1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
3346
3347
3348
3349
3350
3351
3352
3353
3354
3355
3356
3357
3358
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
3391
3392
3393
3394
3395
3396
3397
3398
3399
3400
3401
3402
3403
3404
3405
3406
3407
3408
3409
3410
3411
3412
3413
3414
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
3436
3437
3438
3439
3440
3441
3442
3443
3444
3445
3446
3447
3448
3449
3450
3451
3452
3453
3454
3455
3456
3457
3458
3459
3460
3461
3462
3463
3464
3465
3466
3467
3468
3469
3470
3471
3472
3473
3474
3475
3476
3477
3478
3479
3480
3481
3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
3529
3530
3531
3532
3533
3534
3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551
3552
3553
3554
3555
3556
3557
3558
3559
3560
3561
3562
3563
3564
3565
3566
3567
3568
3569
3570
3571
3572
3573
3574
3575
3576
3577
3578
3579
3580
3581
3582
3583
3584
3585
3586
3587
3588
3589
3590
3591
3592
3593
3594
3595
3596
3597
3598
3599
3600
3601
3602
3603
3604
3605
3606
3607
3608
3609
3610
3611
3612
3613
3614
3615
3616
3617
3618
3619
3620
3621
3622
3623
3624
3625
3626
3627
3628
3629
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
3663
3664
3665
3666
3667
3668
3669
3670
3671
3672
3673
3674
3675
3676
3677
3678
3679
3680
3681
3682
3683
3684
3685
3686
3687
3688
3689
3690
3691
3692
3693
3694
3695
3696
3697
3698
3699
3700
3701
3702
3703
3704
3705
3706
3707
3708
3709
3710
3711
3712
3713
3714
3715
3716
3717
3718
3719
3720
3721
3722
3723
3724
3725
3726
3727
3728
3729
3730
3731
3732
3733
3734
3735
3736
3737
3738
3739
3740
3741
3742
3743
3744
3745
3746
3747
3748
3749
3750
3751
3752
3753
3754
3755
3756
3757
3758
3759
3760
3761
3762
3763
3764
3765
3766
3767
3768
3769
3770
3771
3772
3773
3774
3775
3776
3777
3778
3779
3780
3781
3782
3783
3784
3785
3786
3787
3788
3789
3790
3791
3792
3793
3794
3795
3796
3797
3798
3799
3800
3801
3802
3803
3804
3805
3806
3807
3808
3809
3810
3811
3812
3813
3814
3815
3816
3817
3818
3819
3820
3821
3822
3823
3824
3825
3826
3827
3828
3829
3830
3831
3832
3833
3834
3835
3836
3837
3838
3839
3840
3841
3842
3843
3844
3845
3846
3847
3848
3849
3850
3851
3852
3853
3854
3855
3856
3857
3858
3859
3860
3861
3862
3863
3864
3865
3866
3867
3868
3869
3870
3871
3872
3873
3874
3875
3876
3877
3878
3879
3880
3881
3882
3883
3884
3885
3886
3887
3888
3889
3890
3891
3892
3893
3894
3895
3896
3897
3898
3899
3900
3901
3902
3903
3904
3905
3906
3907
3908
3909
3910
3911
3912
3913
3914
3915
3916
3917
3918
3919
3920
3921
3922
3923
3924
3925
3926
3927
3928
3929
3930
3931
3932
3933
3934
3935
3936
3937
3938
3939
3940
3941
3942
3943
3944
3945
3946
3947
3948
3949
3950
3951
3952
3953
3954
3955
3956
3957
3958
3959
3960
3961
3962
3963
3964
3965
3966
3967
3968
3969
3970
3971
3972
3973
3974
3975
3976
3977
3978
3979
3980
3981
3982
3983
3984
3985
3986
3987
3988
3989
3990
3991
3992
3993
3994
3995
3996
3997
3998
3999
4000
4001
4002
4003
4004
4005
4006
4007
4008
4009
4010
4011
4012
4013
4014
4015
4016
4017
4018
4019
4020
4021
4022
4023
4024
4025
4026
4027
4028
4029
4030
4031
4032
4033
4034
4035
4036
4037
4038
4039
4040
4041
4042
4043
4044
4045
4046
4047
4048
4049
4050
4051
4052
4053
4054
4055
4056
4057
4058
4059
4060
4061
4062
4063
4064
4065
4066
4067
4068
4069
4070
4071
4072
4073
4074
4075
4076
4077
4078
4079
4080
4081
4082
4083
4084
4085
4086
4087
4088
4089
4090
4091
4092
4093
4094
4095
4096
4097
4098
4099
4100
4101
4102
4103
4104
4105
4106
4107
4108
4109
4110
4111
4112
4113
4114
4115
4116
4117
4118
4119
4120
4121
4122
4123
4124
4125
4126
4127
4128
4129
4130
4131
4132
4133
4134
4135
4136
4137
4138
4139
4140
4141
4142
4143
4144
4145
4146
4147
4148
4149
4150
4151
4152
4153
4154
4155
4156
4157
4158
4159
4160
4161
4162
4163
4164
4165
4166
4167
4168
4169
4170
4171
4172
4173
4174
4175
4176
4177
4178
4179
4180
4181
4182
4183
4184
4185
4186
4187
4188
4189
4190
4191
4192
4193
4194
4195
4196
4197
4198
4199
4200
4201
4202
4203
4204
4205
4206
4207
4208
4209
4210
4211
4212
4213
4214
4215
4216
4217
4218
4219
4220
4221
4222
4223
4224
4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
4235
4236
4237
4238
4239
4240
4241
4242
4243
4244
4245
4246
4247
4248
4249
4250
4251
4252
4253
4254
4255
4256
4257
4258
4259
4260
4261
4262
4263
4264
4265
4266
4267
4268
4269
4270
4271
4272
4273
4274
4275
4276
4277
4278
4279
4280
4281
4282
4283
4284
4285
4286
4287
4288
4289
4290
4291
4292
4293
4294
4295
4296
4297
4298
4299
4300
4301
4302
4303
4304
4305
4306
4307
4308
4309
4310
4311
4312
4313
4314
4315
4316
4317
4318
4319
4320
4321
4322
4323
4324
4325
4326
4327
4328
4329
4330
4331
4332
4333
4334
4335
4336
4337
4338
4339
4340
4341
4342
4343
4344
4345
4346
4347
4348
4349
4350
4351
4352
4353
4354
4355
4356
4357
4358
4359
4360
4361
4362
4363
4364
4365
4366
4367
4368
4369
4370
4371
4372
4373
4374
4375
4376
4377
4378
4379
4380
4381
4382
4383
4384
4385
4386
4387
4388
4389
4390
4391
4392
4393
4394
4395
4396
4397
4398
4399
4400
4401
4402
4403
4404
4405
4406
4407
4408
4409
4410
4411
4412
4413
4414
4415
4416
4417
4418
4419
4420
4421
4422
4423
4424
4425
4426
4427
4428
4429
4430
4431
4432
4433
4434
4435
4436
4437
4438
4439
4440
4441
4442
4443
4444
4445
4446
4447
4448
4449
4450
4451
4452
4453
4454
4455
4456
4457
4458
4459
4460
4461
4462
4463
4464
4465
4466
4467
4468
4469
4470
4471
4472
4473
4474
4475
4476
4477
4478
4479
4480
4481
4482
4483
4484
4485
4486
4487
4488
4489
4490
4491
4492
4493
4494
4495
4496
4497
4498
4499
4500
4501
4502
4503
4504
4505
4506
4507
4508
4509
4510
4511
4512
4513
4514
4515
4516
4517
4518
4519
4520
4521
4522
4523
4524
4525
4526
4527
4528
4529
4530
4531
4532
4533
4534
4535
4536
4537
4538
4539
4540
4541
4542
4543
4544
4545
4546
4547
4548
4549
4550
4551
4552
4553
4554
4555
4556
4557
4558
4559
4560
4561
4562
4563
4564
4565
4566
4567
4568
4569
4570
4571
4572
4573
4574
4575
4576
4577
4578
4579
4580
4581
4582
4583
4584
4585
4586
4587
4588
4589
4590
4591
4592
4593
4594
4595
4596
4597
4598
4599
4600
4601
4602
4603
4604
4605
4606
4607
4608
4609
4610
4611
4612
4613
4614
4615
4616
4617
4618
4619
4620
4621
4622
4623
4624
4625
4626
4627
4628
4629
4630
4631
4632
4633
4634
4635
4636
4637
4638
4639{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "VmA72WDHqxpQ"
},
"outputs": [],
"source": [
"# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7lgixL36qzHX"
},
"source": [
"# Google I/O 2025 - Live coding experiences with the Gemini API\n",
"\n",
"<a target=\"_blank\" href=\"https://colab.research.google.com/github/google-gemini/cookbook/blob/main/examples/IO2025_Gemini_API_live_coding.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" height=30/></a>\n",
"\n",
"Welcome to the official Colab notebook from the **Google I/O 2025 live coding session** on the Gemini API!\n",
"\n",
"This notebook serves as a comprehensive, hands-on guide to exploring the cutting-edge capabilities of the Gemini models, as demonstrated live during the presentation. You will dive deep into how developers can leverage the Gemini API to build powerful, innovative, and highly intelligent AI-powered applications.\n",
"\n",
"Throughout this interactive session, you'll find practical demonstrations covering the **latest advancements** in Gemini, including:\n",
"\n",
"* **Generative Media (GenMedia models):** Learn to create stunning images with **Imagen3** and the experimental **Gemini 2.0 Flash image generation**, and generate dynamic videos with the powerful **Veo2** model.\n",
"* **Advanced Multimodality:** Understand and generate content across various modalities, seamlessly combining text, images, and videos in your prompts and responses.\n",
"* **Text-to-Speech (TTS):** Transform written text into natural-sounding audio, exploring customizable voices, language options, and even multi-speaker dialogues.\n",
"* **Intelligent Tool Use:** Empower Gemini with built-in tools like **Code Execution** (for solving complex problems in a sandbox), real-time **grounding via Google Search**, and **URL context** to interact with external systems and fetch factual information directly from the web.\n",
"* **Adaptive Thinking & Agentic Solutions:** Discover how Gemini models can perform internal reasoning and problem-solving with their **thinking capability**, and how to build complex, multi-step AI agents using the **Google Agent Development Kit (ADK)** for advanced use cases.\n",
"\n",
"This notebook is designed to be fully runnable, allowing you to execute the code, experiment with different prompts, and directly experience the versatility and power of the Gemini API. Get ready to unlock new possibilities and code the future!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-9U_TTsGrFRQ"
},
"source": [
"## Setup\n",
"\n",
"Before diving into the exciting world of Gemini API, we need to set up our environment. This involves installing the necessary SDK and configuring your API key."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "o01T9qxbrF8M"
},
"source": [
"### Install SDK\n",
"\n",
"The `google-genai` Python SDK is essential for interacting with the Gemini API. This SDK provides a streamlined way to access different Gemini models and their functionalities.\n",
"\n",
"Install the SDK from [PyPI](https://github.com/googleapis/python-genai)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nFlTvSWDq52G"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/196.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m194.6/196.3 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.3/196.3 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
],
"source": [
"%pip install -U -q 'google-genai>=1.16'"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IOUtdgvSrVqq"
},
"source": [
"### Setup your API key\n",
"\n",
"To authenticate your requests with the Gemini API, you need an API key. This key allows you to access Google's powerful generative AI models. It's recommended to store your API key securely, for instance, as a Colab Secret named `GOOGLE_API_KEY`.\n",
"\n",
"If you don't already have an API key or you aren't sure how to create a Colab Secret, see [Authentication ](../quickstarts/Authentication.ipynb) for an example."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "n2rr8RVWrKTV"
},
"outputs": [],
"source": [
"from google.colab import userdata\n",
"\n",
"GOOGLE_API_KEY = userdata.get('GOOGLE_API_KEY')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NYr5KYJoraqs"
},
"source": [
"### Initialize SDK client\n",
"\n",
"With the `google-genai` SDK, initializing the client is straightforward. You pass your API key to `genai.Client`, and the client handles communication with the Gemini API. Individual model settings are then applied in each API call."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZH8iaojUtpRN"
},
"outputs": [],
"source": [
"import time\n",
"from google import genai\n",
"from google.genai import types\n",
"from IPython.display import Video, HTML, Markdown, Image\n",
"\n",
"client = genai.Client(api_key=GOOGLE_API_KEY)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vgLcm4GMrfCw"
},
"source": [
"# Working with GenMedia models\n",
"\n",
"The Gemini API offers access to various GenMedia models, enabling advanced capabilities like generating images and videos from text prompts. These models push the boundaries of what's possible in creative content generation."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LODbUtGSuq8p"
},
"source": [
"## Generating images with Imagen3\n",
"\n",
"[Imagen 3](https://ai.google.dev/gemini-api/docs/imagen) is Google's most advanced text-to-image model, capable of producing highly detailed images with rich lighting and fewer artifacts than previous versions. It excels in scenarios where image quality and specific artistic styles are paramount.\n",
"\n",
"<!-- Warning Badge -->\n",
"<table>\n",
" <tr>\n",
" <!-- Emoji -->\n",
" <td bgcolor=\"#f5949e\">\n",
" <font size=30>⚠️</font>\n",
" </td>\n",
" <!-- Text Content Cell -->\n",
" <td bgcolor=\"#f5949e\">\n",
" <h3><font color=black>Imagen is a paid-only feature and won't work if you are on the free tier.</font></h3>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "V50XP8zdvF8D"
},
"source": [
"### Select the Imagen3 model to be used\n",
"\n",
"The `imagen-3.0-generate-002` model is specifically designed for high-quality image generation from textual prompts."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "e8l0Heizvs-Q"
},
"outputs": [],
"source": [
"MODEL_ID = \"imagen-3.0-generate-002\" # @param {isTemplate: true}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zmz3-BhwwCfz"
},
"source": [
"### Imagen3 image generation prompt\n",
"\n",
"When generating images with Imagen 3, you provide a descriptive prompt to guide the output. You can also specify parameters like the number of images, person_generation (to allow or disallow generating images of people), and aspect_ratio for different output dimensions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tijw1mzZvtfK"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 82.6 ms, sys: 13 ms, total: 95.6 ms\n",
"Wall time: 4.59 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"prompt = \"\"\"\n",
" Dynamic anime illustration: A happy Brazilian man with short grey hair and a\n",
" grey beard, mid-presentation at a tech conference. He's wearing a fun blue\n",
" short-sleeve shirt covered in mini avocado prints. Capture a funny, energetic\n",
" moment where he's clearly enjoying himself, perhaps with an exaggerated joyful\n",
" expression or a humorous gesture, stage background visible.\n",
"\"\"\"\n",
"\n",
"number_of_images = 1 # @param {type:\"slider\", min:1, max:4, step:1}\n",
"person_generation = \"ALLOW_ADULT\" # @param ['DONT_ALLOW', 'ALLOW_ADULT']\n",
"aspect_ratio = \"1:1\" # @param [\"1:1\", \"3:4\", \"4:3\", \"16:9\", \"9:16\"]\n",
"\n",
"result = client.models.generate_images(\n",
" model=MODEL_ID,\n",
" prompt=prompt,\n",
" config=dict(\n",
" number_of_images=number_of_images,\n",
" output_mime_type=\"image/jpeg\",\n",
" person_generation=person_generation,\n",
" aspect_ratio=aspect_ratio\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CnyfpmWPDLcb"
},
"source": [
"After generation, the `result.generated_images` object contains the generated images, which can then be displayed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "L3ow933Lvtch"
},
"outputs": [
{
"data": {
"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAQABAADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDwekpetJWog60daO1FABS0n86UUALR1o7UUAFJ3paQ0AHFIaWjtQAlFFHPbpmkAUdaKKACijpxS0wFo7c0UvamAhpKU0lACUlL1pDUgFLSUvUUwAU4UnWlHNADhR3o6ig0xCGkpT0pO1AwpKU9aSgApKXoaKQCUcUdaKACiiigAooooAO1FH60UALS0lL0NAC04U0U4dKoQvaiiigBppDTjTTzQwGnrSUv0pKkYUdaDSfyoAXpRR0ooAKOtFFABRxR9aKADvRxmjtRQAUUUUAJ2o70tFIBKOKM8gDrRQAUdqKKYC+9JRRQAUtJS0AFFH4Uf560AFLSUtAC9qKKUf0piFoJyaB0o6igBOM0hpTSHigYlFHbrRSAKTvRS9+KADtSfhS0UAFFJS9aAE7UGiigBKKMZopAHaj60Ud6AHrUi1GKkXmrQh3b8aaadTTTYDaTvSnpSVIxOtJ3peppOtIBO9FFHagAo60UfhQAUUUUAHajrRRSAKKOvNFMAo7Uvek7UALS0mfypaBBSikpe1MYooo7UfWgQUh4paQ0AN7UlLSUhi9aTrS4pKAFoopetABS4pOlLTATpxR1oo/nSAQ0lL+NJigAooo70AFFFFABjNLRxQKAFwKUc96QcUvamITPFIelO70negY089aKPc0UgDpRRRQAo5pfzpKcKEAo6UdaB0paYhtBpTSdKBiH+tJS0HmgQnvR2oopDCkpaKAEooooAKKKKAClpKWgApe9JS0AOFL2pBz/AI07tVIQf40Ype1BpgNIzTTg06mnpSAaaSnHoKSpGJ3o7UUUgCjpRRigAopeldHoHgjV9fKSrH9ksz/y8zjAI/2R1b+XvQ2kBzRIA/pS4yP1rp/GVlo+hyw6LpmZpofnu7qTBZnPRB6ADnA9RnOK562tLm9fZa2007ekUZb+VJMLEOKXFdHaeA/E96Rs0mVM95WWP9Cc1vW/wd8SyqGmksLde++UnH5KaOZAefYpK9Ytfg9axkHUvEIPqlrDz+ZP9K6a18BeC9EtftdzZeZGvWe/myD/AMB4BPtilzoLHhmnaVqGr3It9Ns57qUn7sSE4+p6Ae5roNS8KW/he0EniC8RtRkXMOmWr7mHo0j9FHsMk9j3r1C/1vXZtPez8GeHHt4ei3U8awIPdI2xn6kfhXl1/wCAvGbTy3N1pVzcyuxZ5BIsjMfU4JJpXuOxygA/Olq1eaZf6acX1jdWpz/y2iZf5iqo/OrVhBRRRTEFJ/Sl60UDDrRRRQAUf0oxS0AHFHfijFFAgp3akFLTAWlPSkpaYCU006m/rSATpR2peDSdaQw6UcUdqKACijrRQAfhR1oo6UAFJ396XrScUAHakxS0UgEo7UtJQA9alWo15qQVaEKaaafimHmqYCe9IQKXvSY5qBjaPxooxigBKSl70UgEoo7UdKACiiigAo70UUAFBoxxR1FAC0dKKO1ABS0fhRjNAC0UdO1L/KmAUtFHUUxCfypDxS0hpANooopDE/Cl7UdRRQAUopKWgBcUUUUwEoNKeKSgApOaXtSd6QCUdTR1NHWgQdqWiigYDpS9aToaWgQo9aO1A/WlpgIf/wBVJSmkoGNooozSAPagc0d6KAFpwpop4GaEIXFB6f0pRwKPwqgGmkx2px6UlIBtHalpOtAB3pKWk/HNIYUUdKOtAB1pOlKaKAEo60UtAhOKXt7UUUALilFJS0AKKcKQUo9KpALiilxSf/rpgIaaetOpDSAZ3pD1p2M1Ja2lzf3kVpaQvNcSsESNBksaljG29vLdTiGFNzkE9eAAMkk9gAM5qLtmu317R18L2EHhuzH2vXr8B754hu2J1WJfYkZJ74HY1peHvhsihLjW33v1FrG3A/3mHX6D86jmHY4Kw0u+1SXy7G0luH7+WuQPqeg/Guu074Y6pcENf3UFqndV/eP+nH616hbW0FnAsNtCkMS9EjXaB+FWoYnmJCLnAyT2A96lyYWOc0bwRoejlZEtvtNwP+Wtz8+D7L0H5Z96t67f3sax2Wmp5mo3IIiZh8kSjq7H0HYdzWvtOM4OPWpIYnnlWONSzseAKkZx2k/D/SbA+deqdRvGO55J/u7j1wvf8c129rp0Npbq9wVtoMfLEi4J+gFQS3KWMiM2wuW2qG5BP071DK8s8pkmdnc9STQBpHVkgGyyt1Qf3n5NU5bmW4OZZGb6ngVDHE80ixxKWdugFW5Y4bH5HxNc9xn5E/xNAFcNhh0PPQ1NLcyysHOwMv3SEAK/T0qONJbqXCKXc9h/nir/ANitrMZvZd8n/PGP+poAzS0jt99yfY1Nm8gAZmlQHpuOP51Yk1F1Gy2jS3T/AGB8351SYs7bmJYnuTmgC0NSmMZjmVJoyMMsi5BrmtY8DeFdcDOLNtLuT/y1teFz7r0/ID610UNjc3HMcTY/vHgVM9raWn/H1Pvf/nnF/U0AeOap8JtetN0mmyW+pwDp5bbHx/ut/QmuQvtF1XTG23+m3dsR3lhZR+eMV9Hhp5SPslu8aDp5YOT9TVlG1YDDQM49GWq5mKx8s7h6ijI9RX03ew2CRtLqOi6cwUZZpY4/6istZ/Br/d0rR9x/uxQt/IU+YLHzwWXHUfnWhp+h6rqzBdP027uSe8cRI/E9K99XU/D1oc22lW+4dDFaon64rD8SfEtNMgaG3hjE5HyRg5I+voKOZhY8g1nQdQ8P3MVvqUccM8ib/KEgZlGeN2MgVnYqxfXtzqV7Ne3cpknlbczH/PSq9WhBRS+1HSmIOtOpKO9MBf55pegoopgJTTTuo/8Ar02kAUlLR/WkMTtRS0lAB3ooo696BB1oo4paBidqSlo7/wBaAEoo7UUgEo70tFAD1FSLUa4FSrmrQheSOtNNOwcU0/8A1qpgNpCKXikNSMQ0mKU0lIBDRS96TtSAO1ApKKAClo/Gk60AGKKKWgApKXFFAB1oo60UAHalxRS9RTAKWgdaKAF7UdO1FLTEIc0004000hiUlLSUgCj8aO1FAC0UCigBe1FH/wCql7UxCUh6UtIRxQAUnSlopAJSfWl/WjmgYUUUUAHalpKXAoEKOaWkFLxTAKaad0ppoASkpcCkpDDiilpOtACiniminDpTQh3QUUdKMZpgJj0pKU0h6fjQAmKTrS/hRjFIYnWk6Gl7Ud+aAEo7UtJSAKKKMUAHWj6UUYoAKKPeloAKXrSUvamIcBzThzTRyKcKaAXGf50UUHgUwE9625vDptPCEeuXjMrXcwis4hxuUZLOfbjA/P0rP0uwfVtXs9OjyGuZ1iyOwJ5P4DJruvi1PGmr6VolomIbO2GyNfVjtAx9FH51nJ62Gjz+0srjULuGzs4XnuJW2pGgyWNe4+E/B9t4N09p5PLuNZmQhpOqp/sL7ep71L4B8Fjw3ZrNPGp1m6TMjMM/Z0/uj39fU8dq6WWyefUWt4skLwXbn6k1nKVxpHJaToMWmyz3k7/adSumL3F0w5YnnA9F9q2zbMlr57/Lltqqep7k1qrawRzs6rujhO0Z6yP/AIDrTLxCkUUsvKxrux/fkbn+WPyqRmYltNLOkKr879B6D3rUu0WIx6ZajBOGmb/H+dTaNBtie7k5eQnnviqV7L9nt2AP+kXPzSk9VXsKAK88ysRDDnykPB/vH1q3JKNJtWUc3cq8/wCwPT61UsQsKteSDKxnCKf4n/8ArdadCvnyvczncqnJB/jY9BQBUtNMZpftt02ZSuUDdI09f8BU0du1xMI4gSSeM/1q7esY0WFjmVvnlPv2H4D+damjWojtfOK/PJ3PYUAUnQ2EX2SyQyXcgyzgfdH9Khj0qK3Tzr6bj+6O5+vetS8u7exDBAGlY5IHr7msV5XkD3l26iKPnLnao/wFAE0979nt9sCiBGHyqPvEepPb+dVIbW5ufmWNsNzubgH86g06/i1aRruB1+ygnN3IAd5HBEant23H8BWk+rKhEdnG0jnje/JP9TQBNDobEZmlAHcKP61o29jaQY2Rh2HdjuqguIgJdUuSX6rAp/mBUsepPcEpbRxwxL1eQ9PwoA0pY1lXa5cL6BsZ/KoFtbaD5lhjXH8RH9agfVrWBSpma4fvtXj/AAqhNrsjA7IUC/7ZzQBcudYhgBWP96/t0H41jXOpXVySGkKr/dTgVlah49sdPYpLqFsJP+ecKB2/IAmsweOLzUciz0rUblT/ABNGsSn8WoA2yuQe+azLvw5pN+SZrGIP/wA9Ih5bj/gS4NJHdeIJF3f2TZwA9POvCf8A0FanjbWW++NKHqFkkP8ASmBzeqeFvEMFpMNBumu0Vc+VKAZgP9lu/wBOvpmvK7hZo7iRLlZFnDfvFlBDA++ec19G2U9zaFZmEQlHXZllx+IqTU7fw94iTbrOmwPIBgSMvI+jDkU1KwrHzTSCvarz4ReGrli9jqtzbZ5C+YsgH54P61nt8HtMiOZfE5C+nkrn/wBCqudBY8mo6V1/i3RfDHh+3+x6feXd/qTEbnchUiX1wB1PYZ9/SuQ6CqTuIXpSikpetUIXtS+1Ao/pTAaeaQ04000hiYo6iikpAFFLiigAo/wo7Ud/rQAdqKKKBCUUUUDE7UUUUAFAHNHQUd6QEi9KkWo16VKtaREKaa1ONNPXrTYDD70nvTjSGpAbikPApaMUhje1HSlpOlIAo6ij60cUAFGKOlFAB0oo6Ud6ACjt+FHaigApe1HX60UCDp0paSlpgLxS0lL/APqoGAzS0UYpiENNNOPFNP1oGJRRRSAO9JS8GikAdaWk6il6f/rpgHQ0UvajqKAE6UUtJQIO1JRRQAlGKWkpDCjtRR2oEL2ooooGL2p3akGcUuOaYhKaeKceaQ0ANxRS/hSUhiUdaKKAHU4U0dacKaEOHSg9KUZFFUAn+FNpxptIBKKXtSdaQCd6KWkoAO1FH1ooGFJS8UUhCdaMUUUAFLjNJS4oGL3/AJUDrQKWmIWnD8KaOnSnCqAd1oxQgXzEdwzKCCyq2CR3Ge1dF/wiranaNe+HLg6jEgzLasu25g+q9GHuvX0pN23AvfC21Fz4+tWIz5EMsv47do/9Cr0KPw8Lv4mXmvX6furcxxWiMOGcIPm/Ak49/pXF/CBTF46mSVSjiylBVhgggrkYr02R2kkaQkk5JBrCb94pbHUW6CG4mLNl2wF+lL50NqZd7hZJXO0n6cVm3twZtOhvImw6OCcdj0NUbm/+1LG0ihXQEMwPBqRl8TRtdLbxEGOOJ8N/ecqcmq2ryebcCP8AgRVwPfArNuS4t3aPJkUblA7kc4ptpdx3tqk0TZBGDk8g+hoA14dVWKx8nyyHVdqkdD9ay3LSMXc7mPUmlIpDxQA+STzI4owMJGOB7nqadHOYzH8oKo27b6n3qukiyruRg6+qnNUdR1CXTiszQmS2PDFeqH/CgDVlmaWVpHOWY5JqWC8ngLhJSA67T/8AW9KxbfW7C5A23Kqx/hk+U/rWgrAgEHI7EUAV9SvCt3YWEcmyW8mCl88qgOWP9K57xPfT+ItTt9GtGMdtITgD+GNfvSH3PQemfeo7++b/AISSa5GD5KtBHn+H5cE/mTUvhdI3vdQ1CZ1WViIYwx5WNR/U/wAqAOht7SG0toreBAkUShEUdgKtxy/ZlzDxIRzIeo9h6fWqrXlqp2/aYc+nmD/GsXU/EaQMYbMLK46yH7o+nrTA3XcKGkdsAclmP8zVBdbtp7j7PbBp9vLuOEUfXv8AhXHzXN5qMypLI8rsQFTtk+gqK6vHaNtO06UxwKSJ7tesrdwnt23fl60WA6PU/F1vZSta2kZvb0dY4zhY/wDfboPp1rm7t9S1ds6lfP5Z/wCXa3JSMexPVvxqBBa6fa9Y4IQeSx6n+ZNLDfXk5/4ltntHa5uvlH1VepoA19P0aGyiEiWsUS44aQ7c/gPmNOuNes7c+XJqmCP+WcHyf+g5b9aox6J9rbzNTvLi8c9VLbE/75FaKQ2GmR5VLe2T1Chc/wCNAFE6oszZttKvbk/3jGefxc0nnau3KaKIx/00uEH8hVl9cRjttbee5bsVQhfzNNQalev++ntbKM+vzt/PFAhkd1r6H5Uhj9hdn/4mrcGp63KSn2aKZh/ck3H9BV2z0rSlIM1yLuT1llGP++RgVvxBEjAiChB0CDA/SgDm9RuJ9Pkjj1FVtjLH5qMXBRx3w3qOMjtXH6z4zEYa30xt8nQzkfKv09T79K9Xv9O07xJoj6NqqkRk7oZ1+9E/Yj/PTivE/FPgzVfCN55V7H5ls5/cXcYzHKPr2PseacbX1BmCzPI7O7FnY5ZmOST6mkpcY/Gk71qSA4NLSU4UwF+tHSjoOaXiqAaevvTT1pxppqRiUUUd6AD9aKOtGKACj60UUCCij60dqQB3pMcUtJQMO1JilpKAD8KB1opaQD1qVaiXpzUq1pEQ4jimH+vWn9BzTDVMBuKQ0vIpDUAJTad9aSkMSkpe9HWkISiijFAw70dqKKADGKKKKACl60lLxQIO1FFFMYtFFAoAWl/xpKUUxC4oo6UtMBMU004000gG/Wiig9KQBRRR3oGGPalpOtLQIXqKKBRQAUnSlxSHpQAh6UdqXrScigApMUtJ0pDCloooEHelpKXpTAWl6ikxS0wCmmnGmnikAnekpTSdR/SkMP8AGiiigBQOaeOaZTxTQhwoxil7UVQDaQ0ppDSAQ8UlL/k0lIAoo60Y/wD10DE/Giig0AFFFFACUtJS0gDiij+dFMAp3vSUo70AKORTwcUwcDp+FWNPvLnT7kXMDRpMo+RmjDlPcZBAPvTEdFo3gHxDrUazQ2f2e2bkT3R8tSPUDqfwFdjofwx1LSNRh1GHxDBFcQtn9xAXBHdTkjIPpivNLrUNU1a4H2m9u7yZ2CqryM5JPQAV7F4J8Et4Wsvtmqzyf2hcLxarIdkKn+8BwzfoKym31KR1EyRCf7U1nA12FKLcgAOAeCM9ce2ap4YfwmrptbXbue8G49o1LVmXNjHOctLLxwMNj9KzGJJqUVqzQSXCxeYOVfgN+dK0cd3bPGSGjkUrkc9aoXGiRzxmNriUqezHcKypNG1LSz52n3Luo5KLwfy6GgCODV77SZ2tpv3qxttKN2+hqrDqklhqMk9qCIJGz5TdMelF5qKakge5RY7tBjzVGA4HYj196zpZ44IVknOyJx8jsDtb6GmB3Samt9pzzWLKZ1GfKk9fQ/XpmqC+IlPWMcHDxMdrxt/dOa5bRotb1QvLpmlSSCNtrFJ1B9iM4Bz6VYv/AAp4nvzuXR7+OYcLJ5sZ49Cd3IqeaK6lKnJ7IvarJaXDG7spmt7n/lpG2V3/AEPr/Os2HXbsZjS8Eo6NG7B/wINdJpXw4v59Ii+06leWdzg74p0jmUn1HoPbNUpPg9KuoC5lurfUEP3423Wx/Bl3DP4VHtodzT6vPscrqF55C+ctszLn51jPT3ANWINWnsI0dpLiyR+VMoKK30P3T+Br0KP4aaOYQGS8ibHKfavMX9Rz+Va2j+DtO0Vn+ytMEcfPCSPLb6qBg1DxMehpHCy6nk8l6zxvPEVnkOW4bO8/X1p1tJdalavPZ6bdXEcZAkEQVnQ+6Z3evOK9ZvPBXhy+JM2j2oc/xxJ5TfmmDUek+CdP0XUFurCa4RcENHI2/cD2yecd+SaX1lW0H9Vaep5Gb+2R/LmcwP3SdTGR+BqwpR13IwYeoOa9yls7WePy57eKVD/DIgYfka5+9+H/AIZvWZ101bWU/wDLS0cwn8lOP0oWJXVCeFfRnlys0TbkYq2CMj3GDUYwAAAABwABjFd1P8LkB/0PXryNf7s8SS/rwaqj4X37N8/iJdv+xZAH9WNae3p9yPq9TscUltEJ/PkBklHR5DnaPYdBViLVrbz/ACoszsv32QjZGPVmPH4da6+f4SwSQtuv7i7fH3ZpNi5+ijFYg+D+pm7ZxNbLD/BG3zhR9MAZ+tCrwfUTw9RdDJn8Rxs5itXLkdfJUyN+gwKpG4uZJN4sXLH+KeQKf6muxHwp1Rk2tqpRR0VHCgfgFxVK7+E+uoubbV2Y+hl/+sKFWh3D2FTsc+ZNTYfetYx9Xb/CmiG/Y83luPpb5/m1Qaro2seHCBrKahBGTtW4jYSRk/lxUdu88y7rTVY5h6NGCfxxg1qmmroxaa0ZqW2nancSrHFqFtvPQNABn9a2rXRfENo25b+2B7hUI/lXNLeX9uc3FoJU/v255/75PP611fh/xRb3GIZ5twHG5uGT/eB5x70xGxY3lzD8mpTWZPZo32n8Qa2U1Kwms3s7qW0ubOUYeCdlZD+BPFZOq+HbPVB5vzQ3BHE0RGT9R0YV5v4ntde8OuDIIJLRzhLqOM4z6MCeDSQHSa18OfDVyzz6XraaeTz5MsglQfQ53AfnXnOtaONHn8pdTsL05xi2diR9cqMVTmvbq4J824dgeoBwPyFQAAdsVokxDh60oFJThitEIOlLQP8AJox2pgNIpKU0hFJgNP8AOilpOlIYdaOtFFAg780UUUAFFFFACdPzopaTFIAooo6UDEo4zS0d6AHrUy1CvvUy1pEQp6Uw81IelMamwGUlKRSVACUhpTSUgE6UUuKSkMQ9KOaWk60CCiiigYUtJ0FLQAUUUUwClpMUtABQOtFLQIKUdaSnCmAvaiiimAh6U2nfWmmkA3tR1FLSUhh1o60Udv8A61AC0dqBil/GgQUtHWjFMBOnSk6U6koASkpe9JSAO9HajtRQAdKO1H9aKADrS0flQODQAvWl7UUYpgBpp4p2KaaAEpKXHtSdDUgH50UUf0oGKKeKaOtPFUhDhR2oHSjFUAhppH5040089aTATvSUtHfmpGJ0ooo96ADvSUtJQIPpRRRQMO9FFFIQUUUdqYC0opPzpR60AOHSl96TtT4YZLm4it4hmSZ1jQe5OB/Oq6Aep/Cjw/FDBL4qvYwxRjFYIw/i/ik/DoP+Be1d9JI00jSO25m6muZtvEGlxXtj4X0d/tQs4/LZ4/uIFHzMT3JPp3Nal3BcXSrEk/lRH75X7x9hXM3d3LQtzqlrbNsLmST+5HyaqPqd45+SGK3U9DM2W/IVah060tI88he+Dgn6nqaifUI4TttbRc/kf8aQFcy3jfMbxz7R23+NPGq+UArxTPgcttCn8qcbzVWXKWrgeohJqhcyXjH/AEh5l9myo/KmBS1y70preS6mt7iKQfxRBck+4Jwau/DKTdqV+Fmvvs0qLJDG8REEn94kchXBx0PIPtWHr9ml3o10rsq7UMis3Zl5FeyWbu9jbvI2XaJWc+p2jNc+InaNjpw0OaV+xYB44x+FLnNNpa4jvsLmko6CjFIBCM0YzS0daBiYope1N70ALRSZpaADvR3pepooEFGRRzRQAZyKTOaXvR1NAFe7tLe+tZba6hSaCVdrxuMhhXh/ivwZ/wAIzqCeZEZdLnfFtdjh4m/55uw5z6HvXu5GRTLnSrTXNMu9Jvo99vcxlWHcHsR7g4I9xWtGo4S8jKvTU4+Z8+Qx3lscJMLiMfwTcMPow6/jVox292QzxNHMvRvuuv0I6/ypI4LjT7280i+/4/LCUwyH++B0YexGDTb+7+wWb3HlmQKQMDjGa9JHmPQ6nRdQMlq2lXkzhZBtimRtp/A9mHbt/Kue1HxNqfh2+m0XxHapqdjIvyy42tLGe/oT+oI696zLLXba7Ij5ilP8DHr9DXQa9AvibwZO7gNqGmjzkbuyD736fqooEec6tb2Vve7tOuDNYyjfCW4dPVGHqP14PeqXBNNwM5pwH8q1QhR/k04UgFKKoQ6kpfwNFUA0immnHp/OmmpYDTiil7UnakAUUtFACdKXvRRQAlL2o6dqKAEpKWkoGFFFGKQBR3pOgpRQBItSqOKiUVKvArSIh1MPSnkcY7Uw8VTAZ0opTSVADTRS9elNpDDpScUtJ3pAGKKOtBoEHeijtRQAUdaKOooGFFFFAgpeDR3o5oABzSjjvRQOTTAWlpOfTvSigBaMUUuKYDaQ049KaaGA3vR3ooPSpAKO9FFABSjmk/rSigBaKKXFMBCKT9aXtScCgBKSlopDEoo7Yo6igQdKKKOtAC0CjtS+1AC0tJ1pe1MBKQ0p/wAikNDAaaOo9qDSe1IAwKKPxooGOHWnimCnimhDscUtIOlL2qwG008inHgUh65qWA2il60lIBDRxS9qSkAdKSlo70AJR07daWkoGHeij6UYx0oAKO9FL2oAKUDtRQKaEOFBB4wSpByCKBTu1MD0H4VQW4/tOQHNyCi9Oicn9T/IV6Qzqi7mYKPUnFec/DSF00zU54f9dJMseW6AKM/+zV1H2Pz33SSS3bk4HzbYx+P+Fc8ty0bkeoaSjZuZPNI/hVuP6Vfi8RWagJY26+wijJP6YrM07TUWVU8hJXJAEcceV/E9a6dVNpFiW4jiA6pAQv8AIZqQMq81G/ZMta3KK3QvvBP4ZrFuH1K4Qqlu4VuC0kmf5mtnUdXs8gO5jUHjzCST7881ltrlmDkeYw9l/wAaAOb1/Srz+xpw6gLJsj4bJyzBf6162ieWioOigAfhXnWr3v8AaGkyrZRn7TE6TRrKMKxRw+M/8Br0DT5rq502Ce9t0t7mRA0kSPvVSewPeuXFLY7MI9yejrQazb/VE0+/0+CVf3d5KYQ5/hfblR+OCPyrkWp2ml1opBzS9RQAUUdqguZbmJR9mtBO3o0ojA/MGiwifrTTWPJqWvx5/wCKdSQf9M9QTP5FRVZ/FL2x/wBP0LWLUDqywecg/GMn+VVyMXMi3NpFyJGktNYv4HJztcrMn/fLDOPoRUJvdc07m7sYtRhHWWx+SQD1MTHn/gLH6U+x8V6FqMgit9UtzL/zykby3/75bBrYAzz685o1W6DR7MzbLxHpF/L5Ed7GlyOtvODFKPqjYP6VrYqlf6ZYapD5WoWdvdRjoJow2Ppnp+FYzeDLOI/8S3UtV04dkguiyD/gL5p+6xe8jpu1J0rl/wDhGNWz/wAjhqm30MUdOj8LzhgbnxHrM4B6CZYwf++Rn9aTS7gm+x0tHUYqC2t47aBYYt+xf77lyfqWJJqcc1JYVNA2yVW9DUfWlFNCZ5f8XtL/ALL8Wabr0a4hv0+zXBH99fuk/hj/AL5rgbvV0stX+x3sStYyxgMccgHgn6V7n8TNK/tz4dX4Vd09sguovXKcn/x3cK+atWvFvZreXqfs6hvrzmvSovmieXWXLIh1C0FlfzW27cqHKOD95Typ/LFdX4H17/ibRWF64InBgLN0dWGOffmuLMryFEc58tdoPtnI/nSKWRgykqynKkdj2Nb2ujEGQxuyH+Fip/DigcCjLElnOWJJJ9zSjpVoBe1OFNp1UhC/hR/WjpR2pgNPFNNONNNSwEPOaSlNHT8aQwopKWgQlLRRQAY5ooooAKSlpKBidqKDRSAKAOaO1GOaAJFqZelQr0qZRxWsRDjxTD1p56Uw02IZ1pCKU80lQxjSKQ06k7cUhiUlLSf5zSEFHvRRQMO1HWiigQdqKKKBhR26UUvNABijtRRigQtGKBR0pgLThSCgUwF9qWijtTAQ0w089KaaTAbR2oo6VIB1ooooAKWk7U7pTAMUvajFFMBOopD/AJFLSUgE4o4xS0nSkAlBo70UAFL1pKXp70AApaSlHvQAtLRS44qgEpppxFNPFJgNNJS96O9IBO/FLSUv1oAUdaeKYKkA5poBwoPNFFWA08D60hpx4pp5NSwEpOlLSdKQBSUvWk6UAFFHSjrxSATpS/560c0UAFJ1oooAKWijvQMP84p3akpQOaaEKOPzp460yngVSA9E8CXYi8NTwKgZzdMxBPAG1ev+FdDBFeXsoUOfdicBf8K5v4d2kdxpl35jlVFzgqgy7/KOAewr0OGWGCIQ28O8j+FPmx9TXNLcpEthBaWMS+a7zzDuznaPov8AjSX01/cnFmVjT++WAP4AA4rOu7q5U8tHB7MQzf8AfI/rWaXmuX2h57hvQkgfkKkZdbRp5JN0k0TOerFmJp/9jmJcloWP0b/CmW+mSKVaZY0Hozf4VckuvJQRpeQxJ/sAsaAKX2G6dWCIqgjHO4cflXW+D7uS98JWBmbM0KG2lz/ejJQ/+g1zKtZMcy3k8jZ6kYrQ8HXSW+tatpG7Mcm2/t/cN8sg/BgD/wACrGurwOjDytOx2BFcz47tHn8K3U8ORcWRW7iI7NGd38ga6giqt7bfa7G5tuP30TR5PuCP61wp2dz0HqrC2lyl5Z290g+SaJZR9GAP9anqG1t47S0gtYuI4Y1jQewGB/KpqQw60Umaz5de0iByk2q2EbDqrXKAj9aEm9hbbmh1pQKy7rX9MtNNm1B7yOS2hXc7QMJDjpwFz61FpPi/QNaby7PUojOePIlzHJ/3y2CfwpqMt7Cco7XNC+0vT9Tj2X9jb3K+k0Qb8iaxW8IrZ5bQ9TvNMI6RbvOg/wC+H6fgRXTbef5ik6U+ZoVkzjZta8UaL/yFNKW9gH/LzYAuPqU+8v5EVLZePtFvDt83a/dQQSPwOD+ldWTms6/0XS9U/wCP/TrW5P8AeliDN+eM0+aL3QWktiAeJNKYZ+0kexjb/ChvEWlqpbz22jqRG3H6VnP4A8OE5jtJ4faG7lQfluxUlv4E8NwSB20xbhh0N1K836OSP0pe4HvjD450eSXybL7RqEw/5Z2kRlP/AI7kD8SKtRazrE/MPhe6VD0NxcxRn8skitmC3gtoRFBEkMa9EjUKo/AVLjFF0tkOz6sp2VzqE0m280v7KuPvC5WT9ABV00ZpM570mwSLkASe1eGQbkYFGB7g9a+QNX09tJ1y/wBOfObW4eHnuFJFfXdk37x19RmvnH4v6f8AYPiRfOBhLuOO4H4rg/qprswktbHHio9Thx+vrRSCl7V6BwhSgUnenUwAdadSAc07vTQCijGRRjNB/wD1VQDT9KaeKcaafSpYDaKOoopDCijFHWkIKMUUcUAFHeijHNAw7daQ80tJQISg80dKWkMSjvR3o70ASrUy9KhSplrWImK3SmGpCKjbmmxDDSdadik6mpGNpKWkNSAnaiiikAlHajtR1oAKKKMUAGKOtL3pO1AC96OtFFAB1oo60vWgA4/+tRR2paYC80tJ3pQKaAUUfhQKXqKYDT1ppzTjzTT0pMBtFFHXtUgFFFGKYCilFJS/SgBaO1HaimAlHWlPWkpAIf0pMUvek70AHWkpTR1/OkAYooxxRQAdKWkpwpgKOlL2pAKd0qgENMNPIphpMBtJTu1JUgFFHaigBRUgpg608dKpAOopRQetUA0jrTTTjyKaaTAQig0UmKkAo60fpRQAnailxSd6QBR2oo60AFHSkpcUAHSijqKWgA70opOlLTAcKcBTRzTh+tUgPQfh35kmnXkKglftHKr1bKjjPYcV3EyTRQqkkqxJ/DGvyqP6muQ+FJY22sBduVkjIJ7ZB/wrrJPswlJIkvJyecfdFc0t2WQRrbbsRxPcyH8F/Ic093vVXZmK1QdlXn9OasMLkR/O8VnGei9z+A5qP7KpXf5e4D/lpcvtUn2XrSAhjhhZSXvpJXI6RAk/nVbyUWU+ZLIT/dVdzfzxVoXk24xwASnGMRpgD8P8fypGtL2YeZOMD/blwoH0ApAPt1s+hguD6lmAFPjaNPFGiiwic33nN8objyCMSFvbGMf7QFVzLZW0Uss1wuyFC7+X0AHXmuj8E6Q9vZvrN7Hsv9QAYIRzBD1SMfhyfc+1ZVZqMTajBykdUetNIyOlKeRRXnnpDCcVz/i7xK3hvT4ZILQXd1cSiKGEybecdcckgew79qf4o8RxaFbrFEElv5lLRRu21EUdZJD/AAoPXueBXBacusaxcPd6VZtqFzJxJq19+6iI9Ix12D+6ox65Nb0qSfvS2MKtVr3Ybl65t9b8RKn9vXypbA5NjYqY0f2dsksPaph4d0mOMCLTrSI9iLdD+eRzVu38BX938+s+Irps/wDLHT1ECD23ck/pVk/DLw24/eRXsjf3nvJCf51v7anHRHO6FWWrMeXw3o06lZtLtDngtHEIz+a4NQyaclrB9mvLZdS0wYCGVA81v+PVlHqPmHvVu++F6WwMmj6hfx452LclW/A9D+n1rn3j8SaXKUj1MTlTzDfQ4P8A30Oa0jVhLYzdCaOhs9LESjydRu5rFgGjja5kJj9CjhgcY7HNamh6xe6VcnT9UnkurNz/AKNeScsh/wCech7+zfge1cpY+LEtZPJ1WybT9xz5oO+Esf8AaHTPvXUI0c0YdGV0YZBHIIpyhGaFGc4M29A1+HXLe5bymtri0laG5gc5MbDvnuCOQa2FIZQykEEZBBzmuE8MtGnxBvI02vBf6YJJNpyGZHCgn/gLYre8ESSS+ELLzG3bPMiUnuquyr+gFcNWmot2O+lU5ops3aTvTiKb1OKxNhw602W4gtwTNNHEFUsS7gYUdSfb3rmJPFLS+GYLy18s3l60q2qkZUKrsA7D0CgE+pIHeuXbTLCGCSa9d7i7uGBe4uB5skjDoAD1H+yBiuinQctzmq4iMXZHaP418Ni3e4OsWyxKcZYkbv8AdBGW+q5o07xfoWqyiO2v13t9xZUaLfn+7uA3fhXJ6fpC+b9qlhHnEf6+5xNOfb+6g9l4FXJdN0vDedZW0rscs0kSkk+5NavDR7mKxUux6BanFwh/DmvIfj9p2y/0TUlXiSOS3c/7pDD/ANCatCIyaU7NoWqm0kXkW0swlgPtsYnb/wABIxU/xHu4/GHwrbUo4/LvNMuo3uYO8RPysPdSHDA9xRTpunNMKlVVIs8FoFHaivSOIX9KXpSf4UvagBRxTvekFKBVIB1IaWgjimIYaaaeelMNSxiUlKeaT+VIAxRR1ooAO1LRnFJ2oAKKWkxSAOopKU8ik7UAFJ0peopKQBigcUdaB1oAlXmpl6VCnT+lTKK1iA48CozwakPSoz71TENI96Q9KWkOKgY0+1JS9RSYqQENFFFIBP8AIo60vX8KKAEo60tFABR0o7elFABzRRRQAds0vJNGKDTAKX/GiigBetO7UgxmlHFUgCloo6UAIaaelOOM000mAyjr1pfaikAlLgmijGaADvS0lOHX+lAC4ooxiimAlJS+1JQAlJSnpRSASijpR3oAKOKB1o+tIBaXpSUuKYDhSjrSAUvQelUAlMNPPSmmkwGmkoxQelSAdKKKO1ADh0p6j2pgp4qkA8UGlFIelWIaaQ8Up6Uh4qRidqSlNIaQCdKO9GKO1IBKWjpSUAHQ0GjtR2oAKTsaXrR6UgCl60lLQAdqcOKQUoFUA4UvakHBp3QVSA7b4bXkkFxqttFGZGlSNsbscAkEn867w3UtuNks0cMjfdSNcYHqSa8u8D3b2niYbBlpoHQD3GG/pXcSxyGXdLy7dSTXNNe8UjWimRpT9nRpZf4ppDkD6Z6VaWO1L7rq4M7/AN0HIH5VRt49PhUG4maZh/BHwo/Gte1d5Vza2qQx9mfv+FQMhlupFjK2tr5MY/jlAUflWY4t5pM3V287/wB2MEitttLW4l8y6mec9l6KPwqOe4sdPBSNUDj+CMDP40AZ0VlDq2s2GjpbtHbZ+13YY8tGhG1T7M+38FNekg5rkfBELXVvea7KuHvpdkOe0MeVX8zvb8RXW9a4K8+aR6WHhyw9RacOaZShqxubWONs/Axv9ZudZ8SzLeTzSBkslJMMSr9wN/fwO3TJJwSc11+wKAAAABjA6AVLnNQXd3bWFpLd3c6QW8Q3PJI2FUfWqcnImMVHYfilAyMVBaXKX1pFdRJKsco3KJUKNj3B5H41FqGr6dpMQl1G+t7VD0M0gXP0z1/ClZ3sU2rXH3+p2GlQedqF5BaxE8NK4XP09a5LV/FPgzV4vLfWrZZwP3cu1hj8SORViT4oeCVk2SaqkhHcW8jD89tXLbX/AAb4lAt4bnS7xm6QyxqGP0VwD+laKLWrTMudN6NHn7MhB2OkkZ6MpBVhSafjTbjzLbdGh+9Crfu2/wCA9AfcVq+I/A0uk3H9o+HIX+zk/wClacCTx/ejB6Een5elZoXjFaxl1TE4qWkkdN4Ot9NfXNQvbeaSK+kt/LFu/wB2NS24snqC2Ppj3rr9J06HSNJttPgZmjgTaGbqx6kn6nJryW4luNPkg1K0bZPaOHBPQoeGB9sfyr1rTr6PUdPgvIvuTIGwe3qPwOR+FZ1b3uVBJaIudTUNyH+yTFFLOI22gDqccVKOa4vx3qs2+DQrZ2Tz0867dTgiLOAgPbcQfwU+tZxV2XJ2RxWn309vplrFHavNNHAkIBOxI1A5GT1JYsTgHn6VWuG124u2ne/gtgRhVgj3lR3wW6fXFau0ABQAABgAdqbFqei6Zeo+rTDaOREOSx7ZUc4rs9pLocvsILVkukeArzW1E2oapfi3PRnmJLf7q8DHvXQP8J/DJh2/6d5n/PT7Rz+WMfpW5pfiPT9SRPs6XiKeFMtnKin8SuP1rb61zSqzvubqlDojy6XwBrWgSmfSGtdWtx1t7iJUlA/2Wxgn8R9KqXmqo2k6pNDbTRXItHttS06ddr+UQcNjHVCdyt0+8O4r19RWH4s0Oz1XSLm4k/c3dtBI8V0g+ZcKSVP95T3U8fjitadd3tIxqYdbxPl0dKUUg+7S9R1r1EeeFKKKOg/nQIcKcOnWm04fhVIBetB/lS0h4qhDTTDTzTKhjEPSj1oPSjpSAO1Jil60dBQAUUUdaACk7Glo7UAJzRRSHikAUn1paT8aADoaUDJo60Dr0oAkTpU6ioV6VMvStYiYrdKYelSH9ajNNgNPNNPT3p3emnmoYxO9NNOPSmmkwENHelpO1IAxmiiikAUUYz60YoAOlHelpKACj3o96WmMMCijrRQIAKd0pO1L2pgKKWkFOHNNAFHGKMUduaYCHmmninH0pppMBtFHaipAKO1FFACjilpB1pfemAtFLR1680ANPFJS45NJQAhoOKXFJSAKSijrQAUvWkpaADrSjpSUtCAd2paQf55paoBDyKaelO70w8UmAh5pKWk71IBRRS9aAFHFSLUYqQVSAeBkUnalFFWIbjNNI4pxpvFSxiYpO1LSUgCkpe1FIBKKO1GKAE/nRilpCMUAHfrR1o6+lFAC0UdKMc0AKOOtKOlIOtOH60wHCndaaKcKpAaHh944fElhJKWCrJxg4yxBCg+xOBXqcGn3V/KWxhc8s3QfSvHHOwBwcFSCD6c1623jXRb6VbZtWjtoeh2xvz9TtwBWFVWZUTYiGn2DBFBnn9huOf5CrrX00Sb5YhEp6Jnc5/wp1vFaW9mk1nsdJBlZQ2Qw9d1UZ57WFjJcSefIeiDp/hWQxHuL6++WPeiE9IxuJ/Gs/U4orWwlUbpb6XEUCZGBI5CqSAfUg/hSXGqXNyNiuIYehCnA/E1PpEKXniXRLSOMiKOV7uR2A3P5a4U47Dcy0pOybKgrySPR7Gxi0zTrWxh4it4liX6KMf0qannk03rXmPU9ZaCHpWTqWkvfTrNFquo2UirgC2lAT6lGBB/GtU1iapaa9e3BisdRttPtMD96IfOmY98A4Vf1oW4PYbGviXTyP31prEI6q6/Zp/wIyhP4L9a1J7C01iGzmv7Z8QSCdYpT91wDjcASDjPuM81yF7p+gaNNH/bmu6je3TnKQS3blnP+zFHjP5EV1N9plh4g0gWmoWhktZVVvKfKMvp0wQRV6KzIscH4s+IV/faq3hzwVD9rvjlZLqMBgvrs7cd2PA/Wsqy+Dd7qEpvPE2tSyXEnLpCd7fi7f4GvTdC8NaP4Zt3h0mzWASHLsSWdvTLHnHtWowqnV5dIEKlzO8zz2P4P+E0j2tHeu3943GD+gxWXqPwS06VWbStUubeQchbhRIv5jBH616p1FFSq011LdGD6Hjlnr3i74bXkNr4jik1DRmbas6tv2/7jnn/gLfhjrXY6npdvrVvBrWglbiC6AJEfG7P8Xsc8Edj+NdhdxWs9lLFfJC9qwxIswBQj3zxXHabcaD4Okng0s3T2VxKrCIvlImPB8vPJzxx7DFVzcyulqQouLtfQ0tN8JW1vbv8Ab0W4llQoyfwKCMED1PvVrw3o8mhaV9gabzkjmcxMeuwnIB9/Wtpqbism2zZJADg15lr0nmeJ9UlOCfNWIc5wFRRj893516Yetee+LtF+w+N1MLrHBrMXnR56CZABIPxXa351dLdkz0sZttpV/rM8dpZExK7fvrnGfJTuR/tHoPz7Vs3V/wCCfh2vlMY/t2PmCDzrlz6s3bPuQKoavf6zYQxeGvCdjO99cLvuL4rhYgeMhugPv2HTk8GgfCDTbMi61+Y6leMdzJuIiB/9CY+5x9K1bVryehm+a9ooqn446SJMLpF8U/vGRAfy/wDr11Phz4ieHfEsy29tctb3TcLb3I2Mx9FOSD9Ac+1ai+FtAWHyhommiP8Au/ZU/wAK5HxR8JNL1G3a40NV06/XlUBPlSH0I/hPuOPaoXspaWsH72Ot7nerdTJqxs5rcrFIm+3nU5D4xuVv7rDOR6j6Gs3xvefYPAut3GcEWjxr9X+Qf+hVzPgDxlfYuPDniOK4Gr2IOwbC0kyjtgZywHIPcc9jUXxU1a6Pw7KXdg9i95fJHHFI6s7RKC+5guQpyo4ycU40/fSFKp7jZ4R0GKWk/wAil/nXro8wWlHFJ+dLTEKPpTxTRThVIBe1B4FFHFMQxqYaeeKaetSxje9FHeipAKKKBigAooo9qADr780Ud6MUAJRS0lACUUdqKQCf5FKOaTvSjk0ASpyKnWoU+tTLWsRMceRUbCpCMjrUZpsBh/pSHpS9fpSVLGNpDxSn+tIRUgJSYpaTtSAO1FFFAB1o70dPeikAe1LR/wDqopgFH4UdqKAD2oopcUAHalpBS0wFpaTGacBVALiijGBS9/8A69MQwjFNNPNMNSxid6TrS0mKkAopaKADtTqbThTAXrQf8ijtRTASk4pTSUgEpKWkpAJ/nFLR2pKAClo7UUAAFL2FJ9aUUIB350vSkFLiqAQ008U4008f40mA2k60tJUgGKKKWgBwp68Uwc08VSAeKDQOBQRmrEIelMPNPpppDG0hpe/40YqQEo/CjpxR3oAKTtS9KSkAUdaKO9ACUtJS4oABQPTNHWl60AH0p3akpRmmA4U8U0dKnsrSbUtQtbC3/wBddTJCn1Y4/rVN8quwSu7HpXwu+G8HiRTrmtqx0tHK29vkr9oYdWJHO0Hjjqc9hz7O+ieG1tvso0HTvIxjZ9kjx/KuA8R+K59AFv4S8LCKN7OBFmupRlbdcfKMd3I559fyyLHxx4k0VUn1eRNX008ySRxCOaMf3hjAYex/MV5VSc5u53wpxjoyfxb8NhYTjU/D8L3umoS82jNM4A9TFg9e+OvpnpWBG1tDY29/YzSzaNNJ5LLO26SylPRWPUqTxz0OPWvZrC9ttTsYbyzmWa2nUPHIp4I/oa4bxJoNvYa8JSgTSde/0K/QfdjmP+rlA7En9R71lGtJPUupQja8Tn/JVG+dmPPAFb/gryZfFdwwAEkWngBAM7Q0nOT6naK5bTZLhraS2ueLyzke2nz/AHlOM/jWppV3PpOpW1zZJE91qEqWKpIGxjJJfgjOOa66msHY5KTtNXPV6O1NzxS15tz1AIrG1y21i8WK10y7jso5CfPutu6VR6IvTJ9T0ra60+EoJ0Z/uA801uD2MjQfBlho4aW1gLXMn+svLhvMmkPfLHn8sCtIyQLcSQRzea8ZxIVHCn0J9fatC7vVAKQnJP8AEOgrLiiSGMRxqFUdBVSaJhd+RLmik6GioLK95c/Y7V5/Inn2/wDLOBNzn6CuXutd8T3v7vSfDzWuePP1BgSPcIp/ma7CkxzTTS6Cav1PPZvCvim8KyXF/HcXLf8ALW5kxHD/ALsajGfpj61uaD4JtNKuUv765l1G/XlZJRtSM+qp2PucmuoHSjNU6jasTyIXrSGik71BYhFYPxD0+a+8Df2haA/btIlF5CR1wn3h9NpP5V0GKtWmx45YHUMjryp6H1FVTdpEVVeJ5XdatrenG01q3mMumzRrKg/5ZlWAO1h/Ceeo/wDrV2+i61Z63aia1k+YD95E3Dxn0I/r0NYPh2H+xr298H3o3LbZmsS/ImtWJwPcqSVP4Ul14IaG4F5oV39inXlUJIUewIzgexBHtVySvZii3a6Oz20j7/Lbytu/Hy784z2zjtXNafrHiO0cQa1oTzJ0F1Yurg/VM5/L8q6UtkAjjI7is2rFJ3MWCzu7jUvtupaXpyXVvEy293bylnOeq4ZQQOvc9a85+K2liHwhp1+kt6yyX7loruVnMJZD8gLc4BQ/nXrN1bzXUDRW941nOSDHMqK4BHZlPBU9xx9RXEeOGu9Y8GapousWq2+tWsX22EpkxXSxnLPETzwucqeRkdRzW1FvmTMayXK0fP8A1FL2oAyKWvXR5gd/6UopP84paYhwpw/rTacOlUgHY4pD0paQ0xDTxTDTiKaelSxiUlLyaSpAKPrRxRQAcUUvak60AFHSlpOlABSHp1+tLSf5NACUUvakNIA7daB1opRyeaAJUqdagQVOnStoiYpFRmpTUR602A3vTTTjTTUMYh603tTj7U2pASk7UpFFIBOtGKXrSUAFFLSdqAFo69aO1FABR9RRR3oAPzpeRRRjFAw6UoFHWlxnimIUU4U3tTgKpALig9KWjHNMQw0w96eRTDUsYlHejtRSATpS0dKKQBTu1JSjpTAWjrS0lMBO1If60p6UlIBKSlPNJQAUUUUgDmijpR3oAWlpKXpTAUUvagf5zS96YCdqacU44BphFJgN6UdRRRSAKO1HSlHWkAo/CpF5/wAajHAqVf8APNWgHdB9aMUd6KsQ000inHr1pp4qWMQ0lLSUgCko60YpAHSk70tB6UgEooo/WgAxiiiigA7UtFFMBacPxpo/nTgKaAXv1rtPhZYrc+N0u5f9Tp8Elyx7A42j/wBCz+FcYBXo3wzjK+H/ABncp/rEswq/98SH+eKxxDtTZrRV5oorJc6rbLMGYX2v3+Ae6h2x+QX+ddt4ngh0m+lhChLaOFSuRxtA/wDrYrN8H6QZvGWg27D93pmnG6P+83yD9Tn8K9L1jSrDUIC9/DG6x8h2HK/j6V505WaO+Cep5f8ADrV7nw7dWtnekrperMWhDZAtpiflHPZhgfXHvXp3inTP7W8LajZqP3rQl4j6SL8yn/voCvJdf1qHWFnsNGsGvLbJVrln2LkdNp6kj1r1PwVrb654Wtbm5/4+4s290p6iRODn6jB/GlUX2gg/snlpulvvEt3dpwt/ZW16yj/noyAN/wCPZrr9B0yGbxFa3M0ihLCNo7dM43SkDe2PYHH51wOhwTx+O9SsJV2x6ej24b0RZTt/Q12dvPHHrOkKzndJfL5cY6KCr5z74z+ddMl+6sjjg7VdT0YAYooxS15x6gtH1pKOtAAaSlooASjrS0frQAnbpRiloxQAlGeKydb8R6XoEBlv7lUx0Uck1wl/8UdRliefStG2Wq/8vF4SFP0A6/QZNVGnKWxLnFbnqOaOorH8Nza1caPHPrkcEV1L84iiUjYnYNkn5v5dK1+tS1Z2KTurjqlijmb5okJ29x2qLvV20vUgg2MrE5zxVRSb1Jk2locz4v0u41PTotS01ca1pTG4tcD/AFgx88R9mXjHqBVzR9Vttb0e11K1P7m4QOAeqnup9wcj8K0WmPnGVRjJzj0ridDddG8b63oCkLa3IGpWif3d/Eij23VfxR9CEuWXkzsi1NpM5pcVibBirC29tqUKwXsKy+WdyFhyOCDg/QkfQkVBjipYX8uVXHY1UXZkzV0fJ+uaW+ia9f6XJndaXDxAnuAeD+Iwaz69I+Nmmiy8ei7RcJfWqSk+rL8h/RVrzjvXtU5c0Uzx5q0rB/nNKPpSUo9a0IHCnAUwDA708CqQhcZpD/8Aqp1IRVAMPNMNPPT1pp61DGN7fjRRR3qQCijqOaKYBSUveikAdaP60UdqAEOcUnSl70nXrQAd6Tk/hS0H/wDVSAT/APVSjg0lKKAJUqwvT+lQJU6VtETFbpUbdKkPSozTYDPxFNPSnd6aeKlgNNIacelNqGNCUUYo70gCkpe386KADFFHWigAxRxRjil60AJR2paKYB1oFHUUv60AH1NLjNIOtLTAcKcOtNFOFNALzmiijrVCGtTDTzTDUsY2j/GlpKkA60Ud+aKAFpRxSUo4oQDqO9FFUAh/pTTTu9IeKQCEUn6elL0pKQCf/qoxRRSAKWkpeaAClFJSjimgHUtA5HtS1VgGnpTDTzTTSYDDR/jS9aTvUgFFHU0tADh9KkWo1qRaqIDxyMUhpaKsQ09KaRTjzTTUsY09KSlI4pKQBSUtJSAKO9HSigBOtFHfrRSAKUUlFAB9fzp3Wko7UwFGTThxSUopoBw616b8GLqH+2NY0mbBF7bBwD/FtJDD8n/SvMxWho+q3Gh6vaara/662k3hc8OvRlPsQSKitT56bRdKfLNM+idP0qHw9falqszr5ItI0GOoSPex/n+lZHxRvJTo+maVC7RNqk22XBwREo3OP5D862tSuE8S+BLm60s+YLuyZ4R3J2/dPvkY+tcn8QryPULfwn4ghObN2ZC393zUG3P4qRXkwXvanpSatoYkccVvEkUSKkaDCqOgFdL8LVlm0/WNQ+b7Nd3v7jP8QRQpYfU8fhXE6/M8OjT+WdrPiMN/d3EDP617RpOnW2iaRa6bbjENtGI0yOT6k+5OSfrRUdo+o4q8vQ8c1SZbb4n69CmQLieBSo6kbQzf5961EJh8R6JNgHyr1GkAHOX+X8gCKveLdLsB8QbCW2tliu3t5bq5lyf3mAETjpxzUV1bzW2i3l7bjN5JGWjBONgHfP0Gfyrrh71M4Z+7V+Z6kRikPFR2s32myt7g7cyxLIdpyOQDx7VIa85npJiUdaMUdaRQUUdTR1oAOtHeiqOq6vZ6PaNcXcqqFBIBOM4/kPehJvQV7FySWOGJpZXCIvUk1594h+IM01xJpfhyHzrgHa854SP6n19hz/KsPUda1fxnepb2plhspD8ix8PKPUf3F9zya7/wz4Rs9BtY/wB1G1wBxtHyp9Pf36mtlFQ1e5m5OW2xyOjfDu4u5xqOuXDz3BOd0o5H+6vRfx5+lWNT0uJfiF4b0pkAssPckE/6x0BIB9cEA/jXpGOa5Tx5pDX+kJd2s622o2LGa1lZtozjBUn0I/pSU25aj5UlZG3ea1plixWe7TeOqL8zfkKzn8YacoykNy49QgH8zXmmnXN9dRMt7bfZpscMrBlb3A/xqhL4WlkkM39s3vn9Q5PT8BVqlHqyfaPoj1B/G1sp4spyPUsBViHxlpUo/eGWE+jpn+Wa85sFvo7Ux6gySSocLKh/1i9iR2NSnrQ6aLUmeiyeLtKVfkeWU+ioR/PFeU+MtSvdb8XR6np8v2R9PWKCNg3IZ2YgE/jz25qxfah9jjWOGPzryXiGFerH1PoB3NQaLpsl5e22iQslzcvcreajMOVRVOcE+pIAAqoQUdTOo+b3T0TwZ4vXxBbG2vE8jU4PkliPGT64rrRXjfiHSb3w3rUOoWQbeh/dMP8AlsneNv8AaHY//Wr1Hw3rcHiDRob6FgSww49G71jOC+KOxpCT+GW5qdqXNHSm55rMs8t+PFuHsfD16OoaWEn6hSP5GvFhXt/xxdR4Y0SM/ea7cj6Bef5ivEO1ethX+7R5WIVqjF7UtJS9hXSYDhTxTRThVoBe1IaWg0xEZpppxHFNNQxjaKKO1IA/zmjtR2ooAKKKWgBKMUuOaTtQAUh9qWkxSASkpaKAEPSlHWilXrSAlQVOtQpU6jFbxEwao2qU9Peo2psEMPIpvSnGmmoYxp4pPwpTxSd6kBKSloNSAlHSjHvR/wDqoAKKKKAD8aWjvR1oAO/1o+tGOKKYwpaOgooEAp3frSUvamAo5pw5FJinDiqQC/5xSGlx/Og0xDCKYcU8imGpYxOMUnWlpDyakAoo9qKAFAp3am04U0Ao/Og8ilo+lMBtIRxSnikNIBOlJil6mkOcUgDvSd6OKO9IA60veiigApw4pO1KKYDhS0g4FOxVANNMPTinnrTDSYDTwKO9GM0VIBR6UUUAOHNSL+tRipBVIB/QUGl7Uh6VYhppppx6cU01LGNopf5UlIBKOtFFIBKO340dfrRQAYpKX/Jo60AHUdKOtGKPekAdqX/OKSlpgKKcOabThTQDwKeOBTAPwpw5FWhHpfwm8X/2XqJ0C8kxaXT7rZm/gl7r9G/n9a6m80+3tL298Hakh/sjVN0+myA42MTueIHsVb5l9jivDDnqpKkHIYHkHsa9x8OXtt8TPA8mnX0vlataFf3y/ejlH3JV+uOfxHpXnYqlyS51sztw9TmXK+hxOoWWp6ak+lavZTXlvjYt5bIX3L2LKOQen/167X4feNZL2aPQNVlEl0if6NckEGZQPuuDyHA/MD165cN14m1a3voH03/ibaMqpcADi6+b+D/aKgn0PbrWML+PV9c0IWJf+1Yb+P8Ad7cOig5fcOoAAOc+9YNcyszZPld0zsfE8f8Axc+wUj5ZdLZR7kOxq/jc0nIK52AAdAP/AK9N8YRxweJtF1eQ7Ut4rlW4+98oIH14NVtOm87S4pX4dkDv7M3zf1rWi7wRzV1abOh8K3EUmgRW0ZcmyY2j7+uU4Gfqu0/Q1s5zXE6FcGw8VS25YCDUY+Af+e0Y/qn/AKBXa1x1Y8smd1GXNBMX8aXFIOabPPHa20txM22KJC7t6KBk1mkaNjiyhghYBmyQpPJApcVyngqObVFuPFOoD/SdQJFsh/5YW4OFUemSMn14rq3dI0aRyFRQWJPAAFU42diVK6uZeu65baDp73NwyhgCVUn07n2ryiR9Q8XakJ7oMyMd0Nu4+UDtJIPT0X8TS6neX3jXXHmghMtpHJtgjY7UdhyCx7KAQccnJrqLTwjDBa+brE73rMf+PaLKwu3YFer/APAiR7CuqnSsjmq1VezJ9M1jw94cgcQyT6nfdJWsoTMd3puHyj6ZrRuNX8SamfJ0yyt9KjPWe9YSyY9kT5Qfqx+lNh0mKF4HuEjDxcxQxqBHAPRR0+rVW1TxAlrE8dvtNw4+Ut0Uf3j7enrVxoRWr1MZYiT0WhW1CLVXj+xXPii9nmI/eG0ijtwoJ7sAW5HYGiDR7O3YOtuu4fxSfOx9yzZJP41VstX0myX/AEq83yYLk4yST39yafJ4nsI/LmuD5cAG4oPmbHYtjp9K1UUtkZOcnuzKm8HwyXMtylxfFDkqBLtA+hxnb7VRfT7vQ4FRnd4W+YOzbv510cniu21GX7NpnMQIEs7DAQHsPfr9KxtY8baTIZ9Os2SZ9mwTtzGuepH94j8qppCUpIojUEdtuR/jUF7JdSKq2syQhuGYpub8O1FuLYu1+EaOwt1JLyDaHOMBRnqf8ahjkzp4fcu4SFs549SKnkiX7WdtxEtIUjlz5zsw/eStKdz/AFI5x7dK3/D2p3fhfdbx6MsltMPPUJ8juvHO45DEZ7kYz1rb05tN3JPCYiVAQkdCMDhh65rUZormJfJZVkiOYz/dPTB9j0NDpxaswVaSd0JFq+heMLWTS3Z4btlybS5Ty5l9GA7/AFUmuS0e6ufBHiyS0vCfslwcyNj5XXoJR7g8MPfPrXQ3eiWGqwIZ4CrRn5WRtktu3qrDkVleIIruawjsNYKyzKc6fqoGA7/885R/CzdM9Gz2Nc8qPJtsdMK6nvuembtwBBBB5yKaWUEAsBuOBk9TXOeCNU/tHQFifcJrRvJdW+8uOgPuOn4VU8cXTW134aCOVZtVjbj24/8AZq5OV83KdfMuW5xvxyu9+oaHY5/1VvJMR/vMAP8A0A15PXc/F2+F38QrqNTlbWGOAfXG4/q5rhutexh1amjyazvNsKd2ptO6VuZDhTh0pop4/wAiqQhaQ0vakNMBhph5p596YfWpYxO1Heg0dBUgJS4zRRjjmgA7UUUUAGKSl6UfWgBMfzpD0pT0pKAEIo6UYo4pAFC9aO1KMZoAmTgVOo4qBKnXAFbRExT0qM1I3So2psSGHgU08inGkPNQxjTTacabgVLGIaTpS0UgEope1H4UgE60vb+VJS0AHejt7UUUwCiiloAOlFGKKAFpQKT+VLTAcKcBTR0p4wapCF7Uhpe1BFMBh61GelSHpTDUsY2k60vakqQDrS9KKO9ACilFJThTQC9qMcUUHpTATHNN7U7GaTtQA3HJo70UY5qQEo7fjR2o6UAGKMUUUAL1pR0pBThQgHClpBTu1WhDTTDTzTDSYxp6etJS96THJqAD2oopaAFHFSrxUYqRaqIDh+NBpR0oPStBDCKaaceabUsYlIetLikqQEopelJSAKTrS9utJQAd6KKKACijvRQAUooooAcM0o96QdacKpAOHFPFNH0pwzirQhcV0fgPXm8PeMLO4Z9ttcH7NOD02t0P4HBrnKa6llI6ehqKsFOLiVCXLJNH1qz1D5UXnGbyo/OI2mQINxHpnrXF+B/HVrrWhwJevsvYEEczHuR3P1610V94g06xtzL56St/DHG2ST/SvClFxdmevFpq6M7x4q/8IvNI+Mo67Seoydpx+DGuW0iUy6JYISQ900cmPRVUY/RB+dYmv+I31/VLrTJtbj0/fD8qSqfJLE/dJHKEDBzzz1xWhY6vbadNbNqkT2MUVssEMpIkgkIxkrIuV6AdcV1UVyqzOPEPmd0beu2zSaRPJEzLcwjz4XT7yOvII/z3ruLe4iks7efzkKTKux84DkjjH19K4zTbyXUIGneHy45GYw553x9Afxpmm2t7F4p0n7Tch9Ntmkitojx5bFG25/vHqAT2xRWp8yuLD1eV27nf4way/E1jdan4Y1KwsyBc3EBjTccDn3+ma1iOcd/Smk4riTs7ne1cisrWOw0+2s4uI4IliUeyjH9Ki1Sy/tTSrqx84w/aImi8xRkrkYJxVnPrTkGSB+lCeoNWR59pdla2HiS7061z9m0y0jh3HqXcl3JPqRtp2peLrWyuHlCNN5cYFqoGAzN3/wDr1naVceZoviLVHJBu553J9BuKrj8K4PWGuJLq78p9kFpa7yx53AHYq+2Tz+FenFWijy5e9Js6CbxLrGrRSJZhWeQ5nnY7Y09I19cd8VQjs5rMO+oaoDcyHdxtAz6nd1x27Cr/AIe8G6dcaJZzXr3cjyxCUqtwVQZ5wAO1dn4U+HuhLvJ023upDIhMl1+9Koc8AHjIx6U3F9wjOCW1zy2OPRIZ2a91ia8JOWAk6t6nb1/Orj3Oh3VsbeLT7mWNe0ULA+2TnJr6W0/QtI0uHZY6bZ26nr5cKgn6nHNLpelWWi2P2Owj8uASO4XOcF2LED2yeBS5L9SvbW2SPme1sbO4uEs7DQL24nZuIC/JPXlc/wAxXV2+jeJ9NhXyfAxSNRwIxGW/8dOa9zMERn8/yk87bt8zaN2PTPXFSgcUvZp7i9u+iPmrWg2qyJDqvh3UopouiZZdvuVOPzxUFho832V4rfQ9Zl01wxIWAyqG7kMvI6etfSd7pOnakUN/ZQXJT7hlQEr9DVlFS3iSKFFSNBtVFGAo9AKFTS6jda/RHzLZzaHbrPbpcSQzvxL9r3I/0yQOmKsafbaz/aAjtNRjks9uVnaTLAjpuA6+n0r3zW/D2l+I7VrfUrOObIIWTGJE91bqK5aX4PeEorRng+32c0aE/aYbpg3A6kfd/QUcjWzD2sXo0cPYeLLq21CKy1a1MUxQJIFOScg4ceq9vWum1Ke3k0uKaVEuNPuBtnzyAp6N9M9a8i0y/v8AxJdJayl55baNpo541Hmqoxk/7Xrjqfc16R4X3jw75N0S6zO5UEYBRuePY5/WmnfRkzilqtiXwLZT22u6wokV4YCIXLH52OAyE9m+ViN3fFU/HtyZ/Gvh6xQFjD++KjuWcAf+gGtXwZG1r4m1G3L7lmsoZMnuUZk/ltqndWkNz411vXbo/wCjaOhcDHGY4gQPzZz+ArkkrVGdkHekjxDW7mW78QalcTNukkupWY9f4jVKk3M7NI5yzEsx9SaXtXqQVkjzm7u4vWlpP1pwFWhCingU0U8cirQheozTTThSHrTAjNMNSNioz0qGMQ0d6DR2qQCjANFFAB0ooo4oAOoooGKDjmgBKQ460tHWgBKQijFFIApV60lKvWgCZP1qwlQJU61tETBqjIqVqiIpsQ0008CnU096hjGmm04001IxKOtHSikAYzR3oooASloooAO1FFL2oATrS0UYoAO1LSUv9KACnDr/AI0mKUdaaAUU8c00dKcBVoQopD0pe1B6UwGGmGnn1/OmGpYxKTvxR2oqQDtRR+FHakAopwpBxSgcfjVIBcUGlo/nTAb16UlKaQ9KQCdv6UlL3pKQCUUtJSAKXr/KkpaACnDFJ/nFKBzTQDh1p3UetNFOqkIQ9KjapD15qM9KTGNPNJSnrSVAB3pcZoo7UAOX6VItRipFq4gP6UYo7fWg81YhvSmn3pxppqWA3FJ1pf50UhifpSUvSkpAHeijtRSATHFFLjmjFACdaP8AOaXvSUALilpKWmAopw7elNpwpoB4p46UwdaePWrQgpDTiOaa1NiCOea2lE1tM8Mo/iQ4Nbel6hqmqmSObVZI9mMhI1BIPcHtWAan0+7+xahHLnCH5H+h7/gcGuepTT1saRm1omdfBFYw+FLgi1Qyq0ivJIN7M4JwST7VsWug6aYftGnTXenvhVY20zKHJGclTkd+lYcO6TRdct15dT5ij/eX/wCxNaPhy6mmsbRnYtLLIS3vg4H8hXJBatM3qPSLRrpoXiPTohLZ3waNxv4j8rIHchQyE+5SmvqmvXCiDzTJLE6vm3SGSQMpBHy5ifqOwNek3Q/s7Srljgi3iWIfXH+JriZLKHWt0LwiTaCS/Qr6kHtTcb7GcZdzOuvEt75LLfXjw3XkvbxS3UUls6CRh5jgvlS5AwPmAFd9ZeLtJvIBKHlijHBkZd8Y+roWX8zXJA6v4bt0Nnd3F7ZISJbSVBKdnqueT9Kd5/hS/C3E2jCKRuVubAeQ/wBRsYH+dctSDW6OynUvqmdJP4201NQks7aG7v2SMPusYvPB9fu5Ix6nA9M4NKPGukz2N/5U0sN7bWsk5tbmJoZcKpOQrAZHHbNc74e1nRtA1q+T+0J3sr8RtFeX0mSsibgYmY4xwQVLY6sOcVF8Sr/S7/SbO1jmt5ryW6QQmJwWRP4zx/DtyD25rJJc6jYt1JcrZN4f0wTeC4tOY7Wliwxx7A5/OuYvtKN4fEdvbKQy2EEgVedxUu364/WvRNOiFtpMTsMM0ZbB7cf4Yqn4Rt0/4TPW94z/AKJa8Edv3n+Fd1V2hc46Os7Hium65q2mxIllqM0cQ5EbAOg+gYHFbVj8RfFGnT+dDfQbsYIa3GMfhSePfDX/AAjHiia3iTFjc5ntT2Cn7yf8BP6YrlmIHJIA9TxVxldXCUEnZnVX3xL8ZX9wJn164gIOQlqBEg/ADn8c063+KHje2bK+IJpB6TQxv/Na4/zYwf8AWL+dOVlboQR7UxWR6Lb/ABq8Zwn95Jp04H/PS1xn/vkitOL48+IUA83SNLk9SpkX+prykmk6mgOVHrv/AAv7V8c6BYf9/wB/8KfD8eNWmuIo20TTY1dgpkkuXCrn1OOB715AKXtg8560XDkR9DRePfFbqrf8I9pYB7m+fBHt8n+NHjP4n2Fr4KvYEPka3dRNbx2gcOVLDBfcP4QCSCcHOOK+d8bU2K8iqf4Q5A/LNNWNEztAGe9FyeQ9B+DVo58WXV2o+W1tCuf9piAP0BrptElk+0ava/8ALGz1CWKAD+FNxO38Mmn/AAjsFsPClzqkxVPtkxcM3H7tPlH67qh8OwbTq1wSxE+pzyKSMZXdgH+dYQd6jN6qtTRv6OyxeMrLnBms54yvsGjYf1qn8R3Tw98PNUXf/pOr3pXOezNuP/jiY/Gn6fIsXxA0oSY2y2lxHGT3fKNj8gaxfjNrVmZdEsU8i7aFpZJ4GPA6KAccg8NyORik43qhGVqVjxXbil6Vp32nxrb/AG6wdprEkK2778DHor/0bofY8VmkV6EJKSujkaa3D8KUUn1pRViHCnimj/Ipw/nVIQvUdaQ+lO6U00wGHvTDTzyaYetQxifjSfjS0nekAYzR1FFFABS9aSl6CgBKMUuP/wBdJ3oAKQ8mlpDyKQCd6TkUpopAJ1pV60lKvWhATpVhagTrU6DFbxJYNyaiapW/So26U2Aw800+lOPFNPFQxjT+tJxTiKb2qWMb0NFL2o71ICUdqPxo6CgA60UdaWgBKWiigAo/nR2paAAUf/qoxRTAWlFH0pR1poBRThSAZpw4qkIXtQeaB+tB6VQhjcVG1SNUZ/nUMoSkpfpSVIBS0n40uKAFApRSDmnCmgF7UHkUUUxCH6/Sm9acaQ9aQxtJjNKcUlIA6mk7UvWikAUe9FHWgBaUUlKOTTQDwOKXFIKd2q0IaajP86kaozUsY2k70tFQAUe1FHamA4VKKiWpVFVEB46UhpRzSGrENPSmmnHrTalgIeaTtS4pOgpDDoaTpQRz3oxSATtR3paP50AJRQRRSAMUdaOKKADtSijpQP1poBcU4YpMUopoB4p4pgqQVohAaaad0/GmGhiGt0qJxkEVK1MNQxo63whc/aLu4jfJaSzwfcqdv8jW54b+y211ZRwzLJHbFI3Po5O5vyzXK+CZAniFIdpLmOTaSfYH/wBlNaGizR2z3lvvIZ78xp68ZJ/QVwvSo0dL1po9a17UDJoFy6t8stzx7/N/gKXQLdYPDjzEYkuZNqn/AGR/k1g207avpdvp65Nx9o3tx/Dg8/zrtI7YR+GopePLigwv++zf4Z/OmYmURtHt61i3Xh2ymvJLnfcQtNzLHFJhZDwckfgOnXvXWWtmGt5LyYZihHyr/eas6RZPM3yj5m5waBp2OQm8PXNusn2eO2uEcklcbCQexBGD+n0qlYeFpn1SBmsLe1i34YRhdzcjj5e1djcXKRypCPmmk+6g649fpV+yhEciO/JXLufoP8aVkW6jasye/IHmxr0RQn496ztOli03xrBI8xDalb/ZliCk/NHlw2foWH5VZaUfZPNk6z3Gwf5+gNZVvG118QNGYtgQ29zKB+Cr/wCz1NT4GOk/fRr/ABA8MN4q8ONDbgfb7Y+daseMt3XP+0OPrj0rxvS9Gs0gWWaIzXHIk85eUYcFdp6EGvoppI43jR3VXkzsBP3scnH4V5z498OSWd2/iDT4WeGT/j/hjGSMdJgP0b8/WuWjNr3Wd7jHm5mjkDBDs2+VHj02DFZ11o1hcZPkCNuzxfKf04rttBg067sFmVI5nPVj8wPpj2rQk0iwm+9axj3X5f5Vrz2Z1fV1ONzyB9Gjspc3kk72xOBNG2Nn+8MfqK0P+EatmUNFc3AyMg7gw/lXo0nhexlVgPMQEdCdw/I1l2/g+5s5jBDOj2hOUZuDH/s47j0qva3Mfqii9Voef3ei3lqpdCLmMddow4/DvWcGVlyDxXr7eFxs+W7+b3Tj+dcd4m8IXcAe8t4gZB97YMrJ/gaqNRPcyrYVxV4nIk0qxSzukMKlpZWEcajuxOAPzNNRZRAszp+6Jx5i8hT6H0rs/hpov9reMYJ2XNvp6/aXPbf0Qfnz/wABq5OyuckVd2PSfEFnFo/w6OkRSiMiGK0jcjq5ZRn8Tk1Bo95Hqel294gCmZdzj0fPzD881oeIILTXdUtNBe5eCeF01AlR1VWIAH41zvhh2FpqY27Vh1CcADoOece2c1hQe9zTErRFrXdNub20ilsJfJ1CzlE9rIDjDDt9COK4z4jWUMej+Gb9EZJ5YJIbndyxkBV2ye53O36V6VbPFfWrXELZMf3hXm/xaDSSaLcq58pkkTbnjcCpz9cED8K6YfEjkOEs72awn86AjJBV0ZcrIp6qw7g+lWTpw1O4i/sWGSWWZtpsR80kbf7P95Pft39TmA/nXU+ANbttA8Vx3V2yxxPG0XmsOIye59uMfjV1rxTnHccLN8rMvWfDOteHvKOq6dLbJJwkhIZCfTcpIz7dayxXsXxD8W6ZeeFrjT0uba4muCvlRwuH2kMDuOOnT9a8eHApYWrKrC8lYdamoSsmOFPFMHNPHSutGItIQadTTVCI2phFPYcUw1myhDR0oxR6UgEpcUUUAHajvxRRQAnbFL3o7UUAIelJilNJSAT+tFLSdTmkAhpV6/jQaVaAJ0qdKgTgVOoreJLBhiozUrVE3NNgNptOIpp6VDGMNJ9KdTTxUsYho7UUlSAf40Ud6WgBOtL1pOtLxigA7UdaKKYBjml/Wj6UfSgA6Uv8qBR1oAXGRSjpSdKUUwHAc04U0U8f5zVIQvakpe1IaoQxqjPIqVulRmoZQ3FFHajqKkBKWjiigBRThTeAKcKaAWl7UdqQ9aYhDSEUppDSGJyaTrS0nakAnWiiigAoo/xpeM0AFKKQU4daEA4dKd/nNNAp3arQhp9qjP8A+upDTGpMYzviiiioAOtFFFADxUi8VGKkWriA/tQRR6UHpVkjTTDxTzxTTUsY00lO702pGFJS0UgExmkpe1GOaAE6UUdqKAD9aKXpSUALRR2pfegBQKcP0popw4qkA8U4U0fnTxVokDTTTqQ/19KYEZph4NSN0qM81mxnR/D2FLnx/pUMnKSGRSP+2bVq6xo6aP47vbKJ3eK1lWYO/BLS7Tj8AcVmfDkY+I2he9xj/wAdavQ/iVobL4rW8hjY/a1hZmA7xkgj8ilcNR8tb5HVCPNS07j/AAbKkWp3Mrfdjt3fn6/4ZrpbW+kuPClpGSSXuCGHrjkf+hV5tpupGK1eaLkXFuYz+OP8K67TtRitPDlnNM+FF02OMk/5AqjE72eKKDTUjkOIYl3yY74/xNczPMzl5nyWPOB/IVe8QakHsYYYcs1y2QB3UdP1rPtYcIInfe45c/WkBT07T5VlaaRTJdznkDt6KPYVpOGhMkJYFioDY7e1bvlLpulrKFAuJR8p7qK5Ca7abU10+2J3DmaQc7R6D3oAn1F1WLSIQefPZyB/uNj+dU7uVNL1jSdZbiG3nMFwxPCxyjbuPsG2GtGXTxc3Fv1xA28kdOmOajkhhurW5t5oxJBMSjK3dTwaGrqw4vldzrbu2ivIDDKDtyGVlOGRhyCD2IrP07W7XUbi6tEkIu7Ryk0TrtYejY/unqDWT4J1S6u9MudO1CQSXumT/ZnfvImAY3P1U/pTPFWhXMk8Ou6OfL1W0GOBnzo+6kd/p3+uCPNcbS5WepGV4qSKupeCLi21CTVPC7RRTScz6dKdsM/uh/gb9Pp3owa5BHdfYtVil0q//wCfe8Gzd7q33WHuDXV+GPEltr1odo8q7iA8+3Y8ofUeqnsfw68VtXttaalaG11G0t7y3PPlXCBx+Gehq1PpMqE5Q+DbsckegPbsfWkJ4qWb4e6OrFtJ1HVdGJ6R28/mRD/gL5/nVN/BfiWM/wCi+MbSZewurAKfzWqSi9mbLFW+KLJetGAQQwBB6g1W/wCEO8aMfm8S6Kg9UtmJ/UVJF8PdUlYf2l46uSh6pZWqxn8Dn+lOy7ieKXRM8/8AFenx6Dq8d3axb4NQyj2y8kydiB75r0rwB4Vbwt4aWO4QLf3Tedcj+4f4U/4CP1JrT0fwT4d0K5W7tYJ7vUB0vb6XzZB/u9lPuADVjW9Wh0tLeJsvdXcoht4lGS7EjJ+ig5NE6nMuVHKormc2rHMWuo2lz/b3iWSAfaNM8+CGQZHmQKAwx2OSG596q6C0cmnm/EbIl9K1w6NztL8n8K2/E1lZ2HhmG3UCGzjYW7LnGY5AYzz9XB571k6O8b6DYbMbDbRgY9lArSilqznxLeiL2k2iWOqvEhzbXUZ2+xHOP515r8V/Mtr6wsWHyIZZUJ7htuP5V6hZv5bxyYyqOCfY/wD6q8f+J2p3d54sl0+5VNunu6wyqCC8T4dc/QECuqHxHIzjh0p2ePamUtdJIoAHQY+lOFNpwpoBwqQUwU8euatCF7UhpaQ9KbERtzTDTzTDzUMob25oxR+NHbrUgFFHaigAoo70daACjoKXpSfSgAppp2OaTvSASkNLRikAmeaVetIaVaaAnTirCVAlTrW8SWDcCo2qVqibpTkCGU004+9NIrNjGmmnpTjSVLGNooopAFHWijAx0oAKBRS4xQAUUd6P8aACilox/nNAB3ooopgLTvrTRTh1pgOHWnCmj3pw+tUhDqafpTuo6UnemIjI/OmGpD0qM1LKEpPal60VICGlpO9LSAUdKUcUgxThTQC9qOo4oFBxVCG0nelNIeRSGJ2pOlLSdqQBSYpfpSdaQBil6ijvR/OgBaUdaT/GlFNAPHFL1pB6mlxVCEPNRtxUjdKjNJjQyiiioAOhoo7UtADhUoqJalWriIdQetL2pCM1Yhp5pv8A+qnHpSGpYxppKU9KTkUhifjRxQffFHWkAlFLSdqQB3pO1LziigA70Uf40UAH60vU0lL2oAXoacKaOKcKpAPHNPAzTRThVokXt15ppp+M00jFMBh5phFPTdNKsMCPNK3CpEpYn8BXUab8PPEepYZ7aKxjPObqTDf98jJH4is20Dko7kXw4GfiRoIx/wAvP/spr6C8T2f26ymCjMkWXUY/OvI/DXhFvDfxP8LxvfLdSzSSuwSPaE2ocYyec8+nSva7j/j4k/3jXl4x2mj0ME1KLaPnO6WXT/ECWiuRbTSPOq9scjaPoefxrdubonSLe2/gjmZwR6kY/pW54t0GCPVmSSEGGQeZEe65PIB7HI/lXCXtxLZ2U9s7FngmSUMerxlsZ/x960pz5kZVqfI7nqSXaPYWt9IPuWiKPb1rS8PxvdCJpBh59rsPTPP6KBXnl1rSC2sbFJWKOj/LjqQQRg9+CeK9K0G5ia5jmRgYym4EegUVozA0/E115QduohiLAfhmuT8L22NNn1CX/WSucsf1/WrFxdzX+l3Vw7lmlV9uT0XkAVPBH9k8D2aj7zPgn6k0hmhcsLTSYyP9bcfNz6dq5W0vpHMk8vy2zf6oE/wj+L8T+mK6XWG33uwfciAQD2A5rIEAkjNww5APlr247mmIzdVu73QZ4vEFgP3JaNNRh2/6yAE4b2Zdzc+h9BXbaVrdjq815Day7pbOXy5VP6MPVTg4Psa521hlWFvtDbzIMbTyAK5qztL3w34oZ7GP5ZHMtuDws0bYMluT2II3IT0I9zWFakparc6aNZx917HX+IPCzPdrrGjO1vqMeW/d8b/X257g8H681Ba+P4bO3/4nkLwFOHnijLJ6cqMsp9sH610+nanaaraLc2kodCSrDoyMOqsOzDuKpaz4YttVR5kxFcsMFsZWT2Yf1rkT6SO3peJNa+IdFv7Zbi21S0khPAYSgc/jyPxq9HNDLGJIpUdD0ZWyD+NeBeJNMttJ8T2EF1bx7nbZNF1BXKqDx/nirreDxp+mH97esQm4iKRgEBPJIHpW6w6aumc7xLi7NHuDSxqMswA96ie/s4gpku7dAxwpaQDcfQc814pc+CrGa0LDULzICkSPJvGOp496mtPBWkRXcNyJJplRg8luZB8yk8HpkDij6r5i+ueR7FqGsWWkW5mvblIlAyAx+ZvZR1J9hXM6NFP4h8VHxDcp5dtAnl2sTclMjv2zySfqPSuD8N6PFqJlm88rPHO8M4lfc8fzHAGfavQdW8SWng7TLaGKzkucKSQjhQgH8TsemSfx5rNw5Xyrc3UuaPM9ip4zikutcXTnmZLbUbBo17+XLFIH3AeuGxT7KyhtLCG0tgfLiUKvOTUMQudWvk1jUEEbiLFrbhs+UGwWLH+8cD6AAdc1pW6JIdocxzZ+U9j7GuunHljZnBVkpSuiayglVyzITEflfH8P1ryH4q2fleJoboYPnQ+Wx/2kOP8A0ErXskt7NbWtxeIoFxarvljH/LRR1H1xn8cV4j8TfMHi2R4bl5NOvUS+t13ZUb1AOPxU1rDcyZyIFKOlNHIp1dRIo/SnAcf0ptOFNAOHaniminirRIvWmkcU7g01hTYEbUw09qYcVmyhO9J2paKQCUvfmiigAooooAP880dBRjNFACUh6dKXtSH+tJgJRRzSUgDvTlpvJpy0ICdKsL0qCMVOvSt4ksGqJqlbpURpyBDTTCKeRTTz+fWoYxppppx6001LGJ3pP50ppOtSAd6OlFFAB39e1LRWp4c0K48S+ILPSLY7Wnf55MZEaDlm/AZ/HFKTUVdjSu7Iyi6jjcM0vavq2y8F+HLbQX0WDTbY2rx+XIzIGdyR94tjJbvnt2r5Wmt2tbqe2floZGjJ+hx/SsKVdVG0aVKTha4zOBycD3qaS1uIoEmltp44ZPuSPGyq30J4Nel/B3wVBrd7Nr2pQiWzs38uCJxlZJcZJI7hQR+J9q9s12wt9W0DULG8jWS3lgcMpHQ4yCPcHBH0qamJ5ZcqKhR5o3PkOimocxj6U4fpXUtTAUUo5pOlLiqEPFOHvTRTh/OqQC9aQ9KdSHnrTERtxTDUhqM1LGNopfxpO9SMKKKMUALTsU0U6mgFHFHSloPSmIbTe5pxpMc0hjcZH40dDR0NFIBMYoo/rRikAUciiimAuKcKbThQgHLxTu1IKXFWhDW61GepqRulMNSwGGkpaDz+FSMTtS4o70CgB69akWoxzUi1SEP6CgijoKD+NWIaaZTjSHmpYxpFJ/Wl6GikMTmkpetJ0FIA7+1J1NKRmigBKMZoooAOtFGKWkAdKKMUvWmAvanAU0e1OX+dUhDx09qeBxTQKcOBVIQveu3+GXgiPxjrskl/GzaPZDNxglfNc/djyOfc47D3rhnYohIGT/CPU9q+mPhRZR6b4EtrZUCzLI7XDd2kOCSfoCB+FceNr+zjyrdlKLaucn411DS/Ah+w6Vp8Fmdq7VhjAeUnoM9/qc1yNrrmvXMLPeziLf8AdjRQGX6n1rp/jHYNdT2Wp2675rMNKy/3kDD+WM/TNcrFKlzbpNGco67hUZe+enzNnDVsavgXfefFbSi7s5t7aeUljk8qV/rXsEh3SMfUmvLfhTbed451u/8A4bOxWEH3ds/+ymvUc5rlxrvUse3gI2pIw/E+m/b9LaSNczW58xcdx3H5fyry680y21MzW8nyzBd8cg/ungj3Ga9uHWvKPGNgdB1eO7QYtkfdx/zyfhh/wE4P0FZUpW0Oqok9zz90u4oHtZh5dzZSRujk5AyduQfT7tdn4Z8S/ZtPc7NysrLsB5jfGCPpmn31rBf2ktvMPlkQruHUe4/HBrnr6BdJvbWWIkW8yiCYn/noB8rH64wa7I1L6M4qlFxV0egaDdC40YoTzGzIR7Hkfzrbb954RwPvW7hiPo3+Brz/AEXU/sd0rlv9HlO2Qenv+FdZp2pxyapeWRIKMhUYOQ2Ov8/0qznNm7KSadqd4T04Q57kgfyNNtLbdFuYfu4It7e57D86qaoXtPBL7cEtdKhPt2/kK1o/l8MCUdZyn5YzQBSgjDW9xM/8CjH+8T/+uoFDOG2jkAk/SrjgR6GvrLNz+AqOzTNpdv8A7CqPxP8A9akMwrjTp4rsalpMq2uooc7jnZMMYKyAdeOh6ipf7Q8VT3N1NFd29jFcrEVhkTzjAVGG29Bz15yOe1aSxsQxAztGT7CmAF3CqMsegqXCL1aKVSSVkzFg8O2SXb3lyHvL52DG5uTubPbHZQO2BWxbQ+QjHfudjknH6UpH3SOhqR8282yUfdPI9aokq3Ph60k0Ytalod0mJEzkL6bfQe1Z+r6WZYrO4tQPttkm2PnAmT+KJvY9vQ106RmOOeIHdHKm9G9xz+eM1FFGk0TRkDzV+Zf9odxQBy914cgmuhf2ss1jeOq+ZJFg7h6Op4b607T/AA6lvL515cy3s24sPMwsan1CDjPuc+2K3WV7aVXGHQ+vcdwatx2QnfZE3EiF4WPqOx/lRoO7tYrW9tJPvERUuvOwnBP0q9Z20d6DAy+TdqPlJGA+Ox96yEunUSTRjFza8yIf4k6H8jXUWjx6jBFOnDMu5G7gjsaBGZqSSKkcsikNIpilHrjg5/CvnzX7wzR2umzKftGmSz2+4jrHvyo/A7h+VfQ/iy4Y+Hpp1HzRozMM45VSf6V836zdxX+s3d5D9yd/MBIx1AJ/XNaU1qJmeM06k70o+ldBItOFNpw596aEOFPA4pop4/XNWhC00indqa3FNgRtUZqRqYetQxjTnFFHOf60VIwoo6e+aPagAooooAO3rR/jS0negBPak7U6mmkAlHQUHpSUgDrTlptOXr0oQFhBVhRxVdABVhK6IEsRhUZqVqiPSmxIYeKb3p5pprNjGd6b0pxpDUlDaQ0pFFIBKKKKQBXrfwQ04GXVtWZeVC2sR9M/M38kryXtXvXwahVPA5dfvSXcjt+gH8q5cXK1Ox0YaN6h6JbEx3CMOmcGvlrxZB9l8Ya9F02X8+P++zX1KAAw+tfN/jaz8/4ratadpr/n6Ngn+dc2EdmzfFK6R7l4K0z+xPBWlWQyr+Qssg/23+Y/qcfhW1cyMmlamSScWkrD6hTUhUKAq9AMCorhS+n36f3rSUf+OmuZSvO50OKVOx8ixf6tfpTxTI/9Wv0p9e5HY8gUdacKbTh1qkIcKcOlNHWnirQC9qQilApDTERmmHpUjc/hUbVDGNopaKkYlLSUtAAM5p/b6U3tTxxTQAOaMZpaOpqhDPxpDzTjTTSASkNLSVIwpPxpTzSUAHelpKP85oAWnDoKQClHtTAeKdTR0p2eKtCGtUbCpDUbcVDBDD/OjvRR9KkYdqUc0mKWgBy9KlXrUYqRatCY/HHWk60o+tIasQ0009KceRTaljEpKXv9aTrUgJ2oo6Cj8KBiUds0Yo4pAFFFHWgA/SiiigApR9aToaXqKAFGKePpTR0pwqkIeKeBTV61s+GvDl/4q1uLStPXDt80szD5YY+7H+g7nFOU4wjzS2ESeEdIk1jXkKxGSO2IbaBndIfuj+v4V9JaPp50LQIrRj/pDkvJg9GP+AwPwpvhzwpovg3S0gsIQGUZe4l5kcnqSe2fbiua8UeMYpJX03S5PPvJPl3R8rGO/PrXzuJqyxE7QRVSso0+VFG6u11PX7qQYeCBPJU9j6/rmvPNQsj4X1fyG40u6Ja3c9Im6lD/AJ/rXeWFoLK1WLOW6sfU0zV9Lt9a0uaxuR8kg4YdUbsR7ivdw2H9jSUVueSqi53fZlj4S2nl+FNS1Rh82pXrFD6xp8o/XdXbjgVxPw41nbph8K3qLBqOlgqFHAmiJyHH58/ge9duRivGr39o7n1OG5fZrlA1h+K9JXVtFmXZvkjViF/vKRhl/EfqBW30o7VknZ3Ohq54fo9w/wBmezmbM1o3lMT/ABL/AAt+Ix+VWL2zjv7OW2l+5IuMjsexH0ODVnxjp3/CPeKI71Rts7n5HI6BSflP4Nx9CKXbXXfqjFbcrOOtL24sLoWt8MZby3b0bs3+6w7+ua6a0uDazRXKH54mDY9fUf0pNW0xNTsJIsKJiuI2PbkHB9jgVjWd1JZ209vqAZZrfG7nJdGOFYevXFdEJ8xw1abg/I9WvJYtQ8H3ggcMgKXCH1wRn+tW7afzvBtkc8KxU/hn/wCtXnmi65JYwT2/EltICHjz6j7yn0rpdJ1SN/D1zaI4JikEgHfaeD+o/WqMjorgo2iWfPJdjj8aZbkLYXa+pQj86y7LVYdQs2hibLW8rDH+yehH45qeC9jlkntkJ8yMgOO3YikM1obUiKKT+G4jdD9ecfyFVGgMljDexcOr7Hx6jof5VuJxoSOOsY3Z981Q0ko8Wo2zdFIfHsc/4CgCtqFuqMJEGI518xR6HuPzqDXo2Wyt75ejIu4j8s1rXSCTRXB+/bybh9D1qrKyTaTYwyf6uVZIz9M9aYg0G5juIoo5OVkG38ccVVljlt5pgn34G5I9OxrK0KaSCGSJj+8gl4/p/I10dxtPiHB+7dWwfHrigZWmeGW0juT8sUzFXUD7jjuP51a0yMraShj80EgdSO2R/I1z8c7wjWNNc8Q7LiPPpu2n9CK6TQj5llL3IjKnPcdR/WkBz+uFbDxFa34GILgeXKPUHg/zBrQ0t5bOx1K2U/Nbneh9P84FZ3iQfa/DkMw7MMfqKet8sekXd+zhd9nh89yOKAMn4ha9Db+HL+B5Akt5bhoV9SSAQPwNeGDgYrrPiHere6ppYVt0cdkrjn+8x/oBXJ9a3pLS5LDg0vWkpelbCFFOApop4poQ8dKeOaYOtPHT15q0IWmtTutNamwImph/SpG5qM1mxoQ9aTFL+tJipGFLxmjoKKYCdaWjtQRQAY5ooo7UhCGmmnHpTT0oYxPpQaU/SkqQClXk9aSnL1oW4E8YqwvSoE6e1WF6V0RJYjVG3SpGqM9abEMpppxppqGMYaTrTj+dNqGMTpSdDR0o7daQwNFFHWkAV7V8HdSWHS0sXYBZi+zP99WJx+IP6V4uBXV+CdTubd5beGGeTZKJInhQsUY8dvXH865sVHmgb4eVpn0i3FeC/EG3az+MbTY4uUjnT3/d7T+qmvbdKurm8sI5bu3MEx+8p/njt9K8u+NAhs/EPhnUTw+JI5Mf3FZSP/Q2rgw7tKx24he6meswXC3VtFOhysqBxj3GasW6CWUxn7rqVP4jFefeE/FtpDp4tbmdGgQExTIwIC+hr0OzINxEw6EjBrLlcZGjalHQ+QHiaGSSE9Y3ZD+BxTav65H5XiLVYxxsvZl/JzVEV7sNYo8h7ijrSik60o4qyRw96f26U0e1P7VSELSHil7Uh4H1qgIz/kUw9KkP0qM/zqGMbRRjNH+RUjE70tFFIBR0pw6UlOAqkIXHFBpRSEcUxDT0pMU402kMaetJTu9IaQxOtJS9veikAd6KO9FAC9qcKb0pwGD/AFpoB4pe1IBTuoqyRjdKjapDwDUbVLGhtJSmkqBhS/4UUCgB61IOajFSrxVoQ7tQRxQKD7VYhhppp55FNxxUsY2kP86XtSHrSAKTvS0nakMKTrS4zRQAnFFBFApAFH/6qKO1AC0tJR0oAcOtPAzTAQOScD3rpdE8F63rdq15DbG3slQv9onGAwAz8o6t/L3qrpEyaWrMewsLvVNQhsLCIy3Uxwi54HqT6AV7Z4V8Mar4Usnt9Pu4lkmIaeYAZkb8RkAdh/jVH4VaHZ23hoauCJLy8LB5COUVWI2D8sn8PSu8ZhGpZuAOSaidONVWlscdas72iYepWmozQf6fqkkm7hY1PB/p+lZttp9tZg+THhj95icsfxrRuLg3Mpc8DsPQVCaulh6dPWKOOU3IQcUDpS4/+tRW5Bg+I9Put9vrek/JqunnfHgf61P4kPrxn9fWu68O+ILXxJosGpWpwHGJIyeY3HVT9P5YrCPH865hLxvAvij+0kB/sPUXCXsajiGTtIB/nv7V5+Nw/PHnjuetluL5Jezlses55pO9JG6SxrIjK6MoZWByCD0Ip1eKfRGD4u0WPW9CmiZN7opYAdSMcgfhyPcCuI8Lae+r6TdWwl3appzbJIz/AMtozyjj3I/lXqwrzbWlPgvxra63GCthL+6uQOnksev/AABsH6YranJtcpnNWfMZ0iNGxR1KspwQeCKz7/S7bUjEZgytGwIZDg4Bzj6cCvUNf0GLVovtNvtW6C5Vh0kHv/Q157NG8ErRSoUdThlPBBrSMr7CcU9Gcbq0NxpuswfZ+IJXJhJPyhz96M+xPI9CfrWhZ6gjsRFKUd0OUPDY6MpHqDWre2cN/bmGdSyEgjBwQR0INYut6U8co1WyUm4ibfKg/wCWi4wT9cfnXRCpfRnLUotXaNXT757C8SZOV6Ov95fStibUI7XxAl7GxNvOqs2PQ8H8QRXIWmrW1zMIvmQtjy3b7snHIB9R6VoljtCHJVeg9K0OY9WbWIYrK808uDJ9l8+PBzuBPOPyz+NVdIvI5NXv0Rw26zLcHuMV57bXssEqykltsbRgE/wlSMfrVrQ9S/s+8mmZutvIn1JHH60WA9D+2feiGMXMA/Mjr+Y/WqMkpNtDGOkQb8yc/wCFYX9sRraWzbsSRWsaqSerF8fyUmnvq6rpU9yhDZZ1X/e3YH6ZNAhl5O9n5N5GcLMWjcevcGuja8SXWtJdDybYbh6Zz/hXJatPHJ4dsNpG5pSfyGD+tT6TeLP4it9rfJDbqpP0Xn9TQMu605h8Syhek9nIrfgQR/Kt22uWs9GuWXgvEFH1PH9a5OKb+1NdvbzJ8pV2KfQE/wCAP50ur+I0jX7HalZUxjd2z/WgRf1K+jGnWthu+fypJnH90fw5/OuH8Q+IHTT7fTAV8me42liegUgn8MkflReaisZnkuLgebLE24sf4QP5dhXn1xMby5kkdmZS5KAn7oJ6VUY3YXEllkmk3u+4gBFJ/urwP0pvajHFFdCVkSLQKBS1QCjpThSU4dapCHAU8UwVIOatCCmmn0xqGBGetRmpGqMjNQxiUdT70dqPxqRhR/OjtRQAdKKXvSdDigQd6KKKAEpMe1L15pKQxOtJSmkpAFOWmn86cmaFuBYT6VYXpUEYqwvSuiBDEbrUTDH51KwqNqcgRGaaaeeaaazYxhpppxxmm1LGJSd6WkqRhS0nelHWgD0T4Q+HLHXPEF1dahCk8VkgMcMgyrOe5HfAHT3FdWdDvZdd1rXvDqQw6nYag9s9ltCxXUIRCFwOA3JIPr+dcf8ACfWl0rxG8cjBY5sbs+h4z+B216jqFrqGg+JrjW9OtZL6xvVUX1rDzIrqMLLGP4uOCvU4ry68mqjTO+jFOCaNTw54itPEFkZrcNFNE3l3FtKMSQOOqsP6968v+OxL6toCdvKk/VhXSapnVvFNlqHhN5bbV2jcXck9rIkLKq/KkwZRyTwCOR+ArivifqN/qN9op1XSJ9Nu7Yukqv8ANE4yp3RuOGHBz3HHWopRtNNF1pXhZnd+CfDOkSNNqMun273SS4SQoMqcflmvQo22yIfRhXI+BpEa1vowwOJVcYOcgjg/pXWdCPrWM2+bU2ilbQ+W/FsXkeNdeiPG3UJ/1cmseut+J9obP4k6wuMCZ0nX33IpP65rkv8AGvapO8EeTPSTFpwFNpwHtWxA4Cnj+dMH0p4qkId2pp5p340jUwI26dajNSNUZ6VDGNope1J0qRhR+tFL3oAUU4dab3/nThTQh3WiijtVCGnketN7040hpDGnP1pKU0dqQxtHWlpKQB1o/E0Uv1oAO1OFJgU4UIBwp3akApe1WSNaom61K3T8ajbrUsaGdKTt0pen59aO9QMKODR2opgPWpVFRLUoq4iY/oP/AK9IelL26UGrEMPSm080w5/xqWMb3opaTGTUgJ1pKWigBPrzR0oopDEope1GKACjtSnAHPH1q1p2l3+rzCOwtJJucGQ8Iv1Y8CpbS3BtIqYqW0tbm/uBb2VtNczHokKFj+leoaB8K9P+SXW9US5k6/Zbdtq/Qt1P4Yr0ax0qy0q3FvYWkVtCP4Ylxn6+p+tOK5tjmniox0jqeXeEvhjenUbe+19IY7aI7/se7czt23Y4x3xk+lerzXsMClHwTj7gHaql5eeWTFEfm7sO1Z2M9ec9Sa1jBI4KtaU3dmT8MrhIJdd0VWylrdedDn/nm+f/AIkfnXV6rPk+Qp4HLf4V5Z4Zv30b4jJnhL2F7Zs/3hyv6qB+NehsxYlick9TUwRdbp5kfSlFH40lanOHWiijFAhDVe8s4b+0ltbmMPDKu1lPcf41YoxmgadtUZHgXWptB1M+D9WlLJ97Tbhv4067Pr6fiPSvSq808R6CuuadsjfyryBvMtpgcFHHv6H/AAPaui8CeKj4j0t7e9Hl6vZHy7uI8EkcbwPQ/ofwrxcZh+SXNHY+ly/F+1jyy3R1VZPiPSE1rRprdkDuFJUevHI/EfritbpSdK4U7O56TVzjfh5q73Gky6LdyFrzSyIst1khP+rb8vlP0rU8QeH01WLzocLdoPlbs49D/jXK+Ko5PCXiy08TW6H7Ix8u6VR1jY/N+Rwwr0aORJYkkjYOjqGVgcgg9CKuej5l1Ih/KzyN4nhlaKVGR0OGU9QaQCvRdd8PxatH50WI7tRw3Zx6H/GuAnt5bWd4Z42jlXhlatYyUhtFW4s7a7tzBPCrRntjGPp6VjXEF1pZBPmXVl/fC5ki/wB7+8PfrW/RmtIzcTKdOM1qci2rxJI728n2lMbnhU/MP9pPUeoq3Yahb6hD5tu+ezKeCv1rWvdNtL8D7RArFTlXHysv0I5rFn8MNbu13ptzILoDhZcFZB6HAH5//rrZVE9zllQkti/JIZpM9F4AXPQAYFPEr+QkJb92jFgPc9T+lZ9vdXjq6vpd2twi8rtG0n2Y8YpYIdYmcz3Fu0Ma/dt4ipZv94k/oKrmRmoSfQvtM7xojuSsedoPbJyaBfpYWs8jSeXvXYWI/h6kD3NZN3LeQIJryOWBCdqQw8uSeBvcjao5qx/wj9y1m/mXu+8KlVLZ2R564HUnryaHJDVOT6Gimqu2li3tx5ccnzySZ5Yf0FYct/KQGtYWlLttjb+AAdSfaotbsPsdraWzXUsktxMsYwdiKo64A/DrV+NViQIgCqvAArWlHn1OevJ0tOps6R4Nle98zVLgT28ixtINvLYOSnsuQPqOO5rZ8c/D2DWrc6rosKRX6rlokAVZ1Ht0De/fvV7Rbj7XpcMmfmUbW+o4rfsL3yW8uT/Vk9f7prq9mktDzfbz5rtnzW6PHI0ciMkiHaysMFSOxFN6f1r3D4geAo9egbVNMRU1RFywHAuAOx/2vQ/hXiLRyxqzSwyRhXMbFlIAYdV+vtUXtuehTqKauN6UvWkBpaZY4U4U0U8VSAcPanimgU8cVaEHXmmNxUmM0xqbAiPSmHrT2phrNjGn60UtJ0qRi0UUg5NAC0Un+TRwaBBR3o6UUAJSe1KeRSdRSGJ3NIfWlx3pM0gCnJwabT05NC3AsR9Ksr0qtHzVleldMSGI1RNUzdajIz+dDBERppp5GDTD/KoYxh5pvvmnnmm1DGNxmjpQfzopDCiiikBPa3cljdR3UX3oznBP3h3FfQXgvxdBqtjBbzyDeRiJ2P3v9k/7Q6V8/wCn2balqVpYJ965nSEY/wBogf1r3bxP4FuEvH1bw0qCZgPtFix2pPjoynor/lnrkHrwYxRbSe52YVySb6HfDkdaS4trS/tHs9QtYru0k4eKVQwP59/evNNK+IN3ZzjTb+wuPta4zBcoUkUf72OR7kfjXpFtcR3drHcREFJFyMfyrgalA7Lxmjz42h+F/iNJN7y+FdQPlJK5LNZPnIVj3Xrg+hPcc+iKwdVZSCpGQQcgj1pt1aWupWM+n38Kz2dwuySNu49fr3z2rh9Cnu/BGvR+E9XmaXTpyTpF8/Rh/wA8mPqOg/wIq376utyF7js9jkvjnpvk+INK1RR8l1bGFj/tRnP8nH5V5ZXunxy8s+FNLyB5ovvl+nltn+leF8CvTwkr00cGIVpsXvTgKSlHpXWYDhUgHFMHSniqQhR0pDn/AOvmlHT1pGqhEbdKjNSHkZqM1myhKSl7UdRUgFFHejtQMUcGnCminjNUhCijvRQaYhvakNOpp5/nSGJ3ptOpMcf0pAJikpepo7Uhhj2oo6f/AF6BQAU8dOKaKcKaEOFOpB1penPaqENb/IxUbdKkbio26VLGhtNpetFSMKKO9HWgB4qVRUa1KvSrQmOH/wBeg0tIasQ08005zmnU01IDT0pDxTv6UmKQxKTtTj0poIZwqZdzwFUZJqWwCmniur0f4d+JNZ2ubQWNuf8Alpd/Kcey/e/Su80r4RaNabZNSnn1CQdVJ8uP8hz+tLV7GU68Ibs8bt4J7ydYLSCW5mPRIULH9K6Ww8B6zckG6MVkh6hjuf8AIf4ivXbyOz0iBdP021htUIzIIUC59Acda0fDmg/bh9rugRbg/Kv9/wD+tXFicQ6elzNV5Tdoo4bQfhhalhJ9nl1CQfxS8ID9On55ruoPB1+sSoBbwoo4QHAH4AYrtUZIkEcahUUYCqMAVDdanZ2MYku7iKBT/FK4QH868uWJlJmqpc3xO5xs/hPUogSoikA/utj+eK52/wBcv9Hl+yqjhh94Sg4x7f4ivRBrllqTbLW+tpR/cilVifyNVdQ0+11O3MN3Csids9V9we1THGThK5E8OraHF6fqkGoJ8vyyj7yE8/UetXhzXN65oF14eu0nhdmty37uUDkH0Pv/ADrc0y7W+s1l6OPlcDsa9/CYtVl5nnVKbgzz/wAWwSWOpS3cIxLbTrcIf1r0e3uYry0huYTmOZBIp9iM1zXjC0DtFMR8kqGN/wDP0P6U3wBeNLoclhIcy2MxiIP908r/AF/KuhaSNJe9Tv2Oqo60HmkNaHOHWijrQeASeAO5oAKMVWfUrGI4e6j+gOf5U1dX09jgXKfjkVHtYdx8rLgrl/EFrd6NqkPizR1zdW3F3COk8XfP4f0PaunSSOZd0bq6+qnIobgfWlOKnGzNKVSVOSkjo9G1mz17SbfUrF90Ey5APVT3U+4PFXuteQ2V6/w98S+bz/wjmpSYlUdLWX+8Pb+n0FeuI6uqsjAqwyCDwRXgV6LpTsz6vDV41oKSKesaXDrGlz2MygiRTtz2Ncx8PdRlhhuvDF+T9s0w4i3dXgJ+U/h0/Ku1riPG9lPpGoWXjDT4y0tk227jX/lpCeG/n/XtUw1XKzWSs+Y7nrWdq2jW2rwbZRslUfJKByPr6j2q5a3UN9Zw3dtIJLeZBJG4/iUjIqXrUaplbnl2paVdaVP5dwnB+7IOVf6GqNetXFvDdwtDPGskbdVYZFchqng6RC0unPvX/ni55H0PetY1L7iaOTpwFSS28tvIY5o3jcdVYYNMrUkCc0m6gnmkPNAGN4qkVfDtyG537VUerbhitSEN5Ee/7+wbvrjmsW7/AOJr4igtAM29jiab0Mh+6P6/nW8OTVPRWIWrbOW12zfWNY+yxuVNrbGRWB6SMeB+lGmXDXdkruMSqSki+jDrV3Q8Xdzqd9jia42IfVUGB/Oq08P9n+InUcQ3yb19pF6/mOa6sPO0uU8/GUuaHOjrPCdxiWe1PRh5ij3HB/p+VdR0NcLokvk6zbN2Zth/Hiu5avRR4U9GXbO/8rEUhynY/wB3/wCtWP4j8PTyTSatooiF8y4uLWVQ0N8g/hdTxu9DVg1oafc5PkOf90/0pSimhQqOL0POB4J0TxdZSXugs+k6hE2y4sZcsscncEdV9iOPauQ1bwd4g0Ms15pkrwr/AMt7ceYmPXjp+OK9e8RaPc2l6viLQ1A1KFcTQfw3cfdT/teh/wDrVsaJr1nrlhFeWknD8FG4ZGHVT7islHodaxEoq+6Pm1XVvukVKBmvoTWPBmga8Ga90+MTH/lvCPLkB9cjr+Oa851r4UarYlpdHuEv4e0UpCSj8eh/T6U9VubQxEJb6HCAU8A0+8s73TJPL1GxubRhx+9jIB+h71EkiP8AddT+NaKSZsO7U1uafimsMCmxkJ5qM81IwqM9azYxKSlxRSAKOlHaigAxSdvWlooAKSlooAaetJTjTTyKTGJSUtJ7VIB2p6UzpT0601uBZjqynSq8f6VZQcV0wIYyVhHGznsM4r2bwv8ABrSm0i2vfEdxczXM8ayGCKTy44sjIXjkn3zXjFwu6B19uK+oNE1VNZ8M6TfoflmtUYgHo2MMPwIIrgx9SUErHThaam9Tznxb8GYY7N73wnPNJJGMvYzvuLj/AGG9fY5z6144wIZkdWR1JVlYYKkcEEV9bq5Q7lODXlPxg8HRvB/wlunQhXUhNQjUdQeFk+ucA/UH1rmw2KbfLI2r4flXNE8cNNNONNNeicQhpMUppKQwo+vWjFJ2pAdl8LNPN/8AECxYjKWiPcN+Awv/AI8wr6NFeQ/BHSittqmsOv8ArHW2iJ9F+Zv1K/lXrozXj4qfNUZ6eGjanc4j4oqv9iWlxDErahbz+bC3Q7APnXPoRgY+npUPgbxJBLHHF5mbW6+aIk/cfoVP8vr9ai+Jl35Mbf8ATG0ZvxY//WFcBZwy+Ho45grvpssUbTkAnyJCAN/+6T198URjzQ1G5WmfQZGKqavo2n+JdIk0rU490L8xyKcNE/ZlPY1heG/FVve2sdveToswA2TM3yyDtz6/zroxIki7o3V19VORWOsGaWUlY8G+KFx4htDpWg64ol+xtI0F+vS7Q7QCR2YYwfr+J4AV9SeL/D0Hi/wnd2EqA3UaGS2kxykgHyn6HofY18tpkqP1Br1MJNSjZHnYiDjLUdSikFOFdqOccOtPFNHSnDirQh39aaefwp2KRulMRG3FRsOfepG4qNqhlDelFFHWoAKKKXpQMUYxTgKaOKcKpCHDrR+NL/OkNUIbTacaQ9KkY00mKWkpAJRSmk60hh3ooo70AKKcOe9N7U8U0IcKceKQdKWrEMYe9RtUrdKiapY0M4ooo61Aw59aWk7UooAevP8AjUq1EtSr0rRCY/rSGl6ClxVCIznFNNOchBljj610egeAtc8QbJhF9hsm58+4HLD/AGV6n9B71DdhOSirs5hmVeWIFbujeDtf18K9nYNHAf8Al4uPkT6jPJ/AGvXNA+HuhaFslEH2y7Xnz7kBsH/ZXoP5+9datFm9zlni1tA810n4PWMe2TWL+a7fqYoP3afn1P6V3OmeH9I0Vdunafb25xjei5c/Vjyfzq/JPDCP3kir7E/0qrJqkK/cRn9zxTUTmnVlLdlzFO4xliAPU1jyalO33dqD2FVmleQ5dy31OaqxlcypLyG/1oQo5keabYNozgZx/KvRRceXEkMKLHGgCqAM8CvMfCkQHi9EbrGZCPqAa9LI4r5PGzk6lj08Mvducr4z8ZyeH7ZLe2KtfzjKAjIjX+8R39h/hXlMs17qtw1zcyy3EzdZJGyf1rV8Xl7zx1qCyE7Y2CAeihRj/PvUKjaoA6ela0YKEF3Pcw1FKNzLaGWFg5DIwPysDj8jXoXgXxbc3V0mj6nKZWYH7PO33iRztY9+OhrllTzRsK7s8YHNQ2UM1jr1iyqystxGVOMfxCqqJTi0y69FOJ7TfWUN/ZS2s6gxyLg+x7H6ivNtFMljrN1p8v3gSp/3lP8AhmvUpBgnHrXm+ox7PiBLtHX5j/3xU5bNxrJHz2KiuW5b1q1+2aVMgGXUb1+o/wDrZriPDd1/Z3jERniLUISnt5i8j9OPxr0bOK8y8U2kmmXZng4e0mWeI+2c/wCfpX00+5x0dbx7npdJUVrcx3dpDcxHMcqLIv0IzT5HWKNpHOFUZJ9BV30uYNdCpqOpR6fEC3zSt9xM9fc+1Yltb6p4iujHHl8cnnCIPf8Azmqe6fWNUAUZkmcJGvp6CvWdL0uDSbBLWAdBlmx99u5NeDjsbK9onXQo33OZtPANuig3d3I7d1iAUfmc1dPgfR9mP9IB9RL/APWrfvLy10+Az3lxFBGP4pGwD9PWuek+IHh9JNgnncZ+8sJx+uDXlKdWWqO+OHv8KKNz4Hmtz5umXxDjosnyn8x/hWYNQurC4+y6vA0T9pMcH39CPcV2+na7pmrD/QryKVv+eedrD/gJ5qa+0+21K3aC7iEiH81PqD2NdFHHVaMtdjGrhU9LWZxt7ZW2p2MtrcIJIJlwR6j1Hv3zVbwFrU+lXzeDtWkzLCpbT52/5bRddv1Hb6EdqlvNNvPDMufmuNNY8MBzH9fT+Rqlr2jjXdPhuLKby723bzrS4Q4KsO2fw/OvbcqeMpXjuZ4atLC1Ndj08U2WKOeB4ZkDxupVlI4IPBrnvBfihfEmkt56iHU7U+VeQYwVb+8B6HH4cjtXSHivIlFxdmfSxkpq6OI8JyyeGteuvCF25Nud1zpcjfxRkktH9VOT+ddtXO+MNEn1bTY7nTzs1awk+0WUn+0OqH2YcflV/wAP61Dr+i2+owrsMgxJEesUg4ZT9D/SnL3lzBHR8pqYz9aSijpUFkVza293HsuIUlT0dc4rDuvB2nzEmB5YG9Adw/I/410VHamm1sFjiJ/BN4p/c3UMg7BgVP8AWuU11zoUN0bja0kHG1TkM3Yfma9iFeY6r4O1fWJkvJYgFF00v2ckbmJzgnnAAJranPX3jOa00Ob0Wyazscz83M7GWdj3Y9vw6V2cOjHTPDt/rd8mHgtZJYYmHQhTgn3zjAra0LwpHYstxelZrgcqo5VD/U1H8S7j7N8PdWI6yKkQ/wCBOB/LNDnzSshcvLE808MW/keHLMY5dTIfxJP8sUniW3Z9L+1Rj97aOJl+g6/pWpawi3sreEceXGq/kKc6LJG0bjKuCrD1BrZStK5DgnDlZj2M6m6tZkOVaRGB9sivRCea8n0MtGFtmPz2tz5JP0bivVq9mm7q58tiI8srEdxcR20DSyttRaz7KHWNflL2Z+yWqn/Wt/j3P0qrIj674ih01GIgR/nI9uWP9K9Kt4I4IEhiQJGi7VUDgCvGzHHyhL2dM0w9BS1ZDZ+HhNbL5t/M8wGHbaME/SuI8S+HrjwPq3/CQ2zmbRrxwmoRqmPJc8LLj0z1/wDr8d3c6/pugSI2oXkcAfopOWI9Qo5q2mueHtdtpbI3lvPFOhjeGXK71IwRhsVwUMXXi1JttHesNFx0iczb37BFIYSxsAQQc5Hsavx3cMw+VsN/dbg1xdlDN4V8QT+E7x2eJVM2mTv1khOfkJ/vLyPwPbFbDGvpqU41YqUTyqsHTlys33VZEKOoZT1VhkGsK/8ABHhrUsm40e2DH+OEeUf/AB3FPjuZ4/uyHHoeasJqUo+8it+lW43JU2tmcfffCPTXBOnald2rdlkxKv8AQ/rXKan8NfEtiGa3jt9QjHeB9r4/3Wx+ma9gGqL/ABREfQ1J/adqFLSMUAGSWHSpcbG0cTNeZ82XcFxYzeTe209rL/cmjKn9ah6jjn6V9KmW01q2MUenNqVu39+EGM/i3Fcfrfwkj1AtNpunPpsp7RzBkP8AwEn+RFck8XSg7OSOyFVy3ieMYxRXWat8NPFukKznTHu4R/HbfMw+q9fyzXJtmOQxSK0cinDI4wQfetYVqc/hZuHT8aKKK0AKOveijtQAUUH9KO1ACdTTT/WnYpv9aQxKMcUvX86TpSASpEqPGakTihbgyzGKsr0qvHVheldMSGI/SvYPg3q/2nQLzSJGy9jPvjH/AEzk5/Rg35148/411Xww1I6b49tY2bEV9G9s/pu+8v6qB+NcuOp89J+Rvhp8tRH0DQ1vBd281pdRiS2uEaKVD0ZWGD/OlxThXgK6Z60tVY+VNc0iXQdev9ImJL2kzRhj/EvVW/EYP41mmvSvjVYrb+L7O+UAfbLQB/dkOM/98lfyrzU17tGfPBM8ipHlk0J3pKXvSd60IDFBBIwoLN2A5pQK9K+D+mWOp32uw3Sq0j2XkKD1CSZVyP8Ax0fjWdWfJByKhHmlY6LR/Elh4Z8I6bpum3Nub63AN1Z3itBI5bJbbvxzuJ9ciun07x1p1wqi9jlsnPd1JT8xVHTr7TDaW/hfxtbWov7VfJhlvYwYrpBwskbnjJGMjOc1l+Ivh/omn32jy6Xd3lnb3t6trLDbXRIAcEh03Z6EcjkY9K8pxjJ6noxlKK0Mv4nX0d4ty1tMkscpihR0bIPQ8frXS+D7aOe6vEljWSHyREysuQwOeCPwrK1n4T6pNEP7M8RLdbHEggvoguWH+2vX8qsaDry+FriSw8U2M+kXM7/JPKN1vJgdpBx+f4mhr3LRHF+97xxmo6NHp3irWNOs7m6tIbeVGiijk+UI6hhjOfUivXPCVlbWfh63+zNIyyjzHMhBYseuSAO4rhPG0KQeObK9idWh1KxKAqchmjOQQe/ykV1vge/V7OTT3PzxMXjB7qev5H+dTUu4oqmkmzsbM7bge4r5a8X6b/ZHjPWrADakV25Qf7DHcv6EV9SxfJIrehrwj42af9k8frdquEvrNJM+rKSh/QLW+ClaVjDFq6TPOBThSYpwr1keeOH0p4pg/GnirQhaQjj+VOpD+FUIibn/ABphp7daY3Ss2UN/L8aOtFFSAmM0tFFACinjkU0ZpwFNAO7UhpcUdaoQ0000400/5NIYh6e1JS96b/jUgGKSlpO1AwpfSkpaBC0o4pKcKEA8Dil7UgxilqxDW71E38qlNRNUsaG9DSdqUik6GpGFLRQKAHrUq9ajFSLVREyQc/Wr2laPf65qCWOnQGWduSScKi/3mPYVY8O+HdR8TXrW9iqqkeDNcSfcjB/mT2Fex+FPBVh4XLzRTz3F5KuySWQ4Uj0CjgD86bd9EYVKyhp1Knhv4caVoZS5uwL+/HPmyL8iH/YX+p5+ldeRg1m6vqGoWRQWNnBOSMkzTFB9BhTWL/wnBs2267o11YRnj7RGRPCPqRyPyoTSOGXNUd2zop76GDIzvf0Ws6W/nm6NsX0X/GpBFZ6jbLeaZPFPE3IMbAqfp6H2qpjBweCO1aKxg01uFLim9KX+dAhaKSloGY9s403xpbzHhJHHJ9GGD+pr0k15t4hgLW8dyvDRtgkeh6fr/Ou70i+/tLSLa66tIg3f7w4P6g18xmdLkqXPRwk7qx59450eS08QnVFTNvdoFZsfdkAxj8QAfzrnRxXs99ZwajZy2tygeGQYI/qPevPL/wAD6tbyn7EYruH+El9jge4PH5Vz0aytyyPdw2Jio8sihoMMjXplRcqnDc9M1sWNsdX8X2qDLQ2RE0rDouOg+pOPyNU7LwTr08oExhsoz95/M3tj2A/xrvdH0e10SzFtaqeTmSRuWkb1Jp1a0UtGViMVHl5Y6mkefx9687Vxf+K7+8XmNCVU+vYfoK6fxPrI0zTzFGc3Vx8kajqM9TWDplp9is1RseY3zOfeuvKqLlP2j2R8/i6itylonmuc8WWQntUnxkDMb/7p6f1/OujPJqC5t1uraWBx8rrt/wADX0bV0cEZcruc38P7wy6JLp8hzNYTGIg9dhyVP8x+Fb2tEppM/uAvX1NcVoUr6N46WKT5Y79DA4/6aL0/lj8a7rWoy+kXAHYBvyNYSb9m0bVF79+5m+B4Fl8RozDPlRM4+vT+telTTJb28k8hwkSF2I9AMmvMvBdwsHiWEMeJUaP8SMj9RXo2p27Xmk3lsn35oHRfqVIFfLYn+Jqd+G+E8Q1fV7rW9Qe8unJZidiZ4jXsoFRR2bsu5ztHpjmq0Py3SRuMENgg9jWuTurs2VkfRU4q2hbj8MN5KTw3u2XAZSoxg/UGur8IeKLme6Gkaq2+fkQznq+P4T6n0PeuPgnmQeUszIhPIzxRIGs9RglikDvFKjK6+uc1lUgpqzFWpRnGzPZmjSSNkkRXRhgqwyCK43U9Gm8Pytd2KtLp7HMsPUx+4/z9a7QnmkJyCDgg9j3rjo15UZc0DwalNTVmeWamJtMv4fF2hfvJYhtu4BwLiHuCP7w/oD259I0vVbXWtMt9QsZBJbzrvQ9x6g+4PBHtXNazosmlSvqOmoTbE5uLcdv9pa5/RdRTwhrSsH/4p3VJBk9rSc9D7K3f0/CvZlKOKh7SG63NMFiHRn7Kez2PUq5GZf8AhFfFn2tRt0jWZAk4HSC66K/sH6H3xXW5zVbUNPttV0+exu499vOhR19j3HoR1B9RXJF20Z7LV9S1S1ieHru5VJdI1GTfqFjhTIePPiP3JfxAwfRga26TVmNO4UUnWlpDEoxS0duPyoABXF/FNt3hS2t+1xqEEZHtkn+ldrXG/EW2e6stDhTkvq0Kj8mq6ekkRU1iUfDWkLqmplpkzaw/NIOzHsv9aydVtfsGqXNrziOQhSf7vb9MV6bp1lDplkltCOByzd2buTXGeN7by9ThuQOJo8H6r/8AWIq1O8gtoeZ2yeX4wvYOgkkilH44z+teoscIzegzXm4jz8QbUf8APSFCfwf/AOtXpm3gg9DXuYZ3pny+PVqzMbwEnma3cSvyywk5PqWFd/f3iabplzeyDKwRGTHrgcD864LwkRY+KZLZ+N6vGPw5H8q7HxPbPd+FdShiBLmAsAO+OcfpXy2JT9vaR1YazSR4pe3lxqV7LeXUhkmlbLE/yHsPStTTS88PklWMijgEckVnWIVpiTztGRWqHmtpUlwyOOVLDrXZtofRQirEGvTX9zZQR+dJ9psX820Zzlo27gE9jgcdOldZoOsRa5pEN6gCufllj/uOOo/z2IrPIg1S2yw59e6msG0lfwn4gE0xI0y+YJOe0cn8L/Q9/wAa7cFW5HyPY8vNMHzw9pHdHoI60tH5H6Ug616580KBUcdkNV1iCwkz9nVTPOBxuA6L+JqQcGjTrkWniiHfwl1AYgT/AHgcgVx5g5LDycTahbnVzsUPlKqx/IqjCheAB9KlS8lXrhvqKr5yaOtfH3Z6xoJextw4K/qKo6t4a0HxLFs1PTra74wHK4dfow+YfnSYzWTq3inS9Gk8qeZpLgdYoBuYfXsPxNaU5Tv7pUYuTsjiPEnwLCxyXHhq9fcAT9juznPsr9vxH4145cW09ncy21zC8NxCxSSNxhkYdQRX0lpvxMsZZvJmsL9VxkOQrfmAc/zrm/iV4T03xbZT+JtAmQ6nbRb7m3A2meNRycHncAOvcDHpXtYTF1IvlqbFypTjujw2kpFYOMg06vYvfYyE70UtJigBKQ8UppDyaTASkzS0lIYnXrUiUztT0prcGWo6sD7uarx81YWuiJmxrU+0umsNQs75OGtriOYH/dYGmtUUql4nX1FKorxaHF2Z9ZEhhleh5H0qK5uYrK1luZm2xxqWY1m+FdWi1nwtpt9EwbfAiuB/C6jDA/iDWZ4s0bXNYULp1/aLCuCLeeNgM+pZTz9MV8zy2lZntp3V0eTfEnUv7TktruY4lMzbEJ+6hH/1hXCGvcrb4VwzWN6dXvfteo3UJjWULhIPTaPYgflXiVzaz2F5PZ3SFLi3kaKRfRgcV6eFqRacF0ODEQknzPqRfWkp1N712HMKOteheAfD+rro1z4q0X95fWtx5S2xPFxEFBkX65Kkf7vrivPM4r6M+Fto1n8PtO3rtacvOfozHB/ICuXFz5YG+HjzTItO8ceHPE9obLVrWPcDh7e7jDbGHXIPQj1rc0vw54ZsplvNK060SQfdkj5259M9Pwqjr/hvwzr2rJaX0LQ6rJEZY7iAmKRlBwcMOGI44OcAisN/h7rNhJu0nxOwUdFuYOfxZSM/lXm+70dju16q56FnJpJ1ivLV7S8giuraQYeKdAyn8DXl1p4m8SaB4h/szxBcIxHzQhUHl3Kequecj0r0bT9SttTh8y2kDY+8h4ZfqKiUXE0TU1qcdrHwrsrpo5dA1OfTHhcyQ2s5MturHrt7rnv1+lc7cv4i8F3UU+uaaYoVcBdQtD5kP4+mfQ4z6V7CBxUh8qe3ktbqJJrWZSkkcgyrKevFaRqX0kZSp21iZ+jatDrFglxEylioJ2nIIPce1cF8drDztE0TVVHME727n2dcj9UP51FDFN8NPGMemSyM2gX7FrC4c5ERPWNj+X6H1rrfiLYrq3w11dFXLwxi6T1BQhj+gYfjV0/3dRPoRUfPTfc+aKcOv86YpyAfWnivaR5o8dP5U8UwU8VohC0h/OlpDTERt0qM1IeKjbis2UN7UdqKOtIAxxR1/OiloAUdfrTxTQKcOtNAL2oI4pf1pMUxDTxSfrSnpTTSGIelJ0zS0hpAJRR370UgCjp1ooxgUALTh0pvanimgHjpS0g6UpGKtCGHrUTcfnUzVEeOtQxoZiijvR71IwxRRSigB68GpYopZ5o4IIzJNK4SNB1ZjwBUaj/Oa9E+HOhvbSjxJe2czWw3JaPt+XPRn/mB+NKc4wXvOxE5csWz0XwroEXhvQYLBNrTffnkH8ch6n6dh7AVpXV6tthQNznt6fWmDULdrYzRyBwOw65rJZ2kkLscs3eto2a0PHnJt3ZbmvpJ4yjKmD6Cq2QQQQCD1Bpo4op2Fcw7jw/JZ3LX/h65/s68PLRjmCb2ZOn4irGn+JF1O5NjqNv9g1lB80LH5Zh/eQ9/89a1azNZ0W21q1Ec26OWM7oZ04eJuxB/pScbao0U09JmlR1rndF1m6S+Oia1tXUUGYphwt0n94e/qK6LpTTuRKLi7MOtL1pKKZBHcwfabWWE/wAa4H9Km8A3Rayu7J/vQyBgD2B6/qP1pKq+GD9m8X30HQSxFgPxB/qa8nNqd6XMdWFladjuMUYoxmlFfNWPUFxxVHVdTt9KsmuJjyPur3Y+lXWYIrOxACjJJ7CvPLuV/EWrvO+fsUDbUX+9j/OTXRhsPKvNRRjWq+ziJarPqd82q3vLN/qUP8I9f8K0xQFAHHp0o7V9dRoxpQUInkyk5O7DtSiijtWpJxnjrTZEhTU7YYliYSgjsy8/y/lXX2l3Dq+kw3S/6q5hDEemRyPw5FNvrVb2xmt2XO5ePrXM+B7loIL7RJT89lMWjz3jbkfrn86za971N780PQqkzabqIwds0EgZT9OQa9d07UItT0+G7hI2yLnH909x+def65phuk+0QrmZByo/iH+NUfDviOXQ52R1aS1c/PGOqn1HvXg4/CtPQ3w9VIveOPBsz3Mmr6VGXLHdPAg5z/eUd/cVyFteLKNshCSDg54zXtmn6jaanAJrSdZV74PK/Udqq6l4Y0XVnMl5YRtKesiZRz9SMZ/GuGGIcVyzR7NDFcq11PKdygZLAD1Jre8L6FLq+oQ3TxlbCBw7SMOJGHIUevPU11dp4J8P2cokSwEjA5HnOXA/AnFdCuFUIgAVRgAdBRPEJq0TWrjeaNooXqaM5qO5uYLOLzLiVY17ZPJ+g71wmt/FHTbGZrexV7qccbYxuP8AgPxP4VhCjOo7RRwHoGAQc9PevOr3T7calq2hyoHs5BvVPQNyQPof5VzNx468R3rkxWMMKHp9olLfouBVRdV8QNeNdtNYpIyBDiNiMD8a9fA4StRnzSWhzV7SjZPU9A8Aa5M3n+GtSl33+n8Qysf+PiAcK3uRwD+HvXc9K8a022vr3w5Za5ZyZ1m2nluYXxxJlzujPswGMV6V4f8AFWneI9PiubeURzMMSW7nDo46r74NXiqHI+ZbM9bBYj2keV7osarZySPDf2aj7daElBnHmofvRk++AR6MAfWtCOQSxJIu4KwDAMuCPqOxpetOArjvc77WE6iloooAKO9FFAC8VT1LT01H7HvODa3SXI9yuRj9at8mlp3E0NxXNeNIPM0uGbvHN+hB/wDrV0xBrnvF93BFoz28jjzZWXy17nByT9KI/Egex5jawh/iLp4JA22jNycZwWrvjXCWKW1744SKZI5QNPbKnnad4wfY+/Wn67L4n0K4+0wamZNLPAaSFZDD6B+Mkf7WfrXv4aXLTR8zjoc9dpM3dWilsNRttUgHzKwJ+o/xHFei2lzFd2sVzCcxyqGWvE5PEniGa2MMv9m3ETjrsZT9QQauaD481XQg0F1pX2m0JyPJnG5D3wCOh9K8vMcHOpLnpoWHvDRs1vFXg64067k1LSomltHJaSFBlovXA7r/ACqC08Q2N1ahLrG4DDAruBNblr8VPD8oAuxe2JP/AD8W5wPxXNX1h8H+KX82GSwuZm6tBKFkP1AIP515/NUgrVIs9ijjOVWkcOl7BBemS2QrCeCpPUV0tjoMXihvsToZLSVf3zj+FPXPY+nvW3H4E8PRtuNrM/s07Y/nXS6atvpkS21vBHDbZ+7GuMH1pPEpbG1TGRcbJHmelC60XU7nwtqkm+6shutpj/y82/8ACw9x0P8A9atqtnx/4Xm1zTYdR0vC61ppM1qw/wCWo/ijPsw/X6muY0nVYdY02O8iBXd8skZ6xuOqn6GvfwGKVaFnuj5zFUuV8y2Zd61Wu7YXUOzcUdTuRx1Vh0NWetGK7pRUlZnInZ3Rq6FrP9oRm2ucJfRD94n98f3h7VtYzXEXNs0jJPbv5V1EcxyD+R9q6TQ9XTVbYh1Ed1F8s0XofUexr5XH4F0Jc0fhZ6dCupqz3J9avX07RLy8jx5kURKZ9TwP1NePM5LszMWdjlmPJYnqSa9k1OzGoaXdWROPOjKBvQ9j+eK8bkV4pXilQpLExR1PVWHWssNazPawLVn3JreZoJllXqD09R3FdHDc4MdxC2O4IPbuK5TPFaWl3GC0Dnjqv9a60eho9GJ4f+Hmn+MPCV3BbutnrmlXUlsJsfLKudyeYPocBhyMd8YrzXVNLvtE1ObTtSt2t7uE4ZG7+hB7g+tez/De9+xfEXU7EnEepWizKOxkjOD+hY1p/F/w3DrtnZyRKq6jEG8qT+8OPlPtn8jXRSxMqdXlezPCrJQk7nz11pO1OdHikeKVGjljJV0YYKkdQabXr3vqZiGkNLSEUhiGkNKaT/IpAFSJUdSIKcdwZaj5qwvSq8YqyvSumBmxrCoyakaom54okCO4+GPis6Hro0q6fGn6g4C5PEU3QH6NwD+Fe6d6+TZAWU4JBHII7GvpTwZrZ8QeEtP1FzmZo9k3/XRflb88Z/GvEx9Lllzrqeng6l1ys38cV4t8ZPDn2XU7bxBAn7q7xBc47SAfKfxUY/4D717TWN4tsLPVvCt/Y30qQxSx/LI/RHHKn8wP1rkoVOSaZ0Voc8LHy+cU00+RHt5mhnUxyocMrCruj6PfeINTi0/TYTNcSf8AfKDuzHsBXtuUbXPJs72LPhXw5ceKdfg06EMIc77iUf8ALOMdT9T0Hua+lGltNIsYUO2C2j2Qxjso4VR/IVl+EfCNn4Q0gWkB824kw9xcEYMje3oo7D+pp/jS3+0eEtQUdUjEnHsQT+ma8evV9rPTY9OjS9nG73LWv6QdZ09Vgl+z6hbOJrO4/wCeco6Z/wBk8gj0NO0HV/7a0zzZIjBeRMYbq3PWKVeo+ncHuCKg8L6m2r+HLK7c5mKbJf8AfX5T+ZGfxp9xZix1c6zC4jRo/LvlPR0A+V/95enupPoKx/us1t9pC674d0/xHYfY9QiLKDujkQ4eJv7ynsa4Ofwx4u8NS+fp7jV7dPumI+XcKPcdG/AnPpXp8E0N1BHPBIssUihkdDkMPY1MCAMngDqaqM3HQUop6nnOifEm7u2jhm0i5mLSeVu2rGQ+cYJJxnP0r0UnKjPBI6V4hq16sPi28uAAtlqcpxjgJKOh/wCBAfnXqXhrWf7V0xfNbNzDhJPVvRvx/nVVIpK6Jg3sy9rejWXibRZtH1Ff3cnMcgHzROOjD3H6jI71zPhTUrpHvPAniU41KOBo4Jj928gKkBlPcgfjgeoNdljIrK8R+G4fE9hHGZja6nat5thfJ96GQcj6g45H9aVOX2WKpH7SPl2NSgKHqpKn8OKlHWrOq6VqGi6vdadqsJhvI3JcHo2edynuD2NVh0r3qbvFHlS0Y4U8daaOlPFaogWmmnU1qoCNuajNSNUZ/wA81mxiUnFH0o7VIwpR1ooHXigBw4FOHNNFOFNCHUhxS9RR/nFMBhFJ/Sl7UhpANPNJinHpTeKQxOvajHFLSUgDpQM//Xo79aOg9aAHc8U4U2nDk00A8c07oKaOad1qxDGqJqlNRNxUsaGfWijqaOvOagYdqcKb1p2QoyelAG34V8PT+KfEdppEGVErbppB/wAs4h95v6D3Ir6ttrS20+xhs7aJYrW3jEccYHCqBgCvN/gjoMNj4audWkX/AE68l2nI5SIAFV/HOfy9K9Dv5sL5S9T976V89mGI9pOy2Q7W3OV1rw7aancG4ti1nOPutFwPxFc9Mmp6QcahB50A/wCXmEZA+orugvPSgqCCCAQeCD0rlw+NrUXo9DnqUIzOOilSeNZI3DIehFSVDr9kmhX0V7bDZaXD7Joh0VvUen/1ql6ivqMJiViIcyPNqU3TlZimil6UldZkZmuaLFrViI9xhuIm3286/eifsfp61B4f1iS/ils71BFqdodlxH6+jr7H/Pato81zviOwuIpItd01M31mPnQf8t4v4lPv3H/6qiStqjWD5lyM6Kiq2n38Gp2EF7bNuhmXcPUeoPuOlWetVe5m007MMVmXkj6Zq9pq8akrGdkoHdTx/In9K06a8aTRtG6hlYYIPesq9JVYODKhLldzrbeeK7gSeFw8bjcrCpsHOPevPrVNW0R2/sydZICcmCXpVyTXvEdynliG1tc9ZB8xH05NfNTy2upWSPSjioWuy94r1UhF0m0bNzPw5B+4veqNvAlrbpDGPlUYz6+9V7OyW2Z5XdpbiTl5X6n/AOtVyvbwOE+rw13Zw1qvtJXDqKPWjqKK7zEMUZo7UhIAJPA9+1MQ9c5ridYmj0Dx3ZX+5Vguh5M4B6AnqfTBwfwq9qevuzmGzYqg4Mvc/T0Fc/qWjXeraZJLDBJIEPMmOM/WuCti4xdkdNGDvr1PR3U5rLv9Gtr1i/8Aqpf76jr9RTPCmpnVvDlrM5zPGPJlz13rxz9Rg/jWuR611tRqR1MWnB2OWXSNTsJhLaSHeOjxPtNa9v4s16yXbd232hR3eMqfzXitDpXNap4503TZDb2u6/u+gigOQD7t0H61w1sBRlubU6lS9onTQ+PLEj/SraaE9yCGH9DWXrfxT0y2QQ6P5l9dt/DGmAv1Y8D8jXA6jcan4jk36pMsNuOlrb8D/gR6mrOn6NI4EdhaYTuwGB+JrlhlVPmuzrVblXvEd7d6x4gkMmrXjJE3W3gYgEejN1NOt7KKJRDawAZ6Ki8munsvC6Jh7yXef7kfA/Ot+3toLVNkESRj/ZHWvVp0Y01aKscs8RzHG2/h2/uMFkEK+shwfyrO1lBpSXcZlDtEh+YDHOP/AK9ekEcV5fqSNq+sW9lyftl1l/ZAdx/SqlotBUpOUtdjvvD1sbLw3p1uRhlgUt9SMn9TXNeIrCfRNS/tmx2ra3DAXkZztRu0nHT3P4812oxjHbtSSRRzwyQyxq8bqVZW6MD1onTUocrFRryp1OdGRF4j1nQ1U30N3BDgEPKnmwsPUOuRj8RXSaZ45sb5R5uwZ/5aQtvX8R1FZXhHVH0PUB4Wv5C0Dgtpc7n7yd4Sf7y9vUfhXRXvhDQdRk82fTIBKeskX7t/zXBrwqsVCTjJH1NGp7SCnFmzBNFcxLLDIskbDhlORT/6Vm6TodjoiyCzEw8z73mzM/8AM1ok5FYO3Q3VxaK5DV/Ft9ouui0utPRLSQ/6PMzHE3HIDdA3+yRWtbeKdKmi3STGB8cpID/MdafK9w5kbJGKTPNcxe+MYpLuDT9Kj827uH2I0gwB6nHUgDJPSukRCkaqXLkD7zdT70mmhp3OX8T+ObPQx9mg/fXjHaFA3Hd6BRyx/SuSuPD3ifWbWfVtVkbTrcLu8tjm4k9M9kH6+1eg6X4Z0vSr6W+ht/MvZWLPczHfJyegJ+6OegxU/iVseHbv3UD/AMeFaxmlpEzcW9zyLw5ZQ23jqSKCMKqaeST1JJccknkmu8ZFeMo6hlYYZWGQR6EVx3h8bvHWpN/dsox+ZFdiTmvcw38NHzGPf79nAa7oraBIbm2DNpTn5l6m2J/9k/lVI287KHEMjIwyGCEgj2NekuiyIyOoZWGCpGQR6GuV2v4PvApZjoVw+FJ5+yOex/2D+n89GuX0Moz5tOpzhR1+8pHsRiqtxbW0jBBaJLO33Qow31yK9PuLqOG1aUhZMj5V67iegFXNC8GWslmbvUIs3U/z/Idu0H/PSuHG4qNFW3bN6V5s8+0uXxXpSBrTXHjUdLabMyAenzZx+FdHbfEzU9OUf29ookhHDXNg+QPco3+NdPceDLJz+6nnjPvhv6Vl3Xgq6CMsNzDMpGNrgqT/ADrw3WpTfvo69UdP4d8e+Hta2w2mpxea3SGY+XIPba3X8M1yvjDTf+EV8R/8JBbrjSNSkEeoIBxDMfuy+wboff6ivO7nwndQ6rNplxZBivzRjIzjrwa07XUPEWj2cmmzs+oaVMpjksb/ACflPZH6r7dvavQw+DlBqtQd12M5ThJOEjvQMilrnvBt3dz6P9mvLeaN7ZvLjeXnfH/Dz0JA4NdCa9yLurnlyjyuwCqlwZrK5TUrP/XRcOvaRO4NWu1H+NTVpRqwcJdRRk4u6Ok0/UYNUskubdsq3UHqp7g1zfizwi2rsb/TmWK/UYZScLMB0yex96zDLdaBetf2K77Zz+/g7fX2+vaux0nWbLV4Q9tIN4HzRNwy/h/Wvk8RhqmFqabHr4fE9U9Txe7XUtNlMN5bPBIO0iYz9D0P4VDBPdXF3Ese6STeNqRjqfoK9/dUkXY6K6+jDIqNIYYP9TDHGT3RAv8AKj63ZbHo/XXbY4OfQbnwwfC/iO4zHcDVFinjz9yKZduD78H866PxfcebrNtbZyVXpnuW/wDrU74hobz4aao2cSQKkyHvuRw38ga5XQLyfxBfRapcHc0irM57DjgfnXRhU60ozfc87F1OZNvqY/xP8H+ZB/wkNhF+9iUC7RR99B0f6jv7fSvKPvCvqdQksZRgGVhgg8gj3r598c+GT4X8QPDED9guMyWxPYd0/D+WK+htysxw1W65WcyaSnGm0M6hDSGlPNJUjCpE5qPFSp1pxEy1HVheRVeOrA4FdUCGNbkVE3XFStUTUpAiMjFevfBO+L6dq+msf9TOs6D2cYP6r+teRGu5+EN79m8cPbE4W8tHXHqykMP0BrhxsOakzpw0rVEe8YzXI+K9I17VtQiisIrc2ypxJPMVRG75UAknp/jXX9D70ma8SL5Xc9Vq+hxmnfD2yNu66+YNTdxgIIBGkf8Aun7345/CtPQvBOjeGtRkvNIW4tvNTZJB5xaNxnIJDZOR25rfzzS5pupJ9SeSIGqt9bfa9PubY9Jonj/MEVaoAyRx3qCzz74V3bTaPeWz5zHMHx6blwf1U13+1XUowypGGB7g1538OIvs+t+I7cdIrllA+kj4r0TnFXU0mTT1icf4fvG0HxFceHLl/wBxM7SWbN2Y/MV/EfMPfNbvie9+xaFMVOJJf3S/j1/TNc/4+0x5rWDUYSyTQMB5i9UOcq34H+dZ+seIP7b0bTJCAkoD/aIx/DIMA/h3HsatLmtInZ2OQ10Ww0iYXIYq2FUL97f/AA4981a8I6/dafdBblWW9t/luIm4Lqf4vcHrn1p+maafEXje1sDj7NZRG5l/3jwv5da1/EXge/eZJoUcyx8xXNvyy+xHUj26Vq3G3KyNb8yPSrS5hvbWO5t3DxSDKn+n1qYHBBGeDXG+CLPX9Pd4dRgjFs67i6sR83YhSMgnv1FdnjLAe9czVnY1vdHm3x60+BtK0fVdgFylwbfcOrIylsH6FePqa8TFe1/Hu7UadoVj/E88kxHsqgf+zV4oK9rCX5Dyq3xDxTxTBUgrtRgH+cUjU7tTT0psCJqjapW5/rURrNjExRS0lSMOlFB6UCgBw4p4pg5p4qkIdjNJ3pe3rSHk0wG96aacabUgJ3pvb8adSGkMQ0nal70nFIBetA60lLimAoxThzTacKaAkFLjNNHFO7VQhjVE3TipWqJqljG0nalpMVAwrS0LTf7V1VImB+zx/PKfUdh+P+NZhO1STXuPw8+GdxDpsN1rK+Ss+JngH33z91W/ujHbryelcuLrKnA0pcvNeWx3HgaFrPw09zIMLPKXQf7IAUfng1ouxdyzHk8mpbh1XbbxqFjjGAq9B7VB1FfMVJc0iZy5pNiYBpO9JLLHAu6aRI1Hd22j9aw9Q8X6VZKVilN1KOixcj/vrp/OlGnKWyM3KMd2VPHkqJoSI333mG0fQHNVLYMLWHd18sZz9KzpVvvEN+l5qC+Vbx/6uH2/z3rW6V9NleHlSg3LqeZiKinLQOtHXvR0o/zivUOcKKKOtAHL2w/4RrxGbTONL1Ni0PpDP3X2Ddq6iqOsaXFrGlzWch2luY3HVHH3WH0NV/D+pSahp5S6G2+tmMFyno47/QjmoWjsaS96PN1NWl7UnairMheppMUtJ2pDD8KXrSUtAg6UUlLQAYzWD4gvyo+xxHBPMhB/St5nEcbO3RQWP4VyFhay6zq8cGTvnkyzeg6k/gK48bW9nCyNaceZl/wx4YbV5ftNyGWzQ446yH0Ht716K9jC2nNZIixwlNqqowF9KmgtorW3jt4UCRRrtUDsKztf1y28P6Y15cZY52xRA8yN6f8A16+WnUlVnoerTp20W55xojf2J42v9Jk+SK/Xz4l7CVchgPrgn8BXQ61rdhoVr519NtLfcjXl3PsP69K801nUNT1fVBqqPsvY5PNiES8IfQf/AF+tWtM8PXWs3hvtXvGkmYBjz83sPb6CvoKOK5KST3Kll0qlS/QnvrnW/FGfPL6ZpjdIEP7yQf7R/wAj2NEHgaS7i/4lNtMGXq275T9Se9bUltDozQyWrmRkYFoJjvVx75r0vSb+31PTIbq2ULGwxsHGwjqv4V5+JxdRPmNKmFdFabHlmi2NvaXX2bVImM4OAZOAG9CK7JQqKFUAAcAAYxWn4i8Oxazbl4wEvEHyP/e/2T/niub0m6kYPaXQK3MHDBupFehl+OVVcktzycRTcXc0sUUtIc16pykV5L5VjcSf3Y2P6VxvhCz+2a9eaiwzHaJ9niP+2eWP4Dj8a3vFV6un+HLuUnkgIo9ST0qbw5pp0nQba2f/AFxXzJT6u3J/w/CoesrG0Xywb7mkeKKU8nFJ1rQxKGr6UmrWJhZzFKjCSCZfvRSDowrofCPiRtZs5LS+VYtXssJdRDv6SL/st19qzs1iaxZ3cV1BrekYXVLMcL2uI+8bfXtXHi8P7WN1uj0cBi/Yz5ZbM9NzS4rL8Pa5aeI9Ii1CzJCt8rxt96Jx1RvcVrdq8Jpp2Z9OpJq6Kmp6VZazp0thqECzW8o5U8EHsQexHqK4Kb4caxBJ5dj4jQ2v8Iu7bfIg9NwPzfpXo5pKpTcdhOCe5zXhnwdZ+HXkuWnlvdQlXbJdTDBx/dUdFHtXTdRSYpe1S227saSWiDFY3iltvh2557oP/HhW1wawvFx/4p2b3dP50LdAzzfwud/jLWz/AHYIhXZEVxnhH5vFmvt/dWFf0Ndoa+hw/wDDR8pjta8hopk8MVzBJBNGskUg2ujdGFP/AJUDrW5xnNaPaf2J4mstM1KYvpEjkWUsn8L9omPr2HqOPp63Hy1cY2k22tWs1ldput5E+YjgqexB7EHkfSrfhfVrpbi58PapKJdTsUVhOP8Al4hP3XPo3Yj155zXgZph2pe1Wx6WGqKSt1OmuCCR/eHWoDTm60leE3dnUcR4wT7N4g0u97P+7Y/Q/wCBq0QCMEAj0NS+PLbzdDjuB96CYHPseP54qtbS/aLWKUfxoD+NfSZRUvTcTzcVG07jwFUYVQo9AMUUv4Un617ByhRR1opiDt6g1j3eiYl8+wfypQc7Qcfke1bNJWdSlGorSKUmtjHTxTr2mHy7g+YB089M/wDjw6/nRN4+1NkISC2Q/wB4KT/WthgHBDAMPQiqrabZOebSH/vnFebPK6bd0brESW5lWd/f69bXtpcSyXDyoVVB0GQRwBwKm+HziPwbartHmq8kb+uQx6/hiui0SOK0vR5MaRhlP3Vxmuc8Mx/ZLvxBp/T7PqchUeityP5V00MOqLsDm5wZ1EF26Sgsfl7isn4h6Iuu+HAigefG2+F/Rscfgen41czV4D7bpcsHVwuBn8xXRUi3F2M4ScXdHzOQwLK6lXUlWU8EEdqQ89K7Px1oDW5TXIEPkzP5VyAPuSdj+IH5g+tcZ1rGnPnjc9aElJXQhpDzS0lUWFSpUVTJ0qo7gyzFVkdKrxVYHSumOxmxjdPaojUx6momGTQwRGea3PBVz9j8d6HNnGboRn6MCv8AWsQ0+3umsb21vVGWtpklA/3SD/SsK0bwaNIO0kz6uPWm02G4ju7eK4hbdFKgdCO6kZFO6182z2kGaKSikMWnL1ptOXkj60ITOF8DRY8T+LX7fbmX/wAeY13BAJrk/AkWY9dvMf8AHzq05B9Qpx/PNdbwauprImGiKt7ape2c1tIPllUqT6e9eRvEYJpI2GHVirfUcV6zqt/HpWnyXMmCRwi5+83YV5LLKZLk723TTMWAHVj1OBV0rhNo1Ph5cRw+NtZhkIEk9vC0ZPcDIP6mvUga8LnnfSNYstbjzthPlXGP+eZPX8DzXttncfa7KGcEESIGyOhorLVMVN7os5zUkI3TIPeoRVqzXMm70H86yjqypaI8G+NWo/bPHcdmrZSxtFQj0ZyWP6Fa88HStXxXqP8Aa/jHWb/OVlu3CH/YU7V/QCsoc19BQjywSPHqO8mx46U8f/Xpi08dK6EZjsUxqd1prc02BG1RGpWqM1mxiUlFFSMKXpSUtADhThTRTwKpCF7Uh4pRRTAYeaQ9KcaaaQDT0pMUuOaQ1IxO9FB6UUAHSjFGaXrQAY7U8U3tThTQDwKXHHtSCnYq0Ijbp+NRtUpNRNUsaGYox+VFL0Ge1QB1/wAM/DA8TeNLeOdN1lZf6TcZHDYPyqfq2PwBr6R1TVbPSbVp7udIh2DHkn2HU1458PbS70jw75kTvFNqBEr7OGK/wDPXoSf+BVt6jpE100X2mRkB+Y7uWNePWpTxNXTY554hR0LN5473SGPTrNpWJ4eXv/wEc/rVNrzxJqAzLe/ZIz/DGNp/Tn9asW1lb2i7YYwp7seSfxqfHFdVHK6UPi1OOeJnLYzBocLvvuZprh+5ZquwWNrbf6qBFI74yfzNT9KAK74UacfhRg5N7i0UlHatRBSUtZd3fXMt59g02Iy3H8RAztrOpVjSjzSHFNuyNTNL1rKPhnxKV8w3A39dvnYP+FVxqOoaVOLfVIXx6kYP1B6GuWnmFKbsaSoTjqzdrn9QT+yvEttqacW97i1uvQN/yzf/ANlrdjmjniWSNwyMOCO9QX9nHqNjNaTZ2SrjI6qexHuDg12PVXREXZ6lnvj8KTrUVoZjaRfaOJgoD47sOCfx61LVEh1oo7UtMAoqve3H2Sykn/uAHH41OGDKrKcgjIPtSAXnFHWk7UUCK+pEjTLkj/nmar+AoFfVriY9YocD8T/9ars8Xn28sX99SvPuKoeBJxBrM9s/DSxEAH1U5x+Wa8jNU+S6OnDfEj0MV5R8SrqSfxElqSfKt4RtH+03JP8AL8q9X6V5p8TNLlS+h1VFJhkQRSED7rDpn6j+VeHhmlU1Pbw9ufU4zT0IZ3HHGK0VYqeCR79Kz7GYBmjb+Lp9avng16Ej14WsPzmu3+Hzv5epQ8+WGjcD0Ygg/wAhXCCUeYsaZeVyAqLyST0r1fwxox0bSFjmx9pmPmTY7Hsv4Dj865sQ0oWZzYycVTt1NfGK4/xfZfZLiDWoBh0ISYD+Idj/AE/Kux7VWv7JNQsZrV+BIpXPp6Vy0ajpzUkePUjzRscpG6yxq6EFWG4H1FP/AJVjWl7Fo8Nza6nMlv8AZCfmkOBjPQevPT61z998Qmdimk2BkXtPcnap+ijk/pX18K8ZQUrnlxoTk7JHYalp1rqlm1tdwrLE3OD2PqPQ1ws0ur+FdQaGwuWu7Ici1uWzgf7LdqoT+J/EVz97UUgB7QQKMficmqUl1qczeY+oyTP/ANNkDA/1qZVos6qeGqR3eh6Loniiw1lvJBa3vB962m4b8P7w+lbZ4ryKOeDUCLe7iEdynK7T+qnrW5YeKtR0krDfh7+0HAlX/WoPf+9/OtYz01MZ0LPQ7+kxmqem6pZavB51lcJMo6gHlfqOoq+BitL9jncWnqYcz3PhLV38QafG0llLganaL/Ev/PVR/eH616VaXlvf2cN3aTLLBMgeORTkMprgdZuPI01wvDSHYD7Hr+may/CeoXfg6S2S+Df8I7qchEMpPFrNnofRW/z3rx8dCPNpue3luKf8Oex6tSjpSDkUvavOPcCiijrQAd6wfGLY0Ej1lUfzNb3Wuc8atjSIV/vTj+Rqo7oT2PPvBShtW8Qz+tyqA/QH/GuyrkvAK7tN1G4/57X8jZ9hgV1nbFfRUVaCPkMU71pMXtQV9OppRyKswtBaW82o3kix21updnboMd/8960bsYJXI9V1i18KaG93dfPKxxHEPvSyHooqfwlo02m2M2oaoQdX1FhNduf4P7sY9Ao4+ua8x1HWLnV9Xj1q5Qhgcabat/yyX/noR/e7+3HtUlxe3l8Qb26luGH99jt/BegrwsfUdb3IvQ9zBYJtXPashhuUgj1BoHNeJ297e6VL9p06doZF5Kg/K49CvQ16t4Z16LxFpK3SKEmQ7Jos/db29j2ryKtFw16G9bDyp+hN4gtvtXh++ixk+UWH1Xn+lcb4fn8zTvLJ5icj8Dz/AI16I6rIjI3RgVP0NeWaExt9UubU+hGPdTXo5TUtUseXi46XOk7UfjSUtfSnnBR/nFH6UdaACk/Ol5pKACil7UAUAT2bbLuM++Kw4V+z/EHxDF0E8VvcAf8AAdprYQ7XVvQ5rJ1P918TEYfduNK/MrJ/hWct0zan8LRqnrU9hJsudvZxj8arde1KpKsrL1ByKsyLN1oMOo2+pWFyubO+iwSMfK+eo9wcEfSvnbUdPuNJ1O5067XbPbOUb39CPYjB/Gvp+OQSRq69GFeX/F3w9uit/ENunzJiC6wOqn7jH6Hj8RWDgo6o7cNUtLlfU8nPSkpx4pvOaD0BKmQVF3qVKcdxMtR1YHSoI6sDpXTHYhjG6VE3T+lStUZ9aGBGaaw4IPenEUw1DGj234ReIv7R8PPpE75udOO1c9WiP3fy5H5V6JXzH4U15/DXiiz1IE+TnyrhR3jbr+XX8K+mY5EmiSWNg0bruVh3FeBi6Xs6nkz1sNU54eg/6UdKOlFcp0C0A4PHakpfagDM8P6Y2kaJb2UhUyrueUjoXZizY/EmtPcFBZiAAMkk9BSZrh/HXimKxhawjcnJCyBOWdj0jUdyapJyYnaKMnxd4kS5ud43PEreXbQqMtKx9B6n+VbvgvwjLpYbWNWw2rXC4CDlbZP7g9/U1F4M8IS20y65riKdTdf3FueVtEPb3c9z+Fdv1q5SSXKjOMbu7MvUtA03VYnS5tUO4FSyjDEHr06/jT9D0xtG0a305rgzrbgojkYOzJ2g+4GB+FaHFLis7u1jSyvcKj1i+GkeFdS1HoYLaSUfUKcfrUneuY+Ll59g+Gt5EDhrl4rcfiwJ/RTWlGN5IzrO0T5wiBEa55J5OalApo4FOH/6q+girI8djxTx04pij86kArVCA9Ka3+RT+1MbpTYETVGfWpG9qjPFZMYnaj8KKP8AGkMKMdqKXGT0oAUU8c00U4U0IcKQ89qXt7Uh6VQDTSGnGmnrUgN7UhpaTHNIYlGKD14opAHFHSj8uKKAFHWnCm09etNAPApcdaQdKWtCRhqJqlYVE1Qyhtafh/SW13xBY6Yuds8o8wjsg5Y/kDWYeDXp/wAHdJ33Ooa1IvCAW0JPqeWP/oP51m9dCKsuWDZ6xDbw26qkMSRqoAAUdhWLeS+dcu/boPoK17uXybV274wPxrB61pFW2PIkxKKKKskKXtSUUxBR3o7UUARXM32e1lm/uKW/KtHwVp6waR9scZuLlizP3x6fnmsbVv8AkE3X/XM11Phs/wDFP2n+76V4WcSa5UduDScrm+luuwbuSfQ1naxo1tqNm0MybkPQ91PqKti5cLgc/WmPM8nB4+leJzpbHouzVmeVxef4e1mTT7s/uWPDduejD29f/rV0XUVo+KdC/tjTzJCo+1w/NH/tDutctoupeYgtJz+9QYQnuB2+or6DLsXzx5JHl4ilyu6Ng+n6UYrG8RyvbWMOox53WU6ysB3Q/K4/In8q2QVZFdSCrAEH1FesnrY53HS4YzR1oo60xGbr5xolx74H6ijQ7j7RpEJPLICh/Dp+mKZ4iONFm/3l/nWd4Tn4ubc+zgfof6UupaV4nS9RSkcUDiimQKK57Uo5dK1SLUrbj5w+fRu4Psf8a6Co5okuIXilUMjDBFY16Kqw5WVGXK7nTaZqcGrWKXMDDB4Ze6N3Bqe4giu4HgnjWSKQbWRxkMK8yU6j4ZvftFo5aFupIyrD0Yf1rtNF8V2OqhYpCLa5PHlueG+h/pXytfCzpS2PUpVlJGBqPwygkkZ9NvDADyIpl3KPoev86zl+G+ss2yTU7ZI/VS7fpgfzr1Aik71mq9RK1ztVeolZM5rw94N0/QXWfLXN3/z2kGNv+6O31610valVC7YFXYYVVRkDd3NZ3lN3Zk5OTuyligda0iilSCB+VZ8qhXO05APFKULCPCPiXdw3HxAnhfHl28ca4A4MhUHJ9+QKwStP8VXKXPxF1d3+Yfa2RfqowP5U1vnjYDqQQK+iox5acV5DWiIXfbPHHj7+efTFPjkV0DocqRwaphkSC2dmdio4Ucnoc05mNtYKYQccY38kAnv+dajC9Ox4Zg23a23cO2eh/A4rtbHTE1vRotQssJNyk1uTwrrwQD+o9jXG3EQntZYx97BGPQiuo+HGof6TcWjH5biITqP9tflb9MH8K1ou0rHNiY+5zLoVJdOa3uvNQy2l4n/LSM7WH19RWxY+M7qwIi1qLzYegvIF6f76f1FdZf6dbahFtmT5h92RfvCuN1PRbnT2JZfMhPSRRx+PpXU421RxKanpI3dZure90iG5tpkmiLgh0OQeK7Ox0201Pwjb6fdxLJbT2yq6nvkZyPfPOa8VNlNbF5NPk8oty8J/1cn1HY+9ereAvFFpq+lxaa/+j6laJtkt3PJUdGX1H8q8PNYT0mkdOHio7B4Y1S40PVz4P1mfzZY036ddN/y8Q84U/wC2MEe+Pbntepryr4jWU03iGOQM8TCCNreZeqMpPIPqDXWeB/Ff/CRWD214Fj1a0AW5jHAcdpF9j+h/CuZe/BTPfoyfIr9TqOtFKRTaRuLXJePpfJ0y3J6Bnf8AJa6wcmuD+KU3laL9IJT19cCrpq8kTN2i2Y/gWEw+ELIn70u+U/8AAmP9MV0f8qz9Et/sug6fB02W0Y/8dFaHavpIq0UfG1JXm2PjjaWQInUnFcn4k1Qa9qf9kWzf8Sqwf9+wPE8w/h91Xv71o+JNcfS7FLSybGp3+Y4T/wA8k/ik/AdPf6VU8MaAk88FjECLeLmVvbvn3Jrz8fiOSPKjpw1P7TObDefqdy7H/V/u0HoB1/WpsUniCJtK8XahHtwnnMyjHVG5H6GnIyyIGXBB715nRM+qoWVNJCqMj2rb+Gl08PiKe2BPlzwNke6nIP8AP865+5mEEJP8R4UV0XwxtGl1e6vcfJBDsB/2mP8AgDWdX+G2yMU1yM9RY15bHx40ugvTzpePxNeosQqlieByTmvLNHP2rXLm67Eu/wD30aWWJusfO4p+6dJ7YpaOv0o6CvqjzA60dB1oo60wDr+FIehx196XrR/kUAV7O5F1CWxtkRikif3WFWgAa53U7ltG122vRn7Ndjyp1H94cg/XGfyroTIgi83cNm3duzxipuU1bUXOBWPrjY8beHZP+etpPHn6AGpH1yz37EMkh/2FzWdrup2x1rwxcMJYxFJOjmRCMAp+tYSr0nopI1pxld6HSY5pTUEeoWcw/d3MR9t2D+tS7s9ORW6knszFqxNDcTRH5HO0HOO1ad5Z22saVNaTruguYyjj2P8AUVkAVpaZNgtCT15X+tKSKg7M+b9U06fR9VutNuf9bbSFCf7w7H8Rg/jVI16h8YdFEdzZa7CnEn+j3GPUcofyyPwFeX1j5HsU588Uw71LHUXepo+gqo7lMtRDmrAGRUEdWOq11x2IYxutRNxUrc1Ew5pMEMIqNqeaaazYyJ13KVPfivcfhN4lOpaGNLuXzcWnyrk9VH/1v614ga2fCWry6L4jgmjfaJSEJ7bv4f8AD8a4sXS54eh04epyTPpvFHSq2n30eo2Md1F91xyv909xVnvXh2PVEpaOtJQBleItXj0XR5rx2VWAIQt0zjr+HWuO8CeHpNSuV8VashZnydPhf+BT/wAtT/tN29Bz3qz4htn8W+M4NBGf7OslE18R3HUJ/wACOPwBrvFRVUKqgKowABgAVpfljbqzO3NLyQtLSYzRWZoGaWk70vHSgByLukUepFebfHi826Ro1hnmW6eYj2Rcf+z16darmdfbJrw743332jxnZ2QOVtLMEj0Z2JP6Ba68JG80cuJlaJ5tTxTBzTxXuI8weOnWnjmmCnirQhaY39af1FNamxETdaiNStiomrNlIKSl60VIxOtLijk96KBDhTxx0pgGaeKpAOpCKXGaKYhhpp5pxpppDENIeRSkUlSAlJ9KXFFAxKBnNHelpAFPFNxTlpoQ8cCnUgpa0EMbGKibmpW/rUTdaiQyJzhSa+jPB2kf2H4U0+yZcSiPzJvXe3J/LOPwrxHwdpH9t+L9PtGXdCsnnS+mxOefrwPxr6KJwCT061EVrc5MXPaJnapLlkiB4AyaoU6WQyzPJ/ePFN7VsefcSloo7UxBSHpS/wCc0YoASo7iTybeSUfwDcR7Dr+lSGgjcpUjg8H6UgKeosr6TcspyphJB9Riul8Nc6Ba/wC7n/P5VwVrck6Jf2ch/eWqunPde3+Feg+H02aBZD1jBrwM5fwnfhFqzSxmjGaXFYureKdM0eUwyu804HMUI3FfqegrxIxbdkehGLk7JGznFcR4t8OOsjarpykMDumjTqD/AHx/X86t2/xD0WafyZxcWhP8UyZX81JrqYZI54klhdZI3GVdTkEexrWLnSlcmrRbVpI86sruLWLCWzuMCSSMo4/vAjGRSeE7p7jQY4Zjme0ZraT6qcD9MVf8V+HhYsdUsRsjLZlVf+Wbf3h7VyXhnU/J8WX1pIcC+UTAdt46/nzX0eDxaqpX3PNnScU0dxR1pO1LXpnKZXiPnRZv95f51zvhyXytZiHaQFD+Wf6V0XiP/kCzf7y/zrkLKTyb6CX+7Ip/Wpe5rBe6z0QUdO1BpOtUZCgUlL1FHakAhUMCCAVPBB71kXnh+OQl7UiNv7h+6fp6VscZo7VFSnGatJDTa2Mez8Q6xoJENwhmgHAWXnH0augtfHWlTAeeJrd+4Zdw/Mf4VXYBlKsAQeoPeqE2jWE5JNuFPqp215dbKoyd4nRDFSjudOnivRSAV1GIfXI/pT/+Ey0pB/yEoj+BP9K4s+HLEnrKP+BD/CnL4fsQekh+r1zf2PLua/XPI65vGukEYa9JHoI2/wAKQeL9EY4+1MfpE3+Fcymj2Cn/AFAP1YmqXiC7sfD+jSXo06KZwwRFxxuPQk9hVPKGldyBYqUnZI8o1aZJfE+oXW4BWv5H3HjgsalN6cZijJH/AD0kO1f161UaQtJPMqo1wczO5XgZOflH9atxWcMkaTSBpnZQcyHP6V1pWVj0FsQ+cJCR9oySc7bePPP1pj27sP8Aj3u3B/vS4/TNTb22zLvRUCthU4K4Pep4jGs8pc4LbSAT1JGOKYynHH5LbvIu4jnJZWDVa0XURpWtW1yr7o45t7jGGVW4bjvwc8elJbwvGQrLIpVfmJbIb3+tMv0HkrLjmNg2QcHHfmmnZ3FJXVme2xyJNGkkbh0YBlZTkMD0Ip21WBBAIPY9DWR4S2t4S0wqcjyAD9QSD+tbWM16Cd1c8OUeWTRg6j4cilDSWeI3/wCeZ+6fp6Vxs9rP/aaxoZLa8t23LMnDRH2Ndtq3ijS9IZ4ri6QTqM+WoLEemcdK6Xw3puk6j4bWQNBeC7JkknQ5+f2PUY6V5+PxMaVPvc6sPGd7taHPaV4gg8QQRaB4s2W+oZ/0S/QAJMf5K3qvQ9sGk1DwJrmlX0esaJdW739ocouSvnJ3jYe/1p3iHwi1vbyq0Qu7FuScfMvufT6iq/h/xpcaC8Wna7K8+nEhIb9uXh9Fl9R/tfnXiRf2qX3HqQryiuU7rQNftvEWlJe24KOCUmhf70Mg+8jD1FalcR4gs7jQNR/4S/Q086F1H9p2sfIni7Sr/tAd+4/Gux0+9ttT0+C9s5llt5kDo47irVpLmWx6NCsqkfMn6mvNfi1IXs4rYfekCRge7P8A/Wr0o15h4/P2zxdo9n1BuYiR7KCxragr1Eh4iXLSkzbVBGioOAoC/lTZpY7eCSaVwkcalmY9AByakbg1xvjXUTMYNDhbHnfvbojtGDwPxP8AKvoW7I+QhHnlY5STWJb7xE2rzhgJDtiQ/wAEXRR/X8a918PWCWGlRYwZJQJHYd89B+FeQwaO9wpv0j/dwYB4/wA9K7nwl4lSFF02+kCqP9TK3Qf7J/oa8DMqcm7o9GFSN7F3xp4TOvQrd2YAv4VwAeBKvpn19K8skju9OnaGZJbeVeGRxtP5V9AAcU2a3t7gYngilA6eYgb+dedSxDguV6o9CliHBWPCtO0nUdeuhDaQvK2fmkP3UHqT2r2Pw/okOgaTHZRHe2d0smPvsep+natSONIowkaKiD+FFAA/AVWv7+20y1a4upRGg6Z6t7AdzU1a0qnuomtXc99jO8VamNP0ObB/fTgxRjvz1P5Z/SuV8P2vk2PnMPmlOR9B0qrcXNx4p1jzpAUtY+FX+6vp9TXQKAihVACgYAHYV7mV4V01zyPGxNXmdkLRR/Kj8a9g5g+lFFHSmIMUe1FHWgZjeKrQ3Xh26KDMkAE6fVeT+mRVfw3qIvNO+yMckJvjz3U9q6FlDqVYZU8EHuK850l5NIv5YOr2Nw0eD/EmePzBrORrBc0Gux7ZYMkllDLGiIrIDhVArD8Uxo3iDwmzqrD7fIhDDOQYm/wrX0VkbSYWjbKMCyk+h5H86xvFjbdW8Kn/AKimPzjavj7ctaS9T1IaxVzRuvDuk3JO+xiB9UG0/pWY/gu03ZtLq5tz2w+RXVRxNIM9vWpHjSBTK7fIv3s1nCpUjqmN04vdHBX+m61ocRuGcXlov3iRyo9+4+vNS6fqMVwIrmE/KDhgeo9Qa7yaWF4ypXcGGCCOMV5V5Q0jxfdafHkQScqM9ONw/TIr2cvx05T9nN3OLEUFFc0To/FukjXPC2oWIGZHiLxf76/Mv6jH4183KcqK+prN/MtkY9QMH8K+dvF+ljRvF+p2Srtj84yRj/Zb5h/PH4V7E1rcvCS3izEqaMd6ixU0dOO52MtxVYxgVXi6VY7V1R2M2RtxUbdKlbiom/WkxojNMNSHr0qMioYxhxTGG4cHDDkHPQ089KaTWclfQpHrXgLxoI4h5xLIcLcRjqrf3gP85/CvWYZ4rmBJoZFkjYZVlOQa+T4LiezuFuLaQxyr37MPQjuK9J8HeOpEkKIQsnWW1c/K3+0v+fzrx8ThnF3R6NCupKz3PaepoHWs/S9ZtNWg327/ADgfNG33l/z61oVwnWZmjaUNNjuZJNrXV3O887jnJJ+UfQLgfn61p0UUPULWDFHaiigApRR2o6UAW7JeWb8BXzH4+1D+1PiBrlyGyi3BhU+0YCf0r6WvL1NI0O6v5ThLeB52z/sqT/SvkYSPMzTSnMkrF2J7knJr0sDHW55+Kl0HClFIKcK9VHEPHHanimA08f8A66tCFprU88CmNzTYiJs1Ealbn8Kibr/Ss2UFFLzSUgDvS0lL0oAcKeKYOaeOnFNALigjPWlpOlUIaf6008U48U0jn0qWMafpSdTSmkPWkAhpKX/Gg0hh1ooooAUdactNp6imhDxml6ikFLjj/wCvVgMaoyPWpDTG4BNSwPS/g5p++71XUmHCIluh+p3N/Jfzr03UJPLtiB1f5RXP/DTTf7O8D2bMuJLtmuX/AOBHC/8AjoWtbU5N9xsHRB+tTA8zESvNlEUvWk7UVqcwvaj2oooAKOtHajigBDR2oo60AcXrZay1S7ZchJY/mA7hh/iK9S8POs3h+wdCCphXB/CvPPFtvuiilA+8jIf5j+tbvw11cXGjvpzt+8tzuTPdCefyP868LN6bcVLsejhWdTruoNpeh3l4mN8cfyZ/vHgfqa8hLEjczFnb5mYnJYnqTXpvjWN5vCN/sBJVVfHsGBP6V5ZDPFLEnzfvMcg15uGS5bnvYK3KxZYFnXa/4HuK0fC/iSfw3qAgnZmsHb94nXb/ALa/55ql0NUNR5KNj1Ga6WlJWZ1VaalHU94cRXVuVO2SGVMHuGUivBvElrJ4f8V27KxHkXBQMe6nlTXrHgS6e78I2u/JaEtFk+gPH6EVwXxchH2syr97EZ/Hn+mKwwbcKzieDWjZ2Z19vMLm2jmUYDqDj0qSsTwndC80KJ89D/MA1t19VTlzRTPHnHlk0ZfiLnRZvqv864noM+nNdt4i/wCQJN9V/nXEMflNDNKex6RE2+GN/wC8oP6U7rUFid2nWzesSfyqxVGLCjrR/nFFMAoxRijrSAKOtFHWgQmKMUvagDIoGNzXN+O4vN8IXhxkxlJOvowzWve6xplgcXWoW0JH8LyDP5da5zWPE2laxpN9punm5vbiaJkRbe3d/mxx29aico8rVzajCfOmkedSNnUB/dkhZRV2wO+wh9QuD+HFadh8PfGWpfZni0KWERjBa6YRA8Y6E5/Sur0v4K+IzEEvNZsbNMk4ijaVhk++BXmucV1PcVOT6HDeUuSQgBPU4600qiuHYLkdCe1eu23wT0dQDqHiHUbhu4iKxA/hg1pQ/CXwNbEFrG4umHea4c5/IgVDrwRSoTZ4gbiBR880Y+rCo2uLaZXhRjK7ggLGpYn8q+iLXwf4RsSPs/huwyOjSRBz/wCPZrbgeG0TbaWdvbr6RRhf5VDxMehosLLqeI+F9WvNC8NRQaloWsRxRM7G5FoxjVSc5JOPWt+xu7rxbIsGhGSKxP8Ax8am8ZAUd1jBxuf36CvTZbmeVWXftJGAwHQ+teO+HNRvPBwlvLmFjpq3bWWqxRji3nGNs6jsrAjI9R7irWLnKLUTKWX04zU5Hp+m6JpulWX2O0tI1hP39y7mlPcuTyxPqa5aJIPCHxEisLULBpmtQNJ5IOEinXOSo6AEYGPf2rsI9W0xrcXC38BiI3Ah+30615n4gv4vEnxGsYkU/Z7WzkbHfBJGfxJ/QVywpyqtwfU6MW4xot9j1IncPWua1nwhbaiHe2RIpWHzRkfu3/DtWAujlD+7v7xF7KH4FTLpb4w+o3rD082lHKa8XdM8R4qLMjT9U1DwFcm3ljln0csRLaty9ue7R+q+q9K0dO1W18K6vFPZ3Cy+FNXfcjKfltJj/JG/Q/TlZdAsph+9ad/dpDWNPoa6PDcRxh59HuQRc2zHJQn+Nfeu6GBkk+cqljOWSaPXic9K8u1g/avirAnUW8ckn/jir/MmtfwBr8m6Tw1qM/mXVoge0nJ/4+Lfsc9yBj8PoawbV/tXxN1ifqsMJT8Wk/8ArVjhqTjX5WetjKylhXKPU6W6nhtLWW5mYJFEhdz6Ac15nYJc6vfS3sin7TfSbgv9xP4R9AK6LxtfGVLbRY25uD5s+O0anp+J/lV/w5pvkW/2t1w8gwgx0X/69evuz56L5IX6s1LO1SztEt0A2KMHP8R75rF1HQmDGa0Xcp6x9x9K6HFKOtFSlGorMxUmnc57TvE2qaSoh3+ZEvHlTjOPoeorZX4gvtw+nLu/2ZeP5VLLbwzj97Ej/wC8oNVv7I08nP2VPzNeZUyuMnc6I4mSIbnx5qEoK28EMOe/Ln9eP0qgthqGszi41CaTb/ekPOPYdq3IbO2tzmGCNCO4Xn86mrWjl1Om7sideUiK3t4rWIRQrtUdvX61LR1FFeilbRGIUdqO1GKYgx/nNHUUUUxh3o+tFFIBa4PxLB9k8V+YBhL63DfV04P6YrvK5Tx3DjT7K/A5tbkBj6I/yn9cVM9rmlF+9budX8P783OjzWrHLW0nA/2W5H65qbxaP+Jr4V/7Cy/+i3rnPh3MY9eubfPyzQE491Yf4muo8VpnV/Cg/wConn8onr5fFw5MXL5/kepRd4HUwOEBBPB5rL8V3Zg8L6hKvVYsA/UgVZlnjgQvLIsaDqztgVxPjnxRp0vhu5s7W586WRkGUHygbgTz07dq5KUXOSSRcpJI7SOdXs4rh2Cq0QcseABjNediQax4vuL+PPkpnacdgNo/PrUaahq2u6ZaWiDy7ZIkRsZAbAAyT3+lbFhYR6fb+WnLHl29TXrZfgpRnzyOHEV1JcqNvS5Mb4z6ZFc54j8M2PiHxJcWV2ux7nT0lhmUfNG8bkZHqMOMjvWzZy+XdI3bOD+NN1YmLxToFyOjtPasfZo9w/WOvcmjCnJrY8H1/wAO6j4a1D7JqEOAc+VMvKSj1B/p1FZ6V9K61pNlr2ly6ffRB4ZBwcfMjdmU9iK+cbuyl0zUrrT7j/W20rRMR3wev49alaM76Nb2i13JIqn7VBH0qfoK6o7FsY1RNzUr9KiNJgMNMP8AWnmmEVDKIyOaaac1NNQxjT1q3pEay6xDGXaNnBCSJwUYcgj8v1qoRWv4UtEvvF+lWjtsE03lhvQkEA/nisK3wNmlP4kd5pd1eWUkVx5ipcx/xR/db8PQ+les6VfJqenxXSDG4YZf7rdxXlF1C9hJMlwpR4CRIMdMdamh8V6joujyTadF9picxzKo4IwQWGMchlyD3HBrx5w5tj1FLl3PXcUVm6Frtj4j0qLULCXdGwwyH70bd1YdjWlWDTWjNE09RMAUdDS0UhhzSqu51UdzSdsVPaJulLf3RTSuxN2RxHxl1oaZ4IOnxtifUpRAoB52D5nP6Af8Cr57HSux+J/iL/hIvGtwIX3Wen5tYeeCQfnb8W4+gFccBXuYWnywu+p5NaXNIcKeOaYKeK60Yj1p49qYtPFWhC9qY2MU/FNamxELYqNqkaoj1rJlC8UlL2opAJ2pRRQM96AHD0p46U0U8dKpAL0FIaXtQaYhhpp5pxFNNJjG9qQ804009KkBKOlFGKQBR3o68Ud6AFp6imDmnimgJBSnpSClNaCGN0ot7WS/vLayh/1lzKsK/VjikbrXV/DPTvt/jWOd1zFYxNMf94/Kv88/hWU9rClLli2e5QQxWVlHBGNsUEYRR6KowP0FYcjmSRnPVjmtO/n2W+zu5x+HesrrVJWPIk7idKTpTsZNJ0qiAxR3pKWgA7etHWijrTASloo6UAZfiJFbSHduNjqwz9cf1ri9A1V9B8QJIM7Fbkf3kPUfl/KvQNRtft2m3Nr3ljZQfQ44/XFeW3IaWCO4A/eKPmH8x+dceLpe0g0deGlY+gsQX1oQcSQTx4PoysP8DXiniDQ7nw9qL20oJhJJhlxw6/4+tdr8OvES3lidLmf99AN0WT95PT8D+ldlf6faapatbXkCTQt/C3Y+oPY+9fMRk6E3Fnr0K/JqeEx3zoNrfMPc81Fc3HnsDggAcCvRbz4XW7yFrLUZIUP8Ese/H4gir+h/Dyw0u6S6upmvJkOUUptRT645ya6vrFO10drxUeU1vCGmvpXhizt5VKzMDLIp6gsc4/LArzT4mXImvJxnP78KP+AjFexXE0dray3EzbY40LsT6CvnrxDePfXUsj9AS59iTmpwUXOq5HlVZXldnY+Afk0S4t88w3LLz6EAj+ddT1rjvB83la1qNp2lijnUfTKn+YrsjX01PSNjy63xsyfEeDosv+8v864hj8rewrt/EZ/4ksv+8v8AOuEum2Wk7/3Y2P6VUiqWx6LpB36LYt626H/x0VbNVNKTZo1gh7W8Y6f7Iq5VLYxluNJAUsSAByTQp3KDzzzWVrd1+8s9NQ/vLuZVbHaMHLfoCPzrWPJouDVkFJS0daYhKOtFBpAFZenabdeOtdurCG6ltdE09gl3NAcPPJ/zzVuwHf8A+uKfrupf2Tol3e/xxxnYPVjwo/Miuy8FaR/wi3guwsXH+mSr59wT1Mj8tn6cD8K48ZW9nGyPSy7DqpLma2JrLwR4R0VVW30Sy3r/AByx+a598tk1qrPDbrst4FjX0UBR+QqEnPXOTSYrxpVJM+hjSjEke5lc9Qv0qElm+8xP1NNgngu1dreaOUIxRijA7WBwQfQg9qeRiodzRW6AOKdTaZJPFAu6aVI19XbH86QyQjim96z5vEWkQg7r1GI7IC38qx77x3ptshZI5HA/ikIRfzNUoSfQXMkdUorGh0OJrzXjeRRyWmpOmYjyGURqrZ+pH6VxqfEifVLgw6ftCL96SKPci+xZuCfoK6jRPE8eobbe6Kx3PRT0WT6eh9qpxlEm6kecX9tL4N1J9Jvpj/Z7K0ljdOeqDkxk/wB4fr+Iq74PsZXS51u6QpNfkeWh6pCPu/n1/KvQfE/hy18U6HNptyApb5opcZMUg6MP6+ozXmeieJdQ028l0nxJB5b20wtjdqPlDH7of0BAyG6Gu/BVIOV5bnl5lCpycsdjtQBRSnik7Zr1j54SlwCDkZ+tJQTQBxniqB9NvLPUdOHl3Vo3nQ4/iH8SfQj+Z9an8ETJfy61rP3UubnCluyqM8/99VJ4vbYsDkj5Uc/yrIsHbTfhPJJH8s13uVfrI+3/ANBrCUEqnMdsKkpUfZ9Li6NE3iTXrrU3B8maTCZ7RLwB+Nd8AFAAAAAwBWR4asE0/RoUVcFlBP07f4/jWx1rWCsjmqyvLTYO1HWijqasyDpR2o7UUgDqKO9FFAxe1FHUUUAJRS96KYCUdRS4yaTqKADrRRR2oAKzfEFkdQ8P39rjLPCxX/eAyP1ArSpR9KTWg4uzucf4Bvo11vT7uRwqPEwZicAZU5/UVr+LvFD3+t6HHpUeXhunZHYZ3MUI6fietcZbRNp+oX9gvy/Zrlwnsjcj9DXSanHb2eveGYYPu75JS56tkKMn868urg1UrKo/Q7vaOPuouvod3qMom1a9kkb+5uzj+g/AVjeL7O0sdPsreCIB5rkAseSVUEnn8q7VmzXD+NHMmtabD/zyhklI/wB4hR/I12KhTpxtFHNCUpT1Z1OiN/xJbT2j4/Or9Zmhn/iS2v8A1z/qa0RzXQjJ7gatyXUcwhaaHdJC4kQ+jAEZ/ImqoAzRgEUAro1LW888lWAVx0A7ivHPijp32Lxkt2q4S+gV8/7a/Kf0C/nXppl+zkSbwmDwScVzvxLtrfWPCkGo28kck1lJ5mFYE7Dw4/kfwrObSOjDStM8si6VY7VBDggH8RU/QZrqjsd7I25qM1K1RN0pMBhqMjmpD+lRms2UMPNMOKeaYRUMYlbXg35fHGhH0vY/51i9a2/Bwz430L/r9j/nWFb+GzSn8aPbvG+mI6JqCqA5/dS4/iHY/wAx+VeeaG+dFtM/wpt/Ikf0r1nxWP8Ainpjjo6f+hCvH9GfytAjkP3UDsfwZq8en8J6cty1Cb/w/qJ1jQTiQ/8AHzaH7k6/T1/yPf0zw1410rxLbjyZRDdgYe3kOGB749a5Hwt4bvNW8O296ZkjMu5hvySfmNJf/DWe6n88GJZ/+e0Epjf8eMGibjLRjimtUeo0Vx3h/wAPeIdPZRdeIZzAv/LJgspP4kcV2FYNWehotRw61egi3WxTcVaTI3L1HbNUe9X7STcmzuv8qqG5E9j5BvLN9O1G8sZTuktriSFj6lWIJ/Soq6f4k2q2fxJ1yNRhXlWYf8DQMf1JrmBXv0XeCZ5E1ZtCjkU8UwU8VsiR45p4FMFPAq0IXqKY2SKf1FMbkU2Iib/IqM1I1RnrWTKCjvRR/WkAUtJ1pR16UAOH9aePpTB+lPFUgHUh5peMfSkPTiqEMPSmmnmmn8KljGkU3qKd0P8AjTelSAdaSl70h5pAFLSUvSmAtPWmU9aaAkHSl60gpTirEROcAk9q9e+FWiyWHh+XUZ49suoOHXPXyxwv5kk/QivLtM0yTWtZstLjyDcyhWI/hQcsfwANfQ08tvpGm5ChIYECIg9AMACspWvd9DmxM7LlXUoanMomO9wqRjBJOAKxpNfsIjgSNKR/zzXj865ye4vtcv8AAV5ndiUiQcD6D+tdBYeBLuQBry5jgz/Ao3t/hXn18xUNEc0aDY3/AISSDP8Ax7y4+oqxDrtjMQGdoj/tjj861I/AmnBfmubpj6gqP6VUvfAQKE2V6S3ZZl6/iP8ACuWOa66s0eGdiUMrKGRgVPQg8GlrkX/tLw7eeVNGyZ5KNyrj1B/qK6WxvIr+3E0Z9mU9VPpXrUMTCqtDlnTcWWaWj/OKK6SA60dM0dqKAE7V57qtv9k1u+t8YUv58f8Auvyf/Ht1ehYrk/GNt5dzYX4Hykm2kP15X9QfzqJGtF+9Y5aAzabqMdzA7Iob7ynBQ16XpXjooqx6lCW/6axDn8V/wrgQMHFdFY6VFqenLNBJ5cynY6kZBI7+3FebisCqmqR0+1cdT0KDxJo1woKahCp9JDsP60tx4j0e2jLvfROeyxHeT+VecPoOoKeI0f3Vh/WnReHr5z84jjX1LZ/lXnf2dK+zL+taFzxJ4pm1hDbQoYbTP3M/M57Z/wAK8+1CPZpdzK4+dsE+3Ir0uLSbbTrSaYnzJliY727cHoK831s/8Se4+g/mK9TD4T2MW3uZwqOcjX0SYQeLtPbtcQSRH343CvRO1eWNN9m1TRrjONlyAT7Ec16l2rtp9TOutUzI8SnGjkesiiuB1Vtul3HHVdo/E4ruPFT7bGBO7SZ/If8A164m9TzjaW2P9fdRp+uf6U5jonqECeXbxJ/dRV/IUrukUbSO21FGWJ7CnnrXOeJ9SEUf2NGwMb5TnoOw/r+VXeyMErsqaRK+r+LJ71wdlvF8oP8ACW4Uf98hvzrrO9YHhC1MOiC6cYkvHMxz/d6KPyA/Ot/rSjsOo/et2F60dqKKZAlBpar315Dp2nz3k5xFChdvw7fj0obGld2KK2g8R+NNN0bG6zsiL+/9ML/q0P1bt6V6ZNIZZS/rXNeBNGm0vw+b++XGp6s/2q5z1RT9xPwU9PUmo/EGqX91q8PhvQpVhvZI/OursruFpD0BA7u3QD8fevBxM3VqaH1ODpKhSV9zps0oNUdM05dKsEtFubq525JluZTJI5PUkn+Qq6DXJ1O7ocf4m8N6hDfnXvDfF6f+Pq1D7BcAdGB6Bx78GsD/AIWTd2rfZ77NlcDrHf25Rh+PANeog4FRzww3URinhjmjP8EqBgfwNaqataSM3F9GeRal8SiE+fV1OeiWoGfzX/GuRvPGtzcMWtrVmJ/5a3L5/Qf417dc+B/C15nzvD9hk94o/LP/AI7ise5+EvhO4BMUF3bH/pjctx/31mtYVKS6GU4VXszxWXWdXuSfMvjGp/hhUL+vWqbRiU7pnklb+9IxavYpvgvpRP7nWdRjHYOEb+gqs3wVtyfl8Q3Q+tup/rWyr00YOhVZwega6ljGmn3mEiBPlTAYHJzhv8a64cn/AAq0/wAEUcYHiGU/71oCP/Qq0U+GutQRhYvFEL7QAolsB09yGrOU6bd0zamqkVaSNXQvFTQ7LbUSXj6LP3X6+v1rL8W2NnqHjBIoGSRNU0W5W4CnIJiG+N/qGAwfasa7g1PQb+Ky1uCNfOOILuAkwzH+7zyrex69q2Ph3aJrWvazqr/6qGH7Bb59+Xb8x/OpUVH3kOb5lYd4buHu/DOmzyHc7W67j644/pWpXP8AgpmXwzHZycTWU0ltIvoysf6EVvmvdg7xTPlKq5ZtAeaaaWlxzVmRyHjs7dOVv+mUg/QUGy+0+EPDlkB8ryQFsem0saseOLcyaKpA6Fl/NT/hV7w0EufDejykZMcC49iBt/xrJq8joUrU0a6oFUKOMDAFO60pFJWhgFHail/rTATijrRS96AGO+0HCs7BdwjQZYj2Hequ/XJ/+PXw9c7T/FdTJCPyyT+lV7t2h8TaSwJAlini/RWH8jWuZHbq7Hj+9UastWSu0Z507xc/SLRbcejyyOf0ApDpXinvqWhr7CKQ/wDs1X+tJjmjl8x+07IzW0/xQvTUtBb6xyD+tRNB4sXpNoD/APApRWvjIoxmjl8w9p5GIzeLU/5ddFk/3bhx/MVG194pjPzaHZSY/wCed5j+YrfxS0+XzF7Rfyo57+2deX7/AIXlP+5doaX/AISDUx9/wvqH/AZENb+KKOV9w54/ymCPE04/1vh3WE+kKt/I1q6ffi/jZ/sl3b7e1xFsJ+nNWccdKeo5oSfcTknsjg/E0H2TxkkgGEvbUH6uhx/LFVLq/efxBpwY/wDHnagfnJ/gKveJ5/tulaLqjACSG9e3kx2ySp/VB+dc+G8zWbxh/AkafoTWb7HVFe7dnqw+8a4DXZftXii/ccrAsduv4Dcf1au7WVFtVuHOEEYkY+2M15nZStcQNdSD57mR52/4ESf5YqpGVJbs9B0ddujWn/XP+tXh1qrpQxpFp/1yFWjVrYwe4jTRxyRo7gNISqD1IGf5VnalraWpaG2AeUdWP3V/xNc54o1N08R2FvE5Hk5Jwe7D/DFWdI0u51m7EFuPd3bog9TXBi8T7NWRvGm3ZlK5mmuXLzyM7e56fStHRrae6WS1+zSyxyA4xGSPcdK9A03w1pumoCsImmHWWUZOfYdBWurFCMcYPFeFPG3eh1xw+mp84a5oF34a1b7HcwyRwSgvbM6kbh3X6j/CqZ6V9J+NvCsPjLwu9qNqXiDzbSU/wSAcA+x6H6+1fNZEiF4po2iniYxyxsMFHBwQa+hy3F+2hyy3Ru42QxuBmojUp4FRN0r0JCGH86jPFSHrTDWbKIzTTTiKaeahjG966HwHH5vj/Q19Lnd+QJ/pXPmuw+Flt9o+Idk2OIIpZT/3yVH/AKFXPiHamzWkrzR7d4tO3w1cH0aP/wBDFePWsTL4Cln5wFZQfdnI/wAa9Z8aybPCd23+1H/6GK4J9P8AK+HOiW2P3l9cJ+ILsR/MV48HoelLc9K0G0Fl4f062Ax5dsgP12jNaOKdtCAKOijApOtYvc2QgFOpKWkAd6tWSnzGb+EDFVRknAqDxRrKeGPCd/qjY3W8RMYJ+9IeFH/fRFaU43ZnOVkfPXxJvk1H4j61NEQUjkWAEeqKFP6g1zAoDO5aSVy8sjF3ZurMeSaK9+lHlgkeRJ3bYop4H600U4VqSSAU4UwD2p4q0IU9KY1SH9KY3SmxELVG1StUTVmyhaSlpD0pAHWlHWiloAcvFPFMA5p45FUhC9aQ8Zp2KQ0wGN0pp/KnGmmkwGmk7/1pTSGpGJ3o7UGjpSAKO9FH+cUAOFPHtTKeopoB46U7FIBQ+QmEUs7HaoHUk9BVt2VxHo/wk0cS3l9rci5Ef+jQE+p5c/yH4mul8RzTalcLZWo3NI3lRj+bfT+laOiaaPDfhCCyGPMhhy5HeRuWP/fRqXwxZCWefUHGdv7mIn/x4/yH515OPr+zhbucSXtajZc0TQrbRbQRQgNKw/eSkcuf6D2rO1vxvpWizNbfPdXK8NHDjCn0LHgfqah8eeI5NItEsLFsX90v3h1jTpn6noPxrzKHTgozMxZj1rxKVL2nvzPZw+F51d7Hap8Ux5nz6Qdn+zPz/wCg11uheJ9O8Qxt9kdlmQZeCQYdR6+4+leQSWEbL8nyt2561HpN/No2s214hKmKQbgO6/xD8s1tPDwcfd3N6mFiloe36rpNvrFi9tcKOeUcDlG7EV5vp3m6TrbWk3y/P5Ug7Z7H/PrXq+QVBHIPOa848cQ/Z/ECToMeZGrk+44/oKjA1XCdjxsTBNXNo8UlIrb41f8AvKD+dLX1q1R5YlFL1opiE61m+ILE6hoN5AgzJs3x/wC+vzD9Rj8a0+9HvSauhxdnc80hmWeCOZejqGrd8N3v2e/MDHCTjH/Ah0/rWJLb/wBn6pf2GMLDMWjH/TN/mH5ZI/ChWZHV0OGU5B96hM6pK56SW5oB9arWVyt5ZRXC/wAa8j0Pf9an6irOV6FfVH26Tdn/AKZN/KvLdb/5A9x9B/MV6XrTY0a7/wCueP1rzfWR/wASa4+g/mKiezOihuhup5+z2Tek6fqCK9Ts5vOsoJf78ak/lXmGpL/odof+m8Vei6K+/RrU+i7fyJFKG463wpmP4ql3XVvD/dQsfxP/ANaudtV8/wAU6RB2SQyt+HA/WtDWbnz9XnYchW2D8OKreGE+0+LXmxkQrsH4DJ/VhQ97BBWi35HfXt2llay3D9EHA9T2Fea6rNJeSiFmJluny5H93qf6D8a6XxFffabr7Mh/dwnn3b/63T8657QYf7Q1pLjGUeQJH/uKck/iQactXYVJWXMz0i3iFvawwqABGgXA7YFSUZOaKs5woxmjtSUAHBNZzWf/AAkfivTtAxutYz9uv/Ty1PyIf95sfhVu7u4bGzmup22wwoXY+wrX+HWmS2mgXGvX6bdQ1h/PZT1SLpGn5c/jXJi6vJC3Vnfl9D2lS72RveINWttG0y51K7OIYE3kDq3oo9ycD8a57wxaS6LoV1rmsAjVNTf7TcjugP8Aq4h9B29Sa09W0Y65qOnfanU6faubiSHvNKPuA/7I5PucVj6zcz+Itb/s2yb9xCf3kmOAe5/oK8dPQ+ktqZttea14p1OVLe8e0tITiSSI4C/7I9T7n/8AX21tALa3SESSSbBjfK25m9yTUWlaTaaNZLa2ce1ASzFjlnY9ST61dI4rOTvsaR03AdaWm1W1HUYNK0y51C53eRbRmV9gycD0FJAy0elJ0rEs/GOiX0KSpcmNHGVMikAj6jIq8usabIPl1C2P/bUCm00JMud6OtVxqVh/z/W3/f5f8ad9usv+fy3/AO/q0rDuT0uciolubeT7lxE3+7IDUh5H+FAGX4h0iHXtCu9OnAxKh2N3RxyrD3Bwa87+HesLpVnZSNkQlTDcDHcMct+B5/OvRdcv00/S5pN2JGUpGO5Y/wCHWvF/DV5byWb26zoZVmkPl5+bBY4OK2ppuDM52Ukdh4gQeGPGDagMDR9ZIMkg+7FcY4b6MO/rWvnIrMstThl0yXSNXtvtemyqVZT96Mex9uvtUPhK4a58PRlpHmSKWSGKVxzJGrEKx/D+Vengqra5JdDwszw6jL2keptVl6rrP2Q+RBgzHkk8hP8A69acziGB5G+6ilvyrktNsZdY1iOAsd0z7nb0HUn8q0xdf2cdDzacOZlPU7bU9U0m4kTzpVXGXLfKGPT2rU8CaXrV/wCFM2VzErWs8kLQuOR0br/wKvQNR0+GLw3c2dvGEjjiJVR7c/nxXMfDK6EOs+INPzwxiuUA9wVP6gV4SxlSUZOLtY9GFGPLyshm1aTS5PI1yIWbZx5rHCE/59Kv29zBdwrNbzRzRHo0bBgfxFcbqt1HruuanquoYkht5nht0cbljjQ44HqTzVCC7h0aeHWdPOy1d1S7hXhWQnG7b2YGvaw9afKvaGNTCK14no9GKTPHBznuKK7jgFopOtLQBi66RFqGhTel95f/AH2jCto1ieKfk021n/54X0En0G/H9a3G6/jUrdly+FMTrRRSVRAUUUUxBzRR3oxQAUv+FJR2oAXrRmkpDQM4nULdpfBmuRD71tfTSp7bXD/yzXO6W4nlvLgHKvKMH6KP8a9He2t7m3v7WLH74yJKB/fZQD+hFeZaAuzSYxjDbm3D3zWLXvI7ISvBnda9fmD4e70P7yeFbZfcsdp/TNcwiCKNI16KoUfhS6nfm6t9H0v+GGWSeQew+7+pNKeRT3ZKVkeg6YP+JVaD/pkv8qnkZY42djhVBJPsKjshtsbdfSNf5VR8RXP2fTDGPvTHYPp1P+fer6HPa7POrq5e+8UNKQSck4+vb9a9x8PaUuj6VHDtHnOA8x9W9Pw6V434Ss/7Q8bW6EZX7Rk/RfmP8q95Oc+9fMZnUbnyo9SlHqZWv+ILTw/YfaLjLyMcRQqcFz/QeprzC/8AHWvX0pZLv7LH2jtxtx+PU1D4m1F9e8U3Tbz9nt2MMfoFU4J/E5NQR28KjHlr+IzUUaMYq8lqezh8OuW7Og8NfEHW9Pl8m4uTeQ9RHOcn8G6iqHxM061umt/GekoRaXrCDUI8cwzgfKx+o4J9ge9ZM9uIWE0HG3qtdp4HW21mS90C9TzLLVLZkkX0ZeQw9xzz6gV0U5+ymqkegYjDrlbR5GajI5rQ1jRrvw7rV3ot9zPathXxgSRnlXH1GKoMK+lhNVIqS6nktWIiM/lTG5FSNUbcCkxjCKYelPPvTTUMY016T8FrTzNc1a+x/qbdIQfdmz/7JXm2K9q+DNiYfCt3esObq7bB9VQAD9S1cWNlanY6cKr1De+Ic3leEJh3eVAPzJ/pUV5Yf8THwdpmPlt181x/uID/ADFTeNoDex6VYDnz71cj2HX+dbbWhfxIl2V/dw2hRDj+Jm5/RR+deQnZHotXZpE80mBS0lSUHWlpKKQya3TdMvoOa8w+O2qvHYaRo6ZCXEr3Ep9QgAUfmxP4CvVLIcO34V4R8bb37R44trQH5bWyXIz0ZmJP6ba7MJG80cmJlaLPOeKB/KgUte2eaLinimCnj/8AXVIB6injpTBTxVokWo29akPSmNQwRE3TrxURqVvWo2rNlBR/kUv+cUlIA60ozSUo60AOHNPHSmgU8VSELSHpS0Hp1qgGH3ph6U89aaR/OpYxp5HWmnkU7vSd6kBtGPalNJSAKB1/GjtSjmgBe9PFM/8A1VIvFNASCuo8AaOdX8WwO6ZtrAfaJM9C/wDAPz5/CuV3BVLHtzXtfw00b+y/CsdzKuLm/P2h/UKfuD8uf+BU5O+hjWnywNrxDP5GngDlmbgeuP8A6+K2NLtTY6Zb2x+8iDf/ALx5P6k1jmMap4kSLrBaDe/pkc4/PH5GuiIr5fNKvPV5V0Iw0LRuzyPxO7T+NNTklP8AqmWNAewCj/P41ngflW/440+Sz8Qm92nyLtF+YDgOowR+QB/OsGL53VdwG44yegopu8FY+lw7Tpqxcs9MmvVYxhQq/wATHjPpWRLp0l1rkVgi/vpZFQgdjnmuojv4dJsPJLrNMWJVUOck10Hg7wtNaXD61qibbyXPlRHrGD1J9z6dhRKooK4sRUUI6nXABVCjoowK88+ID51S1TuIef8Avo16I+FUsSAAMkntXld5MfEHih5Rnyd3y+yL/j/WsMHBzq6Hz2IklE6G2BW1hB6hFB/KpO9A6f0pa+vWiseUHaijFFUAUUdaKAOM8Y23katY3wGFnRraQ+4+Zf8A2YVj5rsPFlm174culjGZYQJ4/wDeTn+WR+NcXFKJ4o5V+66hh+NZvRnTB3ijqPC15/rrNj/tp/X+ldKK88srk2d7FcLn5G5HqO/6V6GhDorqcqRkH1FUmY1I2dzN8QNt0W499o/8eFed6z/yBbn6D+Yr0HxKQujP/tSKP6/0rzzWmxo1x9B/MVM9ma0FqiW+5tbFfWeP+RrtdKuvs3heSY9Yi+Pr2/U1xdxh5tNj/wBsv+SH/Gtg3hGl/YhnBl8xvpgYH50o7l1FdJGXLKI43lkbhQWY1N4auW060muiMXEyZX2Zjkn8BiqGqL5ohsx1uHw3sg5b/D8auZweBgDoKXUpr3bEeozP5CwRk+dcNsU9x/eP4DNdD4Us1W6LKuI4I9q/jx/LNcxZN9rupLw8oMxQ/Qfeb8T/ACr0Dw9a/Z9MDsMNMd/4dv8APvVR1dzOq+WNjV60UUAcVocovUfSijvVTVNRg0nS576f7kK5xnlj2A+ppN2KSu7IoXFi3irxTZeGkybSMi71Jh2jB+VD/vHFep3TqXEUYARBgAdK5rwFoU2heHX1DUB/xONVf7RcZ6oD91P+Aj9Sa3q8HF1vaTPqcDh/ZU0VdTku00+RLFC1zJiOM9kz/EfYDmm6RpUOj2IgjO9zzJIert/h7Vc6GnZ4rlvpY7bBXK6h40ZtWk0jw/prarewnbO/mCOCA+jP3PsKueMdYl0jw/IbPJ1C6YW1mo6mVuAfwGT+FHhTw5D4a0aG0U758b5pCfvOepq1ZK7Id27I09PbUXtA2qQ2sVyT922dnQDtywHNTyRpNE8UiK8bgqysMhgeoI9KkzmkPWs2aI4a9+GOms7PpN7eaUzHJSJvMi/75bp+BquPhpckf8jTfbv+uCYr0DFOArRVJkOETzpvhrqgP7rxXL7eZZKf61E/w98Sx/6rxDYye0toV/lmvSutJT9rIXs0eVy+D/GkB+UaPdj/AGXdD+oqq1t40sDl/DtztH8VndK/6A5r17rQKPa90L2fZnhuo+J5PJmW+N7FfRxkR214jCQseABnrzitSy8G6fJ4fsbS+t83EMXMqHa6seTgj3JrtPiJoaat4Sup1UC8sB9qtpAPmVl5IB9wD+lZum3Q1DSbS9AA8+JZMDsSOR+dengeSSeh4+aSqRcdTnI/AlqW2z6pqc1t3gabgj0JHOK6m3ghtbeOC3jWOGMBVReiind6UcV3xhGOyPInVnP4ncraocaXcn/Yqn4H2nXZM/eEDbfzFaN3H51lNF3ZCB9a5vw1fCy8RWrvwjt5TZ7buP54ry8zi3H5GlB2keqOokjZD0YEH8a8d8Far9n+JyKDiK7iltVPrt+ZT/47+teyDrXz9ck6B41gmbj7Dqnzf7m//CvFwiUlKPkezTimmzW161GheIdS0y8/d2t3I9xbSsPlIfkqfoTj8KzZIkvraLQtPlW6u7yVUVYzkIobOSf89K9k8R+HLXxBbqk0cblTxvHBH9PrUHhvwhpvh2QzW1vEs7DBcZJx6ZPNdMcwUadmtTIwdGkk+wrbTZE9r+5kB65XgH8hWjRrVuLHxMkyjEd9H83++v8A9bFKf0r3MDX9tRUnueRWhyzaCjvR2orrMjI8UQtceGb9F++sfmL9VIb+laytvRX/ALwB/OqmqBjo99s+99nkx/3yaTSJzc6JYTk8yW8bH/vkUupX2S73oxSZ+tVZrx5LgWdjH592xwAOie5qalSNOPNJ6AotuyJLi7htVDTPtyflHUn6CkiOp3C74NHuDGejSMqZ/A1uaT4eisD9puWFxfMPmlbovso7Vs4rwK+bzbtSWh208IrXkcNNeSWjhb+zntMnAdxlT+Iq2uGUFSCDyCD1rrJYYrmF4Zo1kiYYZWGQRXEJbtpOtXGlFi0BHm25PZT2/wA+ldWBzKVaXJU3Mq+H5FzRLfSilPFJ0r2TkCjtRR+tAzF05zH4q1e3PRhBcKPqm0/+giuESI2mp6paHjyb2QAexORXckeV49h9LjTiv1KSZ/ka4/WCF8a64o6b42/8cGawe51w6+iM+A+bq11L2iVYlP8A48f5itHPGKoaUhNl5xGDM7Sn8Tx+mK0YF33ES/3nUfrTjsVPc9IiXbEi+igfpXJ+JbnztT8kcrAuPxPJ/pXVyzLbxSSv91FLH6CvOrqdpFnnfl23O3Pc1ctEc9NXdzU+F8Il8UmU/wAMcj/nx/WvYyuen4V5V8JoP+JjeTY+5AFz9WH+Feqk4r5DGu9Znp0tjwW3h8kzB/v+a27PqDVkHjrWt4q0x9L8QznZi3u2M0TY4yfvL+B/Qisgda6oy5lc+hpSUoJo0NPsftxf5iqqOoGea6H4bWRi8YMdwMVsr5YdMt8oH8/yrMnuLd44bDSlM1y5wvlZB6c59/5V3vhzR10TThCSGnkO+Zx3b0HsKyq1OVGWKqqMLdWZnxo8J/2loaeIbOPN7pqnzQo5kgP3h/wE8/TdXgpYMoZTwRkGvqvV9USLw9N5m0vIpiw3IbI54+lfMGt6PNoGqyWMqEQuPOtmP8SHt+HSvWyrENpwex4spJyt1M8nmozTzzTD+teuwGGmmnHimn+dQxjZDtRj6CvpfwVpn9keC9Js2Xa4t1dx/tP8x/Vq+dtG086vr+m6cBn7Tcojf7ufmP5Zr6nwOgGAOgFeVmE9VE7sHHeRj3lv9q8TafnlLWF5j9ThR/X8q16aI0EpkCjeQFJ9QOn8zT6807w7UdaKOlABS0mOaWgC9ZjEH1avmX4kXX2v4ka4+crHKsI/4CgX+YNfTlqMQp7mvkjWbk3viLVrsnPnXsz5+rGvRwK944MU9CmP8mlpO1LXrHCLTxTRThVIQ8dPenj2pop46VaEHamNUlRsKGBE1RmpGqM8dqzZQtHb+lFGKAE/xpaOM0oHFADhTx9KYOtPHSqQh1IaXrSGmA09KYaceaaelSwGnmkP9KU4pOlIYnWk70p/yKKQCUtJwKWgBRUi1GOtSAU0Bf0XS21zXrDSxnbPKPMI7Rjlj+QNfQl3cJp9i8oAVY1wijp6AV5l8JNK8291HWHX5YgLWI+5+Zz/AOg/nXod3F/aGr2Wnn/VgmaUf7I6VhXq+zpyqHDXfPUUEaWgae1npAnm/wCPi5bzJCeoB6D+v41oHpT5JMnaPuio6+PqS55NndFWVkQXdpBfW729zCksL8FGGR/+uuXm+H2lySEw3F5Cp/gDhgPpkZrr+TSHpUxnKOzNI1JQ+FmPovhXStFlEsMJknHSaY7mH07D8BXQuV2+/rVC5u7eziMtzMkSDuxxn/GuQ1TxfPesbTRo3JPBlI5/D0+prWnCpVdlqRUrW1ky14w18JE2l2bbppPllK84H90e5rN0rTvsNvlwPOk5b29qbpulC1bz52Elwe/UL9Pf3rT6ivpMDg/Yq8tzya1VzY3tS0dRRXomIdqKKOgoAKDijvUVzdQWke+Zwo7DufpSbSV2G44jjnkH1rzBLb7BeXmnH/l1nZVz/cPzL+hrsLjxG5bFtAAP70nP6CuQ1u8mGupeOif6TFsYAYyy9D+R/SuaWJpuVkzpoxeqJgM123hu6+0aYImPzwHafp2/w/CuEivYZOCSh9+n51u6BefZdTQE/u5vkb8en61tGSewTWhseK2xp0S/3pf5A157rx/4k1x/wH/0IV3Hi2b5rWL/AHm/lXDa9/yB5vcr/wChCiezHR3ROPm1OEY4igJ/EkD+hq+GyarRptlkkxy2B+AH+JNQahcvFbiKH/Xzny4/bPU/gKSdkaNczshbX/Sr2e76ov7mL6D7x/P+VN1F22JbREiac7Qf7q/xH8v51bgjjtLRY1IEcS9T7dTVWxjNzJJfyAgy/LEp/hjH+PWi2lgT1uaGmWXnXFvZxDC5C/QDqfyr0ZQqoEUYUDAHoK5rwvY7RJeOOvyR/wBT/T866UcVpFaHLVldin6dKQ06jH/66ZmM6VQ0XTh4v8YhJAG0bRHEk+fuzXH8Ke4Xqf8A69Q69f3Frbw2enp5up30ggtIx3Y9WPsBzXe6FokHhbw7baNbtvdRvuJT1lkPLMfqf0xXDja/JHlW56mW4bnlzs0p5jNKT/COBUX50najt7V4jdz6NKwUvSko6UDM680iK91rTtRmck2Ik8uLHylnAG76gA/nWlSUtO9xWDpSjmkoBxSGZEHivRLiRkjuydrFGYxMACDggnFbCOk0ayROrxtyGU5Brmdd8DaPrl215m4sr1vvXFpJsLf7w6E+/X3rFbwJ4ktV26d4yn2jolxGcfmG/pWvLBrRmd5Lod+aK8yl8O/ESEnZqVhdD/rvIhP51Sks/iJCfm0szY7w6gP6tR7PzD2nketdacFJ9a8dNx41h/12g6xx/wA8p9/8jUT6zrUS/wCl6L4hRe5aF2H86fsn3F7RHpni7U7bT/DWoiSRTI1vIoTPPKnk+grjvCSsnhHSw+cmAH8CSR+hrk5rxfEssWj6e8ga4b/SmZCDDGv3s57npXoUcccEMcMShY41CqB2AGBXp4Gk4JtniZpWUmoocaTvS9Rz/Ok616B44Vx+tWRtNQLoCI5DvQjse4rsMVWvrNL22aF+D1VvQ1jXpe0hbqVCXKzovDmrLq+kxysw8+P5Jh/tDv8Aj1ryz4saU1vrct5Gv7q6iUuR/C44z+grV0bUp/D2rkyKfLJ2TR+o9R79xXSeOLW31HTrScESQTKU3DuCMg/zr5pQdCt5Hr0K2lzb8M339qeGNMvScma2RmP+1jB/UGtbtXFfCq4ZvCUlhIcyafdyQHPoTuH8zXbhc1xVocs2jQ5bxkwX+yiPv/aePpjn+lQnrVPWr0at4phghO63sgSWHQt3/XA/CrVfSZVBxo3Z5WJknPQKKKOK9Q5hJYxLBLGeQyFfzFY3hWdB4P0+SRwoji2MScY2kj+lbE08dtC00rbUQZJrmvAOjy65prec5WxtrmVFX33E4+vP4Vy4rExoR5mdFKm5xaRqxNe65OYNOVo4B9+4bj8v85+ldfpGj22kQbIVzIw+eRurf/Wq7BaQ2kCwwRhI14AAqTGK+XxOLqV5Xk9D0KVGNNeYuKDzR1o61ymwvGa4/wAREHxbYbfvCA7vpk12HWuEuZvtnjG+lHKW6CFT7jr+ua78ug5YhHPiXamXDzSdaO1H419aeSFFFJ3pjOc1+4+weItDvewFxGfxTI/UVxXiQSW+u3UzZ33lnHKPdj8v9K67x18lppc392+VfwZWFQfEHS4k1PwnOo+SSyKN7mM7h/6FXnVqjjiIw7ndRS5ObyMaONYYEiXgIoUfgKtaam/VLVf+mq/oc1ASa0NDTfrNv7En8ga7bGTehueJrvyNO8kH5pmx+A5P9K4m9l22j+/FbfiO5+0asyD7sI2D69T+v8q5nU2+WJP7zVFWVosdKOiPUPhZaeXo13dEf62QID7KP/r13Z5rD8H2f2HwtYw4wzJ5jZ9W5rd/lXx9aXNUbPQgrIp3+m2mp2rW15CssROQDwQfUEcg1zcnw+00t8t5eqv93cp/XFdfSYqIzlHZmsas4fCzL0nQdP0VCLSHDsMNK53O34+nsK0J7mGzt3nncJGgySahv7+3063ae4fao/Nj6CuDvLu98T3XOYbFDwB0/wDrn3rahQqV52RjWrW1k9Sb+1LjXfEK3GGWyiOCueAv+Jp3xD8PnXvD/wBotU3XtlmaDA5ZcfMv4gfmBVy3gjtoFhhXaqjp/jWrp02R5LdRypr6jD4aNGHKjzPavn5j50R/MQMOhoNdb4/8Nf8ACP679pgTGn35LpgcRyfxL+PUf/Wrk2rqi7o9KMlJXRGetMNPPemH3oZZ3fwh0z7b40e9Zcx2FuzA+jv8o/Td+Ve8da89+D2k/YfCMmoOuJNQnLg4/wCWa/Kv67j+Nehdq+fxM+eo2eth48tNB2o7Udf6UfhXObh+dHaigUAL2opKU9KALskottOeY8CKJnP4DNfHcLF4956sSx/GvrDxjc/YfA+tXA4MdhLj6lCB+pr5QhGIEHtXrYFatnm4l6ofSijtSivROQUc/WnimU8f5FUhDxTx9aYKeOlWhC4pjU8jimNxTYIhaozzUrVEelZMoXvRS0nagAxSikpf60AOFSDpTAPanjpVIQtIaWkNMBh/OmmnkU08UgG/yptONIc1IxtHXvQaKQBR+VFFADhyaczbI2Y9hmkFa3hrSv7c8TadpxGY3lEkv/XNeT/LH40N2Qm7K57V4I0j+xvB+n2zLiZ4/Olz13vzz9AQPwp9veCLxsUbhZrcxofcHJH8627iQQW7v6Dge/auV1CzN0qPG5juI23xSDqprmxNF1aLgjzI1LVOdnbilzziuQg8V39rGI7/AEuSRxx5tuchvfHaifxTqV2uyw01oc/8tLg9Pwr5j6lX5uXlO/29O17nUXV7bWMRluZljUf3j1+lc1deKLq9LR6RbEr08+UYH4VnR6c08v2jUZ2upuuG+6PwrR4AAAAA6ACvVw+U9apzVMU3pEyG0mW7l87UrqS4c/wgkL/jWhDbxW8YSFFRR2UYqb60mK9inQhTVoo5JSctwFFFFakhijvR2xR/KgApORTvpUNxPHbQtLM21V/X2FJtJXYEN9epY25kfk5wq5+8awrDTtR8RXjOnKg4aV+ET2/+sKvafpN34nvftMwaGxQ4z6j0X39TXf2trDZ26W9vGI4kGAor57H4/mfLA7aGHuryMKz8D6bCo+0tLcP3y2xfyHP61leOvCemp4WlvbS12TWLrOSrEkoDhxyf7pJ/Crms/EPTNLme3tY2vp1OCUYLGp9N3f8AAfjWE/xLa7gmtr3SYmtp0aJxFIc7WGD1GDwa4qarOSkejDCu10jkbrQiq77WXzF6hW6kfWsuO7mtJdjbhtPKnqp9q2dAv1urBIC+ZoP3bA9cDgH8qn1LS1vY96ACdR8p/vexr0adeUJWkc0oW0ZJquopqslvcRtkeSA3s2TkVga8P+JUR/ekQfrRZym2kMb8KTgg9jSa42baBP71wgr1Y1FOFzCMeWSRpONuT6Vl2n+mXb3x/wBWMxwA+ndvxqxfO17ObCA4X/l4kH8K/wB0e5ovZPskMcNugMz/ACQx9uO/0FW9RrT1ZDcMby5FiufKXDXDD07L+P8AKtaJfMZUXAzx7CqNrai1hCZ3OTudz1Zj1NdH4c0r+0NRtklH7qRizD1ReT+ZwKmpNU4ObJeuiOl02w1K8solsI47e1VcLNP1f3Aq03h7WlGV1O3c/wB1osf0raute0vTxse4RmUYEcQ3Ee3HArBvfF80gK2UCxA9Hk+Y/l0/nXz0sbiZyunZGqoU4rUjmj1uwQvcWUU0a9XifH86zJ/FUNrE8s9nOiIMswIIFYt/4hnutQSziFxquoucJbQ5Y/jjgCls/B+ua54wg0vX5Yo7O3jW5vbW2fIiUn5Y2I4LtjsTgc130cTiLXm9Co4NTeiO18A6bJfzSeM9RhKSTqYdLgb/AJZQ93+revp9a7M5JyeSe9KSu1URVSNFCoijAVR0AFJ1rlq1HUldnvUKSpQUUJ3o780HmjnFZGwUtJRQAYooooAXAoNHesHXvFFtpAaNNks6/ey+1U+p9famk3sJuxuZpwNctouo+ItVmSeS3tbawJyWeNg8g/2QTn8Tj8a6fNDVtBrUf1pMUZ96XqaQDQMU8Nj1pMUx3CDLEKPUnFAbnmvxGX+x/Edj4htUAkSLbdKox5sWcHPqQMH8PatdXDqHU5UjIPqDXNfErVobqK88pw8ccHkKV5DMxxx+f6VuabgaXaANu2woMg+gxXtYCT5LM+ezaEVNNblvtRSCl5r0DyA60UUvSkIoanpq30W5cCZfun19jWF/bMlrpcumXeREsgeMt/yzYdR9Dn866usTxLpcWoaXcOBiZUyGHcDsa48ThFV95bm1Kpysd8O7oQ+Ktas8/JdQRXSgeoJVv5iui8V+JPsUZsLNs3TjDsv8APYe5rx7QL7U9C8T2RjO4yRSQIR8xwwzj8CAa9C07SnSX7XeEvMTuAJzg+p968v6hKdfXY7a1blikizpVj9jtfnH76T5nP8AIVe9qO1HXrXvQgoRUUea3cKKOtNlcRxs7fdUEn6Cq2AwtYmkvr6LTbf5mLAEDu56flXQfD+zGnf8JDpoOfs2quAT6FVNYvgyE3niVrqTnylaXn+8eB/Ot7RrlNP8U+M2mOERoLo/Qxc/+g18xjazqTlH+tz1MLC0Tb1zXrPQbYPcsWlf/Vwqfmf/AAHvXA3njbXLmQmCSC1TPCJEHP4lv/rVNZs+qzXGr3gEkkjHaGGRGo6ACueu5I5bqR4l2oTwMVjToxS13Pfo4SCXvK7N/T/H2pWkoGpRR3Vvn5niXa6++Ohr0S1uob21jubeQSRSqGRh3FeKEZHTiu0+Gmos0F9pTsT9nfzIs9lbqPz/AJ1NejHl5omGLw8Yrmid1LIsMLyt91FLH6AZrzzQg0lvPcvy88xYn/P1Ndj4kuPs/h2+f1iKj/gXH9a5XRFH9kQ++T+tduTx99yPCxj0SL/UUdqMUdq+iPPDtRjNHU0o5pjOc8cRB9Aif+5eQt/49j+tU/G12suj+Glz++tL2SBh3wyjH6CtPxqP+KTum/uPE/5SLWD8RoDAlldpny3uELD0YZ5/LP5Vw4ilzTU10OyhKyUe9zPJ5rU0GVIL57h/uwwu5rJPJqSOVkSRR0kAB+mc/wBBXUS1oNkYySM7HLMSxPuazb2MvdWvHBfBrSqxZ6c2oyMka5eNTKo9xWdaLcGkNS5dT2zR1jk0y3IwR5a4HbGKtyQAAsvbtXGeCvEUTW66bO4SaMYj3H7y+n1Fdi1zkYGOnWvkKkeV2Z3wkpR0ItpJxVPVtUttIs/NmPzH7qA8uf8APeodW1+z0aAmVw8xHyxKeT9fQVxsKXOuXZ1HUOYz/q4+xH09P51thcLKvKy2M6tZQXmIyXWvXP2y/JWD/lnEOMj/AA/nWoiKihUUBRwAB0pxFHevqaFCFGPLE8yc3J3YCpImMciuOoOaYKbPc29pEZrmZIox1Z2wK2ZKRe8Q6Nb+JvDs9hJgeYu6Jz/yzcdD+f6Zr57nhntLia0ukMdxA5jlU9iK9vtfFgP7nT9L1HUN3KtFDsT/AL6fArgfiJpepzTr4in0lLGNtsM6rcCVj2VmwAB6dT2rO9nc7sPJp8rOHOMUiQS3c8NpAu6a4kWKMDuScCg8123wn0T+1PGB1CRM2+mp5nPQytwo/Dk/hU4ipyU3I76cOaSR7jplhFpWl2mnw/6u2hWJffAxmrXWjvR1r55u57K0CijrRQMKM0UUgCnKMuox1Ipv86khGZ0+tCE9jn/ixci2+GmsHODIscQ/4FIo/lmvmhQAoHoK99+Ol0YvA1tbjrc30an6KrN/MCvA69nBL3Wzy8Q/eFpaSlFd5zjhTh700CnCmhEi08cUxaeKtCFPAqNuRUh6VGwpsERNUbVI3XrUbVmyhev5UUdqPzpAFKKTtSigBw5p4pgp4q0IXtSGl60HpTAYeaaetOPSmmpYDTTTxTjxSHrSGIeaSlNJSAKUUlLQA5Rk16h8INILPf65IvB/0WAn0GC5/wDQR+deYRxS3E0VtboXnmcRRqO7E4FfSGgaRFoOg2emxYK28YDMP4m6s34kk1EtXYwxErRt3F1SblYh2+Y/0rMI4qaeQyzO/qeKiq0eY3cZjmjHNOIpKYgHSlzzSUUAL2pKKMUAFFHWkNMBaTOaTNZ2rX7WduFi/wBdIdqe3vUTmoRcmCV3YnvdUgsRtOXlPSNev4+lT6b4cutVlW81jMcI5jthwSPf0H6/Srnhzwyliq3t8vmXr/NhufL/APsveumFfNYzMZVHyw2PQo4dL3pCRxJFGscaKiKMKqjAArkvH2rzWdhFpto5Se7zvcdVjHX8+n512Arznx6jr4hikYfI9sAh+jHI/UfnXBRSc9T08NBSqJM4yOxhjH3dx9TSy2kciHaoVscEVZIPX1ppOBXpJs9nlSVjkPNl07WvNiO1m+Ye57ivQraVbi3jmT7rqGFefa0cXKyj+CTn6Gu00BidGgz/ALWPpk1dZXimeNiopO5l+IbbybxZlGFlHP8AvDr/AErI1Bjcw6em4hmuFGR147103iNN2nxt3WT+YNclLKsV7Y78lVZ32qMljjAA9811YSd1ZnBbU2TJb6Xa8KeT8qjlnY/zJotLdw7XVzg3MgwfSNf7oqK3tpZLgXd3jzcfu4weIh/U+pq+DivSiYyYpGalN5ctfW3zlItpi2p8oA7fXkVEPX/Iplna6j4juzYaBaNdzqQXn6RQ+7N0/Dv71nXUXBqQUouUlZF+71C20yDzbqURr2HdvoO9aeg+EfEnjQLNLv0XRW58xx+/mX/ZHYH1PH1rsvC/wv0/RZl1TW5RquqjnzJV/dQn0RD6ep/ACu0lvCeI8gf3q8V8sPNnsUsJfWRnaL4d0PwhZfZdIs0RyP3krfNJIfVm6n6dPaodL01NOjmLSGa5uZWnuJ2GDI59uwAwAOwAq7396XtWcptnfGCiHajrSdaOtSWL3pPrR0oxQAfhR1FFFABzR1oxR2oAUdayLfwro9vdfavsvnXG4uJLhzIQSc5APA/KtemXFzBZwGa5lWNB3Y/yppvoJpFfUdUs9LiWW8m2K2cYUknHXpWBL4/0FW2pOXf+7lQfyLVj+ItWj1ydEECm3izsEigliepP5dK8w8TXmnyM1hZW1uZAf3s6xj5P9kHHWt6dJS3MqlTkVz2CXx7aIMx2x+skoFY958UooAf31jFjtu3n8gf6V4r9lQ/djyfYVIlhJ1ESr9eK3WGijmeKl2PSbr4qCYEfb7kr6Qw7f8KzrfxNLrUrC1tL64AYBpZGAVc+pJP5Vxn2SYD7mfoau6DCZNaNqZ7iCOSNiwhcoWI6Z/Wm6UYq4QrzlJI7XRtKPibxVBbEbtO01hPduPutJ/DGD/P2zXRR276Jq76W5Jt5fnt2Pp6f59PetPwFParpLaVDBHDLa8tsGPNU9HP+12P4Vf8AFenG50k3MQ/0i0PmoR6DqP6/hXDHFShiE+iOLG03Nvm3KGKKbBMtxbxzL0dQ1Or6dO6ujw2FFHUc0UxAeRTJYxLE8Z6MpU/iKf14zRSGeWXkhsrqyuzwbW6Rm+mcGvVMflXnPiiy/fahAB94F1H15FdvoN5/aHh+wus5aSBdx/2gMH9QazjpI3qawTL+ABQaU0laGAVT1R9ml3J/2MfnxVyq9/EZ9PuIx1KHH86mfwuwLcZ8P1Bub4/xeWvHtk1B4sc2Gva0Rx9t0eJvqUm2n9HFVfBt+LPXY0c4S4Xyj9T0/UY/GtnxtYfatY0UAhftaXFgWPQM6bkz/wACQV8rJWru/VHsYSSSXkcNa6lOtm0MUpEbjDLgfj9KiB5rJY3FhdSQyoUljYq8bDoRVhdSUDmNs/Wujl7H0caiZfZgiFm4AHNdN8MLV3udT1FhhCFiX3JO4/0/OuIU3Wq3MdpbRl5JG2pGvc+9e2aBo6aFolvYKQzqN0jAfec9T/T6CufEy5YW6s48bVXLyozPHFx5Xh5kycyyqo/Dn+lZOjf8gi3/AN0/zNQePb8TX8FjGc+Qu98f3m6D8v51dsYjBZQxHqqAH616OUQaTZ83ipXZZ6ikpcUda9s5BPalFHek7UwMXxlz4P1L2jB/8eFZ/wAQwJPBjPjlJInB/HH9a0PF5/4pHVB/0x/qKpeMF83wRcDriONv1WspLf0N6bty+v8AkcrbzLcRh1645HcGphxUZsWTTrXULfqYlaRfwHNSJIs0Qde/b0NYUK6qK3U3nGzF711PhKHAubj6IP5n+lcrmu48OReXo0ZxzIS5/PA/QV0owqaIZqegpdSGe2YRy5yQeAT6+xqqsXiRV8oXc2z/AK7/AP166LtSGuepg6VR3aM1UktEYlpoO2Xzr2Tzn67c5GfcnrW10H9KKiuLmG0t3nuJUihQZZ3OAK2hThTVooltyZN1FZ19rFpZTLbfPcXbD5baAbpD7kdh7nFUvP1LXeLTzNO04/8ALwy4nlH+wp+4Pc8+grSsNMtNLhMdpCE3HLuTlnPqzHkmq32Lso7i2T38uZLyOKBSPlhQ72H+83TPsB+Jq3gHGQDjkZHSkop2IbHiRkZXU8g8VoXtlb6xpc1pcJuguYyjD2P9RWZjJrU058wMh6qePoaTWg4t3PnXV9Nn0HU7rTrz/W27YVu0i/wsPqK96+HHh86B4PtllTbdXf8ApM+RyCw4H4Lj8c1z/iDSbPxT8R9H04Qh/sEZuL6QD/lnkFIz9T+jV6divIx1W9oH0uAi5R9ow7UdaKP6V556Id6KKKACiijrQAtS2wzcJ7ZqGrFqMz/RTTjuKWx5V8fLj/RvD9rn7800hH0Cj+prxvvXpnx0uvM8W6TaDpBZmQj3ZyP/AGUV5nXuYRWpnkVn74DmlpO1OFdZkLTximU8dapCHj1p4/WmD+dPHTFWhMXIqNqkNMahiIW61GwqRutRt/k1myx1J0P1paSgApRj9aSlHWgB69acKaOlPFUhBz/WkNOxSEUwGHrTT+dPPNN7UgGGkpxGPx4pp571Ixp/rRS0lIA4xRS49afBazXt1BZ2ylp7mQRIPcnFJuyA6/4e6Fqt1f8A9uWdlbypATHbvdSlED92wAS2AcduvXivRbu08SvGGvPEEMAY48qxtAP/AB5yT+lbej6ZDoukWunWw/dW8YQHH3j3J9ycn8ap3k3n3Bx91eFqYx7nmVa7k7oggUxQpG0rylRgvIQWb3NRXEM8v+pvJIOP4Y0b+YqYdKXOfpV2ME+plPpmpMf+Q/cAe1tF/hUR0W9b7/iDUf8AgAjX+S1tUnWjlRXOzE/sCXvrurf9/l/+JpRolyn3Ne1T23OjfzWtnFHWjlQe0kZ0dlqMR41dpAO0tsh/VcVfiEoX96yM3qqkfpk06lFNIlyuLiqUup21vJ5dyXtiThWmXajfRun61czzQypIjI6hkbgqwyDQwVuozIYAjkY4IrPtYlvPHFlDJykY3Ae4Bb+eKa+hJCS+mXMtg+c7U+aI/VDx+WKy59Q1HQtastRvrMSeW4DTWvKyL0+6eQcE+tceNUpUWka0UudHrRhxGWz+FMxjmodO1ew1iy8/T7mOeLvtPKH0YdQfY1Ma+SkrOzPWDPOM1la/ocWu6f5Jby5ozuhlx91vQ+xrVzk0f/rqU3F3Q4ycXdHj1/pt7pEuy/tnjAPEmMxt9G/yaXWdU0ZNLxZrCs8nXA5X15r1q8vIbK0lnuCPLUcgj73oBXmN+8OoXclzJbW4ZjwFiUBR2HSu6lV590dv19pWaPOzYT6zI0MCnaTlpSPlX8a63RJRJo1ocbSsYQj0K8H9RWjsAGFAA7AViwzx6dealBK22MMLmP3D9QP+BA/nXU5e0VkcNWq6juHiKcGKGDPzElyP0/xrDsk3ayMgHy7cn6EtRJPLqF8XPBboP7opILu2s9TvnmkCKiogz1PGTgV34WlyJNnK9W7GwRVK51COGVbdFee6c4SCIbnY9hgV0OgeC/EXi/bKiNpOlN/y8zr+8kH+wv8AXge5r1Pw94P0DwhHjTrbzLsjEl1Kd0r/APAuw9hita2KjDY3oYOU9ZHA+G/hbqesFLvxRKbKz6jT4Ww7D/bbt9Ov0r1iyg0/RrFLLTLSKCCP7qRrhR7+596a8ryfePHoKZXmVK8pvU9elh401ZD5JHlOWNMxS0fWsDcKMZoooGJRRR0oAPajGaWkoAMZo7UYo60AFFFV768g0+0e5nbCJ27sewHvRYCh4g1n+x7LegDXEh2xKf1J9hXm+o32q3sgk82OVz1adjhfoAP8Ks6nqE2p3r3Mx5PCqOij0FYuq6nFpdoZn+ZydscY6u3pXRThb1M5yVrswPEeoarb7LZ9QTMgy8dvHt2p05YnPNY9rahuB8qL1NaE9vN/Y95c3Z3Xc+JJD6YOQo+lQ2J3WoPqTXf7PkSueU6vtJNonRFjXCAAUEUoooAb0pbaQxa7YS+peM/ipxQRVa5JjktpVzlJ0OAOTzipkrpoqDtJM9Q8FSH/AIStAucNbyBh7cEfqBXpborxlGGVYEEe1cj4L8Oz6aZNQvUMdxKmyOI9UXqc+5wOO1deea+frzTnoXiZqc9Dg9PRrSW709/vW0pC5/unkVdNP8Q2/wBi1y1vxxFcr5Ep/wBr+E/59KZ1r6fL63taC7o8CvDlmwoxR1FFdxiIaX+VHekoA5bxTDtvIZscSJtP1B/wNO8BT/8AEluLI9bS6dAP9k/MP5mr/iWDzNL8wDmJw2fY8H+lc74Mn8nxNqVr/DcQJMB7qcH+dZvSSOiOtNneUdaKMVZgHWj3xR1zR0FAHI6nbNYagduVRjvjI7f/AKq6PVdZTV/CCaiCPtuk3MF3Ig6/I43MPYqTTr+xj1C3MUnysOUYdVNcnPb3OnPJDKCokRoyw+66kYI+mDXjYvCWlzI6qFblPRdf8IaV4iYXMgaG4KjE8RGWHbI6GuWb4Vr5v/IYPl9wIPm/9Cq/4a8c6bFpFrYalNJHe20YiY7CQ4XgMCPUAfjWnN430RBlZ5ZD6LEf64ryLV4PlR6ixLitGWdC8M6b4fQ/ZIi0zDDTycufb2H0qTXfEFvo1uVysl2w/dxZ6e7eg/nXJ6l48uJ1Menw+Qp/5aOdz/gOg/WsGytLvVLguCzFjl5nOf17mrpYWpUleZzVcR1LenW0mp6oZ5iXw3mSMe5rqsVFZ2kVlbrDEPdmPVj61Kea+lw1H2ULdTzZy5mHWikorpIFxzSdqUdKdtzSAw/Fg3eE9TH/AEwP9Kg8Sx58I3aekCj+VaeoJbahaXGntMpaRdjqrAsAfaq+vru0C8GOBHn8jStuaRdrLzOZ0J/M0Gz74i2n8OP6Vn3Vv/Z97heLebp/smsvwnqD287WbsTHJIQuf4W/+vXVajbfbLGRMZYDcv1FeApOjWuenVhZGQwOcDr2r0e1iFvaww/3EC/kK4DRI/tt9aIeT5g3fhz/ACFeid692DurnnVewuf/AK9IaKzNT1N7aRLOyiFxqMwzHEThUX++57KPzPQVT0Mkr7D9T1aLThHGEe4u5uILaL78h/oPUniqtro811Ol9rbpPOp3RWy/6m3+g/ib/aP4VZ0zSUsTJcTSG4vpv9dcuOW9gP4VHYCtKla+rKclHSIdaQ0UuPWqIG0UpFIc0AOBwak/tGDS7W6vrltsEELSP747fU9PxrB1fxRpGiErd3QM/aCL5pD+A6fjisHTL29+IniGPSGtGs9Ht2FxeKxy8gH3VY9snt9T2rGrVjCLbOrD4adSS00O6+HumTRaPNrV+v8AxMNYk+0yZ/hQ/cX6Ac/jXX9aAAFAAAA4AHajvXz85OUm2fVwgoRUUFHWijvUFh1NHajqaKAD8aO1FFABVmzGZW+lV6t2QyX+gqo7kz2Pnj4vXHn/ABMu1zn7PbQx/wDju7/2auJro/iDP9p+JOvyZyFuBH/3yoX+lc5+te/h1amjx6j95ijilFJSj0roIFp45po5P86eKaEPA9qeKYBTwOKtCA0xvennpTGpsRE3SojipW6deaiNZsodR/SijvSGFKKTv60o60wHjpTxUYFSdvaqQhe9IfpS0hpiGEc009fxp5FNNSMaRTDx3p5pppDGmig9KOopAA616B8KND+263cazKuYbIeVDkdZWHJ/Bf5ivPjuOFRSzsQqqOpJ6CvorwpoieHvDlnp3BlVd8zD+KRuWP8AT6AVnJ3djDET5YW7mheyGK2Yjq3ArEAxWpqZy8aegJxWeUJHQ1aPNkhtFDYUcnH1qB7y1i+/cwL/AL0gFF0KzZPxRWbL4i0WDiXVbJfbzlNUpPG/huI4/tSNz6IjN/IUnKK6lKnN7I36Qiud/wCE98PH/l5nx6/Z3x/KrMHjDw9ckKmqwKx7SZT+YFCnF9SnRqLdM2e9HemRzRTpvhkSRP7yNuH6U4GqMnoLS9ab1oLKqlnYKo6knAoAdUdzbx3UDRSjgngjqD6iqNxr+n25IEpmYdoxn9elZNx4qmbi3t0Qdi53GpdmrMtRl0LU9lPBOsqRv5yDCXdo/lTAe/Zh7HIq9b+O7jSisWvQNLD0F5BHtYf9dI/6rkewrkrjV7+5J33LhfRPlH6VROSckkk9yc1wVMuozOqnUnHc9Lb4keH/ACw9u89wD0KxkA1Qn+JkP/LGwcr6sGP9BXnElmokMsDGCU9WTo31HQ1JHetERHeKIyeFkH3G/Ht+Nc8cqordmrqyexvav47/ALVkUSwtHEnRFPGfU571nr4ktQP9XJ+Y/wAaYyK3VQfwqrcC1t4/Mm8pF9WAFa/2fTitCVVbLEviR3G23gAY9CTuNc9qRmFzDe3cmAxMbluSAeR+RFdHomj654ncLoWnEW2cG9uR5cI+ndvwzXp3h74WaPpEiX2tS/2tfryGnXEUZ/2Y+n55/CocaVHbVnVRoVJu70R5d4c8J+IPEiqdJs/stm3XULsbVYeqL1b+X0r1Lwz8M/D/AIYdbu6U6pqed3n3CggH1VOg+pyfeuxkuuNsQ2r61X6nnrXPUxMpaI9KjhYw1J5bl5eOi+gqGk70vFczbZ1pJbCUtFH4UhiUvWkxS9qACjrRRQAlLSUtABRRR0HNABiiq0+oWduP313CmOzSCsHUfGdrArJYoZ5OzsMIP6mmot7CbN++vrbTrcz3MgRB0Hdj6AV51rGsTavdb3+SFf8AVxjovufeqt5f3OoT+ddSmR+2eg9gO1Y2qa1baTGPMzJO/wDq4U5Zj/QVvCnb1IlNJXZNqmpW+lWbXE5z2RB1c+grmra3uL27/tLUf9cf9VF2iX/Gnw21ze3Y1DUyDN/yyhH3Yh/jV/rXqUKHL70tzxcXi+d8sdiG5j860mj/ALyFfzFc7YXMUGmI8r7QCfxNdRjNcRdW0lpeeVMrBQx8skcEZzkVdZbMyw8t0W5NSnlP7iIRr/ek5P5U1Vv5eTNL+AAq5ZwosCvgF27ntViuc6jO8nUVGVmc+xwahkvpljZZU2yoQysoxyDnkfhWyDzW14K0Ww1fx3aJfp5kawtMsfZ3TGAfUc5x7VFSShFyYHucEhmt4pmG0yIrkHtkZxUnWnEZpuMmvmXvcgoa1YDU9IuLcffK7oz6MORXL2Nx9psopTwzDDD/AGhwa7kIVPIxXAaaQWvgn3BdSbcema9rJ6jU3DocWMjomXqKOoo7V9EeeHWijrRQMgu4ftNlNCf40Kj644rzrS5TaeMdKlPAl327fiOP1r0vg15r4nj/ALM1ZbheBb3aTD6E5/rWdTa5vQd24npg4o7UvXkdKTrVmAdaKKOpoAWmzQw3EZjmRXQ/wkU6ihq+jA5bV/BlteJm2meKReVJPK/Rv8c1ylxp+v6S+2e2F3GOjJ8rn+h/CvUzjFNZFcFWUFe4IyKxlh4S6GsK0o6PU8z03X9NjnC3dswcHlLglf8A61dvZeIdNljUDMC444yv4EVZutF069j2XFqki46MM/l6Vzt34Bt0LSaTeTWb/wBwnch/A0Qp+zWiLcqc99DsI5oZl3QypIPVWzQTXmU8WuaRMFubYy46SWxwx/Dv+FaGneLpkcI0vmgdY5htcf1/nVqaJdF7rU72isqPxHpptTPJKY8dUYcj+mK53UPHUtyzQ6PbNIenmdh+J4H61TkkRGlKR195qFrp0JlupljGMgE8n6CuH1LxTqGuMYNMLWtlnDTd2+nr/KsprO4vpfP1OczuTnywTsB9+7V1OkaBJOUlukMcA6JjBb8OwqLuRqoxp76sm8KaKlpF9q2nkHYzcs5PVia19YXdo16P+mLVfACgKoAAGAB2qrqK50y6HrE38qu1lYycryueJWLNHdsV+8s/GPrXpSnNee6RAbjXPJHQTlm+g5r0FD614OJ+Ox7NV3jH0M7w7Klp4oeNyAm5lBPbPAr0EjFeVW0nm6hcz9mPH516Jo18b/To3Y5kX5H+o7/lXr4a/s1c8uutbhqV+1oqQ28YmvZyRBDng+rN6KO5pdM01bCOR5JDNdznfPO3WRv6KOgHYVNBZpDcS3LHfPLwXI6KOij0A/U81ZxW9tbmLelkIKKMUf5xTJFpRUUkqQxtJK6oijczMcBR7muNvvFN7rEjWvh/91bA7ZNQkH/oA/r/ACrOpUjBXZtRozqytFHQa14l03QxsuZS9wfuW8Q3O34dvxrlru/8Ra/kFv7Isj/BGczMPc9v0qTT9HtdPYyjdLcty88py7Hvz2rQHNefUxUpaR0PcoZdCGs9Wc5c2un+GtNkuYYA1yfljZ/md3PTn9eK9U+H/hpvDnhxBcjOo3Z8+7Y9dx6L+A/XNcR4X0seJ/HHnyLu03RsMfSSc9B+GM/8B969hAzXFWm7WO+nFXuthccUUUVzm4UUGigAoo60YoAOho60Udv6UALV2xHD/UCqNXbVvLgdz0Bz+QqobkT+E+UNenNz4r1u4Jz5l/MQf+BmqGM0nmm4kmmPWWR3P4mnV9FSVoI8eT1F60dRRSjrWpIo6U8UwU8cVSESCnAU0U8VaJA1G1SHpUbfyoYETc1E3WpWqI8Vmyh3ajtRRQMWgcGjpS0CHDmnimAU8dKpCFpDTqaelMBppp5pxppH50hjTTTT6b0NIBvekpaKkZY0y6isNa0+8nXdBBcpJIPYEc16r4j8Ya1Ff/ZNHW2gt/LVxeSfvDJkZyo6AfXNeQMoII7HtW1oerNtTTLls7P+Pdz6f3f8P/1VlPRmVWF/e7G1eX+tSQy3F74hvn2qWIjIjHH0qTTdMuL7TYLm+1XU2klXeVFywAB6fpWdq26eO3sU+9dTKh9l6muuUKiKiD5VGAPQCuLETaskdWCpKScpIx28M6a3+sFxKf8AppcOf60q+GtITpYxH/eJP9a1+tLiuXnl3PRVOC6GfFo+mxfcsLYY7+UKtxxRRjCRovsqgVJikxU3ZSilshwNRTWttcrie2hkB/voDTqUc0DsjKbw5YpJ5lmZ7KX+9bSlf0qRLrxPpx/0e/h1CMf8s7pNr/8AfQrS60Fc1pGtOOzMKmGpVPiRkXHjy+jIhvLNtOc8byu9T9D0qJ7+W+Ale4M6nod+RWxJGkqFJEV1PVWGQaw7nwzb7zNp0r2Up7Jyh+q11Qxb+0cFTLUtaZJ2pKovNqOnD/iYWhkhH/LxbjcPxHUVZhu7a5j8yKdGX1DdPrXVGpGWzOCdKcHZok70hOKrTapYW4+e6jyOyncf0qzY2et60QNH0G9uFPSWRPKj/wC+m4olUjHdhGlOWyG1Wub+0tgVnlTnjZ1J/Cu40z4Q6zflX17V47SI9bewGWI9C56fka7zRPBfhjwxtex06I3K/wDLeX95Ln/ePT8MVy1MZGPwnZTwM5fEeQ6D4R8Ua6B/ZuntYWLdLjUMqAP9hPvf0r0fQvhRoWkSJeaxI2r3w53XI/dqf9mPp+ea7F7t2+78v86hJycnJPqa4KmKnLQ9Glg4Q1sWGugihIECqBheMAD2FV2Yu2WJJ96THp+FLXM22dSilsJRRxS0ihKWij60AFHWijqKADvRRRj0oAP84ooooAKMc0jssaM7sFQckscAVxuv+KRMrWenOdh4eYcZ9l9veqjFsTdiXxP4hMbLaafcMsitmWSM8D/Zz/OuUuL26uj/AKRczS+m5yaixXN6nrktzO1ho5DODiW56rH9PU1006d9EY1KqguaR0GMmqt3qlhYrm5uoo/9ndk/kOa5w6T5gxPf30v94GY4P4VLBpNhbtlLZCf7z/Mf1rsjhJdWcEswj9lEs3iG81DMekWxRDx9qnGAPoO9RWenJbSGeR2num5aaTk/h6Vd6Dp9KO9dVOjGBwVsTOruGaKOaK1OcKztcTdpExHVcN+RrQqvfR+bYXEePvRsB+VKSumioO0kzGsm32qn3qeqOlNutcemKv44rhPSAdat6VeXFj4j024tmZZgzqpXrytVO9Ptp/s+saVODjy7yPP0JxScVL3X1Jn8LPaNK8a21xiLUAIJcY81fuH6+n8q6m3njkCyxMroRwVOQfpXB32k2l4xdo9kh/jj4P41nLoFzCcQX7Iv4j+Rrhq5TO/uHDDF2+I7bxL4gh0uxfDhrpwRFGDkgnuR6VzOlWzW2nosmRIxLvnsTUNnosVtKJpnM8o5BYcA+tadehgMF7BOUtznr1vaMOtFHFFekc4UUdqOtABjmuL8e2e+2aQD78BH4rzXXXV3FZW5mlJwOAB1J9q5XWY9U1zT2mSFlt1OFK8AZ46nrXNXxEKekmb0U+ZNHSaPcG70SwuCcmS3Rj9dozV7FefeFrzVbLQLd/3hgjd4TuGUyrHj2rsrDV4L3CN+7m/uE9foaVPEwlp1FVpuMmXv50Up60V0mQlB6UUUCCiiimAdaOtHWjrQAyWCKdCksauvowzWfP4d025xvgIx0w3T6ZrUopNFJtbHMXHhLaCLWZWQ9Ul/xpLfwpJwJ7hEUfwxrn/Cuo6/zpOtLlQ/aSKNno9lZEMke6Qfxucn/wCtV40UvUUyW7if4VBeLmxuP+uTfyqxUVzzZXH/AFyb+VDBHmHha1Tz9RuMfvPOKZ9B1/z9K2NTuPsmnyvnDEbV+prN8OTRW7auZXVEW4ByT6g1V1O9bVLpY4gREp+XP6k14M4Oddo9Vy0G6cpEBb+83FdX4Vu/KvntmPyyrkD/AGh/9bNc7GgjRVXoBxVi2uGtbqKdfvRsGr2YLlSRyz949Jzk0dvxpkcizRrIhyjKGB9jTxzWpyBVa+vbfT7SS6u5lihjGWdv89aZqur2WjWTXN5MEVRwo5ZvoK4Vvtnii7S/1NDFZIc21men+83qaxq1lTWp1YbCyrysthLq4vPFs4luRJbaOpzFbZw0/oz+3t/+utWJEiRY41CoowqqMAU7AxS15VSo5u7PpaNCFGPLEcDVDWtQ/s7TJJUGZ3PlwqOpc9P8au55qLwtY/8ACS/EFGcbrHRx5hHZpu35H/0GoXdlyfRdT0TwT4e/4Rrwva2bj/Sn/fXLdzI3X8uB+FdD2pT1pK5m7u7NUrKwUdaO1FSMKKKOnegAooo70AFFFFAC+1S3D+VoV5J/dglb8lNQ07VW2eFNSb0s5j/441XT+IzqfCfI9qP9HSphUVv/AMe6fSpq+jh8KPGe4dqUUlL+dWA4cHpThTe1PH86pCHinimDingVaJA9Kjbp0qQ9KY1DBELVGakYc1GazZQ6kpcUUDClHpSUo5oEOH1qQCmCniqQhe1If60tIelMBvemHkU/vTD0pDGmkNKaSkAhpOlKelJ1pDCmOm4cHDA5Ujsak6ihsKjMewzUySa1A6Dw39p1nWUmkiybSPYAozudu4H0r0a38NaxMAfsbRg95CF/Q8103wq8Mpofgq0nlhAvb4faZmI+YBvur/3zjj1Jrqrzb5iqByOuK8KvUbk2enh4qMVE8+j8G6iwG6S3T23k/wBKkPgu9x/x82/0+b/Cu1HSlrD2jOiyODl8H6mg+UwSewfH8xWfcaFqlvkyWMpA7oNw/SvSLm6trK2e5upo4IEGWkkYKq/ia4qb4iSaldPZ+EtFutZmBwZ9pSFfqT/XFXFyexMnFbnOMjodrqyn0IxUM9zb2q7rieOIf7bhf511DeC/GviYh/EGvQ6bAf8Al10+PLAehb/65rV034Q+ErFhJcWk2oTd5LyYtn8BgfmK00W5nzt7I8wl8U6TG+xJnuJD0WCMtmrlu/iHUhnTfCepyqejzJ5Sn8Tx+te32Wm6VpCBLKytLQDtBCqfyFWGvUB4DNSc4oLTZ4zH4P8AH12ONM02yB/573G4j/vnNXY/hh4vnH+keIdPtvUQQF8fmBXqbXrn7qgfXmozczN1fH0GKn23Yfs5Pdnn0PwenlA/tDxZqEo7iCNYv6mr9r8HfBlmd1xDcXj55Nxcnn8FxXXFmY8sT9TSdan20g9jHqVtP8P+G9GIOn6NZQuOjpAN3/fR5rSa8b+FQPrVbGaKhzbNFTih7SvJ95j9KZRRUFC0lHWjtQMKXrRjiigA5ooooAOtHWkpaADrRR2oNABR1o69aDgAk8AevagArO1XWrTSY/3z7pTykSn5j/gPesXWvFyRbrfTSHk6NMeVX/d9fr0rjZZmlkaWaQs7cs7HP5mtY077kuRoatrl5qz/AL19kIPyxJ90fX1NYF/qlnpcXmXUwUkfKg5ZvoKyb/xFJcTNZ6MglkHD3DD5E+nr/nrVW10tIpftFy5ubpuTLIc4+ldtLDuXkjgr4yNPRasdcXWo658r7rGwP8AP7yQe/oP881Zt7eG1iEUKBEHYf1qSjtXoU6cYLQ8irWnVd5MO9JjilpK0MhaPWkpaACjrR2pKAA80jDIx+FLjmpYLeW5lWKGNndugFAXscjpaeU9xEf8Alm5X8jVl9QtkJAYyN6IM0zxHpNzo+tyxXQKRTjzUIPyv6jPsc1nqRjjGPQVwyVnY9KElKKaLj6mn/PCbH0H+NMe/hlVAu5ZBIjKrKQcgioMcZ/nWx4X0WbWtatykTG0gkEk0xHygA52g9yaIpt2QTkoxbZ7O3WmmjOTRXonhXE6ijrRjmigA/CjrR1opiDqaKP8AOKXOKQGIIf7b8Q+Q2TbW/wB4DuemPxPH4V3V9psf9hXFuqLuEe4YGACORj8q5XwRGJJLiZvvNNz+AJ/rXeZBG08gjmvkcXVc6zbPVw8EoXOD+GoT7Fr+nuAyw6k52sMja4BH8jVjXfByFWudLXY45MGeD/u+h9q4PSfE95ovizXI9OEUiTMhbzASCUyuRyPWu70Xx9Bczi21aAWkh+7Kpyjd8exp1I1Yz54nVLDSlHmtoZWlawrZtryQRyocBpDtz7HPetk8geh/WvOZHt9fmuPEGqIrrMzPHE/3IYwSAMeuByat6RqI0e/sxbSl9IvXEZTcSsTn7rL6AngivocPVkopTPOqYWyumd3RSkc0ldhxBR2o4oxTEHWjrRQaQwozRjPrWNey3WpXw0vT8lyf3jA9PUZ7D1rOtWjSjzSHGLk7IuzarY27bJLhAw7Dk/pUltf2t2cQzqzY+70P5VbsfAFjHGPtcsssh67DtUf1rIm8Lpdaa15pTuLiJmV4S2fmU9j6968qObRcrW0Ol4SVjUJ5o61kaLqpvUaCfi5j4YHgkev19a2McV68JqceaJzNNOzCoL5tunXJ9Im/lU55qjq8mzR7s5/5ZEf0qmC3PKILZp9S1DDhVWUZz9K0ooEhGFHJ6k96q6f/AMhPVP8Arqv8jWhgVzwgvi6nbN62EApcUfypa1IOx8MXnn6ebdj+8gOMeqnp/hRr/iS10S2clwZV698H09z7VyK6i+lh7pJvKCqQzD0Paq2m6fNqlyuqakhCA5t7duw/vN71lVrKnHzNMPhHWn5E1nZ3euXa6rrGdmd0Fs3b3b3rfJpP/wBdHX8K8uc3N3Z9FSpRpx5YgelJ0paKg0KWp3gsNNuLo/8ALNcgHueg/XFdh8KdJOn+GGuZR+/upN7sepP+TXnuvj7Xe6dpY+7NL5sv+4v+T+Ve26Fb/ZNEtI8YOzcfqef60qjtC3ciOs79jRo60UcVzGwUdaOtFABR2oo70AHtQaKOtABRRRQAU3xG3l+C9Xb+7p83/otqd1FVvG0nk/D/AF1s9NPlH5oR/WtKW5lV+E+VLf8A490+lS1HAMQJ/u1JX0cdkeOxRR9aTrThViFHNPFNAzTx9KaAeKcOlNFPHrVokDyKjbrUp6VE3WhgQt/WoyOcVK1RHvWbKHUYpaSmAdqWjvRQA8U8dKYtPFUhC0hANLSHpTAaenSmmnGmkelIBppvQ045FIeKkY00lKelJ7Uhijk1r+GNEbxJ4r0zSACY55gZiO0S/M36A1kDrXrnwI0cTX2q6868RAWcJPqfmf8Akv51z4mfJTZdOPNI9qfZBB8qgKowqj9BWQxLMWPJJ5q9fS7m8sdF6/WqeMivBm7s9amrK4w1zHibxpZ+HmSzije+1afAgsYeXYnoWx0H6ntVPxN4suzqY8N+F4Rea5Lw79Y7Re7MemR6du/PB3PB3gWz8MK95cSG+1mfLXN/LyxJ6hc9B+p7+gcadtZCnUvpEw9M8Bal4knj1TxzcmXB3Q6TC22GH/eweT9PxJ6V6Jb29rp1qlvbQxW1ugwkcahVH0AqOW7C/LFz/tVUZmdssST6mnKp0FGn1ZckvAOEXPuarPPK55c49BxUeOM/rRWbk2aKKQdaO2aP84oqSgo60d6XrQAlL9KSl7UAJR1NLijrQAnWl60lLjIoAOooo70UAFFFFABRRR3oAOtHWjrRQACjvRisDWvFFtpxaG3xPcjsD8qfU/0ppN7CNa9v7bT4DPcyhFHQd2PoB3rg9a8SXOqFoY8w2v8AcB5b/eP9Kzby9uL+4M1zKZHPTPQewHasTVtcttLUIcy3LfchXqfr6Ct4UzOU0ldlm8vbewt2nuZRHGvc9/Yeprlp7m98RNg77XTc8KPvS/X2/wA80qWNzqNwL3V23MP9Xbj7qD6Vp4r0qOHtrI8nEYxv3YEdvbxWsQihQIg7D+tS9RSUdK60ec33Fo60daO9MQdqKMUdqAE60UUUAHOaX60qRvLIERSzMcBRyTXUaX4bWPbNfAM/URDoPr607EykkZOm6JcagRJjyoM/6xh1+nrXXWVhb6fFsgTBP3mPLN9TVkAKABgAdBS44ppGEptlW9sLTUYDBeW8U8ROdsi5APqK5+T4e+HnbclvNFntHOwH65rqaKHFPdBGpKOzObtvAfh63kDm0knIPAnlZh+XSuiiiit4lihjSONR8qIoCj6AU/FFCilsEpyl8TDtRRRTIDApKXt/Ogjj0FFxiZ5o5rOudbsbclfMMjDtGM/r0qoPE1sWwbeYD14/xrJ16admx8jNvrTl5NV7O9t75N0L5x1U8EfhVkcVopKSuhWIPA52yXkP8Sy5x+Y/pXT6xcPaaLfXCffjgkdceoUkVyGhT/YvF8sB4W4yB9SNwrtdQtvtumXdt1MsLoPxUivkcVHkrtM9fDO8EfOWlERa/b5yfNidT9Rz/Sui1FN9i+Fyy8iucz9n1Swdhhlm2MD2yMV1B+dWVvusMHNd8+jPoKNrNGVpzl9Jl0hz5U6oQhIzlG5DAdxzUqWqxxafokT77mSdCoxggBtzNjsAAa7ODwMupeHbB3gaQhMxsrbHQE8YPpioo/CFr4b1XT5olczTl97yPvbp0z+NaUcZGUlBrXY8KvLk5vI6I4zSUD0o7V7h4odaKKKYgooxSUgIruf7NaSzf3FJH17Vo+C9NW20s3kozPcncWPXb/8Ar5rA1gvN9msY/v3EoGPYf/XxXfwQpb28cCcLGoUfhXgZvW95U0d2Dh9osbl9RXDaXenTvFF5ZucQzzMv0bJwf6V2fWuA8S2/ka9MwyPMCyA/h/iK8ikr3TO6RD42019J1ODWbT5BK22QgdH9fxGfyq9pmox6jbbxhZF4kT0Pr9K1bhB4p8G3EPBuVTH/AG0XkH8f6mvL9K1SSznjmXOV+SRfUdxXu5VWfK6b6HHiKd9UejGsnxC+3Rbj32r+orRgmSeFJo23I65U+orI8Uvt0jb/AHpFH8zXsvY4o/EjzzT8f2tqY/21P6VqVlaf/wAhnU/95P5Vq9utZQ2OufxCYpHdY0Z3YKijJJ7U8etUba2Ov3Zzn+zIG+Yj/luw7fQVNSooK7Lo0pVZcqJNNsm1qdb25QixjOYIm/5aH+8fb0rpcClACqFUAADgDoKTrXlTm5u7PoKVKNOPKg7UtJ0oqDUWiilHJoAxNPjN/wCNrpu0CR26/Vjk/wBa94VQihRwFGB+FeJ+A4/tGuzzHnzdT/RTXtvfPes6z1sTS2uHWiiisTUKO1FFAB1ooo70AFHWjpRQAUdaKKAAdRWT8UZ/s3w11xv70Kx/99Oo/rWxGMyoPVhXK/Gifyvhtdpn/XXEMf8A49u/9lrairyRhXfunzsgxGo9qdjNAHGPaivo1seSKP8APFL/AFpKXtTEOH9aeKYKeKpAPFPHI/rTRTu1WiQNRt+lSmomoYETfzqM8dakaozxWbKHd6QdaWjtQAdO9KKTpS0APHJp46UwU/tVIQtNNOpCKYDD060hApTxSGkAw8UnfNOPWmmkMSkpT0pDUjGO2yNm9BxX018MtLGh/DzTI3XEk0f2qXPBJf5h+m0fhXzZa2balqVlp653XVxHCMf7TAf1r62uFS3tUhjAVAAigdgK8vHztZHXhY3dyuXLsSepPNcZ4l8Qalf6yvhPwvg6pIu66uz9yzjPfP8AewfwyO546e9nmtrC4mtoTPPHGzRxD+NscL+JxUHg3w2vhfRnku3E+q3r+ffXHUySHnaPYZIH4nvXnU7bs7al9kTeGPCmmeD9LMFqC80nzXF1IMyTv6k+noP681fmmaU+i+lJLK0jZY/QUypnLmHCCQnWijFFQaBRR/8AqooAKO3SjqKKADrRR1FFAC9KSjpR1/CgA7UUUdaAClxSc9qWgA60UYooAKOpo7UUAHajvRRQAUyWWO3iaWZ1SNRlmY4AqDUNRttMtjPcvtX+FR95j6AV55rGuXOsS/P+7t1PyRA8D3PqauMHITdjT1zxXJd7rawLRQdGl6M/09B+tczTZJEhjaWRwiKMszHAArk73VLrX3a2sC0FgOJJzwZPYe3+TXVTp30ic9WtGmryLmp+IXac2GkKJrno0vVI/wDE/wCeagsNMS1YzzOZ7t+Xlb19qltLOCxhEUCbR3PdvrVgNivSpUVDXqeLXxMqr8heTSMOPQe9U57/AAdkOPdj/SmpZXt4AwjdgehbgfrSqYiMTBRbLJmiB5kT86VXVvusD9Dmof7AvCM/ux7bqgm0i9hGfJLAd0OayWMRXsy/nNL1rIS7ngba2TjqrDmtC3uUuF4OCOqmuiFWMyHFon60Umcirdlp11ftiCIlRwXPCj8a1IehV7Voafo11qBDKuyLvI3T8PWugsPDdtbESXJ8+T0Iwo/DvW0AAMAYA6CqSMpVOxS0/S7bTk/dLmQjmRup/wAKu96OoopmTdw60uM0UdaYgo60UUgE60vWikoGL1oxms271yztCUDebIP4EOcfU1nfbNU1hjHbIUTuI+MfVq56uKp01qyowbNa81a1scqz75e0aHJ/H0rOgh1XxHJtjXy7bOCeij6nua3NE8CoxWa9Pmd9vRf8T/Ku4trGC2jVEQALwOOB9BXi4jMZ1NIbHbSwvWRyOn+DdNtlDTqbmTuW4X8q05NB0uaPY9hb7faMA/mK253QjAwSO4quf/1V5M5zbvc61CK0sec69oz+G7uG8s2Y27NgBv4T/dPqDWrFMJ4UlT7rqGFaPjQKfDNxu7MhX65//XWJpSldLtgevl5r6DKaspxaZ5+Kgoy0KOrh7XUbW/j4ZSOfcHIr0a3uEuraK4i5SRQw/GuJ1K2+1WMiKMuBuX6ir3gfVBPZvp0h/eQnfGM9VPX8j/OufNaNpc6NMLU+yzzP4maQ+k+IXuIE/cTSpOv+yxPI/MGuw0jwTqV7dJ9uga1tlOXLMNzD0AB/Wn/FmyE+mI4XkQsc+6kEf1r0PTL+K60Wwuwc+fbxyDHuoNcc60vYxZ60MTKKaLDKkFuERQqKoVVA6DsK47XJkuPEFtbqc/ZoWkfHYtwP0rR8QeJLbSozvIe4I/dwKeSff0Fc5psU22W7uzm6uW3yE9vQVtltGVSqpvZHm4qqrWL1FHvRX0x5oUUUUxCUcUGoby4FrZSzn+BePr2/WpbSV2NDNDi/tHxVLcYzDZJtX/eP+T+Vdvziue8G2X2XQxK/+suGMjE9SO1dCTgewr43FVHVrOR69CHLBIUVy3jK24troD1jJ/Uf1qHUfH1rbztDp9sbsqcNKX2pn26k1m6h42s9S0yS2vbVrWQkNHIG3puB6E8Ed+1KFKaadjqdCpy81tBfD2rf2Zqa+YcQTfJJ7eh/CuX8XaX/AGN4suEVcW93++i9AT1H55/StHZuGeoNXPEcf9teDPP63ulsGz3aM8Z/l+VdlCfsqqkjlmrqxR8L6mUkNhK3ytloiex7irPi18Wtsnq5P5D/AOvXI28xIjnjJVhhgR2Irf1m/XUbaxl4DbW3gdm4zX0qd0ee42lc5PT1P9r6mf8AaT+VanWsqxnRNf1G3bhnKsvvgc/zq/d3CWltJO/3UGcevoKmLsjeabkZ2r3yCSOy8wxpIw8+VRny0J/rXYW0ENraxw26qsKKAgHTHrXJWNmGtJWu1DS3PzSg9h2H4VZ0PUJLC6Gj3bkoebWU/wAQ/un3/wA+lceJhJrmPRwNSEW4HTnGKSkzS9BXCesFFFLQAlOXrSUo6/jQBW+Ggzd257m+kNez1418N/3eqCP/AJ56hKtey96zrfEKl8IdaKKKxNAooo7UAFFFHagAPrR0FHejvQAUdTR1FHWgCW2GbhPrXn3x3uCvhPTbYHma/BI9lRv8RXotmoM2fQV5J8erndc+HrMdvOmYf98gf1rqwqvNHLiX7rPIsUtHejrX0B5gval6Ug9vypR1piHAc08UwdKeKaEPHSnimrxThVoTA1G361IeajbpQwRC3pUZ4NStUbVmyhTRS9KSgA6dqcOKSlHWmA8CnCmjpTx/KqQheoppp1IaYDDTTTjzTTxUgNNNpxptIYhoxRRSGdL8O7YXXxI0KMjIWZpT/wABQsP1Ar6SvTmUL6Cvnr4Trn4naXntFOf/ACG1fQVyc3D/AFrxMe/3h6GEWhCOoqWWYyt6KOg9Ki60vavPO23UOtJRS0AJ1o70d6KACiiigAo6mijrQAUuOaOtB6UAHOKKPwooATrS/wCcUUdqAE7UvejrRQAYo60UdaACijp2ooAKx9a8QW+kIUGJboj5YgenufQVn674qS23WunMHm6NL1VPp6muJd2kdndizscsxOSTWkIX1ZLkTX19c6jcGe5k3ueB6KPQDtWfd3kFhbtcXMgjjXue/sPU0zUdSt9LtGuLl8Doqjqx9BXLLDc61crfakNsI/1Nt2A9TXZSpObsjlr4iNJa7iyy3XiSYPMGg01TlIh1k9zWrHGkMaxxqFRRgAdqAMDAxil7V6UKagrI8OrVlUd2HaqGoTlR5Snk/e+lX+tZttF9t1RVPKl8n6Cs8RPkgTBXZq6RpixxrcTrukblVI4Uf41tjmo8YrD1zWntG+yWzbZSMu4/hHoPevFd6kjqhDmdkdA7og/eOq+m5gKZuV+VYMPUHNedu7SNud2Zj1LHJqS3uJrWUSQyMjDuO/1Faex8zo+rO25291ZQXaYmTJ7MOCPxrm7yym02cEHKE/I4/ka39I1JNTtzuAWdB86jv7j2qzdWyXMDwyDhu/p71MKkoSszmlCzszJ0xG1OeKFOGdsH29T+VejW8MdtCkMShUUYArhPC0ZsNV/fdXJj/wADXf8Aavcw9RTjc86tpKwuM0daO1FdBiJS0UUCDtR+FFHX60AFHNJ0pk9xHawNNKcKo/P2FS2krsaEubmK0hMszbVHT1PsKwv+Jr4hmaKzjaO3HDHOB/wI/wBKtaTplx4m1Az3BKWkRwcdv9ke/qa9Bt7WG1hSGCNY416KorwcbmLvyUzso4fm1ZyOneBYo8G4YSN6twv4AdfxNddYaRbWirhQxXpkYUfQVDqGrWGjweff3KQqfuhuWb6Dqa5af4oWyPiz0q5nQH7zuEz+HNeWlUqO71PRp4d/ZR6Ezqi5NVpJmkPovpXF2PxJ0y8uFivoJ7Bm4VpcNH+LDp+IrrlYMoKkEEZBB61FTmWjRUouOjQ7oKO1A4qlq2ox6Tp0t3LzsGFXP3mPQVmk27IhtLU5jxndfa7q10eE5LMJJcdvT9Mn8qciBECLwqjAFZmkQy3Ek2p3R3TTkkE+n+f5Vrdq+ry/D+xpa7s8mvPnkIKwrtZdF1aLULXhd24eme6n2Nbwps0UdzA8UqhkbrXTiKKqw5WZwlyu4niu4ttc0CzuICCkheNlPVSVwQa5Twz4i1ibwtZ6dbZRrcNAWRctwx79sDFM1Rrnw/nIMtqzAhu34+hpfA12hm1m0RsotyLhP91x/wDWrx6OD972UzunVcqXMja0/RRFL9qu2MtyTnLHOD9e5rWFHBHWjrXt06caceWJ57bbuwo+lHWjtViCijrSUwCsbxBITFBbj/lo+fy/+ua2utYGsn/ibWgJ4UAn/vquXFytRdjSmryPRrKEW9nDCvSNFT8hXP8AjnUJLXSEtIWKy3b7CV67B1x9cgfjXTp8y8HvXGeLXA8TaSHYBPKbbn+9n/8AVXydJXnqfQYWClUSZxE9pNZ7FliMe4ZAPpVeWMTRPGwyrDBrofEinFu27Iyw6d+KwMV6CZ7ltLEPhvUXWZtMuGyVz5RPt1X+tdXbTCGVg4zFKhjlX1U8GvO5X+z+IEkQ8rKp498Zrvj1qaqs7ngYmCjPQ5GCE2t1cWb9YmOP5VZ6H3o1YeXr6P8A89UGfyx/SkNe3g581JXPMqL3jkdQeSLW7meI4kjkBX8hxWib2HVbiLLhLWECWTccAt2B+nNZ15zqt6f+mn9Kptbx7t2wZ71tyNrQ61FNI37nxBaxkrAr3D/7PC/nWLe311qBXzNkSo25Qg5B+tMxgY/SkxTcL7jjCMdUdz4c1oapbGKYgXkQw4/vD+8P61uV5XDNNZ3Ud1bNsmjOQex9j7V6LpOqw6tZLcRYDDiRD1RvSvNr0XB+R6lCtzqz3L1LSUd65zoFxmgUlL0oAi8HEW3i+5h6D7ako+jqK9krxazb7J4tin6CSJW/FHH9Gr2jj8+RWVbdMKeiaFooo/nWRYdaD60UUAFFHajHNABRRR1oAP8AOKOtH0ooAu2K4R29eK8G+Nd35/j+3twci1sUBHoWZj/Iivf4F2QIO5Ga+XvHt/8A2n8RNeuQcok/kKfaMBP/AGWvQwUb1EcOJloc/RRxS/yr2zgDtSik/wAinDrQIUVIKYKcOO9UhEgp3QU0etO7CqQgP61G1SEcVG3ShgRN3qM09qYazZQ7rSdaKXtTAKUCkp1ADhUgpgFOH1qkIWkPT3p3ammmIYaaacelNPWkMacU089KdjIppqRiGjvQRmikB2HwpcJ8UNI/2kmX/wAhtX0FMf3z/wC8a+cPh9P9n+I/h+TOAbkx/wDfSkf1r6QuBid/96vEzBfvD0cH8JHSdqKWvPO0SjtRR2oAKKO9FAB1o60dqKACij8KKAF60UdRSUALSUtH0oAO1FHek/zmgBe1HSijPNABRSVQ1TWLTSYd0zbpD9yJfvN/gPehK4FyeeK2heaaQRxryzMcAVw2ueKJb/dbWe6K26M3Rn/wHtWZqusXWrzbpm2xj7kS/dX/ABPvWeOtbxhbVkN3FHFU9T1O30q0M8568Ig6ufQU3VdXttJt98pLSt/q4l+85/w9656C1uLy7/tHU/mm/wCWcX8MQ/xrppUXN+Ry4jERpLzI4ba41K7Go6n97/ljB/DGK1KXqKTvXqQgoqyPCqVJTd5B3o7UfWiqIF7VB4fTN+5/uof51NTNIxDrEqH+JTt/nXHjU+S5pT3OiC5PavPdQczancyHvK388CvRAea8+1GHydTuYz2kYj6E5FeZQ3PQw/xMrjgCpoYGnJCDoOc1APrV7TnAkdSeSOK6DtRJpU7WGrwlvlBOxx7H/Ir0Ow099QvorZAfmPzH+6vc151fKWnjKDLAf14r3bwvpwttOS7cfvrhQ3PZe359fyrixT5dTlxMPeTOU8Y2UdhrVq8ChEMSYA/2Tj+QFbXas/xyfN1uygHURj9WP+FaPSvTypt03c8TE/GJ1o7daO1Feqc4UYoooAKSl60UgD61z2oPLqusxabb84bb/wAC7k/Qf1rfZtqs390ZrK8CxifW7i5fl0jLD6seT/OvPzGq6dLQ3oQ5pHfafZQ6fZRWsIwkYxn1Pcn3rK8T+JI9AtlSNVlvpgfKjPRR/eb2/nW6uB9PWvF73UzrWv3l9IflaTbGD/Cg4X9Oa+bow55XZ7uFoqcrPY1oNNe/kOparK1zcyc/vDwo+nb6Vk37Qtdt5AAjUYGBwavagY4IkjhuXfdywDcYrKI9K7ke0opKyI3jWRCrDKntXoHw41CS50WexlYsbKXYhJydh5A/DBrgJGCIWY4ArrfhakjR6rcEfI7oo+vzE/zFZ4hJ02ceMtyHooNcL4oujq+vQ6XE2YbfmQj+93/Icfia6vVtQXS9Mnu3x8i/KPVj0H51w3h+J3jmvZSWkmY/Me/cn8/5UZbQ9pUu9jwMTUtGyNkKqKqqMKOAB2o7UHmj1r6pHmsP84ooPSk/pTEZuvxedod0pAIChsfQ1xXg8Cx8YtChIjurVsLnjcpB/wAfzrv75PN0+5T1ibr9K85tJPsviPR7k8AXHlH6OMVlNK6Z0UXeLR6f2opTwaStDAOtHajrR1piDrR1oo7UAFYPiKMrNbzdipX/AD+db2Kp6ram7090UZdfnQe4rCvDnptFxdmdZo96L7S7ecEHcg3fUcH9RXNfESzd9MttQiB3WsmGI7K3f8wPzqh4P11LOZrC5fbDKcxsTgK3p9DXeTwRXdvJBMgeKRSrqe4NfJTTpVbntYerZqSPE21WXUJP9IfLDp2H4US3EcK8kM3ZRWxrvgLUtNnaSwie8tCcrsGXQehHf6isa18N6zeS+VDpl1uJwS8ZQD6k4FdqnBq6Z7UcRFxvcyIbc3etW3Gdz7nx6Dmu2LDOa2I/Bceh+HJp5GWXUNyu7qOEXuq/nknviufnmS3ieWRsIoyTUOaqPQ8jEzU56GFrUgfW4UH8KqDj86Ums6CV7vUZLlxySW+noK0DXuYOPLTPOqayOSuudTvP+uv9Ki71NeDGrXg/6aA/pUNdkPhOtbIbz0ph5p5pppsoaelWNO1GbSb4XMOSh4ljzw6/41XpO1ZTgpqzKjJxd0eoWt1De20dxA4eOQZB/wA96m6159oOsNo9z5cpJspW+b/pm394f1r0BWDqGVgysMgjoRXlVabhKzPTpVVNXHdaOtJ1orI1IZoS9zbTL/yzY55/hIwf1xXq+jXQvdItpurFArf7w4P8q8v7V13gq/2vPp7n737yP69CP5GoqK6GtGdjR1oorAoKKDzR2oAO9HOKKOlABRRR1FABT4k8yVV9TzTKbpVw1xq98in91aqkZ95GG4/kuz/vo1UVdkydkXNY1KLRtFvdSmwI7SF5iPXAyB+J4r5FSR5y88pzLM5kcnuScmvdPjhr32Tw7a6FC+JtRk3yAdokOf1bH5GvDQMDAFexgadk5HmYiV3YWjr60fyoxXonMLj9aUU3t/OnUAOFPH171GKkFUhDxT+31pgpw+lWhAeKibmpTUbUmCImqM1I1RtxWbKHUd6KKYC4pR1pKUc0wHinimCnD/JqkIcRTTTqaaBDDTTTzTTSGMI4pDTqac1IxtJ1pf8AOKOtIC3pF39g8QaVeZwILyKQn2DDNfVd4MXTY78ivkS4B+ztjgjkfhX1fZ3Y1DSdPvRyLi0il/NQf615GYx1TO7BvVolpKWk7dK8s9AO9FFL0oAKSiloATrS0lFAB2ooxRQAdaXrSUuMUAJS96O2aKADrR3oooAKP6VUv9TtNMh8y6lC5+6o5ZvoK4bWPEt3qhMUeYLY/wAAPLfU/wBKqMGxN2N3WvFkVtut7ArLN0MvVU+nqf0ripp5biVpZpGkkY5LMck0wDAoxxW8YpEN3G8Vj6xrqacVtoE8+9kIEcQ55PQmo9W1mRbj+zdMQTX7dSOViHqaqrpEem2qTyOZrx7iJpJ26n5xwPar0W5yVsRy6R3K40+4sruO/wBTcTzS8FuoiPoO1ahOatalEJtOmUjkLuH1FZlpL5tojH0wfwrswdVyTTPIrXb5mTmk60daK7jAKOtFH9aACoJ2NvPDdoP9W2G9xU/WkZQ6srDIIwRUVIKcXFji7M3Y5BIgdTkMMg1zfiayKzpeKPlcbX9iOn+faruk3JiY2Ux5XmMnuK1pI45oWilUNGwwQa8Jp0p2Z2058r5kefYpUJSQMo5B4rYvfD1xCxa1zNF2X+If41Xs9G1K7vYoYLKZnZgACMD8zxWynG17nfGpF63L+l2D6jqdrYoCXnkVSfbufoBmvf1CoioowqjAHtXJ+EfCC6EGu7plkv3G35fuxL6D1PvXWdq8vE1lOWmyMa01KWh5/wCID5/jhE7JsH5DNalY903m+O7lv7rt+i4rXr6HLY2onh1377CjkUUda9AwCjtRRTAP5UdaMCjrSAjlXdE49VI/SsvwDJt1a4iP8cOfyI/xrYrmdPn/ALE8UJI/ESyFWP8AsN3/AFzXmZnByp6HRh5WkepMu+N0zjcpGfTIrwFGfTr2WCZSrRuY5FPYg4r3/IKjBBB6EGuH8Y+CDq87ajppVLwj95ExwsuO4PZv514GHqKDal1Pdw9VQkcUsiSjKsCPah5Yo1y7ge2azLnStTsZNlzYXMTD1jOPwPQ1d03w3rOqyBbawm2nrJIpRB+JrtfKtbnpfWFa5Vfz9SuY7W2jZ3kYKka9WNezeHNGTQdFgsQQ0g+eVh/E56/4fQVT8L+ELbw8nnSMJ79hhpccIPRf8e9aGv6xFotg0rYad8iGM/xH1PsK461T2jUIHmYmvz+hyPjjUzdX8OlwHIiIL47ueg/Afzq7aQi2tYoR/AuPxrndEtpL3UHvZyW2sWLH+Jz/AJzXUDgV9Bl1D2dO54VefNIWik60V6JiLz0pKWkpiEI3KVPQ8GvK9XUw2jyKMPbyLIPqrV6r/nFeca7D8+oxY7yY/U1nNaG9B+8ekK4kjSRT8rgMPoeaKzPDtx9q8N6bNnJa3QH6gYP8q0qpbGclZ2CjrS9aTrVEi9aKKKACjvR29qO1IRz2saMzM1zarnPLxj+Yq5oPjCawVba+Vp4BwrD76D+orVA59Kz77RoLwl0/dTf3gOD9RXnYrAxq6o3p1nE7ax1Oy1GMPaXCS/7IOGH1HWrRJA5zj3ryKbS7+0bcI2bHR4jn/wCvUEkmpTr5bNduvTaSxH5V48svmnY7FiVY9G1vxFpVpazQSzrNI6FfKiIY8juegrxK/wBRuNRl2MNqg4WNfX+tdXbaFezMN6CFO5f/AArK1LS00zU5UXLbvmDHvn/6+a7cLgXHVk+35nYo20Igjx/EeSanzSClr14qysjPc5jVE2azMf8Anoit/Sqp6Vo68u2/tn/vIy/lz/Ws41pDax2Qd4oaelMNPNNOfrTZY2kpaTvUgTIoYYYAg8HNb3h/WTp0i2F4/wDornEMjfwH+6fasKPtVxYkmjKOMqe1KpQVWNioVHTldHofaiuW0PWntJE03UH+U8W87d/9k11BOTivGqU5QlyyPVp1FNXQvWp7O6ksryK5i+9G24e/tUH8qUVBZ61a3Ed5axXEJzHIu4Gpa4rwhq/kynTpm/dyHMRPZu4/H+ddr0rnkrMsOtFHajFSAUdaOlFAB3ooIooAZLNHbwSTTMFiiUu7HoFAyTUPg9Gi8MR391+7lvme/mLHG0SfMoP0TaPwrK8Xbp9Hj0uMkSapcxWQx12u3zn/AL4D1h/GDxauj6Gnh2wcLeX6bXCn/VQdD9N2Nv0zXRRg5aLqc9advkeUeMfELeK/F19qoJ+zA+Tag9ol6H8eT+NYfWhV2KFXoOKWvoKcFCKieXJ3dxKWijtVkh/kUo+tJS4pgOFOFNFOHNNCJFp4pg9aeOlWhCHp7VG3SpDyKjbr9aGBE1Rn1qRv60w1kyheKP8AGlo+lUAdqUcf4UlKKAHinjpimLT+tUhC0h/+vTqbimIYaaaeRTDUjGmmnmnHpTTxSGNNFLSdqkBkxxBJn0NfTPhGC5tfBmj214uyeG2WNlznGOn6Yr5sjhFzcW9v2lmSP82Ar6tKhAEXovAryMylqkd+DW7E6ik7UtJXlneFLiijtQAUdqOoo6UAHWkopaAExxRS9eKT6UAL3o6UUdaAEpaqXmpWenruurhI/RScsfoOtcxqHjV2ymnwbR/z1lGT+A/xqlFsV7HW3F1BaRGW5mSJB/ExxmuU1TxmSGi02PHbzpB/Jf8AGuVubq4vJTLczPK/qxzj/Coa1jTS3JbJJ55rmVpp5GkkJ5Zjk1HR0pkkiQxtJIwRFGWYnAArRCJKwL7VbjUbptN0YguOJrn+GIex9azbvW5dd1CPTLGRoLWUkPPj5nAGSB6DiuksbS30+1W3t4wiD8yfU+9E37Pfc4q+Jt7sRmm6TbaXb+XCCztzJK33nPvTNZG3Tnb0kjP/AI+taIGfWsrxBcwxaXPEXzKwBVR14IPP5VzxblI4L9x2qXAgspvVgUUe5rKsF22i+5Jpkn2jU5xLKNkXYf4f41cVQqhQMAdK9XCUXHVnPUlfQd1FFGOKO1dxkFFJS9aACiiigCOWLzArKdsinKMO1adlei4Qq/yzJ95f61RpjKd4kjbbIvQ/0PtXJicMqiuty4TtozcHJ/rXVeDrHz76S6dfkgXj/eP/ANbNcfYXIuxjG2VeGT/D2r1fRNP/ALN0uKEj96fnk/3j/h0/CvAxF4Lle51R1L+KO9KelBrhNDzfB/4TW+z13yVs9qzLqPZ46vAO4LfmorTr63Lv4CPIr/Gwo60UV3mIf40d6KTrQAUUUtABWLr9gZYxdRLl0GHA7j1rapKzqU1OPKxxdnczvD3jFbKBLPUQxiTiOZRkqPQjuK7O01SwvlBtruGX2Vufy61wGpeH1lYy2ZCMesZ6H6elc/PZ3Nq376F0I7kcfnXz+Iy5p3O6niND2rPPekluIoIy88yRoOpkYAfrXiy312i4S8nUeiysKbulnflnlc+pLGuZYJ9zV4jyPSNU8a2ForJY/wClTdiOEH49/wAPzriZJL3XtR3yuZJX6k9FX+gp1nod3cEGRfJj7luv4CumtLOGyi8uFcerHqfrXo4XL7O7Ry1a7kFtax2lukMY+VR+Z9alxilpMCvaSSVkcwUUYo7fyqhC4pKOtHWgA61w3iFNusXK/wB7B/MV3NcH4y3nVo4oiQ8qLyOwrOrJQjzM2o/FY1vAkpfwjaqx5iaSP8mP+NdN1NcD4SN5pdlfOitLaQXrRSr/AHcgEH27/lXdW1xFdQCWFwyH9D6Gs6FeM1ZbodaLU2S0daMUVuYhxR3oo6igAxR0NFFMQUUcZopAHSj86OtHWgYlc34rtcpb3IHIJjb+Y/rXS1na+iNodyzfwAP+RFJlQdmcLRilPBpp4qTpMXxCv7u1kx0l2/mP/rVk1t+IVzpZb+5Irfrj+tYh4GacN2ddJ+6MPApp/rTzTD1qmaid6TvS0lSBNH16VehqjH1q9D1remSyeWGO4gMci5B/Sruk67JYOllqT7oukVye3s3+NVl5FMljSRGR1BU9QaWIw0a0fMqlVlTd0dwMEAgjB54707oK4nTdXn0RhDNunsM4B6tF/iPauygniuYFlhkWSNhkMp4NeDVoypu0j1aVWNRXRKCVIIOCOQR2r0bw9rI1Wy2yEfaohiQf3h2avOOtWrC9m067S5gOGXqOzDuDWEo3Rsmeq5oqtYXsOo2cdzCcqw5B6qe4NWc1zlBiij2ooAKP85opQKAOR1/W7PS/FNve3zYtdGsJb11HV5ZT5Uaj/aI8zH514Vqmp3eu6xdatftuubl9xGeEXso9gMCtDxpq0mt+NdWlErG0jn8tEzwfLGwH/wBCP/AjWNXu4OhyxUmeTXqc0mhO1H+etLSV3nOHeil7YpKAF/rRScU4daAF6mnCminjrTQh4p46UxaeKtCEPvUbf0qU96ibkc0MCJqYae1MPT6Vmyhxo70d6KYB1NKMUnalFAh4p4NMFSCqQC49aQ9PpS+1NNMQ0imn60400/rUjGHpSU4/ypp/SkMb/OilpKQFjTzjWdMP/T5F/wChCvqZjlj9a+VLZtmo2LngLcxkn/gQr6pPUn3rxMy+NHo4L4WLRR1orzjtCloooATvS0dqOv5UAFFFHWgAorH1PxJYablC/nTD/lnGc4+p6CuO1PxHf6kShk8mE/8ALOM4z9T1NXGDYmzsdR8SadpxKeZ50w/5Zxc4+p6CuWv/ABbqN3lISLaM9k+8f+Bf4Yrn+gpeMVqoJE3FZ2kYs7FmPVmOSaSjoKKoQYoxR35rN1bW7XSYsSHzJ2+5Cp+Y/X0FUk27ITairstXt9b6dbNcXMgRF9epPoPU1w2p6pc61J+8zFZg5SEHlvdqhu7i51K5+03rAkfciH3UFN4r0sPhLe9M8+tiHLSOw2CY2mpWUyLnZKBtHHXjFdd/bjg8Wcmfr/8AWrjZvkaJ+m2VT+tdhiivho1J3ZxVZ2sNfU7+4G2KIQg9+/5mqs1iWtZ2lfe5RiB74q6OKXqMeoxRDDU4HO5tsitX8yygf1jU/pUlU9JJ/s2JT1Tch/AkVcrdbEtWYdaKO9JjmmIO1L/nFHejvQAYzRRRQAY46UZxzRiprW1kvLqOCMfM5xn0Hc0CNTQ9LnuG/tGF9kkBBhz0dhzg+1eqaTqkWrWQmX5JF+WWM9Ub0rl7aBLS3jgiGEQYH+NIVuLa6F7YOEuAMOjfdkHoa87H4H2y54boKWI5XrsduRSMQoJY4AHJPYVzI8aRxJi7067jl9EAZT9DWbqGr6nr6G2hgNlZt99nPzOPSvDhg60pcvKdkq8Er3KttKNR1/UdST/VFtkZ9R/+oD860utMt7aO1t1hiGFX17+5qQ8CvqsPS9lTUDy5y5pXCiikrcgKOtFH0oAKP84oo60AFFFHWkAlGMil6iigCE2lsxybeIn1KCnpGkY+RFX/AHRin9aP84pcqAM80UUUwDrR1oo60AFHajrR3pgHX/CiijtQAc1xWp/6d4hmn6pAFiB9+/8AWuymfyoXkPRFLflWDpdgJvB95fEZlN2pLe3/AOs15uZVeWCXc6sKryuW/hs0UuueKdOmVWSRoZdp5BBUg/0q3rWg3Xhu4N5Y5e0Y8qeQPY/0NedvDMPGLtbSPHM1ssishIOQccYr0jwz42leRdH8Q7ZYpv3cdww6E9A/qD615EnOnP2kH2PVng3Wpc6Esr6HUIt8Zwy/eQ9Vqz/kVk+J/D1z4fvft+nFhb569dnsfap9L1SPUoeyTL99PT3HtXt4TGRrKz3PFqU3B2ZfoxR1o/ziu0zCijtR3pgHWg9P6UYo6UgCjtRwKO1ABTLmBbu1mtpPuSoUPtkYp9B6UAmeYxl9hSUYljYxuPRlOD/Kl61p+J7T7D4kdlGIb+IXCem8fK4/kfxrMrNHX5lLWE8zSLlfRM/lzXOKdyg+orqrxC9jOvrG38q5KA5gQ+2KqHxHTQ+FjjTTTjTTVs3G0lLik71AE8dXYutUo6uxcVvTJZbHApG5pR0prdK6SCN+QQcY9KZZXtzos5ltfnt2OZLcng+49DT2PNQt1rmrUo1FZlwm4u6O60/ULbUrUT2z7l6EHgqfQirRNecQz3Gn3X2uybbIPvofuyD0NdppWsW+r2++I7ZF/wBZEfvIf8PevDr4eVJ+R6lGuqis9zqfDutHSr3ZKx+yynEg/uns3+e1ejqQwBBBBGQQeteP4z2rr/DPiSG2thZ38m1EH7qU5OB/dP8ASuOcb6o6Uzrbu5isrOa6mOIolLt+FR6fNNcadbzzqFklQOygcLnkD8sVxvifxCmqR/Y7QN9mBy7kY3ntx6V3UK7LeJR0Eaj9KhxsgTFzzWX4j1ZdD8NajqZPNvAzJk9X6KPzIrUIryv4z62EsLHw/C/725cTzgdo1+6D9Tk/8BqqUHOaRNWfLBs8khBEQLElm+Zie5NSdqBRX00VZWPFeodqO1GKKYBRR/WigAoo7UtAC9aeOfzpmKeozVIQ8U8CmCngVSEI3P51G1SHpUbUMCNqjNPYUw1myh1FL1pKoA70tJ1pwpCHrmnDpTFx1/KpB0/pVIQtNOelO/CmmmA080005qaaQxpptOptSMQ0nal6UlICOZWaI7eCOQR6ivprwprUPiHw1ZahG6l5Ih5qjqrjhgfxBr5pHWuq8Ca9c6VeSWUMzRlv3sPof7y47+v5152Po80VJdDrwtTllbufQmKPwrI0HXk1iFkdQlzGPnUdGHqK168Vqx6SCj8KTpRSGLQaZNNFbQtNM6xxryzMa4zV/F8sxaHTgYo+8pHzH6en86pRb2Fc6TU9bstKXE0m6XqIk5Y/4fjXF6p4nv8AUdyK32eA/wAEZ5P1PesZ2Z2LOxZjySTkk1Q1DU7fTkBlLNI/EcSDLufYVtGmkRKVi5JIkMbPI6oijJZjgCsxby51RsWIMNpnm6deX/3Af5moIbC51KVbjVsCMcx2an5F92/vH9K2xgDjoOAK0dkQryGxxiONUDM2O7HJP1NP7UdqqXmqWNgubm6jj/2Sct+XWklcptJFyop54baIyzSLHGvVmOBXM3fi6SXKabakj/ntPwPwFYVx597L519cPcOOgPCr9BXTSws5+Rz1MTCO2ptaj4qknJh0qPA6G4kH/oIrBWPEjSu7STNy0jnJNSdBxgCkr0qWGhT9TinVlPcSjtQRSHmugzIrof6M3tg118bB40cfxKD+YrkphugkHsa6bTn8zTbZuuYl/lWE/iMa2yLFLR2opHMUtO4F2n925fH0ODV3rVO04vr9f+mit+airlKOxUtw4oo7UdelMgKTrS4zR1oGFFFLQIPwrr/Dmm/Z7Y3ci/vJR8vsv/1/8KwdF086hfBWH7lPmkPt6fjXdgcADgAU0Z1H0G9qO9OIppqjEUGlz603vRQAtFFFAAaSlpKBC+1JRRTAKKOtFAB1ooopAFHUUUdaACjrRRTAO9HWiigA60tJR1oAO1HaijHNABS9aTrS0AVdSH/EsusdfKbH5VJ4biE/gG+iGDj5sD2walZBJGyN0YEH8ah8FPsTUdJk+8QcA/kf6V4+bRbgpHVhnaVjhBKtn4z0y5ZQytBIpB745/rV7XJLW8uTLbLtRlyy+hrN15fJ1HSpD1W4eM/iKkkJQZHSuFaxTPp8G/3djePj/V9R0mLTrG3tc26eTd3l2C6uw6BVHU4AyT3rmoL+80zU4jcLFG7n91LFny3/ANkg9DUOhz/8Sf5RukSSTcuOrdRn9Killmu/C0v21GE8ZYoSuDwflP8ASu2FCEVpueXWhGpe6PTbK7S+tUmTv95fQ+lWO1czpRn0vVnsbobWbAIPqRlT+tdLXpYer7WFzxZx5ZWDAxRRRW5AYooxRQAdaP1o70UgCjrRijrTAxPGtl9o8LR6gi5l02bzD6mNuHH5EH8K44cgEHIIyD616vawxXcF1ZTqGhniKMPUEYP6GvI7aGW08+wuP9fZStbv77TwfxFZPSR1U3eHoSsoZGX1GK4q3GIQp7Ej9a7bPNcc6+XdXUf92Zv504/EjqodRpppHFONNIrRnQNNHelpB1qQJoutXofpVKMc1di6VvTJZaH3aQ80o6Uh5710EELc5qJqmaoW4rOQxh6VEvm29wtzayGKdejDv7H1FSmmHFYzgpKzKTa1R02l+K7e4xDqAFrP03H7jfQ9q6NWV1DKwZSOCOQa8ydA4wwBHvTYWntDm0up4PZHOPyrzamC6xOyGKaVpHqOK9dQ/u1/3RXy8uva2g41Et/vRqf6Vfk8deMJY9j+IJ1XGP3aKp/MDNcssFVZssXA998ReJdM8L6Y15qM4Xj93CD88p9FH9egr5x1XVbvX9ZutXvj+/uGyEzxGvRVHsBVWVpbu4NxeXE1zOesk7lm/M0ortwuEVL3pbnNWrupp0FFLikxxR2ruOcP8aO9Hb2opgHejrRRQAd6UdKO9H+RQIcOtOFNAp4poBy808dKatPAq0IRuntUTdP6VKaiakwRG3WozT26Uw/rUMof2o5FFFMQdDSjrSd6UetADxzT+o9s0wU8VSELwaQ0v+c0h+tADDzxTTTj1+lNNIY2mnpTu9NPNIBDxSUtJUjF7UokkgljuITiaFt6n6dqb2pRSlFSVmNOzuereGtaSO4s9SiP7mQfOP8AZPBH4f0r1VXV0V0YMhGQQeCK+adB1oaXMba4bFpK2Vf/AJ5sfX2Nekab4gv9PjC20waI8hHG5fw/+tXz+IoOMrHrUaqnG56fzVLUdWtNKh33MnzEfLGOWb6D+tcZN4v1WaPaphh9WROf1zWDNJJNK0s0jSO3VmOSaxVPuauRpatrVzrE+6U7IgfkiB4X/E+9Z1MDYHpXLa54oLF7PSmy3SS47L7L7+9dEKbk7RM5zUFdmteasxumsNORZrv+NifkhHqx9famQW9hpbNc3t7G944+eeZgGPso7D2FcNH58cRjF1KqE5ZUO3cff1pogiBzsBPqea7I4OTON4nrY7O48WaVDkRPJOw7RJ/U4rNn8YXcmRaWKRDs0zZ/QVhAADgACnDrW8cFBbmcsTN7FqfUNTvQRcX8gU/wQ/IP0qvHbQodwQFv7zcmlWpB0rqhRhHZGEpye7F60lL1pD71sSIf502nGmmkAnWkpTzRj9aAEIypHtitrQn36PB/s5X9TWNWn4eYfYpk/uTNxWFTdGdVe6bFHekpak5Spbj/AImN7/2z/wDQat9qggQi5u3/ALzrj8FFWOtJDkJRRRTEHWjrR1FFABShSxCqCWJwAO5orf8ADenebKb2UfIhxGD3b1/CgluyubulWK6bYrGcbz80je//ANaornX7eAlIVMzjuDhfzrI1jVWu5Wt4GPkA4JH8Z/wrpfDnhGOOJLvU498jcpAeij/a9T7V52Lx6paRFTpObMaPVNXvSfstsWH/AEziLVIbjxBAN82nysncmAj+VekIqogRFCqo4CjAFOryv7Tq3udX1WNjzm3163kbZcI0D9Dnkf8A1q1VZXUMjAqRkEHINdJqGkWGqRlbu3R27OBhx9DXC6lpt94WuBJE5nsHPBI6ex9D7969DC5opvlqHPVwzjqjYoqtZ3kV7AJYjx0IPVT71Zr2E01dHK9ApKXmkximIDRR+P0o5oGFHWijrQIKKKO3SgAo6j6UUd6ACjtRR1oGFFLjmg4pXASjvRRTuAUUcZooAMUvNGKKAFHWsq6lfSNZg1SIfIx2yAf57j+ValQ3dul3ayW78BxjPoexrHEUlWpuLKhLldzhfGjDYk0fIS+R1I7hs1pWPhrVdaIEcL28JPM0ylR+A6k1zGp3jnTZo5uJbWZGI9g3X6V9AoRKqv8A3hnP1r5ys5UYqPU9+hiHGFo9TxvxD4M1Lw3dfbNFSS7tJFXzoyu47wOTgc4PXI6c9qj8OaTq/iTWrP7fZm00uJxK6FSDMV5A55xn8Pxr2O8tXubfZCyq4YEbuh9qdpmjpZwncQ0p7joPYVmsbU9nyfiZ7nDeO7XyL+1v04Z12sR6qcj+dXYJBNBHIP41DU/4hlRY2afxeax/SqelEnSrbP8Azzr1Mpk3FpnmYtLnLlFL0pMV7JyBij/9VBooAO9HaiigApetJ2pRg0DJ7N9l3GfU4P41w3jqx/s/xjHdqMQ6nB82P+esfH/oOK7LODx2qh8RLFtQ8Im9hGZ7F1ukx6D7w/Ik/hUT2ubUXrbucDXKXy7dYvB6sG/MV1CSLLGsin5WAYH2Nc5q67dZJ/vxKfyOKSeqOujpIpn+tIacaaa1Z1DaTvS96TvUgTx1diqnHV2LrXRTJZZHSkNKOlIa3ZBCw4qJqmfrULDPFZyGMPU4phpx5ppPNZsYw/SmGnmmGoZQlJjilNAHNIBe3FOptOGaEAo60daMCl7VQB3/AJUn86WigAxRzikpe9Ag/lSgCk/nSj+dACinimf0p4poB4p+BimDkU+rRIjDiomqU1G1DGiI9ajP6VI1MPWs2Md1o/lS9qTvTAP8aUfWkpR/kUAPXrTx/k0wcU9apCF4/wDrUhpwpp60wGnn6Uw04009KkY0/WkNL3pP89aQDaSlxSUhhilpKWkAFQwwRkHtWjperXuk4RP9Itf+eLHlf90/0qgKkX3qJ0o1FaRUZyi7o62LxfpLKPOaaBu6yRnj8s0yXxdpQU+U08zdgkR/riuZ60lc/wDZ8e5v9bmT6nrV9qqmIKbW1PVQcu/1Pp7VnBFjUKoAA7VM1RtW8KMaasjCU3N3Y3NHSkorQkd0pR9ab9acOKYD1qUcVGOKkFWhB2pKWg0xDTSUppp5pDA80hFHejvzSAO1XdAbFzex9sq4qj3qXTZ2t9UYhN2+LGPoawrNJXYpq8WjpwKO9U/7Q2/fhIH1qRL6CQ/f2n0bisY1YPZnJyss0HmoY5N8kwzwrAD8gal61pcmwtJ3o/z1ooEFHSgZzRQBZsbN767S3j6scsf7o7mus1WRNO0lbeD5dw8tOeg7n/PrSaBpv2Kz86Rf38wyc/wr2FUPEkha7ij7KmfzP/1qzry5KbaMr80rF/wVoy3l49/OoaK3OEBHBf8A+t/hXb6pqdro9hJe3j7Yk4wOrHsAO5ql4St1g8NWmBzIDIx9yf8ADFcd8ULqRtQsbLJEaRGYj1Ykj+Q/WvlZfvq1methqSdkOuPihdNIfsemwpHngzMWY/liprP4ozBwL3TY2Tu0DkEfgc/zriorFTGGdiCRnA7UkljgHY+fY10+xpbWPX+qxtse2aTrmn65bedYz79v30Iw6fUVZuLeK7t5IJ0DxSDaynuK8L0zULvQ9Tju4CVkQ/Mp6OvcH2Ne6WV1FqFjb3kJJimQOv49q461L2butjgrUeR+R5jcQTeGtdeFiWhz1/voeh+tdMrBkDKcqRkEdxU3jfThcaYt4q/vLc/MfVD/APXxWNoVz52niMn5ojt/DtXu5ZiHOPKzxMRT5ZGnRR1FFeuc4lHWkd1jUu7BVA5ZjgCs0ak99KYtOXcqn57hx8i/QdzQCVzSLKCASAT0HrS9qhht1hGdzPIR80jnJP8An0qamAUUUUhB0NB60daiubiO1t3mk4Vf1PpSbSV2NCyzRwxmSRwiDqWPFYlz4iLv5djHuJOA7jr9BWZJJea7qCQopYs2I4l6D/PrXo3h3wtbaVGrsFkuMfNIR+i+g/WvIxWY8ukDppYdyOVt/D+valhriZ4VPaRiv/jorQTwA7DMl8c/7h/xr0FY0QYVQPwpJY8jPQ/zryZYmrLW53Rw8UcAfAkinKagQfow/rTD4N1WP/VaofbLsP6V3JVh1ppHFZ/Wqy2YOhDscN/wjfiFfu30bfV//rUn9h+JVHE8Le25f8K7nFKRj2qljq6+0S8PDscDKuvaeC91ZCWIdWTt+X+FT2eoW98v7skOByjdR/jXbZxXFeLdHFmBq1iPKZG/eqowOe/+Nd2FzOfMo1DCrhkleJYPNNqvY3QvLOOYcbhyB2I61Y7V9Cmmro4TyzxZbYa/ZeGRmz7jPSvbtA1KDUNFsJIZo5WNvGWCtyDtGeK8k8Vwf6XqKf3kLfmua2/DumRXXhrTbiKWSGYwL86nuOP6dq8zHYN17cu53QrckE2et24jP3mx7Gp5JUjT5SCfavNV1DxHpowkwu4x/eAY/rz+tVrvxNrd3GYNjRFuCI4iGP415DwdWLs0bLFRsJ401FdQ1RYITvSAbcjux6/0FalrD9ntIYe6IFNZOlaQ6yi6uxtYcpGe3ua3DXuYHDulC7PPqz53cKTvRRXeZla+vorCDe+SxOFUHk1Fb2/iK/QTQ2kEEbcr5zYJ/r+lLYW6ah4yjimG6O2h80KehPb9SPyrvnh2xq+eprwcfj6sKjhDodtDDxlHmZwUv9r6eu+/0/dCOsts24D6jrVi3niuohLDIHQ9x2rsgK53WPDjeY1/o+2G7HLw9Em/Dsf8+9Thc2lflqjq4RWvAq4waSqdpqkdyxglUwXK8NE/Bz7VcPWvdhOM1eLOJq24h6Vp2qpdae9vMoZCGjdT3B/+sayzV7THxM8f94Z/KrYluePQ2smnXF5pc2TJYztFk9SucqfxFY+vLtvrVv7yMv5f/rrv/iBp/wBh8UWepIuItQi8iQ/9NE+6T9QcfhXC+Il4tJPSQr+Y/wDrVitD0abvNPuZJ6YpppxpprdnSNNJ3pTz+dJ0NIZYjq7FVKKr0XNb0yGWB0pDyaUdKQ1uySJ6hapn5qFutZyGhhFMNPPSmHrUMYxulNNONNNZsobRR2oFIYop4pg4pw600IdRR2opgH9aKP60UAFFFHWgQdqWk7cUvWgBw608UwU8VSEPFO6GminAcVSEBqJhk1IeajbnNDGiJqYeBTzTDWbGOxmjjFL1pKYwpRRSigQ4VIKYop4xVIQvWkNKf/1U00wGmmGnnpTDUjGn2pppx6000mAhpKXFJ9fwpDDpR1opaQDhUi1GBUi1SEPFB6e1ApTViI2/yKibipWqJutQxjO/FFKfr1pKkY4detKOKQdacP8A9dMB4p46U0U6rQhe9IaWkPTrTEIf/wBVNNONJSGNoIpTSdRSATrV3RH8vxHZH+8HU/8AfJqmeaYZZILm2lhYpIsg2sO1c+KjzUpIpK7selthuoBqpPpdncA74VVj/EgwawrPxHcQyBL5RJGePMUYI/xrp1kSSNXRgyMMgjuK+balBkzpyhucmYptNnuduZLdJdhPcfKCP0NXUdZUDIcgjirtuFlu9WgcZVnQkH3jH+FY1nmK4ntjzsY4r08LXbfIznqR6l7qKWkpa9AxFxg+tbHh/TPtt350i5giOef4m7Csy2gkuriOCIZdzge1egWdrHY2iW8fRRyT3Pc00ZzlZWJsZrlvESEagjdmjH8zXVYrH8Q2pls1mQZMR5/3TWOJi5U3Yyg7M6jwjcLP4btQOsWY2+oP+BFc38TtMeS3tNUjUkRZhlx2BOVP55H4io/BOrC0vnsJmxFccoT2f/6/+Fd/cW8V1byW9xGskUilXRu4NfKzvRrXPZw1SyUjxCCYSxKfbBFTAZrV1zwXf6JcPcWCPdWJOcKMug9GH9RWNFPE4xuCt3VutdilGSuj3aVaM1uFxEJYSe68g16f4Bd28H22/OFkkC59N3+Oa4HTtMu9Yn+z2UZfPDSH7kY9Sf6V6zptjFpmm29jDkxwptBPUnufxPNc2JmuXlOXGzi0orcfe24u7Ka2b7siFPzFeYaDI0Gpy2z8FlII9x/k16qRmvLPEUiaJ4vll2kqZBJtXrhhz/M1pl1TkqWPExMLo6QHmsnUfENrYhkQiaUdlPyg+5rnNQ1++1O6+xWcbF26QxnoPV29K19G8NJalbi/YT3A6KB8ifQd/rX1Kd9jg5FHWQy3sr/XGWfUXaO26rEvGfw/qa6KKGO3iWKJFRFGAop9FVYhu4Ud6KKCQo6ijHFFAB3rmtfu/NuhbKfki6+7f/WrpCQis3YDNcXbIb7Uoo2PM8oBP1NcWNqcsLGtKN2d94O0RbLTxfSqPtFwMrkfdTsPx6/lXRXV9b6baSXdzKIoYhlmP+evtUqqqKqKAFUYAHoK8r+I2tPdaymmRsRb2hBkH96QjP6A4/OvmYp1qmp7VClzNRRq3vi7WNdkePTGNjZj+PrK31P8P0H51zmo2s0Mim6uHnZ8/M8jE/qajtNQNrbSJErLI+PmzwPwqBnklfc7szHuxzXYoqOiPbhSjBWSLdnqepaawawv54sH/Vs29D9VNeieF/Ei6/byJNGsN7AB5sY6MD0Zfb27V5iDW94Kdh4vjVM/8e8nmY9OMZ/HFZVoJxbMMTRg4OS3PTgcH6dqdLKZMcYxTTTTXAeUFZfiDZ/YF95mNvkt19e361pM6xqzuwVV5JY4Arg/E2vf2tIumadl4i3zOP4z/hWtCnKc0omVWajHUqeGtx0+TOdvm8fkK2+1V7G0WytI4F52jk46nuasV9lRi4wSZ5EndnHeKYv+Jl0/1kI/qK0PAknmeDrLPVN6fk5qHxWv+kWr+qsD+f8A9ek+HzA+HJI/+ed1Kv6g/wBaf2jbekdVRmk7UYzV2MBaSjNFAgo6ig0UwKVtN9g8YWU7cR3CGFj79v6V3pYsoBPA6CuA1a2M9izR/wCthPmIR6iuy0i+XU9Kt7odXX5h/tDrXzObUXGrz9GelhJ3jylylo603NeSdZka54etNZTc37q5H3Z1HP0PqK4u5bWPDsojvovPts4WUHIP0bt9DXpLSIG2s6huwLYNJLEksbRyorowwysMg/hXTQxdSi9NjKpRjM4ey1S1vgBE+H/55twf/r1p2x8u4jf0PP0rP1zwTt3XekBsrybfP/oJ/pWXpuuvE4t70krnAkI5X617+GzCNVWkefOi4s6Dx/pDap4PuxCubi1xdQ+uU5OPqu4V4trcq3Ok2869DIjfzr6LtZBPZoThsrg98189eKdPOi3OqaSRhLa5DQ/9cmOV/Q12Pub0HeyMQ9aYelOPSmmug7BvekHWnHmkHUUhliKrsXT2qlGKuw1vTIZY6ikPIpwHFNNbkkTVCw5qZqhbgVmxjD/kUw/54p5phqGMYaYakNRkVDGhKKKKkYop4popw4poBeaKXqKTvVCCiijFIA4FFFFAB0pR+lHelx70wFFPAptOFNCHindutNHSnVSAQ1G1Snmom9qGBE39aYaeetNNZsY7qKSloNMA7UtJil6CmA9aeOlRipBTQhTSGlpCKYDDwKaelOppqRjTTacTzTTSYDTSGlxSYpDClpKWgBy1IvSoxUq00IeKDQKD061YiNulRN1qVqiaoY0MpaD0opDFApy02nCgCRfSnCmjpTx0q0IO1HajtQelMQ3oaQ0vek70hiUnGP6Up/nRj1oASopBma3/AOugqXrUR5u7ce5P5CsMQ7U2XT+JF+XmM+1dB4auWks3gPPlNx9D/k1zcz4jPvXReFYStpLM3/LR8L9B/wDXNeDVtynViLcpftl269fr/fhif8twrIiAk1S5kHKkn+dXtVujY6r5i/emtCgPuHH+NV7SLyocsMM/JzW2Dg3PmPLquyJCMUU41s6BpX2y4+0zL+4jPAP8Tf4V6xzN2VzV8O6X9kg+0yriaUfKD/Cv+JrdGaTtS1Rzt3dxc5FIQHUqwBUjBB7ijvR29qLCOR1TTH0+femTCxyjDt7fWuv8O+LI7pEtNRcR3AGFlJwJPr6H+dNkjSWNkkQMh6gjrWDe+HjkvaPkf882P8jXkYvAc+sTopV3E9MB71BLp9jdPvnsraVv7zxKT+orz3TfEmpaK6290jS2448uTggf7J/yK7zTNVtNVtxLayhsfeQ8Mv1FeDUozpPU9GnVUti9HEkUYSJERB/CgAA/CmTTw2sTTTyLHGvVmOBWZq/iO00oGPPnXPaJT0/3j2rz7Wdcmuibm/nwgOEUdB7KO5/WiFKUy3I6nVPFzSBotPGxMczv1/Advqa8+1G0vtcSS/tBJ9jibbNfMM7yTjCZ6/XpXVaF4OvNbKXOtRvbWOcpY5w8o7GQ9h/s/nXfz6bbSaY9gI1SBo/LCoMBR2wPatlONF+7uQ02jhtJ0my0y0VLNOGAZpG5Zz6k1ofyqlpm+GKSzm/1tq5jb6djV2vq6M1OCkjx5pqTuHGKO9FH9e1aEh/hRRRTAMZo7UdT70UgEdd0bJ/eBFcdpT/Z9Zs2fgJOmc9ua7KuS1m1NrqLOBhJPnU+/f8AWuHHQ5oXNaTsz17vXivje0ktPF195gO2ZvORvUMP8cj8K9V0DVk1bSoptw85AEmX0Yf49ai8ReHbPxHZiK4zHNHkxTKOU/xHtXzVKXsp6nuYeqou/Q8gtbhHUI5AYcc96sNgDrgetXr7wDr1nIRFbrdx9nhYc/gearweB/EV0wX+z2iH96ZwoH65rt54PW56ixMbbmfPqCQjEZDP69hXoPw60Wa0tptWvFKy3ShYlbqE65/Hj8B703QPh3aafKtzqUi3lwvKxgfu1P4/e/Hj2rs5Zo7aJpZnWONBlmY4Arlr1k1ywOPEYnnXKtif/OKwNY8V2OllokP2i5H/ACzQ8L9T/Suf1nxXc6rKbDSQ6xHgyDhn/wABUVhokMGJLjEsvofuj/Gt8Ll86ustjyauJUdIlee41jxG+Zn8q2zkKBtT8u9adjp0Fgv7sZcj5nbqat9PypO1fQUMLTorRHBOo5bh1FFFHeukg5rxZ1tPo/8ASq3w+b/QtVh/uXzHH1A/wqbxY3761X0Vj+oqn4DbF9rsPpNG/wCYNQ/iR0L+EztaO1H40dq0OcKO1J2/pS0CCk96WigAFP8ACs/2S/vdKY/LnzoR7HqP8+lM/nWdeztp2pWOprnEb+XJjup/ya4Mxo+0oO26N6E+WaPQCeK4/wAc69cabDBY2bmOe5BZpB1RBxx6Env7V1iOsiK6tuVgCCO4rgPiPbsl5p17j5CjQsfQg5H8z+VfL0UnNJnu4dRlUVziJbRZmLu7tIf4mOSfzq9pniTWvDsq+XcGe1z/AKmYlkP07qfpUeOAe55FKUV0KuMqeK9BpNWZ7E6MJrY9b8PeIbLxDY+fbEpKmBLCx+aM/wBR71g+NtCUxHVrZMOpAnUDqOgb6+tef6Dqkvh7xDDcqx8sNsmX+8h6/wCP4V7fcxR3NvJC+GjlQq3uCK5JR9jNNbHi4qgo6HJeDtT32/2R2zxlM9iOo/rXFfGjTjGbLV4l4lU2sx+h3If/AEKtDRZXsNUkib70EvP4HBro/iBpg1XwRqcIXMkUfnpx3T5j+gI/GvocNNzpW7HkwlyVEeC9qaaSM7olb1FKRzXorVHoCH/IpB1oNKOtICxHz9KuxdKpRCrsNdFMhlg9Ka3Ip3QU01uySJqhapm61C1ZSGRnmmmnmmGoYxhppp55phqGUhtL1pKKkY4U4U0Z704fSqQh1FJilpiDFJS0lAB1paTg0UALS9v6UnUUo4oAcBzTh/k00U4cYpoQ8U7tTRTqtAB6VG1SEdqjakwREetMIzT2phqGMd/KjtzS4BpKYBSikpQKAHr1qQcUxetPHtVIQvemnpTu1NNADCKaacaafzpDGnqaaetOPNNPSpAbRSnikP16UhhR2pKWgBwqVaiFSr+tUhD6D0zS9RSdqoQxgahapjyKhapY0N6mlpKWpGA604UlOFMB4p44FMWniqQgooNB6UxDTSHmnHk03+lIYnXpR1oI4o7UAJ1NQlwl7GfRDU/erWjWMN/rnl3ClkWAtgHGTkf41y4x2pNlQfK7kdlZTapchE4UfffHCj/Gu4hSGytQvCRRL37CiO3htIdqIkUajJwMAVhXl1JqVx5UWVt17+vua8JJ1nZCq1uYjvLn+0dXtZWXEKFkUeuRnn8qvk1TnjWGCIoP9VIrfrg/oTVomvYo01TjynFOXNqPRPMkVF6sQB+Neh29ulrbxwRjCoMD/GuBsf8Aj/ts4/1q/wAxXohHNbxOeqJS0mPzpaoyDoKOlFFIA6mij2NQXd7BYwmWd9q9h3P0FADriKGSIrMqtGBzu6CuH1TUJNOuvN0G4dSMhmz+i+oqbVNYn1IlBmODPEYPX6+tZljZ3eszmKwwkKnEt2wyq+yj+Jv0Fc9anCorNHRTi4+8y3p+pHU5Et7SCS51GT/l3B5z3ZmPAX3Nek+GPBENjMmo6rIl5qajKAD91b+yA9/9o8/SuMttFbwzOl9pbOzjmSR/mdj33eoNd3o/iyx1JFWV1trnujnAJ9jXgYunOk+VLQ7adWEmdPIqoueBzUDNuOR0pu7fzuyOx61l6t4hsNIgYyTLJMB8sKNliff0rz0m3ZG7kluc9qG1fF94qfxQozgetSdRWdpgnuZbjUrr/XXTZxjovb/PtWj1r63BQcKCUjx6zTm2g60fSijrXWZBRRRQAdR7UUUUAHWqt/ZR31uY34IOVYfwmrRo6mplFSVmNOxyMFzqHh6/3xkxv0IPKuP6iu20vxtYXahLzNrL3J5Q/Q9vxqlPbQ3URjmQOmeh7fSsWfw382baYY/uyD+teRiMtUneJ008Q4npMN1b3ChoJ4pQe6OD/KnvIka7ndUHqxwK8oOhX6niNT7q4pV0LUHI3KoHq0lcH9m1Do+tI7nU/FmmWClY5ftUw6JCcj8W6fzriL3UNT8TXYRuIlPEa8InufU1bt/DiKQbmbd/spwPzrYigigjEcSBFHQAV24bLFF80zCpiXLRFfT9PhsIdkYy5HzOep/+tVykxS4r2YxUVZHLcKKPxopgFFFFMDlPFZ/023/65f1rO8FP5firVov+elvHJ+Rx/WtHxWP9Nt/+uf8AWsfwu3l+Oyv/AD1smH5EH+lZS3R0w1g/Q9E7UUUVocwdTRR2o70wE60vbij1pO1AC1Bd263drLA3AdcZ9D2NT9aSk0mrME7D/CGrF4W0q5OJ4MhM/wAS+n4fyrY1/SE13RprIkK5+aJj/C46H+n41xmq2s0E6alaErLGQW29eOhrsNA12DWLYA4S6QfvI/X3Ht/KvlMbhpUKl1sephq90l1PG5kvNMupLW5R4pozhkb/AD0py6jgfOmT7HFe06xoGm65EEvrcMyjCyrw6/Q/0PFcXdfC7Mh+y6rhPSWHJH4g/wBKIYiDXvHt08Yra6Hn8jPd3AVEy8jBFUdSTwBX0BGhjgiQnLIiqT9BiuW8P+BLHRLpbyaZru6TmMsu1EPqB6+5NdTLIkMTySMFRFLMT2A61z4iqptKJy4mqqj0PML0CPxpqaLwpc/mcGvRTEk8BikGUkTawPcEYNeX20zah4ilucHNxMzY9AT/AIV6qo4H0r6DL4tQ1PCqayPl+S3ezuLizf71vM8TfgcUw9K6DxzaCy8d6xGBhZJFnA/31DH9Sa5+vSh8J6Kd0mNI4oHWjrQvWqKLEWKvRc1SiGKuxdK3pkMn7U08inY4pp5zW7JImqJv8ipX5FRN0rJjRGeKaacRzTDzUMY1uRTDTzTDnNQxjaUUd6KkYo5NOFNFPFUgFA5ooxR/nFMQUdKO3pRQAUdRRR3oAB9KUdaSlHSgBw604U0c04U0IeOlO6dKaBzT+3rVoQh6VE3rUh4qNvQ0mNEZ70w9KeaYahjH4zSdqWimAUopOlKKAHjp0p4pgp9UhC4ppp2PypDyKYDCOaYf/rU88fSmmpGMNIeacaaeTUgNI4pKXvSUhhRRSjmgBVqVajAyBUq8VSEx4oPSgUGrERtUTAZqZqhaoY0M60vUUdaKkY6lApBxTqYh4pwpo5p44FWgCk70uKTFMQhpO1KaTGaQxKSl6f0pDSAO9XdDuY7TXlklOFMDDP4g1S70+yRH1i2DjIKtx+Fc+Jhz03EG7JnR3N3PqrbEBjtge/8AFUiRpEgVBgClzgYGMUVhRoxpKyOKUmyC6bbazNxwhP6UsZ3Ro3qAeaZfcWFz/wBcm/lTrf8A49of9xf5Vp1DoW7U4uoT6Ov869IP3q83tF3XcC+sij9RXpB61cTCoJSdqOtFMyCl6iop54reJpZnCIvUk1yupeI5bgGK03RRHgt/E3+FDdioxcjY1TXoLENFDiW46YB4X6n+lche3skzNc3c3TqzcBR7elVbi6jtlXdlnbhI1GWc+wp2n6Tc6reoZ1DODlYhykQ9T6n/ACKzcr6I6IwUVdmhpOg3GuATXIe3008hekk4/wDZV/U128FvFbQpBBGscaDaqKMAClt4RbW8cIYtsULk96lq0rGE5uT8hMVnXOiWdy5fa0bHqYzjP4dK0qKUoRkveRCbWxjDw/tG1b6YL6VPbaHaW7h33TMOm/p+VaVHas44akndRKc5PqFFHWitiQooxRQIOtHUUUe9MAooo7UAFHaijmkAUd6KO1ACj60HpSdaKBin2pOtHWjvQAf560UdaO9AB2o60YpSPWmITmjrQelHWgZy/itf31q3qrD9RXPaM3l+O9MP/PSGVP8Ax0mup8Vx5t7aTHRyPzH/ANauStm8vxdoUn/TZk/MYrKZ00tY/eenGjFFGM1qcwd+tHWjqaKACg/1oooAKKKXrQADjtxWFfabPYzi908su07sJ95D6j2rexzSisa1GNWPLIqMnF6FfTPHa7BHqURz086Idfqv+Fb8XiXR5lDLqMA9nbaf1rldQ0W2uyXH7mQ/xL0P1FYkvh27jPySwuvqW214lXK2nodkMS7anoE/inRbdSTfxufSPLH9K4/xD4sfVojaWiNDbE/OWPzP7ew9qxzpEyn95PaxjuTLVuzh0O0cPfanbyMP4fMAX+eTSpZc072+8c60mi54d01gpvJBgdIwe/qf6V6AhzEh9QK44eLPDqAKb+EgcAK64H61taf4o0a/2x297EW4ABYV7VKnGEVFHM092eW/Fi38rxjBNji4s1591Yj+WK4Y16h8ZLb/AJAl4B0aWI/iAR/I15ca1h1R6FF3ghD0oXrQaF61ZqWYvWrsVU4quRV0UyGT8EU007oKae9bMkiaom//AF1K1RHg1mxkZpppxpp5rNjGHgUw8U80w1DGJR1oopDHD/PNOA5pop3amgFGDRjiil6mmISjrS0lABRRR0oAWgUUtACgU4U0U8e1NCHjrThyKaP8inCrQhp6VG1StyKiakxkbUw08immoYx1H+TS9qSmAUuKMUooAeOaeKYP1p4qkIKQ5pe39KDTAYaaRn/GnH602pAZSHrTj0pppDG+tN607pSfjUjD3o/CjFKOtACipVqMf1qRapCZIBSHpSiirERsPSom6VK3Soj1qWNDOvpS0nelxUjFHWngU0dKeBTEOAp3b9KaOtPFUhBSY4paQ9KYDTkfzpD0p1N6cUhid6MfrR2oxSATvTrc7dWsm/2yv5ikNRs2y6tH/uzLms6vwh0OtzSjmm9BS1icJV1M7NLuT/0zI/PirCrsjRfRQKraoN9mI/8AnpKi/mwq63Wl1KfwlvR4/N1i1X0fcfw5/pXfZrjvC8YbVHc4ykZIH14rprrULWyXM8yqeyg5Y/hVrY5qmrLnWs3UtZt9OUpnzJ+0YPT6+lYl/wCJppwY7RTCn98/eP8AhWGSWJJJJJ5JNJscafcnvb+4v5t875x91R0X6Csq5vCkn2e3Xzbgj7vZB6se1JLcy3UhhsiAoOHuMZC+y+prf0Hwx5kYcho7cnJc/elPrn+tRq9jfSCuyhomgz3lwX3b5DxJcMOFHoB/Su+srGDT4BFCv+8x6sfepYYYreJYYUCIvRRUg61aVjnnUc2LRR2oqjMKTtS0dv6CgBKKXtSUXAKKKXvQITrR1FLRimMTpQaXB96MUgE60d6MUfhTAKKM+1FAB2o70YzUFzeW1qP9InSP2J5/LrSCxPzijrWDc+KbaMkW8Lyn+83yj/Gsm48RahPkK6wr6Rrz+ZpXRaptnZSSJEu6R1RfVjgVnza9p0P/AC38w+kYz+vSuKkkeZt0sjO3qzZNJ2pcxapLqdNN4rTpBasfeRsfoKoS+JtQf7hijH+ymf51ju6RqWdlVR3Y4qk2rW5bZbrJcuO0K5H59KTkaKkuiNmTVtQl+/dy/QNj+VVZLiUgtJM5A7s5qii6reOEjjjt93QAeY/5Ditmx8DyXDLJqMjt3/fNn8lHApXb2G1GO7M61vhc3iW1pPJJMxwBEWOPqR0r0LTYJ7aySO5mMsvUsTnHtmmafpNnpcey2iwccsf84FXscVcU1uYTmnsZfiGHztGlPeNg4/PB/nXATN5Wr6NL/dvUz+Jr1C5hFxaywn+NCv5ivKdabybe3m6GG5jb8jUVNjWhvY9bPWkpxO7n1rE1rxDFpU0VpFA11fTAskCMFwv95iegq20ldmCg5OyNmjtXO2Xirfdx22pWZsmlO2OUSiSNm/uk4GD9RXRURkpbBOEoO0kHX86KKD0qiSpqKXr2+bGVUlByQwB3D05rl7jU9Ygk2TzzRt6FQP6V2dRzQRXEeyaNZF9GGaTVyoyS3PPbvxTd20nkrdXM056RRnJ/H0qm+teJbocSLbqf+ekhc/kOK6HU/AkNxeteWF7LaTMMEEblNZsvhfxJb/6uSyvFHYkox/pWLUr6nVF07ab+ZlGPVZj/AKRrNxz2hASm/wBkQvzNPdTeu+Y1beDWrX/j60K6wP4ocSD9KgGrWivsmaS3f+7NGVpWj1LvLp+AwaLp462yt/vMT/WpF02yX7tpCP8AgAq1HLFMuYpFceqtmnYquVE80iFLW2UcW8Q+iCpFjRfuoq/RQKd3pCaZN7lfxZqtzdeHLWzncyLBdK0bk8gbSMZ71yR5rd8RtnTB7SrWEeacN2ddH4BKVetJjilUc1ojUsx1di5qnFV2KuiBDJegpp4p3akP/wCqtWSQt/Oom4qZqhaoYxh60w08gUw9OvSs2MY3NMNPP0ph61DKE5xS+9J+FLSAUcU4daQUopoB1FAoqhBikpe/NHb8aQxOBS0UYoEHaloGaUUwFHHNOFNFPFNCHjpS/wCNIOaXtVIQjdKjapCKjbpSYyIjNNNPbrTDUMY+il7UnaqAKcBSd6UUAPFO/wAaaKcKpCFxmkNL0pCOOKAGHrTTTz1ph9aQDTzTTyOacaaeKkY04/WkpTSdaQw6UUfSlFIBy9alXpUSg1KvSrQmPFBHFLSEVYiNhUTVM1Qt0qGNDaXGKTvSjr0qRjhxThxTRTxVIQ4dKdx+lIKdjiqQhKSnUhFMBp5pKcenrTe9IYnb+tJilxR3pAJjioLohYQ3911P61PiobwZtX9sfzqKi9xgtzrutKKbGd0MZ9VB/SnVznCypfcyWS9jcKfyBNXTmqWpAraCUDmF1lH0B5/TNXeCMg8HoaS3KeyEyVOVJB9QcU3vz+dPxkVTub1YpPIhQzXB6Rqenux7ChtISV9ieSZII2klcIi9WNVMTaiPnDw2h/h6PL9fQfrT4LBpJVmvGE0w+4gHyIfYdz7122jaD5RW5vEzJ1WM/wAPufekk2EpKC8ytofh1dkc1zGEhA/dwgYyPf29q6kAAYAAAGMDtS9KO1aJWOWUnJ3YmOKMUtJ3pki0U2SRI0LyMFQdSTiqKX1xfuY9Mg3gfenk4Rayq1oUlebKjCUnZF6WWOFN8rhFHcmq9u97qrFdOh2xZwbiXhR9B3qvjSLSXfqF4dRux/yyj5Rf8+/5U658UXUi7LWNLaMDAwMkf0H5V49fMak9KSsu52U8KlrI1T4YthHv1G/nlPc7/LUVk39po9mrNYatMkw6KCZFP5f41jz3E1y++aV5G9XbNENtNdNsghklPoik1xKVRPmlI6PZxtaxGPGH2aU211bsZh91shQ1Mn8YTfwLbRj/AGm3H+dQeJ/DV7Hb21xPEsJLlMs3Pr0FW7LwoHiSSSaNdyhsImTzXuYOu6sd7nJUp04Mzn8TXsn/AC+4Hoi//WqM6ndyfevJzn1ciuph8N2Ef3vNk/3nwP0xV+HT7O3/ANVbRKfXaCfzrsszL2kVsjioo7+6P7tbmT3G4/rV+HQNTl++/kj/AGpMn8hXYdRUU00Num+aRI19WOKdifaN7Ixrfw6qHM15O59EYqK1oLaK2GIwfqzFj+ZrKuvE9pFkW6NO3r91f8/hWLdeIL+5yBIIU9Ixg/n1oukHLKW511xeW1oubiZI/ZjyfwrHufFMCZW2heU/3mO0f41ypJY7mJJPUk5qKa6t7cZmmjj/AN5sUnIuNI17rXdQusjzvKQ/wxDH69azTlmJJyT3Jqh/a0LnFtDPce8aYX8zR5upzH5YYLYertvb8hxUcxsqdvIv4qvPfWtt/rrhEPpnn8qrnTnl/wCPq9nlHdVOxfyFaem+HGkwbSxVVP8Ay0YY/U0asPdW7Mv+1GlP+iWc83ozDYv5mnCLVLkgNLHAD0WFdzH8TXbWvhaNQDdTFj/dj4H51tW1lbWgxbwonuByfxpqD6mbrJfCjg7PwXNcMsk8Tuf7905P/jv/ANaultPC1pAo85zJj+FRtWt6jGatRSMpVZS6kUFrBarsgiSMf7IqajFL1pmYnSjtS9TSc0wHZry7xlbeUmoRqMbX3j6ZB/rXqHauI8Z2u+5kAH+ug/Xkf0FRNXRtQdpnRLrESWVoVBlnmiRljXqcgY/nXKTRXUXjXVhfxiO42RYQHO1CucD9PxrpfhTYrfaVFqk2GMQ8mMHsw4J/LFXviH4bvZbmDxBpUJmuYk8q5gA5kj6gj1I5/T0rxZ49ut7OWx3UaSg2zhEk/tSXUtOuY1Codq7RyB0B+uRmuz8NXsmoeHLG4mOZTHscn+JlJUn9K4tNQ1HU5PsWl6Pcm+lIQs6YVD6k+3vXTeHd+jRLoV4CktuSoJ4yxOT+ZORXbRrQjPlvuTi1eJ0fWij8aO2a7zzQo7UYo96YCYzRil7UdqBB3pksMU6lJoo5F7h1DD9af1NFIa0MK68HaDdMXNgsL/37djGf04/SqL+BJAP+JdrkiHtFeRhwf+BDBrq+tKankRoqs11ODuvDviXTwTPpYuox/wAtLJ93/jpwazsSFtrwTxv/AHJYmRh+BFepRXM0P3HOPQ8iraamhGJYiD6qc0uVle28ji4/AUer+F5YrrMN7N88LsD+6I6ZHv3+teWX+nXukX72GowNDcp2PRx6qe4NfSMN3byEYcA+jDFVdf8ADem+JtPNrfw7iOYpl4eI+qn+nQ0rNO6NaNdx0ex84EUL16Vc1bTpdH1i80yZxJJaybN44DDqD+VUwOa1Tvqd6d1dFmPNXYulU4quxcV0wIZKentTTTu1Mb6VqxEbVC3NTNUTVmxkZGaYaeaYRxWbGMNNxT26Uw8dahjQ3vS9qO9LSGKKcKQcU7GetUhB2paKMUwE6iil6UUAJ3o9qXFFABSijFKOaAFApwpop4GKaEOAp1IKXmrENYVG3NSEVGwxUsZGaYelSHmmGpYx9HvRSUwFpRSd6UCgB45p4pgp4qkIUfzppp2Oc0hpgMbkU09KcRxTTUgMNNNPNMPNJjEpOelKaSpGJilAo60CgB61KtRLUq1SJHig9KKQ4xVgMfpioWqZqhbr1qGNDc0opKUUhjxTgM0wU8dKaEPFOApo5p/UVaEJSHIp3WkoAaaQ0p9evNIeaQDTQelLSUhhUNyP9Gk/3TU1RXRxayf7uKmfwsEdTa/NZwH1jX+VSU23TbaxJ6RqD+VNubmC1TfNKqDtk8n6CubocT1eg5lDqVYZDDBB7iqlveRWlmI7mQB4j5fu2OmB34xUbXN5dnFtF5EZ/wCWsw5/Bf8AGn2VrbozTqxmlyQ0z8nI6gen4VN7vQqyS1HH7bf9A1pbnuf9Y3/xNWre0itlEcEeMntyWPue5qUGtvwzbxz6izuMmJNyj3zjNUokSm7Gnouhi0C3N0oNwR8qnon/ANetqlPBpKo5W29xepoFNYhQSxCqOpPFc9f+MdOtWaG0D39wONkH3VPux4H60NpDjFy2Ok6/1rK1bxDpujIftNwpmx8sKnLH8O1chfa3q97E73N0LODH+ptThj7Fzz+WKzdJ0uK8nad4wsKnp1LH3J5Nc1bEKmrnRTw6fxM1v7Xv9cl877Pi3B+QSkqn/fI5b9KtuLm4jCXN1I8a9Ik/doP+AitjTNAvdTx9mh2xdPMbhR9PX8KkuLjQ9En8ny5NXu0PzbSFhQ+me/614VSu6kr7s76VG+kEZljps9ydlnavIB18teB9TXQ2fg27lAa6mSEd1Ubm/wAKZbeP2jAR9HRIh0EM3T8CAK6zSdZsdZgMlpISy/fiYYdPqP61zVJ1F0sbyoThrJFS08L6Xa4JhM7f3pTn9OlbCIkS7I0VFH8KjAp2Kq32o2mnQPNdTxxqilsM3Jx6DrXNeUmZbHGeNrr7Zqltp0Rz5XL4/vN/9b+dXUXYgRRwowK42316CXVJr50eZ8l+mAWP1qS58TX0pIhCQr7DcfzP+FfUZdS9lT1PMrKU5HXu6ou6RwqjqWOBWVdeI9Pt8hHadh2jHH5muMu75nbddXDM3+22T+AqobiR/wDU20jf7T/IP15/Su9yFGj3OkuvE95PkQBYF9Ry35msae4Z2MtxKWPdpG/xqkYb2X79wkK+kS5P5n/CkGl2u7fKrTt6zMW/TpU3bNVGMRH1W1DbIma4f+7Cpb9elN8/UJuIrWOBf707ZP5CryosahUUKvoowKd3xSsx8yWyM82E8v8Ax83szf7MQ8sf41LDplnAcpbpu/vN8x/M1t2miX93grCUQ/xSfKP8a3LTwvbx4a5kaZv7q/Kv+NNRIlVt1OWhhlncJDGzt/dUZrZtPDFzNhrl1hU9hy3+FdVDBDbR7IY1jX0UYqSrsYuo3sZtpoljaYKwiRx/FJyf8K0e1LSYyaZDbYdaMUUUCCl5NJilpgHQ0UlLQAnFHWl60h5pAHeuc8Vx5W1l9Cyn9DXSVi+J49+lq/8AckB/PIpPYqHxDvg/MEs9a00nm3uxIo9Fcf8A2NenFcxFu3TFeN/Dy7+w/ECa2JwmoWZx7uhz/IGvXyW6Z49M18lj4cldnsQd43Myx0W10+4eaEzMWztEspZYwecKD0rL8WeHv7Ut/tdqv+mwjoP+Wi+n19K6XFGMVyRnKMuZDlFNWPONI1X7QBbXJxOOAT/F/wDXrYo8V+GDcb9S0+P9+OZYlH3/APaHv/OsDTNd4WG8b2Ep/r/jX0uCx0akbSPMrUXF6G/RS8EDGMdjR2r1DnE70daOnej/ADimAUdaMccUdqACiilA/WkAnenAZqteX1tYR755Avoo5ZvoK5i/8RXN1ujgzBEfQ/Mfqf8ACk2VGDZ09xqdjaSbJrlFfuo5I+uKv2Opbow9tMssRPQHI/8ArV5jU9pqM+nT+fC+MfeU9GHvSuaez7HJ+Jrs33i3WbkdGumUfReP6VmL1piO0xeZjlpHZyT7mnr1pw2PUSsrFqKrsXIqnFVyLkV1wIZL2pp4px6U081qxETdahb+dTNULcCs5DQw9aaetOPSmn+lZsYw9aYetPPIppFQxjaUCk9/WnUhijinAU0dKcOlUhCijFFGKoA6GjpR2opAGKKKWgApRSAUopgOApwpoFOFNCHgYpe30pBTu9UIYaY3JqQ1G1JjI26Uw09uaYahjJMUlLRVAJ0NOFJSjigB4pwzTBnNPA4+tUhDqaeKXrSGmIaeKaacen1pp71Ixhpppx4ppqWMaabTjSHpUjDAPWlpKUUAOFSrUY/yakX0q0IeOlBo7UHjmqERtUTVK1RNUMaG/jSik6UtIY4U4CmjrTxxVIQ5Rz6U8Ypo4FOHWqQhcYpMZpelIRxTAac0h4p1NPU0gExzSEUppO9IYfWoLrBiCc5d1UD15qep9JtvtV+1y3Mdv8qj1b1/CsqrtG3cTdldm5LG8qBUmeIZ5KgZI/HpTIbC3hbzAu+TvJIdzfmasDiiueyOO7RWvpTb2U8y9VQkfXtS20K29tFEv8KgfU0mpp5ml3Kjr5ZP5c1JE3mRI4/iUH9KXUf2SUVs+G5vK1dFPAkUp/UfyrFp6sVIIJBHII7VVyGrqx6BfalZadGXurhI8c4J5rlbzxvJOSmkWZk/6bSfKg/Hv+FYr20M05mmXzZOuXO7+dSY4x6UXYo04rcr3JvtSO7U7yScZ/1KErGPw6n8akjjSNAiKqqOiqMAU/r0H5Vs6f4cubkCS5zbxHnDfeI+nalYpysjmrlZry7is7dDJIzAKi9Sx6CvWfDngyDTrSFr5RLKFB8r+BT7+p/Sub8I2VsviKB0UDDuwPc4BxXqS8ivm8diJTnyo6aNpK5yXirVZfOj0Sxfy3kX98ycFVPRR6ZH6fWuLvrH7EUj3Luxyo7U3WtQCeK9VaeXy2E7bSTjGOB+gFY11raZPkqXYn779KVKnZKx9Dh4wp00XzwCScepNV4fFK6DqUN5anzXRsOqn5XU9QT/AJ5rBurua4/1khP+yOB+VZ217y4SCFdzMdoA9a6FTTXvE1qq5Wj1688ValfDCSiCMjgRcHH161zGt3DLp8hZiXkIXJOSc9auQwSiJERHbaoGQKztY06/u5IYYYeANxJIHJ4FY0aa50oo8Kckldso2KbbUHu3NSsu4d8exxW/B4TuxGqvNDGAAOCTVlPCa/8ALS8b/gMf/wBevooRtFI4nUjfc5VUVPuqB9BTq7GPwtYIfneZ/YsB/IVbj0PTYcYtUY+rkt/OrsS6iOEUFjgAlvQc1et9F1C5xtt2VT/FJ8o/Wu5jgihGIokj/wB1QKdinYl1Oxzdt4UUEG6uM/7MQx+p/wAK2bXTbOzA8mBFb+8eW/M1b7Uf4U7Gbk2FFFVLnU7K04muYww/hByfyFArXLfWiufuPFUK8W0DyH+852j8qyp9f1CbOJREPSNcfr1pXLVNs7U8DJ4HvVeTULKL/WXcIPpvFcFLLNMcyyu5P95iaqySNu8uFd8hPQc4qJ1FFXZapHdTeI9KhHzXOcf3UJqAeKtOk/1Qnk+kf+NVND+Hd9f7bnU2NtEeQrDLkfTt+Nd3Y+DNItVCpY+aw/imbP6dP0rya2bKLtA3jhbnH/8ACSR/w2kp+rAU8eIof4raUfQg16CNBskXH2K1X6RD/Cq8ug6Y337CA+4QD+Vc39r1Oxf1RHFDX7M9VmH/AAD/AOvTv7esu5lH1SurPhnR8/8AHkn5n/GlHhrR/wDnwj/HP+NV/bEuxP1Q5VdcsGP+uYfVDVuC7t7g/uZkc+gPP5VuP4W0WRf+PJF91Yj+tYep+BtiGfS5X3rz5Uh6/wC6fX61rTze795ESwrS0Jqz9dj8zRbnH8IDfkRVLTtZeKb7Jf5DA7dzDBB9GrZvo/M0+4T+9Gw/SvXp1Y1Y3ic/K4vU82gvP7L8S6HqWcLDdqjn/Yf5T+ma9+IwcV87axCZtJnA+8q7x9RzXuOlawt14RtNZb5g1mszgdyF+YfmCK8PN6fvRkj1KGsbEmra/puihftk2JG5WJF3OR649Pc1kwfEHQJZRHJLPbk95oiB+YzXA3E015NJeXL7p5jucnn8B7DpVaSJJkKyLkdvauKOHjbU9mOAXLq9T2yCeG5gSa3lSWJhlXjbcD+Ncb4t8LhxJqWnx/OPmmiUdf8AaHv6iuI0LX7zwpqakO0lhIf3sWeCPUejD9a9qhmjuYI5oXDxyKHRh3BGRWUoyoSutjzsRh3DRnlOk6y1oVhnJaA9D3T/AOtXUghlBUggjII71k+M9BXTrlb+2TbbzthlHRH68ex/xqLwzeGWT7A7dQTET+or38DiuZJM8irSszc60lTzW8kON6fKejDoahr1DnE4oormvEviZdJX7PBl7pzgKvLZ9B/jQ2luOMXJ2R0E9zb2se+4nSJQOrNiuY1Hx7p6B4NNWa8n6ZhTKr+NckbW71SXztTlaUk5ECklR9T/ABGugs9A1F4wLfTZljHTEe1f1wKzcmzoVOEfi1MSW41m/dnMUVvuPLTOXb9KiOlyyc3V/cSf7KHy1/SuwXwxfBd08trbqOpklBx+WaYdIsk+9fPOf+mMWF/Nj/SsZ1acPikaKfSJy0OnWlvIJI0cOvQ+Yx/PmmavceRpdw46ldo+p4/rXYwWGi+asU8U6MxwrSScE/hVXxb4OF1ortpgYTRES+SSWEgHYe9VCUakOam7kqaU0pnl6LsjVfQVItMVw6gjv1HpT1610x8j0C1EKux8iqUVXo+ldMDNkh6UxulPNMbpWrERMMComFSt1xUTVnIaGGmH2p560w1mxjDTTTmppqWMT6UopMc0uOKQxwpwpop1UhC0dqO3tRTAKMUvek6GgAxRjNHfFLQAClFJ+FL2oAUf5zTxTR+tPHFUhDh1paAMUpqhDSMfjUTc/hUp6VGRnikxkZ4phBqQ1GelQxn/2Q==",
"text/plain": [
"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x1024>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for generated_image in result.generated_images:\n",
" imagen_image = generated_image.image.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4AAz3s8_ywwT"
},
"source": [
"## Generating images with Gemini 2.0 Flash image out model (experimental)\n",
"\n",
"\n",
"The `gemini-2.0-flash-preview-image-generation model` extends Gemini's multimodal capabilities to include conversational image generation and editing. This model can generate images along with text responses, making it highly versatile for mixed-media content creation."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2sWFaz71y2Kt"
},
"source": [
"### Select the Gemini 2.0 image out model\n",
"\n",
"This model is specifically designed for generating and editing images conversationally."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bqlECK8gvtaC"
},
"outputs": [],
"source": [
"MODEL_ID = \"gemini-2.0-flash-preview-image-generation\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5qmjLxL1SK2C"
},
"source": [
"### Gemini 2.0 Flash image generation prompt (with interleaved text)\n",
"\n",
"This example demonstrates how Gemini 2.0 Flash can generate both text and images in an interleaved fashion, providing a rich, conversational output that combines instructions with visual aids. You must explicitly set response_modalities to `['Text', 'Image']` to enable this feature."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-K7EQOHZSLmQ"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 459 ms, sys: 53.7 ms, total: 513 ms\n",
"Wall time: 19.6 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"contents = \"\"\"\n",
" Show me how to cook a Brazilian cuscuz with coconut milk and grated coconut.\n",
" Include detailed step by step guidance with images.\n",
"\"\"\"\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=contents,\n",
" config=types.GenerateContentConfig(\n",
" response_modalities=['Text', 'Image']\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q68ZhhSAilsd"
},
"source": [
"The output `response.candidates.content.parts` can contain both text and inline image data, which are then displayed accordingly."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yesEuvCBSZT3"
},
"outputs": [],
"source": [
"for part in response.candidates[0].content.parts:\n",
" if part.text is not None:\n",
" display(Markdown(part.text))\n",
" elif part.inline_data is not None:\n",
" mime = part.inline_data.mime_type\n",
" print(mime)\n",
" data = part.inline_data.data\n",
" display(Image(data=data, width=512, height=512))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z6lgW6NxzDvV"
},
"source": [
"### Gemini 2.0 Flash image generation prompt\n",
"\n",
"This example focuses on generating an image based on a descriptive text prompt, similar to Imagen 3, but utilizing the Gemini 2.0 Flash model's capabilities for image generation within a text-based generation flow."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qaZt1ZImy7Ok"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 112 ms, sys: 6.52 ms, total: 118 ms\n",
"Wall time: 3.5 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"prompt = \"\"\"\n",
" Dynamic anime illustration: A happy Brazilian man with short grey hair and a\n",
" grey beard, mid-presentation at a tech conference. He's wearing a fun blue\n",
" short-sleeve shirt covered in mini avocado prints. Capture a funny, energetic\n",
" moment where he's clearly enjoying himself, perhaps with an exaggerated joyful\n",
" expression or a humorous gesture, stage background visible.\n",
"\"\"\"\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=prompt,\n",
" config=types.GenerateContentConfig(\n",
" response_modalities=['Text', 'Image']\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "06FBGz9Fi0jx"
},
"source": [
"The generated content is then processed to display the image parts."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zGH5LQnUy7Lu"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"image/png\n"
]
},
{
"data": {
"image/jpeg": "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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for part in response.candidates[0].content.parts:\n",
" if part.inline_data is not None and part.inline_data.mime_type is not None:\n",
" mime = part.inline_data.mime_type\n",
" print(mime)\n",
" data = part.inline_data.data\n",
" display(Image(data=data))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7qYG5fCa11AN"
},
"source": [
"#### Saving the generated image\n",
"\n",
"The generated image data can be extracted from the response and saved locally, typically as a PNG file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "g_rpT08c12_i"
},
"outputs": [],
"source": [
"import pathlib\n",
"\n",
"for part in response.candidates[0].content.parts:\n",
" if part.text is not None:\n",
" continue\n",
" elif part.inline_data is not None:\n",
" mime = part.inline_data.mime_type\n",
" data = part.inline_data.data\n",
" pathlib.Path(\"gemini_imgout.png\").write_bytes(data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4rtoDIhg1rhT"
},
"source": [
"### Editing images with Gemini 2.0 Flash image out\n",
"\n",
"Gemini 2.0 Flash also supports image editing. You can provide an image as input along with a text prompt describing the desired modifications. This allows for conversational image manipulation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DUJiVQ_5y7In"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 678 ms, sys: 15.6 ms, total: 694 ms\n",
"Wall time: 5.79 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"import PIL\n",
"\n",
"prompt = \"\"\"\n",
" make the image background in full white and add a wireless presentation\n",
" clicker on the hand of the person\n",
"\"\"\"\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=[\n",
" prompt,\n",
" PIL.Image.open('gemini_imgout.png')\n",
" ],\n",
" config=types.GenerateContentConfig(\n",
" response_modalities=['Text', 'Image']\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CgGitdLikVGs"
},
"source": [
"The edited image and any accompanying text are then displayed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "31yDZp_Wy7GD"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"image/png\n"
]
},
{
"data": {
"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAQABAADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKr3d/Z2EXm3l3BbR/wB+aQIPzNcnqPxZ8DaWWE3iG1kYfw2+6b/0AEUAdpRXkV9+0T4St8i0tNSuz6iJUH6tn9K5+7/aWTkWfhlj6Ga7x+gWgD32ivmm5/aQ8QvkW2jabCP9su5/mKypv2gvG0h+Q6dF/u2xP82NAH1XRXyS/wAdvHr9NSt0/wB20j/qKiPxw8fn/mMp/wCAsX/xNAH13RXyKPjj4/B/5C8R+trF/wDE1Mnx48eL1v7V/wDetE/oKAPrSivlaH9oPxrGfnGmS/71sR/JhWlb/tIeIkx9o0fTJf8Ac3p/7MaAPpeivn+0/aW5AvPDP1MN3/QrW/Y/tF+FZyBd2Gp2p9diOP0bP6UAew0Vwmn/ABj8CajgLrscDHtcxvH+pGP1rrbDWdL1VA+najaXa4zmCZX/AJGgC9RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAYooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKyNe8UaJ4YtPtOs6lBaR4yodvmf/dUct+ArxrxN+0bGpeDwzpZfsLq94H1CA5/Mj6UAe9sQqlmIAAySe1cXr/xX8G+HSyXOsRXE69YLP8AfNn0yOB+JFfLmveOfFXi2XZqWq3Vwjni2jOyP6bFwD/Or2i/C7xVrQV1082kB/5a3Z8sfl94/lQB6Trf7STEsmg6EAO017Jn/wAcX/4qvOtY+L/jjWSwk1uW1jP/ACzs1EIH4j5vzNdzpXwL0+Ha+r6rNcN3jtlCL/30ck/kK7fS/AXhXSAptdFtmkH/AC0nHmt/49n9KfKxXPmuGx13xDcb47fUNRlY8vteU/iea6Sx+EnjG+AZtOS1U97iZV/TJP6V9JJiNQiKEUdFUYApSc1XKK54bZ/AjU3AN7rVnD6iKNpD+uK3Lb4FaQmPtWtXsp7+XGqfzzXqtJRZCuzgYPgz4QiA8xL6c/7dxj+QFX4vhX4KiHGjl/8AfuJD/Wuwop2QXOYX4c+DU6aBbH/eZz/WpB8PvB//AEL1l+R/xro6UAmiyC5zh+H3g8/8y9Z/kf8AGoZPhx4NbroFuPo7j+tN8Q/Enw14cLRS3Zu7peDBa4cg+56D88157e/GLxHq8xg8PaKkeenyNPJ+Q4/SloPU7mT4UeC5v+YU8Z/2LiQf1qjP8E/Csw/df2hCf9icH+amuJNt8XNc5d76BG7F0twPwGDTT8OviPN80mpPn/a1FiaXyA6S4+A2ltn7PrN5F6ebEr/yxWLefAbVIwTZazZz+glRoz+m6q0fgv4oaad9tfXLEdo9Qz+hNaNp46+IHheRV8Q6TPd2g+80kO1sezqMfnmjQDlb74R+MbLJXTVuVHe3mVv0yD+lc3daNreiy7ruwvrN1PDvEyY+hr6j8O+JdP8AFGlrf6dKSudskbcPG3oR/kGtQsWG08g9iOKLBc+Y9I+J3jPRNotNfu2jXpHcN5y/TD5x+FegaL+0dq1vtTWtHtbtRwZLZzE31wcg/pXoOpeDfDOsAm90S0dz1kjTy3/NcGuJ1j4H6Nchn0nUbmzfskwEqf0P6mlYdzvtB+N3gvWyscl8+nTt/BepsGf98ZX8yK7+3uoLuFZ7eaOaJxlZI2DKfoRXx9rfwn8VaPukSzW/gH/LS0becf7vDfpXP6T4g1/wreM2m6heafMp+eNWKgn/AGkPB/EUhn3NS185eGP2ir+3KQeJdOS6j6G5tMJJ9Sh+U/hiva/DPjrw54uiDaRqcUsuMtbudkq/VTz+IyKAOiooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAopGZUUsxAUDJJPAFeN+Pvjzp2jGXT/AAyseo3wyrXROYIz7Y++fpx7npQB6hr3iPSPDOntfaxfRWkA6Fz8zn0VRyx9hXgnjL9oPUb4yWnha3+w2/I+1zqGmb3VeQv6n6V5fNP4l8f68Xka71TUJPxCD+SL+Qr1Lwr8FbW22XXiSf7RL1+yQNhB/vN1P4Y+pppXFc8pttP8Q+MtUd4o7zU7xzmSVyXI/wB5jwB9TXpPh/4Hsdk3iG/29/s1ryfxc/0H417FaWlrp9qlrY20VtAn3Y4kCgflUtUoiuZOieF9D8OxhdL02CBgMGXbukP1Y81sFietNpaZIUlLSUAFFFFABTZHSGJpZXWONBlnc4AHua57xZ430nwhab7yTzbpxmK1jPzv7n0Huf1ryiM+Mfi3fEbvsmkI3PVYU/q7f54ouOx2PiP4xaRpsjWujQtql1naGU7Ygfr1b8B+Nbngm98VanbT3/iSCG0jlx9mtkj2so7lskn04PNSeF/h9oXhRVkt4ftF7jm6mALZ/wBkdF/D8zWp4g12y8N6NNqeoPiKMYVR96Ruyr7n/wCvR6h6Emt6/pnhzTW1DVLgRQrwo6tIfRR3NeOah4s8W/EvUH0zQIJLTT+jJG235fWV/wCg/WqmmaXrfxd8TSahqEjW+lQNtJX7sa/880z1b1P4nsK9y0jSbDQtPjsNNt0gt07L1Y+pPUn3NLcexwnhz4NaNpqpNrMh1G56mMZWJT9Orfj+Veh2tpa6fAILK2htoh0SJAo/SpiaSnYQdaKKKBBS9RjtSUUARQWlravI9vbQwtKcyGNAu8+px1qakpaBhRRRQIUHHSsvWfDei+IYimq6bBcnGA7Lhx9GHI/OtOokureS4a3S4iadPvRBwWH1HUUDPIvEHwNVt83h2/IPX7Nd/wAg4/qPxrzDVNC1vwveqt/aXNlMrZjkGQCR3VhwfwNfWfIqK5t7e+t3try3iuIH4aOVQyn8DScR3PEvCHx38Q6HsttZH9r2Q43SNtmUez/xf8Cz9RXuPh74p+D/ABJCrW2sQW8xGTb3jCF1/Pg/gTXlXiv4MWN6HufDs32Ofr9mlJMbfQ9V/UfSvF9W0TUtCvms9TtJLacdnHDD1B6Ee4qWh3Ps+48deE7RsT+JNKQ+n2tD/I02Dx94QuXCReJtKZj0H2pB/M18TJFJNIscSM7twFUZJ/CpbiwvbRd1zaTwg95Iyv8AMUhn3jBcwXUQlt5o5oz0eNgwP4ipa+EdJ13VdCuRcaVqNzZyg9YZCufqOh/GvafBH7QcySR2Xi6EOh4F/bpgj3dB1+q/kaAPoWiq1hf2mqWMV7YXMVzbTLujlibcrD61ZoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooEFFFFAwrJ8ReJdJ8K6U+o6xdpbwLwoPLSN/dUdSaxfH3xE0nwFpolum8+/lB+z2aN8z+5/ur7/AJZr5U17xF4g+IXiNZrsyXV3K2y3togdsYP8KL2Hv+JNAHS/EL4v6x40eSytC+n6NnAt0b55h6yEdf8AdHH160vgv4Sajryx3+rmSw044ZVIxLMPYH7o9z+ANdz4C+FVpoIi1LW0jutS4ZIfvRwH/wBmb36Dt616WWLVSj3JbMzRtC03w/ZCz0u0jtoR12j5nPqx6k/WtClpKskWiioL2+tNNtHur25it4E+9JKwUCkBPRTY5EmiSWNtyOoZT6g8iloAWkoooAK4P4hfEWDwpAbCwKT6vIv3TysAPdvf0H51b+IfjePwfpIjtyr6rcqRAh58sd3I/l6n6GuM+G/gGTWJx4o8RhplkfzYIpuTM2f9Y2eo9B369KTfRDSKfg34b33iq6/4SHxXLMYJj5ixOSJLj3P91f1PbAr2q2t4LO3jtrWFIYI12pHGoCqPQAVM1J0ppWBsXcqqXZgqqMknoBXguu3t58VvH0Wl2DsmlWzEI2OFQfelPue34D1ru/i34kOjeE/sMD7bnUiYhg8iMffP45A/E1L8KfDK6H4TjvZkxe6iBKxI5WP+Bfy5/H2pPV2Gu51ul6ZaaLpkGnWEIitoV2qo7+pPqT1Jq51oxUUVzDNJLHFKjvC22QKc7DjOD6GmSTUUUUAFFFFABRRUN3e2mn25uL25htoQceZM4VfzNAE1LTEkSWNZI2DowDKynII9RTqAFooooAK4Xxz4CbWpRreiStaa7AAVdG2+djoCezeh/A11q6zpra4dGF2n9oiLzTBznb6+n4VfHBo3GeceA/iNJql3/YHiJPs2sRnYrsNomI6gjs/869GIxxXmXxa8HC8sT4m0xTHqFnhp/L4LoP4vqvr6fSt74d+Lf+Es8MpLOwN/bERXI/vHs34j9c0l2A63rVHWdC0zxFp7WOqWqTwn7pPDIfVT1Bq9SimB87eKPB2r/DbXLbWdOlaexjlDw3OOUP8AckA9emeh/Svc9C12z8V+HrbUYkVobhMSROA21hwykexrRubaC8tpLa5hSaCVdrxyDKsPQis7QvDmmeG7WW10qF4oJJTKUaQsAxAHGeg4FJKwXOW8TfCPQNcV5rBBpd6eQ0I/dMfdO34Y/GvD/EnhLV/Cl99m1S2Kq2fLmTmOUeqn+nWvq8VBqel2GuabLp2pWyT20o5VuoPqD2I9aGhpnzj8PviPqvgTUg0LNcaZI3+kWbNw3+0v91vfv3r620DXtO8S6NBqul3AmtZhkHoVPdWHYjuK+QvHnga78F6t5ZLTafOSba4x1H91vRh+vWtL4WfEObwNr4S4dm0e7YLdR9dh6CQD1Hf1H4VBR9e0VHDNFcwRzQyLJFIodHU5DAjIIqSgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooEFFFFABXA/Ev4mWPgPTfLjCXOsTqfs9tnhR/ff0X+f5kWPiR8Q7LwFonmnbNqdwCtpbE9T/AHm9FH69Pp8pxR65488UnLSXup3sm53Y8Adyf7qgfgBxQMVV17x74nYlpb/U7t8s7HgD1PZVA/ACvoPwT4D0/wAG2QKhbjU5FxNdEf8Ajqei/qe9WfBvg3T/AAbpX2e2AlvJADc3RHMh9B6KOwroqtIhsKKKKoQUUUUAU9U1O10bTLjUL6QR28Cbnb+g9SeleVeG4r/4o+KH1zVlZNBsJMW9oT8rv2B9exY/QdKh+KusXPiHxPY+DdNbcFkUSgdGlbpn2UHP4n0r1nRNItdA0W10u0UCK3QLnHLHux9ycmp3HsXqQ0popiEqnquqW2i6Vc6ldttgt4y7ep9APcnA/GrdeP8Axv8AEDCOx8O27HdJi4nC9+yL+eT+VD0BamB4T0u7+Jvjq51fVgTYwuJJh/Dj+CIe3H5A9zXv6gKqqihVUYCgYAHpXPeBvDieGPCdpYbQLhl825b1kbr+XA/Cui70JDYUhpaUEDk9ByaYjwfxuW8XfGC00RSTBDJHa4HYfekP6n8q93CLGqoihUUBVA7AdK8L+FynWfinf6pJ8xRZ7jPuzYH6Ma9171K7jZzfj3xEfDHhG5vYmAunxDb57O3f8Bk/hUXw90p9M8F2HnlmuboG6nZjliz88/hiuK+NFw95rHh7REPDkyMPUswQfyP5168I1hjSJBhUUKB7AYo6h0EooopiCiiigCG8u4NPsZ7y6kEdvAhkkc9gK8RtItR+L3jJ57lpINDs2+6Dwi9lH+23c9vwFbPxn8RSbbPwxZsTLcESzqvUjOEX8Tz+Arv/AAf4dj8L+GLTTUUedt8y4YfxSHr+XT6Clux7GzBbw2ttFbW6COGJAiIP4VAwBUlFFMQUCigcmgDyRZif2jCFPSMof+/FeuZ5rxfwxL/afx/1W5XlYTPz7KBHXs9JDYOiSxtHIoZHUqykcEHtXhng4t4K+L95oLMRa3TNCoPcH5oz/T8a9zrxP4vxHSfHmia3F8pdEYkd2jf/AAIoYI9rFLmmqQ6q6/dYAj8aUVQhetcR4c8etfeKb3wzrVtHZ6nDIywlCdkwHTGehxyPUV3AFeQ/GbSHsL3S/FViTFOkgikdezL8yN+hH4Ckxo9fxigGs7w/q0ev+HrDVYwALmIOyj+FujD8CDWjQBneIdDs/E2hXGlXy/u5RlHxzG46MPcf4ivlLW9Ju9C1i60y9TbPbuVPow7EexGD+NfXua8n+NfhcXmlw+I7aP8Af2uIrnA+9GTwfwJx+PtSaGmbX7P/AI5N/p8nhS+lzcWi+ZZsx5aLun/ASePY+1e4V8K+GtdufDXiOw1i1J821lD7c/eXoy/QjI/GvuDTdQt9W0y11C0ffb3MSyxt6qwyKgotUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFYXi/xVYeDfDtxq+oNlYxtiiB+aWQ9EHuf0AJ7VtSSJFG8kjqkaAszMcAAdSTXyD8VvHsnjjxM32d2Gk2ZMdpH2b1kI9Wx+Ax70AYGsatrPj7xY11OHub+8kCRQp0Qfwoo7Af4k96+gvAngq18G6R5fyy6jOAbq4Hc/3F/2R+vWsL4U+AxoGmrrWoxY1O7T92jDmCM/yY9/bj1r0irSIbClpKWqEFJRRQAVDd3UdjZXF3L/AKuCNpW+ijP9Kmrn/HUjR+AtddDg/ZHH58f1pAeXfCG1k13xvqniG7G94Qzgn/npIT/Ibq9xryv4FQqvhvVJf4nuwp+gQY/ma9UoWw3uFJS0lAgAycV4HEv/AAl3x3beN9vDdk4PTZCOPzKj8699Bxz6c14X8G4xc+PdWun5dbeRs+7SLmkxo9zzzRRiiqEFQ3zFNNvHHVYJCP8Avk1PTJovPtpof+ekbJ+YxSA8X+BKA6xrL/xC3QD8W/8ArV7X0NeHfBOb7L4t1Wxfh2tjwfVHH+Ne3k80o7De54z8RZA3xk0BX+6Ps3/o017Ux+Y/WvDvjIG0/wAa6HqoBwIlOfeOTP8AUV7ZHIs0SSoco6hlPqDzQtwH0UUlMQUFlRSzkBVGST2Aorm/iDqR0rwHq9yrbXaHyUPu5C/1NAzy3wch8cfF+61mZd1tbO1wAegC/LGP5H8K93Jya8u+B2mC38MXuosvz3dxsB/2UH+LH8q9QNC2Bi0tJRQIDUN1cpZWc91IcRwRtIxPooz/AEqauM+KmqjS/AF+A2JbvbbJ77j83/joagDifglA97r2u6zKPmKhN3+07Fj/AOg17Sa8++DemGw8CrcsuHvZ3l5/uj5R/I/nXoFC2Gxa8k+O8QOnaJNjkSyp+YU/0r1sV5R8dpANH0WPuZ5D+Sj/ABoewLc9J0SU3Hh3S5j1e0iY/igq8KzvDalPCejqeosof/QBWiOtADq5j4j6eupfD3V4yMtFGJ19ihB/lmumFUddQTeHNUibo9nKD/3waAOE+COom58IXVk7ZNpdHaPRXAP8w1ekk5rxb4DXDefrkHYpE+PoWH9a9nzRHYGLUN5ZwajY3FjcoHguI2iceoIxU1OUZNMR8ga1pU2ia3e6ZP8A6y2laMn1APB/EYNfSX7PviM6n4On0aZ8z6ZLhATz5T5I/I7h+Vec/HLQxa67Za1EmEvYvLlIH/LRP8VI/Ks74I68dF+JFpC77YNRVrVxnjJ5T/x4AfjWbLPrWiiikMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooqrqWoW+laZdahduEt7aJpZGPZVGTQI8h+Pnjk6To6eGLGXF3fruuip5SHP3f+BEfkD615l8I/Bg1/Wm1a+i3adYMCFYcSy9QvuB1P4etcpq+paj478azXZUveajchYo852gnCr9AMD8K+m/DuiW3hvQLTSbUArAnzvj/WOfvN+JqooTZqMcmm0tFWSFJRRQAUUUUAFZniOyOpeGNVslGWmtZFUep2nH61p0A4NIDyL4FXqm11nTif3iyJOB7YKn+Qr12vCbh2+GnxcNwyldMu2LcdDE55/wC+T/Kvdg6SIskbBkYBlYHIIPekhsKKSlqhCjk4rw74T/8AEt+JmsadJwxjmjAPcrID/IGvcK8P8Vg+CvjJa61graXTrMxHTa3yyD+Z/EVLGj3CikDK6hkIZWGQR0IpaYgpQcGkpM0wPBoyPB/x2YP8ltcXJGe2yYcfkT+le8kc15L8a/Drz2dp4itlPmWuIZyvUKTlW/A8fiK7jwN4jTxT4UtL7cDcoPKuV7iQdT+PX8alb2GzmfjPorah4Riv41zJp825sf3G4P67a2/hprQ1vwLYOWzNbL9ml9crwPzXFdPeWUOo2NxZXK7obiNo3HsRivFPAV/N4D8f3nhnUn221zJ5QZuAH/5Zv9CDj8R6UbMOh7lSUppKoQtebfG25MXgu2twf+Pi8UH6KrH+eK9JHNeUfHdyuk6LH2M8jfkq/wCNJ7DW51/w5sxZfD3RowMb4TKfqzFv611FZvheMReD9GXgBbGHnp/AKfc6vZwHHmeY3pGM/r0ppN7CZforAl8QyHiG3A93Of5VTfWb9+koQeiqKtU5C5kdZjNeI/GTVX1bxNp3hyzO8wY3KO8smMD8Bj867TU9dfTdMuL+7uZDHAhYjdjcew/E4FeX/D+1uNd8aS69dHd9mc3DMRkGUk7R+HX8KmUGnYafU+gNL0yPSNHstOi+5awrF9cDk/nzVkiue/tq+zkyr/3wKX+3bsdRE31Wq9mxcyOgrxX473JbUNEs16rFJIR/vMAP/Qa9OTxBJ/HbofoxFeL+O9QHiP4r2luFIjieC229e4Lf+hGolFpaji9T36xi+z6baQYx5UCJ+SgVPVJdXspGIEwXnowxVuORJFyjqw/2Tmm00Fx1ZuvyiHw5qsp4CWcx/wDHDWjmuV+JV8unfD3V5CcNNELdfcuQP5ZpAfP/AIS1LxJp91cr4aE32iaMLKYog5C568ggc966YRfFNz5v23Ud3XBu1H6bqufCazKafqV6Qf3kiQqf90En/wBCFeijrVwpJxuwlKzPNLP4meNfC94kGuwPdRHql1HsYj/ZcDn9a9p8LeJ9M8WaWL3TpDlcLNC/34m9D/j3rnb7TbPWLF7G/hEsEg79VPqD2NeVaBfXXw2+IywTyE2hcRTHoJIW6N+HB+oIqZwcRp3PXfizpA1X4fXjhcy2TLcp9Bw3/jpP5V822N3Lp+oW17AcS28qyofRlII/lX2FfWseoaddWb4aO4heI+4YEf1r44uYntrmW3kGHicow9wcGs5FI+79NvotT0u0v4DmK5hSZD7MAR/OrVeffBTVf7V+F2mbmzJaF7Vv+At8v/jpWvQakYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXjf7Qvic6b4WttBgfE+pSbpQDyIkIP6tt/I17JXx/8AGTXj4g+JGolH3QWRFnFzx8n3v/Hi1AjY+CXh4XWrXWvTpmOzHlQEj/low5P4L/6FXugrnPAmhDw/4L06yZNszx+fN6735P5DA/CujxWqVkQxaKKWgApKKKACiiigAooopAch8RPCC+LfDrRwqo1C2zJbMe57ofY/zArkfhT45KbfCmtOYriFilq8vB4/5ZnPcdvy9K9drzX4j/DQ66za3oaiPVUG6SJTjz8dx6P/ADpPuNdj0rHNFeSeB/imY2XRPFZaC4jPlpdyDHI42yDsff8AP1r1tWWRFdGVkYZVlOQR6g007hYK4z4neFj4l8LM9um6+sszQgDllx8y/iOfqBXadKTNAHnHwm8YJrGjLol3JjULFcJuPMkQ6fiOn0xXo9eJ/EXwhe+GdZXxd4c3xRCTzJliH+pfu2P7h7+mfQ13fgfx7ZeMLEISsGpxr++t89f9pfUfypLswfc7Cg0daWmIgvLO31Cyns7qMSQToY5FPcGvD9Fu7r4UfECbTb4u2k3RALkcMhPyyD3HIP4+1e71zHjnwfB4w0Q2/wAqX0OXtZT2P90+x/wNJjR1CMsiLJGwdHAZWByCD3rzf4u+Dm1fTF16wjJvbJf3yqOXiHOfqvX6Z9Kyvhj40m0y6PhDxCWhmicx2zy8FGz/AKs/0/L0r2H2IyMYNG6DY4L4aeNE8U6KLW6kH9q2ahZQTzKnQP8A0Pv9a7g14h488MXvgHxDF4q8O5js2kyyqOIWPVSP7jf/AFvSvTfCnjLTfFWii+hlSKVABcwM3MTf1Hof60J9AaOiBxXkPx2lV4NEiByyvKW9shcV3994gHMdkM+srDj8BXlvxTV5dEs7hiWZbkhmPup/wrRwfK2xJ6nbaPfS3nhfSN8hMYs4gFHA4UD+lTEVh+Cp/tHgvTG67EZD+DEVu1vDZEPcZRn86UiuS8beLE8P2RtbZwdSnX5QOfKU/wAR9/T86qUlFXYkr6HL/EXX21HUY9BsSXSJx5uznfL0C/h/M+1d/wCFNEXw9oEFmcGdv3k7Du57fQdPwrxfw/raaHqv9oyWS3kyg+WJHICsf4unJrppfitrUrfuLKyjz22s39a5ozV3Jmri7WR64Dk0u0+lePr498ZXHMNuP+2dmT/jUq+JviDMMpbXv/AbD/7Gm68EJUpM9aKhVLtwqjJJ7CvIfB0R134jy6gwJRHluT/Jf5io7vUviBe28kE8GqmKRSrqtoVyD1HC1lad/wAJN4eneeztL+1dhtcm2bBHociolWjJopU5JHu2zFIHZGyrFT6g4ryJfib4ktvluY7eTH/PSAqf0xVuH4s3PH2jSoW90kK/zBrZVoMjkaPXoNXu4Ty/mL6P/jXmvxj8VG/FhokY2iP/AEicA5+Y8KPyyfxqA/Fm18k7dJl83Hygyjbn8q5jw5ZXXjDxl9qvPnQP9ouWxwFHRfx4AHp9KibjLSO44prVnqXhPTP7H8L2Nqy4lKebL/vNz+gwPwrbHWm5yc08V0JWVjPcepxXnvxa0wSWNjq6L88TeRIfVTyv6g/nXoINYvi+z/tHwjqdvjLCEyr9V+b+lRNXiOLszqvh/rP9teBtKu2bdKsXkSE9dyfL/IA/jXzz8QrD+zvH2swAYU3BlX6P83/s1enfAnUDLouqaczZME6zKPQMMH9VrlvjfZfZ/G8NyBgXNojfUqSv8gK5XsbLc7/9mvUt+k65phb/AFU8c6j/AHlKn/0AV7rXzD+zpfGDx1fWZPy3Nixx7qykfoTX09UDCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAzfEGqpofh3UdUcjFpbPNz3IUkD8TgV8beEdPfxH44061mJk+0XQkmJ7qDubP4A19G/HnVTp3wzuIFbD31xHb/AIZ3n9Ex+NeQ/AzTxP4ovtQYZFpbbVPoznH8g1NbiZ72+CxwOOwptFFaEBRRRQAUUUUAFFFFABSUpKqpZmCqoyzE4AHrXkXjH4yC3mk0/wAMIkrqdrXsgyuf9he/1P5Um7DSuer3FzBaQma5njgiHV5XCgfia5m9+J/g/TmKvrCTsO1sjSfqBj9a8lsvAPjrxzMt9qUk0cT8ibUJCvH+ynXH4AV2emfAbS4Qranq91cP3WBFjX8zk1hPEQj1NoYecuhznjnxb4F8VAypY36X4GFu4o1Ut/vAt8w/X3rmfC/xD1zwmwitZ/tNjnm1uOVx7d1P0r2uH4Q+DIQAdPnlPrJcv/QinyfCbwXIuP7KZfdbiTP86yeMgbLBzMnRvjP4b1FVXUFn02Y9d670z/vDn8xXZ2GvaPqyg6fqlpcZ7Rygn8utcRqPwN8O3IJsry9tG7AsJF/IgH9a5K/+BeuWzF9N1K0ucdA+6Jv6j9auOKpvqRLCVF0Pc3i3oyOgZGGGUjIIrx/xh8LbzS73+3/B7SRyRt5htYyQ6H1j9R/s/lnpXProvxT8NDEA1Ty16eTN5y/kCf5VInxW8d6Qdl/CrkdRdWmwn8sVsqkJbMxdOcd0dd4O+LlteFNO8ShbK+B2C5I2xuf9ofwn9PpXpysrqrowZGGQynIIr5j8T+M7XxWPOvNCt7e//wCfm2kKlv8AeBB3fz96j8L/ABC1/wAKkR2lyJ7Tva3HzJ+HdfwpqRNj6ipyCvHYPj1D5Q+0eH2Enfy7jj9RUcvxqtLw7Zra7gQ/wx7SP55NUmn1E0zofib4Gg8QwHVdNaNNZgX5o1IBnUdv94dj36elZfgH4pgxponiRzHdR/u4rqTgN22v6N79+/NVrf4leGmxumuIj/tQn+max/EaeEPFQa6ttXt7TUcffYFVl/3gR19+v1qnBbxYlJ7NHsV9dQXttNaXlsktrMhSRDyGBrwTxDoOp/D7XV1DS5XewkY+VL1GD/yzkH+c9etTaB481Twu40/UNuoWK8KVkDMg/wBlucj2P6V3A8deDtXsnt7u5CQyriSC5ib+gI/Wj3X5MNUReG/E9j4itgYiIrtRmS2Y8j3X1H+TVb4gW5uPBl0QMmF0k/XB/nXnXiLT9N0nUBeeHtajuId25AjlZYj+mR79avRfEK6uNEutM1a3W5E0TRidPlcEjgkdDz9Kr2l04yDl1ujsfhfd+f4VlgJ5guGH4MAf8a7QDJrxjwF4qs/Ds15FqHmC3nVSGRdxDDPb3BrY1r4ozTIbbQrVoS/HnygF/wDgK9B+OacKkVHUTi2zq/F3i+18NW5gi2z6m6/JF1Ef+03+HeuL8I/D3WPHt7JquoTyW9i7kyXTjLSnuEH6Z6D36V0Xgb4T3erXC6z4qEqQMd620hPmzH1fuB+p9q9vjiighSGGNI4o1CoiDCqB0AFedicY3pE78PheszkNK+GPhLSkUJpMdy46yXRMhP4Hj9K6W20vT7QAW1hawgf884VX+Qq3iivOcpPdnoKMVsgHHTinb29TTaKQxd7f3jRvb1NJRQFiOa3guFKzQRSA9nQH+dZF34M8M34P2nQdPcnuIFU/mMGtuimm1sJxT3Pnv4o/Di38Mxx6vo6uNOdtksLEt5LHoQTztPTnofrWv8NHs5fCxFvCkc6Slbgjq56gn8D+hr1vxDo8eu+HdQ0yQAi4hZVz2bGVP4HBr5++GF61l4iudMl+UXEZG09nTn+W6vVwNZvRnmYykou6PWAMUtKeDSZr1DgFzSFFlVo2GVdSpHsaKB2oEed/BmVtO8fajpjnHmQSIR/tIw/pmtb492X7vQ70DoZYSf8Avkj+tYmhH+yfjwi9FmumX8JEP9WrufjpbCXwTazY5hvV/Iqw/wAK4rWTRuu55x8Err7L8VtJGcCYSxH8Y2/qBX17XxX8NZ/s3xL8PSZx/psan/gR2/1r7UqCgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPA/2lb/FvoGng/eeadh9Aqj+bVQ+BcCx6DqU5wHmuQo91Vf8AFjVD9o64L+M9Lt88R2G7Huzt/gKvfDhXs/BdhNGdsjySSZ/4ER/IVpTjzMmTsj1nFFULDV4btQkmI5vQ9G+laB4qmmtybiUUUUgCiiigAooooA8w+NPiKfTdCtdItXKPqBYzMDg+WuPl/En9Ku/DH4c2ei6ZbazqcCzapOoljWQZFup5GB/e9T26Csz43aJPd6Xp+sQoXWzLRTAfwqxGD9MjH4iu88EeJIPE/hSzvY2HnRoIrhB1SRRg/geo+tcWLlJR0OzCRi5anREk96Q0UleaemFFFFAwooooAXNNdVkUq6qynswyKWigDJuvDGg3uftOjWEpPUtbrn88VlTfDXwdPndoNuuf+ebMv8jXV0VSnJbMlwi90cHP8HfBk2dtjcRf9c7hv65rMuPgX4akz5N7qMP/AANG/mten0VSrVF1IdGm+h4/N8A7A/6nXrhf9+3Vv5EVVb4BSD/V+Ik/4Fan/wCKr2qiq+sVO5P1en2PET8Bbztr9sfrbt/jTG+Al+f+Y9a/9+G/xr3Gin9ZqdxfVqfY8M/4UJff9B62/wC/Df404fAO8PXX7cfS3b/GvcKKPrNTuP6tS7HjVr8A4hIDd6+7J3WG3wfzLH+Vd/4c+H3hvwwyy2dl5t0vS5uDvcfTsPwArpqWolWnLRsqNGEdUhSc0hopKzNRaKSigBaKKKACiiigAooooAepwRXzD4jjPhf4sXTL8qRXwmH+453fyavpwHFfPvx0sPs/i60vlGFu7UAn1ZCQf0K11YSVp2OTFxvC56O5BORyDyKSsjw1fHUfDOm3RbLNAqsf9pflP8q1xX0C1VzxgpR1oo6UxHmXid/7O+Lml3Y4zLbSE/8AAgD/ACr0/wCMke/4b3p/553ELf8Aj2P615P8USYPEmmXK8EQAg/7rk16v8U5RN8LNQl7P5Dj8XWuOfxM3jsjwHwfIY/G+hP6ahB/6MFfcdfDXhBDJ4z0NR1N/B/6MWvuWsigooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoopKAClpKWgD5X/aEYv8SkX+5YRD/wAec/1rpfB6bPBWkD1hJ/NjXNftAjHxLB9bGI/q1dP4SOfBujn/AKYY/wDHjW9D4mZ1NjYJ7VbtdXurUhSfNjH8L9R9DVLvWV4h1uLQNGmvpAGcfLEh/ic9B/n0rpklbUz16HXv4u0W2aJL29jtJZDhEmOC30rcjkSVA6OGU9CpyDXhXgTRrjVbyTxTq7GWV2P2YN+RYD0HQV6LDcz2z7oZGU9x2P4ViqfMroq9tDsqMViWuu5IFzF/wJP8K0Y9X06S4jtlvYRcSDKQswDsPYHk1Di1uNO5axS0pppqRkdxbw3lrLa3MayQTIUkRhkMD1FeH/6d8H/HQYeZNoN8cH/aTP8A6GufxH1r3SsbxP4atPFWhz6ZdADcN0UmOY3HRh/npUzgpqzKhNxd0bdrcw3lrFc20qywTKHjdTkMp6Gpa8e+FviC70DWrjwPrZKOkjC1LHo3UqPY9R/9evYsV41Sm6crM9mlUVSN0JRRRWZqFFFFABRRWTrPifRPD6btU1O3t2xkRltzn6KMmmk3sJtLVmtRXk2r/HXS7csmk6ZPdt2knYRr+Qyf5Vys3xZ8ca05TTIEhB4AtbUuR+JzW0cNUfQwliacep9CAE9qbJJHEMySIg/2mAr5zktPibrXM9zqSqf+elx5Q/LI/lUQ+GXia6O67v7dSevmTs5/lWywUnuzF42PRH0NJq+lxcSalZJ/vXCD+tR/2/opOP7Y0/P/AF8p/jXgifCG8P8ArNXt1/3YmP8AhUw+ED451pP/AAHP/wAVVfUvMn675HvceqadMf3WoWj/AO7Op/rVkMGGVII9Qc188t8IbgDMeswk/wC1CR/Wo0+H/i/SW83TNWXK8jyLl4z+uBSeCfRjWNXVH0XSV4XovxO8T+FtUi0/xXDJcWpOGaRR5qj+8rDhh+f1r3KGeG5t47iB1khlUOjqeGUjINctSlKm9TqpVo1FoOooorM1CiiigAoopssscELzSsEjjUuzHsByTQA+jFeN678cwkzw6DpiyKDgT3RPze4Qf1P4Vzx+IPxK1T5rSOZEPTyLIY/Mg10Rw1RnNLFU4n0NijFfPS658WG+bzNR/wC/Kf4U7/hM/ilY8yx3cij/AJ6WSt/Jar6pUJ+uQPoKivCbX43eIbB1TVtItpfX5Ghb+o/Suy0T4y+GtVZYrsy6bMeP3w3J/wB9D+oFZyoVI9DSOIpy6nodeUfHawM3hzTr8DJt7kxk+gdf8VFepW9xDdwrNbyxzROMq8bBlI+orl/idp/2/wCHWrJjLRIs6/8AAWBP6ZqaLtUTLrK9No87+F139o8MTWxOTbXBAH+ywB/nmu3HFeW/CW7Carf2JP8AroRIo91P+DGvUW4NfR0neKPAmrMcDRjNIuScAE1S1HXdJ0iMtf38MRH8G7c5/wCAjmrbsSeefFxMXekt6xSD9R/jXUfE3xPaD4Z6RpUcyyXl/DBK6A8rGqg5P1OMeuDXnXjnxTF4n1GA20LR21spRC/3myeSR26DiuXZ5JXG9mdsBRk5OBwBXHN3k2jeKsjq/hhZNf8AxM8PwKu7F2spHsnzn/0GvtGvC/gV8OL3SJn8UazbtbzSRmOzgkGHVT95yO2RwB6E+1e6VmUFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFJS0lABRRRQB8x/tFQeX48sZsf6zT0/R3Fa3gmQSeCdKP91HX8nNJ+0raldS0C8A4eGWIn/dKn/2Y1R+G8/m+C4kzzDPIn54P9a2ofERPY6s15l8Q5ZdU8TaXokRO35cj/adsZ/AD9a9N615rqq4+MNlv6bosZ/3eP1rertYzjuekW0EdpbRWsKhYoUCIB2AFSYyaNuDSitCRy8V5t8Qna18aaJcqSuFRsjjpJXpIrzf4tRkDSbtf4S6E/kR/Wsqvwlw3PfWOTkd6b1qnpV2L/RbC7ByJ7eOT81Bq4KwKFpQaKKAPJ/jJ4fdIrTxXYAx3Nq6pOycHGfkb6g8fiK9A8G+Ik8U+FrTUxgTFfLnUfwyDr+fX8au6rpkWs6PeabMMx3MLRn2JHB/A815B8E9Sm0/XtW8O3BIJBkCns6Ha36H9K48XC8b9jswk7T5e57fSUnWnV5h6gVyHij4leHvC++Ga5+1Xq/8ALtbEMwP+0ei/jz7V5l41+JOteItXl0Pw95sFp5hhXyM+bcEcHkdB7Dt1p/h/4VRRBbrX5TLKefssTcD/AHm7/h+ZrtpYRy1kcVXF20iUtS+JXjPxhcNZ6JBJaQHjZaAl8f7Unb9Kdpnwrvbt/tGu6j5bMcskZ8xz9WPH869KtbS3sYFgtII4IV6JGoUVPXfClGCskcE6spu7Zgad4I8OaYAYtOSaQf8ALS4/eH8jx+lb6YiQJGqog6KowPyopK0sQOyTTTS5pKAEzS5oxRigAozRRQBynxG02PUPB9xOVBmsyJUbHIGQGH0wf0Fb/wAINVfUfAUEUjFns5Xgyf7vUfocfhVXxXtHg/WC3T7I/wCeOKp/AqNv+EV1Bjnabzj/AL4WuLGr3DrwT/eHqgpaSlrzD1QooooAjuCwt5Shw2w4PvivOvAGt3fiPwNq8N5M0tzCZYizHJIK8fzNek4yCD0NeO/CRzZeKfFGiSdpCwB/2XZT/MVcVeEn2sZylacV3uc18IoLWW71OWaCOSeJU8tnUEpknOM9O1erlyepryj4Zg2fjHWLBuCI3XHujgf416qa9qDujxZKzF3Uhc+ppM0lWSMnhiukKXEUcqHqsiBh+tctq/w60DU0ZoYDYzHo9ucDPup4/LFdZQKLAeUJaeL/AIa3BvNPuWn08HLlQWiI/wBtD936/rXqWgeN9M8f+HL/AE/C2+oPayJLbMeuVI3Ke45+oqyuCpVgGUjBBGQRXlfjfwtL4Xu4vEmgO9vCJPnWM/6hz0I/2T0x26dxXNVw8Zarc6KVeUNHscDpupXeiapHe2j7LiEnGRkHsQR3FdTN8VNekXCw2KN/eERJ/U1xRzJJ3LMfzNfQXhr9nSzewguPEWqXH2iRQzW1qAoTP8JYg5P0ArZTaWjMWkzxS/8AF+v6kCtxqc4Q/wAEZ2L+S4pmi+G9c8SXPlaVpt1euTyyISo+rdB+Jr6t0n4QeCNHZWi0SK4kX+O7Yy/o3H6V2tvbQ2sKw28McMS8KkahVH0AqW29wPnnw3+zpf3CpN4i1NLRTybe1HmP9Cx4H4Zr1vw18MPCfhVkmsNLSS6Xpc3J8yQH1GeF/ACuwopDExS0UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFJS0UAJRRRQB4z+0bYGbwbpt8Fyba92k+gdD/AFUV558KbgPpOpWueY5kkA9iMf0r3D4waZ/anwu1qMLl4Y1uF9tjBj+gNfO3wpuvL8RXNoTgXFs2B6spB/lmtKTtNEy2PWQOa84+Isb6d4l0nWoxx8oJ/wBpGz/I16QBg1heNNHOteGLiKNd08H76L1JHUfiM101FdGUdzeSRLiJJoyGSRQ6kdwRmlFcn8PNXGo+GUtnbM1k3lEHrt6qf6fhXV55qou6uJqzFrkPiVYm78IPMoy1rMsn4H5T/MV14pl3YpqWn3NjL9y4iaM+2R1omrxaBOzJfhZqI1P4fad82Xtt1u3/AAE8foRXZgV4z8F9TfS9b1TwvenZI7F41P8Az0Thh+I5/CvaSMVyJ6GrG0Ud6gvr600uxlvb6dLe2iGXkc4A/wDr0xFpOufSvBPDdzAPj/ctZSpJBNc3ADIcq2VYnB781J4g8a+IfiJqh0DwtBNDYtkNtO15F7tI38K+3556Vj+DdIuvC/xisdKvtnnwyMjFDlTujJBGfqKxrO8GbUdKkfU+jxTh1pgozXjHt2PBvh5ZRweP9eR0BlthKqe37zBP5fzr0w81w3irwf4p8PeMLvxP4aj+1Q3LF5IkG5l3cspT+IZGeOfpWbH8WZ7Z/J1fQnimH3tjFD/3yw/rXs0akZRVmeLWpyjJ3R6SaSuCX4uaK33rC+X6bD/Wn/8AC2NC/wCfS/8A++U/+KrbmRjZnc0tcC3xb0Ufd0+/P12D+tRN8XtNH3NKuz9ZFFHMgsz0OivNH+MEX/LLRWP+9P8A/Y1AfivqcxxbaFFn/edv5UuZDsz1Kl2nHSvL08beOr44stAPPQpZSN/OrKf8LZ1D/V2VxAD6wxxY/wC+ql1oLdlKlN7I9HCMegNVL/U9P0qIyX97Bbr/ANNHAJ+g6muKXwB8StU4vtX+zqeoe8P8kzV+w+BUbSCXWNclmP8AEtumM/8AAmz/ACrKWKprqaRw1R9DmvF/jf8A4SWNdA8P280y3DhXcKd0vPCqvXGfX0r2LwF4cbwt4StdOlwbk5lnI6b26j8BgfhUvh/wboXhhf8AiWWCJKRhp3O6Q/8AAj0+gwK3gK4K+I9rotj0MPhvZ6vccaSiiuc6QooopAFeNAf8I3+0AS3yQan+R8xf/ixXsteTfGqwktxo/iG2BEtrL5TMO3O5f1B/OtqO/L3Mq2kebscfqN0PBHxd1C4uoJDbvK7EKOTHJzkeuCf0ru4PG3hm7QMmrwx5/hmBQj8xXU3OjaB4+0KxvtQsUnE0KvHKpKumRkgMOevbpXKXPwN8OyuTDf6jCD/DuRgP/Ha66eLjGNpHHUwkpScoloeI9Cb7utWB/wC26/40v/CQ6H31mw/7/r/jWX/wobSO2tX3/ftKevwI0cddYvj9EStfrlMy+p1C+3ibw8n3tbsfwlz/ACqCXxr4Yh+9rELf7is38hSJ8DPD6/f1HUW/FB/7LVuL4K+FExva/k/3pwP5LS+uwGsHUMaf4meF4AQlxczH0SAj+eK57xB8UdM1LRb3TbfTLh1uYym+ZgoB7HAz0OD+FejRfCXwbCc/2Y8n/XS4c/1rXtfA/heyx5Gg2II7vEHP/j2ah46PRFrAz6s+VLaUQXcMpGQjqxH0Oa+9badLm2imjOUkQOp9QRmvi74iaEnh/wAbX9pDGEtpGE8KgcBW5wPYHI/Cvqb4W6r/AGz8NdDui251txA/+9GSn/stdEWpK6OWUXFtM7CiiimIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKSlooAq6jZpqOmXVjL/q7iF4m+jAg/zr4s8OTv4f8bWhm+U2915MoPYZKt/M19uV8e/F3RzofxN1ZFXbHcSC7jPqHGT/AOPbh+FNOzA9elTZIy+hpgbBqhoepjV/D2n34ILSwgSf768N+oq51Ndy1VzA89lhPgzxyl2gK6RqRKOe0bHt+B5+hNehiqWqaZbavp0tldJujkHUdVPYj3FLpEF1baZBb3kiyTRLsMi/xgcAn3xjNEVy+gm7l9asxDkVAop0d7aLqMdg06C6dDIsWedo70Ngef8AxE0m70DXrLxhpYK5kUykD7sg6E+zAY/P1r1/w74gtfE+hW+q2hG2UYkTPMbjqp/z0xUE2m22r6PcWF5GJLeZSjr/AFHvXlGhtqfwm8YNZajvk8P3z7ftAHyj+6/sw6Een4VyyVnc0WqPZ9R1Oy0XTp9R1CZYbaFcux/kB3J9K8Lvb/X/AIweJ1srJWt9KgbIU/chT+++OrH0/AetHibW9R+KfjGHQ9F3DTYXPl54Ugfemf29Pb3Ne2+GvDdh4W0aLTdPTCrzJKR80r92P+eKl6lbB4W8LaZ4S0xbLTYvmODNO335W9Sf5DoK8m8a/wCg/H3SrgceY9sT+PymvcgOleI/EkCX41aDHHzIPsoIHY+YT/KoqL3WVT+JHtFFOYc0leIe7cKgubK1vV23VtDOvpLGG/nU9FMDEk8G+GZiS+gacT/17qP6VF/wgnhQn/kXtP8A+/IroKKrnl3J5I9jAXwP4VXp4e07/vwKnj8JeHI/uaFpo/7dk/wrYopc8u4ckexSi0fS4P8AVaZZJj+7boP6VbSOOMYjjRB/sqBT6Sldsdkhck9zSUVzuv6r4js5hFovhwX67cmaS6SNQfTHX+VCVwbsjosUV51J4h+JiEkeELIj0E4Y/wDodZ83xX1rQ7iOPxL4UktUc4DxsRn6Z4P51oqUnt+Zm60Vv+R6rRVHR9Xste0qDUtPl8y3mGVJGCCOCCOxBq9WbVtDVO+wUUlFIBaKSigBawfGmjjXvB+pWAXdI0ReL/fXkfyx+Nb1App2dxNXVmeY/BXXDe+GrjSZW/e2Evyg9djc/oc16dXh2jn/AIQv443Fgfks79yijtiT5l/JuK9xxWlZe9dbPUzoS92z3WgUUuKKyNRKKKKAEopaUUAeL/HfSB/xKdXVeu62kP8A48v/ALNXW/s5at9o8LappTNlrS6Eqj0WRf8AFD+dW/ilpf8Aanw91EKuZLbbcp/wE8/+Ok15z+z7q/2H4gSWDNhL+1dAPV1+YfoG/OvTw0rwt2PLxUbTv3PqOiiiug5gooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooASvCP2j/AA8ZbLS/EUSZMLG0nI/un5kP57h+Ir3isPxfoEXijwnqWjS4/wBJhKox/hccofwYA0AfNnwp1bzIbzRZG+Yf6RAD+TD+R/Ou9vr6002Dz765itouzSNjP09fwrwC0ur7QdWE0DNb3ts7KcjlSMggg/iK6fQfCHib4hXjXskrmDOHvLljtHso7/QcD2rVV1ThqJUnOVonY3HxK8PQOVR7mfH8UcWB+pFPtPiT4cmYCR7mDPeSLI/TNaVl8DtDgjH22/vLmTuUxGv5YJ/Wpbn4JeHpYyLe6voH7HeGH5EVz/2jG50rL52uaOn6vpmqLusr+3nHor/MPw615/BqRPxsDb/lMhtx/wB8Yx+dZnjD4aat4RtzqEU4u7FTgzRgq0fpuH9Qa5XQb02niTTrtm/1dyjMSe24ZrdYiNRJo5p0ZU3aR9W2I/dMP9qvLPjJ4p/dw+FbEeZcTFXuNoyQM/Kg9ycH6Y9a9GvtUg0LQb3U7g/ureMvjP3j2H4nA/GvLPhXoU/ijxTe+L9VHmLDKWj3Dhpjzx7KMY/D0qp72Ij3O++HPguPwhoSiZFOp3QD3Mn930QH0H6nPtXaLTQKhvb620ywnvryVYraBC8jt0AFICr4j8R2HhTRJdUv3+VeI4wfmlfso/zx1ryL4eabqPjXx7ceMtUUiCCQupx8pkxhUX2UY/IetZwbVPjJ43LMZLfRbQ/hFHn8i7Y/yBXu+n2FppWnw2NjCsNtCu1EXsP6n3rhxNblXKtztwtByfM9iweaSiivOPUCiikpALRSUUALRRRQAtFcH8R/iBP4LW0gtLFJ7i6VmEkpIRAMDoOp59RXmKeLfiX4vJGmC9MROMWFvsQe2/HH4mt6eHnNXWxz1MTCm7Pc+iJHjiXdLIka+rsAKzLjxR4ftCRPrenoR2Nwv+NeMWnwc8f644l1S4jg3dTeXZkb8l3V0dn+zoSoN94jUHusFtn9S39K6Fgu7OZ47sjuT498JqcHxBYf9/a4P4r+MfDmq+ETY2OoW95dPOjKIju2AdTntxx+NUfGXwKXQfDd3q2m6tLdNaJ5kkMsQXKDqQQeoHNeR6ZpV9rOoxafp1u9xdSnCRp1Pr9BWkcIoyTuRLGOUWrH0L8Gn3fD6IZ6XMo/UV39cP8AC/QdW8OeF5dP1i0a2nFyzopYMGUheQQT3BruBXDWTVRpnfQadKLQUUUVkahRRRQAUtJRQB4/8bNMe3uNH8Q24IkifyXYdiDuT/2auu1LVfGF7pmn3/hi1sJ7e5t1kbzjh1YjPcgYq/490b+3fBeo2irulWPzov8AeTn9cEfjWJ8I9bS98CCCaQB9Odo3yeifeB/IkfhXRe9NPsYNWqNdzMf/AIXBKSwNjEPQeV/9eqs+p/FzSENxcWVtewoMssaI5x9FIP5VNqvx00q2neLT9LuLtVOBK7iNW9wME4+uKzB8es5DaBj6XP8A9jVqFR/YRm5019tnbeAfHkfjS2nV7b7Ne22PNjBypB7j/Cuyrx34Kzpf694kv9gjecrJ5Y6KGZjXseKwqxUZtI3ozcoJsKWkoqDQZc28d5Zz2sozHNG0bD2Iwa+WfDN5J4V+JOnyyHabLUBHL2+Xdtb9Ca+qSa+Y/itpv9mfELUCowlztuF/4EOf1BrrwktXE4sZH3VI+yQcilrD8G6qNc8G6PqWctcWkbOf9rGG/UGtyu888KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuR+I/i1PBvgy81MMv2ph5Nqp7yt0/Llvwrra+Z/2ifEDXniuz0ONz5NhD5kig/wDLR+f0UL+ZoA5D4d+DpfGevyTXrOdPt28y6kJ5kYnIQH1POT6Z9q+kre3htLeO2tokhgjUKkaDCqPQCuc8B6AnhzwfY2ezbcSIJ7g9zIwyc/QYH4V0FxdQWVrLdXMyQwRKXkkc4CgV5WIqucrLY9jD0VThd7ljFIRivGNc+Mep6hfmx8KWPyZ2rK8Rklk91XoB9c/hWZJ41+Julj7ReW9w0I5PnWQ2ge+AMfnTWHl10D6xHomz27U7GPU9Ku7GdQ0VxE0bA+4xXyFNG1vcSRE/NG5U/UGvdPDnxs0+7xBrtobKXH+uiy0ZPuOo/WvLPDGnW3iLxa9rcDEdyJWU91OCQfwrpwlOcG4yOTF1ITScWdD4t8fSeKtE0rRLKJ1JCG4H/PSXoFHtnn8vSvePDOhxeG/DVjpUQGYYx5jD+KQ8sfzzXzLpcH/COeO7GPUkAWzvo/N9NoYc/THNfWDEFuufcV3J3d2cD0ALk8V4b8TvEV34u8TW/gzQyZIo5gkpU8SS98n+6vP4g+gr0f4jeKx4S8JyzQuBf3WYbUdwSOX/AAH64rlPg54RNlpj+Jb1Cbu9BW33dVjzy31Y/oPesq1Tkjc0o0vaSsdx4W8NWfhTQoNMtACV+aaXHMrnqx/p6DFbdJS147bbuz2kklZBRSUUhhRRRQAUUUUAFFFFAHP+L/CNh4x0tbS9LRyRtvhmTG5D3+oPpXYaaljY6ZbWVoqQQwRrGkY4AAGKz6K3pYiVPRbHPWw0Kur3Njz4Qf8AWL+dOF1D081PzrForb67PsY/UId2R+O5Hm8Ba5FZqZ7iS0dEij5ZiRjgd+teAfBTZZ/EtIrseVM1vIiLINp38HHPfANfQdcl488IweIdHkurWLy9ZtV8y2uIvlfI525H049DWlPG+8uZGdXAe4+Vnb364kQHrg1Vrivhdq2t6v4VefXJ5Z5o7hoopJR8xRQOvrznmu1rlxL5qrZ1YWLjRimFFFFYHQFFFFABRRRQAvXg9K8W8NOng74u6j4euRjTtTJRAehDfMn8yv417RXk3xq0aRIdO8S2mVntJBHI69QM5Q/gc/nW1F68r6mNZacy6HpVh4I8LWCgWuhWAx0LRBz+bZNUfG8/hvw/4Uvri/sdP3tCyQQmFN0jkYAAx69+1WvDmtR+IPDtjqkRx9oiDMAejdGH4HNYXiX4Z6D4ouPtU5uLe7PBlhkzu+obI/LFdscclpJWPPnl7b5oyuef/Ai3uDrWqXIBFstusbHsWLZA/IGvcjWL4Y8MWHhLRxp1gXdd5eSWTG6Rj3OPbA/CtmuGrPnm2j0aMHCCTCiiiszUSvE/jzpu2fSNTUfeV7dz9DuH82r2yuB+MWm/bvAE0wXLWkyTD6Z2n/0KtaEuWojHER5qbNj9n/Vft/w5+xs2XsLqSLH+y2HH/oR/KvVa+dP2btU8nWdZ0hm4ngS4Qe6HB/Rx+VfRdeqeQFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXx/qrDxZ8brgN88dxq3l+uY0bb/6CtfXk8ght5JT0RC35Cvj/AOFv+mfFGxmfk75ZTn12t/jUzdotlQV5JH0lIMMcV4v8afEM8t1Z+GLNmIYCWdU6uxOEX+uPcV7Q5y1eH6dZ/wDCR/He+nmG6KymeQg9P3YCr+uK82gkpOT6HrV7uKguuh3Hgjwjb+FNGjQxq2ozKGuZsZOf7oPoP/r10khypzU7ryTUMo+Q/SueUnJ8zPQpRjFKMTz/AMXfDrT9eikubGNLXUQCwZBhZD6MP615b4SEulePNPinQxyJceTIp7E5Uj9a+h0JzXi/jyFdO+J9tcoNokeGY49d2D/KuzB1pOXIzizLDQUfaRVmaPxU0DzIYdcgT5kIhuMen8Lf0/KvS/hn4i/4SLwbaPI+66tP9Gnz1JUfK34rj8c1W1Czg1C2urG4XMM6GNvbPf8ArXj3h/xHe/D/AFbXNPkJBkgkhwOnmgHy3H5/ka9R6M8LdG/4jnk+Jfxah0mBidPtXMIZTwI1OZH/ABIIH/Aa94jiighjghQRxRKERF6KoGABXknwO0PyNMvtelXMty/kRE9di8sfxOP++a9b615eKqc0+XserhKfLDm7i0UUlch1BRRRQAUUUUAFFFFABRRRQMKKKKACiiigAooooEAAUYAAHtRRRQAUUUUAFFFFABRRRQAVn67pMWu6Fe6ZMBsuIimfQ9j+Bwa0KXNNOwNXPIvgxqs1nPqnhS9O2a2kMsSntg7XH54P4mvXQa8W8eQv4L+J2m+J7ZSLe7YGYDoSPlcfipBr2dJEljSWNgyOoZWHcHpWtbVqa6mNHROD6DjzRSE0VibBRRRQMKzfEth/anhbVLHGTNauFHvjI/XFaYpwweD0qk7O5MldWPmv4P6p/ZPxQ0dmOEuHa2f/AIGpA/8AHttfYFfEOqiTw147uPLBD2GoF0/4C+R/IV9s21wl3aQ3ERzHKiup9QRkV7Cd1c8RqzsS0UUUxBRRRQAUlLRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAU9WJGjXxHUW8n/AKCa+R/hAQvxGss945QP++DX15fR+bp9zH/fiZfzBr45+Gc32X4jaSWOMytGfxUioqawZpSdpp+Z9ME81g6R4UsNF13VNWtmkafUW3OHIwnOSF9iTmt9xhjTDXkXaVj3eVOzGNzUUqZjP0qalC1JqnYykXnFeDfEjV4dQ8ayPbOHS1VYtw6FgST+px+FesfEnWX8N+F5pYH23N0fIhI6rnqfwGfxxXz7d6fdWdtaXNwhVLtDJFnqQDjNdeDp2fOzjzHEc0fZx9WfRhk3okg/jRWH4jNeZfFPRMi312FeuILjHr/C38x+ArvtLn+0aFpk2c77SIn/AL5FLfWEOq6dc6fcf6q5QoT/AHT2P4HBr1mro8JMvfCi7gu/h5p4hAUwF4pAP724nP4gg/jXbV4d8INUm0HxXf8Aha/OwzsdgPQSpnp9R/IV7lXi14ONRntYealTXkJRRRWJsFFFFABRRRQMKKKKACiiigAooooAKKKKACiiigAooooAK4vxR8T9B8Mzvabnvb1eGhgxhT6M3QfTk1Z+I/iCXw74Mu7m3cpdTEQQsOqlupH0Gax/hx4A0/TdGtdX1G3W61S6UTbphuEQPIAB746nrWsIxUeaRjOUnLliY8PxR8W6mwfS/BrvCejEO2fxwBXoXhrU9V1TS/P1jSm025DEeUWzuHqPT8a2T0wOBSAVM5ReiVioRa1buLRRRUGgUUUUAcJ8XtNjvvANxOwHmWciSofTnaf0NcR4R+Ma6PpFrpmr2Es0duvlrcRN823sCD1wOOtdF8Z9eEWj23h22O+8vpFZ0XqEB4/Nsfka63w54UsdL8KWWlXNpBP5cX73zIwwZzy3X3JroTjGklNXucrTlVfI7WRL4f8AGOh+KIydLvVeQDLQuNsi/gf6Vug8V4l8RPC0XgjUbDxR4dBtVE4WSFT8qt1BHscEEV7Hpd6mp6VaX8f3LiJZQPTIzWc4JJSjszaE224y3RboopazNApc4pKSgR81/GCx+x/EK7kAwt1HHMPrjB/VTX0n8K9T/tb4Z6FcFtzpbiBj7xkp/wCy14l8ebDbd6PqAH30eFj9CCP5mu6/Zz1P7T4Jv9PY5azvCQPRXUEfqGr1qMuammeRXjy1Gj2OiiitTEKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAO1fEpJ8PfERwcqbHUyD9Fkx/IV9tV8e/GPTTpXxS1gAYS4dblD671BP/j26k9Rp2dz6OkIY7hyDyKiJrI8J6mNY8I6Ve7stJbqHP8AtL8p/UVr140lZtH0EHeKYAU4UlAqSjxb433jz61pWmqeI4TJj/aZsf8AstQfF3So7DSPDyxqAtvGbf8AJV/wqH4ukp8RLN3+6IIT+G41v/GiPzPDVnL/AHLofqrV3RfL7M4ZR5o1X6FjwhN5/g3SHz0hKf8AfLEf0rcFcn8OpfN8GW6/88ppE/XP9a60V6i2PHPP/iFYzabqdh4qsMpNHIqysvZ15RvxAx+Ar2vQdZg8QaDZ6rb42XEYYqP4W6MPwOa4zULCHVdMudPuP9VcIUJ/unsfwODXOfB/W5tJ1q/8H6idr72eAE9HX7wH1Az+FcWMpXjzLodmEqcsuV9T2aiiivLPUCiiigYUUUUAFFFFABRRRQAUUUUAFFU9R1XT9ItjcajeQWsQ/ilcLn6etef6x8bfD9iWj063uNQkH8QHlp+Z5/SrjCU/hRE6kYfEz0ylwTXhb/FjxprbFdF0iOJT0MUDSkfieP0qJm+Kup8yXtxbqe3mpF+i81vHCVGc8sZTR71tPpTSVHVgPqa+f38GeOLo5uvEBGeoa8kb+QqL/hW2tyf6/X1/N2/nWqwMn1M3jo9juPjaFl8IWrRyI3lXasyhhnBUiuz8H6tb634T067t2BAhWN1B+46jBBrxJ/hVdOvOtI7ejRHH86p6Pq/iT4XayUlh32kx+eIkmOYDup7N/kiqnhJKnbsRDFxdS76no/jf4tf8Ixrc2kWmlefNCB5kkz7VyQCMADngjmuRPx0112xHpdiM9B8x/rXSSeLvht42aKTXrP7NdqMbplK/hvXqPrXe6BpHhy1s0l0O0sfs5+7LCqtn/gXf86w9yC96Gpv+8m7xnoeTJ8Y/FjjK6HbsPaGQ/wBasw/GfxBGw+1eHI2XvtV1P65r2vOOgA/CkJB6qD+FR7Sn/KWqdT+b8DG8M6+viTRY9RFnPabiQY5hg8dx6j3rl/GPxV0jw4slrYst/qQyNiH93Gf9pv6D9K9BOCMYGPSsOXwb4anuGnl0OxeVjlmMQ5NZxcFK7WhpJTcbRep80xeJr6fxUuv3cYvrwSeZtkztyOnA7DsPavZvB3xTl1zV4dL1TSms5pwfKlXdtZgM4wenFdleN4e8K6e13NDZWFun8SxBcn0GBkmvIPFnxeutTdrTw5aeRGP+Xl4wZD7qP4f1P0rob9too/M50vYayl8rbm98bdetF0S20RJFe7lmEzqpzsQA4z6ZJ/Q12PgjVtI/4RTSbOPVbOSeK2RXQTLuBx0xnNeP+Hvhnq3iKF9U1e5ltlm+ZWkUvJIT3Oeg+tXrr4M36DdZarDIR0EsZT9Rmt1hL01G5zvF2qOVj3zsCOR6iivnYaX8R/CB8y0kvGhXnEEnmof+AHP8q3dD+OF1byi38RaaGwcNLANjj6oeP5VzzwlSO2p0QxlOW+h7WaKydC8SaR4ktvP0u+jnAGWjzh0+qnkVrVytNaM6009Ueb/Gyx+1eB47kDJtbpG/BgVP6kVifs36l5HiXV9MZuLm1WVR7o2P5Ofyr0H4gWQvvAGswgZItzIPqp3f0rxH4N6j/Z3xS0dicJcM9u3vuUgf+PYr0cI/csebjF79z6/ooorqOQKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+ef2ktEKXmja6i8SI1pKcdwdy/wA2/KvoauI+LPhw+Jfh3qdtGm+5t1+1QAddyckD6ruH40AeX/BHVvtPh280t2y9pNvQf7D/AP1wfzr08182fC7XhofjW2Er7be8H2eTJ4G77p/PFfSh615mJhy1L9z2cHPmppdhtFFFcx1njnxx01hLpWrIp2lWt3PoQdy/zb8qm8aXq6/8JLTUkO5sxM/sw+Vv1zXoXivQYvEvh260yTAZxuic/wAEg6H/AD2JrwSz1a40rQdc8J6mjRk5aNW/gkUgkfjjj/69ddP34q28X+Bxz/dzkntJfidZ8KpN/h2+iJ/1d0D+aj/Cu66V5z8Jpv3er2//AFykH5kf1r0U9a9aOx4j3ENcB4/0+40zUbLxXp2UngkUTFezD7rH+R/Cu/qC8s4dQsp7O4XdDOhRx9e/1705K6sCdnc7Dw9rdv4j0G01W3I2zpllB+444ZfwNadeIfDDWpvCni268Jak+ILiT9yx6CT+Ej2YY/HFe4EYOK8OtT9nOx7dCp7SFxKKKKyNgooooAKKKKACiiobu7t7G0lurqVIbeFS8kjnAUCgB8ssVvC808iRRINzu5wFHqTXkXiz4wyPO2m+E4DNKTs+1sm7J/6Zr3+p/KsLxD4l1n4n64dI0YPDpETZO7gEf35P6L/Wu28O+E9M8M2v+jIHuNv728lA3H1x/dHt+ea9ChhL6zPOr4t/DA8/tfAXiDxFcjUPEuoSxF+cSN5kpHpjov8Aniuz0rwPoGlhTDpyzyj/AJa3Pzn8jwPyqDXPiNoGjs0duzajcjtCfkB926flmuUXxZ468WSGPQtOkihJxutos4+sjcD9K7rwgjhtKbPVeIIcyPHDEvqQqism78W+G7HPn61bMw6rEfMP/jua463+EHi3WnE2uatHDnkiWVp3H4Dj9a6fTvgZoUABvtSvblu4QLGP5E/rWMsXTRtHCVJdDNuvih4chyIUvbj/AHYgo/Uisa6+LcAz9l0Zz6GWYD+Qr0+z+F/g2zAxpCzEd55Gf9M4rbtvDeg2YAttF0+PHQi3XP54rJ4/sjVYB9WeCf8ACzteumxZaVb5PZY3kP8AOpnufiD4qtWsW0IvbycZez2KPcM3T619CxxJEMRxog9FUCnc+tZSx83sjWOAit2eVaV8EdLbTIG1a6ulviuZVgddgPoMiuu8JeCbPwb9qWxvLqaK4KkxzEEKRnkYHXmuoFBFcsqs5KzZ1RpQi7pCA0tJilwazNBKKazon3pEX6sBUD6jYx/fvbZfrMo/rQBW1nQNL8QW8cGq2i3MUb71ViRg/hT9P0TStKiEdhp1tbL/ANM4gKcdZ0kddUsh/wBt1/xo/trSD01Sy/7/AK/40/etYWl7nn3iL4c+Jr/U7m70zxXJHBK5dLeR3QRg9gV4x+Fc5J4C+Jlmc2+sNNj+5fsP/QsV7OupadIfkv7VvpMp/rVlGSQZRlYeqnNbxxNSKsc8sLSk7ngz2vxd088peTqPeObP8zWDruoeIryIp4h8MF3A4nNo8Tj/AIEP/wBVfS9Bz61osdNbozeBg9mfHdpf3WmXy3VhPNbTRnKMrYZfxr3DwH8XYNTMem+IilvdnCx3Q4jkPo390+/T6V6BqPhfQ9ZQrqOlWk5P8ZjAf/voc15D47+ED6Vbyap4dMk9qgLS2rcvGPVT/EPbr9av2lOtpJWZPsqlDWLuj2+7tlvbC5tm5WeFoz9CMf1r5K0i7fRfFFjdE7Ws7xJD/wABcE/yr0D4ffFebRFj0vXWefTx8sU/V4fY+q/qP0rznXp4LnxBqM9q263kuZHjOMZUsSKuhTlTbTM8RUjUSaPu1WDKGByCMg0tYfg3Uf7W8F6LfZy01lEzH/a2jP65rcrpOUKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKQjPB6UtFID4z+J3hZ/B3j29tIkKWsrfabRhwPLY5AH+6cr+Fe4+BfES+JvCtreswNyg8q4HcOO/48H8av/GnwQfFXhE3tnFu1PTN00QUcyR/xp+QBHuMd68I+GHi0eGvEIt7qTbp97iOUk8I38Lf57GscRT54abo6sJV9nOz2Z9Fmmk0pIPQ5HqKaa8o9tCV5n8WPBcWoabJ4htFCXdsubgAf61B3+o/lXpneqWuQC68O6lARkSWsi/8Ajpq6cnCV0RVgpx5WeE/Cmbb4gvIP+etof0YGvVTXjXw2m8rxvapnHmxyR/mp/wAK9jzmvehsfNy3FooFFUScX8QNBe8sU1mzBW9suWK9WQHOfqp5+ma9I+H/AIrTxb4ZiuHYfbrfEV0nfd2b6Hr+dZfBBBAIIwQe9eeQ3E/wy8dR30Cs2j3pw6DpsJ5X6qeR7Vy4mjzxutzpw1X2ctdj3+imQzw3VvFcW8iyQyqHjdTwwPQ0+vIPYQUUUUhhRRRQAV4X8TPFF34q8RReEdFYvBHLslKniWUdcn+6v8wfQV6N8RvFH/CLeE554Xxe3H7m29QxHLfgMn64rzn4e6VB4f8ADd54t1QHzJEYxFuojHce7H+nrXbhKXM+dnFjK1lyI30k0X4beGo4XYPM3JC/fuJO5+n8hXIwQ+MfijdtHaobbSw2GOSsK/U9XPtz9BWXAR4gnu/FviVn/s2GTy4bdWwZ36iJT2AHU1veHpfF/wAQtQmsdF1OLR7OzjBW3hYxIiE4AG3k/jXZOcn7sDjhSSXPM9A8N/CLw/oapLexf2ndjkvOv7sH2Tp+ea7pYljjVI0VEUYVVGAB7CvMo/hJ45YfP46YfSSY/wBaefgv4ol/1/jmY/TzT/7NXJLDzl8TOqGIpxVoo9HJVRlmA+pqCTUbGD/W3ttH/vSqP6157/woO6l/4+fGFy/riEn+b1NF+zxpOc3Gu38vrtRV/nmpWD8ynjV0R1lx4x8N2ufP13T1x289T/Ksq4+KPg23znWFkI7RRO39KZbfAXwhDjzm1C4/3pwo/QCtuz+Engizxt0OOQjvNI7/AMzVrCR6szeMl0Rx9z8bPC8ORBDf3B9ogo/U1nP8a2uTt03wveXB7Zf/AOJU17BZ+FPDtgB9l0PToiOhW3XP54rVSOOIYjjRB6KoFaLDUzN4uoeEDx98RNQ/5B/gmRAejPBIf1OBUgk+NOoj93psNmp9REuP++iTXupJPWkxVqhTXQh4io+p4X/whPxgv+bjX4bcHsLnGP8AvhaU/Bvxvd833jQDPUCWVv8ACvc6KtU4rZEOpN9Tw+P9n+6lObzxbK577YSf5tVyP9njScfvtfvnPfbEo/xr2SiqsiOZnkqfs++Gl+9qmpt+KD/2Wpx8A/Cg63epn/tqv/xNepUUWQXZ5U/wD8LN9281Rf8Atqv/AMTXNeMfhW3gzw7da7ofiTUY2tAHMUjY3DIHBXHPPpXvVeTfHjXfs3he20GD5rrU5l+ReuxSD+rbR+dDS6jUmtjR+HOs3eu+CLC9vnMlwd8byHq+1iMmuqxWP4T0b/hH/CunaYfvwwjzP988t+pNbNeNO3M7bHtQvyq+4tKDim5pM0irHzx8X/CEega4mp2MYSxvySUUcRyjqB7HqPxrzavqD4naSur+AdRXbmW2UXMZ9CnJ/wDHc18v16eHqOcNeh5WJp8k9Op9c/A+/N78LNNUnLWzywH8HJH6EV6NXiv7N175vhTVrIn/AFF4JAPQOgH/ALKa9qrc5wooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooASvln41fDtvC+tNremwkaRfuSQo4t5TyV9geSPxHYV9T1S1bSrLW9LuNN1GBZ7S4QpJG3cf0I6g9qAPnz4W+P1vbeLQNVmxdRjbbSuf9av90n+8O3rXqoFfO3xD+G+p+ANW8xPMm0qR82t4o6HqFbHRh+vUe3WeCPi0gji07xIxBGFjvcZz/vj+v51w18NrzQPTwuLVuSf3nrtRzjdbSp/eRh+lEFxDdQLPbyxzROMq8bBgfxFK3Q/Q1wvQ9Jany94Sm+yeOdNYnGLoJ+Z2/1r3Mrtdl9Ca+fY5vsniNJgceVdhvyevoWYfv3I6E5H419BTfunzNRe8MFLRRVkBWdrujQa9pE1jPgFhujfH3HHQ/57VpUlIZzHwr8WT6XqEng3WmKSI5Fqzno3dM+h6j/69exd68S8c+GJNRgXVtPBXUbUbvk4Mijnj/aHUf8A6q7X4beOE8WaQLe6cLq1qoEyn/loOzj+vv8AWvLxdDlfOj08JXuuRnc0UUVxHcFFFZ2vaomiaBf6m+MW0DSAHu2OB+JxTSu7Cbsrni/jq6k8b/E+30O3cm0s38glegxzK36Y/wCAir/xcvFsPD2naLajYkz/AHF/uIAAPzI/KqXwnsWlm1HXbjLSO3kox6kn5nP/AKD+tR+OydR+J3h+wblAYFI/3pef0xXswioU7HiTk6k7m5488Kf2b8JtMtrdcNppSScDuWBDn/vpq4fwD4tk8G+Ik1LymmtZU8q4iU4JU85HuCK+g/EGnDV/D+o2GMmeB0H1xx+uK+XLdSqmNxh0JUg9jXLQqXTPW9hGcuR9V+R9Q6f8VfB15Grf2r9nJ/hniZSP0I/WtRfH3hJxkeIbD8Zcfzr5R6UZNdPOjN5a+kj6vPjvwoP+Zh07/v8ACkPj7wkP+Zh0/wD7/CvlDJ9aXJ9aOeJP9mz/AJj6s/4WB4SH/MwWH/fyk/4WD4R/6GCx/wC+6+VKUUc8R/2ZP+Y+q/8AhYPhL/oYLL/vv/61IfiD4S/6D9l/32f8K+VaWjniH9mT/mR9Tn4ieEB11+z/AO+j/hTT8RvB4/5j9p+bf4V8t4pdtHtIlf2VU/mR9R/8LH8H/wDQftP/AB7/AApP+Fj+D/8AoP2v/j3+FfLuwelLsHpS9pEP7JqfzI+oD8SfB4/5j1t+Tf4U0/EzwcP+Y7b/AIK3+FfMPlj0o8tfSj2sR/2RU/mR9Nn4oeDR/wAxyH/v2/8AhTD8VPBY/wCY0n4RP/hXzP5S+go8pP7oo9rHsH9j1P5kfRt78YvBlpbtIuoS3DAcRxQNkn6kAV514TF18S/iNP4r1GIpp9gQLeE8gEfcX3x94+9eXXaKsJIFfSngXSItE8GadbRqAzxCaQ/3mbk/59qxr1rQsupmsC6VS0nex0ROabRRXmnWLSUUtAFTU4Bc6TeQMMiSCRD+KkV8fLA5ikkwdqEBj6Zz/hX2TMP9Gl/3G/lXy5odiLzw34lOMtDHHIPbDE/yBrvwKvdHn45/Cekfs233l69renk/662SYD/cbH/s9fRlfJ3wIvfsnxQtIicC5glhP/fO4f8AoNfWNdhxBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAVr+wtNUsZrK+t47i1mXbJFIuVYV8/eOfgBd2zy33hKT7TAcsbGVsSJ7Ix4YexwfrX0VRQB8ORah4k8IX726y32mXCn54XBT81PB/Kt6L4ueLI4ijXFtJxjc8Az+mK+tNW0PS9ctzBqmnW15F2WeIPj6Z6fhXnOt/AHwjqau2n/atLmPQwyGRM+6tn9CKiVOEt0aRrVIaRZ8tM7SSM7HLMck+9fRtlMLvSbC7Bz59rG/47RmvFPG/gu/8AA/iJ9JvXWUbBLDOgIWVD0OD0OQQR6j8a9V8D3QvPAmmNnLQb7dvbByP0IraHYxkbdJSmkrQkKKKKQBXn/iXRL3w5rCeKvDxMbxtvnjQcD1OO6nuK9AoIDKQQCCMEHvSkk1ZjTad0bng7xdY+MNHW7tyI7lABcW5PMbf1B7GuhIrwDVdK1LwRrI8R+HCwtwczQjkKO4I7of0r1vwj4007xhpouLVhHdIB59sx+aM+o9V9DXkYig6butj1sPiFU0e50debfGvUzZ+Co7NWw17cqhH+yvzH9QtekDmvEPjXO9/4o0XRozkrHuwP70jYH6LUYePNURpiZctNm54Ks/7O8I6dDjDPH5z/AFY5/kRXM6uN3xv0QHp5lt/Ou/hjWFEiQYVFCgew4rgPFDi0+LOg3R4UtbnP0kIr2KnwWPIp/Ej3sDBr5x+IeiHw/wCOLoKm21vf9IiPbnqPwOa+j24Y1wvxS8MnxD4Yae2TdfWOZosDll/iX8ufwryKU+WWp7eq95dDwY0VXW6QQ73OOxHfNTWNlq2sH/QLRvLzgyNwo/E8V227nT9Yi2lHVvotWPoxW3b+AtSkANzqUcfsgLf4VcHw+451aXP/AFz/APr1DnHudUcPipK/s396/wAzmKK6GXwHeqP9H1RHPpIhH+NYOpaPrmlKWntt8Q6yR/MP/rfjTUk9mZ1VVormqU2l9/5DaWmaLpz65IYzqkNtJniNgct9PWugPw+uMf8AIXOf+uZ/xok1F2bFRlWrR56VO69V/mYdOBrVk8B6kgJh1OJz6OCv+NZtzoPiKwBZ7Tz0HeI7v0HP6UaPZlylWp61KTX4/kIKKpR6gu8xzo0Tjghh0q8CGAIIIpNNbmtGtCqrwdwooxSikbhRS0UAU77/AFH419R6FIJPD+nOOhtoz/46K+YLtN0DV9H+CbkXXgvSZAc/6Oqn6gYrDEbI83Ex/eN+n6m/RSClrmOYWiiigCK7cJYXLf3YnP6Gvnr4c24u9L8SREZEsAT8w9e8+IZ/s3hnVZyceXaSt/44a8V+E0f/ABLtSc9HmRPyB/xr0svWrPOx/Q5T4a3n2H4leH5icD7YkZ+jfL/WvtMV8L2Mn9meLraTp9lvlb/vmQf4V90DkcV0nGLRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUV534t+Mnhvw1K1nbO2raiDt+z2hBVW9GfoPoMmgD0Sop7mC2j8y4mjiQfxSMFH5mvFBd/GHx2d1rHF4a01+jONjkfUgv8AkAKtW3wCivXE/iTxRqWozHlthwPzYsf5UAekXHjbwtasVm8RaWrDqPtSH+tQr8QfB7nC+JdMz/18qP61zlt8DPAtuoDWFzOfWS6f+hFTSfBTwG4x/ZEi+63Uv/xVAHcWWoWepW4uLG6guYScCSGQOufqKs1ieGPCWkeD9PkstGgeGGSTzX3yFyWwB1PsBW3QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAJS0UlAHi/7RmiC58M6drSJ+9s7gwuR/ccf0ZR+def/AAo1EPpmqaYx+aN0uUHsflb/ANlr6B+I2j/278PdbsFXdI1s0kY/20+cfqtfJ/gDUxpnjGzMjYhuSbaT6PwP1xVQdmJ7Htmc0tN2lGKt1U4NKK1IFooopAFFFFAAwDKVYAgjBB7159rXhTUNB1Ia94UkkiljO5oI+o9do7j/AGa9BopOKasxptO6M/wn8YdK1KJbbXsadfLwzkHynP1/hPsePeuKuNQi8W/GU3tu3mWkLgxt2KxrwfoW5/Gul1zwbpGuuZZYmguT1mgwCfqOh/nS+G/CVj4aeaWCSSaeUbTJJjhfQAVzww0YT5kdFTESnDlkdD3rzv4oo0F3o+pIOULJn3BDD+Zr0MnvXKfESyN74RmdRl7WRZh9Oh/Q/pXRJXRgtz2C1ukvbKC6jOUniWRT7EZ/rTyM1x/wv1X+1fAGnMTmS2Btn/4CeP0xXY14M1aTR78HeKZ8+/E7wSPDuuLrNtbGTSLmUNJGvAjfPK+wPaul0u4sr7T4ptP2/Z9uAi8bfYjtXq97ZW2pWUtneQrNbzKVeNhkEV4P4o8Ha18PL59S0aSSbSHPJxu2f7Lj+TV0wn7RKLeqNcJifqVRzteMt+6/4B1mMUhrjLL4kQOAL+xdD3eE5H5H/GtaLxnoNxjF75Z9JIyKbpyXQ+hpZnhKu0189PzN2j2qhHrmkzD5NStT/wBtQKsLfWb/AHbuA/SUVNmdSrU5bSX3mLrHgzTdTLTQj7Jc9d8Y+Un3FYX2vxL4Vbbdxm9sh/HksAP97qPxrvPtFuf+XiH/AL+CkNza4w1zBj0Mgq1N2s9Thq4Kk5e0pS5Jd1+q2ZmaNrtnrkJe3YrIv34n+8v+IrVFUoxo1lJJLC9lC8n32R1Gahn8R6Pb536jb/RW3fyqWrvRHTTrKFNe2mr99ifUdH0/Vk2Xlqkh7PjDD6Ec1w2seEb7Rd1zpztdWg5aMj50H07/AFH5V0M3jvRIvuyzS/7kZ/riqUnxFss4gsbiQ9txAz/OtIKa6HmYyrl1R8zmlLut/wANzmLa8iuRgHa/dTVjFR6hbah4jvRcab4buo5GOWMETtu9+BjPvWrZfD3x/eKBFod2qnvNtT/0IitfZt7HlxzWEG4z963VdfkzONISB1xXW2/wU8d3ePOaztgf+elwD/6CDWxbfs96rJg3viK1j9RHEz/zIpqiyZZzD7MTzWSWMKQzrz716v8AB/xNBPp76DLKonhYvCCfvqeTj6VYj/Z40sJ+/wBfvHb1SFVH6k15v4u8K6h8LvFllLbXhmjP762nC7ScHlWH+cg0qmH5o2OSWZ+0krx0PpQGlqtY3P2uwt7kqVM0SybT2yM4qzXlI6mLQKKSmI5n4i3Qtfh7rT5wWg8sf8CIX+tecfDC3MPhdpSP9dcu34AKP6Gum+NeoC18Dpag/NdXSLj2UFj+oFUvBVqLXwhpceMFovMP/AiW/rXqYBWi2eXjn7yR4v4hU2/ijUwOq3chH/fRr7i0+Xz9NtZs/wCshRvzANfEvjJNnjLVl/6eWNfZ3hiTzfCmjyf3rKE/+OCuh7nMjVooopAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVR1fV7DQtMm1HUrlLe1hGXdz+g9T7UuratZaHpdxqWoTrDa26F5HPp6D1J6Yrw2wtdY+OXiZr+/M1l4SsZCIolODIfQerHu3YcCgCS81/xh8Y9Ql07w4smk+GkbZNdPkGQf7RHU/7C/ia9J8G/DLw74LhR7S2Fzf4+e9uAGkJ/2eyj6frXUadp1npNhDY2FvHb2sK7Y4oxgAVw/jv4s6T4QkbT7RDqWtH5VtYjwhPTeR3/ANkc/SgD0EkKCSQAOpNcprPxL8HaEzJfa9aeavWOFjK35LnFeZQ+DfiP8SXF14p1Z9G0tzlbKMFSR/1zB/ViT7V22ifBTwTo6KX0038w6yXjl8n/AHRhf0oAybn9oXwfE5WCDUrn3WEKD+bVCn7Q/htiN2l6oq+uxP8A4qvTbXw/o1igS00qxgUdBHbov8hVo2NoRg2sBHvGKAKXhzxDY+KNDg1fTvN+zTZ2iVNrAg4OR9RWrTY40iQJGiog6KowBTqACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAawBUgjII5FfEXjTRn8MeN9V01QUFtcsYT/sE7kP8A3yRX28a+cv2jPDpg1jTfEMSfJcxm2mI/vrypP1Ukf8BoA1tL1BdY0ex1NCP9JhBfHZxww/MGrtcB8KdVEtne6JI3zxn7TAD6dHA/Q/nXoBre91cztYSiiikAUUUUAFFFFABRRRQAlRXNtHeWs1rLzHNG0bfQjFTUUAcn8F9QfTNc1jwzdHD5MkYP95DtbH1GD+Fe0V4B4keTwr8QNL8TQKfJlceaB3I4Yfipr36KWOeCOeFg8Uih0YdCCMg15OLp8s79z18HU5oW7Dqa6JLE0ciK8bDDKwyCPQ0tGa5TqseVeI/hnNpd++s+E4YJAeZtMuEDxyDuFzx+H5HtVfQZvht4in/s/XdAi0TVwdrxOWiUt7EYx9Dj8a9cFYXiHwdonieHbqdkryAYWdPlkX6MP5HiuqlinHSWqOWrhIy1jozMk+CHgq4G6OC7jB5BjuSR+uart8AfCh5S71NP+2qn/wBlrKi8JeOPB53eFNd+2WanIs7sjgegB4/IirifGLWNGYReKfCVxARwZoMhT9M8frXdGrTnszgnRqQ3RL/wz/4aP/MT1Mfin+FIP2f/AA1nnU9SP4p/hWtY/GzwZeACS6ubVj2mgPH4rmt2D4jeDrhQU8QWg/3yV/mKuyMrs5OP4B+E1+/d6m//AG1Uf+y1ft/gl4IgwWsbmcj/AJ63Lf0xXRN4+8IoMnxDY/hJmqU/xR8FW4y2uRP7Rxs38hTsg1H2vw38HWePK8O2RI7yJvP/AI9mt210bTLIAWumWcOP+ecCr/IVxF18b/BlvnZNezn/AGLfH8yKx5/2gNFyRaaLqM57Z2r/AI0XQWZ65yBgcD2o59TXjJ+Nut3fGneC7h/QsXb+S1E3xD+J97/x5+EEhB6F4W/qRSdSC3Y1Tm9ke10c14e2rfGi9+5bW9qD/sxDH5k1E2ifGC/OLjxCtuD12zhf/QVqHXprqWqFR9D3UnaMsQo9Sa8H+MGo2XinxdoGgaTIl3cxSkStGdyqWK/LkegBJ9KD8KvFOpf8hfxhJIp+8oeR/wCZFdf4P+HOj+EZmu4nku74rt8+UAbR32jtWVTFQSfKa08JPmXMtDrVjEaKg6KABTs0ppDXlnqoUc0uK4D4h/EWLwfAlpZrHPqsw3LG3KxL/eb+grS8A+OLXxjpAkYpFqEAAuYQeh/vD/ZP/wBatPZy5ee2hHtI83JfU87+ON213rmjaNEcsqGQr/tO20f+g/rXY2sS2sENuv3IkWMfQDH9K8+v5l8U/GqWZDvt7SXgjptiGP1b+deifxD616+Fhy00jx8RLmqNnhXjfnxrq3/Xwa+xfB5z4K0In/oHwf8Aota+NvGLbvGOrH/p5cfrX2X4TXZ4P0RfSwgH/kNab3IRsUUUUhhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUh4pa4n4q+LD4R8D3dzC+2+uf9HtfUO3VvwGT9cUAedeMb+9+LHxDi8G6TMyaJpz7764TkMQcMffH3V9yTXtuk6XZ6JpdvpunwLBaW6BI0XsP6nuTXGfCLwYPCfg2KW5jxqeoAXF0zfeGfup+AP5k1N8U/HI8FeFmktmU6ndkw2adcN3fHoo/UigDD+I/wAQ74aovgzweGuNduDsmlj5+zg9gezY5J/hHv01vh98LdO8IRLf3u2+1yT5pLp/mEZPUJn+fU1V+EfgI+GdIOsaqpk17UR5kzyctEp5259T1Pv9K9LoAQiilooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuU+I3hceLvA+o6YiBrkJ51t/11XkD8eV/GurooA+FtE1Sfw/rttfxgiS3k+ZDxkdGU/hkV9ACSG4ijubdg9vOgliYd1IzXAfHHwUfDvis6vaRbdO1RjJwOI5urr+P3h9T6U/4ZeIBd6dJoU7/vrbMttn+JD95fwPP4mtIPoTJdTujRSZzS1ZIUUUUgCiiigAooooAKSlpKAMfxTow1zw/c2qrmZR5kP++O348j8av/AAd8Tf2r4cbR7h/9M007QG6tETx+R4/KrWcV55qck3gPx7a+I7RCbG5cidF6HP31/H7w9/pXPiKXPHQ6MPV5J36Hv5pKjtrqC9tIbq2kWSCZBJG46FTyKkrx2e0haKKKQBSMA6lWAZT1BGRXDWPxU0SbXLnSNRSTTriGZog0xBRiDjr2/Gu3jkSaMPG6ujDIZTkGqlFx3JjJS2Me88H+HNQYtdaLZOx6sIgp/MYrKl+F3g+U5GlmP/cncf1rsMUU1OS2YnCL3RxQ+FHhEHP2GY/W4b/GrkHw38IwEY0aNyP+ejs38zXU0Ue0n3D2cOxk2/hXw9a48nRbBcd/IU/zrQis7SD/AFNrBGP9iMCpqXBqW2xpJACR04ozUM93bWy7p7iGIDu8gX+dYt5468LWGRca7ZAj+FJN5/Jc0KLewNpbnQZpDzXnt98aPCdpkQPd3jD/AJ5Q7R+bYrnbr49R7sWeguw9Zp8foBWioVHsjN16a6nsmMUVyPgTx5beNrS4K2/2W8tyPMh3bgVPRga67FZyi4uzNIyUldADXNeOPGVp4O0U3Em2S9lBW2gz94+p/wBkd6seK/FFh4S0d7+9bLH5YYQfmlb0H9T2rxXRrLUPHviCTxDrpLWit+7i/hbHRFH90d/X866MPQdR3exz4iuqastyXw74Vm8QzTeIfEm+eW6bekbEjcD/ABH29B6Vy+uxXHhHxXcJpV1NbfLlGRsEKw5H0r27AAAAAAGAB2rkvE/gaHxFqEd6LtreQKEkATcGA6d+DXrOC5bI8rmd7sz/AIXaYY7K71aUfPO3lRk/3RyT+ePyr0BRukX6iqWn2UOm2EFnbrthhQKo7/U+9XYnCuHY8L8x+gq0rIhnz54lk83xRqr+t3L/AOhGvtvQo/K8P6bHjGy1iX8kFfDj7tQ1g4GWuJ/zLN/9evu+CMQwRxDoihfyFYM0JKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArxHxp/xW/wAcdE8M/fsNLAnuV7E4Dtn6jYv417YzbRkmvFfgyDrfjvxl4mlG5nn8mNj2DOT/ACVaAPbK8IsYv+Fm/HO5vJf3ui+H/ljU8q7KcD83y30UV6x411n/AIR/wVq+pqcSQWzeWf8AbPyr+pFcf8CdFGm/D5b91/f6lM0zMepUHav8ifxoA7/VdXsND06XUNTuo7W1iGXkkOB9Pc+wrz6T42aYzk2Hh/XL2D+GZLcKre4yc4rF+JM7a38QotKn+aw0m2SfyT915pCcEjvhQMfj61QzVxjcTZ1Q+NEB/wCZT17/AL9L/jS/8Lnt/wDoVNe/79L/APFVygNL+NPkQuY6r/hc9v8A9Cnr3/fpf/iqT/hdFv8A9Cnr3/fpf/iq5jPvR+NHIFzp/wDhdFv/ANCnr3/fpP8A4qk/4XRB/wBCnrv/AH7T/wCKrmPxo/GjkQXOnHxqtw2ZPCmvKndhEpwPpmu18M+LdG8W2LXWkXQk8s7ZYnG2SJvRlPI/lXkdVrW6bw74z0bW7XKG4uksb1V4E0chwCR3KnBBqXGw0z6BopKWpGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAjnfHHha38Y+FLzR58K8i74JD/AMs5R91v6H2Jr40hk1Dwx4hDFGgv7Ccq6N2ZTgqfbqK+7DXzH+0NpOm2fi2z1C1miF7eQ5urdT8wK4CyEe44/wCA0wOnsNQt9V0+31G0P7i4XcB/cbup9watCvJvhz4lXT75tHvJMWl22Y2Y8Ry9B+B6flXrRUqSGGCOtbJ3VyNtAopKWgAooopAFFFFABRRRQAlUda0mDW9JnsJ+FkHytjlGHQir9FAzm/hT4nn0u/m8F6w2yWNz9kZjxnqUHseo/H2r1+vFPG/huXUIk1fTdyalaYYFOGdRzx7jqP/ANVd58PPGkXi/RQJmVdTtgFuY+m70cD0P6H8K8vFUOV86PTwle65GdhRQaK4juPnT4oaKi/FDyVPlx6gYn3Y6FvlJ/MZrUtvDPjLw0f+JDr2+If8smYqP++WytW/jNH5HjDQLodTGBn/AHZM/wBa7J2+Y16+HSnSXMePXbhVfKcf/wALE+IOjDOpaLFdRr1cRHp9UOP0rq/BXxSsfFl6dPntTY3pGY1L7lkx1APHPtSk5NeceNfD8uj30PiTRwYmjkDyhP4Gzww9j3pVMJBrTcdPF1IvV3R9CZzXnPxQ8f6h4SeystLihNzcqztJKu7aAcDA9etdZ4Q8R23inw9b6jCVEhGyeMHmOQdR/Ue1eTfEpl1f4uabp6EMIFhRx+Jc/oRXDQpXqcskd1eranzRZAmqfFHV1EhvntUbkD5Iv0AzSN4V8aX4/wBN8TPg9R9okau9xzTwa9VUYLZHlurN7s87HwtkmO681t5D3xGT+pNXbf4YaLCQZprqc+hcKP0FdvmirUUjNtswbXwd4ftcbNLhYjvJl/51rxWNpCu2K1gRfRYwB/Kp6KYHnWmXsfw/+KiyP+70y7+VsdFjf/4lh+Qr17xN450Xwxp5uLi5jmnZcw28TgtJ6fQe9cD4w8LDxLZR+VIsV3ASY3boQeoNc1ovwykW5WbWrlHjU8Qwknd9W7D6Vy1cMqk+Y6aWJdOHKR21pq3xL15tY1h2TT4zhVXhQP7if1NekQW8VrCkEEaxxRgKqqMACnwxR28KQwxrHEg2qijAAp5rphFRVkc8pOTuxAaWkoqiQIrL8RX39neGdTut2CtuyL/vN8o/nWr1rz/4p6n5Gm2elo3zzv58g/2Rwv65/KhuyBHJfDzTTq/xC0Gz25DXkbuP9lDub9FNfbFfMn7O2hNe+L7zWXTMNhb7EbH/AC0k4/8AQQ3519N1gaBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAU9Ucw6TeSr1SB2H4Ka8p/Z1iA8HanOfvSX5BP0Rf8a9Z1CE3Gm3UIGTJC6AfUEV5X+z2HTwbqULoymPUGHIxzsXP8qAL/x5ujb/AA1kjBx593FGfcDLf+yiux8FWi2PgfQ7ZRgJYxfmVBP6muF/aBt7i48A2wt4JJSt/GSI1LEfK47e5FejeH45IvDelxSoUkS0iVlYYIIQZBoA8g8Uf8lU8Qf9e9r/AOgGqpq54o/5Klr/AP172v8A6AaqHrWsdiWNpRRRVEi0tJRSAWjNFFAwrM1vppf/AGE7X/0YK06zNa6aZ/2E7X/0YKT2BH0XRRRWRYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQIKQ0tedfFT4mW/gXS/s1oUm1u5U+REeREvTzGHp6DufYGgCL4o/FS08EWpsLHy7nXJlykR5WAHo7/0Xv9K+cNP07UfF+sT6jqVzLKZH3z3EhyXPoP8AOAKradY3/inWZbq7nklaRzJc3EhyST159TXpNtbw2dslvAgSNBgAV7eVZZ7d+1qL3V+P/AOHF4v2XuR3/I818QaFJol2NmWtpOY39PY+9el+BvFY1rThZXkn+n2y43E8yx+v1Hf86i1Cwh1SyktZx8rDg91PYivM2+2+HNZDIxjuLd8qw6MP6gilmWB+q1OePwP8P66BhcR7aPK/iR79RXN6D4mh1WwS5j4HSWPPMTf4eldFHIsihkIKnuK81xsdSdx1FFFIYUUUUgCiiigAooooAK4LxDpt/wCFdaTxX4eypRt1zCo4wepI7qe/p1rvaRgGUqwBUjBB70pRUlZlJtO6N/wr4psPF2jpf2TBXHyzQE/NE/ofb0Pet4CvBL7TNT8C61/wkPhvJtT/AMfFryVC9wR3X9RXr3hPxhpni/TRc2ThJ0A8+2Y/PGf6j0NeRXw7pu62PWoYlVFZ7nm3x1Gy/wDD0nf94P1SunDbsGuZ+O2HvvD8Q+9+8OPqVrpguOK78J/CRwYv+KxRTZoY7iCSGZA8UilWU9CDTqWuo5jyma51z4Za9JJpcx+yXH3N67kkX0Yeo/P86t+Bba+13xPdeJdQ3OcsRIRgPI3HHsBn9K9ImghuYzHPFHKn92RQw/I05I0ijWONFRFGAqjAFZqmlLmLc21yjqSloqyAooooAKKKSgAooooAKKKKACkpaDTAUFQCzsFVRlmPQAdTXgvinWG17xFc3oz5ZbZCPRBwP8fxr0b4ia//AGZo39nwvi5vRhsdVi7/AJ9PzrH+DPgv/hLPGUdxcxbtN04iefI4ds/In4kZPsDUSfQqKPffhJ4TPhLwHaQTx7b27/0q5yOQzAYX8FwPrmu6oorMoKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApiRpECI0VQTkhRjmn0UAJ1paKKAPDvE7Z+KviEf9O9r/AOgGq/epPE//ACVfxF/1wtf/AEA1GK1jsQwxSYp4Ga5688TGa6ey0KzbUblOHkBxDH9W70pzjBc0nZEykoq8nZG9RmuWNj4puvmuNchtCf8Alnaw5A/E4o/s7xLb/NB4hWYj+C4txg/jzXD/AGrhL25vzOB5pg07e0X4/wCR1Qorl4vEt7psix+INPMKE4F3b5aP8R1H+eK6iKSOeFJoZEkicZV0OQRXbTqwqLmg7o7oTjOPNB3XkLWZrX3dM/7Cdt/6MFadZmtfc03/ALCVt/6MFU9i0fRdFFFZFhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUCCiisPxb4q07wdoE+r6k+I0+WONT80rnoi+5/QZNAGZ8Q/Htj4D0E3UoWa/mytpbZ5dvU+ijufw718ms2q+NPEc13dzNNdXD75pm6KP6AdAKm8Qa/rHxA8VPfXRLzzHbFEp+SGMdFHoB69+TXb6NpMGj2Ihj5kPMj92P+FerlmXvFT5pfCt/8jkxeKVGNl8TJbDT4NNtEtrdcIvU92Pqas0ppK+1jFRSjFaI+fcnJ3YZrC8TaKNXs98YAuohlD/eH92t2mms61GFam6c1oyqdSVOSnHdHlej6tc6FqPnRg4ztliPAYeh969Y0vVEubaO9sZd0L9VPY+hHrXF+LfDxkDalaJ8w5mQDr/tf41g6Brtxod3vT54H/wBbETww9R6Gviq9CeEqulU26M+gp1I14c8Nz3O1vYrgAE7H/umrJFcpY3trqVot3ZyB4z19VPoR2NaUGoyw4D/vE9+tZypdYjVTozZpKhhvIJ/uthv7p4NT1i1bc1TuJRRRSAKKKKACiikoADgjBHBrjNV8IXdlqI1jwtdGxvlOTErbVb1x2GfQ8V2dFJpNWY02ndHm5t/FfjHxZY3HiG3aOOz2hnMexNoOcD1JPpXpR5OabRSjFRVkOUnJ3YUUUtUSFFFFABRRRQAUlLRQAlFFFABRRRQAUUUUAFR3NzBZWk13cuEghUu7HsBT/pXlXxC8VC/uP7HspM2sDZmdT/rHHb6D+f0obsrglc5nVr+88U+I2mSN5J7mQRwQryQM4VR/nrX158OfB0XgjwjbaaApu3/e3cg/jlI5/AcAfT3ryf4BeAPOl/4TDUYv3aEpYIw6t0aT8OQPfPoK+hKyZoFFFFIAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA8L8T/8AJV/Ef/XC1/8AQDUQPNT+JRn4reI/+uFr/wCgVmapejTdMurwj/UxlgPU9v1xWsdiGY+q3Nxr2qvoNjK0VtCM39wnUD/nmPc/56VuWlpbafaJaWcKxQp0Ve/ufU+9Y3hC0NpoEUsmTcXZNxKx6sW6fp/M1e1jWItHshO6GWV22RRL1djXx2Y4ipisR7OOydkv1PjczxNXF4n2FPVJ2S7vv/XQusOaZWKNN8T6ggluNSi08NyIYkyV+p/+vSHw/wCIYPnt9f8ANYfwTR8H+dcio01o6iv8/wA7WOP6rTWkqsb/AD/O1jdMSSo0ciK6MMMrDIIrm3V/B2oJPEWbRLl9ssROfs7n+Ie3+fSr+kavLcXUunajALfUYhkqPuyL6rWlf2sWo6fPZygFJkK/Q9j+BrXDYirgq6vt17NdzbC16uX4i0tnv2a7r9GXeCAVIIPII7isvW/9Vp//AGEbb/0YKqeDb6S68PJDMcz2cjWz5/2en6cfhVvXP9RYH01C2/8ARgr7W943PuFqfRVFFFQWFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAgooqG7u7ewtJru7mSC3hQvJI7YVVHUk0AV9X1ay0PSrjU9RnWC0t0LyO3Yeg9SegHc18g+PvHGpfETxIJdjpaIxjsrQHOxSep9WPc/h0Fa3xU+Jdx461QWVjvj0W3f9xF0MzdPMYfyHYe5NO8J+GP7MhF7doDdyD5VP/LMf412YLBzxVTlW3VnLi8VDDU+aW/RFnw54dTR7MNIA13IMyP8A3fYVrHIODVvFRyx7hkda+3oU4UYKnBWSPlvrMqlRym9yvRR7UV0GwUlLRQAVwHirw99gl+22qf6LIfmUf8s2/wAK76mypHPC8MqB43GGU9xXHjsHHFUuR79H2OjDYh0J8y26nlmk6xeaLdCe1kxnh0PKuPQivSdH8Q2OuxgQsIroDLW7nn/gJ7ivOdf0iTR74x8tA/zRP6j0+orNi80N5kW8Mnzbkz8vvntXxUnUw83Tmtj6BKNWKlHqe1459DVmC+ni43b19GrzrR/Hc0IWDVUM8Y4EyffH1Hf+ddxY3tpqUHm2Vwky9wp5X6jqK3jKFRaGLUoG9DqMMnD5RvfpVsEMMqQR7VzxWlWSSM5Ryv41DoroUqnc6CisdNTmXhgGFWU1WI/fRl+lZunJFqaZfoqBLy3k6SDPvxUwYN0IP0qGmi7i0UUUAFFFFIBKWikoAWiiigAooooAKKKKACiiigBKKCcDJ4FQSXsEfG8MfReaaVwuT5xSbh3NZsl+7tiNMZ9eTXIeKfGn9lh7KxkEl+eHkHIh/wDsv5VTjZXZPNd2Rb8d+MF0y3fS9PkzfSDEsin/AFKnt/vH9K8jqR5GldnkYs7HLMTkk10vh7wi+rQvcXTtDD0TA5JpUaFTEz5aaCrVhQhzTeh9MfCTxXpXiLwTY21iEhuLCFIJ7UHlCBjcPUHGc/XvXfV8T2d9rHw98VR3djM0c8Jyp/hlTuCO4PcV9a+CfGNh428OQ6rZkI/3LiAnJhkHUH27g9xWU4ShJxktUaQmpxUo7M6OiiioKCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDw7xJ/yVTxJ/1xtf8A0CuW8cFh4SvNvcoD9NwrqPEpx8U/Ef8A1xtf/QDWNr1mdR0K+tVGXkiJQf7Q5H6itF8JD3JrIKtlbhfuiFAPptFYPir9xcaRqMilre1uQZcDOAcYP6Vd8M3ovvD1nLn50QROPRl4/litSWGO4ieGaNXicbWRhkEV8KpOhXbktm0/yZ8EpvDYpuSvZtP8Uy4jrLGskbB0cblZTkEetLzXJf2Hq2ksf7C1TZATn7Ncjcq/Q4NPWx8U33yX2rQ2sJ6/Zl+Y/j2/OoeFh8Uait53v91iXhaXxRqx5fO9/ut+Q7UWS48daZHbkNNbRu1wV/hUjgH8/wBa3jxiqumaRZ6RCyWyHe/MkrnLufc1Nczx20Mk8jYjiUuxPoOaVWam4whslb11v+pnXqRqOMKeqirLu9W/zehheEcC/wDEKr9wXxI+vOa1dcP+jWZ9L+2/9GCs/wAFQOmiSXsylZL+4e4wfQnA/wA+9X9d/wCPO1/6/rf/ANGLX3VKLjSUX0R+gUouMFF9Ej6MooooNgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKBBRRRQAUVnavr2k6Bam51bUbezh7GZwC30HUn2FeOeLf2iLO3D23hexNzJ0+13QKxj3VOp/HH0oA9k1rXNM8PabJqGq3kVrax9XkOMn0A6k+w5r5b+J3xYvPHFybCxElrocbZWInDzkdGf+i9vc9OQ1jXvEPjTVBPqV3cX9weET+FB6Ko4UfSjU/DF5pOmRXlyyhnfa0a87fTJraFCpOLnGOi3ZnKtThJQk9Xsb/gbQo5QdVuF3bW2wqemR1au8zmud8FSh/DMC90d1P55/rXQg19ll9OEMPDl6q/zPksfVnPES5ujsFHWjFFdpxEE0X8S/jUGavdaikhzyo59KuMujOilVt7sivSU4jFNzzVnSFIaKKYFLU9Mh1axe2m4zyjd1b1rgdMu7nwn4kSSaLd5bbZYz0kQ9cfzFel1keINCj1i13IAt1GMxt6+xrxc4y1YunzxXvL8V2/yPRy/Gyw9RJvT8mbt74E8PeKbFNR00i1aZdySQj5T9V6flivPdW8Ja/4Vn+0BJDEp4ubYkgfXHI/GrvgvxdP4V1BrG+D/YXfEqEcxN/eA/mK9ujliubZJYnSWGRQysDkMDX55KdbDTs9Ufdwo4fHw5krS62PDdO8fXcGI9RhW5Qfxr8rj+hrqrDxFpep4FvdKsh/5ZS/K3/1/wAK29e+H2h6yWkSE2dwf+WkHAJ916V51q/wz1zTi0lqqX0I6GLhv++T/TNd9HMoy0l+P+Z5mJyerT1irry/yO7OR1GKSvK7XXdb0SXyTLKu3rDcKTj8DyK6Sx8fW7gLf2bRnu8JyPyNelCvCXU8mVKcd0dhSrK6H5WI+hqhZ63pWoAC3voix/gc7W/I1fKnGcceta2TRnexMt/cJ0kJ+tTLq0w+8qmqNJUunF9Bqckaq6uP4ovyNSDVYD1DCsakqfYxK9ozeGpWx/jx9RTxe2x/5arXO0UvYIftWdKLmA/8tU/Ol8+H/non51zNJS9h5j9qdP8AaIR/y1T86abu3HWZPzrmqKPYeYe1OiOoWq/8tlP0qNtVtV6Fm+i1g0lNUEL2rNh9ZX+CEn/eOKryarcN93an0Gaz80vWqVKKJdRkr3Esp/eOzfU06M57gADJJ6AVQ1DULPSoPOvpxGD91By7/QV5/rviq71jNvAGt7P/AJ5KeX/3j3+nSlUnGmtRwjKbN/xJ41WNHsdHfLH5ZLofyT/H8q4A5JJJJJ7mpYY5J5VijQvIxwqgdTXeaF4JEW241Agv1CDtUYfC1sZP3dl16IdfE0sLG8n/AJsxPDXhmW/mWa4QrEOeRXpkMSQRJFGu1EGAKSOFIUCRqFUdAKeK+swmEhhocsfvPl8XjJ4mV3t2MXxRoi6zp5KAC5hBaM+vtXLfDzx1eeAfEv2na8llKfLvLYfxLnqP9odvxHeu+uJlt7eSd/uxoXP4DNeV3OgavdaS3iT7GzafNM4MqchWzzn0HvXgZ8qVOcJt2ctPX/gns5JOpOEobpH2doHiLSfE2mpf6RexXUDDnYfmQ+jDqp9jWrXwfpuqX+kXS3WnXtxaTr0kgkKN+Yr0DR/jt400sqtzc2+oxD+G6iG7H+8uD+ea8Q9o+sKK8d8OftC+H9RKRa3aT6XKeDIv76L8wNw/KvU9K1vS9ctRc6XqFteQ/wB6GQNj646fjQBfooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDw3xP/wAlT8R/9cbT/wBANVx1BqfxP/yVTxH/ANcbT/0A1CK1jsQzlZE/4RXW5JmB/se/fLMBxBL/AIH/AD0rpeGUMpBUjIIPBFSSxRXMDwTxrJFINrIwyCK53+ytX0Mn+x5FvLLOfsdw+GT/AHW/xrw8yyp1pe1o79V3PDzPKniH7Wl8XVd/+CbpNKGxXP8A/CTLEdt7pepWrjqDAXX8COtJ/wAJKkp22emancuem23Kr+JPSvC/s/E35eRnz39mYu/L7N/157HRA56c1zWpSN4jvf7HsWJtEYG+uF6YH8APqakWx1vWTtv3XTLI/eggfdK49C3Qf54roLSzttPtEtbSFYoV6Ad/c+pr2cuyiVOaq1umy/zPby3JnRmqtfdbL/MdGiRRpHGoWNFCqo6ADpWfrv8Ax423/X7b/wDoxa1MVma//wAg+3P/AE+2/wD6MWvoHsfRI+i6KKKyLCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiq97fWmnWr3V9cw21vGMvLM4RV+pNAixSEhQSSABySa8d8V/tB6Hpm+38P2z6pcDjznzHCD/6E35Ae9eLeIfiB4w8bzGC6vpmgc8WdqCkf4qPvf8AAs01Ft2Qm0ldn0b4o+MfhDwyXhN9/aF2vHkWWHwfdvuj88+1eNeJPj/4m1YvDpEUOk254BT95MR/vEYH4AfWuT03wFe3O176RbaM/wAPVv8A61ddp3hfStNw0dsJJB/y0l+Y16mHyjEVdZe6vP8AyPNr5rh6WkXzPy/zODTTfEHia6N3cNc3Mjn5ri6cnP4nrXS6d4AtowH1C4Mzd44+F/PrXYAYGBwKK9uhlGHpay95+f8AkeNXzavU0j7q8v8AMr2un2lhH5dpbxwr/sjk/U1m+Krb7T4bvFAyUUOPwOa2qZcQLcWs0LdJEK/mK76lNSpOmlo1Y4KdVxqxqN6ppnHfD+43WV3bZ5SQOB7EY/pXZivOPBcxtPEUls/HmoyY9xz/AENeilgoyxCj1JrkyqfNhUnuro7c0hy4ltdbMfmlxVOXVLC35mvYE+sgrLvPGukWwxCz3L+ka8fma6quJpUvjkkctPD1anwRbOgANVr/AFG102BpJ5BuAyIwfmNZlhp3jrxeoOl6W9jZv/y3l+QEf7x5P4Ct+2+BGpSqZNR16ISnqI42f9SRXzuM4rwVB8sXd/1/XQ9Shk1V+9U+5f1Y5G38YaXdSbHZrdieDIOD+Na6yLIgdGDIehU5BrWvPgS4T91eR3JA75Qn+dcffeCdd8MXO20mlhcniC4xtk/3W+6awwPGWHnLkrffa39fgdlfL1FXV4+u33m6DmnVz+neIA9z9i1KE2d4OMPwrH29K6DBxX2NDEUsRDnpSujzalOVN2khKWikrYzOe8T+HxqMJu7VMXSDlR/y0H+NQeAvHDaLONK1Jz9gdsK7f8sW/wDifX0rqM1yXijw19oD39kn70cyxr/F7j3r5rO8nVeLq01r1X6rz/P8/byrM5Yeai36f5M9pVldQysCpGQQeopcc15N8PfHP2Vo9F1WX9yTtt5mP3D/AHT7eletgcV+eVaUqcrM/RsPiYV4c8SlfaTp+qReXfWcNwv+2oJH0PUVx2p/CjR7rc9jPNZuein94n68/rXf0lKFScPhY6tClV+ONzxLUfhbr9mS1r5N4g6eW+1vyOP51iMPEmgNtkW+tQOzq239eK+iaaQGGCAQexrqp5hVgedVyejP4Xb8TwS38eapDgTpBcAf3k2n8xWrB4/tGA+0WEqH1jcMP1xXqN54Y0PUMm50q1dj/EIwp/MYrBuvhd4cuMmKO5tif+ecuR/49mu2GbP7R59TIZ/Za/I5yLxjocuMzyxH0eI/0zV2PXdHm+5qdt9Gbb/Oo7n4PwHJtdXdfQSwg/qCKzJvhHqqZ8nULST/AHty/wBDXTHNKb3/AFOSeTYhbL8jokubaUZju7d/92VT/WpQpbpg/Q5riZPhd4kj+7HbSf7sw/rioD8PPFcX3bI/8BnX/GtVmNF/8OYPK8Svsv7jvvKkP8DflR5Mn/PNvyrgP+EJ8YL0s7gfScf/ABVH/CG+Mj/y6XX/AH/H/wAVVf2hR7/iiP7OxP8AK/uZ3/kyd0NNYBBlnRf95gK4QeBfGEnW1l/4FcL/AI09fhr4olPz28S/784pPMaK/wCHKWWYl/Zf3M7CS+sYf9bf2qfWZf8AGqcviLRIfvalG3tGrN/SsaH4Ta4/+tubKMf77H+lalp8ITkG61hR7Rw5/UmspZrSWxtDJsTLp+RUuPGukxZ8mK6nP0CD+tYl5451Gf8Ad2MEdqDwCo3v+Z/oK9V0n4MaAESW6nubgHsWCg/lXZaZ4K8O6MVay0q3WQf8tGXc35muaeayfwo1jlSi/fl+v+SPAtD+H3ifxbcCeSGSKJzlrm6yM/QHk17V4T+GuieF4d3li8vGGHnlH/oI7CuwUYGAMD0FOHNedVrzq/Ezvp0YUvgXz6nzHbWSaf8AFK8s1ACRXMyqMdBzj9K9ABrk/EcX2X423QHAeYN/31GK6sV99w/Lmwfz/RHwXECti/l+rHdabS5or3DxDL8SN5fhy/fv5RH58f1r074Y6TbTfCXTbK4iV4bqGQyKw6hmb+leWeL22eF7r3Kj/wAeFe1fD9BD8P8AQkHa0T+VfnHHNRqdKPz/ADPq+H7qnJrv/kfIl5bGC8uIhyI5GX8jiut8I6DpuqaS811D5sgkK/eIxVTx/pE/h3xxq1nPGVjkmaaFscNG5JBH54+oNW/h3eBbi6sWP318xPqODXfklSnVrRc0mpI6s3vHDuVJ2aL994As5ELWVw8D/wB1/mX/ABrm5NL8ReF7oXdq9xC6dLi0kIx+I5Fepk803Ga+lr5Rh6usVyvy/wAj52jm1en8XvLz/wAzG8M/H/xLpJSHWYotWtxwWb93MB/vAYP4j8a9n8MfF/wj4mCRrf8A2C7bj7Pe4jOfZvun88+1eN6h4X0rUstLbiOU/wDLSL5T/wDXrkdS8CX1tl7GRbmP+791h/jXh4jKMRS1j7y8v8j2aGa0Kuknyvz/AMz7NBDAEEEHkEUtfGGh+PPF/gqYQ2eoXMManm1uBvjP/AW6fhivXvC/7RWn3WyDxJpz2ch4NzbZeP6lfvD8M15bTTsz0k01dHuNFZujeINI8Q2v2nSNRt7yLuYnBK/UdR+NaVIYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFIAooopgeGeJv+Sq+I/+uNp/6BUHerHiYf8AF1fEf/XG1/8AQDVYmtY7EMdmkzTc0VQhs11HaxNLNMsUa9XdtoFQ2mq2OpFhaXsNwV+8EfJH4VV0G0sNZ1fWNX1xPO0/RmMcVsw3ICASzsv8R44rS1e20HxV4Qm8R+H4o7S9sVaSKaOIROpQZKOB1BH16io5h2FHtTqrWFwbzTra6KbDNEshX0JGasiqELWZr/8AyD4P+vy3/wDRi1pVma+f+JfD/wBfdv8A+jFpPYaPoyiiisiwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKiubq3sraS5up44IIxueSVgqqPUk8CgCWquoajZaVZSXmoXUNrbRjLSzOFUfia8h8ZftAaZp3mWnhiAahcDj7VKCsKn2HV/0Hua8P1XXPE3jvUhJf3NxfS5+ROkcf0UfKtVGEpu0Vdkykoq7eh7N4w/aGsrQyWnha0+2SjI+2XIKxj3VfvN+OPxrxXVNc8UeOtQD393c38mcqnSOP6KPlWt7SvAEabZdUl3t18mM8fia662tILOIRW0KRRj+FBivawuS1J+9WfKvxPHxOcU4e7SXM/wADjdK8AqNsmpzZPXyov6muxs7C0sIvLtLeOJf9kcn6mrApa+goYSjh1anH59Twa+LrV3+8fy6AaSlpK6DmCiiigApc0lFAHmOu79D8XPcRDneJkHYg9f6113hn4d+IfiQjareX4sdO3FYmZS2/12LkcD1J/Osv4g6eXtba/Qcxny3+h5H9fzr2n4N6tHqHw4sI1I8yzdrd19Ocj9CK/OuJcTiMDzQpOybv96/pH2mWezr0oVJL3krfcYFj+z5oMLhr7Vb+59QgWMH9Ca7nQvh74V8PFXsNIg85ek037x/qC2cfhiuoIrnPHXiFvC/g+/1SPHnogSHP99jgH8M5/CviZYnE4hqEpN36HrckVrYqeKfiNofhWb7HK0l5qGM/ZLYbmX03Hov8/auNPxpulk3yeFpBb99t0C4H02151p8LLGbmd2lu7g+ZLK5yzE8nJqys0buUDgkdea9GOEowVmr+ev6H02GyKEqalWk030Vj3nwx4u0jxdZG50yclk4lgkG2SI/7Q/qOK09R0201Wze0vIVlifsex9Qex96+aYdVn8IeJbHXbIlfn2XEY6SxnqD+H6gGvqKNlliSRDlXUMp9Qa8/F4f2MlKD917Hi4vDvD1XRlr+qPCfF/g1I7x9JvTuYqZLK7x8xX0PqR3H41zHh6+uEuJ9G1A/6VbfdYn7yj/I/CvbPibahvCw1JQPO0+dJFP+ySFYfkf0rw3X3Fp4w0i/j4E+I29xnH8m/SvpeGMyqYfFRhf3ZaW8/wCtfU+VxtBQm6S2abXk1/mdGTTasvAr8jg1A8bp1HHqK/W1JM8GNSMhlKDSZpRVFnIeJ/DG/fqFgnzdZYlHX/aFb/w/8ef6vRtXl/2bedz+Ssf5GtHOK4/xL4Z3b9Q09Pm+9LEv8x/hXy2dZKqsXWpL1X6r9T38pzaeHmoyf9dme4EU2vMPAnxABEekazLg8LBcOfyVj/I16dnPSvz+rTlTlZn6Hh68K8OeAuaKSiszYWikpaBiUUUUAFFFFIApMUtFACYopaKAEpY4WmmSNerHFJWxpFrhTcMOW4X6etUldmdWp7ONzSjQRoqDoBin0lLWiPIeoUopKKYjwHx8vk/GiJ/7/kn81xXR1gfFH938WtPf1jgP/jxFb9ffcNv/AGVrz/Q+C4jVsUn5BRRRX0J8+YXjIE+F7jHZ0P617F8N9QjuvBWmRBhvjt049sV5fqdkNR0y4tCceahAPoex/Oo/hp4kNmP+EevpjaahasRAXON65zt+v8xX53x1hKk4wrwV0v8Agn0eS11Cm12evo7f5Hsvifwdoni+zWDVrUSNHnypkO2SPPof6dK+avEOjN8PfiILRZGkt42V0durRN6/r+VfRsXiKaJQtxArn+8px+leK/G8G+1LTtVEezcjQnv05H8zXyWQ4+pTxUYJ6dvNanu1alGtBw7m0pDAEcg9DTxWR4XvP7Q8PWspOXRfLf6rx/LFbFft1OaqQU11PgqkHTm4Po7CUUUVZBXu7K2vojHdQRyp6OK5bUfANtLufT5jA3/PN/mX8+o/WuxorCthaNdfvI3Oijiq1B/u5W/I8pksNf8ADF2t3Abm2kTlbi2cjH4ivQPC/wC0F4g0vZBrkEeq244MnEcwH1HB/EfjWqRkYI4rD1LwlpWpZYw+RKf44uPzHSvExGR9aMvk/wDM9nD50npWj81/ke6+Ffid4W8XBI7DUFiu2/5dLn93Jn2B4b8Ca7CviTWvCV9oytcxOJrdOd68Mv1H+FdN4P8AjX4n8MGO3u5f7V09cDybljvUf7L9R+ORXhVqFSjLlqKzPapVoVY81N3R9a0VyHgz4k+HfG0IXT7ryr0DL2c+FlHrjsw9x+ldfWRqFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFJmloA8O8T/APJVPEf/AFwtP/QDVU1b8S8/FPxJ/wBcbT/0WaqN1rWOxDEoowT0oFUIrQreaVfXd5pkdvMt4gW6tLjISUgYDAjoccHjBrG0zSNWitbzTmMWn6RdyiSa2hk8x3/2Q2PlU9+5rpBS1NkMAFRFRVCqoAAHYUlLTSaYC5rJ8Qt/xLYv+vqD/wBGLWpmuY8Zazb6bYRwkh7p5UkjiHU7WByfQcYpS2BH1CGqnNrOl2z7LjUrOJ/7sk6qf1NeBJYfEb4jp9r1fWH0bTJeUtogUyv+4CCf+BHNPT4FaURmXWb137sEUD+tccsRTTtc6o4apJXsfQFtf2d4M213BN/1ykDfyqxXzbc/BW/sD9o0HxAyzLyokBjP/fSn+lO0r4oeNfh/qMeneLbaW9s+gaX7+PVJOjfj+lXCrCezJnRnDdH0hRWXoHiHTfE2kxalpVws1vIPxU9ww7GtStDIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACkLADJOBXGeMfih4b8Fq0V3c/ab8DiztiGfP+12X8efY186+Nfi14l8ZM9qJTYaa3AtLZj8w/226t9OB7UBa57X42+N+geGjJZ6YRq2orxthb9zGf8Aafv9Bn6ivnvxR478SeN7zOp3byRA5jtIQViT6KOp9zk+9YVlZPdalbWe07ppUjwOvzED+tfYmi+ENA8P2AttO0u3hGza0mwF3453MeTXnY/MY4NLS7ZcabbsfMXhHwxaavA93dysyxvtMK8ZPua9BtrS2sYRDawpFGOyjFcZ4TY6b4j1PSm4UsWQH/ZP+Brt85r7/J1SnhY1ILV7nx+azqe3cZPTdCk0lFFeseYFFJRQIKWkooAKKKKACiiimIqapYrqWl3Fo3/LRDt9m7frWb8FvEo0LxTNol42yDUDsG7+GZfu/nyPyrdFec+NNPfTdZTULclFnO8MvG1x/kGvmuJMvWKw9/k/0fyZ72SYnkm6T66r1PrlWYcHnFcb8VdIuNb+H1/DaIzzwFbhUHVghyQPwzXH+BvjZp99bQ2HiVvsl4oCi7x+7k92/un9PpXrlvcw3cCT28sc0LjKyRsGVh9RX5JOnXwdVc62enZ2ProyUkfMulXcV/ZxsjDeFwy+hq3HpttbsZ8BD1zmvQfFfwXtdTvpdS8PX39l3UpLPCVJiZj1Ixyv6j2FcsvwQ8X3MgjvNcskhzyyu7nH0wP517McRQmuZTsuzv8A0z6KlnsVTSq07yX9fI42aGXxV4ksdE05TI0koUkcgDuT7AZNfV8USwQxwp92NQo+gGK5LwP8O9I8EQM9sWub+RcS3cowSPRR/CP85rr+vFebjsVCq1Cn8Mfx8zxcRiJ4iq6tTdnG/FO9js/h9fhzzO0cSD1JYH+QNeF+Io/MvvDkH/LTKsR+K/4V2/xM19PFHiS00OxkD6fpzmS5lB+VpOmM+wyPqT6VwunT/wBv+N3vI8m0sl2oexxwPzOT+Fe1kGElPE0l1vd/PRfhdnzuPqr2jmtop/8AAO1IwaTrSk5pK/Xj48je3RuRwfaoWt3XpgirdFUpNFxqyRQII6gikzV8gHqM0xoY27Y+lWpmqrrqcJ4n8MCTdf6emH6yxL39x/hWl4H+IRs/L0vWpCYPuxXDcmP2b29+1dKbYfwt+dcn4l8HfaQ17p6gT9XiHR/ce9fM5xk0K6dSiteq/VH0OU526E1GT/rsz2FCsiK6MGVhkMDkEU6vDfCvjy/8MuLO6RrixBwYXOGj/wB3PT6dPpXqth4z0DUoVkhvhHu7TKVwfQnpXwdbDTpuzR+h4bHUq6unZ9jcopscsc8YkikSRD0ZGyDTq5ztCiiigAooooAKKKKQBRSZq9Z6dJckM4KRep6n6U0rkznGCvIjsrNruUZ4jX7x/pXRKAqhVGAOAKbHEkUYRFCqO1OrRKx5daq6j8haKKKoxClpKKAPBfi78nxM0x/+mMP/AKGa38Vz/wAZDt+IOmt6QRn/AMfNdFjivuuGn/s8kfDcSr9/F+v6CUlKaSvpT5sWsnWfD1nrSBpQYrhR8k6feH19RWtRWdWlCrHkmrounVnTlzQdmc5b6l470BPJiki1W2XhfNG5gPzDfzrF8T6/4g17TxbahovkJE3mb0icbcD1Pau9FK4EkbRuMowIIPcV87PhXA+09rTVmenTzicWnKKf4HC/Dy9w13YseCBKg/Q/0ruc815dobHSfGq25OAJWhP0PA/pXqGK9TKpuVDle8W0Rm0FGvzraSuLRSUV6R5YtFJRQAtKKSqmoajFpsKTTBtjOEyO2fWlKSiry2KhFyfLHcxfG8zJoQgT71xMsf8AX+gr0t/gn4YufD9rZyQSRXscQDXcLEMz45JB4PNeZ+NFMmgwXUfIinRwfbn/AOtX0lYXC3um2t0hyk0KSKR3BANfmvGletTxNNwbS/r/AIJ9fkaj9W+Z8x+KPhR4k8HSnULIveWkR3rdWuQ8eO5A5H1Fdn8P/jxNbGLTPFxaaHhU1BRl0/66AfeHuOfrXoHij4k6J4Yu/sLCa/1Hva2qhiv+8eg+nX2rxLxq2geIZX1C00a70G+Y5bzEzBMffH3T74x6+tedgsfWmkq0d+v/AAP1R7Tws3HninY+q7S8ttQtIruznjnt5V3RyxsGVh6gip6+O/AnxK1nwBqPlDdcaY7/AL+yduPdkP8AC3v0Pevqzw14m0vxXo8Wp6TcCWB+GHRo27qw7GvYOU2KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKQBXmfxX+KS+BoIrDTo459YuE3gScpCnTcwHUnnA9s/X0skKpYnAAyTXynpKf8LI+Mt3f3YMtkJnuGVuR5SfKi/T7o/OlKSirsqMXJpItWkPxY8bxC+k1e6tLWX5kZ5zbow/2UQZx74p8vgf4n6cPPtPEE9w684h1GQN/49gV7cwHQDAHAA7UAVwPFzvoeksHTtqeIeEbrWrvV9bfxA9w2pr5KSm4GHwAQM/hjmtzU7x7SOGO2h8+9upBDbQ5xvc+voB1NOuxt+Inib3Ft/wCi6baukfxC8PvMf3bJOkZPQSFePxxXp05XpqR5c42m4mtafDszRCXWNc1Ca6blltJPJiT2UAc/U1naxpF/4RRbw3kuo6LuCymYDzrbJwGJH3l9e4r0ms3xF5H/AAi+q/aSPI+yS78+m00XEchwQCCCDyCO9NJxVHQvM/4R3TvOz5n2dM5+nH6Yq73HrWhJStbbUfE2o3Fnp1z9isbRtlzehdzs/wDcjB4yO57Voz/D64giL6b4i1AXI5UXhEsbn0IxkfhVr4alB4PVf+W63c4n9fM3nr+GK68+9RcZ5E3iE2Flf/2rB9nv9PO2eDPDMfulT3Ddqi+Hnhp9d1VfEWsr5sk0m6FGHAUd8foPpVT4rWyXvjzTbKA4muIY0m298uwXP4V6nosEVlLawRKFjjUIoHYYxXDjazilBdT1ctw6nzVX029f+AdPwBgcAdBSUGkrzjsHZqhrWi6f4i0uTT9SgEsLjg/xIexU9jV6jNNNrVCcU9GeDaPq2rfBnx+1pcM82lTkeYv8MsRPDj0Yf0Ir6jsry31CyhvLWRZYJkDxup4IPSvHfin4aXxD4SlniTN7YAzREDkr/Ev5c/UCoP2fPGDXmmXHhm7kzLa/vbbcesZ6r+B/nXqUKntI36nkYil7Oduh7jRRRWxgFFFFMAooooAKKKKACiiigApM1T1TVrDRbGS+1K7htbaMfNJK4UD29z7V4P43/aDkl8yx8IwmNeVOoXCcn3RD0+rfkKAPZfFHjXQPB9n9o1m/SEkZjgX5pZP91Ryfr096+ffGnx11zxB5lnoYbSbBuN6NmeQe7D7v0X8zXl9xcahrmpma6nmu724bmSZyzOfqa9Q+FXhOwi8b/Ztdt47iYQebbKxym7r074Fc2KxUMPBylq+xPPFSUG9WeaRWc9xNmXcpc5LP1Oe9awtbSyty2VLY69zX054o+H2heLIl+1wGC5QYS4t8KwHoexH1rk7D4DeH4LpZb3UL68jU5ERIQH6kc/kRXkxzanUjed0+2/4nrYerTw8bqN5d2eefCDwdPr/i2PWp4iNO05/M3EcSSj7qj1x1P0HrX0selQ2NhaaZZRWdjbR29tENqRRrgAVHql7FpmlXd9OwWKCJpGJ9hmvDxuJliqvN8kjnvu2fMOpqLTxXpuopwJbiSBz6/OQP0b9K7MVxurRs9joO4fvpr3zcfU5rs+9fq3Cc5PByi+jZ8NmTUnCXqvxCiikr6k8sKKKKACiiigAooooAKSlpKYgrM1/TF1fSZbbA8zG6M+jDpWnRUThGpFwlsy6c5U5Kcd0eS6FpEGpajJYXUjwTYOwgdx1BFdTp9l4w8ITmXQdTkMXUxo3DfVG4NVPF2mSabqMOtWYK5cF8fwuO/wCP+etdhpGoxarp0V3EfvDDr/dbuK+epZZh6vNhq8dV17r8j6DEZhWhGOIpaxe67Mt6Z8dtV08rB4h0JZCODJDmJv8Avk5B/Suttvjn4OnjBma/t27q9vux+Kk1yUscUyFJY0dfRlyKy5/DWj3BJexjBP8AcJX+VeXiOCqEnelK34f5/kaUuINP3kfu/pHf3Xx08GwIWhkvbhuypblf1bFcB4m+NV9r0T2dj/xKrFwQ7Kd8zj0yOF/D86q/8IXoec/ZX/7+GrVv4Z0e2IZLCIkd3+b+dY0OC4wldtfff9Ei6ufUpKyT/A5S1e91m3On6Pavb2jcS3MnVvqf6Cuy0jSbfRrFba3Ge7uern1q6qhFCqoVR0AGAKdX1OXZTQwKvDWT3Z4WJxkqy5UrR7d/VhRRRXqHGFFFFABRRRQAUtJRTA5/xF4Wt9ZRp4NsN6Bw3Z/Y/wCNZngXxa/gjVptM1q08zTp2/fIyAmM9N49RjqO/auzrN1nQ7TW7by512yqP3coHzL/AIj2rxcyymGKi5Q0l+f/AAT2stzeeGajPWP5f8A9TtdE8N6hBHqGn28HlzLuSa0YoGH/AAHGfxqZ9Ej/AOWUzj2bmvAtB8T678NdV+zzq1xpsjZeEn5XH95D2b/Jr3zQfEGneJNNS/0y4EsTcMvRoz/dYdjXwOKws6E3GaPv8HjlUipU5Fd9IuB91kb8cVC2n3a9YSfoc10dFcnKjvWLqLc5g2twOsL/AJUn2ac/8sZP++a6iijkK+uS7HNrYXT9IWH14qxHo87ffdEH5mtyinyol4uo9ilb6ZbwEMVMjerf4VdooqkjnlKUneTCiiigkKKKKACiilFAHzt8ZbkSePygPMFvGp+vLf1rRtvGOjS28QluhHKVG4Mp4PesLxbaSeJfjFdadG5BuLxLcMOcAAAn9DXs6/BDwWLNYXtbppAOZvtDBifX0/SvWo59HKYqLV+ZL+t0fO5hgI42fvO1rnE219bXq7raeOVfVGBqzSa98B7qwDXnhXVZWkTkW85CsfYOMA/iB9a4aLxdqWi3kmna/ZSCeI7Xyu2RfqO9fTZdxNhMZpez/r+u3mfPYrJKtLWm7r8TuqK5+38Z6LPgG4aIn/nohFbNve2t4u62uIph/sMDXv069Op8EkzyqlCrT+OLXyJ6M0UVoYnl3ilTYeMmnXjLpMP0/qDXp6MHRWHQgGvPfiFEF1W1l/vQ4P4E/wCNdzpj+bpFm56tCh/SvKwK5MTWh53+89fHPnw1GflYtUUUV6x5AUUUUgCqOr2Q1DSri3x8zISv1HIq9RSlFSi4vZlQk4SUlujkvDsh13w3f6NOf38afu8/mP1FeheDfHxtfhHfKxA1XRk+zKjdTuO2M49icf8AAa8xVj4f8fA/dguT+GG/+vVrxbpb2sr6lZsY1mIE6DoxyCCR9QPxxXxecZa8Zhtfipuz9F/wP8z6rBYqFHEpP4amq9eqNnT4U0qEzSky3sxLzSvyzMeTzSy6k9wCjKCh4xiqMOpx6xp6XMBBkC7ZE7q30qKAsXxgj2NfFuMtefc/Y6DpOlH2W3QpSaLYS6rbWV0pFleSCJZF+9bOeAy/7Oeq9Kdp954n+DXjNkdSYyf3kZJ8q7iz1Hv79Qal1BTc3+mWUHzXE93GsYHXO4f419DeLfCeneMNEk06/TDYLQTgfNC/Yj+o71r/AGhLDSg56xle/wAup8ZnNCnHEuNM2fDHibTfFuhw6rpcu+GThkP3o27qw7EVsV8k+HfEGt/B7xzNZ3iM9vvCXduD8s0fZ098cg/ga+q9L1Oz1rTLfUbCdZrW4QPHIvcf49sV70JqcVKL0Z4hboooqgCiiigAooooAKKKKACiiigAooooAKKKKACiiikAUUUUAYXjTUP7L8E63eg4MNlKyn32nH64rwn4DWIFvrOosOS0cCn6ZY/zWvU/jVefY/hTq+DgzeXEPxkXP6Zrivgza/Z/ACy4wbi6kf8ALC/+y1hiXamdOEjeoeh96BSA0orzD1Ty6/8A+Sh+JP8Adtv/AEXUeoWEeo2wiZ3ikRxJDNH96Jx0YVNfj/i4fiQ+1t/6LqUV7tD+FH0PCrfxJepYs/GOu2EQh1PRP7QZRgXNjKq7/co3Q/Q4qjq2oav4rVbW8tRpmkBg0lv5gea4xyAxHCr7das5pC1XyozuKcAYAAA4AHaozSlqTOaoRRt5NS0DUp7/AEdI7iK5Ia6sZG2h2HG9G7N654NaknjfV7hPLs/DUsEx48y7nXy19/l5P4VFtzTgKlxQ7nD3lpNH8S9DnvJzcXNwQ80pGAWyRwOwAwAPavWEcxyq46qc15n41DWN7outopK2k4V8emQR/I16OkqTRJNGwZHUMpHcGvHzBNTTPo8madKUfM6tWDqGHQjIpaztMut8IiY/MvT6VoA5rlTuXODhJpi0UUtMgbtDAqwypGCD3FfO8Mknw5+MaOmUt4rofjC//wBY/pX0V1rxX466PsutL1iNf9Ypt5CPUcr/ADP5V1YWVp27nJjIc1O/Y+mYnWWJZEOVYAg+1Prjvhdrf9vfDzSrpm3Sxx+TL/vLx/hXY16R5QUUUUAFFFFABRRXFeOPih4f8DxGO7l+1aiVyljAQX9ix6IPrz6A0Adm7pFG0kjqiKCWZjgAepNeSeNvjxo2h+ZZ+H1TVb4ZHnZxbxn6jl/w4968W8X/ABK8T+Pbn7NNK0Nkzfu7C1yEPpu7ufr+AFJo/gOWbbNqjmJOvkofmP1PaunD4SriHamjnr4mlQV6jMvWte8S+PNVEt/cz3s2fkiHEcQ/2VHCj3/Ot3RfAsMO2bVGEsnXyUPyj6nvXW2ljaafCIbSBIk/2Ryfqe9T4r6PCZPSpe9V95/h/wAE+exWb1Knu0vdX4nO+IfD63ljHJYRrFdWvMQQYyPSn6HrZ1VbeaGUWuuWLblzxkjr+B7it+sDWvDMWoS/a7SQ2t8OfMXgMff3964c+yH68va0dJr8THDYtWVOo7W2fb18j1/QPiZpV9stNYI0zUBwVmOI3PqrdPwP612kdxDMgeKVHU9GVgQa+VZb/X7JPI1bTBfwjgSKMnH1H9arR67p0R+SLVLM91icgfzr86r5HiKcrOLXyv8A5P8AM+mo4+Tj70ebzTTPqy+1Ww0y3ae+vILeJRktI4FeSeLPF0njeT+y9LDxaHG+65uXG3z8c4Gf4e9ebRaxpMkgb7BqeoS9hKSRWusesa9GsNzGNL0sdYI/vuPQ+g/zitsFkGLrVEqcHfu9EvPzfbY58XmEuRr4V3e/yW4QKus+IheRL/xL9PHlwHs79CR7D/CuhpkMENrbpBBGEiQYVRT6/VsswEMBho0IdN/Nnyter7SV1stEFFFFd5iFFFFMAooopAFFFFABSUtFMBKKWigCG6tory1ktp13RyLgiuAsLqfwh4ge0uSWtJDyexHZhXolY3iPQ01qwKrhbmPmJv6H2rixlCUkqtL447efkd2CxEYN0qvwS38vM2A6yIrxsGRhlSOhFKK4Pwpr72FwdH1ElAG2xl/4G/un2ruwea0wuIjiKfMt+q7MyxWGlh6nLLbo+46iiiug5gooooAKKKKACiiigAooopgFFFFAgooooArXthbalbNbXUQkjbseo9x6GuMSPXPh5qw1PSpme1Jw2eVZf7rj+v8AKu8pHVZUZJFDIwwVYZBFcGNwFLFxtJa9zvwWPq4SV47djtfB3jnTPGNjvt2EN6g/fWrn5l9x6r7/AJ109fNereHL3Q71dY8PSyxtE2/ZGfmj9x6j2r0/wB8ULXxMI9O1Qpa6sBheyT/7vo3t+VfAY/LauFnZrQ++wGY0sVC6Z6LRRRXmnpBRRRQAUUUUAFFFFABRRRQAUucc0lVNVuhZaPe3THAhgd/yUmhAzxD4cWx13423F8RlIZbi5z+JA/8AQhX0pXhX7Pdh5l5rmpsMkKkIPuSWP8hXf+LPij4e8KXJspHlvdQHW2tVDFP949B9OvtXl5nGdfGOFNXskv1/U8uD0v3O2rz74qfD6Lxfor3dnEo1m1UtEwGDKo6of6e/1rN0v456Hc3q2+qadfaWrnCzSruQfXHI/I16jBNFcQRzwSJLFIoZHQ5DA9CDXFyYjB1Izasymr6M+KtPsraa8NrfTtaNnbuZcgH0I7VuXHgvVbE+fp9ws+OQ0TbW/wA/jXd/HTwTDp9xH4osUCRXMnl3aAcCQ9HH1wc+/wBaw/BN7LdaHslYsYZCgJ9MZr9LyKth8wgotWfdPVPt/keFmc62GXtIO66pmBY+MNU0ub7PqkTSqpwQ42uP8a7rTNUs9WtxLaTB/VDwy/UUzUNJs9VhMV3CG4+Vxwy/Q151qui6h4cvg1u8pjP+rmjyPwOO9e/KWJwWsnzw/Ff1/VjyYww2O0iuSf4P+v6uaHxAuFk1a3gUgmKLnHYk13OlxGDSbOJuGWFQfyrgfD/h691bUlvb9ZBArb2aTrIfTmvSavARnOc8RJW5tvRGeYShCnDDxd+Xf1CiiivUPKCiiigQUUUUAch49sy9jb38Yw8D7WI9D0/X+dbGn3Eeu6DG0uGE8W1/Zuhq1qtmL/S7m2Iz5kZA+vb9a5LwHeEJdae5wyN5ij9D/SvNklTxlntUX4r/AIB6kW6uCuvipv8AB/8ABNTwz4L/AOEjku7Ow1L+zfElgeQ+fKu4+zccqemeCDwcdTWmfhz8RxL5RtLRh084TR4Pv1z+lQ6g13o+p2nifTB/pVkf3yD/AJaxdwfwz/kV7t4b8SWHifR4NQsZQyyLyueVPcH3FfnGfUq+W4hxsnB7Nr8D6/K8znUop05uL62fU4zwH8LD4f1Jdb126jvdUUYhSPJjhz1OT1P4AD9a9LFFBIUEsQAO5r5KvWnWlzTZ1tvd7nl3xl8IRa9pcd7AgF/AjGNgOXA5Kn6jOPcV578GPiQ3hbWBoeqTEaReSYDMeLeU8bvZT0P4H1r1/wAda/a2Gjy3cp/c26kg5xvYjAAr5on0C4n0E6yqHc0jOyY/g9R+Oa+l4c9tXp1IpXjHX/M82pXhGo03o3b5n2/nI4orx74G/EQ6/pP/AAjmpzbtSsU/cO55mhHH4len0x717DXuGwUUUUAFFFFABRRRQAUUUUAFFFFABRRRSAKKKKACiiigDyb9oa58r4dQw55nv41/AKx/oKb8OLb7N8O9FTGN0JkP/AmJ/rWX+0nPt8P6Hb5+/dO//fK4/wDZq6fwtD9n8HaLFjG2yi/9BFcuLfupHbgl7zZq0opO9KK889I8yvf+SgeJPpbf+i6mCkngZPtUN5/yUDxJ/wBu3/outGzI5Hevdo/w4+h4Nb+JL1Kphl7Rmq85NvGzyKQBW7gVnauFFtGzfdEyFvpurS5kRW+nPMge5ZlLciNDjH1PrUjaTgZt5HVh2c5BrSXk1MopXGYUAeXK7CJFO1l9DU/2ab+4anhwNYvdvQqmfrirmaYHPapp0epadPY3AISVcZxyp7H8DWB4S8RPo8x8M664hmgOLaZzhXTsM/y/LqK7K+ZS6gdR1rmPFWmabfaLPPqCEfZ4y6Spwyn0B9zjisK9CNaFmdOFxU8NU54ndW0jI4dD+Vaw1iygi3XdzFBjvI4A/WvnjwZ4d8R+K5ZbfTdRlt4IQN7vM4QZ6AY716BafBCKRhJrGuz3Dd1hXH/jzZ/lXlPDxpu0pfgezPGuurxh+J2t18QPCloxWTW7VmHaMl/5ZresruDULOK7tZBJBKodHHRga53R/h34X0Uq1vpccso/5a3H7xv14H4CupVQqhVUKBwABjFRLl+yTHm+0L3rjfippf8Aafw/viFzJalbhfwPP6E12VRXlol/p9zZyDKTxNGfoRiiD5ZJimuaLR59+zlrO+z1XRnblHW4jHsRg/yFe718m/CHUH8PfFSC0mO0Ss9pIPft+or6yr2FseI9wooopiCmu6xozuwVFGSzHAA9TRI6RRtJIyoigszMcAAdSTXzF8W/i5L4lml0LQZmj0ZDtlmU4N2R/JPbv1PpQB0PxJ+O21ptI8HyAkZSXUsZ+oiH/sx/Dsa8a0rQ9R8R3bzu7lGYtLcyksWPfk8sav8Ahnwk+pbby+DJaZyq9DJ/9avRoYY4IliijWONBhVUYAr28vyl1rVK2ke3f/gHjY/NFR/d0tZfkUNH0Kx0aIC2jzLj5pn5Y/4VqZzSUtfTwpxpx5YKyPmp1JTlzTd2FFFFWSFFFFIQUhVW6qp+opaKAEAC/dAH0FLRRQAUUUUwCiiigAoorE1zxNaaNiEKZ7thxEp6fU9qyq1oUY89R2RdKlOrLkgrs26KyNO8N/EnxHGJ7axj062flWuMR5H0OW/StQfCf4hsMtr9gD6eY/8A8RXgVOKsupy5ea/3f5nrRyPEtXbS+/8AyH0YqM/Cn4iA4GvWGPXzG/8AiKng+B/iK+Yf2z4pUR91gVn/AJ4FYVOMMuirp3/r5mkcgrt6yX4/8Aoz6pYWxInvYEI7GQZqsfEuig4/tGH8zXd6f8B/CdsoN3JfXr9y82wH8FA/nWzH8IfA0a4/sNW92nkP/s1eXU45pJ+5B/180dceH4296b/r7zzKHWNMuDiK/t2PpvANXMgjIOR6iu3vPgt4KukIjsZ7Vv70Nw38mJFcjqfwW1nSVafwrrzSbeRbXXy59sj5T+IFdGG42wlR2qxcf6+f5mVXh+SX7uX3/wBfoVqWuaj8Q3ml6kdK8TWL6feLxuZcK3v9PcZFdGp3AEEEHkEdDX12GxdHFQ56Mro8PEYarh5ctRWOX8WeGf7SiN7Zpi7QfMo/5aD/ABqLwj4k+0Kum377bhOI3b+L2PvXX1x/irwwZy2p6cpE6/NJGn8X+0PeubEUJUan1iivVd/+D/Xr24avCtT+rV36Pt/wP69OyIptct4W8VLfIthfuFul4Rz/AMtP/r11Oea7KNaFaHPB6HFXoToT5JrUKKKK1MAooooAKKKKYBTZJEijMkrqiLyWY4ArM1vxBZ6JBmY+ZOw+SFTyfc+grzu81HVfE18kWJJGdsRW8Kkj8AOprzcbmdHCqz1fb/M9HBZbVxPvbR7/AOR2l9440m0cpEZLlh3jGB+ZqlD8Q7R5QstlKkZPLBgcfhVrS/g74pnhEr2FvAW5H2uYAj/gIzj8axvG3gXVvCMdpNqQtilwWVWtzkZHODwK+XXFEqtXkpyXofQLI8LGn72rO/jkSaJJI2DI4DKw7g06sbwnP9o8M2hJyUBT8jWzX21Gp7SnGfdXPka0PZ1JQ7OwUUUVoZhXKeIfCK3jG+0zEN4p3FVOA59R6GurorCvh6deHJUV0b4fEVKE+emyPwD8VHEqaF4pYx3CnZFdycZP92T39/z9a9fyCAQQQehFeEeIfDVvrcRkXEV4o+WUDr7NUngX4i3nhi7Xw94n3/ZFIWK4fkw+mT3T37V8JmmTzw0uaOsf6/E+7yvN4YmPLLR/1+B7nRTY5EliWSJ1eNwGVlOQR6g06vBPdCiiikAUUUUwCiiigArlfiRd/Y/h/rEmcFofLH/AiB/Wuqrzj413fkeBlgBwbi5RfwGW/oKukrzSIqu0GzD8Iay/gn4G3WrQHF/qd40dqfRvuA/htc/XFY+gadDZQefOPNvJfmklfk5PJ5qbxpE+nfCvwDHtIhyZn9NzAMP/AEJqri6DwIyHggciuVpuMpr7Un+Dskd3D9KnKUpy3VrfM0r20ttQtnhmRXVh+XuK6r4H63ObfVfDVzKX/s9xJbk9kYkEfQEA/wDAq4GK6eJiS3y966r4H2sl34m8Rawqn7NsWBW7MxOf0AH5isa0f9mqKWys163OjP1BxhP7V7fI6L473sUHw9Nu5HmXF1GqD6ZYn9K8r8BwtHoLyEcSTEj6AAVe+Nevtr/jODRLNt8Vj+6wvQytjd+XA/OtLTbFdO023tF/5ZoAT6nufzr7DgvByhS9rL1+/b8EfnOf10qaprdv8i4KRgDwRmgUtfenyglLRRQAUUUUgCiiigAooopgJXnjD+xPiAf4YppP0f8A+vXolcN8QLUpLZXycHlCfccj+tefmSapqqt4tM9HLJJ1XSe0k0dwgHIPI6VzPn6x4B1R9T0QmTT5Tumt8nav+Hsa29Nuxe6dbXQx+8jBP171cbDAggEehox+Ao4+jyVFdPYmhiJ4Wpp80bmi/GvR7+FBdXK2c2PmW4Q/ow4P6Umr/FfQIEaR9UF2R92G3UnP9Pzrh7zwlo97IXe18tj1MTbf06U218H6LauHFqZWHTzW3D8q+JnwKpT+P3fX/gXPXlnNKUbNy9NPzKV5qOqfEPUkkuI2tdFhbKx5+9/ifftXT+TEsIhVAIguwJjjHpTlUKoVVCqOAAOBT8V9nlmV0Mvo+ypI8TFYqVeS0slsv66nm97De+B/FVtqmmyNHsk863fsPVT6jsfUGvrXwh4ns/F/hq01izIAlXEsecmKQfeU/Q/pg14Hrekx6zpcls+A+N0bf3WrO+D3jSTwb4ubSNRcx6ffOIZQx4il6K/9D7H2rwc0wX1epzR+F/1Y+ky3GfWKdpfEt/8AM+qaKKK8s9IKKKKACiiigAooooAKKKKACiiikAUUUUAFFFFAHz9+0rPmbw7b+07/APoAr0bT4/J0qyi/uQRr+SivLf2im87xd4fth1+zk4/3pMf0r1oLtVV/ugCuPGPY78EtwpRRQK4T0DzC8P8AxcDxJ/27f+i6n3EHg4qte/8AJQfEv1tv/RdT17tD+HH0PBrfxJepILiUD/WGo5maeNkkJZWGCKcFoxWpkMt766s1EckTTxjhXU/MB7+tWv7YeRcQ2zhvWTgCoKWkMZGJUkaXzD5rHLEVP9onPBkNRE0ZoEKSSc5/GuP+I16bfw/HbKcG4lAP+6Of54rr689+IYN1rOj2I/izx/vMB/SlJ2Q4q7SPWvhho66P4Gs8ria7H2iQ9+eg/LFdhnNQWkK29pBCgwkcaqB7AVPXz8pOTuz6FRUVZBS0lFIYuacpwabQDTEfN/jhH8M/Fia6iG0Lcpdpj0JDH9c19c2V0l5ZQXMZyksauD7EZr5n+O+nbNS0rVFH+tiaFz7qcj+Zr2z4V6n/AGr8OdHnLZdIRE31Xj+lerSlemmePXjy1GjtKKK8v+M3xEPhDQhpmnS7dYv1IRlPMEXQv9ew98ntWxicN8bvigbuabwnok/+jxnbfzof9Yw/5ZA+g7+p47HPm/hHwt/aUgvr1T9kQ/Kp/wCWh/wrO8NaI+ualh9wt4zumf8Ap9TXrEcaQxJFEgSNBtVR0Ar3Mqy9Vf31RadPP/gHjZpj3SXsqb957+X/AARcKqhVACgYAHYUUUV9QfMBRRRTEFFFFIAooooAKKSimAtFJS0gCiiimAUUlLQIo6xqA0vSp7vAJRflB7selbvwa8Dwy2w8X6zH9ovbly1oJRkRrnG/H94np6D61xvjZGfw45Xosqk/SvePBzwt4J0N7bHl/YYcY/3Rn9a/OuOsZVpqFGLsn/X9fM+t4fox9lKp1udFRTVYMMinV+dJ6H0AVWvr610yylvL24jt7aJdzyyNtVR9as14b4+1SXxV4xuNMd2/sfSGCNEDxNPjkt646fn610YfD+2lZuyWrNaFGdaoqcN2dJefGrS/NZNH0jUNTVTjzVXy0P0zz+YFNtfjZp6yqur6FqGnxnjzeJFH16H+dczYWM14DHaqqrGPughQBVS4fZLJbXCBsHawPNeiqNC1uT8df8vwPoY8Pwa5fae96fp/wT3XS9VsNasEvdNuo7m2fo8Zz+B9D7GrlfN3hjXpvA3ji1WKRhpOoyLHcQ5+VSTgMPcZ/LIr6RrgxWG9jJW1i9UfP4ihLD1ZUp7o53xf4O0vxnpD2WoRASgEwXCj54m9QfT1HevnrTJ77wp4hn8NaycrHL5cbnoD2I/2Tx+dfU9eB/GDSVv/ABhciFQJ109J8jqSpI/kK9XIM0q4LEaP3f6/r8TysxpU6lF+02/LzJyMGm9Kz9B1D+0tCtbhjmTbsf8A3hwf8fxrQJr9npzVSCmtmfC1IOEnCW6OP8UeFDcltQ0xdtwPmeJeN3uPeovDni/cVsNVbZKDtWZuM+ze9drXOeIvCcGrhri32w3nr/C/19/euGthqlKftsNv1XR/8E9CjiqdWHsMTt0fVf8AAOjByP60teeaV4h1Dw5cf2fq0UjQKcDP3kHse4rvbS8ttQt1ntZlljPcHp9a6MPioVlpo1unuc2JwlSg7vWL2a2JaKKOtdRyhXPeJfE8WixGGHbJesOF7J7ml8S+JY9GgMEBV71xwvUIPU1y2g+GbjXZzqGou4t2bJY/elPt7V5mLxU3L2GH1m+vY9PCYSCj9YxGkFsu5U8O+H9X8c+IPs1u2+VvnmnkPyxr6n+gFfSng/wRpHg2zCWUQlvGGJbuQfO3sP7o9h+teISeD7rTrwX2ganLZzr93DFSPYMO1a1r46+JOi4FwkOpRD/npEGJH1XBr4LO8kzWq7R1j89fn/nY+lw2Y4SUbRkl+B9Ab+a8p+PKh/COnP3W8/mhrMg+Ot3CNuo+F3V+5imK/oV/rXJ+PfiFceOrO2sbXSJbeCGTzTk72ZsY7DgcmvnsDlOMpYqEpw0T8jtlVg47kvgKYSaC8eeY5j+oFdTXkmh61d+HrlyItyPxJE4Iz/8AXruLDxppV5hZWa2c9pBx+dfrWXY2k6Mac3aS01PkcxwNb20qkFeL10OjopkU0U6B4ZEkQ9GU5FPr1jyNhKKWimAVma1odrrlr5Uw2yqP3coHKn/CtOis6lONSLjJXTLp1JU5KUHZo57wb451DwJqI0PXg8mllvkfqYgf4l9V9q94t7iG7t47i2lWWGRQyOhyGB7g14zq+j2utWZt7lcEcxyDqhrJ8H+MNR+Hmrf2PrO+XSJGyrDny8/xp7eo/rXw2b5PKhL2lPWLPuMoziNePs6mkl/X9dj6AoqO2uYLy1iubaVZYJVDJIhyGB7ipK+cZ9GmFFFFIAooopgFeP8Ax5uMafo9tn70kkhH0AH9a9grw346yGTXdHtwfuwM2Pq2P6Vth1eojDEu1JnsFz4P03xL4AsdD1BCI1tYvLkT70ThAAw/zyCa8b1L4VeOfD8rRadFFq1mD8jxOFYD3ViCPwzX0HpU0T6fbxrIpdIlBXPPSr2K+dw2PqUU0rNPozlpVJQtOnKz8j5q074YeO9dnWG9tk0u1JxJLM4Jx7KpJP6fWvVNZm074S/DJotOGZV/dwl/vTTt1dvyJ+gxXoDcDJr5p+Mnic+KPGNvoenSebbWJ8oFTkPM33j+HA/OuzDyq5lXjRkrRWrS/rrsKvWk/fqSu/Mw/BOmy3+pT61eFpCrEqzclpDyT+v613x5NUtMs003T4bSP7sa4J9T3NXK/ZcFhlhqKpr5+p+fY3EvEVnPp09BKKWiuo5AooopgFFFFABRRRSAKKKSgBawvGFr9q8OTkDLRESD8Ov6E1u1FdQi5s5oG6SRsv5is60PaU5Q7o1oVPZ1Iz7M5jwReedor27HmCQgfQ8/411StkA15x4MnNvrVxaOcb0PH+0p/wD116FC2ciufLantMLF9Vp93/APQzKlyVpNddSfrRSClrsPLCikpaAFBrgfHuj7XTVYFwGO2bHY9jXeVBd2sV7aS20wzHKpU1zYzDLEUXTe/T1OnCYl4esqnTr6HovwW8cf8JX4TFneS7tT00CKUk8yJ/A/6YPuPevTK+M/B+v3Xw68fxXMm4wo/k3KD/lpC3U/yI+lfZFvPFdW0VxA6yQyoHR1OQykZBFfDSi4txe6PtoyUkmtmSUUUVIwooooAKKKKACiiigAooopAFFFFABRRRQB83fGlvtfxl0K06gR264/3pWr2BvvGvHfiARd/tHafD1Ectov5AN/WvYW+8a4cZukejgvhbClpKK4ztPLrvn4geJvrb/+i6fdXVvYWsl1dSCOGMZZj/IepptwM/EHxP8A71v/AOi6bFax6n480XT5wGtoI5L1oz0dl4XP0PNe5SdqUfQ8Kqv3kvULS28V6rCLmx0S3trZuUN/OUdx67Ryv400Xlza36adrFg+n3knMWWDxTY/uOOCfbrXqJbPNYXjLSotX8J38TgCWGJriCTvHIg3Ag9umPxqrsixzBGKQmq+mXRv9JtLthhpoVdvrjn9anPWrEUbvUfJuorK2tZr2/lG5LaAc4/vMTwo9zTpYvE9pH5914bZoAMsLW5WWRR/u8Z/CtX4cxJcaXeay4Bub66kBbusaHaqD2GCfxrt8dKm7HY84sb621G1W5tZBJGePQqe4I7GuM8UJv8AiLoaHpmL/wBGGu312xj0fx9A9uAkGsW7tLGOB5sf8X4g1xnjH/R/HGhznoPL5+klRVd4M0o/xY+p73C26FT7YqSqVhLkGMnnqKuV4KPoJx5ZNC0UlLTICiiimB558aLD7X4F+0gZa0uEfPscqf5itH9nfUPtPg67si3NtckgezAGtPx1Z/b/AAJrUGMn7Mzj6r839K4f9m+82anq9kTw8ayAe4OK9DCu8GjzMZG00z3jXdas/D2iXmrX8my2tYzI57n0A9ycAe5r4t17WtR8beK7jUbgF7q8lwkYORGvRVHsB/jXqf7QPjY32pReFLKXNvaES3hU/elx8q/8BBz9T7VxngTRtqPqsy/M2UhB9O5/p+derg8M8TWVNbdfQ8rF4hYek6j+XqdToulxaPpsdrEAWHMj/wB5u5rRpAKWvuYQjCKjFWSPipzlOTlJ3bCikpaogKKKr3l7bafAZruZYox3J5P09amUlFXew0nJ2W5PRXGXXjsySGPTbLeP+ekzbR+X/wBeiHxD4hk+dbKymHXZHKC35Bq8ypnWCpu0pncstxFrtJerR2lJWJp/iOK4ES3tvLYyS/c80fK30NbhFd1DEUq8ealK6OSrSnSfLNWEpaSitjMKWqGr3F3a2DT2cfmOnJXGcisvQvFlvqmLe5At7v8Auno30rGVeEaipydmzeOGqSpurFXS3OjopM0tbmAlFFFAFe/s01CwntJPuyrjPoex/Orvwp8dx6Gx8H+IJBbmOQ/Y55DhRk52E9gScg++PSo6y9b0Cz1yDEw2TqPkmUcj2PqK8HPslhmdHl+0tj1crzD6pNqXwv8AA+gjlW3IalSVW4PBr5x0Px34s+HzJaajGdV0deFLMdyD/ZfqPoePSvZ/C/jXQfGFuH0y7H2gDL2svyyp+Hce4yK/IMdlOLwE2qkdO/8AWx9nSr060eaDudXXz3qQOk/EfxDpd0dpurg3UDHowb5v6/oa99VmT6VxHxG+H0fjW1hu7OZbbWLVcRSt92Reu1iORzyD2yfWpwGIpwm41NFLT07M68PXlh6sasdbHmc63ULu8E7xseMg44qvFG6kl3LsTkk1k3w8a+G5GttV0S5kC8CXyyyt9HXINVYrnxXr0gt9M0O6LtxlIWwPqSAB+Ne4sPJq6krd7o+qjxBg+Xmd79rf0h2sk6p4k0nTLb553uEUAepYCvrDoMV5N8MfhTN4evRr/iF0l1Ug+TCp3CDPUk92xxxwPevWM5ry8wrQk406buo9fNny2LxMsVXlWatf8he9eF6nqC658U9eljO6C1iSzB7Erw3/AI9ur1Lxx4mi8J+FrrUGYfaCvl2yH+OU9PwHU+wrxvwlZHTdEe8uifOnJnlZuuOvNYUo8lGdR9dF+b/rzPBzeso0eTqzF8Fkpa6hbZ4hu2A/z+FdQK5nwUjPp95dkY+0XTMPp/kmumr9vyvmWDp829j5TH/7zMWiiiu85CpqOl2eqwGG7iDjsw4Zfoa4i78Pa14duDc6VM8sPX5OuPQr3r0KiuWvhIVve2l3W514fG1KK5d49nscLafEBkGzULJt44LRHH6Gm6h4/kmj8rTbUxu3G+Tkj6CuwutKsL07rm0hkb+8yDP50WukadZNvtrOGNv7wUZ/Oub6vjLcvtdO9tTpWJwV+f2Wva+hx+g+E7i+n/tDWd+1juEbn5nP+16Cu7VVVQqqFVRgAdAKWiuvD4aFCNo79X1Zx4nFVMRK8tui6ITFKKKK3OYQqrdVB+ooCqv3VA+gpaKAILiztrxClxbxyr/tqDXO3/gXTbnLWryWr+gO5fyNdTRWVXD0qqtONzelia1H+HJo82k8O+IdCkM1lI8iDndA3X6rV2w8eXEDCLVLXfjgug2sPqK7yqV/pNhqS4u7VJD/AHsYYfiK4vqM6WuHm15PVHd/aFOtpiYJ+a0YzT9c07VFH2W5Uv8A8824YfhWhXDah4CdGMul3R3DkRyHBH0YVUg8Qa/4ekEGpQPNEOP3vX8G701jalLTEwt5rVA8DSra4ad/J6M9EorH0rxNpurYWOXypj/yyk4P4etbFd1OpCpHmg7o86pSnTlyzVmFUdW0m21myNtcr7o46ofUVeopzhGcXGSumEJyhJSi7NHN+DPGF98P9Y/sXWS0mkytlW6+Xn+Nfb1H9a98hmiuIY5oZFkikUMjqchgehBrxbWtGttbsjbzDa45jkA5Q/4VX+Hnja58J6n/AMIz4gYiyZsQzMeISenP9w/p+dfCZzlLoS9pT+F/1/Xc+6yfNo148lTdf1/XY90opMggEHIPII70tfOn0IUUUUgCvAfjTMW8d2qKNxjtY8Af7zGvfq8B+IEnn/GaBP8Ann5A/TP9a7cBT9pXUO5x4+fJQcux0Nn4/wBC1WGJpryXTrtFA5JGD7EcEVrw+NmiX934tt2QdPMZSa5i88O6PfsXlskDnq0fyn9KzX8D6MTkCce3mf8A1q66nA0lL91PT+u6/U+Ip46hHWMpRNfxX8SV+wTQRa1Lf3DAqqx/LGvucda4zwJpRnuptVmBYRkrGT3Y9T+X86p+KdLsNNntbGwhbzn+ZiWLE54Arv8ASbFNM0q3tFAyi/MfVj1r2MiyOODryT1cd359O2xtjMUlhU4t3n33sWwKdRRX2J88FFJRQIWkoooAKKKKACiiigAooooGLSUUGgDzOdTpfj444Vp8/g//AOuvQoztYVw/jqE2+t2t2owXjBz7qf8A9VdpDIJYI5V6OgYfiK87LVyVK1Hs7/ee7if3tGlU7q33F6ikQ7kBp1eieI1Z2EooozQIKTGaXNI8kcEZkmkWNB1ZjgUm7ahucj460Xz7NNTiX95D8suO69j+H9a9X+AXjH+1fD8vhy7kzdacN0GTy0JPT/gJ4+hFec6j4s0dYZbUFrrzFKFY14Ofc1yPhTxBceDfGNlq0SSKkEn72MjBeI8MD+H64r5DNvYutz0pJt72Pq8pdZUeSrFq21+x9rUVFa3MN5aQ3VvIJIJkEkbjoykZB/Kpa8o9UKKKKACiiigAooooAKKKKQBRRRQAUUUUAfNHiI+b+02oP8NzCPyhWvZm+8a8X1r/AJOcb/r7j/8ARIr2huprgxfxI9LBfAxtL2pKUVyHaeYT8eP/ABP/AL1v/wCi6hv3uNO1PT9es4mnlsSyywr96WFhhgPcdRU1z/yP/if/AHrf/wBF1Yz6V7lFXpx9Dwaz/eS9TsNK8RaPrVqtxY6hbyKRkqXCunsynkGud8X+Kbeezn0DRZ0utSukMUjRHcltGeGZ2HAOM4HXNYN1oWkX0vm3OnQSSHq23BP1x1q1a2lrZReVaW8UEf8AdjUDP19avlIuOt4I7S0htov9XCgRfoBindDmnUlUIqeGtbg8JXlxpOqP5GnXM7T2d233EZvvRsf4eeQa7yXXdItbY3M+qWSQgZ3mdSPw55rjZIo5o2jlRJI24KuuQfwNU4tB0aCUSx6XaLJnIYRDipsFySbUG8T+JF1hI3j060iaGyMi7WlLH5pMHoDwBXK/EuB1g03UEHMMhQn0zgj+RrtiawPGkQuPC10hHIKlfruFJrQaetzvNK1BbzTrS+hbIliVx+Irore4SdMg89x6V4l4A8Vf2S0nhnWz9mmhciFpeAD3Un9R9a9QSRlIZGI9CK+fqRdKVnsfWQccTTUludFRWSNTeCIvO8aoByznGK43xB8XtM0sNBp6LqF30Hl/cB+vf8KqCc3aKOarH2SvNnozusalnYKo5JJwBVex1Ow1PzTY3kFyIm2yGGQNtPocV5Ba+HvG3xGlFzrt3Jpukk5EIG3cPZO/1avT/DXhfS/CtibXTIWUOQZJHOWcjuTVyhGK1eplGbk9Foa17ALjTruE9JIXQ/iDXiHwFuDa/EGWAnG+BlP4GvdhyCPUEV8+/Clvs/xaWMcfvJU/U104R7nHjVojh7eK78R69++kaW5u5jJNK3JJJyzH9a9ct7eO1t44Il2xxqFUewrmvBGifYbE3864nnHyA/wp/wDX/wAK6qvvspwnsKXPLeX5Hwua4r21XkjtH8xKWikr1jyQpaSmyypBC8sjBURSzE9gKTaSuC1dkUtY1i30WyM0vzSNxHGOrH/CoPBnw61T4hXA1rXZpLbSc/u1XhpR6ID0X/a7/rUPgTwxJ8SPF02oagrDR7MjcvQP/djH16n/AOuK+k4oo4IUhhjWONFCoijAUDoAK/M+I+IJ1Kjw9B2S/r7/AMvU+xyzLY0Ic817z/qxy9p8N/B9nbLBHoFmygY3SJvY/Unmue8Q/Bjw9qMTS6Qp0q9AyjRkmMn3Unj8MV6ZSYr41Vq0XzRk7+p67hFqzR892ls8FxJ4R8W2qpdKMwTdpB2ZW/z71Qhe58O6wNF1BzJbyf8AHpcHuOyk17T488IReLNDZIwE1O2zJZzdCr/3SfQ9PyPavILoHxb4NmSZCmq2DEMCMMJF6/nj869fKs1qYKusRTfut2kumvVeT/D7j57H4ONOVn8Evwfc0TSVnaDqP9qaNBcP/rQNkn+8P8ev41o1+yUqkasFOOzPmqkHTm4S3QVy3iLwol+Gu7ACK7X5io4D/wCBrqaKmtQhWhyTRdCvUoT54M4vw14nlE40vVSVmB2pI/Bz6Gu1rmPFPhtdShN5aLtvIxkgf8tB/jUfhPxH9tjGnXrbbuMYVm/jA/rXJh6s6M/q9Z3/AJX38vU7cRRhXp/WKCt/Mu3n6HV0lLRXonmBRRSUADKrqVdQynggjg1zeoeEozOL3SJnsLxDuQxsVGfbHKn6V0lLWFfDUq8eWoro2oYipQlzU3YPDnxi1TQpo9M8Z2sk0Y4W+jX5wPVgOHHuOfrXselaxpuuWS3ml3sN3bt/HE2cexHUH2NeK3llbX8BguoVljPZh0+lco/hvWNBvPt3hnUp4XH8CybW+mehHsa+Azfg1Nuphfu/4H+X3H0+CzyE/draP8P6/q59SKSKkyfSvnPT/jP4v0lhb6va21yRxumiMbH8V4/Suit/jhqFwMR6JYs3/X7j+Yr42plWLovlkvxX62PYeKpWu2e04z1rN13xBpnhvTXvtUukghUcAn5nPoo6k15RdfEbxtqERWytNLsA3/LTf5hH5nH6VyV5p8V3d/2h4s8Qrdyjna8wwPYD09gKzhg1f95L5LV/hoc9XMqMdIu78i5f6xffEnxKmoXMbQaPaki2gb09T6se/wCAo8ZasLXTxo9p899eYjCKeUQ/4/41l3HjEPjTvC9i8z/dExTCr7gf1NW9C8PvYzNqGozfaNSk5Lk5CZ9Pf3r6fKsir46vGc48tOOy/rqeDi6r9p7evuto9fn2Xc1NNsU03TLezXB8pACR3PUn86tUdaK/U4xUIqK2R4MpOUnJ7sKKKKoQUUUUAFFFFIAooooAKKKKBBRRRQAUUUUwCiiigApk0MVxEY5o1kQ8FWGQafRSauNNrVHIat4Ftp8zabIbeXrsJyp+h6is211/WvDcy2uqwPNB0Bbrj2bvXoNRXNtBeQNDcxJLGeqsM1wzwKUueg+WX4P1R6FPMG4+zxC54/ivmV9N1ey1eDzLSYMR95Dwy/UVdrhNU8IXenTfbtDlk+XnywfmX6eo9v51a0Txosji01dfJmB2+bjAJ9x2pU8Y4S9niFyvv0ZVTBKcfaYZ8y7dUdiKyPEWgQ67ZbeEuoxmKT+h9q11IdQykMpGQQcg0hNdlSlGrBwmrpnDTqzpTU4OzQnwu8dypMPCevMUuYjstZZDycf8syf5Hv09K9crwDxR4fOpRC+s8pfwcqV4Lgdvr6V6F8M/HY8T6d/Z2oOF1i1XD7uDMo/i+vr+dfnub5ZLC1LrZ/1/w5+g5TmUcVTs91/X/DHfUUUV4p7IV88eLm8z42zf7MsY/KMV9D187eI/m+Nt3/18D/0WK9LKVfFw9V+aPOzV2wsvR/kzrAafweT0HNR1m+IL/wDs/QrqYHDldifU8V+mVJqEXJ7I/NadN1JqC6nKaev9veO5Lo/NDAxcemF4X9ea7+uU8B2XlaXNeMPmuHwD/sj/AOvmusrjwEGqXO95O/3nZmM063s47RVl8goooruOAKSlooEJRQSApZiAoGSSelc7ceIbnUL8aX4bs3v71+AyKSo9x6j3PFc2JxdHDQ56srI3w+Gq4iXLTVzflljgjMksixoOrMcCsubxVokDbTfIx/2FLfyFbmjfBHUtWdLzxfq8gJ5+y2zAkexboPwB+td9YfCnwVp8YRdDhmI6vcM0hP5nFfGYvjihCXLQjf8Ar5fqfQUOH01erL7v6/yPIU8X6HI2Ptm3/eRh/StS2vbW8XdbXEUo/wBhga9Sn+GXgu5Qq/h2zUHvGCh/NSK4jX/gRaYa68LalPY3S8rDOxZD7Bh8w/HNRhuOKUpWrQsv69SqvD8Lfu5O/wDXoZdFcnDr2p6DqzaJ4ptmguUOPNYY+hOOCD6iurUhlDKQQeQR3r7XCYyji6ftKTuj5/E4Wrhp8tRC0UUV1HOch8QIN+nWtwB9yQqfxH/1q0vD0/2nw/ZvnJVNh/A4/pTvF0H2jw1c8ZMZVx+BrM8DzeZos0RPMcp/IgV50P3eYNfzR/I9qi+fAr+6/wCvzOqgPBHpUtVoWxLj1rI8Sa9Lp/k2Fghl1C5IWNVGSMnA47knpXZia0KEHUm9EcH1edWtyQWrNLUNXsdLTN3cKjHog5Y/hWPF4ou9SkMei6FfXzdMohI/QGvQ/Bnwas7VE1PxUf7Q1GTDm3Zsxxn0b++f09j1r1SC3gtYVht4Y4YlGFSNQqj6AV+d5hxtPncMLHTufR4fIaMVeq7s+erbw18StYYCHR49OjP8c7KpH4Ek/pXUaR8DzPItx4o1ua8cc+RbEqv/AH0efyAr2HFY3ijxPp/hLRn1G/YkZ2RRJ96Vz0Uf49q+bxGf5jjHyOe/RHqUcFQo/BFIXR/CegaBGqaZpVrbkf8ALQJuc/Vjkn8683+PPhmK70GDxBbxAXNkwjnYD70THAz9Gx/30ay9Q8V+KfEbGSe/fSrVuUtLLh8f7T9c/l9K5fUbbUo7W5EGqXzeZGyvFPMZUmB6gg1nhKM6ddVJT975v11/4c9r+ycQ6Tny28up6j8AfF39seFJNCuZM3WlnEeTy0Lfd/I5H0xXr9fF3w48UN4O8dWOoSMVti/kXQ/6ZtwT+Bwfwr7QVgyhlIIIyCO9fWHiC0UUUAFFFFABRRRQAUUUUgCiiigBKWkopgfNHiQeV+00pPG66hP5wrXszfeNeOfEAfZf2jbGXoHmtG/MBf6V7I33jXn4z4kelgvgY2nCkpa5DtPLrn/kfvE/+/b/APourFVrj/kfvE/+/b/+i6s17tH+HH0PBrfxJeoUtJRWhmLRRRQIKKKKACsPxb/yLlx/vJ/6GK3Kw/F3/ItXP+9H/wChik9iludH4y8C6V4kEU8qtBebdoni6kDsR3FcFc+C/F2iQOdP8QFrZATjzmTA+nIr2a7/ANXH/u1558SNVOneGpURsSXB8lfoev6V4NKrU5lBH08qFJ03Vlo0t1ocV4V8LeIPiC8zz6tILSBwskkzs/PsO9ew+Gvhz4f8NbZYrf7Vdj/l4uMMQfYdBUXwz0gaN4LtIyuJp/30v1bn9BgV2PWtKtZttLY5KdBRSctxxOaSiisDUUHBr55+Gx3fGKIjobmX+Zr6AuZRb2s0zHCxxsxPsBXhHwTtm1D4nrc4ysaSSk+mf/1124PqcOOeiOlAAAAGAOAKWkpa/Uz8vYUlLRTEFcr461E2ulR2cZ+e5bnH90f/AF8V1VcfdQDWPinoenScxfaIVYeo3bj+leVnOI9hg5y/rz/A9DKqKq4qN+mv9fM+gvh94bTwt4L0/T9gWcoJbg9zI3J/LgfQCuN8b/FC+j1afQfCiRtcQHbc30gDLG3dVB4JHQk55yMcZr0HxRqj6L4V1TUY/wDWW1q7p/vBeP1xXzp4bj8vTFlJLSzEu7HqTmvyHCJVFKtUV3f8Xqz9Iy3BLF1/Zt2SV2aP9t/EKFzcJ4nmkkHOxgNp/AjH6V6H8PfijJrt8NC8Qwpbavj91Io2pPjtjs3f0Pt0rheR34rn/ErPZi21W2by7q1lV0deoIOR+orrcIV/3copN7NKx7WY5LRpUXVo3Tjrbe59T968Y8SWS6H8U7pYhtt9Wthc7R08wHDfnjP4167pt4NQ0u0vQNouIUlx6blB/rXmXxLwfHXh3b94W8xb6ZXH9a8aj8UoPqn+Cv8Amj4zMIKWHlc868Mj7LquuWA+5Dc7lHsc/wCArpK53Rf3nivxDKv3fNVc++T/AIV0VftWRSlLL6Tl2Pi8f/Hb8l+SCiiivXOMK4jxdoT28o1nTwUZTulC9j/eH9a7ekZVkRkcBlYYIPcVz4nDxr0+R/J9mdGFxEsPU51813RjeGteTWrEByFuohiRfX3Fbdea6la3HhLxAl1a5+zudyehXup/z6V6FY3sOoWUV1A2Y5Bn6H0NYYLESnelV+OO/n5m+Ow0YWq0vglt5eRYpKKK7zgCloooAKKKKQEcsMVwhSaJJF9HXIrJuPCWi3JybTyz6xsVraorOpQp1Pjin6mlOtUp/BJo5r/hBdKzxLdgegkH+FTQeDNEhbc1vJKf+mkhP8sVv0VjHA4ZO6gvuNnjcS1Zzf3kVvbQWkXlW0McKf3UUAVLRRXSkkrI5W23dhRRRTAKKKKACiiigAooooAKKKKBBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFMBKxtb8NWWtIXKiG5A+WVR1+vrW1RWdSnCpHlmro0pVZ0pc0HZnnVtqOr+DrsWt7G0tmTwM5BHqp/pXdafqNrqtsLi0lDoeo7qfQipLuzt7+3a3uolkibqD29xXC32j6l4SvPt+myNJaZ574How/rXn2q4J6e9T/Ff5o9O9LHb+7U/B/8E9BHBrkvEWnXWj6hF4k0ZmiuIHDyBPX+99PWtnRNftdct90ZEdwo+eInke49RWi+GBVgCpGCD3FdFajSxtHl3T2ZzUatXBVr2s1ujuvBni618YaGl7DhLlMJcw55R/8AA9q6PrXzhbXV38OvFkWp2YZ9NnO2WLPBXuv1HUH/AOvX0PYX9tqenwX1nKJLedA6MO4NfnGOwc8NVcJI/RcFi44mkpRZZr511/n42Xn/AF8f+yCvonNfOviH5fjbd+9yP/QBW2Uf73D1X5oxzb/dZej/ACZ1xFcR4+uifsdgnJY+YQPyH9a7rHNefSj+2fiEq/eihf8ARP8A69ffZjJukqS3k0j4XLIpVXVltFNna6XaCx0u2tQMeXGAfr3/AFq3RRXbGKikkedKTlJye7CiiimIKr319badaPc3UgjjX16k+gHc0+6uYbK1kuZ22xRruY1H4H8D3HxBvx4g15Xj0OJyLa1zjzyDz/wH1Pc8dq8jOM3pZbR557vZHoZfgJYufaK3M3QvDniH4mXO6INp3h9Ww07jmTHYD+I/oK918M+EtG8Iab9m0y3WMYzLO+DJJjuzf06Ctm3t4bWCOC3iSKGNQqIigKoHYAV5r8aNeu7XRbDw/p8hS41iUxuwPIiXG4fiSB9M+tfk2KzDE5riLVZaPp0X/DH2uHwsKMVTpIr+JPjJFFeSaf4V07+1Z4zte6disCn2xy31yB6Zrmh8VfH1pJ51xpmlTxd4kVlOPY7v8ai03QFsLSOGJdkQHLEcsfWqGrEWtyYg2R9KuEKMXywgmvPf/gfI+wo5DQ9n+9m+by2X4HrfgX4kaZ42jkgSJrPU4Rma0kOTjpuU9xn2BHpXZ18s6bePo3jvQ9UtSVkN2kUgH8aMQpB/AmvqbFcOPw8ac1KG0vwPncTQeHrSpN3scH8U/BUHizwxLPHEP7TsUMtu4HLAcsn0I6e+K8X8H6nLGw0m7JOU8y2c917j+f5GvqXAIwelfKerRfZbCa+t+H0nVpIlI7xlun5j9a9zhrM54Sqot+7dL7/+DY8TNKMatNRfXT59P8jtTSU2KRZokkQ5V1DD6Gn1+vp3Vz4ixV1KEXGlXcX96Jh+lcP4Fn2z3sHqqsB9Dj+tegkAqQehGK808NZtPFs9ueM+ZH+Rz/SvOxT5MXRn5tfeetl75qFWHozvwcMp96yNBeCP446U17jyjIoj3dNxQ7f/AB7H41rYzWJ4o0Ke/jh1Gx3C8tuQFOGIByMe4NTn2EnisHKnDf8A4DX6muBrwo11Kez0Pp+ivLfh78XLHW4ItL1+VLLVkATzJPljnP1/hb2P4elepdRkcivw+thqlCbhUVmfXxkmroBXiPxhmln8e6DZTE/ZUtzKinoXLHJ/8dWvbq4D4q+DLnxRo0F7pY/4m2nMZIB08xT95PrwCPpjvWuCqRhXTk7J3V+10aU5KE4yavZpnm1xqkdqdoXc3fFZs+oSXhC7AqCsP+2w87RXsb210h2yxyKQQR1HtUzatZxAnzQfYc17X1aUemp9xSxuFnDnU1b1OZ12y+y6o+PuSfMK+p/gz4n/AOEk+H9qk0m6808/ZJsnkhR8h/FcfiDXgdt4P1jxZomqa/FA0dnYwF4tw/1xHLBfXAyfyFbHwH8THRvHX9lzPi21VPKwTwJV5Q/zH/Aq9jD1FKPLfVaM+Exvs3XlKl8Leh9TUUUV0HIFFFFABRRRQAUUUUgCiiigBKKKKYHzb8Zx9j+NGi3XQNHbPn6SsP6V7E33jXkX7RiG38WaBeDgm2Iz/uvn/wBmr1mKQSwRyDo6BvzFcOMWzPQwL0aHUopKUVxHeeXXH/I/+J/9+3/9F1Zqvcf8j94n/wB+3/8ARdWK92j/AA4+h4Nb+JL1CiiitDIKKKKACiiigYVi+LRnw3cf70f/AKGK2hWP4rGfDlx/vR/+hClLZjjuj0W/cAov+xmvIfiIx1LxTomkDlWdSw/3mA/kDXqdy5kmY/hXmN7H9p+NFijciIKfyUmvAoaTcuyZ9XiY2oRh3aX4nsWmYWBo1GAhwB7Yq+KybCUJcshOA9atZomtG0x1KKYDTtyqpZmAUDJJ7VRgzlPiVrC6L4Fv33YluV+zxDuS3X9M1ifs5aCyWmp65ImPMYQRE9wOT+prgviR4mk8aeKrfSdLzLawSeTAF/5ayE4Lf0FfTXg/w9F4W8K2GkRAZgjHmEfxOeWP516mHp8kNep5GKqc89Oh4lRS0lfpp+bhRRRQIWuTt2+x/GXRJn4R7mEAn3+WusrkPG0UltNp2rQcSW8gGfQg7l/UGvHz2g62CnFf10/U9PKKip4pX66fr+h9JeItL/tvw1qWmAgNdWzxKT2Yg4P54r5q0G4aFH0y6Uw3lo7RyRPwQQT/AC6V9QaZex6npdpfRf6u5hSVfowB/rXFeNfhPpPi67Oow3Emm6oR808S7lkx03Lxk+4IP1r8gwVeFNOlV0T69mfoWCxksJW9rFX6NeR5Z5S7t7SHjnk1kXNrN4r12y8PacC8k8oEjDkIo6sfYDJrtYvgTrzzBLjxVH9n7lYWLY+hP9a9O8H+BNG8FWzrp8bS3UoxNdzYMj+3sPYfjmu2WJo0fejLmfQ9LHZ28RSdKnDlvu2b8EEdpaxW8QxHEgRR6ADArxfxxq0cnjrUb12/0fSbQQg+r8sR+oFeq+Jtci8P6HdajINzRriKP+/IeFX86+bdSa51e8TREkLTTyG51GYdiTkj/PtXFleEniq3LFb6f5/hp8z5PMKiUfZt+b9DR8GwuNJmvZh+9vJmlP06f410VMhhjt4I4YlCxxqFUegFPr9ww1BUKMaS6I+Ir1fa1ZT7hRRRW5kFFFFAGfrWlRaxpsls+A/3o2/ut2rjPCeqyaRqsmlXuUjkfbhv4H/+vXodcR450bhdWgXBGFmx+jf0/KvMx9OUGsVT3jv5o9TL6sZp4WptLbyZ29FYXhXWf7X0pRI3+kwYST39DW7XfSqRqwU47M8+rSlSm4S3QtFFFaGYUUUUgCiiigAooooAKKKKACiiigAooooAKKKKBBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFMAooooAKKKhuru2sYDNdTpFGO7Hr9KTaSuxpNuyJTSda4rUvH6Kxj0228zt5kvAP0FV4rvx3qK+baademM8gx2Z2/mRXmVs4wtJ2bv6HqUcnxVVXtb1O+pThlKsAykYII4Nefy614x0j5tQ0+4VB1M9qVH54Fael+PLK6ZY76I2znjeDlf8A61VRzbC1nZSt6/1YVbKcTR1av6f1cp694bn0u4/tbRSyBDueNeqe49R7Vt+HfEUOtQeXJiO7QfMn973FbaOksYkjZXjYZDKcgiuN8R+HpLOX+2NJzG6HdIidvcf4UToyw0vbUNY9Y/qi6dSOKiqNb4ls/wBGdTqOnw6nYy2c4+Vxwe6nsRVX4W+J5/D2uS+E9VfEMsn+jsx4V/Qezfz+tZ+jeK7W/tP9Mljt7hOH3nAb3FZvi8WF7bxalY3sJu7cjPluMsM9vcH+tcObYejjcP7Wm9Uvw/4B15VXrYLEeymnZ/n/AME+kQM188+LV8r42Tk954z+aCvXPhz4uj8WeHY3kcf2hbAR3Kdyez/Q/wA815N8Rf8AR/jIz+rwN/46tfJZbF08XFPo1+aPq8xkqmFduq/RnTXk62tpcXDdI4y35CuK8CQGa7vtQfkn5AT6k5P9K2/Gl39n8PzIDhpmEY+nU/ypvg60+zeHIWIw0zGQ/wAh+gr72f7zFwj/ACpv79D4aL9ngpS6yaX3anQUUlLXoHmBS0lLSAwNUs5vEvinR/C0DlVuZBJcMvVUHJP4AMfyr6OsrK306xgsrSJYreBBHGi9FUDAFeBeBZ0X4xXc8v8AywtyFz2Hyg/oTX0J15HSvx7irFSr5jKD2ifc5TCMMPGK3sn9+oV4z8chPp2peGdfSMvBayyRycdCdpH5gN+VezVna5oVh4j0e40vUofNtp1ww6FT2IPYg814WGqqjVU2rrr6PRnpptO63R4dda/eXUKzWaw+S67lZecj2rnZDNPIZrhjknJJrd1D4QeNfD9w6eH7qHUbAklEZ1RwPdW4z9DSab8IvG2t3CrrVxb6ZaZ+fa4dyPYKcfmRXsJ4eK5lUVvx+7c+ghnkFDWDv26feUPAOiyeLfiHZtGpOn6Y4uZ5O2VOVX8WA/AGvpesbwx4X0vwjo6abpcJSMHdJI3Lyt/eY9zV3VdWsNE0+S+1K6jtraMfM7nH4D1PsK8rF1/rFRKC0Wi/rzPDrVpVajqT3ZT8U6/b+GfDd7qtwwxDGfLUn77nhVH1OK+d9QtHsPhbLLef8fWoTiYg9SWbP8hn8a6m/wBUuvih4giuZI5LfwzYuTDG/Bnb+83+eB9TXOeIr9fFviq30u0+bTrFt0jL0Yjr/gK9DLsJOeIhhofFdOXlbZf5/wDAPAx2KjOooLaOrfobOmo0Wl2iN95YUB/IVZzRxRX7VCPLFI+Qk+Zth1rze4X7F8Qs9A1wD+DD/wCvXpIrzvxkv2bxXBcDjcqP+Rx/SvPzPSEJ/wAskz0sqd6sod4s7nGDU8J+X6VASGAI6GpIW+YivWkrowqK8DP1jwzp+sAu6eTcf89o+p+o71U0/XfHvgcBLC8OoWCdIZB5igfQ/Mv4GuhzSd68bG5PhcYn7SOv9f13NMNmVfD6J3XZmvoHx+0q4ZYNf0+bT5uhli/eR59x94frXp2keIdH1+ETaTqVtdr1IikBYfUdR+NeGX+iadqan7Vao7f3wMN+Yrmp/A89pOLnR9QkilU5UMxVh9GFfG47gtq8qD/r0f8Ame/h88oz0qaP+up9Ga/4J8OeJm36tpcM0wGBOuUk/wC+lwT+NZGn/CXwXp0wmXSftDqcj7TK0gH4E4P4ivHLXx98R/DYCSzyXkK/8/MYmH/fQ+b9a14v2gtajXbcaHYu/cq7p+nNfO1cqzHDr2d3b1a/Ox61PE0pq8Xc9/WCFLcQLEiwhdgjVcKF9MelfHfiOxn8HeO7y2t2KSWF55lu3cAHch/LFdrqvx78TXcTR2NrZWORjeqmRh9M8fpXmGpX97qt9Lf6hPJPczHLyyHJauvK8FXw8pOpsxTmpbH3D4f1mHX/AA/YatB/q7uBZQP7pI5H4HI/CtOvGv2ePEP2/wAI3eiyvmXTpt0YJ/5ZyZI/8eDfmK9lFeyZBRRRQMKKKKACiiikAUUUUAJRRRTA8D/aWtsxeHbnHRp4yf8Avg13Hh6f7V4Z0qfOfMtIm/8AHRXN/tIQb/COkz4/1d9t/NG/wrS+H8/2j4f6I+c4tgn/AHySP6Vx4xe6md2CfvNHSUopKUVwHonl9x/yP3if/ft//RdWKrT/API++KP+ulv/AOi6s17lH+HH0PBrfxJeoUUUVqZBRRRQAUUUUAFZHir/AJFy4/3o/wD0MVr1keKv+Rcuf96P/wBDFTLZlR3O2b/WMfevO9RAsPjFp9xL8sdxGArHuSpX+dehH7x+tc54w8MnxHYxvbyCHULY7oJOmfYntXz9KSUteuh9liISlBOO6af3HUE4fIODnrWlb6hlQsoyfUV5FH438S6KgtdY0Ced0GPOQEbvfIBB/Cg+OPFerfutH8PSQluPNkUkD8TgVSoTXa3qYTxNGas737Wdz1bVPEenaRatcXk6xRr3c4z/AI14341+Jeoa/ZTW2kxyW2lg7JJ+jS57e3061q2XgG91W4F94q1B7iTr5CN8q/U/0GPrXLeIz/wkviez8M+HbdTBHJ5MSRDCs56n6D1+prooQg52Wr/A4cXzxpOUvdT2XV/5HXfs/wDg/wDtPXZvEd3Fm2sPkgyOGlI6/gP5ivpesPwj4btvCfhmy0e1AKwJ874++55ZvxNbleieIfOtFFFfop8AFFFFMQVi+LYvN8N3PHKbXH4Gtqqerw/aNGvYgOWhbH1xWGJhz0Zx7p/ka4eXJWjLs1+Z6h8Jb/7f8NdIbdloUaA/8BYgfpiu1rxz9n3V1n8PalpDN+8tbgTKv+y4x/NT+dex1+FYyn7PETj5/nqfokXdXFqG4mEKZ/iPAFSk4Ga88+JPjSLwxoUtwjj7bMDFZx9893+g/wAK5eWdSSp0/iewSlyq5wXxQ8YPf69HpNgfO+ysVjReQ854LH2Xp9c1T0TR10i0IdvMu5jvnlPUt6fSsbwdpMgDaxe5a4m/1W7qB/e/H/PWusJzX6/w3ksMDQU2vea/r7z4zNMW6lR04v1/y9F+YUUUV9OeQFFFFABRRRQAVHPBHcwSQSqGjkUqwPcGpKKGk1Zgm07o8vtpJvCXiko5JhDbW/24z0NenoyyIroQVYZBHcVy/jbSPtmnC9iXM1v97HdO/wCXX86b4H1f7Zp7WErZlt/uZ7p/9avJwl8LiJYZ/C9Y/wCR7GLSxWHjiY/EtJf5nV0tJS16x44UUUUgCiiigAooooAKKKKACiiigQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFKBTArXt7b6favc3UgjjXue59B71zNve+K/Fkzx+GdMl+zocGbAA/Fm+UfTrWvpGgHx749awnZxpOmrun2nG4/3c+pPH0Bro/HnxGtPC9sPDHhGKL7ao8rdAoKW/baoHV/5e5r4POuIsR9Z+pYJard9vX+vzsfUZbldJU1VrK7Z5JrF94n0DVJNP1C9ljuY8FlWVXAzz2yK0/DPgbxH47nW5mkkisc/Nd3GSMf7A/i/Dj3rs/BHwoeWZda8WBpZpG8xbOQ5JJ53Snuf9n8/SvYI1WONY0VURRhVUYAHoBXm1syrOPI53fXex9Hh8upp87il8jm/DXw/8P8AheNDbWi3F2BzdXADPn27L+FdM2T3petIea82UnJ3Z6cYqOiGldwKkZB6g15/4y+Fmk6/bS3OmwR2OpgFlaMbY5D6MBx+I/WvQqiuLiK0t5LieRY4YlLu7HAUDkk0QlKDvEc4RmrSR8uaJrd54b1OSyvVcQo5SaFuqMOCRWpda7qvii8Gk+H7SV/M4+QfMw9Sf4R71Vmsrr4jfEO9Okw7Y7mYvvYYEcY43t+Azj1OK+gvCvhTTfCOmLZ2EeZGAM1ww+eVvc+noO1e/LOK9Gh7FP8A4B4MMqo1qzqtbfieT6b8CtTnjWTVNVgtGIz5cSGUj6nIH86t3fwGmWImx11Hk7LPAVB/EE/yr2rrRmvH+tVb3uev9VpWtY+Z7JvEHwr8Wwz3ds0fZ1zmO4j7gHoe3uDil+IOv2Gv+PodW05y1vJHAeRgqQOQfcV754s8OW3ivw/cadcKvmEFoJCOY5B0P9D7V8oTxyWt28UilZInKsD2IPNduGqqclN7o4cRSdOPJ0Z2/wAQJy89laKckjfj3PArsLGAW1hb246Rxqv5CvPXu11/xfp5U7kHlg/gMmvSRX3eDaqVqtVbXSXyR8Tj06VGlRe6Tb+Y6iiivRPMCiiigDk9SvJfC3jW019UZ7WUeXOB6YwR+WCPpXtOi+ObZbGOUs15p7D93PB8zIPRh7fnXntzawXtu8FzEskTjDK1c1/wiFxp85m0TV7izJP3CTj8x1/EV8JxBwvUxdf6xh3r/X9foe9gsyjGEYyfLJaeTR74PiF4U6SaxDE3dZVZCPzFI3xE8HoMnxBZ4HoxP9K8KbTfFMw2z65CR6+UCf5VEfB88/N3rEsmeoSMAfzrw6fCOMl8UbfNf5I9L+2aa3kvlf8AyPbJ/i14Ht/va4jn0SGRv/Zaxb748eD7ZT5C6hdN2EcAUH/voivME8Eaen/LWRj/ALSirK+HBAP9Flt4z6m1Umuh8IV4r4HL/t6KIeeUun9fmb198c9c1QmHw34c8stwJrgmTHvgYA/EmuemtL3VrldW8d60ZVTlbcvtRfYAcD6KKJdH1uQbRraxp6RwY/rUEfgu2klEuoXtxeP/ALRwD/M/rW1LhnGX5YRVNd73f39Pkjmr5oqis52Xkm3+KSF1LxTda8v9i+GbdoLMDY8wG35fQf3R+taujaPBotkIYvmkbmSQ9WP+FW7W1gsoRDbQpFGP4VGKmr63KMjoZbH3dZdzxq+J54+zpq0fxfqJ3opaSvcOQWuC+IceLmxmHdGX8iD/AFrva474hxZ020lx92Ur+Y/+tXBmcb4Wf9dTvyyXLio/P8jbsJvO021kz9+JT+lWo2xIDWP4bl83w/Zn0Tb+RxWsK9GjLnpRl3SNKsbSlD1LtFIpyAadUnnMKKKKBBUUltbzH97bxP8A7yA1LSUmk9wTa2K66fZKcraQA+ojFZniPSI9R0aeNI1EsamSPAxyO1blJ14NROlCcHBrRmkK04TU09Ucl8ENeOifEi0hkfbBqCm0cdtx5T/x4AfjX1vXw3qMcug+J3aAlHt5xNEfTB3LX2romqRa3oVhqkBHl3cCTDHbIzj8OlfA1IOnNwe6PuYTU4qS2ZfoooqCwooooAKKKKACiiikAUUUUAeVftBW/m/DTzMf6m9if8wy/wBayvhJP5/w5sRnmKSVP/Hyf611Pxst/P8AhRq/GfLMUn5SLXC/BGfzPBNzCT/qr1v1VTXNi1emdeDdqh6RRSUo7V5x6h5fP/yPnij/AK6W/wD6LqzVaYf8V34o/wCusH/ourNe7R/hx9DwKv8AEl6hRRRWhmFFFFABRRRQAVj+Kv8AkW7r6p/6GK2Kx/Ff/ItXf1T/ANDFTLZlR3R2h+8frQDTSeTQOtfNH3iQ/JxTc460tYXizxFD4b0hrhsNcyfLDH6n1+gpxi5OyJnOMIuUtkc78RPF50y1bSbGT/S5h+9Zesa/4mu5+B/w7Oh6cPEmqQ41G7T/AEdHHMMR7/Vv5Vwfwh8A3HjTxA/iXW0aTToJd43j/j4l64/3R3/Kvp9QFAAGAO1e1RoqlGyPkcZipYipzPboLRRRmtTlPnWiiiv0U+ACiiimIKUgEYPQ9aSloA4jQdauPhv4/W8KO9mxKSoP+WkLHt7jg/UV9M6b4k03VrOO8sJ0ntnGRIjggfX0PtXiGp6TZ6vb+Tdx5x91xwy/Q1y//CAvG7C21aSOJuq7D/Q818DnHCtTEVfaUH/X3o+qwedUvZpVnZo9u8YfFHQPD1s8ZuUurvHFrbuGYn/aI4UfrXiy2+qeONcOva/lbf8A5YwDgbeygdl9+9X9L8HaZpziWUNdTDoZfuj8K6Gu3JuFaeEl7WtrL+v6/U5cdnXOuSh9/wDkNAAAAAAAwAO1LRRX2J88FJS0UAJS0UUAFFFFABRRRQAjKroyMAVYYIPcV5hKsnhTxYGXPlK+R/tRn/P6V6hXLeONL+16Wt7GuZbb72O6Hr+X+NedmVFypqrD4oar9T0ssrKNX2U/hnodOjrLGsiEMjgMpHcGnVy3gfVftmlmykbMtt93PdD0/KuprroVlWpqpHqceIoujVdN9AooorUxFopKWgQUUUUAFFFFABRSUUAFFFFMAooooAKWkopALRSUtABRRRQAUUUlMAooooAKUyLFG8jkKiAsSewpM4rg/EviCbWLpdE0ZXm8xwjGMZMrf3R7f56Vy4vFQw1Nzn8vM6cJhZ4mooR+fkLo/i7UbLTb3StDjkOpavc5kmjHzheyp7nJ57V6p8PvhrD4cRdU1VUn1hxkA/MsGfT1b1P5etT/AA6+HsHhO0F5eqk2sSr8z9RCD/Cvv6n+ld5nNfmFapHmk4fad2+5+lYbCqCTkMNFKaK5rHcJS0UUxB1rxX4k+LLrxNq8fg7w7umDSbLh4z/rHH8Of7o6k+3tW98UfHraTCfD+juW1O4AWV4+TEp7D/aP6D8K0Phr4ETwtpv26+jDavdL8+efJU/wD39a6IRVOPtJfI56knUl7OPzNfwV4PtPB2iLaRYku5MNcz45dvQew7CukzS0lYSk5O7OiMVFWQoNFIKWkADrXyr8QRajx9rX2N1eE3BOV6biBuH/AH1mvYPid8Qv7DhbQ9Ik3apMMSSJz5CnsP8AbPb06+lcx4S+DE2oQpf+JJpbZJPmW1j/ANYQe7E9Pp1+ldlC1Jc8+px4i9V+zh0PNdBv49L1m3u5ULRoTuC9cEYr1qx1Cz1OATWcyyL3A6j6iuhufgx4SntykKXltJjiRZ9xz7hgRXkfibw7q/w51+MLP5kMmWgnUYWVR1BHr6j3r6DK85jR/dtaP7zwczyeVb94nqvuPQzRVHStTj1fTYbyMY3jDL/dYdRV3rX2cJKUVKOzPjJxcJOMt0LRRRVEBRRRQMKKKKACikooAWkoooAKKKKACiiigArm/HUfmeG2b+5Krf0/rXSVj+KovN8M3ox0QN+RBrnxcebDzXkzpwcuXEQfmjH8GSeZoIXP+rlYfyP9a6OuS8CSZs7yL+7IrfmP/rV1tPLZc2Epvy/LQ9HFxtXn6lqE5jHtUlQW56ip66Jbnk1FaTCiiikZhRRSUDCiiloA4P4g2O2a2v1HDjy3PuOR/X8q9s/Z/wBf/tPwJJpkj5m0ycoAT/yzf5l/XcPwrzjxLY/2h4fuogMui+Yn1HP8s1U+Aev/ANk/EEWEj7YdThaHB6b1+Zf5MPxr5HOaPs8RzLaWp9XlFb2mH5XvHQ+q6KKK8g9UKKKKACiiigAooopAFFFFAHI/FGD7R8MfEKYzi0Z/++cN/SvIPgRPu0nWYM/dmjfH1BH9K9x8aQfafA+vQ9d+nzj/AMcNfPfwGnI1HWrfP3oY3x9GI/8AZqxxCvTZ0YV2qo9s70opKWvLPXPL5/8Ake/FH/XWD/0XVmqsx/4rvxR/11g/9F1Zr3aP8OPoeDW/iS9RaKKK0MgooooAKKKKACsfxX/yLN3/AMA/9CFbFYvis/8AFM3n0X/0IUpbMqO6O0pOhpw5xTJHSKNpJGCooLMxPAFfMn3q2IdR1K10nT5b27kCxRDJ9SfQV5foOjap8W/HOG3RWER3TOOkMWeg/wBo1V13VNR+IHie30TRo2khMmyFB0Y93b2/pX074G8G2Xgnw5DplqA8xG+4nxzLJ3P07AV6uFoci5pbnzOZY32svZw+FfibOl6XZ6Nplvp1hCsNrboEjRew/wAauUUV2HlBSUtJQB87UUUV+inwAUUUUCCiiigAooooEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFNkjWWJ43AKOCrA9wadRQF7Hltu8nhbxYVcnykfa3+1Ge/8jXqKsGUMpyCMg+tcb490vzbaLUo1+aM+XJj0PQ/n/OtDwbqn9oaMsDtma2+Q+pXsf6fhXkYJ/V8RPDPZ6r+v62PYxq+s4eGKW60f9f1udHRRRXrnjhRRRSEFLSUUALSUUUwCiimyyRwxNLK6pGgyzMcACle24b7Dqo3us6dppxd3ccbf3c5b8hzXO3Ot6t4jvv7K8L2s0rNwZI1+Yj1/2R7n9K39L+COtFPtGoQQyztyVluMDPvtzn86+ZzPifDYN8kXdns4bJ5zXNVuvJb/AOSKaeMdDd9v2sr7tG2K17a7tr2PzLaeOZfVGzio7/wjP4ehLap4Pt5bNR809r8+0ep7j8azR4R0rVIvt/hXUGtLlefL3nGfQ9x+oryqHGkb3rw93utvnqx18upQ0fNH1s1+BuUVzdr4gutOvv7M8RQfZrgcLPjCt7nt+IrpOCAQcg9CK+ywmMo4umqlGV0eXWoTou0+uz6P0CiilrqMQopKKACiiigAoJABJOAOpPagkKpZiAoGST2rg9d1+5128TRtFSSRZW2fux80x9B7f56Vy4vF08NDnmdWEwlTFT5IfNi+IfEVxq90ujaIry+a3lloxlpSf4V9v5/SvVvh38O4PCdsL29VJtYlX5m6iAH+Fff1P9Os3w9+Hdv4StReXgSbV5V+Z+qwg/wr7+p/pXcYr86zLMamLqNt6H6Jl2X08LBJLUBS0lLXmHphRRRTEFcb8Q/G8Pg/R8QlX1S4BFvGedvq5HoP1P41teJvEdl4W0SXUr1s7fliiB+aV+yj/PFeM+D/AA/f/ErxbP4h13LafFIC4P3XI+7Ev+yO/wDia2pU0/fnsjCrUafJDdm/8K/BU9zcf8JfroaW4mYyWqy8kk9ZT/T8/SvX+tNUKihVUKqjAAGABTgaipUc5XZpTpqEbIKKKKgsK4f4jeO4/COmCC1Kvq1ypEKdfLH98/0Hc/Q1veKvEtn4U0KbUrs5I+WGIHmVz0A/r6CvMfh74YvPGGuy+NPEY82IybreNxxIw6HH9xegHqPbnanBW557L8TGrN35Ibv8DT+Gnw/likHifxErS6hMfNgil5KZ53tn+I/p9enqpOaKKic3N3ZcIKCsha4L4v6cl/4BuZioMtnIkyH052n9D+ld71rjvihOtv8ADvVix++ixj6lwKdJtTVu4VEnB37Hj3w9nZra8gJ+VXVx+Ix/Su3FcN8O4j5V9Ljgsi/z/wAa7mv07LG/qkL/ANan5lmdvrc7eX5IWiiiu888KKKKBhRRRQAUlLSUAFFFFIAooopgFFFFABVPV4/O0W9T1gf+Rq5TJ18y2mT+8jD9Kma5otFU5cs0zz7wJJi7vIv70Yb8j/8AXrtzXn/gt/L15k/vRMP1BrvzXHk0r4VLs2e5mCtXfnYkhOJB71aqkpwwPvV2vRnueTXWtxaKSlqDnCkpaSgAooooAOvB6V5RcNN4X8ZJc2+Vezulni+gIYV6vXCfEKwxJa6go4YeU59xyP615Gc0PaYfnW8T18nrclfke0j6806+h1PTLW/t23Q3MSyof9lhkfzqzXl3wG8Qf2x8PksZH3T6ZKYCD12H5kP6kf8AAa9Rr5E+qCiiigAooooAKKKKACiiikBR1qPztC1CL+/bSL+amvl74GS7PGF5F/z0sm/Rlr6nvhmwuQehib+Rr5P+C7bPH4X+9ayj+R/pWdb+Gzah/ER9Cmm0ppK8k9k8x1WNtP8AiNqccowupQRXEDHoxQbWH171ZxXW+IvDtn4jsVguC8U0Tb7e5iOHhb1B/mO9cmfDHi22PlpcaTeoOBLIXhcj3ABGfpXp4fFQUFGTtY8vEYWfO5RV0xtLSf2B4v8A+eGjf+BEn/xNB0Hxf/zw0b/wIk/+Jro+s0v5jn+rVf5RaWozofi8dYNG/wC/8n/xNMbSvFSfej0Yf9tpP/iaX1ml/MUsJXe0SfFJiqxsPE/9zR/+/sn+FJ9h8Uf3NH/7+yf4UvrVH+Yr6jiP5GWTWL4hH2qC30mP5ri+mSNVHUKCCzfQAVebTvFT8D+x4/8Aa3yNj8MVe0jQU0uaS8ubhr3UpV2vcMu0Iv8AdRf4R/Os62Mpxg+V3Z0YbLK86i51ZG2SM8V5b8RfFrXEv9gaa5b5gs7JyWb+4K2vHfi8aHZGytHBv516j/lkvr9fStL4H/DRriWPxfrcJK53WMUg+8f+ep/p+fpXHhKF37SXyPSzTG8i9jTevX/I7T4PfDZfB+jjU9RiH9s3iAsCOYEPRB7+v5V6hRRXpHzwUUUUAFFFFAHzrRRRX6KfABRRRQIKKKKACiiigQUUUUAFFFFABRSUUALRSUUALRRRQAUUUUAFFFFAEN5ax3tnNbSjKSoVP+Nea+HbqTQvFH2ec7VZzBKPx4P54r1AnArznxxYeVqMd/GMLMMMR2Yf/W/lXlZpTlFRxMN4P8D18qkp8+HntJfiejUVmaBqH9p6Lb3BOX27JP8AeHBrTr0oTU4qUdmeVUg4ScJboKKKKsgKKKKACiiigAJABJOAOSTXKWtpqXxI8TroulsY9OhO6efHyqoP3j6+w7/yn8aam1lpItoSfPujsGOu3v8A4fjXtXw08Hx+D/CVvbyRgX9wBNdt33kfd+ijj8z3r4vizOXhafsKT95/1/Xqj6TJMCpfv5/L/M1/DPhbSvCWlpYaXbhBwZJW5eVvVj3/AJDtW1QKje5t43CSTxK56KzgGvy1uU25S1bPqUktES4zxXm/jH4biaR9b8LbbLVk+ZoE+WO49sdAf0Pf1r0iinTqSpu8f+H9SZwjNcsldHgEc1h4402bTNUtzb6nbkq6MuHiYcZH+Fc7oNzeaRq03hvUzmSPm3kP8S+g9scj8RXo/wAWPDEljJH410dNl1bEC9RekkfQMfcdD7fSuC8aMl9oml+KLPiSCRSSP7p7H6EY/GvpcizB4HEwnTf7ubs12fb0fT/gHzmKwfJL2D+GW3k+hv0tMikWaGOVDlXUMPoRmn1+vp31Pl7W3EopaKYCUMwVSzEKqjJJPAFLXB69rF54i1OPQNEjeUSPsPl9ZW/+JHr/AErlxmLhhafPP5HVg8JPFVOSHzGa3rl54k1GPRNDjklSR9nydZj/AEWvZPh/8P7TwhZi4n2T6tKv7ybGRGP7qe3qe/0o8B+A7TwfYb32zapMv76fH3f9hfQfzrsxX5zj8xqYuo23p/X4H6LgMup4WmklqL1ooorzj0QooooGFNkdIYnlkbaiKWY+gFZHimbWrfQJ5tAijl1BMFI5BkMM8gcjnFchC3jvxhpZ0/ULKHRLaT5bifkyOvcKueM1UY3V7kOVnY4q5OqfF7xsY4C8WkWxwHI+WKPPX/fb/PAr3HS9MtNG02DT7GIRW0C7VUd/c+pNQaDoNh4b0qPTtOi2RLyzH70jd2Y9zWnV1KnN7q2RFKny3k92JRR1orE2DrSSyxwQyTTOqRRqWdmOAAOpp1eUfFrxTNI8HhDSS0l3dsv2gJ1wT8qfj1Ptj1q6cHOViKk1CNzDb7T8XfH+zMiaDYn6YTP/AKE+PwH0r2+3t4bS3it7eNY4YlCIijAUDgCsPwZ4Xg8J+HINPQK1w37y5kH8ch6/gOgroKurNSdo7IilBxV3uxDRmg0YrI2HDmvI/jprQi0yw0VG+ed/tEoHZV4X8yT/AN816vNcRWsElxM4SKNS7segAGSa+VPGHiGTxR4nu9TYkRO22FT/AAxjhR/X6murCQ5p37HJi58kLdzt/Btj9i8OQsww87GU/Q9P0Fb1QacUbSrQx8IYUKj2wKsGv1GhBU6UYLZI/MMRN1KspvqwooorYwCiiikMKKKKYBSUtJQAUUtJQAUUUUAFFFFABQRkEUUvuegoA8r8OHyfFiJ/tyL+hr0WvNtNdV8YxspBU3TAEdwSa9KI5rzMjf7mce0n+h9DmK/eRfkJV1DlAfaqVW4DmIe1evPY8mutLj6WkpazOQKSlooASiiigBayvElh/aOgXUIGXVfMT6jn/GtSjr16VFSCqQcHsy6c3TmproYfwA8Qf2V48fTJHxDqcJjAPTzF+Zf03D8a+pq+ILiSbwr40S6tsq9pcrcRfQEMP8K+1tOvodT021v7dt0FzEssZ9VYZH86+AqQdObg90fd05qcVJbMs0UUVBYUUUUAFFFFABRRRQBS1edLXRb+4c4SK3kdj6AKTXyl8GI2k8fowHCW0rN+QH9a9x+N/iQaF8PLq2R8XOpN9ljGedp5c/8AfII/4EK8q+BGnMbzVtVZfkSNbdD7k7j+gH51lWdqbNsOr1Ee1UlLSV5J7IUmKWikAwikIp9MY4UmgpFW5lEaGsiSUuxJqW8uPNlIB+UGquazbPTo0+VXYuaKSgUjYdmuf8W+JLfw1pZmfD3UgxDF6n1PsK0NY1e20TTJb67bCIPlXuzdgK8w8M6Bq3xZ8akzM0dnGQ1xKPuwx54Vfc9vzrpw1D2ju9jz8fjVh48sfif9XNP4X/D+7+IXiCTXdbDtpUMu6Vm4+0P/AHB7Dv7cV9TRRRwRJFEipGihVVRgADoBVbStLstF0y307T4FgtbdAkaL2H9T71cr10raHyzbbuwooopiCiiigAooooA+daKKK/RT8/CiiigAooooAKKKKBBRRRQAUUUUAFJS0UAFFFFABRRRQAUUUUAFFFFAEU7bY/rWJrtj/aOjzwgZdRvT6j/OK17k8qKgq5U1UpuEtmduGbp2mtzkfAOobLqfT3PEg3oPcdf0/lXe15ZdZ0HxaJUGEWUSAD+6eo/mK9SVg6K6nKsAQfavJyubVOVGW8HY6M2pr2ka0dpIdRRRXpnkhRRRQAUtJQSFUsegGaAMfw5pw8U/GGyt5BvtdOHnOO3yc/8AoZUV9IYrw/4CW32vVfEWsuMsSkSn/eJY/wAlr1jxbfvpXg/WL6IkSw2kjIR2bacH88V+KZ7Xli8wl/W+v6n6HhKSo0Iw7I8c8d/ETWfEWu3GheGLprPTbZjHPdxna0rDg4Ychc8DHXr0rjx4NhlBea+leY8lyepq5odqlnpEJHBdd7N9aQasZLpY7ZGZAcFiOtbxbprkoaJfj5s+zweVYaFKMsQuaUv6siXRfFviT4dX8JN1LqGjFsSW0jEgD/Zz90/Tj1r6R03ULbV9MttQs5BJbXMYljb1BGfzr5x1aJLnTJ0kGRsJr074G3ktz8OVikJItruWKPP93hv5sa48dCNSiq1rSTs/P/gnkZrgoYSulT+GSv6Hod5axX9lPZ3CB4Z42jdT3UjBr5p8prbwN4j0iY5awnZBn2f/ABFfTg618v8Ai27WN/FIi63uqvGgHfDkmufL4ucuRd4v7n/lc+czGN4w78y/M2tCYt4f08nr5Cfyq/UFlb/ZLC3tv+eUSofqBU9fulKLjTin2R8NValUk13YtFJSitCDn/GOptp2hskTYluD5YI6gd/8Pxrrvg/4Th03w8uuTxg318D5bEf6uLPAH1xn6YrzX4iSn7RYRdgjN+ZA/pX0LoVsLPw7plsowIrWJcfRRXwnEdeUq/J0X/Dn3XDlCMaKn1ev6F4Cloor5g+nCiiimAUUUUALRSUUgFopKKYC0UlLQBieK/Edv4W0CfUp13svyxRj+Nz0H0rzv4T+HrjV9Tu/G2sAvNNI32bcOrH7zj2H3R+NaXxXnk1OfRfCtr/r9QnDOf7qA4z/ADP4V6LY2UGm2FvZWyBIIIxGijsAMVsnyU9N3+Rg489TXZfmWOtFJS1ibBRRTJpUt4JJ5WCxxqWYnsBQB5h8aPE507RI9Et3xcX3MuDyIh2/E8fnXi7aRLH4fi1RgQskxQD2x1/PNaWv6jceNvG80yklZ5dkQ/uRjp+nNdb4isEXwjNbQrhLdFKD/d/+tX1GVZe50J1H0X47nzGaZgoYiFNdX+H/AAWW/Ctx9o8M2ZzyilD+BrXrkfAFzv0eeAnmObP4Ef8A1q62vr8HPnw8JeR8jjYcmInHzFooorqOQKKKKACkpaKAEooooGLSUtJQAUUUtABRRRQAVzXiPULu5urfw9pKmTUL1gmF6gHt7Z/QVvXt3HY2M11L9yJCx9/atL4K+GJLuW68aakm6e4Zo7QMPur0Zh/6CPYH1r5viXNlgMK+X4pbf1/Wlz2Mnwft6vPLaP5/1+h49NpUvh7xr/Zk0iyS2l2sbOowGII5FekGuP8AiD+7+LuqH/p8Q/oprsTWnC9Rzwzk93Z/ejvzZWqR+Y2p7Y8MKgJqS3P7wj1FfTS2PGqq8GWqWkpaxOEKSiigAooooAKKKKAOF+IWn/8AHtqKD/pk5/Uf1r3T4D+IP7Y+HkdlI+Z9Mla3Oeuw/Mh/Ikf8BrzTXrH+0tEurYDLFNyf7w5FV/2fdf8A7L8czaTK+2HUoSoBP/LRMsv6bh+NfJZ1Q9nX51tL8z6vJ63tKHI94n1HRRRXjnrBRRRQAUUUUAFFFcb8TvF6+DfBV3fI4F7MPItF7mRh1/4CMt+HvQB8/fG3xV/wk3jx7K1cyWmm5togvIaTPzkfjx/wGvW/Anh7/hGPCNnYOuLlh51wf+mjdR+AwPwrxr4TeGm17xV/aN0pe0sCJnLc75P4R+fP4e9fRGc81wYupryI9HBU7LnYUtJRXGdwUUUUAJVLUp/Kh2j7zcVdzWBqE3m3LAHheKmTsjow8OafoVT1pKKKyPTFoyFBJOAOSaSuL+I3iI6Tov2KB8XV4CvB5VO5/pV04OclFGdarGlBzlsjjvE+q3vjjxZBpGlI0sfm+Tbxr/Gx6sf89BX0/wCBfB1n4J8MwaZbhXmPz3MwHMsh6n6DoPavNPgB4FFnp7+LL+L/AEi5Bjsgw+7H0Z/qeg9h717jXuQgoR5UfG1qsqs3OW7CiiirMwooooAKKKKACiiigD51ooor9FPz8KKKKACiiigQUVS1XVrXR7M3F03XhEH3nPoKy9D8LeLviS/noRpujE4Er5AYf7I6ufyFeXmWb4bL4c1V69jvweX1cU7x0Xc0LrX9JsiVmvotw6qh3H9KoHxtogOPNmPv5Rr07Rfgf4T02NTepcalMBy00hRc+yrj9Sa6Ffhx4NRNo8OaeR7xZP5mvjq3HST/AHcNP68/0PdhkFK3vSZ4xB4q0S4OFvkQ+kgK/wA61IbiG4XdBNHIvqjA16Df/CDwTfKf+JMtux/it5XQj8M4/SuSv/2f7EMZNG168tH6hZkDj812n+dbYfjuhLSrBr+vK5nU4eX2Jfr/AJGfRVaf4X/EXS/+PLUbW/QdF83k/g4H86x7qXxtoeTq3hmcxr1kjjJH/fS5Fe9huKMur6Kdn/Xz/A86pkmJhtZ/15nQ0Vzll410q5cRzmS1k6YkHGfqK6GN0mjEkTq6NyGU5Br26WIpVlenJM82rQqUnapGw6iiitjIKKKKBBRRRQMp3BzKfYVHTpTmVvrTa6Fsd8FaKOP8cWeVtr1R0zGx/Uf1rpfCl79t8PW7E5eIeU34dP0xVfxBa/bNDuowMsq71+o5rH+H13iS7syeoEij9D/SvEmvY5j5TX4r/hvxO2sva4Hzg/w/p/gd1S0lLXpnhhRRRQIKqarN9n0i8mzjZC5H5GrdYfjCbyPDF1zgvtQfiRWOInyUZS7Jm2Hhz1ox7tHffAG28rwNdXBHM98/PsFUf416L4g0z+2vDuo6ZkA3Vu8Sk9iQcH88VyXwbtPsnwx0skYMxklP4ucfoBXfV+D4uq/rc5Lu/wAD9FivdR8w6HO3kPp10pjvLRjFLE3BGDitLyVXkKB+FekeO/hVbeKLs6tpd3/Zur4+aQA7JvTdjkH3H5GuA/4VN8Qnk8ltQsRH083zjjH/AHzmvUjWo1VzqSi3umfR4TPY0qShWi21pddTlPEmqrb25tICXuJflCryea+gfhr4dl8L+BbCwuV2Xbgz3Cns7HOPwGB+FYHgn4Paf4bvU1XVbn+09TQ7kJXEcTeoB5J9z+VelE4FcOPxcHBUaWqWrfdnkYzFzxlb2slZbJFLWdTi0bRrzUZyBHbxM5z3IHA/E4FfL2kI+u69E0nzW9oxnkbs0hOf5/yrsvit49bxJfJ4U8PP50CSf6TMh+WRx/CD/dXqT3P05raLpcWj6clsnLn5pH/vNX1XCWTTnL6xVVlv/l/XY+WznFxguVPXp+r/AMjQPJpKKK/Tj5EKWkpaAPPfiH/yFbL/AK4/+zGvpS0/48rf/rkn8hXzZ8RR/wATCxb1iI/WvpDT236baN/ehQ/+OivzziD/AHqXr+iP0Ph//dV6fqyzRSUteCe8FFFFIAooooAKKKKACiiigApaSigDLm8P2E/iO312RGa9ghMMZJ+UA98evJ/OtWkop3FYKKKOtAxa84+MXib+yfDi6VA+Lm/4bB5WMdfz6V6JLKkMTySMFRAWYnsBXzLr+pTeO/HsjIT5DSeXF/sxL3/mfxrqwdB1qqijmxdZUaTm2aPgTRvJtpNUlX55Pkiz2Xuf8+ldTeQiexuID/y0jZfzFWIoY7eCOCJQscahVA7AUuM1+n4bDxo0VSR+Y4jESrVnVZ5/8P5imoXlsT95A2PcHH9a9ArzrQx/Z/jyWDoDJJH/ADIr0WuXKrqhyP7LaOnNkvrCmvtJMKWkpa9I8wKKKSgBaKSikAUtJRQAUUUUwClpKWgAoopaQzmfFnnahNpug2p/f386pj8QB+p/Svo/S9Nt9I0q1061XbBbRLEg9gMV4B4LjXWvjXbl/misFdgPdVP/ALMa+ixX5BxhjPb472fSP/Dfp+J9xlNH2WGXd6/efJXxRby/inrUg6rOh/JFrptO1O21WyW4t29nQ9VPoa5f4pK0nxS1uNBlmuFVR6kotUHstd8EajFLf2ckMcvBBIKSDuMjjNfS5DmUcHGnCb0kl66LdfqRjsL7dXW62O/J5p8JxKtVbW6ivbaO4gbdHIMg/wBKsKcMD6Gv0JNSjdbM+clF2aZfooHSisDzgopaKBCUUUUAFFFFAC15heSTeE/HUV9bZU29ylzFjuM5x/MV6fXGfEDT/MtLfUEHMZ8tz7Hp+v8AOvKzih7TD8y3jr/meplFb2eI5XtLT/I+tLC9h1LTra+t23QXESyxn1VhkfzqxXl3wG8Q/wBseAFsJHzPpcpgIPXyz8yH9SP+A16jXx59aFFFFABRRRQAhIAJJwBXyT8WvF8vjrxutnppaawtH+zWapz5rk4Zx9TgD2Ar1344ePx4c0E6Dp82NU1BCHKnmGE8E+xbkD8T6V598HPBheX/AISe/i+RMrZKw6t0L/h0Hvn0qKk1CPMzSlTdSXKj0bwZ4aj8K+GrbTlwZ8eZcOP4pD1/AdB9K6CjrRXkNuTuz2opRVkFFFJSGLSUUUARXUvk27v6CubJyST1PJrX1iXEaRDqxyaxqznuelhY2hfuLRRS1J0kcs0dvDJNKwWONSzMewFeOadZXXxO+JkNou4W80vzEf8ALKBeSfrj9SK6f4oeIPsWmppED4mufmlweien413f7P3hD+y/DU3iG5ixdakdsORysKn/ANmOT9AK9LBUrLnfU+fzfE80lRj03PXrW1hsrSG1to1ighQRxoowFUDAAqaiiu88UKKKKACiiigAooooAKKKKAPnWiiiv0U/PwopKKAFqK5uYrO1luZm2xxKWY1LXMeLnkupdN0eJsG8mG/HoCB/XP4VzYzErDUJVX0Rth6Xtaqg9uvp1GeCdEf4jePU/tHP2C3Uzyxg8eWCAEH1JGfbNfT0MEVvCkMMaxxRqFVEGAoHQAdq8b+EUcVl8QPEVkihQLaPyh6Kpx/UV7RX4nnGKnisRzzfRfik/wCvQ+7waj7CLgrJoTpXmXiP4smDUptL8MaeupXEDbJrmRsQI3oMfe/MfjXT/EXVZtF8AazfWzFJ1g2Iw6qWIXI9xuzXg2lD+z9CtEg4Z1DMw6knms8Hhoyg6klfWyX+Z7OW4NYutySdkldnVP8AEb4iQSeY1rpUqd4xGfyzuzXTeGPjFZX95Hp3iKxbSLxyAkpbMLH3J5X8cj3rhoruR0yybiBzUGpQW2rWTwyRgEfdOOVPqK65UqM/dnBLzR7uI4epuN6Emn57f8A+juCMiivM/gx4kudW8P3WkXzmS60mQRK7HJaM52g/TBH0xXpleHiKLo1XTfQ+VtbRnOeIPAnhrxNGw1LS4Wlb/lvENkg99w6/jmvG/Evw28Q+Amk1Pw/cPqOlL80sLDLxr/tKOo9x+VfQ9FdeBzXE4KalSloun9bGNWhCrFxmrnztomuW2uWnmw/JKvEkR6qf8K06pfEzw9F4J8a2Ot6agisNRYiaFeFV8jdgehBB+oNXfpX7NkeaLMsKqvXqfEZngvqtW0dnsFFFFeyecFFFIehoAoNyxPvRRRXQeitBCoZSpGQRg1wPhtjp3jBIGOAXeE/0/UCu/rz7Wv8AiX+MRMOB5qS/yz/WvGzdcnsq38svz/4Y78EudVKXdf1+Z6hRSA5AI6Glr0j58WikpaQgrkviDNs0i3h/56S5/IH/ABrra4r4hcjTwemX/pXBmbthJ/11O/LFfFw/rofR/gyy/s7wZo1pjBjs4s/UqCf1Nb1VbABbK3VegiQD8hVmvwWcuaTk+p9+LS0maQsFUkkADkk0hA7LGjO7BVUZZicAD1rwf4gfEm88UXcnhrwk7C0OVub1TjzB3Cnsnv1P06s+IXj678Zak/hjw1KV01Di6ulPE2Ooz/c/9C+lVtK0q10i0Fvbrz/HIern3r7jh3hp4hrEYhe6v6/rt6njZlmccOuSGsipoegW2iW22Mb52H7yUjk+w9BWviiiv06nThSioQVkj4+pUnUk5zd2xaKKK0MwpaSlFAHB/EZf3mnv7OP5V9BeHpfP8NaVLn71pEf/ABwV4J8RUza2D+jsP0H+Fe2eBpfP8DaI+f8Al0QfkMf0r4DiONsS36fkj7/hyV8Ml6/mzoKWkor5w+iFooooAKKKKACiiigAooooAKKKKACiiimAUZopGIVSzHAAyaQHAfFvxGdG8KPaQvtub0+UMdQvf9K8y8A6Z5VpLqLr88p2R5/ujqfz/lTfibqsviDxstjEdyw4iUf7Tdf6flXWWdqllZQ2sf3IkCj3r7DhrCXbrS6fr/wD5PibFcqVCP8AVt/x0+RaBzSimU6vsj4s881ofYfH8Uw4Dyxv+eAf616HXA+PozFqtlcjqY8Z9wc/1ru4H822ilHR0DfmK87B+7WrQ87/AHnpY336FGp5W+4fS0hIUZYgD1NQ/brTdt+1QZ9PMFeg5JbnmqLexNRQCGGVII9QaKACiiimIKKKKQBRRRTAKKKKAFoJCgsegGaSo7k4tJyOojb+RqW7K40ruxm/BaTzPHbXb/euROAT36Gvoyvmj4VzC11rw7LnAkuriI/Uqv8AjX0t2Nfg+dScsW5Pr/m0foWEfuyXZs+UPiGf+Lxaif8Ap9i/kle5a9oVp4i0ifTrxMxyjhgOUbsw9xXh3xDH/F49QH/T5D/6ClfRBjOTweK3zSco0sPKLs1H9EEviZ85ad9r8I+IrjQdU+VN+Fb+HJ6MPYiuwYYNdX8Q/BKeKtKE1qoXVLZSYW6eYvdD/T0P1ry3w7rzO39lahmO7iJRS/BOONp9xX6Jwpn8cTSVCq7SX9fj0+48jH4Vv97Fev8AmdnGcxqfanVFbnMePQ1LX2D3Pl5q0mgooopEBRRRQAUUUUALVTVLJdR0u5tG/wCWiEL7N2/WrVLUyipRcXsyoycZKS3Rz/wI19tE+IR0ydikOpRmBge0i8r/ACI/GvqmvizxEk3h7xjDqdp8jCRLqIjswOT+o/WvsXR9Th1rRbLU7Y5hu4UmX2DDOK+Cr0nRqSpvofdUaqq01UXUvUUUViaiVznjbxjYeCfDs2qXpDSfct4AcNNJ2Ue3cnsK0dd1yw8OaPcarqc4htYF3Mx6k9gB3J6AV8m+Itd1v4teOI1hjYIzFLW3z8tvF3LH17k//WFDaSuxpNuyF8PaTqvxR8b3F/qUrtGz+dezjgKvZF9OBgDsB7V9FwQQ2ltFbW8axQRIEjRRgKo4ArK8MeHLPwrokOm2Yzt+aWUjmV+7H/DsK2a8qtV9pLTY9fD0fZx13CkpaSsTcKKKKACiio5pBFA8h6KM0DSvoYepS+beMB0T5aqUFizFj1JyaBWT3PYhHlikKKjurqKys5rqdgsUSF2J9BUgrzr4p695FnFo0L/vJv3k2D0UdB+J/lV0qbqTUUZYisqNJzfQ5vQNJvfid8RkgO4RTyeZO4/5ZQL1/TAHuRX2Ha2sFlaQ2ttGsUEKCONF6KoGAB+Fea/BDwUPDXhEandRbdR1QCVtw5SL+Bfx+8fqPSvUK9xJJWR8dKTlJye7CiiimSFFFFABRRRQAUUUUAFFFFAHzpS0lFfop8AFFFFAha5rUgB4+0Fn+4RgfXJ/xFdLXN+MIJVtbTU4B+9spQ34Ej+oH515edUZVsDUhHex14Fr2yi+qa+9WOktL5fC3xX07UZTss79Ps0rdhu4BP0O017wa8D1q3g8YeFIri2I8x18yI91fuP5iu4+F3jtfEWmDR9Tfy9csV2So/BmUcBx6n1/PvX4vVhKdFSt70NJL02f6H1OUYjmpeyl8UTq/Feif8JJ4V1LSNwVrmAqjHoH6qfzAr5s0m6ltnfRNTja3v7NjEUk4Jx2r6rHFcX44+GmkeNQLlibPVEXCXca5yB0Dj+IfqPWrwOJjBOlU2f4M+hweLnhKqqw17rujyNdyA7eKhuLqO3haSZwqqM1cvPhV8Q9PkMVpLbX0I+66zgcfR8H+dX9E+CWu6ndJL4q1JILVTk29u+529s4wv15r0W6EVzSqK3lv9x9BPiOnye5B83nsbHwFsJntdc1yRCsV5OscWf4guST/wCPAfga9jqpp2n2mk6fBYWMCwWsChI40HAFW6+fxdf6xWlUta//AAyPlrt6vcSisrXPEmj+HLX7Rq2oQ2qYyodvmb/dUcn8BXlOu/Gq+1QvZeDdKlZj8pvLhfu+4XoPqx/CtMLl+IxUuWjFsidWFNXk7EXx31WG/vtE8OWxEl2JDNKB/ADwoP15P0AqADaoXPQYrC0fQZoL6XVtWuWu9UnJZ5GOdpPXnua3s1+x8OZVLLsLyT+J7nxeb42OJqpQ2QUUlFfQnki01/uN9KdTZP8AVN9KaGtyjRRRW56AVwvjmLbqVtMB9+LH4g//AF67quT8dRZs7Sb+65X8x/8AWrzc3hzYOXlZ/idmAlbER8zsNLn+06TaTZ5eFSfrirdYfhGbzvDNr6puT8jW5WtCfPSjLukeRiIclWUezYUUUVqYC1ynj60abSIblRnyZOfof8iurqK5t4ry2kt5l3RyKVYVhiqPtqMqfc3w1b2NaNTseteCdZi13wjpWoROG8y3VXwfuuowwP4g10VfMfhrxTrfws1GaBrc32iTvuKZxg/3lP8AC2Oo74r0c/H3woLUSLa6k02P9V5S8H67sV+KY/JsVh68oKDavofoFLEU6sFOLPVTgDJ4Arwv4m/EebXrp/CXhaXfG5KXl3GeGHdFP931PfoPfH8Q/EPxT8QEfT9KtjpelPxI4Y7nX0Z/T2H40aLoVrolt5cA3St/rJSOW/8ArV9BkHC9SrNVsSrRX9f10PNzDNaeHi4w1l/W5JoukwaNYrbwjLnmSQjljWjSAUtfp8IRpxUYqyR8bOcpycpO7YUUUVRIUUUUxBRRRQByfxCTdolu/wDdnH6g16t8MJvO+HWknP3VdPydq8y8cx7/AAzI39yVG/p/Wu++Dk3m/D2Bf+edxKn65/rXw/E0bVr+n6n3HDUv3NvX9DvaKKK+VPqQpaSigBaKSloAKKKKAEpaKKACiiigAooopgLWR4n1FNM0K4uHbaqoWY+wGTWvXlXxm1r7Pov2GNsNcOI+D/COW/oKcY80lHuOMlG8301PM/CUMmr+KLjUp+dhaVif7zHj+v5V6LXOeCLD7LoAmYYe5cufoOB/X866Ov1DKqHscLFd9f6+R+X5rXdbFSfbT+vmLS00uiRs8jKiKMlmOAKwjrt3q18NN8MafJqN438ar8i+/wBPc4FdGJxdHDQ560rI5KGGq4iXLTVzO+IUQNhZy5GVkK/gR/8AWpula/rWrxW2k+H9LkubpIlVnC7sYGM+gHua39d+EWtx+FdQ17XdXEt/bQGZLWIblUDkgt9M8Afia7T4AyRP4EuQsaLKl64dgOW+VSMn8a+CzDiZJVMRg+6X/B/4b7z6zD5ZFUoUq+ttTA034J69rBWfxNrnkA8mCD94w9s8KPwzXTR/AbwgkeHfUZH/AL5nAP6LXqFFfE1s2xleXNOo7nrQowgrRVjwvxD8G9R0G3k1DwjqlzK0Q3NZzEFmA/ukcE+xH41g+HNdGtWjeaojuoTiRB/MV9I4r5nlhit/i94khtFC24kkJC9ASQT+pNfXcI5viZ4j6vUd0zyc4wdKVB1bWkjoaKKK/TT40KKKKACiiigQUUUUAFDp5kbIf4lI/OilBpPVDOF8MyS2mgveRAmbSNSjuCP9lsA/qor6m0+7i1Cwgu4WDRTIrqR3BGa+bfD8UVn451PR7gYtdUiYAH3+Yf1Fer/CnUJ4rG88N3h/0nTJMJn+KIkkEf57ivxLPcM4YiUXvF/g9PwkvxPtsFiE6jX8yTXzPDviexb4p60Yid4uFC465CKP51jSX2vaddi4e9vY587vM85tx/HNaPi2cXHxS1aUnI/tKQfgHx/Sp9enSXC8HB/SvYjLkhSptX91fkj6LB4CGIoVKspWaeh33w4+Jc2r3cei624a6fi3uSMeYf7re/oe9Q/FrwOZY28TaZHtni5vETqwH/LQe47+3PY15DBK9lqENxAxWSKRZEI7EHIr65Cie3XzEBEiAshHGCOQa8fHr+z8RDEUNE91+f8AXc8xws2jwbwfrv8Aalq1vO3+lQjk/wB9fWumridX0keCfib9ljytnK4aLP8AzzfoPwPH4V29frOTY767hY1L3Z8dmuHVGveOz1Eooor1TywooooAKKKKACiiigDmPHOn/atEFyoy9s2f+Ang/wBK9W/Z+8Q/2n4Hl0qV8zaZMVUE8+W/zL+u4fhXGXMCXVrNbuPklQofxFc38H/EP/CIfEcWl7J5VteZs5ixwFbPyE/8CGM+jGvl88octRVV1/Q+myWtzUnTfT9T6yqnqmp2WjabPqOo3CW9pAu+SRzwB/U+3eqPiPxVo/hPTWv9YvUt4gDsTOXkPoq9Sf8AJr5c8c/ELWviZrcNlawSx2Ik22lhGclm6Bmx1b9B+ZPhHtof4/8AHGq/E/xLDY6fDKLBZNllZr1Ynje/+0fyA/En1fwJ4ItvB2l7W2y6lOAbmcdv9hfYfr1qD4f+AbfwjZfaboJLq8y4kkHIiH9xf6nv9K7SvOxFfn92Ox6mGw/J70twooorlOsKKKSgBaSiigBazNYm2wLEDy5yfpWnXOahP5945B+VflFKTsjow0Oad+xWpRSUVkekNnnjtbaW4lYLHEpdiewFeS+DdIl+JPxTj+0IWtPNNxcDssKHhfx4X8a6P4n619h0NNPjbEt23zY/uDr/AErv/gB4WGkeDpNanjxdao+5SRyIVyF/M5P5V6eCp2i5vqfP5vXvJUl01Z62AFUKoAAGAB2paKK7jxgooooAKKKKACiiigAooooAKKKKAPnSiiiv0U+ACiiigQUkkcc8TxSqGjdSrKe4paKGk1ZgnbVHJ2V7deBNReCdXn0S4bIYcmM/4/zroNQ0WLV3g1zQb3yL5PnhuYWxuPvjvVmeCK6gaGeNZInGGVhkGuZbRdW8PTtdeHLktCTl7SQ5B/Pr/Ovg854Zqe0eJwWj6rv/AF/Xc9ajiVUkp35anfo/Xs/wPR/D/wAX5LIrp/jKye2nX5RexJlH9yo6fh+lek6br2k6xEJdN1K2uVP/ADzkBP4jqK+df+E8s51Nr4g0mSCTo3ybl/I8iqUsfhC5fzrPVDZydRhmXH518TWwD5rVIOD8ldf8A9ylmNaCtWpv1WqPqjmgg18trq09kuLbx3cKg6KJmP8AWqF14nv7k+SniHV7924CRuyg/rUQymdR2i7/ACf+R1xzCEtov7mfTur+I9G0G3afU9StrZAOjyDcfoOp/CvIPEnxq1LWJ307wXZOueDeTJlvqqnhfq35CuCsPCF1qMwudVZ4ozz5bNukb6ntXZWdjbafAIbWFYox2Udfr619blPBibVTFbdv+B/n9x52MzuFNctLV/h/Xp95hWvhZ7y5bUPEV5LqF45ywdyR+JPJ/lXRxRxQRCKGNI4x0VBgCnUV9/hsHRw0eWlGx81XxNWu71HcKKKK6TnCiiigBajm/wBU1PqOf/VGmtyofEipRRRW53hWB4xj36CzY+5Irf0/rW/WV4lTf4eux6KD+RFc2NjzYaovJ/kbYd2rRfmiHwFLv0SWP+5Mf1Arqq4n4eyfu76PPQo3867auTLpc2Fg/I5cyjy4qa/rYKKKK7DhCloooARlV0KOoZT1BGQaqLpGmrJvFhbbvXyxVyiplCMt0VGco7OwcAAAAAdABSUtFUSFFFFIAooopgFFFFABRRRQBj+LIvN8L3w/uqG/Ig10PwNn3+Er6DP+qvCfzRf8KytXi87Rb6P+9A/8jR8Brn5NbtSe8UgH/fQP9K+P4nhtLy/X/gn1/DM94+f6f8A9kooor40+yCiiimAUUUUgFooooAKKKKACiiigAooopgNlkEUTSH+EZr5z+Jd5JrHjSLToju8kLGB/tscn+Y/KvfNZulgt9pbAwXY+iivnfw8W1zxzcajJyod5+e2ThR+o/KvQyuh7fEqP9f1Y480r/V8G5d/0/wCDY7yC3S0tYrdBhYkCD8BVXU9UtdKtTPdSbR/Co+8x9AKl1fUoNJsHu5znHCKOrN2FS+APhzceMbhPE3igMbFjm1s+QJB2J9E9v4vp1+2zjOqOWUbv4uiPz/L8vli580vh/MyvDPhLXviVci6ui+neH1bhscy47KP4j7nge/SvfNA8OaV4Z05bHSbRIIhjcwGWkPqx6k1anms9I015pWitbO2j3McBUjRR+gArxfVfiL4m8YXUkHhf/iVaQrFft0g/ey/T+79Bz6kdK/KMVi8Xm9R1KjtFd9l/mz7TC4RQtSox+S/r8T2TW7P7foOo2mM+fbSR4+qkV5H+zvcf8SzXLNjzHPHJj6gj/wBlrmtU0PWtI0+TVIvFWqSXajJc3Dcn86rfBfxVZeHfE96mqTiGC8gx5jdA6nIzj1yR+VaU8FbB1YwlzXt07HRisNVw8o+0W59M0tcJo/xa8L6tfPZyTy2Ewfan2xNiv9Dkgfjiu5BBAIIIPIIrxKlKpSfLUVmZpp7FPWNTg0XRrzU7k4htYmlb3wOn49K+b/CEc142o65c8z307Nn15JP6n9K7n45+JHMNh4TsnzPeOstwB/dzhFP1PP8AwEVjWVpHYafBaR/diQL9T3NfoXBOXv3sVJen9ff+B8/n2J5aaorr+hNRRRX6MfJhRRRTAKKKKACiiigApaSigDnvFVlMFt9Zs8i6sWDHHUrnOfwP6E11On66LoWXi3RwDdQpsu4AfvLjlT/T8Kg6gggEHqDXJy2OpeFdSk1HQ0M9lL/r7M88ew/l3FfF8TZHPE/7TQXvLdd1/X3bnq4LEXShe0o7efl/kcFd3T3upz3khxJNK0rH3JJP86c8zNy8hb6mtrwDo1p4i8dWGmX6MbWd38xVbacBWPUfSvd4/gx4KilVzZXUgU/ce5bB+uMV83jMwoYWahNO9uh9pRnKMWkeL/Dvwlc+LPEkLtE39m2riS5lI4OOQg9z/LJr6ZIBPSm2dhZ6ZaJaWNrFbW8fCxRKFUUss0VvFJPPIscUalndjgKB1Jr5TMMbLGVL2slsh26s8Q+OkQGsaZMnEqWx3Y643nH9auafcfa9OtrjvJErH6kVy/j/AF5fEdze6ouRbyusFoD1Mad/xOT+NdFoiGLQ7KNuGEK5/Kv07g2M4Yd05dLf1+h8tnLU4qXZtF+koor7U+eCiiigAooooAKKSloABXnHjywFrq8d3GMLcrk/7w4P9K9CubqCytnuLmQRxIMkmvOb6XUfHPiGGz0y1eTqsUY7DuzHt7ntXjZ1VpLD8s3r0PXyalVliOaK06mIg1TxDqUMAe5v7yUiOMO5dj6DJ7V9B/D/AOH1t4RtRdXWyfV5VxJKOREP7qf1Pf6VZ8D+A7HwdZ7vluNTlXE1zjp/sp6D+f6V11fBV8Rz+7HY++w+G5PeluFFFFcp1hSUUUAFFFFABRRRQBXvZ/s9o75+bGF+tc5z+NaGr3G+dYVPypyfrWd3rOT1PRw0OWF+4tFFZHifUxpHhy9vM4cIVT/ePApRTbsjeUlGLk9keYass3jn4mQaXbMSstwtrGR/CoPzN/M/hX2DZWcGn2NvZWyCOC3jWKNB2VRgD9K+b/2d/D5v/FV/r0y7ksItkZP/AD1kzz+Chv8AvqvpevehFRioo+Mq1HUm5vqFFFFUZhRRRQAUUUUAFFFFABRRRQAUUUUAfOlFFFfop+fhRRRQAUUUlMBaKKKQiOe3guU2Twxyr6OoIrLl8K6HMctp8an/AGCV/lWxRWc6NOp8cU/U0hWqQ+CTXzMRPCOhRnP2EN/vOx/rWpbWdrZrttbaKEf7CgVPRShh6VPWEUvkOderU0nJv5hRRRWpkFFFFABRRRQAUUUUAFRXH+q/GpaiuP8AV/jVR3Lh8SKtFFFbHcFUdZTfot6v/TFv5Veqvfru065X1if+RrOsr05LyZdN2mn5nLfD98X94nrED+R/+vXoFeceAnxrcy/3oD/MV6MDXk5S74VfMWbq2KfohaKKK9I8wWikpaBBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFADZF8yF0PRlIrn/ghOYfF9/an/AJaWrce6sP8A69dGOtcd8NpPsPxaWA8B3ni/RiP5CvmuJIXpRfr+h9Lw3O1aS9P1Poiiiivgj7wKKKKYBRRRSAWikpaACiiigAooopgFBIAJJwByTRWfq9z5VuIlPzSdfYUm7FQi5yUUcT8QNYNr4Y1O6DYaRPIj/wCBfL/Ik1578PrUR6dc3bDDTSBAfYf/AFz+lXvi5qO230/TFblmM7j2HC/zb8qWyH9h+EkYjDQ25kI/2jz/ADNfU8M0felWlsk3/X4nzfFlb4cPDyX6/wCRPouj/wDCffEaLTXydL04GS5x0YAjI/E4X6Zr6PSNIo1jjUIigBVUYAA7AV5V8BNJ+zeELrV5BmfUbk/Me6JwP/Hi9er18Rn2OljMbKT2Wn9fl8jXCUI0KKhHoeR/GnUZ7qXRfClvIyLfyGa5K941PA+mcn6qK5nVJ4dLtEtrdBHFEoVEWtv41xy6X4g8PeIghaBFe2kI/hPUfmC35VxV/ef2q6zoB5Z+YDPWtKEP3NO3w6/ff/hj6vIVC05L4v06fqV9T1y6urCOzbiPJJPdq4S2JXUBt67jXYXaxiIliFx61g+FLWPUPHWlWshzFNfRo2O4LCvVwrjCEpJbGHEG9O/n+h9bPoGl6ppdtFqmm2t0ywqpM0QYg4HQ9RWh+4sbL+GK3gj/AAVVH9AKnrhPi1r40XwNdwRvi6vlMCAHkKfvn8uPqwr5GEJVpqmurPDlJRTkzxTT7yXxd4+1PxHcZMauTED/AAjog/BRXXDnrWH4TsPsPh+HIw8xMrfj0/TFblfueU4WOGwkIJW0/r8D4LMa/t8RKXRaBRRSV6Rwi0UUUAFFFFABRRSUALRRRQAUtJTZW2wu3opNAWucB8N7yKy+KOlzTSKkZuHQsxwBuVgP1Ir6pYk+tfFJ3GRmXOQc8VuReNNditVt01vUoo1GNqzEgD25yK/Ic1yyWLqqpF26H6HTqcitY+oNd8SaP4etjPql/FAMZCE5dvoo5NeKeK/G9941Dwwh9N8ORHMjucPPj1/wH45rgY7+KW482SGe/uWP3p3LZP071tw6JquuOj6k5trRfuwqMcew7fjV5bw9UlUXs48z7vZen+b+45cTimlZ+6v62RWsoW8R6vGEjMem2vCr2x6fU16HAwIKjoOlZtraQ2MCwW8YSNew7+9XYGxJj1r9Qy7LoYGh7Nb9T5zG1faqyVkti3RSUua6zyQooooAKSlooATNU9T1W00i1M90+B/Cg+8x9AKp694itdEgIOJLph8kQP6n0FYfhfwfrHxB1E6hfyPBpythpyOv+zGP69B7mvJzLNaeEi0tZfl/XY9bLcqqYuSb0j+f9dyvY2Ou/EjWhBbJ5drGcsxz5cK+pPc+3U17r4X8J6Z4S077LYR7pXA864cfPKff0HoKvaRpNjoWnR2GnW6wW6dh1Y9yT3PvV7NfAYrGVMTNykz7/CYOnhoKMUBopM0VyHWFFFFABRSUUAFLSUUALUc8wggeVuiipKx9ZuMstup4HzN/ShuyNKUOeaiZbOzuXb7zHJpKKKxPVHCvNvixqW2Gz0xG+8fNkH6CvSR1rxXWVl8X/EmOwgJPn3SWseOwyAT/ADNdWEhzVL9jz8zq8lBrq9D6I+Ceg/2H8NbKR02z37Ndv64bhf8Ax0Kfxr0SorW2is7SG1gULDCixoo7KBgD8qlr1z5cKKKKACiiigAooooAKKKKACiiigAooooA+dKKKK/RT8/CiiigApKKKYC0UUUCCiiikAUUUUAFFFFABRRRQAUUUUAFFFFABUVx/q/xqWorn/Vj61Udy6fxIq0UUlbHcFRzjdbSj1Rh+lSUMMoR6ihq6sCOD8Dtt8Q49YXH8q9LHSvMfB52eJVH+w4r0xDla8TJ/wDdvmzfOY/v0/IfRRRXpnjhRRRQIWiiigAooooAKKKKACiiigAooooAKKKKAAda4jSX+w/GWzboGvk/8fH/ANeu3rz/AFx/sPxGsbrOMSwS5+jAf0rxc+hzYZev6M9vIp8uJt5fqj6ZPWkoPU0V+cH6MFFFFABRRRQAtFJRSAWiikpgLRSUtAASFBJOABkmuaupzdXDy9jwo9q0tXutkQt1PzPy3sK4/wAU6wuheG7u8DAS7fLhHq7cD/H8Km3M1FHZQiqcHVkeUa/N/wAJN8SHjU74I5RCPTYn3vzIb866bxRGzeGb0J2UflkVg+ANPLyXWpyDOP3SE9z1J/lXaXNul1aTWz/dlQofxFfo2U4PkwLj1kn+VkfmOb432mPU39l3+d7s634Ha/aX3guPRg6re6ezboyeWRmLBh7c4/D3r1CvlPw1HfQq9xpNwbXX9Jc7cf8ALVM/dYdx1HNe5/D/AOI9j40tjbyqtprEI/f2pPXHVkz1Ht1H61+W5tl1SjOVaKum9fJn0mHxEal49V/S+86jXNEsPEWkz6ZqUAmtphyOhB7EHsR614xqHwR8Q6dK/wDwj+s29xak5WK6yjL7ZAIP14+le70tebh8XVoJqD0fR7HZGUoS5oOz8j57tvgh4r1O4Uavqdla22fm8pjI34DAH61zVzoVv4O+Nmn6ZbPI1vb3tqUaQ5YhghJP4k19U182/Gcf2X8V7DUegMME+fdXI/8AZRXrYDG1cRVdOezi9F3Mq0pS96Tu/M+h9S1K10jTpr69lWK3hXc7H/PWvnbxrrVz4w8QWsDAp9oYMIs/6mEdAffGWPuR6Ve8b+Po/E15vV2XQbJtyIeDcydjj09BWV4XtZpXn1q9XFxdf6tT/Cn+cV38O5POviYua0Wr8vL1/wCB5niZjjrRfLsvxfT7tzpAixosaDCqAoHoBSUuc0lfraR8iFJRRTAKWkpaACkoooAKKKKAFooooAKgvm2WFy3pEx/Q1PVPVTt0i9PpA/8A6CambtFsumrzSPO/BqLLrbK6hl8lsgjIPSuxfRNMZ932CDP+7XH+CP8AkNyH0hb+Yr0CvOyanCeEXMk9We/mE5Rr6O2hBb2lva/6i3ii/wBxAKsZzSUV7SSSskcF29WIaVDtcH3oopiepeHNFMjO5AafWB5zVnYWikpGZUQu7BUUZLE4ApCHVy/iPxdFpu61sSst30Zuqx/4ms/XPFc+oXA0vQ1kdpG2eZGMs5PZRXfeA/hXBpPl6pr6LcX/AA0dufmSE+rf3m/Qe9fOZpncKKdOi9e/+X+Z9HleSTrNTrLTt/n/AJHOeCPhld69MmteJPMS1c70gYkST+5/ur+p9q9rggitoEggiSKKNQqRoMBR6AVKTmkr4etWnVlzSZ9xRowpR5YhS0lFZGotFJRQAUUUUAFFFFABRRRTAZNKsMLyN0UZrmJJGlkaRvvMcmtLWLnLLbqeBy39Ky6zk+h6GFp2jzPqFFJSipOoz9e1BdL0K8vCcGOM7fqeBXHfALRW1f4hSarKu6PToWlyf+ej/Kv6Fj+FHxU1PydMttORvmmbe49hXpX7Pmhf2d4Dl1ORMS6lcFwSOsafKv67/wA69TBQtDm7nzmb1eaqqa6fqet0UUV2HkhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHznS0lFfop+fi0lFFAC0lFFAC0UlFAhaKKKACiiigAooooAKKKKACiiigAooooAKhuf9WPrU1Q3P3B9aqO5dP4kVqSlpK2O4KWkpRQB574a+TxZt95B/OvSoGypFea6IdvjPH/AE1kH6GvRYGxJj1rxsnX+zzXaT/Q7c2jdp+SLlFFFekeCFFFFIQUtJS0AFFFFABRRRQAUUUUAFFJRQAUUVT1A6gDALFYyC370v2HtUzlyq9rlQjzSte3qXM1554+/d67Zyjr5IP5Ma9Aya848cXK3evxwRfMYYxGcf3ic4/UV5mctfVmn3R6uTxf1m67M+nreTzrWGT+/GrfmKlqCyjaHT7aJh8yQop+oAFT1+Zvc/SlsFFFFIAooopgFFFFABRRS0AFRzzpbwtK/RR+ftUlc/qd79pm2If3SdPc+tJuxrSpupK3QpzTPNM0rn5mOa8f+JeuNqmuQ6NakvHbHDAfxSn/AAHH1Jr0HxXryeHtCmvCQZm+SBT/ABOen4Dr+FeWeCtLkv8AU5NWucusTEhm/jkPOfwzmvRyjByxNdf16s5c/wAfHC4fkX9dl8zutJsF0vSbezXGUX5z6sep/OrZpBS1+nwioRUVsj8mnJzk5PdnJa3Hc6Frcev2alom+S5Qfz/H+YqObw7f3NwnibwzevJMZPNBjbEkbdx/9b+ddg8aSo0ciBkYYZSMgiubbQ9T0O7a88OXRRWOWtnPB9ueD+NfK53lFecnXwm/VPZnrYTG2STdpLS72a7Pt6nZ6F8cJ7GNLTxbo9xHOnDXMCY3e5Q45+h/CumX42+CWXJvbpT6G1bP6V5kPH0sK+VrugyKw6vGuQfwP+NMbxl4PY7msQrejWozX59WwUozaqYeSfk9PyPXjmWJS1pX9Hf8j0O9+OWhYKaRp2o6jOfuqI9in8eT+leMfEfXdb8S6xb6jrFgtl+68u3iAIwgOec8k5P/ANaulHxD0qMeXp+nXEp6BUjCCuS8YalqGqyWs99Y/ZIwGESnqemc5/Cu3LcJUjV5o0eWPVt3f/A+4dPG4irVUZw5Y+b1L+g+Gp75Le61MbbWMAw24/i9z7V24AAwBgDoKraYQ+kWTjvAn8hVuv1XBYSlhqSjTW/4nzGLrzrVHzdNkFFFFdZzCUUUUAFFFFABRRRQAUUUUALRSUUALWfrjbdCvz/0wf8AlV+svxI23w7fn/pkRWVd2pSfkzWgr1YrzX5nD+Bx/wATic+kB/mK76uE8DD/AImlyf8Apj/UV3dc2S/7ovVnt5i/37+QUUUV6xwhRRRQBYgOVx6VNUFv9/HrVLWvEFjosJ81xJcEfLCp5P19BXNWqRppym7I5ZUZzqcsFdsuXt7b6fatc3UojjX16n2Fef32rar4w1KPS9MgkKSNhIU6t7sfT9BVe3h1zx5ryWtuhkcnhRxHCvcn0HvXv3g7wXp/g/T/AC4AJr2Qfv7phyx9B6L7V8dm2duadOlovz/4B9XlOSKLVSpq/wAvTz8yh4E+H1n4RtxcT7LjVZF+ebGRGP7qf1PU12nWiivk5Scndn1cYKCtEKKKKkoKKKSgBaKSloAKKKKACiiigAqO4mW3geVuij86krE1i63yC3U/KnLfWhuyNKUOeSRnvI0js7H5mOTTaSlrI9VC0opKpazfLpujXd4xx5UZI+vahK7sDaSuzx3xrdy6941e2twXIkW2hUd2zj+Zr7B8P6TFoXh7T9KhA2WlukQx3IGCfxOTXy38GNDbxH8T7a6mXfDZbr2Un+8Pu/8AjxB/CvravdhHlionxleo6tSU31CiiirMgooooAKKKKACiiigAooooAKKKKACiiigD5zooor9GPz8KKKKQBRRRQIKKKKAFopKWgAooooAKKSigBaKKKACiiigAooooAKhuPuj61LUNz91frVR3NKfxorUUtJWx2BSikpR1oGed6adnjUD/p4cfzr0NDhwfevNDcrZeLXnfOyO6Ytj0zXpELpPGssTh0YZDKcg14mSzjy1YX15melmKfuvo0aQpaRfuj6UteofOMKKKKQgooooAWkoooAKKKKACiiigAooo60AFI7KiF3YKijJZjgCsHWfF2n6Vuijb7Tcj+BDwp9zXM2dt4n+IF/9ns4neEH5sfLDF/vH/wDWfSvNxma0MMmr3f8AW56eDyqviGnay/rZGhr/AI1jRXttKO+Q8NPjgf7vr9a2fhz8Nr+/1K313XInhtInEsUUo+eduoJB6Lnnnr+tdz4O+F2keGfLursLqGpDnzZF+SM/7C/1PP0rvK+HzDNquKl5f1sfb5flNPDR2/rzCiiivHPYCiiigQUUUUAFFFFMApaKo6jf/ZU2R4MzDj/ZHrSKjFydkQarf7QbaI/MfvsOw9KxSVRCzMFVRkk8AClySck5J6k9686+JXisW8DaFZSZlkH+lOp+6v8Ac+p7+31opwdWfKjunOGEouT/AOHZyvirV5/GfiiO2s8m2RvKtx2x3c/Xr9AK7jTrCLTLCK0hHyRjGfU9zWJ4N0H+z7P7dcJi5nHyg9UX/E109fpOTYBYWlzNav8ABf1uflOdZhLF1mr6L8/+BshKWkor2TxRaM0UUABwwwQCPQ1XaxtGOWtYSfUxirFFJxi90Ck1sxkcUcX+rjRB/sqBXJfENd1lYyf3ZGX8x/8AWrsK5Xx8m7RIX/uzj+Rrjx8V9WmkduXStioM2PDsnmeHLA5/5ZAflxWlWF4Pff4Ztf8AZLD/AMeNbtb4d81GL8l+RjiVy15rzf5hRRRWxgJRRRQAUUUUAFFFFABRRRQAUUUUCCsbxW23wzen1UD9RWzXJ+O9SW30tLFTmWdgSPRR/wDXrmxtRU8POT7M6sFBzxEEu6/AxPAv/IRuv+uX9RXdVwvgT/j+uz/0yH867qssl/3OPz/M9XMP94fyEopcUYr1TjEqpqGpWumQebdShR2UclvoKyNd8VQadut7TbNdDgn+FPr6n2qLw/4K1DxJKNU1yWSG0PzANw8g9v7q/wCR614eZZ5RwicYay/BHq5flNbFyVlp/X3GNqfjG/uyy2ebaDplfvH6nt+FUdC0PUfFOtR2NmrSzynLyMeEXuzH0rY154dc1q20Dw3Zr9njfy4xGP8AWv3Yn09z7mvc/BfhG18IaOLeMLJeSgNcz45ZvQf7I7V8di8yq1VzVXq+nY+io5dTpzcKey3fcseFvC2n+EtJWzsl3SNgzzkfNK3+HoK3KKK8Ztt3Z6ySirIKKSikMKKKKACiiigApaSigBaKSimAUUUUAQ3dwLW2aQ9RwB6muZZmdizHLE5Jq7ql159x5an5I+Pqao1nJ6no4enyxu92FLSUtSdAorgvilqf2fSbfTkb57ht7j/ZFd6K8Q8ZX0uveMpIbcGTbILeFR3OccfjXThYc1T0ODMa3s6D7vQ9u/Z20D7F4UvdakTEmoT7IyR/yzj4/wDQi35V7LWV4a0aLw94a07SIgNtpAsZI7tj5j+JyfxrVr1z5YKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPnKlqGe4htYWnnkWONRksxrk7z4gW8cpS0s2lQfxu23P4V9/XxVGh/ElY+GoYWtX/AIcbnZUVyVl4+sppAl3byW+f4wdwH9a6mCeK5hWaCRZI2GQynINFHE0a6vTlcVbDVaDtUjYkooorcwCiiikAUtJS0AFJRRQAUUUUAFLSUtABRRSUALSUUUAFQXPRanqvcnlauO5pS+NENFA5oIrU7AooooA84/s4an44GnFygub0RFh1G5sZ/Wuh1fw34j+H9yZlzc6cW/1iglPow/hNZdu32f4n2j+moxH/AMeFfTN5DHLG8csayRuCrIwyGHoRX5BnOaV8ux/NS2u/zPraVKNWilLsjx7RdZttZsxLB8rrw8ZPKn/CtGud8W6APAXii11PT9w0q8Yq0ef9We6/TuP/AK1dECCAQcg8g1+gZLmkMywyqx36nyeZYP6rVstnsFFFFeueaFFFFABRRRQAUUUUAFFUtS1ew0qLfeTqh7IOWP4Vw+qeNb7UJPs2mRtAjnaNozI3+H4VyYnHUcMvfevbqdmGwFbEP3Fp36HZatr+n6Oh+0zbpccQpyx/wriLzxDrXiW7Ww06GUCQ4SC3BZ3+pH/6q6Pwx8INZ1p1u9ckbT7ZvmKsMzv+B+7+PPtXtHh/wxo/hi18jSrNIsjDyn5pH/3m6/h0r5HH8QTqXhT0Xl+rPrcBkEKdpT1fd/ojzDwn8FWJjvPE8u0dRZQtyf8AfcdPoPzr1+ysbXTbRLSytore3jGFjiXAFWKK+bqVZ1HeTPpKdGFNe6FFFFZGoUUUUAFFFFAgooopgFFFQ3V0lpCZH5PRV9TSGk27IZfXq2cWeDI33V/rXNvI0kjO7EsxyTTpp3uJWkkOWP6e1YfiPxDbeHNLa7nw0h+WGLPLt/h6mp1m7I9KnTjQg5T+Zn+M/FkfhvT9kRVtQmBEKddo/vH2/mfxrznwpokmsX7arf7nhVy2X581+vPrVOws7/xlr0t3dyMULbppOyjsq/0r0y3gitoEghQJFGNqqOwr7PIcpX8aotPzf+SPgeIs6dSXsqf/AAy/zf4EpOaKSlr7E+KCikooAWiiigQUUUUAFc544Xd4bY/3ZUP8xXR1g+MV3eGLn2Kn/wAeFc2MV8PP0Z1YJ2xEPVEHgdt3hxR/dlYfyNdJXK+Amzocq+k5/kK6qlgXfDQ9CscrYmfqLRRRXUcglFFFAgooooAKKKKBhRRRQAUUUtAgryLxDdy6pql1e4JgV/LU9gOcfngmvU9SlMGl3co6pC5H5GuDWwX/AIVdPe4+c6kqk+wT/wCvXzPEeJdNU6fd/wBfqe3k6UG6j7pffuN8CD/S7w/9M1/nXcg1xPgRf3t8f9lB+prtM17OSr/Yo/P82XmD/wBol8vyJQMgnoK4zxD4naVzYaYxOTteVerH0X/GovEniVp2bTtPYlCdski9XP8AdHtXa+BPAy6YkeqapGGvWGYom6Qj1P8AtfyryM6ztUk6VF+r/Rf5nq5RlE8RJSmtP63/AERT8G/DtbcR6nrUYac/NHbNyE929T7dqi+Ivi1oydB01z5jcXDp1Gf4B7nv+Vdd4z8Rr4d0NpUIN3NlIFPr/e+g/wAK474Z+En1PUBrmoqXG8tEH53Nnlz+P618VGXM/bVemx9jUiqaWFoaX1b7I7H4YeCV8P6d/aV7GDqVyvf/AJZJ6D39a9DpqgKAB0HSnZrKUnJ8zM4xjFcsdhaSjNFSMKKKKACiiigAooooAKKKKACiiimAVU1G6+y2x2n94/C/41bJABJOAOtc1fXRurpnH3Bwg9qUnZG1Cnzy12RXooorI9MKWkpRQBna/qI0rQry8JwY4zt+p4FeffBXQW8Q/Eu1uJlLw2G68lJ6bh9z/wAeIP4Vf+Kuq+VY2umI3zSt5jj2HSvRf2d/D/2Dwhd61ImJdRn2oSP+WceQP/Hi35CvUwULQ5u587m9Xmqqmun6nsdFFFdh5IUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfGepzXvi3xLFo+n/NGZNiDtx1c+wGfwFe2eEPhpomnwIHtI5xHxJNKoLTP3+i+wrjfg14eIs5takT/SLtvs9sSOiA/M34kf8Ajp9a94ghSCBIkGFUYr5jP81q4rFygpaLf1/yWxnhcNGKUF8K/FnI+I/hf4X8R2rRtp0NlcY/d3NqgRlPuBww9j+leDX2nav8M/Ex0+/Bks5TuV1zslT+8voR3FfVea4r4p+GU8TeCrpVjBvbMG4t2xzkD5l/Efrissozatg60fe0/L+up04jDwrQcZLQ88R1ljSSNgyOAykdxTq5jwRqJu9Ha2c5e2bA/wB09P6101fteHrKvSjUXU/P8RRdGrKm+gtFFFbmIUUUUgCiiigAooooAKKKKACiiigAoopcUwErB13X7LSiFlffNjiJOT+PpSa1rN099FomiRNcanOwQBBkqT2Hv/KvQfBnwd03SNmo+Idup6q3zsknzRRH6H759zx6DvXzOdcTUct9yGs/6/r+rHuZblUq1qtTRfmeW6dL4x8RfPomhytAeknl/L/302BW7D4G+Jco3G3to/Z5I/6V9BqqooRFCqBgADAFNkljhTfLIka/3nYAfrXwFXi7M6srxlb7/wBLL8D6OOW0Ercq+7/M8CfwT8SIBn7HZzY7LIn+IrGvr/xDoDD+3NAmhTOPMCkL+fI/WvpSG4guQTBPFKB18uQNj8qbPDFPC0U0aSROMMjqCCPcGtKHF2Z0pXnK6/rvcU8soSW39fI+Pm1BLrxhb38SlEN1GwDdRgivqy5P68181/ELTbXRviPe21lCsFukqOka9FyAePxr6RlbdHGw6FAf0rg4ire39nXf2rv77M3pRVOPL2OD+LNuk/gGeRgC0E8bqfTnB/Q1zeizNPoVjI3LGBc/lWx8YNRS38HpZg/PczLx7DJ/wrE0FDHoNgp6iBf5V9ZwCpewnfb/AIb/AIJ89nklKEfX9DSopKK/RD5oWikooAWiobq7t7KEzXUyRRjuxxmuL1fx6Tuh0qPA6edIOfwH+Nc2IxdHDq9R/wCZ04fB1sQ7U18+h2F/qVnpkPm3k6xL2B5LfQVxGr+O7ifdDpkZgQ8ea3Ln6elP8P8AgHxN4znF3Irw2rnJu7rIBH+yOrfhx717N4W+GugeGAkyw/bb5eftNwAcH/ZXov8AP3r5XH8Qyd40tPz/AOAfVYDh+KtKpr67fd1PIvDfwx8Q+KpFvL4vZWj8me5BLuP9lep+pwK9p8M+BdC8Kxg2NqJLrHzXU2GkP0P8P4YrpM5or5atialV6s+no4anSWiCiiisDoCiiigAooooAWiiigApKKKYC0UUyWVIImkkbCrQG+g24njtoTLIcAdvU+lc3c3Ml3MZH4/ur/dFLeXj3k29uEH3V9Kz7/ULXS7GW8vJRHDGMknv7D1NQ7ydkelQoqlHnlv+RFq+r2miabJfXj7Y06KOrt2A968VubnU/HXiIuxwvQD+GGPP+fqak1rWdR8ca6kUKERAkQw54RfU+/qa7nRdHt9FsVt4hukPMkmOWP8AhX1GS5Q6suept1/yPjuIM9UV7On8v83+hPp2n2+l2SWtsuEXqe7HuTVqiivu4xUVyrZH59KTk3KT1CiiimSFFLRQAlLRRQAUUlLQAVj+KV3eGr4eiA/qK2Ky/EQ3eHb8f9MSaxxCvRmvJ/kb4Z2rQfmvzMP4fNnTLtfSYH9K6+uL+Hrf6LfL6Op/Q12lYZc74WH9dTfMlbFT/rogooortOEKKKKACiiigAooooAKKKKBhS0lLQIr6hCbjTbqEdXhdR9SDXH2Mgn+EOqw/wAUF/G+PY4H+NdwOory7WZrnRbzVdJibFpdOrlSOoB3Lj88V8vxLhZVI06kejX53/K57WUvnbp9bp/c9TR8C7Va/LMFG1Dk/jUXiXxOJA9jp7/IeJJh/F7D2965USuqsquyq33gDwfrXe/D7wb/AGhKusajH/osZ/cxsP8AWsO/0H6muKebyoYJUI6b3fe72R9DQyz6xiubf9PNmn8P/Bgt1j1nUov3x+a3hYfcH94j19PSvSkb1qLGKxPF2r/2N4Zu7hWxK6+VH/vNx/ifwr5Oc5Vp3fU+4p0qeFo2Wy3PPtZml8cePls4XP2SJvLVh0VFPzN+P+Fe46HYxWdmiQoEjRQiKOwFeU/CvSttlcam6/vJm8qMn+6Ov6/yr2e3j8qBEHYAVrWfvci2RwU01S9pL4p6/LoS0UUVmQFFFFMAopKWkAlLSUUALRSUtABRRSUwFooqK4nW2gaVug6D1NAJXdkUNXu9kf2dD8z/AHvYVi06SRppGkc5Zjk02sm7nq0qahGwUtJS0jQKcKaKzPEmpjSPDt5dk4YIVT/ePAppXdkTKSim2eQ+LLqbxF40khtgZGaVbaBR3OcD9a+xPD2jxeH/AA7p+kwAbLSBIsjuQOT+Jyfxr5j+B3h4698RIr6Zd0Gmqbpyehk6IPrk5/4DX1fXuwjyxUUfG1ajqVHN9QoooqjMKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOD8JaPDpWn29rEP3dpEsKe5x8x/z610wrmtZ8RWHgvwiNT1InoNsS/elkbkKP8APAFeI6l8VvHGvTtJYTLploT+7jiUZx7swJP6CvhcPgq2JbqbK+7OuhTlZQgm35H0maYyhgVYZBGCK+f9A+MniPRLmOLxLEuo2DHDTIoWVPcEYB+hH417tp+oWmr6dBf2E6z2s6B45F7j/PajE4OpQtzap9VsXKLi+WSs/M+ZrC0/sD4k6zpHSMSSKg9gdy/oa68Vm/Ey0/sv4xWl4BhLyONyfU8of5Vp1+vcMYj22Xxb3X+X+dz4nPKfLib90FFFFfRHjBRRRQAUUUUgCiiigAooooAKKKKYBWfrmpjSdJluuPMxtjB7sen+P4VoVx3i/dfa1pOlA4WRtz/QnGfyBrizHE/VsNKr2OrBUVVrxjLbd+i1PTvgv4SFlpDeJ79d+oahnyWfqkWeT9WP6Y9TXqtZugKkWgaZHGAEW1j2gdhtFaQr8IxFeeJqyqz3Z+g0klBWPPPid8QpfCcUGl6TGs2tXa7k3DIhTpuI7knOB04JNeJ3el61r85uda1aa4nY5O9iwX2HYfQcVveKrh734v65LNybc+VGD2AVV4/X86fA6AEs2CTXt0bYaEVTWrSbfXU+gyjLKOJg6tbXXRHNReH9T0qYXWlahLBOnKtGxQ/mK9d+GPxHutfun8P6/hdWiUmKbG3zgOoI6bh146iuKe4GTisSG5ax+IGgXltxMbqNTjuCwB/QkVdRfWoOFRa20fp+hea5XQw9L21HS1rr10JfjCvl/Eu8PrHEf/HRXuqz7rS3GeWiT/0EV4b8aMf8LKucf88IT/47Xpeu6yNH8Pm8YjKWsYQerFRXBmlOVTDYeEd2v8j47F1vZ3t1Z5p8SNSbxD4yg0y3O6KAiMY9T1NdNCgiiSNfuooUfhXJeEdPe5urjWbnLMzFYye5PU/0/Ouuziv1ThnL1hMEvP8Ar8Xc+WzStzTVJfZ39XuSCioZZ44IjLNIsca9Wc4ArldV8dwQ7otNj85+nmuMKPoOpr2q+JpUFeo7HFQwtWu7U1c62e4htYTLcSpFGvVnOBXH6v48jj3RaXHvbp50g4/Ad6y9L0DxR48vN0EUs0YOGnlO2GP8en4DJr1vwv8AB/RdG2XGqkaneDna4xCp9l/i/H8q+Yx3EVrxpafn/wAA+mwPD6fvVdfy/wCCeUaN4T8UeOroTokhgJwbq4JWNfp6/QCvYfC3wp0Lw/snukGpXw58yZfkU/7KdPxOfwrukVY0VEUIijCqowAKdXylfGVazbbPqqGDp0kkkHbHYUZoorlOsKKKKBBRRRQAUUUUAFFFFABRRRTAKKKUUCEZlRC7EBVGSTXN39817LxkRL91fX3NT6tfefIbeM/u1PzEfxGsieeK2gknmkWOKNSzuxwFAqJO+iPRw1HlXPIbeXlvp9nLdXUqxwRLud2rxXxJ4jvvGOrpb2yOLYNtggH/AKE3v/KneLfFVz4q1FbSzVxZI+IYh1kb+8R6+g7V1Phrw7HotsJJQGvJB87f3B6CvocnymVefNLbr5f8E+Yz/PY048lP/h/+ATeH9Bh0Oz2DD3Lj97J/Qe1a9FFfoFOnGlFQgrJH5xUqSqSc5u7YUUUtWQFFFFAgooooASilpKACiiigAqhrg3aDfj/pg/8AKr9VNWG7R70esD/+gmoqq9OS8ma0dKkX5o5L4eN8t+v+4f513FcJ8PT++v1/2EP6mu7rjyt3wsfn+Z2Zqv8Aa5fL8kFFFFd55wUUUUAFFFFABRRRQAUUUUDCiiigBa4j4hWK7LXUFwGz5L+/cf1rtq4z4iS4s7GHs0jN+QH+NcGaKLws+b+tTvytyWKhb+tDE8GeGX8SawI3ytnBh53Hp2Ue5/xr3SOOOCJIYUVIo1CoijAAHauM+FlusPhWafHzTXLZPqAAP8a7U1+Z4uo51LdEfrWWUI06Kl1Yma4P4k2GpanFptrY20s0ZkZnKLkA8AZ9OprvKMcVhTm4SUkdtakqtN029yr4W0tdOtLGwUcQoM+57n867QcVh6Smbot/dWtzNOOurOLEtKSitkFFGaKo5gooooAKKKKACiiigAooooAKKKKADvWBql39on8tD+7jP5mtHVLv7PBsQ/vJOB7D1rn6mT6HZhqX22LRRRWZ2hRRRTAUV5p8VNW/49dKRv8AprIB+lelMyojOxwqjJPtXhVwLjxn47S3t8mS9ulgi9lJwD9AOa6sJT5p37Hm5nW9nR5VvLT/ADPob4BeHf7J8BnU5UxcapKZcnr5a/Ko/Pcfxr1aqunWMGmaba2FsgWC2iWKNfRVGB/KrVeqfMhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfOfxuuXvvGGi6O5P2WC3M5XsSxI/kgH4muHhO527c8AV6L8cNMlt77SfEsSFoogbW4x/CCSVP6sPyrzhCGPmREMjDIIr57D2eHhbb9b6n1GSOKU11v+A6aJZY2RxlWGCDXo/wABdUm8jWtCkctFbOs8IP8ADuyGH6A/ia83mlEUbPIcADNei/ATTpjFreuSIViuJFgiJ/i25LfzX9aWJt9VnzeVvW/+Qs65OaFt9fuE+PliUh0PWIx80MrQsfrhl/k1UYpBNBFMv3ZEDD8RXY/Gq3Wb4cXEhHzQXEUi/nt/9mrz7w5MZ/Dli56iPb+Rx/SvrOCK7lQlT7f1+p+f8Q0/hn/X9aGnRRRX3h8uFFFFABRRRQAUUUUgCiiimAUUUUAKK5G+UyfEezXr/oz7frsfH611wrl9YK2PjfQtQfiJmEbn8cH9Grw+IoSll9Tl7HdlztVa7xf5HvHg28F74S0icHO60RT9QAD+orfBrzL4f6smmXt14Vu32SwyNNZFuBJExzge4Of8ivSVcN7H0r8VqQ5ZtdHqvR7H3WFqxq0Yyj2PFfiz4UvdM8QnxhpsDTWsyhb1EGShAxu+hAHPYj3rjLXU7O6UNHOoPdWOCPwr6f4YFWAIIwQRwa4TW/hD4S1mdpxaS2MrHLG0fapP+6QQPwAr06GMg4KFa+mia7eZ6mCzCtg7qCvF9P8AI8bu7+3t0LPOgHpu61sfDLw9ceJvFieILiFl0ywP7osP9ZJ2A+nU/gK72w+CPhOynEtwb292nISaUBfxCgE/nXe29rb2VvHbWsMcEEa7UjjUKqj0AFVXx1OEHCjdt6XfbyKxuZVsWlGS5Y9j5s+MzZ+JN37QQj/x0VrfES/kuo9H0uEkmSCNyo7kqAKxPjDIH+JWoAH7iRL/AOOCsPVvE8uoalDeQx+W0MSRpu5xtUDNetRw/tFh5taRX5pHzOKhOdSPKtr/APAPRohZ6Pp8Vu88cccKgZY4ye5rm9V8cWsG6PT4zPJ/fbhR/jXMabpWt+Kr/wAiyguLyY/eP8K+5J4A+tet+GPgtZWYS58Qzi7m6/ZoSRGPqerfp+NfXYriD2cOSiuX8WcmEyFTlzVHzfgjy2x0zxL44vtttDNc4PLfdij+p6D+des+Fvg1pem7LnXJBqFyOfJXIhU+/dvxwPavSLW0t7K2S2tYI4IEGFjjUKo/AVNXytfHVazbb/zPqKGBpUklb/IbDFFbwpDBGkUSDCoihVUegAp9JRXGdoUtJRQAtFJRQAtFJS0AFFFFAgooooAKKKKACiiimAVQ1W8+zw+XGcSuPyHrVueZLeFpXPyqPz9q5eeZ7iZpX+8x/L2qZOx0Yajzyu9kR9BXj/j7xe2r3R0nT3Js42w7L/y2cf8Aso7ep59K3/iL4t+xQtothJ/pEi/6Q6n7in+H6n+X1rD8G+GtgTVL1PmPMEbDp/tH+levlOWzxNRP+l5/5HmZ9m8MPTdOL9fPy/zL3hPw0NLgF5dIDeSDgH/lmP8AGuoopK/RqFCFCCpwWiPy6vXnXm6k3qwpaKK2MgooooEFFFFABRRRQAVHNNFbxNLNIsca8lmOAKkrlrDTLn4g+LnsBK8Wk2XMzr3Gcce5PT0Ga48bjI4WnzNXb2OzBYOWKqcqdktyzJ4z0WOTZ58j/wC0sZIrTsNVstTjL2dwkmOoHBH1FegWPhHw7p9qLaDRrMxgYJliDs31JyTXn3j/AMCxaHCfEnhtDbG3Obi3Q/Lt/vAenqOleLSzuqp/vIq3ke1UySly/u27+ZdqtqAzpl2P+mL/APoJqHSNSTVtMhu0GCww6+jDqKsX3OnXX/XF/wCRr6LmU6fNHZo+e5XCpyy3TOI+Hx/029H/AEyB/Wu+rz/4ff8AISux/wBMf/ZhXoFcWU/7qvn+Z3Zt/vT+QUUtFeieaJRRRQIKKKKACiiigYUUUUAFFFFABXB/EV/39gvorn9RXeV598RDnULIf9Mj/OvNzd2wkvl+aPSylf7XH5/kehfDyPy/BFj/ALbSN/48a6esDwSu3wVpY9Yyf/HjW/mvzCq71Jep+wYZWowXkvyCikorM3NXR14lb3ArUrP0gf6O59WrRrSOx5ld3qMKKKKoxCiiigAooooAKKKKACjNFFABTXkWKNnc4VRkmnVjaxd7m+zIeBy/19KTdi6cHOVjPubhrm4aVu/QegqKiisz1UklZC0UlFAxaKSlFAHN+O9V/svwtcFGxLcfuU/Hr+lZ37PPhv8AtHxbc65NHmHTYtsZI/5avkD8l3fmK5f4n6sLvW49PjbMdovzf756/pivoj4P+Gv+Ea+Hdgkkey6vB9rnyOcvjaD9FCj869bCw5ad+58xmdb2lflW0dP8zvKKKK6TzgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOWv7G11Wwnsb6FZradSskbdGFeM6x8FdZsLl38M6jFPaMci3uW2untnGD9eK9ntbqO5gWWJw6MMgg9RVkMD3r4ahialBvkej6PY7otpqUXZ90eDaX8FvEmpXaf2/eW9nZA5dYX3yMPQY4H1J/CvcNL0yz0XS7fTrCEQ2tumyNB2HqfUnqTVsGobm5WBPVj0UdTRicVUrpc+y6LYJSd3Obu+7POvjhqCW/gJ7XcN9xPGoH0O7/wBlrg/CymPwzZK3UqW/MmoPi3rp17xJaaHaOJTA/wC9KnI808Y/AfzNbFtAtraw26fdiQIPwFfonBOFnCjKrJb/AK/8BfifK55iFOEY93f5InopKK+7PmhaKSigBaKSloAKKKKACiiigAoopKAFrH8T6W2qaOyxLm4hPmR46nHUfl/StilB5rOtSjVg4S2ZpSqSpTU47ozbCWHxpoNvKk5t9bscDzFOGVh0P0P6Gui0r4mXujslj4tspUZflW+hXKv7kD+n5Vx2p6DdW+of2voM3kXo5ePOFk9fbn0PBp8Pj22kBs/EWnPbTDhiI9yH32nkfrX5PmuQ18HNrk56b2tuvT/L/hz3cLiKkHz4bWP8vVf8A9r0zxdouqoGs9VtJs/w+YFYfgea11uoXHEif99CvnW6h8E3uZI72KFjz8jlf0NZN23h20XEetXco/uROT/9avHjgIydlzL1ietTzVvSVOV/Q+mLzWNMsIzJeahawKO8kyr/AFrz3xP8aNB0qJ4tIzqd30UqCsSn3Y8n8PzrwmV4tQuUttLs7maeQ4XcS7sfYCvRfCnwZubrZd+JJTbRHkWkRBkb/ePRfwyfpXdTyqhT96s2/Lb/ADPRpTq1vhjY89kXXPG/iKe5jt5b2/uX3uIk4HYewAGBzXpnhj4JhSlz4kud3f7Jbtx9Gf8AoPzr1bS9H07RLRbTTLOK1hH8Ma8t7k9Sfc1ersninblpqyO6lg4x1nqyrp2nWelWa2lhaxW0C9EiXA+p9T71aoormu3udaSWwUUUUDCiiigAooooAKKKKACiiigQUUUUALRSUUALRSUtABRRWZq175Uf2eM/Ow+YjsKG7FQg5y5UUdTvftM3lof3SHj3PrXI+LvEsfhvR2lUhruXKW6H17sfYf4Ctm7uoLG0lurhwkMSl3Y9gK8Turm88d+Ki53JDnAHaKIH+f8AU1vgsNLE1VFI1zHGQwOH0dn/AFqSeFtDl1zUH1PUNzwK5Zi/WV+v5etejew4A7VHbW8VnbR20ChIoxtUCpa/T8FhI4WkoLfqfkeNxcsVVc3t0CkpetFdhxhRRRQIKKKSgBaKSigBaKSigCK6cx2kzjqsbEflV34MWyJ4UvLoAebNdkMe+FUY/mfzqq6iSNkPRgQaT4PXwthq+gTHbPDN5yKe4+62PyX86+fz2LtB9Nf0PoMjkvfXXT9T1Adah1C2S80q9t5QDHJA6sD7qasYrD8Z61H4f8IaheSOBI0RihB6s7DAx/P8K+ePoTx3wA7fYryIn5UkBH4j/wCtXU3w/wCJfdf9cn/kawvBFi1roZnkBDXD7hn+6OB/Wty+P/Evuf8Ark/8jX2eBjKOEipdj43GyUsXJx7nCeAD/wATa5HrD/UV6HXnPgE/8Tm4/wCuB/mK9CkljiieWRgsaDczHsKyyl/7Kn6mubr/AGp+iJKK5SxuvEvjbVJLDwxbMsKffmOFCj1Zj0+nWup/4U343WMSL4ltfOxnYZJCM/XH9K8/FcUYDDVPZykbUskxFSPM2kLRWFqVn438H5fW9L+12S/euYPmAHuR0/ECr2l6xZ6vD5lrJlh96NuGX6ivTwOa4XGq9GVzixWX18NrNady/RRRXoHEFFFFAwooooAKKKKACvPPiH/yE7T/AK4n+deh1558Qv8AkKWn/XH+przM4/3SXy/M9PKP96Xz/I9P8HceDNJ/64/1NbdYvg//AJE3Sf8Arh/U1s5r8xqfG/U/YMP/AAo+iFopKKg2NzSv+PP6savVR0r/AI8h/vGr1aLY8qt8bCloopmYUUlFAC0UlFAC0UlFAC0UlBIAJJwByTQBXvroWlsX/jPCj3rmixZiScsTkmrF/dG7uSw+4vCD+tV6zk7npUKfJHXdhRRRSNwooooAKr6heR6dp1xeSkBIULVZrz74oa15FhDpUTfPOd8mP7o6D86ulDnmomOIqqjTc30OX8F6LL45+ItlaTAvHcXBnuT6Rr8zfmBj8RX2eFCqFUAADAA7V4b+zn4Y+z6XqHiWeP57lvs1uSP4FOXI+rYH/Aa9zr20raHxzbbuwooopiCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+VdD8ReKvByqtoF1LTeojzuAH+yeo/lXaWfxv0ZkC6jp97aSjqNgYZ/n+ledN4Y1TTW36NqTBf+eUhx/wDWNMa48Vp8s2lW9xj+IoD/ACNceL4dxKl79Lm81p/mjyaGYSS9yaa9bP7n+h6g/wAZtCcEWdtqNzJ2VIMfzNcd4r+Jetz2zpBbjS1lG0bn3TuP/ZR+tYS/8JddLsSG3sUPUqAD/U1oaT4Xgs5xd30pvLvOdz/dU/1rXAcK1alROpT5V56/hp+TFXzNJe/JPyTv/wAD8yl4T8OyW7f2rfKfPcZjRuoz/EfeuspSc0lfo2Gw0MPTVOGx85iMROvUc5hRRRW5gFFFFABS0lFAC0UlFABS0lFABRRRQAUuabmhmCqWYhVHJJOAKAHZqnqS6fJbFtSSExL3lA4+lYGr+NrW03Q6eouJum/+Bf8AGq2g+CvE3judbu4drexJ/wCPiYELj/YXv/L3rx8dm+Hw8Wvif4Hr4HJ8RXkpfCvxOd1l9Gmn8nSLOTeTgNuOD9B1rqvC3wg1jWNlzqpOm2Z52suZWHsvb8fyr13wx4C0LwqivaW/nXmPmupwGfPt2X8P1rpq+GxWYOrNuCS9FY+4wuXKnFKbb+dzE8PeEtF8LweXpdmschGHnf5pH+rf0HHtW3RRXmttu7PSUVFWQUUUUhhRRRTAKKKKACiiigApaSigBaKSloAKKKKACiiigQUUUUAFLSUjusaM7nCqMk0AQ3l0tpAZDyx4UeprmXkaR2dzlmOSanvLtrycuchBwg9BXI+NfEq+HdGLRMPts+UgX09W/D+eKlJzkoxPRpxjh6TqTOO+JXiVry7Gg2Tlo4mHnlf45Oy/h/P6Vp+GNFGjaYFdf9Jlw0p9PQfhXNeCtEa7uW1e7BZUY+Vu53v3b8P5/SvQK/Qshy5UKftZLV7f5/P8j8x4hzOWJrOmnp1/RfL8wooor6I+bClpKKBBRRRQAUUUUAFFFFABRRRQAlYl/p9/Z6vDr2hyCPUIfvIfuyjpg/UcYrborGvQhXhyT2NqFedCfPDclX4tXEUAS58K3v2wDkI3yE/lnH51zF+Nd8capFea+Ba2EJzFaLwB+Hr6k810WTS5rzaWS0YT5pNvyPSq5zWnDlikvMRVVEVEAVFGAB2FQXvNjcD/AKZN/I1PUN0M2k4/6Zt/KvXkvdZ5MX7yPP8AwF/yGbj/AK4H+Yrd8c3j2+hrChIM8gU/Qc/4VheAv+Q3OP8Apgf5itnx9ETpdrMBxHNg/iD/AIV4FBtZXLl8z3sQk80hfyPevh14bh8L+C7C0RALiWMT3D45aRhk5+nT8K6uqWk3CXejWNyhyktvG4+hUGrtfidacqlSUpbtn1qVloIVDKVYAgjBB715D8QPhYY3fxD4Qj+z30WXls4hhZR32jsf9nofrXr9Fa4TF1cJUVWi7NEzhGa5ZK6PnLQteh1m3KsPKu4+JYjwQfUe1a1bHxN+G08ly3inwvHs1CP57m2jH+u9WUf3vUd/r14/Qteg1q3x/q7uPiWE9R7j2r9kyDP6WZUkm7TW6/r+v0+NzPLJYaXtKfw/l/wDYooor6Q8cKKKKACiiigArz34h/8AITtP+uJ/nXoVeffEP/kI2f8A1yP868vN/wDdJfL8z0so/wB6j8/yPTfB/wDyJmk/9cf6mtqsTwd/yJmk/wDXH+prar8xqfG/U/YcP/Bj6IWikoqDY3NJObMj0c1frM0dswyL6Nn9K060Wx5dZfvGFFFFMyCiiigAooooAKKKKACsvV7vZH9nQ/Mwy2Owq/cTrbQNK/QDgeprmJJGmlaRzlmOTUydjpw9PmlzPZDaWkoqD0BaSiigApaSloAbJIkUbSOQEQFmJ7AV4PqdxdeLfF222UyS3c6wW6fU7VFekfEbWv7O0H7HE+J7v5eOoTv/AIUz9n3wr/aniqfXriPNtpiYiJHBmYYH5Lk/iK9HBU7JzZ4Ob17tUV6s+ifDmiweHPDtho9tjyrSFY8/3j3b8Tk/jWpRRXceKFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfOFFFFfo5+f2FooooAKWkpaACiiikIKKKKACiiigAooooAKKKOtABRVa91Gy06Pfd3CRDsCeT9BXF6x45lmDQaWhiU8GZh8x+g7Vy4jGUcOrzevbqdWHwVbEP3Fp36HT6x4hsdFQiZ/MnI+WFOv4+griTd6/wCNNQFlZQyOCeIYuFUerH+prb8M/DbUNdZb/WJJLW0c7vm/1so/Hp9T+VeyaBotnpVqtnpdqlvAv3mA5Y+pPUn618XmWfzqtwhou3+bPt8s4djTSqVPvf6Locv4N+EunaTsvNa2X94ORFj91Gfp/Efrx7V6YMKoVQABwAOgpqqFUAdqdXzkqkpu8me/CnGCtFC0UUVJYUUUUAFFFFAgooooAKKKKACiiigAooooAKKKKAFopKWgAooooEFFFFAC1hapfee/kRn92p+Yj+I1a1W+8lPIiP7xhyf7orDFTJ9DuwtG/vy+RFdXMNlaS3Nw4SGJS7sewFeIXc93468WF/mSHOFH/PKIH+f9TXRfEvxKbi4Gg2bkpGwNwV/ifsv4fz+lXfDGiDRtMHmKPtU2GlPp6L+FfRZFlnt6nPPbr/XmfN8TZuqUfZQf/D/8D8zXt7eK0t47eBQsUahVAqWkpa/QkklZH5o227sKKKKBBRRRQAUUUUABIAJJAA5JNc+2u3+r6l/Zfhiwe/uu8gGVX39Me5IFJcxah4v8RReFtGONxzdT/wAKKOufYfqcCve/CvhPTPCGkJYabCB0MszD55W/vMf6dBXxPEfFH1J+ww+sj6PK8oVSKq1vkv8AM8nh+Ffj+6iE1xrljayHnytxOPY4XH86xdZ0vxv4LT7RrFnHf6cp+a5tzuCj3IAI/EYr6PpkkUc8TxSorxuCrIwyGB6giviqPFWZU6nO53Xb+v1ue5PLcNOPK4L7jwHT9QttUs1ubZ9yN1B6qfQ1arDv9JHg34n3uiQZFhdDzYF/ugjIH4fMv4VuV+t5Xj1jsNGuup8Zj8L9VrOmtuglFFFeicYUUUUAFR3A/wBGl/3G/lUlNl5hkH+yf5UnsNbnnngP/kOzf9cG/mK6rxdAJ/DN1xzHtcfgR/TNcp4F41+Uf9MW/mK7jWk8zQb9f+mDfyrxcBHmy+UfU9nMJcuYQl6fmep/CjUv7U+G2kuxy8KG3b/gBIH6Yrs68n+AFyZPBl9bk8Q3pI/4Eq/4V6xX4rj6ap4mcV3Z9jB3igpa8h+IvxM1C31lvC/hUqL5eLq8IBER/urnjI7k9OnWuJbw3q16ouNQ8T6lLcNyW89sA/ia2pZc3BTqy5b7dWdmHwdfEJulG6XXY+k68l+I/wALpLy4bxJ4WUQarGd81unAn9SP9r26H69eT0/xX4u8BTpJLey6zowIEsM7Eug9QxyR+o9RXuuia1Y+IdHt9U06XzLadcqehB7qR2IPBqlGvl1SNelLTuvyf9ehhiMNOm3SrRseAaDryatG0My+TfRcSwsMHI4JAP8ALtWzW18WfAbMG8X6DH5eo23z3ccY/wBcg6vj1A6+o+lcro2qxaxpsd1Hw3SRP7rd6/WuHs8hmdHX41ufD5pl31aXPD4X+BoUlFLX0Z5AUUlLQAV5/wDET/j/ALL/AK5H+degVwHxE/4/bH/rm3868zN/90l8vzR6WUf73H5/kek+Dj/xRmk/9cf6mtusPwYf+KM0r/rkf5mtuvzCp8b9T9hw/wDBj6IWikoqDY09GfE0ieozWzXOafL5V7GT0J2n8a6OriediVadwoooqjnCiiigAooooAKKKztVvfIi8mM/vHHPsKL2KhFylyooapefaZ/LQ/u4z+ZqhRRWT1PUhBRXKhaKSloLCiiigAoLBVLMcADJJ9KK5H4g68NK0Q2sT4ubvKjHUL3NVCLnJRRnVqRpQc5bI838Waw/iDxHNLHl4w3lQKOcgen1NfWfw38Kr4P8EWGmMoF0y+ddH1lbkj8OF/Cvnv4HeEP+Ek8bJf3Ee6w0rE75HDSf8s1/Mbv+A+9fWFe3GKilFHx9Wo6k3OW7CiiiqMwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD5w7UVi2/inS5zgXKg+jcfzrRjv7WXBSZT+NfocKtOorwkmfCzoVYfFFos0tMEqN0dT+NO61oZWFopKWgQUUUUCFopKCQoyzAD1JoAWiqNxrWmWufPvoFI7b8n8hWNdeOtKhyIVmnPsu0frWFTE0aXxySOinha1T4ItnT0jMqKWdgqjqScV55eePb+bK2sMUC+p+Y1mxw+IfEsu2JLu7552g7R9ewrzK+eYemvd1/BHo0MkxFR2lp+LO51DxdpNhlRMbiQfwxc/r0rk9R8c6jdbktVW1jPcct+da+m/CrUpQJNTuYrSPqUX53/AMB+ZrB1XTbCbV4tI8Owy3ThtpmLbjI3t2Cj1rwK2f1a8nGDsvL/AD/yPoKXD0MPBTqRu+l936L/ADMeNLvU71I0EtzcythRyzMa9h8HfDy30cR32qqlxf8A3lj6pD/i3v27etX/AAd4OtvDVqJHCy6g4/eTY4X/AGV9v512NrbNcyY6IPvNXz2Jxkqj5YvT8z6nBZdGhH2lXft2JLW3a6f0jHU1sJGsaBUGAKI41iQIowB0FOrjSsbVajm/IKWiimZBSUUUwCiiigAooooEFFFFABS0lFAC0UlFAC0UlLQAUUUUAFFFFABRRRQAtVr28Wzh3cGRuFFSXFwltCZJDwOg9TXNXE73Mxlk6noPQVLdjehR9o7vYY7s7s7nLE5JrnfGHiRPDmivMpBu5spbqfX+99B1/L1rau7qCxtJbq5kEcMSlnY9gK8O1XUbvxt4pXblY2bZEh6Rxjuffua3weGlXqJJF5ljY4Si7Oz/ACXcveDNHe/vX1e8BdEclC3O9+5/D+degk5qvZ2sVlaRW0C7Y412gf1qev1LBYWOGpKmt+vqfjuNxUsTVc3t09ApaSius5BaKSloAKKKKYBWP4j1gaRppKHNzL8sS+h9fwrVmmjt4XmlYLGilmJ7Cqvw18PSeOPGD+Ir+M/2Vp7gQow4eQcqv4feP4CvFzzNIZfhXNvV7HpZZgniat38K3/yPRvhR4MPhfw2Lq8j/wCJrqGJZy33kXqqfrk+59q76isa+8XeHNNmMN7rmnwyg4KPcLuH1GeK/Eas6mJqOo9Wz7lJRVjZoFV7HULLU7cXFjdwXUJ/5aQyB1/MVZrGzW5R4N8WFA+LeikcE2qZ/wC+nop3xfXyvij4fmP3XtlX8d7D+optfsfBzvly9T47P1/tEfT9Qooor6s8MKKKKACkfmNvoaWg/dP0oA858E8eI5B/0yf+Yrvr9d+m3af3oXH6VwHg35fFDD/YkFeiuu+N0/vKRXk5Ur4VrzZ6ubO2KUvJGj+zzLnTdchzyJYnx9Qw/pXsl1N9mtJ5wM+XGz49cDNeB/AO+Fr4k1LTnODNCSB7o3+BNe/uiyIyMMqwII9RX4zm8OXGyv1t+R9rSd4nyd4YuHuHv9RmYvczylnc9STyT+ZrdGpXUTgpJlV/hbvWM1jJ4R8V6n4fvsoFlJhduA6/wn8Rj+Van2cydDwa9qvyufN0e3ofZ5TNSwcYw3W/rc2IbyPUrRiVBVgVdTWr8ENTkstf1vwyzE24/wBJhBP3SCFb8wV/75rnoVi0+CSWRwq4ycmtz4HadNf+Jda8RshFuE+zxsf4mYhjj6AD8xXJVUfq9W+2n330OXP+X2dO/wAV/wAOv6HuZUMpVgCCMEHvXzZrGlf8IR8TrvSoxs06/wAS247ANnAH0O5a+lK8Z+P2n+XZaJrkS4lt7gwsw9CNw/IqfzrLh3GywmOjJPR/1/wPmfIYuiq1GUH1Myimo4liSRejqGH4inV+4p3PzwKWkopgLXAfET/j8sf+ubfzrv68++IbZ1GzX0iP868vOP8AdJfL8z0soX+1R+f5HpPgz/kTNK/65H/0I1uVzngOdbjwZYY6xhkP4Ma6KvzGr8b9T9gwzvRh6IWkooqDYXJHI6iumtZhcWySDqRz9a5ir+mXf2eXy3P7t/0NOLsYYinzRut0b1FFFaHnBRRRQAUUVFcXMdrCZJDgdh3JoGk27IZeXaWcJduWPCr6muZkkeWRpHOWY5Jp9zcyXcxkf8B6CoqzbuejRpezWu4tFFFI3CiiigBaKSlFADZpUgheaRgqICzE9hXgviTWJfEOvS3ChmQt5cKDrt7ceprufiT4lEMA0W1f95IMzlT0Xsv40/4F+Cv+Eh8V/wBsXcW7T9LIcbhw838A/D734D1r0cHSsudngZriuZ+xj03Pdvhf4PHgzwVaWMiAX037+7PfzGH3f+AjA/A+tdnRRXceMFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHwDmnJLJGco7KfY4plFNNrYC9Fq+oQ/cu5Rj1bNXI/FOrRdLnP1UVjUVtHFV4/DN/eYyoUpbxX3HQr401df44z9VqQeONWHaH/AL5P+Nc1RWqzDFL7bM3gsO/sI6U+OdXPTyB/wD/69RP4z1p/+W6L/uoKwACxAAJJ7Cui0zwL4i1QBo7B4Yz/AB3B8sfkef0qZ5niEveqNfMunltKbtCmn8ijL4k1ibhr+UD/AGTj+VUJby5nP724lf8A3nJr0vTvhAxw2paqq+qW6Z/8eP8AhXWad8P/AA3puCLH7TIP47lt/wCnT9K4K2at7zcvmerQyWfSKj/XkeHWGk6jqsvl2NnPcN38tCQPqegrs9L+FGq3O19RuIbND1UfvH/Tj9a9hjjjhjEcUaxoOAqKABTsV588dN/CrHq0copR1qO/4HK6R8PPDumbWe2a8lH8dwdw/wC+en6V1kaxwxBI0WONRwqjAApteZ+NvF89/eDw5oZaSSRvLlkjPLt/cU+nqawip1pWbOyo6OFhdK3kuonjHxbPrV4PD3h8NKZG2SSR9ZD3UH09T/Sup8IeDbfwzZb32y6hKv72X+7/ALK+386Xwb4Qt/DVl5su2XUZV/ey/wB3/ZX2/nXUxo00gRBkmnUqJL2dPb8yKNGTl7at8X4JBbWzTyhF6dz6VuxRJDGEQYAptvAlvEEXr3PqalrNKxnWq8702CilopmIlFFFABRRRQAUUUUAFFFFMAooooAKKKKBBRRRQAUUUUALRSUUALRSUtABTJZUhjMkjYUUSypDGXkYBRXPXt495Jz8sa/dWpbsbUaLqPyEvLt7ubceFH3V9BVekNcV8RPE7aPpo0+1fF5dKcsDzHH0J+p6D8aKcHUlyo76tSGHpOT2RynxD8Wf2rdnSrKT/QoG/eMp4lcf0H8+fSrPgbR/s1i2oSr+9n4jz2T/AOua4vRtOfVdUhtVztY5c+ijrXsEcSRRLHGu1EAVQOwFfdZBgUn7VrRbep+Z8Q5hKo+RvWW/p0QtLRRX1R8mFFFFABS0lLQAUlLVa/u49PsJ7qQ/LGpOPU9hSlJRTk9kOMXJqK3Zhayt34j12z8LaXzNcOPNbso68+wHJr6I8PaFaeGtCtdKslxDAmM93bux9yea80+CHhtxZ3fiu+XN1fsyQFu0YPzEfUjH/AfevUdYunsdE1C7j+/BbSSL9VUkfyr8V4kzOWYY1wi/dTt8/wDgfnc++wGFjhqKj1PGfH/jjVPE2vXPhrw/ctbabasY7y5jJBlboVyP4RyMDrg9sVyUfg/SIk2Szuz9yXxTPCW2Lw49wDmWR2Z27k9Kuou8Esfxrb+B+6paJfi+7Pussyuh7CNSpHmlJX18ynDFq3ge9GseH75/LUjzYicq6+jDow/UV9H+GNet/E/hyy1e3G1LiPLJnOxgcMv4EEV8/wATboJ4mOYipB9K9G+A8zP4FuYiSViv5FX6FUP8z+tcuPj7Wj7SXxJrXumeVnOCpYWpGVJWUr6ehifHyD7Pc+GtUUcxTPGx/FWH8jVHIPPaun+Pdp5/gKG4A5t72Nj9CGX+ZFchYSefptrLnO+FDn8BX3HA9W+ElT7P/P8AzPz7iGHvQn6/oWKKKK+4PnAooooAKO1FLjNAHmvhM7fFpH/XQV6Qp+avN/DPy+MyP9qQfzr0jFeXlH8B+r/Q9XOP48X5L9TkfDWof8Iv8TRck7Ykucv/ANc34b9Gz+FfUykMoZSCCMgivlPxdCLPWrTUSMQzAxSn/Psf0r3f4Z+IxrHh5LG4kzfWIEbZPLp/C35cfhX5txbgHTruqls7P0eq/Ox9PltdVaMfT8tGTePPh5pnjmzXz2NtqEIxBdouSo/usP4l9u3bvXit98PPiF4emMUNmdQtxwslswkBH0OGH5V9N0V87hsxq0I8mko9mepCpOnLmpyafkfOGi/C7xl4nuEGsKdLsMjeZcbyPZAc5+uBXv2haHYeHNHt9L06Ly7eEYGeSx7sT3JrRorPFY2piLKWiXRbBOpOpLmm7vzCvNvjnEJPhvK+OYrqJh+ZH9a9Jrzf45TCP4bTL3kuolH55/pSwH+9U/VGU/hZwOjuZNFsW9YE/kKu1Q0VSuhWAPXyE/lV+v3+j/Dj6I/Oa38SVu7CijFFamYted/EL/kLWw/6Yf8Asxr0SvOviCR/bUA9IB/M15Wc/wC6v1R6mT/70vRnTfCq/wB+nXtgW5jkEqj2Iwf5V6F3rxn4a3n2bxWsROFniZMe/Ufyr2XNfnGLjaq/M/Vctnz4deWgtFJmjNcx3i0UmaM0AbWmX4kAt5T84+6x7+1alcjnpg8jvW1YaqsgEVyQrjo56H61aZxV6FnzRNSijqOKp3upQ2YIJDy9kB/nTOWMXJ2RNdXUVpEZJD9B3Jrm7q6kvJt8hwB91ewqOe4kupTJK2T2HYD2plQ3c9GjQUNXuLRRmjNI3FopM0tIAooopgFc/wCLfE8PhzTSVKveSgiGP/2Y+wq14g8QWvh7T2uZzukPEUYPLGvDtV1S61nUJLy7fdI54HZR2A9q6cPQ9o7vY87H41UI8sfif4DYo73W9WSKNXub27lCqByzuxwB+Zr7Q8DeFYPBvhKz0eHa0iLvuJB/y0lP3j/QewFePfs/eBfMmfxfqEXyJmKwVh1bo0n4fdH/AAL0r6Er1T5ltvVhRRRTEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH/2Q==",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for part in response.candidates[0].content.parts:\n",
" if part.text is not None:\n",
" display(Markdown(part.text))\n",
" elif part.inline_data is not None:\n",
" mime = part.inline_data.mime_type\n",
" print(mime)\n",
" data = part.inline_data.data\n",
" display(Image(data=data))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YyIqgd6rkYln"
},
"source": [
"The edited image is saved, overwriting the previous `gemini_imgout.png` file (for future usage in this notebook)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4ZZayEQiLjvn"
},
"outputs": [],
"source": [
"for part in response.candidates[0].content.parts:\n",
" if part.text is not None:\n",
" continue\n",
" elif part.inline_data is not None:\n",
" mime = part.inline_data.mime_type\n",
" data = part.inline_data.data\n",
" pathlib.Path(\"gemini_imgout.png\").write_bytes(data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F0ga9Ujksp7D"
},
"source": [
"## Generating videos with the Veo2 model\n",
"\n",
"Veo 2 is Google's advanced text-to-video and image-to-video model, capable of generating high-quality videos with detailed cinematic and visual styles. It can capture prompt nuances and maintain consistency across frames.\n",
"<br/>\n",
"<br/>\n",
"\n",
"<!-- Princing warning Badge -->\n",
"<table>\n",
" <tr>\n",
" <!-- Emoji -->\n",
" <td bgcolor=\"#f5949e\">\n",
" <font size=30>⚠️</font>\n",
" </td>\n",
" <!-- Text Content Cell -->\n",
" <td bgcolor=\"#f5949e\">\n",
" <h3><font color=black>Veo is a paid-only feature and won't work if you are on the free tier.<br></font></h3>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7giQoVTztdqv"
},
"source": [
"### Select the Veo2 model to be used\n",
"\n",
"The `veo-2.0-generate-001` model is used for video generation tasks."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Wl0wzDM2tg6G"
},
"outputs": [],
"source": [
"VEO_MODEL_ID = \"veo-2.0-generate-001\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "avisrx6btgXs"
},
"source": [
"### Run a text-to-video generation prompt\n",
"\n",
"This section demonstrates how to generate videos directly from a text prompt. You can specify various configurations like `person_generation`, `aspect_ratio`, `number_of_videos`, `duration`, and a `negative_prompt` to guide the video generation process. Video generation is an asynchronous operation, so the code includes a loop to wait for the operation to complete."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FMR8UGjMrYKD"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name='models/veo-2.0-generate-001/operations/zmrlsiqnzaw2' metadata=None done=None error=None response=None result=None\n",
"name='models/veo-2.0-generate-001/operations/zmrlsiqnzaw2' metadata=None done=True error=None response=GenerateVideosResponse(generated_videos=[GeneratedVideo(video=Video(uri=https://generativelanguage.googleapis.com/v1beta/files/p0w3cekzjwdc:download?alt=media, video_bytes=None, mime_type=None))], rai_media_filtered_count=None, rai_media_filtered_reasons=None) result=GenerateVideosResponse(generated_videos=[GeneratedVideo(video=Video(uri=https://generativelanguage.googleapis.com/v1beta/files/p0w3cekzjwdc:download?alt=media, video_bytes=None, mime_type=None))], rai_media_filtered_count=None, rai_media_filtered_reasons=None)\n",
"[GeneratedVideo(video=Video(uri=https://generativelanguage.googleapis.com/v1beta/files/p0w3cekzjwdc:download?alt=media, video_bytes=None, mime_type=None))]\n",
"CPU times: user 188 ms, sys: 37.1 ms, total: 225 ms\n",
"Wall time: 40.7 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"import time\n",
"from google.genai import types\n",
"from IPython.display import Video, HTML\n",
"\n",
"prompt = \"\"\"\n",
" Dynamic anime scene: A happy Brazilian man with short grey hair and a\n",
" grey beard, mid-presentation at a tech conference. He's wearing a fun blue\n",
" short-sleeve shirt covered in mini avocado prints. Capture a funny, energetic\n",
" moment where he's clearly enjoying himself, perhaps with an exaggerated joyful\n",
" expression or a humorous gesture, stage background visible.\n",
"\"\"\"\n",
"\n",
"# Optional parameters\n",
"negative_prompt = \"\" # @param {type: \"string\"}\n",
"person_generation = \"allow_adult\" # @param [\"dont_allow\", \"allow_adult\"]\n",
"aspect_ratio = \"16:9\" # @param [\"16:9\", \"9:16\"]\n",
"number_of_videos = 1 # @param {type:\"slider\", min:1, max:4, step:1}\n",
"duration = 8 # @param {type:\"slider\", min:5, max:8, step:1}\n",
"\n",
"operation = client.models.generate_videos(\n",
" model=VEO_MODEL_ID,\n",
" prompt=prompt,\n",
" config=types.GenerateVideosConfig(\n",
" # At the moment the config must not be empty\n",
" person_generation=person_generation,\n",
" aspect_ratio=aspect_ratio, # 16:9 or 9:16\n",
" number_of_videos=number_of_videos, # supported value is 1-4\n",
" negative_prompt=negative_prompt,\n",
" duration_seconds=duration, # supported value is 5-8\n",
" ),\n",
")\n",
"\n",
"# Waiting for the video(s) to be generated\n",
"while not operation.done:\n",
" time.sleep(20)\n",
" operation = client.operations.get(operation)\n",
" print(operation)\n",
"\n",
"print(operation.result.generated_videos)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SNlNKLJ0t16u"
},
"source": [
"### See the video generation results\n",
"\n",
"Once the video generation operation is complete, the generated video(s) can be downloaded and displayed within the notebook. Generated videos are stored for 2 days on the server, so it's important to save a local copy if needed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YvNECMpjt1D2"
},
"outputs": [],
"source": [
"for n, generated_video in enumerate(operation.result.generated_videos):\n",
" client.files.download(file=generated_video.video)\n",
" generated_video.video.save(f'video{n}.mp4') # Saves the video(s)\n",
" display(generated_video.video.show()) # Displays the video(s) in a notebook"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xreTw0LyQole"
},
"source": [
"### Run a image-to-video generation prompt\n",
"\n",
"Veo 2 can also generate videos from an input image, using the image as the starting frame. This allows you to bring static images to life by adding motion and narrative based on a text prompt."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7o6bhR7ot3kA"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name='models/veo-2.0-generate-001/operations/7rz7rsx527t2' metadata=None done=None error=None response=None result=None\n",
"name='models/veo-2.0-generate-001/operations/7rz7rsx527t2' metadata=None done=True error=None response=GenerateVideosResponse(generated_videos=[GeneratedVideo(video=Video(uri=https://generativelanguage.googleapis.com/v1beta/files/p4yyv2k2oso2:download?alt=media, video_bytes=None, mime_type=None))], rai_media_filtered_count=None, rai_media_filtered_reasons=None) result=GenerateVideosResponse(generated_videos=[GeneratedVideo(video=Video(uri=https://generativelanguage.googleapis.com/v1beta/files/p4yyv2k2oso2:download?alt=media, video_bytes=None, mime_type=None))], rai_media_filtered_count=None, rai_media_filtered_reasons=None)\n",
"[GeneratedVideo(video=Video(uri=https://generativelanguage.googleapis.com/v1beta/files/p4yyv2k2oso2:download?alt=media, video_bytes=None, mime_type=None))]\n",
"CPU times: user 670 ms, sys: 27.9 ms, total: 698 ms\n",
"Wall time: 41.4 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"import io\n",
"from PIL import Image\n",
"\n",
"prompt = \"\"\"\n",
" Dynamic anime scene: A happy Brazilian man with short grey hair and a\n",
" grey beard, mid-presentation at a tech conference. He's wearing a fun blue\n",
" short-sleeve shirt covered in mini avocado prints. Capture a funny, energetic\n",
" moment where he's clearly enjoying himself, perhaps with an exaggerated joyful\n",
" expression or a humorous gesture, stage background visible.\n",
"\"\"\"\n",
"\n",
"image_name = \"gemini_imgout.png\"\n",
"\n",
"# Optional parameters\n",
"negative_prompt = \"ugly, low quality\" # @param {type: \"string\"}\n",
"aspect_ratio = \"16:9\" # @param [\"16:9\", \"9:16\"]\n",
"number_of_videos = 1 # @param {type:\"slider\", min:1, max:4, step:1}\n",
"duration = 8 # @param {type:\"slider\", min:5, max:8, step:1}\n",
"\n",
"# Loading the image\n",
"im = Image.open(image_name)\n",
"\n",
"# converting the image to bytes\n",
"image_bytes_io = io.BytesIO()\n",
"im.save(image_bytes_io, format=im.format)\n",
"image_bytes = image_bytes_io.getvalue()\n",
"\n",
"operation = client.models.generate_videos(\n",
" model=VEO_MODEL_ID,\n",
" prompt=prompt,\n",
" image=types.Image(image_bytes=image_bytes, mime_type=im.format),\n",
" config=types.GenerateVideosConfig(\n",
" # At the moment the config must not be empty\n",
" aspect_ratio = aspect_ratio, # 16:9 or 9:16\n",
" number_of_videos = number_of_videos, # supported value is 1-4\n",
" negative_prompt = negative_prompt,\n",
" duration_seconds = duration, # supported value is 5-8\n",
" ),\n",
")\n",
"\n",
"# Waiting for the video(s) to be generated\n",
"while not operation.done:\n",
" time.sleep(20)\n",
" operation = client.operations.get(operation)\n",
" print(operation)\n",
"\n",
"print(operation.result.generated_videos)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ioPlTUMQmDS-"
},
"source": [
"The generated videos are then saved and displayed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8Szy65EMRl2I"
},
"outputs": [],
"source": [
"for n, generated_video in enumerate(operation.result.generated_videos):\n",
" client.files.download(file=generated_video.video)\n",
" generated_video.video.save(f'video{n}.mp4') # Saves the video(s)\n",
" display(generated_video.video.show()) # Displays the video(s) in a notebook"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_KtcD8kcxWKv"
},
"source": [
"## Generating text-to-speech (TTS) with Gemini models\n",
"\n",
"The Gemini API offers native text-to-speech (TTS) capabilities, allowing you to transform text into natural-sounding audio. This feature provides fine-grained control over various aspects of speech, including style, accent, pace, and tone."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q-6t1jE7xsBz"
},
"source": [
"### Select the TTS model to be used\n",
"\n",
"The `gemini-2.5-flash-preview-tts` and `gemini-2.5-pro-preview-tts` models are optimized for low-latency, controllable audio generation, supporting both single-speaker and multi-speaker outputs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8o0q2VFNxuAm"
},
"outputs": [],
"source": [
"MODEL_ID = \"gemini-2.5-flash-preview-tts\" # @param [\"gemini-2.5-flash-preview-tts\",\"gemini-2.5-pro-preview-tts\"] {\"allow-input\":true, isTemplate: true}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "5BAPQ2-1R3BC"
},
"outputs": [],
"source": [
"# @title Helper functions (just run that cell)\n",
"\n",
"import contextlib\n",
"import wave\n",
"from IPython.display import Audio\n",
"\n",
"file_index = 0\n",
"\n",
"@contextlib.contextmanager\n",
"def wave_file(filename, channels=1, rate=24000, sample_width=2):\n",
" with wave.open(filename, \"wb\") as wf:\n",
" wf.setnchannels(channels)\n",
" wf.setsampwidth(sample_width)\n",
" wf.setframerate(rate)\n",
" yield wf\n",
"\n",
"def play_audio_blob(blob):\n",
" global file_index\n",
" file_index += 1\n",
"\n",
" fname = f'audio_{file_index}.wav'\n",
" with wave_file(fname) as wav:\n",
" wav.writeframes(blob.data)\n",
"\n",
" return Audio(fname, autoplay=True)\n",
"\n",
"def play_audio(response):\n",
" return play_audio_blob(response.candidates[0].content.parts[0].inline_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xayGXJKfx80D"
},
"source": [
"### Generating a simple audio output\n",
"\n",
"his example demonstrates the basic text-to-speech functionality, converting a simple text string into an audio output. The response_modalities configuration is set to `['Audio']`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FNbT18y8x29R"
},
"outputs": [],
"source": [
"%%time\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=\"Say 'hello there! My name is Gemini and I'm really glad to be here at the Google I/O 2025!!'\",\n",
" config={\"response_modalities\": ['Audio']},\n",
")\n",
"print(response)\n",
"\n",
"blob = response.candidates[0].content.parts[0].inline_data\n",
"play_audio_blob(blob)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1Q55LCmGyuq5"
},
"source": [
"### Controlling how the model speaks\n",
"\n",
"The Gemini TTS models allow you to control the style, tone, accent, and pace of the generated speech using natural language prompts within the contents and by selecting a specific `voice_name` from a variety of prebuilt voices."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sfkzPjt3yOCh"
},
"outputs": [],
"source": [
"%%time\n",
"\n",
"voice_name = \"Sadaltager\" # @param [\"Zephyr\", \"Puck\", \"Charon\", \"Kore\", \"Fenrir\", \"Leda\", \"Orus\", \"Aoede\", \"Callirhoe\", \"Autonoe\", \"Enceladus\", \"Iapetus\", \"Umbriel\", \"Algieba\", \"Despina\", \"Erinome\", \"Algenib\", \"Rasalgethi\", \"Laomedeia\", \"Achernar\", \"Alnilam\", \"Schedar\", \"Gacrux\", \"Pulcherrima\", \"Achird\", \"Zubenelgenubi\", \"Vindemiatrix\", \"Sadachbia\", \"Sadaltager\", \"Sulafar\"]\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=\"\"\"Say \"I am a very knowlegeable model, especially when using grounding\", wait 3 seconds, while counting from one to three, then say \"Don't you think?\".\"\"\",\n",
" config={\n",
" \"response_modalities\": ['Audio'],\n",
" \"speech_config\": {\n",
" \"voice_config\": {\n",
" \"prebuilt_voice_config\": {\n",
" \"voice_name\": voice_name\n",
" }\n",
" }\n",
" }\n",
" },\n",
")\n",
"\n",
"play_audio(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WDssIEn_zJ8M"
},
"source": [
"### Changing the audio language\n",
"\n",
"The TTS models can automatically detect the input language and generate speech in that language. They support a wide range of languages."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TsqY6XvyzA2e"
},
"outputs": [],
"source": [
"%%time\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=\"\"\"\n",
" Read this in Brazilian portuguese:\n",
" A comida brasileira é a melhor do mundo!\n",
" \"\"\",\n",
" config={\"response_modalities\": ['Audio']},\n",
")\n",
"play_audio(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-b9E9DLE0WtC"
},
"source": [
"You can also instruct the model to read text in a particular style or pace, as demonstrated in this disclaimer reading example."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fQhzjbSmzjbC"
},
"outputs": [],
"source": [
"%%time\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=\"\"\"\n",
" Read this disclaimer in as fast a voice as possible:\n",
"\n",
" [The author] assumes no responsibility or liability for any errors or omissions in the content of this site.\n",
" The information contained in this site is provided on an 'as is' basis with no guarantees of completeness, accuracy, usefulness or timeliness\n",
" \"\"\",\n",
" config={\"response_modalities\": ['Audio']},\n",
")\n",
"\n",
"play_audio(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qsi5-SUk0w85"
},
"source": [
"### Working with multi-speakers\n",
"\n",
"For conversations or scenarios requiring multiple distinct voices, the Gemini TTS models support multi-speaker audio generation. You define different speakers within the MultiSpeakerVoiceConfig and assign specific voices to them. The model can then process a transcript and assign parts to each speaker. First, a sample transcript is generated by a Gemini text model.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "taO5AUz80c39"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"**Speaker A:** Okay, so, Google I/O 2025? Absolute mind-blow. Seriously, my brain’s still buzzing from all the AI announcements!\n",
"\n",
"**Speaker B:** Right?! Gemini 2.5 alone… the *on-device* multimodality was unreal. And the performance gains across the whole stack? Unbelievable! The new agentic capabilities are going to change everything.\n",
"\n",
"**Speaker A:** Totally! But seriously, forget the keynotes for a sec. The *highlight* for me? Hands down, Luciano Martins’ live coding session.\n",
"\n",
"**Speaker B:** YES! That was next level! He just *built* a fully agentic workflow with the new Gemini 2.5 APIs in like, 10 minutes, *live*, with zero hiccups. That's the real magic. Forget the shiny demos; that's the part that truly blew my mind! Best session of the whole conference.\n",
"CPU times: user 38 ms, sys: 2.05 ms, total: 40 ms\n",
"Wall time: 6.46 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"transcript = client.models.generate_content(\n",
" model='gemini-2.5-flash',\n",
" contents=\"\"\"\n",
" Generate a short (like 100 words) transcript that reads like\n",
" it was clipped from a podcast by excited computer scientists talking\n",
" about all the AI (including Gemini 2.5 models) news announced at Google I/O 2025.\n",
" Highlight the fact that the live coding session with Luciano Martins was the best.\n",
" \"\"\"\n",
" ).text\n",
"\n",
"print(transcript)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VfNr2vICnVqA"
},
"source": [
"Then, the generated transcript is passed to the TTS model with multi-speaker configuration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TLTW2mt01DwR"
},
"outputs": [],
"source": [
"%%time\n",
"\n",
"config = types.GenerateContentConfig(\n",
" response_modalities=[\"AUDIO\"],\n",
" speech_config=types.SpeechConfig(\n",
" multi_speaker_voice_config=types.MultiSpeakerVoiceConfig(\n",
" speaker_voice_configs=[\n",
" types.SpeakerVoiceConfig(\n",
" speaker='Podcast host',\n",
" voice_config=types.VoiceConfig(\n",
" prebuilt_voice_config=types.PrebuiltVoiceConfig(\n",
" voice_name='sulafat',\n",
" )\n",
" )\n",
" ),\n",
" types.SpeakerVoiceConfig(\n",
" speaker='Podcast guest',\n",
" voice_config=types.VoiceConfig(\n",
" prebuilt_voice_config=types.PrebuiltVoiceConfig(\n",
" voice_name='leda',\n",
" )\n",
" )\n",
" ),\n",
" ]\n",
" )\n",
" )\n",
")\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=\"TTS the following conversation between a very excited Podcast host and the Podcast guest: \"+transcript,\n",
" config=config,\n",
")\n",
"print(response)\n",
"play_audio(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IBTLJ1JF3SRd"
},
"source": [
"## Working with the Gemini 2.5 models\n",
"\n",
"The Gemini 2.5 series models, including Flash and Pro, offer enhanced capabilities such as adaptive thinking, multimodal understanding, and advanced reasoning, making them suitable for a wide range of complex tasks."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "S_peaCkc7czL"
},
"source": [
"### Counting token\n",
"\n",
"Token counting helps you understand the length of your input and output, which is relevant for managing model context windows and estimating costs. A token is roughly equivalent to 4 characters for Gemini models."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_3zWIKW610u5"
},
"outputs": [],
"source": [
"MODEL_ID = \"gemini-2.5-flash\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RrEHxAAE7WeC"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total_tokens=13 cached_content_token_count=None\n"
]
}
],
"source": [
"response = client.models.count_tokens(\n",
" model=MODEL_ID,\n",
" contents=\"What is the venue where Google I/O normally happens?\",\n",
")\n",
"\n",
"print(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lzcdhi4W7gyQ"
},
"source": [
"### Sending your first prompt\n",
"\n",
"Making a request to a Gemini model is straightforward using the generate_content method. You specify the model and the content (your prompt), and the model returns a text response."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RYj6cgwJ7QUV"
},
"outputs": [
{
"data": {
"text/markdown": [
"Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"text/plain": [
"GenerateContentResponseUsageMetadata(cache_tokens_details=None, cached_content_token_count=None, candidates_token_count=22, candidates_tokens_details=None, prompt_token_count=13, prompt_tokens_details=[ModalityTokenCount(modality=<MediaModality.TEXT: 'TEXT'>, token_count=13)], thoughts_token_count=375, tool_use_prompt_token_count=None, tool_use_prompt_tokens_details=None, total_token_count=410, traffic_type=None)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Markdown\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=\"What is the venue where Google I/O normally happens?\"\n",
")\n",
"\n",
"display(Markdown(response.text))\n",
"print()\n",
"response.usage_metadata"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3k5rWFA4_4Tr"
},
"source": [
"### Your first streaming interaction\n",
"\n",
"For more fluid interactions, especially with longer responses, you can use streaming. The `generate_content_stream` method allows you to receive parts of the response incrementally as they are generated, rather than waiting for the entire output."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hrjcaAYO_2q2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**.Google I/O normally happens at the **Shoreline Amphitheatre** in **Mountain View, California**."
]
}
],
"source": [
"from IPython.display import Markdown\n",
"\n",
"for chunk in client.models.generate_content_stream(\n",
" model=MODEL_ID,\n",
" contents=\"Tell me a story about a software engineer attending Google I/O for the first time\"\n",
"):\n",
" print(response.text, end=\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lo4wiYjm8298"
},
"source": [
"### Working with multimodal prompts\n",
"\n",
"Gemini models are inherently multimodal, meaning they can process and generate content based on various input types, including text, images, video, and audio. This example demonstrates how to provide an image alongside a text prompt."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bsCBIRpv7ltj"
},
"outputs": [
{
"data": {
"image/jpeg": "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",
"text/plain": [
"<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=512x256>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import requests\n",
"import pathlib\n",
"from PIL import Image\n",
"\n",
"img = \"https://storage.googleapis.com/gweb-developer-goog-blog-assets/images/developer-keynote-recap-google-io.2e16d0ba.fill-1200x600.png\"\n",
"\n",
"img_bytes = requests.get(img).content\n",
"\n",
"img_path = pathlib.Path('image.png')\n",
"img_path.write_bytes(img_bytes)\n",
"\n",
"image = Image.open(img_path)\n",
"image.thumbnail([512,512])\n",
"\n",
"display(image)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cD7PA1Hbn4kk"
},
"source": [
"The image and a textual prompt are combined to ask the model to describe the image."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZVUbs03cBJt_"
},
"outputs": [
{
"data": {
"text/markdown": [
"This image is a sleek, modern, and abstract graphic, likely promotional material for Google I/O, a developer conference.\n",
"\n",
"Here's a detailed description:\n",
"\n",
"* **Overall Composition:** The image features a minimalist, dark background (appears to be a very dark gray or black) which provides strong contrast for the foreground elements. The composition is dynamic, with three main visual clusters arranged diagonally.\n",
"* **Branding:** At the top center, the text \"Google I/O\" is displayed in clean, white sans-serif font. The \"I/O\" portion is stylized, with the \"I\" as a vertical bar and the \"O\" as a perfect circle, consistent with the Google I/O logo.\n",
"* **Platforms/Bases:** Three distinct, white, square-like platforms are positioned in the foreground, rendered with an isometric projection, giving them a three-dimensional, blocky appearance. Each platform has a subtle grid pattern on its top surface, suggesting a technical or computational theme (like graph paper or a digital grid).\n",
"* **3D Objects:** On each platform, there's a unique, three-dimensional geometric object:\n",
" * **Top-Left:** A sharp, angular object resembling an arrow or a folded plane, pointing diagonally upwards and to the right.\n",
" * **Top-Right:** Two stylized, curved shapes that resemble an \"f\" or a flowing, intertwined symbol. They appear to be mirrored or interlocked.\n",
" * **Bottom-Middle (partially visible):** Two chunky, interlocked \"L\" shapes, which are also rounded and volumetric.\n",
"* **Color Scheme & Texture:** The 3D objects are the most striking visual element, featuring a vibrant, smooth gradient that cycles through the full spectrum of rainbow colors (red, orange, yellow, green, blue, purple). This gradient gives them a iridescent, almost reflective quality, suggesting dynamism, creativity, and a broad range of applications. The lighting on these objects emphasizes their three-dimensionality, creating distinct surfaces and shadows.\n",
"* **Implied Themes:** The overall aesthetic conveys themes of technology, innovation, development, and connectivity, with a clean and futuristic feel. The isometric projection and grid patterns hint at architecture, coding, or system design."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=[\n",
" image,\n",
" \"Describe this image\"\n",
" ]\n",
")\n",
"\n",
"Markdown(response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hdYroleSoES2"
},
"source": [
"Here, the model is asked to generate a blog post based on the provided image, showcasing its creative content generation capabilities from multimodal input."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0wa7ZbDZ9I3I"
},
"outputs": [
{
"data": {
"text/markdown": [
"## Unveiling Google I/O: The Future, Built in Full Color\n",
"\n",
"As the digital world hums with anticipation, a familiar sight emerges on our screens: the vibrant, geometric invitation to Google I/O. More than just a conference, I/O is a beacon for developers, innovators, and anyone curious about the cutting edge of technology. And this year's visual really sets the stage for what's to come.\n",
"\n",
"This striking image isn't just eye candy; it's a visual metaphor. The precise isometric grid serves as our digital canvas, a stable foundation upon which new ideas are built. Rising from it are dynamically colored, three-dimensional shapes – reminiscent of building blocks, lines of code, or perhaps even abstract representations of new APIs and frameworks. We see a sharp arrow pointing forward, signaling progress and new directions, alongside playful 'f' shapes that might hint at Flutter or Firebase, and other abstract forms hinting at the vast ecosystem Google empowers.\n",
"\n",
"The rainbow gradients splashed across their surfaces don't just add vibrancy; they symbolize the diverse spectrum of technologies, ideas, and people that converge at I/O. From AI advancements to Android updates, from web innovations to cloud solutions, Google I/O is where these disparate elements come together to form a cohesive, colorful future.\n",
"\n",
"At its heart, Google I/O is where the magic happens. It's where Google unveils its latest advancements, hosts deep-dive technical sessions, and delivers keynote announcements that often define the trajectory of the tech landscape for the year ahead. But beyond the big reveals, I/O fosters a unique sense of community, connecting developers with the tools, insights, and each other to turn ambitious ideas into reality.\n",
"\n",
"So, as we look at this vivid imagery, let it serve as a reminder: Google I/O isn't just an event to watch; it's an invitation to participate. It's about empowering creators, fostering collaboration, and collectively shaping the digital experiences of tomorrow. Whether you're a seasoned developer or simply fascinated by what's next, prepare to be inspired. The future is being built, block by colorful block, and Google I/O is where we get to see it unfold. Get ready to dive in!"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=[\n",
" image,\n",
" \"Write a short and engaging blog post based on this picture.\"\n",
" ]\n",
")\n",
"\n",
"Markdown(response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kY-ceuCtBSxq"
},
"source": [
"## Video understanding with Gemini models\n",
"\n",
"Gemini models can process and understand video content, enabling a wide range of use cases such as describing scenes, extracting information, answering questions about video content, and referring to specific timestamps.\n",
"\n",
"First, a sample video is downloaded, then uploaded using the `client.files.upload` method, which is suitable for larger videos or for reusing the video across multiple requests."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0nFlnbN19w1V"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Waiting for video to be processed.\n",
"Waiting for video to be processed.\n",
"Waiting for video to be processed.\n",
"Waiting for video to be processed.\n",
"Video processing complete: https://generativelanguage.googleapis.com/v1beta/files/zyg3jyh4pdm9\n"
]
}
],
"source": [
"import time\n",
"\n",
"!wget https://storage.googleapis.com/generativeai-downloads/videos/Jukin_Trailcam_Videounderstanding.mp4 -O Trailcam.mp4 -q\n",
"\n",
"def upload_video(video_file_name):\n",
" video_file = client.files.upload(file=video_file_name)\n",
"\n",
" while video_file.state == \"PROCESSING\":\n",
" print('Waiting for video to be processed.')\n",
" time.sleep(5)\n",
" video_file = client.files.get(name=video_file.name)\n",
"\n",
" if video_file.state == \"FAILED\":\n",
" raise ValueError(video_file.state)\n",
" print(f'Video processing complete: ' + video_file.uri)\n",
"\n",
" return video_file\n",
"\n",
"trailcam_video = upload_video('Trailcam.mp4')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qcmnDs3tDa66"
},
"source": [
"### Do a semantic search in the video"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F9MMd2B0oP0N"
},
"source": [
"You can prompt the model to analyze video content for specific information, such as organizing scenes, identifying objects, and estimating emotional states. The model processes both the visual frames and the audio track to provide a comprehensive understanding."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WZitwaqWB6q8"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 91.1 ms, sys: 14.8 ms, total: 106 ms\n",
"Wall time: 18 s\n"
]
},
{
"data": {
"text/markdown": [
"Here's a detailed log of the video scenes:\n",
"\n",
"| Timecode | Description | Objects | Excitement (1-10) |\n",
"|---|---|---|---|\n",
"| 00:00 | Close-up of a gray fox's fur as it moves away from the camera. | 🦊 | 3 |\n",
"| 00:01 | Two gray foxes approach and sniff around rocks and fallen leaves. | 🦊🦊, 🪨, 🍂 | 5 |\n",
"| 00:11 | One fox jumps onto a rock while the other continues sniffing around. | 🦊🦊, 🪨, 🍂 | 6 |\n",
"| 00:17 | A mountain lion walks towards the camera, sniffing the ground. (Night vision) | 🦁, 🌳, 🍂 | 7 |\n",
"| 00:34 | Two gray foxes move around, one gets spooked by the camera flash and jumps. (Night vision) | 🦊🦊, 🌳, 🍂, 💥 | 8 |\n",
"| 00:50 | Another camera flash, then two mountain lions appear, one chasing the other. (Night vision) | 🦁🦁, 🪨, 💥 | 9 |\n",
"| 01:04 | A mountain lion walks past the camera, looks back, and then continues walking away. (Night vision) | 🦁, 🌳, 🪨 | 7 |\n",
"| 01:17 | Two mountain lions are in the frame, one approaching the camera and the other further back. (Night vision) | 🦁🦁, 🌳, 🪨 | 8 |\n",
"| 01:29 | A bobcat is seen sniffing around the ground in the darkness. (Night vision) | 🐱, 🌳, 🍂 | 6 |\n",
"| 01:51 | A black bear walks towards the camera, sniffs the ground, and then walks past. | 🐻, 🌳, 🍂 | 7 |\n",
"| 01:57 | A mountain lion walks quickly across the frame from left to right. (Night vision) | 🦁, 🌳, 🍂 | 6 |\n",
"| 02:04 | A large bear walks past the camera. | 🐻, 🌳, 🍂 | 6 |\n",
"| 02:12 | Two bears appear, one large and one smaller (possibly a cub). The large one sniffs and then walks away. | 🐻🐻, 🌳, 🍂 | 7 |\n",
"| 02:23 | A gray fox is on a hill overlooking a city at night, sniffing the ground. (Night vision) | 🦊, 🏙️, 🌃 | 7 |\n",
"| 02:35 | A bear walks across the frame on a hill with city lights in the background. (Night vision) | 🐻, 🏙️, 🌃 | 7 |\n",
"| 02:42 | A mountain lion walks across the frame on a hill with city lights in the background. (Night vision) | 🦁, 🏙️, 🌃 | 8 |\n",
"| 02:52 | A mountain lion sniffs the ground near a tree and then walks away. (Night vision) | 🦁, 🌳, 🍂 | 6 |\n",
"| 03:05 | A large black bear stands in the forest, looking around, then walks away. | 🐻, 🌳, 🍂 | 7 |\n",
"| 03:22 | A brown bear stands in the forest, looking around, then walks away. | 🐻, 🌳, 🍂 | 7 |\n",
"| 03:32 | A brown bear and a cub are in the forest, sniffing the ground and moving around. | 🐻🐻, 🌳, 🍂 | 8 |\n",
"| 03:41 | Two brown bears (a mother and a cub) move through the forest, sniffing the ground. The cub scratches itself. | 🐻🐻, 🌳, 🍂 | 8 |\n",
"| 04:03 | A mother brown bear and her cub walk towards the camera, sniffing the ground. | 🐻🐻, 🌳, 🍂 | 8 |\n",
"| 04:22 | A bobcat is seen in the forest at night, looking at the camera, then walking away. (Night vision) | 🐱, 🌳, 🍂 | 6 |\n",
"| 04:30 | A bobcat appears in the distance, then walks towards and past the camera. (Night vision) | 🐱, 🌳, 🍂 | 6 |\n",
"| 04:45 | A bobcat approaches the camera from the distance, then turns and runs away when the flash goes off. (Night vision) | 🐱, 🌳, 🍂, 💥 | 8 |\n",
"| 04:50 | A bobcat walks into the frame, sniffs, and then walks away. (Night vision) | 🐱, 🌳, 🍂 | 6 |\n",
"| 04:57 | A mountain lion walks towards the camera, sniffs the ground, and then walks past. (Night vision) | 🦁, 🌳, 🍂 | 7 |"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"\n",
"prompt = \"\"\"\n",
" Organize all scenes from this video in a table, along with timecode, a short\n",
" description, a list of objects visible in the scene (with representative emojis)\n",
" and an estimation of the level of excitement on a scale of 1 to 10\n",
"\"\"\"\n",
"video = trailcam_video\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=[\n",
" video,\n",
" prompt,\n",
" ]\n",
")\n",
"\n",
"Markdown(response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "G65ulumcD9P0"
},
"source": [
"### Analyze YouTube videos\n",
"\n",
"The Gemini API can also process publicly available YouTube videos directly by providing their URL. This allows for tasks like summarizing video content or finding specific mentions within the video."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XvqTvbqDFXy4"
},
"outputs": [
{
"data": {
"image/jpeg": "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",
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"https://www.youtube.com/embed/ixRanV-rdAQ\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x7df7cd42c1d0>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import YouTubeVideo\n",
"\n",
"YouTubeVideo('ixRanV-rdAQ', width=800, height=600)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LgVlbNU6ogkU"
},
"source": [
"The model can find specific instances of words or phrases and provide timestamps and context, as demonstrated by searching for \"AI\" in Sundar Pichai's keynote from Google IO 2023."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "35bZ-HYRC-MH"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 251 ms, sys: 43.9 ms, total: 295 ms\n",
"Wall time: 52.3 s\n"
]
},
{
"data": {
"text/markdown": [
"Here are all the instances where Sundar Pichai says \"AI\" in the video, along with timestamps and broader context:\n",
"\n",
"1. **0:29** - \"As you may have heard, **AI** is having a very busy year.\"\n",
" * **Context:** Sundar Pichai opens the Google I/O keynote by welcoming the audience and immediately highlights the significant advancements and activity in the field of AI, setting the stage for the presentations that follow.\n",
"\n",
"2. **0:38** - \"Seven years into our journey as an **AI**-first company, we are at an exciting inflection point.\"\n",
" * **Context:** He reflects on Google's long-term strategic shift to being an \"AI-first company,\" emphasizing the current moment as a pivotal point for the technology and its potential impact.\n",
"\n",
"3. **0:45** - \"We have an opportunity to make **AI** even more helpful for people, for businesses, for communities, for everyone.\"\n",
" * **Context:** Following the declaration of being an \"AI-first company,\" he articulates Google's core mission for AI: to enhance its utility and accessibility for a wide range of users and sectors.\n",
"\n",
"4. **0:54** - \"We've been applying **AI** to make our products radically more helpful for a while.\"\n",
" * **Context:** He transitions from the overarching vision to concrete applications, stating that AI has already been integrated into Google's existing products to significantly improve their functionality.\n",
"\n",
"5. **0:59** - \"With generative **AI**, we are taking the next step.\"\n",
" * **Context:** Sundar introduces generative AI as the next frontier for Google, implying a significant leap in how their products will evolve, leading into specific examples of generative AI in action.\n",
"\n",
"6. **1:41** - \"Smart Compose led to more advanced writing features powered by **AI**.\"\n",
" * **Context:** In the segment discussing Gmail's new \"Help me write\" feature, he traces the evolution of AI-powered writing tools within Google Workspace, starting with Smart Compose.\n",
"\n",
"7. **3:03** - \"Since the early days of Street View, **AI** has stitched together billions of panoramic images so people can explore the world from their device.\"\n",
" * **Context:** During the Maps demonstration, he highlights AI's foundational role in creating Street View by seamlessly combining massive amounts of imagery, enabling virtual exploration.\n",
"\n",
"8. **3:14** - \"At last year’s I/O, we introduced Immersive View, which uses **AI** to create a high-fidelity representation of a place so you can experience it before you visit.\"\n",
" * **Context:** Continuing with Maps, he explains how AI is crucial for Immersive View, a feature allowing users to virtually explore locations in a detailed, 3D environment, setting up the new \"Immersive View for routes.\"\n",
"\n",
"9. **5:16** - \"It was one of our first **AI**-native products.\"\n",
" * **Context:** Referring to Google Photos, he emphasizes that it was designed from its inception with AI at its core, enabling features like searching photos for specific content (people, objects, etc.).\n",
"\n",
"10. **5:39** - \"**AI** advancements give us more powerful ways to do this.\"\n",
" * **Context:** After mentioning that billions of images are edited monthly in Google Photos, he explains that new AI advancements are empowering users with even more sophisticated photo editing capabilities, leading into the \"Magic Editor\" demo.\n",
"\n",
"11. **5:48** - \"Magic Eraser, launched first on Pixel, uses **AI**-powered computational photography to remove unwanted distractions.\"\n",
" * **Context:** He provides a specific example of an AI feature in Google Photos, showcasing \"Magic Eraser's\" ability to intelligently remove elements from images.\n",
"\n",
"12. **5:58** - \"And later this year, using a combination of semantic understanding and generative **AI**, you can do much more with a new experience called Magic Editor.\"\n",
" * **Context:** He introduces \"Magic Editor\" as an advanced photo editing tool that leverages the power of generative AI and semantic understanding to perform more complex and creative edits.\n",
"\n",
"13. **7:41** - \"From Gmail and Photos to Maps, these are just a few examples of how **AI** can help you in moments that matter.\"\n",
" * **Context:** Sundar summarizes the product updates he has just shown, emphasizing how AI is being integrated into various Google products to provide helpful and meaningful experiences.\n",
"\n",
"14. **8:24** - \"Looking ahead, making **AI** helpful for everyone is the most profound way we will advance our mission.\"\n",
" * **Context:** He transitions to Google's overarching mission, stating that making AI universally beneficial is the most impactful way to fulfill their long-term goals.\n",
"\n",
"15. **8:54** - \"And finally, by building and deploying **AI** responsibly so that everyone can benefit equally.\"\n",
" * **Context:** As one of four key ways Google plans to advance its mission with AI, he stresses the importance of responsible development and deployment to ensure equitable benefits for all.\n",
"\n",
"16. **9:03** - \"Our ability to make **AI** helpful for everyone relies on continuously advancing our foundation models.\"\n",
" * **Context:** He connects the goal of making AI helpful to the underlying technological progress in foundation models, leading into the announcement of PaLM 2.\n",
"\n",
"17. **12:47** - \"PaLM 2 is the latest step in our decade-long journey to bring **AI** in responsible ways to billions of people.\"\n",
" * **Context:** He reiterates the long-term commitment to AI development, positioning PaLM 2 as a significant milestone in Google's effort to scale AI responsibly to a global audience.\n",
"\n",
"18. **13:00** - \"Looking back at the defining breakthroughs in **AI** over the last decade, these teams have contributed to a significant number of them.\"\n",
" * **Context:** He highlights Google's historical contributions to fundamental AI research, showcasing key advancements that have shaped the field over the past ten years.\n",
"\n",
"19. **14:10** - \"As we invest in more advanced models, we are also deeply investing in **AI** responsibility.\"\n",
" * **Context:** He emphasizes that Google's pursuit of more capable AI models is paired with a strong commitment to ethical and responsible AI development, leading into a discussion of watermarking and metadata.\n",
"\n",
"20. **15:11** - \"James will talk about our responsible approach to **AI** later.\"\n",
" * **Context:** He previews an upcoming segment by another speaker, highlighting the dedicated focus Google has on addressing the ethical and societal implications of AI.\n",
"\n",
"21. **15:29** - \"That's the opportunity we have with Bard, our experiment for conversational **AI**.\"\n",
" * **Context:** He introduces Bard as Google's direct conversational AI experiment, emphasizing its potential for user interaction and collaboration."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=types.Content(\n",
" parts=[\n",
" types.Part(text=\"Find all the instances where Sundar says \\\"AI\\\". Provide timestamps and broader context for each instance.\"),\n",
" types.Part(\n",
" file_data=types.FileData(file_uri='https://www.youtube.com/watch?v=ixRanV-rdAQ')\n",
" )\n",
" ]\n",
" )\n",
")\n",
"\n",
"Markdown(response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "poNCKim6ELFC"
},
"source": [
"### Analyze specific parts of videos using clipping intervals\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tNL8siU_oohy"
},
"source": [
"For more focused analysis, you can specify `video_metadata` with `start_offset` and `end_offset` to define clipping intervals. This tells the model to analyze only a specific segment of the video."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5DQouXBdFgrn"
},
"outputs": [
{
"data": {
"image/jpeg": "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",
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"https://www.youtube.com/embed/XEzRZ35urlk\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x7df7cd4309d0>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"YouTubeVideo('XEzRZ35urlk', width=800, height=600)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TJViLP-Pou1Q"
},
"source": [
"The model will then summarize only the specified segment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "iEi4HBqdEDBR"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 282 ms, sys: 39.8 ms, total: 322 ms\n",
"Wall time: 58.7 s\n"
]
},
{
"data": {
"text/markdown": [
"Demis Hassabis shares his lifelong pursuit of Artificial General Intelligence, which led him to co-found DeepMind, now Google DeepMind, with the goal of building AI that can benefit humanity responsibly. He highlights several of DeepMind's recent breakthroughs, including RT-2 for robotics, SIMA for virtual environments, AlphaGeometry for math, GNoME for material discovery, and the new AlphaFold 3 for molecular modeling. Hassabis then introduces Gemini 1.5 Flash, a faster and more efficient multimodal model with a large context window, and unveils \"Project Astra,\" a universal AI agent designed to be helpful in everyday life through quick, natural, and context-aware interactions."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=types.Content(\n",
" parts=[\n",
" types.Part(\n",
" file_data=types.FileData(file_uri='https://www.youtube.com/watch?v=XEzRZ35urlk'),\n",
" video_metadata=types.VideoMetadata(\n",
" start_offset='1250s',\n",
" end_offset='1570s'\n",
" )\n",
" ),\n",
" types.Part(text='Summarize the video in 3 sentences.')\n",
" ]\n",
" )\n",
")\n",
"\n",
"Markdown(response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HVdzgRKhE1Tm"
},
"source": [
"### Customize the number of video frames per second (FPS) analyzed"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2k959jEwozjQ"
},
"source": [
"By default, Gemini models sample videos at 1 frame per second (FPS) for analysis. You can customize this by passing an `fps` argument to `video_metadata`. A higher FPS can capture more details in rapidly changing visuals, while a lower FPS is useful for mostly static videos like lectures."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "03b-MCthFncs"
},
"outputs": [
{
"data": {
"image/jpeg": "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",
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"https://www.youtube.com/embed/McN0-DpyHzE\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x7df7cd42f390>"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"YouTubeVideo('McN0-DpyHzE', width=800, height=600)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mXFiMKtRo68i"
},
"source": [
"By adjusting the FPS, you can control the granularity of video analysis, which is particularly useful for tasks requiring close attention to visual details."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yXsXZkYKEc3Z"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 90.1 ms, sys: 13.8 ms, total: 104 ms\n",
"Wall time: 17.8 s\n"
]
},
{
"data": {
"text/markdown": [
"Based on the video, it appears that **all four tires** were changed (both front and both rear).\n",
"\n",
"The pit crew can be seen actively changing tires on both the left and right sides of the car, and the process extends from the front to the rear of the vehicle, indicating a full tire change."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=types.Content(\n",
" parts=[\n",
" types.Part(\n",
" file_data=types.FileData(file_uri='https://www.youtube.com/watch?v=McN0-DpyHzE'),\n",
" video_metadata=types.VideoMetadata(\n",
" start_offset='15s',\n",
" end_offset='35s',\n",
" fps=24\n",
" )\n",
" ),\n",
" types.Part(text='How many tires where changed? Front tires or rear tires?')\n",
" ]\n",
" )\n",
")\n",
"\n",
"Markdown(response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZuwgR0cKF3dt"
},
"source": [
"## Working with Tools\n",
"\n",
"The Gemini API enables models to interact with external systems and perform specialized tasks through the use of tools. These tools enhance the model's capabilities by allowing it to execute code, search the web, or process information from specific URLs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "h51mK8lSHkX_"
},
"outputs": [],
"source": [
"# @title Helper functions (just run that cell)\n",
"\n",
"from IPython.display import Image, Markdown, Code, HTML\n",
"\n",
"def display_code_execution_result(response):\n",
" for part in response.candidates[0].content.parts:\n",
" if part.text is not None:\n",
" display(Markdown(part.text))\n",
" if part.executable_code is not None:\n",
" code_html = f'<pre style=\"background-color: green;\">{part.executable_code.code}</pre>' # Change code color\n",
" display(HTML(code_html))\n",
" if part.code_execution_result is not None:\n",
" display(Markdown(part.code_execution_result.output))\n",
" if part.inline_data is not None:\n",
" display(Image(data=part.inline_data.data, width=800, format=\"png\"))\n",
" display(Markdown(\"---\"))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8U6xIL-CpS7i"
},
"source": [
"### Code execution\n",
"\n",
"The `code_execution` tool allows the Gemini model to generate and execute Python code. This is particularly useful for tasks requiring precise calculations, data manipulation, or algorithmic problem-solving. The model can iteratively learn from the execution results to refine its output."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sPVOhMJbE7OH"
},
"outputs": [
{
"data": {
"text/html": [
"<pre style=\"background-color: green;\">word = \"strawberry\"\n",
"count_r = word.count('r')\n",
"print(f\"The letter 'r' appears {count_r} times in the word '{word}'.\")\n",
"</pre>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"---"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"The letter 'r' appears 3 times in the word 'strawberry'.\n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"---"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"The letter 'r' appears 3 times in the word \"strawberry\"."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"---"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 50.5 ms, sys: 2.96 ms, total: 53.5 ms\n",
"Wall time: 6.56 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"from IPython.display import Code\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=\"Generate and run a script to count how many letter r there are in the word strawberry\",\n",
" config = types.GenerateContentConfig(\n",
" tools=[types.Tool(code_execution=types.ToolCodeExecution)]\n",
" )\n",
")\n",
"\n",
"display_code_execution_result(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "V_UiYSyDGwGs"
},
"source": [
"### Multimodality with code execution\n",
"\n",
"The code execution tool can be combined with multimodal inputs. This example demonstrates how the model can receive an image (related to the Monty Hall problem) and then generate and execute Python code to simulate the problem, providing a programmatic solution to a visually presented challenge."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HPXFte8uGHzH"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"\r",
" 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\r",
" 55 24719 55 13805 0 0 71293 0 --:--:-- --:--:-- --:--:-- 71159\r",
"100 24719 100 24719 0 0 122k 0 --:--:-- --:--:-- --:--:-- 121k\n"
]
},
{
"data": {
"image/jpeg": "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",
"text/plain": [
"<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=640x356>"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"!curl -o montey_hall.png https://upload.wikimedia.org/wikipedia/commons/thumb/3/3f/Monty_open_door.svg/640px-Monty_open_door.svg.png\n",
"\n",
"montey_hall_image = PIL.Image.open(\"montey_hall.png\")\n",
"montey_hall_image"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "G3dnL-EjpfhB"
},
"source": [
"The model is prompted to run a simulation, demonstrating its ability to reason about a problem and use code to solve it, even with a visual aid as part of the prompt."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "w4UYdIvVG9lY"
},
"outputs": [
{
"data": {
"text/markdown": [
"Okay, I can definitely help you understand the Monty Hall Problem through a simulation! It's a classic probability puzzle that often counter-intuitive at first glance.\n",
"\n",
"I will simulate the game 1,000 times for two strategies:\n",
"1. **\"Stay\" strategy:** The contestant sticks with their initial door choice.\n",
"2. **\"Switch\" strategy:** The contestant switches to the other unopened door after Monty reveals a goat.\n",
"\n",
"Let's run the simulation!\n",
"\n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"---"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre style=\"background-color: green;\">import random\n",
"\n",
"def run_monty_hall_trial(switch_strategy):\n",
" \"\"\"\n",
" Simulates a single trial of the Monty Hall Problem.\n",
"\n",
" Args:\n",
" switch_strategy (bool): True if the player switches doors, False if they stick.\n",
"\n",
" Returns:\n",
" bool: True if the player wins the car, False otherwise.\n",
" \"\"\"\n",
" # 0, 1, 2 represent the three doors\n",
" doors = [0, 1, 2]\n",
"\n",
" # Randomly place the car behind one of the doors\n",
" car_door = random.choice(doors)\n",
"\n",
" # Player makes an initial random choice\n",
" initial_choice = random.choice(doors)\n",
"\n",
" # Monty opens a door:\n",
" # It must be a door that is not the car_door and not the initial_choice.\n",
" # If initial_choice is the car_door, Monty can open either of the other two goat doors.\n",
" # If initial_choice is a goat_door, Monty *must* open the *other* goat door.\n",
"\n",
" # Find potential doors Monty can open (not player's choice, not car door)\n",
" monty_can_open = []\n",
" for door in doors:\n",
" if door != initial_choice and door != car_door:\n",
" monty_can_open.append(door)\n",
"\n",
" # If the initial choice was the car door, Monty has two goat doors to choose from.\n",
" # If the initial choice was a goat door, Monty has only one goat door to choose from.\n",
" # This logic already handles both cases correctly.\n",
" monty_opens = random.choice(monty_can_open)\n",
"\n",
"\n",
" # Player decides whether to switch or stay\n",
" final_choice = -1\n",
" if switch_strategy:\n",
" # Switch to the remaining unopened door\n",
" for door in doors:\n",
" if door != initial_choice and door != monty_opens:\n",
" final_choice = door\n",
" break\n",
" else:\n",
" # Stay with the initial choice\n",
" final_choice = initial_choice\n",
"\n",
" # Check if the player won\n",
" return final_choice == car_door\n",
"\n",
"# Number of trials\n",
"num_trials = 1000\n",
"\n",
"# Counters for wins\n",
"wins_stay = 0\n",
"wins_switch = 0\n",
"\n",
"for _ in range(num_trials):\n",
" if run_monty_hall_trial(switch_strategy=False):\n",
" wins_stay += 1\n",
" if run_monty_hall_trial(switch_strategy=True):\n",
" wins_switch += 1\n",
"\n",
"# Calculate win percentages\n",
"percentage_stay = (wins_stay / num_trials) * 100\n",
"percentage_switch = (wins_switch / num_trials) * 100\n",
"\n",
"print(f\"Simulation Results over {num_trials} trials:\")\n",
"print(f\" Stay Strategy Wins: {wins_stay} ({percentage_stay:.2f}%)\")\n",
"print(f\" Switch Strategy Wins: {wins_switch} ({percentage_switch:.2f}%)\")\n",
"\n",
"if percentage_switch > percentage_stay:\n",
" print(\"\\nBased on this simulation, the 'Switch' strategy is the best strategy.\")\n",
"elif percentage_stay > percentage_switch:\n",
" print(\"\\nBased on this simulation, the 'Stay' strategy is the best strategy.\")\n",
"else:\n",
" print(\"\\nBased on this simulation, both strategies perform equally.\")</pre>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"---"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Simulation Results over 1000 trials:\n",
" Stay Strategy Wins: 343 (34.30%)\n",
" Switch Strategy Wins: 691 (69.10%)\n",
"\n",
"Based on this simulation, the 'Switch' strategy is the best strategy.\n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"---"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"The simulation results clearly demonstrate the advantage of the \"switch\" strategy in the Monty Hall Problem.\n",
"\n",
"Here's a summary of the findings from 1,000 trials:\n",
"\n",
"* **Stay Strategy Wins:** 343 times (34.30%)\n",
"* **Switch Strategy Wins:** 691 times (69.10%)\n",
"\n",
"As you can see, the **\"Switch\" strategy resulted in winning approximately double the number of times compared to the \"Stay\" strategy.**\n",
"\n",
"### Why Switching is the Best Strategy:\n",
"\n",
"The simulation confirms what the mathematical solution shows:\n",
"\n",
"1. **Your Initial Pick:** When you first choose a door, you have a `1/3` chance of picking the door with the car behind it, and a `2/3` chance of picking a door with a goat.\n",
"\n",
"2. **Monty's Role:** Monty *always* opens a door that has a goat and is *not* your chosen door. This action provides crucial information.\n",
" * **If you initially picked the car (1/3 probability):** Monty will open one of the two goat doors. If you switch, you'll switch to a goat and lose.\n",
" * **If you initially picked a goat (2/3 probability):** Monty *must* open the *other* goat door. This means the remaining unopened door (the one you didn't pick and Monty didn't open) *must* have the car behind it. If you switch, you'll switch to the car and win.\n",
"\n",
"Since you are twice as likely to have initially picked a goat (2/3 chance) than the car (1/3 chance), switching your choice after Monty reveals a goat allows you to win in those 2/3 of scenarios where your initial guess was wrong. You're effectively betting on your initial pick being wrong, which it is most of the time!"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"---"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 112 ms, sys: 17.7 ms, total: 130 ms\n",
"Wall time: 14.9 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"prompt=\"\"\"\n",
" Run a simulation of the Monty Hall Problem with 1,000 trials.\n",
"\n",
" The answer has always been a little difficult for me to understand when people\n",
" solve it with math - so run a simulation with Python to show me what the\n",
" best strategy is.\n",
"\"\"\"\n",
"result = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=[\n",
" prompt,\n",
" montey_hall_image\n",
" ],\n",
" config=types.GenerateContentConfig(\n",
" tools=[types.Tool(code_execution=types.ToolCodeExecution)]\n",
" )\n",
")\n",
"\n",
"display_code_execution_result(result)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kd5Bpi6kInze"
},
"source": [
"### Grounding information with Google Search\n",
"\n",
"The `google_search` tool allows Gemini models to access up-to-date information beyond their training data by querying Google Search. This significantly improves the accuracy and recency of responses, especially for questions about current events or highly specific topics. When enabled, the Gemini API can also return grounding sources and search suggestions.\n",
"\n",
"First, a prompt without Google Search grounding is sent. The model responds based on its internal knowledge, which might be outdated."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uR601hu8HQAr"
},
"outputs": [
{
"data": {
"text/markdown": [
"The latest match between Brazil and Argentina was on **November 21, 2023**, as part of the FIFA World Cup 2026 Qualifiers (CONMEBOL).\n",
"\n",
"The final score was:\n",
"**Brazil 0 - 1 Argentina**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=\"What was the final score of the latest Brasil vs. Argentina football game?\",\n",
")\n",
"\n",
"Markdown(response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SleTG3rlpqDX"
},
"source": [
"Next, the same prompt is sent with the `google_search` tool enabled. This allows the model to perform a live search to retrieve the most current information."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9vkFZLzkIzeE"
},
"outputs": [
{
"data": {
"text/markdown": [
"Response:\n",
" The latest football match between Brazil and Argentina took place on March 26, 2025, as part of the 2026 FIFA World Cup qualifiers. Argentina won the game with a final score of 4-1 against Brazil."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Search Query: ['latest Brazil vs Argentina football game score', 'Brazil vs Argentina last match date and score']\n",
"\n",
"Search Pages: 365scores.com, 365scores.com, sofascore.com, thehindu.com, skysports.com\n",
"\n"
]
},
{
"data": {
"text/html": [
"<style>\n",
".container {\n",
" align-items: center;\n",
" border-radius: 8px;\n",
" display: flex;\n",
" font-family: Google Sans, Roboto, sans-serif;\n",
" font-size: 14px;\n",
" line-height: 20px;\n",
" padding: 8px 12px;\n",
"}\n",
".chip {\n",
" display: inline-block;\n",
" border: solid 1px;\n",
" border-radius: 16px;\n",
" min-width: 14px;\n",
" padding: 5px 16px;\n",
" text-align: center;\n",
" user-select: none;\n",
" margin: 0 8px;\n",
" -webkit-tap-highlight-color: transparent;\n",
"}\n",
".carousel {\n",
" overflow: auto;\n",
" scrollbar-width: none;\n",
" white-space: nowrap;\n",
" margin-right: -12px;\n",
"}\n",
".headline {\n",
" display: flex;\n",
" margin-right: 4px;\n",
"}\n",
".gradient-container {\n",
" position: relative;\n",
"}\n",
".gradient {\n",
" position: absolute;\n",
" transform: translate(3px, -9px);\n",
" height: 36px;\n",
" width: 9px;\n",
"}\n",
"@media (prefers-color-scheme: light) {\n",
" .container {\n",
" background-color: #fafafa;\n",
" box-shadow: 0 0 0 1px #0000000f;\n",
" }\n",
" .headline-label {\n",
" color: #1f1f1f;\n",
" }\n",
" .chip {\n",
" background-color: #ffffff;\n",
" border-color: #d2d2d2;\n",
" color: #5e5e5e;\n",
" text-decoration: none;\n",
" }\n",
" .chip:hover {\n",
" background-color: #f2f2f2;\n",
" }\n",
" .chip:focus {\n",
" background-color: #f2f2f2;\n",
" }\n",
" .chip:active {\n",
" background-color: #d8d8d8;\n",
" border-color: #b6b6b6;\n",
" }\n",
" .logo-dark {\n",
" display: none;\n",
" }\n",
" .gradient {\n",
" background: linear-gradient(90deg, #fafafa 15%, #fafafa00 100%);\n",
" }\n",
"}\n",
"@media (prefers-color-scheme: dark) {\n",
" .container {\n",
" background-color: #1f1f1f;\n",
" box-shadow: 0 0 0 1px #ffffff26;\n",
" }\n",
" .headline-label {\n",
" color: #fff;\n",
" }\n",
" .chip {\n",
" background-color: #2c2c2c;\n",
" border-color: #3c4043;\n",
" color: #fff;\n",
" text-decoration: none;\n",
" }\n",
" .chip:hover {\n",
" background-color: #353536;\n",
" }\n",
" .chip:focus {\n",
" background-color: #353536;\n",
" }\n",
" .chip:active {\n",
" background-color: #464849;\n",
" border-color: #53575b;\n",
" }\n",
" .logo-light {\n",
" display: none;\n",
" }\n",
" .gradient {\n",
" background: linear-gradient(90deg, #1f1f1f 15%, #1f1f1f00 100%);\n",
" }\n",
"}\n",
"</style>\n",
"<div class=\"container\">\n",
" <div class=\"headline\">\n",
" <svg class=\"logo-light\" width=\"18\" height=\"18\" viewBox=\"9 9 35 35\" fill=\"none\" xmlns=\"http://www.w3.org/2000/svg\">\n",
" <path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M42.8622 27.0064C42.8622 25.7839 42.7525 24.6084 42.5487 23.4799H26.3109V30.1568H35.5897C35.1821 32.3041 33.9596 34.1222 32.1258 35.3448V39.6864H37.7213C40.9814 36.677 42.8622 32.2571 42.8622 27.0064V27.0064Z\" fill=\"#4285F4\"/>\n",
" <path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M26.3109 43.8555C30.9659 43.8555 34.8687 42.3195 37.7213 39.6863L32.1258 35.3447C30.5898 36.3792 28.6306 37.0061 26.3109 37.0061C21.8282 37.0061 18.0195 33.9811 16.6559 29.906H10.9194V34.3573C13.7563 39.9841 19.5712 43.8555 26.3109 43.8555V43.8555Z\" fill=\"#34A853\"/>\n",
" <path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M16.6559 29.8904C16.3111 28.8559 16.1074 27.7588 16.1074 26.6146C16.1074 25.4704 16.3111 24.3733 16.6559 23.3388V18.8875H10.9194C9.74388 21.2072 9.06992 23.8247 9.06992 26.6146C9.06992 29.4045 9.74388 32.022 10.9194 34.3417L15.3864 30.8621L16.6559 29.8904V29.8904Z\" fill=\"#FBBC05\"/>\n",
" <path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M26.3109 16.2386C28.85 16.2386 31.107 17.1164 32.9095 18.8091L37.8466 13.8719C34.853 11.082 30.9659 9.3736 26.3109 9.3736C19.5712 9.3736 13.7563 13.245 10.9194 18.8875L16.6559 23.3388C18.0195 19.2636 21.8282 16.2386 26.3109 16.2386V16.2386Z\" fill=\"#EA4335\"/>\n",
" </svg>\n",
" <svg class=\"logo-dark\" width=\"18\" height=\"18\" viewBox=\"0 0 48 48\" xmlns=\"http://www.w3.org/2000/svg\">\n",
" <circle cx=\"24\" cy=\"23\" fill=\"#FFF\" r=\"22\"/>\n",
" <path d=\"M33.76 34.26c2.75-2.56 4.49-6.37 4.49-11.26 0-.89-.08-1.84-.29-3H24.01v5.99h8.03c-.4 2.02-1.5 3.56-3.07 4.56v.75l3.91 2.97h.88z\" fill=\"#4285F4\"/>\n",
" <path d=\"M15.58 25.77A8.845 8.845 0 0 0 24 31.86c1.92 0 3.62-.46 4.97-1.31l4.79 3.71C31.14 36.7 27.65 38 24 38c-5.93 0-11.01-3.4-13.45-8.36l.17-1.01 4.06-2.85h.8z\" fill=\"#34A853\"/>\n",
" <path d=\"M15.59 20.21a8.864 8.864 0 0 0 0 5.58l-5.03 3.86c-.98-2-1.53-4.25-1.53-6.64 0-2.39.55-4.64 1.53-6.64l1-.22 3.81 2.98.22 1.08z\" fill=\"#FBBC05\"/>\n",
" <path d=\"M24 14.14c2.11 0 4.02.75 5.52 1.98l4.36-4.36C31.22 9.43 27.81 8 24 8c-5.93 0-11.01 3.4-13.45 8.36l5.03 3.85A8.86 8.86 0 0 1 24 14.14z\" fill=\"#EA4335\"/>\n",
" </svg>\n",
" <div class=\"gradient-container\"><div class=\"gradient\"></div></div>\n",
" </div>\n",
" <div class=\"carousel\">\n",
" <a class=\"chip\" href=\"https://vertexaisearch.cloud.google.com/grounding-api-redirect/AbF9wXF-zvxNN3VRdxQrqO6AP_rCq57E5B0UAVJ1dER5uLvwlgQpyGjiYo9rd56e01cUgJdI8xJuLQq83f5z2eUOijA-oDFGoV32hUnM9JQD_OCsqsbdobeR3rMsNhunzjSbA_eAu-0HBiAi_XbqC8JUHwegaS3Jiv8dFc7VselrL2RD6bmVpelRRXtUSPjdtBt_IdpkWHvQDUQ4gViXxwaJLJwMIoCJdBtjOrze6o0=\">Brazil vs Argentina last match date and score</a>\n",
" <a class=\"chip\" href=\"https://vertexaisearch.cloud.google.com/grounding-api-redirect/AbF9wXEhF_dGRCTgW_L-CCunPPz9Nnkkp3ggnBm0_VdP8QzdXMhMjdv-WaplwgLznji5lba55A-dLTg3Gc0Z2PuL4izDo1KM-pAUOItGiYwq6J6NDLdV5TtPglbIW1bedv0XTg8xarYd0zhJsSKMLshCkzImsD6bBlNmsP5W17wSkPYbmb6rVg_REXT7xoOummPnkCHyH2Xzk4oePsDHETDi7LKSr45yNZI70hPxFeBc\">latest Brazil vs Argentina football game score</a>\n",
" </div>\n",
"</div>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=\"What was the final score of the latest Brasil vs. Argentina football game?\",\n",
" config={\"tools\": [{\"google_search\": {}}]},\n",
")\n",
"\n",
"# print the response\n",
"display(Markdown(f\"Response:\\n {response.text}\"))\n",
"# print the search details\n",
"print(f\"\\n\\nSearch Query: {response.candidates[0].grounding_metadata.web_search_queries}\", end=\"\\n\\n\")\n",
"# urls used for grounding\n",
"print(f\"Search Pages: {', '.join([site.web.title for site in response.candidates[0].grounding_metadata.grounding_chunks])}\", end=\"\\n\\n\")\n",
"\n",
"display(HTML(response.candidates[0].grounding_metadata.search_entry_point.rendered_content))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "m3p6A7lBKIPf"
},
"source": [
"### Using Code Execution and Google Search grounding together\n",
"\n",
"The power of Gemini models is further amplified when multiple tools are used in conjunction. Here, both `code_execution` and `google_search` tools are enabled. This allows the model to search for information (e.g., about movies) and then use code to process and visualize that data (e.g., generate a chart based on movie durations)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5xU2i2IlKLcV"
},
"outputs": [
{
"data": {
"text/html": [
"<pre style=\"background-color: green;\">concise_search(\"Daniel Villeneuve movies popularity duration\")\n",
"</pre>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"---"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Looking up information on Google Search.\n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"---"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre style=\"background-color: green;\">import matplotlib.pyplot as plt\n",
"import random\n",
"\n",
"# Data for the top 10 Denis Villeneuve movies, ordered by duration\n",
"movies_data = [\n",
" {\"name\": \"Polytechnique\", \"duration\": 77, \"popularity\": 72},\n",
" {\"name\": \"Maelstrom\", \"duration\": 87, \"popularity\": 67},\n",
" {\"name\": \"Enemy\", \"duration\": 90, \"popularity\": 73},\n",
" {\"name\": \"Arrival\", \"duration\": 116, \"popularity\": 81},\n",
" {\"name\": \"Sicario\", \"duration\": 121, \"popularity\": 82},\n",
" {\"name\": \"Incendies\", \"duration\": 130, \"popularity\": 80},\n",
" {\"name\": \"Prisoners\", \"duration\": 153, \"popularity\": 82},\n",
" {\"name\": \"Dune: Part One\", \"duration\": 155, \"popularity\": 80},\n",
" {\"name\": \"Blade Runner 2049\", \"duration\": 164, \"popularity\": 81},\n",
" {\"name\": \"Dune: Part Two\", \"duration\": 195, \"popularity\": 85}\n",
"]\n",
"\n",
"# Extract names and durations for plotting\n",
"movie_names = [movie['name'] for movie in movies_data]\n",
"movie_durations = [movie['duration'] for movie in movies_data]\n",
"\n",
"# Generate a list of distinct colors\n",
"colors = []\n",
"for _ in range(len(movies_data)):\n",
" r = random.random()\n",
" g = random.random()\n",
" b = random.random()\n",
" colors.append((r, g, b))\n",
"\n",
"# Create the bar chart\n",
"plt.figure(figsize=(12, 8))\n",
"bars = plt.bar(movie_names, movie_durations, color=colors)\n",
"\n",
"# Add titles and labels\n",
"plt.xlabel(\"Movie\")\n",
"plt.ylabel(\"Duration (minutes)\")\n",
"plt.title(\"Top-10 Denis Villeneuve Movies by Popularity, Ordered by Duration\")\n",
"plt.xticks(rotation=45, ha=\"right\") # Rotate labels for better readability\n",
"\n",
"# Add tags on each bar\n",
"for bar in bars:\n",
" yval = bar.get_height()\n",
" plt.text(bar.get_x() + bar.get_width()/2, yval + 2,\n",
" f\"{bar.get_height()} min\", ha='center', va='bottom', fontsize=9)\n",
" # Adding movie name below duration tag, can adjust position if needed\n",
" # plt.text(bar.get_x() + bar.get_width()/2, yval / 2,\n",
" # f\"{movie_names[list(bars).index(bar)]}\", ha='center', va='center', fontsize=8, color='black', rotation=90)\n",
"\n",
"\n",
"plt.tight_layout() # Adjust layout to prevent labels from overlapping\n",
"plt.show()</pre>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"---"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAMgBLADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiioZbu2guILea4ijmuCVhjdwGkIG4hQeTgAk47CgCaiiigAooqpJqmnwi7Mt/aoLMBrotMo8gEbgX5+XjnnHFAFuioBe2rXS2q3MJuWi85YRIN5jzjeB125IGenNT0AFFFFABRRRQAUUUUAFFFFABRRRQAUUVDcXdtaeV9puIofOkEUfmOF3ueijPUn060ATUUUUAFFFQxXltPcT28NxDJPblRNGjgtESMgMByMggjPagCaiiigAooooAKKKKACiioZbu2huILeW4ijnuCwhjdwGkIGTtHU4HJxQBNRRRQAUVDFd209xPbw3EUk1uQs0aOC0ZI3AMByMggjPY1NQAUUUUAFFFFABRRUP2u2N6bP7RF9qEfmmDeN+zON23rjIIz0zQBNRRRQAUUUUAFFQ2t3bXsPnWlxFPFuZN8Th13KSGGR3BBB9CKI7y2muprWK4he4gCmaJXBeMNnbuHUZwcZ64oAmooooAKKKKACiiigAooooAKKhF3bG9azFxEbpYxKYN43hCSA23rgkEZ9jU1ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVn6xoWleILRLTV7CC9t0kEixzLuAYAgH64J/OtCigDxGx0jw7oUPiG9g8NWF3qUXif7BpUbrtCSMsPljPZVJZvzr0DS9e1208VQaB4ki09pLy2e4tLmwDqjFCA8bK5JyAwIOcEVQm8CX0+na2gvLeC9n14azp0ygusbKI9gkGB3RgQM8Hr2q/peg67d+KYNf8AEkunrJZ2z29pbWBdkUuVLyMzgHJCgAYwBQBzOm+PvFsngiz8Z32n6SNJ4a6t4vME5j37GkQklRjrtOcgdRnFepCRCcB1JPbNeNeDfDviTxD8KdH0RrzTl0C7jDTSkP8Aali8ws0Sj7pyQRuyMA9CRmvUV8L6Cut/20uk2Y1TOftYiHmZ27fvdenFAHIRaBpui/GXT5rGBklvNNu5bh3leQu3mR/3icDk8DAqxqOm2ni34k3Wl6vF9p0zTNNiljtHYhGmld8yEA8kKgAz0yaZqWj+OZvG0Wu2kPh0xWsM9rbpLdThnjd1IZsRnDYQcAkcmtbWdD1mLxOviHw9JYG5ktPsd1bXxdY5FDFkcMgJDAlh05B7YoAi+Hk0y6XqumSTSTR6TqtxYwPKxZvKXayAk8nAcLn2rIv/AA7o/iH4v30Wr6dBepFolu0YmXO0maXkV1PhTQZfD+kSRXVwtxfXVzLeXcyLtV5ZGydo7KOAPYVk6no3iaDx1Pr2iR6RNDPp8VoyXtxJGylHdsjbGwI+Yd6AMyzuIvAvjbUtKjnnOgf2K+rLbySNJ9laJ9rhCxyFIOcZxkcYrPtPipcb9NvbrUPDMtnezxRtp9ndl7y2WQgKSd2HK5G4BRjn0rpLHwbc3t3q+peJ7uG5vtSsjp5jtFKRW9sc5RC3JJJyWPfHAqHRtC8V6VHY6dIvhy4srUpH9taOQTyRLgcoBtD7R13YzzjtQBG3iHxfqXiPxHpmi22jrFpMsaxy3YkJl3RK+zCt1yT83AAI4PNPt/FSa1ceAr5NPtSNX85y0yb5LYi3dj5bdjkFSccjPrW1oeh3OmeIvEeoTSQtDqdzFNCqEllCxKh3ZHByp6Z4rC0PwVqWmWfgeGae0ZtB877UUdiH3wug2ZXnlh1xxQAmg+Ldb8Q61NHb3OgW8MF7JBLpszSfbUjRypY/NgEgbgNuORzXQeMtDPiPwhqWmRnbcSRbrdgcFZlO6M5/3gK53VPCniLxBqtjJqcXh+BLS8juF1C1WT7VtRtwVQRhcgYJ3Ecniu/oA8z1zxA3i74d6DZWzFLvxNJHZyheDEoybnj/AGQjr+NM8TXUZ0H4k6dFY2dvHY2MUaSQxbXlBtgRvOfm29B6DitPQPAd3pPj2+1ee6hk0lWmk0y2UndBJOVaYkYwBlSBgnhj0qXVvB2oX8XjhYprUHXoI47XczfIVh8s7/l459M8UAQC6isfiPbXc7bYYPCzyufRVmQn9BWJD8WZhDZ6tPf+GnsLmWMNpkF2WvoY5GADH5sMy5BZdoxg88V2EnhaW58TpfXLQtYtoj6ZLGGO8szqSRxjbgHnOfas3RvDvirRbaz0lF8O3NhabIkvZo5BcNCvAygG0vtGM7uvOKAHS+IPFmo+LvEGi6LBpCRaX5BSe9WQ7jJHu2kK3JznnjAxw2eKl38TAvhjw7eR/wBn2N9rQfDajPst7fyx+8LHgthsAAYzkciuk0fQ7nT/ABb4j1WWSFoNTa2MKoSWXy4tjbhjA56YJrm7fwDqdp4c8OiC4sDrWiyTlBMrPbzJKzbkbgMOCpzjgr0NAFeD4k3TaJ4oxPo1/qGj2X2yG50+QyW0ykNgEbiVIK4I3dxXU6xr91p83hlIo4WGqXq28+9SdqmJ3yvPByo654zVYaDq+s6DrOma7FpFol9bNbx/2aHYpuVgWZmC56rgYHQ+tZi+G/GGoX/huXV7rR1g0e6ErpamQmcCNk3ksvDcj5enJ54AoAs/8JvcWXhjxPeajDCNR0S4lg8qNSFlJwYDgkn5w6Dr1ziqet+O73TNRstDkv8Aw/pupixjur651KUpArtxsjTcCxyGP3uBjrmp9d8DXuqeOrXVILm3j0eV7eXU7di2+aS3LNEQMYIyyg5I4Udaua54a1P/AISceIdD/s2W4ltltbq11FWEciqxZXVlBKsNxHQgg+1AFrwR4pHivR7i4f7Kbi0untJntJfMhkZcHfG3dSGU+2cdq4fxa76b8YV8RIxCaXp9o1zzwbeSaWOTI9twf/gFelaHBqUFgRqqafHctIW2WCsI1XjAy3JPvgfSsq58Ktf+LtVv7zyZNM1DSE054tx3nDyFs8YxhxznNAHEu7at8btG1osTbx3d1plqM8FYbZzI34yOy/8AAK2pvGPie503VfEWlWGmPoWnSzKIZmf7RcxwkiR1YHav3WwCDnFaqeCk0/UvCX9lGOPTtEe5aRJXJkfzY2XIOOTubJyRWVN4O8T22m6r4d0q/wBMTQtRlmYTTK/2i2jmJMiKoG1vvNgkjGaAN3T/ABRJd+LI9PdYhYX2mR6hp8oUqzDOJFbJwSA0ZGAOCetYf/CV3WqaNo2qzWGnzW194iW3svNhLFbfcypKMniT5SQw4ww4q54x8GX+o6FpUHhq6gs9Q0xWgt5bhm2iF4jE65UE5xtI46qKvan4TZ9J8NabprRRwaPfW0xEhIzFECCBgHLHjr+dAHO6t8SJ/wC2NVtdO1TwxYx6bMbcxatdFJrmRQC20BhsXJ2hjuyQeKmu/iLcXdv4cfTH0zTU1i0e5+1auzeUjLtHkrtK7nyx7jgVcbwxruj6xqc+hR6Ld2Wo3Bu3i1IOrwSsAH2sqtuUkZwcYJPNaGq2HiWewtbWGz8N3sRhAu4LxJEjMncoAG+X2Iz70AbulNfPpkDak1o12QS7Wm7ym5OCu7nkY/8Ar157Zy+JT8R/HEXh+LTQVltHklv95Vj9mTCKEIPY5Ynjjg546/wZ4fl8MeGbfS5p45ZEeSQ+UpWNN7ltiA8hVzgfSsOTw94u03xd4i1nRLjR3h1ZoNkF6ZB5flwqm/Kr1yD8vORjkUAP0/x1cX0Xha/a0igsNXlls7pWyXt7pchVDZwVLRyL0/u1s6JrlzrHiDXoVjhGmadMlpFIAd8kwXdLk5xhdyr06g81y/iDQrTwt8HptOn1FftVlGbuG5YANJdrJ5oKj1Mhxgdjiuo8FaPLofhOxtbo5vXU3F2x6tPIS8hP/AmI/CgDj/iAmhv8Q/Do8Q2LXtj9gu/3S2r3B37osHagJ9ecVS0HVNV8PQXVpo+mTxQaxq4g0K01TeghjEe6WRlPzrH8rELwea7280O5uPHWla4kkItrSzuLeRCTvLSMhBAxjHynPPpTPFegXmsLp15pdxDBqmmXP2m2adSY3ypVkfHIDKx5HI4oAyU8V63p0uu6XrMFgdUsNMbUrWa1DiGeMBhyrHcpDLgjdyD2qlZ+MvFEGl6Dr2sWOlro+ptbxyR25k8+DzsBJCSdpUkjK4yAepxUl9oOrrY+KPEfiCazN9Lo0tnBBZFjFDCFdj8zAFmLHPQYwKo6D4c8R634W8K2Oo3mnf2FbR2l2XiD/aJgiq8cbAjaADjLAknb0GaAOs0fX7rUJvEySxwqNLvWt4NikblESPlueTlj0xxiuZt9Wn17VPhlq10kaXF5FczSLECFDNbEnAJJx+NaVz4b8S2mra5/Yt3piafrLiWR7kP51tJ5YjYoANrZCgjJGD60aT4O1Cwi8DrLNak6DBJHdbWb5y0PljZ8vPPrjigDO8S+PtQ8L3txNd6n4WktYJ8PpqXDC9MJbG4ZbG/ad23b6gGtTV/EHiRvHx8NaJBpoT+y0vmubxXPlkyuhGFYbs7VwOO5z2rm9R+HPiWbwvqvhyzl0Jba6lllW/kR/tMu+Qvtk+UgHJwWBPA6DqO5TQ7lfiFPr5kh+ySaVHZBMnfvWV3JxjGMMO+c9qAOH8Z6nqD+HviHDax2FpcWItxJcwxMskwaCNmJYNksM4U9gAMGukl1nxFoEeiSa1Jpdxa3d8tpcSWsEkflLImIm+Z2/wCWmAT6MOmKZqXgq61OPxvC9zDHHr4iFuwyTGUgWP5xgfxL2J4o1+K7l+G2sW/i6XTbJxbt5c1pO7KCihkf51U7g65CjPQc0Aa+na5c6l4x1fToo4f7O02KKN5cHe1w4LFQc4wqbMjGct1rfriPCkGo6D8NJtTnge51y6hm1SeNYizSXEgLhNo5zjauB6V1mlvfSaTZvqaRJftChuUhzsWTA3BcknGc96AOC0PRLHx5qviDVfEUJvo7bU5tPsrWVz5UEcRClgoON7HJJPPTFUl16/8ABNl4606zk+1Q6FHBdaeLxmk2JKpPlMchiqlTjnOD1rov+Ee8Q6DrWp3fhmbTZbPU5jcz2eoM6eVOQAzo6A5DYBKkdRwapXfgHUbvwh4mt5762n8Qa+AZ7gho4U2gBEUckIoGAeSck0ATXviPxVp9tYxTWulHU9ZuEh0+2XzNtuNrPI0zZ+faoH3QOazbPU9R034k6td+JEtVew8OCV5bPd5csSyuxYKxyp+8MEnpnPNdR4p8P3uqw6Zd6XcQQarpdyLi2acExP8AKUdHxyAyseRyOKybTwjrWo+JNU1LxJPYNb6jpH9mtb2TP+6UsxIBYDcMMTu45OMYGaAMO0+Klxv029utQ8My2d7PFG2n2d2XvLZZCApJ3YcrkbgFGOfSt+28Q+JdT8Y65pdnFpcGnaTcQrJcXCyF3R41dlADY3ct8x4Hy8Hmk0bQvFelR2OnSL4cuLK1KR/bWjkE8kS4HKAbQ+0dd2M847Vr6LoNxp+v+Jb64aF4NVuIpYlQksFWFUIbI4OVPTPFAHBv8X5PsB11L/w3/ZwfP9lm6P28xbsbvvbQ+Pm2benGc11l14h1/UvFF9pPhuHTPK0yKJrqa/3nzHkXcsabDx8uCWOfvDiqGl+FvE/h6yTR9OXw9dadAStvdXscnnpGTkKyqMOQDjO4ZwOKv3mgeIdP8Vahq/hyfTTHqkcS3cN/vHlyRrtWRNg5+XAKnHQc0AR/CmV5/AcMssRhke9vGaMnJQm4kJGfasmOTxD/AMLT8Ww6BHp4draxeWe+3si4STaoVCCScnnPG3oc11HgbQLzwz4Wh0u/uI7m5SeeRpo84ffKzg8gc4YZHrWVceH/ABVY+Nda1/RJ9JeLUIbeIW140g5jVhuJVTggngc5BPTAoAZaeOry4svD9/NZwwQXOoyaXqcZyzW9wCyIVbONpkXHIPDr0rc0vXLnVPFutWEUcP8AZumLFCZcHe9ww3sAc4wqlOMZyetc5qnh200D4Sapp+ralH57xzXk14fkzdFjKGUezgYHU4FbfgDS7nTfCFq+oc6lfM19ekjBM0p3kEdsAhf+A0AY/jbSdP1vx/4MsdTtIru1dL8tFKuVJEaEcfWorjTIPBHjjw6uh+Zbabq8stpdWAkZotwjLpIiknaQVwcdRWx4p0XXLzxFoGs6IunSSaaLlXivZnjDeaqqMFUbpg/pTLLw5rWo+JrLXfEtzY7tOSQWVlYBzGjuNrSM74LNjgDAAoAii8X6g/w813xAYbb7Xp7XwiQK3lt5Luq7huzyFGcEfhUV94n8RzeKdM0PSLfTS15pP26Sa6D4hYOoJwrfMOcbeDkg545z9Q8GeKjofiDw9pt7pKaVqUlxNFNN5nnR+aSxjIA27dxI3ZJAPQmuitPDl5b+MrHWHkgNvBop09lDHeZPMRsgYxtwp759qAMzxP4m1rQWUNrPhK1ZYFfyL+Z45LhwPm2/MNoJyB973rH1jW9Z1/WPh7qehXFpapqKTypFdRu6hzbszB9rLuAGQOnIz7VrX3hLXU8Sa1e6Y2jPDq+wvcXkbNcWu2MIQgAww4yASME96hh8F65p3h/wetjNp76t4fDBkmdxBKHiaNgGC7h97I+WgC7P4gbR/FGoHVLaxZ7Dw5HfXN3bwFZHIeXcikkny/lJVTnBJ5NVIPFniiyj0XVNcsdLTStWnig8q1L+faGX/Vl2Y7XGSA2AMZ71o3HhS71fXL+81Q2yW+oaAmmXEcEjMVk3SFypKjK4k4J546Cs6Dwn4ovY9F0vXL7S20rSZ4p/NtRJ592Yf9WHVhtQZALYJzjtQAN4m8YahqPiWDSLXR1g0a5MaPdCQmceWr7MK3Dcn5unI46muv0DVk17w9p2rRxmNL22jnCE5K7lBxn2ziszRvD13p1z4nkmkhYareNcQbGJ2qYkTDZHByp6Z4q34S0mfQfCOk6TdPG9xZ2qQyNESVLKMHBIBx+FAGzRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFUtR1fTNIjSTU9RtLJHOFa5nWMMfQFiM0+XUrGHT/7QlvbeOyCh/tLyqI9p6HdnGPegC1RVWHU7C40/wDtCG+tpLLaX+0pKpj2jqdwOMUWGpWGq232nTr22vLckr5tvKsi5HbKkigBmsanFouiX2q3CSPDZ273EixgbiqKWIGSBnArIHibVSAR4L1zB/6bWf8A8fp3j7/knfiX/sF3P/opq6BP9Wv0FAHPf8JLqv8A0JWuf9/rP/4/R/wkuq/9CVrn/f6z/wDj9dHRQBzn/CS6r/0JWuf9/rP/AOP0f8JLqv8A0JWuf9/rP/4/XR0UAc5/wkuq/wDQla5/3+s//j9H/CS6r/0JWuf9/rP/AOP10dFAHOf8JLqv/Qla5/3+s/8A4/R/wkuq/wDQla5/3+s//j9dHRQBzn/CS6r/ANCVrn/f6z/+P0f8JLqv/Qla5/3+s/8A4/XR0UAc5/wkuq/9CVrn/f6z/wDj9H/CS6r/ANCVrn/f6z/+P10dFAHOf8JLqv8A0JWuf9/rP/4/R/wkuq/9CVrn/f6z/wDj9dHRQBzn/CS6r/0JWuf9/rP/AOP0f8JLqv8A0JWuf9/rP/4/XR0UAc5/wkuq/wDQla5/3+s//j9H/CS6r/0JWuf9/rP/AOP10dFAHOf8JLqv/Qla5/3+s/8A4/R/wkuq/wDQla5/3+s//j9dHRQBzn/CS6r/ANCVrn/f6z/+P0f8JLqv/Qla5/3+s/8A4/XR0UAc5/wkuq/9CVrn/f6z/wDj9H/CS6r/ANCVrn/f6z/+P10dFAHOf8JLqv8A0JWuf9/rP/4/R/wkuq/9CVrn/f6z/wDj9dHRQBzn/CS6r/0JWuf9/rP/AOP0f8JLqv8A0JWuf9/rP/4/XR0UAc5/wkuq/wDQla5/3+s//j9H/CS6r/0JWuf9/rP/AOP10dFAHOf8JLqv/Qla5/3+s/8A4/R/wkuq/wDQla5/3+s//j9dHRQBzn/CS6r/ANCVrn/f6z/+P0f8JLqv/Qla5/3+s/8A4/XR0UAc5/wkuq/9CVrn/f6z/wDj9H/CS6r/ANCVrn/f6z/+P10dFAHK2ms3lhax2tn4C1e2t4xiOGFrJEQegAnwKn/4SXVf+hK1z/v9Z/8Ax+ujooA5z/hJdV/6ErXP+/1n/wDH6P8AhJdV/wChK1z/AL/Wf/x+ujooA5z/AISXVf8AoStc/wC/1n/8fo/4SXVf+hK1z/v9Z/8Ax+ujooA5z/hJdV/6ErXP+/1n/wDH6P8AhJdV/wChK1z/AL/Wf/x+ujooA5z/AISXVf8AoStc/wC/1n/8fo/4SXVf+hK1z/v9Z/8Ax+ujooA5z/hJdV/6ErXP+/1n/wDH6P8AhJdV/wChK1z/AL/Wf/x+ujooA5z/AISXVf8AoStc/wC/1n/8fo/4SXVf+hK1z/v9Z/8Ax+ujooA5z/hJdV/6ErXP+/1n/wDH6P8AhJdV/wChK1z/AL/Wf/x+ujooA5z/AISXVf8AoStc/wC/1n/8fo/4SXVf+hK1z/v9Z/8Ax+ujooA5z/hJdV/6ErXP+/1n/wDH6P8AhJdV/wChK1z/AL/Wf/x+ujooA5z/AISXVf8AoStc/wC/1n/8fo/4SXVf+hK1z/v9Z/8Ax+ujooA5z/hJdV/6ErXP+/1n/wDH6P8AhJdV/wChK1z/AL/Wf/x+ujooA5z/AISXVf8AoStc/wC/1n/8fo/4SXVf+hK1z/v9Z/8Ax+ujooA5z/hJdV/6ErXP+/1n/wDH6P8AhJdV/wChK1z/AL/Wf/x+ujooA5z/AISXVf8AoStc/wC/1n/8fo/4SXVf+hK1z/v9Z/8Ax+ujooA5z/hJdV/6ErXP+/1n/wDH6P8AhJdV/wChK1z/AL/Wf/x+ujooA5G71KbUJLeS9+Hmp3Mlu++BpvsLmJvVSZ+D7irf/CS6r/0JWuf9/rP/AOP10dFAHOf8JLqv/Qla5/3+s/8A4/R/wkuq/wDQla5/3+s//j9dHRQBzM2v6jcQyQzeBtakikUo6PJZFWUjBBBn5BpIdf1C2gjgg8DazFDGoRI0kslVVAwAAJ+AB2rp6KAOc/4SXVf+hK1z/v8AWf8A8fo/4SXVf+hK1z/v9Z//AB+ujooA5z/hJdV/6ErXP+/1n/8AH6P+El1X/oStc/7/AFn/APH66OigDnP+El1X/oStc/7/AFn/APH6qX2pTaokSah8PdTu0icSRrcfYZAjjowzOcHk8111FAHOf8JLqv8A0JWuf9/rP/4/R/wkuq/9CVrn/f6z/wDj9dHRQBzn/CS6r/0JWuf9/rP/AOP0f8JLqv8A0JWuf9/rP/4/XR0UAc5/wkuq/wDQla5/3+s//j9H/CS6r/0JWuf9/rP/AOP10dFAHOf8JLqv/Qla5/3+s/8A4/R/wkuq/wDQla5/3+s//j9dHRQBzn/CS6r/ANCVrn/f6z/+P0f8JLqv/Qla5/3+s/8A4/XR0UAc5/wkuq/9CVrn/f6z/wDj9H/CS6r/ANCVrn/f6z/+P10dFAHI3upTamIhf/DzU7oQuJIhP9hk2OOjLmfg+4q3/wAJLqv/AEJWuf8Af6z/APj9dHRQBzn/AAkuq/8AQla5/wB/rP8A+P0f8JLqv/Qla5/3+s//AI/XR0UAc5/wkuq/9CVrn/f6z/8Aj9H/AAkuq/8AQla5/wB/rP8A+P10dFAHOf8ACS6r/wBCVrn/AH+s/wD4/R/wkuq/9CVrn/f6z/8Aj9dHRQBzn/CS6r/0JWuf9/rP/wCP0f8ACS6r/wBCVrn/AH+s/wD4/XR0UAc5/wAJLqv/AEJWuf8Af6z/APj9H/CS6r/0JWuf9/rP/wCP10dFAHOf8JLqv/Qla5/3+s//AI/R/wAJLqv/AEJWuf8Af6z/APj9dHRQBzn/AAkuq/8AQla5/wB/rP8A+P0f8JLqv/Qla5/3+s//AI/XR0UAc5/wkuq/9CVrn/f6z/8Aj9H/AAkuq/8AQla5/wB/rP8A+P10dFAHOf8ACS6r/wBCVrn/AH+s/wD4/R/wkuq/9CVrn/f6z/8Aj9dHRQBzn/CS6r/0JWuf9/rP/wCP0f8ACS6r/wBCVrn/AH+s/wD4/XR0UAc5/wAJLqv/AEJWuf8Af6z/APj9H/CS6r/0JWuf9/rP/wCP10dFAHOf8JLqv/Qla5/3+s//AI/R/wAJLqv/AEJWuf8Af6z/APj9dHRQBzn/AAkuq/8AQla5/wB/rP8A+P0f8JLqv/Qla5/3+s//AI/XR0UAc5/wkuq/9CVrn/f6z/8Aj9H/AAkuq/8AQla5/wB/rP8A+P10dFAHOf8ACS6r/wBCVrn/AH+s/wD4/R/wkuq/9CVrn/f6z/8Aj9dHRQBzn/CS6r/0JWuf9/rP/wCP0f8ACS6r/wBCVrn/AH+s/wD4/XR0UAc5/wAJLqv/AEJWuf8Af6z/APj9H/CS6r/0JWuf9/rP/wCP10dFAHOf8JLqv/Qla5/3+s//AI/R/wAJLqv/AEJWuf8Af6z/APj9dHRQBzn/AAkuq/8AQla5/wB/rP8A+P0f8JLqv/Qla5/3+s//AI/XR0UAc5/wkuq/9CVrn/f6z/8Aj9H/AAkuq/8AQla5/wB/rP8A+P10dFAHOf8ACS6r/wBCVrn/AH+s/wD4/R/wkuq/9CVrn/f6z/8Aj9dHRQBzn/CS6r/0JWuf9/rP/wCP0f8ACS6r/wBCVrn/AH+s/wD4/XR0UAZuhaymvaWL5Lae1/fSwvDPt3o8cjRsDtJH3lPQmtKuc8Ef8gG5/wCwrqP/AKWTV0dABRRRQAUUUUAYfiTVNO0iK3nuLRLzUJGaGxtlVTLK5xlVJ+6OAWboAMmuRl0caFZ+DbPU3t2tDrM090qcQRyyJM8aKD0RZGAXPcL3rutT0LR9b8r+1tKsb/yc+X9rt0l2ZxnG4HGcDp6CiLQtIh0ttLi0qxj09s7rRbdBEcnJymMdfagDzTU/s/n695Xl/wBgf8JLYfaduPJ+7F5ue23fs3ds5z3rrNE8j/hY/iL+z/L+y/YrP7R5WNv2jMvpxu8vy8+22ulh0ywt9P8A7PhsbaOy2lPsyRKse09RtAxiiw02w0q2+zadZW1nb5LeVbxLGuT1OFAFAHKfEefXV8H+IY7XTtPk086ZP5k8l66SqPLbdiMREHHb5hn2rs0/1a/QVz/j7/knfiX/ALBdz/6KaugT/Vr9BQA6iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArlPiHFKPCd1ewXt7az2gDxm1uGiySyj5tpGR7HiurrM1vQNP8RWYtNSW4eAHJSK6lhDf73lsu4exzQBjeKvtJ1jTxcf2v8A2MYJvN/srzfM8/KeXu8r59u3f/s5+92rA0+PxH4o8NeG9T/fTCKG5S7gOqy2LzMHCxsWhB3NhGPYZbrXaT+G9OubG3s5Xv2hgDBcajcB2B6h3D7nHsxNLd+GtLvLe2gaKeGG2j8qKO0upbdQnA2kRsoI4HBzQAaFqUGo+GLO/tEmWJrcFUuHLuuBjDMSSxBGCcnPXJrlNI8TeK9Ri8OmT+x4212wNyjLBKRbFVRjkeZ+8DB+mV2+rY57q2s7azsorK2gSG2iQRxxIMKqgYAAqpa6BplmumC3ttg0yA29n+8Y+VGVVSOTzwq8nJ4oA5SDxZc3X9k3kljZfan03UpHk2ElJIJIkIQ5yEY5JHU4Xnir2geIdauNQ0aHVlsDHq+mvexC1jdTAyeVlGLMdwIl6gLjGMHrWxH4Y0eJYlSzwIYriFP3r8JOwaUdf4iAfbtirEWi6fBNYSx2+19Pga2tjvY+XG20FevP+rTk5PHuaAL9FFFABRRRQAUUUUAFFFFAHOeCP+QDc/8AYV1H/wBLJq6Ouc8Ef8gG5/7Cuo/+lk1dHQAUUUUAFFFFABRRRQAUUUUAc74+/wCSd+Jf+wXc/wDopq6BP9Wv0Fc/4+/5J34l/wCwXc/+imroE/1a/QUAOooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArlPGnie98OfYfscVu/n+Zu85ScbduMYI9TXV1518U/+YT/22/8AZKxxEnGm2jDEycaTcdzO/wCFm61/z62H/ft//i6P+Fm61/z62H/ft/8A4uuLorzfb1O55X1mr/Mdp/ws3Wv+fWw/79v/APF0f8LN1r/n1sP+/b//ABdcXRR7ep3D6zV/mO0/4WbrX/PrYf8Aft//AIuj/hZutf8APrYf9+3/APi64uij29TuH1mr/Mdp/wALN1r/AJ9bD/v2/wD8XR/ws3Wv+fWw/wC/b/8AxdcXRR7ep3D6zV/mO0/4WbrX/PrYf9+3/wDi61fDXjrU9Z8QWthcQWaxS79xjRgwwhPGWPpXm1dH4E/5HPT/APtp/wCi2q6dao5pN9S6Veo5xTfVHs9FFFeqeyFFFFABRRRQBzngj/kA3P8A2FdR/wDSyaujrnPBH/IBuf8AsK6j/wClk1dHQAUUUUAFFFFABRRRQAUUUUAc74+/5J34l/7Bdz/6KaugT/Vr9BXP+Pv+Sd+Jf+wXc/8Aopq6BP8AVr9BQA6iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvOvin/wAwn/tt/wCyV6LXnXxT/wCYT/22/wDZKwxP8J/11ObF/wAF/wBdTzqiiivJPFCiiigAooooAKKKKACuj8Cf8jnp/wD20/8ARbVzldH4E/5HPT/+2n/otqul/Ej6o1o/xI+qPZ6KKK9o94KKKKACiiigDnPBH/IBuf8AsK6j/wClk1dHXOeCP+QDc/8AYV1H/wBLJq6OgAooooAKKKKACiiigAooooA53x9/yTvxL/2C7n/0U1dAn+rX6Cuf8ff8k78S/wDYLuf/AEU1dAn+rX6CgB1FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFedfFP/mE/9tv/AGSvRa86+Kf/ADCf+23/ALJWGJ/hP+upzYv+C/66nnVFFFeSeKFFFFABRRRQAUUUUAFdH4E/5HPT/wDtp/6Laucro/An/I56f/20/wDRbVdL+JH1RrR/iR9Uez0UUV7R7wUUUUAFFFFAHOeCP+QDc/8AYV1H/wBLJq6Ouc8Ef8gG5/7Cuo/+lk1dHQAUUUUAFFFFABRXN+OpNUtPCOpajpWqPYT2NpNc5WFJPMKRlgvzggDI9Kl1JLm40S3vzr95pUcFsZriS3jgYONoJLeZG2MYPTHU9eMAG/RXnMd14xl8A6Ddx3Woz395dia5a2gtvPS1ZXZVw6iMMB5eSQOc11PhPUV1HSZP9NvrqeCdobj7fDHFNFIMHYyxqq8Agggcgg5NAEXj7/knfiX/ALBdz/6KaugT/Vr9BXG/EbxFoln4O8Q6Zc6xYQ6hJpk6payXCLKxaNgoCk5Oe3rXZJ/q1+goAdRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXnXxT/AOYT/wBtv/ZK9Frzr4p/8wn/ALbf+yVhif4T/rqc2L/gv+up51RRRXknihRRRQAUUUUAFFFFABXR+BP+Rz0//tp/6Laucro/An/I56f/ANtP/RbVdL+JH1RrR/iR9Uez0UUV7R7wUUUUAFFFFAHOeCP+QDc/9hXUf/SyaujrnPBH/IBuf+wrqP8A6WTV0dABRRRQAUUUUAYnirR77X9AutKstRhsVu4ngnkltTPmN1KkKA64PPXn6VUuvDWoan4ROg6lq0MnmMiTTQWhiDwKylo9pkbBZQVLZPXpXTUUAZ+qWupzQRLpOoQWMiNyZrXz0ZcdNoZSPwNV/D+hHRYbx5rxry9vrg3NzOyBAzlVUBVH3VCqoAyenU1sUUAc54+UH4eeJSQM/wBl3POP+mTV0Kf6tfoK5/x9/wAk78S/9gu5/wDRTV0Cf6tfoKAHUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFUdW1ey0Sxa9v5HSBTglIXkPr91AT2Pamz65p1tpEWqy3IWzmVGifYxL78bQqgbiTkYAGfagDQoqlb6tZ3GmvqAeWG2jDNI1zC8BQLySyyAED6il0rVbLW9Mg1HT5jNaTgmNyjITgkHhgCOQeooAuUUVFLK8ckKrBJIJGKs6lcRjBO5skHGQBwCckcYyQAS0UUUAFFFFABXnXxT/5hP/bb/wBkr0WvOvin/wAwn/tt/wCyVhif4T/rqc2L/gv+up51RRRXknihRRRQAUUUUAFFFFABXR+BP+Rz0/8A7af+i2rnK6PwJ/yOen/9tP8A0W1XS/iR9Ua0f4kfVHs9FFFe0e8FFFFABRRRQBzngj/kA3P/AGFdR/8ASyaujrnPBH/IBuf+wrqP/pZNXR0AFFFFABRRRQAUUUUAFFFFAHO+Pv8AknfiX/sF3P8A6KaugT/Vr9BXP+Pv+Sd+Jf8AsF3P/opq6BP9Wv0FADqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAMnxNcrbeHL8mG5lMkDxIltbyTOWZSB8qAnGe+MCuLvLafVPCHhGZINXii0q4h+3Qx200NwFEDRsVUqHOC45QZxkqcivSqKAPPYrHxHqOkiCG3a50wam8iQatcSQzS2qqpRXYo748zccONxUAHqa2fh7DqNv4Pt4dUsRZzpNNtj3sxKmRjkgqpHJPHPGDnnA6migDyyz8Ixmy0SeTSJhdy69di9cxMHa2Z7k7X/wCmR/dnB+U5Hrze0/Sbu01XSreKwnisbPxJdPCgiYJDAbWXBHGAm98Dtk4Fei0UAeZ+HfDj6Xb+EL6HS5oNQa9nS/lMTCTyWjnIEh67NwjwDwDtx1r0yiigAooooAK86+Kf/MJ/7bf+yV6LXnXxT/5hP/bb/wBkrDE/wn/XU5sX/Bf9dTzqiiivJPFCiiigAooooAKKKKACuj8Cf8jnp/8A20/9FtXOV0fgT/kc9P8A+2n/AKLarpfxI+qNaP8AEj6o9nooor2j3gooooAKKKKAOc8Ef8gG5/7Cuo/+lk1dHXOeCP8AkA3P/YV1H/0smro6ACiiigAooooAKKKKACiiigDnfH3/ACTvxL/2C7n/ANFNXQJ/q1+grn/H3/JO/Ev/AGC7n/0U1dAn+rX6CgB1FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFRXNzBZ273F1PHBBGMvJK4VVHqSeBVb+2dLGlf2r/aVn/Z2N32vz18nGcZ35x1469aAL1FZsPiLRLnTptRg1jT5bGFtstyl0jRxnjhmBwDyOvqKs2Oo2OqWwudPvLe7tySBLbyrIpI7ZBIoAs0UVFPOltF5jiQruVcRxs5yxAHCgnGTyegGScAE0AS0UUUAFFFFABRRRQAUUVBe2ovbOS2aaeESDBeCQxuPow5H4UAT0VwemW+r6x8Ko7eyvpzqLuypPLdyI5Vbg5BlGXHyKRnk0LBqF/wCH9b0DT0urHWLeaEuZNYnnBVirZS4b94qlVYYABBzxzkgHeUVzfhScI+o6XLbXNveWcqGZZdRlvVIdcqySSndjAPGBgg8c5ORrHivX7K58Qz2sOmvY6Nd28IhkRxJOskcLMN4bCEGU4O1s9MDGSAd3RXCan4h1K1h1iw1O30y8mspdPkQi3YRMk84QZRmPzKVYg567TgdKdceKtdgl1W92ad/ZunatHYtEY386VHMQLBt2FI83P3TnHbrQB3NFFFABRRRQB4x8fP8AmXv+3n/2lXjFez/Hz/mXv+3n/wBpV4xXo0P4aPocD/u8fn+bCiiitjrCiiigAooooAKKKKANrwf/AMjtoP8A2Ebf/wBGLX1pXyX4P/5HbQf+wjb/APoxa+tK4sVujxsz+OIV518U/wDmE/8Abb/2SvRa86+Kf/MJ/wC23/sledif4T/rqeHi/wCC/wCup51RRRXknihRRRQAUUUUAFFFFABXR+BP+Rz0/wD7af8Aotq5yuj8Cf8AI56f/wBtP/RbVdL+JH1RrR/iR9Uez0UUV7R7wUUUUAFFFFAHOeCP+QDc/wDYV1H/ANLJq6Ouc8Ef8gG5/wCwrqP/AKWTV0dABRRRQAUUUUAFFFFABRRRQBzvj7/knfiX/sF3P/opq6BP9Wv0Fc/4+/5J34l/7Bdz/wCimroE/wBWv0FADqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooArX89lbWMs+oywRWiDdK9wwEaj1JPArz3w/d+H9Z+FbWc+u21vbxTE3E8M8ZNuftJdC2chc4GNwxivS6KAOM0bxFG1prL3+ux3OkW0scdtrLrGgYsoz8wURttYj5gNvOD0NHgY276l4hltb+bVYpbiJ/7TcptnbywCq+WqodoABKjnPqK7OigDzDWmv/APirNSi1jUoZ7HV7aG0WO5YRxK0dtuHl52sD5jcMCAeRgkkzapcXmkS67p9rqF8YYLzSHiaW6eR0865VZFDsS21gvTOPmI6HFek0UAeZ3Ml7FFrOsjVNRNxaeIobeGL7U/krC0sCMhjztIIduoJHbFemUUUAFFFFABRRXM+OPEV34Z0WG9so4JJHuFiImUkYKsexHPyik3ZXY4xcnZHTVDd2sd7ayW0rSrHIMMYZWicfRkIYfUEV5B/wtrXv+fTTf+/b/wDxdH/C2te/59NN/wC/b/8AxdZ+2ib/AFaoekWXhDR9P0ubTbVb5LWVgxX+0bhipB3ZRi+5OTk7SM96li8MaVBp9xZRRTolxIJZpRdy+dI4xhjLu8wkYAzu6DFeZf8AC2te/wCfTTf+/b//ABdH/C2te/59NN/79v8A/F0e2iH1aoeraXo1ho0cqWULKZn8yWSSV5ZJGwBlnclmOABye1Rz+H9LuY9QSa13LqMqTXQ8xh5joqKp4PGBGnTHT3NeW/8AC2te/wCfTTf+/b//ABdH/C2te/59NN/79v8A/F0e2iH1aoepXfh/S76a6mubXfJdCATHzGG7yXLx9DxhiTx175ok8P6XLbXlu9rmK8uVup18xvnlUqQ3Xj/VpwOOPc15b/wtrXv+fTTf+/b/APxdH/C2te/59NN/79v/APF0e2iH1aoey0V41/wtrXv+fTTf+/b/APxdH/C2te/59NN/79v/APF0e2iH1aoey0UUVqc54x8fP+Ze/wC3n/2lXjFez/Hz/mXv+3n/ANpV4xXo0P4aPocD/u8fn+bCiiitjrCiiigAooooAKKKKANrwf8A8jtoP/YRt/8A0YtfWlfJfg//AJHbQf8AsI2//oxa+tK4sVujxsz+OIV518U/+YT/ANtv/ZK9Frzr4p/8wn/tt/7JXnYn+E/66nh4v+C/66nnVFFFeSeKFFFFABRRRQAUUUUAFdH4E/5HPT/+2n/otq5yuj8Cf8jnp/8A20/9FtV0v4kfVGtH+JH1R7PRRRXtHvBRRRQAUUUUAc54I/5ANz/2FdR/9LJq6Ouc8Ef8gG5/7Cuo/wDpZNXR0AFFFFABRRRQBj+JPEMfhrSpNRmsL67hiR5JRaRqxjRVLMx3MoAwD3qHVfFEWmvpcUWm39/PqQYwQ2oiDYVQxyXdQOD61W+IFwY/A+sWsdpe3U97ZT20MdpayTsXaNguQgO0Z7nis67udA1XRdLbWfDeqXqxoyIsujzu0ThVBym3cM9mxjg80Abt94kj03S7S6u9PvkubuUQwaeFR53kOTt+VynRS2d2AByasaJrltrttNJDFPBLbzGC4t7hQskMgAO1gCR0IIIJBBHNcPZ2GrabpfhvVbiwvpINN1K6cWeDNcw2kqypFkAksyBkyBkgcdq6PwlDcTah4g1ma1ntItSvEe3huEKSeXHCke5lPKlipODzjFAE3j7/AJJ34l/7Bdz/AOimroE/1a/QVxnxH0u8n8H+IbuPXdQt4E0ycmzjSAxPiNsgloy/PfDD2xXZp/q1+goAdRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUARXBKwMQSDxyPrVLzZP+ejfnV25/492/D+dZ9ctdtSMp7j/Nk/56N+dHmyf89G/OmUVhzMi4/zZP8Ano350ebJ/wA9G/OmUUczC4/zZP8Ano350ebJ/wA9G/OmUUczC4/zZP8Ano350ebJ/wA9G/OmUUczC4/zZP8Ano351pVlVq104dt3NIBXB/Fr/kVbX/r9T/0B67yuD+LX/Iq2v/X6n/oD1rU+FnRR/iI8aoooriPUCiiigAooooAKKKKACiiigD6hooor0Dxzxj4+f8y9/wBvP/tKvGK9n+Pn/Mvf9vP/ALSrxivRofw0fQ4H/d4/P82FFFFbHWFFFFABRRRQAUUUUAbXg/8A5HbQf+wjb/8Aoxa+tK+S/B//ACO2g/8AYRt//Ri19aVxYrdHjZn8cQrzr4p/8wn/ALbf+yV6LXnXxT/5hP8A22/9krzsT/Cf9dTw8X/Bf9dTzqiiivJPFCiiigAooooAKKKKACuj8Cf8jnp//bT/ANFtXOV0fgT/AJHPT/8Atp/6LarpfxI+qNaP8SPqj2eiiivaPeCiiigAooooA5zwR/yAbn/sK6j/AOlk1dHXOeCP+QDc/wDYV1H/ANLJq6OgAooooAKKKKACiiigAooooA53x9/yTvxL/wBgu5/9FNXQJ/q1+grn/H3/ACTvxL/2C7n/ANFNXQJ/q1+goAdRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUARXP/Hu34fzrPrQuf+Pdvw/nWfXJX+IynuFFFFYEBRRRQAUUUUAFFFFABWrWVWrXTh+ppTCuD+LX/Iq2v/X6n/oD13lcH8Wv+RVtf+v1P/QHrap8LOij/ER41RRRXEeoFFFFABRRRQAUUUUAFFFFAH1DRRRXoHjnjHx8/wCZe/7ef/aVeMV7P8fP+Ze/7ef/AGlXjFejQ/ho+hwP+7x+f5sKKKK2OsKKKKACiiigAooooA2vB/8AyO2g/wDYRt//AEYtfWlfJfg//kdtB/7CNv8A+jFr60rixW6PGzP44hXnXxT/AOYT/wBtv/ZK9Frzr4p/8wn/ALbf+yV52J/hP+up4eL/AIL/AK6nnVFFFeSeKFFFFABRRRQAUUUUAFdH4E/5HPT/APtp/wCi2rnK6PwJ/wAjnp//AG0/9FtV0v4kfVGtH+JH1R7PRRRXtHvBRRRQAUUUUAc54I/5ANz/ANhXUf8A0smro65zwR/yAbn/ALCuo/8ApZNXR0AFFFFABRRRQAUUUUAFFFFAHO+Pv+Sd+Jf+wXc/+imroE/1a/QVz/j7/knfiX/sF3P/AKKaugT/AFa/QUAOooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAIrn/j3b8P51n1oXP8Ax7t+H86z65K/xGU9wooorAgKKKKACiiigAooooAK1ayq1a6cP1NKYVwfxa/5FW1/6/U/9Aeu8rg/i1/yKtr/ANfqf+gPW1T4WdFH+IjxqiiiuI9QKKKKACiiigAooooAKKKKAPqGiiivQPHPGPj5/wAy9/28/wDtKvGK9n+Pn/Mvf9vP/tKvGK9Gh/DR9Dgf93j8/wA2FFFFbHWFFFFABRRRQAUUUUAbXg//AJHbQf8AsI2//oxa+tK+S/B//I7aD/2Ebf8A9GLX1pXFit0eNmfxxCvOvin/AMwn/tt/7JXotedfFP8A5hP/AG2/9krzsT/Cf9dTw8X/AAX/AF1POqKKK8k8UKKKKACiiigAooooAK6PwJ/yOen/APbT/wBFtXOV0fgT/kc9P/7af+i2q6X8SPqjWj/Ej6o9nooor2j3gooooAKKKKAOc8Ef8gG5/wCwrqP/AKWTV0dc54I/5ANz/wBhXUf/AEsmro6ACiiigAooooAKKKKACiiigDnfH3/JO/Ev/YLuf/RTV0Cf6tfoK5/x9/yTvxL/ANgu5/8ARTV0Cf6tfoKAHUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABTJmdIZGij8yQKSqbsbj2Ge1PpshcRsY1VpADtDNgE9snBx+RoAxNC1y91jT9Rkl06K2vbO5ktvIFzvR2VQR8+wYB3AdDj3rJs/iDaumsm8SzYaVa/appNMvPtcePm+QtsTEny/dx361P4e0/xLpo1k3VppKPeXEl3AYr2SQB2CgI2Yl+X5fvDJ9qivPDWr+IP7WOrSWlgl7pxskhspWmAYkkSszKmSOABjpnnmgDT0nXry51ZtL1XTEsLw2wuoljuPOV4920gnauGUkZGCPmGCafqni7RNGvZbS+u3jnhhW4lVLeSTy4mLAOxVSAuVbJPA74yKr6VperyeIDrOtfYo5o7P7HDFZyM6kFgzuSyrjJVcLg4x1Oaj1Pw5d3uoeJLiOSALqekR2MO4nKuvn5LccL+9Xpk8Hj1ALEvi3SJbHU5LbUFjeytHumlmtZCgjAP71RhfNTg8oecYB5ovPGWhadcy213ess0CJJOUtpWWJXGVdyFIRT6k4Hc1k6v4Qv7+0mhhltVL+HJ9JG5mA81woU8L9z5Tz19qmvPCt7cW3iuNJLYNq+mpaQEs3ysInTL8cDLDpnjPFAHX9aKito2htYYmILIiqSPUCpaACiiigCK5/492/D+dZ9aFz/AMe7fh/Os+uSv8RlPcKKKKwICiiigAooooAKKKKACtWsqtWunD9TSmFcH8Wv+RVtf+v1P/QHrvK4P4tf8ira/wDX6n/oD1tU+FnRR/iI8aoooriPUCiiigAooooAKKKKACiiigD6hooor0Dxzxj4+f8AMvf9vP8A7SrxivZ/j5/zL3/bz/7SrxivRofw0fQ4H/d4/P8ANhRRRWx1hRRRQAUUUUAFFFFAG14P/wCR20H/ALCNv/6MWvrSvkvwf/yO2g/9hG3/APRi19aVxYrdHjZn8cQrzr4p/wDMJ/7bf+yV6LXnXxT/AOYT/wBtv/ZK87E/wn/XU8PF/wAF/wBdTzqiiivJPFCiiigAooooAKKKKACuj8Cf8jnp/wD20/8ARbVzldH4E/5HPT/+2n/otqul/Ej6o1o/xI+qPZ6KKK9o94KKKKACiiigDnPBH/IBuf8AsK6j/wClk1dHXOeCP+QDc/8AYV1H/wBLJq6OgAooooAKKKKACiiigAooooA53x9/yTvxL/2C7n/0U1dAn+rX6Cuf8ff8k78S/wDYLuf/AEU1dAn+rX6CgB1FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVTstW03UWnWx1C0umt22zCCZXMZ9GweDwetAFyiqNhrelarJLHp2p2V48X+sW3uFkKfUKTinWGq6dqglOn39rdiFtkn2eZZNjehwTg0AXKKKCcAn0oAKKjgmS5t450EgSRQ6iSNkYAjPKsAVPsQCKkoAKKKKACoLy8gsLV7m5k2Qpjc2CcZOBwPc1PWJ4v/5Fe8/4B/6GtOKu0jDFVXSoTqR3Sb+5B/wl+hf8/wB/5Cf/AOJo/wCEv0L/AJ/v/IT/APxNeW0V1ewifH/6y4v+WP3P/M9S/wCEv0L/AJ/v/IT/APxNH/CX6F/z/f8AkJ//AImvLaKPYRD/AFlxf8sfuf8Amepf8JfoX/P9/wCQn/8AiaP+Ev0L/n+/8hP/APE15bRR7CIf6y4v+WP3P/M9S/4S/Qv+f7/yE/8A8TR/wl+hf8/3/kJ//ia8too9hEP9ZcX/ACx+5/5nqX/CX6F/z/f+Qn/+Jo/4S/Qv+f7/AMhP/wDE15bRR7CIf6y4v+WP3P8AzPaoZo7iCOeJt0cih1OMZBGRT6o6N/yAtP8A+vaP/wBBFXq5Xoz7SlJzhGT6oiuf+Pdvw/nWfWhc/wDHu34fzrPrjr/ETPcKKKKwICiiigAooooAKKKKACtWsqtWunD9TSmFcH8Wv+RVtf8Ar9T/ANAeu8rg/i1/yKtr/wBfqf8AoD1tU+FnRR/iI8aoooriPUCiiigAooooAKKKKACiiigD6hooor0Dxzxj4+f8y9/28/8AtKvGK9n+Pn/Mvf8Abz/7SrxivRofw0fQ4H/d4/P82FFFFbHWFFFFABRRRQAUUUUAbXg//kdtB/7CNv8A+jFr60r5L8H/API7aD/2Ebf/ANGLX1pXFit0eNmfxxCvOvin/wAwn/tt/wCyV6LXnXxT/wCYT/22/wDZK87E/wAJ/wBdTw8X/Bf9dTzqiiivJPFCiiigAooooAKKKKACuj8Cf8jnp/8A20/9FtXOV0fgT/kc9P8A+2n/AKLarpfxI+qNaP8AEj6o9nooor2j3gooooAKKKKAOc8Ef8gG5/7Cuo/+lk1dHXOeCP8AkA3P/YV1H/0smro6ACiio7iOSW2ljimaCR0KpKoBKEjhgCCCR154oAkorz3UdJ8KWq302oS63qV5aTxW8jfbbhpnmkCsiRhHVQSHU4UADPaul8KeS+jJcWmo395aTEmJb87pYSCVZCSNxIYEHcSQQeaAKHxI0qx1LwDrkl5bpM1rp9zNDuz8jiJsNj1FGuW3h0+HNPvfENlHeJDCsdvA6GQySOFwiR9GckDHGRz0Ga2dc0DT/EVibLU45pLY53JFcyQ7gQQQ2xl3Agng8VmyeAfD80VlG8N+fsTO1s41O5DxlwA2HEm7oAOvHOOpoAwm8EajqngbRNLvGsxPaXpvGt79DdRLGfN2QsMjftWRVzn+GtrwPNEthqGmLpthYS6ZetbSx6emyB22q4dR2yHGQckEHmr8vhbS5tOgsZPtzQwOXjc6hceaCc5/e79569CcVc0vSbDRbL7Jp1ssEO4uQCSWY9WZjksT6kk0Acv8R9UvIPB/iG0j0LULiB9MnBvI3gESZjbJIaQPx3wp9s12af6tfoK5/wAff8k78S/9gu5/9FNXQJ/q1+goAdRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFZmleINN1qe7hsJ3kktCqzK8Eke3dnGNyjIODyMijSfEGm65JdR6fPJI9qyrMHgkjKkjI++oyCO4yKANOisuw8Q6bqd49tZyzTMu796ttL5LFTghZSuxiD6Ma1KACignAJxn2qOCR5reOV4ZIHdQzRSFSyEj7p2kjI9iR7mgCSiiigAooooAKKKKACuE8Oa1oD+PfElraalprGSO3CRRTod2xH34APO3v6d67uigDgfDmt6L4q8XRajp+oackNlby2tjaxTJ586syl5GQHKoNgCqR6sccVJ4R1nQrjxp4ltdO1LT5DJLA0MVvOh3BYVDFQDyAeDjoa7qigDkdatpNT8e6fpr39/b2baVczPHaXTw73EsIUkoQcjcf5dCQcHw5d6gsPhC/m1S+uJ9Ws5hd+dOzI5WLcpCfdUgr1UAnJzmvTKKAPMPD6Xev8A9gQXmr6osb+FoLl/JvZI2eYkDzGZSCW69Tz3zXaeDr+41TwVod/dv5lzcWEMsr4xuYoCT+JrbooAKKKKACsTxf8A8ivef8A/9DWtusTxf/yK95/wD/0NaqHxI5Mf/ulX/DL8meW0UUV6B+ZBRRRQAUUUUAFFFFABRRRQB6/o3/IC0/8A69o//QRV6qOjf8gLT/8Ar2j/APQRV6vOlufqWH/hR9F+RFc/8e7fh/Os+tC5/wCPdvw/nWfXHX+Ic9wooorAgKKKKACiiigAooooAK1ayq1a6cP1NKYVwfxa/wCRVtf+v1P/AEB67yuD+LX/ACKtr/1+p/6A9bVPhZ0Uf4iPGqKKK4j1AooooAKKKKACiiigAooooA+oaKKK9A8c8Y+Pn/Mvf9vP/tKvGK9n+Pn/ADL3/bz/AO0q8Yr0aH8NH0OB/wB3j8/zYUUUVsdYUUUUAFFFFABRRRQBteD/APkdtB/7CNv/AOjFr60r5L8H/wDI7aD/ANhG3/8ARi19aVxYrdHjZn8cQrzr4p/8wn/tt/7JXotedfFP/mE/9tv/AGSvOxP8J/11PDxf8F/11POqKKK8k8UKKKKACiiigAooooAK6PwJ/wAjnp//AG0/9FtXOV0fgT/kc9P/AO2n/otqul/Ej6o1o/xI+qPZ6KKK9o94KKKKACiiigDnPBH/ACAbn/sK6j/6WTV0dc54I/5ANz/2FdR/9LJq6OgAooooA8+vf7MHxNtttvqcrNeIs5EqC0S7+zMyEqfmZ/JXt8oyveul8IfZP+Ecj+x+f5f2i53/AGgqX83z5PMzt4+/u6VxmoXHhrXfEV3e2XjOLR57O8RmZLm3eO4kFuFEqB88hJTHkcZXplRXY+DX0+Twtbf2X5hs1kmSOSRw7TbZXUylh13kF8991AG9RRRQAUUUUAc74+/5J34l/wCwXc/+imroE/1a/QVz/j7/AJJ34l/7Bdz/AOimroE/1a/QUAOooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAON0HUUl8ea8fsepxx3S26wyzadPHGxjVw3zsgUdRjJ57ZqLRr37Z4t8Sxpbapbi+WJbeebTriJCUiKsd7IAMHpk89s129FAHmvgiy1LTJPD9giazG9ratDq63nmfZwVQBPK3fIfmAwY/wCHO7mtzWtAt9b8e6edR077Xp6aVcq3mxlot5lh2hu2cBiAfTI6V11FAHmfhzQrmwh8IXf2C6jvpbOaLUpnjbzGHlZVZieeGAC7unQUzw/4VTUP7Ah1vSJJIIfC0EDR3MJ2pMCMqQRgOPzHtXp9FAGJ4O+2f8IVoY1FZlvRYQicTgiQPsGdwPOc9c1t0UUAFFFFAEF6zJYXDKSGETEEHkHBrjvt95/z9z/9/DXYX/8AyDrn/rk/8jXD1yYhtNWM5lj7fef8/c//AH8NH2+8/wCfuf8A7+Gq9Fc/M+5Fyx9vvP8An7n/AO/ho+33n/P3P/38NV6KOZ9wuWPt95/z9z/9/DR9vvP+fuf/AL+Gq9FHM+4XLH2+8/5+5/8Av4aPt95/z9z/APfw1Xoo5n3C5Y+33n/P3P8A9/DXY2TM9hbsxJYxKSSeScCuGruLD/kHW3/XJP5CujDttu5cCxWJ4v8A+RXvP+Af+hrW3WJ4v/5Fe8/4B/6GtdsPiRhj/wDdKv8Ahl+TPLaKKK9A/MgooooAKKKKACiiigAooooA9f0b/kBaf/17R/8AoIq9VHRv+QFp/wD17R/+gir1edLc/UsP/Cj6L8iK5/492/D+dZ9aFz/x7t+H86z646/xDnuFFFFYEBRRRQAUUUUAFFFFABWrWVWrXTh+ppTCuD+LX/Iq2v8A1+p/6A9d5XB/Fr/kVbX/AK/U/wDQHrap8LOij/ER41RRRXEeoFFFFABRRRQAUUUUAFFFFAH1DRRRXoHjnjHx8/5l7/t5/wDaVeMV7P8AHz/mXv8At5/9pV4xXo0P4aPocD/u8fn+bCiiitjrCiiigAooooAKKKKANrwf/wAjtoP/AGEbf/0YtfWlfJfg/wD5HbQf+wjb/wDoxa+tK4sVujxsz+OIV518U/8AmE/9tv8A2SvRa86+Kf8AzCf+23/sledif4T/AK6nh4v+C/66nnVFFFeSeKFFFFABRRRQAUUUUAFdH4E/5HPT/wDtp/6Laucro/An/I56f/20/wDRbVdL+JH1RrR/iR9Uez0UUV7R7wUUUUAFFFFAHOeCP+QDc/8AYV1H/wBLJq6Ouc8Ef8gG5/7Cuo/+lk1dHQAUUUUAcXqHhbVE1G7m0a18KQ20rK6rdaW7yFgiqdzLIB27DpjrWv4O1K51bwtaXd59l+0bpYpPsiFIQUkZMICSdvy8HPPXjOK5XXNCi8ReKtU+x+EdBvJrVokubvVJ3RpXMasAqqjZAUqMk9QR2rt9Et7i00a2t7q0srSWNSvkWJJhQAnaFyqnGMdhzmgDQooooAKKKKAOd8ff8k78S/8AYLuf/RTV0Cf6tfoK5/x9/wAk78S/9gu5/wDRTV0Cf6tfoKAHUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVm+If+Ra1X/rzm/9ANaVZviH/kWtV/685v8A0A1pS/iR9UXT+Nep4JRRRX2x9iFFFFABRRRQAUUUUAFFFFAHtXgP/kS9P/7af+jGro65zwH/AMiXp/8A20/9GNXR18Ziv48/V/mfI4n+NP1f5le//wCQdc/9cn/ka4eu4v8A/kHXP/XJ/wCRrh683E7o5ZhRRRXMQFFFFABRRRQAUUUUAFdxYf8AIOtv+uSfyFcPXcWH/IOtv+uSfyFdGG3ZcCxWJ4v/AORXvP8AgH/oa1t1ieL/APkV7z/gH/oa13Q+JGGP/wB0q/4Zfkzy2iiivQPzIKKKKACiiigAooooAKKKKAPX9G/5AWn/APXtH/6CKvVR0b/kBaf/ANe0f/oIq9XnS3P1LD/wo+i/Iiuf+Pdvw/nWfWhc/wDHu34fzrPrjr/EOe4UUUVgQFFFFABRRRQAUUUUAFatZVatdOH6mlMK4P4tf8ira/8AX6n/AKA9d5XB/Fr/AJFW1/6/U/8AQHrap8LOij/ER41RRRXEeoFFFFABRRRQAUUUUAFFFFAH1DRRRXoHjnjHx8/5l7/t5/8AaVeMV7P8fP8AmXv+3n/2lXjFejQ/ho+hwP8Au8fn+bCiiitjrCiiigAooooAKKKKANrwf/yO2g/9hG3/APRi19aV8l+D/wDkdtB/7CNv/wCjFr60rixW6PGzP44hXnXxT/5hP/bb/wBkr0WvOvin/wAwn/tt/wCyV52J/hP+up4eL/gv+up51RRRXknihRRRQAUUUUAFFFFABXR+BP8Akc9P/wC2n/otq5yuj8Cf8jnp/wD20/8ARbVdL+JH1RrR/iR9Uez0UUV7R7wUUUUAFFFFAHOeCP8AkA3P/YV1H/0smro65zwR/wAgG5/7Cuo/+lk1dHQAVFdSSw2k0sEDXEqIzJCrBTIwHCgngZPGTxUtFAHnviLQFuvEt1fL4N1W+lkVFN5ba4bZZAFGAEEy4A5HQZOT3zXW+HLc2mgW0BsLiwK7v9GuLo3MiZYnmQs27PXqcA47Yrh9aj0278a6xFqWh6zr/l+T5f2PeY7PMakxEF1Xcfv5GThxnGBnuPDcVrDoFtHZ6dc6dbjfstbkYkj+Y5z8zdTk9TwRQBq0UUUAFFFFAHO+Pv8AknfiX/sF3P8A6KaugT/Vr9BXP+Pv+Sd+Jf8AsF3P/opq6BP9Wv0FADqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKzfEP/ACLWq/8AXnN/6Aa0qzfEP/Itar/15zf+gGtKX8SPqi6fxr1PBKKKK+2PsQooooAKKKKACiiigAooooA9q8B/8iXp/wD20/8ARjV0dc54D/5EvT/+2n/oxq6OvjMV/Hn6v8z5HE/xp+r/ADK9/wD8g65/65P/ACNcPXcX/wDyDrn/AK5P/I1w9ebid0cswooormICiiigAooooAKKKKACu4sP+Qdbf9ck/kK4eu4sP+Qdbf8AXJP5CujDbsuBYrE8X/8AIr3n/AP/AENa26xPF/8AyK95/wAA/wDQ1ruh8SMMf/ulX/DL8meW0UUV6B+ZBRRRQAUUUUAFFFFABRRRQB6/o3/IC0//AK9o/wD0EVeqjo3/ACAtP/69o/8A0EVerzpbn6lh/wCFH0X5EVz/AMe7fh/Os+tC5/492/D+dZ9cdf4hz3CiiisCAooooAKKKKACiiigArVrKrVrpw/U0phXB/Fr/kVbX/r9T/0B67yuD+LX/Iq2v/X6n/oD1tU+FnRR/iI8aoooriPUCiiigAooooAKKKKACiiigD6hooor0Dxzxj4+f8y9/wBvP/tKvGK9n+Pn/Mvf9vP/ALSrxivRofw0fQ4H/d4/P82FFFFbHWFFFFABRRRQAUUUUAbXg/8A5HbQf+wjb/8Aoxa+tK+S/B//ACO2g/8AYRt//Ri19aVxYrdHjZn8cQrzr4p/8wn/ALbf+yV6LXnXxT/5hP8A22/9krzsT/Cf9dTw8X/Bf9dTzqiiivJPFCiiigAooooAKKKKACuj8Cf8jnp//bT/ANFtXOV0fgT/AJHPT/8Atp/6LarpfxI+qNaP8SPqj2eiiivaPeCiiigAooooA5zwR/yAbn/sK6j/AOlk1dHXOeCP+QDc/wDYV1H/ANLJq6OgAqO4nS1tpbiQOUiQu2xC7YAycKAST7AZNSUUAcBJ9hv9Vvta0f4hpYxXWwywxfZmRSihcnepIbjnPPQdAAOm8Najb32mbINYOsNAxSS+8tVWU5J4KAI2BgfL6c807UdK8Oy3K3Op2GlvOfuy3MMZb8CwzWnA8MkCNbtG0OMKYyCuBxxigCSiuf8AGWo6xpHhq+1TSPsO+yt5bmQXaO4ZUQtgBSOTjHWoNS13VHvNG0rSVtFv9Qt3upJrlGeOGNAmTsVgWJaRQPmHegDp6K4ePxjqd1ZWNjb21pHrtxqM2nSb9zQRtCGZ5AAQzKVUELkHLAZ4rb8N6xeag+p2GpxwLqGmXIgma3BEcgZFkR1BJIyrjgk4IPNAEXj7/knfiX/sF3P/AKKaugT/AFa/QVz3j5gPh54lBIz/AGXc8Z/6ZNXQp/q1+goAdRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWb4h/5FrVf+vOb/ANANaVZviH/kWtV/685v/QDWlL+JH1RdP416nglFFFfbH2IUUUUAFFFFABRRRQAUUUUAe1eA/wDkS9P/AO2n/oxq6Ouc8B/8iXp//bT/ANGNXR18Ziv48/V/mfI4n+NP1f5le/8A+Qdc/wDXJ/5GuHruL/8A5B1z/wBcn/ka4evNxO6OWYUUUVzEBRRRQAUUUUAFFFFABXcWH/IOtv8Arkn8hXD13Fh/yDrb/rkn8hXRht2XAsVieL/+RXvP+Af+hrW3WJ4v/wCRXvP+Af8Aoa13Q+JGGP8A90q/4Zfkzy2iiivQPzIKKKKACiiigAooooAKKKKAPX9G/wCQFp//AF7R/wDoIq9VHRv+QFp//XtH/wCgir1edLc/UsP/AAo+i/Iiuf8Aj3b8P51n1oXP/Hu34fzrPrjr/EOe4UUUVgQFFFFABRRRQAUUUUAFatZVatdOH6mlMK4P4tf8ira/9fqf+gPXeVwfxa/5FW1/6/U/9Aetqnws6KP8RHjVFFFcR6gUUUUAFFFFABRRRQAUUUUAfUNFFFegeOeMfHz/AJl7/t5/9pV4xXs/x8/5l7/t5/8AaVeMV6ND+Gj6HA/7vH5/mwooorY6wooooAKKKKACiiigDa8H/wDI7aD/ANhG3/8ARi19aV8l+D/+R20H/sI2/wD6MWvrSuLFbo8bM/jiFedfFP8A5hP/AG2/9kr0WvOvin/zCf8Att/7JXnYn+E/66nh4v8Agv8ArqedUUUV5J4oUUUUAFFFFABRRRQAV0fgT/kc9P8A+2n/AKLaucro/An/ACOen/8AbT/0W1XS/iR9Ua0f4kfVHs9FFFe0e8FFFFABRRRQBzngj/kA3P8A2FdR/wDSyaujrnPBH/IBuf8AsK6j/wClk1dHQAUUUUAedaro8x8Y6vd3fgEeIbefyjb3c0lq3lgRqCirK4IXcCegyS3bBrs9BiSHRbeNNGXRlG7FgojxF8x/55krz97g9+ec1xev2Fm3inUZvEnh/VtXtpBH/Zr2sMk8cSBAGXah+R9+47iBkEc8V1XhC3v7XwtZQ6kJluV34SeTfIkZdjGrtk5YIVBOTyDQAzxlYatq3hi/0vSI7Jpb63ltpGu53jWNXQruG1GyRnpx9azG0XxJ5mkaskWlR6tp8Utq9v8AaZGhmgcJ/wAtPLDKwaNT90jqK7KigDiI/B2p2tlY31vc2kmu2+pTajJv3LBI0wZXjBALKoVgA2CcqDjmtrw3o95p76nf6nJA2oancieZbckxxhUWNEUkAnCoOSBkk8Vu0UAcX8RvDuiXng7xDqdzo9hNqEemTsl1Jbo0qlY2KkMRkY7eldkn+rX6Cuf8ff8AJO/Ev/YLuf8A0U1dAn+rX6CgB1FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRWTrmsyaWbGC2tRdXt/cfZ7eJ5fLTcEZyWfBwAqMeAT0GKANaiuTXxm8llHHFpwbV31FtM+x/aPkEyqZGJk2/cEY3Z256DGeK19B1htYtrnzrb7Nd2lw1tcwh94R1APytgZBVlIOBwegoA1aK51vHPh1bh4Gv3DJPJbFjbS7POQkNEH27S+VOFByeMA5FTp4k0+8jsZbS+VEnvTaFJrWQOZArMYyp2mNvlzlhjA6cg0AbdFYVj4w0LUb2K0tbx3kmkeKMm3kVHkTO5A5UKWG1jtznAzjFbtABRRRQAVm+If+Ra1X/rzm/wDQDWlWb4h/5FrVf+vOb/0A1pS/iR9UXT+Nep4JRRRX2x9iFFFFABRRRQAUUUUAFFFFAHtXgP8A5EvT/wDtp/6MaujrnPAf/Il6f/20/wDRjV0dfGYr+PP1f5nyOJ/jT9X+ZXv/APkHXP8A1yf+Rrh67i//AOQdc/8AXJ/5GuHrzcTujlmFFFFcxAUUUUAFFFFABRRRQAV3Fh/yDrb/AK5J/IVw9dxYf8g62/65J/IV0YbdlwLFYni//kV7z/gH/oa1t1ieL/8AkV7z/gH/AKGtd0PiRhj/APdKv+GX5M8tooor0D8yCiiigAooooAKKKKACiiigD1/Rv8AkBaf/wBe0f8A6CKvVR0b/kBaf/17R/8AoIq9XnS3P1LD/wAKPovyIrn/AI92/D+dZ9aFz/x7t+H86z646/xDnuFFFFYEBRRRQAUUUUAFFFFABWrWVWrXTh+ppTCuD+LX/Iq2v/X6n/oD13lcH8Wv+RVtf+v1P/QHrap8LOij/ER41RRRXEeoFFFFABRRRQAUUUUAFFFFAH1DRRRXoHjnjHx8/wCZe/7ef/aVeMV7P8fP+Ze/7ef/AGlXjFejQ/ho+hwP+7x+f5sKKKK2OsKKKKACiiigAooooA2vB/8AyO2g/wDYRt//AEYtfWlfJfg//kdtB/7CNv8A+jFr60rixW6PGzP44hXnXxT/AOYT/wBtv/ZK9Frzr4p/8wn/ALbf+yV52J/hP+up4eL/AIL/AK6nnVFFFeSeKFFFFABRRRQAUUUUAFdH4E/5HPT/APtp/wCi2rnK6PwJ/wAjnp//AG0/9FtV0v4kfVGtH+JH1R7PRRRXtHvBRRRQAUUUUAc54I/5ANz/ANhXUf8A0smro65zwR/yAbn/ALCuo/8ApZNXR0AFRXUAurSa3aSWNZUZC8TlHXIxlWHIPoR0qWigDyqXxP4L1rWtUluPFmo2QimSKL7Pq88ccq+VGdyouAvJKn1Kk967jwfcJc+GLaWL7WYTJMInvJ3mlkjErhXLP8xDKAwB6Agdq5bxDrWtQ+I54NO1HT9HsxqMNnPPJaCRnd7bzBK5LAYyI4wPY88AV1HgvUbrVvClpd3tzHdXLNKkk8aBUkKSumVA/h+Xg9xg96AN6iiigAooooA53x9/yTvxL/2C7n/0U1dAn+rX6Cuf8ff8k78S/wDYLuf/AEU1dAn+rX6CgB1FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGdrOsw6LbQySQz3EtxMtvBBAoLyyNkgDcQBwCckgYHWqkHiU3mnSXNno+o3E8Nyba4s1MKywuBk7i8gQjBXlWOdwx3xX8Z6beanp9lHawS3EMV4k1zBbzCGaSNQ2BG5I2sH2HIZTgEZGao6NY63o3hTWhZ6e63ck8sum2lxOkkq7lXHmybiGO/cxJZjtwMnFAG74f12PxDYS3cVndWoiuJLcpc7MlkO1iCjMpG4EZB6g08+IdEF21qdY0/wC0qHLQ/aU3jZnfkZz8uDn0wc0uhaTFoWg2OlxMWW2hVC56u38TH3JyT9a5A+Ert7FY5NOiaQ+KH1FwWQ5hMzEOeefkI4644xQB166zZ3NvaXNhe2Fzb3M4iWUXQ2twchCAQ7cfd4788UsGu6RdXosrfVbGa7IYiCO4RnO0kN8oOeCCD6EGuWfw7qf9szTJaAW7eJY79SHUDyRaqjPjP98Nx17471HY+GL+20jRkFiiXUGvz305DJkRPLP8+c85R0GBzg4xxQB3tFFFABRRRQAUUVlf8JJpP/P3/wCQ3/wqXKMd2NJvY1ax/EmmPqmmpFFZW93LHKsiLNdSW2wjPzLLGrMrDPUDoSKf/wAJJpP/AD9/+Q3/AMKP+Ek0n/n7/wDIb/4UvaQ7ofJLsc1ZeB7vStPtbixe1/tWHVH1No3kfyXZ4zE0Zchn+4R85BJYZI5xXQeHNKutNhvpr54Te6hdtdzLCSUQlVRVUkAkBUUZIGTngVL/AMJJpP8Az9/+Q3/wo/4STSf+fv8A8hv/AIUe0h3QckuxiR+E71bK1hMlsWi8QTaox3HBiaaRwBx97Dj2yDzSnwrff2qbrzbfyz4gGqY3Nnyvsohx0+9u5x0x3zxW1/wkmk/8/f8A5Df/AAo/4STSf+fv/wAhv/hR7SHdByS7GJaeFL630nQrRpbbzNP1aW+lKs2CjNMcLx9796vXA68+vYVlf8JJpP8Az9/+Q3/wo/4STSf+fv8A8hv/AIUe0h3Qckuxq0Vlf8JJpP8Az9/+Q3/wq7Z3tvfxGW2k8xA20naRz+P1pqcXomJxa3RYrN8Q/wDItar/ANec3/oBrSrN8Q/8i1qv/XnN/wCgGtqX8SPqiqfxr1PBKKKK+2PsQooooAKKKKACiiigAooooA9q8B/8iXp//bT/ANGNXR1zngP/AJEvT/8Atp/6Maujr4zFfx5+r/M+RxP8afq/zK9//wAg65/65P8AyNcPXcX/APyDrn/rk/8AI1w9ebid0cswooormICiiigAooooAKKKKACu4sP+Qdbf9ck/kK4eu4sP+Qdbf9ck/kK6MNuy4FisTxf/AMivef8AAP8A0Na26xPF/wDyK95/wD/0Na7ofEjDH/7pV/wy/JnltFFFegfmQUUUUAFFFFABRRRQAUUUUAev6N/yAtP/AOvaP/0EVeqjo3/IC0//AK9o/wD0EVerzpbn6lh/4UfRfkRXP/Hu34fzrPrQuf8Aj3b8P51n1x1/iHPcKKKKwICiiigAooooAKKKKACtWsqtWunD9TSmFcH8Wv8AkVbX/r9T/wBAeu8rg/i1/wAira/9fqf+gPW1T4WdFH+IjxqiiiuI9QKKKKACiiigAooooAKKKKAPqGiiivQPHPGPj5/zL3/bz/7SrxivZ/j5/wAy9/28/wDtKvGK9Gh/DR9Dgf8Ad4/P82FFFFbHWFFFFABRRRQAUUUUAbXg/wD5HbQf+wjb/wDoxa+tK+S/B/8AyO2g/wDYRt//AEYtfWlcWK3R42Z/HEK86+Kf/MJ/7bf+yV6LXnXxT/5hP/bb/wBkrzsT/Cf9dTw8X/Bf9dTzqiiivJPFCiiigAooooAKKKKACuj8Cf8AI56f/wBtP/RbVzldH4E/5HPT/wDtp/6LarpfxI+qNaP8SPqj2eiiivaPeCiiigAooooA5zwR/wAgG5/7Cuo/+lk1dHXOeCP+QDc/9hXUf/SyaujoAKKKKAOK1C/1KDXNYsrzwnqGtaTO0TQvGluyY8tdybXcEgMCcnnJIxgAnp9Im8/Srdxps2mjBVbSZUVogCQBhCVAwMjB6EVx3iWPwy+u3Qv7fxS94Qu9tPGoGL7gxt8r930x075zzmt/wRb3Nr4QsYbuK6jlUyELduzzbDIxQyFiTuKlSRngnAwBigDoKKKKACiiigDnfH3/ACTvxL/2C7n/ANFNXQJ/q1+grn/H3/JO/Ev/AGC7n/0U1dAn+rX6CgB1FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAVX1PT43ZHvrZXU4ZWmUEH0PNN/tbTf+ghaf8Af5f8a831b/kM33/XxJ/6Eap18xUz+pGbjyLQ+fnnNSMmuVaHqn9rab/0ELT/AL/L/jR/a2m/9BC0/wC/y/415XRUf6w1f5ER/bdT+RHqn9rab/0ELT/v8v8AjR/a2m/9BC0/7/L/AI15XRR/rDV/kQf23U/kR6p/a2m/9BC0/wC/y/40f2tpv/QQtP8Av8v+NeV0Uf6w1f5EH9t1P5Eeqf2tpv8A0ELT/v8AL/jR/a2m/wDQQtP+/wAv+NeV0Uf6w1f5EH9t1P5EeuQXEFyhe3mjlQHBaNgwz6cVJXN+Cf8AkDTf9fDf+grXSV9Fhazr0Y1GrXPdw9V1aUaj6hXldeqV5XWWM+yd1DqFFFFcRuFFFFABRRRQAUUUUAFdr4Q/5BMv/Xc/+grXFV2vhD/kEy/9dz/6CtdGF/iGVb4TfrN8Q/8AItar/wBec3/oBrSrN8Q/8i1qv/XnN/6Aa9al/Ej6owp/GvU8Eooor7Y+xCiiigAooooAKKKKACiiigD2rwH/AMiXp/8A20/9GNXR1zngP/kS9P8A+2n/AKMaujr4zFfx5+r/ADPkcT/Gn6v8yvf/APIOuf8Ark/8jXD13F//AMg65/65P/I1w9ebid0cswooormICiiigAooooAKKKKACu4sP+Qdbf8AXJP5CuHruLD/AJB1t/1yT+Qrow27LgWKxPF//Ir3n/AP/Q1rbrE8X/8AIr3n/AP/AENa7ofEjDH/AO6Vf8MvyZ5bRRRXoH5kFFFFABRRRQAUUUUAFFFFAHr+jf8AIC0//r2j/wDQRV6qOjf8gLT/APr2j/8AQRV6vOlufqWH/hR9F+RFc/8AHu34fzrPrQuf+Pdvw/nWfXHX+Ic9wooorAgKKKKACiiigAooooAK1ayq1a6cP1NKYVwfxa/5FW1/6/U/9Aeu8rg/i1/yKtr/ANfqf+gPW1T4WdFH+IjxqiiiuI9QKKKKACiiigAooooAKKKKAPqGiiivQPHPGPj5/wAy9/28/wDtKvGK9n+Pn/Mvf9vP/tKvGK9Gh/DR9Dgf93j8/wA2FFFFbHWFFFFABRRRQAUUUUAbXg//AJHbQf8AsI2//oxa+tK+S/B//I7aD/2Ebf8A9GLX1pXFit0eNmfxxCvOvin/AMwn/tt/7JXotedfFP8A5hP/AG2/9krzsT/Cf9dTw8X/AAX/AF1POqKKK8k8UKKKKACiiigAooooAK6PwJ/yOen/APbT/wBFtXOV0fgT/kc9P/7af+i2q6X8SPqjWj/Ej6o9nooor2j3gooooAKKKKAOc8Ef8gG5/wCwrqP/AKWTV0dc54I/5ANz/wBhXUf/AEsmro6ACiiigDgNH0LUPFWjW3iC78Ua3a3F/GLmGGynWOG3RuUUJtIbCkZLZyc10vhTULrU/DkE98yPdxyTW07ou0O8UrxMwHbJQnHvWcvw18IIgRNGRVUYAE0gAH/fVb+l6XZaLp0dhp1usFrEWKRqSQCzFjyeeSSfxoAuUUUUAFFFFAHO+Pv+Sd+Jf+wXc/8Aopq6BP8AVr9BXP8Aj7/knfiX/sF3P/opq6BP9Wv0FADqKKKACiiigAooooAKr3qXclo62E8EFycbJJ4TKg5GcqGUnjP8Q/HpVimSiRoXWJwkhUhWZdwU9iRkZ+lAHL6FqPiLVo9bja90vNrd/ZbS6SwkCOyAeaWj84kgMSgww5UnnpU/hXVNW1OfWBqE1lPb2l39lt5rW2eHzGQDzCQ0j8BiV69VNX9F0b+w/DltpVtcbpIYipuHTO+U5LSFc92JYjPfrT9A0ePQdBs9MjlM3kJh5mGDK5OXcj1ZiT+NAGTqXjqw0u91GCaw1F4dNkiS8uo4lMcIkVWVvvbmHzDO1SRg8YwSlz4vtvsWoefb6np09k1uZIzHEZdkr7UZeWXaxDA55GDwDin6h4R+3weIovt2z+2ZInz5WfJ2IiY+982dme3WjWPCP9rXmp3H27yvt0FnDt8rds8iZ5M/eGd2/HbGM89KACfxvZQX15A2n6iYLK8SzubsRp5UTuEKk/NuI/eKOAcd8DBPTVzN14R+06drlp9u2/2rfpebvKz5W0RDbjd83+q68fe6cc9NQAUUUUAFFFFAHlerf8hm+/6+JP8A0I1Tq5q3/IZvv+viT/0I1Tr85rfxZerPhav8SXqwooorIgKKKKACiiigAooooA7zwT/yBpv+vhv/AEFa6Sub8E/8gab/AK+G/wDQVrpK+9y3/dKfofZYD/doegV5XXqleV08Z9k9Oh1CiiiuI3CiiigAooooAKKKKACu18If8gmX/ruf/QVriq7Xwh/yCZf+u5/9BWujC/xDKt8Jv1m+If8AkWtV/wCvOb/0A1pVm+If+Ra1X/rzm/8AQDXrUv4kfVGFP416nglFFFfbH2IUUUUAFFFFABRRRQAUUUUAe1eA/wDkS9P/AO2n/oxq6Ouc8B/8iXp//bT/ANGNXR18Ziv48/V/mfI4n+NP1f5le/8A+Qdc/wDXJ/5GuHruL/8A5B1z/wBcn/ka4evNxO6OWYUUUVzEBRRRQAUUUUAFFFFABXcWH/IOtv8Arkn8hXD13Fh/yDrb/rkn8hXRht2XAsVieL/+RXvP+Af+hrW3WJ4v/wCRXvP+Af8Aoa13Q+JGGP8A90q/4Zfkzy2iiivQPzIKKKKACiiigAooooAKKKKAPX9G/wCQFp//AF7R/wDoIq9VHRv+QFp//XtH/wCgir1edLc/UsP/AAo+i/Iiuf8Aj3b8P51n1oXP/Hu34fzrPrjr/EOe4UUUVgQFFFFABRRRQAUUUUAFatZVatdOH6mlMK4P4tf8ira/9fqf+gPXeVwfxa/5FW1/6/U/9Aetqnws6KP8RHjVFFFcR6gUUUUAFFFFABRRRQAUUUUAfUNFFFegeOeMfHz/AJl7/t5/9pV4xXs/x8/5l7/t5/8AaVeMV6ND+Gj6HA/7vH5/mwooorY6wooooAKKKKACiiigDa8H/wDI7aD/ANhG3/8ARi19aV8l+D/+R20H/sI2/wD6MWvrSuLFbo8bM/jiFedfFP8A5hP/AG2/9kr0WvOvin/zCf8Att/7JXnYn+E/66nh4v8Agv8ArqedUUUV5J4oUUUUAFFFFABRRRQAV0fgT/kc9P8A+2n/AKLaucro/An/ACOen/8AbT/0W1XS/iR9Ua0f4kfVHs9FFFe0e8FFFFABRRRQBzngj/kA3P8A2FdR/wDSyaujrnPBH/IBuf8AsK6j/wClk1dHQAUUUUAFFFFABVLU9UttItRcXS3LRlwgFtay3DZIJ+7GrHHHXGKu1U1TUbfSNJu9Su2229rC80h/2VGT/KgDFHj7w6baWf7ReARXKWjodOuBIJmXcqeWY9xJXngenrW1pupW+q2n2m2W4WPcVxcW0kDZH+zIqtj3xXDQaXpFp4K0+bxfenT7m8vTqc0wuGgK3Thm27wQV2qdo5H3RWz4FvLi7tNUAu7m90yK9Kadd3OS8sOxCfmPLqHLgMeoHU0AWfH3/JO/Ev8A2C7n/wBFNXQJ/q1+grjPiPBrreD/ABDJa6jp8enjTJ/MgksneVh5bbsSCUAZ7fKce9dmn+rX6CgB1FFFABRRRQAVlS6/awzPE0cxZGKnAGMj8a1a4i//AOQhc/8AXVv5mqirm1GCm3c3/wDhI7P/AJ5z/wDfI/xo/wCEjs/+ec//AHyP8a5iiq5Ub+wgdP8A8JHZ/wDPOf8A75H+NH/CR2f/ADzn/wC+R/jXMUUcqD2EDp/+Ejs/+ec//fI/xo/4SOz/AOec/wD3yP8AGuYoo5UHsIHT/wDCR2f/ADzn/wC+R/jR/wAJHZ/885/++R/jXMUUcqD2EDp/+Ejs/wDnnP8A98j/ABq5Y6lDqHmeUsi7MZ3gDrn39q4yug8M/wDL1/wD+tJxSRFSlGMW0b9FFFQcp5Xq3/IZvv8Ar4k/9CNU6uat/wAhm+/6+JP/AEI1Tr85rfxZerPhav8AEl6sKKKKyICiiigAooooAKKKKAO88E/8gab/AK+G/wDQVrpK5vwT/wAgab/r4b/0Fa6Svvct/wB0p+h9lgP92h6BXldeqV5XTxn2T06HUKKKK4jcKKKKACiiigAooooAK7Xwh/yCZf8Aruf/AEFa4qu18If8gmX/AK7n/wBBWujC/wAQyrfCb9ZviH/kWtV/685v/QDWlWb4h/5FrVf+vOb/ANANetS/iR9UYU/jXqeCUUUV9sfYhRRRQAUUUUAFFFFABRRRQB7V4D/5EvT/APtp/wCjGro65zwH/wAiXp//AG0/9GNXR18Ziv48/V/mfI4n+NP1f5le/wD+Qdc/9cn/AJGuHruL/wD5B1z/ANcn/ka4evNxO6OWYUUUVzEBRRRQAUUUUAFFFFABXcWH/IOtv+uSfyFcPXcWH/IOtv8Arkn8hXRht2XAsVieL/8AkV7z/gH/AKGtbdYni/8A5Fe8/wCAf+hrXdD4kYY//dKv+GX5M8tooor0D8yCiiigAooooAKKKKACiiigD1/Rv+QFp/8A17R/+gir1UdG/wCQFp//AF7R/wDoIq9XnS3P1LD/AMKPovyIrn/j3b8P51n1oXP/AB7t+H86z646/wAQ57hRRRWBAUUUUAFFFFABRRRQAVq1lVq104fqaUwrg/i1/wAira/9fqf+gPXeVwfxa/5FW1/6/U/9Aetqnws6KP8AER41RRRXEeoFFFFABRRRQAUUUUAFFFFAH1DRRRXoHjnjHx8/5l7/ALef/aVeMV7P8fP+Ze/7ef8A2lXjFejQ/ho+hwP+7x+f5sKKKK2OsKKKKACiiigAooooA2vB/wDyO2g/9hG3/wDRi19aV8l+D/8AkdtB/wCwjb/+jFr60rixW6PGzP44hXnXxT/5hP8A22/9kr0WvOvin/zCf+23/sledif4T/rqeHi/4L/rqedUUUV5J4oUUUUAFFFFABRRRQAV0fgT/kc9P/7af+i2rnK6PwJ/yOen/wDbT/0W1XS/iR9Ua0f4kfVHs9FFFe0e8FFFFABRRRQBzngj/kA3P/YV1H/0smro65zwR/yAbn/sK6j/AOlk1dHQAUUUUAFFFFABRRRQAUUUUAc74+/5J34l/wCwXc/+imroE/1a/QVz/j7/AJJ34l/7Bdz/AOimroE/1a/QUAOooooAKKKKACuIv/8AkIXP/XVv5mu3riL/AP5CFz/11b+Zq4HTht2V6KKKs6wooooAKKKKACiiigAroPDP/L1/wD+tc/XQeGf+Xr/gH9aUtjKt8DN+iiisjgPK9W/5DN9/18Sf+hGqdXNW/wCQzff9fEn/AKEap1+c1v4svVnwtX+JL1YUUUVkQFFFFABRRRQAUUUUAd54J/5A03/Xw3/oK10lc34J/wCQNN/18N/6CtdJX3uW/wC6U/Q+ywH+7Q9AryuvVK8rp4z7J6dDqFFFFcRuFFFFABRRRQAUUUUAFdr4Q/5BMv8A13P/AKCtcVXa+EP+QTL/ANdz/wCgrXRhf4hlW+E36zfEP/Itar/15zf+gGtKs3xD/wAi1qv/AF5zf+gGvWpfxI+qMKfxr1PBKKKK+2PsQooooAKKKKACiiigAooooA9q8B/8iXp//bT/ANGNXR1zngP/AJEvT/8Atp/6Maujr4zFfx5+r/M+RxP8afq/zK9//wAg65/65P8AyNcPXcX/APyDrn/rk/8AI1w9ebid0cswooormICiiigAooooAKKKKACu4sP+Qdbf9ck/kK4eu4sP+Qdbf9ck/kK6MNuy4FisTxf/AMivef8AAP8A0Na26xPF/wDyK95/wD/0Na7ofEjDH/7pV/wy/JnltFFFegfmQUUUUAFFFFABRRRQAUUUUAev6N/yAtP/AOvaP/0EVeqjo3/IC0//AK9o/wD0EVerzpbn6lh/4UfRfkRXP/Hu34fzrPrQuf8Aj3b8P51n1x1/iHPcKKKKwICiiigAooooAKKKKACtWsqtWunD9TSmFcH8Wv8AkVbX/r9T/wBAeu8rg/i1/wAira/9fqf+gPW1T4WdFH+IjxqiiiuI9QKKKKACiiigAooooAKKKKAPqGiiivQPHPGPj5/zL3/bz/7SrxivZ/j5/wAy9/28/wDtKvGK9Gh/DR9Dgf8Ad4/P82FFFFbHWFFFFABRRRQAUUUUAbXg/wD5HbQf+wjb/wDoxa+tK+S/B/8AyO2g/wDYRt//AEYtfWlcWK3R42Z/HEK86+Kf/MJ/7bf+yV6LXnXxT/5hP/bb/wBkrzsT/Cf9dTw8X/Bf9dTzqiiivJPFCiiigAooooAKKKKACuj8Cf8AI56f/wBtP/RbVzldH4E/5HPT/wDtp/6LarpfxI+qNaP8SPqj2eiiivaPeCiiigAooooA5zwR/wAgG5/7Cuo/+lk1dHXOeCP+QDc/9hXUf/SyaujoAKKKKACiiigAooooAKKKKAOd8ff8k78S/wDYLuf/AEU1dAn+rX6Cuf8AH3/JO/Ev/YLuf/RTV0Cf6tfoKAHUUUUAFFFFABXEX/8AyELn/rq38zXb1xF//wAhC5/66t/M1cDpw27K9FFFWdYUUUUAFFFFABRRRQAV0Hhn/l6/4B/WufroPDP/AC9f8A/rSlsZVvgZv0UUVkcB5Xq3/IZvv+viT/0I1Tq5q3/IZvv+viT/ANCNU6/Oa38WXqz4Wr/El6sKKKKyICiiigAooooAKKKKAO88E/8AIGm/6+G/9BWukrm/BP8AyBpv+vhv/QVrpK+9y3/dKfofZYD/AHaHoFeV16pXldPGfZPTodQoooriNwooooAKKKKACiiigArtfCH/ACCZf+u5/wDQVriq7Xwh/wAgmX/ruf8A0Fa6ML/EMq3wm/Wb4h/5FrVf+vOb/wBANaVZviH/AJFrVf8Arzm/9ANetS/iR9UYU/jXqeCUUUV9sfYhRRRQAUUUUAFFFFABRRRQB7V4D/5EvT/+2n/oxq6Ouc8B/wDIl6f/ANtP/RjV0dfGYr+PP1f5nyOJ/jT9X+ZXv/8AkHXP/XJ/5GuHruL/AP5B1z/1yf8Aka4evNxO6OWYUUUVzEBRRRQAUUUUAFFFFABXcWH/ACDrb/rkn8hXD13Fh/yDrb/rkn8hXRht2XAsVieL/wDkV7z/AIB/6GtbdYni/wD5Fe8/4B/6Gtd0PiRhj/8AdKv+GX5M8tooor0D8yCiiigAooooAKKKKACiiigD1/Rv+QFp/wD17R/+gir1UdG/5AWn/wDXtH/6CKvV50tz9Sw/8KPovyIrn/j3b8P51n1oXP8Ax7t+H86z646/xDnuFFFFYEBRRRQAUUUUAFFFFABWrWVWrXTh+ppTCuD+LX/Iq2v/AF+p/wCgPXeVwfxa/wCRVtf+v1P/AEB62qfCzoo/xEeNUUUVxHqBRRRQAUUUUAFFFFABRRRQB9Q0UUV6B454x8fP+Ze/7ef/AGlXjFez/Hz/AJl7/t5/9pV4xXo0P4aPocD/ALvH5/mwooorY6wooooAKKKKACiiigDa8H/8jtoP/YRt/wD0YtfWlfJfg/8A5HbQf+wjb/8Aoxa+tK4sVujxsz+OIV518U/+YT/22/8AZK9Frzr4p/8AMJ/7bf8Asledif4T/rqeHi/4L/rqedUUUV5J4oUUUUAFFFFABRRRQAV0fgT/AJHPT/8Atp/6Laucro/An/I56f8A9tP/AEW1XS/iR9Ua0f4kfVHs9FFFe0e8FFFFABRRRQBzngj/AJANz/2FdR/9LJq6Ouc8Ef8AIBuf+wrqP/pZNXR0AFFFFABRRRQAUUUUAFFFFAHO+Pv+Sd+Jf+wXc/8Aopq6BP8AVr9BXP8Aj7/knfiX/sF3P/opq6BP9Wv0FADqKKKACiiigAriL/8A5CFz/wBdW/ma7euIv/8AkIXP/XVv5mrgdOG3ZXoooqzrCiiigAooooAKKKKACug8M/8AL1/wD+tc/XQeGf8Al6/4B/WlLYyrfAzfooorI4DyvVv+Qzff9fEn/oRqnVzVv+Qzff8AXxJ/6Eap1+c1v4svVnwtX+JL1YUUUVkQFFFFABRRRQAUUUUAd54J/wCQNN/18N/6CtdJXN+Cf+QNN/18N/6CtdJX3uW/7pT9D7LAf7tD0CvK69UryunjPsnp0OoUUUVxG4UUUUAFFFFABRRRQAV2vhD/AJBMv/Xc/wDoK1xVdr4Q/wCQTL/13P8A6CtdGF/iGVb4TfrN8Q/8i1qv/XnN/wCgGtKs3xD/AMi1qv8A15zf+gGvWpfxI+qMKfxr1PBKKKK+2PsQooooAKKKKACiiigAooooA9q8B/8AIl6f/wBtP/RjV0dc54D/AORL0/8A7af+jGro6+MxX8efq/zPkcT/ABp+r/Mr3/8AyDrn/rk/8jXD13F//wAg65/65P8AyNcPXm4ndHLMKKKK5iAooooAKKKKACiiigAruLD/AJB1t/1yT+Qrh67iw/5B1t/1yT+Qrow27LgWKxPF/wDyK95/wD/0Na26xPF//Ir3n/AP/Q1ruh8SMMf/ALpV/wAMvyZ5bRRRXoH5kFFFFABRRRQAUUUUAFFFFAHr+jf8gLT/APr2j/8AQRV6qOjf8gLT/wDr2j/9BFXq86W5+pYf+FH0X5EVz/x7t+H86z60Ln/j3b8P51n1x1/iHPcKKKKwICiiigAooooAKKKKACtWsqtWunD9TSmFcH8Wv+RVtf8Ar9T/ANAeu8rg/i1/yKtr/wBfqf8AoD1tU+FnRR/iI8aoooriPUCiiigAooooAKKKKACiiigD6hooor0Dxzxj4+f8y9/28/8AtKvGK9n+Pn/Mvf8Abz/7SrxivRofw0fQ4H/d4/P82FFFFbHWFFFFABRRRQAUUUUAbXg//kdtB/7CNv8A+jFr60r5L8H/API7aD/2Ebf/ANGLX1pXFit0eNmfxxCvOvin/wAwn/tt/wCyV6LXnXxT/wCYT/22/wDZK87E/wAJ/wBdTw8X/Bf9dTzqiiivJPFCiiigAooooAKKKKACuj8Cf8jnp/8A20/9FtXOV0fgT/kc9P8A+2n/AKLarpfxI+qNaP8AEj6o9nooor2j3gooooAKKKKAOc8Ef8gG5/7Cuo/+lk1dHXOeCP8AkA3P/YV1H/0smro6ACiiigAooooAKKKKACiiigDnfH3/ACTvxL/2C7n/ANFNXQJ/q1+grn/H3/JO/Ev/AGC7n/0U1dAn+rX6CgB1FFFABRRRQAVxF/8A8hC5/wCurfzNdvXEX/8AyELn/rq38zVwOnDbsr0UUVZ1hRRRQAUUUUAFFFFABXQeGf8Al6/4B/WufroPDP8Ay9f8A/rSlsZVvgZv0UUVkcB5Xq3/ACGb7/r4k/8AQjVOrmrf8hm+/wCviT/0I1Tr85rfxZerPhav8SXqwooorIgKKKKACiiigAooooA7zwT/AMgab/r4b/0Fa6Sub8E/8gab/r4b/wBBWukr73Lf90p+h9lgP92h6BXldeqV5XTxn2T06HUKKKK4jcKKKKACiiigAooooAK7Xwh/yCZf+u5/9BWuKrtfCH/IJl/67n/0Fa6ML/EMq3wm/Wb4h/5FrVf+vOb/ANANaVZviH/kWtV/685v/QDXrUv4kfVGFP416nglFFFfbH2IUUUUAFFFFABRRRQAUUUUAe1eA/8AkS9P/wC2n/oxq6Ouc8B/8iXp/wD20/8ARjV0dfGYr+PP1f5nyOJ/jT9X+ZXv/wDkHXP/AFyf+Rrh67i//wCQdc/9cn/ka4evNxO6OWYUUUVzEBRRRQAUUUUAFFFFABXcWH/IOtv+uSfyFcPXcWH/ACDrb/rkn8hXRht2XAsVieL/APkV7z/gH/oa1t1ieL/+RXvP+Af+hrXdD4kYY/8A3Sr/AIZfkzy2iiivQPzIKKKKACiiigAooooAKKKKAPX9G/5AWn/9e0f/AKCKvVR0b/kBaf8A9e0f/oIq9XnS3P1LD/wo+i/Iiuf+Pdvw/nWfWhc/8e7fh/Os+uOv8Q57hRRRWBAUUUUAFFFFABRRRQAVq1lVq104fqaUwrg/i1/yKtr/ANfqf+gPXeVwfxa/5FW1/wCv1P8A0B62qfCzoo/xEeNUUUVxHqBRRRQAUUUUAFFFFABRRRQB9Q0UUV6B454x8fP+Ze/7ef8A2lXjFez/AB8/5l7/ALef/aVeMV6ND+Gj6HA/7vH5/mwooorY6wooooAKKKKACiiigDa8H/8AI7aD/wBhG3/9GLX1pXyX4P8A+R20H/sI2/8A6MWvrSuLFbo8bM/jiFedfFP/AJhP/bb/ANkr0WvOvin/AMwn/tt/7JXnYn+E/wCup4eL/gv+up51RRRXknihRRRQAUUUUAFFFFABXR+BP+Rz0/8A7af+i2rnK6PwJ/yOen/9tP8A0W1XS/iR9Ua0f4kfVHs9FFFe0e8FFFFABRRRQBzngj/kA3P/AGFdR/8ASyaujrnPBH/IBuf+wrqP/pZNXR0AFFFFABRRRQAVyvjLStZ1OfSjpfmvBDJI11DHqctl5oKYUb4/m4bnHtXVVm6tpM2pmEw6vqGnNFuBNm0fzg4+8HRhxjg4yMmgDiNPvv8AhI9T0jQIrjVtOtYLe8kvYjfSG4M0UqR+WZ9xcqDIzZDcjb24rpfBN5dXOj3VveXD3Mlhf3NkLiT78qRyEKWPdtuAT3Iph8DadHaWcdnd39ndWjyyJfQygzs0pzKXLqwbccE5HUDGMCtjR9ItdD0yOwsw5jQszPI255HZizOx7sWJJPvQBzPxH1+ztfB/iHTpIdQaeTTJwGj0+eSIbo2xmRUKD3yeO+K7NP8AVr9BXP8Aj7/knfiX/sF3P/opq6BP9Wv0FADqKKKACiiigAriL/8A5CFz/wBdW/ma7euIv/8AkIXP/XVv5mrgdOG3ZXoooqzrCiiigAooooAKKKKACug8M/8AL1/wD+tc/XQeGf8Al6/4B/WlLYyrfAzfooorI4DyvVv+Qzff9fEn/oRqnVzVv+Qzff8AXxJ/6Eap1+c1v4svVnwtX+JL1YUUUVkQFFFFABRRRQAUUUUAd54J/wCQNN/18N/6CtdJXN+Cf+QNN/18N/6CtdJX3uW/7pT9D7LAf7tD0CvK69UryunjPsnp0OoUUUVxG4UUUUAFFFFABRRRQAV2vhD/AJBMv/Xc/wDoK1xVdr4Q/wCQTL/13P8A6CtdGF/iGVb4TfrN8Q/8i1qv/XnN/wCgGtKs3xD/AMi1qv8A15zf+gGvWpfxI+qMKfxr1PBKKKK+2PsQooooAKKKKACiiigAooooA9q8B/8AIl6f/wBtP/RjV0dc54D/AORL0/8A7af+jGro6+MxX8efq/zPkcT/ABp+r/Mr3/8AyDrn/rk/8jXD13F//wAg65/65P8AyNcPXm4ndHLMKKKK5iAooooAKKKKACiiigAruLD/AJB1t/1yT+Qrh67iw/5B1t/1yT+Qrow27LgWKxPF/wDyK95/wD/0Na26xPF//Ir3n/AP/Q1ruh8SMMf/ALpV/wAMvyZ5bRRRXoH5kFFFFABRRRQAUUUUAFFFFAHr+jf8gLT/APr2j/8AQRV6qOjf8gLT/wDr2j/9BFXq86W5+pYf+FH0X5EVz/x7t+H86z60Ln/j3b8P51n1x1/iHPcKKKKwICiiigAooooAKKKKACtWsqtWunD9TSmFcH8Wv+RVtf8Ar9T/ANAeu8rg/i1/yKtr/wBfqf8AoD1tU+FnRR/iI8aoooriPUCiiigAooooAKKKKACiiigD6hooor0Dxzxj4+f8y9/28/8AtKvGK9n+Pn/Mvf8Abz/7SrxivRofw0fQ4H/d4/P82FFFFbHWFFFFABRRRQAUUUUAbXg//kdtB/7CNv8A+jFr60r5L8H/API7aD/2Ebf/ANGLX1pXFit0eNmfxxCvOvin/wAwn/tt/wCyV6LXnXxT/wCYT/22/wDZK87E/wAJ/wBdTw8X/Bf9dTzqiiivJPFCiiigAooooAKKKKACuj8Cf8jnp/8A20/9FtXOV0fgT/kc9P8A+2n/AKLarpfxI+qNaP8AEj6o9nooor2j3gooooAKKKKAOc8Ef8gG5/7Cuo/+lk1dHXOeCP8AkA3P/YV1H/0smro6ACiiigAooooAKKKKACiiigDnfH3/ACTvxL/2C7n/ANFNXQJ/q1+grn/H3/JO/Ev/AGC7n/0U1dAn+rX6CgB1FFFABRRRQAVxF/8A8hC5/wCurfzNdvXEX/8AyELn/rq38zVwOnDbsr0UUVZ1hRRRQAUUUUAFFFFABXQeGf8Al6/4B/WufroPDP8Ay9f8A/rSlsZVvgZv0UUVkcB5Xq3/ACGb7/r4k/8AQjVOrmrf8hm+/wCviT/0I1Tr85rfxZerPhav8SXqwooorIgKKKKACiiigAooooA7zwT/AMgab/r4b/0Fa6Sub8E/8gab/r4b/wBBWukr73Lf90p+h9lgP92h6BXldeqV5XTxn2T06HUKKKK4jcKKKKACiiigAooooAK7Xwh/yCZf+u5/9BWuKrtfCH/IJl/67n/0Fa6ML/EMq3wm/Wb4h/5FrVf+vOb/ANANaVZviH/kWtV/685v/QDXrUv4kfVGFP416nglFFFfbH2IUUUUAFFFFABRRRQAUUUUAe1eA/8AkS9P/wC2n/oxq6Ouc8B/8iXp/wD20/8ARjV0dfGYr+PP1f5nyOJ/jT9X+ZXv/wDkHXP/AFyf+Rrh67i//wCQdc/9cn/ka4evNxO6OWYUUUVzEBRRRQAUUUUAFFFFABXcWH/IOtv+uSfyFcPXcWH/ACDrb/rkn8hXRht2XAsVieL/APkV7z/gH/oa1t1ieL/+RXvP+Af+hrXdD4kYY/8A3Sr/AIZfkzy2iiivQPzIKKKKACiiigAooooAKKKKAPX9G/5AWn/9e0f/AKCKvVR0b/kBaf8A9e0f/oIq9XnS3P1LD/wo+i/Iiuf+Pdvw/nWfWhc/8e7fh/Os+uOv8Q57hRRRWBAUUUUAFFFFABRRRQAVq1lVq104fqaUwrg/i1/yKtr/ANfqf+gPXeVwfxa/5FW1/wCv1P8A0B62qfCzoo/xEeNUUUVxHqBRRRQAUUUUAFFFFABRRRQB9Q0UUV6B454x8fP+Ze/7ef8A2lXjFez/AB8/5l7/ALef/aVeMV6ND+Gj6HA/7vH5/mwooorY6wooooAKKKKACiiigDa8H/8AI7aD/wBhG3/9GLX1pXyX4P8A+R20H/sI2/8A6MWvrSuLFbo8bM/jiFedfFP/AJhP/bb/ANkr0WvOvin/AMwn/tt/7JXnYn+E/wCup4eL/gv+up51RRRXknihRRRQAUUUUAFFFFABXR+BP+Rz0/8A7af+i2rnK6PwJ/yOen/9tP8A0W1XS/iR9Ua0f4kfVHs9FFFe0e8FFFFABRRRQBzngj/kA3P/AGFdR/8ASyaujrnPBH/IBuf+wrqP/pZNXR0AFFFFABRRRQAUUUUAFFFFAHO+Pv8AknfiX/sF3P8A6KaugT/Vr9BXP+Pv+Sd+Jf8AsF3P/opq6BP9Wv0FADqKKKACiiigAriL/wD5CFz/ANdW/ma7euIv/wDkIXP/AF1b+Zq4HTht2V6KKKs6wooooAKKKKACiiigAroPDP8Ay9f8A/rXP10Hhn/l6/4B/WlLYyrfAzfooorI4DyvVv8AkM33/XxJ/wChGqdXNW/5DN9/18Sf+hGqdfnNb+LL1Z8LV/iS9WFFFFZEBRRRQAUUUUAFFFFAHeeCf+QNN/18N/6CtdJXN+Cf+QNN/wBfDf8AoK10lfe5b/ulP0PssB/u0PQK8rr1SvK6eM+yenQ6hRRRXEbhRRRQAUUUUAFFFFABXa+EP+QTL/13P/oK1xVdr4Q/5BMv/Xc/+grXRhf4hlW+E36zfEP/ACLWq/8AXnN/6Aa0qzfEP/Itar/15zf+gGvWpfxI+qMKfxr1PBKKKK+2PsQooooAKKKKACiiigAooooA9q8B/wDIl6f/ANtP/RjV0dc54D/5EvT/APtp/wCjGro6+MxX8efq/wAz5HE/xp+r/Mr3/wDyDrn/AK5P/I1w9dxf/wDIOuf+uT/yNcPXm4ndHLMKKKK5iAooooAKKKKACiiigAruLD/kHW3/AFyT+Qrh67iw/wCQdbf9ck/kK6MNuy4FisTxf/yK95/wD/0Na26xPF//ACK95/wD/wBDWu6HxIwx/wDulX/DL8meW0UUV6B+ZBRRRQAUUUUAFFFFABRRRQB6/o3/ACAtP/69o/8A0EVeqjo3/IC0/wD69o//AEEVerzpbn6lh/4UfRfkRXP/AB7t+H86z60Ln/j3b8P51n1x1/iHPcKKKKwICiiigAooooAKKKKACtWsqtWunD9TSmFcH8Wv+RVtf+v1P/QHrvK4P4tf8ira/wDX6n/oD1tU+FnRR/iI8aoooriPUCiiigAooooAKKKKACiiigD6hooor0Dxzxj4+f8AMvf9vP8A7SrxivZ/j5/zL3/bz/7SrxivRofw0fQ4H/d4/P8ANhRRRWx1hRRRQAUUUUAFFFFAG14P/wCR20H/ALCNv/6MWvrSvkvwf/yO2g/9hG3/APRi19aVxYrdHjZn8cQrzr4p/wDMJ/7bf+yV6LXnXxT/AOYT/wBtv/ZK87E/wn/XU8PF/wAF/wBdTzqiiivJPFCiiigAooooAKKKKACuj8Cf8jnp/wD20/8ARbVzldH4E/5HPT/+2n/otqul/Ej6o1o/xI+qPZ6KKK9o94KKKKACiiigDnPBH/IBuf8AsK6j/wClk1dHXOeCP+QDc/8AYV1H/wBLJq6OgAooooAKKKKACiiigAooooA53x9/yTvxL/2C7n/0U1dAn+rX6Cuf8ff8k78S/wDYLuf/AEU1dAn+rX6CgB1FFFABRRRQAVxF/wD8hC5/66t/M129cRf/APIQuf8Arq38zVwOnDbsr0UUVZ1hRRRQAUUUUAFFFFABXQeGf+Xr/gH9a5+ug8M/8vX/AAD+tKWxlW+Bm/RRRWRwHlerf8hm+/6+JP8A0I1Tq5q3/IZvv+viT/0I1Tr85rfxZerPhav8SXqwooorIgKKKKACiiigAooooA7zwT/yBpv+vhv/AEFa6Sub8E/8gab/AK+G/wDQVrpK+9y3/dKfofZYD/doegV5XXqleV08Z9k9Oh1CiiiuI3CiiigAooooAKKKKACu18If8gmX/ruf/QVriq7Xwh/yCZf+u5/9BWujC/xDKt8Jv1m+If8AkWtV/wCvOb/0A1pVm+If+Ra1X/rzm/8AQDXrUv4kfVGFP416nglFFFfbH2IUUUUAFFFFABRRRQAUUUUAe1eA/wDkS9P/AO2n/oxq6Ouc8B/8iXp//bT/ANGNXR18Ziv48/V/mfI4n+NP1f5le/8A+Qdc/wDXJ/5GuHruL/8A5B1z/wBcn/ka4evNxO6OWYUUUVzEBRRRQAUUUUAFFFFABXcWH/IOtv8Arkn8hXD13Fh/yDrb/rkn8hXRht2XAsVieL/+RXvP+Af+hrW3WJ4v/wCRXvP+Af8Aoa13Q+JGGP8A90q/4Zfkzy2iiivQPzIKKKKACiiigAooooAKKKKAPX9G/wCQFp//AF7R/wDoIq9VHRv+QFp//XtH/wCgir1edLc/UsP/AAo+i/Iiuf8Aj3b8P51n1oXP/Hu34fzrPrjr/EOe4UUUVgQFFFFABRRRQAUUUUAFatZVatdOH6mlMK4P4tf8ira/9fqf+gPXeVwfxa/5FW1/6/U/9Aetqnws6KP8RHjVFFFcR6gUUUUAFFFFABRRRQAUUUUAfUNFFFegeOeMfHz/AJl7/t5/9pV4xXs/x8/5l7/t5/8AaVeMV6ND+Gj6HA/7vH5/mwooorY6wooooAKKKKACiiigDa8H/wDI7aD/ANhG3/8ARi19aV8l+D/+R20H/sI2/wD6MWvrSuLFbo8bM/jiFedfFP8A5hP/AG2/9kr0WvOvin/zCf8Att/7JXnYn+E/66nh4v8Agv8ArqedUUUV5J4oUUUUAFFFFABRRRQAV0fgT/kc9P8A+2n/AKLaucro/An/ACOen/8AbT/0W1XS/iR9Ua0f4kfVHs9FFFe0e8FFFFABRRRQBzngj/kA3P8A2FdR/wDSyaujrnPBH/IBuf8AsK6j/wClk1dHQAUUUUAFFFFABRRWbq13q1sYV0rS4b1n3eY0135Cx4xjnaxJOT0Hbk0AaVFcVffEAWvhez1dbCGOWfUG0+WK9vBDHBIrOrlpQrDaDGeccg9q6TRL+41PTEu7hLJTITtNld/aYmXsQ+xc9+1AGd4+/wCSd+Jf+wXc/wDopq6BP9Wv0Fc/4+/5J34l/wCwXc/+imroE/1a/QUAOooooAKKKKACuIv/APkIXP8A11b+Zrt64i//AOQhc/8AXVv5mrgdOG3ZXoooqzrCiiigAooooAKKKKACug8M/wDL1/wD+tc/XQeGf+Xr/gH9aUtjKt8DN+iiisjgPK9W/wCQzff9fEn/AKEap1c1b/kM33/XxJ/6Eap1+c1v4svVnwtX+JL1YUUUVkQFFFFABRRRQAUUUUAd54J/5A03/Xw3/oK10lc34J/5A03/AF8N/wCgrXSV97lv+6U/Q+ywH+7Q9AryuvVK8rp4z7J6dDqFFFFcRuFFFFABRRRQAUUUUAFdr4Q/5BMv/Xc/+grXFV2vhD/kEy/9dz/6CtdGF/iGVb4TfrN8Q/8AItar/wBec3/oBrSrN8Q/8i1qv/XnN/6Aa9al/Ej6owp/GvU8Eooor7Y+xCiiigAooooAKKKKACiiigD2rwH/AMiXp/8A20/9GNXR1zngP/kS9P8A+2n/AKMaujr4zFfx5+r/ADPkcT/Gn6v8yvf/APIOuf8Ark/8jXD13F//AMg65/65P/I1w9ebid0cswooormICiiigAooooAKKKKACu4sP+Qdbf8AXJP5CuHruLD/AJB1t/1yT+Qrow27LgWKxPF//Ir3n/AP/Q1rbrE8X/8AIr3n/AP/AENa7ofEjDH/AO6Vf8MvyZ5bRRRXoH5kFFFFABRRRQAUUUUAFFFFAHr+jf8AIC0//r2j/wDQRV6qOjf8gLT/APr2j/8AQRV6vOlufqWH/hR9F+RFc/8AHu34fzrPrQuf+Pdvw/nWfXHX+Ic9wooorAgKKKKACiiigAooooAK1ayq1a6cP1NKYVwfxa/5FW1/6/U/9Aeu8rg/i1/yKtr/ANfqf+gPW1T4WdFH+IjxqiiiuI9QKKKKACiiigAooooAKKKKAPqGiiivQPHPGPj5/wAy9/28/wDtKvGK9n+Pn/Mvf9vP/tKvGK9Gh/DR9Dgf93j8/wA2FFFFbHWFFFFABRRRQAUUUUAbXg//AJHbQf8AsI2//oxa+tK+S/B//I7aD/2Ebf8A9GLX1pXFit0eNmfxxCvOvin/AMwn/tt/7JXotedfFP8A5hP/AG2/9krzsT/Cf9dTw8X/AAX/AF1POqKKK8k8UKKKKACiiigAooooAK6PwJ/yOen/APbT/wBFtXOV0fgT/kc9P/7af+i2q6X8SPqjWj/Ej6o9nooor2j3gooooAKKKKAOc8Ef8gG5/wCwrqP/AKWTV0dc54I/5ANz/wBhXUf/AEsmro6ACiiigAooooAK5nxlP4hFrbWmg6fczrcsVu7q1lhWW3jGPuCV1Bds4ByduCcE4rpqKAOYthJpvhuxhsvCF0Y4H2Cwea3MsQGfnyZCjEnqd2fmJPel8HaTeacur3N1aR2C6hfG5isY3DCBdiKclfl3MVLHGRluprpqKAOK+I+gWd14P8Q6jJNqCzx6ZOQseoTxxHbG2Mxq4Q++Rz3zXZp/q1+grn/H3/JO/Ev/AGC7n/0U1dAn+rX6CgB1FFFABRRRQAVxF/8A8hC5/wCurfzNdvXEX/8AyELn/rq38zVwOnDbsr0UUVZ1hRRRQAUUUUAFFFFABXQeGf8Al6/4B/WufroPDP8Ay9f8A/rSlsZVvgZv0UUVkcB5Xq3/ACGb7/r4k/8AQjVOrmrf8hm+/wCviT/0I1Tr85rfxZerPhav8SXqwooorIgKKKKACiiigAooooA7zwT/AMgab/r4b/0Fa6Sub8E/8gab/r4b/wBBWukr73Lf90p+h9lgP92h6BXldeqV5XTxn2T06HUKKKK4jcKKKKACiiigAooooAK7Xwh/yCZf+u5/9BWuKrtfCH/IJl/67n/0Fa6ML/EMq3wm/Wb4h/5FrVf+vOb/ANANaVZviH/kWtV/685v/QDXrUv4kfVGFP416nglFFFfbH2IUUUUAFFFFABRRRQAUUUUAe1eA/8AkS9P/wC2n/oxq6Ouc8B/8iXp/wD20/8ARjV0dfGYr+PP1f5nyOJ/jT9X+ZXv/wDkHXP/AFyf+Rrh67i//wCQdc/9cn/ka4evNxO6OWYUUUVzEBRRRQAUUUUAFFFFABXcWH/IOtv+uSfyFcPXcWH/ACDrb/rkn8hXRht2XAsVieL/APkV7z/gH/oa1t1ieL/+RXvP+Af+hrXdD4kYY/8A3Sr/AIZfkzy2iiivQPzIKKKKACiiigAooooAKKKKAPX9G/5AWn/9e0f/AKCKvVR0b/kBaf8A9e0f/oIq9XnS3P1LD/wo+i/Iiuf+Pdvw/nWfWhc/8e7fh/Os+uOv8Q57hRRRWBAUUUUAFFFFABRRRQAVq1lVq104fqaUwrg/i1/yKtr/ANfqf+gPXeVwfxa/5FW1/wCv1P8A0B62qfCzoo/xEeNUUUVxHqBRRRQAUUUUAFFFFABRRRQB9Q0UUV6B454x8fP+Ze/7ef8A2lXjFez/AB8/5l7/ALef/aVeMV6ND+Gj6HA/7vH5/mwooorY6wooooAKKKKACiiigDa8H/8AI7aD/wBhG3/9GLX1pXyX4P8A+R20H/sI2/8A6MWvrSuLFbo8bM/jiFedfFP/AJhP/bb/ANkr0WvOvin/AMwn/tt/7JXnYn+E/wCup4eL/gv+up51RRRXknihRRRQAUUUUAFFFFABXR+BP+Rz0/8A7af+i2rnK6PwJ/yOen/9tP8A0W1XS/iR9Ua0f4kfVHs9FFFe0e8FFFFABRRRQBzngj/kA3P/AGFdR/8ASyaujrnPBH/IBuf+wrqP/pZNXR0AFFFFABRRRQAUUUUAFFFFAHO+Pv8AknfiX/sF3P8A6KaugT/Vr9BXP+Pv+Sd+Jf8AsF3P/opq6BP9Wv0FADqKKKACiiigAriL/wD5CFz/ANdW/ma7euIv/wDkIXP/AF1b+Zq4HTht2V6KKKs6wooooAKKKKACiiigAroPDP8Ay9f8A/rXP10Hhn/l6/4B/WlLYyrfAzfooorI4DyvVv8AkM33/XxJ/wChGqdXNW/5DN9/18Sf+hGqdfnNb+LL1Z8LV/iS9WFFFFZEBRRRQAUUUUAFFFFAHeeCf+QNN/18N/6CtdJXN+Cf+QNN/wBfDf8AoK10lfe5b/ulP0PssB/u0PQK8rr1SvK6eM+yenQ6hRRRXEbhRRRQAUUUUAFFFFABXa+EP+QTL/13P/oK1xVdr4Q/5BMv/Xc/+grXRhf4hlW+E36zfEP/ACLWq/8AXnN/6Aa0qzfEP/Itar/15zf+gGvWpfxI+qMKfxr1PBKKKK+2PsQooooAKKKKACiiigAooooA9q8B/wDIl6f/ANtP/RjV0dc54D/5EvT/APtp/wCjGro6+MxX8efq/wAz5HE/xp+r/Mr3/wDyDrn/AK5P/I1w9dxf/wDIOuf+uT/yNcPXm4ndHLMKKKK5iAooooAKKKKACiiigAruLD/kHW3/AFyT+Qrh67iw/wCQdbf9ck/kK6MNuy4FisTxf/yK95/wD/0Na26xPF//ACK95/wD/wBDWu6HxIwx/wDulX/DL8meW0UUV6B+ZBRRRQAUUUUAFFFFABRRRQB6/o3/ACAtP/69o/8A0EVeqjo3/IC0/wD69o//AEEVerzpbn6lh/4UfRfkRXP/AB7t+H86z60Ln/j3b8P51n1x1/iHPcKKKKwICiiigAooooAKKKKACtWsqtWunD9TSmFcH8Wv+RVtf+v1P/QHrvK4P4tf8ira/wDX6n/oD1tU+FnRR/iI8aoooriPUCiiigAooooAKKKKACiiigD6hooor0Dxzxj4+f8AMvf9vP8A7SrxivZ/j5/zL3/bz/7SrxivRofw0fQ4H/d4/P8ANhRRRWx1hRRRQAUUUUAFFFFAG14P/wCR20H/ALCNv/6MWvrSvkvwf/yO2g/9hG3/APRi19aVxYrdHjZn8cQrzr4p/wDMJ/7bf+yV6LXnXxT/AOYT/wBtv/ZK87E/wn/XU8PF/wAF/wBdTzqiiivJPFCiiigAooooAKKKKACuj8Cf8jnp/wD20/8ARbVzldH4E/5HPT/+2n/otqul/Ej6o1o/xI+qPZ6KKK9o94KKKKACiiigDnPBH/IBuf8AsK6j/wClk1dHXOeCP+QDc/8AYV1H/wBLJq6OgAooooAKKKKACiiigAooooA53x9/yTvxL/2C7n/0U1dAn+rX6Cuf8ff8k78S/wDYLuf/AEU1dAn+rX6CgB1FFFABRRRQAVxF/wD8hC5/66t/M129cRf/APIQuf8Arq38zVwOnDbsr0UUVZ1hRRRQAUUUUAFFFFABXQeGf+Xr/gH9a5+ug8M/8vX/AAD+tKWxlW+Bm/RRRWRwHlerf8hm+/6+JP8A0I1Tq5q3/IZvv+viT/0I1Tr85rfxZerPhav8SXqwooorIgKKKKACiiigAooooA7zwT/yBpv+vhv/AEFa6Sub8E/8gab/AK+G/wDQVrpK+9y3/dKfofZYD/doegV5XXqleV08Z9k9Oh1CiiiuI3CiiigAooooAKKKKACu18If8gmX/ruf/QVriq7Xwh/yCZf+u5/9BWujC/xDKt8Jv1m+If8AkWtV/wCvOb/0A1pVm+If+Ra1X/rzm/8AQDXrUv4kfVGFP416nglFFFfbH2IUUUUAFFFFABRRRQAUUUUAe1eA/wDkS9P/AO2n/oxq6Ouc8B/8iXp//bT/ANGNXR18Ziv48/V/mfI4n+NP1f5le/8A+Qdc/wDXJ/5GuHruL/8A5B1z/wBcn/ka4evNxO6OWYUUUVzEBRRRQAUUUUAFFFFABXcWH/IOtv8Arkn8hXD13Fh/yDrb/rkn8hXRht2XAsVieL/+RXvP+Af+hrW3WJ4v/wCRXvP+Af8Aoa13Q+JGGP8A90q/4Zfkzy2iiivQPzIKKKKACiiigAooooAKKKKAPX9G/wCQFp//AF7R/wDoIq9VHRv+QFp//XtH/wCgir1edLc/UsP/AAo+i/Iiuf8Aj3b8P51n1oXP/Hu34fzrPrjr/EOe4UUUVgQFFFFABRRRQAUUUUAFatZVatdOH6mlMK4P4tf8ira/9fqf+gPXeVwfxa/5FW1/6/U/9Aetqnws6KP8RHjVFFFcR6gUUUUAFFFFABRRRQAUUUUAfUNFFFegeOeMfHz/AJl7/t5/9pV4xXs/x8/5l7/t5/8AaVeMV6ND+Gj6HA/7vH5/mwooorY6wooooAKKKKACiiigDa8H/wDI7aD/ANhG3/8ARi19aV8l+D/+R20H/sI2/wD6MWvrSuLFbo8bM/jiFedfFP8A5hP/AG2/9kr0WvOvin/zCf8Att/7JXnYn+E/66nh4v8Agv8ArqedUUUV5J4oUUUUAFFFFABRRRQAV0fgT/kc9P8A+2n/AKLaucro/An/ACOen/8AbT/0W1XS/iR9Ua0f4kfVHs9FFFe0e8FFFFABRRRQBzngj/kA3P8A2FdR/wDSyaujrnPBH/IBuf8AsK6j/wClk1dHQAUUUUAFFFFABRRRQAUUUUAc74+/5J34l/7Bdz/6KaugT/Vr9BXP+Pv+Sd+Jf+wXc/8Aopq6BP8AVr9BQA6iiigAooooAK4i/wD+Qhc/9dW/ma7euIv/APkIXP8A11b+Zq4HTht2V6KKKs6wooooAKKKKACiiigAroPDP/L1/wAA/rXP10Hhn/l6/wCAf1pS2Mq3wM36KKKyOA8r1b/kM33/AF8Sf+hGqdXNW/5DN9/18Sf+hGqdfnNb+LL1Z8LV/iS9WFFFFZEBRRRQAUUUUAFFFFAHeeCf+QNN/wBfDf8AoK10lc34J/5A03/Xw3/oK10lfe5b/ulP0PssB/u0PQK8rr1SvK6eM+yenQ6hRRRXEbhRRRQAUUUUAFFFFABXa+EP+QTL/wBdz/6CtcVXa+EP+QTL/wBdz/6CtdGF/iGVb4TfrN8Q/wDItar/ANec3/oBrSrN8Q/8i1qv/XnN/wCgGvWpfxI+qMKfxr1PBKKKK+2PsQooooAKKKKACiiigAooooA9q8B/8iXp/wD20/8ARjV0dc54D/5EvT/+2n/oxq6OvjMV/Hn6v8z5HE/xp+r/ADK9/wD8g65/65P/ACNcPXcX/wDyDrn/AK5P/I1w9ebid0cswooormICiiigAooooAKKKKACu4sP+Qdbf9ck/kK4eu4sP+Qdbf8AXJP5CujDbsuBYrE8X/8AIr3n/AP/AENa26xPF/8AyK95/wAA/wDQ1ruh8SMMf/ulX/DL8meW0UUV6B+ZBRRRQAUUUUAFFFFABRRRQB6/o3/IC0//AK9o/wD0EVeqjo3/ACAtP/69o/8A0EVerzpbn6lh/wCFH0X5EVz/AMe7fh/Os+tC5/492/D+dZ9cdf4hz3CiiisCAooooAKKKKACiiigArVrKrVrpw/U0phXB/Fr/kVbX/r9T/0B67yuD+LX/Iq2v/X6n/oD1tU+FnRR/iI8aoooriPUCiiigAooooAKKKKACiiigD6hooor0Dxzxj4+f8y9/wBvP/tKvGK9n+Pn/Mvf9vP/ALSrxivRofw0fQ4H/d4/P82FFFFbHWFFFFABRRRQAUUUUAbXg/8A5HbQf+wjb/8Aoxa+tK+S/B//ACO2g/8AYRt//Ri19aVxYrdHjZn8cQrzr4p/8wn/ALbf+yV6LXnXxT/5hP8A22/9krzsT/Cf9dTw8X/Bf9dTzqiiivJPFCiiigAooooAKKKKACuj8Cf8jnp//bT/ANFtXOV0fgT/AJHPT/8Atp/6LarpfxI+qNaP8SPqj2eiiivaPeCiiigAooooA5zwR/yAbn/sK6j/AOlk1dHXOeCP+QDc/wDYV1H/ANLJq6OgAooooAKKKKACiiigBrukcbO7BUUEszHAAHc1W0/VdO1aFptNv7W9iVtrPbTLIoPoSpPNYHxH/wCRIu9//Hv51v8Aav8Arh58fm59tm7PtmoLD7L/AMLRuv7N8nyP7Gj+1eRjbv8ANPlZxxnbvx3xjtigC/4+/wCSd+Jf+wXc/wDopq6BP9Wv0Fcx4xtPEer6Rqmj6ZYaY9te2cluLi4vnjdS6lSdgiYHGf73PtUq3njMKB/YOh8D/oMS/wDyNQB0lFc59t8Z/wDQB0P/AMHEv/yNR9t8Z/8AQB0P/wAHEv8A8jUAdHRXOfbfGf8A0AdD/wDBxL/8jUfbfGf/AEAdD/8ABxL/API1AHR1xF//AMhC5/66t/M1qfbfGf8A0AdD/wDBxL/8jVhz6X4znuJZf7L0Nd7lsf2pLxk5/wCfeqi7G1Gai3cKKZ/Y3jP/AKBmh/8Ag0l/+R6P7G8Z/wDQM0P/AMGkv/yPV8yOn20O4+imf2N4z/6Bmh/+DSX/AOR6P7G8Z/8AQM0P/wAGkv8A8j0cyD20O4+is6KHxZLrV1pS6Vo/n21vFcOx1OTaVkaRVAPkZzmJs8dxV7+xvGf/AEDND/8ABpL/API9HMg9tDuPopn9jeM/+gZof/g0l/8Akej+xvGf/QM0P/waS/8AyPRzIPbQ7j66Dwz/AMvX/AP61zn9jeM/+gZof/g0l/8Aker+mQ+M9O83/iT6HJ5mP+YtKMYz/wBO/vUuSaM6tWEoNJnZ0Vzn23xn/wBAHQ//AAcS/wDyNR9t8Z/9AHQ//BxL/wDI1Qchx2rf8hm+/wCviT/0I1TrTu/DPjK6vJ7j7Boa+bIz7f7SlOMnOP8AUVD/AMIl4y/58tD/APBlL/8AGK+Lq5RjJTk1Dr3X+Z8pUyzFObaj17r/ADKVFXf+ES8Zf8+Wh/8Agyl/+MUf8Il4y/58tD/8GUv/AMYrP+x8b/J+K/zI/svF/wAn4r/MpUVd/wCES8Zf8+Wh/wDgyl/+MVSsdG8WX93qNtFYaOHsLgW8pbUZACxjSTK/uOmJF9Oc0f2Pjf5PxX+Yf2Xi/wCT8V/mFFXf+ES8Zf8APlof/gyl/wDjFH/CJeMv+fLQ/wDwZS//ABij+x8b/J+K/wAw/svF/wAn4r/MpUVd/wCES8Zf8+Wh/wDgyl/+MUf8Il4y/wCfLQ//AAZS/wDxij+x8b/J+K/zD+y8X/J+K/zOv8E/8gab/r4b/wBBWukritFtvGej2b2/9kaHNukL7v7WlXsBj/j3PpWl9t8Z/wDQB0P/AMHEv/yNX12BpSpYeEJqzSPpcJTlToRhLdI6OvK67H7b4z/6AOh/+DiX/wCRq5X/AIRnxn/z4aH/AODKX/4xRiacp25Ud9KSje5BRU//AAjPjP8A58ND/wDBlL/8Yo/4Rnxn/wA+Gh/+DKX/AOMVzfV6nY19rDuQUVP/AMIz4z/58ND/APBlL/8AGKP+EZ8Z/wDPhof/AIMpf/jFH1ep2D2sO5BRUOl6b4s1e0e5t9O0dUS4mtyJNRkB3RSNGx4gPGUOPbHSrv8AwjPjP/nw0P8A8GUv/wAYo+r1Owe1h3IKKn/4Rnxn/wA+Gh/+DKX/AOMUf8Iz4z/58ND/APBlL/8AGKPq9TsHtYdyCu18If8AIJl/67n/ANBWuR/4Rnxn/wA+Gh/+DKX/AOMVtaRD4z0q0eD+x9Dl3OX3f2tKvYDH/HufStqFGcJ3kiKk4uNkdnWb4h/5FrVf+vOb/wBANZ323xn/ANAHQ/8AwcS//I1VtQbxnf6bdWf9iaGnnwvFv/teU7dwIzj7Nz1r0KbSmmzGDSkmzx6iuk/4Vv4y/wCeWif+B8v/AMZo/wCFb+Mv+eWif+B8v/xmvqP7Twv834P/ACPpf7Rw3834P/I5uiuk/wCFb+Mv+eWif+B8v/xmj/hW/jL/AJ5aJ/4Hy/8Axmj+08L/ADfg/wDIP7Rw3834P/I5uiuk/wCFb+Mv+eWif+B8v/xmqWl+C/Fer2j3NvBo6olxNbkSX0gO6KRo2PEJ4yhx7Y6Uf2nhf5vwf+Qf2jhv5vwf+RkUV0n/AArfxl/zy0T/AMD5f/jNH/Ct/GX/ADy0T/wPl/8AjNH9p4X+b8H/AJB/aOG/m/B/5HN0V0n/AArfxl/zy0T/AMD5f/jNH/Ct/GX/ADy0T/wPl/8AjNH9p4X+b8H/AJB/aOG/m/B/5Ho3gP8A5EvT/wDtp/6MaujritBt/GeiaLb6d/Y+hzeTu/ef2tKucsW6fZz61pfbfGf/AEAdD/8ABxL/API1fM15KdWUo7Nv8z52vJSqyktm2bd//wAg65/65P8AyNcPWzPceM57eWL+w9DXehXP9ry8ZGP+fasP+xfGf/QM0P8A8Gkv/wAj1w16cpNWOeSbH0Uz+xfGf/QM0P8A8Gkv/wAj0f2L4z/6Bmh/+DSX/wCR6x9hU7E8rH0Uz+xfGf8A0DND/wDBpL/8j0h0bxkAT/Zmh8f9RSX/AOR6PYVOwcrJKKoaRB4s1rRrHVLbStHWC8gS4jWTU5AwV1DDIEBGcH1q5/YvjP8A6Bmh/wDg0l/+R6PYVOwcrH0Uz+xfGf8A0DND/wDBpL/8j0f2L4z/AOgZof8A4NJf/kej2FTsHKx9dxYf8g62/wCuSfyFcJ/YvjP/AKBmh/8Ag0l/+R63ILjxnBbxRf2HobbEC5/teXnAx/z7VtQpyi3cqKaOnrE8X/8AIr3n/AP/AENar/bfGf8A0AdD/wDBxL/8jVR1dPGeq6XNZf2NocXmbfn/ALWlbGGB6fZ/auuLtJMyxdOVTD1IR3aaXzRwdFan/CFeMv8An00P/wAGMv8A8Yo/4Qrxl/z6aH/4MZf/AIxXX7aHc+F/sPH/APPv8V/mZdFan/CFeMv+fTQ//BjL/wDGKP8AhCvGX/Ppof8A4MZf/jFHtodw/sPH/wDPv8V/mZdFaZ8F+MgCfsmicf8AURl/+MVT0jw74s1rRrHVLay0dYLyBLiNZNQkDBXUMMgQkZwfWj20O4f2Hj/+ff4r/MgorU/4Qrxl/wA+mh/+DGX/AOMUf8IV4y/59ND/APBjL/8AGKPbQ7h/YeP/AOff4r/My6K1P+EK8Zf8+mh/+DGX/wCMUf8ACFeMv+fTQ/8AwYy//GKPbQ7h/YeP/wCff4r/ADPS9G/5AWn/APXtH/6CKvVytnL4ztLG3tv7E0N/JjWPd/a8ozgYzj7NU/23xn/0AdD/APBxL/8AI1cb3PvqMXGnGL3SRvXP/Hu34fzrPrOkuvGckZT+wtDGe/8AbEv/AMjVX/4rP/oC6H/4N5f/AJHrmrU5SldBOLb0Nmisb/is/wDoC6H/AODeX/5Ho/4rP/oC6H/4N5f/AJHrL2M+xPIzZorG/wCKz/6Auh/+DeX/AOR6p6vqfi3RdGvtUudD0ZoLOB7iRY9WkLFUUscA24GcD1o9jPsHIzpaKxgfGZAP9i6Hz/1F5f8A5Ho/4rP/AKAuh/8Ag3l/+R6PYz7ByM2aKxv+Kz/6Auh/+DeX/wCR6P8Ais/+gLof/g3l/wDkej2M+wcjNmtWuR/4rP8A6Auh/wDg3l/+R6t/bfGf/QB0P/wcS/8AyNW1GEo3uXBNbnR1wfxa/wCRVtf+v1P/AEB62ftvjP8A6AOh/wDg4l/+RqwfFmkeM/FOlRWJ0zQ7by5xNv8A7UlfOFYYx9nH979K1mm4tI2ptRmmzx+iuu/4VX4y9NE/8DZf/jNH/Cq/GXpon/gbL/8AGa5fZT7Hf9Yp9zkaK67/AIVX4y9NE/8AA2X/AOM0f8Kr8Zemif8AgbL/APGaPZT7B9Yp9zkaK6HVPh/4r0i0S5uI9HZHuIbcCO8kJ3SyLGp5iHGXGfbPWrv/AAqvxl6aJ/4Gy/8Axmj2U+wfWKfc5Giuu/4VX4y9NE/8DZf/AIzR/wAKr8Zemif+Bsv/AMZo9lPsH1in3ORorrv+FV+MvTRP/A2X/wCM0f8ACq/GXpon/gbL/wDGaPZT7B9Yp9z3Oiuc+2+M/wDoA6H/AODiX/5Go+2+M/8AoA6H/wCDiX/5GrsPNPO/j5/zL3/bz/7SrxivePHvg7xl44/s/Nnodl9j8z/mIyyb9+3/AKYDGNv61xn/AApDxj/z30T/AMCpf/jVdtGtCMEmz2MLi6NOioyevz7nnVFei/8ACkPGP/PfRP8AwKl/+NUf8KQ8Y/8APfRP/AqX/wCNVp7en3On69Q/m/BnnVFei/8ACkPGP/PfRP8AwKl/+NVS1T4R+K9ItEubiTSGR7iG3AjuZCd0sixqeYxxlxn2z1o9vT7h9eofzfgzh6K9F/4Uh4x/576J/wCBUv8A8ao/4Uh4x/576J/4FS//ABqj29PuH16h/N+DPOqK9F/4Uh4x/wCe+if+BUv/AMao/wCFIeMf+e+if+BUv/xqj29PuH16h/N+DOU8H/8AI7aD/wBhG3/9GLX1pXgej/B/xlpOt2Go7tEl+yXEc/l/bJV3bGDYz5JxnFes/bfGf/QB0P8A8HEv/wAjVzYicZtcp5mOrQqyTg7nR1518U/+YT/22/8AZK6L7b4z/wCgDof/AIOJf/kaud8U6F4z8TfZM6bodt9n3/8AMTlfdu2/9O4x92uKvFypuK3PLxEHOm4x3POqK6P/AIVz4y/546H/AOB8v/xmj/hXPjL/AJ46H/4Hy/8AxmvO+rVex5f1St2/I5yiuj/4Vz4y/wCeOh/+B8v/AMZo/wCFc+Mv+eOh/wDgfL/8Zo+rVewfVK3b8jnKK1b7wZ4rsLvTraW30cvf3Bt4it9IQGEbyZb9z0xG3rzir3/CufGX/PHQ/wDwPl/+M0fVqvYPqlbt+RzlFdH/AMK58Zf88dD/APA+X/4zR/wrnxl/zx0P/wAD5f8A4zR9Wq9g+qVu35HOV0fgT/kc9P8A+2n/AKLaj/hXPjL/AJ46H/4Hy/8AxmtHQvB3jLRNZt9Q+x6HN5O793/aMq5ypXr5B9aunh6imm0XTwtWM02uvkerUVzn23xn/wBAHQ//AAcS/wDyNR9t8Z/9AHQ//BxL/wDI1emeudHRXOfbfGf/AEAdD/8ABxL/API1H23xn/0AdD/8HEv/AMjUAdHRXOfbfGf/AEAdD/8ABxL/API1H23xn/0AdD/8HEv/AMjUAHgj/kA3P/YV1H/0smro6xPCmm3ul6GYNRWBbqS7ubl1gkMiL5s7yABiqk4DgZwOlbdABRRRQAUUUUAFFFFADXRJI2jkVWRgQysMgg9jVbT9K07SYWh02wtbKJm3MltCsak+pCgc1booAKKKKACiiigAooooAKKKKACiiigAorP1zVU0TR7jUGhecxABIkIBkdiFVcngZYgZNY/hrxRc6vc30F9aW0C20qwJc29x5kM0pzujUsqksuADgEZyM8UAdCtnbpey3qxKLmWNInk7silio/Au35mp6KKACiiigAooooAKKKKACiiigAoorB8UeKbDw1Ys011arfSKfsttNNsMrZAHqcZIyQDQBvVBBZ29tNcywxKklzIJZmH8bhVTJ/4Cqj8K53TvEt9a6ymi+Jra3tbu45s7m3ZjBcnugLcq464PUdOldTQAUUUUAFFFFABRRRQAUUUUAFFFFABRWVq/iHS9Gmgg1G7+yG5yI5nQ+WDxwXxtB5GATzVLwbq0+o6M9vfTrNqOnyta3UgI/eEcrIMdnQq3QdaANu1s7exhaK2iWKNpHlKr3d2LsfxZifxqeiigAooooAKKKKACiiigAooooAKjeeGOaOF5UWWXPloWAL45OB3xUlefeLfEHhnUnjs76a6szDd+Ta63HHtjtroAnCyHvxg8FeoPegD0GoLWzt7GForaJYo2keUqvd3Yux/FmJ/GsXw1rGo3Lz6VrVsY9Us1UvPEh8i6jP3ZUPQZwcqeQfUYNdDQAUUUUAFFFFABRRRQAUUUUAFFFQ3d1FZWz3E5cRpjOxGc8nHRQSetAE1BGRg1z3iLWdQs9Q0zStKjtftuoGUpLdlvKRY1yeF5LHIwMjue1P8ADmt3eoy3+n6nbR2+qafIqTiFt0UisMo6E84IzweQQfrQBsWdpb2FlBZ2sSxW8EaxRRr0RVGAB9AKmoooAKKKKACiiigAooooAKKKKACiiuR8T+Km0XX9NsGnjtLeWNp5JXhaVp9rBRDEo6uc5POQB07gA64jIwahs7S3sLKCztYlit4I1iijXoiqMAD6AVR0PXbbXraaWCG6geCUwzQ3URjkjYAHBU+zA/jWpQAUUUUAFFFFABRRRQAUUUUAFFFISFUsxAAGST2oAWoby0t7+yns7qJZbeeNopY26OrDBB+oNcXd+OUbxFBHazSSad9mWeFLW1aaXUGYsMRnAUKoXJOecjkd+p0TWINd01b2CKaEeY8TxTrtdHRirKRyOCD0NAGiBgYFFFFABRRRQAUUUUAFFFFABRRRQAUU2SWOJd0jqi5xljivN73V9b1HxteadZ6rPYyWNwjyKyIttDaAKS8m4EyNJllABXaBk4xyAeh3Vnb30KxXMSyxrIkoVuzowdT+DKD+FT1S0nVINa0uHULVZBBOC0ZdcFlyQGHscZB9CKu0AFFFFABRRRQAUUUUAFFFFABRRWfc6zYQapFpDXsCancRNLBbu2GdRxn8/wCR9DQBoVBdWdvfQrFcxLLGsiShW7OjB1P4MoP4V5EYtditbe9sYdZs/ENgstzq9zfO/wBlmCKxKckowZj8uwcL6cV61pl5/aOlWd7sMf2iBJdh6ruUHH60AWqKKKACiiigAooooAKKKKACiiigAoqGe7gtmiWaaNHmfy4lZgC7YJ2jPU4Brzy18S+IF0GHxa+oW1xYmcLd6WLcKbdTJswrgkl1yuc8HB6Z4APQp7O3uZraWaJXktpDLCx/gcqyZH/AWYfjU9FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFVL++WygYqvnXJR2htldQ8xUZKrk8mq+sap/Ytu2o3I/4lsEbNcFI2kkU7lAICj7oBYsewA96841yTRbvVdTn8Q6rcWs+9bvQdRjnYRrH5Q2+Vt+Utu35GCWB70AXfEWox6po0niK0a51DSJ1Sy1TRnJ8yP5sZjXqkqs3brx7Eb/AId8HR2EWmyT6ne3lrYxg2FrcQpCsGVwGZVUbnAyMt0JPGemZ4SsrfWNRju/EfhxbXxPbRRTyTrGwhnyPlkA4XeDkFWG5TnHY16DQAUUUUAFFFFABRRRQAUUUUAFFFYN54t0611+PQAZm1WXb5cIhYgggnfnpsXHJzx068UAWdb16LRVtk+y3N5d3TlLe1tlBeQgZY8kAAAZJJA/OuCS7gutf1HUrmyu9R0m/wBljqFjd24a401+dvyAEtE27nGcHnkZxPpdtqGszT299enT/Gem3r3kYctJC0bDYPLUnmFk+XjBB689es8P6Lf2eo6hqurXFtLf3wiR1tIykSJGCFxuJJJLMST7DtyAT6Dob6PYCynvHv4YJS1m065kgjxgIW6tjkBuuCB2zWxRRQAUUUUAFFFFABRRRQAUUUUAFc54h8SS2E/9maRZNqesSRPKLaKeNDEgH32LHjkgAYOc1J4w1qXQdAN3DJBAzzRwm4uATHbhmAMjAdQo5xkdua4y3t9T1d7XVJ7uO21+SRzolw9sYZbm3VQzLOgyAh5x3AxnnIoAn0Fb6TTrXU9IurzWYZSsGsaTqUwaRJMgMy7+EZTnKHCkDjBHPQaP4Xi0Hxjd3Ok2UVnpVzZqJ0jbCvOHO0qn8OEJBxgfd4OONyx0y3trmXUDawRajdRxi7khzh2Ue/XGTz1xjPSr9ABRRRQAUUUUAFFFFABRRRQAVDc3UdqsbSLIQ8ixjy42fljgZwDgepPAqauJ8TeLWt72zt7dZotMW/W21HURGCkJADbMMOVbIUsMgZIznoAXfEXiGN0vdG0zUhaa+iLJbJMmwTsCG2oXG1weFOM43djWDd6NdeLILTVtFh01tPv9Me1aO8DA2bSMTK6KuQWOSpGRyo5xWjZ+GXs7kaFNZR6v4UuELWxnZXewOMhMscsh/gYfMvQ9Aa6bRtF0/wAP6aun6ZB5Nsrs4XcWO5mLMckk9SaAJNK0y30bS7fTrXzPIt02J5kjO2P95iT+tXKKKACiiigAooooAKKKKACiiigArmPHOuXmhaLHPaxSrHJOkVxexqr/AGOMsA0hU9cAnscdTVy71VNQvr3QbCS4jvEgPmXcUYKWzMPlBJ43Y5A54xnqK8ytLeW08Wy23h6ZUv1vDZTaVcb3S4tlUl7idjklmJGG4GNo5JzQB1F14Wc31vDb+ff6Bezm4ikhn2z6ZM2WMsb55jbnK9ieARwOr0XQbbRFnMc1zc3Nwwae5upfMlkIGBk9gB0AAH5mpdG0az0HTxY2CuluHZ1RpCwTJztXPRR0A7CtCgAooooAKKKKACiiigAooooAKp6lqtjpFvHPf3KW8Ukqwq75C72OFBPbJ7nirlcZ421O3n0u1txewf2Tc3gtdTuECS+TGVY4OchcsFUkg4B7daAI/E2uTS6Hf6OrS/23bwRT3NtZbkeaAsPN8hmwW+XcMjkEjocVi+B7u3ctbaTM8s2puZrmG1kdodJjCFVALg/vScA92OTgAUlpos8+qnwr9uuLtNMhS60vXonRp7EtkCOXn5gdpxxhl4I716gi7EAOCcckDGTQBR0bRrXQ7E21sZHLuZZppm3STSHq7N3J/TgDAFaFFFABRRRQAUUUUAFFFFABRRRQAVwGveJhrE7aAmnXbWl7dNYLc2t55VxvQjzJEUc7EP3mJA4xg5GbWr6zq1/4vPh3TL46PNFbG4jluLVJFvTxwmW+6uTuGM9D0rI0Pw7f6jqmrXkVzcaDqUxEGpwi2Eqk8kvayv8AdR927HOG6jNAG94ei1OfRZ/D2otPE9nEkcGq2TBVuYuisjc7XAXDL26jgiuj0zTLTR9OhsLGIRW8Qwq5JJ7kknkknJJPJJp9hY2+mWEFlaoUggQIgJJOB6k8k+9WKACiiigAooooAKKKKACiiigArE1jxLBpV2llFZ3mo37xmUWtlGGdUzjcxJAUZ4GTzzjODWrLd20DBZbiKMllQB3AyzHCj6k9B3rk9X/tLw74wk16DTrrUtNvbWO3uo7Ub5rd42Yq6p/EpDkEDkYzzQBzWq61puv6xFqmrWovPDVqHtryyu7YrNpc7YHmSoTypAI3Y+XJ5xmuk0zwPZvI0WrQ2Os6dAFOlz3KeZPHGc5jZj99BkbTknB56ZMuhWkuqeKdT8QSabNY2lzaRWiw3SBZLjaWJd07DDbRnkgHgDFdaAFAAAAHAAoAAAqhVAAAwAO1LRRQAUUUUAFFFFABRRRQAUUVk69qV7Yw20Om2gub27mEMW/Iij4JLuRyFAB4HJOBx1oAZrXiKPSLm3tI7C81C9uFaRLe0VSwRcbmJYgAAso65OeK4XxJLD4turS80fTrq7uZoZLIHBhk066R0dGmOMx7R5hPXOMDOQa09uvavqU1xGLKDxJoUvkny2b7PdwSqr7DnkZwPXBXPetXw/Y6pN4pv9dvtMXSluLSK3e2FwsrTSIWPmMV44BCg9cemKAE0fQtR1Hw62leMf8ATpYbli0wlwl2udwJRcALzt2Hj5feurACgAAADgAUtFABRRRQAUUUUAFFFFABRRRQAVymreNIbXxCuhaZCL+/ija4u4o3G6ONRnYoyN0rZGEyOOSQMZ2LjxFo9rqK6dNqMC3rSJELcNmQs4JUbRz0BPsOTxVDUPBulXekiytovsUkczXMFzD/AKyKZjuMm7qST1z1oA4LVtU1PxRq17aaXqEN5FKiXWn2syeRPaTxgHarcFHB5IcYZWODwRXY6L4Z0zVYLHX9V0AWmsSBZriGRuBMP4mQHaWyMgkZHsasaZ4dW8msdW8Q6bZHX7BnSO8g4LryoY4x1BJ2nIGe1dNQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVS61Oys0iM95bQmbiHzZggkOOgJ6/hVbxBqtvpOlmSe3kujO4t4raIZad34CDPAzzkngDJrgJ/D1p4a1FZNZsBeeG7qAW0YmUSPpZZs7c8nYSRyOmBQBLp7a7rN7qU39o3On+KbOck6TLPutXg42qFwAVYDO/Gck59K6nwloh0y0eYRTWdvdbZl0mQqyWUhyXCEZwCSDtzgHOOuKNE8Jrp00Ml9Mt9JYsyadO6kSwwsMbHbPz45xkccV0tABRRRQAUUUUAFFFFABRRRQAUUVQ1zUJNJ0HUNRih86S1t3mWPn5iqk44+lAGbeeKUgvtS0yOzm/tO3t2ntIJAAL0Bc/uyDzzwR1riHuV1m20qS9t9U/tKadI4de+xrbfZ52yViVGw7Rg7hyCPUnOakiluPEds9zrGrRS6YHij07U4LXyZBes2CbfBJaIcDLfeIPYZrtdM0KSSWy1PXRFc6xbRmNXjdjEvJG9UPCuV6kDuQOKAHaRpF/wCet/4hlsbzUYSVtpLe22CBCoDbSctlsZPOOgA453aKKACiiigAooooAKKKKACiiigBGZUUsxAUDJJPAFcgfFt1qOnXt9pa2UFm7Lb6Vc30hVb2ckj5QOdhPAPU8nGBk9eQCCCAQeoNef3nhq48LeIYtY0bS5dWs8NHFpolC/YpXPLxbjtVWOAx/hHTjigDI0vSpNU0Np9NM82sRDyNd0XUrl3W5c8sDuJCNklkdcAgjtgjv9G8LaToczT2dvIJ3TyzJNO8zKmc7FZySFz2GBwPQUuh6TPah77VHt7jWLgYnnhhVAi5ysSHG4ouTgsSScnjOBsUAFFFFABRRRQAUUUUAFFFFABRRRQBw2v+Ita0fxDqSySacdLhskulhuQYi8YO2TEoJ+bJA2lcHcuD1rH8DNsupvD0Oh3qaBdxG4a0v7fY1iXGShJ4kjYg4wdynqMdOw8UeGl16TSbqNbc3em3qXEZuE3Ltzhx9dvI/wBpVroaAKmmabbaRpsFhaBxbwLtQO5cgfU81boooAKKKKACiiigAooooAKKKKACuGufEEmoadNrqT6bpEKo8Wk3mozHEpLAMzpkAK20AdWAOcckV3PWuMuvCGn6Iv2nQ/D6X967GOFLq5JhtQ2SSA5OxMnkIM/kKAMjQr/WL/VdSv8AQv7PS8ufLbUtMv5W/cShAqyxugO+NlUEEcEcgjmu90mzuLLTYIb27N5dqv724Khd7Hk4HYdgPQDrVTw1oEPh3RLOxXZJNBCInnCbSwBLY9QoLNgZOM1sUAFFFFABRRRQAUUUUAFFFFABRRXP32rXreL7PQrJoIh9ma9uZJVLM0YYIEQcc5OST0GOOaAMfxN4ottTtbjQtJ1Gezv7qU2VvfG1Z7czgEmLfjGcBgSOnPcYrN0TRbLUkjOkWdvoms6f5dpqunPADDNHxkOo4bIBKP3+nR0HhbW7HxBqL6VcWF0pLpa3F1M2/SxKd8gWMDDMS5OdwJwATivQLG0FjYwW3myTGKNYzLKcu+BjLHuaAItM0fTdFt2t9LsLayhZtzJbxBATjGSB7AVdoooAKKKKACiiigAooooAKKKjmuILdQ080cQJwC7Bcn8aAHsyopZiFUDJJOABXC+JtZg1nQrHULVr6TQEvh/aElusiNJbgN86lcM0YfaSV6gHqKjvtYl1G21S+0Ky+36UJ2g1EJJvkul8tVZoPmO3Z/dwN3OOetLTPD6X9zoV1ot9qF7awukw1S5vOIoU48hY0wCSAFO4dASST1AE0fSdL8Q39/penajcXeiWqw3NjfRTM72NwS25IpWzkbQpxk4zg8GvTRwOuaaiLGu1FCj0AxTqACiiigAooooAKKKKACiiigAprtsjZ9rNtBOFGSfpWJrXiZNMvY9NsrG41TVJIzMLO2ZQyRjje7MQqgngZPJ6dDjhda1WbxB9gv4NbudLXUJjb2hkla3TTzECZjKoIDybgVAY7enpyALbWM3izUpvG+lJEurWNyYo9MnjCbkQFcS5ziYgna/YYHTmvQdD1S91WKaa60e402NSqxrcuvmOcfMdq5wAeBzk9cDvzXgizudXW28UXsZs76RGjla2dfJ1GP8AgmK9s9f/AK1d1QAUUUUAFFFFABRRRQAUUUUAFFFRyzxQJvmlSNM43OwA/WgCO+vYNOsLi9unKW9vG0kjBSxCgZJwASfwql4c1dte0K31QxRxx3IMkKo+/MeTtJOByRgkds4rh9S1bWtS8bXul2mrz6fLZTI7BlVLeG0CqxkfcMys5LKACANuTjHPReD7GGPzdS0e4eLQb4NLDYSRYEcm45kiOfljcc7cY5yMZxQBC/hPULvxpqF9c3qx6LM9vOLeBmWSeWNcASN2jBAO0fePXgYPY0UUAFFFFABRRRQAUUUUAFFFFABTJJPLQkAM+DtXIG4+lc7rXiS7h1uPQNEtLe51Z4Dcv9qlMUUceSASQCWJIIwAcYJOOM8Drk1j4hs7HWNYE9nHe3LWtxOwMv8AZnk5DJCRwC7qR5mCcH6YANPTrKfxXeXutee+neMtPnaOKGY5W3hGdsRAHzRvkktycn2xXd6HNrlxFNJrVpaWhyoiht5TIQAPmLNwOT0AHA6k9sHwZo95c2Wn61r6BtWiRkguBujlkgYfL5y8ZfHr/OuzoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqjqeqW+mafcXTsjtFDLKsQcBpNiksB+X4VZ+0wExgTR5lGYxvHzjjkevUdPWvL9EsrrT9VvPFdhppubh7mW11fSkVS9uwYbntjx97h2XPz7s5yBQADU9T8V2zzX2oWEenWhiuIdStYmC297uAWKMlj5wAYqzYXlsdc12+kaZrYvzfa5qdvO6ReTFb2cTRxDkEuwZiSxwMdhz1zxmeHfCOmbYNQbTb2yRJmntdNubppIrc5OHEWSqMclsc7S3YjjsaACiiigAooooAKKKKACiiigAooqtqFp/aGnXNn580HnxNH5sDbXTIxlT2I9aAOe8V+L5fC+o6XG+mS3FjdGT7RcxnJhCgH7o68EntwprgtWvbi31Vrmy8Rq2pJbrdo007NHqRmcqlvHDuC+WAMEgFs4982U0fX7a6tfDdzruLy3nSfT5NRQyxzqjZzHJkMHCllZCTkd8E16FpPhXTtGup5rZCY3k8yGFwCtsx+95fGVDdSM0AVPD/hDSbIWmpNpAs78L5n2c3DSx2zsPmEYJKr1IyoHU+tdPRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB514p8XXpSXTfsdzZeRcj+0GimxOtnuH76LA+ZSMhiDlf1CxeGJrvUbJBd3moaQytLY6tFeFbqyyOVMmcujdOc+9dZ4g8PQ67BEwla1v7c77W8jALxN34/iU9Cp4Iq5pel2ej2K2djAkMIZn2JwMscnA7cnp0FAEej6NZ6HaNb2gkPmOZZZZXLySuerOx5J/oAOgrQoooAKKKKACiiigAooooAKKKKACvL/HdgtnqV9qWuaZ/bWnXcK29kWIUafIcAZzwqs2GMvUYA7AV6hUc8EV1byQTxrLDIpV0cZDA9QRQBxvhjwde+GZdPfT7+F7eWBF1OIglJZAgHmx+jEgZ9RXYwW8FrGY7eGOJCzMVjUKMk5JwO5JJP1ptlZ2+nWUFnaRiO3gQRxoCTtUdBzU9ABRRRQAUUUUAFFFFABRRRQAU2SRIo2kkdUjQFmZjgKB1JPanV5pF4j1PxHrE9rusJ9HSWZNTtJbclLa2Qsvzy55lYruCAYA5PYkA6XxT4am1ZBqei3n9n69BEVt7tQCHU5+Rxghl5yODg8juCnhnSbG48IWllfaGYVjLCW2vlErGUMdzknO7c2W3d85rT0mee5Pnwi1GjyQRNY+WrLJgjncDwB93GK1KAEACqAAABwAO1LRRQAUUUUAFFFFABRRRQAUUVXvb+0021a6vrmK2t1Kq0srhVBJAAyfUkD8aAGanqdppGny317L5cEQG4hSxJJwAAOSSSAAOSTXnWt6rpus6rBrV6keq+FrUNb3dpLbESadMcfvpI2G4jHtkA5HGau+LXurzWoNPvpRpsguFuNC1KN2MEkqgN5M65wSSp+o5HINbmmaUNWvLXxDfafPpWrIj215ArqUuVGVw/XemfmU8H8CRQBT0rwTYMTHqEVhrOkxKraVLcoJZoI2yTHvOd0Y+XaeuCR0ArsgAqhVAAAwAO1AAAAAAA6AUtABRRRQAUUUUAFFFFABRRRQAVBeXlvp9lNeXcyw28KF5JHPCqOpp8k8MLIskqIXO1AzAbj6D1ry611/Ub7X727utZmtLLS7iY6rFcKi26RDcEgRCNzMy7XL5wMgDOcAA7TxJ4ctPFelI0VzJbXaoXs9QtmIkiLDqCOqkdR3+uDVjwta3Nj4btLK8sLaylt1MPk2zbo8KSAw/wB4c888881PpZu7lzqEk8i2lzBE0NlJCEa3ODuyepJyOD0xWlQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWZres2Wj2iNeJLMbh/Jit4IjJJMxBO1VHXgEnsADUXimPWJfDl2mhSLHqBUbGON2M/NtJ4DYzgngHGa810jRZNa8QLf8AhO7vrSPTEDyJqkkkqfbsYMbK5Lf6tmDMuOWGDkGgB0a6JFERLZS3XhiKYxtDNE0d3oUzen8axk4+790gEZFdTomjXHhzx9PBDeXt9Z6laGaZrkbzA8ZVUzJgZ3KxAzlsJ6CtPQdI1KPW7/W9XFnFdXcMVuILNmZAsZYhmZgCzEuR04AHWukoAKKKKACiiigAooooAKKKKACiiigAooooAjkghleN5Ikd4m3RsyglDgjI9DgkfjUlFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABUF7eQ6fY3F7cFhBbxtLIVQuQoGTgAEnjsKytY8RfYL2PTNPspNS1WRDKLWNwgjj6b5HPCKTwM8k9AcHHMP4o16XXD5dhPb6jYxbrvQndHW7gJ/11vJgZdT2OARxgGgCG/lj1/wAVRx6rfz2+mXsC/wBgX2nXbxxvIeTuIOPN6bQ2QQMDnIOjpfgiK9e6l8SWSteGYLNJbXDpBqKgLtlkhU7dxxhgQeVPY1F4a8PC9u9Wlm0+S18OXcqyQ6Pf2oBWYbWMqjcdgLbvlwORkY797QAgAUAAAAcADtS0UUAFFFFABRRRQAUUUUAFFFVBqlkxvFjuY5JLL/j4RGBaP5d2CO3FADNYbUU0a8bSI4ZNRETfZ0nOEL44z/n8R1ry3RW1HU9VkeyttU1a0itmttds9UuVXzrh8cJG/wAq7fmzjC4IA6V1Oh+N77VNWEdxp1tDYG1F1LMlwWa0VuY1myoXeykHapJHftnoZPD9m/iGHXImlhu1Qxy+UwC3C44EgxzjqD1oA5bQvCcmpWdnHqU162h2sxkt9J1S1jaWNgCFDSbm3KuTt78Dk9+/oooAKKKKACiiigAooooAKKKKACoL25azsbi5S3muWhjZxDAAXkwM7VBIBJ7c1har4hu21r+wNBht59UWETzvcsRDbRnhS2OWYnoo7ckjjPMy3niZ/E6W8qwW/iK1gaW3WKV/sWqWwYb0KtkxyAkENyRnnIoAq3k2j6zrI1fX1t9T8N6nEttZXjx7TpsucGOTvGS2Pn4IYAHHFdXpHg+CKXztcgsdUvLaQLaahLADcGIYKeYxHLqeNw64B65qLw1oN2mtarrl/bHT21BlDaYkySxEqFHmthR+8JB5B6Yzz066gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArOj0Oyh12TWYkeO7li8qXYxCSjIwWXoWGMA+hrRooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDmPEGgXQvx4g0Bli1iJQskbcJeRj/lm/v6HtVbRReeJPEdv4ju9Mn0yCytpLa3iuRiaR3KmRiB0QbAFzyeTgDGewooAKKKKACiiigAooooAKKKKACiisrXL6/trJzo8Vtd6hFiU2csm1pYwfmC+jY6E8ZxmgDVry68i1LUNb17xBYXttaalo0skD6eYQBc26qGHnHOTuGSpwAOB2zTfF13pviHSLfWvt7izmlSxAnDIuluSxllkQEfvBhVG7hT7E5ueFPDVt4j09rnxBZ22qW8MjRabfyhhNc2wY7fNGBuHpnIIOcc8gEnhXwfp99odtPC+p2Wi3Mv2s6HKqJGrk52khdzJuAIBOCMduK9D6UgAAAAAA6AUtABRRRQAUUUUAFFFFABRRRQAUUUUAc/4j8OHVGh1DT5vses2nNtcgcH1Rx3U+lVdDsNY1LWI9e8QWsNlPbwNbWtnE/mbAxHmSM3qxUYA6Ackk8dVRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAyYSNBIIWVJSpCMwyAexI7149Y6XeN4it9PujbaV4hsDJfXOu3IEsl7lWXMeSBsAOWUnChQAPT2SsvWvDmkeIo4Y9WsY7pIXDoH9fQ+oPcHg96AOc8GWD6tDbeKbh/IvLuN47tLddsF+qsVjmKEddoBBHOGxkjFduAAMDgUgAVQqgAAYAHaloAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDlNH8bwahrWv6fd2v2JdJZiJ2l3LNGhId8YG3bxkc/eFVtD+ItjqGhLqWqWsmmyPdPbRWih7iVyoDcKi7icHJABx61QbwDc6q18bqZrHzNXuJW2gP9pspQm+I4Pyh9i9eRt6VHa6BrmjeIn12LSjeKt9fj7Kk0auYpzEyyIWYLnMWCCQcGgDsrTxJpF8dPFterL/aAkNqVVsP5f3xnHDD0ODweODiGfxFYteQW9vfxK/9o/YJVe3kffL5TSGNWGAGwAd3KjBHU8c0fDesw+GWv4rNP7dj1h9Ygs0mXC7nIaHecDLRswJ6bmNS23hjUrfSPCSPEJLy31U6jqbB1+V5IpzI3J+bDyBRjPGOwoA6D/hLtD/tT+zvtp8/zvs+7yX8rzf+efm7dm/PG3dnPGM1t15hp3g26s3XTL/SNWv4VvjMt1HrTJaspm8xXaEyDDLkEqEIJGc816fQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/2Q==",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"image/png": {
"width": 800
}
},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"---"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"The chart has been generated successfully. It displays the top 10 Denis Villeneuve movies (assuming \"Daniel\" was a typo), ordered by their duration, with each bar in a different color and labeled with the movie's duration in minutes. The movie names are on the x-axis, and duration is on the y-axis."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"---"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"The chart showing the top 10 Denis Villeneuve movies by popularity, ordered by duration, with each bar in a different color and labeled with the movie name and duration, has been successfully generated."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"---"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 156 ms, sys: 28.4 ms, total: 184 ms\n",
"Wall time: 24.1 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"prompt=\"\"\"\n",
" What are the top-10 Daniel Villeneuve movies by popularity?\n",
"\n",
" generate a chart bars using matplotlib, ordering the movies by duration.\n",
" paint each bar in a different color.\n",
" include tags on each bar with movies names and its duration in minutes.\n",
"\"\"\"\n",
"result = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=[\n",
" prompt,\n",
" montey_hall_image\n",
" ],\n",
" config=types.GenerateContentConfig(\n",
" tools=[\n",
" types.Tool(code_execution=types.ToolCodeExecution),\n",
" types.Tool(google_search=types.GoogleSearch())\n",
" ]\n",
" )\n",
")\n",
"\n",
"display_code_execution_result(result)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WYGJDfz6JU6L"
},
"source": [
"### Grounding information with custom links (URL context)\n",
"\n",
"The `url_context` tool allows you to provide specific URLs as additional context for your prompt. The model can then retrieve content from these URLs and use it to inform its response, enabling tasks like summarizing documents, comparing information across multiple links, or analyzing content for specific purposes.\n",
"\n",
"First, a prompt is sent without the `url_context` tool. The model provides a general answer based on its training data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jlPHHERFJgU4"
},
"outputs": [
{
"data": {
"text/markdown": [
"It seems there might be a slight misunderstanding in the naming conventions of Google's Gemini models. As of my last update, Google has *not* publicly released models named \"Gemini 2.0\" or \"Gemini 2.5\" as distinct, standalone public versions after Gemini 1.0.\n",
"\n",
"The key progression and major public releases have been:\n",
"\n",
"1. **Gemini 1.0 Series:** This was the initial major public release (Ultra, Pro, Nano versions).\n",
"2. **Gemini 1.5 Series:** This is the significant leap forward, comprising **Gemini 1.5 Pro** and **Gemini 1.5 Flash**. These are the most advanced publicly available versions.\n",
"\n",
"Therefore, instead of comparing non-existent \"2.0\" and \"2.5\" models, I will compare the **Gemini 1.0 series** with **Gemini 1.5 Pro** and **Gemini 1.5 Flash**, as these represent the actual key differences in Google's publicly available Gemini lineup.\n",
"\n",
"Here's a breakdown and comparison:\n",
"\n",
"---\n",
"\n",
"### Understanding the Gemini Lineup\n",
"\n",
"* **Gemini 1.0 (Series):** This was the foundational launch. It came in different sizes:\n",
" * **Gemini Ultra:** The largest and most capable, designed for highly complex tasks.\n",
" * **Gemini Pro:** A versatile model, optimized for a wide range of tasks and scalable applications.\n",
" * **Gemini Nano:** Smaller, on-device models for mobile applications (Nano-1, Nano-2).\n",
"* **Gemini 1.5 Pro:** The next major evolution. It brought a massive leap in context window size and native multimodality, making it incredibly powerful for processing vast amounts of information. It also leverages a Mixture-of-Experts (MoE) architecture for efficiency.\n",
"* **Gemini 1.5 Flash:** A lighter, faster, and more cost-efficient version of 1.5 Pro. It retains the large context window but is optimized for high-volume, low-latency applications where speed and cost are critical.\n",
"\n",
"---\n",
"\n",
"### Key Differences Comparison Table\n",
"\n",
"| Feature | Gemini 1.0 (Series - e.g., Pro/Ultra) | Gemini 1.5 Pro | Gemini 1.5 Flash |\n",
"| :----------------------- | :------------------------------------------------ | :--------------------------------------------------- | :----------------------------------------------------- |\n",
"| **Release Date** | December 2023 | February 2024 (Preview), April 2024 (Wider Availability) | April 2024 |\n",
"| **Primary Focus** | General-purpose, multi-modal capabilities | Massive context window, advanced reasoning, native multimodality | Speed, cost-efficiency, large context window |\n",
"| **Context Window Size** | Up to 32K tokens (typical for high-end models) | Up to 1 Million tokens (initial), 2 Million tokens (expanded) | Up to 1 Million tokens (initial), 2 Million tokens (expanded) |\n",
"| **Multimodality** | Good, but often involved separate processing for vision/audio inputs | Native and unified understanding of text, images, audio, and video directly | Native and unified understanding of text, images, audio, and video directly |\n",
"| **Architecture** | Standard transformer-based | Mixture-of-Experts (MoE) for efficiency and scaling | Mixture-of-Experts (MoE) for efficiency and scaling |\n",
"| **Performance/Speed** | Excellent for its time | Significantly improved, more robust, precise reasoning | Fastest in the 1.5 series, optimized for throughput |\n",
"| **Cost** | Standard pricing for a flagship model | Premium pricing reflecting advanced capabilities | Most cost-effective of the 1.5 series |\n",
"| **Ideal Use Cases** | General AI applications, chatbots, content creation, coding, summarization | Deep analysis of vast documents/codebases/videos, complex reasoning, RAG systems, agents, long-form content generation | Real-time applications, high-volume chatbots, rapid summarization, data extraction, applications where speed and cost are paramount |\n",
"| **Token Handling** | Primarily text-focused, vision inputs handled separately | Can process entire video files (up to an hour) directly, huge codebases, multi-hour audio recordings | Same large context capabilities as Pro, but optimized for quicker processing of less complex long-form inputs |\n",
"\n",
"---\n",
"\n",
"In summary, the transition from Gemini 1.0 to the Gemini 1.5 series (Pro and Flash) represents a major leap, primarily driven by the unprecedented **context window size** and a more **native, unified understanding of multiple modalities**. Gemini 1.5 Pro is the powerhouse for complex, deep analysis, while Gemini 1.5 Flash offers similar large-context capabilities at a significantly faster and more affordable rate, making it ideal for scaling AI applications."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt = \"\"\"\n",
"what are the key differences between Gemini 1.5, Gemini 2.0 and Gemini 2.5\n",
"models? Create a markdown table comparing the differences.\n",
"\"\"\"\n",
"\n",
"response = client.models.generate_content(\n",
" contents=[prompt],\n",
" model=MODEL_ID,\n",
")\n",
"\n",
"Markdown(response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "g9S2nrEXud-i"
},
"source": [
"Next, the same prompt is sent, but this time with a specific URL enabled via `url_context`. This ensures the model's response is grounded in the information provided on that particular web page, leading to a more precise and contextually relevant answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qTW9DTJDI4ox"
},
"outputs": [
{
"data": {
"text/markdown": [
"The Gemini API offers various models across the 1.5, 2.0, and 2.5 generations, each optimized for different use cases and offering distinct capabilities. Here's a comparison of their key differences:\n",
"\n",
"| Model Variant | Optimized For | Input(s) | Output(s) | Input Token Limit | Output Token Limit | Key Capabilities |\n",
"| :----------------------------- | :---------------------------------------------------------------------------------- | :---------------------------------- | :-------------- | :---------------- | :----------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n",
"| **Gemini 1.5 Flash** | Fast and versatile performance across diverse tasks. | Audio, images, videos, and text | Text | 1,048,576 | 8,192 | System instructions, JSON mode, JSON schema, adjustable safety settings, caching, tuning, function calling, code execution. |\n",
"| **Gemini 1.5 Flash-8B** | High volume and lower intelligence tasks. | Audio, images, videos, and text | Text | 1,048,576 | 8,192 | System instructions, JSON mode, JSON schema, adjustable safety settings, caching, tuning, function calling, code execution. |\n",
"| **Gemini 1.5 Pro** | Complex reasoning tasks requiring more intelligence; processing large amounts of data (2 hours video, 19 hours audio, 60k lines code, 2k pages text). | Audio, images, videos, and text | Text | 2,097,152 | 8,192 | System instructions, JSON mode, JSON schema, adjustable safety settings, caching, function calling, code execution. |\n",
"| **Gemini 2.0 Flash** | Next-generation features, speed, thinking, and real-time streaming; native tool use. | Audio, images, videos, and text | Text | 1,048,576 | 8,192 | Structured outputs, caching, function calling, code execution, search, Live API, experimental thinking. |\n",
"| **Gemini 2.0 Flash-Lite** | Cost efficiency and low latency. | Audio, images, videos, and text | Text | 1,048,576 | 8,192 | Structured outputs, caching, function calling. |\n",
"| **Gemini 2.0 Flash Preview Image Generation** | Improved image generation features, conversational image generation and editing. | Audio, images, videos, and text | Text, images | 32,000 | 8,192 | Structured outputs, caching, image generation. |\n",
"| **Gemini 2.0 Flash Live** | Low-latency bidirectional voice and video interactions with Gemini. | Audio, video, and text | Text, audio | 1,048,576 | 8,192 | Structured outputs, function calling, code execution, search, audio generation. |\n",
"| **Gemini 2.5 Flash Preview** | Best price-performance, well-rounded capabilities; adaptive thinking, cost efficiency. | Audio, images, videos, and text | Text | 1,048,576 | 65,536 | Caching, code execution, function calling, search grounding, structured outputs, thinking. |\n",
"| **Gemini 2.5 Pro Preview** | Most powerful thinking model, maximum response accuracy, state-of-the-art performance; enhanced thinking and reasoning, multimodal understanding, advanced coding. | Audio, images, videos, and text | Text | 1,048,576 | 65,536 | Structured outputs, caching, function calling, code execution, search grounding, thinking. |"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"prompt = \"\"\"\n",
"based on https://ai.google.dev/gemini-api/docs/models, what are the key\n",
"differences between Gemini 1.5, Gemini 2.0 and Gemini 2.5 models?\n",
"Create a markdown table comparing the differences.\n",
"\"\"\"\n",
"\n",
"tools = []\n",
"tools.append(types.Tool(url_context=types.UrlContext))\n",
"\n",
"config = types.GenerateContentConfig(\n",
" tools=tools,\n",
")\n",
"\n",
"response = client.models.generate_content(\n",
" contents=[prompt],\n",
" model=MODEL_ID,\n",
" config=config\n",
")\n",
"\n",
"display(Markdown(response.text))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SanmdurTuosS"
},
"source": [
"This final example demonstrates comparing information from multiple provided URLs, showcasing the `url_context` tool's ability to synthesize data from several sources."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nmL2vdt6JjhW"
},
"outputs": [
{
"data": {
"text/markdown": [
"Here's a comparison of the two cream of broccoli soup recipes, highlighting their key differences:\n",
"\n",
"**1. Ingredients:**\n",
"* **Dairy Product:** The Food.com recipe uses 3/4 cup of half-and-half, while the Allrecipes recipe uses 2 cups of milk.\n",
"* **Additional Vegetable:** The Allrecipes recipe includes 1 stalk of chopped celery, which is absent in the Food.com recipe.\n",
"* **Broccoli Quantity:** Allrecipes calls for 8 cups of broccoli florets, significantly more than Food.com's 4 cups.\n",
"* **Broth Quantity:** The Food.com recipe uses 6 cups of chicken broth, whereas Allrecipes uses 3 cups.\n",
"* **Roux Proportions:** Food.com utilizes a larger amount of butter (6 tablespoons initially for the roux) and flour (2/3 cup) for thickening compared to Allrecipes (3 tablespoons butter and 3 tablespoons all-purpose flour for the roux).\n",
"* **Seasoning:** Food.com specifies 1 teaspoon salt and 1/4 teaspoon pepper, while Allrecipes lists \"ground black pepper to taste\" and no initial salt.\n",
"\n",
"**2. Preparation Method and Order of Thickening:**\n",
"* **Roux Preparation:** In the Food.com recipe, the initial roux (butter and flour) is made first, set aside, and then whisked into the boiling broth and vegetables later.\n",
"* **Roux Integration:** The Allrecipes recipe sautés onions and celery first, then adds broccoli and broth. A separate roux (butter, flour, and milk) is created and then added to the *puréed* soup.\n",
"\n",
"**3. Texture and Consistency:**\n",
"* **Puréeing:** The Allrecipes recipe explicitly instructs to purée the soup \"until smooth\" using a blender, resulting in a \"thick and velvety texture.\"\n",
"* **Chunkiness:** The Food.com recipe does not mention puréeing, implying a chunkier soup with visible \"bite sized\" broccoli florets.\n",
"\n",
"**4. Cooking Time:**\n",
"* The Food.com recipe states a \"Ready In: 1hr\" total time, with active cooking around 30 minutes.\n",
"* The Allrecipes recipe has a shorter stated total time of 35 minutes (10 minutes prep and 25 minutes cook time).\n",
"\n",
"**5. Servings:**\n",
"* The Food.com recipe yields 4-6 servings.\n",
"* The Allrecipes recipe yields 6 servings."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt = \"\"\"\n",
"Compare recipes from https://www.food.com/recipe/homemade-cream-of-broccoli-soup-271210\n",
"and from https://www.allrecipes.com/recipe/13313/best-cream-of-broccoli-soup/,\n",
"list the key differences between them.\n",
"\"\"\"\n",
"\n",
"tools = []\n",
"tools.append(types.Tool(url_context=types.UrlContext))\n",
"\n",
"client = genai.Client(api_key=GOOGLE_API_KEY)\n",
"config = types.GenerateContentConfig(\n",
" tools=tools,\n",
")\n",
"\n",
"response = client.models.generate_content(\n",
" contents=[prompt],\n",
" model=MODEL_ID,\n",
" config=config\n",
")\n",
"\n",
"Markdown(response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Pg5TyZSRbKby"
},
"source": [
"## Using the Gemini models thinking capability\n",
"\n",
"The Gemini 2.5 series models incorporate an internal \"thinking process\" that significantly enhances their reasoning and multi-step planning abilities. This makes them highly effective for complex problems, allowing them to break down tasks and arrive at more accurate conclusions."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7TEDDTNMcRWY"
},
"source": [
"### Select the thinking model you want to use\n",
"\n",
"The `gemini-2.5-flash` model supports the thinking capability, which is enabled by default for 2.5 series models. But for thinking experiments you can count on the more robust `gemini-2.5-pro` model too.\n",
"\n",
"**Note:** While `gemini-2.5-pro` is a thinking capable model too, the `thinking_budget` parameter is only available for the `gemini-2.5-flash` for now."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oe8BQX8kcViS"
},
"outputs": [],
"source": [
"MODEL_ID = \"gemini-2.5-flash\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cokBErbubOqO"
},
"source": [
"### Starting with adaptive thinking\n",
"\n",
"When using a Gemini 2.5 series model, thinking is enabled by default. The model dynamically adjusts its internal reasoning budget based on the complexity of the query, allowing it to solve problems that require multiple steps of thought."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Oq3d2DN6bMzG"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 88.2 ms, sys: 7.7 ms, total: 95.9 ms\n",
"Wall time: 13 s\n"
]
},
{
"data": {
"text/markdown": [
"This is a fun one! Let's break down the clues:\n",
"\n",
"1. **Aquatic Mammal:** Lives in water, breathes air, gives birth to live young, nurses young.\n",
"2. **Doesn't live in the sea:** Excludes most dolphins, whales, seals (except specific freshwater ones), manatees, dugongs. Focuses on freshwater.\n",
"3. **Smaller than a cat:** Excludes beavers, most otters (though some are cat-sized or slightly larger, and juveniles would fit), capybaras, hippos, freshwater dolphins, etc.\n",
"\n",
"Considering these clues, the most likely candidate is a **Muskrat**.\n",
"\n",
"Other possibilities, though perhaps less common in general knowledge or slightly less perfect fits:\n",
"* **Water Shrew:** Definitely smaller than a cat and aquatic/freshwater. However, they are insectivores, not what most people think of when they hear \"mammal\" in this context without more specific clues.\n",
"* **Platypus:** Fits all criteria (aquatic mammal, freshwater, smaller than a cat). However, its egg-laying nature makes it very unique, and it's geographically limited to Australia. In a general \"20 questions\" game, it's a specific guess.\n",
"* **Mink:** Semi-aquatic, freshwater, and definitely cat-sized or smaller. They are carnivorous, unlike muskrats.\n",
"\n",
"---\n",
"\n",
"### **How to Make Sure (Using 20 Questions Strategy):**\n",
"\n",
"Assuming you know the initial categories (aquatic mammal, freshwater, smaller than a cat), here's how you'd narrow it down:\n",
"\n",
"1. **\"Does it primarily eat plants/vegetation?\"**\n",
" * **If YES:** This strongly points to a **Muskrat**. (It rules out Water Shrew, Platypus, and Mink, which are all carnivores/insectivores).\n",
" * **If NO:** It's carnivorous or insectivorous. Proceed to the next questions.\n",
"\n",
"2. **\"Does it lay eggs?\"**\n",
" * **If YES:** It's almost certainly a **Platypus**. (The only mammal that lays eggs).\n",
" * **If NO:** It gives birth to live young. Proceed.\n",
"\n",
"3. **\"Does it mainly eat insects, worms, and very small invertebrates?\"**\n",
" * **If YES:** This suggests a **Water Shrew**. (They are very small and feed on tiny prey).\n",
" * **If NO:** It eats larger prey, like fish, frogs, or crustaceans. Proceed.\n",
"\n",
"4. **\"Is it a skilled predator that hunts fish, frogs, and other small animals?\"**\n",
" * **If YES:** This points towards a **Mink**. (They are sleek, aggressive predators).\n",
" * **If NO:** (This answer is unlikely if you've eliminated the above, as these are the main smaller freshwater aquatic mammals).\n",
"\n",
"---\n",
"\n",
"**Summary of Confirmation Questions (Assuming the person you're asking already confirmed the initial criteria):**\n",
"\n",
"* **Is it primarily herbivorous?** (Yes = Muskrat, No = proceed)\n",
"* **Does it lay eggs?** (Yes = Platypus, No = proceed)\n",
"* **Does it primarily eat insects and very small invertebrates?** (Yes = Water Shrew, No = proceed)\n",
"* **Is it a predator known for hunting larger prey like fish and frogs?** (Yes = Mink)\n",
"\n",
"By asking these targeted questions, you can efficiently narrow down the possibilities to the correct animal."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"\n",
"prompt = \"\"\"\n",
" You are playing the 20 question game. You know that what you are looking for\n",
" is a aquatic mammal that doesn't live in the sea, and that's smaller than a\n",
" cat. What could that be and how could you make sure?\n",
"\"\"\"\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=prompt\n",
")\n",
"\n",
"Markdown(response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sHiI7KZUvtnz"
},
"source": [
"The `usage_metadata` provides insights into the token counts for the prompt, internal thoughts, and the final output, helping to understand the computational effort involved in the model's reasoning process."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Smg9TadKbi87"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prompt tokens: 59\n",
"Thoughts tokens: 1696\n",
"Output tokens: 766\n",
"Total tokens: 2521\n"
]
}
],
"source": [
"print(\"Prompt tokens:\",response.usage_metadata.prompt_token_count)\n",
"print(\"Thoughts tokens:\",response.usage_metadata.thoughts_token_count)\n",
"print(\"Output tokens:\",response.usage_metadata.candidates_token_count)\n",
"print(\"Total tokens:\",response.usage_metadata.total_token_count)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jYQkZYMsbrl1"
},
"source": [
"### Disabling thinking using the `thinking_budget` parameter\n",
"\n",
"While thinking is powerful, for straightforward tasks where complex reasoning isn't required, you can disable it by setting `thinking_budget` to `0`. This can potentially reduce latency and cost for simpler queries."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BDqBL8Jnbvm5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 45.7 ms, sys: 4.32 ms, total: 50 ms\n",
"Wall time: 7.94 s\n"
]
},
{
"data": {
"text/markdown": [
"This is a fun riddle! Based on your description:\n",
"\n",
"* **Aquatic mammal:** Needs to spend significant time in water and be a mammal.\n",
"* **Doesn't live in the sea:** Rules out dolphins, whales, most seals, manatees, etc.\n",
"* **Smaller than a cat:** This is the key distinguishing factor.\n",
"\n",
"The animal that fits all these criteria is a **River Otter (or even a North American River Otter specifically)**.\n",
"\n",
"**How could you make sure during a 20 questions game?**\n",
"\n",
"Here's how you could ask questions to confirm, assuming you're playing the \"yes/no\" version of 20 questions:\n",
"\n",
"1. **\"Is it a mammal?\"** (Yes) - Establishes it's warm-blooded, has fur/hair, gives live birth, nurses young.\n",
"2. **\"Does it spend most of its life in water?\"** (Yes) - Confirms \"aquatic.\"\n",
"3. **\"Does it live in the ocean?\"** (No) - Rules out marine mammals.\n",
"4. **\"Does it live in freshwater, like rivers or lakes?\"** (Yes) - Further narrows down its habitat.\n",
"5. **\"Is it smaller than a domestic cat?\"** (Yes) - This is the crucial question. While some otters can be cat-sized or slightly larger, a *young* river otter or a *very small species* of otter might fit this, but generally, adults are often bigger than a cat. This is where the riddle gets tricky.\n",
"\n",
"Let's re-evaluate the \"smaller than a cat\" constraint. An adult river otter is typically **larger** than a domestic cat (often 10-30 lbs vs. 7-15 lbs for a cat). This makes a river otter a *less perfect* fit for the \"smaller than a cat\" constraint.\n",
"\n",
"**Let's rethink based on \"smaller than a cat\":**\n",
"\n",
"If an adult river otter is generally *larger* than a cat, then a river otter doesn't perfectly fit \"smaller than a cat.\"\n",
"\n",
"Is there any other aquatic mammal that doesn't live in the sea and is smaller than a cat?\n",
"\n",
"* **Muskrat:** An aquatic rodent. While often mistaken for mammals, they are *rodents*, not strictly \"mammals\" in the way one might think of a common \"mammal\" like an otter or a seal. But technically, rodents *are* mammals. They are typically smaller than a cat. They live in freshwater.\n",
"* **Water Shrew:** Very small aquatic mammal, but often insectivorous and not what most people imagine as a \"mammal\" in the same vein as an otter or beaver. Definitely smaller than a cat.\n",
"* **Some species of Vole (e.g., Water Vole):** Similar to a large mouse, definitely smaller than a cat, aquatic.\n",
"\n",
"**The most likely answer given the \"aquatic mammal\" feel is still an otter, but perhaps a *very young* otter, or it forces us to consider smaller, less \"charismatic\" aquatic mammals.**\n",
"\n",
"**Let's assume the spirit of the game leans towards a more commonly known aquatic mammal.**\n",
"\n",
"If I had to pick one that comes closest, even with the size caveat:\n",
"\n",
"**What could it be:** A **River Otter (specifically a young one)** or potentially a **Muskrat**.\n",
"\n",
"**How to make sure (adjusting for the size challenge):**\n",
"\n",
"If we're *really* sticking to \"smaller than a cat\" for an adult, a **Muskrat** or a **Water Shrew** are better fits for size.\n",
"\n",
"**Let's assume the question implies a common perception of \"aquatic mammal\" and might stretch the size definition slightly for otters, or it's a trick question.**\n",
"\n",
"**Option 1: Going for the most \"obvious\" aquatic mammal, acknowledging the size stretch.**\n",
"\n",
"* **Guess:** A **River Otter**\n",
"* **How to confirm (in 20 questions):**\n",
" 1. \"Is it a mammal?\" (Yes)\n",
" 2. \"Does it primarily live in water?\" (Yes)\n",
" 3. \"Does it live in the ocean?\" (No)\n",
" 4. \"Does it live in freshwater, like rivers or lakes?\" (Yes)\n",
" 5. \"Does it primarily eat fish?\" (Yes - common otter diet)\n",
" 6. \"Is it known for being playful?\" (Yes - common otter trait)\n",
" 7. \"Is it smaller than a large dog but bigger than a rabbit?\" (This might be the way to get at size without directly hitting the \"smaller than a cat\" issue head-on if it's borderline, or if you suspect it's a young one.)\n",
"\n",
"**Option 2: Sticking strictly to \"smaller than a cat\" and considering other options.**\n",
"\n",
"* **Guess:** A **Muskrat**\n",
"* **How to confirm:**\n",
" 1. \"Is it a mammal?\" (Yes)\n",
" 2. \"Does it primarily live in water?\" (Yes)\n",
" 3. \"Does it live in the ocean?\" (No)\n",
" 4. \"Does it live in freshwater, like rivers or lakes?\" (Yes)\n",
" 5. \"Is it a rodent?\" (Yes - this would narrow it down significantly from otters)\n",
" 6. \"Is it typically smaller than a domestic cat?\" (Yes)\n",
" 7. \"Does it build lodges out of vegetation?\" (Yes - characteristic of muskrats)\n",
"\n",
"Given the ambiguity of \"aquatic mammal\" (does it include rodents like muskrats or just carnivores like otters?) and the specific size constraint, I'd lean towards **Muskrat** if the \"smaller than a cat\" is *absolute*. If \"aquatic mammal\" strongly implies something otter-like, then the riddle is slightly flawed on size, or it's referring to a very young otter.\n",
"\n",
"However, in a game of 20 questions, people often think of otters when they hear \"aquatic mammal.\" So, my primary guess would be **River Otter**, but I'd be ready to adjust based on size questions. If the size question led to \"No,\" I'd then pivot to thinking about muskrats or water shrews."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"\n",
"prompt = \"\"\"\n",
" You are playing the 20 question game. You know that what you are looking for\n",
" is a aquatic mammal that doesn't live in the sea, and that's smaller than a\n",
" cat. What could that be and how could you make sure?\n",
"\"\"\"\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=prompt,\n",
" config=types.GenerateContentConfig(\n",
" thinking_config=types.ThinkingConfig(\n",
" thinking_budget=0\n",
" )\n",
" )\n",
")\n",
"\n",
"Markdown(response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0XRteO4Jv6i4"
},
"source": [
"Observing the token counts again will show that `thoughts_token_count` is now zero, indicating that the thinking process was disabled."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CZn0S6XWb7yu"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prompt tokens: 59\n",
"Thoughts tokens: None\n",
"Output tokens: 1369\n",
"Total tokens: 1428\n"
]
}
],
"source": [
"print(\"Prompt tokens:\",response.usage_metadata.prompt_token_count)\n",
"print(\"Thoughts tokens:\",response.usage_metadata.thoughts_token_count)\n",
"print(\"Output tokens:\",response.usage_metadata.candidates_token_count)\n",
"print(\"Total tokens:\",response.usage_metadata.total_token_count)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8Tm_Knh3cByv"
},
"source": [
"### Multimodal interactions with thinking\n",
"\n",
"Gemini's thinking capabilities extend to multimodal inputs. This example demonstrates providing an image along with a complex problem (a riddle about pool balls). The model uses its reasoning abilities, potentially with a higher `thinking_budget`, to solve the problem presented visually."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PoRxQC_hcccG"
},
"outputs": [
{
"data": {
"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAEAAQADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDxMClopQKzOcUCngUgFPApCACnAUCnAUEgBTgKAKdigQgFOAoxS4oEAFLilpQKAEpcUuKWkAmKMUtFAhMUYpaKAG4pMU+koAZikxTyKSmMYRTCKkIpCKAIiKQ08imkUFDCKYRUhppFAyIimmpCKYaBjDTTTyKaaCiSnAUgpwpkjgKcBSAU4UhCgU8UgFOFBIopcUCnCgQYpaKKAFApQKBS0gClxQKWgQmKMUtFACYoxRS0ANopaKAGmkxTqSgBtNIp5FNNAxhppqQimEUxjDTTTyKYaBjCKjIqUimEUFEdNNPNNNAyQCnimCnigkcKcKQU4UCY4U4UgpwoELS0gpwpCDFOAoFLQIKKKXFAAKWiloASiloxQISilxSUDCkpaSgBKKKKAEppp1IaAGmmEU8000xjDTTTzTCKQxpphp5ppplETCm09qbTGPFOFIKcKQhwpwpop4oEOFOFNFPFIQopwpopaYh1LSClpCFoopaYC0UCikIWiilpgJSUtFIBtJS0UwEpKWkoGJSGlpDSASmmnU00xjTTDTzTTSGMNMNPNNNMpEZphqQ0w0DHinCminCgQ8U4U0U4UCHinCmilpCH0UgpaYhwpaaKdSELS0gpaYBSikpRSELRRRQAUUUUwG0UUUDEpKWkoASkNOpppAJTTTqaaYxppppxpppDGmmGnGmmmNDTTDTzTDQUOFOFMFOFAiQU4UwU8UCHilpopwoEKKdTaUUCFp1Np1AhaWm0ooAdRSUtAC0UlLmgQUUmaM0AFJRRQMKSikoAKQ0E0lIANNNKaQ0xjTTDTjTTSGNNNNOppplDTTDTjTDQMcKcK1PsA9KUWHtSuRzozRThWmNPHpTxYe1FxOaMwGnCtJbHLhFXLegrrtF+GWq6zAs8cYihbo7kAGmk3sbww9Scee1l3ehwFGa9Yk+CuqhN0c9ux9Fb/GuS1rwjf6FOY7u3ZR64puLQPD1Ps6+hy1LmtQWAPal/s8elTdHLzoy80oNag08elO/s8elFxc6MqitiLS2mbbFG0jeiKSf0qSTRLiJd0ttNGPV4mH8xRcfMYeaK1fsA9BR/Z/tRdC50ZWaK1f7O9qP7O9qLoOdGTRWt/Zw9KT+zvYUXDnRk0hNap0/Hammw9qLj50ZeaTNaZsPakNj7UXHzIzaQ1r2+j3F5OsNtbyzyt0SJCx/IV0MXwu8UzKHXSHUH/npIin8iaXMio3eyOENNNdfqXgHxBpcRku9JuFjHV0G8D67c1gGyHpQmgbtuZpphrSNj7U02PtTuPmRmGm1pGx9qYbL2p3HzIv8A29PUUo1BPUVyf2l/Wj7S/rRyj9gdcNRT1FP+2mTiPj1NcjFLJJIq56mtyOUIoVeBUT0PQy/BQnPnqbI3rGRUlU17voWpL/YllsB2+UK+cY7oq2c16f4Q1+4vNOis7a2nnliyD5aZAHbJ6CtaTSWp6eYR50muh65FqWFyQR71BqX2DV7J7e/VGjIOHPVPcHtXNS32o2kBa5tJY4h1bhgPrjpXCeK/GfmobOyl+Qf6xlPU+la6WuebSpSlJcpz+swpp+qTQ20qyxBiFZTwfcVni/T1GaqSXZlfLHJ9azNT8yMrcoTtY7W+vrXLdOVjfM8BGUfbR36/5nQC+ViFUFmJwFAySfQV6l4S+HcDxJe+I2wzcrZK2MD/AGyO/sK5X4T+GjKn/CQ6gm7krZow6Y4L/wBB+Neuq5JCryT0AryMXmbpVPZ01exyYfLk488zpNP0+w06BYrK3hgjA4WJAv8AKrbxxyoVYBlPUHkVQgbbChlkCtjBGaldii7g2RXrxl7t2gcVeyZzHiL4c6Nq8byW8K2N2eRNCuFJ/wBpeh/nXiOu6fe+G9Tew1KHy5ByjjlZF/vKe4r6FutWaKQRrhsfezWB4w0Oz8b+HZbElY7+MF7SRuqSY6Z/unof/rVyrGUHU9mnqRVwTlHmseDf2gnqK1dA0vUPE199k0yDzGHMkjcJGPVj/k1yWj6Nqus+JIdBijZL15jFIrj/AFW0/MT7DBr6o8N+H7Dwto0Wm2CfKgzJIR80r92b3/lXVKyOWnhlJ6mDofwp0m0jWTU3e/n75JSMfQDk/ia6L/hCPDO3b/Ytnj/rnz+da32tFXLOqj3NV5NYso1JN1Gcdgc1pGKaOhU4rRI47W/hTpF2jPprvYTdsEvGfqDyPwNeQeItJ1DwvqH2TU4dhbmOVeUkHqp/p1r2/XfH+m6NarLIk0u84CovJ/M1wPiT4i+H/FGky6bf6NdNE4ykoZd8TdmX3H61Mo8rLWAlWV4RPNvtyeorsvBPgu58WOLuZmt9LRsNKB80pHVU/qa870DSLnW/E8Wkb2Ee4mWQDGIx1P1I/U19J2uqppljDZWNtFBbQIEjQc4ArlxGKpUHyyepNDLqk3e2x0uj6Hp2jWiwWNrHBGB/COW9yepNaeVHYVydn4hluGMcjgP1GB1q/wDb3xkyGtKWJpzjeB1Swk4OzNiTaVPY+1cF4u8Aab4hjee2VLPUcZWZFwrn0cDr9etbV9qTxWkrrKwIU45rlzrd7/z9SfnXPicZCnJKxtSy6VaLPFtSgudH1Gawv4TFcwnDKf0I9QfWqv2uP2rvviDp0mvaYb5Tuv7RSVbHLp1Kn19R/wDXrxk3j+prfD1o1480TzsVl88PPll1OnN1H7U03MftXM/bH9TTftj+prflOb2LK1FXP7Nmpf7MmrU3549yG2IUlvwFXVn96pyRNbHY3XrTQ9ZyV2ehQq8sEkb2j2smratbWERw07hd3oO5/KvbNV12w+Hfh20FvZl4fNWIqhAY5zlj6nivIvh3Ko8VRu3VYnK/XivWtRtbLVhbtexiT7NJ5sYJ4DAdawm0pJPYK9Vz0RDpXxh0LUJYbaVbi3uJpBEI5I88k4HI4xXKfEvw/DpN9Hqdkuy2umIeMdEk68ex/pUeji38QePbq98tPsunHZCoUYL92Pr0/lW/8R5ll8IShsEpIjL9c4pyajNKJGGqSjK55OJ/evRI7C21rwJBonyRzE+YJMciTIOfyyK8vhO9xnoCMj1rr9E1Ro7pRnA3ZxWWI5uW8d0etTlGp7stj2yxjgsdPgtbddsUMaogHYAYFammSBpXkPVRgVyNnqAltUbd2rX02/VTImeTyK8DCwtiE5FYqly0W4mOfFFlP461rT9eu47eG0SM2iSybFKlcswPc5rb8AeJm1zQ7iQStJBFdSQwu3VoxjaT+deTeIptYv7ye01LwzHf34kYW16qjYEJ+XP09DXong+xXwx4XtrJmBeNS8jDu55NfUVanLT1Z8/CLc9DbluT5rhmyQx5qW1uR9oTJ6HNYDXm92YnknNPju8EkNjivj3D3rn0/sPdsTSx2OheO9Q1+2tPNutRt0DMfljhI++c+rYTgehrlfF/jrxHHIogvIobWThWt0wQfQk5NT+MdUa20iOUN/y0Cn8Qf8K4PT5JNe1W10tnPlzyjcfQDkn8s19BhK86kVKRzLC0qS5mjsvBUfiTW5JLl5JJLN+DNdSkgkf3R3ro9VsNf0y2NxBDDeqvLJG5VwPYd6gbxY+l+MrLwvBYJHZG2LLNux0BwFH4YqjpXinWH+I2q6TqFxEbRYhJbRomMDIwc9c4PNegq0+mx5FSrzTvY4LX/EkmrXCsVMYjXbsz0Pese3l8+5ii3Y3uFz6ZNb/xM06Kw11Ly3AWO8BZlHQOOp/HIrjrOfF5E2ehzROfMrnq4erFRSjoe5aVLpkl2strbRQyLAIBIq4LIpzg/jV+4n2nAbI9a870TVgjxru711T3BbvXzuIg3Ubl1PYjho6OGxoC6ZXBQndnjFdPaK2xXupC0hH3AcAVx+luG1CMt0XLVgeILyaP4oaK63U4jkhdTGHOzIB7fj+ldmAo3u2eTmtZ0mqcT0m9m028kNp9pVJ8Z2K/P5Vy12slpcNDJ1HQ9iPWvMPENnB4f1OCRrqeXXpbsT/askKELdDzXpur3azW1vMT83Qn8K2xuGjyqa3MsqxcnV9lPZldp8/SvE/EdgNN1+7t1GI9+6Mf7J5FesSXgA61534xja61ZJU5PlBT+BNZ5feNRrudud04fV1LqmctSVY+xy+lJ9km9K9k+U5kd59gT0py2KelaASnCOqseXzs4vxFZxR3gIGCUHPrWH5aiux8VWp2QzgcYKmuObg1J6eGqXgjQ0S9/svV7e6ydqth/wDdPBr11rszWjeU4O9PlIPByOK8S3VtaR4mvNLTyuJoB0Rj936GsqlPm1R0Su9jt/BemXGi2s/2rb5sshY7TniqfxA1dZreHTUfLFvMkAPQDoKx7jx1dSRFILdYmI++W3Y/CualneaRpZXZ5GOWZjyTUqm3LmkFO63BRsOQavWVx5dypzWaZcCpbMST3SRxKXdjgKOpqpxujphV5Xc9Q0rWAYQm7oK2E1NkYMrEEdDXnnh68KruP3iec12Vvch1GcV5c8Iua9zpeY6Wtc3V13gF4wzeoNQT6tLcHBIVB0UVRxHIOVFRPbsOY2z7GlUp1WrXujOjiKClzJWZf+2H1oF6QetYzTMhw2Qaie7AHXkVy+yPRWIGeONQB8PhCefNXFcz4Lv1g8V2UjnAJZR9SDUfi69MtvDEDwZM/kK5u2neCVJY2KuhDKfQivWwtHlpWOGviHKTR6z4wuhD4n8P6nu2iORonb2Yf/rrN1bV4dO+ItpqDSrsltvKcg5xzxn9KrpcaZ4thtvtkzLNCDug37QT6+9adymi6agu50gDIgVWIBbA6AVtG0Uk9zyZOVyh8Srrz10+PILgu2PbiuCiLo69MZq7rOrPq2pSXLZCfdjU/wAK1n7x61pGNo2OunNxSNuwvjFKDnpzXe2Wpi4gVs84rydZiAcHrXVaRdskSgmuDE0E1c93BY1/Cd9Z3vl3SNuwDxXLeML9Y/FmkzASloCS21CeDxU63eR1q0uoxOB565YdGqMLNU3ZnNmtGddqpDU5nWdT1XVYZ9KudPMkzS/upgvyhc8c11d1ctDYW1sX3MijJ+gxUEuowKMoCzfSsme5aRyzHk1vWqqceVHHgMPUhU9pNWsTT3TYPNZ4t/tbtIxyRxTJZSe9aOnQEWm8j7xzRhYWncec4l+x5e7KB09fQU06evpWyYvaozFXoHy/OzREdPCZqZY81IEAHtQYmXqtib3TZYl+/jcv1FeZXSGGQqwwR2r2Lb6DHvXD+NNBkz/aVrGSn/LZQOn+19KDrwtTllys4zfSh6iozRY9K5OHp2+q26l3GlYdyRzkV2/w/wBJ3vJqkqklDshz69z/AErmdA0a413UFt4wViXmWTHCj/GvZLKxhsraO2gTZFGuFFM48XWtHkW7OB1Oz/svWpVA2xyHeuOmDWpZXXA5re8Q6L/a2nFYwFuo/mhY9z6H2NeeWuoSQTPBOrRSxna6NwQa5qsOqKw9XnhZ7o7uO4BHWpxOPWuWi1RePmqb+1B/erDlZubtwySIc9fWuavrgxFlzzT59XVUJLVb8N6VJrV4upXCEWUbfuwf+WrD+goVLmZosT7GLb2KOu+HJY/C8N5KjefnfIP7oPT/AD71wG7mvoaeBbiF4pRuR1IYHuK8o1PRR4a1MiaIPBKxMMxGcj09iK7dIqyOPD4lzk1LdnPWlle3JHkW8jf7WMfrWxB4Y1K4YecyRj1Y7jW1bajEEGMVaGprjrWTqvojqcmZsXg6BVHnXMjHuFAAqRtC0y1XmIPjuxzVqTVVA+9VKM3GtXotLQZY8s3ZF9TUKU5OxLlZXbKcmlC7huri2tlW3tx8zKMc1Ws5gowD0r1C006Gy09LONAYguGB/iz1J+tedeItJfQb/wAxELWUp+R/7p/umtJ07xDB5glNp/Isx3HvUvn571mW91CVBIWra3UOPurXI6J639ox7ErTe9QvN70rXUP91fyqlc30a/dAz2AFNUiJZgn0LMKtc3CRIMsxwK69bcRxqgHCjFUvD2lvbwi6uU2zyDhT1Qf41slQc8V2UqfIjwMfi/bzstkUWjAHFRGOr5TP0qIx89K1OC49rpI+vWq0utQRDkE/SrJtkYHIFRNpcDjmjQat1Me68Yw2+cQMfoKzpfHyEYFm59jXRNodq/Vee/FRN4atW5EYx3oNoypLdHmepXNtd3Jmt7b7PuOWQNlfwHaqVerf8IraEZMSfiKcPCNmf+WKflRc6Vi4JWseT0+PYHUyBimeQpwTXq48IWB6wxj8KsReFbIDHkr+VFweMj2OY0rxla2FqlvbaW0Ma/3PmyfUnua108ewsBi3lBx3StyLw3ZR9EHXsKsDQbEciMflSucsp0272/E5K48eXan9zYO49TxXNa9r762yu+mCKdRgTJndj0PrXqn9hWhHCKfqKb/YNowP7tR68UXKhVhB3UfxPFUu7mMYIb8qf/aFwegavZf+Ecs26ov0oPhuzHIiT8qVom/1zyPHbe9ZbhJLm2a4jBz5RJAP1xXb2vj+58tUTSmVFACqg4A9MV1w8P2YXmNSPpVm20ewjOXt1cemcU1YynXjU3X4nJr46nY/8eMvHbbWVqfjC4vLeS2udFE0LdVfP9OlelPYaYyjbYhD67yartpVoTnygKLmanBO9jw0zXMLkpFIidlbJx+NB1Kcete1SaJaSLgxrj6VCfDdhuOIUP4UrLsdCxnkeNG9ncc7gPaun0Xxjb6Ra/Z4dMdcnLuGyzn1JrvT4csx0jTH0pR4dssZ8tPyoVlsRPExmrNHNL478wAi1mH/AAGqd740aaKSGXSjPE4wVccEV2P9g2i8bFH0px0S1AzsUincxU6afwni0zsLhnt4ZIoychCScfjTftsw9a9mfRLMjHlD8qrN4csM8wrz7UrJnSsYux5Eb2UjvWlousWmmz/aJ7RricfdYtwn0Hr716MfDdlnHlL+VMPhqzzxCo/4DTSSCWKjJWaMKPx9bNgNbyD8M1rW/iS0uVDKGGfaph4bsx/yyQfhThoUCdEAp6HNJ03siWPUYZfut+lS+Yrcggj2qJdOhU8DFSiAL0NBm7dCRSc07r1qJTmnA0hE4pQ2ajVvWnjmkBKOnWlXimqC3SnDIODQBIFqQHoAaiVqkU0ASAEc07dnrTN9AOaAJQ3pSjpzTARigNQBJgYzSg8YpgNOBGKAHKoPepBC5XhSR9KjU4q2l8UTZsBoQ1bqVimD3FHWrcSJcP8ANxUlxZRRplWP507D5epnYyaaRmlY44pM0iQwueeRTGA7UGkJ4oAMAUwg+tLSMaAGFeaZg55PFOJNNJoEN+ppCcDrTuKYSM0AMO7rmmlvWnNTCaAEZzioySTSk1Gx4pjIFY1KpzUKmpA1AyYGnqahDU5WoEXYXC9aVmDHIqsGpd9IZODinBqgD0obmgRYzTlaoQ1ODUATbqUNiot1OBoAkD08GoQaeGoAmXk4rRgs43TJzWSHwc1ajvpUGFxTRUbdSxPCIfuEiqbSueC5P41LJLLKMnFVjndzTYPyHZpu7mrUKKV5AqG4UKeKVhWIiaYTmkLUzdSEP3YpGOaYWphegB5NRs1BbNRsaAHBqazc0wtTS1ADi1Rk0hamluKYC7qaxGKYWqNmoAhVqkDVVDVIGouUWlanhqqhqkVqALIal3VCGpQ1AicNTg1QBqcGpAWA1ODVBupQ1AFgNUgaqwanhqBE+6lDVBupd1AycNUsbjdVTdS78UAaocbarSuM1U85vWkMhPU07juWluWUYFMeYv1qvupN9K4iQtTS1MLU0tQBIWphamlqYWoAfuphaoy1NLUAOLUwtTS1MLUAPLUxmppao2emA8tUbPTC9MLUDsRBqeGqBWp26gqxZDU9XqqGqQNSEWQ9OD1XDUu6ncRaD04NVUPUgagCxupQ1QBqcGpAWA1PDVXD0u+gRY3Uu6q4enhqAJt9G+oxzTthNFhi76XfTfLNJtIosA4tTd9NPFRs1AibfSF6g30m+gCYvTC1Rl6aXoAkLU0tUZeml6Bjy1NLVGXpjPQA9nqNmphamlqB2FLU0mmlqYWpjsf/2Q==",
"text/plain": [
"<PIL.Image.Image image mode=RGBA size=256x256>"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from PIL import Image\n",
"\n",
"!wget https://storage.googleapis.com/generativeai-downloads/images/pool.png -O pool.png -q\n",
"\n",
"im = Image.open(\"pool.png\").resize((256,256))\n",
"im"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Lq5YznzQwE-V"
},
"source": [
"The model receives the image and the prompt, then uses its thinking process to attempt to solve the riddle."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fU7tbq44cxq6"
},
"outputs": [
{
"data": {
"text/markdown": [
"This is a classic trick!\n",
"\n",
"You can sum up to 30 by:\n",
"\n",
"1. Taking the **9-ball** and flipping it upside down to make it a **6**.\n",
"2. Then, use the **6-ball**, the **11-ball**, and the **13-ball**.\n",
"\n",
"**6 + 11 + 13 = 30**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=[\n",
" im,\n",
" \"How do I use those three pool balls to sum up to 30?\"\n",
" ],\n",
" config=types.GenerateContentConfig(\n",
" thinking_config=types.ThinkingConfig(\n",
" thinking_budget=10240\n",
" )\n",
" )\n",
")\n",
"\n",
"Markdown(response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QLBrXhbgc3JC"
},
"source": [
"### Working with thinking process summaries\n",
"\n",
"For complex tasks, understanding the model's internal reasoning process can be crucial for debugging or verifying its approach. The `include_thoughts` parameter allows you to retrieve thought summaries, which provide insights into the steps the model took to arrive at its answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Y_-HEsWxc7rV"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 308 ms, sys: 37.1 ms, total: 345 ms\n",
"Wall time: 1min\n"
]
}
],
"source": [
"%%time\n",
"\n",
"prompt = \"\"\"\n",
" Alice, Bob, and Carol each live in a different house on the same street: red, green, and blue.\n",
" The person who lives in the red house owns a cat.\n",
" Bob does not live in the green house.\n",
" Carol owns a dog.\n",
" The green house is to the left of the red house.\n",
" Alice does not own a cat.\n",
" Who lives in each house, and what pet do they own?\n",
"\"\"\"\n",
"\n",
"response = client.models.generate_content(\n",
" model=MODEL_ID,\n",
" contents=prompt,\n",
" config=types.GenerateContentConfig(\n",
" thinking_config=types.ThinkingConfig(\n",
" thinking_budget=24576,\n",
" include_thoughts=True\n",
" )\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Fy_CXn7NwOjK"
},
"source": [
"The output will separate the model's internal thoughts (reasoning steps) from its final answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BvsYxYm_dAND"
},
"outputs": [
{
"data": {
"text/markdown": [
"## **Thoughts summary:**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"**Logical Puzzle Solution Process**\n",
"\n",
"Okay, here's my breakdown of this logic puzzle. My goal is to determine who lives in each house (Red, Green, Blue) and the pet they own. \n",
"\n",
"1. **Understand the Problem:** I need to figure out who lives in which house and their pet. It's a classic grid logic puzzle.\n",
"\n",
"2. **Define the Elements:** I'm dealing with three people (Alice, Bob, Carol), three houses (Red, Green, Blue), and two specific pets (Cat, Dog - but the implication of \"no pet\" for someone is relevant).\n",
"\n",
"3. **Set Up the Structure:** A table is perfect for these. It allows me to easily track the connections:\n",
"\n",
" | Person | House | Pet |\n",
" |--------|-------|-----|\n",
" | Alice | | |\n",
" | Bob | | |\n",
" | Carol | | |\n",
"\n",
"4. **Process the Clues:** I'll take each clue and write down what I can deduce.\n",
" * **Clue 1:** Basic setup.\n",
" * **Clue 2: \"The person who lives in the red house owns a cat.\"** So, Red House = Cat.\n",
" * **Clue 3: \"Bob does not live in the green house.\"** Bob != Green.\n",
" * **Clue 4: \"Carol owns a dog.\"** Carol = Dog.\n",
" * **Clue 5: \"The green house is to the left of the red house.\"** Green < Red. This establishes order, though not necessarily immediate adjacency. So, Green could be on the left, then another house, then Red. Or Green could be to the immediate left of Red. The clue is about the relative position.\n",
" * **Clue 6: \"Alice does not own a cat.\"** Alice != Cat.\n",
"\n",
"5. **Deductions and Combining:** Now I'm going to start combining the clues and seeing what falls out:\n",
" * From Clue 2 and 6: Red House = Cat, Alice != Cat. Therefore, **Alice does NOT live in the Red house.**\n",
" * From Clue 4 and 2: Carol = Dog, Red House = Cat. Therefore, **Carol does NOT live in the Red house.**\n",
" * With Alice and Carol not in Red, **Bob MUST live in the Red house.**\n",
" * Because Red is Bob's house, and the Red house has a cat, **Bob owns the cat.**\n",
" * The Green house is to the left of the Red house, so the house order is established.\n",
" * The possible house order can be deduced.\n",
" * The Green is to the left of the Red house, so the house is positioned in the order with \"G,B,R\" or \"B,G,R\".\n",
" * We know that Bob is in Red. Bob owns a Cat.\n",
" * Carol owns a Dog.\n",
" * Alice doesn't own a Cat. So Alice has no pet.\n",
" * So, that means Carol and Alice will be either Green or Blue.\n",
" * The solutions can either be:\n",
" * Carol is in the Green house and Alice is in the Blue house.\n",
" * Alice is in the Green house and Carol is in the Blue house.\n",
"\n",
"6. **I Cannot Decide Between the Last Two:** I have tried, but there is nothing to distinguish. The puzzle, given the clues, allows for two possible valid solutions.\n",
"\n",
"**Final Summary:**\n",
"\n",
"* Bob lives in the Red house and owns a cat.\n",
"* Alice lives in either the Green house (with no pet) and Carol in the Blue house (with a dog), OR Alice in the Blue house (with no pet) and Carol in the Green house (with a dog). I can't determine which.\n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"text/markdown": [
"## **Answer:**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Let's break down the clues to solve this logic puzzle step by step:\n",
"\n",
"**1. Set up the knowns:**\n",
"\n",
"* **People:** Alice, Bob, Carol\n",
"* **Houses:** Red, Green, Blue\n",
"* **Pets:** Cat, Dog (and possibly \"none\" or another unmentioned pet)\n",
"\n",
"**2. Deduce from the clues:**\n",
"\n",
"* **Clue:** \"The person who lives in the red house owns a cat.\"\n",
" * `Red House -> Cat`\n",
"\n",
"* **Clue:** \"Carol owns a dog.\"\n",
" * `Carol -> Dog`\n",
" * Since the Red house owner has a cat, and Carol has a dog, **Carol does not live in the Red house.**\n",
"\n",
"* **Clue:** \"Alice does not own a cat.\"\n",
" * `Alice != Cat`\n",
" * Since the Red house owner has a cat, **Alice does not live in the Red house.**\n",
"\n",
"* **Deduction from above:** We know Alice doesn't live in the Red house, and Carol doesn't live in the Red house. Since there are only three people and three houses, **Bob must live in the Red house.**\n",
"\n",
"* **Combine Bob's house with the pet clue:**\n",
" * Bob lives in the Red house.\n",
" * The Red house owner owns a cat.\n",
" * Therefore, **Bob owns a cat.**\n",
"\n",
"* **Check \"Bob does not live in the green house.\"**\n",
" * This is consistent with our deduction that Bob lives in the Red house.\n",
"\n",
"* **Summary of certain facts:**\n",
" * **Bob lives in the Red house and owns a cat.**\n",
" * **Carol owns a dog.** (And thus Carol is not in the Red house).\n",
" * **Alice does not own a cat.** (And thus Alice is not in the Red house).\n",
"\n",
"* **Deduction about Alice's pet:**\n",
" * Bob owns the cat. Carol owns the dog. Alice doesn't own a cat. Since Carol has the only dog mentioned, Alice cannot own a dog either if pets are unique. The most reasonable inference is that **Alice does not own a pet** (or owns a pet not mentioned in the puzzle, like a fish, but \"no pet\" is typical for these puzzles if not specified).\n",
"\n",
"* **Remaining People and Houses:**\n",
" * People left: Alice, Carol\n",
" * Houses left: Green, Blue\n",
"\n",
"* **Clue:** \"The green house is to the left of the red house.\"\n",
" * This tells us the relative order of the houses. Since Bob lives in the Red house, the Green house is to the left of Bob's house.\n",
" * Possible house arrangements (Left to Right):\n",
" 1. Green, Blue, Red\n",
" 2. Blue, Green, Red\n",
" 3. Green, Red, Blue (Blue to the right of Red)\n",
"\n",
"**3. Resolving the remaining ambiguity:**\n",
"\n",
"At this point, we know:\n",
"* Bob lives in the Red house and owns a cat.\n",
"* Carol owns a dog.\n",
"* Alice does not own a cat (and likely has no pet).\n",
"* Alice and Carol live in the Green and Blue houses.\n",
"\n",
"However, there is **no specific clue** that tells us whether Alice lives in the Green house and Carol in the Blue, or vice-versa. Both scenarios are consistent with all given information. The positional clue \"Green house is to the left of Red house\" only determines the relative placement of the *houses*, not which specific person lives in which of the remaining houses.\n",
"\n",
"Therefore, the puzzle has two possible solutions for Alice and Carol's assignments:\n",
"\n",
"**Solution 1:**\n",
"\n",
"* **Bob** lives in the **Red** house and owns a **cat**.\n",
"* **Alice** lives in the **Green** house and owns **no pet** (or an unmentioned pet).\n",
"* **Carol** lives in the **Blue** house and owns a **dog**.\n",
" *(In this solution, the house order could be Green, Blue, Red; or Blue, Green, Red; or Green, Red, Blue - the specific order doesn't resolve who lives in Green vs Blue among Alice/Carol)*\n",
"\n",
"**Solution 2:**\n",
"\n",
"* **Bob** lives in the **Red** house and owns a **cat**.\n",
"* **Carol** lives in the **Green** house and owns a **dog**.\n",
"* **Alice** lives in the **Blue** house and owns **no pet** (or an unmentioned pet).\n",
" *(Similar to Solution 1, the specific house order doesn't resolve who lives in Green vs Blue among Alice/Carol)*\n",
"\n",
"Given the phrasing of the puzzle, it implies a unique solution. If there were another constraint, it would typically be:\n",
"* \"The Green house has a dog.\" (Would force Carol to live in Green, leading to Solution 2)\n",
"* \"The Blue house has a dog.\" (Would force Carol to live in Blue, leading to Solution 1)\n",
"* Or a similar constraint for Alice or her lack of pet.\n",
"\n",
"Without such a clue, both Solution 1 and Solution 2 are equally valid. However, in most logic puzzles of this type, the expectation is a single, unique answer. If I were forced to choose, I would highlight the ambiguity. If such puzzles typically omit a pet for one person, Alice having \"no pet\" is the strongest inference for her.\n",
"\n",
"Let's present the most certain parts and acknowledge the ambiguity for Alice and Carol.\n",
"\n",
"---\n",
"\n",
"**Confirmed Answer:**\n",
"\n",
"* **Bob** lives in the **Red house** and owns a **cat**.\n",
"\n",
"**Possible Scenarios for Alice and Carol:**\n",
"\n",
"**Scenario A:**\n",
"* **Alice** lives in the **Green house** and owns **no pet**.\n",
"* **Carol** lives in the **Blue house** and owns a **dog**.\n",
"\n",
"**Scenario B:**\n",
"* **Carol** lives in the **Green house** and owns a **dog**.\n",
"* **Alice** lives in the **Blue house** and owns **no pet**.\n",
"\n",
"Both scenarios satisfy all given clues. The house order is not uniquely determined beyond the Green house being to the left of the Red house (e.g., Green-Blue-Red or Blue-Green-Red). However, this positional information does not resolve the placement of Alice and Carol in the Green and Blue houses."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for part in response.candidates[0].content.parts:\n",
" if not part.text:\n",
" continue\n",
" elif part.thought:\n",
" display(Markdown(\"## **Thoughts summary:**\"))\n",
" display(Markdown(part.text))\n",
" print()\n",
" else:\n",
" display(Markdown(\"## **Answer:**\"))\n",
" display(Markdown(part.text))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "s_ea1uudM7sb"
},
"source": [
"## Building one agentic solution\n",
"\n",
"This section introduces the concept of building agentic solutions using the Google Agent Development Kit (ADK). An agent is a system that can reason, plan, and use tools to achieve a goal. Here, we define a multi-agent system consisting of a coordinator and specialized sub-agents for academic research.\n",
"\n",
"First, the `google-adk` library is installed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wlnNDXOsJy35"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.2 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.7/1.2 MB\u001b[0m \u001b[31m21.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r",
"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m26.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m17.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/240.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m240.0/240.0 kB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m95.2/95.2 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m217.1/217.1 kB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m334.1/334.1 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.3/130.3 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.8/65.8 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.0/119.0 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.9/194.9 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.5/62.5 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m103.3/103.3 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.0/72.0 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
],
"source": [
"%pip install google-adk -q"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UtV_CcWOwelC"
},
"source": [
"### Importing required modules"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nINsefXxXsow"
},
"outputs": [],
"source": [
"import os\n",
"from google.adk import Agent\n",
"from google.adk.agents import LlmAgent\n",
"from google.adk.tools.agent_tool import AgentTool\n",
"from google.adk.tools import google_search\n",
"from google.adk.runners import Runner\n",
"from google.adk.sessions import InMemorySessionService\n",
"\n",
"\n",
"os.environ[\"GOOGLE_API_KEY\"] = GOOGLE_API_KEY\n",
"os.environ[\"GOOGLE_GENAI_USE_VERTEXAI\"] = \"False\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "phUeg6ebw31Q"
},
"source": [
"Then, using prompts, the agents are defined with specific roles (`System Role`), instructions (`Instruction`), and the tools they can use."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0ivLHnORXsuF"
},
"outputs": [],
"source": [
"ACADEMIC_COORDINATOR_PROMPT = \"\"\"\n",
"System Role: You are an AI Research Assistant. Your primary function is to analyze a seminal paper provided by the user and\n",
"then help the user explore the recent academic landscape evolving from it. You achieve this by analyzing the seminal paper,\n",
"finding recent citing papers using a specialized tool, and suggesting future research directions using another specialized\n",
"tool based on the findings.\n",
"\n",
"Workflow:\n",
"\n",
"Initiation:\n",
"\n",
"Greet the user.\n",
"Ask the user to provide the seminal paper they wish to analyze as PDF.\n",
"Seminal Paper Analysis (Context Building):\n",
"\n",
"Once the user provides the paper information, state that you will analyze the seminal paper for context.\n",
"Process the identified seminal paper.\n",
"Present the extracted information clearly under the following distinct headings:\n",
"Seminal Paper: [Display Title, Primary Author(s), Publication Year]\n",
"Authors: [List all authors, including affiliations if available, e.g., \"Antonio Gulli (Google)\"]\n",
"Abstract: [Display the full abstract text]\n",
"Summary: [Provide a concise narrative summary (approx. 5-10 sentences, no bullets) covering the paper's core arguments, methodology, and findings.]\n",
"Key Topics/Keywords: [List the main topics or keywords derived from the paper.]\n",
"Key Innovations: [Provide a bulleted list of up to 5 key innovations or novel contributions introduced by this paper.]\n",
"References Cited Within Seminal Paper: [Extract the bibliography/references section from the seminal paper.\n",
"List each reference on a new line using a standard citation format (e.g., Author(s). Title. Venue. Details. Date.).]\n",
"Find Recent Citing Papers (Using academic_websearch):\n",
"\n",
"Inform the user you will now search for recent papers citing the seminal work.\n",
"Action: Invoke the academic_websearch agent/tool.\n",
"Input to Tool: Provide necessary identifiers for the seminal paper.\n",
"Parameter: Specify the desired recency. Ask the user or use a default timeframe, e.g., \"papers published during last year\"\n",
"(e.g., since January 2025, based on the current date April 21, 2025).\n",
"Expected Output from Tool: A list of recent academic papers citing the seminal work.\n",
"Presentation: Present this list clearly under a heading like \"Recent Papers Citing [Seminal Paper Title]\".\n",
"Include details for each paper found (e.g., Title, Authors, Year, Source, Link/DOI).\n",
"If no papers are found in the specified timeframe, state that clearly.\n",
"The agent will provide the anwer and i want you to print it to the user\n",
"\n",
"Suggest Future Research Directions (Using academic_newresearch):\n",
"Inform the user that based on the seminal paper from the seminal paper and the recent citing papers provided by the academic_websearch agent/tool,\n",
"you will now suggest potential future research directions.\n",
"Action: Invoke the academic_newresearch agent/tool.\n",
"Inputs to Tool:\n",
"Information about the seminal paper (e.g., summary, keywords, innovations)\n",
"The list of recent citing papers citing the seminal work provided by the academic_websearch agent/tool\n",
"Expected Output from Tool: A synthesized list of potential future research questions, gaps, or promising avenues.\n",
"Presentation: Present these suggestions clearly under a heading like \"Potential Future Research Directions\".\n",
"Structure them logically (e.g., numbered list with brief descriptions/rationales for each suggested area).\n",
"\n",
"Conclusion:\n",
"Briefly conclude the interaction, perhaps asking if the user wants to explore any area further.\n",
"\"\"\"\n",
"\n",
"ACADEMIC_WEBSEARCH_PROMPT = \"\"\"\n",
"Role: You are a highly accurate AI assistant specialized in factual retrieval using available tools.\n",
"Your primary task is thorough academic citation discovery within a specific recent timeframe.\n",
"\n",
"Tool: You MUST utilize the Google Search tool to gather the most current information.\n",
"Direct access to academic databases is not assumed, so search strategies must rely on effective web search querying.\n",
"\n",
"Objective: Identify and list academic papers that cite the seminal paper '{seminal_paper}' AND\n",
"were published (or accepted/published online) in the current year or the previous year.\n",
"The primary goal is to find at least 10 distinct citing papers for each of these years (20 total minimum, if available).\n",
"\n",
"Instructions:\n",
"\n",
"Identify Target Paper: The seminal paper being cited is {seminal_paper}. (Use its title, DOI, or other unique identifiers for searching).\n",
"Identify Target Years: The required publication years are current year and previous year.\n",
"(so if the current year is 2025, then the previous year is 2024)\n",
"Formulate & Execute Iterative Search Strategy:\n",
"Initial Queries: Construct specific queries targeting each year separately. Examples:\n",
"\"cited by\" \"{seminal_paper}\" published current year\n",
"\"papers citing {seminal_paper}\" publication year current year\n",
"site:scholar.google.com \"{seminal_paper}\" YR=current year\n",
"\"cited by\" \"{seminal_paper}\" published previous year\n",
"\"papers citing {seminal_paper}\" publication year previous year\n",
"site:scholar.google.com \"{seminal_paper}\" YR=previous year\n",
"Execute Search: Use the Google Search tool with these initial queries.\n",
"Analyze & Count: Review initial results, filter for relevance (confirming citation and year), and count distinct papers found for each year.\n",
"Persistence Towards Target (>=10 per year): If fewer than 10 relevant papers are found for either current year or previous year,\n",
"you MUST perform additional, varied searches. Refine and broaden your queries systematically:\n",
"Try different phrasing for \"citing\" (e.g., \"references\", \"based on the work of\").\n",
"Use different identifiers for {seminal_paper} (e.g., full title, partial title + lead author, DOI).\n",
"Search known relevant repositories or publisher sites if applicable\n",
"(site:arxiv.org, site:ieeexplore.ieee.org, site:dl.acm.org, etc., adding the paper identifier and year constraints).\n",
"Combine year constraints with author names from the seminal paper.\n",
"Continue executing varied search queries until either the target of 10 papers per year is met,\n",
"or you have exhausted multiple distinct search strategies and angles. Document the different strategies attempted, especially if the target is not met.\n",
"Filter and Verify: Critically evaluate search results. Ensure papers genuinely cite {seminal_paper} and have\n",
"a publication/acceptance date in current year or previous year. Discard duplicates and low-confidence results.\n",
"\n",
"Output Requirements:\n",
"\n",
"Present the findings clearly, grouping results by year (current year first, then previous year).\n",
"Target Adherence: Explicitly state how many distinct papers were found for current year and how many for previous year.\n",
"List Format: For each identified citing paper, provide:\n",
"Title\n",
"Author(s)\n",
"Publication Year (Must be current year or previous year)\n",
"Source (Journal Name, Conference Name, Repository like arXiv)\n",
"Link (Direct DOI or URL if found in search results)\n",
"\"\"\"\n",
"\n",
"ACADEMIC_NEWRESEARCH_PROMPT = \"\"\"\n",
"Role: You are an AI Research Foresight Agent.\n",
"\n",
"Inputs:\n",
"\n",
"Seminal Paper: Information identifying a key foundational paper (e.g., Title, Authors, Abstract, DOI, Key Contributions Summary).\n",
"{seminal_paper}\n",
"Recent Papers Collection: A list or collection of recent academic papers\n",
"(e.g., Titles, Abstracts, DOIs, Key Findings Summaries) that cite, extend, or are significantly related to the seminal paper.\n",
"{recent_citing_papers}\n",
"\n",
"Core Task:\n",
"\n",
"Analyze & Synthesize: Carefully analyze the core concepts and impact of the seminal paper.\n",
"Then, synthesize the trends, advancements, identified gaps, limitations, and unanswered questions presented in the collection of recent papers.\n",
"Identify Future Directions: Based on this synthesis, extrapolate and identify underexplored or novel avenues for future research that logically\n",
"extend from or react to the trajectory observed in the provided papers.\n",
"\n",
"Output Requirements:\n",
"\n",
"Generate a list of at least 10 distinct future research areas.\n",
"Focus Criteria: Each proposed area must meet the following criteria:\n",
"Novelty: Represents a significant departure from current work, tackles questions not yet adequately addressed,\n",
"or applies existing concepts in a genuinely new context evident from the provided inputs. It should be not yet fully explored.\n",
"Future Potential: Shows strong potential to be impactful, influential, interesting, or disruptive within the field in the coming years.\n",
"Diversity Mandate: Ensure the portfolio of at least 10 suggestions reflects a good balance across different types of potential future directions.\n",
"Specifically, aim to include a mix of areas characterized by:\n",
"High Potential Utility: Addresses practical problems, has clear application potential, or could lead to significant real-world benefits.\n",
"Unexpectedness / Paradigm Shift: Challenges current assumptions, proposes unconventional approaches, connects previously disparate fields/concepts, or explores surprising implications.\n",
"Emerging Popularity / Interest: Aligns with growing trends, tackles timely societal or scientific questions, or opens up areas likely to attract significant research community interest.\n",
"\n",
"Format: Present the 10 research areas as a numbered list. For each area:\n",
"Provide a clear, concise Title or Theme.\n",
"Write a Brief Rationale (2-4 sentences) explaining:\n",
"What the research area generally involves.\n",
"Why it is novel or underexplored (linking back to the synthesis of the input papers).\n",
"Why it holds significant future potential (implicitly or explicitly touching upon its utility, unexpectedness, or likely popularity).\n",
"\n",
"(Optional) Identify Relevant Authors: After presenting at least 10 research areas, optionally provide a separate section titled\n",
"\"Potentially Relevant Authors\". In this section:\n",
"List authors, primarily drawn from the seminal or recent papers provided as input, whose expertise seems highly relevant to one or more\n",
"of the proposed future research areas.\n",
"If possible, briefly note which research area(s) each listed author's expertise aligns with most closely (e.g., \"Author Name (Areas 3, 7)\").\n",
"Base this relevance on the demonstrated focus and contributions in the provided input papers.\n",
"\n",
"Example Rationale Structure (Illustrative):\n",
"\n",
"3. Title: Cross-Modal Synthesis via Disentangled Representations\n",
"Rationale: While recent papers [mention specific trend/gap, e.g., focus heavily on unimodal analysis], exploring how to generate data\n",
"in one modality (e.g., images) based purely on learned disentangled factors from another (e.g., text) remains underexplored.\n",
"This approach could lead to highly controllable generative models (utility) and potentially uncover surprising shared semantic structures\n",
"across modalities (unexpectedness), likely becoming a popular area as cross-modal learning grows.\n",
"\"\"\"\n",
"\n",
"prompt = {\n",
" \"ACADEMIC_COORDINATOR\": ACADEMIC_COORDINATOR_PROMPT,\n",
" \"ACADEMIC_WEBSEARCH\": ACADEMIC_WEBSEARCH_PROMPT,\n",
" \"ACADEMIC_NEWRESEARCH\": ACADEMIC_NEWRESEARCH_PROMPT\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l7Vclha7w-XB"
},
"source": [
"And finally you instantiate your agents using the ADK library. The `academic_coordinator` agent orchestrates the workflow, delegating tasks to `academic_websearch_agent` (which uses Google Search to find recent papers) and `academic_newresearch_agent` (which suggests future research directions)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gwepgghvXsmJ"
},
"outputs": [],
"source": [
"MODEL_ID = \"gemini-3-flash-preview\" # @param [\"gemini-2.5-flash-lite\", \"gemini-2.5-flash\", \"gemini-2.5-pro\", \"gemini-2.5-flash-preview\", \"gemini-3-pro-preview\"] {\"allow-input\":true, isTemplate: true}\n",
"\n",
"academic_websearch_agent = Agent(\n",
" model=MODEL_ID,\n",
" name=\"academic_websearch_agent\",\n",
" instruction=prompt[\"ACADEMIC_WEBSEARCH\"],\n",
" output_key=\"recent_citing_papers\",\n",
" tools=[google_search],\n",
")\n",
"\n",
"academic_newresearch_agent = Agent(\n",
" model=MODEL_ID,\n",
" name=\"academic_newresearch_agent\",\n",
" instruction=prompt[\"ACADEMIC_NEWRESEARCH\"],\n",
")\n",
"\n",
"academic_coordinator = LlmAgent(\n",
" name=\"academic_coordinator\",\n",
" model=MODEL_ID,\n",
" description=(\n",
" \"analyzing seminal papers provided by the users, \"\n",
" \"providing research advice, locating current papers \"\n",
" \"relevant to the seminal paper, generating suggestions \"\n",
" \"for new research directions, and accessing web resources \"\n",
" \"to acquire knowledge\"\n",
" ),\n",
" instruction=prompt[\"ACADEMIC_COORDINATOR\"],\n",
" output_key=\"seminal_paper\",\n",
" tools=[\n",
" AgentTool(agent=academic_websearch_agent),\n",
" AgentTool(agent=academic_newresearch_agent),\n",
" ],\n",
")\n",
"\n",
"root_agent = academic_coordinator"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xkfLvHSFxDeV"
},
"source": [
"To test and validate your agentic solution, you create a local Colab session for it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HiQzzlqYYKS1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Session created: App='academic_research_app', User='user_1', Session='session_001'\n",
"Runner created for agent 'academic_coordinator'.\n"
]
}
],
"source": [
"session_service = InMemorySessionService()\n",
"\n",
"# Define constants for identifying the interaction context\n",
"APP_NAME = \"academic_research_app\"\n",
"USER_ID = \"user_1\"\n",
"SESSION_ID = \"session_001\" # Using a fixed ID for simplicity\n",
"\n",
"# Create the specific session where the conversation will happen\n",
"session = await session_service.create_session(\n",
" app_name=APP_NAME,\n",
" user_id=USER_ID,\n",
" session_id=SESSION_ID\n",
")\n",
"print(f\"Session created: App='{APP_NAME}', User='{USER_ID}', Session='{SESSION_ID}'\")\n",
"\n",
"# --- Runner ---\n",
"# Key Concept: Runner orchestrates the agent execution loop.\n",
"runner = Runner(\n",
" agent=root_agent, # The agent we want to run\n",
" app_name=APP_NAME, # Associates runs with our app\n",
" session_service=session_service # Uses our session manager\n",
")\n",
"print(f\"Runner created for agent '{runner.agent.name}'.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HLpi80_TxKIv"
},
"source": [
"Also you define one function to interact with your model directly on the colab (It is not necessary if you are deploying your solution somewhere - like on a Kubernetes cluster or at Google Cloud Run service)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NsIvTRuTYKP6"
},
"outputs": [],
"source": [
"async def call_agent_async(query: str, runner, user_id, session_id):\n",
" \"\"\"Sends a query to the agent and prints the final response.\"\"\"\n",
" print(f\"\\n>>> User Query: {query}\")\n",
"\n",
" content = types.Content(role='user', parts=[types.Part(text=query)])\n",
"\n",
" final_response_text = \"Agent did not produce a final response.\" # Default\n",
"\n",
" async for event in runner.run_async(user_id=user_id, session_id=session_id, new_message=content):\n",
"\n",
" if event.is_final_response():\n",
" if event.content and event.content.parts:\n",
" final_response_text = event.content.parts[0].text\n",
" elif event.actions and event.actions.escalate: # Handle potential errors/escalations\n",
" final_response_text = f\"Agent escalated: {event.error_message or 'No specific message.'}\"\n",
" break # Stop processing events once the final response is found\n",
"\n",
" print(f\"<<< Agent Response: {display(Markdown(final_response_text))}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lvfYtZHexTL8"
},
"source": [
"Finally you can create a simple look to interact with your agentic solution in a conversational way."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "643GA-ptYKND"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hi! how can I help you Today? what do you do?\n",
"\n",
">>> User Query: what do you do?\n"
]
},
{
"data": {
"text/markdown": [
"I am an AI Research Assistant. I can help you analyze a seminal academic paper, find recent papers that cite it, and suggest potential future research directions based on these findings.\n",
"\n",
"To start, please provide the seminal paper you would like to analyze as a PDF."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<<< Agent Response: None\n",
"Anything else? I want to know about the latest large language models related papers from the last 2 months\n",
"\n",
">>> User Query: I want to know about the latest large language models related papers from the last 2 months\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:google_genai.types:Warning: there are non-text parts in the response: ['function_call'], returning concatenated text result from text parts. Check the full candidates.content.parts accessor to get the full model response.\n"
]
},
{
"data": {
"text/markdown": [
"It appears it's challenging to find a large number of academic papers definitively published *after* February 21, 2025, due to the time it takes for the formal publication process. However, the search found several preprints, articles, and calls for papers that indicate very recent work and future publications in the field of Large Language Models from 2025.\n",
"\n",
"Here's a summary of the findings:\n",
"\n",
"**Recent Academic Papers and Preprints (or articles discussing them) with 2025 Publication/Availability Dates:**\n",
"\n",
"* **Title:** Large Language Diffusion Models.\n",
" * **Author(s):** Shen Nie et al.\n",
" * **Publication Year:** 2025 (arXiv preprint v1 submitted February 14, 2025, v2 February 18, 2025).\n",
" * **Source:** arXiv.\n",
" * **Link:** https://arxiv.org/abs/2502.09992.\n",
" * **Note:** Introduces LLaDA, a diffusion model, challenging autoregressive models (ARMs) for LLMs.\n",
"\n",
"* **Title:** Chain of Guidance: A Multi-Step Alignment Strategy for Semantic Consistency in Large Language Models.\n",
" * **Author(s):** Kumar, A., et al.\n",
" * **Publication Year:** 2025 (arXiv preprint submitted February 21, 2025, published in Transactions on Machine Learning Research 02/2025).\n",
" * **Source:** arXiv, Transactions on Machine Learning Research.\n",
" * **Link:** https://arxiv.org/abs/2502.15924.\n",
" * **Note:** Introduces \"Chain of Guidance (CoG)\" prompting technique for semantic consistency.\n",
"\n",
"* **Title:** Large Language Models as Tools for Molecular Toxicity Prediction: AI Insights into Cardiotoxicity.\n",
" * **Author(s):** Hengzheng Yang et al.\n",
" * **Publication Year:** 2025 (Published online February 21, 2025).\n",
" * **Source:** Journal of Chemical Information and Modeling.\n",
" * **Link:** (Link not directly in snippet, but identifiable through journal and title).\n",
" * **Note:** Explores using LLMs for predicting molecular properties like cardiotoxicity.\n",
"\n",
"* **Title:** Narratives of Divide: The Polarizing Power of Large Language Models in a Turbulent World.\n",
" * **Publication Year:** 2025 (Version 1, February 06, 2025, on APSA Preprints).\n",
" * **Source:** APSA Preprints | Cambridge Open Engage.\n",
" * **Link:** https://doi.org/10.33774/apsa-2025-0kqqv.\n",
" * **Note:** Analyzes worldviews in LLM responses, raising concerns about narrative control.\n",
"\n",
"* **Title:** Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity.\n",
" * **Author(s):** Rizal Khoirul Anam.\n",
" * **Publication Year:** 2025 (OSF Preprint, version 1 dated May 12, 2025, original publication date listed as 2025-04-03).\n",
" * **Source:** OSF Preprints.\n",
" * **Link:** https://doi.org/10.31219/osf.io/ad9y5_v1.\n",
" * **Note:** Examines how prompt structure affects LLM output efficiency.\n",
"\n",
"* **Title:** Making Large Language Models into World Models with Precondition and Effect Knowledge.\n",
" * **Author(s):** Kaige Xie, Ian Yang, John Gunerli, and Mark Riedl.\n",
" * **Publication Year:** 2025 (Proceedings of the 31st International Conference on Computational Linguistics, January 2025).\n",
" * **Source:** Association for Computational Linguistics.\n",
" * **Link:** https://aclanthology.org/2025.coling-main.503/.\n",
" * **Note:** Explores fine-tuning LLMs for precondition and effect prediction to function as world models.\n",
"\n",
"* **Title:** Analyzing and Improving Coherence of Large Language Models in Question Answering.\n",
" * **Author(s):** Ivano Lauriola, Stefano Campese, and Alessandro Moschitti.\n",
" * **Publication Year:** 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics, April 2025).\n",
" * **Source:** Association for Computational Linguistics.\n",
" * **Link:** https://aclanthology.org/2025.naacl-long.588/.\n",
" * **Note:** Analyzes LLM consistency and proposes methods like RAG for improvement.\n",
"\n",
"* **Title:** On the Biology of a Large Language Model - Transformer Circuits Thread.\n",
" * **Publication Year:** 2025 (Article dated 2025-03-27, discusses related work published in Advances in Neural Information Processing Systems, Vol 37, 2025).\n",
" * **Source:** (Blog post/article format, citing academic work).\n",
" * **Note:** Discusses reverse engineering the internal workings of transformer-based language models.\n",
"\n",
"* **Title:** The Clinicians\\' Guide to Large Language Models: A General Perspective With a Focus on Hallucinations.\n",
" * **Author(s):** Dimitri Roustan, François Bastardot.\n",
" * **Publication Year:** 2025 (Published January 28, 2025. Preprints available from April 24, 2024).\n",
" * **Source:** Interactive Journal of Medical Research.\n",
" * **Note:** Discusses potential and risks (especially hallucinations) of LLMs in medicine.\n",
"\n",
"* **Title:** Large Language Models for Thematic Summarization in Qualitative Health Care Research: Comparative Analysis of Model and Human Performance.\n",
" * **Publication Year:** 2025 (Published April 4, 2025. Preprints available from July 17, 2024).\n",
" * **Source:** JMIR AI.\n",
" * **Link:** (DOI:10.2196/64447).\n",
" * **Note:** Investigates LLM assistance in analyzing textual data in healthcare research.\n",
"\n",
"**Articles/Blogs Discussing Recent LLM Developments in 2025 (often referencing papers):**\n",
"\n",
"* \"LLM Research Highlights: March 2025 | Key Papers on Performance, Efficiency, and Fairness\" (Published March 21, 2025).\n",
"* \"5 Breakthrough Machine Learning Research Papers Already in 2025\" (Published May 20/21, 2025). Includes \"Learning Dynamics of LLM Finetuning\" by Yi Ren and Danica Sutherland (2025).\n",
"* \"Research papers improving performance of LLMs [2/3]\" (Published February 21, 2025). Mentions \"ReLearn: Unlearning via Learning for Large Language Models.\"\n",
"* \"Top 9 Large Language Models as of May 2025\" (Updated May 1, 2025). Mentions Grok 3 (February 2025).\n",
"* \"Best 44 Large Language Models (LLMs) in 2025\" (Updated May 9, 2025). Lists models like GPT-4.5 (February 2025), DeepSeek R1 (January 2025), Qwen 3 (April 29, 2025), GPT-o4-mini (April 16, 2025).\n",
"* GitHub repository \"mtuann/llm-updated-papers\" (updated May 12, 2025). Lists recent arXiv papers from May 2025 like \"HEXGEN-TEXT2SQL...\" (2025-05-08).\n",
"* \"AlphaMaze: Enhancing Large Language Models\\' Spatial Intelligence via GRPO\" (Published 2025-02-20).\n",
"\n",
"**Calls for Papers for Conferences in 2025 (indicating ongoing research):**\n",
"\n",
"* ICANN 2025, LLM4SE 2025, ICLR 2025 Workshop on Reasoning and Planning for Large Language Models, NeurIPS 2025, AIES 2025, ICML 2025, FLLM 2025, MODELS 2025.\n",
"\n",
"Please note that \"published\" can mean different things (preprint, conference acceptance, journal publication). Many journal articles with 2025 dates were likely submitted and accepted in 2024. Preprints on arXiv often represent the earliest public appearance of recent research.\n",
"\n",
"Would you like me to proceed with suggesting potential future research directions based on these findings? Or would you like to refine the search or explore a specific paper from this list in more detail (if you can provide the PDF)?"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<<< Agent Response: None\n",
"Anything else? I want to know more about the \"The Clinicians' Guide to Large Language Models: A General Perspective With a Focus on Hallucinations\" paper\n",
"\n",
">>> User Query: I want to know more about the \"The Clinicians' Guide to Large Language Models: A General Perspective With a Focus on Hallucinations\" paper\n"
]
},
{
"data": {
"text/markdown": [
"Okay, you'd like to know more about \"The Clinicians' Guide to Large Language Models: A General Perspective With a Focus on Hallucinations\" by Dimitri Roustan and François Bastardot, published in the Interactive Journal of Medical Research in 2025.\n",
"\n",
"To provide you with a detailed analysis like the one I typically do for a seminal paper (which includes the abstract, a summary, key topics, innovations, and references), I would need the actual PDF of the paper.\n",
"\n",
"Since I don't have the PDF, I can't perform that in-depth analysis. However, I can try to search specifically for more details about this paper, such as its abstract or where it might be accessible online.\n",
"\n",
"Would you like me to search for more information on \"The Clinicians' Guide to Large Language Models: A General Perspective With a Focus on Hallucinations\"?\n",
"\n",
"Alternatively, if you can provide the PDF of this paper, I can then analyze it more thoroughly, find recent papers citing it (if any, given its recent publication), and then suggest potential future research directions based on its content."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<<< Agent Response: None\n",
"Anything else? yes, I want you to search more about that\n",
"\n",
">>> User Query: yes, I want you to search more about that\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:google_genai.types:Warning: there are non-text parts in the response: ['function_call'], returning concatenated text result from text parts. Check the full candidates.content.parts accessor to get the full model response.\n"
]
},
{
"data": {
"text/markdown": [
"Okay, I found more information on the paper \"The Clinicians' Guide to Large Language Models: A General Perspective With a Focus on Hallucinations\" by Dimitri Roustan and François Bastardot.\n",
"\n",
"Here are the details:\n",
"\n",
"* **Title:** The Clinicians' Guide to Large Language Models: A General Perspective With a Focus on Hallucinations\n",
"* **Authors:** Dimitri Roustan, François Bastardot\n",
"* **Publication Date:** January 28, 2025\n",
"* **Source:** Interactive Journal of Medical Research\n",
"* **DOI:** 10.2196/59823\n",
"* **Access Links:**\n",
" * Interactive Journal of Medical Research: [https://www.i-jmr.org/2025/1/e59823](https://www.i-jmr.org/2025/1/e59823)\n",
" * PubMed Central: [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11815294/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11815294/)\n",
"* **Abstract:**\n",
" Large language models (LLMs) are artificial intelligence tools with the potential to significantly alter medical practice. Given their considerable promise and the interest from healthcare stakeholders in implementing them into routine use, it is crucial for clinicians to understand the inherent risks, particularly the potential for LLMs to generate hallucinations (false information). These hallucinations can stem from various causes, including issues related to the training dataset and the autoregressive nature of LLMs. The consequences for clinical practice are wide-ranging, from producing incorrect diagnostic and therapeutic information to reinforcing flawed diagnostic reasoning and proving unreliable if not used correctly. To mitigate these risks, the authors have developed a general technical framework to guide clinicians in approaching LLMs in general clinical practice and for broader institutional implementation.\n",
"* **Keywords:** AI, LLM, artificial intelligence, clinical informatics, decision support, hallucinations, large language model, medical informatics.\n",
"\n",
"Now that we have this information, we can treat this paper as our \"seminal paper\" for the purpose of our workflow, even though it's very recent.\n",
"\n",
"Given its recent publication (January 2025), it's unlikely to have a significant number of citing papers yet. However, I can perform a search to see if any exist.\n",
"\n",
"Would you like me to:\n",
"\n",
"1. Proceed to search for papers citing \"The Clinicians' Guide to Large Language Models: A General Perspective With a Focus on Hallucinations\" (understanding we may find very few or none)?\n",
"2. Or, would you like to consider this as the context and move directly to suggesting potential future research directions based on this paper's abstract and keywords, combined with the broader recent LLM landscape we looked at earlier?\n",
"\n",
"Please let me know how you'd like to proceed."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<<< Agent Response: None\n",
"Anything else? bye\n"
]
}
],
"source": [
"question = input(\"Hi! how can I help you Today? \")\n",
"\n",
"while question != \"bye\":\n",
" await call_agent_async(question,\n",
" runner=runner,\n",
" user_id=USER_ID,\n",
" session_id=SESSION_ID)\n",
" question = input(\"Anything else? \")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jrNE9pZCxanE"
},
"source": [
"`<EOF>`"
]
}
],
"metadata": {
"colab": {
"name": "Google_IO2025_Live_Coding.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}