<
   ir_version: 10,
   opset_import: ["" : 23],
   producer_name: "pytorch"
>
main_graph (uint8[batch,336,336,3] image) => (float[batch,768] image_embedding) 
   <
      float[batch,577,1024] add_1000
      float[batch,577,1024] add_1029
      float[batch,577,1024] add_1144
      float[batch,577,1024] add_1173
      float[batch,577,1024] add_1288
      float[batch,577,1024] add_1317
      float[batch,577,1024] add_136
      float[batch,577,1024] add_1432
      float[batch,577,1024] add_1461
      float[batch,577,1024] add_1576
      float[batch,577,1024] add_1605
      float[batch,577,1024] add_165
      float[batch,577,1024] add_17
      float[batch,577,1024] add_1720
      float[batch,577,1024] add_1749
      float[batch,577,1024] add_1864
      float[batch,577,1024] add_1893
      float[batch,577,1024] add_2008
      float[batch,577,1024] add_2037
      float[batch,577,1024] add_2152
      float[batch,577,1024] add_2181
      float[batch,577,1024] add_2296
      float[batch,577,1024] add_2325
      float[batch,577,1024] add_2440
      float[batch,577,1024] add_2469
      float[batch,577,1024] add_2584
      float[batch,577,1024] add_2613
      float[batch,577,1024] add_2728
      float[batch,577,1024] add_2757
      float[batch,577,1024] add_280
      float[batch,577,1024] add_2872
      float[batch,577,1024] add_2901
      float[batch,577,1024] add_3016
      float[batch,577,1024] add_3045
      float[batch,577,1024] add_309
      float[batch,577,1024] add_3160
      float[batch,577,1024] add_3189
      float[batch,577,1024] add_3304
      float[batch,577,1024] add_3333
      float[batch,1,1024] add_3333_pooled
      float[batch,1,1024] add_3448
      float[batch,1,1024] add_3477
      float[batch,577,1024] add_424
      float[batch,577,1024] add_453
      float[batch,577,1024] add_568
      float[batch,577,1024] add_597
      float[batch,577,1024] add_712
      float[batch,577,1024] add_741
      float[batch,577,1024] add_856
      float[batch,577,1024] add_885
      float[batch,1] clamp_min
      float[batch,1024,24,24] conv2d
      float[batch,3,336,336] image_chw
      float[batch,336,336,3] image_f32
      float[batch,577,1024] layer_norm
      float[batch,577,1024] layer_norm_1
      float[batch,577,1024] layer_norm_10
      float[batch,577,1024] layer_norm_11
      float[batch,577,1024] layer_norm_12
      float[batch,577,1024] layer_norm_13
      float[batch,577,1024] layer_norm_14
      float[batch,577,1024] layer_norm_15
      float[batch,577,1024] layer_norm_16
      float[batch,577,1024] layer_norm_17
      float[batch,577,1024] layer_norm_18
      float[batch,577,1024] layer_norm_19
      float[batch,577,1024] layer_norm_2
      float[batch,577,1024] layer_norm_20
      float[batch,577,1024] layer_norm_21
      float[batch,577,1024] layer_norm_22
      float[batch,577,1024] layer_norm_23
      float[batch,577,1024] layer_norm_24
      float[batch,577,1024] layer_norm_25
      float[batch,577,1024] layer_norm_26
      float[batch,577,1024] layer_norm_27
      float[batch,577,1024] layer_norm_28
      float[batch,577,1024] layer_norm_29
      float[batch,577,1024] layer_norm_3
      float[batch,577,1024] layer_norm_30
      float[batch,577,1024] layer_norm_31
      float[batch,577,1024] layer_norm_32
      float[batch,577,1024] layer_norm_33
      float[batch,577,1024] layer_norm_34
      float[batch,577,1024] layer_norm_35
      float[batch,577,1024] layer_norm_36
      float[batch,577,1024] layer_norm_37
      float[batch,577,1024] layer_norm_38
      float[batch,577,1024] layer_norm_39
      float[batch,577,1024] layer_norm_4
      float[batch,577,1024] layer_norm_40
      float[batch,577,1024] layer_norm_41
      float[batch,577,1024] layer_norm_42
      float[batch,577,1024] layer_norm_43
      float[batch,577,1024] layer_norm_44
      float[batch,577,1024] layer_norm_45
      float[batch,577,1024] layer_norm_46
      float[batch,577,1024] layer_norm_47
      float[batch,1,1024] layer_norm_48
      float[batch,577,1024] layer_norm_5
      float[batch,577,1024] layer_norm_6
      float[batch,577,1024] layer_norm_7
      float[batch,577,1024] layer_norm_8
      float[batch,577,1024] layer_norm_9
      float[batch,1] linalg_vector_norm
      float[batch,577,4096] linear_10
      float[batch,577,1024] linear_11
      float[batch,577,4096] linear_14
      float[batch,577,1024] linear_15
      float[batch,577,4096] linear_18
      float[batch,577,1024] linear_19
      float[batch,577,4096] linear_2
      float[batch,577,4096] linear_22
      float[batch,577,1024] linear_23
      float[batch,577,4096] linear_26
      float[batch,577,1024] linear_27
      float[batch,577,1024] linear_3
      float[batch,577,4096] linear_30
      float[batch,577,1024] linear_31
      float[batch,577,4096] linear_34
      float[batch,577,1024] linear_35
      float[batch,577,4096] linear_38
      float[batch,577,1024] linear_39
      float[batch,577,4096] linear_42
      float[batch,577,1024] linear_43
      float[batch,577,4096] linear_46
      float[batch,577,1024] linear_47
      float[batch,577,4096] linear_50
      float[batch,577,1024] linear_51
      float[batch,577,4096] linear_54
      float[batch,577,1024] linear_55
      float[batch,577,4096] linear_58
      float[batch,577,1024] linear_59
      float[batch,577,4096] linear_6
      float[batch,577,4096] linear_62
      float[batch,577,1024] linear_63
      float[batch,577,4096] linear_66
      float[batch,577,1024] linear_67
      float[batch,577,1024] linear_7
      float[batch,577,4096] linear_70
      float[batch,577,1024] linear_71
      float[batch,577,4096] linear_74
      float[batch,577,1024] linear_75
      float[batch,577,4096] linear_78
      float[batch,577,1024] linear_79
      float[batch,577,4096] linear_82
      float[batch,577,1024] linear_83
      float[batch,577,4096] linear_86
      float[batch,577,1024] linear_87
      float[batch,577,4096] linear_90
      float[batch,577,1024] linear_91
      float[batch,1,4096] linear_94
      float[batch,1,1024] linear_95
      float[batch,768] matmul
      float[batch,577,4096] mul_1078
      float[batch,577,4096] mul_1083
      float[batch,577,4096] mul_115
      float[batch,577,4096] mul_1185
      float[batch,577,4096] mul_1190
      float[batch,577,4096] mul_120
      float[batch,577,4096] mul_1292
      float[batch,577,4096] mul_1297
      float[batch,577,4096] mul_1399
      float[batch,577,4096] mul_1404
      float[batch,577,4096] mul_1506
      float[batch,577,4096] mul_1511
      float[batch,577,4096] mul_1613
      float[batch,577,4096] mul_1618
      float[batch,577,4096] mul_1720
      float[batch,577,4096] mul_1725
      float[batch,577,4096] mul_1827
      float[batch,577,4096] mul_1832
      float[batch,577,4096] mul_1934
      float[batch,577,4096] mul_1939
      float[batch,577,4096] mul_2041
      float[batch,577,4096] mul_2046
      float[batch,577,4096] mul_2148
      float[batch,577,4096] mul_2153
      float[batch,577,4096] mul_222
      float[batch,577,4096] mul_2255
      float[batch,577,4096] mul_2260
      float[batch,577,4096] mul_227
      float[batch,577,4096] mul_2362
      float[batch,577,4096] mul_2367
      float[batch,577,4096] mul_2469
      float[batch,577,4096] mul_2474
      float[batch,1,4096] mul_2576
      float[batch,1,4096] mul_2581
      float[batch,577,4096] mul_329
      float[batch,577,4096] mul_334
      float[batch,577,4096] mul_436
      float[batch,577,4096] mul_441
      float[batch,577,4096] mul_543
      float[batch,577,4096] mul_548
      float[batch,577,4096] mul_650
      float[batch,577,4096] mul_655
      float[batch,577,4096] mul_757
      float[batch,577,4096] mul_762
      float[batch,577,4096] mul_864
      float[batch,577,4096] mul_869
      float[batch,577,4096] mul_971
      float[batch,577,4096] mul_976
      float[batch,577,1024] node_scaled_dot_product_attention_10_k
      float[batch,577,1024] node_scaled_dot_product_attention_10_out
      float[batch,577,1024] node_scaled_dot_product_attention_10_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_10_q
      float[batch,577,3072] node_scaled_dot_product_attention_10_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_10_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_10_v
      float[batch,577,1024] node_scaled_dot_product_attention_11_k
      float[batch,577,1024] node_scaled_dot_product_attention_11_out
      float[batch,577,1024] node_scaled_dot_product_attention_11_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_11_q
      float[batch,577,3072] node_scaled_dot_product_attention_11_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_11_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_11_v
      float[batch,577,1024] node_scaled_dot_product_attention_12_k
      float[batch,577,1024] node_scaled_dot_product_attention_12_out
      float[batch,577,1024] node_scaled_dot_product_attention_12_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_12_q
      float[batch,577,3072] node_scaled_dot_product_attention_12_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_12_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_12_v
      float[batch,577,1024] node_scaled_dot_product_attention_13_k
      float[batch,577,1024] node_scaled_dot_product_attention_13_out
      float[batch,577,1024] node_scaled_dot_product_attention_13_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_13_q
      float[batch,577,3072] node_scaled_dot_product_attention_13_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_13_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_13_v
      float[batch,577,1024] node_scaled_dot_product_attention_14_k
      float[batch,577,1024] node_scaled_dot_product_attention_14_out
      float[batch,577,1024] node_scaled_dot_product_attention_14_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_14_q
      float[batch,577,3072] node_scaled_dot_product_attention_14_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_14_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_14_v
      float[batch,577,1024] node_scaled_dot_product_attention_15_k
      float[batch,577,1024] node_scaled_dot_product_attention_15_out
      float[batch,577,1024] node_scaled_dot_product_attention_15_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_15_q
      float[batch,577,3072] node_scaled_dot_product_attention_15_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_15_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_15_v
      float[batch,577,1024] node_scaled_dot_product_attention_16_k
      float[batch,577,1024] node_scaled_dot_product_attention_16_out
      float[batch,577,1024] node_scaled_dot_product_attention_16_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_16_q
      float[batch,577,3072] node_scaled_dot_product_attention_16_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_16_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_16_v
      float[batch,577,1024] node_scaled_dot_product_attention_17_k
      float[batch,577,1024] node_scaled_dot_product_attention_17_out
      float[batch,577,1024] node_scaled_dot_product_attention_17_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_17_q
      float[batch,577,3072] node_scaled_dot_product_attention_17_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_17_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_17_v
      float[batch,577,1024] node_scaled_dot_product_attention_18_k
      float[batch,577,1024] node_scaled_dot_product_attention_18_out
      float[batch,577,1024] node_scaled_dot_product_attention_18_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_18_q
      float[batch,577,3072] node_scaled_dot_product_attention_18_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_18_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_18_v
      float[batch,577,1024] node_scaled_dot_product_attention_19_k
      float[batch,577,1024] node_scaled_dot_product_attention_19_out
      float[batch,577,1024] node_scaled_dot_product_attention_19_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_19_q
      float[batch,577,3072] node_scaled_dot_product_attention_19_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_19_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_19_v
      float[batch,577,1024] node_scaled_dot_product_attention_1_k
      float[batch,577,1024] node_scaled_dot_product_attention_1_out
      float[batch,577,1024] node_scaled_dot_product_attention_1_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_1_q
      float[batch,577,3072] node_scaled_dot_product_attention_1_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_1_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_1_v
      float[batch,577,1024] node_scaled_dot_product_attention_20_k
      float[batch,577,1024] node_scaled_dot_product_attention_20_out
      float[batch,577,1024] node_scaled_dot_product_attention_20_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_20_q
      float[batch,577,3072] node_scaled_dot_product_attention_20_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_20_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_20_v
      float[batch,577,1024] node_scaled_dot_product_attention_21_k
      float[batch,577,1024] node_scaled_dot_product_attention_21_out
      float[batch,577,1024] node_scaled_dot_product_attention_21_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_21_q
      float[batch,577,3072] node_scaled_dot_product_attention_21_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_21_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_21_v
      float[batch,577,1024] node_scaled_dot_product_attention_22_k
      float[batch,577,1024] node_scaled_dot_product_attention_22_out
      float[batch,577,1024] node_scaled_dot_product_attention_22_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_22_q
      float[batch,577,3072] node_scaled_dot_product_attention_22_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_22_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_22_v
      float[batch,577,1024] node_scaled_dot_product_attention_23_k
      float[batch,1,1024] node_scaled_dot_product_attention_23_out
      float[batch,1,1024] node_scaled_dot_product_attention_23_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_23_q
      float[batch,1,1024] node_scaled_dot_product_attention_23_q_pooled
      float[batch,577,3072] node_scaled_dot_product_attention_23_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_23_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_23_v
      float[batch,577,1024] node_scaled_dot_product_attention_2_k
      float[batch,577,1024] node_scaled_dot_product_attention_2_out
      float[batch,577,1024] node_scaled_dot_product_attention_2_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_2_q
      float[batch,577,3072] node_scaled_dot_product_attention_2_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_2_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_2_v
      float[batch,577,1024] node_scaled_dot_product_attention_3_k
      float[batch,577,1024] node_scaled_dot_product_attention_3_out
      float[batch,577,1024] node_scaled_dot_product_attention_3_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_3_q
      float[batch,577,3072] node_scaled_dot_product_attention_3_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_3_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_3_v
      float[batch,577,1024] node_scaled_dot_product_attention_4_k
      float[batch,577,1024] node_scaled_dot_product_attention_4_out
      float[batch,577,1024] node_scaled_dot_product_attention_4_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_4_q
      float[batch,577,3072] node_scaled_dot_product_attention_4_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_4_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_4_v
      float[batch,577,1024] node_scaled_dot_product_attention_5_k
      float[batch,577,1024] node_scaled_dot_product_attention_5_out
      float[batch,577,1024] node_scaled_dot_product_attention_5_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_5_q
      float[batch,577,3072] node_scaled_dot_product_attention_5_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_5_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_5_v
      float[batch,577,1024] node_scaled_dot_product_attention_6_k
      float[batch,577,1024] node_scaled_dot_product_attention_6_out
      float[batch,577,1024] node_scaled_dot_product_attention_6_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_6_q
      float[batch,577,3072] node_scaled_dot_product_attention_6_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_6_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_6_v
      float[batch,577,1024] node_scaled_dot_product_attention_7_k
      float[batch,577,1024] node_scaled_dot_product_attention_7_out
      float[batch,577,1024] node_scaled_dot_product_attention_7_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_7_q
      float[batch,577,3072] node_scaled_dot_product_attention_7_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_7_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_7_v
      float[batch,577,1024] node_scaled_dot_product_attention_8_k
      float[batch,577,1024] node_scaled_dot_product_attention_8_out
      float[batch,577,1024] node_scaled_dot_product_attention_8_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_8_q
      float[batch,577,3072] node_scaled_dot_product_attention_8_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_8_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_8_v
      float[batch,577,1024] node_scaled_dot_product_attention_9_k
      float[batch,577,1024] node_scaled_dot_product_attention_9_out
      float[batch,577,1024] node_scaled_dot_product_attention_9_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_9_q
      float[batch,577,3072] node_scaled_dot_product_attention_9_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_9_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_9_v
      float[batch,577,1024] node_scaled_dot_product_attention_k
      float[batch,577,1024] node_scaled_dot_product_attention_out
      float[batch,577,1024] node_scaled_dot_product_attention_out_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_q
      float[batch,577,3072] node_scaled_dot_product_attention_qkv
      float[batch,577,3072] node_scaled_dot_product_attention_qkv_mm_out
      float[batch,577,1024] node_scaled_dot_product_attention_v
      float[batch,576,1024] permute
      float[batch,577,1024] scaled_dot_product_attention
      float[batch,577,1024] scaled_dot_product_attention_1
      float[batch,577,1024] scaled_dot_product_attention_10
      float[batch,577,1024] scaled_dot_product_attention_11
      float[batch,577,1024] scaled_dot_product_attention_12
      float[batch,577,1024] scaled_dot_product_attention_13
      float[batch,577,1024] scaled_dot_product_attention_14
      float[batch,577,1024] scaled_dot_product_attention_15
      float[batch,577,1024] scaled_dot_product_attention_16
      float[batch,577,1024] scaled_dot_product_attention_17
      float[batch,577,1024] scaled_dot_product_attention_18
      float[batch,577,1024] scaled_dot_product_attention_19
      float[batch,577,1024] scaled_dot_product_attention_2
      float[batch,577,1024] scaled_dot_product_attention_20
      float[batch,577,1024] scaled_dot_product_attention_21
      float[batch,577,1024] scaled_dot_product_attention_22
      float[batch,1,1024] scaled_dot_product_attention_23
      float[batch,577,1024] scaled_dot_product_attention_3
      float[batch,577,1024] scaled_dot_product_attention_4
      float[batch,577,1024] scaled_dot_product_attention_5
      float[batch,577,1024] scaled_dot_product_attention_6
      float[batch,577,1024] scaled_dot_product_attention_7
      float[batch,577,1024] scaled_dot_product_attention_8
      float[batch,577,1024] scaled_dot_product_attention_9
      float[batch,1024] select_72
      float[batch,577,4096] sigmoid
      float[batch,577,4096] sigmoid_1
      float[batch,577,4096] sigmoid_10
      float[batch,577,4096] sigmoid_11
      float[batch,577,4096] sigmoid_12
      float[batch,577,4096] sigmoid_13
      float[batch,577,4096] sigmoid_14
      float[batch,577,4096] sigmoid_15
      float[batch,577,4096] sigmoid_16
      float[batch,577,4096] sigmoid_17
      float[batch,577,4096] sigmoid_18
      float[batch,577,4096] sigmoid_19
      float[batch,577,4096] sigmoid_2
      float[batch,577,4096] sigmoid_20
      float[batch,577,4096] sigmoid_21
      float[batch,577,4096] sigmoid_22
      float[batch,1,4096] sigmoid_23
      float[batch,577,4096] sigmoid_3
      float[batch,577,4096] sigmoid_4
      float[batch,577,4096] sigmoid_5
      float[batch,577,4096] sigmoid_6
      float[batch,577,4096] sigmoid_7
      float[batch,577,4096] sigmoid_8
      float[batch,577,4096] sigmoid_9
      float[batch,577,1024] val_100
      float[batch,577,4096] val_101
      float[batch,577,1024] val_102
      float[batch,577,4096] val_103
      float[batch,577,1024] val_104
      float[batch,577,4096] val_105
      float[batch,577,1024] val_106
      float[batch,577,4096] val_107
      float[batch,577,1024] val_108
      float[batch,577,4096] val_109
      float[batch,577,1024] val_110
      float[batch,577,4096] val_111
      float[batch,577,1024] val_112
      float[batch,577,4096] val_113
      float[batch,577,1024] val_114
      float[batch,577,4096] val_115
      float[batch,577,1024] val_116
      float[batch,577,4096] val_117
      float[batch,577,1024] val_118
      float[batch,577,4096] val_119
      float[batch,577,1024] val_120
      float[batch,577,4096] val_121
      float[batch,577,1024] val_122
      float[batch,577,4096] val_123
      float[batch,577,1024] val_124
      float[batch,577,4096] val_125
      float[batch,577,1024] val_126
      float[batch,1,4096] val_127
      float[batch,1,1024] val_128
      float[batch,1024] val_129
      float[batch,577,1024] val_80
      float[batch,577,4096] val_81
      float[batch,577,1024] val_82
      float[batch,577,4096] val_83
      float[batch,577,1024] val_84
      float[batch,577,4096] val_85
      float[batch,577,1024] val_86
      float[batch,577,4096] val_87
      float[batch,577,1024] val_88
      float[batch,577,4096] val_89
      float[batch,577,1024] val_90
      float[batch,577,4096] val_91
      float[batch,577,1024] val_92
      float[batch,577,4096] val_93
      float[batch,577,1024] val_94
      float[batch,577,4096] val_95
      float[batch,577,1024] val_96
      float[batch,577,4096] val_97
      float[batch,577,1024] val_98
      float[batch,577,4096] val_99
      float[batch,1024,576] 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_1353_fused_bias)
   view = Reshape <allowzero: int = 1> (conv2d, view_target)
   [node_permute] permute = Transpose <perm: ints = [0, 2, 1]> (view)
   val_80 = Pad (permute, val_4, val_5)
   add_17 = Add (val_80, val_79)
   [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_3x1024)
   [node_scaled_dot_product_attention] scaled_dot_product_attention = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_q, node_scaled_dot_product_attention_k, node_scaled_dot_product_attention_v)
   [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_81 = MatMul (layer_norm_2, val_8)
   linear_2 = Add (val_81, "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_82 = MatMul (mul_120, val_9)
   linear_3 = Add (val_82, "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_3x1024)
   scaled_dot_product_attention_1 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_1_q, node_scaled_dot_product_attention_1_k, node_scaled_dot_product_attention_1_v)
   [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_83 = MatMul (layer_norm_4, val_11)
   linear_6 = Add (val_83, "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_84 = MatMul (mul_227, val_12)
   linear_7 = Add (val_84, "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_3x1024)
   scaled_dot_product_attention_2 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_2_q, node_scaled_dot_product_attention_2_k, node_scaled_dot_product_attention_2_v)
   [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_85 = MatMul (layer_norm_6, val_14)
   linear_10 = Add (val_85, "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_86 = MatMul (mul_334, val_15)
   linear_11 = Add (val_86, "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_3x1024)
   scaled_dot_product_attention_3 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_3_q, node_scaled_dot_product_attention_3_k, node_scaled_dot_product_attention_3_v)
   [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_87 = MatMul (layer_norm_8, val_17)
   linear_14 = Add (val_87, "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_88 = MatMul (mul_441, val_18)
   linear_15 = Add (val_88, "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_3x1024)
   scaled_dot_product_attention_4 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_4_q, node_scaled_dot_product_attention_4_k, node_scaled_dot_product_attention_4_v)
   [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_89 = MatMul (layer_norm_10, val_20)
   linear_18 = Add (val_89, "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_90 = MatMul (mul_548, val_21)
   linear_19 = Add (val_90, "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_3x1024)
   scaled_dot_product_attention_5 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_5_q, node_scaled_dot_product_attention_5_k, node_scaled_dot_product_attention_5_v)
   [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_91 = MatMul (layer_norm_12, val_23)
   linear_22 = Add (val_91, "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_92 = MatMul (mul_655, val_24)
   linear_23 = Add (val_92, "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_3x1024)
   scaled_dot_product_attention_6 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_6_q, node_scaled_dot_product_attention_6_k, node_scaled_dot_product_attention_6_v)
   [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_93 = MatMul (layer_norm_14, val_26)
   linear_26 = Add (val_93, "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_94 = MatMul (mul_762, val_27)
   linear_27 = Add (val_94, "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_3x1024)
   scaled_dot_product_attention_7 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_7_q, node_scaled_dot_product_attention_7_k, node_scaled_dot_product_attention_7_v)
   [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_95 = MatMul (layer_norm_16, val_29)
   linear_30 = Add (val_95, "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_96 = MatMul (mul_869, val_30)
   linear_31 = Add (val_96, "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_3x1024)
   scaled_dot_product_attention_8 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_8_q, node_scaled_dot_product_attention_8_k, node_scaled_dot_product_attention_8_v)
   [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_97 = MatMul (layer_norm_18, val_32)
   linear_34 = Add (val_97, "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_98 = MatMul (mul_976, val_33)
   linear_35 = Add (val_98, "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_3x1024)
   scaled_dot_product_attention_9 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_9_q, node_scaled_dot_product_attention_9_k, node_scaled_dot_product_attention_9_v)
   [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_99 = MatMul (layer_norm_20, val_35)
   linear_38 = Add (val_99, "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_100 = MatMul (mul_1083, val_36)
   linear_39 = Add (val_100, "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_3x1024)
   scaled_dot_product_attention_10 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_10_q, node_scaled_dot_product_attention_10_k, node_scaled_dot_product_attention_10_v)
   [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_101 = MatMul (layer_norm_22, val_38)
   linear_42 = Add (val_101, "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_102 = MatMul (mul_1190, val_39)
   linear_43 = Add (val_102, "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_3x1024)
   scaled_dot_product_attention_11 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_11_q, node_scaled_dot_product_attention_11_k, node_scaled_dot_product_attention_11_v)
   [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_103 = MatMul (layer_norm_24, val_41)
   linear_46 = Add (val_103, "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_104 = MatMul (mul_1297, val_42)
   linear_47 = Add (val_104, "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_3x1024)
   scaled_dot_product_attention_12 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_12_q, node_scaled_dot_product_attention_12_k, node_scaled_dot_product_attention_12_v)
   [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_105 = MatMul (layer_norm_26, val_44)
   linear_50 = Add (val_105, "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_106 = MatMul (mul_1404, val_45)
   linear_51 = Add (val_106, "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_3x1024)
   scaled_dot_product_attention_13 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_13_q, node_scaled_dot_product_attention_13_k, node_scaled_dot_product_attention_13_v)
   [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_107 = MatMul (layer_norm_28, val_47)
   linear_54 = Add (val_107, "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_108 = MatMul (mul_1511, val_48)
   linear_55 = Add (val_108, "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_3x1024)
   scaled_dot_product_attention_14 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_14_q, node_scaled_dot_product_attention_14_k, node_scaled_dot_product_attention_14_v)
   [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_109 = MatMul (layer_norm_30, val_50)
   linear_58 = Add (val_109, "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_110 = MatMul (mul_1618, val_51)
   linear_59 = Add (val_110, "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_3x1024)
   scaled_dot_product_attention_15 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_15_q, node_scaled_dot_product_attention_15_k, node_scaled_dot_product_attention_15_v)
   [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_111 = MatMul (layer_norm_32, val_53)
   linear_62 = Add (val_111, "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_112 = MatMul (mul_1725, val_54)
   linear_63 = Add (val_112, "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_3x1024)
   scaled_dot_product_attention_16 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_16_q, node_scaled_dot_product_attention_16_k, node_scaled_dot_product_attention_16_v)
   [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_113 = MatMul (layer_norm_34, val_56)
   linear_66 = Add (val_113, "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_114 = MatMul (mul_1832, val_57)
   linear_67 = Add (val_114, "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_3x1024)
   scaled_dot_product_attention_17 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_17_q, node_scaled_dot_product_attention_17_k, node_scaled_dot_product_attention_17_v)
   [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_115 = MatMul (layer_norm_36, val_59)
   linear_70 = Add (val_115, "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_116 = MatMul (mul_1939, val_60)
   linear_71 = Add (val_116, "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_3x1024)
   scaled_dot_product_attention_18 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_18_q, node_scaled_dot_product_attention_18_k, node_scaled_dot_product_attention_18_v)
   [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_117 = MatMul (layer_norm_38, val_62)
   linear_74 = Add (val_117, "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_118 = MatMul (mul_2046, val_63)
   linear_75 = Add (val_118, "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_3x1024)
   scaled_dot_product_attention_19 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_19_q, node_scaled_dot_product_attention_19_k, node_scaled_dot_product_attention_19_v)
   [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_119 = MatMul (layer_norm_40, val_65)
   linear_78 = Add (val_119, "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_120 = MatMul (mul_2153, val_66)
   linear_79 = Add (val_120, "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_3x1024)
   scaled_dot_product_attention_20 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_20_q, node_scaled_dot_product_attention_20_k, node_scaled_dot_product_attention_20_v)
   [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_121 = MatMul (layer_norm_42, val_68)
   linear_82 = Add (val_121, "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_122 = MatMul (mul_2260, val_69)
   linear_83 = Add (val_122, "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_3x1024)
   scaled_dot_product_attention_21 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_21_q, node_scaled_dot_product_attention_21_k, node_scaled_dot_product_attention_21_v)
   [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_123 = MatMul (layer_norm_44, val_71)
   linear_86 = Add (val_123, "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_124 = MatMul (mul_2367, val_72)
   linear_87 = Add (val_124, "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_3x1024)
   scaled_dot_product_attention_22 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_22_q, node_scaled_dot_product_attention_22_k, node_scaled_dot_product_attention_22_v)
   [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_125 = MatMul (layer_norm_46, val_74)
   linear_90 = Add (val_125, "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_126 = MatMul (mul_2474, val_75)
   linear_91 = Add (val_126, "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_3x1024)
   [pool_hoist_node_scaled_dot_product_attention_23_q] node_scaled_dot_product_attention_23_q_pooled = Slice (node_scaled_dot_product_attention_23_q, val_2, val_6, val_6)
   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_pooled, 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")
   [pool_hoist_add_3333] add_3333_pooled = Slice (add_3333, val_2, val_6, val_6)
   add_3448 = Add (add_3333_pooled, 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_127 = MatMul (layer_norm_48, val_77)
   linear_94 = Add (val_127, "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_128 = MatMul (mul_2581, val_78)
   linear_95 = Add (val_128, "visual.transformer.resblocks.23.mlp.c_proj.bias")
   add_3477 = Add (add_3448, linear_95)
   val_129 = Squeeze (add_3477, val_6)
   select_72 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (val_129, "visual.ln_post.weight", "visual.ln_post.bias")
   [node_matmul] matmul = MatMul (select_72, "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_3x1024 INT64[3] 0ec6f5651fd5
node_Conv_1353_fused_bias FLOAT[1024] 6786cf84c40f
node_scaled_dot_product_attention_10_wo_t FLOAT[1024,1024] d482f64f1b48
node_scaled_dot_product_attention_11_wo_t FLOAT[1024,1024] 55c80d107fab
node_scaled_dot_product_attention_12_wo_t FLOAT[1024,1024] e2c5b96575c0
node_scaled_dot_product_attention_13_wo_t FLOAT[1024,1024] b38c6f758e4f
node_scaled_dot_product_attention_14_wo_t FLOAT[1024,1024] eba6c247516a
node_scaled_dot_product_attention_15_wo_t FLOAT[1024,1024] 098edbc5d7df
node_scaled_dot_product_attention_16_wo_t FLOAT[1024,1024] aafc6e1c3d79
node_scaled_dot_product_attention_17_wo_t FLOAT[1024,1024] 1d9fd3b83d42
node_scaled_dot_product_attention_18_wo_t FLOAT[1024,1024] d03b2643cb03
node_scaled_dot_product_attention_19_wo_t FLOAT[1024,1024] ee5b6821bbc0
node_scaled_dot_product_attention_1_wo_t FLOAT[1024,1024] b76e4286a9da
node_scaled_dot_product_attention_20_wo_t FLOAT[1024,1024] 1380e8eb02dc
node_scaled_dot_product_attention_21_wo_t FLOAT[1024,1024] 4c131f4966f1
node_scaled_dot_product_attention_22_wo_t FLOAT[1024,1024] f4228c06a678
node_scaled_dot_product_attention_23_wo_t FLOAT[1024,1024] 48e7df5d5214
node_scaled_dot_product_attention_2_wo_t FLOAT[1024,1024] 0cc4d31cb422
node_scaled_dot_product_attention_3_wo_t FLOAT[1024,1024] 06c34d107fe4
node_scaled_dot_product_attention_4_wo_t FLOAT[1024,1024] 019b31f30653
node_scaled_dot_product_attention_5_wo_t FLOAT[1024,1024] df8d4744ace2
node_scaled_dot_product_attention_6_wo_t FLOAT[1024,1024] f9a78fc8e84e
node_scaled_dot_product_attention_7_wo_t FLOAT[1024,1024] a5599b63c5b6
node_scaled_dot_product_attention_8_wo_t FLOAT[1024,1024] 9dd5caa32ca9
node_scaled_dot_product_attention_9_wo_t FLOAT[1024,1024] 9b27454af542
node_scaled_dot_product_attention_wo_t FLOAT[1024,1024] 8c40dcce1fe6
val_0 INT64[1] 12a3ae445661
val_1 FLOAT[] 6708d9be4956
val_10 FLOAT[1024,3072] a02b5b8faf85
val_11 FLOAT[1024,4096] 9c9c4e6576a9
val_12 FLOAT[4096,1024] bf58fcb48c0e
val_13 FLOAT[1024,3072] e12579419907
val_14 FLOAT[1024,4096] bbdc1d965d71
val_15 FLOAT[4096,1024] d56d9298c0e3
val_16 FLOAT[1024,3072] ac20b17cb3d6
val_17 FLOAT[1024,4096] df0a401eeea9
val_18 FLOAT[4096,1024] ae72f8b8a7ea
val_19 FLOAT[1024,3072] fe7612144a74
val_2 INT64[1] af5570f5a181
val_20 FLOAT[1024,4096] 7f0af9d5f5ce
val_21 FLOAT[4096,1024] 70b38b9f5624
val_22 FLOAT[1024,3072] b1c245f2e4f3
val_23 FLOAT[1024,4096] 8d978d39ac73
val_24 FLOAT[4096,1024] 2e1103a1bac8
val_25 FLOAT[1024,3072] 667080e60916
val_26 FLOAT[1024,4096] 6ad404cf42f7
val_27 FLOAT[4096,1024] 33197edccdbf
val_28 FLOAT[1024,3072] 6400b05ea1c4
val_29 FLOAT[1024,4096] d6bbd7ed0d0b
val_3 FLOAT[] c2e7ddfe3114
val_30 FLOAT[4096,1024] da8f6265464d
val_31 FLOAT[1024,3072] 0140ecd1a91b
val_32 FLOAT[1024,4096] 48eb2802c5f0
val_33 FLOAT[4096,1024] c0823162f8a4
val_34 FLOAT[1024,3072] 57bc599b924f
val_35 FLOAT[1024,4096] 006f935bba16
val_36 FLOAT[4096,1024] 2205872e56d0
val_37 FLOAT[1024,3072] 8d5c2d092080
val_38 FLOAT[1024,4096] ddd2cf502b94
val_39 FLOAT[4096,1024] b0ba012ec9ec
val_4 INT64[6] 6b7d92eaae70
val_40 FLOAT[1024,3072] af2d92060fdc
val_41 FLOAT[1024,4096] a15b2b6becde
val_42 FLOAT[4096,1024] 6943d8829e65
val_43 FLOAT[1024,3072] 573f00648a02
val_44 FLOAT[1024,4096] 407cf01ffa9c
val_45 FLOAT[4096,1024] f0db8643ba2f
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