<
   ir_version: 10,
   opset_import: ["" : 23],
   producer_name: "pytorch"
>
main_graph (uint8[batch,224,224,3] image) => (float[batch,1024] image_embedding) 
   <
      float[batch,257,1280] add_1000
      float[batch,257,1280] add_1029
      float[batch,257,1280] add_1144
      float[batch,257,1280] add_1173
      float[batch,257,1280] add_1288
      float[batch,257,1280] add_1317
      float[batch,257,1280] add_136
      float[batch,257,1280] add_1432
      float[batch,257,1280] add_1461
      float[batch,257,1280] add_1576
      float[batch,257,1280] add_1605
      float[batch,257,1280] add_165
      float[batch,257,1280] add_17
      float[batch,257,1280] add_1720
      float[batch,257,1280] add_1749
      float[batch,257,1280] add_1864
      float[batch,257,1280] add_1893
      float[batch,257,1280] add_2008
      float[batch,257,1280] add_2037
      float[batch,257,1280] add_2152
      float[batch,257,1280] add_2181
      float[batch,257,1280] add_2296
      float[batch,257,1280] add_2325
      float[batch,257,1280] add_2440
      float[batch,257,1280] add_2469
      float[batch,257,1280] add_2584
      float[batch,257,1280] add_2613
      float[batch,257,1280] add_2728
      float[batch,257,1280] add_2757
      float[batch,257,1280] add_280
      float[batch,257,1280] add_2872
      float[batch,257,1280] add_2901
      float[batch,257,1280] add_3016
      float[batch,257,1280] add_3045
      float[batch,257,1280] add_309
      float[batch,257,1280] add_3160
      float[batch,257,1280] add_3189
      float[batch,257,1280] add_3304
      float[batch,257,1280] add_3333
      float[batch,257,1280] add_3448
      float[batch,257,1280] add_3477
      float[batch,257,1280] add_3592
      float[batch,257,1280] add_3621
      float[batch,257,1280] add_3736
      float[batch,257,1280] add_3765
      float[batch,257,1280] add_3880
      float[batch,257,1280] add_3909
      float[batch,257,1280] add_4024
      float[batch,257,1280] add_4053
      float[batch,257,1280] add_4168
      float[batch,257,1280] add_4197
      float[batch,257,1280] add_424
      float[batch,257,1280] add_4312
      float[batch,257,1280] add_4341
      float[batch,257,1280] add_4456
      float[batch,257,1280] add_4485
      float[batch,1,1280] add_4485_pooled
      float[batch,257,1280] add_453
      float[batch,1,1280] add_4600
      float[batch,1,1280] add_4629
      float[batch,257,1280] add_568
      float[batch,257,1280] add_597
      float[batch,257,1280] add_712
      float[batch,257,1280] add_741
      float[batch,257,1280] add_856
      float[batch,257,1280] add_885
      float[batch,1] clamp_min
      float[batch,1280,16,16] conv2d
      float[batch,3,224,224] image_chw
      float[batch,224,224,3] image_f32
      float[batch,257,1280] layer_norm
      float[batch,257,1280] layer_norm_1
      float[batch,257,1280] layer_norm_10
      float[batch,257,1280] layer_norm_11
      float[batch,257,1280] layer_norm_12
      float[batch,257,1280] layer_norm_13
      float[batch,257,1280] layer_norm_14
      float[batch,257,1280] layer_norm_15
      float[batch,257,1280] layer_norm_16
      float[batch,257,1280] layer_norm_17
      float[batch,257,1280] layer_norm_18
      float[batch,257,1280] layer_norm_19
      float[batch,257,1280] layer_norm_2
      float[batch,257,1280] layer_norm_20
      float[batch,257,1280] layer_norm_21
      float[batch,257,1280] layer_norm_22
      float[batch,257,1280] layer_norm_23
      float[batch,257,1280] layer_norm_24
      float[batch,257,1280] layer_norm_25
      float[batch,257,1280] layer_norm_26
      float[batch,257,1280] layer_norm_27
      float[batch,257,1280] layer_norm_28
      float[batch,257,1280] layer_norm_29
      float[batch,257,1280] layer_norm_3
      float[batch,257,1280] layer_norm_30
      float[batch,257,1280] layer_norm_31
      float[batch,257,1280] layer_norm_32
      float[batch,257,1280] layer_norm_33
      float[batch,257,1280] layer_norm_34
      float[batch,257,1280] layer_norm_35
      float[batch,257,1280] layer_norm_36
      float[batch,257,1280] layer_norm_37
      float[batch,257,1280] layer_norm_38
      float[batch,257,1280] layer_norm_39
      float[batch,257,1280] layer_norm_4
      float[batch,257,1280] layer_norm_40
      float[batch,257,1280] layer_norm_41
      float[batch,257,1280] layer_norm_42
      float[batch,257,1280] layer_norm_43
      float[batch,257,1280] layer_norm_44
      float[batch,257,1280] layer_norm_45
      float[batch,257,1280] layer_norm_46
      float[batch,257,1280] layer_norm_47
      float[batch,257,1280] layer_norm_48
      float[batch,257,1280] layer_norm_49
      float[batch,257,1280] layer_norm_5
      float[batch,257,1280] layer_norm_50
      float[batch,257,1280] layer_norm_51
      float[batch,257,1280] layer_norm_52
      float[batch,257,1280] layer_norm_53
      float[batch,257,1280] layer_norm_54
      float[batch,257,1280] layer_norm_55
      float[batch,257,1280] layer_norm_56
      float[batch,257,1280] layer_norm_57
      float[batch,257,1280] layer_norm_58
      float[batch,257,1280] layer_norm_59
      float[batch,257,1280] layer_norm_6
      float[batch,257,1280] layer_norm_60
      float[batch,257,1280] layer_norm_61
      float[batch,257,1280] layer_norm_62
      float[batch,257,1280] layer_norm_63
      float[batch,1,1280] layer_norm_64
      float[batch,257,1280] layer_norm_7
      float[batch,257,1280] layer_norm_8
      float[batch,257,1280] layer_norm_9
      float[batch,1] linalg_vector_norm
      float[batch,257,5120] linear_10
      float[batch,257,5120] linear_102
      float[batch,257,1280] linear_103
      float[batch,257,5120] linear_106
      float[batch,257,1280] linear_107
      float[batch,257,1280] linear_11
      float[batch,257,5120] linear_110
      float[batch,257,1280] linear_111
      float[batch,257,5120] linear_114
      float[batch,257,1280] linear_115
      float[batch,257,5120] linear_118
      float[batch,257,1280] linear_119
      float[batch,257,5120] linear_122
      float[batch,257,1280] linear_123
      float[batch,1,5120] linear_126
      float[batch,1,1280] linear_127
      float[batch,257,5120] linear_14
      float[batch,257,1280] linear_15
      float[batch,257,5120] linear_18
      float[batch,257,1280] linear_19
      float[batch,257,5120] linear_2
      float[batch,257,5120] linear_22
      float[batch,257,1280] linear_23
      float[batch,257,5120] linear_26
      float[batch,257,1280] linear_27
      float[batch,257,1280] linear_3
      float[batch,257,5120] linear_30
      float[batch,257,1280] linear_31
      float[batch,257,5120] linear_34
      float[batch,257,1280] linear_35
      float[batch,257,5120] linear_38
      float[batch,257,1280] linear_39
      float[batch,257,5120] linear_42
      float[batch,257,1280] linear_43
      float[batch,257,5120] linear_46
      float[batch,257,1280] linear_47
      float[batch,257,5120] linear_50
      float[batch,257,1280] linear_51
      float[batch,257,5120] linear_54
      float[batch,257,1280] linear_55
      float[batch,257,5120] linear_58
      float[batch,257,1280] linear_59
      float[batch,257,5120] linear_6
      float[batch,257,5120] linear_62
      float[batch,257,1280] linear_63
      float[batch,257,5120] linear_66
      float[batch,257,1280] linear_67
      float[batch,257,1280] linear_7
      float[batch,257,5120] linear_70
      float[batch,257,1280] linear_71
      float[batch,257,5120] linear_74
      float[batch,257,1280] linear_75
      float[batch,257,5120] linear_78
      float[batch,257,1280] linear_79
      float[batch,257,5120] linear_82
      float[batch,257,1280] linear_83
      float[batch,257,5120] linear_86
      float[batch,257,1280] linear_87
      float[batch,257,5120] linear_90
      float[batch,257,1280] linear_91
      float[batch,257,5120] linear_94
      float[batch,257,1280] linear_95
      float[batch,257,5120] linear_98
      float[batch,257,1280] linear_99
      float[batch,1024] matmul
      float[batch,257,5120] mul_1078
      float[batch,257,5120] mul_1083
      float[batch,257,5120] mul_115
      float[batch,257,5120] mul_1185
      float[batch,257,5120] mul_1190
      float[batch,257,5120] mul_120
      float[batch,257,5120] mul_1292
      float[batch,257,5120] mul_1297
      float[batch,257,5120] mul_1399
      float[batch,257,5120] mul_1404
      float[batch,257,5120] mul_1506
      float[batch,257,5120] mul_1511
      float[batch,257,5120] mul_1613
      float[batch,257,5120] mul_1618
      float[batch,257,5120] mul_1720
      float[batch,257,5120] mul_1725
      float[batch,257,5120] mul_1827
      float[batch,257,5120] mul_1832
      float[batch,257,5120] mul_1934
      float[batch,257,5120] mul_1939
      float[batch,257,5120] mul_2041
      float[batch,257,5120] mul_2046
      float[batch,257,5120] mul_2148
      float[batch,257,5120] mul_2153
      float[batch,257,5120] mul_222
      float[batch,257,5120] mul_2255
      float[batch,257,5120] mul_2260
      float[batch,257,5120] mul_227
      float[batch,257,5120] mul_2362
      float[batch,257,5120] mul_2367
      float[batch,257,5120] mul_2469
      float[batch,257,5120] mul_2474
      float[batch,257,5120] mul_2576
      float[batch,257,5120] mul_2581
      float[batch,257,5120] mul_2683
      float[batch,257,5120] mul_2688
      float[batch,257,5120] mul_2790
      float[batch,257,5120] mul_2795
      float[batch,257,5120] mul_2897
      float[batch,257,5120] mul_2902
      float[batch,257,5120] mul_3004
      float[batch,257,5120] mul_3009
      float[batch,257,5120] mul_3111
      float[batch,257,5120] mul_3116
      float[batch,257,5120] mul_3218
      float[batch,257,5120] mul_3223
      float[batch,257,5120] mul_329
      float[batch,257,5120] mul_3325
      float[batch,257,5120] mul_3330
      float[batch,257,5120] mul_334
      float[batch,1,5120] mul_3432
      float[batch,1,5120] mul_3437
      float[batch,257,5120] mul_436
      float[batch,257,5120] mul_441
      float[batch,257,5120] mul_543
      float[batch,257,5120] mul_548
      float[batch,257,5120] mul_650
      float[batch,257,5120] mul_655
      float[batch,257,5120] mul_757
      float[batch,257,5120] mul_762
      float[batch,257,5120] mul_864
      float[batch,257,5120] mul_869
      float[batch,257,5120] mul_971
      float[batch,257,5120] mul_976
      float[batch,257,1280] node_scaled_dot_product_attention_10_k
      float[batch,257,1280] node_scaled_dot_product_attention_10_out
      float[batch,257,1280] node_scaled_dot_product_attention_10_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_10_q
      float[batch,257,3840] node_scaled_dot_product_attention_10_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_10_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_10_v
      float[batch,257,1280] node_scaled_dot_product_attention_11_k
      float[batch,257,1280] node_scaled_dot_product_attention_11_out
      float[batch,257,1280] node_scaled_dot_product_attention_11_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_11_q
      float[batch,257,3840] node_scaled_dot_product_attention_11_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_11_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_11_v
      float[batch,257,1280] node_scaled_dot_product_attention_12_k
      float[batch,257,1280] node_scaled_dot_product_attention_12_out
      float[batch,257,1280] node_scaled_dot_product_attention_12_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_12_q
      float[batch,257,3840] node_scaled_dot_product_attention_12_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_12_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_12_v
      float[batch,257,1280] node_scaled_dot_product_attention_13_k
      float[batch,257,1280] node_scaled_dot_product_attention_13_out
      float[batch,257,1280] node_scaled_dot_product_attention_13_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_13_q
      float[batch,257,3840] node_scaled_dot_product_attention_13_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_13_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_13_v
      float[batch,257,1280] node_scaled_dot_product_attention_14_k
      float[batch,257,1280] node_scaled_dot_product_attention_14_out
      float[batch,257,1280] node_scaled_dot_product_attention_14_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_14_q
      float[batch,257,3840] node_scaled_dot_product_attention_14_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_14_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_14_v
      float[batch,257,1280] node_scaled_dot_product_attention_15_k
      float[batch,257,1280] node_scaled_dot_product_attention_15_out
      float[batch,257,1280] node_scaled_dot_product_attention_15_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_15_q
      float[batch,257,3840] node_scaled_dot_product_attention_15_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_15_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_15_v
      float[batch,257,1280] node_scaled_dot_product_attention_16_k
      float[batch,257,1280] node_scaled_dot_product_attention_16_out
      float[batch,257,1280] node_scaled_dot_product_attention_16_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_16_q
      float[batch,257,3840] node_scaled_dot_product_attention_16_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_16_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_16_v
      float[batch,257,1280] node_scaled_dot_product_attention_17_k
      float[batch,257,1280] node_scaled_dot_product_attention_17_out
      float[batch,257,1280] node_scaled_dot_product_attention_17_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_17_q
      float[batch,257,3840] node_scaled_dot_product_attention_17_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_17_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_17_v
      float[batch,257,1280] node_scaled_dot_product_attention_18_k
      float[batch,257,1280] node_scaled_dot_product_attention_18_out
      float[batch,257,1280] node_scaled_dot_product_attention_18_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_18_q
      float[batch,257,3840] node_scaled_dot_product_attention_18_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_18_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_18_v
      float[batch,257,1280] node_scaled_dot_product_attention_19_k
      float[batch,257,1280] node_scaled_dot_product_attention_19_out
      float[batch,257,1280] node_scaled_dot_product_attention_19_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_19_q
      float[batch,257,3840] node_scaled_dot_product_attention_19_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_19_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_19_v
      float[batch,257,1280] node_scaled_dot_product_attention_1_k
      float[batch,257,1280] node_scaled_dot_product_attention_1_out
      float[batch,257,1280] node_scaled_dot_product_attention_1_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_1_q
      float[batch,257,3840] node_scaled_dot_product_attention_1_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_1_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_1_v
      float[batch,257,1280] node_scaled_dot_product_attention_20_k
      float[batch,257,1280] node_scaled_dot_product_attention_20_out
      float[batch,257,1280] node_scaled_dot_product_attention_20_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_20_q
      float[batch,257,3840] node_scaled_dot_product_attention_20_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_20_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_20_v
      float[batch,257,1280] node_scaled_dot_product_attention_21_k
      float[batch,257,1280] node_scaled_dot_product_attention_21_out
      float[batch,257,1280] node_scaled_dot_product_attention_21_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_21_q
      float[batch,257,3840] node_scaled_dot_product_attention_21_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_21_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_21_v
      float[batch,257,1280] node_scaled_dot_product_attention_22_k
      float[batch,257,1280] node_scaled_dot_product_attention_22_out
      float[batch,257,1280] node_scaled_dot_product_attention_22_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_22_q
      float[batch,257,3840] node_scaled_dot_product_attention_22_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_22_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_22_v
      float[batch,257,1280] node_scaled_dot_product_attention_23_k
      float[batch,257,1280] node_scaled_dot_product_attention_23_out
      float[batch,257,1280] node_scaled_dot_product_attention_23_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_23_q
      float[batch,257,3840] node_scaled_dot_product_attention_23_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_23_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_23_v
      float[batch,257,1280] node_scaled_dot_product_attention_24_k
      float[batch,257,1280] node_scaled_dot_product_attention_24_out
      float[batch,257,1280] node_scaled_dot_product_attention_24_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_24_q
      float[batch,257,3840] node_scaled_dot_product_attention_24_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_24_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_24_v
      float[batch,257,1280] node_scaled_dot_product_attention_25_k
      float[batch,257,1280] node_scaled_dot_product_attention_25_out
      float[batch,257,1280] node_scaled_dot_product_attention_25_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_25_q
      float[batch,257,3840] node_scaled_dot_product_attention_25_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_25_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_25_v
      float[batch,257,1280] node_scaled_dot_product_attention_26_k
      float[batch,257,1280] node_scaled_dot_product_attention_26_out
      float[batch,257,1280] node_scaled_dot_product_attention_26_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_26_q
      float[batch,257,3840] node_scaled_dot_product_attention_26_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_26_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_26_v
      float[batch,257,1280] node_scaled_dot_product_attention_27_k
      float[batch,257,1280] node_scaled_dot_product_attention_27_out
      float[batch,257,1280] node_scaled_dot_product_attention_27_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_27_q
      float[batch,257,3840] node_scaled_dot_product_attention_27_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_27_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_27_v
      float[batch,257,1280] node_scaled_dot_product_attention_28_k
      float[batch,257,1280] node_scaled_dot_product_attention_28_out
      float[batch,257,1280] node_scaled_dot_product_attention_28_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_28_q
      float[batch,257,3840] node_scaled_dot_product_attention_28_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_28_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_28_v
      float[batch,257,1280] node_scaled_dot_product_attention_29_k
      float[batch,257,1280] node_scaled_dot_product_attention_29_out
      float[batch,257,1280] node_scaled_dot_product_attention_29_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_29_q
      float[batch,257,3840] node_scaled_dot_product_attention_29_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_29_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_29_v
      float[batch,257,1280] node_scaled_dot_product_attention_2_k
      float[batch,257,1280] node_scaled_dot_product_attention_2_out
      float[batch,257,1280] node_scaled_dot_product_attention_2_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_2_q
      float[batch,257,3840] node_scaled_dot_product_attention_2_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_2_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_2_v
      float[batch,257,1280] node_scaled_dot_product_attention_30_k
      float[batch,257,1280] node_scaled_dot_product_attention_30_out
      float[batch,257,1280] node_scaled_dot_product_attention_30_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_30_q
      float[batch,257,3840] node_scaled_dot_product_attention_30_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_30_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_30_v
      float[batch,257,1280] node_scaled_dot_product_attention_31_k
      float[batch,1,1280] node_scaled_dot_product_attention_31_out
      float[batch,1,1280] node_scaled_dot_product_attention_31_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_31_q
      float[batch,1,1280] node_scaled_dot_product_attention_31_q_pooled
      float[batch,257,3840] node_scaled_dot_product_attention_31_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_31_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_31_v
      float[batch,257,1280] node_scaled_dot_product_attention_3_k
      float[batch,257,1280] node_scaled_dot_product_attention_3_out
      float[batch,257,1280] node_scaled_dot_product_attention_3_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_3_q
      float[batch,257,3840] node_scaled_dot_product_attention_3_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_3_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_3_v
      float[batch,257,1280] node_scaled_dot_product_attention_4_k
      float[batch,257,1280] node_scaled_dot_product_attention_4_out
      float[batch,257,1280] node_scaled_dot_product_attention_4_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_4_q
      float[batch,257,3840] node_scaled_dot_product_attention_4_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_4_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_4_v
      float[batch,257,1280] node_scaled_dot_product_attention_5_k
      float[batch,257,1280] node_scaled_dot_product_attention_5_out
      float[batch,257,1280] node_scaled_dot_product_attention_5_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_5_q
      float[batch,257,3840] node_scaled_dot_product_attention_5_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_5_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_5_v
      float[batch,257,1280] node_scaled_dot_product_attention_6_k
      float[batch,257,1280] node_scaled_dot_product_attention_6_out
      float[batch,257,1280] node_scaled_dot_product_attention_6_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_6_q
      float[batch,257,3840] node_scaled_dot_product_attention_6_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_6_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_6_v
      float[batch,257,1280] node_scaled_dot_product_attention_7_k
      float[batch,257,1280] node_scaled_dot_product_attention_7_out
      float[batch,257,1280] node_scaled_dot_product_attention_7_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_7_q
      float[batch,257,3840] node_scaled_dot_product_attention_7_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_7_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_7_v
      float[batch,257,1280] node_scaled_dot_product_attention_8_k
      float[batch,257,1280] node_scaled_dot_product_attention_8_out
      float[batch,257,1280] node_scaled_dot_product_attention_8_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_8_q
      float[batch,257,3840] node_scaled_dot_product_attention_8_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_8_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_8_v
      float[batch,257,1280] node_scaled_dot_product_attention_9_k
      float[batch,257,1280] node_scaled_dot_product_attention_9_out
      float[batch,257,1280] node_scaled_dot_product_attention_9_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_9_q
      float[batch,257,3840] node_scaled_dot_product_attention_9_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_9_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_9_v
      float[batch,257,1280] node_scaled_dot_product_attention_k
      float[batch,257,1280] node_scaled_dot_product_attention_out
      float[batch,257,1280] node_scaled_dot_product_attention_out_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_q
      float[batch,257,3840] node_scaled_dot_product_attention_qkv
      float[batch,257,3840] node_scaled_dot_product_attention_qkv_mm_out
      float[batch,257,1280] node_scaled_dot_product_attention_v
      float[batch,256,1280] permute
      float[batch,257,1280] scaled_dot_product_attention
      float[batch,257,1280] scaled_dot_product_attention_1
      float[batch,257,1280] scaled_dot_product_attention_10
      float[batch,257,1280] scaled_dot_product_attention_11
      float[batch,257,1280] scaled_dot_product_attention_12
      float[batch,257,1280] scaled_dot_product_attention_13
      float[batch,257,1280] scaled_dot_product_attention_14
      float[batch,257,1280] scaled_dot_product_attention_15
      float[batch,257,1280] scaled_dot_product_attention_16
      float[batch,257,1280] scaled_dot_product_attention_17
      float[batch,257,1280] scaled_dot_product_attention_18
      float[batch,257,1280] scaled_dot_product_attention_19
      float[batch,257,1280] scaled_dot_product_attention_2
      float[batch,257,1280] scaled_dot_product_attention_20
      float[batch,257,1280] scaled_dot_product_attention_21
      float[batch,257,1280] scaled_dot_product_attention_22
      float[batch,257,1280] scaled_dot_product_attention_23
      float[batch,257,1280] scaled_dot_product_attention_24
      float[batch,257,1280] scaled_dot_product_attention_25
      float[batch,257,1280] scaled_dot_product_attention_26
      float[batch,257,1280] scaled_dot_product_attention_27
      float[batch,257,1280] scaled_dot_product_attention_28
      float[batch,257,1280] scaled_dot_product_attention_29
      float[batch,257,1280] scaled_dot_product_attention_3
      float[batch,257,1280] scaled_dot_product_attention_30
      float[batch,1,1280] scaled_dot_product_attention_31
      float[batch,257,1280] scaled_dot_product_attention_4
      float[batch,257,1280] scaled_dot_product_attention_5
      float[batch,257,1280] scaled_dot_product_attention_6
      float[batch,257,1280] scaled_dot_product_attention_7
      float[batch,257,1280] scaled_dot_product_attention_8
      float[batch,257,1280] scaled_dot_product_attention_9
      float[batch,1280] select_96
      float[batch,257,5120] sigmoid
      float[batch,257,5120] sigmoid_1
      float[batch,257,5120] sigmoid_10
      float[batch,257,5120] sigmoid_11
      float[batch,257,5120] sigmoid_12
      float[batch,257,5120] sigmoid_13
      float[batch,257,5120] sigmoid_14
      float[batch,257,5120] sigmoid_15
      float[batch,257,5120] sigmoid_16
      float[batch,257,5120] sigmoid_17
      float[batch,257,5120] sigmoid_18
      float[batch,257,5120] sigmoid_19
      float[batch,257,5120] sigmoid_2
      float[batch,257,5120] sigmoid_20
      float[batch,257,5120] sigmoid_21
      float[batch,257,5120] sigmoid_22
      float[batch,257,5120] sigmoid_23
      float[batch,257,5120] sigmoid_24
      float[batch,257,5120] sigmoid_25
      float[batch,257,5120] sigmoid_26
      float[batch,257,5120] sigmoid_27
      float[batch,257,5120] sigmoid_28
      float[batch,257,5120] sigmoid_29
      float[batch,257,5120] sigmoid_3
      float[batch,257,5120] sigmoid_30
      float[batch,1,5120] sigmoid_31
      float[batch,257,5120] sigmoid_4
      float[batch,257,5120] sigmoid_5
      float[batch,257,5120] sigmoid_6
      float[batch,257,5120] sigmoid_7
      float[batch,257,5120] sigmoid_8
      float[batch,257,5120] sigmoid_9
      float[batch,257,1280] val_104
      float[batch,257,5120] val_105
      float[batch,257,1280] val_106
      float[batch,257,5120] val_107
      float[batch,257,1280] val_108
      float[batch,257,5120] val_109
      float[batch,257,1280] val_110
      float[batch,257,5120] val_111
      float[batch,257,1280] val_112
      float[batch,257,5120] val_113
      float[batch,257,1280] val_114
      float[batch,257,5120] val_115
      float[batch,257,1280] val_116
      float[batch,257,5120] val_117
      float[batch,257,1280] val_118
      float[batch,257,5120] val_119
      float[batch,257,1280] val_120
      float[batch,257,5120] val_121
      float[batch,257,1280] val_122
      float[batch,257,5120] val_123
      float[batch,257,1280] val_124
      float[batch,257,5120] val_125
      float[batch,257,1280] val_126
      float[batch,257,5120] val_127
      float[batch,257,1280] val_128
      float[batch,257,5120] val_129
      float[batch,257,1280] val_130
      float[batch,257,5120] val_131
      float[batch,257,1280] val_132
      float[batch,257,5120] val_133
      float[batch,257,1280] val_134
      float[batch,257,5120] val_135
      float[batch,257,1280] val_136
      float[batch,257,5120] val_137
      float[batch,257,1280] val_138
      float[batch,257,5120] val_139
      float[batch,257,1280] val_140
      float[batch,257,5120] val_141
      float[batch,257,1280] val_142
      float[batch,257,5120] val_143
      float[batch,257,1280] val_144
      float[batch,257,5120] val_145
      float[batch,257,1280] val_146
      float[batch,257,5120] val_147
      float[batch,257,1280] val_148
      float[batch,257,5120] val_149
      float[batch,257,1280] val_150
      float[batch,257,5120] val_151
      float[batch,257,1280] val_152
      float[batch,257,5120] val_153
      float[batch,257,1280] val_154
      float[batch,257,5120] val_155
      float[batch,257,1280] val_156
      float[batch,257,5120] val_157
      float[batch,257,1280] val_158
      float[batch,257,5120] val_159
      float[batch,257,1280] val_160
      float[batch,257,5120] val_161
      float[batch,257,1280] val_162
      float[batch,257,5120] val_163
      float[batch,257,1280] val_164
      float[batch,257,5120] val_165
      float[batch,257,1280] val_166
      float[batch,1,5120] val_167
      float[batch,1,1280] val_168
      float[batch,1280] val_169
      float[batch,1280,256] 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)
   conv2d = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [14, 14]> (image_chw, "visual.conv1.weight", node_Conv_1785_fused_bias)
   view = Reshape <allowzero: int = 1> (conv2d, view_target)
   [node_permute] permute = Transpose <perm: ints = [0, 2, 1]> (view)
   val_104 = Pad (permute, val_4, val_5)
   add_17 = Add (val_104, val_103)
   [node_layer_norm] layer_norm = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_17, "visual.ln_pre.weight", "visual.ln_pre.bias")
   layer_norm_1 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (layer_norm, "visual.transformer.resblocks.0.ln_1.weight", "visual.transformer.resblocks.0.ln_1.bias")
   [node_scaled_dot_product_attention_qkv_mm] node_scaled_dot_product_attention_qkv_mm_out = MatMul (layer_norm_1, val_7)
   [node_scaled_dot_product_attention_qkv_bias] node_scaled_dot_product_attention_qkv = Add (node_scaled_dot_product_attention_qkv_mm_out, "visual.transformer.resblocks.0.attn.in_proj_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> (node_scaled_dot_product_attention_qkv, attn3d_split_3x1280)
   [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)
   [node_scaled_dot_product_attention_out_mm] node_scaled_dot_product_attention_out_mm_out = MatMul (scaled_dot_product_attention, node_scaled_dot_product_attention_wo_t)
   [node_scaled_dot_product_attention_out_bias] node_scaled_dot_product_attention_out = Add (node_scaled_dot_product_attention_out_mm_out, "visual.transformer.resblocks.0.attn.out_proj.bias")
   add_136 = Add (layer_norm, node_scaled_dot_product_attention_out)
   layer_norm_2 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_136, "visual.transformer.resblocks.0.ln_2.weight", "visual.transformer.resblocks.0.ln_2.bias")
   val_105 = MatMul (layer_norm_2, val_8)
   linear_2 = Add (val_105, "visual.transformer.resblocks.0.mlp.c_fc.bias")
   mul_115 = Mul (linear_2, val_3)
   [node_sigmoid] sigmoid = Sigmoid (mul_115)
   mul_120 = Mul (linear_2, sigmoid)
   val_106 = MatMul (mul_120, val_9)
   linear_3 = Add (val_106, "visual.transformer.resblocks.0.mlp.c_proj.bias")
   add_165 = Add (add_136, linear_3)
   layer_norm_3 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_165, "visual.transformer.resblocks.1.ln_1.weight", "visual.transformer.resblocks.1.ln_1.bias")
   [node_scaled_dot_product_attention_1_qkv_mm] node_scaled_dot_product_attention_1_qkv_mm_out = MatMul (layer_norm_3, val_10)
   [node_scaled_dot_product_attention_1_qkv_bias] node_scaled_dot_product_attention_1_qkv = Add (node_scaled_dot_product_attention_1_qkv_mm_out, "visual.transformer.resblocks.1.attn.in_proj_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> (node_scaled_dot_product_attention_1_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_1_out_mm] node_scaled_dot_product_attention_1_out_mm_out = MatMul (scaled_dot_product_attention_1, node_scaled_dot_product_attention_1_wo_t)
   [node_scaled_dot_product_attention_1_out_bias] node_scaled_dot_product_attention_1_out = Add (node_scaled_dot_product_attention_1_out_mm_out, "visual.transformer.resblocks.1.attn.out_proj.bias")
   add_280 = Add (add_165, node_scaled_dot_product_attention_1_out)
   layer_norm_4 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_280, "visual.transformer.resblocks.1.ln_2.weight", "visual.transformer.resblocks.1.ln_2.bias")
   val_107 = MatMul (layer_norm_4, val_11)
   linear_6 = Add (val_107, "visual.transformer.resblocks.1.mlp.c_fc.bias")
   mul_222 = Mul (linear_6, val_3)
   sigmoid_1 = Sigmoid (mul_222)
   mul_227 = Mul (linear_6, sigmoid_1)
   val_108 = MatMul (mul_227, val_12)
   linear_7 = Add (val_108, "visual.transformer.resblocks.1.mlp.c_proj.bias")
   add_309 = Add (add_280, linear_7)
   layer_norm_5 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_309, "visual.transformer.resblocks.2.ln_1.weight", "visual.transformer.resblocks.2.ln_1.bias")
   [node_scaled_dot_product_attention_2_qkv_mm] node_scaled_dot_product_attention_2_qkv_mm_out = MatMul (layer_norm_5, val_13)
   [node_scaled_dot_product_attention_2_qkv_bias] node_scaled_dot_product_attention_2_qkv = Add (node_scaled_dot_product_attention_2_qkv_mm_out, "visual.transformer.resblocks.2.attn.in_proj_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> (node_scaled_dot_product_attention_2_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_2_out_mm] node_scaled_dot_product_attention_2_out_mm_out = MatMul (scaled_dot_product_attention_2, node_scaled_dot_product_attention_2_wo_t)
   [node_scaled_dot_product_attention_2_out_bias] node_scaled_dot_product_attention_2_out = Add (node_scaled_dot_product_attention_2_out_mm_out, "visual.transformer.resblocks.2.attn.out_proj.bias")
   add_424 = Add (add_309, node_scaled_dot_product_attention_2_out)
   layer_norm_6 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_424, "visual.transformer.resblocks.2.ln_2.weight", "visual.transformer.resblocks.2.ln_2.bias")
   val_109 = MatMul (layer_norm_6, val_14)
   linear_10 = Add (val_109, "visual.transformer.resblocks.2.mlp.c_fc.bias")
   mul_329 = Mul (linear_10, val_3)
   sigmoid_2 = Sigmoid (mul_329)
   mul_334 = Mul (linear_10, sigmoid_2)
   val_110 = MatMul (mul_334, val_15)
   linear_11 = Add (val_110, "visual.transformer.resblocks.2.mlp.c_proj.bias")
   add_453 = Add (add_424, linear_11)
   layer_norm_7 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_453, "visual.transformer.resblocks.3.ln_1.weight", "visual.transformer.resblocks.3.ln_1.bias")
   [node_scaled_dot_product_attention_3_qkv_mm] node_scaled_dot_product_attention_3_qkv_mm_out = MatMul (layer_norm_7, val_16)
   [node_scaled_dot_product_attention_3_qkv_bias] node_scaled_dot_product_attention_3_qkv = Add (node_scaled_dot_product_attention_3_qkv_mm_out, "visual.transformer.resblocks.3.attn.in_proj_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> (node_scaled_dot_product_attention_3_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_3_out_mm] node_scaled_dot_product_attention_3_out_mm_out = MatMul (scaled_dot_product_attention_3, node_scaled_dot_product_attention_3_wo_t)
   [node_scaled_dot_product_attention_3_out_bias] node_scaled_dot_product_attention_3_out = Add (node_scaled_dot_product_attention_3_out_mm_out, "visual.transformer.resblocks.3.attn.out_proj.bias")
   add_568 = Add (add_453, node_scaled_dot_product_attention_3_out)
   layer_norm_8 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_568, "visual.transformer.resblocks.3.ln_2.weight", "visual.transformer.resblocks.3.ln_2.bias")
   val_111 = MatMul (layer_norm_8, val_17)
   linear_14 = Add (val_111, "visual.transformer.resblocks.3.mlp.c_fc.bias")
   mul_436 = Mul (linear_14, val_3)
   sigmoid_3 = Sigmoid (mul_436)
   mul_441 = Mul (linear_14, sigmoid_3)
   val_112 = MatMul (mul_441, val_18)
   linear_15 = Add (val_112, "visual.transformer.resblocks.3.mlp.c_proj.bias")
   add_597 = Add (add_568, linear_15)
   layer_norm_9 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_597, "visual.transformer.resblocks.4.ln_1.weight", "visual.transformer.resblocks.4.ln_1.bias")
   [node_scaled_dot_product_attention_4_qkv_mm] node_scaled_dot_product_attention_4_qkv_mm_out = MatMul (layer_norm_9, val_19)
   [node_scaled_dot_product_attention_4_qkv_bias] node_scaled_dot_product_attention_4_qkv = Add (node_scaled_dot_product_attention_4_qkv_mm_out, "visual.transformer.resblocks.4.attn.in_proj_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> (node_scaled_dot_product_attention_4_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_4_out_mm] node_scaled_dot_product_attention_4_out_mm_out = MatMul (scaled_dot_product_attention_4, node_scaled_dot_product_attention_4_wo_t)
   [node_scaled_dot_product_attention_4_out_bias] node_scaled_dot_product_attention_4_out = Add (node_scaled_dot_product_attention_4_out_mm_out, "visual.transformer.resblocks.4.attn.out_proj.bias")
   add_712 = Add (add_597, node_scaled_dot_product_attention_4_out)
   layer_norm_10 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_712, "visual.transformer.resblocks.4.ln_2.weight", "visual.transformer.resblocks.4.ln_2.bias")
   val_113 = MatMul (layer_norm_10, val_20)
   linear_18 = Add (val_113, "visual.transformer.resblocks.4.mlp.c_fc.bias")
   mul_543 = Mul (linear_18, val_3)
   sigmoid_4 = Sigmoid (mul_543)
   mul_548 = Mul (linear_18, sigmoid_4)
   val_114 = MatMul (mul_548, val_21)
   linear_19 = Add (val_114, "visual.transformer.resblocks.4.mlp.c_proj.bias")
   add_741 = Add (add_712, linear_19)
   layer_norm_11 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_741, "visual.transformer.resblocks.5.ln_1.weight", "visual.transformer.resblocks.5.ln_1.bias")
   [node_scaled_dot_product_attention_5_qkv_mm] node_scaled_dot_product_attention_5_qkv_mm_out = MatMul (layer_norm_11, val_22)
   [node_scaled_dot_product_attention_5_qkv_bias] node_scaled_dot_product_attention_5_qkv = Add (node_scaled_dot_product_attention_5_qkv_mm_out, "visual.transformer.resblocks.5.attn.in_proj_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> (node_scaled_dot_product_attention_5_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_5_out_mm] node_scaled_dot_product_attention_5_out_mm_out = MatMul (scaled_dot_product_attention_5, node_scaled_dot_product_attention_5_wo_t)
   [node_scaled_dot_product_attention_5_out_bias] node_scaled_dot_product_attention_5_out = Add (node_scaled_dot_product_attention_5_out_mm_out, "visual.transformer.resblocks.5.attn.out_proj.bias")
   add_856 = Add (add_741, node_scaled_dot_product_attention_5_out)
   layer_norm_12 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_856, "visual.transformer.resblocks.5.ln_2.weight", "visual.transformer.resblocks.5.ln_2.bias")
   val_115 = MatMul (layer_norm_12, val_23)
   linear_22 = Add (val_115, "visual.transformer.resblocks.5.mlp.c_fc.bias")
   mul_650 = Mul (linear_22, val_3)
   sigmoid_5 = Sigmoid (mul_650)
   mul_655 = Mul (linear_22, sigmoid_5)
   val_116 = MatMul (mul_655, val_24)
   linear_23 = Add (val_116, "visual.transformer.resblocks.5.mlp.c_proj.bias")
   add_885 = Add (add_856, linear_23)
   layer_norm_13 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_885, "visual.transformer.resblocks.6.ln_1.weight", "visual.transformer.resblocks.6.ln_1.bias")
   [node_scaled_dot_product_attention_6_qkv_mm] node_scaled_dot_product_attention_6_qkv_mm_out = MatMul (layer_norm_13, val_25)
   [node_scaled_dot_product_attention_6_qkv_bias] node_scaled_dot_product_attention_6_qkv = Add (node_scaled_dot_product_attention_6_qkv_mm_out, "visual.transformer.resblocks.6.attn.in_proj_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> (node_scaled_dot_product_attention_6_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_6_out_mm] node_scaled_dot_product_attention_6_out_mm_out = MatMul (scaled_dot_product_attention_6, node_scaled_dot_product_attention_6_wo_t)
   [node_scaled_dot_product_attention_6_out_bias] node_scaled_dot_product_attention_6_out = Add (node_scaled_dot_product_attention_6_out_mm_out, "visual.transformer.resblocks.6.attn.out_proj.bias")
   add_1000 = Add (add_885, node_scaled_dot_product_attention_6_out)
   layer_norm_14 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1000, "visual.transformer.resblocks.6.ln_2.weight", "visual.transformer.resblocks.6.ln_2.bias")
   val_117 = MatMul (layer_norm_14, val_26)
   linear_26 = Add (val_117, "visual.transformer.resblocks.6.mlp.c_fc.bias")
   mul_757 = Mul (linear_26, val_3)
   sigmoid_6 = Sigmoid (mul_757)
   mul_762 = Mul (linear_26, sigmoid_6)
   val_118 = MatMul (mul_762, val_27)
   linear_27 = Add (val_118, "visual.transformer.resblocks.6.mlp.c_proj.bias")
   add_1029 = Add (add_1000, linear_27)
   layer_norm_15 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1029, "visual.transformer.resblocks.7.ln_1.weight", "visual.transformer.resblocks.7.ln_1.bias")
   [node_scaled_dot_product_attention_7_qkv_mm] node_scaled_dot_product_attention_7_qkv_mm_out = MatMul (layer_norm_15, val_28)
   [node_scaled_dot_product_attention_7_qkv_bias] node_scaled_dot_product_attention_7_qkv = Add (node_scaled_dot_product_attention_7_qkv_mm_out, "visual.transformer.resblocks.7.attn.in_proj_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> (node_scaled_dot_product_attention_7_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_7_out_mm] node_scaled_dot_product_attention_7_out_mm_out = MatMul (scaled_dot_product_attention_7, node_scaled_dot_product_attention_7_wo_t)
   [node_scaled_dot_product_attention_7_out_bias] node_scaled_dot_product_attention_7_out = Add (node_scaled_dot_product_attention_7_out_mm_out, "visual.transformer.resblocks.7.attn.out_proj.bias")
   add_1144 = Add (add_1029, node_scaled_dot_product_attention_7_out)
   layer_norm_16 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1144, "visual.transformer.resblocks.7.ln_2.weight", "visual.transformer.resblocks.7.ln_2.bias")
   val_119 = MatMul (layer_norm_16, val_29)
   linear_30 = Add (val_119, "visual.transformer.resblocks.7.mlp.c_fc.bias")
   mul_864 = Mul (linear_30, val_3)
   sigmoid_7 = Sigmoid (mul_864)
   mul_869 = Mul (linear_30, sigmoid_7)
   val_120 = MatMul (mul_869, val_30)
   linear_31 = Add (val_120, "visual.transformer.resblocks.7.mlp.c_proj.bias")
   add_1173 = Add (add_1144, linear_31)
   layer_norm_17 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1173, "visual.transformer.resblocks.8.ln_1.weight", "visual.transformer.resblocks.8.ln_1.bias")
   [node_scaled_dot_product_attention_8_qkv_mm] node_scaled_dot_product_attention_8_qkv_mm_out = MatMul (layer_norm_17, val_31)
   [node_scaled_dot_product_attention_8_qkv_bias] node_scaled_dot_product_attention_8_qkv = Add (node_scaled_dot_product_attention_8_qkv_mm_out, "visual.transformer.resblocks.8.attn.in_proj_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> (node_scaled_dot_product_attention_8_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_8_out_mm] node_scaled_dot_product_attention_8_out_mm_out = MatMul (scaled_dot_product_attention_8, node_scaled_dot_product_attention_8_wo_t)
   [node_scaled_dot_product_attention_8_out_bias] node_scaled_dot_product_attention_8_out = Add (node_scaled_dot_product_attention_8_out_mm_out, "visual.transformer.resblocks.8.attn.out_proj.bias")
   add_1288 = Add (add_1173, node_scaled_dot_product_attention_8_out)
   layer_norm_18 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1288, "visual.transformer.resblocks.8.ln_2.weight", "visual.transformer.resblocks.8.ln_2.bias")
   val_121 = MatMul (layer_norm_18, val_32)
   linear_34 = Add (val_121, "visual.transformer.resblocks.8.mlp.c_fc.bias")
   mul_971 = Mul (linear_34, val_3)
   sigmoid_8 = Sigmoid (mul_971)
   mul_976 = Mul (linear_34, sigmoid_8)
   val_122 = MatMul (mul_976, val_33)
   linear_35 = Add (val_122, "visual.transformer.resblocks.8.mlp.c_proj.bias")
   add_1317 = Add (add_1288, linear_35)
   layer_norm_19 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1317, "visual.transformer.resblocks.9.ln_1.weight", "visual.transformer.resblocks.9.ln_1.bias")
   [node_scaled_dot_product_attention_9_qkv_mm] node_scaled_dot_product_attention_9_qkv_mm_out = MatMul (layer_norm_19, val_34)
   [node_scaled_dot_product_attention_9_qkv_bias] node_scaled_dot_product_attention_9_qkv = Add (node_scaled_dot_product_attention_9_qkv_mm_out, "visual.transformer.resblocks.9.attn.in_proj_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> (node_scaled_dot_product_attention_9_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_9_out_mm] node_scaled_dot_product_attention_9_out_mm_out = MatMul (scaled_dot_product_attention_9, node_scaled_dot_product_attention_9_wo_t)
   [node_scaled_dot_product_attention_9_out_bias] node_scaled_dot_product_attention_9_out = Add (node_scaled_dot_product_attention_9_out_mm_out, "visual.transformer.resblocks.9.attn.out_proj.bias")
   add_1432 = Add (add_1317, node_scaled_dot_product_attention_9_out)
   layer_norm_20 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1432, "visual.transformer.resblocks.9.ln_2.weight", "visual.transformer.resblocks.9.ln_2.bias")
   val_123 = MatMul (layer_norm_20, val_35)
   linear_38 = Add (val_123, "visual.transformer.resblocks.9.mlp.c_fc.bias")
   mul_1078 = Mul (linear_38, val_3)
   sigmoid_9 = Sigmoid (mul_1078)
   mul_1083 = Mul (linear_38, sigmoid_9)
   val_124 = MatMul (mul_1083, val_36)
   linear_39 = Add (val_124, "visual.transformer.resblocks.9.mlp.c_proj.bias")
   add_1461 = Add (add_1432, linear_39)
   layer_norm_21 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1461, "visual.transformer.resblocks.10.ln_1.weight", "visual.transformer.resblocks.10.ln_1.bias")
   [node_scaled_dot_product_attention_10_qkv_mm] node_scaled_dot_product_attention_10_qkv_mm_out = MatMul (layer_norm_21, val_37)
   [node_scaled_dot_product_attention_10_qkv_bias] node_scaled_dot_product_attention_10_qkv = Add (node_scaled_dot_product_attention_10_qkv_mm_out, "visual.transformer.resblocks.10.attn.in_proj_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> (node_scaled_dot_product_attention_10_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_10_out_mm] node_scaled_dot_product_attention_10_out_mm_out = MatMul (scaled_dot_product_attention_10, node_scaled_dot_product_attention_10_wo_t)
   [node_scaled_dot_product_attention_10_out_bias] node_scaled_dot_product_attention_10_out = Add (node_scaled_dot_product_attention_10_out_mm_out, "visual.transformer.resblocks.10.attn.out_proj.bias")
   add_1576 = Add (add_1461, node_scaled_dot_product_attention_10_out)
   layer_norm_22 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1576, "visual.transformer.resblocks.10.ln_2.weight", "visual.transformer.resblocks.10.ln_2.bias")
   val_125 = MatMul (layer_norm_22, val_38)
   linear_42 = Add (val_125, "visual.transformer.resblocks.10.mlp.c_fc.bias")
   mul_1185 = Mul (linear_42, val_3)
   sigmoid_10 = Sigmoid (mul_1185)
   mul_1190 = Mul (linear_42, sigmoid_10)
   val_126 = MatMul (mul_1190, val_39)
   linear_43 = Add (val_126, "visual.transformer.resblocks.10.mlp.c_proj.bias")
   add_1605 = Add (add_1576, linear_43)
   layer_norm_23 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1605, "visual.transformer.resblocks.11.ln_1.weight", "visual.transformer.resblocks.11.ln_1.bias")
   [node_scaled_dot_product_attention_11_qkv_mm] node_scaled_dot_product_attention_11_qkv_mm_out = MatMul (layer_norm_23, val_40)
   [node_scaled_dot_product_attention_11_qkv_bias] node_scaled_dot_product_attention_11_qkv = Add (node_scaled_dot_product_attention_11_qkv_mm_out, "visual.transformer.resblocks.11.attn.in_proj_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> (node_scaled_dot_product_attention_11_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_11_out_mm] node_scaled_dot_product_attention_11_out_mm_out = MatMul (scaled_dot_product_attention_11, node_scaled_dot_product_attention_11_wo_t)
   [node_scaled_dot_product_attention_11_out_bias] node_scaled_dot_product_attention_11_out = Add (node_scaled_dot_product_attention_11_out_mm_out, "visual.transformer.resblocks.11.attn.out_proj.bias")
   add_1720 = Add (add_1605, node_scaled_dot_product_attention_11_out)
   layer_norm_24 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1720, "visual.transformer.resblocks.11.ln_2.weight", "visual.transformer.resblocks.11.ln_2.bias")
   val_127 = MatMul (layer_norm_24, val_41)
   linear_46 = Add (val_127, "visual.transformer.resblocks.11.mlp.c_fc.bias")
   mul_1292 = Mul (linear_46, val_3)
   sigmoid_11 = Sigmoid (mul_1292)
   mul_1297 = Mul (linear_46, sigmoid_11)
   val_128 = MatMul (mul_1297, val_42)
   linear_47 = Add (val_128, "visual.transformer.resblocks.11.mlp.c_proj.bias")
   add_1749 = Add (add_1720, linear_47)
   layer_norm_25 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1749, "visual.transformer.resblocks.12.ln_1.weight", "visual.transformer.resblocks.12.ln_1.bias")
   [node_scaled_dot_product_attention_12_qkv_mm] node_scaled_dot_product_attention_12_qkv_mm_out = MatMul (layer_norm_25, val_43)
   [node_scaled_dot_product_attention_12_qkv_bias] node_scaled_dot_product_attention_12_qkv = Add (node_scaled_dot_product_attention_12_qkv_mm_out, "visual.transformer.resblocks.12.attn.in_proj_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> (node_scaled_dot_product_attention_12_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_12_out_mm] node_scaled_dot_product_attention_12_out_mm_out = MatMul (scaled_dot_product_attention_12, node_scaled_dot_product_attention_12_wo_t)
   [node_scaled_dot_product_attention_12_out_bias] node_scaled_dot_product_attention_12_out = Add (node_scaled_dot_product_attention_12_out_mm_out, "visual.transformer.resblocks.12.attn.out_proj.bias")
   add_1864 = Add (add_1749, node_scaled_dot_product_attention_12_out)
   layer_norm_26 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1864, "visual.transformer.resblocks.12.ln_2.weight", "visual.transformer.resblocks.12.ln_2.bias")
   val_129 = MatMul (layer_norm_26, val_44)
   linear_50 = Add (val_129, "visual.transformer.resblocks.12.mlp.c_fc.bias")
   mul_1399 = Mul (linear_50, val_3)
   sigmoid_12 = Sigmoid (mul_1399)
   mul_1404 = Mul (linear_50, sigmoid_12)
   val_130 = MatMul (mul_1404, val_45)
   linear_51 = Add (val_130, "visual.transformer.resblocks.12.mlp.c_proj.bias")
   add_1893 = Add (add_1864, linear_51)
   layer_norm_27 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1893, "visual.transformer.resblocks.13.ln_1.weight", "visual.transformer.resblocks.13.ln_1.bias")
   [node_scaled_dot_product_attention_13_qkv_mm] node_scaled_dot_product_attention_13_qkv_mm_out = MatMul (layer_norm_27, val_46)
   [node_scaled_dot_product_attention_13_qkv_bias] node_scaled_dot_product_attention_13_qkv = Add (node_scaled_dot_product_attention_13_qkv_mm_out, "visual.transformer.resblocks.13.attn.in_proj_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> (node_scaled_dot_product_attention_13_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_13_out_mm] node_scaled_dot_product_attention_13_out_mm_out = MatMul (scaled_dot_product_attention_13, node_scaled_dot_product_attention_13_wo_t)
   [node_scaled_dot_product_attention_13_out_bias] node_scaled_dot_product_attention_13_out = Add (node_scaled_dot_product_attention_13_out_mm_out, "visual.transformer.resblocks.13.attn.out_proj.bias")
   add_2008 = Add (add_1893, node_scaled_dot_product_attention_13_out)
   layer_norm_28 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_2008, "visual.transformer.resblocks.13.ln_2.weight", "visual.transformer.resblocks.13.ln_2.bias")
   val_131 = MatMul (layer_norm_28, val_47)
   linear_54 = Add (val_131, "visual.transformer.resblocks.13.mlp.c_fc.bias")
   mul_1506 = Mul (linear_54, val_3)
   sigmoid_13 = Sigmoid (mul_1506)
   mul_1511 = Mul (linear_54, sigmoid_13)
   val_132 = MatMul (mul_1511, val_48)
   linear_55 = Add (val_132, "visual.transformer.resblocks.13.mlp.c_proj.bias")
   add_2037 = Add (add_2008, linear_55)
   layer_norm_29 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_2037, "visual.transformer.resblocks.14.ln_1.weight", "visual.transformer.resblocks.14.ln_1.bias")
   [node_scaled_dot_product_attention_14_qkv_mm] node_scaled_dot_product_attention_14_qkv_mm_out = MatMul (layer_norm_29, val_49)
   [node_scaled_dot_product_attention_14_qkv_bias] node_scaled_dot_product_attention_14_qkv = Add (node_scaled_dot_product_attention_14_qkv_mm_out, "visual.transformer.resblocks.14.attn.in_proj_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> (node_scaled_dot_product_attention_14_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_14_out_mm] node_scaled_dot_product_attention_14_out_mm_out = MatMul (scaled_dot_product_attention_14, node_scaled_dot_product_attention_14_wo_t)
   [node_scaled_dot_product_attention_14_out_bias] node_scaled_dot_product_attention_14_out = Add (node_scaled_dot_product_attention_14_out_mm_out, "visual.transformer.resblocks.14.attn.out_proj.bias")
   add_2152 = Add (add_2037, node_scaled_dot_product_attention_14_out)
   layer_norm_30 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_2152, "visual.transformer.resblocks.14.ln_2.weight", "visual.transformer.resblocks.14.ln_2.bias")
   val_133 = MatMul (layer_norm_30, val_50)
   linear_58 = Add (val_133, "visual.transformer.resblocks.14.mlp.c_fc.bias")
   mul_1613 = Mul (linear_58, val_3)
   sigmoid_14 = Sigmoid (mul_1613)
   mul_1618 = Mul (linear_58, sigmoid_14)
   val_134 = MatMul (mul_1618, val_51)
   linear_59 = Add (val_134, "visual.transformer.resblocks.14.mlp.c_proj.bias")
   add_2181 = Add (add_2152, linear_59)
   layer_norm_31 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_2181, "visual.transformer.resblocks.15.ln_1.weight", "visual.transformer.resblocks.15.ln_1.bias")
   [node_scaled_dot_product_attention_15_qkv_mm] node_scaled_dot_product_attention_15_qkv_mm_out = MatMul (layer_norm_31, val_52)
   [node_scaled_dot_product_attention_15_qkv_bias] node_scaled_dot_product_attention_15_qkv = Add (node_scaled_dot_product_attention_15_qkv_mm_out, "visual.transformer.resblocks.15.attn.in_proj_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> (node_scaled_dot_product_attention_15_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_15_out_mm] node_scaled_dot_product_attention_15_out_mm_out = MatMul (scaled_dot_product_attention_15, node_scaled_dot_product_attention_15_wo_t)
   [node_scaled_dot_product_attention_15_out_bias] node_scaled_dot_product_attention_15_out = Add (node_scaled_dot_product_attention_15_out_mm_out, "visual.transformer.resblocks.15.attn.out_proj.bias")
   add_2296 = Add (add_2181, node_scaled_dot_product_attention_15_out)
   layer_norm_32 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_2296, "visual.transformer.resblocks.15.ln_2.weight", "visual.transformer.resblocks.15.ln_2.bias")
   val_135 = MatMul (layer_norm_32, val_53)
   linear_62 = Add (val_135, "visual.transformer.resblocks.15.mlp.c_fc.bias")
   mul_1720 = Mul (linear_62, val_3)
   sigmoid_15 = Sigmoid (mul_1720)
   mul_1725 = Mul (linear_62, sigmoid_15)
   val_136 = MatMul (mul_1725, val_54)
   linear_63 = Add (val_136, "visual.transformer.resblocks.15.mlp.c_proj.bias")
   add_2325 = Add (add_2296, linear_63)
   layer_norm_33 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_2325, "visual.transformer.resblocks.16.ln_1.weight", "visual.transformer.resblocks.16.ln_1.bias")
   [node_scaled_dot_product_attention_16_qkv_mm] node_scaled_dot_product_attention_16_qkv_mm_out = MatMul (layer_norm_33, val_55)
   [node_scaled_dot_product_attention_16_qkv_bias] node_scaled_dot_product_attention_16_qkv = Add (node_scaled_dot_product_attention_16_qkv_mm_out, "visual.transformer.resblocks.16.attn.in_proj_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> (node_scaled_dot_product_attention_16_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_16_out_mm] node_scaled_dot_product_attention_16_out_mm_out = MatMul (scaled_dot_product_attention_16, node_scaled_dot_product_attention_16_wo_t)
   [node_scaled_dot_product_attention_16_out_bias] node_scaled_dot_product_attention_16_out = Add (node_scaled_dot_product_attention_16_out_mm_out, "visual.transformer.resblocks.16.attn.out_proj.bias")
   add_2440 = Add (add_2325, node_scaled_dot_product_attention_16_out)
   layer_norm_34 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_2440, "visual.transformer.resblocks.16.ln_2.weight", "visual.transformer.resblocks.16.ln_2.bias")
   val_137 = MatMul (layer_norm_34, val_56)
   linear_66 = Add (val_137, "visual.transformer.resblocks.16.mlp.c_fc.bias")
   mul_1827 = Mul (linear_66, val_3)
   sigmoid_16 = Sigmoid (mul_1827)
   mul_1832 = Mul (linear_66, sigmoid_16)
   val_138 = MatMul (mul_1832, val_57)
   linear_67 = Add (val_138, "visual.transformer.resblocks.16.mlp.c_proj.bias")
   add_2469 = Add (add_2440, linear_67)
   layer_norm_35 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_2469, "visual.transformer.resblocks.17.ln_1.weight", "visual.transformer.resblocks.17.ln_1.bias")
   [node_scaled_dot_product_attention_17_qkv_mm] node_scaled_dot_product_attention_17_qkv_mm_out = MatMul (layer_norm_35, val_58)
   [node_scaled_dot_product_attention_17_qkv_bias] node_scaled_dot_product_attention_17_qkv = Add (node_scaled_dot_product_attention_17_qkv_mm_out, "visual.transformer.resblocks.17.attn.in_proj_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> (node_scaled_dot_product_attention_17_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_17_out_mm] node_scaled_dot_product_attention_17_out_mm_out = MatMul (scaled_dot_product_attention_17, node_scaled_dot_product_attention_17_wo_t)
   [node_scaled_dot_product_attention_17_out_bias] node_scaled_dot_product_attention_17_out = Add (node_scaled_dot_product_attention_17_out_mm_out, "visual.transformer.resblocks.17.attn.out_proj.bias")
   add_2584 = Add (add_2469, node_scaled_dot_product_attention_17_out)
   layer_norm_36 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_2584, "visual.transformer.resblocks.17.ln_2.weight", "visual.transformer.resblocks.17.ln_2.bias")
   val_139 = MatMul (layer_norm_36, val_59)
   linear_70 = Add (val_139, "visual.transformer.resblocks.17.mlp.c_fc.bias")
   mul_1934 = Mul (linear_70, val_3)
   sigmoid_17 = Sigmoid (mul_1934)
   mul_1939 = Mul (linear_70, sigmoid_17)
   val_140 = MatMul (mul_1939, val_60)
   linear_71 = Add (val_140, "visual.transformer.resblocks.17.mlp.c_proj.bias")
   add_2613 = Add (add_2584, linear_71)
   layer_norm_37 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_2613, "visual.transformer.resblocks.18.ln_1.weight", "visual.transformer.resblocks.18.ln_1.bias")
   [node_scaled_dot_product_attention_18_qkv_mm] node_scaled_dot_product_attention_18_qkv_mm_out = MatMul (layer_norm_37, val_61)
   [node_scaled_dot_product_attention_18_qkv_bias] node_scaled_dot_product_attention_18_qkv = Add (node_scaled_dot_product_attention_18_qkv_mm_out, "visual.transformer.resblocks.18.attn.in_proj_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> (node_scaled_dot_product_attention_18_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_18_out_mm] node_scaled_dot_product_attention_18_out_mm_out = MatMul (scaled_dot_product_attention_18, node_scaled_dot_product_attention_18_wo_t)
   [node_scaled_dot_product_attention_18_out_bias] node_scaled_dot_product_attention_18_out = Add (node_scaled_dot_product_attention_18_out_mm_out, "visual.transformer.resblocks.18.attn.out_proj.bias")
   add_2728 = Add (add_2613, node_scaled_dot_product_attention_18_out)
   layer_norm_38 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_2728, "visual.transformer.resblocks.18.ln_2.weight", "visual.transformer.resblocks.18.ln_2.bias")
   val_141 = MatMul (layer_norm_38, val_62)
   linear_74 = Add (val_141, "visual.transformer.resblocks.18.mlp.c_fc.bias")
   mul_2041 = Mul (linear_74, val_3)
   sigmoid_18 = Sigmoid (mul_2041)
   mul_2046 = Mul (linear_74, sigmoid_18)
   val_142 = MatMul (mul_2046, val_63)
   linear_75 = Add (val_142, "visual.transformer.resblocks.18.mlp.c_proj.bias")
   add_2757 = Add (add_2728, linear_75)
   layer_norm_39 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_2757, "visual.transformer.resblocks.19.ln_1.weight", "visual.transformer.resblocks.19.ln_1.bias")
   [node_scaled_dot_product_attention_19_qkv_mm] node_scaled_dot_product_attention_19_qkv_mm_out = MatMul (layer_norm_39, val_64)
   [node_scaled_dot_product_attention_19_qkv_bias] node_scaled_dot_product_attention_19_qkv = Add (node_scaled_dot_product_attention_19_qkv_mm_out, "visual.transformer.resblocks.19.attn.in_proj_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> (node_scaled_dot_product_attention_19_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_19_out_mm] node_scaled_dot_product_attention_19_out_mm_out = MatMul (scaled_dot_product_attention_19, node_scaled_dot_product_attention_19_wo_t)
   [node_scaled_dot_product_attention_19_out_bias] node_scaled_dot_product_attention_19_out = Add (node_scaled_dot_product_attention_19_out_mm_out, "visual.transformer.resblocks.19.attn.out_proj.bias")
   add_2872 = Add (add_2757, node_scaled_dot_product_attention_19_out)
   layer_norm_40 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_2872, "visual.transformer.resblocks.19.ln_2.weight", "visual.transformer.resblocks.19.ln_2.bias")
   val_143 = MatMul (layer_norm_40, val_65)
   linear_78 = Add (val_143, "visual.transformer.resblocks.19.mlp.c_fc.bias")
   mul_2148 = Mul (linear_78, val_3)
   sigmoid_19 = Sigmoid (mul_2148)
   mul_2153 = Mul (linear_78, sigmoid_19)
   val_144 = MatMul (mul_2153, val_66)
   linear_79 = Add (val_144, "visual.transformer.resblocks.19.mlp.c_proj.bias")
   add_2901 = Add (add_2872, linear_79)
   layer_norm_41 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_2901, "visual.transformer.resblocks.20.ln_1.weight", "visual.transformer.resblocks.20.ln_1.bias")
   [node_scaled_dot_product_attention_20_qkv_mm] node_scaled_dot_product_attention_20_qkv_mm_out = MatMul (layer_norm_41, val_67)
   [node_scaled_dot_product_attention_20_qkv_bias] node_scaled_dot_product_attention_20_qkv = Add (node_scaled_dot_product_attention_20_qkv_mm_out, "visual.transformer.resblocks.20.attn.in_proj_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> (node_scaled_dot_product_attention_20_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_20_out_mm] node_scaled_dot_product_attention_20_out_mm_out = MatMul (scaled_dot_product_attention_20, node_scaled_dot_product_attention_20_wo_t)
   [node_scaled_dot_product_attention_20_out_bias] node_scaled_dot_product_attention_20_out = Add (node_scaled_dot_product_attention_20_out_mm_out, "visual.transformer.resblocks.20.attn.out_proj.bias")
   add_3016 = Add (add_2901, node_scaled_dot_product_attention_20_out)
   layer_norm_42 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_3016, "visual.transformer.resblocks.20.ln_2.weight", "visual.transformer.resblocks.20.ln_2.bias")
   val_145 = MatMul (layer_norm_42, val_68)
   linear_82 = Add (val_145, "visual.transformer.resblocks.20.mlp.c_fc.bias")
   mul_2255 = Mul (linear_82, val_3)
   sigmoid_20 = Sigmoid (mul_2255)
   mul_2260 = Mul (linear_82, sigmoid_20)
   val_146 = MatMul (mul_2260, val_69)
   linear_83 = Add (val_146, "visual.transformer.resblocks.20.mlp.c_proj.bias")
   add_3045 = Add (add_3016, linear_83)
   layer_norm_43 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_3045, "visual.transformer.resblocks.21.ln_1.weight", "visual.transformer.resblocks.21.ln_1.bias")
   [node_scaled_dot_product_attention_21_qkv_mm] node_scaled_dot_product_attention_21_qkv_mm_out = MatMul (layer_norm_43, val_70)
   [node_scaled_dot_product_attention_21_qkv_bias] node_scaled_dot_product_attention_21_qkv = Add (node_scaled_dot_product_attention_21_qkv_mm_out, "visual.transformer.resblocks.21.attn.in_proj_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> (node_scaled_dot_product_attention_21_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_21_out_mm] node_scaled_dot_product_attention_21_out_mm_out = MatMul (scaled_dot_product_attention_21, node_scaled_dot_product_attention_21_wo_t)
   [node_scaled_dot_product_attention_21_out_bias] node_scaled_dot_product_attention_21_out = Add (node_scaled_dot_product_attention_21_out_mm_out, "visual.transformer.resblocks.21.attn.out_proj.bias")
   add_3160 = Add (add_3045, node_scaled_dot_product_attention_21_out)
   layer_norm_44 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_3160, "visual.transformer.resblocks.21.ln_2.weight", "visual.transformer.resblocks.21.ln_2.bias")
   val_147 = MatMul (layer_norm_44, val_71)
   linear_86 = Add (val_147, "visual.transformer.resblocks.21.mlp.c_fc.bias")
   mul_2362 = Mul (linear_86, val_3)
   sigmoid_21 = Sigmoid (mul_2362)
   mul_2367 = Mul (linear_86, sigmoid_21)
   val_148 = MatMul (mul_2367, val_72)
   linear_87 = Add (val_148, "visual.transformer.resblocks.21.mlp.c_proj.bias")
   add_3189 = Add (add_3160, linear_87)
   layer_norm_45 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_3189, "visual.transformer.resblocks.22.ln_1.weight", "visual.transformer.resblocks.22.ln_1.bias")
   [node_scaled_dot_product_attention_22_qkv_mm] node_scaled_dot_product_attention_22_qkv_mm_out = MatMul (layer_norm_45, val_73)
   [node_scaled_dot_product_attention_22_qkv_bias] node_scaled_dot_product_attention_22_qkv = Add (node_scaled_dot_product_attention_22_qkv_mm_out, "visual.transformer.resblocks.22.attn.in_proj_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> (node_scaled_dot_product_attention_22_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_22_out_mm] node_scaled_dot_product_attention_22_out_mm_out = MatMul (scaled_dot_product_attention_22, node_scaled_dot_product_attention_22_wo_t)
   [node_scaled_dot_product_attention_22_out_bias] node_scaled_dot_product_attention_22_out = Add (node_scaled_dot_product_attention_22_out_mm_out, "visual.transformer.resblocks.22.attn.out_proj.bias")
   add_3304 = Add (add_3189, node_scaled_dot_product_attention_22_out)
   layer_norm_46 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_3304, "visual.transformer.resblocks.22.ln_2.weight", "visual.transformer.resblocks.22.ln_2.bias")
   val_149 = MatMul (layer_norm_46, val_74)
   linear_90 = Add (val_149, "visual.transformer.resblocks.22.mlp.c_fc.bias")
   mul_2469 = Mul (linear_90, val_3)
   sigmoid_22 = Sigmoid (mul_2469)
   mul_2474 = Mul (linear_90, sigmoid_22)
   val_150 = MatMul (mul_2474, val_75)
   linear_91 = Add (val_150, "visual.transformer.resblocks.22.mlp.c_proj.bias")
   add_3333 = Add (add_3304, linear_91)
   layer_norm_47 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_3333, "visual.transformer.resblocks.23.ln_1.weight", "visual.transformer.resblocks.23.ln_1.bias")
   [node_scaled_dot_product_attention_23_qkv_mm] node_scaled_dot_product_attention_23_qkv_mm_out = MatMul (layer_norm_47, val_76)
   [node_scaled_dot_product_attention_23_qkv_bias] node_scaled_dot_product_attention_23_qkv = Add (node_scaled_dot_product_attention_23_qkv_mm_out, "visual.transformer.resblocks.23.attn.in_proj_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> (node_scaled_dot_product_attention_23_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_23_out_mm] node_scaled_dot_product_attention_23_out_mm_out = MatMul (scaled_dot_product_attention_23, node_scaled_dot_product_attention_23_wo_t)
   [node_scaled_dot_product_attention_23_out_bias] node_scaled_dot_product_attention_23_out = Add (node_scaled_dot_product_attention_23_out_mm_out, "visual.transformer.resblocks.23.attn.out_proj.bias")
   add_3448 = Add (add_3333, node_scaled_dot_product_attention_23_out)
   layer_norm_48 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_3448, "visual.transformer.resblocks.23.ln_2.weight", "visual.transformer.resblocks.23.ln_2.bias")
   val_151 = MatMul (layer_norm_48, val_77)
   linear_94 = Add (val_151, "visual.transformer.resblocks.23.mlp.c_fc.bias")
   mul_2576 = Mul (linear_94, val_3)
   sigmoid_23 = Sigmoid (mul_2576)
   mul_2581 = Mul (linear_94, sigmoid_23)
   val_152 = MatMul (mul_2581, val_78)
   linear_95 = Add (val_152, "visual.transformer.resblocks.23.mlp.c_proj.bias")
   add_3477 = Add (add_3448, linear_95)
   layer_norm_49 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_3477, "visual.transformer.resblocks.24.ln_1.weight", "visual.transformer.resblocks.24.ln_1.bias")
   [node_scaled_dot_product_attention_24_qkv_mm] node_scaled_dot_product_attention_24_qkv_mm_out = MatMul (layer_norm_49, val_79)
   [node_scaled_dot_product_attention_24_qkv_bias] node_scaled_dot_product_attention_24_qkv = Add (node_scaled_dot_product_attention_24_qkv_mm_out, "visual.transformer.resblocks.24.attn.in_proj_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> (node_scaled_dot_product_attention_24_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_24_out_mm] node_scaled_dot_product_attention_24_out_mm_out = MatMul (scaled_dot_product_attention_24, node_scaled_dot_product_attention_24_wo_t)
   [node_scaled_dot_product_attention_24_out_bias] node_scaled_dot_product_attention_24_out = Add (node_scaled_dot_product_attention_24_out_mm_out, "visual.transformer.resblocks.24.attn.out_proj.bias")
   add_3592 = Add (add_3477, node_scaled_dot_product_attention_24_out)
   layer_norm_50 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_3592, "visual.transformer.resblocks.24.ln_2.weight", "visual.transformer.resblocks.24.ln_2.bias")
   val_153 = MatMul (layer_norm_50, val_80)
   linear_98 = Add (val_153, "visual.transformer.resblocks.24.mlp.c_fc.bias")
   mul_2683 = Mul (linear_98, val_3)
   sigmoid_24 = Sigmoid (mul_2683)
   mul_2688 = Mul (linear_98, sigmoid_24)
   val_154 = MatMul (mul_2688, val_81)
   linear_99 = Add (val_154, "visual.transformer.resblocks.24.mlp.c_proj.bias")
   add_3621 = Add (add_3592, linear_99)
   layer_norm_51 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_3621, "visual.transformer.resblocks.25.ln_1.weight", "visual.transformer.resblocks.25.ln_1.bias")
   [node_scaled_dot_product_attention_25_qkv_mm] node_scaled_dot_product_attention_25_qkv_mm_out = MatMul (layer_norm_51, val_82)
   [node_scaled_dot_product_attention_25_qkv_bias] node_scaled_dot_product_attention_25_qkv = Add (node_scaled_dot_product_attention_25_qkv_mm_out, "visual.transformer.resblocks.25.attn.in_proj_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> (node_scaled_dot_product_attention_25_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_25_out_mm] node_scaled_dot_product_attention_25_out_mm_out = MatMul (scaled_dot_product_attention_25, node_scaled_dot_product_attention_25_wo_t)
   [node_scaled_dot_product_attention_25_out_bias] node_scaled_dot_product_attention_25_out = Add (node_scaled_dot_product_attention_25_out_mm_out, "visual.transformer.resblocks.25.attn.out_proj.bias")
   add_3736 = Add (add_3621, node_scaled_dot_product_attention_25_out)
   layer_norm_52 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_3736, "visual.transformer.resblocks.25.ln_2.weight", "visual.transformer.resblocks.25.ln_2.bias")
   val_155 = MatMul (layer_norm_52, val_83)
   linear_102 = Add (val_155, "visual.transformer.resblocks.25.mlp.c_fc.bias")
   mul_2790 = Mul (linear_102, val_3)
   sigmoid_25 = Sigmoid (mul_2790)
   mul_2795 = Mul (linear_102, sigmoid_25)
   val_156 = MatMul (mul_2795, val_84)
   linear_103 = Add (val_156, "visual.transformer.resblocks.25.mlp.c_proj.bias")
   add_3765 = Add (add_3736, linear_103)
   layer_norm_53 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_3765, "visual.transformer.resblocks.26.ln_1.weight", "visual.transformer.resblocks.26.ln_1.bias")
   [node_scaled_dot_product_attention_26_qkv_mm] node_scaled_dot_product_attention_26_qkv_mm_out = MatMul (layer_norm_53, val_85)
   [node_scaled_dot_product_attention_26_qkv_bias] node_scaled_dot_product_attention_26_qkv = Add (node_scaled_dot_product_attention_26_qkv_mm_out, "visual.transformer.resblocks.26.attn.in_proj_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> (node_scaled_dot_product_attention_26_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_26_out_mm] node_scaled_dot_product_attention_26_out_mm_out = MatMul (scaled_dot_product_attention_26, node_scaled_dot_product_attention_26_wo_t)
   [node_scaled_dot_product_attention_26_out_bias] node_scaled_dot_product_attention_26_out = Add (node_scaled_dot_product_attention_26_out_mm_out, "visual.transformer.resblocks.26.attn.out_proj.bias")
   add_3880 = Add (add_3765, node_scaled_dot_product_attention_26_out)
   layer_norm_54 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_3880, "visual.transformer.resblocks.26.ln_2.weight", "visual.transformer.resblocks.26.ln_2.bias")
   val_157 = MatMul (layer_norm_54, val_86)
   linear_106 = Add (val_157, "visual.transformer.resblocks.26.mlp.c_fc.bias")
   mul_2897 = Mul (linear_106, val_3)
   sigmoid_26 = Sigmoid (mul_2897)
   mul_2902 = Mul (linear_106, sigmoid_26)
   val_158 = MatMul (mul_2902, val_87)
   linear_107 = Add (val_158, "visual.transformer.resblocks.26.mlp.c_proj.bias")
   add_3909 = Add (add_3880, linear_107)
   layer_norm_55 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_3909, "visual.transformer.resblocks.27.ln_1.weight", "visual.transformer.resblocks.27.ln_1.bias")
   [node_scaled_dot_product_attention_27_qkv_mm] node_scaled_dot_product_attention_27_qkv_mm_out = MatMul (layer_norm_55, val_88)
   [node_scaled_dot_product_attention_27_qkv_bias] node_scaled_dot_product_attention_27_qkv = Add (node_scaled_dot_product_attention_27_qkv_mm_out, "visual.transformer.resblocks.27.attn.in_proj_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> (node_scaled_dot_product_attention_27_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_27_out_mm] node_scaled_dot_product_attention_27_out_mm_out = MatMul (scaled_dot_product_attention_27, node_scaled_dot_product_attention_27_wo_t)
   [node_scaled_dot_product_attention_27_out_bias] node_scaled_dot_product_attention_27_out = Add (node_scaled_dot_product_attention_27_out_mm_out, "visual.transformer.resblocks.27.attn.out_proj.bias")
   add_4024 = Add (add_3909, node_scaled_dot_product_attention_27_out)
   layer_norm_56 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_4024, "visual.transformer.resblocks.27.ln_2.weight", "visual.transformer.resblocks.27.ln_2.bias")
   val_159 = MatMul (layer_norm_56, val_89)
   linear_110 = Add (val_159, "visual.transformer.resblocks.27.mlp.c_fc.bias")
   mul_3004 = Mul (linear_110, val_3)
   sigmoid_27 = Sigmoid (mul_3004)
   mul_3009 = Mul (linear_110, sigmoid_27)
   val_160 = MatMul (mul_3009, val_90)
   linear_111 = Add (val_160, "visual.transformer.resblocks.27.mlp.c_proj.bias")
   add_4053 = Add (add_4024, linear_111)
   layer_norm_57 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_4053, "visual.transformer.resblocks.28.ln_1.weight", "visual.transformer.resblocks.28.ln_1.bias")
   [node_scaled_dot_product_attention_28_qkv_mm] node_scaled_dot_product_attention_28_qkv_mm_out = MatMul (layer_norm_57, val_91)
   [node_scaled_dot_product_attention_28_qkv_bias] node_scaled_dot_product_attention_28_qkv = Add (node_scaled_dot_product_attention_28_qkv_mm_out, "visual.transformer.resblocks.28.attn.in_proj_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> (node_scaled_dot_product_attention_28_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_28_out_mm] node_scaled_dot_product_attention_28_out_mm_out = MatMul (scaled_dot_product_attention_28, node_scaled_dot_product_attention_28_wo_t)
   [node_scaled_dot_product_attention_28_out_bias] node_scaled_dot_product_attention_28_out = Add (node_scaled_dot_product_attention_28_out_mm_out, "visual.transformer.resblocks.28.attn.out_proj.bias")
   add_4168 = Add (add_4053, node_scaled_dot_product_attention_28_out)
   layer_norm_58 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_4168, "visual.transformer.resblocks.28.ln_2.weight", "visual.transformer.resblocks.28.ln_2.bias")
   val_161 = MatMul (layer_norm_58, val_92)
   linear_114 = Add (val_161, "visual.transformer.resblocks.28.mlp.c_fc.bias")
   mul_3111 = Mul (linear_114, val_3)
   sigmoid_28 = Sigmoid (mul_3111)
   mul_3116 = Mul (linear_114, sigmoid_28)
   val_162 = MatMul (mul_3116, val_93)
   linear_115 = Add (val_162, "visual.transformer.resblocks.28.mlp.c_proj.bias")
   add_4197 = Add (add_4168, linear_115)
   layer_norm_59 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_4197, "visual.transformer.resblocks.29.ln_1.weight", "visual.transformer.resblocks.29.ln_1.bias")
   [node_scaled_dot_product_attention_29_qkv_mm] node_scaled_dot_product_attention_29_qkv_mm_out = MatMul (layer_norm_59, val_94)
   [node_scaled_dot_product_attention_29_qkv_bias] node_scaled_dot_product_attention_29_qkv = Add (node_scaled_dot_product_attention_29_qkv_mm_out, "visual.transformer.resblocks.29.attn.in_proj_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> (node_scaled_dot_product_attention_29_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_29_out_mm] node_scaled_dot_product_attention_29_out_mm_out = MatMul (scaled_dot_product_attention_29, node_scaled_dot_product_attention_29_wo_t)
   [node_scaled_dot_product_attention_29_out_bias] node_scaled_dot_product_attention_29_out = Add (node_scaled_dot_product_attention_29_out_mm_out, "visual.transformer.resblocks.29.attn.out_proj.bias")
   add_4312 = Add (add_4197, node_scaled_dot_product_attention_29_out)
   layer_norm_60 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_4312, "visual.transformer.resblocks.29.ln_2.weight", "visual.transformer.resblocks.29.ln_2.bias")
   val_163 = MatMul (layer_norm_60, val_95)
   linear_118 = Add (val_163, "visual.transformer.resblocks.29.mlp.c_fc.bias")
   mul_3218 = Mul (linear_118, val_3)
   sigmoid_29 = Sigmoid (mul_3218)
   mul_3223 = Mul (linear_118, sigmoid_29)
   val_164 = MatMul (mul_3223, val_96)
   linear_119 = Add (val_164, "visual.transformer.resblocks.29.mlp.c_proj.bias")
   add_4341 = Add (add_4312, linear_119)
   layer_norm_61 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_4341, "visual.transformer.resblocks.30.ln_1.weight", "visual.transformer.resblocks.30.ln_1.bias")
   [node_scaled_dot_product_attention_30_qkv_mm] node_scaled_dot_product_attention_30_qkv_mm_out = MatMul (layer_norm_61, val_97)
   [node_scaled_dot_product_attention_30_qkv_bias] node_scaled_dot_product_attention_30_qkv = Add (node_scaled_dot_product_attention_30_qkv_mm_out, "visual.transformer.resblocks.30.attn.in_proj_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> (node_scaled_dot_product_attention_30_qkv, attn3d_split_3x1280)
   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)
   [node_scaled_dot_product_attention_30_out_mm] node_scaled_dot_product_attention_30_out_mm_out = MatMul (scaled_dot_product_attention_30, node_scaled_dot_product_attention_30_wo_t)
   [node_scaled_dot_product_attention_30_out_bias] node_scaled_dot_product_attention_30_out = Add (node_scaled_dot_product_attention_30_out_mm_out, "visual.transformer.resblocks.30.attn.out_proj.bias")
   add_4456 = Add (add_4341, node_scaled_dot_product_attention_30_out)
   layer_norm_62 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_4456, "visual.transformer.resblocks.30.ln_2.weight", "visual.transformer.resblocks.30.ln_2.bias")
   val_165 = MatMul (layer_norm_62, val_98)
   linear_122 = Add (val_165, "visual.transformer.resblocks.30.mlp.c_fc.bias")
   mul_3325 = Mul (linear_122, val_3)
   sigmoid_30 = Sigmoid (mul_3325)
   mul_3330 = Mul (linear_122, sigmoid_30)
   val_166 = MatMul (mul_3330, val_99)
   linear_123 = Add (val_166, "visual.transformer.resblocks.30.mlp.c_proj.bias")
   add_4485 = Add (add_4456, linear_123)
   layer_norm_63 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_4485, "visual.transformer.resblocks.31.ln_1.weight", "visual.transformer.resblocks.31.ln_1.bias")
   [node_scaled_dot_product_attention_31_qkv_mm] node_scaled_dot_product_attention_31_qkv_mm_out = MatMul (layer_norm_63, val_100)
   [node_scaled_dot_product_attention_31_qkv_bias] node_scaled_dot_product_attention_31_qkv = Add (node_scaled_dot_product_attention_31_qkv_mm_out, "visual.transformer.resblocks.31.attn.in_proj_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> (node_scaled_dot_product_attention_31_qkv, attn3d_split_3x1280)
   [pool_hoist_node_scaled_dot_product_attention_31_q] node_scaled_dot_product_attention_31_q_pooled = Slice (node_scaled_dot_product_attention_31_q, val_2, val_6, val_6)
   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_pooled, node_scaled_dot_product_attention_31_k, node_scaled_dot_product_attention_31_v)
   [node_scaled_dot_product_attention_31_out_mm] node_scaled_dot_product_attention_31_out_mm_out = MatMul (scaled_dot_product_attention_31, node_scaled_dot_product_attention_31_wo_t)
   [node_scaled_dot_product_attention_31_out_bias] node_scaled_dot_product_attention_31_out = Add (node_scaled_dot_product_attention_31_out_mm_out, "visual.transformer.resblocks.31.attn.out_proj.bias")
   [pool_hoist_add_4485] add_4485_pooled = Slice (add_4485, val_2, val_6, val_6)
   add_4600 = Add (add_4485_pooled, node_scaled_dot_product_attention_31_out)
   layer_norm_64 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_4600, "visual.transformer.resblocks.31.ln_2.weight", "visual.transformer.resblocks.31.ln_2.bias")
   val_167 = MatMul (layer_norm_64, val_101)
   linear_126 = Add (val_167, "visual.transformer.resblocks.31.mlp.c_fc.bias")
   mul_3432 = Mul (linear_126, val_3)
   sigmoid_31 = Sigmoid (mul_3432)
   mul_3437 = Mul (linear_126, sigmoid_31)
   val_168 = MatMul (mul_3437, val_102)
   linear_127 = Add (val_168, "visual.transformer.resblocks.31.mlp.c_proj.bias")
   add_4629 = Add (add_4600, linear_127)
   val_169 = Squeeze (add_4629, val_6)
   select_96 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (val_169, "visual.ln_post.weight", "visual.ln_post.bias")
   [node_matmul] matmul = MatMul (select_96, "visual.proj")
   [node_linalg_vector_norm] linalg_vector_norm = ReduceL2 <keepdims: int = 1, noop_with_empty_axes: int = 0> (matmul, val_0)
   [node_clamp_min] clamp_min = Clip (linalg_vector_norm, val_1)
   [node_div] image_embedding = Div (matmul, clamp_min)
}

weights:
attn3d_split_3x1280 INT64[3] eab0797de1a4
node_Conv_1785_fused_bias FLOAT[1280] c4e039ff3d99
node_scaled_dot_product_attention_10_wo_t FLOAT[1280,1280] c30c57658c23
node_scaled_dot_product_attention_11_wo_t FLOAT[1280,1280] 6224ca224fe8
node_scaled_dot_product_attention_12_wo_t FLOAT[1280,1280] 503936a0fe55
node_scaled_dot_product_attention_13_wo_t FLOAT[1280,1280] 08f524890a2d
node_scaled_dot_product_attention_14_wo_t FLOAT[1280,1280] a651b04d6c06
node_scaled_dot_product_attention_15_wo_t FLOAT[1280,1280] 371b1ba07863
node_scaled_dot_product_attention_16_wo_t FLOAT[1280,1280] 5de612597a28
node_scaled_dot_product_attention_17_wo_t FLOAT[1280,1280] d3f98f2ff010
node_scaled_dot_product_attention_18_wo_t FLOAT[1280,1280] 7403ad5fa22b
node_scaled_dot_product_attention_19_wo_t FLOAT[1280,1280] 35eb0cbbe18a
node_scaled_dot_product_attention_1_wo_t FLOAT[1280,1280] 0b06aae12118
node_scaled_dot_product_attention_20_wo_t FLOAT[1280,1280] f722d13046ab
node_scaled_dot_product_attention_21_wo_t FLOAT[1280,1280] b12e4f6b5997
node_scaled_dot_product_attention_22_wo_t FLOAT[1280,1280] 4f2e45a13b7d
node_scaled_dot_product_attention_23_wo_t FLOAT[1280,1280] 9976891db0da
node_scaled_dot_product_attention_24_wo_t FLOAT[1280,1280] 4f6e679a5bad
node_scaled_dot_product_attention_25_wo_t FLOAT[1280,1280] aa8190bf546f
node_scaled_dot_product_attention_26_wo_t FLOAT[1280,1280] 6e315b75ee1a
node_scaled_dot_product_attention_27_wo_t FLOAT[1280,1280] 432b466f319b
node_scaled_dot_product_attention_28_wo_t FLOAT[1280,1280] 3816d38b0180
node_scaled_dot_product_attention_29_wo_t FLOAT[1280,1280] 596d85ad9537
node_scaled_dot_product_attention_2_wo_t FLOAT[1280,1280] 374d7959df87
node_scaled_dot_product_attention_30_wo_t FLOAT[1280,1280] 24b6edd5fa2f
node_scaled_dot_product_attention_31_wo_t FLOAT[1280,1280] 1fe372c828c0
node_scaled_dot_product_attention_3_wo_t FLOAT[1280,1280] b6e678a4533c
node_scaled_dot_product_attention_4_wo_t FLOAT[1280,1280] faa44ca5311b
node_scaled_dot_product_attention_5_wo_t FLOAT[1280,1280] 2f5b980b82c7
node_scaled_dot_product_attention_6_wo_t FLOAT[1280,1280] e579c11d1451
node_scaled_dot_product_attention_7_wo_t FLOAT[1280,1280] 21f091ff790e
node_scaled_dot_product_attention_8_wo_t FLOAT[1280,1280] fdd45e050406
node_scaled_dot_product_attention_9_wo_t FLOAT[1280,1280] 97480bb5674d
node_scaled_dot_product_attention_wo_t FLOAT[1280,1280] 2760f192aae3
val_0 INT64[1] 12a3ae445661
val_1 FLOAT[] 6708d9be4956
val_10 FLOAT[1280,3840] 31a82655fb9f
val_100 FLOAT[1280,3840] c20042686cfd
val_101 FLOAT[1280,5120] 0cef76462e89
val_102 FLOAT[5120,1280] a859c9bb27aa
val_103 FLOAT[1,257,1280] f73fbd000d3c
val_11 FLOAT[1280,5120] 2563f9de4b8c
val_12 FLOAT[5120,1280] c466e56e86e7
val_13 FLOAT[1280,3840] da02aa6268f1
val_14 FLOAT[1280,5120] 1561b150a080
val_15 FLOAT[5120,1280] 0e735b13f80c
val_16 FLOAT[1280,3840] 1583f59a9b02
val_17 FLOAT[1280,5120] 70b0f2e071cc
val_18 FLOAT[5120,1280] deabb67c08ec
val_19 FLOAT[1280,3840] c3f3a78b8f4a
val_2 INT64[1] af5570f5a181
val_20 FLOAT[1280,5120] 4b1b40e39886
val_21 FLOAT[5120,1280] 1469568745b1
val_22 FLOAT[1280,3840] 4ef0007810f6
val_23 FLOAT[1280,5120] ab433c56a206
val_24 FLOAT[5120,1280] 7a6174f79b2a
val_25 FLOAT[1280,3840] c683481289e8
val_26 FLOAT[1280,5120] 5c0940e9df0d
val_27 FLOAT[5120,1280] 1596a9a68740
val_28 FLOAT[1280,3840] c9a7ab628ba0
val_29 FLOAT[1280,5120] 69f054ba562b
val_3 FLOAT[] c2e7ddfe3114
val_30 FLOAT[5120,1280] 134fb84daa4a
val_31 FLOAT[1280,3840] 7beeb420c29c
val_32 FLOAT[1280,5120] 4735eaafb89b
val_33 FLOAT[5120,1280] 2daa0e00f88f
val_34 FLOAT[1280,3840] 341ee494b0d0
val_35 FLOAT[1280,5120] 89f6233d3818
val_36 FLOAT[5120,1280] 9077280dbdae
val_37 FLOAT[1280,3840] 57db9223cdeb
val_38 FLOAT[1280,5120] 638896d2b4ee
val_39 FLOAT[5120,1280] a068ec7b64c8
val_4 INT64[6] 6b7d92eaae70
val_40 FLOAT[1280,3840] dbafe0e3a3ec
val_41 FLOAT[1280,5120] de03ecafb2f0
val_42 FLOAT[5120,1280] 5ff675bbbb07
val_43 FLOAT[1280,3840] c158de5050be
val_44 FLOAT[1280,5120] afca12823fb8
val_45 FLOAT[5120,1280] 1b9b59678126
val_46 FLOAT[1280,3840] b68a8e1a5a1d
val_47 FLOAT[1280,5120] 8b10f4de10ab
val_48 FLOAT[5120,1280] 764ff3cfb114
val_49 FLOAT[1280,3840] b94def858852
val_5 FLOAT[] df3f619804a9
val_50 FLOAT[1280,5120] ad667bd7d32c
val_51 FLOAT[5120,1280] 7262ddf9d046
val_52 FLOAT[1280,3840] 0ea7ed4a7ab8
val_53 FLOAT[1280,5120] 770e90f1f641
val_54 FLOAT[5120,1280] be8b3fbe5d07
val_55 FLOAT[1280,3840] 2ed0d07b4293
val_56 FLOAT[1280,5120] 44eadb489d72
val_57 FLOAT[5120,1280] cc26e7cad9f0
val_58 FLOAT[1280,3840] 8699b276f75b
val_59 FLOAT[1280,5120] 086edb2734b3
val_6 INT64[1] 7c9fa136d441
val_60 FLOAT[5120,1280] 45562dd8214b
val_61 FLOAT[1280,3840] 7bb00cf1b8a8
val_62 FLOAT[1280,5120] 590cd20bc626
val_63 FLOAT[5120,1280] 32059862916e
val_64 FLOAT[1280,3840] 0a6577e9a88e
val_65 FLOAT[1280,5120] 47b2bc95d9f8
val_66 FLOAT[5120,1280] bd9312850850
val_67 FLOAT[1280,3840] 901a9ab210e7
val_68 FLOAT[1280,5120] cd6ddf877908
val_69 FLOAT[5120,1280] fb7a3ada5aef
val_7 FLOAT[1280,3840] 0e57657d7be5
val_70 FLOAT[1280,3840] 9a17eeac1b56
val_71 FLOAT[1280,5120] b0e1a40f0096
val_72 FLOAT[5120,1280] 009538c7351d
val_73 FLOAT[1280,3840] 6f7752692b72
val_74 FLOAT[1280,5120] b482146b8145
val_75 FLOAT[5120,1280] 3525e340d451
val_76 FLOAT[1280,3840] a6ede6819369
val_77 FLOAT[1280,5120] 9de627e6af21
val_78 FLOAT[5120,1280] a0bcb555998b
val_79 FLOAT[1280,3840] 0acf17a64b8b
val_8 FLOAT[1280,5120] a96c47114017
val_80 FLOAT[1280,5120] cf6a53532cd8
val_81 FLOAT[5120,1280] 2d47e4e38126
val_82 FLOAT[1280,3840] f9435225a8f9
val_83 FLOAT[1280,5120] 0039690c72ca
val_84 FLOAT[5120,1280] 63abbb36ba5c
val_85 FLOAT[1280,3840] 4221c32dde8f
val_86 FLOAT[1280,5120] 7465bf8af52c
val_87 FLOAT[5120,1280] 6e9080e3b3e2
val_88 FLOAT[1280,3840] e835ed54a5d3
val_89 FLOAT[1280,5120] 91b82e68fb1b
val_9 FLOAT[5120,1280] f3faaf9dcb16
val_90 FLOAT[5120,1280] bbf2b9411d86
val_91 FLOAT[1280,3840] df49f9453718
val_92 FLOAT[1280,5120] 6444f06809d0
val_93 FLOAT[5120,1280] 41797f8eb176
val_94 FLOAT[1280,3840] 4ac75b1d39b3
val_95 FLOAT[1280,5120] cc24becb2c5a
val_96 FLOAT[5120,1280] c1213387e20e
val_97 FLOAT[1280,3840] 5e09380541b3
val_98 FLOAT[1280,5120] 2228457af912
val_99 FLOAT[5120,1280] f8d5c25dab1d
view_target INT64[3] a4be54622af5
visual.conv1.weight FLOAT[1280,3,14,14] c9ac43ae5e4b
visual.ln_post.bias FLOAT[1280] df737d840f85
visual.ln_post.weight FLOAT[1280] e4d2f388386f
visual.ln_pre.bias FLOAT[1280] 781ea8594e74
visual.ln_pre.weight FLOAT[1280] b06de5263f60
visual.proj FLOAT[1280,1024] 372868fd66af
visual.transformer.resblocks.0.attn.in_proj_bias FLOAT[3840] d36da7881ccf
visual.transformer.resblocks.0.attn.out_proj.bias FLOAT[1280] d3bcea10e118
visual.transformer.resblocks.0.ln_1.bias FLOAT[1280] c8c1f7f8b216
visual.transformer.resblocks.0.ln_1.weight FLOAT[1280] 7678d04be25e
visual.transformer.resblocks.0.ln_2.bias FLOAT[1280] 6223c6078cca
visual.transformer.resblocks.0.ln_2.weight FLOAT[1280] 2c27a582c5d5
visual.transformer.resblocks.0.mlp.c_fc.bias FLOAT[5120] 3915abeaf1f9
visual.transformer.resblocks.0.mlp.c_proj.bias FLOAT[1280] cfdf4123f957
visual.transformer.resblocks.1.attn.in_proj_bias FLOAT[3840] 7a366644d725
visual.transformer.resblocks.1.attn.out_proj.bias FLOAT[1280] 185aef712042
visual.transformer.resblocks.1.ln_1.bias FLOAT[1280] 79bc4d4dd00e
visual.transformer.resblocks.1.ln_1.weight FLOAT[1280] 1412e672abf4
visual.transformer.resblocks.1.ln_2.bias FLOAT[1280] 68354fe7c771
visual.transformer.resblocks.1.ln_2.weight FLOAT[1280] 74e2493a7864
visual.transformer.resblocks.1.mlp.c_fc.bias FLOAT[5120] ba52a2bf7b2d
visual.transformer.resblocks.1.mlp.c_proj.bias FLOAT[1280] ce4320d170de
visual.transformer.resblocks.10.attn.in_proj_bias FLOAT[3840] ca527604a681
visual.transformer.resblocks.10.attn.out_proj.bias FLOAT[1280] 63aa81e81717
visual.transformer.resblocks.10.ln_1.bias FLOAT[1280] 148db999e656
visual.transformer.resblocks.10.ln_1.weight FLOAT[1280] c61b4929c756
visual.transformer.resblocks.10.ln_2.bias FLOAT[1280] 835dafb50c9a
visual.transformer.resblocks.10.ln_2.weight FLOAT[1280] 8d6403249858
visual.transformer.resblocks.10.mlp.c_fc.bias FLOAT[5120] c8e2280a9a20
visual.transformer.resblocks.10.mlp.c_proj.bias FLOAT[1280] 26aa5f7021ee
visual.transformer.resblocks.11.attn.in_proj_bias FLOAT[3840] 1dbf5a86af0c
visual.transformer.resblocks.11.attn.out_proj.bias FLOAT[1280] 01f8632b36be
visual.transformer.resblocks.11.ln_1.bias FLOAT[1280] 3d9a5e1a30d7
visual.transformer.resblocks.11.ln_1.weight FLOAT[1280] f6357079c87b
visual.transformer.resblocks.11.ln_2.bias FLOAT[1280] 22442d2ac83f
visual.transformer.resblocks.11.ln_2.weight FLOAT[1280] fb249fe555e7
visual.transformer.resblocks.11.mlp.c_fc.bias FLOAT[5120] 1ec12e319819
visual.transformer.resblocks.11.mlp.c_proj.bias FLOAT[1280] 83079400e640
visual.transformer.resblocks.12.attn.in_proj_bias FLOAT[3840] 9491e4cc5ad9
visual.transformer.resblocks.12.attn.out_proj.bias FLOAT[1280] 2e18ce3be47f
visual.transformer.resblocks.12.ln_1.bias FLOAT[1280] 7db8e98daa26
visual.transformer.resblocks.12.ln_1.weight FLOAT[1280] a37b1fb970d1
visual.transformer.resblocks.12.ln_2.bias FLOAT[1280] f994d5e5c413
visual.transformer.resblocks.12.ln_2.weight FLOAT[1280] d3a03b882438
visual.transformer.resblocks.12.mlp.c_fc.bias FLOAT[5120] 170b73c1b125
visual.transformer.resblocks.12.mlp.c_proj.bias FLOAT[1280] a34617c71534
visual.transformer.resblocks.13.attn.in_proj_bias FLOAT[3840] 99c17d305dd5
visual.transformer.resblocks.13.attn.out_proj.bias FLOAT[1280] a7388bd3da22
visual.transformer.resblocks.13.ln_1.bias FLOAT[1280] b299ab70337d
visual.transformer.resblocks.13.ln_1.weight FLOAT[1280] 03c11ba24dc8
visual.transformer.resblocks.13.ln_2.bias FLOAT[1280] 3d995c307a8a
visual.transformer.resblocks.13.ln_2.weight FLOAT[1280] 4e8a54d609e6
visual.transformer.resblocks.13.mlp.c_fc.bias FLOAT[5120] bd9f683e7716
visual.transformer.resblocks.13.mlp.c_proj.bias FLOAT[1280] ebce530401bf
visual.transformer.resblocks.14.attn.in_proj_bias FLOAT[3840] 974372372c5c
visual.transformer.resblocks.14.attn.out_proj.bias FLOAT[1280] d1e7c273be96
visual.transformer.resblocks.14.ln_1.bias FLOAT[1280] 8052497b28f8
visual.transformer.resblocks.14.ln_1.weight FLOAT[1280] 3ccb18b822e4
visual.transformer.resblocks.14.ln_2.bias FLOAT[1280] deb75b395fde
visual.transformer.resblocks.14.ln_2.weight FLOAT[1280] cf93226a358b
visual.transformer.resblocks.14.mlp.c_fc.bias FLOAT[5120] 7317a6d89b48
visual.transformer.resblocks.14.mlp.c_proj.bias FLOAT[1280] 1011b683c72f
visual.transformer.resblocks.15.attn.in_proj_bias FLOAT[3840] da06bcadd581
visual.transformer.resblocks.15.attn.out_proj.bias FLOAT[1280] 9ade6ea20361
visual.transformer.resblocks.15.ln_1.bias FLOAT[1280] 50ac4d8da796
visual.transformer.resblocks.15.ln_1.weight FLOAT[1280] 1b341a44fe98
visual.transformer.resblocks.15.ln_2.bias FLOAT[1280] ab268d97e2ee
visual.transformer.resblocks.15.ln_2.weight FLOAT[1280] 543c73cf8fa7
visual.transformer.resblocks.15.mlp.c_fc.bias FLOAT[5120] 82bd81508bce
visual.transformer.resblocks.15.mlp.c_proj.bias FLOAT[1280] 5d570e00ec17
visual.transformer.resblocks.16.attn.in_proj_bias FLOAT[3840] e62b683837a3
visual.transformer.resblocks.16.attn.out_proj.bias FLOAT[1280] e6337a3bbca9
visual.transformer.resblocks.16.ln_1.bias FLOAT[1280] 87775916e67c
visual.transformer.resblocks.16.ln_1.weight FLOAT[1280] 548102c3fe89
visual.transformer.resblocks.16.ln_2.bias FLOAT[1280] 830c5c1c042b
visual.transformer.resblocks.16.ln_2.weight FLOAT[1280] fe6629d0a1f4
visual.transformer.resblocks.16.mlp.c_fc.bias FLOAT[5120] 090d501eac32
visual.transformer.resblocks.16.mlp.c_proj.bias FLOAT[1280] 5a50a8782d02
visual.transformer.resblocks.17.attn.in_proj_bias FLOAT[3840] 5096dfb437ad
visual.transformer.resblocks.17.attn.out_proj.bias FLOAT[1280] 1f396d0f6752
visual.transformer.resblocks.17.ln_1.bias FLOAT[1280] 572691c8e260
visual.transformer.resblocks.17.ln_1.weight FLOAT[1280] 079943c0e674
visual.transformer.resblocks.17.ln_2.bias FLOAT[1280] 19e3cc2964eb
visual.transformer.resblocks.17.ln_2.weight FLOAT[1280] 831e90e29663
visual.transformer.resblocks.17.mlp.c_fc.bias FLOAT[5120] 07ba4bb7d472
visual.transformer.resblocks.17.mlp.c_proj.bias FLOAT[1280] e6f98e19f86b
visual.transformer.resblocks.18.attn.in_proj_bias FLOAT[3840] 203dbf9cfc93
visual.transformer.resblocks.18.attn.out_proj.bias FLOAT[1280] c067bd2dddff
visual.transformer.resblocks.18.ln_1.bias FLOAT[1280] a82ab542242e
visual.transformer.resblocks.18.ln_1.weight FLOAT[1280] 3ab1f320de5f
visual.transformer.resblocks.18.ln_2.bias FLOAT[1280] 468376a871ce
visual.transformer.resblocks.18.ln_2.weight FLOAT[1280] 896f9c0a4368
visual.transformer.resblocks.18.mlp.c_fc.bias FLOAT[5120] c3affae39fd2
visual.transformer.resblocks.18.mlp.c_proj.bias FLOAT[1280] a08393c515ba
visual.transformer.resblocks.19.attn.in_proj_bias FLOAT[3840] b9cc798cfcea
visual.transformer.resblocks.19.attn.out_proj.bias FLOAT[1280] b377474b97e3
visual.transformer.resblocks.19.ln_1.bias FLOAT[1280] e361211ab3f7
visual.transformer.resblocks.19.ln_1.weight FLOAT[1280] f932715fb86b
visual.transformer.resblocks.19.ln_2.bias FLOAT[1280] 95e155cd9024
visual.transformer.resblocks.19.ln_2.weight FLOAT[1280] 164086144a1e
visual.transformer.resblocks.19.mlp.c_fc.bias FLOAT[5120] 8a5d5e6e4b01
visual.transformer.resblocks.19.mlp.c_proj.bias FLOAT[1280] 96142e890197
visual.transformer.resblocks.2.attn.in_proj_bias FLOAT[3840] 42dc8f7af799
visual.transformer.resblocks.2.attn.out_proj.bias FLOAT[1280] 16ab7bff9907
visual.transformer.resblocks.2.ln_1.bias FLOAT[1280] 38746b6f5a34
visual.transformer.resblocks.2.ln_1.weight FLOAT[1280] 35eb64fec5a2
visual.transformer.resblocks.2.ln_2.bias FLOAT[1280] e5193a28e02d
visual.transformer.resblocks.2.ln_2.weight FLOAT[1280] a2e89e344ae5
visual.transformer.resblocks.2.mlp.c_fc.bias FLOAT[5120] 035443a88e08
visual.transformer.resblocks.2.mlp.c_proj.bias FLOAT[1280] b349a7dfcf6f
visual.transformer.resblocks.20.attn.in_proj_bias FLOAT[3840] 634cbd5aef02
visual.transformer.resblocks.20.attn.out_proj.bias FLOAT[1280] ba99d49f74b3
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