rl/massive.py

import sys
import torch as th
from math import log10 as math_log10

TEN = th.Tensor

NodesSycamoreN12M14 = [
    [10, 11, 1], [12, 13, 0], [6, 7, 3], [8, 9, 2], [6, 7, 5], [8, 9, 4], [10, 21, 2, 4], [11, 16, 2, 4],
    [12, 16, 3, 5], [13, 22, 3, 5], [17, 19, 0, 6], [14, 15, 0, 7], [14, 15, 1, 8], [18, 20, 1, 9], [17, 18, 11, 12],
    [19, 20, 11, 12], [21, 22, 7, 8], [27, 28, 10, 14], [29, 30, 14, 13], [23, 24, 10, 15], [25, 26, 15, 13],
    [23, 24, 6, 16], [25, 26, 16, 9], [27, 38, 19, 21], [28, 33, 19, 21], [29, 33, 20, 22], [30, 39, 20, 22],
    [34, 36, 17, 23], [31, 32, 17, 24], [31, 32, 18, 25], [35, 37, 18, 26], [34, 35, 28, 29], [36, 37, 28, 29],
    [38, 39, 24, 25], [44, 45, 27, 31], [46, 47, 31, 30], [40, 41, 27, 32], [42, 43, 32, 30], [40, 41, 23, 33],
    [42, 43, 33, 26], [44, 36, 38, 50], [45, 50, 36, 38], [46, 50, 37, 39], [47, 50, 37, 39], [34, 40, 48, 49],
    [48, 49, 34, 41], [48, 49, 35, 42], [48, 49, 35, 43], [44, 45, 46, 47], [44, 45, 46, 47], [40, 41, 42, 43]
]
NodesSycamoreN14M14 = [
    [12, 6, 1], [7, 15, 0], [8, 9, 3], [10, 11, 2], [8, 9, 5], [10, 11, 4], [16, 13, 7, 0], [16, 14, 6, 1],
    [12, 24, 2, 4], [13, 19, 2, 4], [14, 19, 3, 5], [15, 25, 3, 5], [20, 22, 0, 8], [17, 18, 6, 9], [17, 18, 7, 10],
    [21, 23, 1, 11], [26, 27, 6, 7], [20, 21, 13, 14], [22, 23, 13, 14], [24, 25, 9, 10], [32, 26, 12, 17],
    [27, 35, 17, 15], [28, 29, 12, 18], [30, 31, 18, 15], [28, 29, 8, 19], [30, 31, 19, 11], [36, 33, 16, 20],
    [36, 34, 16, 21], [32, 44, 22, 24], [33, 39, 22, 24], [34, 39, 23, 25], [35, 45, 23, 25], [40, 42, 20, 28],
    [37, 38, 26, 29], [37, 38, 27, 30], [41, 43, 21, 31], [46, 47, 26, 27], [40, 41, 33, 34], [42, 43, 33, 34],
    [44, 45, 29, 30], [52, 46, 32, 37], [47, 55, 37, 35], [48, 49, 32, 38], [50, 51, 38, 35], [48, 49, 28, 39],
    [50, 51, 39, 31], [53, 36, 40, 47], [46, 54, 36, 41], [52, 42, 44, 58], [53, 58, 42, 44], [54, 58, 43, 45],
    [55, 58, 43, 45], [40, 48, 56, 57], [56, 57, 46, 49], [56, 57, 47, 50], [56, 57, 41, 51], [52, 53, 54, 55],
    [52, 53, 54, 55], [48, 49, 50, 51]
]

NodesSycamoreN53M12 = [
    [19, 20, 3], [20, 43, 6], [21, 22, 19, 7], [22, 25, 0, 11], [23, 26, 21, 12], [24, 28, 13], [25, 29, 1, 14],
    [26, 30, 2, 15], [27, 31, 23, 55], [28, 32, 36], [29, 33, 24, 16], [30, 34, 3, 17], [31, 35, 4, 58],
    [33, 36, 5, 39], [34, 37, 6, 18], [35, 38, 7, 60], [37, 39, 10, 41], [38, 40, 11, 61], [40, 41, 14, 127],
    [0, 128, 104, 2], [62, 42, 1, 0], [42, 63, 2, 4], [43, 44, 3, 2], [44, 65, 4, 8], [66, 45, 5, 10], [45, 46, 6, 3],
    [46, 47, 7, 4], [47, 69, 8, 51], [70, 48, 9, 5], [48, 49, 10, 6], [49, 50, 11, 7], [50, 51, 12, 8], [74, 52, 9],
    [52, 53, 13, 10], [53, 54, 14, 11], [54, 55, 15, 12], [78, 56, 9, 13], [56, 57, 16, 14], [57, 58, 17, 15],
    [81, 59, 13, 16], [59, 60, 18, 17], [83, 61, 16, 18], [62, 63, 20, 21], [85, 64, 1, 22], [64, 65, 22, 23],
    [66, 67, 24, 25], [67, 68, 25, 26], [68, 69, 26, 27], [70, 71, 28, 29], [71, 72, 29, 30], [72, 73, 30, 31],
    [73, 93, 31, 27], [74, 75, 32, 33], [75, 76, 33, 34], [76, 77, 34, 35], [77, 97, 35, 8], [78, 79, 36, 37],
    [79, 80, 37, 38], [80, 100, 38, 12], [81, 82, 39, 40], [82, 102, 40, 15], [83, 103, 41, 17], [129, 84, 20, 42],
    [84, 106, 42, 21], [85, 86, 43, 44], [86, 109, 44, 23], [133, 87, 24, 45], [87, 88, 45, 46], [88, 89, 46, 47],
    [89, 113, 47, 27], [137, 90, 28, 48], [90, 91, 48, 49], [91, 92, 49, 50], [92, 93, 50, 51], [201, 94, 32, 52],
    [94, 95, 52, 53], [95, 96, 53, 54], [96, 97, 54, 55], [118, 98, 36, 56], [98, 99, 56, 57], [99, 100, 57, 58],
    [122, 101, 39, 59], [101, 102, 59, 60], [125, 103, 41, 61], [105, 104, 62, 63], [107, 105, 43, 64],
    [108, 106, 64, 65], [110, 107, 66, 67], [111, 108, 67, 68], [112, 109, 68, 69], [114, 110, 70, 71],
    [115, 111, 71, 72], [116, 112, 72, 73], [117, 113, 73, 51], [118, 114, 74, 75], [119, 115, 75, 76],
    [120, 116, 76, 77], [121, 117, 77, 55], [122, 119, 78, 79], [123, 120, 79, 80], [124, 121, 80, 58],
    [125, 123, 81, 82], [126, 124, 82, 60], [127, 126, 83, 61], [147, 130, 19, 84], [128, 131, 84, 85],
    [149, 132, 63, 86], [129, 134, 85, 87], [130, 135, 86, 88], [152, 136, 65, 89], [153, 138, 87, 90],
    [131, 139, 88, 91], [132, 140, 89, 92], [69, 93, 196, 200], [133, 141, 90, 94], [134, 142, 91, 95],
    [135, 143, 92, 96], [136, 159, 93, 97], [137, 160, 94, 78], [138, 144, 95, 98], [139, 145, 96, 99],
    [140, 163, 97, 100], [141, 164, 98, 81], [142, 146, 99, 101], [143, 166, 100, 102], [144, 167, 101, 83],
    [145, 168, 102, 103], [146, 169, 103, 18], [170, 148, 19, 105], [171, 150, 62, 107], [147, 151, 104, 108],
    [148, 154, 105, 111], [149, 155, 106, 112], [175, 156, 66, 114], [150, 157, 107, 115], [151, 158, 108, 116],
    [152, 159, 109, 117], [179, 160, 70, 118], [153, 161, 110, 119], [154, 162, 111, 120], [155, 163, 112, 121],
    [156, 164, 114, 122], [157, 165, 115, 123], [158, 166, 116, 124], [161, 167, 119, 125], [162, 168, 120, 126],
    [165, 169, 123, 127], [170, 172, 104, 130], [170, 173, 128, 131], [190, 174, 106, 132], [171, 176, 129, 134],
    [172, 177, 130, 135], [192, 178, 109, 136], [193, 180, 110, 138], [173, 181, 131, 139], [174, 182, 132, 140],
    [175, 183, 133, 141], [176, 184, 134, 142], [177, 185, 135, 143], [178, 204, 136, 117], [179, 205, 137, 118],
    [180, 186, 138, 144], [181, 187, 139, 145], [182, 207, 140, 121], [183, 208, 141, 122], [184, 188, 142, 146],
    [185, 209, 143, 124], [186, 210, 144, 125], [187, 210, 145, 126], [188, 146, 127], [189, 128, 148, 147],
    [189, 129, 150, 191], [190, 191, 147, 151], [191, 194, 148, 154], [192, 195, 149, 155], [193, 197, 133, 156],
    [194, 198, 150, 157], [195, 199, 151, 158], [196, 200, 152, 159], [197, 201, 137, 160], [198, 202, 153, 161],
    [199, 203, 154, 162], [200, 204, 155, 163], [202, 205, 156, 164], [203, 206, 157, 165], [204, 207, 158, 166],
    [206, 208, 161, 167], [207, 209, 162, 168], [209, 210, 165, 169], [171, 170, 190], [189, 172, 149],
    [171, 173, 172, 192], [191, 174, 152], [175, 153, 194], [193, 176, 173, 195], [194, 177, 174, 196], [195, 178, 113],
    [179, 175, 198], [197, 180, 176, 199], [198, 181, 177, 200], [199, 182, 178, 113], [74, 179, 202],
    [201, 183, 180, 203], [202, 184, 181, 204], [203, 185, 182, 159], [160, 183, 206], [205, 186, 184, 207],
    [206, 187, 185, 163], [164, 186, 209], [208, 188, 187, 166], [167, 188, 168]
]
NodesSycamoreN53M14 = [
    [19, 20, 3], [20, 43, 6], [21, 22, 19, 7], [22, 25, 0, 11], [23, 26, 21, 12], [24, 28, 13], [25, 29, 1, 14],
    [26, 30, 2, 15], [27, 31, 23, 55], [28, 32, 36], [29, 33, 24, 16], [30, 34, 3, 17], [31, 35, 4, 58],
    [33, 36, 5, 39], [34, 37, 6, 18], [35, 38, 7, 60], [37, 39, 10, 41], [38, 40, 11, 61], [40, 41, 14, 127],
    [0, 128, 104, 2], [62, 42, 1, 0], [42, 63, 2, 4], [43, 44, 3, 2], [44, 65, 4, 8], [66, 45, 5, 10], [45, 46, 6, 3],
    [46, 47, 7, 4], [47, 69, 8, 51], [70, 48, 9, 5], [48, 49, 10, 6], [49, 50, 11, 7], [50, 51, 12, 8], [74, 52, 9],
    [52, 53, 13, 10], [53, 54, 14, 11], [54, 55, 15, 12], [78, 56, 9, 13], [56, 57, 16, 14], [57, 58, 17, 15],
    [81, 59, 13, 16], [59, 60, 18, 17], [83, 61, 16, 18], [62, 63, 20, 21], [85, 64, 1, 22], [64, 65, 22, 23],
    [66, 67, 24, 25], [67, 68, 25, 26], [68, 69, 26, 27], [70, 71, 28, 29], [71, 72, 29, 30], [72, 73, 30, 31],
    [73, 93, 31, 27], [74, 75, 32, 33], [75, 76, 33, 34], [76, 77, 34, 35], [77, 97, 35, 8], [78, 79, 36, 37],
    [79, 80, 37, 38], [80, 100, 38, 12], [81, 82, 39, 40], [82, 102, 40, 15], [83, 103, 41, 17], [129, 84, 20, 42],
    [84, 106, 42, 21], [85, 86, 43, 44], [86, 109, 44, 23], [133, 87, 24, 45], [87, 88, 45, 46], [88, 89, 46, 47],
    [89, 113, 47, 27], [137, 90, 28, 48], [90, 91, 48, 49], [91, 92, 49, 50], [92, 93, 50, 51], [201, 94, 32, 52],
    [94, 95, 52, 53], [95, 96, 53, 54], [96, 97, 54, 55], [118, 98, 36, 56], [98, 99, 56, 57], [99, 100, 57, 58],
    [122, 101, 39, 59], [101, 102, 59, 60], [125, 103, 41, 61], [105, 104, 62, 63], [107, 105, 43, 64],
    [108, 106, 64, 65], [110, 107, 66, 67], [111, 108, 67, 68], [112, 109, 68, 69], [114, 110, 70, 71],
    [115, 111, 71, 72], [116, 112, 72, 73], [117, 113, 73, 51], [118, 114, 74, 75], [119, 115, 75, 76],
    [120, 116, 76, 77], [121, 117, 77, 55], [122, 119, 78, 79], [123, 120, 79, 80], [124, 121, 80, 58],
    [125, 123, 81, 82], [126, 124, 82, 60], [127, 126, 83, 61], [147, 130, 19, 84], [128, 131, 84, 85],
    [149, 132, 63, 86], [129, 134, 85, 87], [130, 135, 86, 88], [152, 136, 65, 89], [153, 138, 87, 90],
    [131, 139, 88, 91], [132, 140, 89, 92], [69, 93, 196, 236], [133, 141, 90, 94], [134, 142, 91, 95],
    [135, 143, 92, 96], [136, 159, 93, 97], [137, 160, 94, 78], [138, 144, 95, 98], [139, 145, 96, 99],
    [140, 163, 97, 100], [141, 164, 98, 81], [142, 146, 99, 101], [143, 166, 100, 102], [144, 167, 101, 83],
    [145, 168, 102, 103], [146, 169, 103, 18], [170, 148, 19, 105], [171, 150, 62, 107], [147, 151, 104, 108],
    [148, 154, 105, 111], [149, 155, 106, 112], [175, 156, 66, 114], [150, 157, 107, 115], [151, 158, 108, 116],
    [152, 159, 109, 117], [179, 160, 70, 118], [153, 161, 110, 119], [154, 162, 111, 120], [155, 163, 112, 121],
    [156, 164, 114, 122], [157, 165, 115, 123], [158, 166, 116, 124], [161, 167, 119, 125], [162, 168, 120, 126],
    [165, 169, 123, 127], [170, 172, 104, 130], [170, 173, 128, 131], [190, 174, 106, 132], [171, 176, 129, 134],
    [172, 177, 130, 135], [192, 178, 109, 136], [193, 180, 110, 138], [173, 181, 131, 139], [174, 182, 132, 140],
    [175, 183, 133, 141], [176, 184, 134, 142], [177, 185, 135, 143], [178, 240, 136, 117], [179, 205, 137, 118],
    [180, 186, 138, 144], [181, 187, 139, 145], [182, 243, 140, 121], [183, 208, 141, 122], [184, 188, 142, 146],
    [185, 245, 143, 124], [186, 210, 144, 125], [187, 210, 145, 126], [188, 146, 127], [189, 128, 148, 147],
    [189, 227, 129, 150], [190, 191, 147, 151], [191, 194, 148, 154], [192, 195, 149, 155], [193, 197, 133, 156],
    [194, 198, 150, 157], [195, 199, 151, 158], [196, 200, 152, 159], [197, 201, 137, 160], [198, 202, 153, 161],
    [199, 203, 154, 162], [200, 204, 155, 163], [202, 205, 156, 164], [203, 206, 157, 165], [204, 207, 158, 166],
    [206, 208, 161, 167], [207, 209, 162, 168], [209, 210, 165, 169], [225, 211, 171, 170], [211, 226, 172, 149],
    [227, 212, 173, 172], [212, 228, 174, 152], [229, 213, 175, 153], [213, 214, 176, 173], [214, 215, 177, 174],
    [215, 232, 178, 113], [233, 216, 179, 175], [216, 217, 180, 176], [217, 218, 181, 177], [218, 236, 182, 178],
    [237, 219, 74, 179], [219, 220, 183, 180], [220, 221, 184, 181], [221, 240, 185, 182], [241, 222, 160, 183],
    [222, 223, 186, 184], [223, 243, 187, 185], [244, 224, 164, 186], [224, 245, 188, 187], [167, 188, 168],
    [225, 226, 189, 190], [227, 228, 191, 192], [229, 230, 193, 194], [230, 231, 194, 195], [231, 232, 195, 196],
    [233, 234, 197, 198], [234, 235, 198, 199], [235, 236, 199, 200], [237, 238, 201, 202], [238, 239, 202, 203],
    [239, 240, 203, 204], [241, 242, 205, 206], [242, 243, 206, 207], [244, 245, 208, 209], [189, 211, 226],
    [225, 211, 190], [212, 228, 171, 191], [227, 212, 192], [193, 213, 230], [229, 213, 214, 231], [230, 214, 215, 232],
    [231, 215, 196], [197, 216, 234], [233, 216, 217, 235], [234, 217, 218, 236], [235, 218, 200, 113], [201, 219, 238],
    [237, 219, 220, 239], [238, 220, 221, 240], [239, 221, 204, 159], [205, 222, 242], [241, 222, 223, 243],
    [242, 223, 207, 163], [208, 224, 245], [244, 224, 209, 166]
]
NodesSycamoreN53M16 = [
    [19, 20, 3], [20, 43, 6], [21, 22, 19, 7], [22, 25, 0, 11], [23, 26, 21, 12], [24, 28, 13], [25, 29, 1, 14],
    [26, 30, 2, 15], [27, 31, 23, 55], [28, 32, 36], [29, 33, 24, 16], [30, 34, 3, 17], [31, 35, 4, 58],
    [33, 36, 5, 39], [34, 37, 6, 18], [35, 38, 7, 60], [37, 39, 10, 41], [38, 40, 11, 61], [40, 41, 14, 127],
    [0, 128, 104, 2], [62, 42, 1, 0], [42, 63, 2, 4], [43, 44, 3, 2], [44, 65, 4, 8], [66, 45, 5, 10], [45, 46, 6, 3],
    [46, 47, 7, 4], [47, 69, 8, 51], [70, 48, 9, 5], [48, 49, 10, 6], [49, 50, 11, 7], [50, 51, 12, 8], [74, 52, 9],
    [52, 53, 13, 10], [53, 54, 14, 11], [54, 55, 15, 12], [78, 56, 9, 13], [56, 57, 16, 14], [57, 58, 17, 15],
    [81, 59, 13, 16], [59, 60, 18, 17], [83, 61, 16, 18], [62, 63, 20, 21], [85, 64, 1, 22], [64, 65, 22, 23],
    [66, 67, 24, 25], [67, 68, 25, 26], [68, 69, 26, 27], [70, 71, 28, 29], [71, 72, 29, 30], [72, 73, 30, 31],
    [73, 93, 31, 27], [74, 75, 32, 33], [75, 76, 33, 34], [76, 77, 34, 35], [77, 97, 35, 8], [78, 79, 36, 37],
    [79, 80, 37, 38], [80, 100, 38, 12], [81, 82, 39, 40], [82, 102, 40, 15], [83, 103, 41, 17], [129, 84, 20, 42],
    [84, 106, 42, 21], [85, 86, 43, 44], [86, 109, 44, 23], [133, 87, 24, 45], [87, 88, 45, 46], [88, 89, 46, 47],
    [89, 113, 47, 27], [137, 90, 28, 48], [90, 91, 48, 49], [91, 92, 49, 50], [92, 93, 50, 51], [202, 94, 32, 52],
    [94, 95, 52, 53], [95, 96, 53, 54], [96, 97, 54, 55], [118, 98, 36, 56], [98, 99, 56, 57], [99, 100, 57, 58],
    [122, 101, 39, 59], [101, 102, 59, 60], [125, 103, 41, 61], [105, 104, 62, 63], [107, 105, 43, 64],
    [108, 106, 64, 65], [110, 107, 66, 67], [111, 108, 67, 68], [112, 109, 68, 69], [114, 110, 70, 71],
    [115, 111, 71, 72], [116, 112, 72, 73], [117, 113, 73, 51], [118, 114, 74, 75], [119, 115, 75, 76],
    [120, 116, 76, 77], [121, 117, 77, 55], [122, 119, 78, 79], [123, 120, 79, 80], [124, 121, 80, 58],
    [125, 123, 81, 82], [126, 124, 82, 60], [127, 126, 83, 61], [147, 130, 19, 84], [128, 131, 84, 85],
    [149, 132, 63, 86], [129, 134, 85, 87], [130, 135, 86, 88], [152, 136, 65, 89], [153, 138, 87, 90],
    [131, 139, 88, 91], [132, 140, 89, 92], [69, 93, 197, 221], [133, 141, 90, 94], [134, 142, 91, 95],
    [135, 143, 92, 96], [136, 159, 93, 97], [137, 160, 94, 78], [138, 144, 95, 98], [139, 145, 96, 99],
    [140, 163, 97, 100], [141, 164, 98, 81], [142, 146, 99, 101], [143, 166, 100, 102], [144, 167, 101, 83],
    [145, 168, 102, 103], [146, 169, 103, 18], [170, 148, 19, 105], [171, 150, 62, 107], [147, 151, 104, 108],
    [148, 154, 105, 111], [149, 155, 106, 112], [175, 156, 66, 114], [150, 157, 107, 115], [151, 158, 108, 116],
    [152, 159, 109, 117], [179, 160, 70, 118], [153, 161, 110, 119], [154, 162, 111, 120], [155, 163, 112, 121],
    [156, 164, 114, 122], [157, 165, 115, 123], [158, 166, 116, 124], [161, 167, 119, 125], [162, 168, 120, 126],
    [165, 169, 123, 127], [189, 172, 104, 130], [170, 173, 128, 131], [191, 174, 106, 132], [171, 176, 129, 134],
    [172, 177, 130, 135], [193, 178, 109, 136], [194, 180, 110, 138], [173, 181, 131, 139], [174, 182, 132, 140],
    [175, 183, 133, 141], [176, 184, 134, 142], [177, 185, 135, 143], [178, 225, 136, 117], [179, 206, 137, 118],
    [180, 186, 138, 144], [181, 187, 139, 145], [182, 228, 140, 121], [183, 209, 141, 122], [184, 188, 142, 146],
    [185, 230, 143, 124], [186, 211, 144, 125], [187, 231, 145, 126], [188, 296, 146, 127], [189, 190, 128, 148],
    [190, 213, 129, 150], [191, 192, 147, 151], [192, 195, 148, 154], [193, 196, 149, 155], [194, 198, 133, 156],
    [195, 199, 150, 157], [196, 200, 151, 158], [197, 201, 152, 159], [198, 202, 137, 160], [199, 203, 153, 161],
    [200, 204, 154, 162], [201, 205, 155, 163], [203, 206, 156, 164], [204, 207, 157, 165], [205, 208, 158, 166],
    [207, 209, 161, 167], [208, 210, 162, 168], [210, 211, 165, 169], [170, 147, 274, 275], [232, 212, 171, 170],
    [212, 233, 172, 149], [213, 214, 173, 172], [214, 235, 174, 152], [236, 215, 175, 153], [215, 216, 176, 173],
    [216, 217, 177, 174], [217, 239, 178, 113], [240, 218, 179, 175], [218, 219, 180, 176], [219, 220, 181, 177],
    [220, 221, 182, 178], [244, 222, 74, 179], [222, 223, 183, 180], [223, 224, 184, 181], [224, 225, 185, 182],
    [248, 226, 160, 183], [226, 227, 186, 184], [227, 228, 187, 185], [251, 229, 164, 186], [229, 230, 188, 187],
    [253, 231, 167, 188], [232, 233, 190, 191], [255, 234, 171, 192], [234, 235, 192, 193], [236, 237, 194, 195],
    [237, 238, 195, 196], [238, 239, 196, 197], [240, 241, 198, 199], [241, 242, 199, 200], [242, 243, 200, 201],
    [243, 263, 201, 113], [244, 245, 202, 203], [245, 246, 203, 204], [246, 247, 204, 205], [247, 267, 205, 159],
    [248, 249, 206, 207], [249, 250, 207, 208], [250, 270, 208, 163], [251, 252, 209, 210], [252, 272, 210, 166],
    [253, 273, 211, 168], [254, 190, 212, 277], [254, 276, 212, 191], [255, 256, 213, 214], [256, 279, 214, 193],
    [257, 194, 215, 283], [257, 258, 215, 216], [258, 259, 216, 217], [259, 217, 197, 263], [260, 198, 218, 287],
    [260, 261, 218, 219], [261, 262, 219, 220], [262, 263, 220, 221], [264, 202, 222], [264, 265, 222, 223],
    [265, 266, 223, 224], [266, 267, 224, 225], [287, 268, 206, 226], [268, 269, 226, 227], [269, 270, 227, 228],
    [291, 271, 209, 229], [271, 272, 229, 230], [294, 273, 211, 231], [275, 274, 232, 233], [277, 275, 213, 234],
    [278, 276, 234, 235], [280, 277, 236, 237], [281, 278, 237, 238], [282, 279, 238, 239], [283, 280, 240, 241],
    [284, 281, 241, 242], [285, 282, 242, 243], [286, 239, 243, 221], [287, 283, 244, 245], [288, 284, 245, 246],
    [289, 285, 246, 247], [290, 286, 247, 225], [291, 288, 248, 249], [292, 289, 249, 250], [293, 290, 250, 228],
    [294, 292, 251, 252], [295, 293, 252, 230], [296, 295, 253, 231], [189, 254, 278], [189, 254, 255, 281],
    [233, 256, 282], [232, 255, 257, 284], [274, 256, 258, 285], [235, 259, 286], [257, 260, 288], [275, 258, 261, 289],
    [276, 259, 262, 290], [236, 260, 264, 291], [277, 261, 265, 292], [278, 262, 266, 293], [279, 263, 267],
    [240, 264, 248], [280, 265, 268, 294], [281, 266, 269, 295], [282, 267, 270], [283, 268, 251], [284, 269, 271, 296],
    [285, 270, 272], [288, 271, 253], [289, 272, 273], [292, 273, 169]
]
NodesSycamoreN53M18 = [
    [19, 20, 3], [20, 43, 6], [21, 22, 19, 7], [22, 25, 0, 11], [23, 26, 21, 12], [24, 28, 13], [25, 29, 1, 14],
    [26, 30, 2, 15], [27, 31, 23, 55], [28, 32, 36], [29, 33, 24, 16], [30, 34, 3, 17], [31, 35, 4, 58],
    [33, 36, 5, 39], [34, 37, 6, 18], [35, 38, 7, 60], [37, 39, 10, 41], [38, 40, 11, 61], [40, 41, 14, 127],
    [0, 128, 104, 2], [62, 42, 1, 0], [42, 63, 2, 4], [43, 44, 3, 2], [44, 65, 4, 8], [66, 45, 5, 10], [45, 46, 6, 3],
    [46, 47, 7, 4], [47, 69, 8, 51], [70, 48, 9, 5], [48, 49, 10, 6], [49, 50, 11, 7], [50, 51, 12, 8], [74, 52, 9],
    [52, 53, 13, 10], [53, 54, 14, 11], [54, 55, 15, 12], [78, 56, 9, 13], [56, 57, 16, 14], [57, 58, 17, 15],
    [81, 59, 13, 16], [59, 60, 18, 17], [83, 61, 16, 18], [62, 63, 20, 21], [85, 64, 1, 22], [64, 65, 22, 23],
    [66, 67, 24, 25], [67, 68, 25, 26], [68, 69, 26, 27], [70, 71, 28, 29], [71, 72, 29, 30], [72, 73, 30, 31],
    [73, 93, 31, 27], [74, 75, 32, 33], [75, 76, 33, 34], [76, 77, 34, 35], [77, 97, 35, 8], [78, 79, 36, 37],
    [79, 80, 37, 38], [80, 100, 38, 12], [81, 82, 39, 40], [82, 102, 40, 15], [83, 103, 41, 17], [129, 84, 20, 42],
    [84, 106, 42, 21], [85, 86, 43, 44], [86, 109, 44, 23], [133, 87, 24, 45], [87, 88, 45, 46], [88, 89, 46, 47],
    [89, 113, 47, 27], [137, 90, 28, 48], [90, 91, 48, 49], [91, 92, 49, 50], [92, 93, 50, 51], [202, 94, 32, 52],
    [94, 95, 52, 53], [95, 96, 53, 54], [96, 97, 54, 55], [118, 98, 36, 56], [98, 99, 56, 57], [99, 100, 57, 58],
    [122, 101, 39, 59], [101, 102, 59, 60], [125, 103, 41, 61], [105, 104, 62, 63], [107, 105, 43, 64],
    [108, 106, 64, 65], [110, 107, 66, 67], [111, 108, 67, 68], [112, 109, 68, 69], [114, 110, 70, 71],
    [115, 111, 71, 72], [116, 112, 72, 73], [117, 113, 73, 51], [118, 114, 74, 75], [119, 115, 75, 76],
    [120, 116, 76, 77], [121, 117, 77, 55], [122, 119, 78, 79], [123, 120, 79, 80], [124, 121, 80, 58],
    [125, 123, 81, 82], [126, 124, 82, 60], [127, 126, 83, 61], [147, 130, 19, 84], [128, 131, 84, 85],
    [149, 132, 63, 86], [129, 134, 85, 87], [130, 135, 86, 88], [152, 136, 65, 89], [153, 138, 87, 90],
    [131, 139, 88, 91], [132, 140, 89, 92], [69, 93, 197, 221], [133, 141, 90, 94], [134, 142, 91, 95],
    [135, 143, 92, 96], [136, 159, 93, 97], [137, 160, 94, 78], [138, 144, 95, 98], [139, 145, 96, 99],
    [140, 163, 97, 100], [141, 164, 98, 81], [142, 146, 99, 101], [143, 166, 100, 102], [144, 167, 101, 83],
    [145, 168, 102, 103], [146, 169, 103, 18], [170, 148, 19, 105], [171, 150, 62, 107], [147, 151, 104, 108],
    [148, 154, 105, 111], [149, 155, 106, 112], [175, 156, 66, 114], [150, 157, 107, 115], [151, 158, 108, 116],
    [152, 159, 109, 117], [179, 160, 70, 118], [153, 161, 110, 119], [154, 162, 111, 120], [155, 163, 112, 121],
    [156, 164, 114, 122], [157, 165, 115, 123], [158, 166, 116, 124], [161, 167, 119, 125], [162, 168, 120, 126],
    [165, 169, 123, 127], [189, 172, 104, 130], [170, 173, 128, 131], [191, 174, 106, 132], [171, 176, 129, 134],
    [172, 177, 130, 135], [193, 178, 109, 136], [194, 180, 110, 138], [173, 181, 131, 139], [174, 182, 132, 140],
    [175, 183, 133, 141], [176, 184, 134, 142], [177, 185, 135, 143], [178, 225, 136, 117], [179, 206, 137, 118],
    [180, 186, 138, 144], [181, 187, 139, 145], [182, 228, 140, 121], [183, 209, 141, 122], [184, 188, 142, 146],
    [185, 230, 143, 124], [186, 211, 144, 125], [187, 231, 145, 126], [188, 296, 146, 127], [189, 190, 128, 148],
    [190, 213, 129, 150], [191, 192, 147, 151], [192, 195, 148, 154], [193, 196, 149, 155], [194, 198, 133, 156],
    [195, 199, 150, 157], [196, 200, 151, 158], [197, 201, 152, 159], [198, 202, 137, 160], [199, 203, 153, 161],
    [200, 204, 154, 162], [201, 205, 155, 163], [203, 206, 156, 164], [204, 207, 157, 165], [205, 208, 158, 166],
    [207, 209, 161, 167], [208, 210, 162, 168], [210, 211, 165, 169], [170, 147, 313, 274], [232, 212, 171, 170],
    [212, 233, 172, 149], [213, 214, 173, 172], [214, 235, 174, 152], [236, 215, 175, 153], [215, 216, 176, 173],
    [216, 217, 177, 174], [217, 239, 178, 113], [240, 218, 179, 175], [218, 219, 180, 176], [219, 220, 181, 177],
    [220, 221, 182, 178], [244, 222, 74, 179], [222, 223, 183, 180], [223, 224, 184, 181], [224, 225, 185, 182],
    [248, 226, 160, 183], [226, 227, 186, 184], [227, 228, 187, 185], [251, 229, 164, 186], [229, 230, 188, 187],
    [253, 231, 167, 188], [232, 233, 190, 191], [255, 234, 171, 192], [234, 235, 192, 193], [236, 237, 194, 195],
    [237, 238, 195, 196], [238, 239, 196, 197], [240, 241, 198, 199], [241, 242, 199, 200], [242, 243, 200, 201],
    [243, 263, 201, 113], [244, 245, 202, 203], [245, 246, 203, 204], [246, 247, 204, 205], [247, 267, 205, 159],
    [248, 249, 206, 207], [249, 250, 207, 208], [250, 270, 208, 163], [251, 252, 209, 210], [252, 272, 210, 166],
    [253, 273, 211, 168], [315, 254, 190, 212], [254, 276, 212, 191], [255, 256, 213, 214], [256, 279, 214, 193],
    [321, 257, 194, 215], [257, 258, 215, 216], [258, 259, 216, 217], [259, 217, 197, 263], [287, 260, 198, 218],
    [260, 261, 218, 219], [261, 262, 219, 220], [262, 263, 220, 221], [264, 202, 222], [264, 265, 222, 223],
    [265, 266, 223, 224], [266, 267, 224, 225], [287, 268, 206, 226], [268, 269, 226, 227], [269, 270, 227, 228],
    [291, 271, 209, 229], [271, 272, 229, 230], [294, 273, 211, 231], [275, 274, 232, 233], [277, 275, 213, 234],
    [278, 276, 234, 235], [280, 277, 236, 237], [281, 278, 237, 238], [282, 279, 238, 239], [283, 280, 240, 241],
    [284, 281, 241, 242], [285, 282, 242, 243], [286, 239, 243, 221], [287, 283, 244, 245], [288, 284, 245, 246],
    [289, 285, 246, 247], [290, 286, 247, 225], [291, 288, 248, 249], [292, 289, 249, 250], [293, 290, 250, 228],
    [294, 292, 251, 252], [295, 293, 252, 230], [296, 295, 253, 231], [312, 297, 189, 254], [313, 298, 254, 255],
    [314, 299, 233, 256], [315, 300, 255, 257], [297, 301, 256, 258], [317, 302, 235, 259], [318, 303, 257, 260],
    [298, 304, 258, 261], [299, 305, 259, 262], [321, 306, 260, 264], [300, 307, 261, 265], [301, 308, 262, 266],
    [302, 324, 263, 267], [264, 248, 240], [303, 309, 265, 268], [304, 310, 266, 269], [305, 327, 267, 270],
    [306, 328, 268, 251], [307, 311, 269, 271], [308, 330, 270, 272], [309, 331, 271, 253], [310, 332, 272, 273],
    [311, 333, 273, 169], [312, 316, 274, 278], [313, 319, 275, 281], [314, 320, 276, 282], [315, 322, 277, 284],
    [316, 323, 278, 285], [317, 324, 279, 286], [318, 325, 280, 288], [319, 326, 281, 289], [320, 327, 282, 290],
    [321, 328, 283, 291], [322, 329, 284, 292], [323, 330, 285, 293], [325, 331, 288, 294], [326, 332, 289, 295],
    [329, 333, 292, 296], [274, 297, 316], [298, 319, 189, 275], [276, 299, 320], [300, 322, 232, 277],
    [312, 297, 301, 323], [279, 302, 324], [280, 303, 325], [313, 298, 304, 326], [314, 299, 305, 327],
    [306, 328, 236, 283], [315, 300, 307, 329], [316, 301, 308, 330], [317, 302, 286], [318, 303, 309, 331],
    [319, 304, 310, 332], [320, 305, 290], [321, 306, 291], [322, 307, 311, 333], [323, 308, 293], [325, 309, 294],
    [326, 310, 295], [329, 311, 296]
]
NodesSycamoreN53M20 = [
    [19, 20, 3], [20, 43, 6], [21, 22, 19, 7], [22, 25, 0, 11], [23, 26, 21, 12], [24, 28, 13], [25, 29, 1, 14],
    [26, 30, 2, 15], [27, 31, 23, 55], [28, 32, 36], [29, 33, 24, 16], [30, 34, 3, 17], [31, 35, 4, 58],
    [33, 36, 5, 39], [34, 37, 6, 18], [35, 38, 7, 60], [37, 39, 10, 41], [38, 40, 11, 61], [40, 41, 14, 127],
    [0, 128, 104, 2], [62, 42, 1, 0], [42, 63, 2, 4], [43, 44, 3, 2], [44, 65, 4, 8], [66, 45, 5, 10], [45, 46, 6, 3],
    [46, 47, 7, 4], [47, 69, 8, 51], [70, 48, 9, 5], [48, 49, 10, 6], [49, 50, 11, 7], [50, 51, 12, 8], [74, 52, 9],
    [52, 53, 13, 10], [53, 54, 14, 11], [54, 55, 15, 12], [78, 56, 9, 13], [56, 57, 16, 14], [57, 58, 17, 15],
    [81, 59, 13, 16], [59, 60, 18, 17], [83, 61, 16, 18], [62, 63, 20, 21], [85, 64, 1, 22], [64, 65, 22, 23],
    [66, 67, 24, 25], [67, 68, 25, 26], [68, 69, 26, 27], [70, 71, 28, 29], [71, 72, 29, 30], [72, 73, 30, 31],
    [73, 93, 31, 27], [74, 75, 32, 33], [75, 76, 33, 34], [76, 77, 34, 35], [77, 97, 35, 8], [78, 79, 36, 37],
    [79, 80, 37, 38], [80, 100, 38, 12], [81, 82, 39, 40], [82, 102, 40, 15], [83, 103, 41, 17], [129, 84, 20, 42],
    [84, 106, 42, 21], [85, 86, 43, 44], [86, 109, 44, 23], [133, 87, 24, 45], [87, 88, 45, 46], [88, 89, 46, 47],
    [89, 113, 47, 27], [137, 90, 28, 48], [90, 91, 48, 49], [91, 92, 49, 50], [92, 93, 50, 51], [202, 94, 32, 52],
    [94, 95, 52, 53], [95, 96, 53, 54], [96, 97, 54, 55], [118, 98, 36, 56], [98, 99, 56, 57], [99, 100, 57, 58],
    [122, 101, 39, 59], [101, 102, 59, 60], [125, 103, 41, 61], [105, 104, 62, 63], [107, 105, 43, 64],
    [108, 106, 64, 65], [110, 107, 66, 67], [111, 108, 67, 68], [112, 109, 68, 69], [114, 110, 70, 71],
    [115, 111, 71, 72], [116, 112, 72, 73], [117, 113, 73, 51], [118, 114, 74, 75], [119, 115, 75, 76],
    [120, 116, 76, 77], [121, 117, 77, 55], [122, 119, 78, 79], [123, 120, 79, 80], [124, 121, 80, 58],
    [125, 123, 81, 82], [126, 124, 82, 60], [127, 126, 83, 61], [147, 130, 19, 84], [128, 131, 84, 85],
    [149, 132, 63, 86], [129, 134, 85, 87], [130, 135, 86, 88], [152, 136, 65, 89], [153, 138, 87, 90],
    [131, 139, 88, 91], [132, 140, 89, 92], [69, 93, 197, 221], [133, 141, 90, 94], [134, 142, 91, 95],
    [135, 143, 92, 96], [136, 159, 93, 97], [137, 160, 94, 78], [138, 144, 95, 98], [139, 145, 96, 99],
    [140, 163, 97, 100], [141, 164, 98, 81], [142, 146, 99, 101], [143, 166, 100, 102], [144, 167, 101, 83],
    [145, 168, 102, 103], [146, 169, 103, 18], [170, 148, 19, 105], [171, 150, 62, 107], [147, 151, 104, 108],
    [148, 154, 105, 111], [149, 155, 106, 112], [175, 156, 66, 114], [150, 157, 107, 115], [151, 158, 108, 116],
    [152, 159, 109, 117], [179, 160, 70, 118], [153, 161, 110, 119], [154, 162, 111, 120], [155, 163, 112, 121],
    [156, 164, 114, 122], [157, 165, 115, 123], [158, 166, 116, 124], [161, 167, 119, 125], [162, 168, 120, 126],
    [165, 169, 123, 127], [189, 172, 104, 130], [170, 173, 128, 131], [191, 174, 106, 132], [171, 176, 129, 134],
    [172, 177, 130, 135], [193, 178, 109, 136], [194, 180, 110, 138], [173, 181, 131, 139], [174, 182, 132, 140],
    [175, 183, 133, 141], [176, 184, 134, 142], [177, 185, 135, 143], [178, 225, 136, 117], [179, 206, 137, 118],
    [180, 186, 138, 144], [181, 187, 139, 145], [182, 228, 140, 121], [183, 209, 141, 122], [184, 188, 142, 146],
    [185, 230, 143, 124], [186, 211, 144, 125], [187, 231, 145, 126], [188, 297, 146, 127], [189, 190, 128, 148],
    [190, 213, 129, 150], [191, 192, 147, 151], [192, 195, 148, 154], [193, 196, 149, 155], [194, 198, 133, 156],
    [195, 199, 150, 157], [196, 200, 151, 158], [197, 201, 152, 159], [198, 202, 137, 160], [199, 203, 153, 161],
    [200, 204, 154, 162], [201, 205, 155, 163], [203, 206, 156, 164], [204, 207, 157, 165], [205, 208, 158, 166],
    [207, 209, 161, 167], [208, 210, 162, 168], [210, 211, 165, 169], [170, 147, 298, 274], [232, 212, 171, 170],
    [212, 233, 172, 149], [213, 214, 173, 172], [214, 235, 174, 152], [236, 215, 175, 153], [215, 216, 176, 173],
    [216, 217, 177, 174], [217, 239, 178, 113], [240, 218, 179, 175], [218, 219, 180, 176], [219, 220, 181, 177],
    [220, 221, 182, 178], [244, 222, 74, 179], [222, 223, 183, 180], [223, 224, 184, 181], [224, 225, 185, 182],
    [248, 226, 160, 183], [226, 227, 186, 184], [227, 228, 187, 185], [251, 229, 164, 186], [229, 230, 188, 187],
    [253, 231, 167, 188], [232, 233, 190, 191], [255, 234, 171, 192], [234, 235, 192, 193], [236, 237, 194, 195],
    [237, 238, 195, 196], [238, 239, 196, 197], [240, 241, 198, 199], [241, 242, 199, 200], [242, 243, 200, 201],
    [243, 263, 201, 113], [244, 245, 202, 203], [245, 246, 203, 204], [246, 247, 204, 205], [247, 267, 205, 159],
    [248, 249, 206, 207], [249, 250, 207, 208], [250, 270, 208, 163], [251, 252, 209, 210], [252, 272, 210, 166],
    [253, 273, 211, 168], [299, 254, 190, 212], [254, 276, 212, 191], [255, 256, 213, 214], [256, 279, 214, 193],
    [303, 257, 194, 215], [257, 258, 215, 216], [258, 259, 216, 217], [259, 283, 217, 197], [307, 260, 198, 218],
    [260, 261, 218, 219], [261, 262, 219, 220], [262, 263, 220, 221], [371, 264, 202, 222], [264, 265, 222, 223],
    [265, 266, 223, 224], [266, 267, 224, 225], [288, 268, 206, 226], [268, 269, 226, 227], [269, 270, 227, 228],
    [292, 271, 209, 229], [271, 272, 229, 230], [295, 273, 211, 231], [275, 274, 232, 233], [277, 275, 213, 234],
    [278, 276, 234, 235], [280, 277, 236, 237], [281, 278, 237, 238], [282, 279, 238, 239], [284, 280, 240, 241],
    [285, 281, 241, 242], [286, 282, 242, 243], [287, 283, 243, 221], [288, 284, 244, 245], [289, 285, 245, 246],
    [290, 286, 246, 247], [291, 287, 247, 225], [292, 289, 248, 249], [293, 290, 249, 250], [294, 291, 250, 228],
    [295, 293, 251, 252], [296, 294, 252, 230], [297, 296, 253, 231], [317, 300, 189, 254], [298, 301, 254, 255],
    [319, 302, 233, 256], [299, 304, 255, 257], [300, 305, 256, 258], [322, 306, 235, 259], [323, 308, 257, 260],
    [301, 309, 258, 261], [302, 310, 259, 262], [239, 263, 366, 370], [303, 311, 260, 264], [304, 312, 261, 265],
    [305, 313, 262, 266], [306, 329, 263, 267], [307, 330, 264, 248], [308, 314, 265, 268], [309, 315, 266, 269],
    [310, 333, 267, 270], [311, 334, 268, 251], [312, 316, 269, 271], [313, 336, 270, 272], [314, 337, 271, 253],
    [315, 338, 272, 273], [316, 339, 273, 169], [340, 318, 189, 275], [341, 320, 232, 277], [317, 321, 274, 278],
    [318, 324, 275, 281], [319, 325, 276, 282], [345, 326, 236, 284], [320, 327, 277, 285], [321, 328, 278, 286],
    [322, 329, 279, 287], [349, 330, 240, 288], [323, 331, 280, 289], [324, 332, 281, 290], [325, 333, 282, 291],
    [326, 334, 284, 292], [327, 335, 285, 293], [328, 336, 286, 294], [331, 337, 289, 295], [332, 338, 290, 296],
    [335, 339, 293, 297], [340, 342, 274, 300], [340, 343, 298, 301], [360, 344, 276, 302], [341, 346, 299, 304],
    [342, 347, 300, 305], [362, 348, 279, 306], [363, 350, 280, 308], [343, 351, 301, 309], [344, 352, 302, 310],
    [345, 353, 303, 311], [346, 354, 304, 312], [347, 355, 305, 313], [348, 374, 306, 287], [349, 375, 307, 288],
    [350, 356, 308, 314], [351, 357, 309, 315], [352, 377, 310, 291], [353, 378, 311, 292], [354, 358, 312, 316],
    [355, 379, 313, 294], [356, 380, 314, 295], [357, 380, 315, 296], [358, 316, 297], [359, 298, 318, 317],
    [359, 299, 320, 361], [360, 361, 317, 321], [361, 364, 318, 324], [362, 365, 319, 325], [363, 367, 303, 326],
    [364, 368, 320, 327], [365, 369, 321, 328], [366, 370, 322, 329], [367, 371, 307, 330], [368, 372, 323, 331],
    [369, 373, 324, 332], [370, 374, 325, 333], [372, 375, 326, 334], [373, 376, 327, 335], [374, 377, 328, 336],
    [376, 378, 331, 337], [377, 379, 332, 338], [379, 380, 335, 339], [341, 340, 360], [359, 342, 319],
    [341, 343, 342, 362], [361, 344, 322], [345, 323, 364], [363, 346, 343, 365], [364, 347, 344, 366], [365, 348, 283],
    [349, 345, 368], [367, 350, 346, 369], [368, 351, 347, 370], [369, 352, 348, 283], [244, 349, 372],
    [371, 353, 350, 373], [372, 354, 351, 374], [373, 355, 352, 329], [330, 353, 376], [375, 356, 354, 377],
    [376, 357, 355, 333], [334, 356, 379], [378, 358, 357, 336], [337, 358, 338]
]

Node2sSycamoreN53N20Test1 = [
    (32, 9), (360, 359), (362, 322), (19, 0), (364, 363), (43, 1), (371, 349), (339, 316), (316, 297), (378, 334),
    (28, 5), (380, 337), (368, 367), (376, 375), (366, 365), (42, 20), (20, 62), (167, 144), (144, 125), (66, 45),
    (45, 24), (248, 206), (261, 242), (206, 226), (361, 341), (118, 78), (293, 271), (231, 168), (340, 318), (318, 298),
    (96, 77), (155, 140), (185, 158), (276, 233), (317, 274), (111, 88), (274, 300), (44, 22), (369, 347), (212, 190),
    (190, 232), (65, 23), (157, 134), (247, 225), (214, 193), (126, 103), (225, 267), (193, 235), (220, 201), (87, 67),
    (244, 202), (202, 222), (251, 229), (229, 209), (333, 291), (291, 310), (17, 11), (25, 6), (319, 302), (326, 311),
    (275, 254), (303, 284), (315, 296), (296, 338), (238, 217), (35, 12), (100, 80), (80, 58), (196, 177), (171, 129),
    (129, 150), (266, 246), (240, 198), (237, 216), (198, 218), (26, 7), (245, 223), (255, 213), (213, 234), (281, 258),
    (81, 59), (59, 39), (199, 176), (170, 128), (128, 148), (324, 301), (114, 90), (138, 119), (115, 91), (98, 79),
    (9, 52), (359, 342), (354, 335), (9, 74), (352, 325), (175, 133), (133, 156), (208, 187), (68, 46), (51, 31),
    (169, 146), (146, 127), (355, 328), (162, 145), (49, 30), (188, 165), (94, 75), (143, 124), (124, 166), (280, 260),
    (370, 283), (137, 70), (236, 215), (215, 194), (152, 136), (136, 109), (228, 163), (221, 113), (105, 84),
    (178, 159), (174, 149), (350, 331), (294, 272), (153, 110), (305, 278), (320, 304), (147, 130), (282, 262),
    (130, 104), (192, 173), (141, 122), (122, 164), (269, 249), (379, 358), (224, 204), (83, 61), (61, 41), (270, 250),
    (112, 92), (93, 73), (38, 15), (180, 161), (313, 286), (179, 160), (332, 309), (82, 60), (60, 102), (295, 253),
    (69, 47), (47, 27), (288, 264), (56, 36), (289, 265), (312, 285), (277, 257), (85, 64), (154, 139), (123, 101),
    (348, 329), (207, 184), (33, 10), (37, 16), (72, 50), (330, 307), (55, 8), (106, 86), (135, 108), (259, 239),
    (203, 183), (230, 210), (71, 48), (373, 351), (205, 182), (117, 97), (372, 353), (53, 34), (95, 76), (219, 200),
    (21, 4), (323, 308), (307, 349), (263, 243), (191, 172), (40, 18), (120, 99), (346, 327), (306, 287), (377, 357),
    (64, 1), (334, 292), (363, 343), (5, 13), (322, 279), (297, 273), (337, 356), (124, 158), (36, 78), (365, 344),
    (73, 31), (284, 311), (284, 345), (239, 217), (217, 197), (165, 127), (250, 163), (0, 2), (308, 331), (41, 103),
    (23, 22), (159, 109), (67, 24), (302, 233), (243, 113), (327, 304), (50, 30), (287, 329), (58, 121), (325, 291),
    (160, 70), (271, 252), (110, 119), (75, 9), (342, 341), (16, 57), (77, 54), (145, 181), (84, 131), (190, 299),
    (6, 14), (20, 63), (86, 132), (287, 279), (168, 211), (173, 195), (129, 107), (249, 227), (309, 290), (298, 189),
    (104, 151), (351, 374), (91, 142), (108, 116), (46, 89), (278, 321), (193, 256), (242, 241), (265, 268), (253, 314),
    (286, 336), (48, 29), (11, 3), (184, 186), (347, 367), (335, 375), (145, 139), (308, 260), (91, 134), (307, 264),
    (84, 128), (54, 76), (252, 209), (36, 79), (104, 172), (286, 272), (193, 213), (110, 161), (290, 296), (113, 201),
    (304, 285), (109, 97), (60, 15), (189, 274), (241, 200), (20, 4), (46, 27), (92, 140), (223, 246), (176, 198),
    (257, 194), (122, 90), (99, 101), (182, 204), (258, 216), (86, 149), (357, 358), (8, 12), (183, 206), (210, 187),
    (344, 283), (34, 10), (264, 202), (284, 292), (253, 337), (18, 39), (301, 254), (110, 125), (182, 225), (30, 31),
    (27, 7), (24, 133), (351, 353), (22, 1), (197, 262), (108, 124), (227, 163), (29, 5), (168, 273), (347, 328),
    (86, 92), (4, 0), (341, 343), (36, 70), (283, 279), (91, 88), (10, 9), (104, 177), (189, 278), (223, 265), (6, 16),
    (190, 233), (357, 335), (184, 127), (193, 173), (3, 41), (8, 54), (99, 139), (97, 58), (200, 113), (18, 15),
    (187, 209), (253, 260), (1, 107), (216, 194), (0, 84), (183, 90), (182, 291), (189, 341), (351, 284), (254, 290),
    (328, 272), (7, 30), (202, 176), (36, 5), (104, 108), (24, 110), (187, 163), (335, 285), (15, 3), (279, 197),
    (6, 9), (194, 173), (253, 168), (0, 1), (182, 223), (127, 88), (58, 8), (104, 86), (189, 190), (6, 5), (197, 113),
    (24, 90), (272, 284), (3, 99), (189, 254), (8, 7), (168, 285), (24, 5), (182, 176), (88, 163), (86, 0), (113, 173),
    (168, 272), (5, 3), (0, 7), (168, 189), (168, 113), (113, 176), (0, 3), (0, 88), (0, 113)
]  # log10(multiple_times) = 25.6106868931126
Node2sSycamoreN53N20Test2 = [
    (32, 9), (360, 359), (13, 5), (3, 0), (358, 339), (43, 1), (362, 344), (372, 371), (367, 345), (375, 330),
    (366, 283), (380, 338), (379, 378), (363, 323), (117, 97), (294, 270), (37, 16), (84, 63), (150, 134), (319, 276),
    (276, 302), (76, 53), (312, 293), (245, 222), (234, 214), (115, 91), (177, 151), (332, 315), (228, 208), (51, 27),
    (189, 147), (218, 198), (198, 240), (72, 49), (135, 108), (103, 61), (61, 83), (61, 41), (50, 31), (213, 171),
    (326, 303), (210, 187), (299, 232), (200, 181), (286, 262), (9, 36), (143, 116), (348, 322), (333, 310), (310, 291),
    (304, 285), (343, 318), (347, 328), (370, 352), (219, 199), (275, 255), (178, 152), (296, 272), (57, 38), (54, 34),
    (153, 138), (138, 110), (185, 166), (300, 274), (274, 317), (48, 28), (28, 70), (30, 11), (162, 145), (204, 184),
    (21, 2), (309, 290), (114, 94), (227, 207), (23, 8), (179, 160), (160, 137), (137, 118), (100, 80), (80, 58),
    (279, 235), (122, 98), (233, 212), (212, 191), (127, 18), (376, 356), (182, 163), (220, 201), (197, 113),
    (252, 229), (104, 19), (237, 216), (248, 226), (226, 206), (45, 24), (24, 66), (123, 101), (211, 167), (89, 69),
    (297, 273), (60, 15), (68, 47), (159, 136), (368, 346), (292, 251), (165, 146), (62, 20), (20, 42), (176, 157),
    (93, 73), (26, 7), (107, 85), (112, 92), (337, 295), (295, 314), (278, 256), (269, 250), (180, 161), (109, 65),
    (148, 128), (128, 170), (154, 131), (111, 88), (238, 217), (246, 224), (156, 133), (133, 175), (334, 311), (25, 6),
    (209, 164), (223, 203), (44, 22), (12, 4), (306, 287), (287, 329), (349, 307), (331, 308), (373, 351), (361, 342),
    (261, 241), (52, 33), (321, 305), (149, 132), (132, 106), (225, 205), (288, 264), (120, 96), (303, 345), (374, 355),
    (244, 202), (155, 140), (99, 79), (67, 46), (192, 173), (324, 301), (263, 243), (243, 221), (289, 268), (188, 169),
    (257, 236), (336, 313), (193, 174), (280, 260), (59, 39), (39, 81), (335, 316), (231, 168), (95, 75), (259, 239),
    (126, 102), (341, 320), (90, 71), (77, 55), (316, 339), (215, 194), (267, 247), (40, 17), (354, 327), (340, 298),
    (144, 119), (377, 357), (281, 258), (365, 364), (86, 64), (78, 56), (29, 10), (307, 330), (323, 350), (221, 201),
    (47, 69), (239, 217), (290, 315), (140, 163), (140, 121), (301, 318), (75, 53), (320, 232), (136, 152), (169, 146),
    (161, 110), (157, 134), (313, 270), (55, 97), (56, 9), (247, 205), (287, 322), (71, 28), (202, 222), (58, 38),
    (311, 251), (20, 63), (305, 328), (194, 236), (18, 41), (73, 27), (85, 105), (167, 253), (102, 124), (16, 14),
    (46, 87), (268, 265), (295, 271), (171, 190), (264, 284), (131, 139), (174, 196), (206, 249), (191, 254),
    (133, 141), (164, 183), (187, 230), (166, 158), (173, 172), (352, 369), (39, 82), (119, 125), (33, 74), (4, 35),
    (316, 273), (323, 308), (64, 1), (357, 378), (287, 235), (194, 216), (194, 195), (53, 34), (158, 116), (46, 24),
    (187, 229), (205, 224), (205, 266), (20, 2), (217, 113), (131, 88), (206, 207), (38, 79), (27, 31), (167, 168),
    (39, 101), (133, 94), (18, 17), (301, 298), (171, 129), (352, 325), (134, 142), (164, 186), (201, 242), (327, 285),
    (273, 293), (308, 260), (357, 356), (1, 22), (264, 307), (28, 5), (271, 338), (206, 250), (201, 262), (187, 272),
    (298, 255), (116, 108), (27, 8), (325, 291), (24, 6), (194, 277), (113, 282), (134, 91), (119, 110), (1, 65),
    (264, 353), (235, 283), (264, 303), (6, 10), (108, 151), (108, 130), (206, 208), (260, 371), (17, 14), (205, 203),
    (34, 11), (38, 96), (5, 49), (203, 184), (235, 344), (1, 0), (194, 258), (235, 364), (206, 265), (260, 198),
    (14, 15), (38, 98), (5, 92), (198, 241), (0, 106), (184, 202), (194, 256), (198, 199), (0, 2), (0, 19), (235, 214),
    (14, 146), (206, 270), (38, 145), (5, 9), (0, 85), (14, 39), (194, 274), (184, 181), (5, 33), (0, 128), (14, 102),
    (198, 118), (214, 276), (0, 147), (214, 359), (194, 305), (214, 342), (118, 264), (14, 7), (118, 346), (118, 251),
    (214, 355), (7, 4), (214, 291), (214, 351), (214, 255), (4, 38), (214, 113), (4, 55), (113, 174), (113, 194),
    (4, 11), (4, 8), (113, 201), (113, 290), (4, 47), (113, 136), (113, 232), (113, 191), (113, 172), (113, 129),
    (4, 121), (4, 5), (4, 6), (4, 110), (4, 167), (4, 94), (4, 164), (4, 88), (4, 91), (4, 0), (0, 108), (0, 187),
    (0, 113), (0, 181), (0, 206), (0, 118), (0, 356), (0, 271), (0, 273), (0, 285)
]  # log10(multiple_times) = 25.888611588740623

EdgeSortStrH2OSycamoreN12M14 = """
[22 96 78 74 84 92 87 72 45 57 94 91 89 24 26  8  2 73  0 23 35 76 97  7 36 14 59 19 75 21  4 63 12 66 10  3 30 20 80 34
 90  6 52 11 77 79 53 31 16 49 54 25 70 62 56 33 83 95 15 18 47 43 93  9 55 71 32 29 64 46 67 17 44 38 86 61 13 40 51 58
 65 85 37 41 5 81 28 27 68 39 48 42  1 82 60 88 69 98 50]
"""
EdgeSortStrH2OSycamoreN53M12 = """
[263 165 127 209 233 160  88 291 344 207 269 128 396 124 397 161 388 125 266  73  56 155   5  81  26  80  98 346 340 323
  95  59 384 158   0 258 292 222 308  34 210  71  55 300 283 275 332 315 409 206 316 110  38   2  35 144 117 173  15 247
 195  37 257 244 135 309 302 162 113 120 331  16 121 134  12 142 212 343 261 153  72 299 141 400 339 150 208  46  40  52
 274 129  43 259  51 152  68 221  50 306 236  60 362 183 237 268 307 321 136  65 119 118 354 370 177 232  83 298  92  45
 360 199 350 218  17   1  89  96 231  49 172 336 166  20 369  94 159 337 107  84  28 187 241 196 372 239  82 289 131 198
 216 408  36 228 282 382 361 205 102 356 191 189 193  57 334 278 293 304 277 320  29 280 279  93 103 227 248 140 287 168
 32 355 380 254 174 170   13 403 281 214  19 229  63 371 410 246 352 224 353 303 378 157 333 376 325  76  97 167 116 171
 264 178   4 391 138  75 115   9 347  66 411  18 201 357 181 285 146 367 290 245  53  14 330 179 345 390 130 256 203 175
 101 106 392 255 365 385 329 322  47 348 311 194 393 211 251 250  10 226  54 412 133 375  23 260 123 137 200 399 267 328
 358 111 149 413 122 284  86 318 402 310 295 169 294 317 270 253 230  62  87 374 319 406 394 381 405 148 100 108  22 338
 342 234 252  61 114  77  30  41   8 176 276 197 147  58 297   3 154 180  69 364 265  39 182 217 164 126 215  25 373  24
 312 225 313 213 262 407  33 220  67 202 386 351  70 249  74 104  48  21 143  78 314 335 395 272 341 151 238 184 190  90
 286 223 271 398 188  64 163 377 240  79 109 112  99 366 349 379 145 105  44 389 192 359 324 186 301 273 326  42 383 401
 132 288  11   6 243 242  85  27 404 363 204 296 368 387 185   7  31 327 235 219 139 156 305  91]
"""
EdgeSortStrH2OSycamoreN53M14 = """
[445  85  19  49 133  17  95 140   4 216 441 434 320 457 272 289 475 427 259 126 221  48 127  92  86 229 197 470 483
 407 260 116 354 310  40 377  60 149 306 442 378  68 430 348 344  32 452 390 258 312 227 297 218 352 307 161  46 202
 194 135  43 300 308 478 295 178 365 425  72 112 179 388 162  47 422 443  96 186 305 293 410 326 262 339   0 459 255
 137 141 153 219 154 109 181 471 228  12 236 423  63 249 263  52 192  13 184  33  36  78 429  16 108 444 476  57 317
 271 129 436 404 264  55 405  56  74  91 146 177 125 455 217 118 187 424   2  99 150 274 107 460 253 298 389 171 353
  24 472 292  93 473 342 188 180  53 416 311 222  97 355  73 169 346  61 128 409  77 376 433 463 174 421 155 418 270
  14 435 234 195 235  98  90 176 119 357 438 175 428 294 208 351  54 115 393 120 166 261  94 299 134 143 230 124 469
 462 324  25 479  15 384 101 329 420  30  18 304  80 447 233 408 139 193 145 316 301 347 220 466  88  82 335 431   1
 415 419  27 382 163 100  28 268  50   3  34 212 231 132  67 242  44 314 448 383 122 396 103 276 224 102 361  76 437
  62 451 391 461 136 458 156 394 191 159 239 160 254 164  45 189 144 111 245  64 303 243 331 282 333 464 474 148 373
 467 414 157 277 392  79 321 213  22 401 232 205 267 395 440 142 170 319 403 360 340 456 426  59 323 413 291  75 362
  84 454 110  65 121 345 240 190 225 172  31 158 343 356  26 432 211 105 257 199 371  66 296 251 265 302 417  70  23
 114  42 341 123  51  37 131  20 398 247 397 370 465 183 334 363 283 381 453 374 385  38 185   8 288 325 315 446 482
 359 364 367  71 226 200 256  58  41  39  21 318 368 468   7 309 439 412   5 400 337 214 152 223 481 106 198 278 210
 281 290 402 275 206   6 113 449 330 204   9 372 406 285 480 375 237 151 167 284  69 387 269 279 273 266 477 369 117
 165 379 147  81 104 332  10 173 450 241 411 203 399 358  29  83 207 168 336 248 201 215 182 313 130 252 338 138 350
 246 196 327 287 328 250 349 244 286  35 322 238  87 209  11 366  89 280 386 380]
"""
EdgeSortStrH2OSycamoreN53M16 = """
[ 12  69 231 397 392 268 576  28 477 195 266 393 570 391  14 340 333 559 367 418 419 139 543  47 304   9 514 429 155
 439 158 152 553  45 560 129 486 556 188 101 416 535 575 498 551  15 504 124 580 321 156  91 177 371 554  72 121 166
 577 395 487 545  39 345  86 409 136 516 202 460 422 567 306 447 217 509 118 544 273 502 434 561  59 281 285 548 402
 364 322 491 334 414 473 494 119 113 542 381 465   5 483 305 518 270 448  46 503 342 350 203 496 566 573 534 383 138
 541 260 470 198 480 240  34 525 555 165 303 579 493 572 537 110 201  36 415 578 445 299 581 412 478 583 111 249 427
  84 511  16 313 248 168 479  18  10 550 469 222 436 505   1 160 265 557 507 225 360 302 289 378 571 183 115  89 146
 510  73 489 108 399 435 209 425 344 193 584 157 454 423  87 182 175 403 247 574 552 547 314 453 223  98  56 387  50
 290 320 512 170  20 134 328 404 236  23 295 234 428 324 251 140 291 259 294 528 329 292 267 354 457 122  11 382 262
 369 208 452 521 224  60 533 463 229 476 374 173 300 524 242 366 315 215 243 159 472 196 269 471 449 287  27  92 440
  53 216 286 351 370 438 353 441 357 325 197 191 563 130 390 330 501  76 406 145  63  58 417 176 327  79 218  13  95
 539 317  62  90 274   0  43 426 297 380 169 464 279 220 379  96 213 163 513 458 181 227 272 482 356 257 254 326 141
 190 278  80 481 132 398 459 373 385 123 421 362 348 347 275 411  48 310 400  93  41  97 517 150 396  74 312 245 443
 408 241 125 239  85 131 205 432 311 582 137  71 526 346 226 244 184 401 319 214 135 558 282 102  83  49 186 104  94
 180 128  31  37 343 109 405  51 204 532 256 475  54 171 519 529 430 250 298 413  75 280 112 336 468 359 309 189 221
 410   7 161 258  38 232 538 568 271  78 361  82 149  64 151  19 376 335 253 133 126   3 219 499 523   4  22 164  55
   6 308 337 237 143 207 536 365 349 338 252  32 105 355 332 194  66   8 114 284  21 144 444 530 261 167  52 106 339
 307  35 462 148 467 283 549 433 187  33 446 263 497  44 495 527  88 116 485 162 255 368 200 352 127 384 117 238 461
  65  61 288 103 488 437 424 569 147 178 185  17 500 301 420 199 431  70 331 323 564  67  57  30 233 562 296   2 490
 389 375 316 515 456 506 455  42 531 363 546 142 230 474 492 318  77 212 179 246 358  68 341 192 276 228 100  81 565
  24 484 153 451 442 372  40 394 377 508 210 206 293  25 174 172 235 466 388 450 264 107  99 120 211 386 520  29 154
 407 522 277 540  26]
"""
EdgeSortStrH2OSycamoreN53M18 = """
[163 140 102  95 310 130 398 231 327 145 585  60 212 464 318 380 197  70 498  29 407 356  76  13 346 467 564 216 389
 570  15  72 458 422 546 111 550 206 529 319 114 351 115 276  69 518 395 631 643 580  46  55 462  12 579   6 366 406
 298 483 470  32 189 181 495 259 460 215 656 393 603 371 630 414 431 506  73 281 350 635 345 452 402 183 420 139 598
 325 594  11 244 239 628  80 543 519 122 175 173 257 341 539 293 569 648 142  54  14  89 567 438 613 301  83 233 373
 479 105  36 562 377 651  75 315 136 221  84 222 223 242 266 617 295 205 316 638  85  58 496 435 306 466 324 533 639
 538 508 504 465 155  18 595  43 292 394 188 364 390 514 303 330 509 286 208 196 425 586 131 555  98 191 125 253 652
 317 418 347 193 624 476 375 300 642 107 261  92 329  34 174 472 645 305  35 502 297 108 379  63   7 201  41 124 307
 535 264 536 186 359 484 583 228 185  42 158  23 283 360 311 290 463 157 204 245 232 118 209   5 203 119 166 199 453
  97 461 591 396 609  88 187 482   2 279 337 410 436 110 250  40 592  74 593 511 134 384  81 252   4 497 411 408 106
 247  21 475 486 447 219 641 525  96 171 568 614  64 610 471 226 128 443 604 646 625  19 449 285 135  99 270 178  26
 577 133  77 430 571 304 282 116 649 530 553 265 399 391 655  82 344 616 104 214 101 403 224 254 621 433 561 491 540
 531 382 374 123  10 622 363 273 271 620 578 632 647 368 485  17 412 177 348 565 505 636 549 627 441 388 451 241 454
 444 354 289 321 168 417 294 654 154 237  38 493 260 428 322 291 590 355 558 302  28 268 151 320 132 113 236 331 607
 499 522 597 545 532  37 587 109  33 500 198 528 141 365 127  65  94  87 332 432 349 492 450 556 507 619 588 612 280
 439 164  93 367  68 563 165 195 633 551 160 256 573 473 637  86 159 513 162 129 477 448 581 376 600 269 629  48 370
 575 313 339  24 657 172 299  47  59 566 446 169 369 336 263  53 190 510 542 421 103 147 618 387  16 211 312  52  30
 326 413 220 358 143 608 176 179 487 544 170 138 494 386 137  79 148 584 599  20 267 167 401 457 602 338 392 238 378
 623 527 521 361 288 419 126 335 278 248 437 520   8  78  22 442 560 149 400  91 182  50 548 372 456 606 184 309 434
 192 383 474   3 217 342 429 251 314 227 397 589 296  25 404 481 459 469 547 455 328 501  27 523 559 426 352 100 274
  71  90 445 572  57 582  61  45 488 605  39 526 121 334   1 576 287 323  66 249 161 440 200  31 416 478 112 246 146
   9 381 424 512 277 213 658 554 634 557 120 405 653  56 611  67 272 234 117  49 534 225 255 230 202 515 517  62 229
  44 262 235 144 362 218 194 210 640 343 644 385 503 152 516 552 240 490 156 333 353 150 415 615 524 541 574 650 284
 427 480 601 423 153 258 308 626 275 409 596 489   0 180 243 357  51 468 537 340 207]
"""
EdgeSortStrH2OSycamoreN53M20 = """
[747 288 513 330  73 124 267 560 212  91 732 397 653 107 402 566 449 329 724  43 424 496 668 521  59 225 643 222 478
 201 130 634 177 215 202 749 699 530 155  61 681  69 230 573  88  53 733 570 290 692 226 438 142 163 671 204 665 119
  34 455 379 260 698  10 741 630 637 679 437 607 445 497 403 173  74 140 709 615 229  80 281 464  37 296 647 198 399
 488 629 151 582 580  72 419 264 176 138 609 680 406 197 103 221  32 602  49 529 743 722 145 538 137 517 328 738 126
 178 723 500  66 695 645  20 710 180 320  77 113 465 321 129 640 136 405 683  25 469 706 412 691 186 340 418 626 245
 308 355 287 132 490  46 286 131 133  42 493 540 303 289 476 752 711 456 473 712 670 311  84 584 746 336 617  97  70
 703 484 650 586 744 552 611 656 217 263 335 168 458 495 175 726 514 414  12  57 690   5 502 664  92 114 144 216 249
 228 209 576  31 390 535  39  18 247 639 613 348 704 331 420   4 471 224  76 567 595 707  85 426 659 305 282 605  13
 737 295  68 519 601 111 122 700 597  55 227 416 165 278 532 729 655 646 509 280 256 232 583 598 666  22 306 687 254
 638 171 432 250 658  54 578 258 565  58 211 210 585 660 619 559 448 179 657 241 522 652 697  36 543 728  94 624 199
 685 694 184 627  15 127 713 688 693 395 524 332 391 721 533 266 115 269 401 674 537 467 654 677 554  44 161 220 720
 636 369 170  11 590 219   9 734  96 705 143 105 672  52 292 148 622 523 166 223 172 262  60  17 592 371 409 150 135
 347 587 548 187 139 392 481 128 633 248 185 635 383 678 708   6 159 106 526 457 153  35 477 676 717  82   8 189 470
 604 434 479  28 594 345 499 188  45 719 206 237 271 621 190  65 727 588  40 596 195 378 682 572  33 302 442 244 581
 745 367 651 352  21 536  89  29 441  75 718 346 277 203 147 531 623  56 315  23 251 101  86  93 577 599 162 466 453
 663 309 525 275 317 492 549 528 253 158 427 297 363 483 474 100 374  19 157 459 501 642 417 527 503 661   0 109 284
 174 351  14 386 339 673 614 430 472 731  38 327 257 505 489 751  30 736 407 400 632 388 146 242 200 662  83 239 117
 353  79 574 547 553 511 550 542 600   1 246 610 562 301 618 435 118 696 429 534 491 358 116 539 259 141 684 508 382
 192  67 294 443 725 208 546 450 606 307 411  50 293 394 183 408  87 272 372 591  99 218 121 480 648 125 716 104 120
 740 512 134 541 349 556 160 431 193 515 164 628 276 316 240 268 102 616 750 344 422 396 380 359 389 568 551 462 243
 714 428 366 545  64 486 735 667 444 748 446 425 468 181 167 231 440 506 364 236 715 612 361  27 421 593 191 337 463
  98 433 235 575 354 384 410 558 494   3 357 313 561 291 398 557 255 299 343 279 485 314  26  24 487 300 415 169 393
 563 252  51  63 742 686 214  71 196 341  48 589 387 368 325 579 274 413 516 404 644 461 631 319 304 385 423 112 356
 373 675 641   2 149 213 283 564 265 234 156 285 381 475 110  47 701 326 362  62 273 451 649 342 182 233 452 238 689
 324 518 625 454 298 376 439 375 510 312 436 569 108 338 498 350 730 207  16 360 194 669 310   7 152 205 620 460 323
 154 555 482  95 507  81 318 504 334 333  90 377 447 753 370 739 608 520  78 571 544 365 123 261 702  41 603 322 270]
"""


def get_nodes_list_of_tensor_train(len_list: int = 4):
    nodes = [[] for _ in range(len_list)]  # 初始化邻接表
    for i in range(len_list):
        if i > 0:
            nodes[i].append(i - 1)
        if i < len_list - 1:
            nodes[i].append(i + 1)
        nodes[i].append(i + len_list)
        nodes.append([i])
    return nodes

def get_nodes_list_and_ban_edges_of_tensor_tree(depth: int = 3):
    # 初始化二叉树的二维列表
    depth -= 1
    num_nodes = 2 ** (depth + 1) - 1
    ban_edges = 2 ** depth
    tree = [[] for i in range(num_nodes)]

    # 添加二叉树的边
    def add_edges(_depth, node=0, org_node=-1):
        left_node = node * 2 + 1
        right_node = node * 2 + 2

        tree[node].append(org_node) if org_node >= 0 else None
        if _depth == 0:
            return
        tree[node].append(left_node)
        tree[node].append(right_node)

        org_node = node
        _depth -= 1
        add_edges(_depth, left_node, org_node)
        add_edges(_depth, right_node, org_node)

    add_edges(depth)
    return tree, ban_edges



def get_nodes_ary(nodes_list: list) -> TEN:
    # nodes_list = NodesSycamore
    nodes_ary = th.zeros((len(nodes_list), max([len(nodes) for nodes in nodes_list])), dtype=th.int) - 1
    # # -1 表示这里没有连接
    for i, nodes in enumerate(nodes_list):
        for j, node in enumerate(nodes):
            nodes_ary[i, j] = node
    return nodes_ary


def get_edges_ary(nodes_ary: TEN) -> TEN:
    edges_ary = th.zeros_like(nodes_ary, dtype=nodes_ary.dtype)
    edges_ary[nodes_ary >= 0] = -2  # -2 表示这里的 edge_i 需要被重新赋值
    edges_ary[nodes_ary == -1] = -1  # -1 表示这里的 node 没有连接另一个 node

    num_edges = 0
    '''get nodes_ary'''
    # for i, nodes in enumerate(nodes_ary):  # i 表示节点的编号
    #     for j, node in enumerate(nodes):  # node 表示跟编号为i的节点相连的另一个节点
    #         edge_i = edges_ary[i, j]
    #         if edge_i == -2:
    #             _j = th.where(nodes_ary[node] == i)
    #             edges_ary[i, j] = num_edges
    #             edges_ary[node, _j] = num_edges
    #             num_edges += 1
    '''get nodes_ary and sort the ban edges to large indices'''
    for i, nodes in list(enumerate(nodes_ary))[::-1]:  # i 表示节点的编号
        for j, node in enumerate(nodes):  # node 表示跟编号为i的节点相连的另一个节点
            edge_i = edges_ary[i, j]
            if edge_i == -2:
                nodes_ary_node: TEN = nodes_ary[node]
                _j = th.where(nodes_ary_node == i)

                edges_ary[i, j] = num_edges
                edges_ary[node, _j] = num_edges
                num_edges += 1
    _edges_ary = edges_ary.max() - edges_ary
    _edges_ary[edges_ary == -1] = -1
    edges_ary = _edges_ary
    return edges_ary


def get_node_dims_arys(nodes_ary: TEN) -> list:
    num_nodes = nodes_ary.shape[0]

    arys = []
    for nodes in nodes_ary:
        positive_nodes = nodes[nodes >= 0].long()
        ary = th.zeros((num_nodes,), dtype=th.int)  # 因为都是2，所以我用0 表示 2**0==1
        ary[positive_nodes] = 1  # 2量子比特门，这里的计算会带来2个单位的乘法，因为都是2，所以我用1 表示 2**1==2
        arys.append(ary)
    return arys


def get_node_bool_arys(nodes_ary: TEN) -> list:
    num_nodes = nodes_ary.shape[0]

    arys = []
    for i, nodes in enumerate(nodes_ary):
        ary = th.zeros((num_nodes,), dtype=th.bool)
        ary[i] = True
        arys.append(ary)
    return arys


class TensorNetworkEnv:
    def __init__(self, nodes_list: list, ban_edges: int, device: th.device, if_vec: bool = True):
        self.device = device

        '''build node_arys and edges_ary'''
        nodes_ary = get_nodes_ary(nodes_list)
        num_nodes = nodes_ary.max().item() + 1
        assert num_nodes == nodes_ary.shape[0]

        edges_ary = get_edges_ary(nodes_ary)
        num_edges = edges_ary.max().item() + 1
        assert num_edges == (edges_ary != -1).sum() / 2

        self.nodes_ary = nodes_ary
        self.edges_ary = edges_ary.to(device)
        self.num_nodes = num_nodes
        self.num_edges = num_edges
        self.ban_edges = ban_edges

        '''build for get_log10_multiple_times'''
        node_dims_arys = get_node_dims_arys(nodes_ary)
        assert num_edges == sum([(ary == 1).sum().item() for ary in node_dims_arys]) / 2

        node_bool_arys = get_node_bool_arys(nodes_ary)
        assert num_nodes == sum([ary.sum() for ary in node_bool_arys])

        self.dims_ten = th.stack(node_dims_arys).type(th.float32).to(device)
        self.bool_ten = th.stack(node_bool_arys).type(th.bool).to(device)
        default_num_envs = 1
        self.dims_tens = th.stack([self.dims_ten.clone() for _ in range(default_num_envs)])
        self.bool_tens = th.stack([self.bool_ten.clone() for _ in range(default_num_envs)])

        self.update_pow_counts = self.update_pow_vectorized if if_vec else self.update_pow_vanilla

    def get_log10_multiple_times(self, edge_sorts: TEN, if_acc: bool = False) -> TEN:
        # edge_argsort = th.rand(self.num_edges).argsort()
        device = self.device
        edges_ary: TEN = self.edges_ary
        num_envs, run_edges = edge_sorts.shape

        if not (self.dims_tens.shape[0] == self.bool_tens.shape[0] == num_envs):
            self.dims_tens = th.stack([self.dims_ten.clone() for _ in range(num_envs)])
            self.bool_tens = th.stack([self.bool_ten.clone() for _ in range(num_envs)])
        dims_tens = self.dims_tens.clone()
        bool_tens = self.bool_tens.clone()

        pow_counts = th.zeros((num_envs, run_edges), dtype=th.float64, device=device)
        for i in range(run_edges):
            edge_is = edge_sorts[:, i]
            self.update_pow_counts(i, edge_is, edges_ary, dims_tens, bool_tens, pow_counts)

        # pow_counts += 1  # todo WARMING (Maybe opt_einsum is wrong)
        result = self.get_multiple_times_vectorized(pow_counts) if if_acc \
            else self.get_multiple_times_accurately(pow_counts)
        return result.detach()

    @staticmethod
    def update_pow_vanilla(i: int, edge_is: TEN, edges_ary: TEN,
                           dims_tens: TEN, bool_tens: TEN, pow_counts: TEN):
        num_envs = pow_counts.shape[0]

        for j in range(num_envs):
            edge_i = edge_is[j]
            dims_arys = dims_tens[j]
            bool_arys = bool_tens[j]

            '''find two nodes of an edge_i'''
            node_i0, node_i1 = th.where(edges_ary == edge_i)[0]  # 找出这条edge 两端的node
            # assert isinstance(node_i0.item(), int)
            # assert isinstance(node_i1.item(), int)

            '''whether node_i0 and node_i1 are different'''
            if_diff = th.logical_not(bool_arys[node_i0, node_i1])

            '''calculate the multiple and avoid repeat'''
            ct_dims = dims_arys[node_i0] + dims_arys[node_i1] * if_diff  # 计算收缩后的node 的邻接张量的维数以及来源
            ct_bool = bool_arys[node_i0] | bool_arys[node_i1]  # 计算收缩后的node 由哪些原初node 合成
            # assert ct_dims.shape == (num_nodes, )
            # assert ct_bool.shape == (num_nodes, )

            # 收缩掉的edge 只需要算一遍乘法。因此上面对 两次重复的指数求和后乘以0.5
            pow_count = ct_dims.sum(dim=0) - (ct_dims * ct_bool).sum(dim=0) * 0.5
            pow_counts[j, i] = pow_count * if_diff

            '''adjust two list: dims_arys, bool_arys'''
            # 如果两个张量是一样的，那么 `ct_bool & if_diff` 就会全部变成 False，让下面这行代码不修改任何数值
            ct_dims[ct_bool & if_diff] = 0  # 把收缩掉的边的乘法数量赋值为2**0，接下来不再参与乘法次数的计算
            dims_tens[j, ct_bool] = ct_dims.repeat(1, 1)  # 根据 bool 将所有收缩后的节点都刷新成相同的信息
            bool_tens[j, ct_bool] = ct_bool.repeat(1, 1)  # 根据 bool 将所有收缩后的节点都刷新成相同的信息

    @staticmethod
    def update_pow_vectorized(i: int, edge_is: TEN, edges_ary: TEN,
                              dims_tens: TEN, bool_tens: TEN, pow_counts: TEN):
        num_envs = pow_counts.shape[0]
        env_is = th.arange(num_envs, device=pow_counts.device)

        '''find two nodes of an edge_i'''
        vec_edges_ary: TEN = edges_ary[None, :, :]
        vec_edges_is: TEN = edge_is[:, None, None]
        res = th.where(vec_edges_ary == vec_edges_is)[1]
        res = res.reshape((num_envs, 2))
        node_i0s, node_i1s = res[:, 0], res[:, 1]
        # assert node_i0s.shape == (num_envs, )
        # assert node_i1s.shape == (num_envs, )

        '''whether node_i0 and node_i1 are different'''
        if_diffs = th.logical_not(bool_tens[env_is, node_i0s, node_i1s])
        # assert if_diffs.shape == (num_envs, )

        '''calculate the multiple and avoid repeat'''
        ct_dimss = dims_tens[env_is, node_i0s] + dims_tens[env_is, node_i1s] * if_diffs.unsqueeze(1)
        ct_bools = bool_tens[env_is, node_i0s] | bool_tens[env_is, node_i1s]
        # assert ct_dimss.shape == (num_envs, num_nodes)
        # assert ct_bools.shape == (num_envs, num_nodes)

        # 初始化的时候，给每个节点它自己多送了一个 2 ** 1 标记，排除重复的乘法时，会多减了它自己，下面的代码把它加回去
        pow_count = ct_dimss.sum(dim=1) - (ct_dimss * ct_bools).sum(dim=1) * 0.5
        pow_counts[:, i] = pow_count * if_diffs

        '''adjust two list: dims_arys, bool_arys'''
        for j in range(num_envs):  # 根据 bool 将所有收缩后的节点都刷新成相同的信息
            ct_dims = ct_dimss[j]
            ct_bool = ct_bools[j]

            ct_dims[ct_bool & if_diffs[j]] = 0  # 把收缩掉的边的乘法数量赋值为2**0，接下来不再参与乘法次数的计算
            dims_tens[j, ct_bool] = ct_dims.repeat(1, 1)
            bool_tens[j, ct_bool] = ct_bool.repeat(1, 1)

    @staticmethod
    def get_multiple_times_accurately(pow_timess: TEN) -> TEN:
        num_envs = pow_timess.shape[0]
        # 缓慢但是完全不损失精度的计算方法
        multiple_times = []
        pow_timess = pow_timess.cpu().numpy()
        for env_id in range(num_envs):
            multiple_time = 0
            for pow_time in pow_timess[env_id, :]:
                multiple_time = multiple_time + 2 ** pow_time
            multiple_time = math_log10(multiple_time)
            multiple_times.append(multiple_time)
        return th.tensor(multiple_times, dtype=th.float64)

    @staticmethod
    def get_multiple_times_vectorized(pow_timess: TEN) -> TEN:
        device = pow_timess.device
        # 快速，但是有效数值有 1e-7 的计算方法（以下都是 float64）
        adj_pow_times = pow_timess.max(dim=1)[0] - 960  # automatically set `max - 960`, 960 < the limit 1024
        # 计算这个乘法个数时，即便用 float64 也偶尔会过拟合，所以先除以 2**temp_power ，求log10 后再恢复它
        multiple_times = (th.pow(2, pow_timess - adj_pow_times.unsqueeze(1))).sum(dim=1)
        multiple_times = multiple_times.log10() + adj_pow_times / th.log2(th.tensor((10,), device=device))
        # adj_pow_times / th.log2(th.tensor((10, ), device=device))  # Change of Base Formula
        return multiple_times

    def convert_edge_sort_to_node2s(self, edge_sort: TEN) -> list:
        edges_ary: TEN = self.edges_ary.cpu()
        edge_sort = edge_sort.cpu()

        run_edges = edge_sort.shape[0]
        assert run_edges == self.num_edges - self.ban_edges

        node2s = []
        for i in range(run_edges):
            edge_i = edge_sort[i]

            '''find two nodes of an edge_i'''
            node_i0, node_i1 = th.where(edges_ary == edge_i)[0]  # 找出这条edge 两端的node
            # assert isinstance(node_i0.item(), int)
            # assert isinstance(node_i1.item(), int)
            node2s.append((node_i0.item(), node_i1.item()))
        return node2s

    def convert_node2s_to_edge_sort(self, node2s: list) -> TEN:
        edges_ary: TEN = self.edges_ary.clone().cpu()
        # nodes_ary: TEN = self.nodes_ary.clone().cpu()

        edges_tmp = [set(edges.tolist()) for edges in edges_ary]
        for edges in edges_tmp:
            edges.discard(-1)

        edge_sort = []
        for node_i0, node_i1 in node2s:
            edge_i0_set = edges_tmp[node_i0]
            edge_i1_set = edges_tmp[node_i1]
            print(f"{node_i0:4} {str(edge_i0_set):17}    "
                  f"{node_i1:4} {str(edge_i1_set):17}    ")

            edge_is = edge_i0_set.intersection(edge_i1_set)
            edge_i = sorted(list(edge_is))[0]  # ordered
            #  edge_i = edge_is.pop()  # disordered
            edge_sort.append(edge_i)

            edge_01_set = edge_i0_set.union(edge_i1_set)
            edges_tmp[node_i0] = edge_01_set
            edges_tmp[node_i1] = edge_01_set

        edge_sort = th.tensor(edge_sort)
        return edge_sort


def convert_str_ary_to_list_as_edge_sort(str_ary: str) -> list:
    str_ary = str_ary.replace('   ', ',').replace('  ', ',').replace(' ', ',').replace('[,', '[ ')
    return eval(str_ary)


'''unit tests'''


def unit_test_get_log10_multiple_times():
    gpu_id = int(sys.argv[1]) if len(sys.argv) > 1 else 0
    device = th.device(f'cuda:{gpu_id}' if th.cuda.is_available() and gpu_id >= 0 else 'cpu')

    # nodes_list, ban_edges = NodesSycamoreN12M14, 0
    # nodes_list, ban_edges = NodesSycamoreN14M14, 0
    # nodes_list, ban_edges = NodesSycamoreN53M12, 0
    nodes_list, ban_edges = get_nodes_list_of_tensor_train(len_list=8), 8
    # nodes_list, ban_edges = get_nodes_list_of_tensor_train(len_list=100), 100
    # nodes_list, ban_edges = get_nodes_list_of_tensor_train(len_list=2000), 2000
    # from TNCO_env import get_nodes_list_of_tensor_tree
    # nodes_list, ban_edges = get_nodes_list_of_tensor_tree(depth=3), 2 ** (3 - 1)

    env = TensorNetworkEnv(nodes_list=nodes_list, ban_edges=ban_edges, device=device)
    print(f"\nnum_nodes      {env.num_nodes:9}"
          f"\nnum_edges      {env.num_edges:9}"
          f"\nban_edges      {env.ban_edges:9}")
    num_envs = 6

    # th.save(edge_arys, 'temp.pth')
    # edge_arys = th.load('temp.pth', map_location=device)

    edge_arys = th.rand((num_envs, env.num_edges - env.ban_edges), device=device)
    multiple_times = env.get_log10_multiple_times(edge_sorts=edge_arys.argsort(dim=1))
    print(f"multiple_times(log10) {multiple_times.cpu().numpy()}")

    edge_arys = th.rand((num_envs, env.num_edges - env.ban_edges), device=device)
    multiple_times = env.get_log10_multiple_times(edge_sorts=edge_arys.argsort(dim=1), if_acc=True)
    print(f"multiple_times(log10) if_vec=True  {multiple_times.cpu().numpy()}")
    multiple_times = env.get_log10_multiple_times(edge_sorts=edge_arys.argsort(dim=1), if_acc=False)
    print(f"multiple_times(log10) if_vec=False {multiple_times.cpu().numpy()}")


def unit_test_convert_node2s_to_edge_sorts():
    gpu_id = int(sys.argv[1]) if len(sys.argv) > 1 else 0
    device = th.device(f'cuda:{gpu_id}' if th.cuda.is_available() and gpu_id >= 0 else 'cpu')

    # nodes_list, ban_edges = NodesSycamoreN12M14, 0
    # nodes_list, ban_edges = NodesSycamoreN14M14, 0
    nodes_list, ban_edges = NodesSycamoreN53M12, 0
    # nodes_list, ban_edges = get_nodes_list_of_tensor_train(len_list=8), 8
    # nodes_list, ban_edges = get_nodes_list_of_tensor_train(len_list=100), 100
    # nodes_list, ban_edges = get_nodes_list_of_tensor_train(len_list=2000), 2000
    # from TNCO_env import get_nodes_list_of_tensor_tree
    # nodes_list, ban_edges = get_nodes_list_of_tensor_tree(depth=3), 2 ** (3 - 1)

    env = TensorNetworkEnv(nodes_list=nodes_list, ban_edges=ban_edges, device=device)
    print(f"\nnum_nodes      {env.num_nodes:9}"
          f"\nnum_edges      {env.num_edges:9}"
          f"\nban_edges      {env.ban_edges:9}")
    num_envs = 6

    # th.save(edge_arys, 'temp.pth')
    # edge_arys = th.load('temp.pth', map_location=device)

    edge_arys = th.rand((num_envs, env.num_edges - env.ban_edges), device=device)
    edge_ary = edge_arys[0]
    print(edge_ary.argsort().shape)
    print(edge_ary.argsort())
    node2s = env.convert_edge_sort_to_node2s(edge_sort=edge_ary.argsort(dim=0))
    edge_sort = env.convert_node2s_to_edge_sort(node2s=node2s).to(device)
    print(edge_sort.shape)
    print(edge_sort)

    print(edge_sort - edge_ary.argsort())
    edge_sorts = edge_sort.unsqueeze(0)
    multiple_times = env.get_log10_multiple_times(edge_sorts=edge_sorts.to(device))
    print(f"multiple_times(log10) {multiple_times.cpu().numpy()}")


def unit_test_convert_node2s_to_edge_sorts_of_load():
    gpu_id = int(sys.argv[1]) if len(sys.argv) > 1 else 0
    device = th.device(f'cuda:{gpu_id}' if th.cuda.is_available() and gpu_id >= 0 else 'cpu')

    # nodes_list, ban_edges = NodesSycamoreN53M20, 0
    nodes_list, ban_edges = get_nodes_list_of_tensor_train(), 0
    env = TensorNetworkEnv(nodes_list=nodes_list, ban_edges=ban_edges, device=device)
    print(f"\nnum_nodes      {env.num_nodes:9}"
          f"\nnum_edges      {env.num_edges:9}"
          f"\nban_edges      {env.ban_edges:9}")

    node2s, ground_true_log10 = Node2sSycamoreN53N20Test1, 25.6106868931126
    # node2s, ground_true_log10 = Node2sSycamoreN53N20Test2, 25.888611588740623
    edge_sort = env.convert_node2s_to_edge_sort(node2s=node2s).to(device)
    print(edge_sort.shape)
    print(edge_sort)

    edge_sorts = edge_sort.unsqueeze(0)
    multiple_times = env.get_log10_multiple_times(edge_sorts=edge_sorts.to(device), if_acc=True)
    multiple_times = multiple_times.cpu().numpy()[0]
    print(f"multiple_times(log10) {multiple_times:20.16f}    "
          f"diff (if_vec=True)    {multiple_times - ground_true_log10:9.3e}")
    multiple_times = env.get_log10_multiple_times(edge_sorts=edge_sorts.to(device), if_acc=False)
    multiple_times = multiple_times.cpu().numpy()[0]
    print(f"multiple_times(log10) {multiple_times:20.16f}    "
          f"diff (if_vec=False)   {multiple_times - ground_true_log10:9.3e}")


def unit_test_edge_sorts_to_log10_multiple_times():
    gpu_id = int(sys.argv[1]) if len(sys.argv) > 1 else 0
    device = th.device(f'cuda:{gpu_id}' if th.cuda.is_available() and gpu_id >= 0 else 'cpu')

    # edge_sort_str = EdgeSortStrH2OSycamoreN12M14  # 05.5792907356870209, otherSOTA 5.83

    # edge_sort_str = EdgeSortStrH2OSycamoreN53M12  # 14.7798258185512630, otherSOTA 12.869
    # edge_sort_str = EdgeSortStrH2OSycamoreN53M14  # 15.5812799843876064, otherSOTA 14.420
    # edge_sort_str = EdgeSortStrH2OSycamoreN53M16  # 19.8684635668356293, otherSOTA 17.012
    # edge_sort_str = EdgeSortStrH2OSycamoreN53M18  # 23.7903614350502544, otherSOTA 17.484
    edge_sort_str = EdgeSortStrH2OSycamoreN53M20  # 21.7853530599482035, otherSOTA 18.544

    edge_sort = convert_str_ary_to_list_as_edge_sort(edge_sort_str)
    edge_sort = th.tensor(edge_sort, dtype=th.long).to(device)

    '''auto choose NodesSycamore'''
    num_edges = max(edge_sort) + 1
    nodes_list, ban_edges = None, 0
    for nodes_list in (
            NodesSycamoreN12M14, NodesSycamoreN14M14,
            NodesSycamoreN53M12, NodesSycamoreN53M14, NodesSycamoreN53M16, NodesSycamoreN53M18, NodesSycamoreN53M20,
    ):
        env = TensorNetworkEnv(nodes_list=nodes_list, ban_edges=ban_edges, device=device)
        if num_edges == env.num_edges:
            break
    else:
        raise ValueError(f"num_edges {num_edges}")
    print(f"\nnum_nodes      {env.num_nodes:9}"
          f"\nnum_edges      {env.num_edges:9}"
          f"\nban_edges      {env.ban_edges:9}")

    '''get multiple_times'''
    edge_sorts = edge_sort.unsqueeze(0)
    multiple_times = env.get_log10_multiple_times(edge_sorts=edge_sorts.to(device), if_acc=False)
    multiple_times = multiple_times.cpu().numpy()[0]
    print(f"multiple_times(log10) {multiple_times:20.16f}")

    # import numpy as np
    # np.set_printoptions(linewidth=120)
    # print(edge_sort.cpu().numpy())


def unit_test_warm_up():
    gpu_id = 0
    warm_up_size = 2 ** 6  # 2 ** 14
    target_score = -th.inf  # 7.0
    """
    你可以选择 target_score = 负无穷，然后设置你想要的 warm_up_size ，直接测试仿真环境 并行与否 的耗时
    也可以选择 warm_up_size = 正无穷，然后设置你想要的 target_score ，直接测试仿真环境 并行与否 的耗时
    """
    if_vec = True  # 设置 if_vec = True。可以在有GPU的情况下，高效快速地并行计算乘法次数
    if_acc = True  # 设置 if_acc = True。这种设置虽然慢，但是它使用int计算，能非常精确且不溢出地算出结果（能避免nan）
    env_nums = 2 ** 2
    """
    在启用 if_vec = True 的情况下，你可以设置 env_nums 来主动设置并行子环境数量。
    越好的GPU，就可以设置越大的 env_nums，设置到 GPU使用率无法再提高的情况下，会得到一个接近最高性价比的 env_nums
    
    
    1. gpu接收一次指令 然后运行n个计算任务，而不是编译n个指令 运行n个计算任务

    2. 计算任务里几乎都是加乘操作，用gpu这种里面很少逻辑电路的设备去做，而不是cpu去做，电路利用效率高，分支预测简单

    3. gpu的计算缓存就是设计用来并行的，CPU是通用计算的，没有gpu这么适应并行
    
    
    
    
    """

    # nodes_list, ban_edges = NodesSycamoreN12M14, 0
    nodes_list, ban_edges = get_nodes_list_of_tensor_train(1000), 1000
    # nodes_list, ban_edges = get_nodes_list_and_ban_edges_of_tensor_tree(depth=10)

    """
    这里你可以选择不同的张量电路无向图，在这里跑不同的任务。注意选择正确的 ban_edges 以及对应的 target_socre
    
    
    """

    device = th.device(f'cuda:{gpu_id}' if th.cuda.is_available() and gpu_id >= 0 else 'cpu')

    env = TensorNetworkEnv(nodes_list=nodes_list, ban_edges=ban_edges, device=device, if_vec=if_vec)
    dim = env.num_edges - env.ban_edges

    min_score = th.inf

    from time import time as timer
    start_time = timer()
    for i in range(warm_up_size // env_nums):
        thetas = th.rand((env_nums, dim), dtype=th.float32, device=device)
        thetas = ((thetas - thetas.mean(dim=1, keepdim=True)) / (thetas.std(dim=1, keepdim=True) + 1e-6))

        scores = env.get_log10_multiple_times(edge_sorts=thetas.argsort(dim=1), if_acc=if_acc)
        min_score = min(scores.min(dim=0)[0].item(), min_score)
        print(f"MinScore {min_score:16.9f}  UsedTime {timer() - start_time:9.3f}  SearchNum {(i + 1) * env_nums:9.0f}")
        if min_score < target_score:
            break
    """
    min_score 是这一次 warm_up 搜索到的最优分数
    UsedTime 是开始训练到此刻的耗时
    SearchNum 是开始训练到此刻的搜索次数

    实验结果：
    nodes_list, ban_edges = get_nodes_list_of_tensor_train(100), 100
    if_acc = False
    if_vec = False       MinScore 30.404030  UsedTime   151.614  SearchNum      4096
    env_nums = 2 ** 2    MinScore 30.404030  UsedTime    79.989  SearchNum      4096
    env_nums = 2 ** 4    MinScore 30.404030  UsedTime    55.738  SearchNum      4096
    env_nums = 2 ** 8    MinScore 30.404030  UsedTime    52.963  SearchNum      4096
    env_nums = 2 ** 12   MinScore 30.404030  UsedTime    52.031  SearchNum      4096
    """


if __name__ == '__main__':
    # unit_test_get_log10_multiple_times()
    # unit_test_convert_node2s_to_edge_sorts()
    # unit_test_convert_node2s_to_edge_sorts_of_load()
    # unit_test_edge_sorts_to_log10_multiple_times()
    unit_test_warm_up()