Genus 2 curve data - 277.a.277.1

Formats: - HTML - YAML - JSON - 2024-04-19T09:05:14.322207

• 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': '639653774064676620', 'abs_disc': 277, 'analytic_rank': 0, 'analytic_rank_proved': True, 'analytic_sha': 1, 'aut_grp_id': '[2,1]', 'aut_grp_label': '2.1', 'aut_grp_tex': 'C_2', 'bad_lfactors': '[[277,[1,-7,269,277]]]', 'bad_primes': [277], 'class': '277.a', 'cond': 277, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[0,-1,-1],[1,1,1,1]]', 'g2_inv': "['-33554432/277','524288/277','475136/277']", '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': True, 'igusa_clebsch_inv': "['64','352','9552','-1108']", 'igusa_inv': "['32','-16','-464','-3776','-277']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '277.a.277.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.1431366605510114901557152099923829602231949697145570071', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.6.1', '3.80.1'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_maximal_primes': [2, 3, 5], 'non_solvable_places': [], 'num_rat_pts': 5, 'num_rat_wpts': 1, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '32.205748623977585285035922248', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 10}, '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': 1, 'torsion_order': 15, 'torsion_subgroup': '[15]', 'two_selmer_rank': 0, 'two_torsion_field': ['5.1.4432.1', [1, -1, 2, 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': '277.a.277.1', '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': '277.a.277.1', 'mw_gens': [[[[0, 1], [1, 1], [1, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [15], 'num_rat_pts': 5, 'rat_pts': [[-1, 0, 1], [0, -1, 1], [0, 0, 1], [1, -1, 0], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 8
    {'conductor': 277, 'lmfdb_label': '277.a.277.1', 'modell_image': '2.6.1', 'prime': 2}
  • id: 9
    {'conductor': 277, 'lmfdb_label': '277.a.277.1', 'modell_image': '3.80.1', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  {'cluster_label': 'c4c2_1~2_0', 'label': '277.a.277.1', 'p': 277, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '277.a.277.1', 'plot': 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xl4VOX9/vF7kkBAyAw7AYmIC2AQ9wh1pVZRvioqbhQFl7aiooILrlipG%2BIGiLEVN1So1AUtCmrxJ6VWxKIERaSAAopCkHWGNev5/fFpSMIayMw8M3Per%2Bt6rnMmmeWTcQx3nvMsAc/zPAEAAMA30lwXAAAAgPgiAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQABATHiep0gkIs/zXJcCYDsEQABATGzYsEGhUEgbNmxwXQqA7RAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQBRlZ%2Bfr9zcXOXl5bkuBcAuBDz26AEAxEAkElEoFFI4HFYwGHRdDoAq6AEEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEwDgpK3NdAQAAgCEAxtj330uXXCL16OG6EgAAAJPhuoBUV7euNHGi9QB%2B8410%2BOGuKwIAAH5HD2CM5eRI551n508/7bYWAAAAiQAYFzfeaMdXX5XWr3dbCwAAAAEwDk491S79bt4svfSS62oAILby8/OVm5urvLw816UA2IWA53me6yL8YMwYqX9/6aCDpIULpfR01xUBQGxFIhGFQiGFw2EFg0HX5QCogh7AOLnsMqlRI2nxYmnKFNfVAAAAPyMAxkmDBtLvf2/nTz3lthYAAOBvBMA4GjBASkuTPvpImjfPdTUAAMCvCIBxdOCBlUvCjB7ttBQAAOBjBMA4GzjQjq%2B8Iq1d67YWAADgTwTAODvlFOnII6UtW6TnnnNdDQAA8CMCYJwFApW9gPn5Ummp23oAAID/EAAd%2BO1vpebNpWXLbJ9gAACAeCIAOlCvnnTddXY%2BYoTbWgAAgP8QAB257jqpbl1p5kxrAAAA8UIAdCQ7W%2BrTx87pBQQAAPFEAHRo0CA7vvWW9OOPbmsBAAD%2BQQB06MgjpdNOk8rKWBgaAADEDwHQsVtuseOYMdKGDW5rAQAA/kAAdKxHD6lDBykSkV54wXU1AADADwiAjqWlSTffbOejRrEwNAAAiD0CYALo109q1kxaupSFoQEAQOwRABNA/frS9dfb%2BRNPSJ7nth4AqI38/Hzl5uYqLy/PdSkAdiHgecSNRPDLL9IBB0hFRdK//iWdfLLrigCgdiKRiEKhkMLhsILBoOtyAFRBD2CCaNHCLgVL0uOPu60FAACkNgJgAqlYEmbSJGnBAre1AACA1EUATCAdO0o9e9r5E0%2B4rQUAAKQuAmCCue02O77yilRY6LYWAACQmgiACeakk6SuXW0yCNvDAQCAWCAAJphAQBo82M6feUbauNFtPQAAIPUQABPQeedJ7dtL69dLzz3nuhoAAJBqCIAJKD29cizgiBFScbHbegAAQGohACaovn2l7Gxp2TJpwgTX1QAAgFRCAExQ9epJAwfa%2BaOPSuXlbusBAACpgwCYwK67TsrKkubNkyZPdl0NAABIFQTABBYKSddfb%2BfDhkns2gwAAKKBAJjgBg2SMjOlzz6TPvnEdTUA/GrYsGEKBAIaNGiQ61IARAEBMMFlZ0tXXmnnw4Y5LQWAT82aNUtjxozREUcc4boUAFFCAEwCgwdLaWnSBx9IBQWuqwHgJxs3btRll12m5557To0bN3ZdDoAoIQAmgYMPli691M7pBQQQTwMGDNDZZ5%2Bt008/3XUpAKIow3UBqJm77pJee016801pwQKpQwfXFQFIdRMmTNDs2bM1a9asGt2/qKhIRUVF225HIpFYlQaglugBTBKdO0vnnmszgYcPd10NgFS3bNkyDRw4UOPGjVO9evVq9Jhhw4YpFAptazk5OTGuEsC%2BCngei4ski5kzpV/9SsrIkL77Tmrb1nVFAFLVO%2B%2B8owsuuEDp6enbvlZWVqZAIKC0tDQVFRVV%2B5608x7AnJwchcNhBYPBuNUOYM8IgEnmN7%2BRPv5YGjBAevpp19UASFUbNmzQDz/8UO1rV111lTp27Kg77rhDhx9%2B%2BB6fIxKJKBQKEQCBBEQATDLTpkmnnWZrAy5ZIrVq5boiAH7RrVs3HXXUURo5cmSN7k8ABBIXYwCTTLdu0gknSEVF0hNPuK4GAAAkI3oAk9CUKdLZZ0v77Sf98IPUrJnrigBgR/QAAomLHsAk1KOHdMwx0ubN0ogRrqsBAADJhgCYhAIB6d577Xz0aGndOrf1AACA5EIATFI9e9ragBs2SKNGua4GAAAkEwJgkkpLq%2BwFHDlSCofd1gMAAJIHATCJXXihlJtr4e%2Bpp1xXAwAAkgUBMImlpUlDhtj5iBES224CAICaIAAmuUsukTp2tIkgo0e7rgYAACQDAmCSS0%2Bv7AV88kl6AQEAwJ4RAFNA795Shw7S2rX0AgIAgD0jAKaA9HTpj3%2B08yeeoBcQAADsHgEwRVx6aeVYQGYEAwCA3SEApojtewHXr3dbDwAASFwEwBRyySW2LuD69bY4NAAAwM4QAFNIero0dKidjxhhk0IAIN7y8/OVm5urvLw816UA2IWA53me6yIQPeXl0tFHS19/Ld19t/TQQ64rAuBXkUhEoVBI4XBYwWDQdTkAqqAHMMWkpUl/%2BpOdjxolrVrlth4AAJB4CIAp6LzzpGOPlTZtkoYPd10NAABINATAFBQISPffb%2Bf5%2BdLy5W7rAQAAiYUAmKJ69JBOOEHaupVxgAAAoDoCYIoKBCqD33PPSUuXOi0HAAAkEAJgCuvWTTr9dKmkpHJiCAAAAAEwxT34oB1feUWaP99tLQAAIDEQAFNcly42K7i8XLr3XtfVAACAREAA9IEHH7QxgW%2B9JX3xhetqAACAawRAHzj8cOmyy%2Bz87rvd1gIAANwjAPrE/fdLdepIU6dK06a5rgYAALhEAPSJdu2k/v3t/M47JXaABgDAvwiAPjJkiNSggfSf/0gTJ7quBgAAuEIA9JGWLaVbbrHze%2B6RSkvd1gMAANwgAPrMbbdJzZpJCxZIL77ouhoAAOACAdBngkG7FCxJQ4dKmzc7LQcAADhAAPSha6%2BVDjxQWrFCGjnSdTUAACDeCIA%2BlJkpPfSQnQ8fLq1e7bYeAKklPz9fubm5ysvLc10KgF0IeB4LgvhRebl03HFSQYF0003SqFGuKwKQaiKRiEKhkMLhsILBoOtyAFRBD6BPpaVJjz5q5888I333ndt6AABA/BAAfez006WzzrLlYNgiDgAA/yAA%2Btzw4VIgIL3xhjRzputqAABAPBAAfe6II6Qrr7Tz225jizgAAPyAAAg98IBUv7706adsEQcAgB8QAKH997feP0m64w6puNhtPQAAILYIgJAkDR4sZWdL339vs4IBAEDqIgBCkpSVJd1/v53ff7%2B0dq3begAAQOwQALHN1VdLnTtL69ZVhkEAAJB6CIDYJj1deuIJO8/PlxYudFsPAACIDQIgqjnjDOnss21x6MGDXVcDAABigQCIHTz%2BuPUGTpok/b//57oaAAAQbQRA7KBjR%2Bn66%2B385pulsjK39QAAgOgiAGKn7rtPatxYmjtXev5519UAAIBoIgBip5o2lYYOtfMhQ6T1652WAwAAoogAiF267jrpsMOk1atZFgYAgFRCAMQu1akjjRhh56NHSwsWuK0HQHLIz89Xbm6u8vLyXJcCYBcCnud5rotAYjvnHGnyZKlHD2nKFNfVAEgWkUhEoVBI4XBYwWDQdTkAqqAHEHs0YoT1Br7/PgEQAIBUQADEHh16qDRokJ0PGiQVF7utBwAA1A4BEDUyZIjUsqW0aJE0cqTragAAQG0QAFEjwaA0fLidP/CAtHy523oAAMC%2BIwCixvr2lbp0kTZulO64w3U1AABgXxEAUWNpabYcTCAgjRsnffqp64oAAMC%2BIABir%2BTlSVdfbec33sg%2BwQAAJCMCIPbaww9LjRpJBQXSmDGuqwEAAHuLAIi91qKFTQSRpHvusa3iAABA8iAAYp9ce610xBHSunXS3Xe7rgYAAOwNAiD2SUaGlJ9v588/L82a5bYeAABQcwRA7LOTTrKlYTxPGjBAKi93XREAAKgJAiBq5dFHbZHoWbOsJxAAACQ%2BAiBqJTtbuv9%2BO7/rLiaEAKlk2LBhysvLU1ZWllq0aKHzzz9fCxYscF0WgCggAKLWBgywCSFr11oIBJAapk%2BfrgEDBmjmzJmaOnWqSktL1b17d23atMl1aQBqKeB5nue6CCS/f/9bOvlkO//sM6lrV7f1AIi%2BVatWqUWLFpo%2BfbpOOeWUPd4/EokoFAopHA4rGAzGoUIANUUPIKLipJOkK66w8%2BuvZ4cQIBWFw2FJUpMmTXb6/aKiIkUikWoNQGIiACJqHn20coeQP//ZdTUAosnzPN1yyy066aSTdPjhh%2B/0PsOGDVMoFNrWcnJy4lwlgJriEjCi6plnbExgKCT99782SQRA8hswYIAmT56sf//732rTps1O71NUVKSioqJttyORiHJycrgEDCQgegARVf37S8ceK4XD0uDBrqsBEA033nijJk2apGnTpu0y/ElSZmamgsFgtQYgMREAEVXp6Xb5NxCQxo2Tpk93XRGAfeV5nm644QZNnDhRH3/8sdq1a%2Be6JABRQgBE1OXlWU%2BgJF13nVRc7LYeAPtmwIABGjdunP76178qKytLhYWFKiws1JYtW1yXBqCWGAOImFi3TurQQVq1SnrkEemOO1xXBGBvBQKBnX79pZde0pVXXrnHx7MMDJC4CICImZdflq68UtpvP2n%2BfOmAA1xXBCCeCIBA4uISMGKmXz9bHHrzZmngQNfVAACACgRAxEwgYMvCZGRI77wjTZ7suiIAACARABFjhx8uDRpk5zfeKDF2HAAA9wiAiLn77pPatJGWLJEefth1NQAAgACImGvYUBo50s4ffVRauNBtPQAA%2BB0BEHHRq5d05pm2JuCNN0rMPQcAwB0CIOIiEJCeflrKzJT%2B8Q/prbdcVwQAgH8RABE3hxwi3X67nd98s7Rpk9t6AADwKwIg4uquu6QDD5R%2B%2Bkl64AHX1QAA4E8EQMRV/frSqFF2/uST0oIFbusBAMCPCICIu3PPlf7v/6SSEtshhAkhAADEFwEQcRcIWC9g3brShx9Kf/%2B764oAAPAXAiCcOOQQ6dZb7fyWW6StW93WAyB68vPzlZubq7y8PNelANiFgOdxAQ5ubNwodeggLV8uPfSQdPfdrisCEE2RSEShUEjhcFjBYNB1OQCqoAcQzjRsaDuDSLZF3PLlbusBAMAvCIBwqk8fqWtXWxNwyBDX1QAA4A8EQDgVCEgjRtj52LHSnDlOywEAwBcIgHCua1epd29bDmbwYNfVAACQ%2BgiASAgPP2zLwnz0ke0VDAAAYocAiITQrp10/fV2fvfdLA4NAEAsEQCRMO6%2B22YGf/ml9PbbrqsBACB1EQCRMJo3lwYNsvOhQ6XycqflAACQsgiASCi33CJlZUlz50qTJrmuBgCA1EQAREJp3Fi64QY7f%2BQRxgICABALBEAknIEDpcxM6fPPpRkzXFcDAEDqIQAi4bRsKV1%2BuZ2PHu22FgAAUhEBEAlpwAA7TpworVrlthYAAFINARAJ6eijpWOPlUpKpAkTXFcDAEBqIQAiYVVcBn79dbd1AACQagiASFi9etlxxgxp7Vq3tQAAkEoIgEhYBxwgHXaYLQj9r3%2B5rgYAgNRBAERCO/FEO37xhds6ANRcfn6%2BcnNzlZeX57oUALtAAERC69TJjgsXuq0DQM0NGDBA3377rWbNmuW6FAC7QABEQsvOtuMvv7itAwCAVEIARELLyLBjWZnbOgAASCUEQCS0lSvt2LSp2zoAAEglBEAktJkz7Xj44W7rAAAglRAAkbDWr5feecfOzzzTbS0AAKQSAiAS1r33Shs3Wu/fSSe5rgYAgNRBAERCeukl6emn7fyJJ6RAwG09AACkEgIgEkp5uTR8uPS739ntO%2B6Qund3WxMAAKkmw3UBQIVvv5UGDJD%2B%2BU%2B7feON0sMPOy0JAICURA8gnPv6a6lfP6lzZwt/%2B%2B0nPfusNGqUlMYnFACAqKMHEE6sXi1NnCi9/LI0Y0bl1y%2B4wMb8tWvnrjYAAFIdARBxUV5uPX1Tp0pTpkiffFK5u0d6utSrl3T77dJxx7mtEwAAPyAAIibWrZNmz5Y%2B/1z67DPp00/ta1UddZT0299KfftKrVq5qRMAAD8iAKJWNm%2BWFi6U5s%2BX5s2TvvlG%2BuoraenSHe/bsKF0yinSWWdJ55zDZV4AAFwhAGK3ysulwkLpxx8t1C1ZIi1eLH3/vbRokfTTT7t%2BbLt2Ul6e1LWrdOKJ0tFHS3XqxK10AACwCwRAn/I822pt5UoLeCtWWFu%2BXPr5Z2vLltmxpGT3z9WkidSxo9Spk%2B3a0bmzXd5t3Dg%2BPwsAANg7BMAUUVQkrV0rrVljx9Wr7Xz1amurVln75ZfKVlxcs%2BdOS5P231868EBrBx0kHXywdMgh0qGHSs2axfInAwAA0UYATBBFRVI4LEUidqza1q/fsa1bV9nWrrWxePsiGJSys20SRqtWUuvW1tq0sZaTY7cz%2BKQAqKH8/Hzl5%2BerrGKqP4CEE/A8z3NdRKp7/31b827DhsoWiVQeI5Ga98btTlqaXXZt2rSyNWtmrXnzytaihdSypbV69Wr/ugCwM5FIRKFQSOFwWMFg0HU5AKqgXycOvv5aev75mt23YUMpFKreGjWqPDZqZCFv%2B9akid2HnTMAAMCeEADj4JRTpAcekLKyKlswWHmsaFlZtigyAABALHEJGAAQE1wCBhIXFwwBAAB8hkvAABKC50lbtkibNtlx82Y7bt1qs%2BSLiytbaWllKyuzBcvLy%2B05ql7TCASspaXZ8Iq0NJvRnpFhi5JnZEiZmVLdutYyM21iVEXbbz%2Bpfn37eiDg7r0BgGgjAAKIqrIyW3OysNAWGv/ll8r1KNessaWLKpYzqlj6aMMGC36JOiAlLc3CYIMG1rKybMJWxTjeUMiOFRO1qk7WatLEZuQ3bsxySgASB7%2BOAOwVz7NdYxYskL77zrYGXLLEtgtctsy%2BV9vl3zIzLXBV9MRV9NJlZlrPXUXvXUZG9d69ih6/qrVW9A5WtIqew5ISa1V7FouKrMdx61brfaz4OcrLpY0brdVG48aVyzK1aGHHiiWZsrOrr8m53361ey0A2B0CIIBdKiuTvvlGmjVL%2BvJLW9Lom2%2Bs1253AgELN9nZFnQq1qNs2tR6xCp6yarOgM/KquxhS5TljIqLKy9Hb95cGQIrWsU6nlUXcK/o3Vy7tnKh9nDYnq9i8fZFi/b82qFQ9UXZKxZmP%2BAAqW1bOxISAewrZgEDqGbRImnyZGnqVOmTT%2Bzy7PbS06V27WwrwIMOsvO2bS2gtGljPVpc7qxUWlq5RWPVrRkrLpFX3ZO7sLDmO/s0b27bM7ZrZ/8dKrZpPPhg%2B2/hOkgzCxhIXARAAFq9Who7Vnr1VevlqyorS8rLk447TjrySOmIIyz4ZWY6KTXleZ71KC5fLv38s/TTT3Zctszajz9a21MvbGamBcFDD5Xat5c6dJA6drTWtGl8fhYCIJC4CICAj61aJT34oDRmjI17k6zn7tRTpR49pF//2kIfC5QnnnXrpKVLrS1ZYmMxFy%2BWvv/ebpeU7PqxzZtLublSp07WDj/cWpMm0a2RAAgkLgIg4FOvvy5de60FCUk65hjpmmukiy%2BOfhBAfJWVWS/hokXSwoU2YWfBAum//7VexF3Zf3/r4T3ySOmoo6wdeui%2BX0omAAKJiwAI%2BMWcOdJTT0kzZ6pww34a%2BdNFelb9deBRjfXYY9JvfsNad36wcaMFwW%2B/tfbNN9Z%2B%2BGHn92/Y0ALhscfaMIDjjrPLybsNheGwNGaMIq%2B9plBBgcK9eyt46632YAAJgQAI%2BMFrr0n9%2BtlshCpWNzpYjeZMV0bb/R0VhkQRiUhz50pffVXZvv7aZkFvLyvLAmGXLta6drWlayTZjJZTT5UWLFBEUkhSWFIwPV164QXpiivi90MB2CUCIJDq1q61qbk7%2B5dcki66SHrjjfjWhKRQVmaXjr/80toXX0gFBTufpdy2rXTCCdIfv%2BurjrPGSVL1ACjZYo7LltnaQACcIgACqW70aOmmm3b9/YwMm3LavHn8akLSKi21S8ezZkmffy7NnGmXkD1PCmm9CpWteiqStJMAKEmPPioNHuymeADbsFJXLXiepw07WyQNSCT//e/uv19aavdhXRfU0IEHWrv4YrsdiVgP4YJJ81X8YpGK/3e/yHZHSSr42wIVHxXRMcfYji6AS1lZWQr4dPAzPYC1UDHDDQAAJB8/z1AnANbC9j2AeXl5mjVr1l4/TyQSUU5OjpYtW7bPH8R9fe1oPN7Va7t%2B35LmPV%2B92lb/3dXCcD16SBMmxOa1o/x4l69d28%2BbH963oqIipV19teq8954k6/nLkbRMdgm4LJCuQafP07tftNq2/FCF7GzptNNsNvppp1UuRZQMP3e0H5vMn7XaPj7e75ufewC5BFwLgUCg2ocsPT29Vn9JBIPBfX58bV%2B7No93%2BdqSu/ctad7zYFDKz5f697eBWlV4%2B%2B%2BvQH6%2B3ScWrx3lx7v%2BrEn7/nnzzfv2zDM2nbjKujJBScFAQBo9Si8P6KDycptl/NFH1v71L9sC769/tRYISMcfb3%2BbFBcfoYYNg/u0FmEyv%2BdScn7Wavt4l%2B%2Bb3yTIluupYcCAAUn72rV5vMvXrq1k/bn3%2BrF/%2BIM0fbrUq5dKW7bSIrXTw7pL95/7hW0kG8vXjuLj/fpZq%2B3j4/raOTk2Q2TIEJX977NV0r279PHH0v%2BeJy1NOvpomwvy4Ye2GPnUqdJtt9mOJJ5nE0yGDpWWLn1DrVtLV18tTZy4872po1J3lB/v189abR/v8n3zGy4BJwBWy983vG/7xt63fpLekWT7/15%2BuduakgGft733008/bbsk16ZNm714nPTBB9KUKRYMN26s/F7dunaJuGdPa/un4BKWfNb2De/b3kkfOnToUNdFwLq9u3XrpowMrsrvDd63fZOe/p3y8k7SjBlpevdd2%2BmhY0fXVSU%2BPm97p6ioSI899pjuuusuNWjQoMaPCwZta8JLL7VewW7dpMaNbTjr6tXSd99JkydLTz5px9WrpaZNpWbNUmc3Gz5r%2B4b3reboAQR8qrzcNmUYN86WAnz5ZalPH9dVIZVEu0fG82zFokmTrH32WfVhre3bS716WTvuuNQJg0AsEAABHystla68Uho/3m4/%2BKB09938w4noiPUluZUrLQi%2B845NJikurvzeAQdYELzwQtuhZF8mkQCpjAAI%2BFx5uXTrrdLIkXb7ootsy1aG0KC24jkmKxKxMYMTJ9px06bK77VqZUHwooukk06S0tNjWgqQFAiAACRJzz4r3XijLRd4yCHSa6/ZZTRgX7kalL9li/SPf0hvvim9%2B64UDld%2Br2VLC4MXXyydfDJhEP5Fp7hjJSUluuOOO9S5c2c1aNBArVu3Vr9%2B/bR8%2BXLXpSWEiRMn6swzz1SzZs0UCAQ0Z86cPT5m7NixCgQCO7StW7fGoeLEt6v3tH9/W48tJ8cG2f/qV9IDD%2Bx6/ehU5Hmehg4dqtatW6t%2B/frq1q2b5s2bt9vHDB06dIfPWnZ2dpwqxs7Ury%2Bdd57NcF%2B5UnrvPRvq0KiR3X7mGenXv7YZxNddJ02bJpWVxbfGZ555Ru3atVO9evV07LHH6pNPPtnlffmdtnv/%2Bte/dO6556p169YKBAJ65513XJeUFAiAjm3evFmzZ8/Wvffeq9mzZ2vixIlauHChevbs6bq0hLBp0yadeOKJeuSRR/bqccFgUCtWrKjW6tWrF6Mqk8vu3tOuXaU5c%2BxSWWmp9Mc/Sl26SLNnOyjUgUcffVRPPvmknn76ac2aNUvZ2dk644wz9rjnd6dOnap91ubOnRunirEnmZnS2WdLL70k/fKL9P770lVX2azilSulv/zFlpVp3drC4P/7f/bZj6W//e1vGjRokO655x4VFBTo5JNPVo8ePfTjjz/u8jH8Ttu1TZs26cgjj9TTTz/tupTk4iHh/Oc///EkeT/88IPrUhLGkiVLPEleQUHBHu/70ksveaFQKA5VJbfdvafl5Z43bpznNW7seZLnpaV53k03ed769Q4KjZPy8nIvOzvbe%2BSRR7Z9bevWrV4oFPL%2B8pe/7PJx9913n3fkkUfGo8SkEw6HPUleOBx2XcoOios97/33Pe/qqz2vSRP7nFe0Zs087w9/8LwPP7T7Rdvxxx/vXXvttdW%2B1rFjR%2B/OO%2B/c6f35nVZzkry3337bdRlJgR7ABBQOhxUIBNSoUSPXpSStjRs3qm3btmrTpo3OOeccFRQUuC4pqQQC0mWXSfPnS71720SRp56yZTZeeCH%2Bl8viYcmSJSosLFT37t23fS0zM1OnnnqqZsyYsdvHLlq0SK1bt1a7du3Uu3dvLV68ONblopbq1JHOOss%2Bz4WFtvD0735TddqfAAAZdElEQVRn6wmuXi0995x05plSixa2XNLf/25jC2uruLhYX375ZbXPmSR17959t58zfqch2giACWbr1q2688471adPH1Yy30cdO3bU2LFjNWnSJL322muqV6%2BeTjzxRC1atMh1aUmnZUubDPLhhxb%2BfvlF%2Bv3vbZHeDz7YYWvhpFZYWChJatmyZbWvt2zZctv3dqZLly565ZVX9OGHH%2Bq5555TYWGhTjjhBK1Zsyam9SJ66tSxsPf88xYGp061MbEtWkjr10uvvCKdf74tNN2rlzR2rIXEfbF69WqVlZXt1eeM32mIBQJgnI0fP14NGzbc1qoO/C0pKVHv3r1VXl6uZ555xmGVbuzuvdkbXbt21eWXX64jjzxSJ598sl5//XW1b99eo0ePjnLFiS9a72n37tLcudITT9hA%2Bq%2B/lnr0sIH0n34a5aLjZPv3puR/s10C2y2C6HneDl%2BrqkePHrrwwgvVuXNnnX766Zo8ebIk6eWXX45d8YiZjAzp9NNtbODy5baF9sCBtq7g5s3S22/bGMKWLW1JmUcesf8f9vaPob35nPE7DbHAXilx1rNnT3Xp0mXb7f3/t5FlSUmJLrnkEi1ZskQff/yxL3v/dvXe1FZaWpry8vJ8%2BddyNN/TunWlW26xy2EPPyzl59s/jiedZP9g/vGPtqxGstj%2BvSkqKpJkPYGtWrXa9vVffvllh96a3WnQoIE6d%2B7sy89bqklPl045xdqIEVJBgV0K/vvfpa%2B%2Bsj9%2BPv1Uuusum1F85pn2x9JvfmO9hTvTrFkzpaen79DbtzefMz//TkP00AMYZ1lZWTrkkEO2tfr1628Lf4sWLdJHH32kpk2bui7TiZ29N9HgeZ7mzJlT7R91v4jFe9q0qfUELlok/eEP1mPy0UeV/1BOmZIcl4a3f29yc3OVnZ2tqVOnbrtPcXGxpk%2BfrhNOOKHGz1tUVKT58%2Bf78vOWygIBG/rwpz/ZTPkffrA/gv7v/2zZmZ9/ll580cbMNm8uHXWUdPPNtktJ1cvFdevW1bHHHlvtcyZJU6dOrfHnzM%2B/0xBFbuegoKSkxOvZs6fXpk0bb86cOd6KFSu2taKiItflObdmzRqvoKDAmzx5sifJmzBhgldQUOCtWLFi23369u1bbfbc0KFDvQ8%2B%2BMD7/vvvvYKCAu%2Bqq67yMjIyvM8//9zFj5BwavKe7o3Fiz3vmms8r27dylmUubme9/zznrdlS5SLj7FHHnnEC4VC3sSJE725c%2Bd6v/3tb71WrVp5kUhk231OO%2B00b/To0dtu33rrrd4///lPb/Hixd7MmTO9c845x8vKyvKWLl3q4kdICE8//bR32GGHee3bt0/YWcDRtHmzzRi%2B5RbPO/zw6jOKK1rHjjbj%2BNlnPW/YsA%2B8jIz63gsvvOB9%2B%2B233qBBg7wGDRps%2B8zwO23vbNiwwSsoKPAKCgo8Sd6TTz7pFRQUsJLGHhAAHatYimNnbdq0aa7Lc%2B6ll17a6Xtz3333bbvPqaee6l1xxRXbbg8aNMg74IADvLp163rNmzf3unfv7s2YMSP%2BxSeomryn%2B%2BKnnzzv1ls9Lyur%2BnIad93lecmShcrLy7377rvPy87O9jIzM71TTjnFmzt3brX7tG3bttp7demll3qtWrXy6tSp47Vu3drr1auXN2/evDhXnpgSeRmYWCos9LzXXvO8/v0977DDdh4IMzJKvLp1v/LS0l7w2rR5zHviia%2B8n3%2B2JZj4nbZ3pk2bttPfaVXfQ%2ByIreAARFU4bEtojB4tVaxrm5Zml8quucYmj2Qw%2BtgXXG0Fl2jWrJFmzJBmzpQ%2B/1z64ovq29NV1bChdOihth1ju3ZS27bW2rSxxaqbNrX/n/zC86SiIpuAs2mTHTdutPNdte7dpb0YteFbBEAAMVFaKk2aZOOkPv648uutW0v9%2BtnWXB06OCsPcUAA3DnPkxYvtkklX39tM%2By//Vb6/vs9r7GZkWHL07RsaRNNmja1XU0aNZJCISkYtBDZsKHUoIGNT6xXz1rdutbq1LHnSU%2B3FghYqAwEKsfvep6t/1lebjVVtNJSayUlla24uLIVFVW2rVutVT3fsqXyuGWLBbrtj9u38vK9e3%2BHD5duv33f/tv4CQEQQMwtWCCNGWPrqVUdEH/88dLll0uXXmr/qCG1EAD3TnGxhcBFiywgLl5sk01%2B/NEmmaxa5bpCt%2BrUsVC7q1YRes8/33oBsXsEQABxU1xsvYJjx9pC0hW9HWlpth/rpZdWLriL5EcAjK7iYtu/eOVKC4OrV9vl5bVrbcHqcFjasMEukW7cWNmrtmWL9cJV9NKVlFgv3t7861/RW5ieXtmDWKdO9V7FzMwdW716dqzaE1m/fuXt/faz8/32q2xVb1eEu/32Y%2BhItBEAATixcqU0YYI0frw0a1bl19PTpW7dpAsukHr2lHJynJWIWiIAJjbPsz/Cyst3DIMVl4UrGlIPARCAc999J73%2BuvTmmzYuqqqjj5bOOUc6%2B2zpuOMsICI5EACBxEUABJBQFi%2BWJk60Lbc%2B%2B6x6z0TTpja2p3t36YwzbPcFJC4CIJC4CIAAEtYvv9jOIpMnS//4hxSJVP9%2Bhw62H/Gvfy2deqrNjETiIAACiYsACCAplJTYOmoffihNnWprqW2/PET79rY38Ykn2jpg7dszfsklAiCQuAiAAJLSunXS9OnStGnSP/9pa6lt/9uscWNbaqZLFykvz8YQZmc7KdeXCIBA4iIAAkgJ69ZJn34q/fvfdvziC1twdnutWknHHCMddZS1I46QDj6YySWxQAAEEhcBEEBKKimRvvrKtt6aNcva/Pk7X/usXj0pN1fq1MmOhx0mdewoHXSQrW%2BGfUMABBIXARCAb2zcaKGwoECaM8fO582zhXJ3Jj3dQuChh1o7%2BGC7fdBBtj/rfvvFt/5kEykoUOiYYxResUJBrr0DCYUACMDXysps%2B61586zNn29twQLbSWF3WrSwIHjAAbZgdU6OLU2z//6253GrVrarge/84x/SnXdaAJQUDgYVvOYa6aGHbNsIAM4RAAFgJzzP9l9duND2Zv3uOwuKixdLS5bsuCTNroRCNvGkZUsLjC1aSM2b23Z3zZrZ2oZNmlhr3FgKBpN85vJHH0k9ekilpYpIFgAlBSWpVy/prbeclgfAEAABYC95nk06%2BeEHaz/%2BKC1bJv30k4XGn3%2BWli/f%2BSSUPQkELASGQtaCQWtZWdYqNryvukdqxf6p9etX7rtasRdr3bp2rNi3teKYnm57qwYCUX5zjj9%2B295%2BOwRAyQZlHn98lF8UwN4iAAJADHieFA5LK1bYvscrV9rC1r/8Iq1aJa1eLa1ZU9nWrdv1WMRYSk/feau6D2zFvrCBQPUmVT9vXfqj/r2s7bbn3lkAHBMarOFNH912n%2B2fp%2BprVbx%2B1boyMqzVqVMZZitCbkX4rVfPQnGDBhaYGzasDNKNGllr0sQCdlL3tgK1kOG6AABIRYFAZdg47LCaPWbrVmn9emvhsF1mrjhu3GhtwwZp06bKtmWLjVXcssXa1q3WiorsWFxc2Xb2535ZmbVoqKM9J9ji8GYtDkfn9WorPd0uw7doYZfpW7Wy8Ztt2tjYznbtrPlyHCdSHj2AAOATpaW2PE5JiZ1XbRVBsKzMdlip2jyv8li1SduFypISHX1ejuquXSlp5z2Ai4aO05ozL6v2uO2ft%2BK1qtZSVla93oqfoSLcVgTerVsrQ/GmTZXBORKxtn699bbuaYJPVW3a2LaDFUsFHXGEtQYN9v2/BeAaARAAED0PPijde6%2BknQTAnBybTZMAM4GLiuwy/OrVdnm%2BsNDGbf78s43p/OGH3U/2SUuznt0uXaRf/Uo6%2BWTbejDqYyqBGCEAAgCip7xcuuYa6YUXqgfAtm2lyZOtCy1JeJ6Nz1y0SPrvf6Vvv7UtB7/6ygLj9rKzpdNOk7p3l846y2Z%2BA4mKAAgAiL758xV55RWFHnlE4ZdeUvCyy1JqW5UVK2yy88yZtvXg559br2JVxx8vXXCBdOGFtpA4kEgIgACAmPDTVnBbt1oYnDpV%2BvBD6csvq3//mGOkyy%2BX%2BvShZxCJgQAIAIgJPwXA7a1YIU2aZOtef/xx5UzrjAzpnHOk/v3tUjHL0MAVAiAAICb8HACrWrVKeuMN6eWXpf/8p/LrBx8s3XijdPXVtsg3EE8EQABATBAAdzRvnjRmjIXB8P/WQwyFpOuukwYN4vIw4ocACACICQLgrm3aJL36qjRihO03LdmC09deK91%2Bu80oBmKJ0QcAAMRZgwYW9ubPl95%2B22YMb9ligfDgg6W77rJFq4FYIQACAOBIWpp0/vk2g/iDD2xh6c2bpUcesSA4cqTtdAJEGwEQAADHAgHpzDOlzz6T/v53Wy977Vrp5pulzp2lKVNcV4hUQwAEACBBBAJSz56228iYMVKLFjZG8Oyz7etLl7quEKmCAAgAQIJJT5f%2B8Afbhu6222z9wHfftZ7Bxx6TSktdV4hkRwAEACBBBYMW%2BL76Sjr1VBsfePvtNmlkzhzX1SGZEQABAEhwubnStGnSiy9KjRtLBQVSXp40dKhUUuK6OiQjAiAAAEkgEJCuukr69lupVy%2B7DPynP0ldu9pyMsDeIAACAJBEsrOlN9%2BUXntNatJEmj1bOuYYKT9fYmsH1BQBEAAQVfn5%2BcrNzVVeXp7rUlJWICD17i3NnWvLx2zdKt1wg3TeedLq1a6rQzJgKzgAQEywFVx8lJdLo0fb5JDiYqlNG%2BsdPOkk15UhkdEDCABAEktLkwYOlD7/XGrfXvrpJ6lbN5s9TBcPdoUACABACjjqKOmLL6Q%2BfaSyMusRvPBCKRJxXRkSEQEQAIAUkZUljRsn/fnPUt260ttv25qB//2v68qQaAiAAACkkEBAuvZa6ZNPbDzgggUWAt9913VlSCQEQAAAUtDxx0tffimdcoq0YYPNEB4%2BnHGBMARAAABSVIsW0kcfWY%2Bg50l33ildeaVUVOS6MrhGAAQAIIXVqWNjAvPzpfR06ZVXpDPOkNascV0ZXCIAAgDgA9dfL73/vhQM2vjAX/1K%2Bu4711XBFQIgAAA%2BccYZ0owZUtu20qJF0gknSP/5j%2Buq4AIBEAAAH%2BnUSfrsM9s/eNUq6de/lqZMcV0V4o0ACACAz7RqJf3zn9JZZ0mbN0s9e0ovv%2By6KsQTARAAAB/KypImTZL69rWdQ668UnrySddVIV4IgACAakpKSnTHHXeoc%2BfOatCggVq3bq1%2B/fpp%2BfLlrktDlNWpI40dK91yi92%2B9VZpyBDWCvQDAiAAoJrNmzdr9uzZuvfeezV79mxNnDhRCxcuVM%2BePV2XhhhIS5Mef1x6%2BGG7/dBD0sCBUnm527oQWwHPI%2BcDAHZv1qxZOv744/XDDz/ogAMOqNFjIpGIQqGQwuGwgsFgjCtENPz5z9KAAdYD%2BLvfSc8%2Ba2sHIvXQAwgA2KNwOKxAIKBGjRq5LgUxdN11NhkkLU164QXpiiuk0lLXVSEWMlwXAABIbFu3btWdd96pPn367LYnr6ioSEVV9hiLRCLxKA9R1revVK%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%2BVgHxEAAQDAXrnppsrev4EDpVdfdVsP9h4BEAAA7LUhQyz8SdYrOGWK23qwdwiAAABgrwUC0pNPSpdfbjODL7pImjnTdVWoKQIgAADYJ2lp0osvSmedJW3ZYmsGLljguirUBAEQAADsszp1pDfekPLypDVrLAwWFrquCntCAAQAALXSsKE0ebJ0yCHS0qXS2WdLGze6rgq7QwAEAAC11ry59MEHUrNm0uzZ0qWXSqWlrqvCrhAAAQBAVBx8sPTee1K9ejYr%2BKabbNcQJB4CIAAAiJouXaS//tVmCf/5z7ZjCBIPARAAAETVBRdIjz9u57fdJk2a5LYe7IgACAAAou7mm6X%2B/e0ScJ8%2B0pw5ritCVQRAAEBU5efnKzc3V3l5ea5LgUOBgDR6tHT66dKmTdK557I8TCIJeB7DMwEA0ReJRBQKhRQOhxUMBl2XA0fWr5e6drUFort2laZNs0kicIseQAAAEDONGknvvis1bmxbxVVcFoZbBEAAABBThx4qvf66lJ4uvfKK7SEMtwiAAAAg5k4/vTL43X67NHWq23r8jgAIAADi4sYbpauuksrLbaeQxYtdV%2BRfBEAAABAXFYtDd%2BkirVsnnX%2B%2BzRBG/BEAAQBA3GRmSm%2B9JbVsKc2dK/3%2B90wKcYEACAAA4mr//aU33pAyMqQJE6RRo1xX5D8EQAAAEHcnn1w5KeS226RPPnFbj98QAAEAgBM33GDbxJWVSZdcIq1Y4boi/yAAAgAAJwIBacwYqVMn2ybut7%2BVSktdV%2BUPBEAAAOBMgwbSxIlSw4bS9OnSkCGuK/IHAiAAAHCqfXvpxRftfPhwafJkt/X4AQEQAAA4d/HFtlC0JPXrJ/34o9t6Uh0BEAAAJITHHpPy8qS1a6XevaWSEtcVpS4CIAAASAiZmdLf/iaFQtJnnzEeMJYIgAAAIGG0aye98IKdP/qo9MEHbutJVQRAAEBU5efnKzc3V3l5ea5LQZK68ELp%2BuvtvF8/WyIG0RXwPHbgAwBEXyQSUSgUUjgcVjAYdF0OkszWrVKXLtLXX0unny59%2BKGURrdV1PBWAgCAhFOvnu0TXL%2B%2B9NFH0uOPu64otRAAAQBAQjrsMOmpp%2Bz8nnukL75wW08qIQACAICE9bvfSRddZFvE9ekjbdzouqLUQAAEAAAJKxCQnn1WatNGWrRIuvlm1xWlBgIgAABIaE2aSK%2B%2BamHw%2Beeld95xXVHyIwACAICE162bNHiwnf/%2B9ywNU1sEQAAAkBTuv1866ihpzRobG8hCdvuOAAgAAJJCZqY0bpwdp0yRxoxxXVHyIgACAICk0amTNGyYnd9yi/Tdd27rSVYEQAAAkFQGDrQxgZs3S1dcIZWVua4o%2BRAAAQBAUklLk8aOlbKypBkzpCefdF1R8iEAAgCApNO2rTRypJ0PGSLNm%2Be2nmRDAAQAAEnpqquks8%2BWioulK6%2B03UJQMwRAAACQlAIBmwncqJHtEzx8uOuKkgcBEAAAJK3WraXRo%2B38T3%2BSvvnGbT3JggAIAACS2mWXSeeeK5WU2GVhLgXvGQEQAAAktUBA%2Bstf7FJwUZG0cqXrihJfhusCAACpJT8/X/n5%2BSpjcTbEUevW0scf20LRdeu6ribxBTyPnfQAANEXiUQUCoUUDocVDAZdlwOgCi4BAwAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABALvVv39/BQIBjRw50nUpAKKEAAgA2KV33nlHn3/%2BuVq3bu26FABRRAAEAOzUzz//rBtuuEHjx49XnTp1XJcDIIoyXBcAAEg85eXl6tu3rwYPHqxOnTrV6DFFRUUqKiradjsSicSqPAC1RA8gAGAHw4cPV0ZGhm666aYaP2bYsGEKhULbWk5OTgwrBFAbBEAA8Lnx48erYcOG29r06dM1atQojR07VoFAoMbPc9dddykcDm9ry5Yti2HVAGoj4Hme57oIAIA7GzZs0MqVK7fdfuONN3TPPfcoLa2yj6CsrExpaWnKycnR0qVLa/S8kUhEoVBI4XBYwWAw2mUDqAUCIACgmjVr1mjFihXVvnbmmWeqb9%2B%2Buuqqq9ShQ4caPY/nedqwYYOysrL2qicRQOwxCQQAUE3Tpk3VtGnTal%2BrU6eOsrOzaxz%2BJCkQCNDzByQoxgACAAD4DJeAAQAAfIYeQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DP/Hxto7Cr7QetqAAAAAElFTkSuQmCC'}