{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dbamman/anlp25/blob/main/10.llms/Shot%20Selection.ipynb)"
   ]
  },
  {
   "attachments": {
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fhsMOOyzECVCIbiqBNDiPetFFFw2f/vSnw4orrmij9XwP9hy7FC2CIW72C4sttljg2MT11lsv5OpNGUZRGqIVszhPnDPF6VdLL710iD7kRZ22OrOUuqHt0PfpM8SJm3HDkUceWRwHxfF0POOccvqPJTDheDhL4AVxpjntYpZZZrGvQ1Q4w5lnnln0tzgJDXPNNVd5pNPcc89dhr377rvDJZdcUv62N/BEW4wrShhG7KuivOpvHDdJnf/lL38pwyDXkCfQSiutFBZZZJHyXbsbjhT8/Oc/XwaLFt4QN/SWv9Mb8t9ss81CXIFpkuuUi/LliOP8OK6QtpbS2muvnZUZHA9I/XEeN2WNClpxFjfhOWqz6rxvjtQ655xziriMKXHCEpCr1E/0Py3qiuP6optU03GR999/f/Ge88SRK/DMcZNVZ7OTpvoPR4txxBbpM86CzcwzzxzWXXfdop3PMMMMabGL33bMpg419uQC33777cUxo9GAUbRl+hE4IPtnmmmmXJS2zxZccMGmdhRXvkI0DpRtKU0ADBlX6TtVFCeLRb+l3jjCjLESPpErU001VTbasHCgjhkbGb+4p/yLL754IffscZrUJfVB+xHRjtAdIOp2ww031KviSv2gC3Gm//e///2md/YHsok2Aj7wgR4CnuhJ9PFWR79yLn00chTfDqDfwgdt7ec//3mIfsTFWEj/jxOISqwtLwO/b60Pd/+WGX5kvjgqKgqVclbCM/1hsapLdSy8cZAsLMdKn2tU6hoskeWOrFK4dIaIT5DNj5kUfmX4bSoO79kxagnLpd1IprBcsXxZ/yT7LirVTcdd2XdxwCmziB20zN+GSe+jMC7j5Cy8Vf6uSmf//fcv47P7PypmTXgoHLzlTh7QezCKg3pxbJqe5a7xQxtlft3eDNrCi7uBeE+PigMj2ojec+U4sZSsBZZ3+GzbNqX48UzbNGrxuxML7yDSzzKVPGxn4aU9/OAHP2hydWK1B8t/VVuKyl2SS3c/2dgkyw1tk40vtk/RV6r8BuHPbliMynFTXOqODaa0BUtxEGgQVnXLNU4OG3EC3PRM77HeWr99ZIreVV1Zyhdh9WKTl+RX2v9TucXqDsfjpWljrWVFA6K+7Hswi4pf8Y5VGuVlw9j7OCEuwtb916lLg/yG05UllrzlBmT5YYWFMQmrNHVm31FXcdAfwyo8qZy0U8YM6kpx2eRk3dDgBdeztH/j9sGJK0pL8bnyjDEMOZHKE4WjXjixQsQRVNRF2sbY31LVn2gTdoxQWlzVP8gvKlj2VXnPSh7+0uIpbWPgYnksI9a4satoNv3f//73NWI3B6GMts+CndLkijylLDkaNA6cOBGV15IfXNrStmLd5tKNnLYcuqccrGgTD5mq5+gkVcRKpdxO0U8Ia9s1abAPA7kion2y0hyNf2UehEO+Vh2FOipHow3MpUH+ZnR6On/a2FQZVFAdygkIu2kNhcQOXqSPsBPRAZWnroSnItj8JEpPSqARiugcaTlyykkqAJRf1RWMIHZVpmGswksnocxpGH4j+GiI/FlXgSpeKAcb5NTY0zTTpSQmDmkY/QZnluP1216FF/lwPF2OHzqY5Vl4d3IdtMJr/RPj6kHJGrthKRNlYALEebMqv22fRKBueMeAK2IDQloHYBZnzgpSXjtVeInYz/RLRlrctFN4wZHBJO3PahfRSlEoYGo3wtIqgS2yr3yFcqM8yT9aNcqw3/jGN8o6I790sEYBUtzVV1+9wFSRo5W3aSLLQIMvooid5tFa0pS+ysoknD6helVZUTAtIT9RavSetsYz/dm+o0GUPim3AmSj3QxLOnE1xmbRsO2b96uuumr5HkVO9ZE74oiJG/iKP65MEMSfHSzLRFvcdKLwcjSZDAnklyONReLvhz/8YRkMFyyeU78pJgokX1HCffe739Xj4mqP3LSTXDDnnGz6uvLlqr7O2MORiih4alu8R3HWOBZX0BoYHzBQ2DDkKWKCpvZk85HSgiJFXdp33INZrk+1U/SoS6WHQkw5IeSVVbDhF9nTKTHGiveUZ5Qx2lUdgh+1WcZ39RH6sp6TPv05R4PEgXaqOkYG4E4osv0c/ugLEPwTz55igXKpPqYruNu2QhpVCq/VdZBTtp9ihLP42zbHuMbvtN1JycaQQNtP5V6VMUFlH8Z1IAovwEkI4a8K7bnnnk0ACkysh3UorUTiW4UCK6HS1DUuFzYlLZ70nkZtiU6S5qMGp3Dp4EjnSc9ERYlWHvaKopTuQOY9Vhwo984qvIQhL5um7nmXo7RREh4hiCUBoqMoDXvFZ9BSXCLLhoNnkR0YbFqUQYIRIWjf6Z4BsxcatMJrreIoBxCDLfWP0GTCwYcpVB6uzHgt5RRe3mNls/G4RyCm59dKMarjw2vz7Vf6Ns2q+3YKr+KlbR3FwE5+UwUqLpEpasdX2rgGGCawGvyUUIqP3WhHXCktyIbUgksanBhiB9GcNSPth1itLR9WISWtlBi01UZQCHIkpZX4VukmLG3Jyj8Go5SQT8qDqxRABkN+pythNv7f//73prhStm2YuvepwotSgAXT/mG5SxUj6ipH4CxLH+WgHhmXCI8s5Bln0FYRCgNh+EsVfuLpHXymxGTAjinUTZqXTUNpIUstpbLVTtgIZ8/pJg2wiUvaZRIYMFRW5YHCnVI7RQ8FnPgo4ymlRqXo0pYGqfWbvq+JgXjVFSzpO+BaRdS3rKW2Lyt8KntSrAk3SBx2/p/xiLaQWpjRnXiu8qY4WwsqqwA5svKOdHIKLxN15WEntza9tM3JMKcwWNCVBlfqxh4RZ2Un71NdRukM8zoQhVcbLDbeeOOyLMzMLDi6B6RUYSwjmRsrNBTXKrx0Lj3XFSXNkgY9vedqlQo7W1eYdEc8y4V6p2v8dKzNpqmzKAyzHxECnUZNmeyyEekovK5pI+mHwisFW/zYmbnyTc/4ywnltKOg4Cm+vab1q5mgDcMXw3qhQSu8KLXiF+Ud4U4dYklT+ViuVxgG55SqFF7CscytuLqmAq1bhbdf6aflyf2uq/BG3/Gm8oJNSli7hIXtJ2m4dr/Zxa10mJTkyCpE1hLB4Kq4qeXVppOuzrDJ1ZJd/mPylFI6WVKbUrg6Ci8KF7xGv0tFa7qisKosXFNLdjpA0U/BgrAoUClPNvFBKryW51b38F9FTKZsXCxlsoZjjWpF9HHFTc8Exkiid8jzHFl5hytLSvCtNLimeRA+DZOetJO6vqTvSSNdoYRfGSJ4D7VS9DCSiE/bR16L+dp/tUHCIR+7JQwIqXFJeXNlElr1AStr/EonfvCTGl2sniJ+B4WDHadyyjj5248bpX25jsJLGrav5xRejSVgWVWXpCNrvurT6kus/Ng60QSZeCLrioEMHG8aiMIrMDGLW7IzZQtUqjDaOLpvp/DmrKppRdrDqpW/tZbl8kgtOumsh3RQGC3ZzqJ8ctYRu4RA/GEpvKkwtMvw4jc9Ciin8DIQWEJIKb692jDcp5YGwuY6Sxqv1W8rSGzevVqOydN2bBRflBmEOe3cCgDCsmQWN5k0LQ/xHGql8PIei4vlnXssNyL1K9tm9Y7roNO3eVXd11V44yaJsqxVioL1Y6SNdktSNrFwVhHKAJM8We8VzrZVylZF1mJCveHjaMlOKtOVJ8Jpz4Pqn/QstVN4rTKCNRkLV/qXWplz55cywRcP9ppOki1v3A9S4WXQpH7sH8YO5Bj+heKzlcILjzljBW0ipxQRXgQmYEl7RMZZSuvdvtO9VQLTFUOFsXVT1c7s+JS2U6y5woFrlQU0XWnFpcOSHbvSSabdi0OfStsXvy0P3Nd1QbA82HvkhJ0w2PTBg6MiU7JyI8djyifW5JQGhQOYqgxpHxcPtGOMdRhB0glJXYVXkznyShVejRPio9VqDDJC4bjCkyidhKVtiXB2PGglf5XmoK99V3hlyaVwqUJnBzkLIrO1dmQ7u+JaC69tSHpPB7VkBy+FkR+T+NZzXdMGlztqLR2wbWdROlWCzvI3XgpvbibtCu/rNYMyrnrUlc0u7QbY11N47U6Cpqq901+sVVN5aRNNrwpvr+mn5cn9tgIO/qusGFYWpP1H6VorRTsrnOKkV5QZ4YgVtxNiadTKnXarEHJ9IL/UN7CdwsuSs/jkat074Lmdwht3q5fxGcApa7u/1CAhbOzRR/BS9cUrhec6SIU3lUU2X+6lBLTrj7R/Ww+ULTWKpGlX/cYNxrqhqO6sm4ri1lF47RJ+lcJrleJ0w3RdhTd1O0iX8+3YlSq8trzt2hbvUSxpF70S9WqtnsKaK/2TlTVLVkGuw6f1T1U6g8LB6iCpfqS8W13V1il7ugJo47VSeNPVJBsvvbfykzzt6lQdhdcayialwitfR6ypLFvaP7k62Aar+3bnmtqBR3GswstOZT3X1e4ORhBhldM7rggQEbtA7Tvd672ubFLTO3vF+iCynUVhtKtZYXJXV3hzqNR7NkgLL5tcVI+64u/YzuqVct5O4SU8Kwp28CM/2ikTs14V3l7TT8uT+z0shRfhqbrIXWXVsK4T4NcJsZnEpt1OOVL9ECcV7lbRyll4e1V47UkKqRLTSZkJyyTf+sfi71tlMVTa46nwovyBeTuFF16xCts6raPMq4yMIWAr9yaUF2thJt1RV3hZkbLlT33j7diVKrx2E1I7q7gw6+eVurM8qBwo1iK7ylhlWFDYVtdB4CD5D99Vk/xWPPGuHwpv6iMPX63I6l7W8DDFK7wIRTsLVYOsc019VdMKsKArPavwEp7K0DuuNHgJwUsuuaTpHe/tcVjW78emkfKRzo4UVpvzCG87i967wvs6knaWK3xG2aXB+jGh6Fr+Gfxs3b9eyrF3EnjtBDEuC2l7hwdZf7t1aRBH3aav+K2uw1J428kZDYLWMoqlrRNK/SLZ1dyK2MGv9pwukw5a4bUDYTs+W5WBd8hxa62mTBxD1IrGU+GlX+FGlFM2U55zK3m5k3bSeFhQ1e+pW1b6IOtKAk45HkbJwgvP1vCTTuLs2JUqvHYinvp/k26vxEQrh59Nl4lh2jbBnY2jkG2HvfgQDwIHyxs8d3OKhe3n3Vp4rV8tfLSrS9t+8X0XTfEKr8zXCAaEbu6P5SlAzv2xAaCKUgWA+KnCSwXYZRfCsFFt6623HqNApC4GVb5rKT/aCW35TzuW7SwK5wrv60hq4BA2XEdV4WWjjvhkeUxkj49JN+8pTHqtq/AST31Jedtrrwpvt+mn5cn9HpbCi9Bmo1jVH+ffQulEN/W5zpVBz1JlJrerXWG5WiurLMx6P2iF1/p/5zbhiI8616rl49T336aVDuat/AJtvNx9aoFq59KQpoHilLNIo0zRh21f4h5rduq6ZtO07jf4rtrNe2kbySlsVmFIxx3lY5XJQbo0kJ8dS6Uoig87dqUKr930zdjeTwI3+LKn/lSlj4EhnfBqVYN6tPXbTpmrymMQONi9IPDYzkUqx1s/FF7riw0fTBZbkbWq29WpKV7h1W7Wqg4rUHMHnQM8DvVVZDupGnSq8BKXjoMrg8IQD58e/nCzwC8w1wnSJUXFt8KN9HNuGVYRIoztLErHFV6QeY0mksJrfbZRBCzJ4kod035EDJD2eBY970ThJU7OlYK8+qHwdpO+ytHqOiyFtxUP9h07udUHuR599NH29Zh7BlMrh6zcod22ovg1sDIve9YrcQat8KbuUNbFKscz7hpYymy7JZwm/ihgtDOLHWWo8jscFYWXpXYMELkTNbRpjfEnHfTxW84RS+TWIpqeDDDRFF7aha3TtD7t2JUqvPYjHbSFdsSkvRM3ItyA+KszKU39zO1qrVWG8WVtRxyNmq5gDAoH25Y4c7wVoctwtrPVc/qh8KYrV+2OhrU8W8PUFK3wamBhgGAm04rw17WdTvfETRVMpWMHHoW3DYFw7Hq0yx1Vu+WVZnpNd2+ST2p1PuWUU8bwnlp+bGcRr4NWeCW4aIQWf9v5xUtqqZkMm9aYLat89lq1Ezat+6rfdldzuiPY7ga2u5GZEecErQZHlIk6hMBLv6BE2aoU3kGnX4fnUVN4GTyt7EB4p0qeyoVVEBlgN5ylblKpwqO4XO1Ezg4MvOunwpv6B5N+uvJEu5FM4L0lrGBYKtOJOnJDG350CoA9lo22lzsyi7RThbeX01F6sfDu/L8zTpHTluS7S/3LBcn6XFO2W2+91UYp7lnyt/KEPmkpdZFI3xN2lCy8dsWDcSolO3alCi/Kl8UC/+UqYkJFv0uNBFXheS7re3reay5Oema0lfPysRavrSypWklLwwwKB2skgT/7lcS0nJQRDK28sgov43YVtdq0hlxAhggfdIQqWWE3rcGLnUhP0QqvXAn44lYdyiliVECVQFXl2CsDvIhGYSsRpSMnfBQ+d2VZxKbPffpJw/S839wAyswtTafOUk16eDhppL7N6bKI8pHVGiUMnmiMUA7nOgpvagnL+TiP0rFkElzCQ9f0PNRcvbd6ZhWV1PJglVG1NQQveacClDw06CI46hICx24gIu0qhXfQ6dfhOW3D9utTNr79kk8VHpIplNlulrDp1LlP+ywTDvUXxceSp/q0Z/WmPvu5iQxp2I9XpO4MvLfLwXZZkHcQmxLVZrmmpzRoYxbvwCtdsqe/22Vxwu2www5jZCDKnnzSdfrHaxw0Gttvv33BA4OqiIFQx7qJv5Q3wqaWwzRtpVfniuxRXlzrujRYGWVdKsBGkxF8ukUo6WCpvDCWSG4qjD5brDBpv7Z5EsYqBEqjU4U3d1wcaVlZ3u6UhirDkVXy+XBASq0Uvfvvv7/ESnggd1NC5sIr4xCrWnXJGhBS40KaBh9kEA/kZSmdMMEHvKck/36U0JQGhUOqY9D+0rqkz0mhP/TQQ5tYUx+l7On4awNitRU+yLWUUj9eViVzZPtiKrcYD5UH19yxZPb0mNxEPZfnIJ/15VgyK+zt959bMa7BxQLGPQ3AzmhIg8EpDcdve6yOGq8NB8A0ZgagjeNHMPhjEEX5ZOkuVWBQWlAwbRoMGiLeWwsy4XKW21RBIVw6W1aa9pru+CUeintK6cBGOAYxWaO0nMegaAU64fhLO5gdjBUmdYjX6Rt6zzW1VGIhse91b/3EwDDHU50JQYqD/W2FpfLlmjvo3cZrdc/XjJRWag0jnj6nzIAmAnusZDnSkippdqIQoMhazKoU3kGnnytT+sxaFigny/w5SpeUcwM0/Vb455TIXLq5Z1gbLX5Kkz5D/6Ye9T7XT3mmOFzTwRglySoSOeumlQn2aB/xq0/cKp908EgVbyyvDIwMKPRD7tOBnrQY7DnDGMWNPqJy7hwtoZZYDVP4VGnji4LiiyvKY/qlL9KyYah3JhHEhT/GiLpkN/+RJp/fbUUcS2VXYmx/JJ7aZE5BQO5YvtNPB+OaZN9jPcR6yZiULqsTjnNKUYplISd/q6imCgzvIepJ+VSFUd0RLpVr6bFkuXHYfi0zdy48fFjraDoG8F5jjHjlStumH6FE2T4rv1ri1SHKrXTBI+cWRjrpJCMdz3KTP9LFeEFbwuqsSRz52PFJfA4KB/qp9YlVedErwJt60fgOv+kELFVUkUWkybhjZa1dtWV8T4l0rTGHPK0BkfBMwsUL/KWTbK0oqgzpqhZpoCTrPe13vKknhZdBitm37awUCsUNX1cqwhLWKjpB+v1wAaIrAlWNHZO/LBJ6ryuNVoLFbtrQ+3ZXKjOd5VIm29hJgw5G5adfU0qXaxjoqvwuwYVBJ3eUFVZbBK/F0fJOvgweIg61t+/tPcoWYRGAOeFEWBov9cPsO12mUlrwglAlDBuArNBWGK4oAxylxYAsH277nntm4wx84EPHTN/zG55y1gKVOb0y4FL31E1VOUkX3BkMWZpMrXppmulv67+bG0C0fE8eCAOED3najSk8Zxn82LjJg3AqO4MEPFlLVJq//W0tA1bhHXT6loeqe5bIsfox+bNlVFkR0mAgaw/tRcuXCsNgZwVqeswg6eaWnKt4Sp/TVpRX1RXfTlnqbXzkmP04C7wg91C0UG40eUee5CYy6bI44awLDG05/cojG22taxIDVJV80KSbMHagqyqn3WlN2bDuqN6s/7IwIEwqE+nTqaXXKjs2b5uf0kyv1D140k9sXN0zJoAzgzq+pEww4MlOJBQWKxiELLcWKp2sYPMmjOLpioKsDw7RtoWN3tsrcsGuLOqdFNJ0YoeyYw06YJse7o8Mt5Mm6jVVzBkTsVCLUoUXPsALuYEiYpVzlHrStMTvVJGkvaUTFZQcWctV1txVbdLm0e4exTVNC0sr7in0K1ZbaUs2TJXvNcdUVvUXxade04nlMHBA5ufajPjiyqQtd4qDNS7a8CoL8ouyp22W4xlpa5bSumQMpr3S9pBZ6lvI6nScAqfUEEBdScaTDzLOrm7AL7pEyofladD3PSm892eWOGwlIEwsaUOEDdPqnrh2oMmF1Xfr7ew1F67qGQ0jbVhUCIKyqsNQibmBzW6Wq8qPRpVSlQXbpoFAE2GBsUsuCocQlM9xO9wQWlUWWaXHNXVwt+90TwdLLeN6pysWADv46Lm9pktTKm/uitJn49a5Z5DshKx1B4U9JQZpLbtLwGiwVVgGm1a80cbqkpa5rMI76PTr8FZlXU/LjeCscj1RWFxC0kFN77iiCHZL9KHUh440aXdYStsRCkGq+BGfOoRnJvQpWSuKLYfKIheY9J1+W8Un/fw0+abnqJL/4Ycfnh1QUaRSq5v1CVSeqZVbA5/e2ytjgAg5lk6Mqa86A1y7dmzzbHfPYA2lgy3x9I73qf+tTdcuv6IUpeVCfsp4wYYnGxcDRbuxkfZC3dp46T0ynn0q6XP7W3WVKrypMkIc6p/NjSlVnWFu87FjOfe5k48Ij9EjVZLT/Kp+ayWBiRP8t1KsqVvaTCuib1UZYTAapXuAhokDymaV4Qo5n64+23KmLlr0T3hv1+aoH8JYQnFFb8lZnZmY81n2tP9av17bRnTPBCWdxOkdV/gdL5qKjCMTE54oRqy4sO2223ZcljjYhbXXXjsbL3aaEAVbiMtYYdFFFw1LL710eMtb3pINO+yH8BUHmTDNNNOEhRdeOCyxxBLDZmHS53f++eeH+eabLyy++OKVZY2DTVEPtI0FFligMlyvL2jjcWIXohIVooLda3Jj4g86/TEZjtODOCkM0U0kxIG76NNRmemIkzjRCXFQD48++mjR5+aZZ56O4vcSOA6UhTyac845i7ynmmqqyuTiUm2I/pRh1llnDXHwCnPMMUdl2H69iNbqEFd7MKSEOLkP008/fb+SHtd0ogIS6OdRSQrLLbdcgallKCqvRZtYZpllwuyzz25fDeU+KuVNMoo2Gg0j4e677w7R6haigtj3cStaE4v2FS39Ye655y7GoF7kEhgfd9xxIRomSnzBOypzIVqzi3IsuOCCYbHFFgv02VZt34IeV0qKNMDjne98Z4iTmb62y15wgCdkETIJvijftNNOa9nP3seV6RANH4UM6FR+ZROMD9V3aTtLLbVUmGWWWaqCTtjnk0bhjbOOEC0bIS7XFJURZ7Nh3nnnHVMxcWm1aCj2RfSbCdGvzj7ye0fAEXAEHAFHYEIgkFN4MYQ4OQKOwOsITBqFNy7hFwovRYvLjoUlrMq6EHcqF9YHZklQPAYlRL+w4t7/OQKOgCPgCDgCEwkBV3gnUm05r+OFwKRReO3yRvQ7CdFHqiWmVkFmqSr6lbQM7y8dAUfAEXAEHIFRRCBVeKNvZq2l8VEsi/PkCAwKgUmj8C6//PIhfhO9wAlfmLjBpPD1SYHDTyg67odNN920eOXuDClC/tsRcAQcAUdgIiEQTzUpVi3FM6uYo7LXRDz51REYbwQmjcIbd3iGeFROYKYrwlGfzUZ0fBz3cXw/55xzitc41++8885dbXJT+n51BBwBR8ARcATGEwE2CDKWxaM5SzbisXkhHuFW/vYbR8ARCGHSKLyqzHgWaojnoxanKrBzlt2PEDsZ55prrmI36WqrrVbsdK+zG1Lp+tURcAQcAUfAERglBOKXDUM8CjFwmlBKrHRi8OFkFydHwBGYhAqvV6oj4Ag4Ao6AIzAlIMBxmRwLVkWzzTZb1rWvKrw/dwQmMwKTzsI7mSvLy+YIOAKOgCPgCDgCjoAj0DkCrvB2jpnHcAQcAUfAEXAEHAFHwBGYQAi4wjuBKstZdQQcAUfAEXAEHAFHwBHoHAFXeDvHzGM4Ao6AI+AIOAKOgCPgCEwgBFzhnUCV5aw6Ao6AI+AIOAKOgCPgCHSOQE8KL+fa3n333Z3n6jEcAUfAEXAEHAFHwBFwBByBDhDg+wp8R6Eb6knhvfHGG8OHP/zhbvL1OI6AI+AIOAKOgCPgCDgCjkBtBG6++eaw6KKL1g5vA/ak8NqE/N4RcAQcAUfAEXAEHAFHwBEYRQRc4R3FWnGeHAFHwBFwBBwBR8ARcAT6hoArvH2D0hNyBBwBR8ARcAQcAUfAERhFBFzhHcVacZ4cAUfAEXAEHAFHwBFwBPqGgCu8fYPSE3IEHAFHwBFwBBwBR8ARGEUEhqLwPvroo4Gddf/4xz/CY489Ft7ylreEt7/97WGxxRYL888/fxMut912W+C4s1VWWaXpuX68/PLL4ayzzgpPPvlkWG655QJHVDg5Ao6AI+AIOAJ1EXj11VcDY8273/3u8IY3vKFutI7DDSufjhnzCH1FAL3kiSeeCC+88ELx9+KLL4app546zDTTTMURWm984xsDf9NOO21f8/XEOkNgYArvf/7zn/CLX/wiHHXUUeHaa6+t5AqFdYMNNgif+MQnwjve8Y6w7LLLBhTkZ555ZkycV155Jay00krhqquuKt/tvffe4bvf/W75228cAUfAEXAEHIEqBBhHll566XDHHXcUysitt946xvBSFbeT58PKpxOePGz/EOAbBL/5zW/C2WefHW644YZaCS+55JKFMW/99dcPH/jAB2rF8UD9Q2AgCu/VV18dNt1003DPPfeUnHJe74Ybbhg+8pGPhFlmmaWw+KIQn3TSSWUY3XCocE7hPe+888LHP/5xBSuvzK6mm2668rffOAKOgCPgCDgCOQQuvvjisPLKK5evjjjiiPC1r32t/N2vm2Hl0y9+PZ16CDzwwAPhe9/7XlZ3qZfCa6HWXHPNsP/++xcr3Z3E87A9INDoM/3yl79sRHaa/lZcccXG888/n80puic03va2tzWFjwpvNmxUkJvCKZ+//e1v2fD+0BFwBBwBR8ARsAjcd999TePIn/70J/u6b/fDyqdvDHtCbRH48Y9/XLadzTffvHHBBRc07rrrrsa3vvWt8vlWW23ViNb9RnS7bFx33XWNQw45pPGFL3xhjJ6D/oKuE1ca2ubrAfqDQOhPMq+lcs0115SVLmU0mvAb0VrbMhsaDBWvOFUK77333luGUdh3vetdLdP2l8NBYNttt62c1AyHg7G5/OxnP2vcdNNNY1/4kwmHwCi2rwkH4ggwPCp9MrrFNXbdddcG10HSsPIZZBkmQtrDaFc77bRToX9goENnsRRdFErd5MADD7SvynuU4J///OdjFF/SY3LkNHgE+qbwRiftRtwAUFa6FNLjjz++Vimsslyl8JIQ6ckijDLtCk0teAca6Kc//WlR7//85z8Hmk8nicfPXhc8nX766Z1E87AjiMAotq8RhGnkWfI+OfJVNCEZHEa7wpIrnebYY48dg9P73//+8v0ZZ5wx5r198MgjjzTQXZQe1+juaYP4/YAQ6JvCu9tuuzVVoCrz6aefrs36D3/4wyKNVgqvEnvuued069dxRMBOVEZF4X388ccb8RSQoi25wjuOjaMPWY9i++pDsaa4JLxPTnFVPpQCD6tdSUH94Ac/2IgnMYwpm12hvuWWW8a8Tx/Ejflj9KV2K+FpGv67cwT6ovBi3peCa6+Y+Tsh/Hyx3tZReDtJtx9h//rXvzZwqfjvf//bc3J//vOfiyWMl156qVZadIR4pFutsMMMxExV1nbqfRQUXjBdffXVy/boCu8wW0R/8xrF9tXfEk4ZqXmfnDLqedilHGa72mabbRrsIYob5McUE+Ob1XtY7a5De+yxR1O8eFpInWgepgcE+nJKQ/SFCrvsskus82aKztrhm9/8ZvPDNr+OO+648Otf/zqcf/75TSE32WST4hiZpofxR1S4imNBeL777ruH3/72t2mQpt8ckcYuyy9+8YshKq9N704++eSm42muv/76sNdeewV22/773/8uw6666qphv/32C3HWVz4755xzAjjk6NBDDw3zzjtvwR9HmDz88MNlsOgXFH7wgx9kz+fjDMfouxgOOuigIjxnRh5++OHF0WxlAuN0Ezd6hE996lPhL3/5S8kB5ac+II6PW2SRRcp3urnoootCtNoVp3RwEkdcCiqOaYkbG8NUU02lYE3Xp556qjjijjw565Dzm5dYYokw55xzhhlmmCGssMIKRfg42w+0E+pCBI+rrbZa8RN+4KvfBH+nnnpqiEtZxRnRHMl35ZVXhssvv7w4km/mmWcO6667blhvvfUKfnP5c/b0r371qxAnVuHggw8OUdkrdvByVuiiiy4a4gpKmHXWWZuixolmkX60KITbb789LLTQQuFDH/pQcT41ZeUcyCoiDiek0Ad+9KMfFcE4XeX3v/99kSbxOSrwox/9aNk2OVuS/kPZCDv33HMH+sLnPve5qmxC3LQR4kbW4ggeTml58MEHwyWXXFL0qThBCu95z3vC17/+9TFtpdv2VclIFy84Mxz+wZlzwxdccMGwzDLLNOFx4YUXBo4neutb3xqWX375ojxdZDVpo3TaJzvtB8hSxgtOBEKuc84p57Ovs846xRGXVcDS7mnHxF1rrbWKtp6G5dz4OGEuxhfC0Y9p/9EPM0SjRSHTkScbbbRRpewaVj7i/c477yz4/cMf/hCiIlaMUUsttVTRj+eaay4FG+gV+QdejJ/UyQILLFDIa+R23GBeHENaxUDd8aHTdlWVXz+egzXjGMQxq9HFolayHIdndYjoNlGOVbUS8ECdI9CDslxGjZXcNFOJXBS/Y6Mvw/R6w05HLVMrfa5YGEX4zuT8iAkXj0VrRMWzEZXIBqc64IJhrZNbbLFFQ+4XhCE/m89hhx3WOPLII5s211lfHnyJ2Ylp4+j+hBNOqOSLMF/5yldUhKYrDu5Kw17He+kDfOwSjuVN96njflSMG3FgKcpDXIs9cVgNyJUrDkplXvEYl8Y+++zT+MxnPlPiwk5ZUVQoy+fiw14/+clPKmjP12effbYRFcZGmmdUyBsf+9jHsnzQfsFB9K9//atx4oknNuLgXIZnyYwVkxSfOPFStAYWhO22266Mg0XbpkGZae+sSFhiNzqbv9joKVyIy/JanBiUz/SOa5xEFklQDzaeDROP6LHZNMiHDUG2L+65556NU045JZsHae28885lGt20rzJyH26i8tSgHmwZdQ8GcQLciBOMrDyiPUYlpw9cTI4k0v4hHHWlT3bTD8D4O9/5TllH5LP11ls39Zt4RvsYEJHTtFfbv5DrIqx10cDQiJPoMm14pY+w+15822uc8Cl6eR1WPsowKpnFmCa+8Am1fqU8p+0iO3nH3yBON2IlUsv/6AXID+pFfMFDjjodH+q0q1w+g3gWj7Ury/fVr361dhapZThOEGrH9YDdIdCzSwMmfjXm9BqtRd1xVRGLQTfNA8FlKc7Ax4QhzjHHHGODFfcM/rzjaunoo49uSoPBW6QNNOKDwdGSOrve2+uWW245RpDqfW45I1oOmvhQ2DgLtlmOyz0CliVn8cQVAcpOVP7soI8Q1AATLaHFpAOmGRT0nPgoX5aYgOh9qkBHq2SRd7TollHIk7ytooWSJZ7guV8ULXtZhSdaLAu+UDij9bMJH8qIwMfvDEI5SicO8E6Y9DlLahCTArUxFGgULxG+ZXYyQH7RQqzXDSaEG2+8cRNP5KM/+GGilyq/8s+Ht29/+9sNJn+pgm0V+fQdfKgeufKeMvLc/nHkj6iT9qU4/bieeeaZJU8oMtFK32AgqprMojiwE1/lozx1N+r2g99RT6NOn+ymH8RVsbKe4qpZCQN93bYtfMBFaT2p7VmFlwkrBojUuKI+x6QbftMJEfJANKx8lB/XuOJY4hEtjuUrDAQqZ3qNqy1luH7dMIEhn9S4oLGbfpJSN+NDnXaV5jOo3yi5wjauwNbOhom94nGl7TkNFoGeFV6UHFtp9r6O83Ynxbv//vvH5JXrQDlFg7OAU0Ipgd+HHnqofBVdF5oGL95boYmCa8uY+imrw9swCE+rGKczb8KiaKSEJcKmo3vLTxpnmL/j8mMTfzkfXjq1MMmVEeVA5eLKblhR/NBI+S4VzlJsc9YVq/AO2oc3PXcahdcqoSjtamcqp6ymlBPBnSpThCdeXJ4sJgEMrupL1qrFIJIS1l/auvJi8KdNW0Jh1XuuX/7yl5uELTylbRTrvD1Lm3q1g761QJMXYaX8Ky8sbta/DQu53umqVRbSqNO+CNcv4jxM8WFXb0g/t08BSz5EuRSPK6tFTs0ItOuTnfQDwtoJBisKluzqR/wKp31V3HM+qq0vq/AqMBM4G4ZJoe1vyB/bR+zEW2kMKx87LtLfU7L9lAmxDABpuF5/Y6kXZrlJH/0CHC31Mj6QTrt2ZfMa1L2Vc52cqYuCK7xSXAbF6zDSje6tDYx1/fxjjMJA1iv1rPBimVSlpddUSemVWdJL88gpvMy203D8tku8OtMXa5aldPAiHoJLhIUtTdvOzHLWLQZ2S1jr0jTSZWHCY1lKw9G56m52s3kO4r6OQoL1RWVIFS94iv6v5XvCYYEU2UO+UwWEMPvuu29hjVR4XYcpBFFEVT6uufMUaTN2gEa42TpM3Weq+o3Ni0GsimRNEV8suVmKfu4lz/DFoJMS2Co+EzbLr8LqVBXC5QZ8u+yYe086to5Jx06K6rQv8dKPqxQYrIcpIWyFh66aeKYTGjajODUjUKdP1u0HtEX6kOqBiaGl0047rXzHZDJH1g0vp/AyMVP6XLHapmT5xQUiR8PIxxoNvvGNb4xh46ijjirLMkjFyvaRXH/X6qtlsJfxgXTqtCubX7/vrT6Q00Va5Yf8UBuz7RSDBPK92z9WqMeL6Dd2rFP5+nEFl16pZ4U3p4SqcFbB7JVR4ufyyjUyBnA76xI/diDTkpi1KJKH/EwVhysWBRH39h33djkrp/Cee+65il5craBUWiwV5wj3BZRyltRYSsOXbFSojkJilXssvbk/YcAVASa64oormrBGcbN1gUuFXc5UvGEKQay5lv8qtwl8WG04awmwXxBkgKwiZs5KQy4OubC0f4sBbccS7VHp5CxChMXvXGHoSzmyrj+pOwrhsb4rDcqfIztgENYK/jrtK5dmN8/sV7HsBFZpWT89+LT1ZJeTwXMQvpHiY6JebXusWnWp2w/AAKWWlbyf/OQnYyBh74jaHS4IObJ+9jmFFyuo0uBq+6vSoxwKU9WPhpEPY4f4yCmaMu4oDGUbBKU+qYxX1siB7E4n372MD5ShTrsaRFmVpp1cxc9T63Gtq1wqqRe5oTB+SGFk3MfQgysaOoY9C5hJNmMf+gHynA9vyJKPu994EmVArvfzz64u9lK2nhXeVDCoU3G9+eabs7zZZVkbvupey7l1FV4ytbNapUtDgl/8jrnHcpUqKKnvFnEZ3Oyf0tPVzqjqKLxsnlNcXasU3iyAI/KwjkIi3zfKyWSi3Z/dwEddWUsOadCpJRyqYBimEKyr8KaDjp1o2YEeQVZF1rcWJasV7bDDDk1tzA5ydRRefK3VNqsUXuvOgXU0pToKL3EYpJWXTadO+0rz7PY3rhR8SQnrd45StxOWzUVYHPlEOi44qTxRGPy2OY5wGDTMvOqWp06frNsPcnkyybvkkkuKza9qS1xRinPUD0W0Tj8aRj5WccrJD9qnxSSHR7+eWRlFnoyzrHDmVpHIs5fxgfh12hXhBkX48AvbeMpT7Wx0BCtxaSOiSy+9tEgPRTclVjKUV7pqTFi5lLBvxSmPQM8KL8kyu1VF2Ksd1G32VByVUuWjqjT4PjUDLzvioU4U3tRypDQZ0Jgx8TvdxUsjVDh7pVO2Wl5gdiVyhVdINIp6E45WkXk9RPs7O4NWWlyxHlhXE5vSMIVgXYU33dzJCRyiugO97WcoZ62IzRMWL+unXmegrqPwWitwrn7rKrz4NItXBkjRMBVe5Zm7MljDl3jkinJbl7BssbJh67xu3E7DDTOvTnir0yfr9gObLzIb+av0mZiwOVh1NSUovJRf5eWaulXZfSe0w0ESeaVGCnhiX0FqJWdcF985+VGHT9U76VStHNRJp5swbBIW/1zjMYa1k2EjvOLKoEdkxjX8rHNkjXisbuYI7Pvh65pLezI864vCa78jrUrkyvEu7YgwNo7ucxbPThRe8kVhVnq6opDKbSFderRO5ArP1e50b1ceV3hfR8gqLFaReT1EvTtO+0gVDuqFZ/KjtCkNUwjWVXjhz5bBusHUHehtm8wtXVoMrG8c8awrzKgpvHbAthYq234oQ25TpC3zoO5zG9bs5rp2+cqdZRgK7zDzaldu+75On6zbD5QubVwrcsj0++OmZkgGDdrMlKDwphutGY/tSgMn3Eh2VBmhCuD69I+VDBRY5Wmv1n3E9u9ux4c67apPxRqTjJWjOZeuMRH+98DuxeBYShH+ryisWHlzpFUmypwjGew6kU25dCbzs74ovJxLaRu17u0GpCoQrV+K4nG1DUFxO1V4bcOyaXOfnq6gPNJw/KZh1yVXeF9HKl1K68Wnm2Xa3ASG+rFHYpH7MIVgJwqvtXzQlkV1B3rrl547dUTpcbUbN8nXkhXUVb6Hw7Tw2qP+rB+cHRCp5/FSeDnS0MoFVnvqkj0RgxUtlt3TSRruJjzHHxXlhM2quSVgBjSsWAcccECxTMxE3J5QUCevunz3O1ydPlm3H8AbGKhO0g2/U5rCCx7IISn/4MI4RB+2fSvn70zcQRArWhizrMxTfcEX1I/xoU67GkT5SBP3O5WJ1a46xBgow0fabnEBZTU51/d5JiwZB3OEUYM2MN6EpRsjQT//wM265XVbxr4ovFSGHYzVCKggewxRjskqZTm3GalThZf85MgtnnStOsvW+hQpbNowc+XQs34rvAzyCHeWQLB09qPSxWuv1zoKiRXCdXavszFLKwMszaTKAQM8VkDVDdfUn3WYQrCuwpu62NjNd3UH+vSMXXy2qsimmVq5Rk3h3X777cv6tOct12lfVeXv5jlCFV7o73bjWnoetvXftfnsuOOOxTnbsqbTfm1bZXLBb9ITEQaZg6y0VjHqmjYj4tPm9CXCoMTIb1P+f3XyUlrjca3TJ22btZb+lF/Kqv4PJqlMnBIVXjCy46OUI9oc7S09ySLFtB+/GatS31KeMYlVfXHFGi/qZXwgjTrtSnn184pCL4wpk92cV5UPclfK7v77758NZscFGwB3EGGYYmzDVcW3YQZ5j5uHxUU89+Nqx4Zuy9AXhZfM7YHttnDtHLkHrfDaI1vEF0KgqmHgQ6NwulKBzEZTQtFn8EEAi/qp8OKnow4iXuBvVChVSHJ+RSz1iHeu8VOtlexLEVMYTrMAz5SoC1YPlK4GfYWzQrCVcFD4Xq51FV4seOLXKjzkXXegZ9VDaXAlXhXh46uw6a5d4cz7UbDw2q9a2YG5TvuqKn+nz2lzwourTsFAmUr7YG5jm20HqW+5BoDUpQFlgLQ5Y5Id7hATOvFh3bqkdMdP6ZZF2zl+nS5VDKvyKiON002dPlm3H+iEHXDK+aQy5gjDdLKn4g9jMxl5DSsf+gpGJww88uGtGuOEQb+vrKjS/tIJCPmkewpYrYB6GR+IX6ddEa7fhDypekAAAEAASURBVMFMbaydexmygdNCFD4nP9rxZ0+JyY2z7eIP8z0rYvRRDAD9+sPg18sKscrfN4WXBPVFJlUsV2ZwEubK1F6xWtrwus9ZeON3uceEZcBoReStQUBpYzGtIpYJFc5eUbCsXxSdWsoxX7MR5RTetCx1jyWjsVgedD+s3d4qU9U1tVpaxV9xUiWN+rr/f752CsNVS+js8hUJp5xPkv3gibUYENdayuzXu5RuP69W0aF+mOHmyPq5py4YKEKqW3ivItK2yleq6CueVdJy/a9ThZdBJUednNKA5T5H9hzv9IMNddpXLs1untmjnagLKVJWCVMdYW21hFxQv0d5TUnyJ1V4dToFbciSPb8YSxKkgd3uJ0BhTttLVV42fYwT8MumwnQFxYbr532dPlm3H1h8KG+6ioiFXnWV4qMyWcs7p+aklG4yTTdcEd4aeaomjsPIh81fWmEdz7FBLoRVX1hVG6ZupBT3Mj5QB3XaFeH6Tda9rqq8KKb2c9T44NoJayc8aQNwlSzuJK0pOWxfFV4snhLiEjhcUUj4wECOcGK3YXWfKonEtUtZCse11dIu8bT8pzgMFK0oXTpWPCxRKLdYVjimjOcMjHYmbQWc4qUKtu0sCqMB1vIlJ3WF0RWFZVRIPHFlsEf44rtDx8Rah2Czgk7hUdbAEncEWfhQ5qxvqxRejrFLye7wTV0a7NE41BMKCm2EWXbuuJc07U5+pwpvWtekxZFVKnfOPUblJEy7CVw6QSRuSja9XD+ypyugMNiJnNICJ/FMGNvGFcZabXJ8S0iTDgoBVihLTEbtV+HSTaSEFQ9cc+3LptfLfdonscIwwbBLruLFTuwII5lH2NzkLKeEgqee49Jg/5QPV/noShaAs+3/6dGPSjNVroUNn3G16XOPojJoqtMnbbvNtSfxyFf9bBlwQ0Gm33DDDU1+lYQBDybYWH3lakI6tEelYb98qDzwQ9R7rrkPT9ixi3xyNIx87KZP8mM1gDGKZXMsg0xMcfPoh4UsV0Y9k8KLEpqTKXIvtAaKXsYH8q3TrsRfv66p4YFJIx/7AWOOKGXyrAkIbQedoGojWh2erKyo8t+tk46HiePJIEBgQJXgldBA6UFQW3M8X15BsCmMrjxLhTCbcHLKJHHo4Fh/q8j6v6TLybk4WJbsQCy+0ithNMBx7JM9XsmGZSBkJsuEgA6RYqOwuEegyImqTrDQkpXCjedVA7HKoCtHA4luvPHGbD0rLFcwSa0odgBEgGulgKNvtMSLoqDlMeVnj2+xeSBwrV+kwvdyTRVe8sMCxWZMBkm76sFnTmXZIE8EGWcKW+FIfAaqtEyWR6uMEp7lHiylTDTscTe0uZRYbrfLrMRP3T5QHtL2T9u0Si/9OO2PHDVIGxdZhZd86NdY3FFMyFPx6R9pf1caddqXwvZytZtn4YdJFFf4ZtJk5RS/KQcnIug5z6r6pfq7VULVblBQ2KRW9Sf5kn6EhQlyLr9cXhaXnAtZuyPubPxu71v1ScrYST9AnlAvuT/qwyqANgz9g/5nVyZ4z9hkP/1OGNuPCIO8kfwBAyZn1LlNnw9eqI8MKx94SctjeUrvwWdQ57RK4SVPVhA0gUWe0J/ES6p4dzs+UPZW7arfsp78sOqrz6s8VVe+elcl10irLklWkE8qq+um4eFeQ2AgCi9JY81hgM81DgnltKGgKKAsp0tUpJdLx8ZPv+BCHEsawC+//HL7uPIe4YZluIpXymb9eu2nWC1fuod/hKB+V13tkiVWcbtkQxwE8SgRwkuKgcq09dZbl4JfvLKUo2VfhdMVC67dJKQ4TA7ADaVWYW07wOKXs9ZTd/juKQ5XfMVyeSivbq9WGJFPOlDyDP5zu3hT/zXLL/fyI83xxuQAhdLiQRzaKzjnlCG71JvmxW/6bLoakoZDsbabzNL35C8fVqvw0rdtPRKPsCi0uTpUmeu2L4Xv5cpEIsUTRYcNKUyord+4ys1kBdejVhMUyRCr8F522WVF++RdXcKSpLSUv7X2ko7e27xs+kyo03qgbgZNrfpkN/1ALlDCgSt1pbNQreWPd0xorAuCjad7FF/qWr9zV5RF6yOchqE9DCsf1VlOQU/5Sn+n7UZp9XJlQkE+suRyb/sTE9wqBbCb8QFeW7WrXsqSi0teKY4qI5Mf5C56AIY8uSLl0un0mV1JswbDTtPx8I3GVIAQK21gFC0+IVp0QlzeDnFJKcRZX5huuulCVJLCvPPOW/zNM888xe/pp59+YHzEWWSIG1NC9BMMU001Ve18ovIdooN6iBbcMO200xb8xg4d3vKWt9ROo5eA5B87UIjL8mGZZZYJ733ve3tJbiBx42Af4tIqk6cQFfTQqh4pD3URZ9/hne98Z4gWrsrwcVksEH7mmWcO5HH33XeHuCRb/F588cVb1gFxo1IYojJV8DTLLLMMpOzkAS8i8qVs8BoFZIiCsCWfitfLNSpjRf8Cy4UXXjhMPfXUvSTXt7if//znQ7R6FelFa2iICneIy8shKuNhvvnmC4sttlgtXjtpX70yHweqgsc42QwLLbRQeOtb39qUZLTgFbIgTg4KrGefffam97kfb37zm0NUpEJUQsOXvvSlIgjyMCpHxX2cRIQlllhiTFTyipbHAie9jMfzhTipCnH1Q49CtI6GZZddtvidy6sM+L8b2ihthn4YJyUhWniLNNNw/f7d7z4JplGBKtoQ5Z9hhhmaWI7uIAXuyM2ZZpqp6d1k+hEnmEU9gkU8ti7QhqPiX5SdvhMnOYUsQhZGl6gQ9xCEOHkLxx13XN9hoN8wNlLX0Z+46CtkQl9nvG9HnYwPSqvf7Urpjsp1gw02CNGyG+KErBhXRoWvCcmHa/2OgCPQPQKphTcK3+4Tm2QxrYWX5f8plWR1ZflVZH3QsbBqKVzvuXK6hny+sZrZtmWtvbj7iHJ56V161WqE9UlOw/jv0UaAvQlYpqPykfUzTrnHGowlkjhOo48AckF92v13e6+vgbk09M6ap+AIjD4CrvBW15ErvK9hIx9t3EUgNr1Ey1uTiw9n+7KcDuESwmfPUWLk7oGvLxtjLGnz1g477FA+rsqrDPC/G50+gZ+h08RFQB8/oH3UJRRe/pxGHwF7io377/ZeX67w9o6hpzAFI5AqvFhQnF5DwCq8dT46Mllx0wkQWGrYkIgvI5vHOGEBpdb+SWHl2XnnnVdCgkKDP7/8o3khP1U2fImq8tJ7rhzDRfr4T3t7tchMvHvbduzGu6qSaCOwbVtVYf35+CDAxJcNf4wt2pxNPaPw8nwQe1HGp6TDz9UV3uFj7jlOIgQ4ccAOOlYhmUTF7KoonCQgbNjkOaUSm1hQWMECpZfNcSKU1XQTGYptas1lCZoNQPyhqPKbtNIP+7TKS3lyMHzuBA+99+vEQcBuGKY94KaCTOL4OTZZsckJlxVOFdEGaFYGnEYTAZRdyUzJC+qVP/ucky2cOkdg4JvWYiU5OQKTEoHY3UL0nwzxOLqyfFGZCPH0iPL3lHrD5tRFF1202DgDBlFBKzavsqlqSiQ21rBRjc07bH5NCbz4m3/++cMcc8yRvi42bbLxio2Q8azq4j0bFNkAnFK7vNLw/nviIhDP2Q5xYlmrAGygja4yYZVVVqkV3gM5ApMNAVd4J1uNenmGgkA8+zLE47lCPE5nTH7Rmlec3BA/ODHm3WR/gLIVjwAM8QzibFE54ST6nNYepLOJ+ENHwBEoEWCixMkd0WpfTjD1Mq4IFCd4RF/fEF1gap2Korh+dQQmGwKu8E62GvXyDAUBjonjqLoqmm222ZqOk6oKN9meY/W++uqrWxZr6aWXDm984xtbhvGXjoAj0DkCHD3GsWMcTciqyqyzztp5Ih7DEZikCLjCO0kr1ovlCDgCjoAj4Ag4Ao6AI/AaAq7wektwBBwBR8ARcAQcAUfAEZjUCLjCO6mr1wvnCDgCjoAj4Ag4Ao6AI+AKr7cBR8ARcAQcAUfAEXAEHIFJjYArvJO6er1wjoAj4Ag4Ao6AI+AIOAKu8HobKBH4+9//Huaee+7yNzfxsPJwzTXXhGmmmabpOT+++MUvht/+9rdhxhlnLN+xS5jzRjmD1ckRcAQcAUfAEXAEHIFRQMAV3lGohRHiIX6hpzgAn0PKRWeccUaI317Xz/L6zDPPhFtuuSWstdZaxfmPxx9/fPi///u/wDm0To6AI+AIOAKOgCPgCIwKAq7wjkpNjBAfV1xxRfjIRz4S4mcriw8r8LEArLxVtPjii4cNN9yw+BBDVRh/7gg4Ao6AI+AIOAKOwHghMDSF9+mnnw633XZb8VnMJ598svj8qh8+33u1n3feecVB43xRhwP9F1pooTDVVFP1lPCee+4Z/vjHP4Y111wzbLbZZkVaKMH/7//9vzHpPvbYY4G8+dpP6g4xJrA/cAQcAUdgCkeArxH+5je/KeQrH6jphJD3fFIaOe/kCDgCHSIQv4w0UIpffWlsvvnmjchW8femN72pERWpxqOPPjrQfKeUxA877LBGdCEo8eU+fva2EYVq1xB87GMfaxxyyCGN559/vhGV2SLt+L32bHrR3aERv+iTfecPHQFHwBFwBF5HAJkaP/HbYByMX2t8/UXNu2233baQx5dccknNGB7MEXAEhEDQzSCu0b+zVJji8njjoosuavz3v/8dRFZTfJpg/a1vfatUfL/85S83Xn311Y5xQSAzOSE9aJ999inTjBb6Melts802Rb5jXvgDR8ARmPAIIEPuuOOOxssvvzzhyzLeBcAIEfc7FPL03HPP7YqdV155pbHqqqsWaUQ3s67SmJIixZXlxv333z8lFbkvZX3xxRcbd911V1/SGqVEBqrwLrnkkkXHZDbrFt3hVPuWW25ZKqjxBIWOM7388ssL64MsxNH9pEzvK1/5ypj03vOe9zROOeWUMc/9gSPgCExMBBjsLr300sZ2223XwFDBBPihhx6amIUZIa7BEyy33377nriKp+kUhiRW37qxEveU+QSI/MADDzR+9rOfNViVBG9WmJ3aI8BYz1i+ySabFDpAPKGpfaQJFmJgCi+NjsbG3/e+970JBsvEZffhhx8ucd955507Lsgee+zRWG+99ZriUX+qSzvwxRMdiuc+mWmCy384AhMWAZbK1dft1fb7CVu4cWT8ggsuKHBdYYUVGlhpe6WrrrqqSG/FFVfsaiWv1/xHNT7ukrbdcu8Kb/vaOuCAA8bg5gpve9zKEDfddFMJ4NFHH10+95vBI6AO/9WvfrXjzPDfPfTQQ5viWSXaTl5OP/10999tQsp/OAITG4F///vfDVyXvvOd75TyG3niCm/39Yo7CPscwPG+++7rPqEk5kYbbVSkeeKJJyZvptyfWLwvvPDC0pXSFd56beG5555rXHnllaW7DLi5wlsPuyKUVXiPPfbYDmJ60F4RoLHyt/HGG3eUVOq/ayOjPCtdlj4g/HdxoXAaHwRYtqOfOTkC/UaAzcbq71xd4e0e4Z/85CcFlmxW6ydpjMW1gYmK0+sI4H6n9usW3tdxaXeHribcXOFth5Z5r84IeK7wGmCGcKsG26nCG48eK3x3chsL2biidNnIBuG/e+qppw6hRJ5FisCNN95Y1AdWdidHoN8IsNlH/Z2rK7zdIYx1lz0sYHjxxRd3l0iLWNrA5quozSBZNzxXeJuxafXrhBNOKPu9K7ytkEreucKbADLEnxqoOlV44/m7Y/x3LdtYKEgbi4J8tNlA4TRcBB5//PFyM5ErvMPFfkrJzRXe/tQ0G4eRmRgHujk1px0XZ555ZpH+ZFRO2pW91XtXeFuhU/3OFd5qbFq+cYW3JTwDfdmtwov/7k9/+tNK3jgGR2kT1s/frYRqYC9eeumlxuqrr17Wgyu8A4N6ik7YFd7+VL+MBBgTBkFPPPFEKQt0lOQg8ploabrC212NTXaFd2BfWuNLXe973/uifhRCdGkI8aiL4r7uv+hPGv7whz+EuHQbZp111uLLMksttVR485vfXCYRnayLL4JNO+20YYkllii+BNbrV8bKxLu8uf766wu+o1tAWGyxxcJKK60UpplmmsrU4hFA4c477wzzzz9/6PSrO1WJCoNo4Q3HHXdcVbCm5/Ax44wzhj/96U/hve99b9M7+4NPDvPVNWjrrbcOBx54oH09Ie/58tGvf/3rQN1Fy3VYYIEFivZE/fEFuQ022KCyXPFs6eKzyzfffHN45plnQrS0hFVWWSXE3dOVX7yjvk8++eQQT7cI8cMhIVp+QnQNCfEjHoEv1y266KLh05/+dJGGzThadot+dM4555SP44AaVlttteL3IossUrS38uX/bjrlkbZ79dVXh5NOOinw2ei4JFiUjX4cd/GHuEwblllmmbDpppu2/eITaZ1//vllX6av0r74XPUaa6xRiRGs//Of/wzRglW0yehSE+aaa66w8sorF1+o6udX/W6//fYQN7qEqDAU9U+9U4fwN9NMM6VwFr/jCSUhTjbC2WefXbSdmWeeOVx77bXh5z//efjzn/8c5p133qIu4sailmUksXhOaPjd734XopGguF9wwQUL3GkD88wzTzZ/HsYhrWh7yFr+4ubS4muLYEs7hId2FDerhLjjv+jTN9xwQ/G1xA996ENh3XXXDXPOOWcZPbo0FJ8aLx+Ym27wiytD4Ve/+lWIm4zCwQcfHB555JGw//77F1/jpP3vtttuhdw32XR1C9/I1fH4qmc8jSFMP/30Bd/xlIayn3ZVkBaR6KP0D2QxMnlY9OyzzxYyAtkXfb4LucAX4NZee+2i/9DXc9TPvnP33XcXbfeyyy4LTz31VCFbPv/5z4eouIW99967yB75hZztlrqVh4wH8JHShhtuGJAXEDKfvmAJXYCvnKYU/bQDsh+5HE9TCnHVoCgzMhJdCHlJvHgcW1NfRb7wHowYz5ZffvlCdpNPSnEDZPjCF75QPEaGMCbCH3IN+fSvf/2rkE3kg4xsR52OPaTHF3nRWyhf/J5AQC7FjfQF79HYU7TxVjpVS566mwe0j9WthTcC2tDu08h4OXvlHl8ovgBGGPku2TAs6zDjHQ/i7EqO87L8cI8VtMr/7Z577in9u3ATwKrSDxIPnbg0HH/88ZX+u5anOMCXZTzttNPsqwl5z9FqOi86TtAau+++eyMOGmUZ+XJdjtjUo0PkaZfUn3Dnuv766zeiwCuj4gLygx/8oNytTRjyjQKogbXcxtU9X9GzlGtfCss1/Rpepzxiwd9iiy2aysJu/ajIlS4UNj/K3erwe2QAfZI44POZz3ymWNpVGhzRdO+999oiFvdxgGkcddRRZd/QWbCKR77xE6tj4nX6gPqxpxGk+bAMbfljJ/NBBx3UgG/xwpVj+bbaaqumZ3r/uc99rpKtF154ocExgAr74Q9/uDw7VM84LihHtBvb/mgbHFGleFy/9rWvFbIyF59nHCyv0wOonzjQlW0xxSInwzrFD7nNiQLrrLNOyWdUzgs+0v6z1157VbFd+3lUoIt8GCuqiH4Jjny0p98UlYWynHEi2+/ky/TU9sB1WEQ90g9pZ8gvLKr0F7U/LNuMiaJ+9x1kxK677lrmR5+MimQpu8Qb/HTrw9urPKR/MJ6kbduenXzEEUeM0RvoeyKOsCOMzhQWvvDGF1VtOfWOZ7Q3MGdjuZ7bKzxZ2ab8UgtvNMxk45MWdVy1WbLTsQdMfvzjH5fjBekzXrJHyPLNPadJdEsDO4eXQVKM1t20Fi1f5WdyGSj5bG20ODYQfkqLqyoZYYmPFF8V0/vcxxG6BaduPDYmSIgz6Fx33XVNjZyzAXNERxTfXGnA/SClWUfhjdaoBvWjTgmeOoUhxwuCRoINZXGikwRJqizSqcARXFKi3MKLDinfPBQ8PScurgciBBQTObVd1ZEUC4Q1J2HY+ITBX1cE9ghAKSm856BwnvGnj4UQvhseaQdqx+JP/MAn/czufiYMSlqOdO4oYRBkIvhU++EdZeGZJSmB8BKtFsUrXDm+/e1vN/UXziLtlsBSk2YGTtKHwM1OQKivaDkq3jGZpvyqM2GkCRP9nD6NXNI7rhyTlBJlloLKRMsOgtGC0xQ/PXaKdqZ2RBsTf+TBEYL2U+PwlhuU+LCE0qDd2bYDv5Z/7lOFtxv8wEZ5Kn3qH37T5wzUvZLqifZWRVYhQCHvJ2GcoZxVk+Z+5RVXp4p8wHAYZPv2d7/73aYsrXyIFtbyXT/7DuOtJv+Umf4gog6ZWKt9ce1W4e2XPEyVNtvXxTdYiWfarQi5BP8yHCiMxgBkCEo1G8ltH0IZlXwBDyZ/P/rRj5rC5HQlq/AqL67IEY0F9jlpIwssdTP2UB7LP3lIPqfP0Qu7pYEpvNYSiABvR8xGVIlUhBXAKBQp2ICAoNcB3KqEQQuXXDk4t1aNIi7zFDNb8aOrlCLFp9Pqna7svO8HCSsaYyvCSkTeYKk/fjNgtyIGYAbpiU4IR2GPhTslrJ3gYol6lJLMgJYS6ShNrgwOltL3CDKrTMSl3ab4cSnJRi/u1U9IP+fD2yuPdtAiD37TP0XaKKNypmeLUgZw433OQon1VnG5Wt9DJgY8ow2nihp9xipz7dqp+M1dZf3L9REsH5a/HXfcsSkJrBf2PWW1VgeUWTtARXeupvj82Hnnncty2pUA3jGAqA+Tj+XRpk2+OSXtwQcfbIqfWpk5flDKIINiOmDBQ1webyqjbaO87xY/8sKSbPFj0sTqFgYPFHjq1bYJ8uuUsNwqj1ZHJ6ov1R03MAagwGB1y1nILJ877LBDwYOtP/u+X/eanFPeVsaKfuVn6y/d88Enk4V7bozoR985/PDDyzwuiR9KSYn+ZPtPtwqv0u1VHmqcFS45hdfqS1bhFQ/oQ5KppEP50s9TW+yVVzr+2MkKYZAFllKFF/kQXSKKIIwr4G35IA07Ie9l7GGMkVwS/6y2IjPAh/aEESQn82wZWt0PTOFVZ4fxXAWnTCk8wi8VwPxOQdbscZdddikbP3lhnakiZkuY6O2MsCps3edUknijMUDamatKo3GmZC3ghCON1NJFnG54thaqfrlJpPxPht+0A9VRTilRHdmyWsGUKmSEYxKmNLlunJyFzNFE9r1VJJUPs3OFwfqYkgZpwuQU3l55tEuF1kotPtL+mB63JCsx7T5XPgSW+gxlsG5ICDWe7bTTTsqu6Wo3oxCundLRFPl/P1juIy5/OesrwcQHYdL+y1Kt4nPNWZpl3eM9y62WdKQc73KTJsLut99+ZR4WC9xclDdLflWUrorxMQnRvvvuW6Zx1lln6XHTNVX6rcLbK34WG8qCgt5vYrVMOCHzc2QVkTof6cFwQ7vF2qU2jqyvIilKuJYMkqhblbXXiUIdPlV28mTyagnrnngBq5R67TtWdlAPVYQ8Fx+9Kry9ysN0A2hOH7KKaE7hpZyUV2XiDPaU0B/0nmtaN4RPw6TGilThtatHyk9GCeVlV/l6HXvs2Mc4l9OJxEc314EovLbyWHqoQwwqABidnMcEj5simiqScBokcR8Q8HSw3//+92Pi64EGkdzMU2E6vaqsLJsxu4FspcEbs5SU7KBDGIRIjrrhOTqnl5jgS9rvRpPjcyI+QxlT2+GKYLRKLIodlhxLdgkUS2/uz6ZJp7V0+eWXl3nmBgTC2jywMqfUTuG18XP8yUItPlMe5VLAe6w5ObJuCSypiiymOd4VDiWHs0NxqRFZRQqhn+M9tQBUKTNKM3dlSUxlRxnN5aP3uqIcidJBgzOqU2Iiorip9dAOoLgg5Ig8UEZRqORuQTgGF6Wbm+woLft1RMLjqwxhJafdKY0q2ZAO0lbh7RW/X/ziF2X+/ZTFKjtX+6EcW3c2jPVPZKBvRay0gBnjmVYfqQuwtPVj05CPdbrsb8Ok9/QLVkUYV+qSXRWqmsDVTatOONwM6TPIGVY0LaXtzr7jvte+Y1ceZPRK8+C3nRj3qvD2Ig/hJe1L3Sq8dhJurarkIbLysUo+2P6P8mrJKryt+iaGEMkQrkx0oF7HHuue2km/sWVodd83hZeOSmezyh6DZZUwSJliMwh/OUqXQFkutISFhQZglRX7XvdaCtGHE/S8lyvlZslTVh7OpbUNgXuU8pQkDBW2quzd8mwtHCg0xxxzTLFMWDXApfxNKb9te6UumHhhydHkJcXBzrKpw3Z/WHks1VF42wnrdgpvrzzWEfBVwpcJp9o0y96dkHWVoIztsOU9vqidkvUFrpMHgzv9WlRn0LbLi6nCa5XWdDVLeeSutEk7WOXkio1n3SpkqWejmuon5cvGTQdpq/D2ip9VeK11yObf670G/nQyZ9Nlo5qwwAWiiuIu8SIcadkVC01c0qVlpaM+UvdIMsYvW792Iqk0c1c7yayy2Ofi9fMZ7ii2XQjXVI722nfY66C0W+HTToZ2UvZe5CH5pH1pkApvu7EBftQ3wDHd/GsV3lTPIq7IThZJJ56oVbzqdeyxCq9d2VK+vV77ovDSiG1HBYBtt922trLbrhC2kZP29ttv3y5K5Xt2Rg+S8GmCR/0xqKSdPue/qwaT461bntOlB3jK+YTm8pxSntmTMlRnXPFdSi13WDMUppUwaIXdoBXefvDYi4CXEgBOTNY6IZbohW8nFq5O8iCs3VTWbpKcS7uXQdsqJ8jMToiBUvhwbWfNY3VH4aXcohDpWepqYXlJB2mr8PaK36AVXowQKmOr0xekHKAAVBGyWuHSvShYYskHq2OOwLzV+zQOq5vim2snA77iVSnfaV79+M24Rj+VtY/Jiz6jLH7Ssa+XvgPPVqFKXalsmVzhzbu7gVE/FF7rRkNdI1f6MfZMCIUXEBEMt956a9NRIcxwq5aTiFOH6DByd1AnGuRgWIenVmEYRMQn1x/+8Idjgls3DIVFEPST2GGqtKkHfGtYMm5HWMxa+aW1i9/Ne9oOVvx2Fqtu0q4Th2V1aw0TblyPPPLIMglrvU/9OstAbW4GrfD2g8deFF4dkQR2DDqdkI1L+x0USYGBx258gHsZtG39kH/OR66q3PhnEkd/7TBiSVBhKTPE6lb6LJdfK4W3V/wGrfCy2qcyVrm8WDeAVkvenIJCWpRZrgzCS0fapZsa9V5uP/SnOoSbnjUcadWwXVw2Hqm8OZfAdvG7eY+boVYqwIbP0kPWLQme+qnwgr/KyZXNg1XkCu9gFV42R9q6YOy2sq3b8XHCKLy24dljc7CU9aLM2SU4AdzNLr26rhW2HJ3epwMh/DIJSEl+uSoPS6Y56pZn6z+I/3AqdHJ58cwq4vKRrgrb63NmgwzgDEiatW+33Xa9Jtt1fJRu3ErsgKP6kWClPvSMazfK0qAV3n7w2IvCay28uAt0QhyvI3w3Tjb7dZJOu7DIJOXTTmnMpZX283QlgDhVLg3ppp1OJnmpMgFerchazOWLbRVeMKiiVgpvr/gNWuFFgVX9VhlcsNYqTKulcSl1HOmUko7GqqoHTaI78UXEFxLe7v/fzvg0z9xvq2gMY9OalWEYeOwKZNpG07Gnl76TKrxVboBg5ArvYBVeu1JFP+JkjH6MPRNS4aXB2YGv1XI9YSFmDDTgb3zjG01H0uB7KsHEtWopmZk4wild5kNwyE+TmWg3Pn+vcdj+P35MllfuUaRS4rxOGy49uqlXnrWLllmWPfw75SP9zWYE+KpSwNPw3f62nYU6kwV/2AovZ7ymy5Q8Y1e1rR+ruNnloDqWG04RsYLZDhZVS9rthLW1sOU2JvTKYy8Kr1VmwDCnDNp2w4kFqgPrP0bc9LguG497lviRByhnnZCt31anuihNlFfcA0S9DNqkofZOGXHXakUoDGzO0QTUTsjoO63IDh5aabKKHvnb0xtsWq0U3l7xs22kXRksT3Xv5UrQyspk/Xex9kIcmWU3A9nTNHKGC/Uztd+UP407gz6lwZ6oobKkvPTrN4YK237tplPyGKTCS/pW9tk+yTtL7WSoDdvuvhd5SNppX5oMPry2zcldirKqTyBbuhkfrczqxKWHvOtQ9RS/TuwWYaz/aJVAUHSW9WwnwuKnzRycIQl4+tNuY8XlilKn+NaHiQGTDgLw6oj8HhTZTTfwm1POUwsP4dgUIeoHzxoUW31hSPmlVztbT9/16zeDOFYJ5aVGPmyFF2sIWKHApKTNgmp3Oq9Q/mp63spCJyufDdNvhTfXt3rlsRcBb496AyMGffXlFGMmFwhIWc+szCAuFrSquFgTsC7ZyUiaftVve8g7+eB3WEUMTrQRVmVEvSq8UoTUhloZBDjNhfyl1FvrJfFThUM8ckWZVB5aHrdKHO8wMOSo1SDdK36DVHjxNVaZtVEvVz4pxXawZoC1EyCwUVpM3DBo6A889a7qmEv2sRCmmzaa47nqmV2Zo18MkjAoqdxcUwsuFvVW73vtOzLmKI+c8kj5R1nhza0MynWGciETc6RNkISxEzMb1k4IcsYQwlqltNtNaxrb4MXuqep17JEuQLoTSuG1grXdsmGqKFJYOoadRfCMv1xFy0UA4WUtmgwWEnryf2PwaEXsqKVBYOWUVaVVePsu3XSQm4GmuxtVVqXTDc+Kq6uwamc9UvjxvqqR11F443fbiy9+MYjkPhbRSVnUJqqOsrPCQ0qx9Q8EZyZaWORT0td1UG4sscKg+qlqi3a3c86/UEulpGO/YqZ8euVxt912K3nUUrjS1lVuKPCQ9km7qYn3HLyfbg7DmoaPI/hhNYLA2GJOXM7nTgdVVoP0FZ4qZUN85q7UF2nbPztRVhysn8gCeGRVQsSqjY2bs2JbmWaVKtJgD4KNTztIBx4UfR1dyIdtRLR/G7fKimJXm2wdgqWtO9LKYWgVOsLgsynqFT8d8UW6OaOA8unmatt+7mxt0rQniVjXGXCxJyrYfkYd2T9bB1Uudpo0V/XzbsqXi6NTjGinOcK4gFKCKworr70oxemmbDuZJ297DisYpas0vfYdeyQe6fP1ytS3Gj6s4pV+eIX3nVCv8jBV8tNxi75OH1WbqmovnSq8Vf7r/VB4GX/hl7SsbLf9j/edjo/2/GRkf79pYBZea+lpp/Cmy2yy4tKY1Qh0tdYYhLc9iD21lDCgaiCjw5OGFf4pmAxcyodr7vzcNI79bX2piE/+drBmAKUB2DzS2X+nPNv8da/0rTDXu6ornbLKmlYVp1/PO1F408Eal5duSQovA1tOaEpxs3WUU8rAG8sQ/pFMvrRxkbpmx7ilVGDLym3DWOGXa6/WQogQxMrBoIurDH2tVx7tWYqaMFr+aNMIZbUzu7GPcChveqcr4bHY7rzzzsUyvp6nk41UYBIOHDnTl8EW3pQ3aXVLqaWUfOh7CFk+jGDrIN0kq9UilUHWU8sLmOh9OoDRz9S2FIYr7ZDyYZ2S4k+7oj4tWXcx4vGBFEuEtyc0pMvc+KzafOGPjapqi8huOygSlj6AnJbi3wt+9sMT1G0/yfKVTjTIh/5oZTAf6IAkC5goQLRxYYT1LSUNzMijKrKTk6rzlqvidvJcLiZVip0216k8jKvdkv2qG+khH5A/GKesYqi8aFcoxTrvtde+g5xW31Ae9FXJWZR5NhHqHVfaN3KZ+uiGepWH5GnHLfjBUs4KF/LPrsSIb+SgVnXEs+2TdhKs91xt264KQ/7KJ/2AhXUrI62cS6Y12qXH4PU69tixrWrCasvb6f1IKLwoirYSEIgaEADdzrT5jcAnjJ4TNx2UAIIGTgehEyj9XDiBpi9rqTHkBKbCVl3xlVN8rnRGPl5gBbF9n55V2inPOT6Ufh2Fl0GOT2+CD385i2Euj34+q6vw2t3IKmMvPoAa5EiLpTJtcEEQadWAd+kSFKsXVrCIF3sFy9Tyh1KqmbHCIpSsss2kTe+4kk7qPyiLjg3HPX1GFpVueWSJ3ApW0rUTSXjlgxE2b+oAzCzRjmyY3D3KeUoITLuUnIvHs1afi03TzP1m4M0NNGl+qcsI/KUKJwqEFEHyoh1ZawxpomTaCSV4IV/S/Oxv0sid4kA6TAAUljbCxI+zZFEutJkKxSBnvYVHfdlSaeiqdp0qFbynXVjFpVP84Ju2xMRC+XFFBsplCN56oRRTGTwYuLE+ghUrDhpfUGZ4x8TCyhL7AYV02Zw2QDrwnioMlncbjrwHRcIz/cyv8tME3GJurXIKV+dqx1Kbnu7Zj5LWgXDqV99BRqmdKl+u9pnqx75nnOmU+iUP00mm5Yt7e26+3ln9IzWU0H6tQkzfsoooaaBk28ku+KeftmelDP1LlI4/6FjoWkzq0Z2k/INvVZvuZuxBD2FypLLrysTAjo/is9vrSCi8MI8gtbMgCkylckoDgwmbf9JGTANnFtBu9qxTCwhPpVcR7/SFHoQI+XVD5Jfr9Gn5KGO6JKT86vKs8PaqxlJH4WWwRtlDURJ/UvxIk8EAH0v8abr9Y+aPglFFdRVe4qt+VMZu64i0dAKIBj/StEKTARClOEe0udSfTDxhUUndYazPk8LZK+mhxNln9t6uNtAftKSvMFha0jw75bFKCSIP+gOUU4TEw2mnndYEFWVWm1IYrgzAbJBsRSwHV/WhVpPWVmmm7xCy4JrKFXikbmXtUzyUBFuO9B5LYDro2DDCUOnRJ1Lrm8LjzpCzriguVwYcu3SruLRh2lI7pQbXglx9Yh1moCQ9sGGDFwOeVdjJv1P8cryKZ64Mpr0Q7V3p0e40cbPtCOs5cp72Z59TTtxsRPZrlXqmq94Rx0509N5etVIwiOVZ8mFCpDJb/i0PqZJD+Hb9z8ZP75nIC1vlbWUlm3T1nCtW9H73Hdxq9OlmmxdtH7kjH174YrWl1fiTlk+/+y0Pseba8QW+keOUhckqv8EVXYd9PfQ33tnypff0c/XV9J1+M8YzNuh37sokXoTRDXfInFyEf9KzOoLi2WsnYw9yLseTfZZbZbH51b0fGYUXhllGouIRrlytO4AKxGwESywNoS7Jqrbz/5ZAmaW2UnxJFwWcyu2UrD8XyywotPBLA8CiZSuRwaZq9tINz+JVebRTeOGLRs1METzUwK1F8fHHHy+sVfDa7R8DfZWlCZ47UXgJz8YUWWetVYZ3nRL+oBDlRwFmRskf9VWHGPRZ5kPIoiDRtoZB8AsG8Gpn+rm8x4tHeEFoMxAj7DmrU3jn+Mw9Qw6g2IFxakXOhe/mGTyCJflgnZCVvJu0uomDzCBf2hBtsJ1sSvMgPAoMEwFr0UnD5X5TduLihnL99deXeyBI85JLLqnVnscbP5XLusYxQFIGxhL86VFS0wkhyqr6e9pv5WuPzEtJ7iIc+9aOaPfIY8aSdhOYdmnl3rN6SPp2s10uHGW3Z+fKbz4Xts4z8EKmg19uFQL5yTvGj0ESMoE6pv1amU1ZO9ERBsmjTRtZTVsEG32Ol/fIaPoffWmUiIkCegL4pisddfgcz7Enx99IKbw5Bnt9RiVJAdTSNL6OLBG0IpRTwtUlBkyEI3lxTYUryjvKmXjhmjqvK69ueVZ85dFO4cWioo1i8rtEMHc64Crfbq+dKrzkw1d2KCfLLU6OgCPgCFhXmEcffbQnQLSfg9UfS0wOkDsYB+xSsA1j7zFoyBpatWvehu/knjFFYw4+n+1I1uB0I227eP7eEZgsCEx6hVc7gtmhCmk5qt0SBxZWe9xGuwpPl25TZ267uQ6ByXJa1Yy/W57FY12FF/8kzc7l9zeopTfxlrt2qvBiWUAxB3N7KkcubX/mCDgCUwYCclHIWWU7RYBJP0qtXeXjmVyJOlFedZY8Lh39JE36KW87IwWWVpR3ysTn1J0cgSkRgYEpvPZby60c+wcNus58RHllyQMnbHxkWpEEiSzCrcLyjqUxKZm62m/P4x6h51yZ8bdKuxuexSOzfuXVzsKrOFhDFIcl1WFTJwqvsMRNoo6FZdhl8fwcAUdg+AhYP0Z8jvtBrH4hFxnLmFhr43GnG3ux8mqDdSvXrk555uhM+GN5vBWh7KIUo+yy/O/kCEypCAxM4aWTSYnqdUd1L5WDzyC7nens8MMRLlV+s+SDjw0KKY7ZnZCWrchjq622KqKCQfopTwSP9TXK5dEpzzYNZu/Cveob7zY89zqRQP6wKM3aNc09uyfxj+v2Dx+6VpbYThRe/In4lCe+QU6OgCPgCICA9d+tOn+0U6SQWVIqZe1t5wpXlQd7OZDLyP92mwmr0rDPZTWu457AmIa7R7uNRjZ9v3cEJiMCA1N4UZTsMv94LqPACxuKUEDbEbN4lsY63VzDhhEpmlxZCrO/UYjZYCBFsh0fnfCstHB414YK8sYJvg5pKRC/ZQglXTumreXElqfT+1aWhU4U3oJB/+cIOAKOgEHAnnDS75UfZCArX71uKOLILuRm3ZU3U7ymW/kRdzNONSXkPxyBKQyBqShv7IQDoej4H6LfUIgz2hAVwBCPtQrRNzYssMACA8mvH4nGzVthqaWWCvPMM0/HycUdoyGepxcod5xNF2kstthiIS6/h1VWWSVMN910HadZJ0K0WIe4KzXEo1hCVLyLKPEDHSEu7bWNHv14w0ILLVSEi7vTA2nNP//8Ie5yDquttlrxPAr6EHfltk2rKgDlblX2TTfdNBx33HEF//EItKpk/Lkj4Ag4AlkEvv/974d4/FWIG41DPO0iG2YUHm622WYhWmdDdFsLcRWxY5YYS5dbbrkQ3dBCtBoHxhcnR8ARqInAoBV8rKoc3yKXgshW4WLQ6fE5g+ZzoqafnuPJEhcb8+oSG9eoE3zM8EXGws05qViYB0kc/UQbwKcNf1x4wKUC6wVLb9pMN0gePG1HwBGYHAjg4sRxir1aYQeNBu50+Bgj79q5tuV44dx5Vg/t0ZG5cP7MEXAExiIwUAtv7NQlRQUqRLeGEI/cCvEMwLDGGmuEmWaaqXzvN90hEL+MEuISXphvvvkKy+yss87acULxAO8QNxaGOCkJ0Q0lxB3IYY455ug4nU4iLLjggiGez1rkmcbDihEV8BBdMtJX/tsRcAQcgQmPwOWXX15YajsdA+MehrDwwguH2WeffcJj4AVwBIaNwNAU3mEXzPPrDIFobS3cFqJfWGcRPbQj4Ag4Ao6AI+AIOAIjjoArvCNeQc6eI+AIOAKOgCPgCDgCjkBvCLjC2xt+HtsRcAQcAUfAEXAEHAFHYMQRcIV3xCvI2XMEHAFHwBFwBBwBR8AR6A0BV3h7w89jOwKOgCPgCDgCjoAj4AiMOAKu8I54BTl7joAj4Ag4Ao6AI+AIOAK9IeAKb2/4eWxHwBFwBBwBR8ARcAQcgRFHwBXeEa8gZ88RcAQcAUfAEXAEHAFHoDcERl7h5VO3d999d5h22mnDO97xjjDjjDPWLvHzzz9ffNyAT/tOPfXUteN5QEfAERgeAvFrjMUZ0PPOO+/wMu0xp9tuuy3ce++94R//+Efgk+JPP/10+OQnPxlWWmmlHlPuLTo88RGaN7zhDb0l5LEdAUfAEZhkCIyswnvRRReFfffdN1xwwQVNkMdP6YZ99tmn6Vnux1133RWWX375wFe7PvjBD4YrrriiUJpzYf2ZI+AIDA8BJrE333xzuPTSS8NZZ51V9M1dd9017LTTTsNjosec3vzmNxeyxSZzyCGHhG9+85v2Ud/vX3rppbDXXnsFvtR1ySWXhPgp3cDXFs8///xwwgknFF+zjJ/nDu9973v7nrcn6AhMVgS23HLLYuJ62GGHhdlmm22yFtPLNfZrw+P/5LTTTiu+NR4/L9s499xzG9tss03xO9ZWcT377LPbMrnLLrs0xbn99tvbxvEAjoAjMFgErrnmmkb8hHVT36RfR4V3sBn3OfX4ifRGVDQbq666almWm266qc+5NCd3//33N973vvcV+F111VWNaFXOYhkV3uaI/ssRcARaIhANZI34ldHG2972tsYNN9zQMqy/nLgIhFFj/dZbby0HEIQ6FK0YjfXWW698fuKJJ7Zl+/jjjy/D04ijZaRtHA/gCDgCg0Xgueeea6C4MZG1iu9EU3iF0lprrVXIGcryyiuv6HHfrwzI5IEsu+eee8r0H3roocYRRxxRyjomD67wlvD4jSNQG4EnnniinFAOevJamykP2FcERs6lIVpzw0EHHVSY3uPgGGaaaabi/tlnnw0sGbLcsMkmm4Tpp5++eF71L6IUTjrppMKHd/311w8LLrhgVVB/3iEC+CvibrLnnnt2GHN4wY899tiw9NJLh2WWWWZ4mXpOHSHw6U9/Opx++ulFnInm0gDTUcEt5dA666wTzjjjjI7KXzdwnCAUbln4CkeDQFhiiSWaor788sthhhlmKJ+5S0MJhd9MEARuvPHGcMcdd4SNNtpoXDmmj33oQx8Kjz32WIhKb1hooYXGlR/PvM8I9FV97jGx6NtXWn2wZDiNHgKvvvpqI04gGnEj4Ogx9z+OovAsLF5RmRpZHp2xRiMObqVlciJaeK+99tqS/wMPPHAgVfrCCy8UfS2K/UacYFbmwXv9uYW3EiZ/MYIIPPXUU4U7Aa6Lo0DRN77oS7gP0f+cJg8CI+XSgF+chLYrvKPZyA444ICijkZV4Y07/htvf/vbCx5d4R3NNiSuJrrCG1c5Snk1KL+/7bbbrsgD/8JWg6/kJldXeNXC/DrqCOCuGFdHijY+KgovmMlVaautthp1CJ2/DhAYKYX3lltuKQcQV3g7qMUhBY0nZ5T1M4oKL37aq6++esmjK7xDahhdZjPRFd4111yzbGuD8N9FcZUie8wxx7REWeFc4W0Jk78cMQR22223so2PksIbT4cq+XJ/3hFrND2w03cfXs6+5cgcjh3CB+bFF18s/M8+8IEPhGWXXTbMOuusUSY3U9x0geIdHnjggbD33nuXLzkiRMS5kl/60pc6Ok8XXs4555xw8sknh/322684x1fp6XrnnXcW7x999NFAfnHJPpx66qmFPx5+PIsuumjA13DFFVdUlL5c8cv73e9+V2DEPT7Giy++eJHXPPPMU5kHOMWd7uGPf/xj8ffwww8XvqocvRZPtQitzjIFj/POO6/wbcZXmjhgfuSRRxb1BcY822KLLULcINPEA5h89rOfbXqm+uGM4w022CDMMsssTe/xhzrzzDMDPoX4Z80111xh5ZVXDlFRCHPPPXcZ9plnnimOVCofJDekHTcwBspqibJy9inEWa74dlPfok996lNhtdVWK34ussgiXZ2RGk/3CBdeeGGIk7ECq8UWWyysssoqYY011ij9y8mAs6I5JipH4Apvauc2DHVGv0iJ46aoqyuvvDLcd999IS77Ffh9/OMfD5/4xCfGYK34HMMHBviv77HHHoEzqIlLPZAWdQL+4BYt4YoWaIO8v+yyy8Lf/va34ki/TTfdNMw///xlGHtDmuonHC3GUWPEp+/Hpf4w88wzh3XXXTfEzaZN/qU2Dfpz3FxaPGrnw1u3Hmz63FMn9DP6+eyzz174vy655JKB/k6/1h6BNF6739ZvNlqDQjw5ZkwUMHryySfDAgssEKaZZpox79s9AP/jjjuuCPbggw9m5ZfSmGqqqXRb9DeOJUP+chwjdco55vi0b7755k3HLtGukSc5QvZZuXf11VcXaduw+C7bvnzdddeFX/7ylwF5v+GGGwb4pg4uvvjiou3RHr/+9a8H+mMrqis7bBrIN3zCf/Ob3xQynT506KGHFvnTHnffffcyX+qPIy/hdccddyyOcQMv2i/H5dGeV1hhhbDZZpsV7cbmY+/ZW0JfY7z7y1/+EkgXv8+11167kBPgnqO///3v4Ve/+lWIq5rh4IMPDo888kjYf//9A+c7g3tUBJvGym7zId1TTjmlKGvcKFqwgjxBRiBT8f2G12goCBy1BzHGUB7egweEXyv11q6/cJwo7Yn4yHXkG/KSdmTbKPjS55FRItoGx4NB1BftJ6Vu5ECnWJMneovO/GcciSdHpaz47//P3nmA21FVe3zTO8KjBALIJ0gJoRhAaSpFkC4iAYEXiiDFwMMI36MESCAoAQzlgUAEiRDyKYgxSu9BOggiKEJQQCka6U1p4rz12881b519Z+7p555zs9b33TtzZnb97z17/nvN2mv3IgJNkOU+UR988MFs9dVXjzMjPsHtvffeuf2ZYBOvn3feedHrgo2s96odpRPaaIXnaFqEoGRCMGJ+mqYQrjy8kLzsxBNPzMtKGHkJZvKiz7baaquKeBpfyF0ev5kTPkvKQ57nsdlmm2VCPvLf5IfZQJFQPv3UwoptPFfIQFIR9+CDD87efPPNiuhXXHFFhZcL8rjmmmuyKVOmVMTVuqK9xTRAxWreNUx6xEWTCp+pLr744tweW00MNA5lFzKnwWN5+XSLVl/D6PHoo4/OhDRkeN3QvsU9zq3dp/XioXHtEYzrERmsM/H5nJcnrQMYiZP/PEkhVNlBBx2Uh9e8qZP4jY7h6HN6nSNtJy/LPA09AW+eH8JQz6OOOqpCc809zH9U6POs1E/7ES7A5IWet4PNmzaQCV2G1wQ0K/aenlN2W0d56cbyplizurnsuQE3IQJa1IpjLRreettBM2A9gLwwY72oB21Jf7F9DA8HjQrYKk4yma5IRghFRVuANV9H6hEhKnn69IFqomXhiG2xxdbeoz0Y/1SEoGaMQTYM53xmloWfGiwehbTmn58Jg40j3iPQRIOtfT6xNxai1SddzWf8+PEVaeuPescOIZhxvGfcA2dNX0hOJoQ1/811IV4ZdTj88MMrwsqEIDvmmGMqwmo6HPEoUiR4C9I86f+kwbigcYUoZfadxbhMHP2ETzhRMEQMbb/kuvhazrOsNx/ZDCXGJ20tC0fKIuS+4prep/ysz2Bcsa729D5H8GS8KBKecftuSuvDug+eZRVZgF5YDpufhuVY7zjQKNY2T/tuxXuUS+8j0DKTBgZH7ayQFwYuFZkx5/cIc+CBB+qteOSFzZ9oiPJwDCR6XY8VkUp+QC5SckKelvDysuKFoIOVllvj8aKEvKQPrSWBJdn3e5l66EPEy8KSFoiPloNj6nqNzypaXj7bi/Yoz0s0nzlBIi7kXTR9+X3aIx38tK6Ug/ZgQLL5Q86sUHZbRgZ2bReODJYqSugZ2EVTEy9jbnDkkUdW5KFu5zQeYbVcWhY7wGoZabvUzRz9jXLYly4vXC0jBKhWIS0d9HmRa168SCyxoz1sO5C+JULUgXRUeOFon7rgggv0csURoqPtjB9qyq8yderUHD/6gArlE81dRnjFjaNiQZ8T7VYk3po293kha3/cbbfdMtEwZaeddlqeP2Hss8pEMm0fwuhLHuKkuHFd/yDoRc+OJWV28qL1aqYdWERG/tTX9iHaUNsAYtqoyJeovH4QTBXIEfnS92fOnJn3F57JeuSyyy7L0+e5qSaKNUetH+3Bc2xx5r58walIDtKYkl7RllaE0R/yJSDHVfu+JXBaDi0DR+7rBE7vczz33HM12fxY79jBM6F52bSVfNlrPA/aV+11jc99Jo32GdFwPNdW7CdvJuVWeGY0Hv1EhWc0TZs8wSa9rp/3G8mHSQhlSJ9VxnrKteeee8Zn3U7ouc5vsKAsxBfteJ/JMIqHVOwzJV998ncB7yzFlvTtmMX7grFNNLo5VtjL6nhtx71GxoFGsE7rxZhU1I5pOP/dOwi0hPBCuLRj2Be8hcE+uIRFI5WKJQs8dM0IL2ctE0dLeDVd66uXMBAGq/WxWhbu8xJqRsaLVoN0GATsbJc0eajt4AABUeHhVzIDLqkGl3Dy6bAiPoNaKukAiNbaElVLSClLKgxmlJ8/XhxFom1IfEu6CcuL1b74IOGpaHzNBwKGKJEgjh0M0/hK8ojfqA0vxI/4tg00HzSeWjaOaExSsYuZCKPEXhcgoQkqEzEvydNHk2oFTbfNG82qFUi9fXHSBql2SnG06fBsWkmf1ZT8oDW28ekLdmMXNkRICRSEOxVLxIoIbzPtoM+LJeyaP188KL8ts96r9ciAVvM8AABAAElEQVTLWzHQCRH9jWvaJ6wWq+x5KcvPfqEinWqiZdFjOlalmr10ApiOl2Wb+/CFjjzS9qSPpGQSsmc1nHbCrOWkr6jos9/I2MH7RNPUI+3LM4P2l7Lp1xQl7RqOZ8bWF2zSrx5MDK3w9VLjp5NX+4xBMq0wztu4pMGzAg58JaJfMcbxlQexYevJh7jWpz35kG76Hkw124zP4GNl7NixeV3T9zvvD/2yVNRP03dsOtZAchVHJfk2b84bHQfqxTrNlz6hZUPR4dL7CLSE8Krmjc7BwFkmVvvDoAYBsqIDHuk0S3jRPmhn5Zg+6OTL5y0bxmqCtFxowTRM0UtZw1U7qqss0ioaGIjPp1HNS7ZZzZOEmOr1VPOaB5ITPoNpOI5iD2ZvV3ziUyJpA4jdVUX8dFV4LYRXtQi2/DaPdIC1n8w1HJ/wbT347KpErii8xuPYLOHlM7/mXdaXtY6Eox+nYicohEG7p4SCF2+Kq40vPqjz/HnZpWJJRVGfJi8tPyYrqVA2vc8R05NU0jCpJjQ1cUnvkx4TOrDRvGg/JYaaX3+Et9l20HzpD5hiWAF/yoMGqhGBxGn6qrUSG+l4beLEiXmS9pN+SlbyQCUndmIoWwaXhPr/y1oejuSbCp+cbRjMo6xAXGzfSQmehtWJRJGZijV1gbAXCVpdWw47Fupz1cjYkY5dTGr6Ex1PKEs6OSAeeNixn3BKQrlvtdrpM4TGU+tIPqlQZ73PEWVFmTSTDxNim4/dsETzs671CAvpToUJu6aTjneWFKYKDtJJ38P77bdfRfLVCG+z40A9WFcUTH5YPkL9IdAuvY1A04QXkqgPA0f9hF0EiyweqwiLHakV28GKBgobttp5+tIuIgdikJ+Xpyw/O9NPPwVWK4O9bz+PoBEvEsosC4Cifa0lB1Zb1p/W0mraaQs+UVmxn+PRJKZC29m2TMtZjfDawQltMjP/9C/VMtMnUqFP2Re+lqnoq0Aat1nCK5sH5Bhgs5aWX7UZWiaOKXmgTHaFvQ1rX5pp2fmN9p6XAhr69AXFgGvrx/OSipIG8kzNYjSsbYOy/mQJQZoPmjNbp1RbqPlgx2nDpc9gf4S32XawfZ3nx9qtUj60fWj/GhFLAJg46rjGZCUVNFrpxDMNU/Tb4pZq6auFp++lko6HRWRfNd+ad+pqjf7IvTJtF31W45b5DGYipGE46qSu2bHj6aefrki3Wtva/i0L+1K44m+u27LaMROMGQt4P6QTqnQcThO3pkk8r/1JM/mgNbbltyZ0midk24Ypen9jImHDWOJn349FY2U6XjJ+WalGeJsdB+rB2paL83ScS7+opeH9d/cj0DThTWfW/VWZGaB9cFItYzcSXquRxC6oUbGk1Q4Y1dJD02AHZ7Sd/YlqYMBZtU8a3pIAO3jr/XSAtOYdhKlGeFXLRd4MbNjRVfvDvrtIZor9o+0r1AssqoklhGVkrr80rFlHtbJznwGdBTJFYl3uUBdMGhoRXkJo/9NJgJpK2DRrIby1YGRJsV1gSF7pi6CM8KbmH+nnzP4Ib7PtkGp2wB/70NQ8w2JX6zlaXO2b+mz+4Ac/qDV61XBooDV9jumEoygBG76I8BLHhknJLPchxbbdlYxyD1H7WkxaiqQWwks8a67Bc400O3akhLfoa13M6N//tN3ApIzwMt7YrxR82u9P0I7afqt4p+OWJWG8F+qVWvNJx/MiwsvYpeXkWER4U1JszfHsV4FaxsvUxKga4bV41pJ+Oh43g3U6can2dbHedvTwnUegacKLFsk+MPUMNCmB7DXCmxIQiwPn+sKwWnAG2nqEQcqmW/aZXdO05iWUz0q7Ca/1QpCSG1uOWs/pH1p3cMOmuprUQub6SwM7N82z6BNdf3HTe2jprQkCaZeRwzQuv7HBU88PtCULseyLutsJLyZLiiXH9NNxf4S32XbgmbOTTC0HhK4WjWlRe+g1a5pFurTN5MmTCzX9GqeeY2qrXaSNTdPT+nFslPCSJqYXNi1ruqDPVqrR1LLUSnix/9U89BN5s2NHOwgv9dJFnZS3yEwDMstYpzbd9Dm1c9Y6toLwNpJPuwkv/UDrqBMX7Qu1HqsR3mbHgWYIb2qOgVmiS28j0DThTTUp1WZBVgPFCk0rvUZ4rTZEH3x7ZLaJpLNoXd1s6152ntpL4g2jP2HFsJaBF5SVdhNeO3hVK6ctV9m51a5TJ+zZqom+lAnfiIbXxq/Wl6uVBXJrNe6Uqeizd5oOhAdNCOEhuNjiKlG2BLrbCS/1shqydLLWH+FtRTswYbHaRPDUPyZT6RqCtB2KflvtK2MZRM1OfLE/17Yqil/LtdT8ANOraqL14tgM4WWiYCdVupBI1yCwAKxMaiW81h2iajibHTvaRXhtH01ti/nSoZMq+qtqiq15Bu3RLOFtNJ92E177XtOJS1nfKLtu2137mg3b7DjQDOHFVM0+V5h2uPQ2Ak0TXmtjQ+fAp2F/Ymds6Wf1XiO8kH0WipX96cIJu8gFjKqZJVj80sEzXR1tw3JuNSWqYdYw7Sa8VnOTLk7QMtR6tH3BDjqKaVk6doBshPBajU6zpN0uQrR1KFrkpfWRDRFyAgXxeDZZMd1rhNeSp3RxjiUT6YLQVrYDC2ssKdW24GVbr1jXiaweRyCodqGlXbhWb/oa3uKWrnXQMPaodeLYDOElTTVd0DSZgOnks2iSpeWolfBaLbIS6GbHjnYRXrsQzy4etOs/sPW3C1HTMbsZwttMPu0mvHzB0j7CsREFQTXC2+w40AzhZc2BrR/t6tLbCDRNeFMN5EknndQvIlbjkw6eluQw4DcjqZYkXTBD2nYwKctPB3o6fmqCUU/5bL3x89ufMEDi0keN5O3LTzUiZfEPOOCA/CFNX7ztJry8EOwAYW29isqLuQYaUAZmK3wqU4KCdsN+FQCLIjszjd8s4eUFrHUAr2rC53HMSFKBdJAO5dHFPjbdMjtuuzKcwTqVXiK86QKltM79Ed5m2wEfolbTyqRTffNqO3C0i0NTrIt+24V4duLKeKPPafp5F9dMRfaTRenrNdvORb5PNZwebZ2aJbz4TLbpUWfGL57JlLxp/hxrJbzHHXdcnj5tgjQ7drSL8NovNLq5DuOTHc/J20qrCG+z+bSb8FJn+5WTiVI1gR/Yr1zVCG+z40AzhBd+Yp8DnnGX3kagacLLS0zJCZ2DByB9sSlEdtEaL4eUEA1mwmuJDDjp4KnY2CN+XMFHiaC1ZSVuOsDauPqJjXDphKKVhJc2T8W2H/mjHSnrCxANNCMsREhl9OjRcaDRHcrSSVWqubbxLeFVv5v2frVzu6EAdcAer0wgMbRTutMW5EoXc4AJYl3LkW7qyogwKUEsWlhk65e2L2nYyUGZlwabRpkW3L7IGl20ZhceQm5T6Y/wNtsOYFxk+0p7KDElTC3mArbc9hlKTSKUpIKvipKOevOxREBJoaZZdKQu+tcs4SV9u/pe000XGaflqJXw8txrmuoGq9mxox2EF48pWk6eB55rRF0M6r10EpB+Ck/v10rCms1H+56Ws2jSZc0SCFekTOhv0ZraLmsedhKY9g/1T2zD2H7OuJ9Ks+NArVin+fLbulxD0+zS+wg0TXiBILXjLRvcWcmsD0ZqzkA6mEPofY7NiLW1I60id1B2BzhegkViV4k2o+FlYYOtG/mlRAJyqJsWoKFSST1hlM2keXloHkWk0H4eKsI/1eykXhoor6ZP+a0GjbIyA7ZkirCsbE4HfD6R6sKflJRoHhBGSyjGj/+/TTs0/7LPvFYjU7STk2JadsSEQPPQY9EiJ1xN8RJE05Mu1FQNFoO5Cm1rX/SkneKrWmHNNyXSqeYo7T/kVS/hLXILRzr1EF77OZe4KnYBpV38pPf7I7zNtgMY0meKhK1lFWO2Q69V8PCg8bbffvs+0ZRIM7lVYdLF9Xq9Q+CeUPMq+oKg6XNMv2alz5SG1fQ46kRM76XHdNMd4hQRJhvPEt6yL312MwTr5rHZsQMzIVu/agtOta2IU7bts52kWlMqa5JBfEvgwMMSJe6nih0Wb2pZ068BFs9m82mE8Ba5WMSln5Y3rU+6aJ3xkGc3FfXwY58NwuhYSbpFazSaHQdqxTotL7+tT/QiU0JsejFlYnJYVOeiNP3awCLQHKv8d9kZrKzmA9KT2rtAppQM8ZCnZImkeDnYB0s1nI1AlA7YRbbFqf1x0Ysb4qhlKiKRtZYNwmPtlzVNsOCBwXRC8QFLMLVi/fgS125nSjjCW4JR5NFANVDEL9LWpDZLqRlISrx5IVAvXApRds7TAZC8GAR5uTGAU1d92aSEBG2DfipMNeC0jeJDmqRRpOm2mnTIHy9pNDUQlFptclONOvmBHeSdyZ3tE6k3Cp20UY/0RZf6s0Qbb4lQOkmDdLJYiLak7PZLCmWiz6B9saTVElU7abJ9STEmjbIw2kaESTewSN2SnXXWWTb5eG4JG+UsEqsdol+k0kw7UG7+Ul/S5MHiI72vWrs076Lf1gQqnYwQXvun9X9NG/NSrFfos9oGHNNJo00v3WAgXRxI2NTzQ9FkyabJud16NnVxmIbltyW89FWeZytMDO2kOyVXzYwd6djUn508ZVJs6QfsqGgn19zHHlWfE8ZoxjYVa8dNfLBhnCFO6oqQ+0zOIcU6ybAKIvIok2bzSTWzRUofyqzPAseiMEzubRjrH7xookJY3mF8oeM50Yk+dU3t+C0WxOO9BtasRcE8D2lmHLDp94d1URvY8akIF/2KR7n7m7gUpe3XBgaBlhBeig7BtZ/T6QAQSl7WDMBKtviEXfTZhBlSSgh5CTZCerF9GjNmTMVDyud1Bn0VXiiUxT7Iqd0fpMveZ5BEQ9GoUO+UtNj0OYekFXlxYBCANGp4ynLJJZdEh/oMprq4gpdukYYn/TxGOPvCgXipZwDNAwwtIWBw05eAhtGjmg8QRk0S9F7RMfXQwQtK25/BskjSneSK6oqpQFF+pJ0S0KI8uJb25aL0uKZ1Jg7tAyHSF2mR433C2EGUNHjZWk2v3VkpzRePA9h/p9fVXCCdwFFn+/yQv26SoGkwaNvJEe2XbgeKNt6Sl5Twkhb9D/KPmYV96eM1hDSt8DvVgtGv9PO2hm2kHTSu1o8+opMn+jiTM20j234ar7+jtd8tMjdhvCJf1cjqy7aRxTyUw05yy1aIM0lCm6r15ci4ZhUO1NuuRSAMHmRQQvQn1kygFqws4SUP2pSvLGBFfH0/MCkrIhCNjh1oIO1kn7yZlBa9Z7S+2gcIyx/jLoSbsrLBjY5z9H1dS6FxMcdK42s6HJkAFo3zjNf0RX0XahwWP9qJb7P5MEEFy7TNGc/ts8jYbr9gUh7GGPvFinep/SJCGNK15WVSrnhpndIjeKUKFOppv0raODZ8I+MAY109WCvmegQDLQ99IJXUZRnldel+BFpGeKkqDxP2ZkpctMNw5MXD4EdHtEIcG67onM5U7ROVpmndchWlxcOrtkRF97mGVghCVnafgaNR4eG1mhObB+YMqaYhzQeikJIm0mDAocxFOFntu82Pc+qSOthOw1hChLbC3iff1L8qZcYvadGgD8FKtaJ2C1ZNG62AFSUTet8e7edGBio1l9Aw4JW+tGzaRecQCbCh72k6eoSUpuTM2qJpuNTzQPqi03Ac9ZMYBDVtL3DU7WLThRRotFPNvE2Xc/oFbZhet78hzamm0N7nXOuTEl5LzDQO7WxXtSvG6t5KwxUdwV6l3nbQeLQbk25tPz2SH32WyUG9ohMuCJslDpoOE2rVYGp+PK+NCulpOkV2vGgVi/DTazyDaVvpPT0WTY61vLoKnzJYEqT306MlvEyArAaM/EinGhElzXrGjtTnrdZLj5ShSBRXwvHs2t9co43RMha1M+nxzNmvKcSB0CuRt5/DuUffKRq3uad/RV85GsnHfl3RtO2RyQe2+/Zaeo7ioNp70n5J4B1SNlnnfdff+HvCCSdUlIVxknHCSr3jQCNY2/xs3ctMXmwe1MGl+xGYiyJKZ2+5yAwwyGemIKYLYb311gsf+9jHWp5HLyco2sYgWs0g+64HITRh1VVXDfPOO2/NVQJXIV1B3FiFtddeOwwdOrTmuM0GFNIeZGAPQ4YMiXnPNddcpUnKJ6wgn8TCEkssEWTSE5ZZZpnSsK26ATbyogii4QlCeprqe/IpOZZftLBh+eWXD6uttlqQl2OrilqYDo+kaNdi/xg+fHhYaaWVKsLJpCbI59FYlk984hMV9zrxA2wplwp4059nzZoVhBgF0ZaFJZdcUm+35FhvO4gGJpYBLIXsB7EhjmVr5FnTClC3W2+9NWy00UZhueWW08sVRyFIQV7WQV7wYdNNN20aB9G2h3333TcIuQoyKaprjKgoWAM/brzxxiCmQEGUBEGIZdUU9tprryCTzxhOtOFBNIGxzIxz9OFhw4aFueeeu2o6GqCdY8fiiy8eeI4Q8Z8bNthggyCa+NhXGE/TZ07LZI+inAgyoYhtveGGG8Yxzt6n3zFGjxgxIiy11FL2Vl3nncqnrkKVBBatcez/jAeMTTxv888/f0no/7/M8ymmEvF9Ql8vk3rHgbJ0+rsuSrmwySabBNH2B/nqGWTtUWFwxj0xOwn/8R//EWRiVRjGL3YXAm0jvN1VTS+NI+AItAqBIsI7zzzztCp5TydB4Gtf+1q49NJLg5hIhMMOOyy5276fX/nKV4JoAuOEz05wynJMCa/YvJcFHfDrKeH97Gc/O+Bl8gJ0BwKyoDsccsghcZKJssaVdd3RLq0ohRPeVqDoaTgCcxACTng729hiRxnkc3kQLx5BTBCixrDdJSAvvsyJeVC4+eaba8rOCW9NMHmgLkYATfOnPvWpWEJZQBfEvKKLS+tFqxcBJ7z1IubhHYE5HIGU8PIZvx5znDkcvoaqjxmR2AcHTGvEjjuss846DaVTFIm0ZTFj/DTL5/eFF144bL311vETs9gyRrOGonjpNUt4xXViOP7449MgXfPbanhlsWmQNQNdUzYvyMAggAkNmn5Z0B7E205HJpYDU9M5N1cnvHNu23vNHYGGEJDVz9E2WiPL4qqmbVU1LT+WIyCeMsLIkSMj4a2HiJan+H93tttuuyALSfsEk0VIQRb39bleduFLX/pSkEWt8bYsHg6yaUBZ0AG/btcdyMLCIJ4tBrxMXoCBQ4AxjT6Abb74DA6f/OQnB64wnnPbEHDC2zZoPWFHYPAhwCKw8ePHB3HEnldO3C0F8ZmZ//aT9iHAQhnxbxrEPWIQN1otyYgFWrLKviItFmay4GqFFVaouF72Q1wchjXXXDNfCMYCVRb9oEntNsFEQ1bY58USV1xBvBL4V4ockTnvRHxlhwUWWCCIj+6w4IILznkAzCE1dsI7hzS0V9MRaBaBadOmBdkZqQ85Il1WY7OwSVwiNZuNx+8wArL9avTEoF4LsFsUf8U1abkg4JhaYGZRJOKiMrB4Tfz+Ft3u6DXsksX9XrSFTjMWV3VhjTXWCOJiMXoXSO/7b0fAEeh9BJzw9n4beg0cgY4gIH5fow1pWWa458H1lEvvIYDrKxbELb300tFFov3k319t0Pjfe++9/QWJi4AWWWSRfsN04qb4uI5uxMrywtMIBN3FEXAEBicCTngHZ7t6rRwBR8ARcAQcAUfAEXAE/o2AE17vCo6AI+AIOAKOgCPgCDgCgxoBJ7yDunm9co6AI+AIOAKOgCPgCDgCTni9DzgCjoAj4Ag4Ao6AI+AIDGoEnPAO6ub1yjkCjoAj4Ag4Ao6AI+AIOOH1PuAIOAKOgCPgCDgCjoAjMKgRcMI7qJvXK+cIOAKOgCPgCDgCjoAj4ITX+4Aj4Ag4Ao6AI+AIOAKOwKBGwAnvoG5er5wj4Ag4Ao6AI+AIOAKOgBNe7wOOgCPgCDgCjoAj4Ag4AoMaASe8g7p5vXKOgCPgCDgCjoAj4Ag4Ak54vQ84Ao6AI+AIOAKOgCPgCAxqBJzwDurm9co5Ao6AI+AIOAKOgCPgCDjh9T7gCDgCjoAj4Ag4Ao6AIzCoEXDCO6ib1ys30AjcfvvtYemllw7rrrvuQBelo/k/9thj4bXXXgtbbLFFR/P1zBwBR8ARcAQcgSIEnPAWoeLXHIEWIHD66aeHY489Nlx77bVhxx13bEGKvZPE1VdfHXbZZZcwadKkcNRRR/VOwb2kjoAj4Ag4AoMSASe8g7JZvVIDjcAZZ5wRjjnmmEh4J06cONDFGZD8J0yYEMaPHx/OPvvsMGbMmAEpg2fqCDgCjoAj4AiAgBNe7weOQIsRuOGGG8IOO+wQNttss3DHHXeEeeedt8U59EZyH330UdRs33TTTYG/L37xi71RcC+lI+AIOAKOwKBDwAnvoGtSr9BAIjB79uyw+uqrxyI88cQTYYUVVhjI4gx43q+88koYPnx4ePfdd8MzzzwT7ZkHvFBeAEfAEXAEHIE5DgEnvHNck3uF24nAIYccEi666KIwderUsM8++7Qzq55Je8qUKeHAAw8Mhx56aLjwwgt7ptxeUEfAEXAEHIHBg4AT3sHTll6TAUbgkUceCeuvv35YbLHFwksvvRQWXHDBAS5Rd2SPdnfIkCHh7bffDg8//HDEqDtK5qVwBBwBR8ARmFMQ6HrC+/e//z18//vfD9ddd1247bbb5pR28Xr2IAJ77LFHuOqqq8LJJ58cxo0b14M1aF+RWbg3duzYMGrUqHD55Ze3LyNP2RFwBBwBR8ARKECgawnvP/7xj3DxxReHE088MWqGVllllfD0008XVMEvOQIDj8DLL78cll122ViQF198MQwdOnTgC9VFJbD4YNe71FJLdVHpvCiOgCPgCDgCgx2BriS8v/jFL8KRRx4ZFl100YADe8QJ72Dvir1dv/PPPz8cfvjhYauttvIvESVNud5668XnGaxGjx5dEsovOwKOgCPgCDgCrUegKwnvX/7yl7DkkkuGhRZaKNr+YQ/ZTYT3wQcfDA899FDA7dKwYcPClltuGeaZZ54+rTNz5sxwzz33hI033jhsvfXWfe77hdYh8Oqrr8b2UC1r61KuLSXckOGO7IQTTginnHJKbZH+HQrt5wMPPBCefPLJsOKKK4ZVV101jBgxIndn9t5774VbbrklzJo1K3o5+MxnPhPWWmutuvLohsBMYvHJu/POOwc2pnBxBBwBR8ARcAQ6hkDW5SJkMRMwMiG8A15SIR7Z7rvvHstDmfRP3FBlzz//fEX5ZJet/D7hzjnnnIr7/qN1CNx3330Ra1kslr3++uuFCb///vvZQQcdlG277bbZG2+8URim0Ysffvhh3tbydaLmZJ566qlM+7f2JT3S32Vb4uzxxx/PhATn6et98XiQyYSr5ry6IeD06dPzdvrnP//ZDUXyMjgCjoAj4AjMIQiEbq+nEoKBJrwffPBBJlulxhe2fLbORMubiTYxJyLbb799DqWsRs+vK0EhrEt7EBCXVzne8jWgMJMZM2bkYa6//vrCMI1efPTRR/O004lPWZoQY+0be+65Z3bnnXfGPrX33nvn1/U+R1nslcnXgoo+J4u/ypLvyusvvPBCXrff/e53XVlGL5Qj4Ag4Ao7A4ESgK00a5AWfyyabbBLuv//+qiYNfO7FzydeHX7605+Gj3/843karTi54IILwmGHHRbWXXfdIBrF+Ll5gQUWqEj6X//6V5hrrrkCJhlFGw6IVqvQ9KEikS79ccUVV4Qbb7wxUMdWydxzzx134hKteVNJrrTSSkHIVNzwgX5QJEcccUQ477zz4i0hpdF0oChcI9duvvnmIJrj6I7srbfeqpoEG1KoScIPf/jDsP/+++dxKP+aa66Z/+ZE7YJ1u2K9+Y1vfCPQL3tFMAHSXeeuueaasNNOO/VK0b2cjoAj4Ag4Ar2OQLfz+Fo1vEJocu3Rt771rZZWS0h0xudyaevsl7/8ZUxbXth5flxPNbhCRiruE0bIYkvL1cnExOVWn/pQp2b/SLcZ+dOf/pSXQRaNlSaF2QllxTygFnnttdcyPsGLS7zsD3/4Q79R0LTWk/anP/3pGF48kPRJ99e//nVeH8UWkw1EtiquuPftb3+7T/xuv6DP0fe+971uL6qXzxFwBBwBR2AQITBoTBouu+yynAzIbk4tbaKbbroppi1auZy07rrrrnl+EJP/+q//6pPnT37ykzzMQJtk9ClcnRcg65hqiAazZX+k16xMmzYtx1h84BYmJxr3PMzBBx9cGMZepN0gZqLNz81YmOCUyZlnnhnTl00nyoLk1//4xz/mZZGFdvl1PYFgK9HlaNOcNGlSfo/+RL16TSg39RI/xb1WdC+vI+AIOAKOQA8jMGgILwuHsM284447clLaqnb585//nMnq+2hDSZqWQCk5kVX2hdnJ5+r4gsc20woLjn71q1/ZS20772RebatEScIQWG2DMgIIEdYwEOT+RCdOLE7UhVVoViHALHwrkmOOOSamj213NWHBHGSvjECnNrzHHntsniT5i3eDTLxB5GXLb/77RNz4ZeLDOr3clt/iTzcT39h1pa1fbI466qi64nlgR8ARcAQcAUegGQQGDeFtBoR64/I5VgkURz6Xl8k+++wTw4qtZkUQtHWQ4U5IJ/PqRH1sHurBoL82wNRB2wsTiDJhAqLtiRmLiuycFq+XLXbDYwLxxN2WRmnoiBbdLoQkTchtrQIBBQ+0yO0WJlHUlwlCPYKXDOrFJMHFEXAEHAFHwBHoFAJOeBtAOrWlxAVZkaBpU5tF6y4LLTQv/U4Q3k7mVYRBO68999xzEUewrMV+tz+zErxwqJ0vJg1WzjrrrJiP+JC1l/PzMWPGxPu1aHjzSAUn4oc3rw914q9WF2poo5VMdoLwfuc734nlq5fw6rNz0kknFSDglxwBR8ARcAQcgfYgMGi8NAg56IgIAYmbYtjMxMVSGD58uL0Uz/FssNdeewVZmBWuvPLKeO3ee+8N2223XdwumZX98sk6XpdPvWHBBRfM03jkkUcCf2y6seGGGwYhCnEjjjyAnEiXCLKILpA/u9JpGWRRVAxWa142zbJzvF/gCaHVIhrJsMgiizSUrOJLZDFbCCNHjuyTjpg55B4z+vNqIAvUYnwhveH3v/99hTeNo48+Onz3u98t3VTi+OOPD6eeemoQe9vw8MMP9ylDrRemTJkSxMVaHpw+gUeQavLuu+/GXd6Ij8giuuiFYvnllw9rrLFGHp2+y0Yo1I+NXdgwhU0uUiE9sVsPzz77bEwHjxJ451hnnXVi0MmTJwewRERTG/szfZfyVhPKI/6Hg++2Vg0pv+8IOAKOgCPQUgTaw6Nbl6ra/PWnnWtdbtVTwheqNEDFH/bDRfK5z30uhtPNCNi4Ai2gan05ovEinC5gwo+sbm6h2jDyY8Gc9RaARhLfv3wCF7KVsXBK00XbV0teRWUuu7bjjjtW1DnFoNHfzZgBqCkBebOJQ5FY+13V3IIN2lrbbor1aaed1icZbQ/ZQa3PPS6Iu7OITa0eIGjH4447Ln7W13YnHTV/USyt/S73VbAnp8/89a9/jZcwl2Fxm8bjnPrYzU50IR5l1IVjhEdTawWtOWHwJCEuzzJsbQmn2mu8SChWXCctflP2WkRNNrQtaonjYRwBR8ARcAQcgWYRcA2vvLXrEbaPZRtZFbSpbDWcys9+9rOw2267Rf/B+F2df/758yD/+Z//GX70ox8FMWkIQlby62jRSBs/sbfddltYbrnlgphChM9+9rNRK4cGUexMA/5riXfAAQeEH//4x0E2LohpEIctjNHGLrzwwvFaWV55pjWesE2ymEe03A/v5ptvHv3M1liMimBoJ5955pl4Tchr7uPVBsJ3svqqFTIX8Nl76623hm222SYI8Q34UhYSFzbYYIMY7be//W1Ye+21bRIxDtptIWmhyGcwPmW/9KUvxTjVfC3TVzbaaKM8fXGhF8RkIhAP381o9FVIN/VVi3ZWNfniOi3/2mB9+4pJQ4Xm9q677gqf//zng3iACF//+tejr2j6j2qTZUKWl3/fffeNGmLqq76kxfwgYnb33Xdr0cLiiy8ev1KISUMgTi1i/fDSl2h7F0fAEXAEHAFHoCMINMuY2x2/Hg0vC2n4a6eww5o0TP5X5Ef2xRdfzLWt1157bZ/i6Er81IZXPQTgmsqKfP7N81OfrLoQSz4v26DRlZa1Fy7LqyJSD/5gRzNth9QHslbnb3/7W94ONgxaS9VYEnb06NF5WkIoMyGP+Z/V6KPdLBJ2DdOyYIfbnxx55JF5WOKohnvq1KkV17mHttUKmnvd7U8mO/ZWRrm1DNaGl+cBjS31txptrusXAWx/VdSO2e6ExlcHNL5WNG49NrzgqmW0Xytsun7uCDgCjoAj4Ai0A4GuX7SmL+Bqn4tZYU8YXsSPP/54O7CKaeI7Vl/2vLwhEu+8806eH2Rziy22iC92PrkXSRkJ1c0dSBMfsPrHbyUKfGZGLrroovwan64xcUBwSwUxUinLS+/36lE023n9wSYlhywYFC1uHoZzBNdetN/FF1+cVx0yp/hyz/7pdY5vvvlmHseegLf2CbErtrf6nOtERdPFPZnYzMa+q9f0aAk2YZggcY9+ni5mKyO8orHO66b9SY+aD2XHQwSi/YU+Z71S/OY3v6moi9a3HsIrXzViWWx+FYn6D0fAEXAEHAFHoE0IdDXhtS9rXs5oTsvE7myGxq6dkpItCNO5556bsfMVZISyoomzGjVbHiUVqYZXScQtt9ySoUku+pNPzTEptJeaF/lh4ztz5kybTTwvy6tPwB67YNub+mPvCmnj79FHH402rUyW5LN9bA/sWpkU4B0AnF9++eVYY8ITnz/ZkroPCkoyIYn9iWpeq7nb0k1MyI/2w2WctiNltJMbftOvsLPV61yzGlwtUxnh1Y0s6AdF/Umv6ZcRMX/I8aCMaKCL8tO+Wg/hZQdE0sQm2sURcAQcAUfAEegkAl1JePE9arVuvCT1jwUyM2bM6IPRz3/+8zxMJxbEQKrQ5OqLX8sHMUF7qASiT0HlQhEJhRxrGtU+i2uaLFqCAGk8jiysUm0d4Yry0vi9fNSFV+Cvi6jAXokhBHT27NkZGnd116U46SJC6s8EQq+nWmLaRNtXPCD0C5dq3NNP/0WRxH42L6fmzaIvvh7gJ3i//fbLy6T3mdCwWK1sU4kywkt/II1Ro0YVFaXwGmYzWm/N32p7iaT36yG8+kyzFbOLI+AIOAKOgCPQSQS6kvA2CgB2h/Xu/NRoXjYemmdIAjuy1SJlJFRJRH9bI6P1toLWEpMGjQtBQSuoUpaX3u/FoyWpuqUzphwQWSZL6eYSTABoH+5b+2bqLm7dcnKZYqH3wNZuRJGG4zfpKjlM26goPKYVTGwol2qbbTjMJJ599tl4nw0lqkkZ4VUiTh3KvjjI4rc+X08oU2p+8dBDD+XF0P5WK+HFzEjxacWW0nlB/MQRcAQcAUfAEagBgUFFeGuob1cEURLK0YpdoAcJSYXPzapBxGwDkqfCtrqqQUPjqVKWl97vxSMafCVPRWYI9dRJvAXEtDB/SEVtqk888cT0VuFvFpJRLj7dd1os4WVxmArmMYoVOwSmArHGvZ0urqQPWhtwq+0dP358Hl0Jr7WFzm8WnDAxoRxldu0FUfySI+AIOAKOgCPQMgSc8LYMytoTUltGaxcKwUVTq+SERVaQWIQFS5hsQDKwAUWwYcX/rhVxGxXjKynmXlleNl6vnVuvCopRo3VQogjRs8JkgrYAc0wjahHVYhKnmka4lvTqCYMnBe07V199dYxKn6IcSk65j19i1fSKS7do8kLd1QwGUxH6kRX6GXHHjh2bX8bEgmt4vEDoo2XmFlb7TZ4ujoAj4Ag4Ao5ApxFwwttpxCU/uxkCJIKFURMmTIjkRG1Tlbyolwp+jxs3Li8thBciYz1SoMEjnDVpKMsrT6gHTxSTIq1svdVRO11sf1W4ph4eiuzFNVzRUXZli23AZhSdFsUFu27yh5SyuFHNGrRP0W+0n1FvawLCdSZM9gvDrrvuGutkTRrU3IG02PSErwq33357YZW5T961asoLE/GLjoAj4Ag4Ao5AEwg44W0CvEajYh+JH1glIJAHXeSGpo6V8XqPI6SEhU5WjjjiiHzhE4REd3WDVGhahO8vL5ter5xjL63YgFsrBA8PpIkNOLuw6Q5uduJQaz64LtOFcyxs7KSwsE61uXh+sG7N8POr5VL8ZMOSCrJLWSHNhOMPcxh+kyYTJyv3339/Tpq5n/ZPDauactJ766239LIfHQFHwBFwBByBjiLQ9Tutyct50IosWoq7WQlh6FNHWdgTuD9kyJAg5CXurmYD6S5hYm8Z2EFMyEQQchIWWWQRGyw/7y+vPFAPnEyfPj2MHDkylpTd7ITsN11qWUAWd1BjZzPaYqGFFgqiFQ3i6aGhtIUMhk022SS2B7u4lbVJQ4lXiUQ/EO8dYY011ugTUkaWIFswB+ormtyw6KKL9gkjZglxlz5265NFmPH+aqutFuabb74+Yel77PA2bNiwwl3u6MMbbrhhzFO0w/ludn0S8guOgCPgCDgCjkCbEXDC22aAPfnWInDzzTcHcTMWRGMYZs2aFZZYYomWZSD2wHG7XAge2zc3I+J6K265y/bPl1xySTNJ9Wxc3abYbn/ds5XxgjsCjoAj4Aj0NAJOeHu6+ebMwovpQdRQLrzwwl0NwLRp04L41w2t0kR3dWWTwokJRRB/wkF2ngtf/epXk7v+0xFwBBwBR8AR6CwCTng7i7fnNochIF4gopnESiutNEfVXHZnC5g8rLnmmnNUvb2yjoAj4Ag4At2JgBPe7mwXL5Uj4Ag4Ao6AI+AIOAKOQIsQcMLbIiA9GUfAEXAEHAFHwBFwBByB7kTACW93touXyhFwBBwBR8ARcAQcAUegRQg44W0RkJ6MI+AIOAKOgCPgCDgCjkB3IuCEtzvbxUvlCDgCjoAj4Ag4Ao6AI9AiBJzwtghIT8YRcAQcAUfAEXAEHAFHoDsRcMLbne3ipXIEHAFHwBFwBBwBR8ARaBECTnhbBKQn4wg4Ao6AI+AIOAKOgCPQnQg44e3OdvFSOQKOgCPgCDgCjoAj4Ai0CAEnvA0C+c477wS2uP3Nb34Td5T6+Mc/HjbaaKMwZMiQBlP0aL2CwN///vfw5ptvhn/84x/h3XffDe+9915YYIEFAlsdL7roovGP87nnnrtXquTldAQcAUfAEXAEBjUCTnjrbF62S/3Wt74Vvve97xXG/O///u8wceLEMM888xTe94u9h8C//vWvcO+994Yrr7wy3HzzzeGpp56qqRJbbLFF2GqrrcJ+++0XmBB1u2y33XbhpptuKizmNttsE+teeNMvOgKOgCPgCDgCXY6AE946GghN3siRI8N1112Xxxo7dmwkwMsss0x+7eSTTw7jxo3Lf/tJ7yJw9913h29+85vh17/+dVOV+MY3vhFOPfXUsMQSSzSVTjsjz5o1K361mDRpUrj//vsrslp33XXDo48+WnHNfzgCjoAj4Ag4Ar2CgBPeOlrqqquuCnvssUdFDD5tQw623Xbb/Prqq68eIA8uvYvA+++/Hw499NBw6aWXhmWXXTYcdthhUVu70korhR122CH8/ve/j5WbPn162GmnncLrr78eySJ94Z577gk33HBDn8pvvPHGUUu62GKL9bnXTRfuvPPOsPnmm1cUyQlvBRz+wxFwBBwBR6DHEHDCW0eDQWysdpeoWZaFc845J2p5Nam11lorPP744/rTjz2GALa5X/nKV+LnfcwSrr766mBJ6uKLLx7efvvtWKuHHnoobLDBBn1qCAE+77zzwvjx4yvukd71118fFlpooYrr3fTjscceC+utt15FkZzwVsDhPxwBR8ARcAR6DIGuJbwzZ84MaFQfeOCB8MYbb4R11lknjBo1Knz5y18O884774DAbImOFgDC+/TTT4cRI0bkJOiHP/xh2H///TWIH9uAALbULBh8/vnnw8svvxwXDtpsIJTzzTdf7C8sJKtHjj/++Gh+QBzsdVdbbbU8OosVLfkl76WXXjq/n55g+4ttrBJk7v/gBz8IBx54YBq0a3474e2apvCCOAKOgCPgCLQIga4jvB9++GE45phjwtlnn11YRRYATZkypeMr4CFYEKhUILwIq/UffvjhMGzYsLDUUkulwfx3ixBAc4rWFFMDSyLLkv/xj38c9txzz7Lbfa7/7W9/C8stt1y8ftRRRwXsWa08+eSTsY25BvF966237O3Cc8wbMINQ2WyzzQK2wd0qTni7tWW8XI6AI+AIOAKNItB1hHfChAmR0Hz605+OtpG4gLrxxhsDL2EVFop95zvf0Z8dOVKOIk2hEt5qhcDW94UXXgif/OQnowurauGr3Yf4vfjii2GVVVaJ7rCqhR8M93/6059GW9qXXnopks2tt9468Kl9+PDh0c42rSOuwjA3KJqopGH1N2303e9+N+y8886B9FO59dZbAx4LEDww3HbbbWmQwt9f+MIXwu233x7v1UqUCxNqw0W05HiiWHnllWPqzRBebJ//+Mc/hhVXXDF87GMfa0NpPUlHwBFwBBwBR6ABBISwdY384Q9/QF2aiWuv7KOPPsrLJVrf7IQTToj3uC+EoeJ+HrBNJ7vssku2/vrr5/lTBv0TYp7xt+uuu/bJ/bnnnssOPvjgTBax5eGJ97nPfS7WR4hbnziiDYzpabr2+Nvf/jYjjiycq0hPvEL0SaeeC0JQsmnTpmVnnnlm9vOf/zx74oknKqKL1jP70Y9+lJ1xxhmZaEyz2bNnV9xv9w/6wt57753X+cQTT8xEs9rubAvTP+uss/JyHH300YVhii5ecMEFeTz6gJhGFAXr2DWZMGWHH354JsQ0L5cszstkIpmJR4r8mvZzmViUlk0mg9lJJ52UyaK8inhiyx7zENOT0rh+wxFwBBwBR8AR6AQCLLrqGuFlC7GE4KYiJgWRCOoLWFwkpUHa9lvz7O8omtaK/MX+OBJzjSO+e7PLL788E81hTgog7hBcK9TLkjuNz3HGjBkZ+dhrev6rX/3KJlPTuSyeyiA5moY9ivYy+9Of/pRRD3tdzy+55JKa8mhFoHPPPTeWAQIl9tKtSLLhNMQ8IseDSUKtcsstt+TxwNBO6GpNo1XhaFew1LZMj0V9oozw0l9tWttvv302derUjMmATff0009vVfE9HUfAEXAEHAFHoG4Euorw7rjjjlHDWFYL8WOav0TlU3JZsJZfR4Ml5gN53vZFzj3904xl0VpFWEiuCprJlFAUkVWrebP5lZ1///vf1yxqOqIl1bSOO+64qNWTz/UZxEavQ8j1HA17Sto6QT7l83osA2XhC8BAi20X2Wmv5uI8+OCDOZZl5LHmxJoIKL6kK7S62r5MYMQGPTv22GPzcuo9jkVlFtdsFWHp17LANC8dXz1sGrJZS37PTxwBR8ARcAQcgU4i0FWEV+wI+637hRdemL9AU/Iji4kycfkUtcB//vOf+02nkZu8yO3LW8/TtPj8b4ki4TADsDJ69OiKtCAKsujNBsmsJlHz4ij2pVFTbK9xXo/G+7LLLov5U04+X1sRDwIVZSNtNO+I+BquuHfFFVfYqG05VwIuO4C1Jf16EhWPDHn9wa4eLa24NsvjosEfKJk8eXJeDu1DqUmMeJDoEyYlvNQd0xxNg6P4La6o1k9+8pOK+4Spp59WJOY/HAFHwBFwBByBJhDoKsJbrR5HHnlkfIHyWT8lx0cccUT+csV8oNVSK+EtIgtoKa1cfPHFeVmVMGAja2WfffbpE8aSDkg0RBnilRIWm056jg2w5onGNhXxHZvfJxzpy4K7THzTVlznnmxQkEbPf4s7rwytYZF5Sh6oygnkiHywY+4GgXQrduJ2rq4i8Ulf41500UV5XPoLtq+N/okf4Dytaidod9OvC5QptdnG5lbLqkfb98gH8xy9p0fslK2Qrt7TIzbtLo6AI+AIOAKOQKcR6BnCiw2vvqyLPo2q1pIXK5rgVkuthBeCqC93PUIYrVjipGGwmbVSRHjFXZsNEs/r0TISQXES11h90uJCqlm2C7MwOdHyYqZBmxQJJh6Kw7XXXlsUpKZr4pou5nfKKafUFL7dgcaMGZPXP52gVMtbNdXgAj4IXyXAk2uy9XD8EnDNNddkmJZo3bkPSWRyghkPmmLu6bMgu7pVyzq/X7QYjbzTyaNONLStOaaEt8jOHJtwKyzMs2noudbfhvVzR8ARcAQcAUegnQh0nVsyeSkWihCn6CpKFrXFzSjSzSfwkyukILro+vznPx/mmmuuwnQavYhbsSWWWKJPdGmc/Npf//rXMHTo0Py3nuCqaf7559ef0QerfA7Of3MixKPCp+u+++4bRItWEaZen7IVkf/9Q+xOw5VXXhm++tWvhrXXXrsiCHXBlZT1b8uuYLIQKYZ79dVX49a4a6yxRqAdyoSdyghDndl4YciQIWVB+72OH1zxihA3adh00037DVt0E3dku+22W0vctuG2a/nllw+4REP+8pe/xN9F+abXhECGT33qU/Ey7vRwq4cIkQ/jxo0L3BdCGa/pPzanOOigg+JP3M+l/Urdo9Hn1G+wxi074tZt9913r7gtC8767ApYi1sy2pdNOaywWQw7yanIZKxwkxh2ISRfF0fAEXAEHAFHoGMItJNNtyptPqerd4J6Fgq1Kn/SqUXDi2ZOGq7PX1oOFqkVhbOutoo0vNhEtlNU42jLRr0bkQ8++KBUA1xrepgN2LI0cl6vJrasbPfdd19ellTbWRaH62hPsS2n7LinU20/1+nTRV8riKcaVOIUiS7mS7WzRWH12mmnnZbXQbHEDjeVahpeNLQa3x7BKBV7X89TzyRpHP/tCDgCjoAj4Ai0GoGeMGnALy8vy4FcuFQL4eXzvb7U7TFttCIbScK/9tpredCBILzpgrWBtp3FewS44DmAtq/3TzSOGXarrRDMSbRN63Gxhb2uxmNCoaKTnqIJBSQWUwPi4Su3SDBvwIVfPYJtu5ZFj6kpDelVI7z48NX49pgugCQte1/PMdtwcQQcAUfAEXAEOolA1xNeXRzDcSClFsKrWjd9sesxLbfVFmoYbDKtDAThTfPEFVm9AllrZqGazQ9Xa+AjJgD2csfPqZN1R4Yf21qETTy0fVOSh2a3bHGldfdVptWfPn16JiYPtRQjD8PGIVoePRaR5mqElwSVkGs6HGvV8KYeVvIC+okj4Ag4Ao6AI9AmBLqa8KLF4kVar4/ZdmBVC+EtC5O6HNN6WbLAgjArKfkkbBn5sfFqOb/77rvjIqlJkyZVLFjShVBarnQREmmjMWXBGru9pQKJU9MTvA+I7XIapK7fuvMeGxsMpNjFXpgnVBPMOcACHCGGsqVwnyiQaP6KxGqF8f9cJvUuWCxyEwaRT6UWwov2X/uJHunXVuj3es8eWzUhsnn5uSPgCDgCjoAj0B8CXUt4VQvKVq5Fwsu+Hf52i/LiWhmZTcPbndT0JY9vXiuyeKgPEUg9EbSL8NqtcSkfWkhEFhL1KZMsUrPFjudKxlLN4JQpUyLZZZtiNUFhl7ZmRe1fB1LDz7a52pZ4uehPmCToVtKQf2vG0F88e089ZZTZ79qw9Zxj/671sMe0nYtMblK75bQfkd7PfvaziuLIgro++dUyYahIxH84Ao6AI+AIOAItQKArCS8aJjRjEyZMKK0irpnwvWsFElyv1svG7+8c+1pLEvQ81dIVuRxjMZsVTAU0Pkc0qxBqK6NGjaoIQ7hWkD6Ii81bJxQHHHBAxXXIWiqvvPJK7g6L7WNVxKtDbC/9pC2eEWJarXAPpxMfysxOYAMhSmApQ9pOWh5IItvqKrb4Rm7E/Rb9Sc0Fyux3Nc9GjulmEZQX8wgrluBrfeijtq9DkvWeHsePH2+TiTbXek+PDzzwQEUY/+EIOAKOgCPgCHQCga4jvGjEeOHzgmWL3vQP5/aq/bSLZFgExOdZ4qKtbLVY4qUvb47PP/98RVaQgtS7gLVB5X76ObjIVIHP+DYfzuvZYKKiUOaHJW9ghT1q0QYB3OPTvIq44cq3HYbQWpkxY0a+OYQlQumGBjZOPedKwOgTSqrrid9MWKvtZLJAP2MxHNpr+iITBcqlbYV3hWa2XLaa9qJ+0UxdiGt3fNMy89zMnj07ThbTjUc0DMe77rqrInueTXufzTMsKU63KWaHQRdHwBFwBBwBR2AgEOgqwouJgl0cZF+m6XnqQQDH/Rqm1S9WCI7V3mk+HFnljncGuxAH28V0G17CoCE99dRT83ISHztaK9hsTpw4sSKM5gcJhWShAW9UrHYZLbKaH5BHWsddd9012k/b1f3EQaNrBe33s88+Gy+df/75sexp+9jw9Z6jtbdeEpjwvPDCC/UmU1d48vz2t79d2A7aHnqE8LL9cn/2trVmrgv1SLsV6RXla/uA1oEj/cv+LjrfZZddKpJEq2vDgRn9HNMOe32//fbr028qEvIfjoAj4Ag4Ao5AGxHoKsKbkkT7wkzP0S5ZsSviW60ZS/Mu+g05tAJhws2X1f7ZeNjAWg21xrU7bNnw9rwZMsmCIdnQoYKMkDYkG+0cO3ep3azNk53ZUsy1zPaommnq3mrBjMJOiMAQbfqZZ56ZTZs2rc8f/QAfzo3IL3/5yz4YgQf5o8mE+LNFNFpsq9VsJC8bh8WA5NNq+12bB+XFY0MRweUZxHRE257+u80222SHHnpohju2Ih+6bDGdmsrY+GyD7eIIOAKOgCPgCAwkAj2z05q8QKsKOzgttNBCQexPq4btZADRRsbdtJ577rmw5pprxl23llxyyU4WoU9e7Kb2zDPPBCHmcVe0RRZZpCKMeGMIYu4Qd10bPnx4TbuVPfjgg2GjjTaK6Yita9y1jV3XFl544Yq0m/nBrnWycCz8z//8TxD3XVWTko0nwl577VU1XDcEECIad/OjbcR+N4h5QVuLRduIzXncNW7llVcOI0aMCCussEIQU5bAzoYbbLBB4HqtIv55Yz8Xs6SYDunJBKHW6B7OEXAEHAFHwBFoGwKDivC2DSVPuCYExJQkyEK1IFrXINrguK2wmJpEElRTAnUGEm11JOViMxtJWhqdrY0333zzOAlK73XjbyZsut2zaKf7bAPcjWX2MjkCjoAj4Ag4Ar2AgBPeXmilHinjkCFDwksvvRTuuOOOSDS33HLLsOmmmwaxb+2RGgxsMSdPnhyYICBivxuGDh06sAXy3B0BR8ARcAQcgUGCgBPeQdKQ3VANJbxvvvlmEF/DYcyYMeGpp54Kyy23XDcUr+vKILZMAdMPTAvEC0b4+te/HmS3vlhO8ZkblllmmaidFlvbriu7F8gRcAQcAUfAEeglBJzw9lJrdXlZL7300vC1r30tyEKngDmBuO4KssCry0s9cMVDE44WHElJLXa8el1cvYX55psv/vZ/joAj4Ag4Ao6AI1A/Ak5468fMY/SDANpdFudhizrXXHP1E9JvOQKOgCPgCDgCjoAj0BkEnPB2BmfPxRFwBBwBR8ARcAQcAUdggBBwwjtAwHu2joAj4Ag4Ao6AI+AIOAKdQcAJb2dw9lwcAUfAEXAEHAFHwBFwBAYIASe8AwS8Z+sIOAKOgCPgCDgCjoAj0BkEnPB2BmfPxRFwBBwBR8ARcAQcAUdggBBwwjtAwHu2joAj4Ag4Ao6AI+AIOAKdQcAJb2dw9lwcAUfAEXAEHAFHwBFwBAYIASe8AwS8Z+sIOAKOgCPgCDgCjoAj0BkEnPB2BmfPxRFwBBwBR8ARcAQcAUdggBBwwjtAwHu2joAj4Ag4Ao6AI+AIOAKdQcAJb2dw9lwcgbYhcPvtt4ell146rLvuum3Lo96EH3vssfDaa6+FLbbYot6oHt4RcAQcAUfAEWg5Ak54Ww6pJ+gIdA6B008/PRx7lmKf2gAABHpJREFU7LHh2muvDTvuuGPnMq6S09VXXx122WWXMGnSpHDUUUdVCe23HQFHwBFwBByB9iLghLe9+HrqjkDbEDjjjDPCMcccEwnvxIkT25ZPowlPmDAhjB8/Ppx99tlhzJgxjSbj8RwBR8ARcAQcgaYRcMLbNISegCPQeQRuuOGGsMMOO4TNNtss3HHHHWHeeeftfCGq5PjRRx9FrfNNN90U+PviF79YJYbfdgQcAUfAEXAE2oOAE9724OqpOgJtQ2D27Nlh9dVXj+k/8cQTYYUVVmhbXs0m/Morr4Thw4eHd999NzzzzDPR1rjZND2+I+AIOAKOgCNQLwJOeOtFzMM7AgOMwCGHHBIuuuiiMHXq1LDPPvsMcGmqZz9lypRw4IEHhkMPPTRceOGF1SN4CEfAEXAEHAFHoMUIOOFtMaCenCPQTgQeeeSRsP7664fFFlssvPTSS2HBBRdsZ3YtSRvt7pAhQ8Lbb78dHn744Vj+liTsiTgCjoAj4Ag4AjUi0LWE96GHHgqTJ08OvODfeeedsNVWW4UvfOELYeTIkTVWzYM5AoMPgT322CNcddVV4eSTTw7jxo3rmQqyqG7s2LFh1KhR4fLLL++ZcntBHQFHwBFwBAYHAl1JeI8//vhw6qmnFiLMau8zzzwzzD333IX3/aIjMFgRePnll8Oyyy4bq/fiiy+GoUOH9kxVbdmx611qqaV6puxeUEfAEXAEHIHeR6DrCC+2idgo7r///uHLX/5yXPBy1113hW9+85vxkyiQ33fffWHjjTfuffS9Bo5AHQicf/754fDDD49fO2677bY6YnZH0PXWWy+wIQX1GD16dHcUykvhCDgCjoAjMEcg0FWE97333gsrr7xyOOecc8Jee+1V0QDXXXdd2GmnneI1Po/ibN+lOQQefPDBgOkI7qOGDRsWttxyyzDPPPP0SXTmzJnhnnvuiZOMrbfeus/9OeXCq6++GrFSLWun640bMtyRnXDCCeGUU06pK3s0rA888EB48sknw4orrhhWXXXVMGLEiNydGc/eLbfcEmbNmhU9KXzmM58Ja621Vl15VAt85JFHRp+8O++8c2BjChdHwBFwBBwBR6BjCGRdJLJbVCaan8IS/fOf/8wElPgntouFYfxibQgIucl23333HE/FVVxdZc8//3xFIrKTV0U4mYxU3J9TfshXhYiDLBbLXn/99cJqv//++9lBBx2Ubbvtttkbb7xRGKbRix9++GHeDr/4xS9qTuapp57K5GtIHlfbmuMqq6ySybbE2eOPP54JCe4TRrwqZDIZqjmvagGnT5+eY8jz7OIIOAKOgCPgCHQKgdCpjJrNR1Z45y9k0XI1m9wcG/+DDz7IZMvXiKUsBMxEy5uJxjLHdvvtt8+xsZgrUSLsnCjiVivHSLwjFEIwY8aMPMz1119fGKbRi48++miedjopKUsTYqzttueee2Z33nlnbO+99947v673OcqCskw0+RX9QRaYlSVf9/UXXnghz/d3v/td3fE9giPgCDgCjoAj0CgC/wsAAP//aQ09RQAAQABJREFU7J0HmDVFsfebLMFLzuEiomQUEAURQRSQLBIELkkQBAQu4SOHl5yDkjMiOeecc0ZyhpecMygo4Hz9a625dXp7zpk5YXfObtXz7M6cmZ4O/07/rq7uHiPz4vpATjrpJLfRRhu5tdZay5155pkDYvzMM8+4TTbZxP3tb39zF1xwgZtpppkGuLEHzh1zzDHuD3/4g5t33nnd3Xff7cYee2w33njjNUDzr3/9y40xxhjujTfecNNPP33DO3589dVXbqyxxhrwvNMH55xzjrvmmmsc4XdLxhxzTLfccsu51VZbrSMvZ5xxRvfaa6+57373u46ylpItt9zSHXnkkeHVq6++6maYYYaUs/zZP//5T3fbbbcF/xZYYAH3ox/9KOCeO1A31113nVt66aXdN7/5TffJJ5+oN+nbp556ys0555zh5amnnurWX3/93CHxn3322fPf3CyxxBLuxhtvdAcddJDbYYcd8nebbrppKDP5gw5uvv7661De8OLyyy93yy+/fAe+2aeGgCFgCBgChkAFBCC8dZdbb70VUp550pJ9+eWXyeh6shHc4G7rrbdOuhnpD/1gIPOEKeAEpognHjluYDfVVFM1wOQJT8N73HhC2uBGftxyyy3BP/ld9br66qsPCIvwOv3D307kpZdeyuOw+eabF3rlyXBw54luoRt58cgjj2SekAa811lnnfCdJ5ryesD19NNPL+03Hy+44ILB/W677TbAr4ceeii807j6wU9wt8giizS822effQZ8Lw/ef//9zJPp7M0335RHLa9S/o466qiWbs2BIWAIGAKGgCHQLQRctzzqhT9eo5ZBAnTHPGrUqMxrGAcEd9ppp+Xujj322AHv7UGWXXvttQEjiJaQ1pVXXjnHDZy32GKLAVCdd955uZtZZpllwHse3HvvvbkbiFA7Qpw+/fTTzGswu/aHf53KGWeckaft/PPPT3rnteG5m4033jjpRh4+/PDDYeABlu+++254TLkGf96l5NBDDw3v559//tTrhmfPP/98HpdUXhx//PH5e8LUfh5yyCH5O+JHuorEa86D2+23377IyYDn+EmYu++++4B39sAQMAQMAUPAEOgVArUlvFdccUWujaSD1H977bXXADzQ/F511VUZWkYhcwMcjfAHL7/8crbrrrtmd955Z0BCkzTBF+KaEj8lHvLAm5SkXmePPvpoeL/CCisk3/fzQwis4FNEACHC4gaCXCQffvhhhgYYt08//XTu7Oabbw7PirS8MvDzpgf5N0U3H330USCUaO9TQh5KXLnuuOOOubN//OMf2WWXXZZdffXVyYFl7tDfoDlHY+tNIfTjpvcLLbRQCHvbbbdt6s5eGgKGgCFgCBgC3USgtoRXEvnFF18EIstUu+6kX3/9dXFi1zYRYFpZY8qUfJHItDtT2EXy+eefF73q6+dCUJvhg6mDYIkJRJEIed5ss80anNx///3h+6IBg7dPb/q+wbMmPxgMxnUJctuOeJvcDIJcRbwdckhHEbGv4pe5NQQMAUPAEDAEyiJQe8IrCcFOUNsXXnjhhfLKrm0ioPGErB144IFJn/7+97/n2nY0lCNJXnnllZzIlrHfLTL5ADNMdIpIsdhSowFNyVZbbRW+LaPhTX0vz9AqSxzkikZ4sETK3B577DFYQVo4hoAhYAgYAoZANgYY+I6vL0SvLt9///2dn4rti3jXMZKe5LhJJ520IWqPP/64m2uuuRqe8YPdE9Zcc03np7DdueeeO+A9uzawGwJ/nQg7bLATQreF3RImnHDCtryVtPOxN1twq6666gB/vJlDvptFs10N9txzT+eJXthtgd0otHi7c+e1vm7xxRd33rxBvwr3u+yyi9tvv/2ct7d1Dz744ID3ZR+ccsopbsMNN8yde4IdduvIH5S8YYeJcccdt6Tr/3M222yzuWeffdYdffTRIb3/98buDAFDwBAwBAyBHiLQb6RfFr0ccMAB/Rb1WsUXO15frBr+inbAWHTRRYO7Sy+9tCENmDCwyA07Tv6OOOKIhvdVf8giqDhenf4uMhMoEz8xJSAOnqglP9H2uyzwQzDFOfzww/NdRbSWPLWo0pPQgHFs6iAB+u3OwvsyO0DwzXPPPZfttNNOYdGnXrgmpimCqbbflbC4YutNvsc7MGCvK3a4vK9qWiTmFIKTDtPuDQFDwBAwBAyBXiHQVxpe30k70RDdfvvt7ic/+QmPTNpAwNttumWXXTb/0m9j5e677778t9xcdNFFbpVVVnF+oOHY21Vr9dZdd92wJ6zfMivkhV+4FvbunXbaaeXzSlc0m37RYdf34V1sscXCPrOVIvMfx9/+9rfdiy++GH75AUG+j6z2i32N2d8Y8SYQjj17b7jhBrfkkks6T3zDPsdoh9GQI35xpfvWt74V7uUfewUTjh80OD+IkMf5lX1rV1xxxfC71T7I5CN7+or4bfrcYYcdFvZPZl/ld955R14l98N98sknc03/Bx98kM8EkL/UOcqEH+yE+Hgzj3zv4dzTghu9Dy/5TL6YGAKGgCFgCBgCg4JAr5h0L/zF1tCDErSJfkp1QBAsouHPpDUCnhQFLMGTv9RetWjvZN9Uds3Qcs8994R35AmaYXH32GOPaWd9fe8Pj8gxivcnloS9/fbbedq1G3Yh0Pa2aFIFa9GIy1Wecy1aQObNTfLv9e4OEg993WabbXK3+Cka7r/85S8Nz3mHjbIWtvxbaaWVgrsNNthAv8qWWWaZ3M5bFjymyk3DR+qHHzDl4aOBNjEEDAFDwBAwBAYLgVotWvvss88ydgFge7HUXruyNZPXMA3Ah1XuTPdCIp544okB7+1BIwLsTyskFeIDWQN/ERaneXvSQFCY1o+Fwz1kOhySJn4UmUXE3/fD77PPPjsnaClyiJmC1+LmbrhH2LkAbE888cQ8mUIiU/scU+bxn7+33nor/0bfUB8kv7xdsX414F7vGIGfLIjD/ER2m5CwuHIIhQhuZPs53OrFbF4rHOL33nvvBedysIVOo/hTdD3rrLOCH6TDtg4sQsmeGwKGgCFgCPQCgVoRXr+gJu/4ORwBrSIECk0jm9vTQbMBf0r0iWBFdpCp70bys5jQQWKww+V0LSFHELUUiX3hhRcy2bGBE/DIm5133nlYwanLFOmD4EPU+OOkNA5sYKsysb/lNzMP7EAAqZNDJQBFbM/BPBYhw9jXNhNx12pLLzlghDiTjxwmIflJHMWOlvf8Js/33Xff/DnPOLxCC4RbDsUg7XzLnzd50M6a3jNI4hvKi4khYAgYAoaAITCYCNSK8LJtk2ixpEOVK6SCKeYiueSSS/JO2BbEFKE08DnkBU1ujDukCO1dKxMRFjVJHrWaah8Yer2fCEkFG9lOC1yEMEJA0chC/GV/WcFCL/BD4yvPtUaV1KNVl3eYmTSTE044IbhlYNJK0BpLPMV/CDWaffYJXm+99fJw5T2DTBarobluJkJc11577WbOBrwTrTDHJJsYAoaAIWAIGAKDiUDtFq2xGOaBBx5wnkiExTJeg+a8dqrUllfelMGNP/74YYGV78RNKiLA9losuppuuuncTDPNVOprr3F3/+///T/nCaG74447GECFhVrkQz+Ln1UI5Y40sIiMxWTePtmNHj06LNybY4453H//93/nSSTd/pS6sCDspz/9qZtkkknyd15Dni/28wMEN8000+TvjjvuOMdWZmW2B9NbyRGXueeeO/cndcPWYcTXE3I366yzuimmmKLBGYvI/CAy1LXvfOc7bvLJJ294n/pB/Zx66qmdJ85hYd7Pf/5z5wmym2CCCVLO82d6IRzfTjTRRPk7uzEEDAFDwBAwBHqOwGCyawtr+CEgWlA0ishBBx2UoQHsd2GWwFe+8HfBBRd0nBzRoGs7aWxmRQvrd3UoFQYLyYjXUGGM/TDhYyKBmQPabdKgzTdSCcF2me9S9uAp9/bMEDAEDAFDwBDoJgK1MmnoZsLMr94jgJ2nkMJPPvkk2HNC7LAh7XfBDlzS5jXfHSeH3QzwT+9O4A+iCM+qkFcWZOIPOPuDOjqOV1UPZFHbXnvtFT7de++9s1YmFpBiwdJvvVY1SHNvCBgChoAhYAh0jIAR3o4hHLkesHANIgPhgXyhveMwguGwAp/FaKSNazeEnUfw7/jjjw/4oBHnN0Q4tSiwWZiygJPDKAZbRMPMzhzYHEO8r7/++qbRYKEpad1tt92aurOXhoAhYAgYAoZArxConQ2v7xhN+ggBjqnluFpPfNy8887rLr74YjfllFP2UQoGRlUfFVzlYIWBPjU+8VpRN2rUKOdNAIKtrz8t0Pk9c90444zT6LDFL69Nd9jccoCEX3QYcG/xSdde//Wvfw0HRmCjjT2v3wHCbbzxxoX+Y2tMuSDNfkYglJNCx/bCEDAEDAFDwBDoEQJGeHsE7EjylgVefieCYbNY8MILL3SrrrpqyEJOFVt55ZW7lp0s2GIhGScGjjfeeG376w/+cAsvvLBjUaff+cFNOOGEbftV9UMWw/kdOULY3/jGNwo/J60/+MEPnD+SOSxEXWCBBQrd2gtDwBAwBAwBQ6CXCBjh7SW65ndfInDdddc5v81Y0Eo+88wzDTsu1ClBHOnM8c7ezMCdfPLJdYpaiAtxI45+72G3xhpr1C5+FiFDwBAwBAyBkYOAEd6Rk9eW0goI+KN8g8a61XZbFbzsidMzzjjD+f11Xbc10Z1G1h9j7Pxev87v6uB+85vfdOqdfW8IGAKGgCFgCHSEgBHejuCzjw2BoUeAPW6xoZ5xxhmHPjL/iQH2un7bMjf77LPXJk4WEUPAEDAEDIGRi4AR3pGb95ZyQ8AQMAQMAUPAEDAERgQCRnhHRDZbIg0BQ8AQMAQMAUPAEBi5CBjhHbl5byk3BAwBQ8AQMAQMAUNgRCBghHdEZLMl0hAwBAwBQ8AQMAQMgZGLgBHekZv3lnJDwBAwBAwBQ8AQMARGBAJGeEdENlsiDQFDwBAwBAwBQ8AQGLkIGOEduXlvKa8JAl9++aU788wz3Yorrugmm2yyhlhxlO+7777rllpqqYbn9sMQePTRR91bb701oGxwEt5ZZ53lVlppJTfppJMaUIZA7RDgZM777rvPsc/5fPPN58Ycc8yGOHJs+t133+2WXHLJ4KbhZU1+DIc01ATKwYtGZmIIGAJDioBv2DNf47OTTjppQDyWW265zO+xO+C5PTAE/GmAybLhiUQoT8cee6yBZAjUDgF/KE0on7R5/NG+nXfeeXk8pfzybpFFFsmf1+lmOKShTngOVlzcYAVk4RgChkAagZtvvjk0/Mccc8wAB17DEd4NeGEPRjwCiy66aJLw3nnnnaHM/PGPfxzxGBkA9UIAYguRXWWVVbLnnnsu3Avp/de//hUiu/766+fPeffSSy/VKhHDIQ21AnQQI2OEdxDBtqAMgRQCRnhTqNizVggY4W2FUG/en3322dlUU02VffLJJ70JYJj6+re//S3gNsMMM2R///vfs2uvvTYntmh5v/7665ByBmoQXfl75JFHaoNIN9PwwQcfZFdccUVt0lYmIt/97nezffbZJ/NmeGWc186NEd7aZYlFaKQhYIR3pOV4d9JrhLc7OJb1BbKzzjrrBCKWmo0p689IdSczD0cccUSAQLCE2G6wwQYNsFxwwQU54YUYIuAv3zY47uDHvvvuW/j1qaeemq288srZQw89lLvpNA25R/5GNNkvvviiflzr+8svvzzkC6Ym3s661nFNRc4IbwoVe2YIDCICRngHEexhFJQR3sHLzE8//TTYk0LOLrnkksELeBiF9NVXX2VPP/100A5CYkWDy/XWW28dkNJZZpklaITlxXrrrZettdZa8rPjK4S2aH2Ejh/rKEQ6TYP4w/W2227LTj75ZP2oL+4ZAIDbvPPOm8lgpC8i7iNphLdfcsriOWwRMMI7bLO2pwkzwttTeHPPmX5fYoklAkE75ZRT8ud20z4Cp59+ek54MQ8RcwbxcfTo0eH9dtttl0EyN9988/B7/vnnz4466qiMhb4i119/fbbjjjtm22yzTXbDDTfI43D9/PPPs8suuyzbeuutw3cyWNHh49/555/f8B32xAsttFAIc4cddmh4Jz+0H63SIN8Ml6touhdccMEMjPtF+obwMpKYc845MxbxmBgCwwkBI7zDKTcHLy1GeAcH60022SQQH/oeWVg1OCEP31BkOh/t7hZbbDEgobvuumvA/LHHHguESnZFYCr90ksvzZ588snwzcYbbxzMDlhIttpqq4VvDjnkkNw/NMIQ4VdeeSXbaaedglaSlxBjSCqaSvy7/fbb82/kBhMKNNJFUiUN2g+/bWB2zz33ZO+9955+3Hf3e+65Z8A7lX91TUzfEN7VV189gIsa3cQQGE4IGOEdTrk5eGkxwtt7rK+66qrQ70DMmpGf3sdkeIVA2RWThhNPPLEhca+//nogonpLsg8//DC41yYNF154YXAHMUXQxIufPMPGlN/YAyN+39xs8cUXD/f8Q1tcZNKQO2pyUzUNePXUU09lmGqQNuKWMuVoEmStXoEx+JEO8qIfpC8Ir546MMLbD8XK4lgFASO8VdAyt4KAEV5BojfXL774ImgB6dBZYGXSPQS23HLLnJxiSyuC+QLmIxCpF154QR5nKcIL+SVvMD2QP77j7+GHH87QpMpvwsDv1157LfezU8JbNQ2YbcBf7rjjjjB4Iu6jRo3K49OPNzvvvHPIA7TlMvCoczpqT3hZwUjBkD8jvL0pTmwzgl0OU0dMCbEaFrsmbKlMeotANwjvRx99lJ1zzjnZQQcdFPLw5ZdfTkaaaTRWmJPH/bbgIJmgmjy89957s6OPPjrUG2wK6VxTctNNN2V77713hptOxQhvpwg2//60007L+x1/cl1zx/95i8kD7Sh2oRtuuGHG4SDYgF588cUNZCvlGYSIqXbI9QMPPBDMJ6jT7BSwzDLL9N0WVqk0yjPqge7TscklzaSV5/F2XSnCi6YU980E+10JB3vT559/PnfeKeGtmga0uyussEIIf//99w/xYgahn4WFd4LvkUceWfuk1JrwQsJQ/TMNQeMBsEZ4u1+mmI5ghCYFN75iG9UPo7fuIzM4PnZKeGl40WTE+XbggQc2JODtt99uyGf2w6QjMWkfAbSAYjuo8We/yldffbXBY/JDu+n0YAgjvA3wdv0Ha0Ykv954442W/uOGFf18Q32kXNB3iR9csTn9+OOPG/xi1ftuu+2WUR/FLQMibE/lt1yZth8ucvXVVwezAkkbVzDApjaWFOElf1KLxfhW5xf7+Eo+kC9SLzslvIRTJQ24R+A1pJO49+t+tv9Oyb+3ipP864f01Jrw7rfffqHh4KQVWThghFeKWneukF0psGgRsHd64oknwik4Bx98cP5uq6226k6A5ssABDohvNiASf6huT3ggAPy3zxncYQI02fiVq5osUzaQ4Ap05VWWilgyjQsR6LqgSP1SYRtrQRzueK2EzHC2wl6zb9FEyj5VKbPka2a+Aatrh5IMo2ONlL7R3lAsAvWZUbcaOIsz7j2496nzZH+NwEEBxaWFYkQXq3RlXU98RHa7K3L7CR5yIwXguadAxPAUI5wh/DyuxsLESGurdIgaYMkEy7xQfpdmUQ7RHr407tnSHrrdK0t4ZXztLHfRVoRXgobozimLYqmc+sEfB3i8uabb+YFlaMe42lYsZGiIJdp9DtJ0x577BGm8pjO69Yfm5nrTcM7iV8vv22X8NJQSmfJ6U9IrEU89NBD86jL1j7SOHHVK5pzh3ZTCgFMGKRukBcsitHYci+dKQtx4nf8jutcqYD/48gIbxW0qrnV5gy0jc0EskO/Q36iQYw1uHxLnyR1FXdrrLFGg5coGeLyAWnDbxQPzBiw9dZIFvDjD2J83HHHBfMPwQxtOHnGzglibw3hBTdmYZBnn302YCxmBLjje0xQMB159913BwVemRFC08zxytgf97Nsv/32edkVEl/X9NSS8H722WdhRMwITqQV4dUG5Oy5Z9IaAciQNBhnnHHGgA/kHVexPRrg6D8PbrnlloxTWNoRvThEh9mN+3j03078ev1Nu4T38MMPD/nHQE9EtBaCnd7YnKk93enihs7UpDoCEFwxI5GV1nIKkWAP1lo23XTTvL6JGyHE2h33ZepTp4SXo3GxGT3ssMPCH3amKbIWx20k/N5ss83yvMIMoZnQxkh+YppQJMxYijuujz/+eO6UcqDfUVZMGhHYfffdc4yEtDKrJfUQ/CCTsjZBtPSYD0DKmHFBsSJ1TtdXng+WoO1ngITNNhrr448/frCC7kk4WsmiZ7V6EliHntaS8EJu6Szef//9PHmtCK8ekfcDyckTNoQ3MtKkoaBRAEO9Mlb2GYRENTvPnAU70ljrPKuSNDRddMDd/OsXe7d2Ca9sjC5bwqDNlnyQK5pFLSxaYz9ReY9mw6Q6Atdee23AEDtC6UBlwY1gm9qfkv1C5T0dX0rK1qdOCC+EgTaWPzRdelqyU9viVJr67ZnOy1aaVdliinxl0FAkmDZI3nPlUAUt5IW8jxdtaXcj+Z5ZSUyJtKAFR3tL36EFQolbZl7o17jGgqnEYA/yUEKQz/SrKOeIZz8LhF3Kba9ngjvFaQw88JGtjfhRl1txxRWdN9p3v/jFL/J4+RGv89MYzgPqPPnKn8uNJ0zhmwkmmMD99Kc/dWOMMYa8smsBAn/4wx+cHyEPeOsb3oC9J8TOaw/dJJNMMsCNfuA3Bw/54rXAzq+K1a/69t6b1Dg/zciAsOM0UBYptwsssEDSL6/Ncz/72c9CXlDOtSy11FKhXKfi8ec//9l5Quv8KnA39thjO994Ok9W8s+9HaDzHWf+W278zifu29/+dvjpVw672WefXV458nLWWWd1448/fv6sVzd+cOR8Z+M88WsrCMqaJ/BtfRt/NOaYY4Yy7wd+8avkbz+t6vz+oc5rNNyPf/xj5ztiN9100zW49cTV/fCHP2x4xo/f/va3jrzzJkPuzDPPHPC+bH2infPbLznf0Tf4cddddzlPwkJZ+N///d+Gd/y4//77Q7y8vb7z0/X5e29b6JZddtnw29s+Ok/I8ncj7eYnP/mJ81PdLbGgXk488cTO2+QGt0V5LvhRHsAf8ba+7pprrpFXbuqpp3beRjf8jvu/3JHdDAsE/K46oW+ZdNJJ+z495557rvMmOiEdcAe/OLq2aaoV4X3rrbect7lxv/vd75yfZmsArRXhbXBsP0ohcN1114VGt5ljOk6IxWSTTdbMmfNmCe4b3/hGUzf99HKuueZy/jSfrkXZawIDgU552C7h1X55LYebfPLJ846Xd16b6Bi0xPLMM88Ekgu587aFDrKHQEC///3vO+IjhDj+tlu/vVbU/epXv3KrrrqqW3fddSt7S1sx7bTTVv6u2QcMGOJ2p5l7/c7b8zpvI50/oh0D55SQXr82wfm9QZ2fRUk5KVWf2iW8kDkUA9TruM7usssuzk+9hzh5m0Y3xRRTJOM33B9+73vfc48++mhIpt9uqSFvddq9HaabaaaZ8ketiOpvfvObUC/5gIGe1zzm33ab8BL/X/7yl7n/dmMIVEEAElumPbzyyivd8ssvn3udUs7kL4f4pjaEF5DQEPp9X53fj2+AhskIb29Kire/cX7Krqnnw0lz2zSh6iXl0C8oUE86u0VrWqTJ7Abh9baYzpsqNETS25kGYtPw0P+gEdt2222dtzd0e+21V3jtp9UcGmE/Ve+87VvPCS+kCnLlzWjaIrxE2ptwdE3DO9ZYY7n55puv5cAugJX4pzWCvKZeebvBAS79ufNBk4dG0E+ntpw9GeCBetAO4fXTujnJhaBD5rQwe8agB7noooucn9rXr0fM/c9//nPnt/sL6fULcUJZTSUeUgk5Fmk2iMENszFoz5F4UNRtwsuAC629iSHQLgJ+d6aWnzJTJIqVumt4a2PDy+IJj2yGTVxqlb7YN2GgLu/jIwk9aTYpiQB2h7JC3JO7jAVs2PQKzuSF/pOFALH32E91wwaJFbLstNHNP1bAEr+6S7s2vDpd2Bnq/NILPrU78grbUdxyTj2CrTM7Wsj37IxCnMgLLdi7YVvIVj/UPRaFpAT/sGX0xDrDZpXFOZ4Y5E71Ih825Sesum9nk0c+cSNbJgl+XPWCJP0Ju2nwvih/qtSndmx4WSAq8WRhViysHJf3e+65Z/x6xPxmFwXBIba11SDQbok7rhwq0kxY1Cbu9TG5fKPbXvbh7bawaE7vT4v/J5xwQt4OdDu8wfKPPAB33Q9hr+sH8xkH8nRLHnzwwXCoj/aP3TeGeqeboc5XypCUafhbnaU2hFevmBTwWl032mijOmNb27hBOIT0cCJQLBAhViZr/KnsWrymKmNRDgMQ/jiZrV3BL73SVofb6T07GdRdIHykkxXHscgCs/h5/Ju9PzVW7MebEglLLy7wWqmGDeBZTMFCHL14CeJKHrG4UcoO4bHnpRa2DMINq5BJj9ckh3ixTy3Cwjq9yAe/+M0gtl+FbY009twXDbRkcVi8uX479akdwgvGxJdTkVJ7ukr5IA0sYh2pwmIiyVMOPWomuu2iLDcTPbDktC0tvSS8LOgiPd6MRQcZ6mpchxsc9MEP2ddWL5iGiJLe22+/vWsp2GmnnQZsz8kJfIQjyqOuBVbSozrkq977nX6ozlIbkwYWgcgUki9AAwSbN977xsV5chXeYxeZWhQy4GN7kCMg0+fyIJ5Wk+dcseNkgRPiNbxOG9hjh8hiGfKF6Vym9rz2oG27ylNOOcX5A0a6skgsRNj/G2ecccLCIMwJ6iySJywgrLJoTaeJBXFM8Yv4VfhhQZX85upXLLuFF144uPNHSDtPMvPXLF6jPiGxSYPvNMJCUL8aN9jXswiPqVtPBIJ7T97CQlN+iH2qX5Hupp9++vDeb/njMLnwZ8iH3/z7r//6r2Bv3IlJQ+7ZEN/oxV5ExZN9x6LHWDARYJGYJ/kOvMcdd9zcSTv1qR2ThjzAghu9kLWT+lzgfd88lrwiwq1MusTcThKHXS55nBJt+sKiOBY8inTbpEH85criUBYf67rK8xlnnNH9/ve/d7vuuis/+1Kk/rGAlXUMCGsTZp55Zue3DAxtVzcShukfYelF8yw6XXvttZ0f4IaFw90Ip4ofdchXcMGEC8FEDlO52kqd2biOm+yzqzVT+j33TGnoaY34vf3OMjnD2xfIMDIt2jeP0bK4YXsyLZzehVaD6SI0WaLhkCly7dbuWyMgWrVONLxyMpPkWSovdt5555CnsgekjplfoJfntzZVoD6hsUX7pLWWPJd816N6P4AK/ugpfTSJhKlFvh0OWkQ5JEewT5krsD2cpDnecqrd+tSuhlfng773JDwvA1q7r92MlHu275P8JN9k67lU+v1OGblbvinafF/jG5sz4K/W8LLtXTeFtpq4xTML1O1WZhjdjEcv/GKLPdJGnolwOivPZI9sed7JFfOrmH+wfz3h6LaxkzCqfluHfJXTJruNd1UsyrivjUlDq8i2Irx+q5fQMdM4cWqNSRoB7DMpmPKXIkbYeclUOu5iN0z3YTOKyHRSTIjSodvTFALdILx6Wok8Y8pai9dYhTynfkBuYykivOS9lBUae/0nzzUhkNP5KA+yOTxhQQq08A3fDwfCyzGxkh7SRNo5PEcEG1+/vV9IL/uJx9Jufeom4WVqFPs74s/AyCTLMMORMp6qMxoj7J3FLVd9pDfuIEQMhMRNvD82hFrece12vagDMdJ4dfPeCO/QDWT0mgDaPX7XWYYN4fXTSnmDkVqMUedMGMy4sfhMtHA0rNjhckQqNpqcMc7xs9J5c01pGtjEm04cYaEb/lgn2X4udoPwkh/aNpY84XQhjuDUC3AIKyVFhFc2FYfIosks+pOZFWzmCFv+/HRwcnGblLFud+yptA3GM1mMJulGo41dO9o+tGg8RxOS0gS1W5+6RXiJkxy0YPX4/0qL1EvyjraxmVD+dR9E+eaAATSNHCQi7SRtL3bsWmiTWXgkZYcrg49WJFv70ereCG8rhFq/Nw3vQIy8OV5ebmOb9IGuh/5J3xHeeGpUILzkkkty4CFvJsUIMMXsbbYaptB0Y0sHzWpiTrVpJryX7+IV/c2+s3eNCEjH2olJAz5CXDgiUy8qk/yBeNL5FkkR4WW6Ez+8nVrRpwOes+OCEFoJX2t7+UDeDxfCS5o4jRBNrqRN0o7mg10tZFCA25RUrU/dILzECZMl4sriRZNGBMAYbKg/ZQRzlXgBKd9TBlAuMBugBfIr5aToys4ZnYoR3k4RzDIjvAMxBBPKLW2eXjQ40GU9nvQN4S0DF3aDaEtMyiFAZ+dPRQlbRrFyG9JDoW1mr6Z9ZjsWCrusTOa7fjnOV6djqO+7RXh1OsgHzBH8ntal8qSI8MqWMzRoKe0kYaKhiqdo2SqI2QLdiRMXESGFw4nwStq4ggfEn9XiZaVqfeqU8FJfJY9uvPHGhmiiNMAMZqRLUb1ohQt1ha34mCGL60arb3vxvl3Cy8wRZmvarl/HD/PBXmyhpsNodV9nkwbqGANhv+A+2Q6zOws7VLVbRtrN11aYlnmvj8rGVLIfZFgR3n4AfDjFUTSJohlif1bsEU2qIdALwlstBlkY7Ag5ZXGNCJ2ZPE9tYcd2PCx8lIVYCy20UMMWPVrbO2rUKPE214LaXto5JLlmvmx96pTwsmUcA494ip0YodGUePxfDEfmHThQB/wOG30LQFViRL2WdTNS/zHH0OtjaA/k3e677z5k2NSV8FKvxJxJcMLMTAQ7fzT/vKMetppRle/0tWq+6m87vRdTOcyh+kWM8PZLTtUsnoz4pRKz4AUtH5U2ZfNbs6jXLjp1ILyYuUh+yl6d5Kk/rS0np7w///zzc02v37Iuw7YXkiuzAgyC/PZjDRiLfaK2D5UFUpAuBE3HSJ4daKc+dUJ4MWkiP9mTG+2u/FF/ZeV5Px8G0lAAu/BD9iXv1wFaVWIkmn/qrj9JK28b9O4SQtYoRxC7oZI6El4OPQIXBgmY+7HYl9/8MWuAMLslz7hS76pK1Xyt6n+RezEhpe2n/+8XMcLbLzlVs3hiOkIlxaYaUsTqczpgIT41i26to1MHwgtAspiRgyfY5QFSismLmDVI48zARrT7dHraNpjnlAnIsogsiNImDdKh4hen/GEWw7TfSJV26lO7hFfssiU/i679YJM3WOUFEwXZgYTDBvpNqhAjsSsWciuDI8oJi+9EmAXQZUeeD/a1HcJLP4XJF2Qek4Iya1Cq2PBKm4dGXLAXrOTUSXbykGdcUzNoYIm5IcqFlIjfVbebw9SKWTkUGOxwVeXgDJn1g8Trdj4Vv7o9M8Jbtxzpo/jIqUGQFghL6uSmPkrOkEW1LoTXH/6Ra3PR2Oipbn9QRT79Jo00U1qa7AIgpBkSzB8dJr8pHzSsWmjshTTz3qbPs/x457L1qR3CizZG8q/ZlfwzaUQA0is7MTBo6KfBfRViJHu1U/9JM2VBygrtgAjvZNESg2QRFCCdnLwp/uhrs9PgqhLe0aNHB+UMA3oG3rLIkAWnzWxpyxJef8BPwItdWRDaVcGPdlUvXoV0C75iFqbTLZriotP+quQr/jKThl+ESf8tgzja4jKn0ol5D1vs9ZNmVzA1witI2LUtBDBct4WCbUGXf1QXwkuE/Mk9hdoOOng0ISzCiFebS2Lo7BDs09Bu8EcHkBI6TLQdXE3+jUCV+tQO4TWcO0cAbRraLb3Xcue+9taHKsSIdEmbjomLkDWuMcmRhUv6cKL11lsvEKlupQiSxSCwSKoQXjSZEN3DDjuswTu2kCN9DNAhhSkpS3j5FlItWDHtLximDvmQQRTkNhbaRsxKUu9wWyVfcc8am3h7RNpoiZ8+MAj3saDYasf0IvZnqH4b4R0q5C1cQ+A/CNSJ8Fqm9A8CRnj7J6+GOqZViZHEF22gkCGIbCxi741ZAGRSTJXQ+DJFr+3AmQrnwKJtttkm80eNN3jFKWnsdYxNPxpRpvERfVAS/sUzRbipQnjlIB1tmoEfDMoh1aSVvWVTUoXwyvcQX8GPa0xc0faiXWUA1Y5UyVcUFqQR7W48WJN9ov3RwO1Eo2++McLbN1llER2uCLAxPY0hDX0sjMabaTdi9/Z75CDAcdJ0XrFgK0156tcFVnF67HfnCFQhRjo0mW6nPGHvGgvaS9xAGNGMYvKAWzSBaMJlgRaL/rBrZbs7IVdsw4fwLX6cc8452bPPPptRrmUxK8SYd7SB+Jeadq9CeMVEgDBiEZtktL0paYfwigkAmKA9jkU06CwejeWVV14JZmXaBCJ2UyVfIbyYVIClzMSJf4cffnjIt3XWWUceDcurEd5hma2WqH5CgIYI7UdqlwIWF2BCYGIIxAjQIabKBh0k5anux3zG6bHfvUOgCjGSWPzjH/8IJAiyxp/erhA3cqw8h92IsG8vbrENFbnwwgsbSBbtnPgJ8WJXGH7LtlxMq6MpFkFb3GzQX4Xw4icLreJFWqSVMIgHU/wpaYfw6iOnNSb4j7kCAwPC1Iu/6A8g/Cz+JU5LLrlkKjrhWdV8Be+Y7OIRAwDiwcmaw1mM8A7n3LW0GQKGgCFgCIx4BKoSIwEMrSREiD89Hf/GG28EMgYp0wOrFOGF6PE92mD5g8jx9/DDD2cPPvhgeE9YosHFNlik24RX/NVXdqUhjsyoQThT0g7hhewLfrFJiJDhiy++uCE4frOQjoErGle+TylD+KjdfNUBclw8YaD91cRbuxku90Z4h0tOWjoMAUPAEDAEDIEEAu0SI2w6hbBBvuT0OBZ+QVjjkwRThBcb1WaHE0AwOcpewoEYahvTXhNe2WYLkglORdIO4YVAghNp44pZBhpsSS9+xsICQGyfwYBvGFQUSbv5Kv5xbDUmI5DdIs22uB0OVyO8wyEXLQ2GgCFgCBgChkABAu0SI8jowQcfHOxPhZByZcEkC8BiSRFeyDGkKmWLiqZYhMMMIF74r0leLwkvB64QHovpWu0W0w7hJW1oxiHTQnwFxwMPPLBpmGL/W2RTjN/t5ivfMlhBq85JmSwaHAlihHck5LKl0RAwBAwBQ2DEItAJMRLQsP2E5DY7kEQIr9bosmcrJC9emMXeuuy6wIljspsDYcjiMTkMAsLL90WmBlVteCU9LMKDhLLDhAh2yUUH4LRLeMVv4o/dPelqRa75BtJPutnejIV9qW/azVcWEzIIQYMv9sxsRRfnkcR9uFyN8A6XnLR0GAKGgCFgCBgCCQTaJUYJr1o+gkjxB7k77rjjwhZkotVkSzIILtP2siMAv/XBCnJQgxBrsWNlqzJ2cnj33Xcb4tAO4WVxFtrNeCHeKqusErY5awjgPz86JbwpP4ueYToCZptttllwwkmmcuS7/qadfOXQHzTpsX9s+4ameziLEd7hnLuWNkPAEDAEDIERj0A7xKhd0HbfffdA1iBskFHkmGOOaZjSZ2syWSAF4UXTiiYXUsmuBGxfJoIGVgiz3hFC3lclvPvss0/wjzAhvfxBAMWcougY38EkvGeffXaII9uWcfQvcUstXKuar7INGnjqtMupl+x7PJzFCO9wzl1LmyFgCBgChsCIR6AqMeoUMLYYYxpeC1Py7LMrJ5DJO7YEw76X0xuLyCamEpwCmZIqhFf2PBcCnbrKFH8c1mASXkw7IKQQUWygU9sPEr8q+Uq6UunVz9ixYTjLGCTOJ9jEEDAEDAFDwBAwBIYhAp4sukkmmcT5XQLciiuumKdwxhlndL///e/drrvumj/rtxtvd+uWXXZZ5xdeucknnzxE3y/IcjPPPLO79dZb3U9/+tOuJMlP9zvC8uQz9+/MM890a6+9tvNk3o099tj5827cQM38LgqOPBpjjDGSXg7nfE0muMOHRng7BNA+NwQMAUPAEDAE6oyA17a68cYbz/ljc91iiy2WR3WBBRZw/hS0QHrzh31247W2zu/v6/w2Xm7CCScMsYf8TjnllM7bwrp55pmnKyk69NBD3RVXXOFuvvnm3L8rr7zSrbnmmg7iWURKc8c9uBnO+doDuJwR3l6gOgz9ZATrV5eG0e0777wTRp3+/G8322yzDcPUWpIMAUPAEBheCHgbUDfBBBM0JIpn448//pCQtYaIdPijKG1xejsJxu+y4Lz5RcBL+5MKW7/v9X0q/OGSr93GzghvtxEdIv/8yku38MILF4Z+xx13OH+MYeH7Zi/8+ejObwaedOKPJHR+0UE+lZR0ZA8NAUPAEDAEDAFDwBAYQgSM8A4h+N0MmhGdX83p/NYtbpdddhngtd+CxPn9DQc8b/XA75XYYN8FwfVbzTi/VUyYHuN7vwm5u+2221p5Ze8NAUPAEDAEDAFDwBAYEgSM8A4J7L0N9Gc/+1lORiWkdgjv66+/7vx2KOJFuPoVscFearLJJmt4jtvpppuu4Zn9MAQMAUPAEDAEDAFDoA4I9CXhldWLM800Ux0wrF0cVl11VXfhhRc2xKsdwnvYYYe5bbfdtsEftMgY5//gBz9oeP7WW2+5qaeeuuGZ/TAEDAFDwBAwBAwBQ6AOCNSW8Pp9+Zw/wzts9xED9eCDDzq/P52DkJkMRKBbhPd//ud/3FlnndUQANjPPffc7ic/+UkwoeClPyXHnXTSSQ3u7IchYAgYAoaAIWAIGAJ1QaC2hPeSSy5x/jzuQpz8WdBujjnmKHw/kl90i/CmTCMgvP5EHMeAhP0I/+u//svNOuusIxluS7shYAgYAoaAIWAI1ByB2hJedhxg54GUQIQvuuii1KtBffb55587fzJMsHOdeOKJOwqbRWf4Ne2001ba8eCLL75wL7zwgvvWt76VbznTLcKbygMhvK0SyxYupIdtYbph2ws+bCbO3opTTDFFq+DtvSFgCBgChoAhYAgYAv+HACet1U38in9Of8vOOeecukUt8wQz4zxvzv0mjvLHEYB77rlnxvGJWvzOCNmCCy444G+jjTYKzvyWX9kSSyyR+4N/fvuwcH629ie+9yfmhDPHJXyufreEzO+Vm62yyioN/vHO2/DGXhT+3mabbUJ8td9yz3GHkh6Oj9TiSW526qmnZksvvXTDuemcA+5Po8m81l47D/deU5/7J/7KlXO9wXO33XZrSM9KK62UcfSiiSFgCBgChoAhYAgYAmUQcGUcDbYbSCLEKiaPgx2POLyXXnqpgQhC7C644IJs9dVXzwkZpPPtt9/OPz3ooIOyqaaaKn8vxBFSe9RRRw14Lu+5PvbYY7k/+uaQQw4p/O6b3/xm8l0Vwrvaaqsl/dBx4/6VV17Jo/Xuu+9mXvOef0f+nXDCCQPSuNZaa4Uz0+VDvuOM8thvfu+///4Nfmo33n5bvLCrIWAIGAKGgCFgCBgCTRGoHeF9/PHHc/LjjwvMtttuu+ymm24acvLrzRcyNJWadH300UcBXLSNmtSuv/76DaBDNvV3+h7iu8UWWyTfQ6Rj8YvDBrhdcsklM2/+kV188cXZnHPOOeA94VUhvP40maBB9SepDfDLH2AR3pFmNLqINzcIAxSdLr+bQx71I488ssEfCLV8K47QeOvvW92vscYa8qldDQFDwBAwBAwBQ8AQaIpA7QivX/GfJD5oDO++++7CxDCVv/jiiwcNrLf1LHTX7ovDDz+8IV7LLLNMg1f+IIaG9/4M7/z9Aw880PBOyNzuu++e+cVfwd3VV189wA3aWi2Qbk2s8Qc3/tzw3Bk4iP/6WoXwimex2Qb+eRteeZ1fR40a1RAm5h1a/B69De/xxx9eoZ1kxx9//AA3uIPYYuISa66PPvrohu/thyFgCBgChoAhYAgYAkUI1I7wbr755gO0hZq4Mc2dki233DInTFtvvXXKSdvPPv300wGEa/vtt2/wb6eddsrDJ75opkUeeuihhne8h8BrLec777wzwA3uRIuMX5BEjQX3mAjEok0LxH2vCO9zzz03IE5ocGOJCSukWMg+bk855ZQB/hB3TB4QBhCivUaD/sEHH4Tn9s8QMAQMAUPAEDAEDIFWCNSO8EqEmTJnmj411X3zzTeLs/x62mmn5YTp2GOPzZ934+a+++7L/RYCGRNvfss7rphjiKQIL9rTWPT3cq+1tyzWkudyRTMaS6eL1sS/MhreE088cUCcdtxxR/Eiv4KHxFmuaG5FUoQ3hdFXX30ln9jVEDAEDAFDwBAwBAyBUgjUlvDq2LM4SpMvbGm1dhC3LHDzx95mt9xyS4PmVPvT7r0/fGEAWUNjSZzkL2VqIOGVJbyxFhRiqAkvWmEhi3LFbjeWwSS8aOQlLnJlt4pY4p0ocKvJeorwbrLJJrE39tsQMAQMAUPAEDAEDIHKCPQF4SVVaHz1IqrRo0dXTmy7H0DghMzJFXKK1rLZn2gju0F4IfQStr6yoC+WwSS8LLrT8eGenSliYeeN2B1mICIpwrvZZpvJa7saAoaAIWAIGAKGgCHQNgJ9Q3hJ4bXXXpuTJhZ5DZZsu+22ebhC2lI7KBTFpxuEt8jGV5sFSPiDSXi15l2wOeKIIyQq+TVlV6ztnI3w5lDZjSFgCBgChoAhYAh0GYG+IrxslyWk6uyzz+4yFMXepfbLheiVlW4QXrTFknZ9xYwjlsEkvKk9e1MaXna10PHmnkMqRIzwChJ2NQQMAUPAEDAEDIFuI9BXhJfEi60sW30Nllx++eUDyBqETdvXNotLNwgv/qdseLEvjmUwCS8L1GIiy0l0saRMH+69997cmRHeHAq7MQQMAUPAEDAEDIEuI9BXhJd9aIVcffbZZ12Gotg7TliTcPW1aC9Y9sLlsAU00ki3CK8+0U3ioc0CJAUp84FebUvGARMSF7lutdVWEpX8miLrbPcmYoRXkLCrIWAIGAKGgCFgCHQbgb4ivHI4AxrMlLBzQ7x7Q8pdO884AEEInVxZuBYfxMBvnqOJlr1ieSbfyDVlEiHv9FVrkVmgpt9xz960ej9fjjVO7fYAoawqKfvc1OEfCy64YEO80OZqgdjG8Y6PBk6dIMchJCaGgCFgCBgChoAhYAh0ikCtCO/tt9+ebbrpphk2s+zKoEV2aYDMvfHGG/pVuEfTyHZlvH/iiScGvO/0QUpLC4kjPLbmIs76tLU777wzD/L6668fQPj4lp0XREhTTAr5rU03ILZ6pwpxf+ihhwbSywlzqfe4W2655bJ//vOfElzLq7aXlnC4pmynH3nkkQFxF7JPQNgZaz8g6XFcOCxEu+F+0UUXbRlPc2AIGAKGgCFgCBgChkArBGpFePXWVZBXpuHlAAq0iJzOpYmkThxEWQhTr7azuuiii/IwJKzU9fzzz8+jdsUVVzTsIazdb7zxxhkkldPKUou/cAsmHHwh8uKLL+Z2zNqvlFZXv+ceNwwqWsnjjz8eToqLv+c3eXDGGWdkDDC0kE7tHrOK1157LcMvvpF35OPzzz+ff0r+nn766UmtNN+wddmtt96ayRZv+Yd2YwgYAoaAIWAIGAKGQEkEakV4izShEDUWR8VaX53GSy65JCdV5513nn7V1fsbb7wxSy3AgpxhAhBP+QvRK7qefPLJ2SGHHJLHPeUu3gLtqaeeypZccsnkN+x8oI9ZRuPL9+DHQQ9vvvlmSzxStsKpeMUesTdy6jQ4+XbnnXfO7ZrlW7Tx8r7Z9eOPP5ZP7GoIGAKGgCFgCBgChkAlBMbAtScatRFPbJyfInfeFtVNOeWUbrbZZnPTTjttqfh58uTGH3985zWKpdx34uiZZ55xXvPqPvnkEzfFFFO473znO84T3k68rPyt1546v9NB+O573/uem3vuud03vvEN580vnF/gF+IDHoMthE1ePProoyE/5ptvvoDPWGONNdhRsfAMAUPAEDAEDAFDwBBwtSO8lieGgCFgCBgChoAhYAgYAoZANxEwwttNNM0vQ8AQMAQMAUPAEDAEDIHaIWCEt3ZZYhEyBAwBQ8AQMAQMAUPAEOgmAkZ4u4mm+WUIGAKGgCFgCBgChoAhUDsEjPDWLkssQoaAIWAIGAKGgCFgCBgC3UTACG830TS/DAFDwBAwBAwBQ8AQMARqh4AR3tpliUXIEDAEDAFDwBAwBAwBQ6CbCBjh7Saa5pchYAgYAoaAIWAIGAKGQO0QMMJbuyyxCBkChoAhYAgYAoaAIWAIdBMBI7zdRNP8MgQMAUPAEDAEDAFDwBCoHQJGeGuXJRYhQ8AQMAQMAUPAEDAEDIFuImCEt5toml+GgCFgCBgChoAhYAgYArVDwAhv7bLEImQIGAKGgCFgCBgChoAh0E0EjPB2E03zyxAwBAwBQ8AQMAQMAUOgdggY4a1dlliEDAFDwBAwBAwBQ8AQMAS6iYAR3m6iaX4ZAoaAIWAIGAKGgCFgCNQOASO8tcuS8hF677333D/+8Q83/fTTl/+oT13+61//ck8++aSba6653BhjjNGnqRg+0X788cfdc889595++233zjvvuI8++sitsMIK7mc/+1mtEkn8Pv/8czfzzDMPebz+/ve/uxdffNHNOeecbswxx2wan7rU7W7hR/2lzHz3u9913/jGN5qm3V72LwLdKi9DgcDHH3/sPvzww1q0FUOR/sEMc8iwzkwqI/C3v/0t22+//bLFF188++Y3v5n5gpL5TizbYostsquuuir77LPPshNOOCG8q+x5kw++/PLL7P77788OPvjgbNFFFw3h7rnnnk2+6O9Xb731VnbBBRdkm2yySTbVVFOF9H7xxRf9nahhEnsp95R9+TvqqKNqlbqzzjorj9sOO+wwpHF76qmn8rZioYUWyqjLWupYt7uF3z//+c/QPlJOKDcvvfSSTrrdJxCgj1lvvfVC+Z133nmzu+++O+Fq6B6dffbZoU3+5JNP8kh0q7zkHg7CDWXxlFNOyfxgPWBNX2PSGwTqgLXrTdKGr680PDPMMEPekdKAQ3al04+vkN9uCOGmSMZwJbyrrbZaElMjvN0oTZ378corr2QPPPBAtuSSS+b59NBDD3XucRd9WGSRRfK4UXeGUvbYY488LrQRTzzxRB6dutbtbuF34403NqT9+OOPz9NuN2kEzjjjjAbMKDMoAIZaIOLrrLNOiNsxxxzTEJ1ulZcGT3v4Y5lllhmAsRHe3gBeF6yN8FbI39dffz3XNNIA0XB/9dVXwQc/bZpdffXVDWQYN88//3yFEIqd0tCMHj06aJA18R2uhPfVV1/N0IptuOGGDY2SEd7iMjIUb5ZbbrmQP5TJWGs5FPHRYeqywwBqKOX000/PyzGzFd4UKY9OXet2t/CjDaQtlL9HH300T7vdpBE49NBDc7wEt/POOy/teJCefvrpp5mQ2ksuuWRAqN0qLwM87tEDBu3XX399Q59uhLc3YNcF674kvF9//XXm7QZ7kzNNfMVkQRqfHXfcMenygw8+yPRo5o477ki66+ThyiuvnMejjoQXzd9f/vKXTpKYf4vWUDDnaoQ3h6b0TTfzQwfKVLXkzUorraRf1eKe6VbMLI499tiMejmU4m1Ys3POOSeYQjUbBNepblfBj2nhZhr+O++8M6Ot4mrSGoE33ngjmMxJ/eJ69NFHt/6wRy68/Xm2xBJLhPpOXqekSnlJfT9UzzRRN8Lb21wYaqz7hvAyLbbZZpvl5gNUvsGWWWaZJe/gGRkWiTd8zzW9559/fpGztp/LlBKNYN0IL2kHp6233rrt9OkPn3322Rxz0muEV6PT+r7b+aFDvOeee/K8Ofzww/Uru28TgTrX7aIkPfjgg6EcXHzxxUVO7HkHCBxyyCEB31tuuaUDXzr7FCJI+4sJE4O34STY95M2/ozw9jZnhxrr2hNeNAIsDqMw+hW+2amnnjoktkxM50il4HrSSSc1LRnXXnttcH/EEUc0ddfOy7p2imje0fSBjxHednK2u9/0Ij90DFk8KXWCxUAvhk4AAEAASURBVJQmnSNQ17pdlDK/m0Q+uDfCW4RS+88Z4LNojb5vqEyGWIgt9fzpp59uPzE1/XKoSVhNYelJtIYa61oT3p122imvaJtvvnmGrdtQiZ6+pfKzcM1vxVQYHcgGbsjgbktdO8W99torzy8jvN3O9er+9SI/dCy06c5QdcY6PsPhvq51O4UtdshLL710XueN8KZQav8ZszNiRjBUpiAQbtkhh7I5HGWoSdhwxLQoTUONdW334fU2su7AAw/03NI5r9V166+/frgfyn/f/va3wz6aEgdvwO8uuugi5xsEedRwvfTSS93UU0/t/DZEDc/1D2+q4fwqbffwww87bwPlFlxwQfeLX/zCea124X6z6667rvOLYII33qTB7b777trLhnu/Gtx58wv3yCOPOL8tiJtjjjmC/7/85S/dBBNM0OA2/uEXybnrrrvOeds8xz3pZx/cX//612666abLnfuFe4547LPPPvkz9hr1Ns/h90QTTeTWXnvt/F2VG/Z6Ze9OEd8Au/HGG09+Nlxfe+015+0knTeDCGkde+yx3Q9+8APntc5ugQUWaHCrf7A/6J///OewP+oGG2zgvLbSeXs55+3onO/Q3VZbbeXGGmus/BP2SPWmKu6vf/1rCGf22Wd33//+90M+k+4y0k6++w7Qedto5xf9uPfffz/k5dxzzx3KGJj4repC0L3MD0mbHwDm+eAXrrkrrrhCXuVX4uttZ8O+lhq/3EHFm2eeecb5hTthP1c+nWeeeZzXfjFoD+Vy1llnHeCjX9nuLrzwQnfbbbe5c889d8B7HlA38NcPUt0BBxwQ3Nx1113uhhtucN5sw80222xu+eWXd4sttpijTCGUQ955G32H22mnndb56V63xhprhPepf+zDe+WVV4ay46ep3UwzzTTAWdm6TVz9ItkQvrcJDvuHTjPNNG7ZZZcNcZ144okH+M0D4uCJacCCMow/lPWbb77ZUU/33nvvkF75uAg/6gBtMukR8fbHbqmllgo/wUzvyUw4YEX9pLyAZ5G022aRBtorv9jVTT755I66Qfl48803Q5vVqr0rik/Rc+rZfffd51544YWwF/W4444bygFtTjf2ff7hD3/ovEY19DH0CSkhDmBKPF7y7Tvhkm7aedqv3/zmN6nPSj+jvfHbowX3ftsxt+aaazb9tqi86I/IH8oe+eLt6x17NFNH/UI49+677zraU/oY+sBuCu3H7bff7m699dZQX2g/SA/pEq7hTRpCnFLh0uZRd2j3aYNpC2gT6CPAvEjaabfwizpzzTXXOL8Gw3mzoRAecYZP0H8324+evdHhH8ST/etpG37+8587r6QIZTQVV/ZQJn205ZQp2gPauNNOOy2Ucfb7p077gU/TsPG7U6xT8evKsyImPpTPd91111xrsNFGGw1lVBrCPuigg/J4efDDPVpc3zE2uCvzw29An+kV7jKKFn9XX331jEUAKSmjBeLb7bbbLo+v3kqNMNhKzZPJlPcZO0548pp/y8pc2adQ4nfYYYfl37IwSJ4XXXPHFW/K2PD6hqEhrazI9yQ110wQJ9+gNYTMqlHMTfwAI4/7brvtlvmGN/8tadELD9FiyS4ZmG9oLSfuyUfy1RP88Oc74YZw28134iDhEiZlcZVVVsnjqm3Pepkfkhi20hJ8sDHUwqIsXV6INzb4nYhOkydamSem2fzzz5/HgX1BRfym5hnbOum8IQ5a2Clg2223Dfbmkg60lb4TzvTCMXnH1ZPB4AV5oe35tZt4RgfNN/b+xFm7852Qjk5+X6Zue4Keh89UN+nQmlbiRvkWYXaKOGy88cZ5GSIubHMl+3lL3LDRLINf0baB4g/5j7CQDUx0+8Ye5Slpt83ypC/UNcImHNo91jboMNn1pZtCO6H9l3TLFXw6nZGkTDdbnO0JSjB3IEzqAuWTdk/iQDnoVPSWmyykS0mZ8uLJeEb7SnmV+GGqgZ+ixZbncmWxaTeE/oHyIP5S5mmfJf+kXeW9bkd12J4k53HnO/0N39EepWybq7RbOjzqjfRNhEdbr/OCNKT6b9J64okn5vGL+33izW5SIpTRP/7xjwPaAdrBLbfcMsdMsOPqB/Xy+YBrN7Ae4GkXH9TOpEETHCpsp41GF7EKBFQXOl0I1lprrVB5y4RHQyWVjYZTKgqFXJ7jN51YSlp1ihQ62R+Vii5bIBGublwo/EybaaGD9iPrUNBpRHXHybY4Os1CMog/3+ldLKgsPJM/HUaVe10eCJsptlhoSCVefnSavyZsTUwgaSI0vHGjJZjFz2ULHk3y9CItrz3Mw5d4yJXN2EXazXdMZ6Rc6HDxl4aWsCBUIr3MDwmDAYSkkcVrImLvx2DgZk/2pbzRubUrXnuVh8WOEyIQOSHWXgshj8MzwUviSJ5qIU+95ir3F3e4kT86PjqrmPyKmQjlZ5tttgk7QIjduoTFoEaEgUnc6eCuXcLL1ohSPukQKeMi7IwicdBtB89jPHAnA275hivtG5jG7mP8aGMIWxMYDomR+g4JZRo+9ocwUoS3kzaLOoG/xFH3F7q+NdsZQ/Are9WDYgZVpJt9lSEg2q4d8tlLkbIvgwsJiwEZeIB9JwJm+MNfs/pbprzQdtJvSdkVf6VuQEBRbsXlBRvxToQ2QgZnhE0fKwJR10oD4pQivJBd3lHWGWyKeC1ojg/vaYu1VG235FtZ/4Ofeg0QdUvzD+Kj6z/fi6KKNkkGS/T/tFX4J39iIuNnCsPWn5IP8p785p7yDSYcliPvuKYW7XcDa8GgV9faEV7AFWA5zQwBSIiZEMMUGBjTQ9ToBF5++eWUk648o8PRjbzElSsVilEpjX2RkAZpqOhQY9H7deInhT+WVoRX9nCkMsdCo6zjjDZdy6hRo8J7Gp5Yw0ynpBuk2H89IhwsG944TmjutLB9nKR3++23169C5xhXdDop/IQ4Q/hpOGgYyTed75QDLTvvvHMeDuUALTmNkZTZTvKdEbmkIS7bhEG8UqPuXuSHpFlrFGVAhfabeEqZ0toNGup2RWz5yatYwIMwU4cZeFOfHDfyJCXUV8GWqzdpyegERCgLomkRdxBFtmkSIW91hyDtlrznSgch33Ntl/BCFsUfOnItbL0m77jqdOAOjPR77iFqfIf2lzzSe72WwU/XCfI/JXG8UoS3kzZL8octj2IRUkI6uyFovgRD2r+4rUfxIe+bkcRO40KbJOHQZ8Sy6aabhv4ofl7lN4NICSNu61P+lCkvcf9G3mntO+2qhMlVD2RTYbZ6dtxxx+X+3RzNtvEtfZzu02LCS9mV95osS7h6my3iKyST9+20W3pAq2dRJTy0txofTcBFIUN8WWSvBQ6llT+0V1oYpGt/aS/1zCb9jNQz3GkFi/jTKdbiTy+vtSK83gazAXSmwmSkAchkAoREVxABp5edu4QhV05PY4s0XUD0PQXD23WJ84YrRErcxoUShxB7ec91Pa+FiqUZ4fU2UPn3qVEYfumpYCqHiGwvRLgpMo472SIHNzRwWnqRB600vJAtyoVgxmEVWryNdf6OzigWrcGj845HzOJeNwipqULClThwjePRSb4zyhe/vT27RCm/olUiHbH0Ij8IAy27xEc0id5eLDzbf//982ig+RJ38alMuaMSN1qDmloljhbiyCOPHOCTxryI8F5++eV5HKkLMkDRnmmtHaRbCL52Q7olranOIK7X7RJeph8lnFR51hqgOAy/TiD/Fj8gg82kDH5lCC9h6DYnJrydtFn4LXgQl/hkSwae5H2KrPBtVRFiTpiYzcQiceEaa161W8oQW1a2Gy++k7BS5Y1ZF953IrqPY0DUSsqUl5tuuimPN/HTGnnxX7fJzFC2KwwKpG9oNvgAP8EyJrwoSXiHWV9KZEZLvtftczvtlnxDW5TCRqeJMPWgVupY3C9LvPWCMb7VJhG6TeedaIDlW65agYFJhRYdr3ax1v716r6zGtHlWMWjP4gFnTajKD06odPRmUU09Gi0W7Y/rZLHVLYm5FLouVLR9FSv+IXmU9zRIKb+5D1XGvFYmhFepmrlewplK/9xK7ZZ2s6JwUdKIISXXXZZBlGIO/5eEKxWhJc4QmoxR0iRHtHwkE6IUSxo9ASvWAOs3Qqhw60eJGg3PBe//OII/Sps0ybvUnnCM3nPVec7ful3aOrQPIqgFaCziaUX+UEYNIYSH6bx6Lj5DRmLhRkKvygwflzp97777puHB8Yxtmg2tKZDPNemJkWEV3dYqYEMfmGWIukt0lSzTaG4kUGAxIMr9Ubec43JqLhtVrdxQ8fCIBiNPnVDC2VCE1Bw0cIgXMcB7VUzKYOfDq9Iw0sYYtpC+DHh7aTNiv2GmGAvqgWtdau0avfN7mV6nHTQD9HvaOWGkCfIR6pMit+iOMBdOwIZ0nkJUdMKFMpCatajSliaeBYdtKT9K1NeyrjRfSSa6nZFTF3AKV7Dof3URDAmvHoAmWq3tdaTcNhNSqRqu6XztFm6mdWivdHlTg8aKZepuPJclxnabZEy7ZPM4OFH3FZ2A2uJSy+vtSK8mnAx0tHaNkYgeroo7lRwS+fF5twpLU2vQCRcabx0YeKeTjZufDVBZmq01V9qmq5Zp6htdVr5zXsqBotXEDoLSYMmVGWx6wXBKkN44/iR/zf76SsW/kl6uEKKY9GEt2hkzDeQfO1XavTNAEPcoC3X0km+U8ZEUyH+MyWl7Vl1WHLfi/zAb63NlHi12pda4tTONdZMggF1IDYrif0u07mWIbzaZrOI8KLtk7yhE4yFPJT3XNslvLG//GYalTZIKwUII9bSxIQ3VYa1/2Xw6wbh7aTNIr5a8yQYY8uozU50ujq511pPCYsrAzH6J0hBvC4iFZ5M/2Lj3a5oQipxgNx3q//T/UGZeJYpL2XcNCOgVbDCLljyqNlsRlF42A/L97RzZfpTPTNatd1i8buEx0xCFdEKGepkmbjqg0zKtE/N2spOsa6S1k7c1orwyuiYTE/tfMB0lXSwuHnsscc6SXtXv8XAX9vxScGFUIoQf3me6hTFXatrM8Kr46BH/K381KNLMG5HekGwqhBeOjiOvZROmA5IL6TrhPAylS55x5WFQFoYIIiGF/ywmRLpRr5r0wwdDzQSRdqrXuQHaZLFfRIPiBYduMwUSLq7edVaHwmXK3ZuNNYpKdO5NmvExc8yhFdrgVN1u0yHQnjN6rbER660fyz0AQfyAC2Lbh/7hfC222YJDrRdmpxJ+UCjRf52U/SCIgknvhIXPdVcFD7mFp0IbaPOb4kHa1mKBlRVwtOD9NTsWexXmfpWxk0RAY3Da/Vbxx9TiiIpCk/b3bNGox2p0m5phR/taRXRC7dT635a+VWmfWrWVnaKNfGLB+xSnuWaMuFqla74fa0Ir9bIxYuPJOK6AMkuAfKul1em71uNnFlNH09xkFmy0AhNqmQe5KhdadYpCtkjnNjso1l4Om58W0ZLEfvXC4JVlvAypS9TNoxuR48eHaKnp0s7Ibx4JvZV4APOegWxtn+WRVshAv6fxraTfGcQKKRayhFXnsXT14Tdi/ygk5awmY6lodUNFVtkxQt5BIdOrtQ9bb8qceAKwUiR7TKda7NGXOJbN8LLAEcWy0B4GOQJ5noKtl8Ib7ttluQPVwb3WmGiyweDQj0A1d+1cy87o+gw4nut6GgnjLLfoLVP9TnEJzYdKeunuNNmKGjMW0mZ+lbGTREBbRW+fk990HlCHS6SovB0vadstSNV2i3dXhOnKqK/1XbEZf3ohPB2A2viKf23zjd93406VSvCq0cpcWMtGXfyySfnBTneBkTcdPtKoaVjSa2GjcOiM4ozTkZckGadgVUIqQ6nGeGVLcUIp0rBj43W7733Xh1kqXtd6RiYdEPKEF60fIJr3FB0k/BiP6k7AcgFGDONKJ02o9BY49itfAdPSDZ2YpJefdXbYeG2F/khWx4Rrky7kV6IrsRFL1wjHt0UVtvHGmbCJS+E9El4ZTrXfiO87BIgAwzaJBnYSZr7kfC222ZJmvWVga/gI+WRK3WhU6EfkDIG7pR/bHpTg1DCLJp5idc+dBovyDyDwZS2txnRaxUuduKCIXsbt5Iy9a2MmyIC2ip8/T4mYan1BeK+KDzZjgwMyON2zPwkjDLtltbworSpIuzBLHmFjX9V6SbhbQdr4os5CDvcFP01M0spm95aEV69j2TRAgjdQQ3W4jTApBHlr4ymQPbqlAKoFxRpMlxm1LzHHnsMWAzUjPCymlbChZy1EvBEs47ohhubnGZC489CAD111wuC1Yrw6tXKYBuTzW4SXvDAf+lQpYPhSgMFlkXSSb6TxliDywxIPI2LHaeWXuSHXoihB0XgInjEU/oMFPV+zjqOZe6xn46/Z9GkLq+UeR0f/C3Tuer2hHxNidb0QChTMlgmDdpuMzarIV79SHg7abNIsz8pLiei/GbwrhfRSHvYCdGkr5F6r+00CQ/BvESngzBjO35IsuQfA2RtQ/lvX8r/x25bbyHHlzyL41CVOOkY6NnU1FoS7Zb7MvWtjJsiAhqH1+q3KCHIC+njUt8UhSdbHkr5Ie7NBG6AFlKUdVXbLc1/CLOVWQrlS8qAbn/4Nt5SNI437SntNLPSSCeEl+87xRo/BkNqRXjp1KVwFWlvdefTzC6n2+DRcBC3Mitf9TZGfKN3PND7l/Iu7qR1vKUzjt00I7z6QAD8b2Z7RaGHpAhRksaY7/hrtiiKNPKtVBjirQkWizu6Ia0Ir54VSE15yA4CpKdTkwYaBTS4EC05OQziX0Y6yXc6WMwpYqEDZzQv+RUPcHqRH1rDHQ/+hGzR+IlQPohfq85C3Keu7AGqN2AXN5gxaNuxeABZpnOVOkYc60546cQkr7n6I7AFivyqOx7peOVlrxetSecr4emrLjfxVHsnbRZhgEVqey/6ExmE4abdMsgCWI27Lt86jdwLKca91vCSd3xHGZUV9c38if2Nf7MDBGmjTYpFFsRJnNtdvKf72lTbGodbpr6VcVNEQOPwWv3WJmhgEQ+a5fui8GISSN5qMzb5Xq7sZEGeyNqZqu2WVt4QX/rjIq0ygxuUKMKTNHfiW2Yeir6l32CBtR4MxWlNke1mbWWnWAuGvb7WivCSQdKBFWlS9Midk3S08H1RJmt37dzr0W5quzHtJwVdGhsKpRbsjuUdV8gTI/9YpLGh0MfSjPDil/afewpqLGwVRdwIX1ZrxwsyqLz6GEL8AF8h9GhWtMhG24SZImjabdn7eLEY2hstepsg4hu/141ZrHnEH23312phgthNYlZTVTrJdwgvmOrBhYQP6ZP81g0Y77udH3ScElZqizfw570us5Agnrfb6ZIO6hPa7JToAY2YWIg7BiUSX+KQkmaNuLiXuohfRSSl1S4N2vYZf4q2rGpWt9HqS3q4ykBV4ilEStzEdVefnIUb6Zjl+/haBj9tP5oalIifejYibjc6abPwn7SMGjVKgmq4avMfTr5qR/SuJISVKvv4y2yXYB/bfNJmys5C5D3uispkmTiKH6nF3XxPOZW4pEhxmTDiXQpaDe7LlBe02hKvovTrXTvibcLKxFvc6Nk9wqRuiUmKuOGqlRHxAT6EL/HlivlNvNgQfKWt1QqmdtotvYCT8PAjrqeYrsGP6Ltl32nioPOcb+nP4jxjECYmYXqQiPJCpzNFePVOELFyoBtY6zzp1X2tCC+JZPpfgE9pcKWBhchoQdsBgaMSdetUHe0/jbTEi4KmTyHR7nT8cR93OqmCiTs0IGz9Qicm21sRjix402HoCpqylY0rKf5TQagAkCe0lJIWsS/Gf8hsXOFwB+aEA+ZSqYhv3JAKMRO/GRjgJ1pYtv9qR+KRKyNbLdj7SHhcaXhwQ3kQgirvKRt0rpAkbCERrdWOOykdjtY04w8NEXiQZxyqwHQUBy2gFY9xwZ9O8l1wTdnR6R0gYgIk30n6O80PrZ2JwyKNUjZ0PCE62Pd2ImBNGjBjiEWvpI5nQiDbknauqSltPRVIvqY6ROy0xR/cpAbVWqtGvY0lPkEK+8CUNKvbMWmmvWNaE7+Jo9YuEl/KJ+2H7LcZb5PU6rjdMvjp+sMiRrRo2LpDComTiI4b9oaxtNtm4Y/kjZ5JE//16V/xYFjctLrG+8Ondgdi4ClEgvjEbmh/RfEg5KiTVedCeGmbU2VW2vF4ENwqrfF7rZlPkSDtvkx5iYlRTB7xT/dPnWAELtImSRnBP+lTaQ9YYCzvuFK/aduoK4g+cEi7Q9FC+4rdrZRt8l+XsXbaLX2qpoRHnAiPQZ3e/ise7MRKFb6nLWJPX/oo+nD84jl+aYkHy/HsEG6ZmdFx0t93A2vtX6/ua0d4GZFIQSFz9LSdLE6iENOoaiFTJTO6NZ2u/U8VREaMkBxGShQ+vQUWccGWNyV0UhREiW/qStrjBgbSFBNq/IlP9aLwao1Kyn+e0UDFAlmUClz0HR1baheH+LQx+T6Vljjc1G86kXiqhA5MN5JgKeHEV7BhBXv8nN90GPqULXEDgUp1IOynLG7KXBlcxMSo3XzXxJWGSjTykHDRCDIzEmtRu50f2n5X10vJO5kFEXs5iXe7izPFX90eUGbJH7BlMERZJD/iOg/5kYGj5BfTf5r0ou3QHTru4jpBfdCLqnBD56HzFsIZ1zfaBdGuMCjhhEiJB1c6MD3lXbZux/VB+8mATXeI8o4yQvnVu+Dwjs4/HkAK5mXww2181KmECeFiGp90ae0372m/KbtaOmmzJEz8FTMs6gIaKeng43zVYbe6J580caKdRwGCn+w/jRZZwuGqlQjiNwSKckv5E7cpd+K+1VUIL2mnTNBWIuQnhE0w6bTu3azMOZrttV2mvNBni2mgxA8cdXtL/sk7rmAVDx5aYaPfY8aT6mv1M8kPHa5W0MS2tdqd3FPeY01sO+0Wcdcna4r/8VUPJiW91LWifaL195RfLXynF8zhljZD+hncUr6krRW/WESm28FuYK3j1Yv72hFeEgmIQm4BFwKGNoN7iK3OCAFFjxw7adzEv/gqU+t0EhSOuIOTQsCVgpEaIWk/aSCKOi80ZHoxGN81I3YStiaC3NPJpioz4cYkWceNzoc4iL/6ytRcbLupv41HzGg2iHtV0aYKOny5F9sl/NVTzvKeCks6EK2F4v2VV16ZTJt8yxXCEkuKIOtv4ns0ObFUzXe+p6OhgRZzH8LRDTaNcxFx6VZ+EA/RplMXaSRjgRgIOZRypxdsxu7L/obEQTj0QEz8BwtsQDWR1eQkzhN+01Cj/Uy9k2dsJad3npDn+kpHLNo6/VzuiaO2o5bn+gpZqlK3MWuJSTq4SHml3dH+U/Yx9dDP4nvKlZYy+Il72mKt2cRvtNS0X3oKNA6T34Sjpd02C5zRdEqZkCthUE/oGzoV6hd1Sdc7nSbqBDNZMnNUFJ6cVoU/qTpU9F38XPoj0eRKWiVO9E+Q4m6IDByL7HjLlBdtOiRx1FfaxVhhpN/Tl7UrzOrFs334TR4QL2kLwAwtpvQbOjxOd5TZZR0vyhoLGlN9YtV2S4dHvHR7L2GSF0Vbtsr3zDbptlK+xb94kAVJl/epK+2KNhuL3dC/a+kG1tq/bt+PgYc+EbUUP/JzvmK7Z555xs0444xu7rnndhNMMEFhXL0pgxt//PGdz+xCN+2+8AXa/fnPf3ZeS+ImmWSS4I1v1J3vrJzvHJ1v+N23v/1tN8ccczjf+LkxxhijVFB+CiT44bUh7lvf+laI+7jjjlvq2zKOvJbJeXtd9+qrr7ppp53Wfec733G+kpb51BEnP+3pvK10iBfpG3vssVt+66eBnNfghPwCi8EQX3Gdb+DdmGOO6RZYYAE33njjNQTrGwmHm/nmm69pGWr4SP2gLO6yyy7OmzA4P6By008/fcDFay1C3nuNUrjy22s/nScezjeooWykykKVfCds3E800USOcKgP5Am/55prLjfppJOqmA687VZ+UMb9TIb70Y9+5KaZZpqBAfknvhMP5Zm68eMf/7hl3JKeRA8JlzoxzjjjOE+qQ9nC/+mmm87NPvvsoc5HnwzrnzTZXpsS6ib5T9uohXLutd+hrtOm9Foon342ynlS6DwpcBNPPHFHQVZts/xsUyhn4OK17Y7yTpmhHyjbZpWNMHHztq2h/oEzdW/qqacO11Q9j/1dfvnlnR9wu1GjRjm/A4+jX6HNKtOuxn5JusGf8kAbj9AH0T51S7xyxHliE7yjPwDTfhRPZJ3XeId+bZ555skxouzCK2aeeeaWyQJz+nyEvpS6R/6lpNN2i7LmzS9CeaY/K9PW63jwLX0/fMUPStyUU06pX/f0vhtY9yKCtSa8vUiw+WkItIPAuuuu67wtn/MLWJxfjdvSCz9D4byGMBBeOkQTQ8AQGNkIQEhnmmmmAALEa9ZZZ3XLLrus+/3vf+/8rFutwUHZ89vf/tb5aXrntX61jqtFzhAoQsAIbxEy9twQ+A8C3gbReROJ8AsNEprFVvKnP/3J+SlOhyYSzaSJIWAIjGwEvC2o89sIusUXX9x521jnF4G6xRZbzKENm2KKKWoPDsTcT/k7b7ftfve739U+vhZBQyBGwAhvjIj9NgQiBH796187b3sXnqLl9bZZkYvGn3RkTF16+0zn7V4bX9ovQ8AQGJEI0HYwU+Ttvt0f/vAH5+2rnV/EFcwb+gEQTCcg7H5nk/C35ppr9kO0LY6GQI6AEd4cCrsxBNIIoM3we+/mL/0WSm7VVVcNtnLYR2GrhZYGG+Jzzz3X+cU6oSO76KKLgu1p/qHdGAKGwIhFAPvPX/ziF8GOFPtffyqa8zt+uLHGGqtvMIH0+oWYzi/Ucn57ubCuoYztct8k0CI6rBEwwjuss9cS1w0EWASz8MILh0U5rfxjoZrfvitoQsyUoRVa9t4QGFkIsLCORacsIJp88sn7NvH+2NxgsnXXXXe5CSecsG/TYREfWQgY4R1Z+W2pbRMBdkfw+8o6v8XTAOLLrhd+y5cwRem3zWtrF4g2o2WfGQKGgCFgCBgChkAJBIzwlgDJnBgCGgGmI9meR7Y+KrOITX9v94aAIWAIGAKGgCEwuAgY4R1cvC00Q8AQMAQMAUPAEDAEDIFBRsAI7yADbsEZAoaAIWAIGAKGgCFgCAwuAkZ4BxdvC80QMAQMAUPAEDAEDAFDYJARMMI7yIBbcIaAIWAIGAKGgCFgCBgCg4uAEd7BxdtCMwQMAUPAEDAEDAFDwBAYZASM8A4y4BacIWAIGAKGgCFgCBgChsDgImCEd3DxttAMAUPAEDAEDAFDwBAwBAYZASO8gwy4BWcIGAKGgCFgCBgChoAhMLgIGOEdXLwtNEPAEDAE+hKBjz76yD3++OPu5Zdfdh988IHbYIMNhvRY2a+++sqde+65bplllnGTTTZZ1zB99NFHQ/oWX3zxrvlpHrWHwHPPPeeeffZZt9xyy7XnQZOvKMPXXXedW2211dxYY43VxGXjq6uvvtpx3DzHyH//+993s846qxtjjDEaHdmveiLgz/Y2MQQMAUPAEDAEkgj4zj3bZJNNMt+DhT9/lHbmSWb25ptvJt0PxkN/1He28sorZ8TllVde6WqQl156aUjnIYcc0lV/zbNqCNxwww0hH3bYYYdqH5Z0PXr06FB+POHNPv/885JfZdmxxx6bzTLLLHl94P6MM87I/ACstB/mcGgQcEMTbP+G+tZbb2VUFJPeIPCvf/0re/LJJ7N//vOfvQlgGPrqjzjOHnvssezrr7/uSuq8RqVSB1A10DrVoVblzWuYsvfff79qEoeN+0ceeSTzmqzQuc8wwwzZjTfe2LVy1i5IEAuv8Qtxuuqqq9r1pul3e+65Z/D/8MMPb+rOXvYGgdtuuy3gv8giixT2BbR3t99+e3beeedlRx55ZLbbbrtlW2+9deZnIEpHivLMQI7BU1XCSt3YfPPNw/f44Wc8MtoTk/oiYIS3Qt6cddZZeeHu1aizQnSGjdMvvvgiu+WWW7Idd9wxo1Ol8Xj11VcHPX00XquvvnpfEZynnnoqaCnAbKGFFsq+/PLLyrjR0N9zzz3ZHnvskX33u98N+Ptp3cr+lPmgDnWobHnTndnll19eJnnDzs28884bygOa1KHU6GpgaSco7zvttJN+3PIeLRyEqIxQJ5ZeeukQzrXXXlvmE3PTJQQgrJQ3/pr1AwzyKQfxH0S0iuy3337BDwhzO7LFFlvkcRip7UQ7uA3FN0Z4K6DOaFMqF5XRpHMEbr755hxTwZZrs4au81DTPjz99NNhqgqN1v333592VLOnkFSN2xNPPFEpht4uMyfM2p9eEd6hrkNly9tnn33WgOsaa6xRCdfh4Pill17KMajLAB/ySTlddNFFSw/uGBSut956eVrKzh69++67QbtNW8+9Se8RQGsrbYS3lW0ZIHl755135m0YeVVVU0uYMmNA+1BVXnvttbxsjRo1qurn5n4QETDCWwHsDTfcMC/Y2P2YdI7Ap59+mvmFMNl2222XYztUhJfUMH09//zzhwb0oYce6jyBPfbh9NNPz3GDqP/jH/+oHCKDi+OPPz73B/x7RXiHug6VLW9MTcpsA3gcc8wxlXHt9w8o/6Sdv5NOOmnIkwNRlRmI559/vjA+5DEmCUwxi4Za0sG1Sh05+eSTQ/qxYTbpPQIyA0TelRXKheTvSiutVPazBncoO/BjzjnnLDShaPgg+iHhb7TRRtEb+1knBIzwVsiNTz75JDvqqKOC0bpf4VnhS3PaCgEWxkijwXUoNLwSx7fffjtoetEWYMNZZ4GYnXPOORnTcs1IAGl44IEHsr/85S/J5EACNP69Irx1qUNlytsLL7wQcL3gggva6gSTQPfRQ014Tz311CGPOXaalFHsLZsJU90MViC7EFUWoenBSxXCy+I42gHCffDBB5sFa+86RIBZFbEXr6JsuPfee/O2649//GPbsYAsk89HHHFEZT+k7WQmwaS+CBjhrW/ejKiYMbUujQbXoSS8AM/UFvFA21tlBW9dM+3DDz8MJL6ZDaPGv1eEty741K281QUXHY86EV60eEI8b7rpJh3NUvda01uF8OK52HiuvfbapcIyR+0hwCwKbRDmKlXk4IMPzvsOBvXtyh133BH8gXRXXQshbacR3nbRH5zvjPAODs4WSgsE6khAxK5ryy23bBH7er/GRk20F0Z4/51XdSxvdStFdSK8LAaCVDDl3M5K+E4I7zvvvJMTqvfee69u2TRs4iN5xIxKFWGLPCGcVYlqHI7EoeruHxK+Ed4Y0Xr9NsJbr/wYsbGpIwGRBTI0ZlWm2OqWiXvttVfeIRjh/Xfu1LG81a3c1InwYsZAPdx3333bgkmIDH5U1fASoHx/9NFHtxW+fdQcgYcffjhvo6rMqGlTLBQUnYqsJam6RscIb6fID873dtKaL6lVxO8h6i688ELn9wkMp/zE33ptmrvrrruc3xvQzTXXXM7bkDlvt+i8DZzz0+TOT825+eabz/32t78NJ7TE3+vffrQawrrvvvvCCUfTTz+981PsboEFFnA/+tGPKp0Oo/3V995GLaTFNzjOd3DOb9nk/PZWwX/CmWSSSbTzhnu+vfjiiwMO559/viPtvkMI6Zxooonc3nvv7WabbbaGb/yesc6vqnV+/0Tnd0Jw0047rVt44YXdr371Kzf11FPnbr1Jg/N2d/lvfeN3InDXX3+989vPOL+S3M0xxxzuF7/4hfvlL3/pJphgAu00vyffzjzzTOc3qXd/+tOf3Ouvv+4OPfTQgOvss8/uPCkckFawGH/88YMfvsN1F110Ue5fq5u//vWvztuWDXBG/NZdd11HGsAgJaR7+eWXD6/A9JRTTgnYitvFFlsspFl+kw9XXnmlIw/8ZvlupplmCq/8amXnF++4ffbZR5w6ryFzfhud8Js88tO0+Tt9WpA3aXDzzDNPKBPE89Zbb3Vjjz12OFmIMt3JyVaDWYc6LW/kE7iOM844bpdddsmxim/Ip2uuucb5KVXnbT0DVuBHXaJcamzjb70G0Xk7Uwfmfg9qN80007if//zn4QQx6keR0J5wUpRfqe4mn3xyN/fccztPzJzfPsz9+te/LqwLRf7FzynDtDcI7df6668f7gf7H+3guOOOG4L1g1C31FJLVY7C9773vYAvH3qSlPtX1qNtttnG+T153QorrOAuu+yysp+Zu5IIeLMEt/3224f6cvfdd5f8yjm/nWLoP/iA9px8iuWNN95wflagsD/R7qmH9EUI5Y42r4xI/fYaXvfnP/+5zCfmZigQGBxe3d+hfPzxx+EkFT11gj2ZFl9Js0033TQ3uvd5GXYeYH9TvWCC5/zxPd8UCTaU2I/idsEFFwz7w4oNm/iBrRN2Zfy1s4rcE+l81TOnxay11lphylD858pCEd+Z59HEls6TzWzjjTfObepwx2ECxEd/u+SSS+bfccNKWFlljZ0U4S2xxBLhmxijlA0vC55kBE448TdMd+pFZuTb2WefnU/n8w171RIPWRwh8cVOLyX+eNE8TSyGKSuejAwIgzBZPY6w7Rn5KuHLFczYL1SEQyX0zgaUAcoUU3fkgychDX5waIcICyzF36KruOWq3RDGOuus0/BM3oM7W1ZVkaGoQ+2WN8reYYcdlmv1SDdltUjQhEpekserrLJKQz2iXuhyKf5Qr0488cS8HsXlmbxObc3EtkvUeeJFeNQJypUu06n6I+GWvXZDw8tCSj/wDe0XdQ+civ7YjooyHQvtlJS9drcHEw0t/rSj4fVKjhAH8qTqtldxeobbb7YF23bbbTM/GCjMW8lz2nuvbBgAgZiPecI64F2zBwceeGBeNli8poWT2nS+0z+whVgzoR+Tskb5LyvyjZk0lEVsaNyZSUMJ3KnIujOhcNPwaWEVs9hJSuGXb+jI2GZFExfc0MCnhB0g5Fv8FMLJxu9xpyhhQUCrCPGVb9nIXcLADw6BkHdcibcIq/wlbtqNNFj6GQ2MCH6CGe/ZukV3GnRy+jvu4w6b+EEGeUfnLp0WOyoIaeYdYbBAC2GFtoQp/kO4Iffx86KpfsKSb2lcqwikVr7l6jWtAz6XqVreEyeIbEpksMW2PchBBx2ULAua8GLriH96Y3TskXkmfzosHVfJY8ooG7LH5JfBXRUZ7DrUbnkDF/IhLh9FhFebvejV3fhD+RdMKXc800J54D11HDtRhHJNpy/fcYVQaOH0L54TRwZEItQFybdWO3bIN82uDHokHrQXVYS0xukQv5pdKauxyMCNetuuaOIjbUcVv/Req2yjaJJl2DPrtrdZvup3KRtdqW8oKKqIHA6C/yhjRKTdZv9odumQesHgsJVQzvDvuOOOa+U0fy/pM8KbQ1LLGyO8FbJl9913zzsAKmhKYlLLb90pUfmkcnBNdUyQCXHDsYladAeLG2yf6Fw0YdXuU/e68Y61sOI+Dod9WrXE+7YSFw49gKxDvunsJe5s7SNEHY1pKq7SiUu6Y8Lrp6sCJmjPYkF7Jt9x3XXXXXMnhAVZ0e8hcdhwsmk5DSaap6LTea644or8Wxr3qqIb5NR2Slp7RRxT2g/SQIPNn27UiUs8WNCEV+IKyZX0FxF73IobuZ522mniRbiCq7zjqgctDQ6b/BiMOtSN8kad0tr9FOElr6SjRiMcC9pbjZcuY8zu8I48Zd9YLeSxdLq4oXxqEW2yHojKe7ao4xvqYqeiy77U5TJ+Urf0ABiyTxkEQyEexJE0ov2VP4gtpD2WnXfeOaQpVfdjt0W/OyW8lHXizJ+dppWFtlOXUWYxaFt0voMV7Z/kLwM8FCZxG0b5F2x1HSnKS3nOiYnyHQoBhEG+N40Izym/iFYqbLXVVuFZs3+iWClDjsUfKdedlFHxy669Q8AIbwVsdQdQRHhlZCmVPfYe8iKdJG5SW+wIOeR9TIDoiKVy8Z5OtapwfC7f8peaQhT/pOLjjjB1Q6UXGfCejrZI9LYx3v4t6SwmrZrwMo3ZKr5i/iFx1YGIhkj8qHLWuhAT+TZF1nVY8b23cc7jjh+pDl3MPHifIk7efjb4MWrUqNj7oM2WuHHtFuFNaWHivWu9bdyA+LR6MBh1qNPyJmlAOyTYpgivzOhQN/SgVr7HjEPXdQ41EZHyygAgJTps4qBNIiROlBv2LtXCgh/CrDIdq7/X90I0Cc/bvutXhfeQF9FsE49YY0ddFvJJO0d71kpEiVB1Fkv7K2GSlnY0vPgleUl7MpIFUipYUAbj/Yn5LWWUmaFWMnr06Nx9lbaZmQ8JR2bfpN5gziCiZ7jK1AvpH6uQV63pZsBnUk8EjPBWyBe/UC2vYFT4lMg0JRUx1UnyjXQIuImJItNEUom5pjTA2m6z6qplOmbtv0ylptLiF+s0uNWaDTbl1/4UHcQBSZbGEfdFHVyzVfOXXHJJHhaaBKbH4z8dF+41GUOrIO8hGlUETZl8y1WTljL+0Lnq9Otpb/le5ydlIxax14RwxgKeOn7dIrypfXjjsMp0HnF8e12HulHeJM7N6rKuR83MO+jAOaWM+iKiB3CQvrgs81sPeslf6qKI7lyZrYjtqdHGFtVH8aPVVc/wVFmxrkkyZiUp0e1KbK6Rci9aQzR37Uo3CK9oNIsGKe3GrZ++Y8DPjANlkoEeZnYp2XzzzfN2qajNl+/0LFeVcnvAAQfkYaAUELLLfrpaWPuBogX73DKy2WabBX+LTA5TfohSAlwgzK3SnPLDnvUeASO8FTDuVmct2h0qR6wBgVDxXP70SFWiqrXIVU+WiTWz4mfqqqeaiA8NjEhMeFMaLtzKkY18T4dRJM0Ir7YFpPNr9Qdh0I2bJrxVGjHiqs0/SIPWtBWlJX6uSUBMaLE3lryWK7a/ImiEeS5TdvJcrjEJ7SXhJUyJI1cdT4lPq2uv61A3ypukoRnhpV4KFpjbVBFt1oSGrFV55r0mj/GMBfEgrphydCKQc2Z89BQwg/ayGlGNvTYriuPE4k/BjsVgrUTay6qDe+1vNwivED0WaI1U0W0pg6IiOfbYY/M81m1xyj173kp5qEIU9Qwk7SoEHLvzToU1C8SHMlNFWGws6aBecyw12vAqaaoSnrmtjoAR3gqY9bqzlqhQcaXipM4U1wuIqp4sA8EWv7kWEVWJi9ZOsghMpCzhZWQt4aGdLZJmhFc6GvyJ7R2L/NPPdSNdlfDGhDSevtPhFN0/++yzOQakQeeZ30as4R3v9UEX8h7TiJQY4W0cNHajvAnOzQivHnRWWdyC39KhktfNSIPEI75SZynHfK//0ApX3TBf/KYc6bqOvxC7smQXf5gClvg0GxjqqWhmb1qJaFax829XukF4xR4fTeJIFD1bRVlrZsO///7752Wh1c4auk8qiyumO1LW5IpmlRnTVn1aqzBk54eqs4H4G5vAEbd4LUSr8O197xAwwlsB28EivEx7SyXmqjVpesqWTq9Zo5NKWqwdatYx8b1oV4iHXkFdlvCym4CkhVFvkTQjvHwnfrSKb8r/TggvphESNle0WO2InoYWQsuiCwY3EA1suSUcftOgM31IZ48bbT+twzfC20h4u1HeBN9mhFcvBKxKgPS3VXc/kLgx8NOmMFJ2uDIwLSov8n3qyjdoXzWZp/5r86DUdzzTi7pY7NdMGCBIfMto5MQEjPxoV7pBeGWQsccee7Qbjb7+DhMmyTcOs2kmYgeL+1brHvz+5rm/ZbWhcgww/hMWZggyYOOqt3ZsFs/UO9ZK4G9V5Yje+Yh6w3qFVmQ/Fb496x0CRngrYDtYhFevNKXiUYHRblCBmK7nGQ14kf1UsyRpe1j8wfaomWjt6gknnJA7bYfwEl6RNCO8erV8OwShE8KLiQDxlr92GzBZPY8/QmglXhAMRHfKdAKyAwMawSIxwltMeNstb4J1M8KrSSEmB1WEVetSnjrdxog2QTSg4idXGVRViZd2C7kRAkH9a0VEtDkDU7nNRJtMYF/ZSmRXiqG24ZWB90g9bQ1bdCljzFoVidYEN5vVk++1vXhZG172TZe4YAOMYA6oFQuxLa+E1+oqA9Iqtut6cTI7QdCHm9QPgWIGUr+4DnmMBovwSkJldbJ0PFRwRp1oaVt1QOJHfMWmSBoKrq20Fdq8Qi8wKUt4WUCjwyvaw7IZ4WV1tvhBg9ZKmNZl1C8ixBI/qo7a9fQr37eLO/aVOh/BRTRXsnCQjlTSycCGBpffYF0kRngbCW83yptg3Yzw6jJFHqVsp8UfrpjCEDeEvZQln7m2In3skADpo44glBM9s8NMQbytH/5WMUcIHkf/NKnXZjiRs/BTD+hYJ1AkLLCTtJddBS8Eeah3aZC2UPKxKI3D9blsl0k71ozQaROFMjtaaM1x2V0aNLHVsxnaL9ZOiEgdKdN+MwiljLLwrqzoHVsIy6SeCBjhrZAvg0l4hfzQYMiUULNGpmwyZJpcOh1sscT/2A+9aI1GTnfMZQmv3qKGMJl6SklMePU2SGJTJXHm9Lci4Tviiu2riCYnVQmv3kar1VSthFd0xSZS0iBXrYmLd+jADXaDzaQq4S3CnzAkTlxTuzTEbrSpTbM46ne9rkPdKG8S32aEV3es4AUpK6pHDGioZ7LoM7bzY2BT9C2kFS2Z1iITHuHHgr96UAXWnYiOZyuSpwcazWZBOORGylkzLaGOt9QbjYF+X+Zez560MxDQJht6AWGZsIeLG9m9IF54G6dPNPK4wzSrleiF2qKtbfaNtt+NF/NS16R8acIqs2Vl4iOH/LQy29BxlHpXtK+9dmv3Q4eAEd4K2N944415ZaKAp4RKIhWuaFsy3fjGuzTg5+mnnx78KLOHYSoOrZ7FdrxFHaOewtLmDPjPdmmSTq5Fi8kg6Tq9uE111rEmVW+cr/dplDBTi3PQHkMs0MTohQssGpDvaIyrCLtgyLdMRXcixE/8kmu81RhlRt5xbbWKXTf+uE9t3L7TTjvlfqKJSElMnFN5xHc6bhCiqtLrOtSN8iZpYvZD0pua3tTmPrhDYxnXA/KXjp8yKXvmgrVMj4v/aKPiAS3Tu7ISXecH32BnmBK9HVQZ8pDyQ57pwUMrUyI0wJKWIjt3djwRYpDank/Cja9i81vU5sbuU791G9QO4eWQGklf0ToCFrgyK0des7OM1sKn4qSfgRlEi3JRtMiJ8sMplfjPoQhF/jNIJ72Uz6IygK024WEOUxSejh/3MotA+EWid1yoMiCWckGf00puv/32PC/Yc1uLnsHU2mXIetkBk8SlVZnX4UrZYAtJk/oiYIS3Qt5oLQYFPNVwctqMFP6Udo5OTSoU7mIiSXRoUMQPGiVsglj1SgWmEhIPtAxx51o2KXS4ekqIRjbWyqBtlE4Zkhg3rvH2Zqn9giU+erqTdJF+9vSV0TadOURV0syVxol0CnFlIY5+zz1EAqIALpooxivfNcFv1lhLfPVVVmYTXopMardl7tEwSzpSA5r/396ZQG83VX/8vGayQjIT4c0rMmQolSkypaJSXhkyZIW0KMSboULGsIRoML4ZIqSkzC0K1RsNVKsSy0poIs3T89+fs/77rvOc37nT89z7PPf52Xut3+/e594zfs+55+yzz977qCSCMOCU6mNhPhxMoulxTelkh/UnDIZCSBTRDVYvIKGUhTCUIyYYsDCvW265JQ5S+nsU31AT/Y2KqESLOqek+9Q/xIN72gzmGIZU/SfzPHYvGG77ahr0TbaNL7jgAn9qlY4TMXOr4WEgYwpPsht2a5XvUvMqm/wZ13S8oPwxwQyqASzfVDyexOHD3+FYk6pzGDZ1T9nC8UVViFJh856pGgptEi9MNA5jueLFFT38qhR63iFuqu3QjQ7TTwkqGNvD+SNPBSwcL0mzimEiu2eaf2oHCCZa+2xdF3KqtlLk01qxDHdeHnjgAX3sr6GRsfqN17Et79CjMAFUKrSOodAlDJO61zjD6uSn0rZnzSFgDG9FLBlo2VrUjs2VLcqQIWGbPxxYCRPqvjHIh1JT3sMAxQNwnEaYZ3yPxE51+ypWxQeDwQ2ZL5haDNpgoGB2YCbJC8YzLh96eKEVLuEYQONwYXlCX7RhHXRw1skyfAcOKkWMyxuGC+9hqJRg6sBf66Lh8JtaxWcpzLbGaWqrKlSvSDHQ9BFt/yJfptQRiQ+LIS0jV5it2PAjlE6FYZmc0D1l4RFKMwlDu4eLIPBSx+6aBnrGLIyq0ii/oWH6GwwN0k2dvLW+eNKIVQ9ijyoaNrymmEUYk5ChDsOH96FnFMVZ3/PN6PhC++DfV8scfgcar+61DsNL2jCmfM+UQb9b+ijjihrWwbxT9zpEeK0X0ssyov0YZ2G8UYeiLytmXBnrYMhUgBCO4XlpqyAjJenXOOEJf+RThXnTuLqNTjzqmsIo9EBCuFDqr+mgz8o7/cvb0QnzIyyLxCqkvmbBUBcfGE+rCh5lDw9JqZImYfQ7YlFURqGwJoWTjvf4AuY9cxXfSipsnJcan1GP+FuPw4a/FW9jeENUundvDG+FNkkxY9rBdbDIm2B5zwcIFaUTSgNgQpQRDPPJu2fwGYQYANimirdmyYeyMgjFHz36s3nl4DlbaXnE1lkKAwYklVQy0LAti4pDnDeMGQweYeIyMLDD2IUUSmfj8PxmEiuicHuOrfgmCCaa8udNROShPizjE7TC/PW8+FS9eBbqrxEP5jkMS5+EqQulNuF7vWc7OT5tTt/pNTXxhmXlPtXuGp8rE26T3xB5DtLffh0ccxqWL7wnTEj0k3DLXMOyQE5JwsK44KvMoMbjSnrxToXGo//wzet3oFfiMW5U8W2raRVd6zK8pAU22tZhvWBSUrsPRfmH71QiSR8pI8YNZbzBhvvUn+JWpf+CN/iibpZHHBKjQhEWrZS5KrFA4JuknHkqBixAVcWlaOyC2Scd+lAsAdXykB/tQzhwSO00atj4ikBEsdM25vfuu+8+5dS/OG7e79AbDrsBRaQY4JYvRXxz1Iv2olzcx6pjqXg8Ux1zVFPqEHnxZwxvHdRGH3YGWUpDGXUIAVkhO2ECnXx8TiQKTlbQTqR2Tpilvj9hRNzNN9/sSy56ZW7NNdccuBYiIXIioXEiYXTrr7++W2KJJQZOqyiiSF8c5RYG1y299NJOBmW38MIL+3zFjYx73ete5xZaaKGiJBxpiD6se+KJJ9wKK6zgZs6c6WRgK4xT96VMmm6zzTZzoofmZPBzMonUTSI3PPVcddVV3SqrrJIMIxJ7J9uV7q1vfWvy/aAPZdB3Yijk1l13XScT8qDJTFS8JvpblQqTj2yHOjCmP6+zzjpuqaWWqhLVhyEufXrJJZd0wjC6ZZZZJjeuMAQ+bYZuviPyZGwQ5sOtscYaboEFFsiNW+fFgw8+6ETi5qOIlNoJg1EpOuWSxZr/zl/60pe6DTfc0GNSKXJOIFlwum233dYJ8+JEsugWXHDBnJDNPxZmzLcnKYsamVt88cVLMxFprxO9XCdqCKVhxx1gp512crNnz3aiVlG5KMwTImBwsqvp++usWbPcfPPNVzl+KuAb3vAGJ4IOJ5JZJypsqSD+GeO+7I45WUS5xRZbLBnuueeec7L74cvEnMI3WUYiAPLjouxUOtETdpSnKs2YMcMHFYbXXXrppVWjWbhRIwDDa9QdBFTlAemkTKKlBWNLU/pMqXFTaUIWoA+Biy66yOOKpGYQlZG+xOyHITCBCAwi4W2rmqGqT96pg23ljVoJY2x40mRZXkgfY4OqsjjjeM8cgwS0ygEgbZdP1b10R7Tt/OL0r7vuOt/OSK2rzL1hfPoHfybhDVHp3r2pNHSoTULn7SnvDami6qTE9rRRMwigi81WGH91DBeayd1SMQS6gYCOLUzkKT3kUZdSjbYQBoyK2F5XZqbqtjiqV4wdgxh1jqpemg8qD9Qv1vvX96O8ooeuKhKqAz7K/FUdpao+c1g27SPG8IaodO/eGN59DHQ1AAA8wklEQVQOtUmoH4uT9bJVJvqXKPkPqsPboap3piiyFeuNxpiwquj2dabgVhBDoGEEQjd6F198ccOp108OKa/q0o7q28TAFWam6LTDuCYc2MHOUBUjqTjuKH+j64z3kTxd2FGWRfNSv+d4bRgl4b6NdsaQu2zejctFeGN4Y1S6+dsY3g61i/rf1Y8H5XysrX8thiAc+sAqHB+QGMnAEMOUMbCqtWyHqjKRRcFvJNt7GHvk+dqcyIpZoQ2BARAID0JJeYsYIMmho6hEEsO4Qd0yVi0Exk+MxYwJ4aE7RfFVxazseOWiNEb1TgUsGIx1ifD+Au6o942C6EdqaKleT+rkywEqOmeXedapk66FbR4BY3ibx3TgFPG9GLpc0Y8o78qBAvgYNGoGAZzFg6n6B24mVUvFEJhMBJBcsfjT8afqyWht1/ass87yZWpz+xgGty4ThMSULXlcl9WVEraNWZw+C3raFddsXSO8USDIoXzsMrRN6gMZ12p1iTYPXXTmHfRRN10L3w4CxvC2g+vAqTJQ4scwnGh0wuGKCgMuaXDjZWQIGAKGQJsIoMPOThJjD5JOGCR2nMZNuI2iTBg6tUHKBFW1paAMjN3s0k3CghnpfVPu69rAXw+xwIANV45tkRqJ1/GZTFlQV8HvdOj6sujI+7bKb+nWQ8AY3np4jTQ0El8M2e666y6/xd51nbCRgmOZGQKGwEgQgDlCh1UZX114j3PRjT6vHqPctEoXfnCp4yDGSyNpkBdIJnqQCb7X2yA9kAcBEpLaqsSxzvQP/UPfeBgf01XztXDDI2B+eKXXGhkChoAhYAgUIyASTO/HWYxlnZyg5nbYYYdcP6jFKTX3Fn/VG2+8caPlkGPSvV9wfMsajRcBUW/w/tbVH3STpcFXr+hpO/HOUCtZ/Ps+9dRT3o86/tTxnW00GQgYwzsZ7WSlNAQMAUPAEDAEDAFDwBAYEAFjeAcEzqIZAoaAIWAIGAKGgCFgCEwGAsbwTkY7WSkNAUPAEDAEDAFDwBAwBAZEwBjeAYGzaIaAIWAIGAKGgCFgCBgCk4GAMbyT0U5WSkPAEDAEDAFDwBAwBAyBAREwhndA4CyaIWAIGAKGgCFgCBgChsBkIGAM72S000SU8stf/rLDZVFIs2fPdssvv3z4yO4NAUPAEDAEpgECP/jBD5z4oO2rCW6+cBVnZAh0DQFjeLvWIhNcHvxyfvOb3+yrAT4LN9poo75n9sMQMAQMAUNg8hH4zGc+4w4++OC+ipx88sluzpw5fc/shyHQBQSM4W24Ff71r3+5O+64w910003eSbucP+/kJCAn57K7DTbYwO27775uu+22c/PNN5/PWU4xcttss4079dRT3Y477thwaUabnDK89913X5bxuuuu6xZffPHst90YAoaAIWAITA8E/va3v/nDG6jNvHnznJx+54zhnR5tOx1rYQxvQ6365z//2Z122mlOztN2zz//vE+V02HY2llppZWcnD/vOBXo0UcfdW95y1ucnLnu5p9/fvfGN77Rfe9733OPPPKIW3vttRsqzXiSUYZXDgAcTwEsV0PAEDAEDIGxIPDggw865jxjeMcCv2VaAQFjeCuAVBZk7ty57sMf/rB75plnfNAPfehD7oADDpjCwMIInnXWWe6II45wr3/96/1xmLfddpuTM+odxxzOmDGjLKtOvzeGt9PNY4UzBAwBQ6A1BIzhbQ1aS7ghBIzhHQJI1BcOPfRQ99nPftansskmm7gvfOEL7lWvelVhqoSBIVZ617ve5a655hr9ObFXY3gntums4IaAIWAIDIWAMbxDwWeRR4CAMbwDgowKw6677uruvPNOn8Lb3vY2d/XVV7tFFlmkNMX//Oc/bp111vE6vgRG8f/9739/abyuBzCGt+stZOUzBAwBQ6AdBIzhbQdXS7U5BDrH8H7jG9/wOq1VqnjssceORQ0AhnWXXXZxN998sy/mm9/8ZnfDDTe4BRdcsEqxfZhzzjnHHX744f7+xz/+scO4a9LJGN5Jb0ErvyFgCBgCgyFgDO9guFms0SHQKYb3v//9r1tttdW8V4MyCJZddln39NNPlwVr5T06umeffbZPG/3bn//8526FFVaolRfM8s477+z1d5999tnMa0OtRDoW2BjejjWIFccQMAQMgREhYAzviIC2bAZGoFMM7+233+7e9KY3+crA0KYOLPjRj37k3yMdxQBs1BSWkbwvueQS9973vrd2MR5++GEv1d1tt93cl770pdrxuxjBGN4utoqVyRAwBAyB9hEwhrd9jC2H4RDoFMOL8dZjjz3mvvjFL7qZM2cma7bpppt6lQdOd9liiy2SYdp6+O9//9szqfjWhXApBgM8iHeFX/ziF94373nnnecOOeSQtoo80nTrMLyohaDK8cADDzgk+7Q3ah0rrrhiVuaf/exn7t577/Vu3tZcc0237bbbukUXXTR7bzdTEcDbB2pBnHjHgnHLLbd0L3vZy6YGDJ488cQTDp30WbNmeVd5wSu7nVAEfvWrX7n777/f74KtscYabq211vLtq9XBowz+wvERvsoqq7itt97aLbfccvrarjkIMG7hWeenP/2p9y+O20lccRXRn/70Jz+v8X1N5/HLGN6iXmDvOoGAuMrqBP3lL3/piVS395vf/Ca3PDKI4+C1J2oEPWE+c8O19UK8Mfj8KQN/X/nKVwbOSvzu+noIUzdwGl2LuP3223tcisolzG1PDtnow1Hx5CrGez1xZt77yEc+MiUM/UN8FhclP/Hvvvvd7/ZE6t/bc889e7IoqlUfMaD0fSrEk3vxD52bjuyUZDifeeaZueEm+QVjijjE74mufU+YlU5XZZj2p2Jf//rX/Tga9wF+ywK9JwKF3rXXXpu1eRhOvMd0GpsmCvfPf/6z98lPfrK31VZb9UTYUCtJWRj25AChKdi94x3v6JFuiuS49Sw8/W86kxwz7OsqfninczWtbhOMgOtS2cuYWFFh8B/UQQcdNJZii9uxbPCC6c4b5MZSuA5kWsbwihTRMx1MsjCvF154YU+kvD1R6chw5R3Ycl155ZV9mHChwbPpSiwGwglVTt6rXFXZ8cgwvOCCCzyTGzIzIu2bkhbMTxhmumLLeBHWs6vf7TDtT+Med9xxWT2POeaYHgyI7ED11ltvvey5flvgIUa/fgEQYoNQYTqTeNLJsKDecipkpeqyaFp99dV9XBZPcoR6XzpnnHHGlHRoT42jGPPNTVcyhne6tuz0qVenGN4yWJXhTElpkJSyaifM448/XpZU7fdI23TQ4nrUUUfVTqPJCB/72Md6e+21V6N/++23n58kBy1nGcMrus4eQ9kC7P3hD3/oy4a6hPhyf8899/RkOzBjgPW9bMP2xZ0uP/7xj3/0YVCV4f3rX/+aSfWuuuoqDwdSXcWL66c+9akpMCHRDcOIa70pYabDAySbYT3Ff3YnqzVo+1OZyy67zNcRhhbGI6TPf/7zffUHC5XC6Ter+MAQTmc66aST+rCoyvCy6wJG7EBBxFPMuKakt6Ku1ReGtikT6kwy9sbwTnLrvTDKPjEMrxzJ6wcPBo2UhOaDH/xgNriwTds0ITULB7gU0910nnnpMTEiIQ3L09S9+ATOy7b0uU6eqYChRET0B6cEEZ/GffUhLUj0DPueU0/q3zZdeeWVPu9h8kHqU1eiw0KGOtLPb7nllkrZi8cQH2fzzTfPwodSPdJLbVfDUIf9RlzlZfHbuBkXpl/72teyRRPb2V2mQdqf70nbMTUuyXHn2XvtW6Lr7VWHNJ5e5fjz1uFpoh9QSHaGWBTXITniPZO6yhHvPaSwZYQaleIjHnV88COPPDJ7xjsW7DHx/Ws8rm0vKNnFueiii+Ji1P4N4yrG2L3//e9/teIaw1sLLgs8BgQmhuHVSR0pZIpUwsHAMgzTlkqbZ0h0w8ErllDmxWvruRhP9FARaPIP3dlhqIjhVekuE3pMDKwweCG+MCkQ9Qvfof/bNrHVS55inDhUVmwrszARA5da6cCM1GHqX/va13rs0BeEHnrooT4swfXJJ5/sKwNSzhBv7ufNm9cXpskf48aURTK4TgLVbX8d++S48mT1dt999762DnenkExqP4ABZFxpk4r6AeMAdf+1MKXf//73e88//3xhUdBXpuzXXXddYbj4Jfn8/ve/jx/n/qbM5INQBaIvhWMS766//vop8Ue5oGS8zCtHXVzZIaV+4n6zFtNrDO+ULmAPOobAxDC8qs6gjFCMI1tFDIB33313rY80Tifvt3iQyCYG9LKMpiJQxPB+9atf7R1//PE9lZCEsWEIGazDP1QZlGA8L7/88t4oVBmuuOIKXw4G7yKCYfzc5z7XY9syj+iTTHowvW2WHWkMW7W6XRoaooHpuLdbJxHTvDbt4nP04GHKuMaUWkwyTirB+CFxbXOxo3mV9QN2KMIxIG+s1/S4IrEnDjtIbRFSb/DVb/jGG2/sKyfM4d///ve+7Ee5oNTxk/EoRYPgqjYBjNlVyRjeqkhZuHEhMBEMLyt+HQiHlUIOCrT4B87KwP0wxLYTUpnpRkUMb1FdYx1DFjfjIPTA6WcpAxTKgyeRH/7wh97KXVUGjj766MKishPAAglsqmyfFiZW4SVMbyx9Sullxvq7SPfaoOmAaRu4jCpNxV/HT66pRWfb5dFy5H1b5M8iF5UMLascLFRaLL4pvCSwqPzd735XGr6JAKrPq+VMGVHH+ruE1QVpE2XQNJgPGYuK9P0HwZX0VRWmqpqLMbzaKnbtKgITwfCiW8iAsccee4wNR92SpxzD6GKxVQ1Dcv7554+tLm1lPCjDiwsunTy4zpkzp60i5qbLVi7GdK985SuTOuIYhmkZ2TpWHeoyhpcMkW4Ttwn9utwK/P8LXJNpOfVK2WMKt7IJh8pQ0zRdMG0al1Gm14XFZFk/CPFQjy18h1Xpqaee8n2eXbi2CTUL/a70+u1vf3tKtqNaUKJ2QDl++ctfTilD+GAQXInPmIj3ltQYEqbPvTG8MSL2u2sITATDqzqKdXW1mgT7E5/4RDbQDaPSwPYzA1QV6UVR+ZFmIDVp8g9PFMNIIQZheNlyVeZRJ5A2tyfzMMVAjPwvvvjiZBDKycSqW5fokhO+CsPLhE+foZ7D4JssWPQw9s6QYgJS261t+DeeLphGEE/Uz9j7CVvzo6ayfhCWB5dffFeHHXZY+Lj0/n3ve5+PV1dfvjThKEDsnQHhBWNDTPGCEpeaTRPqKGCVMpiL8xoU17lz5/o8WDiVkTG8ZQjZ+3Ej0HmGN/QVWmbE0CaYahTAAMPfIGX54x//6JmeYb1IwHTF29ZarmGvw0j6ihheDD2QMuy///59B3boVmdY7tSWK3q8SFaLtkSHaX+doKpui9ZheCmXevloe9HGLkiIZcrID2vuMAz3bTDi0wXTYfrVqOLKiYQ9ttaRLIYMWLyYDPV3tWzsOqHSklocaZhhrnX6gfqhVgPMqvmiakQ/PvTQQ6tGGSgcerLht8OYF9OoFpQqRWYHqYwGxZW+QR8ifhkZw1uGkL0fNwKdZ3jPPfdcP8DgtmqcBEPA1o4Odij11yHiM6EweOB5YFjCzRSO5pHYNPX38Y9/vPbpXmE9ihheJlTFjqueqMc2f/gcfbQUKSPHCWxNEycoUYYiPbg4z7oMrxz16/PAYr5NUt1ixTTF4MRS4Db0d6cTpm22VxNp64E82uYYVUEPP/xw37fF+5R3GT3Yhe3rpqlOP2BM0DqwmxISTHzKHWUYhl0UBAFt6sqrCoGWM7XDE0uBCdvGglLrq7tOIRbh/bC44nuYOtCfisgY3iJ07F0XEOg8w6vqDFgSlxEDXZuDHXq3OtChY1bVgI4tbSSbxMXdznSlPIZXt94UO65soSMJYdAOn6tj9xCjUCKJRDiPaA8YPJhiLJMxfkv9xVJPpK6Uoc5Ro3UZXsrMYocJuWjy47hqmFbKP4hbNKTgIZ6x5T55x0xx6lAKxXg6YAoGLGaQUlWx/Ne6cyU8eDEOcexviuiTmn6eMSoGj2y70wfw4cqYkKJB2j9uT90+1z6q/YFvLSa+TcpEGDyh5NEo+oHqmYbSRIQD7Ijpjpa6BkuVU9U3YLzyCAOuAw880AsvBvHJrH6SFdOUC0xVH9AwSLiLCB3gU045xfeh1HjFM4QlYZ/57W9/69usiiBoWFxR86IuGLEVkTG8RejYuy4g0GmGF3+AOmigDlBEMFBIYBkYy1aiRekUvUPKwMCjZWJgK2OwOTBD3cLUlQoXlaWL7/IY3lDCAHYM4DBeSKkVS73GAzjSEp3sihhScEZCpekUXTFADAkGmfA33HBD+LjwXpmJlIQnLyLW5OSD1CtFYKLMB+EG2WL+8Ic/3IdB6B+UCTN2WUY+eYzcdMAUnMNvlr4UMg6pdtBncXtwYluKlNECS/5SPpTjvp6yfI/zq9r+ul1N3tQPNTB1VaVl0ncsMpXwzazMMn0zj0bVD5RRVLUE3IBRPoQF4ZHAsU9pLTfjA/XU0wb1eXjlOPMQk5/85Cfh69L7m266qS9+rGuszGWYR96CkkVQ3HfCeOE9i5Ww7VTFrop63LC4qrpIShgRAmYMb4iG3XcRgU4zvOoWpWyFDLDor+kAcfDBB7eGNdaq4fY8TNZdd93Vlx8TF0y3bn/BCE73M+oBII/h5Z1K6mkjDABDZ/iq46ftxySCqkPIqMS6iaSphNRcmWIkQPxmwtfDSkgXKTGLJv5CHUfS0PxhrqvSIAyvMtZ5BmKx7h/Mb12CmQ6l5qSBxA81BmVuFGe9hhOp5jddMKU+SF+1rlyrfotxe+R5ZwnTpx/y/cd0+umn95UhJYWM86va/qg0af3weBKeAhaWjTAsKPm2woUPcfJsEkbZD5Rxv/baa71PYeqvi1w9XINnqf4K3soEqoQ7bgN+x6efFUm1U/FZLMWYgj/lDOcFbQ+uqQUlkmb9Hhk38a6C0TCLbh3LYJR1zIrrrPq79KsyGhZXyko9ylSfjOEtawl7P24EOs3w6vbsJeLZoIxCZ+CsstskGCbKpAMTgwH3MHVIL3WwY3Aukja0WcZxpF3E8CKVgWFQbLiCj24xM6GFjJpiyiCbcvuj9UOvD/USwqODHNM+++zj3xX1IWXGfy3+nqvSIAzvySef7MtSZGSixm2KU9XyhOGYJFXnWdNRPMPf3KcWk9MNUxYy9DWte4rZDPEL77FOJy7MSd4hI6RPHyRcnkoDxpDqy7tIKjdI+8Ngx5J96so2OWMV389WW22V1V9xYHwt+i5G2Q/CXSAMw8Ay3J1ApQJd4yI1H44Zpm4w/HmEKoB+74Qt26ZPpQPTy6I9HP8V09SzmFklTRXQsGsQL5BoE9Jj7MojXeRwmEcRNYEr9aU8fANFZAxvETr2rgsIdJrhZSuFE4Cq+AAETLanqkpvmgCfgQpH6SeccEIPBgjmjO03GC/UF+KBrIk8u5xGEcOr5cYdG4zDI488ksQHDw0MnD/60Y8qbT2rxIYJPbVVrdKuFDOsZVIJCJNqVRqE4dUdizymKMwbjFI6l2GY1D1Mihqx8N2AIxb8fBtsoYaMH5NYakE2XTHViRsDwq7TIO2PvivHSjNm0tYx0S/Y+UDiWGVMHWU/QKqrTCNXFod1jXuV4YpVlmIc9DeLvVT/1/epKypsKg2nP6G/zYKHvFlo6gJb65JSAyA872GOY+M88lS/3ajC5ZGqKTD/FFETuJI+5WXsKCLFn7YzMgS6iECnGd4uAmZlykegCsObH3uwNypRT50mRoq6zVgk9dc0UkxCXqkGYXhPPPFEP3FUYXiR8qSkr3nlQZJ31FFHZUyDbgWH4cNTrJjAYKhTizLFY7phypYx9U4tjEKcunBft/3bKPMo+4EycEhfdWcPhjA2MC2qpx6HW5XhhYHLk9qn8mEMUQluSr8aDxj0r/AvdSDEMccc48OkTmgjX/W6ASZ5pGmU+SxvAldVtTGGN6817PmkIGAM76S01ASUc9QML1IqnVxSvnthYHWCKlJXUGMytv+q0iAMr25jYolfRjDqdVywxcY08fYj0rJYZSTFeE9nTNHpbsP1VllbDvK+bvsPkkdRnFH3A91lUf+77Ezot3333XdnRS1alKphG7YTZYRuMukjla1C6hVBy8Q1dvEW2hzwPo/xViPmlMtAyqI6wkXqCmqgV7SQJ60mcMV4kPrEYwrph2QS3hANu+8iAsbwdrFVJrRMo2Z4mawYiPMkD6oPmWddrzBjJEc6dSy2B2F4VdqMAV0RIRWiPLFLsaI4xx9/vI9DPP5Cw028BsQ6vajhpGi6YoqqB5N/yo1UCodxPhuk/Zsu7yj7Afr92m/DLX7VeUZ3F8LYs4jpUskoqhhlhOpZSkqbFy/eHaEvhcavqq6k9YBpzVMbUW8yKX1k9YjAQr3oEBz0m8kLrxN51BSu4E5e1KmIjOEtQsfedQEBY3i70ArTpAyjZniBTSePeGueyYRJg788N2AKu+oq1tHnQ3rDJFBHCstkTZy8iZDyoCOIPjKTfR3C2py09Y/6gwn6moqRvsPCu4g0/HTBlLriP5W+8NxzzxVVfezvBm3/Ngo+qn6geqYwkSHp96JecNCHLTJIU319PB4U0R133OG/kyJj2Dg+tiH6/XBVgzpctsWuxXbbbbekazpNU721xAtfxgVV51BjXo0TX9U3eUpHWMM2havqFBfZQZCnMbyKvF27ioAxvF1tmQks1zgYXj3qk8EYoxwYBoxC8MmM5LeK72OMfJjEiqznaQ7UAlB7YGBXzxBMUGy/Ik3BfU8e6RYxTEQRsY1JWVK6f0XxqLtuhRIf5o5r+IeUl7KW0XTDVHV3YQC6ToO2fxv1GlU/wO8u/TTWWVVGEp/FegQ5DGYeqUS46DtkfGBsiPPKSzN8zhgTfk/xN4b+MWoKoeQ3jK/3yhiy46NSXPqojp8p/XuNq1d2LMifcSiPmsKVAzqod54Khuav9TKjNUXErl1DwBjerrXIBJdHB2wYC/2rqiM3TLUx8GHw5w8mlyunKdXJG/3Wou1Syqc6sJpXeGVCwMgnj/CvSZiyyYAtTSaOQYiJlq1XXFShl4wbOKRJ4MOEWoemE6acJlbkCq4OLm2HHab92yjbKPoBfoD5NpC8hkSf1cOE+NaK1FFQhSCNMvUlvpG5c+fm+vIN80/do/aE+hCLRxaYMJWoTrHIrkPoEOt4olfSq3NoEtJd6kz/TlETuJKuLu5TXjPYSdKxXo8sLxvjUmW1Z4bAKBAwhncUKL9A8lCGl0FY/xjYR0FIbhh8mSTLJCyp8qDTSplTTuJT4es+04M2MH6ZFDJMJ6Wl2i1n2/0AiSynw6UIjxrosqeMUsPwHKbB94s/9kkidoxY5KR89ZbV4/bbb/d1zjvJrQlcYcDBNW/3S43ndLznagxvWcvZ+3EhMIOMpZMaGQJDIyDGDU6Og+5LZ6eddnKLLbZY37Mu/hDfrG7VVVd1so3qRB+20SKKpNktvfTSraTdaEEbTswwbRjQCU2uzX6gkGy22WZOvAk48cbiFlhgAX08ra+yGHAbbbSRE+m2E8a5lXofd9xx7qSTTnKiUuLWWmutKXiKfYQT1259z9dff303c+bMvmf2wxDoBALj4rQtX0Ogawicc845XpqBvmCThDsytmTLjOeazLMraRmmXWmJ8ZajrX5ArdQdWZmO6XgRaCd39aCQcjE4bI5qqHf00UcPm5TFNwQ6gYBJeDux7LBCdAEBJCavec1rnLjx8lKLxRdffOhiiTW1Q/okOqRu5513Hjq9SUvAMJ20FmunvG30A0oq2/Zu1qxZTrymONEhbafwHU9VdPaduGRzovrh1l133UZKK9yJE6M6J76GnRgNugUXXLCRdC0RQ2CcCBjDO070Le/OIcDWoBi+OLblrrnmGjdjxoyBy8j2qlhuu3333dfJiVEDpzPpEQ3TSW/BZsrfZD+gROKZxKEyJe7znLgicwsttFAzBZ2wVMRjg9t7772d2Ev4v6WWWmroGsyZM8eJfq4Try5utdVWGzo9S8AQ6AICxvB2oRWsDJ1CQKye/UQqhzd4ydGghUO/TlwluVNOOWUoxnnQ/LsUzzDtUmuMryxN9QNqIFvtTjyaOPES4JZYYonxVaoDOSNBZ7yC+RfPGkOV6Oabb3biAcLJscVOPDQMlZZFNgS6hIAxvF1qDStLZxBgAhHrdLfwwgsPXCY5BtU1oRYxcAE6FtEw7ViDjKk4TfQDii6+rb1B7DC7MGOCoJVsUUNAHWvRRRcdKn2YZvF0M9TYN1QBLLIh0BICxvC2BKwlawgYAoaAIWAIGAKGgCHQDQSM4e1GO1gpDAFDwBAwBAwBQ8AQMARaQsAY3paAtWQNgemMgBzw4eSgD/fmN795SjW//OUvO3yvhjR79my3/PLLh4/sfowIXHzxxe65557LSoAV/iGHHDJF1xyjJfxIb7XVVllYuzEEDAFDYBIRMIa34VbDKOPSSy91H/rQh9x8883XcOqWnCEwfgTkCFi37bbbOjm2OOl9YocddvAGL2FJsSDHiM+oGwik9F7RrZ1//vn7CnjTTTc5OaLanXnmmQ73V0aGgCFgCEwqAsbwNthy+CzEpRVSEaxczz//fGN6G8TXkho/Avfcc4/bYost3Otf/3p31113Jf1zKsN73333ZQXGP2gdAz45atXBWMNwIUnmj5O0XvGKV7gNNtjAu3rbbrvtsu+LheY222zjGfAdd9wxy9du0gjIEdreMIm3Rx11lKNdUwwv7z/xiU84OXrbnX322e6www7jkZEhYAgYAhOHgDG8DTUZDtCReuEmR07Vcs8//7z7wAc+4M4999wp24QNZWnJJBDA+bqcuORuvfVW99hjj7lHH33Uh1p55ZX90cFbbrml42+TTTZxeFHYeOON3aGHHuqOPfbYLDU5m95bgGcPEjccX7rsssu6ZZZZxq2zzjpuzTXXnBLqmWeecRdeeOGU54M+OOKII2of04y1NYwpuMA0IsHjiFDKDA51diFQU1DH9o888ogD0xQpw4vVeF3685//7A8Q+PSnP+2/IeK/+tWv9u200kor+aNjcYRPu+IY/4orrvB1YqHJ0daUa+21166b7Qs6/Nvf/nZ3ww035DK8eCtBdQU3Vfyx0DAyBAwBQ2DiEOjEeW8TXgjRcesJA+WPpRWpbk8covfe9a53+d+i2jDhtZuM4j/77LO9Aw880GMuH2HfVRimnjCnfc/CMMccc0xfJU8++eSe+M/NDR/G1fvVV1+9J5Kw3t/+9rcsLVn81EpD08q7iuP+LO0qN9/+9rd71J30xJ9mb6+99urttttuWZl4d/vtt1dJqidMT0+kuj7uLbfcUhhn++239+EKAyVeCvPa1058O8LATgkpTHxPtth9HpRJ24rjm3lnVA+BXXfd1WMpEt7ciL/73e9824Ax90aGgCFgCEwaAm7SCty18sJoKbP7la98JSsek8fhhx/uJxLRdcye203zCNx22209kTZ6rJVZPP3003uiWtITn5JZhiJx7ckRnH3hCE87pUikjH1hyYM0+RNJV0+k9z0YXc2Tq2y592A0IdnSz97BKMBYf+lLX/Jx77333t5BBx2UvSfuiSee2OM5aV999dU92WruEU/T/+Uvf5kqZvLZQw895OO94x3v6D3++ON9YX7yk59kaZI2+ZXRlVde6ePst99+ZUF7dRleOSmqb7HC9wTGZfT5z3++rx4sMo3qI1CF4SXVL3zhCx5vUdeqn4nFMAQMAUNgzAgYwztkA8CEID27//77kynBYCGBMmoHgZiBpS3KmCVRdejJkb8Zs4RkOEXiiSALA2MIYxATCxsYX2VK9frEE0/05LhT/xym9cknn4yj9i644IK+eOLdYEoYOZ44CwOjWpU233xzH0/UapISuTPOOCNLl/KFC4M4D1H9yCSvSK3LqA7DK54CeqKOkJVFDKT8DklZHrynzCwwFPPPfOYzVaJZmAiBqgwvuxe6AJs3b16Uiv00BAwBQ6DbCHSW4UVChQSMLUu2o7kiKY2lVd2G10rXJgI//elPM2YHpgfJoJy+VCnLp59+OpMK77nnnsk4v/3tb/vSRx0gj/bff/++sO9973t7okvsn332s59NRqvC8BLxtNNO8+mIp4NkOvFDGFRlArked9xxcZDet771rb4wRYyslhMmugpVZXhhWEU3NCsH92KsViWLLIwYUmXxRU85e2431RGoyvCS4ic/+UmPd943Uz1XC2kIGAKGwGgR6CTDO2fOHD+oMsGKlXYPncETTjjBP0PCUDQ5jxY+y21cCIQ6pcrcwQDXIVQhiJuS3JIOTLGmzbWI4Q3VFwiL+gMSW+7zdG+VkdQ8UhJeyvHDH/7Qp4O6QxUSA8q+cqdUatCN1Xy53n333blJr7feej7sddddlxsmfFGV4VWVH/LPk4KH6abuv/a1r/myEZ8+YVQfgToML2pB2m/EM0b9zCyGIWAIGAJjQqBzDC+6iwyoGNTE0h7V2UPia8YpY+oxHcn2oosuyiZe+ssee+wxUMlQbYBBS1EdhhddbmUE9Przn/+8Jx4fUkn7Z1UZXvo6BnG/+tWvctOKX+iikZ0R8c4Qv+4po6hlFa8iU8LwQHWBCYcxZhWqwvDqYkPzv+SSS6okPSWM6iMXLUamRLIHfQjUYXiJqAsgDHSNDAFDwBCYFAQ6x/CKD03POLCNGxMTrk6Q49y+fOCBB3oM9uhuMnEXWTdTB8qNVFr89MZVst8DIAADGHtdoB0GIdpF3Fwlo9ZheLFc177JtcqirCrDmyxchYdFi0KMz7S8MMV5hPEf4VgYVKUyhpeFbKh7iw5vUVmL8oWZp3znnXdeUbCh36GrP3fuXL+AufHGG3vxbgJ9BcM+8Lrqqqt6Tz311NB5jiqBugyvSubFLdyoimj5GAKGgCEwNAKdY3jVKAIL9ZjER2c2SYtT+vh167//8Y9/9Ll1UoaByRsjpRQxIWudYIKQBBoNh8DDDz+c9QNtAxiOpqkOwxvrxOICrIzaZnjz8keaq7hxLTJAUh3bOu71yhhedJrD/EPvJnllznuOagbf189+9rNkEHaMMBqVU8Iq63eHCaGHHS+utOww6hhAXnvttX310fd4NZgEqsvwqqoOuJct9ieh/lZGQ8AQeGEg0DmGV908MZjGlu1qBMSEkqcX2VazIZXCgpy85Vz5HlLecCJEMp0iXPjoBMgVKZHRcAhcfPHFfZjSV9qgqgwvkz59Imznyy+/vLRI42B42W0IffEijSwisKVeZeHCNMoYXnXjR7qkj1uyNkgOyuhrE6SvdQhjP21TXMqxG4DfYt3S1/JrGDm8xO/46G+uddRQ6pStybB1GV458S7DpY7nkCbLbGkZAoaAIVAXgc6dtCYGNk4mJpkrnJOJxckE40+zCk8yky01J+6ofBj9J/qS/jhfsdJ3YlzjXvayl+mrRq7CnLhDDjnEl+k73/mO46StRRZZpC9t2ZbtO1VNrNDdQgst1BdGpGn+5Ki+hxPy4+Mf/7iTCbzR0i644IL+RLoNN9ywcrpiKe4++tGPZuGFgXIclVpGsoByslAqDMYpXXoELielLbfccll4YRSd+NHNfnMjqgzu+OOP7ztRTYwtnbgk832kL3D0Q9xouYMPPjh7KpIzx6lXTRNH9D744IP+pDUxAPVH9IrU059oVoQ7J9EJQ+qLI4Zzvu9XKVvRSWuiGuBmzpyZJcOxtqK+lP1u8kaPQdY0GVeOPPJI/Vl4lQWL22effXz9RXrvQpxEcusOOOCAvvhyWIkTvWmnddeXImF27373u/VnJ69lJ63FhebkNcY/6Ktf/arbeeed4yD22xAwBAyB7iFQl0NuOzynloX6fUhRcdavJ0ahP5baRvvgBz+YSR0I0yTh6kolXXJMq09aBvosP2lVL+2N88Q3L+/0jzSK/J3G8bv0G3WOUKKtdWriWtd/6mGHHZZhSv5V1AfAEqmr7iCkyk39MOZSiiW8SPYuu+wy/3fSSSf1cD0Wp4N0E0v2KjQqCa8sVHroW4b+btHbZWu6qD+GPoDruAMskvDGdR5U97oKvvRZtQnAP3PVOoSeCFLliw8k4bvGnzB+auP+kKcfXqX8owpTV8JLuXQ8bFt3elQYWD6GgCEw/RHonEoDkDPRphiT1OSjTQQjopNNXQZK08i7chIVaTNpqnGNThKaJ0xYTKFzf8KhEjHJxEIDPeom/8KjeKtic/TRR2dtDa51T9iK/feSBkyhtq2WI2Z4ta1TV040g4Gs4xorZv6I3zbB4Kq3E+rBd5anHiRS8wxnFqJVqYjhRTc/xG8Uhpy4z4rbtqguOpbkGfPtvvvufXUI7Q1U55k6sshILc6L8h7HOx3L6pRVx2fZ3RhHkS1PQ8AQMARqI9BJhpdaMAmHEyP3TEAcBpAiJnJ0fPEnWmdyS6UVP0MyhH6eHhmLBXZcNnR6YwonP8Kfc845fUGYiEel4zfKvPoq2cIPXFiF+CN5rUsx04LxUUwxw0s+GFhhpIQkGN+7GEvlufSK04t/j4Ph1TKgz6oYsnuSqkOoM18kCdY09VrE8LI40XxhmrpIeIDhe095gmFsUemm1gOclPjO8NZQZAiYGis0ftNXTh0sW1QOwvDqSYUYAxoZAoaAITAJCHSS4UWFQBmS0MCFCQaH/nkeEUYFeMyoMHHHTDZGbjoh6jU8KQtJIBIgpElt0yjzarsupM/CQzHVax3JKmngdUDjck1JMGOGt2lfr3E/ypPwwkCFZU3d1zUeQrIapoNKUEwYqmmY+F3R7yKGF48JmuawR27ji3kU309YVxY4Wn691vG8wiKJMWwUBPNNXrhUK6JBGF5t49ShJkV52TtDwBAwBMaFQOcYXqzIOWGNyeRjH/uYx+WKK67om2TQ8a16hGwbwGr5dMI75ZRTpmSDFEff6zWUkomRi38/igl7lHlNAaKFB6GOpWKLRX4ditUiOJ0spq4wvHj20HrmXb/3ve/FxS/9zY6Jppdiwq6//vrsfdh3yxJWZigVLtR7HkbFB/1cJK2jPvwgVAcBOxbkVYmdIfTE+WubUE/QdmiD4dW+o2N02/Wx9A0BQ8AQGBaBznlpOPXUU524AHIyoDqsrGfMmCHzinOyNeewLMdyHjr77LOd6M36+1H+k4nE4VkgJNn6dOuuu274yMkJW+6II47Inok018kxyf73hRde6A466CB/j1cKLLvx+CDbhFl48pGjZJ1I7hz34LHxxhtneGhAWSA40TF2ovfshGlxomfsRNrsXvWqV1XOS9Mqu4rEyIlksCxYrffzzz+/W2211TKr76qR11hjDffoo49mwcWIrM9zQ/Yi54Y+Rl9TwgvIkksuqT/9lb5W5qWhL0LNH1W9NNC/RIWiMHU8Cqy44oo+DN+N6I87UVPw39J2222XjDt79myHFwElUd3p825y6623OmGa/GuRgLulllpKgxZe1VOBDE5Twp144oneqwUvZGdkYK8fl156qdt3332dLEqcMJBT8mnrwd577+1kAZ4lL6oPjjqVER5CGAPEtZn3/KBjAV5Bwj5GG4ixq+/ba665phNDQ7f00ktPSV5Uu7wnELyE4PXi5S9/uXvRi17kVl11VceY8IEPfMCJ+z4fj/IyNqywwgpurbXWmpJWXS8NJEA64mPcyYKjz9PIlMTtgSFgCBgCXUFgWI65yfhIkQQX/5eSHOAHU9/j93QclDJ4io9AplxqHa7lFTdqvrjUQaUjvEMdgt+hpwHCoC+KBCtU6cAwCoMxJaSaSOYIw/Y4+nSkiTU+VCUvTavsiuQ91l3Uug17lcVLWfZT3of6pZp/nn73lMjyYJIkvKnyFz1DgqiY0GZI+1IU6tMSPj68Ifzeqno4IB+VLKbyrHqkcSquPkP9hDo27Y1F0+cqi82eLEp7Z555Zp+6UogtmIX6uxof6TPqSqEx5aGHHtpniMs3zx82BxBjHwZgpIlOtX5rXOM81IgWFRsOt1D/4OzkQOi4q1cbTY+8YhsCH1j+DaLSoDjgQcfIEDAEDIFJQKBTEl6kNcsvv7yM0c77O8XvaUwbbbSRl5IMIx2K06zzG8mMTDBZFGE2p/iAFUf6U3z0ypazl9BqxBe/+MVeAicqDQ6pkRISG6TF+LYU90duscUWc0j4hAH2QUT31EuP+aHSJnEE71ZaaSX/XhYK3ncx0mGlvLz0fdUrEiMx7kINpmqU0nBIy/fYYw+HNKsuvfOd73Si95pFQ2oujH/2u+imioQ37I+klfLDW5RH2TvKim9nJeoyrB/e1A4E/nSR/sWk3xLPhbFySLmRuCsh1VXpIn6O6etVqEjCS/mQRtJnIXzcbrHFFlWS9WGI/573vMcJo+jw6Uu5myZ2j/jOlOQoYf/Ny6lubp111tHH/sqOx0te8pK+Z5/73OfcgQce6P1t43dbSZ8Ls+gl0/qcq/qWlkWG9/kri2gvOcXnL4T/aCT4fHuMkfiKpv7sgLGjIwIAt/XWWzt8ZUOyMPe7PdwTjh2RPKor4RV9+WxHhnbYcsst85K254aAIWAIdAeBLnHlSKJUspEn9ZOtOi8FSZ1shuFSXeOluvXnSGNpvewvlOJoWqmjRmMdSK1nrMOrOo4cnxsS0hzNVyXK6q84NFhCvxWJb0h5eYVhJvEe40XFRK+4F4uxTtUtdo+V0uHFPZ6myzXV51JpV30mKhV96aOr3gSFOuYpYzTyiI0qkUimSPsOuqtVqUjCSxro3SquuPor8yKg+TI+7L///j5uaACq71NXYST9LogsqnrsUlQhdle0fFx1d2a//fbre87uTEwYiqn0Mz5tT49U5n1IHD9OPhjqhiSHfWT5yeEc/hWnTxKWnZ3QjkEW4r3QgIwjl7UOTevwhrtcv/jFL8Ii270hYAgYAp1FAIlBp0i9MzARphgXtuYYyHX7TguP0Q6TABN0zCxqmCausUuy0Dcv6cN86oSnEw7uyWJSRiJkeGHW9TmTbvinaXHF1RDEJM5v8gu3PR966KG+7DTNMK++ABP8gwk3ZPDAgz4Cs5pHbM/TV0JMUwwvh4yEYWBwmlxQxQzUCSeckFfkWs/PPfdcX24Wh3nEgQFh3WJ1Bo2n291s71elMoYXjyahOgXlLMNV9LWzdhapcKWi8C2GdRTd+UrxdCFJXL4dXNaFTJ6myTtdfJIwzKgyy6gfxZTH8OI3XPMKv/mwHCx4IXDS8QXXYDC2EMx82IZtMrzqNYT6x95pfGHsnyFgCBgCHUSgcwwvg7YO6LItmEl/kO7gDYGJASYzlG6AKxOyTkRyXGurUGs5ND8YT1wkvf/978/KoO+4igHblPKkmFAYdcLDWOH0P+9P3SCJcVJffkjpUtKcVF5TCjTBD+gbyuSFuMMQwEwhnWRBcInoNqoEXcOBDcxfyHDhsgumCkm5htMreqMwPym/tVUghDGhjZTJ0XS5UhakgjDr6IEOStRFGUokvHibCInTv7RPkG+RZFlxRSe0KpUxvKTD90t/1fqTPguMkFjw8k2oCznSreO3Oj7pkAVGFcIHr5Zrzz337MlxxNnvWDefBQHfPv0ijJPqH3kMry7y0RfO++aVsaX8jCeaF1d2KzjpLaQ2GV6tqzLhYb52bwgYAoZAVxHoHMMLUEjtVJLLxMy9MsEwlSnjJNGzyyaBURhS3HDDDX1GKDoBqYRHf3NNOZpXhiOUusJkEZ53VQm/npqW5hlKe0lH34d5VU1/ksKxNczCR/uK4hFfwQMJHP0kxZiUxSe9WG2kKk7KLMRlin9jVDcMoSbAwkzbnm8Ihi/0g0tfhcEqopBxSknBU3GrMLzEQzrIIkTLCAbcs1AJFxu0Bz6B6xIMcyhFhzmtQsRTA9CwXcCTMuMHGqPZ8B33YEx98iiP4dW06qiNwGSH+SMNDtULwnZLLYLDMqoUn4VjFdK2KVooVUnHwhgChoAhMEoEOmW0JgN4H4k0x7stEumFd7eDYVNsIBJGEGmQW3TRRb27o/B50/fiKcFhCAaJpwQnag7emES2yZ0cf+zdJWmeMhE5mXz6jIF4lzIkCw1NUq7OiCfSO+8OCHdGSrgLw1hFJJX6yImOo8MoCUrllQWchjfClDjZKfBtg7GPSFW9y7GXvvSl3jgOAx51dzcNq99XJQzRbr75Zt9nwAQjyA022MAbQWIstsACC/SFT/14wxve4ITJc7hRkwVnKkjfsyKjtb6A//9DGC1vhIahpehlOww3hcn1LtIwxnrd615XqZyptPUZbgKF8fN10GdlV9y64fqObw43XLHhn0jhvREn4TBmA9siyjNawwhP1AScSOXdNddck0yCcmCsFubBeCcLKD/mEEkWCx67hRdeuDWjNcYyNdyj3hjPGRkChoAhMBEIjJK7nvS8MCJRvTqubH2HhPRHJdPS+F4CkycFUamWTIJZEmJNn0lt2C4Mt9k1EIZOapyCJCyUyoTS3lAfNJWXpmdXQ6AMAdQs6M+oElWhqhLeKmk1FQZVFlQGxkkq4eV7DOm4447LvvuUMR6GqKg5YT8g3i16sSqBpksb3X777T7pUMKLCk4R1ZHw4l6NfNhpMzIEDAFDYJIQ6KRKQ1cBjNUVsIwOKdbLZJIKjVrCsDAPTBx6Fj26nWxDq09N3rGtrVvu+B7FUpvnTIAQ6eMvNCSs0gkzZ86c7HFeXlkAuzEEChCgX9LX6FcsqsqoawyvnsoWq/qU1aPp96hBgSF/uljmuxbXZdlz1DdYWEMseGGA+X7RK4ZgeImv44J/KP9UVQUfvRBjhOal4xR5pagqw4tKi6aJEaGRIWAIGAKThIAxvBVbCyMbHez1ilssJRgBfc4VLwChTp2G06u6V0PagxEKkuE777yzh4eFMB3ulWHl/pZbbtEkPBOCPl04kenkFUqK8vLKErIbQ6AEAT0wgv5VRl1jeNllgZHMW3yW1aep9+pSjO8Yo0I5oS07JEY9rui3z/hBmfkNM8uCGFKGF1119WLDO8LyFx5Mo7tRGASKT28/jsQGjKSpY0a4W8TzmNRYDom0kSFgCBgCk4aAMbw1Wix0ZaX+TfG7efrpp/cxqUw0TExFhAW5Ss1gekNjF5jVWJoMYxtLc8lHJzomTH6TFn6AQyrKKwxn94ZAEQLqVaHMuKpLDC9qAHwTGJx1gUJvMrjT08VqnqEczCWqTkoY7MIEUycWwnz33DNeqGRYw8pBMf4d4Rm7ODkvRVUYXlwhkk7MVKfSs2eGgCFgCHQRgU4brckA2ym69dZbnUzmWZlk8HeydZj9lknFyQTl5JhgbzyXvci5wVgHQzUM0FLGQyIRcvytuuqqbplllpmSimw1eyMWkT478S3r38+cOdNxellMZXnF4e23IRAjgHHkhhtu6E9JEx+3mfFSHK6u0Vocv8nfBxxwgOOUQ/FG4TDm6gJhSDnffPO5FVZYYUpxhPF1YItxmiyIk9+yfvfCLPvvfqmllvJjRMoQEwNbYZK90d2UzP7/QdlJaxinbbzxxt7wURbjmTFsXnr23BAwBAyBLiJgDG/NVoHBnTt3rve8ADPKcZ8wrCJtcdtuu21ygqqZhQU3BDqLAMwYXhNWWWUVz0iGXgO00Mrwyk6DPnLbbLONgzEbNYkKkD/id7nllht11p3OT/SJvfcJCinSb39cO4vi8GhprYAeYS6u4Zz4DNbHdjUEDAFDYKIQMIZ3oprLCmsIjB8B2Tp32223nZPtdCdHgE8pkDK84QuTDIZojP8+JQ1OMbziocPts88+7uqrr3bvfve7x19wK4EhYAgYAgMiYAzvgMBZNEPghYwAvnLxmSsGUVNgQIVAVWz05U477dTnQ1af23U8CIQSXkqAStUuu+wypTD4LoYRnjVr1pR39sAQMAQMgUlCwBjeSWotK6shYAgYAoaAIWAIGAKGQG0EjOGtDZlFMAQMAUPAEDAEDAFDwBCYJASM4Z2k1rKyGgKGgCFgCBgChoAhYAjURsAY3tqQWQRDwBAwBAwBQ8AQMAQMgUlCwBjeSWotK6shYAgYAoaAIWAIGAKGQG0EjOGtDZlFMAQMAUPAEDAEDAFDwBCYJASM4Z2k1rKyGgKGgCFgCBgChoAhYAjURsAY3tqQWQRDwBAwBAwBQ8AQMAQMgUlCwBjeSWotK6shYAgYAoaAIWAIGAKGQG0EjOGtDZlFMAQMAUPAEDAEDAFDwBCYJASM4Z2k1rKyGgKGgCFgCBgChoAhYAjURsAY3tqQWQRDwBAwBAwBQ8AQMAQMgUlCYCiGd968eW7rrbeepPpaWQ0BQ8AQMAQMAUPAEDAEJhCB7373u27WrFkDlXwohvfJJ590Dz/88EAZWyRDwBAwBAwBQ8AQMAQMAUOgKgKbbrqpW2KJJaoG7ws3FMPbl5L9MAQMAUPAEDAEDAFDwBAwBDqIgDG8HWwUK5IhYAgYAoaAIWAIGAKGQHMIGMPbHJaWkiFgCBgChoAhYAgYAoZABxEwhreDjWJFMgQMAUPAEDAEDAFDwBBoDgFjeJvD0lIyBAwBQ8AQMAQMAUPAEOggAsbwdrBRrEiGgCFgCBgChoAhYAgYAs0hYAxvc1haSoaAIWAIGAKGgCFgCBgCHUTAGN4ONooVyRAwBAwBQ8AQMAQMAUOgOQSM4W0OS0vJEDAEDAFDwBAwBAwBQ6CDCBjD28FGsSIZAoaAIWAIGAKGgCFgCDSHgDG8zWFpKRkChoAhYAgYAoaAIWAIdBABY3g72ChWJEPAEDAEDAFDwBAwBAyB5hAwhrc5LC0lQ8AQMAQMAUPAEDAEDIEOImAMbwcbxYpkCBgChoAhYAgYAoaAIdAcAv8HqwpcFKuqClgAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "id": "kM7cO8y_PKZg"
   },
   "source": [
    "Given a dataset of labeled (x,y) examples, how do you select the examples used as shots in few-shot learning?  [Albalak et al. 2024](https://arxiv.org/pdf/2402.16827) provides a great overview of such data selection strategies for large language models, but in this homework we'll focus on a simple one from [Liu et al. 2021](https://arxiv.org/pdf/2101.06804): given a test data point $x_{test}$, find the $n$ closest points in the training data (by cosine similarity to sentence embeddings of those points).  In this framework, each data point that you want to predict will have a different set of shots selected for it.\n",
    "\n",
    "This is the algorithm we will be implementing:\n",
    "\n",
    "<img src=\"attachment:2d5c41aa-ab93-49a7-bd79-84cd5fb8967f.png\" style=\"max-width: 400px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-b0Z6ptSIzn3"
   },
   "source": [
    "Here's the data we'll use.  This is the subject classification task from the Library of Congress ($x$ = the title of a book and $y$ = a choice of a single subject classification). Be sure to adjust the prompt categories in the example below to reflect the classes you're trying to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hpRtuPLYGdkw",
    "outputId": "10db8023-66a4-4a8a-f670-0ced08962299"
   },
   "outputs": [],
   "source": [
    "!wget https://raw.githubusercontent.com/dbamman/anlp25/refs/heads/main/data/loc/train.200.tsv\n",
    "!wget https://raw.githubusercontent.com/dbamman/anlp25/refs/heads/main/data/loc/dev.100.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lc_classification = {\n",
    "    \"A\": \"GENERAL WORKS\",\n",
    "    \"B\": \"PHILOSOPHY. PSYCHOLOGY. RELIGION\",\n",
    "    \"C\": \"AUXILIARY SCIENCES OF HISTORY\",\n",
    "    \"D\": \"WORLD HISTORY AND HISTORY OF EUROPE, ASIA, AFRICA, AUSTRALIA, NEW ZEALAND, ETC.\",\n",
    "    \"E\": \"HISTORY OF THE AMERICAS\",\n",
    "    \"F\": \"HISTORY OF THE AMERICAS\",\n",
    "    \"G\": \"GEOGRAPHY. ANTHROPOLOGY. RECREATION\",\n",
    "    \"H\": \"SOCIAL SCIENCES\",\n",
    "    \"J\": \"POLITICAL SCIENCE\",\n",
    "    \"K\": \"LAW\",\n",
    "    \"L\": \"EDUCATION\",\n",
    "    \"M\": \"MUSIC AND BOOKS ON MUSIC\",\n",
    "    \"N\": \"FINE ARTS\",\n",
    "    \"P\": \"LANGUAGE AND LITERATURE\",\n",
    "    \"Q\": \"SCIENCE\",\n",
    "    \"R\": \"MEDICINE\",\n",
    "    \"S\": \"AGRICULTURE\",\n",
    "    \"T\": \"TECHNOLOGY\",\n",
    "    \"U\": \"MILITARY SCIENCE\",\n",
    "    \"V\": \"NAVAL SCIENCE\",\n",
    "    \"Z\": \"BIBLIOGRAPHY. LIBRARY SCIENCE. INFORMATION RESOURCES (GENERAL)\"\n",
    "}\n",
    "\n",
    "def read_data(filepath):\n",
    "    labels = []\n",
    "    texts = []\n",
    "    with open(filepath, \"r\") as f:\n",
    "        for line in f:\n",
    "            label, text, _ = line.strip().split(\"\\t\")\n",
    "            labels.append(lc_classification[label])\n",
    "            texts.append(text)\n",
    "    return texts, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX, trainY = read_data(\"./train.200.tsv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "devX, devY = read_data(\"./dev.100.tsv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nearest neighbors\n",
    "\n",
    "We will first embed all of the training examples, then define a nearest neighbors function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RrIEtQc10uej"
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "def create_nn_search(sentences):\n",
    "    model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
    "    embeddings = model.encode(sentences)\n",
    "\n",
    "    def search(query, k=5):\n",
    "        # return the top k nearest indices\n",
    "        query_vec = model.encode(query)\n",
    "        similarities = model.similarity(query_vec, embeddings)\n",
    "        topk = torch.argsort(similarities.squeeze())[-k:]\n",
    "        return list(topk)[::-1]\n",
    "    return search\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_search = create_nn_search(trainX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the three nearest neighbors in the training set for the title \"Flemish paintings in the Metropolitan Museum of Art \"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lQ6f4MLiOfom"
   },
   "source": [
    "## LLM Prompting\n",
    "\n",
    "We will evaluate 3-shot prompting with a random sample as well as with the 3 nearest neighbors to the target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 528,
     "referenced_widgets": [
      "bf825f83a19843aba2f412ccf378fa61",
      "c3ebe77e49ae41de8ec7b41620ae0001",
      "653ef1b1bfec47298ead4f8a56bc530a",
      "89d67feed98b4480a49cadb0ca7069a9",
      "f4d3ac50a910432292aeb5dc265d056c",
      "c4c860fb5ce24277b17b0409db551fcf",
      "a6e7137bfe72474183d59d287a35c12f",
      "8790c4de18a7487283769b4d20bf18ba",
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    "id": "yOz-lch2QK49",
    "outputId": "48a7c705-81f8-47a3-9cdc-c81e49f1b8c8"
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   "outputs": [],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained(\"Qwen/Qwen3-4B\", device_map=\"cuda\", dtype=\"auto\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"Qwen/Qwen3-4B\")"
   ]
  },
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   "execution_count": null,
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    "id": "2KsDUDZKQXDU",
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   "source": [
    "from textwrap import dedent\n",
    "import itertools\n",
    "\n",
    "def classify_with_prompt(labels, shots, target_x, thinking=False):\n",
    "    newline = \"\\n\"\n",
    "    system_prompt = dedent(f\"\"\"\n",
    "        You're a helpful assistant for text classification. You'll be given an input text and need to output a single choice from the following set of categories:\n",
    "        {newline.join(labels)}\n",
    "        Pick one of those labels and do not generate any other text.\n",
    "    \"\"\").strip()\n",
    "    \n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": system_prompt},\n",
    "    ]\n",
    "    messages += itertools.chain(*[\n",
    "        ({\"role\": \"user\", \"content\": shot[\"X\"]}, {\"role\": \"assistant\", \"content\": shot[\"y\"]})\n",
    "        for shot in shots\n",
    "    ])\n",
    "    messages += [\n",
    "        {\"role\": \"user\", \"content\": target_x}\n",
    "    ]\n",
    "    text = tokenizer.apply_chat_template(\n",
    "        messages,\n",
    "        tokenize=False,\n",
    "        add_generation_prompt=True,\n",
    "        enable_thinking=thinking # Switches between thinking and non-thinking modes. Default is True.\n",
    "    )\n",
    "    \n",
    "    model_inputs = tokenizer([text], return_tensors=\"pt\").to(model.device)\n",
    "    \n",
    "    # conduct text completion\n",
    "    generated = model.generate(\n",
    "        **model_inputs,\n",
    "        max_new_tokens=100\n",
    "    )\n",
    "\n",
    "    # let's break this down:\n",
    "    #                      | we take the element of the batch (our batch size is 1)\n",
    "    #                      |  |-----------------------------| skip our original input\n",
    "    output_ids = generated[0][len(model_inputs.input_ids[0]):].tolist()\n",
    "\n",
    "    # decode into token space\n",
    "    return tokenizer.decode(output_ids, skip_special_tokens=True).strip(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LVH5pXRyHnxr"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def predict_random_sample(trainX, trainY, devX, k=3):\n",
    "    predicted = []\n",
    "    for target_x in tqdm(devX):\n",
    "        inds = random.sample(range(len(trainX)), k)\n",
    "        shots = [{\"X\": trainX[i], \"y\": trainY[i]} for i in inds]\n",
    "        labels = lc_classification.values()\n",
    "        prediction = classify_with_prompt(labels, shots, target_x)\n",
    "        predicted.append(prediction)\n",
    "    return predicted\n",
    "\n",
    "def predict_nn_sample(trainX, trainY, devX, k=3):\n",
    "    predicted = []\n",
    "    # search_fn(query_sentence, k) returns the top k indices\n",
    "    # of sentences closest to the query_sentence\n",
    "    search_fn = create_nn_search(trainX)\n",
    "    for target_x in tqdm(devX):\n",
    "        # IMPLEMENT ME\n",
    "    return predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand_1_sample_acc = sum([pred == target for pred, target in zip(predict_random_sample(trainX, trainY, devX, k=1), devY)]) / len(devY)\n",
    "nn_1_sample_acc = sum([pred == target for pred, target in zip(predict_nn_sample(trainX, trainY, devX, k=1), devY)]) / len(devY)\n",
    "\n",
    "rand_3_sample_acc = sum([pred == target for pred, target in zip(predict_random_sample(trainX, trainY, devX), devY)]) / len(devY)\n",
    "nn_3_sample_acc = sum([pred == target for pred, target in zip(predict_nn_sample(trainX, trainY, devX), devY)]) / len(devY)\n",
    "\n",
    "print(f\"\"\"\n",
    "Random 1-shot sample acc: {rand_1_sample_acc}\n",
    "Nearest neighbor 1-shot sample acc: {nn_1_sample_acc}\n",
    "---\n",
    "Random 3-shot sample acc: {rand_3_sample_acc}\n",
    "Nearest neighbor 3-shot sample acc: {nn_3_sample_acc}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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