program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
{
    func main<ios17>(tensor<fp32, [1, 1, 100, 32]> erb_features, tensor<fp32, [1, 2, 100, 96]> spec_features) {
            tensor<fp32, [64, 1, 1, 3]> encoder_erb_conv1_0_weight = const()[name = tensor<string, []>("encoder_erb_conv1_0_weight"), val = tensor<fp32, [64, 1, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp32, [64, 1, 1, 3]> encoder_erb_conv2_0_weight = const()[name = tensor<string, []>("encoder_erb_conv2_0_weight"), val = tensor<fp32, [64, 1, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(896)))];
            tensor<fp32, [64, 1, 1, 3]> encoder_erb_conv3_0_weight = const()[name = tensor<string, []>("encoder_erb_conv3_0_weight"), val = tensor<fp32, [64, 1, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1728)))];
            tensor<fp32, [64, 1, 3, 3]> encoder_df_conv0_1_weight = const()[name = tensor<string, []>("encoder_df_conv0_1_weight"), val = tensor<fp32, [64, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2560)))];
            tensor<fp32, [64, 1, 1, 3]> encoder_df_conv1_0_weight = const()[name = tensor<string, []>("encoder_df_conv1_0_weight"), val = tensor<fp32, [64, 1, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4928)))];
            tensor<fp32, [32, 96, 16]> encoder_df_fc_emb_0_weight = const()[name = tensor<string, []>("encoder_df_fc_emb_0_weight"), val = tensor<fp32, [32, 96, 16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5760)))];
            tensor<fp32, [16, 32, 16]> encoder_emb_gru_linear_in_0_weight = const()[name = tensor<string, []>("encoder_emb_gru_linear_in_0_weight"), val = tensor<fp32, [16, 32, 16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202432)))];
            tensor<fp32, [16, 16, 32]> encoder_emb_gru_linear_out_0_weight = const()[name = tensor<string, []>("encoder_emb_gru_linear_out_0_weight"), val = tensor<fp32, [16, 16, 32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235264)))];
            tensor<fp32, [1]> encoder_lsnr_fc_0_bias = const()[name = tensor<string, []>("encoder_lsnr_fc_0_bias"), val = tensor<fp32, [1]>([-0x1.331464p-2])];
            tensor<fp32, [1, 512]> encoder_lsnr_fc_0_weight = const()[name = tensor<string, []>("encoder_lsnr_fc_0_weight"), val = tensor<fp32, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(268096)))];
            tensor<fp32, []> const_0 = const()[name = tensor<string, []>("const_0"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> input_1_pad_0 = const()[name = tensor<string, []>("input_1_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 2, 0, 0, 0])];
            tensor<string, []> input_1_mode_0 = const()[name = tensor<string, []>("input_1_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 1, 102, 32]> input_1 = pad(constant_val = const_0, mode = input_1_mode_0, pad = input_1_pad_0, x = erb_features)[name = tensor<string, []>("input_1")];
            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<fp32, [64, 1, 3, 3]> const_9 = const()[name = tensor<string, []>("const_9"), val = tensor<fp32, [64, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(270208)))];
            tensor<fp32, [64]> const_10 = const()[name = tensor<string, []>("const_10"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(272576)))];
            tensor<fp32, [1, 64, 100, 32]> input_5 = conv(bias = const_10, 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_9, x = input_1)[name = tensor<string, []>("input_5")];
            tensor<fp32, [1, 64, 100, 32]> e0 = relu(x = input_5)[name = tensor<string, []>("input_7")];
            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<fp32, [1, 64, 100, 16]> input_9 = 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 = encoder_erb_conv1_0_weight, x = e0)[name = tensor<string, []>("input_9")];
            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<fp32, [64, 64, 1, 1]> const_11 = const()[name = tensor<string, []>("const_11"), val = tensor<fp32, [64, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(272896)))];
            tensor<fp32, [64]> const_12 = const()[name = tensor<string, []>("const_12"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289344)))];
            tensor<fp32, [1, 64, 100, 16]> input_13 = conv(bias = const_12, 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_11, x = input_9)[name = tensor<string, []>("input_13")];
            tensor<fp32, [1, 64, 100, 16]> e1 = relu(x = input_13)[name = tensor<string, []>("input_15")];
            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<fp32, [1, 64, 100, 8]> input_17 = 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 = encoder_erb_conv2_0_weight, x = e1)[name = tensor<string, []>("input_17")];
            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<fp32, [64, 64, 1, 1]> const_13 = const()[name = tensor<string, []>("const_13"), val = tensor<fp32, [64, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289664)))];
            tensor<fp32, [64]> const_14 = const()[name = tensor<string, []>("const_14"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306112)))];
            tensor<fp32, [1, 64, 100, 8]> input_21 = conv(bias = const_14, 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_13, x = input_17)[name = tensor<string, []>("input_21")];
            tensor<fp32, [1, 64, 100, 8]> e2 = relu(x = input_21)[name = tensor<string, []>("input_23")];
            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<fp32, [1, 64, 100, 8]> input_25 = 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 = encoder_erb_conv3_0_weight, x = e2)[name = tensor<string, []>("input_25")];
            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<fp32, [64, 64, 1, 1]> const_15 = const()[name = tensor<string, []>("const_15"), val = tensor<fp32, [64, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306432)))];
            tensor<fp32, [64]> const_16 = const()[name = tensor<string, []>("const_16"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322880)))];
            tensor<fp32, [1, 64, 100, 8]> input_29 = conv(bias = const_16, 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_15, x = input_25)[name = tensor<string, []>("input_29")];
            tensor<fp32, [1, 64, 100, 8]> e3 = relu(x = input_29)[name = tensor<string, []>("e3")];
            tensor<fp32, []> const_1 = const()[name = tensor<string, []>("const_1"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> input_31_pad_0 = const()[name = tensor<string, []>("input_31_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 2, 0, 0, 0])];
            tensor<string, []> input_31_mode_0 = const()[name = tensor<string, []>("input_31_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 2, 102, 96]> input_31 = pad(constant_val = const_1, mode = input_31_mode_0, pad = input_31_pad_0, x = spec_features)[name = tensor<string, []>("input_31")];
            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<fp32, [1, 64, 100, 96]> input_33 = 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 = encoder_df_conv0_1_weight, x = input_31)[name = tensor<string, []>("input_33")];
            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<fp32, [64, 64, 1, 1]> const_17 = const()[name = tensor<string, []>("const_17"), val = tensor<fp32, [64, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323200)))];
            tensor<fp32, [64]> const_18 = const()[name = tensor<string, []>("const_18"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(339648)))];
            tensor<fp32, [1, 64, 100, 96]> input_37 = conv(bias = const_18, 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_17, x = input_33)[name = tensor<string, []>("input_37")];
            tensor<fp32, [1, 64, 100, 96]> c0 = relu(x = input_37)[name = tensor<string, []>("input_39")];
            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<fp32, [1, 64, 100, 48]> input_41 = 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 = encoder_df_conv1_0_weight, x = c0)[name = tensor<string, []>("input_41")];
            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<fp32, [64, 64, 1, 1]> const_19 = const()[name = tensor<string, []>("const_19"), val = tensor<fp32, [64, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(339968)))];
            tensor<fp32, [64]> const_20 = const()[name = tensor<string, []>("const_20"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356416)))];
            tensor<fp32, [1, 64, 100, 48]> input_45 = conv(bias = const_20, 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_19, x = input_41)[name = tensor<string, []>("input_45")];
            tensor<fp32, [1, 64, 100, 48]> c1 = relu(x = input_45)[name = tensor<string, []>("c1")];
            tensor<int32, [4]> var_156 = const()[name = tensor<string, []>("op_156"), val = tensor<int32, [4]>([0, 2, 3, 1])];
            tensor<int32, [4]> var_163 = const()[name = tensor<string, []>("op_163"), val = tensor<int32, [4]>([1, 100, 32, 96])];
            tensor<fp32, [1, 100, 48, 64]> var_157 = transpose(perm = var_156, x = c1)[name = tensor<string, []>("transpose_16")];
            tensor<fp32, [1, 100, 32, 96]> var_164 = reshape(shape = var_163, x = var_157)[name = tensor<string, []>("op_164")];
            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, 100, 96])];
            tensor<fp32, [32, 1, 100, 96]> transpose_0 = transpose(perm = transpose_0_perm_0, x = var_164)[name = tensor<string, []>("transpose_15")];
            tensor<fp32, [32, 100, 96]> reshape_0 = reshape(shape = concat_5, x = transpose_0)[name = tensor<string, []>("reshape_0")];
            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<fp32, [32, 100, 16]> matmul_0 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = reshape_0, y = encoder_df_fc_emb_0_weight)[name = tensor<string, []>("matmul_0")];
            tensor<int32, [4]> concat_10 = const()[name = tensor<string, []>("concat_10"), val = tensor<int32, [4]>([32, 1, 100, 16])];
            tensor<fp32, [32, 1, 100, 16]> reshape_2 = reshape(shape = concat_10, x = matmul_0)[name = tensor<string, []>("reshape_2")];
            tensor<int32, [4]> x_3_perm_0 = const()[name = tensor<string, []>("x_3_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, 100, 512])];
            tensor<fp32, [1, 100, 32, 16]> x_3 = transpose(perm = x_3_perm_0, x = reshape_2)[name = tensor<string, []>("transpose_14")];
            tensor<fp32, [1, 100, 512]> input_47 = reshape(shape = concat_11, x = x_3)[name = tensor<string, []>("input_47")];
            tensor<fp32, [1, 100, 512]> b_3 = relu(x = input_47)[name = tensor<string, []>("b_3")];
            tensor<int32, [4]> var_169 = const()[name = tensor<string, []>("op_169"), val = tensor<int32, [4]>([0, 2, 3, 1])];
            tensor<int32, [3]> concat_12 = const()[name = tensor<string, []>("concat_12"), val = tensor<int32, [3]>([1, 100, 512])];
            tensor<fp32, [1, 100, 8, 64]> var_170 = transpose(perm = var_169, x = e3)[name = tensor<string, []>("transpose_13")];
            tensor<fp32, [1, 100, 512]> a = reshape(shape = concat_12, x = var_170)[name = tensor<string, []>("a")];
            tensor<fp32, [1, 100, 512]> x_5 = add(x = a, y = b_3)[name = tensor<string, []>("x_5")];
            tensor<int32, [4]> var_180 = const()[name = tensor<string, []>("op_180"), val = tensor<int32, [4]>([1, 100, 16, 32])];
            tensor<fp32, [1, 100, 16, 32]> var_181 = reshape(shape = var_180, x = x_5)[name = tensor<string, []>("op_181")];
            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, 100, 32])];
            tensor<fp32, [16, 1, 100, 32]> transpose_2 = transpose(perm = transpose_2_perm_0, x = var_181)[name = tensor<string, []>("transpose_12")];
            tensor<fp32, [16, 100, 32]> reshape_3 = reshape(shape = concat_17, x = transpose_2)[name = tensor<string, []>("reshape_3")];
            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<fp32, [16, 100, 16]> matmul_1 = matmul(transpose_x = matmul_1_transpose_x_0, transpose_y = matmul_1_transpose_y_0, x = reshape_3, y = encoder_emb_gru_linear_in_0_weight)[name = tensor<string, []>("matmul_1")];
            tensor<int32, [4]> concat_22 = const()[name = tensor<string, []>("concat_22"), val = tensor<int32, [4]>([16, 1, 100, 16])];
            tensor<fp32, [16, 1, 100, 16]> reshape_5 = reshape(shape = concat_22, x = matmul_1)[name = tensor<string, []>("reshape_5")];
            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_23 = const()[name = tensor<string, []>("concat_23"), val = tensor<int32, [3]>([1, 100, 256])];
            tensor<fp32, [1, 100, 16, 16]> x_7 = transpose(perm = x_7_perm_0, x = reshape_5)[name = tensor<string, []>("transpose_11")];
            tensor<fp32, [1, 100, 256]> input_49 = reshape(shape = concat_23, x = x_7)[name = tensor<string, []>("input_49")];
            tensor<fp32, [1, 100, 256]> input_51 = relu(x = input_49)[name = tensor<string, []>("input_51")];
            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]>([100])];
            tensor<fp32, [101, 1, 256]> concat_25 = const()[name = tensor<string, []>("concat_25"), val = tensor<fp32, [101, 1, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356736)))];
            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<fp32, [100, 1, 256]> transpose_4 = transpose(perm = transpose_4_perm_0, x = input_51)[name = tensor<string, []>("transpose_10")];
            tensor<int32, [1]> while_loop_0_0, tensor<fp32, [101, 1, 256]> while_loop_0_1 = while_loop(loop_vars = (while_loop_0_loop_vars0_0, concat_25))[name = tensor<string, []>("while_loop_0")]
                (tensor<int32, [1]> while_loop_0_loop_vars0_0_x0_1_1_1_0, tensor<fp32, [101, 1, 256]> concat_25_x0_1_1_1_0) {
                    tensor<bool, [1]> less_1 = less(x = while_loop_0_loop_vars0_0_x0_1_1_1_0, 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, [101, 1, 256]> concat_25_x0_1_1_1_1) {
                    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<int32, []> greater_equal_0_y_0 = const()[name = tensor<string, []>("greater_equal_0_y_0"), val = tensor<int32, []>(0)];
                    tensor<bool, [1]> greater_equal_0 = greater_equal(x = while_loop_0_loop_vars0_0_x0_1_1_1_1, y = greater_equal_0_y_0)[name = tensor<string, []>("greater_equal_0")];
                    tensor<int32, []> slice_by_index_34 = const()[name = tensor<string, []>("slice_by_index_34"), val = tensor<int32, []>(100)];
                    tensor<int32, [1]> add_10 = add(x = while_loop_0_loop_vars0_0_x0_1_1_1_1, y = slice_by_index_34)[name = tensor<string, []>("add_10")];
                    tensor<int32, [1]> select_0 = select(a = while_loop_0_loop_vars0_0_x0_1_1_1_1, b = add_10, cond = greater_equal_0)[name = tensor<string, []>("select_0")];
                    tensor<int32, []> gather_2_axis_1 = const()[name = tensor<string, []>("gather_2_axis_1"), val = tensor<int32, []>(0)];
                    tensor<fp32, [1, 1, 256]> gather_2 = gather(axis = gather_2_axis_1, batch_dims = gather_2_batch_dims_0, indices = select_0, validate_indices = gather_2_validate_indices_0, x = transpose_4)[name = tensor<string, []>("gather_2")];
                    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<int32, []> slice_by_index_35 = const()[name = tensor<string, []>("slice_by_index_35"), val = tensor<int32, []>(101)];
                    tensor<int32, [1]> add_11 = add(x = while_loop_0_loop_vars0_0_x0_1_1_1_1, y = slice_by_index_35)[name = tensor<string, []>("add_11")];
                    tensor<int32, [1]> select_1 = select(a = while_loop_0_loop_vars0_0_x0_1_1_1_1, b = add_11, cond = greater_equal_0)[name = tensor<string, []>("select_1")];
                    tensor<int32, []> gather_3_axis_1 = const()[name = tensor<string, []>("gather_3_axis_1"), val = tensor<int32, []>(0)];
                    tensor<fp32, [1, 1, 256]> gather_3 = gather(axis = gather_3_axis_1, batch_dims = gather_3_batch_dims_0, indices = select_1, validate_indices = gather_3_validate_indices_0, x = concat_25_x0_1_1_1_1)[name = tensor<string, []>("gather_3")];
                    tensor<int32, [1]> squeeze_2_axes_0 = const()[name = tensor<string, []>("squeeze_2_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp32, [1, 256]> squeeze_2 = squeeze(axes = squeeze_2_axes_0, x = gather_2)[name = tensor<string, []>("squeeze_2")];
                    tensor<int32, [1]> squeeze_3_axes_0 = const()[name = tensor<string, []>("squeeze_3_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp32, [1, 256]> squeeze_3 = squeeze(axes = squeeze_3_axes_0, x = gather_3)[name = tensor<string, []>("squeeze_3")];
                    tensor<fp32, [256, 256]> linear_6_weight_0 = const()[name = tensor<string, []>("linear_6_weight_0"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(460224)))];
                    tensor<fp32, [256]> linear_6_bias_0 = const()[name = tensor<string, []>("linear_6_bias_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(722432)))];
                    tensor<fp32, [1, 256]> linear_6 = linear(bias = linear_6_bias_0, weight = linear_6_weight_0, x = squeeze_2)[name = tensor<string, []>("linear_6")];
                    tensor<fp32, [256, 256]> linear_7_weight_0 = const()[name = tensor<string, []>("linear_7_weight_0"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(723520)))];
                    tensor<fp32, [256]> linear_7_bias_0 = const()[name = tensor<string, []>("linear_7_bias_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(985728)))];
                    tensor<fp32, [1, 256]> linear_7 = linear(bias = linear_7_bias_0, weight = linear_7_weight_0, x = squeeze_3)[name = tensor<string, []>("linear_7")];
                    tensor<fp32, [1, 256]> add_5 = add(x = linear_6, y = linear_7)[name = tensor<string, []>("add_5")];
                    tensor<fp32, [1, 256]> sigmoid_2 = sigmoid(x = add_5)[name = tensor<string, []>("sigmoid_2")];
                    tensor<fp32, [256, 256]> linear_8_weight_0 = const()[name = tensor<string, []>("linear_8_weight_0"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(986816)))];
                    tensor<fp32, [256]> linear_8_bias_0 = const()[name = tensor<string, []>("linear_8_bias_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1249024)))];
                    tensor<fp32, [1, 256]> linear_8 = linear(bias = linear_8_bias_0, weight = linear_8_weight_0, x = squeeze_2)[name = tensor<string, []>("linear_8")];
                    tensor<fp32, [256, 256]> linear_9_weight_0 = const()[name = tensor<string, []>("linear_9_weight_0"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1250112)))];
                    tensor<fp32, [256]> linear_9_bias_0 = const()[name = tensor<string, []>("linear_9_bias_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1512320)))];
                    tensor<fp32, [1, 256]> linear_9 = linear(bias = linear_9_bias_0, weight = linear_9_weight_0, x = squeeze_3)[name = tensor<string, []>("linear_9")];
                    tensor<fp32, [1, 256]> add_6 = add(x = linear_8, y = linear_9)[name = tensor<string, []>("add_6")];
                    tensor<fp32, [1, 256]> sigmoid_3 = sigmoid(x = add_6)[name = tensor<string, []>("sigmoid_3")];
                    tensor<fp32, [256, 256]> linear_10_weight_0 = const()[name = tensor<string, []>("linear_10_weight_0"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1513408)))];
                    tensor<fp32, [256]> linear_10_bias_0 = const()[name = tensor<string, []>("linear_10_bias_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1775616)))];
                    tensor<fp32, [1, 256]> linear_10 = linear(bias = linear_10_bias_0, weight = linear_10_weight_0, x = squeeze_2)[name = tensor<string, []>("linear_10")];
                    tensor<fp32, [256, 256]> linear_11_weight_0 = const()[name = tensor<string, []>("linear_11_weight_0"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1776704)))];
                    tensor<fp32, [256]> linear_11_bias_0 = const()[name = tensor<string, []>("linear_11_bias_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2038912)))];
                    tensor<fp32, [1, 256]> linear_11 = linear(bias = linear_11_bias_0, weight = linear_11_weight_0, x = squeeze_3)[name = tensor<string, []>("linear_11")];
                    tensor<fp32, [1, 256]> mul_3 = mul(x = sigmoid_2, y = linear_11)[name = tensor<string, []>("mul_3")];
                    tensor<fp32, [1, 256]> add_7 = add(x = linear_10, y = mul_3)[name = tensor<string, []>("add_7")];
                    tensor<fp32, [1, 256]> tanh_1 = tanh(x = add_7)[name = tensor<string, []>("tanh_1")];
                    tensor<fp32, []> sub_1_x_0 = const()[name = tensor<string, []>("sub_1_x_0"), val = tensor<fp32, []>(0x1p+0)];
                    tensor<fp32, [1, 256]> sub_1 = sub(x = sub_1_x_0, y = sigmoid_3)[name = tensor<string, []>("sub_1")];
                    tensor<fp32, [1, 256]> mul_4 = mul(x = sub_1, y = tanh_1)[name = tensor<string, []>("mul_4")];
                    tensor<fp32, [1, 256]> mul_5 = mul(x = sigmoid_3, y = squeeze_3)[name = tensor<string, []>("mul_5")];
                    tensor<fp32, [1, 256]> add_8 = add(x = mul_4, y = mul_5)[name = tensor<string, []>("add_8")];
                    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<fp32, [1, 1, 256]> expand_dims_1 = expand_dims(axes = expand_dims_1_axes_0, x = add_8)[name = tensor<string, []>("expand_dims_1")];
                    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<fp32, [101, 1, 256]> scatter_1 = scatter(axis = scatter_1_axis_0, data = concat_25_x0_1_1_1_1, indices = add_9, mode = scatter_1_mode_0, updates = expand_dims_1, validate_indices = scatter_1_validate_indices_0)[name = tensor<string, []>("scatter_1")];
                } -> (add_9, scatter_1);
            tensor<int32, [3]> x_9_tmp_begin_0 = const()[name = tensor<string, []>("x_9_tmp_begin_0"), val = tensor<int32, [3]>([1, 0, 0])];
            tensor<int32, [3]> x_9_tmp_end_0 = const()[name = tensor<string, []>("x_9_tmp_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<bool, [3]> x_9_tmp_begin_mask_0 = const()[name = tensor<string, []>("x_9_tmp_begin_mask_0"), val = tensor<bool, [3]>([false, true, true])];
            tensor<bool, [3]> x_9_tmp_end_mask_0 = const()[name = tensor<string, []>("x_9_tmp_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [100, 1, 256]> x_9_tmp = slice_by_index(begin = x_9_tmp_begin_0, begin_mask = x_9_tmp_begin_mask_0, end = x_9_tmp_end_0, end_mask = x_9_tmp_end_mask_0, x = while_loop_0_1)[name = tensor<string, []>("x_9_tmp")];
            tensor<int32, [3]> x_9_perm_0 = const()[name = tensor<string, []>("x_9_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [4]> var_200 = const()[name = tensor<string, []>("op_200"), val = tensor<int32, [4]>([1, 100, 16, 16])];
            tensor<fp32, [1, 100, 256]> x_9 = transpose(perm = x_9_perm_0, x = x_9_tmp)[name = tensor<string, []>("transpose_9")];
            tensor<fp32, [1, 100, 16, 16]> var_201 = reshape(shape = var_200, x = x_9)[name = tensor<string, []>("op_201")];
            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, 100, 16])];
            tensor<fp32, [16, 1, 100, 16]> transpose_5 = transpose(perm = transpose_5_perm_0, x = var_201)[name = tensor<string, []>("transpose_8")];
            tensor<fp32, [16, 100, 16]> reshape_6 = reshape(shape = concat_30, x = transpose_5)[name = tensor<string, []>("reshape_6")];
            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<fp32, [16, 100, 32]> matmul_2 = matmul(transpose_x = matmul_2_transpose_x_0, transpose_y = matmul_2_transpose_y_0, x = reshape_6, y = encoder_emb_gru_linear_out_0_weight)[name = tensor<string, []>("matmul_2")];
            tensor<int32, [4]> concat_35 = const()[name = tensor<string, []>("concat_35"), val = tensor<int32, [4]>([16, 1, 100, 32])];
            tensor<fp32, [16, 1, 100, 32]> reshape_8 = reshape(shape = concat_35, x = matmul_2)[name = tensor<string, []>("reshape_8")];
            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_36 = const()[name = tensor<string, []>("concat_36"), val = tensor<int32, [3]>([1, 100, 512])];
            tensor<fp32, [1, 100, 16, 32]> x = transpose(perm = x_perm_0, x = reshape_8)[name = tensor<string, []>("transpose_7")];
            tensor<fp32, [1, 100, 512]> input_53 = reshape(shape = concat_36, x = x)[name = tensor<string, []>("input_53")];
            tensor<fp32, [1, 100, 512]> emb = relu(x = input_53)[name = tensor<string, []>("input_55")];
            tensor<fp32, [1, 100, 1]> input = linear(bias = encoder_lsnr_fc_0_bias, weight = encoder_lsnr_fc_0_weight, x = emb)[name = tensor<string, []>("linear_12")];
            tensor<fp32, [1, 100, 1]> var_210 = sigmoid(x = input)[name = tensor<string, []>("op_210")];
            tensor<fp32, []> var_211_promoted = const()[name = tensor<string, []>("op_211_promoted"), val = tensor<fp32, []>(0x1.9p+5)];
            tensor<fp32, [1, 100, 1]> var_212 = mul(x = var_210, y = var_211_promoted)[name = tensor<string, []>("op_212")];
            tensor<fp32, []> var_213_promoted = const()[name = tensor<string, []>("op_213_promoted"), val = tensor<fp32, []>(-0x1.ep+3)];
            tensor<fp32, [1, 100, 1]> lsnr = add(x = var_212, y = var_213_promoted)[name = tensor<string, []>("op_214")];
        } -> (e0, e1, e2, e3, emb, c0, lsnr);
}