shap-101-kaggle-munich-event-18-04-2023.ipynb

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    "# SHAP 101 - explaining ml models and beyond\n",
    "\n",
    "### Feature Attributions\n",
    "* SHAP (SHapley Additive exPlanations) - https://shap.readthedocs.io/en/latest/\n",
    "* Understand individual predictions - https://www.kaggle.com/code/dansbecker/shap-values/tutorial\n",
    "* Aggregate SHAP values for even more detailed model insights - https://www.kaggle.com/code/dansbecker/advanced-uses-of-shap-values/tutorial\n",
    "* Style SHAP Plots - https://towardsdatascience.com/how-to-easily-customize-shap-plots-in-python-fdff9c0483f2?gi=754319eae2c8\n",
    "\n",
    "* Convert SHAP Score to percentage - https://medium.com/towards-data-science/* black-box-models-are-actually-more-explainable-than-a-logistic-regression-f263c22795d\n",
    "* base value in SHAP (average of the outcome variable in the training set) - https://towardsdatascience.com/explainable-ai-xai-with-shap-multi-class-classification-problem-64dd30f97cea\n",
    "* Additive Feature importance - https://medium.com/@singhvis929/additive-feature-attribution-methods-diving-into-ml-explainability-98c81c656d3d\n",
    "* Kernel in SHAP - https://towardsdatascience.com/one-feature-attribution-method-to-supposedly-rule-them-all-shapley-values-f3e04534983d\n",
    "\n",
    "### Partial Dependence Plot\n",
    "* Partial Dependence Plot Theory - https://christophm.github.io/interpretable-ml-book/pdp.html\n",
    "* Partial Dependence Plots - https://scikit-learn.org/stable/modules/partial_dependence.html\n",
    "* Partial Dependence Plots show how a feature affects predictions - https://www.kaggle.com/code/dansbecker/partial-plots\n",
    "\n",
    "### Additional References\n",
    "* Fairlearn - https://fairlearn.org\n",
    "* squaredev.io - https://github.com/squaredev-io/explainable-ai\n",
    "* Rerun-sdk - https://pypi.org/project/rerun-sdk/\n",
    "\n",
    "### Pandas \n",
    "* Working with missing value - https://pandas.pydata.org/docs/user_guide/missing_data.html\n",
    "* show max columns - https://stackoverflow.com/questions/11707586/how-do-i-expand-the-output-display-to-see-more-columns-of-a-pandas-dataframe/11711637#11711637\n",
    "* DataFrame types with dtypes - https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dtypes.html\n",
    "\n",
    "### Scikit-Learn\n",
    "* Custom Demo Classifier _ https://scikit-learn.org/stable/developers/develop.html\n",
    "* Custom Classification Model - https://towardsdatascience.com/how-to-create-custom-scikit-learn-classification-and-regression-models-70db7e76addd\n",
    "* Custom ensemble Model - https://towardsdatascience.com/how-to-build-a-custom-estimator-for-scikit-learn-fddc0cb9e16e?gi=b6f190dcd368\n",
    "* Custom Regression Model - https://towardsdatascience.com/building-a-custom-model-in-scikit-learn-b0da965a1299\n",
    "\n",
    "### Model validation\n",
    "Confusion Matrix, ROC, AUC - https://towardsdatascience.com/intuition-behind-roc-auc-score-1456439d1f30?gi=c4b96aa0c60e\n",
    "\n",
    "### Explainable AI\n",
    "* The Challenge of Crafting Intelligible Intelligence - https://cacm.acm.org/magazines/2019/6/237004-the-challenge-of-crafting-intelligible-intelligence/fulltext"
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    "# Kaggle Titanic Compitition\n",
    "https://www.kaggle.com/competitions/titanic\n",
    "\n",
    "### Data Description\n",
    "https://www.kaggle.com/competitions/titanic/data?select=train.csv\n",
    "\n",
    "| Variable | Definition\t| Key | \n",
    "| :--- | :--- | :--- |\n",
    "| survival | Survival |\t0 = No, 1 = Yes |\n",
    "| pclass   | Ticket class |\t1 = 1st, 2 = 2nd, 3 = 3rd |\n",
    "| sex |\tSex\t| |\n",
    "| Age |\tAge | in years | \t\n",
    "| sibsp\t| # of siblings / spouses <br>aboard the Titanic |\t\n",
    "| parch\t| # of parents / children <br>aboard the Titanic |\t\n",
    "| ticket |\tTicket number | |\t\n",
    "| fare | Passenger fare | (Ticket price paid)  |\t\n",
    "| cabin\t| Cabin number | |\t\n",
    "| embarked\t| Port of Embarkation |\tC = Cherbourg, <br>Q = Queenstown, <br>S = Southampton |\n",
    "\n",
    "### Variable Notes\n",
    "\n",
    "**pclass:** A proxy for socio-economic status (SES)\n",
    "* 1st = Upper\n",
    "* 2nd = Middle\n",
    "* 3rd = Lower\n",
    "\n",
    "**age:** Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\n",
    "\n",
    "**sibsp:** The dataset defines family relations in this way...\n",
    "* Sibling = brother, sister, stepbrother, stepsister\n",
    "* Spouse = husband, wife (mistresses and fiancés were ignored)\n",
    "\n",
    "**parch:** The dataset defines family relations in this way...\n",
    "* Parent = mother, father\n",
    "* Child = daughter, son, stepdaughter, stepson\n",
    "\n",
    "Some children travelled only with a nanny, therefore parch=0 for them."
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p=this.xlabel.selectAll(\"option\").data(f);p.enter().append(\"option\").merge(p).attr(\"value\",function(t){return t}).text(function(t){return t}),p.exit().remove();var h=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";f=(0,y.map)(this.usedFeatures,function(e){return[t.props.featureNames[e],t.props.featureNames[e]+\" effects\"]}),f.unshift([\"model output value\",h]);var d=this.ylabel.selectAll(\"option\").data(f);d.enter().append(\"option\").merge(d).attr(\"value\",function(t){return t[0]}).text(function(t){return t[1]}),d.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var t=this,e=!0,n=!1,r=void 0;try{for(var i,o=this.props.explanations[Symbol.iterator]();!(e=(i=o.next()).done);e=!0){var a=i.value,c=!0,s=!1,l=void 0;try{for(var 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f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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   ],
   "source": [
    "# %load_ext autoreload\n",
    "# %autoreload 2\n",
    "\n",
    "# load JS visualization code to notebook\n",
    "import shap\n",
    "# used for render kernelshap explainer inference progressbar \n",
    "shap.initjs()\n",
    "\n",
    "# graphic display modes\n",
    "%matplotlib inline\n",
    "\n",
    "# DARK_MODE = True\n",
    "DARK_MODE = False\n",
    "\n",
    "\"\"\"the custom argo presented in this notebook is not suitable for kernel explainer\"\"\"\n",
    "# ALGO=\"custom\"\n",
    "# KERNEL_EXPLAINER = False\n",
    "\n",
    "ALGO=\"xgboost\"\n",
    "KERNEL_EXPLAINER = True\n",
    "# KERNEL_EXPLAINER = False\n",
    "\n",
    "# LINK = \"logit\" # to transfer the shap value from probability space to log(odds) space to have value evenly distributed.\n",
    "LINK = \"identity\" # to get the shap value of probability space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.datahelper import (\n",
    "    KaggleData, \n",
    "    current_dir_subpath,\n",
    "    profile, \n",
    "    feature_correlation, \n",
    "    na_columns,\n",
    "    fill_missing_values_with_mean,\n",
    "    DataVisualizer,\n",
    ")\n",
    "\n",
    "from utils.modelhelper import (\n",
    "    ProbBinaryClassifier,\n",
    "    ModelValidator,\n",
    "    ModelExplainer,\n",
    "    ModelKernelExplainer\n",
    ")\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import numpy as np\n",
    "\n",
    "# set the pandas display\n",
    "pd.set_option('display.max_columns', 15)\n",
    "pd.set_option('display.width', 1000) # for print\n",
    "\n",
    "# create a visualization helper object\n",
    "visual_helper = DataVisualizer(dark_mode=DARK_MODE)\n",
    "\n",
    "titanic_train_path = current_dir_subpath(\"data/train.csv\")\n",
    "titanic_test_path = current_dir_subpath(\"data/test.csv\")\n",
    "label_name = \"Survived\"\n",
    "one_hot_cols = [\"Sex\"]\n",
    "\n",
    "titanic = KaggleData(\n",
    "     train_path = titanic_train_path,\n",
    "     test_path = titanic_test_path,\n",
    "     label_col=label_name\n",
    ")\n",
    "\n",
    "# load all raw unprocessed data as DataFrame, label as Series\n",
    "# one_hot_cols transfers categorical column to one_hot encoded column \n",
    "train_X_raw_df, test_X_raw_df, train_raw_y = titanic.load(one_hot_cols=one_hot_cols)\n",
    "all_X_raw_df = titanic.load_all(one_hot_cols=one_hot_cols)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore numerical features in different data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'Sex_female', 'Sex_male']\n"
     ]
    }
   ],
   "source": [
    "# detect the numerical features for building classifier\n",
    "num_features = all_X_raw_df.describe().columns.to_list()\n",
    "print(num_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profile of Raw Training Dataset\n",
      "Shape: (891, 12)\n",
      "Numerical Columns: ['PassengerId', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'Sex_female', 'Sex_male']\n",
      "Num. Columns ['Age'] has total no. of NaN : 177\n",
      "       PassengerId      Pclass         Age       SibSp       Parch        Fare  Sex_female    Sex_male\n",
      "count   891.000000  891.000000  714.000000  891.000000  891.000000  891.000000  891.000000  891.000000\n",
      "mean    446.000000    2.308642   29.699118    0.523008    0.381594   32.204208    0.352413    0.647587\n",
      "std     257.353842    0.836071   14.526497    1.102743    0.806057   49.693429    0.477990    0.477990\n",
      "min       1.000000    1.000000    0.420000    0.000000    0.000000    0.000000    0.000000    0.000000\n",
      "25%     223.500000    2.000000   20.125000    0.000000    0.000000    7.910400    0.000000    0.000000\n",
      "50%     446.000000    3.000000   28.000000    0.000000    0.000000   14.454200    0.000000    1.000000\n",
      "75%     668.500000    3.000000   38.000000    1.000000    0.000000   31.000000    1.000000    1.000000\n",
      "max     891.000000    3.000000   80.000000    8.000000    6.000000  512.329200    1.000000    1.000000\n",
      "\n",
      "####################\n",
      "Profile of Raw Test Dataset\n",
      "Shape: (418, 12)\n",
      "Numerical Columns: ['PassengerId', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'Sex_female', 'Sex_male']\n",
      "Num. Columns ['Age', 'Fare'] has total no. of NaN : 87\n",
      "       PassengerId      Pclass         Age       SibSp       Parch        Fare  Sex_female    Sex_male\n",
      "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000  418.000000  418.000000\n",
      "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188    0.363636    0.636364\n",
      "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576    0.481622    0.481622\n",
      "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000    0.000000    0.000000\n",
      "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800    0.000000    0.000000\n",
      "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200    0.000000    1.000000\n",
      "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000    1.000000    1.000000\n",
      "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200    1.000000    1.000000\n"
     ]
    }
   ],
   "source": [
    "profile(train_X_raw_df, title=\"Profile of Raw Training Dataset\")\n",
    "print(\"\\n\" + \"#\" * 20)\n",
    "profile(test_X_raw_df, title=\"Profile of Raw Test Dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# titanic.boxplot_dist([\"PassengerId\", \"Fare\", \"Age\"], marker=\"x\", orient=\"h\", legend=\"upper right\", dark_mode=DARK_MODE)\n",
    "titanic.boxplot_dist([\"Fare\", \"Age\"], marker=\"x\", orient=\"h\", legend=\"upper right\", dark_mode=DARK_MODE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.boxplot_dist(['Pclass', 'SibSp', 'Parch'], marker=\"x\", orient=\"h\", legend=\"upper right\", dark_mode=DARK_MODE)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Inputation\n",
    "\n",
    "replace the NaN values of numerical features in training and test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_X_raw_df.describe()\n",
    "# test_X_raw_df.describe()\n",
    "# all_X_raw_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_values(x):\n",
    "    \"\"\"x is tuple with two positions\"\"\"\n",
    "    match x[0]:\n",
    "        case \"Age\":\n",
    "            return (x[0], round(x[1]))\n",
    "        case \"Fare\":\n",
    "            return (x[0], round(x[1], 4)) # returns float with rounded 4 decimals\n",
    "        case _:\n",
    "            return x   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Age': 30}\n",
      "no. of numerical cols. has NaN values: 0\n"
     ]
    }
   ],
   "source": [
    "train_X_raw_df, train_mean_dict = fill_missing_values_with_mean(\n",
    "    df=train_X_raw_df, pop_df=all_X_raw_df, filter_cols=num_features, filter_func=filter_values)\n",
    "print(train_mean_dict)\n",
    "print(f\"no. of numerical cols. has NaN values: {len(na_columns(train_X_raw_df, num_features))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Age': 30, 'Fare': 33.2955}\n",
      "no. of numerical cols. has NaN values: 0\n"
     ]
    }
   ],
   "source": [
    "test_X_raw_df, test_mean_dict = fill_missing_values_with_mean(\n",
    "    df=test_X_raw_df, pop_df=all_X_raw_df, filter_cols=num_features, filter_func=filter_values)\n",
    "print(test_mean_dict)\n",
    "print(f\"no. of numerical cols. has NaN values: {len(na_columns(test_X_raw_df, num_features))}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examining the correlation of numerical features and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.033019</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "      <td>-0.042939</td>\n",
       "      <td>0.042939</td>\n",
       "      <td>-0.005007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.329727</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>-0.131900</td>\n",
       "      <td>0.131900</td>\n",
       "      <td>-0.338481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.033019</td>\n",
       "      <td>-0.329727</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.232440</td>\n",
       "      <td>-0.180330</td>\n",
       "      <td>0.090632</td>\n",
       "      <td>-0.084660</td>\n",
       "      <td>0.084660</td>\n",
       "      <td>-0.070657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.232440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.114631</td>\n",
       "      <td>-0.114631</td>\n",
       "      <td>-0.035322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.180330</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>0.245489</td>\n",
       "      <td>-0.245489</td>\n",
       "      <td>0.081629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.090632</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182333</td>\n",
       "      <td>-0.182333</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_female</th>\n",
       "      <td>-0.042939</td>\n",
       "      <td>-0.131900</td>\n",
       "      <td>-0.084660</td>\n",
       "      <td>0.114631</td>\n",
       "      <td>0.245489</td>\n",
       "      <td>0.182333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.543351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_male</th>\n",
       "      <td>0.042939</td>\n",
       "      <td>0.131900</td>\n",
       "      <td>0.084660</td>\n",
       "      <td>-0.114631</td>\n",
       "      <td>-0.245489</td>\n",
       "      <td>-0.182333</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.543351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.070657</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>0.543351</td>\n",
       "      <td>-0.543351</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId    Pclass       Age     SibSp     Parch      Fare  Sex_female  Sex_male  Survived\n",
       "PassengerId     1.000000 -0.035144  0.033019 -0.057527 -0.001652  0.012658   -0.042939  0.042939 -0.005007\n",
       "Pclass         -0.035144  1.000000 -0.329727  0.083081  0.018443 -0.549500   -0.131900  0.131900 -0.338481\n",
       "Age             0.033019 -0.329727  1.000000 -0.232440 -0.180330  0.090632   -0.084660  0.084660 -0.070657\n",
       "SibSp          -0.057527  0.083081 -0.232440  1.000000  0.414838  0.159651    0.114631 -0.114631 -0.035322\n",
       "Parch          -0.001652  0.018443 -0.180330  0.414838  1.000000  0.216225    0.245489 -0.245489  0.081629\n",
       "Fare            0.012658 -0.549500  0.090632  0.159651  0.216225  1.000000    0.182333 -0.182333  0.257307\n",
       "Sex_female     -0.042939 -0.131900 -0.084660  0.114631  0.245489  0.182333    1.000000 -1.000000  0.543351\n",
       "Sex_male        0.042939  0.131900  0.084660 -0.114631 -0.245489 -0.182333   -1.000000  1.000000 -0.543351\n",
       "Survived       -0.005007 -0.338481 -0.070657 -0.035322  0.081629  0.257307    0.543351 -0.543351  1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X_y_raw_df = pd.concat([train_X_raw_df, train_raw_y], axis=1)\n",
    "threshold = 0.2\n",
    "corr_df, high_corr_df = feature_correlation(train_X_y_raw_df, label=label_name, threshold=threshold)\n",
    "corr_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tRq22LAS1RgMKyZ35SVA5yZHJJBQUWV7KFRRr8W9k/YrY1UVu0d0BUFCkw83Fsim7a3sV86YFo7SRkl+o5dX3YikurSrhtu7JJjapnJJSPS3DHJj0iB9uLgq+Wm/ZbXS75AsweawfIwd5YmcrIzK2lNcXxdWYK4CzoynfwmLLfAuLdTT2UVrdxlUlp7CkeryLqqooOHOxlKNnisnK1eLracOEB7x460U7Xl6YiOGv35d8/eNkXp/qzw+fNsNohMISHfOXpFzT4iRHLpOQX2D5/uQXaglsbL0S5+ZiQ0GhZfdIQaEWNxcb0/OuNuZ1ljEa83P/xLBBPuw7nIVGU5W/ykmBXCahoKh6/gF+NeefXy1/DW6u1rtirmfUEB+emxCMvZ2M5NRyZr51EZ2u5h/4dHaSmc6HauejFn/fms+HguLq8a5XnQ9jhnmhNxjZ+mvudfN9aowvIwd6YKuUERlXxpuLE64bf6flC6ByVjTYOf3jL2nExJdQXKqjVTNnnpsYjLubks9Wxt8w7/p0pwy4ri91/vbdvn07jo6O2Nract999/HII4+wYMEC9u3bx4ABA/Dz88PJyYnHH3+cvLw8ystNV8svvPAC77zzDj169GD+/PmcP3/evM8pU6awYcMG2rVrx+zZszl69Kj5uXPnzhEXF4eTkxOOjo44Ojri5uZGZWUl8fFVJ0fLli2RXdVW6uvra24pio6ORi6X06FDB/PzoaGhuLq6WhyntLQUd3d383EcHR1JTEy0OE5gYGCNFSSA+Ph4NBoNXbt2Na9zc3MjPDz8hu/twoULUalUFssmQ/4Nt7sbnYss4bm5Ucx4K5qT54t5fXoTi3FDm3dlcz6qlMQrFWzfn8tX61MZda8Xiga6EdqN8gXYtD2TKfOieHVhDAYDvPpc7cZI1LfDJ4s5ca6U5DQ1f0aU8NanKYQF29E6vOpqfco4X4pKdLz6QRIz/y+RP8+W8OZ0f4svqztFy3BnggMc2L4388bBt9Cvh3OYPOss0+edJzW9grdeblZtjMq/LTTIjlGDPFn0dcoNY3/Ymc3UN2KY80EcBoORV54JuAUZWrrT8q2LjT+ncvZiEfFJZfy8O4PPVibw0PBGt7xME2OSbqBfv35EREQQGxtLRUUFa9asIScnh+HDh9OmTRs2b97M6dOn+fzzz4Gqwc2TJ08mISGBxx9/nAsXLtCpUyc+/fRTAO677z6Sk5N56aWXSE9PZ8CAAbz88suAqWWmY8eOREREWCwxMTGMGzfOnJdCYXmlJpFIMBhq319bWlqKr69vteNER0fzyiuvmOMcHKw3A9eHOXPmUFRUZLGMkVrvm78TFJXo0OuN1b44XZ0V1a7O/1ZQqKs2ANJVJSf/mqusSrWB9Cw1UXFlLP46GYPByJC+lv33V7scV4ZcLsHbs+bWhobOt7hUT1qmmjMXS3j3swS6tlfV2G1nijfle21ly8VZTkGx9Vk6BUU6XJyqxxcW1TyrJytXS1GJDl8v0+ts28yBzm0ceX95GlHxFcSnVPLld5loNAYGdFNZ5KfTG6u1ori5KMizMpgWIL9Qg6uL5f/I1UVhbp3J/2s712ta6lxdbMzP1dWIe32IiS8hOt6yG6eoRItOb8RVVT3/a1uLrs7frVr+NtVaHmqjrFxPakYl5yKLeePDywT42dGrq/WxcQDFJXrT+VDtfFRUax39W0GRDlfnmuNbhzni4ixn3eKW7FzVlp2r2uLjacPTjzZizaIWlscv1ZOWpebMpVIWfpFM13YqmtfQ7Xsn5gtQVKy9bc7pyJhi5HIpPt7XH6dW3+62WwDUuZLk4OBAaGgoAQEB5mn/p0+fxmAw8NFHH3HPPfcQFhZGenr1WSX+/v4899xzbNmyhVmzZvH111+bn/P09GTixImsW7eOJUuWsHz5cgA6dOhAbGwsXl5ehIaGWiwqlaraMawJDw9Hp9Nx9uxZ87q4uDgKCqr6gDt06EBmZiZyubzacf4eH1UbISEhKBQKjh8/bl5XUFBATEzMDbdVKpU4OztbLHdqVxuATm8kJrGc9i2r+tUlEmjfyonIGqZQR8aV0r6l5XiPDq2ciYqzPgC3ar+S615RhQTaozcYr1sZuJ3y/atnGcV1Wg50eohLrqRtc8vxGG2bO1jcXuBqlxPKadfcsuLVvrkDl68z5drdVY6Tg4z8v947pY0pJ6PRsuvHYMTi6lCnh5i4Ejq2qWqxlUigY1tXLkUXY83Fy8V0ams5iLxzO1cuXjbFp2dVkpuvtoixt5PRIszZHFMXdrZS+vf0ZPuv1VuRdDojMfGldGzjYpF/h9YuXIq2Pt37UnQJHa6KB+jc1oVLMXXP7WqSv46tUNRcHuj0RmKTymnfomr8nEQC7Vo4ElnD+RgVV0a7Fpbj7Tq0dDKfv/v+yOe516OZ8kbVklug4ced2cxbVHM3T9X5+9/JF/46J26Tczo02BG93khhLW4TIvxz9fINHBoailar5dNPPyUhIYFvv/2WZcuWWcTMmDGDPXv2kJiYyJkzZzhw4ADNmzcH4M033+Tnn38mLi6OS5cusX37dvNz48ePx8PDg/vvv58jR46QmJjIwYMHeeGFF0hNrXl8ydWaNWvGwIEDeeaZZzhx4gRnz57lmWeewc7OzjzOaeDAgXTr1o1Ro0axd+9ekpKSOHr0KPPmzePUqVO1fi8cHR156qmneOWVV/jtt9+4ePEiTzzxBNIbjRq+BWQO9ji3bYZzW9P9YeyDG+Pcthm2/r432PKf27wri6H9PBjUy42ARra88GQAtkopew6ZBsjOfi6ISY80Msf/tDubzm1UPDTUC39fJY8/6EtYE3t+3mvqOrVVSpk0phHNQx3w8rChaZA9s54OxMNVweHjpkpv81AHHhjiRZMAO3w8bejf3Y3nHmvM/t/zKS2//sjMhsi3WYg99w/yJCTQDi8PG9q1cGLetCakZVYSFXv9ytbWX/MY3MuF/t1UNPaxYep4X2xtpOz7oxCAmZMaMfGBqpl22/bn06GlIw8McqOxjw3jRngSGmTH9t8K/spXwpMPeRHexA4vdwVtmznwxvP+ZORoOHPJlMvlhApKy/S89KQfwY2VNPK24cmHvPD2sOHUNfer2bA1lRGDfRnS35vAxva8PLUpdrZS8yyy118K59kJVd2KP2xLo2sHV8aOakxAYzsmPRpIs1AnNm9Ps4iZ+EgAPbq40yTQgddnNiMvX82RP6vGoHh7KgkNdsDb0xaZFEKDHQgNdsDO1vJz2L+XFzKZhL0Hrd8/a9O2NIYP8mFIPy8CG9sx69kQ7Gxl7Nxvip/7QhjPPBZojv9xezpd27vwyEg/AvzsePKRAMJDHNmyM8Mc4+QoJzTIgSB/U6tFgJ8doUEO5nFsvt5Kxj/YmLAmDnh5KGkV7sTbrzRDrTFY3G/Jmi27c7ivjzsDe7ji76tk+sTG2Cql7D1i6rZ/5ZkAnny46vO+dW8OnVo7M3qIJ/6+Sh4b5UPTYDt+3md6L0vK9CSnVVosOp2pRSc10zR+MryJPSMHetAk4K9zprkjc6YGmltO/0v5QsOc0y3DnXl4pB+hQQ408rZlUB8vXpgcwt6DWZSU1e7eTvVFIpPU23InqJcBBG3btmXx4sW8//77zJkzh969e7Nw4UImTJhgjtHr9Tz//POkpqbi7OzMkCFD+PjjjwGwsbFhzpw5JCUlYWdnR69evdiwYQMA9vb2HD58mFdffZUHH3yQkpIS/Pz8GDBgAM7OtZ8xsnbtWp566il69+6Nj48PCxcu5NKlS+Zp/BKJhJ07dzJv3jyefPJJcnJy8PHxoXfv3nh7W5/uW5MPP/yQ0tJSRowYgZOTE7NmzaKoqKhO+/g3qDq2otv+b82PWyyaC8CVtVs4/9Scf+WYh/4swMVJzsSHGuGqUhCfXMHc92PNg4293G0sWiQiY8tY+HkCTzzsx5Nj/EjLVLNgcTxJqaZZKnqDEf9Gtgzq5Y6zk5ySUh3RCeW89L9o80wWrc5Av26uTHjQF4VCSmaOmi27s9m888Y3kmyIfCs1Bnp0dmHC6EbYKqXkFWo5db6Y9Vsz0F5noC7AkVPFqJxkPHa/J67OchKuqHnzkxQKS0yVQU83hXmwNcDl+Ao+XJHK46O8mPCAF+nZGt79/ArJ6aYvEIMBghvbMqCbCw72MvILtZyNLGPd1mzzoOHiUj3zP0lhwgNevDsrELlMQkq6mnc+v0JiquXEg99+z8FFpWDy+CDcXG2ISyhl1vwL5kGq3p62FvldvFzMW4uiePqxYJ6ZEExqegVz3r1EYkpVS9f6zVewtZUxe1oYjg5yLkQWMWv+BTTaqh09NT6IoQOqpmSvXmqasDF9TgRnL1Z9FocP8uHQsVxKy6xXnn/7IxcXZwWTxgaY8k8s4+W3L5q7X709lRbnw8XoEt7+OJrJ4wJ5+rFAUjMqmPdelEX+PTq7MfeFMPPjBS+bLlq+2ZDCNxtT0GiMtG3hzMMjGuHkIKegSMu5S0VMfe18tUHO1zp0ohCVs5wJD/riqpKTkFLBvEUJ5vPX082Gq0chRMaV896yJCaO9uWJh3xJz1Lz1ieJtZoV9je1xkCPjioef8AHWxsp+UVaTl0o4d1tyTc8f++0fKFhzmmtzsDAXl5MejQIG4WE9KxKNv6cysattWsoqE+S2+CC/1aSGK9tM79LpKam4u/vbx5wfrvaobjxgO/byZIx3zV0Cv9pSnu7GwfdRgozb3wDz9uJVF6LG+XcRuycrd+aQqgfZfkNf3FbF7//0udfP8bpfj3qbV8dD/xx46AGdudNRfmHfvvtN0pLS2ndujUZGRnMnj2boKAgevfu3dCpCYIgCMId4U6ZlVZf7ppKklarZe7cuSQkJODk5ET37t1Zv359tVlxgiAIgiBYd6fMSqsvd00lafDgwQwePLih0xAEQRAE4Q5x11SSBEEQBEG4OaK7TRAEQRAEwYq7bXbb3fVqBUEQBEEQakm0JAmCIAiCUCuiu00QBEEQBMEKMbtNEARBEATBirutJUmMSRIEQRAEQbBCtCQJgiAIglArd9vsNlFJEgRBEAShVkR3myAIgiAIgiBakgRBEARBqJ27rSVJVJIEQRAEQagVUUkSbitLxnzX0CnUyYxN4xo6hTopOxzV0CnUyaYNSQ2dQp3IlTYNnUKdbHpb0dAp1Mncnxo3dAr/aUMGtmnoFIQGJipJgiAIgiDUipjdJgiCIAiCYMXddsftu6tKKAiCIAiCUEuiJUkQBEEQhFoRA7cFQRAEQRCsEGOSBEEQBEEQrLjbWpLuriqhIAiCIAhCLYmWJEEQBEEQauVua0kSlSRBEARBEGrlbhuTdHe9WkEQBEEQhFoSLUmCIAiCINSK6G4TBEEQBEGwQnS3CYIgCIIgCKKSdLW+ffsyY8aMhk5DEARBEG5PEkn9LXeA/1x32xNPPMGaNWsAUCgUBAQEMGHCBObOnYtc/t94uSMHefLwMG/cVAriUyr4fE0K0QnlNcb37uLCxIf98PGwIS1LzYrvUzlxrtj8/OMP+tK3mxuebgp0eiOxieV8symNy/FV+/x2SSt8PJUW+12xIZWNv2TV/wv8i1vPTjSZ9RSqDq2wbeTFqdFTydq2/187Xk2O71vP77tWUVqUi09AM4Y9No/GTdpYjb10ai+Hty8nPysFvV6Hu3cgPYY8Qbse95tjfvvpMy4c30lRfiYyuYJGQS0YOHoG/iFt/1F+Q3o6M7K/ChdnGclpGlZuziMuRV1jfLd2Dowd6oqnm5yMHB3rfsnjbGSF+XlbGwnjR7jRpY0DjvZSsvN17DpcxN4/Sswxz4zxoE24Ha7OMio1RmISK/l2Wz7p2dob5jtqiDdjRzbCzcWGuOQylq5M4nJcaY3xfbq58dTYAHw8laRmVPLVumSOny00P9+rqxsj7/UmrIkDKicFk18+R1yS5efBzUXBc48H0qmNCjs7GVfSK1i3OY3Dx/NvmK81W3bu5futO8gvLCIkKIAZkyfSIizEauzO3w6x8NPlFutsFAr2b1ptfvzu0mXsPnDEIqZL+zZ89Oar/yi/a/XraMvge+xQOUq5kqXj+71lJKbrrMY28pBxfx97An3keLjI2LC3lH0nKy1i+nawpW8HW9xdTNfZ6Tl6fvm9nIvxN/7//xfz/XPfeo7s/KuM8G/G8Mfn4R9SQxlxci8Hf1lOfnYKep0Od59Aet73BO2vKiOutvWbBZw8sJGh416jx5CJ9ZLvzRBjkv4DhgwZwjfffINarWbnzp08//zzKBQK5syZ09Cp3bQ+97jy7PjGLF2VQlR8GQ8O8WLha02Z9PIlCourFyItmjowd1oTVm5M4/jZIvp1d2PBzBCmzosiKdVUkKRmVvLZ6hQystUobaSMvs+b914LY+LMixSVVO1z9Q9p7DyQa35cUWn4V1+rzMGe4vPRXFm9mU4/fv6vHqsmF47vZNeG9xk5cQGNm7Th2N61rFn0NC++txNHZ/dq8fYOLvQZ8Swevk2QyxVERxzkp5XzcHB2p2nrngC4+wQx/PHXcfX0R6ut5NieNaxZNJmX3t+Dg7NbnfLr3t6BiQ+4s3xTDrFJaob1VfH6FB9eePcKxaXV/z/hQUpmTPBi/fZ8Tl8qp1dHR2Y/5cPsRalcyTB9YUx8wJ1WTe1Y+m022fk62obb8fTDHuQX6Tl10VT5SLii5sjpUnILdDjaSxkzxJU3pvry/FspGIw159uvuztTJwaxeHkCUbGlPDTMlw9fb87jL5y1ev62DHfkzRlhLF+fwrHTBQzs5cE7s8N5ZvZ5Eq+YKna2SikXoko4eDSPV6ZYr6jMmR6Ko72cue9HU1SsZWAvD+bPDOPZ184Tl1jzBYY1+38/xmffrGfWc5NoERbCD7/sZtbb7/HdZ4twdVFZ3cbB3o71ny0yP5ZYuYru2r4Nc6Y/a35so1DUKa+adG5uw5iBDqzbVUpCuo6BXeyYMdaZ15cVUFJe/Z9lo5CQU6DnVJSaRwY5Wt1nQYmBzQfKyMrXI5FA9za2THvYmbdXFJKeq7+r8j3/5052fvc+9z+xAP+QNvyxZy2rP3yalz6wXkbYObrQd+SzePo2QfZXGbHl63k4OrnTtE1Pi9hLp37lSvw5nFy9bipH4Z/7T3a3KZVKfHx8CAwMZMqUKQwcOJBt27YB8Mcff9C3b1/s7e1xdXVl8ODBFBQUWN3Pt99+S6dOnXBycsLHx4dx48aRnZ1tfr6goIDx48fj6emJnZ0dTZs25ZtvvgFAo9Ewbdo0fH19sbW1JTAwkIULF970axt9nze7DuSy53AeKWmVfLIqBbXawOA+1T+MAA8M8eLk+SJ+2JFFSnola35MJy6pnPvvrfrQHThawNlLJWTmaEhOq2TZ+is42MtoEmBnsa/ySgMFRTrzUqn+dytJOXsOEzN/CVk/7/tXj3M9R/esoVOfh+nQ60G8/EIZMXEBChtbzhzeYjU+uHkXWnQchFejENy8Auh27wS8/cNIjjltjmnbbTghLbvj5uWPt19Thjz6GuqKUjJTo+uc34i+KvYdLebA8VJSs7Qs35SLWmOk/z1OVuOH9lERcbmcbb8VkZalZcPOAhJT1dzXq+rLPTzYlkMnSrgUV0lOvo59x0pIStcQGlDVkrjvWAlR8abnE1M1bNhZgKerHE+36193PTzClx37stl9IIfk1AoWL0+gUm1gaH/rXwKjh/pyIqKQjdvSSUmrYNWGK8QmlvHAfT7mmF8P57L2x1ROny+q8bitwpzYsiuDy3GlZGSr+XZzGqXlOsKbWP9SvZ6N23YxYlA/hg3oQ7B/Y15+bhK2SiU79h+qcRsJEtxdXcyLm5XKlEKhsIhxcnSoc27WDOpqx5GISv44ryYjV8+6naVodEZ6trW1Gp+UoePH38o5GalBp7Ne4z0Xq+FCvJbsAgNZ+QZ+OliOWmOkid/NX3ffafn+sXsNnfo+TMfepjLi/icWoFDacvqQ9TKiSfMutOw0CC+/ENy9A+g+2FRGJF1VRgAU5Wex/dt3GfPcB8hkt097hkQqrbflTnBnZHmT7Ozs0Gg0REREMGDAAFq0aMGxY8f4/fffGTFiBHq99SsJrVbL//73P86dO8fWrVtJSkriiSeeMD//xhtvEBkZya5du4iKiuLLL7/Ew8MDgKVLl7Jt2zY2bdpEdHQ069evJygo6KZeh1wmISzYnjMXq7rKjEY4c7GEFk2tF/YtQh05c7HEYt2p88U0D7VeAMtlEob286S0TEd8suUV9tgRPmxe1pYv323Ow8O8uUPO8X9Mp9OQnnSJJi26mddJpVJCWnbjSnzEDbc3Go3ERx4jNyOJoPBONR7j1MFN2No54ePfrE75yWXQxF/J+ZiqrjKjES7EVBAeZP0LJSzYlvPRFRbrIi5XEBZUVQGKTqykU2t73FQyAFqG2tLIU8G5a7b7m9JGQr+uTmTlaskrtN4lAiCXSwhv4sjp84UW+Z6+UEiLcOuVupZhThbxACciCmkRZj2+JhdjSujfwwMnRzkSCfTv4Y6NQkrEpeIbb3wVrVZHTHwiHdu2Mq+TSqV0atOKS9GxNW5XUVnJQ8+8wOjJ05nzfx+RmJJaLSbiYhQjJk5h3PMvs2jZKoqKS6zsqW5kUgj0lROZWNWtZASiErU0aVw/X7wSCXRuYYONQkJ8Ws3//9q40/L9u4wIbWlZRoS26EZKXMQNtzcajcRfMpURwc2qygiDwcCPX71Kr6GT8G7c9KZyrG8SqaTeljvB7VM9/RcYjUb279/Pnj17mD59Oh988AGdOnXiiy++MMe0bNmyxu0nTZpk/rtJkyYsXbqUzp07U1paiqOjIykpKbRv355OnUwn99WVoJSUFJo2bUrPnj2RSCQEBgbeMF+1Wo1abTmWxKDXIJXZAKBykiOTSSgosvxgFxRr8W9k/UvR1UVOYZFlv3tBkQ43F8um/K7tVcybFozSRkp+oZZX34uluLSq8rh1TzaxSeWUlOppGebApEf8cHNR8NX66oX9f0V5SSEGgx5HlWUrnaOzO7kZiTVuV1lewocv9UWn0yCVSBk+4U1CW/WwiImOOMCmL19Gq6nAUeXJxFdW4uDkWqf8nBxkyGQSikosK/mFJXr8vKx31bg4ySi8Jr6oRI+Ls8z8eOWPuTw31pPlbwei0xsxGmHZhhyi4i3HeQzu6cxjI92wU0pJy9Lw9hcZ6K7Tc/H3+Zt/7flYqCXAz87qNm4uCvILrz1/tdXO3xt566MY3pwZxi+rO6PTGahUG3jjw2jSMitvvPFVikpK0BsMuKksW4JcXZxJTku3uk1Ao0a8Nu0ZQoL8KS2rYMPPO5gyZwFrP3kfLw/TudW1fVv63NMZX29P0jKzWb5uI6/87wO+fO8tZLJ/fjXiaC9FJpVQXGbZ6ltcZsDH/ea68/w8Zcx5wgWFHNQaI1/8WEzGTXZd3Wn5msuIa7rVHFXu5NygjHj/xb/KCKmUEdeUEUd2rEAqk9Ht3sdvKr9/w53SAlRf/pOVpO3bt+Po6IhWq8VgMDBu3DgWLFhA586defjhh2u9n9OnT7NgwQLOnTtHQUEBBoPpg5uSkkKLFi2YMmUKo0eP5syZM9x7772MGjWK7t27A6YB5IMGDSI8PJwhQ4YwfPhw7r333useb+HChbz11lsW64JbPU1Im2dr2KL+nIss4bm5Uaic5NzXz4PXpzfhhfmXzeNENu+q6mZMvFKBVmdkxqRAVm1MQ1tDE/fdysbWgalvb0FTWU5C5J/s/v593Dz9CW7exRwT3LwrU9/eQnlJAacO/cDGL17i2Tc3Wh3DcKsN7a2iaaCShcszyS3Q0TzElskPmcYkXbiq1erIqRLORZfj6ixnZD8VM5/05vUl6bfl+TBprD+ODjJmvnWJomIdPbu4sWBmGNPfuERiSt3GJNVVq2ZNadWsqjWgdbOmPDZ9Ntv2/sbkcabyaGCvqpaIkMAAQgMDeGTKS5y9FEmnNq2q7fN2kJmn5+0VBdgpJXRspmTSCCc+WFd00xWPf8vtlK+NrQPT3tmC+q8yYtf37+Pm5U+T5l1IS7zE0b3f8vzbm62OXRNurf9klbBfv35EREQQGxtLRUUFa9aswcHBATs761er1pSVlTF48GCcnZ1Zv349J0+e5KeffgJM440A7rvvPpKTk3nppZdIT09nwIABvPzyywB06NCBxMRE/ve//1FRUcGYMWN46KGHrnvMOXPmUFRUZLEEt3zS/HxRiQ693oiryrJu6+qsoKDI+iyNgkIdLirLKzBXlbza1Xml2kB6lpqouDIWf52MwWBkSF+PGnO9HFeGXC7B29Pmuq/pTmbv5IJUKqO0KM9ifWlxHo6qmt8bqVSKu3cgvoHN6XHfk7TsfC+Hd1wzu0lpj7t3IP6h7XjgqXeRymScPry5TvmVlOnR642onGQW6621Fv2tsESPyzXxKicZhcWmeBuFhEeHu7Fmax6nL5WTnK5h95Fi/jhbysj+lq0n5ZVGMnN0RMVX8tE3Wfh5KejSxr7GfP8+f92uPR+ttBb9Lb+wequRq6rmeGsaeSt5cKgvH3wez5kLxcQnl7Pmh1Si40t5YIh3rfcDoHJyQiaVkl9kOf6poLAY9xoGbV9LLpfTNDiQ1IyaZ4Y28vFC5exE2nViaqO03IDeYMTZwbKod3aQUlR2c2MK9QbILjCQnKlny8FyrmTrGNjZeot2bd1p+ZrLiOJryoii2pURjQKb0/OvMuLQL6YyIin6FGXFeXz4Un/eeKIVbzzRisLcdHZ9/wEfzhxwU/nWh7utu+0/WUlycHAgNDSUgIAAi2n/bdq0Yf/+2k0hv3z5Mnl5ebz33nv06tWLZs2aWQza/punpycTJ05k3bp1LFmyhOXLq74MnZ2deeSRR/j666/ZuHEjmzdvJj+/5inHSqUSZ2dni+XvrjYAnd5ITGI57Vs6m9dJJNC+lRORsdanUEfGldK+peX4jQ6tnImKK7vu65dIJCjkNZ/EIYH26A1GCoturk//diaX29AoqCUJkX+a1xkMBhIi/8Q/pF2t92M0GtFpNdePMRjR3yDmWjq9aZZZ67Cqyr9EAq3D7IhOst6NFJNYaREP0DbcjpgkUzevTAoKuQTjNY1BBgPcqEyTSLjuOaPTGYlOKKVDa5XFNh1bq4iMtj7+5lJMiUU8QKe2LkTG1H68jlJpqhReO+tOb7A+y+x6FAo5YSHBnD5/ybzOYDBw+sJFWobXbuyIXm8gIeUK7q4uNcZk5+ZRXFJ63ZhaHcsAyRk6mgdVVTQlQLMgBQmp9fvZlUhMYxpvxp2W799lRPwlyzIiPvJPAkLb1Xo/RoMRvc70+W/fYyTT393KtHe2mBcnVy96DZ3EE6+suKl868PdVkn6T3a31WTOnDm0bt2aqVOn8txzz2FjY8OBAwd4+OGHzQOu/xYQEICNjQ2ffvopzz33HBcvXuR///ufRcybb75Jx44dadmyJWq1mu3bt9O8eXMAFi9ejK+vL+3bt0cqlfLDDz/g4+ODi4vLTb2GzbuymP1sEDGJZUTHl/PAEC9slVL2HDJdycx+LojcAg2rNprGR/y0O5uPXg/noaFeHD9bRN9uboQ1sWfJymTANH163P0+HDtTRF6hFpWjnJGDPPFwVXD4uGnWX/NQB5qFOnAusoTyCj0tmjry3GON2f97PqXl/15TtczBHofQAPNj++DGOLdthia/iMorGf/aca/WffBEtnw9B7/gVvg1ac2xvWvRqCvo0OsBAH5c/irOrt7c+/BMAA5tX45fUEvcvALQ6TTEnjtMxNFtjJjwJgAadTmHfvmKZu364eTiSVlpISf2f0dJQRYtuwyuc36/HCxi2nhP4lPUxKWoGdZHhdJGwoHjpkrz9PGe5BXp+G676X+581ARb73QiBH9VJy+VE7PDo408VeybGMOABVqI5diK3j8fnc02lxy8nW0CLWlT2dH1mw1nWNe7nJ6tHfk3OVyisv0uKvkjBrogkZr5Ezk9buufvglgznTQomOLyMqznQLAFuljF0HTMefMz2U3DwNX3+XAsDmnRl88lZLxozw5c/TBfTv6UF4Ewc+WhZv3qeToxxvDxvcXU0XFP6NTJXA/EIt+YVaUtIqSM2oYNazTfhybTLFJVp6dnGjUxsVcxZervN7/sjI+/i/pV/RLCSY5k1D+GH7bioq1Qwd0AeAdz75Eg83V557fCwA32zcQsvwUBr7+FBSVsb3W3eQmZPL8EF9ASivqOSbjVvo260zbq4upGVm8eWa7/Hz8aZLe+v32qmLX49XMGmkE8kZOhLTdQzsYotSIeGP86aK9KQRjhSWGNhy0PS/k0mhkaepYimXgYuTFH9vGWqNkewCU2vOg33tuRCvIb/YgK2NhK4tlYQHKljyfd0Gwv8X8u0xZCKb/yojGjdpzdG/yoiOvU1lxA9fmcqIwWP+KiN+WY5f8F9lhFZDzHlTGTFyoqmMsHdyxf6a8YkymRxHlQeevsE3na9QN3dVJSksLIy9e/cyd+5cunTpgp2dHV27duXRRx+tFuvp6cnq1auZO3cuS5cupUOHDixatIiRI0eaY2xsbJgzZw5JSUnY2dnRq1cvNmzYAICTkxMffPABsbGxyGQyOnfuzM6dO5He5KC3Q38W4OIkZ+JDjXBVKYhPrmDu+7HmsUNe7jYYr2oGiIwtY+HnCTzxsB9PjvEjLVPNgsXx5nsk6Q1G/BvZMqiXO85OckpKdUQnlPPS/6JJTjPFaHUG+nVzZcKDvigUUjJz1GzZnc3mnf/ejSQBVB1b0W3/t+bHLRbNBeDK2i2cf+rW3POqddehlJUUsP+npZQW5eIb0JwJs5abm9KL8jKQSqr+p1p1Ob98+zbF+VkobGzx8A3moWfep3XXoQBIJDJyMhI4+/tWyksLsHd0wS+4NU/NXYe3X91nsRw9W4azo4yxQ11xcZaTlKrm3WWZ5sHcHq5yixaU6CQ1n6zNZuxQV8YNdyMjR8sHKzPN90gC+HhNNuNGuPHC41442kvJLdDx/Y4C880ktVojzUNsGdbXGQc7GUUleqLiK5i3JN3qvZmuduBoHi7OCp4c64+bi4K4pDJmvxtl7i729rDBeFXCl6JL+d8nsTw1NoDJ4wJIy6jk9Q+izfdIAujRyZXXpoWaH8+fGQbA6k1XWL0pFb3eyKvvXuaZxwL4v9fCsbOVkZZZycLP4ixuSllbA3p2o7C4hJUbfiS/oIjQ4EAWvfmqeVp/Vk6eRQtVSVkZH3yxgvyCIpwcHQgLCebLhQsI9m8MgEwqJT45hd0HjlBaXoaHqyud27Vm8riH6+VeSSejNDg6lHF/H3ucHUw3Z1yyoZjiMtP77K6SWbQcujhJmT+56kt6SDd7hnSzJzpZy4frTN2MTg5SnhrphMpRSoXaSGq2jiXfF1vMSrtb8m1zz19lxJallPxVRjzximUZIbmqjNCoy9m25m2K/iojPH2DefjZ92lzz9CbzuWWuMsGbkuMxmsb1oXbyaDxp28cdBuZsWlcQ6dQJ2WHoxo6hTrZtCGpoVOok9y0f7ciXd82vV0/N3C8Veb+FNTQKfynDRlYt5u7NrSHuv77FZic15+8cVAteb7zTb3t699yd1UJBUEQBEEQaumu6m4TBEEQBOGfE/dJEgRBEARBsOJOmZVWX+6uKqEgCIIgCHekzz//nKCgIGxtbenatSsnTpy4bvySJUsIDw/Hzs4Of39/XnrpJSor63aXfdGSJAiCIAhC7TRQd9vGjRuZOXMmy5Yto2vXrixZsoTBgwcTHR2Nl1f1H8j+7rvveO2111i1ahXdu3cnJiaGJ554AolEwuLFi2t9XNGSJAiCIAhCrTTUzSQXL17M008/zZNPPkmLFi1YtmwZ9vb2rFq1ymr80aNH6dGjB+PGjSMoKIh7772XRx999IatT9cSlSRBEARBEGpFIpHW26JWqykuLrZYrv2RdzD9FNjp06cZOHCgeZ1UKmXgwIEcO3bMap7du3fn9OnT5kpRQkICO3fuZOjQut2PSlSSBEEQBEG45RYuXIhKpbJYFi5cWC0uNzcXvV6Pt7flby16e3uTmZlpdd/jxo3j7bffpmfPnigUCkJCQujbty9z586tU46ikiQIgiAIQu1IJfW2WPtR9zlz6ufXFA4ePMj//d//8cUXX3DmzBm2bNnCjh07qv282I2IgduCIAiCINRKfd4nSalUolQqbxjn4eGBTCYjK8vyDv5ZWVn4+PhY3eaNN97g8ccfZ/LkyQC0bt2asrIynnnmGebNm1frnwgTLUmCIAiCINy2bGxs6NixI/v37zevMxgM7N+/n27dulndpry8vFpFSCYz/RByXX6NTbQkCYIgCIJQKw11M8mZM2cyceJEOnXqRJcuXViyZAllZWU8+aTpt+QmTJiAn5+feUzTiBEjWLx4Me3bt6dr167ExcXxxhtvMGLECHNlqTZEJUkQBEEQhNqRNEwH1COPPEJOTg5vvvkmmZmZtGvXjt27d5sHc6ekpFi0HL3++utIJBJef/110tLS8PT0ZMSIEbz77rt1Oq6oJAmCIAiCcNubNm0a06ZNs/rcwYMHLR7L5XLmz5/P/Pnzb+qYopIkCIIgCEKt3G2/3SYqSUK9Kjsc1dAp1IlD7+YNnUKddFwf2dAp1ElenvWZJ7eraSuzGzqFOjEaSho6hf+0dWvyGzqFOnmoa9i/f5AG+lmShnJ3vVpBEARBEIRaEi1JgiAIgiDUikQiutsEQRAEQRCqu8u620QlSRAEQRCEWrnbBm7fXVVCQRAEQRCEWhItSYIgCIIg1E4D3UyyoYhKkiAIgiAItSO62wRBEARBEATRkiQIgiAIQq1IRHebIAiCIAiCFaK7TRAEQRAEQRAtSYIgCIIg1IpE3ExSEARBEATBirvsZ0nuriqhIAiCIAhCLYmWpBocO3aMnj17MmTIEHbs2NHQ6VgYOciTh4d546ZSEJ9SwedrUohOKK8xvncXFyY+7IePhw1pWWpWfJ/KiXPF5ucff9CXvt3c8HRToNMbiU0s55tNaVyOr9rn2zNDCAm0x8VZTkmZnrOXilnxfRp5hdo6539833p+37WK0qJcfAKaMeyxeTRu0sZq7KVTezm8fTn5WSno9TrcvQPpMeQJ2vW43xzz20+fceH4ToryM5HJFTQKasHA0TPwD2lb59xuhlvPTjSZ9RSqDq2wbeTFqdFTydq2/5bmANAhVELXcAmOtpBdCHvPGsjItx7btomE1oESPFSmx5kFcOiCZXzPlhJa+Etwsge9wRRz+IKB9Br2WVfdW8rp206Ok72EjDwDP/2u5Uq2wWqst6uEwV0UNPaQ4uYs5ec/NBw5r7OIkUjg3k4KOobJcLKXUFRm5FS0jn2ndVb3eSODezgxoq8KFycZyekaVv2UR/wVTY3x97Sx55H7XPF0lZOZq2X99gLOXq4wP69ylDJ+uBttwmxxsJMSlaBm1U95ZOZW5TfgHkd6tnckuLEN9rZSnpiXQnml9ffEWr4j+1+V75Y84lKuk29be8be54qnm5zMHB3rtudzNqoqX1sbCeOHu9G5tT1O9lKy83XsPFLMr0dLzDELnvehZaidxX73Hi3m6x/y/nP53tdbxQOD3HBxlpGUqubrTTnEJlfWGN+9vSPjRnjg5S4nI1vL2q25nL5UZn5+6xdhVrdbvSWHrfsKAHhoiBudWjkQ3FiJTmdk/MvxN8zzXyO62wSAlStXMn36dFauXEl6ejqNGjVq6JQA6HOPK8+Ob8zSVSlExZfx4BAvFr7WlEkvX6KwuPqXQIumDsyd1oSVG9M4fraIft3dWDAzhKnzokhKNX2wUzMr+Wx1ChnZapQ2Ukbf5817r4UxceZFikpM+4yILOH7bZnkFWrxcFXwzLjGvPFiE2a8FV2n/C8c38muDe8zcuICGjdpw7G9a1mz6GlefG8njs7u1eLtHVzoM+JZPHybIJcriI44yE8r5+Hg7E7T1j0BcPcJYvjjr+Pq6Y9WW8mxPWtYs2gyL72/Bwdnt7q+xf+YzMGe4vPRXFm9mU4/fn7Ljnu15v4SBrSVsPu0kfR8I52bSnikt5TluwyUq6vHB3pCZIqR1DwjOj10ayZhbG8pX+8xUPrX905+Cew9Y6CwDOQy6Bxm2ueyXQYqrOyzLtqGyBjZQ8HmQxpSsg30aqPg6eFKPvi+wnz8q9nIJeQXGzkfr2Vkdxur++zXXk73lnI2/KYms8CIv6eUMf1sqNTA7xfqVlHq1s6eCSPd+PrHPGJT1Azr5cy8Z7yZ8X4axaXVKy1hQUpefMyT73YWcCaygp4dHHjlSS9e/TidK5mmC4pXnvRCp4cPv8mmvNLA8D4q3njWh5kfpqHWGAFQKqRERFcQEV3B+GGutc63ezsHJo5yZ/kPucQlqxnWx5l5z/rw4sLUGvOd8bgX3+0o4PSlcnp2dGD2JG9mf5RmznfiKDdahdqxdF0OOfk62jazY/JodwqK9Jy6VHUhte9YMRt3FZofqzU3rtTdafn26OjIpNGefPl9NjFJlYzs78L86X48vyCJolJ9tfjwJrbMmuTLtz/ncupiGb07OfHas42YtTCZlAxTRfCJ1ywrPB1aODDtMW+OnS01r5PLJPxxpoTohAoGdlfdMM9/lehuE0pLS9m4cSNTpkxh2LBhrF692uL5bdu20bRpU2xtbenXrx9r1qxBIpFQWFhojvn999/p1asXdnZ2+Pv788ILL1BWVsbNGn2fN7sO5LLncB4paZV8sioFtdrA4D7VKxgADwzx4uT5In7YkUVKeiVrfkwnLqmc++/1MsccOFrA2UslZOZoSE6rZNn6KzjYy2gSUHWltWV3NlFxZWTnaoiMLWPjL5k0D3VAJqtb/kf3rKFTn4fp0OtBvPxCGTFxAQobW84c3mI1Prh5F1p0HIRXoxDcvALodu8EvP3DSI45bY5p2204IS274+blj7dfU4Y8+hrqilIyU+tWgbtZOXsOEzN/CVk/77ulx71alzAJ5xKMXEgyklcMu08b0emgTbD1gm3bcSNn4o1kF5oqQztPGZFIIMirKj4yxUhSNhSWQW4x7I8wYmsjwaseyuo+beUcj9RxMlpPVoGRzYc0aLVGOjezfv12JcfA9mNaIuL06PRGqzFB3jIuJumJSjFQUGLkfIKemFQ9/l51L+6G91ax/88SDp4sJS1Ly9eb89BojfTr4mQ1fmgvZyKiK/jlYDFp2Vo27i4kIU3DkB7OAPh6yAkLsmXFZlNrVEaOjhWb87BRSOjR3sG8n51Hivn5tyJik+tWCx3e15n9x0o4eKKU1Cwty3/IQ6Mx0r+r9XyH9XYm4nIF2w4UmfLdVUhCqpohvZzNMWFBthw8WUpkfCU5BTr2HSshOV1DaIDSYl9qjZHCEr15qVBb///cyfne39+VvX8U89ufxaRmavjy+2zUGiMDujtbjR/Rz5UzkWVs3VdAaqaG77bnkXClkqF9XcwxhcV6i6VrW0cuxlSQlVfVSr9hRx6//FZIcnrNLWy3ikQqrbflTnBnZHmLbdq0iWbNmhEeHs5jjz3GqlWrMBpNH6DExEQeeughRo0axblz53j22WeZN2+exfbx8fEMGTKE0aNHc/78eTZu3Mjvv//OtGnTbiovuUxCWLA9Zy5WdZUZjXDmYgktmjpa3aZFqCNnLpZYrDt1vpjmoQ5W4+UyCUP7eVJapiM+2XoXnpODjP493IiMLUNf/eKpRjqdhvSkSzRp0c28TiqVEtKyG1fiI264vdFoJD7yGLkZSQSFd6rxGKcObsLWzgkf/2a1T+4/QCoFH1dIzLIs7JOyjfi51+7qTyEz3QalUmP9C0MqhXYhEio1porVzZBJwc9TSkxq1RW8EYhNMxDo/c+LpqQsPU39pHioTK/Z111CsI+Myyl1OFkBmQyaNLbhQmxVV4rRCBdiKgkLVFrdJixQyYUYy66Xc9EVNA0yxcvlppy0uqr312gErd5Is2DbOuV3LbkMmjRWcj6mqgnOaITzsRU15xtkaxH/d75Xx8ckVdKplT1uKtMVUctQW3w9FZyLttyuV0dHVv4vgI9m+zFumCs2iuufc3diviEBtpyPrrrYNRrh3OUywoPtrG4THmzL+cuW5ejZyPIa41VOMjq2cmDf0aLr5iLcOqK7zYqVK1fy2GOPATBkyBCKioo4dOgQffv25auvviI8PJwPP/wQgPDwcC5evMi7775r3n7hwoWMHz+eGTNmANC0aVOWLl1Knz59+PLLL7G1tV4YqtVq1GrLK0eDXoNUZupWUDnJkckkFBRZdhkUFGvxb2R9n64ucgqLLMcNFRTpcHNRWKzr2l7FvGnBKG2k5BdqefW9WIqvaT6ePNaPkYM8sbOVERlbyuuL4qwesyblJYUYDHocVZatXo7O7uRmJNa4XWV5CR++1BedToNUImX4hDcJbdXDIiY64gCbvnwZraYCR5UnE19ZiYNT7bsp/gvsbUAqlVTrViurBHfrF+bV9GsjobQSErMs14f6wv33SFHIobQCNhwyUHGTF7UOthJkUgmlFZYVspJyI14u/7ySdOCMDluFhNmP2mI0mH6Pc/dxLWdj61ZJcnaQIZNJKCyx3K6wVE8jL4XVbVycZNW6XYpK9Lg4mb6w07O15OTrGDfUleU/5lGpMTC8tzMeLnJcnOvYLHsNp7/yLSqpfny/6+V77esr0ePiXPXVsHJzHs8+4sFXCwLQ6Y0YjbBsYy5RCVWVwd/PlJGTX0RBsY4AXxseG+FGIy8Fi77J/u/k6/jX+VBcPd/G3ta7fl2c5dXOn6ISHa41/K/73+NMRaWBYxGlVp+/LYg7bt/doqOjOXHiBD/99BMAcrmcRx55hJUrV9K3b1+io6Pp3LmzxTZdunSxeHzu3DnOnz/P+vXrzeuMRiMGg4HExESaN29u9dgLFy7krbfeslgX3OppQto8Wx8v7brORZbw3NwoVE5y7uvnwevTm/DC/MsW45w2bc9k18FcvD1sePzBRrz6XHCdK0r/hI2tA1Pf3oKmspyEyD/Z/f37uHn6E9y86n0Pbt6VqW9vobykgFOHfmDjFy/x7JsbrY5zEqy7p5mE5v4S1h80oL9meEZyNqz61YCdDbRrImFUNylr9lsf59TQ2obK6BAm47t9GjLzDTTykHJ/DxuKy42ciq5bRam+6Q2waE02U8Z48M07Aej1Ri7EVnImqpzbdaTHfb2cCQtU8t6KTHLydbQIsTWN8SnWmVvN9h2raq1OydBSWKxn/vO+eLvLycr7ZwPm75Z8rzagm4rDJ4stWhpvO3fZHbdFJekaK1euRKfTWQzUNhqNKJVKPvvss1rto7S0lGeffZYXXnih2nMBAQE1bjdnzhxmzpxpse6BZy6Z/y4q0aHXG3FVWf7bXJ0VFBRZn2VWUKjDRWV5VeaqkpN/zay0SrWB9Cw16VlqouLKWP1RS4b09WDDtkxzTHGpnuJSPWmZalLSE/j+0zY0D3UgKq52Y63snVyQSmWUFlnOICktzsNR5VHjdlKpFHfvQAB8A5uTkxHP4R3LLSpJNkp73L0DcfcOxD+0HR+/OpjThzfTZ/gztcrtv6BcAwaDEftreiocbKG05sk3AHQJl9CtmYTvDxnIsdLSr9VDQSkUAOn5Rp69T0LbYAnHLv/zwrys0ojeYMTRzrLQdbKXUFz+z/c7vJuC387oiIgzVYgy8/W4Omrp315Rp0pScZkevd5obgX6m4ujrFrrwN8KS/SoHC3jVU6W8YmpGmYvTsfOVoJcJqGkzMC7L/iSkHpzNc6Sv/JVOVk5fvF18r329TnJzBdHNgoJ44a58eE3WZyJNHVXpWRoCfJTMrKvqlrX4t9iU0yvxcdDUWOl447Lt/Sv88G5er4FNeVbrKt2/qic5FbjW4TY0djHhkUr063uS2gYd1e72Q3odDrWrl3LRx99REREhHk5d+4cjRo14vvvvyc8PJxTp05ZbHfy5EmLxx06dCAyMpLQ0NBqi42N9WZZAKVSibOzs8Xyd1cbgE5vJCaxnPYtqwYJSiTQvpUTkbHWm2cj40pp39Kyr6VDK+cbVmwkEgkKec1XDH9PcFDcoB//anK5DY2CWpIQ+ad5ncFgICHyT/xD2tV6P0ajEZ32+n09RoMR/Q1i/msMf03PD/K2/J8EeklIy6u50tE1XEKP5hI2HjaQWVC7Y0kk1HnQ/rX0BkjLMdC0cVUxJAFC/aQkZ9Vuurs1CrmEa1+t0Vj3STl6PSSkamjVtKorWyKBVk1tialhQHVMsprWTS27vtuE2RKbVD2+otJISZkBHw85If42nLxY8208akOnh4RUNa3DLPNt3dSu5nyTKmkdZjk+pk1YVbxMahpHZbjm32EwGJFcp0UhyM9UbhVYmXF7J+cbn1JJm3B7i3zbhNsTnWhlKiYQnVhJm2b2FuvaNbceP7C7M3HJlSSl3d7llkQirbflTiBakq6yfft2CgoKeOqpp1CpLKfujB49mpUrV7Jp0yYWL17Mq6++ylNPPUVERIR59pvkr1L41Vdf5Z577mHatGlMnjwZBwcHIiMj+fXXX2vdGlWTzbuymP1sEDGJZUTHl/PAEC9slVL2HDK1zsx+LojcAg2rNpquRn7anc1Hr4fz0FAvjp8tom83N8Ka2LNkZTIAtkop4+734diZIvIKtagc5Ywc5ImHq4LDx03fmM1C7Alv4sDFmFJKyvQ08lLyxMONSMusJCq2bjP2ug+eyJav5+AX3Aq/Jq05tnctGnUFHXo9AMCPy1/F2dWbex82tagd2r4cv6CWuHkFoNNpiD13mIij2xgx4U0ANOpyDv3yFc3a9cPJxZOy0kJO7P+OkoIsWnYZfFPvdV3JHOxxCK1qKbQPboxz22Zo8ouovJJxS3I4EWNkeBcJmfmmFp/OYRIUcjifaKo2DO8ioaQCDl0wPb6nmYReLSVs+9NAUbmp1QlAowOtzjSQu3sLCbFpRkorwV5pug+Tkx1cvnLzXQKHzukY29+G1BwDKVkGerWRY6OQcPKy6ctqbH8bisqM7DpuavmUSU33SgJTJU3lIKGRuwS1FvKKTflEJukZ0EFOYYmBzAIjfh5SerdVmPdZF9sPF/H8WE8SrqiJS9EwtLczShsJB0+Yumuef9SD/CId3+8sBEyz0hZM9WF4H2fORFXQo50DIY2VLL/q/jv3tLGnuMxAboGOAF8FT4xy5+TFcs5f1cqhcpLh4iTDx8NURAf4KqhQG8kt0FFWUXMFcvvBYp4f50H8FY15Sr3SRsKB46Z8p43zIL9Iz3c7TJ/tHYeLeWuaL8P7OnMmsoIe7R0I8Vfy1aZcACrURi7FVfD4SDc02jxyC0zdV306ObLmZ9ONsrzd5fTs4MjZqHJKygwENrJh4ig3IuMqSMm4/n3U7rR8f/6tgBcn+BCXrCY2uZIR/VywVUrZf8w0mebFiT7kFepY97Mpn18OFPDuS/7cP8CVUxdL6dXJmZAAW75Ybznoz85WSvcOTnyzJcfqcT1c5Tg5yPBwlSOVSghubGouzsjRUFmLWXn1SnS33b1WrlzJwIEDq1WQwFRJ+uCDDygpKeHHH39k1qxZfPLJJ3Tr1o158+YxZcoUlErTidumTRsOHTrEvHnz6NWrF0ajkZCQEB555JGbzvHQnwW4OMmZ+FAjXFUK4pMrmPt+rLm52cvdxjwTDyAytoyFnyfwxMN+PDnGj7RMNQsWx5vvkaQ3GPFvZMugXu44O8kpKdURnVDOS/+LJjnNFFOpMdCjswsTRjfCViklr1DLqfPFrN+aUee+89Zdh1JWUsD+n5ZSWpSLb0BzJsxabu5uK8rLQHrVFYZWXc4v375NcX4WChtbPHyDeeiZ92nddSgAEomMnIwEzv6+lfLSAuwdXfALbs1Tc9fh7df0n7/R/4CqYyu67f/W/LjForkAXFm7hfNPzbklOURdMXW39WolwcFWQnYhbDpcNXbI2V5icX60DzF1+TzYw7JZ6MglA79fMmIwgruThNbdJdgpoUIDGfmw7jcDucXctHPxehzttAzurMDJXkJ6roEV29XmeyS5Okq4Kl2cHSTMHFPVktC3nZS+7RTEp+n5cpvpRW79XcPgLgoe7G2Do53pZpJ/Rur49VTdb3x6LKIcZ4d8xgx2Nd08ME3D/32dRdFf9/DxcJFb5BeTpGbpuhzG3ufKo0NdycjR8uE32eZ7+AC4OsuYcL8bLo6mbprDp0v58ddCi+Pe282Jhwe7mB+/Pc0XgM835HLoZM2Deo9GlOHsKOWRIX/nq+bdr67K17V6vp98m82jQ10ZN8yNjBwtH6zKssh3ydocxg1z5cXHPHG0l5JToOP7nQXs/evmjDq9kTZhtuYKTl6hnuPny9i81/I1/Rfy/eN0KSrHXB4d7o6rs4zEVDVvfZZmHkzu6SrHaKhKODqhksWrMhg/0oPHRrqTnqPlva/SzfdI+luvjk5IJHDkpOVM5L+NG+5O/25V30sfzzUNP3j94ytcjLXeiiXUD4nx6hJT+Efeffddli1bxpUrV+p934PGn75x0G3k6WntGzqFOnHobX0Q/e3q/PrIhk6hTvLybsOR3deRElfz7Kbb0dVfyEL906rrXrFuSDXdvbs+VW5aVG/7sh3zcr3t698iWpL+gS+++ILOnTvj7u7OH3/8wYcffnjT90ASBEEQhNveXXbHbVFJ+gdiY2N55513yM/PJyAggFmzZjFnzq3pThEEQRAE4dYQlaR/4OOPP+bjjz9u6DQEQRAE4da6Q35OpL6ISpIgCIIgCLVzh0zdry+ikiQIgiAIQu3cZbcAuLuqhIIgCIIgCLUkWpIEQRAEQagd0d0mCIIgCIJgxV12C4C7q0ooCIIgCIJQS6IlSRAEQRCE2hG3ABAEQRAEQbBCdLcJgiAIgiAIoiVJEARBEITaEbPbBEEQBEEQrLjLxiTdXa9WEARBEAShlkRL0m1OaW/X0CnUyaYNSQ2dQp10XB/Z0CnUSZvxLRo6hTpxOXe6oVOokw1qt4ZOoU6K8soaOoX/tMLs/IZO4fZzlw3cFpUkQRAEQRBqR4xJEgRBEARBsOIua0m6u6qEgiAIgiAItSRakgRBEARBqJ27bHabqCQJgiAIglArRtHdJgiCIAiCIIiWJEEQBEEQakfMbhMEQRAEQbDiLqsk3V2vVhAEQRAEoZZES5IgCIIgCLVytw3cFpUkQRAEQRBqR3S3CYIgCIIgCKIlSRAEQRCE2rnLuttESxIgkUjYunUrAElJSUgkEiIiIho0J0EQBEG47Uil9bfcAe6KlqScnBzefPNNduzYQVZWFq6urrRt25Y333yTHj16kJGRgaura532+dNPP/H+++8TFRWFwWAgICCAQYMGsWTJkn/nRVxlWF9XHhzsjqtKTuIVNV99n0FMUmWN8T06OvHY/V54eyhIz9KwenM2py6Wmp+f8WQjBnZ3sdjm9MVS5n+SYn7cyNuGSQ950zzEDoVcQmKqmnU/Z3MhuvyG+Q7p6czI/ipcnGUkp2lYuTmPuBR1jfHd2jkwdqgrnm5yMnJ0rPslj7ORFebnbW0kjB/hRpc2DjjaS8nO17HrcBF7/ygxxzwzxoM24Xa4Osuo1BiJSazk2235pGdrb5jvtTqESugaLsHRFrILYe9ZAxn51mPbNpHQOlCCh8r0OLMADl2wjO/ZUkILfwlO9qA3mGIOXzCQXsM+/y1uPTvRZNZTqDq0wraRF6dGTyVr2/5bmwSwf+cmdm9dS1FhHv5BTRk/eTZNwlrdcLvjR/bw1eK5tO/Sh+lzFpvXnz72Gwf3/EhS/GXKSotYsPg7AoLD6zXnXm0UDOikxNleQlqugR8PVJCcZbAa6+MmZVg3Jf7eMtydpWw+VMnBs5pqcSoHCff3VNIiSI5CISG30MC6vRVcyba+37oY0MWeoT0dUDnKuJKp5dsdxSSkWf8s+HnJebC/I0GNFHi6ylm/s4g9xyw/5+GBNgzt6UBQIwWuzjKWfJfPmaiaP9P/tXxHDvLk4WHeuKkUxKdU8PmaFKITai4Le3dxYeLDfvh42JCWpWbF96mcOFdsfv7xB33p280NTzcFOr2R2MRyvtmUxuX4qn1+u6QVPp5Ki/2u2JDKxl+y/vHr+CfutoHbd0ZV7iaNHj2as2fPsmbNGmJiYti2bRt9+/YlLy8PAB8fH5RK5Q32UmX//v088sgjjB49mhMnTnD69GneffddtNq6fwHXVa9Ozkwe4833v+Tw4v8SSEyt5O0ZgaicZFbjm4XYMfvpxvz6eyEvvJ3AnxElzHven8BGlq/31IVSHpsVbV4++DrV4vn50/2RSWHeR8nMeCeRxNRK5k8PwMXZ+nH/1r29AxMfcOeHPQXM/jCNpHQNr0/xwdnR+qkXHqRkxgQv9v9ZwisfpnHyQhmzn/LB31dhjpn4gDvtmtuz9NtsZixMZcfBIp4a7UGnVvbmmIQraj7/LocZC1N558sMAN6Y6ou0jp/v5v4SBrSV8PslI6t+NZBVaOSR3lLsazhdAj0hMsXIdwcNrN1voKTcyNjeUhztqmLyS2DvGQMr9xhY95uBojLTPu1qfwrWC5mDPcXno7n4wlu39sBXOfH7XjZ+s5iRjzzD/I/W4x8UxuK3p1FceP0aY252OpvWLCGsRftqz6nVFTRt3o6HJ0z/V3LuECbngd627PpTzQfflZGWo2fqAw442lk/uWwUkFtkYNvvlRSVWa/w2CnhpUcc0Bvgy63l/N/aUn46XEmF2njT+XZtZcu4+5zZeqCUN7/MJSVTxysT3XBysP4ZtFFIyCnQs+nXEgpL9FZjlDYSUjK1rN1edNP53Wn59rnHlWfHN2bdlgymvB5FQko5C19riouz9TaHFk0dmDutCbsP5jJlXhR/nCpkwcwQghrbmmNSMyv5bHUKz7wWyUtvRZOVo+G918JQOVnuc/UPaYyZes68/Lw356Zfj3B9//lKUmFhIUeOHOH999+nX79+BAYG0qVLF+bMmcPIkSMBy+62v12+fJnu3btja2tLq1atOHTokPm5X375hR49evDKK68QHh5OWFgYo0aN4vPPPzfHLFiwgHbt2vHVV1/h7++Pvb09Y8aMoajo5j6kowa5s+dIIfuOFnElQ8Pn6zJQawwM6uFiNX7kADdOXyply948UjM1rPs5h/iUCob3t2w50+oMFBbrzUtZeVVh7uwow89byY+780hKU5OerWHN5mxslVIC/WyvPaSFEX1V7DtazIHjpaRmaVm+KRe1xkj/e5ysxg/toyLicjnbfisiLUvLhp0FJKaqua+XyhwTHmzLoRMlXIqrJCdfx75jJSSlawgNqKpl7DtWQlS86fnEVA0bdhbg6SrH061ujaddwiScSzByIclIXjHsPm1Ep4M2wda/ELcdN3Im3kh2oakytPOUEYkEgryq4iNTjCRlQ2EZ5BbD/ggjtjYSvFRWd/mvydlzmJj5S8j6ed+tPfBV9mxbR+9BD9BrwEj8/Jsw4bm52ChtObL/5xq3Mej1LP/4de4f+yye3n7Vnu/edxgjH3mGFm27/is59+ug5NhFLccjtWTmG9i4vxKNzki3lgqr8SlZBn7+Xc2ZGB0669/hDOqkpLDEwPpfK0nOMpBXbORyip7copuvJA3p7sDBU+UcOVtBeo6O1b8UodYa6dPBzmp8YpqWDXtKOH6hEq3O+vHPx6rZvL+U0/XYenSn5Dv6Pm92Hchlz+E8UtIq+WRVCmq1gcF93K3GPzDEi5Pni/hhRxYp6ZWs+TGduKRy7r/Xyxxz4GgBZy+VkJmjITmtkmXrr+BgL6NJgOVrLq80UFCkMy+V6ptvZawzibT+ljvAnZHlTXB0dMTR0ZGtW7eiVtf+A/LKK68wa9Yszp49S7du3RgxYoRFy9OlS5e4ePHidfcRFxfHpk2b+OWXX9i9ezdnz55l6tSp//i1yGUQGmhLRFSZeZ3RCBFRZTQLsbe6TbMm9kREllmsO3OpjGZNLONbhzuw7qMwlv0vhKnjfXByqGohKi7VcyVDTf97VChtJEilMKSPKwXFOuKSK6iJXAZN/JWcj6mKMRrhQkwF4UHWK1dhwbacj7bcZ8TlCsKCqipA0YmVdGptj5vKlGPLUFsaeSo4F209F6WNhH5dncjK1ZJXqKsx32tJpeDjColZlgVvUrYRP/faNUkpZCCVQKXGeuEtlUK7EAmVGlPF6m6i02pJjr9Mi7ZdzOukUikt2nQhPvpCjdtt2/Q1TipXeg8cdQuytCSTgr+XlOgrVeeREYhO0RHke/1W1etp1UROSpaeSUPt+L9nHJk9zoHuraxXuuqUrwyCGim4lFBV9hmNEBmvJtTf5qb3X99u93zlMglhwfacuVjVVWY0wpmLJbRo6mh1mxahjpy5WGKx7tT5YpqHOtR4jKH9PCkt0xGfbNmFN3aED5uXteXLd5vz8DDvBhnWY5RI6225E/znxyTJ5XJWr17N008/zbJly+jQoQN9+vRh7NixtGnTpsbtpk2bxujRowH48ssv2b17NytXrmT27NlMnz6dI0eO0Lp1awIDA7nnnnu49957GT9+vEW3XWVlJWvXrsXPz3S1++mnnzJs2DA++ugjfHx86vxanB3lyGQSCostv+gLi3U09rHeV+OqklNYUj3eRVX1rz9zsZSjZ4rJytXi62nDhAe8eOtFO15emIjhr+/21z9O5vWp/vzwaTOMRigs0TF/SYpFi9O1nBxkyGQSiq5pAi8s0ePnZf0LwMVJVq3JvKhEb9Gtt/LHXJ4b68nytwPR6Y0YjbBsQw5R8Zbjsgb3dOaxkW7YKaWkZWl4+4uMGq/krbG3AalUQvk1deuySnC33hBWTb82EkorIfGaYQOhvnD/PVIUciitgA2HDFRUH6byn1ZSUojBoMdZZXkF7uziTkZaktVtYiLPcmT/zyxY/N0tyLA6BzsJMqmE4nLLSm9JuRFvt39eSfJQSenZxoYDZzTsPakmwFvG6L626PRwIuqfd+M72UuRySQUl1p+TotKDfh63H7F/+2er8rJVAYXFFmWqQXFWvwbWb/wc3WRU1hk+T8sKNLh5mJZBnZtr2LetGCUNlLyC7W8+l4sxaVVBdbWPdnEJpVTUqqnZZgDkx7xw81FwVfrLYdGCPXrzqjK3aTRo0eTnp7Otm3bGDJkCAcPHqRDhw6sXr26xm26detm/lsul9OpUyeioqIAcHBwYMeOHcTFxfH666/j6OjIrFmz6NKlC+XlVTX/gIAAcwXp730aDAaio6OtHlOtVlNcXGyx6PX//jfn4ZPFnDhXSnKamj8jSnjr0xTCgu1oHV51pTNlnC9FJTpe/SCJmf+XyJ9nS3hzuj+uqltfcA3traJpoJKFyzN5dVEaa7bmMfkhD1qHWTZNHzlVwisfpvLG0nTSs7XMfNIbhfzWDTq8p5mE5v4SNv9hQH9NXTI5G1b9ahq3lJBpZFS3msc5CSYVFWWs+ORNJk55HSfnuk20uN1JJHAlW88vR9Wk5hg4elHL0Qsaera5+dYk4c5wLrKE5+ZGMeOtaE6eL+b16U0sxjlt3pXN+ahSEq9UsH1/Ll+tT2XUvV63tEwDTCdrfS13gLuikgRga2vLoEGDeOONNzh69ChPPPEE8+fPv6l9hoSEMHnyZFasWMGZM2eIjIxk48aN/3h/CxcuRKVSWSzxEV+bny8u1aHXG6sNEHRxllNQbL0bqaBIh4tT9fjCopq7nbJytRSV6PD9q7WnbTMHOrdx5P3laUTFVxCfUsmX32Wi0RgY0K3mgTQlZXr0emO1QeXWWov+Vliix+WaeJWTjMJiU7yNQsKjw91YszWP05fKSU7XsPtIMX+cLWVkf8tcyiuNZOboiIqv5KNvsvDzUtCljfVuSWvKNWAwGKtVXhxsobTmyYQAdAmX0K2ZhA2HDeRYGYam1UNBKaTnm8YtGYzQtoZxTv9VTk4uSKUyiovyLNYXF+ahcvGoFp+TmUpudjpL/+8lJo/uwuTRXTh6cAcRJw8zeXQXsjOu/Os5l1UY0RuMONtb/q+c7CUU1zAouzaKy4xk5ltun1VgwNXp5oroknIDer2x2kQJlaOUotIGGM9yA7d7vkUlpjL42otDV2cFBUXWW/wKCnW4qCwru64qOfmFlvGVagPpWWqi4spY/HUyBoORIX2rfw7+djmuDLlcgrfnre2GbMjuts8//5ygoCBsbW3p2rUrJ06cuG58YWEhzz//PL6+viiVSsLCwti5c2edjnnXVJKu1aJFC8rKymp8/s8//zT/rdPpOH36NM2bN68xPigoCHt7e4t9pqSkkJ6ebrFPqVRKeLj16chz5syhqKjIYglp93RVHnqIS66kbfOqFh6JBNo2d7CYKnq1ywnltGtu2ffdvrkDl68zXdXdVY6Tg4z8vypSShvTF4LRaNnFYDCC5DrTxXR60yyzq1t4JBJoHWZHdA23LIhJrKzWItQ23I6YJFOfl0wKCrmEa1LBYOCGM9ckEup01WX4a3p+kLflNoFeEtLyah5Q2zVcQo/mEjYeNpBZULtjSSSm8Rh3E7lCQWBIM6LOnzSvMxgMRF04SUh462rxvn5BvL1kIwsWf2de2nXuTbNWnViw+DvcPOrehV1XegNcyTYQ5l/1JSkBwvzlJGXUoS/3GgnperxdLYtjLxcp+cU3VzHQ6yEpXUvLJlU1fYkEWjRREnfl9uvfvd3z1emNxCSW076ls3mdRALtWzkRGVtqdZvIuFLat7Tsn+/QypmouJq/f0z7lVy3vAoJtEdvMF73gve/ZOPGjcycOZP58+dz5swZ2rZty+DBg8nOzrYar9FoGDRoEElJSfz4449ER0fz9ddfW/Tu1EbDd/L+y/Ly8nj44YeZNGkSbdq0wcnJiVOnTvHBBx9w//3317jd559/TtOmTWnevDkff/wxBQUFTJo0CTDNXCsvL2fo0KEEBgZSWFjI0qVL0Wq1DBo0yLwPW1tbJk6cyKJFiyguLuaFF15gzJgxNY5HUiqV1W5FIJNZXiVs/TWPlyY1IjapgpjECu4f6I6tjZR9fxQCMHNSI/IKdKz5yXTibNufz3svB/HAIDdOXiild2cVoUF2fPataVq8rVLCoyM8OXqmhIIiHb6eNjz5kBcZORrOXDJ9iC8nVFBapuelJ/3YsD0HtdbI4F4ueHvYcOq85YDEa/1ysIhp4z2JT1ETl6JmWB/T4O8Dx00FyvTxnuQV6fhuu6k2sfNQEW+90IgR/VScvlROzw6ONPFXsmyjaaprhdrIpdgKHr/fHY02l5x8HS1CbenT2ZE1W00tEl7ucnq0d+Tc5XKKy/S4q+SMGuiCRmvkTOSN7+t0tRMxRoZ3kZCZD+n5RjqHSVDI4XyiqZI0vIuEkgo4dMH0+J5mEnq1lLDtTwNF5aZWJwCNDrQ600Du7i0kxKYZKa0Ee6XpPkxOdnD5ys3PZKoLmYM9DqEB5sf2wY1xbtsMTX4RlVcybkkOg0c+xoql8wkKaU5w01b8uv071JUV9Bxgmnn69Sdv4urmyUOPT0dho6RxYKjF9vYOpi+fq9eXlhSRn5tJYb7pnMlMSwZA5eKOyrXmK/PaOnBGzWP32pGSpSc5U0/fDjYoFRL+jDS1DDx+ry2FZUZ++aOqYu/jbqoAyaWm+yH5eUpRa4zm2WsHzqqZOcaBezvbcCZGS6CPjO6tbdiwr+aJEbW1+2gZTz/oQmKaloQ0Lfd2s0dpI+HwGdO+nxmtoqDYwA+/mj7LMhn4eZq+GuQyCa7OMgJ85KbJBfmmiqDSRmIxBsvTRU6Aj56yCgN5RTdXsbvd8928K4vZzwYRk1hGdHw5DwzxwlYpZc8hU/kz+7kgcgs0rNpoukD+aXc2H70ezkNDvTh+toi+3dwIa2LPkpWm89JWKWXc/T4cO1NEXqEWlaOckYM88XBVcPi4qVxsHupAs1AHzkWWUF6hp0VTR557rDH7f8+ntPyfV87/kQbqJlu8eDFPP/00Tz75JADLli1jx44drFq1itdee61a/KpVq8jPz+fo0aMoFKaWvKCgoDof9z9fSXJ0dKRr1658/PHHxMfHo9Vq8ff35+mnn2bu3Lk1bvfee+/x3nvvERERQWhoKNu2bcPDw1TA9unTh88//5wJEyaYb07Zvn179u7da9FKFBoayoMPPsjQoUPJz89n+PDhfPHFFzf1eo6cKkblJOOx+z1xdZaTcEXNm5+kmLuvPN0U5sHWAJfjK/hwRSqPj/JiwgNepGdrePfzKySnmwpwgwGCG9syoJsLDvYy8gu1nI0sY93WbHR/TactLtUz/5MUJjzgxbuzApHLJKSkq3nn8yskpl5/xuDRs2U4O8oYO9QVF2c5Salq3l2WaR7M7eEqt8g3OknNJ2uzGTvUlXHD3cjI0fLBykyuZFQ1TX+8JptxI9x44XEvHO2l5Bbo+H5HgflmklqtkeYhtgzr64yDnYyiEj1R8RXMW5JebUDojURdMXW39WolwcFWQnYhbDpsMA/mdraXWLSwtQ+RIJdJeLCHZbPQkUsGfr9k6lZzd5LQursEOyVUaCAjH9b9ZiC3mFtK1bEV3fZ/a37cYpHp83Bl7RbOPzXnluTQpee9lBQXsHXDMooK8vAPDuOlNz9F5WIazJ2fk4m0joVyxMlDrPq06t5Pyz4yvZaRjzzDqLHP3nTOZ2J0ONpVMqybEqe/bib5xdZySv4azO3qLMVI1XmmcpTw2viqmU8DOykZ2ElJbKqOpT+aKu0pWQa+3l7ByB5KhnRVkldsYMuhSk5F33wrwfGLlTg5FPPgAEdUjjJSMrR8uDbf3D3orpJhvOpj4eok453nPc2Ph/Z0ZGhPR6IS1SxcZbp/VXAjBXOfqhpwP36oqWXlyJlyvv7p5m5zcrvne+jPAlyc5Ex8qBGuKgXxyRXMfT/WPKHGy93GokyIjC1j4ecJPPGwH0+O8SMtU82CxfEkpZpa0/UGI/6NbBnUyx1nJzklpTqiE8p56X/RJKeZYrQ6A/26uTLhQV8UCimZOWq27M5m885beyNJoF6n7qvV6mqzzq01Fmg0Gk6fPs2cOVXlklQqZeDAgRw7dszqvrdt20a3bt14/vnn+fnnn/H09GTcuHG8+uqryOrQbC8xXtuHItSLBQsWsHXr1pv+eZPhT0fWT0K3iK399e+bdLvp2COwoVOokzbjWzR0CnXicu50Q6dQJxv2Nvy4l7ooyrt+l41wczIS0m8cdBv5dX3Hf/0YJad219u+Ptr+J2+9ZXkz2/nz57NgwQKLdenp6fj5+XH06FGLSVWzZ8/m0KFDHD9+vNq+mzVrRlJSEuPHj2fq1KnExcUxdepUXnjhhTqNR/7PtyQJgiAIglA/6vNnSebMmcPMmTMt1tXl1y+ux2Aw4OXlxfLly5HJZHTs2JG0tDQ+/PBDUUkSBEEQBOFfUI/dbda61qzx8PBAJpORlWXZvZiVlVXjGF9fX18UCoVF11rz5s3JzMxEo9FgY1O7WYF37ey2f9uCBQtuuqtNEARBEG4nRiT1ttSWjY0NHTt2ZP/+qh/gNhgM7N+/36L77Wo9evQgLi4Og6GqCz0mJgZfX99aV5BAVJIEQRAEQbjNzZw5k6+//po1a9YQFRXFlClTKCsrM892mzBhgsXA7ilTppCfn8+LL75ITEwMO3bs4P/+7/94/vnn63Rc0d0mCIIgCEKtNNRvrj3yyCPk5OTw5ptvkpmZSbt27di9ezfe3t6A6b6E0qt+zM7f3589e/bw0ksv0aZNG/z8/HjxxRd59dVX63RcUUkSBEEQBKF2GvCHaadNm8a0adOsPnfw4MFq67p162ZxY+h/QnS3CYIgCIIgWCFakgRBEARBqJX6vAXAnUBUkgRBEARBqJWGGpPUUO6uVysIgiAIglBLoiVJEARBEITaEd1tgiAIgiAI1YnuNkEQBEEQBEG0JAmCIAiCUDt1+TmR/wJRSRIEQRAEoVbutu42UUm6zRVm5jR0CnUiV9b+hwNvB3l51n9B+nblcu50Q6dQJ4VtOzZ0CnXiufpSQ6dQJ4EBjg2dwn9aeXFFQ6dw+7nLBm7fXVVCQRAEQRCEWhItSYIgCIIg1IrxLmtbEZUkQRAEQRBq5W77WZK7q0ooCIIgCIJQS6IlSRAEQRCEWhGz2wRBEARBEKy42+6TdHdVCQVBEARBEGpJtCQJgiAIglArortNEARBEATBirttdpuoJAmCIAiCUCtiTJIgCIIgCIIgWpIEQRAEQagdMSZJEARBEATBCtHdJty0gwcPIpFIKCwsbOhUBEEQBEH4h+6KlqQnnniCNWvWAKBQKAgICGDChAnMnTsXufz2fgseHNqIRx/0x83VhvjEUj7+Ko6o2JIa4/v18GDyY8H4eNmSml7Ol6sT+fN0vkXMU+ODGHGvD04Oci5EFbPoi1hSMyrMz08YE0C3Tm40beKIVmvkvkf/sNj+vgHezJvRzOrxR006SWGxznLdEG/GjmyEm4sNccllLF2ZxOW40hpfQ59ubjw1NgAfTyWpGZV8tS6Z42cLzc/36urGyHu9CWvigMpJweSXzxGXVG6xDzcXBc89HkinNirs7GRcSa9g3eY0Dh/Pp666t5TTt50cJ3sJGXkGfvpdy5Vsg9VYb1cJg7soaOwhxc1Zys9/aDhy3vL9kEjg3k4KOobJcLKXUFRm5FS0jn2ndVb3WVf7d25i99a1FBXm4R/UlPGTZ9MkrNUNtzt+ZA9fLZ5L+y59mD5nsXn96WO/cXDPjyTFX6astIgFi78jIDi8XnKtC7eenWgy6ylUHVph28iLU6OnkrVt/y3PA6BTmITuzaU42kFWAew6pSc9z3ps+xAJbZtI8VSZHmfkG/ntnMEcL5VAv7ZSQv0kuDqCWgMJmUb2RxgorbC+z7q6dGw95w+voqI0FzefZnQfOQ8v/zZWYy+f2ETM2W0UZMYC4OHXgs6DX7KIP/jDHGLPbLXYrnHTntw36ev/fL5Dejozsr8KF2cZyWkaVm7OIy5FXWN8t3YOjB3qiqebnIwcHet+yeNsZNU/1tZGwvgRbnRp44CjvZTsfB27Dhex9w9TOe9oL2XMfa60DbfDw1VOcZmBk+fL2LAzn/JKY53zv1l3W3fbXfNqhwwZQkZGBrGxscyaNYsFCxbw4Ycf1nk/er0eg8H6F+S/YdrkEL75PomnZpwmLrGUxW+3xkWlsBrbqpkz819pwfa9GUx68TRH/sxj4byWBAfYm2PGj/bnoeF+LPoilmdePktFpZ7Fb7fGRlHVhCqXSzjwRw5bd6ZbPc7+IzmMfPyoxXL8dD5nLxRWqyD16+7O1IlBrP4hladnnyc+qZwPX2+Oi7P1ymnLcEfenBHGjv3ZTH7lPL+fzOed2eEE+9uZY2yVUi5ElbB8XUqN79uc6aH4N7Jj7vvRTJp5jiPH85k/M4zQYPsat7GmbYiMkT0U/HpKy5IfK0nPM/L0cCWOdtbjbeQS8ouN7DyupbjMegHWr72c7i3l/HREwwcbKtn5p5a+7RT0bH3zFfYTv+9l4zeLGfnIM8z/aD3+QWEsfnsaxYXXrxzmZqezac0Swlq0r/acWl1B0+bteHjC9JvO72bIHOwpPh/NxRfeatA8WgRKuLeDlEMXDCzfqSezwMj4fjLsldbjg7wlXEwysHa/nlV79RSXw2P9ZTj9dQ4p5ODrBkcuGPh6p55Nh/V4OEsY20dWL/nGn9/Jnzvep8OA53lg2mbcfcPZteppKkqt1+rSE04S2mYow59ezf1TvsfRxZddqyZTVpRlEdc4rBfj5x42L/0fXfSfz7d7ewcmPuDOD3sKmP1hGknpGl6f4oOzo/Wv0vAgJTMmeLH/zxJe+TCNkxfKmP2UD/6+VWX4xAfcadfcnqXfZjNjYSo7Dhbx1GgPOrUylVWuKhluKjlrf85n5nupfL4+m3bN7ZjyqGed868PRiT1ttwJ7ppKklKpxMfHh8DAQKZMmcLAgQPZtm0bixcvpnXr1jg4OODv78/UqVMpLa1q5Vi9ejUuLi5s27aNFi1aoFQqSUlJQa1W8+qrr+Lv749SqSQ0NJSVK1daHPP06dN06tQJe3t7unfvTnR0dJ3z/mVPBjv3Z5F0pZwPv4ilUm1g+CAfq7EPj/Tj+Jl8vv8pleTUclasTyImvpTRw/0sYtZuSub343nEJ5XxzseXcXdT0useD3PMqu+S2fRzGvHJZVaPo9EYyC/UmheDATq0cWH7rxnVcxrhy4592ew+kENyagWLlydQqTYwtL+X1X2PHurLiYhCNm5LJyWtglUbrhCbWMYD91W95l8P57L2x1ROny+q8X1rFebEll0ZXI4rJSNbzbeb0ygt1xHexLHGbazp01bO8UgdJ6P1ZBUY2XxIg1ZrpHMz6xWaKzkGth/TEhGnR6e3XkkK8pZxMUlPVIqBghIj5xP0xKTq8fe6+Y/jnm3r6D3oAXoNGImffxMmPDcXG6UtR/b/XOM2Br2e5R+/zv1jn8XT26/a8937DmPkI8/Qom3Xm87vZuTsOUzM/CVk/byvQfPo1kzKmTgj5xKM5BbDjhMGtHpTi5E1Px01cCrWSFYB5BXDL8cNSCQQ7GOKV2th3W8GIlOM5JVAWh7sOqmnkbsE57rV6a26cGQNzTo/THinB3H1DqXnqAXIbWyJPrXFanz/sR/Sots43Bs1x8WrCb0e/B9Go4G0+GMWcTK5DfZOnuZFaae6+WRv83xH9FWx72gxB46XkpqlZfmmXNQaI/3vcbIaP7SPiojL5Wz7rYi0LC0bdhaQmKrmvl5Vxw4PtuXQiRIuxVWSk69j37ESktI1hAaYat1XMrQsWpXF6UvlZOXpuBhbyfc7CujUygHpXfMN3nDu2rfYzs4OjUaDVCpl6dKlXLp0iTVr1vDbb78xe/Zsi9jy8nLef/99VqxYwaVLl/Dy8mLChAl8//33LF26lKioKL766iscHS2/gOfNm8dHH33EqVOnkMvlTJo0qc55njpXYP7baIRTEQW0DHe2GtuqmTOnIgos1h0/m0+rZqb4Rt62eLgpOXlVTFm5nsiYYnPMPzGkvzeVagMH/si1WC+XSwhv4sjp84UWr+H0hUJahFsvVFqGOVnEA5yIKKRFmPX4mlyMKaF/Dw+cHOVIJNC/hzs2CikRl4prvQ+ZFPw8pcSkVrUcGoHYNAOB3v/8o5OUpaepnxQPlelL0tddQrCPjMsp+n+8TwCdVkty/GVatO1iXieVSmnRpgvx0Rdq3G7bpq9xUrnSe+Comzr+3UAqNbX6JGZaVoATM4009qjdlbFCZupiq9DUHKO0kWA0Gqm8Tkxt6HUactMv4RfazbxOIpXiF9KN7JSIWu1Dp63EoNdVq1RkJJzg23d6sOmj+/h96wIqywpq2MN/I1+5DJr4KzkfU9VVZjTChZgKwoNsrW4TFmzL+WjLPtOIyxWEBVU1O0YnVtKptT1uKlPLYctQWxp5KjgXXXNfq72tlPJKA7ewU8PMKJHW23InuL0H5PwLjEYj+/fvZ8+ePUyfPp0ZM2aYnwsKCuKdd97hueee44svvjCv12q1fPHFF7Rt2xaAmJgYNm3axK+//srAgQMBaNKkSbVjvfvuu/Tp0weA1157jWHDhlFZWYmtrfUPlDX5BVrLx4VaAhtbv7x0c7GhoNCyVC0o1OLmYmN63tXGvM4yRmN+7p8YNsiHfYez0GgMyK/qclA5yZHJJOQXXXs8LQF+1vur3FwU5F+bX5EWNxfrXYw1eeujGN6cGcYvqzuj0xmoVBt448No0jIra70PB1sJMqmE0grLL8SSciNeLv/8A37gjA5bhYTZj9piNIBECruPazkbe3OVpJKSQgwGPc4qd4v1zi7uZKQlWd0mJvIsR/b/zILF393Use8W9kqQSiWUXTMWpKwSPJxrV0ka0F5KSQUkZFhvaZRJYUA7KReTjGhucphaZXkhRoMeO0fLc8LOyZ3CnMRa7ePErkXYO3vhF9rdvM4/rCfBLQfh5NaY4rwUTu5dwu7VzzJyyvdIpf+8m/B2ztfJQYZMJqGoxPJzWliix8/Levnk4iSj8Jr4ohI9Ls5Vx1z5Yy7PjfVk+duB6PRGjEZYtiGHqHjrZZWTg5SHBruw72jtL/jq053STVZf7ppK0vbt23F0dESr1WIwGBg3bhwLFixg3759LFy4kMuXL1NcXIxOp6OyspLy8nLs7U2VERsbG9q0qRoEGBERgUwmM1eAanL1Nr6+vgBkZ2cTEBBgNV6tVqNWmwYASiQSnJycMBq0VmNvFy3DnQkOcOCdxZcbOhULk8b64+ggY+Zblygq1tGzixsLZoYx/Y1LJKaU33gH/6K2oTI6hMn4bp+GzHwDjTyk3N/DhuJyI6eib66iVBcVFWWs+ORNJk55HSdn11t23LtZjxYSWgVKWLNPj95KK4BUAg/1kiKRmLrxGlrEwa9JOL+LYU+vQa6ougIKaTvM/LebTxhuvuFs/PBeMhJOWLQC3Wp3Wr4AQ3uraBqoZOHyTHILdDQPsWXyQx7kF+m5EGPZmmSnlDD3GR9SM7Vs2nXzLXfCjd0Z7V31oF+/fkRERBAbG0tFRQVr1qwhJyeH4cOH06ZNGzZv3szp06f5/PPPAdBoqlpk7OzskFz1ezV2djWM2r2GQlF1dfH39tcb9L1w4UJUKhUqlQp3d3d0Oh2Gyj8tYtxcFOQVWG+Dzy/U4Opi2SLk6qIg/6/Wpfy/tnO9plXG1cXG/FxdjbjXh5j4EqLjq89WKyrRodcbcVNde7zqrUVVr6F6q5GrquZ4axp5K3lwqC8ffB7PmQvFxCeXs+aHVKLjS3lgiHet91NWaURvMOJoZ3nl5GQvobj8n88qGd5NwW9ndETE6cnMN3ImRs/hc1r6t69ba9m1nJxckEplFBdZDnAtLsxD5eJRLT4nM5Xc7HSW/t9LTB7dhcmju3D04A4iTh5m8uguZGdcual8/ovK1WAwGHGwtTwnHGyp1uJ4rW7NJfRoKWXdb3qyC6s//3cFSeUgYd1+/U23IgHY2rsgkcqqDXquKMnD3qn6OXG184dXce7Q19w3aQXuvtefzejs5o+tgyvFeTVPprjT8y0p06PXG1E5WbY8WWst+lthiR6Xa+JVTjIKi03xNgoJjw53Y83WPE5fKic5XcPuI8X8cbaUkf0tuwttlRJen+JLhdrAByuzrFaybwWjRFJvy53grqkkOTg4EBoaSkBAgHna/+nTpzEYDHz00Ufcc889hIWFkZ5ufUbX1Vq3bo3BYODQoUP1muOcOXMoKiqiqKiI3NxcJBIJg4eMMj8vkUDHtq5cirbezHrxcjGd2lq2CHRu58rFy6b49KxKcvPVFjH2djJahDmbY+rCzlZK/56ebP810+rzOp2R6IRSOrSu+rBLJNCxtYrIaOu3MbgUU2IRD9CprQuRMTXf9uBaSqWpUDJc852lN2BR2b0RvQHScgw0bVz1MZEAoX5SkrP+eQmlkEu49uvUaDS9NzdDrlAQGNKMqPMnzesMBgNRF04SEt66WryvXxBvL9nIgsXfmZd2nXvTrFUnFiz+DjcP6xME7mYGA2TkVw26/luwj4TU3JorSd1bSOjVSsr63/RkWJlo+HcFyc3JVEG63nilupDJbfBo1JK0+KqLLaPBQHr8n3gFtKtxu3OHVnDmty8Z8uRyPBvf+PYRpUWZVJYXYu90czOubud8dXpIuKKmdVjVRbJEAq3D7IhOst41FpNYaREP0DbcjpgkU4+BTPpXeXDNqWMwmM6Jv9kpJbwxxRedzsh7X2eh1d36qf9/Mxol9bbcCe6a7jZrQkND0Wq1fPrpp4wYMYI//viDZcuW3XC7oKAgJk6cyKRJk1i6dClt27YlOTmZ7OxsxowZ84/zUSqVKJWW84hHDmlMdHw5UTEljLnfDztbKTv2mSolr78UTk6ehq/Wmvrqf9iWxmcL2zJ2VGOOnspjYC8vmoU68cFnMeb9/bAtjYmPBHAlvYKMrEomPxZEXr6aI39WDbr29lTi5CjH29MWmRRCgx0ASMuooKKyqnLQv5cXMpmEvQctp9pe7YdfMpgzLZTo+DKi4kp5aJgvtkoZuw7kAKap+rl5Gr7+znRFt3lnBp+81ZIxI3z583QB/Xt6EN7EgY+WxZv36eQox9vDBve/xlH5NzIVQn/PtktJqyA1o4JZzzbhy7XJFJdo6dnFjU5tVMxZWLduwUPndIztb0NqjoGULAO92sixUUg4edl0mT+2vw1FZUZ2HTe1dMmkpnslAchkoHKQ0MhdgloLecWmgi0ySc+ADnIKSwxkFhjx85DSu63CvM+bMXjkY6xYOp+gkOYEN23Fr9u/Q11ZQc8BIwH4+pM3cXXz5KHHp6OwUdI4MNRie3sH0wD5q9eXlhSRn5tJYb7pf5aZlgyAysUdlev1r+7rk8zBHofQqq5q++DGOLdthia/iMor1WdW/luOXTYwqpuU9DwJ6XlGujaTopBBRILp/3t/N9OYo98iTJ+V7i0k9G0jZcsfBgrLTK1OABodaHWmL8OHe0nxcZOw4aAeiaQqpkLDTQ/Obd1rIod+mIOnXys8/Vtz8Y+1aDUVhHV8AIADm17FwdmbLkNmAhBx6GtO//op/ccuwsnVj/IS0/9dYWOPQumAVl3Gmf1fENRqEPZOnhTnpXBi1yKc3QJoHNbz5pK9zfP95WAR08Z7Ep+iJi5FzbA+KpQ2Eg4cN7WkTx/vSV6Rju+2m7rCdh4q4q0XGjGin4rTl8rp2cGRJv5Klm005VihNnIptoLH73dHo80lJ19Hi1Bb+nR2ZM1WU2uanVLCG1N9UdpI+ODbbOxtpdj/dX4Ul+qrXQz+24x3T9sKcJdXktq2bcvixYt5//33mTNnDr1792bhwoVMmDDhhtt++eWXzJ07l6lTp5KXl0dAQABz586t9xw/XxXP5PFBuLnaEJdQyqz5F8wDr709bS0+IBcvF/PWoiiefiyYZyYEk5pewZx3LcfgrN98BVtbGbOnheHoIOdCZBGz5l9Ao63a0VPjgxg6oKoVYfXSTgBMnxPB2YtV0+6HD/Lh0LFcSstqHkdz4GgeLs4Knhzrj5uLgrikMma/G0XBX4O5vT1sMF71Ii5Fl/K/T2J5amwAk8cFkJZRyesfRJN4papvvkcnV16bVvUlPn9mmCnPTVdYvSkVvd7Iq+9e5pnHAvi/18Kxs5WRllnJws/iLG5KWRvn4vU42mkZ3FmBk72E9FwDK7arzTf5c3W0vAp0dpAwc0zVlWPfdlL6tlMQn6bny22mq8etv2sY3EXBg71tcLQz3Uzyz0gdv566+fFnXXreS0lxAVs3LKOoIA//4DBeevNTVC6mgbD5OZlI69hkFXHyEKs+rbo30bKP5gAw8pFnGDX22ZvOubZUHVvRbf+35sctFpk+b1fWbuH8U3NuWR6RyUYclAb6tpXiaGu6meR3B/SU/dWYoHIwzUz7W6emUuQyCWN6W3a7HDpv4NAFA072EO5v+uJ5dphlkbzmVz3J2Tf3LRjSZiiVpQWc3reU8pJc3H2bc9+Ty83dV2WFGUiummkU9ecGDHot+9a/aLGfDgOep+PAaUikMvIyo4k5sxVNZQn2Tp40btqDjoNeQCb/5xNA7oR8j54tw9lRxtihrrg4y0lKVfPuskzzYG4PV7lFmRydpOaTtdmMHerKuOFuZORo+WBlJlcyqj7rH6/JZtwIN1543AtHeym5BTq+31FgvplkE38lYX/Nnvv8TcvxrFPeSiEnv35uQitYJzEar23oE24nPUfUb5fev02uvPlC8lbqNKBtQ6dQJw/0afjBvHVR2LZjQ6dQJ6dXX2roFOrE3u7uuqq/1f48lNTQKdTJj59Un2Vd32Lib27c2dXCQqxPYrqd3NUtSYIgCIIg1N7/s3ff4U1V/wPH31mdSfeC0lJKKavsvWTvvREUZCqIoogoDoaKDAEBfyrKRtlDZIsgW/aeLRRK6S7du01yf3/kS0psii0rRc7refI8zc25936SJrmf+znnnrxsUwCI0xBBEARBEAQzRCVJEARBEIQiedkqSSJJEgRBEAShSF62JEl0twmCIAiCIJghKkmCIAiCIBTJizIJ5NMikiRBEARBEIpEdLcJgiAIgiAIopIkCIIgCELRvGyVJJEkCYIgCIJQJCJJEgRBEARBMONlG7gtxiQJgiAIgiCYISpJgiAIgiAUiV50twmCIAiCIBT0so1JEt1tgiAIgiAIZohKUgknVyosHUKxbPhCZekQimXs0jhLh1As63JcLB1CsbivuGrpEIqlzhtVLR1CsRz67oKlQ/hPa9q6nKVDKHFetoHbIkkSBEEQBKFIRHebIAiCIAiCICpJgiAIgiAUjehuEwRBEARBMEN0twmCIAiCIAiikiQIgiAIQtGI7jZBEARBEAQz9JYO4DkTSZIgCIIgCEXyslWSxJgkQRAEQRAEM0QlSRAEQRCEInnZrm4TSZIgCIIgCEUiutsEQRAEQRAEkSQJgiAIglA0ErKndiuu77//Hj8/P2xsbGjQoAGnTp0q0nrr1q1DJpPRo0ePYu9TJEmCIAiCIBSJXnp6t+JYv34948ePZ8qUKZw7d44aNWrQvn174uLiHrleWFgYEyZMoFmzZo/1fMWYpP954403WLlyZYHlN2/eJCAgwAIRFa5nx1IM6OGNi5MVoWEZLFgSyvWb6YW2b9HYleGvlsXLw4bI6CwWrQrjxLkk4+OvNHSle3svAsurcdSoGPb+eW6FZZhsY8Jb5alTwwk3ZyuysvVcCU5l0aowwiOzih3/ll17Wbt1J4nJKZT38+W9EUOoEljebNtdfx1ixnc/myyzUqnYv2GF8f70hYvYc+CISZv6taozd/JHxY4NoH0TDV1bOOKkUXA3KpdlvyUQei+30PYNq9vRv6Mz7s5KYu7nsXpHEudv5L8ujmo5g7q4UD3QBntbOddv57DstwRi7muNbVo3VNO0lppyZayws5HzxqfhZGY/3owkzaqraF3XGgc7GZH39Ww6kMXdWPPb8nKR07mRNT6eClwd5Gw+lM3B8wWfq6O9jO5Nranip0SlknE/Wc+ve7O4F/d0Zk2pGyijcWU5aluITYLdZ3REJZhvW6u8jBr+ctwdDfejEyX+uqg3tpfLoGUNOQHeMpzVkJMLt2Mk9l/Qk178t+tjc2laF/8PhuNYOwib0h6c6T2G2G37n18AD2lUVUHzmko0tjKiEyR+P5bLvTjzRylPZxnt6qnwdpfhopGz7VguRy/rTNrIZNC2rpLaFRRo7GSkZkicCdax/5zW7Db/6/FeObaaC4eWkpl2H9dSlWja4zM8faubbXvt5AaCz/5OYsxNANy9q9Kg4/sF2ifFhnJ81xyib59Gr9Ph7Fme9oMXonEu/VRiftHMmzePkSNHMnToUAAWLVrEzp07WbZsGR9//LHZdXQ6HYMGDWLatGkcOXKE5OTkYu9XVJIe0qFDB6Kjo01u5cqVK9Y2dDodev2zm26rVRM33h5ajhXrwxnxgSGZmTM5CCdHldn2QRU1TB5fiZ37YxnxwXmOnExg+seVKedrZ2xjYy3n0nVD0lOY4NB0Zn53k9ffOceEL64gA+ZOqYq8mO+g/UeP83/LV/NG/14smfsVAX6+fPDFTJKSUwpdx97Olq3LvjfeNv68oECbBrWqm7SZOn5s8QL7n0Y17RjczYVNe5P56Nso7kbl8ukoTxzU5p9ooJ81415z56+TaXw0L4rTVzL5cKgHPl75/48Ph3rg4aLkm+VxTJwXRXySls/f9MLaKr/cbK2ScyE4i9/2F/46FEXtQCU9X7Fh94kcZq/JIDJex5ie9qhtzZe2rVRwP0XPtqPZpGSYf9/aWsP7/e3R6eHHrZl8vSqd3w5nk5VTzFPBQlQpK6NdbTmHLuv5eZeOmCSJQS0V2Fmbb+/nKeNKmJ5V+3Us26sjNRNea6VAY2t4XKWEUi5w5LKexbt0bDisw81BxoDmiqcSb1Ep7O1IvRTMlXenPdf9/lON8gq6Nlax74yWBZtziE7QM7yzNfY25turlJCYqmf3CS2pGeb/xy1qKmlURcnWo3nMWZ/DrpN5tKippEnQk7/GL1q8ty7s4tj2mdRt+zZ93tuCa+mK7Fgygsx081l+VOgpKtTsTPc3V9Jr7DrUTl7sWDyc9JRYY5uU++H89sNAnN396fbWKvqN/506bcagUBXyoXiOnmZ3W05ODqmpqSa3nJycAvvMzc3l7NmztGnTxrhMLpfTpk0bjh8/XmisX3zxBR4eHgwfPvyxn69Ikh5ibW2Nl5eXyW3BggVUq1YNe3t7fHx8GDNmDOnp+VWbFStW4OTkxLZt26hSpQrW1taEh4eTk5PDhAkT8Pb2xt7engYNGnDw4MEnjrFfN292/BnD7r/iuBuRxdxFt8jO0dG5tafZ9n26lObU+STWbY3kbkQWS9eGE3I7nV6dShnb7D0Uz8oN9zh7MbnQ/W7/M5aL11KJic8h5HYGi9fcxdPdBi+PQr65CrF+2266tm1J59bNKedThglvDcPG2pqd+w8Vuo4MGa7OTsabi5NjgTYqlcqkjUZtX6y4HujyiiP7T6Rx8HQ6kbF5LN6cQG6eRMv6GrPtOzVz4EJwFtsPphIZl8f6PcncjsylQxMHAEq5KQn0s2HJZkM1Kjpey5LNCVipZDSplR/jriOp/P5XCjfvFvyCKI6Wta05fiWPk9fyiEnUs35/NrlaiUZVzSfR4bF6fj+aw7kQLVqd2Sa0rWtNcpqe1X9mczdWT0KqxI1wHfdTnk6S1KiSnHO3JC7elrifCjtP6cnTGSpG5vz2t54zNyVikyAhFbaf1COTQTkvQ/ucPPj1Lz3XwiUS0iAyAXaf1lHaVYaDndlNPhPxfxwmZMp8Yn/f9/x2akaz6kpOXtdxJlhHXJLElsN55GmhXiXzHQkR8RI7T2i5GKpDW0ifiJ+XnKthOm6E60lKk7h8W09IhB4fjyc/pLxo8V48vIIqDfpSqV5vXDwDaN5rGiqVDTdObTbbvs3AOQQ1Hoibd2WcPfxp0fcrJElP5M38g/2pPfMpW6k5jbp8iLt3FRzdfClXtRV2atcnjvdJSZLsqd1mzJiBo6OjyW3GjBkF9nn//n10Oh2enqbHOU9PT2JiYszGefToUZYuXcrixYuf6PmKJOlfyOVyFi5cyNWrV1m5ciV//fUXEydONGmTmZnJrFmzWLJkCVevXsXDw4OxY8dy/Phx1q1bx6VLl+jbty8dOnTg5s2bjx2LUikjsLyaMw8lM5IEZy8lU7Wi+YN41YqaAsnPqQvJVA10eOw4bKzldGrlSVRMNnH3i35Qz8vTEhJ6hzo1gozL5HI5dasHcTW48NclKzubPqPepfeId5j09VzuhEcUaHPhynW6DhnNwLcnMGfRMlJS04r3pACFAvzLWHH5ZrZxmSTB5ZBsAsuaP4MLLGvN5ZBsk2UXg7Oo4Gdor1QaDtx52vwvb0mCPJ1EpXLFSzD/NX45+HjICb6X34UgAcHhWvxKPf4Zc5C/kvBYHcM62fL1KDUTB9rTOMh80lVccrmh6nMnxvTgdidGooxb0QZ2qhSGLraswntEsbaSIUkS2Y9o81+kkIO3u4xbEfkZsATcjNBR1vPxv/7DYvQElJHj5mj4H5VyleHnJSf43pNV0V+0eHXaXOIjr1KmQmPjMplcjneFRsTevVCkbWhzs9DrtFjbGU7+JL2euzcO4ujmx47Fw1k+tTGbF/bjzhXLJtvPwqRJk0hJSTG5TZo06Ym3m5aWxuuvv87ixYtxc3N7om2JMUkP2bFjB2q12ni/Y8eObNy40Xjfz8+Pr776irfeeosffvjBuDwvL48ffviBGjVqABAeHs7y5csJDw+ndGlD//GECRPYs2cPy5cv5+uvv36s+Bw1KpQKGUkpeSbLE5Pz8PU2f4rs4mRFYrLpkSEpORcX5+If5Hp08OKtweWws1VwNyKT8dOuoNUWvZqQkpaGTq/HxdG0EuTs5MDdyCiz6/iWLs3HY0dR3s+H9Iws1v2+k9GTprJqwSw83AxnVQ1q1aB5w3qU8nQnMiaOn39dz4dfzubHmdNQKIr+xepgr0ChkJGcZlpSSU7XUdrD/OvlpFGQkm7aPiVNh5PGkJRExeURn6hlYCdnft6UQHauni6vOODmpMTJ4el2/9jbylDIZaRmmv5P0jIlPF0ef19ujnKaVrfiwLlc9p7OwddTQe8WNmh1cOp63r9v4BHsrEEul5GRbRpzRja4ORQtSWpdS05aFtyONv9eVMihdU05V8Ikcp/OEJQXhr0NKOQy0v4xFis9S8LD6fGTjoPntdhYwYQB1kh6kMnhj1Nazt8spBz5H403OyMJSa/D9h8VHju1G8lxd4q0jRO75mLv4GFMtLLSE8jLyeT8gcXU7zCOhp0mEB58hD2r3qH7myspXb7+E8X8pKSnU0AGDL031tb/3oXo5uaGQqEgNjbWZHlsbCxeXl4F2oeGhhIWFkbXrl2Nyx4Mg1EqlQQHB1O+vPlxsP8kkqSHtGzZkh9//NF4397enn379jFjxgxu3LhBamoqWq2W7OxsMjMzsbMzJCZWVlZUr54/6O7y5cvodDoCAwNNtp+Tk4Ora+Hl0pycnAL9sXpdLnKF1dN4ek/sz8PxnLmYjKuzFQO6ezNtQiXennSR3Lyn+Kn5h6BKFQiqVMF4v1qlCrz2zkS27f2LEQP7AtCmWSPj4+XL+hJQ1pf+o9/n/NVr1K0eVGCbz5NOD3NWxjG6nxvLv/JFp5O4fDObc9czX5h5a2UyCI/Vsf1vw3szIl5PKVc5TaurnjhJelJNqsgIKitj5T4dOjNFAbkM+jSTI5MZuvGEp6N6eQW1KihYuy+P2CQ9pV3ldG2iIjVD4mzIkyUez0JJjffcXz9z68Iuur+1CuX/xhtJkuF96le1FTVeeQMAN+/KxNw9z9UT6yyeJOkt8M1lZWVFnTp12L9/v/Eyfr1ez/79+xk7tuD400qVKnH58mWTZZ999hlpaWksWLAAHx+fIu9bJEkPsbe3N7mSLSwsjC5dujB69GimT5+Oi4sLR48eZfjw4eTm5hqTJFtbW2Sy/DdOeno6CoWCs2fPolCYnsE/XKn6pxkzZjBtmukgT9+KQylbeRgAKWl5aHUSzv8YpO3ipCpQLXogMTkXFyfTJMvZyYrEpOIf3DIydWRk6oiIzuZqSBo7f2lIswau7D96v0jrO2o0KORyElNMBycnJafiamackTlKpZIK5coSER1baJvSXh44OmiIjI4tVpKUmqFDp5OMVaAHnNSKAtWlB5LTdDiqTds7akzb34nIZeK8KGxtZCgVMtIy9Ex/txS3I55s/NE/ZWRJ6PQSDnamX2KGq3keP0FIzZCISTRdPzZJT80KT97llpkDer2EvY0MQ8eKgb2NoXrwKI0qy2hSVc4v+3XEJRd8/EGC5Ggv45d9upeuigSGipxOLxkHtT+gtpWRlvn4JzedGyk5cN4wDgggJlGHk0ZGy1rKJ0o6XrR4beydkckVZP1jkHZm+n3sNI/u5rlwcCnnDyym66hluJauaLJNuVyJi6fpVdXOHuWJuXP2sWN9Wiw14/b48eMZMmQIdevWpX79+syfP5+MjAzj1W6DBw/G29ubGTNmYGNjQ1CQ6Xe/k5MTQIHl/0aMSXqEs2fPotfrmTt3Lg0bNiQwMJCoKPPdQg+rVasWOp2OuLg4AgICTG7mSoMPmOuf9Ql8zfi4VisREppOnepOxmUyGdSu5sTVYPNjcK4Gp1H7ofYA9Wo4cTUk9V+fx6PI/rdvlarobyGVSklg+XKcvXTVuEyv13P28hWqVqzwiDXz6XR6boffw9XZqdA2cfcTSE1Lf2Qb89uG2xG5BFXIHyskk0FQBRtCChlQHXI3h2oVTMcWVQ+04WZYwfZZ2RJpGXq83JSU97Hi9JXMYsX3r/Hr4V6cnkCf/HMfGRDooyQs+vEPBLejdHg6m/6fPZzkJKY+eWVGr4foxPxB1w+U85IRcb/wg2LjKjKaBclZ/ZeO6MSCjz9IkFw0Mn7dr3vkeKX/Mp0eIuMlArzzE3kZEOCtKHRaiKJQKWUFul0kyfB5eRIvWrwKpRXu3lWJuJU/6FrS64m8dQLPsjULXe/8gSWc3f8jnUcsxsOnWsFt+gSRHG/aXZcSH4b6Jb38H6B///7MmTOHyZMnU7NmTS5cuMCePXuMg7nDw8OJjo5+6vsVlaRHCAgIIC8vj++++46uXbty7NgxFi1a9K/rBQYGMmjQIAYPHszcuXOpVasW8fHx7N+/n+rVq9O5c2ez65nrn/1nV9uGbZFMejeQ4NB0rt9Mo2+X0tjaKNi131BZ+eTdQO4n5vDzr3cB2LQjioVfVaN/N2+On02kdVN3KpZX882Pt4zb1KiVeLpZ4+Zi2Jevt+E0LjE5l8TkPEp5WtOqiTunLySRnKrFw9WKQb3KkJOrN5lvqSj6d+vI1wt/olL5clSuUJ6NO/aQlZ1Dp9bNAfhqwY+4uTjz1usDAFi+fgtVKwZQxsuLtIwM1m7dSUz8fbq0bQFAZlY2y9dvoUWjerg4OxEZE8uPK9fi7eVJ/Vrm5yl5lB2HU3h7gDu37+VwKzyXTq84YG0l4+ApQxL69qtuJKZoWbsrGTBclTZ1jBddmjtw7noWTWraU76MNT9vzD+zbFjdjtQMPfeTtPiWUvFGD1dOX8nk0kMDvh01Cpw0CrzcDB9J31IqsnIk7idpycgq+sHhwLkcXmtnS3isjrsxOlrUtsJaJePENUPl8PV2NiRnSGw/ZkjiFHLwcjUkQEq5YT4kb3c5ObmS8eq1A+dzGN/Pnnb1rDgXkkdZLwWNq1mxbt/TmXTo+A09PRrJiUqQEZUg0aCSHJUCLtw27L97I8OYo78uGF6HxlVktKguZ8sxPckZGC8Nz9VCntaQIPVtJsfLRca6gzpksvw2WbmGxOx5UNjbYR/ga7xvV64MDjUqkZuYQva9p/9lXpgjl7T0a6kiIl7PvTg9TasrsVLBmWBDaa1/SxUpGRJ7ThnuK+Tg4WzIHpRyGY72Mkq5ysjNg4RUw//k+l0drWqrSE6XiE2SKO0qo1l1JadvPHm57kWLt8Yrb/DX+o9xLxOEp091Lh1ZSV5uFpXq9QJg/9qPsHf0oGGnDwA4f2Axp/5YSJuBc3Bw9iYzNR4AlbUdKmvDFa81mw/nz9XjKeVfF+/yDQgPPkLY9QN0f2vVE8f7pJ7mmKTiGjt2rNnuNeBfrx5fsWLFY+1TJEmPUKNGDebNm8esWbOYNGkSr7zyCjNmzGDw4MH/uu7y5cv56quv+OCDD4iMjMTNzY2GDRvSpUuXJ4rpr2P3cXJQMWyALy7OVty6k8GEL64YB3N7ulsjPfQuvhKcxhffBjNiYFlGvlaWiOgsPp15nTvh+VWMJvVc+OTd/PFTUydUMjyHdeEsXx9Obq5EjSoO9O1aGo29kqSUPC5eTWHMx5dITilet13rpo1ITk1j6bpNJCalEFCuLHMmf2S8rD82PsGk6zItI4PZPywhMSkFjdqewPLl+HHGVMr5lAFAIZcTejecPQeOkJ6ZgZuzM/VqVmPEwL5YqYrfHXT8QiYO9on0a++Mk4OCsMhcvl4cS0q64cjq5qQ0+ZIICcth4a/xDOjozKudnImOz+Ob5XHci8l/XZwdFAzu7oKTWkFSqo7DZ9PZ9GeyyX7bNdLQt72T8f4XYw1TNHy/7j6HThc+Ueg/nQvRorbNpnMjazT/m0zyh62Zxq4KZwc5EvlZgqNaxseD8ruA29S1pk1da25GaFm4yfAeCY/Vs3hHFt2aWNOhgTUJqXq2HMo2HrSe1LW7EvbWelrUkKO2MUwmueaAjoz/5ZCO9jKT93TdCnKUChn9XjHt5jx0Sc+hy3o0dlDRx5D4vdnZ9Ctu5Z867hYyKeHT5lgniEb7fzHerzLnEwDurdrCpeFPfgVPUV0M1WFvA+3qKdHYyYi6L7F0Z45xYk0njYyHXxEHexnv982vjjavqaJ5TRWhUTp+2mYoyf1+NI929aBnMxVqW8PkjCevadl39snfEy9avAE1O5GVkcjpP74jMy0et9KV6TJisbG7LT05yuQ77erxteh1eez9ZZzJduq2fZt67d4BwL9aW17pNZXzB37m6NbpOLmXo/3rCylVrs4Tx/ukHufnRF5kMkmyZF4o/JtXeh61dAjFsmn6072s/Vkbu/TJLg993jx9XCwdQrG4uz/HiYmegjpvVLV0CMVy6LsLlg7hP6106Rfr++y9bs8+gdl78en1XberUTIuSnoUUUkSBEEQBKFIivubay86kSQJgiAIglAklrq6zVLE1W2CIAiCIAhmiEqSIAiCIAhF8rKNYhZJkiAIgiAIRWKJGbctSXS3CYIgCIIgmCEqSYIgCIIgFInobhMEQRAEQTDjZbu6TSRJgiAIgiAUycs2T5IYkyQIgiAIgmCGqCQJgiAIglAkYkySIAiCIAiCGS/bD9yK7jZBEARBEAQzRCVJEARBEIQiedkGboskSRAEQRCEIhFjkoQSxdZBbekQiuWT38pYOoRikfRplg6hWFISMiwdQrGU9X2x3r+Hvrtg6RCKpfk7NS0dwn9ancvrLB1CMdWydAD/OSJJEgRBEAShSEQlSRAEQRAEwQz9Szbjtri6TRAEQRAEwQxRSRIEQRAEoUhEd5sgCIIgCIIZIkkSBEEQBEEw42WbJ0mMSRIEQRAEQTBDVJIEQRAEQSgS6SW7uk0kSYIgCIIgFMnLNiZJdLcJgiAIgiCYISpJgiAIgiAUycs2cFskSYIgCIIgFInobhMEQRAEQRBEJUkQBEEQhKJ52SpJL3SS9PPPP/Pll18SGRnJvHnzeO+99ywWi5+fH++9995ziaFrazf6dPTAxVHJ7XtZ/PBrJMG3Mwtt36yeI0N6lcLTzYrI2ByWboji9KU0s23fHVKGzq3cWLQ6kt/2xhuXT32vHOV9bXHSKEnL1HH+ahpLN0SRmKwtdvwt69jQvqEtjmo592K1rN2bwZ0o89sp7aage3M7ynopcXNSsG5vOvtOZ5u0aVHbhha1bXB1MhRGo+J1bD+ayZXQvGLHBtC+iYZurRxx0ii4G5XLsi0J3ArPLbR9wxp2DOjojLuLkph4Lb/uSOT89Szj4zZWMgZ1caFeNTs0dnLiErXsOpLKn3/n/w+mvu1F1QBbk+3u/TuVxRsTih1/6/p2dGpqj6Nawb2YPH7ZmcrtSPOvhbeHkl6t1PiVVuHurGT1rhT+OG76XqpY1opOTe3xK63C2UHB/DWJnLueU+y4HuXq8dVcOryMrPT7uHhVonG3T/HwqW627Y1TGwg5v42kmJsAuHlXoV77903aH9w4iZvntpqsV6ZCUzoOW/xU4m1UVUHzmko0tjKiEyR+P5bLvTjzRw9PZxnt6qnwdpfhopGz7VguRy/rTNrIZNC2rpLaFRRo7GSkZkicCdax/1zxP1+Py6VpXfw/GI5j7SBsSntwpvcYYrftf277L66SFO9vO/9g3dbtJCalUN7Pl3GjhlI5MMBs2937DzJz4SKTZVYqFX9u+sVs+7k/LGHbH/sYO3wwfbt1euqxF5cYk1QM8fHxTJ48mZ07dxIbG4uzszM1atRg8uTJNGnS5GnFaFZqaipjx45l3rx59O7dG0dHx2e6v5KieX0nRr1amu9WRnAjNIOe7d2ZPsGf4R/dICWt4BdqlQA7Jo32Y9nGKE5eSKVlI2emjCvH25NDuBtpmmw0ruNIpfL23E8qmBBcvJ7Ouu2xJCbn4easYuQAbz4fW473v7pZrPjrVbaiXxt7ft2dzu0oLW3q2/LeAAc+W5REWmbBT5+VSkZ8ko4z13Po31ZtdptJaXo2H8ggNlGHTAaNq9swtq8DXyxJJuq+zuw6hWlc054hPVz5eeN9bt3NoXNzBz5904txMyJITdcXaB/oZ817r3uwZmcSZ69m0rSOPROHeTJxbiT3YgyJyZAeLgQF2LLw13jiE7XUqGTLiN6uJKXoOHM1PyHZdzyV9buTjfdzcgvu7980CLJhYEcHVmxLITQij/aN7PlwiAsTF8STllFwew9e31NXsxnU0cHsNq2tZITH5HH4XCbjBroUO6Z/E3ppFyd2zqJpj6l4+FTnyrFV7F42kn4f7MJW7VqgfdTt0wRU74Rn11oolNZcPLyE3ctG0Oe97dg7ehrblQlsRvM+0433FUqrpxJvjfIKujZWseVwHuFxeppVUzK8szXfrM0mI7tge5USElP1XAqV6NpYZXabLWoqaVRFyfoDucQmSZRxl9GvhRXZuRLHrhTvPfy4FPZ2pF4K5t6KzdTd9P1z2eeTKCnx/nXkb75f9gvjR4+gSmAAG7fvYsLUGfz6wzycncwfl+ztbPnlh2+N92WFTD10+PgproXcxM3F+VmELhTBE41J6t27N+fPn2flypWEhISwbds2WrRoQUJC8c9+iys8PJy8vDw6d+5MqVKlsLOze+b7LAl6dXBnz6EE9h5JJDwqh4UrIsjJ1dP+FfMHrx7t3DlzOZVNu+O5F53Dqi0x3ArLonsbN5N2rs4qxrzmzayf7qI1c/L62x/x3AjNJC4hj2u3Mlm/M5ZK5e1QKIoXf9sGthy5kM2xSzlE39fx6650crUSTWvYmG0fFq1l01+ZnL6Wi1Zr/hTm4s1cLofmEZekJzZRz28HM8nJlfD3Lv45QJcWDuw/nsbBU+lExObx88YEcnMlWjXQmG3f+RUHLtzIYtuBFCLj8li/O5nbETl0aJafcAT62XDwdDrXQrOJT9Ky73gad6NyCfC1NtlWTq5EcprOeMvKKf4pW4fG9hw8k8mR81lExWtZsT2FnDyJ5rVtzba/E5nHuj/SOHk5m7xCXt9LN3PYvD+ds0+5evTA5SMrqVSvLxXr9sLZM4CmPaaitLIh+MwWs+1bDfiGKo0G4lq6Mk4e/jTr9SWSpCcy9LhJO4XSCjuNu/Fmbft0TqSaVVdy8rqOM8E64pIkthzOI08L9SqZf79FxEvsPKHlYqgObSGn4X5ecq6G6bgRricpTeLybT0hEXp8PJ7fsNH4Pw4TMmU+sb/ve277fBIlJd4Nv++kS7tWdGrTAj/fMnwwegQ21lbs2new0HVkMhmuzk7Gm4uTU4E28QmJLFy8gs/Gj0WpLOYX7TMkSU/v9iJ47E9gcnIyR44cYdasWbRs2ZKyZctSv359Jk2aRLdu3YxtRowYgbu7Ow4ODrRq1YqLFy8ChiqUl5cXX3/9tXGbf//9N1ZWVuzf/+iS6YoVK6hWrRoA/v7+yGQywsLCAPj999+pXbs2NjY2+Pv7M23aNLQPHfVlMhk//fQTXbp0wc7OjsqVK3P8+HFu3bpFixYtsLe3p3HjxoSGhhrXCQ0NpXv37nh6eqJWq6lXrx779j36g/mo5/64lAoZFfzsOHc13bhMkuD81XSqBNibXadygD3nH2oPcPZKGpUfai+TwcRRvmzaFVegumSOxl5Bq0bOXLuVga4YJ7kKOZQtpeTanfyuHwm4ficP/zJPp+dXJoN6VaywUskIjSxeV4VSAf5lrLkUkt9VJklw6WYWgWWtza4T6Gdj0h7gYrBp+5CwbOoG2eHiaPiiqxpgQyl3FReDTddrVkfN0i99mTvRm4GdnbFSFW9mW4UC/EqruHo7P5mRJLgWmkOAz9OpojxtOm0u96Ou4h3QyLhMJpfjXb4RceEXirQNbV42ep22QBIUffsUv3zVhA1zO3J061SyM5KeOF6FHLzdZdyKyH/jS8DNCB1lPR8/oQmL0RNQRo6bo+F/XspVhp+XnOB7xa8mCs9PXp6WkNA71KlRzbhMLpdTp0Y1rgaHFLpeVlY2/UaMpc+wMXwy/RvuhN8zeVyv1zP92+8Z0LML5Xx9nln8j0Ovf3q3F8FjH5nUajVqtZqtW7fSsGFDrK0LHkT69u2Lra0tu3fvxtHRkZ9++onWrVsTEhKCu7s7y5Yto0ePHrRr146KFSvy+uuvM3bsWFq3bv3Ifffv3x8fHx/atGnDqVOn8PHxwd3dnSNHjjB48GAWLlxIs2bNCA0NZdSoUQBMmTLFuP6XX37JvHnzmDdvHh999BEDBw7E39+fSZMm4evry7Bhwxg7diy7d+8GID09nU6dOjF9+nSsra1ZtWoVXbt2JTg4GF9fX7MxPuq5u7g8XpeFg0aBQiEjOcV0fElSSh4+pcwfxJ0dlSSlFmzv7Jj/r+/X2QOdXmLrn/cfuf/h/UrRrY0bNtYKrt3KYPK828WKX20nRyGXkfqPbp/UDD1erua7IYrK213BpDecUCkNFZkfNqUSXcyuNo294fVNSTNdLyVNh7eH+ficNIoC7ZPTdDg55L++Szcn8GZ/N36a6otWJyFJsGj9fa7fzk9Ij57LID4xhaRULb6lrHitqwulPVTMWR5X9Pjt5CgUsgLdginpekq5lczhh9mZyUh6XYFuNVuNK8nxd4q0jVO752Dn4IF3QGPjMp/AppSr2haNSxlSE8I5vXc+e1a8SbfRa5HLH/+s3N4GFHIZaab5LelZEh5Oj58kHTyvxcYKJgywRtKDTA5/nNJy/ubz6WoTHk9Kaio6vb5At5qzkyPhEZFm1/HxLs3Ed96ivJ8vGZmZrNu6g7c/msyK7+bg4Wb4HKzZsg2FQk7vLh2f+XMorhelAvS0PPY3p1KpZMWKFYwcOZJFixZRu3ZtmjdvzoABA6hevTpHjx7l1KlTxMXFGROoOXPmsHXrVjZt2sSoUaPo1KkTI0eOZNCgQdStWxd7e3tmzJjxr/u2tbXF1dXwZnJ3d8fLywuAadOm8fHHHzNkyBDAUGX68ssvmThxokmSNHToUPr16wfARx99RKNGjfj8889p3749AOPGjWPo0KHG9jVq1KBGjRrG+19++SW//fYb27ZtY+zYsQXiK8pzNycnJ4ecHNMuDb0uF7ni2VUBAvxs6dHWnbenBP9r24274thzKBFPNxWDenjx4ShfJn9btAPZsxaToOOLJUnYWsuoU8maYV01zP41pdiJ0rPQsZkDgWWtmbkkhvhELVXK2xjGJKVquRxiSJT2Hc8fxB0enUdyqo4pb5fC01VJbMLzG7z7orlwcDG3L+2m88iVKFX5Jwrla3Q2/u3iFYhLqYqs/6Yd0bdPmVStSorq5RXUqqBg7b48YpP0lHaV07WJitQMibMhln8PC09PUKVAgioFmtwf/PYHbP9jH8MH9Sf41m02b9/N4nkzkBU2WEl4bp7o9LJ379507tyZI0eOcOLECXbv3s3s2bNZsmQJGRkZpKenG5OZB7Kysky6subMmUNQUBAbN27k7NmzZitSRXXx4kWOHTvG9On5gzV1Oh3Z2dlkZmYaxy1Vr55/FYynp2Gg54PuuwfLsrOzSU1NxcHBgfT0dKZOncrOnTuJjo5Gq9WSlZVFeHh4oXEU5bn/04wZM5g2bZrJMv/qbxJQ8y0AUtN06HQSTo6mVQ1nRxVJKeYPpEkpWpwdCm9fLVCNk4OSX+dVNT6uUMgY+WpperRzZ8iEa8blqek6UtN1RMbmEB51l9Xzq1K5vB3XQwu/su5h6Zl6dHoJB3vTM24HezkpZgYVF4dOD3FJhm3cjcnEr7SSNvVs+GV3RpG3kZZheH0dNaaVBkeNguRU8weq5DRdgfZOGgXJqYbX10olY2BnF75ZHsu5a4byQ3h0Hn7e1nRr4WhMkv7pZrghWfZyUxU5SUrL1KPTSTioTV9fR7WcFDODzksCGzsnZHIFWemm4xiz0hKw07gVspbBpcPLuHhoMZ2GL8O1VMVHtnVw8cHG3pnUhPAnSpIyskGnl9D8Y4iX2lZm9sKDourcSMmB84ZxSwAxiTqcNDJa1lKKJKkEc3RwQCGXk5ScYrI8KTkFF2enIm1DqVQS4O9HRHQsAJeu3SApJZV+I/JPwHV6PT8s/4VN23exfvH/PbX4H4eoJBWTjY0Nbdu2pW3btnz++eeMGDGCKVOmMGbMGEqVKsXBgwcLrOP00CC10NBQoqKi0Ov1hIWFmSQrxZWens60adPo1auX2TgfUKnyk4YHmbq5Zfr/dZpOmDCBP//8kzlz5hAQEICtrS19+vQhN9f8ZeHp6elFeu7/NGnSJMaPH2+yrPeYG8a/tTqJm2GZ1Kqi5vi5lP/FCjWrqNm2z3xX2fVbGdSsoja5nL92VQ3XbxmSh33HEjl31XQ6gK8/9Gf/sST2HkksNNYHJzgqVdG7GHR6uButpbKfigshhtdOBlTyU3HgzL+PhSoOmcwwhqs4tDq4HZFDtUAbTl/JNG6nWgVb9hxNNbtOSFg21QJt2XU4//HqgbaE3DUkOQo5KJWyAv3ver2ETF54fH7ehuphUmrRq0g6HYRF5VHV39p4ib5MBlX8rdl3sujJ4vOkUFrhVroqkaEn8KvaBgBJrycq9ARVGg0qdL2Lh5Zw/sBPdBy2GPcyQf+6n/SUGLIzk7HTuD9RvDo9RMZLBHgruBpm+KfKgABvBX9fefyKn0opK3DwkaTCr3oSSgaVSklg+XKcvXSFZg3rAYbjxrlLV+jZqX2RtqHT6blz9x4N6tQEoF2LZiZjnAA+nPo17Vo0o2PrFk8z/McipgB4QlWqVGHr1q3Url2bmJgYlEolfn5+Ztvm5uby2muv0b9/fypWrMiIESO4fPkyHh4ej7Xv2rVrExwcTECA+fkpHtexY8d444036NmzJ2BIgh4MFC8sjn977uZYW1sXqKT9s6tty554Joz0JeROJsG3M+nZ3h0ba7kxoflwlC/3k/JYvjEagK174/lmUgV6d3Dn1MVUmjdwpkI5W+YvNwwUTMvQkZZheqaq1RoqUBExhgNtRX87KvrbcSUkg/QMLaU8rBnS24uo2BxjslVUf57MYlg3DXejtdyJ0tKmvg3WKhnHLhmSpGFd1SSn6dly0JCkKORQ2t1QqVEqwEkjx8dTQU6uZKwc9Wphx+XQXBJT9dhYyWhQ1ZqKZVXMX2s+sXmUHQdTeXugG6H3co1TAFhbyThw0pBIjh3oRmKKjjU7DYOAdx5OZdrYUnRp4cC5a1k0qWVPeR9rftpgSFqzciSu3sri9W4u5OYlcD/J0N3WvK6alb8b/meerkqa1lZz/nomaRl6ypa2YkgPF67dyiI8unhzPe35O4ORvZy4E5nH7cg82jWyw9pKxuFzhirWqN6OJKXq2fin4fkoFODtrvzf6yvD2UGBr5eS7FyJuETD+8LaSoanS361zN1Jia+XjowsPQkpT16hqtZsCIc2TsLdOwh3n2pcObaKvNwsAusYPm8HNnyEvYMn9TsYTiAuHFrM2T+/o9WAOWicvclMM5wAqKzsUFnbk5eTwbn9P+AX1BY7jTupCeGc2j0HBxdfygQ2feJ4j1zS0q+lioh4Pffi9DStrsRKBWeCDUlS/5YqUjIk9pwy3FfIwcPZkO0o5TIc7WWUcpWRmwcJqYYjzvW7OlrVVpGcLhGbJFHaVUaz6kpO33h+Xa0KezvsA/LHWNqVK4NDjUrkJqaQfS/6ucVRVCUl3n7dOzNjwY9UCvCnUoUANm3fRVZ2Dh3bNAdg+rff4+7qwqjBrwKwYt1mqlYMwLuUF+kZmaz9bTsx8fF0adsKAEcHDY4OplfTKpUKXJyd8C1T+rk9L8HgsZOkhIQE+vbty7Bhw6hevToajYYzZ84we/ZsunfvTps2bWjUqBE9evRg9uzZBAYGEhUVxc6dO+nZsyd169bl008/JSUlhYULF6JWq9m1axfDhg1jx44djxXT5MmT6dKlC76+vvTp0we5XM7Fixe5cuUKX3311eM+VSpUqMCWLVvo2rUrMpmMzz//3FhlMqcoz/1xHTqVjKODksG9SuHsqOR2eBafzrlt7N5xd7EyqVpcu5XJzEVhDOldijf6lCIqNodpC+4U6Sq2B3Jy9TSp48jrPb2wsZKTmJLHmctpTN92t9DLxgtz+nouavsMuje3w8HeMJnk/HWppGYYtuPqqDA5o3bSyJkyIn+OkA6N7OjQyI7gu3l886uhmqaxlzO8mwZHtZysHImIOC3z16aaXEVXVH9fyMBBLad/B2ecHBSEReYw/adYY3eVm7PSJL6QsBwW/BLHq52cGdjZhej4PGYvizXOkQQwf1U8Azs7M+41d9R2cuKTtKzdlcTe/00mqdVJVA+0MSZkCck6Tl7KYPPe5GLHf/JKNhr7VHq1VuOoVhAencc3qxKNg+VdHRVID70/nDUKvno7v7rSqamaTk3VXL+Tw4xlhiSuXGkVnwzP7zoe1MkwvcGRc5ks/s20m+FxlK/eiez0JM7uW0hm2n1cS1Wm49Cfjd1tGcnRyGT5FcvrJ9ah1+Wxb/U4k+3Ubv02ddqMRSZXkBATTMi5reRmp2GncadMhSbUafvuU5kr6WKoDnsbaFdPicZORtR9iaU7c0j/32BuJ42Mhz8VDvYy3u+bX8luXlNF85oqQqN0/LTNUFH9/Wge7epBz2Yq1LaGySRPXtOy7+zzS5Ic6wTRaH/+hIZV5nwCwL1VW7g0fNJzi6OoSkq8rZo1Jjk1lWVrNpKYlExAubJ8M+Vj42X9cffvI3+oapyens433y8mMSkZjdqewPL+fD/rC/x8yzy3mJ+E9FT720p+qVQmPeYzzsnJYerUqezdu5fQ0FDy8vLw8fGhb9++fPLJJ9ja2pKWlsann37K5s2bjZf8v/LKK8yYMYPQ0FDatm3LgQMHaNrUcHYXFhZGjRo1mDlzJqNHj37k/i9cuECtWrW4c+eOSbXmjz/+4IsvvuD8+fOoVCoqVarEiBEjGDlypOEJy2T89ttv9OjRw7jPcuXKcf78eWrWrAnAwYMHadmyJUlJSTg5OREWFsawYcM4ceIEbm5ufPTRR2zcuJGaNWsyf/58oOCM24967j4+Rb+ks/2QC0VuWxL4BL4YH/QHUu6bn3m8pLJVm59PqqSqXsvz3xuVIHFxz2YuqGel+Ts1LR3Cf1qdy+ssHUKxeFWq9cz38d3Op5ckvdP5P5wkCc+HSJKeLZEkPVsiSXq2RJL0bIkkqaCXLUkqmZOnCIIgCIJQ4rwok0A+Lc9vzvtiqlq1qnHCyn/eVq9ebenwBEEQBOGl87L9LEmJrSTt2rWLvDzzA28fzG0kCIIgCILwrJTYJKls2bKWDkEQBEEQhIeIeZIEQRAEQRDMeFG6yZ4WkSQJgiAIglAk0lMtJZX8q9tK7MBtQRAEQRAESxKVJEEQBEEQikSMSRIEQRAEQTDjZRuTJLrbBEEQBEEQzBCVJEEQBEEQikT/kvW3iSRJEARBEIQiEd1tgiAIgiAIgqgkCYIgCIJQNC9bJUkkSYIgCIIgFIn+JcuSRHebIAiCIAiCGaKSJAiCIAhCkUh6S0fwfIkkSRAEQRCEIpFesu42kSQJgiAIglAk+peskiTGJAmCIAiCUOJ9//33+Pn5YWNjQ4MGDTh16lShbRcvXkyzZs1wdnbG2dmZNm3aPLJ9YUSSJAiCIAhCkUiS9NRuxbF+/XrGjx/PlClTOHfuHDVq1KB9+/bExcWZbX/w4EFeffVVDhw4wPHjx/Hx8aFdu3ZERkYWa78iSRIEQRAEoUj00tO7Fce8efMYOXIkQ4cOpUqVKixatAg7OzuWLVtmtv3q1asZM2YMNWvWpFKlSixZsgS9Xs/+/fuLtV+RJAmCIAiC8Nzl5OSQmppqcsvJySnQLjc3l7Nnz9KmTRvjMrlcTps2bTh+/HiR9pWZmUleXh4uLi7FilEkSYIgCIIgFImkl57abcaMGTg6OprcZsyYUWCf9+/fR6fT4enpabLc09OTmJiYIsX90UcfUbp0aZNEqyjE1W2CIAiCIBTJ05wBYNKkSYwfP95kmbW19dPbwf/MnDmTdevWcfDgQWxsbIq1rkiSBEEQBEF47qytrYuUFLm5uaFQKIiNjTVZHhsbi5eX1yPXnTNnDjNnzmTfvn1Ur1692DGK7jZBEARBEIpEr5ee2q2orKysqFOnjsmg6weDsBs1alToerNnz+bLL79kz5491K1b97Ger6gkCYIgCIJQJJaacXv8+PEMGTKEunXrUr9+febPn09GRgZDhw4FYPDgwXh7exvHNM2aNYvJkyezZs0a/Pz8jGOX1Go1arW6yPsVSZIgCIIgCCVa//79iY+PZ/LkycTExFCzZk327NljHMwdHh6OXJ7fOfbjjz+Sm5tLnz59TLYzZcoUpk6dWuT9iiTpKVmxYgXvvfceycnJlg5FEARBEJ4JS/7A7dixYxk7dqzZxw4ePGhyPyws7Kns0yJJ0oNscOfOncTGxuLs7EyNGjWYPHkyTZo0sURIL5Surd3o09EDF0clt+9l8cOvkQTfziy0fbN6jgzpVQpPNysiY3NYuiGK05fSzLZ9d0gZOrdyY9HqSH7bG29cPvW9cpT3tcVJoyQtU8f5q2ks3RBFYrK22PG3rGND+4a2OKrl3IvVsnZvBneizG+ntJuC7s3tKOulxM1Jwbq96ew7nW3SpkVtG1rUtsHVyXAWERWvY/vRTK6E5hU7NoD2TTR0a+WIk0bB3ahclm1J4FZ4bqHtG9awY0BHZ9xdlMTEa/l1RyLnr2cZH7exkjGoiwv1qtmhsZMTl6hl15FU/vw7/38w9W0vqgbYmmx379+pLN6YUOz4W9e3o1NTexzVCu7F5PHLzlRuR5p/Lbw9lPRqpcavtAp3ZyWrd6Xwx3HT91LFslZ0amqPX2kVzg4K5q9J5Nz1gnOZPImrx1dz6fAystLv4+JVicbdPsXDx/wgyxunNhByfhtJMTcBcPOuQr3275u0P7hxEjfPbTVZr0yFpnQctvipxNuoqoLmNZVobGVEJ0j8fiyXe3HmuyE8nWW0q6fC212Gi0bOtmO5HL2sM2kjk0HbukpqV1CgsZORmiFxJljH/nPF/3w9LpemdfH/YDiOtYOwKe3Bmd5jiN1WvIn3nqeSFO9vO/9g3dbtJCalUN7Pl3GjhlI5MMBs2937DzJz4SKTZVYqFX9u+sVs+7k/LGHbH/sYO3wwfbt1euqxF5de/MDts9e7d29yc3NZuXIl/v7+xMbGsn//fhISin9AeNk0r+/EqFdL893KCG6EZtCzvTvTJ/gz/KMbpKQV/EKtEmDHpNF+LNsYxckLqbRs5MyUceV4e3IIdyNNk43GdRypVN6e+0kFE4KL19NZtz2WxOQ83JxVjBzgzedjy/H+VzeLFX+9ylb0a2PPr7vTuR2lpU19W94b4MBni5JIyyz44bNSyYhP0nHmeg7925rvR05K07P5QAaxiTpkMmhc3YaxfR34YkkyUfd1ZtcpTOOa9gzp4crPG+9z624OnZs78OmbXoybEUFqesFTqEA/a9573YM1O5M4ezWTpnXsmTjMk4lzI7kXY0hMhvRwISjAloW/xhOfqKVGJVtG9HYlKUXHmav5Ccm+46ms351svJ+TW/xTtgZBNgzs6MCKbSmERuTRvpE9Hw5xYeKCeNIyCm7vwet76mo2gzo6mN2mtZWM8Jg8Dp/LZNzA4k3EVhShl3ZxYucsmvaYiodPda4cW8XuZSPp98EubNWuBdpH3T5NQPVOeHathUJpzcXDS9i9bAR93tuOvWP+PCplApvRvM90432F0uqpxFujvIKujVVsOZxHeJyeZtWUDO9szTdrs8nILthepYTEVD2XQiW6NlaZ3WaLmkoaVVGy/kAusUkSZdxl9GthRXauxLErxXsPPy6FvR2pl4K5t2IzdTd9/1z2+SRKSrx/Hfmb75f9wvjRI6gSGMDG7buYMHUGv/4wD2cnR7Pr2NvZ8ssP3xrvy2Tmt334+CmuhdzEzcX5WYT+WCw1JslSnvvVbcnJyRw5coRZs2bRsmVLypYtS/369Zk0aRLdunUzthkxYgTu7u44ODjQqlUrLl68CBiqUF5eXnz99dfGbf79999YWVkVabrxqVOnUrNmTZYtW4avry9qtZoxY8ag0+mYPXs2Xl5eeHh4MH36dJP15s2bR7Vq1bC3t8fHx4cxY8aQnp7+yH39/vvv1K5dGxsbG/z9/Zk2bRpa7ZOdGfbq4M6eQwnsPZJIeFQOC1dEkJOrp/0r5g9ePdq5c+ZyKpt2x3MvOodVW2K4FZZF9zZuJu1cnVWMec2bWT/dxVyIv/0Rz43QTOIS8rh2K5P1O2OpVN4OhaJ48bdtYMuRC9kcu5RD9H0dv+5KJ1cr0bSG+bkrwqK1bPork9PXctFqzX84L97M5XJoHnFJemIT9fx2MJOcXAl/7+KfA3Rp4cD+42kcPJVORGweP29MIDdXolUDjdn2nV9x4MKNLLYdSCEyLo/1u5O5HZFDh2b5CUegnw0HT6dzLTSb+CQt+46ncTcqlwBf00tfc3IlktN0xltWTvG/jDo0tufgmUyOnM8iKl7Liu0p5ORJNK9ta7b9ncg81v2RxsnL2eQV8vpeupnD5v3pnH3K1aMHLh9ZSaV6falYtxfOngE07TEVpZUNwWe2mG3fasA3VGk0ENfSlXHy8KdZry+RJD2RoaYz7yqUVthp3I03a1vzB6zialZdycnrOs4E64hLkthyOI88LdSrZP79FhEvsfOElouhOrSFXNHj5yXnapiOG+F6ktIkLt/WExKhx8fj+X1Fx/9xmJAp84n9fd9z2+eTKCnxbvh9J13ataJTmxb4+Zbhg9EjsLG2Yte+g4WuI5PJcHV2Mt5cnJwKtIlPSGTh4hV8Nn4sSmUxv2iFp+a5J0kPRpZv3brV7PTjAH379iUuLo7du3dz9uxZateuTevWrUlMTMTd3Z1ly5YxdepUzpw5Q1paGq+//jpjx46ldevWRYohNDSU3bt3s2fPHtauXcvSpUvp3LkzERERHDp0iFmzZvHZZ59x8uRJ4zpyuZyFCxdy9epVVq5cyV9//cXEiRML3ceRI0cYPHgw48aN49q1a/z000+sWLGiQPJVHEqFjAp+dpy7mp+cSRKcv5pOlQB7s+tUDrDn/FXTZO7slTQqP9ReJoOJo3zZtCuuQHXJHI29glaNnLl2KwNdMU5yFXIoW0rJtTv5XT8ScP1OHv5lnk5RUyaDelWssFLJCI0sXkKqVIB/GWsuheR3lUkSXLqZRWBZ83N5BPrZmLQHuBhs2j4kLJu6QXa4OBq+6KoG2FDKXcXFYNP1mtVRs/RLX+ZO9GZgZ2esVIWcXhZCoQC/0iqu3s7/XEkSXAvNIcDn6VRRnjadNpf7UVfxDsi/jFcml+NdvhFx4ReKtA1tXjZ6nbZAEhR9+xS/fNWEDXM7cnTrVLIzkp44XoUcvN1l3IrIf+NLwM0IHWU9H//rNCxGT0AZOW6Ohv95KVcZfl5ygu9ZcACI8K/y8rSEhN6hTo1qxmVyuZw6NapxNTik0PWysrLpN2IsfYaN4ZPp33An/J7J43q9nunffs+Anl0o5+vzzOJ/HJaYAsCSnnt3m1KpZMWKFYwcOZJFixZRu3ZtmjdvzoABA6hevTpHjx7l1KlTxMXFGSeZmjNnDlu3bmXTpk2MGjWKTp06MXLkSAYNGkTdunWxt7c3O5V5YfR6PcuWLUOj0VClShVatmxJcHAwu3btQi6XU7FiRWbNmsWBAwdo0KABAO+9955xfT8/P7766iveeustfvjhB7P7mDZtGh9//DFDhgwBwN/fny+//JKJEycyZcoUs+vk5OQUSBz1ulzkCsMBzkGjQKGQkZxiOr4kKSUPn1LmD+LOjkqSUgu2d3bM/9f36+yBTi+x9c/7ZrfxwPB+pejWxg0bawXXbmUwed7tR7b/J7WdHIVcRuo/un1SM/R4uZrvhigqb3cFk95wQqU0VGR+2JRKdDG72jT2htc3Jc10vZQ0Hd4e5uNz0igKtE9O0+HkkP/6Lt2cwJv93fhpqi9anYQkwaL197l+Oz8hPXoug/jEFJJStfiWsuK1ri6U9lAxZ7n5X7g2G7+dHIVCVqBbMCVdTym3knmNRnZmMpJeV6BbzVbjSnL8nSJt49TuOdg5eOAd0Ni4zCewKeWqtkXjUobUhHBO753PnhVv0m30WuTyxz8rt7cBhVxGmml+S3qWhIfT4ydJB89rsbGCCQOskfQgk8Mfp7Scv/l8utqEx5OSmopOry/Qrebs5Eh4hPlfm/fxLs3Ed96ivJ8vGZmZrNu6g7c/msyK7+bg4Wb4HKzZsg2FQk7vLh2f+XMorpest81yY5I6d+7MkSNHOHHiBLt372b27NksWbKEjIwM0tPTcXU1/dLMysoiNDTUeH/OnDkEBQWxceNGzp49W6ypzP38/NBo8rtPPD09USgUJpcPenp6EheXf4Dat28fM2bM4MaNG6SmpqLVasnOziYzMxM7O7sC+7h48SLHjh0zqRzpdLpHrjNjxgymTZtmssy/+psE1HyryM+tuAL8bOnR1p23pwT/a9uNu+LYcygRTzcVg3p48eEoXyZ/W7QD2bMWk6DjiyVJ2FrLqFPJmmFdNcz+NaXYidKz0LGZA4FlrZm5JIb4RC1VytsYxiSlarkcYkiU9h3PH8QdHp1HcqqOKW+XwtNVSWzC8xu8+6K5cHAxty/tpvPIlShV+d8B5Wt0Nv7t4hWIS6mKrP+mHdG3T5lUrUqK6uUV1KqgYO2+PGKT9JR2ldO1iYrUDImzIZZ/DwtPT1ClQIIqBZrcH/z2B2z/Yx/DB/Un+NZtNm/fzeJ5M5AVNlhJeG4sdnppY2ND27Ztadu2LZ9//jkjRoxgypQpjBkzhlKlShW4nA/A6aF+29DQUKKiotDr9YSFhVGtWrUC7QujUplWBWQymdller3hjDwsLIwuXbowevRopk+fjouLC0ePHmX48OHk5uaaTXjS09OZNm0avXr1MvvczTH3Oza9x9ww/p2apkOnk3ByNI3V2VFFUor5A2lSihZnh8LbVwtU4+Sg5Nd5VY2PKxQyRr5amh7t3Bky4Vr+/tN1pKbriIzNITzqLqvnV6VyeTuuhxZ+Zd3D0jP16PQSDvamZ9wO9nJSzAwqLg6dHuKSDNu4G5OJX2klberZ8MvujCJvIy3D8Po6akwrDY4aBcmp5g9UyWm6Au2dNAqSUw2vr5VKxsDOLnyzPJZz1wzlh/DoPPy8renWwtGYJP3TzXBDRdHLTVXkJCktU49OJ+GgNn19HdVyUswMOi8JbOyckMkVZKWbXrSRlZaAncatkLUMLh1exsVDi+k0fBmupSo+sq2Diw829s6kJoQ/UZKUkQ06vYTmH0O81LYysxceFFXnRkoOnDeMWwKISdThpJHRspZSJEklmKODAwq5nKTkFJPlSckpuDg7FWkbSqWSAH8/IqINP7lx6doNklJS6Tci/1J3nV7PD8t/YdP2Xaxf/H9PLf7HIb0g3WRPS4mpwVepUoWtW7dSu3ZtYmJiUCqV+Pn5mW2bm5vLa6+9Rv/+/alYsSIjRozg8uXLeHh4PJPYzp49i16vZ+7cucZq04YNGx65Tu3atQkODiYgwPxloOaY+x2bB11tAFqdxM2wTGpVUXP8nOFDKZNBzSpqtu0z31V2/VYGNauoTS7nr11Vw/VbhuRh37FEzl01nQ7g6w/92X8sib1HEguN9cEJjkpV9C4GnR7uRmup7KfiQojhCjoZUMlPxYEz/z4WqjhkMsMYruLQ6uB2RA7VAm04fSXTuJ1qFWzZczTV7DohYdlUC7Rl1+H8x6sH2hJy15DkKOSgVMrQ/yNH0eslZPLC4/PzNvzfk1KLXkXS6SAsKo+q/tbGS/RlMqjib82+k0VPFp8nhdIKt9JViQw9gV9Vw69zS3o9UaEnqNJoUKHrXTy0hPMHfqLjsMW4lwn61/2kp8SQnZmMncb9ieLV6SEyXiLAW8HVMMM/VQYEeCv4+8rjV/xUSlmBbgxJKvyqJ6FkUKmUBJYvx9lLV2jWsB5gGM5x7tIVenZqX6Rt6HR67ty9R4M6NQFo16KZyRgngA+nfk27Fs3o2LrF0wz/sYgpAJ6xhIQE+vbty7Bhw6hevToajYYzZ84we/ZsunfvTps2bWjUqBE9evRg9uzZBAYGEhUVxc6dO+nZsyd169bl008/JSUlhYULF6JWq9m1axfDhg1jx44dzyTmgIAA8vLy+O677+jatSvHjh1j0aJFj1xn8uTJdOnSBV9fX/r06YNcLufixYtcuXKFr7766rFj2bInngkjfQm5k0nw7Ux6tnfHxlpuTGg+HOXL/aQ8lm+MBmDr3ni+mVSB3h3cOXUxleYNnKlQzpb5yw0DBdMydKRlmJ6parWGClREjOFAW9Hfjor+dlwJySA9Q0spD2uG9PYiKjbHmGwV1Z8nsxjWTcPdaC13orS0qW+DtUrGsUuGJGlYVzXJaXq2HDQkKQo5lHY3VGqUCnDSyPHxVJCTKxkrR71a2HE5NJfEVD02VjIaVLWmYlkV89eaT2weZcfBVN4e6EbovVzjFADWVjIOnDQkkmMHupGYomPNTsMg4J2HU5k2thRdWjhw7loWTWrZU97Hmp82GJLWrByJq7eyeL2bC7l5CdxPMnS3Na+rZuXvhv+Zp6uSprXVnL+eSVqGnrKlrRjSw4Vrt7IIjy7eXE97/s5gZC8n7kTmcTsyj3aN7LC2knH4nKGKNaq3I0mpejb+aXg+CgV4uyv/9/rKcHZQ4OulJDtXIi7R8L6wtpLh6ZJfLXN3UuLrpSMjS09CypNXqKo1G8KhjZNw9w7C3acaV46tIi83i8A6PQE4sOEj7B08qd/BUGW9cGgxZ//8jlYD5qBx9iYzzXACoLKyQ2VtT15OBuf2/4BfUFvsNO6kJoRzavccHFx8KRPY9InjPXJJS7+WKiLi9dyL09O0uhIrFZwJNiRJ/VuqSMmQ2HPKcF8hBw9nQ7ajlMtwtJdRylVGbh4kpBoOONfv6mhVW0VyukRskkRpVxnNqis5feP5dbUq7O2wD/A13rcrVwaHGpXITUwh+170c4ujqEpKvP26d2bGgh+pFOBPpQoBbNq+i6zsHDq2aQ7A9G+/x93VhVGDXwVgxbrNVK0YgHcpL9IzMln723Zi4uPp0rYVAI4OGhwdTK+mVSoVuDg74Vum9HN7XoLBc0+S1Go1DRo04NtvvyU0NJS8vDx8fHwYOXIkn3zyCTKZjF27dvHpp58ydOhQ4yX/r7zyCp6enhw8eJD58+dz4MABHBwMl1n/8ssv1KhRgx9//JHRo0c/9Zhr1KjBvHnzmDVrFpMmTeKVV15hxowZDB48uNB12rdvz44dO/jiiy+YNWsWKpWKSpUqMWLEiCeK5dCpZBwdlAzuVQpnRyW3w7P4dM5tY/eOu4uVSdXi2q1MZi4KY0jvUrzRpxRRsTlMW3CnSFexPZCTq6dJHUde7+mFjZWcxJQ8zlxOY/q2u4VeNl6Y09dzUdtn0L25HQ72hskk569LJTXDsB1XR4XJGbWTRs6UEflzhHRoZEeHRnYE383jm18N1TSNvZzh3TQ4quVk5UhExGmZvzbV5Cq6ovr7QgYOajn9Ozjj5KAgLDKH6T/FGrur3JyVJvGFhOWw4Jc4Xu3kzMDOLkTH5zF7WaxxjiSA+aviGdjZmXGvuaO2kxOfpGXtriT2/m8ySa1OonqgjTEhS0jWcfJSBpv3Jhc7/pNXstHYp9KrtRpHtYLw6Dy+WZVoHCzv6qgwmTHXWaPgq7fzqyudmqrp1FTN9Ts5zFhmSOLKlVbxyfD8MYKDOhk+d0fOZbL4N9NuhsdRvnonstOTOLtvIZlp93EtVZmOQ382drdlJEcjk+VXLK+fWIdel8e+1eNMtlO79dvUaTMWmVxBQkwwIee2kpudhp3GnTIVmlCn7btPZa6ki6E67G2gXT0lGjsZUfcllu7MIf1/g7mdNDIe/lQ42Mt4v29+F3vzmiqa11QRGqXjp22GiurvR/NoVw96NlOhtjVMJnnympZ9Z59fkuRYJ4hG+/MnNKwy5xMA7q3awqXhk55bHEVVUuJt1awxyampLFuzkcSkZALKleWbKR8bL+uPu38f+UNV4/T0dL75fjGJSclo1PYElvfn+1lf4Odb5rnF/CRetu42mfSyzQz1gmk/5IKlQygWn8AX44P+QMp98zOPl1S2avPj2Uqq6rU8/71RCRIX92zmgnpWmr9T09Ih/KfVubzO0iEUi1elWs98H2/PSX5q2/p+gtNT29az8tznSRIEQRAEQXgR/OeSpKpVqxonrPznbfXq1ZYOTxAEQRBeWHrp6d1eBCXm6ranZdeuXeTlmR+L4un5YpX+BUEQBKEkednGJP3nkqSyZctaOgRBEARB+E962YYx/+e62wRBEARBEJ6G/1wlSRAEQRCEZ+NF+WHap0UkSYIgCIIgFInobhMEQRAEQRBEJUkQBEEQhKIRV7cJgiAIgiCY8bIlSaK7TRAEQRAEwQxRSRIEQRAEoUj0L9nAbZEkCYIgCIJQJKK7TRAEQRAEQRCVpJIuIzHF0iEUS4c21S0dQrH8ujLR0iEUS3LcixVvZmqWpUMolqaty1k6hGKpc3mdpUP4TztbbYClQyiWznnBz3wfL9s8SSJJEgRBEAShSMSM24IgCIIgCGaIMUmCIAiCIAiCqCQJgiAIglA0YkySIAiCIAiCGZJeb+kQnivR3SYIgiAIgmCGqCQJgiAIglAk4uo2QRAEQRAEM162MUmiu00QBEEQBMEMUUkSBEEQBKFIXrZ5kkSSJAiCIAhCkbxsSZLobhMEQRAEQTBDVJIEQRAEQSgSvfRyzZMkkiRBEARBEIpEdLe9hA4ePIhMJiM5OfmZ7ueNN96gR48ez3QfgiAIgvCsSHrpqd1eBCWqkhQfH8/kyZPZuXMnsbGxODs7U6NGDSZPnkyTJk2e2X4bN25MdHQ0jo6Oz2wfT1OvTqV5tZcPLs5WhN5J59ufbnH9Zlqh7Vs2cWPEa+Xw8rAhIiqTH1fc4cTZRJM2wwf50bWdFxp7JZevpzLnh5tERGcZH9+4pAGlPG1M1lm08ja/brpX7PhP7FvNkV3LSE+5j5dPJbq8/ik+5aubbXv19F4Obv+ZxLhwdFotrl5ladrxDWo16W62/dblUzl9YD2dBn5Mkw5Dih0bQMdXHOnZ1gUnBwVhETks3hDPzbvZhbZvXEvNwK5ueLgqiY7LY9XW+5y9mpEf0w+BZtdbsSWerfuSAOjTwYW6QfaUK2ONVisxaEJokePt1tadvp09cXFUERqexfcrwwm+nVlo+1fqOzGkrzdeblZExuawZG0Epy6mGh9/vVcpWjRywd1FhVYncfNOJss3RHIjNH+bv8wPwsvd2mS7S9ZFsH577L/G26GpA91aOeLkoOBuZC5LNydwKzyn0PaNatozoJMz7i5KouO1/Lo9gfPX8t+bNlYyBnV1oX51e9R2cuIStew+nMLeY4bPhNpOTr+OztSoaIubs5LUDD2nL2WwblcimdmP90V95dhqLhxaSmbafVxLVaJpj8/w9DX/Hr52cgPBZ38nMeYmAO7eVWnQ8f0C7ZNiQzm+aw7Rt0+j1+lw9ixP+8EL0TiXfqwYH/bbzj9Yt3U7iUkplPfzZdyooVQODDDbdvf+g8xcuMhkmZVKxZ+bfjHbfu4PS9j2xz7GDh9M326dnjjWFzHeonBpWhf/D4bjWDsIm9IenOk9htht+5/b/oXHU6KSpN69e5Obm8vKlSvx9/cnNjaW/fv3k5CQ8FjbkyQJnU6HUvnop2llZYWXl9dj7eN5a9XUnbEjyjPn+xCuhaTRr5s3876oxqtvnSY5Ja9A+6BKDkz5sAo/rbzN36cTadvcgxmfVmXYe2e5E2446A3q7UOfLt5Mn3+D6NhsRgzyY94X1XhtzGly8/IPIot/vcP2P6KN9zOzdMWO/9KJXexaM4vub0zFp3x1jv2xihXfjOT92btQO7gWaG+rdqJFtzdxL+WPQqki+MJBtiz+FLXGlQrVm5q0vXrmT+6FXkTj7FHsuB5oUkfNsN7u/Lg2jpCwbLq1cmLKO968PTWMlPSCz7eivw0fDCvFL7/f58yVDF6pq+HjN0vzwYy7hEfnAvDGx6YJT+0q9ox9zZPj59ONy5QKGcfOpRF8O4s2jYuerDdv6Mybg8qwcFk410Mz6NXBgxkfV2DYhKskp2oLtK9SwZ5PxvqzdH0kJ8+n0LKxC1PHl2fMp9cJizAkghEx2fzfinCi43KwtpLTu6MnMz8OZMj4K6Sk5W9zxcZIdh24b7yflf3vYxUa17JnSE9Xft4Qz82wHDq3cOSz0V68O/0eqekF16/oZ817gz1YvSORs1czaVZHzcThXkycE8G9aMP7fUhPV4Iq2LLwlzjiErXUqGjLyL5uJKboOHMlE2dHBS6OSlb9nkhETC7uLkpG9XPD2dGducvjivxaP3Drwi6ObZ9J895T8fCtwaUjK9mxZASvTtyNnbrgezgq9BQVanbGq2wtFCprzh9YzI7Fw+k/YQdqR08AUu6H89sPA6lcrw/12r2DlbWaxNhbKFTWBbZXXH8d+Zvvl/3C+NEjqBIYwMbtu5gwdQa//jAPZyfz7zV7O1t++eFb432ZzPy2Dx8/xbWQm7i5OD9xnC9qvEWlsLcj9VIw91Zspu6m75/7/p8WMZmkhSQnJ3PkyBFmzZpFy5YtKVu2LPXr12fSpEl069aNsLAwZDIZFy5cMFlHJpNx8OBBIL/bbPfu3dSpUwdra2uWLVuGTCbjxo0bJvv79ttvKV++vMl6ycnJpKamYmtry+7du03a//bbb2g0GjIzDYnFvXv36NevH05OTri4uNC9e3fCwsKM7XU6HePHj8fJyQlXV1cmTpz4VN5cA3qUYfsf0ezaH0vYvUy++eEm2Tl6urQ1n+T17ebNyXOJrP0tgrsRmSxZHUZIaDq9u3ibtFm14S5HTyYQGpbBV9/ewNXFmmYN3Uy2lZmlIzE5z3jLzin+AL5je1ZSt0Vf6rzSCw/vALq/MRWVtQ1nD20x296/cn2q1m2Lh3d5XD19adx+MJ4+gYSFnDVpl5IYy45fptPvrdkoFI+f+3dv5czeY6n8dSKViJhcflwbR06uROvGDmbbd23pzLlrGWzdl0RETC5rdiRw+142nVo4Gdskp+pMbg1qqLkSkkVsQn5Su25nAtv/SuZuVG6x4u3d0ZPdB+7zx+EEwiOzWbAsnJwcPe2bFzxYA/Ts4MHpSyls3BlLeFQ2KzdFcSssk+7t8hPLA38ncf5qGjHxudyNzGbR6nvY2ynw97U12VZmtp6kFK3xVpT3Q9cWjuz7O5UDJ9OJiM3j5w33ycmVaNVQY7Z9p+aOXLiRyba/UoiMzWPdriTuROTQsVn+wbJiORsOnUrj6q1s4hO17DueRlhULgG+hgTjXnQec5bFcvZqJrEJWq7czGbtziTqBtkjf4xvwIuHV1ClQV8q1euNi2cAzXtNQ6Wy4capzWbbtxk4h6DGA3Hzroyzhz8t+n6FJOmJvHnc2ObUnvmUrdScRl0+xN27Co5uvpSr2sps0lVcG37fSZd2rejUpgV+vmX4YPQIbKyt2LXvYKHryGQyXJ2djDcXJ6cCbeITElm4eAWfjR+LUql44jhf1HiLKv6Pw4RMmU/s7/ue+76fJr1e/9RuL4ISkySp1WrUajVbt24lJ6fw0ntRfPzxx8ycOZPr16/Tp08f6taty+rVq03arF69moEDBxZY18HBgS5durBmzZoC7Xv06IGdnR15eXm0b98ejUbDkSNHOHbsGGq1mg4dOpCbazjIzZ07lxUrVrBs2TKOHj1KYmIiv/322xM9L6VSRmCAhjMXk4zLJAnOXEiiakXzB/GgSg6cuZBksuzk+USCKhnal/a0wc3FmtMPtcnI1HEtJNXY5oHX+viyc3Vjls2vzas9y6Ao5rtHq80lKuwqAVUbGZfJ5XICqjQi/NaFf11fkiRCrx7nfnQY5SrVNS7X6/Vs+ukjmnUahmeZCsUL6iFKBZT3teFScH5XmSTBxRsZVCxna3adiuVsuHTDtGvr/LXMQts7ahTUCbJn398pjx1nfrwyAsvZce5KfleZJMG5K2lUqaA2u06VADXnrph2zZ65lErlAPtC99GppTvpGVpC75o+zwFdvdi8qAY/Tq9M386e/5pwKBXg72PNpZD8rjJJgsshWVT0szG7TmA5Gy4FZ5ksu3Aji0C//ApL8J1s6lazw8XRcOCrGmBDaXcVF/+x3sPsbORkZusp7ve0TptLfORVylRobFwmk8vxrtCI2LsXirQNbW4Wep0WaztDoifp9dy9cRBHNz92LB7O8qmN2bywH3euPPnBNC9PS0joHerUqGZcJpfLqVOjGleDQwpdLysrm34jxtJn2Bg+mf4Nd8JNu9X1ej3Tv/2eAT27UM7X54njfFHjFf77Skx3m1KpZMWKFYwcOZJFixZRu3ZtmjdvzoABA6he3Xxff2G++OIL2rZta7w/aNAg/u///o8vv/wSgJCQEM6ePcuvv/5qdv1Bgwbx+uuvk5mZiZ2dHampqezcudOY5Kxfvx69Xs+SJUuQ/a+uu3z5cpycnDh48CDt2rVj/vz5TJo0iV69egGwaNEi/vjjj0fGnZOTUyBB1OtykSusAHB0UKFUyEhMMu1WS0zOo2wZO7PbdHGyIinZtDqRlJyHi5Nhmy7OVsZlpm1yjY8BbNoeSUhoGqnpWoIqOfDWkHK4uljzf0uLPnYmMy0ZvV5XoFtN7ehKfPSdQtfLzkxj1rgWaLW5yOVyug6eTEBQ/hi1IzuXIFcoaNTu9SLHYo5GrUChkJGcatqtlpKmo4ynldl1nByUJKf9s70WZwfzZ6qtGjqQla3n+IV0s48Xh6NGiUIhIynFtFstKTUPn9Lmkw5nJ2WBbtmkFC0uTiqTZQ1qOfLp2HJYW8lJTM7jo5k3SX2ou3HrH3HcDMskLV1H1UB7hvX3xsVJxU+rIwqNV2NveH1T/vF6Jafp8PZQmV3HSaMw8/rqcHro9V266T5vDXDn5y/KotVJSBIsWhfP9VDz48g09nL6tHdi39+pZh9/lOyMJCS9Dtt/VHjs1G4kxxX+Hn7YiV1zsXfwMCZaWekJ5OVkcv7AYup3GEfDThMIDz7CnlXv0P3NlZQuX7/YcT6QkpqKTq8v0E3l7ORIeESk2XV8vEsz8Z23KO/nS0ZmJuu27uDtjyaz4rs5eLgZnveaLdtQKOT07tLxsWP7L8T7MnpRBlw/LSWmkgSGMUlRUVFs27aNDh06cPDgQWrXrs2KFSuKtZ26deua3B8wYABhYWGcOHECMFSFateuTaVKlcyu36lTJ1QqFdu2bQNg8+bNODg40KZNGwAuXrzIrVu30Gg0xgqYi4sL2dnZhIaGkpKSQnR0NA0aNDBuU6lUFojrn2bMmIGjo6PJLeLW6keu87ys/z2C81dSCA3L4Pc90fzf0tv06VIalbKQzv+nyMrGnrFfbWH01A207fMeu9fO4vb1UwBE3rnK33t/offIGcaEtSRr3ciRw6dTydOW7C+ai9fSeOuT67w3LZjTl1L57B1/nBzyz6k2747j0vV07tzLYsf++/y0OoIe7Tyey/vhnzq94kiFstbM+DmGj+ZEsnJrAiP6uFEtsGA1z9ZaxiejvIiIyWPD7iQzW3u2zv31M7cu7KLDkP9D+b/xRtL/5p3xq9qKGq+8gZt3ZWq3GkXZyi24emLdc48xqFIgHVq9QgV/P2oGVeGrj8fj5ODA9j8Mla3gW7fZvH03k94dXSI+cy9avC86SdI/tduLoMRUkh6wsbGhbdu2tG3bls8//5wRI0YwZcoUjhw5ApgOGsvLKzhQGcDe3rTrwMvLi1atWrFmzRoaNmzImjVrGD16dKExWFlZ0adPH9asWcOAAQNYs2YN/fv3Nw4AT09Pp06dOgW68ADc3d2L/ZwfmDRpEuPHjzdZ1mHASePfKal5aHUSLs6mZ90uTioSksyPZUlMzsXZybQK4uykIvF/1aXE/63n/I9tODtZcet24dWOayGpKJVyvDxtuBdZeLfGw+w0TsjlCtJTTQfip6ckoHZ0K2QtQ7nd1bMsAKXLViYuKpRD23/Gv3J9woLPkJGawDfvtzK21+t17F47m7/3ruLDeUW/eiQtXYdOJ5lUKcDQRZaUan6QenKqFifNP9srzbavUt6WMl5WzFkaVeSYHiUlTYtOJ+HsaPoxdnZQkWRmED9AUrIWJ0fT94+zo5LEf1QSs3P0RMXmEBWbw/VbGayYW5UOLdxYty3G7HZv3MpAqZTh6W5FRLT57vK0DMPr6/iP18tcteiB5DSdmddXYaz2WalkvNrFhW+WxnDuf1e83Y3Kxc/bim6tHLn8UNeejbWMz0aXIitHz+ylsege4zvaxt4ZmVxBVrrpezgz/T52msLfwwAXDi7l/IHFdB21DNfSFU22KZcrcfE0vXrL2aM8MXfO/nMzxeLo4IBCLicp2bR7Nyk5BRdnpyJtQ6lUEuDvR0S04crFS9dukJSSSr8RY41tdHo9Pyz/hU3bd7F+8f+9NPEK/30lqpJkTpUqVcjIyDAmH9HR+VdXPTyI+98MGjSI9evXc/z4cW7fvs2AAQP+tf2ePXu4evUqf/31F4MGDTI+Vrt2bW7evImHhwcBAQEmtwcVoFKlSnHyZH6Co9VqOXv20V941tbWODg4mNwedLUZtiERciuNOtXzr8yQyaBODWeuBpvvOrhyI5W6NUyv5KhX05krNwzto2KzuZ+YY9LGzlZBlUAHYxtzAsqp0ekkkpPNH4zNUSqtKO1XldCrJ4zL9Ho9oddO4BtQs8jbkfQSOq0hoavVpBvvTN/K2K+2GG8aZw+adRrGGx8uKfI2AbQ6CA3PpnrF/K5LmQyqV7Qj+I75RDD4TjbVK5l2ddasbL59m8YO3LqbTVhk8QZnFx6vRMidTGpVzR87JpNBrSAN126aT3Cv3UqnVlXTQdK1gxy4fivDbPv87coeWSUqX9YOnV4iOaXgFXX58cLtezkmFR6ZDKoF2hIcZr5rLOROdoGKUI2KtoSEGRIxhRxUShn/vCZCrwf5Q+HaWsv4fHQptFqJmYtjH7uSp1Ba4e5dlYhb+YOuJb2eyFsn8Cxbs9D1zh9Ywtn9P9J5xGI8fKqZPKZQWuHuE0RyvGl3XUp8GOonvPxfpVISWL4cZy9dMS7T6/Wcu3SFqhXNT03xTzqdnjt37+H6vySlXYtmLFswmyXzZxlvbi7ODOjRlW+mfPJSxfsyEvMkWUhCQgJ9+/Zl2LBhVK9eHY1Gw5kzZ5g9ezbdu3fH1taWhg0bMnPmTMqVK0dcXByfffZZkbffq1cvRo8ezejRo2nZsiWlSz/6y+eVV17By8uLQYMGUa5cOZOus0GDBvHNN9/QvXt3vvjiC8qUKcPdu3fZsmULEydOpEyZMowbN46ZM2dSoUIFKlWqxLx5857KZJXrtkbw6fuVuHErjeshafTr7o2tjZyd+wxn+J+9X5H4hFx+WmX4wt24LZL/m1GDAT3K8PeZBNo086BSgIbZ/5c/CHLjtkiG9PflXlSWYQqA1/xISMzhyAnD5d1VKzpQpaKG85eSyczSUbWSA++OKM/eg7GkZRR+UDSnSYchbF48Ce9yQZTxr8bfe1eRm5NFnVd6GmL56SMcnD1p389QUTu0/We8y1XFxcMXbV4uIZcOc+HvbXQbMhkAO40zdhrTJFChUKJ2dMO9VLliv76//5XEuMFe3Lqbw8272XRt6YSNtZz9xw0J47ghXiQka/n1d8Nrs/1AEtPf96F7a2fOXEmnWV0Hyvva8MNq0/mCbG3kNK6tYfmWeLP7dXNWorFX4OasRC6XUa6MoSsmOj6X7JzCv0w2745l4pt+hNzJIDg0k54dPLCxlvPHIUOlY+JbftxPymXZekP16rc9ccz9rCJ9Onlw8nwKLRq5EOhvx/yldwGwsZYzsLsXx8+lkJCch6NaSbe27rg5qzh80tA9VTnAnkoB9ly8lkZmlo4qFdS89VoZ9h9NJD3z0dNCbD+YwthB7oSG53ArPIfOzR2xtpJx4KQhqXtnkDsJKVrW7DDsa9ehFKa9W5quLR05ezWTprXV+PtYs2i94XXMypG4ejOL17u7kpt3n/hELVUCbGheT83KrYbXwNZaxudjSmFtJWP2L3HY2cix+9+QrdR0HcX9rq7xyhv8tf5j3MsE4elTnUtHVpKXm0Wleobxh/vXfoS9owcNO30AwPkDizn1x0LaDJyDg7M3mamG2FXWdqisDVXvms2H8+fq8ZTyr4t3+QaEBx8h7PoBur+1qnjBmdGve2dmLPiRSgH+VKoQwKbtu8jKzqFjm+YATP/2e9xdXRg1+FUAVqzbTNWKAXiX8iI9I5O1v20nJj6eLm0N1VpHBw2ODqaJtlKpwMXZCd8yTz6n04sWb1Ep7O2wD/A13rcrVwaHGpXITUwh+170I9YsWV6U5OZpKTFJklqtpkGDBnz77beEhoaSl5eHj48PI0eO5JNPDNn+smXLGD58OHXq1KFixYrMnj2bdu3aFWn7Go2Grl27smHDBpYtW/av7WUyGa+++iqzZ89m8uTJJo/Z2dlx+PBhPvroI3r16kVaWhre3t60bt0aBwfDWf0HH3xAdHQ0Q4YMQS6XM2zYMHr27ElKypNd1fTX0XicHFWMGOSHi7OhS+yDKZeNA6893W1MvvSv3Ehl2pzrjHytHKMGlyMiKotJ068a50gCWL35HjY2CiaODURtr+TytRQ+mHLZOEdSnlZPm2YeDHvVDyuVjKjYbNb/HsH6rYUP0i1M9YadyEhLYv+WhaSl3KeUb2Xe+PBnY3dbSkI0Mll+gTM3J5NtK78gJTEWlZUN7qXK0ffNWVRv+GwmgTt2Nh1H9X1e7eKKs4OCOxE5TPu/SONgY3dnpcmXRPDtbOYti2ZQNzde6+ZKVHweM3+KMs6R9ECzOhpkMjhy2vyknwO7uNKqUf5g1W8/MXQvfvbtPa7cLLw789CJJJw0Sob0KY2zo4rQu1l8MuumcY4kD1crky7qazczmPH9bd7o683Qft5ExuQwdV6ocY4knV7Cp7QNbZu54qBRkpauJfh2Ju9/GczdSEObPK2elo2cGdyrFCqVnJj4HLbsiWPzrn+fSPLv8xk4qBUM6OSMk4OSsIgcpi+KMb6+bs5Kk/dvcFgOC1bFMaCTMwO7uBAdn8fspTHGOZIAvl0Zx8CuLrz7ugdqOzn3k7Ss3ZlknEzS38eawP9dPff95PyDFMDoaeHEJxYv0Q+o2YmsjERO//EdmWnxuJWuTJcRi43dbenJUSZjX64eX4tel8feX8aZbKdu27ep1+4dQ4zV2vJKr6mcP/AzR7dOx8m9HO1fX0ipcnWKFZs5rZo1Jjk1lWVrNpKYlExAubJ8M+Vj42XycffvI3+o7Jaens433y8mMSkZjdqewPL+fD/rC/x8yzxxLP/FeIvKsU4QjfbnT3BZZY7huHZv1RYuDZ9kqbCEfyGTXraZoV4wTbsesnQIxfLeZ80sHUKx/LrylqVDKJaMlMJnVi+JHN2e/6R9T6Jp6+JXHy1pQOAFS4fwn3a22qOHZZQ0nfOCn/k+2g+58NS29cfKmk9tW89KiakkCYIgCIJQsonuNkEQBEEQBDOkF2Sm7KelxF/dJgiCIAiCYAmikiQIgiAIQpGI7jZBEARBEAQzXpSZsp8W0d0mCIIgCIJghqgkCYIgCIJQJHrR3SYIgiAIglCQuLpNEARBEARBEJUkQRAEQRCKRlzdJgiCIAiCYIa4uk0QBEEQBEEQlSRBEARBEIpGdLcJgiAIgiCY8bJd3YYkvHSys7OlKVOmSNnZ2ZYOpUhEvM+WiPfZEvE+WyJe4VmSSZL0ctXOBFJTU3F0dCQlJQUHBwdLh/OvRLzPloj32RLxPlsiXuFZEgO3BUEQBEEQzBBJkiAIgiAIghkiSRIEQRAEQTBDJEkvIWtra6ZMmYK1tbWlQykSEe+zJeJ9tkS8z5aIV3iWxMBtQRAEQRAEM0QlSRAEQRAEwQyRJAmCIAiCIJghkiRBEARBEAQzRJIkCIIgCIJghkiSBEEQBEEQzBBJkiAIgiAIghkiSRKEpyQ3N5fg4GC0Wq2lQymyuLg4jhw5wpEjR4iLi7N0OIIFJScns2TJEiZNmkRiYiIA586dIzIy0sKRPVp2dralQxD+w8Q8ScILIzk5GScnJ0uHUUBmZibvvPMOK1euBCAkJAR/f3/eeecdvL29+fjjjy0cYUFpaWmMGTOGdevWodPpAFAoFPTv35/vv/8eR0dHC0doXm5uLnFxcej1epPlvr6+FoqocKGhoSxfvpzQ0FAWLFiAh4cHu3fvxtfXl6pVq1o6PBOXLl2iTZs2ODo6EhYWRnBwMP7+/nz22WeEh4ezatUqS4doQq/XM336dBYtWkRsbKzxM/f555/j5+fH8OHDLR0ivXr1KnLbLVu2PMNIhCehtHQAwrMzfvz4IredN2/eM4yk+GbNmoWfnx/9+/cHoF+/fmzevBkvLy927dpFjRo1LBxhvkmTJnHx4kUOHjxIhw4djMvbtGnD1KlTS2SSNGLECM6fP8+OHTto1KgRAMePH2fcuHG8+eabrFu3zsIRmrp58ybDhg3j77//NlkuSRIymcyY6JUUhw4domPHjjRp0oTDhw8zffp0PDw8uHjxIkuXLmXTpk2WDtHE+PHjeeONN5g9ezYajca4vFOnTgwcONCCkZn31VdfsXLlSmbPns3IkSONy4OCgpg/f36JSJIePtGQJInffvsNR0dH6tatC8DZs2dJTk4uVjIlWIAk/Ge1aNHC5Obg4CDZ2dlJtWrVkmrVqiXZ29tLDg4OUsuWLS0dagF+fn7SsWPHJEmSpL1790pOTk7SH3/8IQ0fPlxq27athaMz5evrKx0/flySJElSq9VSaGioJEmSdPPmTUmj0VgytELZ2dlJR44cKbD88OHDkp2dnQUierTGjRtLr7zyirRr1y7p/Pnz0oULF0xuJU3Dhg2luXPnSpJk+p44efKk5O3tbcnQzHJwcJBu3bolSZJpvGFhYZK1tbUlQzOrfPny0r59+yRJMo33+vXrkpOTkyVDM2vixInSiBEjJK1Wa1ym1WqlUaNGSRMmTLBgZMK/EZWk/7ADBw4Y/543bx4ajYaVK1fi7OwMQFJSEkOHDqVZs2aWCrFQMTEx+Pj4ALBjxw769etHu3bt8PPzo0GDBhaOzlR8fDweHh4FlmdkZCCTySwQ0b9zdXU126Xm6OhofH+UJBcuXODs2bNUqlTJ0qEUyeXLl1mzZk2B5R4eHty/f98CET2atbU1qampBZaHhITg7u5ugYgeLTIykoCAgALL9Xo9eXl5Fojo0ZYtW8bRo0dRKBTGZQqFgvHjx9O4cWO++eYbC0YnPIoYuP2SmDt3LjNmzDA5ADo7O/PVV18xd+5cC0ZmnrOzM/fu3QNgz549tGnTBjCUrUta10rdunXZuXOn8f6DxGjJkiXGrqyS5rPPPmP8+PHExMQYl8XExPDhhx/y+eefWzAy86pUqVIik4vCODk5ER0dXWD5+fPn8fb2tkBEj9atWze++OILY4Ihk8kIDw/no48+onfv3haOrqAqVapw5MiRAss3bdpErVq1LBDRo2m1Wm7cuFFg+Y0bNwqMrxNKFlFJekmkpqYSHx9fYHl8fDxpaWkWiOjRevXqxcCBA6lQoQIJCQl07NgRMBxkzJ1BWtLXX39Nx44duXbtGlqtlgULFnDt2jX+/vtvDh06ZOnwzPrxxx+5desWvr6+xkHP4eHhWFtbEx8fz08//WRse+7cOYvE+HBlY9asWUycOJGvv/6aatWqoVKpTNo6ODg87/AeacCAAXz00Uds3LgRmUyGXq/n2LFjTJgwgcGDB1s6vALmzp1Lnz598PDwICsri+bNmxMTE0OjRo2YPn26pcMrYPLkyQwZMoTIyEj0ej1btmwhODiYVatWsWPHDkuHV8DQoUMZPnw4oaGh1K9fH4CTJ08yc+ZMhg4dauHohEcRV7e9JAYPHsyRI0eYO3euyYf0ww8/pFmzZsYrs0qKvLw8FixYwL1793jjjTeMZ4fffvstGo2GESNGWDhCU6GhocycOZOLFy+Snp5O7dq1+eijj6hWrZqlQzNr2rRpRW47ZcqUZxhJ4eRyuUl3pfS/QdoPk0rowO3c3FzefvttVqxYgU6nQ6lUotPpGDhwICtWrDDpdilJjh49yqVLl4zv4QcV3JLoyJEjfPHFFyafucmTJ9OuXTtLh1aAXq9nzpw5LFiwwFhhLFWqFOPGjeODDz4ose8HQSRJL43MzEwmTJjAsmXLjCV1pVLJ8OHD+eabb7C3t7dwhIJgqjhVuObNmz/DSIpHkiTu3buHu7s79+/f5/Lly6Snp1OrVi0qVKhg6fAEC3tQIS1p1U/BPJEkvWQyMjIIDQ0FoHz58iU2OVq5ciVubm507twZgIkTJ/Lzzz9TpUoV1q5dS9myZS0cYT5zA17BMK7D2toaKyur5xxR8WRnZ7N+/XoyMjJo27atOJA/Ib1ej42NDVevXi3Rr+XChQuL3Pbdd999hpG8HLRaLQcPHiQ0NJSBAwei0WiIiorCwcEBtVpt6fCEQogkSSiRKlasyI8//kirVq04fvw4bdq04dtvv2XHjh0olcoSNfnaP7uF/qlMmTK88cYbTJkyBbncstdKjB8/nry8PL777jvA0C1Uv359rl27hp2dHVqtlr1799K4cWOLxvlPy5cvR61W07dvX5PlGzduJDMzkyFDhlgoMvOqVq3K0qVLadiwoaVDKVS5cuWK1E4mk3H79u1nHM2/c3Z2LvLVog9mDC8p7t69S4cOHQgPDycnJ8c4+eW4cePIyclh0aJFlg5RKIQYuP0fV9SJykpS0gFw79494wDtrVu30rt3b0aNGkWTJk1o0aKFZYP7hxUrVvDpp5/yxhtvGMd7nTp1ipUrV/LZZ58RHx/PnDlzsLa25pNPPrForHv37uXrr7823l+9ejXh4eHcvHkTX19fhg0bxvTp002u1isJZsyYYTKY/AEPDw9GjRpV4pKkmTNn8uGHH/Ljjz8SFBRk6XDMunPnjqVDKJb58+dbOoTHNm7cOOrWrcvFixdxdXU1Lu/Zs6fJZJhCySOSpP+4kvrzEv9GrVaTkJCAr68ve/fuNc4ebmNjQ1ZWloWjM7Vy5Urmzp1Lv379jMu6du1KtWrV+Omnn9i/fz++vr5Mnz7d4klSeHg4VapUMd7fu3cvffr0MXZfjhs3jk6dOlkqvEKFh4ebrXyULVuW8PBwC0T0aIMHDyYzM5MaNWpgZWWFra2tyeMlrdLxIihpiXBxHDlyhL///rtA17ufn1+J/228l51Ikv7jli9fbukQHkvbtm0ZMWIEtWrVIiQkxHjgvnr1Kn5+fpYN7h/+/vtvs+XyWrVqcfz4cQCaNm1aIg7mcrmch3vYT5w4YTIvkpOTE0lJSZYI7ZE8PDy4dOlSgf/9P8/MS4oXseoRERHBtm3bCA8PJzc31+SxkvazRQ/Lzs4uEG9JGxSt1+vNXoEZERFh8jMwQskjkqSXQF5eHra2tly4cKHElv7/6fvvv+ezzz7j3r17bN682XggPHv2LK+++qqFozPl4+PD0qVLmTlzpsnypUuXGmcNT0hIKBEzWVeuXJnt27czfvx4rl69Snh4OC1btjQ+fvfuXTw9PS0YoXmvvvoq7777LhqNhldeeQUwXP02btw4BgwYYOHoCnrRqh779++nW7du+Pv7c+PGDYKCgggLC0OSJGrXrm3p8ArIyMjgo48+YsOGDSQkJBR4vKRNCdGuXTvmz5/Pzz//DBjGeaWnpzNlypQSWbkVHmKJ30IRnr9y5cqVyN+4+i/4/fffJSsrK6l69erS8OHDpeHDh0s1atSQrKyspO3bt0uSJEk//PCD9P7771s4UknasmWLZGVlJbVq1Ury9PSUunTpYvL4xIkTpb59+1oousLl5ORI/fr1k2QymaRSqSSVSiUpFApp6NChUk5OjqXDe6SsrCwpJSXF5FbS1KtXT5o8ebIkSfm/hZaWliZ169ZN+uGHHywcXUFjxoyRKleuLG3atEmytbWVli1bJn355ZdSmTJlpF9//dXS4RVw7949qUqVKlLlypUlpVIpNWzYUHJ1dZUqVqwoxcbGWjo84RHE1W0viaVLl7JlyxZ++eUXXFxcLB1OkWVmZpot/1evXt1CEZkXFhbGokWLCAkJAQxX57355pukp6eXuOrd/v372bFjB15eXrzzzjvY2dkZH5s2bRrNmzcvUYPjpYfmHYqIiODChQvY2tpSrVq1EjUVxMNetEqHRqPhwoULlC9fHmdnZ44ePUrVqlW5ePEi3bt3JywszNIhmvD19WXVqlW0aNECBwcHzp07R0BAAL/88gtr165l165dlg6xAK1Wy7p160wm6xw0aFCB8WpCCWPhJE14TmrWrCmp1WrJ2tpaCgwMlGrVqmVyK2ni4uKkTp06SXK53OytJEtJSZEWLVok1a9fv8TH+iLQ6XSSSqWSQkJCLB1Kkb1olQ5PT0/p2rVrkiRJUuXKlaXff/9dkiRJunDhgmRvb2/J0Myyt7eX7t69K0mSJHl7e0snT56UJEmSbt++XSLjzcrKsnQIwmMSY5JeEj169LB0CMXy3nvvkZKSwsmTJ2nRogW//fYbsbGxJfYHeQEOHz7M0qVL2bx5M6VLl6ZXr1783//9n6XDKlRSUhJLly7l+vXrgGG80rBhw0pcpVEulxt/w68kT874sO3btxsrHUOHDqVZs2YEBARQtmxZVq9ezaBBgywdoomGDRty9OhRKleuTKdOnfjggw+4fPkyW7ZsKZFzPfn7+3Pnzh18fX2pVKkSGzZsoH79+mzfvh0nJydLh1eAh4cHPXv25LXXXqN169YWny9NKAZLZ2mCYI6Xl5fx7FCj0UjBwcGSJBnG/zRp0sSSoZmIjo6WZsyYIQUEBEgeHh7S2LFjJaVSKV29etXSoT3SoUOHJAcHB8nHx0fq2bOn1LNnT8nX11dycHCQDh06ZOnwCti2bZvUtGlT6fLly5YOpUhetEpHaGiodPHiRUmSJCk9PV168803pWrVqkm9evWSwsLCLBxdQfPmzZMWLFggSZIk/fnnn5KNjY1kbW0tyeVyaf78+RaOrqAtW7ZIffr0kWxtbSUvLy9p3Lhx0unTpy0dllAEIkl6iSQlJUmLFy+WPv74YykhIUGSJEk6e/asFBERYeHICtJoNNKdO3ckSZIkX19f6ejRo5IkGQ4ytra2FowsX5cuXSQHBwfp1VdflXbs2CFptVpJkqQXIkkKCgqSRo4caYxZkiRJq9VKo0aNkoKCgiwYmXlOTk6SlZWVJJfLJRsbG8nZ2dnkVtJUq1ZNOnjwoCRJktS6dWvpgw8+kCRJkhYsWCB5e3tbMrT/pLCwMGnz5s3GRK+kSk1NlZYtWya1bdtWUigUUoUKFaRp06ZZOizhEcTA7ZfEpUuXaNOmDY6OjoSFhREcHIy/vz+fffYZ4eHhrFq1ytIhmqhXrx5fffUV7du3p1u3bjg5OTFjxgwWLlzIpk2bjL8/Z0lKpZJ3332X0aNHm3QDqVQqLl68aDJpY0nzYEqIihUrmiwPDg6mZs2aJXLCzkcpKZfc3759Gz8/PxYsWIBCoeDdd99l3759dO3aFUmSyMvLY968eYwbN87SoRYqPT0dvV5vsqykzTv0X3Dt2jUGDRrEpUuXStxAfiGfGJP0khg/fjxvvPEGs2fPNpm8rFOnTgwcONCCkZk3btw4oqOjAZgyZQodOnRg9erVWFlZsWLFCssG9z9Hjx5l6dKl1KlTh8qVK/P666+XyDl7zKlduzbXr18vkCRdv36dGjVqWCiqwpWUJOjfVKhQgejoaN5//30A+vfvz8KFC7lx4wZnz54lICCgxF2ZCYafKBk7diwHDx4kOzvbuFySJGQyWYk8iJ8+fZoDBw4QFxdXIKkrqZNfZmdns23bNtasWcOePXvw9PTkww8/tHRYwiOIStJLwtHRkXPnzlG+fHk0Gg0XL17E39+fu3fvUrFiRZMvxpIoMzOTGzdu4Ovri5ubm6XDMZGRkcH69etZtmwZp06dQqfTMW/ePIYNG1aiZtO9dOmS8e/r168zceJE3nnnHePA3BMnTvD9998zc+ZM+vfvb6kw/1VJnmFZLpcTExODh4cHgMlnrSRr0qQJkiQxbtw4PD09C/yQbPPmzS0UmXlff/01n332GRUrViwQr0wm46+//rJgdAX98ccfrFmzhq1bt6JUKunTpw+DBg0yTowqlGAW7OoTniN3d3fp3LlzkiTlTxYnSZK0d+9eqUyZMpYM7T/lxo0b0ocffih5eXlJNjY2UteuXS0dkpFMJpPkcrkkk8keeSuJ0xakp6dLb7/9tuTu7l6ip4SQyWQmkwM+/Fkryezt7aUbN25YOowi8/DwkJYvX27pMIrM1tZW6tu3r7R161YpNzfX0uEIxSC6214S3bp144svvmDDhg2A4WwrPDycjz76iN69e1s4OoMHP2JbFCW1nF6xYkVmz57NjBkz2L59O8uWLbN0SEYv2q++P2zixIkcOHCAH3/8kddff53vv/+eyMhIfvrppwI/B2NJMpmsQBXmn/dLonr16nHv3r0C3a8llVwup0mTJpYOo8hiY2NLVFVZKDrR3faSSElJoU+fPpw5c4a0tDRKly5NTEwMjRo1YteuXdjb21s6RJPfEHuUklhOF56tF2WGZblcTseOHbG2tgYM8yW1atWqwOdry5YtlgivUKGhobz11lu89tprBAUFoVKpTB4vaeOoZs+eTVRUVIn+IeHU1FRjN3Bqauoj25aU7mKhIJEkvWSOHj1qMi1+mzZtLB2S8Jxs27aNjh07olKp2LZt2yPbduvW7TlFVTRqtZpr167hXs085AAAEl9JREFU6+tLmTJl2LJlC/Xr1+fOnTtUq1aN9PR0S4cIwNChQ4vUbvny5c84kuI5ceIEAwcONPn5EZlMVmIHbuv1ejp37kxISAhVqlQpkNSVhCRUoVAQHR2Nh4cHcrncbEWxpL6+Qj7R3faSadq0KU2bNrV0GP8qJSUFnU5XYPbnxMRElEqlOPN6DD169DAOKn7UDOwl8Uv7RZlhuaQlP0U1bNgwatWqxdq1a80O3C5p3n33XQ4cOEDLli1xdXUtkfH+9ddfxu+vv/76q0TGKPw7UUl6SSxcuNDscplMho2NDQEBAbzyyisoFIrnHJl5HTt2pGvXrowZM8Zk+aJFi9i2bVuJ6V4Rno9vv/32hZ136EVgb2/PxYsXCQgIsHQoRaLRaFi3bh2dO3e2dCjCf5yoJL0kvv32W+Lj48nMzMTZ2Rkw/HaXnZ0darWauLg4/P39OXDgAD4+PhaOFk6ePGl2cHaLFi349NNPLRDRf8Px48dJSEigS5cuxmWrVq1iypQpZGRk0KNHD7777jvjmBpL0+v1fPPNN2zbto3c3FyioqKYMmVKiZ936EXTqlWrFypJcnFxoXz58pYOo8gqVKjAoEGDGDRo0Avz+4OCgfiVvZfE119/Tb169bh58yYJCQkkJCQQEhJCgwYNWLBgAeHh4Xh5eRknwbO0nJwctFptgeV5eXklbjboF8kXX3zB1atXjfcvX77M8OHDadOmDR9//DHbt29nxowZFozQ1PTp0/nkk09Qq9V4e3uzYMEC3n77bcqWLUuvXr1EgvSUdO3alffff5+pU6eyefNmtm3bZnIraaZOncqUKVPIzMy0dChFMmbMGHbu3EmlSpWoV68eCxYsICYmxtJhCUVhsckHhOfK399fOn/+fIHl586dk8qVKydJkiQdO3ZM8vLyes6RmdeiRQtp7NixBZaPGTNGatq0qQUi+m/w8vIy+WHNTz75xOQHgzds2CBVrlzZEqGZFRAQIC1atMh4/88//5SsrKwknU5nwaj+e160ebNq1qwpaTQaSa1WS0FBQVKtWrVMbiVVcHCwNHnyZKlChQqSUqmU2rZtK61cudLSYQmPILrbXhLR0dFmKzNardZ4RlO6dGnS0tKed2hmffXVV7Rp04aLFy/SunVrAPbv38/p06fZu3evhaN7cSUlJeHp6Wm8f+jQITp27Gi8/2C+nJIiPDycTp06Ge+3adMGmUxGVFQUZcqUsWBk/y3//FmPku5RFx6UZIGBgUybNo1p06Zx4sQJRo8ezdChQxk8eLClQxMKIZKkl0TLli158803WbJkCbVq1QLg/PnzjB49mlatWgGGrpdy5cpZMkyjJk2acOLECWbPns2GDRuwtbWlevXqLF26VPTpPwFPT0/u3LmDj48Pubm5nDt3jmnTphkfT0tLK3A5tSVptVpsbGxMlqlUKvLy8iwU0X9fdnZ2gde8pJkyZUqR2q1du5Zu3bqViHngHjh16hRr1qxh/fr1pKam0rdvX0uHJDyCSJJeEkuXLuX111+nTp06xoOgVquldevWLF26FDDMRTN37lxLhllgoG6rVq1YsmQJtra2Fo3rv6JTp058/PHHzJo1i61bt2JnZ0ezZs2Mj1+6dKlEDYiVJIk33njDZCB5dnY2b731lsmBryTMi/Mi0+l0fP311yxatIjY2FhCQkLw9/fn888/x8/Pj+HDh1s6xMfy5ptv0qBBA4v/dl5ISAirV69m7dq13Llzh1atWjFr1ix69eqFWq22aGzCo4kk6SXh5eXFn3/+yY0bNwgJCQEMP6Hx8M8QFHXG62dp+vTpTJ06lTZt2mBra8vChQuJj48vUT/v8SL78ssv6dWrF82bN0etVrNy5UqsrKyMjy9btox27dpZMEJTQ4YMKbDstddes0Ak/23Tp09n5cqVzJ49m5EjRxqXBwUFMX/+/Bc2SZJKyAw3DwZsv/322wwYMMCky1so2cQ8SUKJUqFCBSZMmMCbb74JwL59++jcuTNZWVnI5eJizKclJSUFtVpdYF6sxMRE1Gq1SeIk/PcFBATw008/0bp1azQaDRcvXsTf358bN27QqFEjkpKSLB3iY3n4uViKTqdj2bJl9OnTxzj9ivDiEJWkl4ROp2PFihXs37+fuLi4AgM1S8pvoYmBus+Ho6Oj2eX/nOFceDlERkaanSNJr9eL8V9PSKFQ8M4779CmTRuRJL2ARJL0khg3bhwrVqygc+fOBAUFldgp8sVAXUF4/qpUqcKRI0coW7asyfJNmzYZL/QQHl9QUBC3b98uMRfGCEUnkqSXxLp169iwYYNJlaYkEgN1BeH5mzx5MkOGDCEyMhK9Xs+WLVsIDg5m1apV7Nixw9LhvfC++uorJkyYwJdffkmdOnUKXG0nfouy5BJjkl4SpUuX5uDBgwQGBlo6lEd6UX9FXRBedEeOHOGLL77g4sWLpKenU7t2bSZPnlyiBvIXV1BQELt377b4Ty09PJ7y4Sq+JEkl8gelhXwiSXpJzJ07l9u3b/N///d/JbarTRCE52fhwoWMGjUKGxsbwsPD8fHxeWG+Gw4cOFDo1bg//fST8cKPkuLQoUOPfLx58+bPKRKhuESS9JLo2bMnBw4cwMXFhapVqxaYMFB0XwnCy0WpVBIVFYWHhwcKhYLo6Gg8PDwsHVaRWFtb8+677/L1118bv8vu37/P0KFDOXr06At7NZ5Q8ogxSS8JJycnevbsaekwBEEoIUqXLs3mzZvp1KkTkiQRERFBdna22ba+vr7PObpHO3DgAIMHD/7/9u4+pub2jwP4G9HBqeyIpgdZMQ+j5VlJ2cgqDwv9oUJZin/MipKnRU1Ks2HiVmnLMCXWxDKMykNbqxwnNipLRYwpdOhR3X+0vrdzd6Lfb7u7zun7fm023+/55z1/3Pvc1/e6rjfu3r2Ly5cvo7q6GiEhIZg6dSrUarXoeL0UFhb+9nd3d/cBSkL/K64kERHJUEpKCnbs2KG307GHIe+Z0Wq12L59O7Kzs9HZ2Ym4uDhERUUZ5CdDfXe8/ZrTEP99qRtXkmSko6MD+fn5eP36NQICAmBmZob6+nqYm5vzanwimQkLC4O/vz9qamrg5OSEe/fuYezYsaJj9VtFRQVKSkpga2uL+vp6vHr1Cj9+/DConrYe//78197ejqdPn+LgwYM4cuSIoFTUH1xJkomamhp4eXmhtrYWra2tUjfTzp070drair/++kt0RCISJCMjAxs2bNC5ekMfQymMTUhIQExMDMLCwpCUlISqqips2rQJ3759w8WLF+Hi4iI0X38VFBQgIiICpaWloqNQHzgkyYSvry/MzMxw/vx5jB07VrqqPz8/H6GhoaisrBQdkYgMnLm5OdRqtfDC2AkTJiA9PR3e3t7Su/b2duzbtw+nTp1Ca2urwHT99/LlS8ybNw9arVZ0FOoDP7fJxMOHD/HkyZNenVyTJk3Cu3fvBKUiImNiKP9PXV5eDktLS513w4cPR1JSElatWiUoVd80Go3Oc1dXF96/f4+EhAQ4OzuLCUX9wiFJJjo7O/VuDnz79i3MzMwEJCIi+v9YWlriy5cvyM7OxuvXrxEZGQmVSoWysjK9HXSiOTs7Y8iQIb2GzEWLFiE9PV1QKuoPDkkysWLFCpw4cQIpKSkAuk9WaLVaxMTEGHxVCRHRrzQaDZYvXw4LCwu8efMGoaGhUKlUuH79Ompra3HhwgXREXVUV1frPA8dOhTjxo3r1VNJhqf3uUQalI4fP47Hjx9jxowZaGlpQUBAgPSpLTExUXQ8IqJ+Cw8PR3BwMCorK3UGDR8fnz/eSTSQioqKcPPmTdjb20t/CgoK4O7ujokTJyIsLMxo9k/JFVeSZMLW1hbPnj1DZmam1M0UEhKCwMBAjBw5UnQ8IqJ+KykpkVbFf2VjY4MPHz4ISKRfbGwsli5dKu2TKi8vR0hICIKDgzF9+nQkJSXB2toahw4dEhuU+sQhSUZMTEwQGBiIwMBA0VGIyAjZ29v3qjQSwdTUFN++fev1vqKiAuPGjROQSD+1Wo24uDjp+cqVK1i4cCFSU1MBAHZ2doiJieGQZMD4uU0mMjIycOvWLek5KioKY8aMgaurK2pqagQmIyLRHjx40Odv586dk/7+/Plz2NnZDUSk31qzZg1iY2PR3t4OoHuPZW1tLfbs2YP169cLTvePxsZGWFlZSc8FBQU61xbMnz8fdXV1IqJRP3FIkon4+Hjps1pRURFOnz6NY8eOwdLSEuHh4YLTEZFIXl5eiIyMlIYOoLswdvXq1YiOjhaYTL/jx49Dq9Vi/PjxaG5uhoeHBxwdHaFUKg3qBmsrKytp03ZbWxvKysqwaNEi6fempiaDWJmjvvFzm0zU1dVJR2NzcnLg5+eHsLAwLF68GEuXLhUbjoiEMrbCWAsLC9y9exePHj2CRqOBVqvF3LlzsWzZMtHRdPj4+CA6OhqJiYnIycnBqFGjsGTJEul3jUYDR0dHgQnpT7iSJBNKpRKfP38GANy5cweenp4AAIVCgebmZpHRiEgwV1dXqNVqzJw5E3PmzMHatWsRHh6O/Px82Nvbi44n6Tkt1sPNzQ2jR4/GmTNn4O/vb3CnxeLi4mBiYgIPDw+kpqYiNTVV50Lf9PR0rFixQmBC+hOuJMmEp6cntm7ditmzZ6OiokK6G+nFixeYNGmS2HBEJJwxFMbqOy0WGhqKoKAggzwtZmlpicLCQnz9+hVKpRLDhg3T+f3q1assFzdwXEmSieTkZLi4uODTp0+4du2a1PZdWloKf39/wemISKSEhAS4uLjA09MTz58/R3FxMZ4+fQonJycUFRWJjidRq9U6n9SuXLmCBQsWIDU1FRERETh16hSysrIEJtTPwsKi14AEACqVqldVFBkWFtwSEcmcsRTGKhQKVFZWSifs3Nzc4O3tjf379wMA3rx5g1mzZqGpqUlkTBpEuJIkE7dv38ajR4+k5+TkZDg7OyMgIACNjY0CkxGRaOXl5ToDEvBPYeydO3cEpeqNp8VooHFIkonIyEjp8rXy8nLs2rULPj4+qK6uRkREhOB0RCRST2FsWloa9u7di4aGBgAwuMLYntNiDx8+xN69e3lajP5z3LgtE9XV1ZgxYwYA4Nq1a1i1ahXi4+NRVlbGglsimTOWwti4uDisW7cOHh4eUCqVyMjI4Gkx+k9xSJKJESNG4MePHwCAe/fuYfPmzQC6Nw7qu96fiOSjpzD22LFjMDMzk977+PggICBAYDJdPC1GA41Dkky4ubkhIiICixcvRnFxMTIzMwF0H/u1tbUVnI6IRDKWwtgeFhYWet+rVKoBTkKDHfckycTp06dhYmKC7OxsnD17FjY2NgCAvLw8eHl5CU5HRCIZS2Es0UDjFQBERDK3detWfP78GVlZWVCpVNBoNBg2bBh8fX3h7u6OEydOiI5IJASHJBlqaWlBW1ubzjtzc3NBaYhItK9fv8LPzw8lJSVoamqCtbU13r9/DxcXF+Tl5RnUrdtEA4lDkkx8//4de/bsQVZWltTh9qufP38KSEVEhsTQC2OJBhr3JMlEVFQU7t+/j7Nnz8LU1BRpaWk4fPgwrK2tDeZ4LxENLGMrjCUaaBySZCI3NxdnzpzB+vXrYWJigiVLluDAgQOIj4/HpUuXRMcjIgFiY2Px4sUL6bmnMNbT0xPR0dHIzc3F0aNHBSYkEotDkkw0NDTAwcEBQPf+o54bdd3c3FBYWCgyGhEJYqyFsUQDhUOSTDg4OEidR9OmTZP+w5ebm4sxY8YITEZEojQ2NsLKykp6Ligo0Olwmz9/Purq6kREIzIIHJJkYsuWLXj27BkAIDo6GsnJyVAoFAgPD0dkZKTgdEQkAgtjiX6PN24Pcp2dnUhKSsKNGzfQ1taG+vp6xMTE4OXLlygtLcXkyZPh5OQkOiYRCdBTGJuYmIicnBwWxhL9C4ekQe7IkSM4dOgQli9fjpEjR+LkyZP4+PEj0tPTYW9vLzoeEQnEwlii3+M9SYPclClTsHv3bmzbtg1Ad7ntypUr0dzcjKFD+bWViNBnYWxDQwOUSqXO4EQkJxySBjlTU1NUVVXBzs5OeqdQKFBVVcViWyIiot/gUsIg19HRAYVCofNu+PDhaG9vF5SIiIjIOHBP0iDX1dWF4OBgmJqaSu9aWlqwfft2nT6m69evi4hHRERksDgkDXJBQUG93m3cuFFAEiIiIuPCPUlEREREenBPEhEREZEeHJKIiIiI9OCQRERERKQHhyQiIiIiPTgkEREREenBIYmIiIhIDw5JRERERHr8DX2KCLfIHeaOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual_helper.display_feature_correlation(corr_df=corr_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.338481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_female</th>\n",
       "      <td>0.543351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_male</th>\n",
       "      <td>-0.543351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Survived\n",
       "Pclass     -0.338481\n",
       "Fare        0.257307\n",
       "Sex_female  0.543351\n",
       "Sex_male   -0.543351\n",
       "Survived    1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the features with high correlation to the label\n",
    "high_corr_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select only numerical features\n",
    "selected_features = num_features.copy()\n",
    "selected_features.remove(\"PassengerId\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the training data\n",
    "X_train, X_valid, y_train, y_valid = titanic.split(\n",
    "    titanic.select_cols(df=train_X_raw_df, cols=selected_features), \n",
    "    train_raw_y, test_size=0.2, random_state=10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algo factory for building different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Any\n",
    "class AlgoML:\n",
    "    \"\"\"Interface\"\"\"\n",
    "    def train_model(self, X_train, y_train) -> Any:\n",
    "        pass\n",
    "\n",
    "class AlgoXgboost(AlgoML):\n",
    "    def train_model(self, X_train, y_train) -> Any:\n",
    "        # shift + tab to unindent multiple lines \n",
    "        param_grid = {\n",
    "            'n_estimators': range(6, 10),\n",
    "            'max_depth' : range(3, 8),\n",
    "            'learning_rate' : [.2, .3, .4],\n",
    "            'colsample_bytree' : [.7, .8, .9, 1]\n",
    "        }\n",
    "\n",
    "        xgb = XGBClassifier()\n",
    "        # Searching for the best parameters\n",
    "        g_search = GridSearchCV(estimator=xgb, param_grid=param_grid, cv=3, n_jobs=1, verbose=0, return_train_score=True)\n",
    "\n",
    "        # Fitting the model using best parameters found\n",
    "        g_search.fit(X_train, y_train)\n",
    "\n",
    "        # print the best parameters found\n",
    "        print(g_search.best_params_)\n",
    "        # valid \n",
    "        # g_search.score(X_valid, y_valid)\n",
    "        model = g_search\n",
    "        return model\n",
    "    \n",
    "class AlgoCustom(AlgoML):\n",
    "    def train_model(self, X_train, y_train) -> Any:\n",
    "        feature_position = ProbBinaryClassifier.feature_position(X_train, \"Sex_female\")\n",
    "        # config model\n",
    "        model = ProbBinaryClassifier(feature_position, 1)\n",
    "        # Train model\n",
    "        model.fit(X_train, y_train)\n",
    "        return model\n",
    "    \n",
    "\n",
    "class AlgoFactory:\n",
    "    def get_algo(algo: str) -> AlgoML:\n",
    "        if algo == \"xgboost\":\n",
    "            return AlgoXgboost()\n",
    "        else:\n",
    "            return AlgoCustom()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traing a model \n",
    "\n",
    "* Xgboost model\n",
    "* Custom model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'colsample_bytree': 1, 'learning_rate': 0.4, 'max_depth': 3, 'n_estimators': 6}\n"
     ]
    }
   ],
   "source": [
    "algo = AlgoFactory.get_algo(algo=ALGO)\n",
    "model = algo.train_model(X_train=X_train, y_train=y_train)\n",
    "predicts = model.predict(X_valid)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model performance validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## conf_mat_df:\n",
      "          Perished  Survived\n",
      "Perished       103        14\n",
      "Survived        14        48\n",
      "\n",
      "## conf_mat:\n",
      "[[103  14]\n",
      " [ 14  48]]\n",
      "\n",
      "## f1:\n",
      "0.7741935483870968\n",
      "\n",
      "## acc:\n",
      "0.8435754189944135\n",
      "\n",
      "## roc_fpr:\n",
      "[0.         0.11965812 1.        ]\n",
      "\n",
      "## roc_tpr:\n",
      "[0.         0.77419355 1.        ]\n",
      "\n",
      "## roc_thresholds:\n",
      "[2 1 0]\n",
      "\n",
      "## auc:\n",
      "0.8272677143644885\n",
      "\n"
     ]
    }
   ],
   "source": [
    "validator = ModelValidator(y_valid, predicts, dark_mode=DARK_MODE)\n",
    "scores_dict = validator.evaluate()\n",
    "validator.print_eval_result(scores_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x292b83f40>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 83 is false positive (false predicted to positive label, which has negative label as ground truth)\n",
    "validator.display_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x293e115d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC: Receiver operating characteristic\n",
    "# AUC -> 1\n",
    "validator.display_roc_curve()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model input feature attribution\n",
    "\n",
    "**local weighted regession surrogate explanation model**\n",
    "Kernel SHAP is based on LIME (local Interpretable Model-agnostic Explanations), which uses its own kernel to create a weighted linear regression surrogate surrounding the local data. \n",
    "\n",
    "Note:\n",
    "* The surrogate model is created for every instance x with sampled coalition. (https://christophm.github.io/interpretable-ml-book/shap.html), therefor the kernel SHAP is quite slow to execute.\n",
    "\n",
    "<!--\n",
    " ![local weighted regession surrogate explanation model](https://dl.acm.org/cms/attachment/ 382dae73-8228-4137-9aae-889784b9d7c5/f5.jpg) \n",
    "-->\n",
    "<img src=\"https://dl.acm.org/cms/attachment/382dae73-8228-4137-9aae-889784b9d7c5/f5.jpg\" width=\"700\" height=\"500\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 712 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9304f433147e4ae6bc561f02f635abb6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/179 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Explainer callable for X_valid data set\n",
    "if KERNEL_EXPLAINER:\n",
    "    explainer = ModelKernelExplainer(model=model, train_data=X_train, inference_data=X_valid, dark_mode=DARK_MODE, link=LINK)\n",
    "else: \n",
    "    explainer = ModelExplainer(model=model, data=X_valid, dark_mode=DARK_MODE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "@ModelExplainer.valid_index\n",
    "def get_details(df: DataFrame, idx: int, org_df: DataFrame) -> DataFrame:\n",
    "    \"\"\"get the passenger info from the original data frame based on the index position of validation set\n",
    "    @param df: \n",
    "    @param idx: index position, this must be idx so that decorator works\n",
    "    \"\"\"\n",
    "    # get the index name from the original raw dataset, from the index position of validation data set\n",
    "    index_name = df.iloc[idx].name\n",
    "    # use slicing on the same index name to get the passenger info as a DataFrame obj\n",
    "    return org_df.loc[index_name: index_name]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base values in SHAP\n",
    "Following the [SHAP paper](https://proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf):\n",
    "\n",
    "the base value $E[f(z)]$ is \"the value that would be predicted if we did not know any features from the current output\". \n",
    "\n",
    "In other words, it is the mean of model prediction using our `background` or `reference` dataset: `X_train`\n",
    "```python3\n",
    "ModelKernelExplainer(model=model, train_data=X_train, ...)\n",
    "```\n",
    "the base value $E[f(z)]$:\n",
    "```python3\n",
    "base_value = np.mean(\n",
    "    model.predict_proba(X_train),\n",
    "    axis=0\n",
    ")\n",
    "```\n",
    "\n",
    "Note: \n",
    "* The kernelShap with `Link=\"identity\"` output the shap value in `probability` unit space\n",
    "* The kernelShap with `Link=\"logit\"` output the shap value in `log(odd)` unit space, which is more evenly distributed for loss and gains in a linear scale.\n",
    "\n",
    "Logit funciton $log_e(\\frac{p_x}{1 - p_x}) = log_e(p_x) - log_e(1 - p_x)$, `np.log()` uses Euler's number $e = 2.71828$ as base, which is the natural logarithm.\n",
    "\n",
    "Derivative of $e^x$ is $e^x$. \n",
    "In other words: $\\frac{d}{dx}(e^x) = e^x$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shap base value: \n",
      "[0.60048749 0.3995125 ]\n",
      "base value from local data predictions: \n",
      "[0.6004875  0.39951256]\n"
     ]
    }
   ],
   "source": [
    "from typing import Iterable\n",
    "def simple_logit(probs: Iterable):\n",
    "    # assumption all the probability in the Iterable shall be 1\n",
    "    return np.array([ np.log(x) - np.log(1-x) for x in probs])\n",
    "\n",
    "if getattr(explainer.explainer, \"expected_value\", None) is not None:\n",
    "    # print(explainer.explainer.expected_value)\n",
    "    print(f\"shap base value: \\n{explainer.explainer.expected_value}\")\n",
    "\n",
    "    # calculate base value for kernelshap with link identity and logit output space mapper\n",
    "    # average of all the local data predictions, X_train\n",
    "    all_local_data_predictions = model.predict_proba(X_train)\n",
    "    base_value = np.mean(all_local_data_predictions, axis=0)\n",
    "    if (LINK == 'logit'):\n",
    "        base_value = simple_logit(base_value)\n",
    "    print(f\"base value from local data predictions: \\n{base_value}\")\n",
    "else:\n",
    "    print(\"no expected_value in explainer\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# examining the shap_values, as Explaination or List, Array from kernelExplainer\n",
    "# explainer.shap_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shap.initjs()\n",
    "# shap.plots.force(explainer.explainer.expected_value[1], \n",
    "#                  explainer.shap_values[1], explainer.data, link=\"identity\")\n",
    "# shap.plots.force(explainer.explainer.expected_value[1], explainer.shap_values[1][0], explainer.data.iloc[0], link=\"identity\")\n",
    "# shap.plots.force(explainer.explainer.expected_value[0], explainer.shap_values[0][0], explainer.data.iloc[0], link=\"identity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PassengerId  Pclass                  Name   Age  SibSp  Parch             Ticket   Fare Cabin Embarked  Sex_female  Sex_male  Survived\n",
      "590          591       3  Rintamaki, Mr. Matti  35.0      0      0  STON/O 2. 3101273  7.125   NaN        S           0         1         0\n",
      "\n",
      "predicted probability for label 0 == Perisched\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "predicted probability for label 1 == Survived\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pclass = 3.0 doesn't play any rules for our passenger at X_valid dataset with rank pos 0\n",
    "idx_pos = 0\n",
    "passager_df = get_details(df=X_valid, idx=idx_pos, org_df = train_X_y_raw_df)\n",
    "print(passager_df)\n",
    "explainer.force_plot(idx = idx_pos)\n",
    "explainer.waterfall_plot(idx = idx_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PassengerId  Pclass                        Name  Age  SibSp  Parch     Ticket    Fare Cabin Embarked  Sex_female  Sex_male  Survived\n",
      "788          789       3  Dean, Master. Bertram Vere  1.0      1      2  C.A. 2315  20.575   NaN        S           0         1         1\n",
      "\n",
      "predicted probability for label 0 == Perisched\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "predicted probability for label 1 == Survived\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pclass = 3.0 doesn't play any rules for our passenger at X_valid dataset with rank pos 100\n",
    "idx_pos = 100\n",
    "passager_df = get_details(df=X_valid, idx=idx_pos, org_df = train_X_y_raw_df)\n",
    "print(passager_df)\n",
    "explainer.force_plot(idx = idx_pos)\n",
    "explainer.waterfall_plot(idx = idx_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# idx_pos = 178\n",
    "# passager_df = get_details(df=X_valid, idx=idx_pos, org_df = train_X_raw_df)\n",
    "# print(passager_df)\n",
    "# explainer.force_plot(idx = idx_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# summary_plot is a wrapper of beeswarm_plot(), kernel shap has no beeswarm_plot\n",
    "explainer.beeswarm_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x430 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explainer.summary_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "predicted probability for label 1 == Survived\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "auto_interact_features is ignored.\n"
     ]
    },
    {
     "data": {
      "image/png": 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UX375hUGDBvHpp59StmzZVG2siIiIiKQfj4LiJ598Qo4cOZg9e7Zbz+Hhw4cZPHgw06dPZ+LEianSSHGyO0yWHjbZGWpSv4hB+zIGhmEku57oOJOvDpj8E27SoYyNukWSX4eIiIhkfx4FxZ07d9KzZ0/L28vlypWjR48eLFq0KMWNE1e9Vjj45qD57yOTp6obTG3tlaw6rsaZNPzKzq5Q5+M3ttiZ1srG0Boerb0uIiIi2ZhH6SAmJoZ77rkn0efz589PTEyMx40Sd3vOmzeFRKdP9picCDcTOcPat4fMhJB43aubHTjM5NUjIiIi2Z9HQbFYsWJs2rQp0ec3bdpEsWLFPG6UuDsV4R7kHCaciUpmPZHuZeejIdbuYcNEREQk2/IoKLZv354tW7bw8ssvc+TIEex2O3a7ncOHD/Of//yHrVu30rFjx9Ru612tSXGDINddEikRAPcXSl49D5U1sN0yJLFtsEGuHBqnKCIiIq48GqP4xBNPcPDgQdasWcOPP/6YMKHCNE1M06RVq1Y8/vjjqdrQu52fj8H3Xb14eq2d3eehbmH4pI0XOW5NfXdwb36Dee1tjP3FwclIaFfaYFZbjU8UERERd4Zpej44bevWrWzYsIFTp04BzlvSzZo144EHHki1BmZlkZGRNGvWjPXr1+Pv759q9TpME5sHs53Tqh4REZFsw+juXmbevRN0k9Sj+Prrr9O9e3eqVq0KOGc9ly5dmnr16lGvXr00baC4S61wp5AoIiKSfVy7do0LFy5QoEABfHx8UqXOJN1zXL58OSdPnkx4PHToULZt25YqDRARERERz+3cuZMWLVoQEBBAyZIlEyYch4aG0rJlS9auXetx3UkKinnz5uXixYsJj1Nwt1pEREREUsmuXbto3LgxR44coU+fPi7PFSxYkKtXrzJ37lyP60/Sref77ruPTz/9lLNnzxIYGAjAzz//zIkTJxI9xzAMBg0a5HHDREREROT2XnnlFYoWLcoff/xBTEwMn376qcvzLVu25JtvvvG4/iQFxZEjR/Laa6/x9ddfY5omhmGwbt061q1bl+g5CooiIiIiaWvjxo2MHTsWf39/rl275vZ8yZIlOX36tMf1JykoFi1alBkzZhAXF8fFixd56KGHGDlyJE2bNvX4hUVEREQkZWJiYsiTJ0+iz4eHh6eo/mSto+jt7U3hwoXp2LEjVatWpUiRIil6cRERERHxXNmyZfn9998Tff7nn3+mSpUqHtfv0UrLr776asJSOSIiIiKSMXr16sW8efNcZjZf3wjlvffeY9WqVTzxxBMe1+/RziwiIiIikvFGjRrFjz/+SNu2balUqRKGYfDCCy9w/vx5zp49S+vWrRk2bJjH9WvvNhEREZEsysfHhx9//JFJkybh6+tLrly5OHToEPnz52fChAksX74cm83zuJeiLfzk9tJqCz8RERFJI9rCz4V6FEVERETEksYoioiIiGQRv/zyi0fnNWnSxKPzUhwUT5w4wcWLFylXrpxur4qIiIikoWbNmiXMak6K6xul2O12j17P46C4ceNGJk2axJkzZwD43//+R506dbh06RIDBgzgmWeeoVWrVp5WLyIiIiK3mDNnTrq+nkdBcceOHYwaNYqKFSvSsWNHZsyYkfBcvnz5KF68OGvWrFFQlBSJijXJmQNy2JL+m5OIiEh21rdv33R9PY8ms8yaNYsKFSrw2Wef0bNnT7fnq1WrxsGDB1PcOEmef8JNomKTNok9KtbkRHjmnPB+Nsqk7bd2/D+0U2iqnQ93OjK6SSIiInclj3oU//zzT4YMGZLoujyFChXiwoULKWqYJN2BiyY9v7ez7wL4e8OrDWyMqpP47wATf3Pw+hYHUXFwXwH45iEvKubLPL12Q9Y4WBPiDLGXYuD5nx3cX9CgUfHM00YREZHMxG63c+DAAS5fvozD4d7Bkq6TWRwOBz4+Pok+HxYWhre3t0cNkuTrt8oZEgEi42D0BgdNixvUKeIerLadMfm/X258gfach/6r7GzulXkmwK885t7TufKYg0bFvTKgNSIiIpnb+PHjGTduHOHh4Yke4+lkFo9uPZcuXZo//vgj0ec3btxIhQoVPGqQJE9krMm2M+7lP/1jfVv5p+Pu5VtOw9W4zHMbOjjQqky9iSIiIreaPXs2Y8eOpUaNGrz11luYpsnw4cMZPXo0+fLlo3bt2nz66ace1+9RUOzcuTM//fQTS5Ys4frGLoZhEBMTw8SJE9m7dy9du3b1uFGSdLm9oUSAe3mlfNbHV7QoLxUIuTJPhyLjmtjwuikX1igIvSsrKIqIiNxq2rRp1KtXj3Xr1jF48GAAOnTowLhx49izZw8hISEe9yaCh0GxR48etGnThrfffpuuXbtiGAYvv/wyTZs25ZtvvqFjx460a9fO40ZJ0tkMg/eb2chx0yf5YLDBQ2Wtg1XncgZtg288522D95vZkrUmU1rrXsHG/v5eTGxqY34HG1t6eeHnk3naJyIikln89ddfCROLr/9bfj0YFilShMGDBzNlyhSP6/e4H+nNN9+kRYsWrFy5kuPHj2OaJvfeey8dOnSgZcuWHjdIkq9HRRsPFDFYc9wkOBBalDQSDX45bAY/dLfx0z8mx8OhbbBB8YDMF8Iq5jMy1QQbERGRzMjLyws/Pz+AhP9evHgx4fng4GD+/vtvj+tP0Q3H5s2b07x585RUIamkRKDBwGpJC1aGYdCqlEKYiIhIVleyZEmOHTsGQM6cOSlRogQbN27k0UcfBWD79u3ky5fIeLQkyEQj00REREQkOZo0acKKFSt49913AejZsycffPABV69exeFw8MUXXzBgwACP6/coKM6cOfOOxxiGwaBBgzypXkRERESS4Pnnn6d69epcvXoVX19fXn/9dQ4dOsTcuXMBaNOmDePGjfO4fsO8Pm05GerUqZN4hYaRsAH1b7/95nHDsoPIyEiaNWvG+vXr8ff3z+jmiIiIyJ0Y3d3LzEXp344UunLlCl5eXinOHx71KC5btsytzG63c/LkSb788ksiIyN57bXXUtQwSTm7w+Sbgybbz5rULWLQs4KBl83AYZr8GHJjMkupPBqvKCIikp3kyZMnVerxKCgWKVLEsrx48eI88MADPPnkk3z//fc8/fTTKWqcpEzvFQ6+Pni9w9hkWSWDz9vbaLfIwdp/F97OYYOvOtjoUdGjlZJEREQkE4iOjiYkJISLFy9idbM4Xbfwux3DMGjZsiXz5s1TUMxAf100bwqJTl8dMKlXxEwIiQDxDhi53kH3CokvqSMiIiKZU1RUFC+88AKff/45cXFxbs9fHw7o6aLbaTLrOS4ujitXrqRF1ckWEhLChAkT2LNnD35+frRv355hw4bddi/qCxcuMH/+fLZt28bJkyfx9/enZs2aPPPMM4n2pmY256Ksh57uu+Be/k8ExMSDr7bnFhERyVKGDh3K/Pnz6dq1K40bNyYoKChV60/1oPjnn3+yYMECgoODU7vqZAsPD2fo0KGULFmSiRMnEhoayuTJk4mJiWHMmDGJnvfXX3+xbt06OnXqRLVq1QgLC2PWrFn07duXr7/+OtU/hLTQoJhBET84E3WjrJg/9KtqMHOva1hsWAx8vdWbKCIiktUsXbqUgQMHJmlFGk94FBQ7d+5sWX7lyhWio6Px8vLiP//5T4oalhoWLVpEVFQUEydOTBjUabfbGT9+PAMGDKBAgQKW59WoUYNvv/2WHDluvD333XcfHTt2ZMWKFTz++OPp0v6U8PEyWNndi2d/srPjHNQpDB+18KJ6QYP3msFrmx1ExDr3UZ7zoFdGN1dEREQ84O3tfdvVaFLKo6BYqFAht/FshmFQsWJFSpUqRdeuXSlatGiqNDAlNm/eTN26dV1m/rRu3Zp3332XrVu38tBDD1meFxAQ4FZWqFAhgoKCOH/+fJq1N7XVKGiw8TH3j3hEbRtPVTe4FAPFMuH2fSIiIpI0LVq0YNu2bQwePDhN6vcoKM6YMSO12wE4B2R++eWXbN26lUuXLvH6669z3333ERYWxsKFC2ndunWybmmHhITQqVMnl7KAgADy589PSEhIstp2/PhxLl26ROnSpZN1Xmbl621QTGMSRUREsrT33nuPJk2aMGXKlDvOwfBEptnC7/LlywwcOJBTp05RokQJTp06xbVr1wDImzcvy5cvJyIighEjRiS5zvDwcMvewYCAAMLDw5Ncj2maTJo0iQIFCtC2bdtEj4uNjSU2NjbhcVRUVKLHioiIiKRUyZIleeedd+jTpw+jR4+mSJEieHm5DikzDIMjR454VH+mCYpTp07l4sWLzJ07l8KFC9O6dWuX55s2bcr27dszpG0zZszgt99+46OPPsLX1zfR4+bMmZNmg0mztetT9r00VlJERCQ5PvvsMwYOHIiPjw8VK1bMmFnPderUSfYae4ZhsG3btiQfv2nTJnr27EmlSpUICwtze75YsWIsX748WW0IDAwkMjLSrTwiIoLAwMAk1bF48WJmzpzJf//7X+rWrXvbY/v370/v3r0THkdFRdGhQ4dktfmuEm+HEXNg1lowDBjSBib2UWAUERFJorfffpsaNWqwevVq8ufPn+r1JykodujQIc0XYw4LC6N48eKJPm+z2RJuRSdVcHCw21jEyMhILly4kKSxjuvWrWPcuHEMHTo00ZneN/Px8cHHxydZbbyrvb8MPlp54/Hk7yG4IDyncC0iIpIUp06d4oUXXkiTkAhJDIrpsW/zPffcw6lTpxJ9/uDBgxQuXDhZdTZo0IA5c+YQERGRMFZx7dq12Gw26tWrd9tzd+zYwcsvv0yXLl0YNGhQsl5Xkuj7He5ly7YrKIqIiCRRxYoVuXTpUprVn2k2+G3YsCFLly7lwoULbs/t27ePFStW0LRp02TV2b17d3Lnzs3IkSPZunUry5YtY8qUKXTr1s1lDcWnnnqKLl26JDw+duwYo0aNokSJErRv3569e/cm/Dl58qTH1yi3KGHx20+Je9K/HSIiIlnUSy+9xNSpU9Msn2SaySxPPvkkv/zyC71796ZJkyYYhsHy5ctZvHgx69ato0CBAvTt2zdZdQYGBjJt2jQmTpzIyJEj8fPzo0uXLgwbNszlOLvd7rIH4r59+4iMjCQyMpKBAwe6HNuxY8d06WG9K7zYFVb8DuHRzsdB/vB/XTO2TSIiIlnIX3/9RbFixahcuTJdu3aldOnSlrOe//vf/3pUv2GapvWmwHewa9cuPvvsM/bt20dERAS3VpPcySwAZ8+eZcKECfz66684HI6Eeho2bMiLL75IoUKFPGlqhomMjKRZs2asX78ef3//jG5O5nT6EizYBDYDHm0EhTP/9ogiIpKNGd3dy8xFiR5+4MABnn32WTZv3kxAQAB9+vThrbfeuuOcheDgYI4fP+5WfvXqVXLlypXk5tpsd745bBiGS4dYcnjUo7hz506GDRuGv78/VatW5ddff6VOnTpER0ezf/9+ypUrR6VKlZJdb+HChXn//feJjIzk+PHjmKZJiRIlXHZWkWymaD4Y0enOx4mIiGQyly9fpkWLFpQvX57vvvuOU6dOMWLECKKjo/n444/veH6PHj0YOXKkS1nOnDmT1YZjx44l6/jk8igofvrpp+TPn5958+ZhGAatW7emf//+1KlTh61btzJmzBjGjBnjcaP8/f259957PT5fREREJK1Nnz6d8PBwFi9eTL58+QCIj49n2LBhvPTSS3fczrhQoUJ3nFx7J6VKlUrR+Xfi0WSW/fv306VLF4KCghKWzbl+q7hevXq0b9+e6dOnJ6vOs2fP3vFPWFiY2y1uERERkYzwww8/0KpVq4SQCPDwww/jcDhYs2ZNurfn8OHD/Prrr1y5ciXV6vSoRzE2NjZh1vD1e/DR0dEJz1eoUIGVK1danpuYhx56KElrNebKlYs6deowZMgQKlSokKzXEBEREUktBw4cYMCAAS5lefPmpUiRIhw4cOCO58+fP5+ZM2fi7e1NkyZNGD9+PNWqVUt2O5YvX87zzz+fsHb0jz/+SIsWLQgNDaVBgwaMGzeOHj16JLte8LBHMX/+/ISGhgLg6+tLQECAyx6CoaGh5MiRvAw6aNAgKlWqhGEY1K9fn0cffZRHH32UevXqYRgGlSpVonfv3tSqVYtNmzYxaNCgJH0IIiIiImnh8uXL5M2b1608KCjojmsbdurUiY8//pi1a9fyv//9j8OHD9OoUSOOHj2arDasX7+erl27ki9fPl599VWXO68FCxakbNmyLFiwIFl13syjHsUqVaqwe/fuhMcPPPAAX375JYULF8Y0Tb755ptkjzEsU6YMCxcu5Msvv6Rs2bIuzx0+fJghQ4bQp08fnn/+ef7++28GDhzIrFmzmDRpkieXICIiIuLGmPi1W1laDHr78MMPE/7euHFj2rRpQ6VKlZg0aRJTp05Ncj1vvPEG1atXZ9u2bVy+fNltCb/69evz+eefe9xOj3oUO3fuTN68eYmJiQHg6aefJmfOnLz++uu88cYbeHt789xzzyWrzjlz5tCzZ0+3kAhQrlw5evbsyaeffgpA+fLl6dKlC3/88YcnzRcRERFJsaCgIMvxgJcvX3YZt5gURYoUoVGjRvz+++/JOm/79u307t070WVyihcvztmzZ5NV58086lGsV6+eyyyd4sWL89133/Hbb7/h5eVFjRo1kr1u4PHjxwkKSnwNvaCgIJf1hkqXLu0yLlJEREQkPVWqVMltGNyVK1c4c+aMR8sEesLhcNx2SZ0LFy7ccU3H20m1Lfx8fX1p2rQpjRo18mhx6Xz58rF+/XrL50zTZP369S7p/PLlywQGBnraXBEREZEUadeuHWvXriUsLCyhbOHChdhsNtq0aZOsuk6fPs2mTZuoU6dOss6rXLkyGzduTPT55cuXU7169WTVeTOPgmLv3r1ZsGCByxuTUp07d2b79u08//zzbN26ldOnT3P69Gm2bNnC888/z++//07nzp0Tjt+0aRMVK1ZMtdcXERERSY6hQ4cSEBBAly5dWLNmDXPmzGH06NEMHTrUZQ3Fli1bUq5cuYTHX331Fb1792b+/PmsW7eO2bNn06RJE7y8vNwW4L6TgQMH8u233zJ79myXXe2io6N57rnn2LJlC4MHD/b4Gj3awq99+/acP3+eHDly0KBBAzp06ECTJk2SPdP5Zg6Hg/Hjx/Pdd9+5LZNjmiZdu3blxRdfxGazce3aNZYvX06lSpUy9cLc2sJPREQkazEmxbuVmaMSzzd//fWX2xZ+b7/9tsvt3mbNmhESEpKwfM3WrVsZO3Ys+/btIywsjLx589KiRQveeOMNjzrBHn/8cb788ksCAwOJiIigQIECXLx4EbvdTv/+/Zk9e3ay67zOo6Bomibbtm1jxYoVbNiwgZiYGAICAmjTpg0dO3ZMUXg7evQoGzdu5PTp04BzcGeTJk0oU6YMsbGxKbrPnt4UFEVERLKW5AbFzGLx4sV88cUXHDhwANM0KV++PH369KF7d4u9q5PBo6B4s6tXr/LTTz+xYsUKdu7ciWmalCxZko4dO9KvX78UNe66v/76i6VLl/Ljjz/y008/pUqd6UFBUUREJGvJ7EHxjTfeoFu3blStWhWAf/75hwIFCuDr65smr5fiySy+vr507NiRadOmsWzZMp566ikuXLjAtGnTUlTvlStXWLBgAb169aJv374sWrTIclFLERERkbvFa6+9xp49exIely5dmsWLF6fZ66VaRD558iQrVqzghx9+ICoqyuPxilu2bGHZsmX88ssvxMXFUbJkSZ588klatGhhucaiiIiIyN0ib968LpOJU3hj+I5SFBQjIyNZs2YNK1asYO/evQn3xIcPH067du2SXM/p06dZtmwZy5cvJzQ0NGFQ5+rVqxk2bBgtWrRISTNFREREsoWaNWsyYcIE4uLiEtaf3rhxI/Hx7rfMb9anTx+PXs+jMYobN25kxYoVbNy4kdjYWPLly0fbtm3p2LEjFSpUSHI9P/zwA8uWLWPnzp3YbDYaNWpEx44dadiwIWfPnqVr166MHz8+ywZFjVEUERHJWjL7GMXdu3fTrVs3jh07BjiXwrlTlDMMA7vd7tHreXTlI0aMwMfHh8aNG9OxY0fq16+Pl5dXsut55ZVXKFasGCNGjKBt27YagygiIiJyG9WrV+fQoUMcPXqUM2fO0KxZM15++WVatWqVJq/nUVAcM2YMbdu2JSAgIEUv7uPjw5kzZ9iwYQOBgYE0b96cXLlypahOERERkezMy8uL8uXLU758eZo2bUqzZs1o2rRpmryWR0GxR48eqfLiq1atYuXKlSxbtoxXXnmF3Llz07JlSzp27EiBAgVS5TVEREREsqt169Yl6/hz585RtGhRfvzxxyQN7cvQm+4BAQE88sgjPPLIIxw4cIClS5eyevVqli9fTlBQEIZhEBkZmZFNFBEREclWkjM9JcXrKKaWSpUqMWbMGFatWsUbb7xBmTJlAHjrrbfo1asXs2bN4siRIxncShEREZG7R6YJitf5+Pjw4IMPMm3aNJYsWcKAAQMIDw/nk08+oVevXhndPBEREZG7RuaZ722haNGiDB06lCFDhiQsxC0iIiIi6SNTB8XrDMOgQYMGNGjQIKObIiIiInLXSFZQjI+PZ8OGDZw4cYK8efPSrFkzrX0oIiIikk0lOSiGh4czZMgQjhw5gmmaGIbBRx99xMcff0zlypXTso0iIiIikgGSPJll9uzZHD58mIYNGzJ69GgefvhhoqOjefvtt9OyfSIiIiKSQZLco7hx40bq16/P5MmTE8qKFCnClClTOHfuHIUKFUqTBoqIiIiI5+Lj48mRwxn58uTJw5w5c7j33nuTdG6SexTPnTtHw4YNXcqaNGmCaZqcOXMmGc0VERERkdTQr18/oqOjE33+8OHDLpOBc+XKRd++fZPcwZfkoBgbG0uePHlcyq7v9RwXF5fUakREREQklcybN49atWqxe/fuRJ87dOiQx/WnyoLbhmGkRjUiIiIikgwrV67k0qVL1KtXjw8//BCAqKgo+vTpQ79+/ahUqRI7d+70uP5kLY/zxRdfsHr16oTHdrsdwzCYOnWqW2+jYRi8//77HjdMRERERG6vbdu27Nmzh8cff5wXXniBH374gcOHD3P06FFGjhzJO++8kzA+0RPJOvPgwYMcPHjQrXzv3r1uZeplFBEREUl7hQoVYs2aNTRp0oTVq1djGAYffvghTz/9dIrrTnJQ3L59e4pfTERERERS16VLl+jfvz+bN2+mcePG7Nmzh7FjxxIYGMgTTzyRorpTZYyiiIiIiKS/X375hRo1arBq1SrGjRvHhg0b2LlzJ1WqVKFfv3706dOHqKgoj+tP9aB48eJF5s6dS8+ePVO7ahERERG5SYsWLfD29mbjxo2MHj0agNKlS/Prr78yevRo5s+fz/333+9x/Z6PbryJw+Fg48aNLF26lM2bN2O328mdO3dqVC0iIiIiiejRowczZswgMDDQpdzLy4tx48bRsmVL+vbt63H9KQqKISEhLFu2LGFqdkBAAO3ataNly5Y88MADKalaRERERO5gwYIFt32+devWlmssJlWyg+LVq1dZs2YNy5YtY+/evXh5eVG9enUuXbrEyy+/TIsWLTxujIiIiIgkX0hICGvXruXcuXP07t2b4OBgYmNjOXv2LIULF/a43iQHxV27drFs2TJ++uknoqOjqVixIiNGjODBBx8kIiKCbt26edwIEREREfHMmDFjeP/99xPWt65fvz7BwcHExMRQpUoV3nrrLYYPH+5R3UkOik8++ST58uWja9eudOzYkXLlyiU8FxkZ6dGLi4iIiIjnPvnkEyZOnMhzzz1Hx44dadOmTcJzgYGBdOrUie+//97joJisWc/Xrl0jMjJSwVBEREQkE5g6dSpdu3blgw8+oGbNmm7P33fffZabpSRVknsUFy5cyJIlS/jhhx9YtmwZRYsWpWPHjnTo0MHjFxcRERERzx06dIinnnoq0ecLFCjAhQsXPK4/yUExODiY4cOH88wzz/DLL7+wdOlSZs6cycyZMylXrhyGYWCapscNEREREZHkyZUr120X1D5+/Dh58+b1uP5kL7idI0cOWrRowZQpU1ixYgVDhgwhOjoa0zR55ZVXGDVqFCtXrtTtaREREZE0VrduXRYvXmz5XExMDPPmzaNhw4Ye15+inVny58/PgAEDWLx4MdOnT6dly5Zs3bqVV1991WUwpWSQq9dgynJ4/AP4cAXExGZ0i0RERCQVjR49mi1btvDEE0+wZ88eAM6ePcvq1atp1qwZJ0+eZNSoUR7Xb5ipfL84MjKSVatWsWzZMj7//PPUrDrLiYyMpFmzZqxfvx5/f//0b0Cb1+HHmxbZfLAm/PDf9G+HiIhIFmFMincrM0elykZ2aWbGjBk8//zzxMbGYpomhmEA4OPjw7Rp0+jXr5/Hdad6UJQbMjQo7j4GNUa6l++ZDNVKpW9bREREsoisGBTB2Yu4cOFCDhw4gGmalC9fnocffphixYqlqN4kX/mVK1eSXXmePHmSfY6kkoiYRMqvpm87REREJM0VLlyYZ599lvj4eH777TdOnTpFWFhY+gXFVq1aJXRlJoVhGGzbts2jRkkqqF8ByhaGI2dvlJUvAvUqZFybREREJMXWr1/Pd999x3/+8x8KFiyYUH7s2DG6dOnCvn37Esr69u3Lp59+6vFrJTkodujQwSUoXrt2jR9//JF69eqRP39+jxsgacTLC1a/AmM+hx1HoE45GP8E2FI0f0lEREQy2GeffcaWLVv48MMPXcr79evH3r17adiwIQ888ACrV69m7ty5NG3alL59+3r0Wh6PUQwLC6N169ZMnTqVOnXqePTi2V2GT2YRERGRZMkKYxSrVKlCy5Yt+eijjxLKDhw4QJUqVWjSpAnr168H4OrVq9SsWZNixYrx008/efRa6l4SERERyULOnj1L+fLlXcrWr1+PYRgMGjQooczX15devXolLJvjCQVFERERkSzk2rVr+Pr6upRt374dgKZNm7qUlyhRwqMJydcpKIqIiIhkISVLlmT//v0uZZs2baJgwYKUKFHCpTw6Ojp9t/ATERERkYzTuHFjPv/884TZzYsXL+bvv/+mXbt2bsfu3bs3RUvkpDgoJmfJHBERERFJmbFjx3Lt2jWqV69OwYIF6dGjBz4+Powc6brRht1uZ9myZTRq1Mjj10ryNJ5HH33U5bHD4cAwDN588023++TgDJBfffWVxw0TEREREXelS5dmw4YNvP766xw+fJi6devyn//8h3vvvdfluHXr1nHPPffQuXNnj18ryUExKirKrfewcOHCmKZJdHS0xw0QERERkeSpXbs233///W2PadWqFXv37k3R6yQ5KN6pMSIiIiKSvWgyi4iIiIhYSpWlxuPj49m/fz/nz5+ndOnSlC1bNjWqTRUhISFMmDCBPXv24OfnR/v27Rk2bBje3t63PW/hwoX8+uuv7Nu3j7CwMMaNG0erVq3SqdUiIiIiGS/JQXHHjh2sW7eOgQMHki9fvoTyU6dOMWrUKI4cOZJQ1qFDB1599dXUbakHwsPDGTp0KCVLlmTixImEhoYyefJkYmJiGDNmzG3PXbFiBQANGzZM+LuIiIjI3STJQXH58uXs2bOH0aNHu5Rfn3FTvXp1qlatypYtW1ixYgW1atWiY8eOqd7g5Fi0aBFRUVFMnDiRPHnyAM6p4uPHj2fAgAEUKFAg0XM//fRTbDYbp0+fVlAUERGRu1KSxyju37+fevXquZSFhITwxx9/ULNmTWbNmsXw4cOZO3cuJUqUyBThavPmzdStWzchJAK0bt0ah8PB1q1bb3uuzabhmyIiInJ3S3IaunjxIiVLlnQp27FjB4Zh0KVLl4SyXLly8eCDD/L333+nWiM9FRISQnBwsEtZQEAA+fPnJyQkJEPaJCIiIpJVJPnWc2xsLDlz5nQp+/PPPwG4//77XcoLFSpEZGRkKjQvZcLDwwkICHArDwgIIDw8PNVfLzY2ltjY2ITHUVFRqf4aIiIiIuklyUGxcOHCHD161KVs165dBAUFUbhwYZfymJgYy4CW3c2ZM4eZM2dmdDNEREREUkWSbz3XrFmTFStWcPjwYcC5LcyJEydo0KCB27GHDx++7USR9BIYGGjZsxkREUFgYGCqv17//v1Zv359wp/MME5TRERExFNJ7lHs168fP/zwA7169SJPnjxcuXIFb29vHn/8cZfj7HY7v/zyCy1atEj1xiZXcHCw21jEyMhILly44DZ2MTX4+Pjg4+OT6vWKiIiIZIQk9ygWK1aMGTNm0LBhQ/LkyUODBg345JNP3BbX3rFjB3ny5KFp06ap3tjkatCgAb/99hsREREJZWvXrsVms7nN4BYRERERV8namaVKlSpMnjz5tsc88MADfP311ylqVGrp3r07X3/9NSNHjmTAgAGEhoYyZcoUunXr5nJr/KmnnuLMmTMsWbIkoezPP//k9OnThIWFAbBv3z4AgoKCqFWrVnpehoiIiEiGSJUt/DKrwMBApk2bxsSJExk5ciR+fn506dKFYcOGuRxnt9ux2+0uZd988w3Lly9PePzFF18AzhneM2bMSPvGi4iIiGQwwzRNM6MbkV1FRkbSrFkz1q9fj7+/f0Y3R0RERO7AmBTvVmaOytb9arel7UdERERExJKCooiIiIhYUlAUEREREUsKiiIiIiJiSUFRRERERCwpKIqIiIiIJQVFEREREbGkoCgiIiIilhQURURERMSSgqKIiIiIWFJQFBERERFLCooiIiIiYklBUUREREQsKSiKiIiIiCUFRRERERGxpKAoIiIiIpYUFEVERETEkoKiiIiIiFhSUBQRERERSwqKIiIiImJJQVFERERELCkoioiIiIglBUURERERsaSgKCIiIiKWFBRFRERExJKCooiIiIhYUlAUEREREUsKiiIiIiJiSUFRRERERCwpKIqIiIiIJQVFEREREbGkoCgiIiIilhQURURERMSSgqKIiIiIWFJQFBERERFLCooiIiIiYklBUUREREQsKSiKiIiIiCUFRRERERGxpKAoIiIiIpYUFEVERETEkoKiiIiIiFhSUBQRERERSwqKIiIiImJJQVFERERELCkoioiIiIglBUURERERsaSgKCIiIiKWFBRFRERExFKOjG6ASHby2xmTabscxDlgQDWDFiX1u5iIiGRdCooiqWTLaZOmC+zEOZyPv/zLZGlXeKiswqKIiGRN+hdMJJV8/IcjISQCmMCU380Ma4+IiEhKKSiKpJKYePeyq/FZOyh++ZeD+z+Pp8LseN7a4sBhZu3rERGR5FFQFEkl/aoabmX9q2bd/8XWHnfQe4WDP0Lh78vw318djNumoCgicjfJuv+KiWQyD5W18VVHG/WLQu1CML21jUH3Zd3/xb740z0UfvGnw+JIERHJrjSZRSQVPVrJxqOVsm44vFmgj0VZzvRvh4iIZJzs8S+aiKS6YTVs+HvfeGwAo2rrR4aIyN1EPYoiSXEtDnJ63/m4bKTSPQY7nvBi+m4HkbHwRBUbTUq4j8MUEZHsS0FR5HY2/QVDp8P+E1CjNMwYCnXKg2nC+n1w4gK0qQGFg5zHX4uDH3ZCnB061ILcmfBe7YGTsPUQ1CwN1Uvf9tCK+QwmN/dKp4aJiEhmo6AoAnAhHJ6b7Qx5pQvCu49D4yrQ7k2IjHEes+sYtHkDTs2ERi/DH8ec5TYDvhwBTatA4//A4TPO8iJBsOFNKF/U+jVPXoBnZ8HP+6BKcZjUFxpWhi0HYdRnsO8ENLsXPhoEJQukznWO/w5e/OLG49FdYEKf1KlbRESyHQ04EgHo8yF8tRHCopwBsNO7MG/DjZB4XVgUjJp7IyQCOEwY9D+YuORGSAQ4cxle/ybx1+w2AZb8BuHRzh6+dm/BsXPw4Juw+aCzfNl26Do+da7x/BV4ZYFr2aSlcORs6tQvIiLZjnoU5e6zbi/MXQ+5feCpByG4IKz6w/WY2HhYsMn6/DW73MsiY5w9gLf606IMICQUth92LYu4Cu8vcwbEm+086gyg5YpY17X5AMz+CbxsMKQN1CrrvDX+2c+wdDsUywcjOjmDYuwtq4KbpvNWdNnC1nWLiMhdLdsHxZCQECZMmMCePXvw8/Ojffv2DBs2DG/v209MME2TuXPnsnDhQsLCwqhQoQIjRoygWrVq6dRySRPfboaH33MGJIDP1sGmtyHA1z2gVSvpHId4qwpF4cg51zLDgJbV3ENk86rW7ciTG7xzQNwtwa34Pe7H5vCCvH7W9azZ5eyJdDhuXM+GN53B942bejO//hV2THSGSftNayEaQJ1y1nVnpD0hznbWLJPRLRERuatl61vP4eHhDB06lPj4eCZOnMiwYcNYvHgx77///h3PnTt3Lp988gm9evVi8uTJ5M+fn2eeeYaTJ0+mQ8slzbz//Y2QCHA1FmauhTFdXI+7t4Rz7F6hvK7lpQpAvYru9RrAM+3g0UZg+/d/q3b3w6uPWLcjyN95/M3qV4RRnaFRZdfyp9pC/kDreiZ/fyMkgjN4frgCpix3Pe5iBHy00jUkgnND6r3/WNedEa5EQZP/QPURcP8oqDPa2RMqIiIZIlv3KC5atIioqCgmTpxInjx5ALDb7YwfP54BAwZQoID1BIFr164xZ84cHn/8cXr37g1AzZo16datG1988QUvvvhiul2DpLJbxxxeL3uph7P36oedUKYQ9G8BuXzg8P+ck1x2HHZONJncH2b+6F5HgK/z+K9GwAcDnIGsaL7bt+W9fs5QuG4vVC4O/VqAlxesecV5a3z/P9D0XuheP/E6oq5ZXM9V91vMgDMVWshMM7MnLYWNf954vOMIvLkQPhyUcW0SEbmLZeuguHnzZurWrZsQEgFat27Nu+++y9atW3nooYcsz9uzZw9RUVG0atUqoczb25vmzZuzbt26NG+3pKE+TWH05+5l4OwBbHe/63P+vvDpM65lvZrAu985J6tcN7zjjZ7EW3shE2MY0K2e88/NfHPC0LZJq6NPU9dgBdC3ORS7Bz5Zc6Mspzc82wEOnYHlO26UN6jo7MnMLLYeSlqZiIiki2wdFENCQujUqZNLWUBAAPnz5yckJOS25wEEBwe7lJcuXZqvvvqKmJgYcuXK5XZebGwssbGxCY+joqI8brukkZGdnf/9bB345YIXHoLWNZJXxz0B7Hm5H17jF3PPlTCOVK9Czec6kRto8XU8G044++6qF4A/+uYg3mHy5hYHCw6Y3OMLYx+w8VBZGxeiTV7c6ODnf0yq3GPwdiMb1QsaHLpk8upmB/sumDQrYfBGQxtBuQxWHHHw7m8OzkfDwxUNXqlvw9vH4n9hnxycj7cxvuMTLK1Sm2Lhlxi7fgltgX3TR9Hzq2iOOXJRzLjG5118aHi7a92wHz743tnr2qcZPNEs8WMjo6HMU3A+Ary94OMnYXCb5L23tcrC2j3uZcl1LgzeW+acTNSimvM2v8/dtWC6iEhqyNZBMTw8nICAALfygIAAwsPDb3uej48POXO63pILCAjANE0iIiIsg+KcOXOYOXNmyhsuaccwYFQX5x8P/fP7KSo+/xE57c7bu4U3bmZLdxvvDX+OdTdNct51Hup+EU+Lkgbjf/v3tu9l6LrEwdbeBqM3OFh/wll+7IrJ1jN29vez0exrB2f+/R1j3wWTPy86mNTURuclDuz/VvPWVpOr8Q5GvbmMW+crn3l1MWMrtGRu02YAHC5QhK0ly/P1RxvoVaI10fiDDY6Sg2bfQ8RzDnLlsBiu/Nvf0Oo1iLc7H6/d47zVnVhvZ94+N8ZAxtlhyHSoEQx1KyT5veX/ujhf5/cjzsf3loBXH076+eBc9Lzxy/D3v0sVrfjdWd/8F5JXj4iIZO/JLOmtf//+rF+/PuHPihUrMrpJkgaOzt6SEBKvq/3LFlYesrsd+/s5+Hy/69hAuwnTd9sTQuJ1F6/C+N/MhJB43c//mEzbZU8IidfN+9PkRLyP22sevurNghqu/YTXvH2YEluC6FuGLsY74H9/JDJ2cdbaGyHxuumrrY/946j7RBmAB16E+15w9kwmRb4A2D4BNr8LG9+GPZNv7HqTVD/svBESr1vwK4SGJa8eERHJ3kExMDCQyMhIt/KIiAgCAxOZRfrvebGxsVy75jpRICIiAsMwLHspAXx8fPD390/44+eXyJImkrXlcf9cI3L5Ylj0ytkM8HfPcuTxAS+LbZMDLI41gMCc7gf7e8P3tRviMG48F2+z8VPtethM99D2d8mS7pUD/t6psH/zyQuJP7f3ODz0jvvyQ4kxDOe4yUaVb4z7TA6riTwOh7OXU0REkiVbB8Xg4GC3sYiRkZFcuHDBbfzhrecBHD9+3KU8JCSEwoULW952lrtH9ecaczJffpeyP/p2Zngt9/+depaH0XVcy/PmhOfu96JfVdeAVi0/vFjXoPItk6Ufrmjw/P028t3ytRtVx8YDvavRduBLfF29PvNrNqLl4P/Ssl81fG9JnF52OzO75+aeW+rw84Yn70vkQp9s5VzD8WaJ3Xa+fIfxuBFX4adbxh6evwLvfAtPz7BexDwp4uPhuVlw7/PQc6JzK8b290PBPK7HPVjTOcEnMbuPwcg58OI8+Pu0Z20REcmGsvUYxQYNGjBnzhwiIiISegHXrl2LzWajXr16iZ5333334efnx9q1a6lQwTm+Kj4+nnXr1tGw4W2H/stdIKiIPzHbJ7D2rVXkOHWBHJ3r0HLYA7QEcnrbeX+7icOEflUNPmzpDFrF/OHrfyezPF3TRqk8Bp+0tvFAEZN1/5hUvsfgmZoGvt4GGx41mPy7g/0XoGkJZ7mPl8H2x7343x8Ozl91hseOZW1QozS+/j7MKt4JLwPeaZGHho0Kc7EV1Po8nt3nIVcO+PhBLx4sk4MDAxz0XmmyO9SkUj6Y286GLbFeuzrl4afX4IPlNyazPN7U+tiGle78xt0c1MKioMZIOH3J+XjqKpgyAJ7rmNSPwem+F+CvU86//3kCVu6ES585FzQPvWn9xaK3uX29bi+0ffPG4ucf/wCb34H7gpPXFhGRbMgwTTORAUpZX3h4OA8//DAlS5ZkwIABhIaGMnnyZB588EHGjBmTcNxTTz3FmTNnWLJkSULZZ599xowZM3j22WcpV64cCxcuZNu2bcyfP5/ixYsn6fUjIyNp1qwZ69evx9/fP7UvT7K4WLvJ//4wWXfCpHI+GFHbRiG/VLgNnBEOn4HyTyf+fKc6sHTsjcdvfQP/vWXfab+cEPlV0l/zrxNQ5Xn38scaO/ftvtXFuc4xkLdq96b7Fo4DWsLs21yPiGRbxiT34SvmqGzdr3Zb2frKAwMDmTZtGhMnTmTkyJH4+fnRpUsXhg0b5nKc3W7Hbncdv9S3b19M0+SLL77g8uXLVKhQgY8++ijJIVHkTgascjD/L+fvad8fgSWH7ezt54WP1eDFzO70RevyAF+YNcx9rchf/nI/Nuqac9ccI4nXv+uYdfnPe63Ldx6FVtXdyy9GJK1MROQulK2DIjjXPpw6deptj5kxY4ZbmWEY9O/fn/79+6dV0+QuFhpl8tUB1878Q5dh1TGTTuWyYFBsUhUMiPHy5vfiZQi+FEqx8Mvw4QB42GK4RtUS8ONu1zKrJXpup1EV6/KKRZzrKN4qT27r4x9tBNsPu5Y9oiEmIiJwFwRFkcwozgEOi0EfMVY772URGxa8TY8/C3HBPxAvu52Rp7Yzvl8j64Nf6ATT1zj32r7uqQeT3psIUCK/c2eZzQdvlOUPgLnPORf+vvn9zevnHHNpZXhH52SbWWvBJwc818F5+1pERLL3rGeRpLoaZ/LiL/FUnB1Ph0Xx7Aq9/dDd386YdF5s5/7P43n1VzuxNy1yuPe8yc5zNx7H2R20X2Qn9wfx3PNxPBN+s1MswKBxMdc68+aEDmUM/r5s0nuFnRpz43n+ZzthMTfqOnzZZOtpE/tNKXPaLgc1P4+n9rx45u67sSxOTJyDz/Y6+O2MxfqGSTRmg53AD+Px/SCezovtOBzWdZmmyYAr5bng71x2yu7lxYSS9dh6OpH3sUR+2PKuM5A1qQLv93fuo51cq1+Bl3s4l9IZ0gZ2vgfBhWDJi1AkyLlDzL0l4PdJiddhs8Grj8CJmXBkGjyfzAk1IiLZWLaezJLRNJkl63jw23hWh9x4nNML/hnsRUGLySUnwk0qz7ETFXejbEh1g4lNbXRafGO3lfsLwcpuXvRcZmfjKdc6FnS0MWi1g8g41/JjTxo0/Mrk9E3Lf7YqZbCqu40nVjoSbleXzgMrunmx+bTJoNWu4e3bTjbiHdB7xY2dXKreA3v6eWEko8fugx0OXljvWneHMgbLu3m5HRsaZVJomvs6hR80t/G8xbJBIiKZlSazuNJPcLnrRcWaLiER4JodJm637j1bcMB0CYkAn+0zmbTd4bLbys5z8NpmB5stluV79Vf3kAjQ7wfXkAiw9rjJx384XMY0HrsCz//sYO5+9zbO3W8ycLXDZSeXfRfhzS3J61mcusv9+LXHrX+vzJ8bSlmsYX9/oSw43lJERBIoKMpdLzzWOvwcT2Q7cB/3DjV8vGDbGfd6tp0xLYfdJdaxd+sWe+DcmWX3effybWdMcudwr8jXyz3IAvyYSMhLTE6LX6BvnpB9880Im+FcF/L6Ot8GMKyGQePiCooiIlmZgqLc9Qr5GQRabJ3Xu7J1yOlV2aCAr2vZ0zUMahd2P/7+Qgady7qXT25uYLOo/v1mUOmWnVl6VDBoYhG47i9k8EIt13q8bfB8LS9yWYTZ+kWTF9reaOD+46FfVYOrcSZPrraTe4qd/P+LZ/w2Z89j29I2Tg31YlV3GwcHevG/VhaNuEWc3eTKNY1+ERHJrBQU5a5nMwzmd7Dhc9P/DT0qGIkuU1Mgt8GW3l4Mq2HwUFmDmW1svNPYxsjaNuoUvnFcpXzwegMb33b24oVaBkX9nGVLu9h4sLQXn7fDJeQNqgaNiudgwyNejKlr0KGMc9zjvPY2eld2vtZ1hf2c4//alrax4REv+t1rMKiawa+PedGgmMF7zW3c3PoSAfB2o+QFxa4VbHzzkI1K+Zy3ld9qaPC/Vl688quDWXtNYuLh4lV4caODRYecYTHAx6BtaRvlg+78WlN+d1Bomp28H9lp/rWdUxEKjCIimY0ms6QhTWbJWi7HmPxy0qRMHoNqBTy7ZWqaJtvOOJe/aVjMGULv9JrbzphUDDIonffOr7k71OTCVZNGxQxyWtx2vtm5KAef7Dapco9Bj4qp9zth8enxnLplHGW38rCoc9IHe28+ZdLwK9fJL+1KG6zsfudeyHT13CxYth0K5YX/PQm1y2V0i1JfXDzM/BF++ROqlYJn2kEeP+ei4x+tgIOnoWU16N8CvDLZ52PFboc5P8NPe6FiUXimPeS3GEArkghNZnF19165yC2Cchl0TuFi14ZhUK9o8l7zwdJJf83qBQ0gaccX8rPxSoOktyWprMY/3joB505WHXOfKLM6xMQ0zWTNzE5TLV6Bdfucfz9+Hh4YA4c+hrJF0v61Q8MgyB+80+FHdN+Pbmx5+PWvsHgbbHoLGr0EB/6drr9gE+w4AtOHpn17UuqZWTB99Y3HX/8Ku98HH++Ma5NIFqZbzyKSLL4W2aVIEvaovnmtydJ53I8PDiTzhMT4+Bsh8TqHCWPnp+3r7v8HaoyAQgOg6CD47Oe0fb2TF5wh8Ga/H4HxS26ExOtmr4XLyfyNIL2FRTkXTr/ZgVOw/PeMaY9INqAeRRFJlp4VDT7c6TpipUdFg9Hr45m040aZt+Hc6KRUAFy1O5f0ub8QzGzjxaOVDN7YAiE3zSx/pb7z99Z3tjr4+A/n8j6D7zN4vaGNc5EmDb5yEBLu/O32iSrwWfvb//jadsbkr4smTYvfuK1/LMxk6i4Hl2LgkUoGbYIT+V05PpGlhCJjbvuaKfbYZNh73Pn3C+EwcCo0rATlk9FNnRzRsc79tW913GKafbwDrll0J2cm1+Ig3n09TyKvpn9bRLIJBUURSZa6hV0f+9igdiGD3itcy+NMiIuDPy/dKNt5DjovsTP3QcMlJAJM/t0ZDl/edCOkvbXVJF8uk/d3ODj5b2eWA5j7J9xX0M6I2tZj5vr/YOez/c4AZDNgemsbbUoZ1P7CzqV/s96n+0xmt4UB1SzCYi4fKFsYjpx1LX+xq+XrpYozl26ExOscDuee2GkVFCsUhQfKw7a/b5QVzgtVilsfH5vJ95gslBfa1IA1u26UBflDp7qJnvL7WZMJ2x2cizLpXsHG0zWNO44tFrmb6NaziCTLR3+49kDFOmDQKotenEScjICXN7n3Yu0+D98ccO/J+/bQjZB4sw9+t56Ht+2MmRASwXnHePQGB9N23QiJ1yW2qDoAW8dBrTLObQCD/ODDgdDk3sSPT6l8Ac5Qc6tyaTwmculY6NfcGYy71IWf37CetBPgCwWywKSQBSOc2zmWLQzt74efXnPu9W3hSJhJkwV2vjlosuEkPPezg9d+9XzLS5HsSD2KIpIsYdfcy5LTAWMzwC+ReQUFc1uVGYB7KLRahxLgr4vux165BgcuuR97KuI2Dc0fCDtus0d0Mmw9bRIdb9KkuEGOxBqe0xvGPQ5DP7lxO7hDLWhdPVXakKhCeWHOs65llYs7Q+OS326Uvfs4+OZM27akhiD/JE+6mbff4bbI/bTdJm80SoN2iWRRCooikiy9Ktl4dbNrr8vQ6ga/nEraSlvP1DR4oopBnS9c62hQFEbV9WLxYXvC9oa5csD/1bWx57ydo1dc6xlZ2zpwNSnuXITccVNzggOtw2limS21RMSaPPitPWEbx9J5YG1PL8okthTS4DbQuAqs3e28LdymRvJSeGr6boyzHQdOQcv7oEqJjGlHGnJOnnL93uqms4grBUURSZaX6xnYTYN5f5r4e8OoOjaalgSrXr/K+WBEbYN8uQz2XnCGwdbBNkzTpFVJWPuP87hcXjChqY37Chjs6evFZ/sdxDugz702KuYzWP+IF02/tnPsinO8TL+q8Oz91uMTy+Q1mN7axugNDq5ccy4W/mVHLxb/7XBrY1CuVH1r3Hy003TZ6/vYFecYzK863mY9wsrFnX9S4GSEyfw/TUycOwmVDPQg/hgGtK7h/JMER8JMFhwwyeUFvasYFE7CTPiM9kQVg4nbXZd8GlYj87dbJD1pwe00pAW35W5ht9vJMdn9R8mjFeCrTu6/jy4/4uChxa49inULw7bHb/+7a8gVkzw5netP3kl0nMnpSCiT17nw+V8XTe6fZyfmpluN45vY+L+6aTdUu8dSO4v+dn1fKgTBwYGp9zv6hWiTizFQMZ/zPdl33rmYeXis83l/b9j4mBc1CjqfP3TJJF8uyJ/7xnsYZzf5+7IzVPv5JD8obT5l0nLhjff2Hl/Y1tuLsrdZRD4sxsGGE9C0BOTNlXHD5f84ZzJph4OzUc4dmYZWNzLPMk2SIbTgtqu798pFJNWsOGL9++aGU5bFbD3jfvxvZ5072xwJgzn7nDOg+1SxUSX/jX+0g/9df3HVMQcrj5qUCoTTESbrT8J9BQw+aG6QJ5eN6DiTz/aZ/HXJpHkJg67lofI9Bhse8eK9HQ4uXoVHKxkMui9tA0q9ooZbUKxXJPVCyKj1dj7caRLngCr3wJIuXkzY7kgIiQCRcTBum4N3GtvossTO3guQw+bcn3xycxs//WPyxEpnUArwgcnNbQy0mgl+G29tdbgE8ItXYfIOBx8nst/36PV23tvh7PE0gJG1TSY2y5hdX2oWMpjfIQvsOCOSQRQURSTFWgZbl5cKsC6vlM99bFiJANhzHhp+ZU+4FfjB73Z+fti5f/V147c5eHGj+8zUnaEmSw6bnB9m0vIbk61nnOUf/2EyopbBe829qFvE4OuH0i8UPF3D4Od/DH445rzW+wrAO41TJ5yuPOrgvR033sM/L8KQNQ7LcZeno0yGrXWw94LzcbwDpuw0aVTc5Om1DkKjneURsc462gYbFA9IeqA9Heke/E9HWR/7T7iDSTe12wQm7TB59n4HJQO1EIdIZqP/K0UkxX4OsS7feta6PCzGPVhExcF7Oxwu48Wu2WHCTUvYxDtM3v0t8eVLwq7B0z/dCInXffSHyWWL10xrvt7O/av/6u/F7094sauPF8WSEcCuC79m8u42B48tt/PRTgexdue+5Lf65aRpuQ1ll3I2y+OXHjYTQuJ1dhN+TeLEpOusXrNzWevr/PaQdd1fHzA5E2ny0kY7vZbbmf/njc95z3mTYT/aGbDKzoYTt2+b3WEya4/zvXrtVzsXojPf6KoDF02e+8lO35V2VltsZymSmahHUURSbOHB5B1/Mca9RzEiFs5b/KN+c1msHcItlue5WcgV97I4h/O81Ji88ucFk2d/drDtjEn5vM5gdeyKc7b1xy1tCbvA3KzSPUkLh+eiTJ772cGaEJPyQc4xlM1KGLT51s62f8PvggMmG05c35fc9f26Nz88XdPgVKTB1F0mpglDqhs8f7/BV3/BjnOur1e/CCz+233/7qr5kxdmX65n4+JVB3P2meTMAc/fb6NvVet+iGYlrJc7eqAIPDDfzol/lyz66oBz6ED38jYafHVj/ONn++ws7mKjcznr+p/5ycH03Tfq//qgnd19vfDxyhzjDg9dMqk7307Ev8MDPv/TZF57eLyK+m0kc9I3U0RSbEJz63+E8/hYH9+tvMGt/273qGDwSCX3H0kPV7xRltvboEOZ2/+DP66xgf8tS+HULQylLPaXTq54h0n77+z8/I9JVBzsOg97LzjHAa48ZtJladIXHrfy2HIH3xw0CbsG289Ch+8cLDlsJoTE6xb9bfJAEWhd6sY1+XvDB81t2AyDcU28CH8uBxHP52BSMy+8bAaTm3sRcNPn0byEwcBqNiY1tbncrh5ey+DeZAZFHy+Dj1t5Efm8F5eeycGrDRL/p+X+QjZalXKtv2VJg4OXjYSQeN2U300++sN1ApIJvL/DuhcuLMZk1l7XEHrgEixPZAxtRvhktyMhJF436XYLv4tkMPUoikiKFfb3wteI5+ot/x7/PdA6cFQrYLCwk41XfnVwKhK6lHNOrMiT0yA0Gj7+w7k8zpDqNp6937WOz9rZeO5nByuOmhTwhdBoCI+F3DlgfFMbNQvb+KG7yegNdv68CC1KGnzUMnV+J95+Fo6HJ/78nvNw8JKZMAM5Oc5Hm6y75bbq1XhY9491yIlzGKzpaWPjSZPTkSZtgo3bzgZvVNzgn8FerAkxKeRn0KS4cx3BoTUM2pU2+PW0SbX8BtUKeB6okzpb+MeeXqw+5mDZEZNOZQ3alrYx2SL8xdghwqIH+dYe0Ouu2a236Y7MRFtUW7UlM7VP5FYKiiKSKqJH5uDR7+NZexyK+MM3DxkU8Et84kjX8ja6lncPcP9X9/ZL1tzje+dZqo2KG2zpnfo/3gr43v75HDYI8nDzktw5wDeHMxzerGExg8WHTU7e1Nt2fyESev0aFzdI6jLReXMZPFzJ/dhSeYxU6XFNjralbbQtfeNxjwoG/9mEy04pD1c06F3Z4Nu/XdNfn3utvx+F/AzaBBusCbkRroNyQadExktmhMer2Ji5x+5y871vItcjkhkoKIpIqlnwUPb+kVIuyKBXZYMv/7Lu5Rta3aCghwtN+/kYvFDL4J1tN+quUdB5m75GQS9e2uhgV6hJw2IG45tkv2BRItBgTU8vXtvsIOSKSceyBm81tOHnY/BFe/jgdwcxdhhQ1cazNRN/jxd0tDF2o4O1x00qBhm81chG3iSsu5leGhc3WNTZxoTfnAvC965i48W6mad9IrfSgttpSAtui2Q/dofJ1wdNtp0xqZYf7A748xI0LW7QtXzKF2tedtg5maVCPoP+VQ0CPFgAW0Q8pwW3Xd29Vy4i4gEvm7NXsVfltKm/UzkbncqlTd0iIsmloCgi4qFYu8nXB0z2XTBpUtygQ1n3W8JnIk0+328SHW/yWCVbkpfKSYnjV0zm/bvX8+OVDcslezx1Ptp5PZevmTxc0bk/d1YQZ3f2BO89b9KouEHHMrfv/T0WZvLFXyYGzj2hU3MMZ2iUyed/mly5ZvJIRRtVs8h7mJhLV53Xcz7apHsFG/cXytrXI6506zkN6dazSPbWZqGdH4/f+BF6fQeY646GmTww386Fq87HPl6wqruN5iXTbozh7lCTxgturNPn5w2/POqVKv94n4k0qf2FndORzsdeBnzX2UanRNY0zEzafWtn1U2TXJ6732BKC+tJUX+cc76H12dX+3vDpse8qF4w5e/hqQjne3j2351rcthgaRcb7ctk/vfQysWrJrXn2Qn5dzUAmwFfdrBZLnWVVejWs6us+0mKiGSgjSdNl5AIzh1gbt4J5MOdjoSQCM4Fw9/amra/m0/c7rpOX1QcjL/NbjbJMX23IyEkgnOx8Te2ZP41ADefMl1CIsDUXSahUdafxfjfXHcIioxzvq+pYeouR0JIBOdyPm9mgfcwMbP3mgkhEcBhwmubs+71iDsFRRERD5y1CBlxDlyC4VmL/Y7PWOyLnJosXzORQJQ6dadK1WnK6rOKd8D5qxYHJ3J8al1nVn0PE5OW75VkDgqKIiIeaF3KfQeYKve4btfXtbz7rcruFdJ2/Jbla1qsV+mJbhZ1W5VlNi1LGQTesktQxXzOz8uK1fqeqXWdVp9PVngPE5OW75VkDgqKIiIeyJvLYHk3L2oWdI4za1nSYEkX1zFvj1SyMb6JjUK5nePcnqlp8N/6aftj96kaBv+pZ5Avl3Ox6RfrGm6723iqbWkbH7e0UczfuUD4oGpZY03HPDldP6vmJQyWdvFKdDLLs/cbjH3AICgX5MsFL9czeKpG6ryHHcvamNLCRlF/5/jRwfcZvN0o87+HiWlc3GBmGxslAyBXDuhTxeCD5ln3esSdJrOkIU1mERERyVo0mcWVYr+IiIiIWFJQFBERERFLCooiIiIiYklBUUREREQsKSiKiIiIiCUFRRERERGxpKAoIiIiIpYUFEVERETEkoKiiIiIiIcOHDhA69at8fPzo3Dhwvzf//0fsbGxdzzPNE3GjRtHyZIl8fX1pX79+mzdujUdWpw8CooiIiIiHrh8+TItWrQgNjaW7777jnfeeYcZM2YwYsSIO547fvx4Xn31VV544QWWL19OkSJFaNOmDUePHk2Hlifd3bsnjYiIiEgKTJ8+nfDwcBYvXky+fPkAiI+PZ9iwYbz00ksULVrU8ryYmBjeffddRo4cyQsvvABA48aNqVChApMmTWLq1Knpdg13oh5FEREREQ/88MMPtGrVKiEkAjz88MM4HA7WrFmT6HmbN28mPDychx9+OKHMx8eHbt26sXLlyjRtc3IpKIqIiIh44MCBA1SqVMmlLG/evBQpUoQDBw7c9jzA7dzKlSvzzz//cPXq1dRvrId06zkNmaYJQFRUVAa3REREJGvy8/PDMIyMboaly5cvkzdvXrfyoKAgLl26dNvzcubMSa5cudzOM02Ty5cv4+vrm9rN9YiCYhqKjo4GoEOHDhncEhERkaxp/fr1+Pv7p9vrmaMUjW6mdyMNFShQgBUrVpA7d26PfhuKioqiQ4cOrFixAj8/vzRoYeag68xedJ3Zx91wjaDrzOwyc1uDgoK4cuWKW/nly5ddxi1anXft2jViYmJcehUvX76MYRgEBQWlSXs9oaCYhmw2G4UKFUpxPX5+fun621RG0XVmL7rO7ONuuEbQdUryVapUyW0s4pUrVzhz5ozb+MNbzwM4ePAg1atXTyg/cOBAwrqKmYUms4iIiIh4oF27dqxdu5awsLCEsoULF2Kz2WjTpk2i5zVo0IDAwEAWLlyYUBYXF8d3331H+/bt07LJyaagKCIiIuKBoUOHEhAQQJcuXVizZg1z5sxh9OjRDB061GUNxZYtW1KuXLmEx7ly5WLs2LFMmjSJKVOm8PPPP/PYY49x8eJFRo0alRGXkijdes7EfHx8ePLJJ/Hx8cnopqQpXWf2ouvMPu6GawRdp3guKCiIn376iWeffZYuXboQEBDAoEGDePvtt12Os9vtxMfHu5SNGTMG0zSZNGkS58+fp0aNGqxevZoyZcqk5yXckWFeX8NFREREROQmuvUsIiIiIpYUFEVERETEkoKiiIiIiFjSZJZMKCQkhAkTJrBnzx78/Pxo3749w4YNw9vbO6Ob5rETJ04wb9489u3bx5EjRyhVqhTffPON23FLlizh888/5+zZs5QqVYphw4bRuHHjDGhx8q1du5aVK1dy4MABwsPDKVmyJI888gidOnVyWXA9K18jwKZNm/j88885evQoUVFRFCxYkKZNmzJ48GCXtdl++eUXpk2bxvHjxylcuDD9+vWjU6dOGdjylImOjqZHjx6Ehoby+eefU6VKlYTnsvJn+v333/P666+7lfft25dnn3024XFWvsabLV++nC+//JKQkBB8fX259957mTBhQsKix1n9ezt48GB27txp+dzbb79N27ZtgezzeUraU1DMZMLDwxk6dCglS5Zk4sSJhIaGMnnyZGJiYhgzZkxGN89jR44c4ddff+Xee+/F4XDgcDjcjlm9ejVvv/02AwYMoE6dOqxZs4ZRo0Yxa9YsqlWrlgGtTp758+dTpEgRhg8fTlBQENu2bePtt9/m3LlzDB48GMj61wjO7+i9997LI488Qp48eThy5AgzZszgyJEj/O9//wNg165djB49ms6dOzNy5Ei2b9/Om2++Se7cuWnVqlUGX4FnZs2ahd1udyvPDp8pwEcffeQS9AsUKJDw9+xyjbNnz+bzzz+nf//+VKtWjbCwMLZv357w8yg7fG9ffPFFoqKiXMq+/PJLfv75Zx544AEg+3yekk5MyVQ+/fRTs1GjRmZYWFhC2aJFi8y6deuaoaGhGdiylLHb7Ql/f/XVV82ePXu6HdO1a1fzpZdecinr37+/+eyzz6Z5+1LD5cuX3creeusts0mTJgnXn9WvMTHfffedWatWrYTv6NNPP23279/f5ZiXXnrJ7NGjR0Y0L8WOHTtmNmrUyPz222/NWrVqmfv37094Lqt/psuWLTNr1apl+f29Lqtfo2k6P8O6deuamzZtSvSY7Pa9va5Tp07mc889l/A4O3yekn40RjGT2bx5M3Xr1iVPnjwJZa1bt8bhcLB169YMbFnK2Gy3/6qdPHmSf/75h9atW7uUt2nThu3btxMbG5uWzUsVefPmdSurWLEiUVFRXL16NVtcY2Kuf1/j4uKIjY1lx44dbj0wbdq04dixY5w+fTojmpgiEyZMoHv37pQqVcqlPDt/ptdll2v8/vvvKVasGA0bNrR8Pjt+bwF2797NqVOnaNeuHZB9Pk9JPwqKmUxISAjBwcEuZQEBAeTPn5+QkJAMaVN6uH5tt157cHAwcXFxWfaH9K5duyhYsCB+fn7Z7hrtdjvXrl3jwIEDzJo1iyZNmlC0aFFOnjxJfHy823WWLl0aIMt9j9euXcuRI0cYNGiQ23PZ6TN9+OGHqVu3Lp07d2bOnDkJt9mzyzXu3buXsmXLMmvWLFq3bk29evUYMGAA+/btA8h239vrVq1aha+vL02bNgWyz+cp6UdjFDOZ8PBwAgIC3MoDAgIIDw/PgBalj4iICAC3jeoDAwMB5ybrWc2uXbtYs2YNw4cPB7LfNT700EOEhoYCzn1Lr+9EcP17euv3+Pp1ZqXvcUxMDJMnT2bYsGFunxtkj880f/78DBkyhKpVq2IYBhs2bGDatGmEhoYyZsyYbHGNABcvXuTAgQMcOXKEMWPGkCtXLubMmcPTTz/N4sWLs9X39rr4+HjWrl1LkyZN8PX1BbLHd1bSl4KiSBo4d+4cY8eOpXbt2jz66KMZ3Zw0MWXKFK5evcrRo0eZPXs2L7zwQsJkluxi9uzZ3HPPPVlq1mty1a9fn/r16yc8rlevHrly5eLLL79k4MCBGdiy1GWaJtHR0YwfP57y5csDUK1aNTp16sQ333xDvXr1MriFqW/btm1cvnyZBx98MKObIlmYbj1nMoGBgURGRrqVR0REJPzGlx1d/y3+1mu//lv8zWM2M7uIiAiee+458uTJw4QJExLGZ2anawQoX7489913H126dOG9995jx44drFu3LuF7mth1ZpXv8ZkzZ/jiiy8YPHgwkZGRREREcPXqVcC5VE50dHS2+0yva9WqFXa7nYMHD2abawwICCBPnjwJIRGcba9YsSJHjhzJNt/bm61atYo8efK4/CKQXT5PST8KiplMcHCw21iYyMhILly44DamJDu5fm23XntISAje3t4UK1Ys/RvlgZiYGIYPH05kZCQffvihy+2d7HKNVsqXL0+OHDk4efIkxYsXJ0eOHJbXCe5jozKrU6dOERcXx/Dhw2nevDnNmzfnhRdeAGDo0KEMGzYsW3+m12WXayxTpkyiz8XGxmab7+11MTExbNiwgVatWpEjx42bh9nl85T0o6CYyTRo0IDffvstYRwJOAfT22y2bHlr5LrixYtTsmRJfvrpJ5fyH3/8kTp16mSJxcbj4+MZO3YsISEhfPTRRxQsWNDl+exwjYnZt28f8fHxFCtWDB8fH2rXrm15naVLl6Zo0aIZ1MrkqVixItOnT3f5M2LECADGjh3Liy++mG0/0zVr1uDl5UXFihWzzTU2btyYK1eucPDgwYSysLAwDhw4QOXKlbPN9/a6X375hejoaLfbztnl85T0ozGKmUz37t35+uuvGTlyJAMGDCA0NJQpU6bQrVs3lwVws5qYmBg2bdoEOG/pRUVFsXbtWgBq1apFUFAQgwcP5r///S/FixenVq1a/Pjjj+zbt4+ZM2dmZNOTbPz48WzcuJHhw4cTFRXF3r17E56rWLEiPj4+Wf4aAUaPHk3lypUpX748OXPm5NChQ8ybN4/y5cvTrFkzAAYNGsSQIUMYN24crVq14vfff2fVqlW8++67Gdv4ZAgICKB27dqWz1WuXJlKlSoBZPnP9JlnnqF27dqUK1cOcAaMxYsX8+ijj5I/f34g618jQLNmzahSpQpjxoxh2LBh5MyZk88++wxvb2969OgBZI/v7XWrVq2icOHC1KhRw+257PB5SvoxTNM0M7oR4urYsWNMnDiR3bt34+fnR4cOHbL8Fn6nT59OdELA9OnTE/5BXrJkCXPnzk3YVurpp5/OMttKPfTQQ5w5c8byuWXLliX0SGTlawT47LPPWLNmDadOncLhcFCkSBFatGjB448/7nKr/frs2Zu3QuvcuXMGtjzlduzYwdChQy238Muqn+mkSZPYvHkz586dwzRNSpYsSZcuXXjkkUfctp7Mqtd4XVhYGO+99x4bN24kLi6OmjVrMmLECJfb0tnhexseHk7btm157LHHeO655yyPyQ6fp6QPBUURERERsaQxiiIiIiJiSUFRRERERCwpKIqIiIiIJQVFEREREbGkoCgiIiIilhQURURERMSSgqKIiIiIWFJQFBERERFLCooikm2Eh4fTsGFDateuzYoVKzK6OSIiWZ6CoohkGz/88AOxsbEUK1aMZcuWZXRzRESyPAVFEck2li5dSu3atXnsscfYuXMnJ0+ezOgmiYhkaQqKIpItHDhwgEOHDtGhQwcefPBBvLy8LHsV7XY7s2bNomPHjjRo0IBHH32UNWvW8Mknn1C7dm1Onz7tcvyFCxd499136dChA/Xq1ePBBx/k7bff5tKlS+l1aSIiGSZHRjdARCQ1LF26lNy5c9OyZUt8fX1p3LgxK1asYOjQodhsN34nnjBhAosWLaJ27do8/vjjhIWFMX78eIoWLepW59mzZ+nfvz9xcXF07tyZ4sWLc+LECRYtWsSOHTuYN28e/v7+6XmZIiLpSkFRRLK8a9eusWrVKlq0aIGvry8AHTp0YN26dWzZsoWGDRsCcOTIERYtWkT9+vWZMmVKQoBs1aoVvXr1cqt3woQJxMfHM3/+fAoVKpRQ3qpVK/r378/8+fMZMmRIOlyhiEjG0K1nEcny1q1bR0REBB07dkwoa9SoEUFBQS63nzdu3AjAo48+6tLLWK5cOerVq+dSZ2RkJJs2baJJkybkzJmTsLCwhD9FixalePHibNu2LY2vTEQkY6lHUUSyvKVLlxIUFETBggU5ceJEQnm9evVYu3YtYWFh5M2bN2H8YalSpdzqKFWqFJs3b054HBISgsPhYOnSpSxdutTydYsVK5bKVyIikrkoKIpIlnbq1Cl27NiBaZp069bN8piVK1da3lpOinbt2rn0VN4sZ86cHtUpIpJVKCiKSJb2/fffY5om//nPfywnlkybNo1ly5bRq1evhAkrx48fp3jx4i7HHT9+3OVx8eLFMQyD+Ph4HnjggbS7ABGRTExBUUSyLIfDwffff0+5cuXo0qWL5TFHjx5lxowZ7N+/n8aNG/Pxxx+zYMEC6tevnzBO8fDhw2zdutXlvLx589KwYUN+/vln9u7dS7Vq1VyeN02TsLAwgoKC0uTaREQyA01mEZEsa+vWrZw7d44WLVokesz155YuXUrZsmXp2rUrW7ZsYdiwYSxYsIDp06czZMgQKlasCIBhGAnnvvjiixQoUIAnn3ySN998k2+++YYFCxbw3nvv0aVLF7755pu0vUARkQxmmKZpZnQjREQ8MWbMGH766ScWLFhAuXLlEj2uW7duXLp0iVWrVuHt7c3s2bNZunQply5dolSpUgwcOJD9+/fzxRdfsGbNGvLly5dwblhYGHPnzmXDhg2cPXsWHx8fChUqRJ06dejWrRtlypRJj0sVEckQCooiIsALL7zA9u3b2bBhA15eXhndHBGRTEG3nkXkrhITE+NW9vfff7N582bq1KmjkCgichNNZhGRu8ry5ctZuXIlDRs2JCgoiJCQEBYvXkyOHDm0y4qIyC1061lE7ir79u1j+vTpHDp0iCtXruDn50f16tUZPHgwlStXzujmiYhkKgqKIiIiImJJYxRFRERExJKCooiIiIhYUlAUEREREUsKiiIiIiJiSUFRRERERCwpKIqIiIiIJQVFEREREbGkoCgiIiIilhQURURERMTS/wOFPwqx+vZNewAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explainer.scatter_plot(feature=\"Age\", auto_interact_features=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# explainer.scatter_plot(feature=\"Age\", interact_feature=\"Sex_female\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "predicted probability for label 1 == Survived\n"
     ]
    },
    {
     "data": {
      "image/png": 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iIqoaGECrOJFIhGU9zGD50n8JQV4ivP26yHhFERERUaVmYewCyPj6eZvhxociHEkT4OEA+NcTQSRiACUiIqLywQBKAIA69iKMaMbQSUREROWPl+CJiIiIyKAYQImIiIjIoBhAiYiIiMigGECJiIiIyKAYQImIiIjIoBhAiYiIiMigGECJiIiIyKAYQImI9GnpXqDeh4DLcGDqBkChNHZFREQmhwvRExHpy7bTwOT1/24v2QM42ACfvWO0koiITBHPgBIR6cuvZ7XbthfSRkRUxfEMKAEArj0VcFCS/yz44PoiWJjxsZxEpebqoN1Ww9HwdRARmTgGUMKWv1UYdkAFlZC/3a0ucGSQOSzNGUKJSuXTEGDrSSAjJ3/b0gKYNcC4NRERmSAG0CpOJQiYduLf8AkAx28Du1MEDGrEAEpUKg1rA5e+AaKOATI5MMQPaOpu7KqIiEwOA2gVJ1MAd6Xa7dczDF4KUeVQzwX4L286IiIqDm9CKoREIkF4eDi6dOmC3r17Y/ny5Xj+/Pkrj9u+fTsmTJiAgIAAtGvXDjExMYXu9+jRI0ydOhV+fn7o0aMHvvzyS0ilhaRAA6hmKUKXOtrtvT159pOIiIjKBwNoAVlZWRg7diwUCgUiIyMRHh6OXbt2YenSpa88Njo6GhkZGejcuXOR+ygUCowbNw43b97E/PnzMWPGDMTHx2POnDn6HEapbHjLHHWq/bv9fU8ztK7JAEpERETlg5fgC9ixYwdycnIQGRkJR8f8u1eVSiUWLVqEkSNHwtXVtchj161bBzMzM9y9exfR0dGF7hMTE4PU1FRs374dnp6eAAAHBweMGzcOSUlJaNasmd7H9CrNo5TIfWmt7I+PqjCkkQqO1fifBxEREelfqc+A5ubm4osvvijy8nJFd+bMGXTo0EEdPgEgMDAQKpUK8fHxxR5rZvbqH+eZM2fg7e2tDp8A4OPjA0dHR5w+fVrnunX1/LlCI3y+4PaDwUshIiKiKqLUAbRatWo4fPiw0eYsljeJRKIRDgHA3t4eLi4ukEgkeunfw8NDo00kEsHDw0Mv/ZfWpr8Lb5epDFsHERERVR06zQGtX78+7t27p+9aTEJWVhbs7e212u3t7ZGVlVXm/rOzswvt38HBodj+5XI5pFKp+k9OTk6ZawGAkS0Lv8xuwymgREREVE50muQ3bNgwREREICgoSOtsHpWPqKgorF27Vu/9Zj1TFP4CAyiR7q7dBbJzgbYNjF0JEZFJ0imASiQS1KxZE//5z3/QpUsXuLu7w9raWmMfkUiEUaNG6aVIQ3JwcCh0ekF2djYcHAp5zF4p2dvbF9p/VlYWatasWeRxYWFhGDJkiHo7JycHwcHBZa7n1J3C23kJnkgHz2RAnQ+B9P+/QmFlAVxalr9APRERqekUQNesWaP+e1xcXKH7VNQA6unpqTUXUyqV4vHjx1pzQ3XtPyUlRaNNEASkpaXBx8enyOPEYjHEYnGZ37+goAYWALTPgno76f2tiCo/v//+Gz4BIE8BdJkFPFxvtJKIiEyRTgF07969+q7DZPj6+iIqKkpjrmZMTAzMzMzQsWNHvfR/4MAB3Lx5E+7u+Y/oS0hIQGZmZrHrh5YnvzrAiZfOhIoAXB3FJZiISu2SRLvtUdnnjhMRVTY6pYxatWrpuw6TMWDAAPzyyy+YPHkyRo4ciYcPH2L58uXo37+/xhqgH330Ee7du4fdu3er2/7++2/cvXsXGRkZAICkpCQAgLOzM9q2bQsACAgIQFRUFKZNm4aPP/4YMpkMy5YtQ5cuXYyyBigAHH/PAhkyBUYfAvq8DgxtxvBJpBMV564QEZVEmZNGRkYG7t69CwCoXbs2nJycytqlUTk4OGDVqlWIjIzE5MmTYWtri9DQUISHh2vsp1QqoVRqLqC5bds27N+/X729efNmAECbNm3U0xYsLCywYsUKREZGYvbs2TA3N0f37t0xadKkch5Z8ZysLbCtr1FLIKr4HKsBTwrM8TbnHX1ERAWJBEEQdDnw2rVrWLJkCS5cuKDR3qpVK0ydOhXe3t76qI+KIJVK4e/vj7i4ONjZ2Rm7HCICgJHfAVHHNNt8vIH4Rcaph4hMi2iA5rawwzh1mACdzoCmpKRg1KhRyMvLQ7du3VC/fn0AQGpqKk6cOIFRo0Zh3bp1eP311/VaLBGRSfsxHLhyG4i/lr/9uhvw2xzj1kREZIJ0CqCrV6+GhYUFfvrpJ60znSkpKRg9ejR++OEHREZG6qVIIqIKwcwMOBsByOSAQgnY2Ri7IiIik6TTk5D++OMPDBo0qNDL7A0aNMDAgQPxxx9/lLk4IqIKyVrM8ElEVAydAqhMJsNrr71W5OsuLi6QyWQ6F0VERERElZdOAbROnTo4depUka+fOnUKderU0bkoIiIiIqq8dAqgQUFBOHv2LGbPno3r16+rlyRKSUnBnDlzEB8fj5CQEH3XSkRERESVgE43Ib3//vu4evUqDh8+jCNHjkAkyl/nThAECIKAgIAADB06VK+FEhEREVHloFMANTc3x8KFC9G3b18cP34cd+7kP8exTp068Pf3L/aZ5mSaTt0WEJ2qgoeDCEObiGAn5uLZREREVD5KFEC/+OILDBgwQP2oyD/++ANeXl7o2LGjXp6PTsa14g8Vxh978QhBAasuAueGmMPagiGUiIioqsvLy8Pjx4/h6uoKsVislz5LNAd0//79uH37tnp77NixOHfunF4KIONSqgR8cVbz+dWXHgHbr+r0gCwiIiKqJP744w/06NED9vb2cHd3V9+A/vDhQ/Ts2RMxMTE6912iAOrk5IQnT56ot3V8eieZILkSePpMu/1ejuFrISIiItNw4cIFdO3aFdevX8ewYcM0XqtRowaePXuGDRs26Nx/iS7Bt2jRAuvWrcP9+/fh4OAAADh27Bhu3bpV5DEikQijRo3SuTAyDBtLEXp7inBQ8u8/KsxFQN8GvPxORERUVf33v/9F7dq18eeff0Imk2HdunUar/fs2RPbtm3Tuf8SBdDJkyfj888/xy+//AJBECASiRAbG4vY2Ngij2EArTjWv2WGj2JU2H9dgKcj8FUXMzSqzgBKRERUVZ08eRIzZ86EnZ0d8vLytF53d3fH3bt3de6/RAG0du3aWLNmDZ4/f44nT57g7bffxuTJk9GtWzed35hMR01bEXb2NTd2GURERGQiZDIZHB0di3w9KyurTP2XahkmS0tLuLm5ISQkBM2aNUOtWrXK9OZEREREZHpef/11/P7770W+fuzYMTRp0kTn/nV6EtLcuXPVSzIRERERUeUyePBgbNq0SeNO9xcPHvr6669x8OBBvP/++zr3r9NC9ERERERUeU2ZMgVHjhxB79690bhxY4hEIkycOBGPHj3C/fv3ERgYiPDwcJ371+kMKBERERFVXmKxGEeOHMGSJUtgY2MDa2trXLt2DS4uLli8eDH2798PMzPdY6RI4KKeFZJUKoW/vz/i4uJgZ2dn7HKIiIjoVUQDNLeFHcapwwTwDCgRERERGRTngBIRERFVcSdOnNDpOD8/P52OK3MAvXXrFp48eYIGDRrwUjARERFRBeTv76++y70kXjyYSKlU6vR+OgfQkydPYsmSJbh37x4A4Pvvv0f79u3x9OlTjBw5EuPGjUNAQICu3RMRERGRgURFRRn0/XQKoImJiZgyZQoaNWqEkJAQrFmzRv1a9erVUbduXRw+fJgBlIiIiKgCGD58uEHfT6ebkH788Uc0bNgQ69evx6BBg7Reb968Oa5evVrm4oiIiIio8tHpDOjff/+NMWPGFLn+U82aNfH48eMyFUZERERExqVUKpGcnIz09HSoVCqt1w16E5JKpYJYLC7y9YyMDFhaWupUEBnH6TsColNV8HAQYegbItiKSz4RmYiIiCqfRYsWISIiAllZWUXuo+tNSDpdgvfy8sKff/5Z5OsnT55Ew4YNdSqIDO+7P1To8rMSC88JGHtEhc4/KyFT8PkEREREVdVPP/2EmTNnolWrVpg/fz4EQcCECRMwdepUVK9eHe3atcO6det07l+nANq3b18cPXoUu3fvxosHKYlEIshkMkRGRuLy5cvo16+fzkWR4ShVAj4/q3lK/eIj4NdrDKBERERV1apVq9CxY0fExsZi9OjRAIDg4GBERETg0qVLkEgkOp/9BHQMoAMHDkSvXr2wYMEC9OvXDyKRCLNnz0a3bt2wbds2hISE4K233tK5KDIcuRJ4+ky7/a7U8LUQERGRabhy5Yr6RvMX64O+CJy1atXC6NGjsXz5cp3713kd0C+//BI9evTAb7/9hrS0NAiCgKZNmyI4OBg9e/bUuSAyLBtLEXp7inBQ8u8ZT3MR0Od1zgElIiKqqszNzWFrawsA6v998uSJ+nVPT0/8888/Ovdfpichde/eHd27dy9LF2QC1r9lhrFHVNifKsDTAfiqqxkav8YASkREVFW5u7vjxo0bAAArKyvUq1cPJ0+exH/+8x8AwPnz51G9enWd+ze5Z8HfunULt27dgq+vr7otKSkJP/30EzIzMxESEoL+/fsbscLKp6atCLtCzY1dBhEREZkIPz8/REdHY+HChQCAQYMGYdmyZXj27BlUKhU2b96MkSNH6ty/TgF07dq1r9xHJBJh1KhRpe7722+/RVZWljqAZmRkYPz48cjNzYWVlRUiIiJQvXp1+Pv7l7pvIiIiInq1Tz/9FC1btsSzZ89gY2ODL774AteuXcOGDRsAAL169UJERITO/esUQF9+9GZBIpFI/YB6XQLolStXNO6gP3jwIHJycrBlyxZ4eHhgzJgx+PnnnxlAiYiIiMpJo0aN0KhRI/W2ra0t9u7di8zMTJibm8POzq5M/esUQPfu3avVplQqcfv2bWzduhVSqRSff/65TgWlp6fD1dVVvX327Fm0bNkSDRo0AJCfuMuy7hQRERER6cbR0VEv/egUQGvVqlVoe926deHj44MPP/wQ+/btw8cff1zqvm1sbJCdnQ0gP9ReuHBBPeEVyJ8Im5OTo0vZRERERFQKubm5kEgkePLkiXrt95cZ9FGcxRGJROjZsyc2bdqkUwCtX78+oqOjERwcjJiYGDx79gw+Pj7q1+/duwcnJyc9VqxNIpFg8eLFuHTpEmxtbREUFITw8PBXPl5UEARs2LAB27dvR0ZGBho2bIhJkyahefPm6n0SExMxduxYrWMDAwPVE32JiIiIjCknJwcTJ07Exo0b8fz5c63XX0y31HUx+nK5C/758+fIzMzU6dhhw4Zh8uTJ6NWrF4D8OQitW7dWvx4fH4/GjRvrpc7CZGVlYezYsXB3d0dkZCQePnyIb775BjKZDNOnTy/22A0bNmD16tUYN24cvL29sX37dowbNw5btmxB3bp1NfadO3cuPD091dvlHaqJiIiISmrs2LHYsmUL+vXrh65du8LZ2Vmv/es9gP7999/43//+pxGuSqNLly5YtWoVjh8/Djs7O7zzzjvqFfgzMjJQs2ZNBAcH67FiTTt27EBOTg4iIyPV8xyUSiUWLVqEkSNHasxPfVleXh6ioqIwdOhQDBkyBADQunVr9O/fH5s3b8aMGTM09n/99dfRpEmTchsHERERka727NmDDz74oEQrH+lCpwDat2/fQtszMzORm5sLc3NzzJkzR+ei2rRpgzZt2mi1Ozk5ITIyUud+S+LMmTPo0KGDxiTbF5fH4+Pj8fbbbxd63KVLl5CTk4OAgAB1m6WlJbp3747Y2NhyrZmIiIhInywtLdG+ffty61+nAFqzZk31WckXRCIRGjVqBA8PD/Tr1w+1a9fWS4EAoFAocPz4cWRlZaFr165wcXHRW98FSSQS9OnTR6PN3t4eLi4ukEgkxR4HQOvMr5eXF37++WfIZDJYW1ur2z/99FNkZmbCxcUFvXv3xpgxYzReL0gul0Mul6u3eSMWERERlZcePXrg3LlzGD16dLn0r/d1QMtq+fLl+P3337Fx40YA+ZNcw8PDceHCBQiCAEdHR6xfv15rTqW+ZGVlwd7eXqvd3t4eWVlZxR4nFothZWWldZwgCMjOzoa1tTXs7OwwbNgwtGnTBlZWVjh//jw2b96MGzduYNmyZUX2HxUVVW6nwYmIiIhe9vXXX8PPzw/Lly8v0Y3YpWVyj+I8e/YsOnTooN4+ceIE/vzzTwwbNgwNGzZEZGQk1q9fX6ZL/MbUuHFjjZuo2rdvDxcXFyxevBhJSUlo1qxZoceFhYWp55YC+WdAy3MuLBEREVVd7u7u+OqrrzBs2DBMnToVtWrVgrm55mO7RSIRrl+/rlP/JhdAHzx4AHd3d/X2yZMnUbt2bXzyyScAgNTUVBw8eLDc3t/BwQFSqVSrPTs7Gw4ODsUeJ5fLkZeXp3EWNDs7GyKRqNCzqi8EBgZi8eLFSE5OLjKAisViiMXiUoyEiIiISDfr16/HBx98ALFYjEaNGhnnLvj27dtrzfl8FZFIhHPnzpW6oOfPn2sk7MTERI0zonXq1MHjx49L3W9JeXp6as31lEqlePz4cbF39r94LS0tDQ0bNlS3SyQSuLm5FTu/k4iIiMiULFiwAK1atcKhQ4fK5d6bEgXQ4ODgUgdQXdWsWROXLl1Cv379cP36ddy5c0dj4fanT5/Cxsam3N7f19cXUVFRyM7OVp+1jImJgZmZGTp27FjkcS1atICtrS1iYmLUAVShUCA2NhadO3cu9j0PHToEAFyWiYiIiEzCnTt3MHHixHK78btEAVTX57rrolevXvjpp5+Qnp6O1NRU2NraagS4q1evltsNSAAwYMAA/PLLL5g8eTJGjhyJhw8fYvny5ejfv7/GGqAfffQR7t27h927dwPIf0RoWFgY1qxZA2dnZzRo0ADbt29HZmYmhg4dqj7us88+Q926ddG4cWP1TUhbt26Fv78/AygRERGZhEaNGuHp06fl1r/JzQENCwvDgwcP1AvRf/HFF+ozkVKpFCdOnMDgwYPL7f0dHBywatUqREZGYvLkybC1tUVoaCjCw8M19lMqlVqPnxo+fDgEQcDmzZuRnp6Ohg0bYsWKFRqBuX79+jhw4AC2bNkCuVyO2rVrIywsDGFhYeU2JiIiIqLSmDVrFj799FOMGDGiXE78iYTCnixvolQqFXJzc2FtbQ0LC5PLzgYllUrh7++PuLg42NnZGbscIiIiehXRAM1tYYdx6iiBefPmYd++fUhOTka/fv3g5eVV6F3wn332mU796xxAL1y4gPXr1yMpKQnZ2dko2I2uNyFRyTCAEhERVTClCKDJycn45JNPcObMGdjb22PYsGGYP3/+K1fE8fT0RFpamlb7s2fPSnVDtJmZ2Sv3EYlEWleDS0qn04h//PEHwsPDYWdnh2bNmuH06dNo3749cnNz8ddff6FBgwYaa13q4smTJ7hy5QqysrKgUqm0Xg8JCSlT/0RERESmKD09HT169IC3tzd27tyJO3fuYNKkScjNzcV33333yuMHDhyIyZMna7QVfFDOq9y4caNU+5eWTgF03bp1cHFxwaZNmyASiRAYGIiwsDC0b98e8fHxmD59OqZPn65TQSqVCosWLcLu3bu1zqq+jAFUv5YmKrH1igBPBxGW9RChrv2r/+VDRERE+vfDDz8gKysLu3btQvXq1QHkr6wTHh6OWbNmvfJx5zVr1ix25Z6S8PDwKNPxr6JTyvjrr78QGhoKZ2dn9fJML85SduzYEUFBQfjhhx90KmjTpk3YuXMnevfujc8//xyCIGDcuHGYNm0a6tWrhzfeeAPff/+9Tn1T4UJ3KTE5TsDvD4Ad/who8KMKT59pn3UmIiKi8nfgwAEEBASowycAvPPOO1CpVDh8+LDB60lJScHp06eRmZmptz51CqByuVy9JNGLuQi5ubnq1xs2bIgrV67oVFB0dDQ6deqEefPmwdfXFwDwxhtvYODAgdi8eTMyMjJ07pu0yRUq7L2ueaY5TwnMOFlh7k0jIiKqVJKTk7WmMjo5OaFWrVpITk5+5fFbtmyBlZUV7OzsEBQUhMuXL+tUx/79+/H666+jUaNG8PPzw++//w4AePjwIRo0aIBff/1Vp34BHQOoi4sLHj58CACwsbGBvb29xrNAHz58qPNd6nfu3FEHzxcTYBUKhfq93n77bfXam1R20udAYVHzTrbBSyEiIiLkzwF1cnLSand2dn7l2px9+vTBd999h5iYGHz//fdISUlBly5dkJqaWqoa4uLi0K9fP1SvXh1z587VmBZZo0YNvP766/jf//5Xqj5fplNKbNKkCS5evKje9vHxwdatW+Hm5gZBELBt2zY0bdpUp4KsrKzU4bVatWoQiUQaP2wXFxc8ePBAp75JW3UbM9SopsLDXM32CW0N8+QrIiKiqkIU+YvGdnlca/z222/Vf+/atSt69eqFxo0bY8mSJVi5cmWJ+5k3bx5atmyJc+fOIT09XeuhRJ06dcLGjRt1rlOnM6B9+/aFk5MTZDIZAODjjz+GlZUVvvjiC8ybNw+WlpYYP368TgW5ubnh9u3bAAALCwvUrVsXZ8+eVb9+7tw5vPbaazr1TYU78a4Z6uWv9Q8rc2BGBxECPXkTEpEudv2jQrf/KeCzWYFVFziXmohKz9nZudD5lunp6RrzQkuiVq1a6NKli/ryeUmdP38eQ4YMKXI5prp16+L+/ful6vNlOp0B7dixo8bdVXXr1sXOnTuRkJAAc3NztGrVSue1Kdu3b4/Y2FhMmDABABAUFITVq1fj0aNHEAQBFy5c0Hi0JZVdo9fMcHMMAydRWcWkqTBgj0p9ViPhvgqCAIS35veLiEqucePGWnM9MzMzce/evTIvc1lSKpWq2KWbHj9+/Mo1SYujt9+KNjY26NatG7p06VKmhdHff/99zJgxA3K5HED+oznfeecd/PPPP0hNTUW/fv0wZswYfZVNRKQ3UUmC1iW1dUk8C0pEpfPWW28hJiYGGRkZ6rbt27fDzMwMvXr1KlVfd+/exalTp9C+fftSHffGG2/g5MmTRb6+f/9+tGzZslR9vkynM6BDhgzB22+/jTfffLPQSbJl4eLiAhcXF/W2ubk5pk6diqlTp+r1fYiI9M3KvGRtRETFGTt2LFasWIHQ0FDMmjULd+7cwdSpUzF27FiNNUB79uyJtLQ0pKSkAAB+/vln7N+/H0FBQahduzZSU1OxcOFCmJubay1M/yoffPABxo8fj4CAAPTp0wdA/pOPcnNzMWPGDJw9e7ZMc0B1ehRnUFAQHj16BAsLC/j6+iI4OBh+fn5V/vnshsRHcRKZnsT7Ajr/rIT8pSfT/RJihnca8xI8EQGiJQqNbWFK0bnpypUrWo/iXLBggcZlb39/f0gkEkgkEgBAfHw8Zs6ciaSkJGRkZMDJyQk9evTAvHnz0KhRo1LXO3ToUGzduhUODg7Izs6Gq6srnjx5AqVSibCwMPz000+l7vMFnQKoIAg4d+4coqOjcfz4cchkMtjb26NXr14ICQkp1R3wuk5gdXNz0+m4yoIBlMg0nb8n4PsLKsgUwIhmIrzpxfBJRPlKE0BNxa5du7B582YkJydDEAR4e3tj2LBhGDBgwKsPLoZOAfRlz549w9GjRxEdHY0//vgDgiDA3d0dISEhGDFixCuPb9++vfppSqWRkJCgQ7WVBwMoERFRxWLKAXTevHno378/mjVrBgC4efMmXF1dYWNjUy7vV+YA+rIHDx7gt99+w4YNG/Ds2TOcO3fulcesXr1apwA6evRoXUqsNBhAiUzTXamAjX8JeKYQMPgNMzSqzjV1iSifKQdQMzMzbN68GYMHDwaQfw/Opk2b1Nv6preR3759G9HR0Thw4ABycnJKPB+Ud7QTUWVxPUNAqw1KSJ/nb38Vr8ThQWbo7s7L8ERk2pycnDTuutfj+clClSmASqVSHD58GNHR0bh8+bJ6bsCECRPw1ltv6atGIqIK4dNj/4ZPAFAIwJgjKlz7gAGUiExb69atsXjxYjx//hzOzs4AgJMnT6ofh16UYcOG6fR+Ol2CP3nyJKKjo3Hy5EnI5XJUr14dvXv3RkhICBo2bKhTIS8cPnwYp06dwrx58wp9fe7cuejatSsCAgLK9D4VHS/BE5meej8ocFuq2WZtDjybaDqX2YjIeEz5EvzFixfRv39/3LhxA0D+kkuviogikQhKpbLYfYqi08gnTZoEsViMrl27IiQkBJ06dYK5uX4Wu9u2bRvq1q1b5OtmZmbYtm1blQ+gRGR6GjpDK4DWrGacWoiISqNly5a4du0aUlNTce/ePfj7+2P27Nnllrd0CqDTp09H7969YW9vr+96cOPGDfTs2bPI1xs1alTsyvxERMayrIc52m5S4vlLDz9a3oM3IRFRxWBubg5vb294e3ujW7du8Pf3R7du3crlvXQKoAMHDtR3HWoymazIB98D/67CT0Rkapq7inBpuDnWXMpfB3RYUzN0rM0ASkQVT2xsbKn2f/DgAWrXro0jR46gR48er9zf5GbG165dGxcuXCjy9QsXLlT5ReiJyHQ1fk2Epd3NsTLQnOGTiKqU0txWZHIB1N/fH0ePHsXu3bu1XtuzZw+OHj2K7t27G74wIiIiItIL07n96v+NGDECx48fx1dffYWtW7eqn1167do13LhxAx4eHggLCzNylURERESkK5M7A2pra4t169ahf//+ePLkCQ4ePIiDBw/i8ePHGDBgANatW8dlh4iIiIgqMJM6A5qeno47d+7AyckJM2bMwPTp09Wr8js5Oen0yE4iImNQqVTF3lBJRFSVlSqAKhQKHD9+HLdu3YKTkxP8/f3h5ORU5iJUKhUiIiKwZ88e9QTW5s2bY8mSJerV+ImIKoLuvyhx/JYAAcAbr6kQ/54ZHKwZRImIXlbiAJqVlYUxY8bg+vXrEAQBIpEIK1aswHfffYc33nijTEVs27YNu3btgqurK5o3b45bt27h0qVL+OqrrxAZGVmmvomIDGXsYSXibv17F+iVJ0DQLhVOvccASkT0shIH0J9++gkpKSno0qULOnXqhJs3b2LHjh1YsGABNm/eXKYioqOj4eXlhaioKNja2gIA5s+fj3379iE7O7tcFrwnItK3fde1lyA5f88IhRARmbgSB9CTJ0+iU6dO+Oabb9RttWrVwvLly/HgwQPUrFlT5yLS0tIwatQodfgEgHfffRd79uxBWloamjVrpnPfRESG8lSm3SZXabcREVUGCoUCFhb5UdLR0RFRUVFo2rRpiY4t8XWhBw8eoHPnzhptfn5+EAQB9+6V7Z/4z549g6urq0bbi22ZrJDf6EREJkimNHYFRET6MWLEiGKfPJmSkgJfX1/1trW1NYYPH17iE5IlDqByuRyOjo4abS8ujT9//ryk3RSpqDvcS7OqPhERERGV3aZNm9C2bVtcvHixyNeuXbumc/96WYZJH8sjnT59Go8fP1Zvy2QyiEQixMTE4OrVq1rvN2TIkDK/JxERERFp++233zBs2DB07NgRixYtwvjx45GTk4OPPvoIW7ZsQbt27fDzzz/r3L9IKOEpxvbt26NRo0Yal8qVSiXi4+PRrFkzrbOjIpEIS5cuLVER7du3L0XJ+X0nJCSU6pjKRiqVwt/fH3FxcVyYn8hEmC1RoLBfqMIUk1pymYiMRLREobFt6r8bHjx4gKFDh+LYsWPo1asXUlJSkJqaismTJ+Orr75Sz//URamOvHr1qtbZSAC4fPmyVltpzor+8MMPpSmDiMgkDX8DWH9Fs62Va+H7EhGZupo1a+Lw4cPw8/PDoUOHIBKJ8O233+Ljjz8uc98lDqDnz58v85sVpW3btuXWNxGRoczrao4jt5S4I83ftjED1vQyN25RREQ6evr0KcLCwnDmzBl07doVly5dwsyZM+Hg4ID333+/TH1zdeRCSCQShIeHo0uXLujduzeWL19eohutBEHA+vXrERwcjM6dOyMsLKzQs8OPHj3C1KlT4efnhx49euDLL7+EVCotj6EQkQHVcxDBQfzvtq0V0NqVt8YTUcVz4sQJtGrVCgcPHkRERASOHz+OP/74A02aNMGIESMwbNgw5OTk6Ny/3gPokydPsGHDBgwaNEjfXRtEVlYWxo4dC4VCgcjISISHh2PXrl0lms+6YcMGrF69GoMHD8Y333wDFxcXjBs3Drdv31bvo1AoMG7cONy8eRPz58/HjBkzEB8fjzlz5pTnsIjIAHr8osCVp/9uP34GuK81Xj1ERLrq0aMHLC0tcfLkSUydOhUA4OXlhdOnT2Pq1KnYsmUL2rRpo3P/epn9qlKpcPLkSezZswdnzpyBUqlEtWrV9NG1we3YsQM5OTmIjIxU31ilVCqxaNEijBw5Umu90hfy8vIQFRWFoUOHqu/Qb926Nfr374/NmzdjxowZAICYmBikpqZi+/bt8PT0BAA4ODhg3LhxSEpK4qL7RBXYidvabfd0P0FARGQ0AwcOxJo1a+Dg4KDRbm5ujoiICPTs2RPDhw/Xuf8ynQGVSCT49ttvERQUhKlTp+LixYt466238M033yAmJqYsXRvNmTNn0KFDB427+gMDA6FSqRAfH1/kcZcuXUJOTg4CAgLUbZaWlujevTtOnz6t0b+3t7c6fAKAj48PHB0dNfYjoorHrOwr0hERmYT//e9/WuHzZYGBgYWuEVpSpT4D+uzZMxw+fBh79+7F5cuXYW5ujpYtW+Lp06eYPXs2evTooXMxpkAikaBPnz4abfb29nBxcYFEIin2OAAawRLIP139888/QyaTwdraGhKJBB4eHhr7iEQieHh4FNs/EZm+Xh5A9A3Ntnr2xqmFiEgfJBIJYmJi8ODBAwwZMgSenp6Qy+W4f/8+3NzcdO63xAH0woUL2Lt3L44ePYrc3Fw0atQIkyZNwptvvons7Gz0799f5yJMSVZWlvoJTy+zt7dHVlZWsceJxWJYWVlpHScIArKzs2FtbY3s7OxC+3dwcCi2f7lcDrlcrt4uy8RfIioftmIRUGAl0OrWxqmFiKispk+fjqVLl0KpVEIkEqFTp07w9PSETCZDkyZNMH/+fEyYMEGnvkscQD/88ENUr14d/fr1Q0hICBo0aKB+jXdwl7+oqCisXcu7GYhMmXMhYdPZmtfliajiWb16NSIjIzF+/HiEhISgV69e6tccHBzQp08f7Nu3T+cAWqo5oHl5eZBKpZU6cDo4OBQ6vuzs7GLnQjg4OEAulyMvL0/rOJFIpD7raW9vX2j/WVlZxfYfFhaGuLg49Z/o6OiSDomIDOST1maws/x321wETGvPAEpEFc/KlSvRr18/LFu2DK1bt9Z6vUWLFoU+nKikSnwGdPv27di9ezcOHDiAvXv3onbt2ggJCUFwcLDOb26KPD09teZiSqVSPH78WGt+Z8HjACAtLQ0NGzZUt0skEri5ucHa2lq9X0pKisaxgiAgLS0NPj4+RfYvFoshFouLfJ2IjK+piwh/DDPH2ksqyBTA+03M0L4WAygRVTzXrl3DRx99VOTrrq6uePz4sc79l/gMqKenJyZMmIDo6GhERETAw8MDa9euRWhoKKZPnw6RSIQSPlbepPn6+iIhIQHZ2dnqtpiYGJiZmaFjx45FHteiRQvY2tpq3P2vUCgQGxuLzp07a/T/zz//4ObNm+q2hIQEZGZmauxHRBWTt7MIi7uZ49ue5gyfRFRhWVtbF3u/SVpaGpycnHTuv9TLMFlYWKBHjx5Yvnw5oqOjMWbMGOTm5kIQBPz3v//FlClT8Ntvv1XYy/QDBgxAtWrVMHnyZMTHx2Pv3r1Yvnw5+vfvr7EG6EcffYTQ0FD1tpWVFcLCwrB582b8/PPPOH/+PGbNmoXMzEwMHTpUvV9AQADq16+PadOm4eTJkzhy5AjmzZuHLl26cA1QIiIiMgkdOnTArl27Cn1NJpNh06ZNZTpxJhL0dNry999/x549e3Ds2DHk5eXB0tISZ86c0UfXBnfjxg1ERkbi4sWLsLW1RXBwMMLDw2Fp+e/krtGjR+PevXvYt2+fuu3Fozh//fVXpKeno2HDhpg0aRJatGih0f/Dhw8RGRmJc+fOwdzcHN27d8ekSZNgZ2dX4hqlUin8/f0RFxdXquOIiIjIOERLFBrbwhS9PA+oXMTExKB3794YPHgwRo4ciZ49e2Lz5s147bXXMHfuXPz+++84ceIEOnXqpFP/egugL0ilUhw8eBB79+7Fxo0b9dk1vYQBlIiIqGKpSAEUANasWYNPP/0UcrkcgiBAJMqfViQWi7Fq1SqMGDFC5771HkDJMBhAiYiIKpaKFkAB4P79+9i+fTuSk5MhCAK8vb3xzjvvoE6dOmXqt8Qjz8zMLHXnLz/OkoiIiIgqFjc3N3zyySdQKBRISEjAnTt3kJGRYbgAGhAQoD71WhIikQjnzp3TqSgiIiIiMqy4uDjs3LkTc+bMQY0aNdTtN27cQGhoKJKSktRtw4cPx7p163R+rxIH0ODgYI0AmpeXhyNHjqBjx45wcXHRuQAiIiIiMr7169fj7Nmz+PbbbzXaR4wYgcuXL6Nz587w8fHBoUOHsGHDBnTr1g3Dhw/X6b10ngOakZGBwMBArFy5Eu3bt9fpzUl3nANKRERUsZj6HNAmTZqgZ8+eWLFihbotOTkZTZo0gZ+fH+Li4gAAz549Q+vWrVGnTh0cPXpUp/cq9TqgRERERFT53L9/H97e3hptcXFxEIlEGDVqlLrNxsYGgwcPxqVLl3R+LwZQIiIiIkJeXh5sbGw02s6fPw8A6Natm0Z7vXr1dLpB/QUGUCIiIiKCu7s7/vrrL422U6dOoUaNGqhXr55Ge25urmEfxUlERERElU/Xrl2xceNG9d3uu3btwj///IO33npLa9/Lly+XaSmmMgfQ0izNRERERESmaebMmcjLy0PLli1Ro0YNDBw4EGKxGJMnT9bYT6lUYu/evejSpYvO71Xi26/+85//aGyrVCqIRCJ8+eWXWvMFgPxg+vPPP+tcGBEREREZjpeXF44fP44vvvgCKSkp6NChA+bMmYOmTZtq7BcbG4vXXnsNffv21fm9ShxAc3JytM52urm5QRAE5Obm6lwAEREREZmGdu3aYd++fcXuExAQgMuXL5fpfUocQF9VDBERERFRSfAmJCIiIiIyKL0swa9QKPDXX3/h0aNH8PLywuuvv66PbomIiIioEipxAE1MTERsbCw++OADVK9eXd1+584dTJkyBdevX1e3BQcHY+7cufqtlIiIiIgqhRJfgt+/fz/Onj2rET4BqO+UatGiBQYPHgwvLy9ER0dj//79ei+WiIiIiCq+EgfQv/76Cx07dtRok0gk+PPPP9G6dWv8+OOPmDBhAjZs2IB69eohOjpa78USERERUcVX4gD65MkTuLu7a7QlJiZCJBIhNDRU3WZtbY0333wT//zzj96KJCIiIqLKo8QBVC6Xw8rKSqPt77//BgC0adNGo71mzZqQSqV6KI+IiIiIKpsSB1A3NzekpqZqtF24cAHOzs5wc3PTaJfJZLC3t9dPhURERERUqZQ4gLZu3RrR0dFISUkBkP8Yplu3bsHX11dr35SUFLi6uuqvSiIiIiKqNEq8DNOIESNw4MABDB48GI6OjsjMzISlpSWGDh2qsZ9SqcSJEyfQo0cPvRdLRERERBVfic+A1qlTB2vWrEHnzp3h6OgIX19frF69WmvR+cTERDg6OqJbt256L5aIiIiIKj6RIAiCsYug0pNKpfD390dcXBzs7OyMXQ4RERG9gmiJQmNbmKKXB1JWSHwWPBEREREZFAMoERERERkUAygRERGZnPi7Anw2K2CzTIGe25S4+pQzBisTBlAiIiIyKVK5gKCdSiTcB2QK4NhNAW/vUkLF21YqDQZQIiIiMimxtwSkyzTb/kkHLj8yTj2kfwygREREZFJqVBNptZmJABcbIxRD5YIBlIiIiEyKTy0R3vLSDKEfthChjr12MKWKqeouQEVEREQma3eoGf6XLODiQwGd64jQz5vhszJhACUiIiKTIzYXYVhTEdDU2JVQeeAleCIiIiIyKAZQIiIiIjIoXoIvxIkTJ7Bq1SqkpaXBzc0NI0aMQJ8+fV55nFQqxdKlSxEXFweFQoGOHTti2rRpcHFxUe+zevVqrF27VuvYGTNmYODAgXodBxEREZEpYgAt4MKFC5g6dSr69u2LyZMn4/z58/jyyy9RrVo1BAQEFHvszJkzkZqaipkzZ0IsFmPlypUYP348Nm7cCAuLf3/UVlZW+OGHHzSOrVOnTrmMh4iIiMjUMIAW8OOPP6Jp06aYNWsWAKBdu3a4ffs2Vq9eXWwAvXTpEs6ePYvvvvsOHTt2BAB4eHhg0KBBiI2NRWBgoHpfMzMzNG/evHwHQkRERGSiOAf0JXK5HImJiVpBs1evXrhx4wbu3r1b5LFnzpyBvb09fHx81G2enp5o2LAhTp8+XW41ExEREVU0DKAvuX37NhQKBTw9PTXavby8AAASiaTIYyUSCTw8PCASaa5T5uXlpXVcXl4eAgIC4OPjg0GDBmHXrl36KJ+IiIioQuAl+JdkZWUBAOzt7TXaHRwcNF4v6tiCx73o6+Xj6tWrh08++QSNGjWCXC7HwYMHsWDBAkilUrz//vtF9i+XyyGXy9XbOTk5JRsUERFRBXTliYAZJ1S4+Ch/IfrFfmZ8ElIlUukDqFQqxePHj1+5n6FuAgoKCtLY7tKlC54/f46ffvoJ7733nsbNSi+Liooq9O55IiKiykamENBzmxL3/v9cS1qWgL+fKPHnsEofW6qMSv9JxsTEYP78+a/c79dff1Wf6ZRKpRqvvTiD+eL1wjg4OODBgwda7dnZ2cUeBwCBgYE4evQobt26pb7cX1BYWBiGDBmi3s7JyUFwcHCx/RIREVVEx24K6vD5woWHQNIjAc1ceRa0Mqj0ATQ0NBShoaEl2lcul8PCwgISiQSdOnVSt7+Yw1lwbujLPD09kZCQAEEQNOaBSiQSNGjQQJfSNYjFYojF4jL3Q0REZOrsxYWHTDv+32ClwZuQXiIWi9GuXTscPXpUo/3IkSPw8vJC7dq1izzW19cXWVlZSEhIULelpaXh6tWr6Ny5c7Hve+jQIdjb26NevXplGwAREVEl0KWIWXGejjz7WVkwgBYwatQoXL58GREREUhMTMTq1atx8OBBjBkzRmM/Hx8fzJs3T73dokULdOrUCfPmzUNMTAxOnDiB6dOnw9vbG927d1fvN3ToUPzvf/9DfHw84uLiMGPGDMTGxmL06NFFzv8kIiKqSqp9oyy0vcNGhYErofLCxFNAq1atsHjxYqxatQp79uyBm5sb5syZo7U2qFKphEql0mhbuHAhli5digULFkCpVMLHxwfTpk3TCJb16tXD1q1b8eTJEwBAgwYN8OWXX+Ktt94q/8ERERFVADJV4e1Xnhq2Dio/IkEQBGMXQaUnlUrh7++PuLg42NnZGbscIiIivXl3hwLbbmi3XxsOeLtW3HNnoiWaZ3CFKRV3LGXFS/BERERkUgoLnwDQa4dh66DywwBKREREFYJE+up9qGJgACUiIqIKwc3G2BWQvjCAEhERkUl5v0nh7V/7G7QMKkcMoERERGRSjkoKb48pop0qHgZQIiIiMil3cwtv35Vi2Dqo/DCAEhERkUlp5lx4+7QOhq2Dyg8DKBEREZmUos6Anr5l2Dqo/DCAEhERkUlJzyu8Pf6hYeug8sMASkRERCaljWvh7dN5Cb7SYAAlIiIik9KuVuHttexEhi2Eyg0DKBEREZkUmaLw9uwiLs1TxcMASkRERCZleXftM51mIuCj1uZGqIbKAwMoERERmRRHG3P8+rYI9paACEB1a+DsYF5+r0wsjF0AERERUUEDGpljQCNjV0HlhWdAiYiIiMigGECJiIiIyKAYQImIiIjIoDgHlIiIiEyOShBwIFXApceAb20RutXjTUiVCQMoERERmZyh0Sr8nCyot6d3ECHCj8swVRa8BE9EREQm5c8Hgkb4BICvEwU8yBGKOIIqGgZQIiIiMilpWdpBU6EC7kqNUAyVCwZQIiIiMin+9USwtdRsq2cPtHA1Tj2kfwygREREZFKcrEXY2dcMDZzyt1u6Arv6msPcjDciVRa8CYmIiIhMTi9PM1z7QITc54CtmMGzsmEAJSIiIpMkEolgKzZ2FVQeeAmeiIiIiAyKAZSIiIiIDIoBlIiIiIgMigGUiIiIiAyKAZSIiIiIDIoBlIiIiIgMigGUiIiIiAyKAZSIiIiIDIoBlIiIiIgMigGUiIiIiAyKj+IsxIkTJ7Bq1SqkpaXBzc0NI0aMQJ8+fYo95vnz51i5ciWSkpJw5coVyGQyxMTEwMnJSWvfixcvYtmyZbh27RqcnZ0xcOBADB8+HCIRn3VLRERElR/PgBZw4cIFTJ06Fc2bN8e3336LwMBAfPnll4iJiSn2OJlMht27d0MsFqN169ZF7nfr1i188skncHFxwTfffIP33nsPq1evxubNm/U9FCIiIiKTxDOgBfz4449o2rQpZs2aBQBo164dbt++jdWrVyMgIKDI4+zt7XHs2DGIRCLs27cPZ8+eLXS/jRs3wtHREV999RUsLS3RoUMHZGRkYN26dXj33XchFovLZVxEREREpoJnQF8il8uRmJioFTR79eqFGzdu4O7du8UeX5JL6GfOnIG/vz8sLS01+s/OzsalS5d0K5yIiIioAmEAfcnt27ehUCjg6emp0e7l5QUAkEgkZer/2bNnePDgATw8PDTaPT09IRKJytw/ERERUUXAS/AvycrKApB/Of1lDg4OGq/rKjs7u9D+LS0tYW1tXWz/crkccrlcvZ2Tk1OmWoiIiIiMpdIHUKlUisePH79yvzp16higGt1FRUVh7dq1xi6DiIiIqMwqfQCNiYnB/PnzX7nfr7/+qj7TKZVKNV57cWbyxeu6enHms2D/z58/h0wmK7b/sLAwDBkyRL2dk5OD4ODgMtVDREREZAyVPoCGhoYiNDS0RPvK5XJYWFhAIpGgU6dO6vYXczMLzg0tLRsbG9SsWVNrrmdaWhoEQSi2f7FYzDvkiYiIqFLgTUgvEYvFaNeuHY4eParRfuTIEXh5eaF27dplfg9fX1+cOHECCoVC3Xb48GHY29ujZcuWZe6fiIiIyNQxgBYwatQoXL58GREREUhMTMTq1atx8OBBjBkzRmM/Hx8fzJs3T6Pt9OnTiImJwd9//w0g/4lKMTExSE1NVe8zbNgwPH36FLNmzcL58+fx888/Y9OmTRg5cqTG0kxERERElZVIEATB2EWYmuPHj2s9irNv374a+7Rr1w4hISH4/PPP1W1vv/027t27p9Xfhx9+qBFgL168iG+++Ub9KM5BgwaV+lGcUqkU/v7+iIuLg52dXekHSURERAYlWqLQ2BamVPqZkEViAK2gGECJiIgqFgbQf/ESPBEREREZFAMoERERERkUAygRERERGRQDKBEREREZFAMoERERERkUAygRERERGRQDKBEREREZVNVdgIqIiIhMV95zYPNx4PTfQO82wCBfwIznzSoLBlAiIiIyPa8NA3Ly8v8eFQdM2QDcWmvUkkh/+E8JIiIiMi2Ldv4bPl+4/QS4LDFKOaR/DKBERERkWhbvKrx9zCrD1kHlhgGUiIiITMtr9oW3+3gbtg4qNwygREREZFqauhfeXt3BsHVQuWEAJSIiItMytnfh7SN7GrYOKjcMoERERGRaercGfBtptr3bGajzmnHqIb3jMkxERERkek4vBK7dBXbFA8P9Abfqxq6I9IgBlIiIiExTw9rA9P7GroLKAS/BExEREZFBMYASERGR6fn+N8B8ICDqD4gHAUcuGrsi0iMGUCIiIjItjzOBcT8CKlX+9nMl0OsL49ZEesUASkRERKblzfmFt09cZ9g6qNwwgBIREZFpyZMX3q5QGLYOKjcMoERERGRaejQvvN2nUeHtVOEwgBIREZFp6d268PZODQ1bB5UbBlAiIiIyLb6NATORZpu1GPCsYZx6SO8YQImIiMi0HP8LUAmabTI5cCnNOPWQ3jGAEhERkWlJKiJoSp8Ztg4qNwygREREZFp+Ty28/eQVw9ZB5YYBlIiIiEzLe10Lb3+ns2HroHLDAEpERESmpb8PYGmh2eZkCzSoZZx6SO8YQImIiMi0HP8beF5g0fmMHCDlnnHqIb1jACUiIiLTolCWrp0qHAZQIiIiMi3dmwHVrDTbajkDjesapx7SOwZQIiIiMi1/3gBy8zTbHmQAd58apRzSPwZQIiIiMi3307XbVALwMNPwtVC5YAAlIiIi09K9ef5d7y973Q1o4WGcekjvGECJiIjItNjbANGzgbavAxbmQLemwL5ZgBljS2Vh8epdqp4TJ05g1apVSEtLg5ubG0aMGIE+ffoUe8zz58+xcuVKJCUl4cqVK5DJZIiJiYGTk5PGfqtXr8batWu1jp8xYwYGDhyoz2EQERFVXL6NgcRIY1dB5YQBtIALFy5g6tSp6Nu3LyZPnozz58/jyy+/RLVq1RAQEFDkcTKZDLt370aTJk3QunVrnD17tsh9rays8MMPP2i01alTR29jICIiIjJlDKAF/Pjjj2jatClmzZoFAGjXrh1u376N1atXFxtA7e3tcezYMYhEIuzbt6/YAGpmZobmzZvrvXYiIiKiioCTKV4il8uRmJioFTR79eqFGzdu4O7du8UeLxKJyrM8IiIiokqBZ0Bfcvv2bSgUCnh6emq0e3l5AQAkEglq165d5vfJy8tDQEAAsrOz4e7ujsGDB6Nfv37FHiOXyyGXy9XbOTk5Za6DiIiIyBgYQF+SlZUFIP9y+sscHBw0Xi+LevXq4ZNPPkGjRo0gl8tx8OBBLFiwAFKpFO+//36Rx0VFRRV68xIRERFRRVPpA6hUKsXjx49fuZ+hbgIKCgrS2O7SpQueP3+On376Ce+99x4sLAr/SMLCwjBkyBD1dk5ODoKDg8u1ViIiIqLyUOkDaExMDObPn//K/X799Vf1mU6pVKrx2oszny9e17fAwEAcPXoUt27dUl/uL0gsFkMsFpfL+xMREREZUqUPoKGhoQgNDS3RvnK5HBYWFpBIJOjUqZO6XSKRAIDW3FAiIiIiKj3eBf8SsViMdu3a4ejRoxrtR44cgZeXl15uQCrMoUOHYG9vj3r16pVL/0RERFSxJCcnIzAwELa2tnBzc8O0adM0bkYuiiAIiIiIgLu7O2xsbNCpUyfEx8cboOLSqfRnQEtr1KhRGDNmDCIiIhAQEIDff/8dBw8exMKFCzX28/HxQXBwMP773/+q206fPo1nz57h77//BpD/RKVq1aqhfv36qF+/PgBg6NChCAkJgaenJ2QyGQ4ePIjY2FhMnjy5yPmfREREVHWkp6ejR48e8Pb2xs6dO3Hnzh1MmjQJubm5+O6774o9dtGiRZg7dy4iIiLQokULfP/99+jVqxcuXLigziKmgImngFatWmHx4sVYtWoV9uzZAzc3N8yZM0drbVClUgmVSqXRFhERgXv37qm3582bBwD48MMPMWbMGAD5d8Fv3boVT548AQA0aNAAX375Jd56663yHBYRERFVED/88AOysrKwa9cuVK9eHQCgUCgQHh6OWbNmFXlFViaTYeHChZg8eTImTpwIAOjatSsaNmyIJUuWYOXKlQYbw6uIBEEQjF0ElZ5UKoW/vz/i4uJgZ2dn7HKIiIjoFURLFBrbwpTCzwP6+fmhevXq2L17t7otIyMD1atXx7p16zBixIhCjzt27Bh69uyJP//8E61atVK3T5o0CTt37lTf02IKOAeUiIiIyIQkJyejcePGGm1OTk6oVasWkpOTiz0OgNaxb7zxBm7evIlnz57pv1gd8RJ8BfXixDWfiERERKQbW1tbk3yMdnp6OpycnLTanZ2d8fTp02KPs7KygrW1tdZxgiAgPT0dNjY2+i5XJwygFVRubi4AcDF6IiIiHRl6GltRl9yrIv4kKihXV1dER0ejWrVqevvX24unK0VHR8PW1lYvfRpbZRtTZRsPwDFVFBxTxVDZxlTe4zHVn5GzszMyMzO12tPT09U3JRV1XF5eHmQymcZZ0PT0dIhEIjg7O5dLvbpgAK2gzMzMULNmzXLp29bWttLd2FTZxlTZxgNwTBUFx1QxVLYxVbbxvErjxo215npmZmbi3r17WvM7Cx4HAFevXkXLli3V7cnJyep1QU0Fb0IiIiIiMiFvvfUWYmJikJGRoW7bvn07zMzM0KtXryKP8/X1hYODA7Zv365ue/78OXbu3ImgoKDyLLnUGECJiIiITMjYsWNhb2+P0NBQHD58GFFRUZg6dSrGjh2rsQZoz5490aBBA/W2tbU1Zs6ciSVLlmD58uU4duwY3nvvPTx58gRTpkwxxlCKxEvwpCYWi/Hhhx9CLBYbuxS9qWxjqmzjATimioJjqhgq25gq23hKytnZGUePHsUnn3yC0NBQ2NvbY9SoUViwYIHGfkqlEgqF5tqi06dPhyAIWLJkCR49eoRWrVrh0KFDJvUUJIAL0RMRERGRgfESPBEREREZFAMoERERERkUAygRERERGRRvQqqEbt26hU2bNiEpKQnXr1+Hh4cHtm3b9srjBEHAhg0bsH37dmRkZKBhw4aYNGkSmjdvrrHfo0ePsHjxYpw7dw4WFhbo3r07Jk6cWG5rtOkynsePH2PLli04d+4cbt++DTs7O7Ru3Rrjxo1DrVq11PslJiZi7NixWscHBgZi4cKFeh/LC7p+Rm+//Tbu3bun1X769GlYWVmptw39GQG6jamonz8AeHh4YMeOHcXuV56fU0xMDH777TckJycjKysL7u7uePfdd9GnT59iH/5gqt8jXcdk6t8lXT8nU/4u6TImU/4uAcCpU6ewceNGpKamIicnBzVq1EC3bt0wevToV/4sd+/ejY0bN+L+/fvw8PBAeHg4unbtqrGPVCrF0qVLERcXB4VCgY4dO2LatGlwcXEptzFR2TCAVkLXr1/H6dOn0bRpU6hUKqhUqhIdt2HDBqxevRrjxo2Dt7c3tm/fjnHjxmHLli2oW7cuAEChUGDcuHEAgPnz50Mmk2H58uWYM2cOli1bZjLjuXLlCmJjY9GnTx80b94cGRkZ+PHHHzF8+HD88ssvWk+DmDt3Ljw9PdXbhT2DV590/YyA/GU3hg4dqtH28h2ixviMAN3G1LhxY0RFRWm05eTkYPz48fD19dXa35Cf05YtW1CrVi1MmDABzs7OOHfuHBYsWIAHDx5g9OjRRR5nqt8jXcdk6t8lXT8nwHS/S7qMyZS/SwCQlZWFpk2b4t1334WjoyOuX7+ONWvW4Pr16/j++++LPO7QoUNYsGABRo4cifbt2+Pw4cOYMmUKfvzxR41/1M2cOROpqamYOXMmxGIxVq5cifHjx2Pjxo2wsGDUMUkCVTpKpVL997lz5wqDBg165TEymUzw8/MTvvvuO3WbXC4XQkJChIULF6rbDhw4ILRr1064ceOGuu3s2bNC27ZthcuXL+tnAAXoMp6srCzh+fPnGm33798X2rVrJ2zatEnddv78eaFt27bCX3/9pb+CS0CXMQmCIISEhAgRERHF7mOMz0gQdB9TQXv37hXatm0rJCUlqduM8Tmlp6drtc2fP1/w8/PTGOvLTPl7JAi6jcnUv0u6jEkQTPu7pOuYCjKV71JRdu7cKbRt21Z4+PBhkfv069dPmDVrlkZbWFiY8Mknn6i3L168KLRt21Y4e/asuu3GjRtCu3bthMOHD+u/cNILzgGthMzMSv+xXrp0CTk5OQgICFC3WVpaonv37jh9+rS67cyZM/D29tb4l7OPjw8cHR019tMnXcZjb2+v9a/emjVrwtnZGY8ePdJXaTrTZUwlZYzPCNDfmA4ePAh3d3c0bdpUL/3pqrAzQo0aNUJOTg6ePXtW6DGm/D0CdBuTqX+XdBlTSVWkz6kwpvJdKoqjoyOA/Cf1FOb27du4efMmAgMDNdp79eqF8+fPQy6XA8j/nOzt7eHj46Pex9PTEw0bNizXz4nKhgGUAAASiQQANH7RAoCXlxfu378PmUym3s/Dw0NjH5FIBA8PD3UfpiotLQ1Pnz6Fl5eX1muffvopOnTogKCgICxfvlw9XlN08OBBdOrUCV27dsX48eORkpKi8XpF/oyePHmCxMRE9O7du9DXjf05XbhwATVq1ICtrW2hr1fE79GrxlQYU/8ulXRMFem7VNrPyVS/S0qlEnl5eUhOTsaPP/4IPz8/jSf7vKyo75OnpyeeP3+Ou3fvqvfz8PDQmh/r5eVl8r/zqjJOjCAA+fNzxGKxxuR7IP/shyAIyM7OhrW1NbKzs2Fvb691vIODA7KysgxVbqkJ//9UCFdXV41fyHZ2dhg2bBjatGkDKysrnD9/Hps3b8aNGzfKdY6Xrvz8/NCsWTO4ubnhzp07WLduHT744AON+YUV9TMCgCNHjkCpVOLNN9/UaDeFz+nChQs4fPgwJkyYUOQ+Fe17VJIxFWTq36WSjqkifZd0+ZxM9bv09ttv4+HDhwDyn1te8Mk+L8vOzlbX/DIHBwcAQGZmJoD8711hn5O9vb3J/86ryhhAqUpYs2YNEhISsGLFCtjY2KjbGzdujMaNG6u327dvDxcXFyxevBhJSUlo1qyZMcot0tSpU9V/b926NTp27IgBAwZg8+bNmDFjhhEr048DBw7gjTfe0DrrZOzP6cGDB5g5cybatWuH//znP+X6Xoai65hM+btUmjFVlO+Srp+TqX6Xli9fjmfPniE1NRU//fQTJk6ciO+//x7m5ubl+r5kengJngDk/4tSLpcjLy9Poz07OxsikUj9r0t7e3tIpVKt47OystT/KjU1u3btwtq1azFr1ix06NDhlfu/mG+UnJxc3qWVmYuLC1q1aoUrV66o2yriZwTkz/f666+/tM7YFMVQn1N2djbGjx8PR0dHLF68uNi5rhXle1SaMb3MlL9Luo7pBVP8Luk6JlP9LgGAt7c3WrRogdDQUHz99ddITExEbGxsofu++L4U/AxenNV8MYfUwcGh0M8pOzvbpH/nVXUMoATg3zk2aWlpGu0SiQRubm6wtrZW71dwTo0gCEhLS9Oap2MKYmNjERERgbFjx6Jv377GLscgKtpn9MLBgwdhZmZW5Jw1Y5DJZJgwYQKkUim+/fbbV65XWBG+R6Ud0wum/F3SdUyvUhE/J8A0v0uF8fb2hoWFBW7fvl3o6y9+xgU/A4lEAktLS9SpU0e9X1paGgRB0NrPlH/nVXUMoAQAaNGiBWxtbRETE6NuUygUiI2NRefOndVtvr6++Oeff3Dz5k11W0JCAjIzMzX2MwWJiYmYPXs2QkNDMWrUqBIfd+jQIQBAkyZNyqs0vXn06BEuXLigUWtF+oxedujQIbRt27bEC0eX9+ekUCgwc+ZMSCQSrFixAjVq1HjlMab+PdJlTIBpf5d0HVNBpvRdKuuYTO27VJSkpCQoFAp1kCyobt26cHd3x9GjRzXajxw5gvbt28PS0hJA/ueUlZWFhIQE9T5paWm4evWqSf/Oq+o4B7QSkslkOHXqFADg3r17yMnJUf8fYtu2beHs7IyPPvoI9+7dw+7duwEAVlZWCAsLw5o1a+Ds7IwGDRpg+/btyMzM1FioOSAgAFFRUZg2bRo+/vhjyGQyLFu2DF26dCm3uUO6jOfGjRuYMmUK6tWrh6CgIFy+fFndn7Ozs/omg88++wx169ZF48aN1RPyt27dCn9//3L9ZazLmA4ePIhTp06hc+fOcHV1xe3bt7F+/XqYm5sb/TPSdUwvJCcn48aNGxgyZEihfRvjc1q0aBFOnjyJCRMmICcnR+O/oUaNGkEsFleo75GuYzL175IuYzL175IuY3rBFL9LQP6c2zfeeAPe3t6wsrLCtWvXsGnTJnh7e8Pf3x8AMG/ePERHR+PcuXPq40aPHq2uuW3btjhy5AiSkpKwdu1a9T4tWrRAp06dMG/ePEycOFG9EL23tze6d+9ebmOismEArYSePn2qNYn+xfYPP/yAdu3aQalUQqlUauwzfPhwCIKAzZs3Iz09HQ0bNsSKFSvU/wcDABYWFlixYgUiIyMxe/ZsmJubo3v37pg0aZJJjScpKQlSqRRSqRQffPCBxrEhISH4/PPPAQD169fHgQMHsGXLFsjlctSuXRthYWEICwsrt/HoOqY6derg0aNH+Prrr9V357Zv3x5jxozROINgjM9I1zG9cOjQIYjFYvTs2bPQvo3xOcXHxwNAoXcG7927F7Vr165Q3yNdx2Tq3yVdxmTq3yVd/9sDTPO7BABNmzbF4cOHsWHDBqhUKtSqVQv9+vXD0KFD1WcyVSqV1pjefPNNyGQybNiwAevXr4eHhweWLFmCFi1aaOy3cOFCLF26FAsWLIBSqYSPjw+mTZvGpyCZMJFQcNIEEREREVE54hxQIiIiIjIoBlAiIiIiMigGUCIiIiIyKAZQIiIiIjIoBlAiIiIiMigGUCIiIiIyKAZQIiIiIjIoBlAiojJYvXo12rVrh7t37xq7FCKiCoOPCCCiKiUxMRFjx47VaLOxsYGHhweCg4PxzjvvwNzc3EjVERFVDQygRFQl9e7dG507d4YgCHj06BH279+Pr7/+GqmpqZg9e7axyyMiqtQYQImoSmrcuDGCgoLU2wMHDsSgQYOwe/dujB07Fq+99poRqyMiqtwYQImIANjZ2aF58+Y4duwY7ty5g9deew1SqRQbNmxAbGws7t69CxsbG3h6euKdd95B7969i+zr0aNH2Lx5M86fP4979+4hLy8PderUQXBwMN5//32NS/x5eXlYv349Dh06hAcPHsDS0hI1a9aEr68vPv30U/V+p06dwsaNG3H9+nXIZDI4OTmhSZMmGDduHDw8PMr1Z0NEpG8MoEREAARBwO3btwEATk5OyM7OxgcffIDU1FT07NkTAwcOhFKpxNWrV3Hq1KliA+g///yD2NhY+Pv7o27dulAoFDh79iy+++473LlzR+MS/6JFi7B3714EBwdjyJAhUCqVuHXrFs6fP6/e5/fff8ekSZPw+uuvIywsDHZ2dnj8+DESEhJw69YtBlAiqnAYQImoSpLJZMjIyIAgCHj8+DF++eUXXLt2Dc2bN4e7uzsiIiKQmpqKWbNmoX///hrHqlSqYvtu06YN9uzZA5FIpG4bPHgwPvvsM+zZswdjxoyBi4sLACAuLg6+vr744osviuzv+PHjUKlU+P7771G9enV1+6hRo3QZOhGR0XEZJiKqklavXo2AgAAEBgbivffew969e+Hn54clS5ZApVLh8OHD8PLy0gqfAGBmVvyvTmtra3X4fP78OTIzM5GRkYFOnTpBpVLh77//Vu9rZ2eH1NRUpKSkFNmfnZ0dAODYsWNQKBS6DJeIyKTwDCgRVUn9+vVDQEAARCIRbGxs4O7uDkdHRwDA06dPkZWVhU6dOunUt0KhwPr16/Hbb7/h1q1bEARB4/WsrCz13ydNmoS5c+fiP//5D+rUqYN27dqha9eu8PPzUwfdd955B8ePH0dERARWrFiBli1bwtfXF71794azs7OOPwEiIuNhACWiKsnd3R0+Pj7l0vc333yDX375BYGBgRg5ciScnZ1hYWGB5ORkrFixQiOQ+vv7Y+/evTh9+jT++OMPJCQkYM+ePWjdujVWrlwJS0tLODk5YePGjfjzzz9x7tw5/Pnnn1i6dClWr16N5cuXo0WLFuUyDiKi8sIASkRUgJOTExwcHPDPP//odPxvv/2GNm3aYOHChRrtt27dKnR/R0dHBAUFISgoCIIgYMWKFdi4cSOOHz+OgIAAAIC5uTnatWuHdu3aAci/0Wno0KH46aefsHz5cp3qJCIyFs4BJSIqwMzMDL1790Zqaip2796t9XrBS+qFHV9wn2fPnmHr1q0abUqlEtnZ2RptIpEIjRo1AgBkZmYCADIyMrTew9PTE9bW1hqX84mIKgqeASUiKsRHH32E8+fPY/78+Th37hxatmwJALh69SoUCgW+/PLLIo/t2bMndu7ciZkzZ6JDhw548uQJ9u3bp55j+kJubi7efPNN+Pn5oVGjRnB2dsbdu3fx66+/wsHBAX5+fgCA+fPn4+HDh/Dx8UGtWrWQl5eHI0eOICcnB8HBweX3QyAiKicMoEREhXBwcEBUVBTWrVuH2NhYxMbGwtbWFl5eXnj33XeLPXbSpEmwtbXFkSNHcPz4cdSsWRP9+vVDkyZNEB4ert7P2toa7733HhISEpCQkIDc3Fy4uLjAz88PYWFhcHV1BQAEBQVh3759iI6ORnp6OmxtbVG/fn0sWrQIPXv2LNefAxFReRAJr7qWRERERESkR5wDSkREREQGxQBKRERERAbFAEpEREREBsUASkREREQGxQBKRERERAbFAEpEREREBsUASkREREQGxQBKRERERAbFAEpEREREBsUASkREREQGxQBKRERERAbFAEpEREREBsUASkREREQG9X9vK6Gxi5ItYQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explainer.scatter_plot(feature=\"Pclass\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "predicted probability for label 1 == Survived\n"
     ]
    },
    {
     "data": {
      "image/png": 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m2lbW32kiIiJFgZJZKRQaljUxq6zrzSuKiIhIoaLhKxERERFxWkpmRURERMRpKZkVEREREaelZFZEREREnJaSWRERERFxWkpmRURERMRpKZkVEREREaelZFZEREREnJaSWRERERFxWkpmRURERMRpKZlNR0REBCNGjKBVq1Z07NiRqVOnkpycfNN2hmHw+eef07VrV1q2bMmgQYPYu3dvunU3b97M4MGDadWqFW3btmXYsGGcPXs2py9FREREpFBTMptGbGwsw4cPJyUlhUmTJjFixAiWLl3KlClTbtp23rx5zJw5k/79+/P+++8TFBTEyJEjOXnypF29VatW8fzzz9OwYUM++OADJkyYwG233UZSUlJuXZaIiIhIoeSW3wEUNEuWLCE+Pp5JkyYREBAAgMVi4Z133mHw4MGUKlUq3XaJiYnMnTuXAQMG8NBDDwHQoEEDevfuzYIFC3jxxRcBiImJ4Z133mH06NH07dvX1r5169a5fGUiIiIihY9GZtPYunUrTZo0sSWyAO3bt8dqtbJt27YM2+3Zs4f4+HjatWtnK3N3d6dt27Zs2bLFVrZ27VqsVis9evTInQsQERERKUKUzKYRERFBSEiIXZmfnx9BQUFERETcsB3g0DY0NJQzZ86QkJAAwL59+wgJCWHFihV069aNpk2b0r9/f7uEV0REREQyR8lsGrGxsfj5+TmU+/n5ERsbe8N2Hh4eeHp6OrQzDIO4uDgALly4QGRkJDNmzGD48OFMnTqVcuXK8dxzz3H06NEM+09KSsJsNtu+4uPjs3mFIiIiIoWH5szmMavVyuXLl3njjTds82QbNWpE7969mTdvHq+//nq67ebOncusWbPyMlQRERGRAk/JbBr+/v6YzWaH8ri4OPz9/W/YLikpicTERLvR2bi4OEwmk22092ofjRs3ttVxc3OjQYMGNxyZHTRokO3BMoD4+Hi6du2a+QsTERERKYSUzKYREhLiMDfWbDYTFRXlMB82bTuAyMhIwsLCbOURERGULVsWLy8vAKpUqZJhHzdamsvDwwMPD4+bX4CIiIhIEaI5s2m0aNGCHTt22Oa4AoSHh+Pi4kKzZs0ybFevXj18fHwIDw+3laWkpLB+/XpatmxpK7vzzjsB2LFjh60sOTmZP/74g5o1a+bkpYiIiIgUehqZTaNPnz4sWrSI0aNHM3jwYM6dO8fUqVPp3bu33RqzTzzxBKdPn2bZsmUAeHp6MmjQID799FMCAwOpVq0aixcvJiYmhgEDBtja1axZk7vvvpu33nqLmJgYgoKCWLx4MRcvXuSRRx7J68sVERERcWpKZtPw9/dn+vTpTJo0idGjR+Pj40PPnj0ZMWKEXT2LxYLFYrErGzhwIIZhsGDBAqKjowkLC+Ojjz6iYsWKdvVee+01Pv74Yz7++GPi4+OpWbMmn3zyCdWqVcv16xMREREpTEyGYRj5HYRkndlspk2bNmzYsAFfX9+8D+Dgv9DrHTh2BsoUh9kjoH39vI9DRETEWZj6OJYZS/I+jkJGc2YlexqPhb9PQmIKHI+Czm9C7OX8jkpERESKGCWzknVrd0HcFfsyixUmLcv5c12Ig0c/ggpDoO0rsPVAzp9DREREnJbmzErW+XqnX+6XQfmteHAKrN2d+v+nLkLH1+HoNChdPOfPJSIiIk5HI7OSdc1rQPkS9mVe7vBc95w9z/mYa4nsVeYEWP5bzp5HREREnJaSWcmevz+EextD2eJwZy3Y9wG45fBAv7cHeLo7lhf3ydnziIiIiNPSNAPJHv9i8P243D2Hrzc82Qmm/HCt7LZK0L1xxm1ERESkSFEyKwXb5EehcXVYswvCysOwDumP1oqIiEiRpGRWCjaTCfq1Sv0SERERSUNzZkVERETEaSmZFRERERGnpWRWRERERJyWklkRERERcVpKZkVERETEaSmZFRERERGnpWRWsm/Fb9DtLZi+Or8jERERkSJK68xK9jR/EbYdSv3/lb/D69/A6Tn5G5OIiIgUORqZlaw7dOpaInvVmUsaoRUREZE8p2RWsu6H39IvX7Ezb+MQERGRIk/JrGTdI23SLx/WIU/DEBEREVEyK1lXKgAeaW1f1rga3Nskf+IRERGRIksPgEn2zHsaJj8Kc3+G7o2gVqX8jkhERESKICWzkn2lAmBsr/yOQkRERIowTTMQEREREaelZFZEREREnJaSWRERERFxWkpmRURERMRpKZkVEREREaelZFZEREREnJaW5hIREZECx2oYfHvQYMspg3qlTAyoZcLTzZTfYUkBpGRWRERECpzha63M2mP898rg6wMm1t7nmq8xScGkaQYiIiJSoPwbZ/DZXsOuLDzSYOu/RgYtpChTMisiIiIFysUEsKaTt56/omRWHCmZFRERkQKlThDULGFfFugF91TWnFlxpGRWREREChSTycQPvVzpHGrC1x1aVoDVfVzx9VAyK470AJg4nXO7LrJrxkEun0sguF15bn+8Oi7u+rtMRKQwqRZoYlUfPfAlN6dkVpxK7PF4Vj68GUuCBYCovZdIuJhI8/H18jkyERERyQ8azhKncnTFSVsie9WhbyPzKRoRERHJb0pmxam4pLNgdnplIiIiUjQomU1HREQEI0aMoFWrVnTs2JGpU6eSnJx803aGYfD555/TtWtXWrZsyaBBg9i7d2+G9a1WKwMGDKBRo0aEh4fn5CUUWtV6VMIzwN2u7LaHq+ZTNCIiIpLflMymERsby/Dhw0lJSWHSpEmMGDGCpUuXMmXKlJu2nTdvHjNnzqR///68//77BAUFMXLkSE6ePJlu/e+++47z58/n9CUUaj5lvOn+TWtqPhBCpbZlufP/GnDHqJq31KdhGFy5kIiR3qKGIiIiUqDpAbA0lixZQnx8PJMmTSIgIAAAi8XCO++8w+DBgylVqlS67RITE5k7dy4DBgzgoYceAqBBgwb07t2bBQsW8OKLL9rVv3TpEtOnT+fpp5/m9ddfz92LKmSKV/Gj1ZsNcqSv0zui2DTuD2KPx+NboRit3qhPxTvL5EjfIiIikvs0MpvG1q1badKkiS2RBWjfvj1Wq5Vt27Zl2G7Pnj3Ex8fTrl07W5m7uztt27Zly5YtDvU//vhjGjZsSKNGjXL2AiTTUhItrBu5ndjj8QCY/73Muqd2kGS++ZQSERERKRiUzKYRERFBSEiIXZmfnx9BQUFERETcsB3g0DY0NJQzZ86QkJBgK9u3bx+rV6/mmWeeyZmgJVui9l0iITrJriw5PoVzf17Mp4hEREQkq5TMphEbG4ufn59DuZ+fH7GxsTds5+Hhgaenp0M7wzCIi4sDUh/6evfddxkwYADly5fPdFxJSUmYzWbbV3x8fKbbFgiXEyHaDEfPQNJ/I5+X4iHxJqOghgFRsWC15nhIfhWKYXJ1XAnBr5JPjp9LREREcofmzOaxZcuWceHCBR599NEstZs7dy6zZs3KnaByk9UKz8yB6ash5b+E1MUElYMg4jz4ecMLveDlvo5tN/0Fgz+BI6chpDTMGAYdc2auLIBPWW/qDq7GnlmHbWW1HgolIMQ3x84hIiIiuUvJbBr+/v6YzWaH8ri4OPz9/W/YLikpicTERLvR2bi4OEwmE35+fly+fJlPPvmEESNGkJycTHJysm2ENSEhAbPZjK9v+onUoEGDbA+WAcTHx9O1a9fsXmbemR0OH62yL7MaqYksQNwVGL8QGlW1T1QTk6HPu3D+v9HwiHNw32Q4OQv8i+VYeE3G1iGkfXnO7bpIUJ3ilG0clGN9i4iISO5TMptGSEiIw9xYs9lMVFSUw3zYtO0AIiMjCQsLs5VHRERQtmxZvLy8OHXqFDExMbz99tu8/fbbdu0nTJhAyZIl+emnn9Lt38PDAw8Pj2xdU7768Y/M1Vv9p30y+8exa4nsVXFXYMsB6HxHzsUHlG5QgtINSuRonyIiIpI3lMym0aJFC+bOnUtcXJxt7mx4eDguLi40a9Ysw3b16tXDx8eH8PBwWzKbkpLC+vXradmyJQAlS5ZkxowZdu0uXLjAyy+/zNChQ2natGkuXVU+qlo2e/WCS4GrC1jSzJWtomWzRERE5Bols2n06dOHRYsWMXr0aAYPHsy5c+eYOnUqvXv3tltj9oknnuD06dMsW7YMAE9PTwYNGsSnn35KYGAg1apVY/HixcTExDBgwABbnbRLcZ06dQqAKlWqcPvtt+fNRealZ7vD4q1wPCrjOreHwMC29mXlS8CYHvDO0mtlwztCjQq5EqaIiIg4JyWzafj7+zN9+nQmTZrE6NGj8fHxoWfPnowYMcKunsViwWKx2JUNHDgQwzBYsGAB0dHRhIWF8dFHH1GxYsW8vISCpYQv1K58LZk1mVJXKHAxwd114fH20KMJeLo7tp34cOqxbYegYRW4q3bexi4iIiIFnskwDO3h6YTMZjNt2rRhw4YNGT40ViBMWgZj52d8fMMb0DrrSeqZnVFsn7iPS0fiKNskiBav3o5fhZx7MExERCTHmfo4lhlL8j6OQkbrzEru2nLgJsf/znKXiTFJ/DTkV87vjiY5PoUT688QPiLj3dlERESk8FIyK7nr9pAbH68fmuUuT246R3J8il3Zhb9iiI10XFJNRERECjcls5K7nukGd1S59vr6DbcevBM6ZX0TBK8SjkuUubib8PBPZ96tiIiIFGp6AExyV6Av7JwEG/+ChKTUB7m2HISQUnB71kdlAco3K0WZRiU5u/OCrey2h6rgFeh5g1YiIiJSGCmZldxnMtk/5NWjya1152Ki89yWHF56nEtH4ijXNIjg9uVuMcjCKSkumch1pzG5mAhuVw73YvonLyIihYt+s4lTcvNypdaD2RvZLSpiIsyseHAjV6ISAfAp6023r+/Sqg8iIlKoaM6sZM/89VBmEPj0g7tehotx+R2RpLFr2gFbIgsQf+YKez49lI8RiYiI5Dwls5J189fDwI/gXAxcToJNf0O1ETdvJ3kq5h/H1R1iIrTig4iIFC5KZiXrxn/lWBYdDz/9mfexSIbKtyjtUFYhnTIRERFnpmRWsi4xOf3yS/F5G4fcUP3hYYR2roDJBUyuJqr1qESdQdXyOywREcmExYsX06NHDypWrIiPjw/169dnzpw53Gzj1gEDBlC9enV8fHwIDAzkrrvuYs2aNXZ1jh07Rrdu3ahYsSJeXl6UL1+e++67j0OH7KeirVq1itatW1OqVCk8PT2pUqUKzz33HDExMTl+vbdCD4BJ1o3qCuMX2pd5usEDrfInHkmXm7cb93zYhIToREwuJjwDHNfnFRGRgmnKlCmEhITw3nvvUapUKdauXcvjjz/OiRMnePXVVzNsl5SUxHPPPUf16tVJSEjgs88+o0uXLqxfv54777wTALPZTNmyZXn77bepVKkSp0+f5u2336Zt27bs3r2boKAgAC5evEjTpk0ZNWoUJUuWZN++fUyYMIF9+/Y5JMj5yWTcLMWXAslsNtOmTRs2bNiAr69v3p7cMGDkLJi1FlIsUDYQVr0M9avcvK2IiEhRZerjWGYsSbdqVFSULam8aujQoSxatIjo6GhcXDL34brFYiE0NJROnTrx6aefZljv8OHDhIWF8eWXX9K/f/8M682aNYuhQ4fy77//Ur58+UzFkNs0zUCyzmSCT4ZC0mKwfgenPlMiKyIikoPSJrIADRo0IDY2lvj4zE/rc3V1pXjx4iQlJd2wXsmSJQFyrF5eynIye/nyZV577TXCw8NzIx4RERERScfmzZupUKECfn5+N6xnGAYpKSlcuHCByZMnc/jwYYYNG+ZQz2q1kpycTEREBCNHjqRSpUr06tXLoZ7FYiEhIYE//viD119/nXvvvZeQkJCcuqxbluVktlixYqxZswazWUv8iIiIiOSFzZs38/XXXzNmzJib1v3ss89wd3cnKCiI1157jUWLFtG8eXOHeo888ggeHh6EhoayY8cOwsPDCQgIcKgXHByMt7c3DRs2pFy5cixcuNChTn7K1pzZhx9+mBYtWvDEE0/kRkySCfk6ZxZS58r+vAeOR8GQ9nl/fhERESczu/pSh7Ihhx1HQtM6efIkTZs2pVatWqxZs+am82WjoqKIiIggKiqKxYsX8+WXX7J06VI6d+5sV++ff/4hKiqK48eP88EHHxAZGcnmzZupXLmyXb09e/YQHx/P/v37efPNN6lSpQpr167F1dU1E1ed+7KVzK5du5aJEycyZ84cgoODcyMuuYl8TWYXb4H737Mve+0BeOWBvI3DGZ2Jhqc/g7V7oEZ5eOdhuKt2fkclIiJ5IDvJ7KVLl7jzzjsxmUxs2rQp3ZHTm+nVqxeHDx9m3759Gda5fPky1atXp1evXnz88ccZ1tu9ezf169dn8eLF9O3bN8ux5IZsLc0VERFBmTJl6NevH61ataJy5cp4eXnZ1TGZTAwZMiRHgpQCJNoMD7znWP7qIiWzmfHAe7Dxr9T/33YIOr8J/0yH0sXzNSwRESl4rly5Qrdu3YiJieHXX3/NViIL0LBhQ3788ccb1ilWrBi1atXiyJEjN6xXr1493N3db1ovL2Urmb1+aYcNGzakW0fJbCG15QBkNJb/xzG4Q6saZOhM9LVE9qrLibDidxh8T/7EJCIiBVJKSgr3338/f//9N5s2baJChQrZ7mvz5s1UqXLj38+xsbHs2bPnpqOt27dvJzk5+ab95aVsJbPLly/P6TjEWVQvl/Gx2wrGenMFlo8XeHlAQprlTEre+KlUEREpekaMGMGKFSt47733iI2NZdu2bbZjDRo0wNPTk3vuuYfIyEjbKOnKlSuZP38+3bp1o1KlSly8eJGFCxfy008/8dVX17ainzBhAjExMbRs2ZJSpUoRERHBhx9+SGJiIs8884ytXu/evWnUqBH16tXD29ub3bt3M2nSJOrVq0fPnj3z6lbcVLaS2XLlbpDQSOFWowLc1wIWb7Uvr1kB0kw1kTT8vGFUF3h32bWyBqHQ5Y58C0lERAqmqztsjR492uHYP//8Q0hICBaLhZSUFFt51apVSUxM5MUXX7RtulCvXj02bNhA69atbfXuuOMOpkyZwhdffIHZbKZChQrcddddLF682G7EtUmTJixatIiJEyditVoJCQnh8ccfZ8yYMXh4FJxdJW95B7BLly5x6tQpAMqXL0/x4sVzIi65iXxfzWDVTnh4KsQnwv0tYe5IKCBPNRZ4S7fB2t2pfxgMvic1yRURkUIvu6sZyI1la2QW4NChQ0yePJldu3bZldevX5/nn3+e6tWr32psUpBt/Bsu/rcDyRe/QNwVWPpi/sbkLHo1S/0SERGRW5atZPbIkSMMGTKExMREWrdubRuSPnbsGBs3bmTIkCHMmTOHqlWr5miwUkCcj4EpP9iXLdsB2w9B07D8iUlERESKpGwlszNnzsTNzY3PPvvMYQT2yJEjDB06lBkzZjBp0qQcCVIKmLOXIDnFsfxElJJZERERyVNZ3s4W4I8//uC+++5LdypBtWrV6Nu3L3/88cctBycF1G2VoEoZ+zIfT7inXv7EIyIiIkVWtpLZhIQESpYsmeHxoKAgEhISsh2UFHAuLlC1rH1Z2UAIKJY/8YiIiEiRla1ktkKFCmzevDnD45s3b76lxX2lgDsRlfo0/vWOnoGf9+ZPPCIiIlJkZSuZ7dKlC7/++isvv/wyR48exWKxYLFYOHLkCOPHj2fbtm1069Ytp2OVgiLtov9XXcmgXERERCSXZOsBsIcffpiDBw+yZs0a1q5di8lkAsAwDAzDoF27dgwYMCBHA5UCpHp5aFEDth68VlahBLS/Pf9iEhERkSIpW8msq6srb7/9Nj169OCXX37h33//BVKnH7Rp04amTZvmaJBSAC17EcYvhI1/Qb1geKN/6latIiIiInkoU8nsa6+9Rp8+fahTpw6QuppBaGgozZo1o1kzLf5eJJUKgJlP5Nvpz8YbvLfTyi8nDBItEBZoYkR9E20qZ2vmjIiIiDipTP3mX7FiBSdPnrS9Hj58ONu3b8+1oERuJNlicNfXFib9ZrDjDOw+D4sPGdyz2Ep4pDW/wxMREZE8lKlktnjx4ly4cMH22jCMXAtI5GZWRxgcinYstxrwyZ96b4qIiBQlmZpmUK9ePebMmcOZM2fw9/cH4Oeff+bEiRMZtjGZTAwZMiRnohS5TrIl42NJNzhWEJyLN1h/wqBKgInG5Uz5EoNhGPxywuBCAnQIMeHnkT9x5AaL1SA8MnXqSYcQE15uhefarvfnWYND0QatK5ko61M4r1EKvqs/z6oWN9GorN6Hkn8ylcyOHj2aCRMmsGjRIgzDwGQysX79etavX59hGyWzkls6h5oo7wunzI7HHqtbcH+gLjtspd8KK4n/Jdz9appY2NXFthpIXricbNDxWwubU5/ZJNALfurjmm+JdU6KumzQ9hsL+6JSX1fwhQ0PuFIt0Pmv7XqDfrTw+f7UTyA8XGF+ZxceqKm54pK3CsLPM5GrMpXMli9fnk8//ZTk5GQuXLhA9+7dGT16NK1bt87t+EQceLub2PCAKxO2Wtn8r0GKFUL84ak7XOgdVjB/qVsNg6d+vvaDH+DrAwaD6hh0CMm7H/5z9xm2RBYgOgHG/GLhl37ZWtikQHn/d6stkQX41wyvbLGysJtr/gWVwzadNGyJLKR+EjHqZyu9q5twd1USIXnDYk3/59ngOgbt8/DnmchVWfoN5u7uTtmyZenWrRt16tShXLlyuRWXyA1VDzTxZdeCm6QcvGiwL8qgWTkTFfxMXEqAk3GO9badMugQkrPnPnDBYP8Fg+blTZT3tf/Fsi/KcU7x9QngVYkpqR/Xe7oapBgmTMDdldNPmCxWgw0nDJIscE+wCY98SqrSu4696VxvfrqcnHpfA71MtKpAlkex9p53vJ5zl1O/KvjlVJQiNxaTmP7Ps71R0D4kz8MRyd46s6+++mpOx1GgRERE8O6777Jnzx58fHzo0qULI0aMwN3d/YbtDMNg3rx5LF68mEuXLhEWFsZzzz1H3bp1bXW2b9/OsmXL2LdvHxcvXqR8+fJ0796d/v374+bm/KNjAi9utPDOjtSkw80FZrR34bG6LtQJcky43txmUDvISp8cGlEe+0vqKg9Xzz2zvQuD617r+/xlxzZ3VrRPqA5HG7RdZOFf2zSO1P6qFU/92L6C37X6F64Y3P2NhT3nU18H+8PP97tSpXjeJ7R3VjSx/Kh9sndXxYIzSrTrnEGHxRbOX0l9fWdFWN3HlWLumY8xvespWwzK++ZUlCI3V8LbRO2SsP+CfXlB+vcmRUvB/Ew2H8XGxjJ8+HBSUlKYNGkSI0aMYOnSpUyZMuWmbefNm8fMmTPp378/77//PkFBQYwcOdJuWbPvvvuOy5cvM2zYMKZOnUrXrl2ZOXMmb731Vm5eluSRvy8YtkQWIMUKz663Yk4y+LyTK2WL2ddPtsLIdVZSrLc+grg/yrAlslfP/cx/5waIiDH47rD9eUzAay3sfwG9vMl6XSJ7zZFL8OY2+6XPpuy02hJZgMhYeHVr/iyP9lQDE32qp44iA9xVEd5oWXB+xI3ZYLUlsgCbTsKcvVn7vl9KdCxLtoKlYA1ASxEwr7MrwanPg+PlBm+2ctFDYJJvNBSYxpIlS4iPj2fSpEkEBAQAYLFYeOeddxg8eDClSpVKt11iYiJz585lwIABPPTQQwA0aNCA3r17s2DBAl588UUAxo0bR/HixW3tGjVqhNVqZfr06Tz99NN2x8Q5nDIbvLPDyv4oKOHleDwuCY7FQMOyJl5uZuKpn+0zjzPx8OXfBgNr39ovgvQ+Uo9LgogYqFMqNdlNW8MALqeY+PIvK1/+bRDgCTvOZJwZ7UnzMff1iWxGdXLbV39bWfCXgZ8HPN/EhQ/uhkQLVM2H0eEbSe/7szuL9yq9e3shAc7Ga5qB5K2GZU0cHeLKgYupnwwEeuXsvzdzksGk36xs+RfqlYIXm7hQWit3SAYKzrBFAbF161aaNGliS2QB2rdvj9VqZdu2bRm227NnD/Hx8bRr185W5u7uTtu2bdmyZYutLL1ktUaNGhiGQVRUOpP+pEBL+m8Dhw//MFh33GDxIcdko5Q31CyR+v8tK6T/w/jRH60sPXxrI5rNy5lwSdN9KW8I++/cjcua8EgzzdjfA7b+a2XAKis//mPw9QGDE+nMhbuqVZr4077OqCy3zNhlpf9KK6v+MVh0MPV7EXWl4CWykP73Pu0Uj5tplU79qsU1zUDyh6uLidpBphxPZAH6LLfy+q+pP1ff/z11pRJLDnyCJYWTktk0IiIiCAkJsSvz8/MjKCiIiIiIG7YDHNqGhoZy5swZEhISMmy7a9cuPDw8KF++fDajlvzyU4TB0UuO5d7/feYR5A3zOrvYHoqKTcr4h/60Xbf2gzo4wMRHd7tQ7Lpzz+9y7dylfUzMbO+Cn0fq8UAvmNvJhdlpPuq2GjhMh4DUB8Beamb/I2PUHSburXrtmlpWgNda5N2PlU922f8BkGSB2XsK5i5w77dxod5/H+y4mGBgbRMP1cpaElCvlIm373TB878/Ssr5pH7cq+WQpDA5Em2wJsL+59JfF2DDCSWzkj5NM0gjNjYWPz/Hz+v8/PyIjY29YTsPDw88PT0d2hmGQVxcHF5ejp9BHz9+nK+//po+ffpQrFg6GcR/kpKSSEpKsr2Oj4/PzOVILrNkkDf9NsCFJIuJ20qC53UL91tusHteRn1lxYgGLjx0m4ljl3A4N8CjdVzoG2bicHTqaLG3u4kXNzmeuG8NE0/Wd8HbDVIMMAzSXa/V293E971ciYhJ3aigRom8TarSu2cFdf5ocICJ3QPd2B9lUNwTuwfpsuLFpi4MrWfieBzUCQK3tMPxIk4uo3/DBfXftuS/W05mT5w4wYULF6hWrRq+vvqsKyvMZjPPP/885cuXZ8SIETesO3fuXGbNmpVHkUlmdQo1UdkPjl/30XynEBO1g9IfnWxd0USNEnDwouOxobfnTFIS4GmiQZmMj/t62B8fWs+F53+5lhW6ucCgOi7ULJn5eEIC8iehGnq7C8+ut499cJ2C/YFT7aBbv1clvE2U8M6BYEQKoBolTNxVETZee3aaasVTPx0SSU+2k9lNmzYxefJkTp8+DcAnn3xC48aNuXjxIoMHD2bkyJF280edhb+/P2az46PccXFxtq18M2qXlJREYmKi3ehsXFwcJpPJYbQ3OTmZ559/nri4OObMmYO3941/Mw0aNMj2YBmkjsx27do1s5clucTLzcQv/Vx541cr+y8YtK5oYnzzjJMpVxcTP9/vyuu/Wll/3CDBAsF+qSOq/fJpF6cxjVOnHnz5t5Xiniaea2jijjLO8UvjmYYu+LjDgr+s+HuYeK5R/m0TLCI55/ueqT9Xt5wyuL2Uif81d9GnEJKhbCWzO3fuZMyYMdSoUYNu3brx6aef2o6VKFGCihUrsmbNGqdMZkNCQhzmxprNZqKiohzmw6ZtBxAZGUlYWJitPCIigrJly9pNMbBarYwfP56///6b2bNnU7Zs2ZvG5eHhgYeHR5aupcA5dRGmroCjZ6DzHTDobnAp2KNomRESYOKzTpnfwKG8r4kZ7QvWhg/Dbndh2O3O+b14vJ4Lj9dzzthFJH3FvUy817Zg/ZyUgitbvwFmz55NWFgYn3/+Offdd5/D8bp163Lw4MFbDi4/tGjRgh07dhAXd+1z4/DwcFxcXGjWrFmG7erVq4ePjw/h4eG2spSUFNavX0/Lli3t6r7zzjts2rSJ9957j2rVquX8RRREcVeg+Yvw7jJYsg2GTIPRn+d3VCIiIuLkspXM/vXXX3Tq1AmXDEbVypQp47TLTF19EGv06NFs27aN5cuXM3XqVHr37m23xuwTTzxBz549ba89PT0ZNGgQCxYs4KuvvuK3337jpZdeIiYmhgEDBtjqzZkzhyVLlvDAAw/g4eHB3r17bV/pTW8oNL7bBsfTvCdmrIEr6awCLyIiIpJJ2ZpmYLVab/iR96VLl2669WtB5e/vz/Tp05k0aRKjR4/Gx8eHnj17OjygZbFYsFgsdmUDBw7EMAwWLFhAdHQ0YWFhfPTRR1SsWNFW5+patV988QVffPGFXfsZM2bQqFGjXLqyfHY5naQ1KSV1myoRERGRbMpWMhsaGsqff/6Z7hQDSH047Pp5o84mNDSUadOm3bDO9fOErzKZTAwaNIhBgwZlqV2R0LsZvLgAYi9fK+vTDPz0SLaIiIhkX7amGfTo0YN169axbNkyjP/WzTSZTCQkJDBp0iT27t1Lr169cjRQcXJlisPPr0G3RlCrIjzbHeY8md9RiYiIiJPL1shs37592b17N2+99RYffPABJpOJl19+mUuXLmG1WunevTudO3fO6VjF2TWsCj+8lN9RiIiISCGS7XVm33jjDe6++25WrVpFZGQkhmFQu3Ztunbtyj333JOTMYqIiIiIpOuWdgBr27Ytbdu2zalYRERERESyRCuNi4iIiEiuOnLkCKtXr7Yr2759O927d6dly5a39IB8tkZmZ82addM6JpOJIUOGZKd7ERERESlEXnjhBS5evEinTp0AiIqKonPnzpjNZry9vXniiScoXbq03Rr+mZWtZPZG2bPJZMIwDCWzkjlfbYKPV6WuNzukHTzePr8jEhERkRy2c+dOhg4danv91VdfERsby65duwgLC6NNmzZMnTo175LZ5cuXO5RZLBZOnjzJwoULMZvNTJgwITtdS1GyfAf0f//a6x2Hwd0VHr07/2ISERGRHHf+/HnKly9ve7169WpatmxJnTp1AOjXrx9vvfVWtvrO1pzZcuXKOXxVrFiRZs2aMXXqVFxcXPjhhx+yFZAUIXN/diybk05ZOsynL3N+TzRWi2Eru3QsjgsHYnIqOhEREckhPj4+XLp0CUgdAN28eTN33XWX7bi3tzexsbHZ6vuWVjNIj8lk4p577uGLL77gySe1KL7cgFc6WyJ73XgbZMMw2DphNwe+/gfDCr7lvWk7tQl/fvg3JzedAyCobnE6zmqBd0nP3IhaREREsqh27drMnz+fRx55hMWLF2M2m2nf/trUwsjISEqVKpWtvnNlNYPk5GRiYjRCJjcxsjO4udqXJSbDY5/AH0fTbXJi/Rn+XpiayAKYT13h56d32BJZgKi9l/j9g79yK2oRERHJoueff569e/dSunRpnnzySRo0aMCdd95pO75mzRruuOOObPWd4yOzf/31F19//TUhISE53bUUNi1rwaY3YcYaOHIathyAjX+lfn25EbZNhPqhdk3O/nnRoZv4U1ccys7tcqwnIiIi+aNr1678/PPPfP/99wQEBDBy5EhMJhMAFy5coGLFijzyyCPZ6jtbyWyPHj3SLY+JieHy5cu4uroyfvz4bAUkRUyzGv99vWBfnpgM01fDzCfsikveVtyhC+9Snlw5n2hfr5ZjPREREck/d911l9082atKlizJd999l+1+s5XMlilTxpZNX2UymahRowbBwcH06tXL7om1rDpz5gwzZ85k+/btXLx4kY8++ojGjRsTHR3Nhx9+SN++faldu3a2+5cCKCHZsexKkkNRSIfyhHYqzz+rTwHgGeBO63casnPKX0TtuwSAf2Uf7ni6Vm5GKyIiIrcoJSWF77//nosXL9K9e3fKli2brX5yfJ3ZW/Xvv/8yaNAgEhMTqVu3LlFRUbZjgYGB/P333yxbtkzJbGHzaFt4du611yZTukt0GRaD6r0qU/qOkvhVLEaFlqVxL+ZGhValOfv7BSxJVso1CcLFTZvbiYiIFBRjx45l/fr1/Pbbb0DqA93t2rVj06ZNGIbBSy+9xLZt26hatWqW+87xObO3atq0aZhMJhYtWoSXl5fdk24ALVu2ZNOmTfkUneSap7uBqwt8vh58vOCZbnB3Xbsql88lsKL/RmIj4wHwKedNty/vxL2YGyaTibKNgvIjchEREbmJ1atX065dO9vrH374gY0bNzJ27Fjq16/PU089xcSJEzO1y2xaBS6Z3bFjBw888ABly5a1rUd2vXLlynH27Nm8D0xyl8kET3VN/crAnlmHbIksQPzpK/zx8QFav9MwLyIUERGRbDpx4gTVq1e3vf7hhx8IDQ1l4sSJAOzfv58vv/wyW31nKplt3LixwxzZmzGZTGzfvj3LAcXHxxMUlPEIW3JyMhaLJcv9ivOLPhznUHbpiGOZiIiIFCxJSUm4uV1LO9evX283UlulShVOnz6drb4zlcx27do1y8lsdpUpU4ajR9NfYxRg7969VKpUKU9ikYKlbJMg/t1yzr6sccl8ikZEREQyq1KlSvz66688/vjj7N+/n2PHjvH666/bjp87dw5fX99s9Z2pZHbChAnZ6jw72rZty5IlS+jRo4dthPZqIr1u3TrWrVvH0KFD8yweKTjqDq5G1L5oIsNPgwEV7yxNgydr5ndYIiIichP9+vXjjTfe4Ny5c+zfvx9/f3+6dOliO/7nn39m6+EvKIBzZgcPHszmzZt59NFHueOOOzCZTHz++ed88skn7N+/n7CwMAYMGJDfYUo+cPNypf20ZsSfuYJhNfAtXyy/QxIREZFMGDduHCdOnGDZsmUEBAQwf/58ihcvDqTuU7B8+XKeffbZbPVtMgzDyMFYc4TZbGbGjBmsXr3ati2un58fnTp1YsSIEdkehi5MzGYzbdq0YcOGDbofIiIiTmB29aUOZUMO98qHSAoWq9VKXFwcxYoVw93dPcvtsz0yu2vXLj7//HP27dtHXFwcaXPi7DwAZrFYOH/+PN7e3owZM4YxY8YQHR2NYRgEBgbm2bxdEREREckbLi4uBAQEZLt9tpLZP/74wzZCWqdOHbZs2ULjxo25fPky+/fvp1q1atSsmfW5jCkpKfTo0YMnn3zStj9vYGBgdkIUkWz6N87g7e1W9kYZtK5oYmwTF3w99IekszhtNvi/7Vb2nDe4s6KJF5q44Kfvn4gUEGfPnmXnzp1ER0djtVodjl/N/7IiW8nsnDlzCAoK4osvvsBkMtG+fXsGDRpE48aN2bZtGy+88AIvvPBClvv19PSkePHieHt7ZycsEblFSRaD1ossHL2U+nrjSYPfz1pZ2cc1X+OSzEmxGrRZZOFQdOrrjScNdpy2suY+ff9EJH9ZrVaefPJJZs+enW4Se1V2ktls7fm5f/9+evbsaffR/9XAmjVrRpcuXZgxY0Z2uqZFixba4Uskn6yNMGyJ7FWr/jE4HlvgptZLOtZFGrZE9qq1kQZHL+n7JyL5a/LkycycOZMHH3yQefPmYRgGEydO5JNPPqF69eo0atSItWvXZqvvbCWzSUlJlCpVCgAPDw8ALl++bDseFhbG33//na2Ann76aaKionj11Vc5cuQIiYmJ2epHRLIuo5Sn4D0mmvMMw+DvCwbRCc57sc4buYgUdvPmzaNTp07Mnz+fzp07A9CwYUOGDx/O77//TlRUFL///nu2+s7WNIOgoCDOnUtdvN7b2xs/Pz+OHj1K27ZtgdSFb6/f5SEr2rdvj8lk4vDhw/z444/p1snu7mIicmMdQkxUCYBjMdfKOoeaCA4o3HMu95436P29hSOXwNMVXmxiYkJL5/to/p7KJqoHwuHrRmfbBZuoWrxwf/9EpOA7duwYw4YNA1If+ILUXV0BfHx8GDRoELNnz+b555/Pct/Zyjhvu+02du/ebXvdtGlTFi5cSNmyZTEMg2+++YbatWtnp+s83W1MROx5uJr4pZ8rb22zsi/K4K6KJsY1zdYHOE5l0OrURBYg0QKv/WrQPsSgZQXn+lnk7mpi/f2utgfAWlUoGt8/ESn4vL29bctu+fr6YjKZbAOjAGXLluXEiRPZ6jtbyWyPHj1YsWIFCQkJeHl58eSTT7Jr1y5ee+01AEqWLMmoUaOyFVBe7jYmIo4q+pmY3t75RiWz63Kywe9nHct/OeF8ySxABT8Tn7QrOt8/EXEOwcHBHD16FAB3d3eqVavG6tWrefjhhwEIDw+nTJky2eo7W8lss2bNaNasme11xYoV+e6779ixYweurq7Ur19fC/mLiFPwdoPQAPgnxr68TlD+xCMiUhjdfffdLF26lMmTJwPw8MMP88orr3Dq1CkMw2DTpk2MGTMmW33n2Ha23t7etG7dOqe6A1IfKktvQwZIHY4WEblVJpOJj+52oe8PVhJSUst6VjPRrarzjcqKiBRUY8aMoUOHDiQmJuLp6cm4ceM4d+4cCxYswNXVlaFDh9o+4c+qbG1n+9BDD9G9e3c6depk21c3J/3000989tlnREREZFhnx44dOX5eZ6LtbEVy1vnLBuuOGwT7m2heXomsiOQ8bWebO7L1ZEB0dDTvvfcenTt3ZvTo0fz888+kpKTkSEAbNmxg/PjxWCwWevfujWEYdOzYkXvuuQc3Nzdq1qzJkCFDcuRcIiJXlSpmol9NFyWyIiJOJlvTDFauXMn27dtZuXIlv/zyC5s2bcLPz48OHTrQrVu3bK9kAPDFF18QGhrKF198wZUrV1iyZAn33nsvjRs35siRIzz22GMMHjw42/1LwXJy01kOfhOBydVEzQdDKd+0VH6HJCIiIrfo+PHj2WpXuXLlLLfJVjJrMplsD4FduXKFdevWsXLlSr777juWLFlC5cqV6datG48++miW+z5y5AiDBw/G09OThIQEACwWCwDVqlWjd+/efP7557Rp0yY7oUtOOxMNft7g45XlppHrTrP2iW22ld7/+fFfOn/eivLNldCKiIg4s5CQkGwttXo158uKW34AzNvbm27dutGtWzfOnj3LqlWrmDdvHtOnT89WMmuxWAgICADA09MTSJ0felVwcDDffvvtrYYtt+pEFNw/GbYdgmKe8GIv+N/9WerirwXH7LYsMqzw15fHlMyKiIg4uVdeeSXP9g3IsdUMTp48ycqVK/nxxx+Jj4/P9g5gpUuX5syZMwB4eXlRokQJDhw4QLt27QCIjIzE29s7p8KW7Bo2IzWRBbicCK98Dc1rQLvbM92FNcXqWJbsWCYiIiLOJS/3DbilZNZsNrNmzRpWrlzJ3r17MQyD6tWr88wzz9j23c2q22+/nR07djB8+HAA7rrrLr766is8PT2xWq1888033HXXXbcS9k1FRETw7rvvsmfPHnx8fOjSpQsjRoyw7VyREcMwmDdvHosXL+bSpUuEhYXx3HPPUbduXbt658+f591332X79u24ubnRtm1bnn32WedalWDt7vTLspDM1ugbwultUfZl94fcYmAiIiJSlGQrmd20aRMrV65k06ZNJCUlUaJECfr160e3bt0ICwvLcn/79u2jUqVKBAQE0LdvXzZs2GDbXWzEiBHs37+fTz/9FIAqVarw9NNPZyfsTImNjWX48OFUrlyZSZMmce7cOd5//30SEhJ44YUXbth23rx5zJw5k5EjR1K9enUWL17MyJEj+fLLL6lYsSIAKSkpjBw5EoA333yThIQEpk6dyvjx4/nggw9y7bpyXFh5+OuEY1kWVOtRCQODg1+nPgBW66EqBN9TLgeDFBERkYJg0aJFrFy5kvnz56d7fODAgXTv3p2+fftmue9sJbPPPfccHh4e3HnnnXTr1o3mzZvj6pr97RMHDx7M66+/TqdOnahduzahoaG88cYbPPbYY1SpUoWFCxdy+PBhXFxcCA0NxcUl9/YaX7JkCfHx8UyaNMk2d9disfDOO+8wePBgSpVKfz5nYmIic+fOZcCAATz00EMANGjQgN69e7NgwQJefPFFIHW7tmPHjrF48WJCQkIA8Pf3Z+TIkezbt486derk2rXlqMkDodc7kJic+rpuZeh/Z5a7qd6jMtV7ZP3JRREREXEeH3/8MVWrVs3wuKurKx999FG2ktlsZYUvvPACq1evZuLEibRq1eqWElnAYYevpKQk1qxZw4ULF2xl1atXp2rVqrmayAJs3bqVJk2a2BJZgPbt22O1Wtm2bVuG7fbs2UN8fLxtbi+k7j3ctm1btmzZYtd/9erVbYksQNOmTQkICLCrV+CVDoCAYtde7z0Ozcelrm4gIiIicp2///6bBg0aZHi8QYMG/PXXX9nqO1uZYd++ffHz88vWCQu6iIgIu0QTwM/Pj6CgoBvuSHb1WNq2oaGhnDlzxrbMWEREBMHBwXZ1TCYTwcHBN+y/wBk2A86l2cx+dwT876t8CUdEREQKrvj4+BsOfppMJuLi4rLVd+4Oczqh2NjYdBN1Pz8/YmNjb9jOw8PDtpzY9e0Mw7B9g+Li4tLt39/f/4b9JyUlYTabbV/x8fGZvaScl2KB34+mf2z74byNRURERAq80NBQNm/enOHxzZs3Z2vDBMjBpbkkd82dO5dZs2bldxip3FyhbjDsjXQ8dkeVvI9HRERECrRevXoxceJE2rdvz2OPPWZ3bM6cOSxevJjnn38+W30XmGR2y5YtREWlLtOUkJCAyWQiPDycgwcPOtQ1mUy2h6xymr+/v90mDVfFxcXh7+9/w3ZJSUkkJibajc7GxcVhMplso7F+fn7p9h8bG0uZMmUy7H/QoEF21xwfH0/Xrl0zdU25YvpQ6P42RF93LdXLwev98i8mERERKZBefPFFvv/+e4YOHcr7779P/fr1Adi9ezd//fUXNWrU4KWXXspW3wUmmV29ejWrV6+2K/vuu+/SrZubyWxISIjD3FWz2UxUVJTDfNi07SB1U4frlyeLiIigbNmyeHl52eodOXLErq1hGERGRtK0adMM+/fw8MDDwyNrF5ObWtaCk7Ng019wIQ4qlIRWNeEWHwYUERGRwsfPz48tW7Ywbtw4Fi1aZHvYKzAwkCeeeII333zzhoOGN1IgktkZM2bkdwg2LVq0YO7cuXZzW8PDw3FxcaFZs2YZtqtXrx4+Pj6Eh4fbktmUlBTWr19Py5Yt7fr/8ccfOX78uG1uyI4dO4iJibGr5xSKeULHjJ9MFBERETl//jzHjh0jKCiIadOm8cknn9g+jQ8KCrrlbW+zlMympKTwyy+/cOLECYoXL06bNm0oXrz4LQUA0LBhw1vuI6f06dOHRYsWMXr0aAYPHsy5c+eYOnUqvXv3tltj9oknnuD06dMsW7YMAE9PTwYNGsSnn35KYGAg1apVY/HixcTExDBgwABbu3bt2jF37lzGjh3Lk08+SUJCAh988AGtWrVynjVmRURERG7CarUyYsQIZs+ebVuGtXnz5ixdujTDdfuzI9PJbGxsLMOGDePo0aMYhoHJZOKjjz7i448/platWjkWUH7z9/dn+vTpTJo0idGjR+Pj40PPnj0ZMWKEXT2LxYLFYrErGzhwIIZhsGDBAqKjowkLC+Ojjz6y7f4F4ObmxkcffcSkSZN4+eWXcXV1pW3btjz33HN5cn0iIiIieeHjjz/m008/pXz58jRv3pzDhw+zdetWhg0bluFU0uwwGWl3LMjA+++/z8KFC2nVqhXNmzfn+PHjLFmyhKpVq7JgwYIcC0gyx2w206ZNGzZs2ICvr29+hyMiIiI3Mbv6UoeyIYd75UMkeaNRo0ZcuXKFbdu22aZuPv7443z++eecP38+Rz7dhyyMzG7atInmzZvz/vvv28rKlSvH1KlTOXv27A2fxBcRERGRouXgwYO88sorduvrP/XUU3z22WccOnSIJk2a5Mh5Mr1pwtmzZx0eULrrrrswDIPTp0/nSDAiWRYVC61eAt8Hocpw+PGP/I5IRERESF1GtHz58nZlV1/n5OZPmU5mk5KSCAgIsCu7mmknJyfnWEBSCD30PnjeD259ocIQKP8YNH8R1u1JPW61wpuLocoTcNso+HTNtbZRsTDwQyg3GFqPhy1/2/fdYDRsOQDxifDPOej6FpyMyrtrExERkQylXang6utMznLNlBxZmutWl1SQQmzM57Bw07XXpy6m/vd0dGri+feHsORX+N9X1+oMmwGlA6BnU3hwCoT/l/SeuQQd34Bj06B0cTh6Gk5esD+fYcDr38Cn9g/siYiISN5btWoVZ86csb2+fPkyJpOJxYsXs2vXLru6JpOJZ599NsvnyFIyu2DBAn766Sfba4vFgslkYtq0aQ6jtiaTiSlTpmQ5IClk5m/I+FhiMny3Db5KZ6/mhZugZc1riexV8Qmw/DcY0j51W930uBeI5ZNFRESKvIULF7Jw4UKH8pkzZzqU5Ukye/DgwXS3l927d2+6AYmQbLnx8eI+EFAs/XJvD/B0T0160x4DCC4NVcrAsbPXjrm6wKv331rMIiIicsvWr1+fJ+fJdDL722+/5WYcUgjFRJr5u+qdBB46TI24o44VSvjC/S2hQgnY+BdYrMS7FuNM8fKU6NGWQF9vGNkZ3lt+rU3tStC98bXXu6fAA+/BtkOp/cx8InUKgoiIiOSr1q1b58l59Hms5Ipd0w+yc8pfQCkoU4qdJevzQMRS3LCmVnB3hTWvgp83dLoDtr7Nwf/7lc1/+WIYJnjmb+rsSqLZpIHQuBqs3Q3Vy8GwjqmjtVf5esPK8flyjSIiIpL/Mr2aQWZduHCBefPmcd999+V01+JE/vjQftWBK27F+D24KZQvAV0bwsY3oWFV2/Hk2qFsiwhMTWT/s+/zo1w8FAsPtILZT8ILva9NMRAREREhh0ZmrVYrmzZt4vvvv2fr1q1YLBaKFUtnHqQUCUnmFKwpjktuRLVqBgueT7eN+dRlkuNTHMovHoylRI2AdFqIiIiI3GIyGxERwfLly1m1ahUXL17Ez8+Pzp07c88999C0adOcilGcjIevG27erqRcsX/4q/LdZTNsExDii3cpT66cT7SVmVygTMOSuRaniIiIOL8sJ7NXrlxhzZo1LF++nL179+Lq6srtt9/OxYsXefnll7n77rtzI05xMq0nN+Lnp7Zj/DdFNqheIHUHV8+wvou7C20mNWL96J0kXEjErZgrTV+oi18FjfCLiIhIxjKdzO7atYvly5ezbt06Ll++TI0aNXjuuefo1KkTcXFx9O7dOzfjFCdzass5WyILcOlwLElxSXj4eWTYpkLL0jy4sRMx/8ThW6EYHr7uGdYVERERgSwks48//jglSpSgV69edOvWjWrVqtmOmc3mXAlOnJPVauXA1//YlaVcsbBj0n5avd7ghm1dPVw0R1ZEREQyLUurGSQmJmI2m5W8yg2lXLbajcpeFfuP3jciIiKSszI9Mrt48WKWLVvGjz/+yPLlyylfvjzdunWja9euuRmfOCEPXzc8AtxJirHfuSusb3A+RSQiIiKFVaZHZkNCQnjmmWdYuXIlEydOJDg4mFmzZtGzZ09eeOEFTCYThuG4HJMUTZ0/a4mH/39zXk1QtXtFqvWonL9BiYiISKFjMm4hA42KimL58uX88MMPnDx5Eg8PD5o3b87dd9/NXXfdha+vb07GKtcxm820adOGDRs2FOj7fPl8Ah4BHrh55Pj+HCIiIk5ldvWlDmVDDvfKh0gKl1taZzYoKIjBgwczePBgfv/9d77//nt+/vlnfvnlF9zd3dm6dWtOxSlOqlgpL/uCiHPwz1loGgbFPDPXSXwC7DgMVctC5VI5H6SIiIg4rRzZAQygYcOGNGzYkLFjx7J69WqWL1+eU11LYfHMZ/DhKjAMCPSFb5+Hu+veuM1Pf8ID70HMZXBxgdH3wruP5E28IiIiUuDl+Ge/vr6+9O3bl/nz5+d01+LMNv0FU1emJrIA0WYYOv3a6/RYLDBkWmoiC2C1wqRl8NvhXA9XREREnEOmR2ZjYmKy3HlAgNYLlf/8dsSx7OgZuGiGkn7ptzlzCU5eSL+vxhnvJiYiIiJFR6aT2Xbt2mEymTLdsclkYvv27dkKSgqhxtUcy6qWhRI3eHitbHGoWNIxoW2UTl8iIiJSJGU6me3atatdMpuYmMjatWtp1qwZQUFBuRKcODfDanB65wVKhPnhdedtWJ7ojMuM1Ziuzpn99Am40R9Irq4we4TdnFnjuXu5XLkiXklWXLVCgoiISJGX6WR2woQJdq8vXbrE2rVreeSRR2jcuHFOxyXO4MlPYfZacHeDtwfAsA7gkbq27L55R9j21l74b0qsyQUMaxAuVR6kcmUTbb7tjFuJYjc/R8cGcHIW/HaEKIsP6ycdJebO1XgGetD0hTqE9dFGDCIiIkWZhrYkeyo8BtNWQ5IF4hNh1Gdw+7NwJRFLosUukQVs29taTa5EnHDhlwl7M38uX2+MNnX4+Z0jxBxL3RI3MTqJTS/9QdyJ+By8KBEREXE2SmYle05FO5YdOAVfbebM7xfsEtn0/LvlfJZOZz55mdgI+8TVsMKpbVnrR0RERAoXJbOSs46dJbCa/02r2ba6zSTvIE/cfR1nxQSEFNzdz0RERCT3KZmVnNWpAcVKe1Hq9sAbVmvyfO0sdevm7UbjMbXhuufFQjtXoGxjPXwoIiJSlN3yDmBZWa5LCpHQ0vDPOfuywXdDq1oA9Pi2DfsXHGX3jEOY3EyUbVySuMjLuLi7cMeompRvmvVtaW97qArlm5fi1K/nKV7NL1t9iIiISOGS6WS2X79+dq+tVismk4k33ngDb29vh/omk4mvvvrq1iOUgmnleLj3bThyOnW0dGgHmD7MrkrtAVWpPaBqjp62eBU/ilfJYJMFERERKXIynczGx8c7jMKWLVsWwzC4fPlyjgcmBVytinDwI9gbCaUDoFyJ/I5IREREiqBMJ7M//PBDbsYhzsjFBW4Pze8oREREpAi75TmzIgVKtBlmh0PEOejWCDrfkd8RiYiISC7KkWQ2JSWF/fv3c/78eUJDQ6laNWfnSYpkSnwCNHsRDp1KfT1tderOZC/2zt+4REREJNdkOpnduXMn69ev57HHHqNEiWvzI//991/GjBnD0aNHbWVdu3bl1VdfzdlIRW7mmy3XEtmrJn4HY3qAm2v+xCQiIiK5KtPrzK5YsYJff/3VLpEFeO211zhy5Aj16tWjf//+hIaGsnLlSlasWJHjwYrc0EWzY1lcAiSn5H0sIiIit+DIkSMMHz6c+vXr4+bmRp06dTLd9t9//2XgwIGUKlUKb29vatWqxZdffulQb9u2bbRr1w4/Pz/8/f1p1qwZu3btsh1v06YNJpMp3a+vv/46Jy4zR2R6ZHb//v00a9bMriwiIoI///yTBg0a8OmnnwIwfPhwHnroIVauXEm3bt1yNlqRG+nZFF76EpKuS167NwJvz/yLSUREJBv279/PypUradq0KVarFavVmql2p0+fpnnz5tSoUYNPP/0Uf39/9u/fT2Jiol29n3/+mS5dujB48GBeeOEFkpOT2bFjh90KVdOmTSM2Ntau3QcffMCSJUto167drV9kDsl0MnvhwgUqV65sV7Zz505MJhM9e/a0lXl5edGpUycWLVqUY0HmtY0bNzJ9+nQiIyMpW7Ysjz76KPfee+9N25nNZqZMmcKGDRtISUmhWbNmjB07lqCga7tULVmyhJ9//pkjR46QkJBAaGgojz76KG3atMnFKyoiqpaF71+ElxdC5Hnocgd8MDi/oxIREcmy7t2706NHDwAeffRRdu7cmal2Y8eOpVKlSqxevRpX19Qpdvfcc49dnZSUFB577DGefvpp3nnnHVt5ly5d7OrddtttDv3v2LGDDh062OU2+S3TyWxSUhKenvYjXH/99RcAd9xh/8R4mTJlMJvT+cjXCezatYvnn3+eHj16MHr0aH777TfeeOMNihUrdtO/QsaNG8exY8cYN24cHh4eTJs2jVGjRjF//nzc3FJv9Zw5c2jevDl9+/bF29ubdevWMWbMGCZMmKCR7JzQ6Y7ULxERESfm4pLpmaA2sbGxfPPNN8yZM8eWyKYnPDyciIgIRo0alaX+t27dyj///MObb76Z5dhyU6aT2bJly3Ls2DG7sl27dhEYGEjZsmXtyhMSEvDzc85dmmbPnk3t2rV56aWXAGjUqBEnT55k5syZN0xm9+zZw6+//srHH39sm44RHBzMfffdx/r162nfvj0AX375JcWLF7e1a9asGadOneKLL74oFMlsSpKVPz/6i+gjZkrU8CP6YBz+wT40HFULt2JaCU5ERCS3/PHHHyQlJeHu7k7r1q3ZunUrJUuWZODAgbz55pu4u7sDqXNlS5Ysyc6dO7n77rs5evQoVapUYfz48TzyyCMZ9r9w4UJ8fHxsI8YFRaaziwYNGrBy5Up69OhBtWrVWL9+PSdOnEg3ATty5AilSpXK0UDzQlJSEjt37nT4S6VDhw789NNPnDp1ivLly6fbduvWrfj5+dG0aVNbWUhICGFhYWzZssWWzF6fyF5Vo0YNDhw4kHMXklc27IOh0+HoGbAapLi4sLDq/SQZqf9YjoeftlX9++sI+m/qiIefR35FKyIikq8eH9bdoWxIDvZ/5syZ1D6HDOHxxx9nwoQJ7Nixg1deeQUXFxfefvttW734+HgGDRrE66+/zm233cbChQsZOHAgZcqUoWPHjg59p6Sk8M0333Dvvffi4+OTg1Hfukwns48++ig//vgj/fv3JyAggJiYGNzd3RkwYIBdPYvFwsaNG7n77rtzPNjcdvLkSVJSUggJCbErDw1N3eUqIiIiw2Q2IiKC4OBghy1/Q0NDiYiIuOF5d+3a5XDOAm//cWg3ASzXJqS7WK14JCWQ9N9fftdLiU/ht8l/0fK1+nkXo4iISBFy9SGxdu3a8d577wHQtm1b4uLimDx5Mq+88gre3t5YrVYSEhJ45513GDlyJAB33303Bw4c4K233ko3mV27di3nz5+nf//+eXdBmZTpCRkVKlTg008/pWXLlgQEBNCiRQtmzpzpsEHCzp07CQgIoHXr1jkebG67+sRe2ikS/v7+dsczapve1Ao/P78btlu9ejV79uzh4YcfvmFsSUlJmM1m21d8fPwN6+e6eRvsEllIfTNVjYvIsEnMsbhcDcmOYdzw8JmdURxeepz4M1fyKCAREZHcFRgYCOAwoHjPPfeQmJjIkSNHblpv//796fa9cOFCSpYsmW6im9+yNInxtttu4/33379hnaZNmxaolQzMZjNRUVE3rVehQoU8iMbe4cOHefvtt+nevftNVzOYO3cus2bNypvAMsM9/YnlVkzplgNU61U5w2M5ZuJ3MPl7MCfAgLvgoyF2S3MZhsG6kTuIWJO6uYKLu4k2kxtRpUvF3I9NREQkF6W3+sD1EhISAKhdu/ZN61zvypUrLFu2jAEDBtjm3RYkhf6JnPDw8Ew9dfftt9/aRmDTrsRwdWT16vH0+Pv7c/bsWYfyuLi4dNudPn2aUaNGUbt2bV5++eWbxjdo0CAeeugh2+v4+Hi6du1603a5ZvA9qUljkv2GBAam1FHR66dbmCC0UwXCegfnbkzfboVxC669/mwdBPrCpIG2opObztkSWQBrssG2t/YS0rECLq4ZJ+IiIiIFXXBwMHXr1iU8PNw2fQBSpwh4e3vbkt2OHTvi7u5OeHi43WYMa9eupWHDhg79Ll++HLPZXCCnGEARSGZ79uxptw7ujSQlJeHm5kZERATNmze3lV+d83qjea0hISHs2LEDwzDs5s1GRERQrVo1u7qXLl1i5MiRBAYG8u6779qW7boRDw8PPDwK0MNTVcvCjndgxKfw+7HUXbY83WjW0oVG77fisocP/pV8iPs3Hu9AzyytZJAQnYiHv0fWk8tlO9Ivuy6ZvXTUcarD5XMJJMUm4RWozRVERKRguHz5MqtWrQIgMjKS2NhYvv32WwBat25NqVKluOeee4iMjLRNHwB466236NGjB8888wxdu3blt99+Y/LkyYwdO9b24FaZMmUYNWoU48ePx2QyUatWLb766iu2bdvG6tWrHWJZuHAhlStXplWrVnlw5VlX6JPZrPDw8KBRo0asW7eOBx980Fa+du1aQkNDM3z4C6BFixbMnj2bHTt22FY0iIyM5ODBgwwceC2Zunz5MqNGjSIlJYWZM2fi6+ubexeU224PhS1vOxS7AVfHov0qZP6Jx6h90fwy9neiD8dRrIwXzf9Xj9COWZj+UaHETcvKN3Vc5Dmwhr8SWRERKVDOnTvHfffdZ1d29fX69etp06YNFouFlBT7T0i7d+/OV199xRtvvMH06dMpV64cr732Gi+++KJdvYkTJ+Lr68ukSZM4f/48tWrVYtmyZXTo0MGuXnR0NKtXr+aZZ55xeMi9oDAZxk2elClidu3axbBhw+jVqxft2rXj999/Z/bs2bz99tt268w2bdqUrl278sorr9jKnnrqKY4dO8azzz5r2zTBxcXFbtOEJ598kp07d/LKK6847KhWt27dTMdpNptp06YNGzZscO6E+D+G1WDRPWswn7y2jZ6Luwv9NnakWJBX5jo5GQVNXoDT0amvPdzgx//B3fb3dc9nh/njw79JuWzBP8SHez5sSslaATl1KSIiIukyTU5xKDPGaFzxVimZTccvv/zisJ1t2gWCGzVqRLdu3ZgwYYKt7Op2tuvXr8disdC0aVPGjh1rt+Zuo0aNMjxvZrequ3quwpTMRh+JZUnndQ7lbac0omr3SpnvKCoWvtyY+gDY/S2gevqj6UnmZK5cSMS/sk+B/UtTREQKFyWzuUPJrJMqbMlsYmwSC1v8iCXRfrmve79tTenb05k+ICIi4mSUzOaOrG/8K5ILPP09uOOpmnZlVbpWUCIrIiIiN6Q/B6TAuH1YDSq0LM3p3y4QWM2PCq1K53dIIiIiUsApmZUCJahOIEF1AvM7DBEREXESSmZFrpMUl8zOKX9xcvNZ/Cv70vDpWpSqp+RaRESkoFIyK3KdX8b+TmT4aQBiI+I5+8cF7g/vgHdJrUMrIiJSEOkBMJH/JMYmEbnutF1ZsjmFyPBTGbQQERGR/KaRWckxMRFmTm48i2+FYlRqUzbr29HmMxc3F1zdXbAk2S8P5uatfyYiIiIFlX5LS444svwEvzy/E+O/PLBskyC6fN4SF3fnGfx3L+ZGzQdD2T/vqK3Mr5IPIe3L5WNUIiIiciNKZuWWGVaDHe/usyWyAGd2RBERfpoqnSvkX2DZ0OylupSsGWB7AKz2wKoamRURESnA9Ftabpkl0cLlswkO5XHHzfkQza0xuZgI6xtMWN/g/A5FREREMsF5PgOWAsvN240yjUo6lFdoVSYfohEREZGiRMms5IjWE++wrcfqGeBOi1dvJ6h28fwNSkRERAo9TTOQHOEf7EuPJW1IuJiIu687rh76O0lERERyn5JZyVFeJbS5gIiIiOQdDZ+JiIiIiNNSMisiIiIiTkvJrIiIiIg4LSWzIiIiIuK0lMyKiIiIiNNSMisiIiIiTkvJrIiIiIg4LSWzIiIiIuK0lMyKiIiIiNNSMisiIiIiTkvJrIiIiIg4LSWzAmeiodoIMPUG1z5w36SM6566CHWfTq17/dcrC/MuXhEREZH/KJkVuPtVOHom9f+tBnz7K0xckn7drm/CvhOO5W98CxZL7sUoIiIikg4lswIH/3UsW7DRsSwmHnZFZNzPpz/lWEgiIiIimaFkVsDT3bGsbHHHsmKe4JVO3asaVc+xkEREREQyQ8mswDPd7F+7usDUxxzrubvBS33S78PdFRormRUREZG85ZbfAUgB8H8DoGFV+HAllPSDSY9A1XLp1/3f/al1J3wNfxxLnWN7ezD8MSXXw4w+EsuaYdswn7yMu58bzV6uS1iv4Fw/r4iIiBRcSmYlVZ/mqV+Z0aVh6lce++GBjSTFJgOQFJPMxrF/ULZREP6VfPI8FhERESkYNM1AnML5HWdoeHQLD/6zhB4nVlE5PnVFhT2zDuVzZCIiIpKflMyKUwiY8jW1Yw7iY7lMqcQLtDv9C4GJ0XgW98jv0ERERCQfKZkVp+CxfJvdaxcMql45ToPhNfIpIhERESkIlMyKcyjuOC+25og6uBXTtG8REZGiTMmsOIdxve1fVyiB11Md8icWERERKTA0rCXO4amuUL0cfLcdygfCsA4Q5J/fUYmIiEg+UzKbjo0bNzJ9+nQiIyMpW7Ysjz76KPfee+9N25nNZqZMmcKGDRtISUmhWbNmjB07lqCgoHTrnz17lr59+3LlyhXCw8MpXrx4Dl9JIdPpjtQvERERkf9omkEau3bt4vnnn6du3bp8+OGHtG/fnjfeeIPw8PCbth03bhzbt29n3LhxvPHGG0RGRjJq1ChSUlLSrf/BBx9QrFixnL4EERERkSJDI7NpzJ49m9q1a/PSSy8B0KhRI06ePMnMmTNp165dhu327NnDr7/+yscff0yzZs0ACA4O5r777mP9+vW0b9/erv5vv/3Gjh07GDRoEB988EGuXY+IiIhIYaaR2eskJSWxc+dOh6S1Q4cO/PPPP5w6dSrDtlu3bsXPz4+mTZvaykJCQggLC2PLli12dVNSUnj33XcZOnQoAQEBOXsRIiIiIkWIktnrnDx5kpSUFEJCQuzKQ0NDAYiIiMiwbUREBMHBwZhMJoe2adt99dVXuLi40Ldv35wIW0RERKTI0jSD68TGxgLg5+dnV+7v7293PKO2adtd7ev6dufPn2f27NlMnjwZV1fXTMeWlJREUlKS7XV8fHym24qIiIgUVoU+mTWbzURFRd20XoUKFfIgmtSHvpo0aULjxo2z1G7u3LnMmjUrl6ISERERcU6FPpkNDw/nzTffvGm9b7/91jYCazab7Y5dHVm9ejw9/v7+nD171qE8Li7O1m7Pnj2sW7eOzz//nLi4OAASEhJs5/Ty8sLLyyvd/gcNGsRDDz1kex0fH0/Xrl1vel0iIiIihVmhT2Z79uxJz549M1U3KSkJNzc3IiIiaN68ua386pzXtHNprxcSEsKOHTswDMNu3mxERATVqlUDIDIykpSUFAYMGJBunO3bt+ftt99Ot38PDw88PDwydR2F2ZVkg13noWoAlPYx3byBiIiIFGqFPpnNCg8PDxo1asS6det48MEHbeVr164lNDSU8uXLZ9i2RYsWzJ49mx07dthWNIiMjOTgwYMMHDgQgObNmzNjxgy7dr/++ivz5s1j8uTJVK5cOReuqvBYE2HlwRVWLiaAuwu82sKFl5vpGUYREZGiTMlsGkOGDGHYsGFMnDiRdu3a8fvvv7N69WqHEdOmTZvStWtXXnnlFQDq1atH8+bNef3113n22Wfx8PBg2rRpVK9enbZt2wIQFBTksBvY6dOnAahfv752ALuBFKvB4NWpiSxAshXGb7bSs5qJ2kEaoRURESmqlMymUb9+fd59912mT5/O999/T9myZRk/frzD2rMWiwWr1WpX9vbbbzNlyhTeeustLBYLTZs2ZezYsbi5FcLbHGOG2s/AmUtQ0hd+mwSVS+Xa6Y7Hwr9mx/Jtpw0lsyIiIkWYyTAMI7+DkKwzm820adOGDRs24Ovrm/cBuPSGtO8c47tcO12SxaDiDAvnr9iX//6wK3eUUTIrIiIFn2my4/b2xphCOOCVxzThULJu/ELHRBag/5RcO6WHq4kZ7V3w/u/fvAl4rqFJiayIiEgRpz8HJOt2R6Rffuh0rp62d5gLJyuZ+PWUQY0SJqoFKpEVEREp6jQyK1m3YFT65Yuey/VTl/A20bWqixJZERERAZTMSnYE+MKTnezLHmwFVcvlTzwiIiJSZGmagWTPx0NTv0RERETykUZmRURERMRpKZkVEREREaelZFZEREREnJaSWRERERFxWkpmRURERMRpKZkVEREREaelZFZEREREnJbWmRWnkXw5hd0zDnHq13MEVPGjwZM18a/sk99hiYiISD5SMitOY8OYnUSuPQ3AuV3R/LvlHPetaY97Mb2NRUREiipNMxCncDkqgcjw0/ZlZxM4vv5MPkUkIiIiBYGSWXEKJpMJkymdcpd0CkVERKTIUDIr11xOhOSUm9dLsUBMPKzYAd/vyP24AO+SnoR2rmBX5lvem8pty+bJ+UVERKRg0mRDgWgzDPoYftgJvl4wtie83Df9uu8uhXFfgDVNed/msPj5XA2z8fO1uXTUzKWjcXiV8KD5K/Vw83LN1XOKiIhIwaaRWYFn56aOsFqtEHsZxi+EFTsd6636HV5IJ5EF+PbXzI3q3oJfnv+diwdisCZbuXw2gQ1jfifhUlKunlNEREQKNiWzkpqkprUynbLXF9+4nynLcyaedMSfucKZ3y7YlSWbUzixQQ+AiYiIFGVKZgVCSqdTVsqx7Mhpx7LrhZXPmXjS4ebtiou748NeHn7uuXZOERERKfiUzAq82R88rps+Xb0cPN7esV6Z4jfup3mNHA3rep4BHtTsF2pXVqJmAJVal8m1c4qIiEjBpwfABDrUhwMfwdLtUMIX7msBPl6O9cb2hEc/Sr+PEr5QNjA3o6T5/+pR+vYSnNp2nuJVfKnZLxQXN/09JiIiUpQpmZVUoWXguXtvXGdgWygTAFNXwM/7IOm/B77qVoY/Jud6iCaTiWo9KlGtR6VcP5eIiIg4ByWzkjWd7kj9EhERESkA9BmtiIiIiDgtJbMiIiIi4rSUzIqIiIiI01IyKyIiIiJOS8msiIiIiDgtJbMiIiIi4rSUzIqIiIiI01IyKyIiIiJOS8msiIiIiDgtJbMiIiIi4rSUzIqIiIiI01IyKyIiIiJOS8msiIiIiDgtt/wOoCDauHEj06dPJzIykrJly/Loo49y77333rSd2WxmypQpbNiwgZSUFJo1a8bYsWMJCgqyq2e1Wvnqq6/47rvvOHXqFP7+/jRu3Jg333wzty5JREREpFBSMpvGrl27eP755+nRowejR4/mt99+44033qBYsWK0a9fuhm3HjRvHsWPHGDduHB4eHkybNo1Ro0Yxf/583Nyu3er/+7//Y9OmTTz22GNUrVqVqKgodu3alctXJiIiIlL4KJlNY/bs2dSuXZuXXnoJgEaNGnHy5Elmzpx5w2R2z549/Prrr3z88cc0a9YMgODgYO677z7Wr19P+/btAdixYwc//PADX375JdWqVbO179ixYy5elYiIiEjhpDmz10lKSmLnzp0OSWuHDh34559/OHXqVIZtt27dip+fH02bNrWVhYSEEBYWxpYtW2xlS5cupWHDhnaJrFO6GAcNx4Bff6j7DBw/n98RiYiISBGkZPY6J0+eJCUlhZCQELvy0NBQACIiIjJsGxERQXBwMCaTyaHt9e327dtHSEgI7733Hm3atKFly5Y89dRTREZG5tRl5I3yQ+CPY2BOgH3HoeoT+R2RiIiIFEFKZq8TGxsLgJ+fn125v7+/3fGM2qZtd7Wv69tduHCBFStWsGfPHt58803efPNNzpw5w1NPPUViYmKG/SclJWE2m21f8fHxWbq2HPVZOCQm25elWOG1r/MnHhERESmyCv2cWbPZTFRU1E3rVahQIQ+iSV3JwGKxMGXKFEqWLAmkjt7ef//9rF69mh49eqTbbu7cucyaNStPYrypUxfTL/83Om/jEBERkSKv0Cez4eHhmVry6ttvv7WNwJrNZrtjV0dWrx5Pj7+/P2fPnnUoj4uLs2vn7+9PmTJlbIkspM6tLV26NMeOHcuw/0GDBvHQQw/ZXsfHx9O1a9ebXFUueakPvJLOKOykh/M+FhERESnSCn0y27NnT3r27JmpuklJSbi5uREREUHz5s1t5VfnvKadS3u9kJAQduzYgWEYdvNmIyIi7B72qlKlSoZTBJKSkjLs38PDAw8Pj0xdR65zdYVvRsMjH0JCMni6wUePQ4BvfkcmIiIiRYzmzF7Hw8ODRo0asW7dOrvytWvXEhoaSvny5TNs26JFC2JjY9mxY4etLDIykoMHD9KyZUtbWatWrTh27Jjd1IeIiAjOnTtHzZo1c/Bqsmb3rEN8deePfNslnLN/Xrh5g/tawpVFYHwHCd/A4+1zP0gRERGRNJTMpjFkyBD27t3LxIkT2blzJzNnzmT16tUMGzbMrl7Tpk15/fXXba/r1atH8+bNef311wkPD2fjxo288MILVK9enbZt29rq9erVi5IlS/Lss8+yYcMGwsPDGT16NBUrVsy3tWbXPrGN397dT/yZBC4djuOH+zfy73YttSUiIiIFn5LZNOrXr8+7777Lrl27eOqpp1i9ejXjx493WHvWYrFgtVrtyt5++22aNm3KW2+9xfjx46lUqRJTp0612/3Lx8eH6dOnExQUxP/+9z/eeOMNqlWrxvTp0/Hy8sqTa0wrct1ph7LNL/6RD5GIiIiIZI3JMAwjv4OQrDObzbRp04YNGzbg63trc1VnV1/qUOYR4M4jO7vdUr8iIiJyjWlyikOZMabQP76U6zQyK3j4uzuUhXTIeH6wiIiISEGhZFbouawN7r7X/jIMqhPAXf93Rz5GJCIiIpI5GtsW/Cv5MvDP7vkdhoiIiEiWaWRWRERERJyWklkRERERcVpKZkVERETEaSmZFRERERGnpWRWRERERJyWklkRERERcVpKZkVERETEaSmZFRERERGnpWRWREREpIA5cuQIw4cPp379+ri5uVGnTp1MtTMMg4kTJ1K5cmW8vb1p3rw527Ztc6h36tQp+vTpg5+fHyVKlGDIkCHExsY61Pvhhx+4/fbb8fLyIiwsjLlz597yteU0JbMiIiIiBcz+/ftZuXIl1apV47bbbst0u3feeYdXX32VZ599lhUrVlCuXDk6dOjAsWPHbHWSk5Pp2LEjhw4dYuHChUyfPp2ffvqJ/v372/W1efNmevXqRfPmzfnxxx954IEHeOyxx/j2229z7DpzgskwDCO/g5CsM5vNtGnThg0bNuDr65vf4YiIiMhNmCanOJQZY9zSrWu1WnFxSR1zfPTRR9m5cyf79u27Yf8JCQmUKVOGJ598kv/7v/8DICkpibCwMLp06cK0adMA+Oqrr3jooYf4+++/qVGjBgBr1qyhY8eObN++nSZNmgDQsWNHzGYzW7ZssZ2jf//+7Nq1i7/++iuLV597NDIrIiIiUsBcTWSzYuvWrcTGxnL//ffbyjw8POjduzerVq2ylf3444/Uq1fPlsgCtG/fnhIlStjqJSYmsn79eu677z67c/Tr14+///6biIiILMeXW5TMioiIiBQCBw4cAKBmzZp25bVq1eL48eNcuXLFVi9tHZPJRM2aNW19HD16lOTk5HT7uv5cBUH6Y9tS4F2dHRIfH5/PkYiIiDgnHx8fTCZTfoeRY6Kjo/H09MTLy8uuPDAwEMMwiI6Oxtvbm+joaIoXL+7QPjAwkIsXL9r6AhzqBQYGAtjqFQRKZp3U5cuXAejatWs+RyIiIuKc8vq5k4zmx8qt0V11UqVKlWLlypUUK1Ysx/6qjI+Pp2vXrqxcuRIfH58c6bMo0f27dbqHt0b379bo/t06Z7uHzhBjVgQGBpKYmEhCQoLd6Gx0dDQmk8k2qhoYGEhMTIxD++joaCpVqmSrAzjUuzpiW6JEiVy5huxQMuukXFxcKFOmTK707ePjoxUSboHu363TPbw1un+3Rvfv1uke5o+r81sPHjzI7bffbis/cOCAbd3Zq/X27t1r19YwDA4ePEj79u0BqFq1Ku7u7hw4cICOHTva9XX9uQoCPQAmIiIiUgi0aNECf39/Fi9ebCtLTk7mu+++o0uXLrayzp07s3v3bg4fPmwrW7duHRcuXLDV8/T0pG3btg5ryi5atIhatWoREhKSuxeTBRqZFRERESlgLl++bFsmKzIyktjYWFti2bp1a0qVKsU999xDZGQkR44cAcDLy4tx48YxYcIESpUqRd26dZk2bRoXLlxgzJgxtr779u3L//3f/9GnTx/+7//+j8uXLzNmzBi6du1qW2MW4H//+x9t2rRhxIgR3H///axfv56FCxeyaNGiPLwTN6dkVmw8PDx4/PHH8fDwyO9QnJLu363TPbw1un+3Rvfv1uke5pxz5845rPF69fX69etp06YNFouFlBT7jRheeOEFDMNg8uTJnD9/nvr16/PTTz9RpUoVWx13d3dWr17NqFGjePDBB3Fzc6N37968//77dn21atWK7777jvHjx/PZZ59RuXJlZs+e7RBXftMOYCIiIiLitDRnVkRERESclpJZEREREXFaSmZFRERExGnpATAhIiKCd999lz179uDj40OXLl0YMWIE7u7u+R1agfPDDz/w2muvOZQPHDiQp556yvZ62bJlzJ8/nzNnzhAcHMyIESO488478zLUAuHEiRN88cUX7Nu3j6NHjxIcHMw333zjUC8z98tsNjNlyhQ2bNhASkoKzZo1Y+zYsQQFBeXV5eS5zNy/oUOH8scffzi0/fbbb+2WzimK9y88PJxVq1Zx4MABYmNjqVy5Mg888AD33nuv3WYzev+lLzP3T+8/KQiUzBZxsbGxDB8+nMqVKzNp0iTOnTvH+++/T0JCAi+88EJ+h1dgffTRR3YLgpcqVcr2/z/99BNvvfUWgwcPpnHjxqxZs4YxY8Ywe/Zs6tatmx/h5pujR4+yZcsWateujdVqxWq1OtTJ7P0aN24cx44dY9y4cXh4eDBt2jRGjRrF/PnzcXMrnD/KMnP/AG6//XaeeeYZu7Jy5crZvS6K9+/LL7+kXLlyPPPMMwQGBrJ9+3beeustzp49y9ChQwG9/24kM/cP9P6TAsCQIm3OnDlGq1atjEuXLtnKlixZYjRp0sQ4d+5cPkZWMC1fvtxo2LChER0dnWGdXr16GS+99JJd2aBBg4ynnnoql6MreCwWi+3/X331VeO+++5zqJOZ+7V7926jYcOGxq+//mor++eff4xGjRoZa9asyYXIC4bM3L/HH3/cePrpp2/YT1G9f+n9O33zzTeNu+66y3Zv9f7LWGbun95/UhBozmwRt3XrVpo0aUJAQICtrH379litVrZt25aPkTmnkydPcvz4cdt2gFd16NCB3377jaSkpHyKLH+4uNz4R0xm79fWrVvx8/OjadOmtjohISGEhYWxZcuWnA+8gLjZ/cusonr/ihcv7lBWo0YN4uPjuXLlit5/N3Gz+5dZRfX+Sd5RMlvERUREOGxJ5+fnR1BQEBEREfkSkzO4//77adKkCT169GDu3LlYLBYA2z1Le09DQkJITk7m1KlTeRxpwZbZ+xUREUFwcLDdPEeA0NBQvU+BP/74g1atWtGiRYt05zDq/l2za9cuSpcujY+Pj95/2XD9/btK7z/Jb5qoUsTFxsbi5+fnUO7n50dsbGw+RFSwBQUFMWzYMOrUqYPJZOKXX35h+vTpnDt3jhdeeIG4uDgAu/m0AP7+/gDExMTkecwFWWbvl96nGWvYsCFdu3alcuXKnD9/ngULFjBixAg+/fRT6tWrB+j+XbVr1y7WrFljm9+p91/WpL1/oPefFAxKZkWyoHnz5jRv3tz2ulmzZnh5ebFw4UIee+yxfIxMiqphw4bZvb7zzju5//77mT17Nh9++GE+RVXwnD17lnHjxtGoUSP69euX3+E4nYzun95/UhBomkER5+/vj9lsdiiPi4uzjU7IjbVr1w6LxcLBgwdtow9p7+nV0Yfr5yYLmb5fep9mnre3N61ateLAgQO2sqJ+/+Li4hg1ahQBAQG8++67trnIev9lTkb3Lz16/0l+UDJbxIWEhDjMWTKbzURFRTnMI5Obu3rP0t7TiIgI3N3dqVChQt4HVYBl9n6FhIQQGRmJYRgO9fQ+vbmifP8SEhJ45plnMJvNfPjhh3ZTCvT+u7kb3b/MKsr3T/KGktkirkWLFuzYscM2dwxSF8p2cXGhWbNm+RiZ81izZg2urq7UqFGDihUrUrlyZdatW2dXZ+3atTRu3FgbUaSR2fvVokULYmNj2bFjh61OZGQkBw8epGXLlnkac0F35coVNm3axG233WYrK6r3LyUlhXHjxhEREcFHH31E6dKl7Y7r/XdjN7t/6dH7T/KD5swWcX369GHRokWMHj2awYMHc+7cOaZOnUrv3r3tNgKQVCNHjqRRo0ZUq1YNgI0bN7J06VL69etn28lm6NCh/O9//6NixYo0bNiQtWvXsm/fPmbNmpWfoeeLhIQENm/eDMDp06eJj48nPDwcSH1wJDAwMFP3q169ejRv3pzXX3+dZ5991rboevXq1Wnbtm2+XFteuNn9i4iIYP78+bRt25by5cvbHsC5cOECEydOtPVTVO/fO++8w6ZNm3jmmWeIj49n7969tmM1atTAw8ND778buNn9279/v95/UiCYjLTj/lLk/PPPP0yaNIndu3fj4+ND165dtZ1tBiZPnszWrVs5e/YshmFQuXJlevbsyQMPPOCwPea8efNs22M++eSTRXI721OnTnHvvfeme2zGjBk0atQIyNz9urod5vr167FYLDRt2pSxY8cW6j+6bnb/ypQpw7vvvsuhQ4eIiYnB29ubevXq8fjjj1OnTh27+kXx/nXv3p3Tp0+ne2z58uWUL18e0PsvIze7fxaLRe8/KRCUzIqIiIiI09KcWRERERFxWkpmRURERMRpKZkVEREREaelZFZEREREnJaSWRERERFxWkpmRURERMRpKZkVEREREaelZFZEREREnJa2sxURSWPnzp0MHz48w+Nz586lbt26eRiRiIhkRMmsiEgGOnbsSMuWLR3KK1WqlA/RiIhIepTMiohkoGbNmnTp0iXH+01ISMDNzQ03N/0IFhG5VfpJKiKSRfv27ePbb79lz549nD17FldXV6pVq8bDDz9M27Zt7epOmDCBFStWsHbtWj788EO2bNlCdHQ033//PeXLl8dsNjNnzhx+/vlnzp49i4+PD02aNGHEiBFUrFgxn65QRMR5KJkVEclAQkICly5dsitzd3dnw4YNRERE0K5dO8qVK0dMTAwrVqzg+eef580336RTp04OfT355JOULFmSxx57jCtXrlCsWDHMZjODBw/mzJkz3HvvvVSpUoWoqCi+/fZbHn30Ub744gvKlSuXR1crIuKcTIZhGPkdhIhIQXKjB8Dat2/PK6+8gre3t115QkIC/fv3x9XVlcWLF9vKr47Mdu7cmTfeeMOuzeTJk1m6dClz584lLCzMVn769Gn69etH27ZtmTBhQs5dmIhIIaSRWRGRDPTq1Yt27drZlZUsWdIukU1ISCAhIQGAxo0bs2TJEsxmM76+vnbtBgwYYPfaMAx+/PFHGjRoQOnSpe1GgL29valTpw7btm3L4SsSESl8lMyKiGSgcuXKNG3a1KH84sWLTJ8+nV9++YWLFy86HE8vmQ0ODrZ7HR0dTUxMDNu2bXNImK9ycdFS4CIiN6NkVkQkCwzDYOTIkfzzzz/069eP2267DV9fX1xcXPjhhx9YvXo1VqvVoZ2Xl5dDPwBNmjRh4MCBeRK7iEhhpGRWRCQLDh8+zKFDh3j88ccZNmyY3bFly5Zlup/AwED8/PyIj49Pd/RXREQyR59hiYhkwdWP/tM+O3vkyBE2bNiQpX46derE/v37CQ8PT7dOelMYRETEnkZmRUSyIDQ0lCpVqjB//nwSEhIIDg7m+PHjfPfdd1SrVo2///470309+eST7N69m3HjxrFu3Trq1q2Lu7s7p0+fZsuWLdSqVUurGYiI3ISSWRGRLHB1dWXq1Kl88MEHrFixgitXrlC1alUmTJjAoUOHspTM+vr6MmfOHBYsWMDatWvZuHEjrq6ulC5dmvr169OzZ8/cuxARkUJC68yKiIiIiNPSnFkRERERcVpKZkVERETEaSmZFRERERGnpWRWRERERJyWklkRERERcVpKZkVERETEaSmZFRERERGnpWRWRERERJyWklkRERERcVpKZkVERETEaSmZFRERERGnpWRWRERERJyWklkRERERcVr/D7mXlJHZHSNVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explainer.scatter_plot(feature=\"Fare\", interact_feature=\"Pclass\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "predicted probability for label 1 == Survived\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explainer.scatter_plot(feature=\"Fare\", interact_feature=\"Sex_female\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Play ground: Naiv gender based approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of women who survied: 73.64%\n",
      "% of men who survied: 19.82%\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Naiv gender based approach\"\"\"\n",
    "X_y_train = pd.concat([X_train, y_train], axis=1)\n",
    "\n",
    "# women = train_X_y_raw_df.loc[train_X_y_raw_df[\"Sex_female\"] == 1][\"Survived\"]\n",
    "# rate_women = sum(women) / len(women)\n",
    "\n",
    "def survived_rate(df: DataFrame, feature_name=\"Sex_female\", feature_value=1, label_name=\"Survived\"):\n",
    "    label_part = df.loc[df[feature_name] == feature_value][label_name]\n",
    "    return sum(label_part) / len(label_part)\n",
    "\n",
    "rate_women = survived_rate(X_y_train, \"Sex_female\")\n",
    "print(f\"% of women who survied: {rate_women:.2%}\")\n",
    "\n",
    "rate_men = survived_rate(X_y_train, \"Sex_male\")\n",
    "print(f\"% of men who survied: {rate_men:.2%}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Play ground: kernel shap\n",
    "uncomment the following comments to play arround with shap.KernelExplainer to calculate the shap value estimate Shapley value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# my_kernel_explainer = shap.KernelExplainer(model=model.predict_proba, data=X_train.to_numpy(), link=LINK, algorithm=\"kernel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# my_shap_values = my_kernel_explainer.shap_values(X_valid.iloc[[0]].to_numpy(), nsamples=\"auto\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Play ground: Data profiling for exploratory data analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from ydata_profiling import ProfileReport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_y_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# profile = ProfileReport(all_X_raw_df, title=\"all raw data without filling the missing value report\")\n",
    "# profile = ProfileReport(X_y_train, title=\"all raw data without filling the missing value report\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# profile.to_widgets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# profile.to_notebook_iframe()"
   ]
  }
 ],
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