<
   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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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 = "none"> (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] d013c45ed4ba
node_scaled_dot_product_attention_11_wo_t FLOAT[1152,1152] 3c87332f214e
node_scaled_dot_product_attention_12_wo_t FLOAT[1152,1152] 5929dc92717a
node_scaled_dot_product_attention_13_wo_t FLOAT[1152,1152] 3bdfb77534ac
node_scaled_dot_product_attention_14_wo_t FLOAT[1152,1152] 68ff753637cd
node_scaled_dot_product_attention_15_wo_t FLOAT[1152,1152] 8e446392f5ea
node_scaled_dot_product_attention_16_wo_t FLOAT[1152,1152] 4d7f15cbe9ef
node_scaled_dot_product_attention_17_wo_t FLOAT[1152,1152] 9ac54c3f96ba
node_scaled_dot_product_attention_18_wo_t FLOAT[1152,1152] 4959424df1ff
node_scaled_dot_product_attention_19_wo_t FLOAT[1152,1152] a104a7d857d0
node_scaled_dot_product_attention_1_wo_t FLOAT[1152,1152] 43b4b5a3f1bb
node_scaled_dot_product_attention_20_wo_t FLOAT[1152,1152] 57eaeb761c7b
node_scaled_dot_product_attention_21_wo_t FLOAT[1152,1152] d42c20779cda
node_scaled_dot_product_attention_22_wo_t FLOAT[1152,1152] ab14b271db51
node_scaled_dot_product_attention_23_wo_t FLOAT[1152,1152] ef709ed08b73
node_scaled_dot_product_attention_24_wo_t FLOAT[1152,1152] 5566c069aece
node_scaled_dot_product_attention_25_wo_t FLOAT[1152,1152] 51320873d758
node_scaled_dot_product_attention_26_wo_t FLOAT[1152,1152] 2b9e2ec5a1ab
node_scaled_dot_product_attention_2_wo_t FLOAT[1152,1152] 5f08c5e00271
node_scaled_dot_product_attention_3_wo_t FLOAT[1152,1152] a76ee38ea879
node_scaled_dot_product_attention_4_wo_t FLOAT[1152,1152] 49d7adff1176
node_scaled_dot_product_attention_5_wo_t FLOAT[1152,1152] 73b2c30ec163
node_scaled_dot_product_attention_6_wo_t FLOAT[1152,1152] 9c179ac49ef2
node_scaled_dot_product_attention_7_wo_t FLOAT[1152,1152] 5b8ed5103f79
node_scaled_dot_product_attention_8_wo_t FLOAT[1152,1152] 182d7cd71ec8
node_scaled_dot_product_attention_9_wo_t FLOAT[1152,1152] 5e04606605b6
node_scaled_dot_product_attention_wo_t FLOAT[1152,1152] a0a5de2e8ac8
text.ln_final.bias FLOAT[1152] 945be7904ab9
text.ln_final.weight FLOAT[1152] 55211fe9e5dd
text.positional_embedding FLOAT[64,1152] fd8aca3ba3c8
text.text_projection.bias FLOAT[1152] b43abb926e57
text.text_projection.weight FLOAT[1152,1152] d29b6e1cedf1
text.token_embedding.weight_fp16 FLOAT16[32000,1152] fcb8398cbab0
text.transformer.resblocks.0.attn.in_proj_bias FLOAT[3456] 465ff56ddfba
text.transformer.resblocks.0.attn.out_proj.bias FLOAT[1152] 22f28fe769a7
text.transformer.resblocks.0.ln_1.bias FLOAT[1152] dc0fe2e82417
text.transformer.resblocks.0.ln_1.weight FLOAT[1152] 90f1ea788d23
text.transformer.resblocks.0.ln_2.bias FLOAT[1152] 8cbbddee7bad
text.transformer.resblocks.0.ln_2.weight FLOAT[1152] 228ac2cd634b
text.transformer.resblocks.0.mlp.c_fc.bias FLOAT[4304] fe5f9772ce10
text.transformer.resblocks.0.mlp.c_proj.bias FLOAT[1152] 8dfb26de5624
text.transformer.resblocks.1.attn.in_proj_bias FLOAT[3456] d62ffe6efb32
text.transformer.resblocks.1.attn.out_proj.bias FLOAT[1152] 33b595eea169
text.transformer.resblocks.1.ln_1.bias FLOAT[1152] 086dc2cff3e7
text.transformer.resblocks.1.ln_1.weight FLOAT[1152] d620b4220209
text.transformer.resblocks.1.ln_2.bias FLOAT[1152] 01619a258185
text.transformer.resblocks.1.ln_2.weight FLOAT[1152] aa56b3c597d4
text.transformer.resblocks.1.mlp.c_fc.bias FLOAT[4304] ef216e52b4d4
text.transformer.resblocks.1.mlp.c_proj.bias FLOAT[1152] 9bcdc4495beb
text.transformer.resblocks.10.attn.in_proj_bias FLOAT[3456] b079015a2f9b
text.transformer.resblocks.10.attn.out_proj.bias FLOAT[1152] 7a4fdca26703
text.transformer.resblocks.10.ln_1.bias FLOAT[1152] 7ff18bef0b35
text.transformer.resblocks.10.ln_1.weight FLOAT[1152] 4e1363f4aafc
text.transformer.resblocks.10.ln_2.bias FLOAT[1152] fb705f98b281
text.transformer.resblocks.10.ln_2.weight FLOAT[1152] 292bb4020f4d
text.transformer.resblocks.10.mlp.c_fc.bias FLOAT[4304] cec4283819f7
text.transformer.resblocks.10.mlp.c_proj.bias FLOAT[1152] 2d99d788693b
text.transformer.resblocks.11.attn.in_proj_bias FLOAT[3456] bea3e4ea7272
text.transformer.resblocks.11.attn.out_proj.bias FLOAT[1152] 4c2af4542190
text.transformer.resblocks.11.ln_1.bias FLOAT[1152] 6b71aaf4a444
text.transformer.resblocks.11.ln_1.weight FLOAT[1152] 33abbba24994
text.transformer.resblocks.11.ln_2.bias FLOAT[1152] ec4c1d18a37d
text.transformer.resblocks.11.ln_2.weight FLOAT[1152] f8a7e8f466eb
text.transformer.resblocks.11.mlp.c_fc.bias FLOAT[4304] cd70589ac1de
text.transformer.resblocks.11.mlp.c_proj.bias FLOAT[1152] 52cb8a5f459c
text.transformer.resblocks.12.attn.in_proj_bias FLOAT[3456] 698dad8f7f09
text.transformer.resblocks.12.attn.out_proj.bias FLOAT[1152] 8adcf65be236
text.transformer.resblocks.12.ln_1.bias FLOAT[1152] f47380499266
text.transformer.resblocks.12.ln_1.weight FLOAT[1152] a05afbda8178
text.transformer.resblocks.12.ln_2.bias FLOAT[1152] 8a46d198c76a
text.transformer.resblocks.12.ln_2.weight FLOAT[1152] 16fe82439687
text.transformer.resblocks.12.mlp.c_fc.bias FLOAT[4304] c010703830f7
text.transformer.resblocks.12.mlp.c_proj.bias FLOAT[1152] b32dc7e649a2
text.transformer.resblocks.13.attn.in_proj_bias FLOAT[3456] fcd8ccbc72f1
text.transformer.resblocks.13.attn.out_proj.bias FLOAT[1152] 23dfa94f76fe
text.transformer.resblocks.13.ln_1.bias FLOAT[1152] 868cbfb9037f
text.transformer.resblocks.13.ln_1.weight FLOAT[1152] f9723706df3b
text.transformer.resblocks.13.ln_2.bias FLOAT[1152] 52d320375ff9
text.transformer.resblocks.13.ln_2.weight FLOAT[1152] 847fa514c58f
text.transformer.resblocks.13.mlp.c_fc.bias FLOAT[4304] 8e09b60a7106
text.transformer.resblocks.13.mlp.c_proj.bias FLOAT[1152] a74245a342df
text.transformer.resblocks.14.attn.in_proj_bias FLOAT[3456] 5e122cba5af3
text.transformer.resblocks.14.attn.out_proj.bias FLOAT[1152] 61092f036118
text.transformer.resblocks.14.ln_1.bias FLOAT[1152] 715d908a065e
text.transformer.resblocks.14.ln_1.weight FLOAT[1152] 4e0e62a362b9
text.transformer.resblocks.14.ln_2.bias FLOAT[1152] 46593b72fea6
text.transformer.resblocks.14.ln_2.weight FLOAT[1152] 3f6c72be120a
text.transformer.resblocks.14.mlp.c_fc.bias FLOAT[4304] 1bb5874d1678
text.transformer.resblocks.14.mlp.c_proj.bias FLOAT[1152] 58a5ab2380c0
text.transformer.resblocks.15.attn.in_proj_bias FLOAT[3456] 3c2f94e52217
text.transformer.resblocks.15.attn.out_proj.bias FLOAT[1152] 8550c886ec6f
text.transformer.resblocks.15.ln_1.bias FLOAT[1152] faae16c006f4
text.transformer.resblocks.15.ln_1.weight FLOAT[1152] d342ad637b1b
text.transformer.resblocks.15.ln_2.bias FLOAT[1152] ff7d6649f678
text.transformer.resblocks.15.ln_2.weight FLOAT[1152] 7a847ef27b59
text.transformer.resblocks.15.mlp.c_fc.bias FLOAT[4304] 363bc15e1094
text.transformer.resblocks.15.mlp.c_proj.bias FLOAT[1152] c15a56d07f03
text.transformer.resblocks.16.attn.in_proj_bias FLOAT[3456] 8ae7107330be
text.transformer.resblocks.16.attn.out_proj.bias FLOAT[1152] 0f48d1e0fa83
text.transformer.resblocks.16.ln_1.bias FLOAT[1152] 36856474699d
text.transformer.resblocks.16.ln_1.weight FLOAT[1152] d9491f49dadf
text.transformer.resblocks.16.ln_2.bias FLOAT[1152] 0c97755e55b4
text.transformer.resblocks.16.ln_2.weight FLOAT[1152] 7e3942a6e29d
text.transformer.resblocks.16.mlp.c_fc.bias FLOAT[4304] 2687b6594aa4
text.transformer.resblocks.16.mlp.c_proj.bias FLOAT[1152] 9ae684721a77
text.transformer.resblocks.17.attn.in_proj_bias FLOAT[3456] 28e5a6e32135
text.transformer.resblocks.17.attn.out_proj.bias FLOAT[1152] a2571282b3a3
text.transformer.resblocks.17.ln_1.bias FLOAT[1152] 3631780a6453
text.transformer.resblocks.17.ln_1.weight FLOAT[1152] 8e36a7904e8c
text.transformer.resblocks.17.ln_2.bias FLOAT[1152] 220e6072b122
text.transformer.resblocks.17.ln_2.weight FLOAT[1152] d750693dd268
text.transformer.resblocks.17.mlp.c_fc.bias FLOAT[4304] 1fa750186834
text.transformer.resblocks.17.mlp.c_proj.bias FLOAT[1152] 467335d6e70a
text.transformer.resblocks.18.attn.in_proj_bias FLOAT[3456] 834d2d45ce2f
text.transformer.resblocks.18.attn.out_proj.bias FLOAT[1152] 74c030c721c5
text.transformer.resblocks.18.ln_1.bias FLOAT[1152] 3b231250c867
text.transformer.resblocks.18.ln_1.weight FLOAT[1152] 58264920e672
text.transformer.resblocks.18.ln_2.bias FLOAT[1152] 6a161508c6b5
text.transformer.resblocks.18.ln_2.weight FLOAT[1152] b302257222cf
text.transformer.resblocks.18.mlp.c_fc.bias FLOAT[4304] d683c1fa513f
text.transformer.resblocks.18.mlp.c_proj.bias FLOAT[1152] 5d530e1a8bdf
text.transformer.resblocks.19.attn.in_proj_bias FLOAT[3456] 87bbb01bdab3
text.transformer.resblocks.19.attn.out_proj.bias FLOAT[1152] 3871ef027956
text.transformer.resblocks.19.ln_1.bias FLOAT[1152] 557d2be59997
text.transformer.resblocks.19.ln_1.weight FLOAT[1152] ce0789843ae9
text.transformer.resblocks.19.ln_2.bias FLOAT[1152] 1962de705f64
text.transformer.resblocks.19.ln_2.weight FLOAT[1152] 33f078bc6c85
text.transformer.resblocks.19.mlp.c_fc.bias FLOAT[4304] e28b77d23783
text.transformer.resblocks.19.mlp.c_proj.bias FLOAT[1152] 997151725313
text.transformer.resblocks.2.attn.in_proj_bias FLOAT[3456] 94c10e932e8d
text.transformer.resblocks.2.attn.out_proj.bias FLOAT[1152] df1ad9ea2b82
text.transformer.resblocks.2.ln_1.bias FLOAT[1152] 416c967e5a99
text.transformer.resblocks.2.ln_1.weight FLOAT[1152] ce40a8a8e7d7
text.transformer.resblocks.2.ln_2.bias FLOAT[1152] 1596a5aca222
text.transformer.resblocks.2.ln_2.weight FLOAT[1152] 7d92b879ba01
text.transformer.resblocks.2.mlp.c_fc.bias FLOAT[4304] 4c1ff5484caf
text.transformer.resblocks.2.mlp.c_proj.bias FLOAT[1152] 3ba72234868b
text.transformer.resblocks.20.attn.in_proj_bias FLOAT[3456] c2d607bd5adc
text.transformer.resblocks.20.attn.out_proj.bias FLOAT[1152] de8e5fa6ed0e
text.transformer.resblocks.20.ln_1.bias FLOAT[1152] a90b1315ed42
text.transformer.resblocks.20.ln_1.weight FLOAT[1152] 89635d7371d4
text.transformer.resblocks.20.ln_2.bias FLOAT[1152] c6f9bd834f77
text.transformer.resblocks.20.ln_2.weight FLOAT[1152] 51e0aa1b9b7b
text.transformer.resblocks.20.mlp.c_fc.bias FLOAT[4304] 5183eacdd26b
text.transformer.resblocks.20.mlp.c_proj.bias FLOAT[1152] 3b5870e949be
text.transformer.resblocks.21.attn.in_proj_bias FLOAT[3456] 0fa25d8cf4de
text.transformer.resblocks.21.attn.out_proj.bias FLOAT[1152] d982fedd105e
text.transformer.resblocks.21.ln_1.bias FLOAT[1152] c9641bcb8f5f
text.transformer.resblocks.21.ln_1.weight FLOAT[1152] 7145ea8440d8
text.transformer.resblocks.21.ln_2.bias FLOAT[1152] efc3c28ef2ff
text.transformer.resblocks.21.ln_2.weight FLOAT[1152] 843eb9acdd49
text.transformer.resblocks.21.mlp.c_fc.bias FLOAT[4304] abd451e32cfe
text.transformer.resblocks.21.mlp.c_proj.bias FLOAT[1152] 3fcf54cae31c
text.transformer.resblocks.22.attn.in_proj_bias FLOAT[3456] 6ca2c82406f1
text.transformer.resblocks.22.attn.out_proj.bias FLOAT[1152] 82c84e8bdd40
text.transformer.resblocks.22.ln_1.bias FLOAT[1152] 49d073be2fca
text.transformer.resblocks.22.ln_1.weight FLOAT[1152] 3dd02102c13a
text.transformer.resblocks.22.ln_2.bias FLOAT[1152] 5c88add3b253
text.transformer.resblocks.22.ln_2.weight FLOAT[1152] 2fa412b67fcd
text.transformer.resblocks.22.mlp.c_fc.bias FLOAT[4304] a749a4d3d723
text.transformer.resblocks.22.mlp.c_proj.bias FLOAT[1152] 9fa64bc5528c
text.transformer.resblocks.23.attn.in_proj_bias FLOAT[3456] ee8d11cbbce4
text.transformer.resblocks.23.attn.out_proj.bias FLOAT[1152] 7c8631153d53
text.transformer.resblocks.23.ln_1.bias FLOAT[1152] b4f730defe19
text.transformer.resblocks.23.ln_1.weight FLOAT[1152] a29cce943d6e
text.transformer.resblocks.23.ln_2.bias FLOAT[1152] a71c6364c7f5
text.transformer.resblocks.23.ln_2.weight FLOAT[1152] a9948f0d86d5
text.transformer.resblocks.23.mlp.c_fc.bias FLOAT[4304] 69a1f437ce6d
text.transformer.resblocks.23.mlp.c_proj.bias FLOAT[1152] 2eaa7cd14aa7
text.transformer.resblocks.24.attn.in_proj_bias FLOAT[3456] 2c434fc07141
text.transformer.resblocks.24.attn.out_proj.bias FLOAT[1152] 067b6069a79b
text.transformer.resblocks.24.ln_1.bias FLOAT[1152] 03a14e36d405
text.transformer.resblocks.24.ln_1.weight FLOAT[1152] 304296ad052e
text.transformer.resblocks.24.ln_2.bias FLOAT[1152] d5cbc2b3ce8b
text.transformer.resblocks.24.ln_2.weight FLOAT[1152] 38118e7c864e
text.transformer.resblocks.24.mlp.c_fc.bias FLOAT[4304] 8cd008cc2abe
text.transformer.resblocks.24.mlp.c_proj.bias FLOAT[1152] 25bfdd0bc276
text.transformer.resblocks.25.attn.in_proj_bias FLOAT[3456] ee374aa51c10
text.transformer.resblocks.25.attn.out_proj.bias FLOAT[1152] 43b54d86bcbc
text.transformer.resblocks.25.ln_1.bias FLOAT[1152] 27ce20be093e
text.transformer.resblocks.25.ln_1.weight FLOAT[1152] 5256c89b2bea
text.transformer.resblocks.25.ln_2.bias FLOAT[1152] bde8eee8fb36
text.transformer.resblocks.25.ln_2.weight FLOAT[1152] 3b6498e955a5
text.transformer.resblocks.25.mlp.c_fc.bias FLOAT[4304] 0cc27452b7c0
text.transformer.resblocks.25.mlp.c_proj.bias FLOAT[1152] c437b8895435
text.transformer.resblocks.26.attn.in_proj_bias FLOAT[3456] 69faa1247cc4
text.transformer.resblocks.26.attn.out_proj.bias FLOAT[1152] bc7cc6ff4812
text.transformer.resblocks.26.ln_1.bias FLOAT[1152] 6ca6347ad375
text.transformer.resblocks.26.ln_1.weight FLOAT[1152] b1d869a88624
text.transformer.resblocks.26.ln_2.bias FLOAT[1152] 39e8671bc8b5
text.transformer.resblocks.26.ln_2.weight FLOAT[1152] d8019d306f82
text.transformer.resblocks.26.mlp.c_fc.bias FLOAT[4304] d73c2ac65467
text.transformer.resblocks.26.mlp.c_proj.bias FLOAT[1152] ddff431f7e9f
text.transformer.resblocks.3.attn.in_proj_bias FLOAT[3456] 1ecc6a9d6573
text.transformer.resblocks.3.attn.out_proj.bias FLOAT[1152] aacd65a9c61a
text.transformer.resblocks.3.ln_1.bias FLOAT[1152] a1a1de7f2d32
text.transformer.resblocks.3.ln_1.weight FLOAT[1152] 0a9edc5cad37
text.transformer.resblocks.3.ln_2.bias FLOAT[1152] 037c99318f53
text.transformer.resblocks.3.ln_2.weight FLOAT[1152] 2d34ce9cf8c3
text.transformer.resblocks.3.mlp.c_fc.bias FLOAT[4304] 261fcd41b446
text.transformer.resblocks.3.mlp.c_proj.bias FLOAT[1152] f605261a8cb5
text.transformer.resblocks.4.attn.in_proj_bias FLOAT[3456] 8334be7f5d33
text.transformer.resblocks.4.attn.out_proj.bias FLOAT[1152] 6658c537f8f4
text.transformer.resblocks.4.ln_1.bias FLOAT[1152] 49261eceb5ed
text.transformer.resblocks.4.ln_1.weight FLOAT[1152] b904d85b4e8f
text.transformer.resblocks.4.ln_2.bias FLOAT[1152] fb29ac46e8ac
text.transformer.resblocks.4.ln_2.weight FLOAT[1152] ea9bf8be9c5b
text.transformer.resblocks.4.mlp.c_fc.bias FLOAT[4304] 4c26d64e33c9
text.transformer.resblocks.4.mlp.c_proj.bias FLOAT[1152] 7762ba747ccb
text.transformer.resblocks.5.attn.in_proj_bias FLOAT[3456] 5f210944a25a
text.transformer.resblocks.5.attn.out_proj.bias FLOAT[1152] 04f2ba63ec16
text.transformer.resblocks.5.ln_1.bias FLOAT[1152] 96a290d12e66
text.transformer.resblocks.5.ln_1.weight FLOAT[1152] fb907f5cb90d
text.transformer.resblocks.5.ln_2.bias FLOAT[1152] a16e9e9249c5
text.transformer.resblocks.5.ln_2.weight FLOAT[1152] d342958ec9f7
text.transformer.resblocks.5.mlp.c_fc.bias FLOAT[4304] c389dd78c71d
text.transformer.resblocks.5.mlp.c_proj.bias FLOAT[1152] 7c50741bdc75
text.transformer.resblocks.6.attn.in_proj_bias FLOAT[3456] b56c36fad32d
text.transformer.resblocks.6.attn.out_proj.bias FLOAT[1152] a68a91548478
text.transformer.resblocks.6.ln_1.bias FLOAT[1152] 78f0181d1a79
text.transformer.resblocks.6.ln_1.weight FLOAT[1152] d5120a28935b
text.transformer.resblocks.6.ln_2.bias FLOAT[1152] 238f81fd9d99
text.transformer.resblocks.6.ln_2.weight FLOAT[1152] 0bcd06461eac
text.transformer.resblocks.6.mlp.c_fc.bias FLOAT[4304] 308c3c3c82d6
text.transformer.resblocks.6.mlp.c_proj.bias FLOAT[1152] 336793e74888
text.transformer.resblocks.7.attn.in_proj_bias FLOAT[3456] 45379548c370
text.transformer.resblocks.7.attn.out_proj.bias FLOAT[1152] 57d967b91cd6
text.transformer.resblocks.7.ln_1.bias FLOAT[1152] 2bfff7cc3b9d
text.transformer.resblocks.7.ln_1.weight FLOAT[1152] 6697d059712c
text.transformer.resblocks.7.ln_2.bias FLOAT[1152] 698462f2982c
text.transformer.resblocks.7.ln_2.weight FLOAT[1152] acfa8275f08e
text.transformer.resblocks.7.mlp.c_fc.bias FLOAT[4304] 891aab1d07bc
text.transformer.resblocks.7.mlp.c_proj.bias FLOAT[1152] b018962f5c1b
text.transformer.resblocks.8.attn.in_proj_bias FLOAT[3456] 2acdea1657c1
text.transformer.resblocks.8.attn.out_proj.bias FLOAT[1152] 8fe8ed770d92
text.transformer.resblocks.8.ln_1.bias FLOAT[1152] 8bb0d1f7525b
text.transformer.resblocks.8.ln_1.weight FLOAT[1152] 96fddc61beb5
text.transformer.resblocks.8.ln_2.bias FLOAT[1152] 7884fd6512b0
text.transformer.resblocks.8.ln_2.weight FLOAT[1152] 90c34fc474cb
text.transformer.resblocks.8.mlp.c_fc.bias FLOAT[4304] 702982725a7b
text.transformer.resblocks.8.mlp.c_proj.bias FLOAT[1152] c7c3ad55b5cc
text.transformer.resblocks.9.attn.in_proj_bias FLOAT[3456] 49c7e86db2fe
text.transformer.resblocks.9.attn.out_proj.bias FLOAT[1152] 2ba91f9da2a4
text.transformer.resblocks.9.ln_1.bias FLOAT[1152] 504693aff804
text.transformer.resblocks.9.ln_1.weight FLOAT[1152] f165623c5e3d
text.transformer.resblocks.9.ln_2.bias FLOAT[1152] cdf6e460a406
text.transformer.resblocks.9.ln_2.weight FLOAT[1152] aefe66e450fc
text.transformer.resblocks.9.mlp.c_fc.bias FLOAT[4304] b112c9598077
text.transformer.resblocks.9.mlp.c_proj.bias FLOAT[1152] 549bb92b7d8d
val_0 INT64[1] 12a3ae445661
val_1 FLOAT[] 6708d9be4956
val_10 FLOAT[1152,3456] acadcfe26698
val_11 FLOAT[1152,4304] b66658ea30f8
val_12 FLOAT[4304,1152] 72d73a8b00e2
val_13 FLOAT[1152,3456] 8c10e520d0ae
val_14 FLOAT[1152,4304] 867fb99aa143
val_15 FLOAT[4304,1152] b3e67c4b95e8
val_16 FLOAT[1152,3456] 53ca67718f2a
val_17 FLOAT[1152,4304] 86afb0ec1e0f
val_18 FLOAT[4304,1152] da1da0ed95e7
val_19 FLOAT[1152,3456] e47c3fd607ce
val_2 INT64[1] 7c9fa136d441
val_20 FLOAT[1152,4304] 614872675d1b
val_21 FLOAT[4304,1152] e338ef326f33
val_22 FLOAT[1152,3456] 7e8fa9dec19c
val_23 FLOAT[1152,4304] 6b86d6d7281d
val_24 FLOAT[4304,1152] 9054765aef28
val_25 FLOAT[1152,3456] dd3ed24a8f0d
val_26 FLOAT[1152,4304] 61d7b8a69385
val_27 FLOAT[4304,1152] e80e1f004914
val_28 FLOAT[1152,3456] e33f3499d765
val_29 FLOAT[1152,4304] f256a471aced
val_3 INT64[1] 6a69a6cc7473
val_30 FLOAT[4304,1152] a5dd2c0af951
val_31 FLOAT[1152,3456] 1eb30b02d8ef
val_32 FLOAT[1152,4304] 2265f124b674
val_33 FLOAT[4304,1152] e49d4ac45b7a
val_34 FLOAT[1152,3456] 5e5078612561
val_35 FLOAT[1152,4304] e2c32536c09c
val_36 FLOAT[4304,1152] 5914ae5d2cba
val_37 FLOAT[1152,3456] f9391b2268bb
val_38 FLOAT[1152,4304] 79c6cb203931
val_39 FLOAT[4304,1152] 32ae3d10afb6
val_4 FLOAT[1152,3456] d3084e5a3731
val_40 FLOAT[1152,3456] a3090d536042
val_41 FLOAT[1152,4304] 266a597c86f4
val_42 FLOAT[4304,1152] 20e41fed1417
val_43 FLOAT[1152,3456] 9c79fdb4daad
val_44 FLOAT[1152,4304] 9807dc3a6687
val_45 FLOAT[4304,1152] 60cf29d83b63
val_46 FLOAT[1152,3456] 2a744c5ad173
val_47 FLOAT[1152,4304] efa22d4944f8
val_48 FLOAT[4304,1152] 85d9f1d85e4b
val_49 FLOAT[1152,3456] f6cefd2edcbe
val_5 FLOAT[1152,4304] 5b96ff8335f1
val_50 FLOAT[1152,4304] 468aea94bfc5
val_51 FLOAT[4304,1152] afb3b50b213d
val_52 FLOAT[1152,3456] ac675a8d0b52
val_53 FLOAT[1152,4304] ee33703ce967
val_54 FLOAT[4304,1152] 835002ca3a84
val_55 FLOAT[1152,3456] 3eb6b06c24fc
val_56 FLOAT[1152,4304] d02c04215a5b
val_57 FLOAT[4304,1152] fb931ee119e9
val_58 FLOAT[1152,3456] 3a81cfefb5d9
val_59 FLOAT[1152,4304] 186e217bee41
val_6 FLOAT[4304,1152] 3f31656e1cc6
val_60 FLOAT[4304,1152] 573a65d187f4
val_61 FLOAT[1152,3456] 56f5ca13326c
val_62 FLOAT[1152,4304] 7d82e91d9c1e
val_63 FLOAT[4304,1152] 8c5958f22f3a
val_64 FLOAT[1152,3456] c984b2e85d56
val_65 FLOAT[1152,4304] fcf90a9a82bb
val_66 FLOAT[4304,1152] 475af149fdd8
val_67 FLOAT[1152,3456] 0c127107bad4
val_68 FLOAT[1152,4304] 6a6af9c70f93
val_69 FLOAT[4304,1152] 4f8dd4b97450
val_7 FLOAT[1152,3456] e199034237f2
val_70 FLOAT[1152,3456] af722549111a
val_71 FLOAT[1152,4304] 9860c1fc9309
val_72 FLOAT[4304,1152] 6fb0a0c44fed
val_73 FLOAT[1152,3456] 7fc5ee3983e0
val_74 FLOAT[1152,4304] 118aa9084321
val_75 FLOAT[4304,1152] 4b4ef29af088
val_76 FLOAT[1152,3456] 4ded279aa997
val_77 FLOAT[1152,4304] 66a130022ae6
val_78 FLOAT[4304,1152] 6b9dab21cbae
val_79 FLOAT[1152,3456] 20aed7814f77
val_8 FLOAT[1152,4304] d16bf22042ac
val_80 FLOAT[1152,4304] 163d7f47f7a2
val_81 FLOAT[4304,1152] 1ef8d3b9b262
val_82 FLOAT[1152,3456] 6cd38c929820
val_83 FLOAT[1152,4304] 81a14e8d32c0
val_84 FLOAT[4304,1152] 204b46b90830
val_9 FLOAT[4304,1152] 1cb9b43cbdc7
