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189,214,226,227,228,229,238,246,247,250,251,252,253,255,258,260,261,295,296,297,353,390,392,397,399,400,441,442,444,446,451,452,453,454,455,458,460,462,467,468,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,502,531,532,533,534,536,537,542,543,549,551,554,557,569,581,611,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,781,782,783,784,785,790,791,792,794,798,799,817,821,822,823,824,831,832,833,834,839,840,841,843,847,863,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,946,947,948,951,953,954,956,959,960,961,969,973,975,998,999,1000,1009,1020,1021,1022,1023,1024,1025,1026,1027,1028,1037,1058,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1110,1111,1113,1114,1115,1116,1117,1122,1124,1126,1127,1128,1129,1130,1132,1133,1134,1136,1137,1139,1140,1141,1142,1143,1145,1146,1148,1158,1162,1165,1166],"short":[82,83,84,86,292,321,444,445,462,618,915,932,960,961,1117,1136,1143,1144],"static":[423,458,460,467,468,1124,1132,1161],"super":[138,303,457,458,462,467,468,1110,1111,1114,1122,1126,1127,1128,1129,1131,1133,1136,1142,1143,1162,1166],"switch":[82,211,1108,1122,1137],"throw":[13,470],"true":[9,11,16,17,19,20,27,43,59,60,70,72,78,80,95,96,139,143,144,145,146,147,148,151,153,157,174,185,187,192,197,198,200,201,202,212,265,277,278,281,282,296,302,303,304,307,326,331,332,386,387,388,389,395,404,405,406,407,408,409,410,413,421,423,428,430,432,433,434,442,444,446,452,453,454,455,458,460,462,466,467,468,469,470,476,478,479,483,484,485,491,496,531,534,543,551,554,581,617,618,621,622,623,625,631,638,639,642,655,664,666,681,683,694,695,696,697,699,700,701,702,705,706,707,714,716,717,718,720,721,725,726,728,729,734,736,741,754,781,785,787,821,831,834,836,869,885,894,895,897,903,904,911,912,916,919,924,934,938,939,940,945,950,952,959,960,1054,1082,1084,1085,1088,1089,1090,1093,1094,1095,1110,1111,1116,1117,1118,1122,1124,1127,1128,1129,1130,1131,1132,1136,1137,1139,1140,1141,1142,1144,1145,1146,1148,1158,1161,1162,1165,1166],"try":[0,260,261,296,459,462,618,699,700,1084,1085,1110,1117,1118,1124,1125,1128,1133,1137,1139,1142,1143,1145,1153,1154,1155,1158,1165,1166],"var":[55,192,428,430,460,469,476,488,491,869,973,1021,1022,1024,1025,1027,1028,1133],"void":8,"while":[80,143,148,179,292,321,405,407,409,444,462,476,482,492,617,674,689,730,756,900,902,937,940,1110,1115,1118,1122,1124,1127,1128,1133,1142,1143,1144,1149,1154,1158,1162,1166],AWS:[759,1102,1108,1132],Adding:1127,And:[1113,1117,1118,1124,1137,1139,1140,1141,1144,1145,1154,1165,1166],Are:[421,458,460,468],Axes:[483,646],BOS:[1146,1148],But:[95,96,121,457,476,937,953,965,1110,1116,1118,1127,1128,1136,1148,1154],Doing:1136,Dying:1125,EOS:[1146,1148],For:[0,8,22,69,80,83,105,106,141,154,179,183,192,195,196,197,201,202,204,238,246,251,268,272,277,278,281,282,283,284,285,286,287,288,289,290,291,302,303,304,306,307,324,325,326,327,328,329,331,332,333,386,387,388,389,392,403,404,405,406,407,408,409,413,415,416,417,418,419,420,426,428,429,432,433,434,442,444,446,458,460,461,462,467,468,469,470,479,480,484,486,489,492,496,502,536,537,617,625,627,642,646,647,655,666,672,674,679,681,683,685,688,694,695,696,697,699,700,714,716,717,729,733,741,747,756,766,768,821,885,894,900,908,912,922,924,926,937,953,954,955,959,960,961,999,1028,1084,1085,1106,1108,1110,1113,1114,1117,1118,1125,1126,1127,1128,1131,1132,1133,1136,1137,1139,1141,1142,1143,1145,1146,1148,1149,1154,1155,1158,1161,1162,1165,1166],Has:[219,423,458,460,467,468],IDs:1106,Into:[1131,1153,1154,1155],Its:[323,619,738,1110,1128,1144],NMS:[660,662],NOT:[667,673,715,718,720,721,725,726,736,895,912,924],Not:[725,726,1122,1161],OPs:1165,One:[82,144,158,175,335,348,462,754,953,975,1000,1106,1110,1114,1116,1118,1124,1126,1128,1130,1133,1140,1144,1155,1158],RMS:1158,Such:[1113,1154],That:[1110,1128,1137,1139,1143,1145,1154,1161,1162,1166],The:[0,4,5,6,10,11,12,17,19,20,21,27,29,33,41,55,57,58,59,71,82,83,84,86,87,89,91,92,94,96,97,99,100,101,102,104,106,108,112,113,115,121,124,127,135,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,156,157,158,159,160,162,163,166,167,168,170,173,174,176,178,179,180,181,182,183,184,187,190,192,193,194,195,196,197,198,199,200,201,202,204,206,208,210,216,222,226,227,238,245,249,253,255,257,260,261,268,276,279,280,293,294,295,296,308,310,311,321,324,325,326,327,328,329,330,331,332,333,334,342,343,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,389,391,392,399,400,403,404,405,406,407,408,409,411,412,422,426,428,430,441,442,444,446,453,454,455,456,458,459,460,461,462,466,467,468,469,470,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,502,519,521,522,523,524,525,526,527,528,529,530,531,534,535,536,537,538,539,540,542,543,544,545,546,547,548,550,552,553,555,556,559,560,561,562,563,564,565,566,567,568,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,587,588,589,590,591,593,594,595,596,597,598,599,600,601,602,604,605,606,607,608,609,610,612,613,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,684,685,686,687,688,689,690,691,692,693,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,715,718,719,720,721,722,723,724,725,726,727,728,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,775,776,777,778,779,780,781,785,786,788,790,791,793,795,796,797,800,801,802,803,804,805,807,808,809,810,811,812,814,815,816,818,820,821,822,823,825,826,827,828,829,830,831,834,835,837,839,840,842,844,845,846,848,849,850,851,852,853,855,856,857,858,859,861,862,864,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,939,940,946,947,948,950,951,952,953,954,956,960,969,987,988,989,990,991,992,993,994,995,996,997,998,1000,1001,1002,1003,1004,1005,1006,1007,1008,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1098,1101,1102,1104,1106,1107,1108,1109,1111,1112,1113,1114,1116,1117,1118,1120,1122,1125,1126,1127,1129,1131,1133,1136,1137,1139,1141,1142,1143,1145,1146,1148,1149,1150,1154,1155,1158,1161,1162,1163,1165,1168,1170],Their:[426,429],Then:[69,168,323,476,663,665,699,700,1084,1085,1108,1111,1114,1115,1118,1129,1134,1141,1146,1148,1154,1161,1165],There:[0,158,479,929,999,1106,1110,1128,1131,1132,1133,1136,1137,1143,1145,1154,1166],These:[140,466,484,821,1126,1132,1135,1141,1143,1147,1154,1158,1161],Use:[0,118,134,144,145,146,147,209,212,213,347,410,493,494,495,643,648,652,679,688,702,705,706,707,733,754,766,822,823,882,908,919,968,970,971,972,974,976,977,978,979,980,982,986,1090,1093,1094,1095,1106,1119,1124,1130,1136,1143,1145,1146,1161,1166],Used:[5,16,197,223,660,662,1054,1076,1110,1128],Uses:627,Using:[296,462,1106,1112,1130,1138,1141,1144,1151,1153,1170],Will:[87,453,470],With:[96,99,238,618,1108,1117,1122,1124,1131,1136,1139,1144,1158,1161,1166],__array__:[641,883,902],__call__:1146,__doc__:1148,__future__:[1110,1128,1136,1142],__getitem__:[297,1127],__global__:8,__init__:[3,4,5,6,7,8,69,82,88,89,90,91,92,93,94,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,140,141,142,150,151,152,153,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,224,225,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,385,386,387,388,389,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,457,458,467,468,469,943,944,945,950,953,954,955,956,957,958,959,1110,1111,1114,1122,1126,1127,1128,1129,1131,1133,1136,1142,1143,1146,1158,1162,1166],__iter__:[300,1162],__len__:[297,300,1127],__next__:1162,__rdiv__:505,__rmod__:506,_arg_param:1162,_autogen:[1133,1140,1143,1145,1149,1158],_aw:1106,_barrier:158,_bia:[960,1111,1129],_build:[0,1106],_children:1143,_compos:969,_constant:1146,_contrib_requant:1165,_exec_group:1165,_init_bia:138,_init_weight:[1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et.ndarray.argmin","mxnet.ndarray.argsort","mxnet.ndarray.array","mxnet.ndarray.batch_dot","mxnet.ndarray.batch_take","mxnet.ndarray.broadcast_axes","mxnet.ndarray.broadcast_hypot","mxnet.ndarray.broadcast_like","mxnet.ndarray.broadcast_to","mxnet.ndarray.cast","mxnet.ndarray.cbrt","mxnet.ndarray.ceil","mxnet.ndarray.clip","mxnet.ndarray.concat","mxnet.ndarray.contrib.AdaptiveAvgPooling2D","mxnet.ndarray.contrib.BilinearResize2D","mxnet.ndarray.contrib.DeformableConvolution","mxnet.ndarray.contrib.DeformablePSROIPooling","mxnet.ndarray.contrib.MultiBoxDetection","mxnet.ndarray.contrib.MultiBoxPrior","mxnet.ndarray.contrib.MultiBoxTarget","mxnet.ndarray.contrib.MultiProposal","mxnet.ndarray.contrib.PSROIPooling","mxnet.ndarray.contrib.Proposal","mxnet.ndarray.contrib.ROIAlign","mxnet.ndarray.contrib.cond","mxnet.ndarray.contrib.count_sketch","mxnet.ndarray.contrib.ctc_loss","mxnet.ndarray.contrib.dequantize","mxnet.ndarray.contrib.fft","mxnet.ndarray.contrib.foreach","mxnet.ndarray.contrib.getnnz","mxnet.ndarray.contrib.ifft","mxnet.ndarray.contrib.index_copy","mxnet.ndarray.contrib.quantize","mxnet.ndarray.contrib.while_loop","mxnet.ndarray.cos","mxnet.ndarray.cosh","mxnet.ndarray.degrees","mxnet.ndarray.depth_to_space","mxnet.ndarray.diag","mxnet.ndarray.divide","mxnet.ndarray.dot","mxnet.ndarray.empty","mxnet.ndarray.equal","mxnet.ndarray.exp","mxnet.ndarray.expand_dims","mxnet.ndarray.expm1","mxnet.ndarray.fix","mxnet.ndarray.flatten","mxnet.ndarray.flip","mxnet.ndarray.floor","mxnet.ndarray.full","mxnet.ndarray.gamma","mxnet.ndarray.gammaln","mxnet.ndarray.greater","mxnet.ndarray.greater_equal","mxnet.ndarray.lesser","mxnet.ndarray.lesser_equal","mxnet.ndarray.linalg.gelqf","mxnet.ndarray.linalg.gemm","mxnet.ndarray.linalg.gemm2","mxnet.ndarray.linalg.potrf","mxnet.ndarray.linalg.potri","mxnet.ndarray.linalg.sumlogdiag","mxnet.ndarray.linalg.syevd","mxnet.ndarray.linalg.syrk","mxnet.ndarray.linalg.trmm","mxnet.ndarray.linalg.trsm","mxnet.ndarray.load","mxnet.ndarray.log","mxnet.ndarray.log10","mxnet.ndarray.log1p","mxnet.ndarray.log2","mxnet.ndarray.log_softmax","mxnet.ndarray.logical_and","mxnet.ndarray.logical_not","mxnet.ndarray.logical_or","mxnet.ndarray.logical_xor","mxnet.ndarray.max","mxnet.ndarray.maximum","mxnet.ndarray.mean","mxnet.ndarray.min","mxnet.ndarray.minimum","mxnet.ndarray.modulo","mxnet.ndarray.multiply","mxnet.ndarray.nanprod","mxnet.ndarray.nansum","mxnet.ndarray.negative","mxnet.ndarray.norm","mxnet.ndarray.not_equal","mxnet.ndarray.one_hot","mxnet.ndarray.ones","mxnet.ndarray.ones_like","mxnet.ndarray.pad","mxnet.ndarray.pick","mxnet.ndarray.power","mxnet.ndarray.prod","mxnet.ndarray.radians","mxnet.ndarray.random.exponential","mxnet.ndarray.random.gamma","mxnet.ndarray.random.generalized_negative_binomial","mxnet.ndarray.random.multinomial","mxnet.ndarray.random.negative_binomial","mxnet.ndarray.random.normal","mxnet.ndarray.random.poisson","mxnet.ndarray.random.randint","mxnet.ndarray.random.randn","mxnet.ndarray.random.shuffle","mxnet.ndarray.random.uniform","mxnet.ndarray.ravel_multi_index","mxnet.ndarray.rcbrt","mxnet.ndarray.reciprocal","mxnet.ndarray.relu","mxnet.ndarray.repeat","mxnet.ndarray.reshape","mxnet.ndarray.reshape_like","mxnet.ndarray.rint","mxnet.ndarray.round","mxnet.ndarray.rsqrt","mxnet.ndarray.save","mxnet.ndarray.shape_array","mxnet.ndarray.sigmoid","mxnet.ndarray.sign","mxnet.ndarray.sin","mxnet.ndarray.sinh","mxnet.ndarray.size_array","mxnet.ndarray.slice","mxnet.ndarray.slice_axis","mxnet.ndarray.slice_like","mxnet.ndarray.smooth_l1","mxnet.ndarray.softmax","mxnet.ndarray.softmax_cross_entropy","mxnet.ndarray.sort","mxnet.ndarray.space_to_depth","mxnet.ndarray.sparse.CSRNDArray.__getitem__","mxnet.ndarray.sparse.CSRNDArray.__setitem__","mxnet.ndarray.sparse.CSRNDArray.abs","mxnet.ndarray.sparse.CSRNDArray.arcsin","mxnet.ndarray.sparse.CSRNDArray.arcsinh","mxnet.ndarray.sparse.CSRNDArray.arctan","mxnet.ndarray.sparse.CSRNDArray.arctanh","mxnet.ndarray.sparse.CSRNDArray.as_in_context","mxnet.ndarray.sparse.CSRNDArray.asnumpy","mxnet.ndarray.sparse.CSRNDArray.asscalar","mxnet.ndarray.sparse.CSRNDArray.asscipy","mxnet.ndarray.sparse.CSRNDArray.astype","mxnet.ndarray.sparse.CSRNDArray.ceil","mxnet.ndarray.sparse.CSRNDArray.check_format","mxnet.ndarray.sparse.CSRNDArray.clip","mxnet.ndarray.sparse.CSRNDArray.context","mxnet.ndarray.sparse.CSRNDArray.copy","mxnet.ndarray.sparse.CSRNDArray.copyto","mxnet.ndarray.sparse.CSRNDArray.data","mxnet.ndarray.sparse.CSRNDArray.degrees","mxnet.ndarray.sparse.CSRNDArray.dtype","mxnet.ndarray.sparse.CSRNDArray.expm1","mxnet.ndarray.sparse.CSRNDArray.fix","mxnet.ndarray.sparse.CSRNDArray.floor","mxnet.ndarray.sparse.CSRNDArray.indices","mxnet.ndarray.sparse.CSRNDArray.indptr","mxnet.ndarray.sparse.CSRNDArray.log1p","mxnet.ndarray.sparse.CSRNDArray.mean","mxnet.ndarray.sparse.CSRNDArray.norm","mxnet.ndarray.sparse.CSRNDArray.radians","mxnet.ndarray.sparse.CSRNDArray.rint","mxnet.ndarray.sparse.CSRNDArray.round","mxnet.ndarray.sparse.CSRNDArray.shape","mxnet.ndarray.sparse.CSRNDArray.sign","mxnet.ndarray.sparse.CSRNDArray.sin","mxnet.ndarray.sparse.CSRNDArray.sinh","mxnet.ndarray.sparse.CSRNDArray.slice","mxnet.ndarray.sparse.CSRNDArray.sqrt","mxnet.ndarray.sparse.CSRNDArray.square","mxnet.ndarray.sparse.CSRNDArray.stype","mxnet.ndarray.sparse.CSRNDArray.sum","mxnet.ndarray.sparse.CSRNDArray.tan","mxnet.ndarray.sparse.CSRNDArray.tanh","mxnet.ndarray.sparse.CSRNDArray.tostype","mxnet.ndarray.sparse.CSRNDArray.trunc","mxnet.ndarray.sparse.CSRNDArray.wait_to_read","mxnet.ndarray.sparse.CSRNDArray.zeros_like","mxnet.ndarray.sparse.Embedding","mxnet.ndarray.sparse.LinearRegressionOutput","mxnet.ndarray.sparse.LogisticRegressionOutput","mxnet.ndarray.sparse.RowSparseNDArray.__getitem__","mxnet.ndarray.sparse.RowSparseNDArray.__setitem__","mxnet.ndarray.sparse.RowSparseNDArray.abs","mxnet.ndarray.sparse.RowSparseNDArray.arcsin","mxnet.ndarray.sparse.RowSparseNDArray.arcsinh","mxnet.ndarray.sparse.RowSparseNDArray.arctan","mxnet.ndarray.sparse.RowSparseNDArray.arctanh","mxnet.ndarray.sparse.RowSparseNDArray.as_in_context","mxnet.ndarray.sparse.RowSparseNDArray.asnumpy","mxnet.ndarray.sparse.RowSparseNDArray.asscalar","mxnet.ndarray.sparse.RowSparseNDArray.astype","mxnet.ndarray.sparse.RowSparseNDArray.ceil","mxnet.ndarray.sparse.RowSparseNDArray.check_format","mxnet.ndarray.sparse.RowSparseNDArray.clip","mxnet.ndarray.sparse.RowSparseNDArray.context","mxnet.ndarray.sparse.RowSparseNDArray.copy","mxnet.ndarray.sparse.RowSparseNDArray.copyto","mxnet.ndarray.sparse.RowSparseNDArray.data","mxnet.ndarray.sparse.RowSparseNDArray.degrees","mxnet.ndarray.sparse.RowSparseNDArray.dtype","mxnet.ndarray.sparse.RowSparseNDArray.expm1","mxnet.ndarray.sparse.RowSparseNDArray.fix","mxnet.ndarray.sparse.RowSparseNDArray.floor","mxnet.ndarray.sparse.RowSparseNDArray.indices","mxnet.ndarray.sparse.RowSparseNDArray.log1p","mxnet.ndarray.sparse.RowSparseNDArray.norm","mxnet.ndarray.sparse.RowSparseNDArray.radians","mxnet.ndarray.sparse.RowSparseNDArray.retain","mxnet.ndarray.sparse.RowSparseNDArray.rint","mxnet.ndarray.sparse.RowSparseNDArray.round","mxnet.ndarray.sparse.RowSparseNDArray.shape","mxnet.ndarray.sparse.RowSparseNDArray.sign","mxnet.ndarray.sparse.RowSparseNDArray.sin","mxnet.ndarray.sparse.RowSparseNDArray.sinh","mxnet.ndarray.sparse.RowSparseNDArray.sqrt","mxnet.ndarray.sparse.RowSparseNDArray.square","mxnet.ndarray.sparse.RowSparseNDArray.stype","mxnet.ndarray.sparse.RowSparseNDArray.tan","mxnet.ndarray.sparse.RowSparseNDArray.tanh","mxnet.ndarray.sparse.RowSparseNDArray.tostype","mxnet.ndarray.sparse.RowSparseNDArray.trunc","mxnet.ndarray.sparse.RowSparseNDArray.wait_to_read","mxnet.ndarray.sparse.RowSparseNDArray.zeros_like","mxnet.ndarray.sparse.abs","mxnet.ndarray.sparse.adagrad_update","mxnet.ndarray.sparse.adam_update","mxnet.ndarray.sparse.add_n","mxnet.ndarray.sparse.arcsin","mxnet.ndarray.sparse.arcsinh","mxnet.ndarray.sparse.arctan","mxnet.ndarray.sparse.arctanh","mxnet.ndarray.sparse.array","mxnet.ndarray.sparse.broadcast_add","mxnet.ndarray.sparse.broadcast_div","mxnet.ndarray.sparse.broadcast_mul","mxnet.ndarray.sparse.broadcast_sub","mxnet.ndarray.sparse.cast_storage","mxnet.ndarray.sparse.ceil","mxnet.ndarray.sparse.concat","mxnet.ndarray.sparse.csr_matrix","mxnet.ndarray.sparse.degrees","mxnet.ndarray.sparse.dot","mxnet.ndarray.sparse.elemwise_add","mxnet.ndarray.sparse.elemwise_mul","mxnet.ndarray.sparse.elemwise_sub","mxnet.ndarray.sparse.empty","mxnet.ndarray.sparse.expm1","mxnet.ndarray.sparse.fix","mxnet.ndarray.sparse.floor","mxnet.ndarray.sparse.log1p","mxnet.ndarray.sparse.make_loss","mxnet.ndarray.sparse.mean","mxnet.ndarray.sparse.negative","mxnet.ndarray.sparse.norm","mxnet.ndarray.sparse.radians","mxnet.ndarray.sparse.retain","mxnet.ndarray.sparse.rint","mxnet.ndarray.sparse.round","mxnet.ndarray.sparse.row_sparse_array","mxnet.ndarray.sparse.sgd_mom_update","mxnet.ndarray.sparse.sgd_update","mxnet.ndarray.sparse.sign","mxnet.ndarray.sparse.sin","mxnet.ndarray.sparse.sinh","mxnet.ndarray.sparse.slice","mxnet.ndarray.sparse.sqrt","mxnet.ndarray.sparse.square","mxnet.ndarray.sparse.stop_gradient","mxnet.ndarray.sparse.sum","mxnet.ndarray.sparse.tan","mxnet.ndarray.sparse.tanh","mxnet.ndarray.sparse.trunc","mxnet.ndarray.sparse.where","mxnet.ndarray.sparse.zeros","mxnet.ndarray.sparse.zeros_like","mxnet.ndarray.split","mxnet.ndarray.sqrt","mxnet.ndarray.square","mxnet.ndarray.stack","mxnet.ndarray.subtract","mxnet.ndarray.sum","mxnet.ndarray.swapaxes","mxnet.ndarray.take","mxnet.ndarray.tan","mxnet.ndarray.tanh","mxnet.ndarray.tile","mxnet.ndarray.topk","mxnet.ndarray.transpose","mxnet.ndarray.trunc","mxnet.ndarray.unravel_index","mxnet.ndarray.where","mxnet.ndarray.zeros","mxnet.ndarray.zeros_like","mxnet.ndarray","NDArray","CSRNDArray","RowSparseNDArray","Routines","Sparse routines","mxnet.callback.LogValidationMetricsCallback","mxnet.callback.ProgressBar","mxnet.callback.Speedometer","mxnet.callback.do_checkpoint","mxnet.callback.log_train_metric","mxnet.callback.module_checkpoint","mxnet.model.BatchEndParam","mxnet.model.FeedForward","mxnet.model.load_checkpoint","mxnet.model.save_checkpoint","mxnet.module.BaseModule","mxnet.module.BucketingModule","mxnet.module.Module","mxnet.module.PythonLossModule","mxnet.module.PythonModule","mxnet.module.SequentialModule","mxnet.monitor.Monitor","mxnet.visualization.plot_network","mxnet.visualization.print_summary","Symbol related modules","mxnet.callback","mxnet.model","mxnet.module","mxnet.monitor","mxnet.visualization","mxnet.symbol.Symbol.__add__","mxnet.symbol.Symbol.__call__","mxnet.symbol.Symbol.__div__","mxnet.symbol.Symbol.__eq__","mxnet.symbol.Symbol.__ge__","mxnet.symbol.Symbol.__getitem__","mxnet.symbol.Symbol.__gt__","mxnet.symbol.Symbol.__iter__","mxnet.symbol.Symbol.__le__","mxnet.symbol.Symbol.__lt__","mxnet.symbol.Symbol.__mod__","mxnet.symbol.Symbol.__mul__","mxnet.symbol.Symbol.__ne__","mxnet.symbol.Symbol.__neg__","mxnet.symbol.Symbol.__pow__","mxnet.symbol.Symbol.__rdiv__","mxnet.symbol.Symbol.__rmod__","mxnet.symbol.Symbol.__rsub__","mxnet.symbol.Symbol.__sub__","mxnet.symbol.Symbol.arccos","mxnet.symbol.Symbol.arccosh","mxnet.symbol.Symbol.arcsin","mxnet.symbol.Symbol.arcsinh","mxnet.symbol.Symbol.arctan","mxnet.symbol.Symbol.arctanh","mxnet.symbol.Symbol.argmax","mxnet.symbol.Symbol.argmax_channel","mxnet.symbol.Symbol.argmin","mxnet.symbol.Symbol.argsort","mxnet.symbol.Symbol.astype","mxnet.symbol.Symbol.attr","mxnet.symbol.Symbol.attr_dict","mxnet.symbol.Symbol.bind","mxnet.symbol.Symbol.broadcast_axes","mxnet.symbol.Symbol.broadcast_like","mxnet.symbol.Symbol.broadcast_to","mxnet.symbol.Symbol.cbrt","mxnet.symbol.Symbol.ceil","mxnet.symbol.Symbol.clip","mxnet.symbol.Symbol.cos","mxnet.symbol.Symbol.cosh","mxnet.symbol.Symbol.debug_str","mxnet.symbol.Symbol.degrees","mxnet.symbol.Symbol.depth_to_space","mxnet.symbol.Symbol.diag","mxnet.symbol.Symbol.exp","mxnet.symbol.Symbol.expand_dims","mxnet.symbol.Symbol.expm1","mxnet.symbol.Symbol.fix","mxnet.symbol.Symbol.flatten","mxnet.symbol.Symbol.flip","mxnet.symbol.Symbol.floor","mxnet.symbol.Symbol.get_children","mxnet.symbol.Symbol.get_internals","mxnet.symbol.Symbol.infer_shape","mxnet.symbol.Symbol.infer_shape_partial","mxnet.symbol.Symbol.infer_type","mxnet.symbol.Symbol.list_arguments","mxnet.symbol.Symbol.list_attr","mxnet.symbol.Symbol.list_auxiliary_states","mxnet.symbol.Symbol.list_outputs","mxnet.symbol.Symbol.log","mxnet.symbol.Symbol.log10","mxnet.symbol.Symbol.log1p","mxnet.symbol.Symbol.log2","mxnet.symbol.Symbol.log_softmax","mxnet.symbol.Symbol.max","mxnet.symbol.Symbol.mean","mxnet.symbol.Symbol.min","mxnet.symbol.Symbol.name","mxnet.symbol.Symbol.nanprod","mxnet.symbol.Symbol.nansum","mxnet.symbol.Symbol.norm","mxnet.symbol.Symbol.one_hot","mxnet.symbol.Symbol.ones_like","mxnet.symbol.Symbol.pad","mxnet.symbol.Symbol.pick","mxnet.symbol.Symbol.prod","mxnet.symbol.Symbol.radians","mxnet.symbol.Symbol.rcbrt","mxnet.symbol.Symbol.relu","mxnet.symbol.Symbol.reshape","mxnet.symbol.Symbol.reshape_like","mxnet.symbol.Symbol.rint","mxnet.symbol.Symbol.round","mxnet.symbol.Symbol.rsqrt","mxnet.symbol.Symbol.save","mxnet.symbol.Symbol.shape_array","mxnet.symbol.Symbol.sigmoid","mxnet.symbol.Symbol.sign","mxnet.symbol.Symbol.simple_bind","mxnet.symbol.Symbol.sin","mxnet.symbol.Symbol.sinh","mxnet.symbol.Symbol.size_array","mxnet.symbol.Symbol.slice","mxnet.symbol.Symbol.slice_axis","mxnet.symbol.Symbol.slice_like","mxnet.symbol.Symbol.softmax","mxnet.symbol.Symbol.sort","mxnet.symbol.Symbol.space_to_depth","mxnet.symbol.Symbol.sqrt","mxnet.symbol.Symbol.square","mxnet.symbol.Symbol.sum","mxnet.symbol.Symbol.swapaxes","mxnet.symbol.Symbol.take","mxnet.symbol.Symbol.tan","mxnet.symbol.Symbol.tanh","mxnet.symbol.Symbol.tile","mxnet.symbol.Symbol.tojson","mxnet.symbol.Symbol.topk","mxnet.symbol.Symbol.transpose","mxnet.symbol.Symbol.trunc","mxnet.symbol.Symbol.zeros_like","mxnet.symbol.linalg.extractdiag","mxnet.symbol.linalg.extracttrian","mxnet.symbol.linalg.gelqf","mxnet.symbol.linalg.gemm","mxnet.symbol.linalg.gemm2","mxnet.symbol.linalg.inverse","mxnet.symbol.linalg.makediag","mxnet.symbol.linalg.maketrian","mxnet.symbol.linalg.potrf","mxnet.symbol.linalg.potri","mxnet.symbol.linalg.sumlogdiag","mxnet.symbol.linalg.syevd","mxnet.symbol.linalg.syrk","mxnet.symbol.linalg.trmm","mxnet.symbol.linalg.trsm","mxnet.symbol","Symbol","mxnet.linalg","Python Documentation","Exporting Models","Export","Deployment","Deploy into C++","Inference","Deploy into a Java or Scala Environment","MXNet on the Cloud","Run on AWS","Run on an EC2 Instance","Run on Amazon SageMaker","How to write a custom layer in Apache MxNet Gluon API","Creating custom operators with numpy","Customization","Manipulate data with <code class=\"docutils literal notranslate\"><span class=\"pre\">ndarray</span></code>","Create a neural network","Automatic differentiation with <code class=\"docutils literal notranslate\"><span class=\"pre\">autograd</span></code>","Train the neural network","Predict with a pre-trained model","Use GPUs","Crash Course","Getting Started","Moving to MXNet from Other Frameworks","PyTorch vs Apache MXNet","Python Tutorials","Automatic Differentiation","Activation Blocks","Custom Layers","Custom Loss Blocks","How to write a custom layer in Apache MxNet Gluon API","Creating custom operators with numpy","Data","MXNet Gluon Fit API","Gluon: from experiment to deployment, an end to end tutorial","Hybridize","Image Augmentation","Image Tutorials","Handwritten Digit Recognition","Using pre-trained models in MXNet","Gluon","Initialization","Kullback-Leibler (KL) Divergence","Loss functions","Naming of Gluon Parameter and Blocks","Layers and Blocks","Normalization Blocks","Parameter Management","Google Neural Machine Translation","Text Tutorials","Machine Translation with Transformer","Trainer","Packages","KVStore","Module","An Intro: Manipulate Data the MXNet Way with NDArray","NDArray Operations","NDArray Contexts","NDArray","ONNX","Optimization Algorithms","Symbol","Data","Build/Install MXNet with MKL-DNN","Using AMP (Automatic Mixed Precision) in MXNet","Accelerated Backend Tools","Use Intel MKL-DNN to accelerate CPU performance","Quantize custom models with MKL-DNN backend","Profiling MXNet Models","Use TVM","Compression","Deploy with 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