data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAKAAAAB4CAYAAAB1ovlvAAAAAXNSR0IArs4c6QAAIABJREFUeF7t3QeUJFX1BvC35BUTQSTnnM2JHJagBDOKgIiIiAqYUMEsKqY1S1xAFAEl54xZUUBBUdKybAA2L7CkZYH/+dX8b091b1fn6Z2e7XfOnJnprvSqvvfde79736tRzz///POp3/p3YCHdgVF9AC6kO98/bXYH+gBsAwizZ89Oc+bMaeMIQ7vr6NGj00orrZQWW2yxoT1RG0evCsDbbrstrbXWWmnxxRdv49DDb9fnnnsuTZkyJa2//vppmWWWafkCge5f//pXeuELX5iWX375lo8z1Ds+/fTT6aGHHkprrLFG1ufh2BYA4B/+8IcMfCO5TZ06NW2wwQbppS99adPd9FD//Oc/p1e84hUt7d/0Cdvc4dlnn0233357WnXVVdPLX/7yNo/W+d3LAIj5VlhhhewsP/jBDzLqftWrXpUmTZqUjaR99903XXzxxeltb3tb8hD/+Mc/pp133jk98cQT6fWvf336yU9+kjEChnjnO9+ZHWf8+PHZA9tnn33SAw88kLbaaqt00kknJebrXe96V/rOd76TvvSlL6WvfOUr6ctf/nJ2rjFjxnS+pxVHdM0bb7xx0+e5//77s33WWWedpvddWDvMnTs3Y+w3velNC+sSCs9bBsBZs2alxx57LNv4r3/9a7rrrrvSqFGj0sorr5z99v0uu+yS/vKXv6Qrr7wyveY1r0kbbrhhmjdvXnrJS16Svv/976d3vOMd6bWvfW026tZcc830pz/9KW2++ebZPhMnTkzbb799Wm655dJll12Wdtttt3TfffdlTPLoo4+mbbbZJjvvBz/4wWTkDmVzvi222KLpUzQCwBAW3LPh0m688ca04447DpfLKV1HGQAx1yOPPJJ96Sb+8Ic/TJ///OfTtddem4FMA7oHH3wwvfjFL0433HBD2nTTTdOMGTMyYPKrmKiTTz45ffSjH03XXHNNWnLJJTMwfvKTn0zPPPNMmj9/fvrb3/6WgY4PBYgf+MAH0o9//OP01re+Nc2cOTMDsO2GshlMzGizrREAfve7301nnnlm+tSnPpV+//vfZwMN2990000Zc3Jx9G+//fZLG220UVp77bXT6173umyA7rTTTumUU07JrIz7a0A7nntz4YUXpm233TZNnz49vfvd704XXHBBxuKeQb3WMwCMqA6YGnHUBSpA9tRTTxXeA8ep9X29m9eJ7+c+9mia/+xzpUNxAYYKgE7ynve8J/36179OW2+9dQYev7/3ve9lg/fee+9Nv/vd7zJAcj+0Y489NvvsoosuSmPHjk0i2De/+c3pm9/8Ztpss81KALz88svTnnvumd7+9rens846Kxvk//znP+veop4D4N0335BG335eGr32K+t2rhc2WHbaHWmlw84ouRjdAuAhhxySuSJY6mc/+1nmMx933HGZNQDSddddN3NRTj311Ex1+M1vfpN++tOfZkGDiH3y5MlZFBsMyNqsuOKKGZOed955meuCZeu1ngGgB6PdcsGJaZs1lkqPv+r99frWE98ve8sZaZkxn8yCH83vV76y+cHViAkejjekJwCYF1b/cf6Jads1BwD4v//9L/NbBCVMSSNt2rRpmQjaiSYwMtJXW221lg8HgEvv+omScNwHYMu3sqM7lgUhHsogA55UAiDnd/fdd0+PP/54Rv+NtKuvvjoRfvfYY49GNq+5Db+JPMNRb7UFAKN/fN1OMCA/TtB08803Z8GEn2rNdjvssEOrl5/OP//8zO9rtfUMA+ZN8LZrLp0xoGhLVBa/690E0soqq6yS6Iqd0J6OP/74LIokHrfaKgGonzTOZlulCaZffuQjH8n8OwALCWaTTTZJP//5z7OolV5KfgJUuqeBZHAC7Wc/+9l0wgknZJfxmc98Jn3729/OtjVABBfUBIM+AhlR8W9/+9ts+1133TVTKGzv3EAukIn/833rCQCSJgKAt16IAQcAOBIaAC61y9Gl/nnAnQIgy/D3v/89E9KBQIugwd8xeP39/ve/P3NnBBvE4fe+973Z39HIYHfeeWcm3N9xxx3pE5/4RJZG23vvvdPXv/71TIpx3Zdcckm27+mnn54OOuigTL5xbm6P40V0HcftOQDecsFJabu1qgOQRNCIRNMIcMkznTpWDJ5q560EoG1f/epXN3KJZdtUY8Ajjjgii1yx0LnnnpuBnH7KYmgHH3xwBhRRL73zRS96URbdAiILQRfV9t9//ywDRTeUqSHiE+W33HLLDGzYEgAffvjhLDN14oknptNOOy37fq+99sqAJ/PknvYsALGgduuFJxcCUCajU0l45+vUsZj+ohYAjP5hwE4AsGkEL6QdeoYBBwGIAZfJTDDV/p577kn8Gq0aAGVKDj/88GyUAoJRyZRI1RmVsh7VHngRAPlPVH4MIXcsa/DVr341Y5Nll102yyBUtj4Ai9HdEwCUBgsA3nYRBhwAoCYSJY4WAdDnHHJRIHNAhuGTiNyYbIUNL3jBCxa4Q0UA/OUvf1kCHeUfkAmvzJSUlexAswBccuejSv3DgNKKzbYiHVBfic5MIlN83XXXpaOOOiqpLpLl4MsBwaGHHrrAKT/2sY9lprmdptDjC1/4QvrFL35R9TA9A0AglERngrdfexCAtDi+Sy0AegiU/agn9FvOUqpOi9/5O1QEwFtvvTXLWmBBDr5iBw46ICpUCDbOH6seAwJg9K/TAPziF7+YDTKZDpGvhrXl0w1MA0YgIauhScu5FnnwI488MvP5+HcA6j7zDzG9PvthYRznQx/6UMb+omXS1GGHHZYdWzbl+uuvHxkAdINuu+iUMgDmH3Sv+oABQH0BQLJIs62IATn92Fn+199XXXVVNlAMQqk3BRpkFREtNsSW0m9ACFikFkDC8tJtpBTHOeaYYzL3B7P95z//yVwcDOs72qBSKy7Odtttl2334Q9/uLcZUOcxIBO8/dqjq8owvQzA6F+nAfjkk09mrMScMrvyv6QSQBGVilpVsWA6AAImeh5Zxt/cE+yuEoZAjgVtR+fzHZMOuPzh1VdfPWNXzTmVlck1O35RYNUTJtjDMSK1f16MAQcBaETLhoQJVmTqBgkuNGaCmaxs//jHP8puCs1MY160elFwnFfynqBbq6atngleYqcjS/1zvZ1kwGaZtNvb9wwAgXAAgKemHdYZAKBcsEqN9dZbL/Nz+GcU+ai0VUXNHyGQEk+NXiwJbMqF+C0euJGLFbTwk2QOimQYOhf9SxDDv1TGJNIuao0AMPrnelpJ7bVajKC2j1hcrdX6zvb8v6WWWiq7B62m83oagMCFgQQYmjIhjjDTwvFVoMAfUXRKDBUtKzRlJm655ZaslB8wZQekloBOrdsSSyyRsWMRAAHOTWemmDX/V4si46EOJwCq61N0ax4G0BLb+YQsB2WAEE2A/tznPpcxe/470x2oCFKP7g/WZ8p/9KMfZVMfpOz4jfLsjbJ4zwCQH6H96xIM+IKSDwg0UWLejA+Y36/a6K9ngpsxVY0AMPqHAT3MZlujDChY4J7wNVkI1oKeSsv81re+leVs5ZB9P2HChGyAx3ckL0znGgUVwAaAflgg1kg2xHaRbanXj54AoIeTN8E7rjsIwHwHezUVt/iOHy/1b6gByHdlWkXBIltl9QAon/uNb3wj/fe//03ve9/7Mib0A4DxHQuDEemUAUBRMdBqTLmUHzeoUZPcMwAcZMDTUhEAm2HAeiOzmwwIgHkGfMMb3lDv8hb4vlEGbPrAuR2U4StUwHjV9M5Wjt2DADw17bjuspkJprIzv6QFjW9jNIcw7LOiKDhulolJ0nOVUXEtAPqOA25W3r///e/M/NSagFPPBPcKAFsBWL19egaAnF/t9ksx4AAANcEAMwCIItmjjz46+5x6L+dLggEUdYAqPwitKqgFK5hGao3JEdnGlEt+kaCiKAjBBDQuzrZgx5RNGYei1ggAo38qrIeKAWl3gg33Sysyk7ZT01fUor6PyNzulMqeAKCHkzfBO603AEAAEwmbeskBFr1iMk78GWeckd0c3ws4RH2kF3lJjKWkSHoqAMgXAko3Vx0baaYIgATau+++OyvFZ/Y9MI57qwBcbIePlZngN77xjfWIoyUT7BrdBwA3iGQzrrjiiiwVaTorC6I/+veWt7wlmelGYzWwpR7JWX7cNwNPOk+wosSLGE2Ytr8IuEjaqbzwngHgIAOOSwHAys5U8wGlgTjW8p3RMBLpptYE7W76gACYZ8ChAqC0G3eDbGKwsQbjxo3LshlAJUI2ZxiQokBD9sPsNoPYINVopeQnx1FjmN9PFfWnP/3prMK6kdaDADwt7bTeC6um4no1Cs4DkM/aynSBRoKQShNMWcB2Bi5gApY5MxhR9dDZZ5+dfWaginRVVofbEyX2/ve3yNhvwGXiHVs0XK/1DACJoNodl2HA6gDs1SgYAKN/fMChAmA9MBR9T+9TXV1v4lc937Hy+FiVy9OuH9lqv2rtVzYrLhR4O9x+6bi08/qDAPza176W+XUa3YovKDpW+0cyILbyU3RWUp3vx8E2qq0LI/CQiPeZ/6XpZEuYkiIf0HlkFGhhWALzxqJH1TpVLwgZtf1Hy0xwKwDkMsjINJqBGIqH1uwx+fDYtp1JXc2es9HtFwDgIAOeXgIgQVXlBl9EABIAVAHCdxEJS7dR5gmwypIo9VJN0miEV5GsdJTPzI2g4BNfzXarVZIvtQd0Rr2Hr76w1SAEAPMMaDGkVhp/l0/2spe9LNs9VsoiIMcaOq0cdyj24WoghVaWIRmK66k8Zg0AYsAXlXxABQmxnBng+BsDin6lnLCeAlLMBnx+6IaWY9OMQKCLz+SGAQqAawFQrZvtgF2RQ62J8Y0wYCcAqD+uiT/o2kW8JCetlTUHh/JBI4wogh3K87R67AUACDjavy/HgIMAzJ+gV31ADBj94wNGRU4rN8+8XsGAgUcqEu3GlIVWjreo7lMGQA8nb4J32WBkATBtd0Spf0wTgbfVhplZAIwHfNXmu7R67EVpvwUAOMiAZ6QAIB/CpJmPf/zj2b3BgJUFqfmbJsiolmXAGApR81W79XTASy+9NAt0BDjcgFoVLPVMMADmGRAARYfcCJFno8uIBPu1ynrMNeB2aj50rwAWuXn++TV+agDw9LTLBi/OfEAalocUFdH5glTmx+jna6imFh3z1+hbMiEWrOQj8RPJAH7LimAPvkmtglSCrod0wAEHZAC0Hl4Mgmo3vRUAOo7jSwu6bpN7+LfAybx2uhmcTPVwWj21032sdTy6peAt/NIFAGjugvafKzDgAACBjLMNvaSQfEEqZsQEZvMrPQJA25NOohyJ3CFlROcCRtGnCToecq2V5k3wkUfFTiJs61O3C8DoHx8wcrX6q8hTdJtv2NkSGQKoAw88sOqUg2YeXkwgikDFQpQKdK2tLS2pf1JvMifuMzOvEtrSHr53LQaFbdxP4jL5C3O7R3E8qzTEoqCxVk3k7pu53qHaVv1juD9lAPRw8iZ41w0HAFjZOhGERKFqPRPczE2ox4DPb/uRMhPcCAA9fIMqJsQ3cz2V25peGkvWMeMYXjORyeAkWcmjcwtorlJzmEIOXs7d52bYGbyeFYviOAYnCwSIhGyarep0Uz8Vf8TiRsMlQgfAuPcLAHCQAc9MRQDs1VQcAEb/SEZxE/ImmGxEsPVdp4XbML8BTOvAKDCwCgRtlAtAJ1WwYPomRmauzREm2mM7vjU5C5iUpgEggd/21qU2/8b3LA2fy28l/woiuEPDoXHnQoNdAIBGE/+ECd51w5cMGQPGjeg2A0b/mOBY0swN4ZeEjztUD0nglpdqhgsjDVV/47iPP/Zomjd/fho1auCNTRIZNQFoozuvxIADACRZEJ8jR8kEKzD1P6ddMwoduNJx96ClgtQJVnsBTj0A5gtY680Ka8QEA6CGARstZ+/UA8oDEPiuOf6gtO5GCy4x0qnzDZfjLDnzvrTaLgeluSsMrOZfCEAPZ5ABz0xjNhoAID9CoMFx5A8BoOBDekcRqjSZJSIARADiewWqplTKm8oVc4qF34pKfSc3zGwAbK16QOaEP8Sc8KHUzxW1egB8bpvDS/0zMLqdnAdAA1EDwPvO/05absdDhgtOhuw6lrnr6rTM0zPSo1sfUAJgJAHKTHAAcIABf1ECIF9E2okzTFoBMFML/QAhrU59m6UmpMo4xMBiyqFiS3lT0a4o2sJCgKekH4NysosASHrhfKvbk/znB3UCgMGAwwmAfD8BinsSa/DUQ4QBZ652u82zRSRLL730kLzYsBKAXJ6qUTAAkli0/14FgC8dUT4gBoz+McGNVhO3+4BjfxVCwYAGcTAgbYykwiooWI0ih1rnJXkJqKwc1m4jl5n8hCQaBX8z58wA+NSM9MjW7ysxYFsA7NUoeDgBkAkef8F3MxMs+mURzKWxWkMjKzaYpMX/rmcVGgHKOeeck53fgByKfHYlADFgVRkGOwwy4Flpt41HFgM++6YPlzEgtulmw4BMnYYBA4DdvIaFca4A4Jyt9i8xYFMAJE4LNAQVghCKvs8EFigbovl1xGUiI79EIakRKo8rYuav8AUJux4Ev45vab8iH1DgIajhsFqerN2C1OECwEg/1gIggHaixboynTpWLC7VzPECgLO3fG8m8cFLKBBlQUi8GsrB/3c1Blyu5AN6O2Yk690cQOS7UOtNPCKI8mGsF6NgQAWzBbaJrKqg5YvlcqWTCLLEX7PA+DBFADTJybkIs0DbbkEqAJIANIOo2wxIEA4G1OdaAOxEcBHBVqf8On5zTOpqFoBLPzU9zdlykAGbBiDURgIZKES0ihR0Tr7SA9XkTU21FNWJYuWBjXiMSX7xWyeASnEqX6hWFGwmmcKGThSkDhcABgPef+H3SjJMiOFRIUN7pS7IqfvO0r/8NHOrVWC7t6yKBZ/4cI7JUrhfCj3cdxOYDOJaFUTyzXRa6kK9l+G0C8DZWwwyYCgQZQwY65QMMOAv0+6bDDJgHvGdyAXH8eoJ0c2MtHo64Pw3HlbGgGSlbjYMGG8tN+jyAAQuP3kAinSBiH4Y0S4Xx6D3Yhy5ZG6KJMGvfvWrjF1NjTCP2NK/Bi3LgxBqtQhkSGSx+kW17dsC4JPTExOswVkfgHPnZrpmNxsARjGCvG4egFwYPzRUDaiUvYlK85U7jsE8s0CsjCmZxH4ZI6tIcIu4Qnw1UgeXKEBf1Fd+Ng2QNaq2yGjs1y4AZ23xnhIAQwJbgAHRvXbXNRhw+ZIPmF9E0Q2pNslFpwUKJlPnK02qddyNURcoKKk1J6STK6RiwOgfl6HWshhDAUz3JcBQCcDK8400H3ApDFgPgB5OOOl5AKJ9voWgQjOhCNXzAYFO9sPoZT7MnlOlITcsAjMR243nnzCRRrjFuAUV/JRaJpj/YoFLfo8ovN0VUp95w4fKTPDCAGCeASdc9P3CVNxIBOCszQfWtYaxCADLGBAABxnwV2mPTQcYEKiYK0o5AAUArXci/SblhvpFyQRSVG0qpvyxWf4WXsSMaNdaeQompeio77WCEMfib3poTE+7K6QCYJ4Bx4wZMxREV3hMA7EIgFKX8uJhAhVumO4a86CZyVoV2nxBOXvSV/6tmvnXa1S7MKQQleCsHN+sqFq7HROMAVsGYOWFA4UfF1pvBVTSC3Nczbm1L22wW6/qGg4AjDSbnG+eARWZshqxXg1QsBQkKwPZbxbD51Km7pntDXwqBHIAWFkUCxsJKKgHXJ1a71lWQ+j4ImUV2QKhotfCtgfAaWnmZgMMSO4rZEAXrt19LQZcYUStDQOA0T8+oGkE3Ww002DASgBWSiCYWgRMfxVgqJ8T/YpoaatABqDcHC4P68DdMfELiAj3KqIFJLWCEC4OMJN2rOQVyYNOR8FLPTEtzcyZ4AgAy0ywhxMmqhYAe1WGmff6Q8tM8MIAYJ4BH7h4bMkHrLQk1XxALAjA+e+8N0QpXGWL49Uzwc0MwLYY8IlpacZmA6taYMBCAA4y4Nlpz80GGNBnfL9IVEcUbGQSlGNR7Yw5C3wVQYqbIoMSRabqB43aIhOshJw8EI0eVmtJtXo6IABG/wRPwwmA1dycZsBRtO1wScUt+cTUkgkWhEQAuAADVgMgoNClmAHBhCCECSElcKw9eMGEIIM2xTTwVZxI9iRewgxsfhyPZkXNN8ehCIDYmG+kVN51KWWqNQG8FwAYVeWYMM+AI12GAcAZmw4yYCEApXG0e67DgCtmDMi/kLKJqDGiYNuZIihCtTClamc0TRylrgtSTDGk5ZFUTH3EYv6WYuKHGaFFAGRypO4wr3wzh7vWot2NADD6hwGHeg5IJaj4Z2GCKwFI0nK/Yg1sA1jwwMKwGvkWk40qjx/rcOtjTH+oZ4IFMiwTYdvzY6mKrEw7JnjJx6eWTDBiCiyVMaALDwbMA7CaeWgmciXX6FS1STjdTMU9/boPlpnghQHAPANOvOQHZTqgCiL6qua+CEL4S2bPsRwAKuIlx7AqBicAWRDAgLY/zRTQgUWlM0DXKroI6YZ1Az6qhTnInQ5CAHD6pu8q+YCFABxkwF+nN28+wICVrVcLUgEwz4CNLsXRCV/MMQCjCICmHrieiFjpgVZRxYQGOx/cvtwSVoB8JRi0RK8yOaBkMQKA5uGIhs01rkUWEgIKSJyXpZK+K7Iy7TJgABADhv+9AAM2AsBejYKHAwD5zZpotpIB80AfaZkQDDhtk3eWGLAQgNJu2r3XY8CXZQxIrORToHtVGVGQyndgVn2OvmlURp5FJ5WXq8hA8TQmAYTol85khNG0+HQmOdUz5wRSo7/eiqb1fEAAjP7xATsxn6IZdhR85Rlw0qU/LEzFjbSC1CUefzhN32TQBFcFoIcTDJgHoBSNIEQxKec5ClIBz2sdwhwAI58F0JgC5kLQoeqZv+IFhcwEeUUdW6MAtGq8aLldAD712kPKTPDCAGCeAWsBcKQxIABO23iQAcP/LjPBADjIgOekt2wxwIAxbQ+wFBVEQSqwkUmo8goRohgBuinsihQAFGNiQdv6LBYZstwaU1SPAZXuc7TrvVywHgMCYPQv2LwZBmt3WwxYBED3gr4aa08bvHwuqTWDmA8onUkX5asZkNJv8V4X+5r+oFEbjjrqqEw9iMlLRddu3rVImzAsWGTFBDydDkKWmPtwmQluCoDtRsG1Hlw3o+A8AJlgjNzNBoAx2Dj9eQa0Rox1E5WnaXK49FQvvbE2tyIOwR+AGNwAyLKQu3xOV1PkEbKXwAJhHHnkkaWpoNX66v6zTqJlVddAXbQOdztBCABO3XjgdRKIKwLABRgw3oNx3w0YcKURNS8YAKN/GLDbAIzl1CIImXzZj8p8QA8mCgfizaWqUzCTRZP43NwX2qkcL0mGDx3rcpNSlNebb8FiAAwg1ipGcD+cy4CQGKA5xnIrlYBtB4CLz32ozARXBaCLyZvgvbYcACATaKWoSI2he+VURq0boEm0C0R0XtZEx6Op5WN6Q4TNp+vqMWD+JYh8xlrv0Khngp98zQfKgpBaqywMBTMCYJ4BKwE4kqNgAJy60SADhv9dxoAAOMiA56YAIH/i+OOPz1Jw/DC/RbtGHN1IpoQPQ4syGo0gyj5/RwbDK6b8AAj/UXGrEiBV1bSqWj4g0yOqpv7T0fxd1BoBYJ4B+wBsbpi1y4BNAvCctNeWL88YkA9iZdJ4VUOk4kyMAQi+BsGUg6xAVam91U1lP0gu5jbwLUyiEbDYFnNaEw9gawEwP1Gm3ioAwx2A1IToqwlEeQaUzmQ1YlUEygLGtOZOFIgauNXmscTLq+1TaSHqpeJAMIpDkEHMSel0EIIBH97w7SUfMNyfBRiQ3KKNvxEDDgCwWhBSVJBar0DVsfLb1DPBzYzRRgAY/eMDRtTYzDna2TYAqP8AOOXyH5d8QC/kEeUGALkt0mtYnw8ne6A2UHU5t8S6MAax9awRAZMmLy8rItfu/StcIgCs5bawXs51+OGHZ0UitZasa4sBH3soPbTh27LBxNetCkAPJ2+C996qOgB7NRX3xKsPLgtCFgYAQ2CuBCArodInXgYUADR5H0OxICFFASBpS6WRB+l/cgvLZGBhUm6Ttad9zw0qaqZ+qmAC9qGclrn4/wPQdSjfKwTgIAOel4oA2A4LLKx9l73ljASAeQbMv1q2G9eFAQEQA8oM5Rmw8vyE6LAUjViVatdvP0AdDisjLPbYg+mhDQYZMPzvMhPs4eRN8N5brVzVBHfjYXX6HMMBgExkMGA9AI60VFwA0HNlgsP6LADA8KPuvwkDjhwArnjnuVlFbvRPINRtBgTAKEkDwAev+MkiMy1zsUcfTA9u8NZSEBL3vgyAHk7eBO+z9cgAoEh73rVj03L7HldmguW2u9lqAVDqTRQab96U8aAq8OmqVYHntVYSFcaMudnRJ9t4K1ORsBzb8S1JZoIQrSgQaScIaRiAgwz4mzQSAAh8Iu1XPPbnrCQ8z4ALA4Ax71eeNs+A8rfSZpG1ELnalvgOGP4GUH+Tt+TQpc7IXj4j8uunyiWpNJGwnLwq6VrvWHZ8+9FwVS6JrL3qodMyDABOWX+g2FYQUsiAeRO8z9ar9LQPyAGPSp15141Ny+/7hRIAMUtUH3eLBTFgEQDHjRuXAaYaAC0MICUn6lUZpFIau7FWot0oxRc5SwCYm2OOsOohOeVagntovKqgpPzojEXvR2mXAQOAfMAY/GUmmBgaCxBO+B0G7F0A5sEHhMGAdC8NEOpV13QamACo8kTDgA9d+dNFxgcc9eiUNGW9QQasCkAaoBJtdP7k1PFpu1lXpnmrDrxwupcanTLyzfF7wu/PT0sefFZWoyiXreRIOrCbTSYjz4C1ADjSouBRj0wpM8FVAehhcFqtU6JCQhV0rdRMNx9eu+eKJS3M/mKalDp1uwFgMCBTu0gx4CNT0uT1BoI+PmC4P2UmuNsPZFE7Xy0AynbwS+P1BZh6fWvrAAAF6UlEQVRSIMKvM1jMvxZcxBIefDxMzx8UeJj5xi+UFeFaKKtSoUT2iSLXavebhRBFKzSW6jMwhqIgNT0yucwE9wG4ENAPgJGV8KAfvupnJR9QAJCfIhAAVJCgReU40yzydRyRZLxM3PwbgFQ6J1rmRgkmLIcc7yap1mVBCnAraB3KglQAnLzuIAOakqH1GbCLQMRmeROcB2C+7tElYTNug0hXRZHgyVxa0x7kb+l28X5mFdKKEGwrx8zHxWiCHtvXmkujwkngiW2HsiC1D8AuAq3oVAAYDEhozgOwcp+RNikJACets3fJB4ypB30G7CIwF2kAzpmcJq3bB2AX4bbgqbwNPc+AU6/+eckHZEaVYonSQ42I11yoE+TXKbOqNNX5s+S/E5gw0xYHrVUNEwtPxWJBRak/52lHiE5zJqeJ6wzM2hMFxyKYfQbsIiRrAVBgIaI1+UiT/+X3MdUiWtkOmRCFqCJlSQM6JlAIJAjvACrqFTUrZJXdENjUKki1IJFziXxFwa6haPt2APj8nEllJrgPwC4CL04FgApLNWDKM6C1YcyxMdUyACh6BSQFCgAo6BDpKh49+uijs7SZAIW4Lr1lrq1oOiqpsR9dt5bgLkqm+QJt5YvJK29RuwCcuPYgA1pBrR8FdxmEtQDYThACQObhAGU10AyHgtTnZ08qM8F9AHYZfE5ntas8A0675sRFZm0YAHxg7YG33fMBo0Kn7wN2EYjNAHCkyTB9AHYRaEWnAsCIckW2eQa0ooG8e7wLBABrRbz5cwg2pNOqpdyKVlPN768ekPBd75VebfmAsyelCWsNLIWCAWN+d58BuwjMWgC0SJDZa1aQ0ADQLDWRqXSbpThUSKt6FrUCKl3x2GOPzQIVdZwqn32m0gR4ZUBUOEWVdVFXvZ7Bmt2Wy6hcDji/TzsAfG72xPTAWoMmuA/ALgIvTmVVqzwDTr/2pJIPaNK+iTqRtwVAqTX6oB8T1AHPq1nNrQVAUzZPOOGEbK4w39KcYp+JfrEhOeWUU05Jhx56aM3eKmJVlAC4sdRKtR3aBeCENQcYkJTUB+AwA2CjUXBUP9e6fDngWBe6kZURGr0VbQFw1sQyExwrcPVNcKN3vwPbYcCYYKTWMs+AlYcfaQWpz82amO5fc88SA/YB2AFANXuIZgA40qLgZ2c9kMIEC0L222/gvXF9BmwWRW1sL6DIM+CM607OfEBCsqmRfD51gIIH0asfQQGfTqSr1k/2wzLHfDY1dX4zt4RoyyILZgjPMhvxjrlaiz+ZPadkS6Di3XL8xqF4TQMA3r/GIAP2AdgGkFrdtQiAjqcuzxRJTUoOA8oFK1w95phj0nHHHZcVmcp2eG+I1VDNfgNMKTg1gGPHjk1HHHFEtkaMpjbQez+8OreoAb6Fj+Sgzz777CwIMgOv00FIHoAY0MpofQZsFUkt7geAcrmaV6kGA/of+DAhBjRjLnRAAQWJRHUzdrM0L5BYzEgRAokGY1o8FJB85zxyxKJnhQvx1qRql50vSCX7iKat2zgUABy/+h4lH7APwBZB1M5ueQACxczrT1lkpmU+O/OBNH6NPgDbwU/b+6pUyTNgLQC2fbJhdIBl7ro6AeB9q+9eYkBTCPomuMsPaVEG4PyZE1LeBPcB2GXwOR0Aijg1UeeixIDzZ0woY8D999+/z4DdxmAegIKQWTecWugDdvvahvJ8THAegKJgklHfBA/lXa9ybK+2yDPgogbAe1fbreQD9gHYZfA5XR+AAwDEgAcccECfAbuNQQBUUqUpvVqUGPCZGfene1cdZMA+ALuNvv9nQAAkHgPg7BtPW2R8QAC8Z5UxWSkZBjzwwAP7DNhtDHrRTp4BFxUAjn7wljRv/N/TPauOKfmAfQB2G30pZW+2DAaUalsUAKiG8Mm/npNWGf1sunuVXUsMGAUP/WqYLgIRAL3hUlsUAAh8VqfdaInp6enxN2cmWFMR3QdgF4EXpzInJBoAzrlp3Ij1AQN8Cmsx4Mqj56e7V941676ih5gm8H9CaZZbfQFFRAAAAABJRU5ErkJggg==

Reports a sorted version of the list in its first input slot, using the comparison function in the second input slot. For a list of numbers, using < as the comparison function will sort from low to high; using > will sort from high to low.

ca:ordena _ segons criteri _ The second input is an "association list," a list of two-item lists. Each of those smaller lists has a "key" as its first item and a "value" as its second. ASSOC reports the first key-value pair in the association list whose key matches the first input.

ca:associació _ _ This block carries out the given script for each item of the given list, like the primitive FOR EACH. What's different is that it provides the # variable, which will contain the item number in the list of each item in turn, 1 while processing item 1, and so on.

ca:_ per cada _ de _ _

1Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation). Will not work on circular lists.

ca:notació textual de _ ( )SENTENCE is the main constructor for sentences, represented as lists of words. It takes zero or more inputs, each of which can be either a list or a text string. If a list, the input is assumed to be a list of words. If a text string, it is converted to a list of words using SENTENCE→LIST. Then all the lists of words are appended to form a new list of words. If the inputs are lists of lists rather than lists of words, SENTENCE, like APPEND, does only one level of flattening, reporting a list of all the items of all the input lists.

ca:frase _ ZIP takes any number of lists as inputs. The lists should all be the same length. ZIP reports a list of lists in which the first item is a list of all the first items, the second item is a list of all the second items, etc. Viewing the inputs as the rows of a matrix, ZIP reports its transpose.

The identity function reports its input.

Takes as input a function of N inputs and N lists. The function is called with item 1 of all the lists as its inputs, with item 2 of all the lists as its inputs, and so on. (The lists should all be the same length.)

ca:multi-mapeja _ sobre _ Takes a sentence in text string form and reports the sentence as a list of its words.

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _ pt:uma lista com as palavras da frase _ 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KBgAAH6CYJECBAgIAA+wECBAgQIBAICHCAbpIAAQIECByxcQFpoRMBzwAAAABJRU5ErkJggg==Team,OddsColorado Avalanche,9.6Calgary Flames,6.9Florida Panthers,7.8Carolina Hurricanes,6.3Dallas Stars,4.1Toronto Maple Leafs,5.8Las Vegas Golden Knights,3.6Pittsburgh Penguins,5.4Tampa Bay Lightning,6Buffalo Sabres,1.1