Genus 2 curve data - 13689.b.13689.1

Formats: - HTML - YAML - JSON - 2025-03-18T05:05:27.978082

• g2c_curves •

  Column	Type
  Lhash	text
  abs_disc	bigint
  analytic_rank	smallint
  analytic_rank_proved	boolean
  analytic_sha	smallint
  aut_grp_id	text
  aut_grp_label	text
  aut_grp_tex	text
  bad_lfactors	text
  bad_primes	integer[]
  class	text
  cond	bigint
  disc_sign	smallint
  end_alg	text
  eqn	text
  g2_inv	text
  geom_aut_grp_id	text
  geom_aut_grp_label	text
  geom_aut_grp_tex	text
  geom_end_alg	text
  globally_solvable	smallint
  has_square_sha	boolean
  hasse_weil_proved	boolean
  igusa_clebsch_inv	text
  igusa_inv	text
  is_gl2_type	boolean
  is_simple_base	boolean
  is_simple_geom	boolean
  label	text
  leading_coeff	numeric
  locally_solvable	boolean
  modell_images	text[]
  mw_rank	smallint
  mw_rank_proved	boolean
  non_maximal_primes	integer[]
  non_solvable_places	jsonb
  num_rat_pts	smallint
  num_rat_wpts	smallint
  real_geom_end_alg	text
  real_period	numeric
  regulator	numeric
  root_number	smallint
  st_group	text
  st_label	text
  st_label_components	integer[]
  tamagawa_product	smallint
  torsion_order	smallint
  torsion_subgroup	text
  two_selmer_rank	smallint
  two_torsion_field	jsonb

  
  {'Lhash': '57410029975317309', 'abs_disc': 13689, 'analytic_rank': 0, 'analytic_rank_proved': True, 'analytic_sha': 1, 'aut_grp_id': '[6,2]', 'aut_grp_label': '6.2', 'aut_grp_tex': 'C_6', 'bad_lfactors': '[[3,[1,0,3]],[13,[1,2,13]]]', 'bad_primes': [3, 13], 'class': '13689.b', 'cond': 13689, 'disc_sign': 1, 'end_alg': 'CM', 'eqn': '[[0,-1,-2,1,3,1],[0,1,1]]', 'g2_inv': "['441025329/169','9461333/169','-641603/1521']", 'geom_aut_grp_id': '[12,4]', 'geom_aut_grp_label': '12.4', 'geom_aut_grp_tex': 'D_6', 'geom_end_alg': 'M_2(Q)', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['516','8073','1250613','1752192']", 'igusa_inv': "['129','357','-347','-43053','13689']", 'is_gl2_type': True, 'is_simple_base': True, 'is_simple_geom': False, 'label': '13689.b.13689.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '1.3980659092426372028403932100339898674514156341356439306', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.240.1', '3.480.12'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 3, 'num_rat_wpts': 3, 'real_geom_end_alg': 'M_2(R)', 'real_period': {'__RealLiteral__': 0, 'data': '22.369054547882195245446291361', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 10}, 'root_number': 1, 'st_group': 'E_6', 'st_label': '1.4.E.6.2a', 'st_label_components': [1, 4, 4, 6, 2, 0], 'tamagawa_product': 1, 'torsion_order': 4, 'torsion_subgroup': '[2,2]', 'two_selmer_rank': 2, 'two_torsion_field': ['3.3.13689.1', [-26, -39, 0, 1], [3, 1], True]}
• g2c_endomorphisms •

  Column	Type
  factorsQQ_base	jsonb
  factorsQQ_geom	jsonb
  factorsRR_base	jsonb
  factorsRR_geom	jsonb
  fod_coeffs	jsonb
  fod_label	text
  is_simple_base	boolean
  is_simple_geom	boolean
  label	text
  lattice	jsonb
  ring_base	jsonb
  ring_geom	jsonb
  spl_facs_coeffs	jsonb
  spl_facs_condnorms	jsonb
  spl_facs_labels	jsonb
  spl_fod_coeffs	jsonb
  spl_fod_gen	jsonb
  spl_fod_label	text
  st_group_base	text
  st_group_geom	text

  
  {'factorsQQ_base': [['2.0.3.1', [1, -1, 1], -1]], 'factorsQQ_geom': [['1.1.1.1', [0, 1], 1]], 'factorsRR_base': ['CC'], 'factorsRR_geom': ['M_2(RR)'], 'fod_coeffs': [-468, 234, 351, -26, -39, 0, 1], 'fod_label': '6.6.2436053373.1', 'is_simple_base': True, 'is_simple_geom': False, 'label': '13689.b.13689.1', 'lattice': [[['1.1.1.1', [0, 1], [0, 0, 0, 0, 0, 0]], [['2.0.3.1', [1, -1, 1], -1]], ['CC'], [1, -1], 'E_6'], [['2.2.13.1', [-3, -1, 1], ['-588/319', '-117/638', '351/319', '-155/638', '-21/319', '9/638']], [['2.0.3.1', [1, -1, 1], -1]], ['CC'], [1, -1], 'E_3'], [['3.3.13689.1', [-26, -39, 0, 1], ['-26/29', '234/29', '109/87', '-38/29', '-3/29', '4/87']], [['2.0.3.1', [1, -1, 1], -1]], ['CC'], [1, -1], 'E_2'], [['6.6.2436053373.1', [-468, 234, 351, -26, -39, 0, 1], [0, 1, 0, 0, 0, 0]], [['1.1.1.1', [0, 1], 1]], ['M_2(RR)'], [3, 1], 'E_1']], 'ring_base': [1, -1], 'ring_geom': [3, 1], 'spl_facs_coeffs': [[['145435095/20416', '264047121/163328', '-17420013/20416', '-36412389/163328', '1485783/81664', '934083/163328'], ['499572811413/1306624', '237407060241/653312', '-59082698025/1306624', '-15223855959/326656', '419505723/1306624', '808340715/653312']]], 'spl_facs_condnorms': [1], 'spl_fod_coeffs': [-468, 234, 351, -26, -39, 0, 1], 'spl_fod_gen': [0, 1, 0, 0, 0, 0], 'spl_fod_label': '6.6.2436053373.1', 'st_group_base': 'E_6', 'st_group_geom': 'E_1'}
• g2c_ratpts •

  Column	Type
  label	text
  mw_gens	jsonb
  mw_gens_v	boolean
  mw_heights	numeric[]
  mw_invs	smallint[]
  num_rat_pts	smallint
  rat_pts	jsonb
  rat_pts_v	boolean

  
  {'label': '13689.b.13689.1', 'mw_gens': [[[[0, 1], [1, 1], [1, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]], [[[1, 1], [1, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [2, 2], 'num_rat_pts': 3, 'rat_pts': [[-1, 0, 1], [0, 0, 1], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 4971
    {'conductor': 13689, 'lmfdb_label': '13689.b.13689.1', 'modell_image': '2.240.1', 'prime': 2}
  • id: 4972
    {'conductor': 13689, 'lmfdb_label': '13689.b.13689.1', 'modell_image': '3.480.12', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 15397
    {'cluster_label': 'c2c3_2~3_0', 'label': '13689.b.13689.1', 'p': 3, 'tamagawa_number': 1}
  • id: 15398
    {'cluster_label': 'c2c3_1~3_0', 'label': '13689.b.13689.1', 'p': 13, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '13689.b.13689.1', 'plot': 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EQOTbtm3Sc89J//qXdZvmaNRIuuIK6dJLrWs0Ut240WTLFunzz6XPPrPxjZ98Ys/PvkIhqXlzG3Ddvr109tk2ExlwjQAYezxPGjlSGjLELvfsaUu9xMe7rQvRiwCII1qzxvaMfP55W45FsskZV15pm4iffnowQt/heJ51Hy9aZBNfPvxQWrVq/9uEQjaGsEMH6dxzpbPOsjGRQHEjAMaWrCzp1lulp56yy3feKT36KMte4fAIgDikH3%2BU/u//pJdfzlt7r3lzacAA%2B3R5zDFu64t2GzdK779va2/Nn29jC/cVH28hsFMnO510EkEaxYMAGDt27rT345kz7f3jiSek225zXRX8gACIP/n1V%2BnBB6UJE2wciWRj%2Bu65x8b1EVKOzsaNNnby3XfttH79/l%2BvXdvW67rwQmshLF/eTZ2IfQTA2LB5s9Sliw1FiY%2BXJk%2B2yR9AfhAAkWvPHtsg/P/%2BT9q%2B3a7r1En6xz%2BsmxdFx/OsRXDOHJs9vXBhXve6ZLOJ27e3N/cuXWyZHKCoEAD9b%2BVK%2B8D44482tnjmTJt4BuQXARCSrKvyxhvzuilPO0365z9ZMb647Nplv4PZs6X//tfWJdxXixbSJZfY6eSTaYVF4RAA/e3DD21rt23bbI2/t96Sjj/edVXwGwJgwG3fLg0aJD3zjF2uVs1mkvXpwwBiV3ImlMyaZaePPrKld3I0bGjdPJddxgQcHB0CoH9NnWrvz3v22Ot/1ix73wYKigAYYB99JP3tb3lr2t1wgzRihO3IgeixZYv0n/9IM2bYav777lhSt67017/aMjynnUYYLA6eZ3tX//CDzZBft852mNm0ya7ftk1KS7NFeHfutH/UOZOoJNvOsEwZqVw5W5/t2GNt68Pq1fP2qW7QwBYaT0oq2g9iqampSk1NVVZWllauXEkA9BHPs5m9gwfb5UsusTF/TMbD0SIABlBWlvTwwzbRIzvbxpe98IJN8EB0S0%2B3MYP//reFwn13XmnUyGYD9upl%2Bxmj8DIypK%2B%2BsjUfly2TvvnGtjj844/i%2Bflly9qe1C1aSKeeKrVqZUMASpYs3P3SAugvmZnSLbdI48fb5YEDbYhONO6qBP8gAAbMli22ft/cuXa5d29p3DhWivejXbts7M/UqdYNtO8kktNOs26inj2tdQn5s3u3tYzPm2cTcz7/3ELggUIhqU4da6mrV89a7mrUsK64SpVsB5jy5a11Jj7eAlsoZB%2B49uyxn7NjhxQOW4vhli3Wgrhhg/Tzzzaw/8cfpb17//yzy5e3nWbOPVc6/3wLiAVt%2BSUA%2Bsf27dbC/9Zb9nsePdrW/AMKiwAYIF99ZQOHf/rJ/jH9618WAOF/6ek2C/CVV6ybOGf5nlKl7Hd%2B3XXSeecxrvNgfv/dutdnzLDlefbd2lCy7QxPO80W8T75ZOnEE617NtKLeGdmWjfzN9/YFoSLF9uuM2lp%2B98uZ0xoz57WUpifMEgA9IcNG6SLLrLW57JlpSlT7PUMFAUCYEDMni316GFBoVEj%2B2d30kmuq0Ik/Pab/aN48UVp6dK86%2BvXl266Sbr2WloFs7OtK/3ZZ60rfd%2BWtpo1rXWtfXtbqPu446JnbGV2tvT119ZCOWeOLTC%2Bbwtls2Y2lrdPn8O36hMAo9/XX9u%2B6uvXW8vyrFksx4WiRQAMgAkT7B9/drb0l79Ir7/OnrRB8eWXtn/zyy/njVuLj7eW3zvukE44wW19xW3vXgvGI0dKq1fnXd%2BsmdS9u9S1q%2B12Ey2B70hyxoTmDAPICYMJCfaav/POg88QJQBGt/fes73Vw2Ebz/vf/9pwA6AoEQBj3IgReZuDX321LfdSqpTbmlD8du2SXn1VSk2VlizJu75bN%2BmBB6zrMNa9%2B64NpF%2B50i5XrChddZV0zTUWAP3u99%2BlSZNsP9ic9TzLlbNlngYN2n%2B2KAEwek2aZH%2BTe/dKZ58tvfEGH9gRGQTAGOV59o/9oYfs8r332rFfWjYQGZ5nkxwef1x68027LNnwgBEjYnPHEc%2BT/v536ZFH7HLVqvah6PrrY3O7vexsG/Lxj3/YJBbJWo%2Beey5vpj8BMPocuMzLFVdIEyfakkFAJDAkPEYNHZoX/h591JZ9IfwhFLIZpG%2B8YZMLevSw61591bqDH3ts/zXrYsH99%2BeFv5tvtq7f22%2BPzfAn2USfnP1hp0612cpr1kgdOuQt/YTokp0t3XZbXvi7806b0EX4QyTRAhiD9u32feIJWzMKOJRly%2BxvZOFCu3zOOdJrU7JUJbRVqlAh8tNdI2j1aqlJE/sHO368tfoFzfbtNt7z2Wftct%2B%2B0hPD1qpSg3q0AEaBPXts0s7UqXaZ92wUF1oAY8yzz%2BaFv5EjeSPBkbVoYbNJn31WqlQuQ%2BfMv1%2BqXcu2pjj22P23i/GZhQst/LVrF8zwJ1mGnzDBuoAviHtb17/YRiUa/K%2Bv/6yzbFYYnNixw1prp061sdlTpvCejeJDC2AMmT3bZjFmZ1sIzOn2AvIlK0vp7S5U%2BY/e%2BfPXataUPvnE9p7zkTfesNmUtWpZl3egtzl87TVl9%2BipOC9bYUmJktIkJUi2GvwttzgtL2j%2B%2BEO68EJp0SKboPPGG1LHjq6rQpAQAGPEV19JbdrYshBXXSU9/zxj/lBAM2bYtOBDufFGWz3cR3bvtp0y1qyxls7XX7d1/QInM9MWgtywQZL%2BHAArVJB%2B%2BSV2B0ZGma1bLewtXWqz0d96S2rd2nVVCBoCYCF4nqft27e7LkNbt1oX17p1tmzA9Oks9YKj0KePTQ0%2BlHLlLCT4zPLl1s22dautgThggNS/f7BaA/fMmaPSf/1r7uWwpCRJ6/S/AChZP/Hllxd/cQGzdav9PS5fbrvMvPlmbCxD5FcVKlRQKKCtJQTAQshZSgEAAPhPkCdCEQAL4WAtgOFwWElJSVq3bl2%2B/qhSUlK0ePHifP/MA2//8MO2zMsxx9j2UAfu7FDY%2Bz%2Bcgj7WSNcT6dvH/OP9%2B9%2BlJ5889Dc3bWqb0RZXPUV8355n274NH26tLzlatJAuumi3HnroNK1btygmf7d7vvtOpVq3Vk47x0FbAN9557D9kH56vAeKhtduerqN0V6yxNaiLFeuu7788t9Fdv85ouGxFuftC/t4g9wCWNJ1AX4WCoUO%2BQeXkJCQrz/GEiVKFOjTx763nzfP1m2TrPemVauivf/8yu9jLY56eLyFuP1tt9kYv303xt3XwIGH32C2qOuJwH336iVdeaU0d6709NMWCJctk5YtS5C0Vp07Z6lLlxLq1Ek64wypdOnI1lNst2/VSjr/fNs3bh8J/zvplFOOOAPBV4/3EFy9dvfssa0GlyyxXT3mzZMuv3x9RB9vzL5PHUIkH2%2BsKjFs2LBhrouIJRkZGRoxYoSGDBmi%2BPj4fH3P6QXc4fv000/Xtm32fh0OS9ddJ913X9Hef34czWONZD2Rvn3MP95KlbStyvGKmz1TJXXAatA33GCri%2Bfjk3Ik6y%2BK%2Bw6FpEaNbBHsm2%2B2uRHbt2dq7VpPmzeX0Icf2g4Mo0bZMjI//2yLY1er9udA6JffbXq6NObb85Tw8Tuqrs3KkDRC0hBJ8Q0aWBLOx6BIvzzeA7l87XqeLUE0fboNo333Xally6K7/wPF/PvUAYrr8cYiuoCLWHFtsXTllbZm1PHH20yycuUi9qMOKWjbScX64920SfrLX6S07zZoSJVndc2ZX%2Bn5mdN11bx5Kp%2Bzh1iMst9tA40fv14LF5bV3LnS5s373yYuTjrpJCklRTr1VOs6Pukkm0Abrb79VnrpJesh2LZNKqm9GnH6dPWq8bpqzpymTY8/rur9%2BsX8lhMuX7v//KftxVyihDRrltS5c2R/Xqy/Tx0oaI%2B3KNEFXMTi4%2BM1dOjQAn0SKagZMyz8lSghvfyym/AnFc9jjSax/Hh//FHq1Ml2zqhTp7a6fDhUJWpkaMvw4Sp15pmuy4s4%2B90OUJ8%2Bcbr%2Bemu1Wb5cWrBA%2BuADW6tt/Xpbbumrr2xR5RwNGthSMyecYB/IGje2FsbatS00FhfPsxo/%2B8xaLt95R1q5Mu/rxx0nPfJIKXXvfoW2bPmLVG2a4nr3jvnwJ7l77S5YIN1zjx2PHh358CfF9vvUwQTt8RYlWgB9Ji3N/tFs3Gj7Rg4f7roi%2BN1HH9nyf7/9Zt2h775rAQb727DBwtXnn1ur%2B5df2uvwUEqVkpKSbO3s2rVtMerq1a0ruUoVGwuWmGjDKsuVsx33SpX6cy%2B759kYsh077PX/%2B%2B/2u9q0yZZ%2B%2BuknC3rffmtLjBxYQ6dO0rXX2tIjJUrY9bSaRN62bba8yy%2B/2PZ7L7zA2qyILgRAnxkwwBbtb9zYWiIC8OEdEeJ50jPPSLfeavM%2BTjnFhoLVrOm6Mv/YssV2GPnuO%2Bn77y2IrVpl4wYzM4/uPkuVygtqWVmHnpNzMCVKWLd0mzbWnX/uuRYyD0QAjLzevaVJk2wv6iVL3PXUAIdCAPSRZcts7FF2trXSdOjguiL4VXq61K%2BfDSGQbIbixIn8kyoqmZnWYvjzz9Ytu2GDtRZu2mStd1u2WEteWpq0fbsFvfwoU8bmalStaq2JdepI9epZ9%2B4JJ1hXdH4%2BFBIAI2v%2BfAvgcXE2fOBgKzQArjEG0Cc8z1bhyM62xfoJfzhaX34pXXGFtGKFtRg98ogNUqd7quiULGnBrF69I9/W86SMDGnXLjvfu9de55L9fkqVslB3zDHs8OMH2dnSnXfa8U03Ef4QvWgB9Ik335QuucT%2BEaxYYeOKgILIzrZ1nu%2B%2B28aU1aplk4nOPtt1ZShutABGzqxZtuBz%2BfK2B3WVKq4rAg6uGOeoxYa9e/fqnnvuUbNmzVSuXDnVqlVLffr00S9H2CN12LBhCoVC%2B51q1KiRr5%2BZmSkNGWLHd9wRHeHvaJ%2BHaDd9%2BnR16tRJVapUUSgU0rJly474PRMnTvzT7zYUCmn37t3FUHH%2B/PKLzUAcONDCX5cu1hJ4qPB3NM%2BDH3iep2HDhqlWrVoqW7as2rdvr%2BX7bgtyEIV57aL4PPXUU2rQoIHKlCmjU089VR988MEhbxvJ1%2BzYsXZ%2B883FH/7ef/99denSRbVq1VIoFNKMGTOKt4AIKOhjWrBgwUF/t99//30xVewfBMAC2rlzp5YuXar7779fS5cu1fTp07Vy5Up17dr1iN974oknauPGjbmnr7/%2BOl8/c/JkG2ReqZK13kSDwjwP0WzHjh1q06aNRowYUaDvS0hI2O93u3HjRpWJkhk6r79usxHnzLEW5NRUa1E%2B3D%2Bno30eot2jjz6qUaNGady4cVq8eLFq1Kih8847709bOh7oaF%2B7KB5Tp07VwIEDdd999%2BmLL77QWWedpc6dO2vt2rWH/J5IvGY3bJDee8%2BOb765UHd1VHbs2KHmzZtr3Lhxxf/DI%2BRoH9OKFSv2%2B902btw4QhX6mIdC%2B%2ByzzzxJ3s8//3zI2wwdOtRr3rx5ge97717Pa9TI8yTPGzmyMFVGXn6eB79Ys2aNJ8n74osvjnjbF154wUtMTCyGqgpm2zbP69XL/nYkzzvlFM/79tuC3UdBnodol52d7dWoUcMbMWJE7nW7d%2B/2EhMTvX/961%2BH/L6jfe1Gs7S0NE%2BSl5aW5rqUInH66ad7N910037XNW3a1Bs8ePBBbx%2Bp1%2Bwzz9hr7YwzivyuC0yS98Ybb7guo0jl5zHNnz/fk%2BT9/vvvxVSVf9ECWATS0tIUCoVUsWLFw95u1apVqlWrlho0aKAePXroxx9/POJ9T5ki/fCDtdbccktRVRwZ%2BX0eYlF6errq1aunOnXq6KKLLtIXX3zhtJ6337blQCZPtpmI990nffyxzRQNqjVr1mjTpk3quM%2Bet/Hx8WrXrp0WLVp02O89mtcuiseePXu0ZMmS/X6vktSxY8fD/l4j8Zp9/30779Sp0HeFQmrZsqVq1qypDh06aP78%2Ba7LiUoEwELavXu3Bg8erCuvvPKwg6lbtWqll156Se%2B8844mTJigTZs26cwzz9TWA1du3YfnSY8%2Basd33BHdS3Tk93mIRU2bNtXEiRM1c%2BZMTZkyRWXKlFGbNm20atWqYq8lHLZtezt3tnF/xx9vy1A89NCf97ENmk2bNkmSqlevvt/11atXz/3awRzNazdapaamKjk5WSkpKa5LKTJbtmxRVlZWgX6vkXrNfvWVnZ92WqHuBoVQs2ZNjR8/XtOmTdP06dPVpEkTdejQQe/npHPkcd0EGe0mTZrklStXLvf0/vvv535tz5493sXhgWiUAAAgAElEQVQXX%2By1bNmywF0p6enpXvXq1b3HH3/8kLd56y3rTqhQwfNct2ZH6nlw6XCPqTBdn1lZWV7z5s29AQMGFGW5R/Tee55Xr15el%2B%2Btt3rejh1H/r5IPQ%2BuHfi4FixY4Enyfvnll/1ud91113mdOnXK9/3m57Ub7WKpC3jDhg2eJG/RokX7Xf/QQw95TZo0ydd9FNVrNiHBXnvffVeouykSCmgX8MFcdNFFXpcuXSJQkb%2BxDuARdO3aVa32Wcipdu3akmwW7OWXX641a9Zo3rx5BW71KleunJo1a3bYT5yjR9v5dddJrntVI/U8uHSox1RYcXFxSklJKbYWwPR02xYwNdUu169v2061b5%2B/74/U8%2BDagY8rIyNDkrUE1txnu5PNmzf/qfXocPLz2kXxqVKlikqUKPGn1r6C/F6L4jWblWUt8JJN2EP0aN26tSZNmuS6jKhDADyCChUqqEKFCvtdlxN6Vq1apfnz56ty5coFvt%2BMjAx99913Ouussw769VWrbDP3UEjq3/%2BoSi9SkXoeXDrYYyoKnudp2bJlatasWZHf94EWLpSuuUbKGZJ20002bKAgDytSz4NrBz4uz/NUo0YNzZ07Vy1btpRk48cWLlyokSNH5vt%2Bj/TaRfEqXbq0Tj31VM2dO1fdunXLvX7u3Lm6%2BOKL83UfRfGa3XdF3Zyt/BAdvvjii/0%2B9MEQAAsoMzNT3bt319KlS/Wf//xHWVlZuZ88K1WqpNL/G2jVoUMHdevWTf3/l97uuusudenSRXXr1tXmzZv10EMPKRwOq2/fvgf9Oc88Y%2BcXXCA1bBj5x1VQ%2BX0e/Gbbtm1au3Zt7nqGK1askCTVqFEjd%2B23Pn36qHbt2ho%2BfLgk6cEHH1Tr1q3VuHFjhcNhjR07VsuWLVNqTpNcBOzYYWtDPvmkXa5bV3ruOdv7tSjk53nwm1AopIEDB%2BqRRx5R48aN1bhxYz3yyCM65phjdOWVV%2BberrCvXRS/O%2B64Q71799Zpp52mM844Q%2BPHj9fatWt10003SSqe12zJkrbM0u7dtr2fi8/D6enpWr16de7lNWvWaNmyZapUqZLqRsMCskfhSI9pyJAh2rBhg1566SVJ0ujRo1W/fn2deOKJ2rNnjyZNmqRp06Zp2rRprh5C9HLcBe07OWOiDnaaP39%2B7u3q1avnDR06NPfyFVdc4dWsWdMrVaqUV6tWLe/SSy/1li9fftCfkZHheVWr2liSN9%2BM8AM6Svl9HvzmhRdeOOhj2vd32a5dO69v3765lwcOHOjVrVvXK126tFe1alWvY8eOfxqPVJTefz9vaSDJ8667zvOKeihXfp4HP8rOzvaGDh3q1ahRw4uPj/fOPvts7%2Buvv97vNoV57fpFLI0BzJGamurVq1fPK126tHfKKad4CxcuzP1acb1m69e31%2BQHHxT6ro5KzhIoB572fex%2Bc6TH1LdvX69du3a5tx85cqTXqFEjr0yZMt6xxx7rtW3b1ps9e7ab4qMcW8FFoRkzpG7dpBo1pHXr7JMlsHOnLecyZoxFvzp1pGefZckJFBxbwUVGx47S3LnS%2BPHS9de7rgY4PJaBiUI5Y1V79SL8wXz0kdSihU0M8jwb9/fNN4Q/IJr8b2ipFi92WweQHwTAKLN9uzR7th336uW2Fri3a5d0553SWWfZxKDataX//tfG%2ByUmuq4OwL7atLFz1h2GHxAAo8x//mODiI8/3lp8EFwff2x/A6NGWavfVVdZq1/nzq4rA3Aw7dtLpUpJq1dLK1e6rgY4PAJglJk%2B3c4vu8yWgEHw7N4t3XOP1Lat/ROpWdM%2BGLzwgvv1IAEcWkKCdM45dvz6625rAY6EABhF9uyxtf8k6ZJL3NYCNxYvlk45xdbyy86WeveWli%2BXLrzQdWUA8uOKK%2Bx80qT91wYEog0BMIp8%2BKGNAaxWjb0kgyYjw2b4tm4tffedVL269Oab0ksvScce67o6APnVvbt0zDHS99/bezoQrQiAUSSn9e/886U4fjOBsXSpBf5HHrFWv549rdWva1fXlQEoqIQEKWdd8XHj3NYCHA4xI4q8956dn3ee2zpQPPbulYYNk1q1sskdVatK06ZJr7ziZhcBxL7U1FQlJycrJSXFdSkxbcAAO582TfrpJ6elAIfEQtBRIi3NNhDPzpbWr7flPhC7vv5a6ttX%2BuILu9y9u/TUUxYCgUhjIejIy1kUul8/KYK7QgJHjRbAKPHJJxb%2BGjYk/MWyzExp%2BHDp1FMt/FWqJE2ZYjMGCX9A7BgyxM6fe07asMFtLcDBEACjxMcf23nOQqKIPd9/b7/fe%2B%2B17t%2BuXW2sX48erisDUNTat7cF3DMypIcfdl0N8GcEwCjx2Wd23qqV2zpQ9LKypCeesG2iPvvMdvCYONH2fK5Rw3V1ACIhFJL%2B7//seMIE6Ycf3NYDHIgAGCWWLrXzU091WweK1g8/2MKwd9xhCzx37GgTPvr2ZaFvINa1a2f7dWdm2jJPQDQhAEaBzZulX3%2B1QNCsmetqUBQ8T3r6aal5c%2BmDD6Ty5aVnnpHefluqU8d1dQCKy8iR9t4%2BdaqN9QaiBQEwCixfbucNG0rlyrmtBYW3bp219PXrJ%2B3YYa0AX30l3XADrX5A0DRvbvt4S9Ltt7M7CKIHATAKfPednZ9wgts6UDieZ/v1nnSS9O67Upky0ujR0rx5UoMGrqsD4MrDD9uH%2B08%2BkSZPdl0NYAiAUWD1ajtv3NhtHTh6GzfarN5rrpHCYdvS7csvpdtuY1cXIOhq1pT%2B/nc7vvtue48AXONfUxRYs8bOGzZ0WwcKzvOkV1%2B1Vr///EcqXdrW%2BfvwQ%2Bn4411XByBa3H67dNxx9mHxwQddVwMQAKPCunV2Xreu2zpQMFu2SFdcYXv3bttmy7wsWSINHiyVKOG6OgDRJD5eGjvWjseMsd2AAJcIgFHgl1/snB1A/GPGDOnEE20HjxIlpKFDpU8/tZZAADiYzp2lbt1sbdCbb7bdnwBXCICOeZ702292XL2621pwZL//LvXubW/imzdbCPzsM2nYMKlUKdfVAYh2Y8bYhJCPPrJJY4ArBEDHwmFbJFSSqlRxWwsO7623rIVv0iSb2HHPPdble8oprisD8ic1NVXJyclKSUlxXUpgJSXljQEcNMg%2BSAIuhDyPVYlc%2BvlnqX59Gx%2Bye7franAwaWnSnXfapu6STe548UWb6Qv4UTgcVmJiotLS0pSQkOC6nMDJzJRSUqRly6S//U16%2BWXXFSGIaAF0bMcOO2cB6Og0d67tzvLcc7aI8%2B2325s24Q/A0SpZUho/3t5TJk2S5sxxXRGCiADoWE6rX5kybuvA/rZvl266yXb0WLfOluhZuFAaNUoqW9Z1dQD8LiVFGjDAjm%2B6Ka8xACguBEDHcsb/MYEgesybZ61%2Bzzxjl2%2B5xbZyO%2Bsst3UBiC0PPWRjAtessZUEgOJEAAT%2BZ/t227%2B3Q4e8sZnz5knjxtFFD6DoVaggPf20HT/xhPT5527rQbAQAB3LWTA4pyUQbsybJ518ct6b8c03W6vfOee4rQtAbLvwQqlHD1sT8LrrpL17XVeEoCAAOpYz9o8ZwG6Ewxb2OnSQfvpJqldPevdd6amn7NM5AETamDFSpUq2f/hjj7muBkFBAHSsfHk7T093W0cQzZljY/3%2B9S%2B7fPPNtj1Thw5u6wIQLNWqSaNH2/E//iGtWOG2HgQDAdCxihXtPCND2rXLbS1B8ccf0rXXSp06SWvXSg0aWBcwrX4AXPnb36Tzz7f/BdddxzZxiDwCoGMJCXkzgHO2hEPkzJxp27c9/7ytwTVggLX6MdYPfrB3717dc889atasmcqVK6datWqpT58%2B%2BiVnQ3H4VihkvRHlykkffpg3HhmIFAKgY6GQVKOGHfMeHjm//Sb17CldfLE9z8cfL73/vjR2LDN84R87d%2B7U0qVLdf/992vp0qWaPn26Vq5cqa5du7ouDUWgXj1pxAg7HjzYeiiASGEruCjQpo20aJE0dap0%2BeWuq4ktnidNniwNHCht3Wp7%2BN51lzRsGAs6IzYsXrxYp59%2Bun7%2B%2BWfVrVs3X9/DVnDRKztbOvts6aOPpM6dpdmzraEAKGq0AEaBRo3sfPVqt3XEmp9/tiUWeve28HfyydJnn0kjRxL%2BEDvS0tIUCoVUMWdAMXwtLk569lmpdGnprbfsAywQCQTAKNCkiZ1/953bOmJFVpbNqDvxRHsDjY%2B3Ffc//1w69VTX1QFFZ/fu3Ro8eLCuvPLKw7bkZWRkKBwO73dC9GraNG9nkNtukzZvdlsPYhMBMAo0a2bnX33lto5YsGyZ1Lq1dPvttrfmWWfZ2lr33cd2e/CfyZMnq3z58rmnDz74IPdre/fuVY8ePZSdna2nnnrqsPczfPhwJSYm5p6SkpIiXToKadAgqUULadu2vD2DgaLEGMAosG6dVLeu7QoSDkvHHOO6Iv/ZscPG9T3xhLUAJiZaV%2B/111uXCuBH27dv16%2B//pp7uXbt2ipbtqz27t2ryy%2B/XD/%2B%2BKPmzZunypUrH/Z%2BMjIylJGRkXs5HA4rKSmJMYBRbulS6fTT7T1txgybxAYUFQJgFPA8qXZtaeNGacECqV071xX5y6xZUv/%2BeTPm/vpXW1m/Zk23dQGRkBP%2BVq1apfnz56tq1aoFvg8mgfjH4MH2YbZWLenbb%2B3DLVAUaBuJAqGQzfqSLAAif9aulbp1k7p2teN69aT//Ed67TXCH2JTZmamunfvrs8//1yTJ09WVlaWNm3apE2bNmnPnj2uy0MEDB0qNW5sy1cNGuS6GsQSAmCUyNl%2B7O233dbhB3v22CfiE06wbpGSJaW775aWL7dZv0CsWr9%2BvWbOnKn169erRYsWqlmzZu5p0aJFrstDBJQtK02YYMcTJtBIgKJDF3CUWL9eSkqy1sBffslbHBr7e%2B896%2B79/nu7fNZZtoXbSSe5rQvwE7qA/eemm6RnnpGOO84mDLKUFQqLFsAoUaeOlJJi4wGnTXNdTfRZu9YWyT73XAt/VatKEydKCxcS/gDEvpEjbaz46tXSgw%2B6rgaxgAAYRXr2tPOXXnJbRzTZtUv6xz9sXazXX7cZvf37SytXSn37skI%2BgGBITLTeDkn65z%2BlL75wWw/8jwAYRXr1svFsn31m69kFmefZ1ng5C6Lu2mUTZZYulZ58UmLTAwBB07WrrXKQlSVdd52Umem6IvgZATCKVKsmXXaZHT/xhNtaXPrsM6ltW6lHD%2Bv6TUqSXn3VBj83b%2B66OgBwZ%2BxY%2BwC8dKnteAQcLQJglLnjDjt/5RVpzRq3tRS3NWusG7xVK2nRIlsQe9gwG/N3xRV09wJAjRrS44/b8QMPSD/%2B6LYe%2BBcBMMqcfrrUsaM17T/wgOtqisdvv0kDB9qeyK%2B%2BakGvb18b5zd0KDujAMC%2Brr5aOuccGxpz8802ZAYoKJaBiUKff24zgiVrCTvjDLf1REo4bF3djz8ubd9u1513nvToo7YHJoDIYBkY/1u1yvaRz8iQXn5Z%2BtvfXFcEv6EFMAqddpp01VV2fMMN9gKPJTt2SI89JjVsaF2827dLLVtK77wjzZlD%2BAOAI2ncOK%2BX6Pbbpa1b3dYD/yEARqnHHrO17r75Rrr3XtfVFI30dFu%2BoGFD27lj61br9p061Vo9O3Z0XSEQ21JTU5WcnKyUnC4G%2BNpdd9k6qFu22DFQEHQBR7E335QuucSOX39d6t7dbT1H6/ffpdRUm7GW8ym1YUPp/vut26JkSbf1AUFDF3Ds%2BPhjqU0bGwf43nvSX/7iuiL4BS2AUezii6U777Tj3r2ljz5yW09BrVtnn0rr1rWwt3Wr1KiR9PzzNrP3qqsIfwBQGGecYRNBJNsubvdut/XAPwiAUW7ECOnCC%2B1FfcEF9mkvmnme9OmntpxLgwY2wSM93QYrT55swe/qq6VSpVxXCgCx4ZFHpJo1bWLI8OGuq4Ff0AXsAzt3WvhbuNCWRJk8Oa9rOFrs2GFj%2BZ5%2B2sbz5WjfXho0SOrcmXX8gGhBF3Ds%2Bfe/bZeQ0qWlL7%2B0XZSAw6EF0AeOOUaaPdtC1M6dUrdu0j33SHv2uK3L86RPPrFuh1q1pGuvtfAXH2/r%2BC1dKs2fb%2BGV8AcAkXPZZfZeu2cPawMif2gB9JHMTBsTOHasXT7xRJtc0a5d8dXgedK330qvvWa7laxenfe1Ro1s2Zqrr7YZzACiEy2Asemnn6TkZFsgeuJE%2ByAOHAoB0IfeeEO68UbbQUOylsEhQ2z/3Ei0tO3ZY2MPZ8%2B2mckrV%2BZ97ZhjpEsvtdDXvr0UR5syEPUIgLFr5Ehp8GCpShUbc125suuKEK0IgD61davNrB0/XsrKsuuSk6VevWz2cHLy0YfB9HRpyRILfe%2B/L33wgV2Xo3RpW7OvRw/7WeXLF/7xACg%2BBMDYtXevLay/fLl0/fX2PwI4GAKgz61ebVunTZpkzf45qlWTWrWy2beNGtkMsUqVpLJlrZVuzx4LdVu3Sr/8Iv38s7Xsffut3eeBfxVVq0qdOkldukjnny/xPwPwLwJgbPvwQ%2Bmss%2Bw4lrcTReEQAGNEWpotFj1tmrRgQeHXgqpdW2rd2hYYPecc6eST6d4FYgUBMPZdc430wgv23r1kCWuu4s8IgDEoI8Nm4y5dai16P/0kbdpkO3Ls2mWte6VKWddt5cpSjRpSUpJ03HHSCSdYq2H16q4fBYBIIQDGvt9%2Bs6Vgtm2TRo2y/YKBfREAASBgCIDBMGGCrcxQvry0YoUt1wXkoFMPAIAYdO21NhY8PT1vW1EgBwEQAIAYFBdnuzPFxUmvviq9957rihBNCIAAEBCpqalKTk5WSkqK61JQTFq2lPr1s%2BP%2B/d3vIIXowRhAAAgYxgAGyx9/SMcfbxNDHn3U9mcHaAEEACCGVaxowU%2BSHnxQ2rDBbT2IDgRAAABiXJ8%2B0plnSjt2SHfd5boaRAMCIAAAMS4uTho3Lm9CyIIFriuCawRAAAACoGVL6cYb7XjAACkz0209cIsACABAQDz0kO0A9c030lNPua4GLhEAAQAIiEqVpIcftuMHHrCZwQgmAiAAAAFy3XXWHZyWJt17r%2Btq4AoBEACAAClRQnrySTt%2B7jlpyRK39cANAiAAAAHTpo105ZWS50m33WbnCBYCIAAAATRypHTMMdJHH9nSMAgWAiAAAAFUp440ZIgd3323tHOn23pQvAiAAAAE1J13SvXqSevXW4sggoMACAABkZqaquTkZKWkpLguBVGibFnpn/%2B040cfldaudVsPik/I8xj6CQBBEg6HlZiYqLS0NCUkJLguB455ntS%2BvfT%2B%2B1LPntIrr7iuCMWBFkAAAAIsFJJGj7bzKVOkjz92XRGKAwEQAICAa9lSuvpqO779dik72209iDwCIAAA0EMPSeXKSZ9%2ByrIwQUAABAAAqlkzb2u4wYOlXbvc1oPIIgACAABJ1v1bt660bp00apTrahBJBEAAACDJloUZPtyOR4yQNm1yWw8ihwAIAABy9eghnX66lJ4uDR3quhpECgEQAADkiouTHn/cjp99Vlq%2B3G09iAwCIAD41I033qhQKKTRo0e7LgUxpm1b6dJLbTmYQYNcV4NIIAACgA/NmDFDn376qWrVquW6FMSoESOkkiWlt96S3nvPdTUoagRAAPCZDRs2qH///po8ebJKlSrluhzEqMaNpZtvtuO77mJx6FhDAAQAH8nOzlbv3r01aNAgnXjiia7LQYx74AEpIUFatkyaPNl1NShKBEAA8JGRI0eqZMmSuvXWW/P9PRkZGQqHw/udgPyoUiVvcei//13avdttPSg6BEAAiFKTJ09W%2BfLlc08LFy7UmDFjNHHiRIVCoXzfz/Dhw5WYmJh7SkpKimDViDW33irVqSOtXSs9%2BaTralBUQp7nea6LAAD82fbt2/Xrr7/mXn799dd13333KS4u77N7VlaW4uLilJSUpJ9%2B%2Bumg95ORkaGMjIzcy%2BFwWElJSUpLS1NCQkLE6kfsmDhRuvpqqWJF6YcfpEqVXFeEwiIAAoBPbN26VRs3btzvuk6dOql37966%2Buqr1aRJk3zdTzgcVmJiIgEQ%2BZaVJbVsKX39tU0Ieewx1xWhsEq6LgAAkD%2BVK1dW5cqV97uuVKlSqlGjRr7DH3A0SpSwZWEuvNC6gQcMsD2D4V%2BMAQQAAEfUubPUrp2UkcEWcbGALmAACBi6gHG0Pv1Uat3atov76iuJlYj8ixZAAACQL61a5W0Rl7M8DPyJAAgAAPLt4YetBXDmTGnRItfV4GgRAAEAQL41bSpdc40dDx4sMZDMnwiAAACgQIYOleLjpQ8%2BkN5%2B23U1OBoEQAAAUCB16thSMJKNBczOdlsPCo4ACAAACmzwYCkhQVq2THr9ddfVoKAIgAAAoMAqV5buvNOO779fysx0Ww8KhgAIAACOyu23S1WqSKtW2X7B8A8CIAAERGpqqpKTk5WSkuK6FMSIChXy1gP8xz9slxD4AzuBAEDAsBMIitKuXVLjxtKGDdKYMdKtt7quCPlBCyAAADhqZcvaGEDJFonescNtPcgfAiAAACiUa66RGjSQNm%2BWxo1zXQ3ygwAIAAAKpVQpadgwO370USkcdloO8oEACAAACq1XL6lJE2nbNmn0aNfV4EgIgAAAoNBKlJAefNCOR42Sfv/dbT04PAIgAAAoEn/9q9SsmZSWJj3%2BuOtqcDgEQAAAUCTi4vJaAceMkbZudVsPDo0ACAAAiswll0gtW0rp6dJjj7muBodCAAQAAEUmFMprBRw3TvrtN7f14OAIgAAAoEhddJF06qm2KDStgNGJAAgAAIrUvq2Aqam2QDSiCwEQAAAUuQsukFJSpJ07aQWMRgRAAAiI1NRUJScnKyUlxXUpCIBQSBo61I6feopWwGgT8jzPc10EAKD4hMNhJSYmKi0tTQkJCa7LQQzzPKlVK2nxYmnQINsmDtGBFkAAABAR%2B7YCpqYyIziaEAABAEDEXHCBzQjeuZPdQaIJARAAAERMKCQ98IAdjxvH7iDRggAIAAAiqksXqUULWxfwiSdcVwOJAAgAACJs31bAJ5%2BUfv/dbT0gAAIAgGJw8cVSs2ZSOCyNHeu6GhAAAQBAxMXFSffdZ8ejR1sQhDsEQAAAUCy6d5eaNJH%2B%2BMMWh4Y7BEAAAFAsSpTIawUcNcqWhoEbBEAAAFBsevaUGja0RaHHj3ddTXARAAEAQLEpWVIaPNiOH3tMyshwW09QEQABAECx6tNHql1b%2BuUX6cUXXVcTTARAAABQrOLjpUGD7HjECCkz0209QUQABICASE1NVXJyslJSUlyXAuj666WqVaU1a6RXX3VdTfCEPM/zXBcBACg%2B4XBYiYmJSktLU0JCgutyEGCPPGKzgk88UfrqK1srEMWDpxoAADjRr5%2BUkCAtXy7NmuW6mmAhAAIAACcqVpRuucWOH3lEok%2By%2BBAAAQCAM7fdJpUpI332mTR/vutqgoMACAAAnKleXbr2WjseMcJtLUFCAAQAAE7ddZdtEzd3rrRkietqgoEACAAAnKpf37aIk6SRI52WEhgEQAAA4Nzdd9v5v/8trVrltpYgIAACAADnmjWTLrzQZgI/9pjramIfARAAfOa7775T165dlZiYqAoVKqh169Zau3at67KAQhs82M5ffFHauNFtLbGOAAgAPvLDDz%2Bobdu2atq0qRYsWKAvv/xS999/v8qUKeO6NKDQ2rSRzjxT2rNHGjPGdTWxja3gAMBHevTooVKlSunll18%2B6vtgKzhEszfflC65REpMlNautZ1CUPRoAQQAn8jOztbs2bN1/PHHq1OnTqpWrZpatWqlGTNmHPb7MjIyFA6H9zsB0apLF6lpUyktTRo/3nU1sYsACAA%2BsXnzZqWnp2vEiBE6//zzNWfOHHXr1k2XXnqpFi5ceMjvGz58uBITE3NPSUlJxVg1UDBxcdKgQXY8erR1B6Po0QUMAFFq8uTJuvHGG3Mvz549W%2B3bt1fPnj31yiuv5F7ftWtXlStXTlOmTDno/WRkZCgjIyP3cjgcVlJSEl3AiFoZGVKDBjYRZOJEqW9f1xXFHloAASBKde3aVcuWLcs9tWjRQiVLllRycvJ%2BtzvhhBMOOws4Pj5eCQkJ%2B52AaBYfb3sES9I//2lLw6BoEQABIEpVqFBBxx13XO4pMTFRKSkpWrFixX63W7lyperVq%2BeoSiAybrxRKl9e%2BuYb6e23XVcTewiAAOAjgwYN0tSpUzVhwgStXr1a48aN06xZs9SvXz/XpQFFqmJF6YYb7JiFoYseYwABwGeef/55DR8%2BXOvXr1eTJk304IMP6uKLL87397MMDPxi7VqpUSMpM1NaskQ65RTXFcUOAiAABAwBEH7Sq5f0yitSz552jqJBFzAAAIhad91l56%2B9Zi2CKBoEQAAAELVatpT%2B8hcpK4vt4YoSARAAAES1O%2B%2B08wkTbIcQFB4BEAAARLXzz5dOOEHavl167jnX1cQGAiAAAIhqcXHSHXfY8ZgxNisYhUMABAAAUe9vf5OqVrWJINOmua7G/wiAAAAg6pUpI%2BWsdz5qFNvDFRYBEAAA%2BEK/frZP8GefSYsWua7G3wiAABAQqampSk5OVkpKiutSgKNSrZp1BUvSE0%2B4rcXv2AkEAAKGnUDgZ998IzVrZhNDfvhBql/fdUX%2BRAsgAADwjZNOks47T8rOlp580nU1/kUABAAAvjJwoJ0/%2B6ytDYiCIwACAABfOf98qUkTKRyWXnjBdTX%2BRAAEAAC%2BEhcn3XabHY8da/sEo2AIgAAAwHf69JEqVrSJIP/9r%2Btq/IcACAAAfKdcOen66%2B149Gi3tfgRARAAAPhS//7WHTxvnvT1166r8RcCIAAA8KW6daVLL7XjsWPd1uI3BEAAAOBbt95q55MmSVu3uq3FTwiAAADAt9q2lVq2lHbvtnUBkT8EQAAA4FuhUF4rYGqqlJnpth6/IAACABFs2y0AAARrSURBVABf69FDqlJFWrdOevNN19X4AwEQAAIiNTVVycnJSklJcV0KUKTKlJFuvNGO2R84f0Ke53muiwAAFJ9wOKzExESlpaUpISHBdTlAkVi/Xqpf33YF%2BfJL6eSTXVcU3Uq6LgAAAKCw6tSRBgyQatWSkpJcVxP9aAEEgIChBRAAYwABAAAChgAIAAAQMARAAACAgCEAAgAABAwBEAAAIGAIgAAAAAFDAAQAAAgYAiAAAEDAEAABAAAChgAIAAAQMARAAAiI1NRUJScnKyUlxXUpABxjL2AACBj2AgZACyAAAEDAEAABAAAChgAIAAAQMARAAACAgCEAAgAABAwBEAAAIGAIgAAAAAFDAAQAAAgYAiAA%2BEh6err69%2B%2BvOnXqqGzZsjrhhBP09NNPuy4LgM%2BUdF0AACD/br/9ds2fP1%2BTJk1S/fr1NWfOHPXr10%2B1atXSxRdf7Lo8AD5BCyAA%2BMjHH3%2Bsvn37qn379qpfv75uuOEGNW/eXJ9//rnr0gD4CAEQAHykbdu2mjlzpjZs2CDP8zR//nytXLlSnTp1cl0aAB%2BhCxgAfGTs2LG6/vrrVadOHZUsWVJxcXF69tln1bZt20N%2BT0ZGhjIyMnIvh8Ph4igVQBSjBRAAotTkyZNVvnz53NMHH3ygsWPH6pNPPtHMmTO1ZMkSPf744%2BrXr5/efffdQ97P8OHDlZiYmHtKSkoqxkcBIBqFPM/zXBcBAPiz7du369dff829XLt2bSUmJuqNN97QhRdemHv9ddddp/Xr1%2Bvtt98%2B6P0crAUwKSlJaWlpSkhIiNwDABC16AIGgChVoUIFVahQIfdyOBzW3r17FRe3f%2BdNiRIllJ2dfcj7iY%2BPV3x8fMTqBOA/BEAA8ImEhAS1a9dOgwYNUtmyZVWvXj0tXLhQL730kkaNGuW6PAA%2BQhcwAPjIpk2bNGTIEM2ZM0fbtm1TvXr1dMMNN%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%3D'}