{"cells": [{"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["# Programmation orient\u00e9 objet"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## 1. Attendus\n", "\n", "- Appr\u00e9hender un nouveau paradigme de programmation,\n", "- \u00c9crire la d\u00e9finition d\u2019une classe,\n", "- Acc\u00e9der aux attributs et m\u00e9thodes d\u2019une classe."]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## 2. Contexte\n", "\n", "<table>\n", "    <tr><td style=\"width:33%;\"><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/b/bc/1980_Chevrolet_Corvette_Stingray_5.7_Rear.jpg/640px-1980_Chevrolet_Corvette_Stingray_5.7_Rear.jpg\" /></td>\n", "  <td style=\"width:33%;\"><img src=\"https://upload.wikimedia.org/wikipedia/commons/7/7f/Renault_Clio_front_20080521.jpg\" /></td>\n", "  <td style=\"width:33%;\"><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/8/82/Mercedes-Benz_G63_AMG_6x6%2C_Monaco_diplomatic_plates%2C_rear_right.jpg/640px-Mercedes-Benz_G63_AMG_6x6%2C_Monaco_diplomatic_plates%2C_rear_right.jpg\" /></td>\n", "    </tr>\n", "    <caption>Source : Wikipedia</caption>\n", "</table>"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["- Un concessionnaire dispose de voitures,\n", "- Une voiture a les caract\u00e9ristiques suivantes :\n", "    - immatriculation,\n", "    - marque,\n", "    - mod\u00e8le,\n", "    - ann\u00e9e de circulation, \n", "    - puissance maximale du moteur (en kilowatts KW)\n", "    - taux d'\u00e9mission de dioxyde carbone (en g/km)\n", "    - consommation (en l pour 100km)\n", "\n", "Il existe diff\u00e9rentes fa\u00e7ons de stocker ces informations."]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["\ud83d\udcbb __\u00c0 Faire 1__ : Proposer une fa\u00e7on de stocker les caract\u00e9ristiques des diff\u00e9rentes voitures du concessionnaire (Avec un tableau, tuple, dictionnaire...)\n", "\n", "\ud83d\udc4d __Indication__ : Pour tester, il est possible de consid\u00e9rer la concession de voitures suivantes :\n", "\n", "| Immatriculation | Marque | Mod\u00e8le | Ann\u00e9e de circulation | Puissance | Taux d'\u00e9mission CO2 | Consommation pour 100km |\n", "| :--: | :--: | :--: | :--: | :--: | :--: | :--: |\n", "| ET-242-GP | Chevrolet | Corvette | 1974 | 430 | 406 | 17,41 |\n", "| C4-874-EL | Renault | Clio | 2011 | 90 | 89 | 4,7 |\n", "| AA-373-HN | Mercedes | G63 | 2018 | 544 | 373 | 13,2 |"]}, {"cell_type": "code", "execution_count": null, "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "outputs": [], "source": "# R\u00e9ponse\n"}, {"cell_type": "code", "execution_count": null, "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "outputs": [], "source": "# R\u00e9ponse\n"}, {"cell_type": "code", "execution_count": null, "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "outputs": [], "source": "# R\u00e9ponse\n"}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["\u2753__\u00c0 Faire 2__ : \n", "\n", "1. Comment r\u00e9cup\u00e9rer la valeur d'un attribut d'une voiture ?\n", "2. Y a-t-il des diff\u00e9rences selon la structure de donn\u00e9es ?"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## 3. D\u00e9finition"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["### 3.1. Paradigme\n", "\n", "Un __paradigme__ est _\"une repr\u00e9sentation du monde, une mani\u00e8re de voir les choses, un mod\u00e8le coh\u00e9rent du monde qui repose sur un fondement d\u00e9fini\"_ (Wikipedia).\n", "\n", "En programmation, plus pr\u00e9cis\u00e9ment, on parle de _paradigmes de programmation_ pour exprimer la mani\u00e8re dont sont con\u00e7us et envisag\u00e9s les programmes."]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["### 3.2. Paradigme de la programmation orient\u00e9 objet (POO)\n", "\n", "En **programmation orient\u00e9e objet**, on fabrique de nouveau types de donn\u00e9es correspondant aux besoin du programme. \n", "\n", "On r\u00e9fl\u00e9chit alors aux **caract\u00e9ristiques** des objets qui seront de ce type et aux **actions possibles** \u00e0 partir de ces objets."]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["Ce paradigme repose sur le principe de l\u2019**encapsulation**, i.e le fait de regrouper des donn\u00e9es brutes avec un ensemble de routines (m\u00e9thodes) permettant de les lire ou de les manipuler.\n", "\n", "Avec ce paradigme, les objectifs sont de :\n", "\n", "- mod\u00e9liser un objet concret ou abstrait;\n", "- masquer la structure interne de stockage;\n", "- fournir une interface \u00e0 l'utilisateur de l'objet."]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["### Illustration avec `list`\n", "\n", "\ud83d\udcbb __\u00c0 Faire 3__ : Qu'indique la s\u00e9quence d'instructions suivante ?\n", "\n", "```python\n", ">>> l = [1, 6, 3]\n", ">>> type(l)\n", "???\n", "```"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "source": "R\u00e9ponse ici"}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["La `list` en python est un __objet__, d\u00e9fini dans une `classe`.\n", "\n", "\ud83d\udcbb __\u00c0 Faire 4__ : Qu'indique la s\u00e9quence d'instructions suivante ?\n", "\n", "```python\n", ">>> dir(list)\n", "???\n", "```"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "source": "R\u00e9ponse ici"}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["Les actions possibles sont disponibles en utilisant la m\u00e9thode `dir`.\n", "\n", "Une **action possible** sur les objets de type `list` est le tri de celle-ci avec la **m\u00e9thode** nomm\u00e9e `sort()`. \n", "\n", "```python\n", ">>> l = [1, 6, 3]\n", ">>> l.sort()\n", ">>> l\n", "[1, 3, 6]\n", "```"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## 4. Impl\u00e9mentation de l'objet `Voiture`\n", "\n", "### 4.1. Cr\u00e9ation de la `classe`\n", "\n", "On utilise le mot cl\u00e9 `class` suivi du nom de la classe :"]}, {"cell_type": "code", "execution_count": null, "metadata": {"slideshow": {"slide_type": "subslide"}}, "outputs": [], "source": ["class Voiture:\n", "    '''\n", "    classe mod\u00e9lisant l'objet Voiture\n", "    '''"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["__N.B__ : Par convention, une classe s'\u00e9crit toujours avec la premi\u00e8re lettre en majuscule."]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["### 4.2. D\u00e9claration du `constructeur`\n", "\n", "On d\u00e9finit une m\u00e9thode `__init__`, dite **constructeur** :"]}, {"cell_type": "code", "execution_count": null, "metadata": {"slideshow": {"slide_type": "slide"}}, "outputs": [], "source": ["class Voiture:\n", "    '''\n", "    classe mod\u00e9lisant l'objet Voiture\n", "    '''\n", "    \n", "    def __init__(self, immatriculation, marque, modele, annee, puissance, taux, consommation):\n", "        '''\n", "          Constructeur de la classe Voiture\n", "        '''\n", "        self.marque = marque\n", "        self.modele = modele\n", "        self.annee = annee\n", "        self.puissance = puissance\n", "        self.taux = taux\n", "        self.consommation = consommation"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["Le **constructeur** est la m\u00e9thode appel\u00e9e lorsque l'on ex\u00e9cute les instructions suivantes :\n", "\n", "```python\n", ">>> voiture1 = Voiture('ET-242-GP', 'Chevrolet', 'Corvette', 1974, 430, 310, 17.41)\n", "```\n", "\n", "- On dit que voiture1 est une **instance** de la classe Voiture,\n", "- le **constructeur** `__init__` est appel\u00e9e implicitement par l'interpr\u00e9teur python lors de  l'instruction `Voiture()`,\n", "- Le param\u00e8tre particulier `self` d\u00e9signe l'objet auquel s'applique la m\u00e9thode,\n", "- Les valeurs des autres param\u00e8tres sont stock\u00e9s sous forme d'**attribut** de l'instance d'un objet."]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["\ud83d\udcbb __\u00c0 Faire 5__ : \u00c9crire les instructions permettant de cr\u00e9er les instances des 2 autres voitures Clio et G63."]}, {"cell_type": "code", "execution_count": null, "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "outputs": [], "source": "# R\u00e9ponse\n"}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["\ud83d\udcbb __\u00c0 Faire 6__ : \n", "\n", "1. Qu'indique l'ex\u00e9cution de l'instruction `type` et `dir` sur les diff\u00e9rentes instances des voitures ?\n", "2. Qu'indique l'ex\u00e9cution de l'instruction `voiture2.modele` et `voiture3.modele` ?"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "source": "R\u00e9ponse ici"}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["La classe `Voiture` permet de d\u00e9crire __l'objet__ avec ses __attributs__ et __m\u00e9thodes__. \n", "\n", "`voiture1`, `voiture2`, `voiture3` sont des __instances__ de cette classe. \n", "\n", "![](https://g.gravizo.com/svg?%40startuml%3B%0Aclass%20Voiture%20%7B%3B%0A%20%20%20immatriculation%20%3A%20str%3B%0A%20%20%20marque%20%3A%20str%3B%0A%20%20%20modele%20%3A%20str%3B%0A%20%20%20annee%20%3A%20int%3B%0A%20%20%20puissance%20%3A%20int%3B%0A%20%20%20taux%20%3A%20int%3B%0A%20%20%20consommation%20%3A%20float%3B%0A%20%7Bmethod%7D%20afficher()%20%3A%20None%3B%0A%20%7Bmethod%7D%20polluer(kms)%20%3A%20float%3B%0A%20%7Bmethod%7D%20couter(reference%2C%20departement)%20%3A%20int%3B%0A%7D%3B%0Aentity%20voiture1%20extends%20Voiture%20%7B%3B%0A%20%20%20marque%3A%20'Chevrolet'%2C%20%3B%0A%20%20%20mod%C3%A8le%3A%20'Corvette'%2C%20%3B%0A%20%20%20immatriculation%20%3A%20'ET-242-GP'%2C%20%3B%0A%20%20%20ann%C3%A9e%20%3A%201974%2C%20%3B%0A%20%20%20puissance%20%3A%20430%2C%20%3B%0A%20%20%20taux%20%3A%20406%2C%20%3B%0A%20%20%20consommation%3A%2017.41%3B%0A%7D%3B%0A%0Aentity%20voiture2%20extends%20Voiture%20%7B%3B%0A%20%20%20marque%3A%20'Chevrolet'%2C%20%3B%0A%20%20%20mod%C3%A8le%3A%20'Corvette'%2C%20%3B%0A%20%20%20immatriculation%20%3A%20'ET-242-GP'%2C%20%3B%0A%20%20%20ann%C3%A9e%20%3A%201974%2C%20%3B%0A%20%20%20puissance%20%3A%20430%2C%20%3B%0A%20%20%20taux%20%3A%20406%2C%20%3B%0A%20%20%20consommation%3A%2017.41%3B%0A%7D%3B%0A%0Aentity%20voiture3%20extends%20Voiture%7B%3B%0A%20%20%20marque%3A%20'Chevrolet'%2C%20%3B%0A%20%20%20mod%C3%A8le%3A%20'Corvette'%2C%20%3B%0A%20%20%20immatriculation%20%3A%20'ET-242-GP'%2C%20%3B%0A%20%20%20ann%C3%A9e%20%3A%201974%2C%20%3B%0A%20%20%20puissance%20%3A%20430%2C%20%3B%0A%20%20%20taux%20%3A%20406%2C%20%3B%0A%20%20%20consommation%3A%2017.41%3B%0A%7D%3B%0A%0A%40enduml)"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["### 4.3. D\u00e9claration de m\u00e9thode\n", "\n", "Une __m\u00e9thode de classe__ est une action possible sur un objet.\n", "\n", "\ud83d\udcbb __\u00c0 Faire 7__ : Qu'indique la s\u00e9quence d'instructions suivante ?\n", "\n", "```python\n", ">>> print(voiture1)\n", "???\n", "```"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["L'affichage m\u00e9rite d'\u00eatre plus pertinent.\n", "\n", "\ud83d\udcbb __\u00c0 Faire 8__ : Copier la s\u00e9quence suivante en tant que m\u00e9thode classe, i.e dans le corps de la classe `Voiture` :\n", "\n", "```python\n", "def afficher(self):\n", "    '''\n", "      :param self: (Voiture) instance en cours\n", "      :return: (None)\n", "      :Effet de bord: Affiche les caract\u00e9ristiques de la voiture\n", "    '''\n", "    print(f\"Voici une {self.marque} {self.modele} de {self.annee} avec une puissance de {self.puissance}.\")\n", "```\n", "\n", "\ud83d\udcbb __\u00c0 Faire 9__ : Qu'indique la s\u00e9quence d'instructions suivante ?\n", "\n", "```python\n", "voiture1 = Voiture('ET-242-GP', 'Chevrolet', 'Corvette', 1974, 430, 310, 17.41)\n", "voiture2 = Voiture('C4-874-EL', 'Renault', 'Clio', 2011, 90, 89, 4.7)\n", "voiture3 = Voiture('AA-373-HN', 'Mercedes', 'G63', 2018, 585, 373, 13.2)\n", "\n", "voiture1.afficher()\n", "voiture2.afficher()\n", "voiture3.afficher()\n", "```"]}, {"cell_type": "code", "execution_count": null, "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "outputs": [], "source": "# R\u00e9ponse\n"}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["\ud83d\udc4d __Indication__ : Il est important de noter que le param\u00e8tre `self` n'est pass\u00e9 pas en argument lors de l'appel.\n", "\n", "Une **m\u00e9thode** est une fonction d\u00e9finie dans le corps de la classe. Comme le constructeur de la classe, son premier argument doit \u00eatre `self`, i.e la r\u00e9f\u00e9rence \u00e0 l'instance sur laquelle elle s'applique.\n", "\n", "Le fait que la m\u00e9thode `afficher` soit une m\u00e9thode de classe, l'interpr\u00e9teur Python passe l'instance de l'objet comme valeur pour le param\u00e8tre `self`. "]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["\ud83d\udcbb __\u00c0 Faire 10__ : \u00c9crire la __m\u00e9thode de classe__ `polluer`, qui prend en param\u00e8tre un nombre de kms sous la forme d'un entier et renvoie le rejet de C02 correspondant \u00e0 la distance parcourue."]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["\ud83d\udc4d __Indication__ : Compl\u00e9ter le tableau suivant suite \u00e0 l'ex\u00e9cution de la m\u00e9thode sur les instances et arguments suivants.\n", "\n", "| Instance | kms | Rejet de cO2 calcul\u00e9 (en g)|\n", "| :--: | :--: | :--: |\n", "| voiture1 | 100    |\u00a0|\n", "|\u00a0voiture2 |\u00a0100    |\u00a0|\n", "| voiture3 |\u00a0100    |\u00a0|\n", "| voiture1 |\u00a011628* |\u00a0|\n", "| voiture2 |\u00a011628  |\u00a0|\n", "| voiture3 |\u00a011628  |\u00a0|\n", "| voiture1 |\u00a0250000** |\u00a0|\n", "| voiture2 |\u00a0250000 |\u00a0|\n", "| voiture3 |\u00a0250000 |\u00a0|\n", "\n", "\\* 11628 km : Parcours moyen annuel des voitures particuli\u00e8res diesel en France en 2020, selon [l'entreprise Statista](https://fr.statista.com/statistiques/484345/distance-parcourue-en-moyenne-par-voiture-france/).\n", "\n", "\\** 250000 km : Parcours moyen effectu\u00e9 par une voiture particuli\u00e8re diesel durant sa vie, selon [le site aramisauto](https://www.aramisauto.com/aide/faq?question=est-duree-vie-moyenne-une-voiture).\n", "\n", "N.B : Pour vous rendre compte da la signification d'une 1 tonne de C02 [Une infographie du site hellocarbo](https://www.hellocarbo.com/wp-content/uploads/2021/07/Equivalence-tonne-co2-597x1024.png)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "outputs": [], "source": "# R\u00e9ponse\n"}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "source": "R\u00e9ponse ici"}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["\ud83d\udcbb __\u00c0 Faire 11__ : \u00c9crire la __m\u00e9thode de classe__ `calculer_puissance_fiscale`, qui prend aucun param\u00e8tre et renvoie la puissance fiscale de la voiture.\n", "\n", "\ud83d\udc4d __Indication__ : Depuis 1998, le calcul de la __puissance fiscale__ $P_F$ d\u2019une voiture se calcule comme suit : $P_F = (\\frac{CO2}{45}) + (\\frac{P}{40}) \\times 1.6)$ o\u00f9 $CO2$ est le taux d'\u00e9mission en CO2 du v\u00e9hicule et $P$, la puissance de son moteur. \n", "\n", "La puissance fiscale $P_F$ \u00e9tant arrondie \u00e0 l'entier inf\u00e9rieur)"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["\ud83d\udc4d __Indication__ : Compl\u00e9ter le tableau suivant suite \u00e0 l'ex\u00e9cution de la m\u00e9thode sur les instances suivantes.\n", "\n", "| Instance | Puissance fiscale |\n", "| :--: | :--: |\n", "| voiture1 |  |\n", "|\u00a0voiture2 |\u00a0 |\n", "| voiture3 |\u00a0 |"]}, {"cell_type": "code", "execution_count": null, "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "outputs": [], "source": "# R\u00e9ponse\n"}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "source": "R\u00e9ponse ici"}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["\ud83d\udcbb __\u00c0 Faire 12__ : \u00c9crire la __m\u00e9thode de classe__ `couter`, qui prend en param\u00e8tre un ensemble de r\u00e9f\u00e9rence de fiscalit\u00e9 (Cf. Tableau ci-dessous) et une r\u00e9gion d'immatriculation et calcule le co\u00fbt de l'immatriculation du v\u00e9hicule.\n", "\n", "| R\u00e9gions | Montant cheval fiscal par r\u00e9gion |\n", "| :--: | :--: |\n", "| Auvergne Rh\u00f4ne Alpes | 43\u20ac |\n", "| Bourgogne-Franche-Comt\u00e9 | 51\u20ac |\n", "| Bretagne | 51\u20ac |\n", "| Centre \u2013 Val de Loire\t| 49,8\u20ac|\n", "|\u00a0Corse\t| 27\u20ac |\n", "| Grand Est | 42\u20ac |\n", "| Guadeloupe | 41\u20ac |\n", "| Guyane | 42,5\u20ac |\n", "| Hauts-de-France | 34\u20ac |\n", "| Ile-de-France\t| 46.15\u20ac |\n", "| La R\u00e9union | 51\u20ac | \n", "| Martinique | 30\u20ac |\n", "| Mayotte | 30\u20ac |\n", "| Normandie | 35\u20ac |\n", "| Nouvelle Aquitaine | 41\u20ac |\n", "| Occitanie\t| 44\u20ac | \n", "| Pays de la Loire | 48\u20ac |\n", "| Provence-Alpes-C\u00f4te d\u2019Azur | 51,2\u20ac |\n", "\n", "Tableau r\u00e9capitulatif des prix des chevaux fiscaux par r\u00e9gions, selon [le site acommeassure](https://www.acommeassure.com/guides/assurance-auto-chevaux-fiscaux/)."]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["\ud83d\udc4d __Indication__ : Compl\u00e9ter le tableau suivant suite \u00e0 l'ex\u00e9cution de la m\u00e9thode sur les instances suivantes et arguments suivants.\n", "\n", "| Instance | R\u00e9gion | Co\u00fbt fiscal |\n", "| :--: | :--: | :--: | \n", "| voiture1 | Hauts-de-France |  |\n", "|\u00a0voiture2 |\u00a0Hauts-de-France |  |\n", "| voiture3 |\u00a0Hauts-de-France |  |\n", "| voiture1 | La R\u00e9union |  |\n", "|\u00a0voiture2 |\u00a0Provence-Alpes-C\u00f4te d\u2019Azur |  |\n", "| voiture3 |\u00a0Martinique |  |\n", "| voiture1 | Corse |  |\n", "|\u00a0voiture2 |\u00a0le-de-France | |\n", "| voiture3 |\u00a0Provence-Alpes-C\u00f4te d\u2019Azur | |"]}, {"cell_type": "code", "execution_count": null, "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "outputs": [], "source": "# R\u00e9ponse\n"}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "source": "R\u00e9ponse ici"}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## 5. M\u00e9thodes sp\u00e9cifiques\n", "\n", "Il existe plusieurs m\u00e9thodes sp\u00e9cifiques d\u00e9finies automatiquement d\u00e8s qu'on cr\u00e9e une classe d'objets. Ces m\u00e9thodes sont toutes de la forme `__nom__()` (c'est-\u00e0-dire que le nom de la m\u00e9thode est pr\u00e9fix\u00e9 et postfix\u00e9 par un double tiret du bas, soit Double UNDERScore, ce qui a donn\u00e9 le nom de m\u00e9thodes __DUNDERS__).\n", "\n", "Ce sont des m\u00e9thodes universelles que poss\u00e8dent toute classe en Python, et qui permettent de g\u00e9rer un certain nombre d'actions. Par exemple l'instruction `Voiture('ET-242-GP', 'Chevrolet', 'Corvette', 1974, 430, 310, 17.41)` fait appel \u00e0 la m\u00e9thode DUNDERS __init__() que nous avons d\u00e9finie.\n", "\n", "Il est ainsi possible de red\u00e9finir un certain nombre de ces m\u00e9thodes selon nos utilisations."]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["Le tableau ci-dessous vous pr\u00e9sente quelques-uns de ces DUNDERS, applicables \u00e0 des objets `t` et `other` instances de la classe :\n", "\n", "| m\u00e9thode | Appel | Int\u00e9r\u00eat |\n", "| :--: | :--: | :--: |\n", "| `__str__(self)` | str(t) | renvoie une cha\u00eene de caract\u00e8res d\u00e9crivant l'objet t |\n", "| `__lt__(self, other)` | t < other | permet de d\u00e9finir la relation plus petit que entre deux objets, renvoie True ou False selon la d\u00e9finition propos\u00e9e |\n", "| `__len__(self)` | len(t) | permet de d\u00e9finir la longueur de l'objet t |\n", "| `__contains__(self, x)` | x in t | permet de d\u00e9finir l'appartenance de x \u00e0 t |\n", "| `__eq__(self, other)` | t == other | permet de d\u00e9finir l'\u00e9galit\u00e9 entre deux objets t et other |\n", "| `__add__(self, other)` | t + other | d\u00e9finit l'addition de deux objets t et other | \n", "| `__mul__(self, other)` | t * other | d\u00e9finit la multiplication de deux objets t et other |"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["\u2753__\u00c0 Faire 12__ : Comparer la s\u00e9quence d'instructions suivante avec la m\u00e9thode `afficher` programm\u00e9e pr\u00e9c\u00e9demment. (Param\u00e8tre d'entr\u00e9e, retour ...)\n", "\n", "```python\n", "def __str__(self):\n", "    return f\"Voici une {self.marque} {self.modele} de {self.annee} avec une puissance de {self.puissance}.\"\n", "```"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["\ud83d\udcbb __\u00c0 Faire 13__ : Copier la m\u00e9thode `__str__` dans la classe `Voiture` et ex\u00e9cuter la s\u00e9quence d'instructions suivante. Que constatez-vous ? \n", "\n", "```python\n", "voiture1 = Voiture('ET-242-GP', 'Chevrolet', 'Corvette', 1974, 430, 310, 17.41)\n", "print(voiture1)\n", "voiture1.afficher()\n", "```"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}, "solution": true}, "source": "R\u00e9ponse ici"}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["\ud83d\udc4d __Indication__ : Nous privil\u00e9gierons toujours l'impl\u00e9mentation des __DUNDERS__ aux m\u00e9thodes de classes sp\u00e9cifiques comme `afficher()`.\n", "\n", "Nous reviendrons sur cette sp\u00e9cificit\u00e9 lors de prochains exercices..."]}, {"attachments": {"synthese.png": {"image/png": 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A9POe7tqqR+nKbZ4L/bH79WWscNa6fxbBPWnYMZRTp/tnUX+Q5kyd7sk6n2b9MZi0y0e950dRX8Y6n10ztPZvhTawf+ijx/jDD0IfYWe8MynR06amrnvOHGvbtq3VqFHDjj32WLvzzjtLfPZlWZYruwW/UnkFfPKl8Yj0jjKUht/JQQSsyRTEXzqPd16X2NbfeU5uX6yzg6gN9+c+35z7fPyGElGEd5HzN4ltPYMIdI9ObGOs8nDnZc6/TWyvKP3dec8t7GOdq29h13auv4XN89B4C5sXMu23sJlobtAW9qQgAsUtaf5WbQv5/84444x/E+X6n//5n0L/85UGv8yEfHGnRn7cLanCNTvU9+nG8X5Ad/GapfbyiI7WaVz3DSnJscsLv5yHCahOeLO2Pxf2qdXPn2x/ffpof+5fOfit89GdG+ovW7PCHu7/rAPvGrZjiyo+mku5nzXdx6dJ7/zM8Xbdpw/ad/MneohnNuehs0Zatbfr+BRq2vNzV5462zkYBvBPePMye23EB/7Y6TaWx6QvN3k8grn+fUMPs0xyddB+DhRvz/j055tvzPjIMJM5EcnFREmJch53ZOhTpkn1Je23lvt5+MFRSnC3T6LU3zjyS3Sz3QuhLVlStB1luTzwSyT1/feiMoypZWztBbWiFOVqJ0Tp2KQXM+6X8sDp5EmhT8VmzC4py/vvnbEqB0Qp2pddFNrzrUMP+VxzXGfChAj0k3UOoc6xGZ8m/oIDYlLEqQPM8mLgsIOj2axJfaavY9NGXjaQln37LaFNnVo0+r106VIbOXKkDRkyxObOnVvscy/LsrwtWPArlVcfB9GXxf3SO8pQEn55kE4NohTqtN4KouPHs7SSnsyX4DM2lIjqE+2NdVMQ1WnmvENi+18TvxMlZiIu0qHTOj+I6l+R3lGCmgRFvzzLsrwNeL/99vOwG6s0+GVSqfpd7/WRV6ASOGQCqXiJI36uWrvaJi6aZpMWTd8QMSUyGy+B9PXs0Tn43c/D8w8rF/l9yfRogLZa+yvtl0RxXTnG4d7Vq4WfnOraT+7z44kj+L3DFqxc7OsAzANnfG239mxiZ7/XwLeNCa6Y7Ap4JlL9uycO86nOjOEltXr28nn2zNDX7bz3G/qJuo5x59mv7en2p6eO9BAMDB/y8tn2/cIphcYul9cLfoiWHwLESNUF1u64NbR6dRxkdsvalMlZD2XAL2m9jEldvChrbVpn/URSLFf09ptRajDLEX0xMGv160VgGMMv0c+WzSOoJvIKDKcBj8/xMkPpfbgs+KXe8G+yVvuSaGxt08ahn2Rr/DjnsaG93C6KPKfhl7TvIUOy9tYb0VJKtzhIv6YeUe9onDBAS+SWiHKhOoNLrgP4t20T1Zk+zbX70WjiMFLKSbt+5qnCJu0cyO7ZI/QzQhe+rvXufIts7NixNmrUKJs1a5af/TndP7Isy9uCBb9SefVSEH1JTEZZy6N05Bf9yfmSIBqD+04QpTYDqBw/htiaQZQezTYiuR2CaF1hxhXH+rPzjFwZQLmv891BFPWMxeRc7B8dFI2kdcvta76hdOlqHBTzpVmW5UrrTYr8MhEVKcSMkwUmGXf72shONnzudzZh4VQbOmuUX17o+k8fspu6P5ZLSQ5tyZpl9t0PE/wySO9++4mfLOvnzfa1qm9c4pctGjl3rAdm4JryM5fOtX8+e0Ju4qwq9uTgV2zqklmuzFRrN7yDrw/Q1up4nV/aCHDmXKRBn/FuPWv3TQe/3FHXCb395Fz7vnCGH4fMZFiM/124arGtXrfaL2t0avu6dkuPxvbpxL7We+pge3XE+3ZFl9v9eTk/k20NnjXCty3dH2WZZY3uvdvBbbWMDf86WqKItXxZk3fliqwHRSK8RCgH9IvSndkGIAOT7R34Mlsz8Mm4VwCaFGMA9JOu0ZrB+JOuWb+NGZ8/6hz6dGsio9Tzx3RwO8zVZdKneHIpPw6YdXFdmVkzsx4cgV+gmtTp5Pq9jN/lfCdVjVK2mUCL43Ns6gK/RJ4Zj8uyS9ThPIO+yNoTLULr766NiDTHJLWZ6yciDLBz/TEwU4f05qdaFq1DlPzB+0KrekzG7rkrdKAapWEzyzT9x0sEIubMdB2v9Uv7GOdLdBlQjscWx+ZLYKdOnaxu3bp2zjnnWKtWrWzatGla7kiW5W3Sgl+pvGISK74sMmtqaWrgfI7zdrnPafi91nl9EB2L8bt9gmgiKyasYlsygkuUuVUQpV7GX1ZZwqR6oszvnG8PohTNGJYZK9w0t/+C3LahQVH4jV0nV7YsPR4U/fJcHgPms7ew6bNBW9ik5bbfwib9uPEWNmnW9bewSSevvoVN2vyeW9h/DzZNtpGeHfyIMb9ES8cvmOwnj/Kpz032cSB6mE9fvuaT++2ENy7zYPzzZvvZrs+dZNOXzvLRXtbMvbzLbVbz3fp27OsX+4gukWMirMDnWa7+jd0ftVnL5nmQZfwu6ctEXlmyqIar98iA1taox2O234tn+PqkMjPDdLMv2vq05Nmu7i6tT/QRW7Y/0LeVg9ue9t533aza21f6aDXpzY+64zA2megvv9NeZnYGpF8d+YF9/H1vaz7oRdc2UqH387NFj/2h8MRd5TFRXJbxYeImwLBzp9CBVQSNwKGfxGlQNOszqblEOoFUorCk/RLNvNuBYd8+oY+EdukcTSxFejHja4l+EnnlPMAjYEi0+IJzM/ZO+2iiLGC3X5+sPdkya7XOCj1UMzsy5wdwmbSKdYSZNIrZpFlyiUgs52M7a+3STuCzd68IumueHo0tHjoktD69svbcs6GftZm0Y8D8iwFR9BowffP1COLrXB56KB8zJgLnQQOzfqwwk1SRtszawfQZUV8m7WJ7vSujOt/l6nDc++5muacowksdrp31kmtfHC0XxZrKg7+M0sbj62rrroeZpon+8jIieY+I9tapU8dPeMXfx3HHHeeXO2IJpPT9lGVZruwW/Erl1R5BFJ1lhuaSxDhexu3OC6IUZZSE3z2d/x1EUdi9cttiMREV//Em4TepXZzvDCLA/S61LxZRYVKZpwVRW/d2PiGIjvtIopwkSduW0nBbnHkJtVlme8ZAIDMin/L2FT59OU5NxkAkY3MPbHem3dmrhS13ULpm/Vp7fODzfmIpypZkwPXr2d/6VGkmpbqtZxP7R+uqbt8BHqaj4x/slyEilZlzMS73xLdq27wVCzwws5RR1BbatL+PTP+m5SF+7V6ixae2r2Mj5o71Y36Xrl5ujQe28QAet592MPHV9u6cwPLebar7dGuiyul+KMukIwOkjI89cB+gNLQXc2NyiUoCaEzCBMiy/9ILQx91JVLZ9aPQznRAefB+UTT3tGqhHXFINLaWKDIAzDq5LZuFflwxEDhyhIO8W6LI6JGHhn4CqEsuiGaBPuowxthm7N32Ueov5/92NGsGs4QQyy1F4MxYZAD3/FrR9vvuCj0AA+wTJ5CC7I51qIPvw6IxtQA60Vsmp2LbMYeHduP1TDoVRb3feTu044+Kro92MNM1AH/FZaFfluiKSzN+0qsVuUm6GFdMmjfHpA7X2+DqXJ1Lo5cEdWpHqdnxxF7MQN3hnWgCsSj9ObRrrorWRyZNmv5liSgmz+KeJO/RmDFjrEGDBrbTTjvZjjvuaKeffrr17NlT8CvL8jZpwa+0MfokiL5EXp/eEUSw2yGI9t+b2J6E34uDaP9dBbu9mHxqYW4fkdztgmgGaCKJaQHOC3K/tw0i2P59wW4vQJdjkaL9a+fVzt87/yJZyOlW55VBFFWTJKnyKg26SVfIOr+YCDApzCxBdHbHBh6Eia5e/GEju6/vk9ZhzCcbJokCZvtNG2L1ut5j53a83kdY02bMLWN1569c6NOeGV87ZfEMd6ynfLQYwD31nbp26Ye3+BmYnxj8sp/Jmcm3Wg9908/cvGjVUj8BFmN3z+pwrVVzPxn3i09/5yq7rtsD9vnkLzZMwrVy3Wp7f+xnduZ711iNd6/26dKsC1z1jUv99Zz3QUN/nhlL52x01Bczi3HTx0O79urQ6jsYu+G60F55KUpjBiaJaBLJZDwr+29rFM1sTIoxEEj09vprQg/FLIdEenCn90Pr2T2K4LLWL+NZ4/GypPqy9u+LbUI/g/SlFzkgPD/jxxezDjDRXCK4lOX8Y8Zk7YH7Cs6fNhFrIqzfjw83pB5/81U0Dpe1dIFp2kBEtW+frLV6IvTrEd9/D2OTQx/FHdA/tFsbZeyGBkBsaJdf4oDcX0/G7rkzgthlywr6jLTnfn2jSG1D6tSPoDeuc+9dGevxWdHlnAB6ItO8bKh7RQT9RIOvvzZaTmpAf/o0LDLeecmSJdatWzdr2LCh1a5d28/8zKRXPN/p+ynLslzZLfiVNkassTs3iL5QMgHWRc7VnC8LorRXtvMzCZlJ+D0oiCKy3wZRFBgdEkQpzyxPRP1/5La/m/sMSDNGGCgGuklpZqkidGGuTHfnfYJonWAivbODKPr8q1y5B3PlgHfawJfgK4Mo/XpOUHgcsSRJlU9p4GV4RBvnQ5OFipP9CPhNeuW6VX5yK2CVSafS+3+MgU6iuozrnbV8XqnHZ7ItxgpPWzLLVjmwBVrHzJ/oPMGnRK9dX7gukL1y7Uq/lBEzVjPJFuOWR80bZ5MWT98wKVb6PFvKwCxQx1hVQDpeBqksx+NtAeGJEyM4Lm/dssyxgeA5c6KxvYxbTgNl0pRd8EPof2Kiwcy4PG9e6MffFleXcgsXFK7DRGHz55dcJzbXyfVSfu6c6EVCaeUx95hJrlatWqWxvrIsb9MW/EobKwAY8AVCk182s84vBEVBMj3mFxAl9Tmu97/ObzjfmPt8ea4cwNs7US52lyCKFMd6LIjOkSzDeNfDEmV4gBnfl0mVA8L3T5STJKlyir9nXqDxbwYv4+JZ48uUbSb4lWVZlmV567fgV9pUET09OYgmlDopiCKzxWkP57+ltjGpTfWcmbEZsZ7vnkEEvUlRv3rOgHdxIu2ZtlQPCkNvWoA5ba3ufHDhXZIkVWL5yavSG8sjE/zKsizLct5Y8CtJkiTlrUzwK8uyLMt5Y8GvJEmSlLcywa8sy7Is540Fv5IkSVLeygS/sizLspw3FvxKkiRJeSsT/MqyLMty3ljwK0mSJOWtTPAry7Isy3ljwa8kSZKUtzLBryzLsiznjQW/kiRJUt7KBL+yLMuynDcW/EqSJEl5KxP8yrIsy3LeWPArSZIk5a1M8CvLsizLeWPBryRJkpS3MsGvLMuyLOeNBb+SJElS3soEv7Isy7KcNxb8SpIkSXkrE/zKsizLct5Y8CtJkiTlrUzwK8uyLMt5Y8GvJEmSlLcywa8sy7Is540Fv5IkSVLeygS/sizLspw3FvxKkiRJeSsT/MqyLMty3ljwK0mSJOWtTPAry7Isy3ljwa8kSZKUtzLBryzLsiznjQW/kiRJUt7KBL+yLMuynDcW/EqSJEl5KxP8yrIsy3LeWPArSZIk5a1M8CvLsizLeWPBryRJkpS3MsGvLMuyLOeNBb+SJElS3soEv7Isy7KcNxb8SpIkSXkrE/zKsizLct5Y8CtJkiTlrUzwK8uyLMt5Y8GvJEmSlLcywa8sy7Is540Fv5IkSVLeygS/sizLspw3FvxKkiRJeSsT/MqyLMty3ljwK0mSJOWtTPAry7Isy3ljwa8kSZKUtzLBryzLsiznjQW/kiRJUt7KBL/yj3QYRk5v35zeEueQZVnOBwt+JUmSpLyVCX6LOBOGtjazztasX+u91nldZvP2Q+jOwTHj88TnYHu67NbsNWvcF6n5WfdFKuuelaL7N4c57tKlfGHL2qpVguDNYZ4z/rbXrVu3wXzemOcvk8kUOsbG1pc3r+l77onugVyWBb+SJElS3soEv4WcCTM2d/kCGzxzuPWdOtj6OPebOsRGzx/vATVdflO83p1j0aol9u38723QjG/8OfpPG2oj5o61xauWevhO19kaDZROmpi111/N2gcds7Z4UdEym8OL3HF79sja22+GNnp0BNzpMrEBY9qVyRTdt7U4Bk9AJb1vS3nlypU2depUGzFihH311Vf29ddf+7/3ZcuWldku2r9q1SqbM2eOjRs3zh9j+PDhNnHiRFu6dGmZ9eXN5/hZWr16tYeZ6dOne7DhZUS6bGVw/EJFAF+xFvxKkiRJeSsT/BbyynWrrON3n1q1t6+0A1+safu1PcOqvHS2NfzsYVuwclGR8htr4Hreih/srVGd7cqP7rDjXr/YDmpX0w57pZZd1vlW6zF5gK1at7pIva3RK1Zk7aPOoZ1wTMYuvTD0IFwRUdmxY7PWsEFoNU7L2Gsvh6VCNhHiKVOytmBBxUWif4z5cr9gwQKbNGmSzZs37yeDlMmTJ1ubNm2sXr16dtFFF9kFF1xgjzzyiI0cOdLWrFlTpHzSK1assG+++cZeeOEFu/nmm+2KK66w2rVr26OPPmqDBw/2+9N15IoxLyt4AdGrVy976aWX7IEHHrAPP/zQFi368f9W/RTmhQr/7/A3km//92xJC34lSZKkvJUJfgt56Zrl9sSXL9veL1S3Pz11hG3XdF/7ZfP97ZS3rrDZy+cXKb+xBq47jethez5/ij/2ji2r2K7PnWh7PH+yh+yWX75ki1cvLVJvazQpyL17hXb5JRm77ebQpk2rGPidPClrjz0S2rVXZ6zT+6EH3HQZvG5d1r76KmstmoX2abfQlpVQ7qc0Xzg/+OADe+yxxzywLF++vEiZLWHgt127dnbTTTfZxRdfbEcffbQH2N69e/uocLp8bCJy3377rd15551WtWpVO/300319fMMNN1iXLl0qLXhVRvOy4t5777Vjjz3W/va3v9kvf/lL/0Ji2rRpRcpWBnfr1s1fD38j8+fPVxZBBVnwu+X1T+ernRs7P+pcy3n7QiUkSZKkLSIT/HqHWdKR19sKB6eDZ46w+/u2srof3WV/ePJw2775AcXCLyAQj9lduW61N7/7sbvZoml7RH1nLZtrN3Z/1H73xKG2Q4uD7KS3atsD/Z62ZoNetGeGvm7fzBnjj5GuuzU5Ti0m/XjG9Kx95kDzyy9CHwkurixQunZt9JN0ZH5SF/N7ScAcl128OGtfDwut9+cAdui3pcvilSuz1rlTaOedk7Enmoc2d2507K1lsiwiqp9//rmdd955Ptrat29fn66aLof9mPB163yd2GvXrvVme3FpoXEdysR1+FwSQMTlSVu+7bbb7Nprry0VfjkO+95//32rWbOmh2UgZezYsT76SNrz4sWLSzzf5jbnia83vs54DHJyW7peui73ANNfxaXcJsviuAw/k32drlvec1AuPnZ8PMrG5yrtHMOGDfMR+0aNGtlZZ51lf//73+3WW2+ttPDbp08fO+ecc/zfBy+HSnoW06ZP6Mf0vZOLt+B3y+n3zm85/6+zpfyDc42Coj+pfhVEYF4zvWML6JfO9zlfnN4hSZJUEbJKAL+NGzf2Y9nS2zeX+cK0fO1KG79gsn05c7j1nvKlH4vbd+oQ2/uFU+1XzQ8sAr/rHPT+sHKRDZs92rpPGuCjuZ3G9rDPJ39ho+aNtyWrl3vYjcsDyVMXz3T7B9kZ79SzX7U40P729DH2+MDn/fhizr1k9bJKMd539eooGkuUdcgQ93NY1iZOKH4s7vLlWftujPuSPjRro0dlbdrUrI0YnrV+fSOzjTG96TG6fF64IGujRmZt6JDoPJRlYq10WcCWcwO7jAs+64yMPfJAaN+Pj46NiRYD4D8lBAMkDz74oB1yyCF29913l/hM8zdH2icR1oEDB3og7devn335pXsuBw2y0aNH25IlSwp90Qey+EIb1wEcgGvSk2fNmuVhqiQw+O677+yuu+6yBg0alAi/gBdp2hyP9OaTTjrJbr/9dn8u/p2YMGGC3w+opetWlOmjUaNG2RdffOH7hP6kHYxhJv2afbNnzy5y7fQvbWV///79rXv37vbpp5/6PqY+0fhkeSKQjGnmWul/ouaAw5QpU/x5evTo4aGN7fHLDM4xd+5cH5lNnoPfeUlAajjnwLSRNnN87jHt+Oyzz/yxx4wZ4579Ib4+95T2Md462Q/xcd58803/bHFfKiv80mekbh900EH28MMP+3ta0guM2Dxz3KMZM2b4ly8/1VCCymTB75bRjs6jgwh0X3c+LLf9d871nJc7/49z1dz2n1IXBVE7z0vv2AKqHkTnJjIuSZJU4bJKAL80c7vttvPRJqJOZY1J3BjzpXHBysXWbUJfu/7TB+2ENy+z/dqebse/cbHV7nyr/anVkUUiv4DshIVT7MVv3rUaHerb7s+dbDu2PNinMO/Vprofu/vB2O72w4qFHmaJKlP30QHPWa2O19nfnz42l/J8sFV94xI7/4Mb7Mbuj1gvB92VYbzv3DlZa9M6tCsuDe28s0O74NzQmjUJHVAULQsU339PaOefE1q9OqE1fjS0uleEduJxGTv+6IzVvyq0Tz7OOpgrDKYA9qCBoTVqmLELaoVW68zQbrwutAH9o3Tr5DmWLcva1w7EO7wb2p23hf64V14WWtvnQ2v/Vtb7oy5ZGzc2Om66jVvCfCEn6nvqqafaiSeeaB06dPAAmy7H88i4R/Zff/31duaZZ/o6p512mn/++Z0oHxAagyZ/D4whfvvtt+26667zZY877jh/nssuu8yeffZZH90FaosD4PLALwDN3x7QTjsOPfRQ37Ybb7zRp0Dfd999G1JViztHRRgQ5fykX9epU8e/JAP8atWq5fvg8ssvt6eeespHR5mIK24X4Nu+fXtfh37ad999bY899rAjjjjCXwvQGfcBdXr27Gn169f35wH6mzZt6q+ViCvn2XvvvX1/PPHEExtgDYh766237Morryx0jiOPPNL34dChQz3E8lx89NFHdumll/rUZY5PO3bffXc75ZRT7KqrrvLnoO5+++3nX54QaS8O8LYF+MUdO3b0/cTzxYuF4p7H2PQ1z2/btm39S5l3333Xv5TYGv7f2pot+N0yahVEUPdYekdOQO//OY9I7/gJJPiVJClvZJUEfpP+85//7FP7iKqky26sV66NJriq0u4s+0Wz/bx3cFD66xYH2c8doAZN/lVozC/R3BlL59i9fZ60vzx9lAPjA23350+2Q1+pZfu/eIb98akj7JfN9vcTZXUY84mtcMcHgEfNG2cHtzvbHxfw5bg/a7qPB2uiwLu0PsGaftHWFq3a+sf7zneQ+9Ybod1+SzTe9/CDM3ZNvdCnQCfLAbNTp2Tt8Ucc7B6bsX/tnrFDD3Qwe44rf1XGzj87Y0dUyVidy0Mf4U2mMxPJ/WpYaI8+FFr9uhmrfmLGap4WjfkFdpPnmTIltOZNM3Z2jYwdfXjG9t0zOs/JVTN26smRa18S2jtvhz4KnL6eLWG+bD799NP2r3/9yy688EIfSSwOYABaon7nnnuuVa9e3UMp8MoXe1JBgRuAlugg8ASc8WW/ZcuWVq1aNQ9MV199ta/HGNwaNWp4qOJzSZNZlQd++bfgmWee8UAJBAJijBMmRZXrYcxv69atPXSVFanbXGaGasa30qd/+tOfbOedd7YDDzzQA+TJJ59sBxxwgIdGwJWoanxdRHwBUPoXwAJQgc8qVap46AQw6dM4okpdxqGecMIJPq34+OOP92OduX76mxcS9DEvAOjLOJX8jjvu8PeEcwDal1xyiR188MG+TdxPQJn7zUsR7ulOO+3kzXkAZs7161//2l8f24Bsruu9994r9sXJtgK//G3wnNFPzZo183876TKxeanBSw/67Wc/+5ntv//+9tprr/mJwNJl5QILfiteOzivDaLU5l+k9iX1tHOLoPD43z85N3Ee4DzG+UPnMxL70eHO7Z13c27k3M95VG7b/oly6I/OTZ2/CKLjfep8pXN8gy/J7eMLFud8Nbf9rCA63q7OXZ2/CiJArZLbno5YE9Fm+xWp7Xs4P+M8NIhA/zXnfXL7OEfvIDo3+6n/89w+dJnzZ87fOvdyviG1/9dBVIdIelpPBdH46lg3BVE/HOM8yLmP88mJ/ZIk5YmsEsJv0kQImHWWdLd0vbIMyE5cNM2qt6/jAPcAD60HvXSmj8Se2eEa27X1iR6Ak/BLZPbDcT38PkAZ8L2nd0t7a1QXP2b33Pev9xFdoPa0d+ralCUzLZMNbY6re3+fp/xxd3omjvxWsRPfqm3ndrzerv3kfusxaWCliPwmPWI4UdmMNbi6KPzGnjQpa7c1Cu3owzJ2Xf3QvhwUpSIP6BfaFQ6ezzw948fqkiKdrotnz8raky1Cu/iC4uGXeiNHhH7ffXeHdoID7auuiGaGZhvu2d1B4uQo9Tl9/C1h0leJ5AKNRBdJ0UyXwXyZB2KAJsCOdF6gjecbMAVsSAsl8gvIsq9z584+GguQ8bcQp9US4XznnXfs7LPP9sd78cUXfWQ2fc7ywC//DvCFGXAESIhG3nPPPT6Nl/qk4/4US+yQLgxY/uMf//CACIADT6R/v/LKK75P6POHHnrIpyUD5kzIRap0165dffSYsoD0/fff74GLscxEi9Mp3EQVicr+8Y9/9C8AiPTyQoH+BpC5V/EEZoAF2zgHqczcf9pKn+22224euJPR+48//tiDLS8RSG+mHvdtr7328u0aMGCAh2mgmGss7j5uK/BL3/GM/+Uvf/EvcHgRUdILFSLsvHQAfrfffnvbdddd/Uua4l4OyAUW/Fa8Tg2iLylvpneUod2d5wZROnSnIILGSUF0rEcS5c7NbfvSeYVzlyCCxP84r3T+R64c4A3wrgkiqH3S+ZsgqtsyV6Yk+L0nt22Y86ogStN+PIjGBbO9bq5crL/ltrdObDs+iF4C0KaXnNvlfud4Bwalw+8bue1DgghaPwmiSDnXuV2uDGOqKcOx05oWRNcai/6c4bzQeYnzOudzEvslScoTWSWH39g77LCDX3KFL+/p+iV59fo19vH3veyvTx/tI7GMwX15eEe/Bu+UxTPtpu6P2W+fOKQQ/C5Ytchu69nEbT/UA+7Rr19ozw17y94Y2dleHfGBjwgzSzRR3T3bVPNjiBkfzPmIIn2/cIqd+d41PtrLjM+s8btkzTK/hnBxk2Rt7d4Y+D3LQW6Hdwpma545I7Smj2fsnJqhvfFa1pYsLloXlwW/sZnw6kMH0eeelbFmjaMJr9JlfioDU0RuSY8lFRcwTZfBgGenTp08zDIxFqADGJF6y7PNbMpECqkPEMycOdNHfY866igfgQWA4jHCmFTounXremgjelkcFJUHfmMD1UTWiExzHYxXTZfZko7hl5dgtIeU8XgfbX355Zc9ENL3ACTRcsYKk9oMHBPl5SUDpr+BJ34yhjfdD8Av5yHySx9wD4pL8fZDKdw5gF7OzzlIVecc9Ns///lPf694cRCP343h95ZbbrHx48d7kOZ+EFXmnnIuro8IM9AN9KXPu63ALybbYZdddvEvIpIvCdLm/yde/nB/eflB2j/PfUkTycmRBb8Vr+uD6MsJALkx6un8b+cTE9sA2B7O/3U+Nrctht/pQQSdsW7Lbb899zmG8PobSkTgCNAuzv2Oikt7juGXiC/jl4HS3wQbB7/jggia90xsOziIrhFgR9WDqF4y7fnC3DbgP6lrcttvzH3eWPilLOOvuW6uJRlFliQpT2TbCPwmTfSmPJNkMcnVC1+1zy1ptI/t37aGTVo03UPo6vWrrfO4nh6Ik/BLBPeiTjf59GWA+RfN9vczQnOMtA9/5Vw/jjc5e/P0JbN9dJj6e7epbkNmjfCzQ6fbVlm8MfB7yYUZG/JluCH6yoRWL7djPHDGXn2p5PV7NxZ+azn4bbqVwS9fyAEZIJT1WPnimS6DASfAh8gX5YFl0neBGr7ckw7K+Eb+NgECQAmopRwmIkuabewzzjjDpwETqWSManFQtCXgt7iZmdNlNsUx/J5//vn+xQCR8+R+XhTQD0RRGVtLeykH4O6zzz4+1Zmx0fT1YYcd5qONpHIXN9Mw8Evklcg9UeTiwBeTcssLDMYep8/BPWLYBudnQrL4HGn4jTMFWH+Z9hL5J6J5zDHHWIsWLX48/DID+np3X9ZE5ne2FSmXcJhxZdfm6jBLe2riuSLehHPE5qUB46S5b7yIKA1m2cczTDR/6tSpJYKyXGDBb8WLFFu+kJCSXF79OYgim53TO4IoSsrxXsx9juH3oQ0lIu2d207KLwKi+fxxEC23FAuYTao0+CVFOqnywu++uc+MfU6LmZ0Bc1Q9iMol4bebczaIADUtIuPAO9oU+N0vsa08ahwkvmDKsixvzS5rkiwPv1/H8LuvHfhiDRs9f7yHVdb7fe+7bj4qTDr0SW9ebjOXzrV5KxbY1V3v9SnLpETv8qwDki632XXdHnB+0K7tdr9d9fFdPnX6js+b2ZTFMzbM+sxSSpMXzbBzOjbw8LtXm2r2xYyvfapzScsjbe3eGPi99KLQz9y8AX4XZu0VB73n19p88Nv5w9DOce15/FEmjirYFy/PhH+KGZ+BSuCVCO3rr79e4lq48WRJpMwCmawHzDJERLSIbjHOlLGqr776qo8ukm4MKBONZEwpkzERLX7uuee8+Z0oGunQAHgaDnFFwy+QyJdtUoSJuAL3tH1zvFiL4RdIYixssl85/ieffOJfCLAMEPBLmjPXydhb6jEpFSnPACd9youCkpbZAX55iVBSBD0218m4a2YsZsIqJtciesk5gDqeA+5VeeCXCDHtJo13c8LvuuVZWzo+a/MHRl7u/kbXu7+fkuAU0F3l/p4WDMvavH5ZWzTcQecPOaAtpnyhcwzInWOyOweT1ZVwjqT5G2H8NveOWbLT9yI2zxZ9wzNMdgUp6OnZuuWiFvxWvGKYTI45LUukCFOHZX+KE+nDpCWjGH7TAPr33PZnc5+5iYxtjb8YfRdEYHxCbn+s0uD36MQ2VF74jduYHgOcVvUgKpeEX9KTgd9BxZiUaVK90cbCLy8X4mh3edU4SHyxlGVZriwmisCX7uQXACC3x+QBtutzJ9nPmuzjJ6tqPuhFn6rMkkVXdrnDQy5je4945TwbMnOkT3tuNeQ1+5uD4u2bHWDHvHaRT50e88MEG7dgsiszwjqN624P93/Wnhj8sv2wcqGHWs7F+OJuE/vZiW9e5lOmmeTqtZEf2JBZI23sDxNt2Zpo+ZP0F5WtyTFEArCYSanOqRlNeDV5csH2GDLxhO+zdstNoV1yQTTel8ms2P7D/Ky99CIzRmfsZfeTSHB8HpYzitcHnj4tay2ahXbReRnr2CG0RQsLrxsc12Em508+Dq3m6Rm76/aC9rAd0GZ5JpZaWvMTzPhMVAoAI22WyC3wly6D+eLODLeADrAKzBEpBTr5cs+4T6KIjz/+uE95Jh32+eef9xFeoIzxq/EswkTAqMdMzcABQBRH0JKRWMbxctxrrrnGp1cDE/Eas/HzyE8+E4EE4IiOkm4dT9oUR3OLe345J2NYGecK4DFZHSnBJQHNxjiGX2CWSawAT/qQPiAK2KRJEw9RDIngnEwmRtuJBvNSDAigLP0I1AO3RJFZWogIbnzdXCOgzEsGXhQAWfHay8l+wnG0mftNBJh7zTkAVlKtSV0GailHW+k3UncB75tuusnfK+5jHPllXzLyy5jr+KVD8j7SfqLM9C/ti9uW7jO8eFRoox8Krc+pztVDG/tE1pa5v9OwhDW01y51fzvvhTbo0tB6VQ1tcN3QZn3q/gZLeBHlzzEytJEPhNa7euSxT7pzTCr5HEnzN8J4Z6LnpIeXFPnlGnlRwRrV8d8Az0RxLzvlAgt+K15x1JMIZmlizCmTOAGO1YOoTpyynBbpw4x/RTFY1i7Y7ZWGXwTsMa73gyCCxviLEWNnWWMXlQa/pCknFcPvVant8blj+AV6+VzW+r3Vg6hcEn4XOC8LojHAxTkeS10a/JISnobfMPG5vGocJL5MyrIsb80uK/LLF9ZpS2bZuR0b2m9aHuIjuX9pdZRfh/fY1y9y26r4iDDjd9l+a88mNnPZXBvqYPWUt6/wYEwE97BXzrW6H99lDT59wKq9faXt/OxxfvvhDpi/XzDF1ocZHzVmYiwm1/qzOxZp1ju0ONiOeu0Cq/Hu1Xb9pw95uE6mSG+NZpkhYPbrYVkbNiRr77YPrdpJGQemoXXrGm1j2SFAE8hdsSJrfXpnre7loZ15emjvvxctlUSUdsy3WR+hPb1axho/EvrPMTgzo/TIEdH6wN27Ze32m0M/2/MTLULr3zfaTvnkGsHU698vikRfdH7oQXnwl1nr3Strb70e2sPui/gLz4U+krylo79AWb169XzaK/CSHJuaNBMZEbFlgipgFHiiLsvbAMWMG2USp1atWnmY4rkGWJmtGOBiO2NbqcP6sEQ+2UZEkchmPFaYL7+8DGLJHZaWIUIJkAEd1I+jaPF6tPxkOSUi0qy/SvuArG7duvnzENlkEq/i/s6AdOow1vU3v/mNj4oyG/LmmBwrhl/SlQE/Joeiz4iWkuYNaALGREtpHy8h6Csi8MAy5YgsEiEHVklJBnDbtGnjo+q8CCDFmX7ieBwLkGapo+S6v0k4o/+IHnMOJmMiqks/Aa/8e8SEWUR/mYCMPqZ/uOeHH364n/AK8AbU42WriNoD8txHynAveWEB+MURddrB2GJmhOb66AOi7PHLiXS/zfwotIEXhPbZoaF9enBoQ65yf5e93d/QiqJ9jJdNzNqoh0P7/HhX/qDQehwZ2rin3b8HDC0o4W9pRufQBpzrznFI7hxXZ21e/1z0t5jysWkv94tJrLi3XF9JzwoAxz3nvvFvPrN1c++Ky3CQCyz43TKaHESTKjF7c0n6OojG8jL7MbM08yCnx7kiII+oZTxOdmPglzHDyZt5nPPAICp3QW7bxsBvjdz2a1PbSSdmewy/8XjjWzaUKNDZQQTHtKt6EJVLwi+zOzMxVVn6bRDVZXKstIDnzQG/kiRtY7I8HvOLV69bY59N7O+B96+tjvbQ+qvmB/rljv7xbFUfnQWCAV0Pswun+ghtx++62Rnv1rOdnznOQyx1MBC98zPH26Ev17K7HewSKSbtmaWODnullp8JmnMkTX3WFn5rdGefip1u49ZkUpuJwrLG78UOMM86I7RDDsjYkYdk7Nyzom11aof23DOkukYQfO/d0dq7h1fJ2LVXhx5GWQLplXahnVMjY4cdxDJFob3UNvQRWmZv/rwH6/pm7GL3Bf08d9wTjgntsIMzVv3kaF1hosj33BHa0MGF1+6dNCm0R92X9GonRksdsT7wKe4nM03XqJ6xpo+HNn0aS9gUvbaKNDPWEpViKRaieyWNGSVK2K5dOw9HpM0yTpQ0XUATMCI6CBSRnhzPLByvKQtwERUGYgFtUpOBP8apkmb7xhtv+C+9wAUptxyTOkAzoMaYV87HeFTO9eSTT3oA5N8A2gugMQFR3DaikNTlPMAzM0sXtywNacXsZzkiAJXrK67cpjiGX8ZFc2zaRR8TNWTyKl42AKDANoAaTxDGtTKhEuUZl0t5tjEzNABFhJooLfW4bvqEskA2/77Ea/ICp+wnEhvfT/7d4QUHKcicA2COzwG8koXCOXiBQLSWSck4PrC35557erglhZs1jDkn47yJErNUFtdDv/MiA3jhPjIjMveNPqCPaR9lgHnSh4vLMpjbJ2tDr3UwWzW0nseGNvx29+wNzVqmBDBdNTtr41uH1q+mK390aH3d3/2kV6PU55LgF5geUj8C5p7HuXPc6WD1K3eOMjIvyFQg1Z/+iFO4SxojTh80b97cQ/9vf/tb319kJmipo9It+N0yahhEX0oYbxtHWJNiPDD7icAiOnye8+wgWsInqXgMcQyS5YVfJsACmgHepOIJpWKAjT/zM1ZJ8Mux2N48tT2+nhh+AVPgP07VjkUkemIQvRxA1YKo3jVxgSB6AcA2ZoNO6q9BdEyWPUIci8mz0udgKSjqC34lSSoi20bgd1Nme45NtJWxt7f0eNzO6Xidnf7OVXZhpxvtgX6t7JH+re3yzrfZZZ1vtaYDWYc3WkJjzfo1NnjmCLu39xN2wQc3ujr1rGaH+la7y21+xuf2337sxwezxi/l+f3xgW38cZgwK+lLPrzZ7uzV3EbOG2trczNDb62eNTPrwbZRw4w1vLZ433Jj6MfyArIsLwQsA7ING2Ts4QdDH50l/Zio8R23hr4Okd14HV7gt2+faNmi9LGTbvJY6CPQSfgl+vv9eNdG90X9poahXV0nY9e7so85IP6oswPf6WGh9YS3lIkgAlN8OQdKiOwVF5EjwkqEl9RaIJnUV+AO6ARoiT4CPOkv96RHU48lfShHedKMmWWYqCZRQFJnATSis0RAORbHB67TbtiwoY8CE+3l3wAABJACmNNlMZASp/gm28X5iHqyHi5gxszTAGtx174pjuGXaGc8xhl4pA8AqA4dOvi2x5FD2jNu3Dg/XpkXAqyDTJozgEz7icZyPbSTsbpEWAFN1grmRUHa9DP7ieQnU8SJ1DMrMy8R4nMQofzwww99v5KmG48Hpo1E9GkP5Ulp598xwJi28NKEKDP3l2g1kXPgOF5OieWTWCc43TbuP8dP3xNMxHZG56xPfR7l/iZnf+b+TXN/ryWBLGN+F36TtXGtQhtxT2gT2rpnekw0kVW6bPIc0ztl/fFHufPM7pG1tczoXsI5YvN8M4ablwb0VVnLFhGVp4/4958+j9daTpeTCyz43TKiA7sG0ZeUCc53Op/mfH4QpSCzHdjdJa4QRNFPtvd1PiqIZkkGfDNBdAzWD0blhV/Sr1k26fsgmvxqzyAawzvaeX1QMAlW9SCqxxrA9XPbSoJfwDxerqiO8wFBBNmkZTNONznbc5MgOkbbIIpsH+T8bm5bfJ7Dcp/757ax5jHt4nhEbznHnkEE3cNzZenDWESxAXwm/+IclwfRC4TVgeBXkqRiZJUcfn/MOr9pE6FljO6kRdNs6ZpovF+6TNqUIVo7dfFMm7509laftpwvJrJLinYcSU6ODf6pTAQUWGJcKdGp4qAEMz6U55lIZTwelZRdygOupT2XfOnnSy3RR+pRv7TyFW2ugWslGsvYW8Ynl7TM08aa6yLlGugB/kgXBpTog7KuGfgmxZx+5cVBRfw7F5+De8H9rIhz/CiHBTMxlwWksf1sz6uzpU50VcicY3X5z8HzzQsIwJeIN/e3uFT6tOlrnrWSIsRyYQt+t5xIOWbSKyAu+eWF9Xg/CgqDbyyWSUqOzaUs0WPANlZ54Rcx5pZ1bZPnnxVEIB4LoB2R2E+7SoJfVCsofE38zjaWT0rCL5HZps7/L1cOA913J8qw3FCcho2r5LYf4jwqsR0DxA1y+2ORbj0pKCjDuR4MonWBBb+SJBWRVUL4ZXwXESjGgqXL5qv9TMbrokmgKsqcI33eymhAmGtJX9/mdHGzSgNZLHPEmFIiq0QHt/Uv64AuKcDAL5BaXpgpjwFKJoMiks44aCK/TBJGv7JvW+/bbdEMDyBbgZRxxkjzwqesFxnyxlvwu+UFBANz1Z1Pcf5job1FRZr0sc4nO/8ltW9TxPFIBa4eRO0oSbsHhdfkLU1EaKsG0SzVv0rtS+s3QXQtJwVROnRx2jWIzp1ee/dfQdRuItalneeIIEqhZny0JElSibJKAr9lTV6Vz2aSKNKIx32XtbFjyvZ332Zt1EjnEdHv6f3Femw063K8TFFlNVC6YEE0aVeRayzBo0expFM0wVZ6X0lmrPOSJUUjzrywIZWVNF3GcW6OGY+3ZvMlm7HGjEvlJ5HQdJlNNemujItlDO1uu+3mx7nGM/6S5r2t9+22aCYfY4Zr0sDJlNC/9RVjwa8kSZKUt7JKAL/lnbwqXz0/t2TQvXeFdudtZfv2W0K74TrG00a/p/cX53vvDO2VdlEacfr8lcmkQPfvG/pZptPXWJIb3RBag/oZu7VR0X0ludWToZ/1mtTr5PlJzWScJmvv9unTp8jY3W3RTMxFBJgZeDfnvyfMcMzYaGCJSbdiMwaWPmb8dLqOvHWbF0JMtMb9I2U/vV/ePBb8SpIkSXkrqwTwK5du4Lft86HddVvGbru5bN/aKGO33MTEVO5zo6L7i/Odt2bsxRdCHzVNn78yefmyrPXtHdojD4ZFrrEkA7233uTKNyp/nSeah345plUri7aBNE7+rtLrw8obZ9KaASSgmjRnzJhftinluXKa8drlGbMt/zgLfiVJkqS8lQl+ZVmWZTlvLPiVJEmS8lYm+JVlWZblvLHgV5IkScpbmeBXlmVZlvPGgl9JkiQpb2WCX1mWZVnOGwt+K07xsjx7pLZXpI50rh9Eyw5VZtFn1Z13zH3eKff5z7nPkiRJm0Um+JU30kxGw6Q0a9eu3eCNeU7iCZ/iY/BTExTJlcU8vzyvemblymrBb8VppDPrMw5K76hA/c15tvOCoPR1cCtarB/8XmrbLs4fOv8utb04PRJEfXdA7nPd3OczN5T48fq1cxPns1LbJUnKI5ngt0LcqlUrmzVrVpHtld188WdG3dGjR/t1VjHrcU6dOtWDbLp82gADs/NOnjzZRo0aZV999ZVf+3bGjBnlqr+5vSkzL3MNlBf85I/jFz7MJL1gwQL/vLN800/xzMryj7Xgt2IE/AFrXzr/1/nAQnsrVpx7nXOj9I4tKIAfCE/qhSDqk/LA73nO7Z3/kftcEfB7YRAd8/z0DkmS8kcm+K0Q07XbbbednXrqqfb666974EuXqYwGAIYPH2633nqrXXLJJXbhhRda7dq1rU2bNh4K0uWTBhYBhq5du9ojjzxi11xzja/LzxdeeMFmz55dbgDdXGbd3WnTptmkSZPKta4o7eNLI38bc+fO9f2RLiNve2Y95u+++8569OhhL774ot1333323nvveYhIl5Xlrd2C34rR887/dq7i/H9BBH5bUkSA/57euAX1Y+E3LcGvJEkVIhP8Vojp2qR32GEHu/TSS+2jjz6q1NEi2j5ixAh74IEHrEGDBnbRRRfZoYcearfcckuZke7ly5dbly5d7IwzzrBjjjnGzjvvPLviiivssssus/vvv9/Gjx+/xZ+36dOnW+vWra1Zs2YegMuK5gK7/fr18+398MMPfRQ8XUbe9kymA8/88ccfbzvttJNtv/321rBhQ//MpMvK8tZuwe/m1/bOS4Mo6ov4uSooGL+a1r7ObZ2/cf7W+VXnvQqVCILfOt/tPMB5gvMnQfEgWMu5q/P3zv2d7w2i9N6k/hBE6b5Dgqhcd+drnLcrKFKi/uj8kHMf50lBdIwXnfdOlHnFeVEQRZ+J3l4aRGnMlOdLUEfnO3Nl+cnxTnH+KoiOe1xQcuQXYH08iFLKv879/vtcGfTzIKp3bWJbrBbOTXO/X+I8MIiOyc/X4kKSJOWXTPBbIaZrS/Kf//xn/8V54MCBReptDnPPgFSimnjNmjX+Mz+Bt+Kiq0Bfuk5JZePylPviiy/stNNOKxV+49RiUp2JmJ1wwgl29913++sHHsaNG2dTpkzx50zX3RjH54mvg0gu5rjFQS3liWID32effba/FsqyPXa6DhHAV1991V/Dww8/7K+5pPJxezhmfA/isaL0LdtoZ7w9fa5yOXTnWe+8rhxeH5UvcowynGxvPD477ueyxmynrzW+H8U9W2X1V/xspvuruPteXLn4GY+PH7cpbk/y+U+3j+ekSZMmPuPh3HPPtX/84x92ww03CH7lSmnB7+YXYMV/8A1znxvkPl+3oUSBTnUOgwiWiRa3c17tvNx5n1yZPzlPdP5f507OTwYRAKeP2Ty3DTDk93eDKPoMJMYADByyf00QQSrlAFjqPZcrU5IA3+lBAdQ2dv44iNpF++PJqDYGfns6T3Zekahzcq48ZdNjfhnLPCeI+oAxxf9xHhsUvFjgxQPl3sx9TgrQ/y73u+BXkiQvE/xWiOna8niPPfbwESVSKtPH2BTzRZ403iFDhljv3r2tZ8+e1rdvXxs0aJCHO+5pEjL5gg8sEAFl/G6vXr18aifRTaJdixYtKvEZoC51Tj/99FLhd8WKFR5uu3fvbpdffrlVr17d2rVr54Fi7NixHiAWL15cIkCV11wHgP3ll1/66/744499ijXXPXPmzEIRd+CGVHSu8/zzz/cAT3mugS+GpHATqY6vnbZxfMYmP/vss3b00UfbbbfdZt9++60vj+krwCmGJn7nuun7Pn36+PHNpHaT+k3fcn+6devm20sKdUn9XJrXr8zayulZWzIma4tHl+7lU9x1L+e+FT1OaeZLMuOy+/fv7+8ZzxfPEfeevmUb/UL/JIGRPua6yBTgGeRa6WOeMV548CIhWZ76EydO9Mekvzgu9efMmePP//nnn/tjDBs2zObPn7+hvzjOhAkTfD/zjHEOytE+6nLfOQ99T13uOS9e6P9PPvnE/8794D5Qj2PQPp7buH3JFxzvvPOOHXHEEdaoUSPBr1wpLfjd/Po8iICWCCsiagvUjdpQIhKdCkwuDAoinOiIIII64BU9E0RfEi7aUCKCWQAYSP5lEM3uzNhi6iRvVo3c9ma5zycG0bFu2FAiKg8ALguiY5Wkx4Ko7rmp7YAs2y9PbBsUlC/tGfhlW+vcZ/qK9pQEv1OCCMJj1cttpzwqL/wipT1LkiT4rSDTtRvrww8//EdNlAVscM9atmzp05Fr1KjhoY6fZ511lv/ZvHnzDWNz+SIP4AHKcZ0TTzzRjjvuOA+o1113nXXs2NGDTXFjW8sDv0Aj6cxPPPGEH9sLNJImfeWVV/oo2u233+5TjgGXlStXFqlfXtMWrr1x48Z25pln2mGHHWZ77723f7lwyimn2NNPP+1BlOcZ8/sHH3zgXzzQ7wceeKBvD2DL+GXGdQJB9BXHBmwHDBjgt9P2Pffc0/dn06ZNfXncvn17P4lXPHYYcCNKTKSQ6ybt/ZVXXvHb6IsjjzzSH6dWrVrWuXPnTUqhXjYpaxNeDO2bW7I27Hrn64r3Vw1DG9fKXccod09WFz1OaeYeA3pVq1b1UfJHH33U9xXtZkw7Y7+5h8Bj8iUGgPruu+9a3bp1/TO13377+evl3tx8882+P3n+4vPwnD3//PN2zjnn2FFHHWVXXXWVHy/ftm1b/zv19tprL3++Tz/91L+84Fy8VCANnefwkEMO8WW49zzz9DXH5YUPYEuaPSn3POf0P88HUXzuKfVp37777mv33HOPB+Lki6LYXJPgV67MFvxuXu0SROD6fmp7+yD6z/3oxLbDc9uAyrSIlgKqaJbzmMS+WMcEEcDtEETRVo61a6ESkUin5hjoqCAq18N5tw0lyjczNOVrBoXhGlUPomPemNg2KNg4+E3CPyoJfotLZ+baxuV+r2j4bRwU84VNlmVZ3nze1ImyADRA4dhjj/UTUQG6QBkpxnyxBzqBFqCM8nyxJxIGiPFlnggoEPvggw9avXr1PBScfPLJPkrLl6VklA6XB36JugEn7AeWAJgDDjjA1wG2AZkbb7zRj4UmgpeuX14DtEQmL7jgAt/miy++2F8X6cwADaADtNCfRGQ/++wzD0f/+te/7A9/+IP99re/9eWA4IMPPthD1PXXX+8j8hybSCBABMTusssutuOOO9rf/vY3D3SUxzVr1rTXXnvNf6mkTfRZp06dfDuqVKli++yzj1WrVs2DIAaeuc/A3ksvvbRJkyct/CprXzcKrXe10D6vWrqHXpO12T2ytm5Z0eOUZoD+zjvv9Nf6xz/+0XbeeWffT7xUoP1cOxAJQBKxJWJKPfqOZ4l7zX0Agon887Jh9913t7vuusvDY/xcEZklqgro8owcdNBB/l7yPAPe9C/9xz3mRQMvJngpwwsanl1e2DCBGs8u5+Q+0cdE/4Fyng/a8M9//tNfw0knneQhmBTmX//61/5Z4DPwy7P/9ttv+7+pdH8IfuXKbsHv5tXDQfSfN2NyGyf8UW77WwVFfaSUbaXBFzBH5LZ9ekdKpC5TblAxXhJE54lTn7vlPmOix0Rdq+X2lSXaw7hixunSJlKo/18QHeumRDnOOzvxGZUEv0TF0yoJfg/bUKJAnwZRejcS/MqyLG9DBjaYWCn95aU4k7rMWGK+uAOspHwSTQUq3nrrLQ9hREDjyC8QTMQOGAEWSQPlyz7wQhQVeGYfkUuiw8m0YVwe+MXAJtE30l0BE4DkjTfe8KmsQCXLxhABTMP1xprrJI2VCB/ADbTxmWgf4Hbvvff6MpyHNGjKcf3AFbBL9JAXDkAPgEM6bPzygZ+8KKAfr732Wg9K9Mtzzz3ny2PORdpuOlrItTO7NdDFZEkAGveUFO0xY8b4qDepxOn+LY9Xzsza1PdCG/1IaCPvLsX3Zm3Sq+46JmQts7boccoyqcv169f3oMikT88884x98803vv1vvvmmf5EANPKihRRkIrIA5+DBgz18xqnF3Bf6gugsL2jYn+4v+vCOO+7w5wJgeWY4BtF62sGzyP2LsxE4H31Puj7LbnHfO3To4F8q8LKFbIr4hQ8vPciGAKApTwSZ37mftB1A5jkAtp966qkN9ZIW/MqV3YLfzac4jbnIf94JZ4KCtN14LHBxE1fFYrwvZZgEqzSRUr0+iIC0JJNSjGgnwNchKABj3DcoPQIMiBJlpSxjfEmVZpzwo7ltmwq/HCutkuB3/w0lCtQ5iKLtXFcMv8mXDLEAfcGvLMvyVu5NjfwCu6SkAiekLAMljH8kfRcThQQ+4rRcwJOIWTxbM9FXABUzZpblhwBDomHARDoyW174jQ0oEHkGNogQAsXpMptqQAjIBGiffPJJD09MSMSM1MDObrvt5j/zgiBZD5gilZdUacAnDWJpA+lEd+ljUqbLumYcwy/RTECcMa3lWVapPF69IGvzB2ZtWsesTXm7ZE9tn7W5vSJYZvKr9HHKcgy/QF+LFi38S4R4Hy9YeNZ4UUJ0n3vLNl6ykApNVJu+uummm7x55rgfADPjeNPp7jH8EvnlOeYZK+lZ4YUBzxVp47ysYRw295kXPUTXgdrHHnvMwzLlY/ilDJFp4J3MA/7eSJHmb4gXGsAvKe3J64wt+JUruwW/m0/MWMx/3PH41bRioLsj95kIKp+LS+W9IIgiw8zAzPhhZmRO66AgGrvLkkZAJBHY9MzOKH3zGNebnNn5SOfeQdSW5LjdtL4MonOcntrOzMzUTa4rPCioGPitHhdIiLTuubnffxFE5QD7tH4Ifjz8SpK0jck05rdCTNdurH/smF8itnF0FWAlHRXg4rh8uefLOoAbj8sEaEj9jNM9AUCAJDZLEgEBLEnEmqZpEN9S8BvP9gvoFPc80g4idIwXZTwn10N6MpBD24AoIq6AFBHWZN0k/DIRUnnhF7DaWPilLS+//PKGtOjN4QXDovG8PY8NrfthpfvLOmGU9ry86HHKcgy/RFN5iZJ+Fug7wJeoPmOp6RdevBDdJSJMZJ00aZ4pxvOSMk4aMy9l4jTp2DH8cjzSoItLPcbcd6Lnjz/+uD8m0X1e1tDPpEDzHADZ9H1J8MsLIMYfA+S0m3Px0oeXG4wfL+7+Cn7lym7B7+bTO0H0HzgTVhUnxuMSoWTWY8TsyNkgGn+bFABHhDWeIIvlf9YGBbMpx2J5JNYQ3sP5riA6N9HkpBgPzAzJ8ZhYYJn06GobSkQ6O4jqJ6O3aTFDdHycpOLxxrclthEVjoE0VpsgKhdPBIY2Fn655qSIBNOnrye2EV0flviMDgyi+kn45QUD25ITiUmSlGcywW+FmK4tjzfnbM+AGymgAAUTKzHeEigl+smXeyAQkGUWXUASoGEMJXBMyifr3caTN2FgMk7r5VlIp+VuCfjl+eOLWpzuWtySSLSLlO34WliCiD6gT0mBJbWZlwDFwS+p16QhA2IcI24T18a508v4AL9E5IEsxgDHUBXXoTz1kincMfzG6d68fEi24cd4ydisjXsqa8MahDakXun+tnFoPwyNZohOH6csx/DLNfA8JAGe66W/2QfcAsekMwOYvIBhLDDp4qQkM/kZvwOnPIulwS/PLC9dSpoIjOcAEI3HphOpjWeSJsOBKDBjrcsDvzyTtFvwK+eDBb+bR6w1S9ox40pLUxxhrZ773Cr3mWgxIHeIc5fcNpbjQScEEeABdKyB+y/ne4JoiSGWDUJEUwFm2nBzEK0TzORW/YLoWPHsznsEUSSZWZNPdd4ziCbOGpHbvnuuXHEaGkTtuDqIzsdSTE85/08QnSOecRl1DaL2AeXH5rY1CaJyXHOc6r2x8MvY3geCqN3VnacG0dJQXG8sZtsG8Bs7V3Gu7TwjiNZaTsJvtSA6JlH1+ontkiTlkUzwWyGma0tyRa3zyxI6fHEHYoEMUmsBQ2CLNGbgEEhgnGO8rAyTC7ENoGF2XsrHa6ECHZQBIOOlZWIojMswXhWYAAQYuxvXTz431GFbMspGe4gecgz2lbTMEV/QmNAIQAWsWGuVaF/y+AApQLX//vv7SbRIZeX62E4/kPZKVJBUWGAleS7GoTIJElFJzsP5AGDaBmizFBP9Go8v5bj0FfADTHN8ypO6S7l46SbKxevK8jme+ImXEpQr67rLa8bvrp6XtaXjQlvybSn+Lkp5Xr+RMz3HjuE3nhWb8b6kwcfLWJFyzMsVJjAjrRsIBSi5ZqK3PD/0CRF6UqTJNCCKzFhd+ppnJO4v+jBeT5eJrahbXH9xrzgvbeK5oh7n4Hi0lxRr2kTf82xyDxk7DHgzmRnPNQAcP5Px/Y/hl+eGFxdx26hPO3jWeMkCQHNOtqVfeMjy1mzB7+YRa/rynzpgVpoAMcoxThWx7u4TQcGkURiYS0dgSS0mbTcuQ8SX1N54fVu0h/OARBlMtPbuRBlEujXR4GQ5wPmMZKFiRPR0WlC4HhNtEenmPP0LigZnBRGIUwYQRoAsyzqxjeOgjYVfwJ4lmeLzc5wYrmPtHURr/8Zl6FvGJbNGchJ+Gd9MynRcrriZsiVJ2sZlgt8KMV2b9A477OCXuiHtOB1B3VwGCplNl9RfolakorIObTwBECACULDEDF/YGZPJ7NCAH9BCRItJnYjQsS4r0MLkP0TQOBZgEa9dSznAlxRgUqOBU8CCMZ5E3Rhby3UCBMA3MEJaKeWADyLK8frD7OOLWHEgCFwA6L///e/tZz/7mU9vJXqXHDPL70wgRWot107Umoji+++/79vPpEd/+ctffBot4J9cIxYwZdbhOJLH2FEmQgJwqUsEkvPF450BIICNyC8vE0hjZtwq95VJoAAilnXi+okixtfNNZCKzaRb1Ofa6UPGmBa3jNTW5hh+//rXv3rYpM94puh3IqukgQOaPHdE18kIIOJLv5IVQB+QBk0fAb3cD+4ln7nHPFd8IScrgeeQ55GILlH8uL8A7uSLCO4J2Qmcl8g9Y4u5vzy3AC3tJN2dYQDAOOfhueO8RJ15XolQA8kMC6AtPA9xGV5Q8bfA30qcfcDfDjBNKjdwz7l4icXfXnkyGWR5a7Dgd+vQb4JoaSPW6yVVuThxEwDN6s47Fd5VSDsHURmgsKRjAd2HBlE5xg6XV4wVZomm6kHhpZKKE9e0ZxBN2hWL8cZsY5zypopxzfRTSenlsQ52PjmIovKlievYI71RkqT8kAl+K8R07aZOXLWpJroFoAADRKYYbwkEMKEPY1ppC5MVAX+Uj8dM8mWefUTjmJAJUAOUARcAD3gBEvhyT10mBqpTp47/8k80lvNRFqhh29VXX+2hgMgxzw+pxbSDNtAuUmEBbsoDIcB1SZMakcZN3V133dUvSUS7AJkk/HIdRHA5L0voYECYpYuIBgPE/M4Mw7Sb9OZ4kiXuC2BG9Dpeg5YJwBgryphRZsHu0qVLoftHtJDIJKnURJQBJWCINnJ99CcvCAAn2g7Ece6///3vvg5RRfoaKGOCrvREYlujY/il/fQP/cvvrKdLX9FvpIEDqNxHJopizWT6hhmbAVH6iPI8K3zmPtEPvIABaoFIXh7wMoXhACxHxLMV9xfRWu593F/cd9LnAWWOxf2iPPeRdOd4SSXuEYDL2GDGsjOLNMfn7wKA517yTHIcXnQA1CyNRdtZ15lnHujlGDwnnAN459i80IjBm2tI95ssb40W/EqSJEl5KxP8Voh/zMRVm2q+pJOySQSTdVmBBVJ6AT6+6AMZRLaSkUairaR2kooKhAK+lGecMJFPxlyS+hxHq4kWs42IKMcszkBQDIw8P8AiEUHgIV0WM1aTiF96LC8mdRlYvf322z18ARlcZzrFFBgmisy5AWrAmmsnBRygIV2aa2M/IJOcYZhror1APmnTpMCSIs09JJJNG5Lnov+IqPMigegm5Zk0i3HTRJvjFFvKEBWlDBHvtImecm3p42+NjuGXFyrAIc8ZzxjPCT+J1hLxTT5bcRSVFyqs88uLBMa3Ey0mY4DngWPGKfe8AOHFQUn9RV0AOdlfPGNE+Xk+uO9E97l3HJ8xvESOue+kMLN0EX8btIfj8UzS/5yftvC8M+M3gP3QQw/5tHbgmOeD8eZs428j3S5eYsTXkH4uZXlrtOBXkiRJyluZ4HebMSALGBBxBUL4HQBnsh8gobi04th8aSdiRxouKbukKm8t9z6eSCoeR5venyxHGaKOXDPjUTcGRjgH182XQkC8rLrsj5f0oa9La1tlNtdJFBvIA35JQ6af0pOBFWfK8EWbFyykgVfEM0X7uNfc83ht67LunSznswW/kiRJUt7KBL+yLJdiXqYwppnx4qTBExEnYkpkmy/Q+jdCliuXBb+SJElS3soEv7Isl2LG8TJ2mbGzjGtmLCxjr0l9Z2bnZAq5LMtbvwW/kiRJUt7KBL+yLJdiZu9mPCyzlZP2HJuZqxk/nV6nV5blrduCX0mSJClvZYJfWZZLMeN647WPSYHG/F7WGGxZlrdOC34lSZKkvJUJfmVZlmU5byz4lSRJkvJWJviVZVmW5byx4FeSJEnKW5ngV5ZlWZbzxoJfSZIkKW9lgl9ZlmVZzhsLfiVJkqS8lQl+ZVmWZTlvLPiVJEmS8lYm+JVlWZblvLHgV5IkScpbmeBXlmVZlvPGgl9JkiQpb2WCX1mWZVnOGwt+JUmSpLyVCX5lWZZlOW8s+JUkSZLyVib4lWVZluW8seBXkiRJyluZ4FeWZVmW88aCX0mSJClvZYJfWZZlWc4bC34lSZKkvJUJfmVZlmU5byz4lSRJkvJWJviVZVmW5byx4FeSJEnKW5ngd9t0mLXMWufVzmuyFq4vpkzKlKFsZlVUl2Oky/zYc8iyLMs/rQW/kiRJUt7KBL/bnh2UrluatYVfZW1296zN65e1FVMcnAK06bI5A7GUmdsvqrPwm+gYJQKw2752ceIc/V39qTloTpeVZVmWtxoLfiVJkqS8lQl+tzkThV0wJGtDrwnt8+NC61cztPGts7ZmYdGysVfPy7oyofV1ZXseH/5/9s4DzIoqW9t1vffOHWccR51sZMw5Z0Ew54w554xZMWLOCXPGHFCUHCWIEk2gZMmooOTQde5/0/r3u/cpuqg+p2mgGxrq+57ne7pPnV21d+1TDeettfYq63eRO8YX4VjZtr6POe4LVK9iH659j8Ni+/5J18dPVdvKsizL9ceCX0mSJCm3MsHvKud5vxRs5KsF69wwtrZbx9Zuu9g+OyP2kd1ykdzpwwrW17Vpv0PYp9sBsY1uVYz+lmgPSI94sWBdGhX72D62z8+LbUY1fciyLMsr3oJfSZIkKbcywe8q5/mzQipyz2Nj67Br7CF48HWxzRpfKAumM38o2ODrXVsHsx13i63PSbFNaFewBTOrtvV9zCjY+I8L1us414dr33nf2L5s7voYW74PWZZlecVb8CtJkiTlVib4XfXs4HPOpIKNfDkA6ZD7Y5vS24Hs7BJti57vIPdH12bIfWGf0a1if4xCRdW2OHZ9ANO+j5tjG/pQbD9+Vn0fsizL8oq34FeSJEnKrUzwu8o6rggwGs+v+l5Jx6Et1Z7LQW/Wi/ShiK8sy3K9t+BXkiRJyq1M8CvLsizLubHgV5IkScqtTPAry7Isy7mx4FeSJEnKrUzwK8uyLMu5seBXkiRJyq1M8CvLsizLubHgV5IkScqtTPAry7Isy7mx4FeSJEnKrUzwK8uyLMu5seBXkiRJyq1M8CvLsizLubHgV5IkScqtTPAry7Isy7mx4FeSJEnKrUzwK8uyLMu5seBXkiRJyq1M8CvLsizLubHgV5IkScqtTPAry7Isy7mx4FeSJEnKrUzwK8uyLMu5seBXkiRJyq1M8CvLsizLubHgV5IkScqtTPAry7Isy7mx4FeSJEnKrUzwK8uyLMu5seBXkiRJyq1M8CvLsizLubHgV5IkScqtTPAry7Isy7mx4FeSJEnKrUzwK8uyLMu5seBXkiRJyq1M8CvLsizLubHgV5IkScqtTPAry7Isy7mx4FeSJEnKrUzwK8uyLMu5seBXkiRJyq1M8CvLsizLubHgV5IkScqtTPAry7Isy7mx4FeSJEnKrUzwK8uyLMu5seBXkiRJyq1M8CvLsizLubHgV5IkScqtTPAry7Isy7mx4FeSJEnKrUzwK8uyLMu5seBXkiRJyq1M8CvLsizLubHgV5IkScqtTPAry7Isy7mx4FeSJEnKrUzwK8uyLMu5seBXkiRJyq1M8CvLsizLubHgV5IkScqtTPAry7Isy7mx4FeSJEnKrUzwK8uyLMu5seBXkiRJyq1M8FvFFXGFzauYb3MXzFvo+RW1Ow9xHPtjzltQ2c981yfbs21XZsdxwRbML9h85wULwutsm7p0RUXoe/688LM2++dYHB9n36vv5jrjb3v+/Pk2b948b14vyfVXUVGx8Bh4SfeXZXnFWPCbLx3lfLbzSdk3MtopCu1Ozr6RUoMotDkks72cToxq3nZZxMXKuBpm35AkScrKBL+LGPAdO32idRjZ0z4Y2tG5g334XSfrO26wzVkwt0r7pfECBw0/zppqn4//0toN72GtXT8ffd/Feo8d4Lczhuw+9c0eahcsHvx+/bVgAwcU7NMesQ0aVLAZM2oXQKvzPAe848YWrO9nBevRPbYB/QvuC9/ix1ydk/OeO7dg06YVbMyYgo0fF15n25ZzAp7AY/a95eUZM2bY0KFDrVevXta9e3fvL7/80p3TtMWOi/HPnj3bxo4dawMHDvTH+PTTT+3rr7+2qVOnLnZ/OR9Obo7ohkj9s+A3X/ra2Zz/z3mTzHtp9YlCu18y29Nq7fyfzntl34gCeH6Y2fajc8/MtrrQv0Vh7K2yb0iSJGVlKwH8tmjRwn744Ycq2+vCM+fNtte+am17vdzUNm25n23csrFt8fSBds7HN3owzbZfUle4L4Ljpk+yx/q9ake/c6Ht+PyRtlnL/W2rZw6xo9zr1g60Z82fXWW/+mag9ttvCjZxQoioZt/HgOJ3Qwt2TbMKO/rwCrvmqtiGD1s2+FwS/+Lg9MPWsZ1zVoUdcUiFXXlZbF98XrA5c6q2ralnzQrn1KFdwZ5/Jrbbbo7txedimzSpattSBggmTZpk33zzjY0bN85HXLNtloeHDx9ujzzyiJ188sl2xBFH2GGHHWY33nijDRgwwIF89Td5fv31Vw+89957r5177rl23HHH2THHHGPXXXed9ejRw4N1dh85fx4zZowNHjzYX+9kBmTfl1ecBb/5EvD7/6IAh80z7yVaz/l/iy4Hv1tE4f1Lsm8U1d95VGab4FeSpHonWwngl2GuttpqdvDBB9s777yz2C/ny+Lpc2fYI5+/bFs8dZD96aFd7F/v2sz+/Z4tbN9XT7EJM6ZUab+knjlvlr3xTRvb8PFG7tib2xr3b2f/fLKxbfbUAbbzC0e7vl+yX+ZMr7JffTKwO3BgwW6+Mbb33o09CGfbYOB31MiC3XlbbA33qLBjjoz9fuVgubY93Y2re9eCXX9tbPs3CgDevl1ss2ZWbVtTjxldsEcfju2YIypst50qbNONKuzs02Mb9v3iI9pEwCZPnmyvvPKKB82OHTuuMFDkZtLrr79uN998s51zzjm2zz772CmnnGJdunRxgD+rSvvEnMOgQYPs8ssvt7333tuOPPJIvz8QfO2111r79u3tl19+qbKfnD9/8skn1qxZM3+9T5gwQRkB9ciC33wJ+AVC+YlL6Rrnuc5DovLw+wfnDbMbUxL8SpK0UshWEvhNe5111rErrrjCRxWybZfFCyoW2Kx5s23AxG/s/j7P2+Ud7vAA/Jt7tiwJv2HdblizO3v+HJs9b87C9cFxoWqqH+nMRH0vbHuLg97t7Xf3bWuHvHm26+s5a9m/lb04+F37esr3fr1xdt/FOVnbSqpv8pNUXH7nveQ1P0tFXv3+xTZJO9bqlgK62bML1q5tbIcdWGEtbgPoQrvE2fbff1ewi86rsBOPC6nH7J/0s7h1uMl5LTKuatYOJ2nJfp1v0USbmztQP7lpafgtee5l+hj7Q8FavRbbA/fGdt1VFbbXrhV27pk1g19ShQGCQw891E444QTr2bOnzZkzp0o77K+t4npcbvbgZG0u20ulkpbah9floCNpz98RkALAVge/HGfmzJn25ptv2kEHHWSnn366ffTRRzZs2DAfSR49erSPCpfrr7ZNP8n5JueZXsdc3bmn92We+Bz4WSpNN902vTaa7em5zu5b0z6S4yROf960ra6P+mxS4ckI4AYJ131Nb/Qkc1vus5OX3YLffCmB35ui8CWqVOrzQOc3ij+z8AtYXl18b4LzYOeritsTvRyF/eZEAUCPKW5P4JeU6K5R5f5XRGGdblprOt8dBQCn3edRiDKvlm7k9K9R2B/Ypt0HUYhKl4JfYP35KEA5bdtGWhcsSbmXrYTwm/bOO+9sLVu29GsVs/vV1HzZmjFvlg2aNMTaj+hp7w3tYO1G9LD2w3vYxk80tv+4Z6sq8AvgTprxo3Ub3dfe/Ppje2bAm95vf9vW+o4bZNPm/LrI2l2g+PufR1mb77tY49dO9cf808O72s3dH7auo/r4vn+ePW2p1vsCbCNGFKxHt4J17VKwnj1i++Sj2N5/1/3+acGDWZdOBXvvndg6dSDqVxl9BdiAUSK03d3+H34Q27tvu/3bFKz/FwX76acAgklbUoZJ8X37rdgObFxh118d+zRg2uFpU8N40iA47PvYLj6/wo4/OnbQXNkPKcmff1awn3+qCuTsP3NmANce3WJr82HBWr8fW8f2Ye0wa3eTcSXm9VTX/zdfF6xXT9YZF+zLwQWf6nzT9bGdkoHfdB/duobjM0ftPg7rlEv1keyHe/eK7dgjK+z8s2sGvyNGjPCpwdtvv72P/LJmNtsGA0ykin7++ef28ccf23vvvWetW7f2UVX82Wefubn+aRE4YJ+JEyfaF1984UHj3Xff9fuQhjxq1CgP3uWg6dtvv7VrrrnGzjvvvLLwC7iNHz/e+vTpY82bN/eRYqK/nTp18vD81Vdf+ffrMisja6LojKdt27Z+rphf1h1369bNR9WZJ1Jvs+fOXJFyzr5t2rTxEfBXX33VPvzwQ38eAHy6PW0BOT4L5n/IkCE2ZcoU++6773w/b731ln3wwQd+Hukr6YPPt3fv3v4GAX289tprvg/GOH36dN8HZoyMmc+tQ4cO/nOjPcfu37+/n2Ne85kyPm5AlPss65OZo9tuu8222WYbu+GGG2zkyJGL/f+E82L+uZHC/K0s57qyWfCbLyXw+88orPvNpj5vXNx+aFQVfgFNoJX3We/bwvkt5/+JAkgmYFod/P7qXHD+yPk+5++j8AXulmIb9Gfn0c7/FYX96Yd+acd+aQB+p7i9Y7Fdtyj0wTb2TbSt84yiH3O+KwpzwdhPTbWTJClnspUcfhOvvvrqC9M2s/tXZ9bgTpr5k73+9Ud2cutmPvWYNOSdXjjKjnj7fPvjAztWifzOcyD79eTv7N4+z1ijV0+2dR/dy1a/dxvvDR5v6Pa7wF4Y9K6HY47Pl7exv06wqzvfa/u9frr9+aFdbbW7NnPtt3b9HWUHvXGWndnmWvt4WNelWu9L4aXXX4ut6bGxHXJAhU/JbbRnhe20XWxHHx7bNc1iO3j/Cttxmwo7oHFsLR+PPdgBa9Onh2JQt99C2wpr6Pbba7cKa7xPbGeeFtvLLwYwBpZJb6Zw1dMtY7v0ogrbZXtSiWO7567YHn80+KUXYg/NAHUyvgR+G+9TYZdeGNtxR1XYnrtU2O47V9hpJztIfyecQ9KecQHEHRzoNr+hwo5zgHlgkwrbf98KO+zgCrvw/Nhee7VgI0eEKC37ANzDhxfc9tiuuDS2E46lH9fvBbFPzz7lxAo74ZhK+KUPQLlLp/D+kYdV+NTsvd2579cotvMc0L7+WsF9Ca+8UZB1nyWAX6J3QFqjRo2sYcOGHmQAoGw7TEryCy+8YGeeeaZP9SfKesABB9j+++/v973ooousX79+C0ETMAW8nnrqKR+Nbdy4se2xxx6211572dFHH2133nmnB8FykbeawC9jAhAvvfRS22+//Wzbbbf14znrrLPswgsv9NuBOwB8ecEKkXPGvOuuu/p1x5wD0WvGlaRk33LLLf4GADfHknEBs88++6zfZ6eddrJNN93UNtxwQ39OnAsgmswV+wC8p512mu2yyy62++67+2O+/PLLdtVVV/mbABtttJFttdVWfp650cBNCfp4+umn7dhjj63Sx8UXX+zHBNgByYAzEdIddtjBdtttN99mvfXW832xP+fC64033tifI4XJuJ6y81HfzNy9//77tueee/rrlxsNnHO2Xdrc0GAfbhIBzNwAII1+eV1TebHgN19K4BcNKr5OCwidHoVIbhZ+L4vCl6wrU9vQBcXtZ6e2lUt7pl0Cw+j3zlOjALuJXooCYB+R2oYeiML+5xRfE7XlNdHctF4sbm+V2sZ4ZkUB+hNxjp9GIcV7rdR2SZJyJFtF4DdtvozWtEgWEd+XBr9nWzx1oP3b3Zt7A7G/vXcrD6jRnZsssuaXyOyoaWPt0g532FoP7uwjuJu23N8XyKJ41Z8f3tX+/e4tbJMn93MA/I5NnzvT7/PV5O9tu+cOd8fdeuFx/+XOTd2xt/THWP+xfeze3s/4iHF2jIszFZQ7OKi74JzYtt7MgeWuATJPOym2LTausM3/WWFHHkp6bgBb4HPIt6F402e9C3bGqRW2644Vvv0tN8V2/72xXX5xWCfbaK/YnnBQO3lSwcaPL9gjD4X99969wjZrEID6oP0clB5UYYc7n+5g9s1WBfeFtXJ8Cfwytt1cP/R39ZUBSHfZIezz5aBKyOR8iBCfeFyF7eOA9NyzKuy+u2PXd+yLZh18ACAc22MPxw4yQnQWSH3gvtgDMqB87lkAeuyPwXxss3mFHXFwJfxiwPe0k8KcnH5KKF4FyF/s4LpxQ84rtmefCedeCmyXBH75Un///fd7gDn++ON9tLTU3xZASwTw8MMP9+B7xx132EsvveSLU51xxhm244472oknnuirMycRTdKOabfvvvv6fa688kq76667fHQZ+AWiWCZAVLhUmnVN4JeoHcAIHAK/wB7gR8EsABjofP755z30LS9QIQrKOW699da29tpr29///nc/PxTwOuqoozx0NmjQwI+xc+fOC4GWYmMALG24YcbccEOBGwYA6vXXX++jyEAs58KNhvvuu88OOeQQ+8c//uGzTbiJwc0FjkEKOzco+AyIBvO5MjYi5AD4qaee6j8T+gBu6YP3iG4CsRQPY930+uuv79+j+BjHA3h/97vfeRgmVZ7z5ObHG2+8sUyZLsvTzAPXLf8mMz/8HVR3fXTt2tXPMzczqfPAvkTKuUmQbSsvvQW/+VIafq+NwheldOrz0KgSJrPw2y8KYJxNPeY1EdUuqW3l4HdcZhtq57yg+DvHAlLZP6vfOVc4dy++fjQK408DLVq/uD2BX6LZvH5yYYtKAdi8d3r2jTJqEZX4oinLslwfvbgiWUDp0J9G2D6vnOSBlQjvri8ea+d8fIOd0rqZbfxkE/tXB8Np+GVN8JvffOyjvYAy4HtHzyfs3SHt7NmBb7n9rrLf37ed/eZuosUn24ipP/gve1Nm/Wz39XnOH3eDx/fxhbT+cP/2dvjb59tZH19v13a5zz794YulfpzSbAeypCoTXaWqMVFR0qCJAAOq77wNYMZ20fkBVHv2DI/pYc3urgCogz9SpIFWjsX+wCZQfOShsYdkIr9Dh8TW7pPY7roj9tB4pgPZt94I2zB9ctx0tDSBXwpENbssrPulH6LIJ58QxtPmw9inINP+eweSRG8BX9KVv/oyRHiBS1KuX3w+9sB9zBGxdexAemXBWn8Q26EHxn6sL71QsBHDAU4HqL1jX/Bqx21DPwn8cn7Nb4g9GF9wbmy9e4UxMQbSuO++M7bd3XhPPTH2j0oispyd8yWBX4ALuNxiiy18VKtcyjMpn0QVAdlLLrnEQzLRMraTQkuBKm7sJNE/3uP6PvDAAz00vPjiix6qAFhSo4kwA8DA2pNPPunhI9tnTeAXoOMxRqT8AoJE8ojMkToMfBPx5P1SQF+XBkyJyq677roeyp977jk/10AokAh8brbZZn6srEsGaIkksh8RXdKZSS3mPGgDLHNzgTTq7L8ZHA+gpuYA0WXmk7kjbZkbCzw6Kpk74JRjtGvXzkeo6YPPj6JggCBAzLYkgktqNJ9RcmOD/ZKxA+qMj30B9Mcee8ynFGfnoj6aebj11lvtL3/5i51//vn+WqluLS/ZEXyOa6yxhv3mN7/xNy7S8yTXjgW/+VIafjeIFk193joKX5j2Lb7Owi9QOt+5dwmzfXxl07Lw+1lmG0oemYT+FoUxPFf59iL6NgrrdVF75zj1XlrAeKvi7wng/hBVHTdrjnmPNOiaqEVU4gumLMtyfTdfIolCpL8AzJk/1z74rqOP1hKJ/esje9hrX31o0+ZMtx98mvI9HlDT8PvTrKl2afvbfZVmgHn7546wW3s8Zg989rzd2/tZu7DdLT5V+l/u2tQ2emJf6/lDv4UFrEiBHvbzaDv0zXPsP+7dyho80di6j/ncV5imcFapIlk1dQK/B+1f4SO3U38OEHfEoSHqy+9ESW+7JfaP/enUsWCDBhZ8ivQOW1c4KI6t1auxffBeMFWcH7wvACDpwK+3CinSft7mhIJXpFjf1jwUvMqOJ+0Efg8/uMI+au0Ac0bYTvGoO251wOrGQ9r2rw4+KxYUrNensR3QuMKOPiy2Xr2qgifgev01Icr7yIOxX5975+0hUn13i9gmTqxsC4SzNrfpsYtWe+77WbFis4P7yy9x5/5K6tzfif24SMs+eH/WQBf82LLntSTwCwiRwkpq60MPPVQWXojmsuaTSB9OIr+sKyUdlJ+sAeXLq0+ndxB9zz33+Ggk0eLHH3/crxGmLQbQmjZt6tcZA0+AcbbPmsBvYmAbwAbMHnzwQV/FN9tmeTqBX6LbQCFrpZP3OA/mjmgwcw9Uso02zCGReNK1iS6SYg7QEz0mmksEMjsPwC+pz02aNPFFv7i5UCqKyTbSv1m/y40CbmIkfQDNf/vb3/x4SH1O1ggn8Ms6asCdf6v4PBgT6eRcL8w358m40+dZn81cPPHEEz6iDdhzkyB7UyFtMgzIcmB+SAXnRlByrWfbyktvwW++lIZf9EVxG6LA1JSoMrKbhV+irj9FASpLuWVl07LwW6racxp+AXK+qKWPlRawmoyfdcDzUu+lxXkwJnR8FI7Zt7itlE8utl2cWkQlvlTKsizXRy8u8stjh57o95qt9eBOPhV5y6cPtpHTxnoIBYw//L6z/eXh3RaB30nOx713qU9fBpj/zQHwOg/u7NfxJqZCNEC924vuC/eYvr7YVdLnD79MsCPfucDvT2S57/hBvnhWdmxL6lLwS5XlY4+qsJOOr/CgOWligETglzTpvn0K1njvkBbdpGGAU95LfOiBFf59wJF1uaXg99YlgN+mxWrPSVSYiC3rj4HQ116OfeQV0OXYrD0mHZr1xtnjTXH9tXwiAPKtN8f2afeCj3Yz5jdaBYhOt2ceml0RUqCBX9Kqu3WN/Tlvtak7932qnjvnRhSdiDjFtko9zmlJ4Jd1pEATkEp0ki+e2TaYL/lEEO+++24Ps0QaSTGmaBDgAxAAQaSCck0DSqTpArdAHhFg0mYTEw2mX9YBsyaVpQBZkFge8EufNanCvKRO4JfoNsWksuuoSXdObiRwU4HzB1yZl80339zPKWnSpKKTUgz88pr9svMA/LJ+lbkC0rLzmJhoZ6tWrXzqMn0QrU36IFX8r3/9qx8v10TSRxZ+iV5fcMEF/vNm3BwTKCRtmucrZ+E3rnCmkjvF5uaH19lxLWJ3rcYLQvsKsioo7FbN9btUfRTN9bLlllv6OWcNb3ZeF+nDzSk3FQYOHOjnIFt8TK4dC37zpSz8NovClyRSn4FVUokTZeGXyGl6bW5a2TWzSwu//xGF5xCTCp0VFyFp1wAworAWkes/LWwR9O9ROF6r4uu9onCOty1sUanfRCGdWpKknMpyvOZ35rzZ9syAN2xtB6/A7+ZPHeirLgOr0+fM8FFgYBb4pbAVjymaMvNnO/fjG+33923rU6IpjnVp+9vsxq4P2o3dHrQbuj5gzTrdZad/dI017/6wjyAnFZwXuJ/ANYW0gF/27T12gH9M0oK49OORaupy8Hvc0RX+ET9jx1aF38/7FjxAEv1kveuLz8X2youL+uUXQjR05MhKaAV+gUj6uumG2KciJ+MAAKncjBMYTOD3xOMXfc7vjw5+n3piUfglvblzxwCmJ50QO8irWnGZCDaPGmLs99wZ+8rOV10e26EOWEmJpphXejykTV90Xryw4BXw27177NcGN9ozrPUtd+4ffxTb6FHhUUjZOV8S+AUqAR8ih0QjSRHOtsH8vbFulqggkEY0k2uZlNyzzz7bHwOQZf0tX2BJ5SUVmqJIpNLSnmhZ2vQHjPXt27cKHOK6ht8kEkraL9FQxsHr2ljHmcAvYEVF5PS8MpdAJfAP/PI7KcjMIxF41i8TJSfKyhpfnkdLujmFsMrBL+t8udlAqnN2LIkHDBjgC4/RB8WtKGjFzQqAmc+Cz5B1wjWBXyKgQD2p2uXgFwid5f6+J3dzfxsfFmzKp+71+ACo2bElnv+rg/TBBRv/cfAv37r5YtlBmWuYPmZyA62r66O168P9zc2eUH0fiZlX1isT9WY9e3VFr7ihQwo91z9roflbqI3rRF7Ugt98KQu/6zr/r/ObUfjStGvqvSz8kopMm/1S29Dmzv8dLQqsRJSB5bRqAr+INb2kM7N2Ny0KZdE/ha/Sr3kucVonFbe3Kr4GqIFmxvPbpFFRLaLQ9sTMdkmSciJbReB3aao9k47cZXQf2/DxRr741JoP7GDXO3jtOLKXvT+0gx397kW+IjOQu8PzR9inY76wn2ZPtce/eNX+8kiICO/w/JEOkltbvwlfOnD+1jqN7OnX/l7W4Q7/GKNJM6eESPKCuf4Zvu8MaWd7vHy8X1/8j0f39JHnTqN6W/8JX/t066WNcpBKTEoxa2EpDvXjjyHtmeJUJ51Q4QtCTZxQCb/t28b+kUDnnBmiu0Ajkdjk2bjz5rovxNMKvjDWF5+HVOIEQgHULp1j29/BJ9WUEzgEiqnSzD5DhwQgBwiHDo3twvNC5Ld/v8r1uxSSevKxUGUa2KT6MscfPCh2oExl6gp79eUA18mzdzlPHn10xikhMk269Heur4fuj/15sFZ40IBQ0Ip9AGyiwYz1cHfebT8O8DtoYGxnnRbWAVMhOn3uRJ8Zy7ffOFjvH9YZpx/3lDxHmDXLgPt5Z8X+fNmWhv60AT8isURyeTQX0a1sGwyc8kgdClaxJpV2fPknPZZoGKnLwNPtt9/uwYB0WI5HxJfqw6wRpi37YIo8keoMJPL3nKTZpp+JC/yxL0WXiMwRbWN7+pmy/OQ1EEbkmkgmKb3cYEr6KhfNpU9SsIFK4A0gZD6SsSyLE/glsgg48ppz5ticLzcGWGdNm2QNblJM7O233/bzSxEwIJ7qzFSOJu0ZSOc4yXlzflS7TiK/3HRIotjpecKkVAPjGLgGMpJHRZECzDUA1CZFuJg3AJnIM2nYrKsGlpPIL++lI79kBaRvOsxz1+pod433Oj62TnvE1rtpbGPfd9tTFdSznjogtsHXx9alsXMj92/Bne7vfUiIAmfb4rmuj1Hu76TXscU+Tow9BM8rsRwga+aVQm/cMEkKtWXbJGZegXvSvYmcc5MH0C93bclLZ8FvvpSFX9QnCl+exmS2Z+GX5+TOjMJ62vOdN3U+IQr78ViiPSqb+vW4ADHVow8sbqsp/O4ehejvyCgALv1cHIVHJ01yXrvYjouSNcTsy7OHiV6fG4Ux0ncCv+jSKJwjqc9NovDoI551TDueJUy0WJKkHMpWcvhdluf88oV13K8T7aQPrvBreIn+rvXAjrb3Kyd62KXqM9tYv0tq9Hmf3OQfWTRg4jd2yJvn+OgvlZq3euZgO+bdi+1EdxzAllRptu/8wjE2bOpoH/kd88t4u7j9bbbnSycU06w39RWlt3v2MGv46kl22odXW48xS1fwCsgjyvvQA7F/VA9Vjvv0ChFPIpNAYod2rKdz8HtHWCv75OOxfwbuc8/Etl+jCt/uhedi/2xcij8R8XzmqdiaXRYqR3frUvn4IgCvXz+KS7EmtsIfo2P7go8Q87ijKy+P/drb8WNDEak27ljHHx2qQr/RKlSNJoUaqL7u6pByzVrkwYMCeBKhBooZF/DO8Ult/qxP6OOKS0MRL85z8OCQktyxXQDRfdx21gO/81bBPnGgS2SZtG2qXlNA6/HHQpSWNHDmgOgvRbd4RBPnzpxtZG8AAIAASURBVHpjim+RVn3FJRV2bbPYevYIlbE5d85nsAPn7l1je+KxEKE+6vDYXnslbBsC9Kce85SYAlVURd5uu+18+jGRz2wbDMyybpcoIMV+iBRS7IhoIgCZrAN99NFHPRQAYEQQifqyHSimaBBRM0CW9aIUHKIaMxCSgATwxOOPuFlEJJe+kmgu0Tm2M+Yk7RQoJxoJkAPggBqRU9Yg0w9QA7iUqiYNIN90002+UvIf/vAHX/iIY9dGRC+B3z//+c8ecllfCzDxqCiqOBN9JSX84YcfXvicYs6V7VRfJjLOHPE8WqK+f/zjH310/oEHHvAACiAQGQdogV4gG4hnzrhJAUzzfjpKDNgT2aUPxkPkE3CmcBWf65prrukhlrXfzClRZ/qjqjOfAWnOFOLis2ZMjJ255bPhXIDi9KOupo9wf+u3OSjdK7a2W8fWfid3Hd4for/lIrnj3DXe85jY2m3v9tkmts/OcH93XYrR3xLtpw931/DNAXyTPoY+5K51sj7K9IEZIzBLOjmfEzdnqitexbVLZsNvf/tb+5d/+Rc76aST/LVf3T7yklvwmy+Vgt8EDFnzm1YWftE2USUsJwZ+D0m1QQdFoUAW779f3FZT+EWNo8pnAGMgtU0UItVprRmF4xO9ph0RYx7JxOOTWqXaofOisGY5OSb7fBKFIluSJOVUthLCL9VW+WLLF6ls2yU1Kc7dR/e1Yx28/vWR3T208vih1e8Lz+ylaNXvHOT+zoHwLi8eY8OnjvHp0m2GdfVrf9d7bG+fwkwkFwPE6z22j+358gnWoldLmzrnFw+/X0/+3j9DmOPSR9pElzd/+kD/rGHWIWfHuDgDZG+8HmCUR/oAhqTyktZ8+skVvpoza2IHObjk8UC8zzrgzh2JDsc+dfiQAwMc8nxc3gPqeI4va19vvD6s1U0XniKKDGwTnaXq8yH7h36pDk2RrfvvcfA7jqhvwS6/JDzSaLutK+ys02Pr0jlUY378kbD/tlvQD88PLviUZeB6+LCCHxeR2UZ78Yze2FdeBrZ5fdnF7jidKotnAczA+9EOgKnsvPfuwHPs1/MC/+zDY5maHlfhC1oR2aVydYvbw6OTWN9LH4DyvnuHytSHHxQKX1EYbO6c0M+Qb2O7tXmA8iaNwtg5LhAN4D/q5pe5yX5GRF95LA+F14AXYKZUlJ9I5DPPPOPTmFnrCyyR0UBUFjDi9WWXXebTdxPg4ossVZ2BC6LCRJgpckXkjEgm2wBv1r7zhRcgIeoI/BEFJS0YUAPMgVqiokR2iT6TggukAmhAGBFLwA0I5MYTlXnph2JGQF6pQl4UOQLO11prLb8uGXAsF/leUifwy9hZW0tUFQhm6QNFlojUcrMByOS8uelAajjzQoVonr9LhWd+MufAJftxLG4WAF6cN3NCG86BRxDRDkhmH95PP+KJGwtsY34A/nQffBaMdYMNNvBjow/mgygxAM/jjvhcSHkHnDkX1gpz04HIb7K2G8BP5nDOZPf38kxsPQ6PrcOusXU/JLaRL7ntUwplwZS05X4XxdZ5bwe0e7pr/Dr3t+f+xiuK13nWs10fw1q6Pg4t9uF+jnzV9fFT+T4wNz74t5p5ILuAdPHqorikO3Nzgbng33nmgM+uNm6UyJUW/EpLI9bZbu+8XvaNlLhoGkTLFlX9RxSqUCfR3nJax3lL5zWyb5TQRlEY+x+zb0iSlD/ZSgK/iytetSwm/bnfhK/t9k8ftzPbXGdN37/cLmh3sz342fP2yOcv2xUd77TLO7awlv1b2S9zwprJuRXzbODEb+2+Ps/a+W2b20mtr7RTPrzKt7u/z3O+WBaVoZMvxD/PmmZPfPGaf//i9rfaJSnzzOB7ej9tQ34csVTFr0jj5Zm1d7WI7eYbwxrWV14Kjzt6vVUAuGeeCs/C5ZFFgOnDD4SUXlKTSWkGCNnv8osr7JILYrumWezTp9t8FPZLP7oIA6isI371ldhuaR7bVVfEdsO14Vm8HdrHPrrLPmNGh3W9PD+YsfF+/y8KNs7t+8H7sU/DZjuPTvrow8rnA3P8CeNDpJfxX3lp7CPQ9PHc0+HxR6RZJ+Mh1ZgU50/axHa7a08FZ8ZEfx+1DpWsiXpTwbpH95D6TPoyj2V6+83YF85iH/q49irX7v7Y2n7ivrzz2KZUKujokQW/Ppi5Ytxps+2dt0LKefYzSh5hBPgAOsBrqesYoCWKCngSKSRiSjovqcLNmjXzAETENrtuknRkooVAF+0AKCCZY9AvKdNEb7keiaIBjRyL583SR9akC7NOGFjh3wCipgA264+zbTGACaBlMzDoj/WsQB8ww3pbxlJbMJPAL7APSBIpp0I20WnG1KZNGz/29L9jrL0ldZvINVFgIqxUUGb8rOvlJgVRXtbaUnyMf3NY5wuIZQ3Y8T7Qn76Zwc0NbmJwo4ObF/RBdJd0auaVNdxJH4yRiD0pzxyT1Ggi1Mw3Y2FsnCefL1FUHpVF1JnPnL5Yj/vLNwUb8UJs37q/7VGvuevtu1CYKjtfiee4a3RCh4INdX8fQx+MbXKPYpp0GZClj2lfuz6eD32Mpvr7sPJp0onJIGC9NTdXAPbs9ZE1QE+aN+fIHLF/8nxmufYs+JUkSZJyK1sJ4Lcmxatqw0Rop86eZj/8Ot5mzJtZowJUfOElEjzu10k2YfoUB8W186V+eRt4JLLL2lfAlVTibKGpUk72mzYtpAaXWu+6LE6O/9OPIbrLc3ir64P3SDvmPIjuZqG9lNlnDvtMDjcCanruS+pBgwb5R94QVSRVny+g2TaY1GFgGTjmd4CAaCLgALiWihgn5u+VdrRP1rNW176uDcwDgUS8AX9+L3feS2rOi+JZRJWJPAOvzFt2DW4pA998+Weekn2ybZbV9JF8dvRRXcSzNgyI+krMFVXfK+m4uA8AW83fVNoV82veB9ce1znFrrhBsbjHHCXms6Pd4q51eekt+JUkSZJyK1sJ4FeWVwUTqSPFlYrBRAJJ51zV/75YY0wEljRpwJ8IZm2t3wQsSQ+m2BfrRImKEhHt37+//3K/qs9tffewYcN8BgLp30SzayvVXV52C34lSZKk3MoEv7K83ExxJNKYiVRSLCmbvryqmS/YpAWTwsrP2gQgCkuxFpr1tBRUIrLMGlzmN70mWl4xJp2b9bukd5P1UFs3PeRlt+BXkiRJyq1M8CvLy82kcxL9ZB0u6zpXdfjFnCPQy5rj2kz9pRgVj/1hXS0QnJgIMHMs+F2xpuI4a6tJd67u8Uby8rfgV5IkScqtTPAry8vVrGPk76o2QTCPZv5YVwpUs6YW8zs3GLRWdMWba7wm66/l5W/BryRJkpRbmeBXlmVZlnNjwa8kSZKUW5ngV5ZlWZZzY8GvJEmSlFuZ4FeWZVmWc2PBryRJkpRbmeBXlmVZlnNjwa8kSZKUW5ngV86xKZqUFOZZmgI9tE0fg9+XZH9ZluXlbcFv3Wl357Odd8y+UYfaz7mF81HZN1aAyl005bantX0U5u6PxdcbF1+vt7BF7akm45EkaRWVCX7lnJpH8AwdOtS6d+9unTp1ss6dO/tH5PClcHGVmAFcKg2PGzfO+vfv74/RtWtXGzhwYI32rwsnIF5T+E7ar4ixyrK84iz4rTt952zOA7Nv1KHWdB7mPN3595n3lqcaOXfKbNvcuZfzHzLbS+nWKMzdlsXXpxdfH7KwxbILsH7e+cjsG5Ik5Ucm+K0TP/TQQzZ+/Pgq2+X64x9++MEee+wxa9q0qR166KF28MEH2xVXXGGff/65B9ts+7RnzJhhn332md1zzz125pln2tFHH21HHHGEXXbZZdaxY0f/yJ3sPnVpQHbixIn25Zdf+utu/vz5VdpkzTkA/999910unjcsy3Kw4LdutFsUYK1j8efOi75dpyJKOsP5uuwby1G9nSdktrWMwlzUBH73jUIE+8/F13UBv8dG4ZhHZ9+QJCk/MsFvnZipXW211Wy//fazl156yT+DNNtGXrGeNGmSvffee9aiRQu75JJLrEmTJh5iO3ToYLNmzarSPjGgOWTIELv66qttzz33tKOOOsouuOACu/DCC+3aa6+1jz/+2H755Zcq+9WlebYt/V5zzTX27rvv2tSpU6u0yRrovfPOO+3ee++1YcOGKQIsyzmx4LduRETxP503cf5v55cWfbvO9Sfnv2U3LkctK/xmJfiVJKlOZILfOjFTm/bqq6/uI4wffvihzZs3r0p7uaqBTK4/ophZl1ubW2qfZC1utm3SnjZEQK+//no7+eSTq4Vf2s+ePdvatGljBx10kJ1wwgn20Ucf2ciRI23UqFE2evRof6OjXH81dfo8uF4wv5dLa6bPJ5980ho1auSBlugv7RJn2+PevXvbMcccYyeddJL16dPHA3S59tl5TcaRrHdOjy+7ryzL9cuC39rX6s5znLsVX3d1XhCFlORSIkr8jvO4KADjR1HVdcJ/cX7Q+fsotOnnfOYiLYLOc/4iCm1GOD/svNYiLaLo71GA85FRaPel843Ov0k3KiPW3D4RVY6DMb/vvFOqDenOs50LUYDgC52fcZ4ShS9BfZ0fKLblJ1AMgDKeb52bOJ8RhX03LLZL4PekKByLfn9wfi5aFPL/LQr73ZDalug155eLv58fhXPgmPzkM5IkKYcywW+dmKkt53XWWcdHCXv27FllPzmY644vaH379rUPPvjAXn/9dXvrrbc8dH7yySd+fe2UKVMWgS32YRv7tG7d2lq1auX3YS3v8OHDPbSWAjs8YsQIu+mmm+yUU04pC7/AIVBJujPR4r322svOO+883561voMHD7YxY8b4dtl9l8ScBynMwCnn/vLLL9tzzz1nb7/9tl+TTFQ5OY8ExknhJoK7++6723XXXWdfffWVnws8bdq0hWDLPkAqsNy2bVuf7k20m6gx65dp/9NPP/k06PTcTpgwwa9rZl6Bfc6Vtvwb0b59e3v11Vf9XA8aNEgp1LJczy34rX0BbvwHn8DpqcXXVyxsUaljohAZBuZaON/h/FMUYJmiTwhYneQcOz/rfH0UAJJj8nuiF4rbOjtf4/yk87worAFOwPvfowCZvzjf63yl84dR2O/VYptyAjJ/jkJKNVDdPArjqYhCP4wT3RwFMAaAOacDogDlrH2mn/udTyu2JS18rPN85yHO06JwM6Dcmt9ZUVhLzXk/HQXAZu4SwAfgafdK8XVaQC5wjfaPArTT9j3nW5JGkiTlSyb4rRMztTXxRhtt5KHr22+/rXKMPBv4I1389NNP9xFWfMABB/jU5IYNG9qpp57qITcBTSKPpPE+9dRT/j0ioLvttpuHQQCvefPmHt5Yi1sKgGsCvwAgkHfRRRfZ/vvvb1tttZUfy2mnnWbnnnuuT32m/yTqmt2/piYdm3M/9thjbYcddrCNN97Y1l9/fdtiiy18X8B/kkbPPJFNcPvtt9shhxzi23HuzZo1s1tuucX78ccf9+uYgVLmCUAlSnzOOefY5ptv7s/jjDPOsBtvvNG351jvv/++H0cS4e3WrZv/LHbaaSfbeeedfXo1Y6QfbgJssMEGtummm9oNN9ygfzdkuZ5b8Fv76hkFGEwKTv02CiAIhKa1WhSioeOjRaOzmzn/VxTgDBHp/L8oVHJORIRzcBT64fgAJl8kiKKmtYfz/zg/VnzNWlranZM0KKq986goHLecgGX2bZzZflFxewK0qHdUs7TnZE30bcXXSf/l4PebKETWE1HVmu33FV/XFH6R0p4lSRL81pGZ2iX1jjvuqEJZhQCygOrhhx/uIfOuu+7yUVzA8vzzz/fzdNhhh/mIbhLNJeJK8SlgFFC+6qqr7MEHH/Qgd+KJJ9quu+7qAZX03lLFrGoCv3wur7zyil166aUeNIHGvffe2wMpEWCi+bUBv4yFKC4pyQAn64hZX8z6cSATyAb0AVNumgD2nPeGG25oa6yxhq277roemjlnfPzxx/uoLOuAOXeit4A141977bW9AetddtnFt99jjz38Mfn7T+CXSDLFwdgPwN5mm218n4Av4+SGA5/XzTff7MdUk4JbsiyvGAt+a1f/jAKoZqOopBnznztVkBPxKCS23ZTalgjQ3br4+49R6YrR2znvFQVgBPY41l8XaRFElBjIRkSTadfHedekQQ0FoKfTmxMdHoVjXpza1juqOfwyX6R1p1UOfvmZFdBOijeqa/htEZX4wibLsizXnvNeKIsIJYWogCsiklQwBnLZTtQSGAbOSL0FlHmPFN4DDzzQz9vzzz/v04ABPVKESctlbS7Fqe6//36bPHlylT5rAr+AIMcDComc0heRT9Kg2Z/1vnyhXNYbRpwnsEnV6B49etgXX3zhU6A5780228xXpf7000991Jvrg3kgUgsUA7SsW+baISJMijLHGDt2rJ8rQJb0ZiK59913nwddAJ4bB8w5+xBZ5phUg86OjfeJpgPMjRs3tieeeMLDLsck9Zu100p7luX6bcFv7eruKPzn3ToKoJT43eJ2fiZK0qGre9QOUV3aZGE6K9b5/m8UoDNrUok5RhIxZd1r8iWD995ybhpVH/VNxOOBzo0CyHaIQtQ6OdYlqXb0OyH1GpWD37mp14nKwS/An9XHUUgd5yIV/MqyLK9CXmuttTzYZL+8rMoG6rp06eKjqwAWUEpElagrEcw33njDwy5f4IA50nMfeeQRn5JLWjQRX9YIv/nmm94AGtFfopUAIoWpsn3WBH4TA9tUVCb6TGS5NiP1nA9rbolqE/0lmgzMUpQKuCeqy5xw/mnITApe7bPPPjUeU69evXzUlrlh/XlN1ioDv9xE4MbEa6+9tvAzyLaTZbn+WvBbeyKNOVmbO6GEWcdLBegkOss6WP5zP7T4upSIttLmxewbGZECDUS2qMa/W9g6ivZ0ftR5eFT5JYPCV9U9G5hI8a9RaMu62w+iELVmLTPblhZ+OWZW5eA3eZ0WNxqy8FvqZgHnKviVZVmu566VyG/sQGqB8/ziz4oSbTKmTXofjpFtU20fi2tfQydpzKSA8xgh0pxJyyWqyXpT1vDecccdPgoMsBFxZa3qdttt5w0kAqaJgWigGGhkXStR4WyfywN+09WRywEja5JJS+aZwayhJcrKHADArHkm5ZjzoEBVOjKbwC9R3JqOaWnhF/Bl3TOPRyp3HrVhfz1ybWGu38VdX5nrcbHtS/VRoo0sr2oW/NaeeAwP/3En60+z4rm7vE+hKJS0pxpyVsAeUAlQUwyqy6Jve+0ShUJN6zm3jcI64VLwCmxznEQUrvrX1Gv2p+gTYylVQToRqdfpQlyJKNrFvpemtvWO6gZ+D1zYolJfR6HoFSJ6TTui2VlNjZYdfiVJWsVkWvNbJ2Zql9S1tubXfemfN61gkz8t2Ji3Czb+YwdH3xesYl6JtkVXzHHg9X1oyz5Terlj/BKOlW2b9DF3qmtHH++4/T5x+w+rvo+ammuOaC7puqTiPv300z5F97bbbrOLL77YgyxrTR9++GH/JY6UXlKCSeEFElu2bOnXCGdN1JgU5VLpvHUNv6yBpR2QSVQbcCy19pi04csuu8xDPGuUOX9SoAF8MgAAedKes/ALNHPeRGVZd0sacvJeAt3ZxxCRSn3cccf5NcGkQafHk7TPwi3wu++++/p1z8xZdvy15fnTCzbty4KN+6hgY98v2NSB7nr8tVDt9ThrXMEmdSnYD++GnzN/qP56pI+pg93xPyz2MThsK9uHLK8iFvzWnihQxX/gW2XfKAoIBVBJFQZGqcAM2PZKN4pCqjMVn0llRu2iEE3+x8IWQaT2EvFcNwrgSd9XL9IiRI6pzgwc8iFeG4V22Wgzqddsp3hVORFZJnU4K0CTfZultlH0a3LqNeIRSbRLF/daUvjlcUVpsQaZNcNPpbZRBIxIeFpErdk/Db9AL9uOT22TJClnMsFvnZiprYnrotrzAgeyU3oW7LPTY+u4e2xdD4htyAMFm/Nj1baJZ08s2JD7Y+t2QNjn83PdF6TeAYqzbX0fsxxgdKvso/tBsX33iOtjStW2S2rSeTt16uTnBdgiDZjrEDgbMmSI3XrrrR7yqDgMUPIoH6owA8WkNQ8YMMC3ZR8MyBLtpeIxIJmAbToSC4wSFQae27Vr52GS7VkA5DXQyWOHiEAD5MB30lcWMBNTKfrZZ5/1RaGInLJfqb8vxgiM0oZHHPEllbW6rFMm7RvopwgY88IYk/04Jx6HROo3YMr5ANxEc6kIzXpc5i4N9fRF1JfzAKyZx+QxSEA4kXX2TZ7fy1h59BLVpC+55JKF46/uvJfW076K7atb3LW7X2yd9olt8I0OTgeUvx7nTivYmLdi632ia79HbD2PjW306257Ndf8tMGxfdnc9dEkts4NXX/NXR+Dyvchy6uKBb+1o3WikNJM6nB1+iQK/+EfVnzNY4F4DUDuE4WqzZ9GYf3uwcU2O0bhkT6AJ9FKCmXxuCDa8KghxHpeUpGB6xZReFwQQAsEcvyTi+0AaCCWAlhso6gWFZMpGEVF6vQzc7PiucWAJs/Q3SQKfQCjRIPp4/bKpj4VmfE9ElWeKxWdafe689nFbUsKv8wD6dr0zZppioHx2Kb0jQGek0xbngHcJAo3BljbnI38NoxCuz5ReMSULnJJyqFM8FsnZmrLua6f80vUd9QrBevsoKHt1rG12y62PqfFNmNUoWxUi6jvZ65N++3DPl33j23UqwWbT7StRPu5PxdsxAuuj0aVffQ9x/UxsnwfNTVViV944QUPejyD9plnnvEFnvr37++hj5Rb4JAiTUAhcEZRqDPPPNNv53E7tGMfil2RPk6BLIpnPfDAAx7ogDX2JfWXSC+gSdVmUqbvvvtuvz/b6TN5ri7Q+8033/iCUByP8XFMosqswWWdLoBJZDh7ThwH0FxzzTX9NcDaXPbJRpiBcyK+pHgzHs6d8ZPWTSo8a8B5j4gzx0z25+8UgCVNmmNT2AtAB4iZD/rmvIggJzDPWC+//HIPzPT54osv+rXSzBGvibIzRqLwtGUuORb9A8xE5IFhimrV9vN9J3YMINuueD32PrFg49sUo78l2s901/a3dweIpX37HWL7+o6CzRhdKHs9Tmjnjtu08prvc7Lb1rb8NS/Lq4oFv7UjnpfLP+hXZd/IKHk0D48WSkQ0dnpxOwboTki9jwBjnoObtEkgMF2k6s9RKKgFhCftgFyeO5wWxwKU019GgGSAsjpt6Nw/qtwH0AY0qXBNdPnzyqa+j6TQFinZaKMoPGOYbUS/0ZLCLz95LnAyhn6pNol43nD3qLLNzChEtEntTsMvc8fYknYNUu9JkpQTmeC3TszUpr366qtb06ZNPVQRycu2r037qGzngvU6LrYOO8cegr9sXrBZEwplQWDm2IJ9eXOAhw67OCg4ObZJncKxsm3x/JnAg+vj+GIfDoK/vs31Mb58HzU1sEkxJdKYWetLlJdoKHBL5JR1rdw8AG4T+GMfoPTss8/27YFYHsvDT9YJA6rsD9wB13wG3Hzg0UmkEZPKS6rxlltu6fcnukqKMbAH+AHYRI953A9rcHkkEJWX2YdIKGDK+lnW3ZaqJk1kn4jseuut5wGYsTH+bOozX0gBccb717/+1Ro0aOCzA/75z3/686bf5FFGFMQiopzsyzivu+46X9iL/TgXHo3EI5C4KUCaOOnQCfwyZ++8844/T9oz15tssok//tZbb+2j6F27dvX/DvCsYOaSZw4zfsYGBDMOnqfMGuw0WC+rf+5XsIHNwrXbcbfYBlzuvrD3KX89EuEd+XJsnx4drt/uB8c2/NmCza7mmv+pb8EGXOH62DtkLwxsFrYtmF21rSyvShb81g+RBt0gCoBZnQC7BlFIjS4nCls1cF4/sz0rHi/UICr9eKTqRPsGzmtktpdSOsU5UaltS6oGUZiL6sTNgAbO/5HZnhXvVzefkiStwjLBb52Yqa2VwlVLY9bjOhj44T3SnR0EPONgopqUUQxU0Gb407ENdfuMbR3SpMsWsXLbZ09ifWXoY8RzYV1mdX3U1ABhv379fGSXtbyAFSnOV1xxhYc7AJTn9WajpkQeiQCz9pUUZqKaPO+3RYsWPi2a1F9ShQE0YJYoLlWkeZ9IatZsB8KBXv4O+MLIWluiqtm2mHXJREpJH86eE/0Cu0RfSecmYsqXz1KwSCo3Fa15jBLATgSWaCyRaKKsjIvzYz0wx0j2Y4wAMNFizht4vfLKK331ax6bBChnn79LSjlp3swxEXVuKrBmmPlKUsRpw3lzjhw3a55DTJQZ6C91Pktjv2a9R8GGtYztu8dim9Q1ZBuUA1mKVbHmfPTr7vp9KKQ8/zq0+uuRNeuTu7s+nozte9fH5G6u32r6kOVVxYJfSZIkKbcywW+duFYKVy2jAQLW//rKtyXeL2Xa+n1qCABL00dNTGSWyCRrVjFf1EhXJmqbBbisuWbZl/aAGzBdW1C2rE7W4S5ujWz6HIDpJfk75FyBVvbj5+L6wsw3N2hw8jzgbJvlbncNxvOCCzWsxEzV5oq5S3A9pvuo4TUvyyu7Bb+SJElSbmWCX1mWZVnOjQW/kiRJUm5lgl9ZlmVZzo0Fv5IkSVJuZYJfWZZlWc6NBb+SJElSbmWCX1mWZVnOjQW/kiRJUm5lgl9ZlmVZzo0Fv5IkSVJuZYJfWZZlWc6NBb+SJElSbmWCX1mWZVnOjQW/kiRJUm5lgl9ZlmVZzo0Fv5IkSVJuZYJfeQkdx7FVVFQsYrZl21Xn7DGWdP+VwdlzzL5f1872X1tzzHHSzr4vy3L9tuBXkiRJyq1M8CsvgYEovjj17NnT2rdv792lSxcbMmSIzZkzp0r7rIGlX3/91bfnGJ06dfI/hw8fbnPnzq3Svj66puA3ZcoU69Gjh7Vp08b69u1rv/zyy2L3qS3Pnj3bvvvuO+vQoYO1bt3aevXq5cezLBDO2PmMJk+ebF9//bX169fP/xsxderUGv87UdO5q69emccuy4kFv5IkSVJuZYJfeQk8b948+/zzz+3ss8+2gw46yA444AA76qij7KGHHvJfqLLt0wa8xo4day+//LJdcsklduyxx9ohhxzif9511102evToZYKz5WHAB/gbOHCgPxfmI9smaQdwnnLKKbbTTjv58/3222+X298TY3zmmWf8Z7T99tvbOeecY7179/ZQnG1bE3M+EydOtI8++shuvvlmf17HHHOMnXXWWfb444/bV199tdhjc4xx48bZ4MGDq527+mpu2nCefI78LgiWV1YLfiVJkqTcygS/8hJ4/vz5PqL4xBNP2G233WYXXnih7bzzznb55Zfb+PHjq7RPm8jnm2++afvtt5/ts88+HpyuvPJKu/TSS+2+++6zkSNH1nv4BdiIpnK+r7/+uo96ZttgwAhIuu6662yzzTazww47zN80YP6ybevC06ZN81H5yy67zLbddlsPwR9//LHNmDGjStuamC/KnC+fHeez7777+mNuscUWtvnmm9uNN97oo/nVnR/gy3Vz0003WdeuXZd6LCvKXN+PPPKIXXvttT5jYfr06VXayPLKYMFv7es/nF9xbuX8j8x79UFbR2FsDbNv1FA3OT+W3VhPdKxz7yiMsZTWzbx+2vnazLa6ENcEc35W9g1JklasTPBbJyYSujgYrCsDXum1njVZ91lqn3JtMe+TAks0cf/9918s/HKsUaNGeRhs1KiR3XHHHTZgwAC/z5gxY2zChAlLHQksNfb0OWRf12T/Uu0wwPPiiy/a3nvvbbfccosfd7ZN2qQ7H3300T5Kyu/M2eL6SLwk4yq3z6BBg+zcc8/1YygFv9n25frgxgSAy2d3zTXXWMeOHa1Pnz4+Yr/ddttZw4YN7a233vLQnd0Xz5w507/PtcJND24ErCxp7omJ9hLl3muvveyqq66yb775plrYl+X6asFv7etE5/9z/i/n2zPv1Qcd4GzOZ2ffqKGAy8nZjfVAXKTDnMc6r515D93h/FVm2wznzpltdaE1ojDnz2ffkCRpxcoEv3Vipna11VbzkbKXXnrJf3HOtqkLAxTDhg2ztm3b2quvvmovvPCCj7aSrsraz/79+1dJT2WfESNGWLt27ey1117z+7z99tsebohsAkTZfjCQBMSQ+lwd/AJcQ4cO9f2fcMIJ1rhxYx8BBJwZDxFSvoiV66c6A1Vffvmlffjhh/bBBx/4n4z/6aef9pFPAJtzefLJJ+29997zEJeG7FmzZnmIef/99/0+wM0rr7zi1yHzBTEZE+fKPHGOjz32mO2yyy7WrFkzv/aVdhjwo00aHpkfwPeII47w42CMpCM///zz1r1790X6SMz+QDbnRfvnnnvO7wM8AtCl5orXrOn94osv7JNPPvGgS9+dO3f2KeqMIQ2/6T6YF86dOeLz/+yzz+ynn35a5G+f64CI9zvvvOOvr2QOWfd72mmn2Y477uij96RGp8eVmDm+6KKLfJbA3XffvdibBvXVn376qZ9L/q5btWpVNvIvy/XZgt/aVxfnIc7dogCJ/7ro2ytcqyr8NnH+wXn7zPZE30Thc0lL8CtJOZcJfuvETG3aq6++ujVt2tTD2dJGOBdnjguM3XDDDXbwwQd7KD3wwAOtSZMmPk0VE3FNr80FyknhJJWTtrvuuqvttttutueee/rxPvXUUx6MS0W4agK/XD8A1vXXX+/X9hIl3GqrrTwMAk2nn366XX311R6sslHJmpg+H3zwQR+N22abbTyEbbDBBvaXv/zFdt99dx/xJDV37bXX9n0/8MADfj0sY2ceAD/gkHWxG264od9344039im9jz76qP8bYF6Bunfffden7DK3//jHP/zxATrmDt97770ejtLnkcAvqcesb2b979///ne/P30Am4Bm9pzYzvwA2VtuuaU/B8bP+mpuHJB6nkROKTRGhBf45Hz5DNnvyCOPtDPOOMNHqUm7TsMv58NNEeaf43Lu66+/vm266aZ2+OGH+8+dImTpaxXA5vNMwz03F+iD64VxMbfZz4hxAu7MF3NXKgK9spjzu/XWW/01TLq+or/yymjBb+1qA+f/cX7I+fwo/Kd/zCItSmsT5wbZjSX0Z+dNnX+XfSOjvztv7vzH7BvRksMv4EafaxVfVwe/f41Cv0nbJdU6zps5/yGznXRljvtvme1pMTe0+VP2jaIWB7+cJ31zDtWJud0iKj23aXEc5o2U58XB70ZRGDttJUlajjLBb52YqS3nddZZx6+VJbqY3W9ZzBcaQHCPPfbwqa6k5xJtBPiAJiAHSE2ic0ATYzjppJM8lAGBABwRQNKTSVEFajjmpEmTqqTD1gR+AQOAHOgGkgAgQPC4446zCy64wC6++GKfPkzFaKK42f0XZyJv3FAgovynP/3Jtt56a59WC2Stueaa9sc//tFHmgHQTTbZxEM3oMj6Y/bjxkCDBg38+82bN/dpvOwPSHOse+65x69V5W+BNc4c65///KetscYaHmCBbc6JOeec3njjjUWigQn8rrXWWn4/xknxK8a33nrr2fHHH++jtQlk8oWUTAFgHug99dRTfaSUzxDQpj/mLykQxt8nRZiYf9oDvRSj4vPnc9loo43sD3/4gx93Ap2cO+MkhZn1u4yBIlYtWrTwwM1xuB6Afz7T7OeemONQvIxz5xxZx0skPduO4lbMLfPPuLgeVtZ/Vxg3Nyb4HJhfMiq09lde2Sz4rV3dFoX/3PeJAgAWohAJzgogpt2pzqOLv2N+PzTVDs1zftL57SikU9Ouwvn+qGpUeXfnr6PK4/2383vRomnANYVfQIw1sf8ZhfakcTOOz6Kq8Luj84Cost//dW7j/Jd0ozKibYsorJNmvOwfO1/qvL7z58VteGoU5jYtQLRXVNkGA7Tsi1bLvIebF98DfonQPxxVnifuEAUQT2s/5++jRc/xk6jquu6NnT+NKtvRx7nF37Pwe4bzT8X38PwozAVjliRpOcgEv3ViprYmBk6IJpL6mz3Gkpq1s0A1kT7SZPmCA3ySjksqLED77LPPLoQzwAbwJBUV8AXCAFBADOAj+gdMHXroob5ycXaNZk3gF3M84JmoKFWHiYACX6RCsw6YFFhSsctB1uIMhJEaDJBQ4InCS6TnEi1lGxHObt26eThlrIA2FYeBb8AXWCQFGzBM0saJ7hEN5QYA+xKdJcoH7FxxxRUeEImMA0LMLWnm3EgA9NKRwAR+AT8ihURKmX+OQ+os0XgiyglAkdbMOAFvouWAbZJKzbEffvhhH6VmXDxC6YcffvCfE+eZpNgTqecGB+M+77zz7G9/+5vvJ4FfKlUD+ER5eZ8++dy5GcJ1yJperktumPCZlXqEFZBLxgBjZTwAOuMr9RkmqdHcUACwubaybVYmM6/MDRF25n5xVc5lub5Z8Ft7YpJYbzomtQ3wBJIAorQS+AWOn41C1G+PKEQn/5/zTpVNPfzS7rsogB/RySeisP+DqXbbRgGKRzkfF4VjXhKF/Qc6/3uxXU3h96UotGsZBbht6jwuCueThl+im3Odx0dhvTOviXrPdB7q/NvKpiXF8WZHYYxHOx8ZhZsAwDY/X4zCmIFh5mFk2M2LiPCvUQBIQJII+pnOP0fhs1iz2K5JFD4XtvH7hsXtgCn9ky7NjQjeoz/O++ViG8S887nQN/Owg/NVzguicNykn9WjcCzm/MIozNs1UYB5jpmG3/OK2wB1io9tFwUI5wbH46l2kiTVoUzwWydmapfUAMyyFMoiJZOIIF/KiTDyO1Fc1sCyPpEoMCnICcwAOkRCgRJgiHWfrHfFQNTtt9/ux0Rkj23Zdcs1hd/EQBngmEQJS0HV0jiBX6KlnDPzwPN1GTfpvgAkQEzklNRuYJX1zbxP9BaAu//++61ly5berPsFjHmPKCsR9KSQE/DIayLi3LRY3Dkn8AuYsn43SfcF/IkAE3lmrgFioBlo5/MgMgx4Zm848Plxo4LILGnOzCPnxes777xzkfFwPIAX8OW8E/gFvEltJ8X7xBNPrHLuROhJzaYN1022iBU3KgB92jE/fPas3S73eQKLpFJzPK6xlR0W+QyIYHODguttZV2/LOfXgt/aU5Mo/Ad+a2rbwcVtpEGnlcBvx8z29aIAeEBzIkAKeOK9tIAm2iYpwu2iAL9JxDPRyVHo67Ti65rA79+ikL7dOrOdKCvb0/BLRBpQBXrTOiIK/Vyc2Z5VAr/pNOIkUto+tQ29VdyetCUyDSymbxagvaPQLonwonJpz0R8s3ML0ALzifpGISqbjfICzPRzS/H1OcXX2bkFlNmewO9vnH+JwpiyqdzPRWGON8psL6cWUeYLpCzL8srspS2UBXwQxaMSLUDKl3MijkQpAT1SaDkmUUzWbxIB5JFDrHEFkIjwAouJATBSYwHpNLglXp7wS1+Js++Vgl/AMRk7a1eJ5hJ5BQKBP9b6EhkmNTpJF+amQeIddtjBv8+cAKT0QV8J/DKfSwK/SbXnJCpMlJ5UY8bD8YBfoqncqCAV+eSTT/Y3n7LHI9LLftyUoPoyhbo4Rz4/otBZUCWaT2SbmxzAL9cTjyzi3EjFLnXuRHJ5nzXapIanr0HGyNxys4R1r4A4IFwq3Tkx8002Ap8PY6xxkajYfe4VzgvC71Xez5o2tK8o/l7DfZaoD2euJa53ouOsV+ezzLaR5fpswW/t6c0ogFwSVUSkrwKKRCfT6zkT+D0htS1R9yhETRMBv21TrxMR4eQYQCYABSCTktwgY4CVcTE+VBP4TaCu1Pi+iBaF31lRqKLcIGOi3cD5x6FZWTE2zjmt/aPQP9CY1iPF7UR8EZFoorkNSnh6FNYnJyoHv0Scs+KmBECOWK9b6kYAIu18ThTgGHEjgLbZNdmknQPpCfwmcM75NMg4+VyJntdELaISXx5lWZZXBQMoAE72y0spE5EjmkuFXyork5LJWlHWqpJuTJQOqAWIiCiS+kvkEeBhbSupyESHs6ZqMimt2SyA5QG/9JmkTDMOIqbZ/aqDX1KTS8Ev88MaaCAPiKT6MjcG0mY9K1Fi4CaBVuCX91jnS5GrbApvFtAT+AUk+b06+OUzIdIKkNKetcnZOQeIeTYykV7WUXMurNkmEl0qqso8UPgKiE/gl8+UawGABuDLnXvyuSdjBnCJqJ955pl+jEA1qc/MSfa80waguVnAjRausSygl/L86QWbOsjB/nturt4q2M/9Cjbvl0JZQAVgZ40t2MQOBRv9evg5c3TBKuZVbbtIHwNdH++GPqYOCNvK9ZGY65jPgAJh/GTtdbaNLNdnC35rR6S9EnUF9npnTISP/8RPX9i6En6zEUv0WhTeI4UWAb+l0mD3i0I7IqtEJKt8aci4Z9itRvB7QxTasIY4KyA6gV+iztl+sh5cbFtOwG860o2aRGHfLACS5s32BH6JOGf7S5sU5ETl4LdLZhsi4gzUIiLaHCsbvU9EajcAjvpE4fMuJW5oJPB7SlR1rFnzaKaaqEVUdV9ZluWV1ksb+WWdJymsQBFFhVhrS4SX9ZxAEJAKEAJBgAxf4oncpdcIJ8955SfrUDkO632BvASCEtChDdFMjstaW9qUi9DyGghN4Jd1t8k632zbtAFZ4Ax4AzhZjwoAp6EQmGItM/BL6m8p+AUaE/hlrSyPGSIVFwBMqhQn586x+WJIKi+wR7Q1OXfGDKASGWa9LHPIfrzPo4ZY00uadQKEzE868psUtgJ+KQLF3BHtpT+2My8UpyIaC8wC/sm4+DyIWPM+58qjrBgjcwKMcoMDwGaM7JNUwgbwucnBeTMubjwQ1QeYuWmQPXeuA47DOmiOwblxTOaUVOekEBfP+uX65LrjhgoVqLOP0cIAMpWtGTOgn61uXcpTv4zty+axdWkSW+e9Yxt8vQPg/g5m51Rti+dNdXP6Zmy9msbWaY/YPj06tlGvFWzOj1XbLuxjoOvjRtdHY9fHPq6Pm1wfDoYXlOkjMWu/SYtnzTSfYfYGiCzXdwt+a0cAKP9pUzypVcbAIlE/CjclSuB319S2RO9EIXqYFLMCfll3mxWFsTgGkULSlPkdaGtSxglo1wR+m0WhTba4FCL1OIHf30ehHQWempRxqXNMC/glYppWkygcd3HwS8oykdsmZbwnjYoqB7+lHnWUht8GUeiz1A0IxBrrZB0yEewkYpwVEfIEfk+KwjHviaqOOfE/I0mS6lymNb91YqZ2Sb2sa355hi3pskQFiUiRqgxwATGAFDBIoR6e4wqEAY2s5WU7Rah4fi2gAhwlVaIBPACZ7QAzcMOXf6KCwNQjjzzioRS4A8bYRlsANSnUxBct1n0CWrRr2LChj9BS6InIJYAJ5JW61uiLCCMVi5M5oo+kMjQ/AS+gGsijsBKRbx6dRKQRA4xAKWtsiXJT0RqgJ9pNajht+J3jEukFRokGM1f0zfrgJOWbMVLcinMGUIFtYJi11aTAJlWj+Sw4J86ZtpgbDEAv8Mf4iNgSfeZ5wZwDc8v7rLUmpZhxUUiKcTEG+gDYSVMHvogMEzEmmkpklRR3orK0AzIpmMUYecwWwMqxmE+i4PRB9JebLNwwoQ/MeNmPNGlgmrlkjrkJws0D1gIT8QR+2S+pDM658Hup5/wC0lyXFFbj+mZesm2yntgxtt4nxtZu+9jabu1+b1qwcW2K0d8S7WeMKti3dzuIbRjat9shtq9vd9tHF8pGcie0dcc9obKPPiexrWDzf63aNm3+flgrTXp4Ugk920aW67MFv7UjAIy046TwUVZ9ovCfO0WpUAK/ZycNUhrmPDz1GvgFLrO6KQrH2CUK6dVA16BFWgRRcApATvquCfwma5Uvy74RhRTnyanXFJsakXqdiFTss6JQHKo6LQv8Mlc/RlWrI3PBEmnfLbVtaeGX86CwFRWls2LtMdFnqkMjblIwvnTqO+LxSGxP4JcbEbymwFVWG0UBjrNrtyVJqgOZ4LdOzNTWxLVZ7ZnKycDP5ptv7osmATZ8SWdNKMBJlBFASaKMRPtIawVIiAQCSkAwgAWsAWa8pvIxKdJJ1WYipcAREUigCxgDroA1tpGyS7QWMOP6odov4yLyB6DSHhCiPWtVgSzWjZaKGgKRQOW6665rv//97/140hWIk+gmIMf7VGimPaninC9FqyhORESV5x9T3TlJfWZczD1AzXbOH3MMII85YB/AOZ1qTbSXKtnMMY8rIgLI+PidGwmMh3nlnHjuLo+2In2dOQOuOR7AyDzwyCTmmSrOyQ0ACioB38wRj0diTMwz/TB/VPTmZkIC5ESmeSwRbXjcE2PhfLgOOAZQzBiYa0CdL76s9yZtG4BjHPRBW4pgsT+fFenyfO6cO7BOhenf/e539tvf/tafD8fE3Jhgzsg4KBUFBba5GcN4AH0+0+qi/Zgo78CrYuu0V2wddo1twBXuC/tnBVswq2pbPPdnd/2/ElvPo2Jrv1Ns3Q6KbfizBZs9sVAWfn/6vGADriz2sVtsA5uFbQtmV22bmHFzU4lrl4yJJVrDLMv1xILfZdc2UfhPPJu6mxYQSBsqO6MEfgHJdDVkqjSz/fbUNuCXRwClU5B5DM/EKFRDTj4cqhOzL8dIi2JMbKfqMKoJ/FIZGqjk+Gmg3z8KUew0/D4RheMB2Gkl0eP0uZTSssAvqcG8zq4NTtbNpqO13BhIp0GjmsAvejMK4yTVPK1kDTKfL9ojCvOTrhSNHotCuwR++cyYW25YbJw0igLEk4bNMbipIUlSHcsEv3Viprac6+o5v3wJ57E+RFWBKWAEwCEtlgglkVbgJP2ZAsCkvQJlRHqJ+tEe4AVySW1Nr/tkfS3biPgSQS1l9iOtmIghxwd2iKZSnTjblm2kd5Mym6QEp020magogM6YgNo0bPA7UWjOGZAF2IhaE6kE9JgDotsAGGOiPyoaEzVlfERa33rrLR+tJZoKKANozAUR3nTKc2Lmj1Rq1sUCqaR8A7NEzomUUv2XcyH6TQVl1vZyfG48AJ3MB58FY2PMRGQ5h6SoFsdnXBTa4pyp5szY6IOILjdK0jAOkNEn65jpi2uLSDjjYRvj5BFDXBN9+vTx0JycA8WvGAPHJ8pPsTTmGthNf+7MH58T46F91uxDND1bFA1zXqw/52YBlbXJRMhWsc563rSCTeldsOHPxzbs6dim9AipzeVANnbbZ4wo2Jh3Yvv+idj9LNiv37vre27Vtov00dP18ZwDZfroWX0fmDRvrg1Anug3mRWLOxdZrm8W/C67ErCh8FQ5UQCJxwFhCigl8EsFYdaM3un8QhQep/NtFNKJEwG/tGNfQPO+KFQiZn1xk8pm/pm6wB2RyNejsBb0/ShAG5HpBLJrAr/osCiMB0gjPZfKykRAAd80/BL9JAJLqjYp2/RLajSvibRyvtVpWeCXddGsKQYWP4xC369GYQ6YC24SJKJgFfsCwRcVt9UUfoncTojCnAOw9MN+HO/dVDuUXA9ALO0o+EXBs2TfRDzeiPlkLTCQTtt+Udi3VJq7JEl1IBP81omZ2rRJPWX9KcV/SkFebRjQBBZZGwq08DvrUIFbQK+6aBvvMS5ScolAcoz69NkzFiCDc8y+t6zm3Ik6M1dAZE3PPdkPuGOua3tsHB/I5Ytq8hlW1wftacN5kNKehfZSZh/GzvFJWa7puS+pk9RnIuNkBdRk3a+vxDzfmaJV1QBp2lRtpsiVr/hc4v0qpg/a17AP1nNzg4QoORH+UmneslzfLfhddpG6SqQv+8iarK6Iwhpg1sAm8HteFKLB46IAvQBQ8uiiREm153OjkLoL1AGZPBc2K6oK3xsFoAbWaE90NA3TW0dhHMDX4kS0+aMowDaQSTou4AjgpUV0mLFzDvQL9DKOcmngaVHgK4HRRFtGYYz7ZrbzHGG2r5Xaxo0FHmn0ZRT6/i4K40uDL2oQhZsBvaPKRxMB9NcmDVIikswjh9L6UxQ+a0Cffljfm0R8szonCmu8aUflaLID+Jyz7anEDaxzg4G2faKwryRJy0km+K0TM7VLW7hKllc1A+NEwUnNJlLOc5dXtn9buDlEZgXp40R9Wate3SOeZLm+WvC7YpTA7/HZN0oogV9JkiSplmWC3zrxshSukuVV0axlvvjii+3000/3ad6lUqTrs8mIYPkARc1IPyfCXl02hSzXVwt+V4wEv5IkSfVAJviVZXk5mLR11m+z3pk11ytb1BRgoOo2LvXca1leWSz4XTES/EqSJNUDmeBXluXl5ORZwslzhbPv12czXsZd3bprWV4ZLPhdMeJROGdH4bE2ixOP7KHKsiRJklTLMsGvLMuyLOfGgl9JkiQptzLBryzLsiznxoJfSZIkKbcywa8sy7Is58aCX0mSJCm3MsGvLMuyLOfGgl9JkiQptzLBryzLsiznxoJfSZIkKbcywa8sy7Is58aCX0mSJCm3MsGvLMuyLOfGgl9JkiQptzLBryzLsiznxoJfSZIkKbcywa8sy7Is58aCX0mSJCm3MsGvLMuyLOfGgl9JkiQptzLBryzLsiznxoJfSZIkKbcywa8sy7Is58aCX0mSJCm3MsGvLMuyLOfGgl9JkiQptzLBryzLsiznxoJfSZIkKbcywe+q7bjEtsV5SfdZ0vayLMvyCrPgV5IkScqtTPC76tnB6LxpBZvYMbaRL8Q25u2C/TKkYBXzSrQtumJuwX4d4tq+FduIFwo2qXM4Rlmwddvn/lTZxw/vuv2HhuNUaSvLsizXGwt+JUmSpNzKBL+rnBfMdvDarWB9Tomt426xdd0vtm/vLticKVXbJp41oWDf3O3a7u/22T22vmcVbHKPcKxsW9/HTAe+nUIfnVz7bgfENuR+18fkqm1lWZbl+mPBryRJkpRbmeB3lTMR25EvF6zzPrG13Tq2dtvFHlJnjCyUjeRO/86B7Kmu7fZhH4B51KsFm/9r1baYqO/w510fDSv76HuW62NE+T5kWZblFW/BryRJkpRbmeB3lTPR2sndCtbr+Nja7xB7CP7q1oLNnlgoC6azxhfs69tC2/Y7Olg+ObbJXauJ/M4KkV/fh2vfpVFsX9/p+phQvg9ZlmV5xVvwK0mSJOVWJvhd9cx63J8LNr5twYY/XbBRrxds2lfVr8cFcmkzqlXYZ2KHcIyyIOu2z/nR9fFJaD/6Tbf/19X3IcuyLK94C34lSZKk3MoEv6us4wUORh3UVswvlIfYtF2beH7Yh32rvF/Cvo85S9CHLMuyvEIt+JUkSZJyKxP8yrIsy3JuLPiVJEmScisT/MqyLMtybiz4lSRJknIrE/zKsizLcm4s+JUkSZJyKxP8yrIsy3JuLPiVJEmScisT/MqyLMtybiz4lSRJknIrE/zKsizLcm4s+JUkSZJyKxP8yrIsy3JuLPiVJEmScisT/MqyLMtybiz4lSRJknIrE/zKsizLcm4s+JUkSZJyKxP8yrIsy3JuLPiVJEmScisT/MqyLMtybiz4lSRJknIrE/zKsizLcm4s+JUkSZJyKxP8yrIsy3JuLPiVJEmScisT/MqyLMtybiz4lSRJknIrE/zKsizLcm4s+JUkSZJyKxP8yrIsy3JuLPiVJEmScisT/MqyLMtybiz4lSRJknIrE/zKsizLcm4s+JUkSZJyKxP8yrIsy3JuLPiVJEmScisT/MqyLMtybiz4lSRJknIrE/zKsizLcm4s+JUkSZJyKxP8yrIsy3JuLPiVJEmScisT/MqyLMtybiz4lSRJknIrE/zKsizLcm4s+JUkSZJyKxP8yrIsy3JuLPiVJEmScisT/MqyLMtybiz4lSRJknIrE/zKsizLcm4s+JUkSZJyKxP8yrIsy3JuLPiVJEmScisT/MqyLMtybiz4lSRJknIrE/zKsiz/f/bOA0yKKvvbZV53dQ1r2NVVMWLEHFCBwYQiKIqgmDDnnDNJSYJEyTDkIDnnKEkySE7DkDPDQFfvfvvfPV+993YxRXVPYEAYps/veX4PTPWtdKtg+r3n3HPV6qSxwq9KpVKpklai8KtWq9VqddJY4VelUqlUSStR+FWr1Wq1Omms8KtSqVSqpJUo/KrVarVanTRW+FWpVCpV0koUftVqtVqtThor/KpUKpUqaSUKv/t5b2SvLNq4XNrO7CkNJ7eTBr+2lUZTOkj/RSNl555dce3z44gbkfU7N8rQJeOk9Yzu0mByW2kyNVX6/D5c0nesN5+H9zlavXFjVEYMd6Vnd1dGjYjK5s1Rcd34dn+EMzKisvB3V/r3c6VbV1eGD3UlfY33jPfGtz0Q79nDl8eozJkdlUkTXZk3NyqbNnnPNRLftqB68+bNMnnyZOnVq5d06dJFunbtKmPGjJF169bl6d98RkaGLF68WEaNGiW//PKL9OzZUyZMmJDn/dVq9ZGzwm9y6TXP1Tx/Gv4gpPsc2+6T8AcBFXNsmyrhD2I6IfTzR56fD237I3SsY6/r0fAHKpVKFZYo/O7nbbt3GBC9oWU5ubBRCbngp7vl0ial5eneH8g6D1jD7Q/UgO3SzSvlkxF1pET7J6Vos/vMeS5uXEpKdnjKg+Ee5hrC+xUkA68bNkTlVw/8li31YDAzvg0GBmfPisprL0XkvlIRees1VxbMP3j4zKsB0i6dXHmiQkRKl4jIqy+5MmG8heJw27yY+16/Lir9+7ry1WeuPP+MK5Ufj8gLz7lSt7Yr06ZGZdeu+P32P4Yrq1evNqC4cOFCA5HhNofDCxYskOrVq8tDDz0kJUqUkJIlS8r7779vgDi3a9q0aZMMGDDAtH/kkUfknnvukdKlS8tLL70k/fr1ky1btsTtk6ymn8eOHSvLli2TzMzMuM/V6iNhhd/k0mzPEvO1oc+CmuHYNlvCHwQ0wvNuz1eFP/BU0fP40Lb1nseGtv0ROt6x154a/kClUqnCkqMAfqtVqyYrVqyI2/5HeHvmDmkxvYtc+/NDcnb9W+T4mlfISbWuklIdqsia7evi2h+ot+7e7h2/qwHeE2oVlVPrFJOrmj8g17d4WO5uX9l8RpvwfgXJmR7s/jopKu+/7Upqe9eDnfg2GFhMT49Ky59deei+iDz+qCu/TT988LtzZ1RmzIhK44auPP5IxHjwQNdsD7fNi7dsjsovPS3wlrrTg95nXXnrdVcefjAid90Rkc8/dWXuHNs/4X19r1q1Sn766Sd55513ZNCgQbJ9+5F51kRoR4wYIW3atJFvv/1WypYtK88884zZtnPnzrj2voF3APnVV1+VlJQUeeGFF+Sbb76Rr7/+WurXry/jx48/YvdUED1kyBB544035Mcff5RFixbJnj174tqo1YfbCr/JJeA34vm/nmuEPvN1qef/ef6Pkz383uzYYzwW/iCmqZ6XhrYp/KpUqgInOQrgl8s89thj5YEHHpBu3brJ7t2749ocKpP2vHjzCukxb5DUn9Ra/t7gDjmx1pVSsv1TkpYD/BLRzUu6MgD9yoAv5ZTaxeSvda6X5/p+LJ3n9JO+v4+QgYtGy5LNK2VP5Mj3eU4mcjrIg8iyZSJS/VvXA6n4Nr4BYNKCX3khIpUec2X6dJs2HG73R5hzA9q/L4jK55+48mTFiLnunTvi2+bFpEwD0txH7VquTJxgU5/bt3Ol/EMRebiMK107WUgO74t37Nhh0oOJlD755JMGIo9UNBCIBcR27dol06ZNMzD+4osvyvDhw3OEX+6hY8eOcv/998vLL79sYHnlypXGRLQB30gk938HyWL69vnnnzf/d6WmppqoebiNWn24rfCbXAJ+gdBfPS8Ofebra8/bPM9ysoff3KTwq1KpjgrJUQK/QZ955pnmy/qMGTPi2ubXO/dkyLgVU6TdzJ5Sd1IraT69swelfeWiRiVM5DcMv4AuP3ebO0Bqjmsq7w2tKe8PrSU/TGhhIHZLxjZxo+6+9hl7d8vMtfOlw+zeUrxtRQPUZ9a9Sd4ZUkO6zu0vI5ZNNMfLD/hm7IrKrJlR6d7Flc4dXenVw5XmTVypX9f+fdZMVzp2cKXuD660a+PKwt/3B9Ddu6Myf54r3bz9G/5owa55U1eGDYnKhvX7z2VlP2CXtveWisgnH7qyYIFNg8aAXxhumXf7+stEXl3p1ycqPbq58lMD14DkoAGuSSVONF+WCO3sWa65L9o2qOdKW+/6x4y2c4nD+wC7bP91kiu9e7nSo7sr48ZGZeL4qHzxqStPPREPv/45unb2jl/flTpeH7Vs7srokfH3zn4zPHinX5Yutf3G5wvmu/Lpxza1u15tV9amx98LJgWW9/aGG24w0VJgMdwG82+OCDGR4SZNmkjt2rVN5LBVq1bSunVr6du3r6Slpe0HmfydfYg0Nm/e3OzToEED6dGjhzlvTqnMc+fOlQ8//NCkLWcHv0DykiVLZNiwYabtXXfdJa+99pqZM8xcYdK4mQNMu/C+yWxSwOvVqyc33nijvPnmmzJr1qw8RX8ZRKA/SZdmwIHBinAbtTq/VvhNLvnw+66TferzAs8tPU934uH3VM8NPa9z7P4bPDfw/JdAm9GOTYcmwkzqc9XYdh9+K3n+3bH7b/Rcz/NJsTa+LnAsvO5ybLs0zz94PjnYyLHnZX+uh2j1ZM93Oonh90bPQxx7XXw+03Pl/VqoVKqkkxyF8Bv0TTfdZACBAj7h/fJiIHb1trVSe2ILua/TcyYF+YKf7pIrm91vIPWU2tfFRX53RzJl0uoZ8ubg7+SGluXl3B9vlz//cK3xeQ2Ky13tKst3YxvJqq3p3vFdc47Fm1bIM30+lJtbPSKn171Bjq1xmYHqK5reK7e2riBlu74sHWb1ztd8X+a2tm7pysNlIlLqrojcnxKR22+KSLGrIwZQn60SkRLFI3LdlfbPWjVcU7CJfSlABSi+/oqdF3vHLRG5+/aIFPf+fKSsB8LfuzJ/vgVa4LabB4mffmjn0XK80ndHvH1dee8d62++dGXwIL68Z12fD78cm6gpkHhzsYjcdF1EHn04Iq1aWGgN3tPatVHpnOrKyy9491M6IveUtPdyT0nO7YF9nViK8W7bHhAFYoHPKpXsPrSvWIFjuFK+bEQeK78//ALx3T0Qf/Ule+/Fb43InbdF5K7bLKg3rG/B3k9jBq7pBx96/WsFfj96PyJlH4hICw+c6afwMyJboU+fPnL77bebOba9e/dOmB4M5DAXuE6dOvLYY4+ZiGG5cuVMpPXuu+82+7/yyisyadKkfRkQgC1RZNKXH374YSlevLhpd+utt5ooM8A9ePDgbOfi5gV+uSZgunLlynLbbbfJFVdcYf4kXbpChQpSsWJFk85NBFhBLcv0xcCBA/c9v/bt2+ca/WUQg4GOZ5991mQItGvXTtLT07Vf1YfMCr/JJR9+/+H5/zzX3P9jA8N8oSrpxMPvnxy7/78dC7xAbW3Prucpnk+MtfvG81rHRo+reU6Jbee8mZ73eG7i2OJbYxx7vh9jbVARz5sdC9CkZnOeto5NsyZi7Z+Hl3KUY6EXWKddO8cePwy/dzv2Old6ft/zS54Hx9rxs0qlSlLJUQ6/vk8++WR56qmnzJf38P45Gdis92trubRxaQO5x9W4Qk6seaWcULOome97TPXLzNxcH34B2d83LpUqvT+QM+rdJCf/cI0Ua/GwlOv6iilYBQif9P3VUqRRKQ+oW5oIMAA8d90iU0TrBO8cHNOpfqnxsTUul+O9c57f4E6pO7GVaR++xtxMkaXJv0blq89dueEaoM+V7z3A/cCD0Ws9QL22aESee9r1PrcQ+dgjEZk92wLqiOFRDxAt+L3vtW/VwjVFon6o6Rr4ZS5rjWoUabJA2saD7Befd+WBeyJy5aUWsgHaZ560fucNV/r19vp1W9b1+fB7zRUeWN9ho8WNGniw/LYrd3jnrVLJlamTsyCTOcRUh360rL3eLz9zpVOqrRhNdBagBfJrfOfK8mU2tXnRwqh8/YUrKR6MP1rOQjhRXObklvSA/+rLLZz68Lvdu75+fV15/NGIpHjH+uh91wwgECEnlbtcGdecm+j56lXZV6jmPnv1tPN+q1S2keZEBbWI8lJg6tJLLzVAM3v27IT/toicAsZAbKVKlQwEEQGmGjMFpm655RZ54oknTJVl2gJEHOuTTz4xRauA07p165rUZCLApDLfcccdJkWZCG0isM0L/K5Zs8ZUdf7uu+8M7F5//fWmWBbX9OWXX5rtXDdziRXS9jeR99dff12KFCkiH330kYnm5tRHPAOe/+mnny4nnniiGVhgLnVO0Xu1+kCs8Jtc8uEXjfe8JPAZ+t6xUVQqJofh9wvHfskKR0upqsz2dwLbskt7BlSJzPqiIvQax0Z2ffVwLJjfHtiGPnDsed6L/Vwm9nOtfS2suAe2p8Z+5uUl0kyU+m9+o5h6e/6X53ND27PTd06CL535MH2cVoDMoML4AuQujn1+BcW8U9UKkF9x7GBPQfF9jh3kKii+zLGDaHnyqlWrMkihDJsv6yNHjjRFWpYuXZpv5yXFLzc78f+H5OiLLrooT0WySBUlFfmW1hUM7P7Jg9b7Oz0vtcY3ky9G1pNrmj/kAXDR/eB3e+ZOszzReQ3vNLB8Y8vy0vK3biZtuffvw+TDYd/LaXVuMMcr3q6iLPRAGfjdvGurdJnbX74Y9aMUbXa/AeszPXh+fdDX3vmaS/NpnWVm+nzJ3Ju/OaDAXO9eUQN/gNySxVEZOyYqJe+ywNnnl6jMnAEMWggcOzpq2jAX9vabbSVmilEBxMBbmge7bVu7BoqZyzp6VFS2bo0a2JwwzkJoCe+4r7wIUEZl0gRrjhFeTsiHX6LJFIWaNSsqmzdFTeXl56pE5KH7Iyb67EeL58+Lesf1oPXOiHzvQTg/79ppo7wm5bqrK+UfJDIdkb69AcuodOnsShkPyJ/wQPyXnlFZsdxWZp7xW9QMBNx6oz2PD7/MA/7Q66e7PTD+6APXpDPThwwkcI+Nf/LA//aIPFnRNf2YCGjZNn6sjSzfl0Kk2Ou3tMSgDKAColdddZV89tlnJroXboO3bt0qbdu2NSBLUanp06cbGCX1lWM0btzYRFjnz59v/m3RHtC99957DZQypxhQBZT4DHCuUqWKiTaTOk0EMXzOvMAvoE52BRFg4JpI5hdffCFTpkwxMEfEl88TAX2ymwj/Dz/8IBdeeKEpKsZ0jZz+X+T/XaL9p5xyihxzzDFmsOPXX3/9Q2sdqJPLCr/JpSD8vuHYL0rXZX3srHBsWjMKwy9pwgBkIm3yPC7wc3bwG4Zt1N+xqciI+bpEboPH8kXEl2jwhNjPzRx7/ef5DWI627GQnRr7mWrUtCM9Oiy+tPPZi+EPslE1J8EXTbVarS6Izq1I1q69GdJt3kA5u/6tJhr7jwbFpdf8IbJx1xZZsTVNvh7d0IJsAH5Zn5eCVX/54ToDsBc1KimVe70jVft9Is/1+djAM9FgoroX/lRChi+dILs9oGX+L3/O27BYynSuKid9f5UUaVzSfL4xY4tZQ3ivm39wAA77942adN8farkmjZh05XIPWUgkfZcIJhFR0qOHDY7K9Gn282uKAo0Rqf6dK/XqWBM1ffct16ROA8Dt2nhgtsWm++7YYaOmRH+JNq9Js7DrOwx/PvyW84B1YH+7P21WrYxKzWq2WBQRV47P/mNGuXJPqYhUKOeaqtKkGfvHYr+lS2yUm7RmlhiaOjUq333tmntnXjPXw3XSlmgyUP50ZY6XBb8TJ9jzXn9NRJ56wkZ7/Xsn1fu1l125/mqbZt25Y3wRq90ZRNvpI5te/dUXrlnWKXitQZOmzLJAREypikyENNzGHNd7T5nT++CDD5o0WSK2QGbNmjUNvDZr1szMu+WLK9HD5cuXm4gy84gBZiKLzPcFUPHHH38s9913n1x55ZXy3nvvmXm74ahjXuAXs9+2bdtMNJrIJKnZDJIxiMRn4eOqremXli1bmmfAO+BH7cPtfDN4wdrLpKszT5gBDY2oqw+lFX6TS0H4BRKp6Fwz9vMtjv3CxJ8oDL8Zjo2SpiUwqdD86Ss7+J0Q2oZ+cexxEenYXANgm0gU4UqP/X2o572Bz4LiulNjfy/v2GPucOKvm2vis3D0ODtVdxJ8wVSr1eqC7ssvv9wUmwl+ASCK23ByOzm97o1yXI3L5ZrmD5pqy3zJpEBVv4Uj5Jwfb9sPftM9P9bjTRMlBnBJYz6n/m3yjx/vMCbt+W/1bva23Sp3ta8sE1ZOk8xIVpRnxZY0kyLN/pc0TpFJq36TPYHP8+t98OsBKfC2aaONbgJ8LM2zYoWNyAJ5wC9L/lAIijnARS9hjqwrFcqzJFCWSXsGKJ95MmKOvSMWmSXiOXCAK2XujZhUY9Khw9cTtA+/zNWdOiWrIBZRXApZMe+3Q1u7ZBLHHtDPNdHqZ56yEezw8YjoNmlk05I5P8Wp3n7D3hcFv4DoYHv6gZTuSo9Z+LXp3jZF+uor4u/9sUcAY9fMnX65quv11f5zmLlGimq997ad8/z+u65JO89pjV+qIhN9ZY46IJTdvE8faH/++WeTHs3c3WuuuUauvvpqA7isp0vkmEggAEUEGMAFqm+++WaTikwE2Hf58uXNAFCZMmUMDCeKOOcVfjERaIpuEZnkeGSJhNscUTPwkiDynqMPdJ8DaRsz0XmKXjEHm/RxBhHCbXzzDhBFJ+uGwQrmamsFbfWhtMJvcikIv4g5s340lnm8ywOfheEX0FzlxKdg+v4kq2m28Juo2nMQfs937Be1plkf7yfgd23s78McGyVOJM6VGvs7yzFxTCLM4Wv2/UCs7eESEe4iBcikiaYUIBORr1qA/IoT/84cSX/vxKdmH0mTpj6+AJk0/rS8+sILL/wvc9ES+bzzzjMpxAfj888/X5wEMHo4nFvkd8eeXSZl+Yy6N8kxNS4zMDptzWwTgd2R6X02vZuc6YHsfpHfHRvl1YF+5LeoFGtRVhr82lbazugZcw9pPq2TfDayrtSf1MYsbRSM2Bj47RaA39XAb/4jvr6zg1+KPD35RERWrtwffocMtMAG3BLZbdQgaqKyQwbFm9TeNYFUZrPU0QAb+SVtmrV8w9cTtA+/lR7ff6kjILZpo/3hl3OMBEzvApYjpop1eF1gEzGuYSO/zE0mffrDd+31tGhm7z3YnmMwT9kveEXkecxo25750Y0aAtzx9z1kkCuTJtq1ioP3PmmiK+++GTHw/M6bdp4vxwzfd9A+/AKowGN28EvasF9VmZRlqikDTi1atJCvvvrKFJgCgEl/JhpIW9bY5djMK2VuMMWtwgZqAeVEEcfDAb9MgSCiTfGm/v37m+tO9G8yvybKv32J9++ruyu//+TKim5R2bYwKpFsIvE4c7P3Do5zZVELVxY2i8q6cXZbdnDLObYt9s7RNXaO7t7Pi3I+h+9OnTqZgQ+eHZFcUtLDbXyz/BXzhHn2gDK1Fw5lX6nVCr/JpTD88qWeL0mkPqc7+xfACsMvSyOtCfwc1NWezwr8nF/4ZQ4whalGZn28TwAj0WeKa6GfHXvtRIuDoiL0/3Psl3LEmsS0AxjCOs1zUccW81KpVEkoKSQFr4IGuvMy55eI7JgVU0x6MvAL0D7X9xPpOLuvNJmaKqU6PGUqMh9X8wq5unkZGbR4jGzYuVlaTO8qZ9W/1Wy/tElpqTm+qQxYNEqGLZ0grTyY/nB4LSnb9SV5vu/HsnzLagO/LKU0cdUMM7fXFL7ywJmo8ndjGpl1focvm2DSrfOb2pgt/D6SDfx6YGfW333RVjf+sa5r5quSJrwn0/5J6jSgRySWCKwPrXw2YpgHqCWY8+vKyhV2O6nGVDqeOsWVKZOzIsUHAr9snzXDpilT1IrPgU8/lZqliYYOJkIbMZHnrl3s0k3MQaZK9UtVXfl1ooVU9iEqzdJFFL3ivv20Z+Y/v/i8TVlu2tg1c5z9eyd1eZ2333gPjDjXsqX2M7abVOc3YxHft10ZP9ZGhWlPX82ZZa8x/HxYCghgBIAAV758httgonzdu3c36cukvgKXzN8FOmfOnCmff/65AV0qOxPF5Qssha1KlSpl5ggTEaYtc0oxEUbgFpCmInSiiOMfDb/+2sBEn4licx76IxGI59cZ6z3Abu89j4quDCvuyphy3jvbxtueQ1bCRu89/e0jV0akePvc7cqMT7xt3nu7N5sIfob3vi5p48o47z0eyjnKu7K0nd0ebhs2fcZ8byLzLEdFn4Tb+J43b54Z0GA5KSp216hRw0SBC8L/xerCYYXf5FIYfs90LCiSQsyXJiDWVxh+6zu2TcXANnSbY+fYArG+iLwQJQ4qL/CLiNByTcFrQS869vxUk0b3xn4OAjuiijTbU2M/A80UmGK+8hl+o5ioOk3bR0LbVSpVkkgKCfzmp9oz83CJ5jKHl2WKjq1+mZxe50a5vsXDckWz++RPP1wtx1S/1MwHPrV2ManQ/Q1ZsmmlzFm3UJ7o+bac/P01pugVEdy72z8pKalPy7U/Pyhn1bvFO941cnubx00a9d5Yhegner1tPudYwDb7XubB8/UeDLPUUf+FIw0kh68zNwN6v0135duvXLntxohUfcY1RagoPvVoOVsxuVsXIDQq1b6NFZKq4Zo5v8y1ZZkfIsBUT2Zd4L59XPm5mWuiuqyN+1wVIDAL6oDcaVNJi7ZLJzHftn0bu0bwh+/ZfajoTOEoYLhTx6xlmJo1tYW2WGJp1EhX3nw1YgpLffSejeCSOsw+bVq5co93XURnq31jr6t/X1eaNXENGFM8i8Je8+bZqCvHqlQhYu7/xedsQS6WHfryM3uMyy6ylalrVXc98LVFsqhcfV+KhWKWf+IcFN7iHJ96UFS5YsTM/aUiNsC8eKErX38ekZuvj5iq2qRl+/OkWUf4hWcjplBWonV+Sbl/4YUX5LrrrjOVkbODRqK5jRo1MoDLvFoKJREpJXJM9JfiR8HIL1FCKgFzbPZ54403TMEsllViWR3ACdh8/PHHzVJFnNePLnNcoJS5u8xFZW4w84sBNbaPHTt239xiItUAK5FbllpimSMKaVF8i20AO4WcEkEdBbE+/fRTk0nCGt1vv/22AbxD+X/LDu9dm/u9K8NLuDLgGu/9v8GV2d/Z7dlFctMGerD8hNe2mN1nwpPee+H9u9kTqFQe9PalUZlTw4Kyf445NbxzLM/+HJioLfO8GZTLS/EqIvVkrJx66qly3HHHyaOPPmoqdR/KwQJ1clvhN7kUhl9E+jBfnuaFtofhl8gu0WEiszUcm55KBWaWJSL9mMJSvvo6dmkiorNPxLblFX6vdGxhK4poveXY8xC1ZV7xfGf/tX7981Ckq7RjwZjllDieD7+Ia6Ad0etnPZf13Nyx901xLX3BVaoklRzl8Huw6/xmRjJlStosk8p87o+3maWHmP974vdXySVNUuSypveYKK2p3ty2oiz1YJbiVGNWTJbXB34tFzUqsW9ZJP788/fXmiJY93eqKi1+6ypbd2831Z7nrV9sYBjgDS91xH6sK9x93iBz7PA15mYqJwOLQOylF0bkxusiBkJNoacnI3LdVRF5+UXXA9aoWWKIyscUexrQ35XFi2xklJRgqj4Dg8DiHTcDkq6J1rLeLVFkP2KLWSeYpYEevD8it9xg1wa+9QYLpc96UNjWux7m9M6ZHZUXnrfLHF1xsa3GPHCAjUrXrunKnbdaMC1R3JUf69njkmJMtWZSmP1lmIDzB++zlavNXN8vbTEsgJwI7wZvvx7dXKlS2YIpgMp+pT0Y4pzcF9dApWuAH8BeusQ1c4cZIPDPgemH4rewXrAHwo3toAGR32lTXLNmMvdx8T/t8knFvL6lMBbHBq5r13JNhD38jMhCACxZ6qhq1aqmanKiKP+GDRtMUSvm+hYtWtRE/oiYEmmloBWAy5zfadOm7Vv6hncfYAJ8KZJFG0D2zjvvNP8+qMzMvGBSqUm3Zb8BAwbI008/bdpyDs512WWXmXmp7Me5uF6iwkSQ+b+Aolsc69prr5WLL77YRDKBYNYVJqJJBDpRNWmKfbEE02mnnWbOBVhnl/adX5OuvKyTK2MruDLYe29HP+TKkraxyG82YLppWtREfofe6cqQ21z5jcivty2SoLI33u2dY6n37ox5xDvHTd45yrqytL2NOofbBg38syQU8PvWW2+ZZ5/THN6pU6eawYxzzjlHzjjjDFP0ikrfDHSE26rV+bHCb3KJtW2nhbY95di5b/4SQr6IwDLHNigqK3d1stbSZUkigPbGYCPHFs1a5Ng2rL2LOG+3fS2yBCAvC20j6guUA7wcg3Tnxp5PDTZybAVoItL+9RDhZY7vDGf/tYMRSyMB9P6XRo7J3OJTgo1UKlVySY5C+CV6RCVUIk3htvlxxt5Mmbt+obSb2UuqjW0sHw//QepMbCld5w6QHvMHS9NpHaXp1FTp8/sw2Z65w0SMM/bslvkbFpuUZdp+OqKOfDGqvjSa0l66zOkvY1dMMSnSgC/nAIJ/WTBUmkzt6LXpsJ+bTEk1xyFFOj8VnzN22Sgv6/O2a+1Kh3auDBtqqx6PGGaju8znJSI5d05UunWxUU7WxmVfUoMpGsXSRvXr2DV+iWBS5Zi03lWrstbg9Q2gcvwhgy14A9VEi3t0t1DKuYBl0oH793OlfVvv2tq48ksv1/vyb6O7RHopUMU1d+lo59f6c2c5PscYMzoq7doCxrYC9c9NXenXJ2qOwbX76dBEo0nTnjDeNfdRr7YrP3nX1L1bVMaNicqwwVlrBVOVmQjz3j12SSfmGAPr9b196njwSqp11y5UhLaf+/dOPw0d4kpqe3vN3E/Q3bu6ZmmlRGnPREQ7d+5s4BKAZDmbROu2sg2wbdOmjYnAUhyLKs9UcCbKSoR1zpw5+6UmA9FA7W+//WbmtxM5JmJMtJHjkPLMfF9SnoEuYBYAY11eosNEbokWY/6O2c41El1mH6B83Lhx5h78tsH2pGgDbSzrE7wfro3zsNbwWWedZaLGRMFzWuonP3apQr7Ce16DPNjsaP9kDnBO83Ezt3jvzNSomR+8vIv9O9uyg2VzjuVEjLPOQWQ5Evq3ETZRe6L4gD8DG9mlvPsm9Z1oPpF+Cp8RKebZJRosUavzY4VfVX51jnN4IqbhVOXsFAbj7HSsYytdq1Qq1VEDv7kVrzpYR9yIAVqAddXWdNmSsdUsTcS6u1R+xswRBnz328fbvmX3Nlm9LV3WbF9vKkizH0Wsgm35e+bePfuOFTb7cLzwdeXFACCwyJxU3wAbQAiA+j/z5d1vx5q5fO7vTztSe4nWUtyKaDKAyHa/Xdjsx3GZ2wt4sg4wxw4ud8Q5/fmy+64r9rl/bb752d8veF0AMRFhYNg/R6Jr8ttv22bvg2vy78Gfyxt3fbF78OftErUlJZtU8kTXE7yXRA4ee/9rc030DvgDggDa7JY7AoABIIAZmOSLKhFVvqyS+pro3yTHZzufE1VluRz24xhEDP3liPz2/Mx2/i1lZwDV34c/+TncJmjOHwY0rp8UbaLKRKEBZkA6fP2HwrxrRG337rB/8nO4zX72npPrPeO9O635e3bgG3eOnRasczsHz4OIeLFixUykfeLEiQkHPfY7R6yveXb+/O1wv6rVB2OFX5VKpVIlreQogN+8FK86VAZS+aIZBNe82OxTCL6gAm6+w5/l5ANtf6Ded015PE9+7yE/++XVgGBqaqpJE3722WdN5kJ2/76C75L/buX1/TrQ9n+kmWP8zTffmLRqUnmJTh/qqG9BNnObX3vtNZNKTuSeQYyC8FzUyW2FX5VKpVIlreQogF+1ujAY6KFqL3NpK1WqlOt6r4XB3B9F6Ij+Dh06NN9z849Ws7wT852p0s1gxx+RtaJWH6gVflUqlUqVtBKFX7X6sBn48Zce4s/cUmCPdgP8pO7yZdufcxxuU5hN5JdnTcp7okrYavWRsMKvSqVSqZJWovCrVh9W828K6E2WuZwFKQ37cJt51cn0rNVHhxV+VSqVSpW0EoVftVqtVquTxgq/KpVKpUpaicKvWq1Wq9VJY4VflUqlUiWtROFXrVar1eqkscKvSqVSqZJWovCrVqvVanXSWOFXpVKpVEkrUfhVq9VqtTpprPCrUqlUqqSVKPyq1Wq1Wp00VvhVqVQqVdJKFH7VarVarU4aK/yqVCqVKmklCr9qtVqtVieNFX5VKpVKlbQShV+1Wq1Wq5PGCr8qlUqlSlqJwq9arVar1UljhV+VSqVSJa1E4VetVqvV6qSxwq9KpVKpklai8KtWq9VqddJY4VelUqlUSStR+FWr1Wq1Omms8KtSqVSqpJUo/KrVarVanTRW+FWpVCpV0koUfgu1XTd+W46mfX72CW9Tq9VqdYG0wq9KpVKpklai8Fv47MHo7g1RWdnLlfl1XVn0syubfotKZHeCtjHv3RWVTdNt2/l1o7Kqjz1GtmDrbc9Y552jpz3H4lbe/jO8c2QkaKtWq9XqAmOFX5VKpVIlrUTht9B5746orBkSlfGVXRl6hysj7nFl9reuZKTHt/W9c2VUZn3jykiv7dDirkx6Nirpw7xj7Yxvi/dsj8rqAbFzeO1H3uvKnBpR2bUmmj0wq9VqtfqIW+FXpVKpVEkrUfgtdM7cEpWl7aMyvIQrA65xZeB1rkx82pXtS6PZgum236My8RlXBl1v9wFml3bwjrU1vi3evTEqS1p75ygZO0cxV359wTvH4uzPoVar1eojb4VflUqlUiWtROG30JnU43XjojKhsgezN7gyzIPgOTWjkrE2mi2Y7kqLytxaFpgH3+gaEOYY2aUxkya9dpSN/A7y2g9PcWVebe84RJezOYdarVarj7wVflUqlUqVtBKF38Jn5vxu9uB0dFSWd4nKqn5R2To/5zm/QC5tVve1+wC+mZvtscJt951jY9Y5Vve30eOczqFWq9XqI2+FX5VKpVIlrUTht3DatSDK3FyitO7eBG1C7WlD273bYxCbHfgG9tmbYc8Rycs51Gq1Wn3ErfCrUqlUqqSVKPyq1Wq1Wp00VvhVqVQqVdJKFH7VarVarU4aK/yqVCqVKmklCr9qtVqtVieNFX5VKpVKlbQShV+1Wq1Wq5PGCr8qlUqlSlqJwq9arVar1UljhV+VSqVSJa1E4VetVqvV6qSxwq9KpVKpklai8KtWq9VqddJY4VelUqlUSStR+FWr1Wq1Omms8KtSqVSqpJUo/KrVarVanTRW+FWpVCpV0koUftVqtVqtThor/KpUKpUqaSUKv2q1Wq1WJ40VflUqlUqVtJIc4HfRokWyZ8+euF+carVarVarjz67rmvgd9OmTQq/KpVKpUo+STbwi9LT083osEZ/1Wq1Wq0+ug347ty5U9atW2f+VPhVqVQqVdJJcoDfSCQiW7ZsMaPEaWlparVarVarj1IzoL1hwwYDvv/+97/Dv/KDUvhVqVQqVeGU5AC/jAr/61//kszMTNm1a5f5halWq9Vqtfroc0ZGhhnU/s9//pNT1Bcp/KpUKpWqcEpygF/EL0j83//+V61Wq9Vq9VHsXKDXl8KvSqVSqQqnJBf4ValUKpVKlVRS+FWpVCpV4ZQo/KpUKpVKpcqSwq9KpVKpCqdE4VelUqlUKlWWFH5VKpVKVTglCr8qlUqlUqmypPCrUqlUqsIpUfhVqVQqlUqVJYVflUqlUhVOicKvSqVSqVSqLCn8qlQqlapwShR+VSqVSqVSZUnhV6VSqVSFU6Lwq1KpVCqVKksKvyqVSqUqnBKFX5VKpVKpVFlS+FWpVCpV4ZQo/KpUKpVKpcqSwq9KpVKpCqdE4VelUqlUKlWWFH5VKpVKVTglCr8qlUqlUqmypPCrUqlUqsIpUfhVqVQqlUqVJYVflUqlUhVOicKvSqVSqVSqLCn8qlQqlapwShR+VSqVSqVSZUnhV6VSqVSFU5ID/P7vf/+TvXv3yubNmyU9PV3WrFmjVqvVarX6KPT69etlx44d8q9//Sv86z4shV+VSqVSFU5JDvCbkZEhGzZskG3btsnWrVvVarVarVYfpeZ3OYPZW7ZskWg0Gv6VH5TCr0qlUqkKpyQb+CXqu3r1atm+fbuJ/kYiEXFdV61Wq9Vq9VFofo/v3r3bRID53Z6DFH5VKpVKVTglOcDvokWLJDMz04wQq9VqtVqtProNBAO/mzZtMr/ns5HCr0qlUqkKpyQH+F24cKGJ+oZ/earVarVarT46Dfxu3LhR4VelUqlUySfJAX5///13hV+1Wq1WqwuRFX5VKpVKlbQShV+1Wq1Wq5PGCr8qlUqlSlqJwq9arVar1UljhV+VSqVSJa1E4VetVqvV6qSxwq9KpVKpklai8KtWq9VqddJY4VelUqlUSStR+FWr1Wq1Omms8KtSqVSqpJUo/KrVarVanTRW+FWpVCpV0koUftVqtVqtThor/KpUKpUqaSVHIfxmZGTI3LlzZfXq1RKJROI+V6vVarVandgKvyqVSqVKWslhgF8AdeXKldKyZUv56aefpH///rJ79+64dnlxZmamDBs2TCpUqCAffvihbNmyJa6Nb9d1ZefOnTJr1iyZNGmSgeZwG/WBmfdh+fLl0qFDB2nYsKE0aNBAmjdvLqNGjZIdO3bEtVfb93D9zk0yZuUU6bJggPRZNFzmblgku/bo+6hWqw+/FX5VKpVKlbSSwwC/HGPEiBFy6623StGiReXZZ5/NEVpz8q5du6RFixby97//Xe666y5Zt25dXJs9e/bIr7/+Kt9//70888wzcu+990r58uUlPT09rm1BNP3FtY4dO1ZGjhwpc+bM2W+wgMEEvrxMmDDBfP7bb78dNNgDrlOnTpWuXbtKo0aNTB8zSMHxsf+8OA+DD6VLl5bLLrtMLr30UilWrJh88sknsmbNmrjjFnZv377dvGu835s2bYr7fK8bkZnr5ssX4xpISrdnpVj78nJT6mPyWN+3pc3snpK+Y72B4/B+ePHixTJo0CDz/A/2+arVarVvhV+VSqVSJa3kMMAvX+5XrVol7dq1k7POOktSUlLML95wu7wYsB03bpw8/fTT8u2338rWrVvj2rANGLv22mvluuuuk9NOO00uvPBCWbFiRVzbgmiun8jqAw88IMWLF5cnn3xSJk+ebKLefM4Xl7p16xqo53MGE5YuXZotROVkni+QVbNmTbn//vvl+uuvl8svv1yuvPJKueWWW+SOO+6QypUry4wZM0zf055n2adPH/M8v/jiCwPBXAPR/fDxC7MZkGBg4KWXXpJvvvkm4f0v27paqk1sKle3LSuXt75fyvR8SUp1fUaKtCwtpbs/Jx3n9ZXNGfHvMAaq33zzTfnss89M//vPX61Wqw/GCr8qlUqlKkw613MVz2eEP0gkOQzwizkOc3TPPfdcKVGiRL7hF8AjCgkMAG2Jrg9ImDhxovTs2dNEMgG5888/36TrhtsWRBPlGz9+vIHO008/3Vz7Dz/8IJs3bzb3P3v2bLn77rvlz3/+s1x00UXSuHFj058HCr+0pw/feustueaaa6RUqVJSo0YNadWqlXz11VcGfE855RS54YYbZPTo0fuiz/Q56eRA+pAhQ8wAA4MRR8vgwqHy/PnzTd/xPpPOzxfKcJvRKyfLPd2el6JtHpSPRteR4csnSv8lo+TFIV/IJS3vkaqDP5M56xeaCHF434ULF8pHH30kJUuWNOnlibIc1Gq1+kCt8KtSqVSqwqTzPYvn//M82fO3nm/Zr0VAcpDwS9pn9+7dpXr16vL666/L+++/b6KSwFs4UkVaKOnKgBvQNXjwYGnWrJmBNyKJpM2GC1gRbRwzZozUq1fPpDHjWrVqmfPVrl3bRIHD84eBOraRyks07rbbbpN//vOfhwR+I5lR2TLXlbT+UVnZKwf/EpX0Ya5krPeuJxJ/nJzM9dMXRBPPPPNMOfXUU+Xhhx82109/d+rUSS644AI54YQT5Oqrr5bp06fve06khZO+TNry119/LW+88YaJHv74449mwCDYV3z5adq0qQHfe+65R3r16mX6C8gmkswxiAKT1jx8+PCE/UxqNp/nBL88UwY+fvnlF/NuMFf7tddeky+//FJ69+5tvoiFwZ374Xjc66effiqvvvqqvP322ybaD6DzznCNwfdz27Zt5n2oU6eOacs52Jf2DB7wvoUzBehPBkron3fffdfsA/gzcJLoffQN/PPeEx1/8MEHTT+E+2dPZK/0+H2wXOmB711dnjLQu3X3duPWs3uaSPDdXaqY7Tv27Ex4jtTUVPP+VqpUyVxn+BxqtVp9oFb4ValUKlVh0vGOBV8AOOjNnjs6oaiw5BN+gRUiX8DCjTfeaFJfiRASBWQeKNEwIDUIG8AvkV+ilaTy3n777XLFFVeYfW+66SYDUMydDEIzMAfkcuxLLrlELr74YuMiRYqY+cOADdATvj7fRMs4D7B4KOB394aozPralXGPuTKmXM6e9Kwrqz1I3rsz/ji5ecOGDSYVmXRt+oY+JsoK8DHAQDSbPmA7z4HnAagxPxRQuuqqq8zn9O15551nngnzn5kfzIAC55g3b56UKVPGPANgki9EHINj8dw57gsvvGCeJfOLw4MZtAM2c4NfIJLIJXO0uQ7/+fFMODYDIMFMAI5L1PPjjz824Mc9kirP39mfPrnvvvsMkPN+sA8DHQMHDpRHH33UXM+dd95pjs37yPkYJPj8888NhPvn4e9NmjQx6eX0E+34k2u7+eabTdSVgYVEwLlo0SL54IMPzHMAtBOlnQO/3RYMksta3S93dX5KBi0dKzv37LLb5w800eDr2pWTVrN7yKaM+DnwHI9CbU888YR5B+rXry9r166Na6dWq9UHYoVflUqlUhU2bXLi4TfofVFh78v97vBvPZQb/BIdfP755+Wcc84xkEGUcMCAAdK3b18TlQVUAQkqPPuAAvzS/qSTTjLgw1xJ0kWJ1JUrV07OPvtsAzUsY+Sflz9nzpxpIoRE2rp16yadO3c20TmiyO+8806OxbMONfxmborK7GqujHrAlRGlPJfMxt5n4yt54DfUu4dd8cfJzXw5+e6770xUlntk/jL3zBxTYO2xxx4z9wUUAbw+tPpQBriRvkw0lz4msstgARFOngMAzLEAX0BvypQpcc+ayCPwN3To0ITR2bzCLyBPBJP74FmTis4z5GdAk2JkpHIHo9dkAlAgDeht3bq1GRRhG/cFAAPCFOTiGtmHd5UoN/3B/dOWwlw///yzPPTQQ2bAhQi4Py+X95drIrWbPn7vvffMPOsePXqY95d3GrBl7jhgG4wAc9/0HRkMDMrw7icqduVGXRm4dIzckvq4B7plpM7kVrJ6e7os37pavp3QWM5rfrcB4x+ntpV1OxNPA+B6ieAD5syrXrBgQdxzUKvV6gOxwq9KpVKpCptmO/HAm62JxlatWtXAJTCBcoJfQIAoG6nEgAgQwi9SoAUDQaS3Ml8VsPKjVQACgAv8ko5KFBI4JTIIRAFBfE66L+mo/vmIOHJcrs2fawpoMxeWOZeATPgafR9q+CXtefPsqKzqE5UV3T13S+yVPaKSPsQDufQDT3vGXDdpwUQ9iU4SoWVOLmm/FLkiDZx0W2AvmPZM/5DeTFSUPiLVGKh78cUXTcT0lVdeMf3tp08Dnww4LFmyJO4agCy/7xOl/+YVfjkGEEf0mOrFDJAA5aQiE6EGZAFVv6Ix1waYknJNES5Sionusx34A2iJigP9fhSbe3788cfNIAHHBVi5br7kETGnfZcuXfZFmFn+ikg45wCap02bZt4rIsjLli0z/cvgDfBNurQP2ZhIMIMwDBwwHxcQzq4a84KNS+Wt4dXlgp9LyrXtykmlfu9Jxb7vmFTok368Voq2LiONp3eU9bvi4RlzXAqLcZ0870QReLVarT4QK/yqVCqVqrBpiJMAcvPi4447zqSnkk7MvMdE8Ms2wIy5qEAVkb1gNApQAj6AAwDYjyr6kV9A1J9/6qfrAhdEBP1q0BwzfF7fAA+wBHwfbvjdszUqi5q7MuUNV359yfOLiT3Z++y3j1xZP9Hrj93xx8nNLHVE2i/wy6AE0T+il6TvAoRAJLDH56TG+pWYGbAgQgwo8RwBS+alMkhxxhlnmHRzqjUDk0Axx6RqNM8rfA25Oa/wy7MEtIFNAJ7nwXWRksz7QYVpIvv+OsH+Ws7+XG2WVSKVm3eNebkMjtAnwWg0wEqUlvvhuAykcE3MFSZazNxmMgiAST9yS2SZOdVEfxn8ITKMSeUnuk5/AefAdjCyy/vG8RhMoJ85bqLBAUyaM0Wvnh/8qfyzeQn5a6Ob5eymd8i5Te+UPze8Xm5KrSCd5vWVLbsTp+5zrUSxiTATjWbgIDgwpFar1QdqhV+VSqVSHQmd6LlIwHd7Tom5oueqMb/puVrM1T2nBjzM8/iYifamxbzXSQC2B2qiilT5Df/iBLSIIP7lL38xKaLBqJhvQMavFkwEEqDx5/wCS+HKtcADQAwcA805zW08kvCbsS4qU16LypBbXRl0Q84eUdqV5V1d2bMj/ji5megsqbjALfcK7NIvpHqz9A1FrZ566ikDRURG6d+0tDSz9BCADNgxX5fnQ5GwKlWqyD/+8Q8DkcAvAw9E74FF4JhIaHYAl53zAr9EUwFVoJe0a+bkAvXM4+b+2Jf3jEiwD78cF/hv27atSe9mP94LQJU/AfmKFSuaPvH3IcrL0kDcP+fieQOv7MN7AkhXq1bNXCODBGQrcFzgm/Mz4AJk+2bggKwF+ou2wXnl9DODQ8Av18GAQ/i+9/VR1JVtmTtk5roF0m3BQGn8W0dpPqOLvDuypvzz55JSsuvTMnjZOA+SE0eOMZFrrodnSp8kSrFWq9XqvFrhV6VSqVRBne5kAWlRJwtI8XNOFpR+4mRBaX0nC0g7OVlAipc4WVC600kAmQXJxxxzjFSoUMFEa7NLe2YbkUjA9uWXX46LRPnwQtoogMwatX7kF/gFRihoFIQtgJaoFpFfoDnRsjHBtkcKfilelTY4KgsauDKvTs5e3MKDvwVef+yJP05uBrBIxwV+qZwNsDZv3txAFym6zO197rnnDPwyH5ZnMGrUKAN5pEUTRQdoSTfmvkmd5Xn48Evf84w5PvOziW6GKyFjPzIf3u5/lhv8EpElWksbIrNU7qbSN/fHwIo/dzkIv7wrvB/MvwVwGTwhC4FUaGAeOCUNmEEABgnYhy9y3D/vEO8G0VLSnLkvBmpI7ybCTcQXUKYNfcd1MQ+aec3s75t2mD5iICb474BzUszNh1+uNXzf+/WTB8B7IntkuwfBG3Ztlmlr58rbI2rIJS3vlaqDPpO5GxZLJMFSR76BXwp4Kfyq1epDYYVflUqlOjrEEj5FYr7RyQLSB50sIMXfOVlQ+rOTBaV9nCwgneRkASn+t5MABJPJzMMFZJn7iXKb88s8SKJqFOIBUILzEAExIO20004zKahU1QWU/LTnk08+2Sx3AwD5cEWRK6KCf/3rX81nOVVwPpLwG41EJXNrVHatjsrOlTk7I93rqwxvHzfBcXIxgEpBMOCUiCbABoQBmP68aiK7ABzPgj4A/HgeFGJiPizPhDRfIuqk8gYjv5yD/gcgmTtNQaxgASmeCZ8DzaS4E132C5f5zgv8UjGaqDNzcRs2bGieB8+PwRGKobEv9xCEXwqYAa6AJXPHScnmM94r7ovIMdFf+sc/JwWzGCzgHQZa6Q/24Z3j2ER3gUf+znEYQGCQh7Rm5lFzDvqKiDj3yaBBv379TLo272aw4jPXR4VqouYsQcU1BdP+c/Lq7WulwbR2Uqx9ebm1Y0VpP6e3bMqIH3QImudKajgAzPXn9G9DrVarc7PC76HTR57fC2/8A3RqeMMRENGgap6vDn+QR5HueFLg50cde7xjA9tUqqNBf2Tq7jonAaQVIgPcaQED5ONjBtRTYwbgq8UM2FcNGPBPiZkBgSIxv+jEny+hPdj8HwDEL8KgcoJf/5cnETWiv0RqAQgic23atDGgBZz87W9/M4ADhABcLHdD+2OPPdYAF3NWOTdzMinoRNQXQCFS7BcySuTc4BeIYQ4p52N+KBE6QJxzUcgIsPJBK192bRGrvDhu3zwYuKLCMdBOP9E/3C/Qxr3Tl1RwJsrLMkYsfQQgE0kFMkmNBkZ5JgAhBa04DoMOwCZL5gCl9AEFlJgfyz6k1nIsIqGkCFeuXNm0Z64p0UegkOgwcOlX6eb4QDWQTsox25gPCxQD0gw4MJjB4AMRaSoxU+yM5Xs4NpkBnBuQBTYBO+YIN2rUyLxDVGJm/WjeK8CPdGmuh8gv1bD99Hjun3m6vBNly5Y17Ui35p3kPWVQgPskkksf8r4DsFwDAzScg5+JFLMkEuBN/z7yyCPmusIF2Dg210C6NPeaXREq0pkXblouw5dPlBYzu8nLQ74yha+o/vzluAbeZ8tkbyTxvzEMdDM3m3PxHHnG2RXXUqvV6rxY4ffQab3nVeGNh1BneR7q+aXwB0dAjzj2i+MT4Q/yoFs9L/N8YWAbX3A5HutzqlSHQkmdunuQ5v7SYua+xwdMv6TGTH9Vi5l+rBoz/ZsSMP1fJGaey+HQLU78fYW9wfMn3hfpXeHfeig3+GU70TaAh7moACbgRSSN1GZACpggwgds8KWd6Nvxxx9vPmfZFoAEeONnAAggIsU10RzioAENv9pzoqWOgGGKK/nXRVSb8/rVqYnW+emyBdGkChOVZD7qiSeeaMCHlGG+sHDvwBbzWomS8znwxrJGpEKTjst9M/DA/dK/QCdwB+gxD5YIIinFAB2wSdo0haF4HoAsadD0G+Y6iP4SvScaDHgzoMA5SSWmPdfAoAbn89fIBT65Xp4lladZyorBDZ4z18Q9AZg8c7YRRWW9XI7P8wNC2cagBc+ZecxEiNmP+2OABZD1I7LMgQZ6uT/eJ1K8iZbyTnI/RLa5D784G+8v0V4gG4DlXByXecCcj30AXyptk6YdHIxhf+6JfuWd5rjhARjfU9PnyGvDvpXinSrLlR7wUvW5RNcqUm9qG5mzfqFk7I1fQzho+h3I59kQQfeXtQq3U6vV6rxa4ffQ6Y+G3xKO/cJWEOD3Asd+yS0S2p4XVXfsfQThl2gZx9MXrfBLU3fzbyLBaTETIR4fM5Hj1ICrO1l9R8S5asxEolNi5t9ckYCJYBcm8Z6F+8/3IsdGhs09e7/g7NpGIeUGvxggANQonMRcRJaYIdpKqihzO/kFyxd1YIEUWn8eJmmczEflZyCHuazM6WTpI4AstxRSIl9EAgEvornhuaoAEfM2ATyWpOGcmL8TsWN+Z26AfSRNHwBX/rUT8SQa7i85BLjT58zr9fuTJYC4J54H/UqKMSnD7du3N3BLpJdjcv8MHAB+PD/6mggwqb1Ey4naMq+W6DBr8XJenqP/HtCWpZVYQonrC/cv5vzBysoMThBhJprNNTEowrlIPeaZ057nP3HiRHN8rovr4Z3q2LGjid42btzY7Mv10R8MvNDWf1e4Ru6LfbhuBgOIYGO2MfhCvwXfZ87De8m7ynl4dzkH6/Zyf9w7nwdTnn3zb4OBF6CcNGw/lTzs5VvTpN2cX+S7iU2k7pRW0mFuH1P9edX2dNm9N9PMBw7vEzTXQKo64E9UncGkcBu1Wq0+ECv8HjrlBX6J3uYkIp/nhjfGlBf4PcfJ+5fY3K7FV3bXk5OI7pwS3hhTdcfeRxB+c9PfwxtyUG73xct8XnhjkktTd/PvPzJ1F4BT5U/8X/p/zv7P6lfHZq3sJzkI+MXAGJADgAIKpKECb/4yRn47juOv1cufgAd/MieTX8Js92EsfI6g+Zx5yaRME90kchdOYaYN1+SvCxy2D2XhYxcUh6+ffgr2J30eXPfY708/okl/MLcWUOK5cCy2cwy/ffC5sh/78xmRUUCO50hUmEhzsK/4oMPx3AAAWSZJREFUe3jd5bD5LLgff3JunjXXxDloRxv/PQjfI9dMe64B82UNeGVfH5DD7xfvHaDNn0RiORfm7xwvUcSUbZyXfuKeOQfn9a8v0T6YzwF9UtOZqw64J0p93h3JlLU7NxoIXrNjvWzdvd1Ab04FrnxzzQxwEEkncq4pz2q1+lBY4ffQKQy/Nzj2y/ADnjt6/pdjv4Bt8vx2VjOjqzyP9vwfx7ZhmY4eThb0EaXYGPtsm2OPe3Lss+sdu6alG/sck1b8TOxzX1zbO4794r3DyTpPU88nBNqhmx0LM/91bLvtnmt6Pi72+f2OvYaysZ8RQJnqOcPJug6u+Rsnay5vB8+7Yp8BRcAB+tGxx/OPjy7zPNDJ6jeuleOfHWjDOdmvkucmniOObcv9fRtoh4hWD3KyjheN/XxpsFEBk6bu5t+FIXVXdXDi/9r/ee7vuXjos32Sg4TfoP0ob3h7TqZ9Tvv483uZW4pJdyW9ldRWomFEDw/kGpPF9Gl+n4e/34Hum5sP9JrC0J3bvsFr9v+e1/vw2+V2jmB7ou2kapP+zBzo7CqURziuB7v86Sb4PDuTbs18b+bUs1wVgxJ5uTa1Wq3OyQq/h05h+L3dsV/C2T7D81sxr45tvzfWjqhbuucVnp/2XNqx4Pb/PE+JtaGw1A+O3a+9Y7+cE9kgerrbscckIpzi+TXHfuEHXAFjX/zMtQCdnzq2PVEqjhkERfYBIrmmlx0bleoca1cv1iY855fiVUsdC74fO/Y6uJdpsXbPx9pxzwNi2ygQ5sMzEMI2f87vJY6FfKCb493jWPgG8DnPabF23L/fx4s9v+vY+18Q2/5srB2a49gvwwwkpDj2uJmelzv7Q3depKm7+bem7qoOlz73fGV4Y1hyCOH3jzDRNCJsFD46++yzzXxM5psy/5F03/AyS2r14TLRXwqBsd4yKdCkkofbHIyJ9DI3niJhDPIkSr9Wq9XqA7XC76FTdvA7z9m/kJO/vVHsZwpA8XM4GkzElAJXfvpworRnIlVsuy2wDQFhbAcGfQG/QO0/Atv+4lh4/i2wra9jo6IAaFAACvsTcQ7DL5FgIiyAZ1BErmkHsPuqHtuWU8Grro69XiLQQVV2bDtAGPnwu9ax9+LrIsdeT8/Yz/90bDsGEIICqkY4eU8vHetkQVxhsqbuqpJWUsDhl0gcy9kwn5O5nMwNBThYvicv84PV6j/KvHukSFNpmrTn8NzzgzWRXpaYoshVcH6zWq1WH4wVfg+dsoNfH9R8ESVlOzCBmFNLujPQAdgG4TSoRPCLgksGIeCUlE3aEuH1BUySWh3WLMemSaNjHZteTBp1WH9zsiKuYfhFvCTB9GmiqYA9ENotsL26Y/fNCX5JjZ6c9fE+cQ4qtM6P/ezDb9t9LbJE6vPI2N//5FjI3+zYQYaL/EYHqPFOPDj+keZZEK3GKx0bSR8fcydHU3dVqoOWFHD4xVwDcx0BAOaG8vfwnE+1+kiYd5CIbHZzig/GvOP6rqvV6kNthd9Dp+zgNxh9RXQm24EXX0RMg3N2f/dcy9kfhLOD38s913FsBBNAog3AyZ+fBdoBv8wjDmuqY1OuEfNp2Y/5szkpEfxSaOprx86jJQWZaKJ/Hd0D7arHtmUHvwA2f+8Q+Dyo8Y6dz4l8+K2d9fE+AYxB2CddNjgfmXRn5hqHI9w5iYiov/+Rtp8+7APxL47tx4aOheAPHAvAPKMUx76PRRwt9qVS7Sc5CuBXrVar1Wr1obHC76FTdvAbTmdOBL/oDMfCClHSLY5tw7zXC2KfJ4Jf5tCSokxUk/TU6p4rOHbeMG3D8BuMwPoKwi9RaPajCFZOCsMvAElUFeBl7iZzg6s49tppdyDw+9fY3zsGPg8KACWqi3z4/T7r430Kwy8iNfopx57Pr0JMdPW6YKNcBJwX8Xyts/8831cdC51E+jk+Hh8z/ZvmZBXbKgjmfUiLeYJjr7OLY6+bwYRqjp2jXtVzecfeK0Xcijg2C0ClKhQShV+1Wq1Wq5PGCr+HTvmFX9JPgVU/pRiRfvytY9uRxooSwe90x8JbeF6lP+f3i8C2vMAvqcp7PA/P+nifyjl2/4ucePhtFfs5JfazL3/Orz/3FlWPbcsOftFWxxYJC4vr4zNStVFe4Zd503c58SnlRNzZn2jp4RRLUhXxfJNj++xRx0Im1birOXbwgD6hv8c7+xfF4noLislWSHNs2vx4z2OcLPCv7th7oWhaVcdWPU9xbGXzIp5PdVSqAiBR+FWr1Wq1Omms8HvolF/4JRKZqN1Doe0s1cHPFGnyle7Y5YSAZV8cv59j2wLQvvICv6iXYyO4VwS2cUxAkggz0dMw/LIkET+H4bJ6bDtFtHz5UH9ZYBuwxDYffts4NmU6xW8Q0yuObUexJZRX+E1xErcjkplo+9EgoupFHPtepDi2GFhVx1axrua5sWP71a8sTVGzNCdryayCYuZ3pzm2Qvd4xxZ5S/Xc2rH3QeG3qjGnxHyJY+/9REelOkiJwq9arVar1Uljhd9Dp/zCL8WYiJwRRSPdlC/5LAPE8Sju5KeYXuTY/dY6Fg6IGLeMbSOtmOjhi57HeV7jWHgMpi/nFX4BC5YYApIomAVwEgnmPEQmURh+/QjqZMdCGOvuci7m2AI3v8baIR9giVr70Mn9sM2HXyLG3CdRaOY+0yfcy388z3Rsn6G8wi99zn2yP/OZOR7PhfvmGi+OtUsmEUUvEnNKzM85tm++cix4tnDssxnsWDClcnmak7VOdEExKfdpjn03xjt2XddUx74z1Rz7Hld17EBTipO1RNJFjirpJUch/FIEaO7cubJ69epDXmRIrVar1erCbIXfQ6fezv5zW0nv5Is4c3DDYvuXgZ9JWwaGmePLl3n+7OrEQ9nnji3UxBd9jk9l52ZOFoys9lzXsfNmKW4FAPgCiomihdXcsecK6lLHRoD9AlFAT3DNXFKIuYdSgW1cG8BKe1KT2zkWLoBXINQH1j87dm4p9+CnL7MvxwPIfFGYqa2TNf95iecajr1nX6QPsx+ptWERbW4Q+Jk51cCcH/mkaBYRcubuqvInnmURx1aRTvF8n2Mh8wUnq/p0e8e+h6Mc+6x4jjx7BjZ4DgXF6Y69LgZwxjs2VT/VsUXRqnl+z7H39phj75VK5kUcO1CjOoolhwF+qVS7cuVKad68ufz4449mfV4ANtwuL2bdX5Y6Kl++vLz33nuyZcuWuDaYc27YsMEsQ9OnTx8ZNWqUrFmzRmH5IM37sGzZMmnXrp3Ur19f6tWrJ02aNJERI0bomss5eNeeDJm/cYn0XTxSevw+RKavnSdbd+tSXWq1+vBb4VelUh1JMShRxHMxx0Llw46FzNcdC51E9YFQBoeAUswgD6D6/5x4iD1S5lrSHHtt42PmmlMdew/VHHtPVR17jymOveciju0D1aETg2lXhjdmJzkM8Msxhg8fLjfddJNcdtll8vTTT2cLrbmZpY5atGgh5557rtx5552ybt26uDYsidStWzepXLmy3HXXXXLjjTfKHXfcIZUqVTLgzTHC+xQk01+A+ujRo02/zZ492yyn438OwPPlZfz48ebzadOm5XswwTfgOnnyZOnUqZMZoGjWrJnpK46P/efFeYYNGyalSpWSSy65RC6++GK59tpr5aOPPjLXHD5uYfe2bdtk0qRJpk9Y8zf8Od6SsU16Lhwqlfu9LzelVpBi7cvLgz1flsbTO8qqbekSyQaAFy1aZNa35vkX9HdWrVYfPVb4ValUR7uIvhZxbDQ2xbHRWSCTaC3QSfQWCCWaC5QS3QVUifaGIfZImmh4mmOj4+MdGy1PdWz0vFrMLzj23oiypzhZazcThVdZMeWBaR+kvzMnPkfJYYBfolukKHfs2FHOOussSUlJMb94w+3yYtY8Bfqee+45qVatmoGP4OdAIsBQvHhx+cc//iFPPvmkfPLJJ/LQQw8ZYAbaiAZznPCxC4oBTSKr99xzj9x6663yxBNPGMAi6s3nAH/t2rVNP/J5lSpVZMmSJfmKIvJ8gSz6snTp0nLNNdcYqL388svNoAHHr1ixovz222+mz2jPsxw4cKB5nl9//bVp++yzz5rofvj4hdkMBDAw8MILL5h+SHT/eyJ7ZMSKSVK216tyeev7pUzPl6TcL6/JlW0elBJdqkib2b1kY0bigSCe+WuvvWbe3xkzZux7/mq1Wn0wVvhVqVQqm6JfxLHzgVMcOz8YyGS+MNDJ/GFAFKACTJlfDKgy3zgMsUfSTIFIc+xUhfGOna+d6tiU/2qOnc9d1bHzu1NiLhJzcNrB0SrqBvyfs3+fUHOAOgUJJYcBfrEPTQBoiRIl8g2/AN7WrVtl1qxZsnTp0rjrI0LWoUMHufLKK+XLL7+UKVOmGDAkLRe4+/vf/y41a9aMg+aCZKBqwoQJBtxPO+00Oe+88+T77783kUXun3snon3yySfLRRddJE2bNt33WfhYOZn2gO8bb7whV199tYFpzgN4f/vtt2YA4ZRTTpEbbrjBpI370Wf6nH6mD0lBv+6660w0f8WKFXHnKMyeN2+evPnmm+Z9btSokflCGW6zevtaqTmpuVzT7mGp1O89GbBktIxdNVU+HF1bLm/1gLww+HOTDp0o+suzIaLO8YnGr127Nq6NWq1WH6gVflUqlergReXpIo4tGJcSM5CJmWsPeFLBGhClojVgSoVrQJWicEFYO9JmXnyaYyuEj3dsxfBUx1YQr+bYiuJVHVvcLsWx0dUiTtaa5EdS1E8I34/vRY4tCrhflXA5SPgFgLp27SrffPONvPzyy/L222+bqOTYsWPjIlUAGvB59913y+LFi2XQoEHSuHFjadiwofzyyy8J5+QSbQS8OGaNGjWMq1evbuCsVq1a5jzhlGDmpPbr18/8yWfcA7/oic6dc845JkqZX/iO7I7K5jmurO4blZU9cvaaIa5krPMgMxJ/nJwMlNIX3OOZZ54pp556qpQtW9bcD/1NxPWCCy6QE044wUArUVn/Oe3cudMAP/OrP//8c3nllVfk1VdflTp16ph2wb6iD+h/or333XefmRvNAAWDC5yrdevWcsUVV0ixYsVMhDO4r3+d9D+f5wS/XNuqVaukZ8+eBq7feecdeemll+Szzz6TXr16mS9iYXBnn+XLl0tqaqoBwBdffFFef/11M6BBv/DOMKgRfD/pG67nhx9+MBFT9mHf7777zrw3ZARwb8HzsA+ZBHXr1jWDAOzDdXXv3l3S0tKyff/pZ1Lrb775ZnnwwQdl3Lhxcf2DJ6+ZJY/3fUduTn1MGk7vIGt2rDfzf3svGi7FO1eW0t2ek36LR8qOzJ1x+3IOnvXtt99uov8MiCQ6h1qtVh+IFX5VKpWqYIi1j4s4tphdimPXRgYyKegGdFZ3LIRi1lQGTKkUD6hSLT4Me0fSaTFPcux1Uv091bFrWFdzbOX4qo6tUp/i2DWvizi2iN3B6BYn/lrCpoo+66dTGDDf8AusEPkCeIEfUmWJEDL/k3mgRA2B1ODcXuCXyO+FF15ovszfcsstZg7wpZdeKtdff72ZowtkBaGZCCMww+dFihTZZ+CPdNtEqc9cM+AQvPbNmzfLW2+9ZeD7q6++ioOgvHr3hqjM/MKDvkddGV02Z0942pVVHiTv3Rl/nNxMsS4i1PQV/UTfMlgA8L377rty1VVXmX4mNXn+/PnmeQD+RIUff/xxA61Ehelb0r9p+9RTT8n06dP39S8Vs++//37TlqJVnJNjcCz6bsGCBQZSiTwCXuHBDNoBfbnBLyBPcSzmXXMdPD+u7fzzzzcRbAA8OBjBcXn3Pvjgg333TlT6tttuM+/ZP//5T5MSzrviz4VlznL//v2lXLly5h3k/eO6+TvnIhMAqAXu/fMA5D/99JPce++95rhcF/1Fn3PO999/38ynTgScCxcuNAXXOC4DPmQhhAEeD1s+Qe7oZCF3yLJxBnzZPjV9jjzZ/wMDwC1mdZONu+JTnzkeKfrMVec5U1xMo79qtfpgrfCrUqlUhUcsjVbEsWtYp3gu71jIfMux0MlyakAoFdeB0glOFqiyHFoYFI+U/+XYa2I5svExp8Zc07H38qpj7+1Bx94rldv9pdTyYqrZ1/N+CWaEf+uh3OAXmCSCevbZZ0vJkiVNlJAUWKJrQBswgYlA+oAC/BJ5PemkkwzAEJUEukjpfOSRR8yxgJo5c+bsOy9/8jNgQ5QQE5Uj0gzIEkXMrXgW0EbaM+m5gBCFpMIgl1fv3hiV2dVcGfWAK8NLZe8RKa6Mr+SB31DvHjLij5Ob+XJCxJKoLLALxBH15D4AVgCXiCBFxCiI5EMrEfUPP/zQzBNt27atiebSxwBe0aJFDUDxHIiojxw50gwgEL0E8sLPmgEE5ppyTsA4DHd5hV/2JYIJLALBPXr0MM+QnwHOhx9+2EC7f37eF64b8CVVvX379jJmzBjzDnz66admwIS5yPzMNbIP7yqRYSCRaC+fcX+tWrUyx+e5k6LsXyPvL8cFqOljQLtz584mA4H3F3Cmvz7++GMz4BDMSOC+OTbgDiS3bNkyYbErN+pK38Uj5Oq2ZaVMz5c94J0te117j6Q6vzm8mtzasaLUmdJa1u7cELc/Zh4x7zqDBvx78wc6wu3UarU6r1b4ValUKlVQLDNWxLFrlac4dj1vIPMDx0JnQ8dC6C+OhdJpjgXVdU48YBZ4A6JEb/mCH1RO8MuXbyCXyB2RMiKSfPlnriomogbQnn766QZG0tPTzX60AXA5Jym5AA+/hPmcyOKjjz5qPicyG1w2B1DjuECRP9fUPz/RXEAmfI2+uX7gkKWRODYwQ8Go/AJEJDMqW+ZGZXU/D0x6ee6Zjb3P0odHJWPtgac9Y67xiy++MP0HvJJayyADqdtEUIFItgG/RHODac/cL8BKHzFYAOQRwSWiyYADkVj6FyAFPoFpIpfha6CP/L4Pp6P7n+cFfhlo4J2YOHGieVcAW1KgieiTtg2ADh06dF/Faq6NdGfAnHRsUpl55lTxJuIK0JI+TeTaL1wGpFOYi0ECjkvaNu8K4E0lZtqTnu9D6syZM801cw6gmD7kHPQf6da8v0A2BdK4Zh+yMZFgjsW+PBPS8hNV2wZ0uy0YKJe1uk8e/uVVmbNhoQFiPlu8eYV8OPoHualDBfluQmNJ2x5ftRxzXJ4f5ypTpkzCCLxarVYfiBV+VSqVSnUoxS+MIjGXdCxAP+NYgGYZomqOXV881fNAxwL0HMcCNGuExwHq4fAxxxwjFSpUkKlTp5rffDnBL9sAM+aiAs78Eg3CpD/vlsgZBZtYQod9/MgvEAZ88CXeT9cFLogIUg0a4ABawuf1DfAANESPc4JfzklqNum+pP5WrVrVgOHBVHrO3BKVhU1dmfyqK79WzdnT33dl/Xg7Tzh8nNzMgACgDvz6kW76jTTbBx54wAAZ0V8+pyqwX4mZVGUGFgBaPzJMG6LwZ5xxhimiBYgCehQGIyJKVDgR/ObmvMIvX7Q4F8+B+d5EdLkuwJf3g/179+5t4Jb23AspzVw/xb6Iwj722GMm8gmoMjjCuxKMRgOsRIW5HyK5VPcmjZ45vER1KQrGOwdM+pFbroM+4Twcm7nRzBVmoADo5jPSy3/++ef90rJ53xiQ4HlwHgZxEg4OeKD7y8JhUrR1GSnb6xWZsX6BRFzbbuGmZfLeyFpyS+rjUuvX5pK+I75YljmGd60MFhBhph9YfkrXU1ar1QdjhV+VSqVSFTT9ybHwfLlj4bm0k1VA7DvHAnRbxwL0CMcC9ELPmU4CsD1QAygAVXbwC5wAvX/5y19MIaFgVMw3IEOEkmrBpKACuv6cX+bvhtfnBR6AE6KzRLlymtuYF/jluokMsgwQAEVaNfMnE0XoDsREcqe8GpUht7oy6IacPaK0K8u7urJ3R/xxcjPRWdKdAVdAcPDgwWZuLqnezF0lTRmQBYr84mIUaOIzgI1oKhBHCi/FwoBTBgCYPwr8Er1kuSJgEQjMDuBycl7gl7nVFIYC3Lh+BliAVApMAaW8C4AwEWoffjHPn4gngM8gCoMmwCjvBynAwDCRbR8Eea4MsgDGzA9mTjjtMc+fe6RQFtfIu8E7yXHJTuD87EOKtW8f0gFo2gaBk35mPjvwS7SZfyfh+/Y9cOkYubHDo3J/jxdk/OppkhmxUdvZ63+Xl4Z8Kbd3qiSNpqfK+p3xadO+mU5AijWp3qSyJ0qxVqvV6rxa4VelUqlUhUVDnAQwmxcfd9xx5gs2sASE5hb5Jf0WsAWwwgWngCIil6SNAshUH/Yjv8Av0Er6ahC2AFqiWkR+geZEy8YE2+YEv36qM+AC8FEEiXm+/tzjgzHFq9KHRmXBT67Mr5eD67uyuFVUti30+mNP/HFyM4Dlz2GlGjGFmphbSoVrUnRZBodoJfBLijNwRvotgHTnnXeayCjwD+gCfIAkz8OHX54RUX7Az5+bnWjutB+ZD2/3P8sNfokoMy+bNoA57UmxB+6ZxwvEEnkNwi/Pl/eDewD8AH8iw126dDHvJ/PCuRcgmn5iH77Icf9U+fZTvtmfuej0I+nd7EfEl/eA94e+Bb6pHM0AA4MIvrk2TF8zUBP8d8C18xx8+OVaw/fte8yqKXJvj6pSvPOT0nl+f9m620I02+/v8aLc1eUps33L7uyX3uL+eaY8W5ahUvhVq9UHY4VflUqlUhUWzXYSgG0O3vzMM8/8m7RaordB5QS/QA9Q8re//c1ABRHE4DxEYJgqzH5KK4DCPn7aM+vTUuwIoPPhiqJWzMv961//agoWhYE66Jzg1091JipKlBS4AmK4P0wKKdebbxB2vfNvi8quNM+rczbLHJmUZzfBcXIxgMrAAoBGRJPIJl9Y2M61A5qk9AK/wBF9wOABUVGilvQnz4T9WOKIub7ByC/nAOJIXycySuQTcPSj+DwTnk+bNm0MtNKH4T7LC/xSoIl0Z+biUlkZkOT5MTjSokULU4SMewjCL9Fiik/x7IhaA9BcF5/zbEkHZ645/eOfk8EOf3kjABeQZx/6BXAG8oFH5hozUEDknOOTRk5KOeegr/w+o9AU/cncY/oyWPGZYzdr1sxEzSmmxSBDMO0/6N83LZN3RtaQq9o+JO+P+kGWblklG3ZtliYzOsnV3rZHe79plkPaE8k+FZ93FkhncIqCXJr2rFarD8YKvyqVSqUqLNrkxANu0P/nebLnbx27LFK+lzrilycRNaK/QAXppkQXKUZESjQQBhwDT0AIkEKRJtofe+yxBrgocAXIsKQMcyxpT7quHykOn9N3TvALOAHWRJAprMXxnn/++X1gRJTxueeey3FOca52bRGr3BzNR6ErzP0Q5eXa6SfSgykIBfRw74BZgwYNTL/zOenRpKkT3QYmgX7gn2cC9BLxpB2DDnxOyjEQyfEoQsWgAxF5lgfiWFSEJkWYlGOglZRlABv4pX+JrHJ+ikjRr0A1AAskU2yKpYuInvrPncEMnhURfY7Pz4Anx/7zn/9szs2zATYZ9ODZNGrUyESkSeHmHniviOQScQUC/WWu/PR4UtqZC00RNApD+QWueCd5HxmkIfWdd4s+5Isfc3kBd+YIcw6um7nBvJOkZ5P2TNZAeJ4t+zNgxDUwaBBeazpo1u9NndvHFLa6tl05eX34t/Lh6NpmiaMr2zwo1SY2ldXbs0/x57jMl/aLf/GMDzZ1X61WJ7cVflUqlUpVGHS8Y+E2DLybPXf0XMXzGX5jX5JP+CUyCECR/sxcTuAGUPHnZxJ9JLIHnAALpKQCSMcff7yBHYAUQAbUaM820pSBrERziIMmOsc8TEAnvNQR0WWABfAFsoFtf+4n8zuJLJMuHCxgVNBMsTBAjes98cQTTYovwMgXFmAI2AJIKTjG50AksEwqNPDJMzjzzDMN8NK/pMxSKIr+55hALvAG0GEipQxkAJs8B0CR50laL5AKfBG955kTNQaqOSef055rIL2d50E0lCgr18H1AszAMoDNgATPGlgG5ljrmWfONXIsrpHjA/+AKccie4D7IEILqBLxZd833njDpG370Mnfy5Yta54z18S9cI3+esgMAHAffnE23muKZFG4ijm+nJ9+xpyP/RmcIcJLmna4SBr3RL9yr8zDDafe+6bo1fKtq01RK+D3H83ulr83u0suaXmPvDW8mkxbO0d2782+ejP9wUAE90B0nShzdmnoarVanRcr/KpUKpWqMOh8J5vobk6SfMIvBgiI7JECTXooEUOiZxQ4Yp4qv1z99WdJsQVYSTsl7Zgv8czLZE4m4EZqJ3ONSW3NLoXUN5Ev9gOiqGxMNNL/DBji3KTREinkfEGTNgooFeTlYvz5u9wD10zkkTRdf8khBhSIBPuf05/MO/UjrfSrv35yp06dTMoyz5KoIffPAAPgx/Ojr+lzqkTzfIBOoqbMgwWQOS/P0X8PaMuyQqRIcyy/nzF/ZxvnJ1XYr6zM8yEySxSWa2JQhHNxHJ457Xn+pHdzD1wX6dLAKtFb5voCoezL3GTa8v7Q1n9X+DLHfbEP18LcWNKsMe8m7wT9Fnyf/fRrlg/iHESbOQfvFvfHvfN5oneFgQai2FSsZmDCTyVP5MzIHgPA/RaPlIbT2ku9KW3MEkhzNiySnXt2eYAcv49v+oRBAgYuiNj7y4ap1Wp1fq3wq1KpVKrCoHOdbKK7OUkOAn4xMAZwkq7KF3N+qQJI/jJGwXbAkG+Oy59EzDARQh/GwucIn2/x4sUmIkkkEdgOVglmf3/epm9/fWD/5/C1FTRzbfRp8B6C1+z3ebg//fnTQCGpw8yvBaRpy+fBfYLP1Y+E0kdEzony8hzZN9xX/J3nlFP/4v/f3nmAWVFk77vWn2tYc86KOeccAROYc85xxZyzYs4BXUwoYkLMAQOICJizCCtIjiIgMCBhWHf1f/79Vt9iemrunbkTGZjve57vmbl9q7urqy/MffucOpW9l+F6iNDTJ84R7nf2WNlz0Z7PRYhOcz3AK1/YAiBn+0X/aQdo83kI58Js43j5IqZs47zsyzVzjuznMd8+OCzNRSo3Ufqq1t9liaMpCeiOLfnVRk8ZZ5N+n2IzZs2cs+5vPof1hIkuk81AxJ9xitvJsixXx4JfSZIkqcnKagm/wQG8qguVtK9sH4CCKCdzMlmHlfmbzOkkfZZiUFTjLbaPTclVjWsh1/Q+FuPQp2KPnW1XzL7Z97PtK9sn3/7FtKcN/z5IuweAmQNdWYXy7H6zOEee92LzkOeSSy7xaeqkkYcCcXE7WZbl6ljwK0mSJDVZWR3Bb32Z6Bdp1KSXUhCLeaUUbWL+I+my2aivLDekiQ7zYIZq1uedd56fGxy3qY1JU2d+OgDMQ55CRbVkWZarY8GvJEmS1GRljRx+Q5El5nMyB5X5rxQbYk5pMfODZbm+zGePFGkqSDOnubLluWpi0s+Zq0xhuaqKwMmyLBdrwa8kSZLUZGWNHH5xmOMKADDnMcxhFfjKc9t8BsNc6kLzg2vqMC8+nt8sy7JcGwt+JUmSpCYrmwfgV5ZlWZblurHgV5IkSWqyMsGvLMuyLDcZC34lSZKkJisT/MqyLMtyk7HgV5IkSWqyMsGvLMuyLDcZC34lSZKkJiubB+GXIkDff/+9rwLdGPsny7Isy43Vgl9JkiSpycoaAH6pVDtkyBB7+OGH7a677rJXX33VA2zcrhhPnz7dunbtavvtt59fW5WlZuI2NTVVe/v162fvvvuuX1Lp66+/rnE/5bnj6bNm2LBJo6zbkI/t6b6v2ss/vWd9xw2waTO1Rq4syzIW/EqSJElNVtYA8Msx3n//fdtqq61snXXWsWOPPdYmTpxYoV0xnjp1qj366KO24oor2s4772xjx46t0AZPnjzZOnfubN98840H5vj9rOlf37597corr7S99trLttlmG9tyyy2tefPmds0119jQoUPrfBmbYs31fvnll/bmm2/ab7/9VuH92DxoYExee+01Hx0H6OM286uHTxptD3zVyQ5+9RzbttPhtnGH1rZ5x4P8644JCI+fVrPPnCzL8vxkwW/96eTEnRIfFb9Rj2ru0nOeF78xF7R0vCHREon/L96YRwe59DpWyb3eOfd68zkt6kZ8sJeKN0qS1HRkDQC/ANnIkSPtueees+WWW85DJX9443bFmDVP+/TpYyeffLLdfPPNNmnSpAptAFVgtkWLFnbjjTdWCY3jxo2zU045xdZaay0PvhdeeKGdeeaZtvHGG3tYB4DrMsJcHY8ePdquvvpqO+ywwzzMVnU/AP2PPvrIDjroILvzzjtrPM7zmmeWzrLuQz+x3V843tZ/fN8EeNvY6e9eY7s9f5yt9PDO1uqlM+y9wb2tZIYi+bIsN20LfutHAN7YxH8mHuZSyGoILZD4vcSzXRk4zg2dmbh/tG2fxL+4FICr0nWJLfFGudcn5F63mtOi9mqW+KvEB0fbJUmat3V94jXijYVkDQC/GCAFgFdaaSXbfffdawxlgDTAC9ySSp2vf2z79NNPbe211/aQXNW5Ro0aZS1btrR99tnHpzsPHDjQfvjhB7vvvvtshRVW8BHr/v37z5Xo77Bhw/w1AOW9evWqMopNmvZbb71lW2yxhU8LB57jNvOjS2eX2qCJw+2Ffm/b6wO621dj+lq/X3+2Tn1fs52ePco26bCf3fvFk/bL1PEV9pVlWW5KFvzWj/ZzKaw9mvu5b/m361VLJv4s8eXxGw2o3olHRtseculYFAO/ayVukfgfudf1Ab+HuvSYgl9Jmr/Ev+u/EndzaebNQuXfLi+rJfwCokR0r732Wjv11FPtnHPOsVtvvdV69uxZAdT4Y7vyyivbbrvt5gETSHvggQfs3nvvtZdfftnDcQyYHOODDz6w2267zUdxg6+77jof+f3www/LpfbSX9J+33jjDVtttdXs0EMP9aDMsTEwGKcCk1rcvXt3D8ykS9MHIsxEWjfccENbffXV/Xni66mOQzpyly5d/PgApieddJJddNFF9uyzz/ovI7TJtp8yZYp99dVXdvjhh9smm2zi9wX4w3WEvoZ96B/vdezY0ff7xBNPtG+//XbOtXN+rj2cp6SkxD755BO75557/Jg++OCDvj0PA3gIwLazzjrL7rjjDvvuu++qff2ls5KxHT3bRr1fakNfnG1DOxf2iNdm24Tvkvs3teJxivXvM6fbxN8nWcmMqTZj1gybWTrTQ/BRb1xomz15gN3+2aM2tqTyByGyLMvzuwW/9aNXE09LvEziWYnfLP92XtF21XhjpIVdGrFcMNqeFTermasaMjlfM1dcGnKs0I8Vou1BNYHfZRMvEm/MKYbfFV3avjYS/ErS/Cn+XWc9OXG7xFtkGwVZDeEXgAIqAd5NN93UR1mZJ8vvpA/vuOOOdsMNN5Sb2zt+/Hgf+V1zzTV9Gi/RTPZr1qyZj1QeccQR9t5775WDU8D09ttvt80339wfl33XWGMND6TrrruuP0dIawYWidYeeOCBtv3229siiyziYZs067333tub9x566CEPjtlrIWIK3AUwBH6BYc5J/yh+VWgsijFp08Al/QrXwU/Gg3EAMENqNedmrjLwSpR8lVVWsSWWWMK23XZb23PPPf11EKUGTH/66ScPwGPGjLG7777bXx+R6sUXX9yPEfuHa2d8O3ToMOee8OWH+dM77LCDH1P6xgMFgJzfeXiw9NJL+/TvW265xX9hiq+rMk//bbYNf322fXxiqfXYL3Hrwu55YKl9f0OpTR1Z8TjVMRHg8HPc1AnW4YeXbZtOh/k5wM/3e9MmTS+777Isy03Rgt+61/KJ/5P4pdzrFxP/L/Hqc1qU11mJh7qyL2pjEh9XroVz2yXu4craANR8mctGNBZL3D7xjFyb/5e4Z+JNMm3QDom/cWXHKnUprK+UbVRAuyX+xKXp3GH/4a58f39yado11zwy8RWJX0v8u0vbj0r8dK4tP19JfI1Lj/lH4sMTn+/SfdfNtQvwe6FLU5XDub9OvH2uDfq7S/e7O7Mt6AOXpoQjouITXXoMfg4MjSRJmucV/n/I528Tt3GZmgRWQ/gF1I477jg/h3ePPfawp556yrp16+YrJQOrzJUFGqnwDMCyD/BLoaqFF17Yg9XZZ59t7du3t/vvv98OOeQQfyzm6WbntvLzxx9/9BWeKeKEiRK3bdvWg+35558/B+aIZNIP+sVxFl10UQ91ACHRU3zCCSfYCy+84NvG1xQMAAOT5557ru8vBbqIhmYjs9U1sP3iiy9amzZtfKT1lVde8dHV66+/3lZddVVfvItUa0AWk2bNte2///4elJdaaikPskSyuY6jjjrKrrrqKp8WTb/4IvPEE0/Y8ccfb7vssostueSS/sEC+9Me8D3jjDP8eQP485CBCDzjefrpp/t+cN+IMgPeROS5lxdffLGv0J1vfnVlnjFlto16Z7b1Oa7UurdI3LywP0wAuO+tpTZtTMXjVNcTp02y1wZ0sxPevsw2e+oA2/CJfe3qXvfZgAlDfDQ4bi/LstyULPite13k0i9Z++dek/LM65vntCgTEMZ7pOfR/sDEX7oUXEOUkyJPwO4olxbRapn4EZfu93iuDdHbjxP/N/FtLk0ZBkiB6imJ1861IyWaKMiPLi0qRburXQqqH+baFNKmLoV65vIe6dJ9T0s82qXgukGuHaD6c+JJiU9JvI1Lry3A+z8T75Vr+27iCS4F9uddCqjruMJzfjlPl8R757Yxr3p6bh/EwwDaPZV7ndW/E/fN/b514odd2paI9ImhkSRJ87xi4M1nHvq9kHhPqwH8AltUHwaW1ltvPQ+8ACgwhUeMGOGBlqgh0eAw7xT4ZQ4t8EsRKWCPbfwhJsoK2C2//PK+wFMWtOhDODYG3oBhIpsAaoiYAo1AKlFaUn+BY5ZEImWZbZj0XcA2Tq/OmkgyUVQiy/Sf64tTpatrroFx+Oyzz+ydd97xEP/SSy/5hwNEVsM4hvMA5yy7RFsAdoMNNrDHHnvMPv/8c38dRIYHDBgwpz1Ra8adytBEmOn7kUce6R9IhPaMN6nP4Z5yH/mdMaM4VojKc2+++OIL35b3AGSKguX7LFTm0pmzbdro2Ta252wb/tpsG/ZKYY98O4HWvsk9nFbxONX1uJIJ9th3nW2rpw+xRe7b3FZ8eCc7p9uN9v0v//bp0HF7WZblpmTBb92rX+JfXVk6MUWoiOaOc+XTlUkdJhpK9DKbekw6MkDXPfca2CMiut6cFqm6uhR2qVZ8vEu/0F1QroVzq7kUnAFLBHTSDojOigIxRGArmxt3o0vn0a0fbQeEOeYpmW29XXFpz8Av2wDZrArBb4jcBpHGSJ+eyL0uFn5RTdKeGYP4S3QxDlHwhjZR8t4NbO5pp7ngmxK3bWCToXBKA/tYlz54amjz/0+zBna+ivHFyKrjBHb+Yg4qQJhVZfDLNpYFIrWWqsgAbDYqCliyPBDzTolYArbsE9KeSfclukt6L/uFtGNgEPglkgxsxecNZj+WTorhN5ybY/Xu3dtHfYnaBuALrgx8gW7SoknfBiBJC46vrybmuM8//7wdffTRPjK79dZb+/TkzTbbzEdpgU6uP0TJA5gOGjTIR3NJCwdkgeJC18FrUr+JKNN/0qIB4uy157sOxppoNBDOeALJYW4w5rj59qvKM0sS8P1otn19aal9cnLikwr7szNLbUD7Uvt9XMXjVNfM/R08cbi9+XMPu7znXbZFxwNtm06H2oNfdfKp0HF7WZblpmTBb92K9GS+UN0Tbb81t52U3qCWuW3nZrYFrZz5vcTlj8oCvYvmfgeQgcDFy96eI6LKRH9RM5e2I1X57MRr5rbXVHw5vdSl10FEN6i3S+Erq0Lwm6/fheCXaHUs0hhH536vb/ht6/J8eZZled73AgssYK1bt/ZpsX/88Uel8At8kkK72GKL+bmg+VKI2UYqL4BMYSsikwF+iabG6/MCWBRbIs14/fXX9zAeHzN7/kLwG96nkBNASQp0VdWeg4n4tmvXzoMmKdvMH6ZQVAyZ1TVjQQEuoBfwJ8X7iiuu8McnygoIk25MWnQ8ljxEIFWbPvXo0aPKolOAP1F5oJq08mKqPQf4Ze4xEXvuU9ymJp4+cbYNeX62fbBXqXXdMvEWhf3OtqX25fmlVjKs4nGq69LEs0pn+WWNfvjlJ7us55229mN7+jTogROG+vfjfWRZlpuKBb91q1DdGdDqnTHRYLZnIfak3LbWmW2xgELadIjfiESqdIUvc5EDdDLfjWhw2D4o8R2u6mJbaCuXziv+IvF4l+5PVJOf52Tacc0jM69RIfgF7mMVgt+N57QoExFrAJoPaX3D702u4rjKsjyfmRRb5pwWgl+2UWgKsD3ttNMqzAUlSgh0kcoLIJOqm438Mt+XY8eVikkFJvILNFdWXKkY+CXaTOSXwlqVRZGDAV9glMJagCi/Dx8+PO/1Zw1ssu4wqcbPPPOMh9V4H0D/kksu8ce97LLLPJgPHjzYbycdmWgwUfJ88MucXqpC0y9SoDlf3Ie4PzxsYN4uxciA97hN7AC/2223nU/DzhYpq41nTU/Gtf9sG9RxtvW/r9T631vYP/0r+cx0T+7d5IrHqY1Z2ui+LzvaGo/sYft0OdW+++XfHozjdrIsy03Fgt+6E5WKpyb+zVVMBcWkQjOXN6QNn+LSL1oH5F7nE3N0aZMP5rIivZXq0hyzkEmzDuK4AOWzLk3H5hwUfqqs6BXpzcy5ZY4u+13m0rm3zOdl/5rCL+MVqxD8Mu841hsuTQtHAX47lr09R8xDri381kZEyZs1sElVbTEXTGpu/Pmrb5OC3LaBfZOrmH7dEObfTe8GNv/HjJwLDg/X6t3VifwCt8AnBaqIrobIbngfGAaOSXkmyguAsU8oeEUVZoo5AZchrZY06AMOOMCnAAOIMVBnXRX8cjzm9gLZAB2pv6Hf9IGK0qQYh8JP/CTVmWgpwM7v7EPqL9WUSX1miaE4AszY0G/mFQO2pDHfdNNNFfrD9VNAiqjvXXfd5fvAvnz5AJg5L8Wp8sEv/SB9eaONNrLHH398zrgAuUTKuV+kKYfx5yfLNhE9b9WqlU+bDinURNO5du5XqJCN6wt+Z5cm9+L32T6Veeqo2b6ScyFT6ArwZXmkCscpwr9Om2g9hn1mb/38oQ2eOMJmzJppMxPI/WZsPzv1nattpYd3tsNfP8/+PX7wnIrQsizLTdGC37pTmHcLFObTKS59/77c6z1yry+Z06JMFK16Mvc7haM+zbwXREEswI9qzlSUJvrJEkCxqNBMhWdEOjVFtagMnVVIy86mLsciQkxfmJOcFaDDvtn07d4u/eKaVV3A7yFzWpRpiEsj64i507RjPLLiA0yEeW7CryRJDaNyUFuVazLnF/OHkzV9ieySLkvl4SeffNIeeeQRH3EE9IBjqi8DdEREAT+ixX/72998MSqqMLOsDiDcsmVLW2aZZfyyOxRtKnReXBX8YqCR5ZbowzHHHOPX1r3gggs8YNNfzgcYApEUxGIbhbhof9BBB/mINqY9UAgwxlFX4JhoLJDNwwOqSwOccbQVuGRdYop9Acis8XvppZf69GfAl/14UEB6c+fOncstwwQoU3WZc7D8EBFk1lRmHjDVn3fddddywAroAsWkWLMPad+s18s4UTiL66T6M1BP5Jk1mukbDz5ozz3h+KSz8xCAdYbj6662EwieDdRWZtrgeN8i/eO4gdam242247NH2r5dTrMz37vOzut+k+370mm2avvdbP0n9rF7vujgI8HxvrIsy03Jgt+6E8sKERldJX4jJ4CTAldUWyZKTJSS1GGWBuJ1EIBKOwpaIaK+HJeqyVkxl3emS+f+hqJT/yrXIq3AzLJDRG3QmS5td/qcFqkCuAOZhQQ8DnMpSAZRwKuPS/elynUQ6d1EurN6wKXtsmsDVxd+GeMFQiOXzqFmOwW7gohgD3XpskdBYXyy8Mv8YbYdndkmSdK8rwqAm8e1qvaMw3I8RHiJMlL5mdRdIqekLgNmLL3DH1lglfmqzFtdcMEFfepzAGSAkPZEhIFUIpOh6FMhE91kXiuwll3qKOtQQAsA5viAMufiJ3DLEkvsRzuW+mE7UE7/WFMXGMXAPf384IMPKsy3ZQyoyLzXXnt5gGUMWLoohnH2I/WbFGwqYNOfUFmZiswsM0TfWM8XWKXCctiXsSOyy4MG+sFDA9oxXjwouPzyyz2gZvvGdT399NO20047+XaMEz+JLgO+PKTgiw99p08cl2tdaKGFfP+4DsaD4xNtzje+jc1jSsZZ+29fsJ2fO8aWbbeDLdNue/9z8fu3so06tLbr+zxo/X4daNNV7VmW5SZuwW/dqJlLI68s1VOZiObyxStUWz7CpWALlBE5vdKlEVPgN6T4AtO/uBQ+qTbMnN2wbFAATm7QW7ltQDOQS1vgmi95O+faMYeYYlfM+b3XpdFo2lEQiwjqP3Lt8ukZlx6/s0uXBqIf37t0P7Yzbzjomdy2911ZOnQojMX8ZCLbqLrwS8r1Ry5dG/kul4L9D66s8BcKSxh9kvgGlz48oHo2a/lm4ZeHCbRjvF9y5StxS5I07yoAbj7X2Tq/mPeIsFKF+Nlnn/Wps0QLu3Tp4ue1AoGhUjBzgN9++22/XixL+oRlfACxDh06eJglhbgq8MUAK1AGBBJxzqbwBnNOoI2qz0Awqcssf0QfgEWinvQNcw30hRRiTB+zpr9Eb/NVPCaqzbWydjDH/vnnnz2wxu24Ls5LmjPziekP0WTSsxln9qVKM/OH4zHgekMbIuksecQ19erVa070Ots3rokvNlw7qdS079Spk79G5hjzxYd7x7hxDM7LdYZrD+spc90scxRDf2M0ac4jJ4+1XiO+ss7937Z7vnjS2n78kLX76hnrOqinDZo43FeBjveTZVluahb81o2OcWmqb1UptFSDph3gGdTCpZFSABQDsZtl3keruRTiRrkU+L5xabQ2K+CNFGoAj4gwUPmmqxgxXt2lc2KBPoAdeH3MVT7fFxG5BixZW5c+kGrMGsFErTkPxwha26WRac7RLreNecbP5baFZZzudOm+sYBrxilUo2ZuMa9J36bAFQ8Cxri0P0S+s2JuM8cl+gv09km8i0tBOKSSBwH+pHMzH7iYgl+SJDV+xcBLtg3/D22RbRRktYBfDGgBR6TqApT8QQXewjJGcTtShfnJcfnJHFYgjO1VnSscByAjZZgUZaozs7xP3A5zfvpBfwBxzhXOU6hv+cx7+cA3ew76QNt4XnDWtGOc+OJBf0JfsuePxy3bR95nrID6MMaFzhf6BZzTnvPG1x7axNebve5Cx2+MnsX1zEruxYyp9kvJrx6Gf506wabN/F1FrmRZlnMW/EqSJEnzkwBeHuzxAO4oV/n65bWG36yBqXzgVhsDYEQswxxc0qVJMyZ1mPmrRDKL6WN99K2mrk1farJvTfaZ101RK3/dKm4ly7JczoJfSZIkaX4SNQDWiDcWktUh/NaHiUCSUky1YwpikepMkSiKQwHFcXVkWZZlWZYLW/ArSZIkNVlZI4ffMCeX6s4UpmJuMMWz+vbt61N/m1pEU5ZlWZZrY8GvJEmS1GRljRx+cTxfmHmqjaFfsizLsjyvWfArSZIkNVnZPAC/sizLsizXjQW/kiRJUpOVCX5lWZZluclY8CtJkiQ1WZngV5ZlWZabjAW/kiRJUpOVCX5lWZZluclY8CtJkiQ1Wdk8CL/Tpk2z77//3oYPH94o+yfLsizLjdWCX0mSJKnJyhoAflmOaMiQIdauXTu788477ZVXXvEAG7crxlR77tq1q7Vq1cratGljEyZMqNAmmPOyDvA333xjvXr1qvE55wWz7NOnn35qDz74oHefPn38trhdfZhxZYz/9a9/2e23324dOnSwQYMG1clnB1Pl++eff7bPPvvMP/Cg2nfcpi48ftpE6zn8C3tlwPv27dj+NnXG/Pt5kWW56VrwWz9aI3GzxI1x4BZxad8Wj7YXq5UTrx5vbCRaNnGLxDtE2wuJ+7RSvLEexOegWeLlou2SJM1lWQPAL8d47733bIsttrC11lrLjj766EqhtTIDdI888oitsMIKtuOOO9rYsWMrtAGQe/fubddff70deuihtvPOO9s+++xjo0aNqtC2sZvr/eKLL+z111+33377rcL7wePGjbN77rnHNttsM9t8883tlltu8V9u4nb14fHjx9vjjz9u22yzja2xxhrWsmVLe+edd2oF3yxvBfAC1Keeeqo/5oEHHmidOnWyiRMnVmifz1OmTLFPPvnE3nrrrSrHYvqsGdZ96Cd2yGttbPtOR9i1ve+3EZPHVGiHOT8PU959910bM2aM1pqWZXmesuC37rVO4v+X2BLvG73XGLSXS/t2SvxGkeqdeEy8sZHoUZde23HxG4lWTHxVtG1y4vejbfUhHjTQr8fiNyRJqjdd79IHXJXKGgB+gQPA84UXXrDlllvO9thjjyphpJCJ+n388cceiAC8SZMmVWgDJF588cW24YYb2qabbmpLLLGErb766j76HLdt7B49erRdddVVdsghh/hU70L3g+jrt99+a9dcc42tv/76/vrzPRioDxOZHThwoHXs2NED6vbbb28vvfSSh8+4bbFm35dfftl22203W2+99fz9437efffdHvTj9rHpU48ePezEE0+0yy67zIYNG1ahTdYDJgyxi3vcZiv/axdb8oFt7OSuV9qgicMrtMN8dh977DE78sgjrX379kX1R5ZlubFY8Fv3ujXx74l/SfxG9F5j0PwKv0SkZyd+IH4jp68T/xhtE/xK0vwr/s39lbhb4qMSL1T+7VTWAPCLAWCiZCuvvLLtvvvuNYZfjgPw9uvXzwNNvv4ByERL3377bXvttddsk002mWfhl2s86aSTbKuttrKPPvrIR7XjNsGTJ0+2F1980Ud/L7roogaDX0ykFgC+4IILbJdddqk1/HIPhw4dat26dbPOnTvbWWed5SPLpM3zxS1uH5vPLmnxZAfcf//9lQLq5OlT7Ll+b1rzF06wdR/by5Zrt4Od2PXyBH7zAzP34M03k/bNm9vhhx/uo8DAdtxOlmW5MVrwW7dawKVg+GbihxP/L/Fq5VrMfc2v8Lt+4pMSLxi/kdMPTvArSU1J/JvLmn/v7RJvUa5RLeEXEH3mmWd8dBJIA1Juvvlm+/DDDyuAGumxwC/RPEAJgLjvvvt8NK9Lly42cuRID1HZfThG9+7d/TFJY8bXXXedXXvttXbTTTf56F4+8KDf7MsxASDScWsLv0RXAdDbbrvN2rZtaw888ICHPa4bKCPF9sYbb7Tjjz/eLr/8cg/g2TGgn2xjXuyZZ55pxx57rJ1//vk+ZZi5rNlrB/KBx6+++soOO+ww23jjjf0YDR482EaMGOEjwox9dh/SjF999VWf9nzGGWfYU089ZVdccYWdfPLJ/v4wjsyBjq+L85Amfscdd9g555zjo+pXXnmlPffcc3nvCX0DrEknZgy4dqCb45933nl54ZffmYfMtdM3xghAJ425UOEyzgsE8/6tt97q72Mx8Ms4cP6tt97a9t57b3/e+LMYXDq71L4Z28+Of+tSa9n5JDvlnSttvcf3rhR+cf/+/e3cc8/1QE4GAvcjbiPLstwYLfitW7V26ZessxPvlPv9pmyDnDZK3DbxComPSNzRpWB0pEsBOqtrEu/t0n2IanZKfIUrPH90n8TtXdru9sQblH+7WvC7SuLrXHqsO1w617cQ/DZ3KfDT9q7Em5Z/u6BuTLxb4rUT3+3S/UlXXDr3Pu89ntt+RuL/y23PirF+0KVt7ku8deY9PryM9a+Jx+d+55gowC/zftnO/g8l3ir3flYc50BXNraMR6Fr3N6V3asLXDoXmTGP4ffvLr0PT7m07WWJlynXQpKkmop/c4X8beI2iZe2GsIvANS3b18PVoDZmmuu6aGL3wFNUl8B1ez8TOB3pZVW8m1J491yyy39HGBek54M5DE3OAuzgAzgw/tEb1dbbTXvVVZZxdZee2274YYbKp0LC6TVFfxyHmCda1x11VWtWbNm/npIq+b4O+ywg9+++OKLe8gHigMUMceZFNkWLVr4trQjnZf9N9hgA5+eS8EqIA3g+/rrrz0g7rrrrv5YHBOYY/8999zTG4jkHgU4DfBLvzgH/eT4Sy21lP/ZunVrP77ZubikowOUROMZz5Aqvs466/gI8umnn+7BOBQL4yf9/Oc//+nBj3Nx/4iu81CDCDXbs/DLGFCEi77TlvvHubiH7Hf22Wf7Ylb5HmKE/XngUCz8MlcYsCb9G5iv7L6PLfnV7v/yadvluWPskh63+983f+rAKuGXKDuFvTbaaCPbf//9Ff2VZXmeseC3bvVK4j8SL597Pdil6c9xNPIQl34BA7xmJn4p8Ue5baToAUVB0xP3STwr8ceJu+S2TXLlIxhAc2eXHuNnlx57XOL/uvJzYIuF311cmr7Nefu49JjA4ghXHn75cHRw6TGHuvS8vP9n4jMz7QqJtMS3Es9w6Tn6ufRY3ye+yKWpzJ+7FF7ZDmBndU9u+0iXnpv+MecakET0j/e4L5jf+dKLuB7uEWM5KvGnLj0fEXtAN4h0ya6ubGx7J/7NpX2/MNMOXe3SdmNdWTu+aLMtC7/MQSYSTV+/cul9Z6wB9M0z7SRJqpn4N1eVS4855pg/iGbGqgp+gblkX1tmmWX8HN6nn37aR2Hff/99H0EEbgCdhx56aA5sAb8rrriiLbTQQh58gB6KVxE9PPjgg23ZZZf1EJad28rPH3/80RdQeuONN7wBPKK+AB2RxsoKINUl/AKlP/zwg49CU3ALSKSCNfNsGYcll1zSzwO96667PKgCj0R6idDSb6AQ6AXMiJRSMOnhhx+2vfbay48HAExkl/MQWeTa9ttvPz+OACxwSQEvUm2POOIIH50lLToUXArwC1zSl4MOOshDJ/cgHOfCCy/06ee05x4SdeYhBBBHNJ1oPKnGTzzxhB1wwAF+3OgXaeahXzzwAHq574wFRamIYAPbQDrjEuCXe0MEedttt/Wwf8kll/hrJyWdAl0AMccCUoHWOMqMqwO/jAWfZ4qcMf6PPvpoweJqM0tn2ofDPrPWL51urRK/O7i3de7f1bbseHCV8Mt5PvjgA99/7jWf45qm8suyLDekBb91JyKx/3Hl5/kSteUL1uGZbSjAb4lLI7pBF+e2X5LZBuiy7fLMtvUST3FpKm9Q2JeoaRCVnd91KdBRiAsVA7+ANCA7wZXth4josm8WfgFctt3pyqpbA4s8CAC8C0VHgwBI9j85sy0UrgIcQ+R60cT9E5e6socJRM1pR/sQEebnky497s65bahQ2jP73+DK+s79CMAddJtL23E/gxZL3MOl5+FBAdos97p74oVz25ZM/IVL98/CL6nxfF64H0EU5wHeAex8EW5JkooX/+aKNgBChBUwQpXBL4BC9WEikgANsATgAUc4RPoANqLBIfoJ/AKNCy+8sE9dBmqBI/4If/755z7yS0EsUnSzhaxC+mswkAcMEwkm9bQQ3OC6hF9M2jDXTmT0tNNO8/NSidISMaU/vPfTTz/51GG20U+A9pRTTvHjBUgC0FwDUV7molIIjPEnAswcZd7jPByHhwlAKO8BpKRBf/fdd/4BAbAYR8mBX66VcQcwOT5fdChGxTmI/oZxAGh5zbWQxk0qOsdjjLkvgDAPI4BICjyxHz+BXKAPoCdyzPjTV6LiRPKz8AssE8HmYQgPO+h7uHY+F8ApnyEyBThfvrTs6sAv/adQ1rrrrusBmPT7QhHZwb+NsKt63Ws7PHOktf34YRs2abS9PrB7UfCLuY98BrhePs+kiMdtZFmWG5sFv3UnIoB8iTo4s435vkRAgaSsAvySUpwVAz3cpVHPIOB3UO69rG526TECXA5xaRQzbsdcWNpRiAsVA79AI22ujLYDnURgs/ALVBLljGGNlGmimqQjVyZg8ado2wEuPT9AnRUpyWwnTRkRLZ/qUsjPagmXRnmfyWwrBL9cSzxmvRNPzP3Oe/wOeMday6X9fz73OkByDPw75rYH+OVzwdgA6bHCwwRS3YtRW5fni7wsyzXzAgss4IEIgODLPSBElCtrtjGXlCgfc3yBn+z7wCoRSaKJRCCZC8s+/LElWgsgxccmnZZzLr/88j7CyR/n+LzBgBNQmIXfuE0wMB/gFwiN36+uATegL0QxgUvWtN1uu+38Uk5AIMDNe1w/MAqokg78j3/8w/887rjjPBxjoqhUSGacGBuixuF6ePBAn4FHjs2cWs7Pdsw4Z/sGOLKGMucFsgFz2tCWhwtEnoFWoJmxBwyBaraHlOvs8biH3GfSh4lWk5pM5Bj4JaUd8A194HhffvmljzZzPOYnkxrMvec1D0KI/jLPOXvtLEG19NJLe1jlgQmfkXjMOU8Mv3GbYMaOKCz3m9Rwxj4eJzxtxu8J6H5guzx7jO3z4mn2zqBePgW6c7+3bbMnD7D9Xz7Lug7qaaOn/GIzZlX8N4C578ztZnwA+7r4fMmyPP87htGGtuC37gRYEZUEWlpkTBovoEO0NijA7x6ZbUGkQJN2G+b+Ar/5ICkA4gmJl8r9PtClMBQbAH8n3a0o+D3XpW1aRNsREcsAv8Awxx7mKp4TMx69c20LCXjMRstRC5een7nTWYXI86q519NcmlYenxcTGWfsgwrBLwAdixRnjo04F+d8pOztcuJhxb9zv7/n0vTtWNxLorwBfg9y6TFJ0477zfxf3stG+isT+9BeluU6NpBKVDP+ww0kMd90scUW88V+iPDFbdhG5A1ABhaJvgX4BQABxGx7AI2oIGnRwATRvviY2fMzd3Vuw++ll17qr4njcg4AGODkiwVQBCQSAQU8ibouuuiiPkWWdOGsgX32JTWZBwDZ8eR4J5xwgodf0mwZx7hPwQF+ifqG9ObwHmPL/Qjwy3UQaSUiy/kHDBhQ4Xhcx7333uvvBxFO0toBdyLBzF+Ox51jMEeYuc/AL3OkAXb2Z140104kOXvtzGkm6ksaN1FzIv5xP4DfuOBV3CZuy/0GxHkYEbfBgO49Xzxpa7Zvbis9tHMCwccmEHyqbd3xEFvi/q1thXY72YEvn20v9n/HJkyr2CdM4THS70knp/9kSsRtZFmW68oxxNbUgt+60bYuzxenyBRiCgrwW67qaE4dXfreP3Kvgd97y96eoxYubXeOS9Nl+T3MM81noqaoGPi9yqVttovfcGlhpgC/FKWiHdHg+HzB+cA9K+D3hWhbC5ce94xoewy/7EtqdHzOYOZHBxWC33zVnrPwS9o154yj0EEck1RlxJxhUsXziX4G+D3RpccEmuM+B5+ea1uV2rqKnzVZlmvobOSXqFmhyC+RP8AWKAJysu8TaQNCiOYByMBfiPwCt8xJJR0W4A37ALSAG2nPQFoMx1nXF/zSH/rNHFXmowKbQGK2TSH43WmnnTzExfDLOLL+LvDKdTP/tmfPnr4CcdasXUxKM/tmx4XjUTgLoO3atWuF/mSdhV8itVn45V5ShTkb+aVIE2AKlOaL/FJlmTVyacO1EsUl0k9kGejL3iPuOddJijbHA34BWa6N14wF86OJNue7dvaNrz24OvDLQxPacL+JqBeC33ElE+yxb1/0Kc9rPdLCmj26Z+KWttLDO9tCd29qi923pbXqcrq9NfBDm/x7xYc7mPEhXRz4Za53oXPJsizXlWOQrYkFv3UjooJ8cdo/cbPI67oUsnBIzw3wu2fudVZEDyk0FQT8dsq8DjrGpcc41KVL6RBdzsJeIRUDv6e5suuJRXGqAL+kOjOvl3nFNVVt4JfobnZubmWqKfwyl5tzPl32djkBuxS0Qq+7NN06W7AMMU7ZyC/jyjF5cCFJUv2oAthWZiKTYckW/iD+9ddfHlDzwS+gQ9oxoAqAhshueB8YZg4kqbwUUwrL5fDHNsz5BVpJqw1puUQmgW72ARxjoM66vuCXcxLpJDIN0LRs2XLOckWhTXXhFxglIkq6L0WtKAzFdYeqzmHeL3ODKX71zTfflBtLAAvgZP4w82PDuNAP2hJZBmzZpzrwyzbm/AKIjA3XwpzeAJ+kLHMsUpU5N8smkebLQwHGhgguc4qJUnMPiYKSmky2ABWcQ9ozFcGPOuoo/yCEAl2cI3vtgC1LJlE5mc8A6e/xfakO/DI+zI2mjxQSI70+X9rz9JnTbehvo6z38K+s+5BPvLsN/thu+bi9X+t37xdPtTcG9rBfSsbbzFkVgRwzR5px47PA0lWMT9xGlmW5Lh2DbE0s+K29AFoKVzEvt5CoUMwXrFDUKcAvMJcV6cvALoAZxOtRrmLFaOaZkh69bO41YAcMMt81q00SD3BlabTFwO/aLoVpCkllRdGpME826DOXpvrGSy9xDM7LUkaVqTbwSyo38L3mnBapWEKKecTZiDnzqLNp0KgY+EUUoCK9OhSxCgrLWbH8ETo/95qU9Kxa5rYH+KV/9PvDOS3KxIMH+sqSVZIk1VwVADePfbVnIpH8Ecy6MvjF/OGkQi/zWAFc5oYCMKTDEqkEqIBjCi0BZUAPVaCJFv/tb3/zEWDSfFknF4Bt3ry5n/sJ4BABLXReXBX8cj4qCgNj9It2QDXzcAF8KlMDZvFxiWSS1ss10UdStJ9//nl/PN4HzIiWAqNcGwAIaAFYpPoCXKxZTAQQ+OW8tCW6CaQCw8AhIHz//ff7sQKeSM8FuIHnzp07l+sb4wyQEzUGRIHaq6++2h8DmCVKTkSVBwzcRyCMwlqkFD/55JO+yBhQxvrD9I+HHIwL4MaxWaMZWGU7x6RoFZDNuIWlm0hx57MAcHMtrVq18nOzuVfMdeVaAVwg8O9//7t/wEE/iPLyMIW1fEmVJoLMZ4Nz0OeLL77YR4r5/PCggTEKac+MOZ8DKkkDzYAs47nvvvv66w/jHsMynw3Sp+lLKHgVtwn2hdQSsMU/jR/slzk65JU2tly7HW3jDq3tlk/aW/9fB3lQjvfFPFBg7jL3lT7yoCJuI8uyXNeOYba6FvzWXiwjxJeobJXlWFu5tA1Vf1GAX+bEhkrQLI9EBBUYbJ7bhkK1Z+aCUmGYKCJL9dAum1Ic+kGl4QCH67g04sm83B1y24qBX0QUGbgGxvgAAObMR2bfLPyyJBDb+ri0ABQCRrlW+tgit62QagO/u7t0fyKvFPZCKyfumWtHVDzoY5cCbStX1s9i4Zex4ngvuzLI39KlRca4P0T3EZWdgeTRrmytYapHU4iM/bPVnlm7mG0s1RRS3IFkHqSQRk40X5Kkmot/X4U8Z53f5I9fSQy+xcAv4MAcR6AK0AiVn6kczLI/gCEwTFSTYzD3k6q4Cy64oIdKqh8HQKY9MAV8UTm6stReTJST6CtASCQ1hl/+qB999NEeNDkXQEY6N8AdAIpIZXxcooZAKf1aZJFFPFwy5zmMARWQATfmyXIdwDpLNBEdDlFroJPXACvnJkUYwCTay08KPNEP+sZxAEXGDqBkH6A0O+bAHJDXpk0bD6/sy3EZN2AZ8KTQFPBL3wKA0jdScTkeY8+cYpaYIurOPFsizURtiQ4Dp/Qf0KVf3EPGFqij8jYQGgCSMSJSGypzc+/oD2DLerdEwBkHrpsoMQ8U6BuwGpZ04hzANtfO/SD6TDo4fQ1Rdj43VJam34wPc4bpPz85Bg9LSE/nnsT3kaWOuEYi4IA8DwDiNrH7jPjaF7la9sEd7P/u2tgWvXcLa/3SGdZ9yMdWMr3i55HPP+PK55wHFxTZos9xO1mW5bnlGHoFv3UnondESZtF22MBoXzpApoC/AJZRAABHl6zzivVfrMCrtiXeaW0JcpKW1JsAzQFEd1lmR7epwoyP1lHOLuMULHwC8h1c2lb0rABaCK5bMvCLzrPpefJnpd+/DPbqIBqA7+IawNUs+dmnOJK1Zfm3sMBQouFX3StS1Oa2Z8IOz9HJd4t0wZxf9nO++G+Mo+bvmXhlyj6c7n3GVvGOByTOeSSJNVO4d97MP/e27mo1kJN4ReTIgtEUgiJCCnwRuou81ypDAyYAAmYCCDARVSOlGnAmegtkWFSakmhZemjQlG6rIFjwAhYA85iAAKOmUdKQSfOFxzWCSYaGKK58fUQvaNfLD9ElDPbjuPSb+behuMRCQa+uV6gkLEI0Vaul7V8iXqzL/0EVEknJqLJeBGdZT8AExDNN96MCanTHIuoNSDJMegfKde8j+lbGGP6xvtEUhl7HiqEcWAuM6nEnItrpv+cn8rUYd1lQJ12oe+hL9xLjskcXSK13G/uBWPCgwLmDnMv6SufH/rFOQBD5n6HfXjIwH1nP9K3geRsejn7AcN8VriW+D6GvsXzlDFjT0SddO2QZh63iU16c+/hX9qbAz+w1wckYzWgu/VKXo+ZMs5HhuP23EvAmnMQvea6KytGJsuyPDccg6/gt25ExebserKFRLXnFolXd2XwS9S3mUtB9GhXlsKcVUiDBpaOdGnbzcq1KC9Sao9yaTvWwV2m/Nv+dQuXRkiLERFjjkUUlRTvjV2a7huLvof+cX76UYyau/SYWS3t0j6yXFJWRLLZzjrCWZHqfZhLz81c6Hi/IL708n4Yv10Tb1729hzxfgy1iDFjf85DVe84FT2I/hERpx3nQBwvrFmcFZ+Lk1zalqh0fG2SJNVM/B/LwzUe2PF/Ut5/W7WBX+xTR5M2QCKAA/gBAXHhotAuOLwmxReHbfHxY3Nc0opJFSbySPpsvhRm2mXPl3Xct6zpA0DF9eS79vg6OFaYt5y9tmy77HXRDrADOBkvQJLzhePE54v7xbWyT1iPN7tPvr7l2x7vF/eLL0VEhfO1y7bnIQT3m2hwuOfZcciOcxijsA/n4Foqu/Z8/a7sGoI5B5BMFJooPw8oqnqoMqt0lp/by7JGwbxme9wWM1+atH+ivhS9ypdJIMuy3Bgs+G0cysJvVQrwK0mSJBUvpqJQCb9SJZBbAujG/vPPPz38AicB5uaGgSoiiaRJY9aGZX4o4Mv6sUQs53Yf5cbnUIyKedLMNSeqHrepqUPaPUtEkfYeMgnidrIsy9VxDK11ZcFv45DgV5IkqRGoMvglujW3wZIoHinVG2ywgZ9LypxXCjSx9m2YtxrvI8t8boBSCpix9jAwHLepqUkjJ2378MMP92n7YYmmuJ0sy3JNHMNrXVnwO3dFJd+RiVvHb+QRVYs7xBslSZKk2quxwy9QQUopc1aZ58r8WKoa0zcAJ24vy8GkYzMPmfWJs/PPa2seuDA/nTnMYZ523EaWZbmuHcNsTSz4lSRJkpq0Gjv84jCPlHRT+hPmk8btZDl2+NzU5eclfB6z85VlWZYbyjHQVteCX0mSJKnJal6AX1mWZVmWyzuG2mIt+JUkSZKarBLILQF0Y//vf//z8Jut4ivLsizLct04htmaOobbqszScoJfSZIkqUlK8CvLsizLc9cx0NbEMeQWMvDLUnuCX0mSJKnJSfAry7Isy43LMdhWxzHsxg7wyxSnAgAs+JUkSZLmTyWQWwLoxv7vf//r4XfatGm+uI8sy7Isy3XjGHYrcwy3xTgGXsGvJEmSJLnK4ZflXAS/sizLsly/joE3n2PArcox9BaC32DBryRJkjTfS/Ary7Isy43LMfjGjkE3n2PorQp+MwAs+JUkSZLmTyWQWwLoxv7jjz+sf//+VlJS4pc7kmVZlmW57hwDbz7H0Bs7Bt58zoIvr4Hf8ePH54XfHAALfiVJkqT5U4XgF48YMcJGjRplU6dOrfBHW5ZlWZblunMMvsVCcAy7lXny5Mk2ZswY/2A7hl7BryRJkjTf648//ighypvPVHr+5ZdfbNiwYfbzzz/bwIEDZVmWZVmuoflbWh0PGjSoggcPHlwj87ecqC/gywPuGHqD//rrL8GvJEmSNH+qMvj9z3/+458q84dy0qRJsizLsizXoYnEVuUpU6ZUMH+X85lMrUKmhgfRX+p6xMAr+JUkSZKahBLALQFyZVmWZVmuX8cPmWPH04+yjgtTBv/555/lzFzeqhwDr+BXkiRJahL6j+BXlmVZlhvcMfgWA8Ex+Ap+JUmSJKkaSv4Al8yePdtkWZZlWa5fxwBcFQzH8FsIgmMALgaCY+gV/EqSJEnzvQS/sizLstzwjuG3MhCO4bcuADiGXsGvJEmSNN+rtLS0JLsGoCzLsizLde8YfmPHABxDcAy/+SA4ht+aQLDgV5IkSZpvVSr4lWVZluUGdwy/hUC4ulHgGHyrC8CCX0mSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJKm+9P8Beo0J0n5WAocAAAAASUVORK5CYII="}}, "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## 6. Synth\u00e8se\n", "\n", "![synthese.png](attachment:synthese.png)"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["| Terme | D\u00e9finitions |\n", "| :--: | :-- |\n", "| __Classe__ | Type de donn\u00e9es avec ses **caract\u00e9ristiques** et ses **actions** possibles |\n", "| __Attribut__ | **Caract\u00e9ristique** de la classe |\n", "| __M\u00e9thode__ | **Action possible** sur la classe |\n", "| __Constructeur__ | M\u00e9thode qui initialise un objet. <br />Un appel au constructeur cr\u00e9e une **instance** d'une classe. |\n", "| __Encapsulation__ | D\u00e9signe le principe de **regrouper des donn\u00e9es brutes** avec un ensemble de **m\u00e9thodes** permettant de les lire ou de les manipuler. |\n", "| **Accesseur** ou **getter** | M\u00e9thode qui renvoie la valeur d\u2019un **attribut** de l\u2019objet. <br />Par convention son nom est g\u00e9n\u00e9ralement sous la forme : *get_nom\\_attribut()*. |\n", "| **Mutateur** ou **setter** | M\u00e9thode qui modifie la valeur d\u2019un **attribut** d\u2019un objet. <br/>Son nom est g\u00e9n\u00e9ralement sous la forme : *set_nom\\_attribut()*. |"]}], "metadata": {"celltoolbar": "Diaporama", "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13"}}, "nbformat": 4, "nbformat_minor": 2}