn+tf//ppTQCAVRjOCABwmei/tCb6PllNAAA27znPeU731FafG97whmlNAAAAAPoTPZjWRO8nq7ltjj322O4d54mfZ/u1isGMsxjQCNQynBG4HIvqpqs0QOJg+hwosY7XXfY1Vnlfi4aO7E9su1+W/ceyqLbjXpz9x73MvrFN9rqLxHtaJq3vfchjX5eDw6EOZojjX/Sas8Rrx7Yz8X9n53z/9Sul9r0Mff/UyN73wdS+v2UyO+cHr8PB7H/t2KaU2vO17HWILHsOFtVc9Zpm5yhL6+vUnJNZ4pj2W5RVz8GqljnG2Cbbd5Ehay97bZY9v6XEMS5bp/R7GT/L9lnWMudzlth23valc973ee3Lspm972Xfeyml87TIsudxlv3HHUppPa5FdfendByLXn+Z11lUo2SZc7vM/Vk6zlWOb2bo36U+z3MpUWPZYxzy869PFtUBAJt0xzvesXuyqM8jH/nItCYAwCoMZwQAuEz0X1oTfZ+sJgAAm3e7292ue2qrz+Me97i0JgAAAAD9iR5Ma6L3k9XcNusczrh/MOMsBjQCNQxnBC7HorrpiSETiwZbZYkBFasMl1jH68Z2ywzs2J/Yp2U4SOzTVw6+/rLvoeXYp3rcofbaLpuWY4l9ao8ntl/2tdZ5Lw8tjqmUOO5sv1X1keyaLZNF16Ll+tbcP8uKenEsNYnjWPaa1dael4Ovt0zdmuNcdI9mmdUPsf/MMscW22XHsYyoX3vvzI4xq1cS+wyROP7s9dZh2Wu0P0Pf88vWHurYl0npfl90TH1c73itVTPvOOL442c1ie3jmLJ6fVt0fpfJwfe+7PmM/eL19++7jD6u18G0HMfMkPdP1K69Rq3nNcTrxf6LEvVj25n4vxftN+89LmvZY9uf2D72y+odtMx7OJjZecjqhWUSrxl1snMaSln1nPbNojoAYNO+853vdE8XdXnf+96X1gMAWIXhjAAAl4n+S0ui35PVAwBgPL75zW92T291+dCHPpTWAwAAAKA/0YNpSfR8snrbqpQ+hifGgMeokyUGNmb7AGQMZwRSFtVNx6IBEsskamS1S9bxujEkY5W0vK9QOyRkf+YN7KitmdVYZKrH3ce9NEscb2lgyzxD32urXJtIy3saWinZ9n2I8zDEfV6TbP9V759IVrfFqvdaVvOgVX9ns/u5puYyn+3zrsmq5+dgot4qv5+rHk/La8c+fZ6HZa7HkIa854esPe8eXTZxbFndMGRWud8PWuUczDuOVc9rJKvbt1Xurey9177vlt/bPs5tJN77vOtXY5XjKb3+kL/3Jat+t+5PH+d41eu96B5b9TzPe39DZtVzOgSL6gCATXvrW9/aPWHU5dJLL9275jWvmdYEAGhlOCMAwB9E3yX6Ly2Jfk9WEwCA8TjjjDO6p7f6XOc610lrAgAAALC66L20Jno+Wc1tNW9w4iwxXDHbbxkxfHFeDGYEahnOCKQsqpuOPgZ5tAxoWcfrDj0UpKT2/S0aglKbrMYypnrcIY69dVBL7LvKwJSh77WhBtBs0rx7bdG56MO8156XPu/zbP9V759IVrfFqvdaVjMT77n2tUr3Rs01XeYey67J/nsgaqxyrhbdU8ta9Xqtcgyxb+3v0ixx3Mtch3VY9RxmNWeGrJ3dozWJY8vqzqx6jx/MUNe79jwset+rntdIVncItcdaugZ91lok9m1JXLs4zqxmq77vn7Dq701Wc1nxflrPbySOva/f1dpzezCLjmPV81y6l+K1V62/P32d075ZVAcAjMEjHvGI7gmjPo95zGPSmgAArQxnBAD4g+i7tCb6PVlNAADG4+/+7u+6p7f6POEJT0hrAgAAALC6Jz7xiV0Xpj7R88lqbqsYvrgotYMUo2Zp6KPBjEALwxmBlEV18AelISl9DkDp21SPO8yOPY5z3mCV2XsoDWZhWHHus2TbDmH/fXIws3tnzPf5tij9rs6uwaZ+T/ffH6VjiO1K7yOy/71s6v0MZfae9p+vg5mdm9gmq8H4HbzP513ryOxah3Xd77N78GBmxxo/y/bbBvuvy8Gs8xrUGtPnxjbeP4vOb2TK72+d4vzMzuXMvMzOZxjr796MRXUAwBhc73rX654w6vOBD3wgrQkA0MpwRgCAP4i/dbXmute9bloTAIDxOOyww7qnt/qcddZZaU0AAAAAVnf22Wd3XZj6XOta10prbrPSIMVZYqBiaajibCDjolqxXbY/wCKGMwIpi+oAAAAAxmeVRXXR78lqAgC0+NjHPtY9ZdTn8MMPT2sCALQwnBEA4Aq/77e0Jvo8WU0AAMbnwx/+cPcUV5/rX//6aU0AAAAA2kXPpTXR68lq7oLaLDOIcX9iW4MZgVUYzgjMZVEdAAAAwHhYVAcAjMmTn/zk7kmjPo9//OPTmgAALQxnBAC4wu/7La2JPk9WEwCA8Xnc4x7XPcXV5ylPeUpaEwAAAIB20XNpTfR6spq7IAYnDpVTTjklfU2AGoYzAnNZVAcAAAAwHhbVAQBj4n/lFQAYC8MZAQCusPeRj3yk67zUJ/o8WU0AAMbH/7grAAAAwLh8/OMf77ov9YleT1ZzV/Q9oPGcc875fc3stQBqGc4IzGVRHQAAAMB4WFQHAIzNWWed1T1t1Oe6171uWhMAoJbhjADAros+S2uiv5PVBABgvD7wgQ90T3P1OfLII9OaAAAAANSLXktroseT1dxFp5xySndW2mIoIzAEwxmBIovqAAAAADbPojoAYIye8IQndE8c9XnSk56U1gQAqGU4IwCw66LP0pro72Q1AQAYr0c/+tHd01x9nvrUp6Y1AQAAAKgXvZbWRI8nq7nLaoY0xkDG2N5QRmAohjMCRRbVAQAAAGyeRXUAwBhd5zrX6Z446nP22WenNQEAahnOCADsuo9+9KNdx6U+0d/JagIAMF7XvOY19373u991T3R1+dSnPpXWBAAAAKBe9FpaEr2d6PFkNfmDGLoYYgDjbAjjTLY9QN8MZwSKLKoDAAAA2DyL6gCAsfrQhz7UPXXU50/+5E/SmgAANQxnBAB2WfRXWhN9nawmAADj9773va97qqvPjW50o7QmAAAAAMuLHktroreT1QRgPAxnBBayqA4AAABgcyyqAwDG7LGPfWz35FGfE044Ia0JAFDDcEYAYJdFf6U10dfJagIAMH6PeMQjuqe6+jzjGc9IawIAAACwvOixtCZ6O1lNAMbDcEZgIYvqAAAAADbHojoAYMwOO+yw7smjPh//+MfTmgAANQxnBAB22Sc+8Ymu01Kf6OtkNQEAGL9DDz1079e//nX3ZFeXT3/602lNAAAAAJYXPZaWRE8nejtZTQDGw3BGYCGL6gAAAAA2x6I6AGDs3v/+93dPH/W5wQ1ukNYEAFiW4YwAwK6Kvkprop+T1QQAYDre/e53d0939Tn66KPTmgAAAAAsFr2V1kRPJ6sJwLgYzggsxaI6AAAAgPWzqA4AmIJHPepR3RNIfZ72tKelNQEAlmU4IwCwq6Kv0pro52Q1AQCYjuOPP757uqvPM5/5zLQmAAAAAItFb6U10dPJagIwLoYzAkuxqA4AAABg/SyqAwCm4BrXuMbe7373u+4ppC6f+tSn0poAAMsynBEA2FX/+q//2nVY6hJ9nOjnZDUBAJiOQw45ZO+Xv/xl95RXl89+9rNpTQAAAAAWi95KS6KXEz2drCYA42I4I7AUi+oAAAAA1s+iOgBgKt773vd2TyL1ufGNb5zWBABYhuGMAMAuin5Ka6KPk9UEAGB63vGOd3RPefW52c1ultYEAAAAYL7oqbQmejlZTQDGx3BGYGkW1QEAAACsj0V1AMCUPPzhD++eROpz4oknpjUBAJZhOCMAsIuin9Ka6ONkNQEAmJ6HPOQh3VNefZ773OemNQEAAACYL3oqrYleTlYTgPExnBFYmkV1AAAAAOtjUR0AMCWHHnro3q9//evuaaQun/70p9OaAADLMJwRANhFn/nMZ7rOSl2ifxN9nKwmAADTc8UrXnHvZz/7Wfe0V5fzzjsvrQkAAADAfNFTaUn0cKKXk9UEYHwMZwSWZlEdAAAAwPpYVAcATM273vWu7omkPsccc0xaEwBgEcMZAYBdE32U1kT/JqsJAMB0nXnmmd3TXn1ucYtbpDUBAAAAuLzopbQmejhZTQDGyXBGoIpFdQAAAADDs6gOAJiihz70od0TSX2e9axnpTUBABYxnBEA2DXRR2lN9G+ymgAATNcDH/jA7mmvPs973vPSmgAAAABcXvRSWhM9nKwmAONkOCNQxaI6AAAAgOFZVAcATNEhhxyy98tf/rJ7KqnLZz/72bQmAMAihjMCALsm+igtib5N9G+ymgAATNtPfvKT7qmvLueff35aDwAAAIDLi15KS6J3k9UDYLwMZwSqWFQHAAAAMDyL6gCAqXrHO97RPZnU52Y3u1laEwCgxHBGAGCXRP+kNdG3yWoCADB9b37zm7unvvrc+ta3TmsCAAAAcJnoobQmejdZTQDGy3BGoJpFdQAAAADDsagOAJiyBz/4wd2TSX2e+9znpjUBAEoMZwQAdkn0T1oTfZusJgAA0/eABzyge+qrzwtf+MK0JgAAAACXiR5Ka6J3k9UEYLwMZwSqWVQHAAAAMByL6gCAKbviFa+497Of/ax7OqnLeeedl9YEACgxnBEA2CXRP2lJ9Guib5PVBABgO/zv//5v9/RXly996UtpPQAAAAAuEz2UlkTPJqsHwLgZzghUs6gOAAAAYDgW1QEAU3fmmWd2Tyj1ucUtbpHWBACYx3BGAGBXRN+kNdGvyWoCALA9zjjjjO7prz63ve1t05oAAAAAXOH3vZPWRM8mqwnAuBnOCDSxqA4AAACgfxbVAQDb4G//9m+7J5T6PO95z0trAgDMYzgjALArom/SmujXZDUBANge97vf/bqnv/r84z/+Y1oTAAAAgCv8vnfSmujZZDUBGDfDGYEmFtUBAAAA9M+iOgBgW/zkJz/pnlLq8uUvfzmtBwAwj+GMAMCuiL5JS6JPk9UDAGD7fO973+ueAuvy1a9+Na0HAAAAwBV+3ztpSfRqsnoAjJ/hjEAzi+oAAAAA+mVRHQCwLd785jd3Tyr1ufe9753WBADIGM4IAOyC6Je0Jvo0WU0AALbP61//+u4psD73v//905oAAAAAuyx6Jq2JXk1WE4DxM5wRaGZRHQAAAEB/LKoDALbJAx7wgO5JpT5nnnlmWhMAIGM4IwCwC6Jf0pro02Q1AQDYPscdd1z3FFifd73rXWlNAAAAgF0WPZPWRK8mqwnA+BnOCDSzqA4AAACgPxbVAQDb5oc//GH3tFKXSy+9dO/www9PawIAHGQ4IwCw7aJPEv2SlkR/JqsJAMD2+va3v909DdbniCOOSGsCAAAA7KLolbQmejRZTQCmwXBGYCUW1QEAAACszqI6AGAbnXHGGd0TS32e8YxnpDUBAA4ynBEA2HbRJ2lN9GeymgAAbK/XvOY13dNgfZ7znOekNQEAAAB2UfRKWhM9mqwmANNgOCOwEovqAAAAAFZnUR0AsI3ufve7d08s9Tn//PPTmgAABxnOCABsu+iTtCb6M1lNAAC2153udKfuabA+X/7yl9OaAAAAALvoK1/5Stc1qU/0aLKaAEyD4YzASiyqAwAAAFidRXUAwLb6whe+0D211Oeud71rWhMAYD/DGQGAbRb9kdZEXyarCQDA9jv33HO7p8L63Pve905rAgAAAOyS6JG0JnozWU0ApsNwRmBlFtUBAAAAtLOoDgDYZieeeGL35FKfM844I60JALCf4YwAwDZ7wxve0HVK6hN9mawmAADb74QTTuieCutz5plnpjUBAAAAdsnb3va2rltSn+jNZDUBmA7DGYGVWVQHAAAA0M6iOgBgm13nOtfpnlzq86tf/Wrvale7WloXAGDGcEYAYFtFXyT6I62JvkxWFwCA7XeNa1xj75JLLumeDOtz7WtfO60LAAAAsAuiN9Ka6Mlc/epXT+sCMB2GMwIrs6gOgG1yv/vdb++xj33sH4l/nJJtCwAAq7KoDgDYBav8L8c++clPTmsCAMwYzggAbKvoi7Qm+jFZTQAAdscb3/jG7umwPs94xjPSmgAAAAC7IHojrYmeTFYTgGkxnBHohUV1AGyLD3zgA9031GU5+uij020BAGBVFtUBALvguOOO655g6vO5z30urQkAMGM4IwCwrT772c92HZL6RD8mqwkAwO64+93v3j0d1uf8889PawIAAADsgi984Qtdl6Q+0ZPJagIwLYYzAr2wqA6AbWE4IwAA62RRHQCwK/6//+//655i6vMXf/EXaU0AgGA4IwCwjaIf0prow2Q1AQDYPasMErjb3e6W1gQAAADYZtETaU30YrKaAEyP4YxAbyyqA2AbGM4IAMC6WFQHAOyS5z73ud2TTH1e/epXpzUBAILhjADANop+SGuiD5PVBABg95x44ondU2J9zjjjjLQmAAAAwDZ74xvf2HVH6hO9mKwmANNjOCPQG4vqANgGhjMCALAuFtUBALvkyCOP7J5k6vPTn/5070pXulJaFwDAcEYAYNtEHyT6Ia2JPkxWFwCA3XOd61yne0qsz69+9au9q1/96mldAAAAgG0UvZBLLrmk647U59rXvnZaF4DpMZwR6I1FdQBsA8MZAQBYB4vqAIBd9J73vKd7mqnPYx/72LQmAIDhjADAtok+SGui/5LVBABgd73tbW/rnhbr85SnPCWtCQAAALCNTjjhhK4rUp/owWQ1AZgmwxmBXllUB8DUGc4IAMA6WFQHAOyiv/7rv+6eaOrzqU99Kq0JAGA4IwCwbaIP0prov2Q1AQDYXccdd1z3tFifz33uc2lNAAAAgG107rnndl2R+kQPJqsJwDQZzgj0yqI6AKbOcEYAANbBojoAYFd9/etf755q6nOb29wmrQkA7DbDGQGAbRL9j9ZE3yWrCQAAX/nKV7qnxvrc8Y53TGsCAAAAbJM73elOXTekPtF7yWoCMF2GMwK9s6gOgCkznBEAgKFZVAcA7LIXvehF3ZNNfU4//fS0JgCw2wxnBAC2SfQ/WhN9l6wmAAA897nP7Z4a6/Oa17wmrQkAAACwTV772td23ZD6RO8lqwnAdBnOCPTOojoApsxwRgAAhmZRHQCwy4455pjuyaY+3//+99OaAMBuM5wRANgm0f9oTfRdspoAAHDkkUd2T431+elPf7p35StfOa0LAAAAsA2i93HxxRd33ZD6RO8lqwvAdBnOCPTOojoApsxwRgAAhmZRHQCw6z74wQ92Tzf1efjDH57WBAB2l+GMAMC2iL5Ha6LfktUEAICZd7/73d3TY30e97jHpTUBAAAAtsHjH//4rgtSn+i5ZDUBmDbDGYFBWFQHwFQZzggAwJAsqgMAuMLeQx7ykO4Jpz5nn312WhMA2F2GMwIA2yL6Hq2JfktWEwAAZh7wgAd0T4/1+dd//de0JgAAAMA2OOecc7ouSH2i55LVBGDaDGcEBmFRHQBTZTgjAABDsqgOAOAPvvWtb3VPOfW5+c1vntYEAHaT4YwAwDaIfkdros+S1QQAgIO+9rWvdU+R9bnd7W6X1gQAAACYsuh5tCZ6LVlNAKbPcEZgMBbVATBFhjMCADAUi+oAAC7zkpe8pHvSqc9pp52W1gQAdpPhjADANoh+R2uiz5LVBACAg174whd2T5H1ecUrXpHWBAAAAJiyV77ylV33oz7Ra8lqAjB9hjMCg7GoDoApMpwRAIChWFQHAHCZW9ziFt2TTn0uuuiitCYAsJsMZwQAtkH0O1oTfZasJgAAHBT/Lr41P/jBD9KaAAAAAFP2wx/+sOt+1McMAoDtZTgjMBiL6gCYIsMZAQAYikV1AAB/7KMf/Wj3tFOfBz/4wWlNAGD3GM4IAExd9DlaE/2VrCYAAMyT/Xv5ZfPIRz4yrQkAAAAwRdHraE30WLKaAGwHwxmBQVlUB8AmPPCBD9x761vf2uR73/te9010Wd773vem2y5y+umnp8cHq4qBoaeccsrlHHusxXy77sQTT7zcfRH/LdsWgPWyqA4A4PIe8YhHdE889fEPmgCAGcMZAYCpW2U4TvRXspoAADCPf8cEAAAA8Acf+9jHuq5HfczEAdhuhjMCg7KoDoBNiEFkY8g3v/nN9PhgVbe73e26u+yP86pXvSrdnt0RnzsH47MIYBwsqgMAyP3whz/snnrqc8wxx6Q1AYDdYjgjADBl0d9oTfRVspoAALDIRRdd1D1V1udWt7pVWhMAAABgSm5961t33Y76RG8lqwnA9jCcERicRXUArJvhjGw7wxmZx3BGgHGyqA4AYL5XvvKV3ZNPfU4//fS0JgCwWwxnBACmLPobrYm+SlYTAAAWOe2007qnyvq85jWvSWsCAAAATMlrX/varttRn+itZDUB2B6GMwKDs6gOgHUznJFtZzgj8xjOCDBOFtUBAMx329vetnvyqc/Pf/7zvatf/eppXQBgdxjOCABMVfQ1or/RmuirZHUBAGCRm9/85t1TZX1++9vf7l3vetdL6wIAAABMQfQ2fve733XdjvpEbyWrC8D2MJwRGJxFdQCsWwxnjGFkLX71q19130KX5dvf/na67SL/9m//lh4frMpwRuaJz56Dif+WbQvAelhUBwCw2DnnnNM9/dTn2c9+dloTANgdhjMCAFMVfY3WRD8lqwkAAMs6++yzu6fL+rzwhS9MawIAAABMQfQ2WhM9lawmANvFcEZgLSyqA2AqPvCBD3TfQJfl6KOPTreFTTGckXkMZwQYH4vqAAAWe/zjH989AdXnwgsvTGsCALvDcEYAYKqir9Ga6KdkNQEAYFkPf/jDu6fL+nz3u99NawIAAABMwfe+972uy1Gf6KlkNQHYLoYzAmthUR0AU2E4I1NgOCPzGM4IMD4W1QEALHbIIYfsXXzxxd1TUH0e/ehHp3UBgN1gOCMAMEXRz2hN9FGin5LVBQCAGt///ve7p8z6PPnJT05rAgAAAIzZU57ylK67UZ/opWQ1Adg+hjMCa2FRHQBTYTgjU2A4I/MYzggwLhbVAQAs7xWveEX3JFSfc889N60JAOwGwxkBgCmKfkZroo+S1QQAgFqnnnpq95RZny996UtpTQAAAIAx+8///M+uu1Gf6KVkNQHYPoYzAmtjUR0AU2A4I1NgOCPzGM4IMC4W1QEALO8mN7lJ9yTUlvve975pXQBg+xnOCABMTfQxVkn0UbK6AABQ6wY3uEH3lNmWBz3oQWldAAAAgDGKXsYqiV5KVheA7WM4I7A2FtUBMAWGM47HoYcemv53DGdkPsMZAcbDojoAgHpvf/vbu6eh+nzwgx9MawIA289wRgBgaqKP0Zron2Q1AQCg1RlnnNE9bdbn4x//eFoTAAAAYIyil9Ga6KFkNQHYToYzAmtlUR0A4dhjj/1/sp9vkuGM4/Cwhz1s79vf/vbeLW5xi/Tnu85wRubZhuGMY/1+AKhlUR0AQL073/nO3RNRW25/+9undQGA7WY4IwAwJdG/WCXRP8nqAgBAq3n/LnnZ3OUud0nrAgAAAIzJXe96166b0ZbooWR1AdhOhjMCa2VRHcDuiQFbp5xyyt4555zTfZrPT2wXsjrrYjjj5j372c/e++lPf/r7c3/++efv/e3f/m263S6b2nDG2e/2OmXHsQumOJwxvifiOyL7nohrueqgxln9LLt8rwDDsqgOAKDdxz72se6pqD7+F2kBYDcZzggATEn0L1oTfZOsJgAArCr7N/TLxv8QLQAAADAF73jHO7puRn2id5LVBGB7Gc4IrJ1FdQC7oTQMa1E2OSzLcMbNuupVr7r3+te/vjvzf8h//ud/7j34wQ9Ot99VUxrOGJ8Fm8iqA/2mamrDGePzfpm0Xs9F9Q1nBIZiUR0AQLsHPvCB3ZNRW25wgxukdQGA7WU4IwAwFdG3WCXRN8nqAgDAqu573/t2T51tudnNbpbWBQAAABiD6F2skuidZHUB2F6GMwJrZ1EdwHZbdtDWMsnqD81wxs370z/9072zzjqrO/t/yFe+8pW9hz70oen2u2hKwxn7/Eyoya4O3ZvScMbaeyOrUbJM/V29T4BhWVQHALC6L33pS93TUX1OPfXUtCYAsL0MZwQApiL6Fq2JfklWEwAA+nLuued2T5/1eeUrX5nWBAAAABiD6F20JnomWU0AtpvhjMBGWFQHsH2OPfbYvXPOOaf7tL584mexzf59Fg3Oin32b78OhjOOw33uc5/fD2TcnwsuuGDv4Q9/eLr9rtnG4Yzx+76sRYltsmPZBVMZzlg7mDFSc12XrZ/tC7Aqi+oAAFb35Cc/uXtCqs+PfvSjvStf+cppXQBgOxnOCABMQfQrom/RmuiXZHUBAKAvj3rUo7qnz/r88pe/3LvWta6V1gUAAADYpMMOO+z3vYvWRM8kqwvAdjOcEdgIi+oAtktpCNaiYVoxsLGUgwMdh2Y443g88YlP3Pv5z3/eXYU/5Gtf+5om1v/flIYzlhKfDy2/46UBjbs8mDFMYThj9p0R122ZgYpZvYNqBj9m+wOswqI6AID+fPe73+2ekurztKc9La0JAGwnwxkBgCmIfkVrok+S1QQAgL59/etf755C6/Pc5z43rQkAAACwSSeddFLXvahP9EqymgBsP8MZgY2xqA5gO5SGYMXPsn0O6qNGXwxnHJdTTz21uwqX5Rvf+Mbe4x73uHT7XbENwxlbf7dLnxeRbJ9dMvbhjAcH8mbDNEvDNxcN81x0f+zPur9fgN1gUR0AQH9e+MIXdk9K9fmv//qvtCYAsJ0MZwQApiD6Fa2JPklWEwAA+vasZz2rewqtz9j+x8QBAAAAwv/8z/903Yv6RK8kqwnA9jOcEdgYi+oApq80BGvRAK39Dg7r2p9scNeQDGccl+te97p7b3vb27orcVkuuuiivSc+8YnpPrtgKsMZ531GtA7FWzR4r+ZzZ1uNfTjjwWTbtH4nzNtvdr8d/HnrfQhQYlEdAEB/oi/0u9/9rntaqs/xxx+f1gUAto/hjADA2EWfojXRH4k+SVYXAAD6dvWrX33vZz/7Wfc0Wp9d/x/gBwAAAMYlehWtiR5J9EqyugBsP8MZgY2xqA5g2kpD0loGXs2L4Yzc4Q532PvMZz7TXY3L8p3vfGfvKU95SrrPtpvycMbWgXilz5xIa91tM+bhjDXDEeel9J2Q5eDAznjNqBEO/gxgVRbVAQD07zWveU33xFSfdfcVAYDNMZwRABi76FO0JvojWU0AABjKy172su5ptD7/8R//kdYEAAAA2IToVbQmeiRZTQB2g+GMwEZZVAcwTaUhaa0D0uZl3Z/3hjOO04Me9KC9//mf/+muyGX5/ve/v/fUpz413WebTWU448HFBa2/z6XPnEjr5842GvNwxjC7louuWWlhSrZ9do8YvgisW+mza1EsqgMAyN3qVrfqnpja4v9vCAC7wXBGAGDMDv4PGdYm+iNZXQAAGEr82/lVcv/73z+tCwAAALBOD3jAA7puRVvMFwDYbYYzAhtlUR3ANM3LKgPS5mXdQ9ce+9jH/v419zv88MPTbVmvf/iHf+juij/O//7v/+494xnPSPfZVlMZzngwLc9u8TtYSvw8229XjX0447JKi1MO3kfZPeK+ANbNojoAgOG85z3v6Z6a6vPud787rQkAbBfDGQGAMYv+RGuiL5LVBACAob31rW/tnkrr8+EPfzitCQAAALBOH/nIR7puRX2iN5LVBGB3GM4IbJxFdQDTUhqUlm2/jNIwGwO22C+GD2b56U9/uvfMZz4z3WcbTWE448Hf65bf5UWDrnw+XN62DGcM87J/OGN2j7gvgE2wqA4AYDj3vOc9uyenttzylrdM6wIA28NwRgBgrKIvsUqiL5LVBQCAod3pTnfqnkrb8hd/8RdpXQAAAIB1iN7EKoneSFYXgN1hOCOwcRbVAUxHaTDjKsOwSnX3D+GCo446au/9739/d3f8cX7+85/vPec5z0n32zZTGM4Y4vd3Jvt5STZ0b3/OOeecdL9dt03DGeMaZ9l/7Q9uYzAjsAkW1QEADO/Tn/509/RUn1e/+tVpTQBgexjOCACMVfQlWhP9kKwmAACsy1lnndU9ndbnzW9+c1oTAAAAYB2iN9Ga6IlkNQHYLYYzAqNgUR3ANJSSbb+sUrLtaXP00UdvhRjaF4MY5+XEE09M3/8mXOMa10jfw6oe9rCHde/2jxPDGbPtV3Xd6143fX9DKmUbBjNm57kP3/72t7uzdFniv2Xb9iF7b31ZNJzx4GBfgxmBTbGoDgBgeMcff3z3BFWf3/72t3vXuc510roAwHYwnBEAGKPoR0RfojXRD8nqAgDAuvz1X/9193Talhvf+MZpXQAAAIAhHXXUUV13oi3RE8nqArBbDGcERsGiOoDxOzgEa39WGYg1VF0u7yc/+Ul3Zrc7v/nNb/Ze+MIXpudg3R74wAd2RzXtvPWtb03f31DmDeWbJQZ0ZvtNybb8PmbvrS9xnecl++7IagAMzaI6AID1ueCCC7qnqPq84AUvSGsCANvBcEYAYIyiH9Ga6INkNQEAYN3OP//87im1Pv/n//yftCYAAADAkF760pd23Yn6RC8kqwnA7jGcERgNi+oAxq2UbPtllAYzxnC2bB/a7Vr+/M//PD0P62Q4Y73S50JkGwYzBsMZFysNZzyYbbkvgOmxqA4AYH2e/vSnd09S9fnOd76T1gQAtoPhjADAGEU/ojXRB8lqAgDAuj3hCU/onlLrE/9W9qpXvWpaFwAAAGAIhx566N5Pf/rTrjtRn+iFZHUB2D2GMwKjYVEdwHiVBmTFILVsn5KoF8MXS8n2YzXbMgxu2cRgxOw8rJPhjHUWDWZs+bwZK8MZl7NMtum+AKbHojoAgPU55JBD9n70ox91T1P1edKTnpTWBQCmz3BGAGBsog/Rmuh/RB8kqwsAAJvwrW99q3tarc8//MM/pDUBAAAAhhC9iNZEDySrCcBuMpwRGA2L6gDGqzQwrWYo1jJDGePnsV22P6vZtYzhPjKccXm7NJgxGM64nEXfGQYzAptkUR0AwPqddtpp3RNVfb74xS+mNQGA6TOcEQAYm+hDtCb6H1lNAADYlJNPPrl7Wq3PV7/61bQmAAAAwBCiF9Ga6IFkNQHYTYYzAqNiUR3AOJWSbR9iMF6IwVmLhmvNYsjWsLZlGNwyee1rX7v3J3/yJ+l5WCfDGZeza4MZg+GMyyndG74zgE2zqA4AYP1ueMMbdk9UbXn0ox+d1gUAps1wRgBgTKL/sEqi/5HVBQCATTn88MP3Lrnkku6JtT4nnHBCWhcAAACgT9GDaE30PqIHktUFYDcZzgiMikV1AOMTAxZLicGLB9Um9onXyV6f/swGZk7dQx7ykL2f//zn3d1z+bz97W/fu/KVr5yeg3U74ogj0vewqhNPPLF7t3+cGKKYbb+qW9ziFun760PUL2VbB/AdPMd9+d73vteducsS/y3btg/Ze+tTvMa8rOP1AeaxqA4AYHPe+MY3dk9V9fnyl7+c1gQAps1wRgBgTKL/0Jroe2Q1AQBg0171qld1T631ufDCC9OaAAAAAH2KHkRroveR1QRgdxnOCIyORXUA4xKD0YbIbJCj4VrUuPa1r7135plndnfR5fOKV7xi77DDDkv33Sa3u93tunf8x5li86+U+IzI9mG+b37zm93Zuyzx37Jtp2JetnVwJzANFtUBAGzO7W9/++7Jqi1PeMIT0roAwHQZzggAjEX0HVZJ9D2yugAAsGk3v/nNu6fWtjzjGc9I6wIAAAD0IXoPqyR6H1ldAHaX4YzA6FhUBzAufQ1n3D+M0UBGWr34xS/u7qjL52Uve9ne1a9+9XS/bbMtwxnjM6GUbB/Kdmk4o+GdwKZYVAcAsHkf/OAHu6er+lxwwQVpTQBgugxnBADGIvoOrYl+R1YTAADG4p3vfGf39Fqfb33rW2lNAAAAgD5E76E10fPIagKw2wxnBEbJojqA8ShlNnDxoBjoGAxipE+Pf/zj9y6++OLu7vvjnHbaaXtXvepV0/220TYMZ4zPilJ8drTZtuGMpQHBcQ9l+wAMzaI6AIDNu9/97tc9YbXlhBNOSOsCANNkOCMAMAbRb1gl0e/I6gIAwFjc7W53655e2/LsZz87rQsAAACwiug5rJLoeWR1AdhthjMCo2RRHcB4lJJtD0O4973vvfflL3+5u/Muy6WXXrr3j//4j3tXutKV0v221dSHM5YG7kXi59l+LLZtwxkXJdsHYEgW1QEAjMd5553XPWXV58ILL0xrAgDTZDgjADAG0W9oTfQ5spoAADA2n/jEJ7qn2Pp8//vf37vKVa6S1gUAAABoEb2G6Dm0JnodWV0AMJwRGC2L6gA279hjj+0+WS+fc845J90H+nazm91s78Mf/nB3512WX//613sveMEL0n223ZSHMxrMOKxtGs4Y3zOLEt9T2b4AQ7GoDgBgPB784Ad3T1ptefrTn57WBQCmx3BGAGDTos+wSqLPkdUFAICxue9979s9xbblec97XloXAAAAoEX0GlZJ9DqyugBgOCMwWhbVAWye4Yxs2iGHHLL32te+trvrLssvf/nLvZNOOindZxdMdTijwYzD25bhjIvulVncM8A6WVQHADA+n/nMZ7qnrfpcdNFFaU0AYHoMZwQANi36DK2J/kZWEwAAxurss8/unmbr8+Mf/3jv6le/eloXAAAAoEb0GKLX0JrocWR1ASAYzgiMmkV1AJtlOCOb9pznPKe74y7LxRdfvPfsZz873X5XTHE4o8GM67ENwxkP3ivxf8+7f3wXAetkUR0AwPj8zd/8TffE1ZZnPetZaV0AYFoMZwQANin6C6sk+htZXQAAGKt73ete3dNsW/ybYQAAAKAPL3rRi7puQ1uix5HVBYBgOCMwahbVAWxWaTijfxTB0I4//vi973znO90d94f86Ec/2jvxxBPT7XfJ1IYzlj5LIj5P+jP14YzZEMb476V76GANgCFYVAcAMF6f/OQnu6eu+nzve9/bO+SQQ9K6AMB0GM4IAGxK9BWiv9Ca6GtkdQEAYOw++MEPdk+19fn5z3++d/jhh6d1AQAAAJYRvYXoMbQmehtZXQCYMZwRGD2L6gA2pzQM65xzzkn3gT7c6U532jv33HO7u+0P+cEPfrD3tKc9Ld1+10xtOGMpLZ8l8dm0X7bNrprycMbsO2c2uDP72SzuAWBoFtUBAIzb/e53v+7Jqy0nn3xyWhcAmA7DGQGATYm+wiqJvkZWFwAAxu5ud7tb91TbltNOOy2tCwAAALCM6C2skuhtZHUBYMZwRmD0LKoD2JzSMKy+hzMarsXMEUccsfeud72ru9P+kO985zt7T3nKU9Ltd9GUhjPGZ0Up2T6LHEy2za6a8nDGg5kNZpz381l8fwBDs6gOAGD8PvrRj3ZPX/X50Y9+tHe1q10trQsATIPhjADAJkQ/IfoKrTn77LPTugAAMBXvfe97u6fb+lxyySV717/+9dO6AAAAACXRU/j1r3/ddRnqEz2NrC4A7Gc4IzAJ8Y/QWmNRHcBq5qXP4YyzIZAHB3Gxm1760pf+/n6Y5aKLLtp74hOfmG67q6YynDF+p0tpGap3sGbrZ9Hhhx++d/TRR6eue93rpvtMwVSHMx4c4pld13mDPvv8PgI4aNVFdTEkKKsLAEC/7nWve3VPYG154QtfmNYFAKbBcEYAYBOin7BKop+R1QUAgKm4053u1D3dtuXlL395WhcAAACgJHoKqyR6GlldANjPcEZgEiyqA9icUrLta80GM85iQONuO/TQQ/dOO+207m7Y27vwwgv3HvvYx6bb7rIpDGccYjBjOJjWz4yTTz65q3D5vPnNb073mYIpDmfM7pVsu3nDGSPZ9vu13m8AFtUBAEzHhz70oe4prD4XX3zx3mGHHZbWBQDGz3BGAGDdoo8Q/YTWRB8jqwsAAFPzzne+s3vKrc+ll166d8Mb3jCtCwAAAJCJXsIqiV5GVhcADjKcEZgMi+oANqM0DGvVYVfzhrdl27JbTjrppL2f//zne3e9613Tn++6sQ9nXDSYsXWgYla3tdaJJ57YVbh8PvKRj6T7TMHUhjPWXNODw3z3Z9H30Syt9wuwmyyqAwCYlrvf/e7dk1hbTj311LQuADB+hjMCAOsWfYRVEn2MrC4AAEzN7W9/++4pty1j+h+mBwAAAMYvegmrJHoZWV0AOMhwRmAy7na3u3WPu22xqA6gTWkYVgxuzPZZJGrOG/q46sBHtscRRxyR/nfGPZwxG7a3P6sMx8uSbbeMJz3pSV2Fy+fzn/98us8UjG044/774eB3RnavlO6P1u+jg9832TYAGYvqAACm533ve1/3NFafX/3qV3vXu9710roAwLgZzggArFP0D6KP0JroX2R1AQBgqt761rd2T7ttOeaYY9K6AAAAAPtFD2GVRA8jqwsAGcMZgUmxqA5gM+YNUozUDlrLhnHNYjAjLGeswxlLw/MitZ8X+837HMq2XcYjH/nIrsLl861vfSvdZwrGPJwxEv/3vAG9pQGLM6Xvo2z77PWz7QAOsqgOAGCa/vIv/7J7ImvLy172srQuADBuhjMCAOsU/YNVEv2LrC4AAEzVrW996+5pty2ve93r0roAAAAA+0UPYZVEDyOrCwAZwxmBSbGoDmAzlhm4Vhp4NW8Q1yzxM4MZYXljHM64icGMq9R80IMe1FW5fH7961/vXec610n3G7uxDWesSbb/QYu+S/ZvG/fH/qxyvwC7x6I6AIDp+pd/+Zfuqaw+v/vd7/aOPPLItC4AMF6GMwIA6xJ9g+gftCb6FlldAACYujPOOKN76m3LzW9+87QuAAAAQPjTP/3TrovQluhdZHUBYB7DGYHJsagOYDNKA7EOJrZddvuDw7SAxcY4nLH0O1/7ex6DHhcNdY2sMmxv0TDJo48+Ot1v7KY6nHHZAb2Lrlsku2981wA1Vl1U9653vSutCwDAetzhDnfonsza8spXvjKtCwCMl+GMAMC6RN9glUTfIqsLAABTt+qAhDe+8Y1pXQAAAIAQvYNVEr2LrC4AzGM4IzA5FtUBbM6iQWm1WWWwGuyysQ1n7PuzYdksO9Avc9xxx3VVLp/f/va3e9e5znXS/cZuTMMZ4zN+mdR+F9TebwYzArUsqgMAmL63ve1t3dNZW4466qi0LgAwToYzAgDrEP2CVRL9iqwuAABsi9e97nXd029bbn3rW6d1AQAAgN0WPYNVEj2LrC4AlBjOCEySRXUAm7PssK1SYlDWKkPVYNeNaThj/C5vKtnxLOv+979/V+Xy+e53v5vuMwVTG87YMqS35p4zmBGoZVEdAMB2uM1tbtM9obXlNa95TVoXABgnwxkBgHWIfsEq+bM/+7O0LgAAbItjjjmme/pty5lnnpnWBQAAAHZb9AxWSfQssroAUGI4IzBJ8Y/UVolFdQCraR3QaCgj9GNMwxnDJrLq0L0HPOABXaXL57zzzkv3mYIxDWeMazQvq34fDDX4EcCiOgCA7fGmN72pe0pry81udrO0LgAwPoYzAgBDiz7BKok+RVYXAAC2Tfxb5lVy+9vfPq0LAAAA7KboFaySTa27BmD6DGcEJsuiOoDNi8FaMfwqhmztH8I1+79D/NxARujX2IYzxu/4JmTHsqwTTzyxO2uXz6mnnpruMwVjGs64//thv1Wv3X6z14jsr9/nawC7w6I6AIDtcotb3KJ7UmvLG97whrQuADA+hjMCAEOLPsEqiT5FVhcAALbNDW94w71LL720exKuzzvf+c60LgAAALCb/uVf/qXrGtQnehTRq8jqAsAihjMCk2VRHQCwq8Y2nHGKSv/LvH/5l3+Z7jMFYxrOCDA1FtUBAGyf17/+9d3TWltuectbpnUBgHExnBEAGFL0B1ZJ9CeyugAAsK1e/vKXd0/DbbnTne6U1gUAAAB2S/QIVkn0KLK6ALAMwxmBSfv/sXfn/16V5d7A/5STkIolYoViwnHGTHPWLBVLw6FE06BBIA+WEw51Aic8YeJRi8c5xVLD6VViKlaaOOaQYM4kaiSZ63kuvHlEvNns/V1rfYf1fX9er/cPJ/a+71Vu+V7XOlwXhuoAgH4Uy5+uuuqqjzjyyCOzX8+HDRs2rLjnnntSRfjhLFiwYPWv576vF8yZM+cjPxfxn+W+FoAPGKoDAGim//zP/0wVW2uZP39+9lwAoLtYzggA1CneD5RJvJ/InQsAAE316U9/unjnnXdSRTz0xJ/lzZ0LAAAA9Jebb745vS0YeuLdRLyjyJ0LAINhOSPQ0wzVAQAwVIcddliqBj+cVatWFcccc0z2ewBoNkN1AADN9bOf/SxVba1lv/32y54LAHQPyxkBgLrEe4EyueSSS7LnAgBA082ePTtVxa0l/qxv7lwAAACgP3zlK19JbwlaS7ybyJ0LAINlOSPQ8wzVAQAwFBdffHGqBD+cOXPmZL8egGYrO1QX7yVy5wIA0B0++9nPpsqttdx9993ZcwGA7mE5IwBQl3gvUCbbbLNN9lwAAGi6kSNHFm+99VaqjIeeBx98MHsuAAAA0B/+8Ic/pLcEQ0+8k4h3E7lzAWCwLGcEel784bUyMVQHANA/4oXqa6+9lirBD7Jo0aJi2LBh2e8BoNkM1QEANN/6/qKGweab3/xm9lwAoDtYzggA1CHeB5RJvI/InQsAAP3iRz/6UaqOW8u0adOy5wIAAADNNn369PR2oLXEO4ncuQAwFJYzAo1gqA4AgMG49tprUwX4QZ566qli9OjR2a8HoNkM1QEA9Ifo+//1r3+lKm7oeeaZZ7LnAgDdwXJGAKAO8T6g1cR7iM985jPZcwEAoF+MGDGiWL58eaqSh55XXnml2GyzzbJnAwAAAM0U7wJeffXV9HZg6Il3EfFOInc2AAyF5YxAI8QfYnv33XdTuTz0GKoDAGi+U045JVV/H8RiRoD+VmaoLt5DGKoDAOgd559/fqrkWss555yTPRcA6DzLGQGAqsV7gDKJ9xC5cwEAoN/MnDkzVcmt5aKLLsqeCwAAADTTnDlz0luB1hLvInLnAsBQWc4INIahOgAA1ie3mPGRRx6xmBGgjxmqAwDoL6NGjSpWrlyZqrmhJ5Zzb7vtttmzAYDOspwRAKhS9P9l/rLweP8Q7yFyZwMAQL8ZPnx48corr6RqubXsuuuu2bMBAACAZol3AGUS7yDiXUTubAAYKssZgcYwVAcAwPr8+Mc/TlXf+7niiiuKHXbYIfu1ADSfoToAgP703//936miay3z58/PngsAdJbljABAlaL/L5N4/5A7FwAA+tVpp52WquXWcvPNN2fPBQAAAJol3gGUSbyDyJ0LAK2wnBFoFEN1AACsz1VXXbW65jv77LOzvw5A/zBUBwDQnzbaaKPi+eefT1VdaznoIMuUAKDbWM4IAFQl+v4yifcO8f4hdzYAAPSzRx99NFXNreVrX/ta9lwAAACgGSZOnJjeArSWePeQOxcAWmU5I9AohuoAAFifYcOGFfvvv3/21wDoH4bqAAD62+TJk1Nl11ruueee7LkAQOdYzggAVCX6/jKJ9w65cwEAoN8dffTRqWpuLQ8//HD2XAAAAKAZovcvk3j3kDsXAFplOSPQOFOmTEnlc2sxVAcAAADNZagOAICyNWH8/6Jy5wIAnWE5IwBQBX/2FAAA6nXrrbem6rm1zJgxI3suAAAA0NtOOeWU1P23lnjnkDsXAMqwnBFoJEN1AAAAwLoM1QEAEL74xS+mCq+1PP/888VGG22UPRsAaD/LGQGAsqLPj36/TOJ9Q+5sAADgfbvvvnuqnlvL3//+92KLLbbIng0AAAD0puj1o+cvk3jnkDsbAMqwnBFopIMOOiiV0a3FUB0AAAA0i6E6AADWNn/+/FTltZaf/OQn2XMBgPaznBEAKCv6/DKJ9wy5cwEAgA+bO3duqqJbS3x/7lwAAACgN11yySWp628t3hUAUBfLGYHGMlQHAAAArGGoDgCAtX32s58t3n333VTttZbtttsuezYA0F6WMwIAZUR/XybxfiHeM+TOBgAAPmzkyJHF8uXLUzXdWvbYY4/s2QAAAEBviR6/TOIdQ7xryJ0NAGVZzgg01rbbbmuoDgAAADBUBwBA1jnnnJMqvtZy7bXXZs8FANrLckYAoIzo78sk3i/kzgUAAPJmzJiRqunWcuutt2bPBQAAAHrLbbfdlrr91hLvGHLnAkAVLGcEGs1QHQAAAGCoDgCA9XnmmWdS1ddaDjnkkOy5AED7WM4IALQq+voyifcKuXMBAICBPfTQQ6mqbi3HHHNM9lwAAACgN0RvXybxbiF3LgBUxXJGoPEM1QEAAED/MlQHAMBAvvnNb6bKr7Xcf//92XMBgPaxnBEAaFX09WUS7xVy5wIAAAM74ogjUlXdWh577LHsuQAAAEBviN6+TL72ta9lzwWAqljOCDSeoToAAADoX4bqAADYkLvvvjtVf63le9/7XvZcAKA9LGcEAFoR/XyZxPuE3LkAAMDgLFiwIFXXreW0007LngsAAAB0t+jpyyTeKeTOBYAqWc4I9AVDdQAAANB/DNUBADAY++23X6oAW8uLL75YbLzxxtmzAYD6Wc4IAAxV9PHRz5dJvE/InQ0AAAzO+PHjU3XdWt5+++3iM5/5TPZsAAAAoDtFLx89fZnEO4Xc2QBQJcsZgb5gqA4AAAD6i6E6AACG4oorrkhVYGs5//zzs+cCAPWznBEAGKro48sk3iPkzgUAAIbmoosuSlV2a7nsssuy5wIAAADdKXr5Mol3CblzAaBqljMCfcNQHQAAAPQPQ3UAAAzFVlttVaxcuTJVg61lp512yp4NANTLckYAYCiify+TeH8Q7xFyZwMAAEMzYsSI4uWXX07VdmvZZ599smcDAAAA3SV6+DKJdwjxLiF3NgBUzXJGoG8YqgMAAID+YKgOAIBWnHnmmakibC2//OUvs+cCAPWynBEAGIro38sk3h/kzgUAAFozbdq0VG23ljvuuCN7LgAAANBd7rzzztTNt5Z4h5A7FwDqYDkj0FcM1QEAAEDzGaoDAKBVTzzxRKoKW8tXvvKV7LkAQH0sZwQABiv69jKJ9wa5cwEAgHIWL16cqu7Wctxxx2XPBQAAALpD9O5lEu8OcucCQF0sZwT6zpNPPpnK79ZiqA4AAAC6l6E6AADKOPbYY1Nl2Fr+8Ic/ZM8FAOpjOSMAMFjRt5dJvDfInQsAAJQzYcKEVHW3lqeeeip7LgAAANAd/vKXv6QuvrUcdthh2XMBoC6WMwJ9x1AdAAAANJehOgAAylq4cGGqDlvL9OnTs+cCAPWwnBEAGIzo18sk3hfkzgUAAKpx/fXXp+q7tZx11lnZcwEAAIDOip69TOKdQe5cAKiT5YxAX7r99ttTGd5aDNUBAABA9zFUBwBAFfbaa69UIbaWV199tdhiiy2yZwMA1bOcEQDYkOjTo18vk3hfkDsbAACoxg477JCq79ayatWqYuzYsdmzAQAAgM6IXj169jKJdwa5swGgTpYzAn3JUB0AAAA0i6E6AACqdOmll6YqsbX8/Oc/z54LAFTPckYAYEOiTy+TeE+QOxcAAKjW7NmzUxXeWm666absuQAAAEBnRK9eJvGuIHcuANTNckagb82bNy+V463FUB0AAAB0D0N1AABU6VOf+lTx5ptvpmqxtXzta1/Lng0AVMtyRgBgINGfl0m8H4j3BLmzAQCAag0fPrxYtmxZqsZby4knnpg9GwAAAGiv6NHLJN4RxLuC3NkAUDfLGYG+ZagOAAAAmsFQHQAAdTj11FNTxdhannrqqey5AEC1LGcEAAYS/XmZxPuB3LkAAEA9vvOd76RqvLW8/PLLxciRI7NnAwAAAO0RvXn06GUS7whyZwNAO1jOCPQ1Q3UAAADQ+wzVAQBQlyVLlqSqsbVcdNFF2XMBgOpYzggArE/05WUS7wVy5wIAAPW69957U1XeWq688srsuQAAAEB7RG9eJvFuIHcuALSL5YxA3zNUBwAAAL3LUB0AAHU6+uijU+XYeg444IDs2QBANSxnBAByoh8vm3gvkDsbAACo15e//OVUlbeeI444Ins2AAAAUK/oycsm3g3kzgaAdrGcEeh7huoAAACgNxmqAwCgHW655ZZUPbaWxYsXZ88FAKphOSMAkBP9eJnE+4DcuQAAQHtcddVVqTpvLU8++WT2XAAAAKBe0ZOXSbwTyJ0LAO1kOSPA/2OoDgAAAHqPoToAANph9913TxVk65k5c2b2bACgPMsZAYB1RR9eNvE+IHc2AADQHmPHji3efffdVKG3lgsvvDB7NgAAAFCPiy66KHXlrSXeBcQ7gdzZANBOljMC/D+G6gAAAKC3GKoDAKCdfvKTn6QqsvWMHz8+ezYAUI7ljADA2qL/Lpt4D5A7GwAAaK9TTz01Vemt54ADDsieDQAAAFQrevCyiXcBubMBoN0sZwRIDNUBAABAbzBUBwBAJzzyyCOpmmwtt99+e/ZcAKAcyxkBgLVF/10m0f/nzgUAADpj0aJFqVpvLYsXL86eCwAAAFQrevAyiXcAuXMBoBMsZwRYi6E6AAAA6H6G6gAA6IRDDz00VZStZ+rUqdmzAYDWWc4IAKwxbdq01IG3nuj/c2cDAACdsddee6VqvfXMnDkzezYAAABQjei9yybeAeTOBoBOsJwRYC2G6gAAAKC7GaoDAKCTLr300lRVtpY333yz2HrrrbNnAwCtsZwRAAhjxowp3nrrrdSBt5bo+3NnAwAAnTVr1qxUtbee8ePHZ88GAAAAyomeu2yi98+dDQCdYjkjwDoM1QEAAEB3iqG66LvLxFAdAABlbLrppsXSpUtTddlarrvuuuzZAEBrLGcEAML111+fOu/WEv1+9P25swEAgM5bsmRJqt5by+233549FwAAACgneu4yiZ4/dy4AdJLljADrMFQHAAAA3clQHQAA3eDYY49NFWbriTNyZwMAQ2c5IwAwadKk1HG3Hr06AAB0twkTJqTqvfVMnTo1ezYAAADQmmnTpqWuu/VEz587GwA6yXJGgAxDdQAAANBdDNUBANBN4i/qKpNYHD5ixIjs2QDA0FjOCAD9LfrrZcuWpY67tfgLuQEAoDdceumlqYpvLW+++WYxZsyY7NkAAADA0ESPHb12mUSvnzsbADrNckaA9TBUBwAAAN0h+uvos8vEUB0AAFXaeuutixUrVqRqs7X4A2UAUA3LGQGgv82bNy912q0l+vvo83NnAwAA3aWKP0d2/fXXZ88GAAAAhiZ67DKxkwWAbmY5I8B6GKoDAACA7mCoDgCAbjR16tRUcbaeCRMmZM8GAAbPckYA6F/RV5dN9Pe5swEAgO507LHHpmq+9UyaNCl7NgAAADA40VuXTfT4ubMBoBtYzggwAEN1AAAA0FmG6gAA6GYLFy5MVWdrWbJkSfZcAGDwLGcEgP4VfXWZ3H777dlzAQCA7nbdddelqr61LFu2rBgxYkT2bAAAAGBgm2222ereukyit8+dDQDdwnJGgA0wVAcAAACdY6gOAIBuNn78+FR5tp5Zs2ZlzwYABsdyRgDoT9FPl0309bmzAQCA7rb11lsXb775ZqrsW8u8efOyZwMAAAADi566TFasWLG6t8+dDQDdwnJGgA0wVAcAAACdYagOAIBecOaZZ6bqs/Xstdde2bMBgA2znBEA+k/00WUzc+bM7NkAAEBvmDp1aqruW8+ECROyZwMAAAB5hx12WOqqW0/09LmzAaCbWM4IMAiG6gAAAKC9DNUBANBLHnjggVSFtpZFixZlzwUANsxyRgDoP9FHl0n08blzAQCA3rJw4cJU5beWJUuWZM8FAAAA8h599NHUVbeW6OVz5wJAt7GcEWCQDNUBAABA+xiqAwCgl+y///6pEm09P/zhD7NnAwADs5wRAPpL9M9lc8ABB2TPBgAAessuu+ySqvzWM3v27OzZAAAAwIdFD1020cvnzgaAbmM5I8AgGaoD6B0TJkwobrrppo674oorss8HAMDADNUBANCLLrzwwlSNtpZ333232H777bNnAwDrZzkjAPSP6Jujfy6T6N9zZwMAAL3pzDPPTNV+69l7772zZwMAAADvi965bKKHz50NAN3IckaAITBUB9AbqljmU0Wee+657PMBALB+huoAAOhlTz75ZKpKW8uvf/3r7LkAwPpZzggA/SP65jKJvj13LgAA0NseeOCBVPW3lnvvvTd7LgAAAPC+6J3LJHr33LkA0K0sZwQYIkN1AN3PckYAgN5lqA4AgF52xBFHpMq09Zx00knZswGAPMsZAaA/RL9cNtG3584GAAB62/7775+q/tYzc+bM7NkAAADQ76JnLpvo3XNnA0C3spwRYIgM1QF0P8sZAQB6k6E6AACa4Morr0zVaWv517/+Veyyyy7ZswGAj7KcEQCaL/rk6JfLJPr13NkAAEAzXHjhhan6bz0HHHBA9mwAAADoV9Erl80FF1yQPRsAupnljAAtMFQH0N2+853vFH/729867pFHHsk+HwAAH2WoDgCAphg5cmTx8ssvpyq1tdxzzz3ZswGAj7KcEQCaL/rkMok+Pfr13NkAAEBzPPHEE6kLaC1LlizJngsAAAD9KnrlMolePXcuAHQ7yxkBWmCoDgAAAKplqA4AgCY58cQTU6Xaen7yk59kzwYAPsxyRgBotuiPyyb69NzZAABAsxx++OGpC2g98+bNy54NAAAA/SZ65LKJXj13NgB0O8sZAVpkqA4AAACqYagOAIAmWrBgQapWW8+hhx6aPRsA+IDljADQXNEXl03057mzAQCAZrriiitSN9B6Jk2alD0bAAAA+kX0xmUTPXrubADoBZYzApRgqA4AAADKMVQHAEBTjRs3rnjnnXdS1dpann766WLTTTfNng8AvM9yRgBopuiHoy8uk+jLoz/PnQ8AADTT5ptvXrz00kupK2gtr7/+erHNNttkzwcAAICmi544euMyefHFF1f36LnzAaAXWM4IUIKhOgAAAGidoToAAJpuxowZqXJtPfPnz8+eDQC8z3JGAGim6IfLJvry3NkAAECznXDCCakraD233HJL9mwAAABouuiJyyZ689zZANArLGcEKMlQHQDUY5NNNsn+5wBAcxiqAwCgH/z2t79N1WvrmTJlSvZsAMByRgBoouiDyyb68dzZAABAf7jppptSd9B6Tj311OzZAAAA0FSnnXZa6opbz4033pg9GwB6ieWMABUwVAcwNAcddND/l/t1mDRpUvG3v/2t2GmnnbK/DjSXzwfoH4bqAADoF5/73OeKf//736mKbS3/+Mc/iu233z57PgD0O8sZAaBZov+NPrhMog+Pfjx3PgAA0B/GjBlTvP7666lLaD377LNP9nwAAABomuiByyZ68ejJc+cDQC+xnBGgAobqANYvFmz9+Mc/LhYtWpR+x1t/4utC7hz6R/ytKitWrFj9M/HQQw8VEydOzH4dvWPNv9vtlHsOulN8TsRnRO5zIv5Zll3UuOb8XPysQOcYqgMAoN9Mnz49VbKt56677sqeDQD9znJGAGiW6H/LJvrw3NkAAEB/mTRpUuoSWk/8efbc2QAAANA00QOXTfTiubMBoNdYzghQEUN1AB820DKsDcWyrP618cYbF//7v/+bfhLez5IlS4qjjz46+/V0v/i9oBMpu9CP9ojf7weTVv95buh8nzfQOYbqAADoRzfccEOqZlvPOeeckz0bAPqZ5YwA0BzR95ZN9N+5swEAgP40b9681C20nrlz52bPBgAAgKaI3rdsogfPnQ0AvchyRoAKGaoDGPyircEkdz7Nt8MOOxS33357+il4P48//njxjW98I/v1dLcqf08YSizd635D/dnInTGQwZzv5wQ6w1AdAAD9atSoUcWyZctSVdt6/KUUAPBhljMCQDNU8Zc/vvDCC6v779z5AABA/1qyZEnqGlrPMccckz0bAAAAel30vGXzyCOPZM8GgF5lOSNAhQzVAf0sfu9atGhR+p3so4lfW/f3tw0tzorvWfvr6R+HHnro6oWMa+epp54qjjvuuOzX070Gu4Av/n0frA0lvib3LHSPoS5mjAzln+tgz899L1AvQ3UAAPS7iRMnpsq29cR7s2HDhmXPB4B+ZDkjAPS+6HPX/XMirST67tz5AABAfzvggANS19B6Xn755WL06NHZ8wEAAKBXRa8bPW/ZRO+dOx8AepXljAAVM1QH9KOBlmBtaJnWhhb0WFjbv7773e8Wb7/9dvpJeD/PPPNMccIJJ2S/nu40UOL3h1b+HR9oQaPFjN0v95kR/9wGs1Axd966hrL4Mff9QH0M1QEAwPvmzJmTqtvWc/nll2fPBoB+ZDkjAPS+6HPLJvrt3NkAAABh5syZqXtoPQsWLMieDQAAAL0qet2yOfPMM7NnA0Avs5wRoAaG6oB+MtASrPi13Pesq4ozaKZZs2aln4QP8te//rWYMmVK9uvpPutLq/9uD/T7RST3PXSPdRfy5pZpDrR8c0PLPDf087F2fL5A+xmqAwCADyxevDhVua3nm9/8ZvZsAOg3ljMCQG+Lv6SzbB588MHs2QAAAGu7/fbbUxfRembMmJE9GwAAAHrNKaeckrrd1rNw4cLs2QDQ6yxnBKiJoTqgHwy0BGtDC7TWtu6yrrWTW9xF/9hyyy2La665Jv00fJBly5YV3/3ud7PfQ/dY3+8RrS7F29DivaH8vkNnrJvc17T6mbC+71vz87bur7f6cwi0JvrbsjFUBwBAk3zhC19IlW7reeONN4ptt902ez4A9BPLGQGgd40dO3Z1f1s20WfnzgcAAFhb9CArVqxInUTr0YMAAADQ66r4c6zRY0evnTsfAHqd5YwANTFUBzTdQEvSWll4tb5Yzsgee+xR/P73v08/ER/kxRdfLKZOnZr9HrpD7veJVhfiDfR7TqTVc2mfoSxHXF8G+kzIZd2FnXFnnBHW/TWgPtHXGqoDAICPquJvHP7Nb36TPRsA+onljADQu6KvLZvor3NnAwAA5Jxwwgmpm2g9ixcvzp4NAAAAvSJ627KJHjt3NgA0geWMADUyVAc01UBL0lpdkLa+WM5IOOqoo4qlS5emn4oP8sorrxTf//73s99D58W/v2un1X+fB/o9J9Lq7zu035p/lhv6Z7buz87ayX197mfE8kXoHobqAABg/RYsWJCq3tZzxhlnZM8GgH5hOSMA9KYzzzwzdbat5+abb86eDQAAMJArrrgidRWtZ86cOdmzAQAAoNtFT1s20VvnzgaAprCcEaBmhuqAJlpfyixIW18sXWONH/zgB+mn4sN5/fXXixkzZmS/h85aN60sy8st3Vs7fo9opvhZWV/W/TnK/Yz4uYDuEf1s2RiqAwCgyUaPHl28/PLLqfptPfvvv3/2fADoB5YzAkDviT62bKKfjr46dz4AAMBAhg8fXjzxxBOpu2g9EydOzJ4PAAAA3Sp62bKJnjp669z5ANAUljMC1MxQHdA0Ay1Ky339YAy0hMuCLdY2d+7c9JPx4axYsaL44Q9/mP0eOmPdf69b+Xd5oN8bIn5/aLb1Ze3ljLmfET8X0D0M1QEAwOB8/etfTxVw6/nzn/+cPRsA+oHljADQe6KPLZvop3NnAwAADMaXvvSl1F20nhdeeKHYcssts+cDAABAt4keNnrZsomeOnc+ADSJ5YwAbWCoDmiKgRYzllmGNdC5ay/hgm233bb49a9/nX46Ppy33367OP3007PfR2fEv79r5H59ILmle2tn0aJF2e+jOeKfcS5r/7Nf92ssZoTuYqgOAAAGb31/KclQ8rOf/Sx7NgA0neWMANBbon8tm0suuSR7NgAAwFCce+65qctoPTfccEP2bAAAAOg20cOWzTnnnJM9GwCaxnJGgDYxVAc0wUDJff1gDZTc19OasWPHNkIs7YtFjOvLKaeckv3v3wmf/vSni/Hjx/e8+O+R++9Xp4HShMWMO++8c/Z/616y3XbbZf+7VWVDyxnXXexrMSN0F0N1AAAwdA899FCqhlvPsccemz0bAJrMckYA6B3Rt5ZN9M+5swEAAFpx9913p26j9UyfPj17NgAAAHSL73//+6mLbT133XVX9mwAaCLLGQHayFAd0MvWXYK1dsosxKrrXD7qjTfeSP/LNjv/+te/Vv8tprn/DdrtmGOOSU/V27nyyiuz//3qsr6lfGsSCzpz39dL3n333fTfpnezYsWK7H+3qsQ/5/Ul99mROwPoDEN1AADQmn333TdVxK3n1VdfLbbeeuvs+QDQVJYzAkBviH41+tayif45dz4AAEArdthhh+If//hH6jhay3vvvVfstttu2fMBAACg06Jnjd61TKJ33n777bPnA0ATWc4I0EaG6oBeNlByXz8YAy1mjOVsue+hdf2WL3zhC9n/HdrJcsahG+j3hUgTFjMGyxk3bKDljOumKT8X0ASG6gAAoJzTTjstVcWt59Zbb82eDQBNZTkjAPSG6FfL5vTTT8+eDQAAUMa3v/3t1HW0nvvvvz97NgAAAHRa9KxlM2XKlOzZANBUljMCtJmhOqAXDbQgKxap5b5nIHFeLF8cKLnvo5w33ngj/a/bHznyyCOz/zu0k+WMQ7OhxYyt/H7TrSxnHJzBpEk/F9AEhuoAAKC8Kurq8847L3s2ADSR5YwA0P2iTy2b2267LXs2AABAFebPn5+6j9ZzxRVXZM8GAACAToletWyiZ86dDQBNZjkjQAcYqgN6zUAL04ayFGswSxnj1+Prct9POf2Wbvg5spxx8PppMWOwnHFwNvSZYTEjdBdDdQAAUI3PfvazxfLly1OV3HqOP/747PkA0DSWMwJAd4v+tGyiT45+OXc+AABAFTbddNPi6aefTl1I65kxY0b2fAAAAGi36FHLJnrl6Jlz5wNAk1nOCNABhuqAXjNQcl8fYjFeiMVZG1qutSaWbNXrjTfeSP9LNz/z5s0rPvOZz2T/d2gnyxkHp98WMwbLGQdnoJ8NnxnQXQzVAQBAtY477rhUKbeef/7zn8XnP//57PkA0CSWMwJA94q+NPrTsvFnJQEAgHY49NBDUxdSLgcffHD2fAAAAGiX6E2rSPTKufMBoOksZwToEEN1QK+IBYsDJRYvrmuoie+Je3L3U501CzN7XSw8fPvtt9NPz0dz7bXXFsOGDcv+b9Bu22yzTbHvvvv2vPjvkfvvV4X4ZzpQmrqAb5999sn+b91Ldt999+x/tyoN9PMRv5b7HqD9DNUBAEA9LrvsslQtt56HH364GD58ePZ8AGgKyxkBoDtFPxp9adlEf5w7HwAAoA4/+clPUjfSepYuXVpstdVW2fMBAACgbtGTRm9aNtEj584HgH5gOSNABxmqA3pBLEarI2sWOVquxVBsscUWxdVXX51+ij6aiy++uNh8882z30t3Gijxe0Tue+gv60tTF3dCrzFUBwAA9dloo42KRx99NFXNreeaa67Jng8ATWE5IwB0p+hHyyb64uiPc+cDAADU5Z577kldSeu54447smcDAABA3aInLZvf/e532bMBoF9YzgjQQYbqgF5Q1XLGtZcxWshIqwb6m0gvvPDCYsSIEdnvozvF7wkDJfc99J/1xfJO6A6G6gAAoF4HHnhgqpzL5YwzzsieDwBNYDkjAHSf6EOrSPTFufMBAADqtMsuuxSrVq1KnUnrmTNnTvZ8AAAAqEv0omUTPfHOO++cPR8A+oXljAAdZqgO6HYDZc3CxXXFQsdgESNV+va3v128+eab6afvw5k9e3ax8cYbZ7+P7hS/VwwUv3cQBloQHD9Due8B2sdQHQAAtMdZZ52VqudyOfzww7PnA0Cvs5wRALpL9J9VJPrh3PkAAADtcNJJJ6XupFwmT56cPR8AAACqFj1oFfne976XPR8A+onljABdwFAd0M0GSu7roQ4HH3xw8dhjj6WfvA/y3nvvFf/93/9dbLTRRtnvozsNtHAvEr+e+z76z4aS+x6gPQzVAQBAe91xxx2pim49r776ajFu3Ljs+QDQyyxnBIDuMXbs2OKVV15JnWjriT44dz4AAEA7XXPNNalLaT3//ve/i7322it7PgAAAFQles/oQcsmeuHc+QDQbyxnBOgShuqAbnTQQQel32E+mkWLFmW/B6q2/fbbF7/5zW/ST94HWbVqVXHOOedkv4fuZTEjgxWfMxtKfE7lvheol6E6AABov9GjRxfLli1L1XTrueeee7LnA0Avs5wRALpH9J1lE/1v9MG58wEAANpp+PDhxcMPP5y6ldbz2GOPFSNGjMjeAQAAAGVFzxm9Z9lEDxy9cO4OAOg3ljMCdAlDdUA3spyRTouXePPmzUs/dR9k5cqVxZlnnpn9HrqXxYwM1oZ+VtbEzwx0hqE6AADojIMPPjhV1OVy6aWXZs8HgF5lOSMAdIfoN6tI9L+58wEAADpht912K955553UsbSeX/7yl9nzAQAAoKzoOcsmet/ogXPnA0A/spwRoIsYqgO6jeWMdNrpp5+efuI+yJtvvlmcdtpp2a+ne1nMyGCt+7MS//f6fn58FkH7GaoDAIDOmjFjRqqqy2Xq1KnZ8wGgF1nOCACdF31mFYm+N3c+AABAJx1//PGpaymXc889N3s+AAAAtCp6zSoSvW/ufADoV5YzAnQZQ3VANxloOaNFatTt2GOPLV588cX0E/d+li9fXpxyyinZr6d7DfR7ScTvJ6yRW8IY//lAP0PrngHUx1AdAAB0hyuvvDJV1+VywAEHZM8HgF5jOSMAdFb0l1Uk+t3c+QAAAN3gvPPOS91LuRx99NHZ8wEAAGCoosesItHz5s4HgH5mOSNAFzJUB3SLgZZhLVq0KPs9UIW99967WLx4cfppez+vvvpqcfLJJ2e/nu42UFr5vSR+b1pb7mvoPbnPnDWLO3O/tiZ+BqA9DNUBAEB3eeCBB1KV3XqeeeaZYosttsieDwC9xHJGAOic6Cujvyyb6HNz5wMAAHSTW2+9NXUxreeNN94odtxxx+z5AAAAMFg77bTT6h6zbKLXzZ0PAP3OckaALmWoDugGAy3Dqno5o+VarDF69Ojil7/8ZfpJez8vvvhiMXXq1OzX093i94qBkvueDVk3ua+h96ybNYsZ1/fra+LzA+pnqA4AALpPDGytWLEiVdut55ZbbsmeDwC9xHJGAOic6CvLJvpbi0kAAIBeMGrUqEr+LN3999+fPR8AAAAGK3rLsokeN3rd3PkA0O8sZwToUobqgG6xvlS5nHHNEsh1F3HRny644ILVPw9rsmzZsuK73/1u9mvpbvHv9EBpZaneume2+nvRyJEji7Fjx2ZtueWW2e+hPusu8cz9c13fos8qP4+APEN1AADQnY4++uhUcZfL7Nmzs+cDQK+wnBEAOiP6ySoS/W3ufAAAgG504IEHpm6mXK688srs+QAAALAh0VNWkehxc+cDAJYzAnQ1Q3VANxgoua8fqjWLGdfEgsb+tskmm3zoD+8/99xzxeTJk7NfS3erYzFjWDet/p4xc+bMdMJHM3/+/Oz3UI/cz0ru69a3nDGS+/q1tfrzBhiqAwCAbnfuueemqrtcjjvuuOz5ANALLGcEgPaLPrKKRF+bOx8AAKCbTZs2LXU15TJjxozs+QAAALA+0UtWkehtc+cDAO+znBGgyxmqAzptoGVYZZddrW95W+5r6S9nnnlm8fbbbxf7779/9tfpbhtazNjqQsXcua2edcopp6QTPpqFCxdmv4fqDeWf6brLfNfOhj6P1qTVnxfoV4bqAACgN9x4442p+m49K1euLD73uc9lzweAbmc5IwC0V/SP0UeWTfSzufMBAAB6wbx581J3Uy6HHHJI9nwAAABYV/SQVeTSSy/Nng8AfMByRoAeYKgO6KSBlmHF4sbc92xInLm+pY9lFz7SHKNHj87+53S33LK9tVNmOV4uua8bjO9973vphI/mT3/6U/Z7GLq1fx7W/czI/awM9PPR6ufRup83ua8BPspQHQAA9I4RI0YUjz/+eKrCW89DDz1UbLTRRtk7AKCbWc4IAO0TfWP0j2UTfWz0s7k7AAAAesU999yTupzWs2zZsmKrrbbKng8AAABrRO8YPWTZRC+bOx8A+DDLGQF6gKE6oNPWt0gxMtRFa7llXGtiMSP0toGW50WG+vvF2tb3+1Duawfjm9/8Zjrho3nhhRey38PQrft7fvzf61vQO9CCxTUG+jzKfX3u/tzXAR9mqA4AAHrPXnvtlSrxcrn66quz5wNAN7OcEQDaJ/rGKhJ9bO58AACAXjJu3Lji1VdfTZ1O67njjjuy5wMAAMAa0TuWTfSwY8eOzZ4PAHyY5YwAPcJQHdBJg1m4NtDCq/Ut4lqT+DWLGaG3dWIxY5kzjzrqqHTKR7Nq1api1KhR2e9jaIaS3Peva0OfJWt/bfx8rJ0yPy/QbwzVAQBAb5oyZUqqxsvl9NNPz54PAN3KckYAaI/oF6tI9K+58wEAAHrREUcckbqdcpkzZ072fAAAAIiesYocfvjh2fMBgI+ynBGghxiqAzppoIVY6ya+drBfv+4yLaA3DfTv/FD/PY9Fjxta6hops2xvQ8sk/e0/1RhsBrugd0P/3CK5nxufNTB4huoAAKC3XXzxxakqL5evfvWr2fMBoBtZzggA9Ys+sYpE35o7HwAAoJedccYZqespF3/uDgAAgHVVtWMketfc+QBAnuWMAD3GUB3QSRtalDbUlFmsBnSPqn9vGGwGu9Av55BDDkmnfDTvvvtuMWrUqOz3MXjxe/xgMtTPgqH+vFnMCINnqA4AAJrhzjvvTNV563n55ZeL7bffPns+AHQbyxkBoF7RH0afWDbRr+bOBwAAaIJrrrkmdT+t57333iv222+/7PkAAAD0n+gRo1csm+hZc+cDAOtnOSNADzJUB3TSYJdtDZRYlFVmqRrQPeLf5U4l9zyDNdACspdeein7PQzNYD4vWlnSO5SfOYsZYfAM1QEAQHNsvfXWxQsvvJCq9Nbz0EMPFSNGjMjeAQDdxHJGAKhP9IXRH5ZN9KnRr+buAAAAaILhw4cXDz/8cOqCWs/SpUuLcePGZe8AAACgf4wdO3Z1j1g28f/rGzZsWPYOAGD9LGcE6EGG6oBOa3VBo6WM0EydSNmle4cffng66aN58MEHs9/D0MQ/o/Wl7OdBXYsfoV8ZqgMAgOY55JBDUqVeLrfddlv2fADoJpYzAkB9oi+sItGn5s4HAABokt1226345z//mTqh1hN/lnnjjTfO3gEAAEDzRU8YvWHZRI8avWruDgBgYJYzAvQoQ3VAN4jFWrH8KpZsrb2Ea83/HeLXLWSEZot/xzsh9yyDdcopp6TfsT6aWbNmZb+HoVn782FtZf/ZrW3NHZG1z6/yDugHhuoAAKCZBnr/MZRcfvnl2fMBoFtYzggA9Yh+sIpEf5o7HwAAoImOP/741A2Vy69+9avs+QAAADRf9IRVJHrU3PkAwIZZzgjQwwzVAQC0Zu7cuakS+mj22Wef7PcANJGhOgAAaLaf//znqWovl3PPPTd7PgB0A8sZAaB60QdWkehLc+cDAAA02XnnnZe6onKZN29e9nwAAACaK3rBKjJ79uzs+QDA4FjOCNDjDNUBAAzNsGHDinvuuSdVQR/OggULVv967vsAmsZQHQAA9IfFixen6r1cvvOd72TPB4BOs5wRAKoV/V8ViX40dz4AAEA/uOWWW1J3VC5nnXVW9nwAAACaJ3rAKhI9ae58AGDwLGcEaABDdQAAg3fYYYel6ufDWbVqVXHMMcdkvwegaQzVAQBA/9h5552LN998M1Xx5fKVr3wlewcAdJLljABQna9+9aupAyyX6EOjH83dAQAA0A9GjRpVPPPMM6lLKpcpU6Zk7wAAAKA5Jk+enLrAcoledIsttsjeAQAMnuWMAA1gqA4AYPAuvvjiVPl8OHPmzMl+PUDTGKoDAID+E38hRRVZsWJFsdtuu2XvAIBOsZwRAKrx+c9/fnXfV0X8xYgAAAD/URx44IGpSyqfCRMmZO8AAACg90XPV1UOOOCA7B0AwNBYzgjQEIbqAAA2bOTIkcVrr72WKp8PsmjRomLYsGHZ7wFoEkN1AADQv370ox+lar5cnnzyyeLTn/509g4A6ATLGQGgvOjzot+rItF/5u4AAADoR1OnTk3dUrksX7682HXXXbN3AAAA0Lui14uer4pED5q7AwAYOssZARrEUB0AwMCuvfbaVPF8kKeeeqoYPXp09usBmsRQHQAAcP3116eqvlx++9vfZs8HgE6wnBEAyvvd736XOr5yib4zdz4AAEA/mzt3buqayuXxxx8vRo0alb0DAACA3hM9XvR6VSR6z9wdAEBrLGcEaBhDdQAAeaecckqqdD6IxYxAPzFUBwAAhEWLFqXqvlziL8HInQ8A7WY5IwCUk/tLDltJ9Ju58wEAAPiP4sYbb0zdU7ncfffd2fMBAADoPdHjVZHoOXPnAwCts5wRoIEM1QEAfFhuMeMjjzxiMSPQNwzVAQAAa8T7kL/85S+pyi+XCy+8MHsHALST5YwA0Lro66pI9Jn+/+8AAADrt9FGGxX33Xdf6qLK5eqrr87eAQAAQO+46qqrUpdXLtFrRs+ZuwMAaJ3ljAANZKgOAODDfvzjH6fq5v1cccUVxQ477JD9WoCmMVQHAACsa4899ijeeuutVO2Xy4wZM7J3AEC7WM4IAK2Jfq6KRH8ZfWbuDgAAAD6wzTbbFM8++2zqpsrl/PPPz94BAABA94ueroo899xzq3vN3B0AQDmWMwI0lKE6AIAPW/M3CZ199tnZXwdoIkN1AADA+hxxxBGp4i+fr3/969k7AKAdLGcEgKH7xje+kTq68on+MncHAAAAH7XXXnsVK1euTB1VuZx88snZOwAAAOhe0ctVkegto8fM3QEAlGc5I0CDGaoDAPjAsGHDiv333z/7awBNZKgOAADYkJNOOilV/eXy7rvveu8CQMdYzggAQxP9W/RxVST6ytwdAAAArN+RRx6ZuqryOeaYY7J3AAAA0H2OPvro1M2VT/SWuTsAgGpYzgjQcIbqAAAA+o+hOgAAYLB+/OMfp+q/XJYtW1aMGzcuewcA1MlyRgAYvOjbon+rItFP5u4AAABgw6ZNm5a6q3JZtWpVse+++2bvAAAAoHtE7xY9XBWJnjJ3BwBQHcsZAfqAoToAAID+YagOAAAYqiuvvDJ1AeXy4IMPFhtvvHH2DgCoi+WMADA40a9F31ZFoo/M3QEAAMDgzZo1K3VZ5bJ06dJi2223zd4BAABA50XPFr1bFYleMncHAFAtyxkB+oShOgAAgOYzVAcAALRq4cKFqRsol1/96lfZ8wGgLpYzAsDg/PrXv06dW7lE/5g7HwAAgKH7xS9+kbqtclm8eHExfPjw7B0AAAB0TvRq0bNVkfnz52fvAACqZzkjQB8xVAcAANBshuoAAIBWfeITnyj+/Oc/p66gXObNm5e9AwDqYDkjAGxY9GlVJPrG6B9zdwAAANCaO+64I3Vd5XLzzTdnzwcAAKBzolerItE75s4HAOphOSNAHzFUBwAA0FyG6gAAgLJ23HHH4uWXX07dQbmcddZZ2TsAoGqWMwLAwKI/qyLRL0bfmLsDAACA1o0cObJYsmRJ6r7K5dJLL83eAQAAQPtFj1ZFomeM3jF3BwBQD8sZAfqMoToAAIDmMVQHAABU5Utf+lLqEMpn8uTJ2TsAoEqWMwLA+kVfVlWiX8zdAQAAQHk777xz8dprr6UOrFxmzpyZvQMAAID2id6sikSvGD1j7g4AoD6WMwL0IUN1AAAAzWGoDgAAqNo3v/nN1CWUz4QJE7J3AEBVLGcEgLzox6pK9Im5OwAAAKjOwQcfnLqw8vnWt76VvQMAAID6RU9WVaJXzN0BANTLckaAPmWoDgAAoPcZqgMAAOpy2mmnpW6hXP7xj38UBxxwQPYOAKiC5YwA8FEHHnjg6n6sipx++unZOwAAAKjeiSeemLqx8vnGN76RvQMAAID6RC9WVaJHzN0BANTPckaAPmaoDgAAoHdFH2aoDgAAqNNPf/rT1DWUy/Lly4svfOEL2TsAoCzLGQHgw/bcc8/VfVgVib4wdwcAAAD1OeOMM1JXVj5HHHFE9g4AAACqFz1YVYneMHcHANAeljMC9DlDdQAAAL0n+i9DdQAAQDvceOONqXsol7/97W/F+PHjs3cAQBmWMwLAB6Lviv6rikQ/mLsDAACA+l1yySWpOyuXf//738UhhxySvQMAAIDqRO8VPVgViZ4wdwcA0D6WMwJgqA4AAKCHGKoDAADaaaONNiruu+++1EWUyzPPPFP853/+Z/YeAGiV5YwA8L7ttttudd9VRaIPjH4wdw8AAADtsWDBgtSllcvbb79d7L///tk7AAAAKC96rui9qkj0grk7AID2spwRAEN1AAAAPSL6LUN1AABAu40ZM6Z47rnnUjdRLo899lix1VZbZe8BgFZYzggA/1FsvfXWq/utKhL9X/SBuXsAAABon2HDhhX3339/6tbK5fXXXy/22GOP7D0AAAC0Lnqt6LmqSPSA0Qvm7gEA2styRgBWM1QHAADQ3aLPMlQHAAB0yl577VWsXLkydRXl8sc//rHYYostsvcAwFBZzghAv4v+KvqsKhJ9X/R/uXsAAABov89+9rPFX//619S1lcsLL7xQ7LLLLtl7AAAAGLrosaLXqiLR+22zzTbZewCA9rOcEYD/z1AdAABAdzJUBwAAdIMjjzwydRblc++99xabbLJJ9h4AGArLGQHoZ9FXRX9VVaLvy90DAABA5+y9997FO++8kzq3cnn66aeLcePGZe8BAABg8KK3ih6rikTPF71f7h4AoDMsZwTgQwzVAQAAdBdDdQAAQDeZPn166i7K56677sreAQBDYTkjAP0s+qqqEv1e7g4AAAA676ijjkrdW/k8+uijxVZbbZW9BwAAgA2Lnip6q6oSPV/uHgCgcyxnBOAjDNUBAAB0D0N1AABAt5k1a1bqMsrnlltuyd4BAINlOSMA/Sr6qaoSfV7uDgAAALrH97///dTFlc8f/vCHYuTIkdl7AAAAWL/opaKnqipmvQCgO1nOCECWoToAAIDOM1QHAAB0q8suuyx1G+Vzww03ZO8AgMGwnBGAfhR9VFWJ/i53BwAAAN3n3HPPTd1c+dx7773FJptskr0HAACAj4oeKnqpqhI9Xu4eAKDzLGcEYL0M1QEAAHSOoToAAKDb/Z//839S11E+cVbuDgDYEMsZAeg3ejEAAID+dv7556eurnzuvPPO7B0AAAB8VPRQVSV6u9wdAEB3sJwRgAH5g5wAAADtpxcDAAB6xS9/+cvUfZSPxfIAtMJyRgD6SZV/4XL0c7k7AAAA6H4//elPU3dXPrfcckv2DgAAAD4QvVNViZ4udwcA0D0sZwRggwzVAQAAtI+hOgAAoNfceuutqQspn4svvjh7BwCsj+WMAPSL6JeqSvRxuTsAAADoHZdffnnq8srnhhtuyN4BAADAf6zumapK9HK5OwCA7mI5IwCDUuUmf0N1AAAAeYbqAACAXnXXXXelbqR8Zs2alb0DAHIsZwSgH8yePTt1TOUT/VvuDgAAAHrP1Vdfnbq98pk/f372DgAAgH4WvVJVueqqq7J3AADdx3JGAAatyqG6+MOiuTsAAAD6laE6AACgl2266abF73//+9SVlM/ZZ5+dvQcA1mU5IwBNF/1RVYm+Lfq33D0AAAD0phtvvDF1feVz2WWXZe8AAADoR9EjVZXo3XJ3AADdyXJGAAbNUB0AAEA9DNUBAABNsMUWWxR/+tOfUndSPj/84Q+z9wDA2ixnBKDJoi+qKtGvRd+WuwcAAIDedtttt6Xur3wuvvji7B0AAAD9JHqjqnLrrbdm7wAAupfljAAMSfzhzD/+8Y+pDSwfQ3UAAEC/M1QHAAA0ydZbb1089thjqUspn2nTpmXvAYA1LGcEoKmmT5+eOqPyiT4t+rXcPQAAAPS+j33sY8Xdd9+dusDymT17dvYeAACAfhA9UVWJXi16ttw9AED3spwRgCGreqgu/hBp7h4AAICmM1QHAAA00XbbbVc8++yzqVspnylTpmTvAYBgOSMATRR9UFWJ/iz6tNw9AAAANMeIESOK++67L3WD5XP22Wdn7wEAAGiy6IWqSvRom266afYeAKC7Wc4IQEsM1QEAAJRjqA4AAGiy8ePHFy+++GLqWspn0qRJ2XsAwHJGAJom+p+qEn1Z9Ge5ewAAAGieUaNGFX/6059SV1g+p556avYeAACAJooeqKpEbxY9Wu4eAKD7Wc4IQMsM1QEAALTGUB0AANAP9txzz2L58uWpeymfiRMnZu8BoL9ZzghAkxx55JGpAyqf6MeiL8vdAwAAQHONGTOmePzxx1N3WD7Tp0/P3gMAANAk0ftUlejJtt566+w9AEBvsJwRgFKqHqqLP1yauwcAAKApDNUBAAD95MADDyxWrlyZupjyOeqoo7L3ANC/LGcEoCmi36kq0YdFP5a7BwAAgObbbrvtimeffTZ1ieVjQSMAANBkVS5mjF4serLcPQBA77CcEYDSDNUBAAAMjqE6AACgH02YMCF1MtXkuOOOy94DQH+ynBGAJog+p8oceuih2XsAAADoH7vuumvx4osvpk6xfE499dTsPQAAAL0sep2qEj1Y9GK5ewCA3mI5IwCViD/MWWUM1QEAAE1jqA4AAOhnEydOTN1MNZkyZUr2HgD6j+WMAPS66G+qTPRfuXsAAADoP3vttVexfPny1DGWz9lnn529BwAAoBdFj1NVovfac889s/cAAL3HckYAKmOoDgAAIM9QHQAAwH8UkyZNSl1NNZk+fXr2HgD6i+WMAPSy6GuqTPRduXsAAADoXwceeGCxcuXK1DmWz+zZs7P3AAAA9JLobapK9FzRe+XuAQB6k+WMAFTq2GOPTS1kNTFUBwAA9DpDdQAAAB+oenn9D3/4w+w9APQPyxkB6FXRz1SZyZMnZ+8BAACACRMmFO+9917qIMvn4osvzt4DAADQC6KnqSrRax166KHZewCA3mU5IwCViz/kWWUM1QEAAL3KUB0AAMBHTZs2LXU51eSss87K3gNAf7CcEYBedPbZZ6eOpppEn5W7BwAAANaYOHFi6iKryWWXXZa9BwAAoJtFL1NlotfK3QMA9DbLGQGohaE6AACg3xmqAwAAWL+ql9nPmjUrew8AzWc5IwC9JvqXKuMvPwYAAGCwJk2alLrJajJ//vzsPQAAAN0oepgqc+yxx2bvAQB6n+WMANTGUB0AANCvDNUBAABsWNVL7efMmZO9B4Bms5wRgF4SfUuV8ZceAwAAMFSTJ09OXWU1ueGGG7L3AAAAdJPoXapM9Fa5ewCAZrCcEYBaxR/+rDKG6gAAgG5nqA4AAGDwql5uP2/evOw9ADSX5YwA9IroV6qMv+wYAACAVk2bNi11l9Xklltuyd4DAADQDaJnqTJTp07N3gMANIfljADUzlAdAADQLwzVAQAADN3FF1+cuqBq8otf/CJ7DwDNZDkjAL0g+pQqE31U7h4AAAAYrB/84Aepy6wmd955Z7Hxxhtn7wIAAOiETTbZZHWvUmWil8rdBQA0i+WMALSFoToAAKDpDNUBAAC0bs6cOakbqibXXXdd9h4AmsdyRgC63fXXX586lWoS/VPuHgAAABiqqhc03nvvvcXIkSOzdwEAALRT9CbRo1QZixkBoH9YzghA2xiqAwAAmspQHQAAQHmzZs1KXVE1+dWvfpW9B4BmsZwRgG4WfUmVib4pdw8AAAC0aurUqanrrCZ/+MMfitGjR2fvAgAAaIetttpqdW9SZaJ3yt0FADST5YwAtJWhOgAAoGkM1QEAAFTnrLPOSt1RNbn99tuLj3/849m7AGgGyxkB6EbRh0Q/UmWiX8rdBQAAAGVNnjw5dZ/V5NFHHy3GjRuXvQsAAKBO0YtET1JlomfK3QUANJfljAC0naE6AACgCQzVAQAA1OOHP/xh6pKqyT333FN88pOfzN4FQO+znBGAbhP9R/QhVSb6pNxdAAAAUJVjjz02daHV5Omnny522WWX7F0AAAB1iB4kepEqE71S7i4AoNksZwSgIwzVAQAAvcxQHQAAQL2mTZuWuqVqsnjx4uIzn/lM9i4AepvljAB0k+g7ov+oMtEf5e4CAACAqk2cOLF47733UkdaPi+88EKxxx57ZO8CAACoUvQe0YNUleiNokfK3QUANJ/ljAB0jKE6AACgFxmqAwAAaI8pU6akrqma/PnPfy623Xbb7F0A9C7LGQHoFtFvRN9RZaIvyt0FAAAAdTn00EOLlStXps60fF5//fVi//33z94FAABQheg5oveoKtETRW+UuwsA6A+WMwLQUYbqAACAXmKoDgAAoL2OO+641D1Vk6eeeqrYaaedsncB0JssZwSgG0SfEf1GlZk0aVL2LgAAAKjbgQceWCxfvjx1qOXz9ttvFwcffHD2LgAAgDIOOeSQ1T1HVYleKHqi3F0AQP+wnBGAjjNUBwAA9AJDdQAAAJ1x5JFHpi6qmjz//PPF5z//+exdAPQeyxkB6LToL6LPqDLRB+XuAgAAgHbZc889ixdffDF1quXz73//uzjiiCOydwEAALQieozoNapK9EDRC+XuAgD6i+WMAHQFQ3UAAEA3M1QHAADQWYcddljxz3/+M3VU5fPKK68U++yzT/YuAHqL5YwAdFL0FdFfVJXoe6L/yd0FAAAA7bbrrrsWzz77bOpaq8nXv/717F0AAABD8Y1vfCN1GdUkep/ogXJ3AQD9x3JGALqGoToAAKAbGaoDAADoDl/84heLN954I3VX5bNixYri4IMPzt4FQO+wnBGATol+IvqKqhL9TvQ9ubsAAACgU7bbbrvi8ccfT91rNfnWt76VvQsAAGAwoqeoMtHzRO+TuwsA6E+WMwLQVQzVAQAA3cRQHQAAQHfZe++9i5deeil1WdXkxBNPzN4FQG+wnBGATog+ospEnxP9Tu4uAAAA6LStt966+NOf/pS62Goyc+bM7F0AAAADiV6iykSvEz1P7i4AoH9ZzghA1zFUBwAAdANDdQAAAN3pc5/7XPHXv/41dVvV5IwzzsjeBUD3s5wRgHaL/qHKRH8TfU7uLgAAAOgWo0aNKu67777UzVaTSy+9NHsXAABATvQQVSZ6nOh1cncBAP3NckYAupKhOgAAoJMM1QEAAHS3HXbYoXjiiSdS11VNLrnkkuxdAHQ3yxkBaKfoG6pM9DXR3+TuAgAAgG6z6aabFnfffXfqaqvJzTffXAwfPjx7HwAAQIieIXqHKhO9TfQ4ufsAACxnBKBrGaoDAAA6wVAdAABAb/jsZz9bPPzww6n7qiY33XRTMWzYsOx9AHQnyxkBaIfoE6JfqDLRz0Rfk7sPAAAAutXHPvax4tZbb03dbTVZvHhxse2222bvAwAA+lv0CtEzVJnbbrttdW+Tuw8AIFjOCEBXM1SAjklyAABrmElEQVQHAAC0i6E6AACA3vOpT32qeOCBB1IXVk3uv//+YptttsneB0D3sZwRgLpFfxB9QpWJ86Kfyd0HAAAAveDGG29MXW41Wbp0abHvvvtm7wIAAPpT9AjRK1SZ6GVydwEArM1yRgC6nqE6AACgbobqAAAAetcnPvGJ4re//W3qxqrJc889V+y9997Z+wDoLpYzAlCn6AuiP6gy0b9EH5O7DwAAAHrJVVddlbrdarJq1ari6KOPzt4FAAD0l2OOOWZ1j1Blrr766uxdAADrspwRgJ5gqA4AAKiLoToAAIDeN2zYsGLhwoWpK6sm//znP4sjjzwyex8A3cNyRgDqEv1A9AVVJvqW6F9y9wEAAEAvuvzyy1PXW11OPvnk7F0AAEB/iJ6g6kTvkrsLACDHckYAeoahOgAAoGqG6gAAAJplwYIFqTurLtOnT8/eBUB3sJwRgDpMmzYtdQTVJfqV3F0AAADQ637605+m7re6nH/++dm7AACAZoteoOpEz5K7CwBgfSxnBKDnGKoDAACqYKgOAACgma699trUpVWXWbNmZe8CoPMsZwSgalH/V53oU3J3AQAAQFPUsUDlqquuyt4FAAA009VXX526gepi8TsA0ArLGQHoSYbqAACAMgzVAQAANNuVV16ZurXqMn/+/OxdAHSW5YwAVOkXv/hF6gCqS/QnubsAAACgac4999zUDVeXu+++uxg1alT2PgAAoBmi5o/av+pEj5K7DwBgQyxnBKBnGaoDAABaYagOAACgP5x33nmpa6sud9xxR7H55ptn7wOgMyxnBKAKUedHvV91oi/J3QcAAABNNX369NQVV5fHH3+82HXXXbP3AQAAvS1q/aj5q070Jrn7AAAGw3JGAHqaoToAAGCwDNUBAAD0n5NPPjl1b9VlyZIlxc4775y9D4D2s5wRgLKivo86v+pEP5K7DwAAAJruqKOOKt55553UIVeT5cuXFxMmTMjeBwAA9Kao8aPWrzLRixx55JHZ+wAABstyRgB6nqE6AABgQwzVAQAA9K+jjz66WLVqVerkqsmrr75aHHzwwdn7AGgvyxkBKOPLX/7y6vq+ykT/ccwxx2TvAwAAgH6x9957F3/9619Tt1xdJk+enL0PAADoLVOmTElVfnWJHiR6kdx9AABDYTkjAI1gqA4AAFgfQ3UAAADsu+++xfPPP5+6uupywgknZO8DoH0sZwSgVVHPV53oO6L/yN0HAAAA/WabbbYp7r///tQ1V5ezzjorex8AANAboqavOtF7RA+Suw8AYKgsZwSgMQzVAQAA6zJUBwAAwBrbbrttsXjx4tTdVZczzjgjex8A7WE5IwCtOP3001NFX12i3xg7dmz2PgAAAOhXw4YNKxYsWJC65+oyb9687H0AAEB3i1q+6tx0002re4/cfQAArbCcEYBGMVQHAACsYagOAACAdX384x8vbr755tTlVZe5c+dm7wOgfpYzAjBUUb9XnV/96ler+43cfQAAAMB/FJdccknqoqtL9OMbb7xx9j4AAKC7RO0eNXzViV4jdx8AQBmWMwLQOIbqAAAAQ3UAAAAM5NJLL03dXnWJv317o402yt4HQH0sZwRgsKJej7q96kR/kbsPAAAA+LAzzjgjddPV5cEHH/SXbgMAQJcbN27c6tq96kSPkbsPAKAsyxkBaCxDdQAA0H8M1QEAADBYM2fOTF1fdbnvvvuKMWPGZO8DoB6WMwIwGFGnR71edc4666zsfQAAAEDeiSeemLrq6rJ06dJiv/32y94HAAB0VtTqUbNXnRNOOCF7HwBAFSxnBKDRDNUBAED/MFQHAADAUE2ePDl1f9Vl2bJlxcEHH5y9D4DqWc4IwIYccsghq+v0qhP9RO4+AAAAYGDx/0t77bXXUoddTd57771iypQp2fsAAIDOiBo9avUq8+qrr/rzeQBA7SxnBKDx6hqqiz+0m7sPAABoP0N1AAAAtOrQQw8tli9fnjrB6jJjxozsfQBUy3JGAAYSdXnVif5hwoQJ2fsAAACAwdl5552LJUuWpG67usyZMyd7HwAA0F5Rm1ed6CGil8jdBwBQJcsZAegLhuoAAKC5DNUBAABQ1q677lo8/vjjqSusLldccUX2PgCqYzkjAOtz5ZVXpsq8ukTfEP1D7j4AAABgaEaOHFnccccdqeuuLnHmVlttlb0TAACoV9TiddX5m2++efZOAICqWc4IQN+oa6gu/hBv7j4AAKB+huoAAACoyqhRo4q77747dYfV5f777y922GGH7J0AlGc5IwDr2nHHHVfX4VUn+oUtt9wyeycAAADQuvnz56fuu7osW7asOPjgg7P3AQAA9TjkkENW1+JV5xe/+EX2PgCAuljOCEBfqXOoLv5Qb+5OAACgeobqAAAAqMvVV1+dusTq8sYbbxRHHXVU9j4AyrGcEYC1Rd0d9XfViT4hdx8AAABQjVmzZqUuvNrMmDEjex8AAFCtqL3rSPQKufsAAOpkOSMAfclQHQAA9C5DdQAAANTt/PPPT91itTnnnHOy9wHQOssZAVgj6u06Ev1B7j4AAACgWtOmTUvdeLW58sors/cBAADViJq7jkSPkLsPAKBuljMC0LcM1QEAQO8xVAcAAEC7/Nd//VfqGqvNDTfcUGy66abZOwEYOssZAYj6OursOhJ9Qe5OAAAAoB5HHnlksXLlytSZV5f777+/2HHHHbN3AgAArYkaO2rtqhM9QfQGuTsBANrBckYA+pqhOgAA6A2G6gAAAOiEY445pvjXv/6VOsjq8uijjxZf+MIXsncCMDSWMwL0tz333HN1fV11og+IfiB3JwAAAFCvvfbaq3juuedSl15d3njjjeLoo4/O3gkAAAxN1NZRY1edZ599dnVPkLsTAKBdLGcEoO/VOVQXf/g3dycAADB4huoAAADopH333bdYunRp6iary7///e/iW9/6VvZOAAbPckaA/hX1dNTVVSfq//322y97JwAAANAe22yzTXHfffelbr3anHPOOdk7AQCAwTn33HNTdV1togcYM2ZM9k4AgHaynBEA/h9DdQAA0J0M1QEAANANxo4dWzz44IOpq6w2F110UfZOAAbHckaA/hR1dB2Juj/q/9ydAAAAQHtttNFGxY033pi69mrzy1/+shgxYkT2XgAAIC9q6Kil60jU/tED5O4FAGg3yxkBIDFUBwAA3cVQHQAAAN3k4x//ePGrX/0qdZfVZuHChcVnPvOZ7L0ADMxyRoD+EnVz1M91JOr9qPtz9wIAAACdM3fu3NS9V5vHHnus2GuvvbJ3AgAAHxa1c9TQdSRq/tydAACdYjkjAKzFUB0AAHSeoToAAAC62XnnnZe6zGrz/PPPF1/60peydwKwfpYzAvSPL3/5y6vr5joye/bs7J0AAABAd5g6dWrq4qvNv//972Ly5MnZOwEAgPdFzRy1cx2JWj93JwBAJ1nOCAAZdQ7VxR8Szt0JAAAYqgMAAKA3HH/88cU777yTOs5qc/LJJ2fvBCDPckaA/hB1ch2Juj7q+9ydAAAAQHc58MADi2eeeSZ19dVmzpw52TsBAKDfRa1cR6K2jxo/dycAQKdZzggA62GoDgAA2stQHQAAAL1kt912Kx5++OHUfVabyy+/PHsnAB9lOSNA80V9XEeino+6PncnAAAA0J1GjRpV3HLLLam7rzZ33HFHMXr06Oy9AADQb7baaqvVNXIdiZo+avvcvQAA3cByRgAYgKE6AABoD0N1AAAA9KLhw4cX11xzTepCq83vf//7Yvvtt8/eC8AHLGcEaK6oh6MuriNRx0c9n7sXAAAA6H7nnXde6vKrzdKlS4uDDz44eycAAPSLqImjNq4jUcvn7gQA6CaWMwLABhiqAwCA+hiqAwAAoAnOPPPM1I1Wm+XLlxcTJ07M3gnA+yxnBGimqIOjHq4jUb/n7gQAAAB6y/HHH1/885//TB1/tZkxY0b2TgAAaLqohetI1O5Rw+fuBADoNpYzAsAgGaoDAIBqGaoDAACgSY444ojitddeS51ptTnrrLOydwJgOSNAE0X9W0eiXo+6PXcnAAAA0Jt222234qGHHkrdf7W54oorsncCAEBTRQ1cRx5++OHVtXvuTgCAbmQ5IwAMgaE6AACohqE6AAAAmug///M/i0WLFqUutdpcf/31xciRI7P3AvQzyxkBmiPq3ah760jU6ePGjcveCwAAAPS24cOHF9dcc016C1Bt7r//fktkAABovKh5o/atI1GrR82euxcAoFtZzggAQ2SoDgAAWmeoDgAAgH5w2WWXpW612ixdutRfSgCwDssZAZoh6tyod+vIvHnzsncCAAAAzXLGGWektwHV5r333iu+//3vZ+8EAIBeF7Vu1Lx1JGr03J0AAN3OckYAaJGhOgAAGBpDdQAAAPST6dOnp661+lx44YXZOwH6keWMAL0v6tu6EnV57k4AAACgmQ4//PDi1VdfTW8Gqs0NN9xQbLnlltl7AQCg10RtGzVuHYma3L4EAKCXWc4IACUYqgMAgMExVAcAAEA/+uIXv1g899xzqYOtNvfff3/x+c9/PnsvQD+xnBGgd0U9G3VtHYk6POrx3L0AAABAs40bN66455570luCavPCCy8UEydOzN4LAAC9ImraqG3rSNTiUZPn7gUA6BWWMwJASYbqAABg/QzVAQAA0O8+9alPFbfddlvqZqvPySefnL0XoF9YzgjQm6KOrStRf0cdnrsXAAAA6B+XXnppeltQfebMmZO9EwAAul3UsnVl3rx52TsBAHqN5YwAUAFDdQAA8FGG6gAAAOADF1xwQepqq8+NN96oTwb6luWMAL0l6taoX+vK+eefn70XAAAA6E/Tpk1Lbw2qz+LFi4svfOEL2XsBAKDbRO0aNWxdido7dy8AQC+ynBEAKmSoDgAADNUBAADA+pxwwgnFqlWrUodbbf72t78VRx55ZPZegCaznBGgd0S9GnVrHYk6O+rt3L0AAABAfzvwwAOLZ555Jr1FqD6nnHJK9l4AAOgWUbPWlai1o+bO3QsA0KssZwSAihmqAwCgnxmqAwAAgIHtsccexSOPPJK63epz8cUXZ+8FaCrLGQF6Q9SpdSXq69133z17LwAAAEAYNWpUceutt6a3CdVnwYIFxejRo7N3AwBAp0SNGrVqXYkaO2rt3N0AAL3MckYAqIGhOgAA+pGhOgAAABicjTfeuLjuuutS11t9/vCHPxR77rln9m6AprGcEaC7RV0a9Wldibo66uvc3QAAAADrOu+889Jbherz8ssvF8ccc0z2XgAAaLeoTaNGrStRW+fuBQBoAssZAaAmhuoAAOgXhuoAAACgNWeddVbqfuvJKaeckr0XoEksZwToXlGP1pmop3P3AgAAAAzk+OOPL9555530hqH6zJ07N3svAAC0S9SkdSVq6aipc/cCADSF5YwAUDNDdQAANJmhOgAAAChn4sSJxeuvv5464epz8803F6NHj87eDdAEljMCdJ+oP6MOrStRP0cdnbsbAAAAYDB222234uGHH05vG6rPQw89VOyzzz7ZuwEAoC5Rg0YtWleiho5aOnc3AECTWM4IAG1gqA4AgKYxVAcAAADV2X777YtFixalrrj6vPLKK8XXv/717N0Avc5yRoDuEnVn1J91JermqJ9zdwMAAAAMxfDhw4trrrkmvXWoJ6eddlr2bgAAqFrUnnUmaueooXN3AwA0jeWMANAmhuoAAGgKQ3UAAABQj1mzZqXuuJ7MnTs3ey9AL7OcEaB7RL1ZZ6Jezt0LAAAAUMb3vve9YtWqVekNRPW55ZZbim222SZ7NwAAlBW1ZtScdSVq5aiZc3cDADSV5YwA0GaG6gAA6GWG6gAAAKBehx12WPHss8+mTrn6PPTQQ8U+++yTvRugF1nOCNB5UV9GnVlXoj6OOjl3NwAAAEAVdt555+J3v/tdehtRfV5//fVi0qRJ2bsBAKBVxx133Opas65EjRy1cu5uAIAms5wRADrAUB0AAL3GUB0AAAC0z2abbVZcddVVqWuuJ6eddlr2boBeYzkjQGdFXVlnoi6O+jh3NwAAAEDVfvKTn6S3EvVk3rx52XsBAGAoPvaxj62uLetM1Ma5uwEA+oHljADQIYbqAADoFYbqAAAAoDO+853vFP/85z9TB119brnllmLMmDHZuwF6heWMAJ0RdWTUk3Ul6uCoh3N3AwAAANTp0EMPLZ5++un0lqL6LFmypDjggAOydwMAwIZELRk1ZV2JWjhq4tzdAAD9wnJGAOgwQ3UAAHQrQ3UAAADQeTvttFPx29/+NnXT1ef1118vJk2alL0boBdYzgjQflE/Rh1ZV6L+jTo4dzcAAABAO2y66abF/Pnz09uKejJz5szs3QAAsD5RQ9aZqIGjFs7dDQDQTyxnBIAuYKgOAIBuY6gOAAAAusuPf/zj1FXXk2uuuaYYPXp09m6AbmY5I0D7bLXVVqvrxjoTdW/ubgAAAIBOmDJlSvGPf/wjvbmoPvfee2+x9957Z+8GAIA1omaM2rGuRM0btW/ubgCAfmQ5IwB0EUN1AAB0WtSLhuoAAACgOx188MHFU089lTrs6vPGG28U3/ve97J3A3QryxkB2iPqxKgX60rUuVHv5u4GAAAA6KTtt9++uOuuu9JbjHoye/bs7N0AABC1Yp2JWjdq3tzdAAD9ynJGAOgyhuoAAOgUQ3UAAADQ/TbZZJPiF7/4Req268ltt91W7LTTTtn7AbqN5YwA9dp5552L3/zmN6lSrCdR30adm7sfAAAAoFucc8456W1GPVmyZEkxYcKE7N0AAPSfqA2jRqwzUePm7gYA6HeWMwJAFzJUBwBAO0VdaKgOAAAAesvkyZOLt99+O3Xe9eT000/P3g3QTSxnBKhP1IN1JurZqGtzdwMAAAB0oy996UvFE088kd5u1JN58+YVI0aMyN4PAEDzRS0YNWGdiZo2atvc/QAAWM4IAF3NUB0AAHUzVAcAAAC9a/vtty/uvPPO1IXXk/vuu6/YZ599svcDdAPLGQGqt++++66uA+tM1LFRz+buBwAAAOhmw4cPL6644or0lqOeLFu2rJg0aVL2fgAAmitqwKgF60zUslHT5u4HAOB9ljMCQJdr11Bd/KHq3P0AADSToToAAABojnPOOSd14/XlvPPOy94N0GmWMwJUK+q+uhP1a+5uAAAAgF5ywgknFCtWrEhvPOrJ9ddfX4wZMyZ7PwAAzRE1X9R+dSZq16hhc/cDAPBhljMCQI8wVAcAQFUM1QEAAEDzHHTQQcXjjz+eOvN68thjjxVf+cpXsvcDdIrljADViDov6r06E/Vq1K25+wEAAAB60dixY4uFCxemtx/15M033yymTp2avR8AgN4Xtd5bb72Vqr96EjVr1K65+wEA+CjLGQGghxiqAwCgDEN1AAAA0GzDhw8vLr/88tSl15fLLrus2GyzzbLPANBuljMClPOJT3yi+N///d9U6dWXqFOjXs09AwAAAECvO/PMM9NbkPpy++23F+PHj8/eDwBA74naLmq8uhO1au5+AADWz3JGAOgxhuoAABiqqOsM1QEAAED/OOGEE4oVK1akjr2evPDCC8Vxxx2XvR+gnSxnBGhd1HNR19WZqEujPs3dDwAAANAkBxxwQLFkyZL0VqS+zJw5M3s/AAC9I2q6uhO1adSoufsBABiY5YwA0KMM1QEAMBiG6gAAAKA/jR07tli4cGHq3uvLDTfcUIwZMyb7DADtYDkjwNBts802q+u4uhP1aNSluWcAAAAAaKp58+altyP1ZfHixRbtAAD0oKjhoparO1GT5u4HAGBwLGcEgB7WzqG6+EPZuWcAAKA7GaoDAAAAwplnnpm6+Pry1ltvFdOmTcveD1A3yxkBhibqtqjf6k7Uobn7AQAAAPrBpEmTitdffz29KakvF154YfZ+AAC6z0UXXZSquPoSNWjUorn7AQAYPMsZAaABDNUBALA2Q3UAAADA2uJvXH/wwQdTR19f7rjjjmLXXXfNPgNAXSxnBBicqNOiXqs7UXdG/Zl7BgAAAIB+MmbMmOKmm25Kb03qy5NPPlkcccQR2WcAAKDzolaLmq3uRO0ZNWjuGQAAGBrLGQGgIQzVAQBgqA4AAAAYyNlnn526+3ozc+bM7P0AdbCcEWDDoj5rR6LezN0PAAAA0M9OOOGE4qWXXkpvUOrLlVdeWYwcOTL7DAAAtF/UZlGj1Z2oNaPmzD0DAACtsZwRABrGUB0AQH8yVAcAAAAMxuc+97nizjvvTJ1+fVm8eLG/3AFoC8sZAdYv6rGoy+pO1JdRZ+aeAQAAAID/KDbffPPiiiuuSG9T6svLL79cnHjiidlnAACgfaImi9qs7kSNGbVm7hkAAGid5YwA0ECG6gAA+oehOgAAAKAV06dPL95+++3U+deXCy+8MHs/QFUsZwTIu+iii1JFVl+invz+97+fvR8AAACAjzr88MOLJ598Mr1dqS833XRTMXbs2OwzAABQn3HjxhULFixIVVl9eeKJJ1bXlrlnAACgPMsZAaDB2jVUF3+YO3c/AAD1MlQHAAAAlPHZz362+OUvf5neAtSXZ555pvjmN7+ZfQaAsixnBPiwqLui/qo7UUdGPZl7BgAAAAAGFn/BWd1ZtWpVcdZZZ2XvBwCgelF7RQ1Wd/xluQAA9bOcEQAazlAdAEDzGKoDAAAAqnT88ccXf/vb39Ibgfpy1113Ffvuu2/2GQBaZTkjwPv222+/4u67706VV32JutGfEQIAAAAob//99y8eeOCB9NalvvzlL38pjjvuuOwzAABQXtRaUXPVnagdo4bMPQMAANWynBEA+oShOgCA3meoDgAAAKjLJz/5yeLyyy9PbwfqTdwzevTo7HMADJXljEC/i7qqnXVc1I255wAAAACgNWeeeWZ6+1Jv7rzzzmKfffbJPgMAAEMXtVXUWO1I1Iy5ZwAAoB6WMwJAHzFUBwDQmwzVAQAAAO3y1a9+tXj88cfTm4L6snLlyuKMM87IPgPAUFjOCPSzqKeirqo7UR9GnZh7BgAAAADKGz9+fHH77bentzH15rLLLis+85nPZJ8DAIANi1oqaqp2ZOHChcUuu+ySfQ4AAOpjOSMA9CFDdQAAvcNQHQAAANAJ559/fnprUG/incTXv/717DMADIbljEA/ivqpHX/2J3LBBRdknwEAAACA6k2dOrV4880305uZ+vL2228Xp512WvYZAABYv6ihopaqO1ETRm2YewYAAOpnOSMA9DFDdQAA3ctQHQAAANBp++23X3H//fenNwj15je/+U3xhS98IfscAAOxnBHoJ1EvRd3UjkQdGPVg7jkAAAAAqM/WW29dXHfddektTb157LHHimOOOSb7HAAAfCBqpqid2pGoBaMmzD0HAADtYTkjAPQ5Q3UAAN3FUB0AAADQbc4444z0NqH+XHLJJcWoUaOyzwGQYzkj0A+iPvrZz36WKqb6c+aZZ2afAwAAAID2OfbYY4ulS5emNzb15rbbbiv22GOP7HMAAPSzqJGiVmpHovaLGjD3HAAAtJfljADAaobqAAA6y1AdAAAA0M122WWXYuHChenNQr154403ilNOOSX7HADrspwRaLqoi6I+akei3ou6L/ccAAAAALTfiBEjinnz5qW3N/UnZr622GKL7LMAAPSTqInmzp2bqqT6c+mll66u/XLPAgBA+1nOCAD8f4bqAAA6w1AdAAAA0CtOOumkYsWKFelNQ73585//XEycODH7HABrWM4INFXUQVEPtSNR30Wdl3sOAAAAADpvwoQJxZIlS9LbnHrz97//3cwXANDXZsyYsbomakeixotaL/ccAAB0juWMAMBHGKoDAGgPQ3UAAABAL9pqq62K6667Lr11qD8333xz8bnPfS77LACWMwJNE3VP1D/tStR1W2+9dfZZAAAAAOgus2bNSm916s/DDz9cfO1rX8s+BwBAE0XtEzVQuxK1Xe45AADoPMsZAYAsQ3UAAPUxVAcAAAA0wbHHHlssXbo0vYGoP3PmzCk222yz7LMA/ctyRqApos6JeqddiTou6rncswAAAADQvfbaa69i0aJF6S1P/Yk/87zrrrtmnwUAoAmi1mnnnFfUclHT5Z4FAIDuYDkjADAgQ3UAANUxVAcAAAA0zaabblpceuml6W1E/XnllVeK6dOnZ58F6E+WMwJNEPVN1DntStRvUcflngUAAACA3nDqqacW7777bnrjU3/MfAEATdPuOa+o3aKGyz0LAADdxXJGAGCDDNUBAJRnqA4AAABosoMOOqi455570puJ+vPggw8Whx12WPZZgP5iOSPQy77yla+srmvalajXom7LPQsAAAAAvWfs2LHFVVddld7+1J9XX33VzBcA0AjTpk1bXdu0K1GzRe2WexYAALqP5YwAwKAZqgMAGDpDdQAAAEA/mTJlSrF06dL0pqL+3HDDDcWOO+6YfRagP1jOCPSiqF+ijmlXoj6LOi33LAAAAAD0vi9/+cvFvffem94G1R8zXwBAr4oapp1zXlGjRa2WexYAALqX5YwAwJAZqgMA2DBDdQAAAEC/GjZsWPGTn/wkvbVoT/7nf/6n2GqrrbLPAzSb5YxAL4l6JeqWdibqsqjPcs8DAAAAQLN85zvfKZYtW5beDNWfm2++udh7772zzwIA0E2iZonapV2Jmixqs9yzAADQ/SxnBABaYqgOACDPUB0AAADA+7bffvvi2muvTW8w6s+7775bXHDBBcWWW26ZfR6gmSxnBHpB1CdRp0S90q5cd911q+ux3PMAAAAA0FzDhw8vZs+end4StSfxF9rvvvvu2ecBAOikqFGiVmlnohaLmiz3PAAA9AbLGQGAUgzVAQC8z1AdAAAAQN6hhx5aPPDAA+mNRv1ZuXLl6r/M4pOf/GT2eYBmsZwR6GZRj0RdEvVJuxJ1V9RfuecBAAAAoH/ssMMOxfXXX5/eGrUnV199dTF+/Pjs8wAAtNOuu+66ujZpZ6L2ihos9zwAAPQWyxkBgEoYqgMA+pWhOgAAAIDBOemkk4qXXnopveGoPytWrCjOOeecYpNNNsk+D9AMljMC3Sjqj6hDoh5pV6LOmjp1avZ5AAAAAOhfhx12WLF48eL0Fqk9+fnPf24xEQDQETvuuOPqWqSdiVprwoQJ2ecBAKA3Wc4IAFTKUB0A0C8M1QEAAAAM3aabblpccMEF6W1He/Laa68VZ5xxRvGxj30s+0xAb7OcEegmUW9E3RH1Rztz4YUXrq6zcs8EAAAAAGHatGnFyy+/nN4otSeXXXZZMW7cuOzzAABUKWqOqD3amaitosbKPQ8AAL3NckYAoHKG6gCAJjNUBwAAAFDeLrvsUtx0003pzUd78uKLLxY/+MEPss8D9C7LGYFuEXVG1BvtTNRTUVflngcAAAAA1jVixIjioosuSm+X2pe5c+cWY8aMyT4TAEAZUWNccsklqepoX6Kmitoq90wAAPQ+yxkBgNoYqgMAmsZQHQAAAEC1Dj/88OJPf/pTehPSnjz//PPF97///ezzAL3Hckag06KuiPqinYn66Ygjjsg+DwAAAABsyPjx44sFCxakt03tSywx+vSnP519JgCAoYiaYs6cOanKaF+ihopaKvdMAAA0h+WMAEDtDNUBAL2uE0N1Dz30kKE6AAAAoG/813/9V7F8+fL0ZqQ9efrpp4vvfe972ecBeofljECnRB0R9UQ7E/VS1E255wEAAACAofra1762+s8stzOrVq0qZs+eXYwcOTL7TAAAA4ka4rzzzltdU7Qz5rwAAPqL5YwAQNsYqgMAeo2hOgAAAID22XzzzYuf/vSn6S1J+/L4448XkydPzj4T0P0sZwTaLeqGqB/anaiTol7KPRMAAAAAlDFjxoy2z3y9/fbbxY9+9KNixIgR2WcCAFhb1Aw//vGPV9cQ7Yw5LwCA/mQ5IwDQVobqAIBeYKgOAAAAoHM+//nPF7feemt6Y9K+/PnPfy6OP/747DMB3ctyRqBdok6IeqHdueWWW4rdd989+0wAAAAAUJWRI0cWc+fOTW+l2pe///3vxVlnnVV8/OMfzz4XANDfokaIWiFqhnYnaqOokXLPBQBAs1nOCAB0hKE6AKAbdWqoLuoiQ3UAAAAAH3b00UcXjz76aHqD0r784Q9/KL7+9a9nnwnoPpYzAnWLuiDqg3Yn6qCoh3LPBAAAAAB1iT/T3ImZr1deeaU47bTTss8EAPSnqA2iRmh3zHkBAGA5IwDQUYbqAIBuYKgOAAAAoHudeuqpxdtvv53eqLQv9913XzFx4sTsMwHdw3JGoC5RB0Q90O689dZbq+uf3DMBAAAAQLt0aubrhRdeKGbMmJF9JgCgP0QtEDVBu2POCwCANSxnBAC6gqE6AKATOjVUF3WPoToAAACAwfv0pz9d/OxnP0tvV9qb3/3ud8Vhhx2WfS6g8yxnBKoWn/vx+d+JRL0TdU/uuQAAAACgE0477bTilVdeSW+w2pfnnnuumDZtWvaZAIBmis/+qAHanah1zHkBALA2yxkBgK5hqA4AaBdDdQAAAAC9abvttisuv/zy9Kalvfntb39bHHPMMdnnAjrHckagKvE5H5/3nUjUN1Hn5J4LAAAAADpt+PDhxcyZM4vly5enN1rty9NPP1384Ac/KDbZZJPsswEAvS0+4+OzPj7z252obaLGiVon92wAAPQvyxkBgK5jqA4AqIuhOgAAAIBm2HnnnYv58+enNy/tzaOPPrr6b+rPPRfQfpYzAmXF53p8vnciUc9EXZN7LgAAAADoNiNGjCh+9KMfFW+99VZ6w9W+vPHGG8UFF1xQbLvtttlnAwB6S3ymx2d7fMa3O1HLRE0TtU3u2QAAwHJGAKBrGaoDAKpiqA4AAACgmXbbbbfi2muvTW9i2ptXXnll9R/U/vSnP519NqA9LGcEWhGf3/E5Hp/nnUjUL1HH5J4NAAAAALrdyJEji9mzZxfvvPNOeuPV3sSfz95zzz2zzwYAdLf4DO/U7HjULlHDRC2TezYAAFjDckYAoOsZqgMAWmGoDgAAAKB/xB/cvvHGG9ObmfbmvffeKy677LJi/Pjx2WcD6mU5IzAUu+666+rP7fj87kSiXjE0DgAAAEBTxJ/Xvuiiizr2vu03v/lNcfjhh2efDQDoLvGZHZ/dnUjUKlGzmBUHAGCwLGcEAHqGoToAYDAM1QEAAAD0r/3226/49a9/nd7UtD8LFiwovvzlL2efDaiH5YzAYMTnc3xOdypRn0Sdkns2AAAAAOh1Y8aMKebOnZvehrU/f/zjH4spU6Zknw0A6Kz4jI7P6k4lapSoVXLPBgAA62M5IwDQcwzVAQA5huoAAAAAWOOggw4qFi5cmN7ctD/33ntvMWnSpOyzAdWynBEYSHwe//73v0+f0O1P1CNRl+SeDQAAAACaZty4cav/kv1O5fnnny/OOOOM4pOf/GT2+QCA9th8881XfybHZ3OnEjVJ1Ca55wMAgA2xnBEA6FmG6gCAYKgOAAAAgPU59NBDi7vvvju9yWl/nnrqqWLGjBnFxz/+8ezzAeVZzgisKz534/M3Poc7lag/og7JPR8AAAAANN0OO+xQXHnlleltWfuzcuXK4n/+53+K7bffPvt8AEA94rM3PoPjs7hTiRokapHc8wEAwGBZzggA9DxDdQDQfwzVAQAAADAUhx9+eLFo0aL0Zqf9Wb58eTF79uxizJgx2ecDWmc5I7BGfM7G52187nYqUW9E3ZF7PgAAAADoN+PHjy+uuuqq9PasM7n22muL/fbbL/t8AEA14rM2PnM7mag5ovbIPR8AAAyV5YwAQGMYqgOA5jNUBwAAAEAZRx11VPHAAw+kNz2dSfwN/bvvvnv2+YChs5wRiM/Vn//85+mTtjOJ+iLqjNzzAQAAAEC/i3d4119/fXqb1pnEX8zvHR4AVCs+W+MztpOJGsOfwwEAoGqWMwIAjWOoDgCax1AdAAAAAFU69thjiz/96U/pzU9ncuuttxaHHXZY9vmAwbOcEfpXfI7G52knE/VE1BW55wMAAAAAPmzvvfcuFixYkN6udSaPPPJIcdJJJ2WfDwAYnPgsjc/UTiZqiqgtcs8HAABlWc4IADSWoToA6H2G6gAAAACo0wknnFAsWbIkvQnqTBYvXlyceOKJ2ecDNsxyRug/8bkZn5+dTNQPUUfkng8AAAAAGNgBBxzQ8T8j/tJLLxXnnntu8alPfSr7jADAh8VnZnx2xmdoJxM1RNQSuWcEAICqWM4IADSeoToA6D2G6gAAAABop29/+9vFk08+md4MdSbPPvtsceqppxabbbZZ9hmBPMsZoT/E52N8TsbnZScT9ULUDblnBAAAAACG5stf/nJxxx13pLdvncm7775bXHrppcUuu+ySfUYA6HfxGRmflfGZ2clEzRC1Q+4ZAQCgapYzAgB9w1AdAPzf9u7E24rqzBvwn9LOIsFZI4KoONOmoXUhKE5ga5yliUoUg40GJU5BsGMUJySKIRE1YTkrDYqiH+I8hMR5iLYGRxTQyPv1e3Jo0S4Zz72n6tTzrvWsROHcW7Vr73ezCvfvlptDdQAAAAC025gxY+LVV19tvilqTy1fvjxuueWWOOSQQwqvEfg24YzQ2XI/zH0x98d2Vv75IP+cUHSNAAAAAMCGOfLII+Phhx9uvo1rX82ZMydGjhxZeI0AUDe5J+be2O7KPyPknxWKrhEAALqKcEYAoHYcqgOAcnGoDgAAAICyyf/AfMGCBc03R+2r5557Ls4777zYdtttC68TEM4InSj3vdz/ch9sd+WfB0477bTC6wQAAAAAWmvw4MFxxx13NN/Ota/+9re/xTXXXBMDBgwovE4A6FS5902ZMqWxF7a78s8E+WeDousEAICuJpwRAKgth+oAoH0cqgMAAACgCg4//PC4++67m2+S2lszZ85sXE/RdUKdCWeEzpH7XO53Zajc/+27AAAAANAe/fv3j+uuu67tP/w/69FHH40zzzwzNtlkk8JrBYCqyz3ujDPOaOx57a7c+/PPAPlngaJrBQCA7iKcEQCoPYfqAKD7OFQHAAAAQBUdcMABcdNNNzXfLLW3Fi1aFBMmTIiddtqp8FqhboQzQrXlfpb7Wu5vZajc73PfL7pWAAAAAKB7bb311nHRRRfFX//61+YbvPbVp59+GjfeeGMMGjSo8FoBoGoGDhwYU6dObexx7a7c63PPz72/6FoBAKC7CWcEAGhyqA4AuoZDdQAAAAB0inzXNWnSpFi8eHHzbVN7a9asWTF8+PDCa4W6EM4I1ZT7V+5jZajc1y+//HL/jQYAAAAAlNjo0aPj2Wefbb7Va28tXLgwzjnnnOjRo0fhtQJAWeXeNWbMmHjiiSeau1p7K/f23OOLrhUAANpJOCMAwHc4VAcAreFQHQAAAACdauONN46xY8fGn//85+YbqPbWa6+9Fpdeemn06dOn8HqhkwlnhOrIfSr3q9y3ylD5g8VyP899veh6AQAAAIDyOeaYY2L27NnNt3ztrWXLlsUtt9wSgwcPLrxWACiL3KumT58ey5cvb+5i7a3cy3NPL7pWAAAoA+GMAADfw6E6AFh3DtUBAAAAUDcnnXRSPProo803Uu2ve+65J3784x8XXit0IuGMUH65L+X+VJbKfTv376JrBQAAAACqYdCgQfH73/+++dav/fXcc8/FuHHjYptttim8XgDobrkn5d6Ue1RZKvfu3MOLrhcAAMpEOCMAwFpwqA4AVs+hOgAAAADqbujQoTFr1qzmG6r219tvvx2TJ0+OPffcs/B6oVMIZ4Ryyv0n96Hcj8pSuU8PGTKk8HoBAAAAgGrq27dv/PrXv44lS5Y03wS2v2bOnBnDhg0rvF4A6Gq5B916663NXan9lXt07tW5ZxddLwAAlJFwRgCAdeBQHQB8w6E6AAAAAPi/9t5777jhhhviq6++ar61an/Nnj07TjnllMLrhaoTzgjlkvtN7jtlqdyPc1/O/bnoegEAAACAztCjR48YP358vP766823g+2vRYsWxYUXXhg77bRT4TUDQKvkXpN7Tu49Zanck3Nvzj266JoBAKDMhDMCAKwHh+oAqDOH6gAAAABgzbbbbru49NJL47333mu+yWp/vf/++3HVVVfFfvvtV3jNUEXCGaH9cl/J/SX3mbJU7r+5D+d+XHTNAAAAAEDn+vd///dYuHBh821hOeqPf/xjDB8+vPB6AWB95d4ya9as5m5Tjso9OPfiousFAICqEM4IALABHKoDoC4cqgMAAACA9Xf22WfHCy+80HyzVY6aN29e47q23377wmuGqhDOCO2R+0fuI7mflKlyvz3rrLMKrxkAAAAAqJcjjzwy7r333ubbw3LUq6++GpdffnkccMABhdcMAGuSe0juJa+99lpzdylH5Z6be2/RNQMAQNUIZwQAaBGH6gDoNGU+VJfXVXTNAAAAAFBmxx13XMydO7f5pqs89eCDD8aZZ54ZW2+9deF1Q5kJZ4Tuk/tE7he5b5Stcn/NfbbougEAAACAehswYEBMnz69+TaxPJX/Xfxll10W++67b+F1A8BKuVfknlG2M8xZucfmXlt03QAAUFXCGQEAWsyhOgCqzKE6AAAAAOh6Bx98cNx2223NN1/lqvvuuy9+8pOfRM+ePQuvHcpGOCN0rdwPcl/I/aGMlftp7qtF1w4AAAAAsKof/vCHMXny5Pjggw+abxjLU88880xcdNFFsddeexVeOwD1k3vCxRdfHM8++2xztyhP5V6ae2rurUXXDgAAVSecEQCgizhUB0BVOFQHAAAAAO2x4447xgUXXBB/+tOfmm/DylMrVqyIu+66K0477bTYcsstC68fykA4I7Re9v2RI0fG3Xff3dgPyla5b+b+mfto0fUDAAAAAKzJqaeeGv/1X//VfOtYrnryySfjwgsvjN13373w2gHoXHvssUdMmDAhnnrqqeauUK7KvTP30KJrBwCATiKcEQCgizlUB0AZOVQHAAAAAOVy2GGHxa233lrK93VfffVVzJo1K04++eTYdNNNC68f2kU4I7RG9vfs89nvs++XrXJ/zH0y98ui6wcAAAAAWB/9+/ePK664It55553m28hy1YIFC+LnP/959O3bt/D6Aai+3XbbLcaPH9/o+WWs3CNzr8w9s+j6AQCgEwlnBADoRg7VAdBODtUBAAAAQPlts802MW7cuHj22Webb87KVUuXLo3bb789TjjhhNhoo40K7wG6k3BGWH/Zx7Of33HHHY3+XsbK/TD3xdwfi+4BAAAAAKBVjj/++Lj33nubbyfLV4899ljjfWnv3r0Lrx+A6shenj09e3tZK/fE3BuLrh8AADqdcEYAgDZwqA6A7uJQHQAAAABU1+DBg2P69OmxbNmy5tu0ctWSJUsaP3Dl2GOPLbx+6A7CGWHdHXfccY3+nX28jJX7Xu5/uQ8WXT8AAAAAQFfq27dvXHbZZfHaa68131qWr+bNmxdjx46NnXfeufAeACif7NnZux955JFmNy9f5d6Xe2DuhUX3AAAAdSGcEQCgzRyqA6ArOFQHAAAAAJ2jR48eMWbMmHjiiSeab9jKVx9//HHMmDEjRowYUXgP0FWEM8Layf6cffqTTz5pdu7yVe5zud/lvld0DwAAAAAA3S3frc6aNav5FrOcNXfu3Ma71R122KHwHgBon+zN2aOzV5e5cq/z33sAAMA3hDMCAJSEQ3UAbKiVh+qyX5e1HKoDAAAAgA0zcODAuPHGG+Ozzz5rvnUrXy1evDhuvvnmOOqoowrvAVpJOCN8v+zD2Y+zL5e1cj/LfS33t6J7AAAAAAAog5133jkmTJgQixYtar7dLGfNnj07Ro8eHdtuu23hfQDQ9bIH//SnP2305DJX7mm5t+UeV3QfAABQZ8IZAQBKyKE6ANaWQ3UAAAAAUE+bbLJJnHnmmTF//vzmm7hy1n//93/HtGnTGu8yN95448J7gQ0hnBG+kX02+2323ffff7/ZictZuX/lPpb7WdG9AAAAAACU1eGHHx4zZ85svu0sb91///1xxhlnxC677FJ4HwC0Tvba7LnZe8teuYflXlZ0HwAAwD8IZwQAKDGH6gD4LofqAAAAAIDvGjBgQFxzzTWl/iEuWcuWLWscRDj33HOjf//+hfcC60o4I3WX/TT7avbX7LNlrtyncr/KfavoXgAAAAAAqmSbbbaJ8847L5577rnmW9Dy1tNPPx1XXHFFDBkypPBeAFh32VOzt2aPLXvlXpV7Vu5dRfcCAAB8m3BGAICKcKgOoL4cqgMAAAAA1tbIkSNj7ty5zTd25a6//OUvcf3118eIESNi0003LbwfWBPhjNRN9svsm9k/s49WoXJfyv2p6H4AAAAAADrBIYccErfcckssX768+Wa0vPXxxx/HrFmzYvTo0bHrrrsW3g8A/1f2zOyd2UOzl5a9ck/KvSn3qKL7AQAAvp9wRgCACnKoDqCzZb8cPnx4XHfddQ7VAQAAAADrZe+9945f/epX8d577zXf4pW7vvzyy3jwwQdj3LhxjWsvuicoIpyROsi+mP0x++RXX33V7Jzlrtx/ch/S0wEAAACAOunRo0ecc845sXDhwubb0vLXc889F1deeWUcdthhhfcEUGfZG7NHZq+sSuUelHtR7klF9wQAAKyZcEYAgApzqA6gc6x6qC77ZRXKoToAAAAAqIYTTzwx7r777uabvWrUK6+8ElOnTo1/+7d/i80337zwviAJZ6QTbbHFFo3+l30w+2GVKveb3HeK7gsAAAAAoE4GDRrUeM/77rvvNt+glr8+++yzuOuuu+Lss8+O3XbbrfC+ADpZ9r7sgdkLsydWpXKvyT0n956i+wIAANaNcEYAgA7hUB1AtWTfc6gOAAAAAOhOP/jBD+L000+P++67r/m2rxr19ddfx5w5c+LnP/957LvvvoX3Rn0JZ6RTZH87//zzG/0u+16VKveV3F9ynym6NwAAAACAujv88MPjN7/5Tbz//vvNN6vVqBdffDGuvvrqOOKIIwrvC6ATZI/LXpc9r0qVe0ruLbnHFN0XAACw/oQzAgB0GIfqAMrLoToAAAAAoCy22WabGD16dMyePbv5FrA69dprr8W0adPiuOOOix49ehTeH/UhnJGqyv6VfSz7Wfa1qlXuH7mP5H5SdH8AAAAAABQ76qijYvr06fHhhx8237hWo7744ou4995745xzzondd9+98N4AqiB7WPay7GnZ26pUuXfkHpJ7SdG9AQAArSGcEQCggzlUB9BeDtUBAAAAAFWwww47xJgxY2Lu3LnNt4PVqoceeiguuOCC2H///Qvvj84mnJEqyT6V/Sr7VhUr94ncL3LfKLo/AAAAAADWzYgRI2LGjBnxySefNN/EVqcWLVoU11xzTSMgbOONNy68P4AyyB6VvSp7VvauqlXuEblX5J5RdH8AAEDrCWcEAKgJh+oAuodDdQAAAABAle28884xduzYeOSRR5pvDatVb775Ztx8881x6qmnRt++fQvvkc4inJEyyz6U/Sj7UvanKlbuB7kv5P5QdI8AAAAAALTGcccdF7feemssWbKk+Ya2OrVs2bK4//77Y9y4cTFw4MDC+wPoTtmLsidlb1q+fHmzW1Wnci/IPSH3hqL7AwAAupZwRgCAGnKoDqB1HKoDAAAAADrVrrvu2jis8NhjjzXfJlav3nrrrbjjjjvi3HPPjQMPPLDwPqk24YyUSfaZ7DfZd7L/VLWy72f/z32g6D4BAAAAAOg6G220UZxwwgmNd81Lly5tvrmtVn3xxRfx8MMPx6RJk+Loo4+OXr16Fd4rQCtkj8lekz1n3rx5jR5Uxcqef/vttzf2gNwLiu4VAADoHsIZAQBqzqE6gHXjUB0AAAAAUEe77bZbjB8/PhYsWNB8y1jNWrJkScydOzcmTpwYRxxxRPTs2bPwfqkO4Yy0S/aP7CPZT7KvZH+pcmV/zz6f/b7ofgEAAAAA6H6bbrppnHzyyTFr1qz48ssvm290q1nPP/98TJs2LUaOHBn9+vUrvF+AtZE9JHtJ9pTsLVWu7O3Z47PXZ88vul8AAKD7CWcEAOB/OVQH8G0O1QEAAAAA/F977LFHTJgwIZ566qnm28dq17PPPhs33HBDnHrqqdGnT5/Ce6a8hDPSXbI/ZJ/IfpF9oxMq+3j28+zrRfcMAAAAAEB5bLnllnHaaafFXXfdFStWrGi+6a1uvfPOO41AsnHjxsXAgQML7xkgZY/IXpE9I3tH1evrr79u9PLs6VtssUXhPQMAAO0lnBE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+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Our Options\n",
+    "\n",
+    "Let us restate the two historical viewpoints, for clarity:\n",
+    "\n",
+    "__Option 1__ (Einstein): The two spins (the electron and positron) are determined, in the sense that the outcome of any measurement along any axis is pre-determined by nature, even if we don't know what it is. One might think of this as the spins having some real, well-defined orientation in space, which is not known to us, but which exists. Or one might think of this as a set of information or instructions that determine outcomes of measurements along $x$, $y$, $z$, or anything in between. Measuring the spin of the positron (say along z) forces it to orient and align in the z or -z direction. This has no causal influence on the electron spin, although we know the electron spin started out opposite the positron spin, so if the positron spin is measured to be along +z, the electron spin is measured along -z. Other than the initial condition of instructions that conserve angular momentum (the spins being anti-aligned), there is no connection between the two spins. This option is sometimes called \"hidden variables\", as in: the projections along different axes are determined, but are hidden from us.\n",
+    "\n",
+    "__Option 2__ (Born): The spins are both undetermined in their initial states… not merely unknown, but ill-defined physically, with no definite orientation or instructions on experimental outcomes, until they are measured. Measuring the positron spin “collapses” the space of all possibilities down to a single determined state, either along the +z or -z axes. This measurement of the positron forces the electron spin to also collapse into a well-defined projection along z, exactly opposite the positron’s. This effect occurs spread out across the space between the positron and electron. This has been called \"spooky action at a distance”, but one might less-dramatically call it \"non-local physics\".\n",
+    "\n",
+    "### Check-in question:\n",
+    "\n",
+    "It would be great to distinguish between the Einstein and Born options experimentally. What are some experiments that would yield the same results regardless of which option is true? Can you think of an experiment that would yield different results for the two options? **Note** It would be very impressive if you could come up with an experiment that would yield different results for Einstein's and Born's options; it took humans decades to come up with one.\n",
+    "\n",
+    "<details>\n",
+    "<summary>Answer:</summary>\n",
+    "\n",
+    "Sticking with the experiment described so far (that is, no net spin with the positron and electron anti-aligned), measurement of both spins along $\\pm x$, $\\pm y$, or $\\pm z$ would always yield opposite signs due to conservation of angular momentum, independent of which option is correct. Measuring one particle's spin (say, the electron) along one direction (say, $+z$) means the spin of the other particle, the positron, would be measured along $-z$. If instead you measure the spin of the positron along the $x$ direction, it will be equally likely to come out $+x$ or $-x$. This could be because that's what the hidden instructions say (Einstein's option 1) or because the probability distribution of the positron's spin updates after the measurement of the electron spin, and the new probability distribution is consistent with a 50-50 split between $\\pm x$ (Born's option 2). These points are explained in more detail below.\n",
+    "\n",
+    "The answer is only slightly different if you consider a decay of a particle with spin-1, such that the two emerging particles (like the positron and electron) must have their spins aligned, rather than anti-aligned. If one is measured along $+y$, a measurement of the other particle along the $y$ axis must also yield $+y$, and so on. As before, this could result from either option.\n",
+    "\n",
+    "The rest of this lesson is devoted to an experiment that can distinguish between Einstein and Born's options, and so we won't go into much detail here. However, part of the trick is measuring the two particles along different directions (like $x$ and $z$, or even some direction between the traditional Cartesian axes). The rest comes from carefully considering the precise probability of obtaining different outcomes given the predictions of quantum mechanics and those of classical information as in hidden variables.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "In either option, if the two observers, Lucas and Rihanna, measure along the same axis, we would expect them to obtain anti-aligned spins, regardless of which option is true. To see why, consider the diagrams below.\n",
+    "\n",
+    "![Bell_Hidden_variables_meas.png](attachment:Bell_Hidden_variables_meas.png)\n",
+    "\n",
+    "The figure above shows Einstein's option. The directions of the spins are opposite and determined. If we measure along the $z$ axis, one will be along $+z$, and one along $-z$. We have no reason to assume that the positron would be along $+z$, and the electron along $-z$; the image merely shows that the spins will be measured to be in opposite directions. In fact, a given spin need not actually have a component of its spin along the direction eventually measured, in the case of Einstein's option. The weakest statement of Einstein's option is that there is some set of instructions that are stored in the spin that determine what the measurements outcomes will be when measured along any axis. We don't need to picture that these instructions are in the form of a simple vector (see diagram below); we'll return to this, later.\n",
+    "\n",
+    "![Bell_Hidden_variables_instructions_meas.png](attachment:Bell_Hidden_variables_instructions_meas.png)\n",
+    "\n",
+    "The figure below shows Born's option, in which the directions of the positron and electron spins are smeared out in a distribution of probability and have no definite direction. Don't read too much into the shape of the distribution. Each spin could actually have a non-zero probability of pointing in any direction as long as they are opposite each other; we have simply drawn them as fractions of the circle so we can visually distinguish them for discussion. Note that in the case of Born's option, it is still true that angular momentum must be conserved. So if one wave of probability is \"collapsed\" such that the spin points along $+z$, the other will point along $-z$ and be deflected in the opposite direction. The options appear identical.\n",
+    "\n",
+    "![Bell_Hidden_variables_prob_dist_meas.png](attachment:Bell_Hidden_variables_prob_dist_meas.png)\n",
+    "\n",
+    "But what happens when observers L and R can measure along any of three axes, with each pair 120 degrees apart, as shown in Figures 4 & 5. Each observer can decide at random along what axis they will measure the spin (a, b, or c). The two do not need to measure along the same axis. When each observer measures, they might find a positive projection on their axis of choice, or they might find a negative projection. For example, Lucas and Rihanna might measure +a and -b or +b and +c. Note that if they happen to choose to measure along the same axis, then they MUST obtain opposite signs in their projections: +a and -a, +b and -b, or +c and -c; they cannot both find, for example +a. In the next section, we will work through how to calculate the probability of Lucas and Rihanna getting the same sign on their measured axes (++ or --) and opposite signs (+-) or (-+).\n",
+    "\n",
+    "![Bell_Hidden_variables_meas_3.png](attachment:Bell_Hidden_variables_meas_3.png)\n",
+    "\n",
+    "The two figures above illustrate possible hidden-variables interpretations in this new, three-axis measurement scenario. That is, either the spins are already determined, as vectors, or a set of physical instructions exists somehow embedded in the system such that the outcomes of all possible measurements are pre-determined, even if they are unknowable to experimenters prior to measurement. The alternative is illustrated below. Some probability distribution of outcomes exists, and this distribution can tell us some things about the likelihood of different measurement outcomes, but the outcomes are undetermined by nature prior to measurement.\n",
+    "\n",
+    "![Bell_prob_dist_meas_3.png](attachment:Bell_prob_dist_meas_3.png)\n",
+    "\n",
+    "We can ask ourselves, “How often should the two players find the same sign of the spin’s projection?” That is, we are not even recording along which axis they chose to measure; we are simply recording whether they found the same sign or a different sign. It is not obvious whether Einstein's and Born's options will yield the same result in this more complicated measurement scheme. But it should be clear from Figures 4 and 5 that it is $possible$ for there to be a difference. For the case shown in Einstein's option, a measurement of the projection of the $e+$ spin on axis $a$ will definitely yield $+a$, and the projection of the $e-$ spin on axis $b$ will yield $-b$ (barely). But in Born's option, the possibilities are wide open. It is true that angular momentum is still conserved. But since the two magnetic fields are not oriented in along the same axis, we force the particles into a situation where they must collapse onto different axes (through interactions with the field). In the next section, we will use quantum mechanics to determine what the probabilities should be, given Born's option, that Lucas and Rihanna obtain the same sign on their measured axes (++ or --), and the probabilities that they will obtain opposite signs (+- or -+)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What does Einstein's option (hidden variables) predict?\n",
+    "\n",
+    "If Einstein's option is true, then any given pair of $e+$ and $e-$ will have a set of vector components to their spins. For example, the electron might have components $(+\\hat{a},-\\hat{b}, +\\hat{c})$, in which case the positron must have components $(-\\hat{a},+\\hat{b}, -\\hat{c})$. We are only specifying here the sign of the projection on each axis, not the magnitude. Imagine we allow a very large number $N$ of such decays to take place, and we collect measurements to populate the table below.\n",
+    "\n",
+    "| Population  | Particle 1                                     | Particle 2                     |\n",
+    "|-------------|------------------------------------------------|--------------------------------|\n",
+    "| $N_1$       | $(+\\hat{a},+\\hat{b},+\\hat{c})$                 | $(-\\hat{a},-\\hat{b},-\\hat{c})$ |\n",
+    "| $N_2$       | $(+\\hat{a},+\\hat{b},-\\hat{c})$                 | $(-\\hat{a},-\\hat{b},+\\hat{c})$ |\n",
+    "| $N_3$       | $(+\\hat{a},-\\hat{b},+\\hat{c})$                 | $(-\\hat{a},+\\hat{b},-\\hat{c})$ |\n",
+    "| $N_4$       | $(+\\hat{a},-\\hat{b},-\\hat{c})$                 | $(-\\hat{a},+\\hat{b},+\\hat{c})$ |\n",
+    "| $N_5$       | $(-\\hat{a},+\\hat{b},+\\hat{c})$                 | $(+\\hat{a},-\\hat{b},-\\hat{c})$ |\n",
+    "| $N_6$       | $(-\\hat{a},+\\hat{b},-\\hat{c})$                 | $(+\\hat{a},-\\hat{b},+\\hat{c})$ |\n",
+    "| $N_7$       | $(-\\hat{a},-\\hat{b},+\\hat{c})$                 | $(+\\hat{a},+\\hat{b},-\\hat{c})$ |\n",
+    "| $N_8$       | $(-\\hat{a},-\\hat{b},-\\hat{c})$                 | $(+\\hat{a},+\\hat{b},+\\hat{c})$ |\n",
+    "\n",
+    "For each case in the table above, there are 9 possible choices for Lucas's and Rihanna's axes: $aa$, $ab$, $ac$, $ba$, $bb$, $bc$, $ca$, $cb$, and $cc$. Reading from this table, the probability of the two observers measuring the same sign for rows 1 and 8 are zero. For rows 2-7, there are 4 ways to obtain the same sign, which we will show only for row 2:\n",
+    "\n",
+    "Same signs: $ac$, $bc$, $ca$, $cb$\n",
+    "Opposite signs: $aa$, $ab$, $ba$, $bb$, $cc$\n",
+    "\n",
+    "So if Einstein's option is the correct interpretation of quantum states, the total probability summed over all possible populations, of Lucas and Rihanna obtaining the same sign of spin projection on their randomly chosen axes would be:\n",
+    "$$ P_\\text{same}=\\frac{1}{\\sum_i{N_i}} \\frac{4}{9} (N_2+N_3+N_4+N_5+N_6+N_7)\\leq \\frac{4}{9}$$\n",
+    "Where equality holds only if $N_1=N_8=0$.\n",
+    "\n",
+    "### Check-in question\n",
+    "\n",
+    "For row 2 of the chart above, we listed all the possible ways for Lucas and Rihanna to obtain the same sign for their measurements, and all the ways they could obtain different signs. Repeat this for the third row.\n",
+    "\n",
+    "<details>\n",
+    "<summary> Answer:</summary>\n",
+    "\n",
+    "Same signs: $ab$, $ba$, $bc$, $cb$\n",
+    "\n",
+    "Opposite signs: $aa$, $ac$, $bb$, $ca$, $cc$\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "### Check-in question\n",
+    "\n",
+    "The table above refers to \"populations\", meaning that we do not know how many of each type of instructions nature produces, if the hidden-variables treatment is correct. Show that no matter what the distribution of $N_1$ through $N_8$, the probability of obtaining the same sign from measurements is always less than or equal to 4/9. \n",
+    "\n",
+    "<details>\n",
+    "<summary>Answer:</summary>\n",
+    "\n",
+    "Let us start by assuming a constant number of total measurement trials, such that $\\sum_i{N_i} = N_{tot}$ is constant. Note that in the special case where $N_1=N_8=0$, the expression reduces to \n",
+    "\n",
+    "$$ P_{same}=\\frac{1}{N_2+N_3+N_4+N_5+N_6+N_7} \\times \\frac{4}{9} \\times (N_2+N_3+N_4+N_5+N_6+N_7) = \\frac{1}{N_{tot}} \\times \\frac{4}{9} \\times N_{tot}= \\frac{4}{9}$$\n",
+    "\n",
+    "Now suppose that either $N_1 \\neq 0$ or $N_8 \\neq 0$. Then \n",
+    "\n",
+    "$$ P_{same}=\\frac{1}{N'_1+N'_2+N'_3+N'_4+N'_5+N'_6+N'_7+N'_8} \\times \\frac{4}{9} \\times (N'_2+N'_3+N'_4+N'_5+N'_6+N'_7) = \\frac{4}{9}$$\n",
+    "\n",
+    "The sum of all trials, $N_tot$, is still the same as before. But since $N'_1$ or $N'_8$ has increased from 0, the sum of $N'_2$ through $N'_7$ must be lower than before. In particular, the sum of $N'_2$ through $N'_7$ is less than $N_{tot}$. Thus\n",
+    "\n",
+    "$$ P_{same}=\\frac{1}{N_{tot}} \\times \\frac{4}{9} \\times (N'_2+N'_3+N'_4+N'_5+N'_6+N'_7) < \\frac{4}{9}$$\n",
+    "\n",
+    "Combining all possible cases, we have $P_{same} \\leq \\frac{4}{9}$.\n",
+    "\n",
+    "</details>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generalization\n",
+    "\n",
+    "In the treatment above, we considered measurements along specific axes. Of course, one could make measurements along any axis. Let us call the two spin vectors of two particles $\\vec{a}$ and $\\vec{b}$. Let $\\lambda$ be some hidden variable such that a state of the two-particle system corresponds to a well-defined value of $lambda$. Let $\\rho(\\lambda)$ be the probability density in $\\lambda$.  Finally, we choose the symbols $A(\\vec{a},\\lambda)$ and $B(\\vec{b},\\lambda)$ to be the pre-determined outcome of a measurement performed on either particle (A or B), given the spin vectors and the hidden variable. Critically, note that $A$ is independent of $\\vec{b}$ and $B$ is independent of $\\vec{a}$. One could now pose any number of questions related to correlations between measurements on A and B. In particular, one could ask about the expectation value given by\n",
+    "\n",
+    "$$E(\\vec{a},\\vec{b})\\equiv\\int{d\\lambda \\rho(\\lambda)A(\\vec{a},\\lambda)B(\\vec{b},\\lambda)}$$\n",
+    "\n",
+    "Given some standard assumptions on these values, like $A(\\vec{a},\\lambda)\\leq 1$, $B(\\vec{b},\\lambda)\\leq 1$, and normalization over $\\rho(\\lambda)$, one can show that correlations between the two particles obey the relation\n",
+    "\n",
+    "$$|E(\\vec{a},\\vec{b})-E(\\vec{a},\\vec{d})|+|E(\\vec{c},\\vec{d})+E(\\vec{c},\\vec{b})|\\leq 2,$$\n",
+    "\n",
+    "where $\\vec{a}$ and $\\vec{b}$ are the spin states of your system and $\\vec{c}$ and $\\vec{d}$ are reference spin states (any other possible spin states of the system). This is one of a whole class of inequalities known now as \"Bell inequalities\". We won't use this general form here. Instead, we will focus on one specific experimental setup, so that we can map that setup onto a quantum circuit."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What does Born's option (non-deterministic quantum mechanics) predict?\n",
+    "\n",
+    "Lucas will pick some axis and find the spin of one particle to be in either the positive or negative direction. Whatever he obtains, let us orient our axes such that the $z$ axis is that direction. Then we can write the initial state after the decay of the meson and before any measurement as\n",
+    "$$|\\psi \\rangle =\\frac{1}{\\sqrt{2}}(|+\\rangle_L|-\\rangle_R-|-\\rangle_L|+\\rangle_R)$$\n",
+    "Rihanna will measure her particle's spin along some other direction at an angle $\\theta$ relative to Lucas's. The spin operator along some arbitrary direction $\\hat{n}$ is given by\n",
+    "$$\\hat{S}_{\\hat{n}}=\\frac{\\hbar}{2}\\begin{bmatrix} \\cos(\\theta) & \\sin(\\theta) e^{-i\\phi} \\\\ \\sin(\\theta) e^{i\\phi} & -\\cos(\\theta) \\end{bmatrix}$$\n",
+    "The eigenstates of this operator are\n",
+    "$$|+\\rangle_{\\hat{n}}=\\cos(\\theta/2)|0\\rangle+\\sin(\\theta/2)e^{i\\phi}|1\\rangle \\\\ |-\\rangle_{\\hat{n}}=\\sin(\\theta/2)|0\\rangle-\\cos(\\theta/2)e^{i\\phi}|1\\rangle$$\n",
+    "\n",
+    "### Check-in question\n",
+    "\n",
+    "Verify that $|+\\rangle_{\\hat{n}}$ is an eigenstate of the operator $\\hat{S}_{\\hat{n}}$ above, and find the eigenvalue.\n",
+    "<details>\n",
+    "<summary>Answer:</summary>\n",
+    "\n",
+    "$$\n",
+    "\\hat{S}_{\\hat{n}}|+\\rangle_{\\hat{n}}=\\frac{\\hbar}{2}\\begin{bmatrix} \\cos(\\theta) & \\sin(\\theta) e^{-i\\phi} \\\\ \\sin(\\theta) e^{i\\phi} & -\\cos(\\theta) \\end{bmatrix} \\begin{bmatrix} \\cos(\\theta/2) \\\\ \\sin(\\theta/2)e^{i\\phi}\\end{bmatrix}\n",
+    "$$\n",
+    "$$\n",
+    "=\\frac{\\hbar}{2}\\begin{bmatrix} \\cos(\\theta)\\cos(\\theta/2) + \\sin(\\theta)\\sin(\\theta/2)e^{i\\phi} e^{-i\\phi} \\\\ \\cos(\\theta/2)\\sin(\\theta) e^{i\\phi} -\\cos(\\theta)\\sin(\\theta/2)e^{i\\phi} \\end{bmatrix}\n",
+    "$$\n",
+    "Using $\\cos(\\theta)=\\cos^2(\\theta/2)-\\sin^2(\\theta/2)$ and $\\sin(\\theta)=2\\cos(\\theta/2)\\sin(\\theta/2)$, we have\n",
+    "$$\n",
+    "=\\frac{\\hbar}{2}\\begin{bmatrix} \\left(\\cos(\\theta) + 2\\sin^2(\\theta/2)\\right) \\cos(\\theta/2) \\\\ \\left(2\\cos^2(\\theta/2) -\\cos^2(\\theta/2)+\\sin^2(\\theta/2)\\right)\\sin(\\theta/2)e^{i\\phi} \\end{bmatrix}\n",
+    "$$\n",
+    "$$\n",
+    "=\\frac{\\hbar}{2}\\begin{bmatrix} \\left(\\cos^2(\\theta/2)-\\sin^2(\\theta/2) + 2\\sin^2(\\theta/2)\\right) \\cos(\\theta/2) \\\\ \\left(2\\cos^2(\\theta/2) -\\cos^2(\\theta/2)+\\sin^2(\\theta/2)\\right)\\sin(\\theta/2)e^{i\\phi} \\end{bmatrix}\n",
+    "$$\n",
+    "$$\n",
+    "=\\frac{\\hbar}{2}\\begin{bmatrix} \\cos(\\theta/2) \\\\ \\sin(\\theta/2)e^{i\\phi} \\end{bmatrix}\n",
+    "$$\n",
+    "This demonstrates that $|+\\rangle_{\\hat{n}}$ is an eigenstate and the corresponding eigenvalue is $\\frac{\\hbar}{2}$.\n",
+    "</details>\n",
+    "\n",
+    "The probability of Lucas measuring a spin to be in the positive direction along the axis he chose $|+\\rangle$ $and$ that Rihanna also measures a positive spin along her chosen direction $|+\\rangle_{\\hat{n}}$ is \n",
+    "$$P_{++}=\\left|\\left(_L\\langle+|_{R,\\hat{n}}\\langle+|\\right)|\\psi\\rangle\\right|^2$$ \n",
+    "$$P_{++}=\\left|  \\left(_L\\langle+|_R\\left(\\cos(\\theta/2)\\langle+|+\\sin(\\theta/2)e^{-i\\phi}\\langle-|\\right)\\right)  \\frac{1}{\\sqrt{2}}\\left(|+\\rangle_L|-\\rangle_R-|-\\rangle_L|+\\rangle_R\\right)    \\right|^2$$ \n",
+    "$$P_{++}=\\frac{1}{2}\\left|  \\left(_L\\langle+|_R\\left(\\cos(\\theta/2)\\langle+|+\\sin(\\theta/2)e^{-i\\phi}\\langle-|\\right)\\right)  \\left(|+\\rangle_L|-\\rangle_R\\right)    \\right|^2$$\n",
+    "$$P_{++}=\\frac{1}{2}\\left|  \\left(\\sin(\\theta/2)e^{-i\\phi}\\vphantom{p}_R\\langle-|\\right) |-\\rangle_R    \\right|^2$$ \n",
+    "$$P_{++}=\\frac{1}{2}\\sin^2(\\theta/2)$$ \n",
+    "\n",
+    "### Check-in question\n",
+    "\n",
+    "Do the same for $P_{--}$. Verify that it also equals $\\frac{1}{2}\\sin^2(\\theta).$\n",
+    "<details>\n",
+    "<summary>Answer:</summary>\n",
+    "\n",
+    "Key:\n",
+    "$$P_{--}=\\left|\\left(_L\\langle-|_{R,\\hat{-n}}\\langle+|\\right)|\\psi\\rangle\\right|^2$$ \n",
+    "$$P_{--}=\\left|  \\left(_L\\langle-|_R\\left(\\sin(\\theta/2)\\langle+|-\\cos(\\theta/2)e^{-i\\phi}\\langle-|\\right)\\right)  \\frac{1}{\\sqrt{2}}\\left(|+\\rangle_L|-\\rangle_R-|-\\rangle_L|+\\rangle_R\\right)    \\right|^2$$ \n",
+    "$$P_{--}=\\frac{1}{2}\\left|  \\left(_L\\langle-|_R\\left(\\sin(\\theta/2)\\langle+|-\\cos(\\theta/2)e^{-i\\phi}\\langle-|\\right)\\right)  \\left(-|-\\rangle_L|+\\rangle_R\\right)    \\right|^2$$\n",
+    "$$P_{--}=\\frac{1}{2}\\left|  \\left(\\sin(\\theta/2) \\vphantom{p}_R\\langle+|\\right) |+\\rangle_R    \\right|^2$$ \n",
+    "$$P_{--}=\\frac{1}{2}\\sin^2(\\theta/2)$$ \n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "Adding these results, we find that the probability of the signs of the two measured axes are the same $P_{\\text{same}}=\\sin^2(\\theta/2)$.\n",
+    "\n",
+    "### Check-in question\n",
+    "\n",
+    "What could you do to check the math of this result? To be clear, we're not asking you to verify yet that it matches nature, just to make sure nothing went wrong in all the math.\n",
+    "\n",
+    "<details>\n",
+    "<summary>Answer:</summary>\n",
+    "\n",
+    "(1) Do the same calculation for $P_{\\text{diff}}=\\cos^2(\\theta/2)$ to verify the conservation of probability.\n",
+    "\n",
+    "(2) Check a known case. Insert $\\theta = 0$. Then $P_{\\text{same}}$ corresponds to the two observers each measuring their spin along the same axis, which would violate conservation of angular momentum. So you would expect that probability to be zero, and indeed inserting $\\theta = 0$ yields $\\sin^2(0/2) = 0$.\n",
+    "\n",
+    "(3) Check a different known case. Try $\\theta = \\pi$. What should you obtain. Careful of that $\\frac{1}{2}$.\n",
+    "</details>\n",
+    "\n",
+    "\n",
+    "We were specifically sketching the case where axes are at $120\\deg$ relative to each other. Remember, whatever direction ($\\pm a$, $\\pm b$, or $\\pm c$) Lucas obtains, we call that $z$. Then Rihanna randomly chooses to measure along either $\\pm a$, $\\pm b$, or $\\pm c$. If her choice is the same as Lucas's (up to a sign), then they are both measuring along $z$, and the probability of Rihanna also measuring $+z$ is zero. This should happen 1/3 of the time, since Rihanna's choice of axis is independent of Lucas's choice. For any other choice, Rihanna will be measuring along an axis either $120\\deg = 2\\pi/3$ radians from $z$ (1/3 of the time) or $240\\deg = 4\\pi/3$ radians from $z$ (1/3 of the time). And of course, along either of those axes, the spin could be measured to be in the positive or negative direction. This gives us a total probability of Lucas and Rihanna obtaining the same sign:\n",
+    "\n",
+    "$$P_{\\text{same}} = \\frac{1}{3}\\left(   0 + \\sin^2(\\pi/3) + \\sin^2(2\\pi/3)     \\right) = \\frac{1}{3}\\left(   0 + \\frac{3}{4} + \\frac{3}{4}     \\right) = \\frac{1}{2}$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Wow\n",
+    "\n",
+    "We just showed that \n",
+    "\n",
+    "$$(P_\\text{same})_\\text{max, Einstein}<(P_\\text{same})_\\text{max, Born}.$$ \n",
+    "\n",
+    "Let's take a step back.\n",
+    "\n",
+    "Einstein's and Born's options seemed like they would always yield the same results, since they only differed in their description of what happens prior to measurement. And yet, assuming there were instructions that pre-determined the sign of the spin measurement along certain axes, we obtained a constraint on the probability for the measurements to yield the same sign $(P_{\\text{same}})_\\text{Einstein}\\leq\\frac{4}{9}$. Then we assumed probability distributions as in quantum mechanics... and obtained a different value for $(P_{\\text{same}})_\\text{Born}=\\frac{1}{2}$. The prediction from quantum mechanics is higher than that allowed by the hidden variables treatment. So we can actually do an experiment and discover whether quantum mechanical states are determined by nature prior to measurement, or if they are truly in a probabilistic superposition of possible states.\n",
+    "\n",
+    "This experiment has been done many times using many different physical systems, often photons. There are many subtle considerations, such as biases in measurement, the timing(simultaneity) of measurements, and many others. Over the decades, concerns about these subtleties have been steadily eroded. Tests are still implemented, as we learn more about reality, but there is now broad agreement that the answer you'll obtain here, using IBM Quantum Computers, is correct. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test using real quantum computers!\n",
+    "\n",
+    "In keeping with our treatment above, let us define the direction of Lucas's measurement to be $+z$. This was convenient even in the algebraic approach, but it is especially convenient for quantum computation, since what is typically measured is the qubit's projection along $z$.\n",
+    "We want to make a quantum circuit that gives us the same probability conditions as those above for $P_{++}$. We are free to orient our plane such that $\\phi=0$, and we obtain\n",
+    "$$P_{++}=\\left|  \\left(_L\\langle+|_R\\left(\\cos(\\theta/2)\\langle+|+\\sin(\\theta/2)\\langle-|\\right)\\right)  \\frac{1}{\\sqrt{2}}\\left(|+\\rangle_L|-\\rangle_R-|-\\rangle_L|+\\rangle_R\\right)    \\right|^2$$\n",
+    "We need to know a few things about IBM Quantum Systems, to guide our discussion. Firstly, qubits start out initialized in the $|0\\rangle = |+\\rangle_z$ state. As mentioned, before, when measurements are made, they are along the $z$ axis. So the goal is to determine what operators we can insert between the measurement basis states $\\langle 0|\\langle 0|$ and the initial states of the qubits $|0\\rangle |0\\rangle$ to obtain the complicated expression above. For that, we'll need to review some basic gates in quantum computing.\n",
+    "\n",
+    "$X$ Gate: Equivalent to a NOT operation. Single-qubit gate.\n",
+    "$$X|0\\rangle = |1\\rangle,\\\\X|1\\rangle=|0\\rangle$$\n",
+    "$$X=\\begin{bmatrix} 0 & 1 \\\\ 1 & 0 \\end{bmatrix}$$\n",
+    "\n",
+    "In Qiskit, creating a circuit with an $X$ gate looks like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 185.453x117.056 with 1 Axes>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit import QuantumCircuit\n",
+    "qc=QuantumCircuit(1)\n",
+    "qc.x(0)\n",
+    "qc.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$H$ Hadamard Gate: Creates a superposition state. Single-qubit gate.\n",
+    "$$H|0\\rangle = \\frac{1}{2}\\left(|0\\rangle+|1\\rangle\\right),$$\n",
+    "$$H|1\\rangle = \\frac{1}{2}\\left(|0\\rangle-|1\\rangle\\right)$$\n",
+    "$$H=\\frac{1}{2}\\begin{bmatrix} 1 & 1 \\\\ 1 & -1 \\end{bmatrix}$$\n",
+    "\n",
+    "A circuit with a Hadamard gate is made as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 185.453x117.056 with 1 Axes>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc=QuantumCircuit(1)\n",
+    "qc.h(0)\n",
+    "qc.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CNOT Controlled-NOT Gate: This gate uses two qubits: a control and a target. Checks the state of a control qubit which is not changed. But if the control qubit is in the state $|1\\rangle$, the gate changes the state of the target qubit; if the state of the control qubit is $|0\\rangle$ no change is made at all. In the notation below, assume the first qubit is the control, and the second is the target.\n",
+    "$$CNOT|00\\rangle = |00\\rangle, \\\\ CNOT|01\\rangle = |01\\rangle \\\\ CNOT|10\\rangle = |11\\rangle \\\\ CNOT|11\\rangle = |10\\rangle$$\n",
+    "\n",
+    "A CNOT gate looks a bit different in a circuit, since it requires two qubits. This is how it is implemented:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 203.683x200.667 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc=QuantumCircuit(2)\n",
+    "qc.cx(0,1)\n",
+    "qc.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the first qubit listed in `qc.cx(0,1)` is the control, and the second is the target. Diagrammatically, the target is the one with the \"+\" sign or cross on it.\n",
+    "\n",
+    "$R_y(\\theta)$ Rotation Y Gate: Rotates the state about the y-axis. This is a single-qubit gate.\n",
+    "$$R_y(\\theta)|0\\rangle = \\cos(\\theta/2)|0\\rangle+\\sin(\\theta/2)|1\\rangle,\\\\R_y(\\theta)|0\\rangle = -\\sin(\\theta/2)|0\\rangle+\\cos(\\theta/2)|1\\rangle$$\n",
+    "$$R_y(\\theta)=\\begin{bmatrix} \\cos(\\theta/2) & -\\sin(\\theta/2) \\\\ \\sin(\\theta/2) & \\cos(\\theta/2) \\end{bmatrix}$$\n",
+    "\n",
+    "Finally, rotation gates are implemented by specifying the type of gate, the amount of the rotation, and the qubit on which the gate is placed, in that order:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 203.683x200.667 with 1 Axes>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from numpy import pi\n",
+    "\n",
+    "qc=QuantumCircuit(2)\n",
+    "qc.ry(pi/2,0)\n",
+    "qc.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The name of the gate `ry` specifies the axis about which the rotation occurs. The first argument $\\pi/2$ refers to the amount of the rotation, and the second argument specifies the qubit on which the gate is to be placed.\n",
+    "\n",
+    "### Check-in question\n",
+    "\n",
+    "<details>\n",
+    "<summary>Using the syntax introduced or refreshed above, make any quantum circuit involving four different types of quantum gates. </summary>\n",
+    "\n",
+    "Answer:\n",
+    "\n",
+    "There are, of course, infinitely many possibilities. Here is one example:\n",
+    "\n",
+    "`qc=QuantumCircuit(2)`\n",
+    "\n",
+    "`qc.ry(pi/2,0)`\n",
+    "\n",
+    "`qc.cx(1,0)`\n",
+    "\n",
+    "`qc.x(1)`\n",
+    "\n",
+    "`qc.h(0)`\n",
+    "\n",
+    "`qc.cx(0,1)`\n",
+    "\n",
+    "`qc.draw(\"mpl\")`\n",
+    "\n",
+    "</details>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## From physical experiment to quantum circuits\n",
+    "\n",
+    "From the operations of these gates, we can see, for example, that the kets in the expressions for $P_{++}$:\n",
+    "$$\\frac{1}{\\sqrt{2}}\\left(|+\\rangle_L|-\\rangle_R-|-\\rangle_L|+\\rangle_R\\right)$$\n",
+    "are likely to involve a Hadamard gate to get the superposition, and a CNOT gate to create the entanglement.\n",
+    "\n",
+    "We will now use the H, X, and CNOT gates to turn $|0\\rangle_L|0\\rangle_R$ into $\\frac{1}{\\sqrt{2}}\\left(|+\\rangle_L|-\\rangle_R-|-\\rangle_L|+\\rangle_R\\right)$:\n",
+    "\n",
+    "$$\\frac{1}{\\sqrt{2}}\\left(|0\\rangle_L|1\\rangle_R-|1\\rangle_L|0\\rangle_R\\right)$$\n",
+    "$$\\frac{1}{\\sqrt{2}}CNOT_{LR}\\left(|0\\rangle_L|1\\rangle_R-|1\\rangle_L|1\\rangle_R\\right)$$\n",
+    "Here $CNOT_{LR}$ means a CNOT gate using L as the control and R as the target. We can now factor out the R part of the state:\n",
+    "$$\\text{CNOT}_{LR}\\frac{1}{\\sqrt{2}}\\left(|0\\rangle_L-|1\\rangle_L\\right)|1\\rangle_R$$\n",
+    "$$\\text{CNOT}_{LR} H_L|1\\rangle_L|1\\rangle_R$$\n",
+    "$$\\text{CNOT}_{LR} H_L X_L X_R|0\\rangle_L|0\\rangle_R$$\n",
+    "\n",
+    "Now we have written the ket entirely as quantum gates operating on the default starting state of the qubits.\n",
+    "\n",
+    "Now we can use the $R_y(\\theta)$ acting on $\\vphantom{p}_L\\langle 0|_R\\langle 1|$ to obtain the bra in the expression for $P_{++}$.\n",
+    "\n",
+    "$$\\vphantom{p}_L\\langle0|_R\\left(\\cos(\\theta/2)\\langle0|+\\sin(\\theta/2)\\langle1|\\right)$$\n",
+    "$$\\vphantom{p}_L\\langle0|_R\\left(|0\\rangle \\cos(\\theta/2)+|1\\rangle \\sin(\\theta/2)\\right)^{\\dagger}$$\n",
+    "$$\\vphantom{p}_L\\langle0|\\left(R_{y,R}(\\theta)|0\\rangle_R\\right)^{\\dagger}$$\n",
+    "$$\\vphantom{p}_L\\langle0|_R\\langle0|R_{y,R}(-\\theta)$$\n",
+    "\n",
+    "Combining these results, we can write the probability $P_{++}$ as\n",
+    "\n",
+    "$$p_{++}=\\left|\\vphantom{p}_L\\langle0|_R\\langle0|R_{y,R}(-\\theta)\\text{CNOT}_{LR} H_L X_L X_R|0\\rangle_L|0\\rangle_R\\right|^2$$\n",
+    "\n",
+    "This gives us explicit instructions for how to construct our quantum circuit. We will apply X, H, CNOT and $R_y$ gates to qubits representing the quantum states of particles measured by Lucas and Rihanna, and make measurements to obtain the probability.\n",
+    "\n",
+    "IBM Quantum recommends tackling quantum computing problems using a framework we call Qiskit Patterns. It consists of the following steps.\n",
+    "- Step 1: Map your problem to a quantum circuit\n",
+    "- Step 2: Optimize your circuit for running on real quantum hardware\n",
+    "- Step 3: Execute your job on IBM quantum computers using Runtime Primitives\n",
+    "- Step 4: Post-process the results\n",
+    "\n",
+    "Basically all the work we did above was step 1. Let's build the resulting circuit using Qiskit!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1: Mapping our results to a quantum circuit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#We'll begin by importing qiskit and a visualization module so that we can plot a histogram of our results.\n",
+    "from qiskit import *\n",
+    "from qiskit.visualization import plot_histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Trig functions are in radians by default, so we will need the number pi\n",
+    "from numpy import pi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember that 1/3 of the time Rihanna's axis of choice will be $2\\pi/3$ radians from Lucas's, 1/3 of the time it will be $4 \\pi/3$ radians from Lucas's, and 1/3 of the time, they will choose the same axis. So we actually need to make 3 quantum circuits for these 3 cases, and add up the results. We'll carefully explain the first one, and the last two we will simply state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 621.739x284.278 with 1 Axes>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#We start by declaring our first quantum circuit, and giving it two qubits (the first \"2\") and two classical bits for storing outputs (the second \"2\")\n",
+    "# Define registers\n",
+    "from qiskit import ClassicalRegister, QuantumRegister\n",
+    "qr = QuantumRegister(2, 'q')\n",
+    "cr = ClassicalRegister(2, 'c')\n",
+    "qc1 = QuantumCircuit(qr, cr)\n",
+    "\n",
+    "#We know from our analysis above that we need an X gate acting on each of the qubits (L and R)\n",
+    "qc1.x([0,1])\n",
+    "#We need a Hadamard gate acting on Lucas's qubit, which we're calling the 0th qubit.\n",
+    "qc1.h(0)\n",
+    "#The controlled-NOT gate uses the 0th qubit (Lucas's) as the control and the 1st qubit (Rihanna's) as the target.\n",
+    "qc1.cx(0,1)\n",
+    "#The rotation gate acts on the 1st qubit (Rihanna's) and has an argument of -2 pi/3\n",
+    "qc1.ry(-2*pi/3,1)\n",
+    "#Finally, we want to measure all the qubits in the circuit to obtain measurement probabilities, and store the results in the classical bits.\n",
+    "qc1.measure([0,1],[0,1])\n",
+    "#Now we can draw the first of the three circuits that will check Bell's inequality for us.\n",
+    "qc1.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The code below quickly constructs all three circuits in a more streamlined way. Note that the only difference between the three circuits is how far we rotate the two qubits about the $y$ axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qcs=[QuantumCircuit(2,2),QuantumCircuit(2,2),QuantumCircuit(2,2)]\n",
+    "for i in range(0,len(qcs)):\n",
+    "    qcs[i].x([0,1])\n",
+    "    qcs[i].h(0)\n",
+    "    qcs[i].cx(0,1)\n",
+    "\n",
+    "qcs[0].ry(-2*pi/3,1)\n",
+    "qcs[1].ry(-4*pi/3,1)\n",
+    "qcs[2].ry(-2*pi/3,1)\n",
+    "qcs[2].ry(-4*pi/3,1)\n",
+    "\n",
+    "for i in range(0,len(qcs)):\n",
+    "    qcs[i].barrier()\n",
+    "    qcs[i].measure([0,1],[0,1])\n",
+    "\n",
+    "counts_list = [None]*len(qcs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 705.35x284.278 with 1 Axes>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qcs[0].draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will use a Qiskit primitive called the StatevectorSampler. A sampler is a primitive designed to sample all the possible states of a system and return probabilities (or in some cases, quasiprobabilities) of obtaining each state. We can specify a number of \"shots\", and look at the \"counts\" for each state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.primitives import StatevectorSampler\n",
+    "\n",
+    "sampler = StatevectorSampler()\n",
+    " \n",
+    "# Start a job that will return shots for all 100 parameter value sets.\n",
+    "for i in range(0,len(qcs)):\n",
+    "    pub = (qcs[i])\n",
+    "    job = sampler.run([pub], shots=10000) \n",
+    "    # Extract the result for the 0th pub (this example only has one pub).\n",
+    "    result = job.result()\n",
+    "    data_pub = result[0].data\n",
+    "    counts = data_pub.c.get_counts()\n",
+    "    counts_list[i]=counts\n",
+    "#    plot_histogram(counts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we look at the counts from each circuit, we see that two of them were basically identical, and the third was quite different."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot_histogram(counts_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us make a list of the possible outcomes and add up all the counts of each state from each of the three circuits to obtain overall probabilities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outcomes=('00','01','10','11')\n",
+    "\n",
+    "#Here we convert \"None\"s into 0's so that we can sum.\n",
+    "\n",
+    "for i in range(0,len(qcs)):\n",
+    "    for j in range(0,len(outcomes)):\n",
+    "        if counts_list[i].get(outcomes[j])==None:\n",
+    "            counts_list[i].update({outcomes[j]:0})\n",
+    "\n",
+    "#Here we create a dictionary that holds all the outcomes and sums over their appearances in each of the circuits.\n",
+    "\n",
+    "total_counts={}\n",
+    "for i in range(0,len(outcomes)):\n",
+    "    total_counts[outcomes[i]]=sum(counts_list[j].get(outcomes[i]) for j in range(0,len(qcs)))  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can print out the total counts for each outcome, and plot the histogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'00': 7399, '01': 7463, '10': 7451, '11': 7687}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(total_counts)\n",
+    "plot_histogram(total_counts)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Check-in question\n",
+    "\n",
+    "Is the above picture consistent with the outcomes predicted by hidden variables and determinism? Or is it consistent with probabilistic (and non-local) quantum mechanics?\n",
+    "\n",
+    "<details>\n",
+    "<summary>Answer:</summary>\n",
+    "\n",
+    "It is consistent with probabilistic and non-local quantum mechanics. The hidden variables treatment predicted that the probability of obtaining the same sign was less than or equal to 4/9. Quantum mechanics predicted a probability of 50%. The histogram above describes a probability of 00 or 11 equal to 49.97%. This is very close to the prediction of probabilistic quantum mechanics, but most importantly, it is greater than the allowed range in the hidden-variables treatment.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "### Check-in question\n",
+    "\n",
+    "Does this prove anything about nature?\n",
+    "\n",
+    "<details>\n",
+    "<summary>Answer:</summary>\n",
+    "\n",
+    "No! We were using a simulator! That is a computer programmed to behave according to the laws of probabilistic quantum mechanics. If we propose a rule, and then program a computer to follow that rule, its ability to follow the rule is not proof that the rule is correct! The only way to prove this is to use a real quantum computer!\n",
+    "\n",
+    "</details>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2: Optimizing your quantum circuit for running on real hardware\n",
+    "\n",
+    "Although we initially used a simulator to debug our code, we really want to run on real hardware. After all, a simulator is just pretending to be quantum mechanical, based on the equations above. If the simulator told us those equations were correct, that wouldn't do much to convince us. We want a real quantum computer to tell us what's happening! So we'll select the quantum computer we want to use. Sometimes it might be important to pick a specific device that has properties you want, but often we simply want to use whatever device is least busy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
+    "\n",
+    "# Syntax for first saving your token\n",
+    "# QiskitRuntimeService.save_account(channel=\"ibm_quantum\", token=\"<MY_IBM_QUANTUM_TOKEN>\", overwrite=True, set_as_default=True)\n",
+    "#service = QiskitRuntimeService(channel='ibm_quantum')\n",
+    "\n",
+    "# Load saved credentials\n",
+    "service = QiskitRuntimeService()\n",
+    "\n",
+    "backend = service.least_busy(\n",
+    "    operational=True, min_num_qubits=qcs[0].num_qubits, simulator=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.transpiler import PassManager\n",
+    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
+    "\n",
+    "target = backend.target\n",
+    "pm = generate_preset_pass_manager(target=target, optimization_level=3)\n",
+    "\n",
+    "qcs_isa=qcs\n",
+    "\n",
+    "for i in range(0,len(qcs)):\n",
+    "    qcs_isa[i] = pm.run(qcs[i])\n",
+    "    qcs_isa[i].draw(output=\"mpl\", idle_wires=False, style=\"iqp\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1379.77x284.278 with 1 Axes>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qcs_isa[2].draw(output=\"mpl\", idle_wires=False, style=\"iqp\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3: Execute your job on IBM quantum computers using Runtime Primitives\n",
+    "\n",
+    "Now that we have optimized our circuits to run on real quantum hardware, and debugged our code using simulators, we are ready to gather statistics from a real quantum computer, and settle the disagreement between Einstein and Born."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_runtime import Options, Session, SamplerV2 as Sampler\n",
+    "#sampler.options.default_shots = 1000\n",
+    "\n",
+    "# Start a job that will return shots for all 100 parameter value sets.\n",
+    "result_list=[None]*len(qcs)\n",
+    "real_counts_list=[None]*len(qcs)\n",
+    "with Session(backend=backend) as session:\n",
+    "    sampler = Sampler(mode = session)\n",
+    "    \n",
+    "    for i in range(0,len(qcs)):\n",
+    "        # Define the primitive unified bloc (pub)\n",
+    "        pub = (qcs[i])\n",
+    "        job = sampler.run([pub], shots=10000) \n",
+    "        # Extract the result for the 0th pub (this example only has one pub).\n",
+    "        result_list[i] = job.result()\n",
+    "        data_pub = result_list[i][0].data\n",
+    "        counts = data_pub.c.get_counts()\n",
+    "        real_counts_list[i]=counts\n",
+    "    #   plot_histogram(counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'00': 7422, '01': 7762, '10': 7261, '11': 7555}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outcomes=('00','01','10','11')\n",
+    "\n",
+    "#Here we convert \"None\"s into 0's so that we can sum.\n",
+    "\n",
+    "for i in range(0,len(qcs)):\n",
+    "    for j in range(0,len(outcomes)):\n",
+    "        if real_counts_list[i].get(outcomes[j])==None:\n",
+    "            real_counts_list[i].update({outcomes[j]:0})\n",
+    "\n",
+    "#Here we create a dictionary that holds all the outcomes and sums over their appearances in each of the circuits.\n",
+    "\n",
+    "real_total_counts={}\n",
+    "for i in range(0,len(outcomes)):\n",
+    "    real_total_counts[outcomes[i]]=sum(real_counts_list[j].get(outcomes[i]) for j in range(0,len(qcs)))  \n",
+    "\n",
+    "print(real_total_counts)\n",
+    "plot_histogram(real_total_counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#This syntax allows you to run the job on a simulator, in case you have exhausted your allotted time on real IBM quantum computers.\n",
+    "#But we strongly advise running this on real quantum computers, since this is meant to be a check of the behavior of real quantum systems.\n",
+    "#from qiskit.primitives import BackendSamplerV2 \n",
+    "\n",
+    "#data_pubs = (result0[0].data,result1[0].data,result2[0].data)\n",
+    "#counts = (data_pubs[0].c.get_counts(),data_pubs[1].c.get_counts(),data_pubs[2].c.get_counts())\n",
+    "#plot_histogram(counts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 4: Post-processing and analysis\n",
+    "\n",
+    "Let’s take a step back and recap: Using a hidden-variables treatment, and the 3 offset axes, we obtained a constraint on the probability for the measurements to yield the same sign $P_{same,hv} =4/9$. Then we assumed probability distributions as in quantum mechanics and obtained a __different value__ for that probability: $P_{same,gm} = 1/2$. The prediction from quantum mechanics is higher than that allowed by the hidden variables treatment. So it can be experimentally known whether quantum mechanical states are determined by nature prior to measurement, or if they are truly in a probabilistic superposition of possible states.\n",
+    "\n",
+    "We engineered our quantum circuits such that there are four possible outcomes corresponding to Lucas and Rihanna measuring one sign of the spin projection or the other: 00, 01, 10, and 11. In the cases of 00 and 11, Lucas and Rihanna measure the same sign, and in the cases of 01 and 10, they measure opposite signs. We see that to a very good approximation, the chance of Lucas and Rihanna getting the same sign are about 50%, definitely greater than $4/9$. This means that there is no set of hidden variables that can account for that distribution of probabilities, and the hidden-variables treatment is not compatible with experiment.\n",
+    "\n",
+    "There are different interpretations of quantum mechanical experimental results, and there are many subtleties to experimental setups that are revisited every so often. But so far the principles of quantum mechanics and the probabilistic interpretation of quantum states have accurately described the results. Max Born seems to have been correct.\n",
+    "\n",
+    "Let's take just one more moment to reflect on the significance of this. Two particles emerge from a decay event, and the two particles travel in different directions, possibly for a long time. Their spins are not in any well-defined state, and they carry no hidden-variable instructions with them to determine the outcomes of future measurements. But a measurement of one (along, say $+z$) necessarily determines the outcome of an experiment on the other particle's spin along the $z$ direction (it must be $-z$). This means that something about the physics of one particle is determined by what is done to the other particle, possibly far away. This is one situation that has led people to refer to reality as being \"non-local\".\n",
+    "\n",
+    "Two particles like those we've been describing are somehow \"connected\" in the sense that measurements on one can influence the other. We refer to such particles as being \"entangled\". Entanglement is more than just correlations. For example, we could construct a classical machine that spits out a magnet to one side with its north end up and a magnet to the other side with its north end down. Such magnets could be perfectly anti-correlated. But a measurement of one would do nothing to the other. In quantum mechanical entanglement, particle A could be in an undefined state (or a mixture of many states), and we can pin it down to a defined state through measurements on a totally different particle (say, B). Nothing like that exists in the classical world.\n",
+    "\n",
+    "This often brings up a whole new world of questions and possibilities. Some of the ideas it conjures are real, like using entanglement to compute, as in quantum computers! Some are deceptively appealing, but turn out to fail, like trying to use entanglement to send information faster than light. We encourage you ask all the questions occurring to you, and read about how others have investigated these phenomena. There is a whole world of quantum mechanics out there to explore, but here are just a few resources you might check out:\n",
+    "\n",
+    "IBM Quantum courses:\n",
+    "- [Quantum computing in practice](https://learning.quantum.ibm.com/course/quantum-computing-in-practice)\n",
+    "- [Basics of Quantum Information](https://learning.quantum.ibm.com/course/basics-of-quantum-information)\n",
+    "\n",
+    "Interesting quantum mechanics papers:\n",
+    "- [Einstein Podolsky and Rosen paradox](https://cds.cern.ch/record/405662/files/PhysRev.47.777.pdf)\n",
+    "- [John Bell's original paper from 1964](https://cds.cern.ch/record/111654/files/vol1p195-200_001.pdf)\n",
+    "- [2019 paper on catching and reversing a \"quantum jump\" mid-transition](https://www.nature.com/articles/s41586-019-1287-z)\n",
+    "\n",
+    "Some quantum mechanics instructional resources:\n",
+    "- [Quantum I course materials](https://www.colorado.edu/sei/departments/physics/activities/courses/quantum-i-course-materials) from the University of Colorado.\n",
+    "\n",
+    "Some quantum mechanics educational research:\n",
+    "- [Review of student difficulties in upper-level quantum mechanics](https://journals.aps.org/prper/pdf/10.1103/PhysRevSTPER.11.020117) by C. Singh and E. Marshman"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Critical concepts:\n",
+    "\n",
+    "- There was a historical disagreement about whether quantum states were merely unknown or undetermined by nature prior to measurement, whether quantum mechanics is deterministic or probabilistic.\n",
+    "- Hidden variables and therefore local realism are not consistent with observations of quantum mechanics. That is, the correlations observed in quantum mechanics cannot be explained by well-defined variables that are simply unknown to us.\n",
+    "- Quantum mechanics is probabilistic.\n",
+    "- Entanglement is real and observable.\n",
+    "- Entanglement is not just correlations.\n",
+    "- We can map real world scenarios to quantum computers.\n",
+    "- Hidden variables refers to quantities specified by nature, but unknown to humans; they do not exist in this context.\n",
+    "\n",
+    "## Questions\n",
+    "\n",
+    "### T/F Questions:\n",
+    "\n",
+    "1. T/F Albert Einstein argued that quantum mechanics was incomplete, as a theory, because it only described probabilities of outcomes, and not the underlying mechanism that determined those outcomes.\n",
+    "2. T/F \"Hidden variables\" refers to the idea that two quantum mechanical particles can be entangled.\n",
+    "3. T/F Any two correlated systems are quantum mechanically entangled.\n",
+    "4. T/F Quantum mechanical entanglement is important for getting math right, but you can't see it in an experiment.\n",
+    "5. T/F In most cases, quantum mechanics can't tell you an exact outcome of an experiment, only the probabilities that certain outcomes will be measured.\n",
+    "6. T/F In quantum mechanics, under certain conditions, the state of particle A can be affected by the state of particle B, even if particles A and B are not in contact and do not exchange any particles.\n",
+    "7. T/F We can map real world experiments onto quantum circuits.\n",
+    "\n",
+    "### MC Questions:\n",
+    "\n",
+    "1. Suppose a spin-0 particle decays into two spin-1/2 particles A and B. A measurement is made on particle A that reveals its spin has a projection along $+z$. Particle B now\n",
+    "    - a. definitely has a spin projection along $-z$\n",
+    "    - b. definitely has a spin projection along $-x$\n",
+    "    - c. definitely has a spin projection along $-y$\n",
+    "    - d. definitely has a negative spin projection along any axis measured.\n",
+    "\n",
+    "2. A spin-0 particle decays into two spin-1/2 particles A and B. If particle A is measured to have a projection along $+z$, which of the following projections are possible for a measurement of particle B? Circle all that apply.\n",
+    "    - a. $+x$\n",
+    "    - b. $-x$\n",
+    "    - c. $+y$\n",
+    "    - d. $-y$\n",
+    "    - e. $+z$\n",
+    "    - f. $-z$\n",
+    "\n",
+    "3. Suppose a spin-0 particle decays into two spin-1/2 particles A and B. What best describes the state of particle A prior to any measurement.\n",
+    "    - a. The spin of particle A is along $+z$.\n",
+    "    - b. The spin of particle A is along $-z$.\n",
+    "    - c. The spin of particle A is along $+x$.\n",
+    "    - d. The spin of particle A is defined along some directions, but not others.\n",
+    "    - e. The spin orientation of particle A is undetermined by nature prior to any measurements.\n",
+    "\n",
+    "4. Which of the following is/are true of the Hadamard gate? Select all that apply.\n",
+    "    - a. $H|0\\rangle = \\frac{1}{\\sqrt{2}}(|0\\rangle+|1\\rangle)$\n",
+    "    - b. $H|0\\rangle = \\frac{1}{\\sqrt{2}}(|0\\rangle-|1\\rangle)$\n",
+    "    - c. $H \\frac{1}{\\sqrt{2}}(|0\\rangle-|1\\rangle)=|0\\rangle$\n",
+    "    - d. $H \\frac{1}{\\sqrt{2}}(|0\\rangle+|1\\rangle)=|0\\rangle$\n",
+    "\n",
+    "5. Which of the following is/are true of the X gate? Select all that apply.\n",
+    "    - a. $X|0\\rangle = |1\\rangle$\n",
+    "    - b. $X|0\\rangle = \\frac{1}{\\sqrt{2}}(|0\\rangle-|1\\rangle)$\n",
+    "    - c. $X|0\\rangle = -|0\\rangle$\n",
+    "    - d. $X|1\\rangle = |0\\rangle$\n",
+    "    - e. $X\\frac{1}{\\sqrt{2}}(|0\\rangle-|1\\rangle)=X\\frac{1}{\\sqrt{2}}(|1\\rangle-|0\\rangle)$\n",
+    "\n",
+    "6. Which of the following is a two-qubit gate?\n",
+    "    - a. X\n",
+    "    - b. $R_y(\\theta)$\n",
+    "    - c. H\n",
+    "    - d. CNOT\n",
+    "\n",
+    "7. Suppose a qubit is in the $|0\\rangle$ state. What is the probability of measuring it to be in the $1\\rangle$ state?\n",
+    "    - a. Exactly 100% on a noise-free simulator, near 100% on a real quantum computer\n",
+    "    - b. Near 100% on a noise-free simulator, exactly 100% on a real quantum computer\n",
+    "    - c. Exactly 0% on a noise-free simulator, near 0% on a real quantum computer\n",
+    "    - d. Near 0% on a noise-free simulator, exactly 0% on a real quantum computer\n",
+    "\n",
+    "### Discussion Questions:\n",
+    "\n",
+    "1. Friends A, B, and C are discussing results from this lab, related to Bell's Inequality. Specifically, they are looking at the image that shows the quantum mechanical probability of measuring the same sign along axes is greater than that allowed by a hidden-variables treatment: $(P_\\text{same})_\\text{max,QM}>(P_\\text{same})_\\text{max,HV}$. Friend A says, \"This means that we didn't know the spin states prior to a measurement.\" Friend B says, \"No, it's more than that. This means the spins are not already pointing in a particular direction, prior to measurement. Although, the spin state might somehow be determined or stored somehow\" Friend C says, \"No, it's even more than that. This means the future spin state wasn't even decided by nature prior to measurement.\" With whom do you agree, and why?\n",
+    "\n",
+    "2. Explain how quantum mechanical phenomena like entanglement indicate that reality is non-local.\n",
+    "\n",
+    "3. What additional experiments would you like to do to convince yourself of the results obtained here?\n",
+    "\n",
+    "4. Could Bell's inequality only be explored with 3 equally-spaced axes $a$, $b$, and $c$? Could it be done with any other number of axes? What would this look like? Would it still yield a difference in probabilities predicted by hidden variables vs. probabilistic quantum mechanics?"
+   ]
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diff --git a/QCBasics/CH3-QuantumProtocols/Protocols.md b/QCBasics/CH3-QuantumProtocols/Protocols.md
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+# Introduction to Quantum Protocols
+
+Created: 2025年6月15日 13:07
+Reviewed: No
+
+**Useful Definitions:**
+
+**Protocol: l** is a set of agreed-upon rules, procedures, or conventions that govern the interaction and communication between two or more entities. Think of it as a shared language and etiquette that allows participants to understand each other and achieve a common goal.
+
+> Like the Japanese custom of removing one’s shoes when arriving at the home, is a standard practice that both members agree upon, and they follow it knowing the meaning of each others
+> 
+
+In **computer science**, a protocol is a formal set of rules, formats, and procedures for transmitting data between electronic devices. It dictates how information is structured, sent, received, and interpreted, ensuring that different systems can "speak the same language" and exchange data effectively
+
+We can remark the following **properties**, and later we see how are their manifested in the specific case of **Quantum Protocols**
+
+- **Rules and Procedures:** They define the specific steps and actions that entities must follow during communication.
+- **Syntax:** They specify the structure and format of the messages exchanged (e.g., data format, encoding, signal representation).
+- **Semantics:** They define the meaning of the exchanged messages, including control information, commands, and error handling instructions.
+- **Timing/Synchronization:** They govern the sequence and timing of events, ensuring that messages are sent and received in the correct order and at the appropriate pace.
+- **Interoperability:** By adhering to a common protocol, diverse systems and devices can communicate and work together seamlessly.
+- **Error Handling:** Many protocols include mechanisms for detecting and recovering from errors that may occur during transmission.
+- **Layered Architecture:** Complex protocols are often designed in layers (e.g., the OSI model), where each layer handles a specific aspect of communication, building upon the services of lower layers.
+- **Standards:** Protocols are often established as standards by international or industry-wide organizations (e.g., TCP/IP, HTTP, SMTP).
+
+| Feature/Characteristic | Classical Protocols (e.g., TCP/IP, HTTPS) | Quantum Protocols (e.g., BB84 QKD, Teleportation) |
+| --- | --- | --- |
+| Underlying Physics | Classical Mechanics, Electromagnetism | Quantum Mechanics |
+| Information Unit | Bits (0 or 1) | Qubits (0, 1, or superposition of both) |
+| Key Principles | Mathematical algorithms, computational complexity | Superposition, Entanglement, No-Cloning Theorem, Measurement Postulate, Uncertainty Principle |
+| Security Basis | Computational hardness of mathematical problems (e.g., factoring large numbers) | Laws of physics; attempt to measure disturbs the quantum state, making eavesdropping detectable. |
+| Detection of Eavesdropping | Difficult to detect; relies on computational assumptions | Inherent detection due to quantum measurement principles; any interception fundamentally alters the state. |
+| Communication Channels | Classical channels (e.g., electrical signals, radio waves, optical fiber) | Quantum channels (e.g., transmitting photons) often alongside classical channels (for public discussion) |
+| Hardware Requirements | Standard computing hardware (processors, memory, network cards) | Specialized quantum hardware (e.g., single-photon detectors, entangled photon sources, quantum gates, cryostats) |
+| Scalability & Range | Highly scalable, global reach (Internet) | Currently limited in range and scalability due to decoherence and loss in quantum channels. |
+| Error Correction | Classical error detection/correction codes | Quantum error correction (more complex due to delicate quantum states) |
+| Purpose Examples | Data transfer, web Browse, email, remote login | Secure key distribution, state transfer, quantum computation building blocks |
+
+**2l Qubit:** 2-Level Qubit, which is a fundamental unit of  Quantum Information , representing the quantum equivalent of a classical binary bit It is a two-state quantum-mechanical system where certain properties can be measured in binary positions, such as 'up' or 'down'.
+
+- When **Measured a Qubit** in the Superposition **State** it collides **truly** **randomly** ( which is why it is useful in cryptography applications ) into either state 0 or 1 having assigned a **direction for the basis**
+- It is defined as a **ray** in 2-**Dimensional Hilbert Space**
+
+**Dirac Notation ( Bra-Ket )** 
+
+- A **ket** is of the form $|v\rangle$  Mathematically it denotes a vector,  ${\displaystyle {\boldsymbol {v}}}$, in an abstract (complex) vector space ${\displaystyle V}$, and physically it represents a state of some quantum system.
+
+- A **bra** is of the form ${\displaystyle \langle f|}$. Mathematically it denotes a linear form ${\displaystyle f:V\to \mathbb {C} }$ , i.e. a linear map that maps each vector in  ${\displaystyle V}$ to a number in the complex plane ${\displaystyle \mathbb {C} }$. Letting the linear functional ${\displaystyle \langle f|}$ act on a vector  ${\displaystyle |v\rangle }$ is written as ${\displaystyle \langle f|v\rangle \in \mathbb {C} }$.
+
+**Superposition**
+
+ ****A cornerstone of quantum mechanics, **superposition** dictates that a **quantum system** can exist in **multiple states simultaneously** until a **measurement** is performed. For instance, a quantum bit, or **qubit**, can represent both '0' and '1' at the same time, being able to be in **any linear combination** representing the **state** of the Qubit, unlike classical bits which can only be one or the other. This principle is empirically validated through experiments such as the double-slit experiment, where particles exhibit wave-like properties, creating interference patterns that demonstrate their simultaneous passage through both slits
+This ability enables great properties like
+
+- Parallel Computing
+
+> Like Schrödinger's cat which **before measuring** the only way to treat him and get information is as it is in a combination of both **alive and death** resulting in probabilistic statements, ( Which in this case may depend on the angle of the
+> 
+
+**Entanglement:** 
+
+- A strong dichotomy appears in this property : entanglement can double the classical communication capacity of a noiseless quantum channel but does not increase the quantum communication capacity at all
+
+**Differences Between Classical Information and Quantum Information**
+
+This report addresses a common point of intellectual curiosity and confusion: the distinction between classical and quantum information, especially in light of classical information theory, and how these concepts intertwine in advanced applications such as quantum teleportation. 
+
+This report aims to provide a clear, in-depth analysis of classical and quantum information, defining their fundamental units, properties, and mathematical representations. It will systematically highlight their distinctions, explore the applicability and conceptual limitations of Shannon's theory when extended to the quantum domain, and introduce its quantum analogues. 
+
+Both paradigms **Classical and Quantum** are intended to deal with the Notion of **data,** following principles like it should be
+
+- **Clonable**
+- **Erasable**
+- **Readable as many times as needed**
+- **Unchanging when untouched**
+
+**Classical Information**
+
+- In classical Information, the information is processed and stored as **bits** . Each **bit** exist in **one** of **two Discrete mutually exclusive states**: either a 0 or a 1, which is **encoded** using **electrical impulses** using **Boolean** **Operations** to perform the a vast **array of task** and **challenges**
+- A feature of classical computing is that **its processing power** scales **linearly** with the number the addition of more bits, which means doubling the bits the number of bits doubles it’s processing power, making it not **suitable** for problems that scale faster, like **exponential** or **factorial**, where one of this is **factorization of numbers used for** cryptography
+- Shannon stablished a mathematical framework for **quantifying** the **information** and understanding communication processes. The Shannon Entropy is a **measure of uncertainty** of the **spread of a probability distribution**
+
+**Quantum Information**
+
+The **fundamental** unit of **quantum information** unit to process and store **information** are the **qubits** which unlike classical bits which are strictly 0 or 1, Qubits **harness the principles of Quantum Mechanics** to exist in  **Superposition States meaning they can exist in a combination of both simultaneously**  
+
+- The mathematical way to represent a Qubit is as a **Vector** ( Single column Matrix ) and must satisfy a “Normalization” requirement: the sum of the squares of the absolute values of it’s components ( probabilities of collapsing to 0 or 1) must be equal to 1 ( 100% ). And note this fact is related to the main trigonometric identity which we will use to describe the probability of collapsing to **either state** which depends on the **angle**.
+
+$$
+\sin^2 x + \cos^2x = 1
+$$
+
+- Due to the properties of **Entanglement** the capacity of the information system grows **exponentially** with each additional Qubit. For instance, e, two entangled qubits can simultaneously represent a superposition of four combinations (00, 01, 10, 11), three qubits can represent eight, and so on. ( Making **Exponential growing problems** solvable with Quantum Computation )
+
+# Quantum Protocols
+
+## Introduction
+
+The advent of quantum mechanics has ushered in a new era of technological advancements, particularly in the realm of information science. Quantum protocols represent a profound shift from classical information processing, leveraging the unique and often counter-intuitive behaviors of quantum systems to enable advanced communication methods that inherently ensure privacy and security. This field explores capabilities previously considered impossible within the confines of classical physics like truly random number generation 
+
+- Particles **DO not have internal information** ( A Function which has a given spin assigned to each angle ) when it is created, and it is neither fully determined form the moment of it’s creation. The **Particle has not definite state before it is measured.**
+
+# Superdense Coding
+
+It is a **Quantum protocol** that allows us to send **two bits of classical information** ( 00, 01, 10, 11 ) by only manipulating a single quantum bit, **1 qubit**. ( Double Efficiency )
+
+The person sending the Qubit is Alice
+
+As for this session you will need to be familiar with 
+
+- Qubits
+- Dirac Notation ( AKA Bra-Ket Notation )
+- Classical Bits
+- Linear Algebra
+
+**References** 
+
+- Wikipedia- [https://en.wikipedia.org/wiki/Superdense_coding](https://en.wikipedia.org/wiki/Superdense_coding)
+- [Paper of Super Dense Coding of Quantum States by Aram Harrow](https://arxiv.org/abs/quant-ph/0307221)
+- Demystifying Superdense Coding - [https://medium.com/qiskit/demystifying-superdense-coding-41d46401910e](https://medium.com/qiskit/demystifying-superdense-coding-41d46401910e)
+- Referenced by the above reference [https://www.monoidal.net/papers/tutorialqpl-1.pdf](https://www.monoidal.net/papers/tutorialqpl-1.pdf)
+
+ 
+
+## How Quantum Information differs from Classical Information
+
+Because an important features that is inherit from the way the **Quantum entanglement** also known as “Entrelazamiento Cuántico”
+
+**As proposed in the slides we have**
+
+Quantum protocols leverage the unique properties of **quantum mechanics -** particularly 
+
+**entanglement** and **superposition**  to achieve tasks impossible with classical communication.
+
+We need to give **Clarity to this words**, where does the origin motivation and all behind it comes.
+
+## Cryptography
+
+### BB84 Protocol
+
+https://www.youtube.com/watch?v=2kdRuqvIaww&pp=ygUQUXVhbnR1bSBQcm90b2Nscw%3D%3D
+
+https://www.youtube.com/watch?v=V3WzH2up7Os&t=546s&pp=ygUQUXVhbnR1bSBQcm90b2Nscw%3D%3D
+
+https://www.youtube.com/watch?v=ZuvK-od647c&t=53s
+
+**Importance**
+
+All the benefits and solutions that classical cryptography provides
+
+- Personal Information storage
+- Secure Communication
+- Commercial Records & Stocks
+- Private Statements
+
+**EJ**
+
+Sending a Secret Color, ( RGB ) each color represented by One byte giving a total of three bytes
+
+### Methods of encryption
+
+Suppose the secret communications want to be done between **Bob** and **Alice**.
+
+**One-Time Path:** Alice takes a **Random ( No information ) Digital Key** composed of **24 bits** shared **Beforehand** with Bob
+
+Given the number they want to transmit now your perform the **XOR Operation,**  which has the purpose of **Flipping** some bits in random position of the original message
+
+- If they are the same bit, two 0’s or two  1’s the output is 0 (  Negative  )
+- If the are not the same bits, one of each is present then the output is 1 ( Positive )
+
+| 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 |
+| 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 |
+
+**Note:** It will be Straightforward for **Bob**  to uncover the message  since the **XOR is its own inverse** with its original meaning just having the same bits as the **shared key** suggests.
+
+**Benefits**:
+
+- No Mathematical way to decode back, since the random key is chosen randomly
+
+**Countereffects**
+
+- **Key Shared beforehand (** Secularity in order to grant security )
+- Quantum Computing may be able to run public key methods in the future, is not the case not since the are only 20 Qubit
+
+**Public Key Cryptography ( Asymmetric Key Cryptography - PKC )**
+
+- Why is there not a Public Key Cryptography in Quantum Computing but they use the one mentioned above?
+
+ **( Asymmetric )** 
+
+n = Private Key
+
+e = Both Binary numbers
+
+**QKD ( Quantum Key Distribution )** 
+
+Given this raising problem there may be a tendency to come back to the One Direction and just leading with the problem of distribution with the keys securely  without physical distribution.
+
+**Light Properties**
+
+Light is made of Photon **which have a** Polarization **( The direction i**n which the **electric field oscillates ), the polarization can be measured at the level of individual photons** relative to some **axis** or **direction.** 
+
+- Measuring in the vertical direction you can whether a given photon is oscillating **up** and **own** or **side to side**. This is binary so Alice and Bob can communicate their **One-Time-Key**. Calling side to side = 0 and up and down = 1.
+
+![image.png](image.png)
+
+**Polarization is fundamentally a Quantum Mechanical property**
+
+- A photon can be in a superposition of the two mutually exclusive outcomes, oscillating in **up and down and side to side**  simultaneously
+- If the key is tried to be stolen by an **eavesdropper**  ( fisgon o ladrón ) they will need to **measure** the state, **collapsing it into one of it**, disrupting the **quantum state** of **superposition of both**, that tells that an eavesdropper is present
+- Only they and no one in the universe knows ( Why ? )
+
+How is this production of polarized photons created?
+
+- Like Sun-Glasses that are measuring the photon , letting trough light of one polarization,
+
+- **Measuring** only allows **Discrete** Solutions, which means that even though the input, the output is limited to a handful of already set possible results
+
+How to make the Quantum State of a Photon into a Superposition of Vertical and Horizontal polarization is quite easy, just turn the emitter of photons 45° degrees **Clockwise** **Relative to the Bob**, which is what he will only get out puts for **Vertical or** Horizontal **in his orientation system**
+
+![image.png](image%201.png)
+
+- Not the probability **Bob** measuring the **direction**, that **Alice** Sent is **Directly**  related to the relative angle between the way they are transmitted.
+
+**Depending**  on the chosen basis for the coordinate system of both the names in the papers are named like this
+
+- Aligned coordinates systems is called **Rectilinear Basis**
+- **Curved 45°** is called **Diagonal Basis**
+
+![image.png](image%202.png)
+
+# Verisatium
+
+According to the theory in developing of Quantum mechanics, an **experiment**  to confirm the following statement emerged
+
+- An event at one point in the Universe Could instantaneously affect another event arbitrarily far away, implying **faster than light communication**
+
+### No-Cloning Theorem
+
+A critical theorem in quantum mechanics, the no-cloning theorem states that it is impossible to create an identical copy of an arbitrary unknown quantum state. This principle is vital for the security of quantum cryptography, as it ensures that eavesdroppers cannot perfectly replicate quantum information without altering it. This inherent uniqueness distinguishes quantum information from classical information.
+
+The no-cloning theorem directly prevents a "copy-and-forward" attack, a common strategy in classical eavesdropping. If an eavesdropper, often referred to as Eve, attempts to copy a quantum state, her action will necessarily disturb the original state, and the resulting copy will be imperfect, leading to detectable errors. This provides a foundational layer of security, making quantum information intrinsically more secure against replication attempts compared to classical information. It acts as a built-in tamper-detection mechanism. For quantum teleportation, this theorem implies that the original state is effectively "destroyed" at the sender's location and recreated at the receiver's, rather than being duplicated.
+
+To understand it we first need to understand **Spin**.
+
+**Spin:** A fundamental property all particles have, although they are not spinning in the strict meaning of the word the analogy is appropriate, since they have **Angular Momentum**, and **Orientation in space.**
+
+- This Spinning can be measured but we have to choose the direction in which we measured it, if it **Aligned with the direction of measurement** we call **Spin up** or it is **Opposite** which is called **Spin down.**
+
+The particle **maintains** the spin it ended up after the **measurement** regardless of the **measurement direction and the implications of** whether **it changed** with 50% either chance the direction or preserving the original spin.
+
+This probability depends on the **Square of half the angle**
+
+$$
+P(\uparrow)=\cos^2(\frac{\theta}{2})
+$$
+
+![image.png](image%203.png)
+
+- Two of these particles can be used for the experiment, but have to be **prepared,** in the sense that **both** have to be formed out spontaneously of energy, since the **energy** in the **universe** stays constant, supposing we chose the correct measurement rule, if **one particle is measured to have spin up**  the other has to have **spin down**.
+- Only if the particles are measured in the same direction their spins must be opposite..
+- You **May Imagine that** each particle is created with a definite **well-defined spin**
+
+Two possible interpretation
+
+- Hidden Information that is intrinsic to the particles once they are created
+- Truly Superposition between different states
+
+Let’s Say you have a way to **create** many **copies** of **an entangled pair or particles**, ( Can be produced trough a decay process where one particles decays into two ) 
+
+For example let’s say a spin 0 particles decays into two 1/2 spin particles ( The magnitude )
+
+¿ How ?
+
+# Quantum Teleportation
+
+For Following and Putting into practice using the IBM’S QPU ( Quantum Processing Units ) , you can download the ipynb to practice yourself with QISKIT, this is taken from this [course](https://www.youtube.com/watch?v=jxqnzltpDdE). [Here](https://ibm.ent.box.com/s/47c8ee73qhdkdq6mmlfljff69lyzjw48)
+
+ 
+
+**Note:** for accessing [quantum.ibm.com](http://quantum.ibm.com), and using the QPU you need yo accept their T&C ( Terms and Condition ) which include being over 18 years old, and have an IBM ( International Business Machine ) account.
+
+For running the interactive module you can use the installation via pip or via conda, as for this demonstration we will be using conda which can be installed from [**here](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html).**
+
+```python
+# Note that we use 3.12 while 3.13 is the most recent to avoid some conflicts mentioned in other forums that may no tbe solved as of jun 2025
+conda create -n Quantum python=3.12
+conda install pip
+conda install conda-forge::qiskit # https://anaconda.org/conda-forge/qiskit
+conda install anaconda::ipykernel # https://anaconda.org/anaconda/ipykernel
+conda install conda-forge::matplotlib # https://anaconda.org/conda-forge/matplotlib
+
+```
+
+## Introduction
+
+Teleportation beyond the sci-fic appearance can be done where one object disappears from one location and instantaneously appears somewhere else that is **indeed impossible.**[https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.70.1895](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.70.1895)
+
+The **Quantum Teleportation**, that is possible comes in the sense that it is information rather than matter that is transmitted between a distant location, using a **pair of entangled particles** one on each sides and a **way to communicate classical information**.
+
+> It is called like that because similar to the way that fictions presents this term , the **Quantum Information**, disappeared from one location and appeared in a distant location without **traveling** the intermediate space.
+> 
+
+### How does it works ?
+
+Take Alice and Bob who are far away from each other.
+
+Say Alice has a **Qubit** called **Q** in some **unknown Quantum State** as follows
+
+$$
+{\left | \psi \right\rangle_Q} = \alpha_0|0\rangle +\alpha_1|1\rangle
+
+$$
+
+Where $\alpha_1,\alpha_2 \in \mathbb{C}$.
+
+ **Note:** This symbol is called “psi” and typed \psi in Latex Notation, this was done using the [following syntax](https://tex.stackexchange.com/questions/214728/braket-notation-in-latex).
+
+```latex
+\left\langle \frac{1}{2} \middle| 1 \right\rangle
+```
+
+Alice wants to send her state to Bob but eh has no way to actually sent the Qubit itself EJ ( Too far away, Does not where Bob is ).
+
+But Previously Bob and Alice already shared a **Pair of Entangled Qubits A and B**, A is owned by Alice and B is owned by Bob, meaning that their **states are correlated** regardless of the **distance** between them, in a way that classical physics cannot work
+
+This **Entangled pair of Qubits is** called **an *ebit***
+
+$$
+{\left | \psi \right\rangle_{BA}} = \frac{1}{\sqrt{2}}(|0\rangle_B|0\rangle_A +|1\rangle_B|1\rangle_A)
+
+$$
+
+This **Ebit**, can be used to transmit **Quantum Information** to Bob
+
+**Note:** In **Qiskit** the standard way to order the **Qubits is right to left,** meaning the **Qubit** at **State 0** will appear to the right
+
+So we will be start by writing the **Joint State** of all 3 of these Qubits ( Note order may see arbitrarily but is respects the note above )
+
+$$
+{\left | \psi \right\rangle} = {\left | \psi \right\rangle_{BA}}{\left | \psi \right\rangle_{Q}} =\frac{1}{\sqrt{2}}(
+|0\rangle_B|0\rangle_A +|1\rangle_B|1\rangle_A)
+(\alpha_0|0\rangle +\alpha_1|1\rangle)
+
+$$
+
+Expanding and rewriting the **State**
+
+$$
+=\frac{1}{\sqrt{2}}
+(\alpha_0
+(
+|0\rangle_B|0\rangle_A +|1\rangle_B|1\rangle_A
+)
+|0\rangle _Q
++
+(\alpha_1
+(
+|0\rangle_B|0\rangle_A +|1\rangle_B|1\rangle_A
+)
+|1\rangle_Q
+
+$$
+
+1. Then, the first step is to Alice to **Entangle** her **Data Qubit** with her half of the **Ebit Qubit** using a **C-Not Gate ( Controlled Note Gate )** Which will **change** the **state of One Qubit ( The Target Qubit  which target )** depending of another Qubit**,  the Control Qubit**
+
+- For this it is useful to remember some of the most common and utilized gates across Quantum Computing
+
+![image.png](image%204.png)
+
+Specifically the C Gate
+
+| Before |  | After |  |
+| --- | --- | --- | --- |
+| Control | Target | Control | Target |
+| |0> | |0> | |0> | |0> |
+| |0> | |1> | |0> | |1> |
+| |1> | |0> | |1> | |1> |
+| |1> | |1> | |1> | |0> |
+
+This table shows how the CNOT gate operates: when the control qubit is 1, the target qubit is flipped (0→1 or 1→0). When the control qubit is 0, the target remains unchanged.
+
+**CNOT(**Control, Target ) = CNOT(Q,A)$| \psi \rang$
+
+- If the Control Qubit is state is 0, nothing happens to the target
+- If the Control Qubit State is 1, the target Qubit is Flipped
+
+$$
+|\psi \rang =
+\frac{1}{\sqrt{2}}
+(\alpha_0
+(
+|0\rangle_B|0\rangle_A +|1\rangle_B|1\rangle_A
+)
+|0\rangle _Q
++
+(\alpha_1
+(
+|0\rangle_B|1\rangle_A +|1\rangle_B|0\rangle_A
+)
+|1\rangle_Q
+$$
+
+1. **The** Second step is to apply a **Hadamard Gate on Q given by:**
+
+$$
+\begin{align*}
+H|0\rang &= \frac{1}{\sqrt{2}}(|0\rangle+|1\rang) 
+\\
+H|1\rang &= \frac{1}{\sqrt{2}}(|0\rangle-|1\rang)
+\end{align*}
+$$
+
+**which does the following after rearranging:**
+
+$$
+\begin{align*} =
+\frac{1}{2}
+(
+(\alpha_0
+|0\rang_B
++
+\alpha_1
+|1\rang_B
+)
+|0\rangle _A
+|0\rangle _Q
+&+
+(\alpha_0
+|0\rang_B
+-
+\alpha_1
+|1\rang_B
+)
+|0\rangle _A
+|1\rangle _Q
+\\+ 
+(\alpha_0
+|0\rang_B
+-
+\alpha_1
+|1\rang_B
+)
+|0\rangle _A
+|1\rangle _Q
+&+
+(-\alpha_1
+|0\rang_B
+-
+\alpha_0
+|1\rang_B
+)
+|1\rangle _A
+|1\rangle _Q
+\end{align*}
+$$
+
+Even Though it looks like a complet mess, look what happens if Alice Measures both her Qubits
+
+**Usefulness**
+
+- Quantum Communication
+- Networking
+
+[Here](https://github.com/Qiskit/textbook/blob/main/notebooks/ch-algorithms/superdense-coding.ipynb) you can see a relation between **Teleportation** and **Superdense Coding**
+
+| **Teleportation** | **Superdense Coding** |
+| --- | --- |
+| Transmit one qubit using two classical bits | Transmit two classical bits using one qubit |
+
+**References**
+
+- **Qiskit Introduction to Teleportation -** [https://www.youtube.com/watch?v=jxqnzltpDdE](https://www.youtube.com/watch?v=jxqnzltpDdE)
+- **Original Teleportation Paper -** [https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.70.1895](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.70.1895)
+- **Quantum Gate Teleportation -** [https://www.nature.com/articles/46503](https://www.nature.com/articles/46503).
+**Note:** UNAL students have access to Nature Magazine
+- **Experimental Quantum Teleportation to Space -** [https://arxiv.org/abs/1707.00934](https://arxiv.org/abs/1707.00934) ****
+- Gemini Chat for Research: [https://gemini.google.com/app/b938dddd173cbb75?hl=es](https://gemini.google.com/app/b938dddd173cbb75?hl=es)
diff --git a/QCBasics/CH3-QuantumProtocols/Teleportation_forstudents.ipynb b/QCBasics/CH3-QuantumProtocols/Teleportation_forstudents.ipynb
new file mode 100644
index 0000000..093d847
--- /dev/null
+++ b/QCBasics/CH3-QuantumProtocols/Teleportation_forstudents.ipynb
@@ -0,0 +1,1219 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quantum Teleportation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Introduction and background\n",
+    "\n",
+    "Quantum teleportation is a technique in quantum physics that allows the transfer of quantum information from one location to another without physically moving particles. Unlike the sci-fi concept of teleportation, this process doesn't involve transporting matter. Instead, it relies on the principle of quantum entanglement, where two particles become linked regardless of distance. Through a series of precise measurements and classical communication, the quantum state of one particle can be recreated in another particle at a distant location, effectively \"teleporting\" the quantum information. In this module, we'll see how this works mathematically, and then we will implement quantum teleportation on a real quantum computer. The introduction here will be brief; for more background on quantum information, and more explanation about teleportation, we recommend John Watrous's course on the [Basics of Quantum Information](https://learning.quantum.ibm.com/course/basics-of-quantum-information), and in particular the section on [Teleportation.](https://learning.quantum.ibm.com/course/basics-of-quantum-information/entanglement-in-action#teleportation)\n",
+    "\n",
+    "Classical bits can be in states 0 or 1. Quantum bits (qubits) can be in quantum states denoted $|0\\rangle$ and $|1\\rangle$ and also linear combinations of these states, called \"superpositions\", such as $|\\psi\\rangle = \\alpha_0|0\\rangle +\\alpha_1|1\\rangle$, with $\\alpha_0,\\alpha_1 \\in \\mathbb{C},$ and $|\\alpha_0|^2+|\\alpha_1|^2 = 1.$ Although the states can exist in this superposition, a measurement of the state will \"collapse\" it into either the $|0\\rangle$ or $|1\\rangle$ states. The parameters $a$ and $b$ are related to the probability of each measurement outcome according to\n",
+    "\n",
+    "$$P_0 = |\\alpha_0|^2$$\n",
+    "$$P_1 = |\\alpha_1|^2$$\n",
+    "\n",
+    "Hence the constraint that $|\\alpha_0|^2+|\\alpha_1|^2 = 1.$\n",
+    "\n",
+    "Another key feature is that quantum bits can be \"entangled\", which means that the measurement of one qubit can affect the outcome of the measurement of another, entangled qubit. Understanding how entanglement is different from simple classical correlations is a bit tricky. Let's first explain our notation. Call two qubits belonging to friend 0 (Alice) and friend 1 (Bob), and each in the $|0\\rangle$ state\n",
+    "$$|0\\rangle_B|0\\rangle_A$$ \n",
+    "or \n",
+    "$$|0\\rangle_1|0\\rangle_0$$ \n",
+    "sometimes shortened to simply\n",
+    "$$|00\\rangle.$$\n",
+    "Note that the lowest-numbered (or lettered) qubit is furthest to the right. This is a convention called \"little-endian\" notation, used throughout Qiskit.\n",
+    "If the two-qubit state of the friends is $|00\\rangle,$ and they measure the state of their respective qubits, they will each find a 0. Similarly if the qubits were in the state $|11\\rangle,$ each of their measurements would yield a 1. That is no different from the classical case. However, in quantum computing, we can combine this with superposition to obtain states like\n",
+    "\n",
+    "$$\\frac{1}{\\sqrt{2}}(|00\\rangle+|11\\rangle)$$\n",
+    "\n",
+    "In a state like this, whether Alice and Bob have qubits in the state 0 or 1 is not yet known, not even yet determined by nature, and yet we know they will measure the same state for their qubit. For example, if Bob measures his qubit to be in the state $|0\\rangle,$ the only way for that to happen is if the measurement has collapsed the two-qubit state to one of the two possible states, specifically to $|00\\rangle.$ That leaves Alice's qubit also in the $|0\\rangle$ state.\n",
+    "\n",
+    "The entangled of qubits in this way does not require that the qubits remain physically close to one another. In other words, we could entangle qubits, then separate them by a large distance, and use their entanglement to send information. An entangled state like the one above is a basic unit of entanglement, and is sometimes referred to as an \"e-bit\", a single bit of entanglement. These e-bits can be thought of as resources in quantum communication, since each e-bit shared between distant partners can be used, as we outline here, to move information from one location to another.\n",
+    "\n",
+    "The first thought for many people learning about this for the first time is about violating relativity: can we use this to send information faster than light? By all means, keep questioning and probing scientific rules, but unfortunately this won't allow us to send information faster than light, for reasons that will become clear through the course of this module. Spoiler: amazingly it is NOT due to the speed at which this collapse propagates, which does appear to happen faster than light[1](https://www.nature.com/articles/nature15759)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start with two collaborators Alice and Bob, who are initially in the same location and can work together on the same qubits. These collaborators will entangle their qubits. Then they will move apart to two different geographic locations, bringing their respective qubits with them. Alice will then obtain quantum information on a new qubit Q. We make no assumptions about the information on Q. The state of Q could be a secret unknown to Alice; it could be unknown to all people. But Alice is given the task of transferring the information on Q to Bob. She will do this using quantum teleportation.\n",
+    "\n",
+    "To accomplish this, we will need to know some quantum operations or \"gates\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Quantum operators (gates)\n",
+    "\n",
+    "Feel free to skip this section if you are already familiar with quantum gates. If you want to understand these gates better, check out [Basics of Quantum Information](https://learning.quantum.ibm.com/course/basics-of-quantum-information), especially the first two lessons, on the IBM Quantum Learning platform.\n",
+    "\n",
+    "For this teleportation protocol we will primarily use two types of quantum gates: the Hadamard gate, the CNOT gate. A few others will play a lesser role: the $X$ gate, $Z$ gate, and the SWAP gate.\n",
+    "\n",
+    "This module can be completed with very limited linear algebra background, but sometimes visualizing quantum mechanical gates using matrices and vectors can be helpful. So we present here the matrix/vector forms of quantum gates/states, as well.\n",
+    "\n",
+    "The states we have already presented are chosen (partly by convention and partly by constraints) to have vector forms:\n",
+    "$$|0\\rangle = \\begin{pmatrix}1 \\\\ 0\\end{pmatrix}$$\n",
+    "$$|1\\rangle = \\begin{pmatrix}0 \\\\ 1\\end{pmatrix}$$\n",
+    "In this way, an arbitrary state $|\\psi\\rangle = a|0\\rangle+b|1\\rangle$ can be written as \n",
+    "$$|\\psi\\rangle =\\begin{pmatrix}a \\\\ b\\end{pmatrix}$$\n",
+    "\n",
+    "There is some choice in how to extend the notation to multiple-qubit states, but the choice below is quite standard:\n",
+    "\n",
+    "$$|00\\rangle = \\begin{pmatrix}1 \\\\ 0 \\\\ 0 \\\\ 0\\end{pmatrix},|01\\rangle = \\begin{pmatrix}0 \\\\ 1 \\\\ 0 \\\\ 0\\end{pmatrix},\n",
+    "|10\\rangle = \\begin{pmatrix}0 \\\\ 0 \\\\ 1 \\\\0\\end{pmatrix},|11\\rangle = \\begin{pmatrix}0 \\\\ 0 \\\\ 0 \\\\ 1\\end{pmatrix}.$$\n",
+    "\n",
+    "With this choice of vector notation in mind, we can introduce our needed quantum gates, their effects on quantum states, and their matrix forms.\n",
+    "\n",
+    "__H Hadamard Gate:__ Creates a superposition state. Single-qubit gate.\n",
+    "$$H|0\\rangle = \\frac{1}{2}\\left(|0\\rangle+|1\\rangle\\right),$$\n",
+    "$$H|1\\rangle = \\frac{1}{2}\\left(|0\\rangle-|1\\rangle\\right)$$\n",
+    "$$H=\\frac{1}{2}\\begin{pmatrix} 1 & 1 \\\\ 1 & -1 \\end{pmatrix}$$\n",
+    "\n",
+    "A circuit with a Hadamard gate is made as follows:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "MissingOptionalLibraryError",
+     "evalue": "\"The 'matplotlib' library is required to use 'MatplotlibDrawer'. You can install it with 'pip install matplotlib'.\"",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mMissingOptionalLibraryError\u001b[39m               Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[28]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m      2\u001b[39m qc=QuantumCircuit(\u001b[32m1\u001b[39m)\n\u001b[32m      3\u001b[39m qc.h(\u001b[32m0\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[43mqc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmpl\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/circuit/quantumcircuit.py:3800\u001b[39m, in \u001b[36mQuantumCircuit.draw\u001b[39m\u001b[34m(self, output, scale, filename, style, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m   3797\u001b[39m \u001b[38;5;66;03m# pylint: disable=cyclic-import\u001b[39;00m\n\u001b[32m   3798\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mqiskit\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mvisualization\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m circuit_drawer\n\u001b[32m-> \u001b[39m\u001b[32m3800\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcircuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   3801\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m   3802\u001b[39m \u001b[43m    \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3803\u001b[39m \u001b[43m    \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3804\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3805\u001b[39m \u001b[43m    \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3806\u001b[39m \u001b[43m    \u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m=\u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3807\u001b[39m \u001b[43m    \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3808\u001b[39m \u001b[43m    \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3809\u001b[39m \u001b[43m    \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3810\u001b[39m \u001b[43m    \u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3811\u001b[39m \u001b[43m    \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3812\u001b[39m \u001b[43m    \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3813\u001b[39m \u001b[43m    \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3814\u001b[39m \u001b[43m    \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3815\u001b[39m \u001b[43m    \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3816\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3817\u001b[39m \u001b[43m    \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3818\u001b[39m \u001b[43m    \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3819\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/visualization/circuit/circuit_visualization.py:339\u001b[39m, in \u001b[36mcircuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, output, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m    324\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m _generate_latex_source(\n\u001b[32m    325\u001b[39m         circuit,\n\u001b[32m    326\u001b[39m         filename=filename,\n\u001b[32m   (...)\u001b[39m\u001b[32m    336\u001b[39m         wire_order=complete_wire_order,\n\u001b[32m    337\u001b[39m     )\n\u001b[32m    338\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m output == \u001b[33m\"\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m339\u001b[39m     image = \u001b[43m_matplotlib_circuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    340\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcircuit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    341\u001b[39m \u001b[43m        \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    342\u001b[39m \u001b[43m        \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    343\u001b[39m \u001b[43m        \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    344\u001b[39m \u001b[43m        \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    345\u001b[39m \u001b[43m        \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    346\u001b[39m \u001b[43m        \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    347\u001b[39m \u001b[43m        \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    348\u001b[39m \u001b[43m        \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    349\u001b[39m \u001b[43m        \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    350\u001b[39m \u001b[43m        \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    351\u001b[39m \u001b[43m        \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    352\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    353\u001b[39m \u001b[43m        \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcomplete_wire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    354\u001b[39m \u001b[43m        \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    355\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    356\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    357\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m VisualizationError(\n\u001b[32m    358\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid output type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m selected. The only valid choices \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    359\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mare text, latex, latex_source, and mpl\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    360\u001b[39m     )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/visualization/circuit/circuit_visualization.py:719\u001b[39m, in \u001b[36m_matplotlib_circuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, plot_barriers, reverse_bits, justify, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m    716\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m fold \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    717\u001b[39m     fold = \u001b[32m25\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m719\u001b[39m qcd = \u001b[43m_matplotlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMatplotlibDrawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    720\u001b[39m \u001b[43m    \u001b[49m\u001b[43mqubits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    721\u001b[39m \u001b[43m    \u001b[49m\u001b[43mclbits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    722\u001b[39m \u001b[43m    \u001b[49m\u001b[43mnodes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    723\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcircuit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    724\u001b[39m \u001b[43m    \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    725\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    726\u001b[39m \u001b[43m    \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    727\u001b[39m \u001b[43m    \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    728\u001b[39m \u001b[43m    \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    729\u001b[39m \u001b[43m    \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    730\u001b[39m \u001b[43m    \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    731\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    732\u001b[39m \u001b[43m    \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    733\u001b[39m \u001b[43m    \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    734\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    735\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m qcd.draw(filename)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/utils/classtools.py:111\u001b[39m, in \u001b[36m_WrappedMethod.__get__.<locals>.out\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    108\u001b[39m \u001b[38;5;129m@functools\u001b[39m.wraps(method)\n\u001b[32m    109\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mout\u001b[39m(*args, **kwargs):\n\u001b[32m    110\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m callback \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._before:\n\u001b[32m--> \u001b[39m\u001b[32m111\u001b[39m         \u001b[43mcallback\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__get__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobjtype\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    112\u001b[39m     retval = method(*args, **kwargs)\n\u001b[32m    113\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m callback \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._after:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/utils/lazy_tester.py:41\u001b[39m, in \u001b[36m_RequireNow.__call__\u001b[39m\u001b[34m(self, *_args, **_kwargs)\u001b[39m\n\u001b[32m     40\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, *_args, **_kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m41\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_tester\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrequire_now\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_feature\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/utils/lazy_tester.py:221\u001b[39m, in \u001b[36mLazyDependencyManager.require_now\u001b[39m\u001b[34m(self, feature)\u001b[39m\n\u001b[32m    219\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[32m    220\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m221\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m MissingOptionalLibraryError(\n\u001b[32m    222\u001b[39m     libname=\u001b[38;5;28mself\u001b[39m._name, name=feature, pip_install=\u001b[38;5;28mself\u001b[39m._install, msg=\u001b[38;5;28mself\u001b[39m._msg\n\u001b[32m    223\u001b[39m )\n",
+      "\u001b[31mMissingOptionalLibraryError\u001b[39m: \"The 'matplotlib' library is required to use 'MatplotlibDrawer'. You can install it with 'pip install matplotlib'.\""
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit import QuantumCircuit\n",
+    "qc=QuantumCircuit(1)\n",
+    "qc.h(0)\n",
+    "qc.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__CNOT Controlled-NOT Gate:__ This gate uses two qubits: a control and a target. Checks the state of a control qubit which is not changed. But if the control qubit is in the state $|1\\rangle$, the gate changes the state of the target qubit; if the state of the control qubit is $|0\\rangle$ no change is made at all. In the notation below, assume the qubit $A$ (right-most qubit) is the control, and qubit $B$ (the left-most qubit) is the target. Below, the notation used is $CNOT(q_{control},q_{target})|BA\\rangle.$\n",
+    "$$CNOT(A,B)|00\\rangle = |00\\rangle, \\\\ CNOT(A,B)|01\\rangle = |11\\rangle, \\\\ CNOT(A,B)|10\\rangle = |10\\rangle, \\\\ CNOT(A,B)|11\\rangle = |01\\rangle$$\n",
+    "\n",
+    "You may sometimes see CNOT written with the order of the control and target simply implied. But there is no such ambiguity in code or in circuit diagrams.\n",
+    "\n",
+    "$$CNOT=\\begin{pmatrix} 1 & 0 & 0 & 0 \\\\ 0 & 0 & 0 & 1 \\\\ 0 & 0 & 1 & 0 \\\\ 0 & 1 & 0 & 0\\end{pmatrix}$$\n",
+    "\n",
+    "A CNOT gate looks a bit different in a circuit, since it requires two qubits. This is how it is implemented:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.10.3\n",
+      "/home/asperjasp/miniconda3/envs/Quantum/bin/python\n"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib as mpl\n",
+    "print(mpl.__version__)\n",
+    "import sys \n",
+    "print(sys.executable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<qiskit.circuit.instructionset.InstructionSet at 0x755eb97fc190>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "qc=QuantumCircuit(2)\n",
+    "qc.cx(0,1)\n",
+    "#qc.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\"> <b>Check-in question:</b>\n",
+    "\n",
+    "Most gates have the same matrix form in Qiskit as everywhere else. But the CNOT gate acts on two qubits, and so suddenly ordering conventions of qubits becomes an issue. Texts that order qubits $|q_0,q_1,...\\rangle$ will show a different matrix form for their CNOT gates. Verify by explicit matrix multiplication that the CNOT matrix above has the correct action on the state $|01\\rangle.$\n",
+    "\n",
+    "<details>\n",
+    "\n",
+    "<summary>Solution:</summary>\n",
+    "\n",
+    "$$CNOT|01\\rangle =\\begin{pmatrix} 1 & 0 & 0 & 0 \\\\ 0 & 0 & 0 & 1 \\\\ 0 & 0 & 1 & 0 \\\\ 0 & 1 & 0 & 0\\end{pmatrix}\\begin{pmatrix}0 \\\\ 1 \\\\ 0 \\\\0\\end{pmatrix} = \\begin{pmatrix}0 \\\\ 0 \\\\ 0 \\\\1\\end{pmatrix} = |11\\rangle$$\n",
+    "\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "**$X$ Gate**: Equivalent to a NOT operation. Single-qubit gate.\n",
+    "$$X|0\\rangle = |1\\rangle,\\\\X|1\\rangle=|0\\rangle$$\n",
+    "$$X=\\begin{pmatrix} 0 & 1 \\\\ 1 & 0 \\end{pmatrix}$$\n",
+    "\n",
+    "In Qiskit, creating a circuit with an $X$ gate looks like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "MissingOptionalLibraryError",
+     "evalue": "\"The 'matplotlib' library is required to use 'MatplotlibDrawer'. You can install it with 'pip install matplotlib'.\"",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mMissingOptionalLibraryError\u001b[39m               Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m qc=QuantumCircuit(\u001b[32m1\u001b[39m)\n\u001b[32m      2\u001b[39m qc.x(\u001b[32m0\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[43mqc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmpl\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/circuit/quantumcircuit.py:3800\u001b[39m, in \u001b[36mQuantumCircuit.draw\u001b[39m\u001b[34m(self, output, scale, filename, style, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m   3797\u001b[39m \u001b[38;5;66;03m# pylint: disable=cyclic-import\u001b[39;00m\n\u001b[32m   3798\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mqiskit\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mvisualization\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m circuit_drawer\n\u001b[32m-> \u001b[39m\u001b[32m3800\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcircuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   3801\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m   3802\u001b[39m \u001b[43m    \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3803\u001b[39m \u001b[43m    \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3804\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3805\u001b[39m \u001b[43m    \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3806\u001b[39m \u001b[43m    \u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m=\u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3807\u001b[39m \u001b[43m    \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3808\u001b[39m \u001b[43m    \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3809\u001b[39m \u001b[43m    \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3810\u001b[39m \u001b[43m    \u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3811\u001b[39m \u001b[43m    \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3812\u001b[39m \u001b[43m    \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3813\u001b[39m \u001b[43m    \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3814\u001b[39m \u001b[43m    \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3815\u001b[39m \u001b[43m    \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3816\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3817\u001b[39m \u001b[43m    \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3818\u001b[39m \u001b[43m    \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3819\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/visualization/circuit/circuit_visualization.py:339\u001b[39m, in \u001b[36mcircuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, output, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m    324\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m _generate_latex_source(\n\u001b[32m    325\u001b[39m         circuit,\n\u001b[32m    326\u001b[39m         filename=filename,\n\u001b[32m   (...)\u001b[39m\u001b[32m    336\u001b[39m         wire_order=complete_wire_order,\n\u001b[32m    337\u001b[39m     )\n\u001b[32m    338\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m output == \u001b[33m\"\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m339\u001b[39m     image = \u001b[43m_matplotlib_circuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    340\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcircuit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    341\u001b[39m \u001b[43m        \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    342\u001b[39m \u001b[43m        \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    343\u001b[39m \u001b[43m        \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    344\u001b[39m \u001b[43m        \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    345\u001b[39m \u001b[43m        \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    346\u001b[39m \u001b[43m        \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    347\u001b[39m \u001b[43m        \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    348\u001b[39m \u001b[43m        \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    349\u001b[39m \u001b[43m        \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    350\u001b[39m \u001b[43m        \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    351\u001b[39m \u001b[43m        \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    352\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    353\u001b[39m \u001b[43m        \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcomplete_wire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    354\u001b[39m \u001b[43m        \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    355\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    356\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    357\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m VisualizationError(\n\u001b[32m    358\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid output type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m selected. The only valid choices \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    359\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mare text, latex, latex_source, and mpl\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    360\u001b[39m     )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/visualization/circuit/circuit_visualization.py:719\u001b[39m, in \u001b[36m_matplotlib_circuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, plot_barriers, reverse_bits, justify, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m    716\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m fold \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    717\u001b[39m     fold = \u001b[32m25\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m719\u001b[39m qcd = \u001b[43m_matplotlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMatplotlibDrawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    720\u001b[39m \u001b[43m    \u001b[49m\u001b[43mqubits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    721\u001b[39m \u001b[43m    \u001b[49m\u001b[43mclbits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    722\u001b[39m \u001b[43m    \u001b[49m\u001b[43mnodes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    723\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcircuit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    724\u001b[39m \u001b[43m    \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    725\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    726\u001b[39m \u001b[43m    \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    727\u001b[39m \u001b[43m    \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    728\u001b[39m \u001b[43m    \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    729\u001b[39m \u001b[43m    \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    730\u001b[39m \u001b[43m    \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    731\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    732\u001b[39m \u001b[43m    \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    733\u001b[39m \u001b[43m    \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    734\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    735\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m qcd.draw(filename)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/utils/classtools.py:111\u001b[39m, in \u001b[36m_WrappedMethod.__get__.<locals>.out\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    108\u001b[39m \u001b[38;5;129m@functools\u001b[39m.wraps(method)\n\u001b[32m    109\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mout\u001b[39m(*args, **kwargs):\n\u001b[32m    110\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m callback \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._before:\n\u001b[32m--> \u001b[39m\u001b[32m111\u001b[39m         \u001b[43mcallback\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__get__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobjtype\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    112\u001b[39m     retval = method(*args, **kwargs)\n\u001b[32m    113\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m callback \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._after:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/utils/lazy_tester.py:41\u001b[39m, in \u001b[36m_RequireNow.__call__\u001b[39m\u001b[34m(self, *_args, **_kwargs)\u001b[39m\n\u001b[32m     40\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, *_args, **_kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m41\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_tester\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrequire_now\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_feature\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/utils/lazy_tester.py:221\u001b[39m, in \u001b[36mLazyDependencyManager.require_now\u001b[39m\u001b[34m(self, feature)\u001b[39m\n\u001b[32m    219\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[32m    220\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m221\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m MissingOptionalLibraryError(\n\u001b[32m    222\u001b[39m     libname=\u001b[38;5;28mself\u001b[39m._name, name=feature, pip_install=\u001b[38;5;28mself\u001b[39m._install, msg=\u001b[38;5;28mself\u001b[39m._msg\n\u001b[32m    223\u001b[39m )\n",
+      "\u001b[31mMissingOptionalLibraryError\u001b[39m: \"The 'matplotlib' library is required to use 'MatplotlibDrawer'. You can install it with 'pip install matplotlib'.\""
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "qc=QuantumCircuit(1)\n",
+    "qc.x(0)\n",
+    "qc.draw(output=\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**$Z$ Gate**: Adds a \"phase\" to a state (a prefactor, which in the cases of the Z eigenstates $|0\\rangle$ and $|1\\rangle$ either a 1, or -1, respectively). Single-qubit gate.\n",
+    "$$Z|0\\rangle = |0\\rangle,\\\\Z|1\\rangle=-|1\\rangle$$\n",
+    "$$Z=\\begin{pmatrix} 1 & 0 \\\\ 0 & -1 \\end{pmatrix}$$\n",
+    "\n",
+    "In Qiskit, creating a circuit with an $Z$ gate looks like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">   ┌───┐\n",
+       "q: ┤ Z ├\n",
+       "   └───┘</pre>"
+      ],
+      "text/plain": [
+       "   ┌───┐\n",
+       "q: ┤ Z ├\n",
+       "   └───┘"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "qc=QuantumCircuit(1)\n",
+    "qc.z(0)\n",
+    "qc.draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python executable: /home/asperjasp/miniconda3/envs/Quantum/bin/python\n",
+      "matplotlib version: 3.10.3\n",
+      "matplotlib location: /home/asperjasp/miniconda3/envs/Quantum/lib/python3.12/site-packages/matplotlib/__init__.py\n",
+      "qiskit version: 2.0.2\n",
+      "qiskit location: /home/asperjasp/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/__init__.py\n"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "import matplotlib\n",
+    "import qiskit\n",
+    "\n",
+    "print(\"Python executable:\", sys.executable)\n",
+    "print(\"matplotlib version:\", matplotlib.__version__)\n",
+    "print(\"matplotlib location:\", matplotlib.__file__)\n",
+    "print(\"qiskit version:\", qiskit.__version__)\n",
+    "print(\"qiskit location:\", qiskit.__file__)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">   ┌───┐\n",
+       "q: ┤ Z ├\n",
+       "   └───┘</pre>"
+      ],
+      "text/plain": [
+       "   ┌───┐\n",
+       "q: ┤ Z ├\n",
+       "   └───┘"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "qc.draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Theory\n",
+    "\n",
+    "Let's lay out the protocol for quantum teleportation using math. Then, in the next section, we'll realize this setup using a quantum computer.\n",
+    "\n",
+    "__Alice and Bob entangle their qubits:__ Initially, Alice's qubit and Bob's qubit are each, separately in the $|0\\rangle$ state (a fine assumption and also the correct initialization for IBM quantum computers). We can write this as $|0\\rangle_B|0\\rangle_A$ or simply as $|00\\rangle$. Let's calculate what happens when Alice and Bob act with the Hadamard gate on Alice's qubit, and then a CNOT gate with Alice's qubit as the control and Bob's as the target:\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "CNOT(A,B)H_A |0\\rangle_B|0\\rangle_A &= CNOT(A,B)|0\\rangle_B\\frac{1}{\\sqrt{2}}\\left(|0\\rangle_A+|1\\rangle_A\\right)\\\\\n",
+    "&=\\frac{1}{\\sqrt{2}}\\left(CNOT(A,B)|0\\rangle_B|0\\rangle_A+CNOT(A,B)|0\\rangle_B|1\\rangle_A\\right)\\\\\n",
+    "&=\\frac{1}{\\sqrt{2}}\\left(|0\\rangle_B|0\\rangle_A+|1\\rangle_B|1\\rangle_A\\right)\n",
+    "\\end{aligned}$$\n",
+    "\n",
+    "Note that now Alice's and Bob's qubits are entangled. Although it is not yet determined by nature whether both their qubits are in the $|0\\rangle$ state or the $|1\\rangle$ state, it is known that their qubits are in the same state."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Alice and Bob separate:__ The two friends move their qubits to new locations, possibly very far apart. This comes with a lot of caveats: it is not trivial to move quantum information without disturbing it. But it can be moved, and indeed you will move it in this module. But keep in mind as a caveat that we expect to encounter some errors when we move quantum information around a lot.\n",
+    "\n",
+    "__Q is introduced:__ The secret state is prepared on qubit Q:\n",
+    "\n",
+    "$$\n",
+    "|\\psi\\rangle_Q = \\alpha_0 |0\\rangle_Q + \\alpha_1 |1\\rangle_Q\n",
+    "$$\n",
+    "\n",
+    "At this point Q is simply adjacent to Alice's qubit (A). There has been no entanglement, so the quantum state of the three qubits together can be written as:\n",
+    "\n",
+    "$$\n",
+    "|\\psi\\rangle_{AB}|\\psi\\rangle_Q = \\frac{1}{\\sqrt{2}}\\left(|0\\rangle_B|0\\rangle_A+|1\\rangle_B|1\\rangle_A\\right)\\left(\\alpha_0 |0\\rangle_Q + \\alpha_1 |1\\rangle_Q\\right).\n",
+    "$$\n",
+    "\n",
+    "The goal is to move the information on Q from Alice's location to the location of Bob. At this point, we are not making any claims or requirements about secrecy or speed of information transfer. We are simply exploring how information can move from Alice to Bob."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because the information begins on Q, we will assume Q is assigned the lowest number in qubit numbers, such that little endian notation causes Q to be the right-most qubit in the math below.\n",
+    "\n",
+    "__Alice entangles qubits A and Q:__ Alice now operates with a CNOT gate with her own qubit as the control and Q as the target, then applies a Hadamard gate to Q.  Let's calculate the three-qubit state after that operation:\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "H_Q CNOT(A,Q)|\\psi\\rangle_{AB}|\\psi\\rangle_Q &= H_Q CNOT(A,Q)\\frac{1}{\\sqrt{2}}\\left(|0\\rangle_B|0\\rangle_A+|1\\rangle_B|1\\rangle_A\\right)\\left(\\alpha_0 |0\\rangle_Q + \\alpha_1 |1\\rangle_Q\\right)\\\\\n",
+    "&= H_Q CNOT(A,Q)\\frac{1}{\\sqrt{2}}\\left(\\left(\\alpha_0 |0\\rangle_B|0\\rangle_A|0\\rangle_Q + \\alpha_1 |0\\rangle_B|0\\rangle_A|1\\rangle_Q\\right)+\\left(\\alpha_0 |1\\rangle_B|1\\rangle_A|0\\rangle_Q + \\alpha_1 |1\\rangle_B|1\\rangle_A|1\\rangle_Q\\right)\\right)\\\\\n",
+    "&= H_Q \\frac{1}{\\sqrt{2}}\\left(\\alpha_0 |0\\rangle_B|0\\rangle_A|0\\rangle_Q + \\alpha_1 |0\\rangle_B|1\\rangle_A|1\\rangle_Q+\\alpha_0 |1\\rangle_B|1\\rangle_A|0\\rangle_Q + \\alpha_1 |1\\rangle_B|0\\rangle_A|1\\rangle_Q\\right)\\\\\n",
+    "&= \\frac{1}{2}\\left(\\alpha_0 |0\\rangle_B|0\\rangle_A|0\\rangle_Q + \\alpha_0 |0\\rangle_B|0\\rangle_A|1\\rangle_Q + \\alpha_1 |0\\rangle_B|1\\rangle_A|0\\rangle_Q-\\alpha_1 |0\\rangle_B|1\\rangle_A|1\\rangle_Q\\right)\\\\\n",
+    "&+\\frac{1}{2}\\left(\\alpha_0 |1\\rangle_B|1\\rangle_A|0\\rangle_Q + \\alpha_0 |1\\rangle_B|1\\rangle_A|1\\rangle_Q + \\alpha_1 |1\\rangle_B|0\\rangle_A|0\\rangle_Q - \\alpha_1 |1\\rangle_B|0\\rangle_A|1\\rangle_Q\\right)\n",
+    "\\end{aligned}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because A and Q are in the same location, let us group the terms above according to the outcomes of measurements on qubits A and Q:\n",
+    "$$\\begin{aligned}\n",
+    "|\\psi\\rangle = \\frac{1}{2}\\left((\\alpha_0 |0\\rangle_B+\\alpha_1 |1\\rangle_B)|0\\rangle_A|0\\rangle_Q +  (\\alpha_0 |0\\rangle_B-\\alpha_1 |1\\rangle_B)|0\\rangle_A|1\\rangle_Q + (\\alpha_1 |0\\rangle_B+\\alpha_0 |1\\rangle_B)|1\\rangle_A|0\\rangle_Q+ (-\\alpha_1 |0\\rangle_B+\\alpha_0 |1\\rangle_B)|1\\rangle_A|1\\rangle_Q \\right)\\\\\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\"> <b>Check-in question:</b> \n",
+    "\n",
+    "Given the expression above for the states of all three qubits, what is the probability that a measurement of qubits A and Q yields $|0\\rangle_A|0\\rangle_Q?$\n",
+    "\n",
+    "<details>\n",
+    "\n",
+    "<summary>Answer:</summary>\n",
+    "\n",
+    "25%. To see this, recall that Bob's state must be normalized, so\n",
+    "$$ |_A \\langle0|_Q\\langle0| \\frac{1}{2} |0\\rangle_A|0\\rangle_Q (\\alpha_0 |0\\rangle_B+\\alpha_1 |1\\rangle_B)|^2 = \\frac{1}{4}|(\\alpha_0 |0\\rangle_B+\\alpha_1 |1\\rangle_B)|^2 = \\frac{1}{4}$$\n",
+    "\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, Alice can measure qubits A and Q . She cannot control the outcome of that measurement, since quantum measurements are probabilistic. So when she measures, there are 4 possible outcomes and all 4 are equally likely: $|0\\rangle_A|0\\rangle_Q,$ $|0\\rangle_A|1\\rangle_Q,$ $|1\\rangle_A|0\\rangle_Q,$ and $|1\\rangle_A|1\\rangle_Q.$ Note that each outcome has different implications for Bob's qubit. For example, if Alice finds her qubits to be in $|0\\rangle_A|0\\rangle_Q,$ that has collapsed the entire, 3-qubit quantum state to $(\\alpha_0|0\\rangle_B+\\alpha_1|1\\rangle_B)|0\\rangle_A|0\\rangle_Q.$ Other measurement outcomes for Alice yield different states for Bob. These are collected together in the table below.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "| Alice outcome | Bob's state  | Instruction to Bob| Result |\n",
+    "| ----------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------- | -------------------------|\n",
+    "| $ \\vert 0\\rangle_A \\vert 0\\rangle_Q$ |     $\\alpha_0\\vert 0\\rangle_B+\\alpha_1\\vert 1\\rangle_B$        |        None           | $\\alpha_0\\vert 0\\rangle_B+\\alpha_1\\vert 1\\rangle_B$| \n",
+    "| $ \\vert 0\\rangle_A \\vert 1\\rangle_Q$ |     $\\alpha_0\\vert 0\\rangle_B-\\alpha_1\\vert 1\\rangle_B$        |        $Z$           |  $\\alpha_0\\vert 0\\rangle_B+\\alpha_1\\vert 1\\rangle_B$| \n",
+    "| $ \\vert 1\\rangle_A \\vert 0\\rangle_Q$ |     $\\alpha_1\\vert 0\\rangle_B+\\alpha_0\\vert 1\\rangle_B$       |        $X$           |  $\\alpha_0\\vert 0\\rangle_B+\\alpha_1\\vert 1\\rangle_B$| \n",
+    "| $ \\vert 1\\rangle_A \\vert 1\\rangle_Q$ |     $-\\alpha_1\\vert 0\\rangle_B+\\alpha_0\\vert 1\\rangle_B$       |        $X$ then $Z$           | $\\alpha_0\\vert 0\\rangle_B+\\alpha_1\\vert 1\\rangle_B$|"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For all the possible measurement outcomes on Alice's qubits, Bob's qubit is left in a state vaguely resembling the secret state originally on Q. In the case where Alice measures $|0\\rangle_C|0\\rangle_A$ (the first row of the table), Bob's qubit is left in exactly the secret state! In the other cases, there is something off about the state. The coefficients ($\\alpha$'s) are swapped, or there is a \"-\" sign where there should be a \"+\" sign, or both. In order to modify Bob's qubit to make it exactly equal to the secret state, Alice must call Bob (use some means of classical communication) and tell Bob to perform additional operations on his qubit, as outlined in the table. For example, in the third row the coefficients are swapped. If Alice calls Bob and tells him to apply an $X$ gate to his qubit, it changes a $|0\\rangle$ to a $|1\\rangle$ and vice-versa, and out comes the secret state.\n",
+    "\n",
+    "It should now be clear why we can't use this setup to send information faster than light. We might get lucky and measure $|0\\rangle_A|0\\rangle_Q,$ meaning Bob has exactly the secret state, instantly. But Bob doesn't know that until we call him and tell him \"We measured $|0\\rangle_A|0\\rangle_Q$, so you don't have to do anything.\"\n",
+    "\n",
+    "In the thought experiment, the qubits are often physically separated and taken to a new location. IBM quantum computers use solid-state qubits on a chip that can't be separated. So instead of moving Alice and Bob to different locations, we will separate the information on the chip itself by using so-called \"swap gates\" to move the information from one qubit to another. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Experiment 1: Basic teleportation\n",
+    "\n",
+    "IBM Quantum recommends tackling quantum computing problems using a framework we call \"Qiskit Patterns\". It consists of the following steps.\n",
+    "- Step 1: Map your problem to a quantum circuit\n",
+    "- Step 2: Optimize your circuit for running on real quantum hardware\n",
+    "- Step 3: Execute your job on IBM quantum computers using Runtime Primitives\n",
+    "- Step 4: Post-process the results\n",
+    "\n",
+    "### 4.1. Step 1: Map your problem to a quantum circuit\n",
+    "\n",
+    "All the math we did above was outlining step 1. We will implement it now, building our quantum circuit using Qiskit! We start creating a quantum circuit with three qubits, and entangling the two qubits of Alice and Bob. We will take these to be qubits 1 and 2, and we will reserve qubit 0 for the secret state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "MissingOptionalLibraryError",
+     "evalue": "\"The 'matplotlib' library is required to use 'MatplotlibDrawer'. You can install it with 'pip install matplotlib'.\"",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mMissingOptionalLibraryError\u001b[39m               Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 50\u001b[39m\n\u001b[32m     47\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m qc.if_test((cr[\u001b[32m0\u001b[39m], \u001b[32m1\u001b[39m)):   \n\u001b[32m     48\u001b[39m     qc.z(Bob)\n\u001b[32m---> \u001b[39m\u001b[32m50\u001b[39m \u001b[43mqc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmpl\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/circuit/quantumcircuit.py:3800\u001b[39m, in \u001b[36mQuantumCircuit.draw\u001b[39m\u001b[34m(self, output, scale, filename, style, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m   3797\u001b[39m \u001b[38;5;66;03m# pylint: disable=cyclic-import\u001b[39;00m\n\u001b[32m   3798\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mqiskit\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mvisualization\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m circuit_drawer\n\u001b[32m-> \u001b[39m\u001b[32m3800\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcircuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   3801\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m   3802\u001b[39m \u001b[43m    \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3803\u001b[39m \u001b[43m    \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3804\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3805\u001b[39m \u001b[43m    \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m=\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3806\u001b[39m \u001b[43m    \u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m=\u001b[49m\u001b[43minteractive\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3807\u001b[39m \u001b[43m    \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3808\u001b[39m \u001b[43m    \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3809\u001b[39m \u001b[43m    \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3810\u001b[39m \u001b[43m    \u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvertical_compression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3811\u001b[39m \u001b[43m    \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3812\u001b[39m \u001b[43m    \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3813\u001b[39m \u001b[43m    \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3814\u001b[39m \u001b[43m    \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3815\u001b[39m \u001b[43m    \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3816\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3817\u001b[39m \u001b[43m    \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3818\u001b[39m \u001b[43m    \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3819\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/visualization/circuit/circuit_visualization.py:339\u001b[39m, in \u001b[36mcircuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, output, interactive, plot_barriers, reverse_bits, justify, vertical_compression, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m    324\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m _generate_latex_source(\n\u001b[32m    325\u001b[39m         circuit,\n\u001b[32m    326\u001b[39m         filename=filename,\n\u001b[32m   (...)\u001b[39m\u001b[32m    336\u001b[39m         wire_order=complete_wire_order,\n\u001b[32m    337\u001b[39m     )\n\u001b[32m    338\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m output == \u001b[33m\"\u001b[39m\u001b[33mmpl\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m339\u001b[39m     image = \u001b[43m_matplotlib_circuit_drawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    340\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcircuit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    341\u001b[39m \u001b[43m        \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    342\u001b[39m \u001b[43m        \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    343\u001b[39m \u001b[43m        \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    344\u001b[39m \u001b[43m        \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    345\u001b[39m \u001b[43m        \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    346\u001b[39m \u001b[43m        \u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjustify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    347\u001b[39m \u001b[43m        \u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m=\u001b[49m\u001b[43midle_wires\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    348\u001b[39m \u001b[43m        \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    349\u001b[39m \u001b[43m        \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    350\u001b[39m \u001b[43m        \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    351\u001b[39m \u001b[43m        \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    352\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    353\u001b[39m \u001b[43m        \u001b[49m\u001b[43mwire_order\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcomplete_wire_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    354\u001b[39m \u001b[43m        \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    355\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    356\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    357\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m VisualizationError(\n\u001b[32m    358\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid output type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m selected. The only valid choices \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    359\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mare text, latex, latex_source, and mpl\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    360\u001b[39m     )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/visualization/circuit/circuit_visualization.py:719\u001b[39m, in \u001b[36m_matplotlib_circuit_drawer\u001b[39m\u001b[34m(circuit, scale, filename, style, plot_barriers, reverse_bits, justify, idle_wires, with_layout, fold, ax, initial_state, cregbundle, wire_order, expr_len)\u001b[39m\n\u001b[32m    716\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m fold \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    717\u001b[39m     fold = \u001b[32m25\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m719\u001b[39m qcd = \u001b[43m_matplotlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMatplotlibDrawer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    720\u001b[39m \u001b[43m    \u001b[49m\u001b[43mqubits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    721\u001b[39m \u001b[43m    \u001b[49m\u001b[43mclbits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    722\u001b[39m \u001b[43m    \u001b[49m\u001b[43mnodes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    723\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcircuit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    724\u001b[39m \u001b[43m    \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    725\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    726\u001b[39m \u001b[43m    \u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreverse_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    727\u001b[39m \u001b[43m    \u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplot_barriers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    728\u001b[39m \u001b[43m    \u001b[49m\u001b[43mfold\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    729\u001b[39m \u001b[43m    \u001b[49m\u001b[43max\u001b[49m\u001b[43m=\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    730\u001b[39m \u001b[43m    \u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    731\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcregbundle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    732\u001b[39m \u001b[43m    \u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwith_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    733\u001b[39m \u001b[43m    \u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpr_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    734\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    735\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m qcd.draw(filename)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/utils/classtools.py:111\u001b[39m, in \u001b[36m_WrappedMethod.__get__.<locals>.out\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    108\u001b[39m \u001b[38;5;129m@functools\u001b[39m.wraps(method)\n\u001b[32m    109\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mout\u001b[39m(*args, **kwargs):\n\u001b[32m    110\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m callback \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._before:\n\u001b[32m--> \u001b[39m\u001b[32m111\u001b[39m         \u001b[43mcallback\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__get__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobjtype\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    112\u001b[39m     retval = method(*args, **kwargs)\n\u001b[32m    113\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m callback \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._after:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/utils/lazy_tester.py:41\u001b[39m, in \u001b[36m_RequireNow.__call__\u001b[39m\u001b[34m(self, *_args, **_kwargs)\u001b[39m\n\u001b[32m     40\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, *_args, **_kwargs):\n\u001b[32m---> \u001b[39m\u001b[32m41\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_tester\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrequire_now\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_feature\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/Quantum/lib/python3.12/site-packages/qiskit/utils/lazy_tester.py:221\u001b[39m, in \u001b[36mLazyDependencyManager.require_now\u001b[39m\u001b[34m(self, feature)\u001b[39m\n\u001b[32m    219\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[32m    220\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m221\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m MissingOptionalLibraryError(\n\u001b[32m    222\u001b[39m     libname=\u001b[38;5;28mself\u001b[39m._name, name=feature, pip_install=\u001b[38;5;28mself\u001b[39m._install, msg=\u001b[38;5;28mself\u001b[39m._msg\n\u001b[32m    223\u001b[39m )\n",
+      "\u001b[31mMissingOptionalLibraryError\u001b[39m: \"The 'matplotlib' library is required to use 'MatplotlibDrawer'. You can install it with 'pip install matplotlib'.\""
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "# Step 1: Map your problem to a quantum circuit\n",
+    "\n",
+    "# Import some general packages\n",
+    "from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister\n",
+    "import random\n",
+    "import numpy as np\n",
+    "from numpy import pi\n",
+    "\n",
+    "# Define registers\n",
+    "secret = QuantumRegister(1, \"Q\")\n",
+    "Alice = QuantumRegister(1, \"A\")\n",
+    "Bob = QuantumRegister(1, \"B\")\n",
+    "\n",
+    "cr = ClassicalRegister(3, 'c')\n",
+    "\n",
+    "qc = QuantumCircuit(secret, Alice, Bob, cr)\n",
+    "\n",
+    "# We entangle Alice's and Bob's qubits as in our work above. We apply a Hadamard gate and then a CNOT gate.\n",
+    "# Note that the second argument in the CNOT gate is the target.\n",
+    "qc.h(Alice)\n",
+    "qc.cx(Alice,Bob)\n",
+    "\n",
+    "# Inserting a barrier changes nothing about the logic. It just allows us to force gates to be positioned in \"layers\".\n",
+    "qc.barrier()\n",
+    "\n",
+    "# Now we will use random variables to create the secret state. Don't worry about the \"u\" gate and the details. \n",
+    "np.random.seed(42) #fixing seed for repeatibility\n",
+    "theta = np.random.uniform(0.0, 1.0) * np.pi    #from 0 to pi\n",
+    "varphi = np.random.uniform(0.0, 2.0) * np.pi    #from 0 to 2*pi\n",
+    "\n",
+    "# Assign the secret state to the qubit on the other side of Alice's (qubit 0), labeled Q\n",
+    "qc.u(theta, varphi, 0.0, secret)\n",
+    "qc.barrier()\n",
+    "\n",
+    "# Now entangle Q and Alice's qubits as in the discussion above.\n",
+    "qc.cx(secret,Alice)\n",
+    "qc.h(secret)\n",
+    "qc.barrier()\n",
+    "\n",
+    "# Now Alice measures her qubits, and stores the outcomes in the \"classical registers\" cr[]\n",
+    "qc.measure(Alice, cr[1])\n",
+    "qc.measure(secret, cr[0])\n",
+    "\n",
+    "# Now we insert some conditional logic. If Alice measures Q in a \"1\" we need a Z gate, and if Alice measures A in a \"1\" we need an X gate (see the table).\n",
+    "with qc.if_test((cr[1], 1)):\n",
+    "    qc.x(Bob)\n",
+    "with qc.if_test((cr[0], 1)):   \n",
+    "    qc.z(Bob)\n",
+    "\n",
+    "qc.draw(output=\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's all we have to do to get Alice's state teleported to Bob. However, recall that when we measure a quantum state $\\alpha_0 |0\\rangle+\\alpha_1|1\\rangle$ we find either $|0\\rangle$ or $|1\\rangle.$ So at the end of all this, Bob definitely has Alice's secret state, but we can't easily verify this with a measurement. In order for a measurement to tell us that we did this correctly, we have to do a trick. We had an operator labeled \"U\" for \"unitary\" which we used to prepare Alice's secret state. We can apply the inverse of U at the end of our circuit. If U mapped Alice's $|0\\rangle$ state into $\\alpha_0 |0\\rangle+\\alpha_1|1\\rangle$, then the inverse of U will map Bob's $\\alpha_0 |0\\rangle+\\alpha_1|1\\rangle$ back to $|0\\rangle.$ So this last part wouldn't necessarily be done if the goal were just to move quantum information. This is only done for us to check ourselves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1694.86x367.889 with 1 Axes>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "# Add the inverse of U and measure Bob's qubit.\n",
+    "qc.barrier()\n",
+    "\n",
+    "qc.u(theta, varphi, 0.0, Bob).inverse()    # inverse of u(theta,varphi,0.0)\n",
+    "qc.measure(Bob, cr[2]) # add measurement gate\n",
+    "\n",
+    "qc.draw(output=\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So if we've done this correctly, our measurement on Bob's qubit should yield a $|0\\rangle$ state. Of course, these measurements are probabilistic. So if there is even a small chance of measuring Bob's qubit to be in the $|1\\rangle$ state, then a single measurement could result in $|1\\rangle.$ We would really want to make many measurements to be assured that the probability of $|0\\rangle$ is quite high.\n",
+    "\n",
+    "### 4.2. Step 2: Optimize problem for quantum execution\n",
+    "\n",
+    "This step takes the operations we want to perform and expresses them in terms of the functionality of a specific quantum computer. It also maps our problem onto the layout of the quantum computer.\n",
+    "\n",
+    "We will start by loading several packages that are required to communicate with IBM quantum computers. We must also select a backend on which to run. We can either choose the least busy backend, or select a specific backend whose properties we know."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ibm_brisbane\n"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "#Load the Qiskit Runtime service\n",
+    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
+    "service = QiskitRuntimeService()\n",
+    "\n",
+    "#Use the least busy backend, or uncomment the loading of a specific backend like \"ibm_brisbane\".\n",
+    "backend = service.least_busy(operational=True, simulator=False, min_num_qubits = 127)\n",
+    "#backend = service.backend(\"ibm_brisbane\")\n",
+    "print(backend.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we must \"transpile\" the quantum circuit. This involves many sub-steps and is a fascinating topic. Just to give an example of a sub-step: not all quantum computers can directly implement all logical gates in Qiskit. We must write the gates from our circuit in terms of gates the quantum computer can implement. We can carry out that process, and others, using a preset pass manager. Setting ```optimization = 3``` (the highest level of optimization) ensures that the mapping from our abstract quantum circuit to the instructions given to the quantum computer is as efficient as our pre-processing can get it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "# Step 2: Transpile\n",
+    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
+    "\n",
+    "target = backend.target\n",
+    "pm = generate_preset_pass_manager(target=target, optimization_level=3)\n",
+    "qc_isa = pm.run(qc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A \"sampler\" is a primitive designed to sample possible states resulting from a quantum circuit, and collect statistics on what states might be measured and with what probability. We import the Qiskit Runtime sampler here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "# Load the Runtime primitive and session\n",
+    "from qiskit_ibm_runtime import SamplerV2 as Sampler\n",
+    "sampler = Sampler(mode = backend)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not all computations on a quantum computer can be reasonably simulated on classical computers. This simple teleportation definitely can be, but it isn't at all surprising that we can classically save information in one place or another. We strongly recommend carrying out these calculations using a real IBM quantum computer. But in case you have exhausted your free monthly use, or if something must be completed in class and can't wait in the queue, this module can be completed using a simulator. To do this, simply run the cell below and uncomment the associated lines in the \"Execute\" steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "# Load the backend sampler\n",
+    "from qiskit.primitives import BackendSamplerV2\n",
+    "\n",
+    "# Load the Aer simulator and generate a noise model based on the currently-selected backend.\n",
+    "from qiskit_aer import AerSimulator\n",
+    "from qiskit_aer.noise import NoiseModel\n",
+    "noise_model = NoiseModel.from_backend(backend)\n",
+    "\n",
+    "# Define a simulator using Aer, and use it in Sampler.\n",
+    "backend_sim = AerSimulator(noise_model=noise_model)\n",
+    "sampler_sim = BackendSamplerV2(backend = backend_sim)\n",
+    "\n",
+    "# Alternatively, load a fake backend with generic properties and define a simulator.\n",
+    "from qiskit.providers.fake_provider import GenericBackendV2\n",
+    "backend_gen = GenericBackendV2(num_qubits=18)\n",
+    "sampler_gen = BackendSamplerV2(backend = backend_gen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.3. Step 3: Execute\n",
+    "\n",
+    "Use the sampler to run your job, with the circuit as an argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "job = sampler.run([qc_isa])\n",
+    "#job = sampler_sim.run([qc_isa])\n",
+    "res=job.result()\n",
+    "counts=res[0].data.c.get_counts()\n",
+    "\n",
+    "#job = sampler_gen.run([qc_isa])\n",
+    "#res=job.result()\n",
+    "#counts=res[0].data.c.get_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.4. Step 4: Post-processing and analysis\n",
+    "\n",
+    "Let's plot the results and interpret them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "# This required 5 s to run on ibm_torino on 10-28-24\n",
+    "from qiskit.visualization import plot_histogram\n",
+    "plot_histogram(counts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<div class=\"alert alert-block alert-info\"> <b>Check-in question:</b> \n",
+    "\n",
+    "Which of the states above indicate successful teleportation, and how can you tell?\n",
+    "\n",
+    "<details>\n",
+    "\n",
+    "<summary>Answer:</summary>\n",
+    "\n",
+    "The states $|000\\rangle,$ $|001\\rangle,$ $|010\\rangle,$ $|011\\rangle$ are all consistent with successful teleportation. This is because we added a gate to undo the initial preparation of the secret state. If the secret state was successfully teleported to Bob's qubit, that additional gate should return Bob's qubit to the $|0\\rangle$ state. So any state above with Bob's qubit (qubit 0, also measured to the 0th component of the classical register, and hence the highest/right-most) in the $|0\\rangle$ state indicates success.\n",
+    "\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "This plot is showing all measurement outcomes for the three qubits, over 5,000 trials or \"shots\". We pointed out earlier that Alice would measure all possible states for qubits A and Q with equal likelihood. We assigned qubits 0-2 in the circuit to Q, A, and B, in that order. In little-endian notation, Bob's qubit is the left-most/lowest. So the four bars on the left correspond to Bob's qubit being $|0\\rangle$, and the other two qubits being in all possible combinations with roughly equal probability. Note that almost all (usually \\~95%) of measurements yield Bob's qubit in the $|0\\rangle$ state, meaning our setup was successful! There are a handful of shots (\\~5%) that yielded Bob's qubit in the $|1\\rangle$ state. That should not logically be possible. However, all modern quantum computers suffer from noise and errors to a much greater extent than classical computers. And quantum error correction is still an emerging field.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Experiment 2: Teleporting across a processor\n",
+    "\n",
+    "Arguably, the most interesting part of quantum teleportation is that a quantum state can be teleported over long distances instantly (though the classical communication of extra gates is not instant). As already stated, we can't break qubits off the processor and move them around. But we can move the information from one qubit to another, until the qubits involved in teleportation are on opposite sides of the processor. Let us repeat the steps we took above, but now we will make a larger circuit with enough qubits to span the processor.\n",
+    "\n",
+    "### 5.1. Step 1: Map your problem to a quantum circuit\n",
+    "\n",
+    "This time, the qubits corresponding to Alice and Bob will change. So we will not name a single qubit \"A\" and another \"B\". Rather, we will number the qubits and use variables to represent the current position of the information on qubits belonging to Alice and Bob. All other steps except the swap gates are as described previously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2044.93x1204 with 1 Axes>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "# Step 1: Map\n",
+    "\n",
+    "# Define registers\n",
+    "qr = QuantumRegister(13, 'q')\n",
+    "\n",
+    "qc = QuantumCircuit(qr, cr)\n",
+    "\n",
+    "# Define registers\n",
+    "secret = QuantumRegister(1, \"Q\")\n",
+    "ebitsa = QuantumRegister(6, \"A\")\n",
+    "ebitsb = QuantumRegister(6, \"B\")\n",
+    "#q = ClassicalRegister(1, \"q meas\")\n",
+    "#a = ClassicalRegister(1, \"a\")\n",
+    "#b = ClassicalRegister(1, \"b\")\n",
+    "cr = ClassicalRegister(3, 'c')\n",
+    "qc = QuantumCircuit(secret, ebitsa, ebitsb, cr)\n",
+    "\n",
+    "# We'll start Alice in the middle of the circuit, then move information outward in both directions.\n",
+    "Alice = 5\n",
+    "Bob = 0\n",
+    "qc.h(ebitsa[Alice])\n",
+    "qc.cx(ebitsa[Alice],ebitsb[Bob])\n",
+    "\n",
+    "# Starting with Bob and Alice in the center, we swap their information onto adjacent qubits, until the information is on distant qubits.\n",
+    "\n",
+    "for n in range(Alice):\n",
+    "    qc.swap(ebitsb[Bob],ebitsb[Bob+1])\n",
+    "    qc.swap(ebitsa[Alice],ebitsa[Alice-1])\n",
+    "    Alice = Alice-1\n",
+    "    Bob = Bob+1\n",
+    "\n",
+    "qc.barrier()\n",
+    "\n",
+    "# Create a random state for Alice (qubit zero)\n",
+    "np.random.seed(42) #fixing seed for repeatibility\n",
+    "#theta = np.random.uniform(0.0, 1.0) * np.pi    #from 0 to pi\n",
+    "theta = 0.3\n",
+    "varphi = np.random.uniform(0.0, 2.0) * np.pi    #from 0 to 2*pi\n",
+    "\n",
+    "\n",
+    "qc.u(theta, varphi, 0.0, secret)\n",
+    "\n",
+    "# Entangle Alice's two qubits\n",
+    "qc.cx(secret,ebitsa[Alice])\n",
+    "qc.h(secret)\n",
+    "\n",
+    "qc.barrier()\n",
+    "\n",
+    "# Make measurements of Alice's qubits and store the results in the classical register.\n",
+    "qc.measure(ebitsa[Alice], cr[1])\n",
+    "qc.measure(secret, cr[0])\n",
+    "\n",
+    "# Send instructions to Bob's qubits based on the outcome of Alice's measurements.\n",
+    "with qc.if_test((cr[1], 1)):\n",
+    "    qc.x(ebitsb[Bob])\n",
+    "with qc.if_test((cr[0], 1)):   \n",
+    "    qc.z(ebitsb[Bob])\n",
+    "\n",
+    "qc.barrier()\n",
+    "\n",
+    "# Invert the preparation we did for Carl's qubit so we can check whether we did this correctly.\n",
+    "qc.u(theta, varphi, 0.0, ebitsb[Bob]).inverse()    # inverse of u(theta,varphi,0.0)\n",
+    "qc.measure(ebitsb[Bob], cr[2]) # add measurement gate\n",
+    "\n",
+    "qc.draw('mpl')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can see in the circuit diagram that the logical steps are the same. The only difference is that we used the swap gates to bring Alice's qubit's state from qubit 6 ($A_5$) up to qubit 1 ($A_0$), right next to Q. And we used swap gates to bring Bob's initial state from qubit 7 ($B_0$) down to qubit 12 ($B_5$). Note that the state on qubit 12 is not even related to Q's secret state until measurements are made on the distant qubits 0 and 1, and the state on qubit 12 is not equal to the secret state until after the conditional $X$ and $Z$ gates are applied.\n",
+    "\n",
+    "### 5.2.  Step 2: Optimize your circuit\n",
+    "\n",
+    "Normally, when we use the pass manager to transpile and optimize our circuits, it makes sense to set ```optimization_level = 3```, because we want our circuits to be as efficient as possible. In this case, there is no computational reason for us to transfer states from qubits 6 and 7 over to qubits 1 and 12. That was just something we did to demonstrate teleportation over a distance. If we ask the pass manager to optimize our circuit, it will realize there is no logical reason for these swap gates, and it will remove them and carry out the gate operations on adjacent qubits. So for this special case, we use ```optimization_level = 0```."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "99\n"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "# Step 2: Transpile\n",
+    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
+    "\n",
+    "target = backend.target\n",
+    "pmzero = generate_preset_pass_manager(target=target, optimization_level=0)\n",
+    "\n",
+    "qc_isa_zero = pmzero.run(qc)\n",
+    "\n",
+    "print(qc_isa_zero.depth())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can visualize where on the quantum processor these qubits are using the ```plot_circuit_layout``` function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.3 Step 3: Run\n",
+    "\n",
+    "As before, we recommend running on real IBM quantum computers. If your monthly free usage has been reached, feel free to uncomment the simulator cells to run on a simulator. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "# This required 5 s to run on ibm_torino on 10-28-24\n",
+    "job = sampler.run([qc_isa_zero])\n",
+    "#job = sampler_sim.run([qc_isa_zero])\n",
+    "counts=job.result()[0].data.c.get_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'counts' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[27]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mqiskit\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mvisualization\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m plot_histogram\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m plot_histogram(\u001b[43mcounts\u001b[49m)\n",
+      "\u001b[31mNameError\u001b[39m: name 'counts' is not defined"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mnotebook controller is DISPOSED. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.visualization import plot_histogram\n",
+    "plot_histogram(counts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.4. Step 4: Classical post-processing\n",
+    "\n",
+    "Again we see that the probabilities for the possible outcomes for Alice's qubits are fairly uniform. There is a strong preference for finding Bob's qubit in $|0\\rangle$ after inverting the secret code, meaning there is a strong probability that we correctly teleported the secret state across the processor from Q to Bob (qubits 0 to 12). However, we note that there is now about a higher chance of *not* measuring $|0\\rangle$ for Bob. This is an important lesson in quantum computing: the more gates you have, especially multi-qubit gates like swap gates, the more noise and errors you will encounter."
+   ]
+  },
+  {
+   "attachments": {
+    "entangled_teleportation_fig.png": {
+     "image/png": 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"
+    },
+    "entangled_teleportation_fig_0110.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Review and questions:\n",
+    "\n",
+    "### Critical concepts:\n",
+    "\n",
+    "- Qubits can be entangled, meaning a measurement of one qubit affects or even determines the state of another qubit.\n",
+    "- Entanglement differs from classical correlations; for example, qubits A and B could be in a superposition of states like $\\alpha_0|00\\rangle+\\alpha_1|11\\rangle.$ The state of A or B could be undetermined by nature, and yet A and B could still be guaranteed to be in the same state.\n",
+    "- Through a combination of entanglements and measurements, we can transfer a state (which can store information) from one qubit to another. This transfer can even be done over long distances, and this is called quantum teleportation.\n",
+    "- Quantum teleportation relies on quantum measurements, which are probabilistic. Thus, classical communication can be necessary to tweak the teleported states. This prevents quantum teleportation from moving information faster than light. Quantum teleportation does not violate relativity or causality.\n",
+    "- Modern quantum computers are more susceptible to noise and errors than classical computers. Expect a few percent error.\n",
+    "- The more gates you add in sequence (especially 2-qubit gates) the more errors and noise you can expect.\n",
+    "\n",
+    "\n",
+    "### T/F Questions:\n",
+    "\n",
+    "1. T/F Quantum teleportation can be used to send information faster than light.\n",
+    "2. T/F Modern evidence suggests that the collapse of a quantum state propagates faster than light.\n",
+    "3. T/F In Qiskit, qubits are ordered in states with the lowest-numbered qubit on the right, as in $|q_3,q_2,q_1, q_0\\rangle$\n",
+    "\n",
+    "\n",
+    "### MC Questions:\n",
+    "\n",
+    "1. Qubits A and B are entangled, then separated by a great distance $d$. Qubit A is measured. Which statement is correct about the speed at which the state of qubit B is affected?\n",
+    "\n",
+    "- a. Qubit B is affected instantly, within experimental tolerance, in experiments run so far. \n",
+    "- b. Qubit B is affected after a time $d/c$, meaning the quantum state \"collapses\" at approximately the speed of light, within experimental tolerance.\n",
+    "- c. Qubit B is affected only after classical communication has occurred, meaning it happens in a time longer than $d/c$. \n",
+    "- d. None of the above\n",
+    "\n",
+    "2. Recall that measurement probability is related to amplitudes in quantum states. For example, if a qubit is initially in the state $\\alpha_0|0\\rangle+\\alpha_1 |1\\rangle,$ the probability of measuring the state $|0\\rangle$ is $|\\alpha_0|^2.$ Not all sets of measurements will exactly match these probabilities, due to finite sampling (just as flipping a coin might yield heads twice in a row). The measurement histogram below could correspond to which of the following quantum states? Select the best option.\n",
+    "\n",
+    "![entangled_teleportation_fig.png](attachment:entangled_teleportation_fig.png)\n",
+    "\n",
+    "- a. $|0\\rangle$\n",
+    "- b. $\\frac{1}{\\sqrt{2}}\\left(|0\\rangle-|1\\rangle\\right)$\n",
+    "- c. $\\frac{1}{\\sqrt{2}}\\left(|0\\rangle+|1\\rangle\\right)$\n",
+    "- d. $\\frac{4}{5}|0\\rangle+\\frac{3}{5}|1\\rangle$\n",
+    "- e. $\\frac{3}{5}|0\\rangle+\\frac{4}{5}|1\\rangle$\n",
+    "\n",
+    "\n",
+    "3. Which of the following states show(s) qubits A and B entangled? Select all that apply.\n",
+    "- a. $\\frac{1}{\\sqrt{2}}\\left(|0\\rangle_B|0\\rangle_A+|1\\rangle_B|1\\rangle_A\\right)$\n",
+    "- b. $\\frac{4}{5}|0\\rangle_B|0\\rangle_A+\\frac{3}{5}|1\\rangle_B|1\\rangle_A$\n",
+    "- c. $\\frac{1}{\\sqrt{2}}\\left(|0\\rangle_B|1\\rangle_A-|1\\rangle_B|0\\rangle_A\\right)$\n",
+    "- d. $\\frac{1}{\\sqrt{2}}\\left(|0\\rangle_B|0\\rangle_A+|1\\rangle_B|0\\rangle_A\\right)$\n",
+    "- e. $|0\\rangle_B|0\\rangle_A$\n",
+    "\n",
+    "4. In this module, we prepared an entangled state: $\\frac{1}{\\sqrt{2}}\\left(|0\\rangle_B|0\\rangle_A+|1\\rangle_B|1\\rangle_A\\right).$ But there are many other entangled states one could use for a similar protocol. Which of the states below could yield a 2-qubit measurement histogram like the following? Select the best response.\n",
+    "\n",
+    "![entangled_teleportation_fig_0110.png](attachment:entangled_teleportation_fig_0110.png)\n",
+    "\n",
+    "- a. $\\frac{1}{\\sqrt{2}}\\left(|0\\rangle_B|0\\rangle_A+|1\\rangle_B|1\\rangle_A\\right)$\n",
+    "- b. $\\frac{4}{5}|0\\rangle_B|0\\rangle_A+\\frac{3}{5}|1\\rangle_B|1\\rangle_A$\n",
+    "- c. $\\frac{1}{\\sqrt{2}}\\left(|0\\rangle_B|1\\rangle_A-|1\\rangle_B|0\\rangle_A\\right)$\n",
+    "- d. $\\frac{4}{5}|0\\rangle_B|1\\rangle_A+\\frac{3}{5}|1\\rangle_B|0\\rangle_A$\n",
+    "- e. $|0\\rangle_B|0\\rangle_A$\n",
+    "\n",
+    "### Discussion Questions:\n",
+    "\n",
+    "1. Describe the quantum teleportation protocol, from start to finish, to your partner/group. See if they have anything to add, or if they have questions.\n",
+    "\n",
+    "2. Is there anything unique about the initial entangled state between Alice and Bob: $\\frac{1}{\\sqrt{2}}\\left(|0\\rangle_B|0\\rangle_A+|1\\rangle_B|1\\rangle_A\\right)?$ If so, what is unique about it? If not, what other entangled states could we have used?\n"
+   ]
+  }
+ ],
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