program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MILHost", "3500.11.1"}, {"coremlc-version", "3500.22.1"}, {"mldb_token", "mldb-27mar97a6j"}})]
{
    func main<ios18>(tensor<fp16, [1, ?, 1, 68]> input_pp_positional_encodings, tensor<fp16, [1, ?, 4]> input_stroke) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"input_pp_positional_encodings", [1, 4, 1, 68]}, {"input_stroke", [1, 4, 4]}}), ("RangeDims", {{"input_pp_positional_encodings", [[1, 1], [4, 10000], [1, 1], [68, 68]]}, {"input_stroke", [[1, 1], [4, 10000], [4, 4]]}})))] {
            int32 var_6 = const()[name = string("op_6"), val = int32(2)];
            int32 var_13 = const()[name = string("op_13"), val = int32(20)];
            tensor<int32, [3]> var_32 = const()[name = string("op_32"), val = tensor<int32, [3]>([0, 2, 1])];
            string input0_15_pad_type_0 = const()[name = string("input0_15_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input0_15_pad_0 = const()[name = string("input0_15_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input0_15_strides_0 = const()[name = string("input0_15_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input0_15_dilations_0 = const()[name = string("input0_15_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input0_15_groups_0 = const()[name = string("input0_15_groups_0"), val = int32(1)];
            tensor<fp16, [32, 4, 3]> convolutions_0_weight_to_fp16 = const()[name = string("convolutions_0_weight_to_fp16"), val = tensor<fp16, [32, 4, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
            tensor<fp16, [32]> convolutions_0_bias_to_fp16 = const()[name = string("convolutions_0_bias_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(896)))];
            tensor<fp16, [1, 4, ?]> input_3_cast_fp16 = transpose(perm = var_32, x = input_stroke)[name = string("transpose_20")];
            tensor<fp16, [1, 32, ?]> input0_15_cast_fp16 = conv(bias = convolutions_0_bias_to_fp16, dilations = input0_15_dilations_0, groups = input0_15_groups_0, pad = input0_15_pad_0, pad_type = input0_15_pad_type_0, strides = input0_15_strides_0, weight = convolutions_0_weight_to_fp16, x = input_3_cast_fp16)[name = string("input0_15_cast_fp16")];
            tensor<fp16, [1, 32, ?]> var_48_cast_fp16 = relu(x = input0_15_cast_fp16)[name = string("op_48_cast_fp16")];
            tensor<int32, [1]> var_49 = const()[name = string("op_49"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> var_50 = const()[name = string("op_50"), val = tensor<int32, [1]>([2])];
            string input_8_pad_type_0 = const()[name = string("input_8_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_8_pad_0 = const()[name = string("input_8_pad_0"), val = tensor<int32, [2]>([0, 0])];
            bool input_8_ceil_mode_0 = const()[name = string("input_8_ceil_mode_0"), val = bool(true)];
            tensor<fp16, [1, 32, ?]> input_8_cast_fp16 = max_pool(ceil_mode = input_8_ceil_mode_0, kernel_sizes = var_49, pad = input_8_pad_0, pad_type = input_8_pad_type_0, strides = var_50, x = var_48_cast_fp16)[name = string("input_8_cast_fp16")];
            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)];
            tensor<fp16, [64, 32, 3]> convolutions_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 32, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1024))), scale = tensor<fp16, [64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7232))))[name = string("convolutions_3_weight_to_fp16_quantized")];
            tensor<fp16, [64]> convolutions_3_bias_to_fp16 = const()[name = string("convolutions_3_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7424)))];
            tensor<fp16, [1, 64, ?]> input_5_cast_fp16 = conv(bias = convolutions_3_bias_to_fp16, dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = convolutions_3_weight_to_fp16_quantized, x = input_8_cast_fp16)[name = string("input_5_cast_fp16")];
            tensor<fp16, [1, 64, ?]> var_61_cast_fp16 = relu(x = input_5_cast_fp16)[name = string("op_61_cast_fp16")];
            tensor<int32, [1]> var_62 = const()[name = string("op_62"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> var_63 = const()[name = string("op_63"), val = tensor<int32, [1]>([2])];
            string output_5_pad_type_0 = const()[name = string("output_5_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> output_5_pad_0 = const()[name = string("output_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
            bool output_5_ceil_mode_0 = const()[name = string("output_5_ceil_mode_0"), val = bool(true)];
            tensor<fp16, [1, 64, ?]> output_5_cast_fp16 = max_pool(ceil_mode = output_5_ceil_mode_0, kernel_sizes = var_62, pad = output_5_pad_0, pad_type = output_5_pad_type_0, strides = var_63, x = var_61_cast_fp16)[name = string("output_5_cast_fp16")];
            tensor<int32, [3]> transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor<int32, [3]>([2, 0, 1])];
            string output0_1_batch_first_direction_0 = const()[name = string("output0_1_batch_first_direction_0"), val = string("bidirectional")];
            bool output0_1_batch_first_output_sequence_0 = const()[name = string("output0_1_batch_first_output_sequence_0"), val = bool(true)];
            string output0_1_batch_first_recurrent_activation_0 = const()[name = string("output0_1_batch_first_recurrent_activation_0"), val = string("sigmoid")];
            string output0_1_batch_first_cell_activation_0 = const()[name = string("output0_1_batch_first_cell_activation_0"), val = string("tanh")];
            string output0_1_batch_first_activation_0 = const()[name = string("output0_1_batch_first_activation_0"), val = string("tanh")];
            tensor<fp16, [1, 512]> output0_1_batch_first_lstm_h0_reshaped_to_fp16 = const()[name = string("output0_1_batch_first_lstm_h0_reshaped_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7616)))];
            tensor<fp16, [1024, 64]> concat_4_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8704))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74304))))[name = string("concat_4_to_fp16_quantized")];
            tensor<fp16, [1024, 256]> concat_5_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76416))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338624))))[name = string("concat_5_to_fp16_quantized")];
            tensor<fp16, [1024]> add_2_to_fp16 = const()[name = string("add_2_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340736)))];
            tensor<fp16, [1024, 64]> concat_6_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342848))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408448))))[name = string("concat_6_to_fp16_quantized")];
            tensor<fp16, [1024, 256]> concat_7_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410560))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672768))))[name = string("concat_7_to_fp16_quantized")];
            tensor<fp16, [1024]> add_3_to_fp16 = const()[name = string("add_3_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(674880)))];
            tensor<fp16, [?, 1, 64]> transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = output_5_cast_fp16)[name = string("transpose_19")];
            tensor<fp16, [?, 1, 512]> output0_1_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> output0_1_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> output0_1_batch_first_cast_fp16_2 = lstm(activation = output0_1_batch_first_activation_0, bias = add_2_to_fp16, bias_back = add_3_to_fp16, cell_activation = output0_1_batch_first_cell_activation_0, direction = output0_1_batch_first_direction_0, initial_c = output0_1_batch_first_lstm_h0_reshaped_to_fp16, initial_h = output0_1_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = output0_1_batch_first_output_sequence_0, recurrent_activation = output0_1_batch_first_recurrent_activation_0, weight_hh = concat_5_to_fp16_quantized, weight_hh_back = concat_7_to_fp16_quantized, weight_ih = concat_4_to_fp16_quantized, weight_ih_back = concat_6_to_fp16_quantized, x = transpose_0_cast_fp16)[name = string("output0_1_batch_first_cast_fp16")];
            tensor<int32, [3]> output0_1_perm_0 = const()[name = string("output0_1_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [3]> var_99_begin_0 = const()[name = string("op_99_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> var_99_end_0 = const()[name = string("op_99_end_0"), val = tensor<int32, [3]>([1, 0, 256])];
            tensor<bool, [3]> var_99_end_mask_0 = const()[name = string("op_99_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp16, [1, ?, 512]> output0_1_cast_fp16 = transpose(perm = output0_1_perm_0, x = output0_1_batch_first_cast_fp16_0)[name = string("transpose_18")];
            tensor<fp16, [1, ?, 256]> var_99_cast_fp16 = slice_by_index(begin = var_99_begin_0, end = var_99_end_0, end_mask = var_99_end_mask_0, x = output0_1_cast_fp16)[name = string("op_99_cast_fp16")];
            tensor<int32, [3]> var_102_begin_0 = const()[name = string("op_102_begin_0"), val = tensor<int32, [3]>([0, 0, 256])];
            tensor<int32, [3]> var_102_end_0 = const()[name = string("op_102_end_0"), val = tensor<int32, [3]>([1, 0, 512])];
            tensor<bool, [3]> var_102_end_mask_0 = const()[name = string("op_102_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, ?, 256]> var_102_cast_fp16 = slice_by_index(begin = var_102_begin_0, end = var_102_end_0, end_mask = var_102_end_mask_0, x = output0_1_cast_fp16)[name = string("op_102_cast_fp16")];
            tensor<fp16, [1, ?, 256]> input0_17_cast_fp16 = add(x = var_99_cast_fp16, y = var_102_cast_fp16)[name = string("input0_17_cast_fp16")];
            tensor<int32, [3]> input0_17_batch_first_transpose_perm_0 = const()[name = string("input0_17_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            string output_7_batch_first_direction_0 = const()[name = string("output_7_batch_first_direction_0"), val = string("bidirectional")];
            bool output_7_batch_first_output_sequence_0 = const()[name = string("output_7_batch_first_output_sequence_0"), val = bool(true)];
            string output_7_batch_first_recurrent_activation_0 = const()[name = string("output_7_batch_first_recurrent_activation_0"), val = string("sigmoid")];
            string output_7_batch_first_cell_activation_0 = const()[name = string("output_7_batch_first_cell_activation_0"), val = string("tanh")];
            string output_7_batch_first_activation_0 = const()[name = string("output_7_batch_first_activation_0"), val = string("tanh")];
            tensor<fp16, [1024, 256]> concat_14_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(676992))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(939200))))[name = string("concat_14_to_fp16_quantized")];
            tensor<fp16, [1024, 256]> concat_15_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941312))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1203520))))[name = string("concat_15_to_fp16_quantized")];
            tensor<fp16, [1024]> add_5_to_fp16 = const()[name = string("add_5_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205632)))];
            tensor<fp16, [1024, 256]> concat_16_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1207744))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1469952))))[name = string("concat_16_to_fp16_quantized")];
            tensor<fp16, [1024, 256]> concat_17_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1472064))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1734272))))[name = string("concat_17_to_fp16_quantized")];
            tensor<fp16, [1024]> add_6_to_fp16 = const()[name = string("add_6_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736384)))];
            tensor<fp16, [?, 1, 256]> input0_17_batch_first_transpose_cast_fp16 = transpose(perm = input0_17_batch_first_transpose_perm_0, x = input0_17_cast_fp16)[name = string("transpose_17")];
            tensor<fp16, [?, 1, 512]> output_7_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> output_7_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> output_7_batch_first_cast_fp16_2 = lstm(activation = output_7_batch_first_activation_0, bias = add_5_to_fp16, bias_back = add_6_to_fp16, cell_activation = output_7_batch_first_cell_activation_0, direction = output_7_batch_first_direction_0, initial_c = output0_1_batch_first_lstm_h0_reshaped_to_fp16, initial_h = output0_1_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = output_7_batch_first_output_sequence_0, recurrent_activation = output_7_batch_first_recurrent_activation_0, weight_hh = concat_15_to_fp16_quantized, weight_hh_back = concat_17_to_fp16_quantized, weight_ih = concat_14_to_fp16_quantized, weight_ih_back = concat_16_to_fp16_quantized, x = input0_17_batch_first_transpose_cast_fp16)[name = string("output_7_batch_first_cast_fp16")];
            tensor<int32, [3]> output_7_perm_0 = const()[name = string("output_7_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [3]> var_137_begin_0 = const()[name = string("op_137_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> var_137_end_0 = const()[name = string("op_137_end_0"), val = tensor<int32, [3]>([1, 0, 256])];
            tensor<bool, [3]> var_137_end_mask_0 = const()[name = string("op_137_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp16, [1, ?, 512]> output_7_cast_fp16 = transpose(perm = output_7_perm_0, x = output_7_batch_first_cast_fp16_0)[name = string("transpose_16")];
            tensor<fp16, [1, ?, 256]> var_137_cast_fp16 = slice_by_index(begin = var_137_begin_0, end = var_137_end_0, end_mask = var_137_end_mask_0, x = output_7_cast_fp16)[name = string("op_137_cast_fp16")];
            tensor<int32, [3]> var_140_begin_0 = const()[name = string("op_140_begin_0"), val = tensor<int32, [3]>([0, 0, 256])];
            tensor<int32, [3]> var_140_end_0 = const()[name = string("op_140_end_0"), val = tensor<int32, [3]>([1, 0, 512])];
            tensor<bool, [3]> var_140_end_mask_0 = const()[name = string("op_140_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, ?, 256]> var_140_cast_fp16 = slice_by_index(begin = var_140_begin_0, end = var_140_end_0, end_mask = var_140_end_mask_0, x = output_7_cast_fp16)[name = string("op_140_cast_fp16")];
            tensor<fp16, [1, ?, 256]> input1_3_cast_fp16 = add(x = var_137_cast_fp16, y = var_140_cast_fp16)[name = string("input1_3_cast_fp16")];
            tensor<int32, [3]> input1_3_batch_first_transpose_perm_0 = const()[name = string("input1_3_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            string output_9_batch_first_direction_0 = const()[name = string("output_9_batch_first_direction_0"), val = string("bidirectional")];
            bool output_9_batch_first_output_sequence_0 = const()[name = string("output_9_batch_first_output_sequence_0"), val = bool(true)];
            string output_9_batch_first_recurrent_activation_0 = const()[name = string("output_9_batch_first_recurrent_activation_0"), val = string("sigmoid")];
            string output_9_batch_first_cell_activation_0 = const()[name = string("output_9_batch_first_cell_activation_0"), val = string("tanh")];
            string output_9_batch_first_activation_0 = const()[name = string("output_9_batch_first_activation_0"), val = string("tanh")];
            tensor<fp16, [1, 256]> output_9_batch_first_lstm_h0_reshaped_to_fp16 = const()[name = string("output_9_batch_first_lstm_h0_reshaped_to_fp16"), val = tensor<fp16, [1, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738496)))];
            tensor<fp16, [512, 256]> concat_24_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739072))), scale = tensor<fp16, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1870208))))[name = string("concat_24_to_fp16_quantized")];
            tensor<fp16, [512, 128]> concat_25_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1871296))), scale = tensor<fp16, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1936896))))[name = string("concat_25_to_fp16_quantized")];
            tensor<fp16, [512]> add_8_to_fp16 = const()[name = string("add_8_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1937984)))];
            tensor<fp16, [512, 256]> concat_26_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1939072))), scale = tensor<fp16, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2070208))))[name = string("concat_26_to_fp16_quantized")];
            tensor<fp16, [512, 128]> concat_27_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2071296))), scale = tensor<fp16, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2136896))))[name = string("concat_27_to_fp16_quantized")];
            tensor<fp16, [512]> add_9_to_fp16 = const()[name = string("add_9_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2137984)))];
            tensor<fp16, [?, 1, 256]> input1_3_batch_first_transpose_cast_fp16 = transpose(perm = input1_3_batch_first_transpose_perm_0, x = input1_3_cast_fp16)[name = string("transpose_15")];
            tensor<fp16, [?, 1, 256]> output_9_batch_first_cast_fp16_0, tensor<fp16, [1, 256]> output_9_batch_first_cast_fp16_1, tensor<fp16, [1, 256]> output_9_batch_first_cast_fp16_2 = lstm(activation = output_9_batch_first_activation_0, bias = add_8_to_fp16, bias_back = add_9_to_fp16, cell_activation = output_9_batch_first_cell_activation_0, direction = output_9_batch_first_direction_0, initial_c = output_9_batch_first_lstm_h0_reshaped_to_fp16, initial_h = output_9_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = output_9_batch_first_output_sequence_0, recurrent_activation = output_9_batch_first_recurrent_activation_0, weight_hh = concat_25_to_fp16_quantized, weight_hh_back = concat_27_to_fp16_quantized, weight_ih = concat_24_to_fp16_quantized, weight_ih_back = concat_26_to_fp16_quantized, x = input1_3_batch_first_transpose_cast_fp16)[name = string("output_9_batch_first_cast_fp16")];
            tensor<int32, [3]> output_9_perm_0 = const()[name = string("output_9_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [3]> var_175_begin_0 = const()[name = string("op_175_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> var_175_end_0 = const()[name = string("op_175_end_0"), val = tensor<int32, [3]>([1, 0, 128])];
            tensor<bool, [3]> var_175_end_mask_0 = const()[name = string("op_175_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp16, [1, ?, 256]> output_9_cast_fp16 = transpose(perm = output_9_perm_0, x = output_9_batch_first_cast_fp16_0)[name = string("transpose_14")];
            tensor<fp16, [1, ?, 128]> var_175_cast_fp16 = slice_by_index(begin = var_175_begin_0, end = var_175_end_0, end_mask = var_175_end_mask_0, x = output_9_cast_fp16)[name = string("op_175_cast_fp16")];
            tensor<int32, [3]> var_178_begin_0 = const()[name = string("op_178_begin_0"), val = tensor<int32, [3]>([0, 0, 128])];
            tensor<int32, [3]> var_178_end_0 = const()[name = string("op_178_end_0"), val = tensor<int32, [3]>([1, 0, 256])];
            tensor<bool, [3]> var_178_end_mask_0 = const()[name = string("op_178_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, ?, 128]> var_178_cast_fp16 = slice_by_index(begin = var_178_begin_0, end = var_178_end_0, end_mask = var_178_end_mask_0, x = output_9_cast_fp16)[name = string("op_178_cast_fp16")];
            tensor<fp16, [1, ?, 128]> input2_3_cast_fp16 = add(x = var_175_cast_fp16, y = var_178_cast_fp16)[name = string("input2_3_cast_fp16")];
            tensor<int32, [3]> input2_3_batch_first_transpose_perm_0 = const()[name = string("input2_3_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            string output_11_batch_first_direction_0 = const()[name = string("output_11_batch_first_direction_0"), val = string("bidirectional")];
            bool output_11_batch_first_output_sequence_0 = const()[name = string("output_11_batch_first_output_sequence_0"), val = bool(true)];
            string output_11_batch_first_recurrent_activation_0 = const()[name = string("output_11_batch_first_recurrent_activation_0"), val = string("sigmoid")];
            string output_11_batch_first_cell_activation_0 = const()[name = string("output_11_batch_first_cell_activation_0"), val = string("tanh")];
            string output_11_batch_first_activation_0 = const()[name = string("output_11_batch_first_activation_0"), val = string("tanh")];
            tensor<fp16, [1024, 128]> concat_34_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2139072))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2270208))))[name = string("concat_34_to_fp16_quantized")];
            tensor<fp16, [1024, 256]> concat_35_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2272320))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2534528))))[name = string("concat_35_to_fp16_quantized")];
            tensor<fp16, [1024]> add_11_to_fp16 = const()[name = string("add_11_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2536640)))];
            tensor<fp16, [1024, 128]> concat_36_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2538752))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2669888))))[name = string("concat_36_to_fp16_quantized")];
            tensor<fp16, [1024, 256]> concat_37_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2672000))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2934208))))[name = string("concat_37_to_fp16_quantized")];
            tensor<fp16, [1024]> add_12_to_fp16 = const()[name = string("add_12_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936320)))];
            tensor<fp16, [?, 1, 128]> input2_3_batch_first_transpose_cast_fp16 = transpose(perm = input2_3_batch_first_transpose_perm_0, x = input2_3_cast_fp16)[name = string("transpose_13")];
            tensor<fp16, [?, 1, 512]> output_11_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> output_11_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> output_11_batch_first_cast_fp16_2 = lstm(activation = output_11_batch_first_activation_0, bias = add_11_to_fp16, bias_back = add_12_to_fp16, cell_activation = output_11_batch_first_cell_activation_0, direction = output_11_batch_first_direction_0, initial_c = output0_1_batch_first_lstm_h0_reshaped_to_fp16, initial_h = output0_1_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = output_11_batch_first_output_sequence_0, recurrent_activation = output_11_batch_first_recurrent_activation_0, weight_hh = concat_35_to_fp16_quantized, weight_hh_back = concat_37_to_fp16_quantized, weight_ih = concat_34_to_fp16_quantized, weight_ih_back = concat_36_to_fp16_quantized, x = input2_3_batch_first_transpose_cast_fp16)[name = string("output_11_batch_first_cast_fp16")];
            tensor<int32, [3]> output_11_perm_0 = const()[name = string("output_11_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [3]> var_213_begin_0 = const()[name = string("op_213_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> var_213_end_0 = const()[name = string("op_213_end_0"), val = tensor<int32, [3]>([1, 0, 256])];
            tensor<bool, [3]> var_213_end_mask_0 = const()[name = string("op_213_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp16, [1, ?, 512]> output_11_cast_fp16 = transpose(perm = output_11_perm_0, x = output_11_batch_first_cast_fp16_0)[name = string("transpose_12")];
            tensor<fp16, [1, ?, 256]> var_213_cast_fp16 = slice_by_index(begin = var_213_begin_0, end = var_213_end_0, end_mask = var_213_end_mask_0, x = output_11_cast_fp16)[name = string("op_213_cast_fp16")];
            tensor<int32, [3]> var_216_begin_0 = const()[name = string("op_216_begin_0"), val = tensor<int32, [3]>([0, 0, 256])];
            tensor<int32, [3]> var_216_end_0 = const()[name = string("op_216_end_0"), val = tensor<int32, [3]>([1, 0, 512])];
            tensor<bool, [3]> var_216_end_mask_0 = const()[name = string("op_216_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, ?, 256]> var_216_cast_fp16 = slice_by_index(begin = var_216_begin_0, end = var_216_end_0, end_mask = var_216_end_mask_0, x = output_11_cast_fp16)[name = string("op_216_cast_fp16")];
            tensor<fp16, [1, ?, 256]> input3_3_cast_fp16 = add(x = var_213_cast_fp16, y = var_216_cast_fp16)[name = string("input3_3_cast_fp16")];
            tensor<int32, [3]> input3_3_batch_first_transpose_perm_0 = const()[name = string("input3_3_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            string output_13_batch_first_direction_0 = const()[name = string("output_13_batch_first_direction_0"), val = string("bidirectional")];
            bool output_13_batch_first_output_sequence_0 = const()[name = string("output_13_batch_first_output_sequence_0"), val = bool(true)];
            string output_13_batch_first_recurrent_activation_0 = const()[name = string("output_13_batch_first_recurrent_activation_0"), val = string("sigmoid")];
            string output_13_batch_first_cell_activation_0 = const()[name = string("output_13_batch_first_cell_activation_0"), val = string("tanh")];
            string output_13_batch_first_activation_0 = const()[name = string("output_13_batch_first_activation_0"), val = string("tanh")];
            tensor<fp16, [1024, 256]> concat_44_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2938432))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3200640))))[name = string("concat_44_to_fp16_quantized")];
            tensor<fp16, [1024, 256]> concat_45_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3202752))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3464960))))[name = string("concat_45_to_fp16_quantized")];
            tensor<fp16, [1024]> add_14_to_fp16 = const()[name = string("add_14_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3467072)))];
            tensor<fp16, [1024, 256]> concat_46_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3469184))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3731392))))[name = string("concat_46_to_fp16_quantized")];
            tensor<fp16, [1024, 256]> concat_47_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3733504))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3995712))))[name = string("concat_47_to_fp16_quantized")];
            tensor<fp16, [1024]> add_15_to_fp16 = const()[name = string("add_15_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3997824)))];
            tensor<fp16, [?, 1, 256]> input3_3_batch_first_transpose_cast_fp16 = transpose(perm = input3_3_batch_first_transpose_perm_0, x = input3_3_cast_fp16)[name = string("transpose_11")];
            tensor<fp16, [?, 1, 512]> output_13_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> output_13_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> output_13_batch_first_cast_fp16_2 = lstm(activation = output_13_batch_first_activation_0, bias = add_14_to_fp16, bias_back = add_15_to_fp16, cell_activation = output_13_batch_first_cell_activation_0, direction = output_13_batch_first_direction_0, initial_c = output0_1_batch_first_lstm_h0_reshaped_to_fp16, initial_h = output0_1_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = output_13_batch_first_output_sequence_0, recurrent_activation = output_13_batch_first_recurrent_activation_0, weight_hh = concat_45_to_fp16_quantized, weight_hh_back = concat_47_to_fp16_quantized, weight_ih = concat_44_to_fp16_quantized, weight_ih_back = concat_46_to_fp16_quantized, x = input3_3_batch_first_transpose_cast_fp16)[name = string("output_13_batch_first_cast_fp16")];
            tensor<int32, [3]> output_13_perm_0 = const()[name = string("output_13_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [3]> var_251_begin_0 = const()[name = string("op_251_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> var_251_end_0 = const()[name = string("op_251_end_0"), val = tensor<int32, [3]>([1, 0, 256])];
            tensor<bool, [3]> var_251_end_mask_0 = const()[name = string("op_251_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp16, [1, ?, 512]> output_13_cast_fp16 = transpose(perm = output_13_perm_0, x = output_13_batch_first_cast_fp16_0)[name = string("transpose_10")];
            tensor<fp16, [1, ?, 256]> var_251_cast_fp16 = slice_by_index(begin = var_251_begin_0, end = var_251_end_0, end_mask = var_251_end_mask_0, x = output_13_cast_fp16)[name = string("op_251_cast_fp16")];
            tensor<int32, [3]> var_254_begin_0 = const()[name = string("op_254_begin_0"), val = tensor<int32, [3]>([0, 0, 256])];
            tensor<int32, [3]> var_254_end_0 = const()[name = string("op_254_end_0"), val = tensor<int32, [3]>([1, 0, 512])];
            tensor<bool, [3]> var_254_end_mask_0 = const()[name = string("op_254_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, ?, 256]> var_254_cast_fp16 = slice_by_index(begin = var_254_begin_0, end = var_254_end_0, end_mask = var_254_end_mask_0, x = output_13_cast_fp16)[name = string("op_254_cast_fp16")];
            tensor<fp16, [1, ?, 256]> input4_1_cast_fp16 = add(x = var_251_cast_fp16, y = var_254_cast_fp16)[name = string("input4_1_cast_fp16")];
            tensor<int32, [2]> var_259 = const()[name = string("op_259"), val = tensor<int32, [2]>([-1, 256])];
            tensor<fp16, [?, 256]> input5_1_cast_fp16 = reshape(shape = var_259, x = input4_1_cast_fp16)[name = string("input5_1_cast_fp16")];
            tensor<fp16, [19942, 256]> final_layer_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [19942, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3999936))), scale = tensor<fp16, [19942, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9105152))))[name = string("final_layer_weight_to_fp16_quantized")];
            tensor<fp16, [19942]> final_layer_bias_to_fp16 = const()[name = string("final_layer_bias_to_fp16"), val = tensor<fp16, [19942]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9145152)))];
            tensor<fp16, [?, 19942]> linear_0_cast_fp16 = linear(bias = final_layer_bias_to_fp16, weight = final_layer_weight_to_fp16_quantized, x = input5_1_cast_fp16)[name = string("linear_0_cast_fp16")];
            tensor<int32, [3]> var_264 = const()[name = string("op_264"), val = tensor<int32, [3]>([1, -1, 19942])];
            tensor<fp16, [1, ?, 19942]> input6_1_cast_fp16 = reshape(shape = var_264, x = linear_0_cast_fp16)[name = string("input6_1_cast_fp16")];
            tensor<int32, [3]> var_266 = const()[name = string("op_266"), val = tensor<int32, [3]>([0, 2, 1])];
            string input1_7_pad_type_0 = const()[name = string("input1_7_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input1_7_strides_0 = const()[name = string("input1_7_strides_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [2]> input1_7_pad_0 = const()[name = string("input1_7_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input1_7_dilations_0 = const()[name = string("input1_7_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input1_7_groups_0 = const()[name = string("input1_7_groups_0"), val = int32(1)];
            tensor<fp16, [256, 128, 2]> principal_points_upscale_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 128, 2]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9185152))), scale = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9250752))))[name = string("principal_points_upscale_0_weight_to_fp16_quantized")];
            tensor<fp16, [128]> principal_points_upscale_0_bias_to_fp16 = const()[name = string("principal_points_upscale_0_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9251072)))];
            tensor<fp16, [1, 256, ?]> input7_1_cast_fp16 = transpose(perm = var_266, x = input4_1_cast_fp16)[name = string("transpose_9")];
            tensor<fp16, [1, 128, ?]> input1_7_cast_fp16 = conv_transpose(bias = principal_points_upscale_0_bias_to_fp16, dilations = input1_7_dilations_0, groups = input1_7_groups_0, pad = input1_7_pad_0, pad_type = input1_7_pad_type_0, strides = input1_7_strides_0, weight = principal_points_upscale_0_weight_to_fp16_quantized, x = input7_1_cast_fp16)[name = string("input1_7_cast_fp16")];
            tensor<fp16, [1, 128, ?]> var_282_cast_fp16 = relu(x = input1_7_cast_fp16)[name = string("op_282_cast_fp16")];
            string input_10_pad_type_0 = const()[name = string("input_10_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_10_strides_0 = const()[name = string("input_10_strides_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [2]> input_10_pad_0 = const()[name = string("input_10_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_10_dilations_0 = const()[name = string("input_10_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_10_groups_0 = const()[name = string("input_10_groups_0"), val = int32(1)];
            tensor<fp16, [128, 64, 2]> principal_points_upscale_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 64, 2]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9251392))), scale = tensor<fp16, [1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9267840))))[name = string("principal_points_upscale_2_weight_to_fp16_quantized")];
            tensor<fp16, [64]> principal_points_upscale_2_bias_to_fp16 = const()[name = string("principal_points_upscale_2_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9268032)))];
            tensor<fp16, [1, 64, ?]> input_10_cast_fp16 = conv_transpose(bias = principal_points_upscale_2_bias_to_fp16, dilations = input_10_dilations_0, groups = input_10_groups_0, pad = input_10_pad_0, pad_type = input_10_pad_type_0, strides = input_10_strides_0, weight = principal_points_upscale_2_weight_to_fp16_quantized, x = var_282_cast_fp16)[name = string("input_10_cast_fp16")];
            tensor<fp16, [1, 64, ?]> var_290_cast_fp16 = relu(x = input_10_cast_fp16)[name = string("op_290_cast_fp16")];
            tensor<int32, [3]> var_291 = const()[name = string("op_291"), val = tensor<int32, [3]>([0, 2, 1])];
            bool x_4_interleave_0 = const()[name = string("x_4_interleave_0"), val = bool(false)];
            tensor<fp16, [1, ?, 64]> upscaled_activations_1_cast_fp16 = transpose(perm = var_291, x = var_290_cast_fp16)[name = string("transpose_8")];
            tensor<fp16, [1, ?, 68]> x_4_cast_fp16 = concat(axis = var_6, interleave = x_4_interleave_0, values = (upscaled_activations_1_cast_fp16, input_stroke))[name = string("x_4_cast_fp16")];
            tensor<int32, [3]> var_295 = const()[name = string("op_295"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<fp16, [?, 1, 68]> x0_5_cast_fp16 = transpose(perm = var_295, x = x_4_cast_fp16)[name = string("transpose_7")];
            tensor<int32, [3]> var_297_shape_cast_fp16 = shape(x = x0_5_cast_fp16)[name = string("op_297_shape_cast_fp16")];
            int32 gather_6_axis_0 = const()[name = string("gather_6_axis_0"), val = int32(0)];
            int32 gather_6_batch_dims_0 = const()[name = string("gather_6_batch_dims_0"), val = int32(0)];
            bool gather_6_validate_indices_0 = const()[name = string("gather_6_validate_indices_0"), val = bool(false)];
            string var_297_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_297_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")];
            uint16 select_6_to_uint16 = const()[name = string("select_6_to_uint16"), val = uint16(0)];
            tensor<int16, [3]> var_297_shape_cast_fp16_to_int16 = cast(dtype = var_297_shape_cast_fp16_to_int16_dtype_0, x = var_297_shape_cast_fp16)[name = string("cast_1")];
            int16 gather_6_cast_uint16 = gather(axis = gather_6_axis_0, batch_dims = gather_6_batch_dims_0, indices = select_6_to_uint16, validate_indices = gather_6_validate_indices_0, x = var_297_shape_cast_fp16_to_int16)[name = string("gather_6_cast_uint16")];
            string gather_6_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_6_cast_uint16_to_int32_dtype_0"), val = string("int32")];
            tensor<int32, [4]> var_300_begin_0 = const()[name = string("op_300_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_300_end_0 = const()[name = string("op_300_end_0"), val = tensor<int32, [4]>([1, 0, 1, 68])];
            tensor<bool, [4]> var_300_end_mask_0 = const()[name = string("op_300_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<bool, [4]> var_300_squeeze_mask_0 = const()[name = string("op_300_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
            tensor<fp16, [?, 1, 68]> var_300_cast_fp16 = slice_by_index(begin = var_300_begin_0, end = var_300_end_0, end_mask = var_300_end_mask_0, squeeze_mask = var_300_squeeze_mask_0, x = input_pp_positional_encodings)[name = string("op_300_cast_fp16")];
            int32 concat_50_values1_0 = const()[name = string("concat_50_values1_0"), val = int32(1)];
            int32 concat_50_values2_0 = const()[name = string("concat_50_values2_0"), val = int32(68)];
            int32 concat_50_axis_0 = const()[name = string("concat_50_axis_0"), val = int32(0)];
            bool concat_50_interleave_0 = const()[name = string("concat_50_interleave_0"), val = bool(false)];
            int32 gather_6_cast_uint16_to_int32 = cast(dtype = gather_6_cast_uint16_to_int32_dtype_0, x = gather_6_cast_uint16)[name = string("cast_0")];
            tensor<int32, [3]> concat_50 = concat(axis = concat_50_axis_0, interleave = concat_50_interleave_0, values = (gather_6_cast_uint16_to_int32, concat_50_values1_0, concat_50_values2_0))[name = string("concat_50")];
            tensor<int32, [3]> var_301_begin_0 = const()[name = string("op_301_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<bool, [3]> var_301_end_mask_0 = const()[name = string("op_301_end_mask_0"), val = tensor<bool, [3]>([false, true, true])];
            tensor<fp16, [?, 1, 68]> var_301_cast_fp16 = slice_by_index(begin = var_301_begin_0, end = concat_50, end_mask = var_301_end_mask_0, x = var_300_cast_fp16)[name = string("op_301_cast_fp16")];
            tensor<fp16, [?, 1, 68]> x1_3_cast_fp16 = add(x = x0_5_cast_fp16, y = var_301_cast_fp16)[name = string("x1_3_cast_fp16")];
            tensor<int32, [3]> var_303 = const()[name = string("op_303"), val = tensor<int32, [3]>([1, 0, 2])];
            int32 var_319 = const()[name = string("op_319"), val = int32(34)];
            tensor<int32, [1]> x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_0_ln1_weight_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_0_ln1_weight_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9268224)))];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_0_ln1_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_0_ln1_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9268480)))];
            fp16 var_307_to_fp16 = const()[name = string("op_307_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, ?, 68]> input8_1_cast_fp16 = transpose(perm = var_303, x = x1_3_cast_fp16)[name = string("transpose_6")];
            tensor<fp16, [1, ?, 68]> x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, beta = pp_transformer_encoder_transformer_0_ln1_bias_to_fp16, epsilon = var_307_to_fp16, gamma = pp_transformer_encoder_transformer_0_ln1_weight_to_fp16, x = input8_1_cast_fp16)[name = string("x_3_cast_fp16")];
            tensor<int32, [3]> var_349 = const()[name = string("op_349"), val = tensor<int32, [3]>([0, 2, 1])];
            string var_357_pad_type_0 = const()[name = string("op_357_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_357_strides_0 = const()[name = string("op_357_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_357_pad_0 = const()[name = string("op_357_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_357_dilations_0 = const()[name = string("op_357_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_357_groups_0 = const()[name = string("op_357_groups_0"), val = int32(1)];
            tensor<fp16, [34, 68, 1]> pp_transformer_encoder_transformer_0_attn_key_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [34, 68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9268736))), scale = tensor<fp16, [34, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9271168))))[name = string("pp_transformer_encoder_transformer_0_attn_key_weight_to_fp16_quantized")];
            tensor<fp16, [34]> pp_transformer_encoder_transformer_0_attn_key_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_0_attn_key_bias_to_fp16"), val = tensor<fp16, [34]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9271360)))];
            tensor<fp16, [1, 68, ?]> input_7_cast_fp16 = transpose(perm = var_349, x = x_3_cast_fp16)[name = string("transpose_5")];
            tensor<fp16, [1, 34, ?]> var_357_cast_fp16 = conv(bias = pp_transformer_encoder_transformer_0_attn_key_bias_to_fp16, dilations = var_357_dilations_0, groups = var_357_groups_0, pad = var_357_pad_0, pad_type = var_357_pad_type_0, strides = var_357_strides_0, weight = pp_transformer_encoder_transformer_0_attn_key_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("op_357_cast_fp16")];
            tensor<int32, [4]> var_358 = const()[name = string("op_358"), val = tensor<int32, [4]>([1, 1, 34, -1])];
            tensor<fp16, [1, 1, 34, ?]> k_2_cast_fp16 = reshape(shape = var_358, x = var_357_cast_fp16)[name = string("k_2_cast_fp16")];
            string var_366_pad_type_0 = const()[name = string("op_366_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_366_strides_0 = const()[name = string("op_366_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_366_pad_0 = const()[name = string("op_366_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_366_dilations_0 = const()[name = string("op_366_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_366_groups_0 = const()[name = string("op_366_groups_0"), val = int32(1)];
            tensor<fp16, [68, 68, 1]> pp_transformer_encoder_transformer_0_attn_query_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [68, 68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9271552))), scale = tensor<fp16, [68, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276288))))[name = string("pp_transformer_encoder_transformer_0_attn_query_weight_to_fp16_quantized")];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_0_attn_query_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_0_attn_query_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276544)))];
            tensor<fp16, [1, 68, ?]> var_366_cast_fp16 = conv(bias = pp_transformer_encoder_transformer_0_attn_query_bias_to_fp16, dilations = var_366_dilations_0, groups = var_366_groups_0, pad = var_366_pad_0, pad_type = var_366_pad_type_0, strides = var_366_strides_0, weight = pp_transformer_encoder_transformer_0_attn_query_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("op_366_cast_fp16")];
            tensor<int32, [4]> var_369 = const()[name = string("op_369"), val = tensor<int32, [4]>([1, 2, 34, -1])];
            tensor<fp16, [1, 2, 34, ?]> q_2_cast_fp16 = reshape(shape = var_369, x = var_366_cast_fp16)[name = string("q_2_cast_fp16")];
            string var_377_pad_type_0 = const()[name = string("op_377_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_377_strides_0 = const()[name = string("op_377_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_377_pad_0 = const()[name = string("op_377_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_377_dilations_0 = const()[name = string("op_377_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_377_groups_0 = const()[name = string("op_377_groups_0"), val = int32(1)];
            tensor<fp16, [68, 68, 1]> pp_transformer_encoder_transformer_0_attn_value_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [68, 68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276800))), scale = tensor<fp16, [68, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9281536))))[name = string("pp_transformer_encoder_transformer_0_attn_value_weight_to_fp16_quantized")];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_0_attn_value_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_0_attn_value_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9281792)))];
            tensor<fp16, [1, 68, ?]> var_377_cast_fp16 = conv(bias = pp_transformer_encoder_transformer_0_attn_value_bias_to_fp16, dilations = var_377_dilations_0, groups = var_377_groups_0, pad = var_377_pad_0, pad_type = var_377_pad_type_0, strides = var_377_strides_0, weight = pp_transformer_encoder_transformer_0_attn_value_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("op_377_cast_fp16")];
            tensor<int32, [4]> var_380 = const()[name = string("op_380"), val = tensor<int32, [4]>([1, 2, 34, -1])];
            tensor<fp16, [1, 2, 34, ?]> v_2_cast_fp16 = reshape(shape = var_380, x = var_377_cast_fp16)[name = string("v_2_cast_fp16")];
            fp16 var_317_promoted_to_fp16 = const()[name = string("op_317_promoted_to_fp16"), val = fp16(-0x1.4p+3)];
            fp16 var_316_promoted_to_fp16 = const()[name = string("op_316_promoted_to_fp16"), val = fp16(0x1.4p+3)];
            tensor<fp16, [1, 1, 34, ?]> clip_0_cast_fp16 = clip(alpha = var_317_promoted_to_fp16, beta = var_316_promoted_to_fp16, x = k_2_cast_fp16)[name = string("clip_0_cast_fp16")];
            tensor<fp16, [1, 1, 34, ?]> k1_2_cast_fp16 = exp(x = clip_0_cast_fp16)[name = string("k1_2_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> x_5_cast_fp16 = mul(x = k1_2_cast_fp16, y = v_2_cast_fp16)[name = string("x_5_cast_fp16")];
            fp16 var_315_to_fp16 = const()[name = string("op_315_to_fp16"), val = fp16(-0x1.ffcp+15)];
            fp16 var_314_to_fp16 = const()[name = string("op_314_to_fp16"), val = fp16(0x1.ffcp+15)];
            tensor<fp16, [1, 2, 34, ?]> clip_1_cast_fp16 = clip(alpha = var_315_to_fp16, beta = var_314_to_fp16, x = x_5_cast_fp16)[name = string("clip_1_cast_fp16")];
            tensor<int32, [3]> concat_51x = const()[name = string("concat_51x"), val = tensor<int32, [3]>([1, 34, -1])];
            tensor<fp16, [1, 34, ?]> x0_2_cast_fp16 = reshape(shape = concat_51x, x = k1_2_cast_fp16)[name = string("x0_2_cast_fp16")];
            tensor<int32, [1]> global_pool_2_axes_0 = const()[name = string("global_pool_2_axes_0"), val = tensor<int32, [1]>([-1])];
            bool global_pool_2_keep_dims_0 = const()[name = string("global_pool_2_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 34, 1]> global_pool_2_cast_fp16 = reduce_sum(axes = global_pool_2_axes_0, keep_dims = global_pool_2_keep_dims_0, x = x0_2_cast_fp16)[name = string("global_pool_2_cast_fp16")];
            tensor<int32, [1]> var_403 = const()[name = string("op_403"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_404 = const()[name = string("op_404"), val = tensor<int32, [1]>([1])];
            string var_405_pad_type_0 = const()[name = string("op_405_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_405_pad_0 = const()[name = string("op_405_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [34, 1, 13]> weight_21_to_fp16 = const()[name = string("weight_21_to_fp16"), val = tensor<fp16, [34, 1, 13]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9282048)))];
            tensor<fp16, [1, 34, ?]> var_405_cast_fp16 = conv(dilations = var_404, groups = var_319, pad = var_405_pad_0, pad_type = var_405_pad_type_0, strides = var_403, weight = weight_21_to_fp16, x = x0_2_cast_fp16)[name = string("op_405_cast_fp16")];
            tensor<fp16, [1, 34, ?]> x1_2_cast_fp16 = add(x = var_405_cast_fp16, y = global_pool_2_cast_fp16)[name = string("x1_2_cast_fp16")];
            tensor<int32, [4]> concat_52x = const()[name = string("concat_52x"), val = tensor<int32, [4]>([1, 1, 34, -1])];
            tensor<fp16, [1, 1, 34, ?]> x2_2_cast_fp16 = reshape(shape = concat_52x, x = x1_2_cast_fp16)[name = string("x2_2_cast_fp16")];
            tensor<fp16, [1, 1, 34, ?]> clip_3_cast_fp16 = clip(alpha = var_315_to_fp16, beta = var_314_to_fp16, x = x2_2_cast_fp16)[name = string("clip_3_cast_fp16")];
            tensor<int32, [3]> concat_53x = const()[name = string("concat_53x"), val = tensor<int32, [3]>([2, 34, -1])];
            tensor<fp16, [2, 34, ?]> x3_2_cast_fp16 = reshape(shape = concat_53x, x = clip_1_cast_fp16)[name = string("x3_2_cast_fp16")];
            tensor<int32, [1]> global_pool0_2_axes_0 = const()[name = string("global_pool0_2_axes_0"), val = tensor<int32, [1]>([-1])];
            bool global_pool0_2_keep_dims_0 = const()[name = string("global_pool0_2_keep_dims_0"), val = bool(true)];
            tensor<fp16, [2, 34, 1]> global_pool0_2_cast_fp16 = reduce_sum(axes = global_pool0_2_axes_0, keep_dims = global_pool0_2_keep_dims_0, x = x3_2_cast_fp16)[name = string("global_pool0_2_cast_fp16")];
            tensor<int32, [1]> var_417 = const()[name = string("op_417"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_418 = const()[name = string("op_418"), val = tensor<int32, [1]>([1])];
            string var_419_pad_type_0 = const()[name = string("op_419_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_419_pad_0 = const()[name = string("op_419_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [2, 34, ?]> var_419_cast_fp16 = conv(dilations = var_418, groups = var_319, pad = var_419_pad_0, pad_type = var_419_pad_type_0, strides = var_417, weight = weight_21_to_fp16, x = x3_2_cast_fp16)[name = string("op_419_cast_fp16")];
            tensor<fp16, [2, 34, ?]> x4_2_cast_fp16 = add(x = var_419_cast_fp16, y = global_pool0_2_cast_fp16)[name = string("x4_2_cast_fp16")];
            tensor<int32, [4]> concat_54x = const()[name = string("concat_54x"), val = tensor<int32, [4]>([1, 2, 34, -1])];
            tensor<fp16, [1, 2, 34, ?]> x5_2_cast_fp16 = reshape(shape = concat_54x, x = x4_2_cast_fp16)[name = string("x5_2_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> clip_4_cast_fp16 = clip(alpha = var_315_to_fp16, beta = var_314_to_fp16, x = x5_2_cast_fp16)[name = string("clip_4_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> q0_2_cast_fp16 = sigmoid(x = q_2_cast_fp16)[name = string("q0_2_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> var_425_cast_fp16 = mul(x = q0_2_cast_fp16, y = clip_4_cast_fp16)[name = string("op_425_cast_fp16")];
            fp16 var_312_to_fp16 = const()[name = string("op_312_to_fp16"), val = fp16(0x0p+0)];
            tensor<bool, [1, 1, 34, ?]> var_426_cast_fp16 = equal(x = clip_3_cast_fp16, y = var_312_to_fp16)[name = string("op_426_cast_fp16")];
            fp16 var_311_to_fp16 = const()[name = string("op_311_to_fp16"), val = fp16(0x1.1p-20)];
            tensor<fp16, [1, 1, 34, ?]> var_427_cast_fp16 = select(a = var_311_to_fp16, b = clip_3_cast_fp16, cond = var_426_cast_fp16)[name = string("op_427_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> x6_2_cast_fp16 = real_div(x = var_425_cast_fp16, y = var_427_cast_fp16)[name = string("x6_2_cast_fp16")];
            tensor<int32, [3]> var_429 = const()[name = string("op_429"), val = tensor<int32, [3]>([1, 68, -1])];
            tensor<fp16, [1, 68, ?]> input3_1_cast_fp16 = reshape(shape = var_429, x = x6_2_cast_fp16)[name = string("input3_1_cast_fp16")];
            string input0_4_pad_type_0 = const()[name = string("input0_4_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input0_4_strides_0 = const()[name = string("input0_4_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input0_4_pad_0 = const()[name = string("input0_4_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input0_4_dilations_0 = const()[name = string("input0_4_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input0_4_groups_0 = const()[name = string("input0_4_groups_0"), val = int32(1)];
            tensor<fp16, [68, 68, 1]> pp_transformer_encoder_transformer_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [68, 68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9283008))), scale = tensor<fp16, [68, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9287744))))[name = string("pp_transformer_encoder_transformer_0_attn_proj_weight_to_fp16_quantized")];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_0_attn_proj_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_0_attn_proj_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9288000)))];
            tensor<fp16, [1, 68, ?]> input0_4_cast_fp16 = conv(bias = pp_transformer_encoder_transformer_0_attn_proj_bias_to_fp16, dilations = input0_4_dilations_0, groups = input0_4_groups_0, pad = input0_4_pad_0, pad_type = input0_4_pad_type_0, strides = input0_4_strides_0, weight = pp_transformer_encoder_transformer_0_attn_proj_weight_to_fp16_quantized, x = input3_1_cast_fp16)[name = string("input0_4_cast_fp16")];
            tensor<int32, [3]> var_439 = const()[name = string("op_439"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, ?, 68]> var_440_cast_fp16 = transpose(perm = var_439, x = input0_4_cast_fp16)[name = string("transpose_4")];
            tensor<fp16, [1, ?, 68]> input0_5_cast_fp16 = add(x = input8_1_cast_fp16, y = var_440_cast_fp16)[name = string("input0_5_cast_fp16")];
            tensor<int32, [1]> input0_7_axes_0 = const()[name = string("input0_7_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_0_ln2_weight_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_0_ln2_weight_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9288256)))];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_0_ln2_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_0_ln2_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9288512)))];
            tensor<fp16, [1, ?, 68]> input0_7_cast_fp16 = layer_norm(axes = input0_7_axes_0, beta = pp_transformer_encoder_transformer_0_ln2_bias_to_fp16, epsilon = var_307_to_fp16, gamma = pp_transformer_encoder_transformer_0_ln2_weight_to_fp16, x = input0_5_cast_fp16)[name = string("input0_7_cast_fp16")];
            tensor<fp16, [272, 68]> pp_transformer_encoder_transformer_0_mlp_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [272, 68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9288768))), scale = tensor<fp16, [272, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9307328))))[name = string("pp_transformer_encoder_transformer_0_mlp_0_weight_to_fp16_quantized")];
            tensor<fp16, [272]> pp_transformer_encoder_transformer_0_mlp_0_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_0_mlp_0_bias_to_fp16"), val = tensor<fp16, [272]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9307968)))];
            tensor<fp16, [1, ?, 272]> linear_1_cast_fp16 = linear(bias = pp_transformer_encoder_transformer_0_mlp_0_bias_to_fp16, weight = pp_transformer_encoder_transformer_0_mlp_0_weight_to_fp16_quantized, x = input0_7_cast_fp16)[name = string("linear_1_cast_fp16")];
            string var_451_mode_0 = const()[name = string("op_451_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, ?, 272]> var_451_cast_fp16 = gelu(mode = var_451_mode_0, x = linear_1_cast_fp16)[name = string("op_451_cast_fp16")];
            tensor<fp16, [68, 272]> pp_transformer_encoder_transformer_0_mlp_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [68, 272]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9308608))), scale = tensor<fp16, [68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9327168))))[name = string("pp_transformer_encoder_transformer_0_mlp_2_weight_to_fp16_quantized")];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_0_mlp_2_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_0_mlp_2_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9327424)))];
            tensor<fp16, [1, ?, 68]> linear_2_cast_fp16 = linear(bias = pp_transformer_encoder_transformer_0_mlp_2_bias_to_fp16, weight = pp_transformer_encoder_transformer_0_mlp_2_weight_to_fp16_quantized, x = var_451_cast_fp16)[name = string("linear_2_cast_fp16")];
            tensor<fp16, [1, ?, 68]> var_456_cast_fp16 = add(x = input0_5_cast_fp16, y = linear_2_cast_fp16)[name = string("op_456_cast_fp16")];
            tensor<int32, [1]> x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_1_ln1_weight_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_1_ln1_weight_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9327680)))];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_1_ln1_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_1_ln1_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9327936)))];
            tensor<fp16, [1, ?, 68]> x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, beta = pp_transformer_encoder_transformer_1_ln1_bias_to_fp16, epsilon = var_307_to_fp16, gamma = pp_transformer_encoder_transformer_1_ln1_weight_to_fp16, x = var_456_cast_fp16)[name = string("x_7_cast_fp16")];
            tensor<int32, [3]> var_474 = const()[name = string("op_474"), val = tensor<int32, [3]>([0, 2, 1])];
            string var_482_pad_type_0 = const()[name = string("op_482_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_482_strides_0 = const()[name = string("op_482_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_482_pad_0 = const()[name = string("op_482_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_482_dilations_0 = const()[name = string("op_482_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_482_groups_0 = const()[name = string("op_482_groups_0"), val = int32(1)];
            tensor<fp16, [34, 68, 1]> pp_transformer_encoder_transformer_1_attn_key_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [34, 68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9328192))), scale = tensor<fp16, [34, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9330624))))[name = string("pp_transformer_encoder_transformer_1_attn_key_weight_to_fp16_quantized")];
            tensor<fp16, [34]> pp_transformer_encoder_transformer_1_attn_key_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_1_attn_key_bias_to_fp16"), val = tensor<fp16, [34]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9330816)))];
            tensor<fp16, [1, 68, ?]> input_13_cast_fp16 = transpose(perm = var_474, x = x_7_cast_fp16)[name = string("transpose_3")];
            tensor<fp16, [1, 34, ?]> var_482_cast_fp16 = conv(bias = pp_transformer_encoder_transformer_1_attn_key_bias_to_fp16, dilations = var_482_dilations_0, groups = var_482_groups_0, pad = var_482_pad_0, pad_type = var_482_pad_type_0, strides = var_482_strides_0, weight = pp_transformer_encoder_transformer_1_attn_key_weight_to_fp16_quantized, x = input_13_cast_fp16)[name = string("op_482_cast_fp16")];
            tensor<int32, [4]> var_483 = const()[name = string("op_483"), val = tensor<int32, [4]>([1, 1, 34, -1])];
            tensor<fp16, [1, 1, 34, ?]> k_4_cast_fp16 = reshape(shape = var_483, x = var_482_cast_fp16)[name = string("k_4_cast_fp16")];
            string var_491_pad_type_0 = const()[name = string("op_491_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_491_strides_0 = const()[name = string("op_491_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_491_pad_0 = const()[name = string("op_491_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_491_dilations_0 = const()[name = string("op_491_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_491_groups_0 = const()[name = string("op_491_groups_0"), val = int32(1)];
            tensor<fp16, [68, 68, 1]> pp_transformer_encoder_transformer_1_attn_query_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [68, 68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9331008))), scale = tensor<fp16, [68, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9335744))))[name = string("pp_transformer_encoder_transformer_1_attn_query_weight_to_fp16_quantized")];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_1_attn_query_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_1_attn_query_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9336000)))];
            tensor<fp16, [1, 68, ?]> var_491_cast_fp16 = conv(bias = pp_transformer_encoder_transformer_1_attn_query_bias_to_fp16, dilations = var_491_dilations_0, groups = var_491_groups_0, pad = var_491_pad_0, pad_type = var_491_pad_type_0, strides = var_491_strides_0, weight = pp_transformer_encoder_transformer_1_attn_query_weight_to_fp16_quantized, x = input_13_cast_fp16)[name = string("op_491_cast_fp16")];
            tensor<int32, [4]> var_494 = const()[name = string("op_494"), val = tensor<int32, [4]>([1, 2, 34, -1])];
            tensor<fp16, [1, 2, 34, ?]> q_4_cast_fp16 = reshape(shape = var_494, x = var_491_cast_fp16)[name = string("q_4_cast_fp16")];
            string var_502_pad_type_0 = const()[name = string("op_502_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_502_strides_0 = const()[name = string("op_502_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_502_pad_0 = const()[name = string("op_502_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_502_dilations_0 = const()[name = string("op_502_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_502_groups_0 = const()[name = string("op_502_groups_0"), val = int32(1)];
            tensor<fp16, [68, 68, 1]> pp_transformer_encoder_transformer_1_attn_value_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [68, 68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9336256))), scale = tensor<fp16, [68, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9340992))))[name = string("pp_transformer_encoder_transformer_1_attn_value_weight_to_fp16_quantized")];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_1_attn_value_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_1_attn_value_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9341248)))];
            tensor<fp16, [1, 68, ?]> var_502_cast_fp16 = conv(bias = pp_transformer_encoder_transformer_1_attn_value_bias_to_fp16, dilations = var_502_dilations_0, groups = var_502_groups_0, pad = var_502_pad_0, pad_type = var_502_pad_type_0, strides = var_502_strides_0, weight = pp_transformer_encoder_transformer_1_attn_value_weight_to_fp16_quantized, x = input_13_cast_fp16)[name = string("op_502_cast_fp16")];
            tensor<int32, [4]> var_505 = const()[name = string("op_505"), val = tensor<int32, [4]>([1, 2, 34, -1])];
            tensor<fp16, [1, 2, 34, ?]> v_4_cast_fp16 = reshape(shape = var_505, x = var_502_cast_fp16)[name = string("v_4_cast_fp16")];
            fp16 var_317_promoted_2_to_fp16 = const()[name = string("op_317_promoted_2_to_fp16"), val = fp16(-0x1.4p+3)];
            fp16 var_316_promoted_2_to_fp16 = const()[name = string("op_316_promoted_2_to_fp16"), val = fp16(0x1.4p+3)];
            tensor<fp16, [1, 1, 34, ?]> clip_5_cast_fp16 = clip(alpha = var_317_promoted_2_to_fp16, beta = var_316_promoted_2_to_fp16, x = k_4_cast_fp16)[name = string("clip_5_cast_fp16")];
            tensor<fp16, [1, 1, 34, ?]> k1_4_cast_fp16 = exp(x = clip_5_cast_fp16)[name = string("k1_4_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> x_9_cast_fp16 = mul(x = k1_4_cast_fp16, y = v_4_cast_fp16)[name = string("x_9_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> clip_6_cast_fp16 = clip(alpha = var_315_to_fp16, beta = var_314_to_fp16, x = x_9_cast_fp16)[name = string("clip_6_cast_fp16")];
            tensor<int32, [3]> concat_55x = const()[name = string("concat_55x"), val = tensor<int32, [3]>([1, 34, -1])];
            tensor<fp16, [1, 34, ?]> x0_4_cast_fp16 = reshape(shape = concat_55x, x = k1_4_cast_fp16)[name = string("x0_4_cast_fp16")];
            tensor<int32, [1]> global_pool_4_axes_0 = const()[name = string("global_pool_4_axes_0"), val = tensor<int32, [1]>([-1])];
            bool global_pool_4_keep_dims_0 = const()[name = string("global_pool_4_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 34, 1]> global_pool_4_cast_fp16 = reduce_sum(axes = global_pool_4_axes_0, keep_dims = global_pool_4_keep_dims_0, x = x0_4_cast_fp16)[name = string("global_pool_4_cast_fp16")];
            tensor<int32, [1]> var_528 = const()[name = string("op_528"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_529 = const()[name = string("op_529"), val = tensor<int32, [1]>([1])];
            string var_530_pad_type_0 = const()[name = string("op_530_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_530_pad_0 = const()[name = string("op_530_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [34, 1, 13]> weight_41_to_fp16 = const()[name = string("weight_41_to_fp16"), val = tensor<fp16, [34, 1, 13]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9341504)))];
            tensor<fp16, [1, 34, ?]> var_530_cast_fp16 = conv(dilations = var_529, groups = var_319, pad = var_530_pad_0, pad_type = var_530_pad_type_0, strides = var_528, weight = weight_41_to_fp16, x = x0_4_cast_fp16)[name = string("op_530_cast_fp16")];
            tensor<fp16, [1, 34, ?]> x1_4_cast_fp16 = add(x = var_530_cast_fp16, y = global_pool_4_cast_fp16)[name = string("x1_4_cast_fp16")];
            tensor<int32, [4]> concat_56x = const()[name = string("concat_56x"), val = tensor<int32, [4]>([1, 1, 34, -1])];
            tensor<fp16, [1, 1, 34, ?]> x2_4_cast_fp16 = reshape(shape = concat_56x, x = x1_4_cast_fp16)[name = string("x2_4_cast_fp16")];
            tensor<fp16, [1, 1, 34, ?]> clip_8_cast_fp16 = clip(alpha = var_315_to_fp16, beta = var_314_to_fp16, x = x2_4_cast_fp16)[name = string("clip_8_cast_fp16")];
            tensor<int32, [3]> concat_57x = const()[name = string("concat_57x"), val = tensor<int32, [3]>([2, 34, -1])];
            tensor<fp16, [2, 34, ?]> x3_4_cast_fp16 = reshape(shape = concat_57x, x = clip_6_cast_fp16)[name = string("x3_4_cast_fp16")];
            tensor<int32, [1]> global_pool0_4_axes_0 = const()[name = string("global_pool0_4_axes_0"), val = tensor<int32, [1]>([-1])];
            bool global_pool0_4_keep_dims_0 = const()[name = string("global_pool0_4_keep_dims_0"), val = bool(true)];
            tensor<fp16, [2, 34, 1]> global_pool0_4_cast_fp16 = reduce_sum(axes = global_pool0_4_axes_0, keep_dims = global_pool0_4_keep_dims_0, x = x3_4_cast_fp16)[name = string("global_pool0_4_cast_fp16")];
            tensor<int32, [1]> var_542 = const()[name = string("op_542"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_543 = const()[name = string("op_543"), val = tensor<int32, [1]>([1])];
            string var_544_pad_type_0 = const()[name = string("op_544_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_544_pad_0 = const()[name = string("op_544_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [2, 34, ?]> var_544_cast_fp16 = conv(dilations = var_543, groups = var_319, pad = var_544_pad_0, pad_type = var_544_pad_type_0, strides = var_542, weight = weight_41_to_fp16, x = x3_4_cast_fp16)[name = string("op_544_cast_fp16")];
            tensor<fp16, [2, 34, ?]> x4_4_cast_fp16 = add(x = var_544_cast_fp16, y = global_pool0_4_cast_fp16)[name = string("x4_4_cast_fp16")];
            tensor<int32, [4]> concat_58x = const()[name = string("concat_58x"), val = tensor<int32, [4]>([1, 2, 34, -1])];
            tensor<fp16, [1, 2, 34, ?]> x5_4_cast_fp16 = reshape(shape = concat_58x, x = x4_4_cast_fp16)[name = string("x5_4_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> clip_9_cast_fp16 = clip(alpha = var_315_to_fp16, beta = var_314_to_fp16, x = x5_4_cast_fp16)[name = string("clip_9_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> q0_4_cast_fp16 = sigmoid(x = q_4_cast_fp16)[name = string("q0_4_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> var_550_cast_fp16 = mul(x = q0_4_cast_fp16, y = clip_9_cast_fp16)[name = string("op_550_cast_fp16")];
            tensor<bool, [1, 1, 34, ?]> var_551_cast_fp16 = equal(x = clip_8_cast_fp16, y = var_312_to_fp16)[name = string("op_551_cast_fp16")];
            tensor<fp16, [1, 1, 34, ?]> var_552_cast_fp16 = select(a = var_311_to_fp16, b = clip_8_cast_fp16, cond = var_551_cast_fp16)[name = string("op_552_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> x6_4_cast_fp16 = real_div(x = var_550_cast_fp16, y = var_552_cast_fp16)[name = string("x6_4_cast_fp16")];
            tensor<int32, [3]> var_554 = const()[name = string("op_554"), val = tensor<int32, [3]>([1, 68, -1])];
            tensor<fp16, [1, 68, ?]> input2_4_cast_fp16 = reshape(shape = var_554, x = x6_4_cast_fp16)[name = string("input2_4_cast_fp16")];
            string input0_11_pad_type_0 = const()[name = string("input0_11_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input0_11_strides_0 = const()[name = string("input0_11_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input0_11_pad_0 = const()[name = string("input0_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input0_11_dilations_0 = const()[name = string("input0_11_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input0_11_groups_0 = const()[name = string("input0_11_groups_0"), val = int32(1)];
            tensor<fp16, [68, 68, 1]> pp_transformer_encoder_transformer_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [68, 68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9342464))), scale = tensor<fp16, [68, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9347200))))[name = string("pp_transformer_encoder_transformer_1_attn_proj_weight_to_fp16_quantized")];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_1_attn_proj_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_1_attn_proj_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9347456)))];
            tensor<fp16, [1, 68, ?]> input0_11_cast_fp16 = conv(bias = pp_transformer_encoder_transformer_1_attn_proj_bias_to_fp16, dilations = input0_11_dilations_0, groups = input0_11_groups_0, pad = input0_11_pad_0, pad_type = input0_11_pad_type_0, strides = input0_11_strides_0, weight = pp_transformer_encoder_transformer_1_attn_proj_weight_to_fp16_quantized, x = input2_4_cast_fp16)[name = string("input0_11_cast_fp16")];
            tensor<int32, [3]> var_564 = const()[name = string("op_564"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, ?, 68]> var_565_cast_fp16 = transpose(perm = var_564, x = input0_11_cast_fp16)[name = string("transpose_2")];
            tensor<fp16, [1, ?, 68]> input_15_cast_fp16 = add(x = var_456_cast_fp16, y = var_565_cast_fp16)[name = string("input_15_cast_fp16")];
            tensor<int32, [1]> input0_13_axes_0 = const()[name = string("input0_13_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_1_ln2_weight_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_1_ln2_weight_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9347712)))];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_1_ln2_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_1_ln2_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9347968)))];
            tensor<fp16, [1, ?, 68]> input0_13_cast_fp16 = layer_norm(axes = input0_13_axes_0, beta = pp_transformer_encoder_transformer_1_ln2_bias_to_fp16, epsilon = var_307_to_fp16, gamma = pp_transformer_encoder_transformer_1_ln2_weight_to_fp16, x = input_15_cast_fp16)[name = string("input0_13_cast_fp16")];
            tensor<fp16, [272, 68]> pp_transformer_encoder_transformer_1_mlp_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [272, 68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9348224))), scale = tensor<fp16, [272, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9366784))))[name = string("pp_transformer_encoder_transformer_1_mlp_0_weight_to_fp16_quantized")];
            tensor<fp16, [272]> pp_transformer_encoder_transformer_1_mlp_0_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_1_mlp_0_bias_to_fp16"), val = tensor<fp16, [272]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9367424)))];
            tensor<fp16, [1, ?, 272]> linear_3_cast_fp16 = linear(bias = pp_transformer_encoder_transformer_1_mlp_0_bias_to_fp16, weight = pp_transformer_encoder_transformer_1_mlp_0_weight_to_fp16_quantized, x = input0_13_cast_fp16)[name = string("linear_3_cast_fp16")];
            string var_576_mode_0 = const()[name = string("op_576_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, ?, 272]> var_576_cast_fp16 = gelu(mode = var_576_mode_0, x = linear_3_cast_fp16)[name = string("op_576_cast_fp16")];
            tensor<fp16, [68, 272]> pp_transformer_encoder_transformer_1_mlp_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [68, 272]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9368064))), scale = tensor<fp16, [68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9386624))))[name = string("pp_transformer_encoder_transformer_1_mlp_2_weight_to_fp16_quantized")];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_1_mlp_2_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_1_mlp_2_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9386880)))];
            tensor<fp16, [1, ?, 68]> linear_4_cast_fp16 = linear(bias = pp_transformer_encoder_transformer_1_mlp_2_bias_to_fp16, weight = pp_transformer_encoder_transformer_1_mlp_2_weight_to_fp16_quantized, x = var_576_cast_fp16)[name = string("linear_4_cast_fp16")];
            tensor<fp16, [1, ?, 68]> var_581_cast_fp16 = add(x = input_15_cast_fp16, y = linear_4_cast_fp16)[name = string("op_581_cast_fp16")];
            tensor<int32, [1]> x_2_axes_0 = const()[name = string("x_2_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_2_ln1_weight_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_2_ln1_weight_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9387136)))];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_2_ln1_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_2_ln1_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9387392)))];
            tensor<fp16, [1, ?, 68]> x_2_cast_fp16 = layer_norm(axes = x_2_axes_0, beta = pp_transformer_encoder_transformer_2_ln1_bias_to_fp16, epsilon = var_307_to_fp16, gamma = pp_transformer_encoder_transformer_2_ln1_weight_to_fp16, x = var_581_cast_fp16)[name = string("x_2_cast_fp16")];
            tensor<int32, [3]> var_599 = const()[name = string("op_599"), val = tensor<int32, [3]>([0, 2, 1])];
            string var_607_pad_type_0 = const()[name = string("op_607_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_607_strides_0 = const()[name = string("op_607_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_607_pad_0 = const()[name = string("op_607_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_607_dilations_0 = const()[name = string("op_607_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_607_groups_0 = const()[name = string("op_607_groups_0"), val = int32(1)];
            tensor<fp16, [34, 68, 1]> pp_transformer_encoder_transformer_2_attn_key_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [34, 68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9387648))), scale = tensor<fp16, [34, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9390080))))[name = string("pp_transformer_encoder_transformer_2_attn_key_weight_to_fp16_quantized")];
            tensor<fp16, [34]> pp_transformer_encoder_transformer_2_attn_key_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_2_attn_key_bias_to_fp16"), val = tensor<fp16, [34]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9390272)))];
            tensor<fp16, [1, 68, ?]> input_6_cast_fp16 = transpose(perm = var_599, x = x_2_cast_fp16)[name = string("transpose_1")];
            tensor<fp16, [1, 34, ?]> var_607_cast_fp16 = conv(bias = pp_transformer_encoder_transformer_2_attn_key_bias_to_fp16, dilations = var_607_dilations_0, groups = var_607_groups_0, pad = var_607_pad_0, pad_type = var_607_pad_type_0, strides = var_607_strides_0, weight = pp_transformer_encoder_transformer_2_attn_key_weight_to_fp16_quantized, x = input_6_cast_fp16)[name = string("op_607_cast_fp16")];
            tensor<int32, [4]> var_608 = const()[name = string("op_608"), val = tensor<int32, [4]>([1, 1, 34, -1])];
            tensor<fp16, [1, 1, 34, ?]> k_1_cast_fp16 = reshape(shape = var_608, x = var_607_cast_fp16)[name = string("k_1_cast_fp16")];
            string var_616_pad_type_0 = const()[name = string("op_616_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_616_strides_0 = const()[name = string("op_616_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_616_pad_0 = const()[name = string("op_616_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_616_dilations_0 = const()[name = string("op_616_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_616_groups_0 = const()[name = string("op_616_groups_0"), val = int32(1)];
            tensor<fp16, [68, 68, 1]> pp_transformer_encoder_transformer_2_attn_query_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [68, 68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9390464))), scale = tensor<fp16, [68, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9395200))))[name = string("pp_transformer_encoder_transformer_2_attn_query_weight_to_fp16_quantized")];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_2_attn_query_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_2_attn_query_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9395456)))];
            tensor<fp16, [1, 68, ?]> var_616_cast_fp16 = conv(bias = pp_transformer_encoder_transformer_2_attn_query_bias_to_fp16, dilations = var_616_dilations_0, groups = var_616_groups_0, pad = var_616_pad_0, pad_type = var_616_pad_type_0, strides = var_616_strides_0, weight = pp_transformer_encoder_transformer_2_attn_query_weight_to_fp16_quantized, x = input_6_cast_fp16)[name = string("op_616_cast_fp16")];
            tensor<int32, [4]> var_619 = const()[name = string("op_619"), val = tensor<int32, [4]>([1, 2, 34, -1])];
            tensor<fp16, [1, 2, 34, ?]> q_1_cast_fp16 = reshape(shape = var_619, x = var_616_cast_fp16)[name = string("q_1_cast_fp16")];
            string var_627_pad_type_0 = const()[name = string("op_627_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_627_strides_0 = const()[name = string("op_627_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_627_pad_0 = const()[name = string("op_627_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_627_dilations_0 = const()[name = string("op_627_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_627_groups_0 = const()[name = string("op_627_groups_0"), val = int32(1)];
            tensor<fp16, [68, 68, 1]> pp_transformer_encoder_transformer_2_attn_value_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [68, 68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9395712))), scale = tensor<fp16, [68, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9400448))))[name = string("pp_transformer_encoder_transformer_2_attn_value_weight_to_fp16_quantized")];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_2_attn_value_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_2_attn_value_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9400704)))];
            tensor<fp16, [1, 68, ?]> var_627_cast_fp16 = conv(bias = pp_transformer_encoder_transformer_2_attn_value_bias_to_fp16, dilations = var_627_dilations_0, groups = var_627_groups_0, pad = var_627_pad_0, pad_type = var_627_pad_type_0, strides = var_627_strides_0, weight = pp_transformer_encoder_transformer_2_attn_value_weight_to_fp16_quantized, x = input_6_cast_fp16)[name = string("op_627_cast_fp16")];
            tensor<int32, [4]> var_630 = const()[name = string("op_630"), val = tensor<int32, [4]>([1, 2, 34, -1])];
            tensor<fp16, [1, 2, 34, ?]> v_1_cast_fp16 = reshape(shape = var_630, x = var_627_cast_fp16)[name = string("v_1_cast_fp16")];
            fp16 var_317_promoted_4_to_fp16 = const()[name = string("op_317_promoted_4_to_fp16"), val = fp16(-0x1.4p+3)];
            fp16 var_316_promoted_4_to_fp16 = const()[name = string("op_316_promoted_4_to_fp16"), val = fp16(0x1.4p+3)];
            tensor<fp16, [1, 1, 34, ?]> clip_10_cast_fp16 = clip(alpha = var_317_promoted_4_to_fp16, beta = var_316_promoted_4_to_fp16, x = k_1_cast_fp16)[name = string("clip_10_cast_fp16")];
            tensor<fp16, [1, 1, 34, ?]> k1_1_cast_fp16 = exp(x = clip_10_cast_fp16)[name = string("k1_1_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> x_1_cast_fp16 = mul(x = k1_1_cast_fp16, y = v_1_cast_fp16)[name = string("x_1_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> clip_11_cast_fp16 = clip(alpha = var_315_to_fp16, beta = var_314_to_fp16, x = x_1_cast_fp16)[name = string("clip_11_cast_fp16")];
            tensor<int32, [3]> concat_59x = const()[name = string("concat_59x"), val = tensor<int32, [3]>([1, 34, -1])];
            tensor<fp16, [1, 34, ?]> x0_1_cast_fp16 = reshape(shape = concat_59x, x = k1_1_cast_fp16)[name = string("x0_1_cast_fp16")];
            tensor<int32, [1]> global_pool_1_axes_0 = const()[name = string("global_pool_1_axes_0"), val = tensor<int32, [1]>([-1])];
            bool global_pool_1_keep_dims_0 = const()[name = string("global_pool_1_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 34, 1]> global_pool_1_cast_fp16 = reduce_sum(axes = global_pool_1_axes_0, keep_dims = global_pool_1_keep_dims_0, x = x0_1_cast_fp16)[name = string("global_pool_1_cast_fp16")];
            tensor<int32, [1]> var_653 = const()[name = string("op_653"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_654 = const()[name = string("op_654"), val = tensor<int32, [1]>([1])];
            string var_655_pad_type_0 = const()[name = string("op_655_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_655_pad_0 = const()[name = string("op_655_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [34, 1, 13]> weight_7_to_fp16 = const()[name = string("weight_7_to_fp16"), val = tensor<fp16, [34, 1, 13]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9400960)))];
            tensor<fp16, [1, 34, ?]> var_655_cast_fp16 = conv(dilations = var_654, groups = var_319, pad = var_655_pad_0, pad_type = var_655_pad_type_0, strides = var_653, weight = weight_7_to_fp16, x = x0_1_cast_fp16)[name = string("op_655_cast_fp16")];
            tensor<fp16, [1, 34, ?]> x1_1_cast_fp16 = add(x = var_655_cast_fp16, y = global_pool_1_cast_fp16)[name = string("x1_1_cast_fp16")];
            tensor<int32, [4]> concat_60x = const()[name = string("concat_60x"), val = tensor<int32, [4]>([1, 1, 34, -1])];
            tensor<fp16, [1, 1, 34, ?]> x2_1_cast_fp16 = reshape(shape = concat_60x, x = x1_1_cast_fp16)[name = string("x2_1_cast_fp16")];
            tensor<fp16, [1, 1, 34, ?]> clip_13_cast_fp16 = clip(alpha = var_315_to_fp16, beta = var_314_to_fp16, x = x2_1_cast_fp16)[name = string("clip_13_cast_fp16")];
            tensor<int32, [3]> concat_61x = const()[name = string("concat_61x"), val = tensor<int32, [3]>([2, 34, -1])];
            tensor<fp16, [2, 34, ?]> x3_1_cast_fp16 = reshape(shape = concat_61x, x = clip_11_cast_fp16)[name = string("x3_1_cast_fp16")];
            tensor<int32, [1]> global_pool0_1_axes_0 = const()[name = string("global_pool0_1_axes_0"), val = tensor<int32, [1]>([-1])];
            bool global_pool0_1_keep_dims_0 = const()[name = string("global_pool0_1_keep_dims_0"), val = bool(true)];
            tensor<fp16, [2, 34, 1]> global_pool0_1_cast_fp16 = reduce_sum(axes = global_pool0_1_axes_0, keep_dims = global_pool0_1_keep_dims_0, x = x3_1_cast_fp16)[name = string("global_pool0_1_cast_fp16")];
            tensor<int32, [1]> var_667 = const()[name = string("op_667"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_668 = const()[name = string("op_668"), val = tensor<int32, [1]>([1])];
            string var_669_pad_type_0 = const()[name = string("op_669_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_669_pad_0 = const()[name = string("op_669_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [2, 34, ?]> var_669_cast_fp16 = conv(dilations = var_668, groups = var_319, pad = var_669_pad_0, pad_type = var_669_pad_type_0, strides = var_667, weight = weight_7_to_fp16, x = x3_1_cast_fp16)[name = string("op_669_cast_fp16")];
            tensor<fp16, [2, 34, ?]> x4_1_cast_fp16 = add(x = var_669_cast_fp16, y = global_pool0_1_cast_fp16)[name = string("x4_1_cast_fp16")];
            tensor<int32, [4]> concat_62x = const()[name = string("concat_62x"), val = tensor<int32, [4]>([1, 2, 34, -1])];
            tensor<fp16, [1, 2, 34, ?]> x5_1_cast_fp16 = reshape(shape = concat_62x, x = x4_1_cast_fp16)[name = string("x5_1_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> clip_14_cast_fp16 = clip(alpha = var_315_to_fp16, beta = var_314_to_fp16, x = x5_1_cast_fp16)[name = string("clip_14_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> q0_1_cast_fp16 = sigmoid(x = q_1_cast_fp16)[name = string("q0_1_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> var_675_cast_fp16 = mul(x = q0_1_cast_fp16, y = clip_14_cast_fp16)[name = string("op_675_cast_fp16")];
            tensor<bool, [1, 1, 34, ?]> var_676_cast_fp16 = equal(x = clip_13_cast_fp16, y = var_312_to_fp16)[name = string("op_676_cast_fp16")];
            tensor<fp16, [1, 1, 34, ?]> var_677_cast_fp16 = select(a = var_311_to_fp16, b = clip_13_cast_fp16, cond = var_676_cast_fp16)[name = string("op_677_cast_fp16")];
            tensor<fp16, [1, 2, 34, ?]> x6_1_cast_fp16 = real_div(x = var_675_cast_fp16, y = var_677_cast_fp16)[name = string("x6_1_cast_fp16")];
            tensor<int32, [3]> var_679 = const()[name = string("op_679"), val = tensor<int32, [3]>([1, 68, -1])];
            tensor<fp16, [1, 68, ?]> input2_1_cast_fp16 = reshape(shape = var_679, x = x6_1_cast_fp16)[name = string("input2_1_cast_fp16")];
            string input0_3_pad_type_0 = const()[name = string("input0_3_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input0_3_strides_0 = const()[name = string("input0_3_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input0_3_pad_0 = const()[name = string("input0_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input0_3_dilations_0 = const()[name = string("input0_3_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input0_3_groups_0 = const()[name = string("input0_3_groups_0"), val = int32(1)];
            tensor<fp16, [68, 68, 1]> pp_transformer_encoder_transformer_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [68, 68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9401920))), scale = tensor<fp16, [68, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9406656))))[name = string("pp_transformer_encoder_transformer_2_attn_proj_weight_to_fp16_quantized")];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_2_attn_proj_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_2_attn_proj_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9406912)))];
            tensor<fp16, [1, 68, ?]> input0_3_cast_fp16 = conv(bias = pp_transformer_encoder_transformer_2_attn_proj_bias_to_fp16, dilations = input0_3_dilations_0, groups = input0_3_groups_0, pad = input0_3_pad_0, pad_type = input0_3_pad_type_0, strides = input0_3_strides_0, weight = pp_transformer_encoder_transformer_2_attn_proj_weight_to_fp16_quantized, x = input2_1_cast_fp16)[name = string("input0_3_cast_fp16")];
            tensor<int32, [3]> var_689 = const()[name = string("op_689"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, ?, 68]> var_690_cast_fp16 = transpose(perm = var_689, x = input0_3_cast_fp16)[name = string("transpose_0")];
            tensor<fp16, [1, ?, 68]> input_4_cast_fp16 = add(x = var_581_cast_fp16, y = var_690_cast_fp16)[name = string("input_4_cast_fp16")];
            tensor<int32, [1]> input0_1_axes_0 = const()[name = string("input0_1_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_2_ln2_weight_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_2_ln2_weight_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9407168)))];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_2_ln2_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_2_ln2_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9407424)))];
            tensor<fp16, [1, ?, 68]> input0_1_cast_fp16 = layer_norm(axes = input0_1_axes_0, beta = pp_transformer_encoder_transformer_2_ln2_bias_to_fp16, epsilon = var_307_to_fp16, gamma = pp_transformer_encoder_transformer_2_ln2_weight_to_fp16, x = input_4_cast_fp16)[name = string("input0_1_cast_fp16")];
            tensor<fp16, [272, 68]> pp_transformer_encoder_transformer_2_mlp_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [272, 68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9407680))), scale = tensor<fp16, [272, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9426240))))[name = string("pp_transformer_encoder_transformer_2_mlp_0_weight_to_fp16_quantized")];
            tensor<fp16, [272]> pp_transformer_encoder_transformer_2_mlp_0_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_2_mlp_0_bias_to_fp16"), val = tensor<fp16, [272]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9426880)))];
            tensor<fp16, [1, ?, 272]> linear_5_cast_fp16 = linear(bias = pp_transformer_encoder_transformer_2_mlp_0_bias_to_fp16, weight = pp_transformer_encoder_transformer_2_mlp_0_weight_to_fp16_quantized, x = input0_1_cast_fp16)[name = string("linear_5_cast_fp16")];
            string var_701_mode_0 = const()[name = string("op_701_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, ?, 272]> var_701_cast_fp16 = gelu(mode = var_701_mode_0, x = linear_5_cast_fp16)[name = string("op_701_cast_fp16")];
            tensor<fp16, [68, 272]> pp_transformer_encoder_transformer_2_mlp_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [68, 272]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9427520))), scale = tensor<fp16, [68, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9446080))))[name = string("pp_transformer_encoder_transformer_2_mlp_2_weight_to_fp16_quantized")];
            tensor<fp16, [68]> pp_transformer_encoder_transformer_2_mlp_2_bias_to_fp16 = const()[name = string("pp_transformer_encoder_transformer_2_mlp_2_bias_to_fp16"), val = tensor<fp16, [68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9446336)))];
            tensor<fp16, [1, ?, 68]> linear_6_cast_fp16 = linear(bias = pp_transformer_encoder_transformer_2_mlp_2_bias_to_fp16, weight = pp_transformer_encoder_transformer_2_mlp_2_weight_to_fp16_quantized, x = var_701_cast_fp16)[name = string("linear_6_cast_fp16")];
            tensor<fp16, [1, ?, 68]> var_706_cast_fp16 = add(x = input_4_cast_fp16, y = linear_6_cast_fp16)[name = string("op_706_cast_fp16")];
            tensor<fp16, [5, 68]> pp_final_layer_weight_to_fp16 = const()[name = string("pp_final_layer_weight_to_fp16"), val = tensor<fp16, [5, 68]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9446592)))];
            tensor<fp16, [5]> pp_final_layer_bias_to_fp16 = const()[name = string("pp_final_layer_bias_to_fp16"), val = tensor<fp16, [5]>([0x1.04p-5, -0x1.d4p-3, -0x1.2dcp-3, -0x1.f58p-4, -0x1.2acp-2])];
            tensor<fp16, [1, ?, 5]> linear_7_cast_fp16 = linear(bias = pp_final_layer_bias_to_fp16, weight = pp_final_layer_weight_to_fp16, x = var_706_cast_fp16)[name = string("linear_7_cast_fp16")];
            tensor<fp16, [1, ?, 5]> output_principal_points = softmax(axis = var_6, x = linear_7_cast_fp16)[name = string("principal_points_1_cast_fp16")];
            int32 input9_1_axis_0 = const()[name = string("input9_1_axis_0"), val = int32(2)];
            bool input9_1_ascending_0 = const()[name = string("input9_1_ascending_0"), val = bool(false)];
            bool input9_1_sort_0 = const()[name = string("input9_1_sort_0"), val = bool(true)];
            bool input9_1_return_indices_0 = const()[name = string("input9_1_return_indices_0"), val = bool(true)];
            string input9_1_output_indices_dtype_0 = const()[name = string("input9_1_output_indices_dtype_0"), val = string("int32")];
            tensor<fp16, [1, ?, 20]> input9_1_cast_fp16_0, tensor<int32, [1, ?, 20]> output_topk_indices = topk(ascending = input9_1_ascending_0, axis = input9_1_axis_0, k = var_13, output_indices_dtype = input9_1_output_indices_dtype_0, return_indices = input9_1_return_indices_0, sort = input9_1_sort_0, x = input6_1_cast_fp16)[name = string("input9_1_cast_fp16")];
            tensor<fp16, [1, ?, 20]> log_softmax_1_softmax_cast_fp16 = softmax(axis = var_6, x = input9_1_cast_fp16_0)[name = string("log_softmax_1_softmax_cast_fp16")];
            fp32 log_softmax_1_epsilon_0 = const()[name = string("log_softmax_1_epsilon_0"), val = fp32(0x1p-149)];
            tensor<fp16, [1, ?, 20]> output_after_topk = log(epsilon = log_softmax_1_epsilon_0, x = log_softmax_1_softmax_cast_fp16)[name = string("log_softmax_1_cast_fp16")];
        } -> (output_after_topk, output_principal_points, output_topk_indices);
}