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, 64, 100, 96]> c0, tensor<fp32, [1, 100, 512]> emb) {
            tensor<fp32, [1]> decoder_df_fc_a_0_bias = const()[name = tensor<string, []>("decoder_df_fc_a_0_bias"), val = tensor<fp32, [1]>([-0x1.ee457p-6])];
            tensor<fp32, [1, 256]> decoder_df_fc_a_0_weight = const()[name = tensor<string, []>("decoder_df_fc_a_0_weight"), val = tensor<fp32, [1, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp32, [8, 64, 32]> decoder_df_gru_linear_in_0_weight = const()[name = tensor<string, []>("decoder_df_gru_linear_in_0_weight"), val = tensor<fp32, [8, 64, 32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1152)))];
            tensor<fp32, [16, 32, 16]> decoder_df_skip_weight = const()[name = tensor<string, []>("decoder_df_skip_weight"), val = tensor<fp32, [16, 32, 16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66752)))];
            tensor<fp32, [10, 32, 5, 1]> decoder_df_convp_1_weight = const()[name = tensor<string, []>("decoder_df_convp_1_weight"), val = tensor<fp32, [10, 32, 5, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(99584)))];
            tensor<fp32, [16, 16, 60]> decoder_df_out_0_weight = const()[name = tensor<string, []>("decoder_df_out_0_weight"), val = tensor<fp32, [16, 16, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(106048)))];
            tensor<int32, [4]> var_39 = const()[name = tensor<string, []>("op_39"), val = tensor<int32, [4]>([1, 100, 8, 64])];
            tensor<fp32, [1, 100, 8, 64]> var_40 = reshape(shape = var_39, x = emb)[name = tensor<string, []>("op_40")];
            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_4 = const()[name = tensor<string, []>("concat_4"), val = tensor<int32, [3]>([8, 100, 64])];
            tensor<fp32, [8, 1, 100, 64]> transpose_0 = transpose(perm = transpose_0_perm_0, x = var_40)[name = tensor<string, []>("transpose_15")];
            tensor<fp32, [8, 100, 64]> reshape_0 = reshape(shape = concat_4, 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, [8, 100, 32]> matmul_0 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = reshape_0, y = decoder_df_gru_linear_in_0_weight)[name = tensor<string, []>("matmul_0")];
            tensor<int32, [4]> concat_9 = const()[name = tensor<string, []>("concat_9"), val = tensor<int32, [4]>([8, 1, 100, 32])];
            tensor<fp32, [8, 1, 100, 32]> reshape_2 = reshape(shape = concat_9, x = matmul_0)[name = tensor<string, []>("reshape_2")];
            tensor<int32, [4]> x_1_perm_0 = const()[name = tensor<string, []>("x_1_perm_0"), val = tensor<int32, [4]>([1, 2, 0, 3])];
            tensor<int32, [3]> concat_10 = const()[name = tensor<string, []>("concat_10"), val = tensor<int32, [3]>([1, 100, 256])];
            tensor<fp32, [1, 100, 8, 32]> x_1 = transpose(perm = x_1_perm_0, x = reshape_2)[name = tensor<string, []>("transpose_14")];
            tensor<fp32, [1, 100, 256]> input_1 = reshape(shape = concat_10, x = x_1)[name = tensor<string, []>("input_1")];
            tensor<fp32, [1, 100, 256]> input_3 = relu(x = input_1)[name = tensor<string, []>("input_3")];
            tensor<int32, [3]> transpose_2_perm_0 = const()[name = tensor<string, []>("transpose_2_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [1]> slice_by_index_10 = const()[name = tensor<string, []>("slice_by_index_10"), val = tensor<int32, [1]>([100])];
            tensor<fp32, [101, 1, 256]> concat_12 = const()[name = tensor<string, []>("concat_12"), val = tensor<fp32, [101, 1, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167552)))];
            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_2 = transpose(perm = transpose_2_perm_0, x = input_3)[name = tensor<string, []>("transpose_13")];
            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_12))[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_12_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_10)[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_12_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_35 = const()[name = tensor<string, []>("slice_by_index_35"), val = tensor<int32, []>(100)];
                    tensor<int32, [1]> add_20 = add(x = while_loop_0_loop_vars0_0_x0_1_1_1_1, y = slice_by_index_35)[name = tensor<string, []>("add_20")];
                    tensor<int32, [1]> select_0 = select(a = while_loop_0_loop_vars0_0_x0_1_1_1_1, b = add_20, 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_2)[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_36 = const()[name = tensor<string, []>("slice_by_index_36"), val = tensor<int32, []>(101)];
                    tensor<int32, [1]> add_21 = add(x = while_loop_0_loop_vars0_0_x0_1_1_1_1, y = slice_by_index_36)[name = tensor<string, []>("add_21")];
                    tensor<int32, [1]> select_1 = select(a = while_loop_0_loop_vars0_0_x0_1_1_1_1, b = add_21, 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_12_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, []>(271040)))];
                    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, []>(533248)))];
                    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, []>(534336)))];
                    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, []>(796544)))];
                    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, []>(797632)))];
                    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, []>(1059840)))];
                    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, []>(1060928)))];
                    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, []>(1323136)))];
                    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, []>(1324224)))];
                    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, []>(1586432)))];
                    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, []>(1587520)))];
                    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, []>(1849728)))];
                    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_12_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]> c_layer_0_tmp_begin_0 = const()[name = tensor<string, []>("c_layer_0_tmp_begin_0"), val = tensor<int32, [3]>([1, 0, 0])];
            tensor<int32, [3]> c_layer_0_tmp_end_0 = const()[name = tensor<string, []>("c_layer_0_tmp_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<bool, [3]> c_layer_0_tmp_begin_mask_0 = const()[name = tensor<string, []>("c_layer_0_tmp_begin_mask_0"), val = tensor<bool, [3]>([false, true, true])];
            tensor<bool, [3]> c_layer_0_tmp_end_mask_0 = const()[name = tensor<string, []>("c_layer_0_tmp_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [100, 1, 256]> c_layer_0_tmp = slice_by_index(begin = c_layer_0_tmp_begin_0, begin_mask = c_layer_0_tmp_begin_mask_0, end = c_layer_0_tmp_end_0, end_mask = c_layer_0_tmp_end_mask_0, x = while_loop_0_1)[name = tensor<string, []>("c_layer_0_tmp")];
            tensor<int32, [1]> slice_by_index_13 = const()[name = tensor<string, []>("slice_by_index_13"), val = tensor<int32, [1]>([100])];
            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<int32, [1]> while_loop_1_0, tensor<fp32, [101, 1, 256]> while_loop_1_1 = while_loop(loop_vars = (while_loop_1_loop_vars0_0, concat_12))[name = tensor<string, []>("while_loop_1")]
                (tensor<int32, [1]> while_loop_1_loop_vars0_0_x0_1_1_1_0, tensor<fp32, [101, 1, 256]> concat_14_x0_1_1_1_0) {
                    tensor<bool, [1]> less_3 = less(x = while_loop_1_loop_vars0_0_x0_1_1_1_0, y = slice_by_index_13)[name = tensor<string, []>("less_3")];
                } -> (less_3)
                (tensor<int32, [1]> while_loop_1_loop_vars0_0_x0_1_1_1_1, tensor<fp32, [101, 1, 256]> concat_14_x0_1_1_1_1) {
                    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<int32, []> greater_equal_2_y_0 = const()[name = tensor<string, []>("greater_equal_2_y_0"), val = tensor<int32, []>(0)];
                    tensor<bool, [1]> greater_equal_2 = greater_equal(x = while_loop_1_loop_vars0_0_x0_1_1_1_1, y = greater_equal_2_y_0)[name = tensor<string, []>("greater_equal_2")];
                    tensor<int32, []> slice_by_index_37 = const()[name = tensor<string, []>("slice_by_index_37"), val = tensor<int32, []>(100)];
                    tensor<int32, [1]> add_22 = add(x = while_loop_1_loop_vars0_0_x0_1_1_1_1, y = slice_by_index_37)[name = tensor<string, []>("add_22")];
                    tensor<int32, [1]> select_2 = select(a = while_loop_1_loop_vars0_0_x0_1_1_1_1, b = add_22, cond = greater_equal_2)[name = tensor<string, []>("select_2")];
                    tensor<int32, []> gather_6_axis_1 = const()[name = tensor<string, []>("gather_6_axis_1"), val = tensor<int32, []>(0)];
                    tensor<fp32, [1, 1, 256]> gather_6 = gather(axis = gather_6_axis_1, batch_dims = gather_6_batch_dims_0, indices = select_2, validate_indices = gather_6_validate_indices_0, x = c_layer_0_tmp)[name = tensor<string, []>("gather_6")];
                    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<int32, []> slice_by_index_38 = const()[name = tensor<string, []>("slice_by_index_38"), val = tensor<int32, []>(101)];
                    tensor<int32, [1]> add_23 = add(x = while_loop_1_loop_vars0_0_x0_1_1_1_1, y = slice_by_index_38)[name = tensor<string, []>("add_23")];
                    tensor<int32, [1]> select_3 = select(a = while_loop_1_loop_vars0_0_x0_1_1_1_1, b = add_23, cond = greater_equal_2)[name = tensor<string, []>("select_3")];
                    tensor<int32, []> gather_7_axis_1 = const()[name = tensor<string, []>("gather_7_axis_1"), val = tensor<int32, []>(0)];
                    tensor<fp32, [1, 1, 256]> gather_7 = gather(axis = gather_7_axis_1, batch_dims = gather_7_batch_dims_0, indices = select_3, validate_indices = gather_7_validate_indices_0, x = concat_14_x0_1_1_1_1)[name = tensor<string, []>("gather_7")];
                    tensor<int32, [1]> squeeze_6_axes_0 = const()[name = tensor<string, []>("squeeze_6_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp32, [1, 256]> squeeze_6 = squeeze(axes = squeeze_6_axes_0, x = gather_6)[name = tensor<string, []>("squeeze_6")];
                    tensor<int32, [1]> squeeze_7_axes_0 = const()[name = tensor<string, []>("squeeze_7_axes_0"), val = tensor<int32, [1]>([0])];
                    tensor<fp32, [1, 256]> squeeze_7 = squeeze(axes = squeeze_7_axes_0, x = gather_7)[name = tensor<string, []>("squeeze_7")];
                    tensor<fp32, [256, 256]> linear_18_weight_0 = const()[name = tensor<string, []>("linear_18_weight_0"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1850816)))];
                    tensor<fp32, [256]> linear_18_bias_0 = const()[name = tensor<string, []>("linear_18_bias_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2113024)))];
                    tensor<fp32, [1, 256]> linear_18 = linear(bias = linear_18_bias_0, weight = linear_18_weight_0, x = squeeze_6)[name = tensor<string, []>("linear_18")];
                    tensor<fp32, [256, 256]> linear_19_weight_0 = const()[name = tensor<string, []>("linear_19_weight_0"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2114112)))];
                    tensor<fp32, [256]> linear_19_bias_0 = const()[name = tensor<string, []>("linear_19_bias_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2376320)))];
                    tensor<fp32, [1, 256]> linear_19 = linear(bias = linear_19_bias_0, weight = linear_19_weight_0, x = squeeze_7)[name = tensor<string, []>("linear_19")];
                    tensor<fp32, [1, 256]> add_15 = add(x = linear_18, y = linear_19)[name = tensor<string, []>("add_15")];
                    tensor<fp32, [1, 256]> sigmoid_6 = sigmoid(x = add_15)[name = tensor<string, []>("sigmoid_6")];
                    tensor<fp32, [256, 256]> linear_20_weight_0 = const()[name = tensor<string, []>("linear_20_weight_0"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2377408)))];
                    tensor<fp32, [256]> linear_20_bias_0 = const()[name = tensor<string, []>("linear_20_bias_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2639616)))];
                    tensor<fp32, [1, 256]> linear_20 = linear(bias = linear_20_bias_0, weight = linear_20_weight_0, x = squeeze_6)[name = tensor<string, []>("linear_20")];
                    tensor<fp32, [256, 256]> linear_21_weight_0 = const()[name = tensor<string, []>("linear_21_weight_0"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2640704)))];
                    tensor<fp32, [256]> linear_21_bias_0 = const()[name = tensor<string, []>("linear_21_bias_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2902912)))];
                    tensor<fp32, [1, 256]> linear_21 = linear(bias = linear_21_bias_0, weight = linear_21_weight_0, x = squeeze_7)[name = tensor<string, []>("linear_21")];
                    tensor<fp32, [1, 256]> add_16 = add(x = linear_20, y = linear_21)[name = tensor<string, []>("add_16")];
                    tensor<fp32, [1, 256]> sigmoid_7 = sigmoid(x = add_16)[name = tensor<string, []>("sigmoid_7")];
                    tensor<fp32, [256, 256]> linear_22_weight_0 = const()[name = tensor<string, []>("linear_22_weight_0"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2904000)))];
                    tensor<fp32, [256]> linear_22_bias_0 = const()[name = tensor<string, []>("linear_22_bias_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3166208)))];
                    tensor<fp32, [1, 256]> linear_22 = linear(bias = linear_22_bias_0, weight = linear_22_weight_0, x = squeeze_6)[name = tensor<string, []>("linear_22")];
                    tensor<fp32, [256, 256]> linear_23_weight_0 = const()[name = tensor<string, []>("linear_23_weight_0"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3167296)))];
                    tensor<fp32, [256]> linear_23_bias_0 = const()[name = tensor<string, []>("linear_23_bias_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3429504)))];
                    tensor<fp32, [1, 256]> linear_23 = linear(bias = linear_23_bias_0, weight = linear_23_weight_0, x = squeeze_7)[name = tensor<string, []>("linear_23")];
                    tensor<fp32, [1, 256]> mul_9 = mul(x = sigmoid_6, y = linear_23)[name = tensor<string, []>("mul_9")];
                    tensor<fp32, [1, 256]> add_17 = add(x = linear_22, y = mul_9)[name = tensor<string, []>("add_17")];
                    tensor<fp32, [1, 256]> tanh_3 = tanh(x = add_17)[name = tensor<string, []>("tanh_3")];
                    tensor<fp32, []> sub_3_x_0 = const()[name = tensor<string, []>("sub_3_x_0"), val = tensor<fp32, []>(0x1p+0)];
                    tensor<fp32, [1, 256]> sub_3 = sub(x = sub_3_x_0, y = sigmoid_7)[name = tensor<string, []>("sub_3")];
                    tensor<fp32, [1, 256]> mul_10 = mul(x = sub_3, y = tanh_3)[name = tensor<string, []>("mul_10")];
                    tensor<fp32, [1, 256]> mul_11 = mul(x = sigmoid_7, y = squeeze_7)[name = tensor<string, []>("mul_11")];
                    tensor<fp32, [1, 256]> add_18 = add(x = mul_10, y = mul_11)[name = tensor<string, []>("add_18")];
                    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<fp32, [1, 1, 256]> expand_dims_3 = expand_dims(axes = expand_dims_3_axes_0, x = add_18)[name = tensor<string, []>("expand_dims_3")];
                    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<fp32, [101, 1, 256]> scatter_3 = scatter(axis = scatter_3_axis_0, data = concat_14_x0_1_1_1_1, indices = add_19, mode = scatter_3_mode_0, updates = expand_dims_3, validate_indices = scatter_3_validate_indices_0)[name = tensor<string, []>("scatter_3")];
                } -> (add_19, scatter_3);
            tensor<int32, [3]> c_tmp_begin_0 = const()[name = tensor<string, []>("c_tmp_begin_0"), val = tensor<int32, [3]>([1, 0, 0])];
            tensor<int32, [3]> c_tmp_end_0 = const()[name = tensor<string, []>("c_tmp_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<bool, [3]> c_tmp_begin_mask_0 = const()[name = tensor<string, []>("c_tmp_begin_mask_0"), val = tensor<bool, [3]>([false, true, true])];
            tensor<bool, [3]> c_tmp_end_mask_0 = const()[name = tensor<string, []>("c_tmp_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [100, 1, 256]> c_tmp = slice_by_index(begin = c_tmp_begin_0, begin_mask = c_tmp_begin_mask_0, end = c_tmp_end_0, end_mask = c_tmp_end_mask_0, x = while_loop_1_1)[name = tensor<string, []>("c_tmp")];
            tensor<int32, [3]> c_perm_0 = const()[name = tensor<string, []>("c_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [4]> var_70 = const()[name = tensor<string, []>("op_70"), val = tensor<int32, [4]>([1, 100, 16, 32])];
            tensor<fp32, [1, 100, 16, 32]> var_71 = reshape(shape = var_70, x = emb)[name = tensor<string, []>("op_71")];
            tensor<int32, [4]> transpose_3_perm_0 = const()[name = tensor<string, []>("transpose_3_perm_0"), val = tensor<int32, [4]>([2, 0, 1, 3])];
            tensor<int32, [3]> concat_19 = const()[name = tensor<string, []>("concat_19"), val = tensor<int32, [3]>([16, 100, 32])];
            tensor<fp32, [16, 1, 100, 32]> transpose_3 = transpose(perm = transpose_3_perm_0, x = var_71)[name = tensor<string, []>("transpose_11")];
            tensor<fp32, [16, 100, 32]> reshape_3 = reshape(shape = concat_19, x = transpose_3)[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 = decoder_df_skip_weight)[name = tensor<string, []>("matmul_1")];
            tensor<int32, [4]> concat_24 = const()[name = tensor<string, []>("concat_24"), val = tensor<int32, [4]>([16, 1, 100, 16])];
            tensor<fp32, [16, 1, 100, 16]> reshape_5 = reshape(shape = concat_24, x = matmul_1)[name = tensor<string, []>("reshape_5")];
            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_25 = const()[name = tensor<string, []>("concat_25"), val = tensor<int32, [3]>([1, 100, 256])];
            tensor<fp32, [1, 100, 16, 16]> x_3 = transpose(perm = x_3_perm_0, x = reshape_5)[name = tensor<string, []>("transpose_10")];
            tensor<fp32, [1, 100, 256]> var_74 = reshape(shape = concat_25, x = x_3)[name = tensor<string, []>("op_74")];
            tensor<fp32, [1, 100, 256]> c = transpose(perm = c_perm_0, x = c_tmp)[name = tensor<string, []>("transpose_12")];
            tensor<fp32, [1, 100, 256]> input_13 = add(x = c, y = var_74)[name = tensor<string, []>("input_13")];
            tensor<fp32, []> const_5 = const()[name = tensor<string, []>("const_5"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [8]> input_5_pad_0 = const()[name = tensor<string, []>("input_5_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 4, 0, 0, 0])];
            tensor<string, []> input_5_mode_0 = const()[name = tensor<string, []>("input_5_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp32, [1, 64, 104, 96]> input_5 = pad(constant_val = const_5, mode = input_5_mode_0, pad = input_5_pad_0, x = c0)[name = tensor<string, []>("input_5")];
            tensor<string, []> input_7_pad_type_0 = const()[name = tensor<string, []>("input_7_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_7_groups_0 = const()[name = tensor<string, []>("input_7_groups_0"), val = tensor<int32, []>(2)];
            tensor<int32, [2]> input_7_strides_0 = const()[name = tensor<string, []>("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_7_dilations_0 = const()[name = tensor<string, []>("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp32, [1, 10, 100, 96]> input_7 = conv(dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = decoder_df_convp_1_weight, 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, []>("valid")];
            tensor<int32, [2]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [10, 10, 1, 1]> const_8 = const()[name = tensor<string, []>("const_8"), val = tensor<fp32, [10, 10, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3430592)))];
            tensor<fp32, [10]> const_9 = const()[name = tensor<string, []>("const_9"), val = tensor<fp32, [10]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3431104)))];
            tensor<fp32, [1, 10, 100, 96]> input_11 = conv(bias = const_9, 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 = const_8, x = input_7)[name = tensor<string, []>("input_11")];
            tensor<fp32, [1, 10, 100, 96]> var_110 = relu(x = input_11)[name = tensor<string, []>("op_110")];
            tensor<int32, [4]> var_115 = const()[name = tensor<string, []>("op_115"), val = tensor<int32, [4]>([0, 2, 3, 1])];
            tensor<fp32, [1, 100, 1]> alpha = linear(bias = decoder_df_fc_a_0_bias, weight = decoder_df_fc_a_0_weight, x = input_13)[name = tensor<string, []>("linear_24")];
            tensor<int32, [4]> var_129 = const()[name = tensor<string, []>("op_129"), val = tensor<int32, [4]>([1, 100, 16, 16])];
            tensor<fp32, [1, 100, 16, 16]> var_130 = reshape(shape = var_129, x = input_13)[name = tensor<string, []>("op_130")];
            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_130)[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, 60]> matmul_2 = matmul(transpose_x = matmul_2_transpose_x_0, transpose_y = matmul_2_transpose_y_0, x = reshape_6, y = decoder_df_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, 60])];
            tensor<fp32, [16, 1, 100, 60]> 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, 960])];
            tensor<fp32, [1, 100, 16, 60]> x = transpose(perm = x_perm_0, x = reshape_8)[name = tensor<string, []>("transpose_7")];
            tensor<fp32, [1, 100, 960]> input = reshape(shape = concat_36, x = x)[name = tensor<string, []>("input")];
            tensor<fp32, [1, 100, 960]> var_134 = tanh(x = input)[name = tensor<string, []>("op_134")];
            tensor<int32, [4]> concat_37 = const()[name = tensor<string, []>("concat_37"), val = tensor<int32, [4]>([1, 100, 96, 10])];
            tensor<fp32, [1, 100, 96, 10]> var_139 = reshape(shape = concat_37, x = var_134)[name = tensor<string, []>("op_139")];
            tensor<fp32, [1, 100, 96, 10]> c0_1 = transpose(perm = var_115, x = var_110)[name = tensor<string, []>("transpose_9")];
            tensor<fp32, [1, 100, 96, 10]> df_coefficients = add(x = var_139, y = c0_1)[name = tensor<string, []>("op_141")];
        } -> (df_coefficients, alpha);
}