diff --git "a/ParakeetEncoder_v2.mlmodelc/model.mil" "b/ParakeetEncoder_v2.mlmodelc/model.mil"
new file mode 100644--- /dev/null
+++ "b/ParakeetEncoder_v2.mlmodelc/model.mil"
@@ -0,0 +1,3400 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3405.2.1"}, {"coremlc-version", "3404.23.1"}})]
+{
+    func main<ios17>(tensor<fp32, [1, 128, 1001]> audio_signal, tensor<int32, [1]> length) {
+            tensor<int32, []> var_25 = const()[name = tensor<string, []>("op_25"), val = tensor<int32, []>(-1)];
+            tensor<int32, [3]> x_1_perm_0 = const()[name = tensor<string, []>("x_1_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> audio_signal_to_fp16_dtype_0 = const()[name = tensor<string, []>("audio_signal_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<string, []> cast_0_to_fp16_dtype_0 = const()[name = tensor<string, []>("cast_0_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, []> var_82_promoted_to_fp16 = const()[name = tensor<string, []>("op_82_promoted_to_fp16"), val = tensor<fp16, []>(-0x1p+0)];
+            tensor<fp16, [1]> length_to_fp16 = cast(dtype = cast_0_to_fp16_dtype_0, x = length)[name = tensor<string, []>("cast_2")];
+            tensor<fp16, [1]> var_83_cast_fp16 = add(x = length_to_fp16, y = var_82_promoted_to_fp16)[name = tensor<string, []>("op_83_cast_fp16")];
+            tensor<fp16, []> _inversed_85_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_85_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1]> _inversed_85_cast_fp16 = mul(x = var_83_cast_fp16, y = _inversed_85_y_0_to_fp16)[name = tensor<string, []>("_inversed_85_cast_fp16")];
+            tensor<fp16, []> var_86_to_fp16 = const()[name = tensor<string, []>("op_86_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> lengths_1_cast_fp16 = add(x = _inversed_85_cast_fp16, y = var_86_to_fp16)[name = tensor<string, []>("lengths_1_cast_fp16")];
+            tensor<fp16, [1]> lengths_3_cast_fp16 = floor(x = lengths_1_cast_fp16)[name = tensor<string, []>("lengths_3_cast_fp16")];
+            tensor<fp16, []> var_90_promoted_to_fp16 = const()[name = tensor<string, []>("op_90_promoted_to_fp16"), val = tensor<fp16, []>(-0x1p+0)];
+            tensor<fp16, [1]> var_91_cast_fp16 = add(x = lengths_3_cast_fp16, y = var_90_promoted_to_fp16)[name = tensor<string, []>("op_91_cast_fp16")];
+            tensor<fp16, []> _inversed_93_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_93_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1]> _inversed_93_cast_fp16 = mul(x = var_91_cast_fp16, y = _inversed_93_y_0_to_fp16)[name = tensor<string, []>("_inversed_93_cast_fp16")];
+            tensor<fp16, []> var_94_to_fp16 = const()[name = tensor<string, []>("op_94_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> lengths_7_cast_fp16 = add(x = _inversed_93_cast_fp16, y = var_94_to_fp16)[name = tensor<string, []>("lengths_7_cast_fp16")];
+            tensor<fp16, [1]> lengths_9_cast_fp16 = floor(x = lengths_7_cast_fp16)[name = tensor<string, []>("lengths_9_cast_fp16")];
+            tensor<fp16, []> var_98_promoted_to_fp16 = const()[name = tensor<string, []>("op_98_promoted_to_fp16"), val = tensor<fp16, []>(-0x1p+0)];
+            tensor<fp16, [1]> var_99_cast_fp16 = add(x = lengths_9_cast_fp16, y = var_98_promoted_to_fp16)[name = tensor<string, []>("op_99_cast_fp16")];
+            tensor<fp16, []> _inversed_101_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_101_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1]> _inversed_101_cast_fp16 = mul(x = var_99_cast_fp16, y = _inversed_101_y_0_to_fp16)[name = tensor<string, []>("_inversed_101_cast_fp16")];
+            tensor<fp16, []> var_102_to_fp16 = const()[name = tensor<string, []>("op_102_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> lengths_13_cast_fp16 = add(x = _inversed_101_cast_fp16, y = var_102_to_fp16)[name = tensor<string, []>("lengths_13_cast_fp16")];
+            tensor<fp16, [1]> lengths_cast_fp16 = floor(x = lengths_13_cast_fp16)[name = tensor<string, []>("lengths_cast_fp16")];
+            tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 128, 1001]> audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = tensor<string, []>("cast_1")];
+            tensor<fp16, [1, 1001, 128]> x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = audio_signal_to_fp16)[name = tensor<string, []>("transpose_314")];
+            tensor<fp16, [1, 1, 1001, 128]> input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = x_1_cast_fp16)[name = tensor<string, []>("input_1_cast_fp16")];
+            tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_3_strides_0 = const()[name = tensor<string, []>("input_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_3_dilations_0 = const()[name = tensor<string, []>("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_3_groups_0 = const()[name = tensor<string, []>("input_3_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 1, 3, 3]> model_pre_encode_conv_0_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_pre_encode_conv_0_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2752))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
+            tensor<fp16, [256]> model_pre_encode_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("model_pre_encode_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3328)))];
+            tensor<fp16, [1, 256, 501, 64]> input_3_cast_fp16 = conv(bias = model_pre_encode_conv_0_bias_to_fp16, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = model_pre_encode_conv_0_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<fp16, [1, 256, 501, 64]> input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
+            tensor<string, []> input_7_pad_type_0 = const()[name = tensor<string, []>("input_7_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_7_strides_0 = const()[name = tensor<string, []>("input_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, []> input_7_groups_0 = const()[name = tensor<string, []>("input_7_groups_0"), val = tensor<int32, []>(256)];
+            tensor<int32, [2]> input_7_dilations_0 = const()[name = tensor<string, []>("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> model_pre_encode_conv_2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_pre_encode_conv_2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3904))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6272))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
+            tensor<fp16, [256]> model_pre_encode_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("model_pre_encode_conv_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6848)))];
+            tensor<fp16, [1, 256, 251, 32]> input_7_cast_fp16 = conv(bias = model_pre_encode_conv_2_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = model_pre_encode_conv_2_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
+            tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 1, 1]> model_pre_encode_conv_3_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_pre_encode_conv_3_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7424))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(73024))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
+            tensor<fp16, [256]> model_pre_encode_conv_3_bias_to_fp16 = const()[name = tensor<string, []>("model_pre_encode_conv_3_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(73600)))];
+            tensor<fp16, [1, 256, 251, 32]> input_9_cast_fp16 = conv(bias = model_pre_encode_conv_3_bias_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = model_pre_encode_conv_3_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
+            tensor<fp16, [1, 256, 251, 32]> input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
+            tensor<string, []> input_13_pad_type_0 = const()[name = tensor<string, []>("input_13_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [4]> input_13_pad_0 = const()[name = tensor<string, []>("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_13_strides_0 = const()[name = tensor<string, []>("input_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, []> input_13_groups_0 = const()[name = tensor<string, []>("input_13_groups_0"), val = tensor<int32, []>(256)];
+            tensor<int32, [2]> input_13_dilations_0 = const()[name = tensor<string, []>("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> model_pre_encode_conv_5_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_pre_encode_conv_5_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(74176))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76544))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
+            tensor<fp16, [256]> model_pre_encode_conv_5_bias_to_fp16 = const()[name = tensor<string, []>("model_pre_encode_conv_5_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77120)))];
+            tensor<fp16, [1, 256, 126, 16]> input_13_cast_fp16 = conv(bias = model_pre_encode_conv_5_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = model_pre_encode_conv_5_weight_to_fp16_quantized, x = input_11_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
+            tensor<string, []> input_15_pad_type_0 = const()[name = tensor<string, []>("input_15_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> input_15_strides_0 = const()[name = tensor<string, []>("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> input_15_pad_0 = const()[name = tensor<string, []>("input_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_15_dilations_0 = const()[name = tensor<string, []>("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> input_15_groups_0 = const()[name = tensor<string, []>("input_15_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 1, 1]> model_pre_encode_conv_6_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_pre_encode_conv_6_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77696))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143296))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
+            tensor<fp16, [256]> model_pre_encode_conv_6_bias_to_fp16 = const()[name = tensor<string, []>("model_pre_encode_conv_6_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143872)))];
+            tensor<fp16, [1, 256, 126, 16]> input_15_cast_fp16 = conv(bias = model_pre_encode_conv_6_bias_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = model_pre_encode_conv_6_weight_to_fp16_quantized, x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
+            tensor<fp16, [1, 256, 126, 16]> x_3_cast_fp16 = relu(x = input_15_cast_fp16)[name = tensor<string, []>("x_3_cast_fp16")];
+            tensor<int32, [4]> var_152_perm_0 = const()[name = tensor<string, []>("op_152_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_153 = const()[name = tensor<string, []>("op_153"), val = tensor<int32, [3]>([1, 126, -1])];
+            tensor<fp16, [1, 126, 256, 16]> var_152_cast_fp16 = transpose(perm = var_152_perm_0, x = x_3_cast_fp16)[name = tensor<string, []>("transpose_313")];
+            tensor<fp16, [1, 126, 4096]> input_17_cast_fp16 = reshape(shape = var_153, x = var_152_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_pre_encode_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_pre_encode_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144448))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4339904))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> model_pre_encode_out_bias_to_fp16 = const()[name = tensor<string, []>("model_pre_encode_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4342016)))];
+            tensor<fp16, [1, 126, 1024]> linear_0_cast_fp16 = linear(bias = model_pre_encode_out_bias_to_fp16, weight = model_pre_encode_out_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
+            tensor<string, []> cast_11_dtype_0 = const()[name = tensor<string, []>("cast_11_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1, 126]> expand_dims_0 = const()[name = tensor<string, []>("expand_dims_0"), val = tensor<int32, [1, 126]>([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125]])];
+            tensor<int32, [1]> var_191_axes_0 = const()[name = tensor<string, []>("op_191_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<int32, [1]> encoder_output_length = cast(dtype = cast_11_dtype_0, x = lengths_cast_fp16)[name = tensor<string, []>("cast_0")];
+            tensor<int32, [1, 1]> var_191 = expand_dims(axes = var_191_axes_0, x = encoder_output_length)[name = tensor<string, []>("op_191")];
+            tensor<bool, [1, 126]> pad_mask_1 = less(x = expand_dims_0, y = var_191)[name = tensor<string, []>("pad_mask_1")];
+            tensor<int32, [1]> var_193_axes_0 = const()[name = tensor<string, []>("op_193_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 126]> var_193 = expand_dims(axes = var_193_axes_0, x = pad_mask_1)[name = tensor<string, []>("op_193")];
+            tensor<int32, [3]> var_194 = const()[name = tensor<string, []>("op_194"), val = tensor<int32, [3]>([1, 126, 1])];
+            tensor<bool, [1, 126, 126]> pad_mask_for_att_mask_1 = tile(reps = var_194, x = var_193)[name = tensor<string, []>("pad_mask_for_att_mask_1")];
+            tensor<int32, [3]> var_196_perm_0 = const()[name = tensor<string, []>("op_196_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<bool, [1, 126, 126]> var_196 = transpose(perm = var_196_perm_0, x = pad_mask_for_att_mask_1)[name = tensor<string, []>("transpose_312")];
+            tensor<bool, [1, 126, 126]> pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_196)[name = tensor<string, []>("pad_mask_for_att_mask")];
+            tensor<bool, [1, 126, 126]> const_7 = const()[name = tensor<string, []>("const_7"), val = tensor<bool, [1, 126, 126]>([[[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, 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, 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, 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, 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, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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], [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, 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, 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, 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, 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, true]]])];
+            tensor<bool, [1, 126, 126]> att_mask = logical_and(x = pad_mask_for_att_mask, y = const_7)[name = tensor<string, []>("att_mask")];
+            tensor<bool, [1, 126, 126]> mask_1 = logical_not(x = att_mask)[name = tensor<string, []>("mask_1")];
+            tensor<bool, [1, 126]> pad_mask = logical_not(x = pad_mask_1)[name = tensor<string, []>("pad_mask")];
+            tensor<int32, [1]> input_21_axes_0 = const()[name = tensor<string, []>("input_21_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4344128)))];
+            tensor<fp16, [1024]> model_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4346240)))];
+            tensor<fp16, []> var_4_to_fp16 = const()[name = tensor<string, []>("op_4_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 126, 1024]> input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = model_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_0_norm_feed_forward1_weight_to_fp16, x = linear_0_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_0_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_0_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4348352))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8546880))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [4096]> linear_1_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_1_bias_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8555136)))];
+            tensor<fp16, [1, 126, 4096]> linear_1_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_0_feed_forward1_linear1_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_25_cast_fp16 = silu(x = linear_1_cast_fp16)[name = tensor<string, []>("input_25_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_0_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_0_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8563392))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12757760))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> linear_2_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_2_bias_0_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12759872)))];
+            tensor<fp16, [1, 126, 1024]> linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_0_feed_forward1_linear2_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")];
+            tensor<fp16, []> var_227_to_fp16 = const()[name = tensor<string, []>("op_227_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_228_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_227_to_fp16)[name = tensor<string, []>("op_228_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_31_cast_fp16 = add(x = linear_0_cast_fp16, y = var_228_cast_fp16)[name = tensor<string, []>("input_31_cast_fp16")];
+            tensor<int32, [1]> query_1_axes_0 = const()[name = tensor<string, []>("query_1_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_0_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_0_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12761984)))];
+            tensor<fp16, [1024]> model_layers_0_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_0_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12764096)))];
+            tensor<fp16, [1, 126, 1024]> query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = model_layers_0_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_0_norm_self_att_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("query_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_0_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_0_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12766208))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13814848))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_3_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_0_self_attn_linear_q_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = tensor<string, []>("linear_3_cast_fp16")];
+            tensor<int32, [4]> var_244 = const()[name = tensor<string, []>("op_244"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_1_cast_fp16 = reshape(shape = var_244, x = linear_3_cast_fp16)[name = tensor<string, []>("q_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_0_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_0_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13816960))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14865600))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_4_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_0_self_attn_linear_k_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = tensor<string, []>("linear_4_cast_fp16")];
+            tensor<int32, [4]> var_248 = const()[name = tensor<string, []>("op_248"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_1_cast_fp16 = reshape(shape = var_248, x = linear_4_cast_fp16)[name = tensor<string, []>("k_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_0_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_0_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14867712))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15916352))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_5_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_0_self_attn_linear_v_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = tensor<string, []>("linear_5_cast_fp16")];
+            tensor<int32, [4]> var_252 = const()[name = tensor<string, []>("op_252"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_1_cast_fp16 = reshape(shape = var_252, x = linear_5_cast_fp16)[name = tensor<string, []>("v_1_cast_fp16")];
+            tensor<int32, [4]> value_1_perm_0 = const()[name = tensor<string, []>("value_1_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_0_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_0_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15918464))), scale = tensor<fp16, [8]>([0x1.554p-8, 0x1.17cp-7, 0x1.e6p-8, 0x1.c8cp-8, 0x1.e28p-9, 0x1.fe8p-8, 0x1.2f8p-8, 0x1.3bp-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_264_cast_fp16 = add(x = q_1_cast_fp16, y = model_layers_0_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_264_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_0_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_0_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15919552))), scale = tensor<fp16, [8]>([0x1.ea8p-11, 0x1.7ep-9, 0x1.8e4p-10, 0x1.4e4p-9, 0x1.9e4p-12, 0x1.d6p-10, 0x1.0bcp-10, 0x1.bc4p-10]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_266_cast_fp16 = add(x = q_1_cast_fp16, y = model_layers_0_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_266_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_1_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_1_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_7_transpose_x_0 = const()[name = tensor<string, []>("x_7_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_7_transpose_y_0 = const()[name = tensor<string, []>("x_7_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_268_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_268_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15920640))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16178048))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_266_cast_fp16)[name = tensor<string, []>("transpose_311")];
+            tensor<fp16, [1, 8, 126, 251]> x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = op_268_to_fp16_quantized)[name = tensor<string, []>("x_7_cast_fp16")];
+            tensor<int32, [8]> x_9_pad_0 = const()[name = tensor<string, []>("x_9_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_9_mode_0 = const()[name = tensor<string, []>("x_9_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_14_to_fp16 = const()[name = tensor<string, []>("const_14_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_9_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = tensor<string, []>("x_9_cast_fp16")];
+            tensor<int32, [4]> var_276 = const()[name = tensor<string, []>("op_276"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_11_cast_fp16 = reshape(shape = var_276, x = x_9_cast_fp16)[name = tensor<string, []>("x_11_cast_fp16")];
+            tensor<int32, [4]> var_280_begin_0 = const()[name = tensor<string, []>("op_280_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_280_end_0 = const()[name = tensor<string, []>("op_280_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_280_end_mask_0 = const()[name = tensor<string, []>("op_280_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_280_cast_fp16 = slice_by_index(begin = var_280_begin_0, end = var_280_end_0, end_mask = var_280_end_mask_0, x = x_11_cast_fp16)[name = tensor<string, []>("op_280_cast_fp16")];
+            tensor<int32, [4]> var_281 = const()[name = tensor<string, []>("op_281"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_1_cast_fp16 = reshape(shape = var_281, x = var_280_cast_fp16)[name = tensor<string, []>("matrix_bd_1_cast_fp16")];
+            tensor<bool, []> matrix_ac_1_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_1_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_1_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_1_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_96_perm_0 = const()[name = tensor<string, []>("transpose_96_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_97_perm_0 = const()[name = tensor<string, []>("transpose_97_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = tensor<string, []>("transpose_309")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_96 = transpose(perm = transpose_96_perm_0, x = var_264_cast_fp16)[name = tensor<string, []>("transpose_310")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_96, y = transpose_97)[name = tensor<string, []>("matrix_ac_1_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_3_begin_0 = const()[name = tensor<string, []>("matrix_bd_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_3_end_0 = const()[name = tensor<string, []>("matrix_bd_3_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_3_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = tensor<string, []>("matrix_bd_3_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_290_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = tensor<string, []>("op_290_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_1_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_1_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_1_cast_fp16 = mul(x = var_290_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_1_cast_fp16")];
+            tensor<int32, [1]> mask_3_axes_0 = const()[name = tensor<string, []>("mask_3_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 126, 126]> mask_3 = expand_dims(axes = mask_3_axes_0, x = mask_1)[name = tensor<string, []>("mask_3")];
+            tensor<fp16, []> var_7_to_fp16 = const()[name = tensor<string, []>("op_7_to_fp16"), val = tensor<fp16, []>(-0x1.388p+13)];
+            tensor<fp16, [1, 8, 126, 126]> scores_3_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_3_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_296_cast_fp16 = softmax(axis = var_25, x = scores_3_cast_fp16)[name = tensor<string, []>("op_296_cast_fp16")];
+            tensor<fp16, []> var_6_to_fp16 = const()[name = tensor<string, []>("op_6_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 126]> input_33_cast_fp16 = select(a = var_6_to_fp16, b = var_296_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_33_cast_fp16")];
+            tensor<bool, []> x_13_transpose_x_0 = const()[name = tensor<string, []>("x_13_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_13_transpose_y_0 = const()[name = tensor<string, []>("x_13_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_1_cast_fp16 = transpose(perm = value_1_perm_0, x = v_1_cast_fp16)[name = tensor<string, []>("transpose_308")];
+            tensor<fp16, [1, 8, 126, 128]> x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_33_cast_fp16, y = value_1_cast_fp16)[name = tensor<string, []>("x_13_cast_fp16")];
+            tensor<int32, [4]> var_300_perm_0 = const()[name = tensor<string, []>("op_300_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_301 = const()[name = tensor<string, []>("op_301"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_300_cast_fp16 = transpose(perm = var_300_perm_0, x = x_13_cast_fp16)[name = tensor<string, []>("transpose_307")];
+            tensor<fp16, [1, 126, 1024]> input_35_cast_fp16 = reshape(shape = var_301, x = var_300_cast_fp16)[name = tensor<string, []>("input_35_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_0_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_0_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16178624))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17227264))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_7_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_0_self_attn_linear_out_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = tensor<string, []>("linear_7_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_39_cast_fp16 = add(x = input_31_cast_fp16, y = linear_7_cast_fp16)[name = tensor<string, []>("input_39_cast_fp16")];
+            tensor<int32, [1]> x_17_axes_0 = const()[name = tensor<string, []>("x_17_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_0_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_0_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17229376)))];
+            tensor<fp16, [1024]> model_layers_0_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_0_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17231488)))];
+            tensor<fp16, [1, 126, 1024]> x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = model_layers_0_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_0_norm_conv_weight_to_fp16, x = input_39_cast_fp16)[name = tensor<string, []>("x_17_cast_fp16")];
+            tensor<int32, [3]> input_41_perm_0 = const()[name = tensor<string, []>("input_41_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_43_pad_type_0 = const()[name = tensor<string, []>("input_43_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_43_strides_0 = const()[name = tensor<string, []>("input_43_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_43_pad_0 = const()[name = tensor<string, []>("input_43_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_43_dilations_0 = const()[name = tensor<string, []>("input_43_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_43_groups_0 = const()[name = tensor<string, []>("input_43_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17233600))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19332928))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = x_17_cast_fp16)[name = tensor<string, []>("transpose_306")];
+            tensor<fp16, [1, 2048, 126]> input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = model_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = tensor<string, []>("input_43_cast_fp16")];
+            tensor<int32, []> x_19_split_num_splits_0 = const()[name = tensor<string, []>("x_19_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_19_split_axis_0 = const()[name = tensor<string, []>("x_19_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_19_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_43_cast_fp16)[name = tensor<string, []>("x_19_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = tensor<string, []>("x_19_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_19_cast_fp16")];
+            tensor<int32, [1]> var_323_axes_0 = const()[name = tensor<string, []>("op_323_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 126]> var_323 = expand_dims(axes = var_323_axes_0, x = pad_mask)[name = tensor<string, []>("op_323")];
+            tensor<fp16, [1, 1024, 126]> input_45_cast_fp16 = select(a = var_6_to_fp16, b = x_19_cast_fp16, cond = var_323)[name = tensor<string, []>("input_45_cast_fp16")];
+            tensor<int32, [6]> input_47_pad_0 = const()[name = tensor<string, []>("input_47_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_47_mode_0 = const()[name = tensor<string, []>("input_47_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_17_to_fp16 = const()[name = tensor<string, []>("const_17_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_47_cast_fp16 = pad(constant_val = const_17_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = tensor<string, []>("input_47_cast_fp16")];
+            tensor<string, []> input_49_pad_type_0 = const()[name = tensor<string, []>("input_49_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_49_groups_0 = const()[name = tensor<string, []>("input_49_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_49_strides_0 = const()[name = tensor<string, []>("input_49_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_49_pad_0 = const()[name = tensor<string, []>("input_49_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_49_dilations_0 = const()[name = tensor<string, []>("input_49_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_248_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_248_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19337088))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19346368))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_249_to_fp16 = const()[name = tensor<string, []>("const_249_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19348480)))];
+            tensor<fp16, [1, 1024, 126]> input_51_cast_fp16 = conv(bias = const_249_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_248_to_fp16_quantized, x = input_47_cast_fp16)[name = tensor<string, []>("input_51_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_53_cast_fp16 = silu(x = input_51_cast_fp16)[name = tensor<string, []>("input_53_cast_fp16")];
+            tensor<string, []> x_21_pad_type_0 = const()[name = tensor<string, []>("x_21_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_21_strides_0 = const()[name = tensor<string, []>("x_21_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_21_pad_0 = const()[name = tensor<string, []>("x_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_21_dilations_0 = const()[name = tensor<string, []>("x_21_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_21_groups_0 = const()[name = tensor<string, []>("x_21_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19350592))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20399232))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = model_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_53_cast_fp16)[name = tensor<string, []>("x_21_cast_fp16")];
+            tensor<int32, [3]> input_55_perm_0 = const()[name = tensor<string, []>("input_55_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = tensor<string, []>("transpose_305")];
+            tensor<fp16, [1, 126, 1024]> input_57_cast_fp16 = add(x = input_39_cast_fp16, y = input_55_cast_fp16)[name = tensor<string, []>("input_57_cast_fp16")];
+            tensor<int32, [1]> input_59_axes_0 = const()[name = tensor<string, []>("input_59_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20401344)))];
+            tensor<fp16, [1024]> model_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20403456)))];
+            tensor<fp16, [1, 126, 1024]> input_59_cast_fp16 = layer_norm(axes = input_59_axes_0, beta = model_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_0_norm_feed_forward2_weight_to_fp16, x = input_57_cast_fp16)[name = tensor<string, []>("input_59_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_0_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_0_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20405568))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24599936))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_8_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_0_feed_forward2_linear1_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = tensor<string, []>("linear_8_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_63_cast_fp16 = silu(x = linear_8_cast_fp16)[name = tensor<string, []>("input_63_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_0_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_0_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24608192))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28802560))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_9_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_0_feed_forward2_linear2_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = tensor<string, []>("linear_9_cast_fp16")];
+            tensor<fp16, []> var_361_to_fp16 = const()[name = tensor<string, []>("op_361_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_362_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_361_to_fp16)[name = tensor<string, []>("op_362_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_69_cast_fp16 = add(x = input_57_cast_fp16, y = var_362_cast_fp16)[name = tensor<string, []>("input_69_cast_fp16")];
+            tensor<int32, [1]> input_71_axes_0 = const()[name = tensor<string, []>("input_71_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_0_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_0_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28804672)))];
+            tensor<fp16, [1024]> model_layers_0_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_0_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28806784)))];
+            tensor<fp16, [1, 126, 1024]> input_71_cast_fp16 = layer_norm(axes = input_71_axes_0, beta = model_layers_0_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_0_norm_out_weight_to_fp16, x = input_69_cast_fp16)[name = tensor<string, []>("input_71_cast_fp16")];
+            tensor<int32, [1]> input_73_axes_0 = const()[name = tensor<string, []>("input_73_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28808896)))];
+            tensor<fp16, [1024]> model_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28811008)))];
+            tensor<fp16, [1, 126, 1024]> input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = model_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_1_norm_feed_forward1_weight_to_fp16, x = input_71_cast_fp16)[name = tensor<string, []>("input_73_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_1_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_1_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28813120))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33007488))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_10_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_1_feed_forward1_linear1_weight_to_fp16_quantized, x = input_73_cast_fp16)[name = tensor<string, []>("linear_10_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_77_cast_fp16 = silu(x = linear_10_cast_fp16)[name = tensor<string, []>("input_77_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_1_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_1_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33015744))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37210112))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_11_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_1_feed_forward1_linear2_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = tensor<string, []>("linear_11_cast_fp16")];
+            tensor<fp16, []> var_390_to_fp16 = const()[name = tensor<string, []>("op_390_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_391_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_390_to_fp16)[name = tensor<string, []>("op_391_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_83_cast_fp16 = add(x = input_71_cast_fp16, y = var_391_cast_fp16)[name = tensor<string, []>("input_83_cast_fp16")];
+            tensor<int32, [1]> query_3_axes_0 = const()[name = tensor<string, []>("query_3_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_1_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_1_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37212224)))];
+            tensor<fp16, [1024]> model_layers_1_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_1_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37214336)))];
+            tensor<fp16, [1, 126, 1024]> query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = model_layers_1_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_1_norm_self_att_weight_to_fp16, x = input_83_cast_fp16)[name = tensor<string, []>("query_3_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_1_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_1_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37216448))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38265088))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_12_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_1_self_attn_linear_q_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = tensor<string, []>("linear_12_cast_fp16")];
+            tensor<int32, [4]> var_407 = const()[name = tensor<string, []>("op_407"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_7_cast_fp16 = reshape(shape = var_407, x = linear_12_cast_fp16)[name = tensor<string, []>("q_7_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_1_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_1_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38267200))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39315840))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_13_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_1_self_attn_linear_k_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = tensor<string, []>("linear_13_cast_fp16")];
+            tensor<int32, [4]> var_411 = const()[name = tensor<string, []>("op_411"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_5_cast_fp16 = reshape(shape = var_411, x = linear_13_cast_fp16)[name = tensor<string, []>("k_5_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_1_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_1_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39317952))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40366592))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_14_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_1_self_attn_linear_v_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = tensor<string, []>("linear_14_cast_fp16")];
+            tensor<int32, [4]> var_415 = const()[name = tensor<string, []>("op_415"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_3_cast_fp16 = reshape(shape = var_415, x = linear_14_cast_fp16)[name = tensor<string, []>("v_3_cast_fp16")];
+            tensor<int32, [4]> value_3_perm_0 = const()[name = tensor<string, []>("value_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_1_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_1_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40368704))), scale = tensor<fp16, [8]>([0x1.0acp-7, 0x1.2f4p-7, 0x1.71p-7, 0x1.654p-8, 0x1.a2cp-7, 0x1.548p-8, 0x1.2ccp-7, 0x1.43p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_427_cast_fp16 = add(x = q_7_cast_fp16, y = model_layers_1_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_427_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_1_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_1_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40369792))), scale = tensor<fp16, [8]>([0x1.188p-7, 0x1.ca4p-9, 0x1.dap-9, 0x1.25p-9, 0x1.e54p-9, 0x1.124p-9, 0x1.0e8p-8, 0x1.b08p-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_429_cast_fp16 = add(x = q_7_cast_fp16, y = model_layers_1_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_429_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_3_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_29_transpose_x_0 = const()[name = tensor<string, []>("x_29_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_29_transpose_y_0 = const()[name = tensor<string, []>("x_29_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_431_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_431_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40370880))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40627968))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_429_cast_fp16)[name = tensor<string, []>("transpose_304")];
+            tensor<fp16, [1, 8, 126, 251]> x_29_cast_fp16 = matmul(transpose_x = x_29_transpose_x_0, transpose_y = x_29_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = op_431_to_fp16_quantized)[name = tensor<string, []>("x_29_cast_fp16")];
+            tensor<int32, [8]> x_31_pad_0 = const()[name = tensor<string, []>("x_31_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_31_mode_0 = const()[name = tensor<string, []>("x_31_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_24_to_fp16 = const()[name = tensor<string, []>("const_24_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_31_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = x_31_mode_0, pad = x_31_pad_0, x = x_29_cast_fp16)[name = tensor<string, []>("x_31_cast_fp16")];
+            tensor<int32, [4]> var_439 = const()[name = tensor<string, []>("op_439"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_33_cast_fp16 = reshape(shape = var_439, x = x_31_cast_fp16)[name = tensor<string, []>("x_33_cast_fp16")];
+            tensor<int32, [4]> var_443_begin_0 = const()[name = tensor<string, []>("op_443_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_443_end_0 = const()[name = tensor<string, []>("op_443_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_443_end_mask_0 = const()[name = tensor<string, []>("op_443_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_443_cast_fp16 = slice_by_index(begin = var_443_begin_0, end = var_443_end_0, end_mask = var_443_end_mask_0, x = x_33_cast_fp16)[name = tensor<string, []>("op_443_cast_fp16")];
+            tensor<int32, [4]> var_444 = const()[name = tensor<string, []>("op_444"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_5_cast_fp16 = reshape(shape = var_444, x = var_443_cast_fp16)[name = tensor<string, []>("matrix_bd_5_cast_fp16")];
+            tensor<bool, []> matrix_ac_3_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_3_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_3_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_3_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_98_perm_0 = const()[name = tensor<string, []>("transpose_98_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_99_perm_0 = const()[name = tensor<string, []>("transpose_99_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = tensor<string, []>("transpose_302")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_98 = transpose(perm = transpose_98_perm_0, x = var_427_cast_fp16)[name = tensor<string, []>("transpose_303")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_98, y = transpose_99)[name = tensor<string, []>("matrix_ac_3_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_7_begin_0 = const()[name = tensor<string, []>("matrix_bd_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_7_end_0 = const()[name = tensor<string, []>("matrix_bd_7_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_7_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = tensor<string, []>("matrix_bd_7_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_453_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = tensor<string, []>("op_453_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_5_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_5_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_5_cast_fp16 = mul(x = var_453_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_5_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_7_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_7_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_459_cast_fp16 = softmax(axis = var_25, x = scores_7_cast_fp16)[name = tensor<string, []>("op_459_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_85_cast_fp16 = select(a = var_6_to_fp16, b = var_459_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_85_cast_fp16")];
+            tensor<bool, []> x_35_transpose_x_0 = const()[name = tensor<string, []>("x_35_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_35_transpose_y_0 = const()[name = tensor<string, []>("x_35_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = v_3_cast_fp16)[name = tensor<string, []>("transpose_301")];
+            tensor<fp16, [1, 8, 126, 128]> x_35_cast_fp16 = matmul(transpose_x = x_35_transpose_x_0, transpose_y = x_35_transpose_y_0, x = input_85_cast_fp16, y = value_3_cast_fp16)[name = tensor<string, []>("x_35_cast_fp16")];
+            tensor<int32, [4]> var_463_perm_0 = const()[name = tensor<string, []>("op_463_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_464 = const()[name = tensor<string, []>("op_464"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_463_cast_fp16 = transpose(perm = var_463_perm_0, x = x_35_cast_fp16)[name = tensor<string, []>("transpose_300")];
+            tensor<fp16, [1, 126, 1024]> input_87_cast_fp16 = reshape(shape = var_464, x = var_463_cast_fp16)[name = tensor<string, []>("input_87_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_1_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_1_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40628544))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41677184))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_16_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_1_self_attn_linear_out_weight_to_fp16_quantized, x = input_87_cast_fp16)[name = tensor<string, []>("linear_16_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_91_cast_fp16 = add(x = input_83_cast_fp16, y = linear_16_cast_fp16)[name = tensor<string, []>("input_91_cast_fp16")];
+            tensor<int32, [1]> x_39_axes_0 = const()[name = tensor<string, []>("x_39_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_1_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_1_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41679296)))];
+            tensor<fp16, [1024]> model_layers_1_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_1_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41681408)))];
+            tensor<fp16, [1, 126, 1024]> x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, beta = model_layers_1_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_1_norm_conv_weight_to_fp16, x = input_91_cast_fp16)[name = tensor<string, []>("x_39_cast_fp16")];
+            tensor<int32, [3]> input_93_perm_0 = const()[name = tensor<string, []>("input_93_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_95_pad_type_0 = const()[name = tensor<string, []>("input_95_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_95_strides_0 = const()[name = tensor<string, []>("input_95_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_95_pad_0 = const()[name = tensor<string, []>("input_95_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_95_dilations_0 = const()[name = tensor<string, []>("input_95_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_95_groups_0 = const()[name = tensor<string, []>("input_95_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41683520))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43780736))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_39_cast_fp16)[name = tensor<string, []>("transpose_299")];
+            tensor<fp16, [1, 2048, 126]> input_95_cast_fp16 = conv(dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = model_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = tensor<string, []>("input_95_cast_fp16")];
+            tensor<int32, []> x_41_split_num_splits_0 = const()[name = tensor<string, []>("x_41_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_41_split_axis_0 = const()[name = tensor<string, []>("x_41_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_41_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_41_split_cast_fp16_1 = split(axis = x_41_split_axis_0, num_splits = x_41_split_num_splits_0, x = input_95_cast_fp16)[name = tensor<string, []>("x_41_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_41_split_1_sigmoid_cast_fp16 = sigmoid(x = x_41_split_cast_fp16_1)[name = tensor<string, []>("x_41_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_41_cast_fp16 = mul(x = x_41_split_cast_fp16_0, y = x_41_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_41_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_97_cast_fp16 = select(a = var_6_to_fp16, b = x_41_cast_fp16, cond = var_323)[name = tensor<string, []>("input_97_cast_fp16")];
+            tensor<int32, [6]> input_99_pad_0 = const()[name = tensor<string, []>("input_99_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_99_mode_0 = const()[name = tensor<string, []>("input_99_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_27_to_fp16 = const()[name = tensor<string, []>("const_27_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_99_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = input_99_mode_0, pad = input_99_pad_0, x = input_97_cast_fp16)[name = tensor<string, []>("input_99_cast_fp16")];
+            tensor<string, []> input_101_pad_type_0 = const()[name = tensor<string, []>("input_101_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_101_groups_0 = const()[name = tensor<string, []>("input_101_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_101_strides_0 = const()[name = tensor<string, []>("input_101_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_101_pad_0 = const()[name = tensor<string, []>("input_101_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_101_dilations_0 = const()[name = tensor<string, []>("input_101_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_250_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_250_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43784896))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43794176))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_251_to_fp16 = const()[name = tensor<string, []>("const_251_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43796288)))];
+            tensor<fp16, [1, 1024, 126]> input_103_cast_fp16 = conv(bias = const_251_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_250_to_fp16_quantized, x = input_99_cast_fp16)[name = tensor<string, []>("input_103_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_105_cast_fp16 = silu(x = input_103_cast_fp16)[name = tensor<string, []>("input_105_cast_fp16")];
+            tensor<string, []> x_43_pad_type_0 = const()[name = tensor<string, []>("x_43_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_43_strides_0 = const()[name = tensor<string, []>("x_43_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_43_pad_0 = const()[name = tensor<string, []>("x_43_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_43_dilations_0 = const()[name = tensor<string, []>("x_43_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_43_groups_0 = const()[name = tensor<string, []>("x_43_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43798400))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44847040))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_43_cast_fp16 = conv(dilations = x_43_dilations_0, groups = x_43_groups_0, pad = x_43_pad_0, pad_type = x_43_pad_type_0, strides = x_43_strides_0, weight = model_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = tensor<string, []>("x_43_cast_fp16")];
+            tensor<int32, [3]> input_107_perm_0 = const()[name = tensor<string, []>("input_107_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_43_cast_fp16)[name = tensor<string, []>("transpose_298")];
+            tensor<fp16, [1, 126, 1024]> input_109_cast_fp16 = add(x = input_91_cast_fp16, y = input_107_cast_fp16)[name = tensor<string, []>("input_109_cast_fp16")];
+            tensor<int32, [1]> input_111_axes_0 = const()[name = tensor<string, []>("input_111_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44849152)))];
+            tensor<fp16, [1024]> model_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44851264)))];
+            tensor<fp16, [1, 126, 1024]> input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = model_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_1_norm_feed_forward2_weight_to_fp16, x = input_109_cast_fp16)[name = tensor<string, []>("input_111_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_1_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_1_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44853376))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49047744))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_17_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_1_feed_forward2_linear1_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = tensor<string, []>("linear_17_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_115_cast_fp16 = silu(x = linear_17_cast_fp16)[name = tensor<string, []>("input_115_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_1_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_1_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49056000))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53250368))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_18_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_1_feed_forward2_linear2_weight_to_fp16_quantized, x = input_115_cast_fp16)[name = tensor<string, []>("linear_18_cast_fp16")];
+            tensor<fp16, []> var_524_to_fp16 = const()[name = tensor<string, []>("op_524_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_525_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_524_to_fp16)[name = tensor<string, []>("op_525_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_121_cast_fp16 = add(x = input_109_cast_fp16, y = var_525_cast_fp16)[name = tensor<string, []>("input_121_cast_fp16")];
+            tensor<int32, [1]> input_123_axes_0 = const()[name = tensor<string, []>("input_123_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_1_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_1_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53252480)))];
+            tensor<fp16, [1024]> model_layers_1_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_1_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53254592)))];
+            tensor<fp16, [1, 126, 1024]> input_123_cast_fp16 = layer_norm(axes = input_123_axes_0, beta = model_layers_1_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_1_norm_out_weight_to_fp16, x = input_121_cast_fp16)[name = tensor<string, []>("input_123_cast_fp16")];
+            tensor<int32, [1]> input_125_axes_0 = const()[name = tensor<string, []>("input_125_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53256704)))];
+            tensor<fp16, [1024]> model_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53258816)))];
+            tensor<fp16, [1, 126, 1024]> input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = model_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_2_norm_feed_forward1_weight_to_fp16, x = input_123_cast_fp16)[name = tensor<string, []>("input_125_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_2_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_2_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53260928))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57455296))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_19_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_2_feed_forward1_linear1_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = tensor<string, []>("linear_19_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_129_cast_fp16 = silu(x = linear_19_cast_fp16)[name = tensor<string, []>("input_129_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_2_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_2_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57463552))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61657920))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_20_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_2_feed_forward1_linear2_weight_to_fp16_quantized, x = input_129_cast_fp16)[name = tensor<string, []>("linear_20_cast_fp16")];
+            tensor<fp16, []> var_553_to_fp16 = const()[name = tensor<string, []>("op_553_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_554_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_553_to_fp16)[name = tensor<string, []>("op_554_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_135_cast_fp16 = add(x = input_123_cast_fp16, y = var_554_cast_fp16)[name = tensor<string, []>("input_135_cast_fp16")];
+            tensor<int32, [1]> query_5_axes_0 = const()[name = tensor<string, []>("query_5_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_2_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_2_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61660032)))];
+            tensor<fp16, [1024]> model_layers_2_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_2_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61662144)))];
+            tensor<fp16, [1, 126, 1024]> query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = model_layers_2_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_2_norm_self_att_weight_to_fp16, x = input_135_cast_fp16)[name = tensor<string, []>("query_5_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_2_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_2_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61664256))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62712896))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_21_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_2_self_attn_linear_q_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = tensor<string, []>("linear_21_cast_fp16")];
+            tensor<int32, [4]> var_570 = const()[name = tensor<string, []>("op_570"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_13_cast_fp16 = reshape(shape = var_570, x = linear_21_cast_fp16)[name = tensor<string, []>("q_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_2_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_2_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62715008))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63763648))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_22_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_2_self_attn_linear_k_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = tensor<string, []>("linear_22_cast_fp16")];
+            tensor<int32, [4]> var_574 = const()[name = tensor<string, []>("op_574"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_9_cast_fp16 = reshape(shape = var_574, x = linear_22_cast_fp16)[name = tensor<string, []>("k_9_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_2_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_2_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63765760))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64814400))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_23_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_2_self_attn_linear_v_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = tensor<string, []>("linear_23_cast_fp16")];
+            tensor<int32, [4]> var_578 = const()[name = tensor<string, []>("op_578"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_5_cast_fp16 = reshape(shape = var_578, x = linear_23_cast_fp16)[name = tensor<string, []>("v_5_cast_fp16")];
+            tensor<int32, [4]> value_5_perm_0 = const()[name = tensor<string, []>("value_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_2_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_2_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64816512))), scale = tensor<fp16, [8]>([0x1.9f4p-7, 0x1.0e8p-7, 0x1.8e8p-8, 0x1.2dp-7, 0x1.524p-7, 0x1.a0cp-7, 0x1.57cp-8, 0x1.c18p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_590_cast_fp16 = add(x = q_13_cast_fp16, y = model_layers_2_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_590_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_2_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_2_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64817600))), scale = tensor<fp16, [8]>([0x1.a14p-10, 0x1.8c8p-10, 0x1.054p-8, 0x1.0b4p-8, 0x1.a9p-9, 0x1.a9p-8, 0x1.b1cp-9, 0x1.e38p-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_592_cast_fp16 = add(x = q_13_cast_fp16, y = model_layers_2_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_592_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_5_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_51_transpose_x_0 = const()[name = tensor<string, []>("x_51_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_51_transpose_y_0 = const()[name = tensor<string, []>("x_51_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_594_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_594_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64818688))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65075776))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_592_cast_fp16)[name = tensor<string, []>("transpose_297")];
+            tensor<fp16, [1, 8, 126, 251]> x_51_cast_fp16 = matmul(transpose_x = x_51_transpose_x_0, transpose_y = x_51_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = op_594_to_fp16_quantized)[name = tensor<string, []>("x_51_cast_fp16")];
+            tensor<int32, [8]> x_53_pad_0 = const()[name = tensor<string, []>("x_53_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_53_mode_0 = const()[name = tensor<string, []>("x_53_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_34_to_fp16 = const()[name = tensor<string, []>("const_34_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_53_cast_fp16 = pad(constant_val = const_34_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = x_51_cast_fp16)[name = tensor<string, []>("x_53_cast_fp16")];
+            tensor<int32, [4]> var_602 = const()[name = tensor<string, []>("op_602"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_55_cast_fp16 = reshape(shape = var_602, x = x_53_cast_fp16)[name = tensor<string, []>("x_55_cast_fp16")];
+            tensor<int32, [4]> var_606_begin_0 = const()[name = tensor<string, []>("op_606_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_606_end_0 = const()[name = tensor<string, []>("op_606_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_606_end_mask_0 = const()[name = tensor<string, []>("op_606_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_606_cast_fp16 = slice_by_index(begin = var_606_begin_0, end = var_606_end_0, end_mask = var_606_end_mask_0, x = x_55_cast_fp16)[name = tensor<string, []>("op_606_cast_fp16")];
+            tensor<int32, [4]> var_607 = const()[name = tensor<string, []>("op_607"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_9_cast_fp16 = reshape(shape = var_607, x = var_606_cast_fp16)[name = tensor<string, []>("matrix_bd_9_cast_fp16")];
+            tensor<bool, []> matrix_ac_5_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_5_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_5_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_5_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_100_perm_0 = const()[name = tensor<string, []>("transpose_100_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_101_perm_0 = const()[name = tensor<string, []>("transpose_101_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = tensor<string, []>("transpose_295")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_100 = transpose(perm = transpose_100_perm_0, x = var_590_cast_fp16)[name = tensor<string, []>("transpose_296")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_100, y = transpose_101)[name = tensor<string, []>("matrix_ac_5_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_11_begin_0 = const()[name = tensor<string, []>("matrix_bd_11_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_11_end_0 = const()[name = tensor<string, []>("matrix_bd_11_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_11_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_11_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = tensor<string, []>("matrix_bd_11_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_616_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = tensor<string, []>("op_616_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_9_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_9_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_9_cast_fp16 = mul(x = var_616_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_9_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_11_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_11_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_622_cast_fp16 = softmax(axis = var_25, x = scores_11_cast_fp16)[name = tensor<string, []>("op_622_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_137_cast_fp16 = select(a = var_6_to_fp16, b = var_622_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_137_cast_fp16")];
+            tensor<bool, []> x_57_transpose_x_0 = const()[name = tensor<string, []>("x_57_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_57_transpose_y_0 = const()[name = tensor<string, []>("x_57_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_5_cast_fp16)[name = tensor<string, []>("transpose_294")];
+            tensor<fp16, [1, 8, 126, 128]> x_57_cast_fp16 = matmul(transpose_x = x_57_transpose_x_0, transpose_y = x_57_transpose_y_0, x = input_137_cast_fp16, y = value_5_cast_fp16)[name = tensor<string, []>("x_57_cast_fp16")];
+            tensor<int32, [4]> var_626_perm_0 = const()[name = tensor<string, []>("op_626_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_627 = const()[name = tensor<string, []>("op_627"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_626_cast_fp16 = transpose(perm = var_626_perm_0, x = x_57_cast_fp16)[name = tensor<string, []>("transpose_293")];
+            tensor<fp16, [1, 126, 1024]> input_139_cast_fp16 = reshape(shape = var_627, x = var_626_cast_fp16)[name = tensor<string, []>("input_139_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_2_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_2_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65076352))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66124992))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_25_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_2_self_attn_linear_out_weight_to_fp16_quantized, x = input_139_cast_fp16)[name = tensor<string, []>("linear_25_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_143_cast_fp16 = add(x = input_135_cast_fp16, y = linear_25_cast_fp16)[name = tensor<string, []>("input_143_cast_fp16")];
+            tensor<int32, [1]> x_61_axes_0 = const()[name = tensor<string, []>("x_61_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_2_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_2_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66127104)))];
+            tensor<fp16, [1024]> model_layers_2_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_2_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66129216)))];
+            tensor<fp16, [1, 126, 1024]> x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, beta = model_layers_2_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_2_norm_conv_weight_to_fp16, x = input_143_cast_fp16)[name = tensor<string, []>("x_61_cast_fp16")];
+            tensor<int32, [3]> input_145_perm_0 = const()[name = tensor<string, []>("input_145_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_147_pad_type_0 = const()[name = tensor<string, []>("input_147_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_147_strides_0 = const()[name = tensor<string, []>("input_147_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_147_pad_0 = const()[name = tensor<string, []>("input_147_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_147_dilations_0 = const()[name = tensor<string, []>("input_147_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_147_groups_0 = const()[name = tensor<string, []>("input_147_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66131328))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68228544))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_61_cast_fp16)[name = tensor<string, []>("transpose_292")];
+            tensor<fp16, [1, 2048, 126]> input_147_cast_fp16 = conv(dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = model_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = tensor<string, []>("input_147_cast_fp16")];
+            tensor<int32, []> x_63_split_num_splits_0 = const()[name = tensor<string, []>("x_63_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_63_split_axis_0 = const()[name = tensor<string, []>("x_63_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_63_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_63_split_cast_fp16_1 = split(axis = x_63_split_axis_0, num_splits = x_63_split_num_splits_0, x = input_147_cast_fp16)[name = tensor<string, []>("x_63_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_63_split_1_sigmoid_cast_fp16 = sigmoid(x = x_63_split_cast_fp16_1)[name = tensor<string, []>("x_63_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_63_cast_fp16 = mul(x = x_63_split_cast_fp16_0, y = x_63_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_63_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_149_cast_fp16 = select(a = var_6_to_fp16, b = x_63_cast_fp16, cond = var_323)[name = tensor<string, []>("input_149_cast_fp16")];
+            tensor<int32, [6]> input_151_pad_0 = const()[name = tensor<string, []>("input_151_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_151_mode_0 = const()[name = tensor<string, []>("input_151_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_37_to_fp16 = const()[name = tensor<string, []>("const_37_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_151_cast_fp16 = pad(constant_val = const_37_to_fp16, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149_cast_fp16)[name = tensor<string, []>("input_151_cast_fp16")];
+            tensor<string, []> input_153_pad_type_0 = const()[name = tensor<string, []>("input_153_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_153_groups_0 = const()[name = tensor<string, []>("input_153_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_153_strides_0 = const()[name = tensor<string, []>("input_153_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_153_pad_0 = const()[name = tensor<string, []>("input_153_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_153_dilations_0 = const()[name = tensor<string, []>("input_153_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_252_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_252_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68232704))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68241984))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_253_to_fp16 = const()[name = tensor<string, []>("const_253_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68244096)))];
+            tensor<fp16, [1, 1024, 126]> input_155_cast_fp16 = conv(bias = const_253_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = const_252_to_fp16_quantized, x = input_151_cast_fp16)[name = tensor<string, []>("input_155_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_157_cast_fp16 = silu(x = input_155_cast_fp16)[name = tensor<string, []>("input_157_cast_fp16")];
+            tensor<string, []> x_65_pad_type_0 = const()[name = tensor<string, []>("x_65_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_65_strides_0 = const()[name = tensor<string, []>("x_65_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_65_pad_0 = const()[name = tensor<string, []>("x_65_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_65_dilations_0 = const()[name = tensor<string, []>("x_65_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_65_groups_0 = const()[name = tensor<string, []>("x_65_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68246208))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(69294848))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_65_cast_fp16 = conv(dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = model_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_157_cast_fp16)[name = tensor<string, []>("x_65_cast_fp16")];
+            tensor<int32, [3]> input_159_perm_0 = const()[name = tensor<string, []>("input_159_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_65_cast_fp16)[name = tensor<string, []>("transpose_291")];
+            tensor<fp16, [1, 126, 1024]> input_161_cast_fp16 = add(x = input_143_cast_fp16, y = input_159_cast_fp16)[name = tensor<string, []>("input_161_cast_fp16")];
+            tensor<int32, [1]> input_163_axes_0 = const()[name = tensor<string, []>("input_163_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(69296960)))];
+            tensor<fp16, [1024]> model_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(69299072)))];
+            tensor<fp16, [1, 126, 1024]> input_163_cast_fp16 = layer_norm(axes = input_163_axes_0, beta = model_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_2_norm_feed_forward2_weight_to_fp16, x = input_161_cast_fp16)[name = tensor<string, []>("input_163_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_2_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_2_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(69301184))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(73495552))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_26_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_2_feed_forward2_linear1_weight_to_fp16_quantized, x = input_163_cast_fp16)[name = tensor<string, []>("linear_26_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_167_cast_fp16 = silu(x = linear_26_cast_fp16)[name = tensor<string, []>("input_167_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_2_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_2_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(73503808))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77698176))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_27_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_2_feed_forward2_linear2_weight_to_fp16_quantized, x = input_167_cast_fp16)[name = tensor<string, []>("linear_27_cast_fp16")];
+            tensor<fp16, []> var_687_to_fp16 = const()[name = tensor<string, []>("op_687_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_688_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_687_to_fp16)[name = tensor<string, []>("op_688_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_173_cast_fp16 = add(x = input_161_cast_fp16, y = var_688_cast_fp16)[name = tensor<string, []>("input_173_cast_fp16")];
+            tensor<int32, [1]> input_175_axes_0 = const()[name = tensor<string, []>("input_175_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_2_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_2_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77700288)))];
+            tensor<fp16, [1024]> model_layers_2_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_2_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77702400)))];
+            tensor<fp16, [1, 126, 1024]> input_175_cast_fp16 = layer_norm(axes = input_175_axes_0, beta = model_layers_2_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_2_norm_out_weight_to_fp16, x = input_173_cast_fp16)[name = tensor<string, []>("input_175_cast_fp16")];
+            tensor<int32, [1]> input_177_axes_0 = const()[name = tensor<string, []>("input_177_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77704512)))];
+            tensor<fp16, [1024]> model_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77706624)))];
+            tensor<fp16, [1, 126, 1024]> input_177_cast_fp16 = layer_norm(axes = input_177_axes_0, beta = model_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_3_norm_feed_forward1_weight_to_fp16, x = input_175_cast_fp16)[name = tensor<string, []>("input_177_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_3_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_3_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77708736))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(81903104))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_28_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_3_feed_forward1_linear1_weight_to_fp16_quantized, x = input_177_cast_fp16)[name = tensor<string, []>("linear_28_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_181_cast_fp16 = silu(x = linear_28_cast_fp16)[name = tensor<string, []>("input_181_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_3_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_3_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(81911360))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86105728))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_29_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_3_feed_forward1_linear2_weight_to_fp16_quantized, x = input_181_cast_fp16)[name = tensor<string, []>("linear_29_cast_fp16")];
+            tensor<fp16, []> var_716_to_fp16 = const()[name = tensor<string, []>("op_716_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_717_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_716_to_fp16)[name = tensor<string, []>("op_717_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_187_cast_fp16 = add(x = input_175_cast_fp16, y = var_717_cast_fp16)[name = tensor<string, []>("input_187_cast_fp16")];
+            tensor<int32, [1]> query_7_axes_0 = const()[name = tensor<string, []>("query_7_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_3_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_3_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86107840)))];
+            tensor<fp16, [1024]> model_layers_3_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_3_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86109952)))];
+            tensor<fp16, [1, 126, 1024]> query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = model_layers_3_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_3_norm_self_att_weight_to_fp16, x = input_187_cast_fp16)[name = tensor<string, []>("query_7_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_3_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_3_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86112064))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(87160704))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_30_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_3_self_attn_linear_q_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = tensor<string, []>("linear_30_cast_fp16")];
+            tensor<int32, [4]> var_733 = const()[name = tensor<string, []>("op_733"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_19_cast_fp16 = reshape(shape = var_733, x = linear_30_cast_fp16)[name = tensor<string, []>("q_19_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_3_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_3_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(87162816))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(88211456))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_31_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_3_self_attn_linear_k_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = tensor<string, []>("linear_31_cast_fp16")];
+            tensor<int32, [4]> var_737 = const()[name = tensor<string, []>("op_737"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_13_cast_fp16 = reshape(shape = var_737, x = linear_31_cast_fp16)[name = tensor<string, []>("k_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_3_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_3_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(88213568))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89262208))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_32_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_3_self_attn_linear_v_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = tensor<string, []>("linear_32_cast_fp16")];
+            tensor<int32, [4]> var_741 = const()[name = tensor<string, []>("op_741"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_7_cast_fp16 = reshape(shape = var_741, x = linear_32_cast_fp16)[name = tensor<string, []>("v_7_cast_fp16")];
+            tensor<int32, [4]> value_7_perm_0 = const()[name = tensor<string, []>("value_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_3_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_3_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89264320))), scale = tensor<fp16, [8]>([0x1.25cp-7, 0x1.224p-7, 0x1.1bcp-7, 0x1.408p-8, 0x1.aa4p-8, 0x1.e88p-8, 0x1.e74p-8, 0x1.92p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_753_cast_fp16 = add(x = q_19_cast_fp16, y = model_layers_3_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_753_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_3_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_3_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89265408))), scale = tensor<fp16, [8]>([0x1.348p-8, 0x1.08cp-9, 0x1.41cp-8, 0x1.73cp-8, 0x1.9b4p-8, 0x1.dc4p-9, 0x1.114p-8, 0x1.1a4p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_755_cast_fp16 = add(x = q_19_cast_fp16, y = model_layers_3_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_755_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_7_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_73_transpose_x_0 = const()[name = tensor<string, []>("x_73_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_73_transpose_y_0 = const()[name = tensor<string, []>("x_73_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_757_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_757_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89266496))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89523584))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_755_cast_fp16)[name = tensor<string, []>("transpose_290")];
+            tensor<fp16, [1, 8, 126, 251]> x_73_cast_fp16 = matmul(transpose_x = x_73_transpose_x_0, transpose_y = x_73_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = op_757_to_fp16_quantized)[name = tensor<string, []>("x_73_cast_fp16")];
+            tensor<int32, [8]> x_75_pad_0 = const()[name = tensor<string, []>("x_75_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_75_mode_0 = const()[name = tensor<string, []>("x_75_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_44_to_fp16 = const()[name = tensor<string, []>("const_44_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_75_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = x_73_cast_fp16)[name = tensor<string, []>("x_75_cast_fp16")];
+            tensor<int32, [4]> var_765 = const()[name = tensor<string, []>("op_765"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_77_cast_fp16 = reshape(shape = var_765, x = x_75_cast_fp16)[name = tensor<string, []>("x_77_cast_fp16")];
+            tensor<int32, [4]> var_769_begin_0 = const()[name = tensor<string, []>("op_769_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_769_end_0 = const()[name = tensor<string, []>("op_769_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_769_end_mask_0 = const()[name = tensor<string, []>("op_769_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_769_cast_fp16 = slice_by_index(begin = var_769_begin_0, end = var_769_end_0, end_mask = var_769_end_mask_0, x = x_77_cast_fp16)[name = tensor<string, []>("op_769_cast_fp16")];
+            tensor<int32, [4]> var_770 = const()[name = tensor<string, []>("op_770"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_13_cast_fp16 = reshape(shape = var_770, x = var_769_cast_fp16)[name = tensor<string, []>("matrix_bd_13_cast_fp16")];
+            tensor<bool, []> matrix_ac_7_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_7_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_7_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_7_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_102_perm_0 = const()[name = tensor<string, []>("transpose_102_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_103_perm_0 = const()[name = tensor<string, []>("transpose_103_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = tensor<string, []>("transpose_288")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_102 = transpose(perm = transpose_102_perm_0, x = var_753_cast_fp16)[name = tensor<string, []>("transpose_289")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_102, y = transpose_103)[name = tensor<string, []>("matrix_ac_7_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_15_begin_0 = const()[name = tensor<string, []>("matrix_bd_15_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_15_end_0 = const()[name = tensor<string, []>("matrix_bd_15_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_15_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_15_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = tensor<string, []>("matrix_bd_15_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_779_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = tensor<string, []>("op_779_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_13_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_13_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_13_cast_fp16 = mul(x = var_779_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_13_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_15_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_15_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_785_cast_fp16 = softmax(axis = var_25, x = scores_15_cast_fp16)[name = tensor<string, []>("op_785_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_189_cast_fp16 = select(a = var_6_to_fp16, b = var_785_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_189_cast_fp16")];
+            tensor<bool, []> x_79_transpose_x_0 = const()[name = tensor<string, []>("x_79_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_79_transpose_y_0 = const()[name = tensor<string, []>("x_79_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_7_cast_fp16)[name = tensor<string, []>("transpose_287")];
+            tensor<fp16, [1, 8, 126, 128]> x_79_cast_fp16 = matmul(transpose_x = x_79_transpose_x_0, transpose_y = x_79_transpose_y_0, x = input_189_cast_fp16, y = value_7_cast_fp16)[name = tensor<string, []>("x_79_cast_fp16")];
+            tensor<int32, [4]> var_789_perm_0 = const()[name = tensor<string, []>("op_789_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_790 = const()[name = tensor<string, []>("op_790"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_789_cast_fp16 = transpose(perm = var_789_perm_0, x = x_79_cast_fp16)[name = tensor<string, []>("transpose_286")];
+            tensor<fp16, [1, 126, 1024]> input_191_cast_fp16 = reshape(shape = var_790, x = var_789_cast_fp16)[name = tensor<string, []>("input_191_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_3_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_3_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89524160))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90572800))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_34_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_3_self_attn_linear_out_weight_to_fp16_quantized, x = input_191_cast_fp16)[name = tensor<string, []>("linear_34_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_195_cast_fp16 = add(x = input_187_cast_fp16, y = linear_34_cast_fp16)[name = tensor<string, []>("input_195_cast_fp16")];
+            tensor<int32, [1]> x_83_axes_0 = const()[name = tensor<string, []>("x_83_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_3_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_3_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90574912)))];
+            tensor<fp16, [1024]> model_layers_3_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_3_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90577024)))];
+            tensor<fp16, [1, 126, 1024]> x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = model_layers_3_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_3_norm_conv_weight_to_fp16, x = input_195_cast_fp16)[name = tensor<string, []>("x_83_cast_fp16")];
+            tensor<int32, [3]> input_197_perm_0 = const()[name = tensor<string, []>("input_197_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_199_pad_type_0 = const()[name = tensor<string, []>("input_199_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_199_strides_0 = const()[name = tensor<string, []>("input_199_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_199_pad_0 = const()[name = tensor<string, []>("input_199_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_199_dilations_0 = const()[name = tensor<string, []>("input_199_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_199_groups_0 = const()[name = tensor<string, []>("input_199_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90579136))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92676352))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_197_cast_fp16 = transpose(perm = input_197_perm_0, x = x_83_cast_fp16)[name = tensor<string, []>("transpose_285")];
+            tensor<fp16, [1, 2048, 126]> input_199_cast_fp16 = conv(dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = model_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_197_cast_fp16)[name = tensor<string, []>("input_199_cast_fp16")];
+            tensor<int32, []> x_85_split_num_splits_0 = const()[name = tensor<string, []>("x_85_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_85_split_axis_0 = const()[name = tensor<string, []>("x_85_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_85_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_85_split_cast_fp16_1 = split(axis = x_85_split_axis_0, num_splits = x_85_split_num_splits_0, x = input_199_cast_fp16)[name = tensor<string, []>("x_85_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_85_split_1_sigmoid_cast_fp16 = sigmoid(x = x_85_split_cast_fp16_1)[name = tensor<string, []>("x_85_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_85_cast_fp16 = mul(x = x_85_split_cast_fp16_0, y = x_85_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_85_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_201_cast_fp16 = select(a = var_6_to_fp16, b = x_85_cast_fp16, cond = var_323)[name = tensor<string, []>("input_201_cast_fp16")];
+            tensor<int32, [6]> input_203_pad_0 = const()[name = tensor<string, []>("input_203_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_203_mode_0 = const()[name = tensor<string, []>("input_203_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_47_to_fp16 = const()[name = tensor<string, []>("const_47_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_203_cast_fp16 = pad(constant_val = const_47_to_fp16, mode = input_203_mode_0, pad = input_203_pad_0, x = input_201_cast_fp16)[name = tensor<string, []>("input_203_cast_fp16")];
+            tensor<string, []> input_205_pad_type_0 = const()[name = tensor<string, []>("input_205_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_205_groups_0 = const()[name = tensor<string, []>("input_205_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_205_strides_0 = const()[name = tensor<string, []>("input_205_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_205_pad_0 = const()[name = tensor<string, []>("input_205_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_205_dilations_0 = const()[name = tensor<string, []>("input_205_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_254_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_254_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92680512))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92689792))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_255_to_fp16 = const()[name = tensor<string, []>("const_255_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92691904)))];
+            tensor<fp16, [1, 1024, 126]> input_207_cast_fp16 = conv(bias = const_255_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_254_to_fp16_quantized, x = input_203_cast_fp16)[name = tensor<string, []>("input_207_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_209_cast_fp16 = silu(x = input_207_cast_fp16)[name = tensor<string, []>("input_209_cast_fp16")];
+            tensor<string, []> x_87_pad_type_0 = const()[name = tensor<string, []>("x_87_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_87_strides_0 = const()[name = tensor<string, []>("x_87_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_87_pad_0 = const()[name = tensor<string, []>("x_87_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_87_dilations_0 = const()[name = tensor<string, []>("x_87_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_87_groups_0 = const()[name = tensor<string, []>("x_87_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92694016))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93742656))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_87_cast_fp16 = conv(dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = model_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_209_cast_fp16)[name = tensor<string, []>("x_87_cast_fp16")];
+            tensor<int32, [3]> input_211_perm_0 = const()[name = tensor<string, []>("input_211_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_87_cast_fp16)[name = tensor<string, []>("transpose_284")];
+            tensor<fp16, [1, 126, 1024]> input_213_cast_fp16 = add(x = input_195_cast_fp16, y = input_211_cast_fp16)[name = tensor<string, []>("input_213_cast_fp16")];
+            tensor<int32, [1]> input_215_axes_0 = const()[name = tensor<string, []>("input_215_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93744768)))];
+            tensor<fp16, [1024]> model_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93746880)))];
+            tensor<fp16, [1, 126, 1024]> input_215_cast_fp16 = layer_norm(axes = input_215_axes_0, beta = model_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_3_norm_feed_forward2_weight_to_fp16, x = input_213_cast_fp16)[name = tensor<string, []>("input_215_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_3_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_3_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93748992))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97943360))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_35_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_3_feed_forward2_linear1_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = tensor<string, []>("linear_35_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_219_cast_fp16 = silu(x = linear_35_cast_fp16)[name = tensor<string, []>("input_219_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_3_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_3_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97951616))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102145984))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_36_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_3_feed_forward2_linear2_weight_to_fp16_quantized, x = input_219_cast_fp16)[name = tensor<string, []>("linear_36_cast_fp16")];
+            tensor<fp16, []> var_850_to_fp16 = const()[name = tensor<string, []>("op_850_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_851_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_850_to_fp16)[name = tensor<string, []>("op_851_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_225_cast_fp16 = add(x = input_213_cast_fp16, y = var_851_cast_fp16)[name = tensor<string, []>("input_225_cast_fp16")];
+            tensor<int32, [1]> input_227_axes_0 = const()[name = tensor<string, []>("input_227_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_3_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_3_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102148096)))];
+            tensor<fp16, [1024]> model_layers_3_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_3_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102150208)))];
+            tensor<fp16, [1, 126, 1024]> input_227_cast_fp16 = layer_norm(axes = input_227_axes_0, beta = model_layers_3_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_3_norm_out_weight_to_fp16, x = input_225_cast_fp16)[name = tensor<string, []>("input_227_cast_fp16")];
+            tensor<int32, [1]> input_229_axes_0 = const()[name = tensor<string, []>("input_229_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102152320)))];
+            tensor<fp16, [1024]> model_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102154432)))];
+            tensor<fp16, [1, 126, 1024]> input_229_cast_fp16 = layer_norm(axes = input_229_axes_0, beta = model_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_4_norm_feed_forward1_weight_to_fp16, x = input_227_cast_fp16)[name = tensor<string, []>("input_229_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_4_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_4_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102156544))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(106350912))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_37_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_4_feed_forward1_linear1_weight_to_fp16_quantized, x = input_229_cast_fp16)[name = tensor<string, []>("linear_37_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_233_cast_fp16 = silu(x = linear_37_cast_fp16)[name = tensor<string, []>("input_233_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_4_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_4_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(106359168))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110553536))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_38_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_4_feed_forward1_linear2_weight_to_fp16_quantized, x = input_233_cast_fp16)[name = tensor<string, []>("linear_38_cast_fp16")];
+            tensor<fp16, []> var_879_to_fp16 = const()[name = tensor<string, []>("op_879_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_880_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_879_to_fp16)[name = tensor<string, []>("op_880_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_239_cast_fp16 = add(x = input_227_cast_fp16, y = var_880_cast_fp16)[name = tensor<string, []>("input_239_cast_fp16")];
+            tensor<int32, [1]> query_9_axes_0 = const()[name = tensor<string, []>("query_9_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_4_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_4_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110555648)))];
+            tensor<fp16, [1024]> model_layers_4_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_4_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110557760)))];
+            tensor<fp16, [1, 126, 1024]> query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = model_layers_4_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_4_norm_self_att_weight_to_fp16, x = input_239_cast_fp16)[name = tensor<string, []>("query_9_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_4_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_4_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110559872))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111608512))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_39_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_4_self_attn_linear_q_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = tensor<string, []>("linear_39_cast_fp16")];
+            tensor<int32, [4]> var_896 = const()[name = tensor<string, []>("op_896"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_25_cast_fp16 = reshape(shape = var_896, x = linear_39_cast_fp16)[name = tensor<string, []>("q_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_4_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_4_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111610624))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112659264))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_40_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_4_self_attn_linear_k_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = tensor<string, []>("linear_40_cast_fp16")];
+            tensor<int32, [4]> var_900 = const()[name = tensor<string, []>("op_900"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_17_cast_fp16 = reshape(shape = var_900, x = linear_40_cast_fp16)[name = tensor<string, []>("k_17_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_4_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_4_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112661376))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113710016))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_41_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_4_self_attn_linear_v_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = tensor<string, []>("linear_41_cast_fp16")];
+            tensor<int32, [4]> var_904 = const()[name = tensor<string, []>("op_904"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_9_cast_fp16 = reshape(shape = var_904, x = linear_41_cast_fp16)[name = tensor<string, []>("v_9_cast_fp16")];
+            tensor<int32, [4]> value_9_perm_0 = const()[name = tensor<string, []>("value_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_4_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_4_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113712128))), scale = tensor<fp16, [8]>([0x1.024p-7, 0x1.1e4p-7, 0x1.a9p-8, 0x1.308p-7, 0x1.fcp-7, 0x1.3a4p-7, 0x1.0ap-7, 0x1.414p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_916_cast_fp16 = add(x = q_25_cast_fp16, y = model_layers_4_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_916_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_4_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_4_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113713216))), scale = tensor<fp16, [8]>([0x1.29p-8, 0x1.1ccp-9, 0x1.e2cp-8, 0x1.17cp-8, 0x1.134p-10, 0x1.b28p-9, 0x1.44p-8, 0x1.29cp-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_918_cast_fp16 = add(x = q_25_cast_fp16, y = model_layers_4_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_918_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_9_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_95_transpose_x_0 = const()[name = tensor<string, []>("x_95_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_95_transpose_y_0 = const()[name = tensor<string, []>("x_95_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_920_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_920_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113714304))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113971392))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_918_cast_fp16)[name = tensor<string, []>("transpose_283")];
+            tensor<fp16, [1, 8, 126, 251]> x_95_cast_fp16 = matmul(transpose_x = x_95_transpose_x_0, transpose_y = x_95_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = op_920_to_fp16_quantized)[name = tensor<string, []>("x_95_cast_fp16")];
+            tensor<int32, [8]> x_97_pad_0 = const()[name = tensor<string, []>("x_97_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_97_mode_0 = const()[name = tensor<string, []>("x_97_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_54_to_fp16 = const()[name = tensor<string, []>("const_54_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_97_cast_fp16 = pad(constant_val = const_54_to_fp16, mode = x_97_mode_0, pad = x_97_pad_0, x = x_95_cast_fp16)[name = tensor<string, []>("x_97_cast_fp16")];
+            tensor<int32, [4]> var_928 = const()[name = tensor<string, []>("op_928"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_99_cast_fp16 = reshape(shape = var_928, x = x_97_cast_fp16)[name = tensor<string, []>("x_99_cast_fp16")];
+            tensor<int32, [4]> var_932_begin_0 = const()[name = tensor<string, []>("op_932_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_932_end_0 = const()[name = tensor<string, []>("op_932_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_932_end_mask_0 = const()[name = tensor<string, []>("op_932_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_932_cast_fp16 = slice_by_index(begin = var_932_begin_0, end = var_932_end_0, end_mask = var_932_end_mask_0, x = x_99_cast_fp16)[name = tensor<string, []>("op_932_cast_fp16")];
+            tensor<int32, [4]> var_933 = const()[name = tensor<string, []>("op_933"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_17_cast_fp16 = reshape(shape = var_933, x = var_932_cast_fp16)[name = tensor<string, []>("matrix_bd_17_cast_fp16")];
+            tensor<bool, []> matrix_ac_9_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_9_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_9_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_9_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_104_perm_0 = const()[name = tensor<string, []>("transpose_104_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_105_perm_0 = const()[name = tensor<string, []>("transpose_105_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = tensor<string, []>("transpose_281")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_104 = transpose(perm = transpose_104_perm_0, x = var_916_cast_fp16)[name = tensor<string, []>("transpose_282")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_104, y = transpose_105)[name = tensor<string, []>("matrix_ac_9_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_19_begin_0 = const()[name = tensor<string, []>("matrix_bd_19_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_19_end_0 = const()[name = tensor<string, []>("matrix_bd_19_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_19_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_19_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = tensor<string, []>("matrix_bd_19_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_942_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = tensor<string, []>("op_942_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_17_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_17_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_17_cast_fp16 = mul(x = var_942_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_17_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_19_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_19_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_948_cast_fp16 = softmax(axis = var_25, x = scores_19_cast_fp16)[name = tensor<string, []>("op_948_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_241_cast_fp16 = select(a = var_6_to_fp16, b = var_948_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_241_cast_fp16")];
+            tensor<bool, []> x_101_transpose_x_0 = const()[name = tensor<string, []>("x_101_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_101_transpose_y_0 = const()[name = tensor<string, []>("x_101_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_9_cast_fp16)[name = tensor<string, []>("transpose_280")];
+            tensor<fp16, [1, 8, 126, 128]> x_101_cast_fp16 = matmul(transpose_x = x_101_transpose_x_0, transpose_y = x_101_transpose_y_0, x = input_241_cast_fp16, y = value_9_cast_fp16)[name = tensor<string, []>("x_101_cast_fp16")];
+            tensor<int32, [4]> var_952_perm_0 = const()[name = tensor<string, []>("op_952_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_953 = const()[name = tensor<string, []>("op_953"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_952_cast_fp16 = transpose(perm = var_952_perm_0, x = x_101_cast_fp16)[name = tensor<string, []>("transpose_279")];
+            tensor<fp16, [1, 126, 1024]> input_243_cast_fp16 = reshape(shape = var_953, x = var_952_cast_fp16)[name = tensor<string, []>("input_243_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_4_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_4_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113971968))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115020608))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_43_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_4_self_attn_linear_out_weight_to_fp16_quantized, x = input_243_cast_fp16)[name = tensor<string, []>("linear_43_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_247_cast_fp16 = add(x = input_239_cast_fp16, y = linear_43_cast_fp16)[name = tensor<string, []>("input_247_cast_fp16")];
+            tensor<int32, [1]> x_105_axes_0 = const()[name = tensor<string, []>("x_105_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_4_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_4_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115022720)))];
+            tensor<fp16, [1024]> model_layers_4_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_4_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115024832)))];
+            tensor<fp16, [1, 126, 1024]> x_105_cast_fp16 = layer_norm(axes = x_105_axes_0, beta = model_layers_4_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_4_norm_conv_weight_to_fp16, x = input_247_cast_fp16)[name = tensor<string, []>("x_105_cast_fp16")];
+            tensor<int32, [3]> input_249_perm_0 = const()[name = tensor<string, []>("input_249_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_251_pad_type_0 = const()[name = tensor<string, []>("input_251_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_251_strides_0 = const()[name = tensor<string, []>("input_251_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_251_pad_0 = const()[name = tensor<string, []>("input_251_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_251_dilations_0 = const()[name = tensor<string, []>("input_251_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_251_groups_0 = const()[name = tensor<string, []>("input_251_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115026944))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117124160))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_249_cast_fp16 = transpose(perm = input_249_perm_0, x = x_105_cast_fp16)[name = tensor<string, []>("transpose_278")];
+            tensor<fp16, [1, 2048, 126]> input_251_cast_fp16 = conv(dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = model_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_249_cast_fp16)[name = tensor<string, []>("input_251_cast_fp16")];
+            tensor<int32, []> x_107_split_num_splits_0 = const()[name = tensor<string, []>("x_107_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_107_split_axis_0 = const()[name = tensor<string, []>("x_107_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_107_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_107_split_cast_fp16_1 = split(axis = x_107_split_axis_0, num_splits = x_107_split_num_splits_0, x = input_251_cast_fp16)[name = tensor<string, []>("x_107_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_107_split_1_sigmoid_cast_fp16 = sigmoid(x = x_107_split_cast_fp16_1)[name = tensor<string, []>("x_107_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_107_cast_fp16 = mul(x = x_107_split_cast_fp16_0, y = x_107_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_107_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_253_cast_fp16 = select(a = var_6_to_fp16, b = x_107_cast_fp16, cond = var_323)[name = tensor<string, []>("input_253_cast_fp16")];
+            tensor<int32, [6]> input_255_pad_0 = const()[name = tensor<string, []>("input_255_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_255_mode_0 = const()[name = tensor<string, []>("input_255_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_57_to_fp16 = const()[name = tensor<string, []>("const_57_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_255_cast_fp16 = pad(constant_val = const_57_to_fp16, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253_cast_fp16)[name = tensor<string, []>("input_255_cast_fp16")];
+            tensor<string, []> input_257_pad_type_0 = const()[name = tensor<string, []>("input_257_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_257_groups_0 = const()[name = tensor<string, []>("input_257_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_257_strides_0 = const()[name = tensor<string, []>("input_257_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_257_pad_0 = const()[name = tensor<string, []>("input_257_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_257_dilations_0 = const()[name = tensor<string, []>("input_257_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_256_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_256_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117128320))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117137600))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_257_to_fp16 = const()[name = tensor<string, []>("const_257_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117139712)))];
+            tensor<fp16, [1, 1024, 126]> input_259_cast_fp16 = conv(bias = const_257_to_fp16, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = const_256_to_fp16_quantized, x = input_255_cast_fp16)[name = tensor<string, []>("input_259_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_261_cast_fp16 = silu(x = input_259_cast_fp16)[name = tensor<string, []>("input_261_cast_fp16")];
+            tensor<string, []> x_109_pad_type_0 = const()[name = tensor<string, []>("x_109_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_109_strides_0 = const()[name = tensor<string, []>("x_109_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_109_pad_0 = const()[name = tensor<string, []>("x_109_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_109_dilations_0 = const()[name = tensor<string, []>("x_109_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_109_groups_0 = const()[name = tensor<string, []>("x_109_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117141824))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118190464))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_109_cast_fp16 = conv(dilations = x_109_dilations_0, groups = x_109_groups_0, pad = x_109_pad_0, pad_type = x_109_pad_type_0, strides = x_109_strides_0, weight = model_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_261_cast_fp16)[name = tensor<string, []>("x_109_cast_fp16")];
+            tensor<int32, [3]> input_263_perm_0 = const()[name = tensor<string, []>("input_263_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_109_cast_fp16)[name = tensor<string, []>("transpose_277")];
+            tensor<fp16, [1, 126, 1024]> input_265_cast_fp16 = add(x = input_247_cast_fp16, y = input_263_cast_fp16)[name = tensor<string, []>("input_265_cast_fp16")];
+            tensor<int32, [1]> input_267_axes_0 = const()[name = tensor<string, []>("input_267_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118192576)))];
+            tensor<fp16, [1024]> model_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118194688)))];
+            tensor<fp16, [1, 126, 1024]> input_267_cast_fp16 = layer_norm(axes = input_267_axes_0, beta = model_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_4_norm_feed_forward2_weight_to_fp16, x = input_265_cast_fp16)[name = tensor<string, []>("input_267_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_4_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_4_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118196800))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(122391168))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_44_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_4_feed_forward2_linear1_weight_to_fp16_quantized, x = input_267_cast_fp16)[name = tensor<string, []>("linear_44_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_271_cast_fp16 = silu(x = linear_44_cast_fp16)[name = tensor<string, []>("input_271_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_4_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_4_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(122399424))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126593792))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_45_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_4_feed_forward2_linear2_weight_to_fp16_quantized, x = input_271_cast_fp16)[name = tensor<string, []>("linear_45_cast_fp16")];
+            tensor<fp16, []> var_1013_to_fp16 = const()[name = tensor<string, []>("op_1013_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_1014_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1013_to_fp16)[name = tensor<string, []>("op_1014_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_277_cast_fp16 = add(x = input_265_cast_fp16, y = var_1014_cast_fp16)[name = tensor<string, []>("input_277_cast_fp16")];
+            tensor<int32, [1]> input_279_axes_0 = const()[name = tensor<string, []>("input_279_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_4_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_4_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126595904)))];
+            tensor<fp16, [1024]> model_layers_4_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_4_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126598016)))];
+            tensor<fp16, [1, 126, 1024]> input_279_cast_fp16 = layer_norm(axes = input_279_axes_0, beta = model_layers_4_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_4_norm_out_weight_to_fp16, x = input_277_cast_fp16)[name = tensor<string, []>("input_279_cast_fp16")];
+            tensor<int32, [1]> input_281_axes_0 = const()[name = tensor<string, []>("input_281_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126600128)))];
+            tensor<fp16, [1024]> model_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126602240)))];
+            tensor<fp16, [1, 126, 1024]> input_281_cast_fp16 = layer_norm(axes = input_281_axes_0, beta = model_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_5_norm_feed_forward1_weight_to_fp16, x = input_279_cast_fp16)[name = tensor<string, []>("input_281_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_5_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_5_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126604352))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130798720))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_46_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_5_feed_forward1_linear1_weight_to_fp16_quantized, x = input_281_cast_fp16)[name = tensor<string, []>("linear_46_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_285_cast_fp16 = silu(x = linear_46_cast_fp16)[name = tensor<string, []>("input_285_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_5_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_5_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130806976))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135001344))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_47_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_5_feed_forward1_linear2_weight_to_fp16_quantized, x = input_285_cast_fp16)[name = tensor<string, []>("linear_47_cast_fp16")];
+            tensor<fp16, []> var_1042_to_fp16 = const()[name = tensor<string, []>("op_1042_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_1043_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1042_to_fp16)[name = tensor<string, []>("op_1043_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_291_cast_fp16 = add(x = input_279_cast_fp16, y = var_1043_cast_fp16)[name = tensor<string, []>("input_291_cast_fp16")];
+            tensor<int32, [1]> query_11_axes_0 = const()[name = tensor<string, []>("query_11_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_5_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_5_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135003456)))];
+            tensor<fp16, [1024]> model_layers_5_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_5_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135005568)))];
+            tensor<fp16, [1, 126, 1024]> query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = model_layers_5_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_5_norm_self_att_weight_to_fp16, x = input_291_cast_fp16)[name = tensor<string, []>("query_11_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_5_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_5_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135007680))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136056320))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_48_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_5_self_attn_linear_q_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = tensor<string, []>("linear_48_cast_fp16")];
+            tensor<int32, [4]> var_1059 = const()[name = tensor<string, []>("op_1059"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_31_cast_fp16 = reshape(shape = var_1059, x = linear_48_cast_fp16)[name = tensor<string, []>("q_31_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_5_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_5_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136058432))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137107072))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_49_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_5_self_attn_linear_k_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = tensor<string, []>("linear_49_cast_fp16")];
+            tensor<int32, [4]> var_1063 = const()[name = tensor<string, []>("op_1063"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_21_cast_fp16 = reshape(shape = var_1063, x = linear_49_cast_fp16)[name = tensor<string, []>("k_21_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_5_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_5_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137109184))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138157824))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_50_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_5_self_attn_linear_v_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = tensor<string, []>("linear_50_cast_fp16")];
+            tensor<int32, [4]> var_1067 = const()[name = tensor<string, []>("op_1067"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_11_cast_fp16 = reshape(shape = var_1067, x = linear_50_cast_fp16)[name = tensor<string, []>("v_11_cast_fp16")];
+            tensor<int32, [4]> value_11_perm_0 = const()[name = tensor<string, []>("value_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_5_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_5_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138159936))), scale = tensor<fp16, [8]>([0x1.1e8p-7, 0x1.b5p-8, 0x1.538p-7, 0x1.63cp-7, 0x1.018p-7, 0x1.54cp-7, 0x1.6a8p-7, 0x1.c04p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_1079_cast_fp16 = add(x = q_31_cast_fp16, y = model_layers_5_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_1079_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_5_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_5_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138161024))), scale = tensor<fp16, [8]>([0x1.fb8p-9, 0x1.31cp-8, 0x1.07cp-8, 0x1.2bp-9, 0x1.0ap-7, 0x1.ff8p-9, 0x1.908p-8, 0x1.5d4p-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_1081_cast_fp16 = add(x = q_31_cast_fp16, y = model_layers_5_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_1081_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_11_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_117_transpose_x_0 = const()[name = tensor<string, []>("x_117_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_117_transpose_y_0 = const()[name = tensor<string, []>("x_117_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_1083_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1083_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138162112))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138419200))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1081_cast_fp16)[name = tensor<string, []>("transpose_276")];
+            tensor<fp16, [1, 8, 126, 251]> x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = op_1083_to_fp16_quantized)[name = tensor<string, []>("x_117_cast_fp16")];
+            tensor<int32, [8]> x_119_pad_0 = const()[name = tensor<string, []>("x_119_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_119_mode_0 = const()[name = tensor<string, []>("x_119_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_64_to_fp16 = const()[name = tensor<string, []>("const_64_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_119_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = x_117_cast_fp16)[name = tensor<string, []>("x_119_cast_fp16")];
+            tensor<int32, [4]> var_1091 = const()[name = tensor<string, []>("op_1091"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_121_cast_fp16 = reshape(shape = var_1091, x = x_119_cast_fp16)[name = tensor<string, []>("x_121_cast_fp16")];
+            tensor<int32, [4]> var_1095_begin_0 = const()[name = tensor<string, []>("op_1095_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1095_end_0 = const()[name = tensor<string, []>("op_1095_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_1095_end_mask_0 = const()[name = tensor<string, []>("op_1095_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_1095_cast_fp16 = slice_by_index(begin = var_1095_begin_0, end = var_1095_end_0, end_mask = var_1095_end_mask_0, x = x_121_cast_fp16)[name = tensor<string, []>("op_1095_cast_fp16")];
+            tensor<int32, [4]> var_1096 = const()[name = tensor<string, []>("op_1096"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_21_cast_fp16 = reshape(shape = var_1096, x = var_1095_cast_fp16)[name = tensor<string, []>("matrix_bd_21_cast_fp16")];
+            tensor<bool, []> matrix_ac_11_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_11_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_11_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_11_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_106_perm_0 = const()[name = tensor<string, []>("transpose_106_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_107_perm_0 = const()[name = tensor<string, []>("transpose_107_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = tensor<string, []>("transpose_274")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1079_cast_fp16)[name = tensor<string, []>("transpose_275")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_106, y = transpose_107)[name = tensor<string, []>("matrix_ac_11_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_23_begin_0 = const()[name = tensor<string, []>("matrix_bd_23_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_23_end_0 = const()[name = tensor<string, []>("matrix_bd_23_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_23_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_23_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = tensor<string, []>("matrix_bd_23_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_1105_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = tensor<string, []>("op_1105_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_21_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_21_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_21_cast_fp16 = mul(x = var_1105_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_21_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_23_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_23_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_1111_cast_fp16 = softmax(axis = var_25, x = scores_23_cast_fp16)[name = tensor<string, []>("op_1111_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_293_cast_fp16 = select(a = var_6_to_fp16, b = var_1111_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_293_cast_fp16")];
+            tensor<bool, []> x_123_transpose_x_0 = const()[name = tensor<string, []>("x_123_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_123_transpose_y_0 = const()[name = tensor<string, []>("x_123_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_11_cast_fp16)[name = tensor<string, []>("transpose_273")];
+            tensor<fp16, [1, 8, 126, 128]> x_123_cast_fp16 = matmul(transpose_x = x_123_transpose_x_0, transpose_y = x_123_transpose_y_0, x = input_293_cast_fp16, y = value_11_cast_fp16)[name = tensor<string, []>("x_123_cast_fp16")];
+            tensor<int32, [4]> var_1115_perm_0 = const()[name = tensor<string, []>("op_1115_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1116 = const()[name = tensor<string, []>("op_1116"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_1115_cast_fp16 = transpose(perm = var_1115_perm_0, x = x_123_cast_fp16)[name = tensor<string, []>("transpose_272")];
+            tensor<fp16, [1, 126, 1024]> input_295_cast_fp16 = reshape(shape = var_1116, x = var_1115_cast_fp16)[name = tensor<string, []>("input_295_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_5_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_5_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138419776))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139468416))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_52_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_5_self_attn_linear_out_weight_to_fp16_quantized, x = input_295_cast_fp16)[name = tensor<string, []>("linear_52_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_299_cast_fp16 = add(x = input_291_cast_fp16, y = linear_52_cast_fp16)[name = tensor<string, []>("input_299_cast_fp16")];
+            tensor<int32, [1]> x_127_axes_0 = const()[name = tensor<string, []>("x_127_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_5_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_5_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139470528)))];
+            tensor<fp16, [1024]> model_layers_5_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_5_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139472640)))];
+            tensor<fp16, [1, 126, 1024]> x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = model_layers_5_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_5_norm_conv_weight_to_fp16, x = input_299_cast_fp16)[name = tensor<string, []>("x_127_cast_fp16")];
+            tensor<int32, [3]> input_301_perm_0 = const()[name = tensor<string, []>("input_301_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_303_pad_type_0 = const()[name = tensor<string, []>("input_303_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_303_strides_0 = const()[name = tensor<string, []>("input_303_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_303_pad_0 = const()[name = tensor<string, []>("input_303_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_303_dilations_0 = const()[name = tensor<string, []>("input_303_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_303_groups_0 = const()[name = tensor<string, []>("input_303_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139474752))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141571968))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_301_cast_fp16 = transpose(perm = input_301_perm_0, x = x_127_cast_fp16)[name = tensor<string, []>("transpose_271")];
+            tensor<fp16, [1, 2048, 126]> input_303_cast_fp16 = conv(dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = model_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_301_cast_fp16)[name = tensor<string, []>("input_303_cast_fp16")];
+            tensor<int32, []> x_129_split_num_splits_0 = const()[name = tensor<string, []>("x_129_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_129_split_axis_0 = const()[name = tensor<string, []>("x_129_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_129_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_129_split_cast_fp16_1 = split(axis = x_129_split_axis_0, num_splits = x_129_split_num_splits_0, x = input_303_cast_fp16)[name = tensor<string, []>("x_129_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_129_split_1_sigmoid_cast_fp16 = sigmoid(x = x_129_split_cast_fp16_1)[name = tensor<string, []>("x_129_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_129_cast_fp16 = mul(x = x_129_split_cast_fp16_0, y = x_129_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_129_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_305_cast_fp16 = select(a = var_6_to_fp16, b = x_129_cast_fp16, cond = var_323)[name = tensor<string, []>("input_305_cast_fp16")];
+            tensor<int32, [6]> input_307_pad_0 = const()[name = tensor<string, []>("input_307_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_307_mode_0 = const()[name = tensor<string, []>("input_307_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_67_to_fp16 = const()[name = tensor<string, []>("const_67_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_307_cast_fp16 = pad(constant_val = const_67_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = tensor<string, []>("input_307_cast_fp16")];
+            tensor<string, []> input_309_pad_type_0 = const()[name = tensor<string, []>("input_309_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_309_groups_0 = const()[name = tensor<string, []>("input_309_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_309_strides_0 = const()[name = tensor<string, []>("input_309_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_309_pad_0 = const()[name = tensor<string, []>("input_309_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_309_dilations_0 = const()[name = tensor<string, []>("input_309_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_258_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_258_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141576128))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141585408))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_259_to_fp16 = const()[name = tensor<string, []>("const_259_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141587520)))];
+            tensor<fp16, [1, 1024, 126]> input_311_cast_fp16 = conv(bias = const_259_to_fp16, dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = const_258_to_fp16_quantized, x = input_307_cast_fp16)[name = tensor<string, []>("input_311_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_313_cast_fp16 = silu(x = input_311_cast_fp16)[name = tensor<string, []>("input_313_cast_fp16")];
+            tensor<string, []> x_131_pad_type_0 = const()[name = tensor<string, []>("x_131_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_131_strides_0 = const()[name = tensor<string, []>("x_131_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_131_pad_0 = const()[name = tensor<string, []>("x_131_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_131_dilations_0 = const()[name = tensor<string, []>("x_131_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_131_groups_0 = const()[name = tensor<string, []>("x_131_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141589632))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142638272))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_131_cast_fp16 = conv(dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = model_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_313_cast_fp16)[name = tensor<string, []>("x_131_cast_fp16")];
+            tensor<int32, [3]> input_315_perm_0 = const()[name = tensor<string, []>("input_315_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_131_cast_fp16)[name = tensor<string, []>("transpose_270")];
+            tensor<fp16, [1, 126, 1024]> input_317_cast_fp16 = add(x = input_299_cast_fp16, y = input_315_cast_fp16)[name = tensor<string, []>("input_317_cast_fp16")];
+            tensor<int32, [1]> input_319_axes_0 = const()[name = tensor<string, []>("input_319_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142640384)))];
+            tensor<fp16, [1024]> model_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142642496)))];
+            tensor<fp16, [1, 126, 1024]> input_319_cast_fp16 = layer_norm(axes = input_319_axes_0, beta = model_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_5_norm_feed_forward2_weight_to_fp16, x = input_317_cast_fp16)[name = tensor<string, []>("input_319_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_5_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_5_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142644608))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146838976))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_53_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_5_feed_forward2_linear1_weight_to_fp16_quantized, x = input_319_cast_fp16)[name = tensor<string, []>("linear_53_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_323_cast_fp16 = silu(x = linear_53_cast_fp16)[name = tensor<string, []>("input_323_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_5_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_5_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146847232))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151041600))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_54_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_5_feed_forward2_linear2_weight_to_fp16_quantized, x = input_323_cast_fp16)[name = tensor<string, []>("linear_54_cast_fp16")];
+            tensor<fp16, []> var_1176_to_fp16 = const()[name = tensor<string, []>("op_1176_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_1177_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1176_to_fp16)[name = tensor<string, []>("op_1177_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_329_cast_fp16 = add(x = input_317_cast_fp16, y = var_1177_cast_fp16)[name = tensor<string, []>("input_329_cast_fp16")];
+            tensor<int32, [1]> input_331_axes_0 = const()[name = tensor<string, []>("input_331_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_5_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_5_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151043712)))];
+            tensor<fp16, [1024]> model_layers_5_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_5_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151045824)))];
+            tensor<fp16, [1, 126, 1024]> input_331_cast_fp16 = layer_norm(axes = input_331_axes_0, beta = model_layers_5_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_5_norm_out_weight_to_fp16, x = input_329_cast_fp16)[name = tensor<string, []>("input_331_cast_fp16")];
+            tensor<int32, [1]> input_333_axes_0 = const()[name = tensor<string, []>("input_333_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151047936)))];
+            tensor<fp16, [1024]> model_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151050048)))];
+            tensor<fp16, [1, 126, 1024]> input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = model_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_6_norm_feed_forward1_weight_to_fp16, x = input_331_cast_fp16)[name = tensor<string, []>("input_333_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_6_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_6_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151052160))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155246528))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_55_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_6_feed_forward1_linear1_weight_to_fp16_quantized, x = input_333_cast_fp16)[name = tensor<string, []>("linear_55_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_337_cast_fp16 = silu(x = linear_55_cast_fp16)[name = tensor<string, []>("input_337_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_6_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_6_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155254784))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159449152))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_56_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_6_feed_forward1_linear2_weight_to_fp16_quantized, x = input_337_cast_fp16)[name = tensor<string, []>("linear_56_cast_fp16")];
+            tensor<fp16, []> var_1205_to_fp16 = const()[name = tensor<string, []>("op_1205_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_1206_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1205_to_fp16)[name = tensor<string, []>("op_1206_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_343_cast_fp16 = add(x = input_331_cast_fp16, y = var_1206_cast_fp16)[name = tensor<string, []>("input_343_cast_fp16")];
+            tensor<int32, [1]> query_13_axes_0 = const()[name = tensor<string, []>("query_13_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_6_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_6_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159451264)))];
+            tensor<fp16, [1024]> model_layers_6_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_6_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159453376)))];
+            tensor<fp16, [1, 126, 1024]> query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = model_layers_6_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_6_norm_self_att_weight_to_fp16, x = input_343_cast_fp16)[name = tensor<string, []>("query_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_6_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_6_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159455488))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160504128))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_57_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_6_self_attn_linear_q_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = tensor<string, []>("linear_57_cast_fp16")];
+            tensor<int32, [4]> var_1222 = const()[name = tensor<string, []>("op_1222"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_37_cast_fp16 = reshape(shape = var_1222, x = linear_57_cast_fp16)[name = tensor<string, []>("q_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_6_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_6_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160506240))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161554880))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_58_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_6_self_attn_linear_k_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = tensor<string, []>("linear_58_cast_fp16")];
+            tensor<int32, [4]> var_1226 = const()[name = tensor<string, []>("op_1226"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_25_cast_fp16 = reshape(shape = var_1226, x = linear_58_cast_fp16)[name = tensor<string, []>("k_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_6_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_6_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161556992))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162605632))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_59_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_6_self_attn_linear_v_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = tensor<string, []>("linear_59_cast_fp16")];
+            tensor<int32, [4]> var_1230 = const()[name = tensor<string, []>("op_1230"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_13_cast_fp16 = reshape(shape = var_1230, x = linear_59_cast_fp16)[name = tensor<string, []>("v_13_cast_fp16")];
+            tensor<int32, [4]> value_13_perm_0 = const()[name = tensor<string, []>("value_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_6_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_6_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162607744))), scale = tensor<fp16, [8]>([0x1.d6cp-9, 0x1.438p-7, 0x1.d1p-9, 0x1.c28p-8, 0x1.038p-7, 0x1.164p-7, 0x1.ca8p-8, 0x1.7d8p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_1242_cast_fp16 = add(x = q_37_cast_fp16, y = model_layers_6_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_1242_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_6_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_6_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162608832))), scale = tensor<fp16, [8]>([0x1.194p-9, 0x1.6b4p-8, 0x1.71p-8, 0x1.a84p-10, 0x1.8bp-8, 0x1.dd8p-9, 0x1.1f8p-7, 0x1.68cp-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_1244_cast_fp16 = add(x = q_37_cast_fp16, y = model_layers_6_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_1244_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_13_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_139_transpose_x_0 = const()[name = tensor<string, []>("x_139_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_139_transpose_y_0 = const()[name = tensor<string, []>("x_139_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_1246_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1246_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162609920))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162867008))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1244_cast_fp16)[name = tensor<string, []>("transpose_269")];
+            tensor<fp16, [1, 8, 126, 251]> x_139_cast_fp16 = matmul(transpose_x = x_139_transpose_x_0, transpose_y = x_139_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = op_1246_to_fp16_quantized)[name = tensor<string, []>("x_139_cast_fp16")];
+            tensor<int32, [8]> x_141_pad_0 = const()[name = tensor<string, []>("x_141_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_141_mode_0 = const()[name = tensor<string, []>("x_141_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_74_to_fp16 = const()[name = tensor<string, []>("const_74_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_141_cast_fp16 = pad(constant_val = const_74_to_fp16, mode = x_141_mode_0, pad = x_141_pad_0, x = x_139_cast_fp16)[name = tensor<string, []>("x_141_cast_fp16")];
+            tensor<int32, [4]> var_1254 = const()[name = tensor<string, []>("op_1254"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_143_cast_fp16 = reshape(shape = var_1254, x = x_141_cast_fp16)[name = tensor<string, []>("x_143_cast_fp16")];
+            tensor<int32, [4]> var_1258_begin_0 = const()[name = tensor<string, []>("op_1258_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1258_end_0 = const()[name = tensor<string, []>("op_1258_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_1258_end_mask_0 = const()[name = tensor<string, []>("op_1258_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_1258_cast_fp16 = slice_by_index(begin = var_1258_begin_0, end = var_1258_end_0, end_mask = var_1258_end_mask_0, x = x_143_cast_fp16)[name = tensor<string, []>("op_1258_cast_fp16")];
+            tensor<int32, [4]> var_1259 = const()[name = tensor<string, []>("op_1259"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_25_cast_fp16 = reshape(shape = var_1259, x = var_1258_cast_fp16)[name = tensor<string, []>("matrix_bd_25_cast_fp16")];
+            tensor<bool, []> matrix_ac_13_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_13_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_13_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_13_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_108_perm_0 = const()[name = tensor<string, []>("transpose_108_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_109_perm_0 = const()[name = tensor<string, []>("transpose_109_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = tensor<string, []>("transpose_267")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1242_cast_fp16)[name = tensor<string, []>("transpose_268")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_108, y = transpose_109)[name = tensor<string, []>("matrix_ac_13_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_27_begin_0 = const()[name = tensor<string, []>("matrix_bd_27_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_27_end_0 = const()[name = tensor<string, []>("matrix_bd_27_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_27_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_27_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = tensor<string, []>("matrix_bd_27_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_1268_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = tensor<string, []>("op_1268_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_25_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_25_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_25_cast_fp16 = mul(x = var_1268_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_25_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_27_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_27_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_1274_cast_fp16 = softmax(axis = var_25, x = scores_27_cast_fp16)[name = tensor<string, []>("op_1274_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_345_cast_fp16 = select(a = var_6_to_fp16, b = var_1274_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_345_cast_fp16")];
+            tensor<bool, []> x_145_transpose_x_0 = const()[name = tensor<string, []>("x_145_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_145_transpose_y_0 = const()[name = tensor<string, []>("x_145_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_13_cast_fp16)[name = tensor<string, []>("transpose_266")];
+            tensor<fp16, [1, 8, 126, 128]> x_145_cast_fp16 = matmul(transpose_x = x_145_transpose_x_0, transpose_y = x_145_transpose_y_0, x = input_345_cast_fp16, y = value_13_cast_fp16)[name = tensor<string, []>("x_145_cast_fp16")];
+            tensor<int32, [4]> var_1278_perm_0 = const()[name = tensor<string, []>("op_1278_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1279 = const()[name = tensor<string, []>("op_1279"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_1278_cast_fp16 = transpose(perm = var_1278_perm_0, x = x_145_cast_fp16)[name = tensor<string, []>("transpose_265")];
+            tensor<fp16, [1, 126, 1024]> input_347_cast_fp16 = reshape(shape = var_1279, x = var_1278_cast_fp16)[name = tensor<string, []>("input_347_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_6_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_6_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162867584))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163916224))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_61_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_6_self_attn_linear_out_weight_to_fp16_quantized, x = input_347_cast_fp16)[name = tensor<string, []>("linear_61_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_351_cast_fp16 = add(x = input_343_cast_fp16, y = linear_61_cast_fp16)[name = tensor<string, []>("input_351_cast_fp16")];
+            tensor<int32, [1]> x_149_axes_0 = const()[name = tensor<string, []>("x_149_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_6_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_6_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163918336)))];
+            tensor<fp16, [1024]> model_layers_6_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_6_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163920448)))];
+            tensor<fp16, [1, 126, 1024]> x_149_cast_fp16 = layer_norm(axes = x_149_axes_0, beta = model_layers_6_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_6_norm_conv_weight_to_fp16, x = input_351_cast_fp16)[name = tensor<string, []>("x_149_cast_fp16")];
+            tensor<int32, [3]> input_353_perm_0 = const()[name = tensor<string, []>("input_353_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_355_pad_type_0 = const()[name = tensor<string, []>("input_355_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_355_strides_0 = const()[name = tensor<string, []>("input_355_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_355_pad_0 = const()[name = tensor<string, []>("input_355_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_355_dilations_0 = const()[name = tensor<string, []>("input_355_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_355_groups_0 = const()[name = tensor<string, []>("input_355_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163922560))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166019776))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_149_cast_fp16)[name = tensor<string, []>("transpose_264")];
+            tensor<fp16, [1, 2048, 126]> input_355_cast_fp16 = conv(dilations = input_355_dilations_0, groups = input_355_groups_0, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = input_355_strides_0, weight = model_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_353_cast_fp16)[name = tensor<string, []>("input_355_cast_fp16")];
+            tensor<int32, []> x_151_split_num_splits_0 = const()[name = tensor<string, []>("x_151_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_151_split_axis_0 = const()[name = tensor<string, []>("x_151_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_151_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_151_split_cast_fp16_1 = split(axis = x_151_split_axis_0, num_splits = x_151_split_num_splits_0, x = input_355_cast_fp16)[name = tensor<string, []>("x_151_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_151_split_1_sigmoid_cast_fp16 = sigmoid(x = x_151_split_cast_fp16_1)[name = tensor<string, []>("x_151_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_151_cast_fp16 = mul(x = x_151_split_cast_fp16_0, y = x_151_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_151_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_357_cast_fp16 = select(a = var_6_to_fp16, b = x_151_cast_fp16, cond = var_323)[name = tensor<string, []>("input_357_cast_fp16")];
+            tensor<int32, [6]> input_359_pad_0 = const()[name = tensor<string, []>("input_359_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_359_mode_0 = const()[name = tensor<string, []>("input_359_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_77_to_fp16 = const()[name = tensor<string, []>("const_77_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_359_cast_fp16 = pad(constant_val = const_77_to_fp16, mode = input_359_mode_0, pad = input_359_pad_0, x = input_357_cast_fp16)[name = tensor<string, []>("input_359_cast_fp16")];
+            tensor<string, []> input_361_pad_type_0 = const()[name = tensor<string, []>("input_361_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_361_groups_0 = const()[name = tensor<string, []>("input_361_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_361_strides_0 = const()[name = tensor<string, []>("input_361_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_361_pad_0 = const()[name = tensor<string, []>("input_361_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_361_dilations_0 = const()[name = tensor<string, []>("input_361_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_260_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_260_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166023936))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166033216))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_261_to_fp16 = const()[name = tensor<string, []>("const_261_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166035328)))];
+            tensor<fp16, [1, 1024, 126]> input_363_cast_fp16 = conv(bias = const_261_to_fp16, dilations = input_361_dilations_0, groups = input_361_groups_0, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = input_361_strides_0, weight = const_260_to_fp16_quantized, x = input_359_cast_fp16)[name = tensor<string, []>("input_363_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_365_cast_fp16 = silu(x = input_363_cast_fp16)[name = tensor<string, []>("input_365_cast_fp16")];
+            tensor<string, []> x_153_pad_type_0 = const()[name = tensor<string, []>("x_153_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_153_strides_0 = const()[name = tensor<string, []>("x_153_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_153_pad_0 = const()[name = tensor<string, []>("x_153_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_153_dilations_0 = const()[name = tensor<string, []>("x_153_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_153_groups_0 = const()[name = tensor<string, []>("x_153_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166037440))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167086080))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_153_cast_fp16 = conv(dilations = x_153_dilations_0, groups = x_153_groups_0, pad = x_153_pad_0, pad_type = x_153_pad_type_0, strides = x_153_strides_0, weight = model_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_365_cast_fp16)[name = tensor<string, []>("x_153_cast_fp16")];
+            tensor<int32, [3]> input_367_perm_0 = const()[name = tensor<string, []>("input_367_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_153_cast_fp16)[name = tensor<string, []>("transpose_263")];
+            tensor<fp16, [1, 126, 1024]> input_369_cast_fp16 = add(x = input_351_cast_fp16, y = input_367_cast_fp16)[name = tensor<string, []>("input_369_cast_fp16")];
+            tensor<int32, [1]> input_371_axes_0 = const()[name = tensor<string, []>("input_371_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167088192)))];
+            tensor<fp16, [1024]> model_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167090304)))];
+            tensor<fp16, [1, 126, 1024]> input_371_cast_fp16 = layer_norm(axes = input_371_axes_0, beta = model_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_6_norm_feed_forward2_weight_to_fp16, x = input_369_cast_fp16)[name = tensor<string, []>("input_371_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_6_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_6_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167092416))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171286784))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_62_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_6_feed_forward2_linear1_weight_to_fp16_quantized, x = input_371_cast_fp16)[name = tensor<string, []>("linear_62_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_375_cast_fp16 = silu(x = linear_62_cast_fp16)[name = tensor<string, []>("input_375_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_6_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_6_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171295040))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175489408))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_63_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_6_feed_forward2_linear2_weight_to_fp16_quantized, x = input_375_cast_fp16)[name = tensor<string, []>("linear_63_cast_fp16")];
+            tensor<fp16, []> var_1339_to_fp16 = const()[name = tensor<string, []>("op_1339_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_1340_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1339_to_fp16)[name = tensor<string, []>("op_1340_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_381_cast_fp16 = add(x = input_369_cast_fp16, y = var_1340_cast_fp16)[name = tensor<string, []>("input_381_cast_fp16")];
+            tensor<int32, [1]> input_383_axes_0 = const()[name = tensor<string, []>("input_383_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_6_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_6_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175491520)))];
+            tensor<fp16, [1024]> model_layers_6_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_6_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175493632)))];
+            tensor<fp16, [1, 126, 1024]> input_383_cast_fp16 = layer_norm(axes = input_383_axes_0, beta = model_layers_6_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_6_norm_out_weight_to_fp16, x = input_381_cast_fp16)[name = tensor<string, []>("input_383_cast_fp16")];
+            tensor<int32, [1]> input_385_axes_0 = const()[name = tensor<string, []>("input_385_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175495744)))];
+            tensor<fp16, [1024]> model_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175497856)))];
+            tensor<fp16, [1, 126, 1024]> input_385_cast_fp16 = layer_norm(axes = input_385_axes_0, beta = model_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_7_norm_feed_forward1_weight_to_fp16, x = input_383_cast_fp16)[name = tensor<string, []>("input_385_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_7_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_7_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175499968))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179694336))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_64_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_7_feed_forward1_linear1_weight_to_fp16_quantized, x = input_385_cast_fp16)[name = tensor<string, []>("linear_64_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_389_cast_fp16 = silu(x = linear_64_cast_fp16)[name = tensor<string, []>("input_389_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_7_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_7_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179702592))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183896960))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_65_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_7_feed_forward1_linear2_weight_to_fp16_quantized, x = input_389_cast_fp16)[name = tensor<string, []>("linear_65_cast_fp16")];
+            tensor<fp16, []> var_1368_to_fp16 = const()[name = tensor<string, []>("op_1368_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_1369_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1368_to_fp16)[name = tensor<string, []>("op_1369_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_395_cast_fp16 = add(x = input_383_cast_fp16, y = var_1369_cast_fp16)[name = tensor<string, []>("input_395_cast_fp16")];
+            tensor<int32, [1]> query_15_axes_0 = const()[name = tensor<string, []>("query_15_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_7_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_7_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183899072)))];
+            tensor<fp16, [1024]> model_layers_7_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_7_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183901184)))];
+            tensor<fp16, [1, 126, 1024]> query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = model_layers_7_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_7_norm_self_att_weight_to_fp16, x = input_395_cast_fp16)[name = tensor<string, []>("query_15_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_7_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_7_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183903296))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184951936))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_66_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_7_self_attn_linear_q_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = tensor<string, []>("linear_66_cast_fp16")];
+            tensor<int32, [4]> var_1385 = const()[name = tensor<string, []>("op_1385"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_43_cast_fp16 = reshape(shape = var_1385, x = linear_66_cast_fp16)[name = tensor<string, []>("q_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_7_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_7_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184954048))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186002688))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_67_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_7_self_attn_linear_k_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = tensor<string, []>("linear_67_cast_fp16")];
+            tensor<int32, [4]> var_1389 = const()[name = tensor<string, []>("op_1389"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_29_cast_fp16 = reshape(shape = var_1389, x = linear_67_cast_fp16)[name = tensor<string, []>("k_29_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_7_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_7_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186004800))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187053440))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_68_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_7_self_attn_linear_v_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = tensor<string, []>("linear_68_cast_fp16")];
+            tensor<int32, [4]> var_1393 = const()[name = tensor<string, []>("op_1393"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_15_cast_fp16 = reshape(shape = var_1393, x = linear_68_cast_fp16)[name = tensor<string, []>("v_15_cast_fp16")];
+            tensor<int32, [4]> value_15_perm_0 = const()[name = tensor<string, []>("value_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_7_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_7_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187055552))), scale = tensor<fp16, [8]>([0x1.b78p-8, 0x1.ep-8, 0x1.268p-8, 0x1.15p-7, 0x1.914p-8, 0x1.ae8p-8, 0x1.f4cp-8, 0x1.168p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_1405_cast_fp16 = add(x = q_43_cast_fp16, y = model_layers_7_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_1405_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_7_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_7_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187056640))), scale = tensor<fp16, [8]>([0x1.888p-9, 0x1.e28p-11, 0x1.398p-8, 0x1.de8p-9, 0x1.5ecp-8, 0x1.c8p-8, 0x1.f2p-9, 0x1.e58p-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_1407_cast_fp16 = add(x = q_43_cast_fp16, y = model_layers_7_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_1407_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_15_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_161_transpose_x_0 = const()[name = tensor<string, []>("x_161_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_161_transpose_y_0 = const()[name = tensor<string, []>("x_161_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_1409_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1409_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187057728))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187314816))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1407_cast_fp16)[name = tensor<string, []>("transpose_262")];
+            tensor<fp16, [1, 8, 126, 251]> x_161_cast_fp16 = matmul(transpose_x = x_161_transpose_x_0, transpose_y = x_161_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = op_1409_to_fp16_quantized)[name = tensor<string, []>("x_161_cast_fp16")];
+            tensor<int32, [8]> x_163_pad_0 = const()[name = tensor<string, []>("x_163_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_163_mode_0 = const()[name = tensor<string, []>("x_163_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_84_to_fp16 = const()[name = tensor<string, []>("const_84_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_163_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = x_163_mode_0, pad = x_163_pad_0, x = x_161_cast_fp16)[name = tensor<string, []>("x_163_cast_fp16")];
+            tensor<int32, [4]> var_1417 = const()[name = tensor<string, []>("op_1417"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_165_cast_fp16 = reshape(shape = var_1417, x = x_163_cast_fp16)[name = tensor<string, []>("x_165_cast_fp16")];
+            tensor<int32, [4]> var_1421_begin_0 = const()[name = tensor<string, []>("op_1421_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1421_end_0 = const()[name = tensor<string, []>("op_1421_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_1421_end_mask_0 = const()[name = tensor<string, []>("op_1421_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_1421_cast_fp16 = slice_by_index(begin = var_1421_begin_0, end = var_1421_end_0, end_mask = var_1421_end_mask_0, x = x_165_cast_fp16)[name = tensor<string, []>("op_1421_cast_fp16")];
+            tensor<int32, [4]> var_1422 = const()[name = tensor<string, []>("op_1422"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_29_cast_fp16 = reshape(shape = var_1422, x = var_1421_cast_fp16)[name = tensor<string, []>("matrix_bd_29_cast_fp16")];
+            tensor<bool, []> matrix_ac_15_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_15_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_15_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_15_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_110_perm_0 = const()[name = tensor<string, []>("transpose_110_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_111_perm_0 = const()[name = tensor<string, []>("transpose_111_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = tensor<string, []>("transpose_260")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_110 = transpose(perm = transpose_110_perm_0, x = var_1405_cast_fp16)[name = tensor<string, []>("transpose_261")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_110, y = transpose_111)[name = tensor<string, []>("matrix_ac_15_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_31_begin_0 = const()[name = tensor<string, []>("matrix_bd_31_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_31_end_0 = const()[name = tensor<string, []>("matrix_bd_31_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_31_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_31_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = tensor<string, []>("matrix_bd_31_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_1431_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = tensor<string, []>("op_1431_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_29_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_29_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_29_cast_fp16 = mul(x = var_1431_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_29_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_31_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_31_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_1437_cast_fp16 = softmax(axis = var_25, x = scores_31_cast_fp16)[name = tensor<string, []>("op_1437_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_397_cast_fp16 = select(a = var_6_to_fp16, b = var_1437_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_397_cast_fp16")];
+            tensor<bool, []> x_167_transpose_x_0 = const()[name = tensor<string, []>("x_167_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_167_transpose_y_0 = const()[name = tensor<string, []>("x_167_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_15_cast_fp16)[name = tensor<string, []>("transpose_259")];
+            tensor<fp16, [1, 8, 126, 128]> x_167_cast_fp16 = matmul(transpose_x = x_167_transpose_x_0, transpose_y = x_167_transpose_y_0, x = input_397_cast_fp16, y = value_15_cast_fp16)[name = tensor<string, []>("x_167_cast_fp16")];
+            tensor<int32, [4]> var_1441_perm_0 = const()[name = tensor<string, []>("op_1441_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1442 = const()[name = tensor<string, []>("op_1442"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_1441_cast_fp16 = transpose(perm = var_1441_perm_0, x = x_167_cast_fp16)[name = tensor<string, []>("transpose_258")];
+            tensor<fp16, [1, 126, 1024]> input_399_cast_fp16 = reshape(shape = var_1442, x = var_1441_cast_fp16)[name = tensor<string, []>("input_399_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_7_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_7_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187315392))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188364032))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_70_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_7_self_attn_linear_out_weight_to_fp16_quantized, x = input_399_cast_fp16)[name = tensor<string, []>("linear_70_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_403_cast_fp16 = add(x = input_395_cast_fp16, y = linear_70_cast_fp16)[name = tensor<string, []>("input_403_cast_fp16")];
+            tensor<int32, [1]> x_171_axes_0 = const()[name = tensor<string, []>("x_171_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_7_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_7_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188366144)))];
+            tensor<fp16, [1024]> model_layers_7_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_7_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188368256)))];
+            tensor<fp16, [1, 126, 1024]> x_171_cast_fp16 = layer_norm(axes = x_171_axes_0, beta = model_layers_7_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_7_norm_conv_weight_to_fp16, x = input_403_cast_fp16)[name = tensor<string, []>("x_171_cast_fp16")];
+            tensor<int32, [3]> input_405_perm_0 = const()[name = tensor<string, []>("input_405_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_407_pad_type_0 = const()[name = tensor<string, []>("input_407_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_407_strides_0 = const()[name = tensor<string, []>("input_407_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_407_pad_0 = const()[name = tensor<string, []>("input_407_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_407_dilations_0 = const()[name = tensor<string, []>("input_407_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_407_groups_0 = const()[name = tensor<string, []>("input_407_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188370368))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190467584))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = x_171_cast_fp16)[name = tensor<string, []>("transpose_257")];
+            tensor<fp16, [1, 2048, 126]> input_407_cast_fp16 = conv(dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = model_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_405_cast_fp16)[name = tensor<string, []>("input_407_cast_fp16")];
+            tensor<int32, []> x_173_split_num_splits_0 = const()[name = tensor<string, []>("x_173_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_173_split_axis_0 = const()[name = tensor<string, []>("x_173_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_173_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_173_split_cast_fp16_1 = split(axis = x_173_split_axis_0, num_splits = x_173_split_num_splits_0, x = input_407_cast_fp16)[name = tensor<string, []>("x_173_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_173_split_1_sigmoid_cast_fp16 = sigmoid(x = x_173_split_cast_fp16_1)[name = tensor<string, []>("x_173_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_173_cast_fp16 = mul(x = x_173_split_cast_fp16_0, y = x_173_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_173_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_409_cast_fp16 = select(a = var_6_to_fp16, b = x_173_cast_fp16, cond = var_323)[name = tensor<string, []>("input_409_cast_fp16")];
+            tensor<int32, [6]> input_411_pad_0 = const()[name = tensor<string, []>("input_411_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_411_mode_0 = const()[name = tensor<string, []>("input_411_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_87_to_fp16 = const()[name = tensor<string, []>("const_87_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_411_cast_fp16 = pad(constant_val = const_87_to_fp16, mode = input_411_mode_0, pad = input_411_pad_0, x = input_409_cast_fp16)[name = tensor<string, []>("input_411_cast_fp16")];
+            tensor<string, []> input_413_pad_type_0 = const()[name = tensor<string, []>("input_413_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_413_groups_0 = const()[name = tensor<string, []>("input_413_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_413_strides_0 = const()[name = tensor<string, []>("input_413_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_413_pad_0 = const()[name = tensor<string, []>("input_413_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_413_dilations_0 = const()[name = tensor<string, []>("input_413_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_262_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_262_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190471744))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190481024))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_263_to_fp16 = const()[name = tensor<string, []>("const_263_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190483136)))];
+            tensor<fp16, [1, 1024, 126]> input_415_cast_fp16 = conv(bias = const_263_to_fp16, dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = const_262_to_fp16_quantized, x = input_411_cast_fp16)[name = tensor<string, []>("input_415_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_417_cast_fp16 = silu(x = input_415_cast_fp16)[name = tensor<string, []>("input_417_cast_fp16")];
+            tensor<string, []> x_175_pad_type_0 = const()[name = tensor<string, []>("x_175_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_175_strides_0 = const()[name = tensor<string, []>("x_175_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_175_pad_0 = const()[name = tensor<string, []>("x_175_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_175_dilations_0 = const()[name = tensor<string, []>("x_175_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_175_groups_0 = const()[name = tensor<string, []>("x_175_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190485248))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191533888))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_175_cast_fp16 = conv(dilations = x_175_dilations_0, groups = x_175_groups_0, pad = x_175_pad_0, pad_type = x_175_pad_type_0, strides = x_175_strides_0, weight = model_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_417_cast_fp16)[name = tensor<string, []>("x_175_cast_fp16")];
+            tensor<int32, [3]> input_419_perm_0 = const()[name = tensor<string, []>("input_419_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_175_cast_fp16)[name = tensor<string, []>("transpose_256")];
+            tensor<fp16, [1, 126, 1024]> input_421_cast_fp16 = add(x = input_403_cast_fp16, y = input_419_cast_fp16)[name = tensor<string, []>("input_421_cast_fp16")];
+            tensor<int32, [1]> input_423_axes_0 = const()[name = tensor<string, []>("input_423_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191536000)))];
+            tensor<fp16, [1024]> model_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191538112)))];
+            tensor<fp16, [1, 126, 1024]> input_423_cast_fp16 = layer_norm(axes = input_423_axes_0, beta = model_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_7_norm_feed_forward2_weight_to_fp16, x = input_421_cast_fp16)[name = tensor<string, []>("input_423_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_7_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_7_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191540224))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195734592))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_71_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_7_feed_forward2_linear1_weight_to_fp16_quantized, x = input_423_cast_fp16)[name = tensor<string, []>("linear_71_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_427_cast_fp16 = silu(x = linear_71_cast_fp16)[name = tensor<string, []>("input_427_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_7_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_7_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195742848))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199937216))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_72_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_7_feed_forward2_linear2_weight_to_fp16_quantized, x = input_427_cast_fp16)[name = tensor<string, []>("linear_72_cast_fp16")];
+            tensor<fp16, []> var_1502_to_fp16 = const()[name = tensor<string, []>("op_1502_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_1503_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1502_to_fp16)[name = tensor<string, []>("op_1503_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_433_cast_fp16 = add(x = input_421_cast_fp16, y = var_1503_cast_fp16)[name = tensor<string, []>("input_433_cast_fp16")];
+            tensor<int32, [1]> input_435_axes_0 = const()[name = tensor<string, []>("input_435_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_7_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_7_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199939328)))];
+            tensor<fp16, [1024]> model_layers_7_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_7_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199941440)))];
+            tensor<fp16, [1, 126, 1024]> input_435_cast_fp16 = layer_norm(axes = input_435_axes_0, beta = model_layers_7_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_7_norm_out_weight_to_fp16, x = input_433_cast_fp16)[name = tensor<string, []>("input_435_cast_fp16")];
+            tensor<int32, [1]> input_437_axes_0 = const()[name = tensor<string, []>("input_437_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199943552)))];
+            tensor<fp16, [1024]> model_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199945664)))];
+            tensor<fp16, [1, 126, 1024]> input_437_cast_fp16 = layer_norm(axes = input_437_axes_0, beta = model_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_8_norm_feed_forward1_weight_to_fp16, x = input_435_cast_fp16)[name = tensor<string, []>("input_437_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_8_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_8_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199947776))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(204142144))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_73_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_8_feed_forward1_linear1_weight_to_fp16_quantized, x = input_437_cast_fp16)[name = tensor<string, []>("linear_73_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_441_cast_fp16 = silu(x = linear_73_cast_fp16)[name = tensor<string, []>("input_441_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_8_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_8_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(204150400))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208344768))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_74_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_8_feed_forward1_linear2_weight_to_fp16_quantized, x = input_441_cast_fp16)[name = tensor<string, []>("linear_74_cast_fp16")];
+            tensor<fp16, []> var_1531_to_fp16 = const()[name = tensor<string, []>("op_1531_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_1532_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1531_to_fp16)[name = tensor<string, []>("op_1532_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_447_cast_fp16 = add(x = input_435_cast_fp16, y = var_1532_cast_fp16)[name = tensor<string, []>("input_447_cast_fp16")];
+            tensor<int32, [1]> query_17_axes_0 = const()[name = tensor<string, []>("query_17_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_8_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_8_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208346880)))];
+            tensor<fp16, [1024]> model_layers_8_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_8_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208348992)))];
+            tensor<fp16, [1, 126, 1024]> query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = model_layers_8_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_8_norm_self_att_weight_to_fp16, x = input_447_cast_fp16)[name = tensor<string, []>("query_17_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_8_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_8_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208351104))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209399744))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_75_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_8_self_attn_linear_q_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = tensor<string, []>("linear_75_cast_fp16")];
+            tensor<int32, [4]> var_1548 = const()[name = tensor<string, []>("op_1548"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_49_cast_fp16 = reshape(shape = var_1548, x = linear_75_cast_fp16)[name = tensor<string, []>("q_49_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_8_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_8_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209401856))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210450496))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_76_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_8_self_attn_linear_k_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = tensor<string, []>("linear_76_cast_fp16")];
+            tensor<int32, [4]> var_1552 = const()[name = tensor<string, []>("op_1552"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_33_cast_fp16 = reshape(shape = var_1552, x = linear_76_cast_fp16)[name = tensor<string, []>("k_33_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_8_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_8_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210452608))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211501248))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_77_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_8_self_attn_linear_v_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = tensor<string, []>("linear_77_cast_fp16")];
+            tensor<int32, [4]> var_1556 = const()[name = tensor<string, []>("op_1556"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_17_cast_fp16 = reshape(shape = var_1556, x = linear_77_cast_fp16)[name = tensor<string, []>("v_17_cast_fp16")];
+            tensor<int32, [4]> value_17_perm_0 = const()[name = tensor<string, []>("value_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_8_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_8_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211503360))), scale = tensor<fp16, [8]>([0x1.cc4p-8, 0x1.a6p-8, 0x1.9bp-8, 0x1.ae8p-7, 0x1.1dcp-8, 0x1.478p-7, 0x1.1fp-7, 0x1.c94p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_1568_cast_fp16 = add(x = q_49_cast_fp16, y = model_layers_8_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_1568_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_8_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_8_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211504448))), scale = tensor<fp16, [8]>([0x1.928p-8, 0x1.e34p-9, 0x1.f14p-9, 0x1.3acp-9, 0x1.dfcp-8, 0x1.cap-10, 0x1.9c4p-8, 0x1.f88p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_1570_cast_fp16 = add(x = q_49_cast_fp16, y = model_layers_8_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_1570_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_17_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_183_transpose_x_0 = const()[name = tensor<string, []>("x_183_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_183_transpose_y_0 = const()[name = tensor<string, []>("x_183_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_1572_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1572_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211505536))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211762624))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1570_cast_fp16)[name = tensor<string, []>("transpose_255")];
+            tensor<fp16, [1, 8, 126, 251]> x_183_cast_fp16 = matmul(transpose_x = x_183_transpose_x_0, transpose_y = x_183_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = op_1572_to_fp16_quantized)[name = tensor<string, []>("x_183_cast_fp16")];
+            tensor<int32, [8]> x_185_pad_0 = const()[name = tensor<string, []>("x_185_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_185_mode_0 = const()[name = tensor<string, []>("x_185_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_94_to_fp16 = const()[name = tensor<string, []>("const_94_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_185_cast_fp16 = pad(constant_val = const_94_to_fp16, mode = x_185_mode_0, pad = x_185_pad_0, x = x_183_cast_fp16)[name = tensor<string, []>("x_185_cast_fp16")];
+            tensor<int32, [4]> var_1580 = const()[name = tensor<string, []>("op_1580"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_187_cast_fp16 = reshape(shape = var_1580, x = x_185_cast_fp16)[name = tensor<string, []>("x_187_cast_fp16")];
+            tensor<int32, [4]> var_1584_begin_0 = const()[name = tensor<string, []>("op_1584_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1584_end_0 = const()[name = tensor<string, []>("op_1584_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_1584_end_mask_0 = const()[name = tensor<string, []>("op_1584_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_1584_cast_fp16 = slice_by_index(begin = var_1584_begin_0, end = var_1584_end_0, end_mask = var_1584_end_mask_0, x = x_187_cast_fp16)[name = tensor<string, []>("op_1584_cast_fp16")];
+            tensor<int32, [4]> var_1585 = const()[name = tensor<string, []>("op_1585"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_33_cast_fp16 = reshape(shape = var_1585, x = var_1584_cast_fp16)[name = tensor<string, []>("matrix_bd_33_cast_fp16")];
+            tensor<bool, []> matrix_ac_17_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_17_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_17_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_17_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_112_perm_0 = const()[name = tensor<string, []>("transpose_112_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_113_perm_0 = const()[name = tensor<string, []>("transpose_113_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = tensor<string, []>("transpose_253")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_112 = transpose(perm = transpose_112_perm_0, x = var_1568_cast_fp16)[name = tensor<string, []>("transpose_254")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_112, y = transpose_113)[name = tensor<string, []>("matrix_ac_17_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_35_begin_0 = const()[name = tensor<string, []>("matrix_bd_35_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_35_end_0 = const()[name = tensor<string, []>("matrix_bd_35_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_35_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_35_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = tensor<string, []>("matrix_bd_35_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_1594_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = tensor<string, []>("op_1594_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_33_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_33_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_33_cast_fp16 = mul(x = var_1594_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_33_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_35_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_35_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_1600_cast_fp16 = softmax(axis = var_25, x = scores_35_cast_fp16)[name = tensor<string, []>("op_1600_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_449_cast_fp16 = select(a = var_6_to_fp16, b = var_1600_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_449_cast_fp16")];
+            tensor<bool, []> x_189_transpose_x_0 = const()[name = tensor<string, []>("x_189_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_189_transpose_y_0 = const()[name = tensor<string, []>("x_189_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_17_cast_fp16)[name = tensor<string, []>("transpose_252")];
+            tensor<fp16, [1, 8, 126, 128]> x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = input_449_cast_fp16, y = value_17_cast_fp16)[name = tensor<string, []>("x_189_cast_fp16")];
+            tensor<int32, [4]> var_1604_perm_0 = const()[name = tensor<string, []>("op_1604_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1605 = const()[name = tensor<string, []>("op_1605"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_1604_cast_fp16 = transpose(perm = var_1604_perm_0, x = x_189_cast_fp16)[name = tensor<string, []>("transpose_251")];
+            tensor<fp16, [1, 126, 1024]> input_451_cast_fp16 = reshape(shape = var_1605, x = var_1604_cast_fp16)[name = tensor<string, []>("input_451_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_8_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_8_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211763200))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212811840))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_79_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_8_self_attn_linear_out_weight_to_fp16_quantized, x = input_451_cast_fp16)[name = tensor<string, []>("linear_79_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_455_cast_fp16 = add(x = input_447_cast_fp16, y = linear_79_cast_fp16)[name = tensor<string, []>("input_455_cast_fp16")];
+            tensor<int32, [1]> x_193_axes_0 = const()[name = tensor<string, []>("x_193_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_8_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_8_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212813952)))];
+            tensor<fp16, [1024]> model_layers_8_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_8_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212816064)))];
+            tensor<fp16, [1, 126, 1024]> x_193_cast_fp16 = layer_norm(axes = x_193_axes_0, beta = model_layers_8_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_8_norm_conv_weight_to_fp16, x = input_455_cast_fp16)[name = tensor<string, []>("x_193_cast_fp16")];
+            tensor<int32, [3]> input_457_perm_0 = const()[name = tensor<string, []>("input_457_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_459_pad_type_0 = const()[name = tensor<string, []>("input_459_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_459_strides_0 = const()[name = tensor<string, []>("input_459_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_459_pad_0 = const()[name = tensor<string, []>("input_459_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_459_dilations_0 = const()[name = tensor<string, []>("input_459_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_459_groups_0 = const()[name = tensor<string, []>("input_459_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212818176))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214915392))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_457_cast_fp16 = transpose(perm = input_457_perm_0, x = x_193_cast_fp16)[name = tensor<string, []>("transpose_250")];
+            tensor<fp16, [1, 2048, 126]> input_459_cast_fp16 = conv(dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = model_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_457_cast_fp16)[name = tensor<string, []>("input_459_cast_fp16")];
+            tensor<int32, []> x_195_split_num_splits_0 = const()[name = tensor<string, []>("x_195_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_195_split_axis_0 = const()[name = tensor<string, []>("x_195_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_195_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_195_split_cast_fp16_1 = split(axis = x_195_split_axis_0, num_splits = x_195_split_num_splits_0, x = input_459_cast_fp16)[name = tensor<string, []>("x_195_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_195_split_1_sigmoid_cast_fp16 = sigmoid(x = x_195_split_cast_fp16_1)[name = tensor<string, []>("x_195_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_195_cast_fp16 = mul(x = x_195_split_cast_fp16_0, y = x_195_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_195_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_461_cast_fp16 = select(a = var_6_to_fp16, b = x_195_cast_fp16, cond = var_323)[name = tensor<string, []>("input_461_cast_fp16")];
+            tensor<int32, [6]> input_463_pad_0 = const()[name = tensor<string, []>("input_463_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_463_mode_0 = const()[name = tensor<string, []>("input_463_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_97_to_fp16 = const()[name = tensor<string, []>("const_97_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_463_cast_fp16 = pad(constant_val = const_97_to_fp16, mode = input_463_mode_0, pad = input_463_pad_0, x = input_461_cast_fp16)[name = tensor<string, []>("input_463_cast_fp16")];
+            tensor<string, []> input_465_pad_type_0 = const()[name = tensor<string, []>("input_465_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_465_groups_0 = const()[name = tensor<string, []>("input_465_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_465_strides_0 = const()[name = tensor<string, []>("input_465_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_465_pad_0 = const()[name = tensor<string, []>("input_465_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_465_dilations_0 = const()[name = tensor<string, []>("input_465_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_264_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_264_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214919552))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214928832))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_265_to_fp16 = const()[name = tensor<string, []>("const_265_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214930944)))];
+            tensor<fp16, [1, 1024, 126]> input_467_cast_fp16 = conv(bias = const_265_to_fp16, dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = const_264_to_fp16_quantized, x = input_463_cast_fp16)[name = tensor<string, []>("input_467_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_469_cast_fp16 = silu(x = input_467_cast_fp16)[name = tensor<string, []>("input_469_cast_fp16")];
+            tensor<string, []> x_197_pad_type_0 = const()[name = tensor<string, []>("x_197_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_197_strides_0 = const()[name = tensor<string, []>("x_197_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_197_pad_0 = const()[name = tensor<string, []>("x_197_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_197_dilations_0 = const()[name = tensor<string, []>("x_197_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_197_groups_0 = const()[name = tensor<string, []>("x_197_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214933056))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215981696))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_197_cast_fp16 = conv(dilations = x_197_dilations_0, groups = x_197_groups_0, pad = x_197_pad_0, pad_type = x_197_pad_type_0, strides = x_197_strides_0, weight = model_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_469_cast_fp16)[name = tensor<string, []>("x_197_cast_fp16")];
+            tensor<int32, [3]> input_471_perm_0 = const()[name = tensor<string, []>("input_471_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_197_cast_fp16)[name = tensor<string, []>("transpose_249")];
+            tensor<fp16, [1, 126, 1024]> input_473_cast_fp16 = add(x = input_455_cast_fp16, y = input_471_cast_fp16)[name = tensor<string, []>("input_473_cast_fp16")];
+            tensor<int32, [1]> input_475_axes_0 = const()[name = tensor<string, []>("input_475_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215983808)))];
+            tensor<fp16, [1024]> model_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215985920)))];
+            tensor<fp16, [1, 126, 1024]> input_475_cast_fp16 = layer_norm(axes = input_475_axes_0, beta = model_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_8_norm_feed_forward2_weight_to_fp16, x = input_473_cast_fp16)[name = tensor<string, []>("input_475_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_8_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_8_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215988032))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220182400))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_80_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_8_feed_forward2_linear1_weight_to_fp16_quantized, x = input_475_cast_fp16)[name = tensor<string, []>("linear_80_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_479_cast_fp16 = silu(x = linear_80_cast_fp16)[name = tensor<string, []>("input_479_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_8_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_8_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220190656))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224385024))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_81_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_8_feed_forward2_linear2_weight_to_fp16_quantized, x = input_479_cast_fp16)[name = tensor<string, []>("linear_81_cast_fp16")];
+            tensor<fp16, []> var_1665_to_fp16 = const()[name = tensor<string, []>("op_1665_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_1666_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_1665_to_fp16)[name = tensor<string, []>("op_1666_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_485_cast_fp16 = add(x = input_473_cast_fp16, y = var_1666_cast_fp16)[name = tensor<string, []>("input_485_cast_fp16")];
+            tensor<int32, [1]> input_487_axes_0 = const()[name = tensor<string, []>("input_487_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_8_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_8_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224387136)))];
+            tensor<fp16, [1024]> model_layers_8_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_8_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224389248)))];
+            tensor<fp16, [1, 126, 1024]> input_487_cast_fp16 = layer_norm(axes = input_487_axes_0, beta = model_layers_8_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_8_norm_out_weight_to_fp16, x = input_485_cast_fp16)[name = tensor<string, []>("input_487_cast_fp16")];
+            tensor<int32, [1]> input_489_axes_0 = const()[name = tensor<string, []>("input_489_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224391360)))];
+            tensor<fp16, [1024]> model_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224393472)))];
+            tensor<fp16, [1, 126, 1024]> input_489_cast_fp16 = layer_norm(axes = input_489_axes_0, beta = model_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_9_norm_feed_forward1_weight_to_fp16, x = input_487_cast_fp16)[name = tensor<string, []>("input_489_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_9_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_9_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224395584))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(228589952))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_82_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_9_feed_forward1_linear1_weight_to_fp16_quantized, x = input_489_cast_fp16)[name = tensor<string, []>("linear_82_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_493_cast_fp16 = silu(x = linear_82_cast_fp16)[name = tensor<string, []>("input_493_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_9_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_9_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(228598208))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232792576))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_83_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_9_feed_forward1_linear2_weight_to_fp16_quantized, x = input_493_cast_fp16)[name = tensor<string, []>("linear_83_cast_fp16")];
+            tensor<fp16, []> var_1694_to_fp16 = const()[name = tensor<string, []>("op_1694_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_1695_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_1694_to_fp16)[name = tensor<string, []>("op_1695_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_499_cast_fp16 = add(x = input_487_cast_fp16, y = var_1695_cast_fp16)[name = tensor<string, []>("input_499_cast_fp16")];
+            tensor<int32, [1]> query_19_axes_0 = const()[name = tensor<string, []>("query_19_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_9_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_9_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232794688)))];
+            tensor<fp16, [1024]> model_layers_9_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_9_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232796800)))];
+            tensor<fp16, [1, 126, 1024]> query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = model_layers_9_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_9_norm_self_att_weight_to_fp16, x = input_499_cast_fp16)[name = tensor<string, []>("query_19_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_9_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_9_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232798912))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233847552))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_84_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_9_self_attn_linear_q_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = tensor<string, []>("linear_84_cast_fp16")];
+            tensor<int32, [4]> var_1711 = const()[name = tensor<string, []>("op_1711"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_55_cast_fp16 = reshape(shape = var_1711, x = linear_84_cast_fp16)[name = tensor<string, []>("q_55_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_9_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_9_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233849664))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234898304))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_85_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_9_self_attn_linear_k_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = tensor<string, []>("linear_85_cast_fp16")];
+            tensor<int32, [4]> var_1715 = const()[name = tensor<string, []>("op_1715"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_37_cast_fp16 = reshape(shape = var_1715, x = linear_85_cast_fp16)[name = tensor<string, []>("k_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_9_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_9_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234900416))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235949056))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_86_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_9_self_attn_linear_v_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = tensor<string, []>("linear_86_cast_fp16")];
+            tensor<int32, [4]> var_1719 = const()[name = tensor<string, []>("op_1719"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_19_cast_fp16 = reshape(shape = var_1719, x = linear_86_cast_fp16)[name = tensor<string, []>("v_19_cast_fp16")];
+            tensor<int32, [4]> value_19_perm_0 = const()[name = tensor<string, []>("value_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_9_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_9_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235951168))), scale = tensor<fp16, [8]>([0x1.1d4p-7, 0x1.4c8p-8, 0x1.cap-8, 0x1.db8p-7, 0x1.d7cp-8, 0x1.1ep-7, 0x1.d98p-8, 0x1.1dp-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_1731_cast_fp16 = add(x = q_55_cast_fp16, y = model_layers_9_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_1731_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_9_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_9_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235952256))), scale = tensor<fp16, [8]>([0x1.324p-8, 0x1.9ap-7, 0x1.eecp-8, 0x1.d4p-9, 0x1.4b4p-9, 0x1.17cp-7, 0x1.0bcp-7, 0x1.46cp-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_1733_cast_fp16 = add(x = q_55_cast_fp16, y = model_layers_9_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_1733_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_19_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_205_transpose_x_0 = const()[name = tensor<string, []>("x_205_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_205_transpose_y_0 = const()[name = tensor<string, []>("x_205_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_1735_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1735_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235953344))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236210432))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1733_cast_fp16)[name = tensor<string, []>("transpose_248")];
+            tensor<fp16, [1, 8, 126, 251]> x_205_cast_fp16 = matmul(transpose_x = x_205_transpose_x_0, transpose_y = x_205_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = op_1735_to_fp16_quantized)[name = tensor<string, []>("x_205_cast_fp16")];
+            tensor<int32, [8]> x_207_pad_0 = const()[name = tensor<string, []>("x_207_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_207_mode_0 = const()[name = tensor<string, []>("x_207_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_104_to_fp16 = const()[name = tensor<string, []>("const_104_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_207_cast_fp16 = pad(constant_val = const_104_to_fp16, mode = x_207_mode_0, pad = x_207_pad_0, x = x_205_cast_fp16)[name = tensor<string, []>("x_207_cast_fp16")];
+            tensor<int32, [4]> var_1743 = const()[name = tensor<string, []>("op_1743"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_209_cast_fp16 = reshape(shape = var_1743, x = x_207_cast_fp16)[name = tensor<string, []>("x_209_cast_fp16")];
+            tensor<int32, [4]> var_1747_begin_0 = const()[name = tensor<string, []>("op_1747_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1747_end_0 = const()[name = tensor<string, []>("op_1747_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_1747_end_mask_0 = const()[name = tensor<string, []>("op_1747_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_1747_cast_fp16 = slice_by_index(begin = var_1747_begin_0, end = var_1747_end_0, end_mask = var_1747_end_mask_0, x = x_209_cast_fp16)[name = tensor<string, []>("op_1747_cast_fp16")];
+            tensor<int32, [4]> var_1748 = const()[name = tensor<string, []>("op_1748"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_37_cast_fp16 = reshape(shape = var_1748, x = var_1747_cast_fp16)[name = tensor<string, []>("matrix_bd_37_cast_fp16")];
+            tensor<bool, []> matrix_ac_19_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_19_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_19_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_19_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_114_perm_0 = const()[name = tensor<string, []>("transpose_114_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_115_perm_0 = const()[name = tensor<string, []>("transpose_115_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = tensor<string, []>("transpose_246")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_114 = transpose(perm = transpose_114_perm_0, x = var_1731_cast_fp16)[name = tensor<string, []>("transpose_247")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_114, y = transpose_115)[name = tensor<string, []>("matrix_ac_19_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_39_begin_0 = const()[name = tensor<string, []>("matrix_bd_39_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_39_end_0 = const()[name = tensor<string, []>("matrix_bd_39_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_39_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_39_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = tensor<string, []>("matrix_bd_39_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_1757_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = tensor<string, []>("op_1757_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_37_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_37_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_37_cast_fp16 = mul(x = var_1757_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_37_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_39_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_39_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_1763_cast_fp16 = softmax(axis = var_25, x = scores_39_cast_fp16)[name = tensor<string, []>("op_1763_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_501_cast_fp16 = select(a = var_6_to_fp16, b = var_1763_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_501_cast_fp16")];
+            tensor<bool, []> x_211_transpose_x_0 = const()[name = tensor<string, []>("x_211_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_211_transpose_y_0 = const()[name = tensor<string, []>("x_211_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_19_cast_fp16)[name = tensor<string, []>("transpose_245")];
+            tensor<fp16, [1, 8, 126, 128]> x_211_cast_fp16 = matmul(transpose_x = x_211_transpose_x_0, transpose_y = x_211_transpose_y_0, x = input_501_cast_fp16, y = value_19_cast_fp16)[name = tensor<string, []>("x_211_cast_fp16")];
+            tensor<int32, [4]> var_1767_perm_0 = const()[name = tensor<string, []>("op_1767_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1768 = const()[name = tensor<string, []>("op_1768"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_1767_cast_fp16 = transpose(perm = var_1767_perm_0, x = x_211_cast_fp16)[name = tensor<string, []>("transpose_244")];
+            tensor<fp16, [1, 126, 1024]> input_503_cast_fp16 = reshape(shape = var_1768, x = var_1767_cast_fp16)[name = tensor<string, []>("input_503_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_9_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_9_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236211008))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237259648))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_88_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_9_self_attn_linear_out_weight_to_fp16_quantized, x = input_503_cast_fp16)[name = tensor<string, []>("linear_88_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_507_cast_fp16 = add(x = input_499_cast_fp16, y = linear_88_cast_fp16)[name = tensor<string, []>("input_507_cast_fp16")];
+            tensor<int32, [1]> x_215_axes_0 = const()[name = tensor<string, []>("x_215_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_9_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_9_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237261760)))];
+            tensor<fp16, [1024]> model_layers_9_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_9_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237263872)))];
+            tensor<fp16, [1, 126, 1024]> x_215_cast_fp16 = layer_norm(axes = x_215_axes_0, beta = model_layers_9_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_9_norm_conv_weight_to_fp16, x = input_507_cast_fp16)[name = tensor<string, []>("x_215_cast_fp16")];
+            tensor<int32, [3]> input_509_perm_0 = const()[name = tensor<string, []>("input_509_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_511_pad_type_0 = const()[name = tensor<string, []>("input_511_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_511_strides_0 = const()[name = tensor<string, []>("input_511_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_511_pad_0 = const()[name = tensor<string, []>("input_511_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_511_dilations_0 = const()[name = tensor<string, []>("input_511_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_511_groups_0 = const()[name = tensor<string, []>("input_511_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237265984))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(239363200))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_509_cast_fp16 = transpose(perm = input_509_perm_0, x = x_215_cast_fp16)[name = tensor<string, []>("transpose_243")];
+            tensor<fp16, [1, 2048, 126]> input_511_cast_fp16 = conv(dilations = input_511_dilations_0, groups = input_511_groups_0, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = input_511_strides_0, weight = model_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_509_cast_fp16)[name = tensor<string, []>("input_511_cast_fp16")];
+            tensor<int32, []> x_217_split_num_splits_0 = const()[name = tensor<string, []>("x_217_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_217_split_axis_0 = const()[name = tensor<string, []>("x_217_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_217_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_217_split_cast_fp16_1 = split(axis = x_217_split_axis_0, num_splits = x_217_split_num_splits_0, x = input_511_cast_fp16)[name = tensor<string, []>("x_217_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_217_split_1_sigmoid_cast_fp16 = sigmoid(x = x_217_split_cast_fp16_1)[name = tensor<string, []>("x_217_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_217_cast_fp16 = mul(x = x_217_split_cast_fp16_0, y = x_217_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_217_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_513_cast_fp16 = select(a = var_6_to_fp16, b = x_217_cast_fp16, cond = var_323)[name = tensor<string, []>("input_513_cast_fp16")];
+            tensor<int32, [6]> input_515_pad_0 = const()[name = tensor<string, []>("input_515_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_515_mode_0 = const()[name = tensor<string, []>("input_515_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_107_to_fp16 = const()[name = tensor<string, []>("const_107_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_515_cast_fp16 = pad(constant_val = const_107_to_fp16, mode = input_515_mode_0, pad = input_515_pad_0, x = input_513_cast_fp16)[name = tensor<string, []>("input_515_cast_fp16")];
+            tensor<string, []> input_517_pad_type_0 = const()[name = tensor<string, []>("input_517_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_517_groups_0 = const()[name = tensor<string, []>("input_517_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_517_strides_0 = const()[name = tensor<string, []>("input_517_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_517_pad_0 = const()[name = tensor<string, []>("input_517_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_517_dilations_0 = const()[name = tensor<string, []>("input_517_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_266_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_266_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(239367360))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(239376640))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_267_to_fp16 = const()[name = tensor<string, []>("const_267_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(239378752)))];
+            tensor<fp16, [1, 1024, 126]> input_519_cast_fp16 = conv(bias = const_267_to_fp16, dilations = input_517_dilations_0, groups = input_517_groups_0, pad = input_517_pad_0, pad_type = input_517_pad_type_0, strides = input_517_strides_0, weight = const_266_to_fp16_quantized, x = input_515_cast_fp16)[name = tensor<string, []>("input_519_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_521_cast_fp16 = silu(x = input_519_cast_fp16)[name = tensor<string, []>("input_521_cast_fp16")];
+            tensor<string, []> x_219_pad_type_0 = const()[name = tensor<string, []>("x_219_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_219_strides_0 = const()[name = tensor<string, []>("x_219_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_219_pad_0 = const()[name = tensor<string, []>("x_219_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_219_dilations_0 = const()[name = tensor<string, []>("x_219_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_219_groups_0 = const()[name = tensor<string, []>("x_219_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(239380864))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(240429504))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_219_cast_fp16 = conv(dilations = x_219_dilations_0, groups = x_219_groups_0, pad = x_219_pad_0, pad_type = x_219_pad_type_0, strides = x_219_strides_0, weight = model_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_521_cast_fp16)[name = tensor<string, []>("x_219_cast_fp16")];
+            tensor<int32, [3]> input_523_perm_0 = const()[name = tensor<string, []>("input_523_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_219_cast_fp16)[name = tensor<string, []>("transpose_242")];
+            tensor<fp16, [1, 126, 1024]> input_525_cast_fp16 = add(x = input_507_cast_fp16, y = input_523_cast_fp16)[name = tensor<string, []>("input_525_cast_fp16")];
+            tensor<int32, [1]> input_527_axes_0 = const()[name = tensor<string, []>("input_527_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(240431616)))];
+            tensor<fp16, [1024]> model_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(240433728)))];
+            tensor<fp16, [1, 126, 1024]> input_527_cast_fp16 = layer_norm(axes = input_527_axes_0, beta = model_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_9_norm_feed_forward2_weight_to_fp16, x = input_525_cast_fp16)[name = tensor<string, []>("input_527_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_9_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_9_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(240435840))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244630208))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_89_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_9_feed_forward2_linear1_weight_to_fp16_quantized, x = input_527_cast_fp16)[name = tensor<string, []>("linear_89_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_531_cast_fp16 = silu(x = linear_89_cast_fp16)[name = tensor<string, []>("input_531_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_9_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_9_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244638464))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248832832))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_90_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_9_feed_forward2_linear2_weight_to_fp16_quantized, x = input_531_cast_fp16)[name = tensor<string, []>("linear_90_cast_fp16")];
+            tensor<fp16, []> var_1828_to_fp16 = const()[name = tensor<string, []>("op_1828_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_1829_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_1828_to_fp16)[name = tensor<string, []>("op_1829_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_537_cast_fp16 = add(x = input_525_cast_fp16, y = var_1829_cast_fp16)[name = tensor<string, []>("input_537_cast_fp16")];
+            tensor<int32, [1]> input_539_axes_0 = const()[name = tensor<string, []>("input_539_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_9_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_9_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248834944)))];
+            tensor<fp16, [1024]> model_layers_9_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_9_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248837056)))];
+            tensor<fp16, [1, 126, 1024]> input_539_cast_fp16 = layer_norm(axes = input_539_axes_0, beta = model_layers_9_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_9_norm_out_weight_to_fp16, x = input_537_cast_fp16)[name = tensor<string, []>("input_539_cast_fp16")];
+            tensor<int32, [1]> input_541_axes_0 = const()[name = tensor<string, []>("input_541_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248839168)))];
+            tensor<fp16, [1024]> model_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248841280)))];
+            tensor<fp16, [1, 126, 1024]> input_541_cast_fp16 = layer_norm(axes = input_541_axes_0, beta = model_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_10_norm_feed_forward1_weight_to_fp16, x = input_539_cast_fp16)[name = tensor<string, []>("input_541_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_10_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_10_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248843392))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253037760))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_91_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_10_feed_forward1_linear1_weight_to_fp16_quantized, x = input_541_cast_fp16)[name = tensor<string, []>("linear_91_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_545_cast_fp16 = silu(x = linear_91_cast_fp16)[name = tensor<string, []>("input_545_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_10_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_10_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253046016))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257240384))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_92_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_10_feed_forward1_linear2_weight_to_fp16_quantized, x = input_545_cast_fp16)[name = tensor<string, []>("linear_92_cast_fp16")];
+            tensor<fp16, []> var_1857_to_fp16 = const()[name = tensor<string, []>("op_1857_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_1858_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_1857_to_fp16)[name = tensor<string, []>("op_1858_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_551_cast_fp16 = add(x = input_539_cast_fp16, y = var_1858_cast_fp16)[name = tensor<string, []>("input_551_cast_fp16")];
+            tensor<int32, [1]> query_21_axes_0 = const()[name = tensor<string, []>("query_21_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_10_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_10_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257242496)))];
+            tensor<fp16, [1024]> model_layers_10_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_10_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257244608)))];
+            tensor<fp16, [1, 126, 1024]> query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = model_layers_10_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_10_norm_self_att_weight_to_fp16, x = input_551_cast_fp16)[name = tensor<string, []>("query_21_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_10_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_10_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257246720))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(258295360))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_93_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_10_self_attn_linear_q_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = tensor<string, []>("linear_93_cast_fp16")];
+            tensor<int32, [4]> var_1874 = const()[name = tensor<string, []>("op_1874"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_61_cast_fp16 = reshape(shape = var_1874, x = linear_93_cast_fp16)[name = tensor<string, []>("q_61_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_10_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_10_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(258297472))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259346112))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_94_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_10_self_attn_linear_k_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = tensor<string, []>("linear_94_cast_fp16")];
+            tensor<int32, [4]> var_1878 = const()[name = tensor<string, []>("op_1878"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_41_cast_fp16 = reshape(shape = var_1878, x = linear_94_cast_fp16)[name = tensor<string, []>("k_41_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_10_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_10_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259348224))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(260396864))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_95_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_10_self_attn_linear_v_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = tensor<string, []>("linear_95_cast_fp16")];
+            tensor<int32, [4]> var_1882 = const()[name = tensor<string, []>("op_1882"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_21_cast_fp16 = reshape(shape = var_1882, x = linear_95_cast_fp16)[name = tensor<string, []>("v_21_cast_fp16")];
+            tensor<int32, [4]> value_21_perm_0 = const()[name = tensor<string, []>("value_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_10_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_10_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(260398976))), scale = tensor<fp16, [8]>([0x1.b8p-8, 0x1.0f4p-7, 0x1.6fcp-7, 0x1.1a8p-8, 0x1.c58p-8, 0x1.268p-7, 0x1.fbp-8, 0x1.b58p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_1894_cast_fp16 = add(x = q_61_cast_fp16, y = model_layers_10_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_1894_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_10_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_10_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(260400064))), scale = tensor<fp16, [8]>([0x1.5e8p-7, 0x1.cd4p-8, 0x1.2bcp-9, 0x1.af8p-9, 0x1.69p-9, 0x1.164p-8, 0x1.42p-7, 0x1.d4p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_1896_cast_fp16 = add(x = q_61_cast_fp16, y = model_layers_10_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_1896_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_21_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_227_transpose_x_0 = const()[name = tensor<string, []>("x_227_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_227_transpose_y_0 = const()[name = tensor<string, []>("x_227_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_1898_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1898_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(260401152))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(260658240))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_1896_cast_fp16)[name = tensor<string, []>("transpose_241")];
+            tensor<fp16, [1, 8, 126, 251]> x_227_cast_fp16 = matmul(transpose_x = x_227_transpose_x_0, transpose_y = x_227_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = op_1898_to_fp16_quantized)[name = tensor<string, []>("x_227_cast_fp16")];
+            tensor<int32, [8]> x_229_pad_0 = const()[name = tensor<string, []>("x_229_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_229_mode_0 = const()[name = tensor<string, []>("x_229_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_114_to_fp16 = const()[name = tensor<string, []>("const_114_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_229_cast_fp16 = pad(constant_val = const_114_to_fp16, mode = x_229_mode_0, pad = x_229_pad_0, x = x_227_cast_fp16)[name = tensor<string, []>("x_229_cast_fp16")];
+            tensor<int32, [4]> var_1906 = const()[name = tensor<string, []>("op_1906"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_231_cast_fp16 = reshape(shape = var_1906, x = x_229_cast_fp16)[name = tensor<string, []>("x_231_cast_fp16")];
+            tensor<int32, [4]> var_1910_begin_0 = const()[name = tensor<string, []>("op_1910_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1910_end_0 = const()[name = tensor<string, []>("op_1910_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_1910_end_mask_0 = const()[name = tensor<string, []>("op_1910_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_1910_cast_fp16 = slice_by_index(begin = var_1910_begin_0, end = var_1910_end_0, end_mask = var_1910_end_mask_0, x = x_231_cast_fp16)[name = tensor<string, []>("op_1910_cast_fp16")];
+            tensor<int32, [4]> var_1911 = const()[name = tensor<string, []>("op_1911"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_41_cast_fp16 = reshape(shape = var_1911, x = var_1910_cast_fp16)[name = tensor<string, []>("matrix_bd_41_cast_fp16")];
+            tensor<bool, []> matrix_ac_21_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_21_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_21_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_21_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_116_perm_0 = const()[name = tensor<string, []>("transpose_116_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_117_perm_0 = const()[name = tensor<string, []>("transpose_117_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = tensor<string, []>("transpose_239")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_116 = transpose(perm = transpose_116_perm_0, x = var_1894_cast_fp16)[name = tensor<string, []>("transpose_240")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_116, y = transpose_117)[name = tensor<string, []>("matrix_ac_21_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_43_begin_0 = const()[name = tensor<string, []>("matrix_bd_43_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_43_end_0 = const()[name = tensor<string, []>("matrix_bd_43_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_43_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_43_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = tensor<string, []>("matrix_bd_43_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_1920_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = tensor<string, []>("op_1920_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_41_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_41_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_41_cast_fp16 = mul(x = var_1920_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_41_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_43_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_43_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_1926_cast_fp16 = softmax(axis = var_25, x = scores_43_cast_fp16)[name = tensor<string, []>("op_1926_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_553_cast_fp16 = select(a = var_6_to_fp16, b = var_1926_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_553_cast_fp16")];
+            tensor<bool, []> x_233_transpose_x_0 = const()[name = tensor<string, []>("x_233_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_233_transpose_y_0 = const()[name = tensor<string, []>("x_233_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_21_cast_fp16)[name = tensor<string, []>("transpose_238")];
+            tensor<fp16, [1, 8, 126, 128]> x_233_cast_fp16 = matmul(transpose_x = x_233_transpose_x_0, transpose_y = x_233_transpose_y_0, x = input_553_cast_fp16, y = value_21_cast_fp16)[name = tensor<string, []>("x_233_cast_fp16")];
+            tensor<int32, [4]> var_1930_perm_0 = const()[name = tensor<string, []>("op_1930_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1931 = const()[name = tensor<string, []>("op_1931"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_1930_cast_fp16 = transpose(perm = var_1930_perm_0, x = x_233_cast_fp16)[name = tensor<string, []>("transpose_237")];
+            tensor<fp16, [1, 126, 1024]> input_555_cast_fp16 = reshape(shape = var_1931, x = var_1930_cast_fp16)[name = tensor<string, []>("input_555_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_10_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_10_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(260658816))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261707456))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_97_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_10_self_attn_linear_out_weight_to_fp16_quantized, x = input_555_cast_fp16)[name = tensor<string, []>("linear_97_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_559_cast_fp16 = add(x = input_551_cast_fp16, y = linear_97_cast_fp16)[name = tensor<string, []>("input_559_cast_fp16")];
+            tensor<int32, [1]> x_237_axes_0 = const()[name = tensor<string, []>("x_237_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_10_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_10_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261709568)))];
+            tensor<fp16, [1024]> model_layers_10_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_10_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261711680)))];
+            tensor<fp16, [1, 126, 1024]> x_237_cast_fp16 = layer_norm(axes = x_237_axes_0, beta = model_layers_10_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_10_norm_conv_weight_to_fp16, x = input_559_cast_fp16)[name = tensor<string, []>("x_237_cast_fp16")];
+            tensor<int32, [3]> input_561_perm_0 = const()[name = tensor<string, []>("input_561_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_563_pad_type_0 = const()[name = tensor<string, []>("input_563_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_563_strides_0 = const()[name = tensor<string, []>("input_563_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_563_pad_0 = const()[name = tensor<string, []>("input_563_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_563_dilations_0 = const()[name = tensor<string, []>("input_563_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_563_groups_0 = const()[name = tensor<string, []>("input_563_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261713792))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263811008))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_561_cast_fp16 = transpose(perm = input_561_perm_0, x = x_237_cast_fp16)[name = tensor<string, []>("transpose_236")];
+            tensor<fp16, [1, 2048, 126]> input_563_cast_fp16 = conv(dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = model_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_561_cast_fp16)[name = tensor<string, []>("input_563_cast_fp16")];
+            tensor<int32, []> x_239_split_num_splits_0 = const()[name = tensor<string, []>("x_239_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_239_split_axis_0 = const()[name = tensor<string, []>("x_239_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_239_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_239_split_cast_fp16_1 = split(axis = x_239_split_axis_0, num_splits = x_239_split_num_splits_0, x = input_563_cast_fp16)[name = tensor<string, []>("x_239_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_239_split_1_sigmoid_cast_fp16 = sigmoid(x = x_239_split_cast_fp16_1)[name = tensor<string, []>("x_239_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_239_cast_fp16 = mul(x = x_239_split_cast_fp16_0, y = x_239_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_239_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_565_cast_fp16 = select(a = var_6_to_fp16, b = x_239_cast_fp16, cond = var_323)[name = tensor<string, []>("input_565_cast_fp16")];
+            tensor<int32, [6]> input_567_pad_0 = const()[name = tensor<string, []>("input_567_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_567_mode_0 = const()[name = tensor<string, []>("input_567_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_117_to_fp16 = const()[name = tensor<string, []>("const_117_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_567_cast_fp16 = pad(constant_val = const_117_to_fp16, mode = input_567_mode_0, pad = input_567_pad_0, x = input_565_cast_fp16)[name = tensor<string, []>("input_567_cast_fp16")];
+            tensor<string, []> input_569_pad_type_0 = const()[name = tensor<string, []>("input_569_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_569_groups_0 = const()[name = tensor<string, []>("input_569_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_569_strides_0 = const()[name = tensor<string, []>("input_569_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_569_pad_0 = const()[name = tensor<string, []>("input_569_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_569_dilations_0 = const()[name = tensor<string, []>("input_569_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_268_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_268_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263815168))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263824448))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_269_to_fp16 = const()[name = tensor<string, []>("const_269_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263826560)))];
+            tensor<fp16, [1, 1024, 126]> input_571_cast_fp16 = conv(bias = const_269_to_fp16, dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = const_268_to_fp16_quantized, x = input_567_cast_fp16)[name = tensor<string, []>("input_571_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_573_cast_fp16 = silu(x = input_571_cast_fp16)[name = tensor<string, []>("input_573_cast_fp16")];
+            tensor<string, []> x_241_pad_type_0 = const()[name = tensor<string, []>("x_241_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_241_strides_0 = const()[name = tensor<string, []>("x_241_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_241_pad_0 = const()[name = tensor<string, []>("x_241_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_241_dilations_0 = const()[name = tensor<string, []>("x_241_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_241_groups_0 = const()[name = tensor<string, []>("x_241_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263828672))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264877312))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_241_cast_fp16 = conv(dilations = x_241_dilations_0, groups = x_241_groups_0, pad = x_241_pad_0, pad_type = x_241_pad_type_0, strides = x_241_strides_0, weight = model_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_573_cast_fp16)[name = tensor<string, []>("x_241_cast_fp16")];
+            tensor<int32, [3]> input_575_perm_0 = const()[name = tensor<string, []>("input_575_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_241_cast_fp16)[name = tensor<string, []>("transpose_235")];
+            tensor<fp16, [1, 126, 1024]> input_577_cast_fp16 = add(x = input_559_cast_fp16, y = input_575_cast_fp16)[name = tensor<string, []>("input_577_cast_fp16")];
+            tensor<int32, [1]> input_579_axes_0 = const()[name = tensor<string, []>("input_579_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264879424)))];
+            tensor<fp16, [1024]> model_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264881536)))];
+            tensor<fp16, [1, 126, 1024]> input_579_cast_fp16 = layer_norm(axes = input_579_axes_0, beta = model_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_10_norm_feed_forward2_weight_to_fp16, x = input_577_cast_fp16)[name = tensor<string, []>("input_579_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_10_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_10_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264883648))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269078016))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_98_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_10_feed_forward2_linear1_weight_to_fp16_quantized, x = input_579_cast_fp16)[name = tensor<string, []>("linear_98_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_583_cast_fp16 = silu(x = linear_98_cast_fp16)[name = tensor<string, []>("input_583_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_10_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_10_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269086272))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273280640))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_99_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_10_feed_forward2_linear2_weight_to_fp16_quantized, x = input_583_cast_fp16)[name = tensor<string, []>("linear_99_cast_fp16")];
+            tensor<fp16, []> var_1991_to_fp16 = const()[name = tensor<string, []>("op_1991_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_1992_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_1991_to_fp16)[name = tensor<string, []>("op_1992_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_589_cast_fp16 = add(x = input_577_cast_fp16, y = var_1992_cast_fp16)[name = tensor<string, []>("input_589_cast_fp16")];
+            tensor<int32, [1]> input_591_axes_0 = const()[name = tensor<string, []>("input_591_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_10_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_10_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273282752)))];
+            tensor<fp16, [1024]> model_layers_10_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_10_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273284864)))];
+            tensor<fp16, [1, 126, 1024]> input_591_cast_fp16 = layer_norm(axes = input_591_axes_0, beta = model_layers_10_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_10_norm_out_weight_to_fp16, x = input_589_cast_fp16)[name = tensor<string, []>("input_591_cast_fp16")];
+            tensor<int32, [1]> input_593_axes_0 = const()[name = tensor<string, []>("input_593_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273286976)))];
+            tensor<fp16, [1024]> model_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273289088)))];
+            tensor<fp16, [1, 126, 1024]> input_593_cast_fp16 = layer_norm(axes = input_593_axes_0, beta = model_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_11_norm_feed_forward1_weight_to_fp16, x = input_591_cast_fp16)[name = tensor<string, []>("input_593_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_11_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_11_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273291200))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277485568))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_100_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_11_feed_forward1_linear1_weight_to_fp16_quantized, x = input_593_cast_fp16)[name = tensor<string, []>("linear_100_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_597_cast_fp16 = silu(x = linear_100_cast_fp16)[name = tensor<string, []>("input_597_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_11_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_11_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277493824))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281688192))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_101_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_11_feed_forward1_linear2_weight_to_fp16_quantized, x = input_597_cast_fp16)[name = tensor<string, []>("linear_101_cast_fp16")];
+            tensor<fp16, []> var_2020_to_fp16 = const()[name = tensor<string, []>("op_2020_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_2021_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2020_to_fp16)[name = tensor<string, []>("op_2021_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_603_cast_fp16 = add(x = input_591_cast_fp16, y = var_2021_cast_fp16)[name = tensor<string, []>("input_603_cast_fp16")];
+            tensor<int32, [1]> query_23_axes_0 = const()[name = tensor<string, []>("query_23_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_11_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_11_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281690304)))];
+            tensor<fp16, [1024]> model_layers_11_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_11_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281692416)))];
+            tensor<fp16, [1, 126, 1024]> query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = model_layers_11_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_11_norm_self_att_weight_to_fp16, x = input_603_cast_fp16)[name = tensor<string, []>("query_23_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_11_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_11_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281694528))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282743168))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_102_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_11_self_attn_linear_q_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = tensor<string, []>("linear_102_cast_fp16")];
+            tensor<int32, [4]> var_2037 = const()[name = tensor<string, []>("op_2037"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_67_cast_fp16 = reshape(shape = var_2037, x = linear_102_cast_fp16)[name = tensor<string, []>("q_67_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_11_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_11_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282745280))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283793920))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_103_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_11_self_attn_linear_k_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = tensor<string, []>("linear_103_cast_fp16")];
+            tensor<int32, [4]> var_2041 = const()[name = tensor<string, []>("op_2041"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_45_cast_fp16 = reshape(shape = var_2041, x = linear_103_cast_fp16)[name = tensor<string, []>("k_45_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_11_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_11_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283796032))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284844672))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_104_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_11_self_attn_linear_v_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = tensor<string, []>("linear_104_cast_fp16")];
+            tensor<int32, [4]> var_2045 = const()[name = tensor<string, []>("op_2045"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_23_cast_fp16 = reshape(shape = var_2045, x = linear_104_cast_fp16)[name = tensor<string, []>("v_23_cast_fp16")];
+            tensor<int32, [4]> value_23_perm_0 = const()[name = tensor<string, []>("value_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_11_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_11_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284846784))), scale = tensor<fp16, [8]>([0x1.dd4p-8, 0x1.b6p-8, 0x1.e8p-8, 0x1.dap-8, 0x1.1a4p-7, 0x1.02cp-7, 0x1.f08p-8, 0x1.f24p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_2057_cast_fp16 = add(x = q_67_cast_fp16, y = model_layers_11_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_2057_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_11_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_11_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284847872))), scale = tensor<fp16, [8]>([0x1.9f4p-8, 0x1.34cp-7, 0x1.fecp-9, 0x1.f3cp-8, 0x1.864p-8, 0x1.57p-8, 0x1.22cp-7, 0x1.248p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_2059_cast_fp16 = add(x = q_67_cast_fp16, y = model_layers_11_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_2059_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_23_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_249_transpose_x_0 = const()[name = tensor<string, []>("x_249_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_249_transpose_y_0 = const()[name = tensor<string, []>("x_249_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_2061_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2061_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284848960))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285106048))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2059_cast_fp16)[name = tensor<string, []>("transpose_234")];
+            tensor<fp16, [1, 8, 126, 251]> x_249_cast_fp16 = matmul(transpose_x = x_249_transpose_x_0, transpose_y = x_249_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = op_2061_to_fp16_quantized)[name = tensor<string, []>("x_249_cast_fp16")];
+            tensor<int32, [8]> x_251_pad_0 = const()[name = tensor<string, []>("x_251_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_251_mode_0 = const()[name = tensor<string, []>("x_251_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_124_to_fp16 = const()[name = tensor<string, []>("const_124_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_251_cast_fp16 = pad(constant_val = const_124_to_fp16, mode = x_251_mode_0, pad = x_251_pad_0, x = x_249_cast_fp16)[name = tensor<string, []>("x_251_cast_fp16")];
+            tensor<int32, [4]> var_2069 = const()[name = tensor<string, []>("op_2069"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_253_cast_fp16 = reshape(shape = var_2069, x = x_251_cast_fp16)[name = tensor<string, []>("x_253_cast_fp16")];
+            tensor<int32, [4]> var_2073_begin_0 = const()[name = tensor<string, []>("op_2073_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2073_end_0 = const()[name = tensor<string, []>("op_2073_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_2073_end_mask_0 = const()[name = tensor<string, []>("op_2073_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_2073_cast_fp16 = slice_by_index(begin = var_2073_begin_0, end = var_2073_end_0, end_mask = var_2073_end_mask_0, x = x_253_cast_fp16)[name = tensor<string, []>("op_2073_cast_fp16")];
+            tensor<int32, [4]> var_2074 = const()[name = tensor<string, []>("op_2074"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_45_cast_fp16 = reshape(shape = var_2074, x = var_2073_cast_fp16)[name = tensor<string, []>("matrix_bd_45_cast_fp16")];
+            tensor<bool, []> matrix_ac_23_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_23_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_23_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_23_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_118_perm_0 = const()[name = tensor<string, []>("transpose_118_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_119_perm_0 = const()[name = tensor<string, []>("transpose_119_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = tensor<string, []>("transpose_232")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2057_cast_fp16)[name = tensor<string, []>("transpose_233")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_118, y = transpose_119)[name = tensor<string, []>("matrix_ac_23_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_47_begin_0 = const()[name = tensor<string, []>("matrix_bd_47_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_47_end_0 = const()[name = tensor<string, []>("matrix_bd_47_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_47_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_47_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = tensor<string, []>("matrix_bd_47_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_2083_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = tensor<string, []>("op_2083_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_45_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_45_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_45_cast_fp16 = mul(x = var_2083_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_45_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_47_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_47_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_2089_cast_fp16 = softmax(axis = var_25, x = scores_47_cast_fp16)[name = tensor<string, []>("op_2089_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_605_cast_fp16 = select(a = var_6_to_fp16, b = var_2089_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_605_cast_fp16")];
+            tensor<bool, []> x_255_transpose_x_0 = const()[name = tensor<string, []>("x_255_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_255_transpose_y_0 = const()[name = tensor<string, []>("x_255_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_23_cast_fp16)[name = tensor<string, []>("transpose_231")];
+            tensor<fp16, [1, 8, 126, 128]> x_255_cast_fp16 = matmul(transpose_x = x_255_transpose_x_0, transpose_y = x_255_transpose_y_0, x = input_605_cast_fp16, y = value_23_cast_fp16)[name = tensor<string, []>("x_255_cast_fp16")];
+            tensor<int32, [4]> var_2093_perm_0 = const()[name = tensor<string, []>("op_2093_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2094 = const()[name = tensor<string, []>("op_2094"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_2093_cast_fp16 = transpose(perm = var_2093_perm_0, x = x_255_cast_fp16)[name = tensor<string, []>("transpose_230")];
+            tensor<fp16, [1, 126, 1024]> input_607_cast_fp16 = reshape(shape = var_2094, x = var_2093_cast_fp16)[name = tensor<string, []>("input_607_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_11_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_11_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285106624))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286155264))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_106_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_11_self_attn_linear_out_weight_to_fp16_quantized, x = input_607_cast_fp16)[name = tensor<string, []>("linear_106_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_611_cast_fp16 = add(x = input_603_cast_fp16, y = linear_106_cast_fp16)[name = tensor<string, []>("input_611_cast_fp16")];
+            tensor<int32, [1]> x_259_axes_0 = const()[name = tensor<string, []>("x_259_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_11_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_11_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286157376)))];
+            tensor<fp16, [1024]> model_layers_11_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_11_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286159488)))];
+            tensor<fp16, [1, 126, 1024]> x_259_cast_fp16 = layer_norm(axes = x_259_axes_0, beta = model_layers_11_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_11_norm_conv_weight_to_fp16, x = input_611_cast_fp16)[name = tensor<string, []>("x_259_cast_fp16")];
+            tensor<int32, [3]> input_613_perm_0 = const()[name = tensor<string, []>("input_613_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_615_pad_type_0 = const()[name = tensor<string, []>("input_615_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_615_strides_0 = const()[name = tensor<string, []>("input_615_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_615_pad_0 = const()[name = tensor<string, []>("input_615_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_615_dilations_0 = const()[name = tensor<string, []>("input_615_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_615_groups_0 = const()[name = tensor<string, []>("input_615_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286161600))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288258816))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_613_cast_fp16 = transpose(perm = input_613_perm_0, x = x_259_cast_fp16)[name = tensor<string, []>("transpose_229")];
+            tensor<fp16, [1, 2048, 126]> input_615_cast_fp16 = conv(dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = model_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_613_cast_fp16)[name = tensor<string, []>("input_615_cast_fp16")];
+            tensor<int32, []> x_261_split_num_splits_0 = const()[name = tensor<string, []>("x_261_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_261_split_axis_0 = const()[name = tensor<string, []>("x_261_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_261_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_261_split_cast_fp16_1 = split(axis = x_261_split_axis_0, num_splits = x_261_split_num_splits_0, x = input_615_cast_fp16)[name = tensor<string, []>("x_261_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_261_split_1_sigmoid_cast_fp16 = sigmoid(x = x_261_split_cast_fp16_1)[name = tensor<string, []>("x_261_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_261_cast_fp16 = mul(x = x_261_split_cast_fp16_0, y = x_261_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_261_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_617_cast_fp16 = select(a = var_6_to_fp16, b = x_261_cast_fp16, cond = var_323)[name = tensor<string, []>("input_617_cast_fp16")];
+            tensor<int32, [6]> input_619_pad_0 = const()[name = tensor<string, []>("input_619_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_619_mode_0 = const()[name = tensor<string, []>("input_619_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_127_to_fp16 = const()[name = tensor<string, []>("const_127_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_619_cast_fp16 = pad(constant_val = const_127_to_fp16, mode = input_619_mode_0, pad = input_619_pad_0, x = input_617_cast_fp16)[name = tensor<string, []>("input_619_cast_fp16")];
+            tensor<string, []> input_621_pad_type_0 = const()[name = tensor<string, []>("input_621_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_621_groups_0 = const()[name = tensor<string, []>("input_621_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_621_strides_0 = const()[name = tensor<string, []>("input_621_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_621_pad_0 = const()[name = tensor<string, []>("input_621_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_621_dilations_0 = const()[name = tensor<string, []>("input_621_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_270_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_270_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288262976))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288272256))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_271_to_fp16 = const()[name = tensor<string, []>("const_271_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288274368)))];
+            tensor<fp16, [1, 1024, 126]> input_623_cast_fp16 = conv(bias = const_271_to_fp16, dilations = input_621_dilations_0, groups = input_621_groups_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = const_270_to_fp16_quantized, x = input_619_cast_fp16)[name = tensor<string, []>("input_623_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_625_cast_fp16 = silu(x = input_623_cast_fp16)[name = tensor<string, []>("input_625_cast_fp16")];
+            tensor<string, []> x_263_pad_type_0 = const()[name = tensor<string, []>("x_263_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_263_strides_0 = const()[name = tensor<string, []>("x_263_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_263_pad_0 = const()[name = tensor<string, []>("x_263_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_263_dilations_0 = const()[name = tensor<string, []>("x_263_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_263_groups_0 = const()[name = tensor<string, []>("x_263_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288276480))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289325120))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_263_cast_fp16 = conv(dilations = x_263_dilations_0, groups = x_263_groups_0, pad = x_263_pad_0, pad_type = x_263_pad_type_0, strides = x_263_strides_0, weight = model_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_625_cast_fp16)[name = tensor<string, []>("x_263_cast_fp16")];
+            tensor<int32, [3]> input_627_perm_0 = const()[name = tensor<string, []>("input_627_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_263_cast_fp16)[name = tensor<string, []>("transpose_228")];
+            tensor<fp16, [1, 126, 1024]> input_629_cast_fp16 = add(x = input_611_cast_fp16, y = input_627_cast_fp16)[name = tensor<string, []>("input_629_cast_fp16")];
+            tensor<int32, [1]> input_631_axes_0 = const()[name = tensor<string, []>("input_631_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289327232)))];
+            tensor<fp16, [1024]> model_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289329344)))];
+            tensor<fp16, [1, 126, 1024]> input_631_cast_fp16 = layer_norm(axes = input_631_axes_0, beta = model_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_11_norm_feed_forward2_weight_to_fp16, x = input_629_cast_fp16)[name = tensor<string, []>("input_631_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_11_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_11_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289331456))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293525824))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_107_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_11_feed_forward2_linear1_weight_to_fp16_quantized, x = input_631_cast_fp16)[name = tensor<string, []>("linear_107_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_635_cast_fp16 = silu(x = linear_107_cast_fp16)[name = tensor<string, []>("input_635_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_11_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_11_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293534080))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297728448))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_108_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_11_feed_forward2_linear2_weight_to_fp16_quantized, x = input_635_cast_fp16)[name = tensor<string, []>("linear_108_cast_fp16")];
+            tensor<fp16, []> var_2154_to_fp16 = const()[name = tensor<string, []>("op_2154_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_2155_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2154_to_fp16)[name = tensor<string, []>("op_2155_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_641_cast_fp16 = add(x = input_629_cast_fp16, y = var_2155_cast_fp16)[name = tensor<string, []>("input_641_cast_fp16")];
+            tensor<int32, [1]> input_643_axes_0 = const()[name = tensor<string, []>("input_643_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_11_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_11_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297730560)))];
+            tensor<fp16, [1024]> model_layers_11_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_11_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297732672)))];
+            tensor<fp16, [1, 126, 1024]> input_643_cast_fp16 = layer_norm(axes = input_643_axes_0, beta = model_layers_11_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_11_norm_out_weight_to_fp16, x = input_641_cast_fp16)[name = tensor<string, []>("input_643_cast_fp16")];
+            tensor<int32, [1]> input_645_axes_0 = const()[name = tensor<string, []>("input_645_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297734784)))];
+            tensor<fp16, [1024]> model_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297736896)))];
+            tensor<fp16, [1, 126, 1024]> input_645_cast_fp16 = layer_norm(axes = input_645_axes_0, beta = model_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_12_norm_feed_forward1_weight_to_fp16, x = input_643_cast_fp16)[name = tensor<string, []>("input_645_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_12_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_12_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297739008))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(301933376))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_109_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_12_feed_forward1_linear1_weight_to_fp16_quantized, x = input_645_cast_fp16)[name = tensor<string, []>("linear_109_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_649_cast_fp16 = silu(x = linear_109_cast_fp16)[name = tensor<string, []>("input_649_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_12_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_12_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(301941632))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306136000))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_110_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_12_feed_forward1_linear2_weight_to_fp16_quantized, x = input_649_cast_fp16)[name = tensor<string, []>("linear_110_cast_fp16")];
+            tensor<fp16, []> var_2183_to_fp16 = const()[name = tensor<string, []>("op_2183_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_2184_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2183_to_fp16)[name = tensor<string, []>("op_2184_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_655_cast_fp16 = add(x = input_643_cast_fp16, y = var_2184_cast_fp16)[name = tensor<string, []>("input_655_cast_fp16")];
+            tensor<int32, [1]> query_25_axes_0 = const()[name = tensor<string, []>("query_25_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_12_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_12_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306138112)))];
+            tensor<fp16, [1024]> model_layers_12_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_12_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306140224)))];
+            tensor<fp16, [1, 126, 1024]> query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = model_layers_12_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_12_norm_self_att_weight_to_fp16, x = input_655_cast_fp16)[name = tensor<string, []>("query_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_12_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_12_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306142336))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307190976))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_111_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_12_self_attn_linear_q_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = tensor<string, []>("linear_111_cast_fp16")];
+            tensor<int32, [4]> var_2200 = const()[name = tensor<string, []>("op_2200"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_73_cast_fp16 = reshape(shape = var_2200, x = linear_111_cast_fp16)[name = tensor<string, []>("q_73_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_12_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_12_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307193088))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308241728))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_112_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_12_self_attn_linear_k_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = tensor<string, []>("linear_112_cast_fp16")];
+            tensor<int32, [4]> var_2204 = const()[name = tensor<string, []>("op_2204"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_49_cast_fp16 = reshape(shape = var_2204, x = linear_112_cast_fp16)[name = tensor<string, []>("k_49_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_12_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_12_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308243840))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309292480))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_113_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_12_self_attn_linear_v_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = tensor<string, []>("linear_113_cast_fp16")];
+            tensor<int32, [4]> var_2208 = const()[name = tensor<string, []>("op_2208"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_25_cast_fp16 = reshape(shape = var_2208, x = linear_113_cast_fp16)[name = tensor<string, []>("v_25_cast_fp16")];
+            tensor<int32, [4]> value_25_perm_0 = const()[name = tensor<string, []>("value_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_12_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_12_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309294592))), scale = tensor<fp16, [8]>([0x1.194p-7, 0x1.f04p-8, 0x1.d6cp-8, 0x1.0e8p-7, 0x1.fb8p-8, 0x1.fdcp-8, 0x1.308p-7, 0x1.5ap-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_2220_cast_fp16 = add(x = q_73_cast_fp16, y = model_layers_12_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_2220_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_12_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_12_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309295680))), scale = tensor<fp16, [8]>([0x1.20cp-8, 0x1.48p-7, 0x1.654p-8, 0x1.77cp-8, 0x1.cb4p-8, 0x1.ef4p-8, 0x1.1c4p-7, 0x1.a9cp-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_2222_cast_fp16 = add(x = q_73_cast_fp16, y = model_layers_12_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_2222_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_25_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_271_transpose_x_0 = const()[name = tensor<string, []>("x_271_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_271_transpose_y_0 = const()[name = tensor<string, []>("x_271_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_2224_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2224_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309296768))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309553856))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2222_cast_fp16)[name = tensor<string, []>("transpose_227")];
+            tensor<fp16, [1, 8, 126, 251]> x_271_cast_fp16 = matmul(transpose_x = x_271_transpose_x_0, transpose_y = x_271_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = op_2224_to_fp16_quantized)[name = tensor<string, []>("x_271_cast_fp16")];
+            tensor<int32, [8]> x_273_pad_0 = const()[name = tensor<string, []>("x_273_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_273_mode_0 = const()[name = tensor<string, []>("x_273_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_134_to_fp16 = const()[name = tensor<string, []>("const_134_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_273_cast_fp16 = pad(constant_val = const_134_to_fp16, mode = x_273_mode_0, pad = x_273_pad_0, x = x_271_cast_fp16)[name = tensor<string, []>("x_273_cast_fp16")];
+            tensor<int32, [4]> var_2232 = const()[name = tensor<string, []>("op_2232"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_275_cast_fp16 = reshape(shape = var_2232, x = x_273_cast_fp16)[name = tensor<string, []>("x_275_cast_fp16")];
+            tensor<int32, [4]> var_2236_begin_0 = const()[name = tensor<string, []>("op_2236_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2236_end_0 = const()[name = tensor<string, []>("op_2236_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_2236_end_mask_0 = const()[name = tensor<string, []>("op_2236_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_2236_cast_fp16 = slice_by_index(begin = var_2236_begin_0, end = var_2236_end_0, end_mask = var_2236_end_mask_0, x = x_275_cast_fp16)[name = tensor<string, []>("op_2236_cast_fp16")];
+            tensor<int32, [4]> var_2237 = const()[name = tensor<string, []>("op_2237"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_49_cast_fp16 = reshape(shape = var_2237, x = var_2236_cast_fp16)[name = tensor<string, []>("matrix_bd_49_cast_fp16")];
+            tensor<bool, []> matrix_ac_25_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_25_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_25_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_25_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_120_perm_0 = const()[name = tensor<string, []>("transpose_120_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_121_perm_0 = const()[name = tensor<string, []>("transpose_121_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = tensor<string, []>("transpose_225")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_120 = transpose(perm = transpose_120_perm_0, x = var_2220_cast_fp16)[name = tensor<string, []>("transpose_226")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_120, y = transpose_121)[name = tensor<string, []>("matrix_ac_25_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_51_begin_0 = const()[name = tensor<string, []>("matrix_bd_51_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_51_end_0 = const()[name = tensor<string, []>("matrix_bd_51_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_51_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_51_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = tensor<string, []>("matrix_bd_51_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_2246_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = tensor<string, []>("op_2246_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_49_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_49_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_49_cast_fp16 = mul(x = var_2246_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_49_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_51_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_51_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_2252_cast_fp16 = softmax(axis = var_25, x = scores_51_cast_fp16)[name = tensor<string, []>("op_2252_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_657_cast_fp16 = select(a = var_6_to_fp16, b = var_2252_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_657_cast_fp16")];
+            tensor<bool, []> x_277_transpose_x_0 = const()[name = tensor<string, []>("x_277_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_277_transpose_y_0 = const()[name = tensor<string, []>("x_277_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_25_cast_fp16)[name = tensor<string, []>("transpose_224")];
+            tensor<fp16, [1, 8, 126, 128]> x_277_cast_fp16 = matmul(transpose_x = x_277_transpose_x_0, transpose_y = x_277_transpose_y_0, x = input_657_cast_fp16, y = value_25_cast_fp16)[name = tensor<string, []>("x_277_cast_fp16")];
+            tensor<int32, [4]> var_2256_perm_0 = const()[name = tensor<string, []>("op_2256_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2257 = const()[name = tensor<string, []>("op_2257"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_2256_cast_fp16 = transpose(perm = var_2256_perm_0, x = x_277_cast_fp16)[name = tensor<string, []>("transpose_223")];
+            tensor<fp16, [1, 126, 1024]> input_659_cast_fp16 = reshape(shape = var_2257, x = var_2256_cast_fp16)[name = tensor<string, []>("input_659_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_12_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_12_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309554432))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310603072))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_115_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_12_self_attn_linear_out_weight_to_fp16_quantized, x = input_659_cast_fp16)[name = tensor<string, []>("linear_115_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_663_cast_fp16 = add(x = input_655_cast_fp16, y = linear_115_cast_fp16)[name = tensor<string, []>("input_663_cast_fp16")];
+            tensor<int32, [1]> x_281_axes_0 = const()[name = tensor<string, []>("x_281_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_12_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_12_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310605184)))];
+            tensor<fp16, [1024]> model_layers_12_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_12_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310607296)))];
+            tensor<fp16, [1, 126, 1024]> x_281_cast_fp16 = layer_norm(axes = x_281_axes_0, beta = model_layers_12_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_12_norm_conv_weight_to_fp16, x = input_663_cast_fp16)[name = tensor<string, []>("x_281_cast_fp16")];
+            tensor<int32, [3]> input_665_perm_0 = const()[name = tensor<string, []>("input_665_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_667_pad_type_0 = const()[name = tensor<string, []>("input_667_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_667_strides_0 = const()[name = tensor<string, []>("input_667_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_667_pad_0 = const()[name = tensor<string, []>("input_667_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_667_dilations_0 = const()[name = tensor<string, []>("input_667_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_667_groups_0 = const()[name = tensor<string, []>("input_667_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310609408))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312706624))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_665_cast_fp16 = transpose(perm = input_665_perm_0, x = x_281_cast_fp16)[name = tensor<string, []>("transpose_222")];
+            tensor<fp16, [1, 2048, 126]> input_667_cast_fp16 = conv(dilations = input_667_dilations_0, groups = input_667_groups_0, pad = input_667_pad_0, pad_type = input_667_pad_type_0, strides = input_667_strides_0, weight = model_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_665_cast_fp16)[name = tensor<string, []>("input_667_cast_fp16")];
+            tensor<int32, []> x_283_split_num_splits_0 = const()[name = tensor<string, []>("x_283_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_283_split_axis_0 = const()[name = tensor<string, []>("x_283_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_283_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_283_split_cast_fp16_1 = split(axis = x_283_split_axis_0, num_splits = x_283_split_num_splits_0, x = input_667_cast_fp16)[name = tensor<string, []>("x_283_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_283_split_1_sigmoid_cast_fp16 = sigmoid(x = x_283_split_cast_fp16_1)[name = tensor<string, []>("x_283_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_283_cast_fp16 = mul(x = x_283_split_cast_fp16_0, y = x_283_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_283_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_669_cast_fp16 = select(a = var_6_to_fp16, b = x_283_cast_fp16, cond = var_323)[name = tensor<string, []>("input_669_cast_fp16")];
+            tensor<int32, [6]> input_671_pad_0 = const()[name = tensor<string, []>("input_671_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_671_mode_0 = const()[name = tensor<string, []>("input_671_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_137_to_fp16 = const()[name = tensor<string, []>("const_137_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_671_cast_fp16 = pad(constant_val = const_137_to_fp16, mode = input_671_mode_0, pad = input_671_pad_0, x = input_669_cast_fp16)[name = tensor<string, []>("input_671_cast_fp16")];
+            tensor<string, []> input_673_pad_type_0 = const()[name = tensor<string, []>("input_673_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_673_groups_0 = const()[name = tensor<string, []>("input_673_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_673_strides_0 = const()[name = tensor<string, []>("input_673_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_673_pad_0 = const()[name = tensor<string, []>("input_673_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_673_dilations_0 = const()[name = tensor<string, []>("input_673_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_272_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_272_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312710784))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312720064))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_273_to_fp16 = const()[name = tensor<string, []>("const_273_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312722176)))];
+            tensor<fp16, [1, 1024, 126]> input_675_cast_fp16 = conv(bias = const_273_to_fp16, dilations = input_673_dilations_0, groups = input_673_groups_0, pad = input_673_pad_0, pad_type = input_673_pad_type_0, strides = input_673_strides_0, weight = const_272_to_fp16_quantized, x = input_671_cast_fp16)[name = tensor<string, []>("input_675_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_677_cast_fp16 = silu(x = input_675_cast_fp16)[name = tensor<string, []>("input_677_cast_fp16")];
+            tensor<string, []> x_285_pad_type_0 = const()[name = tensor<string, []>("x_285_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_285_strides_0 = const()[name = tensor<string, []>("x_285_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_285_pad_0 = const()[name = tensor<string, []>("x_285_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_285_dilations_0 = const()[name = tensor<string, []>("x_285_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_285_groups_0 = const()[name = tensor<string, []>("x_285_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312724288))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313772928))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_285_cast_fp16 = conv(dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = model_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_677_cast_fp16)[name = tensor<string, []>("x_285_cast_fp16")];
+            tensor<int32, [3]> input_679_perm_0 = const()[name = tensor<string, []>("input_679_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_285_cast_fp16)[name = tensor<string, []>("transpose_221")];
+            tensor<fp16, [1, 126, 1024]> input_681_cast_fp16 = add(x = input_663_cast_fp16, y = input_679_cast_fp16)[name = tensor<string, []>("input_681_cast_fp16")];
+            tensor<int32, [1]> input_683_axes_0 = const()[name = tensor<string, []>("input_683_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313775040)))];
+            tensor<fp16, [1024]> model_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313777152)))];
+            tensor<fp16, [1, 126, 1024]> input_683_cast_fp16 = layer_norm(axes = input_683_axes_0, beta = model_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_12_norm_feed_forward2_weight_to_fp16, x = input_681_cast_fp16)[name = tensor<string, []>("input_683_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_12_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_12_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313779264))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317973632))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_116_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_12_feed_forward2_linear1_weight_to_fp16_quantized, x = input_683_cast_fp16)[name = tensor<string, []>("linear_116_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_687_cast_fp16 = silu(x = linear_116_cast_fp16)[name = tensor<string, []>("input_687_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_12_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_12_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317981888))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322176256))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_117_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_12_feed_forward2_linear2_weight_to_fp16_quantized, x = input_687_cast_fp16)[name = tensor<string, []>("linear_117_cast_fp16")];
+            tensor<fp16, []> var_2317_to_fp16 = const()[name = tensor<string, []>("op_2317_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_2318_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2317_to_fp16)[name = tensor<string, []>("op_2318_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_693_cast_fp16 = add(x = input_681_cast_fp16, y = var_2318_cast_fp16)[name = tensor<string, []>("input_693_cast_fp16")];
+            tensor<int32, [1]> input_695_axes_0 = const()[name = tensor<string, []>("input_695_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_12_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_12_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322178368)))];
+            tensor<fp16, [1024]> model_layers_12_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_12_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322180480)))];
+            tensor<fp16, [1, 126, 1024]> input_695_cast_fp16 = layer_norm(axes = input_695_axes_0, beta = model_layers_12_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_12_norm_out_weight_to_fp16, x = input_693_cast_fp16)[name = tensor<string, []>("input_695_cast_fp16")];
+            tensor<int32, [1]> input_697_axes_0 = const()[name = tensor<string, []>("input_697_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322182592)))];
+            tensor<fp16, [1024]> model_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322184704)))];
+            tensor<fp16, [1, 126, 1024]> input_697_cast_fp16 = layer_norm(axes = input_697_axes_0, beta = model_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_13_norm_feed_forward1_weight_to_fp16, x = input_695_cast_fp16)[name = tensor<string, []>("input_697_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_13_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_13_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322186816))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326381184))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_118_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_13_feed_forward1_linear1_weight_to_fp16_quantized, x = input_697_cast_fp16)[name = tensor<string, []>("linear_118_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_701_cast_fp16 = silu(x = linear_118_cast_fp16)[name = tensor<string, []>("input_701_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_13_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_13_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326389440))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330583808))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_119_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_13_feed_forward1_linear2_weight_to_fp16_quantized, x = input_701_cast_fp16)[name = tensor<string, []>("linear_119_cast_fp16")];
+            tensor<fp16, []> var_2346_to_fp16 = const()[name = tensor<string, []>("op_2346_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_2347_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2346_to_fp16)[name = tensor<string, []>("op_2347_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_707_cast_fp16 = add(x = input_695_cast_fp16, y = var_2347_cast_fp16)[name = tensor<string, []>("input_707_cast_fp16")];
+            tensor<int32, [1]> query_27_axes_0 = const()[name = tensor<string, []>("query_27_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_13_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_13_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330585920)))];
+            tensor<fp16, [1024]> model_layers_13_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_13_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330588032)))];
+            tensor<fp16, [1, 126, 1024]> query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = model_layers_13_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_13_norm_self_att_weight_to_fp16, x = input_707_cast_fp16)[name = tensor<string, []>("query_27_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_13_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_13_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330590144))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(331638784))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_120_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_13_self_attn_linear_q_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = tensor<string, []>("linear_120_cast_fp16")];
+            tensor<int32, [4]> var_2363 = const()[name = tensor<string, []>("op_2363"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_79_cast_fp16 = reshape(shape = var_2363, x = linear_120_cast_fp16)[name = tensor<string, []>("q_79_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_13_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_13_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(331640896))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332689536))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_121_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_13_self_attn_linear_k_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = tensor<string, []>("linear_121_cast_fp16")];
+            tensor<int32, [4]> var_2367 = const()[name = tensor<string, []>("op_2367"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_53_cast_fp16 = reshape(shape = var_2367, x = linear_121_cast_fp16)[name = tensor<string, []>("k_53_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_13_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_13_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332691648))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(333740288))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_122_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_13_self_attn_linear_v_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = tensor<string, []>("linear_122_cast_fp16")];
+            tensor<int32, [4]> var_2371 = const()[name = tensor<string, []>("op_2371"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_27_cast_fp16 = reshape(shape = var_2371, x = linear_122_cast_fp16)[name = tensor<string, []>("v_27_cast_fp16")];
+            tensor<int32, [4]> value_27_perm_0 = const()[name = tensor<string, []>("value_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_13_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_13_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(333742400))), scale = tensor<fp16, [8]>([0x1.a54p-7, 0x1.38p-7, 0x1.a78p-8, 0x1.9b4p-7, 0x1.bd8p-7, 0x1.644p-7, 0x1.cc4p-8, 0x1.374p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_2383_cast_fp16 = add(x = q_79_cast_fp16, y = model_layers_13_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_2383_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_13_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_13_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(333743488))), scale = tensor<fp16, [8]>([0x1.1a8p-7, 0x1.4c4p-8, 0x1.914p-8, 0x1.02cp-8, 0x1.f9p-8, 0x1.38cp-7, 0x1.e3cp-9, 0x1.bdp-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_2385_cast_fp16 = add(x = q_79_cast_fp16, y = model_layers_13_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_2385_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_27_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_293_transpose_x_0 = const()[name = tensor<string, []>("x_293_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_293_transpose_y_0 = const()[name = tensor<string, []>("x_293_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_2387_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2387_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(333744576))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(334001664))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2385_cast_fp16)[name = tensor<string, []>("transpose_220")];
+            tensor<fp16, [1, 8, 126, 251]> x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = op_2387_to_fp16_quantized)[name = tensor<string, []>("x_293_cast_fp16")];
+            tensor<int32, [8]> x_295_pad_0 = const()[name = tensor<string, []>("x_295_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_295_mode_0 = const()[name = tensor<string, []>("x_295_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_144_to_fp16 = const()[name = tensor<string, []>("const_144_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_295_cast_fp16 = pad(constant_val = const_144_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = tensor<string, []>("x_295_cast_fp16")];
+            tensor<int32, [4]> var_2395 = const()[name = tensor<string, []>("op_2395"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_297_cast_fp16 = reshape(shape = var_2395, x = x_295_cast_fp16)[name = tensor<string, []>("x_297_cast_fp16")];
+            tensor<int32, [4]> var_2399_begin_0 = const()[name = tensor<string, []>("op_2399_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2399_end_0 = const()[name = tensor<string, []>("op_2399_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_2399_end_mask_0 = const()[name = tensor<string, []>("op_2399_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_2399_cast_fp16 = slice_by_index(begin = var_2399_begin_0, end = var_2399_end_0, end_mask = var_2399_end_mask_0, x = x_297_cast_fp16)[name = tensor<string, []>("op_2399_cast_fp16")];
+            tensor<int32, [4]> var_2400 = const()[name = tensor<string, []>("op_2400"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_53_cast_fp16 = reshape(shape = var_2400, x = var_2399_cast_fp16)[name = tensor<string, []>("matrix_bd_53_cast_fp16")];
+            tensor<bool, []> matrix_ac_27_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_27_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_27_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_27_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_122_perm_0 = const()[name = tensor<string, []>("transpose_122_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_123_perm_0 = const()[name = tensor<string, []>("transpose_123_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = tensor<string, []>("transpose_218")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_122 = transpose(perm = transpose_122_perm_0, x = var_2383_cast_fp16)[name = tensor<string, []>("transpose_219")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_122, y = transpose_123)[name = tensor<string, []>("matrix_ac_27_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_55_begin_0 = const()[name = tensor<string, []>("matrix_bd_55_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_55_end_0 = const()[name = tensor<string, []>("matrix_bd_55_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_55_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_55_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = tensor<string, []>("matrix_bd_55_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_2409_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = tensor<string, []>("op_2409_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_53_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_53_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_53_cast_fp16 = mul(x = var_2409_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_53_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_55_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_55_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_2415_cast_fp16 = softmax(axis = var_25, x = scores_55_cast_fp16)[name = tensor<string, []>("op_2415_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_709_cast_fp16 = select(a = var_6_to_fp16, b = var_2415_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_709_cast_fp16")];
+            tensor<bool, []> x_299_transpose_x_0 = const()[name = tensor<string, []>("x_299_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_299_transpose_y_0 = const()[name = tensor<string, []>("x_299_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_27_cast_fp16)[name = tensor<string, []>("transpose_217")];
+            tensor<fp16, [1, 8, 126, 128]> x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_709_cast_fp16, y = value_27_cast_fp16)[name = tensor<string, []>("x_299_cast_fp16")];
+            tensor<int32, [4]> var_2419_perm_0 = const()[name = tensor<string, []>("op_2419_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2420 = const()[name = tensor<string, []>("op_2420"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_2419_cast_fp16 = transpose(perm = var_2419_perm_0, x = x_299_cast_fp16)[name = tensor<string, []>("transpose_216")];
+            tensor<fp16, [1, 126, 1024]> input_711_cast_fp16 = reshape(shape = var_2420, x = var_2419_cast_fp16)[name = tensor<string, []>("input_711_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_13_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_13_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(334002240))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335050880))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_124_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_13_self_attn_linear_out_weight_to_fp16_quantized, x = input_711_cast_fp16)[name = tensor<string, []>("linear_124_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_715_cast_fp16 = add(x = input_707_cast_fp16, y = linear_124_cast_fp16)[name = tensor<string, []>("input_715_cast_fp16")];
+            tensor<int32, [1]> x_303_axes_0 = const()[name = tensor<string, []>("x_303_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_13_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_13_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335052992)))];
+            tensor<fp16, [1024]> model_layers_13_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_13_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335055104)))];
+            tensor<fp16, [1, 126, 1024]> x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = model_layers_13_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_13_norm_conv_weight_to_fp16, x = input_715_cast_fp16)[name = tensor<string, []>("x_303_cast_fp16")];
+            tensor<int32, [3]> input_717_perm_0 = const()[name = tensor<string, []>("input_717_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_719_pad_type_0 = const()[name = tensor<string, []>("input_719_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_719_strides_0 = const()[name = tensor<string, []>("input_719_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_719_pad_0 = const()[name = tensor<string, []>("input_719_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_719_dilations_0 = const()[name = tensor<string, []>("input_719_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_719_groups_0 = const()[name = tensor<string, []>("input_719_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335057216))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337154432))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_717_cast_fp16 = transpose(perm = input_717_perm_0, x = x_303_cast_fp16)[name = tensor<string, []>("transpose_215")];
+            tensor<fp16, [1, 2048, 126]> input_719_cast_fp16 = conv(dilations = input_719_dilations_0, groups = input_719_groups_0, pad = input_719_pad_0, pad_type = input_719_pad_type_0, strides = input_719_strides_0, weight = model_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_717_cast_fp16)[name = tensor<string, []>("input_719_cast_fp16")];
+            tensor<int32, []> x_305_split_num_splits_0 = const()[name = tensor<string, []>("x_305_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_305_split_axis_0 = const()[name = tensor<string, []>("x_305_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_305_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_719_cast_fp16)[name = tensor<string, []>("x_305_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = tensor<string, []>("x_305_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_305_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_721_cast_fp16 = select(a = var_6_to_fp16, b = x_305_cast_fp16, cond = var_323)[name = tensor<string, []>("input_721_cast_fp16")];
+            tensor<int32, [6]> input_723_pad_0 = const()[name = tensor<string, []>("input_723_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_723_mode_0 = const()[name = tensor<string, []>("input_723_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_147_to_fp16 = const()[name = tensor<string, []>("const_147_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_723_cast_fp16 = pad(constant_val = const_147_to_fp16, mode = input_723_mode_0, pad = input_723_pad_0, x = input_721_cast_fp16)[name = tensor<string, []>("input_723_cast_fp16")];
+            tensor<string, []> input_725_pad_type_0 = const()[name = tensor<string, []>("input_725_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_725_groups_0 = const()[name = tensor<string, []>("input_725_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_725_strides_0 = const()[name = tensor<string, []>("input_725_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_725_pad_0 = const()[name = tensor<string, []>("input_725_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_725_dilations_0 = const()[name = tensor<string, []>("input_725_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_274_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_274_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337158592))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337167872))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_275_to_fp16 = const()[name = tensor<string, []>("const_275_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337169984)))];
+            tensor<fp16, [1, 1024, 126]> input_727_cast_fp16 = conv(bias = const_275_to_fp16, dilations = input_725_dilations_0, groups = input_725_groups_0, pad = input_725_pad_0, pad_type = input_725_pad_type_0, strides = input_725_strides_0, weight = const_274_to_fp16_quantized, x = input_723_cast_fp16)[name = tensor<string, []>("input_727_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_729_cast_fp16 = silu(x = input_727_cast_fp16)[name = tensor<string, []>("input_729_cast_fp16")];
+            tensor<string, []> x_307_pad_type_0 = const()[name = tensor<string, []>("x_307_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_307_strides_0 = const()[name = tensor<string, []>("x_307_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_307_pad_0 = const()[name = tensor<string, []>("x_307_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_307_dilations_0 = const()[name = tensor<string, []>("x_307_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_307_groups_0 = const()[name = tensor<string, []>("x_307_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337172096))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338220736))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_307_cast_fp16 = conv(dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = model_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_729_cast_fp16)[name = tensor<string, []>("x_307_cast_fp16")];
+            tensor<int32, [3]> input_731_perm_0 = const()[name = tensor<string, []>("input_731_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_307_cast_fp16)[name = tensor<string, []>("transpose_214")];
+            tensor<fp16, [1, 126, 1024]> input_733_cast_fp16 = add(x = input_715_cast_fp16, y = input_731_cast_fp16)[name = tensor<string, []>("input_733_cast_fp16")];
+            tensor<int32, [1]> input_735_axes_0 = const()[name = tensor<string, []>("input_735_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338222848)))];
+            tensor<fp16, [1024]> model_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338224960)))];
+            tensor<fp16, [1, 126, 1024]> input_735_cast_fp16 = layer_norm(axes = input_735_axes_0, beta = model_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_13_norm_feed_forward2_weight_to_fp16, x = input_733_cast_fp16)[name = tensor<string, []>("input_735_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_13_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_13_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338227072))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(342421440))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_125_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_13_feed_forward2_linear1_weight_to_fp16_quantized, x = input_735_cast_fp16)[name = tensor<string, []>("linear_125_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_739_cast_fp16 = silu(x = linear_125_cast_fp16)[name = tensor<string, []>("input_739_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_13_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_13_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(342429696))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346624064))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_126_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_13_feed_forward2_linear2_weight_to_fp16_quantized, x = input_739_cast_fp16)[name = tensor<string, []>("linear_126_cast_fp16")];
+            tensor<fp16, []> var_2480_to_fp16 = const()[name = tensor<string, []>("op_2480_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_2481_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2480_to_fp16)[name = tensor<string, []>("op_2481_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_745_cast_fp16 = add(x = input_733_cast_fp16, y = var_2481_cast_fp16)[name = tensor<string, []>("input_745_cast_fp16")];
+            tensor<int32, [1]> input_747_axes_0 = const()[name = tensor<string, []>("input_747_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_13_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_13_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346626176)))];
+            tensor<fp16, [1024]> model_layers_13_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_13_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346628288)))];
+            tensor<fp16, [1, 126, 1024]> input_747_cast_fp16 = layer_norm(axes = input_747_axes_0, beta = model_layers_13_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_13_norm_out_weight_to_fp16, x = input_745_cast_fp16)[name = tensor<string, []>("input_747_cast_fp16")];
+            tensor<int32, [1]> input_749_axes_0 = const()[name = tensor<string, []>("input_749_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346630400)))];
+            tensor<fp16, [1024]> model_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346632512)))];
+            tensor<fp16, [1, 126, 1024]> input_749_cast_fp16 = layer_norm(axes = input_749_axes_0, beta = model_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_14_norm_feed_forward1_weight_to_fp16, x = input_747_cast_fp16)[name = tensor<string, []>("input_749_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_14_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_14_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346634624))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(350828992))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_127_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_14_feed_forward1_linear1_weight_to_fp16_quantized, x = input_749_cast_fp16)[name = tensor<string, []>("linear_127_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_753_cast_fp16 = silu(x = linear_127_cast_fp16)[name = tensor<string, []>("input_753_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_14_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_14_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(350837248))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(355031616))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_128_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_14_feed_forward1_linear2_weight_to_fp16_quantized, x = input_753_cast_fp16)[name = tensor<string, []>("linear_128_cast_fp16")];
+            tensor<fp16, []> var_2509_to_fp16 = const()[name = tensor<string, []>("op_2509_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_2510_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2509_to_fp16)[name = tensor<string, []>("op_2510_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_759_cast_fp16 = add(x = input_747_cast_fp16, y = var_2510_cast_fp16)[name = tensor<string, []>("input_759_cast_fp16")];
+            tensor<int32, [1]> query_29_axes_0 = const()[name = tensor<string, []>("query_29_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_14_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_14_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(355033728)))];
+            tensor<fp16, [1024]> model_layers_14_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_14_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(355035840)))];
+            tensor<fp16, [1, 126, 1024]> query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = model_layers_14_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_14_norm_self_att_weight_to_fp16, x = input_759_cast_fp16)[name = tensor<string, []>("query_29_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_14_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_14_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(355037952))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356086592))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_129_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_14_self_attn_linear_q_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = tensor<string, []>("linear_129_cast_fp16")];
+            tensor<int32, [4]> var_2526 = const()[name = tensor<string, []>("op_2526"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_85_cast_fp16 = reshape(shape = var_2526, x = linear_129_cast_fp16)[name = tensor<string, []>("q_85_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_14_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_14_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356088704))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357137344))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_130_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_14_self_attn_linear_k_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = tensor<string, []>("linear_130_cast_fp16")];
+            tensor<int32, [4]> var_2530 = const()[name = tensor<string, []>("op_2530"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_57_cast_fp16 = reshape(shape = var_2530, x = linear_130_cast_fp16)[name = tensor<string, []>("k_57_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_14_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_14_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357139456))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358188096))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_131_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_14_self_attn_linear_v_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = tensor<string, []>("linear_131_cast_fp16")];
+            tensor<int32, [4]> var_2534 = const()[name = tensor<string, []>("op_2534"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_29_cast_fp16 = reshape(shape = var_2534, x = linear_131_cast_fp16)[name = tensor<string, []>("v_29_cast_fp16")];
+            tensor<int32, [4]> value_29_perm_0 = const()[name = tensor<string, []>("value_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_14_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_14_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358190208))), scale = tensor<fp16, [8]>([0x1.048p-7, 0x1.0ccp-7, 0x1.8f8p-7, 0x1.67p-7, 0x1.014p-7, 0x1.5cp-7, 0x1.0ecp-7, 0x1.57cp-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_2546_cast_fp16 = add(x = q_85_cast_fp16, y = model_layers_14_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_2546_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_14_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_14_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358191296))), scale = tensor<fp16, [8]>([0x1.b98p-8, 0x1.088p-9, 0x1.184p-8, 0x1.44p-7, 0x1.384p-7, 0x1.a44p-8, 0x1.c48p-8, 0x1.bf4p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_2548_cast_fp16 = add(x = q_85_cast_fp16, y = model_layers_14_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_2548_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_29_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_315_transpose_x_0 = const()[name = tensor<string, []>("x_315_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_315_transpose_y_0 = const()[name = tensor<string, []>("x_315_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_2550_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2550_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358192384))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358449472))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2548_cast_fp16)[name = tensor<string, []>("transpose_213")];
+            tensor<fp16, [1, 8, 126, 251]> x_315_cast_fp16 = matmul(transpose_x = x_315_transpose_x_0, transpose_y = x_315_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = op_2550_to_fp16_quantized)[name = tensor<string, []>("x_315_cast_fp16")];
+            tensor<int32, [8]> x_317_pad_0 = const()[name = tensor<string, []>("x_317_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_317_mode_0 = const()[name = tensor<string, []>("x_317_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_154_to_fp16 = const()[name = tensor<string, []>("const_154_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_317_cast_fp16 = pad(constant_val = const_154_to_fp16, mode = x_317_mode_0, pad = x_317_pad_0, x = x_315_cast_fp16)[name = tensor<string, []>("x_317_cast_fp16")];
+            tensor<int32, [4]> var_2558 = const()[name = tensor<string, []>("op_2558"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_319_cast_fp16 = reshape(shape = var_2558, x = x_317_cast_fp16)[name = tensor<string, []>("x_319_cast_fp16")];
+            tensor<int32, [4]> var_2562_begin_0 = const()[name = tensor<string, []>("op_2562_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2562_end_0 = const()[name = tensor<string, []>("op_2562_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_2562_end_mask_0 = const()[name = tensor<string, []>("op_2562_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_2562_cast_fp16 = slice_by_index(begin = var_2562_begin_0, end = var_2562_end_0, end_mask = var_2562_end_mask_0, x = x_319_cast_fp16)[name = tensor<string, []>("op_2562_cast_fp16")];
+            tensor<int32, [4]> var_2563 = const()[name = tensor<string, []>("op_2563"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_57_cast_fp16 = reshape(shape = var_2563, x = var_2562_cast_fp16)[name = tensor<string, []>("matrix_bd_57_cast_fp16")];
+            tensor<bool, []> matrix_ac_29_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_29_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_29_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_29_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_124_perm_0 = const()[name = tensor<string, []>("transpose_124_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_125_perm_0 = const()[name = tensor<string, []>("transpose_125_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = tensor<string, []>("transpose_211")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_124 = transpose(perm = transpose_124_perm_0, x = var_2546_cast_fp16)[name = tensor<string, []>("transpose_212")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_124, y = transpose_125)[name = tensor<string, []>("matrix_ac_29_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_59_begin_0 = const()[name = tensor<string, []>("matrix_bd_59_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_59_end_0 = const()[name = tensor<string, []>("matrix_bd_59_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_59_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_59_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = tensor<string, []>("matrix_bd_59_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_2572_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = tensor<string, []>("op_2572_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_57_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_57_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_57_cast_fp16 = mul(x = var_2572_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_57_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_59_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_59_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_2578_cast_fp16 = softmax(axis = var_25, x = scores_59_cast_fp16)[name = tensor<string, []>("op_2578_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_761_cast_fp16 = select(a = var_6_to_fp16, b = var_2578_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_761_cast_fp16")];
+            tensor<bool, []> x_321_transpose_x_0 = const()[name = tensor<string, []>("x_321_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_321_transpose_y_0 = const()[name = tensor<string, []>("x_321_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_29_cast_fp16)[name = tensor<string, []>("transpose_210")];
+            tensor<fp16, [1, 8, 126, 128]> x_321_cast_fp16 = matmul(transpose_x = x_321_transpose_x_0, transpose_y = x_321_transpose_y_0, x = input_761_cast_fp16, y = value_29_cast_fp16)[name = tensor<string, []>("x_321_cast_fp16")];
+            tensor<int32, [4]> var_2582_perm_0 = const()[name = tensor<string, []>("op_2582_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2583 = const()[name = tensor<string, []>("op_2583"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_2582_cast_fp16 = transpose(perm = var_2582_perm_0, x = x_321_cast_fp16)[name = tensor<string, []>("transpose_209")];
+            tensor<fp16, [1, 126, 1024]> input_763_cast_fp16 = reshape(shape = var_2583, x = var_2582_cast_fp16)[name = tensor<string, []>("input_763_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_14_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_14_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358450048))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359498688))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_133_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_14_self_attn_linear_out_weight_to_fp16_quantized, x = input_763_cast_fp16)[name = tensor<string, []>("linear_133_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_767_cast_fp16 = add(x = input_759_cast_fp16, y = linear_133_cast_fp16)[name = tensor<string, []>("input_767_cast_fp16")];
+            tensor<int32, [1]> x_325_axes_0 = const()[name = tensor<string, []>("x_325_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_14_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_14_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359500800)))];
+            tensor<fp16, [1024]> model_layers_14_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_14_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359502912)))];
+            tensor<fp16, [1, 126, 1024]> x_325_cast_fp16 = layer_norm(axes = x_325_axes_0, beta = model_layers_14_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_14_norm_conv_weight_to_fp16, x = input_767_cast_fp16)[name = tensor<string, []>("x_325_cast_fp16")];
+            tensor<int32, [3]> input_769_perm_0 = const()[name = tensor<string, []>("input_769_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_771_pad_type_0 = const()[name = tensor<string, []>("input_771_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_771_strides_0 = const()[name = tensor<string, []>("input_771_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_771_pad_0 = const()[name = tensor<string, []>("input_771_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_771_dilations_0 = const()[name = tensor<string, []>("input_771_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_771_groups_0 = const()[name = tensor<string, []>("input_771_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359505024))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(361602240))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_769_cast_fp16 = transpose(perm = input_769_perm_0, x = x_325_cast_fp16)[name = tensor<string, []>("transpose_208")];
+            tensor<fp16, [1, 2048, 126]> input_771_cast_fp16 = conv(dilations = input_771_dilations_0, groups = input_771_groups_0, pad = input_771_pad_0, pad_type = input_771_pad_type_0, strides = input_771_strides_0, weight = model_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_769_cast_fp16)[name = tensor<string, []>("input_771_cast_fp16")];
+            tensor<int32, []> x_327_split_num_splits_0 = const()[name = tensor<string, []>("x_327_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_327_split_axis_0 = const()[name = tensor<string, []>("x_327_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_327_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_327_split_cast_fp16_1 = split(axis = x_327_split_axis_0, num_splits = x_327_split_num_splits_0, x = input_771_cast_fp16)[name = tensor<string, []>("x_327_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_327_split_1_sigmoid_cast_fp16 = sigmoid(x = x_327_split_cast_fp16_1)[name = tensor<string, []>("x_327_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_327_cast_fp16 = mul(x = x_327_split_cast_fp16_0, y = x_327_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_327_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_773_cast_fp16 = select(a = var_6_to_fp16, b = x_327_cast_fp16, cond = var_323)[name = tensor<string, []>("input_773_cast_fp16")];
+            tensor<int32, [6]> input_775_pad_0 = const()[name = tensor<string, []>("input_775_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_775_mode_0 = const()[name = tensor<string, []>("input_775_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_157_to_fp16 = const()[name = tensor<string, []>("const_157_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_775_cast_fp16 = pad(constant_val = const_157_to_fp16, mode = input_775_mode_0, pad = input_775_pad_0, x = input_773_cast_fp16)[name = tensor<string, []>("input_775_cast_fp16")];
+            tensor<string, []> input_777_pad_type_0 = const()[name = tensor<string, []>("input_777_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_777_groups_0 = const()[name = tensor<string, []>("input_777_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_777_strides_0 = const()[name = tensor<string, []>("input_777_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_777_pad_0 = const()[name = tensor<string, []>("input_777_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_777_dilations_0 = const()[name = tensor<string, []>("input_777_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_276_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_276_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(361606400))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(361615680))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_277_to_fp16 = const()[name = tensor<string, []>("const_277_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(361617792)))];
+            tensor<fp16, [1, 1024, 126]> input_779_cast_fp16 = conv(bias = const_277_to_fp16, dilations = input_777_dilations_0, groups = input_777_groups_0, pad = input_777_pad_0, pad_type = input_777_pad_type_0, strides = input_777_strides_0, weight = const_276_to_fp16_quantized, x = input_775_cast_fp16)[name = tensor<string, []>("input_779_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_781_cast_fp16 = silu(x = input_779_cast_fp16)[name = tensor<string, []>("input_781_cast_fp16")];
+            tensor<string, []> x_329_pad_type_0 = const()[name = tensor<string, []>("x_329_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_329_strides_0 = const()[name = tensor<string, []>("x_329_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_329_pad_0 = const()[name = tensor<string, []>("x_329_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_329_dilations_0 = const()[name = tensor<string, []>("x_329_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_329_groups_0 = const()[name = tensor<string, []>("x_329_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(361619904))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362668544))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_329_cast_fp16 = conv(dilations = x_329_dilations_0, groups = x_329_groups_0, pad = x_329_pad_0, pad_type = x_329_pad_type_0, strides = x_329_strides_0, weight = model_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_781_cast_fp16)[name = tensor<string, []>("x_329_cast_fp16")];
+            tensor<int32, [3]> input_783_perm_0 = const()[name = tensor<string, []>("input_783_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_329_cast_fp16)[name = tensor<string, []>("transpose_207")];
+            tensor<fp16, [1, 126, 1024]> input_785_cast_fp16 = add(x = input_767_cast_fp16, y = input_783_cast_fp16)[name = tensor<string, []>("input_785_cast_fp16")];
+            tensor<int32, [1]> input_787_axes_0 = const()[name = tensor<string, []>("input_787_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362670656)))];
+            tensor<fp16, [1024]> model_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362672768)))];
+            tensor<fp16, [1, 126, 1024]> input_787_cast_fp16 = layer_norm(axes = input_787_axes_0, beta = model_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_14_norm_feed_forward2_weight_to_fp16, x = input_785_cast_fp16)[name = tensor<string, []>("input_787_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_14_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_14_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362674880))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(366869248))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_134_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_14_feed_forward2_linear1_weight_to_fp16_quantized, x = input_787_cast_fp16)[name = tensor<string, []>("linear_134_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_791_cast_fp16 = silu(x = linear_134_cast_fp16)[name = tensor<string, []>("input_791_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_14_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_14_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(366877504))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371071872))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_135_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_14_feed_forward2_linear2_weight_to_fp16_quantized, x = input_791_cast_fp16)[name = tensor<string, []>("linear_135_cast_fp16")];
+            tensor<fp16, []> var_2643_to_fp16 = const()[name = tensor<string, []>("op_2643_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_2644_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_2643_to_fp16)[name = tensor<string, []>("op_2644_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_797_cast_fp16 = add(x = input_785_cast_fp16, y = var_2644_cast_fp16)[name = tensor<string, []>("input_797_cast_fp16")];
+            tensor<int32, [1]> input_799_axes_0 = const()[name = tensor<string, []>("input_799_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_14_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_14_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371073984)))];
+            tensor<fp16, [1024]> model_layers_14_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_14_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371076096)))];
+            tensor<fp16, [1, 126, 1024]> input_799_cast_fp16 = layer_norm(axes = input_799_axes_0, beta = model_layers_14_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_14_norm_out_weight_to_fp16, x = input_797_cast_fp16)[name = tensor<string, []>("input_799_cast_fp16")];
+            tensor<int32, [1]> input_801_axes_0 = const()[name = tensor<string, []>("input_801_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371078208)))];
+            tensor<fp16, [1024]> model_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371080320)))];
+            tensor<fp16, [1, 126, 1024]> input_801_cast_fp16 = layer_norm(axes = input_801_axes_0, beta = model_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_15_norm_feed_forward1_weight_to_fp16, x = input_799_cast_fp16)[name = tensor<string, []>("input_801_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_15_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_15_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371082432))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(375276800))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_136_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_15_feed_forward1_linear1_weight_to_fp16_quantized, x = input_801_cast_fp16)[name = tensor<string, []>("linear_136_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_805_cast_fp16 = silu(x = linear_136_cast_fp16)[name = tensor<string, []>("input_805_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_15_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_15_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(375285056))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(379479424))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_137_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_15_feed_forward1_linear2_weight_to_fp16_quantized, x = input_805_cast_fp16)[name = tensor<string, []>("linear_137_cast_fp16")];
+            tensor<fp16, []> var_2672_to_fp16 = const()[name = tensor<string, []>("op_2672_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_2673_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_2672_to_fp16)[name = tensor<string, []>("op_2673_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_811_cast_fp16 = add(x = input_799_cast_fp16, y = var_2673_cast_fp16)[name = tensor<string, []>("input_811_cast_fp16")];
+            tensor<int32, [1]> query_31_axes_0 = const()[name = tensor<string, []>("query_31_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_15_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_15_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(379481536)))];
+            tensor<fp16, [1024]> model_layers_15_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_15_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(379483648)))];
+            tensor<fp16, [1, 126, 1024]> query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = model_layers_15_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_15_norm_self_att_weight_to_fp16, x = input_811_cast_fp16)[name = tensor<string, []>("query_31_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_15_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_15_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(379485760))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(380534400))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_138_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_15_self_attn_linear_q_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = tensor<string, []>("linear_138_cast_fp16")];
+            tensor<int32, [4]> var_2689 = const()[name = tensor<string, []>("op_2689"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_91_cast_fp16 = reshape(shape = var_2689, x = linear_138_cast_fp16)[name = tensor<string, []>("q_91_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_15_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_15_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(380536512))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381585152))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_139_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_15_self_attn_linear_k_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = tensor<string, []>("linear_139_cast_fp16")];
+            tensor<int32, [4]> var_2693 = const()[name = tensor<string, []>("op_2693"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_61_cast_fp16 = reshape(shape = var_2693, x = linear_139_cast_fp16)[name = tensor<string, []>("k_61_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_15_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_15_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381587264))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382635904))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_140_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_15_self_attn_linear_v_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = tensor<string, []>("linear_140_cast_fp16")];
+            tensor<int32, [4]> var_2697 = const()[name = tensor<string, []>("op_2697"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_31_cast_fp16 = reshape(shape = var_2697, x = linear_140_cast_fp16)[name = tensor<string, []>("v_31_cast_fp16")];
+            tensor<int32, [4]> value_31_perm_0 = const()[name = tensor<string, []>("value_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_15_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_15_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382638016))), scale = tensor<fp16, [8]>([0x1.c94p-8, 0x1.894p-8, 0x1.b6p-8, 0x1.bb8p-8, 0x1.85p-8, 0x1.068p-7, 0x1.02cp-7, 0x1.c2p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_2709_cast_fp16 = add(x = q_91_cast_fp16, y = model_layers_15_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_2709_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_15_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_15_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382639104))), scale = tensor<fp16, [8]>([0x1.1f8p-9, 0x1.cfp-8, 0x1.f3p-8, 0x1.2f4p-7, 0x1.a6cp-8, 0x1.104p-9, 0x1.234p-8, 0x1.09cp-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_2711_cast_fp16 = add(x = q_91_cast_fp16, y = model_layers_15_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_2711_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_31_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_337_transpose_x_0 = const()[name = tensor<string, []>("x_337_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_337_transpose_y_0 = const()[name = tensor<string, []>("x_337_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_2713_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2713_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382640192))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382897280))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_2711_cast_fp16)[name = tensor<string, []>("transpose_206")];
+            tensor<fp16, [1, 8, 126, 251]> x_337_cast_fp16 = matmul(transpose_x = x_337_transpose_x_0, transpose_y = x_337_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = op_2713_to_fp16_quantized)[name = tensor<string, []>("x_337_cast_fp16")];
+            tensor<int32, [8]> x_339_pad_0 = const()[name = tensor<string, []>("x_339_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_339_mode_0 = const()[name = tensor<string, []>("x_339_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_164_to_fp16 = const()[name = tensor<string, []>("const_164_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_339_cast_fp16 = pad(constant_val = const_164_to_fp16, mode = x_339_mode_0, pad = x_339_pad_0, x = x_337_cast_fp16)[name = tensor<string, []>("x_339_cast_fp16")];
+            tensor<int32, [4]> var_2721 = const()[name = tensor<string, []>("op_2721"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_341_cast_fp16 = reshape(shape = var_2721, x = x_339_cast_fp16)[name = tensor<string, []>("x_341_cast_fp16")];
+            tensor<int32, [4]> var_2725_begin_0 = const()[name = tensor<string, []>("op_2725_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2725_end_0 = const()[name = tensor<string, []>("op_2725_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_2725_end_mask_0 = const()[name = tensor<string, []>("op_2725_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_2725_cast_fp16 = slice_by_index(begin = var_2725_begin_0, end = var_2725_end_0, end_mask = var_2725_end_mask_0, x = x_341_cast_fp16)[name = tensor<string, []>("op_2725_cast_fp16")];
+            tensor<int32, [4]> var_2726 = const()[name = tensor<string, []>("op_2726"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_61_cast_fp16 = reshape(shape = var_2726, x = var_2725_cast_fp16)[name = tensor<string, []>("matrix_bd_61_cast_fp16")];
+            tensor<bool, []> matrix_ac_31_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_31_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_31_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_31_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_126_perm_0 = const()[name = tensor<string, []>("transpose_126_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_127_perm_0 = const()[name = tensor<string, []>("transpose_127_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = tensor<string, []>("transpose_204")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_126 = transpose(perm = transpose_126_perm_0, x = var_2709_cast_fp16)[name = tensor<string, []>("transpose_205")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_126, y = transpose_127)[name = tensor<string, []>("matrix_ac_31_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_63_begin_0 = const()[name = tensor<string, []>("matrix_bd_63_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_63_end_0 = const()[name = tensor<string, []>("matrix_bd_63_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_63_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_63_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = tensor<string, []>("matrix_bd_63_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_2735_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = tensor<string, []>("op_2735_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_61_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_61_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_61_cast_fp16 = mul(x = var_2735_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_61_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_63_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_63_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_2741_cast_fp16 = softmax(axis = var_25, x = scores_63_cast_fp16)[name = tensor<string, []>("op_2741_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_813_cast_fp16 = select(a = var_6_to_fp16, b = var_2741_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_813_cast_fp16")];
+            tensor<bool, []> x_343_transpose_x_0 = const()[name = tensor<string, []>("x_343_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_343_transpose_y_0 = const()[name = tensor<string, []>("x_343_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_31_cast_fp16)[name = tensor<string, []>("transpose_203")];
+            tensor<fp16, [1, 8, 126, 128]> x_343_cast_fp16 = matmul(transpose_x = x_343_transpose_x_0, transpose_y = x_343_transpose_y_0, x = input_813_cast_fp16, y = value_31_cast_fp16)[name = tensor<string, []>("x_343_cast_fp16")];
+            tensor<int32, [4]> var_2745_perm_0 = const()[name = tensor<string, []>("op_2745_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2746 = const()[name = tensor<string, []>("op_2746"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_2745_cast_fp16 = transpose(perm = var_2745_perm_0, x = x_343_cast_fp16)[name = tensor<string, []>("transpose_202")];
+            tensor<fp16, [1, 126, 1024]> input_815_cast_fp16 = reshape(shape = var_2746, x = var_2745_cast_fp16)[name = tensor<string, []>("input_815_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_15_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_15_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382897856))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383946496))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_142_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_15_self_attn_linear_out_weight_to_fp16_quantized, x = input_815_cast_fp16)[name = tensor<string, []>("linear_142_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_819_cast_fp16 = add(x = input_811_cast_fp16, y = linear_142_cast_fp16)[name = tensor<string, []>("input_819_cast_fp16")];
+            tensor<int32, [1]> x_347_axes_0 = const()[name = tensor<string, []>("x_347_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_15_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_15_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383948608)))];
+            tensor<fp16, [1024]> model_layers_15_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_15_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383950720)))];
+            tensor<fp16, [1, 126, 1024]> x_347_cast_fp16 = layer_norm(axes = x_347_axes_0, beta = model_layers_15_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_15_norm_conv_weight_to_fp16, x = input_819_cast_fp16)[name = tensor<string, []>("x_347_cast_fp16")];
+            tensor<int32, [3]> input_821_perm_0 = const()[name = tensor<string, []>("input_821_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_823_pad_type_0 = const()[name = tensor<string, []>("input_823_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_823_strides_0 = const()[name = tensor<string, []>("input_823_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_823_pad_0 = const()[name = tensor<string, []>("input_823_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_823_dilations_0 = const()[name = tensor<string, []>("input_823_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_823_groups_0 = const()[name = tensor<string, []>("input_823_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383952832))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(386050048))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_821_cast_fp16 = transpose(perm = input_821_perm_0, x = x_347_cast_fp16)[name = tensor<string, []>("transpose_201")];
+            tensor<fp16, [1, 2048, 126]> input_823_cast_fp16 = conv(dilations = input_823_dilations_0, groups = input_823_groups_0, pad = input_823_pad_0, pad_type = input_823_pad_type_0, strides = input_823_strides_0, weight = model_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_821_cast_fp16)[name = tensor<string, []>("input_823_cast_fp16")];
+            tensor<int32, []> x_349_split_num_splits_0 = const()[name = tensor<string, []>("x_349_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_349_split_axis_0 = const()[name = tensor<string, []>("x_349_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_349_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_349_split_cast_fp16_1 = split(axis = x_349_split_axis_0, num_splits = x_349_split_num_splits_0, x = input_823_cast_fp16)[name = tensor<string, []>("x_349_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_349_split_1_sigmoid_cast_fp16 = sigmoid(x = x_349_split_cast_fp16_1)[name = tensor<string, []>("x_349_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_349_cast_fp16 = mul(x = x_349_split_cast_fp16_0, y = x_349_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_349_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_825_cast_fp16 = select(a = var_6_to_fp16, b = x_349_cast_fp16, cond = var_323)[name = tensor<string, []>("input_825_cast_fp16")];
+            tensor<int32, [6]> input_827_pad_0 = const()[name = tensor<string, []>("input_827_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_827_mode_0 = const()[name = tensor<string, []>("input_827_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_167_to_fp16 = const()[name = tensor<string, []>("const_167_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_827_cast_fp16 = pad(constant_val = const_167_to_fp16, mode = input_827_mode_0, pad = input_827_pad_0, x = input_825_cast_fp16)[name = tensor<string, []>("input_827_cast_fp16")];
+            tensor<string, []> input_829_pad_type_0 = const()[name = tensor<string, []>("input_829_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_829_groups_0 = const()[name = tensor<string, []>("input_829_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_829_strides_0 = const()[name = tensor<string, []>("input_829_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_829_pad_0 = const()[name = tensor<string, []>("input_829_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_829_dilations_0 = const()[name = tensor<string, []>("input_829_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_278_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_278_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(386054208))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(386063488))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_279_to_fp16 = const()[name = tensor<string, []>("const_279_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(386065600)))];
+            tensor<fp16, [1, 1024, 126]> input_831_cast_fp16 = conv(bias = const_279_to_fp16, dilations = input_829_dilations_0, groups = input_829_groups_0, pad = input_829_pad_0, pad_type = input_829_pad_type_0, strides = input_829_strides_0, weight = const_278_to_fp16_quantized, x = input_827_cast_fp16)[name = tensor<string, []>("input_831_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_833_cast_fp16 = silu(x = input_831_cast_fp16)[name = tensor<string, []>("input_833_cast_fp16")];
+            tensor<string, []> x_351_pad_type_0 = const()[name = tensor<string, []>("x_351_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_351_strides_0 = const()[name = tensor<string, []>("x_351_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_351_pad_0 = const()[name = tensor<string, []>("x_351_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_351_dilations_0 = const()[name = tensor<string, []>("x_351_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_351_groups_0 = const()[name = tensor<string, []>("x_351_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(386067712))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387116352))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_351_cast_fp16 = conv(dilations = x_351_dilations_0, groups = x_351_groups_0, pad = x_351_pad_0, pad_type = x_351_pad_type_0, strides = x_351_strides_0, weight = model_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_833_cast_fp16)[name = tensor<string, []>("x_351_cast_fp16")];
+            tensor<int32, [3]> input_835_perm_0 = const()[name = tensor<string, []>("input_835_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_351_cast_fp16)[name = tensor<string, []>("transpose_200")];
+            tensor<fp16, [1, 126, 1024]> input_837_cast_fp16 = add(x = input_819_cast_fp16, y = input_835_cast_fp16)[name = tensor<string, []>("input_837_cast_fp16")];
+            tensor<int32, [1]> input_839_axes_0 = const()[name = tensor<string, []>("input_839_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387118464)))];
+            tensor<fp16, [1024]> model_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387120576)))];
+            tensor<fp16, [1, 126, 1024]> input_839_cast_fp16 = layer_norm(axes = input_839_axes_0, beta = model_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_15_norm_feed_forward2_weight_to_fp16, x = input_837_cast_fp16)[name = tensor<string, []>("input_839_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_15_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_15_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387122688))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(391317056))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_143_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_15_feed_forward2_linear1_weight_to_fp16_quantized, x = input_839_cast_fp16)[name = tensor<string, []>("linear_143_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_843_cast_fp16 = silu(x = linear_143_cast_fp16)[name = tensor<string, []>("input_843_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_15_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_15_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(391325312))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395519680))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_144_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_15_feed_forward2_linear2_weight_to_fp16_quantized, x = input_843_cast_fp16)[name = tensor<string, []>("linear_144_cast_fp16")];
+            tensor<fp16, []> var_2806_to_fp16 = const()[name = tensor<string, []>("op_2806_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_2807_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_2806_to_fp16)[name = tensor<string, []>("op_2807_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_849_cast_fp16 = add(x = input_837_cast_fp16, y = var_2807_cast_fp16)[name = tensor<string, []>("input_849_cast_fp16")];
+            tensor<int32, [1]> input_851_axes_0 = const()[name = tensor<string, []>("input_851_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_15_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_15_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395521792)))];
+            tensor<fp16, [1024]> model_layers_15_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_15_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395523904)))];
+            tensor<fp16, [1, 126, 1024]> input_851_cast_fp16 = layer_norm(axes = input_851_axes_0, beta = model_layers_15_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_15_norm_out_weight_to_fp16, x = input_849_cast_fp16)[name = tensor<string, []>("input_851_cast_fp16")];
+            tensor<int32, [1]> input_853_axes_0 = const()[name = tensor<string, []>("input_853_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395526016)))];
+            tensor<fp16, [1024]> model_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395528128)))];
+            tensor<fp16, [1, 126, 1024]> input_853_cast_fp16 = layer_norm(axes = input_853_axes_0, beta = model_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_16_norm_feed_forward1_weight_to_fp16, x = input_851_cast_fp16)[name = tensor<string, []>("input_853_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_16_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_16_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395530240))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(399724608))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_145_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_16_feed_forward1_linear1_weight_to_fp16_quantized, x = input_853_cast_fp16)[name = tensor<string, []>("linear_145_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_857_cast_fp16 = silu(x = linear_145_cast_fp16)[name = tensor<string, []>("input_857_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_16_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_16_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(399732864))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403927232))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_146_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_16_feed_forward1_linear2_weight_to_fp16_quantized, x = input_857_cast_fp16)[name = tensor<string, []>("linear_146_cast_fp16")];
+            tensor<fp16, []> var_2835_to_fp16 = const()[name = tensor<string, []>("op_2835_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_2836_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_2835_to_fp16)[name = tensor<string, []>("op_2836_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_863_cast_fp16 = add(x = input_851_cast_fp16, y = var_2836_cast_fp16)[name = tensor<string, []>("input_863_cast_fp16")];
+            tensor<int32, [1]> query_33_axes_0 = const()[name = tensor<string, []>("query_33_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_16_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_16_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403929344)))];
+            tensor<fp16, [1024]> model_layers_16_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_16_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403931456)))];
+            tensor<fp16, [1, 126, 1024]> query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = model_layers_16_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_16_norm_self_att_weight_to_fp16, x = input_863_cast_fp16)[name = tensor<string, []>("query_33_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_16_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_16_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403933568))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404982208))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_147_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_16_self_attn_linear_q_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = tensor<string, []>("linear_147_cast_fp16")];
+            tensor<int32, [4]> var_2852 = const()[name = tensor<string, []>("op_2852"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_97_cast_fp16 = reshape(shape = var_2852, x = linear_147_cast_fp16)[name = tensor<string, []>("q_97_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_16_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_16_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404984320))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(406032960))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_148_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_16_self_attn_linear_k_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = tensor<string, []>("linear_148_cast_fp16")];
+            tensor<int32, [4]> var_2856 = const()[name = tensor<string, []>("op_2856"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_65_cast_fp16 = reshape(shape = var_2856, x = linear_148_cast_fp16)[name = tensor<string, []>("k_65_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_16_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_16_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(406035072))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407083712))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_149_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_16_self_attn_linear_v_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = tensor<string, []>("linear_149_cast_fp16")];
+            tensor<int32, [4]> var_2860 = const()[name = tensor<string, []>("op_2860"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_33_cast_fp16 = reshape(shape = var_2860, x = linear_149_cast_fp16)[name = tensor<string, []>("v_33_cast_fp16")];
+            tensor<int32, [4]> value_33_perm_0 = const()[name = tensor<string, []>("value_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_16_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_16_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407085824))), scale = tensor<fp16, [8]>([0x1.a14p-8, 0x1.64cp-7, 0x1.3b8p-8, 0x1.a18p-8, 0x1.5ap-7, 0x1.384p-7, 0x1.61cp-8, 0x1.4b4p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_2872_cast_fp16 = add(x = q_97_cast_fp16, y = model_layers_16_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_2872_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_16_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_16_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407086912))), scale = tensor<fp16, [8]>([0x1.3d4p-8, 0x1.b7p-9, 0x1.2p-8, 0x1.2b4p-7, 0x1.f84p-8, 0x1.18p-7, 0x1.82p-8, 0x1.2f8p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_2874_cast_fp16 = add(x = q_97_cast_fp16, y = model_layers_16_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_2874_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_33_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_359_transpose_x_0 = const()[name = tensor<string, []>("x_359_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_359_transpose_y_0 = const()[name = tensor<string, []>("x_359_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_2876_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2876_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407088000))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407345088))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_2874_cast_fp16)[name = tensor<string, []>("transpose_199")];
+            tensor<fp16, [1, 8, 126, 251]> x_359_cast_fp16 = matmul(transpose_x = x_359_transpose_x_0, transpose_y = x_359_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = op_2876_to_fp16_quantized)[name = tensor<string, []>("x_359_cast_fp16")];
+            tensor<int32, [8]> x_361_pad_0 = const()[name = tensor<string, []>("x_361_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_361_mode_0 = const()[name = tensor<string, []>("x_361_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_174_to_fp16 = const()[name = tensor<string, []>("const_174_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_361_cast_fp16 = pad(constant_val = const_174_to_fp16, mode = x_361_mode_0, pad = x_361_pad_0, x = x_359_cast_fp16)[name = tensor<string, []>("x_361_cast_fp16")];
+            tensor<int32, [4]> var_2884 = const()[name = tensor<string, []>("op_2884"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_363_cast_fp16 = reshape(shape = var_2884, x = x_361_cast_fp16)[name = tensor<string, []>("x_363_cast_fp16")];
+            tensor<int32, [4]> var_2888_begin_0 = const()[name = tensor<string, []>("op_2888_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2888_end_0 = const()[name = tensor<string, []>("op_2888_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_2888_end_mask_0 = const()[name = tensor<string, []>("op_2888_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_2888_cast_fp16 = slice_by_index(begin = var_2888_begin_0, end = var_2888_end_0, end_mask = var_2888_end_mask_0, x = x_363_cast_fp16)[name = tensor<string, []>("op_2888_cast_fp16")];
+            tensor<int32, [4]> var_2889 = const()[name = tensor<string, []>("op_2889"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_65_cast_fp16 = reshape(shape = var_2889, x = var_2888_cast_fp16)[name = tensor<string, []>("matrix_bd_65_cast_fp16")];
+            tensor<bool, []> matrix_ac_33_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_33_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_33_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_33_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_128_perm_0 = const()[name = tensor<string, []>("transpose_128_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_129_perm_0 = const()[name = tensor<string, []>("transpose_129_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = tensor<string, []>("transpose_197")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_128 = transpose(perm = transpose_128_perm_0, x = var_2872_cast_fp16)[name = tensor<string, []>("transpose_198")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_33_cast_fp16 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_128, y = transpose_129)[name = tensor<string, []>("matrix_ac_33_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_67_begin_0 = const()[name = tensor<string, []>("matrix_bd_67_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_67_end_0 = const()[name = tensor<string, []>("matrix_bd_67_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_67_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_67_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_67_cast_fp16 = slice_by_index(begin = matrix_bd_67_begin_0, end = matrix_bd_67_end_0, end_mask = matrix_bd_67_end_mask_0, x = matrix_bd_65_cast_fp16)[name = tensor<string, []>("matrix_bd_67_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_2898_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = tensor<string, []>("op_2898_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_65_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_65_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_65_cast_fp16 = mul(x = var_2898_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_65_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_67_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_67_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_2904_cast_fp16 = softmax(axis = var_25, x = scores_67_cast_fp16)[name = tensor<string, []>("op_2904_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_865_cast_fp16 = select(a = var_6_to_fp16, b = var_2904_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_865_cast_fp16")];
+            tensor<bool, []> x_365_transpose_x_0 = const()[name = tensor<string, []>("x_365_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_365_transpose_y_0 = const()[name = tensor<string, []>("x_365_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_33_cast_fp16)[name = tensor<string, []>("transpose_196")];
+            tensor<fp16, [1, 8, 126, 128]> x_365_cast_fp16 = matmul(transpose_x = x_365_transpose_x_0, transpose_y = x_365_transpose_y_0, x = input_865_cast_fp16, y = value_33_cast_fp16)[name = tensor<string, []>("x_365_cast_fp16")];
+            tensor<int32, [4]> var_2908_perm_0 = const()[name = tensor<string, []>("op_2908_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2909 = const()[name = tensor<string, []>("op_2909"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_2908_cast_fp16 = transpose(perm = var_2908_perm_0, x = x_365_cast_fp16)[name = tensor<string, []>("transpose_195")];
+            tensor<fp16, [1, 126, 1024]> input_867_cast_fp16 = reshape(shape = var_2909, x = var_2908_cast_fp16)[name = tensor<string, []>("input_867_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_16_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_16_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407345664))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408394304))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_151_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_16_self_attn_linear_out_weight_to_fp16_quantized, x = input_867_cast_fp16)[name = tensor<string, []>("linear_151_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_871_cast_fp16 = add(x = input_863_cast_fp16, y = linear_151_cast_fp16)[name = tensor<string, []>("input_871_cast_fp16")];
+            tensor<int32, [1]> x_369_axes_0 = const()[name = tensor<string, []>("x_369_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_16_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_16_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408396416)))];
+            tensor<fp16, [1024]> model_layers_16_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_16_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408398528)))];
+            tensor<fp16, [1, 126, 1024]> x_369_cast_fp16 = layer_norm(axes = x_369_axes_0, beta = model_layers_16_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_16_norm_conv_weight_to_fp16, x = input_871_cast_fp16)[name = tensor<string, []>("x_369_cast_fp16")];
+            tensor<int32, [3]> input_873_perm_0 = const()[name = tensor<string, []>("input_873_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_875_pad_type_0 = const()[name = tensor<string, []>("input_875_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_875_strides_0 = const()[name = tensor<string, []>("input_875_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_875_pad_0 = const()[name = tensor<string, []>("input_875_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_875_dilations_0 = const()[name = tensor<string, []>("input_875_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_875_groups_0 = const()[name = tensor<string, []>("input_875_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408400640))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(410497856))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_873_cast_fp16 = transpose(perm = input_873_perm_0, x = x_369_cast_fp16)[name = tensor<string, []>("transpose_194")];
+            tensor<fp16, [1, 2048, 126]> input_875_cast_fp16 = conv(dilations = input_875_dilations_0, groups = input_875_groups_0, pad = input_875_pad_0, pad_type = input_875_pad_type_0, strides = input_875_strides_0, weight = model_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_873_cast_fp16)[name = tensor<string, []>("input_875_cast_fp16")];
+            tensor<int32, []> x_371_split_num_splits_0 = const()[name = tensor<string, []>("x_371_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_371_split_axis_0 = const()[name = tensor<string, []>("x_371_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_371_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_371_split_cast_fp16_1 = split(axis = x_371_split_axis_0, num_splits = x_371_split_num_splits_0, x = input_875_cast_fp16)[name = tensor<string, []>("x_371_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_371_split_1_sigmoid_cast_fp16 = sigmoid(x = x_371_split_cast_fp16_1)[name = tensor<string, []>("x_371_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_371_cast_fp16 = mul(x = x_371_split_cast_fp16_0, y = x_371_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_371_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_877_cast_fp16 = select(a = var_6_to_fp16, b = x_371_cast_fp16, cond = var_323)[name = tensor<string, []>("input_877_cast_fp16")];
+            tensor<int32, [6]> input_879_pad_0 = const()[name = tensor<string, []>("input_879_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_879_mode_0 = const()[name = tensor<string, []>("input_879_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_177_to_fp16 = const()[name = tensor<string, []>("const_177_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_879_cast_fp16 = pad(constant_val = const_177_to_fp16, mode = input_879_mode_0, pad = input_879_pad_0, x = input_877_cast_fp16)[name = tensor<string, []>("input_879_cast_fp16")];
+            tensor<string, []> input_881_pad_type_0 = const()[name = tensor<string, []>("input_881_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_881_groups_0 = const()[name = tensor<string, []>("input_881_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_881_strides_0 = const()[name = tensor<string, []>("input_881_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_881_pad_0 = const()[name = tensor<string, []>("input_881_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_881_dilations_0 = const()[name = tensor<string, []>("input_881_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_280_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_280_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(410502016))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(410511296))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_281_to_fp16 = const()[name = tensor<string, []>("const_281_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(410513408)))];
+            tensor<fp16, [1, 1024, 126]> input_883_cast_fp16 = conv(bias = const_281_to_fp16, dilations = input_881_dilations_0, groups = input_881_groups_0, pad = input_881_pad_0, pad_type = input_881_pad_type_0, strides = input_881_strides_0, weight = const_280_to_fp16_quantized, x = input_879_cast_fp16)[name = tensor<string, []>("input_883_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_885_cast_fp16 = silu(x = input_883_cast_fp16)[name = tensor<string, []>("input_885_cast_fp16")];
+            tensor<string, []> x_373_pad_type_0 = const()[name = tensor<string, []>("x_373_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_373_strides_0 = const()[name = tensor<string, []>("x_373_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_373_pad_0 = const()[name = tensor<string, []>("x_373_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_373_dilations_0 = const()[name = tensor<string, []>("x_373_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_373_groups_0 = const()[name = tensor<string, []>("x_373_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(410515520))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411564160))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_373_cast_fp16 = conv(dilations = x_373_dilations_0, groups = x_373_groups_0, pad = x_373_pad_0, pad_type = x_373_pad_type_0, strides = x_373_strides_0, weight = model_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_885_cast_fp16)[name = tensor<string, []>("x_373_cast_fp16")];
+            tensor<int32, [3]> input_887_perm_0 = const()[name = tensor<string, []>("input_887_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_373_cast_fp16)[name = tensor<string, []>("transpose_193")];
+            tensor<fp16, [1, 126, 1024]> input_889_cast_fp16 = add(x = input_871_cast_fp16, y = input_887_cast_fp16)[name = tensor<string, []>("input_889_cast_fp16")];
+            tensor<int32, [1]> input_891_axes_0 = const()[name = tensor<string, []>("input_891_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411566272)))];
+            tensor<fp16, [1024]> model_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411568384)))];
+            tensor<fp16, [1, 126, 1024]> input_891_cast_fp16 = layer_norm(axes = input_891_axes_0, beta = model_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_16_norm_feed_forward2_weight_to_fp16, x = input_889_cast_fp16)[name = tensor<string, []>("input_891_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_16_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_16_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411570496))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(415764864))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_152_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_16_feed_forward2_linear1_weight_to_fp16_quantized, x = input_891_cast_fp16)[name = tensor<string, []>("linear_152_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_895_cast_fp16 = silu(x = linear_152_cast_fp16)[name = tensor<string, []>("input_895_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_16_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_16_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(415773120))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(419967488))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_153_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_16_feed_forward2_linear2_weight_to_fp16_quantized, x = input_895_cast_fp16)[name = tensor<string, []>("linear_153_cast_fp16")];
+            tensor<fp16, []> var_2969_to_fp16 = const()[name = tensor<string, []>("op_2969_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_2970_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_2969_to_fp16)[name = tensor<string, []>("op_2970_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_901_cast_fp16 = add(x = input_889_cast_fp16, y = var_2970_cast_fp16)[name = tensor<string, []>("input_901_cast_fp16")];
+            tensor<int32, [1]> input_903_axes_0 = const()[name = tensor<string, []>("input_903_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_16_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_16_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(419969600)))];
+            tensor<fp16, [1024]> model_layers_16_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_16_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(419971712)))];
+            tensor<fp16, [1, 126, 1024]> input_903_cast_fp16 = layer_norm(axes = input_903_axes_0, beta = model_layers_16_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_16_norm_out_weight_to_fp16, x = input_901_cast_fp16)[name = tensor<string, []>("input_903_cast_fp16")];
+            tensor<int32, [1]> input_905_axes_0 = const()[name = tensor<string, []>("input_905_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(419973824)))];
+            tensor<fp16, [1024]> model_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(419975936)))];
+            tensor<fp16, [1, 126, 1024]> input_905_cast_fp16 = layer_norm(axes = input_905_axes_0, beta = model_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_17_norm_feed_forward1_weight_to_fp16, x = input_903_cast_fp16)[name = tensor<string, []>("input_905_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_17_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_17_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(419978048))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(424172416))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_154_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_17_feed_forward1_linear1_weight_to_fp16_quantized, x = input_905_cast_fp16)[name = tensor<string, []>("linear_154_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_909_cast_fp16 = silu(x = linear_154_cast_fp16)[name = tensor<string, []>("input_909_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_17_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_17_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(424180672))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428375040))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_155_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_17_feed_forward1_linear2_weight_to_fp16_quantized, x = input_909_cast_fp16)[name = tensor<string, []>("linear_155_cast_fp16")];
+            tensor<fp16, []> var_2998_to_fp16 = const()[name = tensor<string, []>("op_2998_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_2999_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_2998_to_fp16)[name = tensor<string, []>("op_2999_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_915_cast_fp16 = add(x = input_903_cast_fp16, y = var_2999_cast_fp16)[name = tensor<string, []>("input_915_cast_fp16")];
+            tensor<int32, [1]> query_35_axes_0 = const()[name = tensor<string, []>("query_35_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_17_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_17_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428377152)))];
+            tensor<fp16, [1024]> model_layers_17_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_17_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428379264)))];
+            tensor<fp16, [1, 126, 1024]> query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = model_layers_17_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_17_norm_self_att_weight_to_fp16, x = input_915_cast_fp16)[name = tensor<string, []>("query_35_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_17_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_17_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428381376))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(429430016))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_156_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_17_self_attn_linear_q_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = tensor<string, []>("linear_156_cast_fp16")];
+            tensor<int32, [4]> var_3015 = const()[name = tensor<string, []>("op_3015"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_103_cast_fp16 = reshape(shape = var_3015, x = linear_156_cast_fp16)[name = tensor<string, []>("q_103_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_17_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_17_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(429432128))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430480768))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_157_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_17_self_attn_linear_k_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = tensor<string, []>("linear_157_cast_fp16")];
+            tensor<int32, [4]> var_3019 = const()[name = tensor<string, []>("op_3019"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_69_cast_fp16 = reshape(shape = var_3019, x = linear_157_cast_fp16)[name = tensor<string, []>("k_69_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_17_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_17_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430482880))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(431531520))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_158_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_17_self_attn_linear_v_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = tensor<string, []>("linear_158_cast_fp16")];
+            tensor<int32, [4]> var_3023 = const()[name = tensor<string, []>("op_3023"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_35_cast_fp16 = reshape(shape = var_3023, x = linear_158_cast_fp16)[name = tensor<string, []>("v_35_cast_fp16")];
+            tensor<int32, [4]> value_35_perm_0 = const()[name = tensor<string, []>("value_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_17_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_17_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(431533632))), scale = tensor<fp16, [8]>([0x1.ee4p-8, 0x1.d1cp-9, 0x1.9e4p-9, 0x1.7cp-8, 0x1.58p-8, 0x1.a4cp-7, 0x1.4e4p-8, 0x1.754p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_3035_cast_fp16 = add(x = q_103_cast_fp16, y = model_layers_17_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_3035_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_17_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_17_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(431534720))), scale = tensor<fp16, [8]>([0x1.d74p-8, 0x1.e74p-8, 0x1.3ecp-8, 0x1.0a8p-7, 0x1.fd8p-8, 0x1.aa8p-9, 0x1.0f4p-8, 0x1.d9p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_3037_cast_fp16 = add(x = q_103_cast_fp16, y = model_layers_17_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_3037_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_35_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_381_transpose_x_0 = const()[name = tensor<string, []>("x_381_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_381_transpose_y_0 = const()[name = tensor<string, []>("x_381_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_3039_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3039_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(431535808))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(431792896))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3037_cast_fp16)[name = tensor<string, []>("transpose_192")];
+            tensor<fp16, [1, 8, 126, 251]> x_381_cast_fp16 = matmul(transpose_x = x_381_transpose_x_0, transpose_y = x_381_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = op_3039_to_fp16_quantized)[name = tensor<string, []>("x_381_cast_fp16")];
+            tensor<int32, [8]> x_383_pad_0 = const()[name = tensor<string, []>("x_383_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_383_mode_0 = const()[name = tensor<string, []>("x_383_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_184_to_fp16 = const()[name = tensor<string, []>("const_184_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_383_cast_fp16 = pad(constant_val = const_184_to_fp16, mode = x_383_mode_0, pad = x_383_pad_0, x = x_381_cast_fp16)[name = tensor<string, []>("x_383_cast_fp16")];
+            tensor<int32, [4]> var_3047 = const()[name = tensor<string, []>("op_3047"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_385_cast_fp16 = reshape(shape = var_3047, x = x_383_cast_fp16)[name = tensor<string, []>("x_385_cast_fp16")];
+            tensor<int32, [4]> var_3051_begin_0 = const()[name = tensor<string, []>("op_3051_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3051_end_0 = const()[name = tensor<string, []>("op_3051_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_3051_end_mask_0 = const()[name = tensor<string, []>("op_3051_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_3051_cast_fp16 = slice_by_index(begin = var_3051_begin_0, end = var_3051_end_0, end_mask = var_3051_end_mask_0, x = x_385_cast_fp16)[name = tensor<string, []>("op_3051_cast_fp16")];
+            tensor<int32, [4]> var_3052 = const()[name = tensor<string, []>("op_3052"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_69_cast_fp16 = reshape(shape = var_3052, x = var_3051_cast_fp16)[name = tensor<string, []>("matrix_bd_69_cast_fp16")];
+            tensor<bool, []> matrix_ac_35_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_35_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_35_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_35_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_130_perm_0 = const()[name = tensor<string, []>("transpose_130_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_131_perm_0 = const()[name = tensor<string, []>("transpose_131_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = tensor<string, []>("transpose_190")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_130 = transpose(perm = transpose_130_perm_0, x = var_3035_cast_fp16)[name = tensor<string, []>("transpose_191")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_35_cast_fp16 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_130, y = transpose_131)[name = tensor<string, []>("matrix_ac_35_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_71_begin_0 = const()[name = tensor<string, []>("matrix_bd_71_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_71_end_0 = const()[name = tensor<string, []>("matrix_bd_71_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_71_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_71_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_71_cast_fp16 = slice_by_index(begin = matrix_bd_71_begin_0, end = matrix_bd_71_end_0, end_mask = matrix_bd_71_end_mask_0, x = matrix_bd_69_cast_fp16)[name = tensor<string, []>("matrix_bd_71_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_3061_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = tensor<string, []>("op_3061_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_69_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_69_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_69_cast_fp16 = mul(x = var_3061_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_69_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_71_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_71_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_3067_cast_fp16 = softmax(axis = var_25, x = scores_71_cast_fp16)[name = tensor<string, []>("op_3067_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_917_cast_fp16 = select(a = var_6_to_fp16, b = var_3067_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_917_cast_fp16")];
+            tensor<bool, []> x_387_transpose_x_0 = const()[name = tensor<string, []>("x_387_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_387_transpose_y_0 = const()[name = tensor<string, []>("x_387_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_35_cast_fp16)[name = tensor<string, []>("transpose_189")];
+            tensor<fp16, [1, 8, 126, 128]> x_387_cast_fp16 = matmul(transpose_x = x_387_transpose_x_0, transpose_y = x_387_transpose_y_0, x = input_917_cast_fp16, y = value_35_cast_fp16)[name = tensor<string, []>("x_387_cast_fp16")];
+            tensor<int32, [4]> var_3071_perm_0 = const()[name = tensor<string, []>("op_3071_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3072 = const()[name = tensor<string, []>("op_3072"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_3071_cast_fp16 = transpose(perm = var_3071_perm_0, x = x_387_cast_fp16)[name = tensor<string, []>("transpose_188")];
+            tensor<fp16, [1, 126, 1024]> input_919_cast_fp16 = reshape(shape = var_3072, x = var_3071_cast_fp16)[name = tensor<string, []>("input_919_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_17_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_17_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(431793472))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432842112))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_160_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_17_self_attn_linear_out_weight_to_fp16_quantized, x = input_919_cast_fp16)[name = tensor<string, []>("linear_160_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_923_cast_fp16 = add(x = input_915_cast_fp16, y = linear_160_cast_fp16)[name = tensor<string, []>("input_923_cast_fp16")];
+            tensor<int32, [1]> x_391_axes_0 = const()[name = tensor<string, []>("x_391_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_17_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_17_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432844224)))];
+            tensor<fp16, [1024]> model_layers_17_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_17_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432846336)))];
+            tensor<fp16, [1, 126, 1024]> x_391_cast_fp16 = layer_norm(axes = x_391_axes_0, beta = model_layers_17_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_17_norm_conv_weight_to_fp16, x = input_923_cast_fp16)[name = tensor<string, []>("x_391_cast_fp16")];
+            tensor<int32, [3]> input_925_perm_0 = const()[name = tensor<string, []>("input_925_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_927_pad_type_0 = const()[name = tensor<string, []>("input_927_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_927_strides_0 = const()[name = tensor<string, []>("input_927_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_927_pad_0 = const()[name = tensor<string, []>("input_927_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_927_dilations_0 = const()[name = tensor<string, []>("input_927_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_927_groups_0 = const()[name = tensor<string, []>("input_927_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432848448))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(434945664))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_925_cast_fp16 = transpose(perm = input_925_perm_0, x = x_391_cast_fp16)[name = tensor<string, []>("transpose_187")];
+            tensor<fp16, [1, 2048, 126]> input_927_cast_fp16 = conv(dilations = input_927_dilations_0, groups = input_927_groups_0, pad = input_927_pad_0, pad_type = input_927_pad_type_0, strides = input_927_strides_0, weight = model_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_925_cast_fp16)[name = tensor<string, []>("input_927_cast_fp16")];
+            tensor<int32, []> x_393_split_num_splits_0 = const()[name = tensor<string, []>("x_393_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_393_split_axis_0 = const()[name = tensor<string, []>("x_393_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_393_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_393_split_cast_fp16_1 = split(axis = x_393_split_axis_0, num_splits = x_393_split_num_splits_0, x = input_927_cast_fp16)[name = tensor<string, []>("x_393_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_393_split_1_sigmoid_cast_fp16 = sigmoid(x = x_393_split_cast_fp16_1)[name = tensor<string, []>("x_393_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_393_cast_fp16 = mul(x = x_393_split_cast_fp16_0, y = x_393_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_393_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_929_cast_fp16 = select(a = var_6_to_fp16, b = x_393_cast_fp16, cond = var_323)[name = tensor<string, []>("input_929_cast_fp16")];
+            tensor<int32, [6]> input_931_pad_0 = const()[name = tensor<string, []>("input_931_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_931_mode_0 = const()[name = tensor<string, []>("input_931_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_187_to_fp16 = const()[name = tensor<string, []>("const_187_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_931_cast_fp16 = pad(constant_val = const_187_to_fp16, mode = input_931_mode_0, pad = input_931_pad_0, x = input_929_cast_fp16)[name = tensor<string, []>("input_931_cast_fp16")];
+            tensor<string, []> input_933_pad_type_0 = const()[name = tensor<string, []>("input_933_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_933_groups_0 = const()[name = tensor<string, []>("input_933_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_933_strides_0 = const()[name = tensor<string, []>("input_933_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_933_pad_0 = const()[name = tensor<string, []>("input_933_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_933_dilations_0 = const()[name = tensor<string, []>("input_933_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_282_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_282_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(434949824))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(434959104))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_283_to_fp16 = const()[name = tensor<string, []>("const_283_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(434961216)))];
+            tensor<fp16, [1, 1024, 126]> input_935_cast_fp16 = conv(bias = const_283_to_fp16, dilations = input_933_dilations_0, groups = input_933_groups_0, pad = input_933_pad_0, pad_type = input_933_pad_type_0, strides = input_933_strides_0, weight = const_282_to_fp16_quantized, x = input_931_cast_fp16)[name = tensor<string, []>("input_935_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_937_cast_fp16 = silu(x = input_935_cast_fp16)[name = tensor<string, []>("input_937_cast_fp16")];
+            tensor<string, []> x_395_pad_type_0 = const()[name = tensor<string, []>("x_395_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_395_strides_0 = const()[name = tensor<string, []>("x_395_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_395_pad_0 = const()[name = tensor<string, []>("x_395_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_395_dilations_0 = const()[name = tensor<string, []>("x_395_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_395_groups_0 = const()[name = tensor<string, []>("x_395_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(434963328))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(436011968))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_395_cast_fp16 = conv(dilations = x_395_dilations_0, groups = x_395_groups_0, pad = x_395_pad_0, pad_type = x_395_pad_type_0, strides = x_395_strides_0, weight = model_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_937_cast_fp16)[name = tensor<string, []>("x_395_cast_fp16")];
+            tensor<int32, [3]> input_939_perm_0 = const()[name = tensor<string, []>("input_939_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_939_cast_fp16 = transpose(perm = input_939_perm_0, x = x_395_cast_fp16)[name = tensor<string, []>("transpose_186")];
+            tensor<fp16, [1, 126, 1024]> input_941_cast_fp16 = add(x = input_923_cast_fp16, y = input_939_cast_fp16)[name = tensor<string, []>("input_941_cast_fp16")];
+            tensor<int32, [1]> input_943_axes_0 = const()[name = tensor<string, []>("input_943_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(436014080)))];
+            tensor<fp16, [1024]> model_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(436016192)))];
+            tensor<fp16, [1, 126, 1024]> input_943_cast_fp16 = layer_norm(axes = input_943_axes_0, beta = model_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_17_norm_feed_forward2_weight_to_fp16, x = input_941_cast_fp16)[name = tensor<string, []>("input_943_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_17_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_17_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(436018304))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(440212672))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_161_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_17_feed_forward2_linear1_weight_to_fp16_quantized, x = input_943_cast_fp16)[name = tensor<string, []>("linear_161_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_947_cast_fp16 = silu(x = linear_161_cast_fp16)[name = tensor<string, []>("input_947_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_17_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_17_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(440220928))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444415296))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_162_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_17_feed_forward2_linear2_weight_to_fp16_quantized, x = input_947_cast_fp16)[name = tensor<string, []>("linear_162_cast_fp16")];
+            tensor<fp16, []> var_3132_to_fp16 = const()[name = tensor<string, []>("op_3132_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_3133_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_3132_to_fp16)[name = tensor<string, []>("op_3133_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_953_cast_fp16 = add(x = input_941_cast_fp16, y = var_3133_cast_fp16)[name = tensor<string, []>("input_953_cast_fp16")];
+            tensor<int32, [1]> input_955_axes_0 = const()[name = tensor<string, []>("input_955_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_17_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_17_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444417408)))];
+            tensor<fp16, [1024]> model_layers_17_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_17_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444419520)))];
+            tensor<fp16, [1, 126, 1024]> input_955_cast_fp16 = layer_norm(axes = input_955_axes_0, beta = model_layers_17_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_17_norm_out_weight_to_fp16, x = input_953_cast_fp16)[name = tensor<string, []>("input_955_cast_fp16")];
+            tensor<int32, [1]> input_957_axes_0 = const()[name = tensor<string, []>("input_957_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444421632)))];
+            tensor<fp16, [1024]> model_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444423744)))];
+            tensor<fp16, [1, 126, 1024]> input_957_cast_fp16 = layer_norm(axes = input_957_axes_0, beta = model_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_18_norm_feed_forward1_weight_to_fp16, x = input_955_cast_fp16)[name = tensor<string, []>("input_957_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_18_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_18_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444425856))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448620224))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_163_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_18_feed_forward1_linear1_weight_to_fp16_quantized, x = input_957_cast_fp16)[name = tensor<string, []>("linear_163_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_961_cast_fp16 = silu(x = linear_163_cast_fp16)[name = tensor<string, []>("input_961_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_18_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_18_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448628480))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452822848))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_164_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_18_feed_forward1_linear2_weight_to_fp16_quantized, x = input_961_cast_fp16)[name = tensor<string, []>("linear_164_cast_fp16")];
+            tensor<fp16, []> var_3161_to_fp16 = const()[name = tensor<string, []>("op_3161_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_3162_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3161_to_fp16)[name = tensor<string, []>("op_3162_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_967_cast_fp16 = add(x = input_955_cast_fp16, y = var_3162_cast_fp16)[name = tensor<string, []>("input_967_cast_fp16")];
+            tensor<int32, [1]> query_37_axes_0 = const()[name = tensor<string, []>("query_37_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_18_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_18_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452824960)))];
+            tensor<fp16, [1024]> model_layers_18_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_18_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452827072)))];
+            tensor<fp16, [1, 126, 1024]> query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = model_layers_18_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_18_norm_self_att_weight_to_fp16, x = input_967_cast_fp16)[name = tensor<string, []>("query_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_18_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_18_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452829184))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(453877824))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_165_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_18_self_attn_linear_q_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = tensor<string, []>("linear_165_cast_fp16")];
+            tensor<int32, [4]> var_3178 = const()[name = tensor<string, []>("op_3178"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_109_cast_fp16 = reshape(shape = var_3178, x = linear_165_cast_fp16)[name = tensor<string, []>("q_109_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_18_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_18_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(453879936))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454928576))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_166_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_18_self_attn_linear_k_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = tensor<string, []>("linear_166_cast_fp16")];
+            tensor<int32, [4]> var_3182 = const()[name = tensor<string, []>("op_3182"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_73_cast_fp16 = reshape(shape = var_3182, x = linear_166_cast_fp16)[name = tensor<string, []>("k_73_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_18_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_18_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454930688))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(455979328))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_167_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_18_self_attn_linear_v_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = tensor<string, []>("linear_167_cast_fp16")];
+            tensor<int32, [4]> var_3186 = const()[name = tensor<string, []>("op_3186"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_37_cast_fp16 = reshape(shape = var_3186, x = linear_167_cast_fp16)[name = tensor<string, []>("v_37_cast_fp16")];
+            tensor<int32, [4]> value_37_perm_0 = const()[name = tensor<string, []>("value_37_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_18_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_18_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(455981440))), scale = tensor<fp16, [8]>([0x1.7acp-8, 0x1.248p-8, 0x1.498p-8, 0x1.248p-7, 0x1.8cp-8, 0x1.8e4p-8, 0x1.17p-8, 0x1.9b4p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_3198_cast_fp16 = add(x = q_109_cast_fp16, y = model_layers_18_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_3198_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_18_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_18_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(455982528))), scale = tensor<fp16, [8]>([0x1.75p-9, 0x1.05cp-8, 0x1.3f8p-8, 0x1.a6p-8, 0x1.294p-8, 0x1.3fp-7, 0x1.168p-8, 0x1.ed8p-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_3200_cast_fp16 = add(x = q_109_cast_fp16, y = model_layers_18_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_3200_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_37_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_37_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_403_transpose_x_0 = const()[name = tensor<string, []>("x_403_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_403_transpose_y_0 = const()[name = tensor<string, []>("x_403_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_3202_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3202_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(455983616))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(456240704))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3200_cast_fp16)[name = tensor<string, []>("transpose_185")];
+            tensor<fp16, [1, 8, 126, 251]> x_403_cast_fp16 = matmul(transpose_x = x_403_transpose_x_0, transpose_y = x_403_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = op_3202_to_fp16_quantized)[name = tensor<string, []>("x_403_cast_fp16")];
+            tensor<int32, [8]> x_405_pad_0 = const()[name = tensor<string, []>("x_405_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_405_mode_0 = const()[name = tensor<string, []>("x_405_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_194_to_fp16 = const()[name = tensor<string, []>("const_194_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_405_cast_fp16 = pad(constant_val = const_194_to_fp16, mode = x_405_mode_0, pad = x_405_pad_0, x = x_403_cast_fp16)[name = tensor<string, []>("x_405_cast_fp16")];
+            tensor<int32, [4]> var_3210 = const()[name = tensor<string, []>("op_3210"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_407_cast_fp16 = reshape(shape = var_3210, x = x_405_cast_fp16)[name = tensor<string, []>("x_407_cast_fp16")];
+            tensor<int32, [4]> var_3214_begin_0 = const()[name = tensor<string, []>("op_3214_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3214_end_0 = const()[name = tensor<string, []>("op_3214_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_3214_end_mask_0 = const()[name = tensor<string, []>("op_3214_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_3214_cast_fp16 = slice_by_index(begin = var_3214_begin_0, end = var_3214_end_0, end_mask = var_3214_end_mask_0, x = x_407_cast_fp16)[name = tensor<string, []>("op_3214_cast_fp16")];
+            tensor<int32, [4]> var_3215 = const()[name = tensor<string, []>("op_3215"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_73_cast_fp16 = reshape(shape = var_3215, x = var_3214_cast_fp16)[name = tensor<string, []>("matrix_bd_73_cast_fp16")];
+            tensor<bool, []> matrix_ac_37_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_37_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_37_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_37_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_132_perm_0 = const()[name = tensor<string, []>("transpose_132_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_133_perm_0 = const()[name = tensor<string, []>("transpose_133_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = tensor<string, []>("transpose_183")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_132 = transpose(perm = transpose_132_perm_0, x = var_3198_cast_fp16)[name = tensor<string, []>("transpose_184")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_37_cast_fp16 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_132, y = transpose_133)[name = tensor<string, []>("matrix_ac_37_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_75_begin_0 = const()[name = tensor<string, []>("matrix_bd_75_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_75_end_0 = const()[name = tensor<string, []>("matrix_bd_75_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_75_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_75_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_75_cast_fp16 = slice_by_index(begin = matrix_bd_75_begin_0, end = matrix_bd_75_end_0, end_mask = matrix_bd_75_end_mask_0, x = matrix_bd_73_cast_fp16)[name = tensor<string, []>("matrix_bd_75_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_3224_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = tensor<string, []>("op_3224_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_73_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_73_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_73_cast_fp16 = mul(x = var_3224_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_73_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_75_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_75_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_3230_cast_fp16 = softmax(axis = var_25, x = scores_75_cast_fp16)[name = tensor<string, []>("op_3230_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_969_cast_fp16 = select(a = var_6_to_fp16, b = var_3230_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_969_cast_fp16")];
+            tensor<bool, []> x_409_transpose_x_0 = const()[name = tensor<string, []>("x_409_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_409_transpose_y_0 = const()[name = tensor<string, []>("x_409_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_37_cast_fp16)[name = tensor<string, []>("transpose_182")];
+            tensor<fp16, [1, 8, 126, 128]> x_409_cast_fp16 = matmul(transpose_x = x_409_transpose_x_0, transpose_y = x_409_transpose_y_0, x = input_969_cast_fp16, y = value_37_cast_fp16)[name = tensor<string, []>("x_409_cast_fp16")];
+            tensor<int32, [4]> var_3234_perm_0 = const()[name = tensor<string, []>("op_3234_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3235 = const()[name = tensor<string, []>("op_3235"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_3234_cast_fp16 = transpose(perm = var_3234_perm_0, x = x_409_cast_fp16)[name = tensor<string, []>("transpose_181")];
+            tensor<fp16, [1, 126, 1024]> input_971_cast_fp16 = reshape(shape = var_3235, x = var_3234_cast_fp16)[name = tensor<string, []>("input_971_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_18_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_18_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(456241280))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457289920))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_169_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_18_self_attn_linear_out_weight_to_fp16_quantized, x = input_971_cast_fp16)[name = tensor<string, []>("linear_169_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_975_cast_fp16 = add(x = input_967_cast_fp16, y = linear_169_cast_fp16)[name = tensor<string, []>("input_975_cast_fp16")];
+            tensor<int32, [1]> x_413_axes_0 = const()[name = tensor<string, []>("x_413_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_18_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_18_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457292032)))];
+            tensor<fp16, [1024]> model_layers_18_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_18_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457294144)))];
+            tensor<fp16, [1, 126, 1024]> x_413_cast_fp16 = layer_norm(axes = x_413_axes_0, beta = model_layers_18_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_18_norm_conv_weight_to_fp16, x = input_975_cast_fp16)[name = tensor<string, []>("x_413_cast_fp16")];
+            tensor<int32, [3]> input_977_perm_0 = const()[name = tensor<string, []>("input_977_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_979_pad_type_0 = const()[name = tensor<string, []>("input_979_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_979_strides_0 = const()[name = tensor<string, []>("input_979_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_979_pad_0 = const()[name = tensor<string, []>("input_979_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_979_dilations_0 = const()[name = tensor<string, []>("input_979_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_979_groups_0 = const()[name = tensor<string, []>("input_979_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457296256))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(459393472))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_977_cast_fp16 = transpose(perm = input_977_perm_0, x = x_413_cast_fp16)[name = tensor<string, []>("transpose_180")];
+            tensor<fp16, [1, 2048, 126]> input_979_cast_fp16 = conv(dilations = input_979_dilations_0, groups = input_979_groups_0, pad = input_979_pad_0, pad_type = input_979_pad_type_0, strides = input_979_strides_0, weight = model_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_977_cast_fp16)[name = tensor<string, []>("input_979_cast_fp16")];
+            tensor<int32, []> x_415_split_num_splits_0 = const()[name = tensor<string, []>("x_415_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_415_split_axis_0 = const()[name = tensor<string, []>("x_415_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_415_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_415_split_cast_fp16_1 = split(axis = x_415_split_axis_0, num_splits = x_415_split_num_splits_0, x = input_979_cast_fp16)[name = tensor<string, []>("x_415_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_415_split_1_sigmoid_cast_fp16 = sigmoid(x = x_415_split_cast_fp16_1)[name = tensor<string, []>("x_415_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_415_cast_fp16 = mul(x = x_415_split_cast_fp16_0, y = x_415_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_415_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_981_cast_fp16 = select(a = var_6_to_fp16, b = x_415_cast_fp16, cond = var_323)[name = tensor<string, []>("input_981_cast_fp16")];
+            tensor<int32, [6]> input_983_pad_0 = const()[name = tensor<string, []>("input_983_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_983_mode_0 = const()[name = tensor<string, []>("input_983_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_197_to_fp16 = const()[name = tensor<string, []>("const_197_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_983_cast_fp16 = pad(constant_val = const_197_to_fp16, mode = input_983_mode_0, pad = input_983_pad_0, x = input_981_cast_fp16)[name = tensor<string, []>("input_983_cast_fp16")];
+            tensor<string, []> input_985_pad_type_0 = const()[name = tensor<string, []>("input_985_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_985_groups_0 = const()[name = tensor<string, []>("input_985_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_985_strides_0 = const()[name = tensor<string, []>("input_985_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_985_pad_0 = const()[name = tensor<string, []>("input_985_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_985_dilations_0 = const()[name = tensor<string, []>("input_985_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_284_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_284_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(459397632))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(459406912))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_285_to_fp16 = const()[name = tensor<string, []>("const_285_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(459409024)))];
+            tensor<fp16, [1, 1024, 126]> input_987_cast_fp16 = conv(bias = const_285_to_fp16, dilations = input_985_dilations_0, groups = input_985_groups_0, pad = input_985_pad_0, pad_type = input_985_pad_type_0, strides = input_985_strides_0, weight = const_284_to_fp16_quantized, x = input_983_cast_fp16)[name = tensor<string, []>("input_987_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_989_cast_fp16 = silu(x = input_987_cast_fp16)[name = tensor<string, []>("input_989_cast_fp16")];
+            tensor<string, []> x_417_pad_type_0 = const()[name = tensor<string, []>("x_417_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_417_strides_0 = const()[name = tensor<string, []>("x_417_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_417_pad_0 = const()[name = tensor<string, []>("x_417_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_417_dilations_0 = const()[name = tensor<string, []>("x_417_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_417_groups_0 = const()[name = tensor<string, []>("x_417_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(459411136))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(460459776))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_417_cast_fp16 = conv(dilations = x_417_dilations_0, groups = x_417_groups_0, pad = x_417_pad_0, pad_type = x_417_pad_type_0, strides = x_417_strides_0, weight = model_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_989_cast_fp16)[name = tensor<string, []>("x_417_cast_fp16")];
+            tensor<int32, [3]> input_991_perm_0 = const()[name = tensor<string, []>("input_991_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_991_cast_fp16 = transpose(perm = input_991_perm_0, x = x_417_cast_fp16)[name = tensor<string, []>("transpose_179")];
+            tensor<fp16, [1, 126, 1024]> input_993_cast_fp16 = add(x = input_975_cast_fp16, y = input_991_cast_fp16)[name = tensor<string, []>("input_993_cast_fp16")];
+            tensor<int32, [1]> input_995_axes_0 = const()[name = tensor<string, []>("input_995_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(460461888)))];
+            tensor<fp16, [1024]> model_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(460464000)))];
+            tensor<fp16, [1, 126, 1024]> input_995_cast_fp16 = layer_norm(axes = input_995_axes_0, beta = model_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_18_norm_feed_forward2_weight_to_fp16, x = input_993_cast_fp16)[name = tensor<string, []>("input_995_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_18_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_18_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(460466112))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(464660480))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_170_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_18_feed_forward2_linear1_weight_to_fp16_quantized, x = input_995_cast_fp16)[name = tensor<string, []>("linear_170_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_999_cast_fp16 = silu(x = linear_170_cast_fp16)[name = tensor<string, []>("input_999_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_18_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_18_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(464668736))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468863104))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_171_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_18_feed_forward2_linear2_weight_to_fp16_quantized, x = input_999_cast_fp16)[name = tensor<string, []>("linear_171_cast_fp16")];
+            tensor<fp16, []> var_3295_to_fp16 = const()[name = tensor<string, []>("op_3295_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_3296_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_3295_to_fp16)[name = tensor<string, []>("op_3296_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1005_cast_fp16 = add(x = input_993_cast_fp16, y = var_3296_cast_fp16)[name = tensor<string, []>("input_1005_cast_fp16")];
+            tensor<int32, [1]> input_1007_axes_0 = const()[name = tensor<string, []>("input_1007_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_18_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_18_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468865216)))];
+            tensor<fp16, [1024]> model_layers_18_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_18_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468867328)))];
+            tensor<fp16, [1, 126, 1024]> input_1007_cast_fp16 = layer_norm(axes = input_1007_axes_0, beta = model_layers_18_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_18_norm_out_weight_to_fp16, x = input_1005_cast_fp16)[name = tensor<string, []>("input_1007_cast_fp16")];
+            tensor<int32, [1]> input_1009_axes_0 = const()[name = tensor<string, []>("input_1009_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468869440)))];
+            tensor<fp16, [1024]> model_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468871552)))];
+            tensor<fp16, [1, 126, 1024]> input_1009_cast_fp16 = layer_norm(axes = input_1009_axes_0, beta = model_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1007_cast_fp16)[name = tensor<string, []>("input_1009_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_19_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_19_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468873664))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(473068032))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_172_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_19_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1009_cast_fp16)[name = tensor<string, []>("linear_172_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_1013_cast_fp16 = silu(x = linear_172_cast_fp16)[name = tensor<string, []>("input_1013_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_19_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_19_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(473076288))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477270656))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_173_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_19_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1013_cast_fp16)[name = tensor<string, []>("linear_173_cast_fp16")];
+            tensor<fp16, []> var_3324_to_fp16 = const()[name = tensor<string, []>("op_3324_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_3325_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3324_to_fp16)[name = tensor<string, []>("op_3325_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1019_cast_fp16 = add(x = input_1007_cast_fp16, y = var_3325_cast_fp16)[name = tensor<string, []>("input_1019_cast_fp16")];
+            tensor<int32, [1]> query_39_axes_0 = const()[name = tensor<string, []>("query_39_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_19_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_19_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477272768)))];
+            tensor<fp16, [1024]> model_layers_19_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_19_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477274880)))];
+            tensor<fp16, [1, 126, 1024]> query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = model_layers_19_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_19_norm_self_att_weight_to_fp16, x = input_1019_cast_fp16)[name = tensor<string, []>("query_39_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_19_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_19_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477276992))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478325632))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_174_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_19_self_attn_linear_q_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = tensor<string, []>("linear_174_cast_fp16")];
+            tensor<int32, [4]> var_3341 = const()[name = tensor<string, []>("op_3341"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_115_cast_fp16 = reshape(shape = var_3341, x = linear_174_cast_fp16)[name = tensor<string, []>("q_115_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_19_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_19_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478327744))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479376384))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_175_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_19_self_attn_linear_k_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = tensor<string, []>("linear_175_cast_fp16")];
+            tensor<int32, [4]> var_3345 = const()[name = tensor<string, []>("op_3345"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_77_cast_fp16 = reshape(shape = var_3345, x = linear_175_cast_fp16)[name = tensor<string, []>("k_77_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_19_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_19_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479378496))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480427136))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_176_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_19_self_attn_linear_v_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = tensor<string, []>("linear_176_cast_fp16")];
+            tensor<int32, [4]> var_3349 = const()[name = tensor<string, []>("op_3349"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_39_cast_fp16 = reshape(shape = var_3349, x = linear_176_cast_fp16)[name = tensor<string, []>("v_39_cast_fp16")];
+            tensor<int32, [4]> value_39_perm_0 = const()[name = tensor<string, []>("value_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_19_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_19_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480429248))), scale = tensor<fp16, [8]>([0x1.ddp-8, 0x1.a74p-8, 0x1.7ccp-8, 0x1.92p-8, 0x1.568p-8, 0x1.aap-8, 0x1.35p-7, 0x1.274p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_3361_cast_fp16 = add(x = q_115_cast_fp16, y = model_layers_19_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_3361_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_19_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_19_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480430336))), scale = tensor<fp16, [8]>([0x1.3c4p-8, 0x1.554p-8, 0x1.0c4p-8, 0x1.dbcp-8, 0x1.2cp-8, 0x1.d88p-8, 0x1.b44p-8, 0x1.81cp-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_3363_cast_fp16 = add(x = q_115_cast_fp16, y = model_layers_19_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_3363_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_39_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_425_transpose_x_0 = const()[name = tensor<string, []>("x_425_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_425_transpose_y_0 = const()[name = tensor<string, []>("x_425_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_3365_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3365_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480431424))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480688512))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3363_cast_fp16)[name = tensor<string, []>("transpose_178")];
+            tensor<fp16, [1, 8, 126, 251]> x_425_cast_fp16 = matmul(transpose_x = x_425_transpose_x_0, transpose_y = x_425_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = op_3365_to_fp16_quantized)[name = tensor<string, []>("x_425_cast_fp16")];
+            tensor<int32, [8]> x_427_pad_0 = const()[name = tensor<string, []>("x_427_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_427_mode_0 = const()[name = tensor<string, []>("x_427_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_204_to_fp16 = const()[name = tensor<string, []>("const_204_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_427_cast_fp16 = pad(constant_val = const_204_to_fp16, mode = x_427_mode_0, pad = x_427_pad_0, x = x_425_cast_fp16)[name = tensor<string, []>("x_427_cast_fp16")];
+            tensor<int32, [4]> var_3373 = const()[name = tensor<string, []>("op_3373"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_429_cast_fp16 = reshape(shape = var_3373, x = x_427_cast_fp16)[name = tensor<string, []>("x_429_cast_fp16")];
+            tensor<int32, [4]> var_3377_begin_0 = const()[name = tensor<string, []>("op_3377_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3377_end_0 = const()[name = tensor<string, []>("op_3377_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_3377_end_mask_0 = const()[name = tensor<string, []>("op_3377_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_3377_cast_fp16 = slice_by_index(begin = var_3377_begin_0, end = var_3377_end_0, end_mask = var_3377_end_mask_0, x = x_429_cast_fp16)[name = tensor<string, []>("op_3377_cast_fp16")];
+            tensor<int32, [4]> var_3378 = const()[name = tensor<string, []>("op_3378"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_77_cast_fp16 = reshape(shape = var_3378, x = var_3377_cast_fp16)[name = tensor<string, []>("matrix_bd_77_cast_fp16")];
+            tensor<bool, []> matrix_ac_39_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_39_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_39_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_39_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_134_perm_0 = const()[name = tensor<string, []>("transpose_134_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_135_perm_0 = const()[name = tensor<string, []>("transpose_135_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = tensor<string, []>("transpose_176")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_134 = transpose(perm = transpose_134_perm_0, x = var_3361_cast_fp16)[name = tensor<string, []>("transpose_177")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_39_cast_fp16 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_134, y = transpose_135)[name = tensor<string, []>("matrix_ac_39_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_79_begin_0 = const()[name = tensor<string, []>("matrix_bd_79_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_79_end_0 = const()[name = tensor<string, []>("matrix_bd_79_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_79_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_79_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_79_cast_fp16 = slice_by_index(begin = matrix_bd_79_begin_0, end = matrix_bd_79_end_0, end_mask = matrix_bd_79_end_mask_0, x = matrix_bd_77_cast_fp16)[name = tensor<string, []>("matrix_bd_79_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_3387_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = tensor<string, []>("op_3387_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_77_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_77_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_77_cast_fp16 = mul(x = var_3387_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_77_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_79_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_79_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_3393_cast_fp16 = softmax(axis = var_25, x = scores_79_cast_fp16)[name = tensor<string, []>("op_3393_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_1021_cast_fp16 = select(a = var_6_to_fp16, b = var_3393_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_1021_cast_fp16")];
+            tensor<bool, []> x_431_transpose_x_0 = const()[name = tensor<string, []>("x_431_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_431_transpose_y_0 = const()[name = tensor<string, []>("x_431_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_39_cast_fp16)[name = tensor<string, []>("transpose_175")];
+            tensor<fp16, [1, 8, 126, 128]> x_431_cast_fp16 = matmul(transpose_x = x_431_transpose_x_0, transpose_y = x_431_transpose_y_0, x = input_1021_cast_fp16, y = value_39_cast_fp16)[name = tensor<string, []>("x_431_cast_fp16")];
+            tensor<int32, [4]> var_3397_perm_0 = const()[name = tensor<string, []>("op_3397_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3398 = const()[name = tensor<string, []>("op_3398"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_3397_cast_fp16 = transpose(perm = var_3397_perm_0, x = x_431_cast_fp16)[name = tensor<string, []>("transpose_174")];
+            tensor<fp16, [1, 126, 1024]> input_1023_cast_fp16 = reshape(shape = var_3398, x = var_3397_cast_fp16)[name = tensor<string, []>("input_1023_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_19_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_19_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480689088))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481737728))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_178_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_19_self_attn_linear_out_weight_to_fp16_quantized, x = input_1023_cast_fp16)[name = tensor<string, []>("linear_178_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1027_cast_fp16 = add(x = input_1019_cast_fp16, y = linear_178_cast_fp16)[name = tensor<string, []>("input_1027_cast_fp16")];
+            tensor<int32, [1]> x_435_axes_0 = const()[name = tensor<string, []>("x_435_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_19_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_19_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481739840)))];
+            tensor<fp16, [1024]> model_layers_19_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_19_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481741952)))];
+            tensor<fp16, [1, 126, 1024]> x_435_cast_fp16 = layer_norm(axes = x_435_axes_0, beta = model_layers_19_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_19_norm_conv_weight_to_fp16, x = input_1027_cast_fp16)[name = tensor<string, []>("x_435_cast_fp16")];
+            tensor<int32, [3]> input_1029_perm_0 = const()[name = tensor<string, []>("input_1029_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_1031_pad_type_0 = const()[name = tensor<string, []>("input_1031_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_1031_strides_0 = const()[name = tensor<string, []>("input_1031_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1031_pad_0 = const()[name = tensor<string, []>("input_1031_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1031_dilations_0 = const()[name = tensor<string, []>("input_1031_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_1031_groups_0 = const()[name = tensor<string, []>("input_1031_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481744064))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483841280))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_1029_cast_fp16 = transpose(perm = input_1029_perm_0, x = x_435_cast_fp16)[name = tensor<string, []>("transpose_173")];
+            tensor<fp16, [1, 2048, 126]> input_1031_cast_fp16 = conv(dilations = input_1031_dilations_0, groups = input_1031_groups_0, pad = input_1031_pad_0, pad_type = input_1031_pad_type_0, strides = input_1031_strides_0, weight = model_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1029_cast_fp16)[name = tensor<string, []>("input_1031_cast_fp16")];
+            tensor<int32, []> x_437_split_num_splits_0 = const()[name = tensor<string, []>("x_437_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_437_split_axis_0 = const()[name = tensor<string, []>("x_437_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_437_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_437_split_cast_fp16_1 = split(axis = x_437_split_axis_0, num_splits = x_437_split_num_splits_0, x = input_1031_cast_fp16)[name = tensor<string, []>("x_437_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_437_split_1_sigmoid_cast_fp16 = sigmoid(x = x_437_split_cast_fp16_1)[name = tensor<string, []>("x_437_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_437_cast_fp16 = mul(x = x_437_split_cast_fp16_0, y = x_437_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_437_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_1033_cast_fp16 = select(a = var_6_to_fp16, b = x_437_cast_fp16, cond = var_323)[name = tensor<string, []>("input_1033_cast_fp16")];
+            tensor<int32, [6]> input_1035_pad_0 = const()[name = tensor<string, []>("input_1035_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_1035_mode_0 = const()[name = tensor<string, []>("input_1035_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_207_to_fp16 = const()[name = tensor<string, []>("const_207_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_1035_cast_fp16 = pad(constant_val = const_207_to_fp16, mode = input_1035_mode_0, pad = input_1035_pad_0, x = input_1033_cast_fp16)[name = tensor<string, []>("input_1035_cast_fp16")];
+            tensor<string, []> input_1037_pad_type_0 = const()[name = tensor<string, []>("input_1037_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_1037_groups_0 = const()[name = tensor<string, []>("input_1037_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_1037_strides_0 = const()[name = tensor<string, []>("input_1037_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1037_pad_0 = const()[name = tensor<string, []>("input_1037_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1037_dilations_0 = const()[name = tensor<string, []>("input_1037_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_286_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_286_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483845440))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483854720))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_287_to_fp16 = const()[name = tensor<string, []>("const_287_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483856832)))];
+            tensor<fp16, [1, 1024, 126]> input_1039_cast_fp16 = conv(bias = const_287_to_fp16, dilations = input_1037_dilations_0, groups = input_1037_groups_0, pad = input_1037_pad_0, pad_type = input_1037_pad_type_0, strides = input_1037_strides_0, weight = const_286_to_fp16_quantized, x = input_1035_cast_fp16)[name = tensor<string, []>("input_1039_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_1041_cast_fp16 = silu(x = input_1039_cast_fp16)[name = tensor<string, []>("input_1041_cast_fp16")];
+            tensor<string, []> x_439_pad_type_0 = const()[name = tensor<string, []>("x_439_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_439_strides_0 = const()[name = tensor<string, []>("x_439_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_439_pad_0 = const()[name = tensor<string, []>("x_439_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_439_dilations_0 = const()[name = tensor<string, []>("x_439_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_439_groups_0 = const()[name = tensor<string, []>("x_439_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483858944))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(484907584))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_439_cast_fp16 = conv(dilations = x_439_dilations_0, groups = x_439_groups_0, pad = x_439_pad_0, pad_type = x_439_pad_type_0, strides = x_439_strides_0, weight = model_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1041_cast_fp16)[name = tensor<string, []>("x_439_cast_fp16")];
+            tensor<int32, [3]> input_1043_perm_0 = const()[name = tensor<string, []>("input_1043_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_1043_cast_fp16 = transpose(perm = input_1043_perm_0, x = x_439_cast_fp16)[name = tensor<string, []>("transpose_172")];
+            tensor<fp16, [1, 126, 1024]> input_1045_cast_fp16 = add(x = input_1027_cast_fp16, y = input_1043_cast_fp16)[name = tensor<string, []>("input_1045_cast_fp16")];
+            tensor<int32, [1]> input_1047_axes_0 = const()[name = tensor<string, []>("input_1047_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(484909696)))];
+            tensor<fp16, [1024]> model_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(484911808)))];
+            tensor<fp16, [1, 126, 1024]> input_1047_cast_fp16 = layer_norm(axes = input_1047_axes_0, beta = model_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1045_cast_fp16)[name = tensor<string, []>("input_1047_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_19_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_19_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(484913920))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(489108288))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_179_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_19_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1047_cast_fp16)[name = tensor<string, []>("linear_179_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_1051_cast_fp16 = silu(x = linear_179_cast_fp16)[name = tensor<string, []>("input_1051_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_19_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_19_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(489116544))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(493310912))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_180_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_19_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1051_cast_fp16)[name = tensor<string, []>("linear_180_cast_fp16")];
+            tensor<fp16, []> var_3458_to_fp16 = const()[name = tensor<string, []>("op_3458_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_3459_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_3458_to_fp16)[name = tensor<string, []>("op_3459_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1057_cast_fp16 = add(x = input_1045_cast_fp16, y = var_3459_cast_fp16)[name = tensor<string, []>("input_1057_cast_fp16")];
+            tensor<int32, [1]> input_1059_axes_0 = const()[name = tensor<string, []>("input_1059_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_19_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_19_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(493313024)))];
+            tensor<fp16, [1024]> model_layers_19_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_19_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(493315136)))];
+            tensor<fp16, [1, 126, 1024]> input_1059_cast_fp16 = layer_norm(axes = input_1059_axes_0, beta = model_layers_19_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_19_norm_out_weight_to_fp16, x = input_1057_cast_fp16)[name = tensor<string, []>("input_1059_cast_fp16")];
+            tensor<int32, [1]> input_1061_axes_0 = const()[name = tensor<string, []>("input_1061_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(493317248)))];
+            tensor<fp16, [1024]> model_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(493319360)))];
+            tensor<fp16, [1, 126, 1024]> input_1061_cast_fp16 = layer_norm(axes = input_1061_axes_0, beta = model_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1059_cast_fp16)[name = tensor<string, []>("input_1061_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_20_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_20_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(493321472))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(497515840))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_181_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_20_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1061_cast_fp16)[name = tensor<string, []>("linear_181_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_1065_cast_fp16 = silu(x = linear_181_cast_fp16)[name = tensor<string, []>("input_1065_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_20_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_20_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(497524096))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(501718464))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_182_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_20_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1065_cast_fp16)[name = tensor<string, []>("linear_182_cast_fp16")];
+            tensor<fp16, []> var_3487_to_fp16 = const()[name = tensor<string, []>("op_3487_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_3488_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3487_to_fp16)[name = tensor<string, []>("op_3488_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1071_cast_fp16 = add(x = input_1059_cast_fp16, y = var_3488_cast_fp16)[name = tensor<string, []>("input_1071_cast_fp16")];
+            tensor<int32, [1]> query_41_axes_0 = const()[name = tensor<string, []>("query_41_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_20_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_20_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(501720576)))];
+            tensor<fp16, [1024]> model_layers_20_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_20_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(501722688)))];
+            tensor<fp16, [1, 126, 1024]> query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = model_layers_20_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_20_norm_self_att_weight_to_fp16, x = input_1071_cast_fp16)[name = tensor<string, []>("query_41_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_20_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_20_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(501724800))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(502773440))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_183_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_20_self_attn_linear_q_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = tensor<string, []>("linear_183_cast_fp16")];
+            tensor<int32, [4]> var_3504 = const()[name = tensor<string, []>("op_3504"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_121_cast_fp16 = reshape(shape = var_3504, x = linear_183_cast_fp16)[name = tensor<string, []>("q_121_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_20_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_20_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(502775552))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503824192))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_184_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_20_self_attn_linear_k_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = tensor<string, []>("linear_184_cast_fp16")];
+            tensor<int32, [4]> var_3508 = const()[name = tensor<string, []>("op_3508"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_81_cast_fp16 = reshape(shape = var_3508, x = linear_184_cast_fp16)[name = tensor<string, []>("k_81_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_20_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_20_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503826304))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504874944))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_185_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_20_self_attn_linear_v_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = tensor<string, []>("linear_185_cast_fp16")];
+            tensor<int32, [4]> var_3512 = const()[name = tensor<string, []>("op_3512"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_41_cast_fp16 = reshape(shape = var_3512, x = linear_185_cast_fp16)[name = tensor<string, []>("v_41_cast_fp16")];
+            tensor<int32, [4]> value_41_perm_0 = const()[name = tensor<string, []>("value_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_20_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_20_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504877056))), scale = tensor<fp16, [8]>([0x1.43p-8, 0x1.448p-7, 0x1.2ecp-8, 0x1.3ecp-8, 0x1.e38p-8, 0x1.36p-7, 0x1.03p-8, 0x1.514p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_3524_cast_fp16 = add(x = q_121_cast_fp16, y = model_layers_20_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_3524_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_20_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_20_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504878144))), scale = tensor<fp16, [8]>([0x1.18cp-8, 0x1.0b8p-8, 0x1.aa4p-8, 0x1.714p-9, 0x1.074p-9, 0x1.0fp-7, 0x1.d9p-8, 0x1.3ccp-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_3526_cast_fp16 = add(x = q_121_cast_fp16, y = model_layers_20_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_3526_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_41_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_447_transpose_x_0 = const()[name = tensor<string, []>("x_447_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_447_transpose_y_0 = const()[name = tensor<string, []>("x_447_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_3528_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3528_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504879232))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505136320))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_3526_cast_fp16)[name = tensor<string, []>("transpose_171")];
+            tensor<fp16, [1, 8, 126, 251]> x_447_cast_fp16 = matmul(transpose_x = x_447_transpose_x_0, transpose_y = x_447_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = op_3528_to_fp16_quantized)[name = tensor<string, []>("x_447_cast_fp16")];
+            tensor<int32, [8]> x_449_pad_0 = const()[name = tensor<string, []>("x_449_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_449_mode_0 = const()[name = tensor<string, []>("x_449_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_214_to_fp16 = const()[name = tensor<string, []>("const_214_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_449_cast_fp16 = pad(constant_val = const_214_to_fp16, mode = x_449_mode_0, pad = x_449_pad_0, x = x_447_cast_fp16)[name = tensor<string, []>("x_449_cast_fp16")];
+            tensor<int32, [4]> var_3536 = const()[name = tensor<string, []>("op_3536"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_451_cast_fp16 = reshape(shape = var_3536, x = x_449_cast_fp16)[name = tensor<string, []>("x_451_cast_fp16")];
+            tensor<int32, [4]> var_3540_begin_0 = const()[name = tensor<string, []>("op_3540_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3540_end_0 = const()[name = tensor<string, []>("op_3540_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_3540_end_mask_0 = const()[name = tensor<string, []>("op_3540_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_3540_cast_fp16 = slice_by_index(begin = var_3540_begin_0, end = var_3540_end_0, end_mask = var_3540_end_mask_0, x = x_451_cast_fp16)[name = tensor<string, []>("op_3540_cast_fp16")];
+            tensor<int32, [4]> var_3541 = const()[name = tensor<string, []>("op_3541"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_81_cast_fp16 = reshape(shape = var_3541, x = var_3540_cast_fp16)[name = tensor<string, []>("matrix_bd_81_cast_fp16")];
+            tensor<bool, []> matrix_ac_41_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_41_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_41_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_41_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_136_perm_0 = const()[name = tensor<string, []>("transpose_136_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_137_perm_0 = const()[name = tensor<string, []>("transpose_137_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = tensor<string, []>("transpose_169")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_136 = transpose(perm = transpose_136_perm_0, x = var_3524_cast_fp16)[name = tensor<string, []>("transpose_170")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_41_cast_fp16 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_136, y = transpose_137)[name = tensor<string, []>("matrix_ac_41_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_83_begin_0 = const()[name = tensor<string, []>("matrix_bd_83_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_83_end_0 = const()[name = tensor<string, []>("matrix_bd_83_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_83_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_83_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_83_cast_fp16 = slice_by_index(begin = matrix_bd_83_begin_0, end = matrix_bd_83_end_0, end_mask = matrix_bd_83_end_mask_0, x = matrix_bd_81_cast_fp16)[name = tensor<string, []>("matrix_bd_83_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_3550_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = tensor<string, []>("op_3550_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_81_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_81_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_81_cast_fp16 = mul(x = var_3550_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_81_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_83_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_83_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_3556_cast_fp16 = softmax(axis = var_25, x = scores_83_cast_fp16)[name = tensor<string, []>("op_3556_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_1073_cast_fp16 = select(a = var_6_to_fp16, b = var_3556_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_1073_cast_fp16")];
+            tensor<bool, []> x_453_transpose_x_0 = const()[name = tensor<string, []>("x_453_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_453_transpose_y_0 = const()[name = tensor<string, []>("x_453_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_41_cast_fp16)[name = tensor<string, []>("transpose_168")];
+            tensor<fp16, [1, 8, 126, 128]> x_453_cast_fp16 = matmul(transpose_x = x_453_transpose_x_0, transpose_y = x_453_transpose_y_0, x = input_1073_cast_fp16, y = value_41_cast_fp16)[name = tensor<string, []>("x_453_cast_fp16")];
+            tensor<int32, [4]> var_3560_perm_0 = const()[name = tensor<string, []>("op_3560_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3561 = const()[name = tensor<string, []>("op_3561"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_3560_cast_fp16 = transpose(perm = var_3560_perm_0, x = x_453_cast_fp16)[name = tensor<string, []>("transpose_167")];
+            tensor<fp16, [1, 126, 1024]> input_1075_cast_fp16 = reshape(shape = var_3561, x = var_3560_cast_fp16)[name = tensor<string, []>("input_1075_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_20_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_20_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505136896))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506185536))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_187_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_20_self_attn_linear_out_weight_to_fp16_quantized, x = input_1075_cast_fp16)[name = tensor<string, []>("linear_187_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1079_cast_fp16 = add(x = input_1071_cast_fp16, y = linear_187_cast_fp16)[name = tensor<string, []>("input_1079_cast_fp16")];
+            tensor<int32, [1]> x_457_axes_0 = const()[name = tensor<string, []>("x_457_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_20_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_20_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506187648)))];
+            tensor<fp16, [1024]> model_layers_20_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_20_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506189760)))];
+            tensor<fp16, [1, 126, 1024]> x_457_cast_fp16 = layer_norm(axes = x_457_axes_0, beta = model_layers_20_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_20_norm_conv_weight_to_fp16, x = input_1079_cast_fp16)[name = tensor<string, []>("x_457_cast_fp16")];
+            tensor<int32, [3]> input_1081_perm_0 = const()[name = tensor<string, []>("input_1081_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_1083_pad_type_0 = const()[name = tensor<string, []>("input_1083_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_1083_strides_0 = const()[name = tensor<string, []>("input_1083_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1083_pad_0 = const()[name = tensor<string, []>("input_1083_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1083_dilations_0 = const()[name = tensor<string, []>("input_1083_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_1083_groups_0 = const()[name = tensor<string, []>("input_1083_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506191872))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508289088))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_1081_cast_fp16 = transpose(perm = input_1081_perm_0, x = x_457_cast_fp16)[name = tensor<string, []>("transpose_166")];
+            tensor<fp16, [1, 2048, 126]> input_1083_cast_fp16 = conv(dilations = input_1083_dilations_0, groups = input_1083_groups_0, pad = input_1083_pad_0, pad_type = input_1083_pad_type_0, strides = input_1083_strides_0, weight = model_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1081_cast_fp16)[name = tensor<string, []>("input_1083_cast_fp16")];
+            tensor<int32, []> x_459_split_num_splits_0 = const()[name = tensor<string, []>("x_459_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_459_split_axis_0 = const()[name = tensor<string, []>("x_459_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_459_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_459_split_cast_fp16_1 = split(axis = x_459_split_axis_0, num_splits = x_459_split_num_splits_0, x = input_1083_cast_fp16)[name = tensor<string, []>("x_459_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_459_split_1_sigmoid_cast_fp16 = sigmoid(x = x_459_split_cast_fp16_1)[name = tensor<string, []>("x_459_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_459_cast_fp16 = mul(x = x_459_split_cast_fp16_0, y = x_459_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_459_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_1085_cast_fp16 = select(a = var_6_to_fp16, b = x_459_cast_fp16, cond = var_323)[name = tensor<string, []>("input_1085_cast_fp16")];
+            tensor<int32, [6]> input_1087_pad_0 = const()[name = tensor<string, []>("input_1087_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_1087_mode_0 = const()[name = tensor<string, []>("input_1087_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_217_to_fp16 = const()[name = tensor<string, []>("const_217_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_1087_cast_fp16 = pad(constant_val = const_217_to_fp16, mode = input_1087_mode_0, pad = input_1087_pad_0, x = input_1085_cast_fp16)[name = tensor<string, []>("input_1087_cast_fp16")];
+            tensor<string, []> input_1089_pad_type_0 = const()[name = tensor<string, []>("input_1089_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_1089_groups_0 = const()[name = tensor<string, []>("input_1089_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_1089_strides_0 = const()[name = tensor<string, []>("input_1089_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1089_pad_0 = const()[name = tensor<string, []>("input_1089_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1089_dilations_0 = const()[name = tensor<string, []>("input_1089_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_288_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_288_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508293248))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508302528))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_289_to_fp16 = const()[name = tensor<string, []>("const_289_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508304640)))];
+            tensor<fp16, [1, 1024, 126]> input_1091_cast_fp16 = conv(bias = const_289_to_fp16, dilations = input_1089_dilations_0, groups = input_1089_groups_0, pad = input_1089_pad_0, pad_type = input_1089_pad_type_0, strides = input_1089_strides_0, weight = const_288_to_fp16_quantized, x = input_1087_cast_fp16)[name = tensor<string, []>("input_1091_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_1093_cast_fp16 = silu(x = input_1091_cast_fp16)[name = tensor<string, []>("input_1093_cast_fp16")];
+            tensor<string, []> x_461_pad_type_0 = const()[name = tensor<string, []>("x_461_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_461_strides_0 = const()[name = tensor<string, []>("x_461_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_461_pad_0 = const()[name = tensor<string, []>("x_461_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_461_dilations_0 = const()[name = tensor<string, []>("x_461_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_461_groups_0 = const()[name = tensor<string, []>("x_461_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508306752))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(509355392))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_461_cast_fp16 = conv(dilations = x_461_dilations_0, groups = x_461_groups_0, pad = x_461_pad_0, pad_type = x_461_pad_type_0, strides = x_461_strides_0, weight = model_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1093_cast_fp16)[name = tensor<string, []>("x_461_cast_fp16")];
+            tensor<int32, [3]> input_1095_perm_0 = const()[name = tensor<string, []>("input_1095_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_1095_cast_fp16 = transpose(perm = input_1095_perm_0, x = x_461_cast_fp16)[name = tensor<string, []>("transpose_165")];
+            tensor<fp16, [1, 126, 1024]> input_1097_cast_fp16 = add(x = input_1079_cast_fp16, y = input_1095_cast_fp16)[name = tensor<string, []>("input_1097_cast_fp16")];
+            tensor<int32, [1]> input_1099_axes_0 = const()[name = tensor<string, []>("input_1099_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(509357504)))];
+            tensor<fp16, [1024]> model_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(509359616)))];
+            tensor<fp16, [1, 126, 1024]> input_1099_cast_fp16 = layer_norm(axes = input_1099_axes_0, beta = model_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1097_cast_fp16)[name = tensor<string, []>("input_1099_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_20_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_20_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(509361728))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513556096))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_188_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_20_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1099_cast_fp16)[name = tensor<string, []>("linear_188_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_1103_cast_fp16 = silu(x = linear_188_cast_fp16)[name = tensor<string, []>("input_1103_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_20_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_20_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513564352))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(517758720))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_189_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_20_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1103_cast_fp16)[name = tensor<string, []>("linear_189_cast_fp16")];
+            tensor<fp16, []> var_3621_to_fp16 = const()[name = tensor<string, []>("op_3621_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_3622_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_3621_to_fp16)[name = tensor<string, []>("op_3622_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1109_cast_fp16 = add(x = input_1097_cast_fp16, y = var_3622_cast_fp16)[name = tensor<string, []>("input_1109_cast_fp16")];
+            tensor<int32, [1]> input_1111_axes_0 = const()[name = tensor<string, []>("input_1111_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_20_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_20_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(517760832)))];
+            tensor<fp16, [1024]> model_layers_20_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_20_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(517762944)))];
+            tensor<fp16, [1, 126, 1024]> input_1111_cast_fp16 = layer_norm(axes = input_1111_axes_0, beta = model_layers_20_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_20_norm_out_weight_to_fp16, x = input_1109_cast_fp16)[name = tensor<string, []>("input_1111_cast_fp16")];
+            tensor<int32, [1]> input_1113_axes_0 = const()[name = tensor<string, []>("input_1113_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(517765056)))];
+            tensor<fp16, [1024]> model_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(517767168)))];
+            tensor<fp16, [1, 126, 1024]> input_1113_cast_fp16 = layer_norm(axes = input_1113_axes_0, beta = model_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1111_cast_fp16)[name = tensor<string, []>("input_1113_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_21_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_21_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(517769280))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(521963648))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_190_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_21_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1113_cast_fp16)[name = tensor<string, []>("linear_190_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_1117_cast_fp16 = silu(x = linear_190_cast_fp16)[name = tensor<string, []>("input_1117_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_21_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_21_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(521971904))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526166272))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_191_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_21_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1117_cast_fp16)[name = tensor<string, []>("linear_191_cast_fp16")];
+            tensor<fp16, []> var_3650_to_fp16 = const()[name = tensor<string, []>("op_3650_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_3651_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_3650_to_fp16)[name = tensor<string, []>("op_3651_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1123_cast_fp16 = add(x = input_1111_cast_fp16, y = var_3651_cast_fp16)[name = tensor<string, []>("input_1123_cast_fp16")];
+            tensor<int32, [1]> query_43_axes_0 = const()[name = tensor<string, []>("query_43_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_21_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_21_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526168384)))];
+            tensor<fp16, [1024]> model_layers_21_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_21_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526170496)))];
+            tensor<fp16, [1, 126, 1024]> query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = model_layers_21_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_21_norm_self_att_weight_to_fp16, x = input_1123_cast_fp16)[name = tensor<string, []>("query_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_21_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_21_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526172608))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527221248))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_192_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_21_self_attn_linear_q_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = tensor<string, []>("linear_192_cast_fp16")];
+            tensor<int32, [4]> var_3667 = const()[name = tensor<string, []>("op_3667"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_127_cast_fp16 = reshape(shape = var_3667, x = linear_192_cast_fp16)[name = tensor<string, []>("q_127_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_21_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_21_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527223360))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(528272000))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_193_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_21_self_attn_linear_k_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = tensor<string, []>("linear_193_cast_fp16")];
+            tensor<int32, [4]> var_3671 = const()[name = tensor<string, []>("op_3671"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_85_cast_fp16 = reshape(shape = var_3671, x = linear_193_cast_fp16)[name = tensor<string, []>("k_85_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_21_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_21_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(528274112))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529322752))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_194_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_21_self_attn_linear_v_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = tensor<string, []>("linear_194_cast_fp16")];
+            tensor<int32, [4]> var_3675 = const()[name = tensor<string, []>("op_3675"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_43_cast_fp16 = reshape(shape = var_3675, x = linear_194_cast_fp16)[name = tensor<string, []>("v_43_cast_fp16")];
+            tensor<int32, [4]> value_43_perm_0 = const()[name = tensor<string, []>("value_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_21_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_21_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529324864))), scale = tensor<fp16, [8]>([0x1.0c4p-8, 0x1.41cp-8, 0x1.08p-8, 0x1.83p-8, 0x1.4cp-8, 0x1.af8p-8, 0x1.44p-8, 0x1.298p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_3687_cast_fp16 = add(x = q_127_cast_fp16, y = model_layers_21_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_3687_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_21_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_21_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529325952))), scale = tensor<fp16, [8]>([0x1.6cp-8, 0x1.414p-9, 0x1.b3p-8, 0x1.c7p-8, 0x1.0ccp-8, 0x1.1cp-8, 0x1.a18p-8, 0x1.8ep-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_3689_cast_fp16 = add(x = q_127_cast_fp16, y = model_layers_21_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_3689_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_43_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_469_transpose_x_0 = const()[name = tensor<string, []>("x_469_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_469_transpose_y_0 = const()[name = tensor<string, []>("x_469_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_3691_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3691_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529327040))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529584128))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_3689_cast_fp16)[name = tensor<string, []>("transpose_164")];
+            tensor<fp16, [1, 8, 126, 251]> x_469_cast_fp16 = matmul(transpose_x = x_469_transpose_x_0, transpose_y = x_469_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = op_3691_to_fp16_quantized)[name = tensor<string, []>("x_469_cast_fp16")];
+            tensor<int32, [8]> x_471_pad_0 = const()[name = tensor<string, []>("x_471_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_471_mode_0 = const()[name = tensor<string, []>("x_471_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_224_to_fp16 = const()[name = tensor<string, []>("const_224_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_471_cast_fp16 = pad(constant_val = const_224_to_fp16, mode = x_471_mode_0, pad = x_471_pad_0, x = x_469_cast_fp16)[name = tensor<string, []>("x_471_cast_fp16")];
+            tensor<int32, [4]> var_3699 = const()[name = tensor<string, []>("op_3699"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_473_cast_fp16 = reshape(shape = var_3699, x = x_471_cast_fp16)[name = tensor<string, []>("x_473_cast_fp16")];
+            tensor<int32, [4]> var_3703_begin_0 = const()[name = tensor<string, []>("op_3703_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3703_end_0 = const()[name = tensor<string, []>("op_3703_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_3703_end_mask_0 = const()[name = tensor<string, []>("op_3703_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_3703_cast_fp16 = slice_by_index(begin = var_3703_begin_0, end = var_3703_end_0, end_mask = var_3703_end_mask_0, x = x_473_cast_fp16)[name = tensor<string, []>("op_3703_cast_fp16")];
+            tensor<int32, [4]> var_3704 = const()[name = tensor<string, []>("op_3704"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_85_cast_fp16 = reshape(shape = var_3704, x = var_3703_cast_fp16)[name = tensor<string, []>("matrix_bd_85_cast_fp16")];
+            tensor<bool, []> matrix_ac_43_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_43_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_43_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_43_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_138_perm_0 = const()[name = tensor<string, []>("transpose_138_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_139_perm_0 = const()[name = tensor<string, []>("transpose_139_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = tensor<string, []>("transpose_162")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_138 = transpose(perm = transpose_138_perm_0, x = var_3687_cast_fp16)[name = tensor<string, []>("transpose_163")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_43_cast_fp16 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_138, y = transpose_139)[name = tensor<string, []>("matrix_ac_43_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_87_begin_0 = const()[name = tensor<string, []>("matrix_bd_87_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_87_end_0 = const()[name = tensor<string, []>("matrix_bd_87_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_87_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_87_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_87_cast_fp16 = slice_by_index(begin = matrix_bd_87_begin_0, end = matrix_bd_87_end_0, end_mask = matrix_bd_87_end_mask_0, x = matrix_bd_85_cast_fp16)[name = tensor<string, []>("matrix_bd_87_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_3713_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = tensor<string, []>("op_3713_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_85_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_85_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_85_cast_fp16 = mul(x = var_3713_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_85_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_87_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_87_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_3719_cast_fp16 = softmax(axis = var_25, x = scores_87_cast_fp16)[name = tensor<string, []>("op_3719_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_1125_cast_fp16 = select(a = var_6_to_fp16, b = var_3719_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_1125_cast_fp16")];
+            tensor<bool, []> x_475_transpose_x_0 = const()[name = tensor<string, []>("x_475_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_475_transpose_y_0 = const()[name = tensor<string, []>("x_475_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_43_cast_fp16)[name = tensor<string, []>("transpose_161")];
+            tensor<fp16, [1, 8, 126, 128]> x_475_cast_fp16 = matmul(transpose_x = x_475_transpose_x_0, transpose_y = x_475_transpose_y_0, x = input_1125_cast_fp16, y = value_43_cast_fp16)[name = tensor<string, []>("x_475_cast_fp16")];
+            tensor<int32, [4]> var_3723_perm_0 = const()[name = tensor<string, []>("op_3723_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3724 = const()[name = tensor<string, []>("op_3724"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_3723_cast_fp16 = transpose(perm = var_3723_perm_0, x = x_475_cast_fp16)[name = tensor<string, []>("transpose_160")];
+            tensor<fp16, [1, 126, 1024]> input_1127_cast_fp16 = reshape(shape = var_3724, x = var_3723_cast_fp16)[name = tensor<string, []>("input_1127_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_21_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_21_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529584704))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530633344))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_196_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_21_self_attn_linear_out_weight_to_fp16_quantized, x = input_1127_cast_fp16)[name = tensor<string, []>("linear_196_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1131_cast_fp16 = add(x = input_1123_cast_fp16, y = linear_196_cast_fp16)[name = tensor<string, []>("input_1131_cast_fp16")];
+            tensor<int32, [1]> x_479_axes_0 = const()[name = tensor<string, []>("x_479_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_21_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_21_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530635456)))];
+            tensor<fp16, [1024]> model_layers_21_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_21_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530637568)))];
+            tensor<fp16, [1, 126, 1024]> x_479_cast_fp16 = layer_norm(axes = x_479_axes_0, beta = model_layers_21_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_21_norm_conv_weight_to_fp16, x = input_1131_cast_fp16)[name = tensor<string, []>("x_479_cast_fp16")];
+            tensor<int32, [3]> input_1133_perm_0 = const()[name = tensor<string, []>("input_1133_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_1135_pad_type_0 = const()[name = tensor<string, []>("input_1135_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_1135_strides_0 = const()[name = tensor<string, []>("input_1135_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1135_pad_0 = const()[name = tensor<string, []>("input_1135_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1135_dilations_0 = const()[name = tensor<string, []>("input_1135_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_1135_groups_0 = const()[name = tensor<string, []>("input_1135_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530639680))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(532736896))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_1133_cast_fp16 = transpose(perm = input_1133_perm_0, x = x_479_cast_fp16)[name = tensor<string, []>("transpose_159")];
+            tensor<fp16, [1, 2048, 126]> input_1135_cast_fp16 = conv(dilations = input_1135_dilations_0, groups = input_1135_groups_0, pad = input_1135_pad_0, pad_type = input_1135_pad_type_0, strides = input_1135_strides_0, weight = model_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1133_cast_fp16)[name = tensor<string, []>("input_1135_cast_fp16")];
+            tensor<int32, []> x_481_split_num_splits_0 = const()[name = tensor<string, []>("x_481_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_481_split_axis_0 = const()[name = tensor<string, []>("x_481_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_481_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_481_split_cast_fp16_1 = split(axis = x_481_split_axis_0, num_splits = x_481_split_num_splits_0, x = input_1135_cast_fp16)[name = tensor<string, []>("x_481_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_481_split_1_sigmoid_cast_fp16 = sigmoid(x = x_481_split_cast_fp16_1)[name = tensor<string, []>("x_481_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_481_cast_fp16 = mul(x = x_481_split_cast_fp16_0, y = x_481_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_481_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_1137_cast_fp16 = select(a = var_6_to_fp16, b = x_481_cast_fp16, cond = var_323)[name = tensor<string, []>("input_1137_cast_fp16")];
+            tensor<int32, [6]> input_1139_pad_0 = const()[name = tensor<string, []>("input_1139_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_1139_mode_0 = const()[name = tensor<string, []>("input_1139_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_227_to_fp16 = const()[name = tensor<string, []>("const_227_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_1139_cast_fp16 = pad(constant_val = const_227_to_fp16, mode = input_1139_mode_0, pad = input_1139_pad_0, x = input_1137_cast_fp16)[name = tensor<string, []>("input_1139_cast_fp16")];
+            tensor<string, []> input_1141_pad_type_0 = const()[name = tensor<string, []>("input_1141_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_1141_groups_0 = const()[name = tensor<string, []>("input_1141_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_1141_strides_0 = const()[name = tensor<string, []>("input_1141_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1141_pad_0 = const()[name = tensor<string, []>("input_1141_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1141_dilations_0 = const()[name = tensor<string, []>("input_1141_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_290_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_290_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(532741056))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(532750336))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_291_to_fp16 = const()[name = tensor<string, []>("const_291_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(532752448)))];
+            tensor<fp16, [1, 1024, 126]> input_1143_cast_fp16 = conv(bias = const_291_to_fp16, dilations = input_1141_dilations_0, groups = input_1141_groups_0, pad = input_1141_pad_0, pad_type = input_1141_pad_type_0, strides = input_1141_strides_0, weight = const_290_to_fp16_quantized, x = input_1139_cast_fp16)[name = tensor<string, []>("input_1143_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_1145_cast_fp16 = silu(x = input_1143_cast_fp16)[name = tensor<string, []>("input_1145_cast_fp16")];
+            tensor<string, []> x_483_pad_type_0 = const()[name = tensor<string, []>("x_483_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_483_strides_0 = const()[name = tensor<string, []>("x_483_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_483_pad_0 = const()[name = tensor<string, []>("x_483_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_483_dilations_0 = const()[name = tensor<string, []>("x_483_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_483_groups_0 = const()[name = tensor<string, []>("x_483_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(532754560))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(533803200))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_483_cast_fp16 = conv(dilations = x_483_dilations_0, groups = x_483_groups_0, pad = x_483_pad_0, pad_type = x_483_pad_type_0, strides = x_483_strides_0, weight = model_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1145_cast_fp16)[name = tensor<string, []>("x_483_cast_fp16")];
+            tensor<int32, [3]> input_1147_perm_0 = const()[name = tensor<string, []>("input_1147_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_1147_cast_fp16 = transpose(perm = input_1147_perm_0, x = x_483_cast_fp16)[name = tensor<string, []>("transpose_158")];
+            tensor<fp16, [1, 126, 1024]> input_1149_cast_fp16 = add(x = input_1131_cast_fp16, y = input_1147_cast_fp16)[name = tensor<string, []>("input_1149_cast_fp16")];
+            tensor<int32, [1]> input_1151_axes_0 = const()[name = tensor<string, []>("input_1151_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(533805312)))];
+            tensor<fp16, [1024]> model_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(533807424)))];
+            tensor<fp16, [1, 126, 1024]> input_1151_cast_fp16 = layer_norm(axes = input_1151_axes_0, beta = model_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1149_cast_fp16)[name = tensor<string, []>("input_1151_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_21_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_21_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(533809536))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(538003904))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_197_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_21_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1151_cast_fp16)[name = tensor<string, []>("linear_197_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_1155_cast_fp16 = silu(x = linear_197_cast_fp16)[name = tensor<string, []>("input_1155_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_21_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_21_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(538012160))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(542206528))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_198_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_21_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1155_cast_fp16)[name = tensor<string, []>("linear_198_cast_fp16")];
+            tensor<fp16, []> var_3784_to_fp16 = const()[name = tensor<string, []>("op_3784_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_3785_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_3784_to_fp16)[name = tensor<string, []>("op_3785_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1161_cast_fp16 = add(x = input_1149_cast_fp16, y = var_3785_cast_fp16)[name = tensor<string, []>("input_1161_cast_fp16")];
+            tensor<int32, [1]> input_1163_axes_0 = const()[name = tensor<string, []>("input_1163_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_21_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_21_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(542208640)))];
+            tensor<fp16, [1024]> model_layers_21_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_21_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(542210752)))];
+            tensor<fp16, [1, 126, 1024]> input_1163_cast_fp16 = layer_norm(axes = input_1163_axes_0, beta = model_layers_21_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_21_norm_out_weight_to_fp16, x = input_1161_cast_fp16)[name = tensor<string, []>("input_1163_cast_fp16")];
+            tensor<int32, [1]> input_1165_axes_0 = const()[name = tensor<string, []>("input_1165_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(542212864)))];
+            tensor<fp16, [1024]> model_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(542214976)))];
+            tensor<fp16, [1, 126, 1024]> input_1165_cast_fp16 = layer_norm(axes = input_1165_axes_0, beta = model_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1163_cast_fp16)[name = tensor<string, []>("input_1165_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_22_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_22_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(542217088))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(546411456))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_199_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_22_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1165_cast_fp16)[name = tensor<string, []>("linear_199_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_1169_cast_fp16 = silu(x = linear_199_cast_fp16)[name = tensor<string, []>("input_1169_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_22_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_22_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(546419712))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(550614080))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_200_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_22_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1169_cast_fp16)[name = tensor<string, []>("linear_200_cast_fp16")];
+            tensor<fp16, []> var_3813_to_fp16 = const()[name = tensor<string, []>("op_3813_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_3814_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_3813_to_fp16)[name = tensor<string, []>("op_3814_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1175_cast_fp16 = add(x = input_1163_cast_fp16, y = var_3814_cast_fp16)[name = tensor<string, []>("input_1175_cast_fp16")];
+            tensor<int32, [1]> query_45_axes_0 = const()[name = tensor<string, []>("query_45_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_22_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_22_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(550616192)))];
+            tensor<fp16, [1024]> model_layers_22_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_22_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(550618304)))];
+            tensor<fp16, [1, 126, 1024]> query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = model_layers_22_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_22_norm_self_att_weight_to_fp16, x = input_1175_cast_fp16)[name = tensor<string, []>("query_45_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_22_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_22_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(550620416))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(551669056))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_201_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_22_self_attn_linear_q_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = tensor<string, []>("linear_201_cast_fp16")];
+            tensor<int32, [4]> var_3830 = const()[name = tensor<string, []>("op_3830"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_133_cast_fp16 = reshape(shape = var_3830, x = linear_201_cast_fp16)[name = tensor<string, []>("q_133_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_22_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_22_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(551671168))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(552719808))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_202_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_22_self_attn_linear_k_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = tensor<string, []>("linear_202_cast_fp16")];
+            tensor<int32, [4]> var_3834 = const()[name = tensor<string, []>("op_3834"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_89_cast_fp16 = reshape(shape = var_3834, x = linear_202_cast_fp16)[name = tensor<string, []>("k_89_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_22_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_22_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(552721920))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553770560))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_203_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_22_self_attn_linear_v_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = tensor<string, []>("linear_203_cast_fp16")];
+            tensor<int32, [4]> var_3838 = const()[name = tensor<string, []>("op_3838"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_45_cast_fp16 = reshape(shape = var_3838, x = linear_203_cast_fp16)[name = tensor<string, []>("v_45_cast_fp16")];
+            tensor<int32, [4]> value_45_perm_0 = const()[name = tensor<string, []>("value_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_22_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_22_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553772672))), scale = tensor<fp16, [8]>([0x1.a6cp-9, 0x1.dbp-9, 0x1.018p-7, 0x1.474p-8, 0x1.3ccp-7, 0x1.8p-8, 0x1.4ep-8, 0x1.17p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_3850_cast_fp16 = add(x = q_133_cast_fp16, y = model_layers_22_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_3850_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_22_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_22_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553773760))), scale = tensor<fp16, [8]>([0x1.dap-8, 0x1.798p-8, 0x1.fd8p-10, 0x1.1ep-8, 0x1.484p-8, 0x1.b3p-8, 0x1.a4p-8, 0x1.9bp-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_3852_cast_fp16 = add(x = q_133_cast_fp16, y = model_layers_22_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_3852_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_45_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_491_transpose_x_0 = const()[name = tensor<string, []>("x_491_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_491_transpose_y_0 = const()[name = tensor<string, []>("x_491_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_3854_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3854_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553774848))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554031936))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_3852_cast_fp16)[name = tensor<string, []>("transpose_157")];
+            tensor<fp16, [1, 8, 126, 251]> x_491_cast_fp16 = matmul(transpose_x = x_491_transpose_x_0, transpose_y = x_491_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = op_3854_to_fp16_quantized)[name = tensor<string, []>("x_491_cast_fp16")];
+            tensor<int32, [8]> x_493_pad_0 = const()[name = tensor<string, []>("x_493_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_493_mode_0 = const()[name = tensor<string, []>("x_493_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_234_to_fp16 = const()[name = tensor<string, []>("const_234_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_493_cast_fp16 = pad(constant_val = const_234_to_fp16, mode = x_493_mode_0, pad = x_493_pad_0, x = x_491_cast_fp16)[name = tensor<string, []>("x_493_cast_fp16")];
+            tensor<int32, [4]> var_3862 = const()[name = tensor<string, []>("op_3862"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_495_cast_fp16 = reshape(shape = var_3862, x = x_493_cast_fp16)[name = tensor<string, []>("x_495_cast_fp16")];
+            tensor<int32, [4]> var_3866_begin_0 = const()[name = tensor<string, []>("op_3866_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3866_end_0 = const()[name = tensor<string, []>("op_3866_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_3866_end_mask_0 = const()[name = tensor<string, []>("op_3866_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_3866_cast_fp16 = slice_by_index(begin = var_3866_begin_0, end = var_3866_end_0, end_mask = var_3866_end_mask_0, x = x_495_cast_fp16)[name = tensor<string, []>("op_3866_cast_fp16")];
+            tensor<int32, [4]> var_3867 = const()[name = tensor<string, []>("op_3867"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_89_cast_fp16 = reshape(shape = var_3867, x = var_3866_cast_fp16)[name = tensor<string, []>("matrix_bd_89_cast_fp16")];
+            tensor<bool, []> matrix_ac_45_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_45_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_45_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_45_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_140_perm_0 = const()[name = tensor<string, []>("transpose_140_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_141_perm_0 = const()[name = tensor<string, []>("transpose_141_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = tensor<string, []>("transpose_155")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_140 = transpose(perm = transpose_140_perm_0, x = var_3850_cast_fp16)[name = tensor<string, []>("transpose_156")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_45_cast_fp16 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_140, y = transpose_141)[name = tensor<string, []>("matrix_ac_45_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_91_begin_0 = const()[name = tensor<string, []>("matrix_bd_91_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_91_end_0 = const()[name = tensor<string, []>("matrix_bd_91_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_91_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_91_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_91_cast_fp16 = slice_by_index(begin = matrix_bd_91_begin_0, end = matrix_bd_91_end_0, end_mask = matrix_bd_91_end_mask_0, x = matrix_bd_89_cast_fp16)[name = tensor<string, []>("matrix_bd_91_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_3876_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = tensor<string, []>("op_3876_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_89_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_89_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_89_cast_fp16 = mul(x = var_3876_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_89_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_91_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_91_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_3882_cast_fp16 = softmax(axis = var_25, x = scores_91_cast_fp16)[name = tensor<string, []>("op_3882_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_1177_cast_fp16 = select(a = var_6_to_fp16, b = var_3882_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_1177_cast_fp16")];
+            tensor<bool, []> x_497_transpose_x_0 = const()[name = tensor<string, []>("x_497_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_497_transpose_y_0 = const()[name = tensor<string, []>("x_497_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_45_cast_fp16)[name = tensor<string, []>("transpose_154")];
+            tensor<fp16, [1, 8, 126, 128]> x_497_cast_fp16 = matmul(transpose_x = x_497_transpose_x_0, transpose_y = x_497_transpose_y_0, x = input_1177_cast_fp16, y = value_45_cast_fp16)[name = tensor<string, []>("x_497_cast_fp16")];
+            tensor<int32, [4]> var_3886_perm_0 = const()[name = tensor<string, []>("op_3886_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3887 = const()[name = tensor<string, []>("op_3887"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_3886_cast_fp16 = transpose(perm = var_3886_perm_0, x = x_497_cast_fp16)[name = tensor<string, []>("transpose_153")];
+            tensor<fp16, [1, 126, 1024]> input_1179_cast_fp16 = reshape(shape = var_3887, x = var_3886_cast_fp16)[name = tensor<string, []>("input_1179_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_22_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_22_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554032512))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(555081152))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_205_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_22_self_attn_linear_out_weight_to_fp16_quantized, x = input_1179_cast_fp16)[name = tensor<string, []>("linear_205_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1183_cast_fp16 = add(x = input_1175_cast_fp16, y = linear_205_cast_fp16)[name = tensor<string, []>("input_1183_cast_fp16")];
+            tensor<int32, [1]> x_501_axes_0 = const()[name = tensor<string, []>("x_501_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_22_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_22_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(555083264)))];
+            tensor<fp16, [1024]> model_layers_22_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_22_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(555085376)))];
+            tensor<fp16, [1, 126, 1024]> x_501_cast_fp16 = layer_norm(axes = x_501_axes_0, beta = model_layers_22_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_22_norm_conv_weight_to_fp16, x = input_1183_cast_fp16)[name = tensor<string, []>("x_501_cast_fp16")];
+            tensor<int32, [3]> input_1185_perm_0 = const()[name = tensor<string, []>("input_1185_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_1187_pad_type_0 = const()[name = tensor<string, []>("input_1187_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_1187_strides_0 = const()[name = tensor<string, []>("input_1187_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1187_pad_0 = const()[name = tensor<string, []>("input_1187_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1187_dilations_0 = const()[name = tensor<string, []>("input_1187_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_1187_groups_0 = const()[name = tensor<string, []>("input_1187_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(555087488))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(557184704))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_1185_cast_fp16 = transpose(perm = input_1185_perm_0, x = x_501_cast_fp16)[name = tensor<string, []>("transpose_152")];
+            tensor<fp16, [1, 2048, 126]> input_1187_cast_fp16 = conv(dilations = input_1187_dilations_0, groups = input_1187_groups_0, pad = input_1187_pad_0, pad_type = input_1187_pad_type_0, strides = input_1187_strides_0, weight = model_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1185_cast_fp16)[name = tensor<string, []>("input_1187_cast_fp16")];
+            tensor<int32, []> x_503_split_num_splits_0 = const()[name = tensor<string, []>("x_503_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_503_split_axis_0 = const()[name = tensor<string, []>("x_503_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_503_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_503_split_cast_fp16_1 = split(axis = x_503_split_axis_0, num_splits = x_503_split_num_splits_0, x = input_1187_cast_fp16)[name = tensor<string, []>("x_503_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_503_split_1_sigmoid_cast_fp16 = sigmoid(x = x_503_split_cast_fp16_1)[name = tensor<string, []>("x_503_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_503_cast_fp16 = mul(x = x_503_split_cast_fp16_0, y = x_503_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_503_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_1189_cast_fp16 = select(a = var_6_to_fp16, b = x_503_cast_fp16, cond = var_323)[name = tensor<string, []>("input_1189_cast_fp16")];
+            tensor<int32, [6]> input_1191_pad_0 = const()[name = tensor<string, []>("input_1191_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_1191_mode_0 = const()[name = tensor<string, []>("input_1191_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_237_to_fp16 = const()[name = tensor<string, []>("const_237_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_1191_cast_fp16 = pad(constant_val = const_237_to_fp16, mode = input_1191_mode_0, pad = input_1191_pad_0, x = input_1189_cast_fp16)[name = tensor<string, []>("input_1191_cast_fp16")];
+            tensor<string, []> input_1193_pad_type_0 = const()[name = tensor<string, []>("input_1193_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_1193_groups_0 = const()[name = tensor<string, []>("input_1193_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_1193_strides_0 = const()[name = tensor<string, []>("input_1193_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1193_pad_0 = const()[name = tensor<string, []>("input_1193_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1193_dilations_0 = const()[name = tensor<string, []>("input_1193_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_292_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_292_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(557188864))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(557198144))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_293_to_fp16 = const()[name = tensor<string, []>("const_293_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(557200256)))];
+            tensor<fp16, [1, 1024, 126]> input_1195_cast_fp16 = conv(bias = const_293_to_fp16, dilations = input_1193_dilations_0, groups = input_1193_groups_0, pad = input_1193_pad_0, pad_type = input_1193_pad_type_0, strides = input_1193_strides_0, weight = const_292_to_fp16_quantized, x = input_1191_cast_fp16)[name = tensor<string, []>("input_1195_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_1197_cast_fp16 = silu(x = input_1195_cast_fp16)[name = tensor<string, []>("input_1197_cast_fp16")];
+            tensor<string, []> x_505_pad_type_0 = const()[name = tensor<string, []>("x_505_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_505_strides_0 = const()[name = tensor<string, []>("x_505_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_505_pad_0 = const()[name = tensor<string, []>("x_505_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_505_dilations_0 = const()[name = tensor<string, []>("x_505_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_505_groups_0 = const()[name = tensor<string, []>("x_505_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(557202368))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(558251008))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_505_cast_fp16 = conv(dilations = x_505_dilations_0, groups = x_505_groups_0, pad = x_505_pad_0, pad_type = x_505_pad_type_0, strides = x_505_strides_0, weight = model_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1197_cast_fp16)[name = tensor<string, []>("x_505_cast_fp16")];
+            tensor<int32, [3]> input_1199_perm_0 = const()[name = tensor<string, []>("input_1199_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_1199_cast_fp16 = transpose(perm = input_1199_perm_0, x = x_505_cast_fp16)[name = tensor<string, []>("transpose_151")];
+            tensor<fp16, [1, 126, 1024]> input_1201_cast_fp16 = add(x = input_1183_cast_fp16, y = input_1199_cast_fp16)[name = tensor<string, []>("input_1201_cast_fp16")];
+            tensor<int32, [1]> input_1203_axes_0 = const()[name = tensor<string, []>("input_1203_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(558253120)))];
+            tensor<fp16, [1024]> model_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(558255232)))];
+            tensor<fp16, [1, 126, 1024]> input_1203_cast_fp16 = layer_norm(axes = input_1203_axes_0, beta = model_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1201_cast_fp16)[name = tensor<string, []>("input_1203_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_22_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_22_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(558257344))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(562451712))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_206_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_22_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1203_cast_fp16)[name = tensor<string, []>("linear_206_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_1207_cast_fp16 = silu(x = linear_206_cast_fp16)[name = tensor<string, []>("input_1207_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_22_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_22_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(562459968))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566654336))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_207_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_22_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1207_cast_fp16)[name = tensor<string, []>("linear_207_cast_fp16")];
+            tensor<fp16, []> var_3947_to_fp16 = const()[name = tensor<string, []>("op_3947_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_3948_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_3947_to_fp16)[name = tensor<string, []>("op_3948_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1213_cast_fp16 = add(x = input_1201_cast_fp16, y = var_3948_cast_fp16)[name = tensor<string, []>("input_1213_cast_fp16")];
+            tensor<int32, [1]> input_1215_axes_0 = const()[name = tensor<string, []>("input_1215_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_22_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_22_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566656448)))];
+            tensor<fp16, [1024]> model_layers_22_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_22_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566658560)))];
+            tensor<fp16, [1, 126, 1024]> input_1215_cast_fp16 = layer_norm(axes = input_1215_axes_0, beta = model_layers_22_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_22_norm_out_weight_to_fp16, x = input_1213_cast_fp16)[name = tensor<string, []>("input_1215_cast_fp16")];
+            tensor<int32, [1]> input_1217_axes_0 = const()[name = tensor<string, []>("input_1217_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566660672)))];
+            tensor<fp16, [1024]> model_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566662784)))];
+            tensor<fp16, [1, 126, 1024]> input_1217_cast_fp16 = layer_norm(axes = input_1217_axes_0, beta = model_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1215_cast_fp16)[name = tensor<string, []>("input_1217_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_23_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_23_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566664896))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(570859264))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_208_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_23_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1217_cast_fp16)[name = tensor<string, []>("linear_208_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_1221_cast_fp16 = silu(x = linear_208_cast_fp16)[name = tensor<string, []>("input_1221_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_23_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_23_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(570867520))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(575061888))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_209_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_23_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1221_cast_fp16)[name = tensor<string, []>("linear_209_cast_fp16")];
+            tensor<fp16, []> var_3976_to_fp16 = const()[name = tensor<string, []>("op_3976_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_3977_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_3976_to_fp16)[name = tensor<string, []>("op_3977_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1227_cast_fp16 = add(x = input_1215_cast_fp16, y = var_3977_cast_fp16)[name = tensor<string, []>("input_1227_cast_fp16")];
+            tensor<int32, [1]> query_axes_0 = const()[name = tensor<string, []>("query_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_23_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_23_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(575064000)))];
+            tensor<fp16, [1024]> model_layers_23_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_23_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(575066112)))];
+            tensor<fp16, [1, 126, 1024]> query_cast_fp16 = layer_norm(axes = query_axes_0, beta = model_layers_23_norm_self_att_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_23_norm_self_att_weight_to_fp16, x = input_1227_cast_fp16)[name = tensor<string, []>("query_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_23_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_23_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(575068224))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(576116864))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_210_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_23_self_attn_linear_q_weight_to_fp16_quantized, x = query_cast_fp16)[name = tensor<string, []>("linear_210_cast_fp16")];
+            tensor<int32, [4]> var_3993 = const()[name = tensor<string, []>("op_3993"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> q_139_cast_fp16 = reshape(shape = var_3993, x = linear_210_cast_fp16)[name = tensor<string, []>("q_139_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_23_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_23_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(576118976))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577167616))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_211_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_23_self_attn_linear_k_weight_to_fp16_quantized, x = query_cast_fp16)[name = tensor<string, []>("linear_211_cast_fp16")];
+            tensor<int32, [4]> var_3997 = const()[name = tensor<string, []>("op_3997"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> k_93_cast_fp16 = reshape(shape = var_3997, x = linear_211_cast_fp16)[name = tensor<string, []>("k_93_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_23_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_23_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577169728))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578218368))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_212_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_23_self_attn_linear_v_weight_to_fp16_quantized, x = query_cast_fp16)[name = tensor<string, []>("linear_212_cast_fp16")];
+            tensor<int32, [4]> var_4001 = const()[name = tensor<string, []>("op_4001"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 126, 8, 128]> v_cast_fp16 = reshape(shape = var_4001, x = linear_212_cast_fp16)[name = tensor<string, []>("v_cast_fp16")];
+            tensor<int32, [4]> value_perm_0 = const()[name = tensor<string, []>("value_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> model_layers_23_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_23_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578220480))), scale = tensor<fp16, [8]>([0x1.8fp-8, 0x1.12p-8, 0x1.494p-8, 0x1.98p-8, 0x1.2e4p-8, 0x1.21p-8, 0x1.9acp-9, 0x1.b88p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_4013_cast_fp16 = add(x = q_139_cast_fp16, y = model_layers_23_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_4013_cast_fp16")];
+            tensor<fp16, [8, 128]> model_layers_23_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_23_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578221568))), scale = tensor<fp16, [8]>([0x1.d18p-9, 0x1.90cp-9, 0x1.548p-8, 0x1.e84p-9, 0x1.6dcp-8, 0x1.1dcp-8, 0x1.41p-8, 0x1.95p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp16, [1, 126, 8, 128]> var_4015_cast_fp16 = add(x = q_139_cast_fp16, y = model_layers_23_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_4015_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> x_513_transpose_x_0 = const()[name = tensor<string, []>("x_513_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_513_transpose_y_0 = const()[name = tensor<string, []>("x_513_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 128, 251]> op_4017_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_4017_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578222656))), scale = tensor<fp16, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578479744))), zero_point = tensor<int8, [251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16177728)))];
+            tensor<fp16, [1, 8, 126, 128]> q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4015_cast_fp16)[name = tensor<string, []>("transpose_150")];
+            tensor<fp16, [1, 8, 126, 251]> x_513_cast_fp16 = matmul(transpose_x = x_513_transpose_x_0, transpose_y = x_513_transpose_y_0, x = q_with_bias_v_cast_fp16, y = op_4017_to_fp16_quantized)[name = tensor<string, []>("x_513_cast_fp16")];
+            tensor<int32, [8]> x_515_pad_0 = const()[name = tensor<string, []>("x_515_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            tensor<string, []> x_515_mode_0 = const()[name = tensor<string, []>("x_515_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_244_to_fp16 = const()[name = tensor<string, []>("const_244_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 126, 252]> x_515_cast_fp16 = pad(constant_val = const_244_to_fp16, mode = x_515_mode_0, pad = x_515_pad_0, x = x_513_cast_fp16)[name = tensor<string, []>("x_515_cast_fp16")];
+            tensor<int32, [4]> var_4025 = const()[name = tensor<string, []>("op_4025"), val = tensor<int32, [4]>([1, 8, -1, 126])];
+            tensor<fp16, [1, 8, 252, 126]> x_517_cast_fp16 = reshape(shape = var_4025, x = x_515_cast_fp16)[name = tensor<string, []>("x_517_cast_fp16")];
+            tensor<int32, [4]> var_4029_begin_0 = const()[name = tensor<string, []>("op_4029_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4029_end_0 = const()[name = tensor<string, []>("op_4029_end_0"), val = tensor<int32, [4]>([1, 8, 252, 126])];
+            tensor<bool, [4]> var_4029_end_mask_0 = const()[name = tensor<string, []>("op_4029_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 251, 126]> var_4029_cast_fp16 = slice_by_index(begin = var_4029_begin_0, end = var_4029_end_0, end_mask = var_4029_end_mask_0, x = x_517_cast_fp16)[name = tensor<string, []>("op_4029_cast_fp16")];
+            tensor<int32, [4]> var_4030 = const()[name = tensor<string, []>("op_4030"), val = tensor<int32, [4]>([1, 8, 126, 251])];
+            tensor<fp16, [1, 8, 126, 251]> matrix_bd_93_cast_fp16 = reshape(shape = var_4030, x = var_4029_cast_fp16)[name = tensor<string, []>("matrix_bd_93_cast_fp16")];
+            tensor<bool, []> matrix_ac_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> matrix_ac_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_142_perm_0 = const()[name = tensor<string, []>("transpose_142_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_143_perm_0 = const()[name = tensor<string, []>("transpose_143_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 126]> transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = tensor<string, []>("transpose_148")];
+            tensor<fp16, [1, 8, 126, 128]> transpose_142 = transpose(perm = transpose_142_perm_0, x = var_4013_cast_fp16)[name = tensor<string, []>("transpose_149")];
+            tensor<fp16, [1, 8, 126, 126]> matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_142, y = transpose_143)[name = tensor<string, []>("matrix_ac_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_begin_0 = const()[name = tensor<string, []>("matrix_bd_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_end_0 = const()[name = tensor<string, []>("matrix_bd_end_0"), val = tensor<int32, [4]>([1, 8, 126, 126])];
+            tensor<bool, [4]> matrix_bd_end_mask_0 = const()[name = tensor<string, []>("matrix_bd_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 126, 126]> matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_93_cast_fp16)[name = tensor<string, []>("matrix_bd_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_4039_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = tensor<string, []>("op_4039_cast_fp16")];
+            tensor<fp16, []> _inversed_scores_93_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_93_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
+            tensor<fp16, [1, 8, 126, 126]> _inversed_scores_93_cast_fp16 = mul(x = var_4039_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_93_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> scores_cast_fp16 = select(a = var_7_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> var_4045_cast_fp16 = softmax(axis = var_25, x = scores_cast_fp16)[name = tensor<string, []>("op_4045_cast_fp16")];
+            tensor<fp16, [1, 8, 126, 126]> input_1229_cast_fp16 = select(a = var_6_to_fp16, b = var_4045_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_1229_cast_fp16")];
+            tensor<bool, []> x_519_transpose_x_0 = const()[name = tensor<string, []>("x_519_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_519_transpose_y_0 = const()[name = tensor<string, []>("x_519_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 8, 126, 128]> value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = tensor<string, []>("transpose_147")];
+            tensor<fp16, [1, 8, 126, 128]> x_519_cast_fp16 = matmul(transpose_x = x_519_transpose_x_0, transpose_y = x_519_transpose_y_0, x = input_1229_cast_fp16, y = value_cast_fp16)[name = tensor<string, []>("x_519_cast_fp16")];
+            tensor<int32, [4]> var_4049_perm_0 = const()[name = tensor<string, []>("op_4049_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4050 = const()[name = tensor<string, []>("op_4050"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 126, 8, 128]> var_4049_cast_fp16 = transpose(perm = var_4049_perm_0, x = x_519_cast_fp16)[name = tensor<string, []>("transpose_146")];
+            tensor<fp16, [1, 126, 1024]> input_1231_cast_fp16 = reshape(shape = var_4050, x = var_4049_cast_fp16)[name = tensor<string, []>("input_1231_cast_fp16")];
+            tensor<fp16, [1024, 1024]> model_layers_23_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_23_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578480320))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579528960))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_214_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_23_self_attn_linear_out_weight_to_fp16_quantized, x = input_1231_cast_fp16)[name = tensor<string, []>("linear_214_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_1235_cast_fp16 = add(x = input_1227_cast_fp16, y = linear_214_cast_fp16)[name = tensor<string, []>("input_1235_cast_fp16")];
+            tensor<int32, [1]> x_523_axes_0 = const()[name = tensor<string, []>("x_523_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_23_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_23_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579531072)))];
+            tensor<fp16, [1024]> model_layers_23_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_23_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579533184)))];
+            tensor<fp16, [1, 126, 1024]> x_523_cast_fp16 = layer_norm(axes = x_523_axes_0, beta = model_layers_23_norm_conv_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_23_norm_conv_weight_to_fp16, x = input_1235_cast_fp16)[name = tensor<string, []>("x_523_cast_fp16")];
+            tensor<int32, [3]> input_1237_perm_0 = const()[name = tensor<string, []>("input_1237_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> input_1239_pad_type_0 = const()[name = tensor<string, []>("input_1239_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_1239_strides_0 = const()[name = tensor<string, []>("input_1239_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1239_pad_0 = const()[name = tensor<string, []>("input_1239_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1239_dilations_0 = const()[name = tensor<string, []>("input_1239_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_1239_groups_0 = const()[name = tensor<string, []>("input_1239_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [2048, 1024, 1]> model_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579535296))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581632512))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19330816)))];
+            tensor<fp16, [1, 1024, 126]> input_1237_cast_fp16 = transpose(perm = input_1237_perm_0, x = x_523_cast_fp16)[name = tensor<string, []>("transpose_145")];
+            tensor<fp16, [1, 2048, 126]> input_1239_cast_fp16 = conv(dilations = input_1239_dilations_0, groups = input_1239_groups_0, pad = input_1239_pad_0, pad_type = input_1239_pad_type_0, strides = input_1239_strides_0, weight = model_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1237_cast_fp16)[name = tensor<string, []>("input_1239_cast_fp16")];
+            tensor<int32, []> x_525_split_num_splits_0 = const()[name = tensor<string, []>("x_525_split_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> x_525_split_axis_0 = const()[name = tensor<string, []>("x_525_split_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 126]> x_525_split_cast_fp16_0, tensor<fp16, [1, 1024, 126]> x_525_split_cast_fp16_1 = split(axis = x_525_split_axis_0, num_splits = x_525_split_num_splits_0, x = input_1239_cast_fp16)[name = tensor<string, []>("x_525_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_525_split_1_sigmoid_cast_fp16 = sigmoid(x = x_525_split_cast_fp16_1)[name = tensor<string, []>("x_525_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> x_525_cast_fp16 = mul(x = x_525_split_cast_fp16_0, y = x_525_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("x_525_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_1241_cast_fp16 = select(a = var_6_to_fp16, b = x_525_cast_fp16, cond = var_323)[name = tensor<string, []>("input_1241_cast_fp16")];
+            tensor<int32, [6]> input_1243_pad_0 = const()[name = tensor<string, []>("input_1243_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            tensor<string, []> input_1243_mode_0 = const()[name = tensor<string, []>("input_1243_mode_0"), val = tensor<string, []>("constant")];
+            tensor<fp16, []> const_247_to_fp16 = const()[name = tensor<string, []>("const_247_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1024, 134]> input_1243_cast_fp16 = pad(constant_val = const_247_to_fp16, mode = input_1243_mode_0, pad = input_1243_pad_0, x = input_1241_cast_fp16)[name = tensor<string, []>("input_1243_cast_fp16")];
+            tensor<string, []> input_1245_pad_type_0 = const()[name = tensor<string, []>("input_1245_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, []> input_1245_groups_0 = const()[name = tensor<string, []>("input_1245_groups_0"), val = tensor<int32, []>(1024)];
+            tensor<int32, [1]> input_1245_strides_0 = const()[name = tensor<string, []>("input_1245_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1245_pad_0 = const()[name = tensor<string, []>("input_1245_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1245_dilations_0 = const()[name = tensor<string, []>("input_1245_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_294_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_294_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581636672))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581645952))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1024]> const_295_to_fp16 = const()[name = tensor<string, []>("const_295_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581648064)))];
+            tensor<fp16, [1, 1024, 126]> input_1247_cast_fp16 = conv(bias = const_295_to_fp16, dilations = input_1245_dilations_0, groups = input_1245_groups_0, pad = input_1245_pad_0, pad_type = input_1245_pad_type_0, strides = input_1245_strides_0, weight = const_294_to_fp16_quantized, x = input_1243_cast_fp16)[name = tensor<string, []>("input_1247_cast_fp16")];
+            tensor<fp16, [1, 1024, 126]> input_1249_cast_fp16 = silu(x = input_1247_cast_fp16)[name = tensor<string, []>("input_1249_cast_fp16")];
+            tensor<string, []> x_527_pad_type_0 = const()[name = tensor<string, []>("x_527_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> x_527_strides_0 = const()[name = tensor<string, []>("x_527_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_527_pad_0 = const()[name = tensor<string, []>("x_527_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_527_dilations_0 = const()[name = tensor<string, []>("x_527_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_527_groups_0 = const()[name = tensor<string, []>("x_527_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 1]> model_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581650176))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(582698816))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 1024, 126]> x_527_cast_fp16 = conv(dilations = x_527_dilations_0, groups = x_527_groups_0, pad = x_527_pad_0, pad_type = x_527_pad_type_0, strides = x_527_strides_0, weight = model_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1249_cast_fp16)[name = tensor<string, []>("x_527_cast_fp16")];
+            tensor<int32, [3]> input_1251_perm_0 = const()[name = tensor<string, []>("input_1251_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 126, 1024]> input_1251_cast_fp16 = transpose(perm = input_1251_perm_0, x = x_527_cast_fp16)[name = tensor<string, []>("transpose_144")];
+            tensor<fp16, [1, 126, 1024]> input_1253_cast_fp16 = add(x = input_1235_cast_fp16, y = input_1251_cast_fp16)[name = tensor<string, []>("input_1253_cast_fp16")];
+            tensor<int32, [1]> input_1255_axes_0 = const()[name = tensor<string, []>("input_1255_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(582700928)))];
+            tensor<fp16, [1024]> model_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(582703040)))];
+            tensor<fp16, [1, 126, 1024]> input_1255_cast_fp16 = layer_norm(axes = input_1255_axes_0, beta = model_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1253_cast_fp16)[name = tensor<string, []>("input_1255_cast_fp16")];
+            tensor<fp16, [4096, 1024]> model_layers_23_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_23_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(582705152))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586899520))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8542720)))];
+            tensor<fp16, [1, 126, 4096]> linear_215_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = model_layers_23_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1255_cast_fp16)[name = tensor<string, []>("linear_215_cast_fp16")];
+            tensor<fp16, [1, 126, 4096]> input_1259_cast_fp16 = silu(x = linear_215_cast_fp16)[name = tensor<string, []>("input_1259_cast_fp16")];
+            tensor<fp16, [1024, 4096]> model_layers_23_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("model_layers_23_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586907776))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591102144))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4338816)))];
+            tensor<fp16, [1, 126, 1024]> linear_216_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = model_layers_23_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1259_cast_fp16)[name = tensor<string, []>("linear_216_cast_fp16")];
+            tensor<fp16, []> var_4110_to_fp16 = const()[name = tensor<string, []>("op_4110_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 126, 1024]> var_4111_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_4110_to_fp16)[name = tensor<string, []>("op_4111_cast_fp16")];
+            tensor<fp16, [1, 126, 1024]> input_cast_fp16 = add(x = input_1253_cast_fp16, y = var_4111_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
+            tensor<int32, [1]> audio_signal_axes_0 = const()[name = tensor<string, []>("audio_signal_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> model_layers_23_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("model_layers_23_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591104256)))];
+            tensor<fp16, [1024]> model_layers_23_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("model_layers_23_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(591106368)))];
+            tensor<fp16, [1, 126, 1024]> encoder_output = layer_norm(axes = audio_signal_axes_0, beta = model_layers_23_norm_out_bias_to_fp16, epsilon = var_4_to_fp16, gamma = model_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("audio_signal_cast_fp16")];
+        } -> (encoder_output, encoder_output_length);
+}
\ No newline at end of file