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g2c_curves • Show schema
{'Lhash': '351152264670607119', 'abs_disc': 200000, 'analytic_rank': 2, 'analytic_rank_proved': False, 'analytic_sha': 1, 'aut_grp_id': '[4,1]', 'aut_grp_label': '4.1', 'aut_grp_tex': 'C_4', 'bad_lfactors': '[[2,[1,2,2]],[5,[1]]]', 'bad_primes': [2, 5], 'class': '40000.e', 'cond': 40000, 'disc_sign': -1, 'end_alg': 'CM', 'eqn': '[[-2,-8,-10,-5,0,1],[0,0,0,1]]', 'g2_inv': "['50000','3750','-125']", 'geom_aut_grp_id': '[48,29]', 'geom_aut_grp_label': '48.29', 'geom_aut_grp_tex': 'GL(2,3)', 'geom_end_alg': 'M_2(CM)', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['20','-20','-40','8']", 'igusa_inv': "['100','750','-2500','-203125','200000']", 'is_gl2_type': True, 'is_simple_base': True, 'is_simple_geom': False, 'label': '40000.e.200000.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.719234727373731540911849298163001704648801924904', 'prec': 167}, 'locally_solvable': True, 'modell_images': ['2.90.7', '3.3240.15'], 'mw_rank': 2, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 8, 'num_rat_wpts': 0, 'real_geom_end_alg': 'M_2(C)', 'real_period': {'__RealLiteral__': 0, 'data': '11.350269367470155555076729393', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.12673438913', 'prec': 44}, 'root_number': 1, 'st_group': 'J(C_4)', 'st_label': '1.4.F.8.2a', 'st_label_components': [1, 4, 5, 8, 2, 0], 'tamagawa_product': 2, 'torsion_order': 2, 'torsion_subgroup': '[2]', 'two_selmer_rank': 3, 'two_torsion_field': ['8.0.4000000.1', [1, 0, -1, 0, 1, 0, -1, 0, 1], [8, 2], True]} -
g2c_endomorphisms • Show schema
{'factorsQQ_base': [['2.0.4.1', [1, 0, 1], -1]], 'factorsQQ_geom': [['2.0.8.1', [2, 0, 1], 1]], 'factorsRR_base': ['CC'], 'factorsRR_geom': ['M_2(CC)'], 'fod_coeffs': [16, 0, -8, 0, 4, 0, -2, 0, 1], 'fod_label': '8.0.64000000.1', 'is_simple_base': True, 'is_simple_geom': False, 'label': '40000.e.200000.1', 'lattice': [[['1.1.1.1', [0, 1], [0, 0, 0, 0, 0, 0, 0, 0]], [['2.0.4.1', [1, 0, 1], -1]], ['CC'], [1, -1], 'J(C_4)'], [['2.0.8.1', [2, 0, 1], [0, 0, 0, 0, 0, '-1/4', 0, 0]], [['4.0.256.1', [1, 0, 0, 0, 1], -1]], ['CC', 'CC'], [1, -1], 'C_4'], [['2.0.40.1', [10, 0, 1], [0, 0, 0, 1, 0, '-1/4', 0, '1/4']], [['2.0.4.1', [1, 0, 1], -1]], ['CC'], [1, -1], 'C_{4,1}'], [['2.2.5.1', [-1, -1, 1], [0, 0, 0, 0, '-1/4', 0, '1/8', 0]], [['2.0.4.1', [1, 0, 1], -1]], ['CC'], [1, -1], 'J(C_2)'], [['4.0.1600.1', [4, 0, 6, 0, 1], [0, 0, 0, '-1/2', 0, '1/4', 0, '-1/8']], [['4.0.256.1', [1, 0, 0, 0, 1], -1]], ['CC', 'CC'], [1, -1], 'C_2'], [['4.4.8000.1', [20, 0, -10, 0, 1], [0, -2, 0, '1/2', 0, '-1/4', 0, '1/8']], [['1.1.1.1', [0, 1], 1]], ['M_2(RR)'], [4, 0], 'C_{2,1}'], [['4.0.125.1', [1, -1, 1, -1, 1], [0, 0, '1/2', 0, 0, 0, 0, 0]], [['1.1.1.1', [0, 1], 2]], ['HH'], [1, 1], 'J(C_1)'], [['8.0.64000000.1', [16, 0, -8, 0, 4, 0, -2, 0, 1], [0, 1, 0, 0, 0, 0, 0, 0]], [['2.0.8.1', [2, 0, 1], 1]], ['M_2(CC)'], [1, -1], 'C_1']], 'ring_base': [1, -1], 'ring_geom': [1, -1], 'spl_facs_coeffs': [[[-1741500, 648000, 631800, -234900], [3684366000, -1369645200, -1332903600, 495501300]]], 'spl_facs_condnorms': [25], 'spl_facs_labels': ['4.4.8000.1-25.1-b2'], 'spl_fod_coeffs': [20, 0, -10, 0, 1], 'spl_fod_gen': [0, -2, 0, '1/2', 0, '-1/4', 0, '1/8'], 'spl_fod_label': '4.4.8000.1', 'st_group_base': 'J(C_4)', 'st_group_geom': 'C_1'} -
g2c_ratpts • Show schema
{'label': '40000.e.200000.1', 'mw_gens': [[[[1, 1], [1, 1], [0, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]], [[[1, 1], [1, 1], [0, 1]], [[-1, 1], [0, 1], [0, 1], [-1, 1]]], [[[2, 1], [2, 1], [1, 1]], [[-2, 1], [-1, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.3559977375349680282262798969018', 'prec': 110}, {'__RealLiteral__': 0, 'data': '0.3559977375349680282262798969020', 'prec': 110}], 'mw_invs': [0, 0, 2], 'num_rat_pts': 8, 'rat_pts': [[-3, 11, 1], [-3, 16, 1], [-1, -2, 2], [-1, 0, 1], [-1, 1, 1], [-1, 3, 2], [1, -1, 0], [1, 0, 0]], 'rat_pts_v': False} - g2c_galrep • Show schema
- g2c_tamagawa • Show schema
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g2c_plots • Show schema
{'label': '40000.e.200000.1', 'plot': 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3XucTfXi//H3NjMGY2a7ZVwal3OSTCgnI3SPSEm6kCOj20Hl0kSK/Io6J5M6irIJXZRcupxIcpz0rSblkjDlUuSkXCcl9riOMbN%2Bf3yOmSYjZszen733ej0fj/WYZa89e793Rt4%2Ba30%2By%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%2BudatW2cxMQAEDwUQQMTZs2ePLrroIsXExOjf//631q9fr7Fjx6pKlSoFz3nqqaf0zDPPaMKECVqxYoVq1aqlq666Svv27bOYHACCw%2BM4jmM7BACUpWHDhunzzz/X4sWLiz3uOI7q1KmjtLQ0PfTQQ5KknJwcJSYmasyYMerXr99J38NxHO3bt0/x8fHyeDxlmh8AAo0RQAARZ968eWrZsqW6deummjVrqkWLFpo6dWrB8c2bNysrK0sdOnQoeCw2NlaXXXaZlixZUuxr5uTkKDs7u2Dbvn27vF4vI4YAwhIFEEDE%2Bf777zVp0iQ1atRI//nPf3T33Xdr0KBBeu211yRJWVlZkqTExMQi35eYmFhw7PfS09Pl9XoLtqSkpMB%2BCAAIIAoggIiTn5%2Bvv/zlLxo9erRatGihfv36qU%2BfPpo0aVKR5/3%2B1K3jOCc8nTt8%2BHD5/f6CbevWrQHLDwCBRgEEEHFq166t5OTkIo81adJEW7ZskSTVqlVLko4b7du1a9dxo4LHxMbGKiEhocgGAOGKAggg4lx00UXasGFDkcc2btyo%2BvXrS5IaNmyoWrVqadGiRQXHjxw5ooyMDLVt2zaoWQHAhmjbAQCgrN1///1q27atRo8ere7du%2BuLL77QlClTNGXKFEnm1G9aWppGjx6tRo0aqVGjRho9erQqVaqknj17Wk4PAIHHMjAAItL8%2BfM1fPhwfffdd2rYsKEGDx6sPn36FBx3HEePPfaYJk%2BerD179ujCCy%2BUz%2BdT06ZNT%2Bn1s7Oz5fV65ff7OR0MIOxQAAGgFCiAAMIZ1wACAAC4DAUQAADAZSiAAAAALkMBBAAAcBkKIAAAgMtQAAEAAFyGAggAAOAyFEAAKAGfz6fk5GSlpKTYjgIApcZC0ABQCiwEDSCcMQIYJLm5thMAAAAYFMAA275d6t1buuIKibFWAAAQCiiAARYVJb39tvT559LChbbTAAAAUAADrlYt6d57zf7IkYwCAgAA%2ByiAQfDgg1KlStKKFdL779tOAwAA3I4CGAQ1a0oDBpj9Rx9lFBAAANhFAQySoUOlypWl1aulOXNspwEAAG5GAQySGjWktDSz/%2BijUl6e3TwAAMC9KIBBNGSIVKWKtG6d9MYbttMAAAC3ogAGUZUq0gMPmP2RI1kcGgAA2EEBDLL77pPOOEPatEmaNs12GgAA4EYUwCCrXFl6%2BGGz//jj0uHDdvMAKBmfz6fk5GSlpKTYjgIApeZxHBYlCbbDh6Wzz5a2bpXGjpUGD7adCEBJZWdny%2Bv1yu/3KyEhwXYcACgRRgAtqFDBXAMoSaNHS9nZdvMAAAB3oQBactttUuPG0u7d0j//aTsNAABwEwqgJdHR0hNPmP1nnpF%2B%2BsluHgAA4B4UQItuvFFq1Uo6cED6%2B99tpwEAAG5BAbTI45HGjDH7kyebpWEAAAACjQJo2eWXS506SUePFi4PAwAAEEgUwBDw5JNmNPCtt6Tly22nAQAAkY4CGAKaNzezgiVp6FCJlRkBAEAgUQBDxOOPm/UBFy%2BW5s2znQYAAEQyCmCISEqS7r/f7D/4oJSbazcPAACIXBTAEDJsmHTGGdLGjdKUKbbTAACASEUBDCEJCdJjj5n9UaOkvXutxgEAABGKAhhi%2BvSRmjSRfvml8E4hAEKHz%2BdTcnKyUlJSbEcBgFLzOA5zTkPNggXStddK5ctL69dLf/6z7UQAfi87O1ter1d%2Bv18JCQm24wBAiTACGII6dZI6dJCOHDHLwgAAAJQlCmAI8niksWOlcuWkOXOkTz6xnQgAAEQSCmCIatpUuvtus3/ffVJent08AAAgclAAQ9hjj0lVqkhffy29%2BKLtNAAAIFJQAENYjRrmDiGSNGKEtGeP3TwAACAyUABD3N13S8nJ0u7d0siRttMAAIBIQAEMcTEx0nPPmf2JE6U1a%2BzmAQAA4Y8CGAbatZNuuslMBBk4UGLlRqBk0tPT5fF4lJaWVvBYTk6OBg4cqBo1aiguLk5dunTRtm3bLKYEgOChAIaJsWOlihWljAzpjTdspwHCx4oVKzRlyhQ1b968yONpaWmaM2eOZs%2Berc8%2B%2B0z79%2B9X586dlceUewAuQAEME/XrS8OHm/0hQ6R9%2B%2BzmAcLB/v37deutt2rq1KmqWrVqweN%2Bv18vvfSSxo4dq/bt26tFixZ6/fXXtWbNGn344YcWEwNAcFAAw8jQoea2cDt2mCViAPyx/v3769prr1X79u2LPL5y5Url5uaqQ4cOBY/VqVNHTZs21ZIlS4p9rZycHGVnZxfZACBcUQDDSIUKhRNCxo%2BX1q61mwcIZbNnz9aqVauUnp5%2B3LGsrCyVL1%2B%2ByKigJCUmJiorK6vY10tPT5fX6y3YkpKSApIbAIKBAhhmrrlG6tpVOnpUuvdeJoQAxdm6davuu%2B8%2Bvf7666pQocIpf5/jOPJ4PMUeGz58uPx%2Bf8G2devWsooLAEFHAQxD48dLlSpJixdLr75qOw0QelauXKldu3bpggsuUHR0tKKjo5WRkaHnnntO0dHRSkxM1JEjR7Tnd6ur79q1S4mJicW%2BZmxsrBISEopsABCuKIBhqF69wkWhhw41i0QDKNSuXTutWbNGmZmZBVvLli116623FuzHxMRo0aJFBd%2Bzc%2BdOrV27Vm3btrWYHACCI9p2AJTO/fdL06eb6wAffFB66SXbiYDQER8fr6ZNmxZ5LC4uTtWrVy94/K677tKQIUNUvXp1VatWTQ888ICaNWt23IQRAIhEjACGqZgY6YUXzP7LL0uffmo3DxBunn32WXXt2lXdu3fXRRddpEqVKum9995TVFSU7WgAEHAex2EaQTjr21eaOlU65xwpM1OKjbWdCHCH7Oxseb1e%2Bf1%2BrgcEEHYYAQxzY8ZIiYnSt99KTz5pOw0AAAgHFMAwV7WqNG6c2R89WvrmG7t5AABA6KMARoBbbjHrAx45IvXpI%2BXn204EAABCGQUwAng80qRJUlyc9PnnhZNDAAAAikMBjBD16hVeA/jQQ9KWLXbzAACA0EUBjCD33iu1bSvt3y/168dt4gAAQPEogBGkXDmzIHRsrLRwofTaa7YTAQCAUEQBjDDnnCONGmX209KkHTusxgEijs/nU3JyslJSUmxHAYBSYyHoCHT0qNSmjfTll1LnztK8eWaiCICyw0LQAMIZI4ARKDpaeuUVqXx5af58c89gAACAYyiAEapp08JTwYMGSdu2WY0DAABCCAUwgg0dKrVqJfn90l13MSsYAAAYFMAIFh0tvfqqVKGC9MEH0uTJthMBAIBQQAGMcOecI6Wnm/0hQ6RNm%2BzmAQAA9lEAXWDQIOmKK6SDB6XUVDNLGAAAuBcF0AXKlZOmTZMSEqRlywpHBAEAgDtRAF2iXj1p4kSz/9hj0hdf2M0DAADsoQC6SM%2BeUo8eUl6edOut5p7BAADAfSiALuLxmFHApCQzGWTQINuJAACADRRAl6la1dwZxOMxdwt5803biQAAQLBRAF3ossukhx82%2B337Sj/8YDUOEFZ8Pp%2BSk5OVkpJiOwoAlJrHcbg/hBvl5poiuHSp1Lq19OmnUkyM7VRA%2BMjOzpbX65Xf71dCQoLtOABQIowAulRMjDRzpuT1mqVhHnnEdiIAABAsFEAXa9BAevFFsz9mjLRwodU4AAAgSCiALnfzzdI995j91FRp%2B3a7eQAAQOBRAKFnnpHOP1/65RezTiC3igMAILJRAKEKFcxyMPHx0mefSSNG2E4EAAACiQIISVKjRtLLL5v9p56S5s2zmwcAAAQOBRAFbr5Zuu8%2Bs9%2B7t/Tf/9rNAwAAAoMCiCKeekpq00by%2B6Ubb5QOHrSdCAAAlDUKIIooX1566y2pZk3p66%2Blfv0klgoHACCyUABxnLp1zaSQqCjp9del55%2B3nQgAAJQlCiCKddll0tNPm/3Bg6WMDLt5AABA2aEA4oTS0qSePaW8PDNBZMsW24kA%2B3w%2Bn5KTk5WSkmI7CgCUmsdxuMILJ3bwoHTxxdLq1VKLFmadwEqVbKcC7MvOzpbX65Xf71dCQoLtOABQIowA4g9VqiTNnSudcYYpgXfcwaQQAADCHQUQJ1WvnvSvf0kxMWZyyN//bjsRAAA4HRRAnJJLLpEmTTL7I0eapWIAAEB4ogDilN11l3T//Wa/d29pxQq7eQAAQOlQAFEiTz8tXXONdPiw1KULM4MBAAhHFECUSFSUNGuW1KyZlJUlde4sZWfbTgUAAEqCAogSS0iQ5s%2BXatWS1qyRunWTcnNtpwIAAKeKAohSqVfPlMBKlaQPPpDuuYflYRA60tPTlZKSovj4eNWsWVNdu3bVhg0bijwnJydHAwcOVI0aNRQXF6cuXbpo27ZtlhIDQHBRAFFqF1wgzZ4tlSsnvfQSy8MgdGRkZKh///5atmyZFi1apKNHj6pDhw46cOBAwXPS0tI0Z84czZ49W5999pn279%2Bvzp07Ky8vz2JyAAgO7gSC0/bCC2YEUDJF8M477eYBfu/nn39WzZo1lZGRoUsvvVR%2Bv19nnHGGpk%2BfrltuuUWStGPHDiUlJWnBggXq2LHjSV%2BTO4EACGeMAOK03X23NHy42e/bV3r/fbt5gN/z%2B/2SpGrVqkmSVq5cqdzcXHXo0KHgOXXq1FHTpk21ZMkSKxkBIJgogEHw/ffShAnS//4OikhPPGHWBszLM5NCli61nQgwHMfR4MGDdfHFF6tp06aSpKysLJUvX15Vq1Yt8tzExERlZWUV%2Bzo5OTnKzs4usgFAuKIABsGkSdLAgVLdumaEbPVq24nKnscjvfii1KmTdOiQdO210rp1tlMB0oABA/T1119r1qxZJ32u4zjyeDzFHktPT5fX6y3YkpKSyjoqAAQNBTAIzj1XSk6WDhyQpk6V/vIXqXVrado06eBB2%2BnKTkyMuUVc69bSnj1Shw7SDz/YTgU3GzhwoObNm6ePP/5YZ555ZsHjtWrV0pEjR7Rnz54iz9%2B1a5cSExOLfa3hw4fL7/cXbFu3bg1odgAIJApgENx%2Bu7R2rfTJJ9Itt5iitHy5dMcdZlRw0CBzPBLExZlrAM89V9qxQ7rqKrNgNBBMjuNowIABeuedd/TRRx%2BpYcOGRY5fcMEFiomJ0aJFiwoe27lzp9auXau2bdsW%2B5qxsbFKSEgosgFAuGIWsAU//SS98oo0ZYq0eXPh423bSn36SN27m/X1wtn27dLFF5sRwGbNTPn93/X3QMDde%2B%2B9mjlzpt599101bty44HGv16uKFStKku655x7Nnz9f06ZNU7Vq1fTAAw9o9%2B7dWrlypaKiok76HswCBhDOKIAW5edLixZJkydL8%2BaZCRSSudPGrbeaMtiihd2Mp%2BO//zUlMCtLatVK%2BvBDKT7ediq4wYmu43vllVd0%2B%2B23S5IOHz6soUOHaubMmTp06JDatWuniRMnnvK1fRRAAOGMAhgidu40o4Ivvlh0VLBFC%2Blvf5N69pSqVLGXr7TWrpUuu0z69VfzdcGC8B/dBCQKIIDwRgEMMfn50kcfmSI4Z4505Ih5vEIF6cYbzSLLV1xh7r4RLr78UmrXTsrONhND3p24XRVmT5O2bJEaNDAXSdaubTklUDIUQADhjAIYwnbvll5/3dxdY82awsfr15duu81sf/qTvXwl8fnnpvzdftCn8Z40RTtHCw/GxEgTJ5qhTiBMUAABhDMKYBhwHDOK9vLL0qxZRReUvvRSM4B2882hf33d6nEZOu/%2BK1ROxfzIlStnWmLr1sEPBpQCBRBAOAujE4nu5fFIKSlmQemdO6WZM83yKh6P9Omn5rRwYqLUq5f0wQeFk0lCTYtPxxdf/iRz7vv554MbCAAAl2IEMIxt3SpNny699pq0YUPh47Vrm0kjqalS8%2BamKIaEpCRp27YTH2/USNq4MXh5gNPACCCAcEYBjACOI33xhSmCs2ebGbfHNG1qlpTp2VOqV89eRklSkybSt9%2Be%2BHiLFtKqVcHLA5wGCiCAcMYp4Ajg8UgXXij5fOYU8Zw5ZsZw%2BfJmGZbhw83EkUsvlV54QfrlF0tBb7nlDw/ndfvj4wAAoGwwAhjB9uyR3n7bXDOYkWFGCiUpKsrMyO3RQ7r%2BesnrDVKg3bvNitDff3/coQ06W092Xa6pb1VRdHSQ8gCl4PP55PP5lJeXp40bNzICCCAsUQBdYts2c3p41qyiZ1ljY6WrrzaDc9ddJ1WuHOAgO3ZIDzwg/etfZpHD2FhtbdtdbRY/re1HE9WtmzRjhlkZBghlnAIGEM4ogC60YYMpg7NnF70kr0IFqVMnqVs3qXPnAC8rs3evuSlyrVqS16v586WbbjKdkBKIcEABBBDOKIAu5jhmgek335TeeEPatKnwWGys1LGjKWXXXSdVrRr4PL8vgTNnitPBCFkUQADhjAIISaYMfvWV9NZb5rrB367GEh1tbj93ww3mmsE6dQKXY/58M4ElN1f661/NMjdRUYF7P6C0KIAAwhkFEMdxHDN7%2BO23pXfeMfu/1aqVKYJdukjnnlv26wzOm2dGAo8eNXc5eeml8Lr3MdyBAgggnFEAcVLffWeWlpkzR1q2rOixBg3MKeLOnc0yMxUqnOKLHjpkZgXXqFHsN/3rX2ZiSl6elJYmPfNMCC1oDYgCCCC8UQBRIjt3Su%2B9Z0bpPvxQyskpPBYXJ115pXTNNWZmcYMGxbzAzz9LI0aYWR4HD5ppx716SU88IVWrVuSp06dLvXub/Weeke6/P2AfCygxCiCAcEYBRKkdOGBK4Pz50oIFZoWX3zr7bLPe4FVXSZdfLiXk75XatCn%2BbiDNmkmff37c1OOxY82qMR6P9J//mNcCQgEFEEA4owCiTDiOlJkp/fvfZlu61Jy%2BPSYqSppQ%2Bwndve3/nfhFxo6VBg8%2B7nX79DHXAdaubbojf9ciFFAAAYQzCiACwu%2BXPvpI%2BuADM0q4aZO0Rk3VVOtO%2BD3OBRfI8%2BWXxz1%2B6JDUvLl5jX/%2BUxoyJJDJgVNDAQQQzlhlDQHh9ZplY264wfx6yxap%2Bvl%2Bac%2BJv%2Be/q7LVu62UnCz9%2Bc9SUpJZfzA/X2rSxBTAYvohAAAoIQoggqJePUkXt5De23bC56xyztfSpeb08Ym0a1f22QAAcBsKIIJn4EAzhfgEUl4dqFnlzXV%2B339vJpXs3WvWADzzTKl7d7M0DAAAOD0UQATPVVdJY8ZIw4aZ2R3HlCsnPfOMGva%2BRA3tpQNOic/nk8/nU95vZzkBQJhhEgiCb9Mm6eWXzYWBDRpId94p/elPtlMBJcIkEADhjAIIAKVAAQQQzrjDKgAAgMtQAAEAAFyGAggAAOAyFEAAAACXoQACAAC4DAUQAACEvT17pBdfNDcSwMlRAAEAQFg6cECaPVu6/nopMVHq00d69VXbqcIDdwIBAABhIydH%2Bs9/pFmzpHnzpIMHC481aybVr28vWzihAAIAgJB29Kj00UdmtO%2BddyS/v/DYn/4k/fWvZjv3XHsZww0FEAAAhJy8PGnxYumNN6S335Z%2B%2BaXwWJ06UvfupvSlpEgej72c4YoCCAAl4PP55PP5lJeXZzsKEHHy86WlSwtL386dhcdq1JBuvlnq0UO65BKpHLMYTgv3AgaAUuBewEDZcBxp%2BXLpzTelt96Stm0rPFalinTDDab0XXmlFM2wVZnhPyUAAAgqx5FWrDCF7803pS1bCo8lJEhduki33CJ16CCVL28vZySjAAIAgIBzHGnlysKRvh9%2BKDxWubJ03XWm9HXsKFWoYC2ma1AAAQBAQBwrfW%2B9ZbbNmwuPxcVJnTubyRydOkkVK9rL6UYUQAAAUGYcR1q1qnCk77elr1IlU/q6dZOuucb8GnZQAAGUnV27zKqsSUlSVJTtNACC5FjpOzbS99vbsVWqJF17rSl9115L6QsVFEAAp%2B/zz6URI6SMDPPrunWlgQOloUNZqwGIUI4jrV5dOJHjt6WvYsWiI31xcfZyongUQACn59NPzVS9nJzCx7Zvl4YNM38jTJ4cuPfOzzf3hPr2W6lmTalrV/6mAQLIcaSvvio8vbtpU%2BGxihXNCF/37pS%2BcMA6gAAK7NwpffedtH%2B/WYW/XDkpJkaKjTWz8ipWNKdv4uLMVqmSFH1JG2nZshO/6Pr1UpMmZR925UozvPDbC4y8Xum556Tevcv%2B/X6HdQDhJuvWmcWZ33hD2rix8PGKFU3Z697dlD9KX/igAAKQZG6sfuut5l/4p6qeftSPavCHz5nf4hH932WPq3Jls9RDXFzRr7/dj48v/PUfnjnetcuUyl9/Pf5YuXLSokVm1dgAogAi0n33nSl8s2ebAnhMbGxh6evc2fyZRfjhFDAASWb071TKX1ycdOiQOfuaoOyTPn/Tar/GrS55nmOF0Ost3KpWNdtN30xR%2B%2BLKn2SCjRkT8AIIRKJt20zpmzXLDLIfU768WZ/vllvMIs3x8fYyomxQAAFIktLSpNq1zf/8MzKkvXuLf15ennT22ea5dRMaKP9dj8rpxM2xVdvyeugSc1r5wIGiX3%2B/v3%2B/6W9S4a9/ey/QY%2B7Uu3/4WZwP/0/cGx44NXv2mPvuzphhLuk99g/BqCipfXtT%2Bm64wdyWDZGDAngaHMfRvn37bMcAysy115otP99c3J2ZaU79fPutue7nxx%2Blw4fNr7/9Vmqsb7T/D8qfJG36fp/WVctWlSpmFK92bXOrp2OneuPizHVEFSqYUYa8POnIEbOazKFDpgT6/eYvqV9/NWd/q72/Xdm5J37P/Pw85e3OVkxM2f23ycnJUc5vJroc%2B7OfnX3yUVAg1OTkmPlTb7xhvub%2B5s9T69bSzTeb0lejRuHjkfijHh8fL4/Hnf9c5BrA03DsGiAAABB%2B3HwNLwXwNJzqCGBKSopWrFhxSq%2BZnZ2tpKQkbd269ZR%2BKEvy2qH0/JJ%2BzkBmCfTzw/2z/uFzHUf6y1%2BKLgD2O58M%2B7e%2Bq9FWe/eakbx9%2Bwq3AwekL79cp4YNz9Xhw2ZU4sgR6ehRMyKRn194OsrjMaekDh3aqzej79FVRxac8D2dqlXl%2Bd%2BNRsvqs/5%2BBHDnzp1q1aqV1q9fr7p1657265/u8wP52oH%2BGQ6ln/dI/Ky//GKu6Zs%2BXdqwofDxxMR8/fTTc1q06G9q1erkMzlC6fepJM//o99TN48Acgr4NHg8nlP6H0RUVFSJ/4WRkJAQkNcOteef6ucMRhY%2Baymf%2B/jjUq9exR%2B78kp1Sb/6D18/OfkurV%2B//pSymOe31k1PPSVdd%2BICqL59zXlmlfFnLUZ8fHxI/L4G%2BmdGCtzPcCj9vB8T7p81P1/6%2BGOzDOfcuYWneCtUkG68Ubr9dqlly2xVqzZSrVql8feNC7FEfxD0798/ZF471J4fyNcOtecH8rUD%2BfyTPvfWW6WpU81CzP/jREVJf/2r%2BZunDLMUPP/YarPFadJEevDBUr1%2BIH9PS/P6gcweSp81lH7eSyOUPuudd96v8ePNH4P27c1izbm5UsuW0gsvSFlZZrLHVVeV/G6Nofb7FOjf10jHKeAQ45a1xdzyOSUXfdYjR3RgwQL1vOEGvf7NN4o/55zAvl9enjRxojRpkjmvVbOmdNttpvxVqxbY95a0bdu2gtNKZ555ZsDfzybX/AwrfD/r999L48ZJr7xiJk5JZqmWXr2kfv2k8847/nvC9bOWlFs%2BZ0lxCjjExMbGauTIkYqNjbUdJaDc8jklF33W8uUV3amTWowcqfINGwb%2B/aKizP2GBw4M/HsV49jvZ8T/vspFP8MKv8/61VdSeroZ6Tu2hFJysjRggJSa%2BseLNIfbZy0tt3zOkmIEEABKgVEF2LRmjfToo0WvsujYURoyxJz6dem8BpQAI4AAAISJbdukESPMjF7HMUWve3dp%2BPDiT/MCJ0IBBAAgxOXmSmPHSn//u1kkXZK6dZMee8xM%2BABKigIIAEAI%2B%2BorqXdv6euvza8vukh69lkpJcVuLoQ3loEBACAEOY6Z6N6qlSl/1atLr74qLV5M%2BcPpowCGqH79%2Bsnj8WjcuHG2owTEqFGjdM455yguLk5Vq1ZV%2B/bttXz5ctuxylxubq4eeughNWvWTHFxcapTp4569%2B6tHTt22I4WEO%2B88446duyoGjVqyOPxKDMz03YknIZPP/1U1113nerUqSOPx6O5p7CuYzhKT09XSkqK4uPjVbNmTXXt2lUbfnvLDAuOHjXLt/Tvb%2B6Oc9110vr1ZiTwdCZ4TJo0Sc2bNy9YFLlNmzb697//XXbBQ1h6ero8Ho/S0tJsRwkJFMAQNHfuXC1fvlx16tSxHSVgzj52qdmQAAAcWklEQVT7bE2YMEFr1qzRZ599pgYNGqhDhw76%2BeefbUcrUwcPHtSqVav0yCOPaNWqVXrnnXe0ceNGdenSxXa0gDhw4IAuuugiPfnkk7ajoAwcOHBA5513niZMmGA7SkBlZGSof//%2BWrZsmRYtWqSjR4%2BqQ4cOOnDggJU8ublSjx5mffVy5aSnn5befbfIWuulduaZZ%2BrJJ5/Ul19%2BqS%2B//FJXXnmlrr/%2Beq1bt%2B70XzyErVixQlOmTFHz5s1tRwkdDkLKtm3bnLp16zpr16516tev7zz77LO2IwWF3%2B93JDkffvih7SgB98UXXziSnB9//NF2lIDZvHmzI8lZvXq17ShlbsKECU6TJk2cs88%2B25Hk%2BP1%2B25GCQpIzZ84c2zGCYteuXY4kJyMjI%2BjvnZ/vOHfe6TiS45Qv7zjB%2BE9etWpV58UXXwz8G1myb98%2Bp1GjRs6iRYucyy67zLnvvvtsRwoJjACGkPz8fKWmpmro0KE699xzbccJmiNHjmjKlCnyer06zwXrGPj9fnk8HlWpUsV2FJRC//79tX79%2BhLdtB7hxe/3S5KqBeGOMr/34ovSyy%2Bbkb%2B335a6dg3ce%2BXl5Wn27Nk6cOCA2rRpE7g3sqx///669tpr1b59e9tRQgqzgEPImDFjFB0drUGDBtmOEhTz589Xjx49dPDgQdWuXVuLFi1SjRo1bMcKqMOHD2vYsGHq2bMniwcDIchxHA0ePFgXX3yxmjZtGtT33rlTGjzY7Kenm%2Bv%2BAmHNmjVq06aNDh8%2BrMqVK2vOnDlKTk4OzJtZNnv2bK1atYp/sBWDEUBLZsyYocqVKxdsGRkZGj9%2BvKZNmyZPhC3h/vvPunjxYknSFVdcoczMTC1ZskRXX321unfvrl27dllOe3pO9FklMyGkR48eys/P18SJEy2mLBt/9FmBcDVgwAB9/fXXmjVrVtDfe/Rocx/fCy%2BUHnggcO/TuHFjZWZmatmyZbrnnnt02223af369YF7Q0u2bt2q%2B%2B67T6%2B//roqVKhgO07I4VZwluzbt08//fRTwa/feustjRgxQuXKFXbyvLw8lStXTklJSfrhhx8spCwbv/%2BsdevWVcWKFY97XqNGjXTnnXdq%2BPDhwYxXpk70WXNzc9W9e3d9//33%2Buijj1S9enWLKcvGH/2%2B/vDDD2rYsKFWr16t888/31bEgHLbreA8Ho/mzJmjroE8J2nZwIEDNXfuXH366adqGIz7Wf/GwYNSYqIpgIsWmdu5BUv79u315z//WZMnTw7emwbB3LlzdcMNNygqKqrgsby8PHk8HpUrV045OTlFjrkNp4AtiY%2BPV3x8fMGv%2B/btq%2Bt%2BN97fsWNHpaam6o477gh2vDL1%2B896Io7jKCcnJwiJAqe4z3qs/H333Xf6%2BOOPI6L8Saf%2B%2BwqEOsdxNHDgQM2ZM0effPJJ0MufJH30kSl/9etL7doF970j4f%2B9xWnXrp3WrFlT5LE77rhD55xzjh566CFXlz%2BJAhgyqlevflwxiImJUa1atdS4cWNLqQLjwIEDeuKJJ9SlSxfVrl1bu3fv1sSJE7Vt2zZ169bNdrwydfToUd18881atWqV5s%2Bfr7y8PGVlZUkyF5iXL1/ecsKy9euvv2rLli0F6xweW0utVq1aqlWrls1oKIX9%2B/dr06ZNBb/evHmzMjMzVa1aNdWrV89isrLVv39/zZw5U%2B%2B%2B%2B67i4%2BML/ox6vd5iz1YEwhdfmK9XXnl66/ydzMMPP6xOnTopKSlJ%2B/bt0%2BzZs/XJJ59o4cKFgXtTS%2BLj44%2B7jjMuLk7Vq1cP%2BvWdoYgCiKCLiorSt99%2Bq1dffVW//PKLqlevrpSUFC1evDjiZj9v27ZN8%2BbNk6TjToV%2B/PHHuvzyyy2kCpx58%2BYVGbHu0aOHJGnkyJEaNWqUpVQorS%2B//FJXXHFFwa8H/2%2BGwm233aZp06ZZSlX2Jk2aJEnH/Xl85ZVXdPvttwclw48/mq%2BB/vf%2BTz/9pNTUVO3cuVNer1fNmzfXwoULddVVVwX2jRFyuAYQAErBbdcAIrBuvln617%2BkCRPM3T%2BAQGMWMAAAlh0703zwoN0ccA8KIAAAlh278%2BeWLXZzwD0ogAAAWHZsHeavvrKbA%2B5BAQQAwLLWrc3XL77gNDCCgwIIAIBlZ58t1asn5eRIH35oOw3cgAIIACXg8/mUnJyslJQU21EQQTwe6dhNVmbPtpsF7sAyMABQCiwDg7K2YoXUqpVUoYK0fbtUrZrtRIhkjAACABACWraUzjtPOnxYeukl22kQ6SiAAACEAI9HGjTI7I8fLx05YjcPIhsFEACAEHHrrVLt2uYU8Guv2U6DSEYBBAAgRMTGSkOHmv1//MPMCgYCgQIIAEAIuftuMwr444/SlCm20yBSUQABAAghFStKjz5q9h9/XPL77eZBZKIAAgAQYu66S2rcWPrlF2n0aNtpEIkogAAAhJiYGGnsWLP/7LPSd9/ZzYPIQwEEACAEXXON1LGjlJsrpaXZToNIQwEEACAEeTxmPcCYGGnBAundd20nQiShAAIAEKIaN5aGDDH7gwZJBw7YzYPIQQEEgBLw%2BXxKTk5WSkqK7Shwif/3/6T69aUtW6THHrOdBpHC4ziOYzsEAISb7Oxseb1e%2Bf1%2BJSQk2I6DCDd/vnTddVJUlLRypblnMHA6GAEEACDEde4s3XSTlJcn9eljvgKngwIIAEAYeO45yeuVVqyQnn/edhqEOwogAABhoE4d6amnzP6IEdLmzXbzILxRAAEACBN/%2B5t06aXSwYNSv34SV/GjtCiAAACEiXLlpBdflCpUkBYtkqZNs50I4YoCCABAGGnUqHA5mMGDpZ077eZBeKIAAgAQZgYPli64QNq7V7r3Xk4Fo%2BQogAAiSm5urh566CE1a9ZMcXFxqlOnjnr37q0dO3YUed6ePXuUmpoqr9crr9er1NRU7d2711JqoGSio6WXXzZf586V3nzTdiKEGwoggIhy8OBBrVq1So888ohWrVqld955Rxs3blSXLl2KPK9nz57KzMzUwoULtXDhQmVmZio1NdVSaqDkmjc3s4ElacAA6eef7eZBeOFOIAAi3ooVK9SqVSv9%2BOOPqlevnr755hslJydr2bJluvDCCyVJy5YtU5s2bfTtt9%2BqcePGJ31N7gSCUHDkiDkVvHat1KOHNGuW7UQIF4wAAoh4fr9fHo9HVapUkSQtXbpUXq%2B3oPxJUuvWreX1erVkyRJbMYESK19eeuUVMzt49mzp3XdtJ0K4oAACiGiHDx/WsGHD1LNnz4KRuqysLNWsWfO459asWVNZWVnFvk5OTo6ys7OLbEAoaNlSGjrU7N99t7Rnj908CA8UQABhbcaMGapcuXLBtnjx4oJjubm56tGjh/Lz8zVx4sQi3%2BfxeI57Lcdxin1cktLT0wsmjHi9XiUlJZXtBwFOw6hRUuPGUlaWmSEMnAwFEEBY69KlizIzMwu2li1bSjLlr3v37tq8ebMWLVpU5Dq9WrVq6aeffjrutX7%2B%2BWclJiYW%2Bz7Dhw%2BX3%2B8v2LZu3RqYDwSUQoUKZlawx2MWh1640HYihDoKIICwFh8fr7POOqtgq1ixYkH5%2B%2B677/Thhx%2BqevXqRb6nTZs28vv9%2BuKLLwoeW758ufx%2Bv9q2bVvs%2B8TGxiohIaHIBoSStm2lQYPMft%2B%2BElcp4I8wCxhARDl69KhuuukmrVq1SvPnzy8yoletWjWVL19ektSpUyft2LFDkydPliT17dtX9evX13vvvXdK78MsYISiAwekZs2kzZule%2B6RfnflA1CAAgggovzwww9q2LBhscc%2B/vhjXX755ZKkX3/9VYMGDdK8efMkmVPJEyZMKJgpfDIUQISqjz6S2rUz%2BxkZ0qWX2s2D0EQBBIBSoAAilPXtK02dKp11lvT111LFirYTIdRwDSAAABHm6aelOnWkTZukkSNtp0EoogACABBhvF7phRfM/tix0sqVdvMg9FAAAQCIQNddZ24Pl58v3XWXlJtrOxFCCQUQAIAINX68VK2a9NVX5rQwcAwFEACACFWzpjRunNl//HFp40a7eRA6KIAAAESwXr2kjh2lnBwzOzg/33YihAIKIAAAEczjMRNCKlUy6wK%2B9JLtRAgFFEAAKAGfz6fk5GSlpKTYjgKcsgYNpL//3ew/%2BKCUlWU1DkIAC0EDQCmwEDTCzdGjUuvWZkmY7t2lN96wnQg2MQIIAIALREebu4NERUlvviktWGA7EWyiAAIA4BItWkj332/2771XOnDAbh7YQwEEAMBFRo2S6teXfvzR7MOdKIAAALhIXJzk85n9Z581i0TDfSiAAAC4zLXXSjfdJOXlSf36sTagG1EAAQBwofHjpfh4aflyacoU22kQbBRAAABcqG5d6R//MPvDh0u7dtnNg%2BCiAAIA4FL33mtmBu/dKz3wgO00CCYKIAAALhUdbW4T5/FI06ebW8XBHSiAAAC4WKtWUt%2B%2BZr9/fyk3124eBAcFEAAAlxs9WqpRQ1q3TnruOdtpEAwUQAAAXK5aNWnMGLM/apS0fbvVOAgCCiAAlIDP51NycrJSUlJsRwHK1O23S61bS/v3MyHEDTyO4zi2QwBAuMnOzpbX65Xf71dCQoLtOECZWL1aatnSLAz98cfS5ZfbToRAYQQQAABIMkvC9Otn9gcMYEJIJKMAAgCAAv/4h1S9upkQMnGi7TQIFAogAAAoUK2a9MQTZn/kSO4QEqkogAAAoIi//c2cDvb7pREjbKdBIFAAAQBAEVFR0vPPm/2XXpJWrrSbB2WPAggAAI5z0UVSz56S40j33We%2BInJQAAEAQLHGjJEqVZI%2B/1yaPdt2GpQlCiAAACjWmWdKw4eb/Ycekg4etJsHZYcCCAAATmjIEKl%2BfWnrVunpp22nQVmhAAIAgBOqWLHwPsFPPSVt22Y3D8oGBRAAAPyh7t3NpJCDB6WHH7adBmWBAggAJeDz%2BZScnKyUlBTbUYCg8XikZ581%2B9OnS19%2BaTcPTp/HcZjYDQAllZ2dLa/XK7/fr4SEBNtxgKDo3dsUwEsukTIyTDFEeGIEEAAAnJLRo801gYsXS3Pm2E6D00EBBAAAp%2BTMM82sYMksC3PkiN08KD0KIAAAOGUPPiglJkqbNkkvvGA7DUqLAggAAE5ZfLz02GNm//HHpb177eZB6VAAAQBAidx1l9SkibR7t5SebjsNSoMCCAAASiQ6unBx6PHjpS1b7OZByVEAAQBAiXXuLF12mZSTIz36qO00KCkKIICI1q9fP3k8Ho0bN67I43v27FFqaqq8Xq%2B8Xq9SU1O1l4uZgFPm8Zhbw0nSa69JX39tNw9KhgIIIGLNnTtXy5cvV506dY471rNnT2VmZmrhwoVauHChMjMzlZqaaiElEL5atZK6dZMcRxo%2B3HYalAQFEEBE2r59uwYMGKAZM2YoJiamyLFvvvlGCxcu1Isvvqg2bdqoTZs2mjp1qubPn68NGzZYSgyEpyeeMNcELlhg7g6C8EABBBBx8vPzlZqaqqFDh%2Brcc8897vjSpUvl9Xp14YUXFjzWunVreb1eLVmyJJhRgbDXqJHUp4/ZHzbMjAYi9FEAAUScMWPGKDo6WoMGDSr2eFZWlmrWrHnc4zVr1lRWVlax35OTk6Ps7OwiGwDjkUekSpWkZcukd9%2B1nQanggIIIKzNmDFDlStXLtgyMjI0fvx4TZs2TZ4/uFN9ccccxznh96SnpxdMGPF6vUpKSiqzzwCEu9q1pbQ0sz9ihJSXZzcPTo4CCCCsdenSRZmZmQXbkiVLtGvXLtWrV0/R0dGKjo7Wjz/%2BqCFDhqhBgwaSpFq1aumnn3467rV%2B/vlnJSYmFvs%2Bw4cPl9/vL9i2bt0ayI8FhJ2hQ6WqVaX166XXX7edBifjcRzO1gOIHLt379bOnTuLPNaxY0elpqbqjjvuUOPGjfXNN98oOTlZy5cvV6tWrSRJy5cvV%2BvWrfXtt9%2BqcePGJ32f7Oxseb1e%2Bf1%2BJSQkBOSzAOHm6afNvYLr15c2bJBiY20nwolQAAFEvAYNGigtLU1px85RSerUqZN27NihyZMnS5L69u2r%2BvXr67333jul16QAAsc7eFA66yxp507p%2BeelAQNsJ8KJcAoYgCvNmDFDzZo1U4cOHdShQwc1b95c06dPtx0LCGuVKpkJIZL0j39IBw7YzYMTYwQQAEqBEUCgeEeOSOecI23eLD35pPTQQ7YToTiMAAIAgDJTvrw0cqTZf%2BopiRWTQhMFEAAAlKlevcwo4K%2B/Ss8%2BazsNikMBBAAAZSoqSnrsMbP/zDOmCCK0UAABAECZu/lmqXlzcwp47FjbafB7FEAAAFDmypUrHAV87jnpl1/s5kFRFEAAABAQ118v/eUv0v790j//aTsNfosCCAAAAsLjkUaNMvvPPy/t2mU1Dn6DAggAJeDz%2BZScnKyUlBTbUYCw0Lmz1LKluUsIo4Chg4WgAaAUWAgaOHXvv2%2BKYKVKZoHomjVtJwIjgAAAIKCuuUZKSWEUMJRQAAEAQEB5PNKjj5p9n0/6%2BWe7eUABBAAAQXDttdIFF5hRwGeesZ0GFEAAABBwvx0FnDBB2r3bbh63owACAICguO466bzzzLqA48bZTuNuFEAAABAUHo/0yCNm//nnJb/fbh43owACAICgueEG6dxzTfl7/nnbadyLAggAAIKmXDlpxAizP26cOR2M4KMAAgCAoOreXTrrLDMRZPJk22nciQIIAACCKipKGj7c7P/zn9Lhw3bzuBEFEAAABF2vXlJSkpSVJb3yiu007kMBBAAAQVe%2BvDR0qNl/6inp6FG7edyGAggAJeDz%2BZScnKyUlBTbUYCw97e/SWecIf3wgzR7tu007uJxHMexHQIAwk12dra8Xq/8fr8SEhJsxwHCVnq69PDDZmmYr782s4QRePxnBgAA1txzj5SQIK1bJ82fbzuNe1AAAQCANVWqmBIoSU8%2BKXFeMjgogAAAwKq0NCk2Vlq6VFq82HYad6AAAgAAq2rVkm6/3eyPGWM1imtQAAEAgHUPPGAmgCxYIK1ZYztN5KMAAgAA6846S7rpJrP/9NN2s7gBBRAAAISEBx80X2fNkrZutZsl0lEAAQBASGjZUrriCnNXkGeftZ0mslEAAQBAyDh2e7ipU6W9e%2B1miWQUQAAAEDKuvlpq2lTav1%2BaPNl2mshFAQQAACHD4zEzgiVp/HjpyBG7eSIVBRAASsDn8yk5OVkpKSm2owAR669/lerUkXbuNBNCUPY8jsNNVwCgpLKzs%2BX1euX3%2B5WQkGA7DhBxxoyRhg2TmjWTvvrKjAyi7DACCAAAQk7fvlJcnFkU%2BsMPbaeJPBRAAAAQcqpWle66y%2ByPHWs3SySiAAIAgJB0333m9nD/%2BY%2B0bp3tNJGFAggAAELSn/4kde1q9seNs5sl0lAAAQBAyBo82HydPl3atctulkhCAQQAACGrbVspJUXKyWFh6LJEAQQAACHL45Huv9/s%2B3ymCOL0UQABRJxvvvlGXbp0kdfrVXx8vFq3bq0tW7YUHM/JydHAgQNVo0YNxcXFqUuXLtq2bZvFxAD%2ByM03S3XrSj/9JL3xhu00kYECCCCi/Pe//9XFF1%2Bsc845R5988om%2B%2BuorPfLII6pQoULBc9LS0jRnzhzNnj1bn332mfbv36/OnTsrLy/PYnIAJxITI/Xvb/bHjZO4hcXp404gACJKjx49FBMTo%2BnTpxd73O/364wzztD06dN1yy23SJJ27NihpKQkLViwQB07djyl9%2BFOIEBw7d4tJSVJhw5Jn34qXXKJ7UThjRFAABEjPz9f77//vs4%2B%2B2x17NhRNWvW1IUXXqi5c%2BcWPGflypXKzc1Vhw4dCh6rU6eOmjZtqiVLltiIDeAUVK8u9epl9sePt5slElAAAUSMXbt2af/%2B/XryySd19dVX64MPPtANN9ygG2%2B8URkZGZKkrKwslS9fXlWrVi3yvYmJicrKyjrha%2Bfk5Cg7O7vIBiC4Bg0yX%2BfMkX5zWS9KgQIIIGzNmDFDlStXLtg2bNggSbr%2B%2But1//336/zzz9ewYcPUuXNnvfDCC3/4Wo7jyPMHd5tPT0%2BX1%2Bst2JKSksr0swA4uaZNpSuvlPLzpYkTbacJbxRAAGGrS5cuyszMLNjOP/98RUdHKzk5ucjzmjRpUjALuFatWjpy5Ij27NlT5Dm7du1SYmLiCd9r%2BPDh8vv9BdvWrVvL/gMBOKljo4BTp0oHD9rNEs4ogADCVnx8vM4666yCzev1KiUlpWAk8JiNGzeqfv36kqQLLrhAMTExWrRoUcHxnTt3au3atWrbtu0J3ys2NlYJCQlFNgDB17mz1LCh9Ouv0syZttOELwoggIgydOhQvfHGG5o6dao2bdqkCRMm6L333tO9994rSfJ6vbrrrrs0ZMgQ/d///Z9Wr16tXr16qVmzZmrfvr3l9ABOJipK%2Bt8fZz3/PEvClBbLwACIOC%2B//LLS09O1bds2NW7cWI899piuv/76guOHDx/W0KFDNXPmTB06dEjt2rXTxIkTS3RdH8vAAPbs2WMWhj50SMrIkC691Hai8EMBBIBSoAACdvXta64D7NZNevNN22nCD6eAAQBA2Dl2Z5B33pG2b7ebJRxRAAEAQNg57zzp4oulvDxpyhTbacIPBRAAAISlAQPM1ylTpCNH7GYJNxRAAAAQlm68UapdW8rKMncHwamjAAIAgLAUEyP16WP2fT67WcINBRAAAIStvn3N2oCLF0tr19pOEz4ogAAAIGzVrSt17Wr2uT/wqaMAAkAJ%2BHw%2BJScnKyUlxXYUAP9zzz3m6/Tp0r59drOECxaCBoBSYCFoIHQ4jtSkibRhgzRpknT33bYThT5GAAEAQFjzeApL36RJ3B/4VFAAAQBA2LvtNqlCBenrr6WlS22nCX3RtgMAAACcrqpVpZEjpVq1pBYtbKcJfVwDCAClwDWAAMIZp4ABAABchgIIAADgMhRAAAAAl6EAAgAAuAwFEAAAwGUogAAAAC5DAQQAAHAZCiAAAIDLUAABoAR8Pp%2BSk5OVkpJiOwoAlBp3AgGAUuBOIADCGSOAAAAALkMBBAAAcBkKIAAAgMtQAAEAAFyGAggAAOAyFEAAAACXoQACAAC4DAUQAADAZSiAAAAALkMBBAAAcBkKIAAAgMtQAAGgBHw%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%3D%3D'}
Genus 2 curve data - 40000.e.200000.1