{
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A8GyIl5bxwV5P0M/iCbtlrauSQgIkQAIkQAIkQAKZQoDKXaacSbaDBEiABEiABEiABCwCVO54GZAACZAACZAACZBABhGgcpdBJ5NNIQESIAESIAESIAEqd7wGSIAESIAESIAESCCDCFC5y6CTyaaQAAmQAAmQAAmQAJU7XgMkQAIkQAIkQAIpJVC6dGlp37691K5dO6XHYeH/IUDljleCIQAntkcccQRpkAAJ5CECvO/z0Mk+jE2tXLmy3HnnnfLWW2/JI488IoULFz6Mtckbh06Jcjdx4kT5+uuvzd8JJ5wQkyS84iPt7NmzpUKFCjHTBh1Zq1Yt+eqrr0z9/vWvfwV9+JQfr3z58jJq1ChZv369bNu2Tfbt2yeTJk3yPW468YDXdFw3Y8aM8a0vI4IlkCn3/RNPPGGurcmTJwcLMKCjheG+/+c//ymlSpWKItKvXz9zXj799NOocO6kL4GTTjpJvvjiC1m3bp306dPHVLRdu3ayceNGGTZsmCA+O4IVdFS3cP/iOHh/QYHE+zp//vzZKVqqV69uv++vueaabOVNy8R+Xo5zskLF3r177WK///77yJFHHunr4frf//63nbZevXq+6Sx4gcedccYZdt0eeOCBwI+f6jZbS8zZ7dON9957z7ed6cTjm2++MVW2HhK+9U01v0wqPzdWqMiU+95SHsy1tX///oy8ttL5vi9TpkxkxIgRkUOHDkWuu+66KP7Wi9ucl4MHD0aFZ9J9mE5tyekKFZUqVYrg+RxLtmzZErGMBgmfz507d8YqLirujz/+iDRo0CDhsq0PCjt/TvSfIM5hWqxQ0bhxY7nvvvus9lLSiUDZsmXl7LPPNlU6cOCAWVP0ueeeM1896VRP1iWcBHjfp+d5S/f7/rzzzpPrr79e8uXLl54AWauECbzwwgsCKzFk9erVMmXKFLO9bNkyWb58udnGOLz+/fub7ez+W7x4sejfkiVLZMWKFfLnn3/axeBa//jjj8X6YLDD8tLGkUE0tm/fvkZ5+Pnnn4M4HI+RAIGqVavaqaZPny6XXnqpvc8NEsgNArzvc4Ni7pbB+z53ebI0fwJnnXWWHXnBBReIZUWTFi1ayOjRo43SNWfOHBP/f//3f1KgQAEzLMjOEGfDsvhJ3bp1s6T6xz/+Iffcc4+gC/+oo44Sq0fCvNusHsIsad0Bc+fONQYPlPHrr7+6o0O3H4hyh77v4cOHy6mnniqwEiUiODHIh/QYC+YWr3gNs7pT5O+//xarO1hgQShXrpx8++23smnTpqhijj76aGnUqJFY5ltzMrdv3x4V797B1+Sxxx4rxx9/vPkS+emnn+K2B18mJ554ohQrVkwWLFhgvlgs26+7aHMhor2oO9p8yimnmDEns2bNkj179mRJ7xeAh7fVvW1uFhwPXzPu4xUqVEhQLxW0o0iRIr6sNZ37NxkemLRx3HHHmb/NmzcLjr1r1y530VH7NWvWNOkRiAcCxgf6CdoB2b17t/liO/PMM+2vO3eeRFi584AdHip4aPz++++CDxbn16KmT9W1iOuoTp06Uq1aNQE/nGM86NJRMuW+B3OMAStYsKD8+OOPZrxQPN6876MJ4VrALEmM28XzfOnSpeYZanWx2gnxgtf7F4HYxh+ef1Y3rZ1ON3CPQWHAWCncB7DeuMV5H8Z7riZ6b7uPkehzxFmX3Hw/peMzARN1MIkC8ttvv5nzjXOlgvfx2LFjzcQKnDew8XrPa/pEf3GdPPXUU+b52KVLF5Pt9NNPFyh3Tv5e1wKuxR9++MFYjf3eubhGoceUKFHCvFtxHceqd6LPgUTbl610diezayMnfc469sYCFMGfyoMPPpil/9tvzN3UqVNNNr8xVTNmzDDxa9eutcscN26cCbMsUZEnn3wy4u6ftzTzCMYBWIpTxDINa7XM719//RXp3r27XZYFMeIcY2ZdiJGVK1dG5UHbrMHXEUthicqHvJZCF7EulKj02Nm6dWvkpptuypJ+woQJJu13330XmTdvnp3PUlIivXr1ypIex3D+WV8/kYULF9r5dMNShCKdOnWKWIqYSW9dlBHrwaLRUb9on7NM93ZOeGDcpTVLKgLOTsHYGoylsR4EWY5dpUqVyOeff+5MbsbijB8/PrJo0SIT7rw+cF5VrMG6EesG1l3DxjLTm2MkysrZfmtwd2TgwIFZ2KH+GKNlfShE1T+3r0XrwRR56aWXou4nbZz1gIlYXexRx3fWPZFtS1k1xWGcTSLpvdJkyn2vY+4AZNCgQRGM8XKKpdCb+9uLAe/76OeSpXhFrIkpUfeisrQ+iiKWlcV+fuo9rfH6e/fdd5trUsfcIfzee+/Nci9gjFWHDh2irt9EnqvZvbf1vGf3ORK2Z0JOxtzheW99tJtTaCk/EUvZi1x22WVm/7HHHos6R8ozkV99p1sftjHLuPLKK82x8A9jTFF2vGvBmvxp57GGkkWVb1khI9ArnPoMEluTESO33nprVFocK7vPgUTa7kyTyJg7WHU8JTeUO+srN4IbUwUDlK3ZMVEg/JQ7a+aLybZjx46o9NpAHVDvPMnOm1+P6f7dsGFDBGV6CV7UTZo0sY/nVGa80msYLhqtF36tLs4ILuhY8vLLL0flwYXjJ507d45K6zwWtjt27OiX1Q63TOGmDOtLwg5zb4CNu2znfrI8cKN/+eWX7sNF7WNgrWURs49vfSFF9BxHJXTtOJU73IB+grZBCc8OK2279UVvK5N+5eOhY33R2fXP7Wvx9ddf9zu0CYcCYo1Xso+vdU/0NzeVu7Df907lzg86PlLOOeecKN6876Ofg3jW68vYjyPC9aPa6mXwTNa7d2/DOZF7Cs/wq6++2j4v8Z6rydzbuKeSeY4kUv/svJ9S/UzIiXIHRs776JNPPol07drVnN8glDvL7Yp9Lb399tvmeoh3LfhNqICiZvXq2eV5bcCAos/aZJ4DmjfR37RQ7qz+64jlTsTmAesZrBDaiFQqd2+++ab5WoAVDxYwp8BC1rJly8jFF18cZSl78cUX7bq5lRkokvgigBKCiwfKqkqrVq1MPqsbIQJrooo1oDNidQ1HrK40o+g686B85eC+8GCZRN5Vq1ZFoJBpOvdv/fr1o74mPvjgA6OgYpaQNe4gyupw+eWXm3KQx/lwgpXr5JNPjjtrKRkeqO8dd9yhOIzlEl/XmCEFZcypwFkDbu12DhkyxM6Dhz7OFb6scFE7LSmxlDurGz5iuXqJzJ8/37BIhhXqb7ldsesCyyseejif1viRiDU2w46DxVWtuO4HeU6uRVxTet3gaxjXLCzQYAJmKiNHjrT5ua+TePu5rdyF+b53vpTA9p133ok0bNgwctppp0Us9wuKO4JniHLlff8fLPocBBdc8yroicGzExxh6XDO2P3ll18Mx2OOOcbc35oHz208l4oXL27i3fcULGF4puFZBq8MKs7zEu+5msy9nexzxF3/dH8m5FS5a9u2rafFFs8svEvwjND7J9Ff/VhwGnXcedGL4VTGtOcr3rXgpdxZE0IilhsXvbTMOxnpcAzM6lZBvfChkOxzwN2GePtpodyhkngROs2ZDz30kH1SU6XcoRtVuyJRB7j4UMHXUdGiRe06QDNXgUKlYJ3KDBQKt9UR2rqKmn7RXaCCm1nL0l+nUoWXiIY7L7z333/fDtd4v99nnnlGDxexfL5lyWeNO7Dj0W2r5cBCqaJfxhrn95sMD1jg0DUMAUOU4SzfGosT1d2NeFj6tIsPv9aYlqg8999/v1bdTLXX8pyWOyhD1njKqHzJsIJrBlgDtP64VvR4+MXN7Oyuv+qqq0y880Ge02sRHwcq06ZNizo+lDx0b+Hvs88+i4pz1jPedm4rdzheWO97p3LnvhdxPTvPt1pLed//5wrV56DlpNbuvcAHGF58zmsQ97TeV3gRa5zln0wvdV9XKEjw4YcfRikH6FrVHhl8AGl5sZ6ryd7byTxHUJ+wPRNyqtyhzZZPO/tZbp/Y/27AcIEPVT1XifyqcociYLnUP+gRlg+8iDWGO+ow+MBX40isawHH9lLuYAVWwXPBqVPgQx5DNFTatGljhgvofnbe/4m03ZkmbZQ7VKpnz57aZmOFwBccwlOl3J1//vlRF023bt3s41suP6Li8MBWcb4gncqMl+IExcQaVG+yavcxuj9VoGxAUXH+WZM77K8Zp9XJeeGde+65UfVznlT3ttPyZU32yJIPx4blCwLlyhoUbtLkVLlLlAd8F6pAMXGy0G1ryrwmidx+++0RfBmrvPHGG1naBMVcv8ycDJ3KHSyfucGqadOmWhVjwXGXif1bbrnFToMxmAhzPshzei1aA6Ztyx0OBCsIjokxiTiWnlOvuiUalgrlDscO433vVO6syVNZriN0I6rAgo928r6Pfg7CKoMPIVhv3Nc/noHousJQDAh6VfQ6TVS5c3eJI79z6Ida0GM9V5O9t5N95obtmZAbyh3OC5QrGBncipfeQ+iu1fMf79ep3Gl+v19cX5dccolddqxrAcf1Uu6syRl28V7jmmHNx1wCKIHWIgxJPwfitdsdn4hyl5IVKqyKZBH4vMFqDxDMWhluzZ7Fb6pE/eho+fCQrWIpO7ppfjHbBbMrY4lO23amsaxDYnUpmCCr68CssIHZtCqY6YpZUc4/zNi1HjwmCXwAIZ9bMAMnUbGUZJMUMya98mFWkNVNYdJYD1x71mmi5fulS4ZHs2bNolgoF6vb1j4M+Dm9lltdnXacbmB2aryp6l4skmGFmccq8IjuJc5wr+n5Ob0WMZsYM8tU4FbA+mIVq/vfzDaG53fMQkxHCfN9j+sMszDdghmzKpj5DeF9H/0ctKxy5trEKh9Wd6sMHTrUPP/xnMIz0OpSjZqxrzwT/YV/M7egbBXM5HeL+5mQ7L2dzHPEXZe89EywhrKINcZcLIXEYICXAefMZvi505m1bk7Z2ce7zrKqi/VhL88++6x511lj/TyLcF8LnomsQD3XiIerFLdYir48+uijZqUny9CQK88B9zGS3Q9MucPNbs0Std164AWuy5EkU3lVkLzyWqq2rFmzxivKhFmDobPEIU8s8XpYIL3TLQemR2NafHakYsWKUcmh8GA5sETFsmCZpLF4OB961ldUokXHTJdKHnBtoOL3AYAXRCyxTP5ZopNh5eTq3HYWHotvbl2LWHAbD0h8UDgF7gWsbnXjkuXaa691RqXFdpjve3wMeYn7nkca3vf/c0+E5yAE7pzgvBYfItY4W4FLitx4/uCegoLglljPcK/nqvN+dm47y/W6t5N5jjjLzOvPBKu3TqyxlOaZBS7W0Baxel2ciOJu4x7Eu8H5B3c7+JCAI2z4unOeO2eBXteCM965DTdcEOTBR3Y8yelzIF752YkPxM+dVgjaOhQ6rIQAsZb0si1fmsb9a3XduYPMPvx8+QluHqsL0i86qXAvhRAFqQduHBOWFCgd8KsHwfp0sXzgII3Tooh91DvWQwppnIKXJyRWHueavfiKyg3JDg89Hqx98EEUS6CU4wZV8WuXvkA0nfsXN6NbkmGleVCWX11i8UWe3LgW8VVqdV+I1QUg1kQK4wwUDkH1+rPGNJm1GrH2IixO6SRhve/9rPnKHIz1I5L3/X9WIsD1juegNXRCLC8CglUCINaYa+NrFJYOa4KTWBMgBFY9vDz9lGiT0eOf333okdQO8nquJntva75Y9cjrzwSrS96sNIKPz+eff95enUJPCO4trAuuvu+8ejw0rdcvzgGeicmI17XgVw6scfDPCCUS/gTdCh4+CuDTD+2xxofn+P3vV49kwr0/TZMpKcE81mxUmT17tkkNxc1p9nQWoScOjkPdCh4gO28eZ75UbVtj1LIUjQeYXpzWJA2BwuM0NyMM3Q/OP7x88UCDI0RY7bSdWQpPMEDNy1hk29nNoNnxNeOsOy7W3BBnmVpePB54wDtZ6Da+wuFs2hqAb7qs4UhSBV/7bkGbrHF57uC4+8mw0jwoHA6RvcQau2MH5xZfu0BrA1xgncNC2/iosWZvmgcnrh9rHIj9hYoHkDVe05k1bbbDeN/jI835oaEwrbG4uilYSgnC+/4/Dmr1OYh7RT9ywQbd1rg2sRSlNavbKIDWOE/Dzs9q5tc7YDLlwr9k723NF/Qz19nkdH8m4L1ojbeV5s2bizXuzVl1e9v5rPT7kLITH6YNvb9xeFgb3YKhRnrNP/7444E/B9z1ce4HrtxB43Z2zzor49xW0ze+6qxBts4oY/GLCghgx5pyb1amcB7K8uFnVpFAmI5rc44Rs2ZVZVFMrcHYZjyANbtOrMH3zuKS2nYez3IdkGVNRlh7rIH3pmyMD3TeUEkd8L+ZEuWBB6HeuHi4WzOKog6LMYdYjgbKC+pvDZQ2q3iohdFyfJnlA8CacWxbrKIKi7OTDCuM7VMr4BVXXGFWKnAeBvWHBVrFmjSim7n2i+V5LN9QYg1wFjxAVGA5sPxBRlm/s2sF0bJS/RvW+949dATrVFqTKGxcvO9FvJ6DzjGIuIfcPRS4jvVaxce7KnJqFQPgeNZ5+yQkuZHsvZ3McyTJKvpmS/dnAqyy2mOBtYKtWf1RbYFCf9ttt9lhOnbdDkiTDaygpILnvF6nGmZ5zDBWPYRjLK7z2gji/a/18Py1XhCeYg1+tGeZWBmzta1uLODM1C+v0/eZVgAzKzU9HB2qWF0exlea9XI1U581HL9Ofzc6G8m6qOxytDz1jo08N998c5Z4TJ+H+M2WRZyloZup3XBW65wOb1nfjA82HMt6IBn3HEgPsZZZiVhfLxHMzLIGXkat0IAytH46kwcuYzQskV+4wnDOIJo4caJxrorZsNagUtvdAOpiKQd22YhXScYVCvImwgNtcLougXNnHA+rSVhjxKL8XaHtuooEVrNQgW856wFh/AVaL1t7tjHiLWXVbpNztixcU7j5JcvKea7BGjN6LYtzBFPfLauEVtP4vFOXD7l5LcKliFMw/R+zDeFn7+mnn7bPMe47zCR2tzuR/VTNlnUfOwz3vXO2LLhjRj9mysFtg/WxYp8KdQyONvK+j5j7Er4owQOrN6jgnocLKKt7y8yctSZXaJT9ixUM3Pngusl6odrP1lj3FPLCPYqKpTCa8uI9V5O5t5N9jsSqf3bfT0E8E/C+gODZAL7Z/bMmFOrpiFhLkEXUdynOq6VY23HWuEzjUiqR8vVd53zvJ5IPaeJdC16zZfE8xSoUKnCVhvcMdBFsq+AdBbdYyT4HEm2DpoN+Fk8wjshTUq3cWV9tEV2FQivgVO6wnJMqiRrv9es8ybl58wCi0xWK17E1zO1xGyceD7RYAkebcHGhJyvehafpvH7hFBQKZizBS9WZNzeUO7/juXnAZYzTfYBXPuuL3SjOWkfrS8j47fNK6wzLjnKHspNhhfPkfBg5j6/bWDrJ6ia1Gef2tYgVTeJJTu7ZoJS7MNz3buXOizsclbtferzv/7esFPzcWTNavdB5hrVu3drcO1AA3fLuu++auFj3FO7tZJS7ZO7tZJ8jseqfXeUOdUj1MyGnyh1c4eD5HEvw3rrooovs5ybaFesvaOUOdYF7HnUi79UWvLucbUjmORCrzV5xiSh3KemW1W44/bUql0VgfrcsaGacGiKxjzFZKpjtiPFWWJjdKZY/OYF7BXThQZzH0G39deZzhjm3NY2G6S/CnZMGMJDd7UIFXZyWk8Yss34th8mm+w6uX9Q0rcdBGzH+CIPhnYMz9bj6q+kT+YWbAYz7wkBl6+Kzs2BmJbrtLJ9o5ph2hLWBtmnaRI+ZLA/UA/Xr27evaHe7sy6YYm55thfrIW4Ho2433HCDDB8+PGpcIq4T6+VrunKR2Fl3v227UGsjGVY4TxgTiOn17skKGFBveZoXy5dX1Aw+rYv+OuvgDHNuaxoN01+E9+jRw/DzmgEGtwqYKWvd8FrEYfnV+uqvVyXCcN9r/THo/9VXX42aoYwxstYXuxkqALcLTuF938fGgWcFxlp9/vnndphuYIwShlZgyIWKZTUxmxjjhLFazueEjnvU86K/mld/Ndzr2aZxmlZ/k7m3kTeZ54jWQX+1Dvh1hjm3NY2G6S/C0/2ZgC7Nc6xhNngH4b5xC9zZYJas1dvkjvLd1/brr29CjwjNo7/uJF7XDdJYjszF8mdnul31nal50bV/4YUXRrUhmeeAlperv1ZlPSUnVgCrgjG17+zGo6sOnuDxJYAv/+zmz830sChaJzNiPXASqgcczGKlBOSBZTI3HM7Gag/MyHCsCNOxdhHGSp/TuOzywPHgfBeOTWEZdVq7/OoC7/OWoh+xHhT2UkR+abMTngwrWBStCTHmfGIJtewcLzfSWpMmzLUHFjjH2p2V07Jzw3KX0zq486fLfY97FtcfrldYpNz19Nrnff+/dwC6MTEsxRpzaxy9evFyh+E+wzWJP1zz7vhU7Cd7byfzHMnN+qfqmZBTy52zjdbY5IgOtcGSkHhvOOPDsg1LL54FuJYt33xx25Cq50AilruUdMuG5USxnv97AJNF3maRjsodr8m8fU3y/B/e85+byh3OpXY9u4ft8Dxn/zwnotx5O5GzaFNIgARIgARIgARIIDcIwM+pNblOrOUTc6M4lhGHAJW7OIAYTQIkQAIkQAIkkDMCcIeDpcYowRBIyYSKYKrOo5AACZAACZAACZAACbgJULlzE+E+CZAACZAACZAACYSYAJW7EJ88Vp0ESIAESIAESIAE3ASo3LmJcJ8ESIAESIAESIAEQkyAyl2ITx6rTgIkQAIkQAIkQAJuAlTu3ES4TwIkQAIkQAIkQAIhJkDlLsQnj1UnARIgARIgARIgATcBKnduItwnARIgARIgARIggRAToHIX4pPHqpMACZAACZAACZCAmwCVOzcR7pMACZAACZAACZBAiAlQuQvxyWPVSYAESIAESIAESMBNgMqdmwj3SYAESIAESIAESCDEBKjchfjkseokQAIkQAIkQAIk4CZA5c5NJA/s/+Mf/5BatWpJ1apVo1pbpkwZadiwoRx55JFR4dwhARLIPALFixeXRo0aCZ4HKtiuX7++VK5cWYP4SwKhJaDXc6FChaLagGu/Xr16UWGZtvO/uzrTWsb2ZCFw4oknyrfffiu7du2SZcuWyTfffCOlS5c26Tp06CCrV6+WuXPnyujRo7PkZQAJkEBmEHj++edl5cqVsmPHDvnxxx/l7rvvNg2rUKGCfP755/LLL7/I2rVrBc8LCgmEkUC3bt1k/PjxsnXrVnM9d+zYMaoZuO4XLFggDz/8cFR4Ju1QucuksxmnLUuXLpVXX31Vpk+fblJWqVJFunfvLjfccIO89tprUrhwYRNerFixOCUxmgRIIKwE3nrrLRkxYoT88ccfpgkPPvigVKxYUT799FM5++yzTVi+fPmEz4GwnmHWu06dOlKqVCmBhQ7Stm3bKCj4kIFcfPHFUeEZtRPxEUujjVgN5V8GMrC6XSPz5883Z96y4kV2794dmTBhQqRatWqRJk2aRCwTNs97Bp73WPdzjRo1zPVw44038tznkXN/xRVX2E//33//PbJv375Iq1atIlZ3VeTYY4/ldZBHrgO/5wKeBRA8G/zSpHv4m2++adpgWaKj2nD55ZdH9uzZE3n//fejwtO9PVo/6GfxhJY7i1ZekwMHDsg777xjml20aFHzBX/VVVfJmjVrTLetddHnNSRsLwnkOQLjxo0zQzTQcFgyevbsKdZHnixcuFBg5aeQQNgJfPfdd6YJsEw7x5YuXrxY8ufPL5988knYm+hbfyp3vmgyO+Kzzz6zGzhgwAD5888/7X33BhTAZs2auYO5TwIkEGIC+MibOXOmacH27dvl5Zdf9mxNwYIF5f/+7//k1FNPlaOOOsozDQNJIB0JYGwp5IgjjpCyZcuabQw5GDRokGzcuFHGjh1rwjLxH5W7TDyrCbQJF7ZK9erVdTPqF+MV+vbtK6tWrcroL5yoRnOHBPIQgZ07d5rWlihRQkqWLJml5b169ZINGzbIpEmTzASsefPmidVNlyUdA0ggHQk433M6zq5Tp05y7rnnSo8ePWzLdTrWPad1onKXU4Ihzf/kk0/aNT/rrLPsbecG3KLUrVtX8OCnkAAJZBaB448/Xtq1a2caBWvGmWeeGdXASpUqSb9+/WTOnDlmGAeGayDPwIEDo9JxhwTSlYBTuStfvrwcc8wx8tRTT8m7774r7733XrpWO1fqReUuVzCGqxB0sVx77bVmijhqDiUOM+PgFqV58+Z2Y2bNmmXSqWnbjuAGCZBAqAlg/BFmyGPsEbpnIfqRhxmz8HNnDag33VZ4XrRv315uvvlmk+700083v/xHAulOYNOmTXYVrQmDxs0X3KN07tzZDs/UDSp3mXpmXe2qXbu2YHo4HtpvvPGGTJw4Ubp27WpSYTwCHuCjRo2SZ555xpVTxJpFlyWMASRAAuEiUKBAAWnatKlgDB3cn0BJgwL35Zdfmoa0bNnSdFdhPC7G1w0ZMkTg/1JFB5+vX79eg/hLAmlNANZm+HWFDBs2zDjohlsU+HjMdOFSBJl+hq32YeUJzIDDyhP79++XzZs3C5w6btmyRXDxw3v3mDFjzAWPh7pbrCnX7iDukwAJhIwALG+vvPKK+ViDovfoo4+acXQzZswwSl2DBg2MD0xY9DCT1i0YogGZMmWKO4r7JJC2BGC9U5+NuAfgqD8vCC13eeAso9vl77//Ni21/FkZx43wQA/F7vXXXzfhlq87ufrqq003TR5AwiaSQJ4jUK5cOdNmfORByXvooYfM/siRI80HH3amTp1qHJubCNc/pF+xYoUZh+eK4i4JpC0BHXeHceZ5afUlWu7S9pLMvYrBBI1uWcsxqemCOXjwoF04VqjABb9o0SJjybMjuEECJJBRBB577DFjmYM/Syw1qLJkyRI54YQT5OijjzZ+LjXc+Xv99dfLRRddJOecc47dzeWM5zYJpCMBrKGOSUBYiqxPnz7pWMWU1YnKXcrQplfBGCfjN1Zm9uzZ6VVZ1oYESCDXCRw6dEj87nVYN9TC4T4wxukNHTpU7r//ft/87jzcJ4HDTQCW6smTJwt6qzCBENd/XhIqd3npbCfZVsysg6sECgmQQN4igDG4sHq8+OKL9mQrzKbFJKzPP/88b8Fga9OeQOvWrcVaQlOsZcVMj1SRIkXMJCL155j2DcjFCnLMXS7CzMSioNjBRQpm2Omg1ExsJ9tEAiQQTQAWO4zBg59LzLT/6KOPBJMvpk2bJujuopBAOhGAc+0PP/xQevfubSZNlCpVSi688EJZvnx5OlUzsLpQuQsMdfgOhDE4GJ+jnr1xk+CriEICJJD5BOA6Qj/o2rRpI7CKYMwdxuxmugPYzD+7mddCXJf4g3eH6dOnm3fVr7/+mnkNTbBF7JZNEFReTIZlx6pUqZIXm842k0CeJ4AJWBQSCAsBGCKqVq0q6G3Cknl5Xajc5fUrgO0nARIgARIggQwg4DcpKAOalu0msFs228iYgQRIgARIgARIgATSl4Cv5a5kyZKCAYoUEiCBzCeA7gxI2bJled9n/ulmC0kgLgE8CyD6bIibgQkCIwD9LJ7kswYfcm2peJQYTwIkQAIkQAIkQAIhIcBu2ZCcKFaTBEiABEiABEiABBIhQOUuEUpMQwIkQAIkQAIkQAIhIUDlLiQnitUkARIgARIgARIggUQI+E6o2L59u+CPQgIkkPkEjjzySDNwevPmzfLnn39mfoPZQhIggZgEihYtaiZYrV27Vg4cOBAzLSODJYAJFfEmVfgqd3/99ZfgQU8hARLIfAIFChQwyh0UO973mX++2UISSIQAZsxu27ZN9u3bl0hypgmIQP78+eMqd+yWDehk8DAkQAIkQAIkQAIkEAQBKndBUOYxSIAESIAESIAESCAgAlTuAgLNw5AACZAACZAACZBAEASo3AVBmccgARIgARIgARIggYAIULkLCDQPQwIkQAIkQAIkQAJBEKByFwRlHoMESIAESIAESIAEAiJA5S4g0DwMCZAACZAACZAACQRBwNfPXRAH5zHSh8D8+fNl6dKlsnXrVqlbt66cdNJJUqJEifSpIGtCAiSQUgILFy6U3377TdavX2/u/Ro1apjnQMGCBVN6XBae+QR27NghH330kfzjH/+Q9u3b+zZ42bJlgusQ12C5cuWkVq1a5hr0zRAnIpny4LD5q6++kpUrV5rSGzRoIMcff7zAF2gsWb16tcydO1d+//13Ofroo029K1WqFCtLSuOo3KUUb/oXjgvxkUceke+//z5LZa+88krp2bOnuSGzRDKABEggIwisWbNG+vfvL99++22W9lSsWFHuueceOfvss7PEMYAEEiXwxBNPyGeffWaSX3vttZIvX76orHCe/swzz8jEiROjwrFz8sknS9++fY2T9SyRPgHJlrdgwQLp06eP4J5wClbrePHFFz0VzUgkIs8995y89957cujQIWc2adGihTz88MOCFYCCFnbLBk08jY63d+9eueuuu2zFrkKFCtKyZUvb8/WYMWPMTZVGVWZVSIAEcpHAnj175I477rAVu0aNGslVV10lzZo1My8kfPxBuYNln0ICyRAYP368rdj55Yfyp4odFKnmzZsLPiwgP/74o3lP4X2VqCRT3oYNG6RHjx5GsYOVDvcAlLNixYqZJRm7dOliLHPuOgwaNEhGjx5tFDtYGtu2bSv169c3yaZMmSK9evVyZwlkP3h1MpBm8SCJEBg7dqzpikXa22+/XW688UZjpYNZ+vHHHxfclJ9++ql07NhRqlevnkiRTEMCJBAiAiNGjBB0J0FgHWndurVde3TR3nrrrbJr1y558sknZdSoUXYcN0ggEQLr1q0zFrlYaRctWiRTp041SWAhhmIG5QpWMFjDnn32WVmxYoXp1sWHRzxJtrzXX39dtm/fLoUKFZJXXnlFTjjhBHMorK2L9yM+dF599VUZMmSIXQW075133jH7UEjRC6ZWOpQxbNgw+eKLL+SHH36Qxo0b2/mC2KDlLgjKaXqMadOmmZphbF27du3s7ldcnM5xEV9//XWatoDVIgESyAmBOXPmmOxNmjSJUuwQCCsEPvggUPTQ1UUhgUQJHDx4UB588EHBOvWxBGPsVHC96dg2jM+DMlelShUT/euvv2qymL/JlIdre8KECabcCy+80FbsEFC1alXp0KGDiYMVcfny5WYb/z744AO7K7Zbt262Yoc4KIRqfYSSGrRQuQuaeBodr1OnTtK9e3djNnYPmsYNpeMiuGh0Gp00VoUEcokAxgphAXIM+vazKlSuXNk+2saNG+1tbpBAPALDhw+Xn376SUqVKiVdu3b1Ta6WLiTANemU/fv32x8V7jhnOud2MuVBcYQyCmnVqpWzOLONLtqjjjrKbKtRBDs6XAH3jypyJpH1D+9PWPMgM2bMEPSIBSnslg2SdpodC1/r+POSkSNH2jfaGWec4ZWEYSRAAiEmgJePs4vJqynz5s0zwXixYQYghQQSIQBlaejQoSZp7969BbNl/cT5foGFC92hsNpBMA5P8zZt2tSviKjwZMpDV66KjpfTffxiHCCGJsGC7bTcLV682CTzyoMIDYfiiOEPNWvWNOmD+EflLgjKITkGbsjZs2ebP8wagmAMTu3atUPSAlaTBEggtwhgxiDG5ULgGslpEcmtY7CczCOASTrojoVCg/fHOeecIx9//LFvQ8uUKSM33HCDYPzn5MmTzaSFc889V37++WfR99A///lPSVS5S6Y8jLWDoEtYu4XdFVbXYJs3bzZRaKf2ammcXx6EIx+VOzch7gdCAINBMXBVBVPQMS2cQgIkkLcIbNu2zcyi/fvvv013FGbMUkggEQIvvPCCsVJhaA9caSUi6LbFRAZMWED3P2afqpx44ony8ssv29Y8DY/1m93ydu/ebYorWbKkb7HFixc3cTprV/Mg0C+f5kEaKINBCsfcBUk7zY9VunRpOfPMM+W4444zNcXgUQwkdZqh07wJrB4JkEAOCWzZskUwOFxn0Xbu3DlQi0MOq8/sh5HAzJkzZdy4cWa8GYwFhQsXjlsbTLiAjzsdIoDu//PPP984DkZmjNvDxwUc7CciyZSn4+0wBtVPoHxC8MED0TzY9suneZCGY+5AgXJYCOCrSeXLL7+Ue++919xYd999t5mSfsQRR2g0f0mABDKQwErLKz98fWGFAMhNN90UNXM+A5vMJuUSAXwUPPbYY6Y0dLOiKz8RgXNgKIQQ+FyE5wadzAdPDXgPzZo1y7giefvtt+04v7KTKU8tbGqV8ypbZ4urwqZ5kNYvn+ZBGs2H7SCElrsgKIfwGBjfcPPNN5uaY+wNxj9QSIAEMpcAlk665ZZbbMUOM+lhtaOQQCIEnn76aeMnDpMhME57+vTp9p/TjQlWqkAcxqDB+gV/qhBY69yrV5x++un2TNslS5YYY0OsuiRbHpY6g+zcudO3eJ3YgckVEChrRYoUMdt++TQPEmk+kyGAf1TuAoCczofAF4eamd31PO200+wgOGukkAAJZCYBOJGFB368pDAzFk7Mr7vuusxsLFuVEgI6XhvOhzFW+7777rP/dGIODqzhcCOC8XX6/jn11FM96+Vc+g6W5ViSbHlly5Y1xWKChJ8VThU1TYsMuq0TMtx1c4ZrWneaVO1TuUsV2TQvF+MSsNTYWWedJVhmzEucJmX4KqKQAAlkHgEodngZ4yWLWX+DBw+2/XNlXmvZolQRgPULS1h6/amFC8cuX768SYPxeM4xec73jbOOTv92fjNZNX2y5cHXo4o69tZ9/GJ1ilWrVpkgdW+CHc3ntS4z4jUc70+nz0jEpVroCiXVhNO0fOeNBV9CV199tbjH1Dmnr9etWzdNW8JqkQAJJEsASys9/PDDxss+XrqYlVijRo1ki2O+PEwA146f4F3Sr18/E41uWB1ThwBcd5s2bTKOfjHezu1yR5cmQ9o6dergx1cwazWZ8uBbDxM5oMBNmjTJuG9xHgQuWlROOeUU3TQOj7/55huBvztMPHS6OoEVEN3PEGceO3OKN2i5SzHgdC4eljsIxjKgGwYPeghm9WBdPKwrC7nooosEM2kpJEACmUUA63ZiFQBImzZtzMsNMx69/vwsK5lFhK0JmsCll15qDolx3fCP57zOoGi99tprJh4TNJwfHlgpArNo8QflUCWZ8qBsQrGEQCHDxA0VTC5Uh8zwJuGswwUXXGCskEjbq1cv082Mbbg9Qb3UXQrGEgYt+SyTZ/R6H/+tAWZL6YypoCvF4wVDAINPsf4dBlKr4MsDN4reYPiaGW4tIxP0YFCtD3+DIYDujgYNGgjGtKiTzmCOzKMcLgLwyp+dcXVvvPGGuUYOV3153GAJYIwYFBkoXeqsN9kaOC136Kp0Wu5gTOjYsaM9WQJxeA9hjJs+izAz9c0337TXmUU94Drl9ddfN1XCikrqbD/Z8tBGrG27bNkyUyYMGliWU/UgdK3iHsBas07BmrRw+wJVCpNJwAxj1JUZFDvMAs5NQRdvvG5eWu5yk3jIykI37MCBA82sWPXTA9MyFDvsX3/99eYLhopdyE4sq0sCCRCAxT47oktCZScP05IACOi141TqlAy6YaGowdCAGahQkrDMFxQ75GvRooUZFw6nyE7xKgvxyZaHD1wob+jRQhnwq6eKHRz6YyyqW7HD8bAW7aBBg4wFD5NJ8A6FYodxhpikBNdCh0NouTsc1NPwmBhMDaelmG2EL4Jq1aplGYOXhtVmlXKJAC13uQSSxZBAhhDITctdokig2EGhwhAhLCOGniPM3vYTKGNQutCV6vQ7p+mzW57mw/sQFjxMPMS7EOP4EhEopFBMYfVDPlj+UiGJWO44oSIV5ENYJm6gWrVqmb8QVp9VJgESIAESCDkBWONgoXNb6byaBbc9mGwBBdBLsUOe7JTnPAbeh/Xq1XMGJbQNhTholyd+FWO3rB8ZhpMACZAACZAACaQlAYwFh289rIZByUqAlrusTBhCAiRAAiRAAiSQxgQwJrx58+ZCN13eJ4nKnTcXhpIACZAACZAACaQpAfi0wx/FmwC7Zb25MJQESIAESIAESIAEQkmAyl0oTxsrTQIkQAIkQAIkQALeBKjceXNhKAmQAAmQAAmQAAmEkgCVu1CeNlaaBEiABEiABEiABLwJULnz5sJQEiABEiABEiABEgglAd8VKkLZGlaaBEiABEiABEiABPI4AV9XKPv37xf8UUiABDKfANZwLFy4sFkTEUvvUEiABPI2AazSgGUJsQQX1kylpA8BrP2u68H71SqmcocF5CkkQAKZT+CII44wyt3evXtlz549md9gtpAESCAmgUKFChnlbvfu3XLw4MGYaRkZLIGiRYvGVe445i7Yc8KjkQAJkAAJkAAJkEBKCVC5SyleFk4CJEACJEACJEACwRKgchcsbx6NBEiABEiABEiABFJKgMpdSvGycBIgARIgARIgARIIlgCVu2B582gkQAIkQAIkQAIkkFICVO5SipeFkwAJkAAJkAAJkECwBKjcBcubRyMBEiABEiABEiCBlBKgcpdSvCycBEiABEiABEiABIIl4OvEONhq8GiHm8D8+fNl0aJFsmbNGilVqpTUrl1bTjnlFOPY9nDXjccnARIIlsCIESNk3bp1ctddd0nBggWDPTiPlnEEtm3bJiNHjpR8+fLJ7bffnlD7ksnjLvi7776TX3/9VTZv3iwnnniieafh/RZLsELP559/LsuWLZNIJCKNGzeWhg0bxr0Pli9fLt988425b2rVqmWOVa1atViHSmmc79qyWJ2CK1SklH1aFL5ixQq57777ZNasWVnqU7VqVXn88celefPmWeIYkFkEsEJFuXLlZMeOHVyhIrNObbZb8/bbb8s999xj8v3yyy9SpkyZbJfBDOEngBUqSpQoIX/88UeOV6i49dZbZfz48QbK+vXrjZIXj1AyebTMtWvXyh133CGzZ8/WIPv35ptvln79+gmWXHTLvHnzpHPnzoL3olOKFy8ub731ljRp0sQZbLahAPbt21eGDRuWZZm2tm3byoABAwRLueWmYIUK/MWSrK2LlZpxGUUAawa2b9/eVuxOO+00ueWWW+SSSy4xFyNukJtuukm+/fbbjGo3G0MCJOBNAC/D+++/3zuSoSSQBIHRo0fbil2i2ZPJo2XjvXbDDTfYil2VKlXk8ssvtz9S3njjDenSpYsmt3/Ra4X3IRQ7KLZ4D0I5g4K7c+dOufrqq41lzs7w3w0oiv/+97+NYle3bl257rrrpFGjRiZ23Lhx5p3qzhPEPpW7ICin6TFeeuklgSkZ8sILLwguxMcee0xee+01mTp1qrmosWB0r1690rQFrBYJkEBuEMCawnhJXXnllXLgwIHcKJJlkICsXr1aevfunS0SyeRxHgAWtgULFpgg9ErBOIF33dy5c831jYgPP/zQfvdp3ueff162bNlihiK9//775j34yiuvyOTJkwW9WFhz++mnn9bk5hd1HTJkiNlu06aNeW8izcSJE6VHjx4mHO/Sr776KipfEDtU7oKgnKbHmDlzpqnZ2Wefbb5KnNWsU6eOdOvWzQQtXrzYfLk447lNAiSQGQTQFdWsWTPBiwwfcxQSyA0CBw8elK5du8ru3bsTLi6ZPO7CP/nkExNUunRpue222+zuV3SNostVBePqVHbt2iVQ6CBQ0k4++WSNkho1apixpwj4+uuvBe9DFYxN1XumT58+cuSR/5vGcO+99xqlEGmHDx+uWQL7pXIXGOr0OhDGCRQoUMBcfGeccYZn5apXr26Hb9iwwd7mBgmQQOYQgJVBLfiw3MHKQSGBnBIYOHCgYEIDxmwmar1LJo+7nlCqMAbuiSeeMN2rzni80zCpAwJrtQqsemqxvuKKKzTY/m3VqpXkz5/f7KvyiB20D4J3KKx7TsFxoChCJk2aJJioEaT8T80M8qg81mEngAtv7NixMesxZ84cE4+LumbNmjHTMpIESCC8BJo2bWqsE6effrpMmTIlvA1hzdOCAJSl5557ztQFv5j5Gk+SyeNV5llnnSX485KhQ4eaGbCIg7Va5aefftJNe7ycHWBtYEIF3oHwKLFkyRI76ueffzbbOsbOjvjvhoZDccQHFHrEghIqd0GRDtlxMKgUYxcgcImS27N9QoaD1SWBjCWAmbG8vzP29AbeMExoQHcsulivueYaadGihYwaNSpmPZLJE7NARySUxunTp5s/DEGAYHIEJj+obN261WxiIoWf6x91obJx40aTFnVW6x+6gL1E8yBu06ZNVO68IDEsOALwCYRZQ/v37zemaLhDoZAACWQmASp2mXleD1erHnroIWOlQhcoJukkIsnkSaRcpIFLFKe1DdZpTJ5wCsbcQfyUNMSVLFkSPwKlDqJ5sO2XT/MgjebDdhDCMXdBUA7RMeDTCF9bOgYHs42CNCWHCBWrSgIkQAIk4CCALn34ScSwn0GDBkmRIkUcsd6byeTxLsk7tGzZsqYLtn79+iYBJkVgLJxzYgSsjBAdV2d2XP8KFy5sQmD0gGgebPvl0zxIo/mwHYSwWzYIyiE5Bjxyt2vXzqxSgSp37949YW/iIWkiq0kCJEACJJACAjAM9OzZ05SMblkvh7/uwyaTx11GvP0PPvjATjJt2jTp0KGDmQgBH65ffPGFwIG7Wtjg7sRP4OsOogqr5kGYXz7N48yH7SCEyl0QlENwDCybgot9+/btpraYbeScNh6CJrCKJEACJEACh4kAnF/DTxxWfqhXr16U42KMe1PBShWw7EH5e+CBB7Kdp3z58lpUtn8vuOAC438Ovugwrvz777+XU089VSpUqGDK0vefV8Eah8kVEFjlsEoEVvLSOHc+Z7jmc6dJ1T6Vu1SRDVG5H3/8sfFpp2PsMB1dp3CHqBmsKgmQAAmQwGEisHTpUnNk+H2LZRiA7zkIZq4mk+fiiy82+WP9gyUNFjmv7tJzzz3Xdka8atUqo9xVrFjRFIcJEsiLiRVu0UkXqggiHvnQ46VxfnkQ7sznTpeKfSp3qaAaojKh2GEhZ9yQGBSK9fESMaeHqImsKgmQAAmQQIoJQNHxW48ekw90AkKlSpWM5Q7dm8nkidUMOEw+88wzBTNaMUmjU6dOWZJrPRCB8XgQp486rLOOGb5OwVKcv/32mwlq2LChHYV8UO681mZHIl0oAL7+qlWrZucLYoPKXRCU0/QY+GrBuDoodrjhxowZI7Vr107T2rJaJEACJEAC6UoA68H6CVyh3HnnnSb6hx9+sB0Jn3feeX5ZjPsUrzy+GawIKIw6Jg4rTtx6663GgufMM3LkSHv3xBNPNNtYkaJWrVpGgcMYPbdyh6U5VeATUgUOj2fMmCG//PKLmaDhnHwIK+CECRNMUmcezZvqX86WTTXhNC4fXsP37dtnaoiJFPgywcwlrz/n104aN4lVIwESIAESyAMEsFLEzTffbP6cKyhdfvnlpvW//vqrmeCxcuVKs48VIvr372/WlUXAv/71L9tyhzGAHTt2NOlQ7uDBg802/mEShjpkhuPjY4891o5r3bq1VK5c2exjosb69evNNtyeoG763vSyINqFpGiDlrsUgU33YuGRG44dVfTi1X33LwbBNm7c2B3MfRIgARIgARIInABWi8CyXpBevXqZ3ids9+jRw8yCxSRBWBPxd9xxx8nvv/9ur5GOHiosT+YUWOEwLGnhwoXy6KOPmrWWMfZuzZo1Jhm6Vt0+X7GWLNyF4ZjonoXDf5S9evVq28ExFDtnV67zmKncpuUulXTTuOwFCxZkq3aYAUUhARLIfALOe925nfktZwtTRQCWMYj+JnIcTau/7jx+4ZhI8e677xrnxVg/HQInxnBLgv0uXbrIp59+KsWKFYsqEitTwIgByx8ce8OZvyp2cHyM7tqjjz46Kg92oBSiq7dKlSpmiBOOhS5ZlI/ZwA8++GCWPEEE5LMWkI94HQgDI/0GR3qlZxgJkEB4CeCBWK5cOdmxY4evz6bwto41JwESyC4BWK1KlCgh8EXndNib3XJSmX7AgAGmqxXWNqffOT0mPEDA5cm6desEK2Ycc8wxWcbgaVrnL/KhTEzQQB6MSU9EMJEDzpExUQP5vGbdJlJOvDRwwYK/WELlLhYdxpFAHiFA5S6PnGg2kwQSJJDuyh18yF122WVm5Ycvv/wywVZlRrJElDv2tWXGuWYrSIAESIAESCDPEMDyZvCT161btzzT5uw0lBMqskOLaUmABEiABEiABA47AYydu/TSS6VBgwaHvS7pWAEqd+l4VlgnEiABEiABEiABXwJwuo8/ijcBdst6c2EoCZAACZAACZAACYSSAJW7UJ42VpoESIAESIAESIAEvAlQufPmwlASIAESIAESIAESCCUBKnehPG2sNAmQAAmQAAmQAAl4E6By582FoSRAAiRAAiRAAiQQSgK+s2Wx/EaqvCuHkhQrTQIZTECXmcqfP38Gt5JNIwESSJSAPguwLNehQ4cSzcZ0ARCAfhZPfFeoiJeR8SRAAiRAAiRAAiRAAulHwNdyt2nTJsE6aRQSIIHMJ4Cv9Dp16sjatWtl27Ztmd9gtpAESCAmgVKlSknVqlXNWqlYa5WSPgQqVKgg5cuXj1khX+XuwIEDsm/fvpiZGUkCJJBZBHjfZ9b5ZGtIIFkCeBZAoNhRF0iWYmry6bmJVTonVMSiwzgSIAESIAESIAESCBkBKnchO2GsLgmQAAmQAAmQAAnEIkDlLhYdxpEACZAACZAACZBAyAhQuQvZCWN1SYAESIAESIAESCAWASp3segwjgRIgARIgARIgARCRoDKXchOGKtLAiRAAiRAAiRAArEIULmLRYdxJEACJEACJEACJBAyAlTuQnbCWF0SIAESIAESIAESiEXA14lxrEyMy2wCO3bskI8++kiw3mj79u0zu7FsHQmQgCEwf/58Wbp0qWzdulXq1q0rJ510kpQoUYJ0SCBbBOD0eNGiRTHz4Lo6+uijs6TJ7Wtw2bJlsnDhQlm/fr2UK1dOatWqZa7rLAd2BMBB8FdffSUrV640oQ0aNJDjjz9eChQo4EiVdXP16tUyd+5c+f33303bcP9UqlQpa8KAQqjcBQQ6TId54okn5LPPPjNVvvbaayVfvnxhqj7rSgIkkA0CeBk98sgj8v3332fJdeWVV0rPnj3Nh16WSAaQgAeBX3/9VW677TaPmP8FnX766TJw4EA7ILevwT///FOeeeYZmThxon0M3Tj55JOlb9++Zmk1DdPfBQsWSJ8+fWTNmjUaZH6LFi0qL774oqdiGIlE5LnnnpP33ntPDh06FJWvRYsW8vDDD8uRRwavarFbNupUcGf8+PG2YkcaJEACmU1g7969ctddd9mKHdasbNmypZQsWdI0fMyYeFkCrgAAQABJREFUMeZFmNkU2LrcJLBkyZJsFZeKaxAGClXsoJg1b95cKlasaOr1448/mmsex3XKhg0bpEePHkaxg5WuWbNmAuWsWLFiAmWxS5cuxjLnzIPtQYMGyejRo41iB8tg27ZtpX79+ibZlClTpFevXu4sgewHr04G0iweJBkC69atM187yeRlHhIggfARGDt2rOmKRc1vv/12ufHGG42VDl1Tjz/+uOBj79NPP5WOHTtK9erVw9dA1jhwAosXLzbHhIIzYMAAz+Pnz5/fDs/taxBdwlOnTjXln3322QJFD8oarGqwrj377LOyYsUKM/Toqquusuvx+uuvy/bt26VQoULyyiuvyAknnGDi1q5da+4NWBdfffVVGTJkiJ0H78x33nnH7EOBhAVcrXQoY9iwYfLFF1/IDz/8II0bN7bzBbFBy10QlENwjIMHD8qDDz4of/31VwhqyyqSAAnkBoFp06aZYjAGql27dnb3K15QzvG2X3/9dW4cjmXkAQJqucM4NVxXXn9QoFRy+xrEGDsVfKzoWDmMIYcyV6VKFRON7mMVWOYmTJhgdi+88EJbsUNA1apVpUOHDiYOVr/ly5ebbfz74IMP7K7Ybt262Yod4vCxpNZCKJVBC5W7oImn6fGGDx8uP/30k5QqVUq6du2aprVktUiABHKTQKdOnaR79+6m66hgwYJRReMlqONt9+3bFxXHHRLwIgCL72+//WaioNwlIrl9DarlDMfGeDinYLIHFDmIMw6KHgwckFatWplf5z900R511FEmSJVR7GACCARWOVXkTID1D/cOrHmQGTNmCNgEKeyWDZJ2mh4LF/bQoUNN7Xr37i2YLUshARLIfAJNmjQR/HnJyJEj7RfgGWec4ZWEYSQQRQDdnarEYKboqFGjjLIHReq4444TXEewhDklt69B57UKixm6V2G1g2Acnr7fmjZtalfDObtXx8vZkdYGxu1hWAIUV6flztkF7Uyv21oWFEfMpq1Zs6ZGpfyXyl3KEaf3Afbs2WO6Y3HxtW7dWs455xz5+OOP07vSrB0JkEBKCOBDb/bs2eYPMwcheC7Url07JcdjoZlFQJUdtAqTE9wWX3SRYmLCNddc49vwnF6DZcqUkRtuuEFGjBghkydPNpMgzj33XPn5559Fr+l//vOf4lTuMNYOgvppN667guhehmzevNn84t2p7dM4E+H45wxHPip3DjjcTC2BF154wXxRoAsGLg8oJEACeZcABoTD+qICtxFwDUEhgUQI6Hg7pIUFr1GjRsZnIvzMffnll0YZev75540l7JJLLvEsMjeuQQwtwrg+TIDYuHGjmc2qBzvxxBPl5Zdftq15CN+9e7eJ1lnimtb5W7x4cbOrs2w1DwL98mkepIEyGKRwzF2QtNPsWDNnzpRx48aZsQG4oQoXLpxmNWR1SIAEgiRQunRpOfPMM00XGo6LAeQYTO7sigqyPjxWuAjAnQikSJEiRrF67bXXjNsRzFDFzFG4FYHAqLBz506z7f6X02sQkwLh405ntcJZ8vnnn28cEeNYGFt+zz33GGfdemwdb+ecxatx+quTQP7++28TpHmw45dP8yCNdldjOwhht2wQlNPwGFu2bJHHHnvM1AwmbHjTppAACeRtArB0qMDScu+995qX4d13323cSBxxxBEazV8SyEIAShUsVJi44OySRMJ69epJ586d5amnnpJdu3bJL7/8YsbguQvJ6TUIZ8MwWkDuuOMOMwtcJwZh1jeu6VmzZpmVJN5++21j3FALm1rl3HXCvk7EUIVN8yDOL5/mQRrNh+0ghJa7ICin4TGefvpp49MHA00xnmb69On2H8Y8qGClCsTpOAMN5y8JkEBmE8CYpJtvvtk0Eh77MWaJQgLxCECJcSt2msc52UFn1Wqc1292r0FY0+CbEQJrnXuFJayMod4g0IUMKx4ES5NB/KyJiNOJGJhcAUE7YaGE+OXTPEij+bAdhFC5C4JyGh5Dx9XAsSPG1Nx33332H5xKqmi4TvnWcP6SAAlkBgFYHbSryd2i0047zQ6Cw1YKCeSEgHbLogxnN2VuXYMYX6fX8qmnnupZVTg2Vln53/Vjy5Yta4IwQcLPCqeKmqZFBt3WCRlarv46wzWtxqX6l8pdqgmnafn4UsFSQ15/+jWCqpcvX96k4Xi8ND2RrBYJJEkAY5Ow1NhZZ50lWGbMS5zdSvCBSSEBPwK4VrAWMZwAw2+ql6xatcoOrlGjhnGan5vXoPM95bx27YNaG07/djozFm5bVObMmaOb9i9Wp9C6q3sTRGq+b7/91k7r3NBw3DuVK1d2RqV8m8pdyhGn5wEwWwjma68/rDWpovEwZ1NIgAQyhwBehPoyhP8v5wBxbaXTLVLdunU1mL8kkIUAuh1h9YK1Ci5InJY5TfzWW2+ZTTgahpKU29cgZq3CIAHxcxysS5MhTZ06dfBjfOFh4gVk0qRJ5tf5D+1ROeWUU3TTdngMFzDuSUewAmJIE8SZx86c4g0qdykGzOJJgARIIF0JwGoCwfgjrCWLdTQheDFjbUysKwu56KKLBLMYKSQQi4CuyIDxdP3797e7OHE9DRw4UDCGGwILnyphyV6DWCkCs17xt2nTJrtal156qdnGGFEsqem04EFxwwxeCCYRwnoIwYQLLL8HgUKGiRYqmFikTv4xk1zzIP6CCy4wPVvY7tWrl3G7gm1MKkG91F0Kxv4FLfksE2X0+hz/rQH80uCPkvcI4Gu9X79+puEwK+tMo7xHIu+0GN0TDRo0EIxB4eSZvHPeYa3DGphz5861Gw1Hq3hZ6ksRFg10swU9INyuEDcOCwGMEYMiAyVJnfXGqwjGu8F1jjoLxhg7rEiB1RlU0cGKFJgxq9dTstcgXJ28/vrrpkpYTUUdbUOR7Nixoz1ZAu8vXNMYM6fPNsx0ffPNN+11ZlEI2oi1aJctW2bKxMcMluRTPQhdq2+88UaWFTawJi1ciUGVwgRFMMP4VGUGxQ6zdnNT0MUbr5uXlrvcJJ4hZelSLVTqMuSEshkk4EMArk1gUcGsWPXVhe4lKHbYv/76640VQ1/EPsUwmAQMAay/OnjwYHPdYBsuTxYuXGgUO3SZwu0Wrjfn9ZTsNej3fkKXLxQ/fLRgRiuULlgSodjh3daiRQszxhSO+52CD1wob7AkooytW7faih2ceaNd7qXTkB9r0Q4aNMhY8DBBEfcPFDuMXcdqHFip43AILXeHgzqPSQJpRoCWuzQ7IYehOrC6wMKCGYewClSrVk3o1+4wnIg0OWQyljtn1WFBQzc/ridYfytWrOiM9tzO7jUIZQxKF7pSnX7ntHAodrC8oR5Ylgz1gNIZT1APWPAw6Qj3gXYhx8sHBRKKJKx+yAfLXyokEcsdnRingjzLJAESIIGQEcBLr1atWuYvZFVnddOQAKxf6KLEX6KSnWsQvuUwOQIKm5dih2PCugcLndtKF68+qAecLmdXoBAH7fLEr47slvUjw3ASIAESIAESIIG0JDDcGgcKf63o6qVkJUDLXVYmDCEBEiABEiABEkhjAhgPitm5dNHjfZKo3HlzYSgJkAAJkAAJkECaEsAEDfxRvAmwW9abC0NJgARIgARIgARIIJQEqNyF8rSx0iRAAiRAAiRAAiTgTYDKnTcXhpIACZAACZAACZBAKAlQuQvlaWOlSYAESIAESIAESMCbAJU7by4MJQESIAESIAESIIFQEvBdoWL//v322mihbBkrTQIkkDABLMuD5XL27t0r8M5OIQESyNsE4MgXKyxgTVgsq0VJHwJYUUiXC/Srla8rFCw7gwIoJEACmU9A12mEV3ldWzjzW80WkgAJ+BHQ5wCUCCzjRUkfAoksC+ir3O3Zs8csHp0+zWFNSIAEUkUAD4ty5cqZr3Tc+xQSIIG8TaBQoUJSokQJ2b59uxw8eDBvw0iz1hctWlTwF0s45i4WHcaRAAmQAAmQAAmQQMgIULkL2QljdUmABEiABEiABEggFgEqd7HoMI4ESIAESIAESIAEQkaAyl3IThirSwIkQAIkQAIkQAKxCFC5i0WHcSRAAiRAAiRAAiQQMgJU7kJ2wlhdEiABEiABEiABEohFgMpdLDqMIwESIAESIAESIIGQEaByF7ITxuqSAAmQAAmQAAmQQCwCvk6MY2ViXOYRmD9/vixatEjWrFkjpUqVktq1a8spp5wihQsXzrzGskUkQAIxCYwYMULWrVsnd911l1mCKmZiRpJAHALbtm2TkSNHClbCuf322+Ok/k90MnncBSfzXsPyi59//rksW7bMrMzRuHFjadiwYdz7YPny5fLNN9+Y+6ZWrVrm/VmtWjV3lQLb911b9s8//+QKFYGdhsN3oBUrVsh9990ns2bNylKJqlWryuOPPy7NmzfPEseAzCKgK1Ts2LFDuEJFZp3b7Lbm7bfflnvuucdk++WXX6RMmTLZLYLpM4CArlDxxx9/5HiFiltvvVXGjx9vqKxfv94oefEQJZNHy0z2vTZv3jzp3LmzIL9TihcvLm+99ZY0adLEGWy2sTRb3759ZdiwYVnW4G3btq0MGDBAsE5vbkoiK1RQuctN4iEr66+//pILL7xQ8MUBOe2006R+/fqyadMmmTx5sllAHusLjhs3zvOiDllzWd0YBKjcxYCTh6Jmz54tV199tRw4cMC0mspdHjr5rqbmlnI3evRoueOOO+zSE1HuksmjB0j2vYZeq5YtW8qWLVsEbb/gggsEa21/9tlngo9ehL377rvmPanHwu+jjz4qgwcPNkF169Y1FjvcN3PnzjVheMe++eabZju3/iWi3LFbNrdoh7Ccl156yVbsXnjhBfNQ12YsXrxY2rRpYy7qXr16yYwZMzSKvyRAAhlGYO/evfLMM8/Iq6++msX6kGFNZXMCJLB69Wrp3bt3to6YTB7nAZJ9rz3//PNGscNQpPfee09OPvlkU+zKlSvliiuukLVr18rTTz8tY8eOtQ+Hug4ZMsTs432JY0MhhPTv399Y7aZOnSpfffWVnHHGGSY8qH+cUBEU6TQ8zsyZM02tzj777CjFDoF16tSRbt26mXgoejt37jTb/EcCJJBZBNAV1axZM3nllVeo2GXWqT2srTl48KB07dpVdu/enXA9ksnjLjyZ99quXbvk/fffN0VBSVPFDgE1atQwY0+x/fXXXwvehyoYm3ro0CGz26dPH1uxQ8C9994rGNoEGT58uPkN8h+VuyBpp9GxME6gQIEC5uLz+6KoXr26XeMNGzbY29wgARLIHAITJ060LfhXXnmlsT5kTuvYksNFYODAgfLdd9+ZMZuJWu+SyeNsX7LvNXSh6lAEWOnc0qpVK8mfP78J/uSTT+xotA+Cd6gqchqJySNQFCGTJk0yw5w0LohfdssGQTkNj4ELz2le9qrinDlzTDAu6po1a3olYRgJkEAGEGjatKmxTpx++ukyZcqUDGgRm3A4CUBZeu6550wV8IuZr/EkmTzuMpN9r/300092UY0aNbK3dQMTKvAOhEeJJUuWaLD8/PPPZtsrDyI0HIojxrajRywooeUuKNIhOw5mC2F2EAQuUXJ7tk/IcLC6JJCxBDAzFmOMoNhRSCCnBDChAd2x6GK95pprpEWLFnGLTCZP3EI9Evi917Zu3WpSY9JEwYIFPXKKcRGGiI0bN5p41BljVSGlS5c2v+5/cCumgomKQQqVuyBph+RYmzdvlvbt28v+/fuNKRruUCgkQAKZSYAfbpl5Xg9Xqx566CFjpcKwnn79+iVUjWTyJFSwI1Gs9xrG3EH8lDTElSxZEj8CpQ6iebDtl0/zII3mw3YQQuUuCMohOgZ8GuFrS92jwAdekKbkEKFiVUmABEiABBwE0KUPP4noHh00aJAUKVLEEeu9mUwe75L8Q+O912BlhOi4Oq+S1KE/jB4QzYNtv3yaB2k0H7aDECp3QVAOyTHgkRsDR+GjB9K9e/eEvYmHpImsJgmQAAmQQAoIQIHq2bOnKRndsl4Of92HTSaPu4x4+4m819TCFsuBu3qMUIVV8+D4fvk0D9JoPmwHIZxQEQTlEBwDy6bcdNNNsn37dlNbeNyGp24KCZAACZAACcQjcP/99xs/cXB8X69ePXtFCuRTh77YxkoVsOxB+XvggQeynad8+fIoJiFJ9L1WoUIFU56+/7wK1zhMroDAKgdnwljNS+Pc+Zzhms+dJlX7VO5SRTZE5X788cfGp52OscN0dJ3CHaJmsKokQAIkQAKHicDSpUvNkeH3LZZh4LbbbjPphg4dKsnkufjiixNqYXbeaxUrVjRlYoIErHCYWOEWnXShiiDikQ+WQY3zy4NwZz53ulTsU7lLBdUQlYkbAAs544bEoFCsj5eIOT1ETWRVSYAESIAEUkwAig6sWF6CyQc6AaFSpUrGcoduymTyeJXvDsvue83pow7rrLtn+GJ1it9++80cpmHDhvbhkA/Kndfa7EikDpWxPnO1atXsfEFsULkLgnKaHmPVqlVmXB0UO9xwY8aMkdq1a6dpbVktEiABEiCBdCWA9WD9ZNSoUXLnnXea6B9++MEod9g577zz/LKIXx7fDP+NSOa9hhUpatWqZRS4Dz74IItyh/XVVeATUgUOj7E0J8apY+UK5+RDWAEnTJhgkjrzaN5U/1K5SzXhNC4fXsP37dtnatiuXTtzYevXibva8MBdrFgxdzD3SYAESIAESCBwAlgpQpUuuOuCgQKSzHsNYwA7duwoWEcd5Q4ePNieTDht2jTbITOW6Tv22GPttrZu3Vpw7PXr10uHDh0ECm7lypWN2xPsq7WyU6dOdp6gNqjcBUU6zY4Dj9zTp0+3a6XexO0A1wYGwTZu3NgVyl0SIAESIAESCJ4AVovAsl4QKGVQ7nLyXoMVDsOSFi5cKI8++qhZaxlj79asWWOOga5Vt8/XI488UuAurEePHqZ7Fg7/0fu1evVq28ExFDtnV64pLIB/dIUSAOR0PMSCBQuyVS3MgKKQAAlkPgHnve7czvyWs4WpIgDLGER/EzmOptVfdx6v8Jy817AyBYwYl19+uVmRCU6PVbHD6i3orj366KPd1RAohSNHjpQqVaqYsetYngxdsujpwmzgBx98MEueIALyWQvtRrwOhIGRfoMjvdIzjARIILwEjjjiCClXrpzs2LHD12dTeFvHmpMACWSXAKxWJUqUEPiiczrszW45qUw/YMAA6d+/v7G2Of3O5fSY8BwBC97u3bvlmGOOsbt845WLpckw9q5s2bImn9es23hlJBIPFyz4iyXslo1Fh3EkQAIkQAIkQAJpRwA+5D766CMzESI3FTs0FCtOnHTSSdluM9ydBO3yxK+S7GvzI8NwEiABEiABEiCBtCSA5c3gJ69bt25pWb/DXSla7g73GeDxSYAESIAESIAEskWgS5cucumll0qDBg2ylS+vJKZyl1fONNtJAiRAAiRAAhlCAE738UfxJsBuWW8uDCUBEiABEiABEiCBUBKgchfK08ZKkwAJkAAJkAAJkIA3ASp33lwYSgIkQAIkQAIkQAKhJEDlLpSnjZUmARIgARIgARIgAW8CVO68uTCUBEiABEiABEiABEJJwHe2LDzWw5EfhQRIIPMJ6DJTWCuR933mn2+2kATiEcCzAHLUUUcJ9AFK+hBI5Hz4Lj+WPs1gTUiABEiABEiABEiABBIl4Gu527Jli2DhXAoJkEDmE8DXec2aNeX3338368tmfovZQhIggVgEsK5sxYoVZfny5fL333/HSsq4gAlg7doyZcrEPKqvcrdv3z7ZtWtXzMyMJAESyAwCBQoUMA3Zu3cv7/vMOKVsBQnkiIA+E3bv3i3QByjpQ6BYsWJxK8MJFXERMQEJkAAJkAAJkAAJhIcAlbvwnCvWlARIgARIgARIgATiEqByFxcRE5AACZAACZAACZBAeAhQuQvPuWJNSYAESIAESIAESCAuASp3cRExAQmQAAmQAAmQAAmEhwCVu/CcK9aUBEiABEiABEiABOISoHIXFxETkAAJkAAJkAAJkEB4CFC5C8+5Yk1JgARIgARIgARIIC4BXyfGcXMyQUYRmD9/vixdulS2bt0qdevWlZNOOkngoZxCAiSQNwgsXLhQfvvtN1m/fr2592vUqGGeAwULFswbANjKlBHYsWOHfPTRR4I1rNu3b+97nEWLFsnixYvNSjkVKlSQxo0bS7Vq1XzTe0X89NNPcujQIa+oLGFHH320lCpVKir8wIED8tVXX8nKlStNeIMGDeT4448Xdeocldixs3r1apk7d66pO8rFO7RSpUqOFMFuUrkLlnfaHQ3LTT3yyCPy/fffZ6nblVdeKT179jQ3ZJZIBpAACWQEgTVr1kj//v3l22+/zdIeLD91zz33yNlnn50ljgEkkCiBJ554Qj777DOT/Nprr5V8+fJFZYVR4fHHH5dZs2ZFhSPdJZdcIr179074PXTLLbdElRFr54EHHpC2bdvaSRYsWCB9+vQR3BNOKVq0qLz44otGYXOGYzsSichzzz0n7733XhalskWLFvLwww/LkUcGr2qxW9Z9pvLQPpaauuuuu2zFDl9KLVu2lJIlSxoKY8aMkb59++YhImwqCeQtAnv27JE77rjDVuwaNWokV111lTRr1sy8kPDxB+UOln0KCSRDYPz48bZi55V///79ctttt9mKXa1ateSyyy6T008/3ShOH3/8sQwePNgra47DnFbpDRs2SI8ePYxiBysd7gEoZ1jq688//5QuXboYy5z7oIMGDZLRo0cbxQ51h7JYv359k2zKlCnSq1cvd5ZA9oNXJwNpFg+SCIGxY8earlikvf322+XGG280X0cwS+MrCjflp59+Kh07dpTq1asnUiTTkAAJhIjAiBEjBN1JEHzItW7d2q49umhvvfVWs9bwk08+KaNGjbLjuEECiRBYt26dPPPMMzGTDh8+XFatWmXS4MPizjvvlCOOOMLsw9p33333CdLUrl3bKFsxC7MikcevWxbXNN5nkCZNmkjz5s3NNv69/vrrsn37dilUqJC88sorcsIJJ5i4tWvXmvcjPnReffVVGTJkiJ0H7XvnnXfMPspCL5ha6VDGsGHD5IsvvpAffvjBdDHbGQPYoOUuAMjpeohp06aZqmFsXbt27WyzNy5O57iIr7/+Ol2bwHqRAAnkgMCcOXNMbrzonIodAmGFwAcfBC9FWC8oJJAogYMHD8qDDz4of/31V8wskydPNvG43pyKHQLPP/98adWqlYmHYpWIwNKGd5r776ijjhJ0D0MwFg7bqkTi2p4wYYKJu/DCC23FDgFVq1aVDh06mLgff/xRli9fbrbx74MPPrAVyW7dutmKHeJgMMGwBgi6bIMWKndBE0+j43Xq1Em6d+9uzMZO8zSqWKVKFXtcxL59+9Ko1qwKCZBAbhDAWKH8+fObFx0GrntJ5cqV7eCNGzfa29wggXgEYG3D5AZMWOjatatncnTJwvoFgeVLlS1n4qZNm5pdWNDQdZqsvPbaa7aF8P7774+aMPjrr78KlFGIKpPO46CLFsohRI0i2NbhCrh/VJFDOATjBdUyOGPGDEGPWJDCbtkgaafZsfC1jj8vGTlypBnvgLgzzjjDKwnDSIAEQkwALx9nF5NXU+bNm2eC8WLDDEAKCSRCAMrS0KFDTVJMhsBsWS/ZsmWLbfnCmG8vKVKkiB28YsWKpGagwtqmwwpgmcN4Pqdglq6KjpfTffxiQgWGJsGC7bTcYWYvxCuPMxyKI4Y/1KxZ06QP4h+VuyAoh+QYuCFnz55t/jBrCIKuGox1oJAACeQtApgxiHG5ELh10LFEeYsCW5tdApikg+5YKDR4f5xzzjmCSRFeUq5cOXNdwaqlFjx3Oox1U9m1a5duZuv35ZdfNvWBZRBdv27BWDsIJlL4uTxR12CbN282adFO7dXSOBPh+OcMR74glTt2yzpORF7fxGBQfG2pYnfyySebaeF5nQvbTwJ5jcC2bdvMLNq///7bdEdhxiyFBBIh8MILLxgrFYb2wJVWLMEHg1qEMX7NrbxBQVSLG8pxx8cqW+MwnODLL780u+hehULplt27d5sg9RThjsd+8eLFTTC8TEA0D7b98mkepIEyGKRQuQuSdpofq3Tp0nLmmWfKcccdZ2qKwaMYSOo0Q6d5E1g9EiCBHBJAVxkGh+ss2s6dOwdqcchh9Zn9MBKYOXOmjBs3zow3g7GgcOHCcWtz3XXXmTTwdYfZ2XjvQIFatmyZcU2CrlAVOEHOrnzyySd21+8VV1zhmV3H22EMqp9gFi0EHzwQzYNtv3yaB2k45g4UKIeFgHM2Er507r33XjMg9u677zazfbwGux6WivKgJEACKSEAr/zw9YVVKiA33XRT1Mz5lByUhWYEAXwUPPbYY6YtN9xwg6fDX6+GYgID3JfAgbHTVYmmhQNtdW7sHH+n8bF+4RIFK2NAjj32WGnYsKFncrWwqVXOK5HOFleFTfMgrV8+zYM0mg/bQUj21eAgasVjHHYCmKF08803m3pg7M3PP/982OvECpAACaSOAJZOgnd/Vewwkx5WOwoJJELg6aefNn7iYF3DOO3p06fbfxjPrQJFDnE6dg3hzz77rPHc4OwyrWEtf4ePC+cYObg5yY7A1Y+O2fvXv/7lm1WPu3PnTt80OikEkysgUNZU2fTLp3mQXvNhOwjhhIogKKfxMfDFAYucTvN2VvW0004zThsRhsGufl89zjzcJgESCB+BqVOnykMPPWS6nPAsePjhh203DuFrDWt8OAhgJisE1jIs4eUncEoMwZJ3GAMHwcxtdM/iD92zKKNs2bIm7rvvvjO/+JddZ/offvihyQvF6qKLLrLLcW/osTBBAu9Et2swpFdFTdMiDNsYe6cTMhDmFGe4M58zTaq2qdylimyalwvHkpdffrn5esLyQ1jvzy1Ok7J7cWV3Wu6TAAmEkwAUO7yM8ULF7D6sk4nZsRQSyA4BWL/8HBbjXaITEMqXL2+UOR2Ph14hrFABx8LwF4ex307B9QkpU6aMcSjsjIu1jetZnXRjxq6Xwqb5cWwV5EF6p8D6p6toON2eIB/CvdZlRn4Nx/vT6TPSWXaqtqncpYpsmpeLG0tvrokTJ8rVV1+dxYGkc/p63bp107xFrB4JkEB2CcAxLKx0eBHipQuXEegOo5BAdgng2vETvEv69etnorGsJSx1KnB2jDF1sGxhfJxzcgJmbWMJTMhZZ52lWRL6hSVRFUqMt4slWGoMs3ahqE2aNCmLcqeraKCMU045xS4K4wW/+eYbgb87TDx0ujqBFRDdz+48duYUb3DMXYoBp3PxLVu2NNVbsmSJWUsWD3oIZvVgXTy9qWDOdn9NmYT8RwIkEGoCGOuEVQIgbdq0MS83zHj0+nNa8kPdaFY+rQjAQwMEY/D+/e9/2y5DsBoFlvCCggaXKToGXCuPlSLgogd/mzZt0mD7d+HChfY2ljaLJVA2sQQnBArZ22+/bSfH5EJ1yIy6Oj9+LrjgAlHny7169RJdxQVuT1AvVS69esbsA6Rog5a7FIENQ7EYrAqzMQZSY7o4/vDlgRtFH+T4mqGPqzCcTdaRBLJHAF754bRcRV9guu/+feONN6RBgwbuYO6TQI4IwNExLHuYdIFr7J133jG+6NTYAMULwwacXac4IGbWYlkvSMeOHY3l2ez8959T4Yun3CELrHBYAxYuWAYMGCBvvfWW6crVCUboWoXnCKdA6YQCCrcvsPqhLVD+MEZdHRxDsTv++OOd2QLZpuUuEMzpeRBMpBg4cKD5IlJTOEzLUOywf/3115svmKBn+aQnLdaKBDKLACz22ZFkfIxlp3ymzVwCeu04u2O1tVCQnn/+eXtNVyhFqtihOxVOkb3We/UqS8vEr05mwAxbnQ3rjHdvY2UKKJfo0UKdMLFDFTs49B88eLDnmD/UbdCgQcaCh+ENeIeiDZhJ26VLF+NayH2sIPbzWYtHR7wOhEZpw7ziGZZZBOCYEU5LYVbGwM9q1aplGYOXWS1ma5wE8GCDVQZ+zpwuCpxpuE0CJJB3CGAMHKxQmPCgVqhUtx4rUMAChhmrVatWNQpTLCUOyhiULnSlOv3O5bSeeB/CgocJIngXYjxqIoJnJyyKGMaEfLEmcSRSnl8avKPjTdBgt6wfvTwWDvcHMF0nYr7OY2jYXBIgARIggQAIwMqGyQ2JCHzLYSYthg7lpmKHY+N9WK9evUSqEZUGCnHQLk+iKuDYYbesAwY3SYAESIAESIAE0p8AZtliRixWw6BkJUDLXVYmDCEBEiABEiABEkhjAhgT3rx5c6GbLu+TROXOmwtDSYAESIAESIAE0pRAyZIlBX8UbwLslvXmwlASIAESIAESIAESCCUBKnehPG2sNAmQAAmQAAmQAAl4E6By582FoSRAAiRAAiRAAiQQSgJU7kJ52lhpEiABEiABEiABEvAmQOXOmwtDSYAESIAESIAESCCUBHxny2IdNXitp5AACWQ+AV0eCA444UiUQgIkkLcJ6PsfK0VgWS1K+hAoXLhw3Mr4Lj+GVcl4QuPyYwISyBgCWGsY97zPioQZ0042hARIID4BLPuFj76DBw/GT8wUgRLAeYm1LBsq42u52717t1lAPtAa82AkQAKHhQAUOyyujbUd9+zZc1jqwIOSAAmkD4FChQpJiRIlZOvWrVTw0ue0mJoULVpU8BdLOOYuFh3GkQAJkAAJkAAJkEDICFC5C9kJY3VJgARIgARIgARIIBYBKnex6DCOBEiABEiABEiABEJGgMpdyE4Yq0sCJEACJEACJEACsQhQuYtFh3EkQAIkQAIkQAIkEDICVO5CdsJYXRIgARIgARIgARKIRYDKXSw6jCMBEiABEiABEiCBkBGgcheyE8bqkgAJkAAJkAAJkEAsAlTuYtFhHAmQAAmQAAmQAAmEjIDvChUhawerm0MC8+fPl0WLFsmaNWsE6wrXrl1bTjnlFElkDbscHprZSYAE0oAAnwFpcBIytArbtm2TkSNHmiWzbr/99oRamUwed8E///yz/PLLL7J27VqpXLmynHHGGXLMMce4k0Xt//333/L555/LsmXLzFKMjRs3loYNG0rBggWj0rl3li9fLt98842sW7dOatWqZd6f1apVcycLbN93bdk///yTy48FdhoO34FWrFgh9913n8yaNStLJbBg9OOPPy7NmzfPEseAzCKgy4/t2LGDy49l1qmN2xo+A+IiypMJdPmxP/74I8fLj916660yfvx4w3H9+vVx10VFwmTy6InavHmz9OzZUz799FMNMr9Yj/Wqq66S5557zqybGxVp7cybN086d+4suCecUrx4cXnrrbekSZMmzmCzjbW4+/btK8OGDTNrczsTtG3bVgYMGCBHHXWUMzjH24ksP0blLseYw1vAX3/9JRdeeKHgiwNy2mmnSf369WXTpk0yefJkwRcMFigeN26c50Ud3paz5m4CVO7cRPLGPp8BeeM8J9PK3FLuRo8eLXfccYddhUSUu2Ty6AH27dsnzZo1k99++80E1alTR0499VTTKwWLHKRbt27ywAMPmG39h16rli1bypYtWwRtv+CCC+TII4+Uzz77TPDRi7B3333XvCc1D34fffRRGTx4sAmqW7eusdjBWjh37lwThnfsm2++abZz618iyh27ZXOLdgjLeemll2zF7oUXXpCrr77absXixYulTZs25qLu1auXzJgxw47jBgmQQGYQ4DMgM85jurZi9erV0rt372xVL5k8zgMMGjTIVuxuueUWeeSRRwQfr5AJEyYYiyDSHH/88XLppZfaWZ9//nmj2GEo0nvvvScnn3yyiVu5cqVcccUVpmv36aeflrFjx9p5UNchQ4aYfbwvcT9BIYT079/fWO2mTp0qX331lekSNhEB/eOEioBAp+NhZs6caap19tlnRyl2CMTXDr5uIFD0du7cabb5jwRIIHMI8BmQOecy3Vpy8OBB6dq1q+zevTvhqiWTx124Kl+wojkVO6Rr1aqVUdSw/dRTT+HHyK5du+T9998321DSVLFDQI0aNeSuu+4ycV9//bV5H5od69+IESPsrtg+ffrYih3i7733XsHQJsjw4cPNb5D/qNwFSTuNjoVxAgUKFDAXHwaZekn16tXt4A0bNtjb3CABEgg/AT4Dwn8O07kFAwcOlO+++07KlCmTsPUumTxOBuiSXbVqlQmCVU4tds406G6FwCKHrlgIulAPHDhgtmGlcwuUwvz585vgTz75xI5G+yB4h6oip5EY3wdFETJp0iQzzEnjgvhlt2wQlNPwGLjw9AvHr3pz5swxUbioa9as6ZeM4SRAAiEkwGdACE9aSKoMZQmTFiD4xczXeJJMHneZGC9+6NAhE1ypUiV3tNkvVqyYHb506VLBjNaffvrJDmvUqJG9rRuYUIF3IDxKLFmyRIMFs3EhXnmc4VAcMbYdPWJBCS13QZEO2XEwWwizgyBwiZLbs31ChoPVJYE8R4DPgDx3ynOlwZikg+5YdLFec8010qJFi7jlJpPHq1AodPquwng4L4GrEhVMlIBs3brV/GLShJ/LE7gIg2zcuNH8os579+4126VLlza/7n+aB+FQPIMUKndB0g7JsTCNvH379rJ//35jioY7FAoJkEDeIcBnQN4517nd0oceeshYqTCsp1+/fgkVn0wer4IxmQE+5iAYD6fKm6aFwjl06FDdteMx5g7ip6QhrmTJkvgRKHUQzYNtv3yaB2k0H7aDECp3QVAO0THg0whfW+oeBT7wgjQlhwgVq0oCGUmAz4CMPK2BNGrKlCny9ttvGz92mJFapEiRuMdNJk+sQuGnDoIPFIy7wyQIKFYLFy6Ua6+9NmpCBFx9QaD0QXRcndlx/VOH/jB6QDQPtv3yaR6k0XzYDkI45i4IyiE5Bjxyt2vXzh5k2r17d0nUm3hImshqkgAJxCDAZ0AMOIyKSQAfBXAcDEG3rJfDX3cByeRxl+Hex4QIuDyB0ogxcpdddllUEjjlV+fGGEsHUQvbnj17otI6d9RjhCqsmgdp/PJpHqTRfNgOQqjcBUE5BMfAsik33XSTbN++3dQWHrf1CygE1WcVSYAEckiAz4AcAszj2e+//37jJw7WsHr16tkrUgCLOvTFNlaqwGQeKH9wJAynwdnJU758eRQTU7BaBBwLowv2999/N2mPPfZYueiii0zPlFu5q1Chgkmj7z+vwjVOFUJY5eBMGKt5aZw7nzNc87nTpGqfyl2qyIao3I8//tj4tNMxdpiOrlO4Q9QMVpUESCBJAnwGJAmO2WwCmHkKwWzVWIaB2267zaSD4pVMnosvvtjkj/UPyiPqgD90z6JOqhR++eWXdlb1AlGxYkUThgkSsMJhYoVbdNKFKoKIRz5YuzXOLw/Cnfnc6VKxT+UuFVRDVCYe6uh6xcWPQaH44knEnB6iJrKqJEACMQjwGRADDqMSJgBFB1YsL8HkA52AgBmtUL7QTZlMHq/ynWE//PCDUbjg4gT+58qWLeuMlo8++sjslytXTmpYDoohTh91WGfdPcN37dq19qoXDRs2NHk0H5Q7r7XZEa9OwuHrD/UJUqjcBUk7zY4FZ48YVwfFDjfcmDFjpHbt2mlWS1aHBEggVQT4DEgV2bxXLtaD9ZNRo0bJnXfeaaKhfEG5g5x33nnm1+ufXx6vtM4w9Dyh2xWWMvhqhbN+FVjxPvzwQ7OLsXcqWJECs2yxHu0HH3yQRbnD+uoqTZs21U2z2gWW5sRasljJyTn5EFZAjP2DOPPYmVO8QeUuxYDTuXis+QeP3hBMpMCFrYstu+uNLyCn80d3PPdJgATCR4DPgPCdM9b4PwSwUoQqXXDXpU6LsQIFlDv4o3vxxRfNkCOMj8NqFNdff72xLsIXXo8ePWyUUDY7duwoWEcd5WK8nk4mnDZtmu2QuVmzZoKxeyqtW7cWHHv9+vXSoUMHgYJbuXJlMzsX+2qt7NSpk2YJ7DeftQRNxOtoMK/6mVj/v71zAbdjuuL4CvFKEM8gQRKCiKYeqUdUVVFpKaW+Rj3bqkoiQksbWqRF9QupkKQtqXjFI0hR73o1qFJaIh7xCuJNkCJCIiTNb7drvrnnzpx777nnTGbO+a/vO+fM2bP3zN6/mXP2mrXXXjspv9KKRYCI3KWm53ItwAm2f//+5bJoX4EJsEwPwxTEhUqb+VXg5qnqCQT0H5AARUkRAfzOunTpYsxojYf9iDK0YSNuhUMRcstduUO0VGb06NE2ZsyYcIipU6caa8kiCxcuDD7jPomDdmDFY7kxhHOPHTs2WmM2JC55w9LGhAtCpiAM51LWlyhjaBVLXI8ePcJ+f5syZUpQFFGlmBjC6BcBlD3AMYodcfyqKUzk4FVOFOeuHJ063jdjxow2tc7jAbWpkDKLgAjkloD+A3J7aequYq7M+WdrGuh5/bO0TFo6VrlJkybZoEGDQhEeVl2x69u3b1h5KWn9WFamwIix//77h1UuGMJ1xW7AgAFhuLZUseMEHGvy5MnWvXv34OLE8mQodox0MRt45MiRpVXP5Lssd5lg1klEIN8EZLnL9/VR7UQgawLVtNzVqu5Y4EaNGhWsbfG4c34+RiJwNULBQzFDAUtTCr0Mn0SOwII3b94869WrVzTkG8+TtM1QML53WP0oB8NaSGssd1LuakFexxSBghGQclewC6bqikCNCeRduSOGHAGKUcTi4U1qjCUXh2+Ncqdh2VxcKlVCBERABERABESgtQRY3ow4ecOHD29tkYbKp9myDXW51VgREAEREAERKD6BYcOGhbVj+/XrV/zG1KAFUu5qAFWHFAEREAEREAERqB0Bgu7zkiQT0LBsMhelioAIiIAIiIAIiEAhCUi5K+RlU6VFQAREQAREQAREIJmAlLtkLkoVAREQAREQAREQgUISkHJXyMumSouACIiACIiACIhAMgEpd8lclCoCIiACIiACIiAChSSQGsS4kK1RpUVABERABERABESgwQmkhkKZO3eu8ZKIgAjUP4GOHTta165djajvH3/8cf03WC0UAREoS6BTp07Gkl6zZ8+2zz77rGxe7cyWAOvW8ionZZW7N954o1xZ7RMBEagTAiussEKk3LFgtkQERKCxCbA+Ksod66UuWLCgsWHkrPXdunVrUbmTz13OLpqqIwIiIAIiIAIiIALtISDlrj30VFYEREAEREAEREAEckZAyl3OLoiqIwIiIAIiIAIiIALtISDlrj30VFYEREAEREAEREAEckZAyl3OLoiqIwIiIAIiIAIiIALtISDlrj30VFYEREAEREAEREAEckZAyl3OLoiqIwIiIAIiIAIiIALtISDlrj30VFYEREAEREAEREAEckYgNYhxzuqp6tSIwKeffmrPPPNM2aN36dLFevToUTaPdoqACBSbwPTp0+3555+3OXPmWJ8+fWzLLbc0fvsSEWgLgfb0KTNnzrSnn37aWEBh7bXXto033jjch205f1reDz74wG644QZbZpll7JBDDknLFlbjeOCBB2zWrFkhT79+/axv375GoPdy8sorr9i0adPsrbfeCv0lv5/11luvXJGa7pNyV1O8+T/4U089ZUceeWTZig4YMMDGjRtXNo92ioAIFJMAndGpp55q//73v5s1YNCgQXb88ceHDrHZTiWIQAKBSvqUjz76yEaPHm233nprsyNus802dsopp9j666/fbF9bEn7729/a3/72t1Dk4IMPtg4dOjQrPmPGDDv55JPt1VdfbbJv5ZVXtnPPPTdR0Vy8eLGdffbZNmXKFFu0aFGTcgMHDrRf//rXxvKOWYuGZbMmnrPzPffcczmrkaojAiKQFYH58+fbcccdFyl266yzjn3zm98My05Rh2uuuSZ0rFnVR+cpPoFK+hQUL1fsUKT22GMPW3fddQOMRx99NNyj3KuVys033xwpdmnHePPNN+3YY48Nih1Wut12281QzljDFeVz2LBhwTJXWn78+PF29dVXB8UOS+N+++1nW2yxRch2++2324gRI0qLZPI9e3Uyk2bpJK0l8Oyzz4as3Ixjx45NLLb88ssnpitRBESg2ASuu+66MBRLK4YOHWo/+MEPgpWOheLPOOMMo1O84447bPDgwbbhhhsWu7GqfSYE2tqn4BZ05513hrrtvPPOhqKHcoUVDGvY7373O3vppZfCkOoBBxzQ5ja8/vrrwSrYUsGJEyfa+++/byuttJL98Y9/tC984QuhyGuvvRZ+G1i4zz//fJswYUJ0KI59xRVXhO8opFjA3UrHMS6++GL7+9//bo888oj1798/KpfFhix3WVDO8Tn8KQufAvxrkl7c7BIREIH6I3DXXXeFRvG7P+igg6LhVzqouF/Sgw8+WH+NV4tqQqCtfQo+di48XLhvG75xKHPdu3cPuxnubat8/vnnNnLkSPv444/LFsUyd8stt4Q8X//61yPFjgSGg4844oiwDyviiy++GLZ5u/baa6Oh2OHDh0eKHft4WHLrI0pq1iLlLmviOTofT+cvvPBCqBHKnUQERKCxCAwZMsSOOeaYMHS04oorNmk8nar7JS1YsKDJPn0RgSQClfQpbuniePivxYXJGSheSOm+eL607UsuucQef/xxW3311e3oo49Oy2YojiiCyF577dUsH0O0yy23XEj3ByK+MAkJwSrnilxIWPLGbwdrHnLPPfeEiRrhS0ZvGpbNCHQeT4Opmx8jwqyeq666Kih7/Ig23XRT23HHHdvtxJrHdqtOIiAC/yOw3XbbGa8kmTx5ctSh8l8gEYGWCFTSp8TvLSxcDIditUPww2OWK7LTTjuFz9a+obBdcMEFIftJJ50UHSepfDxihPvLxfPhB4hbAsaQuOUuPgQdz+/bfiwUR2bTbrTRRr6r5p9S7mqOOL8n8BuTGuJIWvp0jnkcJ9IDDzwwv41QzURABKpGgA7xH//4R3gxcxDZZ599rHfv3lU7hw5UvwQq6VPWXHNN+/73v2+XXnqp/fWvfw2TFnbZZRd74oknzO/BL33pS21S7j755JMwHItSxf371a9+1W688cZU8PjaIfR5PixcmtnDAr377rthF+fwPtP3pZUhnXJS7koJ6XtNCLhvBAfHgrf11luH+FbEGLr//vvDjTtmzBjjqWXvvfeuSR10UBEQgfwQwCEc64sLYSgIDSERgdYQqLRPYcgU324mLLz99tth9qmf74tf/KL94Q9/iKx5nl7u85xzzgmWMlwLCOXTksybNy9kWW211VKzrrrqqmGfz9r1MiSmlfMy5EEZzFJkucuSds7OxdRvpHPnziGGz1ZbbRXVECdXrHZz5841fig8+cRv1CijNkRABOqGwBprrGHdunWzd955x+iocSDHmZxhrSytDnUDtMEaUkmfwmQHlDefdEDAfEKKMDsVyx0+cz//+c/DPcj92ZLce++9dv311wefNx5WOnXq1FKRyN+uXGQIn1i4cOHCcDz30eNLWjkvQx53gWI7C5FylwXlnJ6DoJE8TeC0WmpW3nzzze2oo46yM888Myh4Tz75ZPDBy2lTVC0REIEqEMBy4oL1/oQTTgid689+9rPQ+S677LK+W58i0IxAJX0KwYFRxpCf/OQnYda2T+Rhljb34H333ReUvcsvvzya5NPs5EsS3nvvPfvNb34TdjHUyyoRrRE3XLhVLqmMT+xwhc3LkDetnJchj5djOwvRbNksKOf4HNxwpYqdVzfu6Oqzan2fPkVABOqbAA7shx9+eGgkEfvxgZKIQEsE2tKnYP0iliKy6667WunKEayO5LNcsSRjxSsnZ511VohVx4QM/ETvvvvu6BUPpcJKFexz/zmWOkM+/PDD1MP7xA7clBDayagXklbOy5DHy7GdhchylwXlgp6DyNwuWZuU/bz6FAERqC0BrA5Y5DzUQ/xsO+ywQ/CDIo2ArXHXjXg+bYtAawiU9in41/kw5/bbb594CAIbYxFEZi1Z77WcNc79RQmAXM5X9MQTTwzHGzVqVFiJYq211grfmSDB76E0LBA7XVHzvKSxje+dT8ggLS7x9Hi5eJ5abctyVyuyOT8u5mLWjSRgI7GAkuTll1+Oknv27Blta0MERKD4BPB1Yqmxr3zlK2GZsaQWxYeViBUmEYE0ApX0KXF/uPi9Fj9HPL5d2kxWz48FjiX0kl5uZSNv165dQx4/P6HAXB566CHfjD7x//P+0MObsNPLPfzww1He+Ian89vBlzVLkXKXJe0cnQsTMU8oPFkw/TzJMnfZZZeFGhNkMn5D56gZqooIiECFBOjYvHMjnljcQdwPGQ8f0adPH0/Wpwg0I1BJn8IsUxQtJC3Qry9NRp7NNtuMj1RhYgbDvEkv1lB28f0M+yLE1mMiB3LbbbeFz/gbfaTLtttu65tRwGNCwMTj35EBKyBDv0i8TEjI4E3KXQaQ83oKj56NPx3maXcKRdEbN25ctNAyFj7/Aea1LaqXCIhA2wlguUPwZ2ItWdbRRPgPYG1M1pVF9txzT2vNTMWQWW8NS6CSPmXfffcNvPDpZKmwuAUPRetPf/pT2M9wbHwEiZUimEXLa/bs2e1izgQOlt9DUMiYuOHCxCIPhvzlL3+5SR123333YAEk74gRI0IYF7aZqEi9PFwKvoRZS4clJs+m6338vwbEOuMlqV8C+DoQ5sADReIPwTp6RNL2m5Lo9cyYzdoZtH6p57NlDHf069cv+LS4k3E+a6paVZMA1jrWwJw2bVp0WEKe0Fl6J4tFA9cN/QdEiBpiAx8xlCmULg/W21LDK+lTeJAYPHhwNFkCRYt7EB83/y9iZuqkSZOidWapx4QJE2zixImhSqym0lKgbazQp59+esjPcKnPyA0JS95oI2vbzpw5MyTxMIPvnetBDK1edNFFzVZtYk1aQq6gSjGRA2b4pzozFDtmAVdTGOJtaZhXlrtqEi/YsXCgPu+88+ywww4LztTEtCO+HYod5nKmkmPB0596wS6sqisCrSTARAp+48yK9VhdDC+h2PGd/wasGPoPaCXQBs9WSZ+C2w+KGg8ZzEBFSWI0CcUOZWngwIHBJ5SAxHEpVc7i+5K2fUmztHI84KK8Yc2mTnPmzIkUO4J501di/CgV1qIdP358sOAxkYPfD4odPn7EimX1p6UhstwtDeo5PCdPTwzJMHuJJ/XSRZBzWGVVqYoEZLmrIsyCHgqrC1Z7/gOwCmywwQZhFm1Bm6Nqt5NAJZa7+Ckr6VNQ7LCU0RexLBl9UdIsbj8PyhhKF0Op8bhzvr/ST34LWPCYdMTvoLVuSSikKKZY/SiXNOu20jrFy7XGcqdQKHFiDbzNkwrmZF4SERCBxiNAJ8rKALwkItBeApX0KVjVsNCVWumS6kJsOSZboABWU7HjXPwWCOTfVkEhzjrkSVodNSybRkbpIiACIiACIiACuSSAHyhx7XAfkjQnIMtdcyZKEQEREAEREAERyDEB/EGZnasQPckXScpdMhelioAIiIAIiIAI5JQAk/54SZIJaFg2mYtSRUAEREAEREAERKCQBKTcFfKyqdIiIAIiIAIiIAIikExAyl0yF6WKgAiIgAiIgAiIQCEJSLkr5GVTpUVABERABERABEQgmYCUu2QuShUBERABERABERCBQhJInS3bqVOn3ATjKyRZVVoECkSAgKOIlpkq0EVTVUWghgT8v4A1VVltQpIfAuhnLUnq8mMtFdR+ERABERABERABERCB/BFItdyxphoLyEtEQATqnwALyLMe4ty5c23+/Pn132C1UAREoCwB1kVdZZVVbM6cOfb555+Xzaud2RLo3LmztWS9S1XuFi1apAua7fXS2URgqRPQ736pXwJVQARyQYD/AgTFTspdLi5JVAm/NlFCwoYmVCRAUZIIiIAIiIAIiIAIFJWAlLuiXjnVWwREQAREQAREQAQSCEi5S4CiJBEQAREQAREQAdMlpGUAAA2uSURBVBEoKgEpd0W9cqq3CIiACIiACIiACCQQkHKXAEVJIiACIiACIiACIlBUAlLuinrlVG8REAEREAEREAERSCAg5S4BipJEQAREQAREQAREoKgEpNwV9cqp3iIgAiIgAiIgAiKQQCA1iHFCXiU1CIH//Oc/NnnyZOvQoYMNHTq0QVqtZopAYxOYPn26PfPMM/bqq68a64n27t3btt122xYj4Tc2NbW+lMCCBQvsiSeeKE1u8p37a+ONN26Sxpdq34PU48knn7TXXnvNunXrZjvuuKP16tWr2XnjCQsXLrSpU6fazJkzbfHixda/f3/baqutjBU7ysmLL75o//znP+31118PbeO3s8EGG5QrUtN9Uu5qireYBx8xYoTdfPPNofJDhgwJSl4xW6Jai4AItETgpZdeshNPPNHuu+++ZlnXX399O+OMM2yPPfZotk8JIpBEYNq0abbffvsl7YrSvva1r9mVV14Zfa/2Pfjuu+/a8ccfb3fccUd0DjYwWBxwwAF29tln2zLLNB+4fOyxx+yoo44y6hOXVVdd1S677DLbbrvt4slhGwXwlFNOsYsvvthKV46Aw9ixY2255ZZrVq7WCc1bV+sz6vi5JnD11VdHil2uK6rKiYAItJsAa4gfcsghkWK3ww472I9+9CPbe++9Q4eExeOHP/yhPfzww+0+lw7QGASeeuqpNjW02vcglsN99903Uuw222wzO+ywwwyFEkXsqquuslGjRjWrIxZrfgsodiuttFL4DaCcdenSxT788EP73ve+FyxzpQVPP/10u/DCC4Ni16dPHzv00ENt6623Dtmuv/768HsqLZPFd1nusqBckHO88sordtJJJxWktqqmCIhAewn8/ve/N4aTkHPOOSd0YH7MZ5991r797W/bBx98YFjz77nnHt+lTxFIJeBDsig4V1xxRWK+FVZYIUqv9j04fvx4e+GFF8LxeVA59dRTbdlllw3fb7nlFvvxj39s5Onbt29QAr0iY8aMsffeey+4IUyZMsW22WabsGvWrFn23e9+NwztnnXWWXbdddd5EaPPnDBhQvjOb4W2dOz4P7UKBRKr3Z133mkPPPBAGBKOCmawIctdBpCLcAoWhj766KNt3rx5Raiu6igCIlAFAvfee284ys4779xEsSMRi8fw4cPDfhQ9rBcSEWiJgFvu8FPDty7p1alTp+gw1b4HXfnCihZX7DjhXnvtFRQ1ts8880w+gsydO9f+/Oc/h22UNFfsSOjZs6cdd9xxYd+DDz5o/BZcLr300mgo9uSTT44UO/afcMIJhlsDcskll4TPLN+k3GVJO8fnGjdunP3rX/+yNddcU9a7HF8nVU0EqkWAISosKHRAOJonyYYbbhglv/nmm9G2NkQgiQCTEVz52XLLLZOyNEmr9j3IkOzLL78czsHQrFvs4ifdfffdw1cscgzFIvgJfvbZZ2EbK12poBQuv/zyIfmmm26KdtNnIvx+XJHznfj3oSgit912m8EmS9GwbJa0c3oubmwcTBE+mS0rEQERqG8CdD5u5Uhr6UMPPRR20bFttNFGadmULgKBwPPPPx8pMcwUnThxYpiBjRK3xRZb2G677WY9evSIaFX7Hpw9e3ZkSVtvvfWi88Q3Vllllegr9aWejz/+eJTm/nJRwpINJlRw/zOb/Lnnnot2xYego8TYhh8LxRH3B6zhWYmUu6xI5/Q8OLMyHMuw7IEHHmgDBw4MDqc5ra6qJQIikBEBHMuZIYgQ1mFpzPjLqKk6TZUIuLLD4Zic8MknnzQ5MpMPfvnLXwa/tyY7Ur609R5EoeM+xUqGP1ySEKrEBX9SZM6cOeGTiRRpIU8YXkbefvvt8EnfOX/+/LC9xhprhM/SNy9DOopnlsqdhmVLr0aDff/Vr34VnigYfuGHJxEBERABQknQOX/66adhOIpwKBIRaImA+9uRDwWL2ddMYPjGN74RhkhRhkaOHGlEZWhJKrkHmczg8fPwh3Plzc+FEeOCCy7wr9F+fO6QNCWNfautthofhlKHeBm208p5GfJ4ObazECl3WVDO6Tluv/12u/zyy0PsH2YPde7cOac1VbVEQASyIvDOO+8EK77PoiUGXpYWh6zaqfNUnwChcxCGPpmgQCiQ0047LcSAY6YqYUUQjArvv/9+2E56a889SJw6BOUQvzsmQaBYPf3003bwwQdHPoHk8Vh3KH2I+9WFLyVvPgmEBx7Ey7CdVs7LkMfLsZ2FSLnLgnIOz8GPhyCPCMOyScEZc1htVUkERKCGBIjKj/M4Uf2RY445RqvU1JB3vR36oosuCmFI8NXcfvvtmzSPCRY8KCBY1B599NEm+/1Le+9BJkTgXoTgI/ed73wnWPN23XVXY2ZuPCA3vnSIW9hKh5HDzv+/+WxxN4J4GXanlfMy5PFybGch8rnLgnIOz/GLX/wixPThyWXzzTdvEriYCRYurFSB0yvKX9euXT1ZnyIgAnVGgKWTCFjsFhWi7rsVpM6aqubUkADWqrjFKn4qJlS4oHihcMWlWvcgq0Wcd955YQj2rbfeCqfYZJNNbM899wxWaV+5wpW7ddZZJ+Txez9eJ9/2fV6GNq688sr20UcfRb8Zz+ufXobvXs731fpTyl2tCef0+MwSQlgupdwf+JFHHhny4afwrW99K2zrTQREoL4I3HjjjSGmnfvYERrJwzjUV0vVmqVJIK7geOgRr08170EMEvRrvBiepZ9z48T999/vp4xmgK+77rohDZ9ArHBMrCgVn3ThiiD7KYel0fellSE9Xq40Xy2+S7mrBdUCHJObkieOJMFR1J1FmX3EDyVrk3JSvZQmAiJQfQJ0qkOHDg0dII7hWD3kplF9zvV+RIYgWbaOVR4GDx4cBcCOt9tXjiCtd+/e0a5q3oOPPPJIULgIcUL8ubXWWis6Dxs33HBD+L722mtbzyUBipF4jDrWWPZh3bBzyRu+hF53gjO7UA7lLmldZvJ4gGbix1KfLEXKXZa0c3SucrOVWHvvpz/9aagtPxSUO4kIiED9ESDgK351WDZ4kLvmmmuadLr112K1qFYEsMoxcQHljviJPDD4Ulx+ToZKEcKVeAy4at+DWJ0ZdsVShu9ffKkzrHh/+ctfQh3ivnesSMEsWxS4a6+9tplyx8QQl5122sk3w2oXLMuHjyrBm+MTj7ACMokEiZeJCtd4QxMqagxYhxcBERCBvBJgLWmi+iMHHXRQ6NyYRZ/0cmt+Xtuiei19AsxORfCnY/ktD//BECyhtvDhRg4//PDwMMF2pfcgK0VwHF7x1VN8BQri0Z177rlRHViNgskWjFihXB577LGcPggGDKyNCMd1JZTvd911VxTkH59BfPdc9tlnH+vWrVv4esQRR9gbb7wRtmk39fLfzJAhQ7xIZp8dlkSOXpx0NgCkDdsl5Vda/RCIW+64WWW5q59rm9YSlulhmIJZbGkzv9LKKr2YBIjKXzr8VK4ldMz9+/cvl0X76ogAfmeELiGyQjzsR7km4rOJr+Zjjz0WslGeoU8sYq5PsI4xK1cQLqU99+Do0aNtzJgx4TxTp0411pJFiK9HHXxiIO3AisdyYwj92dixY6M1ZkPikjcsbUy4IGQKwnAuZX2JMoZWscTFV9gg35QpU4KiiCrFBEWGmwmgzPEQFDtCv1RTmMjBq5zIcleOToPuc2XOPxsUg5otAnVNYMaMGW1qn8cEa1MhZW4oAsR7I77dsGHDQuw3HhanT58eFDuUI8JuXXnllUGxA0x77sG0/gmr3KRJk2zQoEGBPQ+rrtj17ds3rLqStH4sK1PwALP//vsHyx5DuK7YDRgwIAzXlip2nIBjTZ482bp37x7cG1ieDMUO5ZXVOAjavDRElrulQV3nFIGcEZDlLmcXRNURgaVMoBLLXbzKWNBQqhj9wZ8tPmkhnq8921jgRo0aFaxt8bhzfkyUS6yGKHgoZihgaUqhl+ETCyQWvHnz5lmvXr2iIeR4nqRthoLxvcPqRzkY1kJaY7mTclcL8jqmCBSMgJS7gl0wVVcEakygvcpdjasXYssRoBhFLB7epNbnzcPxW6PcaVg2D1dKdRABERABERABEWg1AZbMJF7r8OHDW12mkTIqFEojXW21VQREQAREQATqgAB+fczO7devXx20pvpNkHJXfaY6ogiIgAiIgAiIQA0JEHCblySZgIZlk7koVQREQAREQAREQAQKSUDKXSEvmyotAiIgAiIgAiIgAskEpNwlc1GqCIiACIiACIiACBSSgJS7Ql42VVoEREAEREAEREAEkglIuUvmolQREAEREAEREAERKCSB1CDGrCXX2vXkCtlyVVoERCAiQNR2lu1hge9FixZF6doQARFoTAIsN9exY8ewVmvKEvSNCSYHrSboPK9ykqrclSukfSIgAiIgAiIgAiIgAvkkoGHZfF4X1UoEREAEREAEREAEKiIg5a4ibCokAiIgAiIgAiIgAvkkIOUun9dFtRIBERABERABERCBighIuasImwqJgAiIgAiIgAiIQD4JSLnL53VRrURABERABERABESgIgJS7irCpkIiIAIiIAIiIAIikE8CUu7yeV1UKxEQAREQAREQARGoiICUu4qwqZAIiIAIiIAIiIAI5JOAlLt8XhfVSgREQAREQAREQAQqIiDlriJsKiQCIiACIiACIiAC+STwX0v4A9hj3gvQAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple regression analysis with 2 independent variables using SQL\n",
    "#### Home prices `y` based on the number of bedrooms `x1` and the number of bathrooms `x2`.(Obviously not in the Bay Area!)\n",
    "![Screenshot 2024-10-30 at 11.25.44 PM.png](attachment:2839973f-334b-4870-aa68-6567aee80ffd.png). \n"
   ]
  },
  {
   "attachments": {
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     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use regression analysis to estimate the price of another home with 2 bedrooms and 1 bathroom, and the price of another home with 5 bedrooms and 3 bathrooms.\n",
    "#### This is a multiple regression problem because we have more than one independent variables `x1` and `x2`. The regression equation is\n",
    "![Screenshot 2024-10-30 at 11.32.55 PM.png](attachment:c2b36f7b-8cf3-4cd5-8c82-01dafd5c60f4.png)\n",
    "#### Just as we had to calculate linear regression coefficients `m` and `b`, we need to calculate the multiple regression coefficients `β0`, `β1`, and `β2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "from data201 import db_connection, df_query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bedrooms  = [         3,          2,          4,          2,          3,          2,          5,          4]\n",
    "bathrooms = [         2,          1,          3,          1,          2,          2,          3,          2]\n",
    "prices    = [488_000.00, 443_000.00, 538_000.00, 442_000.00, 497_000.00, 449_000.00, 584_000.00, 529_000.00]\n",
    "\n",
    "df = DataFrame({'bedrooms': bedrooms, 'bathrooms': bathrooms, 'prices': prices})\n",
    "\n",
    "pd.options.display.float_format = '${:,.2f}'.format\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = db_connection(config_file = 'homes.ini')\n",
    "cursor = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cursor.execute('DROP TABLE IF EXISTS homes')\n",
    "\n",
    "cursor.execute(\n",
    "    \"\"\"\n",
    "    CREATE TABLE homes\n",
    "    (\n",
    "        id        INT NOT NULL,\n",
    "        bedrooms  INT NOT NULL,\n",
    "        bathrooms INT NOT NULL,\n",
    "        price     DOUBLE,\n",
    "        PRIMARY KEY(id)\n",
    "    )\n",
    "    \"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = list(zip(range(len(prices)), bedrooms, bathrooms, prices))\n",
    "values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the `homes` table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql = ( \"\"\"\n",
    "        INSERT INTO homes\n",
    "        VALUES (%s, %s, %s, %s)\n",
    "        \"\"\"\n",
    "      )\n",
    "\n",
    "cursor.executemany(sql, values)\n",
    "conn.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.float_format = '${:,.2f}'.format\n",
    "\n",
    "df_query(conn, 'SELECT * FROM homes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate correlation coefficients with SQL\n",
    "#### Perform matrix arithmetic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctes = (\n",
    "    \"\"\"\n",
    "    WITH\n",
    "        base AS (\n",
    "    \t\tSELECT\n",
    "    \t\t\tid,\n",
    "    \n",
    "    \t\t\tbedrooms                          AS x1,\n",
    "    \t\t\tbedrooms - AVG(bedrooms) OVER()   AS x1_centered,\n",
    "    \n",
    "    \t\t\tbathrooms                         AS x2,\n",
    "    \t\t\tbathrooms - AVG(bathrooms) OVER() AS x2_centered,\n",
    "    \n",
    "    \t\t\tprice                             AS y,\n",
    "    \t\t\tprice - AVG(price) OVER()         AS y_centered\n",
    "    \t\tFROM homes\n",
    "    \t),\n",
    "     \n",
    "    \tunivariate_regression AS (\n",
    "    \t\tSELECT\n",
    "    \t\t\tAVG(x2) - AVG(x1) * SUM(x2_centered * x1_centered)\n",
    "    \t\t\t\t\t  / SUM(x1_centered * x1_centered)         AS x1_const,\n",
    "    \t\t\tSUM(x2_centered * x1_centered)\n",
    "    \t\t\t\t\t  / SUM(x1_centered * x1_centered)         AS beta1,\n",
    "    \t\t\tAVG(x1) - AVG(x2) * SUM(x1_centered * x2_centered)\n",
    "    \t\t\t\t\t  / SUM(x2_centered * x2_centered)         AS x2_const,\n",
    "    \t\t\tSUM(x1_centered * x2_centered)\n",
    "    \t\t\t\t\t  / SUM(x2_centered * x2_centered)         AS beta2\n",
    "    \t\tFROM base\n",
    "    \t),\n",
    "     \n",
    "        residuals AS (\n",
    "    \t\tSELECT\n",
    "    \t\t\tx1,\n",
    "    \t\t\tx1 - (SELECT beta2    FROM univariate_regression) * x2\n",
    "    \t\t\t   - (SELECT x2_const FROM univariate_regression)      AS x1_resid,\n",
    "    \n",
    "    \t\t\tx2,\n",
    "    \t\t\tx2 - (SELECT beta1    FROM univariate_regression) * x1\n",
    "    \t\t\t   - (SELECT x1_const FROM univariate_regression)      AS x2_resid,\n",
    "    \n",
    "    \t\t\ty,\n",
    "    \t\t\ty_centered\n",
    "    \t\tFROM base\n",
    "    \t),\n",
    "     \n",
    "        multiple_regression_2 AS (\n",
    "    \t\tSELECT\n",
    "    \t\t\tAVG(y) - AVG(x1) * SUM(y_centered * x1_resid)\n",
    "    \t\t\t\t\t / SUM(x1_resid * x1_resid)\n",
    "    \t\t\t\t   - AVG(x2) * SUM(y_centered * x2_resid)\n",
    "    \t\t\t\t\t / SUM(x2_resid * x2_resid)           AS beta0,\n",
    "    \t\t\tSUM(y_centered * x1_resid)\n",
    "    \t\t\t\t/ SUM(x1_resid * x1_resid)                AS beta1,\n",
    "    \t\t\tSUM(y_centered * x2_resid)\n",
    "    \t\t\t\t/ SUM(x2_resid * x2_resid)                AS beta2\n",
    "    \t\tFROM residuals\n",
    "    \t)\n",
    "    \n",
    "    SELECT * FROM multiple_regression_2\n",
    "    \"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_query(conn, ctes)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "β0 = df.iloc(0)[0].beta0\n",
    "β1 = df.iloc(0)[0].beta1\n",
    "β2 = df.iloc(0)[0].beta2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "est_2_1 = β0 + 2*β1 + 1*β2\n",
    "est_5_3 = β0 + 5*β1 + 3*β2\n",
    "\n",
    "print(f'est_2_1 = ${est_2_1:,.2f}')\n",
    "print(f'est_5_3 = ${est_5_3:,.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cursor.close()\n",
    "conn.close()"
   ]
  }
 ],
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