<
   ir_version: 10,
   opset_import: ["" : 23],
   producer_name: "pytorch"
>
main_graph (uint8[batch,256,256,3] image) => (float[batch,1024] image_embedding) 
   <
      float[batch,256,1024] add_1007
      float[batch,256,1024] add_1068
      float[batch,256,1024] add_107
      float[batch,256,1024] add_1097
      float[batch,256,1024] add_1158
      float[batch,256,1024] add_1187
      float[batch,256,1024] add_1248
      float[batch,256,1024] add_1277
      float[batch,256,1024] add_13
      float[batch,256,1024] add_1338
      float[batch,256,1024] add_1367
      float[batch,256,1024] add_1428
      float[batch,256,1024] add_1457
      float[batch,256,1024] add_1518
      float[batch,256,1024] add_1547
      float[batch,256,1024] add_1608
      float[batch,256,1024] add_1637
      float[batch,256,1024] add_168
      float[batch,256,1024] add_1698
      float[batch,256,1024] add_1727
      float[batch,256,1024] add_1788
      float[batch,256,1024] add_1817
      float[batch,256,1024] add_1878
      float[batch,256,1024] add_1907
      float[batch,256,1024] add_1968
      float[batch,256,1024] add_197
      float[batch,256,1024] add_1997
      float[batch,256,1024] add_2058
      float[batch,256,1024] add_2087
      float[batch,256,1024] add_2148
      float[batch,256,1024] add_2177
      float[batch,1,1024] add_2275
      float[batch,256,1024] add_258
      float[batch,256,1024] add_287
      float[batch,256,1024] add_348
      float[batch,256,1024] add_377
      float[batch,256,1024] add_438
      float[batch,256,1024] add_467
      float[batch,256,1024] add_528
      float[batch,256,1024] add_557
      float[batch,256,1024] add_618
      float[batch,256,1024] add_647
      float[batch,256,1024] add_708
      float[batch,256,1024] add_737
      float[batch,256,1024] add_78
      float[batch,256,1024] add_798
      float[batch,256,1024] add_827
      float[batch,256,1024] add_888
      float[batch,256,1024] add_917
      float[batch,256,1024] add_978
      float[batch,1] clamp_min
      float[batch,1024,16,16] conv2d
      float[batch,256,4096] gelu
      float[batch,256,4096] gelu_1
      float[batch,256,4096] gelu_10
      float[batch,256,4096] gelu_11
      float[batch,256,4096] gelu_12
      float[batch,256,4096] gelu_13
      float[batch,256,4096] gelu_14
      float[batch,256,4096] gelu_15
      float[batch,256,4096] gelu_16
      float[batch,256,4096] gelu_17
      float[batch,256,4096] gelu_18
      float[batch,256,4096] gelu_19
      float[batch,256,4096] gelu_2
      float[batch,256,4096] gelu_20
      float[batch,256,4096] gelu_21
      float[batch,256,4096] gelu_22
      float[batch,256,4096] gelu_23
      float[batch,1,4096] gelu_24
      float[batch,256,4096] gelu_3
      float[batch,256,4096] gelu_4
      float[batch,256,4096] gelu_5
      float[batch,256,4096] gelu_6
      float[batch,256,4096] gelu_7
      float[batch,256,4096] gelu_8
      float[batch,256,4096] 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,256,1024] layer_norm
      float[batch,256,1024] layer_norm_1
      float[batch,256,1024] layer_norm_10
      float[batch,256,1024] layer_norm_11
      float[batch,256,1024] layer_norm_12
      float[batch,256,1024] layer_norm_13
      float[batch,256,1024] layer_norm_14
      float[batch,256,1024] layer_norm_15
      float[batch,256,1024] layer_norm_16
      float[batch,256,1024] layer_norm_17
      float[batch,256,1024] layer_norm_18
      float[batch,256,1024] layer_norm_19
      float[batch,256,1024] layer_norm_2
      float[batch,256,1024] layer_norm_20
      float[batch,256,1024] layer_norm_21
      float[batch,256,1024] layer_norm_22
      float[batch,256,1024] layer_norm_23
      float[batch,256,1024] layer_norm_24
      float[batch,256,1024] layer_norm_25
      float[batch,256,1024] layer_norm_26
      float[batch,256,1024] layer_norm_27
      float[batch,256,1024] layer_norm_28
      float[batch,256,1024] layer_norm_29
      float[batch,256,1024] layer_norm_3
      float[batch,256,1024] layer_norm_30
      float[batch,256,1024] layer_norm_31
      float[batch,256,1024] layer_norm_32
      float[batch,256,1024] layer_norm_33
      float[batch,256,1024] layer_norm_34
      float[batch,256,1024] layer_norm_35
      float[batch,256,1024] layer_norm_36
      float[batch,256,1024] layer_norm_37
      float[batch,256,1024] layer_norm_38
      float[batch,256,1024] layer_norm_39
      float[batch,256,1024] layer_norm_4
      float[batch,256,1024] layer_norm_40
      float[batch,256,1024] layer_norm_41
      float[batch,256,1024] layer_norm_42
      float[batch,256,1024] layer_norm_43
      float[batch,256,1024] layer_norm_44
      float[batch,256,1024] layer_norm_45
      float[batch,256,1024] layer_norm_46
      float[batch,256,1024] layer_norm_47
      float[batch,256,1024] layer_norm_48
      float[batch,1,1024] layer_norm_49
      float[batch,256,1024] layer_norm_5
      float[batch,256,1024] layer_norm_6
      float[batch,256,1024] layer_norm_7
      float[batch,256,1024] layer_norm_8
      float[batch,256,1024] layer_norm_9
      float[batch,1] linalg_vector_norm
      float[batch,256,3072] linear
      float[batch,256,1024] linear_1
      float[batch,256,4096] linear_10
      float[batch,1,1024] linear_100
      float[batch,256,1024] linear_11
      float[batch,256,3072] linear_12
      float[batch,256,1024] linear_13
      float[batch,256,4096] linear_14
      float[batch,256,1024] linear_15
      float[batch,256,3072] linear_16
      float[batch,256,1024] linear_17
      float[batch,256,4096] linear_18
      float[batch,256,1024] linear_19
      float[batch,256,4096] linear_2
      float[batch,256,3072] linear_20
      float[batch,256,1024] linear_21
      float[batch,256,4096] linear_22
      float[batch,256,1024] linear_23
      float[batch,256,3072] linear_24
      float[batch,256,1024] linear_25
      float[batch,256,4096] linear_26
      float[batch,256,1024] linear_27
      float[batch,256,3072] linear_28
      float[batch,256,1024] linear_29
      float[batch,256,1024] linear_3
      float[batch,256,4096] linear_30
      float[batch,256,1024] linear_31
      float[batch,256,3072] linear_32
      float[batch,256,1024] linear_33
      float[batch,256,4096] linear_34
      float[batch,256,1024] linear_35
      float[batch,256,3072] linear_36
      float[batch,256,1024] linear_37
      float[batch,256,4096] linear_38
      float[batch,256,1024] linear_39
      float[batch,256,3072] linear_4
      float[batch,256,3072] linear_40
      float[batch,256,1024] linear_41
      float[batch,256,4096] linear_42
      float[batch,256,1024] linear_43
      float[batch,256,3072] linear_44
      float[batch,256,1024] linear_45
      float[batch,256,4096] linear_46
      float[batch,256,1024] linear_47
      float[batch,256,3072] linear_48
      float[batch,256,1024] linear_49
      float[batch,256,1024] linear_5
      float[batch,256,4096] linear_50
      float[batch,256,1024] linear_51
      float[batch,256,3072] linear_52
      float[batch,256,1024] linear_53
      float[batch,256,4096] linear_54
      float[batch,256,1024] linear_55
      float[batch,256,3072] linear_56
      float[batch,256,1024] linear_57
      float[batch,256,4096] linear_58
      float[batch,256,1024] linear_59
      float[batch,256,4096] linear_6
      float[batch,256,3072] linear_60
      float[batch,256,1024] linear_61
      float[batch,256,4096] linear_62
      float[batch,256,1024] linear_63
      float[batch,256,3072] linear_64
      float[batch,256,1024] linear_65
      float[batch,256,4096] linear_66
      float[batch,256,1024] linear_67
      float[batch,256,3072] linear_68
      float[batch,256,1024] linear_69
      float[batch,256,1024] linear_7
      float[batch,256,4096] linear_70
      float[batch,256,1024] linear_71
      float[batch,256,3072] linear_72
      float[batch,256,1024] linear_73
      float[batch,256,4096] linear_74
      float[batch,256,1024] linear_75
      float[batch,256,3072] linear_76
      float[batch,256,1024] linear_77
      float[batch,256,4096] linear_78
      float[batch,256,1024] linear_79
      float[batch,256,3072] linear_8
      float[batch,256,3072] linear_80
      float[batch,256,1024] linear_81
      float[batch,256,4096] linear_82
      float[batch,256,1024] linear_83
      float[batch,256,3072] linear_84
      float[batch,256,1024] linear_85
      float[batch,256,4096] linear_86
      float[batch,256,1024] linear_87
      float[batch,256,3072] linear_88
      float[batch,256,1024] linear_89
      float[batch,256,1024] linear_9
      float[batch,256,4096] linear_90
      float[batch,256,1024] linear_91
      float[batch,256,3072] linear_92
      float[batch,256,1024] linear_93
      float[batch,256,4096] linear_94
      float[batch,256,1024] linear_95
      float[batch,256,2048] linear_97
      float[batch,1,1024] linear_98
      float[batch,1,4096] linear_99
      float[batch,256,1024] node_scaled_dot_product_attention_10_k
      float[batch,256,1024] node_scaled_dot_product_attention_10_q
      float[batch,256,1024] node_scaled_dot_product_attention_10_v
      float[batch,256,1024] node_scaled_dot_product_attention_11_k
      float[batch,256,1024] node_scaled_dot_product_attention_11_q
      float[batch,256,1024] node_scaled_dot_product_attention_11_v
      float[batch,256,1024] node_scaled_dot_product_attention_12_k
      float[batch,256,1024] node_scaled_dot_product_attention_12_q
      float[batch,256,1024] node_scaled_dot_product_attention_12_v
      float[batch,256,1024] node_scaled_dot_product_attention_13_k
      float[batch,256,1024] node_scaled_dot_product_attention_13_q
      float[batch,256,1024] node_scaled_dot_product_attention_13_v
      float[batch,256,1024] node_scaled_dot_product_attention_14_k
      float[batch,256,1024] node_scaled_dot_product_attention_14_q
      float[batch,256,1024] node_scaled_dot_product_attention_14_v
      float[batch,256,1024] node_scaled_dot_product_attention_15_k
      float[batch,256,1024] node_scaled_dot_product_attention_15_q
      float[batch,256,1024] node_scaled_dot_product_attention_15_v
      float[batch,256,1024] node_scaled_dot_product_attention_16_k
      float[batch,256,1024] node_scaled_dot_product_attention_16_q
      float[batch,256,1024] node_scaled_dot_product_attention_16_v
      float[batch,256,1024] node_scaled_dot_product_attention_17_k
      float[batch,256,1024] node_scaled_dot_product_attention_17_q
      float[batch,256,1024] node_scaled_dot_product_attention_17_v
      float[batch,256,1024] node_scaled_dot_product_attention_18_k
      float[batch,256,1024] node_scaled_dot_product_attention_18_q
      float[batch,256,1024] node_scaled_dot_product_attention_18_v
      float[batch,256,1024] node_scaled_dot_product_attention_19_k
      float[batch,256,1024] node_scaled_dot_product_attention_19_q
      float[batch,256,1024] node_scaled_dot_product_attention_19_v
      float[batch,256,1024] node_scaled_dot_product_attention_1_k
      float[batch,256,1024] node_scaled_dot_product_attention_1_q
      float[batch,256,1024] node_scaled_dot_product_attention_1_v
      float[batch,256,1024] node_scaled_dot_product_attention_20_k
      float[batch,256,1024] node_scaled_dot_product_attention_20_q
      float[batch,256,1024] node_scaled_dot_product_attention_20_v
      float[batch,256,1024] node_scaled_dot_product_attention_21_k
      float[batch,256,1024] node_scaled_dot_product_attention_21_q
      float[batch,256,1024] node_scaled_dot_product_attention_21_v
      float[batch,256,1024] node_scaled_dot_product_attention_22_k
      float[batch,256,1024] node_scaled_dot_product_attention_22_q
      float[batch,256,1024] node_scaled_dot_product_attention_22_v
      float[batch,256,1024] node_scaled_dot_product_attention_23_k
      float[batch,256,1024] node_scaled_dot_product_attention_23_q
      float[batch,256,1024] node_scaled_dot_product_attention_23_v
      float[batch,256,1024] node_scaled_dot_product_attention_24_k
      float[batch,1,1024] node_scaled_dot_product_attention_24_q
      float[batch,1,1] node_scaled_dot_product_attention_24_q_col_out
      float[batch,256,1024] node_scaled_dot_product_attention_24_v
      float[batch,256,1024] node_scaled_dot_product_attention_2_k
      float[batch,256,1024] node_scaled_dot_product_attention_2_q
      float[batch,256,1024] node_scaled_dot_product_attention_2_v
      float[batch,256,1024] node_scaled_dot_product_attention_3_k
      float[batch,256,1024] node_scaled_dot_product_attention_3_q
      float[batch,256,1024] node_scaled_dot_product_attention_3_v
      float[batch,256,1024] node_scaled_dot_product_attention_4_k
      float[batch,256,1024] node_scaled_dot_product_attention_4_q
      float[batch,256,1024] node_scaled_dot_product_attention_4_v
      float[batch,256,1024] node_scaled_dot_product_attention_5_k
      float[batch,256,1024] node_scaled_dot_product_attention_5_q
      float[batch,256,1024] node_scaled_dot_product_attention_5_v
      float[batch,256,1024] node_scaled_dot_product_attention_6_k
      float[batch,256,1024] node_scaled_dot_product_attention_6_q
      float[batch,256,1024] node_scaled_dot_product_attention_6_v
      float[batch,256,1024] node_scaled_dot_product_attention_7_k
      float[batch,256,1024] node_scaled_dot_product_attention_7_q
      float[batch,256,1024] node_scaled_dot_product_attention_7_v
      float[batch,256,1024] node_scaled_dot_product_attention_8_k
      float[batch,256,1024] node_scaled_dot_product_attention_8_q
      float[batch,256,1024] node_scaled_dot_product_attention_8_v
      float[batch,256,1024] node_scaled_dot_product_attention_9_k
      float[batch,256,1024] node_scaled_dot_product_attention_9_q
      float[batch,256,1024] node_scaled_dot_product_attention_9_v
      float[batch,256,1024] node_scaled_dot_product_attention_k
      float[batch,256,1024] node_scaled_dot_product_attention_q
      float[batch,256,1024] node_scaled_dot_product_attention_v
      float[batch,256,1024] scaled_dot_product_attention
      float[batch,256,1024] scaled_dot_product_attention_1
      float[batch,256,1024] scaled_dot_product_attention_10
      float[batch,256,1024] scaled_dot_product_attention_11
      float[batch,256,1024] scaled_dot_product_attention_12
      float[batch,256,1024] scaled_dot_product_attention_13
      float[batch,256,1024] scaled_dot_product_attention_14
      float[batch,256,1024] scaled_dot_product_attention_15
      float[batch,256,1024] scaled_dot_product_attention_16
      float[batch,256,1024] scaled_dot_product_attention_17
      float[batch,256,1024] scaled_dot_product_attention_18
      float[batch,256,1024] scaled_dot_product_attention_19
      float[batch,256,1024] scaled_dot_product_attention_2
      float[batch,256,1024] scaled_dot_product_attention_20
      float[batch,256,1024] scaled_dot_product_attention_21
      float[batch,256,1024] scaled_dot_product_attention_22
      float[batch,256,1024] scaled_dot_product_attention_23
      float[batch,1,1024] scaled_dot_product_attention_24
      float[batch,256,1024] scaled_dot_product_attention_3
      float[batch,256,1024] scaled_dot_product_attention_4
      float[batch,256,1024] scaled_dot_product_attention_5
      float[batch,256,1024] scaled_dot_product_attention_6
      float[batch,256,1024] scaled_dot_product_attention_7
      float[batch,256,1024] scaled_dot_product_attention_8
      float[batch,256,1024] scaled_dot_product_attention_9
      float[batch,1024] select
      float[batch,256,1024] transpose
      float[batch,256,3072] val_103
      float[batch,256,1024] val_104
      float[batch,256,4096] val_105
      float[batch,256,1024] val_106
      float[batch,256,3072] val_107
      float[batch,256,1024] val_108
      float[batch,256,4096] val_109
      float[batch,256,1024] val_110
      float[batch,256,3072] val_111
      float[batch,256,1024] val_112
      float[batch,256,4096] val_113
      float[batch,256,1024] val_114
      float[batch,256,3072] val_115
      float[batch,256,1024] val_116
      float[batch,256,4096] val_117
      float[batch,256,1024] val_118
      float[batch,256,3072] val_119
      float[batch,256,1024] val_120
      float[batch,256,4096] val_121
      float[batch,256,1024] val_122
      float[batch,256,3072] val_123
      float[batch,256,1024] val_124
      float[batch,256,4096] val_125
      float[batch,256,1024] val_126
      float[batch,256,3072] val_127
      float[batch,256,1024] val_128
      float[batch,256,4096] val_129
      float[batch,256,1024] val_130
      float[batch,256,3072] val_131
      float[batch,256,1024] val_132
      float[batch,256,4096] val_133
      float[batch,256,1024] val_134
      float[batch,256,3072] val_135
      float[batch,256,1024] val_136
      float[batch,256,4096] val_137
      float[batch,256,1024] val_138
      float[batch,256,3072] val_139
      float[batch,256,1024] val_140
      float[batch,256,4096] val_141
      float[batch,256,1024] val_142
      float[batch,256,3072] val_143
      float[batch,256,1024] val_144
      float[batch,256,4096] val_145
      float[batch,256,1024] val_146
      float[batch,256,3072] val_147
      float[batch,256,1024] val_148
      float[batch,256,4096] val_149
      float[batch,256,1024] val_150
      float[batch,256,3072] val_151
      float[batch,256,1024] val_152
      float[batch,256,4096] val_153
      float[batch,256,1024] val_154
      float[batch,256,3072] val_155
      float[batch,256,1024] val_156
      float[batch,256,4096] val_157
      float[batch,256,1024] val_158
      float[batch,256,3072] val_159
      float[batch,256,1024] val_160
      float[batch,256,4096] val_161
      float[batch,256,1024] val_162
      float[batch,256,3072] val_163
      float[batch,256,1024] val_164
      float[batch,256,4096] val_165
      float[batch,256,1024] val_166
      float[batch,256,3072] val_167
      float[batch,256,1024] val_168
      float[batch,256,4096] val_169
      float[batch,256,1024] val_170
      float[batch,256,3072] val_171
      float[batch,256,1024] val_172
      float[batch,256,4096] val_173
      float[batch,256,1024] val_174
      float[batch,256,3072] val_175
      float[batch,256,1024] val_176
      float[batch,256,4096] val_177
      float[batch,256,1024] val_178
      float[batch,256,3072] val_179
      float[batch,256,1024] val_180
      float[batch,256,4096] val_181
      float[batch,256,1024] val_182
      float[batch,256,3072] val_183
      float[batch,256,1024] val_184
      float[batch,256,4096] val_185
      float[batch,256,1024] val_186
      float[batch,256,3072] val_187
      float[batch,256,1024] val_188
      float[batch,256,4096] val_189
      float[batch,256,1024] val_190
      float[batch,256,3072] val_191
      float[batch,256,1024] val_192
      float[batch,256,4096] val_193
      float[batch,256,1024] val_194
      float[batch,256,3072] val_195
      float[batch,256,1024] val_196
      float[batch,256,4096] val_197
      float[batch,256,1024] val_198
      float[batch,256,2048] val_199
      float[batch,1,1024] val_200
      float[batch,1,4096] val_201
      float[batch,1,1024] val_202
      float[batch,1024,256] 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_103 = MatMul (layer_norm, val_3)
   [node_linear] linear = Add (val_103, "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_3x1024)
   [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_104 = MatMul (scaled_dot_product_attention, val_4)
   linear_1 = Add (val_104, "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_105 = MatMul (layer_norm_1, val_5)
   linear_2 = Add (val_105, "visual.trunk.blocks.0.mlp.fc1.bias")
   [node_gelu] gelu = Gelu <approximate: string = "none"> (linear_2)
   val_106 = MatMul (gelu, val_6)
   linear_3 = Add (val_106, "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_107 = MatMul (layer_norm_2, val_7)
   linear_4 = Add (val_107, "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_3x1024)
   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_108 = MatMul (scaled_dot_product_attention_1, val_8)
   linear_5 = Add (val_108, "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_109 = MatMul (layer_norm_3, val_9)
   linear_6 = Add (val_109, "visual.trunk.blocks.1.mlp.fc1.bias")
   gelu_1 = Gelu <approximate: string = "none"> (linear_6)
   val_110 = MatMul (gelu_1, val_10)
   linear_7 = Add (val_110, "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_111 = MatMul (layer_norm_4, val_11)
   linear_8 = Add (val_111, "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_3x1024)
   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_112 = MatMul (scaled_dot_product_attention_2, val_12)
   linear_9 = Add (val_112, "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_113 = MatMul (layer_norm_5, val_13)
   linear_10 = Add (val_113, "visual.trunk.blocks.2.mlp.fc1.bias")
   gelu_2 = Gelu <approximate: string = "none"> (linear_10)
   val_114 = MatMul (gelu_2, val_14)
   linear_11 = Add (val_114, "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_115 = MatMul (layer_norm_6, val_15)
   linear_12 = Add (val_115, "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_3x1024)
   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_116 = MatMul (scaled_dot_product_attention_3, val_16)
   linear_13 = Add (val_116, "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_117 = MatMul (layer_norm_7, val_17)
   linear_14 = Add (val_117, "visual.trunk.blocks.3.mlp.fc1.bias")
   gelu_3 = Gelu <approximate: string = "none"> (linear_14)
   val_118 = MatMul (gelu_3, val_18)
   linear_15 = Add (val_118, "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_119 = MatMul (layer_norm_8, val_19)
   linear_16 = Add (val_119, "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_3x1024)
   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_120 = MatMul (scaled_dot_product_attention_4, val_20)
   linear_17 = Add (val_120, "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_121 = MatMul (layer_norm_9, val_21)
   linear_18 = Add (val_121, "visual.trunk.blocks.4.mlp.fc1.bias")
   gelu_4 = Gelu <approximate: string = "none"> (linear_18)
   val_122 = MatMul (gelu_4, val_22)
   linear_19 = Add (val_122, "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_123 = MatMul (layer_norm_10, val_23)
   linear_20 = Add (val_123, "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_3x1024)
   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_124 = MatMul (scaled_dot_product_attention_5, val_24)
   linear_21 = Add (val_124, "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_125 = MatMul (layer_norm_11, val_25)
   linear_22 = Add (val_125, "visual.trunk.blocks.5.mlp.fc1.bias")
   gelu_5 = Gelu <approximate: string = "none"> (linear_22)
   val_126 = MatMul (gelu_5, val_26)
   linear_23 = Add (val_126, "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_127 = MatMul (layer_norm_12, val_27)
   linear_24 = Add (val_127, "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_3x1024)
   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_128 = MatMul (scaled_dot_product_attention_6, val_28)
   linear_25 = Add (val_128, "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_129 = MatMul (layer_norm_13, val_29)
   linear_26 = Add (val_129, "visual.trunk.blocks.6.mlp.fc1.bias")
   gelu_6 = Gelu <approximate: string = "none"> (linear_26)
   val_130 = MatMul (gelu_6, val_30)
   linear_27 = Add (val_130, "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_131 = MatMul (layer_norm_14, val_31)
   linear_28 = Add (val_131, "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_3x1024)
   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_132 = MatMul (scaled_dot_product_attention_7, val_32)
   linear_29 = Add (val_132, "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_133 = MatMul (layer_norm_15, val_33)
   linear_30 = Add (val_133, "visual.trunk.blocks.7.mlp.fc1.bias")
   gelu_7 = Gelu <approximate: string = "none"> (linear_30)
   val_134 = MatMul (gelu_7, val_34)
   linear_31 = Add (val_134, "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_135 = MatMul (layer_norm_16, val_35)
   linear_32 = Add (val_135, "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_3x1024)
   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_136 = MatMul (scaled_dot_product_attention_8, val_36)
   linear_33 = Add (val_136, "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_137 = MatMul (layer_norm_17, val_37)
   linear_34 = Add (val_137, "visual.trunk.blocks.8.mlp.fc1.bias")
   gelu_8 = Gelu <approximate: string = "none"> (linear_34)
   val_138 = MatMul (gelu_8, val_38)
   linear_35 = Add (val_138, "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_139 = MatMul (layer_norm_18, val_39)
   linear_36 = Add (val_139, "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_3x1024)
   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_140 = MatMul (scaled_dot_product_attention_9, val_40)
   linear_37 = Add (val_140, "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_141 = MatMul (layer_norm_19, val_41)
   linear_38 = Add (val_141, "visual.trunk.blocks.9.mlp.fc1.bias")
   gelu_9 = Gelu <approximate: string = "none"> (linear_38)
   val_142 = MatMul (gelu_9, val_42)
   linear_39 = Add (val_142, "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_143 = MatMul (layer_norm_20, val_43)
   linear_40 = Add (val_143, "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_3x1024)
   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_144 = MatMul (scaled_dot_product_attention_10, val_44)
   linear_41 = Add (val_144, "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_145 = MatMul (layer_norm_21, val_45)
   linear_42 = Add (val_145, "visual.trunk.blocks.10.mlp.fc1.bias")
   gelu_10 = Gelu <approximate: string = "none"> (linear_42)
   val_146 = MatMul (gelu_10, val_46)
   linear_43 = Add (val_146, "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_147 = MatMul (layer_norm_22, val_47)
   linear_44 = Add (val_147, "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_3x1024)
   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_148 = MatMul (scaled_dot_product_attention_11, val_48)
   linear_45 = Add (val_148, "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_149 = MatMul (layer_norm_23, val_49)
   linear_46 = Add (val_149, "visual.trunk.blocks.11.mlp.fc1.bias")
   gelu_11 = Gelu <approximate: string = "none"> (linear_46)
   val_150 = MatMul (gelu_11, val_50)
   linear_47 = Add (val_150, "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_151 = MatMul (layer_norm_24, val_51)
   linear_48 = Add (val_151, "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_3x1024)
   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_152 = MatMul (scaled_dot_product_attention_12, val_52)
   linear_49 = Add (val_152, "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_153 = MatMul (layer_norm_25, val_53)
   linear_50 = Add (val_153, "visual.trunk.blocks.12.mlp.fc1.bias")
   gelu_12 = Gelu <approximate: string = "none"> (linear_50)
   val_154 = MatMul (gelu_12, val_54)
   linear_51 = Add (val_154, "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_155 = MatMul (layer_norm_26, val_55)
   linear_52 = Add (val_155, "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_3x1024)
   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_156 = MatMul (scaled_dot_product_attention_13, val_56)
   linear_53 = Add (val_156, "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_157 = MatMul (layer_norm_27, val_57)
   linear_54 = Add (val_157, "visual.trunk.blocks.13.mlp.fc1.bias")
   gelu_13 = Gelu <approximate: string = "none"> (linear_54)
   val_158 = MatMul (gelu_13, val_58)
   linear_55 = Add (val_158, "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_159 = MatMul (layer_norm_28, val_59)
   linear_56 = Add (val_159, "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_3x1024)
   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_160 = MatMul (scaled_dot_product_attention_14, val_60)
   linear_57 = Add (val_160, "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_161 = MatMul (layer_norm_29, val_61)
   linear_58 = Add (val_161, "visual.trunk.blocks.14.mlp.fc1.bias")
   gelu_14 = Gelu <approximate: string = "none"> (linear_58)
   val_162 = MatMul (gelu_14, val_62)
   linear_59 = Add (val_162, "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_163 = MatMul (layer_norm_30, val_63)
   linear_60 = Add (val_163, "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_3x1024)
   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_164 = MatMul (scaled_dot_product_attention_15, val_64)
   linear_61 = Add (val_164, "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_165 = MatMul (layer_norm_31, val_65)
   linear_62 = Add (val_165, "visual.trunk.blocks.15.mlp.fc1.bias")
   gelu_15 = Gelu <approximate: string = "none"> (linear_62)
   val_166 = MatMul (gelu_15, val_66)
   linear_63 = Add (val_166, "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_167 = MatMul (layer_norm_32, val_67)
   linear_64 = Add (val_167, "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_3x1024)
   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_168 = MatMul (scaled_dot_product_attention_16, val_68)
   linear_65 = Add (val_168, "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_169 = MatMul (layer_norm_33, val_69)
   linear_66 = Add (val_169, "visual.trunk.blocks.16.mlp.fc1.bias")
   gelu_16 = Gelu <approximate: string = "none"> (linear_66)
   val_170 = MatMul (gelu_16, val_70)
   linear_67 = Add (val_170, "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_171 = MatMul (layer_norm_34, val_71)
   linear_68 = Add (val_171, "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_3x1024)
   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_172 = MatMul (scaled_dot_product_attention_17, val_72)
   linear_69 = Add (val_172, "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_173 = MatMul (layer_norm_35, val_73)
   linear_70 = Add (val_173, "visual.trunk.blocks.17.mlp.fc1.bias")
   gelu_17 = Gelu <approximate: string = "none"> (linear_70)
   val_174 = MatMul (gelu_17, val_74)
   linear_71 = Add (val_174, "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_175 = MatMul (layer_norm_36, val_75)
   linear_72 = Add (val_175, "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_3x1024)
   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_176 = MatMul (scaled_dot_product_attention_18, val_76)
   linear_73 = Add (val_176, "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_177 = MatMul (layer_norm_37, val_77)
   linear_74 = Add (val_177, "visual.trunk.blocks.18.mlp.fc1.bias")
   gelu_18 = Gelu <approximate: string = "none"> (linear_74)
   val_178 = MatMul (gelu_18, val_78)
   linear_75 = Add (val_178, "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_179 = MatMul (layer_norm_38, val_79)
   linear_76 = Add (val_179, "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_3x1024)
   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_180 = MatMul (scaled_dot_product_attention_19, val_80)
   linear_77 = Add (val_180, "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_181 = MatMul (layer_norm_39, val_81)
   linear_78 = Add (val_181, "visual.trunk.blocks.19.mlp.fc1.bias")
   gelu_19 = Gelu <approximate: string = "none"> (linear_78)
   val_182 = MatMul (gelu_19, val_82)
   linear_79 = Add (val_182, "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_183 = MatMul (layer_norm_40, val_83)
   linear_80 = Add (val_183, "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_3x1024)
   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_184 = MatMul (scaled_dot_product_attention_20, val_84)
   linear_81 = Add (val_184, "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_185 = MatMul (layer_norm_41, val_85)
   linear_82 = Add (val_185, "visual.trunk.blocks.20.mlp.fc1.bias")
   gelu_20 = Gelu <approximate: string = "none"> (linear_82)
   val_186 = MatMul (gelu_20, val_86)
   linear_83 = Add (val_186, "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_187 = MatMul (layer_norm_42, val_87)
   linear_84 = Add (val_187, "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_3x1024)
   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_188 = MatMul (scaled_dot_product_attention_21, val_88)
   linear_85 = Add (val_188, "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_189 = MatMul (layer_norm_43, val_89)
   linear_86 = Add (val_189, "visual.trunk.blocks.21.mlp.fc1.bias")
   gelu_21 = Gelu <approximate: string = "none"> (linear_86)
   val_190 = MatMul (gelu_21, val_90)
   linear_87 = Add (val_190, "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_191 = MatMul (layer_norm_44, val_91)
   linear_88 = Add (val_191, "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_3x1024)
   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_192 = MatMul (scaled_dot_product_attention_22, val_92)
   linear_89 = Add (val_192, "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_193 = MatMul (layer_norm_45, val_93)
   linear_90 = Add (val_193, "visual.trunk.blocks.22.mlp.fc1.bias")
   gelu_22 = Gelu <approximate: string = "none"> (linear_90)
   val_194 = MatMul (gelu_22, val_94)
   linear_91 = Add (val_194, "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_195 = MatMul (layer_norm_46, val_95)
   linear_92 = Add (val_195, "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_3x1024)
   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_196 = MatMul (scaled_dot_product_attention_23, val_96)
   linear_93 = Add (val_196, "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_197 = MatMul (layer_norm_47, val_97)
   linear_94 = Add (val_197, "visual.trunk.blocks.23.mlp.fc1.bias")
   gelu_23 = Gelu <approximate: string = "none"> (linear_94)
   val_198 = MatMul (gelu_23, val_98)
   linear_95 = Add (val_198, "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.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_199 = MatMul (layer_norm_48, val_99)
   linear_97 = Add (val_199, "visual.trunk.attn_pool.kv.bias")
   [node_scaled_dot_product_attention_24_qkv_split] node_scaled_dot_product_attention_24_k, node_scaled_dot_product_attention_24_v = Split <axis: int = -1> (linear_97, attn3d_split_2x1024)
   [node_scaled_dot_product_attention_24_q_col] node_scaled_dot_product_attention_24_q_col_out = Unsqueeze (image_ez, node_scaled_dot_product_attention_24_q_col_axes)
   [node_scaled_dot_product_attention_24_q_bcast] node_scaled_dot_product_attention_24_q = Add (node_scaled_dot_product_attention_24_q3, node_scaled_dot_product_attention_24_q_col_out)
   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_200 = MatMul (scaled_dot_product_attention_24, val_100)
   linear_98 = Add (val_200, "visual.trunk.attn_pool.proj.bias")
   layer_norm_49 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (linear_98, "visual.trunk.attn_pool.norm.weight", "visual.trunk.attn_pool.norm.bias")
   val_201 = MatMul (layer_norm_49, val_101)
   linear_99 = Add (val_201, "visual.trunk.attn_pool.mlp.fc1.bias")
   gelu_24 = Gelu <approximate: string = "none"> (linear_99)
   val_202 = MatMul (gelu_24, val_102)
   linear_100 = Add (val_202, "visual.trunk.attn_pool.mlp.fc2.bias")
   add_2275 = Add (linear_98, linear_100)
   select = Squeeze (add_2275, 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_2x1024 INT64[2] d190f758d9d9
attn3d_split_3x1024 INT64[3] 0ec6f5651fd5
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_24_q3 FLOAT[1,1,1024] 6362ed21e9d0
node_scaled_dot_product_attention_24_q_col_axes INT64[2] 0c730b69905c
val_0 INT64[1] 12a3ae445661
val_1 FLOAT[] 6708d9be4956
val_10 FLOAT[4096,1024] 7b7582101b34
val_100 FLOAT[1024,1024] 5b7fcd2859fa
val_101 FLOAT[1024,4096] a77b7144f295
val_102 FLOAT[4096,1024] d5f650a37d46
val_11 FLOAT[1024,3072] 17d27fc3873b
val_12 FLOAT[1024,1024] 1a6e0d597fb7
val_13 FLOAT[1024,4096] f615bc53a2ba
val_14 FLOAT[4096,1024] d3f1c99be163
val_15 FLOAT[1024,3072] 38a6c3cacfb2
val_16 FLOAT[1024,1024] 48c786b9967e
val_17 FLOAT[1024,4096] 537849a44bb6
val_18 FLOAT[4096,1024] 5bc6efdb8029
val_19 FLOAT[1024,3072] c35caefe8fa1
val_2 INT64[1] 7c9fa136d441
val_20 FLOAT[1024,1024] 4f6ea4adab98
val_21 FLOAT[1024,4096] f413dd46a877
val_22 FLOAT[4096,1024] b35c22de4598
val_23 FLOAT[1024,3072] 50a0284f75b0
val_24 FLOAT[1024,1024] 84419d1f7975
val_25 FLOAT[1024,4096] 6be96858f459
val_26 FLOAT[4096,1024] 24ecc54a3b61
val_27 FLOAT[1024,3072] 257f6e94c1a3
val_28 FLOAT[1024,1024] c73d16071b50
val_29 FLOAT[1024,4096] 67d8c9639be0
val_3 FLOAT[1024,3072] c5086a1b65b0
val_30 FLOAT[4096,1024] a8dae8ae073c
val_31 FLOAT[1024,3072] 5a85e808e287
val_32 FLOAT[1024,1024] 420cc5053631
val_33 FLOAT[1024,4096] 8cc999b67523
val_34 FLOAT[4096,1024] 17a4b5771779
val_35 FLOAT[1024,3072] 6c625f3d3e90
val_36 FLOAT[1024,1024] eb197fbc697d
val_37 FLOAT[1024,4096] e6e39f784535
val_38 FLOAT[4096,1024] b9e4371f7efa
val_39 FLOAT[1024,3072] 098238a339a7
val_4 FLOAT[1024,1024] 9456070d60aa
val_40 FLOAT[1024,1024] 691f2371b3e8
val_41 FLOAT[1024,4096] 9111dad56698
val_42 FLOAT[4096,1024] c36d08a9697a
val_43 FLOAT[1024,3072] 7d5c1c62ce07
val_44 FLOAT[1024,1024] 4f4d4e676a93
val_45 FLOAT[1024,4096] ffc9b7df8360
val_46 FLOAT[4096,1024] 1a4d09f9010a
val_47 FLOAT[1024,3072] 4c1e607855a3
val_48 FLOAT[1024,1024] 5ba2b7af6b64
val_49 FLOAT[1024,4096] f10e38e6bd9d
val_5 FLOAT[1024,4096] f334ce978207
val_50 FLOAT[4096,1024] fafe8c859738
val_51 FLOAT[1024,3072] c8f50bf4cb8f
val_52 FLOAT[1024,1024] 26f529e77501
val_53 FLOAT[1024,4096] f61f14965cef
val_54 FLOAT[4096,1024] fa37b899dcf4
val_55 FLOAT[1024,3072] f4786acb5b77
val_56 FLOAT[1024,1024] d6476497c2e0
val_57 FLOAT[1024,4096] b5ccaff4d4bd
val_58 FLOAT[4096,1024] 5367af3f4e58
val_59 FLOAT[1024,3072] 28edf115ca83
val_6 FLOAT[4096,1024] efb067b185c3
val_60 FLOAT[1024,1024] 698422ca4081
val_61 FLOAT[1024,4096] 2bf9dfdba9b5
val_62 FLOAT[4096,1024] 91485f3835c7
val_63 FLOAT[1024,3072] a40ce6e9aeea
val_64 FLOAT[1024,1024] 0dfbe7433892
val_65 FLOAT[1024,4096] 50a08de94510
val_66 FLOAT[4096,1024] b1c95b679046
val_67 FLOAT[1024,3072] 2772bec27348
val_68 FLOAT[1024,1024] f7ded3da99b7
val_69 FLOAT[1024,4096] a8430998a2a0
val_7 FLOAT[1024,3072] c3aa3056add8
val_70 FLOAT[4096,1024] 09ef2ab1b820
val_71 FLOAT[1024,3072] c8fae2b1e728
val_72 FLOAT[1024,1024] ec72aa95436b
val_73 FLOAT[1024,4096] 84a3951fed63
val_74 FLOAT[4096,1024] 3cbbd039a549
val_75 FLOAT[1024,3072] 705caa5d7a3b
val_76 FLOAT[1024,1024] 77f55cdf4eff
val_77 FLOAT[1024,4096] b00ed91446ea
val_78 FLOAT[4096,1024] 34d91bb20693
val_79 FLOAT[1024,3072] fe56d3e0ee2f
val_8 FLOAT[1024,1024] cc3bbd907369
val_80 FLOAT[1024,1024] 8bc314683dcb
val_81 FLOAT[1024,4096] b69ec5e40a09
val_82 FLOAT[4096,1024] 4eaa407a3557
val_83 FLOAT[1024,3072] 6f838a034b6d
val_84 FLOAT[1024,1024] 2a2e9aa498a1
val_85 FLOAT[1024,4096] c1ea0f54051d
val_86 FLOAT[4096,1024] 1915cf96e4ac
val_87 FLOAT[1024,3072] c08dd4c44881
val_88 FLOAT[1024,1024] b16b6870e2f6
val_89 FLOAT[1024,4096] 711a3663a800
val_9 FLOAT[1024,4096] b5f2bd58cd8c
val_90 FLOAT[4096,1024] a1a57c044ba9
val_91 FLOAT[1024,3072] ac76dfe49ed5
val_92 FLOAT[1024,1024] 506dca87ad45
val_93 FLOAT[1024,4096] d3c7d23f068a
val_94 FLOAT[4096,1024] 886bd31fcd10
val_95 FLOAT[1024,3072] 4988f298a880
val_96 FLOAT[1024,1024] b74a8565e52e
val_97 FLOAT[1024,4096] 799d07c2bb7a
val_98 FLOAT[4096,1024] 12992c948187
val_99 FLOAT[1024,2048] 2d5149d42856
view_target INT64[3] 3f85cfe8397f
visual.trunk.attn_pool.kv.bias FLOAT[2048] 97da5b9821c1
visual.trunk.attn_pool.mlp.fc1.bias FLOAT[4096] 225567589307
visual.trunk.attn_pool.mlp.fc2.bias FLOAT[1024] 782ff93aec2b
visual.trunk.attn_pool.norm.bias FLOAT[1024] 133f1ea18571
visual.trunk.attn_pool.norm.weight FLOAT[1024] 5c5d64246f30
visual.trunk.attn_pool.proj.bias FLOAT[1024] 597ffe0ef997
visual.trunk.blocks.0.attn.proj.bias FLOAT[1024] b4c120179fa5
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visual.trunk.blocks.0.mlp.fc1.bias FLOAT[4096] 1d698cacee5e
visual.trunk.blocks.0.mlp.fc2.bias FLOAT[1024] f30ef33932f1
visual.trunk.blocks.0.norm1.bias FLOAT[1024] 973e6e49bae3
visual.trunk.blocks.0.norm1.weight FLOAT[1024] 5abafe61f905
visual.trunk.blocks.0.norm2.bias FLOAT[1024] 2b8f47100ace
visual.trunk.blocks.0.norm2.weight FLOAT[1024] 05e798d3e0e4
visual.trunk.blocks.1.attn.proj.bias FLOAT[1024] 2a8f4856aa7f
visual.trunk.blocks.1.attn.qkv.bias FLOAT[3072] 364d7fb70f3d
visual.trunk.blocks.1.mlp.fc1.bias FLOAT[4096] d172d6f1f8ad
visual.trunk.blocks.1.mlp.fc2.bias FLOAT[1024] 3c7ddd3dde7b
visual.trunk.blocks.1.norm1.bias FLOAT[1024] d8e7e8853ce1
visual.trunk.blocks.1.norm1.weight FLOAT[1024] 54149cbd5d74
visual.trunk.blocks.1.norm2.bias FLOAT[1024] 0f75a50b4555
visual.trunk.blocks.1.norm2.weight FLOAT[1024] 0e347f884793
visual.trunk.blocks.10.attn.proj.bias FLOAT[1024] 571eac45ff41
visual.trunk.blocks.10.attn.qkv.bias FLOAT[3072] b51292f65bd2
visual.trunk.blocks.10.mlp.fc1.bias FLOAT[4096] 026485b23635
visual.trunk.blocks.10.mlp.fc2.bias FLOAT[1024] 0981d8cbccc0
visual.trunk.blocks.10.norm1.bias FLOAT[1024] 8394f98ff8de
visual.trunk.blocks.10.norm1.weight FLOAT[1024] 5f191292adf3
visual.trunk.blocks.10.norm2.bias FLOAT[1024] 4e040b59efc3
visual.trunk.blocks.10.norm2.weight FLOAT[1024] 2f66f53e6530
visual.trunk.blocks.11.attn.proj.bias FLOAT[1024] a05d45a9be22
visual.trunk.blocks.11.attn.qkv.bias FLOAT[3072] a8d90650d010
visual.trunk.blocks.11.mlp.fc1.bias FLOAT[4096] 2745279528ea
visual.trunk.blocks.11.mlp.fc2.bias FLOAT[1024] 8f6faafdc6d0
visual.trunk.blocks.11.norm1.bias FLOAT[1024] dc04597b0786
visual.trunk.blocks.11.norm1.weight FLOAT[1024] 22b00f8cf7e9
visual.trunk.blocks.11.norm2.bias FLOAT[1024] 8e94f3270e66
visual.trunk.blocks.11.norm2.weight FLOAT[1024] 229ad09f8bce
visual.trunk.blocks.12.attn.proj.bias FLOAT[1024] b185f2608439
visual.trunk.blocks.12.attn.qkv.bias FLOAT[3072] 2eab2be5f0fe
visual.trunk.blocks.12.mlp.fc1.bias FLOAT[4096] 1bd8574c00e4
visual.trunk.blocks.12.mlp.fc2.bias FLOAT[1024] e2e3739280d6
visual.trunk.blocks.12.norm1.bias FLOAT[1024] be30fc06c1a3
visual.trunk.blocks.12.norm1.weight FLOAT[1024] 8efff8311d47
visual.trunk.blocks.12.norm2.bias FLOAT[1024] 3acecd002e9c
visual.trunk.blocks.12.norm2.weight FLOAT[1024] 78d9184c890e
visual.trunk.blocks.13.attn.proj.bias FLOAT[1024] 4b3ebd05991a
visual.trunk.blocks.13.attn.qkv.bias FLOAT[3072] 0831a1cf141e
visual.trunk.blocks.13.mlp.fc1.bias FLOAT[4096] 1277ab872198
visual.trunk.blocks.13.mlp.fc2.bias FLOAT[1024] dbdf783a0376
visual.trunk.blocks.13.norm1.bias FLOAT[1024] 54f3ab17832b
visual.trunk.blocks.13.norm1.weight FLOAT[1024] b6e651385d02
visual.trunk.blocks.13.norm2.bias FLOAT[1024] be526bce3695
visual.trunk.blocks.13.norm2.weight FLOAT[1024] a07f40f00868
visual.trunk.blocks.14.attn.proj.bias FLOAT[1024] 5304eef20595
visual.trunk.blocks.14.attn.qkv.bias FLOAT[3072] 652659081d6d
visual.trunk.blocks.14.mlp.fc1.bias FLOAT[4096] 9140490e42e5
visual.trunk.blocks.14.mlp.fc2.bias FLOAT[1024] d35d83f703db
visual.trunk.blocks.14.norm1.bias FLOAT[1024] 46a3977a0ae7
visual.trunk.blocks.14.norm1.weight FLOAT[1024] 860ef7f4b793
visual.trunk.blocks.14.norm2.bias FLOAT[1024] 8323568e39b1
visual.trunk.blocks.14.norm2.weight FLOAT[1024] 9168ca131ff0
visual.trunk.blocks.15.attn.proj.bias FLOAT[1024] 3bca8c3accb7
visual.trunk.blocks.15.attn.qkv.bias FLOAT[3072] c3f87b170c9a
visual.trunk.blocks.15.mlp.fc1.bias FLOAT[4096] f8c0d3bc31cf
visual.trunk.blocks.15.mlp.fc2.bias FLOAT[1024] 007faf257d7e
visual.trunk.blocks.15.norm1.bias FLOAT[1024] 2523a2582672
visual.trunk.blocks.15.norm1.weight FLOAT[1024] c3f6598e2ced
visual.trunk.blocks.15.norm2.bias FLOAT[1024] 96f6a84aa4c4
visual.trunk.blocks.15.norm2.weight FLOAT[1024] 9b688bba1501
visual.trunk.blocks.16.attn.proj.bias FLOAT[1024] 40f603100cf6
visual.trunk.blocks.16.attn.qkv.bias FLOAT[3072] 4ada175831bd
visual.trunk.blocks.16.mlp.fc1.bias FLOAT[4096] 029451aca7e0
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visual.trunk.blocks.16.norm1.bias FLOAT[1024] dbbe7a6f98d3
visual.trunk.blocks.16.norm1.weight FLOAT[1024] a9044e89945b
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visual.trunk.blocks.16.norm2.weight FLOAT[1024] e81f882fd4c7
visual.trunk.blocks.17.attn.proj.bias FLOAT[1024] 65d9ace81106
visual.trunk.blocks.17.attn.qkv.bias FLOAT[3072] b520180425b8
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visual.trunk.blocks.17.mlp.fc2.bias FLOAT[1024] 8344bdf1ee92
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visual.trunk.blocks.17.norm2.bias FLOAT[1024] 2e8f8f31623c
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visual.trunk.blocks.18.attn.proj.bias FLOAT[1024] e80b42a944c3
visual.trunk.blocks.18.attn.qkv.bias FLOAT[3072] 128efd55d6d0
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visual.trunk.blocks.18.norm1.bias FLOAT[1024] fb6a01d53ba0
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visual.trunk.blocks.19.attn.proj.bias FLOAT[1024] 19f901963f27
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visual.trunk.blocks.19.mlp.fc1.bias FLOAT[4096] b68c6613baa1
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visual.trunk.blocks.19.norm2.weight FLOAT[1024] 0b6758d8ee02
visual.trunk.blocks.2.attn.proj.bias FLOAT[1024] 9971ac28a461
visual.trunk.blocks.2.attn.qkv.bias FLOAT[3072] 76a3eb1ddf74
visual.trunk.blocks.2.mlp.fc1.bias FLOAT[4096] 8a8668346f19
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visual.trunk.blocks.2.norm1.bias FLOAT[1024] 0a68c5570005
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visual.trunk.blocks.20.attn.proj.bias FLOAT[1024] 703679246fcf
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visual.trunk.blocks.21.attn.proj.bias FLOAT[1024] 047d6dda06ce
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visual.trunk.blocks.22.attn.proj.bias FLOAT[1024] 61ddf4acd4e2
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