Genus 2 curve data - 249.a.6723.1

Formats: - HTML - YAML - JSON - 2026-01-15T11:19:19.170044

• 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': '1229180703233001291', 'abs_disc': 6723, 'analytic_rank': 0, 'analytic_rank_proved': False, 'analytic_sha': 1, 'aut_grp_id': '[2,1]', 'aut_grp_label': '2.1', 'aut_grp_tex': 'C_2', 'bad_lfactors': '[[3,[1,2,0,-3]],[83,[1,-1,83,-83]]]', 'bad_primes': [3, 83], 'class': '249.a', 'cond': 249, 'disc_sign': -1, 'end_alg': 'Q', 'eqn': '[[2,3,1,1,0,-1],[1,0,0,1]]', 'g2_inv': "['324526850403/83','25281736298/249','-4178776252/747']", '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': "['1932','87897','65765571','860544']", 'igusa_inv': "['483','6058','-161212','-28641190','6723']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '249.a.6723.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.1315495070114787592134013430121752606933130670903651314', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.30.3'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_maximal_primes': [2, 7], 'non_solvable_places': [], 'num_rat_pts': 6, 'num_rat_wpts': 2, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '25.783703374249836805826663230', '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': 4, 'torsion_order': 28, 'torsion_subgroup': '[28]', 'two_selmer_rank': 1, 'two_torsion_field': ['4.2.1328.1', [1, -2, -3, 0, 1], [4, 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': '249.a.6723.1', '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': '249.a.6723.1', 'mw_gens': [[[[-3, 1], [-5, 2], [1, 1]], [[-7, 2], [-15, 4], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [28], 'num_rat_pts': 6, 'rat_pts': [[-1, 0, 1], [0, -2, 1], [0, 1, 1], [1, -1, 0], [1, 0, 0], [3, -14, 1]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  {'conductor': 249, 'lmfdb_label': '249.a.6723.1', 'modell_image': '2.30.3', 'prime': 2}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  local_root_number	numeric
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 51636
    {'cluster_label': 'c4c2_2_0', 'label': '249.a.6723.1', 'local_root_number': -1, 'p': 3, 'tamagawa_number': 4}
  • id: 51637
    {'cluster_label': 'c4c2_1~2_0', 'label': '249.a.6723.1', 'local_root_number': -1, 'p': 83, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '249.a.6723.1', 'plot': 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1vbsqVwy8o69vfweApvHeJvhwt2/tuTTy7mvMmLL5amTJF0mAA4cqTt3I2Q5vNJf/wh/fijbcw%2BZYrNMzxQUpJ06aXSZZdZKIyJcaZWAMGHAIigsXq19Oab0ttv2xyoalqv5WqgWBWRrsaMka6%2B%2BpjfJyvL/qFcv94m52/cWNA2bbLbzZtt5eexiI62DsqEhIK94CpXtvuqVCkIeP7rYoe545GZKd18s/TZZ8rcv98CYMWKin/iCem220rpTeEkn886dr//3tYBTZ5c%2BBeTcuWsh/2qq6Ru3WzhDQD3IgAi6OzaJQ0cKNV%2BZ7Ae2PdE0d/YurX000%2BH3O3zWbhbssRWIa9aZZPt16yxfQoP7Ew8mvh4W3mZlGS3B7aEhMItLi4IR1XXrVPmtGny9umjjC1bFF%2BlitMVIUCys6Xp06UJE6Tx4%2B0XLL%2BoKKlTJ6lXL%2Bnyy207GgDuQgA8AT6fT7t27XK6jLCV0/kyRc36sehv8Hgszf0vdWVmSo8%2Bav/Y/fXXkV87JsY2561WzcLdKafYrb/5Q14oTqjPzs5W9gH7uOzatUvJyclat26d4llu7Uo%2Bn7R4sYXBceMKz7stX96GiK%2B91haTsDUT3CQuLk6eoPvNPTAIgCcgMzNTXq/X6TIAAMBxyMjIcO0vxgTAE3A8PYCZmZmqUaPGCfXGNG/eXPPmzTuu557o84/nuXl5Ntfujz%2Bkm28eqquuGqSVKwuGZnNzD/%2B8XvpIw3WE%2BWq9eknDh%2Bd/OXOmzW3KybGvK1a0CfCdO1vPRrt2zn1uJ/r8Y3nuwT2AmzZt0rnnnqulS5eq2nGsnnby5z7R55/Ic53%2BsxqIz83nk37%2BWfr4Y%2BmzzwqvTq9QYYbee%2B88dehwbL2CbvjcSuP5Tn9uJ/p8J9/7RD47N/cARh79W1AUj8dz3H9Q4%2BPjj/u5ZcqUOaHfWE7k%2BUd67s6dNrT0228F7fffLfj5N3%2BWhuqNNwo/LzradiY5/fSCVqGCdHZyX8X1e0ueBQsOfTOvVxoypNAO0p07204xL78sffSR7Z83apS1MmWksmU/0vPPx6ttW9tI91iP2grWz7244uLijus1nPy5T/T5JfG5OfVnNVCfW7t21l57TRo71hZhTZ4s7dlzqa65xjZsv/126aabjm3hSLh/bqX1/HD8tyEQz5dO7LNzIwJgCOrfv79jz7/99v5av96C1tKlFviWLbN28J5kB4qKkk49VYqKWq2OHevo9NMLzkCtUaOovcqibY%2BYAQOkTz7JP58s9/zzFfnKK3Z46kFOO0165RXphRdsa4zx46Vvv7XVkX//fY4ef9x2kImKkpo1sz3TWrWydsopR/7ZnfzcT/S9T4STP/eJPt/Jz%2B1E3z/Qn1u5cjYP8Npr7c/Lrbcu1MKFZ2v1aju6ccgQ6Z//tD%2BOp556QqUdVSh9biX9fCffO5Q/dxw7hoADzD9vMNjnHfh8tiXK4sW2mnbxYgt8S5ce%2BUSKqlULDrWvV8%2Bu69WTatU6YGPn47F9u3YvWqTktm21%2BDg%2BuzVrLEtOnmzBcOPGQ7%2BnenU7p7hZM9uD8Oyzw%2BPs1fXr1%2BcPj1SvXt3pckJGqPxZLU179lhv%2Bssv298Bkv2y1qOHbcPZpMmhz%2BFzOz58bsePz%2B740AMYYNHR0Xr00UcVHR3tdCn5du2yv9x//dWOn/K3orZLKVPGeu8aNixoDRpYi4srpSIrVlRUy5b653F%2BdrVr2xDWTTdZuF21Spoxw%2BYNzpplIde/N%2BDYsQXPS0y0f%2BTOOENq3NhagwahtW2G//MKpv/nQkEw/lkNtAoV7M/MjTfaL1DPPSd99511yH/yiZ3YM3iw1KJFwXP43I4Pn9vx47M7PvQAuojPJ61bJ6WlWfvlF2srVx7%2B%2ByMibIi2cWOpUSNrycnWo1e2bGBrL227d9sxWz//bLcLFtj8xaL%2BdNSoYUHQ38NZt6612rVteDmY8NsxSlJamvT009Knn9oCL8nm3z7%2BuPWgAwgNBMAwlZdniy/mz7eD5BcssNvt2w///aecIp15pvV0%2BW8bNLB5QW6VlWU9of6e0SVLrB1prmNEhFSzpk2cr1PHAmGtWnZfzZq272DAfkldvlxKSVHmTz/Ju3ChMp5%2BWvG33x5a3ZcIWitWSE89ZWd6799v93XrJj35pP3SCCC4EQDDgM9nvXjz5hXuwTrcDjWRkTZke9ZZ1po0sVa5cuDrDlXbthVe7bxihbWVK4t3fFyVKjbf0L8RddWq1vwbUvs3oT6hXtZx46RrrpH27St8FnBysh0am5BwAi8OFFi5UvrPfywI5uXZvux9%2Bth9NWs6XR2AohAAQ1B6ujR3rjRnjoW%2BefNsC5aDlStn4a5p04JFDY0bB7AHymX8C2dWrbJjt/zHz61da0fQrVt34HY4R3fyyYWPmvOfKey/9p8tXLmyVKnSAb21O3dawvzfQbCFAqAk9e4tpaaW4E8O2C9FgwfbfoKS/T1zzz22WISZB0DwIQAGub17beh21ixp9mwLfX/%2Beej3RUdb2GveXDrnHFvJ2rDhCa68RYny%2Baz30L/YZONG2yB706aClp4ubd5c9ObYRxITY2Hw1rwUPbjujvz7Dw6AeVFltWHeJlWsW1EVKgTh%2BcUIaXPnSg88YKvtJfuF5amnpBtu4Jg5IJgQAAMoJydH//73v/XNN99o1apV8nq9at%2B%2BvYYNG6aqVatKslAwc2ZBW7hQ2rev8Ot4PLYYo0ULC3znnms9e%2BG2MONAX3zxhYYPH6758%2Bdr27ZtWrhwoc466yynyyoVeXm2AnvzZmtbt9q8wy1b7HrrVjvr2H%2B7bVvBHCxJekH36B69lP/1IT2Aks7SQv2isxQdXbgn8cDm73H09zomJNjpKuH%2Bj/iPP/6oZ599VvPnz9emTZs0duxYde/e3emygt7QoUP1xRdfaPny5SpXrrxOPfVubdlyv9assb%2BYzjnHNptu2dLhQoPMG2%2B8oTfeeENr1qyRJDVq1EiPPPKILrnkEmcLCzFDhw7VQw89pLvvvlsvvfTS0Z8AtoEJpD179mjBggUaPHiwmjRpom3bdujWW19Wq1YjdcEFD2nGDBsyPFiVKrZRccuWBaGv1LZbCVJZWVlq06aNevTooX79%2BjldTqmKiLAh3UqVLOgfTV6eHeG1bZsFQu/wU6SRRX//fkVoR1SilGN7a2/YYK24tVWubHMUExJs3mJiYsH8Rf9t1ap2akQo9i5mZWWpSZMm6tu3r6666iqnywkZ06ZNU//%2B/dW8eXPl5ubq4Ycf1qZNyRo6dImGDo3W/Pn299hNN0nDhtn/35CqV6%2BuYcOGqW7dupKk999/X5dffrkWLlyoRo0aOVxdaJg3b55GjBihM8880%2BlSQgo9gAGUk2M9ej/%2BaO2nnw5dlRsRYStwW7e21qqV7bofiv%2BQloY1a9aoTp06Yd0DeMI2bbLZ9/8bRz6kB/CSS%2BT7%2Bhvt3l0QGg9uB/Y0%2Bnsei1pBXpTo6IKFLtWq2bRE/22NGtaSkoK7R9Hj8dADeJy2bt2qhIQETZs2TfXrX6CBA6X337fHKleWnn/eFovwd9uhKlasqGeffVY33nij06UEvd27d6tp06Z6/fXX9cQTT%2Biss86iB7CY6AEMgE8%2Bkd5914Z0d%2B8u/Fi5cvu1d%2B9UDRx4ni6%2BOFotWzJhGifolFNsx94BAw59LCFBeukleTzWixwXZ1vVFEdOTkEg9A9Pb95s8xb9zT%2BXcccO611cvdpaUSIjLRDWqlXQatcu2EKnRg3msYaqjIwMSRZmEhOlkSOt9%2B%2B222zj%2Beuvt5XDI0bYf29I%2B/fv15gxY5SVlaVWrVo5XU5I6N%2B/v7p06aL27dvriSeecLqckMJfrQHw%2B%2B%2B2e75kKzvPO0%2B64ALp3HOzdc89F6hhw9M1bFg7Z4tEeLn7btul%2BsUXbQXRrl3SLbdIDz103HtzREVZtjzamcmSLV7atMmGlv2LXfyLX9avtxXRGzdaJ6V/tfThlCljofC006wn3L/h9umn231u3qcymPl8Pt17770677zz1PiATQHPO8%2B2qHr%2Beemxx%2Bx0kcaNpaFDpTvuKOpM8PC3aNEitWrVSnv37lVsbKzGjh2r5OLM/3C50aNHa8GCBZo3b57TpYQkAmApSk1N1S233KK8vIYqW/ZCvflmb11/fVNFRNiCkB49rpHPl6PXX3/d6VKDiv9z8/v22291/vnnO1hRiOrc2VpmpuT1Ss88E7Du5XLlCjbDLkpuroXEP/%2B0rXLWri0Ig6tX29f79tm2OqtWHfp8j8eybL16BedO%2B48krF6doUUn3XHHHfr11181Y8aMQx6LipIefFC6%2BmqpXz9p6lT7feWzz6T33rNg7zb169dXWlqadu7cqc8//1zXX3%2B9pk2bRgg8gnXr1unuu%2B/Wd999p3L8JnhcmANYinbt2qXNmzfnf12tWjWVL19eOTk56tmzp1atWqXJkyerErOhCynqc5OYA3g8QvUouLw86yVcudIC4MqVdrrNH3/YxtuZmUU/NzbWgmBysrVGjaynqWbNY%2BtlYg7gsbvzzjs1btw4/fjjj6pzlLHdvDxp%2BHDp/vtt28qYGOsdvPlmdwf49u3b67TTTtPw4cOdLiVojRs3TldccYXKHDCJeP/%2B/fJ4PIqIiFB2dnahx3AoegBLUVxcnOIOWq7rD38rVqzQlClTCH%2BHcbjPDe4TEWE9edWrSxdeWPgxn88WpqxYUXAiy2%2B/2WbEK1faXNuff7Z2oNhYC4JnnFFw7GGTJrZiGSfG5/Ppzjvv1NixYzV16tSjhj/J/hvfdpt1VPfta3sH3nqrNGGC9M477j2wxufzKTs72%2Bkyglq7du20aNGiQvf17dtXDRo00MCBAwl/xUAADKDc3FxdffXVWrBggSZMmKD9%2B/crPT1dkk2ULhvOG/mdoO3bt%2BvPP//Uxo0bJUm//fabJCkpKUlJSUlOlgYHeDwFexO2aVP4sX37LAQuXVrQliyxcLh7t22oPnt24efUqmVB8Oyzpfr1/9ZJJ61SUlKOJGn16tVKS0tTxYoVVZOzzYrUv39/ffTRR/ryyy8VFxeX/3eb1%2BvN78EvSp060uTJ0ksv2TTVCRMsoL//voXDcPbQQw/pkksuUY0aNbRr1y6NHj1aU6dO1cSJE50uLajFxcUVml8qSTExMapUqdIh9%2BPwGAIOIP/w5eFMmTJFbdu2DWxBIWTkyJHq27fvIfc/%2BuijGjJkSOALCiGhOgRc0nJyrMdw0SJrv/4q/fLL4U/WMX9JWiDpZ0nzddVVtTRmzAuuHpo8Ek8RH8x7772nG264odivs2iRnVa4eLF9fd990pNPhu9G9zfeeKN%2B%2BOEHbdq0SV6vV2eeeaYGDhyoDh06OF1ayGnbti3bwBwDAiAQ5giAR7ZzpwXBtDRrCxZYr%2BHhjuOrUqXg9J0WLey2YsXA1xzu/v7b5gWmpNjXLVrYdlq1ajlbFxBOCIBAmCMAHru9e60nasECaf58m0u4aNHhQ%2BHpp9spPS1b2ubtjRuzd2FJGTfO5gbu3GlbaI0aJV16qdNVAeGBAAiEqZSUFKWkpGj//v36/fffCYAnaO9e6yGcN0%2BaO1eaM8eGlA8WE1MQBs87T2zufoLWrJF69rTPXZIGD5YefTS4T5ABQgEBEAhz9ACWnu3bLQjOnm37bc%2BZc%2Bj2NBERtsDk/PNtA/jzz3fv6tbjlZ0t3Xuv5N8y9ZJLpNRU6xUEcHwIgECYIwAGzv79Nn/wp5%2BszZhx%2BFNOGjSwrW3atrXb4pyuAhsCvvlmmyNYt6705Ze2zyOAY0cABMIcAdBZGzZYEJw%2BXfrxR5tLeLD69aWLLpIuvthuK1cOfJ2hIi1N6t7dToqJi5M%2B%2Bkjq2tXpqoDQQwAEwhwBMLhs325hcNo0OwYtLc02tj7QWWdJ7dpJ7dvbkHFMjCOlBq2tW6UePewz9HjslMN//cvdp4cAx4oACIQ5AmBw27HDgsyUKbYZsn//O7%2ByZW1BSceO1s4%2B%2B9iOswtXOTnSnXfaUXKSdNNNNkcwKsrZuoBQQQAEwhwBMLRs3mxBcNIkawdvVF25sgXBTp2sJSY6U2cw8PmkV16xBSJ5eVKHDtKYMZLX63RlQPAjAAJhjgAYunw%2B22rm%2B%2B%2Bl776zXsJduwp/T9Omtir20kttw2Q3bo8yYYLUq5eUlWVHyH3zjZ0hDaBoBEAgzBEAw0dOjm0389//ShMn2kbVB6pY0c7O7drVegfddErJggVSly5SerqFv4kTpUaNnK4KCF4EQCDMEQDD1%2BbNFnS%2B/dZC4c6dBY8PZRPPAAAgAElEQVSVKSO1aSNddpm1%2BvWdqzNQ1qyx3tDly22PwAkTbP4kgEMRAIEwRwB0h9xc6x38%2BmsLPkuWFH68Xj3p8sulbt2kVq3Cd6h42zYLvLNmSeXLS59/bqEQQGEEQCDMEQDdac0a6auvrE2dasPHflWqWBC84grbbqZcOaeqLB179tg2Md98Y%2Bcyf/ihzREEUIAACIQpzgKGX2amDRWPH289hAcOFcfG2gKSK6%2B0OXSxsc7VWZJycqTrr5c%2B/tj2BxwxwraKAWAIgECYowcQB8rJsX0Hx461o9Q2bCh4LDraFo9cfbUNo550knN1loS8PKl/f%2BnNN%2B3rV16xvQMBEACBsEcARFHy8qR586QvvrD2xx8Fj0VF2X6DPXrY3MFQDYM%2Bn3T//dLzz9vXzz1np4YAbkcABMIcARDF4fPZOcWffy599pm0dGnBY1FR1jPYs6eFwVD738jnkwYPlp580r5%2B%2BmnpgQecrQlwGgEQCHMEQByPpUvtVI0xYwqvKI6OtjmD115rcwYrVHCuxmP12GPSkCF2/cwz1jMIuBUBEAhzBECcqKVLpU8%2BsfbbbwX3x8ZK3btbGOzQITTO4X38cenRR%2B36hReke%2B5xth7AKQRAIMwRAFFSfD7p11%2Bl0aOtrVlT8FjlyjZEfN11ts%2Bgx%2BNYmUc1ZIj1BkpSSop0%2B%2B2OlgM4ggAIhDkCIEqDzyfNni199JH06afSli0Fj516qgXB//s/24A62Ph80sMPS0OH2tfvvSfdcIOjJQEBRwAEwhwBEKUtN1f64QcpNdVWE2dlFTzWooX0j39I11wjVarkXI0H8/mke%2B%2BVXnpJioiwEHvVVU5XBQQOARAIcwRABFJWlu0vOGqU9N130v79dn9UlNS1q/W0XXJJcMwX9Pmkfv2kd96xer7%2B2uYyAm5AAATCHAEQTklPt5M4PvhASksruL9KFRsi7ttXOvNM5%2BqTLKBee62tdo6JkaZMkZo3d7YmIBAIgECYIwAiGCxaJL3/vvUMbt5ccH/TphYEe/eWKlZ0prbsbDv55PvvbTHLTz8F59xFoCQRAIEwxVnACEa5uXYu8ciRdjZxTo7dX7asnUf8z39K7drZvLxA2r1buugi6eefpTp1pJkzpaSkwNYABBIBEAhz9AAiWG3bZgtH3n1X%2BuWXgvtr17YgeMMNUo0agatnyxapdWtp5UrpnHPszOSYmMC9PxBIBEAgzBEAEQoWLLDFGKmpUkaG3RcRIXXubAs1unQJzMKRP/6wfQz/%2BsuGhceOlcqUKf33BQKNAAiEOQIgQsnff9t5xG%2B/bT1wfqecYr2CN91kPYSladYsGw7OzpYGDJBefLF03w9wAgEQCHMEQISqFSukt96yxSP%2BjaY9HqlTJ%2BmWW2xbmcjI0nnvTz%2B1vQslafhw6eabS%2Bd9AKcQAIEwRwBEqNu3z/YWHDFCmjSp4P6qVW14%2BKabpOrVS/59n3hCGjzYQuakSdKFF5b8ewBOIQACYY4AiHCycqUFwffek7ZutfsiIqRu3aTbbpPaty%2B5FcQ%2Bn%2B1X%2BPHHtj3Mzz9LtWqVzGsDTgvwQnsAfl988YU6deqkypUry%2BPxKO3AnXL/Jzs7W3feeacqV66smJgYdevWTevXr3egWiA4nHaa9PTT0rp1FswuvFDKy5PGjbOh4fr1peeftxXGJ8rjsYUpTZvaopArr7Q5ikA4IAACDsnKylKbNm00bNiwIr9nwIABGjt2rEaPHq0ZM2Zo9%2B7d6tq1q/b7z9cCXCo6WurVS5o6VVqyRLrzTik%2B3lbx3nefDQn37Wu9dieifHlbCVy5sq1U7t%2B/RMoHHMcQMOCwNWvWqE6dOlq4cKHOOuus/PszMjJUpUoVffjhh7rmf7PRN27cqBo1auibb75Rp06divX6DAHDLbKypI8%2Bkl5/vfDRc%2BeeK91%2Buy3qKFfu%2BF77hx%2Bkjh2tt/Hddy1cAqGMHkAgSM2fP185OTnq2LFj/n1Vq1ZV48aNNXPmTAcrA4JTTIwtClmwwE7yuO46O2Fk7lzbVLp6denBB6W1a4/9tdu1kx5/3K7795eWLi3R0oGAIwACQSo9PV1ly5bVySefXOj%2BxMREpaenF/m87OxsZWZmFmqAm3g8tpnzqFE2V/Cpp%2BxEkW3bbP7gqadKl19uZ//m5RX/dQcNkjp0sHmAvXvbPoFAqCIAAgGQmpqq2NjY/DZ9%2BvTjfi2fzyePx1Pk40OHDpXX681vNQJ5lhYQZBISLLitWmVz%2Bdq1s9A3frwN6TZsKL3ySsHpI0cSESF98IHNB/zlF2nIkFIvHyg1BEAgALp166a0tLT81qxZs6M%2BJykpSfv27dOOHTsK3b9lyxYlJiYW%2BbxBgwYpIyMjv61bt%2B6E6wdCXWSk1L277ee3bJl0xx1SXJz0%2B%2B/S3XdL1apJt94q/frrkV8nKcm2oZGkZ56x4WYgFBEAgQCIi4tT3bp181v58uWP%2BpxzzjlHUVFR%2Bv777/Pv27RpkxYvXqzWrVsX%2Bbzo6GjFx8cXagAKNGggvfqqtGGDlJIiJSfbApLhw6UmTaTzzrMziffuPfzzr7jCFpTk5dnikmMZRgaCBauAAYds375df/75pzZu3KguXbpo9OjRql%2B/vpKSkpSUlCRJuu222zRhwgSNHDlSFStW1H333adt27Zp/vz5KlPME%2BpZBQwcmc9n5w6npNh%2Bgrm5dn%2BlStL119tJIw0bFn7Opk1SvXrS7t12dvGVVwa%2BbuBE0AMIOGT8%2BPE6%2B%2Byz1aVLF0lSr169dPbZZ%2BvNN9/M/54XX3xR3bt3V8%2BePdWmTRtVqFBBX331VbHDH4Cj83iktm2lMWNshfBjj9mK4W3bpBdesB7CVq2kN9%2B0DaEl6ZRTpAED7PqllxwrHThu9AACYY4eQODY5eZK334rvfWW9M03kn/v9TJlpAsusNXAkZHSAw/YfdnZdguECgIgEOYIgMCJSU%2B3OYGpqdLChYc%2BnphoQ8JHWJwPBB0CIBDmCIBAyVm50noEp0%2BXdu2y%2BYODB0tt2jhdGXBsCIBAmCMAAgAOxiIQAAAAlyEAAgAAuAwBEAhTKSkpSk5OVvPmzZ0uBQAQZJgDCIQ55gACAA5GDyAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuEyk0wUAAOBqPp/000/S1q1Sw4ZSgwZOVwQXoAcQAACnfP%2B9VK%2BedP750pVXWgC86CJp7VqnK0OYYx9AIMyxDyAQpObNk847T9q379DHTj1VSkuT4uICXxdcgR5AAACcMHTo4cOfJK1apRm3fqgdOwJbEtyDAAgAgBMmTDjiw7s%2B%2BkqnnCL94x/S3LkBqgmuQQAEwhRnAQNBzOeT8vKO%2BC0nxeQqO1v68EOpRQupdWtp7NijPg0oFuYAAmGOOYBAkOrY0RaBFME37GnNu%2BgBpaRIo0cXjBY3aCANGiRde60UFRWgWhF2CIBAmCMAAkHqhx8sBB6uS69KFWnZMqlSJUlSerr06qtSSoqUkWHfUqeO9PDDNkRMEMSxYggYAAAntGsnvfeedNJJhe7eUOF06xn8X/iTpKQk6cknpT//lIYNs3y4erV0001S/frSyJFSbm6A60dIowcQCHP0AAJBbs8eafx4bV2yRX2eaqj/5rXXjBketWlT9FOysqQ335SeeUbassXuq1dPevxxqUcPKYLuHRwFARAIcwRAIHT06ye9/bbUpo00fbrk8Rz5%2B7OypNdfl55%2BWtq2ze5r0sR6Cy%2B99OjPh3sRAIEwRwAEQseGDdLpp0t//y19/rkdDlIcu3ZJL70kPfeclJlp951/vg0Xt25devUidNFJDABAkKhWTfrXv%2Bz6gQek7OziPS8uTho8WFq1Srr/fqlcOetBbNNGuuIKW08CHIgACABAEHngAVv0sXKlrfw9FpUq2bzAFSukG2%2B0uYDjxklnnCHdcoutJgYkAiAAAEElLk566im7/s9/pM2bj/01qle3uYSLF0uXXy7t3y%2BNGCHVrSs99pjNHYS7EQABAAgy118vnXOOzed7%2BOHjf52GDa0H8Mcf7TSRrCxpyBCbZ/juuxYM4U4EQAAAgkxEhPTKK3b97rvS/Pkn9nrnny/NmiV98ol06qnSpk02RHzOObYfNdyHAAiEKc4CBkJb69ZS7952bPBdd9ntifB4pJ49paVLbbXwSSdJv/witW8vdesm/f57ydSN0MA2MECYYxsYIHRt2GAnfWRlSaNGSdddV3KvvW2bzQd8/XUbCo6MlO68U3rkkUMOJ0EYogcQAIAgVa2a9NBDdv3AA9Lu3SX32pUq2TDz4sW2aXRurvTiizY/cPhw5geGOwIgAABB7N57bd7exo0Fq4NLUoMG0tdfSxMn2qKRv/6Sbr1VatpUmjat5N8PwYEACABAECtXTnrhBbt%2B/nnbH7A0dOpkcwJfftmGgH/9VWrb1uYN/vln6bwnnEMABAAgyHXrJnXoIO3bV3BSSGmIirIFJytWSLfdZquRx4yxXsL//Efau7f03huBRQAEACDIeTx21m%2BZMtKXX0qTJpXu%2B1WubItDFiyQLrjAziZ%2B5BEpOdnen%2BWjoY8ACABACEhOlvr3t%2BsBA2zRRmlr0kSaOlX6%2BGNbkLJ6tdS9uy0aWbGi9N8fpYcACABAiBgyxFbvLlkivflmYN7T45F69ZKWL5cefNCGiSdOlBo3lv79b2nPnsDUgZJFAAQAIEScfLL0%2BON2/eij0vbtgXvv2Fhp6FDbNqZTJ5uP%2BOSTBcfNMSwcWgiAAACEkJtvtt637dttI%2BdAq1dP%2BvZb6YsvpJo1bYXwFVdIl10mrVoV%2BHpwfAiAAACEkMhI27BZsoUay5cHvgaPx0Lf0qXSoEE2LPz111KjRrZaODs78DXh2BAAgTDFWcBA%2BGrf3nrccnNLd1uYo4mJsc2pf/1VatfOtol55BHpzDOlH35wri4cHWcBA2GOs4CB8PT779bjlpsrffed7RPoJJ9PGj3aTi5JT7f7eve2zauTkpytDYeiBxAAgBBUr17BtjD33uv82b0ej3TttTYkfccd9vVHH9km0m%2B%2BKeXlOVsfCiMAAgAQoh55xFYGL14svfOO09UYr1d69VVp7lzpnHOkjAw7VaRNGxsqRnAgAAIAEKIqVrQQKNntrl3O1nOgZs2kOXPsbOG4OGn2bAuEDz7I3oHBgAAIAEAIu/12qW5dafNm6ZlnnK6msDJl7GzhZcukK6%2B0%2BYpPP23b2Hz3ndPVuRsBEACAEFa2rIUqyRZcbNjgbD2HU62a9Pnndo5w9ep2pFynTlKfPtLWrU5X504EQMABOTk5GjhwoM444wzFxMSoatWq%2Bsc//qGNGzcW%2Br4dO3aoT58%2B8nq98nq96tOnj3bu3OlQ1QCC1RVXSOedJ/39tzR4sNPVFK1bN9s78O67bZHIqFF2ksgHH3CSSKCxDQzggIyMDF199dXq16%2BfmjRpoh07dmjAgAHKzc3Vzz//nP99l1xyidavX68RI0ZIkm6%2B%2BWbVrl1bX331VbHfi21gAHeYM0dq2dKCVVqa7cUXzObNk266qWBhSIcO0vDhUp06ztblFgRAIEjMmzdP5557rtauXauaNWtq2bJlSk5O1uzZs9WiRQtJ0uzZs9WqVSstX75c9evXL9brEgAB9%2BjZUxozRurc2Y5rC3Y5OTZs/dhjtol0hQrSE0/YvMEyZZyuLrwxBAwEiYyMDHk8Hp100kmSpFmzZsnr9eaHP0lq2bKlvF6vZs6cWeTrZGdnKzMzs1AD4A5PPWVHxU2cGBoncURF2argX3%2BV2ra11cH33iu1aiUtWuR0deGNAAgEgb179%2BrBBx9U796983vp0tPTlZCQcMj3JiQkKN2/zf5hDB06NH/OoNfrVY0aNUqtbgDBpW5d23NPkh54IHQ2Xz79dGnyZGnECNtHcN482zJmyBBp3z6nqwtPBEAgAFJTUxUbG5vfpk%2Bfnv9YTk6OevXqpby8PL3%2B%2BuuFnufxeA55LZ/Pd9j7/QYNGqSMjIz8tm7dupL7QQAEvcGDbd%2B9BQukTz91upri83ikfv1skcjll9vw8GOPSU2b2qbSKFkEQCAAunXrprS0tPzWrFkzSRb%2BevbsqdWrV%2Bv7778vNEcvKSlJmzdvPuS1tm7dqsTExCLfKzo6WvHx8YUaAPeoUkW6/367fvjh0OtBq1pVGjvWzhWuUkVassSGhO%2B/31Y5o2QQAIEAiIuLU926dfNb%2BfLl88PfihUrNGnSJFWqVKnQc1q1aqWMjAzNPeBX3zlz5igjI0OtW7cO9I8AIITcc4%2BUmCitWiW99ZbT1Rw7j0e65hrrDezd24ayn3tOOussacYMp6sLD6wCBhyQm5urq666SgsWLNCECRMK9ehVrFhRZcuWlWTbwGzcuFHDhw%2BXZNvA1KpVi21gABzV669L/ftLCQnSypVSbKzTFR2/r76Sbr1V2rjRwuFdd0lPPinFxDhdWegiAAIOWLNmjeoUsdnVlClT1LZtW0nS9u3bddddd2n8%2BPGSbCj5tddey18pXBwEQMCdcnKkBg2sF/CJJ2w4OJTt3GkrhN97z74%2B7TTp3XelCy5wtq5QRQAEwhwBEHCvjz6SrrtOio%2B349cqVnS6ohM3caItFlm/3r6%2B6y7b/obewGPDHEAAAMJUr152IkhmZsF5waGuc2dp8WI7RUSSXnmFuYHHgwAIAECYioiwuXKSBaWDjhsPWV6vLW6ZOFGqXl364w8bCv7Xv1gpXFwEQAAAwliXLlLr1nbUmj8MhotOnaw3sG9fyeeTXnhBuvhiu8aREQABAAhjHk9B8HvrLWnNGkfLKXFery0GmTBBOuUU6fbb7WfGkbEIBAhzLAIBIEkdOkiTJllv2bvvOl1N6cjMtFNQCIBHRw8gEKZSUlKUnJys5s2bO10KgCDwxBN2%2B/770u%2B/O1tLaYmPJ/wVFz2AQJijBxCA32WX2VBp795SaqrT1cBJ9AACAOASjz9utx9/bGfswr0IgAAAuMTZZ0tXXmmrZB97zOlq4CQCIAAALvLYYzZPbswY6ddfna4GTiEAAgDgIo0bSz162DW9gO5FAAQAwGUeecR6Ab/4QvrlF6ergRMIgAAAuEyjRlLPnnZNL6A7EQABAHAhfy/g2LHMBXQjAiAAAC6UnFzQC%2BjfHgbuQQAEAMClBg%2B2288/lxYvdrYWBBYBEAAAl2rUSLr6arv2HxUHdyAAAmGKs4ABFIe/F/DTT6Xly52tBYHDWcBAmOMsYABH07279OWX0v/9n/Thh05Xg0CgBxAAAJf797/t9uOPpZUrna0FgUEABADA5Zo1kzp3lvbvl4YNc7oaBAIBEAAA5PcCvv%2B%2BtG6ds7Wg9BEAAQCA2rSR2raVcnKk555zuhqUNgIgAACQJD30kN2%2B9Za0ZYuztaB0EQABAIAkqX17qXlz6e%2B/pZdfdroalCYCIAAAkGRnAw8aZNcpKVJGhrP1oPQQAAEAQL7LL5caNrTw98YbTleD0kIABAAA%2BSIipAcftOuXXpL27nW2HpQOAiAAACjk2mulmjWlzZulkSOdrgalgQAIhCnOAgZwvKKipH/9y66ffVbKzXW2HpQ8zgIGwhxnAQM4HllZUq1a0rZt0ujR0jXXOF0RShI9gAAA4BAxMdIdd9j1M89IdBeFFwIgAAA4rDvukMqXlxYskCZPdroalCQCIAAAOKzKlaUbb7TrZ55xthaULAIgAAAo0r332tYw330n/fKL09WgpBAAAQBAkerUkXr0sOvnnnO2FpQcAiAAADii%2B%2B6z29GjpXXrnK0FJYMACAAAjqhZM6ltW9sP8JVXnK4GJYEACAAAjsrfCzhihJSZ6WwtOHEEQAAAcFSXXCI1bGjh7%2B23na4GJ4oACIQpjoIDUJIiImxFsCS9/DLHw4U6joIDwhxHwQEoKXv3SjVrSlu3cjxcqKMHEAAAFEu5clL//nb9wgscDxfKCIAAAKDYbrtNio6W5s6VZs1yuhocLwIgAAAotoQE6brr7PqFF5ytBcePAAgAAI7JPffY7dix0po1jpaC40QABAAAx6RxY6l9eykvT3r1VaerwfEgAAIAgGPm7wV85x1p925na8GxIwACDhkyZIgaNGigmJgYnXzyyWrfvr3mzJlT6Ht27NihPn36yOv1yuv1qk%2BfPtq5c6dDFQNAgc6dpXr1pIwMaeRIp6vBsSIAAg6pV6%2BeXnvtNS1atEgzZsxQ7dq11bFjR23dujX/e3r37q20tDRNnDhREydOVFpamvr06eNg1QBgIiKku%2B6y61deseFghA42ggaChH/D5kmTJqldu3ZatmyZkpOTNXv2bLVo0UKSNHv2bLVq1UrLly9X/fr1j%2Bl12QgaQEnbvVuqVs2Oh/v6a%2BnSS52uCMVFDyAQBPbt26cRI0bI6/WqSZMmkqRZs2bJ6/Xmhz9Jatmypbxer2bOnFnka2VnZyszM7NQA4DSEBsr3XijXb/yirO14NgQAAEHTZgwQbGxsSpXrpxefPFFff/996pcubIkKT09XQkJCYc8JyEhQenp6UW%2B5tChQ/PnDHq9XtWoUaPU6geAO%2B6QPB7pv/%2BVfvvN6WpQXARAIABSU1MVGxub36ZPny5Juuiii5SWlqaZM2eqc%2BfO6tmzp7Zs2ZL/PI/Hc8hr%2BXy%2Bw97vN2jQIGVkZOS3devWlfwPBAD/c%2Bqp0mWX2fVrrzlbC4qPAAgEQLdu3ZSWlpbfmjVrJkmKiYlR3bp11bJlS73zzjuKjIzUO%2B%2B8I0lKSkrS5s2bD3mtrVu3KjExscj3io6OVnx8fKEGAKXpzjvtduRImw%2BI4EcABAIgLi5OdevWzW/ly5c/7Pf5fD5lZ2dLklq1aqWMjAzNnTs3//E5c%2BYoIyNDrVu3DkjdAFAc7dpJDRvaohC2hAkNBEDAAVlZWXrooYc0e/ZsrV27VgsWLNBNN92k9evXq0ePHpKkhg0bqnPnzurXr59mz56t2bNnq1%2B/furatWuxVwADQCB4PDYXUJJSUtgSJhQQAAEHlClTRsuXL9dVV12levXqqWvXrtq6daumT5%2BuRo0a5X9famqqzjjjDHXs2FEdO3bUmWeeqQ8//NDBygHg8Pr0keLjpd9/lyZNcroaHA37AAJhjn0AAQTKXXfZ2cCXXSaNH%2B90NTgSegABAECJ6N/fbidMkNascbQUHAUBEAAAlIj69aX27SWfT3rzTaerwZEQAAEAQInx9wK%2B/ba0d6%2BztaBoBEAAAFBiunaVatSQtm2TxoxxuhoUhQAIAABKTGSkdMstdv36687WgqIRAIEwlZKSouTkZDVv3tzpUgC4zE03SVFR0uzZ0sKFTleDw2EbGCDMsQ0MACf06iV98onUr580YoTT1eBg9AACAIASd/vtdpuaKmVkOFsLDkUABAAAJe7886XkZGnPHmnUKKerwcEIgAAAoMR5PNKtt9r1G2/Y3oAIHgRAAABQKvr0kcqXl5YskX76yelqcCACIAAAKBUnnSRde61dDx/ubC0ojAAIAABKjX8YeMwY2xwawYEACAAASk2zZtLZZ0vZ2dL77ztdDfwIgAAAoNR4PAUng4wYwWKQYEEABAAApap3bykmRvrtN%2BnHH52uBhIBEAAAlLK4OAuBEqeCBAsCIBCmOAsYQDC5%2BWa7/fxzFoMEA84CBsIcZwEDCAY%2Bn9S0qZSWJr34ojRggNMVuRs9gAAAoNR5PFK/fnb91lssBnEaARAAAATEddfZySBLl0qzZjldjbsRAAEAQEB4vVLPnnb99tvO1uJ2BEAAABAw/mHgTz6RMjOdrcXNCIAAACBgWreWGjSQ9uyRRo92uhr3IgACAICA8XikG2%2B0a4aBnUMABAAAAfWPf0iRkdK8edKiRU5X404EQAAAEFAJCVK3bnb9zjvO1uJWBEAAABBw/mHgUaOk7Gxna3EjAiAQpjgKDkAw69RJqlbNjoUbP97patyHo%2BCAMMdRcACC1cMPS089JXXuLH37rdPVuAs9gAAAwBF9%2B9rtd99J69c7W4vbEAABAIAj6taVLrhAysuTPvjA6WrchQAIAAAc889/2u1770lMSgscAiAAAHDMVVdJsbHSH39IM2Y4XY17EAABAIBjYmOlnj3teuRIR0txFQIgAABw1A032O2nn0pZWY6W4hoEQAAA4KjzzpNOO03avVv6/HOnq3EHAiAAAHCUxyNdf71dMwwcGARAAADgOH8AnDJFWrPG0VJcgQAIAAAcV7OmdPHFdv3hh87W4gYEQCBMcRYwgFDj7wX84AP2BCxtnAUMhDnOAgYQKnbvlpKSbCXwjBlSmzZOVxS%2B6AEEAABBITZWuvpqu37/fWdrCXcEQAAAEDT8w8Cffirt3etsLeGMAAgAAILGhRfagpCMDGn8eKerCV8EQAAAEDQiIqT/%2Bz%2B7/uADZ2sJZwRAAAAQVPr0sduJE6UtW5ytJVwRAIEgcMstt8jj8eill14qdP%2BOHTvUp08feb1eeb1e9enTRzt37nSoSgAIjAYNpObNpf37pY8/drqa8EQABBw2btw4zZkzR1WrVj3ksd69eystLU0TJ07UxIkTlZaWpj7%2BX40BIIz5/6pjU%2BjSQQAEHLRhwwbdcccdSk1NVVRUVKHHli1bpokTJ%2Brtt99Wq1at1KpVK7311luaMGGCfvvtN4cqBoDA6NVLioyU5s%2BXli93uprwQwAEHJKXl6c%2Bffro/vvvV6NGjQ55fNasWfJ6vWrRokX%2BfS1btpTX69XMmTMDWSoABFyVKlLnznZNL2DJIwACDnn66acVGRmpu%2B6667CPp6enKyEh4ZD7ExISlJ6eXuTrZmdnKzMzs1ADgFDkXw2cmirl5TlbS7ghAAIBkJqaqtjY2Pw2bdo0vfzyyxo5cqQ8Hk%2BRzzvcYz6f74jPGTp0aP6iEa/Xqxo1apTIzwAAgdatmxQXJ61dK/30k9PVhBcCIBAA3bp1U1paWn6bOXOmtmzZopo1ayoyMlKRkZFau3at/vWvf6l27TEIiDQAAAeZSURBVNqSpKSkJG3evPmQ19q6dasSExOLfK9BgwYpIyMjv61bt660fiwAKFXly0tXXWXXo0Y5W0u48fh8Pp/TRQBus23bNm3atKnQfZ06dVKfPn3Ut29f1a9fX8uWLVNycrLmzJmjc889V5I0Z84ctWzZUsuXL1f9%2BvWL9V6ZmZnyer3KyMhQfHx8if8sAFCafvhBat9eOvlkadMmKTra6YrCQ6TTBQBuVKlSJVWqVKnQfVFRUUpKSsoPdg0bNlTnzp3Vr18/DR8%2BXJJ08803q2vXrsUOfwAQ6tq2lapWlTZulL79Vure3emKwgNDwEAQS01N1RlnnKGOHTuqY8eOOvPMM/Uhy%2BEAuEiZMtK119p1aqqztYQThoCBMMcQMIBQt3Ch1LSpDf9u3ix5vU5XFProAQQAAEHtrLOkhg2l7Gxp7FinqwkPBEAAABDUPB6pd2%2B7Zhi4ZBAAAQBA0PMHwMmTpSPshY9iIgACAICgd%2BqpUsuWdiLIJ584XU3oIwACAICQ4O8F/OgjZ%2BsIBwRAAAAQEnr2lCIipLlzpZUrna4mtBEAgTCVkpKi5ORkNW/e3OlSAKBEJCZK7drZ9ejRztYS6tgHEAhz7AMIIJy89570z39KjRpJixc7XU3oogcQAACEjCuukMqWlZYskRYtcrqa0EUABAAAIeOkk6RLL7Xrjz92tpZQRgAEAAAhpVcvu/3kE4mJbMeHAAgAAEJK165ShQrSqlXSvHlOVxOaCIAAACCkxMRI3brZNauBjw8BEAAAhBz/MPCnn9rpIDg2BEAAABByOneWvF5pwwbpp5%2Bcrib0EAABAEDIiY6Wune3a4aBjx0BEAAAhKRrrrHbzz6T9u93tpZQQwAEwhRHwQEId%2B3bSxUrSlu2SNOmOV1NaCEAAmGqf//%2BWrp0qeaxRwKAMBUVJV15pV1/8omztYQaAiAAAAhZ/mHgzz%2BXcnOdrSWUEAABAEDIattWqlJF2rZNmjzZ6WpCBwEQAACErMjIgmHgTz91tpZQQgAEAAAhrWdPux07VsrJcbaWUEEABAAAIe3CC6WEBGn7doaBi4sACAAAQlqZMgwDHysCIAAACHn%2BYeBx4xgGLg4CIAAACHkXXFAwDDxlitPVBL9IpwsAAAA4UWXKSE88IcXFSa1bO11N8PP4fD6f00UAKD2ZmZnyer3KyMhQfHy80%2BUAAIIAQ8BAmOIsYABAUegBBMIcPYAAgIPRAwgAAOAyBEAAAACXIQACAAC4DAEQAADAZQiAAAAALkMABAAAcBkCIAAAgMsQAAEAAFyGAAgAAOAyBEAAAACXIQACYYqzgAEAReEsYCDMcRYwAOBg9AACAAC4DAEQAADAZQiAAAAALkMABAAAcBkCIAAAgMsQAAGH3HDDDfJ4PIVay5YtC31Pdna27rzzTlWuXFkxMTHq1q2b1q9f71DFAIBwQQAEHNS5c2dt2rQpv33zzTeFHh8wYIDGjh2r0aNHa8aMGdq9e7e6du2q/fv3O1QxACAcRDpdAOBm0dHRSkpKOuxjGRkZeuedd/Thhx%2Bqffv2kqRRo0apRo0amjRpkjp16hTIUgEAYYQeQMBBU6dOVUJCgurVq6d%2B/fppy5Yt%2BY/Nnz9fOTk56tixY/59VatWVePGjTVz5kwnygUAhAl6AAGHXHLJJerRo4dq1aql1atXa/Dgwbr44os1f/58RUdHKz09XWXLltXJJ59c6HmJiYlKT08v8nWzs7OVnZ2d/3VmZmap/QwAgNBEDyAQAKmpqYqNjc1v06dP1zXXXKMuXbqocePGuuyyy/Ttt9/q999/19f/374dqsQShnEc/i%2BrbFoEk4OIF2EQbGK3CHsPVrFosgkWb2Gx2TV5DSYFg0U0CMKiQRC2jG1BDp4Tjrjo%2BzwwYZjhm%2B9rP16Y8/O/rtW2bTqdzqfPDw8PMzc3N7mWlpa%2B%2BjgA/HAmgPANNjc3s7q6OrlfXFz8452mabK8vJzb29skycLCQsbjcZ6fnz9MAZ%2BenrK2tvbpt/b29rKzszO5b9s24/E4/X7/K44CwC8gAOEb9Pv9fwbYaDTKw8NDmqZJkqysrGR2djYXFxcZDAZJksfHx1xfX%2Bfo6OjTdXq9Xnq93tdtHoBfp9O2bTvtTUA1r6%2BvOTg4yNbWVpqmyd3dXfb393N/f5%2Bbm5tJLG5vb%2Bfs7CzD4TDz8/PZ3d3NaDTK5eVlut3ulE8BwE9lAghT0O12c3V1lZOTk7y8vKRpmqyvr%2Bf09PTDpPD4%2BDgzMzMZDAZ5e3vLxsZGhsOh%2BAPgv5gAAgAU4y9gAIBiBCAAQDECEACgGAEIAFCMAAQAKEYAAgAUIwABAIoRgAAAxQhAAIBiBCAAQDECEACgGAEIAFCMAAQAKEYAAgAUIwABAIoRgAAAxQhAAIBiBCAAQDECEACgGAEIAFCMAAQAKEYAAgAUIwABAIoRgAAAxQhAAIBiBCAAQDECEACgGAEIAFCMAAQAKEYAAgAUIwABAIoRgAAAxQhAAIBiBCAAQDECEACgGAEIAFCMAAQAKEYAAgAUIwABAIoRgAAAxQhAAIBiBCAAQDECEACgGAEIAFCMAAQAKEYAAgAUIwABAIp5B4m7f7igo3VGAAAAAElFTkSuQmCC'}