<
   ir_version: 10,
   opset_import: ["" : 23],
   producer_name: "pytorch"
>
main_graph (int32[batch,64] text) => (float[batch,1152] text_embedding) 
   <
      float[batch,64,1152] add_1071
      float[batch,64,1152] add_1092
      float[batch,64,1152] add_119
      float[batch,64,1152] add_1207
      float[batch,64,1152] add_1228
      float[batch,64,1152] add_1343
      float[batch,64,1152] add_1364
      float[batch,64,1152] add_140
      float[batch,64,1152] add_1479
      float[batch,64,1152] add_1500
      float[batch,64,1152] add_1615
      float[batch,64,1152] add_1636
      float[batch,64,1152] add_1751
      float[batch,64,1152] add_1772
      float[batch,64,1152] add_1887
      float[batch,64,1152] add_1908
      float[batch,64,1152] add_2023
      float[batch,64,1152] add_2044
      float[batch,64,1152] add_2159
      float[batch,64,1152] add_2180
      float[batch,64,1152] add_2295
      float[batch,64,1152] add_2316
      float[batch,64,1152] add_2431
      float[batch,64,1152] add_2452
      float[batch,64,1152] add_255
      float[batch,64,1152] add_2567
      float[batch,64,1152] add_2588
      float[batch,64,1152] add_2703
      float[batch,64,1152] add_2724
      float[batch,64,1152] add_276
      float[batch,64,1152] add_2839
      float[batch,64,1152] add_2860
      float[batch,64,1152] add_2975
      float[batch,64,1152] add_2996
      float[batch,64,1152] add_3111
      float[batch,64,1152] add_3132
      float[batch,64,1152] add_3247
      float[batch,64,1152] add_3268
      float[batch,64,1152] add_3383
      float[batch,64,1152] add_3404
      float[batch,64,1152] add_3519
      float[batch,64,1152] add_3540
      float[batch,1,1152] add_3540_pooled
      float[batch,1,1152] add_3655
      float[batch,1,1152] add_3676
      float[batch,64,1152] add_391
      float[batch,64,1152] add_4
      float[batch,64,1152] add_412
      float[batch,64,1152] add_527
      float[batch,64,1152] add_548
      float[batch,64,1152] add_663
      float[batch,64,1152] add_684
      float[batch,64,1152] add_799
      float[batch,64,1152] add_820
      float[batch,64,1152] add_935
      float[batch,64,1152] add_956
      float[batch,1] clamp_min
      float[batch,64,1152] embedding
      float[batch,64,4304] gelu
      float[batch,64,4304] gelu_1
      float[batch,64,4304] gelu_10
      float[batch,64,4304] gelu_11
      float[batch,64,4304] gelu_12
      float[batch,64,4304] gelu_13
      float[batch,64,4304] gelu_14
      float[batch,64,4304] gelu_15
      float[batch,64,4304] gelu_16
      float[batch,64,4304] gelu_17
      float[batch,64,4304] gelu_18
      float[batch,64,4304] gelu_19
      float[batch,64,4304] gelu_2
      float[batch,64,4304] gelu_20
      float[batch,64,4304] gelu_21
      float[batch,64,4304] gelu_22
      float[batch,64,4304] gelu_23
      float[batch,64,4304] gelu_24
      float[batch,64,4304] gelu_25
      float[batch,1,4304] gelu_26
      float[batch,64,4304] gelu_3
      float[batch,64,4304] gelu_4
      float[batch,64,4304] gelu_5
      float[batch,64,4304] gelu_6
      float[batch,64,4304] gelu_7
      float[batch,64,4304] gelu_8
      float[batch,64,4304] gelu_9
      float[batch,64,1152] layer_norm
      float[batch,64,1152] layer_norm_1
      float[batch,64,1152] layer_norm_10
      float[batch,64,1152] layer_norm_11
      float[batch,64,1152] layer_norm_12
      float[batch,64,1152] layer_norm_13
      float[batch,64,1152] layer_norm_14
      float[batch,64,1152] layer_norm_15
      float[batch,64,1152] layer_norm_16
      float[batch,64,1152] layer_norm_17
      float[batch,64,1152] layer_norm_18
      float[batch,64,1152] layer_norm_19
      float[batch,64,1152] layer_norm_2
      float[batch,64,1152] layer_norm_20
      float[batch,64,1152] layer_norm_21
      float[batch,64,1152] layer_norm_22
      float[batch,64,1152] layer_norm_23
      float[batch,64,1152] layer_norm_24
      float[batch,64,1152] layer_norm_25
      float[batch,64,1152] layer_norm_26
      float[batch,64,1152] layer_norm_27
      float[batch,64,1152] layer_norm_28
      float[batch,64,1152] layer_norm_29
      float[batch,64,1152] layer_norm_3
      float[batch,64,1152] layer_norm_30
      float[batch,64,1152] layer_norm_31
      float[batch,64,1152] layer_norm_32
      float[batch,64,1152] layer_norm_33
      float[batch,64,1152] layer_norm_34
      float[batch,64,1152] layer_norm_35
      float[batch,64,1152] layer_norm_36
      float[batch,64,1152] layer_norm_37
      float[batch,64,1152] layer_norm_38
      float[batch,64,1152] layer_norm_39
      float[batch,64,1152] layer_norm_4
      float[batch,64,1152] layer_norm_40
      float[batch,64,1152] layer_norm_41
      float[batch,64,1152] layer_norm_42
      float[batch,64,1152] layer_norm_43
      float[batch,64,1152] layer_norm_44
      float[batch,64,1152] layer_norm_45
      float[batch,64,1152] layer_norm_46
      float[batch,64,1152] layer_norm_47
      float[batch,64,1152] layer_norm_48
      float[batch,64,1152] layer_norm_49
      float[batch,64,1152] layer_norm_5
      float[batch,64,1152] layer_norm_50
      float[batch,64,1152] layer_norm_51
      float[batch,64,1152] layer_norm_52
      float[batch,1,1152] layer_norm_53
      float[batch,64,1152] layer_norm_6
      float[batch,64,1152] layer_norm_7
      float[batch,64,1152] layer_norm_8
      float[batch,64,1152] layer_norm_9
      float[batch,1] linalg_vector_norm
      float[batch,64,4304] linear_10
      float[batch,64,4304] linear_102
      float[batch,64,1152] linear_103
      float[batch,1,4304] linear_106
      float[batch,1,1152] linear_107
      float[batch,1152] linear_108
      float[batch,64,1152] linear_11
      float[batch,64,4304] linear_14
      float[batch,64,1152] linear_15
      float[batch,64,4304] linear_18
      float[batch,64,1152] linear_19
      float[batch,64,4304] linear_2
      float[batch,64,4304] linear_22
      float[batch,64,1152] linear_23
      float[batch,64,4304] linear_26
      float[batch,64,1152] linear_27
      float[batch,64,1152] linear_3
      float[batch,64,4304] linear_30
      float[batch,64,1152] linear_31
      float[batch,64,4304] linear_34
      float[batch,64,1152] linear_35
      float[batch,64,4304] linear_38
      float[batch,64,1152] linear_39
      float[batch,64,4304] linear_42
      float[batch,64,1152] linear_43
      float[batch,64,4304] linear_46
      float[batch,64,1152] linear_47
      float[batch,64,4304] linear_50
      float[batch,64,1152] linear_51
      float[batch,64,4304] linear_54
      float[batch,64,1152] linear_55
      float[batch,64,4304] linear_58
      float[batch,64,1152] linear_59
      float[batch,64,4304] linear_6
      float[batch,64,4304] linear_62
      float[batch,64,1152] linear_63
      float[batch,64,4304] linear_66
      float[batch,64,1152] linear_67
      float[batch,64,1152] linear_7
      float[batch,64,4304] linear_70
      float[batch,64,1152] linear_71
      float[batch,64,4304] linear_74
      float[batch,64,1152] linear_75
      float[batch,64,4304] linear_78
      float[batch,64,1152] linear_79
      float[batch,64,4304] linear_82
      float[batch,64,1152] linear_83
      float[batch,64,4304] linear_86
      float[batch,64,1152] linear_87
      float[batch,64,4304] linear_90
      float[batch,64,1152] linear_91
      float[batch,64,4304] linear_94
      float[batch,64,1152] linear_95
      float[batch,64,4304] linear_98
      float[batch,64,1152] linear_99
      float[batch,64,1152] node_scaled_dot_product_attention_10_k
      float[batch,64,1152] node_scaled_dot_product_attention_10_out
      float[batch,64,1152] node_scaled_dot_product_attention_10_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_10_q
      float[batch,64,3456] node_scaled_dot_product_attention_10_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_10_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_10_v
      float[batch,64,1152] node_scaled_dot_product_attention_11_k
      float[batch,64,1152] node_scaled_dot_product_attention_11_out
      float[batch,64,1152] node_scaled_dot_product_attention_11_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_11_q
      float[batch,64,3456] node_scaled_dot_product_attention_11_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_11_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_11_v
      float[batch,64,1152] node_scaled_dot_product_attention_12_k
      float[batch,64,1152] node_scaled_dot_product_attention_12_out
      float[batch,64,1152] node_scaled_dot_product_attention_12_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_12_q
      float[batch,64,3456] node_scaled_dot_product_attention_12_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_12_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_12_v
      float[batch,64,1152] node_scaled_dot_product_attention_13_k
      float[batch,64,1152] node_scaled_dot_product_attention_13_out
      float[batch,64,1152] node_scaled_dot_product_attention_13_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_13_q
      float[batch,64,3456] node_scaled_dot_product_attention_13_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_13_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_13_v
      float[batch,64,1152] node_scaled_dot_product_attention_14_k
      float[batch,64,1152] node_scaled_dot_product_attention_14_out
      float[batch,64,1152] node_scaled_dot_product_attention_14_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_14_q
      float[batch,64,3456] node_scaled_dot_product_attention_14_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_14_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_14_v
      float[batch,64,1152] node_scaled_dot_product_attention_15_k
      float[batch,64,1152] node_scaled_dot_product_attention_15_out
      float[batch,64,1152] node_scaled_dot_product_attention_15_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_15_q
      float[batch,64,3456] node_scaled_dot_product_attention_15_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_15_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_15_v
      float[batch,64,1152] node_scaled_dot_product_attention_16_k
      float[batch,64,1152] node_scaled_dot_product_attention_16_out
      float[batch,64,1152] node_scaled_dot_product_attention_16_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_16_q
      float[batch,64,3456] node_scaled_dot_product_attention_16_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_16_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_16_v
      float[batch,64,1152] node_scaled_dot_product_attention_17_k
      float[batch,64,1152] node_scaled_dot_product_attention_17_out
      float[batch,64,1152] node_scaled_dot_product_attention_17_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_17_q
      float[batch,64,3456] node_scaled_dot_product_attention_17_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_17_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_17_v
      float[batch,64,1152] node_scaled_dot_product_attention_18_k
      float[batch,64,1152] node_scaled_dot_product_attention_18_out
      float[batch,64,1152] node_scaled_dot_product_attention_18_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_18_q
      float[batch,64,3456] node_scaled_dot_product_attention_18_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_18_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_18_v
      float[batch,64,1152] node_scaled_dot_product_attention_19_k
      float[batch,64,1152] node_scaled_dot_product_attention_19_out
      float[batch,64,1152] node_scaled_dot_product_attention_19_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_19_q
      float[batch,64,3456] node_scaled_dot_product_attention_19_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_19_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_19_v
      float[batch,64,1152] node_scaled_dot_product_attention_1_k
      float[batch,64,1152] node_scaled_dot_product_attention_1_out
      float[batch,64,1152] node_scaled_dot_product_attention_1_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_1_q
      float[batch,64,3456] node_scaled_dot_product_attention_1_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_1_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_1_v
      float[batch,64,1152] node_scaled_dot_product_attention_20_k
      float[batch,64,1152] node_scaled_dot_product_attention_20_out
      float[batch,64,1152] node_scaled_dot_product_attention_20_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_20_q
      float[batch,64,3456] node_scaled_dot_product_attention_20_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_20_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_20_v
      float[batch,64,1152] node_scaled_dot_product_attention_21_k
      float[batch,64,1152] node_scaled_dot_product_attention_21_out
      float[batch,64,1152] node_scaled_dot_product_attention_21_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_21_q
      float[batch,64,3456] node_scaled_dot_product_attention_21_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_21_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_21_v
      float[batch,64,1152] node_scaled_dot_product_attention_22_k
      float[batch,64,1152] node_scaled_dot_product_attention_22_out
      float[batch,64,1152] node_scaled_dot_product_attention_22_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_22_q
      float[batch,64,3456] node_scaled_dot_product_attention_22_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_22_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_22_v
      float[batch,64,1152] node_scaled_dot_product_attention_23_k
      float[batch,64,1152] node_scaled_dot_product_attention_23_out
      float[batch,64,1152] node_scaled_dot_product_attention_23_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_23_q
      float[batch,64,3456] node_scaled_dot_product_attention_23_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_23_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_23_v
      float[batch,64,1152] node_scaled_dot_product_attention_24_k
      float[batch,64,1152] node_scaled_dot_product_attention_24_out
      float[batch,64,1152] node_scaled_dot_product_attention_24_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_24_q
      float[batch,64,3456] node_scaled_dot_product_attention_24_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_24_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_24_v
      float[batch,64,1152] node_scaled_dot_product_attention_25_k
      float[batch,64,1152] node_scaled_dot_product_attention_25_out
      float[batch,64,1152] node_scaled_dot_product_attention_25_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_25_q
      float[batch,64,3456] node_scaled_dot_product_attention_25_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_25_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_25_v
      float[batch,64,1152] node_scaled_dot_product_attention_26_k
      float[batch,1,1152] node_scaled_dot_product_attention_26_out
      float[batch,1,1152] node_scaled_dot_product_attention_26_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_26_q
      float[batch,1,1152] node_scaled_dot_product_attention_26_q_pooled
      float[batch,64,3456] node_scaled_dot_product_attention_26_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_26_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_26_v
      float[batch,64,1152] node_scaled_dot_product_attention_2_k
      float[batch,64,1152] node_scaled_dot_product_attention_2_out
      float[batch,64,1152] node_scaled_dot_product_attention_2_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_2_q
      float[batch,64,3456] node_scaled_dot_product_attention_2_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_2_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_2_v
      float[batch,64,1152] node_scaled_dot_product_attention_3_k
      float[batch,64,1152] node_scaled_dot_product_attention_3_out
      float[batch,64,1152] node_scaled_dot_product_attention_3_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_3_q
      float[batch,64,3456] node_scaled_dot_product_attention_3_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_3_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_3_v
      float[batch,64,1152] node_scaled_dot_product_attention_4_k
      float[batch,64,1152] node_scaled_dot_product_attention_4_out
      float[batch,64,1152] node_scaled_dot_product_attention_4_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_4_q
      float[batch,64,3456] node_scaled_dot_product_attention_4_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_4_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_4_v
      float[batch,64,1152] node_scaled_dot_product_attention_5_k
      float[batch,64,1152] node_scaled_dot_product_attention_5_out
      float[batch,64,1152] node_scaled_dot_product_attention_5_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_5_q
      float[batch,64,3456] node_scaled_dot_product_attention_5_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_5_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_5_v
      float[batch,64,1152] node_scaled_dot_product_attention_6_k
      float[batch,64,1152] node_scaled_dot_product_attention_6_out
      float[batch,64,1152] node_scaled_dot_product_attention_6_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_6_q
      float[batch,64,3456] node_scaled_dot_product_attention_6_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_6_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_6_v
      float[batch,64,1152] node_scaled_dot_product_attention_7_k
      float[batch,64,1152] node_scaled_dot_product_attention_7_out
      float[batch,64,1152] node_scaled_dot_product_attention_7_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_7_q
      float[batch,64,3456] node_scaled_dot_product_attention_7_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_7_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_7_v
      float[batch,64,1152] node_scaled_dot_product_attention_8_k
      float[batch,64,1152] node_scaled_dot_product_attention_8_out
      float[batch,64,1152] node_scaled_dot_product_attention_8_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_8_q
      float[batch,64,3456] node_scaled_dot_product_attention_8_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_8_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_8_v
      float[batch,64,1152] node_scaled_dot_product_attention_9_k
      float[batch,64,1152] node_scaled_dot_product_attention_9_out
      float[batch,64,1152] node_scaled_dot_product_attention_9_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_9_q
      float[batch,64,3456] node_scaled_dot_product_attention_9_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_9_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_9_v
      float[batch,64,1152] node_scaled_dot_product_attention_k
      float[batch,64,1152] node_scaled_dot_product_attention_out
      float[batch,64,1152] node_scaled_dot_product_attention_out_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_q
      float[batch,64,3456] node_scaled_dot_product_attention_qkv
      float[batch,64,3456] node_scaled_dot_product_attention_qkv_mm_out
      float[batch,64,1152] node_scaled_dot_product_attention_v
      float[batch,64,1152] scaled_dot_product_attention
      float[batch,64,1152] scaled_dot_product_attention_1
      float[batch,64,1152] scaled_dot_product_attention_10
      float[batch,64,1152] scaled_dot_product_attention_11
      float[batch,64,1152] scaled_dot_product_attention_12
      float[batch,64,1152] scaled_dot_product_attention_13
      float[batch,64,1152] scaled_dot_product_attention_14
      float[batch,64,1152] scaled_dot_product_attention_15
      float[batch,64,1152] scaled_dot_product_attention_16
      float[batch,64,1152] scaled_dot_product_attention_17
      float[batch,64,1152] scaled_dot_product_attention_18
      float[batch,64,1152] scaled_dot_product_attention_19
      float[batch,64,1152] scaled_dot_product_attention_2
      float[batch,64,1152] scaled_dot_product_attention_20
      float[batch,64,1152] scaled_dot_product_attention_21
      float[batch,64,1152] scaled_dot_product_attention_22
      float[batch,64,1152] scaled_dot_product_attention_23
      float[batch,64,1152] scaled_dot_product_attention_24
      float[batch,64,1152] scaled_dot_product_attention_25
      float[batch,1,1152] scaled_dot_product_attention_26
      float[batch,64,1152] scaled_dot_product_attention_3
      float[batch,64,1152] scaled_dot_product_attention_4
      float[batch,64,1152] scaled_dot_product_attention_5
      float[batch,64,1152] scaled_dot_product_attention_6
      float[batch,64,1152] scaled_dot_product_attention_7
      float[batch,64,1152] scaled_dot_product_attention_8
      float[batch,64,1152] scaled_dot_product_attention_9
      float[batch,1152] select_81
      float[batch,64,4304] val_100
      float[batch,64,1152] val_101
      float[batch,64,4304] val_102
      float[batch,64,1152] val_103
      float[batch,64,4304] val_104
      float[batch,64,1152] val_105
      float[batch,64,4304] val_106
      float[batch,64,1152] val_107
      float[batch,64,4304] val_108
      float[batch,64,1152] val_109
      float[batch,64,4304] val_110
      float[batch,64,1152] val_111
      float[batch,64,4304] val_112
      float[batch,64,1152] val_113
      float[batch,64,4304] val_114
      float[batch,64,1152] val_115
      float[batch,64,4304] val_116
      float[batch,64,1152] val_117
      float[batch,64,4304] val_118
      float[batch,64,1152] val_119
      float[batch,64,4304] val_120
      float[batch,64,1152] val_121
      float[batch,64,4304] val_122
      float[batch,64,1152] val_123
      float[batch,64,4304] val_124
      float[batch,64,1152] val_125
      float[batch,64,4304] val_126
      float[batch,64,1152] val_127
      float[batch,64,4304] val_128
      float[batch,64,1152] val_129
      float[batch,64,4304] val_130
      float[batch,64,1152] val_131
      float[batch,64,4304] val_132
      float[batch,64,1152] val_133
      float[batch,64,4304] val_134
      float[batch,64,1152] val_135
      float[batch,64,4304] val_136
      float[batch,64,1152] val_137
      float[batch,1,4304] val_138
      float[batch,1,1152] val_139
      float[batch,1152] val_140
      float[batch,64,4304] val_86
      float[batch,64,1152] val_87
      float[batch,64,4304] val_88
      float[batch,64,1152] val_89
      float[batch,64,4304] val_90
      float[batch,64,1152] val_91
      float[batch,64,4304] val_92
      float[batch,64,1152] val_93
      float[batch,64,4304] val_94
      float[batch,64,1152] val_95
      float[batch,64,4304] val_96
      float[batch,64,1152] val_97
      float[batch,64,4304] val_98
      float[batch,64,1152] val_99
   >
{
   val_85 = Gather <axis: int = 0> ("text.token_embedding.weight_fp16", text)
   embedding = Cast <to: int = 1> (val_85)
   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_3x1152)
   [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_86 = MatMul (layer_norm_1, val_5)
   linear_2 = Add (val_86, "text.transformer.resblocks.0.mlp.c_fc.bias")
   [node_gelu] gelu = Gelu <approximate: string = "tanh"> (linear_2)
   val_87 = MatMul (gelu, val_6)
   linear_3 = Add (val_87, "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_3x1152)
   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_88 = MatMul (layer_norm_3, val_8)
   linear_6 = Add (val_88, "text.transformer.resblocks.1.mlp.c_fc.bias")
   gelu_1 = Gelu <approximate: string = "tanh"> (linear_6)
   val_89 = MatMul (gelu_1, val_9)
   linear_7 = Add (val_89, "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_3x1152)
   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_90 = MatMul (layer_norm_5, val_11)
   linear_10 = Add (val_90, "text.transformer.resblocks.2.mlp.c_fc.bias")
   gelu_2 = Gelu <approximate: string = "tanh"> (linear_10)
   val_91 = MatMul (gelu_2, val_12)
   linear_11 = Add (val_91, "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_3x1152)
   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_92 = MatMul (layer_norm_7, val_14)
   linear_14 = Add (val_92, "text.transformer.resblocks.3.mlp.c_fc.bias")
   gelu_3 = Gelu <approximate: string = "tanh"> (linear_14)
   val_93 = MatMul (gelu_3, val_15)
   linear_15 = Add (val_93, "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_3x1152)
   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_94 = MatMul (layer_norm_9, val_17)
   linear_18 = Add (val_94, "text.transformer.resblocks.4.mlp.c_fc.bias")
   gelu_4 = Gelu <approximate: string = "tanh"> (linear_18)
   val_95 = MatMul (gelu_4, val_18)
   linear_19 = Add (val_95, "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_3x1152)
   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_96 = MatMul (layer_norm_11, val_20)
   linear_22 = Add (val_96, "text.transformer.resblocks.5.mlp.c_fc.bias")
   gelu_5 = Gelu <approximate: string = "tanh"> (linear_22)
   val_97 = MatMul (gelu_5, val_21)
   linear_23 = Add (val_97, "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_3x1152)
   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_98 = MatMul (layer_norm_13, val_23)
   linear_26 = Add (val_98, "text.transformer.resblocks.6.mlp.c_fc.bias")
   gelu_6 = Gelu <approximate: string = "tanh"> (linear_26)
   val_99 = MatMul (gelu_6, val_24)
   linear_27 = Add (val_99, "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_3x1152)
   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_100 = MatMul (layer_norm_15, val_26)
   linear_30 = Add (val_100, "text.transformer.resblocks.7.mlp.c_fc.bias")
   gelu_7 = Gelu <approximate: string = "tanh"> (linear_30)
   val_101 = MatMul (gelu_7, val_27)
   linear_31 = Add (val_101, "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_3x1152)
   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_102 = MatMul (layer_norm_17, val_29)
   linear_34 = Add (val_102, "text.transformer.resblocks.8.mlp.c_fc.bias")
   gelu_8 = Gelu <approximate: string = "tanh"> (linear_34)
   val_103 = MatMul (gelu_8, val_30)
   linear_35 = Add (val_103, "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_3x1152)
   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_104 = MatMul (layer_norm_19, val_32)
   linear_38 = Add (val_104, "text.transformer.resblocks.9.mlp.c_fc.bias")
   gelu_9 = Gelu <approximate: string = "tanh"> (linear_38)
   val_105 = MatMul (gelu_9, val_33)
   linear_39 = Add (val_105, "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_3x1152)
   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_106 = MatMul (layer_norm_21, val_35)
   linear_42 = Add (val_106, "text.transformer.resblocks.10.mlp.c_fc.bias")
   gelu_10 = Gelu <approximate: string = "tanh"> (linear_42)
   val_107 = MatMul (gelu_10, val_36)
   linear_43 = Add (val_107, "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_3x1152)
   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_108 = MatMul (layer_norm_23, val_38)
   linear_46 = Add (val_108, "text.transformer.resblocks.11.mlp.c_fc.bias")
   gelu_11 = Gelu <approximate: string = "tanh"> (linear_46)
   val_109 = MatMul (gelu_11, val_39)
   linear_47 = Add (val_109, "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_3x1152)
   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_110 = MatMul (layer_norm_25, val_41)
   linear_50 = Add (val_110, "text.transformer.resblocks.12.mlp.c_fc.bias")
   gelu_12 = Gelu <approximate: string = "tanh"> (linear_50)
   val_111 = MatMul (gelu_12, val_42)
   linear_51 = Add (val_111, "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_3x1152)
   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_112 = MatMul (layer_norm_27, val_44)
   linear_54 = Add (val_112, "text.transformer.resblocks.13.mlp.c_fc.bias")
   gelu_13 = Gelu <approximate: string = "tanh"> (linear_54)
   val_113 = MatMul (gelu_13, val_45)
   linear_55 = Add (val_113, "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_3x1152)
   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_114 = MatMul (layer_norm_29, val_47)
   linear_58 = Add (val_114, "text.transformer.resblocks.14.mlp.c_fc.bias")
   gelu_14 = Gelu <approximate: string = "tanh"> (linear_58)
   val_115 = MatMul (gelu_14, val_48)
   linear_59 = Add (val_115, "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_3x1152)
   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_116 = MatMul (layer_norm_31, val_50)
   linear_62 = Add (val_116, "text.transformer.resblocks.15.mlp.c_fc.bias")
   gelu_15 = Gelu <approximate: string = "tanh"> (linear_62)
   val_117 = MatMul (gelu_15, val_51)
   linear_63 = Add (val_117, "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_3x1152)
   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_118 = MatMul (layer_norm_33, val_53)
   linear_66 = Add (val_118, "text.transformer.resblocks.16.mlp.c_fc.bias")
   gelu_16 = Gelu <approximate: string = "tanh"> (linear_66)
   val_119 = MatMul (gelu_16, val_54)
   linear_67 = Add (val_119, "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_3x1152)
   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_120 = MatMul (layer_norm_35, val_56)
   linear_70 = Add (val_120, "text.transformer.resblocks.17.mlp.c_fc.bias")
   gelu_17 = Gelu <approximate: string = "tanh"> (linear_70)
   val_121 = MatMul (gelu_17, val_57)
   linear_71 = Add (val_121, "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_3x1152)
   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_122 = MatMul (layer_norm_37, val_59)
   linear_74 = Add (val_122, "text.transformer.resblocks.18.mlp.c_fc.bias")
   gelu_18 = Gelu <approximate: string = "tanh"> (linear_74)
   val_123 = MatMul (gelu_18, val_60)
   linear_75 = Add (val_123, "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_3x1152)
   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_124 = MatMul (layer_norm_39, val_62)
   linear_78 = Add (val_124, "text.transformer.resblocks.19.mlp.c_fc.bias")
   gelu_19 = Gelu <approximate: string = "tanh"> (linear_78)
   val_125 = MatMul (gelu_19, val_63)
   linear_79 = Add (val_125, "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_3x1152)
   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_126 = MatMul (layer_norm_41, val_65)
   linear_82 = Add (val_126, "text.transformer.resblocks.20.mlp.c_fc.bias")
   gelu_20 = Gelu <approximate: string = "tanh"> (linear_82)
   val_127 = MatMul (gelu_20, val_66)
   linear_83 = Add (val_127, "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_3x1152)
   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_128 = MatMul (layer_norm_43, val_68)
   linear_86 = Add (val_128, "text.transformer.resblocks.21.mlp.c_fc.bias")
   gelu_21 = Gelu <approximate: string = "tanh"> (linear_86)
   val_129 = MatMul (gelu_21, val_69)
   linear_87 = Add (val_129, "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_3x1152)
   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_130 = MatMul (layer_norm_45, val_71)
   linear_90 = Add (val_130, "text.transformer.resblocks.22.mlp.c_fc.bias")
   gelu_22 = Gelu <approximate: string = "tanh"> (linear_90)
   val_131 = MatMul (gelu_22, val_72)
   linear_91 = Add (val_131, "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_3x1152)
   scaled_dot_product_attention_23 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_23_q, node_scaled_dot_product_attention_23_k, node_scaled_dot_product_attention_23_v)
   [node_scaled_dot_product_attention_23_out_mm] node_scaled_dot_product_attention_23_out_mm_out = MatMul (scaled_dot_product_attention_23, node_scaled_dot_product_attention_23_wo_t)
   [node_scaled_dot_product_attention_23_out_bias] node_scaled_dot_product_attention_23_out = Add (node_scaled_dot_product_attention_23_out_mm_out, "text.transformer.resblocks.23.attn.out_proj.bias")
   add_3247 = Add (add_3132, 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_132 = MatMul (layer_norm_47, val_74)
   linear_94 = Add (val_132, "text.transformer.resblocks.23.mlp.c_fc.bias")
   gelu_23 = Gelu <approximate: string = "tanh"> (linear_94)
   val_133 = MatMul (gelu_23, val_75)
   linear_95 = Add (val_133, "text.transformer.resblocks.23.mlp.c_proj.bias")
   add_3268 = Add (add_3247, linear_95)
   layer_norm_48 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3268, "text.transformer.resblocks.24.ln_1.weight", "text.transformer.resblocks.24.ln_1.bias")
   [node_scaled_dot_product_attention_24_qkv_mm] node_scaled_dot_product_attention_24_qkv_mm_out = MatMul (layer_norm_48, val_76)
   [node_scaled_dot_product_attention_24_qkv_bias] node_scaled_dot_product_attention_24_qkv = Add (node_scaled_dot_product_attention_24_qkv_mm_out, "text.transformer.resblocks.24.attn.in_proj_bias")
   [node_scaled_dot_product_attention_24_qkv_split] node_scaled_dot_product_attention_24_q, node_scaled_dot_product_attention_24_k, node_scaled_dot_product_attention_24_v = Split <axis: int = -1> (node_scaled_dot_product_attention_24_qkv, attn3d_split_3x1152)
   scaled_dot_product_attention_24 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_24_q, node_scaled_dot_product_attention_24_k, node_scaled_dot_product_attention_24_v)
   [node_scaled_dot_product_attention_24_out_mm] node_scaled_dot_product_attention_24_out_mm_out = MatMul (scaled_dot_product_attention_24, node_scaled_dot_product_attention_24_wo_t)
   [node_scaled_dot_product_attention_24_out_bias] node_scaled_dot_product_attention_24_out = Add (node_scaled_dot_product_attention_24_out_mm_out, "text.transformer.resblocks.24.attn.out_proj.bias")
   add_3383 = Add (add_3268, node_scaled_dot_product_attention_24_out)
   layer_norm_49 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3383, "text.transformer.resblocks.24.ln_2.weight", "text.transformer.resblocks.24.ln_2.bias")
   val_134 = MatMul (layer_norm_49, val_77)
   linear_98 = Add (val_134, "text.transformer.resblocks.24.mlp.c_fc.bias")
   gelu_24 = Gelu <approximate: string = "tanh"> (linear_98)
   val_135 = MatMul (gelu_24, val_78)
   linear_99 = Add (val_135, "text.transformer.resblocks.24.mlp.c_proj.bias")
   add_3404 = Add (add_3383, linear_99)
   layer_norm_50 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3404, "text.transformer.resblocks.25.ln_1.weight", "text.transformer.resblocks.25.ln_1.bias")
   [node_scaled_dot_product_attention_25_qkv_mm] node_scaled_dot_product_attention_25_qkv_mm_out = MatMul (layer_norm_50, val_79)
   [node_scaled_dot_product_attention_25_qkv_bias] node_scaled_dot_product_attention_25_qkv = Add (node_scaled_dot_product_attention_25_qkv_mm_out, "text.transformer.resblocks.25.attn.in_proj_bias")
   [node_scaled_dot_product_attention_25_qkv_split] node_scaled_dot_product_attention_25_q, node_scaled_dot_product_attention_25_k, node_scaled_dot_product_attention_25_v = Split <axis: int = -1> (node_scaled_dot_product_attention_25_qkv, attn3d_split_3x1152)
   scaled_dot_product_attention_25 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_25_q, node_scaled_dot_product_attention_25_k, node_scaled_dot_product_attention_25_v)
   [node_scaled_dot_product_attention_25_out_mm] node_scaled_dot_product_attention_25_out_mm_out = MatMul (scaled_dot_product_attention_25, node_scaled_dot_product_attention_25_wo_t)
   [node_scaled_dot_product_attention_25_out_bias] node_scaled_dot_product_attention_25_out = Add (node_scaled_dot_product_attention_25_out_mm_out, "text.transformer.resblocks.25.attn.out_proj.bias")
   add_3519 = Add (add_3404, node_scaled_dot_product_attention_25_out)
   layer_norm_51 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3519, "text.transformer.resblocks.25.ln_2.weight", "text.transformer.resblocks.25.ln_2.bias")
   val_136 = MatMul (layer_norm_51, val_80)
   linear_102 = Add (val_136, "text.transformer.resblocks.25.mlp.c_fc.bias")
   gelu_25 = Gelu <approximate: string = "tanh"> (linear_102)
   val_137 = MatMul (gelu_25, val_81)
   linear_103 = Add (val_137, "text.transformer.resblocks.25.mlp.c_proj.bias")
   add_3540 = Add (add_3519, linear_103)
   layer_norm_52 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3540, "text.transformer.resblocks.26.ln_1.weight", "text.transformer.resblocks.26.ln_1.bias")
   [node_scaled_dot_product_attention_26_qkv_mm] node_scaled_dot_product_attention_26_qkv_mm_out = MatMul (layer_norm_52, val_82)
   [node_scaled_dot_product_attention_26_qkv_bias] node_scaled_dot_product_attention_26_qkv = Add (node_scaled_dot_product_attention_26_qkv_mm_out, "text.transformer.resblocks.26.attn.in_proj_bias")
   [node_scaled_dot_product_attention_26_qkv_split] node_scaled_dot_product_attention_26_q, node_scaled_dot_product_attention_26_k, node_scaled_dot_product_attention_26_v = Split <axis: int = -1> (node_scaled_dot_product_attention_26_qkv, attn3d_split_3x1152)
   [pool_hoist_node_scaled_dot_product_attention_26_q] node_scaled_dot_product_attention_26_q_pooled = Slice (node_scaled_dot_product_attention_26_q, val_0, val_3, val_2)
   scaled_dot_product_attention_26 = Attention <is_causal: int = 0, kv_num_heads: int = 16, q_num_heads: int = 16, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_26_q_pooled, node_scaled_dot_product_attention_26_k, node_scaled_dot_product_attention_26_v)
   [node_scaled_dot_product_attention_26_out_mm] node_scaled_dot_product_attention_26_out_mm_out = MatMul (scaled_dot_product_attention_26, node_scaled_dot_product_attention_26_wo_t)
   [node_scaled_dot_product_attention_26_out_bias] node_scaled_dot_product_attention_26_out = Add (node_scaled_dot_product_attention_26_out_mm_out, "text.transformer.resblocks.26.attn.out_proj.bias")
   [pool_hoist_add_3540] add_3540_pooled = Slice (add_3540, val_0, val_3, val_2)
   add_3655 = Add (add_3540_pooled, node_scaled_dot_product_attention_26_out)
   layer_norm_53 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_3655, "text.transformer.resblocks.26.ln_2.weight", "text.transformer.resblocks.26.ln_2.bias")
   val_138 = MatMul (layer_norm_53, val_83)
   linear_106 = Add (val_138, "text.transformer.resblocks.26.mlp.c_fc.bias")
   gelu_26 = Gelu <approximate: string = "tanh"> (linear_106)
   val_139 = MatMul (gelu_26, val_84)
   linear_107 = Add (val_139, "text.transformer.resblocks.26.mlp.c_proj.bias")
   add_3676 = Add (add_3655, linear_107)
   val_140 = Squeeze (add_3676, val_2)
   select_81 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (val_140, "text.ln_final.weight", "text.ln_final.bias")
   linear_108 = Gemm <alpha: float = 1, beta: float = 1, transA: int = 0, transB: int = 1> (select_81, "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_108, val_0)
   [node_clamp_min] clamp_min = Clip (linalg_vector_norm, val_1)
   [node_div] text_embedding = Div (linear_108, clamp_min)
}

weights:
attn3d_split_3x1152 INT64[3] 125b254aeb28
node_scaled_dot_product_attention_10_wo_t FLOAT[1152,1152] ddbfc06e7347
node_scaled_dot_product_attention_11_wo_t FLOAT[1152,1152] 873c5bd24d82
node_scaled_dot_product_attention_12_wo_t FLOAT[1152,1152] 1004b1d24f16
node_scaled_dot_product_attention_13_wo_t FLOAT[1152,1152] d9f5441f8764
node_scaled_dot_product_attention_14_wo_t FLOAT[1152,1152] 1b24e2bf67f3
node_scaled_dot_product_attention_15_wo_t FLOAT[1152,1152] f6c448c9e67a
node_scaled_dot_product_attention_16_wo_t FLOAT[1152,1152] b25180baf84d
node_scaled_dot_product_attention_17_wo_t FLOAT[1152,1152] eb23afeea222
node_scaled_dot_product_attention_18_wo_t FLOAT[1152,1152] d101467fbdc9
node_scaled_dot_product_attention_19_wo_t FLOAT[1152,1152] 22442787024b
node_scaled_dot_product_attention_1_wo_t FLOAT[1152,1152] a25986c44eb4
node_scaled_dot_product_attention_20_wo_t FLOAT[1152,1152] a732eba7a1c9
node_scaled_dot_product_attention_21_wo_t FLOAT[1152,1152] f62b0c1c9788
node_scaled_dot_product_attention_22_wo_t FLOAT[1152,1152] e34b09605b07
node_scaled_dot_product_attention_23_wo_t FLOAT[1152,1152] d84cfee52e47
node_scaled_dot_product_attention_24_wo_t FLOAT[1152,1152] 0df2a5875f8a
node_scaled_dot_product_attention_25_wo_t FLOAT[1152,1152] 33995d7827a8
node_scaled_dot_product_attention_26_wo_t FLOAT[1152,1152] 31a603087a84
node_scaled_dot_product_attention_2_wo_t FLOAT[1152,1152] bdd24de33cd6
node_scaled_dot_product_attention_3_wo_t FLOAT[1152,1152] 913511e8eb93
node_scaled_dot_product_attention_4_wo_t FLOAT[1152,1152] 548b78b249b9
node_scaled_dot_product_attention_5_wo_t FLOAT[1152,1152] 91f3ce53c3b2
node_scaled_dot_product_attention_6_wo_t FLOAT[1152,1152] faa252543fa0
node_scaled_dot_product_attention_7_wo_t FLOAT[1152,1152] d7cca72f64b3
node_scaled_dot_product_attention_8_wo_t FLOAT[1152,1152] a4bd9860ffb5
node_scaled_dot_product_attention_9_wo_t FLOAT[1152,1152] 09e262da8f12
node_scaled_dot_product_attention_wo_t FLOAT[1152,1152] c8f1a81664ce
text.ln_final.bias FLOAT[1152] 9104894f700d
text.ln_final.weight FLOAT[1152] f199c7cfb0fd
text.positional_embedding FLOAT[64,1152] c2977e631fd3
text.text_projection.bias FLOAT[1152] 094d824e3c39
text.text_projection.weight FLOAT[1152,1152] f80826783f67
text.token_embedding.weight_fp16 FLOAT16[256000,1152] eec21125377e
text.transformer.resblocks.0.attn.in_proj_bias FLOAT[3456] 950d7f819c5a
text.transformer.resblocks.0.attn.out_proj.bias FLOAT[1152] 7205665ce58c
text.transformer.resblocks.0.ln_1.bias FLOAT[1152] c3fd07815fb3
text.transformer.resblocks.0.ln_1.weight FLOAT[1152] 2a087f0d0d54
text.transformer.resblocks.0.ln_2.bias FLOAT[1152] a89de47be31b
text.transformer.resblocks.0.ln_2.weight FLOAT[1152] 76f232fd79b3
text.transformer.resblocks.0.mlp.c_fc.bias FLOAT[4304] 698b9cb864fb
text.transformer.resblocks.0.mlp.c_proj.bias FLOAT[1152] ac3e7a88fc27
text.transformer.resblocks.1.attn.in_proj_bias FLOAT[3456] 64c144f95702
text.transformer.resblocks.1.attn.out_proj.bias FLOAT[1152] 6e9e63d1cc5f
text.transformer.resblocks.1.ln_1.bias FLOAT[1152] 6b0369c48824
text.transformer.resblocks.1.ln_1.weight FLOAT[1152] 900d3c1d51cd
text.transformer.resblocks.1.ln_2.bias FLOAT[1152] 7c90f2880838
text.transformer.resblocks.1.ln_2.weight FLOAT[1152] 064bb74fd59e
text.transformer.resblocks.1.mlp.c_fc.bias FLOAT[4304] 3b6fb546a12b
text.transformer.resblocks.1.mlp.c_proj.bias FLOAT[1152] 210420a98c9c
text.transformer.resblocks.10.attn.in_proj_bias FLOAT[3456] c5d22536ab91
text.transformer.resblocks.10.attn.out_proj.bias FLOAT[1152] 90d9510babd5
text.transformer.resblocks.10.ln_1.bias FLOAT[1152] 97f18b7e2635
text.transformer.resblocks.10.ln_1.weight FLOAT[1152] 9066ac1ae442
text.transformer.resblocks.10.ln_2.bias FLOAT[1152] a6eb013b34a1
text.transformer.resblocks.10.ln_2.weight FLOAT[1152] d611a9478fba
text.transformer.resblocks.10.mlp.c_fc.bias FLOAT[4304] 8ab9d583e282
text.transformer.resblocks.10.mlp.c_proj.bias FLOAT[1152] 79a84979d30c
text.transformer.resblocks.11.attn.in_proj_bias FLOAT[3456] 14a10b1e276b
text.transformer.resblocks.11.attn.out_proj.bias FLOAT[1152] 2dc4d7a87bca
text.transformer.resblocks.11.ln_1.bias FLOAT[1152] 9a40ad21789e
text.transformer.resblocks.11.ln_1.weight FLOAT[1152] 9065b1566636
text.transformer.resblocks.11.ln_2.bias FLOAT[1152] 8e31138e04e8
text.transformer.resblocks.11.ln_2.weight FLOAT[1152] adef8b4dd72e
text.transformer.resblocks.11.mlp.c_fc.bias FLOAT[4304] 1d59dda3c03a
text.transformer.resblocks.11.mlp.c_proj.bias FLOAT[1152] db7c0c981c43
text.transformer.resblocks.12.attn.in_proj_bias FLOAT[3456] af1fd0fdad5b
text.transformer.resblocks.12.attn.out_proj.bias FLOAT[1152] 0e8df92674df
text.transformer.resblocks.12.ln_1.bias FLOAT[1152] b9e4def7a9bf
text.transformer.resblocks.12.ln_1.weight FLOAT[1152] 9f32e7b50f1a
text.transformer.resblocks.12.ln_2.bias FLOAT[1152] 5cc084d1d33b
text.transformer.resblocks.12.ln_2.weight FLOAT[1152] 624b9fa0ac61
text.transformer.resblocks.12.mlp.c_fc.bias FLOAT[4304] 8846b7daac75
text.transformer.resblocks.12.mlp.c_proj.bias FLOAT[1152] ee66eec8ae82
text.transformer.resblocks.13.attn.in_proj_bias FLOAT[3456] ce3eab5664ec
text.transformer.resblocks.13.attn.out_proj.bias FLOAT[1152] 5d77cbf412d8
text.transformer.resblocks.13.ln_1.bias FLOAT[1152] 0818a99d9f4e
text.transformer.resblocks.13.ln_1.weight FLOAT[1152] 45c6540a77f3
text.transformer.resblocks.13.ln_2.bias FLOAT[1152] b060ef2397d4
text.transformer.resblocks.13.ln_2.weight FLOAT[1152] ff403b069b7c
text.transformer.resblocks.13.mlp.c_fc.bias FLOAT[4304] 874a54036128
text.transformer.resblocks.13.mlp.c_proj.bias FLOAT[1152] 3c684cb07b72
text.transformer.resblocks.14.attn.in_proj_bias FLOAT[3456] 684997fb941e
text.transformer.resblocks.14.attn.out_proj.bias FLOAT[1152] 2f34ef59e3e0
text.transformer.resblocks.14.ln_1.bias FLOAT[1152] 73bbbc143526
text.transformer.resblocks.14.ln_1.weight FLOAT[1152] 0027e1624996
text.transformer.resblocks.14.ln_2.bias FLOAT[1152] 01fe6b90beda
text.transformer.resblocks.14.ln_2.weight FLOAT[1152] eba7c279a9a5
text.transformer.resblocks.14.mlp.c_fc.bias FLOAT[4304] db4335a71d5a
text.transformer.resblocks.14.mlp.c_proj.bias FLOAT[1152] b1b93729dc09
text.transformer.resblocks.15.attn.in_proj_bias FLOAT[3456] 7a0174dc1704
text.transformer.resblocks.15.attn.out_proj.bias FLOAT[1152] 5523e4866a12
text.transformer.resblocks.15.ln_1.bias FLOAT[1152] bc29ac48c2f5
text.transformer.resblocks.15.ln_1.weight FLOAT[1152] c356fd891178
text.transformer.resblocks.15.ln_2.bias FLOAT[1152] a8bdac670bfe
text.transformer.resblocks.15.ln_2.weight FLOAT[1152] 14b9c33f9c7c
text.transformer.resblocks.15.mlp.c_fc.bias FLOAT[4304] 035ab51be821
text.transformer.resblocks.15.mlp.c_proj.bias FLOAT[1152] f8765d312748
text.transformer.resblocks.16.attn.in_proj_bias FLOAT[3456] 6ab4e8d3acce
text.transformer.resblocks.16.attn.out_proj.bias FLOAT[1152] 4876d3e2629c
text.transformer.resblocks.16.ln_1.bias FLOAT[1152] 33710d6b5fdb
text.transformer.resblocks.16.ln_1.weight FLOAT[1152] addd126ab613
text.transformer.resblocks.16.ln_2.bias FLOAT[1152] 67c8612622c2
text.transformer.resblocks.16.ln_2.weight FLOAT[1152] 9450364aaa19
text.transformer.resblocks.16.mlp.c_fc.bias FLOAT[4304] ad9e20653831
text.transformer.resblocks.16.mlp.c_proj.bias FLOAT[1152] ab881c78244b
text.transformer.resblocks.17.attn.in_proj_bias FLOAT[3456] 2c6a04af8c21
text.transformer.resblocks.17.attn.out_proj.bias FLOAT[1152] 35f822346ed8
text.transformer.resblocks.17.ln_1.bias FLOAT[1152] d7b9cb53d298
text.transformer.resblocks.17.ln_1.weight FLOAT[1152] 0058fe9b3e9e
text.transformer.resblocks.17.ln_2.bias FLOAT[1152] 3f57d568f26a
text.transformer.resblocks.17.ln_2.weight FLOAT[1152] 5cc68dd56181
text.transformer.resblocks.17.mlp.c_fc.bias FLOAT[4304] 9cb443305be9
text.transformer.resblocks.17.mlp.c_proj.bias FLOAT[1152] cc36f893122b
text.transformer.resblocks.18.attn.in_proj_bias FLOAT[3456] 3dff5bb39cc6
text.transformer.resblocks.18.attn.out_proj.bias FLOAT[1152] 9abcc8f10329
text.transformer.resblocks.18.ln_1.bias FLOAT[1152] f80701fa2827
text.transformer.resblocks.18.ln_1.weight FLOAT[1152] d828476ece5c
text.transformer.resblocks.18.ln_2.bias FLOAT[1152] bddaf09459ef
text.transformer.resblocks.18.ln_2.weight FLOAT[1152] 765fc7464f9d
text.transformer.resblocks.18.mlp.c_fc.bias FLOAT[4304] 67ee733eeaef
text.transformer.resblocks.18.mlp.c_proj.bias FLOAT[1152] 932a75c4e8f0
text.transformer.resblocks.19.attn.in_proj_bias FLOAT[3456] d27095e74902
text.transformer.resblocks.19.attn.out_proj.bias FLOAT[1152] 0a07fb5ae0cf
text.transformer.resblocks.19.ln_1.bias FLOAT[1152] 361bab51f1a5
text.transformer.resblocks.19.ln_1.weight FLOAT[1152] ff35c9138148
text.transformer.resblocks.19.ln_2.bias FLOAT[1152] 3a1e9aedb7fb
text.transformer.resblocks.19.ln_2.weight FLOAT[1152] 769faf43dfc3
text.transformer.resblocks.19.mlp.c_fc.bias FLOAT[4304] 21d47d8b40a6
text.transformer.resblocks.19.mlp.c_proj.bias FLOAT[1152] cf37e7b03699
text.transformer.resblocks.2.attn.in_proj_bias FLOAT[3456] acc2235aee6e
text.transformer.resblocks.2.attn.out_proj.bias FLOAT[1152] b3460c1df3ea
text.transformer.resblocks.2.ln_1.bias FLOAT[1152] 8cbf8352a1b2
text.transformer.resblocks.2.ln_1.weight FLOAT[1152] ffe34d2fc62a
text.transformer.resblocks.2.ln_2.bias FLOAT[1152] c7567f88d594
text.transformer.resblocks.2.ln_2.weight FLOAT[1152] 0fdb686ac99d
text.transformer.resblocks.2.mlp.c_fc.bias FLOAT[4304] 47f04a908000
text.transformer.resblocks.2.mlp.c_proj.bias FLOAT[1152] 821eef73809e
text.transformer.resblocks.20.attn.in_proj_bias FLOAT[3456] 36ebc5b60528
text.transformer.resblocks.20.attn.out_proj.bias FLOAT[1152] ab23e10e7a7c
text.transformer.resblocks.20.ln_1.bias FLOAT[1152] cf026987b395
text.transformer.resblocks.20.ln_1.weight FLOAT[1152] 12cae652903e
text.transformer.resblocks.20.ln_2.bias FLOAT[1152] 8a976439443b
text.transformer.resblocks.20.ln_2.weight FLOAT[1152] 36afce3ec55b
text.transformer.resblocks.20.mlp.c_fc.bias FLOAT[4304] cd6d47b03caa
text.transformer.resblocks.20.mlp.c_proj.bias FLOAT[1152] fa42e58d6c31
text.transformer.resblocks.21.attn.in_proj_bias FLOAT[3456] 355a076fa06c
text.transformer.resblocks.21.attn.out_proj.bias FLOAT[1152] 65c7d7c40ede
text.transformer.resblocks.21.ln_1.bias FLOAT[1152] c28dfe577cfc
text.transformer.resblocks.21.ln_1.weight FLOAT[1152] 3d7f5b9a972f
text.transformer.resblocks.21.ln_2.bias FLOAT[1152] b41b2d4900a8
text.transformer.resblocks.21.ln_2.weight FLOAT[1152] 6cf5a257d9ee
text.transformer.resblocks.21.mlp.c_fc.bias FLOAT[4304] f5d23f157df0
text.transformer.resblocks.21.mlp.c_proj.bias FLOAT[1152] 9595517e7509
text.transformer.resblocks.22.attn.in_proj_bias FLOAT[3456] fb01bbd093d7
text.transformer.resblocks.22.attn.out_proj.bias FLOAT[1152] 914a6002b7c7
text.transformer.resblocks.22.ln_1.bias FLOAT[1152] 29ac05d7bba5
text.transformer.resblocks.22.ln_1.weight FLOAT[1152] 0d11528247ed
text.transformer.resblocks.22.ln_2.bias FLOAT[1152] 178ba7ef141e
text.transformer.resblocks.22.ln_2.weight FLOAT[1152] d710213c5a69
text.transformer.resblocks.22.mlp.c_fc.bias FLOAT[4304] a7d87d7b0782
text.transformer.resblocks.22.mlp.c_proj.bias FLOAT[1152] 3712fb1cbb08
text.transformer.resblocks.23.attn.in_proj_bias FLOAT[3456] 1ace487286e7
text.transformer.resblocks.23.attn.out_proj.bias FLOAT[1152] 557a5ef0861b
text.transformer.resblocks.23.ln_1.bias FLOAT[1152] 76904a306aa9
text.transformer.resblocks.23.ln_1.weight FLOAT[1152] 9960e0173393
text.transformer.resblocks.23.ln_2.bias FLOAT[1152] 959da0251c1e
text.transformer.resblocks.23.ln_2.weight FLOAT[1152] 40a1306ebb40
text.transformer.resblocks.23.mlp.c_fc.bias FLOAT[4304] 61a44db6564e
text.transformer.resblocks.23.mlp.c_proj.bias FLOAT[1152] b7741fd91775
text.transformer.resblocks.24.attn.in_proj_bias FLOAT[3456] e4bf194c30aa
text.transformer.resblocks.24.attn.out_proj.bias FLOAT[1152] f712c9d0227b
text.transformer.resblocks.24.ln_1.bias FLOAT[1152] 1a8841fc2a5f
text.transformer.resblocks.24.ln_1.weight FLOAT[1152] 671c0e246c87
text.transformer.resblocks.24.ln_2.bias FLOAT[1152] a42b1b174ab7
text.transformer.resblocks.24.ln_2.weight FLOAT[1152] 53d1592575a0
text.transformer.resblocks.24.mlp.c_fc.bias FLOAT[4304] 11bfdd6744ff
text.transformer.resblocks.24.mlp.c_proj.bias FLOAT[1152] f2c8ec680857
text.transformer.resblocks.25.attn.in_proj_bias FLOAT[3456] 03a299b5cff0
text.transformer.resblocks.25.attn.out_proj.bias FLOAT[1152] 0196fa925695
text.transformer.resblocks.25.ln_1.bias FLOAT[1152] 2564eb0d94cd
text.transformer.resblocks.25.ln_1.weight FLOAT[1152] a26e7f57b8ae
text.transformer.resblocks.25.ln_2.bias FLOAT[1152] 7fca959deda4
text.transformer.resblocks.25.ln_2.weight FLOAT[1152] f5c929f5aab9
text.transformer.resblocks.25.mlp.c_fc.bias FLOAT[4304] 9442b7c5589f
text.transformer.resblocks.25.mlp.c_proj.bias FLOAT[1152] f87bf1cb177c
text.transformer.resblocks.26.attn.in_proj_bias FLOAT[3456] c9d711e5ad74
text.transformer.resblocks.26.attn.out_proj.bias FLOAT[1152] 9e5d6d3ba64a
text.transformer.resblocks.26.ln_1.bias FLOAT[1152] 242f290b526f
text.transformer.resblocks.26.ln_1.weight FLOAT[1152] 04cef709e1a8
text.transformer.resblocks.26.ln_2.bias FLOAT[1152] fadce70b84ef
text.transformer.resblocks.26.ln_2.weight FLOAT[1152] 37e5baf608af
text.transformer.resblocks.26.mlp.c_fc.bias FLOAT[4304] 6651313cdacc
text.transformer.resblocks.26.mlp.c_proj.bias FLOAT[1152] 45a2e16eeb50
text.transformer.resblocks.3.attn.in_proj_bias FLOAT[3456] f594371660fc
text.transformer.resblocks.3.attn.out_proj.bias FLOAT[1152] 64cb98b49cdf
text.transformer.resblocks.3.ln_1.bias FLOAT[1152] e18101ccc51e
text.transformer.resblocks.3.ln_1.weight FLOAT[1152] dd02cc344680
text.transformer.resblocks.3.ln_2.bias FLOAT[1152] 7440473a8ae9
text.transformer.resblocks.3.ln_2.weight FLOAT[1152] 90f47d2e090d
text.transformer.resblocks.3.mlp.c_fc.bias FLOAT[4304] 3905bcf4be8f
text.transformer.resblocks.3.mlp.c_proj.bias FLOAT[1152] 3ea1c7dc0831
text.transformer.resblocks.4.attn.in_proj_bias FLOAT[3456] 9dd7407e35dc
text.transformer.resblocks.4.attn.out_proj.bias FLOAT[1152] b5095c0a9248
text.transformer.resblocks.4.ln_1.bias FLOAT[1152] 2ed61120cdf8
text.transformer.resblocks.4.ln_1.weight FLOAT[1152] 21cc696b6895
text.transformer.resblocks.4.ln_2.bias FLOAT[1152] 95fbb027f11a
text.transformer.resblocks.4.ln_2.weight FLOAT[1152] 6d41edda53ba
text.transformer.resblocks.4.mlp.c_fc.bias FLOAT[4304] 116d41d05e1d
text.transformer.resblocks.4.mlp.c_proj.bias FLOAT[1152] e707e65b760f
text.transformer.resblocks.5.attn.in_proj_bias FLOAT[3456] 16d6b620b306
text.transformer.resblocks.5.attn.out_proj.bias FLOAT[1152] 2d58dfa78328
text.transformer.resblocks.5.ln_1.bias FLOAT[1152] 9036b655f7ec
text.transformer.resblocks.5.ln_1.weight FLOAT[1152] 051c8fbad6ce
text.transformer.resblocks.5.ln_2.bias FLOAT[1152] 28260db311c8
text.transformer.resblocks.5.ln_2.weight FLOAT[1152] fb5913898532
text.transformer.resblocks.5.mlp.c_fc.bias FLOAT[4304] fcfc9afd45d8
text.transformer.resblocks.5.mlp.c_proj.bias FLOAT[1152] 294eb49d3e74
text.transformer.resblocks.6.attn.in_proj_bias FLOAT[3456] c25f1d368d32
text.transformer.resblocks.6.attn.out_proj.bias FLOAT[1152] 516676632d99
text.transformer.resblocks.6.ln_1.bias FLOAT[1152] 8508fd730f54
text.transformer.resblocks.6.ln_1.weight FLOAT[1152] 55dbe6e93628
text.transformer.resblocks.6.ln_2.bias FLOAT[1152] c325042d1f7d
text.transformer.resblocks.6.ln_2.weight FLOAT[1152] 0db19550982f
text.transformer.resblocks.6.mlp.c_fc.bias FLOAT[4304] 031079c7bbda
text.transformer.resblocks.6.mlp.c_proj.bias FLOAT[1152] 49995c05f206
text.transformer.resblocks.7.attn.in_proj_bias FLOAT[3456] ec17b191d4f3
text.transformer.resblocks.7.attn.out_proj.bias FLOAT[1152] 615980540d06
text.transformer.resblocks.7.ln_1.bias FLOAT[1152] d38ea8084011
text.transformer.resblocks.7.ln_1.weight FLOAT[1152] 1ffd40da2e9d
text.transformer.resblocks.7.ln_2.bias FLOAT[1152] 8b23cb379494
text.transformer.resblocks.7.ln_2.weight FLOAT[1152] 9d589a35acf4
text.transformer.resblocks.7.mlp.c_fc.bias FLOAT[4304] 48f1975a077c
text.transformer.resblocks.7.mlp.c_proj.bias FLOAT[1152] 0a0df8b62193
text.transformer.resblocks.8.attn.in_proj_bias FLOAT[3456] 3cbbd80bc3ac
text.transformer.resblocks.8.attn.out_proj.bias FLOAT[1152] 770f41497471
text.transformer.resblocks.8.ln_1.bias FLOAT[1152] 2faf37e1d742
text.transformer.resblocks.8.ln_1.weight FLOAT[1152] 7da8915c7b7a
text.transformer.resblocks.8.ln_2.bias FLOAT[1152] 635aef1049d5
text.transformer.resblocks.8.ln_2.weight FLOAT[1152] 81774f1b87c4
text.transformer.resblocks.8.mlp.c_fc.bias FLOAT[4304] ba9d4813100b
text.transformer.resblocks.8.mlp.c_proj.bias FLOAT[1152] 5b1e2b15ca54
text.transformer.resblocks.9.attn.in_proj_bias FLOAT[3456] 73914b3a1cb7
text.transformer.resblocks.9.attn.out_proj.bias FLOAT[1152] 96bac62a56da
text.transformer.resblocks.9.ln_1.bias FLOAT[1152] e50b5d961056
text.transformer.resblocks.9.ln_1.weight FLOAT[1152] dc78c821d1d6
text.transformer.resblocks.9.ln_2.bias FLOAT[1152] f288f6140468
text.transformer.resblocks.9.ln_2.weight FLOAT[1152] 7f36520a3794
text.transformer.resblocks.9.mlp.c_fc.bias FLOAT[4304] 9eb266d1b3a6
text.transformer.resblocks.9.mlp.c_proj.bias FLOAT[1152] 1c3562e6f083
val_0 INT64[1] 12a3ae445661
val_1 FLOAT[] 6708d9be4956
val_10 FLOAT[1152,3456] 40b62ff61e0c
val_11 FLOAT[1152,4304] 9f95714a66c1
val_12 FLOAT[4304,1152] aaded4be69bc
val_13 FLOAT[1152,3456] 7cb9e895f198
val_14 FLOAT[1152,4304] 6a9f1c346b21
val_15 FLOAT[4304,1152] 1218fee6f284
val_16 FLOAT[1152,3456] 6c6d12806150
val_17 FLOAT[1152,4304] e78a14b57e67
val_18 FLOAT[4304,1152] cffc760837c3
val_19 FLOAT[1152,3456] 572207e95b55
val_2 INT64[1] 7c9fa136d441
val_20 FLOAT[1152,4304] 0217eb58cf9b
val_21 FLOAT[4304,1152] 3f64b356b57f
val_22 FLOAT[1152,3456] 36876121e875
val_23 FLOAT[1152,4304] 6b8c4a400d2f
val_24 FLOAT[4304,1152] de0c4b5d6b7c
val_25 FLOAT[1152,3456] d1612960d7be
val_26 FLOAT[1152,4304] 3088817bc8cb
val_27 FLOAT[4304,1152] 258999dab5d3
val_28 FLOAT[1152,3456] 91eb7eb28c04
val_29 FLOAT[1152,4304] f002611dadce
val_3 INT64[1] 6a69a6cc7473
val_30 FLOAT[4304,1152] 91f13d4618e5
val_31 FLOAT[1152,3456] 32f693b258e4
val_32 FLOAT[1152,4304] dc836b66cfd1
val_33 FLOAT[4304,1152] 274780fe74c5
val_34 FLOAT[1152,3456] 3e6d9932b753
val_35 FLOAT[1152,4304] f09e5d370310
val_36 FLOAT[4304,1152] 00816fc1c18e
val_37 FLOAT[1152,3456] 5e78b5505219
val_38 FLOAT[1152,4304] f75c788fa0af
val_39 FLOAT[4304,1152] 13c14a727bcb
val_4 FLOAT[1152,3456] e16966ed7727
val_40 FLOAT[1152,3456] 53606143ad0f
val_41 FLOAT[1152,4304] bdfd594c2c60
val_42 FLOAT[4304,1152] 7c8752c837b2
val_43 FLOAT[1152,3456] 94cfe3b81867
val_44 FLOAT[1152,4304] b0a9132edc40
val_45 FLOAT[4304,1152] 66e71733a8ff
val_46 FLOAT[1152,3456] b4dae7aa8e0e
val_47 FLOAT[1152,4304] cb324ece8f9d
val_48 FLOAT[4304,1152] bc56706fbef7
val_49 FLOAT[1152,3456] 2c5ef0669d29
val_5 FLOAT[1152,4304] 5513cbff47ee
val_50 FLOAT[1152,4304] 79ba2683d6d7
val_51 FLOAT[4304,1152] 37e6951d5528
val_52 FLOAT[1152,3456] 775c6c6e264f
val_53 FLOAT[1152,4304] 9feb38372050
val_54 FLOAT[4304,1152] 6710a04f8b8a
val_55 FLOAT[1152,3456] 835d1bb5780b
val_56 FLOAT[1152,4304] 2c6ddddd1ce0
val_57 FLOAT[4304,1152] d7877257c7ce
val_58 FLOAT[1152,3456] 855c5c8db8ee
val_59 FLOAT[1152,4304] ab44e821ebec
val_6 FLOAT[4304,1152] 6441b3974737
val_60 FLOAT[4304,1152] b88abe758554
val_61 FLOAT[1152,3456] 83b9f76ba489
val_62 FLOAT[1152,4304] d578d6207f54
val_63 FLOAT[4304,1152] ced3035792a9
val_64 FLOAT[1152,3456] c7573f05b3a1
val_65 FLOAT[1152,4304] f7aa8c80bfc1
val_66 FLOAT[4304,1152] ab160608b2a2
val_67 FLOAT[1152,3456] 329738598b51
val_68 FLOAT[1152,4304] 6c1bf118158f
val_69 FLOAT[4304,1152] e4d866429b6f
val_7 FLOAT[1152,3456] 2a79e64116b9
val_70 FLOAT[1152,3456] b1a91ab1229d
val_71 FLOAT[1152,4304] b44fc15b5af6
val_72 FLOAT[4304,1152] d62133c728cf
val_73 FLOAT[1152,3456] 5616ff92ec49
val_74 FLOAT[1152,4304] a1b81333a22b
val_75 FLOAT[4304,1152] 1b1b06382ec3
val_76 FLOAT[1152,3456] cd50f3acaf92
val_77 FLOAT[1152,4304] f9074d02c9b6
val_78 FLOAT[4304,1152] 5a8a43b4593b
val_79 FLOAT[1152,3456] 7e59100cfb6f
val_8 FLOAT[1152,4304] a4894854471f
val_80 FLOAT[1152,4304] 90d90a40dd52
val_81 FLOAT[4304,1152] 6aecbb87ff92
val_82 FLOAT[1152,3456] 75a6f7d2da34
val_83 FLOAT[1152,4304] 84bd02b7ef6a
val_84 FLOAT[4304,1152] eb91b2cbe718
val_9 FLOAT[4304,1152] 9b8a0cf6afee
