<
   ir_version: 10,
   opset_import: ["" : 23],
   producer_name: "pytorch"
>
main_graph (int32[batch,64] text) => (float[batch,1024] text_embedding) 
   <
      float[batch,64,1024] add_1071
      float[batch,64,1024] add_1092
      float[batch,64,1024] add_119
      float[batch,64,1024] add_1207
      float[batch,64,1024] add_1228
      float[batch,64,1024] add_1343
      float[batch,64,1024] add_1364
      float[batch,64,1024] add_140
      float[batch,64,1024] add_1479
      float[batch,64,1024] add_1500
      float[batch,64,1024] add_1615
      float[batch,64,1024] add_1636
      float[batch,64,1024] add_1751
      float[batch,64,1024] add_1772
      float[batch,64,1024] add_1887
      float[batch,64,1024] add_1908
      float[batch,64,1024] add_2023
      float[batch,64,1024] add_2044
      float[batch,64,1024] add_2159
      float[batch,64,1024] add_2180
      float[batch,64,1024] add_2295
      float[batch,64,1024] add_2316
      float[batch,64,1024] add_2431
      float[batch,64,1024] add_2452
      float[batch,64,1024] add_255
      float[batch,64,1024] add_2567
      float[batch,64,1024] add_2588
      float[batch,64,1024] add_2703
      float[batch,64,1024] add_2724
      float[batch,64,1024] add_276
      float[batch,64,1024] add_2839
      float[batch,64,1024] add_2860
      float[batch,64,1024] add_2975
      float[batch,64,1024] add_2996
      float[batch,64,1024] add_3111
      float[batch,64,1024] add_3132
      float[batch,1,1024] add_3132_pooled
      float[batch,1,1024] add_3247
      float[batch,1,1024] add_3268
      float[batch,64,1024] add_391
      float[batch,64,1024] add_4
      float[batch,64,1024] add_412
      float[batch,64,1024] add_527
      float[batch,64,1024] add_548
      float[batch,64,1024] add_663
      float[batch,64,1024] add_684
      float[batch,64,1024] add_799
      float[batch,64,1024] add_820
      float[batch,64,1024] add_935
      float[batch,64,1024] add_956
      float[batch,1] clamp_min
      float[batch,64,1024] embedding
      float[batch,64,4096] gelu
      float[batch,64,4096] gelu_1
      float[batch,64,4096] gelu_10
      float[batch,64,4096] gelu_11
      float[batch,64,4096] gelu_12
      float[batch,64,4096] gelu_13
      float[batch,64,4096] gelu_14
      float[batch,64,4096] gelu_15
      float[batch,64,4096] gelu_16
      float[batch,64,4096] gelu_17
      float[batch,64,4096] gelu_18
      float[batch,64,4096] gelu_19
      float[batch,64,4096] gelu_2
      float[batch,64,4096] gelu_20
      float[batch,64,4096] gelu_21
      float[batch,64,4096] gelu_22
      float[batch,1,4096] gelu_23
      float[batch,64,4096] gelu_3
      float[batch,64,4096] gelu_4
      float[batch,64,4096] gelu_5
      float[batch,64,4096] gelu_6
      float[batch,64,4096] gelu_7
      float[batch,64,4096] gelu_8
      float[batch,64,4096] gelu_9
      float[batch,64,1024] layer_norm
      float[batch,64,1024] layer_norm_1
      float[batch,64,1024] layer_norm_10
      float[batch,64,1024] layer_norm_11
      float[batch,64,1024] layer_norm_12
      float[batch,64,1024] layer_norm_13
      float[batch,64,1024] layer_norm_14
      float[batch,64,1024] layer_norm_15
      float[batch,64,1024] layer_norm_16
      float[batch,64,1024] layer_norm_17
      float[batch,64,1024] layer_norm_18
      float[batch,64,1024] layer_norm_19
      float[batch,64,1024] layer_norm_2
      float[batch,64,1024] layer_norm_20
      float[batch,64,1024] layer_norm_21
      float[batch,64,1024] layer_norm_22
      float[batch,64,1024] layer_norm_23
      float[batch,64,1024] layer_norm_24
      float[batch,64,1024] layer_norm_25
      float[batch,64,1024] layer_norm_26
      float[batch,64,1024] layer_norm_27
      float[batch,64,1024] layer_norm_28
      float[batch,64,1024] layer_norm_29
      float[batch,64,1024] layer_norm_3
      float[batch,64,1024] layer_norm_30
      float[batch,64,1024] layer_norm_31
      float[batch,64,1024] layer_norm_32
      float[batch,64,1024] layer_norm_33
      float[batch,64,1024] layer_norm_34
      float[batch,64,1024] layer_norm_35
      float[batch,64,1024] layer_norm_36
      float[batch,64,1024] layer_norm_37
      float[batch,64,1024] layer_norm_38
      float[batch,64,1024] layer_norm_39
      float[batch,64,1024] layer_norm_4
      float[batch,64,1024] layer_norm_40
      float[batch,64,1024] layer_norm_41
      float[batch,64,1024] layer_norm_42
      float[batch,64,1024] layer_norm_43
      float[batch,64,1024] layer_norm_44
      float[batch,64,1024] layer_norm_45
      float[batch,64,1024] layer_norm_46
      float[batch,1,1024] layer_norm_47
      float[batch,64,1024] layer_norm_5
      float[batch,64,1024] layer_norm_6
      float[batch,64,1024] layer_norm_7
      float[batch,64,1024] layer_norm_8
      float[batch,64,1024] layer_norm_9
      float[batch,1] linalg_vector_norm
      float[batch,64,4096] linear_10
      float[batch,64,1024] linear_11
      float[batch,64,4096] linear_14
      float[batch,64,1024] linear_15
      float[batch,64,4096] linear_18
      float[batch,64,1024] linear_19
      float[batch,64,4096] linear_2
      float[batch,64,4096] linear_22
      float[batch,64,1024] linear_23
      float[batch,64,4096] linear_26
      float[batch,64,1024] linear_27
      float[batch,64,1024] linear_3
      float[batch,64,4096] linear_30
      float[batch,64,1024] linear_31
      float[batch,64,4096] linear_34
      float[batch,64,1024] linear_35
      float[batch,64,4096] linear_38
      float[batch,64,1024] linear_39
      float[batch,64,4096] linear_42
      float[batch,64,1024] linear_43
      float[batch,64,4096] linear_46
      float[batch,64,1024] linear_47
      float[batch,64,4096] linear_50
      float[batch,64,1024] linear_51
      float[batch,64,4096] linear_54
      float[batch,64,1024] linear_55
      float[batch,64,4096] linear_58
      float[batch,64,1024] linear_59
      float[batch,64,4096] linear_6
      float[batch,64,4096] linear_62
      float[batch,64,1024] linear_63
      float[batch,64,4096] linear_66
      float[batch,64,1024] linear_67
      float[batch,64,1024] linear_7
      float[batch,64,4096] linear_70
      float[batch,64,1024] linear_71
      float[batch,64,4096] linear_74
      float[batch,64,1024] linear_75
      float[batch,64,4096] linear_78
      float[batch,64,1024] linear_79
      float[batch,64,4096] linear_82
      float[batch,64,1024] linear_83
      float[batch,64,4096] linear_86
      float[batch,64,1024] linear_87
      float[batch,64,4096] linear_90
      float[batch,64,1024] linear_91
      float[batch,1,4096] linear_94
      float[batch,1,1024] linear_95
      float[batch,1024] linear_96
      float[batch,64,1024] node_scaled_dot_product_attention_10_k
      float[batch,64,1024] node_scaled_dot_product_attention_10_out
      float[batch,64,1024] node_scaled_dot_product_attention_10_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_10_q
      float[batch,64,3072] node_scaled_dot_product_attention_10_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_10_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_10_v
      float[batch,64,1024] node_scaled_dot_product_attention_11_k
      float[batch,64,1024] node_scaled_dot_product_attention_11_out
      float[batch,64,1024] node_scaled_dot_product_attention_11_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_11_q
      float[batch,64,3072] node_scaled_dot_product_attention_11_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_11_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_11_v
      float[batch,64,1024] node_scaled_dot_product_attention_12_k
      float[batch,64,1024] node_scaled_dot_product_attention_12_out
      float[batch,64,1024] node_scaled_dot_product_attention_12_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_12_q
      float[batch,64,3072] node_scaled_dot_product_attention_12_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_12_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_12_v
      float[batch,64,1024] node_scaled_dot_product_attention_13_k
      float[batch,64,1024] node_scaled_dot_product_attention_13_out
      float[batch,64,1024] node_scaled_dot_product_attention_13_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_13_q
      float[batch,64,3072] node_scaled_dot_product_attention_13_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_13_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_13_v
      float[batch,64,1024] node_scaled_dot_product_attention_14_k
      float[batch,64,1024] node_scaled_dot_product_attention_14_out
      float[batch,64,1024] node_scaled_dot_product_attention_14_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_14_q
      float[batch,64,3072] node_scaled_dot_product_attention_14_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_14_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_14_v
      float[batch,64,1024] node_scaled_dot_product_attention_15_k
      float[batch,64,1024] node_scaled_dot_product_attention_15_out
      float[batch,64,1024] node_scaled_dot_product_attention_15_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_15_q
      float[batch,64,3072] node_scaled_dot_product_attention_15_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_15_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_15_v
      float[batch,64,1024] node_scaled_dot_product_attention_16_k
      float[batch,64,1024] node_scaled_dot_product_attention_16_out
      float[batch,64,1024] node_scaled_dot_product_attention_16_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_16_q
      float[batch,64,3072] node_scaled_dot_product_attention_16_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_16_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_16_v
      float[batch,64,1024] node_scaled_dot_product_attention_17_k
      float[batch,64,1024] node_scaled_dot_product_attention_17_out
      float[batch,64,1024] node_scaled_dot_product_attention_17_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_17_q
      float[batch,64,3072] node_scaled_dot_product_attention_17_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_17_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_17_v
      float[batch,64,1024] node_scaled_dot_product_attention_18_k
      float[batch,64,1024] node_scaled_dot_product_attention_18_out
      float[batch,64,1024] node_scaled_dot_product_attention_18_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_18_q
      float[batch,64,3072] node_scaled_dot_product_attention_18_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_18_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_18_v
      float[batch,64,1024] node_scaled_dot_product_attention_19_k
      float[batch,64,1024] node_scaled_dot_product_attention_19_out
      float[batch,64,1024] node_scaled_dot_product_attention_19_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_19_q
      float[batch,64,3072] node_scaled_dot_product_attention_19_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_19_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_19_v
      float[batch,64,1024] node_scaled_dot_product_attention_1_k
      float[batch,64,1024] node_scaled_dot_product_attention_1_out
      float[batch,64,1024] node_scaled_dot_product_attention_1_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_1_q
      float[batch,64,3072] node_scaled_dot_product_attention_1_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_1_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_1_v
      float[batch,64,1024] node_scaled_dot_product_attention_20_k
      float[batch,64,1024] node_scaled_dot_product_attention_20_out
      float[batch,64,1024] node_scaled_dot_product_attention_20_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_20_q
      float[batch,64,3072] node_scaled_dot_product_attention_20_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_20_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_20_v
      float[batch,64,1024] node_scaled_dot_product_attention_21_k
      float[batch,64,1024] node_scaled_dot_product_attention_21_out
      float[batch,64,1024] node_scaled_dot_product_attention_21_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_21_q
      float[batch,64,3072] node_scaled_dot_product_attention_21_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_21_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_21_v
      float[batch,64,1024] node_scaled_dot_product_attention_22_k
      float[batch,64,1024] node_scaled_dot_product_attention_22_out
      float[batch,64,1024] node_scaled_dot_product_attention_22_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_22_q
      float[batch,64,3072] node_scaled_dot_product_attention_22_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_22_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_22_v
      float[batch,64,1024] 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,64,1024] node_scaled_dot_product_attention_23_q
      float[batch,1,1024] node_scaled_dot_product_attention_23_q_pooled
      float[batch,64,3072] node_scaled_dot_product_attention_23_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_23_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_23_v
      float[batch,64,1024] node_scaled_dot_product_attention_2_k
      float[batch,64,1024] node_scaled_dot_product_attention_2_out
      float[batch,64,1024] node_scaled_dot_product_attention_2_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_2_q
      float[batch,64,3072] node_scaled_dot_product_attention_2_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_2_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_2_v
      float[batch,64,1024] node_scaled_dot_product_attention_3_k
      float[batch,64,1024] node_scaled_dot_product_attention_3_out
      float[batch,64,1024] node_scaled_dot_product_attention_3_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_3_q
      float[batch,64,3072] node_scaled_dot_product_attention_3_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_3_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_3_v
      float[batch,64,1024] node_scaled_dot_product_attention_4_k
      float[batch,64,1024] node_scaled_dot_product_attention_4_out
      float[batch,64,1024] node_scaled_dot_product_attention_4_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_4_q
      float[batch,64,3072] node_scaled_dot_product_attention_4_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_4_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_4_v
      float[batch,64,1024] node_scaled_dot_product_attention_5_k
      float[batch,64,1024] node_scaled_dot_product_attention_5_out
      float[batch,64,1024] node_scaled_dot_product_attention_5_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_5_q
      float[batch,64,3072] node_scaled_dot_product_attention_5_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_5_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_5_v
      float[batch,64,1024] node_scaled_dot_product_attention_6_k
      float[batch,64,1024] node_scaled_dot_product_attention_6_out
      float[batch,64,1024] node_scaled_dot_product_attention_6_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_6_q
      float[batch,64,3072] node_scaled_dot_product_attention_6_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_6_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_6_v
      float[batch,64,1024] node_scaled_dot_product_attention_7_k
      float[batch,64,1024] node_scaled_dot_product_attention_7_out
      float[batch,64,1024] node_scaled_dot_product_attention_7_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_7_q
      float[batch,64,3072] node_scaled_dot_product_attention_7_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_7_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_7_v
      float[batch,64,1024] node_scaled_dot_product_attention_8_k
      float[batch,64,1024] node_scaled_dot_product_attention_8_out
      float[batch,64,1024] node_scaled_dot_product_attention_8_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_8_q
      float[batch,64,3072] node_scaled_dot_product_attention_8_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_8_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_8_v
      float[batch,64,1024] node_scaled_dot_product_attention_9_k
      float[batch,64,1024] node_scaled_dot_product_attention_9_out
      float[batch,64,1024] node_scaled_dot_product_attention_9_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_9_q
      float[batch,64,3072] node_scaled_dot_product_attention_9_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_9_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_9_v
      float[batch,64,1024] node_scaled_dot_product_attention_k
      float[batch,64,1024] node_scaled_dot_product_attention_out
      float[batch,64,1024] node_scaled_dot_product_attention_out_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_q
      float[batch,64,3072] node_scaled_dot_product_attention_qkv
      float[batch,64,3072] node_scaled_dot_product_attention_qkv_mm_out
      float[batch,64,1024] node_scaled_dot_product_attention_v
      float[batch,64,1024] scaled_dot_product_attention
      float[batch,64,1024] scaled_dot_product_attention_1
      float[batch,64,1024] scaled_dot_product_attention_10
      float[batch,64,1024] scaled_dot_product_attention_11
      float[batch,64,1024] scaled_dot_product_attention_12
      float[batch,64,1024] scaled_dot_product_attention_13
      float[batch,64,1024] scaled_dot_product_attention_14
      float[batch,64,1024] scaled_dot_product_attention_15
      float[batch,64,1024] scaled_dot_product_attention_16
      float[batch,64,1024] scaled_dot_product_attention_17
      float[batch,64,1024] scaled_dot_product_attention_18
      float[batch,64,1024] scaled_dot_product_attention_19
      float[batch,64,1024] scaled_dot_product_attention_2
      float[batch,64,1024] scaled_dot_product_attention_20
      float[batch,64,1024] scaled_dot_product_attention_21
      float[batch,64,1024] scaled_dot_product_attention_22
      float[batch,1,1024] scaled_dot_product_attention_23
      float[batch,64,1024] scaled_dot_product_attention_3
      float[batch,64,1024] scaled_dot_product_attention_4
      float[batch,64,1024] scaled_dot_product_attention_5
      float[batch,64,1024] scaled_dot_product_attention_6
      float[batch,64,1024] scaled_dot_product_attention_7
      float[batch,64,1024] scaled_dot_product_attention_8
      float[batch,64,1024] scaled_dot_product_attention_9
      float[batch,1024] select_72
      float[batch,64,1024] val_100
      float[batch,64,4096] val_101
      float[batch,64,1024] val_102
      float[batch,64,4096] val_103
      float[batch,64,1024] val_104
      float[batch,64,4096] val_105
      float[batch,64,1024] val_106
      float[batch,64,4096] val_107
      float[batch,64,1024] val_108
      float[batch,64,4096] val_109
      float[batch,64,1024] val_110
      float[batch,64,4096] val_111
      float[batch,64,1024] val_112
      float[batch,64,4096] val_113
      float[batch,64,1024] val_114
      float[batch,64,4096] val_115
      float[batch,64,1024] val_116
      float[batch,64,4096] val_117
      float[batch,64,1024] val_118
      float[batch,64,4096] val_119
      float[batch,64,1024] val_120
      float[batch,64,4096] val_121
      float[batch,64,1024] val_122
      float[batch,1,4096] val_123
      float[batch,1,1024] val_124
      float[batch,1024] val_125
      float[batch,64,4096] val_77
      float[batch,64,1024] val_78
      float[batch,64,4096] val_79
      float[batch,64,1024] val_80
      float[batch,64,4096] val_81
      float[batch,64,1024] val_82
      float[batch,64,4096] val_83
      float[batch,64,1024] val_84
      float[batch,64,4096] val_85
      float[batch,64,1024] val_86
      float[batch,64,4096] val_87
      float[batch,64,1024] val_88
      float[batch,64,4096] val_89
      float[batch,64,1024] val_90
      float[batch,64,4096] val_91
      float[batch,64,1024] val_92
      float[batch,64,4096] val_93
      float[batch,64,1024] val_94
      float[batch,64,4096] val_95
      float[batch,64,1024] val_96
      float[batch,64,4096] val_97
      float[batch,64,1024] val_98
      float[batch,64,4096] val_99
   >
{
   val_76 = Gather <axis: int = 0> ("text.token_embedding.weight_fp16", text)
   embedding = Cast <to: int = 1> (val_76)
   add_4 = Add (embedding, "text.positional_embedding")
   [node_layer_norm] layer_norm = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_4, "text.transformer.resblocks.0.ln_1.weight", "text.transformer.resblocks.0.ln_1.bias")
   [node_scaled_dot_product_attention_qkv_mm] node_scaled_dot_product_attention_qkv_mm_out = MatMul (layer_norm, val_4)
   [node_scaled_dot_product_attention_qkv_bias] node_scaled_dot_product_attention_qkv = Add (node_scaled_dot_product_attention_qkv_mm_out, "text.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, "text.transformer.resblocks.0.attn.out_proj.bias")
   add_119 = Add (add_4, node_scaled_dot_product_attention_out)
   layer_norm_1 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_119, "text.transformer.resblocks.0.ln_2.weight", "text.transformer.resblocks.0.ln_2.bias")
   val_77 = MatMul (layer_norm_1, val_5)
   linear_2 = Add (val_77, "text.transformer.resblocks.0.mlp.c_fc.bias")
   [node_gelu] gelu = Gelu <approximate: string = "tanh"> (linear_2)
   val_78 = MatMul (gelu, val_6)
   linear_3 = Add (val_78, "text.transformer.resblocks.0.mlp.c_proj.bias")
   add_140 = Add (add_119, linear_3)
   layer_norm_2 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_140, "text.transformer.resblocks.1.ln_1.weight", "text.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_2, val_7)
   [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, "text.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, "text.transformer.resblocks.1.attn.out_proj.bias")
   add_255 = Add (add_140, node_scaled_dot_product_attention_1_out)
   layer_norm_3 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_255, "text.transformer.resblocks.1.ln_2.weight", "text.transformer.resblocks.1.ln_2.bias")
   val_79 = MatMul (layer_norm_3, val_8)
   linear_6 = Add (val_79, "text.transformer.resblocks.1.mlp.c_fc.bias")
   gelu_1 = Gelu <approximate: string = "tanh"> (linear_6)
   val_80 = MatMul (gelu_1, val_9)
   linear_7 = Add (val_80, "text.transformer.resblocks.1.mlp.c_proj.bias")
   add_276 = Add (add_255, linear_7)
   layer_norm_4 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_276, "text.transformer.resblocks.2.ln_1.weight", "text.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_4, val_10)
   [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, "text.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, "text.transformer.resblocks.2.attn.out_proj.bias")
   add_391 = Add (add_276, node_scaled_dot_product_attention_2_out)
   layer_norm_5 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_391, "text.transformer.resblocks.2.ln_2.weight", "text.transformer.resblocks.2.ln_2.bias")
   val_81 = MatMul (layer_norm_5, val_11)
   linear_10 = Add (val_81, "text.transformer.resblocks.2.mlp.c_fc.bias")
   gelu_2 = Gelu <approximate: string = "tanh"> (linear_10)
   val_82 = MatMul (gelu_2, val_12)
   linear_11 = Add (val_82, "text.transformer.resblocks.2.mlp.c_proj.bias")
   add_412 = Add (add_391, linear_11)
   layer_norm_6 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_412, "text.transformer.resblocks.3.ln_1.weight", "text.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_6, val_13)
   [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, "text.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, "text.transformer.resblocks.3.attn.out_proj.bias")
   add_527 = Add (add_412, node_scaled_dot_product_attention_3_out)
   layer_norm_7 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_527, "text.transformer.resblocks.3.ln_2.weight", "text.transformer.resblocks.3.ln_2.bias")
   val_83 = MatMul (layer_norm_7, val_14)
   linear_14 = Add (val_83, "text.transformer.resblocks.3.mlp.c_fc.bias")
   gelu_3 = Gelu <approximate: string = "tanh"> (linear_14)
   val_84 = MatMul (gelu_3, val_15)
   linear_15 = Add (val_84, "text.transformer.resblocks.3.mlp.c_proj.bias")
   add_548 = Add (add_527, linear_15)
   layer_norm_8 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_548, "text.transformer.resblocks.4.ln_1.weight", "text.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_8, val_16)
   [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, "text.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, "text.transformer.resblocks.4.attn.out_proj.bias")
   add_663 = Add (add_548, node_scaled_dot_product_attention_4_out)
   layer_norm_9 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_663, "text.transformer.resblocks.4.ln_2.weight", "text.transformer.resblocks.4.ln_2.bias")
   val_85 = MatMul (layer_norm_9, val_17)
   linear_18 = Add (val_85, "text.transformer.resblocks.4.mlp.c_fc.bias")
   gelu_4 = Gelu <approximate: string = "tanh"> (linear_18)
   val_86 = MatMul (gelu_4, val_18)
   linear_19 = Add (val_86, "text.transformer.resblocks.4.mlp.c_proj.bias")
   add_684 = Add (add_663, linear_19)
   layer_norm_10 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_684, "text.transformer.resblocks.5.ln_1.weight", "text.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_10, val_19)
   [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, "text.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, "text.transformer.resblocks.5.attn.out_proj.bias")
   add_799 = Add (add_684, node_scaled_dot_product_attention_5_out)
   layer_norm_11 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_799, "text.transformer.resblocks.5.ln_2.weight", "text.transformer.resblocks.5.ln_2.bias")
   val_87 = MatMul (layer_norm_11, val_20)
   linear_22 = Add (val_87, "text.transformer.resblocks.5.mlp.c_fc.bias")
   gelu_5 = Gelu <approximate: string = "tanh"> (linear_22)
   val_88 = MatMul (gelu_5, val_21)
   linear_23 = Add (val_88, "text.transformer.resblocks.5.mlp.c_proj.bias")
   add_820 = Add (add_799, linear_23)
   layer_norm_12 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_820, "text.transformer.resblocks.6.ln_1.weight", "text.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_12, val_22)
   [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, "text.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, "text.transformer.resblocks.6.attn.out_proj.bias")
   add_935 = Add (add_820, node_scaled_dot_product_attention_6_out)
   layer_norm_13 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_935, "text.transformer.resblocks.6.ln_2.weight", "text.transformer.resblocks.6.ln_2.bias")
   val_89 = MatMul (layer_norm_13, val_23)
   linear_26 = Add (val_89, "text.transformer.resblocks.6.mlp.c_fc.bias")
   gelu_6 = Gelu <approximate: string = "tanh"> (linear_26)
   val_90 = MatMul (gelu_6, val_24)
   linear_27 = Add (val_90, "text.transformer.resblocks.6.mlp.c_proj.bias")
   add_956 = Add (add_935, linear_27)
   layer_norm_14 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_956, "text.transformer.resblocks.7.ln_1.weight", "text.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_14, val_25)
   [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, "text.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, "text.transformer.resblocks.7.attn.out_proj.bias")
   add_1071 = Add (add_956, node_scaled_dot_product_attention_7_out)
   layer_norm_15 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1071, "text.transformer.resblocks.7.ln_2.weight", "text.transformer.resblocks.7.ln_2.bias")
   val_91 = MatMul (layer_norm_15, val_26)
   linear_30 = Add (val_91, "text.transformer.resblocks.7.mlp.c_fc.bias")
   gelu_7 = Gelu <approximate: string = "tanh"> (linear_30)
   val_92 = MatMul (gelu_7, val_27)
   linear_31 = Add (val_92, "text.transformer.resblocks.7.mlp.c_proj.bias")
   add_1092 = Add (add_1071, linear_31)
   layer_norm_16 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1092, "text.transformer.resblocks.8.ln_1.weight", "text.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_16, val_28)
   [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, "text.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, "text.transformer.resblocks.8.attn.out_proj.bias")
   add_1207 = Add (add_1092, node_scaled_dot_product_attention_8_out)
   layer_norm_17 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1207, "text.transformer.resblocks.8.ln_2.weight", "text.transformer.resblocks.8.ln_2.bias")
   val_93 = MatMul (layer_norm_17, val_29)
   linear_34 = Add (val_93, "text.transformer.resblocks.8.mlp.c_fc.bias")
   gelu_8 = Gelu <approximate: string = "tanh"> (linear_34)
   val_94 = MatMul (gelu_8, val_30)
   linear_35 = Add (val_94, "text.transformer.resblocks.8.mlp.c_proj.bias")
   add_1228 = Add (add_1207, linear_35)
   layer_norm_18 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1228, "text.transformer.resblocks.9.ln_1.weight", "text.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_18, val_31)
   [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, "text.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, "text.transformer.resblocks.9.attn.out_proj.bias")
   add_1343 = Add (add_1228, node_scaled_dot_product_attention_9_out)
   layer_norm_19 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1343, "text.transformer.resblocks.9.ln_2.weight", "text.transformer.resblocks.9.ln_2.bias")
   val_95 = MatMul (layer_norm_19, val_32)
   linear_38 = Add (val_95, "text.transformer.resblocks.9.mlp.c_fc.bias")
   gelu_9 = Gelu <approximate: string = "tanh"> (linear_38)
   val_96 = MatMul (gelu_9, val_33)
   linear_39 = Add (val_96, "text.transformer.resblocks.9.mlp.c_proj.bias")
   add_1364 = Add (add_1343, linear_39)
   layer_norm_20 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1364, "text.transformer.resblocks.10.ln_1.weight", "text.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_20, val_34)
   [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, "text.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, "text.transformer.resblocks.10.attn.out_proj.bias")
   add_1479 = Add (add_1364, node_scaled_dot_product_attention_10_out)
   layer_norm_21 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1479, "text.transformer.resblocks.10.ln_2.weight", "text.transformer.resblocks.10.ln_2.bias")
   val_97 = MatMul (layer_norm_21, val_35)
   linear_42 = Add (val_97, "text.transformer.resblocks.10.mlp.c_fc.bias")
   gelu_10 = Gelu <approximate: string = "tanh"> (linear_42)
   val_98 = MatMul (gelu_10, val_36)
   linear_43 = Add (val_98, "text.transformer.resblocks.10.mlp.c_proj.bias")
   add_1500 = Add (add_1479, linear_43)
   layer_norm_22 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1500, "text.transformer.resblocks.11.ln_1.weight", "text.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_22, val_37)
   [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, "text.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, "text.transformer.resblocks.11.attn.out_proj.bias")
   add_1615 = Add (add_1500, node_scaled_dot_product_attention_11_out)
   layer_norm_23 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1615, "text.transformer.resblocks.11.ln_2.weight", "text.transformer.resblocks.11.ln_2.bias")
   val_99 = MatMul (layer_norm_23, val_38)
   linear_46 = Add (val_99, "text.transformer.resblocks.11.mlp.c_fc.bias")
   gelu_11 = Gelu <approximate: string = "tanh"> (linear_46)
   val_100 = MatMul (gelu_11, val_39)
   linear_47 = Add (val_100, "text.transformer.resblocks.11.mlp.c_proj.bias")
   add_1636 = Add (add_1615, linear_47)
   layer_norm_24 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1636, "text.transformer.resblocks.12.ln_1.weight", "text.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_24, val_40)
   [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, "text.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, "text.transformer.resblocks.12.attn.out_proj.bias")
   add_1751 = Add (add_1636, node_scaled_dot_product_attention_12_out)
   layer_norm_25 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1751, "text.transformer.resblocks.12.ln_2.weight", "text.transformer.resblocks.12.ln_2.bias")
   val_101 = MatMul (layer_norm_25, val_41)
   linear_50 = Add (val_101, "text.transformer.resblocks.12.mlp.c_fc.bias")
   gelu_12 = Gelu <approximate: string = "tanh"> (linear_50)
   val_102 = MatMul (gelu_12, val_42)
   linear_51 = Add (val_102, "text.transformer.resblocks.12.mlp.c_proj.bias")
   add_1772 = Add (add_1751, linear_51)
   layer_norm_26 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1772, "text.transformer.resblocks.13.ln_1.weight", "text.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_26, val_43)
   [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, "text.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, "text.transformer.resblocks.13.attn.out_proj.bias")
   add_1887 = Add (add_1772, node_scaled_dot_product_attention_13_out)
   layer_norm_27 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1887, "text.transformer.resblocks.13.ln_2.weight", "text.transformer.resblocks.13.ln_2.bias")
   val_103 = MatMul (layer_norm_27, val_44)
   linear_54 = Add (val_103, "text.transformer.resblocks.13.mlp.c_fc.bias")
   gelu_13 = Gelu <approximate: string = "tanh"> (linear_54)
   val_104 = MatMul (gelu_13, val_45)
   linear_55 = Add (val_104, "text.transformer.resblocks.13.mlp.c_proj.bias")
   add_1908 = Add (add_1887, linear_55)
   layer_norm_28 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1908, "text.transformer.resblocks.14.ln_1.weight", "text.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_28, val_46)
   [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, "text.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, "text.transformer.resblocks.14.attn.out_proj.bias")
   add_2023 = Add (add_1908, node_scaled_dot_product_attention_14_out)
   layer_norm_29 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2023, "text.transformer.resblocks.14.ln_2.weight", "text.transformer.resblocks.14.ln_2.bias")
   val_105 = MatMul (layer_norm_29, val_47)
   linear_58 = Add (val_105, "text.transformer.resblocks.14.mlp.c_fc.bias")
   gelu_14 = Gelu <approximate: string = "tanh"> (linear_58)
   val_106 = MatMul (gelu_14, val_48)
   linear_59 = Add (val_106, "text.transformer.resblocks.14.mlp.c_proj.bias")
   add_2044 = Add (add_2023, linear_59)
   layer_norm_30 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2044, "text.transformer.resblocks.15.ln_1.weight", "text.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_30, val_49)
   [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, "text.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, "text.transformer.resblocks.15.attn.out_proj.bias")
   add_2159 = Add (add_2044, node_scaled_dot_product_attention_15_out)
   layer_norm_31 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2159, "text.transformer.resblocks.15.ln_2.weight", "text.transformer.resblocks.15.ln_2.bias")
   val_107 = MatMul (layer_norm_31, val_50)
   linear_62 = Add (val_107, "text.transformer.resblocks.15.mlp.c_fc.bias")
   gelu_15 = Gelu <approximate: string = "tanh"> (linear_62)
   val_108 = MatMul (gelu_15, val_51)
   linear_63 = Add (val_108, "text.transformer.resblocks.15.mlp.c_proj.bias")
   add_2180 = Add (add_2159, linear_63)
   layer_norm_32 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2180, "text.transformer.resblocks.16.ln_1.weight", "text.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_32, val_52)
   [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, "text.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, "text.transformer.resblocks.16.attn.out_proj.bias")
   add_2295 = Add (add_2180, node_scaled_dot_product_attention_16_out)
   layer_norm_33 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2295, "text.transformer.resblocks.16.ln_2.weight", "text.transformer.resblocks.16.ln_2.bias")
   val_109 = MatMul (layer_norm_33, val_53)
   linear_66 = Add (val_109, "text.transformer.resblocks.16.mlp.c_fc.bias")
   gelu_16 = Gelu <approximate: string = "tanh"> (linear_66)
   val_110 = MatMul (gelu_16, val_54)
   linear_67 = Add (val_110, "text.transformer.resblocks.16.mlp.c_proj.bias")
   add_2316 = Add (add_2295, linear_67)
   layer_norm_34 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2316, "text.transformer.resblocks.17.ln_1.weight", "text.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_34, val_55)
   [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, "text.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, "text.transformer.resblocks.17.attn.out_proj.bias")
   add_2431 = Add (add_2316, node_scaled_dot_product_attention_17_out)
   layer_norm_35 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2431, "text.transformer.resblocks.17.ln_2.weight", "text.transformer.resblocks.17.ln_2.bias")
   val_111 = MatMul (layer_norm_35, val_56)
   linear_70 = Add (val_111, "text.transformer.resblocks.17.mlp.c_fc.bias")
   gelu_17 = Gelu <approximate: string = "tanh"> (linear_70)
   val_112 = MatMul (gelu_17, val_57)
   linear_71 = Add (val_112, "text.transformer.resblocks.17.mlp.c_proj.bias")
   add_2452 = Add (add_2431, linear_71)
   layer_norm_36 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2452, "text.transformer.resblocks.18.ln_1.weight", "text.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_36, val_58)
   [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, "text.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, "text.transformer.resblocks.18.attn.out_proj.bias")
   add_2567 = Add (add_2452, node_scaled_dot_product_attention_18_out)
   layer_norm_37 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2567, "text.transformer.resblocks.18.ln_2.weight", "text.transformer.resblocks.18.ln_2.bias")
   val_113 = MatMul (layer_norm_37, val_59)
   linear_74 = Add (val_113, "text.transformer.resblocks.18.mlp.c_fc.bias")
   gelu_18 = Gelu <approximate: string = "tanh"> (linear_74)
   val_114 = MatMul (gelu_18, val_60)
   linear_75 = Add (val_114, "text.transformer.resblocks.18.mlp.c_proj.bias")
   add_2588 = Add (add_2567, linear_75)
   layer_norm_38 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2588, "text.transformer.resblocks.19.ln_1.weight", "text.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_38, val_61)
   [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, "text.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, "text.transformer.resblocks.19.attn.out_proj.bias")
   add_2703 = Add (add_2588, node_scaled_dot_product_attention_19_out)
   layer_norm_39 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2703, "text.transformer.resblocks.19.ln_2.weight", "text.transformer.resblocks.19.ln_2.bias")
   val_115 = MatMul (layer_norm_39, val_62)
   linear_78 = Add (val_115, "text.transformer.resblocks.19.mlp.c_fc.bias")
   gelu_19 = Gelu <approximate: string = "tanh"> (linear_78)
   val_116 = MatMul (gelu_19, val_63)
   linear_79 = Add (val_116, "text.transformer.resblocks.19.mlp.c_proj.bias")
   add_2724 = Add (add_2703, linear_79)
   layer_norm_40 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2724, "text.transformer.resblocks.20.ln_1.weight", "text.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_40, val_64)
   [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, "text.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, "text.transformer.resblocks.20.attn.out_proj.bias")
   add_2839 = Add (add_2724, node_scaled_dot_product_attention_20_out)
   layer_norm_41 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2839, "text.transformer.resblocks.20.ln_2.weight", "text.transformer.resblocks.20.ln_2.bias")
   val_117 = MatMul (layer_norm_41, val_65)
   linear_82 = Add (val_117, "text.transformer.resblocks.20.mlp.c_fc.bias")
   gelu_20 = Gelu <approximate: string = "tanh"> (linear_82)
   val_118 = MatMul (gelu_20, val_66)
   linear_83 = Add (val_118, "text.transformer.resblocks.20.mlp.c_proj.bias")
   add_2860 = Add (add_2839, linear_83)
   layer_norm_42 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2860, "text.transformer.resblocks.21.ln_1.weight", "text.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_42, val_67)
   [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, "text.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, "text.transformer.resblocks.21.attn.out_proj.bias")
   add_2975 = Add (add_2860, node_scaled_dot_product_attention_21_out)
   layer_norm_43 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2975, "text.transformer.resblocks.21.ln_2.weight", "text.transformer.resblocks.21.ln_2.bias")
   val_119 = MatMul (layer_norm_43, val_68)
   linear_86 = Add (val_119, "text.transformer.resblocks.21.mlp.c_fc.bias")
   gelu_21 = Gelu <approximate: string = "tanh"> (linear_86)
   val_120 = MatMul (gelu_21, val_69)
   linear_87 = Add (val_120, "text.transformer.resblocks.21.mlp.c_proj.bias")
   add_2996 = Add (add_2975, linear_87)
   layer_norm_44 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_2996, "text.transformer.resblocks.22.ln_1.weight", "text.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_44, val_70)
   [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, "text.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, "text.transformer.resblocks.22.attn.out_proj.bias")
   add_3111 = Add (add_2996, node_scaled_dot_product_attention_22_out)
   layer_norm_45 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3111, "text.transformer.resblocks.22.ln_2.weight", "text.transformer.resblocks.22.ln_2.bias")
   val_121 = MatMul (layer_norm_45, val_71)
   linear_90 = Add (val_121, "text.transformer.resblocks.22.mlp.c_fc.bias")
   gelu_22 = Gelu <approximate: string = "tanh"> (linear_90)
   val_122 = MatMul (gelu_22, val_72)
   linear_91 = Add (val_122, "text.transformer.resblocks.22.mlp.c_proj.bias")
   add_3132 = Add (add_3111, linear_91)
   layer_norm_46 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3132, "text.transformer.resblocks.23.ln_1.weight", "text.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_46, val_73)
   [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, "text.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_0, val_3, val_2)
   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, "text.transformer.resblocks.23.attn.out_proj.bias")
   [pool_hoist_add_3132] add_3132_pooled = Slice (add_3132, val_0, val_3, val_2)
   add_3247 = Add (add_3132_pooled, node_scaled_dot_product_attention_23_out)
   layer_norm_47 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3247, "text.transformer.resblocks.23.ln_2.weight", "text.transformer.resblocks.23.ln_2.bias")
   val_123 = MatMul (layer_norm_47, val_74)
   linear_94 = Add (val_123, "text.transformer.resblocks.23.mlp.c_fc.bias")
   gelu_23 = Gelu <approximate: string = "tanh"> (linear_94)
   val_124 = MatMul (gelu_23, val_75)
   linear_95 = Add (val_124, "text.transformer.resblocks.23.mlp.c_proj.bias")
   add_3268 = Add (add_3247, linear_95)
   val_125 = Squeeze (add_3268, val_2)
   select_72 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (val_125, "text.ln_final.weight", "text.ln_final.bias")
   linear_96 = Gemm <alpha: float = 1, beta: float = 1, transA: int = 0, transB: int = 1> (select_72, "text.text_projection.weight", "text.text_projection.bias")
   [node_linalg_vector_norm] linalg_vector_norm = ReduceL2 <keepdims: int = 1, noop_with_empty_axes: int = 0> (linear_96, val_0)
   [node_clamp_min] clamp_min = Clip (linalg_vector_norm, val_1)
   [node_div] text_embedding = Div (linear_96, clamp_min)
}

weights:
attn3d_split_3x1024 INT64[3] 0ec6f5651fd5
node_scaled_dot_product_attention_10_wo_t FLOAT[1024,1024] ed0f6e2e926d
node_scaled_dot_product_attention_11_wo_t FLOAT[1024,1024] 1dbaa5304d01
node_scaled_dot_product_attention_12_wo_t FLOAT[1024,1024] c61f4a69e2c2
node_scaled_dot_product_attention_13_wo_t FLOAT[1024,1024] f305467dc4be
node_scaled_dot_product_attention_14_wo_t FLOAT[1024,1024] bcca51e3dcc3
node_scaled_dot_product_attention_15_wo_t FLOAT[1024,1024] 409c7f6ad2aa
node_scaled_dot_product_attention_16_wo_t FLOAT[1024,1024] 4337862113d9
node_scaled_dot_product_attention_17_wo_t FLOAT[1024,1024] 3013ce70312c
node_scaled_dot_product_attention_18_wo_t FLOAT[1024,1024] 96fa9391baa2
node_scaled_dot_product_attention_19_wo_t FLOAT[1024,1024] 6b165c5cffc8
node_scaled_dot_product_attention_1_wo_t FLOAT[1024,1024] 083cedf05b53
node_scaled_dot_product_attention_20_wo_t FLOAT[1024,1024] f4fd1d6388bd
node_scaled_dot_product_attention_21_wo_t FLOAT[1024,1024] c9ca14b23459
node_scaled_dot_product_attention_22_wo_t FLOAT[1024,1024] 7fcb3c059c13
node_scaled_dot_product_attention_23_wo_t FLOAT[1024,1024] 55ccdd167ffa
node_scaled_dot_product_attention_2_wo_t FLOAT[1024,1024] 0cf8d1111dcb
node_scaled_dot_product_attention_3_wo_t FLOAT[1024,1024] fe4d36d4ea36
node_scaled_dot_product_attention_4_wo_t FLOAT[1024,1024] 2fcdc7d55efc
node_scaled_dot_product_attention_5_wo_t FLOAT[1024,1024] e65df139901f
node_scaled_dot_product_attention_6_wo_t FLOAT[1024,1024] 7ab21b41a1a0
node_scaled_dot_product_attention_7_wo_t FLOAT[1024,1024] 7e8038969572
node_scaled_dot_product_attention_8_wo_t FLOAT[1024,1024] 56341c166b7f
node_scaled_dot_product_attention_9_wo_t FLOAT[1024,1024] 76d8ab4fe94e
node_scaled_dot_product_attention_wo_t FLOAT[1024,1024] 7415b1b5b072
text.ln_final.bias FLOAT[1024] 743b6fe7fb88
text.ln_final.weight FLOAT[1024] e3e4b3eb303e
text.positional_embedding FLOAT[64,1024] 96298d0e9ef1
text.text_projection.bias FLOAT[1024] b1dd8556709d
text.text_projection.weight FLOAT[1024,1024] fe60b206eee1
text.token_embedding.weight_fp16 FLOAT16[256000,1024] 468e9a4b590f
text.transformer.resblocks.0.attn.in_proj_bias FLOAT[3072] 5f45b5ced947
text.transformer.resblocks.0.attn.out_proj.bias FLOAT[1024] 3a27905d4df1
text.transformer.resblocks.0.ln_1.bias FLOAT[1024] 37f2c2440d41
text.transformer.resblocks.0.ln_1.weight FLOAT[1024] 8e13e0d02872
text.transformer.resblocks.0.ln_2.bias FLOAT[1024] e49e4ed6c36b
text.transformer.resblocks.0.ln_2.weight FLOAT[1024] ce1823d939ad
text.transformer.resblocks.0.mlp.c_fc.bias FLOAT[4096] 9a03f84aab54
text.transformer.resblocks.0.mlp.c_proj.bias FLOAT[1024] 037200e62493
text.transformer.resblocks.1.attn.in_proj_bias FLOAT[3072] f9fedd4d0db5
text.transformer.resblocks.1.attn.out_proj.bias FLOAT[1024] a1bd7719b574
text.transformer.resblocks.1.ln_1.bias FLOAT[1024] 9003a21f1a52
text.transformer.resblocks.1.ln_1.weight FLOAT[1024] 12d9b7df176a
text.transformer.resblocks.1.ln_2.bias FLOAT[1024] 5d115515b2d9
text.transformer.resblocks.1.ln_2.weight FLOAT[1024] c58d3b4e7780
text.transformer.resblocks.1.mlp.c_fc.bias FLOAT[4096] 802439d7797a
text.transformer.resblocks.1.mlp.c_proj.bias FLOAT[1024] a24439ecaa15
text.transformer.resblocks.10.attn.in_proj_bias FLOAT[3072] 387fd880ab37
text.transformer.resblocks.10.attn.out_proj.bias FLOAT[1024] 42879f93e691
text.transformer.resblocks.10.ln_1.bias FLOAT[1024] 927f1c5cadeb
text.transformer.resblocks.10.ln_1.weight FLOAT[1024] e678046c3d25
text.transformer.resblocks.10.ln_2.bias FLOAT[1024] b1899d529952
text.transformer.resblocks.10.ln_2.weight FLOAT[1024] 4bc9ca1ce3cb
text.transformer.resblocks.10.mlp.c_fc.bias FLOAT[4096] d5edbd895808
text.transformer.resblocks.10.mlp.c_proj.bias FLOAT[1024] f6ed5811ceb7
text.transformer.resblocks.11.attn.in_proj_bias FLOAT[3072] b5474d9d2a2e
text.transformer.resblocks.11.attn.out_proj.bias FLOAT[1024] c9f7cac3fdce
text.transformer.resblocks.11.ln_1.bias FLOAT[1024] e0dfc2fbd501
text.transformer.resblocks.11.ln_1.weight FLOAT[1024] 2dc8575bafa2
text.transformer.resblocks.11.ln_2.bias FLOAT[1024] aacbb346c63e
text.transformer.resblocks.11.ln_2.weight FLOAT[1024] 17478dfaba81
text.transformer.resblocks.11.mlp.c_fc.bias FLOAT[4096] b87788ddcf38
text.transformer.resblocks.11.mlp.c_proj.bias FLOAT[1024] 34be3ff7eab3
text.transformer.resblocks.12.attn.in_proj_bias FLOAT[3072] 88091876cf34
text.transformer.resblocks.12.attn.out_proj.bias FLOAT[1024] 09e30ab3949b
text.transformer.resblocks.12.ln_1.bias FLOAT[1024] a9d118756cbb
text.transformer.resblocks.12.ln_1.weight FLOAT[1024] d0b4103c3f87
text.transformer.resblocks.12.ln_2.bias FLOAT[1024] a96ed1fe4724
text.transformer.resblocks.12.ln_2.weight FLOAT[1024] 2c173dbc4121
text.transformer.resblocks.12.mlp.c_fc.bias FLOAT[4096] 4c581e90591f
text.transformer.resblocks.12.mlp.c_proj.bias FLOAT[1024] 2a69b289beb2
text.transformer.resblocks.13.attn.in_proj_bias FLOAT[3072] 23599cac92d0
text.transformer.resblocks.13.attn.out_proj.bias FLOAT[1024] 5201c0bac71a
text.transformer.resblocks.13.ln_1.bias FLOAT[1024] 3a709529488c
text.transformer.resblocks.13.ln_1.weight FLOAT[1024] c76a5454dbd2
text.transformer.resblocks.13.ln_2.bias FLOAT[1024] 29e6a2f67669
text.transformer.resblocks.13.ln_2.weight FLOAT[1024] f8347e7b0b31
text.transformer.resblocks.13.mlp.c_fc.bias FLOAT[4096] 28a11db9000f
text.transformer.resblocks.13.mlp.c_proj.bias FLOAT[1024] e75a0da084f8
text.transformer.resblocks.14.attn.in_proj_bias FLOAT[3072] 8186d39d3275
text.transformer.resblocks.14.attn.out_proj.bias FLOAT[1024] 3918dc86f16c
text.transformer.resblocks.14.ln_1.bias FLOAT[1024] 79fa60bcbe26
text.transformer.resblocks.14.ln_1.weight FLOAT[1024] c81bf39f34f8
text.transformer.resblocks.14.ln_2.bias FLOAT[1024] 9c8373435eaa
text.transformer.resblocks.14.ln_2.weight FLOAT[1024] 506dd9f3ed22
text.transformer.resblocks.14.mlp.c_fc.bias FLOAT[4096] be2a7c027d0d
text.transformer.resblocks.14.mlp.c_proj.bias FLOAT[1024] 5301bf3458f6
text.transformer.resblocks.15.attn.in_proj_bias FLOAT[3072] 2840f953cd55
text.transformer.resblocks.15.attn.out_proj.bias FLOAT[1024] 91668ab3df59
text.transformer.resblocks.15.ln_1.bias FLOAT[1024] 18b27acf5a0e
text.transformer.resblocks.15.ln_1.weight FLOAT[1024] 3369642f6fd2
text.transformer.resblocks.15.ln_2.bias FLOAT[1024] 4c08afb20eb6
text.transformer.resblocks.15.ln_2.weight FLOAT[1024] 774ffe5886f3
text.transformer.resblocks.15.mlp.c_fc.bias FLOAT[4096] ea608e756284
text.transformer.resblocks.15.mlp.c_proj.bias FLOAT[1024] 5eccf01eb52a
text.transformer.resblocks.16.attn.in_proj_bias FLOAT[3072] 2650562d80f6
text.transformer.resblocks.16.attn.out_proj.bias FLOAT[1024] 03bb12ad0b79
text.transformer.resblocks.16.ln_1.bias FLOAT[1024] 8b6c2cb7f527
text.transformer.resblocks.16.ln_1.weight FLOAT[1024] af2ad3fb9061
text.transformer.resblocks.16.ln_2.bias FLOAT[1024] 3dd747342514
text.transformer.resblocks.16.ln_2.weight FLOAT[1024] 72436aa0f606
text.transformer.resblocks.16.mlp.c_fc.bias FLOAT[4096] 9bf00b05b6b9
text.transformer.resblocks.16.mlp.c_proj.bias FLOAT[1024] ab6cb80b4798
text.transformer.resblocks.17.attn.in_proj_bias FLOAT[3072] 120da6a5696a
text.transformer.resblocks.17.attn.out_proj.bias FLOAT[1024] 1b0d07cb5f23
text.transformer.resblocks.17.ln_1.bias FLOAT[1024] 380dff51095b
text.transformer.resblocks.17.ln_1.weight FLOAT[1024] 74e6133ca6d2
text.transformer.resblocks.17.ln_2.bias FLOAT[1024] b6d0429a2a47
text.transformer.resblocks.17.ln_2.weight FLOAT[1024] 9c92c5db9e99
text.transformer.resblocks.17.mlp.c_fc.bias FLOAT[4096] bf59eef54807
text.transformer.resblocks.17.mlp.c_proj.bias FLOAT[1024] ffe50eac4d50
text.transformer.resblocks.18.attn.in_proj_bias FLOAT[3072] 29abd6634f12
text.transformer.resblocks.18.attn.out_proj.bias FLOAT[1024] bd20d12cdea5
text.transformer.resblocks.18.ln_1.bias FLOAT[1024] b0ad97982cdf
text.transformer.resblocks.18.ln_1.weight FLOAT[1024] 0c8d90c10e8b
text.transformer.resblocks.18.ln_2.bias FLOAT[1024] be85f078c338
text.transformer.resblocks.18.ln_2.weight FLOAT[1024] 84dc4683554c
text.transformer.resblocks.18.mlp.c_fc.bias FLOAT[4096] abc17718b090
text.transformer.resblocks.18.mlp.c_proj.bias FLOAT[1024] a2696bcc99ce
text.transformer.resblocks.19.attn.in_proj_bias FLOAT[3072] b84edd840b5a
text.transformer.resblocks.19.attn.out_proj.bias FLOAT[1024] 3bb65521cc40
text.transformer.resblocks.19.ln_1.bias FLOAT[1024] 163db26b490e
text.transformer.resblocks.19.ln_1.weight FLOAT[1024] aebaf5f36380
text.transformer.resblocks.19.ln_2.bias FLOAT[1024] e42f31f9324b
text.transformer.resblocks.19.ln_2.weight FLOAT[1024] ad129c795858
text.transformer.resblocks.19.mlp.c_fc.bias FLOAT[4096] 01b648955db1
text.transformer.resblocks.19.mlp.c_proj.bias FLOAT[1024] e4d26903e45b
text.transformer.resblocks.2.attn.in_proj_bias FLOAT[3072] 58ecceedde55
text.transformer.resblocks.2.attn.out_proj.bias FLOAT[1024] d270051ca331
text.transformer.resblocks.2.ln_1.bias FLOAT[1024] 2bf998093f7b
text.transformer.resblocks.2.ln_1.weight FLOAT[1024] a81d3deeded5
text.transformer.resblocks.2.ln_2.bias FLOAT[1024] 9eebfdf28513
text.transformer.resblocks.2.ln_2.weight FLOAT[1024] 90051d9e268a
text.transformer.resblocks.2.mlp.c_fc.bias FLOAT[4096] d1d487eee260
text.transformer.resblocks.2.mlp.c_proj.bias FLOAT[1024] e698cad27041
text.transformer.resblocks.20.attn.in_proj_bias FLOAT[3072] a4878d31a657
text.transformer.resblocks.20.attn.out_proj.bias FLOAT[1024] 79f78f5eff96
text.transformer.resblocks.20.ln_1.bias FLOAT[1024] e62fb759ea9a
text.transformer.resblocks.20.ln_1.weight FLOAT[1024] 839123fa16eb
text.transformer.resblocks.20.ln_2.bias FLOAT[1024] 6ec2929d820a
text.transformer.resblocks.20.ln_2.weight FLOAT[1024] dfcf4957ee63
text.transformer.resblocks.20.mlp.c_fc.bias FLOAT[4096] becc920ca437
text.transformer.resblocks.20.mlp.c_proj.bias FLOAT[1024] 362e7dedca48
text.transformer.resblocks.21.attn.in_proj_bias FLOAT[3072] f80c3c68c27c
text.transformer.resblocks.21.attn.out_proj.bias FLOAT[1024] 1389f9bfd4ba
text.transformer.resblocks.21.ln_1.bias FLOAT[1024] b0089142602a
text.transformer.resblocks.21.ln_1.weight FLOAT[1024] 89ac1210685c
text.transformer.resblocks.21.ln_2.bias FLOAT[1024] 9032181928ef
text.transformer.resblocks.21.ln_2.weight FLOAT[1024] bc62738dcd94
text.transformer.resblocks.21.mlp.c_fc.bias FLOAT[4096] 61946c462fd6
text.transformer.resblocks.21.mlp.c_proj.bias FLOAT[1024] 4512983a7871
text.transformer.resblocks.22.attn.in_proj_bias FLOAT[3072] 7e3ebcda20fb
text.transformer.resblocks.22.attn.out_proj.bias FLOAT[1024] e48d14c64a67
text.transformer.resblocks.22.ln_1.bias FLOAT[1024] 5e47ccc09f3c
text.transformer.resblocks.22.ln_1.weight FLOAT[1024] f6e4c3288107
text.transformer.resblocks.22.ln_2.bias FLOAT[1024] b5cb003948cf
text.transformer.resblocks.22.ln_2.weight FLOAT[1024] a679865121d5
text.transformer.resblocks.22.mlp.c_fc.bias FLOAT[4096] 3e125ff3d3d5
text.transformer.resblocks.22.mlp.c_proj.bias FLOAT[1024] 4681806332f3
text.transformer.resblocks.23.attn.in_proj_bias FLOAT[3072] daf33a07d7ba
text.transformer.resblocks.23.attn.out_proj.bias FLOAT[1024] 431a8fc196c0
text.transformer.resblocks.23.ln_1.bias FLOAT[1024] 2d8b167df92a
text.transformer.resblocks.23.ln_1.weight FLOAT[1024] 7e0ebabbca30
text.transformer.resblocks.23.ln_2.bias FLOAT[1024] 0e93f4246dad
text.transformer.resblocks.23.ln_2.weight FLOAT[1024] f4122c24c03b
text.transformer.resblocks.23.mlp.c_fc.bias FLOAT[4096] 815ca4412513
text.transformer.resblocks.23.mlp.c_proj.bias FLOAT[1024] 2a55068905ae
text.transformer.resblocks.3.attn.in_proj_bias FLOAT[3072] da189d540682
text.transformer.resblocks.3.attn.out_proj.bias FLOAT[1024] d59ca89c4706
text.transformer.resblocks.3.ln_1.bias FLOAT[1024] afff865cc97d
text.transformer.resblocks.3.ln_1.weight FLOAT[1024] d4f3c38ae478
text.transformer.resblocks.3.ln_2.bias FLOAT[1024] 467466ca253f
text.transformer.resblocks.3.ln_2.weight FLOAT[1024] c64b87ca1a41
text.transformer.resblocks.3.mlp.c_fc.bias FLOAT[4096] 91d3d2500e7e
text.transformer.resblocks.3.mlp.c_proj.bias FLOAT[1024] 3ec45925ee38
text.transformer.resblocks.4.attn.in_proj_bias FLOAT[3072] 24e76aa768fc
text.transformer.resblocks.4.attn.out_proj.bias FLOAT[1024] d6c2e76b4e47
text.transformer.resblocks.4.ln_1.bias FLOAT[1024] 88c0f8be7516
text.transformer.resblocks.4.ln_1.weight FLOAT[1024] d1d41ffacca4
text.transformer.resblocks.4.ln_2.bias FLOAT[1024] ab91b2f1099f
text.transformer.resblocks.4.ln_2.weight FLOAT[1024] da39e9411fdf
text.transformer.resblocks.4.mlp.c_fc.bias FLOAT[4096] 2a68272cdad2
text.transformer.resblocks.4.mlp.c_proj.bias FLOAT[1024] 1606283179c1
text.transformer.resblocks.5.attn.in_proj_bias FLOAT[3072] 4f3804f6419b
text.transformer.resblocks.5.attn.out_proj.bias FLOAT[1024] e350ca689f78
text.transformer.resblocks.5.ln_1.bias FLOAT[1024] abfbe3af6b13
text.transformer.resblocks.5.ln_1.weight FLOAT[1024] b96e8e821422
text.transformer.resblocks.5.ln_2.bias FLOAT[1024] 0a296701fcbd
text.transformer.resblocks.5.ln_2.weight FLOAT[1024] ae0632bd309f
text.transformer.resblocks.5.mlp.c_fc.bias FLOAT[4096] a883478ec777
text.transformer.resblocks.5.mlp.c_proj.bias FLOAT[1024] 447821e84ad2
text.transformer.resblocks.6.attn.in_proj_bias FLOAT[3072] 01a078fd9dab
text.transformer.resblocks.6.attn.out_proj.bias FLOAT[1024] fe4c02a7b074
text.transformer.resblocks.6.ln_1.bias FLOAT[1024] 08a3a61b3629
text.transformer.resblocks.6.ln_1.weight FLOAT[1024] e2a08891ca46
text.transformer.resblocks.6.ln_2.bias FLOAT[1024] c0e5e2547a13
text.transformer.resblocks.6.ln_2.weight FLOAT[1024] 105b92f2d65c
text.transformer.resblocks.6.mlp.c_fc.bias FLOAT[4096] 3e4d3ca457f7
text.transformer.resblocks.6.mlp.c_proj.bias FLOAT[1024] b781cd45fbed
text.transformer.resblocks.7.attn.in_proj_bias FLOAT[3072] 60ea01ffe80e
text.transformer.resblocks.7.attn.out_proj.bias FLOAT[1024] 55eacb892691
text.transformer.resblocks.7.ln_1.bias FLOAT[1024] d2331b0064e3
text.transformer.resblocks.7.ln_1.weight FLOAT[1024] d06ffe956a2e
text.transformer.resblocks.7.ln_2.bias FLOAT[1024] df8f9eb8124a
text.transformer.resblocks.7.ln_2.weight FLOAT[1024] b1e8fcf9c6f1
text.transformer.resblocks.7.mlp.c_fc.bias FLOAT[4096] 732d49a4c4ea
text.transformer.resblocks.7.mlp.c_proj.bias FLOAT[1024] c20e129509d2
text.transformer.resblocks.8.attn.in_proj_bias FLOAT[3072] dcaaa86451bc
text.transformer.resblocks.8.attn.out_proj.bias FLOAT[1024] d13217b84b55
text.transformer.resblocks.8.ln_1.bias FLOAT[1024] 728f2f4b3a60
text.transformer.resblocks.8.ln_1.weight FLOAT[1024] f50a67f1b270
text.transformer.resblocks.8.ln_2.bias FLOAT[1024] 5d6e6ecee751
text.transformer.resblocks.8.ln_2.weight FLOAT[1024] 1996e04df110
text.transformer.resblocks.8.mlp.c_fc.bias FLOAT[4096] 06ba44a367fc
text.transformer.resblocks.8.mlp.c_proj.bias FLOAT[1024] 1c0b5c046f98
text.transformer.resblocks.9.attn.in_proj_bias FLOAT[3072] 69c890994ee5
text.transformer.resblocks.9.attn.out_proj.bias FLOAT[1024] 6fa24b5f13d7
text.transformer.resblocks.9.ln_1.bias FLOAT[1024] 3485ba6b5f2b
text.transformer.resblocks.9.ln_1.weight FLOAT[1024] 574af684e9ec
text.transformer.resblocks.9.ln_2.bias FLOAT[1024] 2e976a4e2344
text.transformer.resblocks.9.ln_2.weight FLOAT[1024] 872338d6bfd5
text.transformer.resblocks.9.mlp.c_fc.bias FLOAT[4096] 2fa1886f401a
text.transformer.resblocks.9.mlp.c_proj.bias FLOAT[1024] b58fa6ca2060
val_0 INT64[1] 12a3ae445661
val_1 FLOAT[] 6708d9be4956
val_10 FLOAT[1024,3072] 575e95b60986
val_11 FLOAT[1024,4096] d3328f759a8a
val_12 FLOAT[4096,1024] ebf2a071cb26
val_13 FLOAT[1024,3072] 549d4236a1c5
val_14 FLOAT[1024,4096] 6eb18939b828
val_15 FLOAT[4096,1024] 045527ee754c
val_16 FLOAT[1024,3072] 72d923170816
val_17 FLOAT[1024,4096] d938a9ed5d4f
val_18 FLOAT[4096,1024] 74f951be9370
val_19 FLOAT[1024,3072] 81500e5ba656
val_2 INT64[1] 7c9fa136d441
val_20 FLOAT[1024,4096] f32a68ea9a24
val_21 FLOAT[4096,1024] 08c53c63809e
val_22 FLOAT[1024,3072] 4747795a5c77
val_23 FLOAT[1024,4096] 9584ecb41051
val_24 FLOAT[4096,1024] 5750a0c79c87
val_25 FLOAT[1024,3072] 444adc2f066a
val_26 FLOAT[1024,4096] 99c59c51c655
val_27 FLOAT[4096,1024] b4dd782b8f49
val_28 FLOAT[1024,3072] 9950d2e48003
val_29 FLOAT[1024,4096] fe1973d1c320
val_3 INT64[1] 6a69a6cc7473
val_30 FLOAT[4096,1024] 16879d7006a1
val_31 FLOAT[1024,3072] d34a6787038d
val_32 FLOAT[1024,4096] b65f9ee2ed2e
val_33 FLOAT[4096,1024] 977f4d597c14
val_34 FLOAT[1024,3072] eb25c6010df5
val_35 FLOAT[1024,4096] b217ded6f8bd
val_36 FLOAT[4096,1024] 00df65dd6905
val_37 FLOAT[1024,3072] a30e76ddd597
val_38 FLOAT[1024,4096] c78937bc4adc
val_39 FLOAT[4096,1024] 99d5f86e1353
val_4 FLOAT[1024,3072] 8cd9a711e84b
val_40 FLOAT[1024,3072] 5ac059d6d15c
val_41 FLOAT[1024,4096] 19f7fd81fb6d
val_42 FLOAT[4096,1024] b934a8229140
val_43 FLOAT[1024,3072] 13221f45c03d
val_44 FLOAT[1024,4096] 405e56a46fc3
val_45 FLOAT[4096,1024] 7035543bcef4
val_46 FLOAT[1024,3072] a9e29da41ae7
val_47 FLOAT[1024,4096] 07c4bab1d158
val_48 FLOAT[4096,1024] 9baee3d0b2fc
val_49 FLOAT[1024,3072] 7c1cf2e4be2b
val_5 FLOAT[1024,4096] 8bcd9fae947e
val_50 FLOAT[1024,4096] 8bc0d0a37fbc
val_51 FLOAT[4096,1024] 14d0a3eb46a7
val_52 FLOAT[1024,3072] 60d54a01a1d0
val_53 FLOAT[1024,4096] 1a8a826d91dc
val_54 FLOAT[4096,1024] c7e5e2d115ad
val_55 FLOAT[1024,3072] 1efa1f15b5a9
val_56 FLOAT[1024,4096] f3ffd2ab8835
val_57 FLOAT[4096,1024] aed77b178b38
val_58 FLOAT[1024,3072] a2d55689a9f0
val_59 FLOAT[1024,4096] b2ec3e01dbcb
val_6 FLOAT[4096,1024] 25d25fff8971
val_60 FLOAT[4096,1024] 8cdf4e4bf579
val_61 FLOAT[1024,3072] 00d230ee9430
val_62 FLOAT[1024,4096] fcbcb027f3ae
val_63 FLOAT[4096,1024] 2c978808dbb5
val_64 FLOAT[1024,3072] a23939509db7
val_65 FLOAT[1024,4096] 84281bf5d221
val_66 FLOAT[4096,1024] 0f6b498a63f4
val_67 FLOAT[1024,3072] 96167490134b
val_68 FLOAT[1024,4096] dee6496963da
val_69 FLOAT[4096,1024] d68995fcbdbf
val_7 FLOAT[1024,3072] d722e54a9c3b
val_70 FLOAT[1024,3072] ef3ce6c92abf
val_71 FLOAT[1024,4096] 37dc4622eacb
val_72 FLOAT[4096,1024] f9241c1e8611
val_73 FLOAT[1024,3072] 2b315016b043
val_74 FLOAT[1024,4096] c950d17d58fc
val_75 FLOAT[4096,1024] d84bbbbee195
val_8 FLOAT[1024,4096] 73a803e7fbbd
val_9 FLOAT[4096,1024] 1743a1e7a689
