diff --git "a/Encoder-v1-fp32.mlmodelc/model.mil" "b/Encoder-v1-fp32.mlmodelc/model.mil"
new file mode 100644--- /dev/null
+++ "b/Encoder-v1-fp32.mlmodelc/model.mil"
@@ -0,0 +1,3642 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
+{
+    func main<ios17>(tensor<fp32, [1, 80, 1501]> audio_signal, tensor<int32, [1]> length) {
+            tensor<int32, []> var_30 = const()[name = tensor<string, []>("op_30"), 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, []> var_86_to_fp16_dtype_0 = const()[name = tensor<string, []>("op_86_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, []> var_87_promoted_to_fp16 = const()[name = tensor<string, []>("op_87_promoted_to_fp16"), val = tensor<fp16, []>(-0x1p+0)];
+            tensor<fp16, [1]> length_to_fp16 = cast(dtype = var_86_to_fp16_dtype_0, x = length)[name = tensor<string, []>("cast_229")];
+            tensor<fp16, [1]> var_88_cast_fp16 = add(x = length_to_fp16, y = var_87_promoted_to_fp16)[name = tensor<string, []>("op_88_cast_fp16")];
+            tensor<fp16, []> _inversed_90_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_90_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1]> _inversed_90_cast_fp16 = mul(x = var_88_cast_fp16, y = _inversed_90_y_0_to_fp16)[name = tensor<string, []>("_inversed_90_cast_fp16")];
+            tensor<fp16, []> var_91_to_fp16 = const()[name = tensor<string, []>("op_91_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> lengths_1_cast_fp16 = add(x = _inversed_90_cast_fp16, y = var_91_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_95_promoted_to_fp16 = const()[name = tensor<string, []>("op_95_promoted_to_fp16"), val = tensor<fp16, []>(-0x1p+0)];
+            tensor<fp16, [1]> var_96_cast_fp16 = add(x = lengths_3_cast_fp16, y = var_95_promoted_to_fp16)[name = tensor<string, []>("op_96_cast_fp16")];
+            tensor<fp16, []> _inversed_98_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_98_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1]> _inversed_98_cast_fp16 = mul(x = var_96_cast_fp16, y = _inversed_98_y_0_to_fp16)[name = tensor<string, []>("_inversed_98_cast_fp16")];
+            tensor<fp16, []> var_99_to_fp16 = const()[name = tensor<string, []>("op_99_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> lengths_7_cast_fp16 = add(x = _inversed_98_cast_fp16, y = var_99_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_103_promoted_to_fp16 = const()[name = tensor<string, []>("op_103_promoted_to_fp16"), val = tensor<fp16, []>(-0x1p+0)];
+            tensor<fp16, [1]> var_104_cast_fp16 = add(x = lengths_9_cast_fp16, y = var_103_promoted_to_fp16)[name = tensor<string, []>("op_104_cast_fp16")];
+            tensor<fp16, []> _inversed_106_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_106_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1]> _inversed_106_cast_fp16 = mul(x = var_104_cast_fp16, y = _inversed_106_y_0_to_fp16)[name = tensor<string, []>("_inversed_106_cast_fp16")];
+            tensor<fp16, []> var_107_to_fp16 = const()[name = tensor<string, []>("op_107_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> lengths_13_cast_fp16 = add(x = _inversed_106_cast_fp16, y = var_107_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, 80, 1501]> audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = tensor<string, []>("cast_230")];
+            tensor<fp16, [1, 1501, 80]> x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = audio_signal_to_fp16)[name = tensor<string, []>("transpose_291")];
+            tensor<fp16, [1, 1, 1501, 80]> 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]> module_pre_encode_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp16, [256]> module_pre_encode_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4736)))];
+            tensor<fp16, [1, 256, 751, 40]> input_3_cast_fp16 = conv(bias = module_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 = module_pre_encode_conv_0_weight_to_fp16, x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> 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]> module_pre_encode_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5312)))];
+            tensor<fp16, [256]> module_pre_encode_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9984)))];
+            tensor<fp16, [1, 256, 376, 20]> input_7_cast_fp16 = conv(bias = module_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 = module_pre_encode_conv_2_weight_to_fp16, 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]> module_pre_encode_conv_3_weight_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_3_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10560)))];
+            tensor<fp16, [256]> module_pre_encode_conv_3_bias_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_3_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141696)))];
+            tensor<fp16, [1, 256, 376, 20]> input_9_cast_fp16 = conv(bias = module_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 = module_pre_encode_conv_3_weight_to_fp16, x = input_7_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> 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]> module_pre_encode_conv_5_weight_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_5_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142272)))];
+            tensor<fp16, [256]> module_pre_encode_conv_5_bias_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_5_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146944)))];
+            tensor<fp16, [1, 256, 188, 10]> input_13_cast_fp16 = conv(bias = module_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 = module_pre_encode_conv_5_weight_to_fp16, 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]> module_pre_encode_conv_6_weight_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_6_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147520)))];
+            tensor<fp16, [256]> module_pre_encode_conv_6_bias_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_6_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(278656)))];
+            tensor<fp16, [1, 256, 188, 10]> input_15_cast_fp16 = conv(bias = module_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 = module_pre_encode_conv_6_weight_to_fp16, x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> x_3_cast_fp16 = relu(x = input_15_cast_fp16)[name = tensor<string, []>("x_3_cast_fp16")];
+            tensor<int32, [4]> var_157_perm_0 = const()[name = tensor<string, []>("op_157_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_158 = const()[name = tensor<string, []>("op_158"), val = tensor<int32, [3]>([1, 188, -1])];
+            tensor<fp16, [1, 188, 256, 10]> var_157_cast_fp16 = transpose(perm = var_157_perm_0, x = x_3_cast_fp16)[name = tensor<string, []>("transpose_290")];
+            tensor<fp16, [1, 188, 2560]> input_17_cast_fp16 = reshape(shape = var_158, x = var_157_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
+            tensor<fp16, [1024, 2560]> module_pre_encode_out_weight_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_out_weight_to_fp16"), val = tensor<fp16, [1024, 2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(279232)))];
+            tensor<fp16, [1024]> module_pre_encode_out_bias_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5522176)))];
+            tensor<fp16, [1, 188, 1024]> linear_0_cast_fp16 = linear(bias = module_pre_encode_out_bias_to_fp16, weight = module_pre_encode_out_weight_to_fp16, x = input_17_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
+            tensor<string, []> padding_length_dtype_0 = const()[name = tensor<string, []>("padding_length_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1, 188]> expand_dims_0 = const()[name = tensor<string, []>("expand_dims_0"), val = tensor<int32, [1, 188]>([[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, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187]])];
+            tensor<int32, [1]> var_196_axes_0 = const()[name = tensor<string, []>("op_196_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<int32, [1]> encoded_length = cast(dtype = padding_length_dtype_0, x = lengths_cast_fp16)[name = tensor<string, []>("cast_228")];
+            tensor<int32, [1, 1]> var_196 = expand_dims(axes = var_196_axes_0, x = encoded_length)[name = tensor<string, []>("op_196")];
+            tensor<bool, [1, 188]> pad_mask_1 = less(x = expand_dims_0, y = var_196)[name = tensor<string, []>("pad_mask_1")];
+            tensor<int32, [1]> var_198_axes_0 = const()[name = tensor<string, []>("op_198_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188]> var_198 = expand_dims(axes = var_198_axes_0, x = pad_mask_1)[name = tensor<string, []>("op_198")];
+            tensor<int32, [3]> var_199 = const()[name = tensor<string, []>("op_199"), val = tensor<int32, [3]>([1, 188, 1])];
+            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask_1 = tile(reps = var_199, x = var_198)[name = tensor<string, []>("pad_mask_for_att_mask_1")];
+            tensor<int32, [3]> var_201_perm_0 = const()[name = tensor<string, []>("op_201_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<bool, [1, 188, 188]> var_201 = transpose(perm = var_201_perm_0, x = pad_mask_for_att_mask_1)[name = tensor<string, []>("transpose_289")];
+            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_201)[name = tensor<string, []>("pad_mask_for_att_mask")];
+            tensor<bool, [1, 188, 188]> const_7 = const()[name = tensor<string, []>("const_7"), val = tensor<bool, [1, 188, 188]>([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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]]])];
+            tensor<bool, [1, 188, 188]> att_mask = logical_and(x = pad_mask_for_att_mask, y = const_7)[name = tensor<string, []>("att_mask")];
+            tensor<bool, [1, 188, 188]> mask_1 = logical_not(x = att_mask)[name = tensor<string, []>("mask_1")];
+            tensor<bool, [1, 188]> 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]> module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5524288)))];
+            tensor<fp16, [1024]> module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5526400)))];
+            tensor<fp16, []> var_9_to_fp16 = const()[name = tensor<string, []>("op_9_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 188, 1024]> input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward1_weight_to_fp16, x = linear_0_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_0_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5528512)))];
+            tensor<fp16, [4096]> module_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13917184)))];
+            tensor<fp16, [1, 188, 4096]> linear_1_cast_fp16 = linear(bias = module_layers_0_feed_forward1_linear1_bias_to_fp16, weight = module_layers_0_feed_forward1_linear1_weight_to_fp16, x = input_21_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_25_cast_fp16 = silu(x = linear_1_cast_fp16)[name = tensor<string, []>("input_25_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_0_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13925440)))];
+            tensor<fp16, [1024]> module_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22314112)))];
+            tensor<fp16, [1, 188, 1024]> linear_2_cast_fp16 = linear(bias = module_layers_0_feed_forward1_linear2_bias_to_fp16, weight = module_layers_0_feed_forward1_linear2_weight_to_fp16, x = input_25_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")];
+            tensor<fp16, []> var_234_to_fp16 = const()[name = tensor<string, []>("op_234_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_235_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_234_to_fp16)[name = tensor<string, []>("op_235_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_31_cast_fp16 = add(x = linear_0_cast_fp16, y = var_235_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]> module_layers_0_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22316224)))];
+            tensor<fp16, [1024]> module_layers_0_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22318336)))];
+            tensor<fp16, [1, 188, 1024]> query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_self_att_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("query_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_0_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22320448)))];
+            tensor<fp16, [1024]> module_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24417664)))];
+            tensor<fp16, [1, 188, 1024]> linear_3_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_q_bias_to_fp16, weight = module_layers_0_self_attn_linear_q_weight_to_fp16, x = query_1_cast_fp16)[name = tensor<string, []>("linear_3_cast_fp16")];
+            tensor<int32, [4]> var_252 = const()[name = tensor<string, []>("op_252"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_1_cast_fp16 = reshape(shape = var_252, x = linear_3_cast_fp16)[name = tensor<string, []>("q_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_0_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24419776)))];
+            tensor<fp16, [1024]> module_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26516992)))];
+            tensor<fp16, [1, 188, 1024]> linear_4_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_k_bias_to_fp16, weight = module_layers_0_self_attn_linear_k_weight_to_fp16, x = query_1_cast_fp16)[name = tensor<string, []>("linear_4_cast_fp16")];
+            tensor<int32, [4]> var_257 = const()[name = tensor<string, []>("op_257"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_1_cast_fp16 = reshape(shape = var_257, x = linear_4_cast_fp16)[name = tensor<string, []>("k_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_0_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26519104)))];
+            tensor<fp16, [1024]> module_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28616320)))];
+            tensor<fp16, [1, 188, 1024]> linear_5_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_v_bias_to_fp16, weight = module_layers_0_self_attn_linear_v_weight_to_fp16, x = query_1_cast_fp16)[name = tensor<string, []>("linear_5_cast_fp16")];
+            tensor<int32, [4]> var_262 = const()[name = tensor<string, []>("op_262"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_1_cast_fp16 = reshape(shape = var_262, x = linear_5_cast_fp16)[name = tensor<string, []>("v_1_cast_fp16")];
+            tensor<int32, [4]> value_3_perm_0 = const()[name = tensor<string, []>("value_3_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28618432)))];
+            tensor<fp16, [1, 188, 8, 128]> var_274_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_274_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28620544)))];
+            tensor<fp16, [1, 188, 8, 128]> var_276_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_276_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, 1, 3])];
+            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, 375]> var_278_to_fp16 = const()[name = tensor<string, []>("op_278_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28622656)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_276_cast_fp16)[name = tensor<string, []>("transpose_287")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_278_to_fp16)[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, 188, 376]> 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_286 = const()[name = tensor<string, []>("op_286"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_11_cast_fp16 = reshape(shape = var_286, x = x_9_cast_fp16)[name = tensor<string, []>("x_11_cast_fp16")];
+            tensor<int32, [4]> var_290_begin_0 = const()[name = tensor<string, []>("op_290_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_290_end_0 = const()[name = tensor<string, []>("op_290_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_290_end_mask_0 = const()[name = tensor<string, []>("op_290_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_290_cast_fp16 = slice_by_index(begin = var_290_begin_0, end = var_290_end_0, end_mask = var_290_end_mask_0, x = x_11_cast_fp16)[name = tensor<string, []>("op_290_cast_fp16")];
+            tensor<int32, [4]> var_291 = const()[name = tensor<string, []>("op_291"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_1_cast_fp16 = reshape(shape = var_291, x = var_290_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_72_perm_0 = const()[name = tensor<string, []>("transpose_72_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_73_perm_0 = const()[name = tensor<string, []>("transpose_73_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_73 = transpose(perm = transpose_73_perm_0, x = k_1_cast_fp16)[name = tensor<string, []>("transpose_285")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_72 = transpose(perm = transpose_72_perm_0, x = var_274_cast_fp16)[name = tensor<string, []>("transpose_286")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_72, y = transpose_73)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_300_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = tensor<string, []>("op_300_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, 188, 188]> _inversed_scores_1_cast_fp16 = mul(x = var_300_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, 188, 188]> mask_3 = expand_dims(axes = mask_3_axes_0, x = mask_1)[name = tensor<string, []>("mask_3")];
+            tensor<fp16, []> var_12_to_fp16 = const()[name = tensor<string, []>("op_12_to_fp16"), val = tensor<fp16, []>(-0x1.388p+13)];
+            tensor<fp16, [1, 8, 188, 188]> scores_3_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_3_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_306_cast_fp16 = softmax(axis = var_30, x = scores_3_cast_fp16)[name = tensor<string, []>("op_306_cast_fp16")];
+            tensor<fp16, []> var_11_to_fp16 = const()[name = tensor<string, []>("op_11_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 188]> input_33_cast_fp16 = select(a = var_11_to_fp16, b = var_306_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, 188, 128]> value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = v_1_cast_fp16)[name = tensor<string, []>("transpose_288")];
+            tensor<fp16, [1, 8, 188, 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_3_cast_fp16)[name = tensor<string, []>("x_13_cast_fp16")];
+            tensor<int32, [4]> var_310_perm_0 = const()[name = tensor<string, []>("op_310_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_311 = const()[name = tensor<string, []>("op_311"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_310_cast_fp16 = transpose(perm = var_310_perm_0, x = x_13_cast_fp16)[name = tensor<string, []>("transpose_284")];
+            tensor<fp16, [1, 188, 1024]> input_35_cast_fp16 = reshape(shape = var_311, x = var_310_cast_fp16)[name = tensor<string, []>("input_35_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_0_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29390720)))];
+            tensor<fp16, [1024]> module_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31487936)))];
+            tensor<fp16, [1, 188, 1024]> linear_7_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_out_bias_to_fp16, weight = module_layers_0_self_attn_linear_out_weight_to_fp16, x = input_35_cast_fp16)[name = tensor<string, []>("linear_7_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_0_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31490048)))];
+            tensor<fp16, [1024]> module_layers_0_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31492160)))];
+            tensor<fp16, [1, 188, 1024]> x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = module_layers_0_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_0_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31494272)))];
+            tensor<fp16, [2048]> module_layers_0_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35688640)))];
+            tensor<fp16, [1, 1024, 188]> input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = x_17_cast_fp16)[name = tensor<string, []>("transpose_283")];
+            tensor<fp16, [1, 2048, 188]> input_43_cast_fp16 = conv(bias = module_layers_0_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_0_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_19_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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_335_axes_0 = const()[name = tensor<string, []>("op_335_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188]> var_335 = expand_dims(axes = var_335_axes_0, x = pad_mask)[name = tensor<string, []>("op_335")];
+            tensor<fp16, [1, 1024, 188]> input_45_cast_fp16 = select(a = var_11_to_fp16, b = x_19_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_248_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35692800)))];
+            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, []>(35711296)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_47_cast_fp16)[name = tensor<string, []>("input_51_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_0_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35713408)))];
+            tensor<fp16, [1024]> module_layers_0_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37810624)))];
+            tensor<fp16, [1, 1024, 188]> x_21_cast_fp16 = conv(bias = module_layers_0_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_0_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = tensor<string, []>("transpose_282")];
+            tensor<fp16, [1, 188, 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]> module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37812736)))];
+            tensor<fp16, [1024]> module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37814848)))];
+            tensor<fp16, [1, 188, 1024]> input_59_cast_fp16 = layer_norm(axes = input_59_axes_0, beta = module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_57_cast_fp16)[name = tensor<string, []>("input_59_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_0_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37816960)))];
+            tensor<fp16, [4096]> module_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46205632)))];
+            tensor<fp16, [1, 188, 4096]> linear_8_cast_fp16 = linear(bias = module_layers_0_feed_forward2_linear1_bias_to_fp16, weight = module_layers_0_feed_forward2_linear1_weight_to_fp16, x = input_59_cast_fp16)[name = tensor<string, []>("linear_8_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_63_cast_fp16 = silu(x = linear_8_cast_fp16)[name = tensor<string, []>("input_63_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_0_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46213888)))];
+            tensor<fp16, [1024]> module_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54602560)))];
+            tensor<fp16, [1, 188, 1024]> linear_9_cast_fp16 = linear(bias = module_layers_0_feed_forward2_linear2_bias_to_fp16, weight = module_layers_0_feed_forward2_linear2_weight_to_fp16, x = input_63_cast_fp16)[name = tensor<string, []>("linear_9_cast_fp16")];
+            tensor<fp16, []> var_377_to_fp16 = const()[name = tensor<string, []>("op_377_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_378_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_377_to_fp16)[name = tensor<string, []>("op_378_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_69_cast_fp16 = add(x = input_57_cast_fp16, y = var_378_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]> module_layers_0_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54604672)))];
+            tensor<fp16, [1024]> module_layers_0_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54606784)))];
+            tensor<fp16, [1, 188, 1024]> input_71_cast_fp16 = layer_norm(axes = input_71_axes_0, beta = module_layers_0_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54608896)))];
+            tensor<fp16, [1024]> module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54611008)))];
+            tensor<fp16, [1, 188, 1024]> input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_71_cast_fp16)[name = tensor<string, []>("input_73_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_1_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54613120)))];
+            tensor<fp16, [4096]> module_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63001792)))];
+            tensor<fp16, [1, 188, 4096]> linear_10_cast_fp16 = linear(bias = module_layers_1_feed_forward1_linear1_bias_to_fp16, weight = module_layers_1_feed_forward1_linear1_weight_to_fp16, x = input_73_cast_fp16)[name = tensor<string, []>("linear_10_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_77_cast_fp16 = silu(x = linear_10_cast_fp16)[name = tensor<string, []>("input_77_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_1_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63010048)))];
+            tensor<fp16, [1024]> module_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(71398720)))];
+            tensor<fp16, [1, 188, 1024]> linear_11_cast_fp16 = linear(bias = module_layers_1_feed_forward1_linear2_bias_to_fp16, weight = module_layers_1_feed_forward1_linear2_weight_to_fp16, x = input_77_cast_fp16)[name = tensor<string, []>("linear_11_cast_fp16")];
+            tensor<fp16, []> var_408_to_fp16 = const()[name = tensor<string, []>("op_408_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_409_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_408_to_fp16)[name = tensor<string, []>("op_409_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_83_cast_fp16 = add(x = input_71_cast_fp16, y = var_409_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]> module_layers_1_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(71400832)))];
+            tensor<fp16, [1024]> module_layers_1_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(71402944)))];
+            tensor<fp16, [1, 188, 1024]> query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_self_att_weight_to_fp16, x = input_83_cast_fp16)[name = tensor<string, []>("query_3_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_1_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(71405056)))];
+            tensor<fp16, [1024]> module_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(73502272)))];
+            tensor<fp16, [1, 188, 1024]> linear_12_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_q_bias_to_fp16, weight = module_layers_1_self_attn_linear_q_weight_to_fp16, x = query_3_cast_fp16)[name = tensor<string, []>("linear_12_cast_fp16")];
+            tensor<int32, [4]> var_426 = const()[name = tensor<string, []>("op_426"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_7_cast_fp16 = reshape(shape = var_426, x = linear_12_cast_fp16)[name = tensor<string, []>("q_7_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_1_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(73504384)))];
+            tensor<fp16, [1024]> module_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(75601600)))];
+            tensor<fp16, [1, 188, 1024]> linear_13_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_k_bias_to_fp16, weight = module_layers_1_self_attn_linear_k_weight_to_fp16, x = query_3_cast_fp16)[name = tensor<string, []>("linear_13_cast_fp16")];
+            tensor<int32, [4]> var_431 = const()[name = tensor<string, []>("op_431"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_5_cast_fp16 = reshape(shape = var_431, x = linear_13_cast_fp16)[name = tensor<string, []>("k_5_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_1_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(75603712)))];
+            tensor<fp16, [1024]> module_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77700928)))];
+            tensor<fp16, [1, 188, 1024]> linear_14_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_v_bias_to_fp16, weight = module_layers_1_self_attn_linear_v_weight_to_fp16, x = query_3_cast_fp16)[name = tensor<string, []>("linear_14_cast_fp16")];
+            tensor<int32, [4]> var_436 = const()[name = tensor<string, []>("op_436"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_3_cast_fp16 = reshape(shape = var_436, x = linear_14_cast_fp16)[name = tensor<string, []>("v_3_cast_fp16")];
+            tensor<int32, [4]> value_5_perm_0 = const()[name = tensor<string, []>("value_5_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77703040)))];
+            tensor<fp16, [1, 188, 8, 128]> var_448_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_448_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77705152)))];
+            tensor<fp16, [1, 188, 8, 128]> var_450_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_450_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, 1, 3])];
+            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, 375]> var_452_to_fp16 = const()[name = tensor<string, []>("op_452_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77707264)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_450_cast_fp16)[name = tensor<string, []>("transpose_280")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_452_to_fp16)[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, 188, 376]> 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_460 = const()[name = tensor<string, []>("op_460"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_33_cast_fp16 = reshape(shape = var_460, x = x_31_cast_fp16)[name = tensor<string, []>("x_33_cast_fp16")];
+            tensor<int32, [4]> var_464_begin_0 = const()[name = tensor<string, []>("op_464_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_464_end_0 = const()[name = tensor<string, []>("op_464_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_464_end_mask_0 = const()[name = tensor<string, []>("op_464_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_464_cast_fp16 = slice_by_index(begin = var_464_begin_0, end = var_464_end_0, end_mask = var_464_end_mask_0, x = x_33_cast_fp16)[name = tensor<string, []>("op_464_cast_fp16")];
+            tensor<int32, [4]> var_465 = const()[name = tensor<string, []>("op_465"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_5_cast_fp16 = reshape(shape = var_465, x = var_464_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_74_perm_0 = const()[name = tensor<string, []>("transpose_74_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_75_perm_0 = const()[name = tensor<string, []>("transpose_75_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_75 = transpose(perm = transpose_75_perm_0, x = k_5_cast_fp16)[name = tensor<string, []>("transpose_278")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_74 = transpose(perm = transpose_74_perm_0, x = var_448_cast_fp16)[name = tensor<string, []>("transpose_279")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_74, y = transpose_75)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_474_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = tensor<string, []>("op_474_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, 188, 188]> _inversed_scores_5_cast_fp16 = mul(x = var_474_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_5_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_7_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_7_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_480_cast_fp16 = softmax(axis = var_30, x = scores_7_cast_fp16)[name = tensor<string, []>("op_480_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_85_cast_fp16 = select(a = var_11_to_fp16, b = var_480_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, 188, 128]> value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_3_cast_fp16)[name = tensor<string, []>("transpose_281")];
+            tensor<fp16, [1, 8, 188, 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_5_cast_fp16)[name = tensor<string, []>("x_35_cast_fp16")];
+            tensor<int32, [4]> var_484_perm_0 = const()[name = tensor<string, []>("op_484_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_485 = const()[name = tensor<string, []>("op_485"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_484_cast_fp16 = transpose(perm = var_484_perm_0, x = x_35_cast_fp16)[name = tensor<string, []>("transpose_277")];
+            tensor<fp16, [1, 188, 1024]> input_87_cast_fp16 = reshape(shape = var_485, x = var_484_cast_fp16)[name = tensor<string, []>("input_87_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_1_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(78475328)))];
+            tensor<fp16, [1024]> module_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(80572544)))];
+            tensor<fp16, [1, 188, 1024]> linear_16_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_out_bias_to_fp16, weight = module_layers_1_self_attn_linear_out_weight_to_fp16, x = input_87_cast_fp16)[name = tensor<string, []>("linear_16_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_1_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(80574656)))];
+            tensor<fp16, [1024]> module_layers_1_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(80576768)))];
+            tensor<fp16, [1, 188, 1024]> x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, beta = module_layers_1_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_1_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(80578880)))];
+            tensor<fp16, [2048]> module_layers_1_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(84773248)))];
+            tensor<fp16, [1, 1024, 188]> input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_39_cast_fp16)[name = tensor<string, []>("transpose_276")];
+            tensor<fp16, [1, 2048, 188]> input_95_cast_fp16 = conv(bias = module_layers_1_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_1_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_41_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_97_cast_fp16 = select(a = var_11_to_fp16, b = x_41_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_250_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(84777408)))];
+            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, []>(84795904)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_99_cast_fp16)[name = tensor<string, []>("input_103_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_1_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(84798016)))];
+            tensor<fp16, [1024]> module_layers_1_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86895232)))];
+            tensor<fp16, [1, 1024, 188]> x_43_cast_fp16 = conv(bias = module_layers_1_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_1_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_43_cast_fp16)[name = tensor<string, []>("transpose_275")];
+            tensor<fp16, [1, 188, 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]> module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86897344)))];
+            tensor<fp16, [1024]> module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86899456)))];
+            tensor<fp16, [1, 188, 1024]> input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_109_cast_fp16)[name = tensor<string, []>("input_111_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_1_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86901568)))];
+            tensor<fp16, [4096]> module_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95290240)))];
+            tensor<fp16, [1, 188, 4096]> linear_17_cast_fp16 = linear(bias = module_layers_1_feed_forward2_linear1_bias_to_fp16, weight = module_layers_1_feed_forward2_linear1_weight_to_fp16, x = input_111_cast_fp16)[name = tensor<string, []>("linear_17_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_115_cast_fp16 = silu(x = linear_17_cast_fp16)[name = tensor<string, []>("input_115_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_1_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95298496)))];
+            tensor<fp16, [1024]> module_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103687168)))];
+            tensor<fp16, [1, 188, 1024]> linear_18_cast_fp16 = linear(bias = module_layers_1_feed_forward2_linear2_bias_to_fp16, weight = module_layers_1_feed_forward2_linear2_weight_to_fp16, x = input_115_cast_fp16)[name = tensor<string, []>("linear_18_cast_fp16")];
+            tensor<fp16, []> var_551_to_fp16 = const()[name = tensor<string, []>("op_551_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_552_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_551_to_fp16)[name = tensor<string, []>("op_552_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_121_cast_fp16 = add(x = input_109_cast_fp16, y = var_552_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]> module_layers_1_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103689280)))];
+            tensor<fp16, [1024]> module_layers_1_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103691392)))];
+            tensor<fp16, [1, 188, 1024]> input_123_cast_fp16 = layer_norm(axes = input_123_axes_0, beta = module_layers_1_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103693504)))];
+            tensor<fp16, [1024]> module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103695616)))];
+            tensor<fp16, [1, 188, 1024]> input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_123_cast_fp16)[name = tensor<string, []>("input_125_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_2_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103697728)))];
+            tensor<fp16, [4096]> module_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112086400)))];
+            tensor<fp16, [1, 188, 4096]> linear_19_cast_fp16 = linear(bias = module_layers_2_feed_forward1_linear1_bias_to_fp16, weight = module_layers_2_feed_forward1_linear1_weight_to_fp16, x = input_125_cast_fp16)[name = tensor<string, []>("linear_19_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_129_cast_fp16 = silu(x = linear_19_cast_fp16)[name = tensor<string, []>("input_129_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_2_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112094656)))];
+            tensor<fp16, [1024]> module_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120483328)))];
+            tensor<fp16, [1, 188, 1024]> linear_20_cast_fp16 = linear(bias = module_layers_2_feed_forward1_linear2_bias_to_fp16, weight = module_layers_2_feed_forward1_linear2_weight_to_fp16, x = input_129_cast_fp16)[name = tensor<string, []>("linear_20_cast_fp16")];
+            tensor<fp16, []> var_582_to_fp16 = const()[name = tensor<string, []>("op_582_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_583_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_582_to_fp16)[name = tensor<string, []>("op_583_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_135_cast_fp16 = add(x = input_123_cast_fp16, y = var_583_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]> module_layers_2_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120485440)))];
+            tensor<fp16, [1024]> module_layers_2_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120487552)))];
+            tensor<fp16, [1, 188, 1024]> query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_self_att_weight_to_fp16, x = input_135_cast_fp16)[name = tensor<string, []>("query_5_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_2_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120489664)))];
+            tensor<fp16, [1024]> module_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(122586880)))];
+            tensor<fp16, [1, 188, 1024]> linear_21_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_q_bias_to_fp16, weight = module_layers_2_self_attn_linear_q_weight_to_fp16, x = query_5_cast_fp16)[name = tensor<string, []>("linear_21_cast_fp16")];
+            tensor<int32, [4]> var_600 = const()[name = tensor<string, []>("op_600"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_13_cast_fp16 = reshape(shape = var_600, x = linear_21_cast_fp16)[name = tensor<string, []>("q_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_2_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(122588992)))];
+            tensor<fp16, [1024]> module_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(124686208)))];
+            tensor<fp16, [1, 188, 1024]> linear_22_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_k_bias_to_fp16, weight = module_layers_2_self_attn_linear_k_weight_to_fp16, x = query_5_cast_fp16)[name = tensor<string, []>("linear_22_cast_fp16")];
+            tensor<int32, [4]> var_605 = const()[name = tensor<string, []>("op_605"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_9_cast_fp16 = reshape(shape = var_605, x = linear_22_cast_fp16)[name = tensor<string, []>("k_9_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_2_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(124688320)))];
+            tensor<fp16, [1024]> module_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126785536)))];
+            tensor<fp16, [1, 188, 1024]> linear_23_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_v_bias_to_fp16, weight = module_layers_2_self_attn_linear_v_weight_to_fp16, x = query_5_cast_fp16)[name = tensor<string, []>("linear_23_cast_fp16")];
+            tensor<int32, [4]> var_610 = const()[name = tensor<string, []>("op_610"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_5_cast_fp16 = reshape(shape = var_610, x = linear_23_cast_fp16)[name = tensor<string, []>("v_5_cast_fp16")];
+            tensor<int32, [4]> value_7_perm_0 = const()[name = tensor<string, []>("value_7_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126787648)))];
+            tensor<fp16, [1, 188, 8, 128]> var_622_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_622_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126789760)))];
+            tensor<fp16, [1, 188, 8, 128]> var_624_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_624_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, 1, 3])];
+            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, 375]> var_626_to_fp16 = const()[name = tensor<string, []>("op_626_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126791872)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_624_cast_fp16)[name = tensor<string, []>("transpose_273")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_626_to_fp16)[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, 188, 376]> 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_634 = const()[name = tensor<string, []>("op_634"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_55_cast_fp16 = reshape(shape = var_634, x = x_53_cast_fp16)[name = tensor<string, []>("x_55_cast_fp16")];
+            tensor<int32, [4]> var_638_begin_0 = const()[name = tensor<string, []>("op_638_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_638_end_0 = const()[name = tensor<string, []>("op_638_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_638_end_mask_0 = const()[name = tensor<string, []>("op_638_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_638_cast_fp16 = slice_by_index(begin = var_638_begin_0, end = var_638_end_0, end_mask = var_638_end_mask_0, x = x_55_cast_fp16)[name = tensor<string, []>("op_638_cast_fp16")];
+            tensor<int32, [4]> var_639 = const()[name = tensor<string, []>("op_639"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_9_cast_fp16 = reshape(shape = var_639, x = var_638_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_76_perm_0 = const()[name = tensor<string, []>("transpose_76_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_77_perm_0 = const()[name = tensor<string, []>("transpose_77_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_77 = transpose(perm = transpose_77_perm_0, x = k_9_cast_fp16)[name = tensor<string, []>("transpose_271")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_76 = transpose(perm = transpose_76_perm_0, x = var_622_cast_fp16)[name = tensor<string, []>("transpose_272")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_76, y = transpose_77)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_648_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = tensor<string, []>("op_648_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, 188, 188]> _inversed_scores_9_cast_fp16 = mul(x = var_648_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_9_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_11_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_11_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_654_cast_fp16 = softmax(axis = var_30, x = scores_11_cast_fp16)[name = tensor<string, []>("op_654_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_137_cast_fp16 = select(a = var_11_to_fp16, b = var_654_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, 188, 128]> value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_5_cast_fp16)[name = tensor<string, []>("transpose_274")];
+            tensor<fp16, [1, 8, 188, 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_7_cast_fp16)[name = tensor<string, []>("x_57_cast_fp16")];
+            tensor<int32, [4]> var_658_perm_0 = const()[name = tensor<string, []>("op_658_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_659 = const()[name = tensor<string, []>("op_659"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_658_cast_fp16 = transpose(perm = var_658_perm_0, x = x_57_cast_fp16)[name = tensor<string, []>("transpose_270")];
+            tensor<fp16, [1, 188, 1024]> input_139_cast_fp16 = reshape(shape = var_659, x = var_658_cast_fp16)[name = tensor<string, []>("input_139_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_2_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(127559936)))];
+            tensor<fp16, [1024]> module_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129657152)))];
+            tensor<fp16, [1, 188, 1024]> linear_25_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_out_bias_to_fp16, weight = module_layers_2_self_attn_linear_out_weight_to_fp16, x = input_139_cast_fp16)[name = tensor<string, []>("linear_25_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_2_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129659264)))];
+            tensor<fp16, [1024]> module_layers_2_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129661376)))];
+            tensor<fp16, [1, 188, 1024]> x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, beta = module_layers_2_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_2_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129663488)))];
+            tensor<fp16, [2048]> module_layers_2_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133857856)))];
+            tensor<fp16, [1, 1024, 188]> input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_61_cast_fp16)[name = tensor<string, []>("transpose_269")];
+            tensor<fp16, [1, 2048, 188]> input_147_cast_fp16 = conv(bias = module_layers_2_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_2_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_63_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_149_cast_fp16 = select(a = var_11_to_fp16, b = x_63_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_252_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133862016)))];
+            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, []>(133880512)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_151_cast_fp16)[name = tensor<string, []>("input_155_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_2_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133882624)))];
+            tensor<fp16, [1024]> module_layers_2_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135979840)))];
+            tensor<fp16, [1, 1024, 188]> x_65_cast_fp16 = conv(bias = module_layers_2_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_2_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_65_cast_fp16)[name = tensor<string, []>("transpose_268")];
+            tensor<fp16, [1, 188, 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]> module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135981952)))];
+            tensor<fp16, [1024]> module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135984064)))];
+            tensor<fp16, [1, 188, 1024]> input_163_cast_fp16 = layer_norm(axes = input_163_axes_0, beta = module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_161_cast_fp16)[name = tensor<string, []>("input_163_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_2_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135986176)))];
+            tensor<fp16, [4096]> module_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144374848)))];
+            tensor<fp16, [1, 188, 4096]> linear_26_cast_fp16 = linear(bias = module_layers_2_feed_forward2_linear1_bias_to_fp16, weight = module_layers_2_feed_forward2_linear1_weight_to_fp16, x = input_163_cast_fp16)[name = tensor<string, []>("linear_26_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_167_cast_fp16 = silu(x = linear_26_cast_fp16)[name = tensor<string, []>("input_167_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_2_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144383104)))];
+            tensor<fp16, [1024]> module_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152771776)))];
+            tensor<fp16, [1, 188, 1024]> linear_27_cast_fp16 = linear(bias = module_layers_2_feed_forward2_linear2_bias_to_fp16, weight = module_layers_2_feed_forward2_linear2_weight_to_fp16, x = input_167_cast_fp16)[name = tensor<string, []>("linear_27_cast_fp16")];
+            tensor<fp16, []> var_725_to_fp16 = const()[name = tensor<string, []>("op_725_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_726_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_725_to_fp16)[name = tensor<string, []>("op_726_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_173_cast_fp16 = add(x = input_161_cast_fp16, y = var_726_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]> module_layers_2_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152773888)))];
+            tensor<fp16, [1024]> module_layers_2_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152776000)))];
+            tensor<fp16, [1, 188, 1024]> input_175_cast_fp16 = layer_norm(axes = input_175_axes_0, beta = module_layers_2_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152778112)))];
+            tensor<fp16, [1024]> module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152780224)))];
+            tensor<fp16, [1, 188, 1024]> input_177_cast_fp16 = layer_norm(axes = input_177_axes_0, beta = module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_175_cast_fp16)[name = tensor<string, []>("input_177_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_3_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152782336)))];
+            tensor<fp16, [4096]> module_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161171008)))];
+            tensor<fp16, [1, 188, 4096]> linear_28_cast_fp16 = linear(bias = module_layers_3_feed_forward1_linear1_bias_to_fp16, weight = module_layers_3_feed_forward1_linear1_weight_to_fp16, x = input_177_cast_fp16)[name = tensor<string, []>("linear_28_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_181_cast_fp16 = silu(x = linear_28_cast_fp16)[name = tensor<string, []>("input_181_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_3_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161179264)))];
+            tensor<fp16, [1024]> module_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169567936)))];
+            tensor<fp16, [1, 188, 1024]> linear_29_cast_fp16 = linear(bias = module_layers_3_feed_forward1_linear2_bias_to_fp16, weight = module_layers_3_feed_forward1_linear2_weight_to_fp16, x = input_181_cast_fp16)[name = tensor<string, []>("linear_29_cast_fp16")];
+            tensor<fp16, []> var_756_to_fp16 = const()[name = tensor<string, []>("op_756_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_757_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_756_to_fp16)[name = tensor<string, []>("op_757_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_187_cast_fp16 = add(x = input_175_cast_fp16, y = var_757_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]> module_layers_3_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169570048)))];
+            tensor<fp16, [1024]> module_layers_3_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169572160)))];
+            tensor<fp16, [1, 188, 1024]> query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_self_att_weight_to_fp16, x = input_187_cast_fp16)[name = tensor<string, []>("query_7_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_3_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169574272)))];
+            tensor<fp16, [1024]> module_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171671488)))];
+            tensor<fp16, [1, 188, 1024]> linear_30_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_q_bias_to_fp16, weight = module_layers_3_self_attn_linear_q_weight_to_fp16, x = query_7_cast_fp16)[name = tensor<string, []>("linear_30_cast_fp16")];
+            tensor<int32, [4]> var_774 = const()[name = tensor<string, []>("op_774"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_19_cast_fp16 = reshape(shape = var_774, x = linear_30_cast_fp16)[name = tensor<string, []>("q_19_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_3_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171673600)))];
+            tensor<fp16, [1024]> module_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(173770816)))];
+            tensor<fp16, [1, 188, 1024]> linear_31_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_k_bias_to_fp16, weight = module_layers_3_self_attn_linear_k_weight_to_fp16, x = query_7_cast_fp16)[name = tensor<string, []>("linear_31_cast_fp16")];
+            tensor<int32, [4]> var_779 = const()[name = tensor<string, []>("op_779"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_13_cast_fp16 = reshape(shape = var_779, x = linear_31_cast_fp16)[name = tensor<string, []>("k_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_3_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(173772928)))];
+            tensor<fp16, [1024]> module_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175870144)))];
+            tensor<fp16, [1, 188, 1024]> linear_32_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_v_bias_to_fp16, weight = module_layers_3_self_attn_linear_v_weight_to_fp16, x = query_7_cast_fp16)[name = tensor<string, []>("linear_32_cast_fp16")];
+            tensor<int32, [4]> var_784 = const()[name = tensor<string, []>("op_784"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_7_cast_fp16 = reshape(shape = var_784, x = linear_32_cast_fp16)[name = tensor<string, []>("v_7_cast_fp16")];
+            tensor<int32, [4]> value_9_perm_0 = const()[name = tensor<string, []>("value_9_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175872256)))];
+            tensor<fp16, [1, 188, 8, 128]> var_796_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_796_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175874368)))];
+            tensor<fp16, [1, 188, 8, 128]> var_798_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_798_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, 1, 3])];
+            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, 375]> var_800_to_fp16 = const()[name = tensor<string, []>("op_800_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175876480)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_798_cast_fp16)[name = tensor<string, []>("transpose_266")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_800_to_fp16)[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, 188, 376]> 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_808 = const()[name = tensor<string, []>("op_808"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_77_cast_fp16 = reshape(shape = var_808, x = x_75_cast_fp16)[name = tensor<string, []>("x_77_cast_fp16")];
+            tensor<int32, [4]> var_812_begin_0 = const()[name = tensor<string, []>("op_812_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_812_end_0 = const()[name = tensor<string, []>("op_812_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_812_end_mask_0 = const()[name = tensor<string, []>("op_812_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_812_cast_fp16 = slice_by_index(begin = var_812_begin_0, end = var_812_end_0, end_mask = var_812_end_mask_0, x = x_77_cast_fp16)[name = tensor<string, []>("op_812_cast_fp16")];
+            tensor<int32, [4]> var_813 = const()[name = tensor<string, []>("op_813"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_13_cast_fp16 = reshape(shape = var_813, x = var_812_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_78_perm_0 = const()[name = tensor<string, []>("transpose_78_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_79_perm_0 = const()[name = tensor<string, []>("transpose_79_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_79 = transpose(perm = transpose_79_perm_0, x = k_13_cast_fp16)[name = tensor<string, []>("transpose_264")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_78 = transpose(perm = transpose_78_perm_0, x = var_796_cast_fp16)[name = tensor<string, []>("transpose_265")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_78, y = transpose_79)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_822_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = tensor<string, []>("op_822_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, 188, 188]> _inversed_scores_13_cast_fp16 = mul(x = var_822_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_13_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_15_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_15_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_828_cast_fp16 = softmax(axis = var_30, x = scores_15_cast_fp16)[name = tensor<string, []>("op_828_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_189_cast_fp16 = select(a = var_11_to_fp16, b = var_828_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, 188, 128]> value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_7_cast_fp16)[name = tensor<string, []>("transpose_267")];
+            tensor<fp16, [1, 8, 188, 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_9_cast_fp16)[name = tensor<string, []>("x_79_cast_fp16")];
+            tensor<int32, [4]> var_832_perm_0 = const()[name = tensor<string, []>("op_832_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_833 = const()[name = tensor<string, []>("op_833"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_832_cast_fp16 = transpose(perm = var_832_perm_0, x = x_79_cast_fp16)[name = tensor<string, []>("transpose_263")];
+            tensor<fp16, [1, 188, 1024]> input_191_cast_fp16 = reshape(shape = var_833, x = var_832_cast_fp16)[name = tensor<string, []>("input_191_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_3_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(176644544)))];
+            tensor<fp16, [1024]> module_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178741760)))];
+            tensor<fp16, [1, 188, 1024]> linear_34_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_out_bias_to_fp16, weight = module_layers_3_self_attn_linear_out_weight_to_fp16, x = input_191_cast_fp16)[name = tensor<string, []>("linear_34_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_3_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178743872)))];
+            tensor<fp16, [1024]> module_layers_3_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178745984)))];
+            tensor<fp16, [1, 188, 1024]> x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = module_layers_3_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_3_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178748096)))];
+            tensor<fp16, [2048]> module_layers_3_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182942464)))];
+            tensor<fp16, [1, 1024, 188]> input_197_cast_fp16 = transpose(perm = input_197_perm_0, x = x_83_cast_fp16)[name = tensor<string, []>("transpose_262")];
+            tensor<fp16, [1, 2048, 188]> input_199_cast_fp16 = conv(bias = module_layers_3_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_3_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_85_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_201_cast_fp16 = select(a = var_11_to_fp16, b = x_85_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_254_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182946624)))];
+            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, []>(182965120)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_203_cast_fp16)[name = tensor<string, []>("input_207_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_3_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182967232)))];
+            tensor<fp16, [1024]> module_layers_3_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185064448)))];
+            tensor<fp16, [1, 1024, 188]> x_87_cast_fp16 = conv(bias = module_layers_3_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_3_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_87_cast_fp16)[name = tensor<string, []>("transpose_261")];
+            tensor<fp16, [1, 188, 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]> module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185066560)))];
+            tensor<fp16, [1024]> module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185068672)))];
+            tensor<fp16, [1, 188, 1024]> input_215_cast_fp16 = layer_norm(axes = input_215_axes_0, beta = module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_213_cast_fp16)[name = tensor<string, []>("input_215_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_3_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185070784)))];
+            tensor<fp16, [4096]> module_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(193459456)))];
+            tensor<fp16, [1, 188, 4096]> linear_35_cast_fp16 = linear(bias = module_layers_3_feed_forward2_linear1_bias_to_fp16, weight = module_layers_3_feed_forward2_linear1_weight_to_fp16, x = input_215_cast_fp16)[name = tensor<string, []>("linear_35_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_219_cast_fp16 = silu(x = linear_35_cast_fp16)[name = tensor<string, []>("input_219_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_3_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(193467712)))];
+            tensor<fp16, [1024]> module_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201856384)))];
+            tensor<fp16, [1, 188, 1024]> linear_36_cast_fp16 = linear(bias = module_layers_3_feed_forward2_linear2_bias_to_fp16, weight = module_layers_3_feed_forward2_linear2_weight_to_fp16, x = input_219_cast_fp16)[name = tensor<string, []>("linear_36_cast_fp16")];
+            tensor<fp16, []> var_899_to_fp16 = const()[name = tensor<string, []>("op_899_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_900_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_899_to_fp16)[name = tensor<string, []>("op_900_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_225_cast_fp16 = add(x = input_213_cast_fp16, y = var_900_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]> module_layers_3_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201858496)))];
+            tensor<fp16, [1024]> module_layers_3_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201860608)))];
+            tensor<fp16, [1, 188, 1024]> input_227_cast_fp16 = layer_norm(axes = input_227_axes_0, beta = module_layers_3_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201862720)))];
+            tensor<fp16, [1024]> module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201864832)))];
+            tensor<fp16, [1, 188, 1024]> input_229_cast_fp16 = layer_norm(axes = input_229_axes_0, beta = module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_227_cast_fp16)[name = tensor<string, []>("input_229_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_4_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201866944)))];
+            tensor<fp16, [4096]> module_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210255616)))];
+            tensor<fp16, [1, 188, 4096]> linear_37_cast_fp16 = linear(bias = module_layers_4_feed_forward1_linear1_bias_to_fp16, weight = module_layers_4_feed_forward1_linear1_weight_to_fp16, x = input_229_cast_fp16)[name = tensor<string, []>("linear_37_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_233_cast_fp16 = silu(x = linear_37_cast_fp16)[name = tensor<string, []>("input_233_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_4_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210263872)))];
+            tensor<fp16, [1024]> module_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218652544)))];
+            tensor<fp16, [1, 188, 1024]> linear_38_cast_fp16 = linear(bias = module_layers_4_feed_forward1_linear2_bias_to_fp16, weight = module_layers_4_feed_forward1_linear2_weight_to_fp16, x = input_233_cast_fp16)[name = tensor<string, []>("linear_38_cast_fp16")];
+            tensor<fp16, []> var_930_to_fp16 = const()[name = tensor<string, []>("op_930_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_931_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_930_to_fp16)[name = tensor<string, []>("op_931_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_239_cast_fp16 = add(x = input_227_cast_fp16, y = var_931_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]> module_layers_4_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218654656)))];
+            tensor<fp16, [1024]> module_layers_4_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218656768)))];
+            tensor<fp16, [1, 188, 1024]> query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_self_att_weight_to_fp16, x = input_239_cast_fp16)[name = tensor<string, []>("query_9_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_4_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218658880)))];
+            tensor<fp16, [1024]> module_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220756096)))];
+            tensor<fp16, [1, 188, 1024]> linear_39_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_q_bias_to_fp16, weight = module_layers_4_self_attn_linear_q_weight_to_fp16, x = query_9_cast_fp16)[name = tensor<string, []>("linear_39_cast_fp16")];
+            tensor<int32, [4]> var_948 = const()[name = tensor<string, []>("op_948"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_25_cast_fp16 = reshape(shape = var_948, x = linear_39_cast_fp16)[name = tensor<string, []>("q_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_4_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220758208)))];
+            tensor<fp16, [1024]> module_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222855424)))];
+            tensor<fp16, [1, 188, 1024]> linear_40_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_k_bias_to_fp16, weight = module_layers_4_self_attn_linear_k_weight_to_fp16, x = query_9_cast_fp16)[name = tensor<string, []>("linear_40_cast_fp16")];
+            tensor<int32, [4]> var_953 = const()[name = tensor<string, []>("op_953"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_17_cast_fp16 = reshape(shape = var_953, x = linear_40_cast_fp16)[name = tensor<string, []>("k_17_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_4_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222857536)))];
+            tensor<fp16, [1024]> module_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224954752)))];
+            tensor<fp16, [1, 188, 1024]> linear_41_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_v_bias_to_fp16, weight = module_layers_4_self_attn_linear_v_weight_to_fp16, x = query_9_cast_fp16)[name = tensor<string, []>("linear_41_cast_fp16")];
+            tensor<int32, [4]> var_958 = const()[name = tensor<string, []>("op_958"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_9_cast_fp16 = reshape(shape = var_958, x = linear_41_cast_fp16)[name = tensor<string, []>("v_9_cast_fp16")];
+            tensor<int32, [4]> value_11_perm_0 = const()[name = tensor<string, []>("value_11_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224956864)))];
+            tensor<fp16, [1, 188, 8, 128]> var_970_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_970_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224958976)))];
+            tensor<fp16, [1, 188, 8, 128]> var_972_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_972_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, 1, 3])];
+            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, 375]> var_974_to_fp16 = const()[name = tensor<string, []>("op_974_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224961088)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_972_cast_fp16)[name = tensor<string, []>("transpose_259")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_974_to_fp16)[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, 188, 376]> 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_982 = const()[name = tensor<string, []>("op_982"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_99_cast_fp16 = reshape(shape = var_982, x = x_97_cast_fp16)[name = tensor<string, []>("x_99_cast_fp16")];
+            tensor<int32, [4]> var_986_begin_0 = const()[name = tensor<string, []>("op_986_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_986_end_0 = const()[name = tensor<string, []>("op_986_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_986_end_mask_0 = const()[name = tensor<string, []>("op_986_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_986_cast_fp16 = slice_by_index(begin = var_986_begin_0, end = var_986_end_0, end_mask = var_986_end_mask_0, x = x_99_cast_fp16)[name = tensor<string, []>("op_986_cast_fp16")];
+            tensor<int32, [4]> var_987 = const()[name = tensor<string, []>("op_987"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_17_cast_fp16 = reshape(shape = var_987, x = var_986_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_80_perm_0 = const()[name = tensor<string, []>("transpose_80_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_81_perm_0 = const()[name = tensor<string, []>("transpose_81_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_81 = transpose(perm = transpose_81_perm_0, x = k_17_cast_fp16)[name = tensor<string, []>("transpose_257")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_80 = transpose(perm = transpose_80_perm_0, x = var_970_cast_fp16)[name = tensor<string, []>("transpose_258")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_80, y = transpose_81)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_996_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = tensor<string, []>("op_996_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, 188, 188]> _inversed_scores_17_cast_fp16 = mul(x = var_996_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_17_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_19_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_19_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1002_cast_fp16 = softmax(axis = var_30, x = scores_19_cast_fp16)[name = tensor<string, []>("op_1002_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_241_cast_fp16 = select(a = var_11_to_fp16, b = var_1002_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, 188, 128]> value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_9_cast_fp16)[name = tensor<string, []>("transpose_260")];
+            tensor<fp16, [1, 8, 188, 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_11_cast_fp16)[name = tensor<string, []>("x_101_cast_fp16")];
+            tensor<int32, [4]> var_1006_perm_0 = const()[name = tensor<string, []>("op_1006_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1007 = const()[name = tensor<string, []>("op_1007"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1006_cast_fp16 = transpose(perm = var_1006_perm_0, x = x_101_cast_fp16)[name = tensor<string, []>("transpose_256")];
+            tensor<fp16, [1, 188, 1024]> input_243_cast_fp16 = reshape(shape = var_1007, x = var_1006_cast_fp16)[name = tensor<string, []>("input_243_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_4_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225729152)))];
+            tensor<fp16, [1024]> module_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227826368)))];
+            tensor<fp16, [1, 188, 1024]> linear_43_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_out_bias_to_fp16, weight = module_layers_4_self_attn_linear_out_weight_to_fp16, x = input_243_cast_fp16)[name = tensor<string, []>("linear_43_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_4_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227828480)))];
+            tensor<fp16, [1024]> module_layers_4_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227830592)))];
+            tensor<fp16, [1, 188, 1024]> x_105_cast_fp16 = layer_norm(axes = x_105_axes_0, beta = module_layers_4_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_4_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227832704)))];
+            tensor<fp16, [2048]> module_layers_4_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232027072)))];
+            tensor<fp16, [1, 1024, 188]> input_249_cast_fp16 = transpose(perm = input_249_perm_0, x = x_105_cast_fp16)[name = tensor<string, []>("transpose_255")];
+            tensor<fp16, [1, 2048, 188]> input_251_cast_fp16 = conv(bias = module_layers_4_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_4_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_107_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_253_cast_fp16 = select(a = var_11_to_fp16, b = x_107_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_256_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232031232)))];
+            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, []>(232049728)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_255_cast_fp16)[name = tensor<string, []>("input_259_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_4_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232051840)))];
+            tensor<fp16, [1024]> module_layers_4_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234149056)))];
+            tensor<fp16, [1, 1024, 188]> x_109_cast_fp16 = conv(bias = module_layers_4_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_4_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_109_cast_fp16)[name = tensor<string, []>("transpose_254")];
+            tensor<fp16, [1, 188, 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]> module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234151168)))];
+            tensor<fp16, [1024]> module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234153280)))];
+            tensor<fp16, [1, 188, 1024]> input_267_cast_fp16 = layer_norm(axes = input_267_axes_0, beta = module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_265_cast_fp16)[name = tensor<string, []>("input_267_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_4_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234155392)))];
+            tensor<fp16, [4096]> module_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(242544064)))];
+            tensor<fp16, [1, 188, 4096]> linear_44_cast_fp16 = linear(bias = module_layers_4_feed_forward2_linear1_bias_to_fp16, weight = module_layers_4_feed_forward2_linear1_weight_to_fp16, x = input_267_cast_fp16)[name = tensor<string, []>("linear_44_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_271_cast_fp16 = silu(x = linear_44_cast_fp16)[name = tensor<string, []>("input_271_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_4_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(242552320)))];
+            tensor<fp16, [1024]> module_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250940992)))];
+            tensor<fp16, [1, 188, 1024]> linear_45_cast_fp16 = linear(bias = module_layers_4_feed_forward2_linear2_bias_to_fp16, weight = module_layers_4_feed_forward2_linear2_weight_to_fp16, x = input_271_cast_fp16)[name = tensor<string, []>("linear_45_cast_fp16")];
+            tensor<fp16, []> var_1073_to_fp16 = const()[name = tensor<string, []>("op_1073_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1074_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1073_to_fp16)[name = tensor<string, []>("op_1074_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_277_cast_fp16 = add(x = input_265_cast_fp16, y = var_1074_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]> module_layers_4_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250943104)))];
+            tensor<fp16, [1024]> module_layers_4_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250945216)))];
+            tensor<fp16, [1, 188, 1024]> input_279_cast_fp16 = layer_norm(axes = input_279_axes_0, beta = module_layers_4_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250947328)))];
+            tensor<fp16, [1024]> module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250949440)))];
+            tensor<fp16, [1, 188, 1024]> input_281_cast_fp16 = layer_norm(axes = input_281_axes_0, beta = module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_279_cast_fp16)[name = tensor<string, []>("input_281_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_5_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250951552)))];
+            tensor<fp16, [4096]> module_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259340224)))];
+            tensor<fp16, [1, 188, 4096]> linear_46_cast_fp16 = linear(bias = module_layers_5_feed_forward1_linear1_bias_to_fp16, weight = module_layers_5_feed_forward1_linear1_weight_to_fp16, x = input_281_cast_fp16)[name = tensor<string, []>("linear_46_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_285_cast_fp16 = silu(x = linear_46_cast_fp16)[name = tensor<string, []>("input_285_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_5_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259348480)))];
+            tensor<fp16, [1024]> module_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(267737152)))];
+            tensor<fp16, [1, 188, 1024]> linear_47_cast_fp16 = linear(bias = module_layers_5_feed_forward1_linear2_bias_to_fp16, weight = module_layers_5_feed_forward1_linear2_weight_to_fp16, x = input_285_cast_fp16)[name = tensor<string, []>("linear_47_cast_fp16")];
+            tensor<fp16, []> var_1104_to_fp16 = const()[name = tensor<string, []>("op_1104_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1105_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1104_to_fp16)[name = tensor<string, []>("op_1105_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_291_cast_fp16 = add(x = input_279_cast_fp16, y = var_1105_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]> module_layers_5_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(267739264)))];
+            tensor<fp16, [1024]> module_layers_5_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(267741376)))];
+            tensor<fp16, [1, 188, 1024]> query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_self_att_weight_to_fp16, x = input_291_cast_fp16)[name = tensor<string, []>("query_11_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_5_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(267743488)))];
+            tensor<fp16, [1024]> module_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269840704)))];
+            tensor<fp16, [1, 188, 1024]> linear_48_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_q_bias_to_fp16, weight = module_layers_5_self_attn_linear_q_weight_to_fp16, x = query_11_cast_fp16)[name = tensor<string, []>("linear_48_cast_fp16")];
+            tensor<int32, [4]> var_1122 = const()[name = tensor<string, []>("op_1122"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_31_cast_fp16 = reshape(shape = var_1122, x = linear_48_cast_fp16)[name = tensor<string, []>("q_31_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_5_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269842816)))];
+            tensor<fp16, [1024]> module_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271940032)))];
+            tensor<fp16, [1, 188, 1024]> linear_49_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_k_bias_to_fp16, weight = module_layers_5_self_attn_linear_k_weight_to_fp16, x = query_11_cast_fp16)[name = tensor<string, []>("linear_49_cast_fp16")];
+            tensor<int32, [4]> var_1127 = const()[name = tensor<string, []>("op_1127"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_21_cast_fp16 = reshape(shape = var_1127, x = linear_49_cast_fp16)[name = tensor<string, []>("k_21_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_5_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271942144)))];
+            tensor<fp16, [1024]> module_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(274039360)))];
+            tensor<fp16, [1, 188, 1024]> linear_50_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_v_bias_to_fp16, weight = module_layers_5_self_attn_linear_v_weight_to_fp16, x = query_11_cast_fp16)[name = tensor<string, []>("linear_50_cast_fp16")];
+            tensor<int32, [4]> var_1132 = const()[name = tensor<string, []>("op_1132"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_11_cast_fp16 = reshape(shape = var_1132, x = linear_50_cast_fp16)[name = tensor<string, []>("v_11_cast_fp16")];
+            tensor<int32, [4]> value_13_perm_0 = const()[name = tensor<string, []>("value_13_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(274041472)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1144_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_1144_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(274043584)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1146_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_1146_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, 1, 3])];
+            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, 375]> var_1148_to_fp16 = const()[name = tensor<string, []>("op_1148_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(274045696)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1146_cast_fp16)[name = tensor<string, []>("transpose_252")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_1148_to_fp16)[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, 188, 376]> 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_1156 = const()[name = tensor<string, []>("op_1156"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_121_cast_fp16 = reshape(shape = var_1156, x = x_119_cast_fp16)[name = tensor<string, []>("x_121_cast_fp16")];
+            tensor<int32, [4]> var_1160_begin_0 = const()[name = tensor<string, []>("op_1160_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1160_end_0 = const()[name = tensor<string, []>("op_1160_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1160_end_mask_0 = const()[name = tensor<string, []>("op_1160_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1160_cast_fp16 = slice_by_index(begin = var_1160_begin_0, end = var_1160_end_0, end_mask = var_1160_end_mask_0, x = x_121_cast_fp16)[name = tensor<string, []>("op_1160_cast_fp16")];
+            tensor<int32, [4]> var_1161 = const()[name = tensor<string, []>("op_1161"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_21_cast_fp16 = reshape(shape = var_1161, x = var_1160_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_82_perm_0 = const()[name = tensor<string, []>("transpose_82_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_83_perm_0 = const()[name = tensor<string, []>("transpose_83_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_83 = transpose(perm = transpose_83_perm_0, x = k_21_cast_fp16)[name = tensor<string, []>("transpose_250")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_82 = transpose(perm = transpose_82_perm_0, x = var_1144_cast_fp16)[name = tensor<string, []>("transpose_251")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_82, y = transpose_83)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_1170_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = tensor<string, []>("op_1170_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, 188, 188]> _inversed_scores_21_cast_fp16 = mul(x = var_1170_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_21_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_23_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_23_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1176_cast_fp16 = softmax(axis = var_30, x = scores_23_cast_fp16)[name = tensor<string, []>("op_1176_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_293_cast_fp16 = select(a = var_11_to_fp16, b = var_1176_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, 188, 128]> value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_11_cast_fp16)[name = tensor<string, []>("transpose_253")];
+            tensor<fp16, [1, 8, 188, 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_13_cast_fp16)[name = tensor<string, []>("x_123_cast_fp16")];
+            tensor<int32, [4]> var_1180_perm_0 = const()[name = tensor<string, []>("op_1180_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1181 = const()[name = tensor<string, []>("op_1181"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1180_cast_fp16 = transpose(perm = var_1180_perm_0, x = x_123_cast_fp16)[name = tensor<string, []>("transpose_249")];
+            tensor<fp16, [1, 188, 1024]> input_295_cast_fp16 = reshape(shape = var_1181, x = var_1180_cast_fp16)[name = tensor<string, []>("input_295_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_5_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(274813760)))];
+            tensor<fp16, [1024]> module_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(276910976)))];
+            tensor<fp16, [1, 188, 1024]> linear_52_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_out_bias_to_fp16, weight = module_layers_5_self_attn_linear_out_weight_to_fp16, x = input_295_cast_fp16)[name = tensor<string, []>("linear_52_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_5_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(276913088)))];
+            tensor<fp16, [1024]> module_layers_5_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(276915200)))];
+            tensor<fp16, [1, 188, 1024]> x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = module_layers_5_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_5_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(276917312)))];
+            tensor<fp16, [2048]> module_layers_5_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281111680)))];
+            tensor<fp16, [1, 1024, 188]> input_301_cast_fp16 = transpose(perm = input_301_perm_0, x = x_127_cast_fp16)[name = tensor<string, []>("transpose_248")];
+            tensor<fp16, [1, 2048, 188]> input_303_cast_fp16 = conv(bias = module_layers_5_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_5_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_129_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_305_cast_fp16 = select(a = var_11_to_fp16, b = x_129_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_258_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281115840)))];
+            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, []>(281134336)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_307_cast_fp16)[name = tensor<string, []>("input_311_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_5_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281136448)))];
+            tensor<fp16, [1024]> module_layers_5_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283233664)))];
+            tensor<fp16, [1, 1024, 188]> x_131_cast_fp16 = conv(bias = module_layers_5_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_5_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_131_cast_fp16)[name = tensor<string, []>("transpose_247")];
+            tensor<fp16, [1, 188, 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]> module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283235776)))];
+            tensor<fp16, [1024]> module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283237888)))];
+            tensor<fp16, [1, 188, 1024]> input_319_cast_fp16 = layer_norm(axes = input_319_axes_0, beta = module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_317_cast_fp16)[name = tensor<string, []>("input_319_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_5_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283240000)))];
+            tensor<fp16, [4096]> module_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291628672)))];
+            tensor<fp16, [1, 188, 4096]> linear_53_cast_fp16 = linear(bias = module_layers_5_feed_forward2_linear1_bias_to_fp16, weight = module_layers_5_feed_forward2_linear1_weight_to_fp16, x = input_319_cast_fp16)[name = tensor<string, []>("linear_53_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_323_cast_fp16 = silu(x = linear_53_cast_fp16)[name = tensor<string, []>("input_323_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_5_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291636928)))];
+            tensor<fp16, [1024]> module_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300025600)))];
+            tensor<fp16, [1, 188, 1024]> linear_54_cast_fp16 = linear(bias = module_layers_5_feed_forward2_linear2_bias_to_fp16, weight = module_layers_5_feed_forward2_linear2_weight_to_fp16, x = input_323_cast_fp16)[name = tensor<string, []>("linear_54_cast_fp16")];
+            tensor<fp16, []> var_1247_to_fp16 = const()[name = tensor<string, []>("op_1247_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1248_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1247_to_fp16)[name = tensor<string, []>("op_1248_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_329_cast_fp16 = add(x = input_317_cast_fp16, y = var_1248_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]> module_layers_5_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300027712)))];
+            tensor<fp16, [1024]> module_layers_5_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300029824)))];
+            tensor<fp16, [1, 188, 1024]> input_331_cast_fp16 = layer_norm(axes = input_331_axes_0, beta = module_layers_5_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300031936)))];
+            tensor<fp16, [1024]> module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300034048)))];
+            tensor<fp16, [1, 188, 1024]> input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_331_cast_fp16)[name = tensor<string, []>("input_333_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_6_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300036160)))];
+            tensor<fp16, [4096]> module_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308424832)))];
+            tensor<fp16, [1, 188, 4096]> linear_55_cast_fp16 = linear(bias = module_layers_6_feed_forward1_linear1_bias_to_fp16, weight = module_layers_6_feed_forward1_linear1_weight_to_fp16, x = input_333_cast_fp16)[name = tensor<string, []>("linear_55_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_337_cast_fp16 = silu(x = linear_55_cast_fp16)[name = tensor<string, []>("input_337_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_6_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308433088)))];
+            tensor<fp16, [1024]> module_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316821760)))];
+            tensor<fp16, [1, 188, 1024]> linear_56_cast_fp16 = linear(bias = module_layers_6_feed_forward1_linear2_bias_to_fp16, weight = module_layers_6_feed_forward1_linear2_weight_to_fp16, x = input_337_cast_fp16)[name = tensor<string, []>("linear_56_cast_fp16")];
+            tensor<fp16, []> var_1278_to_fp16 = const()[name = tensor<string, []>("op_1278_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1279_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1278_to_fp16)[name = tensor<string, []>("op_1279_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_343_cast_fp16 = add(x = input_331_cast_fp16, y = var_1279_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]> module_layers_6_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316823872)))];
+            tensor<fp16, [1024]> module_layers_6_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316825984)))];
+            tensor<fp16, [1, 188, 1024]> query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_self_att_weight_to_fp16, x = input_343_cast_fp16)[name = tensor<string, []>("query_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_6_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316828096)))];
+            tensor<fp16, [1024]> module_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318925312)))];
+            tensor<fp16, [1, 188, 1024]> linear_57_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_q_bias_to_fp16, weight = module_layers_6_self_attn_linear_q_weight_to_fp16, x = query_13_cast_fp16)[name = tensor<string, []>("linear_57_cast_fp16")];
+            tensor<int32, [4]> var_1296 = const()[name = tensor<string, []>("op_1296"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_37_cast_fp16 = reshape(shape = var_1296, x = linear_57_cast_fp16)[name = tensor<string, []>("q_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_6_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318927424)))];
+            tensor<fp16, [1024]> module_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(321024640)))];
+            tensor<fp16, [1, 188, 1024]> linear_58_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_k_bias_to_fp16, weight = module_layers_6_self_attn_linear_k_weight_to_fp16, x = query_13_cast_fp16)[name = tensor<string, []>("linear_58_cast_fp16")];
+            tensor<int32, [4]> var_1301 = const()[name = tensor<string, []>("op_1301"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_25_cast_fp16 = reshape(shape = var_1301, x = linear_58_cast_fp16)[name = tensor<string, []>("k_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_6_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(321026752)))];
+            tensor<fp16, [1024]> module_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323123968)))];
+            tensor<fp16, [1, 188, 1024]> linear_59_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_v_bias_to_fp16, weight = module_layers_6_self_attn_linear_v_weight_to_fp16, x = query_13_cast_fp16)[name = tensor<string, []>("linear_59_cast_fp16")];
+            tensor<int32, [4]> var_1306 = const()[name = tensor<string, []>("op_1306"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_13_cast_fp16 = reshape(shape = var_1306, x = linear_59_cast_fp16)[name = tensor<string, []>("v_13_cast_fp16")];
+            tensor<int32, [4]> value_15_perm_0 = const()[name = tensor<string, []>("value_15_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323126080)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1318_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_1318_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323128192)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1320_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_1320_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, 1, 3])];
+            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, 375]> var_1322_to_fp16 = const()[name = tensor<string, []>("op_1322_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323130304)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1320_cast_fp16)[name = tensor<string, []>("transpose_245")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_1322_to_fp16)[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, 188, 376]> 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_1330 = const()[name = tensor<string, []>("op_1330"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_143_cast_fp16 = reshape(shape = var_1330, x = x_141_cast_fp16)[name = tensor<string, []>("x_143_cast_fp16")];
+            tensor<int32, [4]> var_1334_begin_0 = const()[name = tensor<string, []>("op_1334_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1334_end_0 = const()[name = tensor<string, []>("op_1334_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1334_end_mask_0 = const()[name = tensor<string, []>("op_1334_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1334_cast_fp16 = slice_by_index(begin = var_1334_begin_0, end = var_1334_end_0, end_mask = var_1334_end_mask_0, x = x_143_cast_fp16)[name = tensor<string, []>("op_1334_cast_fp16")];
+            tensor<int32, [4]> var_1335 = const()[name = tensor<string, []>("op_1335"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_25_cast_fp16 = reshape(shape = var_1335, x = var_1334_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_84_perm_0 = const()[name = tensor<string, []>("transpose_84_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_85_perm_0 = const()[name = tensor<string, []>("transpose_85_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_85 = transpose(perm = transpose_85_perm_0, x = k_25_cast_fp16)[name = tensor<string, []>("transpose_243")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_84 = transpose(perm = transpose_84_perm_0, x = var_1318_cast_fp16)[name = tensor<string, []>("transpose_244")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_84, y = transpose_85)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_1344_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = tensor<string, []>("op_1344_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, 188, 188]> _inversed_scores_25_cast_fp16 = mul(x = var_1344_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_25_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_27_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_27_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1350_cast_fp16 = softmax(axis = var_30, x = scores_27_cast_fp16)[name = tensor<string, []>("op_1350_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_345_cast_fp16 = select(a = var_11_to_fp16, b = var_1350_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, 188, 128]> value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_13_cast_fp16)[name = tensor<string, []>("transpose_246")];
+            tensor<fp16, [1, 8, 188, 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_15_cast_fp16)[name = tensor<string, []>("x_145_cast_fp16")];
+            tensor<int32, [4]> var_1354_perm_0 = const()[name = tensor<string, []>("op_1354_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1355 = const()[name = tensor<string, []>("op_1355"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1354_cast_fp16 = transpose(perm = var_1354_perm_0, x = x_145_cast_fp16)[name = tensor<string, []>("transpose_242")];
+            tensor<fp16, [1, 188, 1024]> input_347_cast_fp16 = reshape(shape = var_1355, x = var_1354_cast_fp16)[name = tensor<string, []>("input_347_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_6_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323898368)))];
+            tensor<fp16, [1024]> module_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325995584)))];
+            tensor<fp16, [1, 188, 1024]> linear_61_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_out_bias_to_fp16, weight = module_layers_6_self_attn_linear_out_weight_to_fp16, x = input_347_cast_fp16)[name = tensor<string, []>("linear_61_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_6_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325997696)))];
+            tensor<fp16, [1024]> module_layers_6_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325999808)))];
+            tensor<fp16, [1, 188, 1024]> x_149_cast_fp16 = layer_norm(axes = x_149_axes_0, beta = module_layers_6_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_6_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326001920)))];
+            tensor<fp16, [2048]> module_layers_6_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330196288)))];
+            tensor<fp16, [1, 1024, 188]> input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_149_cast_fp16)[name = tensor<string, []>("transpose_241")];
+            tensor<fp16, [1, 2048, 188]> input_355_cast_fp16 = conv(bias = module_layers_6_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_6_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_151_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_357_cast_fp16 = select(a = var_11_to_fp16, b = x_151_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_260_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330200448)))];
+            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, []>(330218944)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_359_cast_fp16)[name = tensor<string, []>("input_363_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_6_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330221056)))];
+            tensor<fp16, [1024]> module_layers_6_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332318272)))];
+            tensor<fp16, [1, 1024, 188]> x_153_cast_fp16 = conv(bias = module_layers_6_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_6_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_153_cast_fp16)[name = tensor<string, []>("transpose_240")];
+            tensor<fp16, [1, 188, 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]> module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332320384)))];
+            tensor<fp16, [1024]> module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332322496)))];
+            tensor<fp16, [1, 188, 1024]> input_371_cast_fp16 = layer_norm(axes = input_371_axes_0, beta = module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_369_cast_fp16)[name = tensor<string, []>("input_371_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_6_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332324608)))];
+            tensor<fp16, [4096]> module_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(340713280)))];
+            tensor<fp16, [1, 188, 4096]> linear_62_cast_fp16 = linear(bias = module_layers_6_feed_forward2_linear1_bias_to_fp16, weight = module_layers_6_feed_forward2_linear1_weight_to_fp16, x = input_371_cast_fp16)[name = tensor<string, []>("linear_62_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_375_cast_fp16 = silu(x = linear_62_cast_fp16)[name = tensor<string, []>("input_375_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_6_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(340721536)))];
+            tensor<fp16, [1024]> module_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349110208)))];
+            tensor<fp16, [1, 188, 1024]> linear_63_cast_fp16 = linear(bias = module_layers_6_feed_forward2_linear2_bias_to_fp16, weight = module_layers_6_feed_forward2_linear2_weight_to_fp16, x = input_375_cast_fp16)[name = tensor<string, []>("linear_63_cast_fp16")];
+            tensor<fp16, []> var_1421_to_fp16 = const()[name = tensor<string, []>("op_1421_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1422_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1421_to_fp16)[name = tensor<string, []>("op_1422_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_381_cast_fp16 = add(x = input_369_cast_fp16, y = var_1422_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]> module_layers_6_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349112320)))];
+            tensor<fp16, [1024]> module_layers_6_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349114432)))];
+            tensor<fp16, [1, 188, 1024]> input_383_cast_fp16 = layer_norm(axes = input_383_axes_0, beta = module_layers_6_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349116544)))];
+            tensor<fp16, [1024]> module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349118656)))];
+            tensor<fp16, [1, 188, 1024]> input_385_cast_fp16 = layer_norm(axes = input_385_axes_0, beta = module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_383_cast_fp16)[name = tensor<string, []>("input_385_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_7_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349120768)))];
+            tensor<fp16, [4096]> module_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357509440)))];
+            tensor<fp16, [1, 188, 4096]> linear_64_cast_fp16 = linear(bias = module_layers_7_feed_forward1_linear1_bias_to_fp16, weight = module_layers_7_feed_forward1_linear1_weight_to_fp16, x = input_385_cast_fp16)[name = tensor<string, []>("linear_64_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_389_cast_fp16 = silu(x = linear_64_cast_fp16)[name = tensor<string, []>("input_389_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_7_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357517696)))];
+            tensor<fp16, [1024]> module_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(365906368)))];
+            tensor<fp16, [1, 188, 1024]> linear_65_cast_fp16 = linear(bias = module_layers_7_feed_forward1_linear2_bias_to_fp16, weight = module_layers_7_feed_forward1_linear2_weight_to_fp16, x = input_389_cast_fp16)[name = tensor<string, []>("linear_65_cast_fp16")];
+            tensor<fp16, []> var_1452_to_fp16 = const()[name = tensor<string, []>("op_1452_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1453_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1452_to_fp16)[name = tensor<string, []>("op_1453_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_395_cast_fp16 = add(x = input_383_cast_fp16, y = var_1453_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]> module_layers_7_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(365908480)))];
+            tensor<fp16, [1024]> module_layers_7_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(365910592)))];
+            tensor<fp16, [1, 188, 1024]> query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_self_att_weight_to_fp16, x = input_395_cast_fp16)[name = tensor<string, []>("query_15_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_7_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(365912704)))];
+            tensor<fp16, [1024]> module_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(368009920)))];
+            tensor<fp16, [1, 188, 1024]> linear_66_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_q_bias_to_fp16, weight = module_layers_7_self_attn_linear_q_weight_to_fp16, x = query_15_cast_fp16)[name = tensor<string, []>("linear_66_cast_fp16")];
+            tensor<int32, [4]> var_1470 = const()[name = tensor<string, []>("op_1470"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_43_cast_fp16 = reshape(shape = var_1470, x = linear_66_cast_fp16)[name = tensor<string, []>("q_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_7_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(368012032)))];
+            tensor<fp16, [1024]> module_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370109248)))];
+            tensor<fp16, [1, 188, 1024]> linear_67_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_k_bias_to_fp16, weight = module_layers_7_self_attn_linear_k_weight_to_fp16, x = query_15_cast_fp16)[name = tensor<string, []>("linear_67_cast_fp16")];
+            tensor<int32, [4]> var_1475 = const()[name = tensor<string, []>("op_1475"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_29_cast_fp16 = reshape(shape = var_1475, x = linear_67_cast_fp16)[name = tensor<string, []>("k_29_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_7_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370111360)))];
+            tensor<fp16, [1024]> module_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372208576)))];
+            tensor<fp16, [1, 188, 1024]> linear_68_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_v_bias_to_fp16, weight = module_layers_7_self_attn_linear_v_weight_to_fp16, x = query_15_cast_fp16)[name = tensor<string, []>("linear_68_cast_fp16")];
+            tensor<int32, [4]> var_1480 = const()[name = tensor<string, []>("op_1480"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_15_cast_fp16 = reshape(shape = var_1480, x = linear_68_cast_fp16)[name = tensor<string, []>("v_15_cast_fp16")];
+            tensor<int32, [4]> value_17_perm_0 = const()[name = tensor<string, []>("value_17_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372210688)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1492_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_1492_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372212800)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1494_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_1494_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, 1, 3])];
+            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, 375]> var_1496_to_fp16 = const()[name = tensor<string, []>("op_1496_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372214912)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1494_cast_fp16)[name = tensor<string, []>("transpose_238")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_1496_to_fp16)[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, 188, 376]> 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_1504 = const()[name = tensor<string, []>("op_1504"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_165_cast_fp16 = reshape(shape = var_1504, x = x_163_cast_fp16)[name = tensor<string, []>("x_165_cast_fp16")];
+            tensor<int32, [4]> var_1508_begin_0 = const()[name = tensor<string, []>("op_1508_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1508_end_0 = const()[name = tensor<string, []>("op_1508_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1508_end_mask_0 = const()[name = tensor<string, []>("op_1508_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1508_cast_fp16 = slice_by_index(begin = var_1508_begin_0, end = var_1508_end_0, end_mask = var_1508_end_mask_0, x = x_165_cast_fp16)[name = tensor<string, []>("op_1508_cast_fp16")];
+            tensor<int32, [4]> var_1509 = const()[name = tensor<string, []>("op_1509"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_29_cast_fp16 = reshape(shape = var_1509, x = var_1508_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_86_perm_0 = const()[name = tensor<string, []>("transpose_86_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_87_perm_0 = const()[name = tensor<string, []>("transpose_87_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_87 = transpose(perm = transpose_87_perm_0, x = k_29_cast_fp16)[name = tensor<string, []>("transpose_236")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_86 = transpose(perm = transpose_86_perm_0, x = var_1492_cast_fp16)[name = tensor<string, []>("transpose_237")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_86, y = transpose_87)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_1518_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = tensor<string, []>("op_1518_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, 188, 188]> _inversed_scores_29_cast_fp16 = mul(x = var_1518_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_29_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_31_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_31_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1524_cast_fp16 = softmax(axis = var_30, x = scores_31_cast_fp16)[name = tensor<string, []>("op_1524_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_397_cast_fp16 = select(a = var_11_to_fp16, b = var_1524_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, 188, 128]> value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_15_cast_fp16)[name = tensor<string, []>("transpose_239")];
+            tensor<fp16, [1, 8, 188, 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_17_cast_fp16)[name = tensor<string, []>("x_167_cast_fp16")];
+            tensor<int32, [4]> var_1528_perm_0 = const()[name = tensor<string, []>("op_1528_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1529 = const()[name = tensor<string, []>("op_1529"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1528_cast_fp16 = transpose(perm = var_1528_perm_0, x = x_167_cast_fp16)[name = tensor<string, []>("transpose_235")];
+            tensor<fp16, [1, 188, 1024]> input_399_cast_fp16 = reshape(shape = var_1529, x = var_1528_cast_fp16)[name = tensor<string, []>("input_399_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_7_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372982976)))];
+            tensor<fp16, [1024]> module_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(375080192)))];
+            tensor<fp16, [1, 188, 1024]> linear_70_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_out_bias_to_fp16, weight = module_layers_7_self_attn_linear_out_weight_to_fp16, x = input_399_cast_fp16)[name = tensor<string, []>("linear_70_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_7_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(375082304)))];
+            tensor<fp16, [1024]> module_layers_7_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(375084416)))];
+            tensor<fp16, [1, 188, 1024]> x_171_cast_fp16 = layer_norm(axes = x_171_axes_0, beta = module_layers_7_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_7_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(375086528)))];
+            tensor<fp16, [2048]> module_layers_7_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(379280896)))];
+            tensor<fp16, [1, 1024, 188]> input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = x_171_cast_fp16)[name = tensor<string, []>("transpose_234")];
+            tensor<fp16, [1, 2048, 188]> input_407_cast_fp16 = conv(bias = module_layers_7_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_7_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_173_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_409_cast_fp16 = select(a = var_11_to_fp16, b = x_173_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_262_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(379285056)))];
+            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, []>(379303552)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_411_cast_fp16)[name = tensor<string, []>("input_415_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_7_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(379305664)))];
+            tensor<fp16, [1024]> module_layers_7_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381402880)))];
+            tensor<fp16, [1, 1024, 188]> x_175_cast_fp16 = conv(bias = module_layers_7_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_7_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_175_cast_fp16)[name = tensor<string, []>("transpose_233")];
+            tensor<fp16, [1, 188, 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]> module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381404992)))];
+            tensor<fp16, [1024]> module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381407104)))];
+            tensor<fp16, [1, 188, 1024]> input_423_cast_fp16 = layer_norm(axes = input_423_axes_0, beta = module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_421_cast_fp16)[name = tensor<string, []>("input_423_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_7_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381409216)))];
+            tensor<fp16, [4096]> module_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389797888)))];
+            tensor<fp16, [1, 188, 4096]> linear_71_cast_fp16 = linear(bias = module_layers_7_feed_forward2_linear1_bias_to_fp16, weight = module_layers_7_feed_forward2_linear1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor<string, []>("linear_71_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_427_cast_fp16 = silu(x = linear_71_cast_fp16)[name = tensor<string, []>("input_427_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_7_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389806144)))];
+            tensor<fp16, [1024]> module_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(398194816)))];
+            tensor<fp16, [1, 188, 1024]> linear_72_cast_fp16 = linear(bias = module_layers_7_feed_forward2_linear2_bias_to_fp16, weight = module_layers_7_feed_forward2_linear2_weight_to_fp16, x = input_427_cast_fp16)[name = tensor<string, []>("linear_72_cast_fp16")];
+            tensor<fp16, []> var_1595_to_fp16 = const()[name = tensor<string, []>("op_1595_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1596_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1595_to_fp16)[name = tensor<string, []>("op_1596_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_433_cast_fp16 = add(x = input_421_cast_fp16, y = var_1596_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]> module_layers_7_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(398196928)))];
+            tensor<fp16, [1024]> module_layers_7_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(398199040)))];
+            tensor<fp16, [1, 188, 1024]> input_435_cast_fp16 = layer_norm(axes = input_435_axes_0, beta = module_layers_7_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(398201152)))];
+            tensor<fp16, [1024]> module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(398203264)))];
+            tensor<fp16, [1, 188, 1024]> input_437_cast_fp16 = layer_norm(axes = input_437_axes_0, beta = module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_435_cast_fp16)[name = tensor<string, []>("input_437_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_8_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(398205376)))];
+            tensor<fp16, [4096]> module_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(406594048)))];
+            tensor<fp16, [1, 188, 4096]> linear_73_cast_fp16 = linear(bias = module_layers_8_feed_forward1_linear1_bias_to_fp16, weight = module_layers_8_feed_forward1_linear1_weight_to_fp16, x = input_437_cast_fp16)[name = tensor<string, []>("linear_73_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_441_cast_fp16 = silu(x = linear_73_cast_fp16)[name = tensor<string, []>("input_441_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_8_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(406602304)))];
+            tensor<fp16, [1024]> module_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(414990976)))];
+            tensor<fp16, [1, 188, 1024]> linear_74_cast_fp16 = linear(bias = module_layers_8_feed_forward1_linear2_bias_to_fp16, weight = module_layers_8_feed_forward1_linear2_weight_to_fp16, x = input_441_cast_fp16)[name = tensor<string, []>("linear_74_cast_fp16")];
+            tensor<fp16, []> var_1626_to_fp16 = const()[name = tensor<string, []>("op_1626_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1627_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1626_to_fp16)[name = tensor<string, []>("op_1627_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_447_cast_fp16 = add(x = input_435_cast_fp16, y = var_1627_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]> module_layers_8_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(414993088)))];
+            tensor<fp16, [1024]> module_layers_8_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(414995200)))];
+            tensor<fp16, [1, 188, 1024]> query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_self_att_weight_to_fp16, x = input_447_cast_fp16)[name = tensor<string, []>("query_17_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_8_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(414997312)))];
+            tensor<fp16, [1024]> module_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(417094528)))];
+            tensor<fp16, [1, 188, 1024]> linear_75_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_q_bias_to_fp16, weight = module_layers_8_self_attn_linear_q_weight_to_fp16, x = query_17_cast_fp16)[name = tensor<string, []>("linear_75_cast_fp16")];
+            tensor<int32, [4]> var_1644 = const()[name = tensor<string, []>("op_1644"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_49_cast_fp16 = reshape(shape = var_1644, x = linear_75_cast_fp16)[name = tensor<string, []>("q_49_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_8_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(417096640)))];
+            tensor<fp16, [1024]> module_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(419193856)))];
+            tensor<fp16, [1, 188, 1024]> linear_76_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_k_bias_to_fp16, weight = module_layers_8_self_attn_linear_k_weight_to_fp16, x = query_17_cast_fp16)[name = tensor<string, []>("linear_76_cast_fp16")];
+            tensor<int32, [4]> var_1649 = const()[name = tensor<string, []>("op_1649"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_33_cast_fp16 = reshape(shape = var_1649, x = linear_76_cast_fp16)[name = tensor<string, []>("k_33_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_8_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(419195968)))];
+            tensor<fp16, [1024]> module_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421293184)))];
+            tensor<fp16, [1, 188, 1024]> linear_77_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_v_bias_to_fp16, weight = module_layers_8_self_attn_linear_v_weight_to_fp16, x = query_17_cast_fp16)[name = tensor<string, []>("linear_77_cast_fp16")];
+            tensor<int32, [4]> var_1654 = const()[name = tensor<string, []>("op_1654"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_17_cast_fp16 = reshape(shape = var_1654, x = linear_77_cast_fp16)[name = tensor<string, []>("v_17_cast_fp16")];
+            tensor<int32, [4]> value_19_perm_0 = const()[name = tensor<string, []>("value_19_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421295296)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1666_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_1666_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421297408)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1668_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_1668_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, 1, 3])];
+            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, 375]> var_1670_to_fp16 = const()[name = tensor<string, []>("op_1670_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421299520)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1668_cast_fp16)[name = tensor<string, []>("transpose_231")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_1670_to_fp16)[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, 188, 376]> 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_1678 = const()[name = tensor<string, []>("op_1678"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_187_cast_fp16 = reshape(shape = var_1678, x = x_185_cast_fp16)[name = tensor<string, []>("x_187_cast_fp16")];
+            tensor<int32, [4]> var_1682_begin_0 = const()[name = tensor<string, []>("op_1682_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1682_end_0 = const()[name = tensor<string, []>("op_1682_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1682_end_mask_0 = const()[name = tensor<string, []>("op_1682_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1682_cast_fp16 = slice_by_index(begin = var_1682_begin_0, end = var_1682_end_0, end_mask = var_1682_end_mask_0, x = x_187_cast_fp16)[name = tensor<string, []>("op_1682_cast_fp16")];
+            tensor<int32, [4]> var_1683 = const()[name = tensor<string, []>("op_1683"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_33_cast_fp16 = reshape(shape = var_1683, x = var_1682_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_88_perm_0 = const()[name = tensor<string, []>("transpose_88_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_89_perm_0 = const()[name = tensor<string, []>("transpose_89_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_89 = transpose(perm = transpose_89_perm_0, x = k_33_cast_fp16)[name = tensor<string, []>("transpose_229")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_88 = transpose(perm = transpose_88_perm_0, x = var_1666_cast_fp16)[name = tensor<string, []>("transpose_230")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_88, y = transpose_89)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_1692_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = tensor<string, []>("op_1692_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, 188, 188]> _inversed_scores_33_cast_fp16 = mul(x = var_1692_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_33_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_35_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_35_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1698_cast_fp16 = softmax(axis = var_30, x = scores_35_cast_fp16)[name = tensor<string, []>("op_1698_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_449_cast_fp16 = select(a = var_11_to_fp16, b = var_1698_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, 188, 128]> value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_17_cast_fp16)[name = tensor<string, []>("transpose_232")];
+            tensor<fp16, [1, 8, 188, 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_19_cast_fp16)[name = tensor<string, []>("x_189_cast_fp16")];
+            tensor<int32, [4]> var_1702_perm_0 = const()[name = tensor<string, []>("op_1702_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1703 = const()[name = tensor<string, []>("op_1703"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1702_cast_fp16 = transpose(perm = var_1702_perm_0, x = x_189_cast_fp16)[name = tensor<string, []>("transpose_228")];
+            tensor<fp16, [1, 188, 1024]> input_451_cast_fp16 = reshape(shape = var_1703, x = var_1702_cast_fp16)[name = tensor<string, []>("input_451_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_8_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422067584)))];
+            tensor<fp16, [1024]> module_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(424164800)))];
+            tensor<fp16, [1, 188, 1024]> linear_79_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_out_bias_to_fp16, weight = module_layers_8_self_attn_linear_out_weight_to_fp16, x = input_451_cast_fp16)[name = tensor<string, []>("linear_79_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_8_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(424166912)))];
+            tensor<fp16, [1024]> module_layers_8_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(424169024)))];
+            tensor<fp16, [1, 188, 1024]> x_193_cast_fp16 = layer_norm(axes = x_193_axes_0, beta = module_layers_8_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_8_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(424171136)))];
+            tensor<fp16, [2048]> module_layers_8_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428365504)))];
+            tensor<fp16, [1, 1024, 188]> input_457_cast_fp16 = transpose(perm = input_457_perm_0, x = x_193_cast_fp16)[name = tensor<string, []>("transpose_227")];
+            tensor<fp16, [1, 2048, 188]> input_459_cast_fp16 = conv(bias = module_layers_8_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_8_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_195_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_461_cast_fp16 = select(a = var_11_to_fp16, b = x_195_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_264_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428369664)))];
+            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, []>(428388160)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_463_cast_fp16)[name = tensor<string, []>("input_467_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_8_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428390272)))];
+            tensor<fp16, [1024]> module_layers_8_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430487488)))];
+            tensor<fp16, [1, 1024, 188]> x_197_cast_fp16 = conv(bias = module_layers_8_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_8_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_197_cast_fp16)[name = tensor<string, []>("transpose_226")];
+            tensor<fp16, [1, 188, 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]> module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430489600)))];
+            tensor<fp16, [1024]> module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430491712)))];
+            tensor<fp16, [1, 188, 1024]> input_475_cast_fp16 = layer_norm(axes = input_475_axes_0, beta = module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_473_cast_fp16)[name = tensor<string, []>("input_475_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_8_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430493824)))];
+            tensor<fp16, [4096]> module_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(438882496)))];
+            tensor<fp16, [1, 188, 4096]> linear_80_cast_fp16 = linear(bias = module_layers_8_feed_forward2_linear1_bias_to_fp16, weight = module_layers_8_feed_forward2_linear1_weight_to_fp16, x = input_475_cast_fp16)[name = tensor<string, []>("linear_80_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_479_cast_fp16 = silu(x = linear_80_cast_fp16)[name = tensor<string, []>("input_479_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_8_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(438890752)))];
+            tensor<fp16, [1024]> module_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(447279424)))];
+            tensor<fp16, [1, 188, 1024]> linear_81_cast_fp16 = linear(bias = module_layers_8_feed_forward2_linear2_bias_to_fp16, weight = module_layers_8_feed_forward2_linear2_weight_to_fp16, x = input_479_cast_fp16)[name = tensor<string, []>("linear_81_cast_fp16")];
+            tensor<fp16, []> var_1769_to_fp16 = const()[name = tensor<string, []>("op_1769_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1770_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_1769_to_fp16)[name = tensor<string, []>("op_1770_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_485_cast_fp16 = add(x = input_473_cast_fp16, y = var_1770_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]> module_layers_8_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(447281536)))];
+            tensor<fp16, [1024]> module_layers_8_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(447283648)))];
+            tensor<fp16, [1, 188, 1024]> input_487_cast_fp16 = layer_norm(axes = input_487_axes_0, beta = module_layers_8_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(447285760)))];
+            tensor<fp16, [1024]> module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(447287872)))];
+            tensor<fp16, [1, 188, 1024]> input_489_cast_fp16 = layer_norm(axes = input_489_axes_0, beta = module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_487_cast_fp16)[name = tensor<string, []>("input_489_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_9_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(447289984)))];
+            tensor<fp16, [4096]> module_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(455678656)))];
+            tensor<fp16, [1, 188, 4096]> linear_82_cast_fp16 = linear(bias = module_layers_9_feed_forward1_linear1_bias_to_fp16, weight = module_layers_9_feed_forward1_linear1_weight_to_fp16, x = input_489_cast_fp16)[name = tensor<string, []>("linear_82_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_493_cast_fp16 = silu(x = linear_82_cast_fp16)[name = tensor<string, []>("input_493_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_9_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(455686912)))];
+            tensor<fp16, [1024]> module_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(464075584)))];
+            tensor<fp16, [1, 188, 1024]> linear_83_cast_fp16 = linear(bias = module_layers_9_feed_forward1_linear2_bias_to_fp16, weight = module_layers_9_feed_forward1_linear2_weight_to_fp16, x = input_493_cast_fp16)[name = tensor<string, []>("linear_83_cast_fp16")];
+            tensor<fp16, []> var_1800_to_fp16 = const()[name = tensor<string, []>("op_1800_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1801_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_1800_to_fp16)[name = tensor<string, []>("op_1801_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_499_cast_fp16 = add(x = input_487_cast_fp16, y = var_1801_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]> module_layers_9_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(464077696)))];
+            tensor<fp16, [1024]> module_layers_9_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(464079808)))];
+            tensor<fp16, [1, 188, 1024]> query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_self_att_weight_to_fp16, x = input_499_cast_fp16)[name = tensor<string, []>("query_19_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_9_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(464081920)))];
+            tensor<fp16, [1024]> module_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466179136)))];
+            tensor<fp16, [1, 188, 1024]> linear_84_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_q_bias_to_fp16, weight = module_layers_9_self_attn_linear_q_weight_to_fp16, x = query_19_cast_fp16)[name = tensor<string, []>("linear_84_cast_fp16")];
+            tensor<int32, [4]> var_1818 = const()[name = tensor<string, []>("op_1818"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_55_cast_fp16 = reshape(shape = var_1818, x = linear_84_cast_fp16)[name = tensor<string, []>("q_55_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_9_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466181248)))];
+            tensor<fp16, [1024]> module_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468278464)))];
+            tensor<fp16, [1, 188, 1024]> linear_85_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_k_bias_to_fp16, weight = module_layers_9_self_attn_linear_k_weight_to_fp16, x = query_19_cast_fp16)[name = tensor<string, []>("linear_85_cast_fp16")];
+            tensor<int32, [4]> var_1823 = const()[name = tensor<string, []>("op_1823"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_37_cast_fp16 = reshape(shape = var_1823, x = linear_85_cast_fp16)[name = tensor<string, []>("k_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_9_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468280576)))];
+            tensor<fp16, [1024]> module_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470377792)))];
+            tensor<fp16, [1, 188, 1024]> linear_86_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_v_bias_to_fp16, weight = module_layers_9_self_attn_linear_v_weight_to_fp16, x = query_19_cast_fp16)[name = tensor<string, []>("linear_86_cast_fp16")];
+            tensor<int32, [4]> var_1828 = const()[name = tensor<string, []>("op_1828"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_19_cast_fp16 = reshape(shape = var_1828, x = linear_86_cast_fp16)[name = tensor<string, []>("v_19_cast_fp16")];
+            tensor<int32, [4]> value_21_perm_0 = const()[name = tensor<string, []>("value_21_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470379904)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1840_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_1840_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470382016)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1842_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_1842_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, 1, 3])];
+            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, 375]> var_1844_to_fp16 = const()[name = tensor<string, []>("op_1844_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470384128)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1842_cast_fp16)[name = tensor<string, []>("transpose_224")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_1844_to_fp16)[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, 188, 376]> 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_1852 = const()[name = tensor<string, []>("op_1852"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_209_cast_fp16 = reshape(shape = var_1852, x = x_207_cast_fp16)[name = tensor<string, []>("x_209_cast_fp16")];
+            tensor<int32, [4]> var_1856_begin_0 = const()[name = tensor<string, []>("op_1856_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1856_end_0 = const()[name = tensor<string, []>("op_1856_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1856_end_mask_0 = const()[name = tensor<string, []>("op_1856_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1856_cast_fp16 = slice_by_index(begin = var_1856_begin_0, end = var_1856_end_0, end_mask = var_1856_end_mask_0, x = x_209_cast_fp16)[name = tensor<string, []>("op_1856_cast_fp16")];
+            tensor<int32, [4]> var_1857 = const()[name = tensor<string, []>("op_1857"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_37_cast_fp16 = reshape(shape = var_1857, x = var_1856_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_90_perm_0 = const()[name = tensor<string, []>("transpose_90_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_91_perm_0 = const()[name = tensor<string, []>("transpose_91_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_91 = transpose(perm = transpose_91_perm_0, x = k_37_cast_fp16)[name = tensor<string, []>("transpose_222")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_90 = transpose(perm = transpose_90_perm_0, x = var_1840_cast_fp16)[name = tensor<string, []>("transpose_223")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_90, y = transpose_91)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_1866_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = tensor<string, []>("op_1866_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, 188, 188]> _inversed_scores_37_cast_fp16 = mul(x = var_1866_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_37_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_39_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_39_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1872_cast_fp16 = softmax(axis = var_30, x = scores_39_cast_fp16)[name = tensor<string, []>("op_1872_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_501_cast_fp16 = select(a = var_11_to_fp16, b = var_1872_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, 188, 128]> value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_19_cast_fp16)[name = tensor<string, []>("transpose_225")];
+            tensor<fp16, [1, 8, 188, 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_21_cast_fp16)[name = tensor<string, []>("x_211_cast_fp16")];
+            tensor<int32, [4]> var_1876_perm_0 = const()[name = tensor<string, []>("op_1876_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1877 = const()[name = tensor<string, []>("op_1877"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1876_cast_fp16 = transpose(perm = var_1876_perm_0, x = x_211_cast_fp16)[name = tensor<string, []>("transpose_221")];
+            tensor<fp16, [1, 188, 1024]> input_503_cast_fp16 = reshape(shape = var_1877, x = var_1876_cast_fp16)[name = tensor<string, []>("input_503_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_9_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(471152192)))];
+            tensor<fp16, [1024]> module_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(473249408)))];
+            tensor<fp16, [1, 188, 1024]> linear_88_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_out_bias_to_fp16, weight = module_layers_9_self_attn_linear_out_weight_to_fp16, x = input_503_cast_fp16)[name = tensor<string, []>("linear_88_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_9_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(473251520)))];
+            tensor<fp16, [1024]> module_layers_9_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(473253632)))];
+            tensor<fp16, [1, 188, 1024]> x_215_cast_fp16 = layer_norm(axes = x_215_axes_0, beta = module_layers_9_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_9_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(473255744)))];
+            tensor<fp16, [2048]> module_layers_9_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477450112)))];
+            tensor<fp16, [1, 1024, 188]> input_509_cast_fp16 = transpose(perm = input_509_perm_0, x = x_215_cast_fp16)[name = tensor<string, []>("transpose_220")];
+            tensor<fp16, [1, 2048, 188]> input_511_cast_fp16 = conv(bias = module_layers_9_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_9_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_217_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_513_cast_fp16 = select(a = var_11_to_fp16, b = x_217_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_266_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477454272)))];
+            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, []>(477472768)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_515_cast_fp16)[name = tensor<string, []>("input_519_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_9_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477474880)))];
+            tensor<fp16, [1024]> module_layers_9_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479572096)))];
+            tensor<fp16, [1, 1024, 188]> x_219_cast_fp16 = conv(bias = module_layers_9_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_9_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_219_cast_fp16)[name = tensor<string, []>("transpose_219")];
+            tensor<fp16, [1, 188, 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]> module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479574208)))];
+            tensor<fp16, [1024]> module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479576320)))];
+            tensor<fp16, [1, 188, 1024]> input_527_cast_fp16 = layer_norm(axes = input_527_axes_0, beta = module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_525_cast_fp16)[name = tensor<string, []>("input_527_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_9_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479578432)))];
+            tensor<fp16, [4096]> module_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(487967104)))];
+            tensor<fp16, [1, 188, 4096]> linear_89_cast_fp16 = linear(bias = module_layers_9_feed_forward2_linear1_bias_to_fp16, weight = module_layers_9_feed_forward2_linear1_weight_to_fp16, x = input_527_cast_fp16)[name = tensor<string, []>("linear_89_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_531_cast_fp16 = silu(x = linear_89_cast_fp16)[name = tensor<string, []>("input_531_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_9_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(487975360)))];
+            tensor<fp16, [1024]> module_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496364032)))];
+            tensor<fp16, [1, 188, 1024]> linear_90_cast_fp16 = linear(bias = module_layers_9_feed_forward2_linear2_bias_to_fp16, weight = module_layers_9_feed_forward2_linear2_weight_to_fp16, x = input_531_cast_fp16)[name = tensor<string, []>("linear_90_cast_fp16")];
+            tensor<fp16, []> var_1943_to_fp16 = const()[name = tensor<string, []>("op_1943_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1944_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_1943_to_fp16)[name = tensor<string, []>("op_1944_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_537_cast_fp16 = add(x = input_525_cast_fp16, y = var_1944_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]> module_layers_9_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496366144)))];
+            tensor<fp16, [1024]> module_layers_9_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496368256)))];
+            tensor<fp16, [1, 188, 1024]> input_539_cast_fp16 = layer_norm(axes = input_539_axes_0, beta = module_layers_9_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496370368)))];
+            tensor<fp16, [1024]> module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496372480)))];
+            tensor<fp16, [1, 188, 1024]> input_541_cast_fp16 = layer_norm(axes = input_541_axes_0, beta = module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_539_cast_fp16)[name = tensor<string, []>("input_541_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_10_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496374592)))];
+            tensor<fp16, [4096]> module_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504763264)))];
+            tensor<fp16, [1, 188, 4096]> linear_91_cast_fp16 = linear(bias = module_layers_10_feed_forward1_linear1_bias_to_fp16, weight = module_layers_10_feed_forward1_linear1_weight_to_fp16, x = input_541_cast_fp16)[name = tensor<string, []>("linear_91_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_545_cast_fp16 = silu(x = linear_91_cast_fp16)[name = tensor<string, []>("input_545_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_10_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504771520)))];
+            tensor<fp16, [1024]> module_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513160192)))];
+            tensor<fp16, [1, 188, 1024]> linear_92_cast_fp16 = linear(bias = module_layers_10_feed_forward1_linear2_bias_to_fp16, weight = module_layers_10_feed_forward1_linear2_weight_to_fp16, x = input_545_cast_fp16)[name = tensor<string, []>("linear_92_cast_fp16")];
+            tensor<fp16, []> var_1974_to_fp16 = const()[name = tensor<string, []>("op_1974_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1975_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_1974_to_fp16)[name = tensor<string, []>("op_1975_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_551_cast_fp16 = add(x = input_539_cast_fp16, y = var_1975_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]> module_layers_10_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513162304)))];
+            tensor<fp16, [1024]> module_layers_10_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513164416)))];
+            tensor<fp16, [1, 188, 1024]> query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_self_att_weight_to_fp16, x = input_551_cast_fp16)[name = tensor<string, []>("query_21_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_10_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513166528)))];
+            tensor<fp16, [1024]> module_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(515263744)))];
+            tensor<fp16, [1, 188, 1024]> linear_93_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_q_bias_to_fp16, weight = module_layers_10_self_attn_linear_q_weight_to_fp16, x = query_21_cast_fp16)[name = tensor<string, []>("linear_93_cast_fp16")];
+            tensor<int32, [4]> var_1992 = const()[name = tensor<string, []>("op_1992"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_61_cast_fp16 = reshape(shape = var_1992, x = linear_93_cast_fp16)[name = tensor<string, []>("q_61_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_10_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(515265856)))];
+            tensor<fp16, [1024]> module_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(517363072)))];
+            tensor<fp16, [1, 188, 1024]> linear_94_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_k_bias_to_fp16, weight = module_layers_10_self_attn_linear_k_weight_to_fp16, x = query_21_cast_fp16)[name = tensor<string, []>("linear_94_cast_fp16")];
+            tensor<int32, [4]> var_1997 = const()[name = tensor<string, []>("op_1997"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_41_cast_fp16 = reshape(shape = var_1997, x = linear_94_cast_fp16)[name = tensor<string, []>("k_41_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_10_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(517365184)))];
+            tensor<fp16, [1024]> module_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(519462400)))];
+            tensor<fp16, [1, 188, 1024]> linear_95_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_v_bias_to_fp16, weight = module_layers_10_self_attn_linear_v_weight_to_fp16, x = query_21_cast_fp16)[name = tensor<string, []>("linear_95_cast_fp16")];
+            tensor<int32, [4]> var_2002 = const()[name = tensor<string, []>("op_2002"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_21_cast_fp16 = reshape(shape = var_2002, x = linear_95_cast_fp16)[name = tensor<string, []>("v_21_cast_fp16")];
+            tensor<int32, [4]> value_23_perm_0 = const()[name = tensor<string, []>("value_23_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(519464512)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2014_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_2014_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(519466624)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2016_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_2016_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, 1, 3])];
+            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, 375]> var_2018_to_fp16 = const()[name = tensor<string, []>("op_2018_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(519468736)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2016_cast_fp16)[name = tensor<string, []>("transpose_217")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_2018_to_fp16)[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, 188, 376]> 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_2026 = const()[name = tensor<string, []>("op_2026"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_231_cast_fp16 = reshape(shape = var_2026, x = x_229_cast_fp16)[name = tensor<string, []>("x_231_cast_fp16")];
+            tensor<int32, [4]> var_2030_begin_0 = const()[name = tensor<string, []>("op_2030_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2030_end_0 = const()[name = tensor<string, []>("op_2030_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2030_end_mask_0 = const()[name = tensor<string, []>("op_2030_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2030_cast_fp16 = slice_by_index(begin = var_2030_begin_0, end = var_2030_end_0, end_mask = var_2030_end_mask_0, x = x_231_cast_fp16)[name = tensor<string, []>("op_2030_cast_fp16")];
+            tensor<int32, [4]> var_2031 = const()[name = tensor<string, []>("op_2031"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_41_cast_fp16 = reshape(shape = var_2031, x = var_2030_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_92_perm_0 = const()[name = tensor<string, []>("transpose_92_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_93_perm_0 = const()[name = tensor<string, []>("transpose_93_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_93 = transpose(perm = transpose_93_perm_0, x = k_41_cast_fp16)[name = tensor<string, []>("transpose_215")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_92 = transpose(perm = transpose_92_perm_0, x = var_2014_cast_fp16)[name = tensor<string, []>("transpose_216")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_92, y = transpose_93)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_2040_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = tensor<string, []>("op_2040_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, 188, 188]> _inversed_scores_41_cast_fp16 = mul(x = var_2040_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_41_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_43_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_43_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2046_cast_fp16 = softmax(axis = var_30, x = scores_43_cast_fp16)[name = tensor<string, []>("op_2046_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_553_cast_fp16 = select(a = var_11_to_fp16, b = var_2046_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, 188, 128]> value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_21_cast_fp16)[name = tensor<string, []>("transpose_218")];
+            tensor<fp16, [1, 8, 188, 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_23_cast_fp16)[name = tensor<string, []>("x_233_cast_fp16")];
+            tensor<int32, [4]> var_2050_perm_0 = const()[name = tensor<string, []>("op_2050_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2051 = const()[name = tensor<string, []>("op_2051"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2050_cast_fp16 = transpose(perm = var_2050_perm_0, x = x_233_cast_fp16)[name = tensor<string, []>("transpose_214")];
+            tensor<fp16, [1, 188, 1024]> input_555_cast_fp16 = reshape(shape = var_2051, x = var_2050_cast_fp16)[name = tensor<string, []>("input_555_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_10_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(520236800)))];
+            tensor<fp16, [1024]> module_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522334016)))];
+            tensor<fp16, [1, 188, 1024]> linear_97_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_out_bias_to_fp16, weight = module_layers_10_self_attn_linear_out_weight_to_fp16, x = input_555_cast_fp16)[name = tensor<string, []>("linear_97_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_10_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522336128)))];
+            tensor<fp16, [1024]> module_layers_10_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522338240)))];
+            tensor<fp16, [1, 188, 1024]> x_237_cast_fp16 = layer_norm(axes = x_237_axes_0, beta = module_layers_10_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_10_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(522340352)))];
+            tensor<fp16, [2048]> module_layers_10_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526534720)))];
+            tensor<fp16, [1, 1024, 188]> input_561_cast_fp16 = transpose(perm = input_561_perm_0, x = x_237_cast_fp16)[name = tensor<string, []>("transpose_213")];
+            tensor<fp16, [1, 2048, 188]> input_563_cast_fp16 = conv(bias = module_layers_10_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_10_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_239_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_565_cast_fp16 = select(a = var_11_to_fp16, b = x_239_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_268_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526538880)))];
+            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, []>(526557376)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_567_cast_fp16)[name = tensor<string, []>("input_571_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_10_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(526559488)))];
+            tensor<fp16, [1024]> module_layers_10_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(528656704)))];
+            tensor<fp16, [1, 1024, 188]> x_241_cast_fp16 = conv(bias = module_layers_10_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_10_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_241_cast_fp16)[name = tensor<string, []>("transpose_212")];
+            tensor<fp16, [1, 188, 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]> module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(528658816)))];
+            tensor<fp16, [1024]> module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(528660928)))];
+            tensor<fp16, [1, 188, 1024]> input_579_cast_fp16 = layer_norm(axes = input_579_axes_0, beta = module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_577_cast_fp16)[name = tensor<string, []>("input_579_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_10_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(528663040)))];
+            tensor<fp16, [4096]> module_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(537051712)))];
+            tensor<fp16, [1, 188, 4096]> linear_98_cast_fp16 = linear(bias = module_layers_10_feed_forward2_linear1_bias_to_fp16, weight = module_layers_10_feed_forward2_linear1_weight_to_fp16, x = input_579_cast_fp16)[name = tensor<string, []>("linear_98_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_583_cast_fp16 = silu(x = linear_98_cast_fp16)[name = tensor<string, []>("input_583_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_10_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(537059968)))];
+            tensor<fp16, [1024]> module_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545448640)))];
+            tensor<fp16, [1, 188, 1024]> linear_99_cast_fp16 = linear(bias = module_layers_10_feed_forward2_linear2_bias_to_fp16, weight = module_layers_10_feed_forward2_linear2_weight_to_fp16, x = input_583_cast_fp16)[name = tensor<string, []>("linear_99_cast_fp16")];
+            tensor<fp16, []> var_2117_to_fp16 = const()[name = tensor<string, []>("op_2117_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2118_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2117_to_fp16)[name = tensor<string, []>("op_2118_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_589_cast_fp16 = add(x = input_577_cast_fp16, y = var_2118_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]> module_layers_10_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545450752)))];
+            tensor<fp16, [1024]> module_layers_10_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545452864)))];
+            tensor<fp16, [1, 188, 1024]> input_591_cast_fp16 = layer_norm(axes = input_591_axes_0, beta = module_layers_10_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545454976)))];
+            tensor<fp16, [1024]> module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545457088)))];
+            tensor<fp16, [1, 188, 1024]> input_593_cast_fp16 = layer_norm(axes = input_593_axes_0, beta = module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_591_cast_fp16)[name = tensor<string, []>("input_593_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_11_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545459200)))];
+            tensor<fp16, [4096]> module_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553847872)))];
+            tensor<fp16, [1, 188, 4096]> linear_100_cast_fp16 = linear(bias = module_layers_11_feed_forward1_linear1_bias_to_fp16, weight = module_layers_11_feed_forward1_linear1_weight_to_fp16, x = input_593_cast_fp16)[name = tensor<string, []>("linear_100_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_597_cast_fp16 = silu(x = linear_100_cast_fp16)[name = tensor<string, []>("input_597_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_11_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553856128)))];
+            tensor<fp16, [1024]> module_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(562244800)))];
+            tensor<fp16, [1, 188, 1024]> linear_101_cast_fp16 = linear(bias = module_layers_11_feed_forward1_linear2_bias_to_fp16, weight = module_layers_11_feed_forward1_linear2_weight_to_fp16, x = input_597_cast_fp16)[name = tensor<string, []>("linear_101_cast_fp16")];
+            tensor<fp16, []> var_2148_to_fp16 = const()[name = tensor<string, []>("op_2148_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2149_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2148_to_fp16)[name = tensor<string, []>("op_2149_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_603_cast_fp16 = add(x = input_591_cast_fp16, y = var_2149_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]> module_layers_11_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(562246912)))];
+            tensor<fp16, [1024]> module_layers_11_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(562249024)))];
+            tensor<fp16, [1, 188, 1024]> query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_self_att_weight_to_fp16, x = input_603_cast_fp16)[name = tensor<string, []>("query_23_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_11_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(562251136)))];
+            tensor<fp16, [1024]> module_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564348352)))];
+            tensor<fp16, [1, 188, 1024]> linear_102_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_q_bias_to_fp16, weight = module_layers_11_self_attn_linear_q_weight_to_fp16, x = query_23_cast_fp16)[name = tensor<string, []>("linear_102_cast_fp16")];
+            tensor<int32, [4]> var_2166 = const()[name = tensor<string, []>("op_2166"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_67_cast_fp16 = reshape(shape = var_2166, x = linear_102_cast_fp16)[name = tensor<string, []>("q_67_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_11_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564350464)))];
+            tensor<fp16, [1024]> module_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566447680)))];
+            tensor<fp16, [1, 188, 1024]> linear_103_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_k_bias_to_fp16, weight = module_layers_11_self_attn_linear_k_weight_to_fp16, x = query_23_cast_fp16)[name = tensor<string, []>("linear_103_cast_fp16")];
+            tensor<int32, [4]> var_2171 = const()[name = tensor<string, []>("op_2171"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_45_cast_fp16 = reshape(shape = var_2171, x = linear_103_cast_fp16)[name = tensor<string, []>("k_45_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_11_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566449792)))];
+            tensor<fp16, [1024]> module_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(568547008)))];
+            tensor<fp16, [1, 188, 1024]> linear_104_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_v_bias_to_fp16, weight = module_layers_11_self_attn_linear_v_weight_to_fp16, x = query_23_cast_fp16)[name = tensor<string, []>("linear_104_cast_fp16")];
+            tensor<int32, [4]> var_2176 = const()[name = tensor<string, []>("op_2176"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_23_cast_fp16 = reshape(shape = var_2176, x = linear_104_cast_fp16)[name = tensor<string, []>("v_23_cast_fp16")];
+            tensor<int32, [4]> value_25_perm_0 = const()[name = tensor<string, []>("value_25_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(568549120)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2188_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_2188_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(568551232)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2190_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_2190_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, 1, 3])];
+            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, 375]> var_2192_to_fp16 = const()[name = tensor<string, []>("op_2192_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(568553344)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2190_cast_fp16)[name = tensor<string, []>("transpose_210")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_2192_to_fp16)[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, 188, 376]> 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_2200 = const()[name = tensor<string, []>("op_2200"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_253_cast_fp16 = reshape(shape = var_2200, x = x_251_cast_fp16)[name = tensor<string, []>("x_253_cast_fp16")];
+            tensor<int32, [4]> var_2204_begin_0 = const()[name = tensor<string, []>("op_2204_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2204_end_0 = const()[name = tensor<string, []>("op_2204_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2204_end_mask_0 = const()[name = tensor<string, []>("op_2204_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2204_cast_fp16 = slice_by_index(begin = var_2204_begin_0, end = var_2204_end_0, end_mask = var_2204_end_mask_0, x = x_253_cast_fp16)[name = tensor<string, []>("op_2204_cast_fp16")];
+            tensor<int32, [4]> var_2205 = const()[name = tensor<string, []>("op_2205"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_45_cast_fp16 = reshape(shape = var_2205, x = var_2204_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_94_perm_0 = const()[name = tensor<string, []>("transpose_94_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_95_perm_0 = const()[name = tensor<string, []>("transpose_95_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_95 = transpose(perm = transpose_95_perm_0, x = k_45_cast_fp16)[name = tensor<string, []>("transpose_208")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_94 = transpose(perm = transpose_94_perm_0, x = var_2188_cast_fp16)[name = tensor<string, []>("transpose_209")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_94, y = transpose_95)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_2214_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = tensor<string, []>("op_2214_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, 188, 188]> _inversed_scores_45_cast_fp16 = mul(x = var_2214_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_45_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_47_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_47_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2220_cast_fp16 = softmax(axis = var_30, x = scores_47_cast_fp16)[name = tensor<string, []>("op_2220_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_605_cast_fp16 = select(a = var_11_to_fp16, b = var_2220_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, 188, 128]> value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_23_cast_fp16)[name = tensor<string, []>("transpose_211")];
+            tensor<fp16, [1, 8, 188, 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_25_cast_fp16)[name = tensor<string, []>("x_255_cast_fp16")];
+            tensor<int32, [4]> var_2224_perm_0 = const()[name = tensor<string, []>("op_2224_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2225 = const()[name = tensor<string, []>("op_2225"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2224_cast_fp16 = transpose(perm = var_2224_perm_0, x = x_255_cast_fp16)[name = tensor<string, []>("transpose_207")];
+            tensor<fp16, [1, 188, 1024]> input_607_cast_fp16 = reshape(shape = var_2225, x = var_2224_cast_fp16)[name = tensor<string, []>("input_607_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_11_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(569321408)))];
+            tensor<fp16, [1024]> module_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(571418624)))];
+            tensor<fp16, [1, 188, 1024]> linear_106_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_out_bias_to_fp16, weight = module_layers_11_self_attn_linear_out_weight_to_fp16, x = input_607_cast_fp16)[name = tensor<string, []>("linear_106_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_11_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(571420736)))];
+            tensor<fp16, [1024]> module_layers_11_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(571422848)))];
+            tensor<fp16, [1, 188, 1024]> x_259_cast_fp16 = layer_norm(axes = x_259_axes_0, beta = module_layers_11_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_11_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(571424960)))];
+            tensor<fp16, [2048]> module_layers_11_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(575619328)))];
+            tensor<fp16, [1, 1024, 188]> input_613_cast_fp16 = transpose(perm = input_613_perm_0, x = x_259_cast_fp16)[name = tensor<string, []>("transpose_206")];
+            tensor<fp16, [1, 2048, 188]> input_615_cast_fp16 = conv(bias = module_layers_11_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_11_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_261_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_617_cast_fp16 = select(a = var_11_to_fp16, b = x_261_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_270_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(575623488)))];
+            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, []>(575641984)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_619_cast_fp16)[name = tensor<string, []>("input_623_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_11_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(575644096)))];
+            tensor<fp16, [1024]> module_layers_11_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577741312)))];
+            tensor<fp16, [1, 1024, 188]> x_263_cast_fp16 = conv(bias = module_layers_11_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_11_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_263_cast_fp16)[name = tensor<string, []>("transpose_205")];
+            tensor<fp16, [1, 188, 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]> module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577743424)))];
+            tensor<fp16, [1024]> module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577745536)))];
+            tensor<fp16, [1, 188, 1024]> input_631_cast_fp16 = layer_norm(axes = input_631_axes_0, beta = module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_629_cast_fp16)[name = tensor<string, []>("input_631_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_11_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577747648)))];
+            tensor<fp16, [4096]> module_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586136320)))];
+            tensor<fp16, [1, 188, 4096]> linear_107_cast_fp16 = linear(bias = module_layers_11_feed_forward2_linear1_bias_to_fp16, weight = module_layers_11_feed_forward2_linear1_weight_to_fp16, x = input_631_cast_fp16)[name = tensor<string, []>("linear_107_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_635_cast_fp16 = silu(x = linear_107_cast_fp16)[name = tensor<string, []>("input_635_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_11_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586144576)))];
+            tensor<fp16, [1024]> module_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594533248)))];
+            tensor<fp16, [1, 188, 1024]> linear_108_cast_fp16 = linear(bias = module_layers_11_feed_forward2_linear2_bias_to_fp16, weight = module_layers_11_feed_forward2_linear2_weight_to_fp16, x = input_635_cast_fp16)[name = tensor<string, []>("linear_108_cast_fp16")];
+            tensor<fp16, []> var_2291_to_fp16 = const()[name = tensor<string, []>("op_2291_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2292_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2291_to_fp16)[name = tensor<string, []>("op_2292_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_641_cast_fp16 = add(x = input_629_cast_fp16, y = var_2292_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]> module_layers_11_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594535360)))];
+            tensor<fp16, [1024]> module_layers_11_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594537472)))];
+            tensor<fp16, [1, 188, 1024]> input_643_cast_fp16 = layer_norm(axes = input_643_axes_0, beta = module_layers_11_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594539584)))];
+            tensor<fp16, [1024]> module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594541696)))];
+            tensor<fp16, [1, 188, 1024]> input_645_cast_fp16 = layer_norm(axes = input_645_axes_0, beta = module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_643_cast_fp16)[name = tensor<string, []>("input_645_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_12_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(594543808)))];
+            tensor<fp16, [4096]> module_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(602932480)))];
+            tensor<fp16, [1, 188, 4096]> linear_109_cast_fp16 = linear(bias = module_layers_12_feed_forward1_linear1_bias_to_fp16, weight = module_layers_12_feed_forward1_linear1_weight_to_fp16, x = input_645_cast_fp16)[name = tensor<string, []>("linear_109_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_649_cast_fp16 = silu(x = linear_109_cast_fp16)[name = tensor<string, []>("input_649_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_12_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(602940736)))];
+            tensor<fp16, [1024]> module_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(611329408)))];
+            tensor<fp16, [1, 188, 1024]> linear_110_cast_fp16 = linear(bias = module_layers_12_feed_forward1_linear2_bias_to_fp16, weight = module_layers_12_feed_forward1_linear2_weight_to_fp16, x = input_649_cast_fp16)[name = tensor<string, []>("linear_110_cast_fp16")];
+            tensor<fp16, []> var_2322_to_fp16 = const()[name = tensor<string, []>("op_2322_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2323_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2322_to_fp16)[name = tensor<string, []>("op_2323_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_655_cast_fp16 = add(x = input_643_cast_fp16, y = var_2323_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]> module_layers_12_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(611331520)))];
+            tensor<fp16, [1024]> module_layers_12_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(611333632)))];
+            tensor<fp16, [1, 188, 1024]> query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_self_att_weight_to_fp16, x = input_655_cast_fp16)[name = tensor<string, []>("query_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_12_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(611335744)))];
+            tensor<fp16, [1024]> module_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(613432960)))];
+            tensor<fp16, [1, 188, 1024]> linear_111_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_q_bias_to_fp16, weight = module_layers_12_self_attn_linear_q_weight_to_fp16, x = query_25_cast_fp16)[name = tensor<string, []>("linear_111_cast_fp16")];
+            tensor<int32, [4]> var_2340 = const()[name = tensor<string, []>("op_2340"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_73_cast_fp16 = reshape(shape = var_2340, x = linear_111_cast_fp16)[name = tensor<string, []>("q_73_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_12_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(613435072)))];
+            tensor<fp16, [1024]> module_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(615532288)))];
+            tensor<fp16, [1, 188, 1024]> linear_112_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_k_bias_to_fp16, weight = module_layers_12_self_attn_linear_k_weight_to_fp16, x = query_25_cast_fp16)[name = tensor<string, []>("linear_112_cast_fp16")];
+            tensor<int32, [4]> var_2345 = const()[name = tensor<string, []>("op_2345"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_49_cast_fp16 = reshape(shape = var_2345, x = linear_112_cast_fp16)[name = tensor<string, []>("k_49_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_12_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(615534400)))];
+            tensor<fp16, [1024]> module_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617631616)))];
+            tensor<fp16, [1, 188, 1024]> linear_113_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_v_bias_to_fp16, weight = module_layers_12_self_attn_linear_v_weight_to_fp16, x = query_25_cast_fp16)[name = tensor<string, []>("linear_113_cast_fp16")];
+            tensor<int32, [4]> var_2350 = const()[name = tensor<string, []>("op_2350"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_25_cast_fp16 = reshape(shape = var_2350, x = linear_113_cast_fp16)[name = tensor<string, []>("v_25_cast_fp16")];
+            tensor<int32, [4]> value_27_perm_0 = const()[name = tensor<string, []>("value_27_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617633728)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2362_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_2362_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617635840)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2364_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_2364_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, 1, 3])];
+            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, 375]> var_2366_to_fp16 = const()[name = tensor<string, []>("op_2366_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617637952)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2364_cast_fp16)[name = tensor<string, []>("transpose_203")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_2366_to_fp16)[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, 188, 376]> 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_2374 = const()[name = tensor<string, []>("op_2374"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_275_cast_fp16 = reshape(shape = var_2374, x = x_273_cast_fp16)[name = tensor<string, []>("x_275_cast_fp16")];
+            tensor<int32, [4]> var_2378_begin_0 = const()[name = tensor<string, []>("op_2378_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2378_end_0 = const()[name = tensor<string, []>("op_2378_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2378_end_mask_0 = const()[name = tensor<string, []>("op_2378_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2378_cast_fp16 = slice_by_index(begin = var_2378_begin_0, end = var_2378_end_0, end_mask = var_2378_end_mask_0, x = x_275_cast_fp16)[name = tensor<string, []>("op_2378_cast_fp16")];
+            tensor<int32, [4]> var_2379 = const()[name = tensor<string, []>("op_2379"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_49_cast_fp16 = reshape(shape = var_2379, x = var_2378_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_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, 188]> transpose_97 = transpose(perm = transpose_97_perm_0, x = k_49_cast_fp16)[name = tensor<string, []>("transpose_201")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_96 = transpose(perm = transpose_96_perm_0, x = var_2362_cast_fp16)[name = tensor<string, []>("transpose_202")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_96, y = transpose_97)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_2388_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = tensor<string, []>("op_2388_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, 188, 188]> _inversed_scores_49_cast_fp16 = mul(x = var_2388_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_49_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_51_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_51_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2394_cast_fp16 = softmax(axis = var_30, x = scores_51_cast_fp16)[name = tensor<string, []>("op_2394_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_657_cast_fp16 = select(a = var_11_to_fp16, b = var_2394_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, 188, 128]> value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_25_cast_fp16)[name = tensor<string, []>("transpose_204")];
+            tensor<fp16, [1, 8, 188, 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_27_cast_fp16)[name = tensor<string, []>("x_277_cast_fp16")];
+            tensor<int32, [4]> var_2398_perm_0 = const()[name = tensor<string, []>("op_2398_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2399 = const()[name = tensor<string, []>("op_2399"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2398_cast_fp16 = transpose(perm = var_2398_perm_0, x = x_277_cast_fp16)[name = tensor<string, []>("transpose_200")];
+            tensor<fp16, [1, 188, 1024]> input_659_cast_fp16 = reshape(shape = var_2399, x = var_2398_cast_fp16)[name = tensor<string, []>("input_659_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_12_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(618406016)))];
+            tensor<fp16, [1024]> module_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(620503232)))];
+            tensor<fp16, [1, 188, 1024]> linear_115_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_out_bias_to_fp16, weight = module_layers_12_self_attn_linear_out_weight_to_fp16, x = input_659_cast_fp16)[name = tensor<string, []>("linear_115_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_12_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(620505344)))];
+            tensor<fp16, [1024]> module_layers_12_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(620507456)))];
+            tensor<fp16, [1, 188, 1024]> x_281_cast_fp16 = layer_norm(axes = x_281_axes_0, beta = module_layers_12_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_12_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(620509568)))];
+            tensor<fp16, [2048]> module_layers_12_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(624703936)))];
+            tensor<fp16, [1, 1024, 188]> input_665_cast_fp16 = transpose(perm = input_665_perm_0, x = x_281_cast_fp16)[name = tensor<string, []>("transpose_199")];
+            tensor<fp16, [1, 2048, 188]> input_667_cast_fp16 = conv(bias = module_layers_12_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_12_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_283_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_669_cast_fp16 = select(a = var_11_to_fp16, b = x_283_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_272_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(624708096)))];
+            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, []>(624726592)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_671_cast_fp16)[name = tensor<string, []>("input_675_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_12_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(624728704)))];
+            tensor<fp16, [1024]> module_layers_12_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(626825920)))];
+            tensor<fp16, [1, 1024, 188]> x_285_cast_fp16 = conv(bias = module_layers_12_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_12_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_285_cast_fp16)[name = tensor<string, []>("transpose_198")];
+            tensor<fp16, [1, 188, 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]> module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(626828032)))];
+            tensor<fp16, [1024]> module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(626830144)))];
+            tensor<fp16, [1, 188, 1024]> input_683_cast_fp16 = layer_norm(axes = input_683_axes_0, beta = module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_681_cast_fp16)[name = tensor<string, []>("input_683_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_12_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(626832256)))];
+            tensor<fp16, [4096]> module_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(635220928)))];
+            tensor<fp16, [1, 188, 4096]> linear_116_cast_fp16 = linear(bias = module_layers_12_feed_forward2_linear1_bias_to_fp16, weight = module_layers_12_feed_forward2_linear1_weight_to_fp16, x = input_683_cast_fp16)[name = tensor<string, []>("linear_116_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_687_cast_fp16 = silu(x = linear_116_cast_fp16)[name = tensor<string, []>("input_687_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_12_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(635229184)))];
+            tensor<fp16, [1024]> module_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643617856)))];
+            tensor<fp16, [1, 188, 1024]> linear_117_cast_fp16 = linear(bias = module_layers_12_feed_forward2_linear2_bias_to_fp16, weight = module_layers_12_feed_forward2_linear2_weight_to_fp16, x = input_687_cast_fp16)[name = tensor<string, []>("linear_117_cast_fp16")];
+            tensor<fp16, []> var_2465_to_fp16 = const()[name = tensor<string, []>("op_2465_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2466_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2465_to_fp16)[name = tensor<string, []>("op_2466_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_693_cast_fp16 = add(x = input_681_cast_fp16, y = var_2466_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]> module_layers_12_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643619968)))];
+            tensor<fp16, [1024]> module_layers_12_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643622080)))];
+            tensor<fp16, [1, 188, 1024]> input_695_cast_fp16 = layer_norm(axes = input_695_axes_0, beta = module_layers_12_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643624192)))];
+            tensor<fp16, [1024]> module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643626304)))];
+            tensor<fp16, [1, 188, 1024]> input_697_cast_fp16 = layer_norm(axes = input_697_axes_0, beta = module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_695_cast_fp16)[name = tensor<string, []>("input_697_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_13_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(643628416)))];
+            tensor<fp16, [4096]> module_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(652017088)))];
+            tensor<fp16, [1, 188, 4096]> linear_118_cast_fp16 = linear(bias = module_layers_13_feed_forward1_linear1_bias_to_fp16, weight = module_layers_13_feed_forward1_linear1_weight_to_fp16, x = input_697_cast_fp16)[name = tensor<string, []>("linear_118_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_701_cast_fp16 = silu(x = linear_118_cast_fp16)[name = tensor<string, []>("input_701_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_13_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(652025344)))];
+            tensor<fp16, [1024]> module_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(660414016)))];
+            tensor<fp16, [1, 188, 1024]> linear_119_cast_fp16 = linear(bias = module_layers_13_feed_forward1_linear2_bias_to_fp16, weight = module_layers_13_feed_forward1_linear2_weight_to_fp16, x = input_701_cast_fp16)[name = tensor<string, []>("linear_119_cast_fp16")];
+            tensor<fp16, []> var_2496_to_fp16 = const()[name = tensor<string, []>("op_2496_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2497_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2496_to_fp16)[name = tensor<string, []>("op_2497_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_707_cast_fp16 = add(x = input_695_cast_fp16, y = var_2497_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]> module_layers_13_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(660416128)))];
+            tensor<fp16, [1024]> module_layers_13_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(660418240)))];
+            tensor<fp16, [1, 188, 1024]> query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_self_att_weight_to_fp16, x = input_707_cast_fp16)[name = tensor<string, []>("query_27_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_13_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(660420352)))];
+            tensor<fp16, [1024]> module_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(662517568)))];
+            tensor<fp16, [1, 188, 1024]> linear_120_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_q_bias_to_fp16, weight = module_layers_13_self_attn_linear_q_weight_to_fp16, x = query_27_cast_fp16)[name = tensor<string, []>("linear_120_cast_fp16")];
+            tensor<int32, [4]> var_2514 = const()[name = tensor<string, []>("op_2514"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_79_cast_fp16 = reshape(shape = var_2514, x = linear_120_cast_fp16)[name = tensor<string, []>("q_79_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_13_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(662519680)))];
+            tensor<fp16, [1024]> module_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(664616896)))];
+            tensor<fp16, [1, 188, 1024]> linear_121_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_k_bias_to_fp16, weight = module_layers_13_self_attn_linear_k_weight_to_fp16, x = query_27_cast_fp16)[name = tensor<string, []>("linear_121_cast_fp16")];
+            tensor<int32, [4]> var_2519 = const()[name = tensor<string, []>("op_2519"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_53_cast_fp16 = reshape(shape = var_2519, x = linear_121_cast_fp16)[name = tensor<string, []>("k_53_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_13_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(664619008)))];
+            tensor<fp16, [1024]> module_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(666716224)))];
+            tensor<fp16, [1, 188, 1024]> linear_122_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_v_bias_to_fp16, weight = module_layers_13_self_attn_linear_v_weight_to_fp16, x = query_27_cast_fp16)[name = tensor<string, []>("linear_122_cast_fp16")];
+            tensor<int32, [4]> var_2524 = const()[name = tensor<string, []>("op_2524"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_27_cast_fp16 = reshape(shape = var_2524, x = linear_122_cast_fp16)[name = tensor<string, []>("v_27_cast_fp16")];
+            tensor<int32, [4]> value_29_perm_0 = const()[name = tensor<string, []>("value_29_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(666718336)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2536_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_2536_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(666720448)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2538_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_2538_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, 1, 3])];
+            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, 375]> var_2540_to_fp16 = const()[name = tensor<string, []>("op_2540_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(666722560)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2538_cast_fp16)[name = tensor<string, []>("transpose_196")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_2540_to_fp16)[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, 188, 376]> 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_2548 = const()[name = tensor<string, []>("op_2548"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_297_cast_fp16 = reshape(shape = var_2548, x = x_295_cast_fp16)[name = tensor<string, []>("x_297_cast_fp16")];
+            tensor<int32, [4]> var_2552_begin_0 = const()[name = tensor<string, []>("op_2552_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2552_end_0 = const()[name = tensor<string, []>("op_2552_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2552_end_mask_0 = const()[name = tensor<string, []>("op_2552_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2552_cast_fp16 = slice_by_index(begin = var_2552_begin_0, end = var_2552_end_0, end_mask = var_2552_end_mask_0, x = x_297_cast_fp16)[name = tensor<string, []>("op_2552_cast_fp16")];
+            tensor<int32, [4]> var_2553 = const()[name = tensor<string, []>("op_2553"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_53_cast_fp16 = reshape(shape = var_2553, x = var_2552_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_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, 188]> transpose_99 = transpose(perm = transpose_99_perm_0, x = k_53_cast_fp16)[name = tensor<string, []>("transpose_194")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_98 = transpose(perm = transpose_98_perm_0, x = var_2536_cast_fp16)[name = tensor<string, []>("transpose_195")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_98, y = transpose_99)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_2562_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = tensor<string, []>("op_2562_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, 188, 188]> _inversed_scores_53_cast_fp16 = mul(x = var_2562_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_53_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_55_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_55_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2568_cast_fp16 = softmax(axis = var_30, x = scores_55_cast_fp16)[name = tensor<string, []>("op_2568_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_709_cast_fp16 = select(a = var_11_to_fp16, b = var_2568_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, 188, 128]> value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_27_cast_fp16)[name = tensor<string, []>("transpose_197")];
+            tensor<fp16, [1, 8, 188, 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_29_cast_fp16)[name = tensor<string, []>("x_299_cast_fp16")];
+            tensor<int32, [4]> var_2572_perm_0 = const()[name = tensor<string, []>("op_2572_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2573 = const()[name = tensor<string, []>("op_2573"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2572_cast_fp16 = transpose(perm = var_2572_perm_0, x = x_299_cast_fp16)[name = tensor<string, []>("transpose_193")];
+            tensor<fp16, [1, 188, 1024]> input_711_cast_fp16 = reshape(shape = var_2573, x = var_2572_cast_fp16)[name = tensor<string, []>("input_711_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_13_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(667490624)))];
+            tensor<fp16, [1024]> module_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(669587840)))];
+            tensor<fp16, [1, 188, 1024]> linear_124_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_out_bias_to_fp16, weight = module_layers_13_self_attn_linear_out_weight_to_fp16, x = input_711_cast_fp16)[name = tensor<string, []>("linear_124_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_13_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(669589952)))];
+            tensor<fp16, [1024]> module_layers_13_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(669592064)))];
+            tensor<fp16, [1, 188, 1024]> x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = module_layers_13_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_13_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(669594176)))];
+            tensor<fp16, [2048]> module_layers_13_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(673788544)))];
+            tensor<fp16, [1, 1024, 188]> input_717_cast_fp16 = transpose(perm = input_717_perm_0, x = x_303_cast_fp16)[name = tensor<string, []>("transpose_192")];
+            tensor<fp16, [1, 2048, 188]> input_719_cast_fp16 = conv(bias = module_layers_13_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_13_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_305_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_721_cast_fp16 = select(a = var_11_to_fp16, b = x_305_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_274_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(673792704)))];
+            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, []>(673811200)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_723_cast_fp16)[name = tensor<string, []>("input_727_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_13_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(673813312)))];
+            tensor<fp16, [1024]> module_layers_13_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(675910528)))];
+            tensor<fp16, [1, 1024, 188]> x_307_cast_fp16 = conv(bias = module_layers_13_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_13_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_307_cast_fp16)[name = tensor<string, []>("transpose_191")];
+            tensor<fp16, [1, 188, 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]> module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(675912640)))];
+            tensor<fp16, [1024]> module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(675914752)))];
+            tensor<fp16, [1, 188, 1024]> input_735_cast_fp16 = layer_norm(axes = input_735_axes_0, beta = module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_733_cast_fp16)[name = tensor<string, []>("input_735_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_13_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(675916864)))];
+            tensor<fp16, [4096]> module_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684305536)))];
+            tensor<fp16, [1, 188, 4096]> linear_125_cast_fp16 = linear(bias = module_layers_13_feed_forward2_linear1_bias_to_fp16, weight = module_layers_13_feed_forward2_linear1_weight_to_fp16, x = input_735_cast_fp16)[name = tensor<string, []>("linear_125_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_739_cast_fp16 = silu(x = linear_125_cast_fp16)[name = tensor<string, []>("input_739_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_13_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684313792)))];
+            tensor<fp16, [1024]> module_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(692702464)))];
+            tensor<fp16, [1, 188, 1024]> linear_126_cast_fp16 = linear(bias = module_layers_13_feed_forward2_linear2_bias_to_fp16, weight = module_layers_13_feed_forward2_linear2_weight_to_fp16, x = input_739_cast_fp16)[name = tensor<string, []>("linear_126_cast_fp16")];
+            tensor<fp16, []> var_2639_to_fp16 = const()[name = tensor<string, []>("op_2639_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2640_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2639_to_fp16)[name = tensor<string, []>("op_2640_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_745_cast_fp16 = add(x = input_733_cast_fp16, y = var_2640_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]> module_layers_13_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(692704576)))];
+            tensor<fp16, [1024]> module_layers_13_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(692706688)))];
+            tensor<fp16, [1, 188, 1024]> input_747_cast_fp16 = layer_norm(axes = input_747_axes_0, beta = module_layers_13_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(692708800)))];
+            tensor<fp16, [1024]> module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(692710912)))];
+            tensor<fp16, [1, 188, 1024]> input_749_cast_fp16 = layer_norm(axes = input_749_axes_0, beta = module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_747_cast_fp16)[name = tensor<string, []>("input_749_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_14_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(692713024)))];
+            tensor<fp16, [4096]> module_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(701101696)))];
+            tensor<fp16, [1, 188, 4096]> linear_127_cast_fp16 = linear(bias = module_layers_14_feed_forward1_linear1_bias_to_fp16, weight = module_layers_14_feed_forward1_linear1_weight_to_fp16, x = input_749_cast_fp16)[name = tensor<string, []>("linear_127_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_753_cast_fp16 = silu(x = linear_127_cast_fp16)[name = tensor<string, []>("input_753_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_14_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(701109952)))];
+            tensor<fp16, [1024]> module_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(709498624)))];
+            tensor<fp16, [1, 188, 1024]> linear_128_cast_fp16 = linear(bias = module_layers_14_feed_forward1_linear2_bias_to_fp16, weight = module_layers_14_feed_forward1_linear2_weight_to_fp16, x = input_753_cast_fp16)[name = tensor<string, []>("linear_128_cast_fp16")];
+            tensor<fp16, []> var_2670_to_fp16 = const()[name = tensor<string, []>("op_2670_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2671_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2670_to_fp16)[name = tensor<string, []>("op_2671_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_759_cast_fp16 = add(x = input_747_cast_fp16, y = var_2671_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]> module_layers_14_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(709500736)))];
+            tensor<fp16, [1024]> module_layers_14_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(709502848)))];
+            tensor<fp16, [1, 188, 1024]> query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_self_att_weight_to_fp16, x = input_759_cast_fp16)[name = tensor<string, []>("query_29_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_14_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(709504960)))];
+            tensor<fp16, [1024]> module_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(711602176)))];
+            tensor<fp16, [1, 188, 1024]> linear_129_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_q_bias_to_fp16, weight = module_layers_14_self_attn_linear_q_weight_to_fp16, x = query_29_cast_fp16)[name = tensor<string, []>("linear_129_cast_fp16")];
+            tensor<int32, [4]> var_2688 = const()[name = tensor<string, []>("op_2688"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_85_cast_fp16 = reshape(shape = var_2688, x = linear_129_cast_fp16)[name = tensor<string, []>("q_85_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_14_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(711604288)))];
+            tensor<fp16, [1024]> module_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(713701504)))];
+            tensor<fp16, [1, 188, 1024]> linear_130_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_k_bias_to_fp16, weight = module_layers_14_self_attn_linear_k_weight_to_fp16, x = query_29_cast_fp16)[name = tensor<string, []>("linear_130_cast_fp16")];
+            tensor<int32, [4]> var_2693 = const()[name = tensor<string, []>("op_2693"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_57_cast_fp16 = reshape(shape = var_2693, x = linear_130_cast_fp16)[name = tensor<string, []>("k_57_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_14_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(713703616)))];
+            tensor<fp16, [1024]> module_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(715800832)))];
+            tensor<fp16, [1, 188, 1024]> linear_131_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_v_bias_to_fp16, weight = module_layers_14_self_attn_linear_v_weight_to_fp16, x = query_29_cast_fp16)[name = tensor<string, []>("linear_131_cast_fp16")];
+            tensor<int32, [4]> var_2698 = const()[name = tensor<string, []>("op_2698"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_29_cast_fp16 = reshape(shape = var_2698, x = linear_131_cast_fp16)[name = tensor<string, []>("v_29_cast_fp16")];
+            tensor<int32, [4]> value_31_perm_0 = const()[name = tensor<string, []>("value_31_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(715802944)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2710_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_2710_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(715805056)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2712_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_2712_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, 1, 3])];
+            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, 375]> var_2714_to_fp16 = const()[name = tensor<string, []>("op_2714_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(715807168)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2712_cast_fp16)[name = tensor<string, []>("transpose_189")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_2714_to_fp16)[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, 188, 376]> 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_2722 = const()[name = tensor<string, []>("op_2722"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_319_cast_fp16 = reshape(shape = var_2722, x = x_317_cast_fp16)[name = tensor<string, []>("x_319_cast_fp16")];
+            tensor<int32, [4]> var_2726_begin_0 = const()[name = tensor<string, []>("op_2726_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2726_end_0 = const()[name = tensor<string, []>("op_2726_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2726_end_mask_0 = const()[name = tensor<string, []>("op_2726_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2726_cast_fp16 = slice_by_index(begin = var_2726_begin_0, end = var_2726_end_0, end_mask = var_2726_end_mask_0, x = x_319_cast_fp16)[name = tensor<string, []>("op_2726_cast_fp16")];
+            tensor<int32, [4]> var_2727 = const()[name = tensor<string, []>("op_2727"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_57_cast_fp16 = reshape(shape = var_2727, x = var_2726_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_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, 188]> transpose_101 = transpose(perm = transpose_101_perm_0, x = k_57_cast_fp16)[name = tensor<string, []>("transpose_187")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_100 = transpose(perm = transpose_100_perm_0, x = var_2710_cast_fp16)[name = tensor<string, []>("transpose_188")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_100, y = transpose_101)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_2736_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = tensor<string, []>("op_2736_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, 188, 188]> _inversed_scores_57_cast_fp16 = mul(x = var_2736_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_57_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_59_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_59_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2742_cast_fp16 = softmax(axis = var_30, x = scores_59_cast_fp16)[name = tensor<string, []>("op_2742_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_761_cast_fp16 = select(a = var_11_to_fp16, b = var_2742_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, 188, 128]> value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_29_cast_fp16)[name = tensor<string, []>("transpose_190")];
+            tensor<fp16, [1, 8, 188, 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_31_cast_fp16)[name = tensor<string, []>("x_321_cast_fp16")];
+            tensor<int32, [4]> var_2746_perm_0 = const()[name = tensor<string, []>("op_2746_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2747 = const()[name = tensor<string, []>("op_2747"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2746_cast_fp16 = transpose(perm = var_2746_perm_0, x = x_321_cast_fp16)[name = tensor<string, []>("transpose_186")];
+            tensor<fp16, [1, 188, 1024]> input_763_cast_fp16 = reshape(shape = var_2747, x = var_2746_cast_fp16)[name = tensor<string, []>("input_763_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_14_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(716575232)))];
+            tensor<fp16, [1024]> module_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(718672448)))];
+            tensor<fp16, [1, 188, 1024]> linear_133_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_out_bias_to_fp16, weight = module_layers_14_self_attn_linear_out_weight_to_fp16, x = input_763_cast_fp16)[name = tensor<string, []>("linear_133_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_14_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(718674560)))];
+            tensor<fp16, [1024]> module_layers_14_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(718676672)))];
+            tensor<fp16, [1, 188, 1024]> x_325_cast_fp16 = layer_norm(axes = x_325_axes_0, beta = module_layers_14_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_14_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(718678784)))];
+            tensor<fp16, [2048]> module_layers_14_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(722873152)))];
+            tensor<fp16, [1, 1024, 188]> input_769_cast_fp16 = transpose(perm = input_769_perm_0, x = x_325_cast_fp16)[name = tensor<string, []>("transpose_185")];
+            tensor<fp16, [1, 2048, 188]> input_771_cast_fp16 = conv(bias = module_layers_14_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_14_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_327_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_773_cast_fp16 = select(a = var_11_to_fp16, b = x_327_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_276_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(722877312)))];
+            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, []>(722895808)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_775_cast_fp16)[name = tensor<string, []>("input_779_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_14_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(722897920)))];
+            tensor<fp16, [1024]> module_layers_14_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(724995136)))];
+            tensor<fp16, [1, 1024, 188]> x_329_cast_fp16 = conv(bias = module_layers_14_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_14_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_329_cast_fp16)[name = tensor<string, []>("transpose_184")];
+            tensor<fp16, [1, 188, 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]> module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(724997248)))];
+            tensor<fp16, [1024]> module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(724999360)))];
+            tensor<fp16, [1, 188, 1024]> input_787_cast_fp16 = layer_norm(axes = input_787_axes_0, beta = module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_785_cast_fp16)[name = tensor<string, []>("input_787_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_14_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(725001472)))];
+            tensor<fp16, [4096]> module_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(733390144)))];
+            tensor<fp16, [1, 188, 4096]> linear_134_cast_fp16 = linear(bias = module_layers_14_feed_forward2_linear1_bias_to_fp16, weight = module_layers_14_feed_forward2_linear1_weight_to_fp16, x = input_787_cast_fp16)[name = tensor<string, []>("linear_134_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_791_cast_fp16 = silu(x = linear_134_cast_fp16)[name = tensor<string, []>("input_791_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_14_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(733398400)))];
+            tensor<fp16, [1024]> module_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(741787072)))];
+            tensor<fp16, [1, 188, 1024]> linear_135_cast_fp16 = linear(bias = module_layers_14_feed_forward2_linear2_bias_to_fp16, weight = module_layers_14_feed_forward2_linear2_weight_to_fp16, x = input_791_cast_fp16)[name = tensor<string, []>("linear_135_cast_fp16")];
+            tensor<fp16, []> var_2813_to_fp16 = const()[name = tensor<string, []>("op_2813_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2814_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_2813_to_fp16)[name = tensor<string, []>("op_2814_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_797_cast_fp16 = add(x = input_785_cast_fp16, y = var_2814_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]> module_layers_14_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(741789184)))];
+            tensor<fp16, [1024]> module_layers_14_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(741791296)))];
+            tensor<fp16, [1, 188, 1024]> input_799_cast_fp16 = layer_norm(axes = input_799_axes_0, beta = module_layers_14_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(741793408)))];
+            tensor<fp16, [1024]> module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(741795520)))];
+            tensor<fp16, [1, 188, 1024]> input_801_cast_fp16 = layer_norm(axes = input_801_axes_0, beta = module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_799_cast_fp16)[name = tensor<string, []>("input_801_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_15_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(741797632)))];
+            tensor<fp16, [4096]> module_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(750186304)))];
+            tensor<fp16, [1, 188, 4096]> linear_136_cast_fp16 = linear(bias = module_layers_15_feed_forward1_linear1_bias_to_fp16, weight = module_layers_15_feed_forward1_linear1_weight_to_fp16, x = input_801_cast_fp16)[name = tensor<string, []>("linear_136_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_805_cast_fp16 = silu(x = linear_136_cast_fp16)[name = tensor<string, []>("input_805_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_15_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(750194560)))];
+            tensor<fp16, [1024]> module_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(758583232)))];
+            tensor<fp16, [1, 188, 1024]> linear_137_cast_fp16 = linear(bias = module_layers_15_feed_forward1_linear2_bias_to_fp16, weight = module_layers_15_feed_forward1_linear2_weight_to_fp16, x = input_805_cast_fp16)[name = tensor<string, []>("linear_137_cast_fp16")];
+            tensor<fp16, []> var_2844_to_fp16 = const()[name = tensor<string, []>("op_2844_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2845_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_2844_to_fp16)[name = tensor<string, []>("op_2845_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_811_cast_fp16 = add(x = input_799_cast_fp16, y = var_2845_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]> module_layers_15_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(758585344)))];
+            tensor<fp16, [1024]> module_layers_15_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(758587456)))];
+            tensor<fp16, [1, 188, 1024]> query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_self_att_weight_to_fp16, x = input_811_cast_fp16)[name = tensor<string, []>("query_31_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_15_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(758589568)))];
+            tensor<fp16, [1024]> module_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(760686784)))];
+            tensor<fp16, [1, 188, 1024]> linear_138_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_q_bias_to_fp16, weight = module_layers_15_self_attn_linear_q_weight_to_fp16, x = query_31_cast_fp16)[name = tensor<string, []>("linear_138_cast_fp16")];
+            tensor<int32, [4]> var_2862 = const()[name = tensor<string, []>("op_2862"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_91_cast_fp16 = reshape(shape = var_2862, x = linear_138_cast_fp16)[name = tensor<string, []>("q_91_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_15_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(760688896)))];
+            tensor<fp16, [1024]> module_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(762786112)))];
+            tensor<fp16, [1, 188, 1024]> linear_139_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_k_bias_to_fp16, weight = module_layers_15_self_attn_linear_k_weight_to_fp16, x = query_31_cast_fp16)[name = tensor<string, []>("linear_139_cast_fp16")];
+            tensor<int32, [4]> var_2867 = const()[name = tensor<string, []>("op_2867"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_61_cast_fp16 = reshape(shape = var_2867, x = linear_139_cast_fp16)[name = tensor<string, []>("k_61_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_15_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(762788224)))];
+            tensor<fp16, [1024]> module_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(764885440)))];
+            tensor<fp16, [1, 188, 1024]> linear_140_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_v_bias_to_fp16, weight = module_layers_15_self_attn_linear_v_weight_to_fp16, x = query_31_cast_fp16)[name = tensor<string, []>("linear_140_cast_fp16")];
+            tensor<int32, [4]> var_2872 = const()[name = tensor<string, []>("op_2872"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_31_cast_fp16 = reshape(shape = var_2872, x = linear_140_cast_fp16)[name = tensor<string, []>("v_31_cast_fp16")];
+            tensor<int32, [4]> value_33_perm_0 = const()[name = tensor<string, []>("value_33_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(764887552)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2884_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_2884_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(764889664)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2886_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_2886_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, 1, 3])];
+            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, 375]> var_2888_to_fp16 = const()[name = tensor<string, []>("op_2888_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(764891776)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_2886_cast_fp16)[name = tensor<string, []>("transpose_182")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_2888_to_fp16)[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, 188, 376]> 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_2896 = const()[name = tensor<string, []>("op_2896"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_341_cast_fp16 = reshape(shape = var_2896, x = x_339_cast_fp16)[name = tensor<string, []>("x_341_cast_fp16")];
+            tensor<int32, [4]> var_2900_begin_0 = const()[name = tensor<string, []>("op_2900_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2900_end_0 = const()[name = tensor<string, []>("op_2900_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2900_end_mask_0 = const()[name = tensor<string, []>("op_2900_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2900_cast_fp16 = slice_by_index(begin = var_2900_begin_0, end = var_2900_end_0, end_mask = var_2900_end_mask_0, x = x_341_cast_fp16)[name = tensor<string, []>("op_2900_cast_fp16")];
+            tensor<int32, [4]> var_2901 = const()[name = tensor<string, []>("op_2901"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_61_cast_fp16 = reshape(shape = var_2901, x = var_2900_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_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, 188]> transpose_103 = transpose(perm = transpose_103_perm_0, x = k_61_cast_fp16)[name = tensor<string, []>("transpose_180")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_102 = transpose(perm = transpose_102_perm_0, x = var_2884_cast_fp16)[name = tensor<string, []>("transpose_181")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_102, y = transpose_103)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_2910_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = tensor<string, []>("op_2910_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, 188, 188]> _inversed_scores_61_cast_fp16 = mul(x = var_2910_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_61_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_63_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_63_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2916_cast_fp16 = softmax(axis = var_30, x = scores_63_cast_fp16)[name = tensor<string, []>("op_2916_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_813_cast_fp16 = select(a = var_11_to_fp16, b = var_2916_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, 188, 128]> value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_31_cast_fp16)[name = tensor<string, []>("transpose_183")];
+            tensor<fp16, [1, 8, 188, 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_33_cast_fp16)[name = tensor<string, []>("x_343_cast_fp16")];
+            tensor<int32, [4]> var_2920_perm_0 = const()[name = tensor<string, []>("op_2920_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2921 = const()[name = tensor<string, []>("op_2921"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2920_cast_fp16 = transpose(perm = var_2920_perm_0, x = x_343_cast_fp16)[name = tensor<string, []>("transpose_179")];
+            tensor<fp16, [1, 188, 1024]> input_815_cast_fp16 = reshape(shape = var_2921, x = var_2920_cast_fp16)[name = tensor<string, []>("input_815_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_15_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(765659840)))];
+            tensor<fp16, [1024]> module_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(767757056)))];
+            tensor<fp16, [1, 188, 1024]> linear_142_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_out_bias_to_fp16, weight = module_layers_15_self_attn_linear_out_weight_to_fp16, x = input_815_cast_fp16)[name = tensor<string, []>("linear_142_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_15_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(767759168)))];
+            tensor<fp16, [1024]> module_layers_15_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(767761280)))];
+            tensor<fp16, [1, 188, 1024]> x_347_cast_fp16 = layer_norm(axes = x_347_axes_0, beta = module_layers_15_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_15_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(767763392)))];
+            tensor<fp16, [2048]> module_layers_15_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(771957760)))];
+            tensor<fp16, [1, 1024, 188]> input_821_cast_fp16 = transpose(perm = input_821_perm_0, x = x_347_cast_fp16)[name = tensor<string, []>("transpose_178")];
+            tensor<fp16, [1, 2048, 188]> input_823_cast_fp16 = conv(bias = module_layers_15_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_15_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_349_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_825_cast_fp16 = select(a = var_11_to_fp16, b = x_349_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_278_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(771961920)))];
+            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, []>(771980416)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_827_cast_fp16)[name = tensor<string, []>("input_831_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_15_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(771982528)))];
+            tensor<fp16, [1024]> module_layers_15_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(774079744)))];
+            tensor<fp16, [1, 1024, 188]> x_351_cast_fp16 = conv(bias = module_layers_15_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_15_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_351_cast_fp16)[name = tensor<string, []>("transpose_177")];
+            tensor<fp16, [1, 188, 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]> module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(774081856)))];
+            tensor<fp16, [1024]> module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(774083968)))];
+            tensor<fp16, [1, 188, 1024]> input_839_cast_fp16 = layer_norm(axes = input_839_axes_0, beta = module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_837_cast_fp16)[name = tensor<string, []>("input_839_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_15_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(774086080)))];
+            tensor<fp16, [4096]> module_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(782474752)))];
+            tensor<fp16, [1, 188, 4096]> linear_143_cast_fp16 = linear(bias = module_layers_15_feed_forward2_linear1_bias_to_fp16, weight = module_layers_15_feed_forward2_linear1_weight_to_fp16, x = input_839_cast_fp16)[name = tensor<string, []>("linear_143_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_843_cast_fp16 = silu(x = linear_143_cast_fp16)[name = tensor<string, []>("input_843_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_15_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(782483008)))];
+            tensor<fp16, [1024]> module_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(790871680)))];
+            tensor<fp16, [1, 188, 1024]> linear_144_cast_fp16 = linear(bias = module_layers_15_feed_forward2_linear2_bias_to_fp16, weight = module_layers_15_feed_forward2_linear2_weight_to_fp16, x = input_843_cast_fp16)[name = tensor<string, []>("linear_144_cast_fp16")];
+            tensor<fp16, []> var_2987_to_fp16 = const()[name = tensor<string, []>("op_2987_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2988_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_2987_to_fp16)[name = tensor<string, []>("op_2988_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_849_cast_fp16 = add(x = input_837_cast_fp16, y = var_2988_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]> module_layers_15_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(790873792)))];
+            tensor<fp16, [1024]> module_layers_15_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(790875904)))];
+            tensor<fp16, [1, 188, 1024]> input_851_cast_fp16 = layer_norm(axes = input_851_axes_0, beta = module_layers_15_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(790878016)))];
+            tensor<fp16, [1024]> module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(790880128)))];
+            tensor<fp16, [1, 188, 1024]> input_853_cast_fp16 = layer_norm(axes = input_853_axes_0, beta = module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_851_cast_fp16)[name = tensor<string, []>("input_853_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_16_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(790882240)))];
+            tensor<fp16, [4096]> module_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(799270912)))];
+            tensor<fp16, [1, 188, 4096]> linear_145_cast_fp16 = linear(bias = module_layers_16_feed_forward1_linear1_bias_to_fp16, weight = module_layers_16_feed_forward1_linear1_weight_to_fp16, x = input_853_cast_fp16)[name = tensor<string, []>("linear_145_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_857_cast_fp16 = silu(x = linear_145_cast_fp16)[name = tensor<string, []>("input_857_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_16_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(799279168)))];
+            tensor<fp16, [1024]> module_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(807667840)))];
+            tensor<fp16, [1, 188, 1024]> linear_146_cast_fp16 = linear(bias = module_layers_16_feed_forward1_linear2_bias_to_fp16, weight = module_layers_16_feed_forward1_linear2_weight_to_fp16, x = input_857_cast_fp16)[name = tensor<string, []>("linear_146_cast_fp16")];
+            tensor<fp16, []> var_3018_to_fp16 = const()[name = tensor<string, []>("op_3018_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3019_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3018_to_fp16)[name = tensor<string, []>("op_3019_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_863_cast_fp16 = add(x = input_851_cast_fp16, y = var_3019_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]> module_layers_16_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(807669952)))];
+            tensor<fp16, [1024]> module_layers_16_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(807672064)))];
+            tensor<fp16, [1, 188, 1024]> query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_self_att_weight_to_fp16, x = input_863_cast_fp16)[name = tensor<string, []>("query_33_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_16_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(807674176)))];
+            tensor<fp16, [1024]> module_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(809771392)))];
+            tensor<fp16, [1, 188, 1024]> linear_147_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_q_bias_to_fp16, weight = module_layers_16_self_attn_linear_q_weight_to_fp16, x = query_33_cast_fp16)[name = tensor<string, []>("linear_147_cast_fp16")];
+            tensor<int32, [4]> var_3036 = const()[name = tensor<string, []>("op_3036"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_97_cast_fp16 = reshape(shape = var_3036, x = linear_147_cast_fp16)[name = tensor<string, []>("q_97_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_16_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(809773504)))];
+            tensor<fp16, [1024]> module_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(811870720)))];
+            tensor<fp16, [1, 188, 1024]> linear_148_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_k_bias_to_fp16, weight = module_layers_16_self_attn_linear_k_weight_to_fp16, x = query_33_cast_fp16)[name = tensor<string, []>("linear_148_cast_fp16")];
+            tensor<int32, [4]> var_3041 = const()[name = tensor<string, []>("op_3041"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_65_cast_fp16 = reshape(shape = var_3041, x = linear_148_cast_fp16)[name = tensor<string, []>("k_65_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_16_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(811872832)))];
+            tensor<fp16, [1024]> module_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(813970048)))];
+            tensor<fp16, [1, 188, 1024]> linear_149_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_v_bias_to_fp16, weight = module_layers_16_self_attn_linear_v_weight_to_fp16, x = query_33_cast_fp16)[name = tensor<string, []>("linear_149_cast_fp16")];
+            tensor<int32, [4]> var_3046 = const()[name = tensor<string, []>("op_3046"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_33_cast_fp16 = reshape(shape = var_3046, x = linear_149_cast_fp16)[name = tensor<string, []>("v_33_cast_fp16")];
+            tensor<int32, [4]> value_35_perm_0 = const()[name = tensor<string, []>("value_35_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(813972160)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3058_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_3058_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(813974272)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3060_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_3060_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, 1, 3])];
+            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, 375]> var_3062_to_fp16 = const()[name = tensor<string, []>("op_3062_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(813976384)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_3060_cast_fp16)[name = tensor<string, []>("transpose_175")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_3062_to_fp16)[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, 188, 376]> 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_3070 = const()[name = tensor<string, []>("op_3070"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_363_cast_fp16 = reshape(shape = var_3070, x = x_361_cast_fp16)[name = tensor<string, []>("x_363_cast_fp16")];
+            tensor<int32, [4]> var_3074_begin_0 = const()[name = tensor<string, []>("op_3074_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3074_end_0 = const()[name = tensor<string, []>("op_3074_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3074_end_mask_0 = const()[name = tensor<string, []>("op_3074_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3074_cast_fp16 = slice_by_index(begin = var_3074_begin_0, end = var_3074_end_0, end_mask = var_3074_end_mask_0, x = x_363_cast_fp16)[name = tensor<string, []>("op_3074_cast_fp16")];
+            tensor<int32, [4]> var_3075 = const()[name = tensor<string, []>("op_3075"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_65_cast_fp16 = reshape(shape = var_3075, x = var_3074_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_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, 188]> transpose_105 = transpose(perm = transpose_105_perm_0, x = k_65_cast_fp16)[name = tensor<string, []>("transpose_173")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_104 = transpose(perm = transpose_104_perm_0, x = var_3058_cast_fp16)[name = tensor<string, []>("transpose_174")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_33_cast_fp16 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_104, y = transpose_105)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_3084_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = tensor<string, []>("op_3084_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, 188, 188]> _inversed_scores_65_cast_fp16 = mul(x = var_3084_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_65_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_67_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_67_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3090_cast_fp16 = softmax(axis = var_30, x = scores_67_cast_fp16)[name = tensor<string, []>("op_3090_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_865_cast_fp16 = select(a = var_11_to_fp16, b = var_3090_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, 188, 128]> value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_33_cast_fp16)[name = tensor<string, []>("transpose_176")];
+            tensor<fp16, [1, 8, 188, 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_35_cast_fp16)[name = tensor<string, []>("x_365_cast_fp16")];
+            tensor<int32, [4]> var_3094_perm_0 = const()[name = tensor<string, []>("op_3094_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3095 = const()[name = tensor<string, []>("op_3095"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3094_cast_fp16 = transpose(perm = var_3094_perm_0, x = x_365_cast_fp16)[name = tensor<string, []>("transpose_172")];
+            tensor<fp16, [1, 188, 1024]> input_867_cast_fp16 = reshape(shape = var_3095, x = var_3094_cast_fp16)[name = tensor<string, []>("input_867_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_16_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(814744448)))];
+            tensor<fp16, [1024]> module_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(816841664)))];
+            tensor<fp16, [1, 188, 1024]> linear_151_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_out_bias_to_fp16, weight = module_layers_16_self_attn_linear_out_weight_to_fp16, x = input_867_cast_fp16)[name = tensor<string, []>("linear_151_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_16_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(816843776)))];
+            tensor<fp16, [1024]> module_layers_16_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(816845888)))];
+            tensor<fp16, [1, 188, 1024]> x_369_cast_fp16 = layer_norm(axes = x_369_axes_0, beta = module_layers_16_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_16_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(816848000)))];
+            tensor<fp16, [2048]> module_layers_16_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(821042368)))];
+            tensor<fp16, [1, 1024, 188]> input_873_cast_fp16 = transpose(perm = input_873_perm_0, x = x_369_cast_fp16)[name = tensor<string, []>("transpose_171")];
+            tensor<fp16, [1, 2048, 188]> input_875_cast_fp16 = conv(bias = module_layers_16_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_16_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_371_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_877_cast_fp16 = select(a = var_11_to_fp16, b = x_371_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_280_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(821046528)))];
+            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, []>(821065024)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_879_cast_fp16)[name = tensor<string, []>("input_883_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_16_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(821067136)))];
+            tensor<fp16, [1024]> module_layers_16_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(823164352)))];
+            tensor<fp16, [1, 1024, 188]> x_373_cast_fp16 = conv(bias = module_layers_16_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_16_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_373_cast_fp16)[name = tensor<string, []>("transpose_170")];
+            tensor<fp16, [1, 188, 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]> module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(823166464)))];
+            tensor<fp16, [1024]> module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(823168576)))];
+            tensor<fp16, [1, 188, 1024]> input_891_cast_fp16 = layer_norm(axes = input_891_axes_0, beta = module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_889_cast_fp16)[name = tensor<string, []>("input_891_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_16_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(823170688)))];
+            tensor<fp16, [4096]> module_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(831559360)))];
+            tensor<fp16, [1, 188, 4096]> linear_152_cast_fp16 = linear(bias = module_layers_16_feed_forward2_linear1_bias_to_fp16, weight = module_layers_16_feed_forward2_linear1_weight_to_fp16, x = input_891_cast_fp16)[name = tensor<string, []>("linear_152_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_895_cast_fp16 = silu(x = linear_152_cast_fp16)[name = tensor<string, []>("input_895_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_16_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(831567616)))];
+            tensor<fp16, [1024]> module_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(839956288)))];
+            tensor<fp16, [1, 188, 1024]> linear_153_cast_fp16 = linear(bias = module_layers_16_feed_forward2_linear2_bias_to_fp16, weight = module_layers_16_feed_forward2_linear2_weight_to_fp16, x = input_895_cast_fp16)[name = tensor<string, []>("linear_153_cast_fp16")];
+            tensor<fp16, []> var_3161_to_fp16 = const()[name = tensor<string, []>("op_3161_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3162_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_3161_to_fp16)[name = tensor<string, []>("op_3162_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_901_cast_fp16 = add(x = input_889_cast_fp16, y = var_3162_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]> module_layers_16_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(839958400)))];
+            tensor<fp16, [1024]> module_layers_16_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(839960512)))];
+            tensor<fp16, [1, 188, 1024]> input_903_cast_fp16 = layer_norm(axes = input_903_axes_0, beta = module_layers_16_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(839962624)))];
+            tensor<fp16, [1024]> module_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(839964736)))];
+            tensor<fp16, [1, 188, 1024]> input_905_cast_fp16 = layer_norm(axes = input_905_axes_0, beta = module_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward1_weight_to_fp16, x = input_903_cast_fp16)[name = tensor<string, []>("input_905_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_17_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(839966848)))];
+            tensor<fp16, [4096]> module_layers_17_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(848355520)))];
+            tensor<fp16, [1, 188, 4096]> linear_154_cast_fp16 = linear(bias = module_layers_17_feed_forward1_linear1_bias_to_fp16, weight = module_layers_17_feed_forward1_linear1_weight_to_fp16, x = input_905_cast_fp16)[name = tensor<string, []>("linear_154_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_909_cast_fp16 = silu(x = linear_154_cast_fp16)[name = tensor<string, []>("input_909_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_17_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(848363776)))];
+            tensor<fp16, [1024]> module_layers_17_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(856752448)))];
+            tensor<fp16, [1, 188, 1024]> linear_155_cast_fp16 = linear(bias = module_layers_17_feed_forward1_linear2_bias_to_fp16, weight = module_layers_17_feed_forward1_linear2_weight_to_fp16, x = input_909_cast_fp16)[name = tensor<string, []>("linear_155_cast_fp16")];
+            tensor<fp16, []> var_3192_to_fp16 = const()[name = tensor<string, []>("op_3192_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3193_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_3192_to_fp16)[name = tensor<string, []>("op_3193_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_915_cast_fp16 = add(x = input_903_cast_fp16, y = var_3193_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]> module_layers_17_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(856754560)))];
+            tensor<fp16, [1024]> module_layers_17_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(856756672)))];
+            tensor<fp16, [1, 188, 1024]> query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = module_layers_17_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_self_att_weight_to_fp16, x = input_915_cast_fp16)[name = tensor<string, []>("query_35_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_17_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(856758784)))];
+            tensor<fp16, [1024]> module_layers_17_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(858856000)))];
+            tensor<fp16, [1, 188, 1024]> linear_156_cast_fp16 = linear(bias = module_layers_17_self_attn_linear_q_bias_to_fp16, weight = module_layers_17_self_attn_linear_q_weight_to_fp16, x = query_35_cast_fp16)[name = tensor<string, []>("linear_156_cast_fp16")];
+            tensor<int32, [4]> var_3210 = const()[name = tensor<string, []>("op_3210"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_103_cast_fp16 = reshape(shape = var_3210, x = linear_156_cast_fp16)[name = tensor<string, []>("q_103_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_17_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(858858112)))];
+            tensor<fp16, [1024]> module_layers_17_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(860955328)))];
+            tensor<fp16, [1, 188, 1024]> linear_157_cast_fp16 = linear(bias = module_layers_17_self_attn_linear_k_bias_to_fp16, weight = module_layers_17_self_attn_linear_k_weight_to_fp16, x = query_35_cast_fp16)[name = tensor<string, []>("linear_157_cast_fp16")];
+            tensor<int32, [4]> var_3215 = const()[name = tensor<string, []>("op_3215"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_69_cast_fp16 = reshape(shape = var_3215, x = linear_157_cast_fp16)[name = tensor<string, []>("k_69_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_17_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(860957440)))];
+            tensor<fp16, [1024]> module_layers_17_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(863054656)))];
+            tensor<fp16, [1, 188, 1024]> linear_158_cast_fp16 = linear(bias = module_layers_17_self_attn_linear_v_bias_to_fp16, weight = module_layers_17_self_attn_linear_v_weight_to_fp16, x = query_35_cast_fp16)[name = tensor<string, []>("linear_158_cast_fp16")];
+            tensor<int32, [4]> var_3220 = const()[name = tensor<string, []>("op_3220"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_35_cast_fp16 = reshape(shape = var_3220, x = linear_158_cast_fp16)[name = tensor<string, []>("v_35_cast_fp16")];
+            tensor<int32, [4]> value_37_perm_0 = const()[name = tensor<string, []>("value_37_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(863056768)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3232_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_3232_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(863058880)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3234_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_3234_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, 1, 3])];
+            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, 375]> var_3236_to_fp16 = const()[name = tensor<string, []>("op_3236_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(863060992)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3234_cast_fp16)[name = tensor<string, []>("transpose_168")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_3236_to_fp16)[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, 188, 376]> 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_3244 = const()[name = tensor<string, []>("op_3244"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_385_cast_fp16 = reshape(shape = var_3244, x = x_383_cast_fp16)[name = tensor<string, []>("x_385_cast_fp16")];
+            tensor<int32, [4]> var_3248_begin_0 = const()[name = tensor<string, []>("op_3248_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3248_end_0 = const()[name = tensor<string, []>("op_3248_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3248_end_mask_0 = const()[name = tensor<string, []>("op_3248_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3248_cast_fp16 = slice_by_index(begin = var_3248_begin_0, end = var_3248_end_0, end_mask = var_3248_end_mask_0, x = x_385_cast_fp16)[name = tensor<string, []>("op_3248_cast_fp16")];
+            tensor<int32, [4]> var_3249 = const()[name = tensor<string, []>("op_3249"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_69_cast_fp16 = reshape(shape = var_3249, x = var_3248_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_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, 188]> transpose_107 = transpose(perm = transpose_107_perm_0, x = k_69_cast_fp16)[name = tensor<string, []>("transpose_166")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_106 = transpose(perm = transpose_106_perm_0, x = var_3232_cast_fp16)[name = tensor<string, []>("transpose_167")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_35_cast_fp16 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_106, y = transpose_107)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_3258_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = tensor<string, []>("op_3258_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, 188, 188]> _inversed_scores_69_cast_fp16 = mul(x = var_3258_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_69_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_71_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_71_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3264_cast_fp16 = softmax(axis = var_30, x = scores_71_cast_fp16)[name = tensor<string, []>("op_3264_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_917_cast_fp16 = select(a = var_11_to_fp16, b = var_3264_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, 188, 128]> value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_35_cast_fp16)[name = tensor<string, []>("transpose_169")];
+            tensor<fp16, [1, 8, 188, 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_37_cast_fp16)[name = tensor<string, []>("x_387_cast_fp16")];
+            tensor<int32, [4]> var_3268_perm_0 = const()[name = tensor<string, []>("op_3268_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3269 = const()[name = tensor<string, []>("op_3269"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3268_cast_fp16 = transpose(perm = var_3268_perm_0, x = x_387_cast_fp16)[name = tensor<string, []>("transpose_165")];
+            tensor<fp16, [1, 188, 1024]> input_919_cast_fp16 = reshape(shape = var_3269, x = var_3268_cast_fp16)[name = tensor<string, []>("input_919_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_17_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(863829056)))];
+            tensor<fp16, [1024]> module_layers_17_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(865926272)))];
+            tensor<fp16, [1, 188, 1024]> linear_160_cast_fp16 = linear(bias = module_layers_17_self_attn_linear_out_bias_to_fp16, weight = module_layers_17_self_attn_linear_out_weight_to_fp16, x = input_919_cast_fp16)[name = tensor<string, []>("linear_160_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_17_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(865928384)))];
+            tensor<fp16, [1024]> module_layers_17_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(865930496)))];
+            tensor<fp16, [1, 188, 1024]> x_391_cast_fp16 = layer_norm(axes = x_391_axes_0, beta = module_layers_17_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_17_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(865932608)))];
+            tensor<fp16, [2048]> module_layers_17_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(870126976)))];
+            tensor<fp16, [1, 1024, 188]> input_925_cast_fp16 = transpose(perm = input_925_perm_0, x = x_391_cast_fp16)[name = tensor<string, []>("transpose_164")];
+            tensor<fp16, [1, 2048, 188]> input_927_cast_fp16 = conv(bias = module_layers_17_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_17_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_393_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_929_cast_fp16 = select(a = var_11_to_fp16, b = x_393_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_282_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(870131136)))];
+            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, []>(870149632)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_931_cast_fp16)[name = tensor<string, []>("input_935_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_17_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(870151744)))];
+            tensor<fp16, [1024]> module_layers_17_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(872248960)))];
+            tensor<fp16, [1, 1024, 188]> x_395_cast_fp16 = conv(bias = module_layers_17_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_17_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_939_cast_fp16 = transpose(perm = input_939_perm_0, x = x_395_cast_fp16)[name = tensor<string, []>("transpose_163")];
+            tensor<fp16, [1, 188, 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]> module_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(872251072)))];
+            tensor<fp16, [1024]> module_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(872253184)))];
+            tensor<fp16, [1, 188, 1024]> input_943_cast_fp16 = layer_norm(axes = input_943_axes_0, beta = module_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward2_weight_to_fp16, x = input_941_cast_fp16)[name = tensor<string, []>("input_943_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_17_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(872255296)))];
+            tensor<fp16, [4096]> module_layers_17_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(880643968)))];
+            tensor<fp16, [1, 188, 4096]> linear_161_cast_fp16 = linear(bias = module_layers_17_feed_forward2_linear1_bias_to_fp16, weight = module_layers_17_feed_forward2_linear1_weight_to_fp16, x = input_943_cast_fp16)[name = tensor<string, []>("linear_161_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_947_cast_fp16 = silu(x = linear_161_cast_fp16)[name = tensor<string, []>("input_947_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_17_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(880652224)))];
+            tensor<fp16, [1024]> module_layers_17_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(889040896)))];
+            tensor<fp16, [1, 188, 1024]> linear_162_cast_fp16 = linear(bias = module_layers_17_feed_forward2_linear2_bias_to_fp16, weight = module_layers_17_feed_forward2_linear2_weight_to_fp16, x = input_947_cast_fp16)[name = tensor<string, []>("linear_162_cast_fp16")];
+            tensor<fp16, []> var_3335_to_fp16 = const()[name = tensor<string, []>("op_3335_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3336_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_3335_to_fp16)[name = tensor<string, []>("op_3336_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_953_cast_fp16 = add(x = input_941_cast_fp16, y = var_3336_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]> module_layers_17_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(889043008)))];
+            tensor<fp16, [1024]> module_layers_17_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(889045120)))];
+            tensor<fp16, [1, 188, 1024]> input_955_cast_fp16 = layer_norm(axes = input_955_axes_0, beta = module_layers_17_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(889047232)))];
+            tensor<fp16, [1024]> module_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(889049344)))];
+            tensor<fp16, [1, 188, 1024]> input_957_cast_fp16 = layer_norm(axes = input_957_axes_0, beta = module_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward1_weight_to_fp16, x = input_955_cast_fp16)[name = tensor<string, []>("input_957_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_18_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(889051456)))];
+            tensor<fp16, [4096]> module_layers_18_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(897440128)))];
+            tensor<fp16, [1, 188, 4096]> linear_163_cast_fp16 = linear(bias = module_layers_18_feed_forward1_linear1_bias_to_fp16, weight = module_layers_18_feed_forward1_linear1_weight_to_fp16, x = input_957_cast_fp16)[name = tensor<string, []>("linear_163_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_961_cast_fp16 = silu(x = linear_163_cast_fp16)[name = tensor<string, []>("input_961_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_18_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(897448384)))];
+            tensor<fp16, [1024]> module_layers_18_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(905837056)))];
+            tensor<fp16, [1, 188, 1024]> linear_164_cast_fp16 = linear(bias = module_layers_18_feed_forward1_linear2_bias_to_fp16, weight = module_layers_18_feed_forward1_linear2_weight_to_fp16, x = input_961_cast_fp16)[name = tensor<string, []>("linear_164_cast_fp16")];
+            tensor<fp16, []> var_3366_to_fp16 = const()[name = tensor<string, []>("op_3366_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3367_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3366_to_fp16)[name = tensor<string, []>("op_3367_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_967_cast_fp16 = add(x = input_955_cast_fp16, y = var_3367_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]> module_layers_18_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(905839168)))];
+            tensor<fp16, [1024]> module_layers_18_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(905841280)))];
+            tensor<fp16, [1, 188, 1024]> query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = module_layers_18_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_self_att_weight_to_fp16, x = input_967_cast_fp16)[name = tensor<string, []>("query_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_18_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(905843392)))];
+            tensor<fp16, [1024]> module_layers_18_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(907940608)))];
+            tensor<fp16, [1, 188, 1024]> linear_165_cast_fp16 = linear(bias = module_layers_18_self_attn_linear_q_bias_to_fp16, weight = module_layers_18_self_attn_linear_q_weight_to_fp16, x = query_37_cast_fp16)[name = tensor<string, []>("linear_165_cast_fp16")];
+            tensor<int32, [4]> var_3384 = const()[name = tensor<string, []>("op_3384"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_109_cast_fp16 = reshape(shape = var_3384, x = linear_165_cast_fp16)[name = tensor<string, []>("q_109_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_18_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(907942720)))];
+            tensor<fp16, [1024]> module_layers_18_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(910039936)))];
+            tensor<fp16, [1, 188, 1024]> linear_166_cast_fp16 = linear(bias = module_layers_18_self_attn_linear_k_bias_to_fp16, weight = module_layers_18_self_attn_linear_k_weight_to_fp16, x = query_37_cast_fp16)[name = tensor<string, []>("linear_166_cast_fp16")];
+            tensor<int32, [4]> var_3389 = const()[name = tensor<string, []>("op_3389"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_73_cast_fp16 = reshape(shape = var_3389, x = linear_166_cast_fp16)[name = tensor<string, []>("k_73_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_18_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(910042048)))];
+            tensor<fp16, [1024]> module_layers_18_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(912139264)))];
+            tensor<fp16, [1, 188, 1024]> linear_167_cast_fp16 = linear(bias = module_layers_18_self_attn_linear_v_bias_to_fp16, weight = module_layers_18_self_attn_linear_v_weight_to_fp16, x = query_37_cast_fp16)[name = tensor<string, []>("linear_167_cast_fp16")];
+            tensor<int32, [4]> var_3394 = const()[name = tensor<string, []>("op_3394"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_37_cast_fp16 = reshape(shape = var_3394, x = linear_167_cast_fp16)[name = tensor<string, []>("v_37_cast_fp16")];
+            tensor<int32, [4]> value_39_perm_0 = const()[name = tensor<string, []>("value_39_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(912141376)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3406_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_3406_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(912143488)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3408_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_3408_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, 1, 3])];
+            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, 375]> var_3410_to_fp16 = const()[name = tensor<string, []>("op_3410_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(912145600)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3408_cast_fp16)[name = tensor<string, []>("transpose_161")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_3410_to_fp16)[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, 188, 376]> 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_3418 = const()[name = tensor<string, []>("op_3418"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_407_cast_fp16 = reshape(shape = var_3418, x = x_405_cast_fp16)[name = tensor<string, []>("x_407_cast_fp16")];
+            tensor<int32, [4]> var_3422_begin_0 = const()[name = tensor<string, []>("op_3422_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3422_end_0 = const()[name = tensor<string, []>("op_3422_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3422_end_mask_0 = const()[name = tensor<string, []>("op_3422_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3422_cast_fp16 = slice_by_index(begin = var_3422_begin_0, end = var_3422_end_0, end_mask = var_3422_end_mask_0, x = x_407_cast_fp16)[name = tensor<string, []>("op_3422_cast_fp16")];
+            tensor<int32, [4]> var_3423 = const()[name = tensor<string, []>("op_3423"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_73_cast_fp16 = reshape(shape = var_3423, x = var_3422_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_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, 188]> transpose_109 = transpose(perm = transpose_109_perm_0, x = k_73_cast_fp16)[name = tensor<string, []>("transpose_159")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_108 = transpose(perm = transpose_108_perm_0, x = var_3406_cast_fp16)[name = tensor<string, []>("transpose_160")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_37_cast_fp16 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_108, y = transpose_109)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_3432_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = tensor<string, []>("op_3432_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, 188, 188]> _inversed_scores_73_cast_fp16 = mul(x = var_3432_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_73_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_75_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_75_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3438_cast_fp16 = softmax(axis = var_30, x = scores_75_cast_fp16)[name = tensor<string, []>("op_3438_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_969_cast_fp16 = select(a = var_11_to_fp16, b = var_3438_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, 188, 128]> value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_37_cast_fp16)[name = tensor<string, []>("transpose_162")];
+            tensor<fp16, [1, 8, 188, 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_39_cast_fp16)[name = tensor<string, []>("x_409_cast_fp16")];
+            tensor<int32, [4]> var_3442_perm_0 = const()[name = tensor<string, []>("op_3442_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3443 = const()[name = tensor<string, []>("op_3443"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3442_cast_fp16 = transpose(perm = var_3442_perm_0, x = x_409_cast_fp16)[name = tensor<string, []>("transpose_158")];
+            tensor<fp16, [1, 188, 1024]> input_971_cast_fp16 = reshape(shape = var_3443, x = var_3442_cast_fp16)[name = tensor<string, []>("input_971_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_18_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(912913664)))];
+            tensor<fp16, [1024]> module_layers_18_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(915010880)))];
+            tensor<fp16, [1, 188, 1024]> linear_169_cast_fp16 = linear(bias = module_layers_18_self_attn_linear_out_bias_to_fp16, weight = module_layers_18_self_attn_linear_out_weight_to_fp16, x = input_971_cast_fp16)[name = tensor<string, []>("linear_169_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_18_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(915012992)))];
+            tensor<fp16, [1024]> module_layers_18_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(915015104)))];
+            tensor<fp16, [1, 188, 1024]> x_413_cast_fp16 = layer_norm(axes = x_413_axes_0, beta = module_layers_18_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_18_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(915017216)))];
+            tensor<fp16, [2048]> module_layers_18_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(919211584)))];
+            tensor<fp16, [1, 1024, 188]> input_977_cast_fp16 = transpose(perm = input_977_perm_0, x = x_413_cast_fp16)[name = tensor<string, []>("transpose_157")];
+            tensor<fp16, [1, 2048, 188]> input_979_cast_fp16 = conv(bias = module_layers_18_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_18_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_415_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_981_cast_fp16 = select(a = var_11_to_fp16, b = x_415_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_284_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(919215744)))];
+            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, []>(919234240)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_983_cast_fp16)[name = tensor<string, []>("input_987_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_18_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(919236352)))];
+            tensor<fp16, [1024]> module_layers_18_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(921333568)))];
+            tensor<fp16, [1, 1024, 188]> x_417_cast_fp16 = conv(bias = module_layers_18_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_18_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_991_cast_fp16 = transpose(perm = input_991_perm_0, x = x_417_cast_fp16)[name = tensor<string, []>("transpose_156")];
+            tensor<fp16, [1, 188, 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]> module_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(921335680)))];
+            tensor<fp16, [1024]> module_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(921337792)))];
+            tensor<fp16, [1, 188, 1024]> input_995_cast_fp16 = layer_norm(axes = input_995_axes_0, beta = module_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward2_weight_to_fp16, x = input_993_cast_fp16)[name = tensor<string, []>("input_995_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_18_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(921339904)))];
+            tensor<fp16, [4096]> module_layers_18_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(929728576)))];
+            tensor<fp16, [1, 188, 4096]> linear_170_cast_fp16 = linear(bias = module_layers_18_feed_forward2_linear1_bias_to_fp16, weight = module_layers_18_feed_forward2_linear1_weight_to_fp16, x = input_995_cast_fp16)[name = tensor<string, []>("linear_170_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_999_cast_fp16 = silu(x = linear_170_cast_fp16)[name = tensor<string, []>("input_999_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_18_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(929736832)))];
+            tensor<fp16, [1024]> module_layers_18_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(938125504)))];
+            tensor<fp16, [1, 188, 1024]> linear_171_cast_fp16 = linear(bias = module_layers_18_feed_forward2_linear2_bias_to_fp16, weight = module_layers_18_feed_forward2_linear2_weight_to_fp16, x = input_999_cast_fp16)[name = tensor<string, []>("linear_171_cast_fp16")];
+            tensor<fp16, []> var_3509_to_fp16 = const()[name = tensor<string, []>("op_3509_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3510_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_3509_to_fp16)[name = tensor<string, []>("op_3510_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1005_cast_fp16 = add(x = input_993_cast_fp16, y = var_3510_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]> module_layers_18_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(938127616)))];
+            tensor<fp16, [1024]> module_layers_18_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(938129728)))];
+            tensor<fp16, [1, 188, 1024]> input_1007_cast_fp16 = layer_norm(axes = input_1007_axes_0, beta = module_layers_18_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(938131840)))];
+            tensor<fp16, [1024]> module_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(938133952)))];
+            tensor<fp16, [1, 188, 1024]> input_1009_cast_fp16 = layer_norm(axes = input_1009_axes_0, beta = module_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1007_cast_fp16)[name = tensor<string, []>("input_1009_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_19_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(938136064)))];
+            tensor<fp16, [4096]> module_layers_19_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(946524736)))];
+            tensor<fp16, [1, 188, 4096]> linear_172_cast_fp16 = linear(bias = module_layers_19_feed_forward1_linear1_bias_to_fp16, weight = module_layers_19_feed_forward1_linear1_weight_to_fp16, x = input_1009_cast_fp16)[name = tensor<string, []>("linear_172_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1013_cast_fp16 = silu(x = linear_172_cast_fp16)[name = tensor<string, []>("input_1013_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_19_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(946532992)))];
+            tensor<fp16, [1024]> module_layers_19_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(954921664)))];
+            tensor<fp16, [1, 188, 1024]> linear_173_cast_fp16 = linear(bias = module_layers_19_feed_forward1_linear2_bias_to_fp16, weight = module_layers_19_feed_forward1_linear2_weight_to_fp16, x = input_1013_cast_fp16)[name = tensor<string, []>("linear_173_cast_fp16")];
+            tensor<fp16, []> var_3540_to_fp16 = const()[name = tensor<string, []>("op_3540_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3541_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3540_to_fp16)[name = tensor<string, []>("op_3541_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1019_cast_fp16 = add(x = input_1007_cast_fp16, y = var_3541_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]> module_layers_19_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(954923776)))];
+            tensor<fp16, [1024]> module_layers_19_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(954925888)))];
+            tensor<fp16, [1, 188, 1024]> query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = module_layers_19_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_self_att_weight_to_fp16, x = input_1019_cast_fp16)[name = tensor<string, []>("query_39_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_19_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(954928000)))];
+            tensor<fp16, [1024]> module_layers_19_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(957025216)))];
+            tensor<fp16, [1, 188, 1024]> linear_174_cast_fp16 = linear(bias = module_layers_19_self_attn_linear_q_bias_to_fp16, weight = module_layers_19_self_attn_linear_q_weight_to_fp16, x = query_39_cast_fp16)[name = tensor<string, []>("linear_174_cast_fp16")];
+            tensor<int32, [4]> var_3558 = const()[name = tensor<string, []>("op_3558"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_115_cast_fp16 = reshape(shape = var_3558, x = linear_174_cast_fp16)[name = tensor<string, []>("q_115_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_19_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(957027328)))];
+            tensor<fp16, [1024]> module_layers_19_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(959124544)))];
+            tensor<fp16, [1, 188, 1024]> linear_175_cast_fp16 = linear(bias = module_layers_19_self_attn_linear_k_bias_to_fp16, weight = module_layers_19_self_attn_linear_k_weight_to_fp16, x = query_39_cast_fp16)[name = tensor<string, []>("linear_175_cast_fp16")];
+            tensor<int32, [4]> var_3563 = const()[name = tensor<string, []>("op_3563"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_77_cast_fp16 = reshape(shape = var_3563, x = linear_175_cast_fp16)[name = tensor<string, []>("k_77_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_19_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(959126656)))];
+            tensor<fp16, [1024]> module_layers_19_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(961223872)))];
+            tensor<fp16, [1, 188, 1024]> linear_176_cast_fp16 = linear(bias = module_layers_19_self_attn_linear_v_bias_to_fp16, weight = module_layers_19_self_attn_linear_v_weight_to_fp16, x = query_39_cast_fp16)[name = tensor<string, []>("linear_176_cast_fp16")];
+            tensor<int32, [4]> var_3568 = const()[name = tensor<string, []>("op_3568"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_39_cast_fp16 = reshape(shape = var_3568, x = linear_176_cast_fp16)[name = tensor<string, []>("v_39_cast_fp16")];
+            tensor<int32, [4]> value_41_perm_0 = const()[name = tensor<string, []>("value_41_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(961225984)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3580_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_3580_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(961228096)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3582_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_3582_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, 1, 3])];
+            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, 375]> var_3584_to_fp16 = const()[name = tensor<string, []>("op_3584_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(961230208)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3582_cast_fp16)[name = tensor<string, []>("transpose_154")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_3584_to_fp16)[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, 188, 376]> 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_3592 = const()[name = tensor<string, []>("op_3592"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_429_cast_fp16 = reshape(shape = var_3592, x = x_427_cast_fp16)[name = tensor<string, []>("x_429_cast_fp16")];
+            tensor<int32, [4]> var_3596_begin_0 = const()[name = tensor<string, []>("op_3596_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3596_end_0 = const()[name = tensor<string, []>("op_3596_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3596_end_mask_0 = const()[name = tensor<string, []>("op_3596_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3596_cast_fp16 = slice_by_index(begin = var_3596_begin_0, end = var_3596_end_0, end_mask = var_3596_end_mask_0, x = x_429_cast_fp16)[name = tensor<string, []>("op_3596_cast_fp16")];
+            tensor<int32, [4]> var_3597 = const()[name = tensor<string, []>("op_3597"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_77_cast_fp16 = reshape(shape = var_3597, x = var_3596_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_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, 188]> transpose_111 = transpose(perm = transpose_111_perm_0, x = k_77_cast_fp16)[name = tensor<string, []>("transpose_152")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_110 = transpose(perm = transpose_110_perm_0, x = var_3580_cast_fp16)[name = tensor<string, []>("transpose_153")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_39_cast_fp16 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_110, y = transpose_111)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_3606_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = tensor<string, []>("op_3606_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, 188, 188]> _inversed_scores_77_cast_fp16 = mul(x = var_3606_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_77_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_79_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_79_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3612_cast_fp16 = softmax(axis = var_30, x = scores_79_cast_fp16)[name = tensor<string, []>("op_3612_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1021_cast_fp16 = select(a = var_11_to_fp16, b = var_3612_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, 188, 128]> value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_39_cast_fp16)[name = tensor<string, []>("transpose_155")];
+            tensor<fp16, [1, 8, 188, 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_41_cast_fp16)[name = tensor<string, []>("x_431_cast_fp16")];
+            tensor<int32, [4]> var_3616_perm_0 = const()[name = tensor<string, []>("op_3616_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3617 = const()[name = tensor<string, []>("op_3617"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3616_cast_fp16 = transpose(perm = var_3616_perm_0, x = x_431_cast_fp16)[name = tensor<string, []>("transpose_151")];
+            tensor<fp16, [1, 188, 1024]> input_1023_cast_fp16 = reshape(shape = var_3617, x = var_3616_cast_fp16)[name = tensor<string, []>("input_1023_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_19_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(961998272)))];
+            tensor<fp16, [1024]> module_layers_19_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(964095488)))];
+            tensor<fp16, [1, 188, 1024]> linear_178_cast_fp16 = linear(bias = module_layers_19_self_attn_linear_out_bias_to_fp16, weight = module_layers_19_self_attn_linear_out_weight_to_fp16, x = input_1023_cast_fp16)[name = tensor<string, []>("linear_178_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_19_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(964097600)))];
+            tensor<fp16, [1024]> module_layers_19_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(964099712)))];
+            tensor<fp16, [1, 188, 1024]> x_435_cast_fp16 = layer_norm(axes = x_435_axes_0, beta = module_layers_19_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_19_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(964101824)))];
+            tensor<fp16, [2048]> module_layers_19_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(968296192)))];
+            tensor<fp16, [1, 1024, 188]> input_1029_cast_fp16 = transpose(perm = input_1029_perm_0, x = x_435_cast_fp16)[name = tensor<string, []>("transpose_150")];
+            tensor<fp16, [1, 2048, 188]> input_1031_cast_fp16 = conv(bias = module_layers_19_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_19_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_437_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_1033_cast_fp16 = select(a = var_11_to_fp16, b = x_437_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_286_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(968300352)))];
+            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, []>(968318848)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_1035_cast_fp16)[name = tensor<string, []>("input_1039_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_19_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(968320960)))];
+            tensor<fp16, [1024]> module_layers_19_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(970418176)))];
+            tensor<fp16, [1, 1024, 188]> x_439_cast_fp16 = conv(bias = module_layers_19_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_19_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_1043_cast_fp16 = transpose(perm = input_1043_perm_0, x = x_439_cast_fp16)[name = tensor<string, []>("transpose_149")];
+            tensor<fp16, [1, 188, 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]> module_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(970420288)))];
+            tensor<fp16, [1024]> module_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(970422400)))];
+            tensor<fp16, [1, 188, 1024]> input_1047_cast_fp16 = layer_norm(axes = input_1047_axes_0, beta = module_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1045_cast_fp16)[name = tensor<string, []>("input_1047_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_19_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(970424512)))];
+            tensor<fp16, [4096]> module_layers_19_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(978813184)))];
+            tensor<fp16, [1, 188, 4096]> linear_179_cast_fp16 = linear(bias = module_layers_19_feed_forward2_linear1_bias_to_fp16, weight = module_layers_19_feed_forward2_linear1_weight_to_fp16, x = input_1047_cast_fp16)[name = tensor<string, []>("linear_179_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1051_cast_fp16 = silu(x = linear_179_cast_fp16)[name = tensor<string, []>("input_1051_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_19_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(978821440)))];
+            tensor<fp16, [1024]> module_layers_19_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(987210112)))];
+            tensor<fp16, [1, 188, 1024]> linear_180_cast_fp16 = linear(bias = module_layers_19_feed_forward2_linear2_bias_to_fp16, weight = module_layers_19_feed_forward2_linear2_weight_to_fp16, x = input_1051_cast_fp16)[name = tensor<string, []>("linear_180_cast_fp16")];
+            tensor<fp16, []> var_3683_to_fp16 = const()[name = tensor<string, []>("op_3683_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3684_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_3683_to_fp16)[name = tensor<string, []>("op_3684_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1057_cast_fp16 = add(x = input_1045_cast_fp16, y = var_3684_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]> module_layers_19_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(987212224)))];
+            tensor<fp16, [1024]> module_layers_19_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(987214336)))];
+            tensor<fp16, [1, 188, 1024]> input_1059_cast_fp16 = layer_norm(axes = input_1059_axes_0, beta = module_layers_19_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(987216448)))];
+            tensor<fp16, [1024]> module_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(987218560)))];
+            tensor<fp16, [1, 188, 1024]> input_1061_cast_fp16 = layer_norm(axes = input_1061_axes_0, beta = module_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1059_cast_fp16)[name = tensor<string, []>("input_1061_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_20_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(987220672)))];
+            tensor<fp16, [4096]> module_layers_20_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(995609344)))];
+            tensor<fp16, [1, 188, 4096]> linear_181_cast_fp16 = linear(bias = module_layers_20_feed_forward1_linear1_bias_to_fp16, weight = module_layers_20_feed_forward1_linear1_weight_to_fp16, x = input_1061_cast_fp16)[name = tensor<string, []>("linear_181_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1065_cast_fp16 = silu(x = linear_181_cast_fp16)[name = tensor<string, []>("input_1065_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_20_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(995617600)))];
+            tensor<fp16, [1024]> module_layers_20_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1004006272)))];
+            tensor<fp16, [1, 188, 1024]> linear_182_cast_fp16 = linear(bias = module_layers_20_feed_forward1_linear2_bias_to_fp16, weight = module_layers_20_feed_forward1_linear2_weight_to_fp16, x = input_1065_cast_fp16)[name = tensor<string, []>("linear_182_cast_fp16")];
+            tensor<fp16, []> var_3714_to_fp16 = const()[name = tensor<string, []>("op_3714_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3715_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3714_to_fp16)[name = tensor<string, []>("op_3715_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1071_cast_fp16 = add(x = input_1059_cast_fp16, y = var_3715_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]> module_layers_20_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1004008384)))];
+            tensor<fp16, [1024]> module_layers_20_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1004010496)))];
+            tensor<fp16, [1, 188, 1024]> query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = module_layers_20_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_self_att_weight_to_fp16, x = input_1071_cast_fp16)[name = tensor<string, []>("query_41_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_20_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1004012608)))];
+            tensor<fp16, [1024]> module_layers_20_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1006109824)))];
+            tensor<fp16, [1, 188, 1024]> linear_183_cast_fp16 = linear(bias = module_layers_20_self_attn_linear_q_bias_to_fp16, weight = module_layers_20_self_attn_linear_q_weight_to_fp16, x = query_41_cast_fp16)[name = tensor<string, []>("linear_183_cast_fp16")];
+            tensor<int32, [4]> var_3732 = const()[name = tensor<string, []>("op_3732"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_121_cast_fp16 = reshape(shape = var_3732, x = linear_183_cast_fp16)[name = tensor<string, []>("q_121_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_20_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1006111936)))];
+            tensor<fp16, [1024]> module_layers_20_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1008209152)))];
+            tensor<fp16, [1, 188, 1024]> linear_184_cast_fp16 = linear(bias = module_layers_20_self_attn_linear_k_bias_to_fp16, weight = module_layers_20_self_attn_linear_k_weight_to_fp16, x = query_41_cast_fp16)[name = tensor<string, []>("linear_184_cast_fp16")];
+            tensor<int32, [4]> var_3737 = const()[name = tensor<string, []>("op_3737"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_81_cast_fp16 = reshape(shape = var_3737, x = linear_184_cast_fp16)[name = tensor<string, []>("k_81_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_20_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1008211264)))];
+            tensor<fp16, [1024]> module_layers_20_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1010308480)))];
+            tensor<fp16, [1, 188, 1024]> linear_185_cast_fp16 = linear(bias = module_layers_20_self_attn_linear_v_bias_to_fp16, weight = module_layers_20_self_attn_linear_v_weight_to_fp16, x = query_41_cast_fp16)[name = tensor<string, []>("linear_185_cast_fp16")];
+            tensor<int32, [4]> var_3742 = const()[name = tensor<string, []>("op_3742"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_41_cast_fp16 = reshape(shape = var_3742, x = linear_185_cast_fp16)[name = tensor<string, []>("v_41_cast_fp16")];
+            tensor<int32, [4]> value_43_perm_0 = const()[name = tensor<string, []>("value_43_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1010310592)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3754_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_3754_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1010312704)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3756_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_3756_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, 1, 3])];
+            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, 375]> var_3758_to_fp16 = const()[name = tensor<string, []>("op_3758_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1010314816)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_3756_cast_fp16)[name = tensor<string, []>("transpose_147")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_3758_to_fp16)[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, 188, 376]> 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_3766 = const()[name = tensor<string, []>("op_3766"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_451_cast_fp16 = reshape(shape = var_3766, x = x_449_cast_fp16)[name = tensor<string, []>("x_451_cast_fp16")];
+            tensor<int32, [4]> var_3770_begin_0 = const()[name = tensor<string, []>("op_3770_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3770_end_0 = const()[name = tensor<string, []>("op_3770_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3770_end_mask_0 = const()[name = tensor<string, []>("op_3770_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3770_cast_fp16 = slice_by_index(begin = var_3770_begin_0, end = var_3770_end_0, end_mask = var_3770_end_mask_0, x = x_451_cast_fp16)[name = tensor<string, []>("op_3770_cast_fp16")];
+            tensor<int32, [4]> var_3771 = const()[name = tensor<string, []>("op_3771"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_81_cast_fp16 = reshape(shape = var_3771, x = var_3770_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_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, 188]> transpose_113 = transpose(perm = transpose_113_perm_0, x = k_81_cast_fp16)[name = tensor<string, []>("transpose_145")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_112 = transpose(perm = transpose_112_perm_0, x = var_3754_cast_fp16)[name = tensor<string, []>("transpose_146")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_41_cast_fp16 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_112, y = transpose_113)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_3780_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = tensor<string, []>("op_3780_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, 188, 188]> _inversed_scores_81_cast_fp16 = mul(x = var_3780_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_81_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_83_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_83_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3786_cast_fp16 = softmax(axis = var_30, x = scores_83_cast_fp16)[name = tensor<string, []>("op_3786_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1073_cast_fp16 = select(a = var_11_to_fp16, b = var_3786_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, 188, 128]> value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_41_cast_fp16)[name = tensor<string, []>("transpose_148")];
+            tensor<fp16, [1, 8, 188, 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_43_cast_fp16)[name = tensor<string, []>("x_453_cast_fp16")];
+            tensor<int32, [4]> var_3790_perm_0 = const()[name = tensor<string, []>("op_3790_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3791 = const()[name = tensor<string, []>("op_3791"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3790_cast_fp16 = transpose(perm = var_3790_perm_0, x = x_453_cast_fp16)[name = tensor<string, []>("transpose_144")];
+            tensor<fp16, [1, 188, 1024]> input_1075_cast_fp16 = reshape(shape = var_3791, x = var_3790_cast_fp16)[name = tensor<string, []>("input_1075_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_20_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1011082880)))];
+            tensor<fp16, [1024]> module_layers_20_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1013180096)))];
+            tensor<fp16, [1, 188, 1024]> linear_187_cast_fp16 = linear(bias = module_layers_20_self_attn_linear_out_bias_to_fp16, weight = module_layers_20_self_attn_linear_out_weight_to_fp16, x = input_1075_cast_fp16)[name = tensor<string, []>("linear_187_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_20_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1013182208)))];
+            tensor<fp16, [1024]> module_layers_20_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1013184320)))];
+            tensor<fp16, [1, 188, 1024]> x_457_cast_fp16 = layer_norm(axes = x_457_axes_0, beta = module_layers_20_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_20_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1013186432)))];
+            tensor<fp16, [2048]> module_layers_20_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1017380800)))];
+            tensor<fp16, [1, 1024, 188]> input_1081_cast_fp16 = transpose(perm = input_1081_perm_0, x = x_457_cast_fp16)[name = tensor<string, []>("transpose_143")];
+            tensor<fp16, [1, 2048, 188]> input_1083_cast_fp16 = conv(bias = module_layers_20_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_20_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_459_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_1085_cast_fp16 = select(a = var_11_to_fp16, b = x_459_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_288_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1017384960)))];
+            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, []>(1017403456)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_1087_cast_fp16)[name = tensor<string, []>("input_1091_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_20_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1017405568)))];
+            tensor<fp16, [1024]> module_layers_20_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1019502784)))];
+            tensor<fp16, [1, 1024, 188]> x_461_cast_fp16 = conv(bias = module_layers_20_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_20_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_1095_cast_fp16 = transpose(perm = input_1095_perm_0, x = x_461_cast_fp16)[name = tensor<string, []>("transpose_142")];
+            tensor<fp16, [1, 188, 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]> module_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1019504896)))];
+            tensor<fp16, [1024]> module_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1019507008)))];
+            tensor<fp16, [1, 188, 1024]> input_1099_cast_fp16 = layer_norm(axes = input_1099_axes_0, beta = module_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1097_cast_fp16)[name = tensor<string, []>("input_1099_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_20_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1019509120)))];
+            tensor<fp16, [4096]> module_layers_20_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1027897792)))];
+            tensor<fp16, [1, 188, 4096]> linear_188_cast_fp16 = linear(bias = module_layers_20_feed_forward2_linear1_bias_to_fp16, weight = module_layers_20_feed_forward2_linear1_weight_to_fp16, x = input_1099_cast_fp16)[name = tensor<string, []>("linear_188_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1103_cast_fp16 = silu(x = linear_188_cast_fp16)[name = tensor<string, []>("input_1103_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_20_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1027906048)))];
+            tensor<fp16, [1024]> module_layers_20_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1036294720)))];
+            tensor<fp16, [1, 188, 1024]> linear_189_cast_fp16 = linear(bias = module_layers_20_feed_forward2_linear2_bias_to_fp16, weight = module_layers_20_feed_forward2_linear2_weight_to_fp16, x = input_1103_cast_fp16)[name = tensor<string, []>("linear_189_cast_fp16")];
+            tensor<fp16, []> var_3857_to_fp16 = const()[name = tensor<string, []>("op_3857_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3858_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_3857_to_fp16)[name = tensor<string, []>("op_3858_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1109_cast_fp16 = add(x = input_1097_cast_fp16, y = var_3858_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]> module_layers_20_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1036296832)))];
+            tensor<fp16, [1024]> module_layers_20_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1036298944)))];
+            tensor<fp16, [1, 188, 1024]> input_1111_cast_fp16 = layer_norm(axes = input_1111_axes_0, beta = module_layers_20_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1036301056)))];
+            tensor<fp16, [1024]> module_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1036303168)))];
+            tensor<fp16, [1, 188, 1024]> input_1113_cast_fp16 = layer_norm(axes = input_1113_axes_0, beta = module_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1111_cast_fp16)[name = tensor<string, []>("input_1113_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_21_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1036305280)))];
+            tensor<fp16, [4096]> module_layers_21_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1044693952)))];
+            tensor<fp16, [1, 188, 4096]> linear_190_cast_fp16 = linear(bias = module_layers_21_feed_forward1_linear1_bias_to_fp16, weight = module_layers_21_feed_forward1_linear1_weight_to_fp16, x = input_1113_cast_fp16)[name = tensor<string, []>("linear_190_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1117_cast_fp16 = silu(x = linear_190_cast_fp16)[name = tensor<string, []>("input_1117_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_21_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1044702208)))];
+            tensor<fp16, [1024]> module_layers_21_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1053090880)))];
+            tensor<fp16, [1, 188, 1024]> linear_191_cast_fp16 = linear(bias = module_layers_21_feed_forward1_linear2_bias_to_fp16, weight = module_layers_21_feed_forward1_linear2_weight_to_fp16, x = input_1117_cast_fp16)[name = tensor<string, []>("linear_191_cast_fp16")];
+            tensor<fp16, []> var_3888_to_fp16 = const()[name = tensor<string, []>("op_3888_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3889_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_3888_to_fp16)[name = tensor<string, []>("op_3889_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1123_cast_fp16 = add(x = input_1111_cast_fp16, y = var_3889_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]> module_layers_21_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1053092992)))];
+            tensor<fp16, [1024]> module_layers_21_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1053095104)))];
+            tensor<fp16, [1, 188, 1024]> query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = module_layers_21_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_self_att_weight_to_fp16, x = input_1123_cast_fp16)[name = tensor<string, []>("query_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_21_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1053097216)))];
+            tensor<fp16, [1024]> module_layers_21_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1055194432)))];
+            tensor<fp16, [1, 188, 1024]> linear_192_cast_fp16 = linear(bias = module_layers_21_self_attn_linear_q_bias_to_fp16, weight = module_layers_21_self_attn_linear_q_weight_to_fp16, x = query_43_cast_fp16)[name = tensor<string, []>("linear_192_cast_fp16")];
+            tensor<int32, [4]> var_3906 = const()[name = tensor<string, []>("op_3906"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_127_cast_fp16 = reshape(shape = var_3906, x = linear_192_cast_fp16)[name = tensor<string, []>("q_127_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_21_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1055196544)))];
+            tensor<fp16, [1024]> module_layers_21_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1057293760)))];
+            tensor<fp16, [1, 188, 1024]> linear_193_cast_fp16 = linear(bias = module_layers_21_self_attn_linear_k_bias_to_fp16, weight = module_layers_21_self_attn_linear_k_weight_to_fp16, x = query_43_cast_fp16)[name = tensor<string, []>("linear_193_cast_fp16")];
+            tensor<int32, [4]> var_3911 = const()[name = tensor<string, []>("op_3911"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_85_cast_fp16 = reshape(shape = var_3911, x = linear_193_cast_fp16)[name = tensor<string, []>("k_85_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_21_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1057295872)))];
+            tensor<fp16, [1024]> module_layers_21_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1059393088)))];
+            tensor<fp16, [1, 188, 1024]> linear_194_cast_fp16 = linear(bias = module_layers_21_self_attn_linear_v_bias_to_fp16, weight = module_layers_21_self_attn_linear_v_weight_to_fp16, x = query_43_cast_fp16)[name = tensor<string, []>("linear_194_cast_fp16")];
+            tensor<int32, [4]> var_3916 = const()[name = tensor<string, []>("op_3916"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_43_cast_fp16 = reshape(shape = var_3916, x = linear_194_cast_fp16)[name = tensor<string, []>("v_43_cast_fp16")];
+            tensor<int32, [4]> value_45_perm_0 = const()[name = tensor<string, []>("value_45_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1059395200)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3928_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_3928_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1059397312)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3930_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_3930_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, 1, 3])];
+            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, 375]> var_3932_to_fp16 = const()[name = tensor<string, []>("op_3932_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1059399424)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_3930_cast_fp16)[name = tensor<string, []>("transpose_140")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_3932_to_fp16)[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, 188, 376]> 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_3940 = const()[name = tensor<string, []>("op_3940"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_473_cast_fp16 = reshape(shape = var_3940, x = x_471_cast_fp16)[name = tensor<string, []>("x_473_cast_fp16")];
+            tensor<int32, [4]> var_3944_begin_0 = const()[name = tensor<string, []>("op_3944_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3944_end_0 = const()[name = tensor<string, []>("op_3944_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3944_end_mask_0 = const()[name = tensor<string, []>("op_3944_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3944_cast_fp16 = slice_by_index(begin = var_3944_begin_0, end = var_3944_end_0, end_mask = var_3944_end_mask_0, x = x_473_cast_fp16)[name = tensor<string, []>("op_3944_cast_fp16")];
+            tensor<int32, [4]> var_3945 = const()[name = tensor<string, []>("op_3945"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_85_cast_fp16 = reshape(shape = var_3945, x = var_3944_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_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, 188]> transpose_115 = transpose(perm = transpose_115_perm_0, x = k_85_cast_fp16)[name = tensor<string, []>("transpose_138")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_114 = transpose(perm = transpose_114_perm_0, x = var_3928_cast_fp16)[name = tensor<string, []>("transpose_139")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_43_cast_fp16 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_114, y = transpose_115)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_3954_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = tensor<string, []>("op_3954_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, 188, 188]> _inversed_scores_85_cast_fp16 = mul(x = var_3954_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_85_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_87_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_87_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3960_cast_fp16 = softmax(axis = var_30, x = scores_87_cast_fp16)[name = tensor<string, []>("op_3960_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1125_cast_fp16 = select(a = var_11_to_fp16, b = var_3960_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, 188, 128]> value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_43_cast_fp16)[name = tensor<string, []>("transpose_141")];
+            tensor<fp16, [1, 8, 188, 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_45_cast_fp16)[name = tensor<string, []>("x_475_cast_fp16")];
+            tensor<int32, [4]> var_3964_perm_0 = const()[name = tensor<string, []>("op_3964_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3965 = const()[name = tensor<string, []>("op_3965"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3964_cast_fp16 = transpose(perm = var_3964_perm_0, x = x_475_cast_fp16)[name = tensor<string, []>("transpose_137")];
+            tensor<fp16, [1, 188, 1024]> input_1127_cast_fp16 = reshape(shape = var_3965, x = var_3964_cast_fp16)[name = tensor<string, []>("input_1127_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_21_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1060167488)))];
+            tensor<fp16, [1024]> module_layers_21_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1062264704)))];
+            tensor<fp16, [1, 188, 1024]> linear_196_cast_fp16 = linear(bias = module_layers_21_self_attn_linear_out_bias_to_fp16, weight = module_layers_21_self_attn_linear_out_weight_to_fp16, x = input_1127_cast_fp16)[name = tensor<string, []>("linear_196_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_21_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1062266816)))];
+            tensor<fp16, [1024]> module_layers_21_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1062268928)))];
+            tensor<fp16, [1, 188, 1024]> x_479_cast_fp16 = layer_norm(axes = x_479_axes_0, beta = module_layers_21_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_21_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1062271040)))];
+            tensor<fp16, [2048]> module_layers_21_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1066465408)))];
+            tensor<fp16, [1, 1024, 188]> input_1133_cast_fp16 = transpose(perm = input_1133_perm_0, x = x_479_cast_fp16)[name = tensor<string, []>("transpose_136")];
+            tensor<fp16, [1, 2048, 188]> input_1135_cast_fp16 = conv(bias = module_layers_21_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_21_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_481_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_1137_cast_fp16 = select(a = var_11_to_fp16, b = x_481_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_290_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1066469568)))];
+            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, []>(1066488064)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_1139_cast_fp16)[name = tensor<string, []>("input_1143_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_21_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1066490176)))];
+            tensor<fp16, [1024]> module_layers_21_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1068587392)))];
+            tensor<fp16, [1, 1024, 188]> x_483_cast_fp16 = conv(bias = module_layers_21_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_21_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_1147_cast_fp16 = transpose(perm = input_1147_perm_0, x = x_483_cast_fp16)[name = tensor<string, []>("transpose_135")];
+            tensor<fp16, [1, 188, 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]> module_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1068589504)))];
+            tensor<fp16, [1024]> module_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1068591616)))];
+            tensor<fp16, [1, 188, 1024]> input_1151_cast_fp16 = layer_norm(axes = input_1151_axes_0, beta = module_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1149_cast_fp16)[name = tensor<string, []>("input_1151_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_21_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1068593728)))];
+            tensor<fp16, [4096]> module_layers_21_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1076982400)))];
+            tensor<fp16, [1, 188, 4096]> linear_197_cast_fp16 = linear(bias = module_layers_21_feed_forward2_linear1_bias_to_fp16, weight = module_layers_21_feed_forward2_linear1_weight_to_fp16, x = input_1151_cast_fp16)[name = tensor<string, []>("linear_197_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1155_cast_fp16 = silu(x = linear_197_cast_fp16)[name = tensor<string, []>("input_1155_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_21_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1076990656)))];
+            tensor<fp16, [1024]> module_layers_21_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1085379328)))];
+            tensor<fp16, [1, 188, 1024]> linear_198_cast_fp16 = linear(bias = module_layers_21_feed_forward2_linear2_bias_to_fp16, weight = module_layers_21_feed_forward2_linear2_weight_to_fp16, x = input_1155_cast_fp16)[name = tensor<string, []>("linear_198_cast_fp16")];
+            tensor<fp16, []> var_4031_to_fp16 = const()[name = tensor<string, []>("op_4031_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4032_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_4031_to_fp16)[name = tensor<string, []>("op_4032_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1161_cast_fp16 = add(x = input_1149_cast_fp16, y = var_4032_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]> module_layers_21_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1085381440)))];
+            tensor<fp16, [1024]> module_layers_21_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1085383552)))];
+            tensor<fp16, [1, 188, 1024]> input_1163_cast_fp16 = layer_norm(axes = input_1163_axes_0, beta = module_layers_21_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1085385664)))];
+            tensor<fp16, [1024]> module_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1085387776)))];
+            tensor<fp16, [1, 188, 1024]> input_1165_cast_fp16 = layer_norm(axes = input_1165_axes_0, beta = module_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1163_cast_fp16)[name = tensor<string, []>("input_1165_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_22_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1085389888)))];
+            tensor<fp16, [4096]> module_layers_22_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1093778560)))];
+            tensor<fp16, [1, 188, 4096]> linear_199_cast_fp16 = linear(bias = module_layers_22_feed_forward1_linear1_bias_to_fp16, weight = module_layers_22_feed_forward1_linear1_weight_to_fp16, x = input_1165_cast_fp16)[name = tensor<string, []>("linear_199_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1169_cast_fp16 = silu(x = linear_199_cast_fp16)[name = tensor<string, []>("input_1169_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_22_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1093786816)))];
+            tensor<fp16, [1024]> module_layers_22_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1102175488)))];
+            tensor<fp16, [1, 188, 1024]> linear_200_cast_fp16 = linear(bias = module_layers_22_feed_forward1_linear2_bias_to_fp16, weight = module_layers_22_feed_forward1_linear2_weight_to_fp16, x = input_1169_cast_fp16)[name = tensor<string, []>("linear_200_cast_fp16")];
+            tensor<fp16, []> var_4062_to_fp16 = const()[name = tensor<string, []>("op_4062_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4063_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_4062_to_fp16)[name = tensor<string, []>("op_4063_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1175_cast_fp16 = add(x = input_1163_cast_fp16, y = var_4063_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]> module_layers_22_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1102177600)))];
+            tensor<fp16, [1024]> module_layers_22_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1102179712)))];
+            tensor<fp16, [1, 188, 1024]> query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = module_layers_22_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_self_att_weight_to_fp16, x = input_1175_cast_fp16)[name = tensor<string, []>("query_45_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_22_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1102181824)))];
+            tensor<fp16, [1024]> module_layers_22_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1104279040)))];
+            tensor<fp16, [1, 188, 1024]> linear_201_cast_fp16 = linear(bias = module_layers_22_self_attn_linear_q_bias_to_fp16, weight = module_layers_22_self_attn_linear_q_weight_to_fp16, x = query_45_cast_fp16)[name = tensor<string, []>("linear_201_cast_fp16")];
+            tensor<int32, [4]> var_4080 = const()[name = tensor<string, []>("op_4080"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_133_cast_fp16 = reshape(shape = var_4080, x = linear_201_cast_fp16)[name = tensor<string, []>("q_133_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_22_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1104281152)))];
+            tensor<fp16, [1024]> module_layers_22_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1106378368)))];
+            tensor<fp16, [1, 188, 1024]> linear_202_cast_fp16 = linear(bias = module_layers_22_self_attn_linear_k_bias_to_fp16, weight = module_layers_22_self_attn_linear_k_weight_to_fp16, x = query_45_cast_fp16)[name = tensor<string, []>("linear_202_cast_fp16")];
+            tensor<int32, [4]> var_4085 = const()[name = tensor<string, []>("op_4085"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_89_cast_fp16 = reshape(shape = var_4085, x = linear_202_cast_fp16)[name = tensor<string, []>("k_89_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_22_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1106380480)))];
+            tensor<fp16, [1024]> module_layers_22_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1108477696)))];
+            tensor<fp16, [1, 188, 1024]> linear_203_cast_fp16 = linear(bias = module_layers_22_self_attn_linear_v_bias_to_fp16, weight = module_layers_22_self_attn_linear_v_weight_to_fp16, x = query_45_cast_fp16)[name = tensor<string, []>("linear_203_cast_fp16")];
+            tensor<int32, [4]> var_4090 = const()[name = tensor<string, []>("op_4090"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_45_cast_fp16 = reshape(shape = var_4090, x = linear_203_cast_fp16)[name = tensor<string, []>("v_45_cast_fp16")];
+            tensor<int32, [4]> value_47_perm_0 = const()[name = tensor<string, []>("value_47_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1108479808)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4102_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_4102_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1108481920)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4104_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_4104_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, 1, 3])];
+            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, 375]> var_4106_to_fp16 = const()[name = tensor<string, []>("op_4106_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1108484032)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_4104_cast_fp16)[name = tensor<string, []>("transpose_133")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_4106_to_fp16)[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, 188, 376]> 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_4114 = const()[name = tensor<string, []>("op_4114"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_495_cast_fp16 = reshape(shape = var_4114, x = x_493_cast_fp16)[name = tensor<string, []>("x_495_cast_fp16")];
+            tensor<int32, [4]> var_4118_begin_0 = const()[name = tensor<string, []>("op_4118_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4118_end_0 = const()[name = tensor<string, []>("op_4118_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4118_end_mask_0 = const()[name = tensor<string, []>("op_4118_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4118_cast_fp16 = slice_by_index(begin = var_4118_begin_0, end = var_4118_end_0, end_mask = var_4118_end_mask_0, x = x_495_cast_fp16)[name = tensor<string, []>("op_4118_cast_fp16")];
+            tensor<int32, [4]> var_4119 = const()[name = tensor<string, []>("op_4119"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_89_cast_fp16 = reshape(shape = var_4119, x = var_4118_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_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, 188]> transpose_117 = transpose(perm = transpose_117_perm_0, x = k_89_cast_fp16)[name = tensor<string, []>("transpose_131")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_116 = transpose(perm = transpose_116_perm_0, x = var_4102_cast_fp16)[name = tensor<string, []>("transpose_132")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_45_cast_fp16 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_116, y = transpose_117)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_4128_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = tensor<string, []>("op_4128_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, 188, 188]> _inversed_scores_89_cast_fp16 = mul(x = var_4128_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_89_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_91_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_91_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4134_cast_fp16 = softmax(axis = var_30, x = scores_91_cast_fp16)[name = tensor<string, []>("op_4134_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1177_cast_fp16 = select(a = var_11_to_fp16, b = var_4134_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, 188, 128]> value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_45_cast_fp16)[name = tensor<string, []>("transpose_134")];
+            tensor<fp16, [1, 8, 188, 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_47_cast_fp16)[name = tensor<string, []>("x_497_cast_fp16")];
+            tensor<int32, [4]> var_4138_perm_0 = const()[name = tensor<string, []>("op_4138_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4139 = const()[name = tensor<string, []>("op_4139"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4138_cast_fp16 = transpose(perm = var_4138_perm_0, x = x_497_cast_fp16)[name = tensor<string, []>("transpose_130")];
+            tensor<fp16, [1, 188, 1024]> input_1179_cast_fp16 = reshape(shape = var_4139, x = var_4138_cast_fp16)[name = tensor<string, []>("input_1179_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_22_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1109252096)))];
+            tensor<fp16, [1024]> module_layers_22_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1111349312)))];
+            tensor<fp16, [1, 188, 1024]> linear_205_cast_fp16 = linear(bias = module_layers_22_self_attn_linear_out_bias_to_fp16, weight = module_layers_22_self_attn_linear_out_weight_to_fp16, x = input_1179_cast_fp16)[name = tensor<string, []>("linear_205_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_22_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1111351424)))];
+            tensor<fp16, [1024]> module_layers_22_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1111353536)))];
+            tensor<fp16, [1, 188, 1024]> x_501_cast_fp16 = layer_norm(axes = x_501_axes_0, beta = module_layers_22_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_22_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1111355648)))];
+            tensor<fp16, [2048]> module_layers_22_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1115550016)))];
+            tensor<fp16, [1, 1024, 188]> input_1185_cast_fp16 = transpose(perm = input_1185_perm_0, x = x_501_cast_fp16)[name = tensor<string, []>("transpose_129")];
+            tensor<fp16, [1, 2048, 188]> input_1187_cast_fp16 = conv(bias = module_layers_22_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_22_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_503_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_1189_cast_fp16 = select(a = var_11_to_fp16, b = x_503_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_292_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1115554176)))];
+            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, []>(1115572672)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_1191_cast_fp16)[name = tensor<string, []>("input_1195_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_22_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1115574784)))];
+            tensor<fp16, [1024]> module_layers_22_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1117672000)))];
+            tensor<fp16, [1, 1024, 188]> x_505_cast_fp16 = conv(bias = module_layers_22_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_22_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_1199_cast_fp16 = transpose(perm = input_1199_perm_0, x = x_505_cast_fp16)[name = tensor<string, []>("transpose_128")];
+            tensor<fp16, [1, 188, 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]> module_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1117674112)))];
+            tensor<fp16, [1024]> module_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1117676224)))];
+            tensor<fp16, [1, 188, 1024]> input_1203_cast_fp16 = layer_norm(axes = input_1203_axes_0, beta = module_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1201_cast_fp16)[name = tensor<string, []>("input_1203_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_22_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1117678336)))];
+            tensor<fp16, [4096]> module_layers_22_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1126067008)))];
+            tensor<fp16, [1, 188, 4096]> linear_206_cast_fp16 = linear(bias = module_layers_22_feed_forward2_linear1_bias_to_fp16, weight = module_layers_22_feed_forward2_linear1_weight_to_fp16, x = input_1203_cast_fp16)[name = tensor<string, []>("linear_206_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1207_cast_fp16 = silu(x = linear_206_cast_fp16)[name = tensor<string, []>("input_1207_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_22_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1126075264)))];
+            tensor<fp16, [1024]> module_layers_22_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1134463936)))];
+            tensor<fp16, [1, 188, 1024]> linear_207_cast_fp16 = linear(bias = module_layers_22_feed_forward2_linear2_bias_to_fp16, weight = module_layers_22_feed_forward2_linear2_weight_to_fp16, x = input_1207_cast_fp16)[name = tensor<string, []>("linear_207_cast_fp16")];
+            tensor<fp16, []> var_4205_to_fp16 = const()[name = tensor<string, []>("op_4205_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4206_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_4205_to_fp16)[name = tensor<string, []>("op_4206_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1213_cast_fp16 = add(x = input_1201_cast_fp16, y = var_4206_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]> module_layers_22_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1134466048)))];
+            tensor<fp16, [1024]> module_layers_22_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1134468160)))];
+            tensor<fp16, [1, 188, 1024]> input_1215_cast_fp16 = layer_norm(axes = input_1215_axes_0, beta = module_layers_22_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1134470272)))];
+            tensor<fp16, [1024]> module_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1134472384)))];
+            tensor<fp16, [1, 188, 1024]> input_1217_cast_fp16 = layer_norm(axes = input_1217_axes_0, beta = module_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1215_cast_fp16)[name = tensor<string, []>("input_1217_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_23_feed_forward1_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1134474496)))];
+            tensor<fp16, [4096]> module_layers_23_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1142863168)))];
+            tensor<fp16, [1, 188, 4096]> linear_208_cast_fp16 = linear(bias = module_layers_23_feed_forward1_linear1_bias_to_fp16, weight = module_layers_23_feed_forward1_linear1_weight_to_fp16, x = input_1217_cast_fp16)[name = tensor<string, []>("linear_208_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1221_cast_fp16 = silu(x = linear_208_cast_fp16)[name = tensor<string, []>("input_1221_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_23_feed_forward1_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1142871424)))];
+            tensor<fp16, [1024]> module_layers_23_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1151260096)))];
+            tensor<fp16, [1, 188, 1024]> linear_209_cast_fp16 = linear(bias = module_layers_23_feed_forward1_linear2_bias_to_fp16, weight = module_layers_23_feed_forward1_linear2_weight_to_fp16, x = input_1221_cast_fp16)[name = tensor<string, []>("linear_209_cast_fp16")];
+            tensor<fp16, []> var_4236_to_fp16 = const()[name = tensor<string, []>("op_4236_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4237_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_4236_to_fp16)[name = tensor<string, []>("op_4237_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1227_cast_fp16 = add(x = input_1215_cast_fp16, y = var_4237_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]> module_layers_23_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1151262208)))];
+            tensor<fp16, [1024]> module_layers_23_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1151264320)))];
+            tensor<fp16, [1, 188, 1024]> query_cast_fp16 = layer_norm(axes = query_axes_0, beta = module_layers_23_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_self_att_weight_to_fp16, x = input_1227_cast_fp16)[name = tensor<string, []>("query_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_23_self_attn_linear_q_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1151266432)))];
+            tensor<fp16, [1024]> module_layers_23_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1153363648)))];
+            tensor<fp16, [1, 188, 1024]> linear_210_cast_fp16 = linear(bias = module_layers_23_self_attn_linear_q_bias_to_fp16, weight = module_layers_23_self_attn_linear_q_weight_to_fp16, x = query_cast_fp16)[name = tensor<string, []>("linear_210_cast_fp16")];
+            tensor<int32, [4]> var_4254 = const()[name = tensor<string, []>("op_4254"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_139_cast_fp16 = reshape(shape = var_4254, x = linear_210_cast_fp16)[name = tensor<string, []>("q_139_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_23_self_attn_linear_k_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1153365760)))];
+            tensor<fp16, [1024]> module_layers_23_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1155462976)))];
+            tensor<fp16, [1, 188, 1024]> linear_211_cast_fp16 = linear(bias = module_layers_23_self_attn_linear_k_bias_to_fp16, weight = module_layers_23_self_attn_linear_k_weight_to_fp16, x = query_cast_fp16)[name = tensor<string, []>("linear_211_cast_fp16")];
+            tensor<int32, [4]> var_4259 = const()[name = tensor<string, []>("op_4259"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_93_cast_fp16 = reshape(shape = var_4259, x = linear_211_cast_fp16)[name = tensor<string, []>("k_93_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_23_self_attn_linear_v_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1155465088)))];
+            tensor<fp16, [1024]> module_layers_23_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1157562304)))];
+            tensor<fp16, [1, 188, 1024]> linear_212_cast_fp16 = linear(bias = module_layers_23_self_attn_linear_v_bias_to_fp16, weight = module_layers_23_self_attn_linear_v_weight_to_fp16, x = query_cast_fp16)[name = tensor<string, []>("linear_212_cast_fp16")];
+            tensor<int32, [4]> var_4264 = const()[name = tensor<string, []>("op_4264"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_cast_fp16 = reshape(shape = var_4264, 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, 1, 3])];
+            tensor<fp16, [8, 128]> module_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = tensor<string, []>("module_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1157564416)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4276_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_u_to_fp16)[name = tensor<string, []>("op_4276_cast_fp16")];
+            tensor<fp16, [8, 128]> module_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = tensor<string, []>("module_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1157566528)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4278_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_v_to_fp16)[name = tensor<string, []>("op_4278_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, 1, 3])];
+            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, 375]> var_4280_to_fp16 = const()[name = tensor<string, []>("op_4280_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1157568640)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4278_cast_fp16)[name = tensor<string, []>("transpose_126")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_4280_to_fp16)[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, 188, 376]> 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_4288 = const()[name = tensor<string, []>("op_4288"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_517_cast_fp16 = reshape(shape = var_4288, x = x_515_cast_fp16)[name = tensor<string, []>("x_517_cast_fp16")];
+            tensor<int32, [4]> var_4292_begin_0 = const()[name = tensor<string, []>("op_4292_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4292_end_0 = const()[name = tensor<string, []>("op_4292_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4292_end_mask_0 = const()[name = tensor<string, []>("op_4292_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4292_cast_fp16 = slice_by_index(begin = var_4292_begin_0, end = var_4292_end_0, end_mask = var_4292_end_mask_0, x = x_517_cast_fp16)[name = tensor<string, []>("op_4292_cast_fp16")];
+            tensor<int32, [4]> var_4293 = const()[name = tensor<string, []>("op_4293"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_93_cast_fp16 = reshape(shape = var_4293, x = var_4292_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_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, 188]> transpose_119 = transpose(perm = transpose_119_perm_0, x = k_93_cast_fp16)[name = tensor<string, []>("transpose_124")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_118 = transpose(perm = transpose_118_perm_0, x = var_4276_cast_fp16)[name = tensor<string, []>("transpose_125")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_118, y = transpose_119)[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, 188, 188])];
+            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, 188, 188]> 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, 188, 188]> var_4302_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = tensor<string, []>("op_4302_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, 188, 188]> _inversed_scores_93_cast_fp16 = mul(x = var_4302_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_93_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4308_cast_fp16 = softmax(axis = var_30, x = scores_cast_fp16)[name = tensor<string, []>("op_4308_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1229_cast_fp16 = select(a = var_11_to_fp16, b = var_4308_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, 188, 128]> value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = tensor<string, []>("transpose_127")];
+            tensor<fp16, [1, 8, 188, 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_4312_perm_0 = const()[name = tensor<string, []>("op_4312_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4313 = const()[name = tensor<string, []>("op_4313"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4312_cast_fp16 = transpose(perm = var_4312_perm_0, x = x_519_cast_fp16)[name = tensor<string, []>("transpose_123")];
+            tensor<fp16, [1, 188, 1024]> input_1231_cast_fp16 = reshape(shape = var_4313, x = var_4312_cast_fp16)[name = tensor<string, []>("input_1231_cast_fp16")];
+            tensor<fp16, [1024, 1024]> module_layers_23_self_attn_linear_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1158336704)))];
+            tensor<fp16, [1024]> module_layers_23_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1160433920)))];
+            tensor<fp16, [1, 188, 1024]> linear_214_cast_fp16 = linear(bias = module_layers_23_self_attn_linear_out_bias_to_fp16, weight = module_layers_23_self_attn_linear_out_weight_to_fp16, x = input_1231_cast_fp16)[name = tensor<string, []>("linear_214_cast_fp16")];
+            tensor<fp16, [1, 188, 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]> module_layers_23_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1160436032)))];
+            tensor<fp16, [1024]> module_layers_23_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1160438144)))];
+            tensor<fp16, [1, 188, 1024]> x_523_cast_fp16 = layer_norm(axes = x_523_axes_0, beta = module_layers_23_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_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]> module_layers_23_conv_pointwise_conv1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1160440256)))];
+            tensor<fp16, [2048]> module_layers_23_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1164634624)))];
+            tensor<fp16, [1, 1024, 188]> input_1237_cast_fp16 = transpose(perm = input_1237_perm_0, x = x_523_cast_fp16)[name = tensor<string, []>("transpose_122")];
+            tensor<fp16, [1, 2048, 188]> input_1239_cast_fp16 = conv(bias = module_layers_23_conv_pointwise_conv1_bias_to_fp16, 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 = module_layers_23_conv_pointwise_conv1_weight_to_fp16, 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, 188]> x_525_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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, 188]> 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, 188]> 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, 188]> input_1241_cast_fp16 = select(a = var_11_to_fp16, b = x_525_cast_fp16, cond = var_335)[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, 196]> 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 = const()[name = tensor<string, []>("const_294_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1164638784)))];
+            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, []>(1164657280)))];
+            tensor<fp16, [1, 1024, 188]> 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, x = input_1243_cast_fp16)[name = tensor<string, []>("input_1247_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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]> module_layers_23_conv_pointwise_conv2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1164659392)))];
+            tensor<fp16, [1024]> module_layers_23_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1166756608)))];
+            tensor<fp16, [1, 1024, 188]> x_527_cast_fp16 = conv(bias = module_layers_23_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_23_conv_pointwise_conv2_weight_to_fp16, 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, 188, 1024]> input_1251_cast_fp16 = transpose(perm = input_1251_perm_0, x = x_527_cast_fp16)[name = tensor<string, []>("transpose_121")];
+            tensor<fp16, [1, 188, 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]> module_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1166758720)))];
+            tensor<fp16, [1024]> module_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1166760832)))];
+            tensor<fp16, [1, 188, 1024]> input_1255_cast_fp16 = layer_norm(axes = input_1255_axes_0, beta = module_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1253_cast_fp16)[name = tensor<string, []>("input_1255_cast_fp16")];
+            tensor<fp16, [4096, 1024]> module_layers_23_feed_forward2_linear1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1166762944)))];
+            tensor<fp16, [4096]> module_layers_23_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1175151616)))];
+            tensor<fp16, [1, 188, 4096]> linear_215_cast_fp16 = linear(bias = module_layers_23_feed_forward2_linear1_bias_to_fp16, weight = module_layers_23_feed_forward2_linear1_weight_to_fp16, x = input_1255_cast_fp16)[name = tensor<string, []>("linear_215_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1259_cast_fp16 = silu(x = linear_215_cast_fp16)[name = tensor<string, []>("input_1259_cast_fp16")];
+            tensor<fp16, [1024, 4096]> module_layers_23_feed_forward2_linear2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1175159872)))];
+            tensor<fp16, [1024]> module_layers_23_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1183548544)))];
+            tensor<fp16, [1, 188, 1024]> linear_216_cast_fp16 = linear(bias = module_layers_23_feed_forward2_linear2_bias_to_fp16, weight = module_layers_23_feed_forward2_linear2_weight_to_fp16, x = input_1259_cast_fp16)[name = tensor<string, []>("linear_216_cast_fp16")];
+            tensor<fp16, []> var_4379_to_fp16 = const()[name = tensor<string, []>("op_4379_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4380_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_4379_to_fp16)[name = tensor<string, []>("op_4380_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_cast_fp16 = add(x = input_1253_cast_fp16, y = var_4380_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]> module_layers_23_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1183550656)))];
+            tensor<fp16, [1024]> module_layers_23_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1183552768)))];
+            tensor<fp16, [1, 188, 1024]> audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = module_layers_23_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("audio_signal_cast_fp16")];
+            tensor<int32, [3]> obj_1_perm_0 = const()[name = tensor<string, []>("obj_1_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> obj_1_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("obj_1_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp16, [1, 1024, 188]> obj_1_cast_fp16 = transpose(perm = obj_1_perm_0, x = audio_signal_cast_fp16)[name = tensor<string, []>("transpose_120")];
+            tensor<fp32, [1, 1024, 188]> encoder_output = cast(dtype = obj_1_cast_fp16_to_fp32_dtype_0, x = obj_1_cast_fp16)[name = tensor<string, []>("cast_227")];
+        } -> (encoder_output, encoded_length);
+}
\ No newline at end of file