<
   ir_version: 10,
   opset_import: ["" : 23],
   producer_name: "pytorch"
>
main_graph (uint8[batch,256,256,3] image) => (float[batch,768] image_embedding) 
   <
      float[batch,64,768] add_1007
      float[batch,64,768] add_1068
      float[batch,64,768] add_107
      float[batch,64,768] add_1097
      float[batch,1,768] add_1195
      float[batch,64,768] add_13
      float[batch,64,768] add_168
      float[batch,64,768] add_197
      float[batch,64,768] add_258
      float[batch,64,768] add_287
      float[batch,64,768] add_348
      float[batch,64,768] add_377
      float[batch,64,768] add_438
      float[batch,64,768] add_467
      float[batch,64,768] add_528
      float[batch,64,768] add_557
      float[batch,64,768] add_618
      float[batch,64,768] add_647
      float[batch,64,768] add_708
      float[batch,64,768] add_737
      float[batch,64,768] add_78
      float[batch,64,768] add_798
      float[batch,64,768] add_827
      float[batch,64,768] add_888
      float[batch,64,768] add_917
      float[batch,64,768] add_978
      float[batch,1] clamp_min
      float[batch,768,8,8] conv2d
      float[batch,64,3072] gelu
      float[batch,64,3072] gelu_1
      float[batch,64,3072] gelu_10
      float[batch,64,3072] gelu_11
      float[batch,1,3072] gelu_12
      float[batch,64,3072] gelu_2
      float[batch,64,3072] gelu_3
      float[batch,64,3072] gelu_4
      float[batch,64,3072] gelu_5
      float[batch,64,3072] gelu_6
      float[batch,64,3072] gelu_7
      float[batch,64,3072] gelu_8
      float[batch,64,3072] gelu_9
      float[batch,3,256,256] image_chw
      float[batch] image_ez
      float[batch] image_ez_r
      float[batch,1,1,1] image_ez_s
      float[batch,256,256,3] image_f32
      float[batch,64,768] layer_norm
      float[batch,64,768] layer_norm_1
      float[batch,64,768] layer_norm_10
      float[batch,64,768] layer_norm_11
      float[batch,64,768] layer_norm_12
      float[batch,64,768] layer_norm_13
      float[batch,64,768] layer_norm_14
      float[batch,64,768] layer_norm_15
      float[batch,64,768] layer_norm_16
      float[batch,64,768] layer_norm_17
      float[batch,64,768] layer_norm_18
      float[batch,64,768] layer_norm_19
      float[batch,64,768] layer_norm_2
      float[batch,64,768] layer_norm_20
      float[batch,64,768] layer_norm_21
      float[batch,64,768] layer_norm_22
      float[batch,64,768] layer_norm_23
      float[batch,64,768] layer_norm_24
      float[batch,1,768] layer_norm_25
      float[batch,64,768] layer_norm_3
      float[batch,64,768] layer_norm_4
      float[batch,64,768] layer_norm_5
      float[batch,64,768] layer_norm_6
      float[batch,64,768] layer_norm_7
      float[batch,64,768] layer_norm_8
      float[batch,64,768] layer_norm_9
      float[batch,1] linalg_vector_norm
      float[batch,64,2304] linear
      float[batch,64,768] linear_1
      float[batch,64,3072] linear_10
      float[batch,64,768] linear_11
      float[batch,64,2304] linear_12
      float[batch,64,768] linear_13
      float[batch,64,3072] linear_14
      float[batch,64,768] linear_15
      float[batch,64,2304] linear_16
      float[batch,64,768] linear_17
      float[batch,64,3072] linear_18
      float[batch,64,768] linear_19
      float[batch,64,3072] linear_2
      float[batch,64,2304] linear_20
      float[batch,64,768] linear_21
      float[batch,64,3072] linear_22
      float[batch,64,768] linear_23
      float[batch,64,2304] linear_24
      float[batch,64,768] linear_25
      float[batch,64,3072] linear_26
      float[batch,64,768] linear_27
      float[batch,64,2304] linear_28
      float[batch,64,768] linear_29
      float[batch,64,768] linear_3
      float[batch,64,3072] linear_30
      float[batch,64,768] linear_31
      float[batch,64,2304] linear_32
      float[batch,64,768] linear_33
      float[batch,64,3072] linear_34
      float[batch,64,768] linear_35
      float[batch,64,2304] linear_36
      float[batch,64,768] linear_37
      float[batch,64,3072] linear_38
      float[batch,64,768] linear_39
      float[batch,64,2304] linear_4
      float[batch,64,2304] linear_40
      float[batch,64,768] linear_41
      float[batch,64,3072] linear_42
      float[batch,64,768] linear_43
      float[batch,64,2304] linear_44
      float[batch,64,768] linear_45
      float[batch,64,3072] linear_46
      float[batch,64,768] linear_47
      float[batch,64,1536] linear_49
      float[batch,64,768] linear_5
      float[batch,1,768] linear_50
      float[batch,1,3072] linear_51
      float[batch,1,768] linear_52
      float[batch,64,3072] linear_6
      float[batch,64,768] linear_7
      float[batch,64,2304] linear_8
      float[batch,64,768] linear_9
      float[batch,64,768] node_scaled_dot_product_attention_10_k
      float[batch,64,768] node_scaled_dot_product_attention_10_q
      float[batch,64,768] node_scaled_dot_product_attention_10_v
      float[batch,64,768] node_scaled_dot_product_attention_11_k
      float[batch,64,768] node_scaled_dot_product_attention_11_q
      float[batch,64,768] node_scaled_dot_product_attention_11_v
      float[batch,64,768] node_scaled_dot_product_attention_12_k
      float[batch,1,768] node_scaled_dot_product_attention_12_q
      float[batch,1,1] node_scaled_dot_product_attention_12_q_col_out
      float[batch,64,768] node_scaled_dot_product_attention_12_v
      float[batch,64,768] node_scaled_dot_product_attention_1_k
      float[batch,64,768] node_scaled_dot_product_attention_1_q
      float[batch,64,768] node_scaled_dot_product_attention_1_v
      float[batch,64,768] node_scaled_dot_product_attention_2_k
      float[batch,64,768] node_scaled_dot_product_attention_2_q
      float[batch,64,768] node_scaled_dot_product_attention_2_v
      float[batch,64,768] node_scaled_dot_product_attention_3_k
      float[batch,64,768] node_scaled_dot_product_attention_3_q
      float[batch,64,768] node_scaled_dot_product_attention_3_v
      float[batch,64,768] node_scaled_dot_product_attention_4_k
      float[batch,64,768] node_scaled_dot_product_attention_4_q
      float[batch,64,768] node_scaled_dot_product_attention_4_v
      float[batch,64,768] node_scaled_dot_product_attention_5_k
      float[batch,64,768] node_scaled_dot_product_attention_5_q
      float[batch,64,768] node_scaled_dot_product_attention_5_v
      float[batch,64,768] node_scaled_dot_product_attention_6_k
      float[batch,64,768] node_scaled_dot_product_attention_6_q
      float[batch,64,768] node_scaled_dot_product_attention_6_v
      float[batch,64,768] node_scaled_dot_product_attention_7_k
      float[batch,64,768] node_scaled_dot_product_attention_7_q
      float[batch,64,768] node_scaled_dot_product_attention_7_v
      float[batch,64,768] node_scaled_dot_product_attention_8_k
      float[batch,64,768] node_scaled_dot_product_attention_8_q
      float[batch,64,768] node_scaled_dot_product_attention_8_v
      float[batch,64,768] node_scaled_dot_product_attention_9_k
      float[batch,64,768] node_scaled_dot_product_attention_9_q
      float[batch,64,768] node_scaled_dot_product_attention_9_v
      float[batch,64,768] node_scaled_dot_product_attention_k
      float[batch,64,768] node_scaled_dot_product_attention_q
      float[batch,64,768] node_scaled_dot_product_attention_v
      float[batch,64,768] scaled_dot_product_attention
      float[batch,64,768] scaled_dot_product_attention_1
      float[batch,64,768] scaled_dot_product_attention_10
      float[batch,64,768] scaled_dot_product_attention_11
      float[batch,1,768] scaled_dot_product_attention_12
      float[batch,64,768] scaled_dot_product_attention_2
      float[batch,64,768] scaled_dot_product_attention_3
      float[batch,64,768] scaled_dot_product_attention_4
      float[batch,64,768] scaled_dot_product_attention_5
      float[batch,64,768] scaled_dot_product_attention_6
      float[batch,64,768] scaled_dot_product_attention_7
      float[batch,64,768] scaled_dot_product_attention_8
      float[batch,64,768] scaled_dot_product_attention_9
      float[batch,768] select
      float[batch,64,768] transpose
      float[batch,64,768] val_100
      float[batch,64,3072] val_101
      float[batch,64,768] val_102
      float[batch,64,1536] val_103
      float[batch,1,768] val_104
      float[batch,1,3072] val_105
      float[batch,1,768] val_106
      float[batch,64,2304] val_55
      float[batch,64,768] val_56
      float[batch,64,3072] val_57
      float[batch,64,768] val_58
      float[batch,64,2304] val_59
      float[batch,64,768] val_60
      float[batch,64,3072] val_61
      float[batch,64,768] val_62
      float[batch,64,2304] val_63
      float[batch,64,768] val_64
      float[batch,64,3072] val_65
      float[batch,64,768] val_66
      float[batch,64,2304] val_67
      float[batch,64,768] val_68
      float[batch,64,3072] val_69
      float[batch,64,768] val_70
      float[batch,64,2304] val_71
      float[batch,64,768] val_72
      float[batch,64,3072] val_73
      float[batch,64,768] val_74
      float[batch,64,2304] val_75
      float[batch,64,768] val_76
      float[batch,64,3072] val_77
      float[batch,64,768] val_78
      float[batch,64,2304] val_79
      float[batch,64,768] val_80
      float[batch,64,3072] val_81
      float[batch,64,768] val_82
      float[batch,64,2304] val_83
      float[batch,64,768] val_84
      float[batch,64,3072] val_85
      float[batch,64,768] val_86
      float[batch,64,2304] val_87
      float[batch,64,768] val_88
      float[batch,64,3072] val_89
      float[batch,64,768] val_90
      float[batch,64,2304] val_91
      float[batch,64,768] val_92
      float[batch,64,3072] val_93
      float[batch,64,768] val_94
      float[batch,64,2304] val_95
      float[batch,64,768] val_96
      float[batch,64,3072] val_97
      float[batch,64,768] val_98
      float[batch,64,2304] val_99
      float[batch,768,64] view
   >
{
   [pre_cast] image_f32 = Cast <to: int = 1> (image)
   [pre_nhwc_to_nchw] image_chw = Transpose <perm: ints = [0, 3, 1, 2]> (image_f32)
   [node_conv2d] conv2d = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [32, 32]> (image_chw, "visual.trunk.patch_embed.proj.weight", "visual.trunk.patch_embed.proj.bias")
   view = Reshape <allowzero: int = 1> (conv2d, view_target)
   [node_transpose] transpose = Transpose <perm: ints = [0, 2, 1]> (view)
   add_13 = Add (transpose, "visual.trunk.pos_embed")
   [node_layer_norm] layer_norm = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_13, "visual.trunk.blocks.0.norm1.weight", "visual.trunk.blocks.0.norm1.bias")
   val_55 = MatMul (layer_norm, val_3)
   [node_linear] linear = Add (val_55, "visual.trunk.blocks.0.attn.qkv.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> (linear, attn3d_split_3x768)
   [node_scaled_dot_product_attention] scaled_dot_product_attention = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, 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)
   val_56 = MatMul (scaled_dot_product_attention, val_4)
   linear_1 = Add (val_56, "visual.trunk.blocks.0.attn.proj.bias")
   add_78 = Add (add_13, linear_1)
   layer_norm_1 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_78, "visual.trunk.blocks.0.norm2.weight", "visual.trunk.blocks.0.norm2.bias")
   val_57 = MatMul (layer_norm_1, val_5)
   linear_2 = Add (val_57, "visual.trunk.blocks.0.mlp.fc1.bias")
   [node_gelu] gelu = Gelu <approximate: string = "tanh"> (linear_2)
   val_58 = MatMul (gelu, val_6)
   linear_3 = Add (val_58, "visual.trunk.blocks.0.mlp.fc2.bias")
   add_107 = Add (add_78, linear_3)
   layer_norm_2 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_107, "visual.trunk.blocks.1.norm1.weight", "visual.trunk.blocks.1.norm1.bias")
   val_59 = MatMul (layer_norm_2, val_7)
   linear_4 = Add (val_59, "visual.trunk.blocks.1.attn.qkv.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> (linear_4, attn3d_split_3x768)
   scaled_dot_product_attention_1 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, 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)
   val_60 = MatMul (scaled_dot_product_attention_1, val_8)
   linear_5 = Add (val_60, "visual.trunk.blocks.1.attn.proj.bias")
   add_168 = Add (add_107, linear_5)
   layer_norm_3 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_168, "visual.trunk.blocks.1.norm2.weight", "visual.trunk.blocks.1.norm2.bias")
   val_61 = MatMul (layer_norm_3, val_9)
   linear_6 = Add (val_61, "visual.trunk.blocks.1.mlp.fc1.bias")
   gelu_1 = Gelu <approximate: string = "tanh"> (linear_6)
   val_62 = MatMul (gelu_1, val_10)
   linear_7 = Add (val_62, "visual.trunk.blocks.1.mlp.fc2.bias")
   add_197 = Add (add_168, linear_7)
   layer_norm_4 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_197, "visual.trunk.blocks.2.norm1.weight", "visual.trunk.blocks.2.norm1.bias")
   val_63 = MatMul (layer_norm_4, val_11)
   linear_8 = Add (val_63, "visual.trunk.blocks.2.attn.qkv.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> (linear_8, attn3d_split_3x768)
   scaled_dot_product_attention_2 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, 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)
   val_64 = MatMul (scaled_dot_product_attention_2, val_12)
   linear_9 = Add (val_64, "visual.trunk.blocks.2.attn.proj.bias")
   add_258 = Add (add_197, linear_9)
   layer_norm_5 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_258, "visual.trunk.blocks.2.norm2.weight", "visual.trunk.blocks.2.norm2.bias")
   val_65 = MatMul (layer_norm_5, val_13)
   linear_10 = Add (val_65, "visual.trunk.blocks.2.mlp.fc1.bias")
   gelu_2 = Gelu <approximate: string = "tanh"> (linear_10)
   val_66 = MatMul (gelu_2, val_14)
   linear_11 = Add (val_66, "visual.trunk.blocks.2.mlp.fc2.bias")
   add_287 = Add (add_258, linear_11)
   layer_norm_6 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_287, "visual.trunk.blocks.3.norm1.weight", "visual.trunk.blocks.3.norm1.bias")
   val_67 = MatMul (layer_norm_6, val_15)
   linear_12 = Add (val_67, "visual.trunk.blocks.3.attn.qkv.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> (linear_12, attn3d_split_3x768)
   scaled_dot_product_attention_3 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, 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)
   val_68 = MatMul (scaled_dot_product_attention_3, val_16)
   linear_13 = Add (val_68, "visual.trunk.blocks.3.attn.proj.bias")
   add_348 = Add (add_287, linear_13)
   layer_norm_7 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_348, "visual.trunk.blocks.3.norm2.weight", "visual.trunk.blocks.3.norm2.bias")
   val_69 = MatMul (layer_norm_7, val_17)
   linear_14 = Add (val_69, "visual.trunk.blocks.3.mlp.fc1.bias")
   gelu_3 = Gelu <approximate: string = "tanh"> (linear_14)
   val_70 = MatMul (gelu_3, val_18)
   linear_15 = Add (val_70, "visual.trunk.blocks.3.mlp.fc2.bias")
   add_377 = Add (add_348, linear_15)
   layer_norm_8 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_377, "visual.trunk.blocks.4.norm1.weight", "visual.trunk.blocks.4.norm1.bias")
   val_71 = MatMul (layer_norm_8, val_19)
   linear_16 = Add (val_71, "visual.trunk.blocks.4.attn.qkv.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> (linear_16, attn3d_split_3x768)
   scaled_dot_product_attention_4 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, 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)
   val_72 = MatMul (scaled_dot_product_attention_4, val_20)
   linear_17 = Add (val_72, "visual.trunk.blocks.4.attn.proj.bias")
   add_438 = Add (add_377, linear_17)
   layer_norm_9 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_438, "visual.trunk.blocks.4.norm2.weight", "visual.trunk.blocks.4.norm2.bias")
   val_73 = MatMul (layer_norm_9, val_21)
   linear_18 = Add (val_73, "visual.trunk.blocks.4.mlp.fc1.bias")
   gelu_4 = Gelu <approximate: string = "tanh"> (linear_18)
   val_74 = MatMul (gelu_4, val_22)
   linear_19 = Add (val_74, "visual.trunk.blocks.4.mlp.fc2.bias")
   add_467 = Add (add_438, linear_19)
   layer_norm_10 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_467, "visual.trunk.blocks.5.norm1.weight", "visual.trunk.blocks.5.norm1.bias")
   val_75 = MatMul (layer_norm_10, val_23)
   linear_20 = Add (val_75, "visual.trunk.blocks.5.attn.qkv.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> (linear_20, attn3d_split_3x768)
   scaled_dot_product_attention_5 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, 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)
   val_76 = MatMul (scaled_dot_product_attention_5, val_24)
   linear_21 = Add (val_76, "visual.trunk.blocks.5.attn.proj.bias")
   add_528 = Add (add_467, linear_21)
   layer_norm_11 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_528, "visual.trunk.blocks.5.norm2.weight", "visual.trunk.blocks.5.norm2.bias")
   val_77 = MatMul (layer_norm_11, val_25)
   linear_22 = Add (val_77, "visual.trunk.blocks.5.mlp.fc1.bias")
   gelu_5 = Gelu <approximate: string = "tanh"> (linear_22)
   val_78 = MatMul (gelu_5, val_26)
   linear_23 = Add (val_78, "visual.trunk.blocks.5.mlp.fc2.bias")
   add_557 = Add (add_528, linear_23)
   layer_norm_12 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_557, "visual.trunk.blocks.6.norm1.weight", "visual.trunk.blocks.6.norm1.bias")
   val_79 = MatMul (layer_norm_12, val_27)
   linear_24 = Add (val_79, "visual.trunk.blocks.6.attn.qkv.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> (linear_24, attn3d_split_3x768)
   scaled_dot_product_attention_6 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, 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)
   val_80 = MatMul (scaled_dot_product_attention_6, val_28)
   linear_25 = Add (val_80, "visual.trunk.blocks.6.attn.proj.bias")
   add_618 = Add (add_557, linear_25)
   layer_norm_13 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_618, "visual.trunk.blocks.6.norm2.weight", "visual.trunk.blocks.6.norm2.bias")
   val_81 = MatMul (layer_norm_13, val_29)
   linear_26 = Add (val_81, "visual.trunk.blocks.6.mlp.fc1.bias")
   gelu_6 = Gelu <approximate: string = "tanh"> (linear_26)
   val_82 = MatMul (gelu_6, val_30)
   linear_27 = Add (val_82, "visual.trunk.blocks.6.mlp.fc2.bias")
   add_647 = Add (add_618, linear_27)
   layer_norm_14 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_647, "visual.trunk.blocks.7.norm1.weight", "visual.trunk.blocks.7.norm1.bias")
   val_83 = MatMul (layer_norm_14, val_31)
   linear_28 = Add (val_83, "visual.trunk.blocks.7.attn.qkv.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> (linear_28, attn3d_split_3x768)
   scaled_dot_product_attention_7 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, 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)
   val_84 = MatMul (scaled_dot_product_attention_7, val_32)
   linear_29 = Add (val_84, "visual.trunk.blocks.7.attn.proj.bias")
   add_708 = Add (add_647, linear_29)
   layer_norm_15 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_708, "visual.trunk.blocks.7.norm2.weight", "visual.trunk.blocks.7.norm2.bias")
   val_85 = MatMul (layer_norm_15, val_33)
   linear_30 = Add (val_85, "visual.trunk.blocks.7.mlp.fc1.bias")
   gelu_7 = Gelu <approximate: string = "tanh"> (linear_30)
   val_86 = MatMul (gelu_7, val_34)
   linear_31 = Add (val_86, "visual.trunk.blocks.7.mlp.fc2.bias")
   add_737 = Add (add_708, linear_31)
   layer_norm_16 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_737, "visual.trunk.blocks.8.norm1.weight", "visual.trunk.blocks.8.norm1.bias")
   val_87 = MatMul (layer_norm_16, val_35)
   linear_32 = Add (val_87, "visual.trunk.blocks.8.attn.qkv.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> (linear_32, attn3d_split_3x768)
   scaled_dot_product_attention_8 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, 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)
   val_88 = MatMul (scaled_dot_product_attention_8, val_36)
   linear_33 = Add (val_88, "visual.trunk.blocks.8.attn.proj.bias")
   add_798 = Add (add_737, linear_33)
   layer_norm_17 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_798, "visual.trunk.blocks.8.norm2.weight", "visual.trunk.blocks.8.norm2.bias")
   val_89 = MatMul (layer_norm_17, val_37)
   linear_34 = Add (val_89, "visual.trunk.blocks.8.mlp.fc1.bias")
   gelu_8 = Gelu <approximate: string = "tanh"> (linear_34)
   val_90 = MatMul (gelu_8, val_38)
   linear_35 = Add (val_90, "visual.trunk.blocks.8.mlp.fc2.bias")
   add_827 = Add (add_798, linear_35)
   layer_norm_18 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_827, "visual.trunk.blocks.9.norm1.weight", "visual.trunk.blocks.9.norm1.bias")
   val_91 = MatMul (layer_norm_18, val_39)
   linear_36 = Add (val_91, "visual.trunk.blocks.9.attn.qkv.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> (linear_36, attn3d_split_3x768)
   scaled_dot_product_attention_9 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, 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)
   val_92 = MatMul (scaled_dot_product_attention_9, val_40)
   linear_37 = Add (val_92, "visual.trunk.blocks.9.attn.proj.bias")
   add_888 = Add (add_827, linear_37)
   layer_norm_19 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_888, "visual.trunk.blocks.9.norm2.weight", "visual.trunk.blocks.9.norm2.bias")
   val_93 = MatMul (layer_norm_19, val_41)
   linear_38 = Add (val_93, "visual.trunk.blocks.9.mlp.fc1.bias")
   gelu_9 = Gelu <approximate: string = "tanh"> (linear_38)
   val_94 = MatMul (gelu_9, val_42)
   linear_39 = Add (val_94, "visual.trunk.blocks.9.mlp.fc2.bias")
   add_917 = Add (add_888, linear_39)
   layer_norm_20 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_917, "visual.trunk.blocks.10.norm1.weight", "visual.trunk.blocks.10.norm1.bias")
   val_95 = MatMul (layer_norm_20, val_43)
   linear_40 = Add (val_95, "visual.trunk.blocks.10.attn.qkv.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> (linear_40, attn3d_split_3x768)
   scaled_dot_product_attention_10 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, 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)
   val_96 = MatMul (scaled_dot_product_attention_10, val_44)
   linear_41 = Add (val_96, "visual.trunk.blocks.10.attn.proj.bias")
   add_978 = Add (add_917, linear_41)
   layer_norm_21 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_978, "visual.trunk.blocks.10.norm2.weight", "visual.trunk.blocks.10.norm2.bias")
   val_97 = MatMul (layer_norm_21, val_45)
   linear_42 = Add (val_97, "visual.trunk.blocks.10.mlp.fc1.bias")
   gelu_10 = Gelu <approximate: string = "tanh"> (linear_42)
   val_98 = MatMul (gelu_10, val_46)
   linear_43 = Add (val_98, "visual.trunk.blocks.10.mlp.fc2.bias")
   add_1007 = Add (add_978, linear_43)
   layer_norm_22 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1007, "visual.trunk.blocks.11.norm1.weight", "visual.trunk.blocks.11.norm1.bias")
   val_99 = MatMul (layer_norm_22, val_47)
   linear_44 = Add (val_99, "visual.trunk.blocks.11.attn.qkv.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> (linear_44, attn3d_split_3x768)
   scaled_dot_product_attention_11 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, 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)
   val_100 = MatMul (scaled_dot_product_attention_11, val_48)
   linear_45 = Add (val_100, "visual.trunk.blocks.11.attn.proj.bias")
   add_1068 = Add (add_1007, linear_45)
   layer_norm_23 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1068, "visual.trunk.blocks.11.norm2.weight", "visual.trunk.blocks.11.norm2.bias")
   val_101 = MatMul (layer_norm_23, val_49)
   linear_46 = Add (val_101, "visual.trunk.blocks.11.mlp.fc1.bias")
   gelu_11 = Gelu <approximate: string = "tanh"> (linear_46)
   val_102 = MatMul (gelu_11, val_50)
   linear_47 = Add (val_102, "visual.trunk.blocks.11.mlp.fc2.bias")
   add_1097 = Add (add_1068, linear_47)
   layer_norm_24 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (add_1097, "visual.trunk.norm.weight", "visual.trunk.norm.bias")
   [ez_slice] image_ez_s = Slice (image_f32, image_ez_starts, image_ez_ends, image_ez_axes)
   [ez_flatten] image_ez_r = Reshape (image_ez_s, val_0)
   [ez_mul] image_ez = Mul (image_ez_r, image_ez_zero)
   val_103 = MatMul (layer_norm_24, val_51)
   linear_49 = Add (val_103, "visual.trunk.attn_pool.kv.bias")
   [node_scaled_dot_product_attention_12_qkv_split] node_scaled_dot_product_attention_12_k, node_scaled_dot_product_attention_12_v = Split <axis: int = -1> (linear_49, attn3d_split_2x768)
   [node_scaled_dot_product_attention_12_q_col] node_scaled_dot_product_attention_12_q_col_out = Unsqueeze (image_ez, node_scaled_dot_product_attention_12_q_col_axes)
   [node_scaled_dot_product_attention_12_q_bcast] node_scaled_dot_product_attention_12_q = Add (node_scaled_dot_product_attention_12_q3, node_scaled_dot_product_attention_12_q_col_out)
   scaled_dot_product_attention_12 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, 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)
   val_104 = MatMul (scaled_dot_product_attention_12, val_52)
   linear_50 = Add (val_104, "visual.trunk.attn_pool.proj.bias")
   layer_norm_25 = LayerNormalization <axis: int = -1, epsilon: float = 1e-06, stash_type: int = 1> (linear_50, "visual.trunk.attn_pool.norm.weight", "visual.trunk.attn_pool.norm.bias")
   val_105 = MatMul (layer_norm_25, val_53)
   linear_51 = Add (val_105, "visual.trunk.attn_pool.mlp.fc1.bias")
   gelu_12 = Gelu <approximate: string = "tanh"> (linear_51)
   val_106 = MatMul (gelu_12, val_54)
   linear_52 = Add (val_106, "visual.trunk.attn_pool.mlp.fc2.bias")
   add_1195 = Add (linear_50, linear_52)
   select = Squeeze (add_1195, val_2)
   [node_linalg_vector_norm] linalg_vector_norm = ReduceL2 <keepdims: int = 1, noop_with_empty_axes: int = 0> (select, val_0)
   [node_clamp_min] clamp_min = Clip (linalg_vector_norm, val_1)
   [node_div] image_embedding = Div (select, clamp_min)
}

weights:
attn3d_split_2x768 INT64[2] 6b8b40015c71
attn3d_split_3x768 INT64[3] eca50b2daf34
image_ez_axes INT64[3] e2e2033ae7e1
image_ez_ends INT64[3] 605390e5a369
image_ez_starts INT64[3] 9d908ecfb6b2
image_ez_zero FLOAT[] df3f619804a9
node_scaled_dot_product_attention_12_q3 FLOAT[1,1,768] bda9722f9bdc
node_scaled_dot_product_attention_12_q_col_axes INT64[2] 0c730b69905c
val_0 INT64[1] 12a3ae445661
val_1 FLOAT[] 6708d9be4956
val_10 FLOAT[3072,768] 9f43c5634115
val_11 FLOAT[768,2304] 1309461ea3fa
val_12 FLOAT[768,768] 9a4214e4b976
val_13 FLOAT[768,3072] c29a91aa31d4
val_14 FLOAT[3072,768] 4f1dfe6f6ecd
val_15 FLOAT[768,2304] a1864854abe2
val_16 FLOAT[768,768] de9e59734aaa
val_17 FLOAT[768,3072] df4168dbd39c
val_18 FLOAT[3072,768] af914e29c6ab
val_19 FLOAT[768,2304] 183bcc459292
val_2 INT64[1] 7c9fa136d441
val_20 FLOAT[768,768] 850680662625
val_21 FLOAT[768,3072] e3de08c7b68a
val_22 FLOAT[3072,768] 15124e004991
val_23 FLOAT[768,2304] 613aeb54e7fd
val_24 FLOAT[768,768] 167368f7bbd4
val_25 FLOAT[768,3072] 711029aca159
val_26 FLOAT[3072,768] 6da7afdcef02
val_27 FLOAT[768,2304] 0081a5d58081
val_28 FLOAT[768,768] f3e1c474c77d
val_29 FLOAT[768,3072] a56a572c5368
val_3 FLOAT[768,2304] ff1ebc3c3210
val_30 FLOAT[3072,768] 03cb1082093b
val_31 FLOAT[768,2304] fb5546de4177
val_32 FLOAT[768,768] 4764fa740b50
val_33 FLOAT[768,3072] 0df94103a5d1
val_34 FLOAT[3072,768] d806e881bf1d
val_35 FLOAT[768,2304] 40e88ce8f358
val_36 FLOAT[768,768] 39433c860a49
val_37 FLOAT[768,3072] 5c0b354f2115
val_38 FLOAT[3072,768] 989a98f84bad
val_39 FLOAT[768,2304] 4ad0ef356440
val_4 FLOAT[768,768] c4d4fd036cc6
val_40 FLOAT[768,768] 76e62145f632
val_41 FLOAT[768,3072] ee850dc2e5fb
val_42 FLOAT[3072,768] 23ae694cff16
val_43 FLOAT[768,2304] f0ebb7462d70
val_44 FLOAT[768,768] 33607b34623b
val_45 FLOAT[768,3072] 9959b0ddca62
val_46 FLOAT[3072,768] 0290ca0d170f
val_47 FLOAT[768,2304] 0b6d36e38492
val_48 FLOAT[768,768] bb8fc25506cb
val_49 FLOAT[768,3072] c05b657ac915
val_5 FLOAT[768,3072] 80904515daca
val_50 FLOAT[3072,768] 747139cde8f5
val_51 FLOAT[768,1536] 6c93209fdad4
val_52 FLOAT[768,768] b0315f67b3ef
val_53 FLOAT[768,3072] 82fc951ff927
val_54 FLOAT[3072,768] 12a9d0eb3fa1
val_6 FLOAT[3072,768] 881713839b7d
val_7 FLOAT[768,2304] c9a0c42122f8
val_8 FLOAT[768,768] 6c1bbe3df017
val_9 FLOAT[768,3072] 42b63bec76cd
view_target INT64[3] 288b66080dd3
visual.trunk.attn_pool.kv.bias FLOAT[1536] 604b069237eb
visual.trunk.attn_pool.mlp.fc1.bias FLOAT[3072] 15a5e1e335bc
visual.trunk.attn_pool.mlp.fc2.bias FLOAT[768] 1a095ef28681
visual.trunk.attn_pool.norm.bias FLOAT[768] 5b17db6ba977
visual.trunk.attn_pool.norm.weight FLOAT[768] c16efe78ed6d
visual.trunk.attn_pool.proj.bias FLOAT[768] 9df13752d2c2
visual.trunk.blocks.0.attn.proj.bias FLOAT[768] cd2a1583a685
visual.trunk.blocks.0.attn.qkv.bias FLOAT[2304] ff0e027664ec
visual.trunk.blocks.0.mlp.fc1.bias FLOAT[3072] cd31fad95e94
visual.trunk.blocks.0.mlp.fc2.bias FLOAT[768] 4d4ad7810a30
visual.trunk.blocks.0.norm1.bias FLOAT[768] 9b9ce079e3b3
visual.trunk.blocks.0.norm1.weight FLOAT[768] fcc299cf1c4f
visual.trunk.blocks.0.norm2.bias FLOAT[768] e7e1259c33a9
visual.trunk.blocks.0.norm2.weight FLOAT[768] a493c089b41a
visual.trunk.blocks.1.attn.proj.bias FLOAT[768] be38352fbad5
visual.trunk.blocks.1.attn.qkv.bias FLOAT[2304] de4eb7c9b904
visual.trunk.blocks.1.mlp.fc1.bias FLOAT[3072] e62416bc3c35
visual.trunk.blocks.1.mlp.fc2.bias FLOAT[768] 5b8b0471c423
visual.trunk.blocks.1.norm1.bias FLOAT[768] 92a4080789b6
visual.trunk.blocks.1.norm1.weight FLOAT[768] 26ab161d0298
visual.trunk.blocks.1.norm2.bias FLOAT[768] 16cd88abaf63
visual.trunk.blocks.1.norm2.weight FLOAT[768] 8328bde54d8f
visual.trunk.blocks.10.attn.proj.bias FLOAT[768] 888c54e883b5
visual.trunk.blocks.10.attn.qkv.bias FLOAT[2304] 07dc85863e55
visual.trunk.blocks.10.mlp.fc1.bias FLOAT[3072] 07b0d9ba8e71
visual.trunk.blocks.10.mlp.fc2.bias FLOAT[768] 0c50e59d7b00
visual.trunk.blocks.10.norm1.bias FLOAT[768] 3bf738cc5b1f
visual.trunk.blocks.10.norm1.weight FLOAT[768] 67bb651ec993
visual.trunk.blocks.10.norm2.bias FLOAT[768] 71755d6dce19
visual.trunk.blocks.10.norm2.weight FLOAT[768] e26ee969c3e2
visual.trunk.blocks.11.attn.proj.bias FLOAT[768] 56c8ff181379
visual.trunk.blocks.11.attn.qkv.bias FLOAT[2304] 16c93c391e59
visual.trunk.blocks.11.mlp.fc1.bias FLOAT[3072] 1c5470dd827d
visual.trunk.blocks.11.mlp.fc2.bias FLOAT[768] f8c0d57ce41a
visual.trunk.blocks.11.norm1.bias FLOAT[768] 5350b58341d4
visual.trunk.blocks.11.norm1.weight FLOAT[768] ab6db058ba9e
visual.trunk.blocks.11.norm2.bias FLOAT[768] 7f386788fddd
visual.trunk.blocks.11.norm2.weight FLOAT[768] 2212fc48f125
visual.trunk.blocks.2.attn.proj.bias FLOAT[768] b1c7ae99f4bc
visual.trunk.blocks.2.attn.qkv.bias FLOAT[2304] cceaef89d085
visual.trunk.blocks.2.mlp.fc1.bias FLOAT[3072] 7a70e7b070fd
visual.trunk.blocks.2.mlp.fc2.bias FLOAT[768] 34b1b3effaa3
visual.trunk.blocks.2.norm1.bias FLOAT[768] f3afbd8354da
visual.trunk.blocks.2.norm1.weight FLOAT[768] 57fa66ea869b
visual.trunk.blocks.2.norm2.bias FLOAT[768] 777196519d97
visual.trunk.blocks.2.norm2.weight FLOAT[768] 54e9fa3b2de7
visual.trunk.blocks.3.attn.proj.bias FLOAT[768] db91b01dd600
visual.trunk.blocks.3.attn.qkv.bias FLOAT[2304] e90f80823831
visual.trunk.blocks.3.mlp.fc1.bias FLOAT[3072] 174a9344bf03
visual.trunk.blocks.3.mlp.fc2.bias FLOAT[768] f62cb3886876
visual.trunk.blocks.3.norm1.bias FLOAT[768] 93f2af2c34e6
visual.trunk.blocks.3.norm1.weight FLOAT[768] 9e8219dc765a
visual.trunk.blocks.3.norm2.bias FLOAT[768] d6d195533bc2
visual.trunk.blocks.3.norm2.weight FLOAT[768] 4e0cf077c7c4
visual.trunk.blocks.4.attn.proj.bias FLOAT[768] 3d379cc247d2
visual.trunk.blocks.4.attn.qkv.bias FLOAT[2304] 55e5ed8f78d6
visual.trunk.blocks.4.mlp.fc1.bias FLOAT[3072] 53131341eb49
visual.trunk.blocks.4.mlp.fc2.bias FLOAT[768] b874144be54d
visual.trunk.blocks.4.norm1.bias FLOAT[768] 4d38255cf4ed
visual.trunk.blocks.4.norm1.weight FLOAT[768] bda526e57396
visual.trunk.blocks.4.norm2.bias FLOAT[768] 1c6f59d31409
visual.trunk.blocks.4.norm2.weight FLOAT[768] 891a8de87d99
visual.trunk.blocks.5.attn.proj.bias FLOAT[768] 8f7b6bff1596
visual.trunk.blocks.5.attn.qkv.bias FLOAT[2304] 2a2085c18e34
visual.trunk.blocks.5.mlp.fc1.bias FLOAT[3072] cd9a7ea86ae4
visual.trunk.blocks.5.mlp.fc2.bias FLOAT[768] 2c68c18b2133
visual.trunk.blocks.5.norm1.bias FLOAT[768] ecf0a54c5daa
visual.trunk.blocks.5.norm1.weight FLOAT[768] 7ca80cd5f95b
visual.trunk.blocks.5.norm2.bias FLOAT[768] 1dcc9998c2b9
visual.trunk.blocks.5.norm2.weight FLOAT[768] 0484ac51dce3
visual.trunk.blocks.6.attn.proj.bias FLOAT[768] 8e3d1dff2a7e
visual.trunk.blocks.6.attn.qkv.bias FLOAT[2304] f4854b33404c
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visual.trunk.blocks.6.mlp.fc2.bias FLOAT[768] d3a2aa90f847
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