Genus 2 curve data - 26384.d.422144.1

Formats: - HTML - YAML - JSON - 2025-04-26T13:55:44.518351

• 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': '566572705861761287', 'abs_disc': 422144, 'analytic_rank': 2, 'analytic_rank_proved': False, 'analytic_sha': 1, 'aut_grp_id': '[2,1]', 'aut_grp_label': '2.1', 'aut_grp_tex': 'C_2', 'bad_lfactors': '[[2,[1]],[17,[1,8,24,17]],[97,[1,11,85,-97]]]', 'bad_primes': [2, 17, 97], 'class': '26384.d', 'cond': 26384, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[1,-1,1,0,-1,1],[]]', 'g2_inv': "['-995328/1649','165888/1649','18432/1649']", 'geom_aut_grp_id': '[2,1]', 'geom_aut_grp_label': '2.1', 'geom_aut_grp_tex': 'C_2', 'geom_end_alg': 'Q', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': False, 'igusa_clebsch_inv': "['24','180','1224','-1649']", 'igusa_inv': "['48','-384','-2048','-61440','-422144']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '26384.d.422144.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '1.000796167073714263246106543638469765156417191499', 'prec': 167}, 'locally_solvable': True, 'modell_images': ['2.6.1'], 'mw_rank': 2, 'mw_rank_proved': True, 'non_maximal_primes': [2], 'non_solvable_places': [], 'num_rat_pts': 9, 'num_rat_wpts': 1, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '11.654542817223844547575900645', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.0171743530874', 'prec': 50}, 'root_number': 1, 'st_group': 'USp(4)', 'st_label': '1.4.A.1.1a', 'st_label_components': [1, 4, 0, 1, 1, 0], 'tamagawa_product': 5, 'torsion_order': 1, 'torsion_subgroup': '[]', 'two_selmer_rank': 2, 'two_torsion_field': ['5.1.1649.1', [1, -1, 1, 0, -1, 1], [5, 5], False]}
• 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': [['1.1.1.1', [0, 1], -1]], 'factorsQQ_geom': [['1.1.1.1', [0, 1], -1]], 'factorsRR_base': ['RR'], 'factorsRR_geom': ['RR'], 'fod_coeffs': [0, 1], 'fod_label': '1.1.1.1', 'is_simple_base': True, 'is_simple_geom': True, 'label': '26384.d.422144.1', 'lattice': [[['1.1.1.1', [0, 1], [0]], [['1.1.1.1', [0, 1], -1]], ['RR'], [1, -1], 'USp(4)']], 'ring_base': [1, -1], 'ring_geom': [1, -1], 'spl_fod_coeffs': [0, 1], 'spl_fod_gen': [0], 'spl_fod_label': '1.1.1.1', 'st_group_base': 'USp(4)', 'st_group_geom': 'USp(4)'}
• 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': '26384.d.422144.1', 'mw_gens': [[[[1, 1], [0, 1], [1, 1]], [[0, 1], [-1, 1], [0, 1], [0, 1]]], [[[-1, 1], [1, 1]], [[-1, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.1940295093265112153623679341111', 'prec': 110}, {'__RealLiteral__': 0, 'data': '0.08851607801982760674683602939694', 'prec': 113}], 'mw_invs': [0, 0], 'num_rat_pts': 9, 'rat_pts': [[-1, -1, 1], [-1, 1, 1], [0, -1, 1], [0, 1, 1], [1, -1, 1], [1, 0, 0], [1, 1, 1], [3, -13, 1], [3, 13, 1]], 'rat_pts_v': False}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  {'conductor': 26384, 'lmfdb_label': '26384.d.422144.1', 'modell_image': '2.6.1', 'prime': 2}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 55611
    {'label': '26384.d.422144.1', 'p': 2, 'tamagawa_number': 5}
  • id: 55612
    {'cluster_label': 'c3c2_1~2_0', 'label': '26384.d.422144.1', 'p': 17, 'tamagawa_number': 1}
  • id: 55613
    {'cluster_label': 'c3c2_1~2_0', 'label': '26384.d.422144.1', 'p': 97, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '26384.d.422144.1', 'plot': 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xd4VHXe/vF7CCEJJaETEnqVICJgpAmoIMqCqFgfV8H9rcgquqLuPq7r2nZtW%2ByMurqPFXAtYFldRRQpC4pUQVCKgEgJnUwSSD%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%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARBAVAsGg8rIyFBmZqbbpQBAzAg4nKwOwANCoZBSUlKUnZ2t5ORkt8sBAE%2BjBxAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEENU4CxgAIo%2BzgAF4AmcBA0Dk0AMIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgCAmLB1q8QBt%2BVDAAQQ1YLBoDIyMpSZmel2KQCi2O7d0hlnSFdfLYVCblcT/QKOQ1YGEP1CoZBSUlKUnZ2t5ORkt8sBEEUcR7r8cmnyZKldO2nxYql2bberim70AAIAAE978UULf/Hx0htvEP7KgwAIAAA8a/ly6dZb7fZDD9kwMI6PAAgAADxp717pyiul/Hzp/POlO%2B5wuyLvIAACAABPGjfOegCbNJFefVWqRqopN14qAADgOf/8p839CwSkCRMsBKL8CIAAAMBTVq%2BWRo%2B223ffLQ0a5G49XkQABAAAnpGfb1u%2B5OZK/ftL993ndkXeRAAEAACeceut0jffSI0a2ZYv1au7XZE3EQABAIAnTJggvfCCzfubOFFKS3O7Iu8iAAIAgKi3fLk0Zozdvvde6bzz3K3H6wiAAKIaZwEDyMmRLr3U9v077zzpnnvcrsj7OAsYgCdwFjDgTwee89usmbRokc3/Q8XQAwgAAKLWY4%2BVnfP79tuEv0ghAAIAgKg0fbp05512%2B8knpV693K0nlhAAAQBA1Nmwwc75LS2VRo6UbrzR7YpiCwEQAABElX37pBEjpB07pG7dpOeft61fEDkEQAAAEDUcR/rVr6SFC6UGDaR335WSktyuKvYQAAEAQNR46inptdekuDjpzTelli3drig2EQABVLpHHnlEmZmZqlOnjho3bqyLL75YK1eudLssAFHms8%2BkO%2B6w2489Jg0c6G49sYwACKDSzZw5U2PHjtVXX32ladOmqbi4WIMHD1ZeXp7bpQGIEqtXS1dcYYs%2BRo2Sfv1rtyuKbWwEDaDKbd%2B%2BXY0bN9bMmTPVv3//cn0NG0EDsSs727Z4%2Bf57qWdPacYMKTHR7apiGz2AAKpcdna2JKl%2B/fouVwLAbcXF0lVXWfhr1swWfRD%2BKl91twsA4C%2BO4%2Bj222/XWWedpVNPPfWojysoKFBBQcH%2B%2B6FQqCrKA1DF7rhD%2BuQTW%2Bn7/vtS06ZuV%2BQP9AACqFI333yzli5dqjfeeOOYj3vkkUeUkpKyvzVv3ryKKgRQVZ57Tnr6abv9%2ButS9%2B7u1uMnzAEEUGVuueUWvffee5o1a5Zat259zMceqQewefPmzAEEYsTUqdLQoVJJifTQQ9Lvf%2B92Rf7CEDCASuc4jm655Ra9%2B%2B67mjFjxnHDnyQlJCQoISGhCqoDUNWWLZMuv9zC36hR0l13uV2R/xAAAVS6sWPHatKkSXr//fdVp04dZWVlSZJSUlKUxBb/gK9s3iz97GdSTo40YID0wgsc8%2BYGhoABVLrAUd7dX375ZV133XXleg62gQG8LydH6t9fWrJEOuUUac4cic0A3EEPIIBKx%2B%2BZAIqKbNh3yRKpcWPpo48If25iFTAAAKhUjiPdcIMt/KhZU/rwQ6lNG7er8jcCIAAAqFT33CO98ooUFye99ZaUmel2RSAAAgCAShMM2jYvku37N3Sou/XAEAABAECleOst6ZZb7PYDD0ijR7tbD8oQAAEAQMRNmyZdc43N/7vxRhsGRvQgAAIAgIj66ivpkkts5e8VV0jPPMNef9GGAAggqgWDQWVkZCiTWeOAJyxbZhs95%2BVJgwfbGb9xcW5XhUOxETQAT2AjaCD6rV4t9esnbd0q9e5tw8C1arldFY6EHkAAAFBhP/4oDRxo4a9rV9vomfAXvQiAAACgQjZtks49V/rpJ6ljR%2BnTT6V69dyuCsdCAAQAACctK8t6/tautdM9Pv/cjnpDdCMAAgCAk7Jtm4W/lSulFi2k6dOl9HS3q0J5EAABAMAJ27bNhn1XrLDQN3261LKl21WhvAiAAADghGzdauFv%2BXIpLU364gupbVu3q8KJqO52AQAAwDuysiz8ffedhb8ZM6T27d2uCieKHkAAAFAumzZJAwZY%2BGvWTJo5k/DnVQRAAABwXOvW2SbPq1bZgo%2BZM6V27dyuCieLAAgAAI5p5UoLf%2BvW2Vy/WbNsyxd4FwEQQFTjLGDAXYsXW/jbtEnq1MnCH6t9vY%2BzgAF4AmcBA1Vv9mxp2DApFJK6d5c%2B%2BURq1MjtqhAJ9AACAIDD/Otf0uDBFv769bN9/gh/sYMACAAADvLSS9LFF0v5%2BdKFF0pTp0opKW5XhUgiAAIAAEmS40h/%2BpP0y19KpaXSdddJU6ZISUluV4ZIIwACAAAVFUljxkj33mv377rLegKrc2RETOKvFQAAnwuFpCuvtEUe1apJzzwj3XST21WhMhEAAQDwsQ0bbJ7f0qVSzZrSG29Iw4e7XRUqGwEQAACf%2Bvpr6aKL7Hzf1FRb%2BXvGGW5XharAHEAAAHxo0iQ71zcrS%2BrSRZo3j/DnJwRAAAB8pKTEFnj8/Oe2zcuwYdKcOXa%2BL/yDAAgAgE/s3m3z/R591O7feaf03ntSnTru1oWqxxxAAAB8YNky6ZJLpB9%2BkBITpf/7P%2Bnqq92uCm6hBxBAVAsGg8rIyFBmZqbbpQCeNXGi1KuXhb%2BWLaW5cwl/fhdwHMdxuwgAOJ5QKKSUlBRlZ2crOTnZ7XIAT8jPl267TXr%2Bebs/eLAt/mjQwN264D6GgOFpBQXSrl02ryUUkvLypH377ONFRXaskSQFArabfY0aNvSRlCTVrm3zXlJSpLp1pfh4d78XAIikNWukK66QFi%2B298C775buv1%2BKi3O7MkQDAiCiWk6O9N130sqVNnSxfr3044/S5s22dUEoFLk/q3ZtqWFDqXFja02bSmlpUnq61KyZrZBr2dIeBwDRbNIkO9YtN9d6%2ByZOlM4/3%2B2qEE0IgIgaOTm2D9W8edKCBdKSJRb4jqdaNevBS062cJaUZD198fH2uUDADjUvKrKWny/t3Wu9haGQvUFKds3NPf6f2aCB1KaN1Lat1K6dtQ4dpI4dpfr1K/oqAMDJy8mRbrlFevVVu9%2Bvn53skZ7ubl2IPswBhGsKC23vqalTpenTpYULLagdKjXVwlX79lLr1tYLl55uPXSNG9sQbrUKLGcqKZH27LGh5B07pK1bpW3bpC1brKdx0ybpp5/suKQ9e479XI0aSZ06WevcWTr1VGuNGp18fTDMAQSO7csvpWuukdautffEP/xBuucem/4CHIoAiCqVlyd99JE0ZYr08ceHD%2BG2bCn17i2deabUrZvtTh9Nk5Wzs62HcO1aG5Jes0Zatcrapk1H/7rUVOm006ydfrq1jh15Yz4RBEDgyAoLpQcesL39SkvtfXTCBOmss9yuDNGMAIhKV1oqzZghvfKKBb%2B8vLLPNWli81IGDZLOPltq3tylIiMgN9fmKn73nbR8ubRihe27tW7dkR%2BfmCh17Sp1727tjDOs15DFKEdGAAQOt2SJdN110jff2P1rrpHGj7eREeBYCICoNNnZ0ksvSc8%2Baz1lYa1b28q0Sy6RMjMrNnzrBbm50rffSkuX2pv0kiV2PTAIhyUkWO9gZqb1gp55pg19x/prVB4EQKBMYaH08MPSQw9JxcU2UvL889Jll7ldGbyCAIiI27RJevxx6YUXyhZY1Kljm46OGmWbkQYC7tbottJSC8WLFllbsMCu2dmHPzYlxQJhz5722vXqZauV/YYACJivvpKuv95GGiRpxAj7RbtJE3frgrcQABExWVn2G%2Bnf/26/nUo2pPnrX9uh47VquVtftCsttXmF8%2Bdb%2B/prC4X5%2BYc/tm1bC4K9e1s77bTYn09IAITfZWfbXn7PPmt7nDZqJD3zjI2o%2BP2Xapw4AiAqLC9P%2BtvfpL/8xbZXkWzy8e9/L11wAW9MFVFUZMPH4e1xvvpK%2Bv77wx9Xs6YNF/fuLfXpY9doWjwTCQRA%2BJXjSP/8p3T77faLtmSjKY89Fnv/zlF1CIA4aY5jizpuu822SZFsmPKhh6RzzyX4VZY9eywMfvmlBcKvvjry0HHHjhYG%2B/SR%2Bva1%2B16cSxgMBhUMBlVSUqJVq1YRAOEr335r%2B/rNmGH3O3SwHsCBA10tCzGAAIiTsnGjdOON0ocf2v2WLa0H8PLLCX5VrbTUVh5/%2BaUd8D53rq1GPlT9%2BmU9hH372rzCmjWrvt6TRQ8g/GTHDju27bnn7N94YqIN//72t7ZYDKgoAiBO2MSJ0tix1usUHy/deacN9yYluV0ZwnbuPDgQfv21nZF8oOrVba/Fvn2t9eljR99FKwIg/KCgwLZxefDBso3nR4yw4d5WrVwtDTGGAIhyy8mxXr%2BJE%2B1%2BZqb08su20APRrajIDoSfO9dOX5k71045OVSrVmU9hH362Ebc0XJwPAEQsaykxI5su%2BeesuMou3aVnnhCOuccV0tDjCIAolyWL7ffQletskBw773W6xfrK09jleNIP/5YFgjnzLFNqw89iq92bZvXGR467tVLqlfPnZoJgIhFjiO9/74Fv2%2B/tY%2BlpUl/%2BpMt9IiWX8AQewiAOK7337fd5XNzpWbNbDVa375uV4VIC4VscUm4h3DevMOP6pPsnOPw9jO9e9v9qlhcQgBELHEc6V//siPcFi2yj6WkSL/7nW2d5aX5ufAmAiCOynFs%2BOE3v7Hb55wjvfmm7T2F2FdSYj2/4XmEX3558IkuYSkptgVNeJPqnj0rZ2sKAiBiQUmJ7Z7w0ENlx7fVqiXdequ917rVww7/IQDiiEpL7c3oiSfs/o03Sk89xTm1frdtm2078%2BWX1ubPL9v78UBt21oQ7NnTwuHpp9sqxoogAMLL9u2TXnvNFnOsXm0fq13bFtT95jf%2BPN0H7iIAwiZ/PfaYNG2aVK2aSof8THfvuF2PvttRkm3yfPvtbO%2BCwxUX27yl8J6E8%2BYdeQua%2BHipTZsc5efP1p49nyk7%2BzO9886fdOmlFx3/D/ngA2n8eIUWL1bKjh3K/v3vlfzb30p160b%2BGwJOREGBHX308su2qqptW2nMGGnkyP1vmFu22FYuzz1nW7tI1sv3619bq1/fxfrhawRAv/v0U%2Bmiiw47byxHtTW02ica/UpfXXutS7XBk3bvtm1nvv7aAuHXX0vbtx/%2BuISEYvXoUV09ekhnnCH16CGdcsohk97vvddmw0sKSUqRlC0puVMnadYsuk3gnvx8aciQsh2aD%2BBcc41mX/%2Banns%2BoHfesV%2BUJNsv9bbbpF/%2B0nr/ADcRAP2suNj2/di06YifzknrqDqbjnDuGHACwiuOw%2Bcbz58vzZyZI6nOYY9NSrKtL7p3lwY2WqoRD3Td/7mDAqAk3XSTFAxWzTcBHOqJJ2xo5Cgu1rt6XxdLskVzt94qXXIJOycgehAA/eyjj6Rhw479mNmz7WBfIIICgWp6%2BulPVa/eIC1cKC1YYPsU5uWVPeYJjdM4PbX//mEBsE4d2/Gaialww6mn2iqpo/h3tWF69//9SzfdZBuuA9GG30X8bOPGyDwGOGGOmjfP1cUX2xZDkq2OXL3atsRYvFjq%2BOxK6QgLTPbLydGIQSG16tFAXbrY/8cZGbaiEqhMJSVS6fqNOtavHud33qifvVhlJQEnjADoZ61bR%2BYxQATExdkcwFNOka6%2BWiou6SQ98clRH79T9fXBrBSVzDr4461b2%2Bk0GRnWOnWy52ThMCqioMCm%2B737rrVP8lqrm5Yc9fFx7XjvRHQjAPrZoEEqSG%2BthE3rjvz5006zfTwAF1QfM0Z68kmbRHgEhVdfp%2BfPqa5ly2wl8rff2jY169ZZ%2B/DDgx%2Bfnm5hsGNHax062LV5c05bwJFlZUmffGKzZaZOteMwwybUvEHd9t509C%2B%2B4YbKLxCoAAKgj%2B3OrqYbnAn6h4YoRYcc%2BdCggfTqq%2B4UBkiWzh577MgT7TMz1fT5%2B3X9IetItm%2BXVqywqVnLl9vt776Ttm61tU6bNkmffXbw1yQkSO3aWWvf3q5t21pr3pxJ%2B36SkyP95z/S55/brlhLlx78%2BdRUafhwOxbz3P6jpf%2BZakclHeqWW6QLLqiaooGTxCIQn3IcexN77z2pb/p6Tbt4vJJmfWpneg0daiss09PdLhMxJDc3V2v%2Be5RIt27d9Pjjj%2Bucc85R/fr11aJFi6N/4Zw5UjCo0KJFSlm5UtmPPabkG2%2B0JcPltHu39P331lauLGs//CAVFh7966pXt607WreW2rSxa6tWZa1JE/bH9LKsLNvDcs4cW%2B%2B2cKHN7zvQGWfYbi9Dh0qZmYcce1hSYscjvfSS7QPYrp3tAzh0aJV%2BH8DJIAD6VDAo3XyzVKOGHfPVo4fbFSHWzZgxQ%2Becc85hHx81apReeeWV4359ZZwEUlJiW9SsXm1tzZqytm7dscOhZL2HzZtLLVpYC99u1sxaerrtV01IdN%2BOHba4aNEiW3U%2Bf7793R%2BqVSvp3HOlQYOscfQlYhUB0IdWrLB91goK7Hi3X//a7YqA46vqo%2BBKS23IeO1aC4Ph6/r1Fhw2bbLHHE/NmlJaWllr2rSspaZaa9LEZl0c1LuEE%2BY4tjPQqlU29L9ihc0NXbbMTuQ4VCBgq8f79LHdrvr3twAP%2BAEB0GeKi6Xeve034AsukP79b3on4A3RdhZwUZHtkvTjj9JPP0kbNtg13DZtknbtKv/zxcVZb1PjxnY9sDVsaK1BA2v161urWdNf/34dx4bzN20qe81//NGC%2BQ8/WM/tnj1H//p27WxPvjPOsOHcHj1YHQ7/Ynqzzzz%2BuIW/unWl//s/f/3nAURSfLzNCTzWTkl799rUsM2bLbRs2WJt82abf5aVZQtUdu604ejwx06khvr17d9zuKWklLXkZNsvOznZjh4Lt1q1rNWsaS0pyVpV9UCWlNhrk5trLRSytnu3Bbhdu%2Bw12bHDFvZs3Wpty5bDTq08oubNbeufTp1sS6DwPpF1Dj98BvAtegB9ZO1aezPMz7ezy6%2B7zu2KgPKLth7ASCoqKgs627ZZ277d2o4d1sKBKByOwufLRlJ8vJSYaHMbExJsjnB8vLXq1a3FxVkLBMqaZL1zpaUW7kpKrL6iImuFhTblZN8%2Ba8ebW3k8DRpYyGve3BbptGpli3TCK7hr1qzwSwHEPHoAfcJxbGeC/Hyb4DxqlNsVAQiLjy%2BbI1gejmM9Z7t3l7U9e6Ts7INbKGRbm4RbuMctL8964PLyDg5j4cB24H53lSkQsB7J5GRr4V7M%2BvUt5DVsaEPgTZpYC8%2BdTEysmvqAWEYA9ImPPrL5fvHx0rPPMvQLeFkgYMOZdepUfNFCSUlZz1x%2BvvXUhVthYVkoLCqyXr2SEuvpc5yyFq6pWjVrcXHWWxjuPQz3KCYmlg0316plV96LAHcQAH2gqKhsL93bbrP9dQFAsrAWnhsIwD/YdMAHXnzR9jhr1Ei6%2B263qwFOTDAYVEZGhjIzM90uBQBiBotAYlxenk2K3rpVGj9eGjvW7YqAkxPLi0AAoKrRAxjjgkELf23aSKNHu10NAACIBgTAGJaXJ/31r3b7nntsSwcAAAACYAx78UXbN6xNG%2Bmaa9yuBgAARAsCYIwqKrJTPyTpzjttSwYAAACJABiz3nnHzsps3FgaOdLtagAAQDQhAMaop56y69ix7JoPAAAORgCMQYsWSfPm2Q78Y8a4XQ0AAIg2BMAY9Pe/2/Wyy%2Bz8TAAAgAMRAGNMXp40aZLdvuEGd2sBAADRiQAYY6ZMkXJz7fSPAQPcrgYAAEQjAmCMef11u44cKQUC7tYCRAJnAQNA5HEWcAzZulVKS5NKS6U1a6wXEIgVnAUMAJFDD2AMmTzZwl9mJuEPAAAcHQEwhkyebNcrrnC3DgAAEN0IgDFi1y5p5ky7PWKEu7UAAIDoRgCMER9/LJWUSKeeKrVp43Y1AAAgmhEAY8S//23XYcPcrQMAAEQ/AmAMKC2VPv3Ubg8Z4m4tAAAg%2BhEAY8A330g7dki1a0u9e7tdDQAAiHYEwBgwfbpdBwyQ4uPdrQUAAEQ/AmAMCK/%2BPftsV8sAAAAeQQD0uNJSac4cu92/v7u1AAAAbyAAetzKlbYHYFKS1K2b29UAAAAvIAB63Lx5du3Rg/l/iE3BYFAZGRnKzMx0uxQAiBkEQI%2BbP9%2BuPXu6WwdQWcaOHasVK1ZofviHHQBQYQRAj1u0yK49erhbBwAA8A4CoIeVlEhLl9pt5v8BAIDyIgB62Lp10t69UkKC1K6d29UAAACvIAB62Lff2rVTJ6l6dXdrAQAA3kEA9LDvv7drRoa7dQAAAG8hAHrYypV27djR3ToAAIC3EAA97Icf7Nq%2Bvbt1AAAAbyEAetjatXZt08bdOgAAgLcQAD2qqEjavNlut2zpbi0AAMBbCIAetWmT5DhSjRpS48ZuVwMAALyEAOhRmzbZNT1dqsbfImIYZwEDQOQRHTxqyxa7Nm3qbh1AZeMsYACIPAKgR23bZtcmTdytAwAAeA8B0KO2b7dro0bu1gEAALyHAOhRu3bZtX59d%2BsAAADeQwD0qOxsu9ar524dAADAewiAHhUK2bVOHXfrAAAA3kMA9Ki8PLvWquVuHQAAwHsIgB61d69da9Z0tw4AAOA9BECPKiiwa2Kiu3UAAADvIQB6VFGRXePj3a0DAAB4T3W3C/Ayx3GUk5Pjyp9dWGjX/PyyBSFALCkoKFBBuKtb2v9vLcQPPIAIqVOnjgKBgNtluCLgOI7jdhFeFQqFlJKS4nYZAADgJGRnZys5OdntMlxBAKyAA3sAMzMzK3RW6Yl%2BfZ8%2B0vLl0rvvSueee3LPcahQKKTmzZvrp59%2BqtA/iIrWEYnniIYaouX1jIbX4mSe49AewC1btujMM8/UihUrlJ6eXiU1xPJzROLnMxq%2Bj2h4jmj5tx4rz1GVr6efewAZAq6AQCCw/4czLi6uQj%2BoJ/r1NWrYNTFRCn9ZRWsIS05OrtLvpTKeIxpqCHP79YyW1yJSr2edOnVO%2Bnmi5fuIlueQKvbzGS3fR7Q8h9v/1mPtOaLh9YxlLAKJkLFjx1bp14cXf4TnAkaihkiJRB1V/XpW1nNEQqy8FtHwekbL9xEtzxENNcTSc0RDDbH0HLFQQzRjCNijBgyQZs2S3nxTuuKKyDxneE6jn%2BdERBKvZ2Rt3Lhx/7BQs2bN3C7H8/j5jBxey8ji9awa9AB6VHgD6PCG0JGQkJCg%2B%2B67TwkJCZF7Uh/j9Yys8OvI6xkZ/HxGDq9lZPF6Vg16AD3qssukyZOl8eMlernhB/QKAEDk0APoUeHdZ7Kz3a0DAAB4DwHQo8IBcM8ed%2BsAAADeQwD0qAYN7Lpzp7t1AAAA7yEAelSjRnbdvt3dOgAAgPcQAD2qSRO7bt0aueecMmWKzj//fDVs2FCBQEBLliyJ3JPHKMdxdP/99ystLU1JSUk6%2B%2ByztXz58mN%2Bzf33369AIHBQS01NraKK4RfPPvusWrdurcTERPXo0UOzZ88%2B6mNfeeWVw34mA4GA8vPzq7Bib5o1a5YuvPBCpaWlKRAI6L333nO7pKh3oq/ZjBkzjvjz%2Bf3331dRxbGJAOhRTZvaddOmyD1nXl6e%2Bvbtq0cffTRyTxrj/vKXv%2Bjxxx/X%2BPHjNX/%2BfKWmpuq8887bf0Tg0XTu3FlbtmzZ35YtW1ZFFcMP3nzzTY0bN0533323Fi9erH79%2BmnIkCHasGHDUb8mOTn5oJ/JLVu2KDExsQqr9qa8vDx17dpV48ePd7sUzzjZ12zlypUH/Xy2b9%2B%2Bkir0B46C86jwPrhbtkjFxVL1CPxNXnvttZKk9evXV/zJfMBxHD355JO6%2B%2B67NWLECEnSq6%2B%2BqiZNmmjSpEkaM2bMUb%2B2evXq9PqVUzAYVDAYVElJiduleMbjjz%2BuX/7yl7r%2B%2BuslSU8%2B%2BaSmTp2q5557To888sgRv4ae6JMzZMgQDRkyxO0yPOVkX7PGjRurbt26lVCRP9ED6FFNmljoKy2VNm92uxp/WrdunbKysjR48OD9H0tISNCAAQM0d%2B7cY37t6tWrlZaWptatW%2Buqq67S2rVrK7tczxo7dqxWrFhR4cPp/aKwsFALFy486OdSkgYPHnzMn8vc3Fy1bNl4cCWyAAAeNElEQVRSzZo107Bhw7R48eLKLhU4Id26dVPTpk01cOBAffHFF26X43kEQI%2BKi5NatLDbdNi5IysrS5LUJDwh87%2BaNGmy/3NH0rNnT7322muaOnWqXnzxRWVlZalPnz7ayZJuRMCOHTtUUlJyQj%2BXp5xyil555RV98MEHeuONN5SYmKi%2Bfftq9erVVVEycExNmzbVCy%2B8oMmTJ2vKlCnq2LGjBg4cqFmzZrldmqcRAD2sTRu7nkzn0cSJE1W7du397VgTxGEOfc2Kiook2dDZgRzHOexjBxoyZIguvfRSdenSRYMGDdJHH30kyYaPgUg5kZ/LXr166ZprrlHXrl3Vr18/vfXWW%2BrQoYOeeeaZqigVOKaOHTtq9OjR6t69u3r37q1nn31WQ4cO1d/%2B9je3S/M05gB6WLt20mefSSfzS/rw4cPVs2fP/ffT09MjWFlsOvQ1KygokGQ9gU3Dq3Ikbdu27bDel2OpVauWunTpQm8LIqJhw4aKi4s7rLfvRH4uq1WrpszMTH4mEbV69eqlCRMmuF2Gp9ED6GEdO9r1ZFbC16lTR%2B3atdvfkpKSIltcDDr0NcvIyFBqaqqmTZu2/zGFhYWaOXOm%2BvTpU%2B7nLSgo0HfffXdQiAROVo0aNdSjR4%2BDfi4ladq0aeX%2BuXQcR0uWLOFnElFr8eLF/HxWED2AHtapk11XrIjM8%2B3atUsbNmzQ5v%2BuKlm5cqUkKTU1ldWBRxAIBDRu3Dg9/PDDat%2B%2Bvdq3b6%2BHH35YNWvW1NVXX73/cQMHDtQll1yim2%2B%2BWZL0m9/8RhdeeKFatGihbdu26cEHH1QoFNKoUaPc%2BlYQY26//XZde%2B21OuOMM9S7d2%2B98MIL2rBhg371q19JkkaOHKn09PT9K4IfeOAB9erVS%2B3bt1coFNLTTz%2BtJUuWKBgMuvlteEJubq7WrFmz//66deu0ZMkS1a9fXy3CE7VxkOO9ZnfddZc2bdqk1157TZKtYm/VqpU6d%2B6swsJCTZgwQZMnT9bkyZPd%2BhZigwPP2rjRcSTHiYtznH37Kv58L7/8siPpsHbfffdV/MljVGlpqXPfffc5qampTkJCgtO/f39n2bJlBz2mZcuWB72GV155pdO0aVMnPj7eSUtLc0aMGOEsX768iiv3nuzsbEeSk52d7XYpnhAMBp2WLVs6NWrUcLp37%2B7MnDlz/%2BcGDBjgjBo1av/9cePGOS1atHBq1KjhNGrUyBk8eLAzd%2B5cF6r2ni%2B%2B%2BOKI75sHvr442PFes1GjRjkDBgzY//g///nPTtu2bZ3ExESnXr16zllnneV89NFH7hQfQwKO4zhVHzsRCY4jNWwo7dolLVwode/udkVA5QmFQkpJSVF2draSk5PdLgcAPI05gB4WCEinn2632bILAACUFwHQ48K9fgsWuFsHAADwDgKgx515pl3nzXO3DgAA4B0EQI/r1cuuS5dKeXnu1gJUhmAwqIyMDGVmZrpdCgDEDBaBxIDmzaWNG6XPP5fOPdftaoDKwSIQAIgcegBjQL9%2BduVYRAAAUB4EwBhw9tl2nT7d1TIAAIBHEABjwMCBdv3qKyk3191aAABA9CMAxoA2baTWraWiImnGDLerAQAA0Y4AGAMCAen88%2B32v//tbi0AACD6EQBjxLBhdv3wQzsiDgAA4GgIgDHi3HOlmjWln37iWDgAAHBsBMAYkZQkXXCB3Z482d1aAABAdCMAxpDLLrPrW28xDAwAAI6OABhDLrzQegLXrJEWLnS7GgAAEK0IgDGkdm1p%2BHC7/frr7tYCAACiFwEwxowcaddJk6TCQndrASIhGAwqIyNDmZmZbpcCADEj4DjMFoslxcVSixbSli3S22%2BXzQsEvC4UCiklJUXZ2dlKTk52uxwA8DR6AGNM9erSL35ht//%2Bd3drAQAA0YkAGINGj7bTQT77TFq50u1qAABAtCEAxqBWrcpOBnn6aVdLAQAAUYgAGKPGjbPrK69IO3e6WgoAAIgyBMAYdc45Urdu0t69UjDodjUAACCaEABjVCAg3Xmn3X7qKSknx916AABA9CAAxrDLLpM6dJB27ZLGj3e7GgAAEC0IgDEsLk665x67/de/Snv2uFsPAACIDgTAGPc//yNlZEi7d0t/%2BYvb1QAAgGhAAIxxcXHSww/b7SeekDZscLceAADgPgKgDwwfLvXvL%2BXnly0MAbyCs4ABIPI4C9gnFi%2BWevSQHEf64gvp7LPdrgg4MZwFXHGlpVJeXlnbu1fat8%2Bu%2BfllraBAKiy0a1GRteJiayUl9jylpfZ%2BcqC4OKlaNWvVq0vx8VKNGtYSEqTERCkpSapZ01rt2taSk6WUFPt8IODOawP4DQHQR266SXruOemUU6QlS%2BwNGfAKAqCFtZ07bWV/uO3ebQu8wi0721ooZNecnLK2d6/b38GxxcdL9epJ9etLDRpIDRtKjRpZS02VmjaV0tKk9HRr8fFuVwx4FwHQR3bvljp1krZule69V3rgAbcrAsovFgPgvn327zErS9q2zW5v22Zt%2B3ZrO3ZY6Nuxwx4fCYFAWS9cuEcu3DuXmGi/HIZ77mrUsKBVvbq1A3v5AoGyHrtwr2BpqfUShnsMCwutHdjDGO59zM21YJqbe3hvYnm%2Bh6ZNpZYt7fjLNm2ktm2l9u2ljh0tPNKbCBwdAdBn3n5buuIKeyP/%2Bms7LQTwAi8FwIICafNmaeNGadMmux1uW7aUtVDoxJ%2B7enXrIQv3lNWrZ61uXWspKdaSk8tanTrWateWatWywBdN4chxLAiGezF37SoLvuFAnJVlr9nmzfaaFhYe%2Bznr1bMdEDIypC5dpNNOk7p2tdcIAAHQdxxHuvxyafJkqXNnaf58%2B60fiHbREgAdx3rmfvyxrG3YYO2nn6xt21b%2B50tIkBo3tiHOxo3LWnjos2HDg1udOtEV3txw6N/BunXS2rXSmjXSqlX2d3C0/9lat5bOOMNaz552rVWrausHogEB0Ie2b7ffiLdulcaO5ZQQeENVBsDcXAsUB7Z166ytX1%2B%2BodiEhLK5amlpZXPXmjYta6mp1lvn90AXafv2WRBcsUL69ltp6VJrR9oGKy5OOv10qV8/2y2hXz8L2kCsIwD61CefSEOG2O133pEuvVQ2phIIMLMaUSm0datSUlMjFgDz8qTVqy0orF5tbc0aa1u3HvtrD5x/1rKl1KKFtebNy1qDBgS7aLNrl7RokbRggY1%2BfPWVDSkf6rTTpEGDpMGDLRQed5TEcWxyI8Mp8BACoI/97//aEXGXJH2iCZ0fUc0Fs%2Bx/rEGDpLvvlgYMcLtExIgpU6bo73//uxYuXKidO3dq8eLFOv3004//haWl0jPPSOPHK7RmjVIkZf/850p%2B6CFLXsfhOPYf/HffWfv%2Be2nlSmsbNx77a%2BvXt4UF4cUFrVtba9XKAh6r6GPDhg3SnDnS7NnSzJnWa3igxETp3HOloUOlCy%2B0v/v9du2S/vQn6ZVXbPJi06bSDTdIv/udfSEQxQiAPlZUJP21y2v63crrVE1H2NDr7belSy5xpzjElNdff13r1q1TWlqaRo8eXf4AeN110quvSpJCkgVAScmpqdLcuZbIZEFv0yYb7lu%2B3NqKFRb6jrXQokEDqUMHWzkabu3aWeBjsYA/bd1qe6VOmyZNnWo/Vwfq0UMaMUK6/Lw9an9d38MToySdc459MaMpiGIEQD/bt0%2Bl6c1UbfeuI3%2B%2BWTOb8BQXV6VlIXatX79erVu3Ll8A/M9/bELWfx0UACWtPPNaPdn9NS1bZsEvO/vITxMXZ4GuUyfbA/OUUyz0dexoARA4Gsexn62PPpI%2B/NB%2B5wj/j3mf7tf9OsZeWq%2B%2BKo0cWTWFAieBAOhn77xjS4KPZdo0GxIGIuBEAmDx6NGq/o9/7L9/aADMV4JSlK1C2Vhs9erWg9e5c1nLyLAePYZrEQnbtknvv2%2B7KASntlVbrT36gwcNsvdPIEpVd7sAuGjnzuM/ZseOyq8DvldSYosxFi60SfqLF0u3zNqpEcf4mkQV6A%2B35qrtmQnq0sV69GrUqLKS4UONG0ujR1srTdlpv5UcRd6GHarpsBAI0YsA6GennhqZxwAHmDhxosaMGbP//scff6x%2BBwzlSgGtW5egb7%2B11ZgLF1rgy8s7%2BHkG6DSN0LtH/4PS0nTP4/WkapGtHyiPal1OtdUjRzFl1al6vLtttXX11bb5NhBNGAL2u9NPl7755oifWt%2Biv1qun8lvsDghOTk52vrffVQcR3KcZlq6NFHz50uzZ%2B/Tl18WyQZxD1azpv04du9uJ9T0TN%2BojOFtFfjvkQ%2BHDgHrj3%2BU7rmnir4r4BCTJkk///lRP312jbmaWdhbkp1KcsMN0s0329RqIBoQAP1u5Uqbq3LInhhr1FYD9blGjGupxx6zcz%2BB4wmFbH%2B1efPK2pH21EtMLFX37tX2n8jQvbstzjhsvdHbb0vXXCMVFh4cAIcPtzmsrLKEm2655fCd9AMB6a9/1c7r7tDLL0vPPmsbiEs2T/Wqq2wLri5dqr5c4EAEQNj/2q%2B9ZhOWAwFp6FA9l321bvqtnY909dXSSy8xkR4HKy21HTC%2B%2BqqsrVhx%2BBFc1atLGRnFat9%2Bj9LTN%2Bvpp6/RxIl/UEZGB6Wmpio1NfXYf9D69dILLyi0YIFSpk1T9jvvKHnECCZXITrMnWtvkFu22HLzG244aOpMSYmtIH7iCdtnMGzoUNtutXdvF2oGRADEMUyYIP3iF1Jxse3GMXmynU0Kf9q923r05s61sDdv3pH32GvVys5Y7dlTOvNM6917881X9Itf/OKwx9533326//77y/XnR8tZwMDJWrDANt9/5x37BUqyAZj775f69nW1NPgQARDH9Nln0mWX2R5rLVpIU6bYRqiIbY5jq3LnzLHA9%2BWXR97vtlYtKTNT6tWrrDVpUjk1EQARK1avlh591AZeiovtY%2BefLz34oE2JAKoCARDH9d130kUX2ZtWjRo2lHHjjYzAxZK9e23u3pdfWuj78ssj7xLUrp0NWYXbqafaEG9VIAAi1qxfLz38sPTyy2VB8NJL7WMdOrhaGnyAAIhy2bNHGjVK%2BuADuz98uPTii7YvFrxnyxYLeuEevkWLyv4DCktMtN6IPn1seKpXL3f/vgmAiFVr19ow8IQJ1vseFyeNGSPddx/vsag8BECUm%2BNITz5p55wXFkoNG0rPPCNdeSW9gdGspMTOxj0w8IVXJR6oaVMLen36WOvWLbo2ViYAItZ9%2B6101122aESSkpOlP/xB%2BvWvWYSHyCMA4oR9840dcbl0qd0fMkR6%2BmkbHoT7srNtgUZ4OPerr6ScnIMfEwhIp51WFvj69pVatozuIE8AhF/MmCHdcYf1zEt2xOFTT9l7LRApBECclMJC6ZFHbK5KYaH1FI0bZ7%2B91q3rdnX%2B4TjSDz9Yr154scayZYdvxVK7tg3hHjic67UMRQCEn5SWSq%2B%2BKv3%2B91JWln3soossCLZs6W5tiA0EQFTIypU2PPHpp3a/fn3pzjvt%2BKNatdytLRbl5JQt1gjvvXek45pbty4byu3TxzadPWyTZY8hAMKPQiE79Oapp2yebs2aNl9w3Dj2QUfFEABRYY4jffSRBb/wViENGki33irddJPdxokrLra5e19/XXaqxvLlh/fuJSTY1jy9e1vY693b5vPFimAwqGAwqJKSEq1atYoACF9avtzeT2fNsvtdu0r/%2BAfbxuDkEQARMSUltortT3%2ByYUlJSkqyk7xuusnOecWROY60Zo1tFDt/vrVFi2x7lkO1bGmbLPfubUO53br5Y4I4PYDwO8exYeE77pB27bIjOu%2B4Q3rgAXuvBU4EARARV1xsR7j%2B9a/S4sVlHz/jDNtK5oor/L21QUmJbbK8eLG1hQst7GVnH/7YOnXsdQufrNGzZ2z17p0IAiBgtm%2B3EZY33rD7HTvaXoIcK4cTQQBEpXEcafZsKRiU3n1XKiqyj8fFSWefLV1yiXThhXbCSKzavdu2dli61FZPf/ONLdLYt%2B/wxyYk2LBOZqa1M8%2B0N/Zq1aq%2B7mhEAAQO9q9/2X6BW7bY%2B8Rvf2u9gX4YEUDFEQBRJbZvlyZNsiHiBQsO/lxGhp2Hec450lln2f6CXuI40rZttiDm%2B%2B9tHuSKFTZnZ/PmI39NzZoW9rp3tyHcHj2kzp2Z1H0sBEDgcLt3W2/g66/b/a5dpYkT7f0EOBYCIKrcDz/YmcLvv2%2BrWcOHooe1a2e9X9262V51p55qw55u7lFXUCBt3Cj9%2BKNtorx2rX0fP/xgw7mh0NG/tkULW4V72mn25ty1q%2B3r5fVVuVWNAAgc3bvvSqNH2xGOCQk2Befmm6N7b0%2B4iwAIV%2B3aJU2fLn3%2BuTRzpp07fCR16kht20pt2tgiiPR0C4WNGtkq43r1bF%2B7WrXsze9Yb3rFxba4IjfXtlXJzraj7nbutC1Vtm2Ttm61YZUtWyz4bd167O8jELC6TjlF6tTJejU7d7ZrSsrJvz4oQwAEji0rS/p//0/6%2BGO7P2yYzQ302qgKqgYBEFFl1y7b9mThQmnJEps798MPtnCivAIB25g6Pt7mxQQC1stYXGybVp/Icx0oMdF681q3tta2rfVWtm9vtxMTT%2B55UT4EQOD4HEcaP97mAxYUSGlptlikf3%2B3K0O0IQAi6hUU2JDrmjXS%2BvXShg3Spk322%2B727dZzt2fPkRdWHEu1anZCRkqK9SDWr2%2B/KTdqJDVpYj2MaWnW29ismX2O4RT3EACB8vvmGzunfeVKe6978EHbq5VFZQgjACJmFBdLeXkWBAsK7H5pqf1GXK2azbmrUcN66mrWtCuBzjsIgMCJyc21PVjDC0SGDpVee81%2B2QUIgAA8gQAInDjHkV56yRaE5Ofb9JUpU9iYHxKdwQAAxKhAQPrlL6W5cy38rVtnG0ZPnOh2ZXAbARBAVAsGg8rIyFBmZqbbpQCe1a2bLa4bMsR6Aq%2B5xo6RKy52uzK4hSFgAJ7AEDBQcSUl0r33Sg8/bPcHD5befFOqW9fdulD16AEEAMAn4uKkhx6S3nrLFsN9%2BqnUq5e0erXblaGqEQABAPCZyy%2BX5syRmje3rWJ69bLN%2BOEfBEAAAHzo9NNt4/0zz7RN%2BM87r2zLGMQ%2BAiAAAD6VmirNmGE9gkVF0siR0h//aNvHILYRAAEA8LGkJOmf/7STQiTpvvuk66%2B3QIjYRQAEAMDnqlWTHn1Uev55u/3SS9LFF9vpSohNBEAAACBJGjNGeu896xX897%2BlgQOlHTvcrgqVgQAIAAD2u/BC6fPP7czgefOk/v2ln35yuypEGgEQAAAcpHdv6T//kZo1k777TjrrLGnVKrerQiQRAAEAwGE6dbK9Ajt0kDZskPr1k775xu2qECkEQABRjbOAAfe0aCHNnm1nCW/bJp19tg0Lw/s4CxiAJ3AWMOCe7GzpZz%2BT5s6Vate2BSL9%2BrldFSqCHkAAAHBMKSnS1KnSuedKubnSBRdI06e7XRUqggAIAACOq3Zt6cMPLfzt3SsNHSpNm%2BZ2VThZBEAAAFAuSUm2T%2BCwYVJ%2Bvm0Z8%2BmnbleFk0EABAAA5ZaQIE2eLF10kVRQIA0fTk%2BgFxEAAQDACalRQ3rrrYNDIHMCvYUACAAATlg4BB44HDxrlttVobwIgAAA4KTUqCG9887BC0PYJ9AbCIAAAOCkJSRIU6YcvEUMJ4ZEPwIgAACokKQk6YMPpD59pD17pPPOk1audLsqHAsBEAAAVFitWnZCSPfu0vbt0qBBdoYwohMBEAAARERKivTJJ1KnTtLGjRYCt21zuyocCQEQQFQLBoPKyMhQZmam26UAKIdGjWxz6JYtpdWrpSFDpFDI7apwqIDjOI7bRQDA8YRCIaWkpCg7O1vJyclulwPgOFatks46y4aDzzlH%2BvhjWzCC6EAPIAAAiLgOHSz01a4tffGFdO21Ummp21UhjAAIAAAqRY8ednZwfLz09tvSuHES447RgQAIAAAqzcCB0muv2e1nnpH%2B9jd364EhAAIAgEp11VXSY4/Z7f/9X%2BmNN9ytBwRAAABQBW6/3YaAJem66zg32G0EQAAAUCUee0waMUIqLJQuvlj6/nu3K/IvAiCASlVUVKQ777xTXbp0Ua1atZSWlqaRI0dq8%2BbNbpcGoIpVqyZNmCD16iXt3i0NHWrbxKDqEQABVKq9e/dq0aJFuueee7Ro0SJNmTJFq1at0vDhw90uDYALwucGt24trV1rPYH5%2BW5X5T9sBA2gys2fP19nnnmmfvzxR7Vo0aJcX8NG0EBs%2Bf57qXdvac8e6ec/l15/XQoE3K7KP%2BgBBFDlsrOzFQgEVLdu3aM%2BpqCgQKFQ6KAGIHaccor0zjtSXJw0caL04INuV%2BQvBEAAVSo/P1%2B/%2B93vdPXVVx%2BzJ%2B%2BRRx5RSkrK/ta8efMqrBJAVRg4UHr2Wbt9773S5Mnu1uMnBEAAETVx4kTVrl17f5s9e/b%2BzxUVFemqq65SaWmpng2/6x/FXXfdpezs7P3tp59%2BquzSAbjghhukW2%2B129deKy1e7G49fsEcQAARlZOTo61bt%2B6/n56erqSkJBUVFemKK67Q2rVrNX36dDVo0OCEnpc5gEDsKi6Whg2Tpk6VmjeX5s%2BXmjRxu6rYRgAEUOnC4W/16tX64osv1KhRoxN%2BDgIgENv27JF69pRWrZL69pWmT5dq1HC7qtjFEDCASlVcXKzLLrtMCxYs0MSJE1VSUqKsrCxlZWWpsLDQ7fIARIm6dW17mJQUac4c6ZZb3K4ottEDCKBSrV%2B/Xq1btz7i57744gudffbZ5XoeegABf/j4Y9sg2nGk556TfvUrtyuKTQRAAJ5AAAT849FHpbvukuLjpRkzpD593K4o9jAEDAAAosqdd0qXXy4VFUmXXipt2eJ2RbGHAAgAAKJKICC99JLUubOUlWVhkCnDkUUABAAAUad2bendd6XkZFsU8pvfuF1RbCEAAgCAqNS%2BvZ0RLEnPPCO98Ya79cQSAiAAAIhaw4dLd99tt6%2B/Xlq%2B3N16YgUBEAAARLUHHpAGDZL27rVFITk5blfkfQRAAFEtGAwqIyNDmZmZbpcCwCVxcdKkSVJ6urRypZ0fzCZ2FcM%2BgAA8gX0AAcydKw0YYGcHB4PSTTe5XZF30QMIAAA8oU8f6c9/ttu33SYtWuRuPV5GAAQAAJ5x223SRRfZvoBXXCFlZ7tdkTcRAAEAgGcEAtLLL0stW0o//CCNHs18wJNBAAQAAJ5Sr5705ptS9erS229LL7zgdkXeQwAEAACe07On9MgjdnvcOGnZMnfr8RoCIAAA8KTbb5eGDJHy86Urr7R9AlE%2BBEAAAOBJ1apJr74qNW0qffed9QSifAiAAADAsxo1svOCAwHpxRelyZPdrsgbCIAAAMDTBg6U7rzTbl9/vZSV5W49XlDd7QIAAAAq6o9/lP7zH%2BmSS6TGjd2uJvoRAAFEtWAwqGAwqJKSErdLARDF4uOlmTNtXiCOj7OAAXgCZwEDQOSQkwEAAHyGAAgAAOAzBEAAAACfIQACAAD4DAEQAADAZwiAAAAAPkMABAAA8BkCIAAAgM8QAAEAAHyGAAgAAOAzBEAAUS0YDCojI0OZmZlulwIAMYOzgAF4AmcBA0Dk0AMIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEEBUCwaDysjIUGZmptulAEDMCDiO47hdBAAcTygUUkpKirKzs5WcnOx2OQDgafQAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%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%3D%3D'}