program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
{
    func main<ios18>(tensor<int32, [1, 256]> attention_mask, tensor<int32, [1, 256]> input_ids) {
            tensor<fp32, [64402, 1024]> base_model_embed_tokens_weight = const()[name = string("base_model_embed_tokens_weight"), val = tensor<fp32, [64402, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
            tensor<fp32, [1024]> base_model_layers_0_operator_norm_weight = const()[name = string("base_model_layers_0_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263790720)))];
            tensor<fp32, [3072, 1024]> base_model_layers_0_conv_in_proj_weight = const()[name = string("base_model_layers_0_conv_in_proj_weight"), val = tensor<fp32, [3072, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263794880)))];
            tensor<fp32, [1024, 1, 3]> base_model_layers_0_conv_conv_weight = const()[name = string("base_model_layers_0_conv_conv_weight"), val = tensor<fp32, [1024, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276377856)))];
            tensor<fp32, [1024, 1024]> base_model_layers_0_conv_out_proj_weight = const()[name = string("base_model_layers_0_conv_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276390208)))];
            tensor<fp32, [1024]> base_model_layers_0_ffn_norm_weight = const()[name = string("base_model_layers_0_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280584576)))];
            tensor<fp32, [4608, 1024]> base_model_layers_0_feed_forward_w1_weight = const()[name = string("base_model_layers_0_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280588736)))];
            tensor<fp32, [4608, 1024]> base_model_layers_0_feed_forward_w3_weight = const()[name = string("base_model_layers_0_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299463168)))];
            tensor<fp32, [1024, 4608]> base_model_layers_0_feed_forward_w2_weight = const()[name = string("base_model_layers_0_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318337600)))];
            tensor<fp32, [1024]> base_model_layers_1_operator_norm_weight = const()[name = string("base_model_layers_1_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337212032)))];
            tensor<fp32, [3072, 1024]> base_model_layers_1_conv_in_proj_weight = const()[name = string("base_model_layers_1_conv_in_proj_weight"), val = tensor<fp32, [3072, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337216192)))];
            tensor<fp32, [1024, 1, 3]> base_model_layers_1_conv_conv_weight = const()[name = string("base_model_layers_1_conv_conv_weight"), val = tensor<fp32, [1024, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349799168)))];
            tensor<fp32, [1024, 1024]> base_model_layers_1_conv_out_proj_weight = const()[name = string("base_model_layers_1_conv_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349811520)))];
            tensor<fp32, [1024]> base_model_layers_1_ffn_norm_weight = const()[name = string("base_model_layers_1_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354005888)))];
            tensor<fp32, [4608, 1024]> base_model_layers_1_feed_forward_w1_weight = const()[name = string("base_model_layers_1_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354010048)))];
            tensor<fp32, [4608, 1024]> base_model_layers_1_feed_forward_w3_weight = const()[name = string("base_model_layers_1_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372884480)))];
            tensor<fp32, [1024, 4608]> base_model_layers_1_feed_forward_w2_weight = const()[name = string("base_model_layers_1_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391758912)))];
            tensor<fp32, [1024]> base_model_layers_2_operator_norm_weight = const()[name = string("base_model_layers_2_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410633344)))];
            tensor<fp32, [1024, 1024]> base_model_layers_2_self_attn_q_proj_weight = const()[name = string("base_model_layers_2_self_attn_q_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410637504)))];
            tensor<fp32, [64]> base_model_layers_2_self_attn_q_layernorm_weight = const()[name = string("base_model_layers_2_self_attn_q_layernorm_weight"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(414831872)))];
            tensor<fp32, [512, 1024]> base_model_layers_2_self_attn_k_proj_weight = const()[name = string("base_model_layers_2_self_attn_k_proj_weight"), val = tensor<fp32, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(414832192)))];
            tensor<fp32, [64]> base_model_layers_2_self_attn_k_layernorm_weight = const()[name = string("base_model_layers_2_self_attn_k_layernorm_weight"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416929408)))];
            tensor<fp32, [512, 1024]> base_model_layers_2_self_attn_v_proj_weight = const()[name = string("base_model_layers_2_self_attn_v_proj_weight"), val = tensor<fp32, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416929728)))];
            tensor<fp32, [1024, 1024]> base_model_layers_2_self_attn_out_proj_weight = const()[name = string("base_model_layers_2_self_attn_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419026944)))];
            tensor<fp32, [1024]> base_model_layers_2_ffn_norm_weight = const()[name = string("base_model_layers_2_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423221312)))];
            tensor<fp32, [4608, 1024]> base_model_layers_2_feed_forward_w1_weight = const()[name = string("base_model_layers_2_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423225472)))];
            tensor<fp32, [4608, 1024]> base_model_layers_2_feed_forward_w3_weight = const()[name = string("base_model_layers_2_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442099904)))];
            tensor<fp32, [1024, 4608]> base_model_layers_2_feed_forward_w2_weight = const()[name = string("base_model_layers_2_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460974336)))];
            tensor<fp32, [1024]> base_model_layers_3_operator_norm_weight = const()[name = string("base_model_layers_3_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479848768)))];
            tensor<fp32, [3072, 1024]> base_model_layers_3_conv_in_proj_weight = const()[name = string("base_model_layers_3_conv_in_proj_weight"), val = tensor<fp32, [3072, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479852928)))];
            tensor<fp32, [1024, 1, 3]> base_model_layers_3_conv_conv_weight = const()[name = string("base_model_layers_3_conv_conv_weight"), val = tensor<fp32, [1024, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492435904)))];
            tensor<fp32, [1024, 1024]> base_model_layers_3_conv_out_proj_weight = const()[name = string("base_model_layers_3_conv_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492448256)))];
            tensor<fp32, [1024]> base_model_layers_3_ffn_norm_weight = const()[name = string("base_model_layers_3_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(496642624)))];
            tensor<fp32, [4608, 1024]> base_model_layers_3_feed_forward_w1_weight = const()[name = string("base_model_layers_3_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(496646784)))];
            tensor<fp32, [4608, 1024]> base_model_layers_3_feed_forward_w3_weight = const()[name = string("base_model_layers_3_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(515521216)))];
            tensor<fp32, [1024, 4608]> base_model_layers_3_feed_forward_w2_weight = const()[name = string("base_model_layers_3_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534395648)))];
            tensor<fp32, [1024]> base_model_layers_4_operator_norm_weight = const()[name = string("base_model_layers_4_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553270080)))];
            tensor<fp32, [3072, 1024]> base_model_layers_4_conv_in_proj_weight = const()[name = string("base_model_layers_4_conv_in_proj_weight"), val = tensor<fp32, [3072, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553274240)))];
            tensor<fp32, [1024, 1, 3]> base_model_layers_4_conv_conv_weight = const()[name = string("base_model_layers_4_conv_conv_weight"), val = tensor<fp32, [1024, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(565857216)))];
            tensor<fp32, [1024, 1024]> base_model_layers_4_conv_out_proj_weight = const()[name = string("base_model_layers_4_conv_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(565869568)))];
            tensor<fp32, [1024]> base_model_layers_4_ffn_norm_weight = const()[name = string("base_model_layers_4_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570063936)))];
            tensor<fp32, [4608, 1024]> base_model_layers_4_feed_forward_w1_weight = const()[name = string("base_model_layers_4_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570068096)))];
            tensor<fp32, [4608, 1024]> base_model_layers_4_feed_forward_w3_weight = const()[name = string("base_model_layers_4_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(588942528)))];
            tensor<fp32, [1024, 4608]> base_model_layers_4_feed_forward_w2_weight = const()[name = string("base_model_layers_4_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(607816960)))];
            tensor<fp32, [1024]> base_model_layers_5_operator_norm_weight = const()[name = string("base_model_layers_5_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(626691392)))];
            tensor<fp32, [1024, 1024]> base_model_layers_5_self_attn_q_proj_weight = const()[name = string("base_model_layers_5_self_attn_q_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(626695552)))];
            tensor<fp32, [64]> base_model_layers_5_self_attn_q_layernorm_weight = const()[name = string("base_model_layers_5_self_attn_q_layernorm_weight"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630889920)))];
            tensor<fp32, [512, 1024]> base_model_layers_5_self_attn_k_proj_weight = const()[name = string("base_model_layers_5_self_attn_k_proj_weight"), val = tensor<fp32, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630890240)))];
            tensor<fp32, [64]> base_model_layers_5_self_attn_k_layernorm_weight = const()[name = string("base_model_layers_5_self_attn_k_layernorm_weight"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(632987456)))];
            tensor<fp32, [512, 1024]> base_model_layers_5_self_attn_v_proj_weight = const()[name = string("base_model_layers_5_self_attn_v_proj_weight"), val = tensor<fp32, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(632987776)))];
            tensor<fp32, [1024, 1024]> base_model_layers_5_self_attn_out_proj_weight = const()[name = string("base_model_layers_5_self_attn_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(635084992)))];
            tensor<fp32, [1024]> base_model_layers_5_ffn_norm_weight = const()[name = string("base_model_layers_5_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(639279360)))];
            tensor<fp32, [4608, 1024]> base_model_layers_5_feed_forward_w1_weight = const()[name = string("base_model_layers_5_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(639283520)))];
            tensor<fp32, [4608, 1024]> base_model_layers_5_feed_forward_w3_weight = const()[name = string("base_model_layers_5_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(658157952)))];
            tensor<fp32, [1024, 4608]> base_model_layers_5_feed_forward_w2_weight = const()[name = string("base_model_layers_5_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(677032384)))];
            tensor<fp32, [1024]> base_model_layers_6_operator_norm_weight = const()[name = string("base_model_layers_6_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(695906816)))];
            tensor<fp32, [3072, 1024]> base_model_layers_6_conv_in_proj_weight = const()[name = string("base_model_layers_6_conv_in_proj_weight"), val = tensor<fp32, [3072, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(695910976)))];
            tensor<fp32, [1024, 1, 3]> base_model_layers_6_conv_conv_weight = const()[name = string("base_model_layers_6_conv_conv_weight"), val = tensor<fp32, [1024, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(708493952)))];
            tensor<fp32, [1024, 1024]> base_model_layers_6_conv_out_proj_weight = const()[name = string("base_model_layers_6_conv_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(708506304)))];
            tensor<fp32, [1024]> base_model_layers_6_ffn_norm_weight = const()[name = string("base_model_layers_6_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(712700672)))];
            tensor<fp32, [4608, 1024]> base_model_layers_6_feed_forward_w1_weight = const()[name = string("base_model_layers_6_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(712704832)))];
            tensor<fp32, [4608, 1024]> base_model_layers_6_feed_forward_w3_weight = const()[name = string("base_model_layers_6_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(731579264)))];
            tensor<fp32, [1024, 4608]> base_model_layers_6_feed_forward_w2_weight = const()[name = string("base_model_layers_6_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(750453696)))];
            tensor<fp32, [1024]> base_model_layers_7_operator_norm_weight = const()[name = string("base_model_layers_7_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(769328128)))];
            tensor<fp32, [3072, 1024]> base_model_layers_7_conv_in_proj_weight = const()[name = string("base_model_layers_7_conv_in_proj_weight"), val = tensor<fp32, [3072, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(769332288)))];
            tensor<fp32, [1024, 1, 3]> base_model_layers_7_conv_conv_weight = const()[name = string("base_model_layers_7_conv_conv_weight"), val = tensor<fp32, [1024, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(781915264)))];
            tensor<fp32, [1024, 1024]> base_model_layers_7_conv_out_proj_weight = const()[name = string("base_model_layers_7_conv_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(781927616)))];
            tensor<fp32, [1024]> base_model_layers_7_ffn_norm_weight = const()[name = string("base_model_layers_7_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786121984)))];
            tensor<fp32, [4608, 1024]> base_model_layers_7_feed_forward_w1_weight = const()[name = string("base_model_layers_7_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786126144)))];
            tensor<fp32, [4608, 1024]> base_model_layers_7_feed_forward_w3_weight = const()[name = string("base_model_layers_7_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(805000576)))];
            tensor<fp32, [1024, 4608]> base_model_layers_7_feed_forward_w2_weight = const()[name = string("base_model_layers_7_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(823875008)))];
            tensor<fp32, [1024]> base_model_layers_8_operator_norm_weight = const()[name = string("base_model_layers_8_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(842749440)))];
            tensor<fp32, [1024, 1024]> base_model_layers_8_self_attn_q_proj_weight = const()[name = string("base_model_layers_8_self_attn_q_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(842753600)))];
            tensor<fp32, [64]> base_model_layers_8_self_attn_q_layernorm_weight = const()[name = string("base_model_layers_8_self_attn_q_layernorm_weight"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(846947968)))];
            tensor<fp32, [512, 1024]> base_model_layers_8_self_attn_k_proj_weight = const()[name = string("base_model_layers_8_self_attn_k_proj_weight"), val = tensor<fp32, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(846948288)))];
            tensor<fp32, [64]> base_model_layers_8_self_attn_k_layernorm_weight = const()[name = string("base_model_layers_8_self_attn_k_layernorm_weight"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(849045504)))];
            tensor<fp32, [512, 1024]> base_model_layers_8_self_attn_v_proj_weight = const()[name = string("base_model_layers_8_self_attn_v_proj_weight"), val = tensor<fp32, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(849045824)))];
            tensor<fp32, [1024, 1024]> base_model_layers_8_self_attn_out_proj_weight = const()[name = string("base_model_layers_8_self_attn_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(851143040)))];
            tensor<fp32, [1024]> base_model_layers_8_ffn_norm_weight = const()[name = string("base_model_layers_8_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855337408)))];
            tensor<fp32, [4608, 1024]> base_model_layers_8_feed_forward_w1_weight = const()[name = string("base_model_layers_8_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855341568)))];
            tensor<fp32, [4608, 1024]> base_model_layers_8_feed_forward_w3_weight = const()[name = string("base_model_layers_8_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(874216000)))];
            tensor<fp32, [1024, 4608]> base_model_layers_8_feed_forward_w2_weight = const()[name = string("base_model_layers_8_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(893090432)))];
            tensor<fp32, [1024]> base_model_layers_9_operator_norm_weight = const()[name = string("base_model_layers_9_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911964864)))];
            tensor<fp32, [3072, 1024]> base_model_layers_9_conv_in_proj_weight = const()[name = string("base_model_layers_9_conv_in_proj_weight"), val = tensor<fp32, [3072, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911969024)))];
            tensor<fp32, [1024, 1, 3]> base_model_layers_9_conv_conv_weight = const()[name = string("base_model_layers_9_conv_conv_weight"), val = tensor<fp32, [1024, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(924552000)))];
            tensor<fp32, [1024, 1024]> base_model_layers_9_conv_out_proj_weight = const()[name = string("base_model_layers_9_conv_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(924564352)))];
            tensor<fp32, [1024]> base_model_layers_9_ffn_norm_weight = const()[name = string("base_model_layers_9_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(928758720)))];
            tensor<fp32, [4608, 1024]> base_model_layers_9_feed_forward_w1_weight = const()[name = string("base_model_layers_9_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(928762880)))];
            tensor<fp32, [4608, 1024]> base_model_layers_9_feed_forward_w3_weight = const()[name = string("base_model_layers_9_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(947637312)))];
            tensor<fp32, [1024, 4608]> base_model_layers_9_feed_forward_w2_weight = const()[name = string("base_model_layers_9_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(966511744)))];
            tensor<fp32, [1024]> base_model_layers_10_operator_norm_weight = const()[name = string("base_model_layers_10_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(985386176)))];
            tensor<fp32, [1024, 1024]> base_model_layers_10_self_attn_q_proj_weight = const()[name = string("base_model_layers_10_self_attn_q_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(985390336)))];
            tensor<fp32, [64]> base_model_layers_10_self_attn_q_layernorm_weight = const()[name = string("base_model_layers_10_self_attn_q_layernorm_weight"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(989584704)))];
            tensor<fp32, [512, 1024]> base_model_layers_10_self_attn_k_proj_weight = const()[name = string("base_model_layers_10_self_attn_k_proj_weight"), val = tensor<fp32, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(989585024)))];
            tensor<fp32, [64]> base_model_layers_10_self_attn_k_layernorm_weight = const()[name = string("base_model_layers_10_self_attn_k_layernorm_weight"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(991682240)))];
            tensor<fp32, [512, 1024]> base_model_layers_10_self_attn_v_proj_weight = const()[name = string("base_model_layers_10_self_attn_v_proj_weight"), val = tensor<fp32, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(991682560)))];
            tensor<fp32, [1024, 1024]> base_model_layers_10_self_attn_out_proj_weight = const()[name = string("base_model_layers_10_self_attn_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993779776)))];
            tensor<fp32, [1024]> base_model_layers_10_ffn_norm_weight = const()[name = string("base_model_layers_10_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(997974144)))];
            tensor<fp32, [4608, 1024]> base_model_layers_10_feed_forward_w1_weight = const()[name = string("base_model_layers_10_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(997978304)))];
            tensor<fp32, [4608, 1024]> base_model_layers_10_feed_forward_w3_weight = const()[name = string("base_model_layers_10_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1016852736)))];
            tensor<fp32, [1024, 4608]> base_model_layers_10_feed_forward_w2_weight = const()[name = string("base_model_layers_10_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1035727168)))];
            tensor<fp32, [1024]> base_model_layers_11_operator_norm_weight = const()[name = string("base_model_layers_11_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1054601600)))];
            tensor<fp32, [3072, 1024]> base_model_layers_11_conv_in_proj_weight = const()[name = string("base_model_layers_11_conv_in_proj_weight"), val = tensor<fp32, [3072, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1054605760)))];
            tensor<fp32, [1024, 1, 3]> base_model_layers_11_conv_conv_weight = const()[name = string("base_model_layers_11_conv_conv_weight"), val = tensor<fp32, [1024, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1067188736)))];
            tensor<fp32, [1024, 1024]> base_model_layers_11_conv_out_proj_weight = const()[name = string("base_model_layers_11_conv_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1067201088)))];
            tensor<fp32, [1024]> base_model_layers_11_ffn_norm_weight = const()[name = string("base_model_layers_11_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1071395456)))];
            tensor<fp32, [4608, 1024]> base_model_layers_11_feed_forward_w1_weight = const()[name = string("base_model_layers_11_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1071399616)))];
            tensor<fp32, [4608, 1024]> base_model_layers_11_feed_forward_w3_weight = const()[name = string("base_model_layers_11_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1090274048)))];
            tensor<fp32, [1024, 4608]> base_model_layers_11_feed_forward_w2_weight = const()[name = string("base_model_layers_11_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1109148480)))];
            tensor<fp32, [1024]> base_model_layers_12_operator_norm_weight = const()[name = string("base_model_layers_12_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1128022912)))];
            tensor<fp32, [1024, 1024]> base_model_layers_12_self_attn_q_proj_weight = const()[name = string("base_model_layers_12_self_attn_q_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1128027072)))];
            tensor<fp32, [64]> base_model_layers_12_self_attn_q_layernorm_weight = const()[name = string("base_model_layers_12_self_attn_q_layernorm_weight"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1132221440)))];
            tensor<fp32, [512, 1024]> base_model_layers_12_self_attn_k_proj_weight = const()[name = string("base_model_layers_12_self_attn_k_proj_weight"), val = tensor<fp32, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1132221760)))];
            tensor<fp32, [64]> base_model_layers_12_self_attn_k_layernorm_weight = const()[name = string("base_model_layers_12_self_attn_k_layernorm_weight"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1134318976)))];
            tensor<fp32, [512, 1024]> base_model_layers_12_self_attn_v_proj_weight = const()[name = string("base_model_layers_12_self_attn_v_proj_weight"), val = tensor<fp32, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1134319296)))];
            tensor<fp32, [1024, 1024]> base_model_layers_12_self_attn_out_proj_weight = const()[name = string("base_model_layers_12_self_attn_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136416512)))];
            tensor<fp32, [1024]> base_model_layers_12_ffn_norm_weight = const()[name = string("base_model_layers_12_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1140610880)))];
            tensor<fp32, [4608, 1024]> base_model_layers_12_feed_forward_w1_weight = const()[name = string("base_model_layers_12_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1140615040)))];
            tensor<fp32, [4608, 1024]> base_model_layers_12_feed_forward_w3_weight = const()[name = string("base_model_layers_12_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1159489472)))];
            tensor<fp32, [1024, 4608]> base_model_layers_12_feed_forward_w2_weight = const()[name = string("base_model_layers_12_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1178363904)))];
            tensor<fp32, [1024]> base_model_layers_13_operator_norm_weight = const()[name = string("base_model_layers_13_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1197238336)))];
            tensor<fp32, [3072, 1024]> base_model_layers_13_conv_in_proj_weight = const()[name = string("base_model_layers_13_conv_in_proj_weight"), val = tensor<fp32, [3072, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1197242496)))];
            tensor<fp32, [1024, 1, 3]> base_model_layers_13_conv_conv_weight = const()[name = string("base_model_layers_13_conv_conv_weight"), val = tensor<fp32, [1024, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1209825472)))];
            tensor<fp32, [1024, 1024]> base_model_layers_13_conv_out_proj_weight = const()[name = string("base_model_layers_13_conv_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1209837824)))];
            tensor<fp32, [1024]> base_model_layers_13_ffn_norm_weight = const()[name = string("base_model_layers_13_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214032192)))];
            tensor<fp32, [4608, 1024]> base_model_layers_13_feed_forward_w1_weight = const()[name = string("base_model_layers_13_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214036352)))];
            tensor<fp32, [4608, 1024]> base_model_layers_13_feed_forward_w3_weight = const()[name = string("base_model_layers_13_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1232910784)))];
            tensor<fp32, [1024, 4608]> base_model_layers_13_feed_forward_w2_weight = const()[name = string("base_model_layers_13_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1251785216)))];
            tensor<fp32, [1024]> base_model_layers_14_operator_norm_weight = const()[name = string("base_model_layers_14_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1270659648)))];
            tensor<fp32, [1024, 1024]> base_model_layers_14_self_attn_q_proj_weight = const()[name = string("base_model_layers_14_self_attn_q_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1270663808)))];
            tensor<fp32, [64]> base_model_layers_14_self_attn_q_layernorm_weight = const()[name = string("base_model_layers_14_self_attn_q_layernorm_weight"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274858176)))];
            tensor<fp32, [512, 1024]> base_model_layers_14_self_attn_k_proj_weight = const()[name = string("base_model_layers_14_self_attn_k_proj_weight"), val = tensor<fp32, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274858496)))];
            tensor<fp32, [64]> base_model_layers_14_self_attn_k_layernorm_weight = const()[name = string("base_model_layers_14_self_attn_k_layernorm_weight"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1276955712)))];
            tensor<fp32, [512, 1024]> base_model_layers_14_self_attn_v_proj_weight = const()[name = string("base_model_layers_14_self_attn_v_proj_weight"), val = tensor<fp32, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1276956032)))];
            tensor<fp32, [1024, 1024]> base_model_layers_14_self_attn_out_proj_weight = const()[name = string("base_model_layers_14_self_attn_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1279053248)))];
            tensor<fp32, [1024]> base_model_layers_14_ffn_norm_weight = const()[name = string("base_model_layers_14_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1283247616)))];
            tensor<fp32, [4608, 1024]> base_model_layers_14_feed_forward_w1_weight = const()[name = string("base_model_layers_14_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1283251776)))];
            tensor<fp32, [4608, 1024]> base_model_layers_14_feed_forward_w3_weight = const()[name = string("base_model_layers_14_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1302126208)))];
            tensor<fp32, [1024, 4608]> base_model_layers_14_feed_forward_w2_weight = const()[name = string("base_model_layers_14_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1321000640)))];
            tensor<fp32, [1024]> base_model_layers_15_operator_norm_weight = const()[name = string("base_model_layers_15_operator_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1339875072)))];
            tensor<fp32, [3072, 1024]> base_model_layers_15_conv_in_proj_weight = const()[name = string("base_model_layers_15_conv_in_proj_weight"), val = tensor<fp32, [3072, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1339879232)))];
            tensor<fp32, [1024, 1, 3]> base_model_layers_15_conv_conv_weight = const()[name = string("base_model_layers_15_conv_conv_weight"), val = tensor<fp32, [1024, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352462208)))];
            tensor<fp32, [1024, 1024]> base_model_layers_15_conv_out_proj_weight = const()[name = string("base_model_layers_15_conv_out_proj_weight"), val = tensor<fp32, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352474560)))];
            tensor<fp32, [1024]> base_model_layers_15_ffn_norm_weight = const()[name = string("base_model_layers_15_ffn_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1356668928)))];
            tensor<fp32, [4608, 1024]> base_model_layers_15_feed_forward_w1_weight = const()[name = string("base_model_layers_15_feed_forward_w1_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1356673088)))];
            tensor<fp32, [4608, 1024]> base_model_layers_15_feed_forward_w3_weight = const()[name = string("base_model_layers_15_feed_forward_w3_weight"), val = tensor<fp32, [4608, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1375547520)))];
            tensor<fp32, [1024, 4608]> base_model_layers_15_feed_forward_w2_weight = const()[name = string("base_model_layers_15_feed_forward_w2_weight"), val = tensor<fp32, [1024, 4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1394421952)))];
            tensor<fp32, [1024]> base_model_embedding_norm_weight = const()[name = string("base_model_embedding_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413296384)))];
            tensor<fp32, [128, 1024]> projection_weight = const()[name = string("projection_weight"), val = tensor<fp32, [128, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413300544)))];
            fp32 var_15 = const()[name = string("op_15"), val = fp32(0x1p-3)];
            int32 var_26 = const()[name = string("op_26"), val = int32(-1)];
            int32 inputs_embeds_batch_dims_0 = const()[name = string("inputs_embeds_batch_dims_0"), val = int32(0)];
            bool inputs_embeds_validate_indices_0 = const()[name = string("inputs_embeds_validate_indices_0"), val = bool(false)];
            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
            tensor<bool, [1, 256]> greater_equal_0 = greater_equal(x = input_ids, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
            int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(64402)];
            tensor<int32, [1, 256]> add_6 = add(x = input_ids, y = slice_by_index_0)[name = string("add_6")];
            tensor<int32, [1, 256]> select_0 = select(a = input_ids, b = add_6, cond = greater_equal_0)[name = string("select_0")];
            int32 inputs_embeds_axis_1 = const()[name = string("inputs_embeds_axis_1"), val = int32(0)];
            tensor<fp32, [1, 256, 1024]> inputs_embeds = gather(axis = inputs_embeds_axis_1, batch_dims = inputs_embeds_batch_dims_0, indices = select_0, validate_indices = inputs_embeds_validate_indices_0, x = base_model_embed_tokens_weight)[name = string("inputs_embeds")];
            string attention_mask_5_dtype_0 = const()[name = string("attention_mask_5_dtype_0"), val = string("bool")];
            string cast_1_dtype_0 = const()[name = string("cast_1_dtype_0"), val = string("int8")];
            int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)];
            bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)];
            tensor<int32, [256]> select_1 = const()[name = string("select_1"), val = tensor<int32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413824896)))];
            int32 gather_0_axis_1 = const()[name = string("gather_0_axis_1"), val = int32(1)];
            tensor<bool, [1, 256]> attention_mask_5 = cast(dtype = attention_mask_5_dtype_0, x = attention_mask)[name = string("cast_56")];
            tensor<int8, [1, 256]> cast_1 = cast(dtype = cast_1_dtype_0, x = attention_mask_5)[name = string("cast_55")];
            tensor<int8, [1, 256]> gather_0 = gather(axis = gather_0_axis_1, batch_dims = gather_0_batch_dims_0, indices = select_1, validate_indices = gather_0_validate_indices_0, x = cast_1)[name = string("gather_0")];
            string padding_mask_dtype_0 = const()[name = string("padding_mask_dtype_0"), val = string("bool")];
            tensor<bool, [1, 1, 256, 256]> var_93 = const()[name = string("op_93"), val = tensor<bool, [1, 1, 256, 256]>([[[[true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false], [true, true, false, false, false, false, false, false, false, false, false, false, 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true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true]]]])];
            tensor<int32, [1]> var_99_axes_0 = const()[name = string("op_99_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, [1, 256]> padding_mask = cast(dtype = padding_mask_dtype_0, x = gather_0)[name = string("cast_54")];
            tensor<bool, [1, 1, 256]> var_99 = expand_dims(axes = var_99_axes_0, x = padding_mask)[name = string("op_99")];
            tensor<int32, [1]> var_100_axes_0 = const()[name = string("op_100_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<bool, [1, 1, 1, 256]> var_100 = expand_dims(axes = var_100_axes_0, x = var_99)[name = string("op_100")];
            tensor<bool, [1, 1, 256, 256]> attention_mask_7 = logical_and(x = var_93, y = var_100)[name = string("attention_mask_7")];
            fp32 var_25_promoted = const()[name = string("op_25_promoted"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_139 = pow(x = inputs_embeds, y = var_25_promoted)[name = string("op_139")];
            tensor<int32, [1]> variance_1_axes_0 = const()[name = string("variance_1_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_1_keep_dims_0 = const()[name = string("variance_1_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_1 = reduce_mean(axes = variance_1_axes_0, keep_dims = variance_1_keep_dims_0, x = var_139)[name = string("variance_1")];
            fp32 var_142 = const()[name = string("op_142"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_143 = add(x = variance_1, y = var_142)[name = string("op_143")];
            fp32 var_144_epsilon_0 = const()[name = string("op_144_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_144 = rsqrt(epsilon = var_144_epsilon_0, x = var_143)[name = string("op_144")];
            tensor<fp32, [1, 256, 1024]> hidden_states_3 = mul(x = inputs_embeds, y = var_144)[name = string("hidden_states_3")];
            tensor<fp32, [1, 256, 1024]> x_1 = mul(x = base_model_layers_0_operator_norm_weight, y = hidden_states_3)[name = string("x_1")];
            tensor<fp32, [3072]> linear_0_bias_0 = const()[name = string("linear_0_bias_0"), val = tensor<fp32, [3072]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413825984)))];
            tensor<fp32, [1, 256, 3072]> var_156 = linear(bias = linear_0_bias_0, weight = base_model_layers_0_conv_in_proj_weight, x = x_1)[name = string("linear_0")];
            tensor<int32, [3]> BCx_1_perm_0 = const()[name = string("BCx_1_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [3]> var_158_split_sizes_0 = const()[name = string("op_158_split_sizes_0"), val = tensor<int32, [3]>([1024, 1024, 1024])];
            int32 var_158_axis_0 = const()[name = string("op_158_axis_0"), val = int32(-2)];
            tensor<fp32, [1, 3072, 256]> BCx_1 = transpose(perm = BCx_1_perm_0, x = var_156)[name = string("transpose_43")];
            tensor<fp32, [1, 1024, 256]> var_158_0, tensor<fp32, [1, 1024, 256]> var_158_1, tensor<fp32, [1, 1024, 256]> var_158_2 = split(axis = var_158_axis_0, split_sizes = var_158_split_sizes_0, x = BCx_1)[name = string("op_158")];
            tensor<fp32, [1, 1024, 256]> input_3 = mul(x = var_158_0, y = var_158_2)[name = string("input_3")];
            string var_167_pad_type_0 = const()[name = string("op_167_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> var_167_pad_0 = const()[name = string("op_167_pad_0"), val = tensor<int32, [2]>([2, 2])];
            int32 var_167_groups_0 = const()[name = string("op_167_groups_0"), val = int32(1024)];
            tensor<int32, [1]> var_167_strides_0 = const()[name = string("op_167_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_167_dilations_0 = const()[name = string("op_167_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1024, 258]> var_167 = conv(dilations = var_167_dilations_0, groups = var_167_groups_0, pad = var_167_pad_0, pad_type = var_167_pad_type_0, strides = var_167_strides_0, weight = base_model_layers_0_conv_conv_weight, x = input_3)[name = string("op_167")];
            tensor<int32, [3]> conv_out_1_begin_0 = const()[name = string("conv_out_1_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> conv_out_1_end_0 = const()[name = string("conv_out_1_end_0"), val = tensor<int32, [3]>([1, 1024, 256])];
            tensor<bool, [3]> conv_out_1_end_mask_0 = const()[name = string("conv_out_1_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 1024, 256]> conv_out_1 = slice_by_index(begin = conv_out_1_begin_0, end = conv_out_1_end_0, end_mask = conv_out_1_end_mask_0, x = var_167)[name = string("conv_out_1")];
            tensor<fp32, [1, 1024, 256]> y_1 = mul(x = var_158_1, y = conv_out_1)[name = string("y_1")];
            tensor<int32, [3]> var_170_perm_0 = const()[name = string("op_170_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<fp32, [1024]> linear_1_bias_0 = const()[name = string("linear_1_bias_0"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413838336)))];
            tensor<fp32, [1, 256, 1024]> var_170 = transpose(perm = var_170_perm_0, x = y_1)[name = string("transpose_42")];
            tensor<fp32, [1, 256, 1024]> hidden_states_7 = linear(bias = linear_1_bias_0, weight = base_model_layers_0_conv_out_proj_weight, x = var_170)[name = string("linear_1")];
            tensor<fp32, [1, 256, 1024]> hidden_states_9 = add(x = hidden_states_7, y = inputs_embeds)[name = string("hidden_states_9")];
            fp32 var_25_promoted_1 = const()[name = string("op_25_promoted_1"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_177 = pow(x = hidden_states_9, y = var_25_promoted_1)[name = string("op_177")];
            tensor<int32, [1]> variance_3_axes_0 = const()[name = string("variance_3_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_3_keep_dims_0 = const()[name = string("variance_3_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_3 = reduce_mean(axes = variance_3_axes_0, keep_dims = variance_3_keep_dims_0, x = var_177)[name = string("variance_3")];
            fp32 var_180 = const()[name = string("op_180"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_181 = add(x = variance_3, y = var_180)[name = string("op_181")];
            fp32 var_182_epsilon_0 = const()[name = string("op_182_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_182 = rsqrt(epsilon = var_182_epsilon_0, x = var_181)[name = string("op_182")];
            tensor<fp32, [1, 256, 1024]> hidden_states_13 = mul(x = hidden_states_9, y = var_182)[name = string("hidden_states_13")];
            tensor<fp32, [1, 256, 1024]> input_7 = mul(x = base_model_layers_0_ffn_norm_weight, y = hidden_states_13)[name = string("input_7")];
            tensor<fp32, [4608]> linear_2_bias_0 = const()[name = string("linear_2_bias_0"), val = tensor<fp32, [4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413842496)))];
            tensor<fp32, [1, 256, 4608]> input_9 = linear(bias = linear_2_bias_0, weight = base_model_layers_0_feed_forward_w1_weight, x = input_7)[name = string("linear_2")];
            tensor<fp32, [1, 256, 4608]> var_194 = silu(x = input_9)[name = string("op_194")];
            tensor<fp32, [1, 256, 4608]> var_196 = linear(bias = linear_2_bias_0, weight = base_model_layers_0_feed_forward_w3_weight, x = input_7)[name = string("linear_3")];
            tensor<fp32, [1, 256, 4608]> input_11 = mul(x = var_194, y = var_196)[name = string("input_11")];
            tensor<fp32, [1, 256, 1024]> var_199 = linear(bias = linear_1_bias_0, weight = base_model_layers_0_feed_forward_w2_weight, x = input_11)[name = string("linear_4")];
            tensor<fp32, [1, 256, 1024]> hidden_states_17 = add(x = hidden_states_9, y = var_199)[name = string("hidden_states_17")];
            fp32 var_25_promoted_2 = const()[name = string("op_25_promoted_2"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_207 = pow(x = hidden_states_17, y = var_25_promoted_2)[name = string("op_207")];
            tensor<int32, [1]> variance_5_axes_0 = const()[name = string("variance_5_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_5_keep_dims_0 = const()[name = string("variance_5_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_5 = reduce_mean(axes = variance_5_axes_0, keep_dims = variance_5_keep_dims_0, x = var_207)[name = string("variance_5")];
            fp32 var_210 = const()[name = string("op_210"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_211 = add(x = variance_5, y = var_210)[name = string("op_211")];
            fp32 var_212_epsilon_0 = const()[name = string("op_212_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_212 = rsqrt(epsilon = var_212_epsilon_0, x = var_211)[name = string("op_212")];
            tensor<fp32, [1, 256, 1024]> hidden_states_21 = mul(x = hidden_states_17, y = var_212)[name = string("hidden_states_21")];
            tensor<fp32, [1, 256, 1024]> x_5 = mul(x = base_model_layers_1_operator_norm_weight, y = hidden_states_21)[name = string("x_5")];
            tensor<fp32, [1, 256, 3072]> var_224 = linear(bias = linear_0_bias_0, weight = base_model_layers_1_conv_in_proj_weight, x = x_5)[name = string("linear_5")];
            tensor<int32, [3]> BCx_3_perm_0 = const()[name = string("BCx_3_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [3]> var_226_split_sizes_0 = const()[name = string("op_226_split_sizes_0"), val = tensor<int32, [3]>([1024, 1024, 1024])];
            int32 var_226_axis_0 = const()[name = string("op_226_axis_0"), val = int32(-2)];
            tensor<fp32, [1, 3072, 256]> BCx_3 = transpose(perm = BCx_3_perm_0, x = var_224)[name = string("transpose_41")];
            tensor<fp32, [1, 1024, 256]> var_226_0, tensor<fp32, [1, 1024, 256]> var_226_1, tensor<fp32, [1, 1024, 256]> var_226_2 = split(axis = var_226_axis_0, split_sizes = var_226_split_sizes_0, x = BCx_3)[name = string("op_226")];
            tensor<fp32, [1, 1024, 256]> input_13 = mul(x = var_226_0, y = var_226_2)[name = string("input_13")];
            string var_235_pad_type_0 = const()[name = string("op_235_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> var_235_pad_0 = const()[name = string("op_235_pad_0"), val = tensor<int32, [2]>([2, 2])];
            int32 var_235_groups_0 = const()[name = string("op_235_groups_0"), val = int32(1024)];
            tensor<int32, [1]> var_235_strides_0 = const()[name = string("op_235_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_235_dilations_0 = const()[name = string("op_235_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1024, 258]> var_235 = conv(dilations = var_235_dilations_0, groups = var_235_groups_0, pad = var_235_pad_0, pad_type = var_235_pad_type_0, strides = var_235_strides_0, weight = base_model_layers_1_conv_conv_weight, x = input_13)[name = string("op_235")];
            tensor<int32, [3]> conv_out_3_begin_0 = const()[name = string("conv_out_3_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> conv_out_3_end_0 = const()[name = string("conv_out_3_end_0"), val = tensor<int32, [3]>([1, 1024, 256])];
            tensor<bool, [3]> conv_out_3_end_mask_0 = const()[name = string("conv_out_3_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 1024, 256]> conv_out_3 = slice_by_index(begin = conv_out_3_begin_0, end = conv_out_3_end_0, end_mask = conv_out_3_end_mask_0, x = var_235)[name = string("conv_out_3")];
            tensor<fp32, [1, 1024, 256]> y_3 = mul(x = var_226_1, y = conv_out_3)[name = string("y_3")];
            tensor<int32, [3]> var_238_perm_0 = const()[name = string("op_238_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<fp32, [1, 256, 1024]> var_238 = transpose(perm = var_238_perm_0, x = y_3)[name = string("transpose_40")];
            tensor<fp32, [1, 256, 1024]> hidden_states_25 = linear(bias = linear_1_bias_0, weight = base_model_layers_1_conv_out_proj_weight, x = var_238)[name = string("linear_6")];
            tensor<fp32, [1, 256, 1024]> hidden_states_27 = add(x = hidden_states_25, y = hidden_states_17)[name = string("hidden_states_27")];
            fp32 var_25_promoted_3 = const()[name = string("op_25_promoted_3"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_245 = pow(x = hidden_states_27, y = var_25_promoted_3)[name = string("op_245")];
            tensor<int32, [1]> variance_7_axes_0 = const()[name = string("variance_7_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_7_keep_dims_0 = const()[name = string("variance_7_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_7 = reduce_mean(axes = variance_7_axes_0, keep_dims = variance_7_keep_dims_0, x = var_245)[name = string("variance_7")];
            fp32 var_248 = const()[name = string("op_248"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_249 = add(x = variance_7, y = var_248)[name = string("op_249")];
            fp32 var_250_epsilon_0 = const()[name = string("op_250_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_250 = rsqrt(epsilon = var_250_epsilon_0, x = var_249)[name = string("op_250")];
            tensor<fp32, [1, 256, 1024]> hidden_states_31 = mul(x = hidden_states_27, y = var_250)[name = string("hidden_states_31")];
            tensor<fp32, [1, 256, 1024]> input_17 = mul(x = base_model_layers_1_ffn_norm_weight, y = hidden_states_31)[name = string("input_17")];
            tensor<fp32, [1, 256, 4608]> input_19 = linear(bias = linear_2_bias_0, weight = base_model_layers_1_feed_forward_w1_weight, x = input_17)[name = string("linear_7")];
            tensor<fp32, [1, 256, 4608]> var_262 = silu(x = input_19)[name = string("op_262")];
            tensor<fp32, [1, 256, 4608]> var_264 = linear(bias = linear_2_bias_0, weight = base_model_layers_1_feed_forward_w3_weight, x = input_17)[name = string("linear_8")];
            tensor<fp32, [1, 256, 4608]> input_21 = mul(x = var_262, y = var_264)[name = string("input_21")];
            tensor<fp32, [1, 256, 1024]> var_267 = linear(bias = linear_1_bias_0, weight = base_model_layers_1_feed_forward_w2_weight, x = input_21)[name = string("linear_9")];
            tensor<fp32, [1, 256, 1024]> hidden_states_35 = add(x = hidden_states_27, y = var_267)[name = string("hidden_states_35")];
            fp32 var_25_promoted_4 = const()[name = string("op_25_promoted_4"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_275 = pow(x = hidden_states_35, y = var_25_promoted_4)[name = string("op_275")];
            tensor<int32, [1]> variance_9_axes_0 = const()[name = string("variance_9_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_9_keep_dims_0 = const()[name = string("variance_9_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_9 = reduce_mean(axes = variance_9_axes_0, keep_dims = variance_9_keep_dims_0, x = var_275)[name = string("variance_9")];
            fp32 var_278 = const()[name = string("op_278"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_279 = add(x = variance_9, y = var_278)[name = string("op_279")];
            fp32 var_280_epsilon_0 = const()[name = string("op_280_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_280 = rsqrt(epsilon = var_280_epsilon_0, x = var_279)[name = string("op_280")];
            tensor<fp32, [1, 256, 1024]> hidden_states_39 = mul(x = hidden_states_35, y = var_280)[name = string("hidden_states_39")];
            tensor<fp32, [1, 256, 1024]> hidden_states_43 = mul(x = base_model_layers_2_operator_norm_weight, y = hidden_states_39)[name = string("hidden_states_43")];
            tensor<fp32, [1, 256, 1024]> var_296 = linear(bias = linear_1_bias_0, weight = base_model_layers_2_self_attn_q_proj_weight, x = hidden_states_43)[name = string("linear_10")];
            tensor<int32, [4]> var_297 = const()[name = string("op_297"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 16, 64]> hidden_states_45 = reshape(shape = var_297, x = var_296)[name = string("hidden_states_45")];
            fp32 var_25_promoted_5 = const()[name = string("op_25_promoted_5"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 16, 64]> var_301 = pow(x = hidden_states_45, y = var_25_promoted_5)[name = string("op_301")];
            tensor<int32, [1]> variance_11_axes_0 = const()[name = string("variance_11_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_11_keep_dims_0 = const()[name = string("variance_11_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 16, 1]> variance_11 = reduce_mean(axes = variance_11_axes_0, keep_dims = variance_11_keep_dims_0, x = var_301)[name = string("variance_11")];
            fp32 var_304 = const()[name = string("op_304"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 16, 1]> var_305 = add(x = variance_11, y = var_304)[name = string("op_305")];
            fp32 var_306_epsilon_0 = const()[name = string("op_306_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 16, 1]> var_306 = rsqrt(epsilon = var_306_epsilon_0, x = var_305)[name = string("op_306")];
            tensor<fp32, [1, 256, 16, 64]> hidden_states_49 = mul(x = hidden_states_45, y = var_306)[name = string("hidden_states_49")];
            tensor<fp32, [1, 256, 16, 64]> var_309 = mul(x = base_model_layers_2_self_attn_q_layernorm_weight, y = hidden_states_49)[name = string("op_309")];
            tensor<int32, [4]> q_1_perm_0 = const()[name = string("q_1_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [512]> linear_11_bias_0 = const()[name = string("linear_11_bias_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413860992)))];
            tensor<fp32, [1, 256, 512]> var_312 = linear(bias = linear_11_bias_0, weight = base_model_layers_2_self_attn_k_proj_weight, x = hidden_states_43)[name = string("linear_11")];
            tensor<int32, [4]> var_313 = const()[name = string("op_313"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 8, 64]> hidden_states_53 = reshape(shape = var_313, x = var_312)[name = string("hidden_states_53")];
            fp32 var_25_promoted_6 = const()[name = string("op_25_promoted_6"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 8, 64]> var_317 = pow(x = hidden_states_53, y = var_25_promoted_6)[name = string("op_317")];
            tensor<int32, [1]> variance_13_axes_0 = const()[name = string("variance_13_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_13_keep_dims_0 = const()[name = string("variance_13_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 8, 1]> variance_13 = reduce_mean(axes = variance_13_axes_0, keep_dims = variance_13_keep_dims_0, x = var_317)[name = string("variance_13")];
            fp32 var_320 = const()[name = string("op_320"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 8, 1]> var_321 = add(x = variance_13, y = var_320)[name = string("op_321")];
            fp32 var_322_epsilon_0 = const()[name = string("op_322_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 8, 1]> var_322 = rsqrt(epsilon = var_322_epsilon_0, x = var_321)[name = string("op_322")];
            tensor<fp32, [1, 256, 8, 64]> hidden_states_57 = mul(x = hidden_states_53, y = var_322)[name = string("hidden_states_57")];
            tensor<fp32, [1, 256, 8, 64]> var_325 = mul(x = base_model_layers_2_self_attn_k_layernorm_weight, y = hidden_states_57)[name = string("op_325")];
            tensor<int32, [4]> k_1_perm_0 = const()[name = string("k_1_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 256, 512]> var_328 = linear(bias = linear_11_bias_0, weight = base_model_layers_2_self_attn_v_proj_weight, x = hidden_states_43)[name = string("linear_12")];
            tensor<int32, [4]> var_329 = const()[name = string("op_329"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 8, 64]> var_330 = reshape(shape = var_329, x = var_328)[name = string("op_330")];
            tensor<int32, [4]> hidden_states_65_perm_0 = const()[name = string("hidden_states_65_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 1, 256, 64]> cos_5 = const()[name = string("cos_5"), val = tensor<fp32, [1, 1, 256, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413863104)))];
            tensor<fp32, [1, 1, 256, 64]> sin_5 = const()[name = string("sin_5"), val = tensor<fp32, [1, 1, 256, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413928704)))];
            tensor<fp32, [1, 16, 256, 64]> q_1 = transpose(perm = q_1_perm_0, x = var_309)[name = string("transpose_39")];
            tensor<fp32, [1, 16, 256, 64]> var_334 = mul(x = q_1, y = cos_5)[name = string("op_334")];
            tensor<int32, [4]> x1_1_begin_0 = const()[name = string("x1_1_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> x1_1_end_0 = const()[name = string("x1_1_end_0"), val = tensor<int32, [4]>([1, 16, 256, 32])];
            tensor<bool, [4]> x1_1_end_mask_0 = const()[name = string("x1_1_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 16, 256, 32]> x1_1 = slice_by_index(begin = x1_1_begin_0, end = x1_1_end_0, end_mask = x1_1_end_mask_0, x = q_1)[name = string("x1_1")];
            tensor<int32, [4]> x2_1_begin_0 = const()[name = string("x2_1_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])];
            tensor<int32, [4]> x2_1_end_0 = const()[name = string("x2_1_end_0"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<bool, [4]> x2_1_end_mask_0 = const()[name = string("x2_1_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 16, 256, 32]> x2_1 = slice_by_index(begin = x2_1_begin_0, end = x2_1_end_0, end_mask = x2_1_end_mask_0, x = q_1)[name = string("x2_1")];
            fp32 const_22_promoted = const()[name = string("const_22_promoted"), val = fp32(-0x1p+0)];
            tensor<fp32, [1, 16, 256, 32]> var_345 = mul(x = x2_1, y = const_22_promoted)[name = string("op_345")];
            bool var_347_interleave_0 = const()[name = string("op_347_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 64]> var_347 = concat(axis = var_26, interleave = var_347_interleave_0, values = (var_345, x1_1))[name = string("op_347")];
            tensor<fp32, [1, 16, 256, 64]> var_348 = mul(x = var_347, y = sin_5)[name = string("op_348")];
            tensor<fp32, [1, 16, 256, 64]> query_1 = add(x = var_334, y = var_348)[name = string("query_1")];
            tensor<fp32, [1, 8, 256, 64]> k_1 = transpose(perm = k_1_perm_0, x = var_325)[name = string("transpose_38")];
            tensor<fp32, [1, 8, 256, 64]> var_350 = mul(x = k_1, y = cos_5)[name = string("op_350")];
            tensor<int32, [4]> x1_3_begin_0 = const()[name = string("x1_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> x1_3_end_0 = const()[name = string("x1_3_end_0"), val = tensor<int32, [4]>([1, 8, 256, 32])];
            tensor<bool, [4]> x1_3_end_mask_0 = const()[name = string("x1_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 256, 32]> x1_3 = slice_by_index(begin = x1_3_begin_0, end = x1_3_end_0, end_mask = x1_3_end_mask_0, x = k_1)[name = string("x1_3")];
            tensor<int32, [4]> x2_3_begin_0 = const()[name = string("x2_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])];
            tensor<int32, [4]> x2_3_end_0 = const()[name = string("x2_3_end_0"), val = tensor<int32, [4]>([1, 8, 256, 64])];
            tensor<bool, [4]> x2_3_end_mask_0 = const()[name = string("x2_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 256, 32]> x2_3 = slice_by_index(begin = x2_3_begin_0, end = x2_3_end_0, end_mask = x2_3_end_mask_0, x = k_1)[name = string("x2_3")];
            fp32 const_25_promoted = const()[name = string("const_25_promoted"), val = fp32(-0x1p+0)];
            tensor<fp32, [1, 8, 256, 32]> var_361 = mul(x = x2_3, y = const_25_promoted)[name = string("op_361")];
            bool var_363_interleave_0 = const()[name = string("op_363_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 8, 256, 64]> var_363 = concat(axis = var_26, interleave = var_363_interleave_0, values = (var_361, x1_3))[name = string("op_363")];
            tensor<fp32, [1, 8, 256, 64]> var_364 = mul(x = var_363, y = sin_5)[name = string("op_364")];
            tensor<fp32, [1, 8, 256, 64]> hidden_states_61 = add(x = var_350, y = var_364)[name = string("hidden_states_61")];
            tensor<int32, [1]> var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp32, [1, 8, 1, 256, 64]> var_373 = expand_dims(axes = var_373_axes_0, x = hidden_states_61)[name = string("op_373")];
            tensor<int32, [5]> hidden_states_63_reps_0 = const()[name = string("hidden_states_63_reps_0"), val = tensor<int32, [5]>([1, 1, 2, 1, 1])];
            tensor<fp32, [1, 8, 2, 256, 64]> hidden_states_63 = tile(reps = hidden_states_63_reps_0, x = var_373)[name = string("hidden_states_63")];
            tensor<int32, [4]> var_381 = const()[name = string("op_381"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<fp32, [1, 16, 256, 64]> key_1 = reshape(shape = var_381, x = hidden_states_63)[name = string("key_1")];
            tensor<int32, [1]> var_390_axes_0 = const()[name = string("op_390_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp32, [1, 8, 256, 64]> hidden_states_65 = transpose(perm = hidden_states_65_perm_0, x = var_330)[name = string("transpose_37")];
            tensor<fp32, [1, 8, 1, 256, 64]> var_390 = expand_dims(axes = var_390_axes_0, x = hidden_states_65)[name = string("op_390")];
            tensor<int32, [5]> hidden_states_67_reps_0 = const()[name = string("hidden_states_67_reps_0"), val = tensor<int32, [5]>([1, 1, 2, 1, 1])];
            tensor<fp32, [1, 8, 2, 256, 64]> hidden_states_67 = tile(reps = hidden_states_67_reps_0, x = var_390)[name = string("hidden_states_67")];
            tensor<int32, [4]> var_398 = const()[name = string("op_398"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<fp32, [1, 16, 256, 64]> value_1 = reshape(shape = var_398, x = hidden_states_67)[name = string("value_1")];
            string cast_8_dtype_0 = const()[name = string("cast_8_dtype_0"), val = string("fp32")];
            fp32 sub_0_x_0 = const()[name = string("sub_0_x_0"), val = fp32(0x1p+0)];
            tensor<fp32, [1, 1, 256, 256]> cast_8 = cast(dtype = cast_8_dtype_0, x = attention_mask_7)[name = string("cast_53")];
            tensor<fp32, [1, 1, 256, 256]> sub_0 = sub(x = sub_0_x_0, y = cast_8)[name = string("sub_0")];
            fp32 mul_0_x_0 = const()[name = string("mul_0_x_0"), val = fp32(-0x1.d4cp+14)];
            tensor<fp32, [1, 1, 256, 256]> mul_0 = mul(x = mul_0_x_0, y = sub_0)[name = string("mul_0")];
            tensor<fp32, [1, 16, 256, 64]> mul_1 = mul(x = query_1, y = var_15)[name = string("mul_1")];
            bool matmul_0_transpose_y_0 = const()[name = string("matmul_0_transpose_y_0"), val = bool(true)];
            bool matmul_0_transpose_x_0 = const()[name = string("matmul_0_transpose_x_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 256]> matmul_0 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = mul_1, y = key_1)[name = string("matmul_0")];
            tensor<fp32, [1, 16, 256, 256]> add_0 = add(x = matmul_0, y = mul_0)[name = string("add_0")];
            int32 softmax_0_axis_0 = const()[name = string("softmax_0_axis_0"), val = int32(-1)];
            tensor<fp32, [1, 16, 256, 256]> softmax_0 = softmax(axis = softmax_0_axis_0, x = add_0)[name = string("softmax_0")];
            bool attn_output_1_transpose_x_0 = const()[name = string("attn_output_1_transpose_x_0"), val = bool(false)];
            bool attn_output_1_transpose_y_0 = const()[name = string("attn_output_1_transpose_y_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 64]> attn_output_1 = matmul(transpose_x = attn_output_1_transpose_x_0, transpose_y = attn_output_1_transpose_y_0, x = softmax_0, y = value_1)[name = string("attn_output_1")];
            tensor<int32, [4]> var_406_perm_0 = const()[name = string("op_406_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_408 = const()[name = string("op_408"), val = tensor<int32, [3]>([1, 256, -1])];
            tensor<fp32, [1, 256, 16, 64]> var_406 = transpose(perm = var_406_perm_0, x = attn_output_1)[name = string("transpose_36")];
            tensor<fp32, [1, 256, 1024]> var_409 = reshape(shape = var_408, x = var_406)[name = string("op_409")];
            tensor<fp32, [1, 256, 1024]> hidden_states_69 = linear(bias = linear_1_bias_0, weight = base_model_layers_2_self_attn_out_proj_weight, x = var_409)[name = string("linear_13")];
            tensor<fp32, [1, 256, 1024]> hidden_states_71 = add(x = hidden_states_69, y = hidden_states_35)[name = string("hidden_states_71")];
            fp32 var_25_promoted_7 = const()[name = string("op_25_promoted_7"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_416 = pow(x = hidden_states_71, y = var_25_promoted_7)[name = string("op_416")];
            tensor<int32, [1]> variance_15_axes_0 = const()[name = string("variance_15_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_15_keep_dims_0 = const()[name = string("variance_15_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_15 = reduce_mean(axes = variance_15_axes_0, keep_dims = variance_15_keep_dims_0, x = var_416)[name = string("variance_15")];
            fp32 var_419 = const()[name = string("op_419"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_420 = add(x = variance_15, y = var_419)[name = string("op_420")];
            fp32 var_421_epsilon_0 = const()[name = string("op_421_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_421 = rsqrt(epsilon = var_421_epsilon_0, x = var_420)[name = string("op_421")];
            tensor<fp32, [1, 256, 1024]> hidden_states_75 = mul(x = hidden_states_71, y = var_421)[name = string("hidden_states_75")];
            tensor<fp32, [1, 256, 1024]> input_25 = mul(x = base_model_layers_2_ffn_norm_weight, y = hidden_states_75)[name = string("input_25")];
            tensor<fp32, [1, 256, 4608]> input_27 = linear(bias = linear_2_bias_0, weight = base_model_layers_2_feed_forward_w1_weight, x = input_25)[name = string("linear_14")];
            tensor<fp32, [1, 256, 4608]> var_433 = silu(x = input_27)[name = string("op_433")];
            tensor<fp32, [1, 256, 4608]> var_435 = linear(bias = linear_2_bias_0, weight = base_model_layers_2_feed_forward_w3_weight, x = input_25)[name = string("linear_15")];
            tensor<fp32, [1, 256, 4608]> input_29 = mul(x = var_433, y = var_435)[name = string("input_29")];
            tensor<fp32, [1, 256, 1024]> var_438 = linear(bias = linear_1_bias_0, weight = base_model_layers_2_feed_forward_w2_weight, x = input_29)[name = string("linear_16")];
            tensor<fp32, [1, 256, 1024]> hidden_states_79 = add(x = hidden_states_71, y = var_438)[name = string("hidden_states_79")];
            fp32 var_25_promoted_8 = const()[name = string("op_25_promoted_8"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_446 = pow(x = hidden_states_79, y = var_25_promoted_8)[name = string("op_446")];
            tensor<int32, [1]> variance_17_axes_0 = const()[name = string("variance_17_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_17_keep_dims_0 = const()[name = string("variance_17_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_17 = reduce_mean(axes = variance_17_axes_0, keep_dims = variance_17_keep_dims_0, x = var_446)[name = string("variance_17")];
            fp32 var_449 = const()[name = string("op_449"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_450 = add(x = variance_17, y = var_449)[name = string("op_450")];
            fp32 var_451_epsilon_0 = const()[name = string("op_451_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_451 = rsqrt(epsilon = var_451_epsilon_0, x = var_450)[name = string("op_451")];
            tensor<fp32, [1, 256, 1024]> hidden_states_83 = mul(x = hidden_states_79, y = var_451)[name = string("hidden_states_83")];
            tensor<fp32, [1, 256, 1024]> x_9 = mul(x = base_model_layers_3_operator_norm_weight, y = hidden_states_83)[name = string("x_9")];
            tensor<fp32, [1, 256, 3072]> var_463 = linear(bias = linear_0_bias_0, weight = base_model_layers_3_conv_in_proj_weight, x = x_9)[name = string("linear_17")];
            tensor<int32, [3]> BCx_5_perm_0 = const()[name = string("BCx_5_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [3]> var_465_split_sizes_0 = const()[name = string("op_465_split_sizes_0"), val = tensor<int32, [3]>([1024, 1024, 1024])];
            int32 var_465_axis_0 = const()[name = string("op_465_axis_0"), val = int32(-2)];
            tensor<fp32, [1, 3072, 256]> BCx_5 = transpose(perm = BCx_5_perm_0, x = var_463)[name = string("transpose_35")];
            tensor<fp32, [1, 1024, 256]> var_465_0, tensor<fp32, [1, 1024, 256]> var_465_1, tensor<fp32, [1, 1024, 256]> var_465_2 = split(axis = var_465_axis_0, split_sizes = var_465_split_sizes_0, x = BCx_5)[name = string("op_465")];
            tensor<fp32, [1, 1024, 256]> input_31 = mul(x = var_465_0, y = var_465_2)[name = string("input_31")];
            string var_474_pad_type_0 = const()[name = string("op_474_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> var_474_pad_0 = const()[name = string("op_474_pad_0"), val = tensor<int32, [2]>([2, 2])];
            int32 var_474_groups_0 = const()[name = string("op_474_groups_0"), val = int32(1024)];
            tensor<int32, [1]> var_474_strides_0 = const()[name = string("op_474_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_474_dilations_0 = const()[name = string("op_474_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1024, 258]> var_474 = conv(dilations = var_474_dilations_0, groups = var_474_groups_0, pad = var_474_pad_0, pad_type = var_474_pad_type_0, strides = var_474_strides_0, weight = base_model_layers_3_conv_conv_weight, x = input_31)[name = string("op_474")];
            tensor<int32, [3]> conv_out_5_begin_0 = const()[name = string("conv_out_5_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> conv_out_5_end_0 = const()[name = string("conv_out_5_end_0"), val = tensor<int32, [3]>([1, 1024, 256])];
            tensor<bool, [3]> conv_out_5_end_mask_0 = const()[name = string("conv_out_5_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 1024, 256]> conv_out_5 = slice_by_index(begin = conv_out_5_begin_0, end = conv_out_5_end_0, end_mask = conv_out_5_end_mask_0, x = var_474)[name = string("conv_out_5")];
            tensor<fp32, [1, 1024, 256]> y_5 = mul(x = var_465_1, y = conv_out_5)[name = string("y_5")];
            tensor<int32, [3]> var_477_perm_0 = const()[name = string("op_477_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<fp32, [1, 256, 1024]> var_477 = transpose(perm = var_477_perm_0, x = y_5)[name = string("transpose_34")];
            tensor<fp32, [1, 256, 1024]> hidden_states_87 = linear(bias = linear_1_bias_0, weight = base_model_layers_3_conv_out_proj_weight, x = var_477)[name = string("linear_18")];
            tensor<fp32, [1, 256, 1024]> hidden_states_89 = add(x = hidden_states_87, y = hidden_states_79)[name = string("hidden_states_89")];
            fp32 var_25_promoted_9 = const()[name = string("op_25_promoted_9"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_484 = pow(x = hidden_states_89, y = var_25_promoted_9)[name = string("op_484")];
            tensor<int32, [1]> variance_19_axes_0 = const()[name = string("variance_19_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_19_keep_dims_0 = const()[name = string("variance_19_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_19 = reduce_mean(axes = variance_19_axes_0, keep_dims = variance_19_keep_dims_0, x = var_484)[name = string("variance_19")];
            fp32 var_487 = const()[name = string("op_487"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_488 = add(x = variance_19, y = var_487)[name = string("op_488")];
            fp32 var_489_epsilon_0 = const()[name = string("op_489_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_489 = rsqrt(epsilon = var_489_epsilon_0, x = var_488)[name = string("op_489")];
            tensor<fp32, [1, 256, 1024]> hidden_states_93 = mul(x = hidden_states_89, y = var_489)[name = string("hidden_states_93")];
            tensor<fp32, [1, 256, 1024]> input_35 = mul(x = base_model_layers_3_ffn_norm_weight, y = hidden_states_93)[name = string("input_35")];
            tensor<fp32, [1, 256, 4608]> input_37 = linear(bias = linear_2_bias_0, weight = base_model_layers_3_feed_forward_w1_weight, x = input_35)[name = string("linear_19")];
            tensor<fp32, [1, 256, 4608]> var_501 = silu(x = input_37)[name = string("op_501")];
            tensor<fp32, [1, 256, 4608]> var_503 = linear(bias = linear_2_bias_0, weight = base_model_layers_3_feed_forward_w3_weight, x = input_35)[name = string("linear_20")];
            tensor<fp32, [1, 256, 4608]> input_39 = mul(x = var_501, y = var_503)[name = string("input_39")];
            tensor<fp32, [1, 256, 1024]> var_506 = linear(bias = linear_1_bias_0, weight = base_model_layers_3_feed_forward_w2_weight, x = input_39)[name = string("linear_21")];
            tensor<fp32, [1, 256, 1024]> hidden_states_97 = add(x = hidden_states_89, y = var_506)[name = string("hidden_states_97")];
            fp32 var_25_promoted_10 = const()[name = string("op_25_promoted_10"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_514 = pow(x = hidden_states_97, y = var_25_promoted_10)[name = string("op_514")];
            tensor<int32, [1]> variance_21_axes_0 = const()[name = string("variance_21_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_21_keep_dims_0 = const()[name = string("variance_21_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_21 = reduce_mean(axes = variance_21_axes_0, keep_dims = variance_21_keep_dims_0, x = var_514)[name = string("variance_21")];
            fp32 var_517 = const()[name = string("op_517"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_518 = add(x = variance_21, y = var_517)[name = string("op_518")];
            fp32 var_519_epsilon_0 = const()[name = string("op_519_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_519 = rsqrt(epsilon = var_519_epsilon_0, x = var_518)[name = string("op_519")];
            tensor<fp32, [1, 256, 1024]> hidden_states_101 = mul(x = hidden_states_97, y = var_519)[name = string("hidden_states_101")];
            tensor<fp32, [1, 256, 1024]> x_13 = mul(x = base_model_layers_4_operator_norm_weight, y = hidden_states_101)[name = string("x_13")];
            tensor<fp32, [1, 256, 3072]> var_531 = linear(bias = linear_0_bias_0, weight = base_model_layers_4_conv_in_proj_weight, x = x_13)[name = string("linear_22")];
            tensor<int32, [3]> BCx_7_perm_0 = const()[name = string("BCx_7_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [3]> var_533_split_sizes_0 = const()[name = string("op_533_split_sizes_0"), val = tensor<int32, [3]>([1024, 1024, 1024])];
            int32 var_533_axis_0 = const()[name = string("op_533_axis_0"), val = int32(-2)];
            tensor<fp32, [1, 3072, 256]> BCx_7 = transpose(perm = BCx_7_perm_0, x = var_531)[name = string("transpose_33")];
            tensor<fp32, [1, 1024, 256]> var_533_0, tensor<fp32, [1, 1024, 256]> var_533_1, tensor<fp32, [1, 1024, 256]> var_533_2 = split(axis = var_533_axis_0, split_sizes = var_533_split_sizes_0, x = BCx_7)[name = string("op_533")];
            tensor<fp32, [1, 1024, 256]> input_41 = mul(x = var_533_0, y = var_533_2)[name = string("input_41")];
            string var_542_pad_type_0 = const()[name = string("op_542_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> var_542_pad_0 = const()[name = string("op_542_pad_0"), val = tensor<int32, [2]>([2, 2])];
            int32 var_542_groups_0 = const()[name = string("op_542_groups_0"), val = int32(1024)];
            tensor<int32, [1]> var_542_strides_0 = const()[name = string("op_542_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_542_dilations_0 = const()[name = string("op_542_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1024, 258]> var_542 = conv(dilations = var_542_dilations_0, groups = var_542_groups_0, pad = var_542_pad_0, pad_type = var_542_pad_type_0, strides = var_542_strides_0, weight = base_model_layers_4_conv_conv_weight, x = input_41)[name = string("op_542")];
            tensor<int32, [3]> conv_out_7_begin_0 = const()[name = string("conv_out_7_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> conv_out_7_end_0 = const()[name = string("conv_out_7_end_0"), val = tensor<int32, [3]>([1, 1024, 256])];
            tensor<bool, [3]> conv_out_7_end_mask_0 = const()[name = string("conv_out_7_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 1024, 256]> conv_out_7 = slice_by_index(begin = conv_out_7_begin_0, end = conv_out_7_end_0, end_mask = conv_out_7_end_mask_0, x = var_542)[name = string("conv_out_7")];
            tensor<fp32, [1, 1024, 256]> y_7 = mul(x = var_533_1, y = conv_out_7)[name = string("y_7")];
            tensor<int32, [3]> var_545_perm_0 = const()[name = string("op_545_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<fp32, [1, 256, 1024]> var_545 = transpose(perm = var_545_perm_0, x = y_7)[name = string("transpose_32")];
            tensor<fp32, [1, 256, 1024]> hidden_states_105 = linear(bias = linear_1_bias_0, weight = base_model_layers_4_conv_out_proj_weight, x = var_545)[name = string("linear_23")];
            tensor<fp32, [1, 256, 1024]> hidden_states_107 = add(x = hidden_states_105, y = hidden_states_97)[name = string("hidden_states_107")];
            fp32 var_25_promoted_11 = const()[name = string("op_25_promoted_11"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_552 = pow(x = hidden_states_107, y = var_25_promoted_11)[name = string("op_552")];
            tensor<int32, [1]> variance_23_axes_0 = const()[name = string("variance_23_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_23_keep_dims_0 = const()[name = string("variance_23_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_23 = reduce_mean(axes = variance_23_axes_0, keep_dims = variance_23_keep_dims_0, x = var_552)[name = string("variance_23")];
            fp32 var_555 = const()[name = string("op_555"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_556 = add(x = variance_23, y = var_555)[name = string("op_556")];
            fp32 var_557_epsilon_0 = const()[name = string("op_557_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_557 = rsqrt(epsilon = var_557_epsilon_0, x = var_556)[name = string("op_557")];
            tensor<fp32, [1, 256, 1024]> hidden_states_111 = mul(x = hidden_states_107, y = var_557)[name = string("hidden_states_111")];
            tensor<fp32, [1, 256, 1024]> input_45 = mul(x = base_model_layers_4_ffn_norm_weight, y = hidden_states_111)[name = string("input_45")];
            tensor<fp32, [1, 256, 4608]> input_47 = linear(bias = linear_2_bias_0, weight = base_model_layers_4_feed_forward_w1_weight, x = input_45)[name = string("linear_24")];
            tensor<fp32, [1, 256, 4608]> var_569 = silu(x = input_47)[name = string("op_569")];
            tensor<fp32, [1, 256, 4608]> var_571 = linear(bias = linear_2_bias_0, weight = base_model_layers_4_feed_forward_w3_weight, x = input_45)[name = string("linear_25")];
            tensor<fp32, [1, 256, 4608]> input_49 = mul(x = var_569, y = var_571)[name = string("input_49")];
            tensor<fp32, [1, 256, 1024]> var_574 = linear(bias = linear_1_bias_0, weight = base_model_layers_4_feed_forward_w2_weight, x = input_49)[name = string("linear_26")];
            tensor<fp32, [1, 256, 1024]> hidden_states_115 = add(x = hidden_states_107, y = var_574)[name = string("hidden_states_115")];
            fp32 var_25_promoted_12 = const()[name = string("op_25_promoted_12"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_582 = pow(x = hidden_states_115, y = var_25_promoted_12)[name = string("op_582")];
            tensor<int32, [1]> variance_25_axes_0 = const()[name = string("variance_25_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_25_keep_dims_0 = const()[name = string("variance_25_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_25 = reduce_mean(axes = variance_25_axes_0, keep_dims = variance_25_keep_dims_0, x = var_582)[name = string("variance_25")];
            fp32 var_585 = const()[name = string("op_585"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_586 = add(x = variance_25, y = var_585)[name = string("op_586")];
            fp32 var_587_epsilon_0 = const()[name = string("op_587_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_587 = rsqrt(epsilon = var_587_epsilon_0, x = var_586)[name = string("op_587")];
            tensor<fp32, [1, 256, 1024]> hidden_states_119 = mul(x = hidden_states_115, y = var_587)[name = string("hidden_states_119")];
            tensor<fp32, [1, 256, 1024]> hidden_states_123 = mul(x = base_model_layers_5_operator_norm_weight, y = hidden_states_119)[name = string("hidden_states_123")];
            tensor<fp32, [1, 256, 1024]> var_603 = linear(bias = linear_1_bias_0, weight = base_model_layers_5_self_attn_q_proj_weight, x = hidden_states_123)[name = string("linear_27")];
            tensor<int32, [4]> var_604 = const()[name = string("op_604"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 16, 64]> hidden_states_125 = reshape(shape = var_604, x = var_603)[name = string("hidden_states_125")];
            fp32 var_25_promoted_13 = const()[name = string("op_25_promoted_13"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 16, 64]> var_608 = pow(x = hidden_states_125, y = var_25_promoted_13)[name = string("op_608")];
            tensor<int32, [1]> variance_27_axes_0 = const()[name = string("variance_27_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_27_keep_dims_0 = const()[name = string("variance_27_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 16, 1]> variance_27 = reduce_mean(axes = variance_27_axes_0, keep_dims = variance_27_keep_dims_0, x = var_608)[name = string("variance_27")];
            fp32 var_611 = const()[name = string("op_611"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 16, 1]> var_612 = add(x = variance_27, y = var_611)[name = string("op_612")];
            fp32 var_613_epsilon_0 = const()[name = string("op_613_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 16, 1]> var_613 = rsqrt(epsilon = var_613_epsilon_0, x = var_612)[name = string("op_613")];
            tensor<fp32, [1, 256, 16, 64]> hidden_states_129 = mul(x = hidden_states_125, y = var_613)[name = string("hidden_states_129")];
            tensor<fp32, [1, 256, 16, 64]> var_616 = mul(x = base_model_layers_5_self_attn_q_layernorm_weight, y = hidden_states_129)[name = string("op_616")];
            tensor<int32, [4]> q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 256, 512]> var_619 = linear(bias = linear_11_bias_0, weight = base_model_layers_5_self_attn_k_proj_weight, x = hidden_states_123)[name = string("linear_28")];
            tensor<int32, [4]> var_620 = const()[name = string("op_620"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 8, 64]> hidden_states_133 = reshape(shape = var_620, x = var_619)[name = string("hidden_states_133")];
            fp32 var_25_promoted_14 = const()[name = string("op_25_promoted_14"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 8, 64]> var_624 = pow(x = hidden_states_133, y = var_25_promoted_14)[name = string("op_624")];
            tensor<int32, [1]> variance_29_axes_0 = const()[name = string("variance_29_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_29_keep_dims_0 = const()[name = string("variance_29_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 8, 1]> variance_29 = reduce_mean(axes = variance_29_axes_0, keep_dims = variance_29_keep_dims_0, x = var_624)[name = string("variance_29")];
            fp32 var_627 = const()[name = string("op_627"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 8, 1]> var_628 = add(x = variance_29, y = var_627)[name = string("op_628")];
            fp32 var_629_epsilon_0 = const()[name = string("op_629_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 8, 1]> var_629 = rsqrt(epsilon = var_629_epsilon_0, x = var_628)[name = string("op_629")];
            tensor<fp32, [1, 256, 8, 64]> hidden_states_137 = mul(x = hidden_states_133, y = var_629)[name = string("hidden_states_137")];
            tensor<fp32, [1, 256, 8, 64]> var_632 = mul(x = base_model_layers_5_self_attn_k_layernorm_weight, y = hidden_states_137)[name = string("op_632")];
            tensor<int32, [4]> k_3_perm_0 = const()[name = string("k_3_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 256, 512]> var_635 = linear(bias = linear_11_bias_0, weight = base_model_layers_5_self_attn_v_proj_weight, x = hidden_states_123)[name = string("linear_29")];
            tensor<int32, [4]> var_636 = const()[name = string("op_636"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 8, 64]> var_637 = reshape(shape = var_636, x = var_635)[name = string("op_637")];
            tensor<int32, [4]> hidden_states_145_perm_0 = const()[name = string("hidden_states_145_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 16, 256, 64]> q_3 = transpose(perm = q_3_perm_0, x = var_616)[name = string("transpose_31")];
            tensor<fp32, [1, 16, 256, 64]> var_641 = mul(x = q_3, y = cos_5)[name = string("op_641")];
            tensor<int32, [4]> x1_5_begin_0 = const()[name = string("x1_5_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> x1_5_end_0 = const()[name = string("x1_5_end_0"), val = tensor<int32, [4]>([1, 16, 256, 32])];
            tensor<bool, [4]> x1_5_end_mask_0 = const()[name = string("x1_5_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 16, 256, 32]> x1_5 = slice_by_index(begin = x1_5_begin_0, end = x1_5_end_0, end_mask = x1_5_end_mask_0, x = q_3)[name = string("x1_5")];
            tensor<int32, [4]> x2_5_begin_0 = const()[name = string("x2_5_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])];
            tensor<int32, [4]> x2_5_end_0 = const()[name = string("x2_5_end_0"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<bool, [4]> x2_5_end_mask_0 = const()[name = string("x2_5_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 16, 256, 32]> x2_5 = slice_by_index(begin = x2_5_begin_0, end = x2_5_end_0, end_mask = x2_5_end_mask_0, x = q_3)[name = string("x2_5")];
            fp32 const_41_promoted = const()[name = string("const_41_promoted"), val = fp32(-0x1p+0)];
            tensor<fp32, [1, 16, 256, 32]> var_652 = mul(x = x2_5, y = const_41_promoted)[name = string("op_652")];
            bool var_654_interleave_0 = const()[name = string("op_654_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 64]> var_654 = concat(axis = var_26, interleave = var_654_interleave_0, values = (var_652, x1_5))[name = string("op_654")];
            tensor<fp32, [1, 16, 256, 64]> var_655 = mul(x = var_654, y = sin_5)[name = string("op_655")];
            tensor<fp32, [1, 16, 256, 64]> query_3 = add(x = var_641, y = var_655)[name = string("query_3")];
            tensor<fp32, [1, 8, 256, 64]> k_3 = transpose(perm = k_3_perm_0, x = var_632)[name = string("transpose_30")];
            tensor<fp32, [1, 8, 256, 64]> var_657 = mul(x = k_3, y = cos_5)[name = string("op_657")];
            tensor<int32, [4]> x1_7_begin_0 = const()[name = string("x1_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> x1_7_end_0 = const()[name = string("x1_7_end_0"), val = tensor<int32, [4]>([1, 8, 256, 32])];
            tensor<bool, [4]> x1_7_end_mask_0 = const()[name = string("x1_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 256, 32]> x1_7 = slice_by_index(begin = x1_7_begin_0, end = x1_7_end_0, end_mask = x1_7_end_mask_0, x = k_3)[name = string("x1_7")];
            tensor<int32, [4]> x2_7_begin_0 = const()[name = string("x2_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])];
            tensor<int32, [4]> x2_7_end_0 = const()[name = string("x2_7_end_0"), val = tensor<int32, [4]>([1, 8, 256, 64])];
            tensor<bool, [4]> x2_7_end_mask_0 = const()[name = string("x2_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 256, 32]> x2_7 = slice_by_index(begin = x2_7_begin_0, end = x2_7_end_0, end_mask = x2_7_end_mask_0, x = k_3)[name = string("x2_7")];
            fp32 const_44_promoted = const()[name = string("const_44_promoted"), val = fp32(-0x1p+0)];
            tensor<fp32, [1, 8, 256, 32]> var_668 = mul(x = x2_7, y = const_44_promoted)[name = string("op_668")];
            bool var_670_interleave_0 = const()[name = string("op_670_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 8, 256, 64]> var_670 = concat(axis = var_26, interleave = var_670_interleave_0, values = (var_668, x1_7))[name = string("op_670")];
            tensor<fp32, [1, 8, 256, 64]> var_671 = mul(x = var_670, y = sin_5)[name = string("op_671")];
            tensor<fp32, [1, 8, 256, 64]> hidden_states_141 = add(x = var_657, y = var_671)[name = string("hidden_states_141")];
            tensor<int32, [1]> var_680_axes_0 = const()[name = string("op_680_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp32, [1, 8, 1, 256, 64]> var_680 = expand_dims(axes = var_680_axes_0, x = hidden_states_141)[name = string("op_680")];
            tensor<int32, [5]> hidden_states_143_reps_0 = const()[name = string("hidden_states_143_reps_0"), val = tensor<int32, [5]>([1, 1, 2, 1, 1])];
            tensor<fp32, [1, 8, 2, 256, 64]> hidden_states_143 = tile(reps = hidden_states_143_reps_0, x = var_680)[name = string("hidden_states_143")];
            tensor<int32, [4]> var_688 = const()[name = string("op_688"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<fp32, [1, 16, 256, 64]> key_3 = reshape(shape = var_688, x = hidden_states_143)[name = string("key_3")];
            tensor<int32, [1]> var_697_axes_0 = const()[name = string("op_697_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp32, [1, 8, 256, 64]> hidden_states_145 = transpose(perm = hidden_states_145_perm_0, x = var_637)[name = string("transpose_29")];
            tensor<fp32, [1, 8, 1, 256, 64]> var_697 = expand_dims(axes = var_697_axes_0, x = hidden_states_145)[name = string("op_697")];
            tensor<int32, [5]> hidden_states_147_reps_0 = const()[name = string("hidden_states_147_reps_0"), val = tensor<int32, [5]>([1, 1, 2, 1, 1])];
            tensor<fp32, [1, 8, 2, 256, 64]> hidden_states_147 = tile(reps = hidden_states_147_reps_0, x = var_697)[name = string("hidden_states_147")];
            tensor<int32, [4]> var_705 = const()[name = string("op_705"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<fp32, [1, 16, 256, 64]> value_3 = reshape(shape = var_705, x = hidden_states_147)[name = string("value_3")];
            tensor<fp32, [1, 16, 256, 64]> mul_3 = mul(x = query_3, y = var_15)[name = string("mul_3")];
            bool matmul_1_transpose_y_0 = const()[name = string("matmul_1_transpose_y_0"), val = bool(true)];
            bool matmul_1_transpose_x_0 = const()[name = string("matmul_1_transpose_x_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 256]> matmul_1 = matmul(transpose_x = matmul_1_transpose_x_0, transpose_y = matmul_1_transpose_y_0, x = mul_3, y = key_3)[name = string("matmul_1")];
            tensor<fp32, [1, 16, 256, 256]> add_1 = add(x = matmul_1, y = mul_0)[name = string("add_1")];
            int32 softmax_1_axis_0 = const()[name = string("softmax_1_axis_0"), val = int32(-1)];
            tensor<fp32, [1, 16, 256, 256]> softmax_1 = softmax(axis = softmax_1_axis_0, x = add_1)[name = string("softmax_1")];
            bool attn_output_5_transpose_x_0 = const()[name = string("attn_output_5_transpose_x_0"), val = bool(false)];
            bool attn_output_5_transpose_y_0 = const()[name = string("attn_output_5_transpose_y_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 64]> attn_output_5 = matmul(transpose_x = attn_output_5_transpose_x_0, transpose_y = attn_output_5_transpose_y_0, x = softmax_1, y = value_3)[name = string("attn_output_5")];
            tensor<int32, [4]> var_713_perm_0 = const()[name = string("op_713_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_715 = const()[name = string("op_715"), val = tensor<int32, [3]>([1, 256, -1])];
            tensor<fp32, [1, 256, 16, 64]> var_713 = transpose(perm = var_713_perm_0, x = attn_output_5)[name = string("transpose_28")];
            tensor<fp32, [1, 256, 1024]> var_716 = reshape(shape = var_715, x = var_713)[name = string("op_716")];
            tensor<fp32, [1, 256, 1024]> hidden_states_149 = linear(bias = linear_1_bias_0, weight = base_model_layers_5_self_attn_out_proj_weight, x = var_716)[name = string("linear_30")];
            tensor<fp32, [1, 256, 1024]> hidden_states_151 = add(x = hidden_states_149, y = hidden_states_115)[name = string("hidden_states_151")];
            fp32 var_25_promoted_15 = const()[name = string("op_25_promoted_15"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_723 = pow(x = hidden_states_151, y = var_25_promoted_15)[name = string("op_723")];
            tensor<int32, [1]> variance_31_axes_0 = const()[name = string("variance_31_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_31_keep_dims_0 = const()[name = string("variance_31_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_31 = reduce_mean(axes = variance_31_axes_0, keep_dims = variance_31_keep_dims_0, x = var_723)[name = string("variance_31")];
            fp32 var_726 = const()[name = string("op_726"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_727 = add(x = variance_31, y = var_726)[name = string("op_727")];
            fp32 var_728_epsilon_0 = const()[name = string("op_728_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_728 = rsqrt(epsilon = var_728_epsilon_0, x = var_727)[name = string("op_728")];
            tensor<fp32, [1, 256, 1024]> hidden_states_155 = mul(x = hidden_states_151, y = var_728)[name = string("hidden_states_155")];
            tensor<fp32, [1, 256, 1024]> input_53 = mul(x = base_model_layers_5_ffn_norm_weight, y = hidden_states_155)[name = string("input_53")];
            tensor<fp32, [1, 256, 4608]> input_55 = linear(bias = linear_2_bias_0, weight = base_model_layers_5_feed_forward_w1_weight, x = input_53)[name = string("linear_31")];
            tensor<fp32, [1, 256, 4608]> var_740 = silu(x = input_55)[name = string("op_740")];
            tensor<fp32, [1, 256, 4608]> var_742 = linear(bias = linear_2_bias_0, weight = base_model_layers_5_feed_forward_w3_weight, x = input_53)[name = string("linear_32")];
            tensor<fp32, [1, 256, 4608]> input_57 = mul(x = var_740, y = var_742)[name = string("input_57")];
            tensor<fp32, [1, 256, 1024]> var_745 = linear(bias = linear_1_bias_0, weight = base_model_layers_5_feed_forward_w2_weight, x = input_57)[name = string("linear_33")];
            tensor<fp32, [1, 256, 1024]> hidden_states_159 = add(x = hidden_states_151, y = var_745)[name = string("hidden_states_159")];
            fp32 var_25_promoted_16 = const()[name = string("op_25_promoted_16"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_753 = pow(x = hidden_states_159, y = var_25_promoted_16)[name = string("op_753")];
            tensor<int32, [1]> variance_33_axes_0 = const()[name = string("variance_33_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_33_keep_dims_0 = const()[name = string("variance_33_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_33 = reduce_mean(axes = variance_33_axes_0, keep_dims = variance_33_keep_dims_0, x = var_753)[name = string("variance_33")];
            fp32 var_756 = const()[name = string("op_756"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_757 = add(x = variance_33, y = var_756)[name = string("op_757")];
            fp32 var_758_epsilon_0 = const()[name = string("op_758_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_758 = rsqrt(epsilon = var_758_epsilon_0, x = var_757)[name = string("op_758")];
            tensor<fp32, [1, 256, 1024]> hidden_states_163 = mul(x = hidden_states_159, y = var_758)[name = string("hidden_states_163")];
            tensor<fp32, [1, 256, 1024]> x_17 = mul(x = base_model_layers_6_operator_norm_weight, y = hidden_states_163)[name = string("x_17")];
            tensor<fp32, [1, 256, 3072]> var_770 = linear(bias = linear_0_bias_0, weight = base_model_layers_6_conv_in_proj_weight, x = x_17)[name = string("linear_34")];
            tensor<int32, [3]> BCx_9_perm_0 = const()[name = string("BCx_9_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [3]> var_772_split_sizes_0 = const()[name = string("op_772_split_sizes_0"), val = tensor<int32, [3]>([1024, 1024, 1024])];
            int32 var_772_axis_0 = const()[name = string("op_772_axis_0"), val = int32(-2)];
            tensor<fp32, [1, 3072, 256]> BCx_9 = transpose(perm = BCx_9_perm_0, x = var_770)[name = string("transpose_27")];
            tensor<fp32, [1, 1024, 256]> var_772_0, tensor<fp32, [1, 1024, 256]> var_772_1, tensor<fp32, [1, 1024, 256]> var_772_2 = split(axis = var_772_axis_0, split_sizes = var_772_split_sizes_0, x = BCx_9)[name = string("op_772")];
            tensor<fp32, [1, 1024, 256]> input_59 = mul(x = var_772_0, y = var_772_2)[name = string("input_59")];
            string var_781_pad_type_0 = const()[name = string("op_781_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> var_781_pad_0 = const()[name = string("op_781_pad_0"), val = tensor<int32, [2]>([2, 2])];
            int32 var_781_groups_0 = const()[name = string("op_781_groups_0"), val = int32(1024)];
            tensor<int32, [1]> var_781_strides_0 = const()[name = string("op_781_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_781_dilations_0 = const()[name = string("op_781_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1024, 258]> var_781 = conv(dilations = var_781_dilations_0, groups = var_781_groups_0, pad = var_781_pad_0, pad_type = var_781_pad_type_0, strides = var_781_strides_0, weight = base_model_layers_6_conv_conv_weight, x = input_59)[name = string("op_781")];
            tensor<int32, [3]> conv_out_9_begin_0 = const()[name = string("conv_out_9_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> conv_out_9_end_0 = const()[name = string("conv_out_9_end_0"), val = tensor<int32, [3]>([1, 1024, 256])];
            tensor<bool, [3]> conv_out_9_end_mask_0 = const()[name = string("conv_out_9_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 1024, 256]> conv_out_9 = slice_by_index(begin = conv_out_9_begin_0, end = conv_out_9_end_0, end_mask = conv_out_9_end_mask_0, x = var_781)[name = string("conv_out_9")];
            tensor<fp32, [1, 1024, 256]> y_9 = mul(x = var_772_1, y = conv_out_9)[name = string("y_9")];
            tensor<int32, [3]> var_784_perm_0 = const()[name = string("op_784_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<fp32, [1, 256, 1024]> var_784 = transpose(perm = var_784_perm_0, x = y_9)[name = string("transpose_26")];
            tensor<fp32, [1, 256, 1024]> hidden_states_167 = linear(bias = linear_1_bias_0, weight = base_model_layers_6_conv_out_proj_weight, x = var_784)[name = string("linear_35")];
            tensor<fp32, [1, 256, 1024]> hidden_states_169 = add(x = hidden_states_167, y = hidden_states_159)[name = string("hidden_states_169")];
            fp32 var_25_promoted_17 = const()[name = string("op_25_promoted_17"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_791 = pow(x = hidden_states_169, y = var_25_promoted_17)[name = string("op_791")];
            tensor<int32, [1]> variance_35_axes_0 = const()[name = string("variance_35_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_35_keep_dims_0 = const()[name = string("variance_35_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_35 = reduce_mean(axes = variance_35_axes_0, keep_dims = variance_35_keep_dims_0, x = var_791)[name = string("variance_35")];
            fp32 var_794 = const()[name = string("op_794"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_795 = add(x = variance_35, y = var_794)[name = string("op_795")];
            fp32 var_796_epsilon_0 = const()[name = string("op_796_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_796 = rsqrt(epsilon = var_796_epsilon_0, x = var_795)[name = string("op_796")];
            tensor<fp32, [1, 256, 1024]> hidden_states_173 = mul(x = hidden_states_169, y = var_796)[name = string("hidden_states_173")];
            tensor<fp32, [1, 256, 1024]> input_63 = mul(x = base_model_layers_6_ffn_norm_weight, y = hidden_states_173)[name = string("input_63")];
            tensor<fp32, [1, 256, 4608]> input_65 = linear(bias = linear_2_bias_0, weight = base_model_layers_6_feed_forward_w1_weight, x = input_63)[name = string("linear_36")];
            tensor<fp32, [1, 256, 4608]> var_808 = silu(x = input_65)[name = string("op_808")];
            tensor<fp32, [1, 256, 4608]> var_810 = linear(bias = linear_2_bias_0, weight = base_model_layers_6_feed_forward_w3_weight, x = input_63)[name = string("linear_37")];
            tensor<fp32, [1, 256, 4608]> input_67 = mul(x = var_808, y = var_810)[name = string("input_67")];
            tensor<fp32, [1, 256, 1024]> var_813 = linear(bias = linear_1_bias_0, weight = base_model_layers_6_feed_forward_w2_weight, x = input_67)[name = string("linear_38")];
            tensor<fp32, [1, 256, 1024]> hidden_states_177 = add(x = hidden_states_169, y = var_813)[name = string("hidden_states_177")];
            fp32 var_25_promoted_18 = const()[name = string("op_25_promoted_18"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_821 = pow(x = hidden_states_177, y = var_25_promoted_18)[name = string("op_821")];
            tensor<int32, [1]> variance_37_axes_0 = const()[name = string("variance_37_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_37_keep_dims_0 = const()[name = string("variance_37_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_37 = reduce_mean(axes = variance_37_axes_0, keep_dims = variance_37_keep_dims_0, x = var_821)[name = string("variance_37")];
            fp32 var_824 = const()[name = string("op_824"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_825 = add(x = variance_37, y = var_824)[name = string("op_825")];
            fp32 var_826_epsilon_0 = const()[name = string("op_826_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_826 = rsqrt(epsilon = var_826_epsilon_0, x = var_825)[name = string("op_826")];
            tensor<fp32, [1, 256, 1024]> hidden_states_181 = mul(x = hidden_states_177, y = var_826)[name = string("hidden_states_181")];
            tensor<fp32, [1, 256, 1024]> x_21 = mul(x = base_model_layers_7_operator_norm_weight, y = hidden_states_181)[name = string("x_21")];
            tensor<fp32, [1, 256, 3072]> var_838 = linear(bias = linear_0_bias_0, weight = base_model_layers_7_conv_in_proj_weight, x = x_21)[name = string("linear_39")];
            tensor<int32, [3]> BCx_11_perm_0 = const()[name = string("BCx_11_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [3]> var_840_split_sizes_0 = const()[name = string("op_840_split_sizes_0"), val = tensor<int32, [3]>([1024, 1024, 1024])];
            int32 var_840_axis_0 = const()[name = string("op_840_axis_0"), val = int32(-2)];
            tensor<fp32, [1, 3072, 256]> BCx_11 = transpose(perm = BCx_11_perm_0, x = var_838)[name = string("transpose_25")];
            tensor<fp32, [1, 1024, 256]> var_840_0, tensor<fp32, [1, 1024, 256]> var_840_1, tensor<fp32, [1, 1024, 256]> var_840_2 = split(axis = var_840_axis_0, split_sizes = var_840_split_sizes_0, x = BCx_11)[name = string("op_840")];
            tensor<fp32, [1, 1024, 256]> input_69 = mul(x = var_840_0, y = var_840_2)[name = string("input_69")];
            string var_849_pad_type_0 = const()[name = string("op_849_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> var_849_pad_0 = const()[name = string("op_849_pad_0"), val = tensor<int32, [2]>([2, 2])];
            int32 var_849_groups_0 = const()[name = string("op_849_groups_0"), val = int32(1024)];
            tensor<int32, [1]> var_849_strides_0 = const()[name = string("op_849_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_849_dilations_0 = const()[name = string("op_849_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1024, 258]> var_849 = conv(dilations = var_849_dilations_0, groups = var_849_groups_0, pad = var_849_pad_0, pad_type = var_849_pad_type_0, strides = var_849_strides_0, weight = base_model_layers_7_conv_conv_weight, x = input_69)[name = string("op_849")];
            tensor<int32, [3]> conv_out_11_begin_0 = const()[name = string("conv_out_11_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> conv_out_11_end_0 = const()[name = string("conv_out_11_end_0"), val = tensor<int32, [3]>([1, 1024, 256])];
            tensor<bool, [3]> conv_out_11_end_mask_0 = const()[name = string("conv_out_11_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 1024, 256]> conv_out_11 = slice_by_index(begin = conv_out_11_begin_0, end = conv_out_11_end_0, end_mask = conv_out_11_end_mask_0, x = var_849)[name = string("conv_out_11")];
            tensor<fp32, [1, 1024, 256]> y_11 = mul(x = var_840_1, y = conv_out_11)[name = string("y_11")];
            tensor<int32, [3]> var_852_perm_0 = const()[name = string("op_852_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<fp32, [1, 256, 1024]> var_852 = transpose(perm = var_852_perm_0, x = y_11)[name = string("transpose_24")];
            tensor<fp32, [1, 256, 1024]> hidden_states_185 = linear(bias = linear_1_bias_0, weight = base_model_layers_7_conv_out_proj_weight, x = var_852)[name = string("linear_40")];
            tensor<fp32, [1, 256, 1024]> hidden_states_187 = add(x = hidden_states_185, y = hidden_states_177)[name = string("hidden_states_187")];
            fp32 var_25_promoted_19 = const()[name = string("op_25_promoted_19"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_859 = pow(x = hidden_states_187, y = var_25_promoted_19)[name = string("op_859")];
            tensor<int32, [1]> variance_39_axes_0 = const()[name = string("variance_39_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_39_keep_dims_0 = const()[name = string("variance_39_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_39 = reduce_mean(axes = variance_39_axes_0, keep_dims = variance_39_keep_dims_0, x = var_859)[name = string("variance_39")];
            fp32 var_862 = const()[name = string("op_862"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_863 = add(x = variance_39, y = var_862)[name = string("op_863")];
            fp32 var_864_epsilon_0 = const()[name = string("op_864_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_864 = rsqrt(epsilon = var_864_epsilon_0, x = var_863)[name = string("op_864")];
            tensor<fp32, [1, 256, 1024]> hidden_states_191 = mul(x = hidden_states_187, y = var_864)[name = string("hidden_states_191")];
            tensor<fp32, [1, 256, 1024]> input_73 = mul(x = base_model_layers_7_ffn_norm_weight, y = hidden_states_191)[name = string("input_73")];
            tensor<fp32, [1, 256, 4608]> input_75 = linear(bias = linear_2_bias_0, weight = base_model_layers_7_feed_forward_w1_weight, x = input_73)[name = string("linear_41")];
            tensor<fp32, [1, 256, 4608]> var_876 = silu(x = input_75)[name = string("op_876")];
            tensor<fp32, [1, 256, 4608]> var_878 = linear(bias = linear_2_bias_0, weight = base_model_layers_7_feed_forward_w3_weight, x = input_73)[name = string("linear_42")];
            tensor<fp32, [1, 256, 4608]> input_77 = mul(x = var_876, y = var_878)[name = string("input_77")];
            tensor<fp32, [1, 256, 1024]> var_881 = linear(bias = linear_1_bias_0, weight = base_model_layers_7_feed_forward_w2_weight, x = input_77)[name = string("linear_43")];
            tensor<fp32, [1, 256, 1024]> hidden_states_195 = add(x = hidden_states_187, y = var_881)[name = string("hidden_states_195")];
            fp32 var_25_promoted_20 = const()[name = string("op_25_promoted_20"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_889 = pow(x = hidden_states_195, y = var_25_promoted_20)[name = string("op_889")];
            tensor<int32, [1]> variance_41_axes_0 = const()[name = string("variance_41_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_41_keep_dims_0 = const()[name = string("variance_41_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_41 = reduce_mean(axes = variance_41_axes_0, keep_dims = variance_41_keep_dims_0, x = var_889)[name = string("variance_41")];
            fp32 var_892 = const()[name = string("op_892"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_893 = add(x = variance_41, y = var_892)[name = string("op_893")];
            fp32 var_894_epsilon_0 = const()[name = string("op_894_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_894 = rsqrt(epsilon = var_894_epsilon_0, x = var_893)[name = string("op_894")];
            tensor<fp32, [1, 256, 1024]> hidden_states_199 = mul(x = hidden_states_195, y = var_894)[name = string("hidden_states_199")];
            tensor<fp32, [1, 256, 1024]> hidden_states_203 = mul(x = base_model_layers_8_operator_norm_weight, y = hidden_states_199)[name = string("hidden_states_203")];
            tensor<fp32, [1, 256, 1024]> var_910 = linear(bias = linear_1_bias_0, weight = base_model_layers_8_self_attn_q_proj_weight, x = hidden_states_203)[name = string("linear_44")];
            tensor<int32, [4]> var_911 = const()[name = string("op_911"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 16, 64]> hidden_states_205 = reshape(shape = var_911, x = var_910)[name = string("hidden_states_205")];
            fp32 var_25_promoted_21 = const()[name = string("op_25_promoted_21"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 16, 64]> var_915 = pow(x = hidden_states_205, y = var_25_promoted_21)[name = string("op_915")];
            tensor<int32, [1]> variance_43_axes_0 = const()[name = string("variance_43_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_43_keep_dims_0 = const()[name = string("variance_43_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 16, 1]> variance_43 = reduce_mean(axes = variance_43_axes_0, keep_dims = variance_43_keep_dims_0, x = var_915)[name = string("variance_43")];
            fp32 var_918 = const()[name = string("op_918"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 16, 1]> var_919 = add(x = variance_43, y = var_918)[name = string("op_919")];
            fp32 var_920_epsilon_0 = const()[name = string("op_920_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 16, 1]> var_920 = rsqrt(epsilon = var_920_epsilon_0, x = var_919)[name = string("op_920")];
            tensor<fp32, [1, 256, 16, 64]> hidden_states_209 = mul(x = hidden_states_205, y = var_920)[name = string("hidden_states_209")];
            tensor<fp32, [1, 256, 16, 64]> var_923 = mul(x = base_model_layers_8_self_attn_q_layernorm_weight, y = hidden_states_209)[name = string("op_923")];
            tensor<int32, [4]> q_5_perm_0 = const()[name = string("q_5_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 256, 512]> var_926 = linear(bias = linear_11_bias_0, weight = base_model_layers_8_self_attn_k_proj_weight, x = hidden_states_203)[name = string("linear_45")];
            tensor<int32, [4]> var_927 = const()[name = string("op_927"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 8, 64]> hidden_states_213 = reshape(shape = var_927, x = var_926)[name = string("hidden_states_213")];
            fp32 var_25_promoted_22 = const()[name = string("op_25_promoted_22"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 8, 64]> var_931 = pow(x = hidden_states_213, y = var_25_promoted_22)[name = string("op_931")];
            tensor<int32, [1]> variance_45_axes_0 = const()[name = string("variance_45_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_45_keep_dims_0 = const()[name = string("variance_45_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 8, 1]> variance_45 = reduce_mean(axes = variance_45_axes_0, keep_dims = variance_45_keep_dims_0, x = var_931)[name = string("variance_45")];
            fp32 var_934 = const()[name = string("op_934"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 8, 1]> var_935 = add(x = variance_45, y = var_934)[name = string("op_935")];
            fp32 var_936_epsilon_0 = const()[name = string("op_936_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 8, 1]> var_936 = rsqrt(epsilon = var_936_epsilon_0, x = var_935)[name = string("op_936")];
            tensor<fp32, [1, 256, 8, 64]> hidden_states_217 = mul(x = hidden_states_213, y = var_936)[name = string("hidden_states_217")];
            tensor<fp32, [1, 256, 8, 64]> var_939 = mul(x = base_model_layers_8_self_attn_k_layernorm_weight, y = hidden_states_217)[name = string("op_939")];
            tensor<int32, [4]> k_5_perm_0 = const()[name = string("k_5_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 256, 512]> var_942 = linear(bias = linear_11_bias_0, weight = base_model_layers_8_self_attn_v_proj_weight, x = hidden_states_203)[name = string("linear_46")];
            tensor<int32, [4]> var_943 = const()[name = string("op_943"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 8, 64]> var_944 = reshape(shape = var_943, x = var_942)[name = string("op_944")];
            tensor<int32, [4]> hidden_states_225_perm_0 = const()[name = string("hidden_states_225_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 16, 256, 64]> q_5 = transpose(perm = q_5_perm_0, x = var_923)[name = string("transpose_23")];
            tensor<fp32, [1, 16, 256, 64]> var_948 = mul(x = q_5, y = cos_5)[name = string("op_948")];
            tensor<int32, [4]> x1_9_begin_0 = const()[name = string("x1_9_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> x1_9_end_0 = const()[name = string("x1_9_end_0"), val = tensor<int32, [4]>([1, 16, 256, 32])];
            tensor<bool, [4]> x1_9_end_mask_0 = const()[name = string("x1_9_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 16, 256, 32]> x1_9 = slice_by_index(begin = x1_9_begin_0, end = x1_9_end_0, end_mask = x1_9_end_mask_0, x = q_5)[name = string("x1_9")];
            tensor<int32, [4]> x2_9_begin_0 = const()[name = string("x2_9_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])];
            tensor<int32, [4]> x2_9_end_0 = const()[name = string("x2_9_end_0"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<bool, [4]> x2_9_end_mask_0 = const()[name = string("x2_9_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 16, 256, 32]> x2_9 = slice_by_index(begin = x2_9_begin_0, end = x2_9_end_0, end_mask = x2_9_end_mask_0, x = q_5)[name = string("x2_9")];
            fp32 const_60_promoted = const()[name = string("const_60_promoted"), val = fp32(-0x1p+0)];
            tensor<fp32, [1, 16, 256, 32]> var_959 = mul(x = x2_9, y = const_60_promoted)[name = string("op_959")];
            bool var_961_interleave_0 = const()[name = string("op_961_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 64]> var_961 = concat(axis = var_26, interleave = var_961_interleave_0, values = (var_959, x1_9))[name = string("op_961")];
            tensor<fp32, [1, 16, 256, 64]> var_962 = mul(x = var_961, y = sin_5)[name = string("op_962")];
            tensor<fp32, [1, 16, 256, 64]> query_5 = add(x = var_948, y = var_962)[name = string("query_5")];
            tensor<fp32, [1, 8, 256, 64]> k_5 = transpose(perm = k_5_perm_0, x = var_939)[name = string("transpose_22")];
            tensor<fp32, [1, 8, 256, 64]> var_964 = mul(x = k_5, y = cos_5)[name = string("op_964")];
            tensor<int32, [4]> x1_11_begin_0 = const()[name = string("x1_11_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> x1_11_end_0 = const()[name = string("x1_11_end_0"), val = tensor<int32, [4]>([1, 8, 256, 32])];
            tensor<bool, [4]> x1_11_end_mask_0 = const()[name = string("x1_11_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 256, 32]> x1_11 = slice_by_index(begin = x1_11_begin_0, end = x1_11_end_0, end_mask = x1_11_end_mask_0, x = k_5)[name = string("x1_11")];
            tensor<int32, [4]> x2_11_begin_0 = const()[name = string("x2_11_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])];
            tensor<int32, [4]> x2_11_end_0 = const()[name = string("x2_11_end_0"), val = tensor<int32, [4]>([1, 8, 256, 64])];
            tensor<bool, [4]> x2_11_end_mask_0 = const()[name = string("x2_11_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 256, 32]> x2_11 = slice_by_index(begin = x2_11_begin_0, end = x2_11_end_0, end_mask = x2_11_end_mask_0, x = k_5)[name = string("x2_11")];
            fp32 const_63_promoted = const()[name = string("const_63_promoted"), val = fp32(-0x1p+0)];
            tensor<fp32, [1, 8, 256, 32]> var_975 = mul(x = x2_11, y = const_63_promoted)[name = string("op_975")];
            bool var_977_interleave_0 = const()[name = string("op_977_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 8, 256, 64]> var_977 = concat(axis = var_26, interleave = var_977_interleave_0, values = (var_975, x1_11))[name = string("op_977")];
            tensor<fp32, [1, 8, 256, 64]> var_978 = mul(x = var_977, y = sin_5)[name = string("op_978")];
            tensor<fp32, [1, 8, 256, 64]> hidden_states_221 = add(x = var_964, y = var_978)[name = string("hidden_states_221")];
            tensor<int32, [1]> var_987_axes_0 = const()[name = string("op_987_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp32, [1, 8, 1, 256, 64]> var_987 = expand_dims(axes = var_987_axes_0, x = hidden_states_221)[name = string("op_987")];
            tensor<int32, [5]> hidden_states_223_reps_0 = const()[name = string("hidden_states_223_reps_0"), val = tensor<int32, [5]>([1, 1, 2, 1, 1])];
            tensor<fp32, [1, 8, 2, 256, 64]> hidden_states_223 = tile(reps = hidden_states_223_reps_0, x = var_987)[name = string("hidden_states_223")];
            tensor<int32, [4]> var_995 = const()[name = string("op_995"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<fp32, [1, 16, 256, 64]> key_5 = reshape(shape = var_995, x = hidden_states_223)[name = string("key_5")];
            tensor<int32, [1]> var_1004_axes_0 = const()[name = string("op_1004_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp32, [1, 8, 256, 64]> hidden_states_225 = transpose(perm = hidden_states_225_perm_0, x = var_944)[name = string("transpose_21")];
            tensor<fp32, [1, 8, 1, 256, 64]> var_1004 = expand_dims(axes = var_1004_axes_0, x = hidden_states_225)[name = string("op_1004")];
            tensor<int32, [5]> hidden_states_227_reps_0 = const()[name = string("hidden_states_227_reps_0"), val = tensor<int32, [5]>([1, 1, 2, 1, 1])];
            tensor<fp32, [1, 8, 2, 256, 64]> hidden_states_227 = tile(reps = hidden_states_227_reps_0, x = var_1004)[name = string("hidden_states_227")];
            tensor<int32, [4]> var_1012 = const()[name = string("op_1012"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<fp32, [1, 16, 256, 64]> value_5 = reshape(shape = var_1012, x = hidden_states_227)[name = string("value_5")];
            tensor<fp32, [1, 16, 256, 64]> mul_5 = mul(x = query_5, y = var_15)[name = string("mul_5")];
            bool matmul_2_transpose_y_0 = const()[name = string("matmul_2_transpose_y_0"), val = bool(true)];
            bool matmul_2_transpose_x_0 = const()[name = string("matmul_2_transpose_x_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 256]> matmul_2 = matmul(transpose_x = matmul_2_transpose_x_0, transpose_y = matmul_2_transpose_y_0, x = mul_5, y = key_5)[name = string("matmul_2")];
            tensor<fp32, [1, 16, 256, 256]> add_2 = add(x = matmul_2, y = mul_0)[name = string("add_2")];
            int32 softmax_2_axis_0 = const()[name = string("softmax_2_axis_0"), val = int32(-1)];
            tensor<fp32, [1, 16, 256, 256]> softmax_2 = softmax(axis = softmax_2_axis_0, x = add_2)[name = string("softmax_2")];
            bool attn_output_9_transpose_x_0 = const()[name = string("attn_output_9_transpose_x_0"), val = bool(false)];
            bool attn_output_9_transpose_y_0 = const()[name = string("attn_output_9_transpose_y_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 64]> attn_output_9 = matmul(transpose_x = attn_output_9_transpose_x_0, transpose_y = attn_output_9_transpose_y_0, x = softmax_2, y = value_5)[name = string("attn_output_9")];
            tensor<int32, [4]> var_1020_perm_0 = const()[name = string("op_1020_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1022 = const()[name = string("op_1022"), val = tensor<int32, [3]>([1, 256, -1])];
            tensor<fp32, [1, 256, 16, 64]> var_1020 = transpose(perm = var_1020_perm_0, x = attn_output_9)[name = string("transpose_20")];
            tensor<fp32, [1, 256, 1024]> var_1023 = reshape(shape = var_1022, x = var_1020)[name = string("op_1023")];
            tensor<fp32, [1, 256, 1024]> hidden_states_229 = linear(bias = linear_1_bias_0, weight = base_model_layers_8_self_attn_out_proj_weight, x = var_1023)[name = string("linear_47")];
            tensor<fp32, [1, 256, 1024]> hidden_states_231 = add(x = hidden_states_229, y = hidden_states_195)[name = string("hidden_states_231")];
            fp32 var_25_promoted_23 = const()[name = string("op_25_promoted_23"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1030 = pow(x = hidden_states_231, y = var_25_promoted_23)[name = string("op_1030")];
            tensor<int32, [1]> variance_47_axes_0 = const()[name = string("variance_47_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_47_keep_dims_0 = const()[name = string("variance_47_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_47 = reduce_mean(axes = variance_47_axes_0, keep_dims = variance_47_keep_dims_0, x = var_1030)[name = string("variance_47")];
            fp32 var_1033 = const()[name = string("op_1033"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1034 = add(x = variance_47, y = var_1033)[name = string("op_1034")];
            fp32 var_1035_epsilon_0 = const()[name = string("op_1035_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1035 = rsqrt(epsilon = var_1035_epsilon_0, x = var_1034)[name = string("op_1035")];
            tensor<fp32, [1, 256, 1024]> hidden_states_235 = mul(x = hidden_states_231, y = var_1035)[name = string("hidden_states_235")];
            tensor<fp32, [1, 256, 1024]> input_81 = mul(x = base_model_layers_8_ffn_norm_weight, y = hidden_states_235)[name = string("input_81")];
            tensor<fp32, [1, 256, 4608]> input_83 = linear(bias = linear_2_bias_0, weight = base_model_layers_8_feed_forward_w1_weight, x = input_81)[name = string("linear_48")];
            tensor<fp32, [1, 256, 4608]> var_1047 = silu(x = input_83)[name = string("op_1047")];
            tensor<fp32, [1, 256, 4608]> var_1049 = linear(bias = linear_2_bias_0, weight = base_model_layers_8_feed_forward_w3_weight, x = input_81)[name = string("linear_49")];
            tensor<fp32, [1, 256, 4608]> input_85 = mul(x = var_1047, y = var_1049)[name = string("input_85")];
            tensor<fp32, [1, 256, 1024]> var_1052 = linear(bias = linear_1_bias_0, weight = base_model_layers_8_feed_forward_w2_weight, x = input_85)[name = string("linear_50")];
            tensor<fp32, [1, 256, 1024]> hidden_states_239 = add(x = hidden_states_231, y = var_1052)[name = string("hidden_states_239")];
            fp32 var_25_promoted_24 = const()[name = string("op_25_promoted_24"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1060 = pow(x = hidden_states_239, y = var_25_promoted_24)[name = string("op_1060")];
            tensor<int32, [1]> variance_49_axes_0 = const()[name = string("variance_49_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_49_keep_dims_0 = const()[name = string("variance_49_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_49 = reduce_mean(axes = variance_49_axes_0, keep_dims = variance_49_keep_dims_0, x = var_1060)[name = string("variance_49")];
            fp32 var_1063 = const()[name = string("op_1063"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1064 = add(x = variance_49, y = var_1063)[name = string("op_1064")];
            fp32 var_1065_epsilon_0 = const()[name = string("op_1065_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1065 = rsqrt(epsilon = var_1065_epsilon_0, x = var_1064)[name = string("op_1065")];
            tensor<fp32, [1, 256, 1024]> hidden_states_243 = mul(x = hidden_states_239, y = var_1065)[name = string("hidden_states_243")];
            tensor<fp32, [1, 256, 1024]> x_25 = mul(x = base_model_layers_9_operator_norm_weight, y = hidden_states_243)[name = string("x_25")];
            tensor<fp32, [1, 256, 3072]> var_1077 = linear(bias = linear_0_bias_0, weight = base_model_layers_9_conv_in_proj_weight, x = x_25)[name = string("linear_51")];
            tensor<int32, [3]> BCx_13_perm_0 = const()[name = string("BCx_13_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [3]> var_1079_split_sizes_0 = const()[name = string("op_1079_split_sizes_0"), val = tensor<int32, [3]>([1024, 1024, 1024])];
            int32 var_1079_axis_0 = const()[name = string("op_1079_axis_0"), val = int32(-2)];
            tensor<fp32, [1, 3072, 256]> BCx_13 = transpose(perm = BCx_13_perm_0, x = var_1077)[name = string("transpose_19")];
            tensor<fp32, [1, 1024, 256]> var_1079_0, tensor<fp32, [1, 1024, 256]> var_1079_1, tensor<fp32, [1, 1024, 256]> var_1079_2 = split(axis = var_1079_axis_0, split_sizes = var_1079_split_sizes_0, x = BCx_13)[name = string("op_1079")];
            tensor<fp32, [1, 1024, 256]> input_87 = mul(x = var_1079_0, y = var_1079_2)[name = string("input_87")];
            string var_1088_pad_type_0 = const()[name = string("op_1088_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> var_1088_pad_0 = const()[name = string("op_1088_pad_0"), val = tensor<int32, [2]>([2, 2])];
            int32 var_1088_groups_0 = const()[name = string("op_1088_groups_0"), val = int32(1024)];
            tensor<int32, [1]> var_1088_strides_0 = const()[name = string("op_1088_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1088_dilations_0 = const()[name = string("op_1088_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1024, 258]> var_1088 = conv(dilations = var_1088_dilations_0, groups = var_1088_groups_0, pad = var_1088_pad_0, pad_type = var_1088_pad_type_0, strides = var_1088_strides_0, weight = base_model_layers_9_conv_conv_weight, x = input_87)[name = string("op_1088")];
            tensor<int32, [3]> conv_out_13_begin_0 = const()[name = string("conv_out_13_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> conv_out_13_end_0 = const()[name = string("conv_out_13_end_0"), val = tensor<int32, [3]>([1, 1024, 256])];
            tensor<bool, [3]> conv_out_13_end_mask_0 = const()[name = string("conv_out_13_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 1024, 256]> conv_out_13 = slice_by_index(begin = conv_out_13_begin_0, end = conv_out_13_end_0, end_mask = conv_out_13_end_mask_0, x = var_1088)[name = string("conv_out_13")];
            tensor<fp32, [1, 1024, 256]> y_13 = mul(x = var_1079_1, y = conv_out_13)[name = string("y_13")];
            tensor<int32, [3]> var_1091_perm_0 = const()[name = string("op_1091_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<fp32, [1, 256, 1024]> var_1091 = transpose(perm = var_1091_perm_0, x = y_13)[name = string("transpose_18")];
            tensor<fp32, [1, 256, 1024]> hidden_states_247 = linear(bias = linear_1_bias_0, weight = base_model_layers_9_conv_out_proj_weight, x = var_1091)[name = string("linear_52")];
            tensor<fp32, [1, 256, 1024]> hidden_states_249 = add(x = hidden_states_247, y = hidden_states_239)[name = string("hidden_states_249")];
            fp32 var_25_promoted_25 = const()[name = string("op_25_promoted_25"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1098 = pow(x = hidden_states_249, y = var_25_promoted_25)[name = string("op_1098")];
            tensor<int32, [1]> variance_51_axes_0 = const()[name = string("variance_51_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_51_keep_dims_0 = const()[name = string("variance_51_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_51 = reduce_mean(axes = variance_51_axes_0, keep_dims = variance_51_keep_dims_0, x = var_1098)[name = string("variance_51")];
            fp32 var_1101 = const()[name = string("op_1101"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1102 = add(x = variance_51, y = var_1101)[name = string("op_1102")];
            fp32 var_1103_epsilon_0 = const()[name = string("op_1103_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1103 = rsqrt(epsilon = var_1103_epsilon_0, x = var_1102)[name = string("op_1103")];
            tensor<fp32, [1, 256, 1024]> hidden_states_253 = mul(x = hidden_states_249, y = var_1103)[name = string("hidden_states_253")];
            tensor<fp32, [1, 256, 1024]> input_91 = mul(x = base_model_layers_9_ffn_norm_weight, y = hidden_states_253)[name = string("input_91")];
            tensor<fp32, [1, 256, 4608]> input_93 = linear(bias = linear_2_bias_0, weight = base_model_layers_9_feed_forward_w1_weight, x = input_91)[name = string("linear_53")];
            tensor<fp32, [1, 256, 4608]> var_1115 = silu(x = input_93)[name = string("op_1115")];
            tensor<fp32, [1, 256, 4608]> var_1117 = linear(bias = linear_2_bias_0, weight = base_model_layers_9_feed_forward_w3_weight, x = input_91)[name = string("linear_54")];
            tensor<fp32, [1, 256, 4608]> input_95 = mul(x = var_1115, y = var_1117)[name = string("input_95")];
            tensor<fp32, [1, 256, 1024]> var_1120 = linear(bias = linear_1_bias_0, weight = base_model_layers_9_feed_forward_w2_weight, x = input_95)[name = string("linear_55")];
            tensor<fp32, [1, 256, 1024]> hidden_states_257 = add(x = hidden_states_249, y = var_1120)[name = string("hidden_states_257")];
            fp32 var_25_promoted_26 = const()[name = string("op_25_promoted_26"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1128 = pow(x = hidden_states_257, y = var_25_promoted_26)[name = string("op_1128")];
            tensor<int32, [1]> variance_53_axes_0 = const()[name = string("variance_53_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_53_keep_dims_0 = const()[name = string("variance_53_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_53 = reduce_mean(axes = variance_53_axes_0, keep_dims = variance_53_keep_dims_0, x = var_1128)[name = string("variance_53")];
            fp32 var_1131 = const()[name = string("op_1131"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1132 = add(x = variance_53, y = var_1131)[name = string("op_1132")];
            fp32 var_1133_epsilon_0 = const()[name = string("op_1133_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1133 = rsqrt(epsilon = var_1133_epsilon_0, x = var_1132)[name = string("op_1133")];
            tensor<fp32, [1, 256, 1024]> hidden_states_261 = mul(x = hidden_states_257, y = var_1133)[name = string("hidden_states_261")];
            tensor<fp32, [1, 256, 1024]> hidden_states_265 = mul(x = base_model_layers_10_operator_norm_weight, y = hidden_states_261)[name = string("hidden_states_265")];
            tensor<fp32, [1, 256, 1024]> var_1149 = linear(bias = linear_1_bias_0, weight = base_model_layers_10_self_attn_q_proj_weight, x = hidden_states_265)[name = string("linear_56")];
            tensor<int32, [4]> var_1150 = const()[name = string("op_1150"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 16, 64]> hidden_states_267 = reshape(shape = var_1150, x = var_1149)[name = string("hidden_states_267")];
            fp32 var_25_promoted_27 = const()[name = string("op_25_promoted_27"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 16, 64]> var_1154 = pow(x = hidden_states_267, y = var_25_promoted_27)[name = string("op_1154")];
            tensor<int32, [1]> variance_55_axes_0 = const()[name = string("variance_55_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_55_keep_dims_0 = const()[name = string("variance_55_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 16, 1]> variance_55 = reduce_mean(axes = variance_55_axes_0, keep_dims = variance_55_keep_dims_0, x = var_1154)[name = string("variance_55")];
            fp32 var_1157 = const()[name = string("op_1157"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 16, 1]> var_1158 = add(x = variance_55, y = var_1157)[name = string("op_1158")];
            fp32 var_1159_epsilon_0 = const()[name = string("op_1159_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 16, 1]> var_1159 = rsqrt(epsilon = var_1159_epsilon_0, x = var_1158)[name = string("op_1159")];
            tensor<fp32, [1, 256, 16, 64]> hidden_states_271 = mul(x = hidden_states_267, y = var_1159)[name = string("hidden_states_271")];
            tensor<fp32, [1, 256, 16, 64]> var_1162 = mul(x = base_model_layers_10_self_attn_q_layernorm_weight, y = hidden_states_271)[name = string("op_1162")];
            tensor<int32, [4]> q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 256, 512]> var_1165 = linear(bias = linear_11_bias_0, weight = base_model_layers_10_self_attn_k_proj_weight, x = hidden_states_265)[name = string("linear_57")];
            tensor<int32, [4]> var_1166 = const()[name = string("op_1166"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 8, 64]> hidden_states_275 = reshape(shape = var_1166, x = var_1165)[name = string("hidden_states_275")];
            fp32 var_25_promoted_28 = const()[name = string("op_25_promoted_28"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 8, 64]> var_1170 = pow(x = hidden_states_275, y = var_25_promoted_28)[name = string("op_1170")];
            tensor<int32, [1]> variance_57_axes_0 = const()[name = string("variance_57_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_57_keep_dims_0 = const()[name = string("variance_57_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 8, 1]> variance_57 = reduce_mean(axes = variance_57_axes_0, keep_dims = variance_57_keep_dims_0, x = var_1170)[name = string("variance_57")];
            fp32 var_1173 = const()[name = string("op_1173"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 8, 1]> var_1174 = add(x = variance_57, y = var_1173)[name = string("op_1174")];
            fp32 var_1175_epsilon_0 = const()[name = string("op_1175_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 8, 1]> var_1175 = rsqrt(epsilon = var_1175_epsilon_0, x = var_1174)[name = string("op_1175")];
            tensor<fp32, [1, 256, 8, 64]> hidden_states_279 = mul(x = hidden_states_275, y = var_1175)[name = string("hidden_states_279")];
            tensor<fp32, [1, 256, 8, 64]> var_1178 = mul(x = base_model_layers_10_self_attn_k_layernorm_weight, y = hidden_states_279)[name = string("op_1178")];
            tensor<int32, [4]> k_7_perm_0 = const()[name = string("k_7_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 256, 512]> var_1181 = linear(bias = linear_11_bias_0, weight = base_model_layers_10_self_attn_v_proj_weight, x = hidden_states_265)[name = string("linear_58")];
            tensor<int32, [4]> var_1182 = const()[name = string("op_1182"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 8, 64]> var_1183 = reshape(shape = var_1182, x = var_1181)[name = string("op_1183")];
            tensor<int32, [4]> hidden_states_287_perm_0 = const()[name = string("hidden_states_287_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 16, 256, 64]> q_7 = transpose(perm = q_7_perm_0, x = var_1162)[name = string("transpose_17")];
            tensor<fp32, [1, 16, 256, 64]> var_1187 = mul(x = q_7, y = cos_5)[name = string("op_1187")];
            tensor<int32, [4]> x1_13_begin_0 = const()[name = string("x1_13_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> x1_13_end_0 = const()[name = string("x1_13_end_0"), val = tensor<int32, [4]>([1, 16, 256, 32])];
            tensor<bool, [4]> x1_13_end_mask_0 = const()[name = string("x1_13_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 16, 256, 32]> x1_13 = slice_by_index(begin = x1_13_begin_0, end = x1_13_end_0, end_mask = x1_13_end_mask_0, x = q_7)[name = string("x1_13")];
            tensor<int32, [4]> x2_13_begin_0 = const()[name = string("x2_13_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])];
            tensor<int32, [4]> x2_13_end_0 = const()[name = string("x2_13_end_0"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<bool, [4]> x2_13_end_mask_0 = const()[name = string("x2_13_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 16, 256, 32]> x2_13 = slice_by_index(begin = x2_13_begin_0, end = x2_13_end_0, end_mask = x2_13_end_mask_0, x = q_7)[name = string("x2_13")];
            fp32 const_78_promoted = const()[name = string("const_78_promoted"), val = fp32(-0x1p+0)];
            tensor<fp32, [1, 16, 256, 32]> var_1198 = mul(x = x2_13, y = const_78_promoted)[name = string("op_1198")];
            bool var_1200_interleave_0 = const()[name = string("op_1200_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 64]> var_1200 = concat(axis = var_26, interleave = var_1200_interleave_0, values = (var_1198, x1_13))[name = string("op_1200")];
            tensor<fp32, [1, 16, 256, 64]> var_1201 = mul(x = var_1200, y = sin_5)[name = string("op_1201")];
            tensor<fp32, [1, 16, 256, 64]> query_7 = add(x = var_1187, y = var_1201)[name = string("query_7")];
            tensor<fp32, [1, 8, 256, 64]> k_7 = transpose(perm = k_7_perm_0, x = var_1178)[name = string("transpose_16")];
            tensor<fp32, [1, 8, 256, 64]> var_1203 = mul(x = k_7, y = cos_5)[name = string("op_1203")];
            tensor<int32, [4]> x1_15_begin_0 = const()[name = string("x1_15_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> x1_15_end_0 = const()[name = string("x1_15_end_0"), val = tensor<int32, [4]>([1, 8, 256, 32])];
            tensor<bool, [4]> x1_15_end_mask_0 = const()[name = string("x1_15_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 256, 32]> x1_15 = slice_by_index(begin = x1_15_begin_0, end = x1_15_end_0, end_mask = x1_15_end_mask_0, x = k_7)[name = string("x1_15")];
            tensor<int32, [4]> x2_15_begin_0 = const()[name = string("x2_15_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])];
            tensor<int32, [4]> x2_15_end_0 = const()[name = string("x2_15_end_0"), val = tensor<int32, [4]>([1, 8, 256, 64])];
            tensor<bool, [4]> x2_15_end_mask_0 = const()[name = string("x2_15_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 256, 32]> x2_15 = slice_by_index(begin = x2_15_begin_0, end = x2_15_end_0, end_mask = x2_15_end_mask_0, x = k_7)[name = string("x2_15")];
            fp32 const_81_promoted = const()[name = string("const_81_promoted"), val = fp32(-0x1p+0)];
            tensor<fp32, [1, 8, 256, 32]> var_1214 = mul(x = x2_15, y = const_81_promoted)[name = string("op_1214")];
            bool var_1216_interleave_0 = const()[name = string("op_1216_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 8, 256, 64]> var_1216 = concat(axis = var_26, interleave = var_1216_interleave_0, values = (var_1214, x1_15))[name = string("op_1216")];
            tensor<fp32, [1, 8, 256, 64]> var_1217 = mul(x = var_1216, y = sin_5)[name = string("op_1217")];
            tensor<fp32, [1, 8, 256, 64]> hidden_states_283 = add(x = var_1203, y = var_1217)[name = string("hidden_states_283")];
            tensor<int32, [1]> var_1226_axes_0 = const()[name = string("op_1226_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp32, [1, 8, 1, 256, 64]> var_1226 = expand_dims(axes = var_1226_axes_0, x = hidden_states_283)[name = string("op_1226")];
            tensor<int32, [5]> hidden_states_285_reps_0 = const()[name = string("hidden_states_285_reps_0"), val = tensor<int32, [5]>([1, 1, 2, 1, 1])];
            tensor<fp32, [1, 8, 2, 256, 64]> hidden_states_285 = tile(reps = hidden_states_285_reps_0, x = var_1226)[name = string("hidden_states_285")];
            tensor<int32, [4]> var_1234 = const()[name = string("op_1234"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<fp32, [1, 16, 256, 64]> key_7 = reshape(shape = var_1234, x = hidden_states_285)[name = string("key_7")];
            tensor<int32, [1]> var_1243_axes_0 = const()[name = string("op_1243_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp32, [1, 8, 256, 64]> hidden_states_287 = transpose(perm = hidden_states_287_perm_0, x = var_1183)[name = string("transpose_15")];
            tensor<fp32, [1, 8, 1, 256, 64]> var_1243 = expand_dims(axes = var_1243_axes_0, x = hidden_states_287)[name = string("op_1243")];
            tensor<int32, [5]> hidden_states_289_reps_0 = const()[name = string("hidden_states_289_reps_0"), val = tensor<int32, [5]>([1, 1, 2, 1, 1])];
            tensor<fp32, [1, 8, 2, 256, 64]> hidden_states_289 = tile(reps = hidden_states_289_reps_0, x = var_1243)[name = string("hidden_states_289")];
            tensor<int32, [4]> var_1251 = const()[name = string("op_1251"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<fp32, [1, 16, 256, 64]> value_7 = reshape(shape = var_1251, x = hidden_states_289)[name = string("value_7")];
            tensor<fp32, [1, 16, 256, 64]> mul_7 = mul(x = query_7, y = var_15)[name = string("mul_7")];
            bool matmul_3_transpose_y_0 = const()[name = string("matmul_3_transpose_y_0"), val = bool(true)];
            bool matmul_3_transpose_x_0 = const()[name = string("matmul_3_transpose_x_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 256]> matmul_3 = matmul(transpose_x = matmul_3_transpose_x_0, transpose_y = matmul_3_transpose_y_0, x = mul_7, y = key_7)[name = string("matmul_3")];
            tensor<fp32, [1, 16, 256, 256]> add_3 = add(x = matmul_3, y = mul_0)[name = string("add_3")];
            int32 softmax_3_axis_0 = const()[name = string("softmax_3_axis_0"), val = int32(-1)];
            tensor<fp32, [1, 16, 256, 256]> softmax_3 = softmax(axis = softmax_3_axis_0, x = add_3)[name = string("softmax_3")];
            bool attn_output_13_transpose_x_0 = const()[name = string("attn_output_13_transpose_x_0"), val = bool(false)];
            bool attn_output_13_transpose_y_0 = const()[name = string("attn_output_13_transpose_y_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 64]> attn_output_13 = matmul(transpose_x = attn_output_13_transpose_x_0, transpose_y = attn_output_13_transpose_y_0, x = softmax_3, y = value_7)[name = string("attn_output_13")];
            tensor<int32, [4]> var_1259_perm_0 = const()[name = string("op_1259_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1261 = const()[name = string("op_1261"), val = tensor<int32, [3]>([1, 256, -1])];
            tensor<fp32, [1, 256, 16, 64]> var_1259 = transpose(perm = var_1259_perm_0, x = attn_output_13)[name = string("transpose_14")];
            tensor<fp32, [1, 256, 1024]> var_1262 = reshape(shape = var_1261, x = var_1259)[name = string("op_1262")];
            tensor<fp32, [1, 256, 1024]> hidden_states_291 = linear(bias = linear_1_bias_0, weight = base_model_layers_10_self_attn_out_proj_weight, x = var_1262)[name = string("linear_59")];
            tensor<fp32, [1, 256, 1024]> hidden_states_293 = add(x = hidden_states_291, y = hidden_states_257)[name = string("hidden_states_293")];
            fp32 var_25_promoted_29 = const()[name = string("op_25_promoted_29"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1269 = pow(x = hidden_states_293, y = var_25_promoted_29)[name = string("op_1269")];
            tensor<int32, [1]> variance_59_axes_0 = const()[name = string("variance_59_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_59_keep_dims_0 = const()[name = string("variance_59_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_59 = reduce_mean(axes = variance_59_axes_0, keep_dims = variance_59_keep_dims_0, x = var_1269)[name = string("variance_59")];
            fp32 var_1272 = const()[name = string("op_1272"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1273 = add(x = variance_59, y = var_1272)[name = string("op_1273")];
            fp32 var_1274_epsilon_0 = const()[name = string("op_1274_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1274 = rsqrt(epsilon = var_1274_epsilon_0, x = var_1273)[name = string("op_1274")];
            tensor<fp32, [1, 256, 1024]> hidden_states_297 = mul(x = hidden_states_293, y = var_1274)[name = string("hidden_states_297")];
            tensor<fp32, [1, 256, 1024]> input_99 = mul(x = base_model_layers_10_ffn_norm_weight, y = hidden_states_297)[name = string("input_99")];
            tensor<fp32, [1, 256, 4608]> input_101 = linear(bias = linear_2_bias_0, weight = base_model_layers_10_feed_forward_w1_weight, x = input_99)[name = string("linear_60")];
            tensor<fp32, [1, 256, 4608]> var_1286 = silu(x = input_101)[name = string("op_1286")];
            tensor<fp32, [1, 256, 4608]> var_1288 = linear(bias = linear_2_bias_0, weight = base_model_layers_10_feed_forward_w3_weight, x = input_99)[name = string("linear_61")];
            tensor<fp32, [1, 256, 4608]> input_103 = mul(x = var_1286, y = var_1288)[name = string("input_103")];
            tensor<fp32, [1, 256, 1024]> var_1291 = linear(bias = linear_1_bias_0, weight = base_model_layers_10_feed_forward_w2_weight, x = input_103)[name = string("linear_62")];
            tensor<fp32, [1, 256, 1024]> hidden_states_301 = add(x = hidden_states_293, y = var_1291)[name = string("hidden_states_301")];
            fp32 var_25_promoted_30 = const()[name = string("op_25_promoted_30"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1299 = pow(x = hidden_states_301, y = var_25_promoted_30)[name = string("op_1299")];
            tensor<int32, [1]> variance_61_axes_0 = const()[name = string("variance_61_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_61_keep_dims_0 = const()[name = string("variance_61_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_61 = reduce_mean(axes = variance_61_axes_0, keep_dims = variance_61_keep_dims_0, x = var_1299)[name = string("variance_61")];
            fp32 var_1302 = const()[name = string("op_1302"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1303 = add(x = variance_61, y = var_1302)[name = string("op_1303")];
            fp32 var_1304_epsilon_0 = const()[name = string("op_1304_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1304 = rsqrt(epsilon = var_1304_epsilon_0, x = var_1303)[name = string("op_1304")];
            tensor<fp32, [1, 256, 1024]> hidden_states_305 = mul(x = hidden_states_301, y = var_1304)[name = string("hidden_states_305")];
            tensor<fp32, [1, 256, 1024]> x_29 = mul(x = base_model_layers_11_operator_norm_weight, y = hidden_states_305)[name = string("x_29")];
            tensor<fp32, [1, 256, 3072]> var_1316 = linear(bias = linear_0_bias_0, weight = base_model_layers_11_conv_in_proj_weight, x = x_29)[name = string("linear_63")];
            tensor<int32, [3]> BCx_15_perm_0 = const()[name = string("BCx_15_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [3]> var_1318_split_sizes_0 = const()[name = string("op_1318_split_sizes_0"), val = tensor<int32, [3]>([1024, 1024, 1024])];
            int32 var_1318_axis_0 = const()[name = string("op_1318_axis_0"), val = int32(-2)];
            tensor<fp32, [1, 3072, 256]> BCx_15 = transpose(perm = BCx_15_perm_0, x = var_1316)[name = string("transpose_13")];
            tensor<fp32, [1, 1024, 256]> var_1318_0, tensor<fp32, [1, 1024, 256]> var_1318_1, tensor<fp32, [1, 1024, 256]> var_1318_2 = split(axis = var_1318_axis_0, split_sizes = var_1318_split_sizes_0, x = BCx_15)[name = string("op_1318")];
            tensor<fp32, [1, 1024, 256]> input_105 = mul(x = var_1318_0, y = var_1318_2)[name = string("input_105")];
            string var_1327_pad_type_0 = const()[name = string("op_1327_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> var_1327_pad_0 = const()[name = string("op_1327_pad_0"), val = tensor<int32, [2]>([2, 2])];
            int32 var_1327_groups_0 = const()[name = string("op_1327_groups_0"), val = int32(1024)];
            tensor<int32, [1]> var_1327_strides_0 = const()[name = string("op_1327_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1327_dilations_0 = const()[name = string("op_1327_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1024, 258]> var_1327 = conv(dilations = var_1327_dilations_0, groups = var_1327_groups_0, pad = var_1327_pad_0, pad_type = var_1327_pad_type_0, strides = var_1327_strides_0, weight = base_model_layers_11_conv_conv_weight, x = input_105)[name = string("op_1327")];
            tensor<int32, [3]> conv_out_15_begin_0 = const()[name = string("conv_out_15_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> conv_out_15_end_0 = const()[name = string("conv_out_15_end_0"), val = tensor<int32, [3]>([1, 1024, 256])];
            tensor<bool, [3]> conv_out_15_end_mask_0 = const()[name = string("conv_out_15_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 1024, 256]> conv_out_15 = slice_by_index(begin = conv_out_15_begin_0, end = conv_out_15_end_0, end_mask = conv_out_15_end_mask_0, x = var_1327)[name = string("conv_out_15")];
            tensor<fp32, [1, 1024, 256]> y_15 = mul(x = var_1318_1, y = conv_out_15)[name = string("y_15")];
            tensor<int32, [3]> var_1330_perm_0 = const()[name = string("op_1330_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<fp32, [1, 256, 1024]> var_1330 = transpose(perm = var_1330_perm_0, x = y_15)[name = string("transpose_12")];
            tensor<fp32, [1, 256, 1024]> hidden_states_309 = linear(bias = linear_1_bias_0, weight = base_model_layers_11_conv_out_proj_weight, x = var_1330)[name = string("linear_64")];
            tensor<fp32, [1, 256, 1024]> hidden_states_311 = add(x = hidden_states_309, y = hidden_states_301)[name = string("hidden_states_311")];
            fp32 var_25_promoted_31 = const()[name = string("op_25_promoted_31"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1337 = pow(x = hidden_states_311, y = var_25_promoted_31)[name = string("op_1337")];
            tensor<int32, [1]> variance_63_axes_0 = const()[name = string("variance_63_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_63_keep_dims_0 = const()[name = string("variance_63_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_63 = reduce_mean(axes = variance_63_axes_0, keep_dims = variance_63_keep_dims_0, x = var_1337)[name = string("variance_63")];
            fp32 var_1340 = const()[name = string("op_1340"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1341 = add(x = variance_63, y = var_1340)[name = string("op_1341")];
            fp32 var_1342_epsilon_0 = const()[name = string("op_1342_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1342 = rsqrt(epsilon = var_1342_epsilon_0, x = var_1341)[name = string("op_1342")];
            tensor<fp32, [1, 256, 1024]> hidden_states_315 = mul(x = hidden_states_311, y = var_1342)[name = string("hidden_states_315")];
            tensor<fp32, [1, 256, 1024]> input_109 = mul(x = base_model_layers_11_ffn_norm_weight, y = hidden_states_315)[name = string("input_109")];
            tensor<fp32, [1, 256, 4608]> input_111 = linear(bias = linear_2_bias_0, weight = base_model_layers_11_feed_forward_w1_weight, x = input_109)[name = string("linear_65")];
            tensor<fp32, [1, 256, 4608]> var_1354 = silu(x = input_111)[name = string("op_1354")];
            tensor<fp32, [1, 256, 4608]> var_1356 = linear(bias = linear_2_bias_0, weight = base_model_layers_11_feed_forward_w3_weight, x = input_109)[name = string("linear_66")];
            tensor<fp32, [1, 256, 4608]> input_113 = mul(x = var_1354, y = var_1356)[name = string("input_113")];
            tensor<fp32, [1, 256, 1024]> var_1359 = linear(bias = linear_1_bias_0, weight = base_model_layers_11_feed_forward_w2_weight, x = input_113)[name = string("linear_67")];
            tensor<fp32, [1, 256, 1024]> hidden_states_319 = add(x = hidden_states_311, y = var_1359)[name = string("hidden_states_319")];
            fp32 var_25_promoted_32 = const()[name = string("op_25_promoted_32"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1367 = pow(x = hidden_states_319, y = var_25_promoted_32)[name = string("op_1367")];
            tensor<int32, [1]> variance_65_axes_0 = const()[name = string("variance_65_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_65_keep_dims_0 = const()[name = string("variance_65_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_65 = reduce_mean(axes = variance_65_axes_0, keep_dims = variance_65_keep_dims_0, x = var_1367)[name = string("variance_65")];
            fp32 var_1370 = const()[name = string("op_1370"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1371 = add(x = variance_65, y = var_1370)[name = string("op_1371")];
            fp32 var_1372_epsilon_0 = const()[name = string("op_1372_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1372 = rsqrt(epsilon = var_1372_epsilon_0, x = var_1371)[name = string("op_1372")];
            tensor<fp32, [1, 256, 1024]> hidden_states_323 = mul(x = hidden_states_319, y = var_1372)[name = string("hidden_states_323")];
            tensor<fp32, [1, 256, 1024]> hidden_states_327 = mul(x = base_model_layers_12_operator_norm_weight, y = hidden_states_323)[name = string("hidden_states_327")];
            tensor<fp32, [1, 256, 1024]> var_1388 = linear(bias = linear_1_bias_0, weight = base_model_layers_12_self_attn_q_proj_weight, x = hidden_states_327)[name = string("linear_68")];
            tensor<int32, [4]> var_1389 = const()[name = string("op_1389"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 16, 64]> hidden_states_329 = reshape(shape = var_1389, x = var_1388)[name = string("hidden_states_329")];
            fp32 var_25_promoted_33 = const()[name = string("op_25_promoted_33"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 16, 64]> var_1393 = pow(x = hidden_states_329, y = var_25_promoted_33)[name = string("op_1393")];
            tensor<int32, [1]> variance_67_axes_0 = const()[name = string("variance_67_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_67_keep_dims_0 = const()[name = string("variance_67_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 16, 1]> variance_67 = reduce_mean(axes = variance_67_axes_0, keep_dims = variance_67_keep_dims_0, x = var_1393)[name = string("variance_67")];
            fp32 var_1396 = const()[name = string("op_1396"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 16, 1]> var_1397 = add(x = variance_67, y = var_1396)[name = string("op_1397")];
            fp32 var_1398_epsilon_0 = const()[name = string("op_1398_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 16, 1]> var_1398 = rsqrt(epsilon = var_1398_epsilon_0, x = var_1397)[name = string("op_1398")];
            tensor<fp32, [1, 256, 16, 64]> hidden_states_333 = mul(x = hidden_states_329, y = var_1398)[name = string("hidden_states_333")];
            tensor<fp32, [1, 256, 16, 64]> var_1401 = mul(x = base_model_layers_12_self_attn_q_layernorm_weight, y = hidden_states_333)[name = string("op_1401")];
            tensor<int32, [4]> q_9_perm_0 = const()[name = string("q_9_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 256, 512]> var_1404 = linear(bias = linear_11_bias_0, weight = base_model_layers_12_self_attn_k_proj_weight, x = hidden_states_327)[name = string("linear_69")];
            tensor<int32, [4]> var_1405 = const()[name = string("op_1405"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 8, 64]> hidden_states_337 = reshape(shape = var_1405, x = var_1404)[name = string("hidden_states_337")];
            fp32 var_25_promoted_34 = const()[name = string("op_25_promoted_34"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 8, 64]> var_1409 = pow(x = hidden_states_337, y = var_25_promoted_34)[name = string("op_1409")];
            tensor<int32, [1]> variance_69_axes_0 = const()[name = string("variance_69_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_69_keep_dims_0 = const()[name = string("variance_69_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 8, 1]> variance_69 = reduce_mean(axes = variance_69_axes_0, keep_dims = variance_69_keep_dims_0, x = var_1409)[name = string("variance_69")];
            fp32 var_1412 = const()[name = string("op_1412"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 8, 1]> var_1413 = add(x = variance_69, y = var_1412)[name = string("op_1413")];
            fp32 var_1414_epsilon_0 = const()[name = string("op_1414_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 8, 1]> var_1414 = rsqrt(epsilon = var_1414_epsilon_0, x = var_1413)[name = string("op_1414")];
            tensor<fp32, [1, 256, 8, 64]> hidden_states_341 = mul(x = hidden_states_337, y = var_1414)[name = string("hidden_states_341")];
            tensor<fp32, [1, 256, 8, 64]> var_1417 = mul(x = base_model_layers_12_self_attn_k_layernorm_weight, y = hidden_states_341)[name = string("op_1417")];
            tensor<int32, [4]> k_9_perm_0 = const()[name = string("k_9_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 256, 512]> var_1420 = linear(bias = linear_11_bias_0, weight = base_model_layers_12_self_attn_v_proj_weight, x = hidden_states_327)[name = string("linear_70")];
            tensor<int32, [4]> var_1421 = const()[name = string("op_1421"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 8, 64]> var_1422 = reshape(shape = var_1421, x = var_1420)[name = string("op_1422")];
            tensor<int32, [4]> hidden_states_349_perm_0 = const()[name = string("hidden_states_349_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 16, 256, 64]> q_9 = transpose(perm = q_9_perm_0, x = var_1401)[name = string("transpose_11")];
            tensor<fp32, [1, 16, 256, 64]> var_1426 = mul(x = q_9, y = cos_5)[name = string("op_1426")];
            tensor<int32, [4]> x1_17_begin_0 = const()[name = string("x1_17_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> x1_17_end_0 = const()[name = string("x1_17_end_0"), val = tensor<int32, [4]>([1, 16, 256, 32])];
            tensor<bool, [4]> x1_17_end_mask_0 = const()[name = string("x1_17_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 16, 256, 32]> x1_17 = slice_by_index(begin = x1_17_begin_0, end = x1_17_end_0, end_mask = x1_17_end_mask_0, x = q_9)[name = string("x1_17")];
            tensor<int32, [4]> x2_17_begin_0 = const()[name = string("x2_17_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])];
            tensor<int32, [4]> x2_17_end_0 = const()[name = string("x2_17_end_0"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<bool, [4]> x2_17_end_mask_0 = const()[name = string("x2_17_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 16, 256, 32]> x2_17 = slice_by_index(begin = x2_17_begin_0, end = x2_17_end_0, end_mask = x2_17_end_mask_0, x = q_9)[name = string("x2_17")];
            fp32 const_96_promoted = const()[name = string("const_96_promoted"), val = fp32(-0x1p+0)];
            tensor<fp32, [1, 16, 256, 32]> var_1437 = mul(x = x2_17, y = const_96_promoted)[name = string("op_1437")];
            bool var_1439_interleave_0 = const()[name = string("op_1439_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 64]> var_1439 = concat(axis = var_26, interleave = var_1439_interleave_0, values = (var_1437, x1_17))[name = string("op_1439")];
            tensor<fp32, [1, 16, 256, 64]> var_1440 = mul(x = var_1439, y = sin_5)[name = string("op_1440")];
            tensor<fp32, [1, 16, 256, 64]> query_9 = add(x = var_1426, y = var_1440)[name = string("query_9")];
            tensor<fp32, [1, 8, 256, 64]> k_9 = transpose(perm = k_9_perm_0, x = var_1417)[name = string("transpose_10")];
            tensor<fp32, [1, 8, 256, 64]> var_1442 = mul(x = k_9, y = cos_5)[name = string("op_1442")];
            tensor<int32, [4]> x1_19_begin_0 = const()[name = string("x1_19_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> x1_19_end_0 = const()[name = string("x1_19_end_0"), val = tensor<int32, [4]>([1, 8, 256, 32])];
            tensor<bool, [4]> x1_19_end_mask_0 = const()[name = string("x1_19_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 256, 32]> x1_19 = slice_by_index(begin = x1_19_begin_0, end = x1_19_end_0, end_mask = x1_19_end_mask_0, x = k_9)[name = string("x1_19")];
            tensor<int32, [4]> x2_19_begin_0 = const()[name = string("x2_19_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])];
            tensor<int32, [4]> x2_19_end_0 = const()[name = string("x2_19_end_0"), val = tensor<int32, [4]>([1, 8, 256, 64])];
            tensor<bool, [4]> x2_19_end_mask_0 = const()[name = string("x2_19_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 256, 32]> x2_19 = slice_by_index(begin = x2_19_begin_0, end = x2_19_end_0, end_mask = x2_19_end_mask_0, x = k_9)[name = string("x2_19")];
            fp32 const_99_promoted = const()[name = string("const_99_promoted"), val = fp32(-0x1p+0)];
            tensor<fp32, [1, 8, 256, 32]> var_1453 = mul(x = x2_19, y = const_99_promoted)[name = string("op_1453")];
            bool var_1455_interleave_0 = const()[name = string("op_1455_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 8, 256, 64]> var_1455 = concat(axis = var_26, interleave = var_1455_interleave_0, values = (var_1453, x1_19))[name = string("op_1455")];
            tensor<fp32, [1, 8, 256, 64]> var_1456 = mul(x = var_1455, y = sin_5)[name = string("op_1456")];
            tensor<fp32, [1, 8, 256, 64]> hidden_states_345 = add(x = var_1442, y = var_1456)[name = string("hidden_states_345")];
            tensor<int32, [1]> var_1465_axes_0 = const()[name = string("op_1465_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp32, [1, 8, 1, 256, 64]> var_1465 = expand_dims(axes = var_1465_axes_0, x = hidden_states_345)[name = string("op_1465")];
            tensor<int32, [5]> hidden_states_347_reps_0 = const()[name = string("hidden_states_347_reps_0"), val = tensor<int32, [5]>([1, 1, 2, 1, 1])];
            tensor<fp32, [1, 8, 2, 256, 64]> hidden_states_347 = tile(reps = hidden_states_347_reps_0, x = var_1465)[name = string("hidden_states_347")];
            tensor<int32, [4]> var_1473 = const()[name = string("op_1473"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<fp32, [1, 16, 256, 64]> key_9 = reshape(shape = var_1473, x = hidden_states_347)[name = string("key_9")];
            tensor<int32, [1]> var_1482_axes_0 = const()[name = string("op_1482_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp32, [1, 8, 256, 64]> hidden_states_349 = transpose(perm = hidden_states_349_perm_0, x = var_1422)[name = string("transpose_9")];
            tensor<fp32, [1, 8, 1, 256, 64]> var_1482 = expand_dims(axes = var_1482_axes_0, x = hidden_states_349)[name = string("op_1482")];
            tensor<int32, [5]> hidden_states_351_reps_0 = const()[name = string("hidden_states_351_reps_0"), val = tensor<int32, [5]>([1, 1, 2, 1, 1])];
            tensor<fp32, [1, 8, 2, 256, 64]> hidden_states_351 = tile(reps = hidden_states_351_reps_0, x = var_1482)[name = string("hidden_states_351")];
            tensor<int32, [4]> var_1490 = const()[name = string("op_1490"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<fp32, [1, 16, 256, 64]> value_9 = reshape(shape = var_1490, x = hidden_states_351)[name = string("value_9")];
            tensor<fp32, [1, 16, 256, 64]> mul_9 = mul(x = query_9, y = var_15)[name = string("mul_9")];
            bool matmul_4_transpose_y_0 = const()[name = string("matmul_4_transpose_y_0"), val = bool(true)];
            bool matmul_4_transpose_x_0 = const()[name = string("matmul_4_transpose_x_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 256]> matmul_4 = matmul(transpose_x = matmul_4_transpose_x_0, transpose_y = matmul_4_transpose_y_0, x = mul_9, y = key_9)[name = string("matmul_4")];
            tensor<fp32, [1, 16, 256, 256]> add_4 = add(x = matmul_4, y = mul_0)[name = string("add_4")];
            int32 softmax_4_axis_0 = const()[name = string("softmax_4_axis_0"), val = int32(-1)];
            tensor<fp32, [1, 16, 256, 256]> softmax_4 = softmax(axis = softmax_4_axis_0, x = add_4)[name = string("softmax_4")];
            bool attn_output_17_transpose_x_0 = const()[name = string("attn_output_17_transpose_x_0"), val = bool(false)];
            bool attn_output_17_transpose_y_0 = const()[name = string("attn_output_17_transpose_y_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 64]> attn_output_17 = matmul(transpose_x = attn_output_17_transpose_x_0, transpose_y = attn_output_17_transpose_y_0, x = softmax_4, y = value_9)[name = string("attn_output_17")];
            tensor<int32, [4]> var_1498_perm_0 = const()[name = string("op_1498_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1500 = const()[name = string("op_1500"), val = tensor<int32, [3]>([1, 256, -1])];
            tensor<fp32, [1, 256, 16, 64]> var_1498 = transpose(perm = var_1498_perm_0, x = attn_output_17)[name = string("transpose_8")];
            tensor<fp32, [1, 256, 1024]> var_1501 = reshape(shape = var_1500, x = var_1498)[name = string("op_1501")];
            tensor<fp32, [1, 256, 1024]> hidden_states_353 = linear(bias = linear_1_bias_0, weight = base_model_layers_12_self_attn_out_proj_weight, x = var_1501)[name = string("linear_71")];
            tensor<fp32, [1, 256, 1024]> hidden_states_355 = add(x = hidden_states_353, y = hidden_states_319)[name = string("hidden_states_355")];
            fp32 var_25_promoted_35 = const()[name = string("op_25_promoted_35"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1508 = pow(x = hidden_states_355, y = var_25_promoted_35)[name = string("op_1508")];
            tensor<int32, [1]> variance_71_axes_0 = const()[name = string("variance_71_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_71_keep_dims_0 = const()[name = string("variance_71_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_71 = reduce_mean(axes = variance_71_axes_0, keep_dims = variance_71_keep_dims_0, x = var_1508)[name = string("variance_71")];
            fp32 var_1511 = const()[name = string("op_1511"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1512 = add(x = variance_71, y = var_1511)[name = string("op_1512")];
            fp32 var_1513_epsilon_0 = const()[name = string("op_1513_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1513 = rsqrt(epsilon = var_1513_epsilon_0, x = var_1512)[name = string("op_1513")];
            tensor<fp32, [1, 256, 1024]> hidden_states_359 = mul(x = hidden_states_355, y = var_1513)[name = string("hidden_states_359")];
            tensor<fp32, [1, 256, 1024]> input_117 = mul(x = base_model_layers_12_ffn_norm_weight, y = hidden_states_359)[name = string("input_117")];
            tensor<fp32, [1, 256, 4608]> input_119 = linear(bias = linear_2_bias_0, weight = base_model_layers_12_feed_forward_w1_weight, x = input_117)[name = string("linear_72")];
            tensor<fp32, [1, 256, 4608]> var_1525 = silu(x = input_119)[name = string("op_1525")];
            tensor<fp32, [1, 256, 4608]> var_1527 = linear(bias = linear_2_bias_0, weight = base_model_layers_12_feed_forward_w3_weight, x = input_117)[name = string("linear_73")];
            tensor<fp32, [1, 256, 4608]> input_121 = mul(x = var_1525, y = var_1527)[name = string("input_121")];
            tensor<fp32, [1, 256, 1024]> var_1530 = linear(bias = linear_1_bias_0, weight = base_model_layers_12_feed_forward_w2_weight, x = input_121)[name = string("linear_74")];
            tensor<fp32, [1, 256, 1024]> hidden_states_363 = add(x = hidden_states_355, y = var_1530)[name = string("hidden_states_363")];
            fp32 var_25_promoted_36 = const()[name = string("op_25_promoted_36"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1538 = pow(x = hidden_states_363, y = var_25_promoted_36)[name = string("op_1538")];
            tensor<int32, [1]> variance_73_axes_0 = const()[name = string("variance_73_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_73_keep_dims_0 = const()[name = string("variance_73_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_73 = reduce_mean(axes = variance_73_axes_0, keep_dims = variance_73_keep_dims_0, x = var_1538)[name = string("variance_73")];
            fp32 var_1541 = const()[name = string("op_1541"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1542 = add(x = variance_73, y = var_1541)[name = string("op_1542")];
            fp32 var_1543_epsilon_0 = const()[name = string("op_1543_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1543 = rsqrt(epsilon = var_1543_epsilon_0, x = var_1542)[name = string("op_1543")];
            tensor<fp32, [1, 256, 1024]> hidden_states_367 = mul(x = hidden_states_363, y = var_1543)[name = string("hidden_states_367")];
            tensor<fp32, [1, 256, 1024]> x_33 = mul(x = base_model_layers_13_operator_norm_weight, y = hidden_states_367)[name = string("x_33")];
            tensor<fp32, [1, 256, 3072]> var_1555 = linear(bias = linear_0_bias_0, weight = base_model_layers_13_conv_in_proj_weight, x = x_33)[name = string("linear_75")];
            tensor<int32, [3]> BCx_17_perm_0 = const()[name = string("BCx_17_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [3]> var_1557_split_sizes_0 = const()[name = string("op_1557_split_sizes_0"), val = tensor<int32, [3]>([1024, 1024, 1024])];
            int32 var_1557_axis_0 = const()[name = string("op_1557_axis_0"), val = int32(-2)];
            tensor<fp32, [1, 3072, 256]> BCx_17 = transpose(perm = BCx_17_perm_0, x = var_1555)[name = string("transpose_7")];
            tensor<fp32, [1, 1024, 256]> var_1557_0, tensor<fp32, [1, 1024, 256]> var_1557_1, tensor<fp32, [1, 1024, 256]> var_1557_2 = split(axis = var_1557_axis_0, split_sizes = var_1557_split_sizes_0, x = BCx_17)[name = string("op_1557")];
            tensor<fp32, [1, 1024, 256]> input_123 = mul(x = var_1557_0, y = var_1557_2)[name = string("input_123")];
            string var_1566_pad_type_0 = const()[name = string("op_1566_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> var_1566_pad_0 = const()[name = string("op_1566_pad_0"), val = tensor<int32, [2]>([2, 2])];
            int32 var_1566_groups_0 = const()[name = string("op_1566_groups_0"), val = int32(1024)];
            tensor<int32, [1]> var_1566_strides_0 = const()[name = string("op_1566_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1566_dilations_0 = const()[name = string("op_1566_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1024, 258]> var_1566 = conv(dilations = var_1566_dilations_0, groups = var_1566_groups_0, pad = var_1566_pad_0, pad_type = var_1566_pad_type_0, strides = var_1566_strides_0, weight = base_model_layers_13_conv_conv_weight, x = input_123)[name = string("op_1566")];
            tensor<int32, [3]> conv_out_17_begin_0 = const()[name = string("conv_out_17_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> conv_out_17_end_0 = const()[name = string("conv_out_17_end_0"), val = tensor<int32, [3]>([1, 1024, 256])];
            tensor<bool, [3]> conv_out_17_end_mask_0 = const()[name = string("conv_out_17_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 1024, 256]> conv_out_17 = slice_by_index(begin = conv_out_17_begin_0, end = conv_out_17_end_0, end_mask = conv_out_17_end_mask_0, x = var_1566)[name = string("conv_out_17")];
            tensor<fp32, [1, 1024, 256]> y_17 = mul(x = var_1557_1, y = conv_out_17)[name = string("y_17")];
            tensor<int32, [3]> var_1569_perm_0 = const()[name = string("op_1569_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<fp32, [1, 256, 1024]> var_1569 = transpose(perm = var_1569_perm_0, x = y_17)[name = string("transpose_6")];
            tensor<fp32, [1, 256, 1024]> hidden_states_371 = linear(bias = linear_1_bias_0, weight = base_model_layers_13_conv_out_proj_weight, x = var_1569)[name = string("linear_76")];
            tensor<fp32, [1, 256, 1024]> hidden_states_373 = add(x = hidden_states_371, y = hidden_states_363)[name = string("hidden_states_373")];
            fp32 var_25_promoted_37 = const()[name = string("op_25_promoted_37"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1576 = pow(x = hidden_states_373, y = var_25_promoted_37)[name = string("op_1576")];
            tensor<int32, [1]> variance_75_axes_0 = const()[name = string("variance_75_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_75_keep_dims_0 = const()[name = string("variance_75_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_75 = reduce_mean(axes = variance_75_axes_0, keep_dims = variance_75_keep_dims_0, x = var_1576)[name = string("variance_75")];
            fp32 var_1579 = const()[name = string("op_1579"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1580 = add(x = variance_75, y = var_1579)[name = string("op_1580")];
            fp32 var_1581_epsilon_0 = const()[name = string("op_1581_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1581 = rsqrt(epsilon = var_1581_epsilon_0, x = var_1580)[name = string("op_1581")];
            tensor<fp32, [1, 256, 1024]> hidden_states_377 = mul(x = hidden_states_373, y = var_1581)[name = string("hidden_states_377")];
            tensor<fp32, [1, 256, 1024]> input_127 = mul(x = base_model_layers_13_ffn_norm_weight, y = hidden_states_377)[name = string("input_127")];
            tensor<fp32, [1, 256, 4608]> input_129 = linear(bias = linear_2_bias_0, weight = base_model_layers_13_feed_forward_w1_weight, x = input_127)[name = string("linear_77")];
            tensor<fp32, [1, 256, 4608]> var_1593 = silu(x = input_129)[name = string("op_1593")];
            tensor<fp32, [1, 256, 4608]> var_1595 = linear(bias = linear_2_bias_0, weight = base_model_layers_13_feed_forward_w3_weight, x = input_127)[name = string("linear_78")];
            tensor<fp32, [1, 256, 4608]> input_131 = mul(x = var_1593, y = var_1595)[name = string("input_131")];
            tensor<fp32, [1, 256, 1024]> var_1598 = linear(bias = linear_1_bias_0, weight = base_model_layers_13_feed_forward_w2_weight, x = input_131)[name = string("linear_79")];
            tensor<fp32, [1, 256, 1024]> hidden_states_381 = add(x = hidden_states_373, y = var_1598)[name = string("hidden_states_381")];
            fp32 var_25_promoted_38 = const()[name = string("op_25_promoted_38"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1606 = pow(x = hidden_states_381, y = var_25_promoted_38)[name = string("op_1606")];
            tensor<int32, [1]> variance_77_axes_0 = const()[name = string("variance_77_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_77_keep_dims_0 = const()[name = string("variance_77_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_77 = reduce_mean(axes = variance_77_axes_0, keep_dims = variance_77_keep_dims_0, x = var_1606)[name = string("variance_77")];
            fp32 var_1609 = const()[name = string("op_1609"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1610 = add(x = variance_77, y = var_1609)[name = string("op_1610")];
            fp32 var_1611_epsilon_0 = const()[name = string("op_1611_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1611 = rsqrt(epsilon = var_1611_epsilon_0, x = var_1610)[name = string("op_1611")];
            tensor<fp32, [1, 256, 1024]> hidden_states_385 = mul(x = hidden_states_381, y = var_1611)[name = string("hidden_states_385")];
            tensor<fp32, [1, 256, 1024]> hidden_states_389 = mul(x = base_model_layers_14_operator_norm_weight, y = hidden_states_385)[name = string("hidden_states_389")];
            tensor<fp32, [1, 256, 1024]> var_1627 = linear(bias = linear_1_bias_0, weight = base_model_layers_14_self_attn_q_proj_weight, x = hidden_states_389)[name = string("linear_80")];
            tensor<int32, [4]> var_1628 = const()[name = string("op_1628"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 16, 64]> hidden_states_391 = reshape(shape = var_1628, x = var_1627)[name = string("hidden_states_391")];
            fp32 var_25_promoted_39 = const()[name = string("op_25_promoted_39"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 16, 64]> var_1632 = pow(x = hidden_states_391, y = var_25_promoted_39)[name = string("op_1632")];
            tensor<int32, [1]> variance_79_axes_0 = const()[name = string("variance_79_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_79_keep_dims_0 = const()[name = string("variance_79_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 16, 1]> variance_79 = reduce_mean(axes = variance_79_axes_0, keep_dims = variance_79_keep_dims_0, x = var_1632)[name = string("variance_79")];
            fp32 var_1635 = const()[name = string("op_1635"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 16, 1]> var_1636 = add(x = variance_79, y = var_1635)[name = string("op_1636")];
            fp32 var_1637_epsilon_0 = const()[name = string("op_1637_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 16, 1]> var_1637 = rsqrt(epsilon = var_1637_epsilon_0, x = var_1636)[name = string("op_1637")];
            tensor<fp32, [1, 256, 16, 64]> hidden_states_395 = mul(x = hidden_states_391, y = var_1637)[name = string("hidden_states_395")];
            tensor<fp32, [1, 256, 16, 64]> var_1640 = mul(x = base_model_layers_14_self_attn_q_layernorm_weight, y = hidden_states_395)[name = string("op_1640")];
            tensor<int32, [4]> q_perm_0 = const()[name = string("q_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 256, 512]> var_1643 = linear(bias = linear_11_bias_0, weight = base_model_layers_14_self_attn_k_proj_weight, x = hidden_states_389)[name = string("linear_81")];
            tensor<int32, [4]> var_1644 = const()[name = string("op_1644"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 8, 64]> hidden_states_399 = reshape(shape = var_1644, x = var_1643)[name = string("hidden_states_399")];
            fp32 var_25_promoted_40 = const()[name = string("op_25_promoted_40"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 8, 64]> var_1648 = pow(x = hidden_states_399, y = var_25_promoted_40)[name = string("op_1648")];
            tensor<int32, [1]> variance_81_axes_0 = const()[name = string("variance_81_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_81_keep_dims_0 = const()[name = string("variance_81_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 8, 1]> variance_81 = reduce_mean(axes = variance_81_axes_0, keep_dims = variance_81_keep_dims_0, x = var_1648)[name = string("variance_81")];
            fp32 var_1651 = const()[name = string("op_1651"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 8, 1]> var_1652 = add(x = variance_81, y = var_1651)[name = string("op_1652")];
            fp32 var_1653_epsilon_0 = const()[name = string("op_1653_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 8, 1]> var_1653 = rsqrt(epsilon = var_1653_epsilon_0, x = var_1652)[name = string("op_1653")];
            tensor<fp32, [1, 256, 8, 64]> hidden_states_403 = mul(x = hidden_states_399, y = var_1653)[name = string("hidden_states_403")];
            tensor<fp32, [1, 256, 8, 64]> var_1656 = mul(x = base_model_layers_14_self_attn_k_layernorm_weight, y = hidden_states_403)[name = string("op_1656")];
            tensor<int32, [4]> k_perm_0 = const()[name = string("k_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 256, 512]> var_1659 = linear(bias = linear_11_bias_0, weight = base_model_layers_14_self_attn_v_proj_weight, x = hidden_states_389)[name = string("linear_82")];
            tensor<int32, [4]> var_1660 = const()[name = string("op_1660"), val = tensor<int32, [4]>([1, 256, -1, 64])];
            tensor<fp32, [1, 256, 8, 64]> var_1661 = reshape(shape = var_1660, x = var_1659)[name = string("op_1661")];
            tensor<int32, [4]> hidden_states_411_perm_0 = const()[name = string("hidden_states_411_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp32, [1, 16, 256, 64]> q = transpose(perm = q_perm_0, x = var_1640)[name = string("transpose_5")];
            tensor<fp32, [1, 16, 256, 64]> var_1665 = mul(x = q, y = cos_5)[name = string("op_1665")];
            tensor<int32, [4]> x1_21_begin_0 = const()[name = string("x1_21_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> x1_21_end_0 = const()[name = string("x1_21_end_0"), val = tensor<int32, [4]>([1, 16, 256, 32])];
            tensor<bool, [4]> x1_21_end_mask_0 = const()[name = string("x1_21_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 16, 256, 32]> x1_21 = slice_by_index(begin = x1_21_begin_0, end = x1_21_end_0, end_mask = x1_21_end_mask_0, x = q)[name = string("x1_21")];
            tensor<int32, [4]> x2_21_begin_0 = const()[name = string("x2_21_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])];
            tensor<int32, [4]> x2_21_end_0 = const()[name = string("x2_21_end_0"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<bool, [4]> x2_21_end_mask_0 = const()[name = string("x2_21_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 16, 256, 32]> x2_21 = slice_by_index(begin = x2_21_begin_0, end = x2_21_end_0, end_mask = x2_21_end_mask_0, x = q)[name = string("x2_21")];
            fp32 const_114_promoted = const()[name = string("const_114_promoted"), val = fp32(-0x1p+0)];
            tensor<fp32, [1, 16, 256, 32]> var_1676 = mul(x = x2_21, y = const_114_promoted)[name = string("op_1676")];
            bool var_1678_interleave_0 = const()[name = string("op_1678_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 64]> var_1678 = concat(axis = var_26, interleave = var_1678_interleave_0, values = (var_1676, x1_21))[name = string("op_1678")];
            tensor<fp32, [1, 16, 256, 64]> var_1679 = mul(x = var_1678, y = sin_5)[name = string("op_1679")];
            tensor<fp32, [1, 16, 256, 64]> query = add(x = var_1665, y = var_1679)[name = string("query")];
            tensor<fp32, [1, 8, 256, 64]> k = transpose(perm = k_perm_0, x = var_1656)[name = string("transpose_4")];
            tensor<fp32, [1, 8, 256, 64]> var_1681 = mul(x = k, y = cos_5)[name = string("op_1681")];
            tensor<int32, [4]> x1_begin_0 = const()[name = string("x1_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> x1_end_0 = const()[name = string("x1_end_0"), val = tensor<int32, [4]>([1, 8, 256, 32])];
            tensor<bool, [4]> x1_end_mask_0 = const()[name = string("x1_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp32, [1, 8, 256, 32]> x1 = slice_by_index(begin = x1_begin_0, end = x1_end_0, end_mask = x1_end_mask_0, x = k)[name = string("x1")];
            tensor<int32, [4]> x2_begin_0 = const()[name = string("x2_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])];
            tensor<int32, [4]> x2_end_0 = const()[name = string("x2_end_0"), val = tensor<int32, [4]>([1, 8, 256, 64])];
            tensor<bool, [4]> x2_end_mask_0 = const()[name = string("x2_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp32, [1, 8, 256, 32]> x2 = slice_by_index(begin = x2_begin_0, end = x2_end_0, end_mask = x2_end_mask_0, x = k)[name = string("x2")];
            fp32 const_117_promoted = const()[name = string("const_117_promoted"), val = fp32(-0x1p+0)];
            tensor<fp32, [1, 8, 256, 32]> var_1692 = mul(x = x2, y = const_117_promoted)[name = string("op_1692")];
            bool var_1694_interleave_0 = const()[name = string("op_1694_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 8, 256, 64]> var_1694 = concat(axis = var_26, interleave = var_1694_interleave_0, values = (var_1692, x1))[name = string("op_1694")];
            tensor<fp32, [1, 8, 256, 64]> var_1695 = mul(x = var_1694, y = sin_5)[name = string("op_1695")];
            tensor<fp32, [1, 8, 256, 64]> hidden_states_407 = add(x = var_1681, y = var_1695)[name = string("hidden_states_407")];
            tensor<int32, [1]> var_1704_axes_0 = const()[name = string("op_1704_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp32, [1, 8, 1, 256, 64]> var_1704 = expand_dims(axes = var_1704_axes_0, x = hidden_states_407)[name = string("op_1704")];
            tensor<int32, [5]> hidden_states_409_reps_0 = const()[name = string("hidden_states_409_reps_0"), val = tensor<int32, [5]>([1, 1, 2, 1, 1])];
            tensor<fp32, [1, 8, 2, 256, 64]> hidden_states_409 = tile(reps = hidden_states_409_reps_0, x = var_1704)[name = string("hidden_states_409")];
            tensor<int32, [4]> var_1712 = const()[name = string("op_1712"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<fp32, [1, 16, 256, 64]> key = reshape(shape = var_1712, x = hidden_states_409)[name = string("key")];
            tensor<int32, [1]> var_1721_axes_0 = const()[name = string("op_1721_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp32, [1, 8, 256, 64]> hidden_states_411 = transpose(perm = hidden_states_411_perm_0, x = var_1661)[name = string("transpose_3")];
            tensor<fp32, [1, 8, 1, 256, 64]> var_1721 = expand_dims(axes = var_1721_axes_0, x = hidden_states_411)[name = string("op_1721")];
            tensor<int32, [5]> hidden_states_413_reps_0 = const()[name = string("hidden_states_413_reps_0"), val = tensor<int32, [5]>([1, 1, 2, 1, 1])];
            tensor<fp32, [1, 8, 2, 256, 64]> hidden_states_413 = tile(reps = hidden_states_413_reps_0, x = var_1721)[name = string("hidden_states_413")];
            tensor<int32, [4]> var_1729 = const()[name = string("op_1729"), val = tensor<int32, [4]>([1, 16, 256, 64])];
            tensor<fp32, [1, 16, 256, 64]> value = reshape(shape = var_1729, x = hidden_states_413)[name = string("value")];
            tensor<fp32, [1, 16, 256, 64]> mul_11 = mul(x = query, y = var_15)[name = string("mul_11")];
            bool matmul_5_transpose_y_0 = const()[name = string("matmul_5_transpose_y_0"), val = bool(true)];
            bool matmul_5_transpose_x_0 = const()[name = string("matmul_5_transpose_x_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 256]> matmul_5 = matmul(transpose_x = matmul_5_transpose_x_0, transpose_y = matmul_5_transpose_y_0, x = mul_11, y = key)[name = string("matmul_5")];
            tensor<fp32, [1, 16, 256, 256]> add_5 = add(x = matmul_5, y = mul_0)[name = string("add_5")];
            int32 softmax_5_axis_0 = const()[name = string("softmax_5_axis_0"), val = int32(-1)];
            tensor<fp32, [1, 16, 256, 256]> softmax_5 = softmax(axis = softmax_5_axis_0, x = add_5)[name = string("softmax_5")];
            bool attn_output_21_transpose_x_0 = const()[name = string("attn_output_21_transpose_x_0"), val = bool(false)];
            bool attn_output_21_transpose_y_0 = const()[name = string("attn_output_21_transpose_y_0"), val = bool(false)];
            tensor<fp32, [1, 16, 256, 64]> attn_output_21 = matmul(transpose_x = attn_output_21_transpose_x_0, transpose_y = attn_output_21_transpose_y_0, x = softmax_5, y = value)[name = string("attn_output_21")];
            tensor<int32, [4]> var_1737_perm_0 = const()[name = string("op_1737_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1739 = const()[name = string("op_1739"), val = tensor<int32, [3]>([1, 256, -1])];
            tensor<fp32, [1, 256, 16, 64]> var_1737 = transpose(perm = var_1737_perm_0, x = attn_output_21)[name = string("transpose_2")];
            tensor<fp32, [1, 256, 1024]> var_1740 = reshape(shape = var_1739, x = var_1737)[name = string("op_1740")];
            tensor<fp32, [1, 256, 1024]> hidden_states_415 = linear(bias = linear_1_bias_0, weight = base_model_layers_14_self_attn_out_proj_weight, x = var_1740)[name = string("linear_83")];
            tensor<fp32, [1, 256, 1024]> hidden_states_417 = add(x = hidden_states_415, y = hidden_states_381)[name = string("hidden_states_417")];
            fp32 var_25_promoted_41 = const()[name = string("op_25_promoted_41"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1747 = pow(x = hidden_states_417, y = var_25_promoted_41)[name = string("op_1747")];
            tensor<int32, [1]> variance_83_axes_0 = const()[name = string("variance_83_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_83_keep_dims_0 = const()[name = string("variance_83_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_83 = reduce_mean(axes = variance_83_axes_0, keep_dims = variance_83_keep_dims_0, x = var_1747)[name = string("variance_83")];
            fp32 var_1750 = const()[name = string("op_1750"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1751 = add(x = variance_83, y = var_1750)[name = string("op_1751")];
            fp32 var_1752_epsilon_0 = const()[name = string("op_1752_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1752 = rsqrt(epsilon = var_1752_epsilon_0, x = var_1751)[name = string("op_1752")];
            tensor<fp32, [1, 256, 1024]> hidden_states_421 = mul(x = hidden_states_417, y = var_1752)[name = string("hidden_states_421")];
            tensor<fp32, [1, 256, 1024]> input_135 = mul(x = base_model_layers_14_ffn_norm_weight, y = hidden_states_421)[name = string("input_135")];
            tensor<fp32, [1, 256, 4608]> input_137 = linear(bias = linear_2_bias_0, weight = base_model_layers_14_feed_forward_w1_weight, x = input_135)[name = string("linear_84")];
            tensor<fp32, [1, 256, 4608]> var_1764 = silu(x = input_137)[name = string("op_1764")];
            tensor<fp32, [1, 256, 4608]> var_1766 = linear(bias = linear_2_bias_0, weight = base_model_layers_14_feed_forward_w3_weight, x = input_135)[name = string("linear_85")];
            tensor<fp32, [1, 256, 4608]> input_139 = mul(x = var_1764, y = var_1766)[name = string("input_139")];
            tensor<fp32, [1, 256, 1024]> var_1769 = linear(bias = linear_1_bias_0, weight = base_model_layers_14_feed_forward_w2_weight, x = input_139)[name = string("linear_86")];
            tensor<fp32, [1, 256, 1024]> hidden_states_425 = add(x = hidden_states_417, y = var_1769)[name = string("hidden_states_425")];
            fp32 var_25_promoted_42 = const()[name = string("op_25_promoted_42"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1777 = pow(x = hidden_states_425, y = var_25_promoted_42)[name = string("op_1777")];
            tensor<int32, [1]> variance_85_axes_0 = const()[name = string("variance_85_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_85_keep_dims_0 = const()[name = string("variance_85_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_85 = reduce_mean(axes = variance_85_axes_0, keep_dims = variance_85_keep_dims_0, x = var_1777)[name = string("variance_85")];
            fp32 var_1780 = const()[name = string("op_1780"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1781 = add(x = variance_85, y = var_1780)[name = string("op_1781")];
            fp32 var_1782_epsilon_0 = const()[name = string("op_1782_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1782 = rsqrt(epsilon = var_1782_epsilon_0, x = var_1781)[name = string("op_1782")];
            tensor<fp32, [1, 256, 1024]> hidden_states_429 = mul(x = hidden_states_425, y = var_1782)[name = string("hidden_states_429")];
            tensor<fp32, [1, 256, 1024]> x_37 = mul(x = base_model_layers_15_operator_norm_weight, y = hidden_states_429)[name = string("x_37")];
            tensor<fp32, [1, 256, 3072]> var_1794 = linear(bias = linear_0_bias_0, weight = base_model_layers_15_conv_in_proj_weight, x = x_37)[name = string("linear_87")];
            tensor<int32, [3]> BCx_perm_0 = const()[name = string("BCx_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [3]> var_1796_split_sizes_0 = const()[name = string("op_1796_split_sizes_0"), val = tensor<int32, [3]>([1024, 1024, 1024])];
            int32 var_1796_axis_0 = const()[name = string("op_1796_axis_0"), val = int32(-2)];
            tensor<fp32, [1, 3072, 256]> BCx = transpose(perm = BCx_perm_0, x = var_1794)[name = string("transpose_1")];
            tensor<fp32, [1, 1024, 256]> var_1796_0, tensor<fp32, [1, 1024, 256]> var_1796_1, tensor<fp32, [1, 1024, 256]> var_1796_2 = split(axis = var_1796_axis_0, split_sizes = var_1796_split_sizes_0, x = BCx)[name = string("op_1796")];
            tensor<fp32, [1, 1024, 256]> input_141 = mul(x = var_1796_0, y = var_1796_2)[name = string("input_141")];
            string var_1805_pad_type_0 = const()[name = string("op_1805_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> var_1805_pad_0 = const()[name = string("op_1805_pad_0"), val = tensor<int32, [2]>([2, 2])];
            int32 var_1805_groups_0 = const()[name = string("op_1805_groups_0"), val = int32(1024)];
            tensor<int32, [1]> var_1805_strides_0 = const()[name = string("op_1805_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1805_dilations_0 = const()[name = string("op_1805_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1024, 258]> var_1805 = conv(dilations = var_1805_dilations_0, groups = var_1805_groups_0, pad = var_1805_pad_0, pad_type = var_1805_pad_type_0, strides = var_1805_strides_0, weight = base_model_layers_15_conv_conv_weight, x = input_141)[name = string("op_1805")];
            tensor<int32, [3]> conv_out_begin_0 = const()[name = string("conv_out_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> conv_out_end_0 = const()[name = string("conv_out_end_0"), val = tensor<int32, [3]>([1, 1024, 256])];
            tensor<bool, [3]> conv_out_end_mask_0 = const()[name = string("conv_out_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 1024, 256]> conv_out = slice_by_index(begin = conv_out_begin_0, end = conv_out_end_0, end_mask = conv_out_end_mask_0, x = var_1805)[name = string("conv_out")];
            tensor<fp32, [1, 1024, 256]> y = mul(x = var_1796_1, y = conv_out)[name = string("y")];
            tensor<int32, [3]> var_1808_perm_0 = const()[name = string("op_1808_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<fp32, [1, 256, 1024]> var_1808 = transpose(perm = var_1808_perm_0, x = y)[name = string("transpose_0")];
            tensor<fp32, [1, 256, 1024]> hidden_states_433 = linear(bias = linear_1_bias_0, weight = base_model_layers_15_conv_out_proj_weight, x = var_1808)[name = string("linear_88")];
            tensor<fp32, [1, 256, 1024]> hidden_states_435 = add(x = hidden_states_433, y = hidden_states_425)[name = string("hidden_states_435")];
            fp32 var_25_promoted_43 = const()[name = string("op_25_promoted_43"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1815 = pow(x = hidden_states_435, y = var_25_promoted_43)[name = string("op_1815")];
            tensor<int32, [1]> variance_87_axes_0 = const()[name = string("variance_87_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_87_keep_dims_0 = const()[name = string("variance_87_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance_87 = reduce_mean(axes = variance_87_axes_0, keep_dims = variance_87_keep_dims_0, x = var_1815)[name = string("variance_87")];
            fp32 var_1818 = const()[name = string("op_1818"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1819 = add(x = variance_87, y = var_1818)[name = string("op_1819")];
            fp32 var_1820_epsilon_0 = const()[name = string("op_1820_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1820 = rsqrt(epsilon = var_1820_epsilon_0, x = var_1819)[name = string("op_1820")];
            tensor<fp32, [1, 256, 1024]> hidden_states_439 = mul(x = hidden_states_435, y = var_1820)[name = string("hidden_states_439")];
            tensor<fp32, [1, 256, 1024]> input_145 = mul(x = base_model_layers_15_ffn_norm_weight, y = hidden_states_439)[name = string("input_145")];
            tensor<fp32, [1, 256, 4608]> input_147 = linear(bias = linear_2_bias_0, weight = base_model_layers_15_feed_forward_w1_weight, x = input_145)[name = string("linear_89")];
            tensor<fp32, [1, 256, 4608]> var_1832 = silu(x = input_147)[name = string("op_1832")];
            tensor<fp32, [1, 256, 4608]> var_1834 = linear(bias = linear_2_bias_0, weight = base_model_layers_15_feed_forward_w3_weight, x = input_145)[name = string("linear_90")];
            tensor<fp32, [1, 256, 4608]> input_149 = mul(x = var_1832, y = var_1834)[name = string("input_149")];
            tensor<fp32, [1, 256, 1024]> var_1837 = linear(bias = linear_1_bias_0, weight = base_model_layers_15_feed_forward_w2_weight, x = input_149)[name = string("linear_91")];
            tensor<fp32, [1, 256, 1024]> hidden_states_443 = add(x = hidden_states_435, y = var_1837)[name = string("hidden_states_443")];
            fp32 var_25_promoted_44 = const()[name = string("op_25_promoted_44"), val = fp32(0x1p+1)];
            tensor<fp32, [1, 256, 1024]> var_1841 = pow(x = hidden_states_443, y = var_25_promoted_44)[name = string("op_1841")];
            tensor<int32, [1]> variance_axes_0 = const()[name = string("variance_axes_0"), val = tensor<int32, [1]>([-1])];
            bool variance_keep_dims_0 = const()[name = string("variance_keep_dims_0"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> variance = reduce_mean(axes = variance_axes_0, keep_dims = variance_keep_dims_0, x = var_1841)[name = string("variance")];
            fp32 var_1844 = const()[name = string("op_1844"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 256, 1]> var_1845 = add(x = variance, y = var_1844)[name = string("op_1845")];
            fp32 var_1846_epsilon_0 = const()[name = string("op_1846_epsilon_0"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1846 = rsqrt(epsilon = var_1846_epsilon_0, x = var_1845)[name = string("op_1846")];
            tensor<fp32, [1, 256, 1024]> hidden_states_447 = mul(x = hidden_states_443, y = var_1846)[name = string("hidden_states_447")];
            tensor<fp32, [1, 256, 1024]> input_151 = mul(x = base_model_embedding_norm_weight, y = hidden_states_447)[name = string("input_151")];
            tensor<fp32, [128]> linear_92_bias_0 = const()[name = string("linear_92_bias_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413994304)))];
            tensor<fp32, [1, 256, 128]> input_153 = linear(bias = linear_92_bias_0, weight = projection_weight, x = input_151)[name = string("linear_92")];
            tensor<int32, [1]> var_1855 = const()[name = string("op_1855"), val = tensor<int32, [1]>([-1])];
            bool var_1856 = const()[name = string("op_1856"), val = bool(true)];
            tensor<fp32, [1, 256, 1]> var_1858 = reduce_l2_norm(axes = var_1855, keep_dims = var_1856, x = input_153)[name = string("op_1858")];
            fp32 var_1859 = const()[name = string("op_1859"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 256, 1]> var_1860 = maximum(x = var_1858, y = var_1859)[name = string("op_1860")];
            tensor<int32, [3]> denom_1_reps_0 = const()[name = string("denom_1_reps_0"), val = tensor<int32, [3]>([1, 1, 128])];
            tensor<fp32, [1, 256, 128]> denom_1 = tile(reps = denom_1_reps_0, x = var_1860)[name = string("denom_1")];
            tensor<fp32, [1, 256, 128]> projected = real_div(x = input_153, y = denom_1)[name = string("projected")];
            tensor<int32, [1]> var_1864_axes_0 = const()[name = string("op_1864_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<int32, [1, 256, 1]> var_1864 = expand_dims(axes = var_1864_axes_0, x = attention_mask)[name = string("op_1864")];
            string mask_dtype_0 = const()[name = string("mask_dtype_0"), val = string("fp32")];
            tensor<fp32, [1, 256, 1]> mask = cast(dtype = mask_dtype_0, x = var_1864)[name = string("cast_52")];
            tensor<fp32, [1, 256, 128]> token_embeddings = mul(x = projected, y = mask)[name = string("token_embeddings")];
            tensor<int32, [1]> var_1875_axes_0 = const()[name = string("op_1875_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_1875_keep_dims_0 = const()[name = string("op_1875_keep_dims_0"), val = bool(false)];
            tensor<fp32, [1, 1]> var_1875 = reduce_sum(axes = var_1875_axes_0, keep_dims = var_1875_keep_dims_0, x = mask)[name = string("op_1875")];
            fp32 var_1876 = const()[name = string("op_1876"), val = fp32(0x1.0c6f7ap-20)];
            fp32 const_128 = const()[name = string("const_128"), val = fp32(0x1.fffffep+127)];
            tensor<fp32, [1, 1]> clip_0 = clip(alpha = var_1876, beta = const_128, x = var_1875)[name = string("clip_0")];
            tensor<int32, [1]> var_1883_axes_0 = const()[name = string("op_1883_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_1883_keep_dims_0 = const()[name = string("op_1883_keep_dims_0"), val = bool(false)];
            tensor<fp32, [1, 128]> var_1883 = reduce_sum(axes = var_1883_axes_0, keep_dims = var_1883_keep_dims_0, x = token_embeddings)[name = string("op_1883")];
            tensor<fp32, [1, 128]> input = real_div(x = var_1883, y = clip_0)[name = string("input")];
            tensor<int32, [1]> var_1887 = const()[name = string("op_1887"), val = tensor<int32, [1]>([-1])];
            bool var_1888 = const()[name = string("op_1888"), val = bool(true)];
            tensor<fp32, [1, 1]> var_1890 = reduce_l2_norm(axes = var_1887, keep_dims = var_1888, x = input)[name = string("op_1890")];
            fp32 var_1891 = const()[name = string("op_1891"), val = fp32(0x1.197998p-40)];
            tensor<fp32, [1, 1]> var_1892 = maximum(x = var_1890, y = var_1891)[name = string("op_1892")];
            tensor<int32, [2]> denom_reps_0 = const()[name = string("denom_reps_0"), val = tensor<int32, [2]>([1, 128])];
            tensor<fp32, [1, 128]> denom = tile(reps = denom_reps_0, x = var_1892)[name = string("denom")];
            tensor<fp32, [1, 128]> pooled_embedding = real_div(x = input, y = denom)[name = string("pooled")];
        } -> (token_embeddings, pooled_embedding);
}