program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.2.1"}, {"coremlc-version", "3520.2.1"}, {"mldb_token", "mldb-jvakw8rano"}})]
{
    func main<ios15>(tensor<int32, [1, ?]> token_ids) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"token_ids", [1, 1]}}), ("RangeDims", {{"token_ids", [[1, 1], [1, 64]]}})))] {
            tensor<int32, []> embedded_input_axis_0 = const()[name = tensor<string, []>("embedded_input_axis_0"), val = tensor<int32, []>(0)];
            tensor<fp16, [10001, 80]> word_embs_weight_to_fp16 = const()[name = tensor<string, []>("word_embs_weight_to_fp16"), val = tensor<fp16, [10001, 80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp16, [1, ?, 80]> embedded_input_cast = gather(axis = embedded_input_axis_0, indices = token_ids, x = word_embs_weight_to_fp16);
            tensor<int32, [3]> embedded_input_batch_first_transpose_perm_0 = const()[name = tensor<string, []>("embedded_input_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<string, []> embedded_input_batch_first_transpose_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("embedded_input_batch_first_transpose_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [256]> add_0 = const()[name = tensor<string, []>("add_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1600320)))];
            tensor<fp32, [256]> add_1 = const()[name = tensor<string, []>("add_1"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1601408)))];
            tensor<fp32, [256, 80]> concat_4 = const()[name = tensor<string, []>("concat_4"), val = tensor<fp32, [256, 80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1602496)))];
            tensor<fp32, [256, 64]> concat_5 = const()[name = tensor<string, []>("concat_5"), val = tensor<fp32, [256, 64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1684480)))];
            tensor<fp32, [256, 80]> concat_6 = const()[name = tensor<string, []>("concat_6"), val = tensor<fp32, [256, 80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1750080)))];
            tensor<fp32, [256, 64]> concat_7 = const()[name = tensor<string, []>("concat_7"), val = tensor<fp32, [256, 64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1832064)))];
            tensor<fp32, [1, 128]> lstm_output_lstm_layer_0_lstm_h0_reshaped = const()[name = tensor<string, []>("lstm_output_lstm_layer_0_lstm_h0_reshaped"), val = tensor<fp32, [1, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1897664)))];
            tensor<fp32, [1, 128]> lstm_output_lstm_layer_0_lstm_c0_reshaped = const()[name = tensor<string, []>("lstm_output_lstm_layer_0_lstm_c0_reshaped"), val = tensor<fp32, [1, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1898240)))];
            tensor<string, []> lstm_output_lstm_layer_0_direction_0 = const()[name = tensor<string, []>("lstm_output_lstm_layer_0_direction_0"), val = tensor<string, []>("bidirectional")];
            tensor<bool, []> lstm_output_lstm_layer_0_output_sequence_0 = const()[name = tensor<string, []>("lstm_output_lstm_layer_0_output_sequence_0"), val = tensor<bool, []>(true)];
            tensor<string, []> lstm_output_lstm_layer_0_recurrent_activation_0 = const()[name = tensor<string, []>("lstm_output_lstm_layer_0_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
            tensor<string, []> lstm_output_lstm_layer_0_cell_activation_0 = const()[name = tensor<string, []>("lstm_output_lstm_layer_0_cell_activation_0"), val = tensor<string, []>("tanh")];
            tensor<string, []> lstm_output_lstm_layer_0_activation_0 = const()[name = tensor<string, []>("lstm_output_lstm_layer_0_activation_0"), val = tensor<string, []>("tanh")];
            tensor<fp16, [?, 1, 80]> transpose_1 = transpose(perm = embedded_input_batch_first_transpose_perm_0, x = embedded_input_cast);
            tensor<fp32, [?, 1, 80]> cast_27 = cast(dtype = embedded_input_batch_first_transpose_cast_to_fp32_dtype_0, x = transpose_1);
            tensor<fp32, [?, 1, 128]> lstm_output_lstm_layer_0_0, tensor<fp32, [1, 128]> lstm_output_lstm_layer_0_1, tensor<fp32, [1, 128]> lstm_output_lstm_layer_0_2 = lstm(activation = lstm_output_lstm_layer_0_activation_0, bias = add_0, bias_back = add_1, cell_activation = lstm_output_lstm_layer_0_cell_activation_0, direction = lstm_output_lstm_layer_0_direction_0, initial_c = lstm_output_lstm_layer_0_lstm_c0_reshaped, initial_h = lstm_output_lstm_layer_0_lstm_h0_reshaped, output_sequence = lstm_output_lstm_layer_0_output_sequence_0, recurrent_activation = lstm_output_lstm_layer_0_recurrent_activation_0, weight_hh = concat_5, weight_hh_back = concat_7, weight_ih = concat_4, weight_ih_back = concat_6, x = cast_27);
            tensor<fp32, [256]> add_2 = const()[name = tensor<string, []>("add_2"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1898816)))];
            tensor<fp32, [256]> add_3 = const()[name = tensor<string, []>("add_3"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1899904)))];
            tensor<fp32, [256, 128]> concat_14 = const()[name = tensor<string, []>("concat_14"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1900992)))];
            tensor<fp32, [256, 64]> concat_15 = const()[name = tensor<string, []>("concat_15"), val = tensor<fp32, [256, 64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2032128)))];
            tensor<fp32, [256, 128]> concat_16 = const()[name = tensor<string, []>("concat_16"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2097728)))];
            tensor<fp32, [256, 64]> concat_17 = const()[name = tensor<string, []>("concat_17"), val = tensor<fp32, [256, 64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2228864)))];
            tensor<fp32, [1, 128]> lstm_output_lstm_layer_1_lstm_h0_reshaped = const()[name = tensor<string, []>("lstm_output_lstm_layer_1_lstm_h0_reshaped"), val = tensor<fp32, [1, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2294464)))];
            tensor<fp32, [1, 128]> lstm_output_lstm_layer_1_lstm_c0_reshaped = const()[name = tensor<string, []>("lstm_output_lstm_layer_1_lstm_c0_reshaped"), val = tensor<fp32, [1, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2295040)))];
            tensor<string, []> lstm_output_lstm_layer_1_direction_0 = const()[name = tensor<string, []>("lstm_output_lstm_layer_1_direction_0"), val = tensor<string, []>("bidirectional")];
            tensor<bool, []> lstm_output_lstm_layer_1_output_sequence_0 = const()[name = tensor<string, []>("lstm_output_lstm_layer_1_output_sequence_0"), val = tensor<bool, []>(true)];
            tensor<string, []> lstm_output_lstm_layer_1_recurrent_activation_0 = const()[name = tensor<string, []>("lstm_output_lstm_layer_1_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
            tensor<string, []> lstm_output_lstm_layer_1_cell_activation_0 = const()[name = tensor<string, []>("lstm_output_lstm_layer_1_cell_activation_0"), val = tensor<string, []>("tanh")];
            tensor<string, []> lstm_output_lstm_layer_1_activation_0 = const()[name = tensor<string, []>("lstm_output_lstm_layer_1_activation_0"), val = tensor<string, []>("tanh")];
            tensor<fp32, [?, 1, 128]> lstm_output_lstm_layer_1_0, tensor<fp32, [1, 128]> lstm_output_lstm_layer_1_1, tensor<fp32, [1, 128]> lstm_output_lstm_layer_1_2 = lstm(activation = lstm_output_lstm_layer_1_activation_0, bias = add_2, bias_back = add_3, cell_activation = lstm_output_lstm_layer_1_cell_activation_0, direction = lstm_output_lstm_layer_1_direction_0, initial_c = lstm_output_lstm_layer_1_lstm_c0_reshaped, initial_h = lstm_output_lstm_layer_1_lstm_h0_reshaped, output_sequence = lstm_output_lstm_layer_1_output_sequence_0, recurrent_activation = lstm_output_lstm_layer_1_recurrent_activation_0, weight_hh = concat_15, weight_hh_back = concat_17, weight_ih = concat_14, weight_ih_back = concat_16, x = lstm_output_lstm_layer_0_0);
            tensor<fp32, [256]> add_4 = const()[name = tensor<string, []>("add_4"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2295616)))];
            tensor<fp32, [256]> add_5 = const()[name = tensor<string, []>("add_5"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2296704)))];
            tensor<fp32, [256, 128]> concat_24 = const()[name = tensor<string, []>("concat_24"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2297792)))];
            tensor<fp32, [256, 64]> concat_25 = const()[name = tensor<string, []>("concat_25"), val = tensor<fp32, [256, 64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2428928)))];
            tensor<fp32, [256, 128]> concat_26 = const()[name = tensor<string, []>("concat_26"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2494528)))];
            tensor<fp32, [256, 64]> concat_27 = const()[name = tensor<string, []>("concat_27"), val = tensor<fp32, [256, 64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2625664)))];
            tensor<fp32, [1, 128]> lstm_output_batch_first_lstm_h0_reshaped = const()[name = tensor<string, []>("lstm_output_batch_first_lstm_h0_reshaped"), val = tensor<fp32, [1, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2691264)))];
            tensor<fp32, [1, 128]> lstm_output_batch_first_lstm_c0_reshaped = const()[name = tensor<string, []>("lstm_output_batch_first_lstm_c0_reshaped"), val = tensor<fp32, [1, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2691840)))];
            tensor<string, []> lstm_output_batch_first_direction_0 = const()[name = tensor<string, []>("lstm_output_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
            tensor<bool, []> lstm_output_batch_first_output_sequence_0 = const()[name = tensor<string, []>("lstm_output_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
            tensor<string, []> lstm_output_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("lstm_output_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
            tensor<string, []> lstm_output_batch_first_cell_activation_0 = const()[name = tensor<string, []>("lstm_output_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
            tensor<string, []> lstm_output_batch_first_activation_0 = const()[name = tensor<string, []>("lstm_output_batch_first_activation_0"), val = tensor<string, []>("tanh")];
            tensor<fp32, [?, 1, 128]> lstm_output_batch_first_0, tensor<fp32, [1, 128]> lstm_output_batch_first_1, tensor<fp32, [1, 128]> lstm_output_batch_first_2 = lstm(activation = lstm_output_batch_first_activation_0, bias = add_4, bias_back = add_5, cell_activation = lstm_output_batch_first_cell_activation_0, direction = lstm_output_batch_first_direction_0, initial_c = lstm_output_batch_first_lstm_c0_reshaped, initial_h = lstm_output_batch_first_lstm_h0_reshaped, output_sequence = lstm_output_batch_first_output_sequence_0, recurrent_activation = lstm_output_batch_first_recurrent_activation_0, weight_hh = concat_25, weight_hh_back = concat_27, weight_ih = concat_24, weight_ih_back = concat_26, x = lstm_output_lstm_layer_1_0);
            tensor<int32, [3]> lstm_output_perm_0 = const()[name = tensor<string, []>("lstm_output_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<string, []> lstm_output_batch_first_0_to_fp16_dtype_0 = const()[name = tensor<string, []>("lstm_output_batch_first_0_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<string, []> lstm_output_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("lstm_output_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, []> split_32_num_splits_0 = const()[name = tensor<string, []>("split_32_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> split_32_axis_0 = const()[name = tensor<string, []>("split_32_axis_0"), val = tensor<int32, []>(1)];
            tensor<string, []> lstm_output_lstm_layer_0_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("lstm_output_lstm_layer_0_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [1, 128]> cast_24 = cast(dtype = lstm_output_lstm_layer_0_1_to_fp16_dtype_0, x = lstm_output_lstm_layer_0_1);
            tensor<fp16, [1, 64]> split_32_cast_0, tensor<fp16, [1, 64]> split_32_cast_1 = split(axis = split_32_axis_0, num_splits = split_32_num_splits_0, x = cast_24);
            tensor<int32, []> stack_0_axis_0 = const()[name = tensor<string, []>("stack_0_axis_0"), val = tensor<int32, []>(0)];
            tensor<fp16, [2, 1, 64]> stack_0_cast = stack(axis = stack_0_axis_0, values = (split_32_cast_0, split_32_cast_1));
            tensor<int32, []> split_33_num_splits_0 = const()[name = tensor<string, []>("split_33_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> split_33_axis_0 = const()[name = tensor<string, []>("split_33_axis_0"), val = tensor<int32, []>(1)];
            tensor<string, []> lstm_output_lstm_layer_1_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("lstm_output_lstm_layer_1_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [1, 128]> cast_20 = cast(dtype = lstm_output_lstm_layer_1_1_to_fp16_dtype_0, x = lstm_output_lstm_layer_1_1);
            tensor<fp16, [1, 64]> split_33_cast_0, tensor<fp16, [1, 64]> split_33_cast_1 = split(axis = split_33_axis_0, num_splits = split_33_num_splits_0, x = cast_20);
            tensor<int32, []> stack_1_axis_0 = const()[name = tensor<string, []>("stack_1_axis_0"), val = tensor<int32, []>(0)];
            tensor<fp16, [2, 1, 64]> stack_1_cast = stack(axis = stack_1_axis_0, values = (split_33_cast_0, split_33_cast_1));
            tensor<int32, []> split_34_num_splits_0 = const()[name = tensor<string, []>("split_34_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> split_34_axis_0 = const()[name = tensor<string, []>("split_34_axis_0"), val = tensor<int32, []>(1)];
            tensor<string, []> lstm_output_batch_first_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("lstm_output_batch_first_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [1, 128]> cast_16 = cast(dtype = lstm_output_batch_first_1_to_fp16_dtype_0, x = lstm_output_batch_first_1);
            tensor<fp16, [1, 64]> split_34_cast_0, tensor<fp16, [1, 64]> split_34_cast_1 = split(axis = split_34_axis_0, num_splits = split_34_num_splits_0, x = cast_16);
            tensor<int32, []> stack_2_axis_0 = const()[name = tensor<string, []>("stack_2_axis_0"), val = tensor<int32, []>(0)];
            tensor<fp16, [2, 1, 64]> stack_2_cast = stack(axis = stack_2_axis_0, values = (split_34_cast_0, split_34_cast_1));
            tensor<int32, []> hn_1_axis_0 = const()[name = tensor<string, []>("hn_1_axis_0"), val = tensor<int32, []>(0)];
            tensor<bool, []> hn_1_interleave_0 = const()[name = tensor<string, []>("hn_1_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [6, 1, 64]> hn_1_cast = concat(axis = hn_1_axis_0, interleave = hn_1_interleave_0, values = (stack_0_cast, stack_1_cast, stack_2_cast));
            tensor<int32, [3]> hn_3_begin_0 = const()[name = tensor<string, []>("hn_3_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> hn_3_end_0 = const()[name = tensor<string, []>("hn_3_end_0"), val = tensor<int32, [3]>([1, 1, 64])];
            tensor<bool, [3]> hn_3_end_mask_0 = const()[name = tensor<string, []>("hn_3_end_mask_0"), val = tensor<bool, [3]>([false, true, true])];
            tensor<bool, [3]> hn_3_squeeze_mask_0 = const()[name = tensor<string, []>("hn_3_squeeze_mask_0"), val = tensor<bool, [3]>([true, false, false])];
            tensor<fp16, [1, 64]> hn_3_cast = slice_by_index(begin = hn_3_begin_0, end = hn_3_end_0, end_mask = hn_3_end_mask_0, squeeze_mask = hn_3_squeeze_mask_0, x = hn_1_cast);
            tensor<int32, [1]> var_90_axes_0 = const()[name = tensor<string, []>("op_90_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 64]> var_90_cast = expand_dims(axes = var_90_axes_0, x = hn_3_cast);
            tensor<int32, [3]> hn_reps_0 = const()[name = tensor<string, []>("hn_reps_0"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp16, [1, 64, 64]> hn_cast = tile(reps = hn_reps_0, x = var_90_cast);
            tensor<string, []> hn_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("hn_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, []> var_98 = const()[name = tensor<string, []>("op_98"), 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, 128]> cast_26 = cast(dtype = lstm_output_batch_first_0_to_fp16_dtype_0, x = lstm_output_batch_first_0);
            tensor<fp16, [1, ?, 128]> transpose_0 = transpose(perm = lstm_output_perm_0, x = cast_26);
            tensor<fp16, [1, ?, 192]> input_1_cast = concat(axis = var_98, interleave = input_1_interleave_0, values = (transpose_0, hn_cast));
            tensor<fp16, [1, ?, 192]> input_3_cast = relu(x = input_1_cast);
            tensor<fp16, [1, 192]> attn_weight_to_fp16 = const()[name = tensor<string, []>("attn_weight_to_fp16"), val = tensor<fp16, [1, 192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2692416)))];
            tensor<fp16, [1]> attn_bias_to_fp16 = const()[name = tensor<string, []>("attn_bias_to_fp16"), val = tensor<fp16, [1]>([-0x1.d94p-3])];
            tensor<fp16, [1, ?, 1]> input_5_cast = linear(bias = attn_bias_to_fp16, weight = attn_weight_to_fp16, x = input_3_cast);
            tensor<int32, []> var_104 = const()[name = tensor<string, []>("op_104"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, ?, 1]> attn_weights_cast = softmax(axis = var_104, x = input_5_cast);
            tensor<fp16, [1, ?, 128]> input_7_cast = mul(x = transpose_0, y = attn_weights_cast);
            tensor<fp16, [6, 128]> tagged_weight_to_fp16 = const()[name = tensor<string, []>("tagged_weight_to_fp16"), val = tensor<fp16, [6, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2692864)))];
            tensor<fp16, [6]> tagged_bias_to_fp16 = const()[name = tensor<string, []>("tagged_bias_to_fp16"), val = tensor<fp16, [6]>([0x1.05cp+1, -0x1.6bp-1, -0x1.744p+0, -0x1.5ep+0, -0x1.5b4p+1, 0x1.38p+0])];
            tensor<fp16, [1, ?, 6]> input_cast = linear(bias = tagged_bias_to_fp16, weight = tagged_weight_to_fp16, x = input_7_cast);
            tensor<int32, []> var_111 = const()[name = tensor<string, []>("op_111"), val = tensor<int32, []>(2)];
            tensor<fp16, [1, ?, 6]> sequence_tags_cast = softmax(axis = var_111, x = input_cast);
            tensor<string, []> sequence_tags_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("sequence_tags_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, [1]> reduce_max_0_axes_0 = const()[name = tensor<string, []>("reduce_max_0_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<bool, []> reduce_max_0_keep_dims_0 = const()[name = tensor<string, []>("reduce_max_0_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, ?]> reduce_max_0_cast = reduce_max(axes = reduce_max_0_axes_0, keep_dims = reduce_max_0_keep_dims_0, x = sequence_tags_cast);
            tensor<string, []> reduce_max_0_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("reduce_max_0_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, []> reduce_argmax_0_axis_0 = const()[name = tensor<string, []>("reduce_argmax_0_axis_0"), val = tensor<int32, []>(2)];
            tensor<bool, []> reduce_argmax_0_keep_dims_0 = const()[name = tensor<string, []>("reduce_argmax_0_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<int32, [1, ?]> reduce_argmax_0 = reduce_argmax(axis = reduce_argmax_0_axis_0, keep_dims = reduce_argmax_0_keep_dims_0, x = sequence_tags_cast);
            tensor<fp32, [1, ?]> reduce_max_0 = cast(dtype = reduce_max_0_cast_to_fp32_dtype_0, x = reduce_max_0_cast);
            tensor<fp32, [1, ?, 6]> sequence_tags = cast(dtype = sequence_tags_cast_to_fp32_dtype_0, x = sequence_tags_cast);
            tensor<fp32, [1, 64, 64]> hn = cast(dtype = hn_cast_to_fp32_dtype_0, x = hn_cast);
            tensor<fp32, [1, ?, 128]> lstm_output = cast(dtype = lstm_output_cast_to_fp32_dtype_0, x = transpose_0);
        } -> (lstm_output, hn, sequence_tags, reduce_argmax_0, reduce_max_0);
}