An example of working with Binary 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An example of working with Binary counters

24-710

37-21-50

§_getVarNamesDicttotal

36-20-50

This block creates new variables on the selected scope: global (for all sprites), sprite (for this sprite only) or script (only for that blocks stack) with the names given (in 'names' list). If there is already a variable with that name in that scope, it does nothing: no errors and no overwrites.

pt:cria as variáveis _ _ ca:crea les _ variables _ es:crear las _ variables _ de:erstellen _ var _ globalglobal sprite script

This block deletes all the variables with the names given (inside input list). Each name will make only one variable deletion, and this will be the variable found following the scope order: 'script' -> 'sprite' -> 'global'. If we have a "testing" sprite variable and also a "testing" global one, deleting "testing" will delete only the sprite one (Yes! we can also do "delete var (testing, testing)" to delete both. If one variable does not exists (in any scope) an error happens, stopping block action in that point. You can check it before with the "does var (name) exists?"block.

pt:remove as variáveis _ ca:esborra les variables _ es:borrar variables _ de:löschen var _

This block sets the given value (last input) to the variable named with the name givent (var input). It looks for that variable following the scope order 'script' -> 'sprite' -> 'global' (the first match it finds). If that variable does not exist (in any scope) an error happens, stopping their script. You can check it before using the "does var (name) exists?" block.

ca:assigna a _ el valor _ es:asignar a _ el valor _ pt:altera _ para _ de:setze var _ auf _ §_getVarNamesDict

This block reports the value of the variable with the name given. It looks for that variable following the scope order 'script' -> 'sprite' -> 'global' (the first match it finds). If that variable does not exist (in any scope) an error happens, stopping their script. You can check it before using the "does var (name) exists?" block.

pt:o valor de _ §_getVarNamesDict

This block reports "true" if there is a variable with this given name (input slot) in that context. It can be a global, sprite or script variable. Otherwise it reports "false".

pt:a variável _ existe ca:existeix la variable _ ? es:existe la variable _ ? de:existiert var _ ? err_reset

This block turns on (show) the watcher view on stage (if it was not already activated) of the variable with the given name (slot input). It can only access to the closest variable scope (if there different variables with the same name in different scopes) following the order 'script' -> 'sprite' -> 'global'. No errors if that variable does not exist.

ca:mostra la variable _ es:mostrar variable _ pt:mostra a variável _ de:zeige var _ §_getVarNamesDict

This block turns off (hide) the watcher view on stage (if it was not already activated) of the variable with the given name (slot input). It can only access to the closest variable scope (if there different variables with the same name in different scopes) following the order 'script' -> 'sprite' -> 'global'. No errors if that variable does not exist.

ca:amaga la variable _ es:esconder variable _ pt:esconde a variável _ de:verstecke var _ §_getVarNamesDict

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p6O3F/GvzOXzB7eXuJ7fw+1So45cuxX7vdnF+zl/eNl57PnTs3M1++fF0cOb81/fzNW7dskdJlysjOP/YZfdgG3ONVq8nWzZuNlgKqVV26duPGVs7an1+A/+L1cXFPOrQ//wCz8rH1sw3gOtWqy+a4OKOl6jVqhsVsWB/syP6opU+AANZnf8+Vr127Zvbw8JBzCQmp+Xx8sqgd169dm1jz8cdzXLlyRTb+8suY+o0avaa+fuzo0W1FixVz6Nmy9HfPU3nHB1r8EhISxMfHx/iZXLtmTerjtWub1Pz+un7D0gaNG7ZUXz944MC5UqVL57OvYsb2Llu8hPnI4cNSvWZNifllvdHf0SNHzOo9VjGbZeK48av79enTUH29RatWqQvClhhr1FHbP/Wn3qOeMG7c6gF9XjP6a96qZcrCsLA7Xklwv/q19FejVi2JXr/O8Dt86JBZ/XGl+psxddrqF3v1Mvp78qmnUmcvXOBQv/s1bo57dwEC+O5G2h+RkpxsdsuSRZ28kVSwUCGP7Vu2NKv42GPLTSaTvNG3nzRt2qR2UKtWa41fjEePHi9WrFgRRzZNf/ZpW/xOnzolhXx9TVu3bg2uXLnyYsv8NmnapEPzVq1+UFUOHTyYVLJUKQ/7KmZs78J585nPnTsndesFysroaNNHw4Z1bNi0aWjNxx+/ESDTp7/xYo+en6mjNgsKMi9eFuGWsQr2PTp9fyOHD2/fuEmT2Zb+Zk6b1rd3z15f6u4vsH59+ennKNMnI0d2CGzYcJalvwVz5w7o/GyHz40/YIJbmRcsWeJQP/v02dseAQLYHj0H7ZuSkmJ2c3NTZ58mFyhY0P1cQsLRPHnzFomNjpE2LVrI+KlTCrdr1+6EaufYsWOnixYtWshBrRll6M8+bYvfmdOnpWChQqYzp09fzl+gQC7L/E6dFVopODh4h6py5PDh5OIlSjj0TPcCXt5mdcOX2nXqSOSaWFNgzZopH378sVvdevWMAJ4TGtqie+cu4aq/pkFB5iUODuD0/dWrWSt55Mejslj6mx0a2qRH5y4rb/T3hHnJsmUODThLf3UCA2VVTLSp/uO1U0b87yOr3+IFC4I6tG23TPXXvFUr88IwAti+n6jMszcBnAnmKikpyZw1a1Z11yvz2fj4r0qWLt03OSlJAipVluPHjsnMWaE1goKDNxgBfPTonqLFij3qyGHRn33aFr+LFy/KyZMnZ5YrV66z7fx+Nyu0bYvg4HmqyoH9+y+XLlPmznfrsK+Nv9271EMPmU8cPyGPVaki/tWqLp3+7ZSWP/0cJZaAC505s//zXbuNVgcIatnS/OPSMIcGnG1/flUDFn83ZWpr2/6+nzHjtRe6dTfOHG/esmXqwqVhDn2J19qfn5/UqFUzbPKEia1s+1swb16/zu2f/UL1F9ymjXnujwsd6neflg2HvQcBAvgekHQ/5Mrly+acuXJJcnKyerYp6jaFH3/4oQwdPERy5colC5YsfrFew4YTVZ9nz56dnz9//raO7Jn+7NO+cuWKOWfOnMb8pqakiIcxvx/J0MGDjfmdv2Tx4PoNG45UVY4fP76mSJEide2rmLG9/cqXN+/etVvy5csn7u7u6lp0+WXLZiOQ1TPg8WPGhPz1Vohx5nPDJo33R6xc6dA7Tt2tv7FjxoQMvNlfg0aNfl8WueqRjAnY92i/8hXMu3ftuuHn4WFc623rN/Wbb0JeffElw69p0BN/LFm27GH7KrJ3ZhEggDPBTJ04cSLR19c3h6XVC+fPJ3R4pm3WqMhIL/UL+petW94vU6bMcPX9xMTEYX99zaGXgdCffYvoxPHjqb6FC1t/FhMSEpK6deyUZeWKFW5qftdv2Tzs4YcfHqprfps1bGiOXh1lHaSfv7/MW7xILDe6GNCnz7AJ48Yb/QVUqzps7caNDl1/zRo0NEdHpenPPG/xIpOz9BfUqLE5KjLS6hdQrZo5dO4ck+U64Ddf7zds3NdjtPnZt3rZ2x4BAtgePQftm3D2bGjefPk6qnLqGce+PXuCX3vl1e/VjTjUL+i9hw6G5vXx6aS+v23btoFVqlT51EGtGWXozz7t0ydPLi1QqFALy/xu37694ztvDvxe3YhDze9v+/aGF/L1Nb6/dfPmoX4BAcYfW47aGtetF7kmNsY4S1fdTvGNQW+/1P35HhMsN+IIathodXT0zw3FLPLOkMEhQ0eMcOh16I0CA1etjYltZOlvwMC3XujxQs9vLP090bBhZMzP0cb3dfTXrEGDyOion61+b7075MWOXTpNtARwy6ZNI1evijT6e2/Y8JDBQ99zqJ+j1hF1bhcggDPBqti5ffvA8pUqfaxaPX3qVGIhX99cljth+eTPL78fOqhewjQlJSXJ8uXLczn6I+zoz75F9Nu2be9XqFzZ+KV78uRJc+HChd0sd8K6Ob+iXqJW8xsZGekTFBTk0I/YG/L220P/uouT8ay2XoP6siIqymR7HXD5UqXl0KFDonr96PPPSnft2vWAfSIZ2zuD/ZXo2rXr4YxVsO/Rw4a8P/SjER8YfupTkJZFrjLZXgdcvnQZdXa74ff1mK9KPdWx40H7KrJ3ZhEggDPBTIWEhLgN6N8/0dPLy/gMwp07dy56oUvXoL379mWbHvq9OrHEGMWvv2xICA9b0iNkxIjFjhwW/dmnPWfOnCzNmjVL8r55s42dO3as7N2zV+Pf9+wx2c7vhvXrkyNXrOr+ztAh39tXMWN7z5kzx+OHGTOv5c2bx9ixY+cuUf4B/g3y+Rg3Z5Pnu3Uz/lvF319q1q79es2aNR16q8x77c/PP8Bcs07tPjVq1BiXMQH7Hq36m/N96DVvby/jQF26d4+qWLlyg/z586fx869aVQICqvepHVh7rH0V2TuzCBDAmWSm5s+eO7xN26ffU5cjqU2dsKM2dXa02tRf0OqOROXLV9gcuSYmwNHDoj/7xOfNmTPiqWeeGXy3+a1WvXrCkuXLbiSfA7cTJ0587Ovra9yP/J+2P/btS324bFmHnmWs+jlx4sQoX1/fQffQX8rDZcs69EYcqqdTp06NKliw4F372717d0r58uUd3t/d3Pj+/REggO+P639+1A5Ptx167frVkL9uhC+WZx6WIuvXrpOeXbqoS1SkxuO1folet67Wf97AXQ5If/aJd3i6bci161eH3m1+mzRrdjJsxfLC9lXL+N5nTp0KyV+woHGi0D9t+/btSy5btqxDr1NW/dxzf3v3JpUtV86hNzLJSH+7d+1KKl+hgsP7u9u88v37I0AA3x/X+3bU1atXZ/X29Hy9UKHCnteTr5tPHD26vXZg4ML7VjCDB6a/DIKle/jUqVOzV6xYcVCRwoXdrl+/LkeOHTtar169yfYd9b/be86cOd4H9u7tt2fPHpN6X7p8+YqXX+37mkNP+vun0SxatMhz944dAyz9PfpI+Yt9+vc17jLlDFt4eLjXnt9+6//b9t9MOXPnlCpVqlzq9eKLxl3E2FxPgAB2vTlnxAgggAACTiBAADvBJNACAggggIDrCRDArjfnjBgBBBBAwAkECGAnmARaQAABBBBwPQEC2PXmnBEjgAACCDiBAAHsBJNACwgggAACridAALvenDNiBBBAAAEnECCAnWASaAEBBBBAwPUECGDXm3NGjAACCCDgBAIEsBNMAi0ggAACCLieAAHsenPOiBFAAAEEnECAAHaCSaAFBBBAAAHXEyCAXW/OGTECCCCAgBMIEMBOMAm0gAACCCDgegIEsOvNOSNGAAEEEHACAQLYCSaBFhBAAAEEXE+AAHa9OWfECCCAAAJOIEAAO8Ek0AICCCCAgOsJEMCuN+eMGAEEEEDACQQIYCeYBFpAAAEEEHA9AQLY9eacESOAAAIIOIEAAewEk0ALCCCAAAKuJ0AAu96cM2IEEEAAAScQIICdYBJoAQEEEEDA9QQIYNebc0aMAAIIIOAEAgSwE0wCLSCAAAIIuJ4AAex6c86IEUAAAQScQIAAdoJJoAUEEEAAAdcTIIBdb84ZMQIIIICAEwgQwE4wCbSAAAIIIOB6AgSw6805I0YAAQQQcAIBAtgJJoEWEEAAAQRcT4AAdr05Z8QIIIAAAk4gQAA7wSTQAgIIIICA6wkQwK4354wYAQQQQMAJBAhgJ5gEWkAAAQQQcD0BAtj15pwRI4AAAgg4gQAB7ASTQAsIIIAAAq4nQAC73pwzYgQQQAABJxAggJ1gEmgBAQQQQMD1BAhg15tzRowAAggg4AQCBLATTAItIIAAAgi4ngAB7HpzzogRQAABBJxAgAB2gkmgBQQQQAAB1xMggF1vzhkxAggggIATCBDATjAJtIAAAggg4HoCBLDrzTkjRgABBBBwAgEC2AkmgRYQQAABBFxPgAB2vTlnxAgggAACTiBAADvBJNACAggggIDrCRDArjfnjBgBBBBAwAkECGAnmARaQAABBBBwPQEC2PXmnBEjgAACCDiBAAHsBJNACwgggAACridAALvenDNiBBBAAAEnECCAnWASaAEBBBBAwPUECGDXm3NGjAACCCDgBAIEsBNMAi0ggAACCLieAAHsenPOiBFAAAEEnECAAHaCSaAFBBBAAAHXEyCAXW/OGTECCCCAgBMIEMBOMAm0gAACCCDgegIEsOvNOSNGAAEEEHACAQLYCSaBFhBAAAEEXE+AAHa9OWfEDhCoLnVLp4iptVnMuU0iWUTEJGJ2N4m0MIupsoi42bZhErmWKrLNTSTWLHJJRJJMIpc3SewYEUlN33JVefzRVMlS1E2kglkkn4jUFZFAEXG/+ViziMSLyAkR+cMkckBEzpsldW2crI28E0ENqVsuRcxF1PdMIllTxa2kSaSYWaS0iNlH/b+I5DTf6P2kiPxuEvMBEVOCWVK3m8RkNokc3yhr9jiAmBIIZHoBAjjTTyEDcEaBqlK3b6Valb5o3K5xhttzy+ImWbJkkaTrSdKqa6vn8hXIEWp7kCpSp2IWMUXIjUDM0OaV11Munbv07SaJfT79jj3r9tm+JXZLpQwdMN2DTWKavkliuttzDPZFwFUECGBXmWnG6VABFcBtX237Rb9P+9pbd1iOHKYQ24PU8GgQknw9eei/ObB/PX/ZEr35l00SWyv9/t+MmJk6fsgEe38nTIqT2Bf/TW/sg4CrCdj7w+ZqXowXgXsSuJ8BXC9PUMjl85fTBHC27NmkQvXy4lM4v1y98qcc3H1Ijv5x9LZefYv7ysnDJ7fGSaxf+m+u/nFd6httBt72O+ER/3LyUMmHxOTmJkf2HZH9O/ZLSkrK3ziYguIkZvk9IfEgBFxcgAB28QXA8O+PQFWpmrN5l6d3dHmzc6n0FTzzekneAnkk8VKinD15Vsxm9XatiHrpuWiZomkePn3UjJdfCuk6wfaLlgBWL1PXax0oLbu1lBy5sktc9GZJSUqRa39ek7wF80qhogVlYsg3cvzAcevuJpNJ1UtOkZSSW2XdMdvjblu/53j3Wr0Kq69VqlVJGj/TSDyyuYu3j7dUqF7BHD4zwlTykRJSplIZ+eT1zyTu57ibu6tfI8YYTpSRwsXmyty/S+f7g81REcikAgRwJp042nZugRAJcVssK6/anBRlNFyvdT0Jmf6+fNBrhKxeEGUEXKO2jWTZ98ulWsOq8mX4F9aBnTt9ToJLPDl+k8S8YjvaJgVbh1SpW2Vop/4dJSYsVpZMWSypKanS9tV2UrF6BRk9YLQc2XdUPPN4Sq8hPWT8kIly7eo16yGyZM0iyckpb2+W2FG2x41ctPbDKSOmvVO7+eNycNdBWTUv0jhG/y/6Ld8cFZdv8dSw6urxHft1kK4Du8grTfvIgZ3q3C7rFhcnsVWde2boDgHnESCAnWcu6OQBEgiQOoEipmjbIRUsWlBmbf1e3nl2sHnDyg3Gz16Pd7uLOdUs0/43Xd6f8p480bGZdZczx85Im4efXhYnsc1tjxN/8nLj2V/PXjnzs1DJ5ZVLOvbtIA2fbiAzPp25ZdHkxX43X2Y2dsmZO6fxvaXfhVsP4ZHdQ65fvR4L+BGqAAAQ7ElEQVQWJ7HBtse9etUc/OWbYxbN/nqO9feCX90qqxu3azxpzKAxs65fSxI3NzfzuFVjf9z/2/6n1DPsN9oMtD1E6lVJyLFTdl5/gKaSoSBw3wQI4PtGy4FdWSBAAvuLmD+3NRj09UDJkTuHDOs+3PiyCsrJsd9I54AuxhnP4UfDJKt7VuN7Vy5dkXGDx8ua8LWpy48tVJcxWTd/CWzsW7TAyk4DOolPoXwyb8ICqdogYN+3H0wtJmLOZhvAaif1zHrj6k3W/Qs8VEDOHD+zKU5iq9ke98TBhBZtKz679M8rfxpfVr080alZ3T+27Qvbvfn3POprXj7eX0ccDTv7cZ9Phqpnwh0qd0ozzW5ibrZR1vzkynPP2BG4VwEC+F6leBwCGRDwl7pD/rr+9wPLLirMwo+FyXP+XUQ9s1Xbhz+MlLjoOJk3br60f62d9P34dWuFuePmScSMCKndora89uHzaX5Oq2WtF1KyXImh6qXkvdv2iXs2d6lQtXzy1rXbjPROH8D1n6wnPy+69WS8eLnicurwiY3rrq42XlK2bDNHLxjxef/PB9/qOcuBoOeC1oZNW/qc+ppHdo/4i1dTS01ePnr4gNYD+n8V8aW81CjNq+NiFtMHmyXm/QxQ8VAEXFaAAHbZqWfg91MgQOqqs5Stlw/VaFJDXhzeW3rVvnH5bc2mNeT593pJ7/ovGSdhhW6ZKSUeKWFtqcfjveTw74dFvc87aeT0J7dJ9GLLNwOk7mwRaW/5twrgR/0fle3rtxthXLi4rxzee8R6rPLVysuujbus/37E/xE5eejkgtUJS5+xNZg0fMaGCe9PtIbyI37lNp84fNL/YsJF42G5vHP1fm5Ax/2/rtwYsTlmi/tHs0eql9PTMZq+j5OYzvfTlmMj8KAIEMAPykwyDqcSSB/Az77WXrzyeck3wyYbfapnxMlJycb/V6pRUSb+nOZEZ6mTQ93USuSTBR/LwKcHhcRJ7DDLAKtK3T9u3J3q1qZOlrp0Xt1A6/ZnwPl9fST+5Fnrgx9+7GHZt23fl3ES28/2GJ/2H5sUOnrWjdfARSR7zuxyNVGdR2Y8+5Wka0nSvHOQhM+IEPXs+6VhvWXs4PFp3P+609eGTRJb06kmg2YQcFIBAthJJ4a2MrdA+gB+88sBsm3ddlnxw+1vj741dpC07nnrfKgje49Ih8duvLc6Y+N06VKtW5oArpG1wZ/JycnZ/07I9iVodw9345pddZa0JfhrNq2p3lueGScxXWyP8V7XD822J2tZvqcuj1J/MATU85c9m3+Xc2fOSbMOTWX7+h1y4qC602Wa7XicxBq3s2RDAIF/FiCAWSEI3AeBAKmrXn623ixDPXOMmLnstkrqBhrLToQbzzDVpsKyZbFg41nxuuXr5ds130ivOi8Mi5NY68vZtbI3Trl+9Vqae0nbHtg2gG2faavH1GxSQ35ZuUE8snksWX8tsrXtfoPaDTWvnLvqth5z5MphBLB/PT+JXhxjPDNW41k46UfrY29eX6z+fTFOYr3vAymHROCBEyCAH7gpZUDOIJA+gG17avpsU3myZ7D89utOKVikoPFs0rJFLYySwc+9JyqYazSuLh36dpBXm/ZJE8A1sjYwJyffePla3bijTovaxvW6WT3cxd0jq/w4eZGoZ9Hpw7dwCV/JkSunHNh1QD1+U9S5cOtZ0GfOmD0/6P72xdila9LwqT8MVC9PPt86dean3xuh36Z3G/nRNnzdTMalVJbtr5fL+b3iDIuQHpxegB8Up58iGsyMAn8XwC+PeEnKVikr73V6T65cSpQpa78VdatHy/Zuh8HWM5ZbdmkhzbsESZ9mr6cJ4L9e3jaXq1JWWnRpYZxstW7ZOjlx6NZLweoZ8JnjZyQl+dYNqdQNP6o3rq5eepZsObJJztw5rqWcuZwnSqKMN3kTE80133jyjfW//LQhDXee/N7mV0e+8sl3n8wYpG7uoW7SoV6GVnfwsmzqJWrLS9zqa16S1T1Kom78hcCGAAJ/K0AAszgQuA8CdwrgUuVLyueLP5NBz7wlh34/LMXLFpPpG6ZZq19IuCAtiwZbb03pmddTFv4+X5oUaJb2GbB7A7MKV8stLNv3aSeP+JWT37fuu3rtz6vZN8dsMe4DbRvATdo1kZVzVxq13NzcRN0U5NThU602ScxS9bVLl8wVhnR697eYJTHWfoxnvzmyjW/Wrknj+ZMWllNnU/95Wd1n+qD1MerZ+6q5kbb3hk6Nk9g01y3fB14OicADIUAAPxDTyCCcTSD9SViqv/e/HWKciKVeIlZbv8/6SrtX2lpbn/nZ9zLhvYnWYFXfmL19ljxbuaNtAJtquDdItZxB7Vu8kPR8t4f88NXsy/t3Hsitzk5WLz2r+0FbtmdeflqWTg+3ntGs3tN1czPJ5UuJ/9ssse+ox5nNZve3nx12/ac5N0Jabe7ZPFL7ffr6pNEDvnhJXTucPUc22WlzOZOq81XEF/JKkz7WfUwiKZsk1nomtbPNC/0g4EwCBLAzzQa9PDACAVJ3o4ikuS/y7B2zpGu17sZ9mVV4RRxfatwq0rJ18ussh/YcSmPw+eJPZUDrN60BXM2t3qupqalfWx7U9a0ucnz/cYlcsNp4H1adDJWaeuOMZ7Wp640DWwXK1++MtQaw+vqNl43Ns+MkpoP694JvIp7eumbr/CXTwqz7lny05Nk8Pt7e8afOZvUp5CNb12y9bX4mRI5LfzMOc5zE/u0JYg/MBDMQBP4DAQL4P0DkEAikF6gqgclmMad5KXb5yQh5wre5qB+65l2ay+BJ71p32x23W53tfBvkm1++oT4U4VJsTFyRPbLmUoDUVRcMWz9vV51YlZyUYrznm34LqB8gp46cMurl9Mwpv2/da32Icda1SU5e/zP5uTiJjvzus7lzTh0+1W7Wlz9YH1OkdBF1z2hRz343Rd26laUKeeMDHZKS5Q4BrO6G9fpmiRnDqkAAgX8WIIBZIQjcB4H0Aaw+OnDhvvnSulQbo9q4lWOlSp3HrJU/6/e5LJi48LZOBozub4Rr6Cc/FP5Vok6mD2DLDup9XdtnvpVrVpLLl64Yn1aUJ38eKVLqIeOsa8uWv3B+48YaFxIu9o6TmG9mfD53bvyJs21nfDLT+hjvfF7yaNXy8stPv1i/pp45q08eVLVUCH8wc7ioE8fSbqbbrjG+D8QcEoFML0AAZ/opZADOKHCnZ8ALfp8nT5dra1w6pF6OtmzqgxiaP9RSLB+CYDueQWMHGje7+KcAVjfbUMewbOX8yor6iOG9N5/xFixSQExubsazYcumQlm9ZG0bwEf2Hm07b/x84yHqfeJqjaqJ7UlZKnBV+KprldWmbszxaNVHJXT0rbHcOD4B7Ixrkp6cT4AAdr45oaMHQCBA6o4tU6nMK3/s+MM6mvGR42TVvFVSvVF1qduyjvXry2ctl+E9R9xx1J8sGKU+23fn54NG1tgm2670CRr4to9vvpHnzlxwM6emyubYLZJ4KdG6b5mKpaVAkYKyfsV669dUUMZFb7b+WwW2qn/66Om4Xb/uenmjxGyIWvTLqMTLV57/uM+n+dQfAoHBgRI5P9K6j/oUJ9W3utzIy8crKWphlHv7V9uJumxp16Zb95muWL3C9R2/7uy+WWLTp/IDMKsMAYH/VoAA/m89ORoCt4LPrW6IpN66G1aTdo3l/anvS+KlK8aNMyxb/+A3ZMPKtNffqu+pE7XemfC2jOj14RObJHqF5fEVpJ2HV/azV9Wbrdev3froXfVebfs+bXeHjv7hkeMHjlt/titUK5/m7GV1CZJ6Jnv62OmecRI71XLcq1fNQcN7johQz3B/mn3rbOisWbOq94uN93zVWc6ztodOevOpQS+//r8+Mqjt2+lnPCJOYluwDBBA4O4CBPDdjXgEAhkW8JfaLxctXeyj82fOe6vP9rVs03+dJg9XKmP9d/yJeHmy9FN3PL666UU5/0euT/5wTr5tssJ6kKa+rWebzdI44VSCj2XHwiULS7e3usaPfHm0X7FS+Y/YBrB6ufl8/HlrjZKPlpBTR0+nJF5OLL5Z1hy3fMNsNpsWTVuRPLzHB9azmI2XndUtMm/e1MMkpkWxf0ZvmTZq+tCtMVtlw6pf0/Vufi1O1ljP0s4wHDsg4EICBLALTTZDdZzAna4DVtXVZUXqwxAs25SRU+XbEVPu2NgHM4bJvh379k4fNaNjnMRaT0Ou6dEwOel6UpozrOu1rifJSUly/vS58deuJ7106dwl6892ztw55GrijeuC1UlUub1zq/eDd6w+u7TKX5cnW69ZmjcxfOKoV0b1trzHqx7vnd/buBWlZcvtmXNhk2ebeCWcPtdY3Rfadrty8Upy4sUrZTZJ7GHHSVMJgcwrQABn3rmjcycWuFMAFyhSQBbunW9cq6s2dSerHrV6ysHdh9KcRKW+F9TpCTl/9oKojzHs3+qNwXES86FluNWz1k9NSU6x62fX5GaSgqm5s0dIhPWOHZOGf7djwvuTKtrDahZpullib71+bc/B2BeBB1zArh/iB9yG4SHwrwXuFMA9h/SQXoN7Wo+pbhnZp9lrt9Wo26KO5CuUTxZPXSKjwz4ngP/1LLAjAs4tQAA79/zQXSYV8JN6td0kdY2601Xi5RtnKS/YO18KqROgbm4fvfQ/CZtu3IrZ2NS1ueqTjZ7o2Mx6e0d1q8e+zfu+u0nWfGR5XLUs9S+lpqTktodGPQP+M/Vstp2y03oW1zcjZmwdP2TirYuT/0UBk5gbbZI1q//FruyCgMsJEMAuN+UM2NEC1aVOxcqB/k97ZPPIcvLwSbly4Yp73kI+BY4fOJrNp7BPBU/PXBFPPv9kaolyxa72bfzyl+tk3Z/30mNIcEhO96LeneKPxz/0x29/uB3Zd8zk7pElW85cOZIfrlIuS/mAckmSKim//bpz1/o1v4XvlKjL93Jc28eYzea8UYvWvzJ/wnz3Hb/uzHL1ylX30hVKXk1OSk45eTT+x+hz4dszekwejwACNwQIYFYCAggggAACGgQIYA3olEQAAQQQQIAAZg0ggAACCCCgQYAA1oBOSQQQQAABBAhg1gACCCCAAAIaBAhgDeiURAABBBBAgABmDSCAAAIIIKBBgADWgE5JBBBAAAEECGDWAAIIIIAAAhoECGAN6JREAAEEEECAAGYNIIAAAgggoEGAANaATkkEEEAAAQQIYNYAAggggAACGgQIYA3olEQAAQQQQIAAZg0ggAACCCCgQYAA1oBOSQQQQAABBAhg1gACCCCAAAIaBAhgDeiURAABBBBAgABmDSCAAAIIIKBBgADWgE5JBBBAAAEECGDWAAIIIIAAAhoECGAN6JREAAEEEECAAGYNIIAAAgggoEGAANaATkkEEEAAAQQIYNYAAggggAACGgQIYA3olEQAAQQQQIAAZg0ggAACCCCgQYAA1oBOSQQQQAABBAhg1gACCCCAAAIaBAhgDeiURAABBBBAgABmDSCAAAIIIKBBgADWgE5JBBBAAAEECGDWAAIIIIAAAhoECGAN6JREAAEEEECAAGYNIIAAAgggoEGAANaATkkEEEAAAQQIYNYAAggggAACGgQIYA3olEQAAQQQQIAAZg0ggAACCCCgQYAA1oBOSQQQQAABBAhg1gACCCCAAAIaBAhgDeiURAABBBBAgABmDSCAAAIIIKBBgADWgE5JBBBAAAEECGDWAAIIIIAAAhoECGAN6JREAAEEEECAAGYNIIAAAgggoEGAANaATkkEEEAAAQQIYNYAAggggAACGgT+D4l2NFmzIidcAAAAAElFTkSuQmCC111214080320160647