<
   ir_version: 10,
   opset_import: ["" : 23],
   producer_name: "pytorch"
>
main_graph (uint8[batch,256,256,3] image) => (float[batch,1536] image_embedding) 
   <
      float[batch,256,1536] add_1007
      float[batch,256,1536] add_1068
      float[batch,256,1536] add_107
      float[batch,256,1536] add_1097
      float[batch,256,1536] add_1158
      float[batch,256,1536] add_1187
      float[batch,256,1536] add_1248
      float[batch,256,1536] add_1277
      float[batch,256,1536] add_13
      float[batch,256,1536] add_1338
      float[batch,256,1536] add_1367
      float[batch,256,1536] add_1428
      float[batch,256,1536] add_1457
      float[batch,256,1536] add_1518
      float[batch,256,1536] add_1547
      float[batch,256,1536] add_1608
      float[batch,256,1536] add_1637
      float[batch,256,1536] add_168
      float[batch,256,1536] add_1698
      float[batch,256,1536] add_1727
      float[batch,256,1536] add_1788
      float[batch,256,1536] add_1817
      float[batch,256,1536] add_1878
      float[batch,256,1536] add_1907
      float[batch,256,1536] add_1968
      float[batch,256,1536] add_197
      float[batch,256,1536] add_1997
      float[batch,256,1536] add_2058
      float[batch,256,1536] add_2087
      float[batch,256,1536] add_2148
      float[batch,256,1536] add_2177
      float[batch,256,1536] add_2238
      float[batch,256,1536] add_2267
      float[batch,256,1536] add_2328
      float[batch,256,1536] add_2357
      float[batch,256,1536] add_2418
      float[batch,256,1536] add_2447
      float[batch,256,1536] add_2508
      float[batch,256,1536] add_2537
      float[batch,256,1536] add_258
      float[batch,256,1536] add_2598
      float[batch,256,1536] add_2627
      float[batch,256,1536] add_2688
      float[batch,256,1536] add_2717
      float[batch,256,1536] add_2778
      float[batch,256,1536] add_2807
      float[batch,256,1536] add_2868
      float[batch,256,1536] add_287
      float[batch,256,1536] add_2897
      float[batch,256,1536] add_2958
      float[batch,256,1536] add_2987
      float[batch,256,1536] add_3048
      float[batch,256,1536] add_3077
      float[batch,256,1536] add_3138
      float[batch,256,1536] add_3167
      float[batch,256,1536] add_3228
      float[batch,256,1536] add_3257
      float[batch,256,1536] add_3318
      float[batch,256,1536] add_3347
      float[batch,256,1536] add_3408
      float[batch,256,1536] add_3437
      float[batch,256,1536] add_348
      float[batch,256,1536] add_3498
      float[batch,256,1536] add_3527
      float[batch,256,1536] add_3588
      float[batch,256,1536] add_3617
      float[batch,1,1536] add_3715
      float[batch,256,1536] add_377
      float[batch,256,1536] add_438
      float[batch,256,1536] add_467
      float[batch,256,1536] add_528
      float[batch,256,1536] add_557
      float[batch,256,1536] add_618
      float[batch,256,1536] add_647
      float[batch,256,1536] add_708
      float[batch,256,1536] add_737
      float[batch,256,1536] add_78
      float[batch,256,1536] add_798
      float[batch,256,1536] add_827
      float[batch,256,1536] add_888
      float[batch,256,1536] add_917
      float[batch,256,1536] add_978
      float[batch,1] clamp_min
      float[batch,1536,16,16] conv2d
      float[batch,256,6144] gelu
      float[batch,256,6144] gelu_1
      float[batch,256,6144] gelu_10
      float[batch,256,6144] gelu_11
      float[batch,256,6144] gelu_12
      float[batch,256,6144] gelu_13
      float[batch,256,6144] gelu_14
      float[batch,256,6144] gelu_15
      float[batch,256,6144] gelu_16
      float[batch,256,6144] gelu_17
      float[batch,256,6144] gelu_18
      float[batch,256,6144] gelu_19
      float[batch,256,6144] gelu_2
      float[batch,256,6144] gelu_20
      float[batch,256,6144] gelu_21
      float[batch,256,6144] gelu_22
      float[batch,256,6144] gelu_23
      float[batch,256,6144] gelu_24
      float[batch,256,6144] gelu_25
      float[batch,256,6144] gelu_26
      float[batch,256,6144] gelu_27
      float[batch,256,6144] gelu_28
      float[batch,256,6144] gelu_29
      float[batch,256,6144] gelu_3
      float[batch,256,6144] gelu_30
      float[batch,256,6144] gelu_31
      float[batch,256,6144] gelu_32
      float[batch,256,6144] gelu_33
      float[batch,256,6144] gelu_34
      float[batch,256,6144] gelu_35
      float[batch,256,6144] gelu_36
      float[batch,256,6144] gelu_37
      float[batch,256,6144] gelu_38
      float[batch,256,6144] gelu_39
      float[batch,256,6144] gelu_4
      float[batch,1,6144] gelu_40
      float[batch,256,6144] gelu_5
      float[batch,256,6144] gelu_6
      float[batch,256,6144] gelu_7
      float[batch,256,6144] gelu_8
      float[batch,256,6144] gelu_9
      float[batch,3,256,256] image_chw
      float[batch] image_ez
      float[batch] image_ez_r
      float[batch,1,1,1] image_ez_s
      float[batch,256,256,3] image_f32
      float[batch,256,1536] layer_norm
      float[batch,256,1536] layer_norm_1
      float[batch,256,1536] layer_norm_10
      float[batch,256,1536] layer_norm_11
      float[batch,256,1536] layer_norm_12
      float[batch,256,1536] layer_norm_13
      float[batch,256,1536] layer_norm_14
      float[batch,256,1536] layer_norm_15
      float[batch,256,1536] layer_norm_16
      float[batch,256,1536] layer_norm_17
      float[batch,256,1536] layer_norm_18
      float[batch,256,1536] layer_norm_19
      float[batch,256,1536] layer_norm_2
      float[batch,256,1536] layer_norm_20
      float[batch,256,1536] layer_norm_21
      float[batch,256,1536] layer_norm_22
      float[batch,256,1536] layer_norm_23
      float[batch,256,1536] layer_norm_24
      float[batch,256,1536] layer_norm_25
      float[batch,256,1536] layer_norm_26
      float[batch,256,1536] layer_norm_27
      float[batch,256,1536] layer_norm_28
      float[batch,256,1536] layer_norm_29
      float[batch,256,1536] layer_norm_3
      float[batch,256,1536] layer_norm_30
      float[batch,256,1536] layer_norm_31
      float[batch,256,1536] layer_norm_32
      float[batch,256,1536] layer_norm_33
      float[batch,256,1536] layer_norm_34
      float[batch,256,1536] layer_norm_35
      float[batch,256,1536] layer_norm_36
      float[batch,256,1536] layer_norm_37
      float[batch,256,1536] layer_norm_38
      float[batch,256,1536] layer_norm_39
      float[batch,256,1536] layer_norm_4
      float[batch,256,1536] layer_norm_40
      float[batch,256,1536] layer_norm_41
      float[batch,256,1536] layer_norm_42
      float[batch,256,1536] layer_norm_43
      float[batch,256,1536] layer_norm_44
      float[batch,256,1536] layer_norm_45
      float[batch,256,1536] layer_norm_46
      float[batch,256,1536] layer_norm_47
      float[batch,256,1536] layer_norm_48
      float[batch,256,1536] layer_norm_49
      float[batch,256,1536] layer_norm_5
      float[batch,256,1536] layer_norm_50
      float[batch,256,1536] layer_norm_51
      float[batch,256,1536] layer_norm_52
      float[batch,256,1536] layer_norm_53
      float[batch,256,1536] layer_norm_54
      float[batch,256,1536] layer_norm_55
      float[batch,256,1536] layer_norm_56
      float[batch,256,1536] layer_norm_57
      float[batch,256,1536] layer_norm_58
      float[batch,256,1536] layer_norm_59
      float[batch,256,1536] layer_norm_6
      float[batch,256,1536] layer_norm_60
      float[batch,256,1536] layer_norm_61
      float[batch,256,1536] layer_norm_62
      float[batch,256,1536] layer_norm_63
      float[batch,256,1536] layer_norm_64
      float[batch,256,1536] layer_norm_65
      float[batch,256,1536] layer_norm_66
      float[batch,256,1536] layer_norm_67
      float[batch,256,1536] layer_norm_68
      float[batch,256,1536] layer_norm_69
      float[batch,256,1536] layer_norm_7
      float[batch,256,1536] layer_norm_70
      float[batch,256,1536] layer_norm_71
      float[batch,256,1536] layer_norm_72
      float[batch,256,1536] layer_norm_73
      float[batch,256,1536] layer_norm_74
      float[batch,256,1536] layer_norm_75
      float[batch,256,1536] layer_norm_76
      float[batch,256,1536] layer_norm_77
      float[batch,256,1536] layer_norm_78
      float[batch,256,1536] layer_norm_79
      float[batch,256,1536] layer_norm_8
      float[batch,256,1536] layer_norm_80
      float[batch,1,1536] layer_norm_81
      float[batch,256,1536] layer_norm_9
      float[batch,1] linalg_vector_norm
      float[batch,256,4608] linear
      float[batch,256,1536] linear_1
      float[batch,256,6144] linear_10
      float[batch,256,4608] linear_100
      float[batch,256,1536] linear_101
      float[batch,256,6144] linear_102
      float[batch,256,1536] linear_103
      float[batch,256,4608] linear_104
      float[batch,256,1536] linear_105
      float[batch,256,6144] linear_106
      float[batch,256,1536] linear_107
      float[batch,256,4608] linear_108
      float[batch,256,1536] linear_109
      float[batch,256,1536] linear_11
      float[batch,256,6144] linear_110
      float[batch,256,1536] linear_111
      float[batch,256,4608] linear_112
      float[batch,256,1536] linear_113
      float[batch,256,6144] linear_114
      float[batch,256,1536] linear_115
      float[batch,256,4608] linear_116
      float[batch,256,1536] linear_117
      float[batch,256,6144] linear_118
      float[batch,256,1536] linear_119
      float[batch,256,4608] linear_12
      float[batch,256,4608] linear_120
      float[batch,256,1536] linear_121
      float[batch,256,6144] linear_122
      float[batch,256,1536] linear_123
      float[batch,256,4608] linear_124
      float[batch,256,1536] linear_125
      float[batch,256,6144] linear_126
      float[batch,256,1536] linear_127
      float[batch,256,4608] linear_128
      float[batch,256,1536] linear_129
      float[batch,256,1536] linear_13
      float[batch,256,6144] linear_130
      float[batch,256,1536] linear_131
      float[batch,256,4608] linear_132
      float[batch,256,1536] linear_133
      float[batch,256,6144] linear_134
      float[batch,256,1536] linear_135
      float[batch,256,4608] linear_136
      float[batch,256,1536] linear_137
      float[batch,256,6144] linear_138
      float[batch,256,1536] linear_139
      float[batch,256,6144] linear_14
      float[batch,256,4608] linear_140
      float[batch,256,1536] linear_141
      float[batch,256,6144] linear_142
      float[batch,256,1536] linear_143
      float[batch,256,4608] linear_144
      float[batch,256,1536] linear_145
      float[batch,256,6144] linear_146
      float[batch,256,1536] linear_147
      float[batch,256,4608] linear_148
      float[batch,256,1536] linear_149
      float[batch,256,1536] linear_15
      float[batch,256,6144] linear_150
      float[batch,256,1536] linear_151
      float[batch,256,4608] linear_152
      float[batch,256,1536] linear_153
      float[batch,256,6144] linear_154
      float[batch,256,1536] linear_155
      float[batch,256,4608] linear_156
      float[batch,256,1536] linear_157
      float[batch,256,6144] linear_158
      float[batch,256,1536] linear_159
      float[batch,256,4608] linear_16
      float[batch,256,3072] linear_161
      float[batch,1,1536] linear_162
      float[batch,1,6144] linear_163
      float[batch,1,1536] linear_164
      float[batch,256,1536] linear_17
      float[batch,256,6144] linear_18
      float[batch,256,1536] linear_19
      float[batch,256,6144] linear_2
      float[batch,256,4608] linear_20
      float[batch,256,1536] linear_21
      float[batch,256,6144] linear_22
      float[batch,256,1536] linear_23
      float[batch,256,4608] linear_24
      float[batch,256,1536] linear_25
      float[batch,256,6144] linear_26
      float[batch,256,1536] linear_27
      float[batch,256,4608] linear_28
      float[batch,256,1536] linear_29
      float[batch,256,1536] linear_3
      float[batch,256,6144] linear_30
      float[batch,256,1536] linear_31
      float[batch,256,4608] linear_32
      float[batch,256,1536] linear_33
      float[batch,256,6144] linear_34
      float[batch,256,1536] linear_35
      float[batch,256,4608] linear_36
      float[batch,256,1536] linear_37
      float[batch,256,6144] linear_38
      float[batch,256,1536] linear_39
      float[batch,256,4608] linear_4
      float[batch,256,4608] linear_40
      float[batch,256,1536] linear_41
      float[batch,256,6144] linear_42
      float[batch,256,1536] linear_43
      float[batch,256,4608] linear_44
      float[batch,256,1536] linear_45
      float[batch,256,6144] linear_46
      float[batch,256,1536] linear_47
      float[batch,256,4608] linear_48
      float[batch,256,1536] linear_49
      float[batch,256,1536] linear_5
      float[batch,256,6144] linear_50
      float[batch,256,1536] linear_51
      float[batch,256,4608] linear_52
      float[batch,256,1536] linear_53
      float[batch,256,6144] linear_54
      float[batch,256,1536] linear_55
      float[batch,256,4608] linear_56
      float[batch,256,1536] linear_57
      float[batch,256,6144] linear_58
      float[batch,256,1536] linear_59
      float[batch,256,6144] linear_6
      float[batch,256,4608] linear_60
      float[batch,256,1536] linear_61
      float[batch,256,6144] linear_62
      float[batch,256,1536] linear_63
      float[batch,256,4608] linear_64
      float[batch,256,1536] linear_65
      float[batch,256,6144] linear_66
      float[batch,256,1536] linear_67
      float[batch,256,4608] linear_68
      float[batch,256,1536] linear_69
      float[batch,256,1536] linear_7
      float[batch,256,6144] linear_70
      float[batch,256,1536] linear_71
      float[batch,256,4608] linear_72
      float[batch,256,1536] linear_73
      float[batch,256,6144] linear_74
      float[batch,256,1536] linear_75
      float[batch,256,4608] linear_76
      float[batch,256,1536] linear_77
      float[batch,256,6144] linear_78
      float[batch,256,1536] linear_79
      float[batch,256,4608] linear_8
      float[batch,256,4608] linear_80
      float[batch,256,1536] linear_81
      float[batch,256,6144] linear_82
      float[batch,256,1536] linear_83
      float[batch,256,4608] linear_84
      float[batch,256,1536] linear_85
      float[batch,256,6144] linear_86
      float[batch,256,1536] linear_87
      float[batch,256,4608] linear_88
      float[batch,256,1536] linear_89
      float[batch,256,1536] linear_9
      float[batch,256,6144] linear_90
      float[batch,256,1536] linear_91
      float[batch,256,4608] linear_92
      float[batch,256,1536] linear_93
      float[batch,256,6144] linear_94
      float[batch,256,1536] linear_95
      float[batch,256,4608] linear_96
      float[batch,256,1536] linear_97
      float[batch,256,6144] linear_98
      float[batch,256,1536] linear_99
      float[batch,256,1536] node_scaled_dot_product_attention_10_k
      float[batch,256,1536] node_scaled_dot_product_attention_10_q
      float[batch,256,1536] node_scaled_dot_product_attention_10_v
      float[batch,256,1536] node_scaled_dot_product_attention_11_k
      float[batch,256,1536] node_scaled_dot_product_attention_11_q
      float[batch,256,1536] node_scaled_dot_product_attention_11_v
      float[batch,256,1536] node_scaled_dot_product_attention_12_k
      float[batch,256,1536] node_scaled_dot_product_attention_12_q
      float[batch,256,1536] node_scaled_dot_product_attention_12_v
      float[batch,256,1536] node_scaled_dot_product_attention_13_k
      float[batch,256,1536] node_scaled_dot_product_attention_13_q
      float[batch,256,1536] node_scaled_dot_product_attention_13_v
      float[batch,256,1536] node_scaled_dot_product_attention_14_k
      float[batch,256,1536] node_scaled_dot_product_attention_14_q
      float[batch,256,1536] node_scaled_dot_product_attention_14_v
      float[batch,256,1536] node_scaled_dot_product_attention_15_k
      float[batch,256,1536] node_scaled_dot_product_attention_15_q
      float[batch,256,1536] node_scaled_dot_product_attention_15_v
      float[batch,256,1536] node_scaled_dot_product_attention_16_k
      float[batch,256,1536] node_scaled_dot_product_attention_16_q
      float[batch,256,1536] node_scaled_dot_product_attention_16_v
      float[batch,256,1536] node_scaled_dot_product_attention_17_k
      float[batch,256,1536] node_scaled_dot_product_attention_17_q
      float[batch,256,1536] node_scaled_dot_product_attention_17_v
      float[batch,256,1536] node_scaled_dot_product_attention_18_k
      float[batch,256,1536] node_scaled_dot_product_attention_18_q
      float[batch,256,1536] node_scaled_dot_product_attention_18_v
      float[batch,256,1536] node_scaled_dot_product_attention_19_k
      float[batch,256,1536] node_scaled_dot_product_attention_19_q
      float[batch,256,1536] node_scaled_dot_product_attention_19_v
      float[batch,256,1536] node_scaled_dot_product_attention_1_k
      float[batch,256,1536] node_scaled_dot_product_attention_1_q
      float[batch,256,1536] node_scaled_dot_product_attention_1_v
      float[batch,256,1536] node_scaled_dot_product_attention_20_k
      float[batch,256,1536] node_scaled_dot_product_attention_20_q
      float[batch,256,1536] node_scaled_dot_product_attention_20_v
      float[batch,256,1536] node_scaled_dot_product_attention_21_k
      float[batch,256,1536] node_scaled_dot_product_attention_21_q
      float[batch,256,1536] node_scaled_dot_product_attention_21_v
      float[batch,256,1536] node_scaled_dot_product_attention_22_k
      float[batch,256,1536] node_scaled_dot_product_attention_22_q
      float[batch,256,1536] node_scaled_dot_product_attention_22_v
      float[batch,256,1536] node_scaled_dot_product_attention_23_k
      float[batch,256,1536] node_scaled_dot_product_attention_23_q
      float[batch,256,1536] node_scaled_dot_product_attention_23_v
      float[batch,256,1536] node_scaled_dot_product_attention_24_k
      float[batch,256,1536] node_scaled_dot_product_attention_24_q
      float[batch,256,1536] node_scaled_dot_product_attention_24_v
      float[batch,256,1536] node_scaled_dot_product_attention_25_k
      float[batch,256,1536] node_scaled_dot_product_attention_25_q
      float[batch,256,1536] node_scaled_dot_product_attention_25_v
      float[batch,256,1536] node_scaled_dot_product_attention_26_k
      float[batch,256,1536] node_scaled_dot_product_attention_26_q
      float[batch,256,1536] node_scaled_dot_product_attention_26_v
      float[batch,256,1536] node_scaled_dot_product_attention_27_k
      float[batch,256,1536] node_scaled_dot_product_attention_27_q
      float[batch,256,1536] node_scaled_dot_product_attention_27_v
      float[batch,256,1536] node_scaled_dot_product_attention_28_k
      float[batch,256,1536] node_scaled_dot_product_attention_28_q
      float[batch,256,1536] node_scaled_dot_product_attention_28_v
      float[batch,256,1536] node_scaled_dot_product_attention_29_k
      float[batch,256,1536] node_scaled_dot_product_attention_29_q
      float[batch,256,1536] node_scaled_dot_product_attention_29_v
      float[batch,256,1536] node_scaled_dot_product_attention_2_k
      float[batch,256,1536] node_scaled_dot_product_attention_2_q
      float[batch,256,1536] node_scaled_dot_product_attention_2_v
      float[batch,256,1536] node_scaled_dot_product_attention_30_k
      float[batch,256,1536] node_scaled_dot_product_attention_30_q
      float[batch,256,1536] node_scaled_dot_product_attention_30_v
      float[batch,256,1536] node_scaled_dot_product_attention_31_k
      float[batch,256,1536] node_scaled_dot_product_attention_31_q
      float[batch,256,1536] node_scaled_dot_product_attention_31_v
      float[batch,256,1536] node_scaled_dot_product_attention_32_k
      float[batch,256,1536] node_scaled_dot_product_attention_32_q
      float[batch,256,1536] node_scaled_dot_product_attention_32_v
      float[batch,256,1536] node_scaled_dot_product_attention_33_k
      float[batch,256,1536] node_scaled_dot_product_attention_33_q
      float[batch,256,1536] node_scaled_dot_product_attention_33_v
      float[batch,256,1536] node_scaled_dot_product_attention_34_k
      float[batch,256,1536] node_scaled_dot_product_attention_34_q
      float[batch,256,1536] node_scaled_dot_product_attention_34_v
      float[batch,256,1536] node_scaled_dot_product_attention_35_k
      float[batch,256,1536] node_scaled_dot_product_attention_35_q
      float[batch,256,1536] node_scaled_dot_product_attention_35_v
      float[batch,256,1536] node_scaled_dot_product_attention_36_k
      float[batch,256,1536] node_scaled_dot_product_attention_36_q
      float[batch,256,1536] node_scaled_dot_product_attention_36_v
      float[batch,256,1536] node_scaled_dot_product_attention_37_k
      float[batch,256,1536] node_scaled_dot_product_attention_37_q
      float[batch,256,1536] node_scaled_dot_product_attention_37_v
      float[batch,256,1536] node_scaled_dot_product_attention_38_k
      float[batch,256,1536] node_scaled_dot_product_attention_38_q
      float[batch,256,1536] node_scaled_dot_product_attention_38_v
      float[batch,256,1536] node_scaled_dot_product_attention_39_k
      float[batch,256,1536] node_scaled_dot_product_attention_39_q
      float[batch,256,1536] node_scaled_dot_product_attention_39_v
      float[batch,256,1536] node_scaled_dot_product_attention_3_k
      float[batch,256,1536] node_scaled_dot_product_attention_3_q
      float[batch,256,1536] node_scaled_dot_product_attention_3_v
      float[batch,256,1536] node_scaled_dot_product_attention_40_k
      float[batch,1,1536] node_scaled_dot_product_attention_40_q
      float[batch,1,1] node_scaled_dot_product_attention_40_q_col_out
      float[batch,256,1536] node_scaled_dot_product_attention_40_v
      float[batch,256,1536] node_scaled_dot_product_attention_4_k
      float[batch,256,1536] node_scaled_dot_product_attention_4_q
      float[batch,256,1536] node_scaled_dot_product_attention_4_v
      float[batch,256,1536] node_scaled_dot_product_attention_5_k
      float[batch,256,1536] node_scaled_dot_product_attention_5_q
      float[batch,256,1536] node_scaled_dot_product_attention_5_v
      float[batch,256,1536] node_scaled_dot_product_attention_6_k
      float[batch,256,1536] node_scaled_dot_product_attention_6_q
      float[batch,256,1536] node_scaled_dot_product_attention_6_v
      float[batch,256,1536] node_scaled_dot_product_attention_7_k
      float[batch,256,1536] node_scaled_dot_product_attention_7_q
      float[batch,256,1536] node_scaled_dot_product_attention_7_v
      float[batch,256,1536] node_scaled_dot_product_attention_8_k
      float[batch,256,1536] node_scaled_dot_product_attention_8_q
      float[batch,256,1536] node_scaled_dot_product_attention_8_v
      float[batch,256,1536] node_scaled_dot_product_attention_9_k
      float[batch,256,1536] node_scaled_dot_product_attention_9_q
      float[batch,256,1536] node_scaled_dot_product_attention_9_v
      float[batch,256,1536] node_scaled_dot_product_attention_k
      float[batch,256,1536] node_scaled_dot_product_attention_q
      float[batch,256,1536] node_scaled_dot_product_attention_v
      float[batch,256,1536] scaled_dot_product_attention
      float[batch,256,1536] scaled_dot_product_attention_1
      float[batch,256,1536] scaled_dot_product_attention_10
      float[batch,256,1536] scaled_dot_product_attention_11
      float[batch,256,1536] scaled_dot_product_attention_12
      float[batch,256,1536] scaled_dot_product_attention_13
      float[batch,256,1536] scaled_dot_product_attention_14
      float[batch,256,1536] scaled_dot_product_attention_15
      float[batch,256,1536] scaled_dot_product_attention_16
      float[batch,256,1536] scaled_dot_product_attention_17
      float[batch,256,1536] scaled_dot_product_attention_18
      float[batch,256,1536] scaled_dot_product_attention_19
      float[batch,256,1536] scaled_dot_product_attention_2
      float[batch,256,1536] scaled_dot_product_attention_20
      float[batch,256,1536] scaled_dot_product_attention_21
      float[batch,256,1536] scaled_dot_product_attention_22
      float[batch,256,1536] scaled_dot_product_attention_23
      float[batch,256,1536] scaled_dot_product_attention_24
      float[batch,256,1536] scaled_dot_product_attention_25
      float[batch,256,1536] scaled_dot_product_attention_26
      float[batch,256,1536] scaled_dot_product_attention_27
      float[batch,256,1536] scaled_dot_product_attention_28
      float[batch,256,1536] scaled_dot_product_attention_29
      float[batch,256,1536] scaled_dot_product_attention_3
      float[batch,256,1536] scaled_dot_product_attention_30
      float[batch,256,1536] scaled_dot_product_attention_31
      float[batch,256,1536] scaled_dot_product_attention_32
      float[batch,256,1536] scaled_dot_product_attention_33
      float[batch,256,1536] scaled_dot_product_attention_34
      float[batch,256,1536] scaled_dot_product_attention_35
      float[batch,256,1536] scaled_dot_product_attention_36
      float[batch,256,1536] scaled_dot_product_attention_37
      float[batch,256,1536] scaled_dot_product_attention_38
      float[batch,256,1536] scaled_dot_product_attention_39
      float[batch,256,1536] scaled_dot_product_attention_4
      float[batch,1,1536] scaled_dot_product_attention_40
      float[batch,256,1536] scaled_dot_product_attention_5
      float[batch,256,1536] scaled_dot_product_attention_6
      float[batch,256,1536] scaled_dot_product_attention_7
      float[batch,256,1536] scaled_dot_product_attention_8
      float[batch,256,1536] scaled_dot_product_attention_9
      float[batch,1536] select
      float[batch,256,1536] transpose
      float[batch,256,4608] val_167
      float[batch,256,1536] val_168
      float[batch,256,6144] val_169
      float[batch,256,1536] val_170
      float[batch,256,4608] val_171
      float[batch,256,1536] val_172
      float[batch,256,6144] val_173
      float[batch,256,1536] val_174
      float[batch,256,4608] val_175
      float[batch,256,1536] val_176
      float[batch,256,6144] val_177
      float[batch,256,1536] val_178
      float[batch,256,4608] val_179
      float[batch,256,1536] val_180
      float[batch,256,6144] val_181
      float[batch,256,1536] val_182
      float[batch,256,4608] val_183
      float[batch,256,1536] val_184
      float[batch,256,6144] val_185
      float[batch,256,1536] val_186
      float[batch,256,4608] val_187
      float[batch,256,1536] val_188
      float[batch,256,6144] val_189
      float[batch,256,1536] val_190
      float[batch,256,4608] val_191
      float[batch,256,1536] val_192
      float[batch,256,6144] val_193
      float[batch,256,1536] val_194
      float[batch,256,4608] val_195
      float[batch,256,1536] val_196
      float[batch,256,6144] val_197
      float[batch,256,1536] val_198
      float[batch,256,4608] val_199
      float[batch,256,1536] val_200
      float[batch,256,6144] val_201
      float[batch,256,1536] val_202
      float[batch,256,4608] val_203
      float[batch,256,1536] val_204
      float[batch,256,6144] val_205
      float[batch,256,1536] val_206
      float[batch,256,4608] val_207
      float[batch,256,1536] val_208
      float[batch,256,6144] val_209
      float[batch,256,1536] val_210
      float[batch,256,4608] val_211
      float[batch,256,1536] val_212
      float[batch,256,6144] val_213
      float[batch,256,1536] val_214
      float[batch,256,4608] val_215
      float[batch,256,1536] val_216
      float[batch,256,6144] val_217
      float[batch,256,1536] val_218
      float[batch,256,4608] val_219
      float[batch,256,1536] val_220
      float[batch,256,6144] val_221
      float[batch,256,1536] val_222
      float[batch,256,4608] val_223
      float[batch,256,1536] val_224
      float[batch,256,6144] val_225
      float[batch,256,1536] val_226
      float[batch,256,4608] val_227
      float[batch,256,1536] val_228
      float[batch,256,6144] val_229
      float[batch,256,1536] val_230
      float[batch,256,4608] val_231
      float[batch,256,1536] val_232
      float[batch,256,6144] val_233
      float[batch,256,1536] val_234
      float[batch,256,4608] val_235
      float[batch,256,1536] val_236
      float[batch,256,6144] val_237
      float[batch,256,1536] val_238
      float[batch,256,4608] val_239
      float[batch,256,1536] val_240
      float[batch,256,6144] val_241
      float[batch,256,1536] val_242
      float[batch,256,4608] val_243
      float[batch,256,1536] val_244
      float[batch,256,6144] val_245
      float[batch,256,1536] val_246
      float[batch,256,4608] val_247
      float[batch,256,1536] val_248
      float[batch,256,6144] val_249
      float[batch,256,1536] val_250
      float[batch,256,4608] val_251
      float[batch,256,1536] val_252
      float[batch,256,6144] val_253
      float[batch,256,1536] val_254
      float[batch,256,4608] val_255
      float[batch,256,1536] val_256
      float[batch,256,6144] val_257
      float[batch,256,1536] val_258
      float[batch,256,4608] val_259
      float[batch,256,1536] val_260
      float[batch,256,6144] val_261
      float[batch,256,1536] val_262
      float[batch,256,4608] val_263
      float[batch,256,1536] val_264
      float[batch,256,6144] val_265
      float[batch,256,1536] val_266
      float[batch,256,4608] val_267
      float[batch,256,1536] val_268
      float[batch,256,6144] val_269
      float[batch,256,1536] val_270
      float[batch,256,4608] val_271
      float[batch,256,1536] val_272
      float[batch,256,6144] val_273
      float[batch,256,1536] val_274
      float[batch,256,4608] val_275
      float[batch,256,1536] val_276
      float[batch,256,6144] val_277
      float[batch,256,1536] val_278
      float[batch,256,4608] val_279
      float[batch,256,1536] val_280
      float[batch,256,6144] val_281
      float[batch,256,1536] val_282
      float[batch,256,4608] val_283
      float[batch,256,1536] val_284
      float[batch,256,6144] val_285
      float[batch,256,1536] val_286
      float[batch,256,4608] val_287
      float[batch,256,1536] val_288
      float[batch,256,6144] val_289
      float[batch,256,1536] val_290
      float[batch,256,4608] val_291
      float[batch,256,1536] val_292
      float[batch,256,6144] val_293
      float[batch,256,1536] val_294
      float[batch,256,4608] val_295
      float[batch,256,1536] val_296
      float[batch,256,6144] val_297
      float[batch,256,1536] val_298
      float[batch,256,4608] val_299
      float[batch,256,1536] val_300
      float[batch,256,6144] val_301
      float[batch,256,1536] val_302
      float[batch,256,4608] val_303
      float[batch,256,1536] val_304
      float[batch,256,6144] val_305
      float[batch,256,1536] val_306
      float[batch,256,4608] val_307
      float[batch,256,1536] val_308
      float[batch,256,6144] val_309
      float[batch,256,1536] val_310
      float[batch,256,4608] val_311
      float[batch,256,1536] val_312
      float[batch,256,6144] val_313
      float[batch,256,1536] val_314
      float[batch,256,4608] val_315
      float[batch,256,1536] val_316
      float[batch,256,6144] val_317
      float[batch,256,1536] val_318
      float[batch,256,4608] val_319
      float[batch,256,1536] val_320
      float[batch,256,6144] val_321
      float[batch,256,1536] val_322
      float[batch,256,4608] val_323
      float[batch,256,1536] val_324
      float[batch,256,6144] val_325
      float[batch,256,1536] val_326
      float[batch,256,3072] val_327
      float[batch,1,1536] val_328
      float[batch,1,6144] val_329
      float[batch,1,1536] val_330
      float[batch,1536,256] view
   >
{
   [pre_cast] image_f32 = Cast <to: int = 1> (image)
   [pre_nhwc_to_nchw] image_chw = Transpose <perm: ints = [0, 3, 1, 2]> (image_f32)
   [node_conv2d] conv2d = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [16, 16]> (image_chw, "visual.trunk.patch_embed.proj.weight", "visual.trunk.patch_embed.proj.bias")
   view = Reshape <allowzero: int = 1> (conv2d, view_target)
   [node_transpose] transpose = Transpose <perm: ints = [0, 2, 1]> (view)
   add_13 = Add (transpose, "visual.trunk.pos_embed")
   [node_layer_norm] layer_norm = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_13, "visual.trunk.blocks.0.norm1.weight", "visual.trunk.blocks.0.norm1.bias")
   val_167 = MatMul (layer_norm, val_3)
   [node_linear] linear = Add (val_167, "visual.trunk.blocks.0.attn.qkv.bias")
   [node_scaled_dot_product_attention_qkv_split] node_scaled_dot_product_attention_q, node_scaled_dot_product_attention_k, node_scaled_dot_product_attention_v = Split <axis: int = -1> (linear, attn3d_split_3x1536)
   [node_scaled_dot_product_attention] scaled_dot_product_attention = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_q, node_scaled_dot_product_attention_k, node_scaled_dot_product_attention_v)
   val_168 = MatMul (scaled_dot_product_attention, val_4)
   linear_1 = Add (val_168, "visual.trunk.blocks.0.attn.proj.bias")
   add_78 = Add (add_13, linear_1)
   layer_norm_1 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_78, "visual.trunk.blocks.0.norm2.weight", "visual.trunk.blocks.0.norm2.bias")
   val_169 = MatMul (layer_norm_1, val_5)
   linear_2 = Add (val_169, "visual.trunk.blocks.0.mlp.fc1.bias")
   [node_gelu] gelu = Gelu <approximate: string = "tanh"> (linear_2)
   val_170 = MatMul (gelu, val_6)
   linear_3 = Add (val_170, "visual.trunk.blocks.0.mlp.fc2.bias")
   add_107 = Add (add_78, linear_3)
   layer_norm_2 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_107, "visual.trunk.blocks.1.norm1.weight", "visual.trunk.blocks.1.norm1.bias")
   val_171 = MatMul (layer_norm_2, val_7)
   linear_4 = Add (val_171, "visual.trunk.blocks.1.attn.qkv.bias")
   [node_scaled_dot_product_attention_1_qkv_split] node_scaled_dot_product_attention_1_q, node_scaled_dot_product_attention_1_k, node_scaled_dot_product_attention_1_v = Split <axis: int = -1> (linear_4, attn3d_split_3x1536)
   scaled_dot_product_attention_1 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_1_q, node_scaled_dot_product_attention_1_k, node_scaled_dot_product_attention_1_v)
   val_172 = MatMul (scaled_dot_product_attention_1, val_8)
   linear_5 = Add (val_172, "visual.trunk.blocks.1.attn.proj.bias")
   add_168 = Add (add_107, linear_5)
   layer_norm_3 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_168, "visual.trunk.blocks.1.norm2.weight", "visual.trunk.blocks.1.norm2.bias")
   val_173 = MatMul (layer_norm_3, val_9)
   linear_6 = Add (val_173, "visual.trunk.blocks.1.mlp.fc1.bias")
   gelu_1 = Gelu <approximate: string = "tanh"> (linear_6)
   val_174 = MatMul (gelu_1, val_10)
   linear_7 = Add (val_174, "visual.trunk.blocks.1.mlp.fc2.bias")
   add_197 = Add (add_168, linear_7)
   layer_norm_4 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_197, "visual.trunk.blocks.2.norm1.weight", "visual.trunk.blocks.2.norm1.bias")
   val_175 = MatMul (layer_norm_4, val_11)
   linear_8 = Add (val_175, "visual.trunk.blocks.2.attn.qkv.bias")
   [node_scaled_dot_product_attention_2_qkv_split] node_scaled_dot_product_attention_2_q, node_scaled_dot_product_attention_2_k, node_scaled_dot_product_attention_2_v = Split <axis: int = -1> (linear_8, attn3d_split_3x1536)
   scaled_dot_product_attention_2 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_2_q, node_scaled_dot_product_attention_2_k, node_scaled_dot_product_attention_2_v)
   val_176 = MatMul (scaled_dot_product_attention_2, val_12)
   linear_9 = Add (val_176, "visual.trunk.blocks.2.attn.proj.bias")
   add_258 = Add (add_197, linear_9)
   layer_norm_5 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_258, "visual.trunk.blocks.2.norm2.weight", "visual.trunk.blocks.2.norm2.bias")
   val_177 = MatMul (layer_norm_5, val_13)
   linear_10 = Add (val_177, "visual.trunk.blocks.2.mlp.fc1.bias")
   gelu_2 = Gelu <approximate: string = "tanh"> (linear_10)
   val_178 = MatMul (gelu_2, val_14)
   linear_11 = Add (val_178, "visual.trunk.blocks.2.mlp.fc2.bias")
   add_287 = Add (add_258, linear_11)
   layer_norm_6 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_287, "visual.trunk.blocks.3.norm1.weight", "visual.trunk.blocks.3.norm1.bias")
   val_179 = MatMul (layer_norm_6, val_15)
   linear_12 = Add (val_179, "visual.trunk.blocks.3.attn.qkv.bias")
   [node_scaled_dot_product_attention_3_qkv_split] node_scaled_dot_product_attention_3_q, node_scaled_dot_product_attention_3_k, node_scaled_dot_product_attention_3_v = Split <axis: int = -1> (linear_12, attn3d_split_3x1536)
   scaled_dot_product_attention_3 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_3_q, node_scaled_dot_product_attention_3_k, node_scaled_dot_product_attention_3_v)
   val_180 = MatMul (scaled_dot_product_attention_3, val_16)
   linear_13 = Add (val_180, "visual.trunk.blocks.3.attn.proj.bias")
   add_348 = Add (add_287, linear_13)
   layer_norm_7 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_348, "visual.trunk.blocks.3.norm2.weight", "visual.trunk.blocks.3.norm2.bias")
   val_181 = MatMul (layer_norm_7, val_17)
   linear_14 = Add (val_181, "visual.trunk.blocks.3.mlp.fc1.bias")
   gelu_3 = Gelu <approximate: string = "tanh"> (linear_14)
   val_182 = MatMul (gelu_3, val_18)
   linear_15 = Add (val_182, "visual.trunk.blocks.3.mlp.fc2.bias")
   add_377 = Add (add_348, linear_15)
   layer_norm_8 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_377, "visual.trunk.blocks.4.norm1.weight", "visual.trunk.blocks.4.norm1.bias")
   val_183 = MatMul (layer_norm_8, val_19)
   linear_16 = Add (val_183, "visual.trunk.blocks.4.attn.qkv.bias")
   [node_scaled_dot_product_attention_4_qkv_split] node_scaled_dot_product_attention_4_q, node_scaled_dot_product_attention_4_k, node_scaled_dot_product_attention_4_v = Split <axis: int = -1> (linear_16, attn3d_split_3x1536)
   scaled_dot_product_attention_4 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_4_q, node_scaled_dot_product_attention_4_k, node_scaled_dot_product_attention_4_v)
   val_184 = MatMul (scaled_dot_product_attention_4, val_20)
   linear_17 = Add (val_184, "visual.trunk.blocks.4.attn.proj.bias")
   add_438 = Add (add_377, linear_17)
   layer_norm_9 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_438, "visual.trunk.blocks.4.norm2.weight", "visual.trunk.blocks.4.norm2.bias")
   val_185 = MatMul (layer_norm_9, val_21)
   linear_18 = Add (val_185, "visual.trunk.blocks.4.mlp.fc1.bias")
   gelu_4 = Gelu <approximate: string = "tanh"> (linear_18)
   val_186 = MatMul (gelu_4, val_22)
   linear_19 = Add (val_186, "visual.trunk.blocks.4.mlp.fc2.bias")
   add_467 = Add (add_438, linear_19)
   layer_norm_10 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_467, "visual.trunk.blocks.5.norm1.weight", "visual.trunk.blocks.5.norm1.bias")
   val_187 = MatMul (layer_norm_10, val_23)
   linear_20 = Add (val_187, "visual.trunk.blocks.5.attn.qkv.bias")
   [node_scaled_dot_product_attention_5_qkv_split] node_scaled_dot_product_attention_5_q, node_scaled_dot_product_attention_5_k, node_scaled_dot_product_attention_5_v = Split <axis: int = -1> (linear_20, attn3d_split_3x1536)
   scaled_dot_product_attention_5 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_5_q, node_scaled_dot_product_attention_5_k, node_scaled_dot_product_attention_5_v)
   val_188 = MatMul (scaled_dot_product_attention_5, val_24)
   linear_21 = Add (val_188, "visual.trunk.blocks.5.attn.proj.bias")
   add_528 = Add (add_467, linear_21)
   layer_norm_11 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_528, "visual.trunk.blocks.5.norm2.weight", "visual.trunk.blocks.5.norm2.bias")
   val_189 = MatMul (layer_norm_11, val_25)
   linear_22 = Add (val_189, "visual.trunk.blocks.5.mlp.fc1.bias")
   gelu_5 = Gelu <approximate: string = "tanh"> (linear_22)
   val_190 = MatMul (gelu_5, val_26)
   linear_23 = Add (val_190, "visual.trunk.blocks.5.mlp.fc2.bias")
   add_557 = Add (add_528, linear_23)
   layer_norm_12 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_557, "visual.trunk.blocks.6.norm1.weight", "visual.trunk.blocks.6.norm1.bias")
   val_191 = MatMul (layer_norm_12, val_27)
   linear_24 = Add (val_191, "visual.trunk.blocks.6.attn.qkv.bias")
   [node_scaled_dot_product_attention_6_qkv_split] node_scaled_dot_product_attention_6_q, node_scaled_dot_product_attention_6_k, node_scaled_dot_product_attention_6_v = Split <axis: int = -1> (linear_24, attn3d_split_3x1536)
   scaled_dot_product_attention_6 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_6_q, node_scaled_dot_product_attention_6_k, node_scaled_dot_product_attention_6_v)
   val_192 = MatMul (scaled_dot_product_attention_6, val_28)
   linear_25 = Add (val_192, "visual.trunk.blocks.6.attn.proj.bias")
   add_618 = Add (add_557, linear_25)
   layer_norm_13 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_618, "visual.trunk.blocks.6.norm2.weight", "visual.trunk.blocks.6.norm2.bias")
   val_193 = MatMul (layer_norm_13, val_29)
   linear_26 = Add (val_193, "visual.trunk.blocks.6.mlp.fc1.bias")
   gelu_6 = Gelu <approximate: string = "tanh"> (linear_26)
   val_194 = MatMul (gelu_6, val_30)
   linear_27 = Add (val_194, "visual.trunk.blocks.6.mlp.fc2.bias")
   add_647 = Add (add_618, linear_27)
   layer_norm_14 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_647, "visual.trunk.blocks.7.norm1.weight", "visual.trunk.blocks.7.norm1.bias")
   val_195 = MatMul (layer_norm_14, val_31)
   linear_28 = Add (val_195, "visual.trunk.blocks.7.attn.qkv.bias")
   [node_scaled_dot_product_attention_7_qkv_split] node_scaled_dot_product_attention_7_q, node_scaled_dot_product_attention_7_k, node_scaled_dot_product_attention_7_v = Split <axis: int = -1> (linear_28, attn3d_split_3x1536)
   scaled_dot_product_attention_7 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_7_q, node_scaled_dot_product_attention_7_k, node_scaled_dot_product_attention_7_v)
   val_196 = MatMul (scaled_dot_product_attention_7, val_32)
   linear_29 = Add (val_196, "visual.trunk.blocks.7.attn.proj.bias")
   add_708 = Add (add_647, linear_29)
   layer_norm_15 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_708, "visual.trunk.blocks.7.norm2.weight", "visual.trunk.blocks.7.norm2.bias")
   val_197 = MatMul (layer_norm_15, val_33)
   linear_30 = Add (val_197, "visual.trunk.blocks.7.mlp.fc1.bias")
   gelu_7 = Gelu <approximate: string = "tanh"> (linear_30)
   val_198 = MatMul (gelu_7, val_34)
   linear_31 = Add (val_198, "visual.trunk.blocks.7.mlp.fc2.bias")
   add_737 = Add (add_708, linear_31)
   layer_norm_16 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_737, "visual.trunk.blocks.8.norm1.weight", "visual.trunk.blocks.8.norm1.bias")
   val_199 = MatMul (layer_norm_16, val_35)
   linear_32 = Add (val_199, "visual.trunk.blocks.8.attn.qkv.bias")
   [node_scaled_dot_product_attention_8_qkv_split] node_scaled_dot_product_attention_8_q, node_scaled_dot_product_attention_8_k, node_scaled_dot_product_attention_8_v = Split <axis: int = -1> (linear_32, attn3d_split_3x1536)
   scaled_dot_product_attention_8 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_8_q, node_scaled_dot_product_attention_8_k, node_scaled_dot_product_attention_8_v)
   val_200 = MatMul (scaled_dot_product_attention_8, val_36)
   linear_33 = Add (val_200, "visual.trunk.blocks.8.attn.proj.bias")
   add_798 = Add (add_737, linear_33)
   layer_norm_17 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_798, "visual.trunk.blocks.8.norm2.weight", "visual.trunk.blocks.8.norm2.bias")
   val_201 = MatMul (layer_norm_17, val_37)
   linear_34 = Add (val_201, "visual.trunk.blocks.8.mlp.fc1.bias")
   gelu_8 = Gelu <approximate: string = "tanh"> (linear_34)
   val_202 = MatMul (gelu_8, val_38)
   linear_35 = Add (val_202, "visual.trunk.blocks.8.mlp.fc2.bias")
   add_827 = Add (add_798, linear_35)
   layer_norm_18 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_827, "visual.trunk.blocks.9.norm1.weight", "visual.trunk.blocks.9.norm1.bias")
   val_203 = MatMul (layer_norm_18, val_39)
   linear_36 = Add (val_203, "visual.trunk.blocks.9.attn.qkv.bias")
   [node_scaled_dot_product_attention_9_qkv_split] node_scaled_dot_product_attention_9_q, node_scaled_dot_product_attention_9_k, node_scaled_dot_product_attention_9_v = Split <axis: int = -1> (linear_36, attn3d_split_3x1536)
   scaled_dot_product_attention_9 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_9_q, node_scaled_dot_product_attention_9_k, node_scaled_dot_product_attention_9_v)
   val_204 = MatMul (scaled_dot_product_attention_9, val_40)
   linear_37 = Add (val_204, "visual.trunk.blocks.9.attn.proj.bias")
   add_888 = Add (add_827, linear_37)
   layer_norm_19 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_888, "visual.trunk.blocks.9.norm2.weight", "visual.trunk.blocks.9.norm2.bias")
   val_205 = MatMul (layer_norm_19, val_41)
   linear_38 = Add (val_205, "visual.trunk.blocks.9.mlp.fc1.bias")
   gelu_9 = Gelu <approximate: string = "tanh"> (linear_38)
   val_206 = MatMul (gelu_9, val_42)
   linear_39 = Add (val_206, "visual.trunk.blocks.9.mlp.fc2.bias")
   add_917 = Add (add_888, linear_39)
   layer_norm_20 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_917, "visual.trunk.blocks.10.norm1.weight", "visual.trunk.blocks.10.norm1.bias")
   val_207 = MatMul (layer_norm_20, val_43)
   linear_40 = Add (val_207, "visual.trunk.blocks.10.attn.qkv.bias")
   [node_scaled_dot_product_attention_10_qkv_split] node_scaled_dot_product_attention_10_q, node_scaled_dot_product_attention_10_k, node_scaled_dot_product_attention_10_v = Split <axis: int = -1> (linear_40, attn3d_split_3x1536)
   scaled_dot_product_attention_10 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_10_q, node_scaled_dot_product_attention_10_k, node_scaled_dot_product_attention_10_v)
   val_208 = MatMul (scaled_dot_product_attention_10, val_44)
   linear_41 = Add (val_208, "visual.trunk.blocks.10.attn.proj.bias")
   add_978 = Add (add_917, linear_41)
   layer_norm_21 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_978, "visual.trunk.blocks.10.norm2.weight", "visual.trunk.blocks.10.norm2.bias")
   val_209 = MatMul (layer_norm_21, val_45)
   linear_42 = Add (val_209, "visual.trunk.blocks.10.mlp.fc1.bias")
   gelu_10 = Gelu <approximate: string = "tanh"> (linear_42)
   val_210 = MatMul (gelu_10, val_46)
   linear_43 = Add (val_210, "visual.trunk.blocks.10.mlp.fc2.bias")
   add_1007 = Add (add_978, linear_43)
   layer_norm_22 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1007, "visual.trunk.blocks.11.norm1.weight", "visual.trunk.blocks.11.norm1.bias")
   val_211 = MatMul (layer_norm_22, val_47)
   linear_44 = Add (val_211, "visual.trunk.blocks.11.attn.qkv.bias")
   [node_scaled_dot_product_attention_11_qkv_split] node_scaled_dot_product_attention_11_q, node_scaled_dot_product_attention_11_k, node_scaled_dot_product_attention_11_v = Split <axis: int = -1> (linear_44, attn3d_split_3x1536)
   scaled_dot_product_attention_11 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_11_q, node_scaled_dot_product_attention_11_k, node_scaled_dot_product_attention_11_v)
   val_212 = MatMul (scaled_dot_product_attention_11, val_48)
   linear_45 = Add (val_212, "visual.trunk.blocks.11.attn.proj.bias")
   add_1068 = Add (add_1007, linear_45)
   layer_norm_23 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1068, "visual.trunk.blocks.11.norm2.weight", "visual.trunk.blocks.11.norm2.bias")
   val_213 = MatMul (layer_norm_23, val_49)
   linear_46 = Add (val_213, "visual.trunk.blocks.11.mlp.fc1.bias")
   gelu_11 = Gelu <approximate: string = "tanh"> (linear_46)
   val_214 = MatMul (gelu_11, val_50)
   linear_47 = Add (val_214, "visual.trunk.blocks.11.mlp.fc2.bias")
   add_1097 = Add (add_1068, linear_47)
   layer_norm_24 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1097, "visual.trunk.blocks.12.norm1.weight", "visual.trunk.blocks.12.norm1.bias")
   val_215 = MatMul (layer_norm_24, val_51)
   linear_48 = Add (val_215, "visual.trunk.blocks.12.attn.qkv.bias")
   [node_scaled_dot_product_attention_12_qkv_split] node_scaled_dot_product_attention_12_q, node_scaled_dot_product_attention_12_k, node_scaled_dot_product_attention_12_v = Split <axis: int = -1> (linear_48, attn3d_split_3x1536)
   scaled_dot_product_attention_12 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_12_q, node_scaled_dot_product_attention_12_k, node_scaled_dot_product_attention_12_v)
   val_216 = MatMul (scaled_dot_product_attention_12, val_52)
   linear_49 = Add (val_216, "visual.trunk.blocks.12.attn.proj.bias")
   add_1158 = Add (add_1097, linear_49)
   layer_norm_25 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1158, "visual.trunk.blocks.12.norm2.weight", "visual.trunk.blocks.12.norm2.bias")
   val_217 = MatMul (layer_norm_25, val_53)
   linear_50 = Add (val_217, "visual.trunk.blocks.12.mlp.fc1.bias")
   gelu_12 = Gelu <approximate: string = "tanh"> (linear_50)
   val_218 = MatMul (gelu_12, val_54)
   linear_51 = Add (val_218, "visual.trunk.blocks.12.mlp.fc2.bias")
   add_1187 = Add (add_1158, linear_51)
   layer_norm_26 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1187, "visual.trunk.blocks.13.norm1.weight", "visual.trunk.blocks.13.norm1.bias")
   val_219 = MatMul (layer_norm_26, val_55)
   linear_52 = Add (val_219, "visual.trunk.blocks.13.attn.qkv.bias")
   [node_scaled_dot_product_attention_13_qkv_split] node_scaled_dot_product_attention_13_q, node_scaled_dot_product_attention_13_k, node_scaled_dot_product_attention_13_v = Split <axis: int = -1> (linear_52, attn3d_split_3x1536)
   scaled_dot_product_attention_13 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_13_q, node_scaled_dot_product_attention_13_k, node_scaled_dot_product_attention_13_v)
   val_220 = MatMul (scaled_dot_product_attention_13, val_56)
   linear_53 = Add (val_220, "visual.trunk.blocks.13.attn.proj.bias")
   add_1248 = Add (add_1187, linear_53)
   layer_norm_27 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1248, "visual.trunk.blocks.13.norm2.weight", "visual.trunk.blocks.13.norm2.bias")
   val_221 = MatMul (layer_norm_27, val_57)
   linear_54 = Add (val_221, "visual.trunk.blocks.13.mlp.fc1.bias")
   gelu_13 = Gelu <approximate: string = "tanh"> (linear_54)
   val_222 = MatMul (gelu_13, val_58)
   linear_55 = Add (val_222, "visual.trunk.blocks.13.mlp.fc2.bias")
   add_1277 = Add (add_1248, linear_55)
   layer_norm_28 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1277, "visual.trunk.blocks.14.norm1.weight", "visual.trunk.blocks.14.norm1.bias")
   val_223 = MatMul (layer_norm_28, val_59)
   linear_56 = Add (val_223, "visual.trunk.blocks.14.attn.qkv.bias")
   [node_scaled_dot_product_attention_14_qkv_split] node_scaled_dot_product_attention_14_q, node_scaled_dot_product_attention_14_k, node_scaled_dot_product_attention_14_v = Split <axis: int = -1> (linear_56, attn3d_split_3x1536)
   scaled_dot_product_attention_14 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_14_q, node_scaled_dot_product_attention_14_k, node_scaled_dot_product_attention_14_v)
   val_224 = MatMul (scaled_dot_product_attention_14, val_60)
   linear_57 = Add (val_224, "visual.trunk.blocks.14.attn.proj.bias")
   add_1338 = Add (add_1277, linear_57)
   layer_norm_29 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1338, "visual.trunk.blocks.14.norm2.weight", "visual.trunk.blocks.14.norm2.bias")
   val_225 = MatMul (layer_norm_29, val_61)
   linear_58 = Add (val_225, "visual.trunk.blocks.14.mlp.fc1.bias")
   gelu_14 = Gelu <approximate: string = "tanh"> (linear_58)
   val_226 = MatMul (gelu_14, val_62)
   linear_59 = Add (val_226, "visual.trunk.blocks.14.mlp.fc2.bias")
   add_1367 = Add (add_1338, linear_59)
   layer_norm_30 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1367, "visual.trunk.blocks.15.norm1.weight", "visual.trunk.blocks.15.norm1.bias")
   val_227 = MatMul (layer_norm_30, val_63)
   linear_60 = Add (val_227, "visual.trunk.blocks.15.attn.qkv.bias")
   [node_scaled_dot_product_attention_15_qkv_split] node_scaled_dot_product_attention_15_q, node_scaled_dot_product_attention_15_k, node_scaled_dot_product_attention_15_v = Split <axis: int = -1> (linear_60, attn3d_split_3x1536)
   scaled_dot_product_attention_15 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_15_q, node_scaled_dot_product_attention_15_k, node_scaled_dot_product_attention_15_v)
   val_228 = MatMul (scaled_dot_product_attention_15, val_64)
   linear_61 = Add (val_228, "visual.trunk.blocks.15.attn.proj.bias")
   add_1428 = Add (add_1367, linear_61)
   layer_norm_31 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1428, "visual.trunk.blocks.15.norm2.weight", "visual.trunk.blocks.15.norm2.bias")
   val_229 = MatMul (layer_norm_31, val_65)
   linear_62 = Add (val_229, "visual.trunk.blocks.15.mlp.fc1.bias")
   gelu_15 = Gelu <approximate: string = "tanh"> (linear_62)
   val_230 = MatMul (gelu_15, val_66)
   linear_63 = Add (val_230, "visual.trunk.blocks.15.mlp.fc2.bias")
   add_1457 = Add (add_1428, linear_63)
   layer_norm_32 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1457, "visual.trunk.blocks.16.norm1.weight", "visual.trunk.blocks.16.norm1.bias")
   val_231 = MatMul (layer_norm_32, val_67)
   linear_64 = Add (val_231, "visual.trunk.blocks.16.attn.qkv.bias")
   [node_scaled_dot_product_attention_16_qkv_split] node_scaled_dot_product_attention_16_q, node_scaled_dot_product_attention_16_k, node_scaled_dot_product_attention_16_v = Split <axis: int = -1> (linear_64, attn3d_split_3x1536)
   scaled_dot_product_attention_16 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_16_q, node_scaled_dot_product_attention_16_k, node_scaled_dot_product_attention_16_v)
   val_232 = MatMul (scaled_dot_product_attention_16, val_68)
   linear_65 = Add (val_232, "visual.trunk.blocks.16.attn.proj.bias")
   add_1518 = Add (add_1457, linear_65)
   layer_norm_33 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1518, "visual.trunk.blocks.16.norm2.weight", "visual.trunk.blocks.16.norm2.bias")
   val_233 = MatMul (layer_norm_33, val_69)
   linear_66 = Add (val_233, "visual.trunk.blocks.16.mlp.fc1.bias")
   gelu_16 = Gelu <approximate: string = "tanh"> (linear_66)
   val_234 = MatMul (gelu_16, val_70)
   linear_67 = Add (val_234, "visual.trunk.blocks.16.mlp.fc2.bias")
   add_1547 = Add (add_1518, linear_67)
   layer_norm_34 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1547, "visual.trunk.blocks.17.norm1.weight", "visual.trunk.blocks.17.norm1.bias")
   val_235 = MatMul (layer_norm_34, val_71)
   linear_68 = Add (val_235, "visual.trunk.blocks.17.attn.qkv.bias")
   [node_scaled_dot_product_attention_17_qkv_split] node_scaled_dot_product_attention_17_q, node_scaled_dot_product_attention_17_k, node_scaled_dot_product_attention_17_v = Split <axis: int = -1> (linear_68, attn3d_split_3x1536)
   scaled_dot_product_attention_17 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_17_q, node_scaled_dot_product_attention_17_k, node_scaled_dot_product_attention_17_v)
   val_236 = MatMul (scaled_dot_product_attention_17, val_72)
   linear_69 = Add (val_236, "visual.trunk.blocks.17.attn.proj.bias")
   add_1608 = Add (add_1547, linear_69)
   layer_norm_35 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1608, "visual.trunk.blocks.17.norm2.weight", "visual.trunk.blocks.17.norm2.bias")
   val_237 = MatMul (layer_norm_35, val_73)
   linear_70 = Add (val_237, "visual.trunk.blocks.17.mlp.fc1.bias")
   gelu_17 = Gelu <approximate: string = "tanh"> (linear_70)
   val_238 = MatMul (gelu_17, val_74)
   linear_71 = Add (val_238, "visual.trunk.blocks.17.mlp.fc2.bias")
   add_1637 = Add (add_1608, linear_71)
   layer_norm_36 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1637, "visual.trunk.blocks.18.norm1.weight", "visual.trunk.blocks.18.norm1.bias")
   val_239 = MatMul (layer_norm_36, val_75)
   linear_72 = Add (val_239, "visual.trunk.blocks.18.attn.qkv.bias")
   [node_scaled_dot_product_attention_18_qkv_split] node_scaled_dot_product_attention_18_q, node_scaled_dot_product_attention_18_k, node_scaled_dot_product_attention_18_v = Split <axis: int = -1> (linear_72, attn3d_split_3x1536)
   scaled_dot_product_attention_18 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_18_q, node_scaled_dot_product_attention_18_k, node_scaled_dot_product_attention_18_v)
   val_240 = MatMul (scaled_dot_product_attention_18, val_76)
   linear_73 = Add (val_240, "visual.trunk.blocks.18.attn.proj.bias")
   add_1698 = Add (add_1637, linear_73)
   layer_norm_37 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1698, "visual.trunk.blocks.18.norm2.weight", "visual.trunk.blocks.18.norm2.bias")
   val_241 = MatMul (layer_norm_37, val_77)
   linear_74 = Add (val_241, "visual.trunk.blocks.18.mlp.fc1.bias")
   gelu_18 = Gelu <approximate: string = "tanh"> (linear_74)
   val_242 = MatMul (gelu_18, val_78)
   linear_75 = Add (val_242, "visual.trunk.blocks.18.mlp.fc2.bias")
   add_1727 = Add (add_1698, linear_75)
   layer_norm_38 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1727, "visual.trunk.blocks.19.norm1.weight", "visual.trunk.blocks.19.norm1.bias")
   val_243 = MatMul (layer_norm_38, val_79)
   linear_76 = Add (val_243, "visual.trunk.blocks.19.attn.qkv.bias")
   [node_scaled_dot_product_attention_19_qkv_split] node_scaled_dot_product_attention_19_q, node_scaled_dot_product_attention_19_k, node_scaled_dot_product_attention_19_v = Split <axis: int = -1> (linear_76, attn3d_split_3x1536)
   scaled_dot_product_attention_19 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_19_q, node_scaled_dot_product_attention_19_k, node_scaled_dot_product_attention_19_v)
   val_244 = MatMul (scaled_dot_product_attention_19, val_80)
   linear_77 = Add (val_244, "visual.trunk.blocks.19.attn.proj.bias")
   add_1788 = Add (add_1727, linear_77)
   layer_norm_39 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1788, "visual.trunk.blocks.19.norm2.weight", "visual.trunk.blocks.19.norm2.bias")
   val_245 = MatMul (layer_norm_39, val_81)
   linear_78 = Add (val_245, "visual.trunk.blocks.19.mlp.fc1.bias")
   gelu_19 = Gelu <approximate: string = "tanh"> (linear_78)
   val_246 = MatMul (gelu_19, val_82)
   linear_79 = Add (val_246, "visual.trunk.blocks.19.mlp.fc2.bias")
   add_1817 = Add (add_1788, linear_79)
   layer_norm_40 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1817, "visual.trunk.blocks.20.norm1.weight", "visual.trunk.blocks.20.norm1.bias")
   val_247 = MatMul (layer_norm_40, val_83)
   linear_80 = Add (val_247, "visual.trunk.blocks.20.attn.qkv.bias")
   [node_scaled_dot_product_attention_20_qkv_split] node_scaled_dot_product_attention_20_q, node_scaled_dot_product_attention_20_k, node_scaled_dot_product_attention_20_v = Split <axis: int = -1> (linear_80, attn3d_split_3x1536)
   scaled_dot_product_attention_20 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_20_q, node_scaled_dot_product_attention_20_k, node_scaled_dot_product_attention_20_v)
   val_248 = MatMul (scaled_dot_product_attention_20, val_84)
   linear_81 = Add (val_248, "visual.trunk.blocks.20.attn.proj.bias")
   add_1878 = Add (add_1817, linear_81)
   layer_norm_41 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1878, "visual.trunk.blocks.20.norm2.weight", "visual.trunk.blocks.20.norm2.bias")
   val_249 = MatMul (layer_norm_41, val_85)
   linear_82 = Add (val_249, "visual.trunk.blocks.20.mlp.fc1.bias")
   gelu_20 = Gelu <approximate: string = "tanh"> (linear_82)
   val_250 = MatMul (gelu_20, val_86)
   linear_83 = Add (val_250, "visual.trunk.blocks.20.mlp.fc2.bias")
   add_1907 = Add (add_1878, linear_83)
   layer_norm_42 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1907, "visual.trunk.blocks.21.norm1.weight", "visual.trunk.blocks.21.norm1.bias")
   val_251 = MatMul (layer_norm_42, val_87)
   linear_84 = Add (val_251, "visual.trunk.blocks.21.attn.qkv.bias")
   [node_scaled_dot_product_attention_21_qkv_split] node_scaled_dot_product_attention_21_q, node_scaled_dot_product_attention_21_k, node_scaled_dot_product_attention_21_v = Split <axis: int = -1> (linear_84, attn3d_split_3x1536)
   scaled_dot_product_attention_21 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_21_q, node_scaled_dot_product_attention_21_k, node_scaled_dot_product_attention_21_v)
   val_252 = MatMul (scaled_dot_product_attention_21, val_88)
   linear_85 = Add (val_252, "visual.trunk.blocks.21.attn.proj.bias")
   add_1968 = Add (add_1907, linear_85)
   layer_norm_43 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1968, "visual.trunk.blocks.21.norm2.weight", "visual.trunk.blocks.21.norm2.bias")
   val_253 = MatMul (layer_norm_43, val_89)
   linear_86 = Add (val_253, "visual.trunk.blocks.21.mlp.fc1.bias")
   gelu_21 = Gelu <approximate: string = "tanh"> (linear_86)
   val_254 = MatMul (gelu_21, val_90)
   linear_87 = Add (val_254, "visual.trunk.blocks.21.mlp.fc2.bias")
   add_1997 = Add (add_1968, linear_87)
   layer_norm_44 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1997, "visual.trunk.blocks.22.norm1.weight", "visual.trunk.blocks.22.norm1.bias")
   val_255 = MatMul (layer_norm_44, val_91)
   linear_88 = Add (val_255, "visual.trunk.blocks.22.attn.qkv.bias")
   [node_scaled_dot_product_attention_22_qkv_split] node_scaled_dot_product_attention_22_q, node_scaled_dot_product_attention_22_k, node_scaled_dot_product_attention_22_v = Split <axis: int = -1> (linear_88, attn3d_split_3x1536)
   scaled_dot_product_attention_22 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_22_q, node_scaled_dot_product_attention_22_k, node_scaled_dot_product_attention_22_v)
   val_256 = MatMul (scaled_dot_product_attention_22, val_92)
   linear_89 = Add (val_256, "visual.trunk.blocks.22.attn.proj.bias")
   add_2058 = Add (add_1997, linear_89)
   layer_norm_45 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2058, "visual.trunk.blocks.22.norm2.weight", "visual.trunk.blocks.22.norm2.bias")
   val_257 = MatMul (layer_norm_45, val_93)
   linear_90 = Add (val_257, "visual.trunk.blocks.22.mlp.fc1.bias")
   gelu_22 = Gelu <approximate: string = "tanh"> (linear_90)
   val_258 = MatMul (gelu_22, val_94)
   linear_91 = Add (val_258, "visual.trunk.blocks.22.mlp.fc2.bias")
   add_2087 = Add (add_2058, linear_91)
   layer_norm_46 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2087, "visual.trunk.blocks.23.norm1.weight", "visual.trunk.blocks.23.norm1.bias")
   val_259 = MatMul (layer_norm_46, val_95)
   linear_92 = Add (val_259, "visual.trunk.blocks.23.attn.qkv.bias")
   [node_scaled_dot_product_attention_23_qkv_split] node_scaled_dot_product_attention_23_q, node_scaled_dot_product_attention_23_k, node_scaled_dot_product_attention_23_v = Split <axis: int = -1> (linear_92, attn3d_split_3x1536)
   scaled_dot_product_attention_23 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_23_q, node_scaled_dot_product_attention_23_k, node_scaled_dot_product_attention_23_v)
   val_260 = MatMul (scaled_dot_product_attention_23, val_96)
   linear_93 = Add (val_260, "visual.trunk.blocks.23.attn.proj.bias")
   add_2148 = Add (add_2087, linear_93)
   layer_norm_47 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2148, "visual.trunk.blocks.23.norm2.weight", "visual.trunk.blocks.23.norm2.bias")
   val_261 = MatMul (layer_norm_47, val_97)
   linear_94 = Add (val_261, "visual.trunk.blocks.23.mlp.fc1.bias")
   gelu_23 = Gelu <approximate: string = "tanh"> (linear_94)
   val_262 = MatMul (gelu_23, val_98)
   linear_95 = Add (val_262, "visual.trunk.blocks.23.mlp.fc2.bias")
   add_2177 = Add (add_2148, linear_95)
   layer_norm_48 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2177, "visual.trunk.blocks.24.norm1.weight", "visual.trunk.blocks.24.norm1.bias")
   val_263 = MatMul (layer_norm_48, val_99)
   linear_96 = Add (val_263, "visual.trunk.blocks.24.attn.qkv.bias")
   [node_scaled_dot_product_attention_24_qkv_split] node_scaled_dot_product_attention_24_q, node_scaled_dot_product_attention_24_k, node_scaled_dot_product_attention_24_v = Split <axis: int = -1> (linear_96, attn3d_split_3x1536)
   scaled_dot_product_attention_24 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_24_q, node_scaled_dot_product_attention_24_k, node_scaled_dot_product_attention_24_v)
   val_264 = MatMul (scaled_dot_product_attention_24, val_100)
   linear_97 = Add (val_264, "visual.trunk.blocks.24.attn.proj.bias")
   add_2238 = Add (add_2177, linear_97)
   layer_norm_49 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2238, "visual.trunk.blocks.24.norm2.weight", "visual.trunk.blocks.24.norm2.bias")
   val_265 = MatMul (layer_norm_49, val_101)
   linear_98 = Add (val_265, "visual.trunk.blocks.24.mlp.fc1.bias")
   gelu_24 = Gelu <approximate: string = "tanh"> (linear_98)
   val_266 = MatMul (gelu_24, val_102)
   linear_99 = Add (val_266, "visual.trunk.blocks.24.mlp.fc2.bias")
   add_2267 = Add (add_2238, linear_99)
   layer_norm_50 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2267, "visual.trunk.blocks.25.norm1.weight", "visual.trunk.blocks.25.norm1.bias")
   val_267 = MatMul (layer_norm_50, val_103)
   linear_100 = Add (val_267, "visual.trunk.blocks.25.attn.qkv.bias")
   [node_scaled_dot_product_attention_25_qkv_split] node_scaled_dot_product_attention_25_q, node_scaled_dot_product_attention_25_k, node_scaled_dot_product_attention_25_v = Split <axis: int = -1> (linear_100, attn3d_split_3x1536)
   scaled_dot_product_attention_25 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_25_q, node_scaled_dot_product_attention_25_k, node_scaled_dot_product_attention_25_v)
   val_268 = MatMul (scaled_dot_product_attention_25, val_104)
   linear_101 = Add (val_268, "visual.trunk.blocks.25.attn.proj.bias")
   add_2328 = Add (add_2267, linear_101)
   layer_norm_51 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2328, "visual.trunk.blocks.25.norm2.weight", "visual.trunk.blocks.25.norm2.bias")
   val_269 = MatMul (layer_norm_51, val_105)
   linear_102 = Add (val_269, "visual.trunk.blocks.25.mlp.fc1.bias")
   gelu_25 = Gelu <approximate: string = "tanh"> (linear_102)
   val_270 = MatMul (gelu_25, val_106)
   linear_103 = Add (val_270, "visual.trunk.blocks.25.mlp.fc2.bias")
   add_2357 = Add (add_2328, linear_103)
   layer_norm_52 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2357, "visual.trunk.blocks.26.norm1.weight", "visual.trunk.blocks.26.norm1.bias")
   val_271 = MatMul (layer_norm_52, val_107)
   linear_104 = Add (val_271, "visual.trunk.blocks.26.attn.qkv.bias")
   [node_scaled_dot_product_attention_26_qkv_split] node_scaled_dot_product_attention_26_q, node_scaled_dot_product_attention_26_k, node_scaled_dot_product_attention_26_v = Split <axis: int = -1> (linear_104, attn3d_split_3x1536)
   scaled_dot_product_attention_26 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_26_q, node_scaled_dot_product_attention_26_k, node_scaled_dot_product_attention_26_v)
   val_272 = MatMul (scaled_dot_product_attention_26, val_108)
   linear_105 = Add (val_272, "visual.trunk.blocks.26.attn.proj.bias")
   add_2418 = Add (add_2357, linear_105)
   layer_norm_53 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2418, "visual.trunk.blocks.26.norm2.weight", "visual.trunk.blocks.26.norm2.bias")
   val_273 = MatMul (layer_norm_53, val_109)
   linear_106 = Add (val_273, "visual.trunk.blocks.26.mlp.fc1.bias")
   gelu_26 = Gelu <approximate: string = "tanh"> (linear_106)
   val_274 = MatMul (gelu_26, val_110)
   linear_107 = Add (val_274, "visual.trunk.blocks.26.mlp.fc2.bias")
   add_2447 = Add (add_2418, linear_107)
   layer_norm_54 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2447, "visual.trunk.blocks.27.norm1.weight", "visual.trunk.blocks.27.norm1.bias")
   val_275 = MatMul (layer_norm_54, val_111)
   linear_108 = Add (val_275, "visual.trunk.blocks.27.attn.qkv.bias")
   [node_scaled_dot_product_attention_27_qkv_split] node_scaled_dot_product_attention_27_q, node_scaled_dot_product_attention_27_k, node_scaled_dot_product_attention_27_v = Split <axis: int = -1> (linear_108, attn3d_split_3x1536)
   scaled_dot_product_attention_27 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_27_q, node_scaled_dot_product_attention_27_k, node_scaled_dot_product_attention_27_v)
   val_276 = MatMul (scaled_dot_product_attention_27, val_112)
   linear_109 = Add (val_276, "visual.trunk.blocks.27.attn.proj.bias")
   add_2508 = Add (add_2447, linear_109)
   layer_norm_55 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2508, "visual.trunk.blocks.27.norm2.weight", "visual.trunk.blocks.27.norm2.bias")
   val_277 = MatMul (layer_norm_55, val_113)
   linear_110 = Add (val_277, "visual.trunk.blocks.27.mlp.fc1.bias")
   gelu_27 = Gelu <approximate: string = "tanh"> (linear_110)
   val_278 = MatMul (gelu_27, val_114)
   linear_111 = Add (val_278, "visual.trunk.blocks.27.mlp.fc2.bias")
   add_2537 = Add (add_2508, linear_111)
   layer_norm_56 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2537, "visual.trunk.blocks.28.norm1.weight", "visual.trunk.blocks.28.norm1.bias")
   val_279 = MatMul (layer_norm_56, val_115)
   linear_112 = Add (val_279, "visual.trunk.blocks.28.attn.qkv.bias")
   [node_scaled_dot_product_attention_28_qkv_split] node_scaled_dot_product_attention_28_q, node_scaled_dot_product_attention_28_k, node_scaled_dot_product_attention_28_v = Split <axis: int = -1> (linear_112, attn3d_split_3x1536)
   scaled_dot_product_attention_28 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_28_q, node_scaled_dot_product_attention_28_k, node_scaled_dot_product_attention_28_v)
   val_280 = MatMul (scaled_dot_product_attention_28, val_116)
   linear_113 = Add (val_280, "visual.trunk.blocks.28.attn.proj.bias")
   add_2598 = Add (add_2537, linear_113)
   layer_norm_57 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2598, "visual.trunk.blocks.28.norm2.weight", "visual.trunk.blocks.28.norm2.bias")
   val_281 = MatMul (layer_norm_57, val_117)
   linear_114 = Add (val_281, "visual.trunk.blocks.28.mlp.fc1.bias")
   gelu_28 = Gelu <approximate: string = "tanh"> (linear_114)
   val_282 = MatMul (gelu_28, val_118)
   linear_115 = Add (val_282, "visual.trunk.blocks.28.mlp.fc2.bias")
   add_2627 = Add (add_2598, linear_115)
   layer_norm_58 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2627, "visual.trunk.blocks.29.norm1.weight", "visual.trunk.blocks.29.norm1.bias")
   val_283 = MatMul (layer_norm_58, val_119)
   linear_116 = Add (val_283, "visual.trunk.blocks.29.attn.qkv.bias")
   [node_scaled_dot_product_attention_29_qkv_split] node_scaled_dot_product_attention_29_q, node_scaled_dot_product_attention_29_k, node_scaled_dot_product_attention_29_v = Split <axis: int = -1> (linear_116, attn3d_split_3x1536)
   scaled_dot_product_attention_29 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_29_q, node_scaled_dot_product_attention_29_k, node_scaled_dot_product_attention_29_v)
   val_284 = MatMul (scaled_dot_product_attention_29, val_120)
   linear_117 = Add (val_284, "visual.trunk.blocks.29.attn.proj.bias")
   add_2688 = Add (add_2627, linear_117)
   layer_norm_59 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2688, "visual.trunk.blocks.29.norm2.weight", "visual.trunk.blocks.29.norm2.bias")
   val_285 = MatMul (layer_norm_59, val_121)
   linear_118 = Add (val_285, "visual.trunk.blocks.29.mlp.fc1.bias")
   gelu_29 = Gelu <approximate: string = "tanh"> (linear_118)
   val_286 = MatMul (gelu_29, val_122)
   linear_119 = Add (val_286, "visual.trunk.blocks.29.mlp.fc2.bias")
   add_2717 = Add (add_2688, linear_119)
   layer_norm_60 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2717, "visual.trunk.blocks.30.norm1.weight", "visual.trunk.blocks.30.norm1.bias")
   val_287 = MatMul (layer_norm_60, val_123)
   linear_120 = Add (val_287, "visual.trunk.blocks.30.attn.qkv.bias")
   [node_scaled_dot_product_attention_30_qkv_split] node_scaled_dot_product_attention_30_q, node_scaled_dot_product_attention_30_k, node_scaled_dot_product_attention_30_v = Split <axis: int = -1> (linear_120, attn3d_split_3x1536)
   scaled_dot_product_attention_30 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_30_q, node_scaled_dot_product_attention_30_k, node_scaled_dot_product_attention_30_v)
   val_288 = MatMul (scaled_dot_product_attention_30, val_124)
   linear_121 = Add (val_288, "visual.trunk.blocks.30.attn.proj.bias")
   add_2778 = Add (add_2717, linear_121)
   layer_norm_61 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2778, "visual.trunk.blocks.30.norm2.weight", "visual.trunk.blocks.30.norm2.bias")
   val_289 = MatMul (layer_norm_61, val_125)
   linear_122 = Add (val_289, "visual.trunk.blocks.30.mlp.fc1.bias")
   gelu_30 = Gelu <approximate: string = "tanh"> (linear_122)
   val_290 = MatMul (gelu_30, val_126)
   linear_123 = Add (val_290, "visual.trunk.blocks.30.mlp.fc2.bias")
   add_2807 = Add (add_2778, linear_123)
   layer_norm_62 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2807, "visual.trunk.blocks.31.norm1.weight", "visual.trunk.blocks.31.norm1.bias")
   val_291 = MatMul (layer_norm_62, val_127)
   linear_124 = Add (val_291, "visual.trunk.blocks.31.attn.qkv.bias")
   [node_scaled_dot_product_attention_31_qkv_split] node_scaled_dot_product_attention_31_q, node_scaled_dot_product_attention_31_k, node_scaled_dot_product_attention_31_v = Split <axis: int = -1> (linear_124, attn3d_split_3x1536)
   scaled_dot_product_attention_31 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_31_q, node_scaled_dot_product_attention_31_k, node_scaled_dot_product_attention_31_v)
   val_292 = MatMul (scaled_dot_product_attention_31, val_128)
   linear_125 = Add (val_292, "visual.trunk.blocks.31.attn.proj.bias")
   add_2868 = Add (add_2807, linear_125)
   layer_norm_63 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2868, "visual.trunk.blocks.31.norm2.weight", "visual.trunk.blocks.31.norm2.bias")
   val_293 = MatMul (layer_norm_63, val_129)
   linear_126 = Add (val_293, "visual.trunk.blocks.31.mlp.fc1.bias")
   gelu_31 = Gelu <approximate: string = "tanh"> (linear_126)
   val_294 = MatMul (gelu_31, val_130)
   linear_127 = Add (val_294, "visual.trunk.blocks.31.mlp.fc2.bias")
   add_2897 = Add (add_2868, linear_127)
   layer_norm_64 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2897, "visual.trunk.blocks.32.norm1.weight", "visual.trunk.blocks.32.norm1.bias")
   val_295 = MatMul (layer_norm_64, val_131)
   linear_128 = Add (val_295, "visual.trunk.blocks.32.attn.qkv.bias")
   [node_scaled_dot_product_attention_32_qkv_split] node_scaled_dot_product_attention_32_q, node_scaled_dot_product_attention_32_k, node_scaled_dot_product_attention_32_v = Split <axis: int = -1> (linear_128, attn3d_split_3x1536)
   scaled_dot_product_attention_32 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_32_q, node_scaled_dot_product_attention_32_k, node_scaled_dot_product_attention_32_v)
   val_296 = MatMul (scaled_dot_product_attention_32, val_132)
   linear_129 = Add (val_296, "visual.trunk.blocks.32.attn.proj.bias")
   add_2958 = Add (add_2897, linear_129)
   layer_norm_65 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2958, "visual.trunk.blocks.32.norm2.weight", "visual.trunk.blocks.32.norm2.bias")
   val_297 = MatMul (layer_norm_65, val_133)
   linear_130 = Add (val_297, "visual.trunk.blocks.32.mlp.fc1.bias")
   gelu_32 = Gelu <approximate: string = "tanh"> (linear_130)
   val_298 = MatMul (gelu_32, val_134)
   linear_131 = Add (val_298, "visual.trunk.blocks.32.mlp.fc2.bias")
   add_2987 = Add (add_2958, linear_131)
   layer_norm_66 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2987, "visual.trunk.blocks.33.norm1.weight", "visual.trunk.blocks.33.norm1.bias")
   val_299 = MatMul (layer_norm_66, val_135)
   linear_132 = Add (val_299, "visual.trunk.blocks.33.attn.qkv.bias")
   [node_scaled_dot_product_attention_33_qkv_split] node_scaled_dot_product_attention_33_q, node_scaled_dot_product_attention_33_k, node_scaled_dot_product_attention_33_v = Split <axis: int = -1> (linear_132, attn3d_split_3x1536)
   scaled_dot_product_attention_33 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_33_q, node_scaled_dot_product_attention_33_k, node_scaled_dot_product_attention_33_v)
   val_300 = MatMul (scaled_dot_product_attention_33, val_136)
   linear_133 = Add (val_300, "visual.trunk.blocks.33.attn.proj.bias")
   add_3048 = Add (add_2987, linear_133)
   layer_norm_67 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3048, "visual.trunk.blocks.33.norm2.weight", "visual.trunk.blocks.33.norm2.bias")
   val_301 = MatMul (layer_norm_67, val_137)
   linear_134 = Add (val_301, "visual.trunk.blocks.33.mlp.fc1.bias")
   gelu_33 = Gelu <approximate: string = "tanh"> (linear_134)
   val_302 = MatMul (gelu_33, val_138)
   linear_135 = Add (val_302, "visual.trunk.blocks.33.mlp.fc2.bias")
   add_3077 = Add (add_3048, linear_135)
   layer_norm_68 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3077, "visual.trunk.blocks.34.norm1.weight", "visual.trunk.blocks.34.norm1.bias")
   val_303 = MatMul (layer_norm_68, val_139)
   linear_136 = Add (val_303, "visual.trunk.blocks.34.attn.qkv.bias")
   [node_scaled_dot_product_attention_34_qkv_split] node_scaled_dot_product_attention_34_q, node_scaled_dot_product_attention_34_k, node_scaled_dot_product_attention_34_v = Split <axis: int = -1> (linear_136, attn3d_split_3x1536)
   scaled_dot_product_attention_34 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_34_q, node_scaled_dot_product_attention_34_k, node_scaled_dot_product_attention_34_v)
   val_304 = MatMul (scaled_dot_product_attention_34, val_140)
   linear_137 = Add (val_304, "visual.trunk.blocks.34.attn.proj.bias")
   add_3138 = Add (add_3077, linear_137)
   layer_norm_69 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3138, "visual.trunk.blocks.34.norm2.weight", "visual.trunk.blocks.34.norm2.bias")
   val_305 = MatMul (layer_norm_69, val_141)
   linear_138 = Add (val_305, "visual.trunk.blocks.34.mlp.fc1.bias")
   gelu_34 = Gelu <approximate: string = "tanh"> (linear_138)
   val_306 = MatMul (gelu_34, val_142)
   linear_139 = Add (val_306, "visual.trunk.blocks.34.mlp.fc2.bias")
   add_3167 = Add (add_3138, linear_139)
   layer_norm_70 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3167, "visual.trunk.blocks.35.norm1.weight", "visual.trunk.blocks.35.norm1.bias")
   val_307 = MatMul (layer_norm_70, val_143)
   linear_140 = Add (val_307, "visual.trunk.blocks.35.attn.qkv.bias")
   [node_scaled_dot_product_attention_35_qkv_split] node_scaled_dot_product_attention_35_q, node_scaled_dot_product_attention_35_k, node_scaled_dot_product_attention_35_v = Split <axis: int = -1> (linear_140, attn3d_split_3x1536)
   scaled_dot_product_attention_35 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_35_q, node_scaled_dot_product_attention_35_k, node_scaled_dot_product_attention_35_v)
   val_308 = MatMul (scaled_dot_product_attention_35, val_144)
   linear_141 = Add (val_308, "visual.trunk.blocks.35.attn.proj.bias")
   add_3228 = Add (add_3167, linear_141)
   layer_norm_71 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3228, "visual.trunk.blocks.35.norm2.weight", "visual.trunk.blocks.35.norm2.bias")
   val_309 = MatMul (layer_norm_71, val_145)
   linear_142 = Add (val_309, "visual.trunk.blocks.35.mlp.fc1.bias")
   gelu_35 = Gelu <approximate: string = "tanh"> (linear_142)
   val_310 = MatMul (gelu_35, val_146)
   linear_143 = Add (val_310, "visual.trunk.blocks.35.mlp.fc2.bias")
   add_3257 = Add (add_3228, linear_143)
   layer_norm_72 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3257, "visual.trunk.blocks.36.norm1.weight", "visual.trunk.blocks.36.norm1.bias")
   val_311 = MatMul (layer_norm_72, val_147)
   linear_144 = Add (val_311, "visual.trunk.blocks.36.attn.qkv.bias")
   [node_scaled_dot_product_attention_36_qkv_split] node_scaled_dot_product_attention_36_q, node_scaled_dot_product_attention_36_k, node_scaled_dot_product_attention_36_v = Split <axis: int = -1> (linear_144, attn3d_split_3x1536)
   scaled_dot_product_attention_36 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_36_q, node_scaled_dot_product_attention_36_k, node_scaled_dot_product_attention_36_v)
   val_312 = MatMul (scaled_dot_product_attention_36, val_148)
   linear_145 = Add (val_312, "visual.trunk.blocks.36.attn.proj.bias")
   add_3318 = Add (add_3257, linear_145)
   layer_norm_73 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3318, "visual.trunk.blocks.36.norm2.weight", "visual.trunk.blocks.36.norm2.bias")
   val_313 = MatMul (layer_norm_73, val_149)
   linear_146 = Add (val_313, "visual.trunk.blocks.36.mlp.fc1.bias")
   gelu_36 = Gelu <approximate: string = "tanh"> (linear_146)
   val_314 = MatMul (gelu_36, val_150)
   linear_147 = Add (val_314, "visual.trunk.blocks.36.mlp.fc2.bias")
   add_3347 = Add (add_3318, linear_147)
   layer_norm_74 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3347, "visual.trunk.blocks.37.norm1.weight", "visual.trunk.blocks.37.norm1.bias")
   val_315 = MatMul (layer_norm_74, val_151)
   linear_148 = Add (val_315, "visual.trunk.blocks.37.attn.qkv.bias")
   [node_scaled_dot_product_attention_37_qkv_split] node_scaled_dot_product_attention_37_q, node_scaled_dot_product_attention_37_k, node_scaled_dot_product_attention_37_v = Split <axis: int = -1> (linear_148, attn3d_split_3x1536)
   scaled_dot_product_attention_37 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_37_q, node_scaled_dot_product_attention_37_k, node_scaled_dot_product_attention_37_v)
   val_316 = MatMul (scaled_dot_product_attention_37, val_152)
   linear_149 = Add (val_316, "visual.trunk.blocks.37.attn.proj.bias")
   add_3408 = Add (add_3347, linear_149)
   layer_norm_75 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3408, "visual.trunk.blocks.37.norm2.weight", "visual.trunk.blocks.37.norm2.bias")
   val_317 = MatMul (layer_norm_75, val_153)
   linear_150 = Add (val_317, "visual.trunk.blocks.37.mlp.fc1.bias")
   gelu_37 = Gelu <approximate: string = "tanh"> (linear_150)
   val_318 = MatMul (gelu_37, val_154)
   linear_151 = Add (val_318, "visual.trunk.blocks.37.mlp.fc2.bias")
   add_3437 = Add (add_3408, linear_151)
   layer_norm_76 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3437, "visual.trunk.blocks.38.norm1.weight", "visual.trunk.blocks.38.norm1.bias")
   val_319 = MatMul (layer_norm_76, val_155)
   linear_152 = Add (val_319, "visual.trunk.blocks.38.attn.qkv.bias")
   [node_scaled_dot_product_attention_38_qkv_split] node_scaled_dot_product_attention_38_q, node_scaled_dot_product_attention_38_k, node_scaled_dot_product_attention_38_v = Split <axis: int = -1> (linear_152, attn3d_split_3x1536)
   scaled_dot_product_attention_38 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_38_q, node_scaled_dot_product_attention_38_k, node_scaled_dot_product_attention_38_v)
   val_320 = MatMul (scaled_dot_product_attention_38, val_156)
   linear_153 = Add (val_320, "visual.trunk.blocks.38.attn.proj.bias")
   add_3498 = Add (add_3437, linear_153)
   layer_norm_77 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3498, "visual.trunk.blocks.38.norm2.weight", "visual.trunk.blocks.38.norm2.bias")
   val_321 = MatMul (layer_norm_77, val_157)
   linear_154 = Add (val_321, "visual.trunk.blocks.38.mlp.fc1.bias")
   gelu_38 = Gelu <approximate: string = "tanh"> (linear_154)
   val_322 = MatMul (gelu_38, val_158)
   linear_155 = Add (val_322, "visual.trunk.blocks.38.mlp.fc2.bias")
   add_3527 = Add (add_3498, linear_155)
   layer_norm_78 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3527, "visual.trunk.blocks.39.norm1.weight", "visual.trunk.blocks.39.norm1.bias")
   val_323 = MatMul (layer_norm_78, val_159)
   linear_156 = Add (val_323, "visual.trunk.blocks.39.attn.qkv.bias")
   [node_scaled_dot_product_attention_39_qkv_split] node_scaled_dot_product_attention_39_q, node_scaled_dot_product_attention_39_k, node_scaled_dot_product_attention_39_v = Split <axis: int = -1> (linear_156, attn3d_split_3x1536)
   scaled_dot_product_attention_39 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_39_q, node_scaled_dot_product_attention_39_k, node_scaled_dot_product_attention_39_v)
   val_324 = MatMul (scaled_dot_product_attention_39, val_160)
   linear_157 = Add (val_324, "visual.trunk.blocks.39.attn.proj.bias")
   add_3588 = Add (add_3527, linear_157)
   layer_norm_79 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3588, "visual.trunk.blocks.39.norm2.weight", "visual.trunk.blocks.39.norm2.bias")
   val_325 = MatMul (layer_norm_79, val_161)
   linear_158 = Add (val_325, "visual.trunk.blocks.39.mlp.fc1.bias")
   gelu_39 = Gelu <approximate: string = "tanh"> (linear_158)
   val_326 = MatMul (gelu_39, val_162)
   linear_159 = Add (val_326, "visual.trunk.blocks.39.mlp.fc2.bias")
   add_3617 = Add (add_3588, linear_159)
   layer_norm_80 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3617, "visual.trunk.norm.weight", "visual.trunk.norm.bias")
   [ez_slice] image_ez_s = Slice (image_f32, image_ez_starts, image_ez_ends, image_ez_axes)
   [ez_flatten] image_ez_r = Reshape (image_ez_s, val_0)
   [ez_mul] image_ez = Mul (image_ez_r, image_ez_zero)
   val_327 = MatMul (layer_norm_80, val_163)
   linear_161 = Add (val_327, "visual.trunk.attn_pool.kv.bias")
   [node_scaled_dot_product_attention_40_qkv_split] node_scaled_dot_product_attention_40_k, node_scaled_dot_product_attention_40_v = Split <axis: int = -1> (linear_161, attn3d_split_2x1536)
   [node_scaled_dot_product_attention_40_q_col] node_scaled_dot_product_attention_40_q_col_out = Unsqueeze (image_ez, node_scaled_dot_product_attention_40_q_col_axes)
   [node_scaled_dot_product_attention_40_q_bcast] node_scaled_dot_product_attention_40_q = Add (node_scaled_dot_product_attention_40_q3, node_scaled_dot_product_attention_40_q_col_out)
   scaled_dot_product_attention_40 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_40_q, node_scaled_dot_product_attention_40_k, node_scaled_dot_product_attention_40_v)
   val_328 = MatMul (scaled_dot_product_attention_40, val_164)
   linear_162 = Add (val_328, "visual.trunk.attn_pool.proj.bias")
   layer_norm_81 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (linear_162, "visual.trunk.attn_pool.norm.weight", "visual.trunk.attn_pool.norm.bias")
   val_329 = MatMul (layer_norm_81, val_165)
   linear_163 = Add (val_329, "visual.trunk.attn_pool.mlp.fc1.bias")
   gelu_40 = Gelu <approximate: string = "tanh"> (linear_163)
   val_330 = MatMul (gelu_40, val_166)
   linear_164 = Add (val_330, "visual.trunk.attn_pool.mlp.fc2.bias")
   add_3715 = Add (linear_162, linear_164)
   select = Squeeze (add_3715, val_2)
   [node_linalg_vector_norm] linalg_vector_norm = ReduceL2 <keepdims: int = 1, noop_with_empty_axes: int = 0> (select, val_0)
   [node_clamp_min] clamp_min = Clip (linalg_vector_norm, val_1)
   [node_div] image_embedding = Div (select, clamp_min)
}

weights:
attn3d_split_2x1536 INT64[2] 6cd12d726747
attn3d_split_3x1536 INT64[3] 44a42bc4131f
image_ez_axes INT64[3] e2e2033ae7e1
image_ez_ends INT64[3] 605390e5a369
image_ez_starts INT64[3] 9d908ecfb6b2
image_ez_zero FLOAT[] df3f619804a9
node_scaled_dot_product_attention_40_q3 FLOAT[1,1,1536] 0b11a3ebad7d
node_scaled_dot_product_attention_40_q_col_axes INT64[2] 0c730b69905c
val_0 INT64[1] 12a3ae445661
val_1 FLOAT[] 6708d9be4956
val_10 FLOAT[6144,1536] 48c427ca51a0
val_100 FLOAT[1536,1536] b6eca0331b99
val_101 FLOAT[1536,6144] a23d96b91f58
val_102 FLOAT[6144,1536] 8b8b822ee7c7
val_103 FLOAT[1536,4608] a6d71b8e196d
val_104 FLOAT[1536,1536] a0d0faed144d
val_105 FLOAT[1536,6144] f3b9c6f84455
val_106 FLOAT[6144,1536] 0e87b37c7aa1
val_107 FLOAT[1536,4608] fd3aa32ec07e
val_108 FLOAT[1536,1536] 7260f4b41ece
val_109 FLOAT[1536,6144] c42a35f84dda
val_11 FLOAT[1536,4608] 613b85c109e2
val_110 FLOAT[6144,1536] bb4616ddad7c
val_111 FLOAT[1536,4608] 87e5e1ca837c
val_112 FLOAT[1536,1536] e5fec3759ee7
val_113 FLOAT[1536,6144] e95f7c716156
val_114 FLOAT[6144,1536] 227e88e4e2b4
val_115 FLOAT[1536,4608] f07be3d8048e
val_116 FLOAT[1536,1536] d4007b0ee2d9
val_117 FLOAT[1536,6144] 7c8885ec4ceb
val_118 FLOAT[6144,1536] fef5eded4dde
val_119 FLOAT[1536,4608] 0d38042d7509
val_12 FLOAT[1536,1536] 4b1761cdbc95
val_120 FLOAT[1536,1536] 8f1c799417a3
val_121 FLOAT[1536,6144] 369487fb3e06
val_122 FLOAT[6144,1536] 88df5255e06f
val_123 FLOAT[1536,4608] e7dfbbcc9933
val_124 FLOAT[1536,1536] 3980950c8b86
val_125 FLOAT[1536,6144] 2d01da1781c1
val_126 FLOAT[6144,1536] 727e89abf3db
val_127 FLOAT[1536,4608] 40bea9693a42
val_128 FLOAT[1536,1536] eeca943cf1b4
val_129 FLOAT[1536,6144] 95310052ecb9
val_13 FLOAT[1536,6144] d6850780071b
val_130 FLOAT[6144,1536] 7ff8f8ef876c
val_131 FLOAT[1536,4608] 3047dc9b7f74
val_132 FLOAT[1536,1536] 34b97bea7a01
val_133 FLOAT[1536,6144] 6eb872dff318
val_134 FLOAT[6144,1536] 95694307f75e
val_135 FLOAT[1536,4608] 477e28fa7c8a
val_136 FLOAT[1536,1536] 236d65850fd5
val_137 FLOAT[1536,6144] af11e63644ef
val_138 FLOAT[6144,1536] aaab7b3752cc
val_139 FLOAT[1536,4608] d2bee69747ed
val_14 FLOAT[6144,1536] e17a07008fb1
val_140 FLOAT[1536,1536] be48b81b1452
val_141 FLOAT[1536,6144] 484c84444150
val_142 FLOAT[6144,1536] 3437c1154db2
val_143 FLOAT[1536,4608] c9323e43223a
val_144 FLOAT[1536,1536] bf8504c082a2
val_145 FLOAT[1536,6144] 561a876fd8f1
val_146 FLOAT[6144,1536] afdd832285a5
val_147 FLOAT[1536,4608] 57c56d5cdea4
val_148 FLOAT[1536,1536] 08214b46cede
val_149 FLOAT[1536,6144] 6e605f7bea1b
val_15 FLOAT[1536,4608] 7efeeeba2f0e
val_150 FLOAT[6144,1536] 6c210322afbd
val_151 FLOAT[1536,4608] ffa006513112
val_152 FLOAT[1536,1536] c0a56508ea5c
val_153 FLOAT[1536,6144] b4e6f7edeb38
val_154 FLOAT[6144,1536] 736d57508a4c
val_155 FLOAT[1536,4608] 14a9943fd20f
val_156 FLOAT[1536,1536] 31dc75fad226
val_157 FLOAT[1536,6144] 82a086a621fc
val_158 FLOAT[6144,1536] 1c1403b009c6
val_159 FLOAT[1536,4608] 6fe593520a7c
val_16 FLOAT[1536,1536] 5eee9558d757
val_160 FLOAT[1536,1536] 7b1827420b74
val_161 FLOAT[1536,6144] 3c107e4b0ed6
val_162 FLOAT[6144,1536] 6695718518c3
val_163 FLOAT[1536,3072] 23d5ea0e4f33
val_164 FLOAT[1536,1536] f66e8c417509
val_165 FLOAT[1536,6144] e8266a651f93
val_166 FLOAT[6144,1536] 3128492fa5a4
val_17 FLOAT[1536,6144] 5e15bf4ac000
val_18 FLOAT[6144,1536] 9866a28e75d4
val_19 FLOAT[1536,4608] 91090c19844f
val_2 INT64[1] 7c9fa136d441
val_20 FLOAT[1536,1536] dd14ec8796d2
val_21 FLOAT[1536,6144] e5b2784031a2
val_22 FLOAT[6144,1536] 0787913a79a0
val_23 FLOAT[1536,4608] 70706280813f
val_24 FLOAT[1536,1536] d04d36069837
val_25 FLOAT[1536,6144] a209902b0d6c
val_26 FLOAT[6144,1536] dd50bc597471
val_27 FLOAT[1536,4608] d1eafb339b0a
val_28 FLOAT[1536,1536] 5d91e7f88b08
val_29 FLOAT[1536,6144] 42afa33cb3e4
val_3 FLOAT[1536,4608] f5dfd61a198d
val_30 FLOAT[6144,1536] be12cf9afa09
val_31 FLOAT[1536,4608] f2378910f0dd
val_32 FLOAT[1536,1536] 81ee7f3d1697
val_33 FLOAT[1536,6144] ecaf707dbd34
val_34 FLOAT[6144,1536] 83ac273ca9b3
val_35 FLOAT[1536,4608] cd26ccdb7aba
val_36 FLOAT[1536,1536] 02bd376e50e7
val_37 FLOAT[1536,6144] 8d46e6f6ff8e
val_38 FLOAT[6144,1536] 2c846a39a19c
val_39 FLOAT[1536,4608] 54f0e659c88c
val_4 FLOAT[1536,1536] dedbcad23028
val_40 FLOAT[1536,1536] 1aa3f724aa9a
val_41 FLOAT[1536,6144] ee8b763b0ca9
val_42 FLOAT[6144,1536] 121468e6ee07
val_43 FLOAT[1536,4608] bf5d585010ef
val_44 FLOAT[1536,1536] 9f555ccaf501
val_45 FLOAT[1536,6144] 920fdb1c57de
val_46 FLOAT[6144,1536] 449edc980f47
val_47 FLOAT[1536,4608] 4e6f95cf44bb
val_48 FLOAT[1536,1536] 01ac1e851e8b
val_49 FLOAT[1536,6144] a69d7ce709ed
val_5 FLOAT[1536,6144] a39990e5c885
val_50 FLOAT[6144,1536] bc28fef08cfe
val_51 FLOAT[1536,4608] c0fc213592df
val_52 FLOAT[1536,1536] e124930371e2
val_53 FLOAT[1536,6144] e6bb986816fa
val_54 FLOAT[6144,1536] 4996adbc1f3d
val_55 FLOAT[1536,4608] b38958d4faba
val_56 FLOAT[1536,1536] 81a85b2d0452
val_57 FLOAT[1536,6144] a2414daf7d8b
val_58 FLOAT[6144,1536] fba6362b0bbd
val_59 FLOAT[1536,4608] f5b9eb7b3f85
val_6 FLOAT[6144,1536] a094e3702b52
val_60 FLOAT[1536,1536] d3b1ab0085b4
val_61 FLOAT[1536,6144] c1d803d9b0ef
val_62 FLOAT[6144,1536] 54730c479d56
val_63 FLOAT[1536,4608] 49ff5a9cac8c
val_64 FLOAT[1536,1536] 33bd51b66260
val_65 FLOAT[1536,6144] e13727339e9a
val_66 FLOAT[6144,1536] f002494dc1e4
val_67 FLOAT[1536,4608] 648a3e5c12cd
val_68 FLOAT[1536,1536] 72d3da444b26
val_69 FLOAT[1536,6144] 3808c36741ea
val_7 FLOAT[1536,4608] f4ab5d682494
val_70 FLOAT[6144,1536] e80883204865
val_71 FLOAT[1536,4608] 5c4d3e7e7e90
val_72 FLOAT[1536,1536] 24e1af77cb74
val_73 FLOAT[1536,6144] cf2fc64e82fb
val_74 FLOAT[6144,1536] 141ec0b586a8
val_75 FLOAT[1536,4608] 700714909b9e
val_76 FLOAT[1536,1536] b8ed7b019d03
val_77 FLOAT[1536,6144] b9a06484feb0
val_78 FLOAT[6144,1536] 8a706f0a822d
val_79 FLOAT[1536,4608] 7d0ff7c17f4f
val_8 FLOAT[1536,1536] df9fff13e8e3
val_80 FLOAT[1536,1536] f33069b1ff93
val_81 FLOAT[1536,6144] 25acc83a30bd
val_82 FLOAT[6144,1536] 318295e9bb91
val_83 FLOAT[1536,4608] c8afc4a44491
val_84 FLOAT[1536,1536] 61ed0cd256fd
val_85 FLOAT[1536,6144] dd1b572ddcf3
val_86 FLOAT[6144,1536] 8c39d68ee059
val_87 FLOAT[1536,4608] a4a1b4765bed
val_88 FLOAT[1536,1536] 72fd01227388
val_89 FLOAT[1536,6144] 32540e59394f
val_9 FLOAT[1536,6144] 9d77aacf9d09
val_90 FLOAT[6144,1536] e7edb810723d
val_91 FLOAT[1536,4608] 67c3e8cec5fe
val_92 FLOAT[1536,1536] d96aac62d561
val_93 FLOAT[1536,6144] 81d97bbc3234
val_94 FLOAT[6144,1536] 8acf1c6a7b75
val_95 FLOAT[1536,4608] 29b5916caf33
val_96 FLOAT[1536,1536] c644e487f8ed
val_97 FLOAT[1536,6144] 1787e1379719
val_98 FLOAT[6144,1536] d35d9a6aa4d3
val_99 FLOAT[1536,4608] c13f5fed652b
view_target INT64[3] 9429ec04284e
visual.trunk.attn_pool.kv.bias FLOAT[3072] 3959a431375a
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visual.trunk.blocks.0.mlp.fc1.bias FLOAT[6144] ec361063ddaa
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visual.trunk.blocks.0.norm2.weight FLOAT[1536] aaba2163d0ad
visual.trunk.blocks.1.attn.proj.bias FLOAT[1536] 08543d3ce265
visual.trunk.blocks.1.attn.qkv.bias FLOAT[4608] e44ea6482aea
visual.trunk.blocks.1.mlp.fc1.bias FLOAT[6144] 77c346f9619c
visual.trunk.blocks.1.mlp.fc2.bias FLOAT[1536] 51a43949b7e4
visual.trunk.blocks.1.norm1.bias FLOAT[1536] 24bc2a874dc6
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visual.trunk.blocks.1.norm2.bias FLOAT[1536] e88c121123ea
visual.trunk.blocks.1.norm2.weight FLOAT[1536] 4776f31dec66
visual.trunk.blocks.10.attn.proj.bias FLOAT[1536] c02de0f8b8dc
visual.trunk.blocks.10.attn.qkv.bias FLOAT[4608] 9dad753b03a5
visual.trunk.blocks.10.mlp.fc1.bias FLOAT[6144] e093bbb5993b
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visual.trunk.blocks.10.norm1.bias FLOAT[1536] 0c6c39f53ac2
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visual.trunk.blocks.10.norm2.weight FLOAT[1536] 300bf484616e
visual.trunk.blocks.11.attn.proj.bias FLOAT[1536] 1cec64b2be9d
visual.trunk.blocks.11.attn.qkv.bias FLOAT[4608] 56c687c040b0
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visual.trunk.blocks.11.mlp.fc2.bias FLOAT[1536] 09d9262a2195
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visual.trunk.blocks.12.attn.proj.bias FLOAT[1536] 9a22579dd6cf
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visual.trunk.blocks.12.norm2.bias FLOAT[1536] 69b2cd81e2c2
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visual.trunk.blocks.13.attn.qkv.bias FLOAT[4608] 0e4e68e467b4
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visual.trunk.blocks.13.norm1.bias FLOAT[1536] daa736988fa9
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visual.trunk.blocks.14.attn.proj.bias FLOAT[1536] 11c51e152264
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visual.trunk.blocks.14.mlp.fc2.bias FLOAT[1536] 7c920ea65401
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visual.trunk.blocks.14.norm1.weight FLOAT[1536] 09ca7faa1d1c
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visual.trunk.blocks.14.norm2.weight FLOAT[1536] 2ab4a52a7bb8
visual.trunk.blocks.15.attn.proj.bias FLOAT[1536] fa507bb73007
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visual.trunk.blocks.15.norm1.weight FLOAT[1536] 4f60490e1306
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