program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.22.1"}})]
{
    func main<ios17>(tensor<fp16, [1, 1, 200, 32]> feat_erb, tensor<fp16, [1, 1, 200, 96, 2]> feat_spec, tensor<fp16, [1, 1, 200, 481, 2]> spec) {
            tensor<int32, [1]> var_48_axes_0 = const()[name = tensor<string, []>("op_48_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 200, 96, 2]> var_48_cast_fp16 = squeeze(axes = var_48_axes_0, x = feat_spec)[name = tensor<string, []>("op_48_cast_fp16")];
            tensor<int32, [4]> var_53 = const()[name = tensor<string, []>("op_53"), val = tensor<int32, [4]>([0, 3, 1, 2])];
            tensor<int32, [4]> var_85_begin_0 = const()[name = tensor<string, []>("op_85_begin_0"), val = tensor<int32, [4]>([0, 0, 2, 0])];
            tensor<int32, [4]> var_85_end_0 = const()[name = tensor<string, []>("op_85_end_0"), val = tensor<int32, [4]>([1, 1, 200, 32])];
            tensor<bool, [4]> var_85_end_mask_0 = const()[name = tensor<string, []>("op_85_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 1, 198, 32]> var_85_cast_fp16 = slice_by_index(begin = var_85_begin_0, end = var_85_end_0, end_mask = var_85_end_mask_0, x = feat_erb)[name = tensor<string, []>("op_85_cast_fp16")];
            tensor<int32, []> var_92 = const()[name = tensor<string, []>("op_92"), val = tensor<int32, []>(2)];
            tensor<bool, []> x_1_interleave_0 = const()[name = tensor<string, []>("x_1_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 1, 2, 32]> zeros_e_to_fp16 = const()[name = tensor<string, []>("zeros_e_to_fp16"), val = tensor<fp16, [1, 1, 2, 32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp16, [1, 1, 200, 32]> x_1_cast_fp16 = concat(axis = var_92, interleave = x_1_interleave_0, values = (var_85_cast_fp16, zeros_e_to_fp16))[name = tensor<string, []>("x_1_cast_fp16")];
            tensor<int32, [4]> var_127_begin_0 = const()[name = tensor<string, []>("op_127_begin_0"), val = tensor<int32, [4]>([0, 0, 2, 0])];
            tensor<int32, [4]> var_127_end_0 = const()[name = tensor<string, []>("op_127_end_0"), val = tensor<int32, [4]>([1, 2, 200, 96])];
            tensor<bool, [4]> var_127_end_mask_0 = const()[name = tensor<string, []>("op_127_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 200, 96]> fs_in_cast_fp16 = transpose(perm = var_53, x = var_48_cast_fp16)[name = tensor<string, []>("transpose_26")];
            tensor<fp16, [1, 2, 198, 96]> var_127_cast_fp16 = slice_by_index(begin = var_127_begin_0, end = var_127_end_0, end_mask = var_127_end_mask_0, x = fs_in_cast_fp16)[name = tensor<string, []>("op_127_cast_fp16")];
            tensor<int32, []> var_134 = const()[name = tensor<string, []>("op_134"), val = tensor<int32, []>(2)];
            tensor<bool, []> x_3_interleave_0 = const()[name = tensor<string, []>("x_3_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 2, 2, 96]> zeros_s_to_fp16 = const()[name = tensor<string, []>("zeros_s_to_fp16"), val = tensor<fp16, [1, 2, 2, 96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(256)))];
            tensor<fp16, [1, 2, 200, 96]> x_3_cast_fp16 = concat(axis = var_134, interleave = x_3_interleave_0, values = (var_127_cast_fp16, zeros_s_to_fp16))[name = tensor<string, []>("x_3_cast_fp16")];
            tensor<int32, []> var_142 = const()[name = tensor<string, []>("op_142"), val = tensor<int32, []>(2)];
            tensor<bool, []> input_1_interleave_0 = const()[name = tensor<string, []>("input_1_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 1, 2, 32]> var_153_to_fp16 = const()[name = tensor<string, []>("op_153_to_fp16"), val = tensor<fp16, [1, 1, 2, 32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1088)))];
            tensor<fp16, [1, 1, 202, 32]> input_1_cast_fp16 = concat(axis = var_142, interleave = input_1_interleave_0, values = (var_153_to_fp16, 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]>([0, 0, 1, 1])];
            tensor<int32, [2]> input_3_strides_0 = const()[name = tensor<string, []>("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            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, [64, 1, 3, 3]> const_43_to_fp16 = const()[name = tensor<string, []>("const_43_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1280)))];
            tensor<fp16, [64]> const_44_to_fp16 = const()[name = tensor<string, []>("const_44_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2496)))];
            tensor<fp16, [1, 64, 200, 32]> input_5_cast_fp16 = conv(bias = const_44_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 = const_43_to_fp16, x = input_1_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
            tensor<fp16, [1, 64, 200, 32]> input_7_cast_fp16 = relu(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, []>("custom")];
            tensor<int32, [4]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [4]>([0, 0, 1, 1])];
            tensor<int32, [2]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [2]>([1, 2])];
            tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(64)];
            tensor<int32, [2]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [64, 1, 1, 3]> enc_erb_conv1_0_weight_to_fp16 = const()[name = tensor<string, []>("enc_erb_conv1_0_weight_to_fp16"), val = tensor<fp16, [64, 1, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2688)))];
            tensor<fp16, [1, 64, 200, 16]> input_9_cast_fp16 = conv(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 = enc_erb_conv1_0_weight_to_fp16, x = input_7_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
            tensor<string, []> input_11_pad_type_0 = const()[name = tensor<string, []>("input_11_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> input_11_strides_0 = const()[name = tensor<string, []>("input_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_11_pad_0 = const()[name = tensor<string, []>("input_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_11_dilations_0 = const()[name = tensor<string, []>("input_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_11_groups_0 = const()[name = tensor<string, []>("input_11_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [64, 64, 1, 1]> const_45_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3136))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6272))), name = tensor<string, []>("const_45_to_fp16_palettized"), shape = tensor<uint32, [4]>([64, 64, 1, 1])];
            tensor<fp16, [64]> const_46_to_fp16 = const()[name = tensor<string, []>("const_46_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6464)))];
            tensor<fp16, [1, 64, 200, 16]> input_13_cast_fp16 = conv(bias = const_46_to_fp16, dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = const_45_to_fp16_palettized, x = input_9_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
            tensor<fp16, [1, 64, 200, 16]> input_15_cast_fp16 = relu(x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
            tensor<string, []> input_17_pad_type_0 = const()[name = tensor<string, []>("input_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_17_pad_0 = const()[name = tensor<string, []>("input_17_pad_0"), val = tensor<int32, [4]>([0, 0, 1, 1])];
            tensor<int32, [2]> input_17_strides_0 = const()[name = tensor<string, []>("input_17_strides_0"), val = tensor<int32, [2]>([1, 2])];
            tensor<int32, []> input_17_groups_0 = const()[name = tensor<string, []>("input_17_groups_0"), val = tensor<int32, []>(64)];
            tensor<int32, [2]> input_17_dilations_0 = const()[name = tensor<string, []>("input_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [64, 1, 1, 3]> enc_erb_conv2_0_weight_to_fp16 = const()[name = tensor<string, []>("enc_erb_conv2_0_weight_to_fp16"), val = tensor<fp16, [64, 1, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6656)))];
            tensor<fp16, [1, 64, 200, 8]> input_17_cast_fp16 = conv(dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = enc_erb_conv2_0_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
            tensor<string, []> input_19_pad_type_0 = const()[name = tensor<string, []>("input_19_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> input_19_strides_0 = const()[name = tensor<string, []>("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_19_dilations_0 = const()[name = tensor<string, []>("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_19_groups_0 = const()[name = tensor<string, []>("input_19_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [64, 64, 1, 1]> const_47_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7104))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10240))), name = tensor<string, []>("const_47_to_fp16_palettized"), shape = tensor<uint32, [4]>([64, 64, 1, 1])];
            tensor<fp16, [64]> const_48_to_fp16 = const()[name = tensor<string, []>("const_48_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10432)))];
            tensor<fp16, [1, 64, 200, 8]> input_21_cast_fp16 = conv(bias = const_48_to_fp16, dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = const_47_to_fp16_palettized, x = input_17_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
            tensor<fp16, [1, 64, 200, 8]> input_23_cast_fp16 = relu(x = input_21_cast_fp16)[name = tensor<string, []>("input_23_cast_fp16")];
            tensor<string, []> input_25_pad_type_0 = const()[name = tensor<string, []>("input_25_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_25_pad_0 = const()[name = tensor<string, []>("input_25_pad_0"), val = tensor<int32, [4]>([0, 0, 1, 1])];
            tensor<int32, []> input_25_groups_0 = const()[name = tensor<string, []>("input_25_groups_0"), val = tensor<int32, []>(64)];
            tensor<int32, [2]> input_25_strides_0 = const()[name = tensor<string, []>("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_25_dilations_0 = const()[name = tensor<string, []>("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [64, 1, 1, 3]> enc_erb_conv3_0_weight_to_fp16 = const()[name = tensor<string, []>("enc_erb_conv3_0_weight_to_fp16"), val = tensor<fp16, [64, 1, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10624)))];
            tensor<fp16, [1, 64, 200, 8]> input_25_cast_fp16 = conv(dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = enc_erb_conv3_0_weight_to_fp16, x = input_23_cast_fp16)[name = tensor<string, []>("input_25_cast_fp16")];
            tensor<string, []> input_27_pad_type_0 = const()[name = tensor<string, []>("input_27_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> input_27_strides_0 = const()[name = tensor<string, []>("input_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_27_pad_0 = const()[name = tensor<string, []>("input_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_27_dilations_0 = const()[name = tensor<string, []>("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_27_groups_0 = const()[name = tensor<string, []>("input_27_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [64, 64, 1, 1]> const_49_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11072))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14208))), name = tensor<string, []>("const_49_to_fp16_palettized"), shape = tensor<uint32, [4]>([64, 64, 1, 1])];
            tensor<fp16, [64]> const_50_to_fp16 = const()[name = tensor<string, []>("const_50_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14400)))];
            tensor<fp16, [1, 64, 200, 8]> input_29_cast_fp16 = conv(bias = const_50_to_fp16, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = const_49_to_fp16_palettized, x = input_25_cast_fp16)[name = tensor<string, []>("input_29_cast_fp16")];
            tensor<fp16, [1, 64, 200, 8]> e3_cast_fp16 = relu(x = input_29_cast_fp16)[name = tensor<string, []>("e3_cast_fp16")];
            tensor<int32, []> var_263 = const()[name = tensor<string, []>("op_263"), val = tensor<int32, []>(2)];
            tensor<bool, []> input_31_interleave_0 = const()[name = tensor<string, []>("input_31_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 2, 202, 96]> input_31_cast_fp16 = concat(axis = var_263, interleave = input_31_interleave_0, values = (zeros_s_to_fp16, x_3_cast_fp16))[name = tensor<string, []>("input_31_cast_fp16")];
            tensor<string, []> input_33_pad_type_0 = const()[name = tensor<string, []>("input_33_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_33_pad_0 = const()[name = tensor<string, []>("input_33_pad_0"), val = tensor<int32, [4]>([0, 0, 1, 1])];
            tensor<int32, []> input_33_groups_0 = const()[name = tensor<string, []>("input_33_groups_0"), val = tensor<int32, []>(2)];
            tensor<int32, [2]> input_33_strides_0 = const()[name = tensor<string, []>("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_33_dilations_0 = const()[name = tensor<string, []>("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [64, 1, 3, 3]> enc_df_conv0_1_weight_to_fp16 = const()[name = tensor<string, []>("enc_df_conv0_1_weight_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14592)))];
            tensor<fp16, [1, 64, 200, 96]> input_33_cast_fp16 = conv(dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = enc_df_conv0_1_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("input_33_cast_fp16")];
            tensor<string, []> input_35_pad_type_0 = const()[name = tensor<string, []>("input_35_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> input_35_strides_0 = const()[name = tensor<string, []>("input_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_35_pad_0 = const()[name = tensor<string, []>("input_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_35_dilations_0 = const()[name = tensor<string, []>("input_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_35_groups_0 = const()[name = tensor<string, []>("input_35_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [64, 64, 1, 1]> const_51_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15808))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18944))), name = tensor<string, []>("const_51_to_fp16_palettized"), shape = tensor<uint32, [4]>([64, 64, 1, 1])];
            tensor<fp16, [64]> const_52_to_fp16 = const()[name = tensor<string, []>("const_52_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19136)))];
            tensor<fp16, [1, 64, 200, 96]> input_37_cast_fp16 = conv(bias = const_52_to_fp16, dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = const_51_to_fp16_palettized, x = input_33_cast_fp16)[name = tensor<string, []>("input_37_cast_fp16")];
            tensor<fp16, [1, 64, 200, 96]> input_39_cast_fp16 = relu(x = input_37_cast_fp16)[name = tensor<string, []>("input_39_cast_fp16")];
            tensor<string, []> input_41_pad_type_0 = const()[name = tensor<string, []>("input_41_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_41_pad_0 = const()[name = tensor<string, []>("input_41_pad_0"), val = tensor<int32, [4]>([0, 0, 1, 1])];
            tensor<int32, [2]> input_41_strides_0 = const()[name = tensor<string, []>("input_41_strides_0"), val = tensor<int32, [2]>([1, 2])];
            tensor<int32, []> input_41_groups_0 = const()[name = tensor<string, []>("input_41_groups_0"), val = tensor<int32, []>(64)];
            tensor<int32, [2]> input_41_dilations_0 = const()[name = tensor<string, []>("input_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [64, 1, 1, 3]> enc_df_conv1_0_weight_to_fp16 = const()[name = tensor<string, []>("enc_df_conv1_0_weight_to_fp16"), val = tensor<fp16, [64, 1, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19328)))];
            tensor<fp16, [1, 64, 200, 48]> input_41_cast_fp16 = conv(dilations = input_41_dilations_0, groups = input_41_groups_0, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = input_41_strides_0, weight = enc_df_conv1_0_weight_to_fp16, x = input_39_cast_fp16)[name = tensor<string, []>("input_41_cast_fp16")];
            tensor<string, []> input_43_pad_type_0 = const()[name = tensor<string, []>("input_43_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> input_43_strides_0 = const()[name = tensor<string, []>("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_43_pad_0 = const()[name = tensor<string, []>("input_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_43_dilations_0 = const()[name = tensor<string, []>("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_43_groups_0 = const()[name = tensor<string, []>("input_43_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [64, 64, 1, 1]> const_53_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19776))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22912))), name = tensor<string, []>("const_53_to_fp16_palettized"), shape = tensor<uint32, [4]>([64, 64, 1, 1])];
            tensor<fp16, [64]> const_54_to_fp16 = const()[name = tensor<string, []>("const_54_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23104)))];
            tensor<fp16, [1, 64, 200, 48]> input_45_cast_fp16 = conv(bias = const_54_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 = const_53_to_fp16_palettized, x = input_41_cast_fp16)[name = tensor<string, []>("input_45_cast_fp16")];
            tensor<fp16, [1, 64, 200, 48]> c1_cast_fp16 = relu(x = input_45_cast_fp16)[name = tensor<string, []>("c1_cast_fp16")];
            tensor<int32, [4]> var_330 = const()[name = tensor<string, []>("op_330"), val = tensor<int32, [4]>([0, 2, 3, 1])];
            tensor<int32, [4]> var_347 = const()[name = tensor<string, []>("op_347"), val = tensor<int32, [4]>([1, 200, 32, 96])];
            tensor<fp16, [1, 200, 48, 64]> var_331_cast_fp16 = transpose(perm = var_330, x = c1_cast_fp16)[name = tensor<string, []>("transpose_25")];
            tensor<fp16, [1, 200, 32, 96]> var_348_cast_fp16 = reshape(shape = var_347, x = var_331_cast_fp16)[name = tensor<string, []>("op_348_cast_fp16")];
            tensor<int32, [4]> transpose_0_perm_0 = const()[name = tensor<string, []>("transpose_0_perm_0"), val = tensor<int32, [4]>([2, 0, 1, 3])];
            tensor<int32, [3]> concat_5 = const()[name = tensor<string, []>("concat_5"), val = tensor<int32, [3]>([32, 200, 96])];
            tensor<fp16, [32, 1, 200, 96]> transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = var_348_cast_fp16)[name = tensor<string, []>("transpose_24")];
            tensor<fp16, [32, 200, 96]> reshape_0_cast_fp16 = reshape(shape = concat_5, x = transpose_0_cast_fp16)[name = tensor<string, []>("reshape_0_cast_fp16")];
            tensor<bool, []> matmul_0_transpose_x_0 = const()[name = tensor<string, []>("matmul_0_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_0_transpose_y_0 = const()[name = tensor<string, []>("matmul_0_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [32, 96, 16]> enc_df_fc_emb_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [36864]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23296))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60224))), name = tensor<string, []>("enc_df_fc_emb_0_weight_to_fp16_palettized"), shape = tensor<uint32, [3]>([32, 96, 16])];
            tensor<fp16, [32, 200, 16]> matmul_0_cast_fp16 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = reshape_0_cast_fp16, y = enc_df_fc_emb_0_weight_to_fp16_palettized)[name = tensor<string, []>("matmul_0_cast_fp16")];
            tensor<int32, [4]> concat_10 = const()[name = tensor<string, []>("concat_10"), val = tensor<int32, [4]>([32, 1, 200, 16])];
            tensor<fp16, [32, 1, 200, 16]> reshape_2_cast_fp16 = reshape(shape = concat_10, x = matmul_0_cast_fp16)[name = tensor<string, []>("reshape_2_cast_fp16")];
            tensor<int32, [4]> x_7_perm_0 = const()[name = tensor<string, []>("x_7_perm_0"), val = tensor<int32, [4]>([1, 2, 0, 3])];
            tensor<int32, [3]> concat_11 = const()[name = tensor<string, []>("concat_11"), val = tensor<int32, [3]>([1, 200, 512])];
            tensor<fp16, [1, 200, 32, 16]> x_7_cast_fp16 = transpose(perm = x_7_perm_0, x = reshape_2_cast_fp16)[name = tensor<string, []>("transpose_23")];
            tensor<fp16, [1, 200, 512]> input_47_cast_fp16 = reshape(shape = concat_11, x = x_7_cast_fp16)[name = tensor<string, []>("input_47_cast_fp16")];
            tensor<fp16, [1, 200, 512]> b_3_cast_fp16 = relu(x = input_47_cast_fp16)[name = tensor<string, []>("b_3_cast_fp16")];
            tensor<int32, [4]> var_357 = const()[name = tensor<string, []>("op_357"), val = tensor<int32, [4]>([0, 2, 3, 1])];
            tensor<int32, [3]> concat_12 = const()[name = tensor<string, []>("concat_12"), val = tensor<int32, [3]>([1, 200, 512])];
            tensor<fp16, [1, 200, 8, 64]> var_358_cast_fp16 = transpose(perm = var_357, x = e3_cast_fp16)[name = tensor<string, []>("transpose_22")];
            tensor<fp16, [1, 200, 512]> a_cast_fp16 = reshape(shape = concat_12, x = var_358_cast_fp16)[name = tensor<string, []>("a_cast_fp16")];
            tensor<fp16, [1, 200, 512]> x_9_cast_fp16 = add(x = a_cast_fp16, y = b_3_cast_fp16)[name = tensor<string, []>("x_9_cast_fp16")];
            tensor<int32, [4]> var_385 = const()[name = tensor<string, []>("op_385"), val = tensor<int32, [4]>([1, 200, 16, 32])];
            tensor<fp16, [1, 200, 16, 32]> var_386_cast_fp16 = reshape(shape = var_385, x = x_9_cast_fp16)[name = tensor<string, []>("op_386_cast_fp16")];
            tensor<int32, [4]> transpose_2_perm_0 = const()[name = tensor<string, []>("transpose_2_perm_0"), val = tensor<int32, [4]>([2, 0, 1, 3])];
            tensor<int32, [3]> concat_17 = const()[name = tensor<string, []>("concat_17"), val = tensor<int32, [3]>([16, 200, 32])];
            tensor<fp16, [16, 1, 200, 32]> transpose_2_cast_fp16 = transpose(perm = transpose_2_perm_0, x = var_386_cast_fp16)[name = tensor<string, []>("transpose_21")];
            tensor<fp16, [16, 200, 32]> reshape_3_cast_fp16 = reshape(shape = concat_17, x = transpose_2_cast_fp16)[name = tensor<string, []>("reshape_3_cast_fp16")];
            tensor<bool, []> matmul_1_transpose_x_0 = const()[name = tensor<string, []>("matmul_1_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_1_transpose_y_0 = const()[name = tensor<string, []>("matmul_1_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 32, 16]> enc_emb_gru_linear_in_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60416))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66624))), name = tensor<string, []>("enc_emb_gru_linear_in_0_weight_to_fp16_palettized"), shape = tensor<uint32, [3]>([16, 32, 16])];
            tensor<fp16, [16, 200, 16]> matmul_1_cast_fp16 = matmul(transpose_x = matmul_1_transpose_x_0, transpose_y = matmul_1_transpose_y_0, x = reshape_3_cast_fp16, y = enc_emb_gru_linear_in_0_weight_to_fp16_palettized)[name = tensor<string, []>("matmul_1_cast_fp16")];
            tensor<int32, [4]> concat_22 = const()[name = tensor<string, []>("concat_22"), val = tensor<int32, [4]>([16, 1, 200, 16])];
            tensor<fp16, [16, 1, 200, 16]> reshape_5_cast_fp16 = reshape(shape = concat_22, x = matmul_1_cast_fp16)[name = tensor<string, []>("reshape_5_cast_fp16")];
            tensor<int32, [4]> x_11_perm_0 = const()[name = tensor<string, []>("x_11_perm_0"), val = tensor<int32, [4]>([1, 2, 0, 3])];
            tensor<int32, [3]> concat_23 = const()[name = tensor<string, []>("concat_23"), val = tensor<int32, [3]>([1, 200, 256])];
            tensor<fp16, [1, 200, 16, 16]> x_11_cast_fp16 = transpose(perm = x_11_perm_0, x = reshape_5_cast_fp16)[name = tensor<string, []>("transpose_20")];
            tensor<fp16, [1, 200, 256]> input_49_cast_fp16 = reshape(shape = concat_23, x = x_11_cast_fp16)[name = tensor<string, []>("input_49_cast_fp16")];
            tensor<fp16, [1, 200, 256]> input_51_cast_fp16 = relu(x = input_49_cast_fp16)[name = tensor<string, []>("input_51_cast_fp16")];
            tensor<int32, [3]> transpose_4_perm_0 = const()[name = tensor<string, []>("transpose_4_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [1]> slice_by_index_23 = const()[name = tensor<string, []>("slice_by_index_23"), val = tensor<int32, [1]>([200])];
            tensor<fp32, [201, 1, 256]> concat_25_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [38592]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66816))), lut = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105472))), name = tensor<string, []>("concat_25_palettized"), shape = tensor<uint32, [3]>([201, 1, 256])];
            tensor<int32, [1]> while_loop_0_loop_vars0_0 = const()[name = tensor<string, []>("while_loop_0_loop_vars0_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [200, 1, 256]> transpose_4_cast_fp16 = transpose(perm = transpose_4_perm_0, x = input_51_cast_fp16)[name = tensor<string, []>("transpose_19")];
            tensor<int32, [1]> while_loop_0_0, tensor<fp32, [201, 1, 256]> while_loop_0_1 = while_loop(loop_vars = (while_loop_0_loop_vars0_0, concat_25_palettized))[name = tensor<string, []>("while_loop_0")]
                (tensor<int32, [1]> while_loop_0_loop_vars0_0_x0_1_1_1, tensor<fp32, [201, 1, 256]> concat_25_x0_1_1_1) {
                    tensor<bool, [1]> less_1 = less(x = while_loop_0_loop_vars0_0_x0_1_1_1, y = slice_by_index_23)[name = tensor<string, []>("less_1")];
                } -> (less_1)
                (tensor<int32, [1]> while_loop_0_loop_vars0_0_x0_1_1_1_1, tensor<fp32, [201, 1, 256]> concat_25_x0_1_1_1_1) {
                    tensor<int32, []> gather_2_axis_0 = const()[name = tensor<string, []>("gather_2_axis_0"), val = tensor<int32, []>(0)];
                    tensor<int32, []> gather_2_batch_dims_0 = const()[name = tensor<string, []>("gather_2_batch_dims_0"), val = tensor<int32, []>(0)];
                    tensor<bool, []> gather_2_validate_indices_0 = const()[name = tensor<string, []>("gather_2_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<string, []> while_loop_0_loop_vars0_0_x0_1_to_uint16_dtype_0 = const()[name = tensor<string, []>("while_loop_0_loop_vars0_0_x0_1_to_uint16_dtype_0"), val = tensor<string, []>("uint16")];
                    tensor<uint16, [1]> while_loop_0_loop_vars0_0_x0_1_to_uint16 = cast(dtype = while_loop_0_loop_vars0_0_x0_1_to_uint16_dtype_0, x = while_loop_0_loop_vars0_0_x0_1_1_1_1)[name = tensor<string, []>("cast_19")];
                    tensor<fp16, [1, 1, 256]> gather_2_cast_fp16_cast_uint16 = gather(axis = gather_2_axis_0, batch_dims = gather_2_batch_dims_0, indices = while_loop_0_loop_vars0_0_x0_1_to_uint16, validate_indices = gather_2_validate_indices_0, x = transpose_4_cast_fp16)[name = tensor<string, []>("gather_2_cast_fp16_cast_uint16")];
                    tensor<int32, []> gather_3_axis_0 = const()[name = tensor<string, []>("gather_3_axis_0"), val = tensor<int32, []>(0)];
                    tensor<int32, []> gather_3_batch_dims_0 = const()[name = tensor<string, []>("gather_3_batch_dims_0"), val = tensor<int32, []>(0)];
                    tensor<bool, []> gather_3_validate_indices_0 = const()[name = tensor<string, []>("gather_3_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<string, []> concat_25_x0_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("concat_25_x0_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
                    tensor<fp16, [201, 1, 256]> concat_25_x0_1_to_fp16 = cast(dtype = concat_25_x0_1_to_fp16_dtype_0, x = concat_25_x0_1_1_1_1)[name = tensor<string, []>("cast_18")];
                    tensor<fp16, [1, 1, 256]> gather_3_cast_fp16_cast_uint16 = gather(axis = gather_3_axis_0, batch_dims = gather_3_batch_dims_0, indices = while_loop_0_loop_vars0_0_x0_1_to_uint16, validate_indices = gather_3_validate_indices_0, x = concat_25_x0_1_to_fp16)[name = tensor<string, []>("gather_3_cast_fp16_cast_uint16")];
                    tensor<int32, [1]> squeeze_2_axes_0 = const()[name = tensor<string, []>("squeeze_2_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 256]> squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = gather_2_cast_fp16_cast_uint16)[name = tensor<string, []>("squeeze_2_cast_fp16")];
                    tensor<int32, [1]> squeeze_3_axes_0 = const()[name = tensor<string, []>("squeeze_3_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 256]> squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = gather_3_cast_fp16_cast_uint16)[name = tensor<string, []>("squeeze_3_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_6_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105792))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155008))), name = tensor<string, []>("linear_6_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_6_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_6_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155200)))];
                    tensor<fp16, [1, 256]> linear_6_cast_fp16 = linear(bias = linear_6_bias_0_to_fp16, weight = linear_6_weight_0_to_fp16_palettized, x = squeeze_2_cast_fp16)[name = tensor<string, []>("linear_6_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_7_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155776))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(204992))), name = tensor<string, []>("linear_7_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_7_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_7_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205184)))];
                    tensor<fp16, [1, 256]> linear_7_cast_fp16 = linear(bias = linear_7_bias_0_to_fp16, weight = linear_7_weight_0_to_fp16_palettized, x = squeeze_3_cast_fp16)[name = tensor<string, []>("linear_7_cast_fp16")];
                    tensor<fp16, [1, 256]> add_5_cast_fp16 = add(x = linear_6_cast_fp16, y = linear_7_cast_fp16)[name = tensor<string, []>("add_5_cast_fp16")];
                    tensor<fp16, [1, 256]> sigmoid_2_cast_fp16 = sigmoid(x = add_5_cast_fp16)[name = tensor<string, []>("sigmoid_2_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_8_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205760))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(254976))), name = tensor<string, []>("linear_8_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_8_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_8_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255168)))];
                    tensor<fp16, [1, 256]> linear_8_cast_fp16 = linear(bias = linear_8_bias_0_to_fp16, weight = linear_8_weight_0_to_fp16_palettized, x = squeeze_2_cast_fp16)[name = tensor<string, []>("linear_8_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_9_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255744))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304960))), name = tensor<string, []>("linear_9_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_9_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_9_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305152)))];
                    tensor<fp16, [1, 256]> linear_9_cast_fp16 = linear(bias = linear_9_bias_0_to_fp16, weight = linear_9_weight_0_to_fp16_palettized, x = squeeze_3_cast_fp16)[name = tensor<string, []>("linear_9_cast_fp16")];
                    tensor<fp16, [1, 256]> add_6_cast_fp16 = add(x = linear_8_cast_fp16, y = linear_9_cast_fp16)[name = tensor<string, []>("add_6_cast_fp16")];
                    tensor<fp16, [1, 256]> sigmoid_3_cast_fp16 = sigmoid(x = add_6_cast_fp16)[name = tensor<string, []>("sigmoid_3_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_10_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305728))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(354944))), name = tensor<string, []>("linear_10_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_10_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_10_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(355136)))];
                    tensor<fp16, [1, 256]> linear_10_cast_fp16 = linear(bias = linear_10_bias_0_to_fp16, weight = linear_10_weight_0_to_fp16_palettized, x = squeeze_2_cast_fp16)[name = tensor<string, []>("linear_10_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_11_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(355712))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404928))), name = tensor<string, []>("linear_11_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_11_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_11_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(405120)))];
                    tensor<fp16, [1, 256]> linear_11_cast_fp16 = linear(bias = linear_11_bias_0_to_fp16, weight = linear_11_weight_0_to_fp16_palettized, x = squeeze_3_cast_fp16)[name = tensor<string, []>("linear_11_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_3_cast_fp16 = mul(x = sigmoid_2_cast_fp16, y = linear_11_cast_fp16)[name = tensor<string, []>("mul_3_cast_fp16")];
                    tensor<fp16, [1, 256]> add_7_cast_fp16 = add(x = linear_10_cast_fp16, y = mul_3_cast_fp16)[name = tensor<string, []>("add_7_cast_fp16")];
                    tensor<fp16, [1, 256]> tanh_1_cast_fp16 = tanh(x = add_7_cast_fp16)[name = tensor<string, []>("tanh_1_cast_fp16")];
                    tensor<fp16, []> sub_1_x_0_to_fp16 = const()[name = tensor<string, []>("sub_1_x_0_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
                    tensor<fp16, [1, 256]> sub_1_cast_fp16 = sub(x = sub_1_x_0_to_fp16, y = sigmoid_3_cast_fp16)[name = tensor<string, []>("sub_1_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_4_cast_fp16 = mul(x = sub_1_cast_fp16, y = tanh_1_cast_fp16)[name = tensor<string, []>("mul_4_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_5_cast_fp16 = mul(x = sigmoid_3_cast_fp16, y = squeeze_3_cast_fp16)[name = tensor<string, []>("mul_5_cast_fp16")];
                    tensor<fp16, [1, 256]> add_8_cast_fp16 = add(x = mul_4_cast_fp16, y = mul_5_cast_fp16)[name = tensor<string, []>("add_8_cast_fp16")];
                    tensor<int32, []> add_9_y_0 = const()[name = tensor<string, []>("add_9_y_0"), val = tensor<int32, []>(1)];
                    tensor<int32, [1]> add_9 = add(x = while_loop_0_loop_vars0_0_x0_1_1_1_1, y = add_9_y_0)[name = tensor<string, []>("add_9")];
                    tensor<int32, [1]> expand_dims_1_axes_0 = const()[name = tensor<string, []>("expand_dims_1_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 1, 256]> expand_dims_1_cast_fp16 = expand_dims(axes = expand_dims_1_axes_0, x = add_8_cast_fp16)[name = tensor<string, []>("expand_dims_1_cast_fp16")];
                    tensor<int32, []> scatter_1_axis_0 = const()[name = tensor<string, []>("scatter_1_axis_0"), val = tensor<int32, []>(0)];
                    tensor<string, []> scatter_1_mode_0 = const()[name = tensor<string, []>("scatter_1_mode_0"), val = tensor<string, []>("add")];
                    tensor<bool, []> scatter_1_validate_indices_0 = const()[name = tensor<string, []>("scatter_1_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<fp16, [201, 1, 256]> scatter_1_cast_fp16 = scatter(axis = scatter_1_axis_0, data = concat_25_x0_1_to_fp16, indices = add_9, mode = scatter_1_mode_0, updates = expand_dims_1_cast_fp16, validate_indices = scatter_1_validate_indices_0)[name = tensor<string, []>("scatter_1_cast_fp16")];
                    tensor<string, []> scatter_1_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("scatter_1_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
                    tensor<fp32, [201, 1, 256]> scatter_1_cast_fp16_to_fp32 = cast(dtype = scatter_1_cast_fp16_to_fp32_dtype_0, x = scatter_1_cast_fp16)[name = tensor<string, []>("cast_17")];
                } -> (add_9, scatter_1_cast_fp16_to_fp32);
            tensor<int32, [3]> x_13_tmp_begin_0 = const()[name = tensor<string, []>("x_13_tmp_begin_0"), val = tensor<int32, [3]>([1, 0, 0])];
            tensor<int32, [3]> x_13_tmp_end_0 = const()[name = tensor<string, []>("x_13_tmp_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<bool, [3]> x_13_tmp_begin_mask_0 = const()[name = tensor<string, []>("x_13_tmp_begin_mask_0"), val = tensor<bool, [3]>([false, true, true])];
            tensor<bool, [3]> x_13_tmp_end_mask_0 = const()[name = tensor<string, []>("x_13_tmp_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<string, []> while_loop_0_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("while_loop_0_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [201, 1, 256]> while_loop_0_1_to_fp16 = cast(dtype = while_loop_0_1_to_fp16_dtype_0, x = while_loop_0_1)[name = tensor<string, []>("cast_16")];
            tensor<fp16, [200, 1, 256]> x_13_tmp_cast_fp16 = slice_by_index(begin = x_13_tmp_begin_0, begin_mask = x_13_tmp_begin_mask_0, end = x_13_tmp_end_0, end_mask = x_13_tmp_end_mask_0, x = while_loop_0_1_to_fp16)[name = tensor<string, []>("x_13_tmp_cast_fp16")];
            tensor<int32, [3]> x_13_perm_0 = const()[name = tensor<string, []>("x_13_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [4]> var_405 = const()[name = tensor<string, []>("op_405"), val = tensor<int32, [4]>([1, 200, 16, 16])];
            tensor<fp16, [1, 200, 256]> x_13_cast_fp16 = transpose(perm = x_13_perm_0, x = x_13_tmp_cast_fp16)[name = tensor<string, []>("transpose_18")];
            tensor<fp16, [1, 200, 16, 16]> var_406_cast_fp16 = reshape(shape = var_405, x = x_13_cast_fp16)[name = tensor<string, []>("op_406_cast_fp16")];
            tensor<int32, [4]> transpose_5_perm_0 = const()[name = tensor<string, []>("transpose_5_perm_0"), val = tensor<int32, [4]>([2, 0, 1, 3])];
            tensor<int32, [3]> concat_30 = const()[name = tensor<string, []>("concat_30"), val = tensor<int32, [3]>([16, 200, 16])];
            tensor<fp16, [16, 1, 200, 16]> transpose_5_cast_fp16 = transpose(perm = transpose_5_perm_0, x = var_406_cast_fp16)[name = tensor<string, []>("transpose_17")];
            tensor<fp16, [16, 200, 16]> reshape_6_cast_fp16 = reshape(shape = concat_30, x = transpose_5_cast_fp16)[name = tensor<string, []>("reshape_6_cast_fp16")];
            tensor<bool, []> matmul_2_transpose_x_0 = const()[name = tensor<string, []>("matmul_2_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_2_transpose_y_0 = const()[name = tensor<string, []>("matmul_2_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 16, 32]> enc_emb_gru_linear_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(405696))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411904))), name = tensor<string, []>("enc_emb_gru_linear_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [3]>([16, 16, 32])];
            tensor<fp16, [16, 200, 32]> matmul_2_cast_fp16 = matmul(transpose_x = matmul_2_transpose_x_0, transpose_y = matmul_2_transpose_y_0, x = reshape_6_cast_fp16, y = enc_emb_gru_linear_out_0_weight_to_fp16_palettized)[name = tensor<string, []>("matmul_2_cast_fp16")];
            tensor<int32, [4]> concat_35 = const()[name = tensor<string, []>("concat_35"), val = tensor<int32, [4]>([16, 1, 200, 32])];
            tensor<fp16, [16, 1, 200, 32]> reshape_8_cast_fp16 = reshape(shape = concat_35, x = matmul_2_cast_fp16)[name = tensor<string, []>("reshape_8_cast_fp16")];
            tensor<int32, [4]> x_15_perm_0 = const()[name = tensor<string, []>("x_15_perm_0"), val = tensor<int32, [4]>([1, 2, 0, 3])];
            tensor<int32, [3]> concat_36 = const()[name = tensor<string, []>("concat_36"), val = tensor<int32, [3]>([1, 200, 512])];
            tensor<fp16, [1, 200, 16, 32]> x_15_cast_fp16 = transpose(perm = x_15_perm_0, x = reshape_8_cast_fp16)[name = tensor<string, []>("transpose_16")];
            tensor<fp16, [1, 200, 512]> input_53_cast_fp16 = reshape(shape = concat_36, x = x_15_cast_fp16)[name = tensor<string, []>("input_53_cast_fp16")];
            tensor<fp16, [1, 200, 512]> x_17_cast_fp16 = relu(x = input_53_cast_fp16)[name = tensor<string, []>("x_17_cast_fp16")];
            tensor<int32, [4]> var_444 = const()[name = tensor<string, []>("op_444"), val = tensor<int32, [4]>([1, 200, 16, 32])];
            tensor<fp16, [1, 200, 16, 32]> var_445_cast_fp16 = reshape(shape = var_444, x = x_17_cast_fp16)[name = tensor<string, []>("op_445_cast_fp16")];
            tensor<int32, [4]> transpose_7_perm_0 = const()[name = tensor<string, []>("transpose_7_perm_0"), val = tensor<int32, [4]>([2, 0, 1, 3])];
            tensor<int32, [3]> concat_41 = const()[name = tensor<string, []>("concat_41"), val = tensor<int32, [3]>([16, 200, 32])];
            tensor<fp16, [16, 1, 200, 32]> transpose_7_cast_fp16 = transpose(perm = transpose_7_perm_0, x = var_445_cast_fp16)[name = tensor<string, []>("transpose_15")];
            tensor<fp16, [16, 200, 32]> reshape_9_cast_fp16 = reshape(shape = concat_41, x = transpose_7_cast_fp16)[name = tensor<string, []>("reshape_9_cast_fp16")];
            tensor<bool, []> matmul_3_transpose_x_0 = const()[name = tensor<string, []>("matmul_3_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_3_transpose_y_0 = const()[name = tensor<string, []>("matmul_3_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 32, 16]> erb_dec_emb_gru_linear_in_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(412096))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(418304))), name = tensor<string, []>("erb_dec_emb_gru_linear_in_0_weight_to_fp16_palettized"), shape = tensor<uint32, [3]>([16, 32, 16])];
            tensor<fp16, [16, 200, 16]> matmul_3_cast_fp16 = matmul(transpose_x = matmul_3_transpose_x_0, transpose_y = matmul_3_transpose_y_0, x = reshape_9_cast_fp16, y = erb_dec_emb_gru_linear_in_0_weight_to_fp16_palettized)[name = tensor<string, []>("matmul_3_cast_fp16")];
            tensor<int32, [4]> concat_46 = const()[name = tensor<string, []>("concat_46"), val = tensor<int32, [4]>([16, 1, 200, 16])];
            tensor<fp16, [16, 1, 200, 16]> reshape_11_cast_fp16 = reshape(shape = concat_46, x = matmul_3_cast_fp16)[name = tensor<string, []>("reshape_11_cast_fp16")];
            tensor<int32, [4]> x_19_perm_0 = const()[name = tensor<string, []>("x_19_perm_0"), val = tensor<int32, [4]>([1, 2, 0, 3])];
            tensor<int32, [3]> concat_47 = const()[name = tensor<string, []>("concat_47"), val = tensor<int32, [3]>([1, 200, 256])];
            tensor<fp16, [1, 200, 16, 16]> x_19_cast_fp16 = transpose(perm = x_19_perm_0, x = reshape_11_cast_fp16)[name = tensor<string, []>("transpose_14")];
            tensor<fp16, [1, 200, 256]> input_55_cast_fp16 = reshape(shape = concat_47, x = x_19_cast_fp16)[name = tensor<string, []>("input_55_cast_fp16")];
            tensor<fp16, [1, 200, 256]> input_57_cast_fp16 = relu(x = input_55_cast_fp16)[name = tensor<string, []>("input_57_cast_fp16")];
            tensor<int32, [3]> transpose_9_perm_0 = const()[name = tensor<string, []>("transpose_9_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [1]> slice_by_index_44 = const()[name = tensor<string, []>("slice_by_index_44"), val = tensor<int32, [1]>([200])];
            tensor<int32, [1]> while_loop_1_loop_vars0_0 = const()[name = tensor<string, []>("while_loop_1_loop_vars0_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [200, 1, 256]> transpose_9_cast_fp16 = transpose(perm = transpose_9_perm_0, x = input_57_cast_fp16)[name = tensor<string, []>("transpose_13")];
            tensor<int32, [1]> while_loop_1_0, tensor<fp32, [201, 1, 256]> while_loop_1_1 = while_loop(loop_vars = (while_loop_1_loop_vars0_0, concat_25_palettized))[name = tensor<string, []>("while_loop_1")]
                (tensor<int32, [1]> while_loop_1_loop_vars0_0_x0_1_1_1, tensor<fp32, [201, 1, 256]> concat_49_x0_1_1_1) {
                    tensor<bool, [1]> less_3 = less(x = while_loop_1_loop_vars0_0_x0_1_1_1, y = slice_by_index_44)[name = tensor<string, []>("less_3")];
                } -> (less_3)
                (tensor<int32, [1]> while_loop_1_loop_vars0_0_x0_1_1_1_1, tensor<fp32, [201, 1, 256]> concat_49_x0_1_1_1_1) {
                    tensor<int32, []> gather_6_axis_0 = const()[name = tensor<string, []>("gather_6_axis_0"), val = tensor<int32, []>(0)];
                    tensor<int32, []> gather_6_batch_dims_0 = const()[name = tensor<string, []>("gather_6_batch_dims_0"), val = tensor<int32, []>(0)];
                    tensor<bool, []> gather_6_validate_indices_0 = const()[name = tensor<string, []>("gather_6_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<string, []> while_loop_1_loop_vars0_0_x0_1_to_uint16_dtype_0 = const()[name = tensor<string, []>("while_loop_1_loop_vars0_0_x0_1_to_uint16_dtype_0"), val = tensor<string, []>("uint16")];
                    tensor<uint16, [1]> while_loop_1_loop_vars0_0_x0_1_to_uint16 = cast(dtype = while_loop_1_loop_vars0_0_x0_1_to_uint16_dtype_0, x = while_loop_1_loop_vars0_0_x0_1_1_1_1)[name = tensor<string, []>("cast_15")];
                    tensor<fp16, [1, 1, 256]> gather_6_cast_fp16_cast_uint16 = gather(axis = gather_6_axis_0, batch_dims = gather_6_batch_dims_0, indices = while_loop_1_loop_vars0_0_x0_1_to_uint16, validate_indices = gather_6_validate_indices_0, x = transpose_9_cast_fp16)[name = tensor<string, []>("gather_6_cast_fp16_cast_uint16")];
                    tensor<int32, []> gather_7_axis_0 = const()[name = tensor<string, []>("gather_7_axis_0"), val = tensor<int32, []>(0)];
                    tensor<int32, []> gather_7_batch_dims_0 = const()[name = tensor<string, []>("gather_7_batch_dims_0"), val = tensor<int32, []>(0)];
                    tensor<bool, []> gather_7_validate_indices_0 = const()[name = tensor<string, []>("gather_7_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<string, []> concat_49_x0_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("concat_49_x0_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
                    tensor<fp16, [201, 1, 256]> concat_49_x0_1_to_fp16 = cast(dtype = concat_49_x0_1_to_fp16_dtype_0, x = concat_49_x0_1_1_1_1)[name = tensor<string, []>("cast_14")];
                    tensor<fp16, [1, 1, 256]> gather_7_cast_fp16_cast_uint16 = gather(axis = gather_7_axis_0, batch_dims = gather_7_batch_dims_0, indices = while_loop_1_loop_vars0_0_x0_1_to_uint16, validate_indices = gather_7_validate_indices_0, x = concat_49_x0_1_to_fp16)[name = tensor<string, []>("gather_7_cast_fp16_cast_uint16")];
                    tensor<int32, [1]> squeeze_6_axes_0 = const()[name = tensor<string, []>("squeeze_6_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 256]> squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = gather_6_cast_fp16_cast_uint16)[name = tensor<string, []>("squeeze_6_cast_fp16")];
                    tensor<int32, [1]> squeeze_7_axes_0 = const()[name = tensor<string, []>("squeeze_7_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 256]> squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = gather_7_cast_fp16_cast_uint16)[name = tensor<string, []>("squeeze_7_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_18_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(418496))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(467712))), name = tensor<string, []>("linear_18_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_18_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_18_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(467904)))];
                    tensor<fp16, [1, 256]> linear_18_cast_fp16 = linear(bias = linear_18_bias_0_to_fp16, weight = linear_18_weight_0_to_fp16_palettized, x = squeeze_6_cast_fp16)[name = tensor<string, []>("linear_18_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_19_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468480))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(517696))), name = tensor<string, []>("linear_19_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_19_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_19_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(517888)))];
                    tensor<fp16, [1, 256]> linear_19_cast_fp16 = linear(bias = linear_19_bias_0_to_fp16, weight = linear_19_weight_0_to_fp16_palettized, x = squeeze_7_cast_fp16)[name = tensor<string, []>("linear_19_cast_fp16")];
                    tensor<fp16, [1, 256]> add_15_cast_fp16 = add(x = linear_18_cast_fp16, y = linear_19_cast_fp16)[name = tensor<string, []>("add_15_cast_fp16")];
                    tensor<fp16, [1, 256]> sigmoid_6_cast_fp16 = sigmoid(x = add_15_cast_fp16)[name = tensor<string, []>("sigmoid_6_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_20_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(518464))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(567680))), name = tensor<string, []>("linear_20_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_20_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_20_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(567872)))];
                    tensor<fp16, [1, 256]> linear_20_cast_fp16 = linear(bias = linear_20_bias_0_to_fp16, weight = linear_20_weight_0_to_fp16_palettized, x = squeeze_6_cast_fp16)[name = tensor<string, []>("linear_20_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_21_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(568448))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617664))), name = tensor<string, []>("linear_21_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_21_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_21_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(617856)))];
                    tensor<fp16, [1, 256]> linear_21_cast_fp16 = linear(bias = linear_21_bias_0_to_fp16, weight = linear_21_weight_0_to_fp16_palettized, x = squeeze_7_cast_fp16)[name = tensor<string, []>("linear_21_cast_fp16")];
                    tensor<fp16, [1, 256]> add_16_cast_fp16 = add(x = linear_20_cast_fp16, y = linear_21_cast_fp16)[name = tensor<string, []>("add_16_cast_fp16")];
                    tensor<fp16, [1, 256]> sigmoid_7_cast_fp16 = sigmoid(x = add_16_cast_fp16)[name = tensor<string, []>("sigmoid_7_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_22_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(618432))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(667648))), name = tensor<string, []>("linear_22_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_22_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_22_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(667840)))];
                    tensor<fp16, [1, 256]> linear_22_cast_fp16 = linear(bias = linear_22_bias_0_to_fp16, weight = linear_22_weight_0_to_fp16_palettized, x = squeeze_6_cast_fp16)[name = tensor<string, []>("linear_22_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_23_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(668416))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717632))), name = tensor<string, []>("linear_23_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_23_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_23_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717824)))];
                    tensor<fp16, [1, 256]> linear_23_cast_fp16 = linear(bias = linear_23_bias_0_to_fp16, weight = linear_23_weight_0_to_fp16_palettized, x = squeeze_7_cast_fp16)[name = tensor<string, []>("linear_23_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_9_cast_fp16 = mul(x = sigmoid_6_cast_fp16, y = linear_23_cast_fp16)[name = tensor<string, []>("mul_9_cast_fp16")];
                    tensor<fp16, [1, 256]> add_17_cast_fp16 = add(x = linear_22_cast_fp16, y = mul_9_cast_fp16)[name = tensor<string, []>("add_17_cast_fp16")];
                    tensor<fp16, [1, 256]> tanh_3_cast_fp16 = tanh(x = add_17_cast_fp16)[name = tensor<string, []>("tanh_3_cast_fp16")];
                    tensor<fp16, []> sub_3_x_0_to_fp16 = const()[name = tensor<string, []>("sub_3_x_0_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
                    tensor<fp16, [1, 256]> sub_3_cast_fp16 = sub(x = sub_3_x_0_to_fp16, y = sigmoid_7_cast_fp16)[name = tensor<string, []>("sub_3_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_10_cast_fp16 = mul(x = sub_3_cast_fp16, y = tanh_3_cast_fp16)[name = tensor<string, []>("mul_10_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_11_cast_fp16 = mul(x = sigmoid_7_cast_fp16, y = squeeze_7_cast_fp16)[name = tensor<string, []>("mul_11_cast_fp16")];
                    tensor<fp16, [1, 256]> add_18_cast_fp16 = add(x = mul_10_cast_fp16, y = mul_11_cast_fp16)[name = tensor<string, []>("add_18_cast_fp16")];
                    tensor<int32, []> add_19_y_0 = const()[name = tensor<string, []>("add_19_y_0"), val = tensor<int32, []>(1)];
                    tensor<int32, [1]> add_19 = add(x = while_loop_1_loop_vars0_0_x0_1_1_1_1, y = add_19_y_0)[name = tensor<string, []>("add_19")];
                    tensor<int32, [1]> expand_dims_3_axes_0 = const()[name = tensor<string, []>("expand_dims_3_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 1, 256]> expand_dims_3_cast_fp16 = expand_dims(axes = expand_dims_3_axes_0, x = add_18_cast_fp16)[name = tensor<string, []>("expand_dims_3_cast_fp16")];
                    tensor<int32, []> scatter_3_axis_0 = const()[name = tensor<string, []>("scatter_3_axis_0"), val = tensor<int32, []>(0)];
                    tensor<string, []> scatter_3_mode_0 = const()[name = tensor<string, []>("scatter_3_mode_0"), val = tensor<string, []>("add")];
                    tensor<bool, []> scatter_3_validate_indices_0 = const()[name = tensor<string, []>("scatter_3_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<fp16, [201, 1, 256]> scatter_3_cast_fp16 = scatter(axis = scatter_3_axis_0, data = concat_49_x0_1_to_fp16, indices = add_19, mode = scatter_3_mode_0, updates = expand_dims_3_cast_fp16, validate_indices = scatter_3_validate_indices_0)[name = tensor<string, []>("scatter_3_cast_fp16")];
                    tensor<string, []> scatter_3_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("scatter_3_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
                    tensor<fp32, [201, 1, 256]> scatter_3_cast_fp16_to_fp32 = cast(dtype = scatter_3_cast_fp16_to_fp32_dtype_0, x = scatter_3_cast_fp16)[name = tensor<string, []>("cast_13")];
                } -> (add_19, scatter_3_cast_fp16_to_fp32);
            tensor<int32, [3]> x_21_layer_0_tmp_begin_0 = const()[name = tensor<string, []>("x_21_layer_0_tmp_begin_0"), val = tensor<int32, [3]>([1, 0, 0])];
            tensor<int32, [3]> x_21_layer_0_tmp_end_0 = const()[name = tensor<string, []>("x_21_layer_0_tmp_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<bool, [3]> x_21_layer_0_tmp_begin_mask_0 = const()[name = tensor<string, []>("x_21_layer_0_tmp_begin_mask_0"), val = tensor<bool, [3]>([false, true, true])];
            tensor<bool, [3]> x_21_layer_0_tmp_end_mask_0 = const()[name = tensor<string, []>("x_21_layer_0_tmp_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<string, []> while_loop_1_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("while_loop_1_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [201, 1, 256]> while_loop_1_1_to_fp16 = cast(dtype = while_loop_1_1_to_fp16_dtype_0, x = while_loop_1_1)[name = tensor<string, []>("cast_12")];
            tensor<fp16, [200, 1, 256]> x_21_layer_0_tmp_cast_fp16 = slice_by_index(begin = x_21_layer_0_tmp_begin_0, begin_mask = x_21_layer_0_tmp_begin_mask_0, end = x_21_layer_0_tmp_end_0, end_mask = x_21_layer_0_tmp_end_mask_0, x = while_loop_1_1_to_fp16)[name = tensor<string, []>("x_21_layer_0_tmp_cast_fp16")];
            tensor<int32, [1]> slice_by_index_47 = const()[name = tensor<string, []>("slice_by_index_47"), val = tensor<int32, [1]>([200])];
            tensor<int32, [1]> while_loop_2_loop_vars0_0 = const()[name = tensor<string, []>("while_loop_2_loop_vars0_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> while_loop_2_0, tensor<fp32, [201, 1, 256]> while_loop_2_1 = while_loop(loop_vars = (while_loop_2_loop_vars0_0, concat_25_palettized))[name = tensor<string, []>("while_loop_2")]
                (tensor<int32, [1]> while_loop_2_loop_vars0_0_x0_1_1_1, tensor<fp32, [201, 1, 256]> concat_51_x0_1_1_1) {
                    tensor<bool, [1]> less_5 = less(x = while_loop_2_loop_vars0_0_x0_1_1_1, y = slice_by_index_47)[name = tensor<string, []>("less_5")];
                } -> (less_5)
                (tensor<int32, [1]> while_loop_2_loop_vars0_0_x0_1_1_1_1, tensor<fp32, [201, 1, 256]> concat_51_x0_1_1_1_1) {
                    tensor<int32, []> gather_10_axis_0 = const()[name = tensor<string, []>("gather_10_axis_0"), val = tensor<int32, []>(0)];
                    tensor<int32, []> gather_10_batch_dims_0 = const()[name = tensor<string, []>("gather_10_batch_dims_0"), val = tensor<int32, []>(0)];
                    tensor<bool, []> gather_10_validate_indices_0 = const()[name = tensor<string, []>("gather_10_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<string, []> while_loop_2_loop_vars0_0_x0_1_to_uint16_dtype_0 = const()[name = tensor<string, []>("while_loop_2_loop_vars0_0_x0_1_to_uint16_dtype_0"), val = tensor<string, []>("uint16")];
                    tensor<uint16, [1]> while_loop_2_loop_vars0_0_x0_1_to_uint16 = cast(dtype = while_loop_2_loop_vars0_0_x0_1_to_uint16_dtype_0, x = while_loop_2_loop_vars0_0_x0_1_1_1_1)[name = tensor<string, []>("cast_11")];
                    tensor<fp16, [1, 1, 256]> gather_10_cast_fp16_cast_uint16 = gather(axis = gather_10_axis_0, batch_dims = gather_10_batch_dims_0, indices = while_loop_2_loop_vars0_0_x0_1_to_uint16, validate_indices = gather_10_validate_indices_0, x = x_21_layer_0_tmp_cast_fp16)[name = tensor<string, []>("gather_10_cast_fp16_cast_uint16")];
                    tensor<int32, []> gather_11_axis_0 = const()[name = tensor<string, []>("gather_11_axis_0"), val = tensor<int32, []>(0)];
                    tensor<int32, []> gather_11_batch_dims_0 = const()[name = tensor<string, []>("gather_11_batch_dims_0"), val = tensor<int32, []>(0)];
                    tensor<bool, []> gather_11_validate_indices_0 = const()[name = tensor<string, []>("gather_11_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<string, []> concat_51_x0_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("concat_51_x0_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
                    tensor<fp16, [201, 1, 256]> concat_51_x0_1_to_fp16 = cast(dtype = concat_51_x0_1_to_fp16_dtype_0, x = concat_51_x0_1_1_1_1)[name = tensor<string, []>("cast_10")];
                    tensor<fp16, [1, 1, 256]> gather_11_cast_fp16_cast_uint16 = gather(axis = gather_11_axis_0, batch_dims = gather_11_batch_dims_0, indices = while_loop_2_loop_vars0_0_x0_1_to_uint16, validate_indices = gather_11_validate_indices_0, x = concat_51_x0_1_to_fp16)[name = tensor<string, []>("gather_11_cast_fp16_cast_uint16")];
                    tensor<int32, [1]> squeeze_10_axes_0 = const()[name = tensor<string, []>("squeeze_10_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 256]> squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = gather_10_cast_fp16_cast_uint16)[name = tensor<string, []>("squeeze_10_cast_fp16")];
                    tensor<int32, [1]> squeeze_11_axes_0 = const()[name = tensor<string, []>("squeeze_11_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 256]> squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = gather_11_cast_fp16_cast_uint16)[name = tensor<string, []>("squeeze_11_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_30_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(718400))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(767616))), name = tensor<string, []>("linear_30_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_30_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_30_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(767808)))];
                    tensor<fp16, [1, 256]> linear_30_cast_fp16 = linear(bias = linear_30_bias_0_to_fp16, weight = linear_30_weight_0_to_fp16_palettized, x = squeeze_10_cast_fp16)[name = tensor<string, []>("linear_30_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_31_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(768384))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(817600))), name = tensor<string, []>("linear_31_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_31_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_31_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(817792)))];
                    tensor<fp16, [1, 256]> linear_31_cast_fp16 = linear(bias = linear_31_bias_0_to_fp16, weight = linear_31_weight_0_to_fp16_palettized, x = squeeze_11_cast_fp16)[name = tensor<string, []>("linear_31_cast_fp16")];
                    tensor<fp16, [1, 256]> add_25_cast_fp16 = add(x = linear_30_cast_fp16, y = linear_31_cast_fp16)[name = tensor<string, []>("add_25_cast_fp16")];
                    tensor<fp16, [1, 256]> sigmoid_10_cast_fp16 = sigmoid(x = add_25_cast_fp16)[name = tensor<string, []>("sigmoid_10_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_32_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(818368))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(867584))), name = tensor<string, []>("linear_32_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_32_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_32_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(867776)))];
                    tensor<fp16, [1, 256]> linear_32_cast_fp16 = linear(bias = linear_32_bias_0_to_fp16, weight = linear_32_weight_0_to_fp16_palettized, x = squeeze_10_cast_fp16)[name = tensor<string, []>("linear_32_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_33_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(868352))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(917568))), name = tensor<string, []>("linear_33_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_33_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_33_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(917760)))];
                    tensor<fp16, [1, 256]> linear_33_cast_fp16 = linear(bias = linear_33_bias_0_to_fp16, weight = linear_33_weight_0_to_fp16_palettized, x = squeeze_11_cast_fp16)[name = tensor<string, []>("linear_33_cast_fp16")];
                    tensor<fp16, [1, 256]> add_26_cast_fp16 = add(x = linear_32_cast_fp16, y = linear_33_cast_fp16)[name = tensor<string, []>("add_26_cast_fp16")];
                    tensor<fp16, [1, 256]> sigmoid_11_cast_fp16 = sigmoid(x = add_26_cast_fp16)[name = tensor<string, []>("sigmoid_11_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_34_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(918336))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(967552))), name = tensor<string, []>("linear_34_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_34_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_34_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(967744)))];
                    tensor<fp16, [1, 256]> linear_34_cast_fp16 = linear(bias = linear_34_bias_0_to_fp16, weight = linear_34_weight_0_to_fp16_palettized, x = squeeze_10_cast_fp16)[name = tensor<string, []>("linear_34_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_35_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(968320))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1017536))), name = tensor<string, []>("linear_35_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_35_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_35_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1017728)))];
                    tensor<fp16, [1, 256]> linear_35_cast_fp16 = linear(bias = linear_35_bias_0_to_fp16, weight = linear_35_weight_0_to_fp16_palettized, x = squeeze_11_cast_fp16)[name = tensor<string, []>("linear_35_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_15_cast_fp16 = mul(x = sigmoid_10_cast_fp16, y = linear_35_cast_fp16)[name = tensor<string, []>("mul_15_cast_fp16")];
                    tensor<fp16, [1, 256]> add_27_cast_fp16 = add(x = linear_34_cast_fp16, y = mul_15_cast_fp16)[name = tensor<string, []>("add_27_cast_fp16")];
                    tensor<fp16, [1, 256]> tanh_5_cast_fp16 = tanh(x = add_27_cast_fp16)[name = tensor<string, []>("tanh_5_cast_fp16")];
                    tensor<fp16, []> sub_5_x_0_to_fp16 = const()[name = tensor<string, []>("sub_5_x_0_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
                    tensor<fp16, [1, 256]> sub_5_cast_fp16 = sub(x = sub_5_x_0_to_fp16, y = sigmoid_11_cast_fp16)[name = tensor<string, []>("sub_5_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_16_cast_fp16 = mul(x = sub_5_cast_fp16, y = tanh_5_cast_fp16)[name = tensor<string, []>("mul_16_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_17_cast_fp16 = mul(x = sigmoid_11_cast_fp16, y = squeeze_11_cast_fp16)[name = tensor<string, []>("mul_17_cast_fp16")];
                    tensor<fp16, [1, 256]> add_28_cast_fp16 = add(x = mul_16_cast_fp16, y = mul_17_cast_fp16)[name = tensor<string, []>("add_28_cast_fp16")];
                    tensor<int32, []> add_29_y_0 = const()[name = tensor<string, []>("add_29_y_0"), val = tensor<int32, []>(1)];
                    tensor<int32, [1]> add_29 = add(x = while_loop_2_loop_vars0_0_x0_1_1_1_1, y = add_29_y_0)[name = tensor<string, []>("add_29")];
                    tensor<int32, [1]> expand_dims_5_axes_0 = const()[name = tensor<string, []>("expand_dims_5_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 1, 256]> expand_dims_5_cast_fp16 = expand_dims(axes = expand_dims_5_axes_0, x = add_28_cast_fp16)[name = tensor<string, []>("expand_dims_5_cast_fp16")];
                    tensor<int32, []> scatter_5_axis_0 = const()[name = tensor<string, []>("scatter_5_axis_0"), val = tensor<int32, []>(0)];
                    tensor<string, []> scatter_5_mode_0 = const()[name = tensor<string, []>("scatter_5_mode_0"), val = tensor<string, []>("add")];
                    tensor<bool, []> scatter_5_validate_indices_0 = const()[name = tensor<string, []>("scatter_5_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<fp16, [201, 1, 256]> scatter_5_cast_fp16 = scatter(axis = scatter_5_axis_0, data = concat_51_x0_1_to_fp16, indices = add_29, mode = scatter_5_mode_0, updates = expand_dims_5_cast_fp16, validate_indices = scatter_5_validate_indices_0)[name = tensor<string, []>("scatter_5_cast_fp16")];
                    tensor<string, []> scatter_5_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("scatter_5_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
                    tensor<fp32, [201, 1, 256]> scatter_5_cast_fp16_to_fp32 = cast(dtype = scatter_5_cast_fp16_to_fp32_dtype_0, x = scatter_5_cast_fp16)[name = tensor<string, []>("cast_9")];
                } -> (add_29, scatter_5_cast_fp16_to_fp32);
            tensor<int32, [3]> x_21_tmp_begin_0 = const()[name = tensor<string, []>("x_21_tmp_begin_0"), val = tensor<int32, [3]>([1, 0, 0])];
            tensor<int32, [3]> x_21_tmp_end_0 = const()[name = tensor<string, []>("x_21_tmp_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<bool, [3]> x_21_tmp_begin_mask_0 = const()[name = tensor<string, []>("x_21_tmp_begin_mask_0"), val = tensor<bool, [3]>([false, true, true])];
            tensor<bool, [3]> x_21_tmp_end_mask_0 = const()[name = tensor<string, []>("x_21_tmp_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<string, []> while_loop_2_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("while_loop_2_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [201, 1, 256]> while_loop_2_1_to_fp16 = cast(dtype = while_loop_2_1_to_fp16_dtype_0, x = while_loop_2_1)[name = tensor<string, []>("cast_8")];
            tensor<fp16, [200, 1, 256]> x_21_tmp_cast_fp16 = slice_by_index(begin = x_21_tmp_begin_0, begin_mask = x_21_tmp_begin_mask_0, end = x_21_tmp_end_0, end_mask = x_21_tmp_end_mask_0, x = while_loop_2_1_to_fp16)[name = tensor<string, []>("x_21_tmp_cast_fp16")];
            tensor<int32, [3]> x_21_perm_0 = const()[name = tensor<string, []>("x_21_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [4]> var_468 = const()[name = tensor<string, []>("op_468"), val = tensor<int32, [4]>([1, 200, 16, 16])];
            tensor<fp16, [1, 200, 256]> x_21_cast_fp16 = transpose(perm = x_21_perm_0, x = x_21_tmp_cast_fp16)[name = tensor<string, []>("transpose_12")];
            tensor<fp16, [1, 200, 16, 16]> var_469_cast_fp16 = reshape(shape = var_468, x = x_21_cast_fp16)[name = tensor<string, []>("op_469_cast_fp16")];
            tensor<int32, [4]> transpose_10_perm_0 = const()[name = tensor<string, []>("transpose_10_perm_0"), val = tensor<int32, [4]>([2, 0, 1, 3])];
            tensor<int32, [3]> concat_56 = const()[name = tensor<string, []>("concat_56"), val = tensor<int32, [3]>([16, 200, 16])];
            tensor<fp16, [16, 1, 200, 16]> transpose_10_cast_fp16 = transpose(perm = transpose_10_perm_0, x = var_469_cast_fp16)[name = tensor<string, []>("transpose_11")];
            tensor<fp16, [16, 200, 16]> reshape_12_cast_fp16 = reshape(shape = concat_56, x = transpose_10_cast_fp16)[name = tensor<string, []>("reshape_12_cast_fp16")];
            tensor<bool, []> matmul_4_transpose_x_0 = const()[name = tensor<string, []>("matmul_4_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_4_transpose_y_0 = const()[name = tensor<string, []>("matmul_4_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 16, 32]> erb_dec_emb_gru_linear_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1018304))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1024512))), name = tensor<string, []>("erb_dec_emb_gru_linear_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [3]>([16, 16, 32])];
            tensor<fp16, [16, 200, 32]> matmul_4_cast_fp16 = matmul(transpose_x = matmul_4_transpose_x_0, transpose_y = matmul_4_transpose_y_0, x = reshape_12_cast_fp16, y = erb_dec_emb_gru_linear_out_0_weight_to_fp16_palettized)[name = tensor<string, []>("matmul_4_cast_fp16")];
            tensor<int32, [4]> concat_61 = const()[name = tensor<string, []>("concat_61"), val = tensor<int32, [4]>([16, 1, 200, 32])];
            tensor<fp16, [16, 1, 200, 32]> reshape_14_cast_fp16 = reshape(shape = concat_61, x = matmul_4_cast_fp16)[name = tensor<string, []>("reshape_14_cast_fp16")];
            tensor<int32, [4]> x_23_perm_0 = const()[name = tensor<string, []>("x_23_perm_0"), val = tensor<int32, [4]>([1, 2, 0, 3])];
            tensor<int32, [3]> concat_62 = const()[name = tensor<string, []>("concat_62"), val = tensor<int32, [3]>([1, 200, 512])];
            tensor<fp16, [1, 200, 16, 32]> x_23_cast_fp16 = transpose(perm = x_23_perm_0, x = reshape_14_cast_fp16)[name = tensor<string, []>("transpose_10")];
            tensor<fp16, [1, 200, 512]> input_59_cast_fp16 = reshape(shape = concat_62, x = x_23_cast_fp16)[name = tensor<string, []>("input_59_cast_fp16")];
            tensor<fp16, [1, 200, 512]> emb_d_1_cast_fp16 = relu(x = input_59_cast_fp16)[name = tensor<string, []>("emb_d_1_cast_fp16")];
            tensor<int32, [4]> var_475 = const()[name = tensor<string, []>("op_475"), val = tensor<int32, [4]>([1, 200, 8, -1])];
            tensor<fp16, [1, 200, 8, 64]> var_476_cast_fp16 = reshape(shape = var_475, x = emb_d_1_cast_fp16)[name = tensor<string, []>("op_476_cast_fp16")];
            tensor<int32, [4]> var_481 = const()[name = tensor<string, []>("op_481"), val = tensor<int32, [4]>([0, 3, 1, 2])];
            tensor<string, []> input_61_pad_type_0 = const()[name = tensor<string, []>("input_61_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_61_groups_0 = const()[name = tensor<string, []>("input_61_groups_0"), val = tensor<int32, []>(64)];
            tensor<int32, [2]> input_61_strides_0 = const()[name = tensor<string, []>("input_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_61_pad_0 = const()[name = tensor<string, []>("input_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_61_dilations_0 = const()[name = tensor<string, []>("input_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [64, 1, 1, 1]> const_55_to_fp16 = const()[name = tensor<string, []>("const_55_to_fp16"), val = tensor<fp16, [64, 1, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1024704)))];
            tensor<fp16, [64]> const_56_to_fp16 = const()[name = tensor<string, []>("const_56_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1024896)))];
            tensor<fp16, [1, 64, 200, 8]> input_63_cast_fp16 = conv(bias = const_56_to_fp16, dilations = input_61_dilations_0, groups = input_61_groups_0, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = input_61_strides_0, weight = const_55_to_fp16, x = e3_cast_fp16)[name = tensor<string, []>("input_63_cast_fp16")];
            tensor<fp16, [1, 64, 200, 8]> var_504_cast_fp16 = relu(x = input_63_cast_fp16)[name = tensor<string, []>("op_504_cast_fp16")];
            tensor<fp16, [1, 64, 200, 8]> emb_d_cast_fp16 = transpose(perm = var_481, x = var_476_cast_fp16)[name = tensor<string, []>("transpose_9")];
            tensor<fp16, [1, 64, 200, 8]> input_65_cast_fp16 = add(x = var_504_cast_fp16, y = emb_d_cast_fp16)[name = tensor<string, []>("input_65_cast_fp16")];
            tensor<string, []> input_67_pad_type_0 = const()[name = tensor<string, []>("input_67_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_67_pad_0 = const()[name = tensor<string, []>("input_67_pad_0"), val = tensor<int32, [4]>([0, 0, 1, 1])];
            tensor<int32, []> input_67_groups_0 = const()[name = tensor<string, []>("input_67_groups_0"), val = tensor<int32, []>(64)];
            tensor<int32, [2]> input_67_strides_0 = const()[name = tensor<string, []>("input_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_67_dilations_0 = const()[name = tensor<string, []>("input_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [64, 1, 1, 3]> erb_dec_convt3_0_weight_to_fp16 = const()[name = tensor<string, []>("erb_dec_convt3_0_weight_to_fp16"), val = tensor<fp16, [64, 1, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1025088)))];
            tensor<fp16, [1, 64, 200, 8]> input_67_cast_fp16 = conv(dilations = input_67_dilations_0, groups = input_67_groups_0, pad = input_67_pad_0, pad_type = input_67_pad_type_0, strides = input_67_strides_0, weight = erb_dec_convt3_0_weight_to_fp16, x = input_65_cast_fp16)[name = tensor<string, []>("input_67_cast_fp16")];
            tensor<string, []> input_69_pad_type_0 = const()[name = tensor<string, []>("input_69_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> input_69_strides_0 = const()[name = tensor<string, []>("input_69_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_69_pad_0 = const()[name = tensor<string, []>("input_69_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_69_dilations_0 = const()[name = tensor<string, []>("input_69_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_69_groups_0 = const()[name = tensor<string, []>("input_69_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [64, 64, 1, 1]> const_57_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1025536))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1028672))), name = tensor<string, []>("const_57_to_fp16_palettized"), shape = tensor<uint32, [4]>([64, 64, 1, 1])];
            tensor<fp16, [64]> const_58_to_fp16 = const()[name = tensor<string, []>("const_58_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1028864)))];
            tensor<fp16, [1, 64, 200, 8]> input_71_cast_fp16 = conv(bias = const_58_to_fp16, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = const_57_to_fp16_palettized, x = input_67_cast_fp16)[name = tensor<string, []>("input_71_cast_fp16")];
            tensor<fp16, [1, 64, 200, 8]> e3d_cast_fp16 = relu(x = input_71_cast_fp16)[name = tensor<string, []>("e3d_cast_fp16")];
            tensor<string, []> input_73_pad_type_0 = const()[name = tensor<string, []>("input_73_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_73_groups_0 = const()[name = tensor<string, []>("input_73_groups_0"), val = tensor<int32, []>(64)];
            tensor<int32, [2]> input_73_strides_0 = const()[name = tensor<string, []>("input_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_73_pad_0 = const()[name = tensor<string, []>("input_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_73_dilations_0 = const()[name = tensor<string, []>("input_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [64, 1, 1, 1]> const_59_to_fp16 = const()[name = tensor<string, []>("const_59_to_fp16"), val = tensor<fp16, [64, 1, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1029056)))];
            tensor<fp16, [64]> const_60_to_fp16 = const()[name = tensor<string, []>("const_60_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1029248)))];
            tensor<fp16, [1, 64, 200, 8]> input_75_cast_fp16 = conv(bias = const_60_to_fp16, dilations = input_73_dilations_0, groups = input_73_groups_0, pad = input_73_pad_0, pad_type = input_73_pad_type_0, strides = input_73_strides_0, weight = const_59_to_fp16, x = input_23_cast_fp16)[name = tensor<string, []>("input_75_cast_fp16")];
            tensor<fp16, [1, 64, 200, 8]> var_557_cast_fp16 = relu(x = input_75_cast_fp16)[name = tensor<string, []>("op_557_cast_fp16")];
            tensor<fp16, [1, 64, 200, 8]> input_77_cast_fp16 = add(x = var_557_cast_fp16, y = e3d_cast_fp16)[name = tensor<string, []>("input_77_cast_fp16")];
            tensor<string, []> conv_transpose_0_pad_type_0 = const()[name = tensor<string, []>("conv_transpose_0_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> conv_transpose_0_pad_0 = const()[name = tensor<string, []>("conv_transpose_0_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> conv_transpose_0_strides_0 = const()[name = tensor<string, []>("conv_transpose_0_strides_0"), val = tensor<int32, [2]>([1, 2])];
            tensor<int32, []> conv_transpose_0_groups_0 = const()[name = tensor<string, []>("conv_transpose_0_groups_0"), val = tensor<int32, []>(64)];
            tensor<int32, [2]> conv_transpose_0_dilations_0 = const()[name = tensor<string, []>("conv_transpose_0_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> conv_transpose_0_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("conv_transpose_0_has_output_shape_output_shape_0"), val = tensor<int32, [4]>([1, 64, 200, 17])];
            tensor<fp16, [64, 1, 1, 3]> erb_dec_convt2_0_weight_to_fp16 = const()[name = tensor<string, []>("erb_dec_convt2_0_weight_to_fp16"), val = tensor<fp16, [64, 1, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1029440)))];
            tensor<fp16, [1, 64, 200, 17]> conv_transpose_0_has_output_shape_cast_fp16 = conv_transpose(dilations = conv_transpose_0_dilations_0, groups = conv_transpose_0_groups_0, output_shape = conv_transpose_0_has_output_shape_output_shape_0, pad = conv_transpose_0_pad_0, pad_type = conv_transpose_0_pad_type_0, strides = conv_transpose_0_strides_0, weight = erb_dec_convt2_0_weight_to_fp16, x = input_77_cast_fp16)[name = tensor<string, []>("conv_transpose_0_has_output_shape_cast_fp16")];
            tensor<int32, [2]> input_79_crop_height_0 = const()[name = tensor<string, []>("input_79_crop_height_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [2]> input_79_crop_width_0 = const()[name = tensor<string, []>("input_79_crop_width_0"), val = tensor<int32, [2]>([1, 0])];
            tensor<fp16, [1, 64, 200, 16]> input_79_cast_fp16 = crop(crop_height = input_79_crop_height_0, crop_width = input_79_crop_width_0, x = conv_transpose_0_has_output_shape_cast_fp16)[name = tensor<string, []>("input_79_cast_fp16")];
            tensor<string, []> input_81_pad_type_0 = const()[name = tensor<string, []>("input_81_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> input_81_strides_0 = const()[name = tensor<string, []>("input_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_81_pad_0 = const()[name = tensor<string, []>("input_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_81_dilations_0 = const()[name = tensor<string, []>("input_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_81_groups_0 = const()[name = tensor<string, []>("input_81_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [64, 64, 1, 1]> const_61_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1029888))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1033024))), name = tensor<string, []>("const_61_to_fp16_palettized"), shape = tensor<uint32, [4]>([64, 64, 1, 1])];
            tensor<fp16, [64]> const_62_to_fp16 = const()[name = tensor<string, []>("const_62_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1033216)))];
            tensor<fp16, [1, 64, 200, 16]> input_83_cast_fp16 = conv(bias = const_62_to_fp16, dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = const_61_to_fp16_palettized, x = input_79_cast_fp16)[name = tensor<string, []>("input_83_cast_fp16")];
            tensor<fp16, [1, 64, 200, 16]> e2d_cast_fp16 = relu(x = input_83_cast_fp16)[name = tensor<string, []>("e2d_cast_fp16")];
            tensor<string, []> input_85_pad_type_0 = const()[name = tensor<string, []>("input_85_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_85_groups_0 = const()[name = tensor<string, []>("input_85_groups_0"), val = tensor<int32, []>(64)];
            tensor<int32, [2]> input_85_strides_0 = const()[name = tensor<string, []>("input_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_85_pad_0 = const()[name = tensor<string, []>("input_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_85_dilations_0 = const()[name = tensor<string, []>("input_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [64, 1, 1, 1]> const_63_to_fp16 = const()[name = tensor<string, []>("const_63_to_fp16"), val = tensor<fp16, [64, 1, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1033408)))];
            tensor<fp16, [64]> const_64_to_fp16 = const()[name = tensor<string, []>("const_64_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1033600)))];
            tensor<fp16, [1, 64, 200, 16]> input_87_cast_fp16 = conv(bias = const_64_to_fp16, dilations = input_85_dilations_0, groups = input_85_groups_0, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = input_85_strides_0, weight = const_63_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("input_87_cast_fp16")];
            tensor<fp16, [1, 64, 200, 16]> var_611_cast_fp16 = relu(x = input_87_cast_fp16)[name = tensor<string, []>("op_611_cast_fp16")];
            tensor<fp16, [1, 64, 200, 16]> input_89_cast_fp16 = add(x = var_611_cast_fp16, y = e2d_cast_fp16)[name = tensor<string, []>("input_89_cast_fp16")];
            tensor<string, []> conv_transpose_1_pad_type_0 = const()[name = tensor<string, []>("conv_transpose_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> conv_transpose_1_pad_0 = const()[name = tensor<string, []>("conv_transpose_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> conv_transpose_1_strides_0 = const()[name = tensor<string, []>("conv_transpose_1_strides_0"), val = tensor<int32, [2]>([1, 2])];
            tensor<int32, []> conv_transpose_1_groups_0 = const()[name = tensor<string, []>("conv_transpose_1_groups_0"), val = tensor<int32, []>(64)];
            tensor<int32, [2]> conv_transpose_1_dilations_0 = const()[name = tensor<string, []>("conv_transpose_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> conv_transpose_1_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("conv_transpose_1_has_output_shape_output_shape_0"), val = tensor<int32, [4]>([1, 64, 200, 33])];
            tensor<fp16, [64, 1, 1, 3]> erb_dec_convt1_0_weight_to_fp16 = const()[name = tensor<string, []>("erb_dec_convt1_0_weight_to_fp16"), val = tensor<fp16, [64, 1, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1033792)))];
            tensor<fp16, [1, 64, 200, 33]> conv_transpose_1_has_output_shape_cast_fp16 = conv_transpose(dilations = conv_transpose_1_dilations_0, groups = conv_transpose_1_groups_0, output_shape = conv_transpose_1_has_output_shape_output_shape_0, pad = conv_transpose_1_pad_0, pad_type = conv_transpose_1_pad_type_0, strides = conv_transpose_1_strides_0, weight = erb_dec_convt1_0_weight_to_fp16, x = input_89_cast_fp16)[name = tensor<string, []>("conv_transpose_1_has_output_shape_cast_fp16")];
            tensor<int32, [2]> input_91_crop_height_0 = const()[name = tensor<string, []>("input_91_crop_height_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [2]> input_91_crop_width_0 = const()[name = tensor<string, []>("input_91_crop_width_0"), val = tensor<int32, [2]>([1, 0])];
            tensor<fp16, [1, 64, 200, 32]> input_91_cast_fp16 = crop(crop_height = input_91_crop_height_0, crop_width = input_91_crop_width_0, x = conv_transpose_1_has_output_shape_cast_fp16)[name = tensor<string, []>("input_91_cast_fp16")];
            tensor<string, []> input_93_pad_type_0 = const()[name = tensor<string, []>("input_93_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> input_93_strides_0 = const()[name = tensor<string, []>("input_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_93_pad_0 = const()[name = tensor<string, []>("input_93_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_93_dilations_0 = const()[name = tensor<string, []>("input_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_93_groups_0 = const()[name = tensor<string, []>("input_93_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [64, 64, 1, 1]> const_65_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1034240))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1037376))), name = tensor<string, []>("const_65_to_fp16_palettized"), shape = tensor<uint32, [4]>([64, 64, 1, 1])];
            tensor<fp16, [64]> const_66_to_fp16 = const()[name = tensor<string, []>("const_66_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1037568)))];
            tensor<fp16, [1, 64, 200, 32]> input_95_cast_fp16 = conv(bias = const_66_to_fp16, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = const_65_to_fp16_palettized, x = input_91_cast_fp16)[name = tensor<string, []>("input_95_cast_fp16")];
            tensor<fp16, [1, 64, 200, 32]> e1d_cast_fp16 = relu(x = input_95_cast_fp16)[name = tensor<string, []>("e1d_cast_fp16")];
            tensor<string, []> input_97_pad_type_0 = const()[name = tensor<string, []>("input_97_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_97_groups_0 = const()[name = tensor<string, []>("input_97_groups_0"), val = tensor<int32, []>(64)];
            tensor<int32, [2]> input_97_strides_0 = const()[name = tensor<string, []>("input_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_97_pad_0 = const()[name = tensor<string, []>("input_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_97_dilations_0 = const()[name = tensor<string, []>("input_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [64, 1, 1, 1]> const_67_to_fp16 = const()[name = tensor<string, []>("const_67_to_fp16"), val = tensor<fp16, [64, 1, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1037760)))];
            tensor<fp16, [64]> const_68_to_fp16 = const()[name = tensor<string, []>("const_68_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1037952)))];
            tensor<fp16, [1, 64, 200, 32]> input_99_cast_fp16 = conv(bias = const_68_to_fp16, dilations = input_97_dilations_0, groups = input_97_groups_0, pad = input_97_pad_0, pad_type = input_97_pad_type_0, strides = input_97_strides_0, weight = const_67_to_fp16, x = input_7_cast_fp16)[name = tensor<string, []>("input_99_cast_fp16")];
            tensor<fp16, [1, 64, 200, 32]> var_665_cast_fp16 = relu(x = input_99_cast_fp16)[name = tensor<string, []>("op_665_cast_fp16")];
            tensor<fp16, [1, 64, 200, 32]> input_101_cast_fp16 = add(x = var_665_cast_fp16, y = e1d_cast_fp16)[name = tensor<string, []>("input_101_cast_fp16")];
            tensor<string, []> input_103_pad_type_0 = const()[name = tensor<string, []>("input_103_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_103_pad_0 = const()[name = tensor<string, []>("input_103_pad_0"), val = tensor<int32, [4]>([0, 0, 1, 1])];
            tensor<int32, [2]> input_103_strides_0 = const()[name = tensor<string, []>("input_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_103_dilations_0 = const()[name = tensor<string, []>("input_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_103_groups_0 = const()[name = tensor<string, []>("input_103_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 64, 1, 3]> const_69_to_fp16 = const()[name = tensor<string, []>("const_69_to_fp16"), val = tensor<fp16, [1, 64, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1038144)))];
            tensor<fp16, [1]> const_70_to_fp16 = const()[name = tensor<string, []>("const_70_to_fp16"), val = tensor<fp16, [1]>([-0x1.e04p-1])];
            tensor<fp16, [1, 1, 200, 32]> input_105_cast_fp16 = conv(bias = const_70_to_fp16, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = const_69_to_fp16, x = input_101_cast_fp16)[name = tensor<string, []>("input_105_cast_fp16")];
            tensor<fp16, [1, 1, 200, 32]> mask_1_cast_fp16 = sigmoid(x = input_105_cast_fp16)[name = tensor<string, []>("mask_1_cast_fp16")];
            tensor<bool, []> mask_3_transpose_x_0 = const()[name = tensor<string, []>("mask_3_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> mask_3_transpose_y_0 = const()[name = tensor<string, []>("mask_3_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [32, 481]> mask_module_erb_inv_fb_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [11544]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1038592))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1050240))), name = tensor<string, []>("mask_module_erb_inv_fb_to_fp16_palettized"), shape = tensor<uint32, [2]>([32, 481])];
            tensor<fp16, [1, 1, 200, 481]> mask_3_cast_fp16 = matmul(transpose_x = mask_3_transpose_x_0, transpose_y = mask_3_transpose_y_0, x = mask_1_cast_fp16, y = mask_module_erb_inv_fb_to_fp16_palettized)[name = tensor<string, []>("mask_3_cast_fp16")];
            tensor<int32, [1]> mask_axes_0 = const()[name = tensor<string, []>("mask_axes_0"), val = tensor<int32, [1]>([4])];
            tensor<fp16, [1, 1, 200, 481, 1]> mask_cast_fp16 = expand_dims(axes = mask_axes_0, x = mask_3_cast_fp16)[name = tensor<string, []>("mask_cast_fp16")];
            tensor<fp16, [1, 1, 200, 481, 2]> spec_m_cast_fp16 = mul(x = spec, y = mask_cast_fp16)[name = tensor<string, []>("spec_m_cast_fp16")];
            tensor<int32, [4]> var_722 = const()[name = tensor<string, []>("op_722"), val = tensor<int32, [4]>([1, 200, 8, 64])];
            tensor<fp16, [1, 200, 8, 64]> var_723_cast_fp16 = reshape(shape = var_722, x = x_17_cast_fp16)[name = tensor<string, []>("op_723_cast_fp16")];
            tensor<int32, [4]> transpose_12_perm_0 = const()[name = tensor<string, []>("transpose_12_perm_0"), val = tensor<int32, [4]>([2, 0, 1, 3])];
            tensor<int32, [3]> concat_67 = const()[name = tensor<string, []>("concat_67"), val = tensor<int32, [3]>([8, 200, 64])];
            tensor<fp16, [8, 1, 200, 64]> transpose_12_cast_fp16 = transpose(perm = transpose_12_perm_0, x = var_723_cast_fp16)[name = tensor<string, []>("transpose_8")];
            tensor<fp16, [8, 200, 64]> reshape_15_cast_fp16 = reshape(shape = concat_67, x = transpose_12_cast_fp16)[name = tensor<string, []>("reshape_15_cast_fp16")];
            tensor<bool, []> matmul_5_transpose_x_0 = const()[name = tensor<string, []>("matmul_5_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_5_transpose_y_0 = const()[name = tensor<string, []>("matmul_5_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [8, 64, 32]> df_dec_df_gru_linear_in_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [12288]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1050432))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1062784))), name = tensor<string, []>("df_dec_df_gru_linear_in_0_weight_to_fp16_palettized"), shape = tensor<uint32, [3]>([8, 64, 32])];
            tensor<fp16, [8, 200, 32]> matmul_5_cast_fp16 = matmul(transpose_x = matmul_5_transpose_x_0, transpose_y = matmul_5_transpose_y_0, x = reshape_15_cast_fp16, y = df_dec_df_gru_linear_in_0_weight_to_fp16_palettized)[name = tensor<string, []>("matmul_5_cast_fp16")];
            tensor<int32, [4]> concat_72 = const()[name = tensor<string, []>("concat_72"), val = tensor<int32, [4]>([8, 1, 200, 32])];
            tensor<fp16, [8, 1, 200, 32]> reshape_17_cast_fp16 = reshape(shape = concat_72, x = matmul_5_cast_fp16)[name = tensor<string, []>("reshape_17_cast_fp16")];
            tensor<int32, [4]> x_25_perm_0 = const()[name = tensor<string, []>("x_25_perm_0"), val = tensor<int32, [4]>([1, 2, 0, 3])];
            tensor<int32, [3]> concat_73 = const()[name = tensor<string, []>("concat_73"), val = tensor<int32, [3]>([1, 200, 256])];
            tensor<fp16, [1, 200, 8, 32]> x_25_cast_fp16 = transpose(perm = x_25_perm_0, x = reshape_17_cast_fp16)[name = tensor<string, []>("transpose_7")];
            tensor<fp16, [1, 200, 256]> input_107_cast_fp16 = reshape(shape = concat_73, x = x_25_cast_fp16)[name = tensor<string, []>("input_107_cast_fp16")];
            tensor<fp16, [1, 200, 256]> input_109_cast_fp16 = relu(x = input_107_cast_fp16)[name = tensor<string, []>("input_109_cast_fp16")];
            tensor<int32, [3]> transpose_14_perm_0 = const()[name = tensor<string, []>("transpose_14_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [1]> slice_by_index_68 = const()[name = tensor<string, []>("slice_by_index_68"), val = tensor<int32, [1]>([200])];
            tensor<int32, [1]> while_loop_3_loop_vars0_0 = const()[name = tensor<string, []>("while_loop_3_loop_vars0_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [200, 1, 256]> transpose_14_cast_fp16 = transpose(perm = transpose_14_perm_0, x = input_109_cast_fp16)[name = tensor<string, []>("transpose_6")];
            tensor<int32, [1]> while_loop_3_0, tensor<fp32, [201, 1, 256]> while_loop_3_1 = while_loop(loop_vars = (while_loop_3_loop_vars0_0, concat_25_palettized))[name = tensor<string, []>("while_loop_3")]
                (tensor<int32, [1]> while_loop_3_loop_vars0_0_x0_1_1_1, tensor<fp32, [201, 1, 256]> concat_75_x0_1_1_1) {
                    tensor<bool, [1]> less_7 = less(x = while_loop_3_loop_vars0_0_x0_1_1_1, y = slice_by_index_68)[name = tensor<string, []>("less_7")];
                } -> (less_7)
                (tensor<int32, [1]> while_loop_3_loop_vars0_0_x0_1_1_1_1, tensor<fp32, [201, 1, 256]> concat_75_x0_1_1_1_1) {
                    tensor<int32, []> gather_14_axis_0 = const()[name = tensor<string, []>("gather_14_axis_0"), val = tensor<int32, []>(0)];
                    tensor<int32, []> gather_14_batch_dims_0 = const()[name = tensor<string, []>("gather_14_batch_dims_0"), val = tensor<int32, []>(0)];
                    tensor<bool, []> gather_14_validate_indices_0 = const()[name = tensor<string, []>("gather_14_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<string, []> while_loop_3_loop_vars0_0_x0_1_to_uint16_dtype_0 = const()[name = tensor<string, []>("while_loop_3_loop_vars0_0_x0_1_to_uint16_dtype_0"), val = tensor<string, []>("uint16")];
                    tensor<uint16, [1]> while_loop_3_loop_vars0_0_x0_1_to_uint16 = cast(dtype = while_loop_3_loop_vars0_0_x0_1_to_uint16_dtype_0, x = while_loop_3_loop_vars0_0_x0_1_1_1_1)[name = tensor<string, []>("cast_7")];
                    tensor<fp16, [1, 1, 256]> gather_14_cast_fp16_cast_uint16 = gather(axis = gather_14_axis_0, batch_dims = gather_14_batch_dims_0, indices = while_loop_3_loop_vars0_0_x0_1_to_uint16, validate_indices = gather_14_validate_indices_0, x = transpose_14_cast_fp16)[name = tensor<string, []>("gather_14_cast_fp16_cast_uint16")];
                    tensor<int32, []> gather_15_axis_0 = const()[name = tensor<string, []>("gather_15_axis_0"), val = tensor<int32, []>(0)];
                    tensor<int32, []> gather_15_batch_dims_0 = const()[name = tensor<string, []>("gather_15_batch_dims_0"), val = tensor<int32, []>(0)];
                    tensor<bool, []> gather_15_validate_indices_0 = const()[name = tensor<string, []>("gather_15_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<string, []> concat_75_x0_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("concat_75_x0_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
                    tensor<fp16, [201, 1, 256]> concat_75_x0_1_to_fp16 = cast(dtype = concat_75_x0_1_to_fp16_dtype_0, x = concat_75_x0_1_1_1_1)[name = tensor<string, []>("cast_6")];
                    tensor<fp16, [1, 1, 256]> gather_15_cast_fp16_cast_uint16 = gather(axis = gather_15_axis_0, batch_dims = gather_15_batch_dims_0, indices = while_loop_3_loop_vars0_0_x0_1_to_uint16, validate_indices = gather_15_validate_indices_0, x = concat_75_x0_1_to_fp16)[name = tensor<string, []>("gather_15_cast_fp16_cast_uint16")];
                    tensor<int32, [1]> squeeze_14_axes_0 = const()[name = tensor<string, []>("squeeze_14_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 256]> squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = gather_14_cast_fp16_cast_uint16)[name = tensor<string, []>("squeeze_14_cast_fp16")];
                    tensor<int32, [1]> squeeze_15_axes_0 = const()[name = tensor<string, []>("squeeze_15_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 256]> squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = gather_15_cast_fp16_cast_uint16)[name = tensor<string, []>("squeeze_15_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_42_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1062976))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1112192))), name = tensor<string, []>("linear_42_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_42_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_42_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1112384)))];
                    tensor<fp16, [1, 256]> linear_42_cast_fp16 = linear(bias = linear_42_bias_0_to_fp16, weight = linear_42_weight_0_to_fp16_palettized, x = squeeze_14_cast_fp16)[name = tensor<string, []>("linear_42_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_43_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1112960))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1162176))), name = tensor<string, []>("linear_43_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_43_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_43_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1162368)))];
                    tensor<fp16, [1, 256]> linear_43_cast_fp16 = linear(bias = linear_43_bias_0_to_fp16, weight = linear_43_weight_0_to_fp16_palettized, x = squeeze_15_cast_fp16)[name = tensor<string, []>("linear_43_cast_fp16")];
                    tensor<fp16, [1, 256]> add_35_cast_fp16 = add(x = linear_42_cast_fp16, y = linear_43_cast_fp16)[name = tensor<string, []>("add_35_cast_fp16")];
                    tensor<fp16, [1, 256]> sigmoid_14_cast_fp16 = sigmoid(x = add_35_cast_fp16)[name = tensor<string, []>("sigmoid_14_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_44_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1162944))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1212160))), name = tensor<string, []>("linear_44_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_44_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_44_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1212352)))];
                    tensor<fp16, [1, 256]> linear_44_cast_fp16 = linear(bias = linear_44_bias_0_to_fp16, weight = linear_44_weight_0_to_fp16_palettized, x = squeeze_14_cast_fp16)[name = tensor<string, []>("linear_44_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_45_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1212928))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1262144))), name = tensor<string, []>("linear_45_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_45_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_45_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1262336)))];
                    tensor<fp16, [1, 256]> linear_45_cast_fp16 = linear(bias = linear_45_bias_0_to_fp16, weight = linear_45_weight_0_to_fp16_palettized, x = squeeze_15_cast_fp16)[name = tensor<string, []>("linear_45_cast_fp16")];
                    tensor<fp16, [1, 256]> add_36_cast_fp16 = add(x = linear_44_cast_fp16, y = linear_45_cast_fp16)[name = tensor<string, []>("add_36_cast_fp16")];
                    tensor<fp16, [1, 256]> sigmoid_15_cast_fp16 = sigmoid(x = add_36_cast_fp16)[name = tensor<string, []>("sigmoid_15_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_46_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1262912))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1312128))), name = tensor<string, []>("linear_46_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_46_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_46_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1312320)))];
                    tensor<fp16, [1, 256]> linear_46_cast_fp16 = linear(bias = linear_46_bias_0_to_fp16, weight = linear_46_weight_0_to_fp16_palettized, x = squeeze_14_cast_fp16)[name = tensor<string, []>("linear_46_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_47_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1312896))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1362112))), name = tensor<string, []>("linear_47_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_47_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_47_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1362304)))];
                    tensor<fp16, [1, 256]> linear_47_cast_fp16 = linear(bias = linear_47_bias_0_to_fp16, weight = linear_47_weight_0_to_fp16_palettized, x = squeeze_15_cast_fp16)[name = tensor<string, []>("linear_47_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_21_cast_fp16 = mul(x = sigmoid_14_cast_fp16, y = linear_47_cast_fp16)[name = tensor<string, []>("mul_21_cast_fp16")];
                    tensor<fp16, [1, 256]> add_37_cast_fp16 = add(x = linear_46_cast_fp16, y = mul_21_cast_fp16)[name = tensor<string, []>("add_37_cast_fp16")];
                    tensor<fp16, [1, 256]> tanh_7_cast_fp16 = tanh(x = add_37_cast_fp16)[name = tensor<string, []>("tanh_7_cast_fp16")];
                    tensor<fp16, []> sub_7_x_0_to_fp16 = const()[name = tensor<string, []>("sub_7_x_0_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
                    tensor<fp16, [1, 256]> sub_7_cast_fp16 = sub(x = sub_7_x_0_to_fp16, y = sigmoid_15_cast_fp16)[name = tensor<string, []>("sub_7_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_22_cast_fp16 = mul(x = sub_7_cast_fp16, y = tanh_7_cast_fp16)[name = tensor<string, []>("mul_22_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_23_cast_fp16 = mul(x = sigmoid_15_cast_fp16, y = squeeze_15_cast_fp16)[name = tensor<string, []>("mul_23_cast_fp16")];
                    tensor<fp16, [1, 256]> add_38_cast_fp16 = add(x = mul_22_cast_fp16, y = mul_23_cast_fp16)[name = tensor<string, []>("add_38_cast_fp16")];
                    tensor<int32, []> add_39_y_0 = const()[name = tensor<string, []>("add_39_y_0"), val = tensor<int32, []>(1)];
                    tensor<int32, [1]> add_39 = add(x = while_loop_3_loop_vars0_0_x0_1_1_1_1, y = add_39_y_0)[name = tensor<string, []>("add_39")];
                    tensor<int32, [1]> expand_dims_7_axes_0 = const()[name = tensor<string, []>("expand_dims_7_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 1, 256]> expand_dims_7_cast_fp16 = expand_dims(axes = expand_dims_7_axes_0, x = add_38_cast_fp16)[name = tensor<string, []>("expand_dims_7_cast_fp16")];
                    tensor<int32, []> scatter_7_axis_0 = const()[name = tensor<string, []>("scatter_7_axis_0"), val = tensor<int32, []>(0)];
                    tensor<string, []> scatter_7_mode_0 = const()[name = tensor<string, []>("scatter_7_mode_0"), val = tensor<string, []>("add")];
                    tensor<bool, []> scatter_7_validate_indices_0 = const()[name = tensor<string, []>("scatter_7_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<fp16, [201, 1, 256]> scatter_7_cast_fp16 = scatter(axis = scatter_7_axis_0, data = concat_75_x0_1_to_fp16, indices = add_39, mode = scatter_7_mode_0, updates = expand_dims_7_cast_fp16, validate_indices = scatter_7_validate_indices_0)[name = tensor<string, []>("scatter_7_cast_fp16")];
                    tensor<string, []> scatter_7_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("scatter_7_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
                    tensor<fp32, [201, 1, 256]> scatter_7_cast_fp16_to_fp32 = cast(dtype = scatter_7_cast_fp16_to_fp32_dtype_0, x = scatter_7_cast_fp16)[name = tensor<string, []>("cast_5")];
                } -> (add_39, scatter_7_cast_fp16_to_fp32);
            tensor<int32, [3]> c_1_layer_0_tmp_begin_0 = const()[name = tensor<string, []>("c_1_layer_0_tmp_begin_0"), val = tensor<int32, [3]>([1, 0, 0])];
            tensor<int32, [3]> c_1_layer_0_tmp_end_0 = const()[name = tensor<string, []>("c_1_layer_0_tmp_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<bool, [3]> c_1_layer_0_tmp_begin_mask_0 = const()[name = tensor<string, []>("c_1_layer_0_tmp_begin_mask_0"), val = tensor<bool, [3]>([false, true, true])];
            tensor<bool, [3]> c_1_layer_0_tmp_end_mask_0 = const()[name = tensor<string, []>("c_1_layer_0_tmp_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<string, []> while_loop_3_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("while_loop_3_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [201, 1, 256]> while_loop_3_1_to_fp16 = cast(dtype = while_loop_3_1_to_fp16_dtype_0, x = while_loop_3_1)[name = tensor<string, []>("cast_4")];
            tensor<fp16, [200, 1, 256]> c_1_layer_0_tmp_cast_fp16 = slice_by_index(begin = c_1_layer_0_tmp_begin_0, begin_mask = c_1_layer_0_tmp_begin_mask_0, end = c_1_layer_0_tmp_end_0, end_mask = c_1_layer_0_tmp_end_mask_0, x = while_loop_3_1_to_fp16)[name = tensor<string, []>("c_1_layer_0_tmp_cast_fp16")];
            tensor<int32, [1]> slice_by_index_71 = const()[name = tensor<string, []>("slice_by_index_71"), val = tensor<int32, [1]>([200])];
            tensor<int32, [1]> while_loop_4_loop_vars0_0 = const()[name = tensor<string, []>("while_loop_4_loop_vars0_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> while_loop_4_0, tensor<fp32, [201, 1, 256]> while_loop_4_1 = while_loop(loop_vars = (while_loop_4_loop_vars0_0, concat_25_palettized))[name = tensor<string, []>("while_loop_4")]
                (tensor<int32, [1]> while_loop_4_loop_vars0_0_x0_1_1_1, tensor<fp32, [201, 1, 256]> concat_77_x0_1_1_1) {
                    tensor<bool, [1]> less_9 = less(x = while_loop_4_loop_vars0_0_x0_1_1_1, y = slice_by_index_71)[name = tensor<string, []>("less_9")];
                } -> (less_9)
                (tensor<int32, [1]> while_loop_4_loop_vars0_0_x0_1_1_1_1, tensor<fp32, [201, 1, 256]> concat_77_x0_1_1_1_1) {
                    tensor<int32, []> gather_18_axis_0 = const()[name = tensor<string, []>("gather_18_axis_0"), val = tensor<int32, []>(0)];
                    tensor<int32, []> gather_18_batch_dims_0 = const()[name = tensor<string, []>("gather_18_batch_dims_0"), val = tensor<int32, []>(0)];
                    tensor<bool, []> gather_18_validate_indices_0 = const()[name = tensor<string, []>("gather_18_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<string, []> while_loop_4_loop_vars0_0_x0_1_to_uint16_dtype_0 = const()[name = tensor<string, []>("while_loop_4_loop_vars0_0_x0_1_to_uint16_dtype_0"), val = tensor<string, []>("uint16")];
                    tensor<uint16, [1]> while_loop_4_loop_vars0_0_x0_1_to_uint16 = cast(dtype = while_loop_4_loop_vars0_0_x0_1_to_uint16_dtype_0, x = while_loop_4_loop_vars0_0_x0_1_1_1_1)[name = tensor<string, []>("cast_3")];
                    tensor<fp16, [1, 1, 256]> gather_18_cast_fp16_cast_uint16 = gather(axis = gather_18_axis_0, batch_dims = gather_18_batch_dims_0, indices = while_loop_4_loop_vars0_0_x0_1_to_uint16, validate_indices = gather_18_validate_indices_0, x = c_1_layer_0_tmp_cast_fp16)[name = tensor<string, []>("gather_18_cast_fp16_cast_uint16")];
                    tensor<int32, []> gather_19_axis_0 = const()[name = tensor<string, []>("gather_19_axis_0"), val = tensor<int32, []>(0)];
                    tensor<int32, []> gather_19_batch_dims_0 = const()[name = tensor<string, []>("gather_19_batch_dims_0"), val = tensor<int32, []>(0)];
                    tensor<bool, []> gather_19_validate_indices_0 = const()[name = tensor<string, []>("gather_19_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<string, []> concat_77_x0_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("concat_77_x0_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
                    tensor<fp16, [201, 1, 256]> concat_77_x0_1_to_fp16 = cast(dtype = concat_77_x0_1_to_fp16_dtype_0, x = concat_77_x0_1_1_1_1)[name = tensor<string, []>("cast_2")];
                    tensor<fp16, [1, 1, 256]> gather_19_cast_fp16_cast_uint16 = gather(axis = gather_19_axis_0, batch_dims = gather_19_batch_dims_0, indices = while_loop_4_loop_vars0_0_x0_1_to_uint16, validate_indices = gather_19_validate_indices_0, x = concat_77_x0_1_to_fp16)[name = tensor<string, []>("gather_19_cast_fp16_cast_uint16")];
                    tensor<int32, [1]> squeeze_18_axes_0 = const()[name = tensor<string, []>("squeeze_18_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 256]> squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = gather_18_cast_fp16_cast_uint16)[name = tensor<string, []>("squeeze_18_cast_fp16")];
                    tensor<int32, [1]> squeeze_19_axes_0 = const()[name = tensor<string, []>("squeeze_19_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 256]> squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = gather_19_cast_fp16_cast_uint16)[name = tensor<string, []>("squeeze_19_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_54_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1362880))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1412096))), name = tensor<string, []>("linear_54_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_54_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_54_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1412288)))];
                    tensor<fp16, [1, 256]> linear_54_cast_fp16 = linear(bias = linear_54_bias_0_to_fp16, weight = linear_54_weight_0_to_fp16_palettized, x = squeeze_18_cast_fp16)[name = tensor<string, []>("linear_54_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_55_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1412864))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1462080))), name = tensor<string, []>("linear_55_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_55_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_55_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1462272)))];
                    tensor<fp16, [1, 256]> linear_55_cast_fp16 = linear(bias = linear_55_bias_0_to_fp16, weight = linear_55_weight_0_to_fp16_palettized, x = squeeze_19_cast_fp16)[name = tensor<string, []>("linear_55_cast_fp16")];
                    tensor<fp16, [1, 256]> add_45_cast_fp16 = add(x = linear_54_cast_fp16, y = linear_55_cast_fp16)[name = tensor<string, []>("add_45_cast_fp16")];
                    tensor<fp16, [1, 256]> sigmoid_18_cast_fp16 = sigmoid(x = add_45_cast_fp16)[name = tensor<string, []>("sigmoid_18_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_56_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1462848))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1512064))), name = tensor<string, []>("linear_56_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_56_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_56_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1512256)))];
                    tensor<fp16, [1, 256]> linear_56_cast_fp16 = linear(bias = linear_56_bias_0_to_fp16, weight = linear_56_weight_0_to_fp16_palettized, x = squeeze_18_cast_fp16)[name = tensor<string, []>("linear_56_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_57_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1512832))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1562048))), name = tensor<string, []>("linear_57_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_57_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_57_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1562240)))];
                    tensor<fp16, [1, 256]> linear_57_cast_fp16 = linear(bias = linear_57_bias_0_to_fp16, weight = linear_57_weight_0_to_fp16_palettized, x = squeeze_19_cast_fp16)[name = tensor<string, []>("linear_57_cast_fp16")];
                    tensor<fp16, [1, 256]> add_46_cast_fp16 = add(x = linear_56_cast_fp16, y = linear_57_cast_fp16)[name = tensor<string, []>("add_46_cast_fp16")];
                    tensor<fp16, [1, 256]> sigmoid_19_cast_fp16 = sigmoid(x = add_46_cast_fp16)[name = tensor<string, []>("sigmoid_19_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_58_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1562816))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1612032))), name = tensor<string, []>("linear_58_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_58_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_58_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1612224)))];
                    tensor<fp16, [1, 256]> linear_58_cast_fp16 = linear(bias = linear_58_bias_0_to_fp16, weight = linear_58_weight_0_to_fp16_palettized, x = squeeze_18_cast_fp16)[name = tensor<string, []>("linear_58_cast_fp16")];
                    tensor<fp16, [256, 256]> linear_59_weight_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1612800))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1662016))), name = tensor<string, []>("linear_59_weight_0_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 256])];
                    tensor<fp16, [256]> linear_59_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_59_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1662208)))];
                    tensor<fp16, [1, 256]> linear_59_cast_fp16 = linear(bias = linear_59_bias_0_to_fp16, weight = linear_59_weight_0_to_fp16_palettized, x = squeeze_19_cast_fp16)[name = tensor<string, []>("linear_59_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_27_cast_fp16 = mul(x = sigmoid_18_cast_fp16, y = linear_59_cast_fp16)[name = tensor<string, []>("mul_27_cast_fp16")];
                    tensor<fp16, [1, 256]> add_47_cast_fp16 = add(x = linear_58_cast_fp16, y = mul_27_cast_fp16)[name = tensor<string, []>("add_47_cast_fp16")];
                    tensor<fp16, [1, 256]> tanh_9_cast_fp16 = tanh(x = add_47_cast_fp16)[name = tensor<string, []>("tanh_9_cast_fp16")];
                    tensor<fp16, []> sub_9_x_0_to_fp16 = const()[name = tensor<string, []>("sub_9_x_0_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
                    tensor<fp16, [1, 256]> sub_9_cast_fp16 = sub(x = sub_9_x_0_to_fp16, y = sigmoid_19_cast_fp16)[name = tensor<string, []>("sub_9_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_28_cast_fp16 = mul(x = sub_9_cast_fp16, y = tanh_9_cast_fp16)[name = tensor<string, []>("mul_28_cast_fp16")];
                    tensor<fp16, [1, 256]> mul_29_cast_fp16 = mul(x = sigmoid_19_cast_fp16, y = squeeze_19_cast_fp16)[name = tensor<string, []>("mul_29_cast_fp16")];
                    tensor<fp16, [1, 256]> add_48_cast_fp16 = add(x = mul_28_cast_fp16, y = mul_29_cast_fp16)[name = tensor<string, []>("add_48_cast_fp16")];
                    tensor<int32, []> add_49_y_0 = const()[name = tensor<string, []>("add_49_y_0"), val = tensor<int32, []>(1)];
                    tensor<int32, [1]> add_49 = add(x = while_loop_4_loop_vars0_0_x0_1_1_1_1, y = add_49_y_0)[name = tensor<string, []>("add_49")];
                    tensor<int32, [1]> expand_dims_9_axes_0 = const()[name = tensor<string, []>("expand_dims_9_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp16, [1, 1, 256]> expand_dims_9_cast_fp16 = expand_dims(axes = expand_dims_9_axes_0, x = add_48_cast_fp16)[name = tensor<string, []>("expand_dims_9_cast_fp16")];
                    tensor<int32, []> scatter_9_axis_0 = const()[name = tensor<string, []>("scatter_9_axis_0"), val = tensor<int32, []>(0)];
                    tensor<string, []> scatter_9_mode_0 = const()[name = tensor<string, []>("scatter_9_mode_0"), val = tensor<string, []>("add")];
                    tensor<bool, []> scatter_9_validate_indices_0 = const()[name = tensor<string, []>("scatter_9_validate_indices_0"), val = tensor<bool, []>(false)];
                    tensor<fp16, [201, 1, 256]> scatter_9_cast_fp16 = scatter(axis = scatter_9_axis_0, data = concat_77_x0_1_to_fp16, indices = add_49, mode = scatter_9_mode_0, updates = expand_dims_9_cast_fp16, validate_indices = scatter_9_validate_indices_0)[name = tensor<string, []>("scatter_9_cast_fp16")];
                    tensor<string, []> scatter_9_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("scatter_9_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
                    tensor<fp32, [201, 1, 256]> scatter_9_cast_fp16_to_fp32 = cast(dtype = scatter_9_cast_fp16_to_fp32_dtype_0, x = scatter_9_cast_fp16)[name = tensor<string, []>("cast_1")];
                } -> (add_49, scatter_9_cast_fp16_to_fp32);
            tensor<int32, [3]> c_1_tmp_begin_0 = const()[name = tensor<string, []>("c_1_tmp_begin_0"), val = tensor<int32, [3]>([1, 0, 0])];
            tensor<int32, [3]> c_1_tmp_end_0 = const()[name = tensor<string, []>("c_1_tmp_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<bool, [3]> c_1_tmp_begin_mask_0 = const()[name = tensor<string, []>("c_1_tmp_begin_mask_0"), val = tensor<bool, [3]>([false, true, true])];
            tensor<bool, [3]> c_1_tmp_end_mask_0 = const()[name = tensor<string, []>("c_1_tmp_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<string, []> while_loop_4_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("while_loop_4_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [201, 1, 256]> while_loop_4_1_to_fp16 = cast(dtype = while_loop_4_1_to_fp16_dtype_0, x = while_loop_4_1)[name = tensor<string, []>("cast_0")];
            tensor<fp16, [200, 1, 256]> c_1_tmp_cast_fp16 = slice_by_index(begin = c_1_tmp_begin_0, begin_mask = c_1_tmp_begin_mask_0, end = c_1_tmp_end_0, end_mask = c_1_tmp_end_mask_0, x = while_loop_4_1_to_fp16)[name = tensor<string, []>("c_1_tmp_cast_fp16")];
            tensor<int32, [3]> c_1_perm_0 = const()[name = tensor<string, []>("c_1_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<bool, []> matmul_6_transpose_x_0 = const()[name = tensor<string, []>("matmul_6_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_6_transpose_y_0 = const()[name = tensor<string, []>("matmul_6_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 32, 16]> df_dec_df_skip_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [6144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1662784))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1668992))), name = tensor<string, []>("df_dec_df_skip_weight_to_fp16_palettized"), shape = tensor<uint32, [3]>([16, 32, 16])];
            tensor<fp16, [16, 200, 16]> matmul_6_cast_fp16 = matmul(transpose_x = matmul_6_transpose_x_0, transpose_y = matmul_6_transpose_y_0, x = reshape_9_cast_fp16, y = df_dec_df_skip_weight_to_fp16_palettized)[name = tensor<string, []>("matmul_6_cast_fp16")];
            tensor<int32, [4]> concat_87 = const()[name = tensor<string, []>("concat_87"), val = tensor<int32, [4]>([16, 1, 200, 16])];
            tensor<fp16, [16, 1, 200, 16]> reshape_20_cast_fp16 = reshape(shape = concat_87, x = matmul_6_cast_fp16)[name = tensor<string, []>("reshape_20_cast_fp16")];
            tensor<int32, [4]> x_27_perm_0 = const()[name = tensor<string, []>("x_27_perm_0"), val = tensor<int32, [4]>([1, 2, 0, 3])];
            tensor<int32, [3]> concat_88 = const()[name = tensor<string, []>("concat_88"), val = tensor<int32, [3]>([1, 200, 256])];
            tensor<fp16, [1, 200, 16, 16]> x_27_cast_fp16 = transpose(perm = x_27_perm_0, x = reshape_20_cast_fp16)[name = tensor<string, []>("transpose_5")];
            tensor<fp16, [1, 200, 256]> var_757_cast_fp16 = reshape(shape = concat_88, x = x_27_cast_fp16)[name = tensor<string, []>("op_757_cast_fp16")];
            tensor<fp16, [1, 200, 256]> c_1_cast_fp16 = transpose(perm = c_1_perm_0, x = c_1_tmp_cast_fp16)[name = tensor<string, []>("transpose_4")];
            tensor<fp16, [1, 200, 256]> x_29_cast_fp16 = add(x = c_1_cast_fp16, y = var_757_cast_fp16)[name = tensor<string, []>("x_29_cast_fp16")];
            tensor<int32, []> var_771 = const()[name = tensor<string, []>("op_771"), val = tensor<int32, []>(2)];
            tensor<bool, []> input_111_interleave_0 = const()[name = tensor<string, []>("input_111_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 64, 4, 96]> op_779_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [18432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1669184))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1687680))), name = tensor<string, []>("op_779_to_fp16_palettized"), shape = tensor<uint32, [4]>([1, 64, 4, 96])];
            tensor<fp16, [1, 64, 204, 96]> input_111_cast_fp16 = concat(axis = var_771, interleave = input_111_interleave_0, values = (op_779_to_fp16_palettized, input_39_cast_fp16))[name = tensor<string, []>("input_111_cast_fp16")];
            tensor<string, []> input_113_pad_type_0 = const()[name = tensor<string, []>("input_113_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_113_groups_0 = const()[name = tensor<string, []>("input_113_groups_0"), val = tensor<int32, []>(2)];
            tensor<int32, [2]> input_113_strides_0 = const()[name = tensor<string, []>("input_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_113_pad_0 = const()[name = tensor<string, []>("input_113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_113_dilations_0 = const()[name = tensor<string, []>("input_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [10, 32, 5, 1]> df_dec_df_convp_1_weight_to_fp16 = const()[name = tensor<string, []>("df_dec_df_convp_1_weight_to_fp16"), val = tensor<fp16, [10, 32, 5, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1687872)))];
            tensor<fp16, [1, 10, 200, 96]> input_113_cast_fp16 = conv(dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = df_dec_df_convp_1_weight_to_fp16, x = input_111_cast_fp16)[name = tensor<string, []>("input_113_cast_fp16")];
            tensor<string, []> input_115_pad_type_0 = const()[name = tensor<string, []>("input_115_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> input_115_strides_0 = const()[name = tensor<string, []>("input_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_115_pad_0 = const()[name = tensor<string, []>("input_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_115_dilations_0 = const()[name = tensor<string, []>("input_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_115_groups_0 = const()[name = tensor<string, []>("input_115_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [10, 10, 1, 1]> const_71_to_fp16 = const()[name = tensor<string, []>("const_71_to_fp16"), val = tensor<fp16, [10, 10, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1691136)))];
            tensor<fp16, [10]> const_72_to_fp16 = const()[name = tensor<string, []>("const_72_to_fp16"), val = tensor<fp16, [10]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1691456)))];
            tensor<fp16, [1, 10, 200, 96]> input_117_cast_fp16 = conv(bias = const_72_to_fp16, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = const_71_to_fp16, x = input_113_cast_fp16)[name = tensor<string, []>("input_117_cast_fp16")];
            tensor<fp16, [1, 10, 200, 96]> var_799_cast_fp16 = relu(x = input_117_cast_fp16)[name = tensor<string, []>("op_799_cast_fp16")];
            tensor<int32, [4]> var_804 = const()[name = tensor<string, []>("op_804"), val = tensor<int32, [4]>([0, 2, 3, 1])];
            tensor<int32, [4]> var_817 = const()[name = tensor<string, []>("op_817"), val = tensor<int32, [4]>([1, 200, 16, 16])];
            tensor<fp16, [1, 200, 16, 16]> var_818_cast_fp16 = reshape(shape = var_817, x = x_29_cast_fp16)[name = tensor<string, []>("op_818_cast_fp16")];
            tensor<int32, [4]> transpose_17_perm_0 = const()[name = tensor<string, []>("transpose_17_perm_0"), val = tensor<int32, [4]>([2, 0, 1, 3])];
            tensor<int32, [3]> concat_93 = const()[name = tensor<string, []>("concat_93"), val = tensor<int32, [3]>([16, 200, 16])];
            tensor<fp16, [16, 1, 200, 16]> transpose_17_cast_fp16 = transpose(perm = transpose_17_perm_0, x = var_818_cast_fp16)[name = tensor<string, []>("transpose_3")];
            tensor<fp16, [16, 200, 16]> reshape_21_cast_fp16 = reshape(shape = concat_93, x = transpose_17_cast_fp16)[name = tensor<string, []>("reshape_21_cast_fp16")];
            tensor<bool, []> matmul_7_transpose_x_0 = const()[name = tensor<string, []>("matmul_7_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_7_transpose_y_0 = const()[name = tensor<string, []>("matmul_7_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 16, 60]> df_dec_df_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [11520]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1691584))), lut = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1703168))), name = tensor<string, []>("df_dec_df_out_0_weight_to_fp16_palettized"), shape = tensor<uint32, [3]>([16, 16, 60])];
            tensor<fp16, [16, 200, 60]> matmul_7_cast_fp16 = matmul(transpose_x = matmul_7_transpose_x_0, transpose_y = matmul_7_transpose_y_0, x = reshape_21_cast_fp16, y = df_dec_df_out_0_weight_to_fp16_palettized)[name = tensor<string, []>("matmul_7_cast_fp16")];
            tensor<int32, [4]> concat_98 = const()[name = tensor<string, []>("concat_98"), val = tensor<int32, [4]>([16, 1, 200, 60])];
            tensor<fp16, [16, 1, 200, 60]> reshape_23_cast_fp16 = reshape(shape = concat_98, x = matmul_7_cast_fp16)[name = tensor<string, []>("reshape_23_cast_fp16")];
            tensor<int32, [4]> x_perm_0 = const()[name = tensor<string, []>("x_perm_0"), val = tensor<int32, [4]>([1, 2, 0, 3])];
            tensor<int32, [3]> concat_99 = const()[name = tensor<string, []>("concat_99"), val = tensor<int32, [3]>([1, 200, 960])];
            tensor<fp16, [1, 200, 16, 60]> x_cast_fp16 = transpose(perm = x_perm_0, x = reshape_23_cast_fp16)[name = tensor<string, []>("transpose_2")];
            tensor<fp16, [1, 200, 960]> input_cast_fp16 = reshape(shape = concat_99, x = x_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
            tensor<fp16, [1, 200, 960]> c_cast_fp16 = tanh(x = input_cast_fp16)[name = tensor<string, []>("c_cast_fp16")];
            tensor<int32, [4]> var_825 = const()[name = tensor<string, []>("op_825"), val = tensor<int32, [4]>([1, 200, 96, 10])];
            tensor<fp16, [1, 200, 96, 10]> var_826_cast_fp16 = reshape(shape = var_825, x = c_cast_fp16)[name = tensor<string, []>("op_826_cast_fp16")];
            tensor<fp16, [1, 200, 96, 10]> c0_proj_cast_fp16 = transpose(perm = var_804, x = var_799_cast_fp16)[name = tensor<string, []>("transpose_1")];
            tensor<fp16, [1, 200, 96, 10]> coefs_1_cast_fp16 = add(x = var_826_cast_fp16, y = c0_proj_cast_fp16)[name = tensor<string, []>("coefs_1_cast_fp16")];
            tensor<int32, [5]> var_838 = const()[name = tensor<string, []>("op_838"), val = tensor<int32, [5]>([1, 200, 96, -1, 2])];
            tensor<fp16, [1, 200, 96, 5, 2]> coefs_3_cast_fp16 = reshape(shape = var_838, x = coefs_1_cast_fp16)[name = tensor<string, []>("coefs_3_cast_fp16")];
            tensor<int32, [5]> var_840 = const()[name = tensor<string, []>("op_840"), val = tensor<int32, [5]>([0, 3, 1, 2, 4])];
            tensor<int32, [5]> var_858_begin_0 = const()[name = tensor<string, []>("op_858_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 0])];
            tensor<int32, [5]> var_858_end_0 = const()[name = tensor<string, []>("op_858_end_0"), val = tensor<int32, [5]>([1, 1, 200, 96, 2])];
            tensor<bool, [5]> var_858_end_mask_0 = const()[name = tensor<string, []>("op_858_end_mask_0"), val = tensor<bool, [5]>([true, true, true, false, true])];
            tensor<fp16, [1, 1, 200, 96, 2]> var_858_cast_fp16 = slice_by_index(begin = var_858_begin_0, end = var_858_end_0, end_mask = var_858_end_mask_0, x = spec)[name = tensor<string, []>("op_858_cast_fp16")];
            tensor<int32, [1]> spec_df_axes_0 = const()[name = tensor<string, []>("spec_df_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 200, 96, 2]> spec_df_cast_fp16 = squeeze(axes = spec_df_axes_0, x = var_858_cast_fp16)[name = tensor<string, []>("spec_df_cast_fp16")];
            tensor<int32, []> var_885 = const()[name = tensor<string, []>("op_885"), val = tensor<int32, []>(1)];
            tensor<bool, []> padded_interleave_0 = const()[name = tensor<string, []>("padded_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 2, 96, 2]> zeros_pre_to_fp16 = const()[name = tensor<string, []>("zeros_pre_to_fp16"), val = tensor<fp16, [1, 2, 96, 2]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1703360)))];
            tensor<fp16, [1, 204, 96, 2]> padded_cast_fp16 = concat(axis = var_885, interleave = padded_interleave_0, values = (zeros_pre_to_fp16, spec_df_cast_fp16, zeros_pre_to_fp16))[name = tensor<string, []>("padded_cast_fp16")];
            tensor<int32, [4]> p_1_begin_0 = const()[name = tensor<string, []>("p_1_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> p_1_end_0 = const()[name = tensor<string, []>("p_1_end_0"), val = tensor<int32, [4]>([1, 200, 96, 2])];
            tensor<bool, [4]> p_1_end_mask_0 = const()[name = tensor<string, []>("p_1_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<fp16, [1, 200, 96, 2]> p_1_cast_fp16 = slice_by_index(begin = p_1_begin_0, end = p_1_end_0, end_mask = p_1_end_mask_0, x = padded_cast_fp16)[name = tensor<string, []>("p_1_cast_fp16")];
            tensor<int32, [4]> pRe_1_begin_0 = const()[name = tensor<string, []>("pRe_1_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> pRe_1_end_0 = const()[name = tensor<string, []>("pRe_1_end_0"), val = tensor<int32, [4]>([1, 200, 96, 1])];
            tensor<bool, [4]> pRe_1_end_mask_0 = const()[name = tensor<string, []>("pRe_1_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> pRe_1_squeeze_mask_0 = const()[name = tensor<string, []>("pRe_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> pRe_1_cast_fp16 = slice_by_index(begin = pRe_1_begin_0, end = pRe_1_end_0, end_mask = pRe_1_end_mask_0, squeeze_mask = pRe_1_squeeze_mask_0, x = p_1_cast_fp16)[name = tensor<string, []>("pRe_1_cast_fp16")];
            tensor<int32, [4]> pIm_1_begin_0 = const()[name = tensor<string, []>("pIm_1_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
            tensor<int32, [4]> pIm_1_end_0 = const()[name = tensor<string, []>("pIm_1_end_0"), val = tensor<int32, [4]>([1, 200, 96, 2])];
            tensor<bool, [4]> pIm_1_end_mask_0 = const()[name = tensor<string, []>("pIm_1_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> pIm_1_squeeze_mask_0 = const()[name = tensor<string, []>("pIm_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> pIm_1_cast_fp16 = slice_by_index(begin = pIm_1_begin_0, end = pIm_1_end_0, end_mask = pIm_1_end_mask_0, squeeze_mask = pIm_1_squeeze_mask_0, x = p_1_cast_fp16)[name = tensor<string, []>("pIm_1_cast_fp16")];
            tensor<int32, [5]> var_927_begin_0 = const()[name = tensor<string, []>("op_927_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 0])];
            tensor<int32, [5]> var_927_end_0 = const()[name = tensor<string, []>("op_927_end_0"), val = tensor<int32, [5]>([1, 1, 200, 96, 2])];
            tensor<bool, [5]> var_927_end_mask_0 = const()[name = tensor<string, []>("op_927_end_mask_0"), val = tensor<bool, [5]>([true, false, true, true, true])];
            tensor<bool, [5]> var_927_squeeze_mask_0 = const()[name = tensor<string, []>("op_927_squeeze_mask_0"), val = tensor<bool, [5]>([false, true, false, false, false])];
            tensor<fp16, [1, 5, 200, 96, 2]> coefs_cast_fp16 = transpose(perm = var_840, x = coefs_3_cast_fp16)[name = tensor<string, []>("transpose_0")];
            tensor<fp16, [1, 200, 96, 2]> var_927_cast_fp16 = slice_by_index(begin = var_927_begin_0, end = var_927_end_0, end_mask = var_927_end_mask_0, squeeze_mask = var_927_squeeze_mask_0, x = coefs_cast_fp16)[name = tensor<string, []>("op_927_cast_fp16")];
            tensor<int32, [4]> cRe_1_begin_0 = const()[name = tensor<string, []>("cRe_1_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> cRe_1_end_0 = const()[name = tensor<string, []>("cRe_1_end_0"), val = tensor<int32, [4]>([1, 200, 96, 1])];
            tensor<bool, [4]> cRe_1_end_mask_0 = const()[name = tensor<string, []>("cRe_1_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> cRe_1_squeeze_mask_0 = const()[name = tensor<string, []>("cRe_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> cRe_1_cast_fp16 = slice_by_index(begin = cRe_1_begin_0, end = cRe_1_end_0, end_mask = cRe_1_end_mask_0, squeeze_mask = cRe_1_squeeze_mask_0, x = var_927_cast_fp16)[name = tensor<string, []>("cRe_1_cast_fp16")];
            tensor<int32, [4]> cIm_1_begin_0 = const()[name = tensor<string, []>("cIm_1_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
            tensor<int32, [4]> cIm_1_end_0 = const()[name = tensor<string, []>("cIm_1_end_0"), val = tensor<int32, [4]>([1, 200, 96, 2])];
            tensor<bool, [4]> cIm_1_end_mask_0 = const()[name = tensor<string, []>("cIm_1_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> cIm_1_squeeze_mask_0 = const()[name = tensor<string, []>("cIm_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> cIm_1_cast_fp16 = slice_by_index(begin = cIm_1_begin_0, end = cIm_1_end_0, end_mask = cIm_1_end_mask_0, squeeze_mask = cIm_1_squeeze_mask_0, x = var_927_cast_fp16)[name = tensor<string, []>("cIm_1_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_962_cast_fp16 = mul(x = pRe_1_cast_fp16, y = cRe_1_cast_fp16)[name = tensor<string, []>("op_962_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_965_cast_fp16 = mul(x = pIm_1_cast_fp16, y = cIm_1_cast_fp16)[name = tensor<string, []>("op_965_cast_fp16")];
            tensor<fp16, [1, 200, 96]> out_re_3_cast_fp16 = sub(x = var_962_cast_fp16, y = var_965_cast_fp16)[name = tensor<string, []>("out_re_3_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_968_cast_fp16 = mul(x = pRe_1_cast_fp16, y = cIm_1_cast_fp16)[name = tensor<string, []>("op_968_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_971_cast_fp16 = mul(x = pIm_1_cast_fp16, y = cRe_1_cast_fp16)[name = tensor<string, []>("op_971_cast_fp16")];
            tensor<fp16, [1, 200, 96]> out_im_3_cast_fp16 = add(x = var_968_cast_fp16, y = var_971_cast_fp16)[name = tensor<string, []>("out_im_3_cast_fp16")];
            tensor<int32, [4]> p_3_begin_0 = const()[name = tensor<string, []>("p_3_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> p_3_end_0 = const()[name = tensor<string, []>("p_3_end_0"), val = tensor<int32, [4]>([1, 201, 96, 2])];
            tensor<bool, [4]> p_3_end_mask_0 = const()[name = tensor<string, []>("p_3_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<fp16, [1, 200, 96, 2]> p_3_cast_fp16 = slice_by_index(begin = p_3_begin_0, end = p_3_end_0, end_mask = p_3_end_mask_0, x = padded_cast_fp16)[name = tensor<string, []>("p_3_cast_fp16")];
            tensor<int32, [4]> pRe_3_begin_0 = const()[name = tensor<string, []>("pRe_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> pRe_3_end_0 = const()[name = tensor<string, []>("pRe_3_end_0"), val = tensor<int32, [4]>([1, 200, 96, 1])];
            tensor<bool, [4]> pRe_3_end_mask_0 = const()[name = tensor<string, []>("pRe_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> pRe_3_squeeze_mask_0 = const()[name = tensor<string, []>("pRe_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> pRe_3_cast_fp16 = slice_by_index(begin = pRe_3_begin_0, end = pRe_3_end_0, end_mask = pRe_3_end_mask_0, squeeze_mask = pRe_3_squeeze_mask_0, x = p_3_cast_fp16)[name = tensor<string, []>("pRe_3_cast_fp16")];
            tensor<int32, [4]> pIm_3_begin_0 = const()[name = tensor<string, []>("pIm_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
            tensor<int32, [4]> pIm_3_end_0 = const()[name = tensor<string, []>("pIm_3_end_0"), val = tensor<int32, [4]>([1, 200, 96, 2])];
            tensor<bool, [4]> pIm_3_end_mask_0 = const()[name = tensor<string, []>("pIm_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> pIm_3_squeeze_mask_0 = const()[name = tensor<string, []>("pIm_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> pIm_3_cast_fp16 = slice_by_index(begin = pIm_3_begin_0, end = pIm_3_end_0, end_mask = pIm_3_end_mask_0, squeeze_mask = pIm_3_squeeze_mask_0, x = p_3_cast_fp16)[name = tensor<string, []>("pIm_3_cast_fp16")];
            tensor<int32, [5]> var_1000_begin_0 = const()[name = tensor<string, []>("op_1000_begin_0"), val = tensor<int32, [5]>([0, 1, 0, 0, 0])];
            tensor<int32, [5]> var_1000_end_0 = const()[name = tensor<string, []>("op_1000_end_0"), val = tensor<int32, [5]>([1, 2, 200, 96, 2])];
            tensor<bool, [5]> var_1000_end_mask_0 = const()[name = tensor<string, []>("op_1000_end_mask_0"), val = tensor<bool, [5]>([true, false, true, true, true])];
            tensor<bool, [5]> var_1000_squeeze_mask_0 = const()[name = tensor<string, []>("op_1000_squeeze_mask_0"), val = tensor<bool, [5]>([false, true, false, false, false])];
            tensor<fp16, [1, 200, 96, 2]> var_1000_cast_fp16 = slice_by_index(begin = var_1000_begin_0, end = var_1000_end_0, end_mask = var_1000_end_mask_0, squeeze_mask = var_1000_squeeze_mask_0, x = coefs_cast_fp16)[name = tensor<string, []>("op_1000_cast_fp16")];
            tensor<int32, [4]> cRe_3_begin_0 = const()[name = tensor<string, []>("cRe_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> cRe_3_end_0 = const()[name = tensor<string, []>("cRe_3_end_0"), val = tensor<int32, [4]>([1, 200, 96, 1])];
            tensor<bool, [4]> cRe_3_end_mask_0 = const()[name = tensor<string, []>("cRe_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> cRe_3_squeeze_mask_0 = const()[name = tensor<string, []>("cRe_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> cRe_3_cast_fp16 = slice_by_index(begin = cRe_3_begin_0, end = cRe_3_end_0, end_mask = cRe_3_end_mask_0, squeeze_mask = cRe_3_squeeze_mask_0, x = var_1000_cast_fp16)[name = tensor<string, []>("cRe_3_cast_fp16")];
            tensor<int32, [4]> cIm_3_begin_0 = const()[name = tensor<string, []>("cIm_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
            tensor<int32, [4]> cIm_3_end_0 = const()[name = tensor<string, []>("cIm_3_end_0"), val = tensor<int32, [4]>([1, 200, 96, 2])];
            tensor<bool, [4]> cIm_3_end_mask_0 = const()[name = tensor<string, []>("cIm_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> cIm_3_squeeze_mask_0 = const()[name = tensor<string, []>("cIm_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> cIm_3_cast_fp16 = slice_by_index(begin = cIm_3_begin_0, end = cIm_3_end_0, end_mask = cIm_3_end_mask_0, squeeze_mask = cIm_3_squeeze_mask_0, x = var_1000_cast_fp16)[name = tensor<string, []>("cIm_3_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1035_cast_fp16 = mul(x = pRe_3_cast_fp16, y = cRe_3_cast_fp16)[name = tensor<string, []>("op_1035_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1037_cast_fp16 = add(x = out_re_3_cast_fp16, y = var_1035_cast_fp16)[name = tensor<string, []>("op_1037_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1038_cast_fp16 = mul(x = pIm_3_cast_fp16, y = cIm_3_cast_fp16)[name = tensor<string, []>("op_1038_cast_fp16")];
            tensor<fp16, [1, 200, 96]> out_re_5_cast_fp16 = sub(x = var_1037_cast_fp16, y = var_1038_cast_fp16)[name = tensor<string, []>("out_re_5_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1041_cast_fp16 = mul(x = pRe_3_cast_fp16, y = cIm_3_cast_fp16)[name = tensor<string, []>("op_1041_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1043_cast_fp16 = add(x = out_im_3_cast_fp16, y = var_1041_cast_fp16)[name = tensor<string, []>("op_1043_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1044_cast_fp16 = mul(x = pIm_3_cast_fp16, y = cRe_3_cast_fp16)[name = tensor<string, []>("op_1044_cast_fp16")];
            tensor<fp16, [1, 200, 96]> out_im_5_cast_fp16 = add(x = var_1043_cast_fp16, y = var_1044_cast_fp16)[name = tensor<string, []>("out_im_5_cast_fp16")];
            tensor<int32, [4]> p_5_begin_0 = const()[name = tensor<string, []>("p_5_begin_0"), val = tensor<int32, [4]>([0, 2, 0, 0])];
            tensor<int32, [4]> p_5_end_0 = const()[name = tensor<string, []>("p_5_end_0"), val = tensor<int32, [4]>([1, 202, 96, 2])];
            tensor<bool, [4]> p_5_end_mask_0 = const()[name = tensor<string, []>("p_5_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<fp16, [1, 200, 96, 2]> p_5_cast_fp16 = slice_by_index(begin = p_5_begin_0, end = p_5_end_0, end_mask = p_5_end_mask_0, x = padded_cast_fp16)[name = tensor<string, []>("p_5_cast_fp16")];
            tensor<int32, [4]> pRe_5_begin_0 = const()[name = tensor<string, []>("pRe_5_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> pRe_5_end_0 = const()[name = tensor<string, []>("pRe_5_end_0"), val = tensor<int32, [4]>([1, 200, 96, 1])];
            tensor<bool, [4]> pRe_5_end_mask_0 = const()[name = tensor<string, []>("pRe_5_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> pRe_5_squeeze_mask_0 = const()[name = tensor<string, []>("pRe_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> pRe_5_cast_fp16 = slice_by_index(begin = pRe_5_begin_0, end = pRe_5_end_0, end_mask = pRe_5_end_mask_0, squeeze_mask = pRe_5_squeeze_mask_0, x = p_5_cast_fp16)[name = tensor<string, []>("pRe_5_cast_fp16")];
            tensor<int32, [4]> pIm_5_begin_0 = const()[name = tensor<string, []>("pIm_5_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
            tensor<int32, [4]> pIm_5_end_0 = const()[name = tensor<string, []>("pIm_5_end_0"), val = tensor<int32, [4]>([1, 200, 96, 2])];
            tensor<bool, [4]> pIm_5_end_mask_0 = const()[name = tensor<string, []>("pIm_5_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> pIm_5_squeeze_mask_0 = const()[name = tensor<string, []>("pIm_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> pIm_5_cast_fp16 = slice_by_index(begin = pIm_5_begin_0, end = pIm_5_end_0, end_mask = pIm_5_end_mask_0, squeeze_mask = pIm_5_squeeze_mask_0, x = p_5_cast_fp16)[name = tensor<string, []>("pIm_5_cast_fp16")];
            tensor<int32, [5]> var_1073_begin_0 = const()[name = tensor<string, []>("op_1073_begin_0"), val = tensor<int32, [5]>([0, 2, 0, 0, 0])];
            tensor<int32, [5]> var_1073_end_0 = const()[name = tensor<string, []>("op_1073_end_0"), val = tensor<int32, [5]>([1, 3, 200, 96, 2])];
            tensor<bool, [5]> var_1073_end_mask_0 = const()[name = tensor<string, []>("op_1073_end_mask_0"), val = tensor<bool, [5]>([true, false, true, true, true])];
            tensor<bool, [5]> var_1073_squeeze_mask_0 = const()[name = tensor<string, []>("op_1073_squeeze_mask_0"), val = tensor<bool, [5]>([false, true, false, false, false])];
            tensor<fp16, [1, 200, 96, 2]> var_1073_cast_fp16 = slice_by_index(begin = var_1073_begin_0, end = var_1073_end_0, end_mask = var_1073_end_mask_0, squeeze_mask = var_1073_squeeze_mask_0, x = coefs_cast_fp16)[name = tensor<string, []>("op_1073_cast_fp16")];
            tensor<int32, [4]> cRe_5_begin_0 = const()[name = tensor<string, []>("cRe_5_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> cRe_5_end_0 = const()[name = tensor<string, []>("cRe_5_end_0"), val = tensor<int32, [4]>([1, 200, 96, 1])];
            tensor<bool, [4]> cRe_5_end_mask_0 = const()[name = tensor<string, []>("cRe_5_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> cRe_5_squeeze_mask_0 = const()[name = tensor<string, []>("cRe_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> cRe_5_cast_fp16 = slice_by_index(begin = cRe_5_begin_0, end = cRe_5_end_0, end_mask = cRe_5_end_mask_0, squeeze_mask = cRe_5_squeeze_mask_0, x = var_1073_cast_fp16)[name = tensor<string, []>("cRe_5_cast_fp16")];
            tensor<int32, [4]> cIm_5_begin_0 = const()[name = tensor<string, []>("cIm_5_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
            tensor<int32, [4]> cIm_5_end_0 = const()[name = tensor<string, []>("cIm_5_end_0"), val = tensor<int32, [4]>([1, 200, 96, 2])];
            tensor<bool, [4]> cIm_5_end_mask_0 = const()[name = tensor<string, []>("cIm_5_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> cIm_5_squeeze_mask_0 = const()[name = tensor<string, []>("cIm_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> cIm_5_cast_fp16 = slice_by_index(begin = cIm_5_begin_0, end = cIm_5_end_0, end_mask = cIm_5_end_mask_0, squeeze_mask = cIm_5_squeeze_mask_0, x = var_1073_cast_fp16)[name = tensor<string, []>("cIm_5_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1108_cast_fp16 = mul(x = pRe_5_cast_fp16, y = cRe_5_cast_fp16)[name = tensor<string, []>("op_1108_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1110_cast_fp16 = add(x = out_re_5_cast_fp16, y = var_1108_cast_fp16)[name = tensor<string, []>("op_1110_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1111_cast_fp16 = mul(x = pIm_5_cast_fp16, y = cIm_5_cast_fp16)[name = tensor<string, []>("op_1111_cast_fp16")];
            tensor<fp16, [1, 200, 96]> out_re_7_cast_fp16 = sub(x = var_1110_cast_fp16, y = var_1111_cast_fp16)[name = tensor<string, []>("out_re_7_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1114_cast_fp16 = mul(x = pRe_5_cast_fp16, y = cIm_5_cast_fp16)[name = tensor<string, []>("op_1114_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1116_cast_fp16 = add(x = out_im_5_cast_fp16, y = var_1114_cast_fp16)[name = tensor<string, []>("op_1116_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1117_cast_fp16 = mul(x = pIm_5_cast_fp16, y = cRe_5_cast_fp16)[name = tensor<string, []>("op_1117_cast_fp16")];
            tensor<fp16, [1, 200, 96]> out_im_7_cast_fp16 = add(x = var_1116_cast_fp16, y = var_1117_cast_fp16)[name = tensor<string, []>("out_im_7_cast_fp16")];
            tensor<int32, [4]> p_7_begin_0 = const()[name = tensor<string, []>("p_7_begin_0"), val = tensor<int32, [4]>([0, 3, 0, 0])];
            tensor<int32, [4]> p_7_end_0 = const()[name = tensor<string, []>("p_7_end_0"), val = tensor<int32, [4]>([1, 203, 96, 2])];
            tensor<bool, [4]> p_7_end_mask_0 = const()[name = tensor<string, []>("p_7_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<fp16, [1, 200, 96, 2]> p_7_cast_fp16 = slice_by_index(begin = p_7_begin_0, end = p_7_end_0, end_mask = p_7_end_mask_0, x = padded_cast_fp16)[name = tensor<string, []>("p_7_cast_fp16")];
            tensor<int32, [4]> pRe_7_begin_0 = const()[name = tensor<string, []>("pRe_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> pRe_7_end_0 = const()[name = tensor<string, []>("pRe_7_end_0"), val = tensor<int32, [4]>([1, 200, 96, 1])];
            tensor<bool, [4]> pRe_7_end_mask_0 = const()[name = tensor<string, []>("pRe_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> pRe_7_squeeze_mask_0 = const()[name = tensor<string, []>("pRe_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> pRe_7_cast_fp16 = slice_by_index(begin = pRe_7_begin_0, end = pRe_7_end_0, end_mask = pRe_7_end_mask_0, squeeze_mask = pRe_7_squeeze_mask_0, x = p_7_cast_fp16)[name = tensor<string, []>("pRe_7_cast_fp16")];
            tensor<int32, [4]> pIm_7_begin_0 = const()[name = tensor<string, []>("pIm_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
            tensor<int32, [4]> pIm_7_end_0 = const()[name = tensor<string, []>("pIm_7_end_0"), val = tensor<int32, [4]>([1, 200, 96, 2])];
            tensor<bool, [4]> pIm_7_end_mask_0 = const()[name = tensor<string, []>("pIm_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> pIm_7_squeeze_mask_0 = const()[name = tensor<string, []>("pIm_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> pIm_7_cast_fp16 = slice_by_index(begin = pIm_7_begin_0, end = pIm_7_end_0, end_mask = pIm_7_end_mask_0, squeeze_mask = pIm_7_squeeze_mask_0, x = p_7_cast_fp16)[name = tensor<string, []>("pIm_7_cast_fp16")];
            tensor<int32, [5]> var_1146_begin_0 = const()[name = tensor<string, []>("op_1146_begin_0"), val = tensor<int32, [5]>([0, 3, 0, 0, 0])];
            tensor<int32, [5]> var_1146_end_0 = const()[name = tensor<string, []>("op_1146_end_0"), val = tensor<int32, [5]>([1, 4, 200, 96, 2])];
            tensor<bool, [5]> var_1146_end_mask_0 = const()[name = tensor<string, []>("op_1146_end_mask_0"), val = tensor<bool, [5]>([true, false, true, true, true])];
            tensor<bool, [5]> var_1146_squeeze_mask_0 = const()[name = tensor<string, []>("op_1146_squeeze_mask_0"), val = tensor<bool, [5]>([false, true, false, false, false])];
            tensor<fp16, [1, 200, 96, 2]> var_1146_cast_fp16 = slice_by_index(begin = var_1146_begin_0, end = var_1146_end_0, end_mask = var_1146_end_mask_0, squeeze_mask = var_1146_squeeze_mask_0, x = coefs_cast_fp16)[name = tensor<string, []>("op_1146_cast_fp16")];
            tensor<int32, [4]> cRe_7_begin_0 = const()[name = tensor<string, []>("cRe_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> cRe_7_end_0 = const()[name = tensor<string, []>("cRe_7_end_0"), val = tensor<int32, [4]>([1, 200, 96, 1])];
            tensor<bool, [4]> cRe_7_end_mask_0 = const()[name = tensor<string, []>("cRe_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> cRe_7_squeeze_mask_0 = const()[name = tensor<string, []>("cRe_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> cRe_7_cast_fp16 = slice_by_index(begin = cRe_7_begin_0, end = cRe_7_end_0, end_mask = cRe_7_end_mask_0, squeeze_mask = cRe_7_squeeze_mask_0, x = var_1146_cast_fp16)[name = tensor<string, []>("cRe_7_cast_fp16")];
            tensor<int32, [4]> cIm_7_begin_0 = const()[name = tensor<string, []>("cIm_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
            tensor<int32, [4]> cIm_7_end_0 = const()[name = tensor<string, []>("cIm_7_end_0"), val = tensor<int32, [4]>([1, 200, 96, 2])];
            tensor<bool, [4]> cIm_7_end_mask_0 = const()[name = tensor<string, []>("cIm_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> cIm_7_squeeze_mask_0 = const()[name = tensor<string, []>("cIm_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> cIm_7_cast_fp16 = slice_by_index(begin = cIm_7_begin_0, end = cIm_7_end_0, end_mask = cIm_7_end_mask_0, squeeze_mask = cIm_7_squeeze_mask_0, x = var_1146_cast_fp16)[name = tensor<string, []>("cIm_7_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1181_cast_fp16 = mul(x = pRe_7_cast_fp16, y = cRe_7_cast_fp16)[name = tensor<string, []>("op_1181_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1183_cast_fp16 = add(x = out_re_7_cast_fp16, y = var_1181_cast_fp16)[name = tensor<string, []>("op_1183_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1184_cast_fp16 = mul(x = pIm_7_cast_fp16, y = cIm_7_cast_fp16)[name = tensor<string, []>("op_1184_cast_fp16")];
            tensor<fp16, [1, 200, 96]> out_re_9_cast_fp16 = sub(x = var_1183_cast_fp16, y = var_1184_cast_fp16)[name = tensor<string, []>("out_re_9_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1187_cast_fp16 = mul(x = pRe_7_cast_fp16, y = cIm_7_cast_fp16)[name = tensor<string, []>("op_1187_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1189_cast_fp16 = add(x = out_im_7_cast_fp16, y = var_1187_cast_fp16)[name = tensor<string, []>("op_1189_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1190_cast_fp16 = mul(x = pIm_7_cast_fp16, y = cRe_7_cast_fp16)[name = tensor<string, []>("op_1190_cast_fp16")];
            tensor<fp16, [1, 200, 96]> out_im_9_cast_fp16 = add(x = var_1189_cast_fp16, y = var_1190_cast_fp16)[name = tensor<string, []>("out_im_9_cast_fp16")];
            tensor<int32, [4]> p_begin_0 = const()[name = tensor<string, []>("p_begin_0"), val = tensor<int32, [4]>([0, 4, 0, 0])];
            tensor<int32, [4]> p_end_0 = const()[name = tensor<string, []>("p_end_0"), val = tensor<int32, [4]>([1, 1, 96, 2])];
            tensor<bool, [4]> p_end_mask_0 = const()[name = tensor<string, []>("p_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 200, 96, 2]> p_cast_fp16 = slice_by_index(begin = p_begin_0, end = p_end_0, end_mask = p_end_mask_0, x = padded_cast_fp16)[name = tensor<string, []>("p_cast_fp16")];
            tensor<int32, [4]> pRe_begin_0 = const()[name = tensor<string, []>("pRe_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> pRe_end_0 = const()[name = tensor<string, []>("pRe_end_0"), val = tensor<int32, [4]>([1, 200, 96, 1])];
            tensor<bool, [4]> pRe_end_mask_0 = const()[name = tensor<string, []>("pRe_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> pRe_squeeze_mask_0 = const()[name = tensor<string, []>("pRe_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> pRe_cast_fp16 = slice_by_index(begin = pRe_begin_0, end = pRe_end_0, end_mask = pRe_end_mask_0, squeeze_mask = pRe_squeeze_mask_0, x = p_cast_fp16)[name = tensor<string, []>("pRe_cast_fp16")];
            tensor<int32, [4]> pIm_begin_0 = const()[name = tensor<string, []>("pIm_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
            tensor<int32, [4]> pIm_end_0 = const()[name = tensor<string, []>("pIm_end_0"), val = tensor<int32, [4]>([1, 200, 96, 2])];
            tensor<bool, [4]> pIm_end_mask_0 = const()[name = tensor<string, []>("pIm_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> pIm_squeeze_mask_0 = const()[name = tensor<string, []>("pIm_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> pIm_cast_fp16 = slice_by_index(begin = pIm_begin_0, end = pIm_end_0, end_mask = pIm_end_mask_0, squeeze_mask = pIm_squeeze_mask_0, x = p_cast_fp16)[name = tensor<string, []>("pIm_cast_fp16")];
            tensor<int32, [5]> var_1219_begin_0 = const()[name = tensor<string, []>("op_1219_begin_0"), val = tensor<int32, [5]>([0, 4, 0, 0, 0])];
            tensor<int32, [5]> var_1219_end_0 = const()[name = tensor<string, []>("op_1219_end_0"), val = tensor<int32, [5]>([1, 5, 200, 96, 2])];
            tensor<bool, [5]> var_1219_end_mask_0 = const()[name = tensor<string, []>("op_1219_end_mask_0"), val = tensor<bool, [5]>([true, false, true, true, true])];
            tensor<bool, [5]> var_1219_squeeze_mask_0 = const()[name = tensor<string, []>("op_1219_squeeze_mask_0"), val = tensor<bool, [5]>([false, true, false, false, false])];
            tensor<fp16, [1, 200, 96, 2]> var_1219_cast_fp16 = slice_by_index(begin = var_1219_begin_0, end = var_1219_end_0, end_mask = var_1219_end_mask_0, squeeze_mask = var_1219_squeeze_mask_0, x = coefs_cast_fp16)[name = tensor<string, []>("op_1219_cast_fp16")];
            tensor<int32, [4]> cRe_begin_0 = const()[name = tensor<string, []>("cRe_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> cRe_end_0 = const()[name = tensor<string, []>("cRe_end_0"), val = tensor<int32, [4]>([1, 200, 96, 1])];
            tensor<bool, [4]> cRe_end_mask_0 = const()[name = tensor<string, []>("cRe_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> cRe_squeeze_mask_0 = const()[name = tensor<string, []>("cRe_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> cRe_cast_fp16 = slice_by_index(begin = cRe_begin_0, end = cRe_end_0, end_mask = cRe_end_mask_0, squeeze_mask = cRe_squeeze_mask_0, x = var_1219_cast_fp16)[name = tensor<string, []>("cRe_cast_fp16")];
            tensor<int32, [4]> cIm_begin_0 = const()[name = tensor<string, []>("cIm_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
            tensor<int32, [4]> cIm_end_0 = const()[name = tensor<string, []>("cIm_end_0"), val = tensor<int32, [4]>([1, 200, 96, 2])];
            tensor<bool, [4]> cIm_end_mask_0 = const()[name = tensor<string, []>("cIm_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<bool, [4]> cIm_squeeze_mask_0 = const()[name = tensor<string, []>("cIm_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 200, 96]> cIm_cast_fp16 = slice_by_index(begin = cIm_begin_0, end = cIm_end_0, end_mask = cIm_end_mask_0, squeeze_mask = cIm_squeeze_mask_0, x = var_1219_cast_fp16)[name = tensor<string, []>("cIm_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1254_cast_fp16 = mul(x = pRe_cast_fp16, y = cRe_cast_fp16)[name = tensor<string, []>("op_1254_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1256_cast_fp16 = add(x = out_re_9_cast_fp16, y = var_1254_cast_fp16)[name = tensor<string, []>("op_1256_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1257_cast_fp16 = mul(x = pIm_cast_fp16, y = cIm_cast_fp16)[name = tensor<string, []>("op_1257_cast_fp16")];
            tensor<fp16, [1, 200, 96]> out_re_cast_fp16 = sub(x = var_1256_cast_fp16, y = var_1257_cast_fp16)[name = tensor<string, []>("out_re_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1260_cast_fp16 = mul(x = pRe_cast_fp16, y = cIm_cast_fp16)[name = tensor<string, []>("op_1260_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1262_cast_fp16 = add(x = out_im_9_cast_fp16, y = var_1260_cast_fp16)[name = tensor<string, []>("op_1262_cast_fp16")];
            tensor<fp16, [1, 200, 96]> var_1263_cast_fp16 = mul(x = pIm_cast_fp16, y = cRe_cast_fp16)[name = tensor<string, []>("op_1263_cast_fp16")];
            tensor<fp16, [1, 200, 96]> out_im_cast_fp16 = add(x = var_1262_cast_fp16, y = var_1263_cast_fp16)[name = tensor<string, []>("out_im_cast_fp16")];
            tensor<int32, []> var_1268_axis_0 = const()[name = tensor<string, []>("op_1268_axis_0"), val = tensor<int32, []>(-1)];
            tensor<fp16, [1, 200, 96, 2]> var_1268_cast_fp16 = stack(axis = var_1268_axis_0, values = (out_re_cast_fp16, out_im_cast_fp16))[name = tensor<string, []>("op_1268_cast_fp16")];
            tensor<int32, [1]> df_out_1_axes_0 = const()[name = tensor<string, []>("df_out_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 200, 96, 2]> df_out_1_cast_fp16 = expand_dims(axes = df_out_1_axes_0, x = var_1268_cast_fp16)[name = tensor<string, []>("df_out_1_cast_fp16")];
            tensor<int32, [5]> var_1275_begin_0 = const()[name = tensor<string, []>("op_1275_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 96, 0])];
            tensor<int32, [5]> var_1275_end_0 = const()[name = tensor<string, []>("op_1275_end_0"), val = tensor<int32, [5]>([1, 1, 200, 481, 2])];
            tensor<bool, [5]> var_1275_end_mask_0 = const()[name = tensor<string, []>("op_1275_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, true])];
            tensor<fp16, [1, 1, 200, 385, 2]> var_1275_cast_fp16 = slice_by_index(begin = var_1275_begin_0, end = var_1275_end_0, end_mask = var_1275_end_mask_0, x = spec_m_cast_fp16)[name = tensor<string, []>("op_1275_cast_fp16")];
            tensor<int32, []> var_1282 = const()[name = tensor<string, []>("op_1282"), val = tensor<int32, []>(-2)];
            tensor<bool, []> var_1283_interleave_0 = const()[name = tensor<string, []>("op_1283_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 1, 200, 481, 2]> spec_enhanced = concat(axis = var_1282, interleave = var_1283_interleave_0, values = (df_out_1_cast_fp16, var_1275_cast_fp16))[name = tensor<string, []>("op_1283_cast_fp16")];
        } -> (spec_enhanced);
}