program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3500.12.1"}, {"coremlc-version", "3500.26.1"}}), mldb_token = string("mldb-rxg5jorao8")]
{
    func main<ios18>(tensor<fp32, [1, 1, 480]> audio, state<tensor<fp16, [1, 1, 540]>> audio_to_fp16_state, state<tensor<fp16, [1, 224, 9]>> input0_3_cast_fp16_state, state<tensor<fp16, [1, 288, 40]>> input_107_cast_fp16_state, state<tensor<fp16, [1, 288, 9]>> input_119_cast_fp16_state, state<tensor<fp16, [1, 288, 10]>> input_13_cast_fp16_state, state<tensor<fp16, [1, 288, 12]>> input_17_cast_fp16_state, state<tensor<fp16, [1, 288, 12]>> input_23_cast_fp16_state, state<tensor<fp16, [1, 288, 16]>> input_27_cast_fp16_state, state<tensor<fp16, [1, 288, 24]>> input_37_cast_fp16_state, state<tensor<fp16, [1, 288, 40]>> input_47_cast_fp16_state, state<tensor<fp16, [1, 288, 72]>> input_4_cast_fp16_state, state<tensor<fp16, [1, 288, 72]>> input_57_cast_fp16_state, state<tensor<fp16, [1, 288, 10]>> input_67_cast_fp16_state, state<tensor<fp16, [1, 288, 12]>> input_77_cast_fp16_state, state<tensor<fp16, [1, 288, 10]>> input_7_cast_fp16_state, state<tensor<fp16, [1, 288, 16]>> input_87_cast_fp16_state, state<tensor<fp16, [1, 288, 24]>> input_97_cast_fp16_state, state<tensor<fp16, [1, 1, 224, 15]>> var_794_cast_fp16_state) [BNNSOptions = dict<string, string>({{"StateMode", "Streaming"}}), UserMetadata = dict<string, string>({{"iteration", "1529290"}, {"taskid", "qsg8bvjn7i"}})] {
            tensor<fp16, [288, 288, 1]> sep_module_3_tcn_0_weight_cast_fp16 = constexpr_lut_to_dense(indices = tensor<uint4, [288, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(64))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(41600))))[name = string("sep_module_3_tcn_0_weight_cast_fp16")];
            tensor<fp16, [288, 288, 1]> sep_module_4_tcn_0_weight_cast_fp16 = constexpr_lut_to_dense(indices = tensor<uint4, [288, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(41728))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(83264))))[name = string("sep_module_4_tcn_0_weight_cast_fp16")];
            tensor<fp16, [288, 288, 1]> sep_module_5_tcn_0_weight_cast_fp16 = constexpr_lut_to_dense(indices = tensor<uint4, [288, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(83392))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(124928))))[name = string("sep_module_5_tcn_0_weight_cast_fp16")];
            tensor<fp16, [288, 288, 1]> sep_module_6_tcn_0_weight_cast_fp16 = constexpr_lut_to_dense(indices = tensor<uint4, [288, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(125056))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(166592))))[name = string("sep_module_6_tcn_0_weight_cast_fp16")];
            tensor<fp16, [288, 288, 1]> sep_module_7_tcn_0_weight_cast_fp16 = constexpr_lut_to_dense(indices = tensor<uint4, [288, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(166720))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(208256))))[name = string("sep_module_7_tcn_0_weight_cast_fp16")];
            tensor<fp16, [288, 288, 1]> sep_module_8_tcn_0_weight_cast_fp16 = constexpr_lut_to_dense(indices = tensor<uint4, [288, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(208384))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(249920))))[name = string("sep_module_8_tcn_0_weight_cast_fp16")];
            string input_113_pad_type_0 = const()[name = string("input_113_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_113_pad_0 = const()[name = string("input_113_pad_0"), val = tensor<int32, [2]>([60, 60])];
            tensor<int32, [1]> input_113_strides_0 = const()[name = string("input_113_strides_0"), val = tensor<int32, [1]>([60])];
            tensor<int32, [1]> input_113_dilations_0 = const()[name = string("input_113_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_113_groups_0 = const()[name = string("input_113_groups_0"), val = int32(1)];
            string audio_to_fp16_dtype_0 = const()[name = string("audio_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [224, 1, 120]> front_end_0_weight_to_fp16 = const()[name = string("front_end_0_weight_to_fp16"), val = tensor<fp16, [224, 1, 120]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(250048)))];
            tensor<fp16, [1, 1, 480]> audio_to_fp16 = cast(dtype = audio_to_fp16_dtype_0, x = audio)[name = string("cast_1")];
            tensor<fp16, [1, 1, 540]> audio_to_fp16_state_input = read_state(input = audio_to_fp16_state);
            tensor<fp16, [1, 1, 540]> audio_to_fp16_state_updated = slice_update(begin = tensor<int32, [3]>([0, 0, 60]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = audio_to_fp16, x = audio_to_fp16_state_input);
            tensor<fp16, [1, 224, 8]> input_113_cast_fp16 = conv(dilations = input_113_dilations_0, groups = input_113_groups_0, pad = tensor<int32, [2]>([0, 0]), pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = front_end_0_weight_to_fp16, x = audio_to_fp16_state_updated);
            write_state(data = audio_to_fp16_state_updated, input = audio_to_fp16_state);
            tensor<fp16, [1, 224, 8]> var_26_cast_fp16 = relu(x = input_113_cast_fp16)[name = string("op_26_cast_fp16")];
            tensor<int32, [1]> mean_y_4_axes_0 = const()[name = string("mean_y_4_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_4_keep_dims_0 = const()[name = string("mean_y_4_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_4_cast_fp16 = reduce_mean(axes = mean_y_4_axes_0, keep_dims = mean_y_4_keep_dims_0, x = var_26_cast_fp16)[name = string("mean_y_4_cast_fp16")];
            tensor<fp16, [1, 224, 8]> var_36_cast_fp16 = sub(x = var_26_cast_fp16, y = mean_y_4_cast_fp16)[name = string("op_36_cast_fp16")];
            tensor<fp16, [1, 224, 8]> var_37_cast_fp16 = square(x = var_36_cast_fp16);
            tensor<int32, [1]> var_39_axes_0 = const()[name = string("op_39_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_39_keep_dims_0 = const()[name = string("op_39_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_39_cast_fp16 = reduce_mean(axes = var_39_axes_0, keep_dims = var_39_keep_dims_0, x = var_37_cast_fp16)[name = string("op_39_cast_fp16")];
            fp16 var_40_to_fp16 = const()[name = string("op_40_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_41_cast_fp16 = add(x = var_39_cast_fp16, y = var_40_to_fp16)[name = string("op_41_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_4_cast_fp16 = sqrt(x = var_41_cast_fp16)[name = string("std_y_4_cast_fp16")];
            tensor<fp16, [1, 224, 1]> front_norm_norm_gamma_to_fp16 = const()[name = string("front_norm_norm_gamma_to_fp16"), val = tensor<fp16, [1, 224, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(303872)))];
            tensor<fp16, [1, 224, 8]> var_44_cast_fp16 = mul(x = front_norm_norm_gamma_to_fp16, y = var_36_cast_fp16)[name = string("op_44_cast_fp16")];
            tensor<fp16, [1, 224, 8]> var_45_cast_fp16 = real_div(x = var_44_cast_fp16, y = std_y_4_cast_fp16)[name = string("op_45_cast_fp16")];
            tensor<fp16, [1, 224, 1]> front_norm_norm_beta_to_fp16 = const()[name = string("front_norm_norm_beta_to_fp16"), val = tensor<fp16, [1, 224, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(304384)))];
            tensor<fp16, [1, 224, 8]> input_115_cast_fp16 = add(x = var_45_cast_fp16, y = front_norm_norm_beta_to_fp16)[name = string("input_115_cast_fp16")];
            string input_119_pad_type_0 = const()[name = string("input_119_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_119_strides_0 = const()[name = string("input_119_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_119_dilations_0 = const()[name = string("input_119_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_119_groups_0 = const()[name = string("input_119_groups_0"), val = int32(1)];
            tensor<fp16, [288, 224, 1]> to_latent_weight_to_fp16 = const()[name = string("to_latent_weight_to_fp16"), val = tensor<fp16, [288, 224, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(304896)))];
            tensor<fp16, [1, 288, 8]> input_119_cast_fp16 = conv(dilations = input_119_dilations_0, groups = input_119_groups_0, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = input_119_strides_0, weight = to_latent_weight_to_fp16, x = input_115_cast_fp16)[name = string("input_119_cast_fp16")];
            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
            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, [288, 288, 1]> sep_module_0_tcn_0_weight_to_fp16 = const()[name = string("sep_module_0_tcn_0_weight_to_fp16"), val = tensor<fp16, [288, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(433984)))];
            tensor<fp16, [1, 288, 8]> input_5_cast_fp16 = conv(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 = sep_module_0_tcn_0_weight_to_fp16, x = input_119_cast_fp16)[name = string("input_5_cast_fp16")];
            fp32 var_98_alpha_1 = const()[name = string("op_98_alpha_1"), val = fp32(0x1.860d4cp-2)];
            tensor<fp16, [1, 288, 8]> var_98_cast_fp16 = leaky_relu(alpha = fp16(0x1.86p-2), x = input_5_cast_fp16);
            tensor<int32, [1]> mean_y_3_axes_0 = const()[name = string("mean_y_3_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_3_keep_dims_0 = const()[name = string("mean_y_3_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_3_cast_fp16 = reduce_mean(axes = mean_y_3_axes_0, keep_dims = mean_y_3_keep_dims_0, x = var_98_cast_fp16)[name = string("mean_y_3_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_104_cast_fp16 = sub(x = var_98_cast_fp16, y = mean_y_3_cast_fp16)[name = string("op_104_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_105_cast_fp16 = square(x = var_104_cast_fp16);
            tensor<int32, [1]> var_107_axes_0 = const()[name = string("op_107_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_107_keep_dims_0 = const()[name = string("op_107_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_107_cast_fp16 = reduce_mean(axes = var_107_axes_0, keep_dims = var_107_keep_dims_0, x = var_105_cast_fp16)[name = string("op_107_cast_fp16")];
            fp16 var_108_to_fp16 = const()[name = string("op_108_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_109_cast_fp16 = add(x = var_107_cast_fp16, y = var_108_to_fp16)[name = string("op_109_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_3_cast_fp16 = sqrt(x = var_109_cast_fp16)[name = string("std_y_3_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_0_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_0_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(599936)))];
            tensor<fp16, [1, 288, 8]> var_112_cast_fp16 = mul(x = sep_module_0_tcn_2_norm_gamma_to_fp16, y = var_104_cast_fp16)[name = string("op_112_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_113_cast_fp16 = real_div(x = var_112_cast_fp16, y = std_y_3_cast_fp16)[name = string("op_113_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_0_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_0_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(600576)))];
            tensor<fp16, [1, 288, 8]> input_7_cast_fp16 = add(x = var_113_cast_fp16, y = sep_module_0_tcn_2_norm_beta_to_fp16)[name = string("input_7_cast_fp16")];
            tensor<int32, [6]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 1])];
            string input_9_mode_0 = const()[name = string("input_9_mode_0"), val = string("constant")];
            fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 288, 10]> input_7_cast_fp16_state_input = read_state(input = input_7_cast_fp16_state);
            tensor<fp16, [1, 288, 10]> input_9_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 2]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_7_cast_fp16, x = input_7_cast_fp16_state_input);
            write_state(data = input_9_cast_fp16, input = input_7_cast_fp16_state);
            string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("valid")];
            int32 input_11_groups_0 = const()[name = string("input_11_groups_0"), val = int32(288)];
            tensor<int32, [1]> input_11_strides_0 = const()[name = string("input_11_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_11_dilations_0 = const()[name = string("input_11_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [288, 1, 3]> sep_module_0_tcn_4_weight_to_fp16 = const()[name = string("sep_module_0_tcn_4_weight_to_fp16"), val = tensor<fp16, [288, 1, 3]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(601216)))];
            tensor<fp16, [1, 288, 8]> input_11_cast_fp16 = conv(dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = sep_module_0_tcn_4_weight_to_fp16, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")];
            fp32 var_124_alpha_1 = const()[name = string("op_124_alpha_1"), val = fp32(-0x1.92f49p-2)];
            tensor<fp16, [1, 288, 8]> var_124_cast_fp16 = leaky_relu(alpha = fp16(-0x1.93p-2), x = input_11_cast_fp16);
            tensor<int32, [1]> mean_y_5_axes_0 = const()[name = string("mean_y_5_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_5_keep_dims_0 = const()[name = string("mean_y_5_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_5_cast_fp16 = reduce_mean(axes = mean_y_5_axes_0, keep_dims = mean_y_5_keep_dims_0, x = var_124_cast_fp16)[name = string("mean_y_5_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_130_cast_fp16 = sub(x = var_124_cast_fp16, y = mean_y_5_cast_fp16)[name = string("op_130_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_131_cast_fp16 = square(x = var_130_cast_fp16);
            tensor<int32, [1]> var_133_axes_0 = const()[name = string("op_133_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_133_keep_dims_0 = const()[name = string("op_133_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_133_cast_fp16 = reduce_mean(axes = var_133_axes_0, keep_dims = var_133_keep_dims_0, x = var_131_cast_fp16)[name = string("op_133_cast_fp16")];
            fp16 var_134_to_fp16 = const()[name = string("op_134_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_135_cast_fp16 = add(x = var_133_cast_fp16, y = var_134_to_fp16)[name = string("op_135_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_5_cast_fp16 = sqrt(x = var_135_cast_fp16)[name = string("std_y_5_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_0_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_0_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(603008)))];
            tensor<fp16, [1, 288, 8]> var_138_cast_fp16 = mul(x = sep_module_0_tcn_6_norm_gamma_to_fp16, y = var_130_cast_fp16)[name = string("op_138_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_139_cast_fp16 = real_div(x = var_138_cast_fp16, y = std_y_5_cast_fp16)[name = string("op_139_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_0_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_0_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(603648)))];
            tensor<fp16, [1, 288, 8]> y_2_cast_fp16 = add(x = var_139_cast_fp16, y = sep_module_0_tcn_6_norm_beta_to_fp16)[name = string("y_2_cast_fp16")];
            tensor<fp16, [1, 288, 9]> input_119_cast_fp16_state_input = read_state(input = input_119_cast_fp16_state);
            tensor<fp16, [1, 288, 9]> input_119_cast_fp16_state_updated = slice_update(begin = tensor<int32, [3]>([0, 0, 1]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_119_cast_fp16, x = input_119_cast_fp16_state_input);
            write_state(data = input_119_cast_fp16_state_updated, input = input_119_cast_fp16_state);
            tensor<fp16, [1, 288, 8]> input_119_cast_fp16_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 288, 8]), x = input_119_cast_fp16_state_updated);
            tensor<fp16, [1, 288, 8]> input_13_cast_fp16 = add(x = input_119_cast_fp16_delayed, y = y_2_cast_fp16)[name = string("input_13_cast_fp16")];
            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)];
            tensor<fp16, [288, 288, 1]> sep_module_1_tcn_0_weight_to_fp16 = const()[name = string("sep_module_1_tcn_0_weight_to_fp16"), val = tensor<fp16, [288, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(604288)))];
            tensor<fp16, [1, 288, 8]> input_15_cast_fp16 = conv(dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = sep_module_1_tcn_0_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")];
            fp32 var_156_alpha_1 = const()[name = string("op_156_alpha_1"), val = fp32(0x1.d3294p-2)];
            tensor<fp16, [1, 288, 8]> var_156_cast_fp16 = leaky_relu(alpha = fp16(0x1.d34p-2), x = input_15_cast_fp16);
            tensor<int32, [1]> mean_y_7_axes_0 = const()[name = string("mean_y_7_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_7_keep_dims_0 = const()[name = string("mean_y_7_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_7_cast_fp16 = reduce_mean(axes = mean_y_7_axes_0, keep_dims = mean_y_7_keep_dims_0, x = var_156_cast_fp16)[name = string("mean_y_7_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_162_cast_fp16 = sub(x = var_156_cast_fp16, y = mean_y_7_cast_fp16)[name = string("op_162_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_163_cast_fp16 = square(x = var_162_cast_fp16);
            tensor<int32, [1]> var_165_axes_0 = const()[name = string("op_165_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_165_keep_dims_0 = const()[name = string("op_165_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_165_cast_fp16 = reduce_mean(axes = var_165_axes_0, keep_dims = var_165_keep_dims_0, x = var_163_cast_fp16)[name = string("op_165_cast_fp16")];
            fp16 var_166_to_fp16 = const()[name = string("op_166_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_167_cast_fp16 = add(x = var_165_cast_fp16, y = var_166_to_fp16)[name = string("op_167_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_7_cast_fp16 = sqrt(x = var_167_cast_fp16)[name = string("std_y_7_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_1_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_1_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(770240)))];
            tensor<fp16, [1, 288, 8]> var_170_cast_fp16 = mul(x = sep_module_1_tcn_2_norm_gamma_to_fp16, y = var_162_cast_fp16)[name = string("op_170_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_171_cast_fp16 = real_div(x = var_170_cast_fp16, y = std_y_7_cast_fp16)[name = string("op_171_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_1_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_1_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(770880)))];
            tensor<fp16, [1, 288, 8]> input_17_cast_fp16 = add(x = var_171_cast_fp16, y = sep_module_1_tcn_2_norm_beta_to_fp16)[name = string("input_17_cast_fp16")];
            tensor<int32, [6]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            string input_19_mode_0 = const()[name = string("input_19_mode_0"), val = string("constant")];
            fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 288, 12]> input_17_cast_fp16_state_input = read_state(input = input_17_cast_fp16_state);
            tensor<fp16, [1, 288, 12]> input_19_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 4]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_17_cast_fp16, x = input_17_cast_fp16_state_input);
            write_state(data = input_19_cast_fp16, input = input_17_cast_fp16_state);
            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [1]>([2])];
            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(288)];
            tensor<int32, [1]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [288, 1, 3]> sep_module_1_tcn_4_weight_to_fp16 = const()[name = string("sep_module_1_tcn_4_weight_to_fp16"), val = tensor<fp16, [288, 1, 3]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(771520)))];
            tensor<fp16, [1, 288, 8]> input_21_cast_fp16 = conv(dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = sep_module_1_tcn_4_weight_to_fp16, x = input_19_cast_fp16)[name = string("input_21_cast_fp16")];
            fp32 var_182_alpha_1 = const()[name = string("op_182_alpha_1"), val = fp32(0x1.fcc07ep-5)];
            tensor<fp16, [1, 288, 8]> var_182_cast_fp16 = leaky_relu(alpha = fp16(0x1.fccp-5), x = input_21_cast_fp16);
            tensor<int32, [1]> mean_y_9_axes_0 = const()[name = string("mean_y_9_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_9_keep_dims_0 = const()[name = string("mean_y_9_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_9_cast_fp16 = reduce_mean(axes = mean_y_9_axes_0, keep_dims = mean_y_9_keep_dims_0, x = var_182_cast_fp16)[name = string("mean_y_9_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_188_cast_fp16 = sub(x = var_182_cast_fp16, y = mean_y_9_cast_fp16)[name = string("op_188_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_189_cast_fp16 = square(x = var_188_cast_fp16);
            tensor<int32, [1]> var_191_axes_0 = const()[name = string("op_191_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_191_keep_dims_0 = const()[name = string("op_191_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_191_cast_fp16 = reduce_mean(axes = var_191_axes_0, keep_dims = var_191_keep_dims_0, x = var_189_cast_fp16)[name = string("op_191_cast_fp16")];
            fp16 var_192_to_fp16 = const()[name = string("op_192_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_193_cast_fp16 = add(x = var_191_cast_fp16, y = var_192_to_fp16)[name = string("op_193_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_9_cast_fp16 = sqrt(x = var_193_cast_fp16)[name = string("std_y_9_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_1_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_1_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(773312)))];
            tensor<fp16, [1, 288, 8]> var_196_cast_fp16 = mul(x = sep_module_1_tcn_6_norm_gamma_to_fp16, y = var_188_cast_fp16)[name = string("op_196_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_197_cast_fp16 = real_div(x = var_196_cast_fp16, y = std_y_9_cast_fp16)[name = string("op_197_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_1_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_1_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(773952)))];
            tensor<fp16, [1, 288, 8]> y_4_cast_fp16 = add(x = var_197_cast_fp16, y = sep_module_1_tcn_6_norm_beta_to_fp16)[name = string("y_4_cast_fp16")];
            tensor<fp16, [1, 288, 10]> input_13_cast_fp16_state_input = read_state(input = input_13_cast_fp16_state);
            tensor<fp16, [1, 288, 10]> input_13_cast_fp16_state_updated = slice_update(begin = tensor<int32, [3]>([0, 0, 2]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_13_cast_fp16, x = input_13_cast_fp16_state_input);
            write_state(data = input_13_cast_fp16_state_updated, input = input_13_cast_fp16_state);
            tensor<fp16, [1, 288, 8]> input_13_cast_fp16_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 288, 8]), x = input_13_cast_fp16_state_updated);
            tensor<fp16, [1, 288, 8]> input_23_cast_fp16 = add(x = input_13_cast_fp16_delayed, y = y_4_cast_fp16)[name = string("input_23_cast_fp16")];
            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
            tensor<fp16, [288, 288, 1]> sep_module_2_tcn_0_weight_to_fp16 = const()[name = string("sep_module_2_tcn_0_weight_to_fp16"), val = tensor<fp16, [288, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(774592)))];
            tensor<fp16, [1, 288, 8]> input_25_cast_fp16 = conv(dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = sep_module_2_tcn_0_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")];
            fp32 var_214_alpha_1 = const()[name = string("op_214_alpha_1"), val = fp32(0x1.126852p-1)];
            tensor<fp16, [1, 288, 8]> var_214_cast_fp16 = leaky_relu(alpha = fp16(0x1.128p-1), x = input_25_cast_fp16);
            tensor<int32, [1]> mean_y_11_axes_0 = const()[name = string("mean_y_11_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_11_keep_dims_0 = const()[name = string("mean_y_11_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_11_cast_fp16 = reduce_mean(axes = mean_y_11_axes_0, keep_dims = mean_y_11_keep_dims_0, x = var_214_cast_fp16)[name = string("mean_y_11_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_220_cast_fp16 = sub(x = var_214_cast_fp16, y = mean_y_11_cast_fp16)[name = string("op_220_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_221_cast_fp16 = square(x = var_220_cast_fp16);
            tensor<int32, [1]> var_223_axes_0 = const()[name = string("op_223_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_223_keep_dims_0 = const()[name = string("op_223_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_223_cast_fp16 = reduce_mean(axes = var_223_axes_0, keep_dims = var_223_keep_dims_0, x = var_221_cast_fp16)[name = string("op_223_cast_fp16")];
            fp16 var_224_to_fp16 = const()[name = string("op_224_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_225_cast_fp16 = add(x = var_223_cast_fp16, y = var_224_to_fp16)[name = string("op_225_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_11_cast_fp16 = sqrt(x = var_225_cast_fp16)[name = string("std_y_11_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_2_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_2_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(940544)))];
            tensor<fp16, [1, 288, 8]> var_228_cast_fp16 = mul(x = sep_module_2_tcn_2_norm_gamma_to_fp16, y = var_220_cast_fp16)[name = string("op_228_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_229_cast_fp16 = real_div(x = var_228_cast_fp16, y = std_y_11_cast_fp16)[name = string("op_229_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_2_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_2_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(941184)))];
            tensor<fp16, [1, 288, 8]> input_27_cast_fp16 = add(x = var_229_cast_fp16, y = sep_module_2_tcn_2_norm_beta_to_fp16)[name = string("input_27_cast_fp16")];
            tensor<int32, [6]> input_29_pad_0 = const()[name = string("input_29_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("constant")];
            fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 288, 16]> input_27_cast_fp16_state_input = read_state(input = input_27_cast_fp16_state);
            tensor<fp16, [1, 288, 16]> input_29_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 8]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_27_cast_fp16, x = input_27_cast_fp16_state_input);
            write_state(data = input_29_cast_fp16, input = input_27_cast_fp16_state);
            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [1]>([4])];
            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(288)];
            tensor<int32, [1]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [288, 1, 3]> sep_module_2_tcn_4_weight_to_fp16 = const()[name = string("sep_module_2_tcn_4_weight_to_fp16"), val = tensor<fp16, [288, 1, 3]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(941824)))];
            tensor<fp16, [1, 288, 8]> input_31_cast_fp16 = conv(dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = sep_module_2_tcn_4_weight_to_fp16, x = input_29_cast_fp16)[name = string("input_31_cast_fp16")];
            fp32 var_240_alpha_1 = const()[name = string("op_240_alpha_1"), val = fp32(-0x1.df13dp-4)];
            tensor<fp16, [1, 288, 8]> var_240_cast_fp16 = leaky_relu(alpha = fp16(-0x1.dfp-4), x = input_31_cast_fp16);
            tensor<int32, [1]> mean_y_13_axes_0 = const()[name = string("mean_y_13_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_13_keep_dims_0 = const()[name = string("mean_y_13_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_13_cast_fp16 = reduce_mean(axes = mean_y_13_axes_0, keep_dims = mean_y_13_keep_dims_0, x = var_240_cast_fp16)[name = string("mean_y_13_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_246_cast_fp16 = sub(x = var_240_cast_fp16, y = mean_y_13_cast_fp16)[name = string("op_246_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_247_cast_fp16 = square(x = var_246_cast_fp16);
            tensor<int32, [1]> var_249_axes_0 = const()[name = string("op_249_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_249_keep_dims_0 = const()[name = string("op_249_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_249_cast_fp16 = reduce_mean(axes = var_249_axes_0, keep_dims = var_249_keep_dims_0, x = var_247_cast_fp16)[name = string("op_249_cast_fp16")];
            fp16 var_250_to_fp16 = const()[name = string("op_250_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_251_cast_fp16 = add(x = var_249_cast_fp16, y = var_250_to_fp16)[name = string("op_251_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_13_cast_fp16 = sqrt(x = var_251_cast_fp16)[name = string("std_y_13_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_2_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_2_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(943616)))];
            tensor<fp16, [1, 288, 8]> var_254_cast_fp16 = mul(x = sep_module_2_tcn_6_norm_gamma_to_fp16, y = var_246_cast_fp16)[name = string("op_254_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_255_cast_fp16 = real_div(x = var_254_cast_fp16, y = std_y_13_cast_fp16)[name = string("op_255_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_2_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_2_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(944256)))];
            tensor<fp16, [1, 288, 8]> y_6_cast_fp16 = add(x = var_255_cast_fp16, y = sep_module_2_tcn_6_norm_beta_to_fp16)[name = string("y_6_cast_fp16")];
            tensor<fp16, [1, 288, 12]> input_23_cast_fp16_state_input = read_state(input = input_23_cast_fp16_state);
            tensor<fp16, [1, 288, 12]> input_23_cast_fp16_state_updated = slice_update(begin = tensor<int32, [3]>([0, 0, 4]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_23_cast_fp16, x = input_23_cast_fp16_state_input);
            write_state(data = input_23_cast_fp16_state_updated, input = input_23_cast_fp16_state);
            tensor<fp16, [1, 288, 8]> input_23_cast_fp16_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 288, 8]), x = input_23_cast_fp16_state_updated);
            tensor<fp16, [1, 288, 8]> input_33_cast_fp16 = add(x = input_23_cast_fp16_delayed, y = y_6_cast_fp16)[name = string("input_33_cast_fp16")];
            string input_35_pad_type_0 = const()[name = string("input_35_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_35_strides_0 = const()[name = string("input_35_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_35_dilations_0 = const()[name = string("input_35_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_35_groups_0 = const()[name = string("input_35_groups_0"), val = int32(1)];
            tensor<fp16, [1, 288, 8]> input_35_cast_fp16 = conv(dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = sep_module_3_tcn_0_weight_cast_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")];
            fp32 var_272_alpha_1 = const()[name = string("op_272_alpha_1"), val = fp32(0x1.3d7856p-2)];
            tensor<fp16, [1, 288, 8]> var_272_cast_fp16 = leaky_relu(alpha = fp16(0x1.3d8p-2), x = input_35_cast_fp16);
            tensor<int32, [1]> mean_y_15_axes_0 = const()[name = string("mean_y_15_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_15_keep_dims_0 = const()[name = string("mean_y_15_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_15_cast_fp16 = reduce_mean(axes = mean_y_15_axes_0, keep_dims = mean_y_15_keep_dims_0, x = var_272_cast_fp16)[name = string("mean_y_15_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_278_cast_fp16 = sub(x = var_272_cast_fp16, y = mean_y_15_cast_fp16)[name = string("op_278_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_279_cast_fp16 = square(x = var_278_cast_fp16);
            tensor<int32, [1]> var_281_axes_0 = const()[name = string("op_281_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_281_keep_dims_0 = const()[name = string("op_281_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_281_cast_fp16 = reduce_mean(axes = var_281_axes_0, keep_dims = var_281_keep_dims_0, x = var_279_cast_fp16)[name = string("op_281_cast_fp16")];
            fp16 var_282_to_fp16 = const()[name = string("op_282_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_283_cast_fp16 = add(x = var_281_cast_fp16, y = var_282_to_fp16)[name = string("op_283_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_15_cast_fp16 = sqrt(x = var_283_cast_fp16)[name = string("std_y_15_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_3_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_3_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(944896)))];
            tensor<fp16, [1, 288, 8]> var_286_cast_fp16 = mul(x = sep_module_3_tcn_2_norm_gamma_to_fp16, y = var_278_cast_fp16)[name = string("op_286_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_287_cast_fp16 = real_div(x = var_286_cast_fp16, y = std_y_15_cast_fp16)[name = string("op_287_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_3_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_3_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(945536)))];
            tensor<fp16, [1, 288, 8]> input_37_cast_fp16 = add(x = var_287_cast_fp16, y = sep_module_3_tcn_2_norm_beta_to_fp16)[name = string("input_37_cast_fp16")];
            tensor<int32, [6]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            string input_39_mode_0 = const()[name = string("input_39_mode_0"), val = string("constant")];
            fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 288, 24]> input_37_cast_fp16_state_input = read_state(input = input_37_cast_fp16_state);
            tensor<fp16, [1, 288, 24]> input_39_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 16]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_37_cast_fp16, x = input_37_cast_fp16_state_input);
            write_state(data = input_39_cast_fp16, input = input_37_cast_fp16_state);
            string input_41_pad_type_0 = const()[name = string("input_41_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_41_dilations_0 = const()[name = string("input_41_dilations_0"), val = tensor<int32, [1]>([8])];
            int32 input_41_groups_0 = const()[name = string("input_41_groups_0"), val = int32(288)];
            tensor<int32, [1]> input_41_strides_0 = const()[name = string("input_41_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [288, 1, 3]> sep_module_3_tcn_4_weight_to_fp16 = const()[name = string("sep_module_3_tcn_4_weight_to_fp16"), val = tensor<fp16, [288, 1, 3]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(946176)))];
            tensor<fp16, [1, 288, 8]> input_41_cast_fp16 = conv(dilations = input_41_dilations_0, groups = input_41_groups_0, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = input_41_strides_0, weight = sep_module_3_tcn_4_weight_to_fp16, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")];
            fp32 var_298_alpha_1 = const()[name = string("op_298_alpha_1"), val = fp32(-0x1.fbe0ep-5)];
            tensor<fp16, [1, 288, 8]> var_298_cast_fp16 = leaky_relu(alpha = fp16(-0x1.fcp-5), x = input_41_cast_fp16);
            tensor<int32, [1]> mean_y_17_axes_0 = const()[name = string("mean_y_17_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_17_keep_dims_0 = const()[name = string("mean_y_17_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_17_cast_fp16 = reduce_mean(axes = mean_y_17_axes_0, keep_dims = mean_y_17_keep_dims_0, x = var_298_cast_fp16)[name = string("mean_y_17_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_304_cast_fp16 = sub(x = var_298_cast_fp16, y = mean_y_17_cast_fp16)[name = string("op_304_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_305_cast_fp16 = square(x = var_304_cast_fp16);
            tensor<int32, [1]> var_307_axes_0 = const()[name = string("op_307_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_307_keep_dims_0 = const()[name = string("op_307_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_307_cast_fp16 = reduce_mean(axes = var_307_axes_0, keep_dims = var_307_keep_dims_0, x = var_305_cast_fp16)[name = string("op_307_cast_fp16")];
            fp16 var_308_to_fp16 = const()[name = string("op_308_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_309_cast_fp16 = add(x = var_307_cast_fp16, y = var_308_to_fp16)[name = string("op_309_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_17_cast_fp16 = sqrt(x = var_309_cast_fp16)[name = string("std_y_17_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_3_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_3_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(947968)))];
            tensor<fp16, [1, 288, 8]> var_312_cast_fp16 = mul(x = sep_module_3_tcn_6_norm_gamma_to_fp16, y = var_304_cast_fp16)[name = string("op_312_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_313_cast_fp16 = real_div(x = var_312_cast_fp16, y = std_y_17_cast_fp16)[name = string("op_313_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_3_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_3_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(948608)))];
            tensor<fp16, [1, 288, 8]> y_8_cast_fp16 = add(x = var_313_cast_fp16, y = sep_module_3_tcn_6_norm_beta_to_fp16)[name = string("y_8_cast_fp16")];
            tensor<fp16, [1, 288, 8]> input_43_cast_fp16 = add(x = input_33_cast_fp16, y = y_8_cast_fp16)[name = string("input_43_cast_fp16")];
            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)];
            tensor<fp16, [1, 288, 8]> input_45_cast_fp16 = conv(dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = sep_module_4_tcn_0_weight_cast_fp16, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")];
            fp32 var_330_alpha_1 = const()[name = string("op_330_alpha_1"), val = fp32(0x1.ce803ep-2)];
            tensor<fp16, [1, 288, 8]> var_330_cast_fp16 = leaky_relu(alpha = fp16(0x1.ce8p-2), x = input_45_cast_fp16);
            tensor<int32, [1]> mean_y_19_axes_0 = const()[name = string("mean_y_19_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_19_keep_dims_0 = const()[name = string("mean_y_19_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_19_cast_fp16 = reduce_mean(axes = mean_y_19_axes_0, keep_dims = mean_y_19_keep_dims_0, x = var_330_cast_fp16)[name = string("mean_y_19_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_336_cast_fp16 = sub(x = var_330_cast_fp16, y = mean_y_19_cast_fp16)[name = string("op_336_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_337_cast_fp16 = square(x = var_336_cast_fp16);
            tensor<int32, [1]> var_339_axes_0 = const()[name = string("op_339_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_339_keep_dims_0 = const()[name = string("op_339_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_339_cast_fp16 = reduce_mean(axes = var_339_axes_0, keep_dims = var_339_keep_dims_0, x = var_337_cast_fp16)[name = string("op_339_cast_fp16")];
            fp16 var_340_to_fp16 = const()[name = string("op_340_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_341_cast_fp16 = add(x = var_339_cast_fp16, y = var_340_to_fp16)[name = string("op_341_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_19_cast_fp16 = sqrt(x = var_341_cast_fp16)[name = string("std_y_19_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_4_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_4_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(949248)))];
            tensor<fp16, [1, 288, 8]> var_344_cast_fp16 = mul(x = sep_module_4_tcn_2_norm_gamma_to_fp16, y = var_336_cast_fp16)[name = string("op_344_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_345_cast_fp16 = real_div(x = var_344_cast_fp16, y = std_y_19_cast_fp16)[name = string("op_345_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_4_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_4_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(949888)))];
            tensor<fp16, [1, 288, 8]> input_47_cast_fp16 = add(x = var_345_cast_fp16, y = sep_module_4_tcn_2_norm_beta_to_fp16)[name = string("input_47_cast_fp16")];
            tensor<int32, [6]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("constant")];
            fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 288, 40]> input_47_cast_fp16_state_input = read_state(input = input_47_cast_fp16_state);
            tensor<fp16, [1, 288, 40]> input_49_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 32]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_47_cast_fp16, x = input_47_cast_fp16_state_input);
            write_state(data = input_49_cast_fp16, input = input_47_cast_fp16_state);
            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [1]>([16])];
            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(288)];
            tensor<int32, [1]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [288, 1, 3]> sep_module_4_tcn_4_weight_to_fp16 = const()[name = string("sep_module_4_tcn_4_weight_to_fp16"), val = tensor<fp16, [288, 1, 3]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(950528)))];
            tensor<fp16, [1, 288, 8]> input_51_cast_fp16 = conv(dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = sep_module_4_tcn_4_weight_to_fp16, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")];
            fp32 var_356_alpha_1 = const()[name = string("op_356_alpha_1"), val = fp32(-0x1.f73f1ep-4)];
            tensor<fp16, [1, 288, 8]> var_356_cast_fp16 = leaky_relu(alpha = fp16(-0x1.f74p-4), x = input_51_cast_fp16);
            tensor<int32, [1]> mean_y_21_axes_0 = const()[name = string("mean_y_21_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_21_keep_dims_0 = const()[name = string("mean_y_21_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_21_cast_fp16 = reduce_mean(axes = mean_y_21_axes_0, keep_dims = mean_y_21_keep_dims_0, x = var_356_cast_fp16)[name = string("mean_y_21_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_362_cast_fp16 = sub(x = var_356_cast_fp16, y = mean_y_21_cast_fp16)[name = string("op_362_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_363_cast_fp16 = square(x = var_362_cast_fp16);
            tensor<int32, [1]> var_365_axes_0 = const()[name = string("op_365_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_365_keep_dims_0 = const()[name = string("op_365_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_365_cast_fp16 = reduce_mean(axes = var_365_axes_0, keep_dims = var_365_keep_dims_0, x = var_363_cast_fp16)[name = string("op_365_cast_fp16")];
            fp16 var_366_to_fp16 = const()[name = string("op_366_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_367_cast_fp16 = add(x = var_365_cast_fp16, y = var_366_to_fp16)[name = string("op_367_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_21_cast_fp16 = sqrt(x = var_367_cast_fp16)[name = string("std_y_21_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_4_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_4_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(952320)))];
            tensor<fp16, [1, 288, 8]> var_370_cast_fp16 = mul(x = sep_module_4_tcn_6_norm_gamma_to_fp16, y = var_362_cast_fp16)[name = string("op_370_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_371_cast_fp16 = real_div(x = var_370_cast_fp16, y = std_y_21_cast_fp16)[name = string("op_371_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_4_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_4_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(952960)))];
            tensor<fp16, [1, 288, 8]> y_10_cast_fp16 = add(x = var_371_cast_fp16, y = sep_module_4_tcn_6_norm_beta_to_fp16)[name = string("y_10_cast_fp16")];
            tensor<fp16, [1, 288, 8]> input_53_cast_fp16 = add(x = input_43_cast_fp16, y = y_10_cast_fp16)[name = string("input_53_cast_fp16")];
            string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_55_strides_0 = const()[name = string("input_55_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_55_dilations_0 = const()[name = string("input_55_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_55_groups_0 = const()[name = string("input_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 288, 8]> input_55_cast_fp16 = conv(dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = sep_module_5_tcn_0_weight_cast_fp16, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")];
            fp32 var_388_alpha_1 = const()[name = string("op_388_alpha_1"), val = fp32(0x1.16a7d6p-2)];
            tensor<fp16, [1, 288, 8]> var_388_cast_fp16 = leaky_relu(alpha = fp16(0x1.16cp-2), x = input_55_cast_fp16);
            tensor<int32, [1]> mean_y_23_axes_0 = const()[name = string("mean_y_23_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_23_keep_dims_0 = const()[name = string("mean_y_23_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_23_cast_fp16 = reduce_mean(axes = mean_y_23_axes_0, keep_dims = mean_y_23_keep_dims_0, x = var_388_cast_fp16)[name = string("mean_y_23_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_394_cast_fp16 = sub(x = var_388_cast_fp16, y = mean_y_23_cast_fp16)[name = string("op_394_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_395_cast_fp16 = square(x = var_394_cast_fp16);
            tensor<int32, [1]> var_397_axes_0 = const()[name = string("op_397_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_397_keep_dims_0 = const()[name = string("op_397_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_397_cast_fp16 = reduce_mean(axes = var_397_axes_0, keep_dims = var_397_keep_dims_0, x = var_395_cast_fp16)[name = string("op_397_cast_fp16")];
            fp16 var_398_to_fp16 = const()[name = string("op_398_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_399_cast_fp16 = add(x = var_397_cast_fp16, y = var_398_to_fp16)[name = string("op_399_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_23_cast_fp16 = sqrt(x = var_399_cast_fp16)[name = string("std_y_23_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_5_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_5_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(953600)))];
            tensor<fp16, [1, 288, 8]> var_402_cast_fp16 = mul(x = sep_module_5_tcn_2_norm_gamma_to_fp16, y = var_394_cast_fp16)[name = string("op_402_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_403_cast_fp16 = real_div(x = var_402_cast_fp16, y = std_y_23_cast_fp16)[name = string("op_403_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_5_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_5_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(954240)))];
            tensor<fp16, [1, 288, 8]> input_57_cast_fp16 = add(x = var_403_cast_fp16, y = sep_module_5_tcn_2_norm_beta_to_fp16)[name = string("input_57_cast_fp16")];
            tensor<int32, [6]> input_59_pad_0 = const()[name = string("input_59_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            string input_59_mode_0 = const()[name = string("input_59_mode_0"), val = string("constant")];
            fp16 const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 288, 72]> input_57_cast_fp16_state_input = read_state(input = input_57_cast_fp16_state);
            tensor<fp16, [1, 288, 72]> input_59_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 64]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_57_cast_fp16, x = input_57_cast_fp16_state_input);
            write_state(data = input_59_cast_fp16, input = input_57_cast_fp16_state);
            string input_61_pad_type_0 = const()[name = string("input_61_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_61_dilations_0 = const()[name = string("input_61_dilations_0"), val = tensor<int32, [1]>([32])];
            int32 input_61_groups_0 = const()[name = string("input_61_groups_0"), val = int32(288)];
            tensor<int32, [1]> input_61_strides_0 = const()[name = string("input_61_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [288, 1, 3]> sep_module_5_tcn_4_weight_to_fp16 = const()[name = string("sep_module_5_tcn_4_weight_to_fp16"), val = tensor<fp16, [288, 1, 3]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(954880)))];
            tensor<fp16, [1, 288, 8]> input_61_cast_fp16 = conv(dilations = input_61_dilations_0, groups = input_61_groups_0, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = input_61_strides_0, weight = sep_module_5_tcn_4_weight_to_fp16, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")];
            fp32 var_414_alpha_1 = const()[name = string("op_414_alpha_1"), val = fp32(-0x1.f6cf74p-5)];
            tensor<fp16, [1, 288, 8]> var_414_cast_fp16 = leaky_relu(alpha = fp16(-0x1.f6cp-5), x = input_61_cast_fp16);
            tensor<int32, [1]> mean_y_25_axes_0 = const()[name = string("mean_y_25_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_25_keep_dims_0 = const()[name = string("mean_y_25_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_25_cast_fp16 = reduce_mean(axes = mean_y_25_axes_0, keep_dims = mean_y_25_keep_dims_0, x = var_414_cast_fp16)[name = string("mean_y_25_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_420_cast_fp16 = sub(x = var_414_cast_fp16, y = mean_y_25_cast_fp16)[name = string("op_420_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_421_cast_fp16 = square(x = var_420_cast_fp16);
            tensor<int32, [1]> var_423_axes_0 = const()[name = string("op_423_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_423_keep_dims_0 = const()[name = string("op_423_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_423_cast_fp16 = reduce_mean(axes = var_423_axes_0, keep_dims = var_423_keep_dims_0, x = var_421_cast_fp16)[name = string("op_423_cast_fp16")];
            fp16 var_424_to_fp16 = const()[name = string("op_424_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_425_cast_fp16 = add(x = var_423_cast_fp16, y = var_424_to_fp16)[name = string("op_425_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_25_cast_fp16 = sqrt(x = var_425_cast_fp16)[name = string("std_y_25_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_5_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_5_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(956672)))];
            tensor<fp16, [1, 288, 8]> var_428_cast_fp16 = mul(x = sep_module_5_tcn_6_norm_gamma_to_fp16, y = var_420_cast_fp16)[name = string("op_428_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_429_cast_fp16 = real_div(x = var_428_cast_fp16, y = std_y_25_cast_fp16)[name = string("op_429_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_5_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_5_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(957312)))];
            tensor<fp16, [1, 288, 8]> y_12_cast_fp16 = add(x = var_429_cast_fp16, y = sep_module_5_tcn_6_norm_beta_to_fp16)[name = string("y_12_cast_fp16")];
            tensor<fp16, [1, 288, 8]> input_63_cast_fp16 = add(x = input_53_cast_fp16, y = y_12_cast_fp16)[name = string("input_63_cast_fp16")];
            string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(1)];
            tensor<fp16, [1, 288, 8]> input_65_cast_fp16 = conv(dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = sep_module_6_tcn_0_weight_cast_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")];
            fp32 var_446_alpha_1 = const()[name = string("op_446_alpha_1"), val = fp32(-0x1.6af746p-4)];
            tensor<fp16, [1, 288, 8]> var_446_cast_fp16 = leaky_relu(alpha = fp16(-0x1.6bp-4), x = input_65_cast_fp16);
            tensor<int32, [1]> mean_y_27_axes_0 = const()[name = string("mean_y_27_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_27_keep_dims_0 = const()[name = string("mean_y_27_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_27_cast_fp16 = reduce_mean(axes = mean_y_27_axes_0, keep_dims = mean_y_27_keep_dims_0, x = var_446_cast_fp16)[name = string("mean_y_27_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_452_cast_fp16 = sub(x = var_446_cast_fp16, y = mean_y_27_cast_fp16)[name = string("op_452_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_453_cast_fp16 = square(x = var_452_cast_fp16);
            tensor<int32, [1]> var_455_axes_0 = const()[name = string("op_455_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_455_keep_dims_0 = const()[name = string("op_455_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_455_cast_fp16 = reduce_mean(axes = var_455_axes_0, keep_dims = var_455_keep_dims_0, x = var_453_cast_fp16)[name = string("op_455_cast_fp16")];
            fp16 var_456_to_fp16 = const()[name = string("op_456_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_457_cast_fp16 = add(x = var_455_cast_fp16, y = var_456_to_fp16)[name = string("op_457_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_27_cast_fp16 = sqrt(x = var_457_cast_fp16)[name = string("std_y_27_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_6_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_6_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(957952)))];
            tensor<fp16, [1, 288, 8]> var_460_cast_fp16 = mul(x = sep_module_6_tcn_2_norm_gamma_to_fp16, y = var_452_cast_fp16)[name = string("op_460_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_461_cast_fp16 = real_div(x = var_460_cast_fp16, y = std_y_27_cast_fp16)[name = string("op_461_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_6_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_6_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(958592)))];
            tensor<fp16, [1, 288, 8]> input_67_cast_fp16 = add(x = var_461_cast_fp16, y = sep_module_6_tcn_2_norm_beta_to_fp16)[name = string("input_67_cast_fp16")];
            tensor<int32, [6]> input_69_pad_0 = const()[name = string("input_69_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
            string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("constant")];
            fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 288, 10]> input_67_cast_fp16_state_input = read_state(input = input_67_cast_fp16_state);
            tensor<fp16, [1, 288, 10]> input_69_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 2]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_67_cast_fp16, x = input_67_cast_fp16_state_input);
            write_state(data = input_69_cast_fp16, input = input_67_cast_fp16_state);
            string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("valid")];
            int32 input_71_groups_0 = const()[name = string("input_71_groups_0"), val = int32(288)];
            tensor<int32, [1]> input_71_strides_0 = const()[name = string("input_71_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_71_dilations_0 = const()[name = string("input_71_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [288, 1, 3]> sep_module_6_tcn_4_weight_to_fp16 = const()[name = string("sep_module_6_tcn_4_weight_to_fp16"), val = tensor<fp16, [288, 1, 3]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(959232)))];
            tensor<fp16, [1, 288, 8]> input_71_cast_fp16 = conv(dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = sep_module_6_tcn_4_weight_to_fp16, x = input_69_cast_fp16)[name = string("input_71_cast_fp16")];
            fp32 var_472_alpha_1 = const()[name = string("op_472_alpha_1"), val = fp32(0x1.9176cep-1)];
            tensor<fp16, [1, 288, 8]> var_472_cast_fp16 = leaky_relu(alpha = fp16(0x1.918p-1), x = input_71_cast_fp16);
            tensor<int32, [1]> mean_y_29_axes_0 = const()[name = string("mean_y_29_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_29_keep_dims_0 = const()[name = string("mean_y_29_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_29_cast_fp16 = reduce_mean(axes = mean_y_29_axes_0, keep_dims = mean_y_29_keep_dims_0, x = var_472_cast_fp16)[name = string("mean_y_29_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_478_cast_fp16 = sub(x = var_472_cast_fp16, y = mean_y_29_cast_fp16)[name = string("op_478_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_479_cast_fp16 = square(x = var_478_cast_fp16);
            tensor<int32, [1]> var_481_axes_0 = const()[name = string("op_481_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_481_keep_dims_0 = const()[name = string("op_481_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_481_cast_fp16 = reduce_mean(axes = var_481_axes_0, keep_dims = var_481_keep_dims_0, x = var_479_cast_fp16)[name = string("op_481_cast_fp16")];
            fp16 var_482_to_fp16 = const()[name = string("op_482_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_483_cast_fp16 = add(x = var_481_cast_fp16, y = var_482_to_fp16)[name = string("op_483_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_29_cast_fp16 = sqrt(x = var_483_cast_fp16)[name = string("std_y_29_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_6_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_6_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(961024)))];
            tensor<fp16, [1, 288, 8]> var_486_cast_fp16 = mul(x = sep_module_6_tcn_6_norm_gamma_to_fp16, y = var_478_cast_fp16)[name = string("op_486_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_487_cast_fp16 = real_div(x = var_486_cast_fp16, y = std_y_29_cast_fp16)[name = string("op_487_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_6_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_6_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(961664)))];
            tensor<fp16, [1, 288, 8]> y_14_cast_fp16 = add(x = var_487_cast_fp16, y = sep_module_6_tcn_6_norm_beta_to_fp16)[name = string("y_14_cast_fp16")];
            tensor<fp16, [1, 288, 8]> input_73_cast_fp16 = add(x = input_63_cast_fp16, y = y_14_cast_fp16)[name = string("input_73_cast_fp16")];
            string input_75_pad_type_0 = const()[name = string("input_75_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_75_strides_0 = const()[name = string("input_75_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_75_pad_0 = const()[name = string("input_75_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_75_dilations_0 = const()[name = string("input_75_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_75_groups_0 = const()[name = string("input_75_groups_0"), val = int32(1)];
            tensor<fp16, [1, 288, 8]> input_75_cast_fp16 = conv(dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = sep_module_7_tcn_0_weight_cast_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")];
            fp32 var_504_alpha_1 = const()[name = string("op_504_alpha_1"), val = fp32(-0x1.6254dp-3)];
            tensor<fp16, [1, 288, 8]> var_504_cast_fp16 = leaky_relu(alpha = fp16(-0x1.624p-3), x = input_75_cast_fp16);
            tensor<int32, [1]> mean_y_31_axes_0 = const()[name = string("mean_y_31_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_31_keep_dims_0 = const()[name = string("mean_y_31_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_31_cast_fp16 = reduce_mean(axes = mean_y_31_axes_0, keep_dims = mean_y_31_keep_dims_0, x = var_504_cast_fp16)[name = string("mean_y_31_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_510_cast_fp16 = sub(x = var_504_cast_fp16, y = mean_y_31_cast_fp16)[name = string("op_510_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_511_cast_fp16 = square(x = var_510_cast_fp16);
            tensor<int32, [1]> var_513_axes_0 = const()[name = string("op_513_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_513_keep_dims_0 = const()[name = string("op_513_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_513_cast_fp16 = reduce_mean(axes = var_513_axes_0, keep_dims = var_513_keep_dims_0, x = var_511_cast_fp16)[name = string("op_513_cast_fp16")];
            fp16 var_514_to_fp16 = const()[name = string("op_514_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_515_cast_fp16 = add(x = var_513_cast_fp16, y = var_514_to_fp16)[name = string("op_515_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_31_cast_fp16 = sqrt(x = var_515_cast_fp16)[name = string("std_y_31_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_7_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_7_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(962304)))];
            tensor<fp16, [1, 288, 8]> var_518_cast_fp16 = mul(x = sep_module_7_tcn_2_norm_gamma_to_fp16, y = var_510_cast_fp16)[name = string("op_518_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_519_cast_fp16 = real_div(x = var_518_cast_fp16, y = std_y_31_cast_fp16)[name = string("op_519_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_7_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_7_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(962944)))];
            tensor<fp16, [1, 288, 8]> input_77_cast_fp16 = add(x = var_519_cast_fp16, y = sep_module_7_tcn_2_norm_beta_to_fp16)[name = string("input_77_cast_fp16")];
            tensor<int32, [6]> input_79_pad_0 = const()[name = string("input_79_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
            string input_79_mode_0 = const()[name = string("input_79_mode_0"), val = string("constant")];
            fp16 const_7_to_fp16 = const()[name = string("const_7_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 288, 12]> input_77_cast_fp16_state_input = read_state(input = input_77_cast_fp16_state);
            tensor<fp16, [1, 288, 12]> input_79_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 4]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_77_cast_fp16, x = input_77_cast_fp16_state_input);
            write_state(data = input_79_cast_fp16, input = input_77_cast_fp16_state);
            string input_81_pad_type_0 = const()[name = string("input_81_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_81_dilations_0 = const()[name = string("input_81_dilations_0"), val = tensor<int32, [1]>([2])];
            int32 input_81_groups_0 = const()[name = string("input_81_groups_0"), val = int32(288)];
            tensor<int32, [1]> input_81_strides_0 = const()[name = string("input_81_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_81_pad_0 = const()[name = string("input_81_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [288, 1, 3]> sep_module_7_tcn_4_weight_to_fp16 = const()[name = string("sep_module_7_tcn_4_weight_to_fp16"), val = tensor<fp16, [288, 1, 3]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(963584)))];
            tensor<fp16, [1, 288, 8]> input_81_cast_fp16 = conv(dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = sep_module_7_tcn_4_weight_to_fp16, x = input_79_cast_fp16)[name = string("input_81_cast_fp16")];
            fp32 var_530_alpha_1 = const()[name = string("op_530_alpha_1"), val = fp32(0x1.d31416p-1)];
            tensor<fp16, [1, 288, 8]> var_530_cast_fp16 = leaky_relu(alpha = fp16(0x1.d3p-1), x = input_81_cast_fp16);
            tensor<int32, [1]> mean_y_33_axes_0 = const()[name = string("mean_y_33_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_33_keep_dims_0 = const()[name = string("mean_y_33_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_33_cast_fp16 = reduce_mean(axes = mean_y_33_axes_0, keep_dims = mean_y_33_keep_dims_0, x = var_530_cast_fp16)[name = string("mean_y_33_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_536_cast_fp16 = sub(x = var_530_cast_fp16, y = mean_y_33_cast_fp16)[name = string("op_536_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_537_cast_fp16 = square(x = var_536_cast_fp16);
            tensor<int32, [1]> var_539_axes_0 = const()[name = string("op_539_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_539_keep_dims_0 = const()[name = string("op_539_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_539_cast_fp16 = reduce_mean(axes = var_539_axes_0, keep_dims = var_539_keep_dims_0, x = var_537_cast_fp16)[name = string("op_539_cast_fp16")];
            fp16 var_540_to_fp16 = const()[name = string("op_540_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_541_cast_fp16 = add(x = var_539_cast_fp16, y = var_540_to_fp16)[name = string("op_541_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_33_cast_fp16 = sqrt(x = var_541_cast_fp16)[name = string("std_y_33_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_7_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_7_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(965376)))];
            tensor<fp16, [1, 288, 8]> var_544_cast_fp16 = mul(x = sep_module_7_tcn_6_norm_gamma_to_fp16, y = var_536_cast_fp16)[name = string("op_544_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_545_cast_fp16 = real_div(x = var_544_cast_fp16, y = std_y_33_cast_fp16)[name = string("op_545_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_7_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_7_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(966016)))];
            tensor<fp16, [1, 288, 8]> y_16_cast_fp16 = add(x = var_545_cast_fp16, y = sep_module_7_tcn_6_norm_beta_to_fp16)[name = string("y_16_cast_fp16")];
            tensor<fp16, [1, 288, 8]> input_83_cast_fp16 = add(x = input_73_cast_fp16, y = y_16_cast_fp16)[name = string("input_83_cast_fp16")];
            string input_85_pad_type_0 = const()[name = string("input_85_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_85_strides_0 = const()[name = string("input_85_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_85_dilations_0 = const()[name = string("input_85_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_85_groups_0 = const()[name = string("input_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 288, 8]> input_85_cast_fp16 = conv(dilations = input_85_dilations_0, groups = input_85_groups_0, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = input_85_strides_0, weight = sep_module_8_tcn_0_weight_cast_fp16, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")];
            fp32 var_562_alpha_1 = const()[name = string("op_562_alpha_1"), val = fp32(-0x1.71d6f4p-3)];
            tensor<fp16, [1, 288, 8]> var_562_cast_fp16 = leaky_relu(alpha = fp16(-0x1.71cp-3), x = input_85_cast_fp16);
            tensor<int32, [1]> mean_y_35_axes_0 = const()[name = string("mean_y_35_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_35_keep_dims_0 = const()[name = string("mean_y_35_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_35_cast_fp16 = reduce_mean(axes = mean_y_35_axes_0, keep_dims = mean_y_35_keep_dims_0, x = var_562_cast_fp16)[name = string("mean_y_35_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_568_cast_fp16 = sub(x = var_562_cast_fp16, y = mean_y_35_cast_fp16)[name = string("op_568_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_569_cast_fp16 = square(x = var_568_cast_fp16);
            tensor<int32, [1]> var_571_axes_0 = const()[name = string("op_571_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_571_keep_dims_0 = const()[name = string("op_571_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_571_cast_fp16 = reduce_mean(axes = var_571_axes_0, keep_dims = var_571_keep_dims_0, x = var_569_cast_fp16)[name = string("op_571_cast_fp16")];
            fp16 var_572_to_fp16 = const()[name = string("op_572_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_573_cast_fp16 = add(x = var_571_cast_fp16, y = var_572_to_fp16)[name = string("op_573_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_35_cast_fp16 = sqrt(x = var_573_cast_fp16)[name = string("std_y_35_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_8_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_8_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(966656)))];
            tensor<fp16, [1, 288, 8]> var_576_cast_fp16 = mul(x = sep_module_8_tcn_2_norm_gamma_to_fp16, y = var_568_cast_fp16)[name = string("op_576_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_577_cast_fp16 = real_div(x = var_576_cast_fp16, y = std_y_35_cast_fp16)[name = string("op_577_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_8_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_8_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(967296)))];
            tensor<fp16, [1, 288, 8]> input_87_cast_fp16 = add(x = var_577_cast_fp16, y = sep_module_8_tcn_2_norm_beta_to_fp16)[name = string("input_87_cast_fp16")];
            tensor<int32, [6]> input_89_pad_0 = const()[name = string("input_89_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("constant")];
            fp16 const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 288, 16]> input_87_cast_fp16_state_input = read_state(input = input_87_cast_fp16_state);
            tensor<fp16, [1, 288, 16]> input_89_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 8]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_87_cast_fp16, x = input_87_cast_fp16_state_input);
            write_state(data = input_89_cast_fp16, input = input_87_cast_fp16_state);
            string input_91_pad_type_0 = const()[name = string("input_91_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_91_dilations_0 = const()[name = string("input_91_dilations_0"), val = tensor<int32, [1]>([4])];
            int32 input_91_groups_0 = const()[name = string("input_91_groups_0"), val = int32(288)];
            tensor<int32, [1]> input_91_strides_0 = const()[name = string("input_91_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_91_pad_0 = const()[name = string("input_91_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [288, 1, 3]> sep_module_8_tcn_4_weight_to_fp16 = const()[name = string("sep_module_8_tcn_4_weight_to_fp16"), val = tensor<fp16, [288, 1, 3]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(967936)))];
            tensor<fp16, [1, 288, 8]> input_91_cast_fp16 = conv(dilations = input_91_dilations_0, groups = input_91_groups_0, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = input_91_strides_0, weight = sep_module_8_tcn_4_weight_to_fp16, x = input_89_cast_fp16)[name = string("input_91_cast_fp16")];
            fp32 var_588_alpha_1 = const()[name = string("op_588_alpha_1"), val = fp32(0x1.ac7562p-2)];
            tensor<fp16, [1, 288, 8]> var_588_cast_fp16 = leaky_relu(alpha = fp16(0x1.ac8p-2), x = input_91_cast_fp16);
            tensor<int32, [1]> mean_y_37_axes_0 = const()[name = string("mean_y_37_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_37_keep_dims_0 = const()[name = string("mean_y_37_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_37_cast_fp16 = reduce_mean(axes = mean_y_37_axes_0, keep_dims = mean_y_37_keep_dims_0, x = var_588_cast_fp16)[name = string("mean_y_37_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_594_cast_fp16 = sub(x = var_588_cast_fp16, y = mean_y_37_cast_fp16)[name = string("op_594_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_595_cast_fp16 = square(x = var_594_cast_fp16);
            tensor<int32, [1]> var_597_axes_0 = const()[name = string("op_597_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_597_keep_dims_0 = const()[name = string("op_597_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_597_cast_fp16 = reduce_mean(axes = var_597_axes_0, keep_dims = var_597_keep_dims_0, x = var_595_cast_fp16)[name = string("op_597_cast_fp16")];
            fp16 var_598_to_fp16 = const()[name = string("op_598_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_599_cast_fp16 = add(x = var_597_cast_fp16, y = var_598_to_fp16)[name = string("op_599_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_37_cast_fp16 = sqrt(x = var_599_cast_fp16)[name = string("std_y_37_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_8_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_8_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(969728)))];
            tensor<fp16, [1, 288, 8]> var_602_cast_fp16 = mul(x = sep_module_8_tcn_6_norm_gamma_to_fp16, y = var_594_cast_fp16)[name = string("op_602_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_603_cast_fp16 = real_div(x = var_602_cast_fp16, y = std_y_37_cast_fp16)[name = string("op_603_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_8_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_8_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(970368)))];
            tensor<fp16, [1, 288, 8]> y_18_cast_fp16 = add(x = var_603_cast_fp16, y = sep_module_8_tcn_6_norm_beta_to_fp16)[name = string("y_18_cast_fp16")];
            tensor<fp16, [1, 288, 8]> input_93_cast_fp16 = add(x = input_83_cast_fp16, y = y_18_cast_fp16)[name = string("input_93_cast_fp16")];
            string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)];
            tensor<fp16, [288, 288, 1]> sep_module_9_tcn_0_weight_to_fp16 = const()[name = string("sep_module_9_tcn_0_weight_to_fp16"), val = tensor<fp16, [288, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(971008)))];
            tensor<fp16, [1, 288, 8]> input_95_cast_fp16 = conv(dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = sep_module_9_tcn_0_weight_to_fp16, x = input_93_cast_fp16)[name = string("input_95_cast_fp16")];
            fp32 var_620_alpha_1 = const()[name = string("op_620_alpha_1"), val = fp32(0x1.0852b2p-2)];
            tensor<fp16, [1, 288, 8]> var_620_cast_fp16 = leaky_relu(alpha = fp16(0x1.084p-2), x = input_95_cast_fp16);
            tensor<int32, [1]> mean_y_39_axes_0 = const()[name = string("mean_y_39_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_39_keep_dims_0 = const()[name = string("mean_y_39_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_39_cast_fp16 = reduce_mean(axes = mean_y_39_axes_0, keep_dims = mean_y_39_keep_dims_0, x = var_620_cast_fp16)[name = string("mean_y_39_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_626_cast_fp16 = sub(x = var_620_cast_fp16, y = mean_y_39_cast_fp16)[name = string("op_626_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_627_cast_fp16 = square(x = var_626_cast_fp16);
            tensor<int32, [1]> var_629_axes_0 = const()[name = string("op_629_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_629_keep_dims_0 = const()[name = string("op_629_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_629_cast_fp16 = reduce_mean(axes = var_629_axes_0, keep_dims = var_629_keep_dims_0, x = var_627_cast_fp16)[name = string("op_629_cast_fp16")];
            fp16 var_630_to_fp16 = const()[name = string("op_630_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_631_cast_fp16 = add(x = var_629_cast_fp16, y = var_630_to_fp16)[name = string("op_631_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_39_cast_fp16 = sqrt(x = var_631_cast_fp16)[name = string("std_y_39_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_9_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_9_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1136960)))];
            tensor<fp16, [1, 288, 8]> var_634_cast_fp16 = mul(x = sep_module_9_tcn_2_norm_gamma_to_fp16, y = var_626_cast_fp16)[name = string("op_634_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_635_cast_fp16 = real_div(x = var_634_cast_fp16, y = std_y_39_cast_fp16)[name = string("op_635_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_9_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_9_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1137600)))];
            tensor<fp16, [1, 288, 8]> input_97_cast_fp16 = add(x = var_635_cast_fp16, y = sep_module_9_tcn_2_norm_beta_to_fp16)[name = string("input_97_cast_fp16")];
            tensor<int32, [6]> input_99_pad_0 = const()[name = string("input_99_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("constant")];
            fp16 const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 288, 24]> input_97_cast_fp16_state_input = read_state(input = input_97_cast_fp16_state);
            tensor<fp16, [1, 288, 24]> input_99_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 16]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_97_cast_fp16, x = input_97_cast_fp16_state_input);
            write_state(data = input_99_cast_fp16, input = input_97_cast_fp16_state);
            string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor<int32, [1]>([8])];
            int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(288)];
            tensor<int32, [1]> input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [288, 1, 3]> sep_module_9_tcn_4_weight_to_fp16 = const()[name = string("sep_module_9_tcn_4_weight_to_fp16"), val = tensor<fp16, [288, 1, 3]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1138240)))];
            tensor<fp16, [1, 288, 8]> input_101_cast_fp16 = conv(dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = sep_module_9_tcn_4_weight_to_fp16, x = input_99_cast_fp16)[name = string("input_101_cast_fp16")];
            fp32 var_646_alpha_1 = const()[name = string("op_646_alpha_1"), val = fp32(0x1.09ec8ep-2)];
            tensor<fp16, [1, 288, 8]> var_646_cast_fp16 = leaky_relu(alpha = fp16(0x1.0ap-2), x = input_101_cast_fp16);
            tensor<int32, [1]> mean_y_41_axes_0 = const()[name = string("mean_y_41_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_41_keep_dims_0 = const()[name = string("mean_y_41_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_41_cast_fp16 = reduce_mean(axes = mean_y_41_axes_0, keep_dims = mean_y_41_keep_dims_0, x = var_646_cast_fp16)[name = string("mean_y_41_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_652_cast_fp16 = sub(x = var_646_cast_fp16, y = mean_y_41_cast_fp16)[name = string("op_652_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_653_cast_fp16 = square(x = var_652_cast_fp16);
            tensor<int32, [1]> var_655_axes_0 = const()[name = string("op_655_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_655_keep_dims_0 = const()[name = string("op_655_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_655_cast_fp16 = reduce_mean(axes = var_655_axes_0, keep_dims = var_655_keep_dims_0, x = var_653_cast_fp16)[name = string("op_655_cast_fp16")];
            fp16 var_656_to_fp16 = const()[name = string("op_656_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_657_cast_fp16 = add(x = var_655_cast_fp16, y = var_656_to_fp16)[name = string("op_657_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_41_cast_fp16 = sqrt(x = var_657_cast_fp16)[name = string("std_y_41_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_9_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_9_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1140032)))];
            tensor<fp16, [1, 288, 8]> var_660_cast_fp16 = mul(x = sep_module_9_tcn_6_norm_gamma_to_fp16, y = var_652_cast_fp16)[name = string("op_660_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_661_cast_fp16 = real_div(x = var_660_cast_fp16, y = std_y_41_cast_fp16)[name = string("op_661_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_9_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_9_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1140672)))];
            tensor<fp16, [1, 288, 8]> y_20_cast_fp16 = add(x = var_661_cast_fp16, y = sep_module_9_tcn_6_norm_beta_to_fp16)[name = string("y_20_cast_fp16")];
            tensor<fp16, [1, 288, 8]> input_103_cast_fp16 = add(x = input_93_cast_fp16, y = y_20_cast_fp16)[name = string("input_103_cast_fp16")];
            string input_105_pad_type_0 = const()[name = string("input_105_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_105_strides_0 = const()[name = string("input_105_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_105_pad_0 = const()[name = string("input_105_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_105_dilations_0 = const()[name = string("input_105_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_105_groups_0 = const()[name = string("input_105_groups_0"), val = int32(1)];
            tensor<fp16, [288, 288, 1]> sep_module_10_tcn_0_weight_to_fp16 = const()[name = string("sep_module_10_tcn_0_weight_to_fp16"), val = tensor<fp16, [288, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1141312)))];
            tensor<fp16, [1, 288, 8]> input_105_cast_fp16 = conv(dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = sep_module_10_tcn_0_weight_to_fp16, x = input_103_cast_fp16)[name = string("input_105_cast_fp16")];
            fp32 var_678_alpha_1 = const()[name = string("op_678_alpha_1"), val = fp32(0x1.a1ac14p-3)];
            tensor<fp16, [1, 288, 8]> var_678_cast_fp16 = leaky_relu(alpha = fp16(0x1.a1cp-3), x = input_105_cast_fp16);
            tensor<int32, [1]> mean_y_43_axes_0 = const()[name = string("mean_y_43_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_43_keep_dims_0 = const()[name = string("mean_y_43_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_43_cast_fp16 = reduce_mean(axes = mean_y_43_axes_0, keep_dims = mean_y_43_keep_dims_0, x = var_678_cast_fp16)[name = string("mean_y_43_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_684_cast_fp16 = sub(x = var_678_cast_fp16, y = mean_y_43_cast_fp16)[name = string("op_684_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_685_cast_fp16 = square(x = var_684_cast_fp16);
            tensor<int32, [1]> var_687_axes_0 = const()[name = string("op_687_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_687_keep_dims_0 = const()[name = string("op_687_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_687_cast_fp16 = reduce_mean(axes = var_687_axes_0, keep_dims = var_687_keep_dims_0, x = var_685_cast_fp16)[name = string("op_687_cast_fp16")];
            fp16 var_688_to_fp16 = const()[name = string("op_688_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_689_cast_fp16 = add(x = var_687_cast_fp16, y = var_688_to_fp16)[name = string("op_689_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_43_cast_fp16 = sqrt(x = var_689_cast_fp16)[name = string("std_y_43_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_10_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_10_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1307264)))];
            tensor<fp16, [1, 288, 8]> var_692_cast_fp16 = mul(x = sep_module_10_tcn_2_norm_gamma_to_fp16, y = var_684_cast_fp16)[name = string("op_692_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_693_cast_fp16 = real_div(x = var_692_cast_fp16, y = std_y_43_cast_fp16)[name = string("op_693_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_10_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_10_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1307904)))];
            tensor<fp16, [1, 288, 8]> input_107_cast_fp16 = add(x = var_693_cast_fp16, y = sep_module_10_tcn_2_norm_beta_to_fp16)[name = string("input_107_cast_fp16")];
            tensor<int32, [6]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("constant")];
            fp16 const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 288, 40]> input_107_cast_fp16_state_input = read_state(input = input_107_cast_fp16_state);
            tensor<fp16, [1, 288, 40]> input_109_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 32]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_107_cast_fp16, x = input_107_cast_fp16_state_input);
            write_state(data = input_109_cast_fp16, input = input_107_cast_fp16_state);
            string input_111_pad_type_0 = const()[name = string("input_111_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_111_dilations_0 = const()[name = string("input_111_dilations_0"), val = tensor<int32, [1]>([16])];
            int32 input_111_groups_0 = const()[name = string("input_111_groups_0"), val = int32(288)];
            tensor<int32, [1]> input_111_strides_0 = const()[name = string("input_111_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_111_pad_0 = const()[name = string("input_111_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [288, 1, 3]> sep_module_10_tcn_4_weight_to_fp16 = const()[name = string("sep_module_10_tcn_4_weight_to_fp16"), val = tensor<fp16, [288, 1, 3]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1308544)))];
            tensor<fp16, [1, 288, 8]> input_111_cast_fp16 = conv(dilations = input_111_dilations_0, groups = input_111_groups_0, pad = input_111_pad_0, pad_type = input_111_pad_type_0, strides = input_111_strides_0, weight = sep_module_10_tcn_4_weight_to_fp16, x = input_109_cast_fp16)[name = string("input_111_cast_fp16")];
            fp32 var_704_alpha_1 = const()[name = string("op_704_alpha_1"), val = fp32(0x1.9e0e02p-3)];
            tensor<fp16, [1, 288, 8]> var_704_cast_fp16 = leaky_relu(alpha = fp16(0x1.9ep-3), x = input_111_cast_fp16);
            tensor<int32, [1]> mean_y_45_axes_0 = const()[name = string("mean_y_45_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_45_keep_dims_0 = const()[name = string("mean_y_45_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_45_cast_fp16 = reduce_mean(axes = mean_y_45_axes_0, keep_dims = mean_y_45_keep_dims_0, x = var_704_cast_fp16)[name = string("mean_y_45_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_710_cast_fp16 = sub(x = var_704_cast_fp16, y = mean_y_45_cast_fp16)[name = string("op_710_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_711_cast_fp16 = square(x = var_710_cast_fp16);
            tensor<int32, [1]> var_713_axes_0 = const()[name = string("op_713_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_713_keep_dims_0 = const()[name = string("op_713_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_713_cast_fp16 = reduce_mean(axes = var_713_axes_0, keep_dims = var_713_keep_dims_0, x = var_711_cast_fp16)[name = string("op_713_cast_fp16")];
            fp16 var_714_to_fp16 = const()[name = string("op_714_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_715_cast_fp16 = add(x = var_713_cast_fp16, y = var_714_to_fp16)[name = string("op_715_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_45_cast_fp16 = sqrt(x = var_715_cast_fp16)[name = string("std_y_45_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_10_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_10_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1310336)))];
            tensor<fp16, [1, 288, 8]> var_718_cast_fp16 = mul(x = sep_module_10_tcn_6_norm_gamma_to_fp16, y = var_710_cast_fp16)[name = string("op_718_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_719_cast_fp16 = real_div(x = var_718_cast_fp16, y = std_y_45_cast_fp16)[name = string("op_719_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_10_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_10_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1310976)))];
            tensor<fp16, [1, 288, 8]> y_22_cast_fp16 = add(x = var_719_cast_fp16, y = sep_module_10_tcn_6_norm_beta_to_fp16)[name = string("y_22_cast_fp16")];
            tensor<fp16, [1, 288, 8]> input_3_cast_fp16 = add(x = input_103_cast_fp16, y = y_22_cast_fp16)[name = string("input_3_cast_fp16")];
            string input_2_pad_type_0 = const()[name = string("input_2_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_2_strides_0 = const()[name = string("input_2_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_2_pad_0 = const()[name = string("input_2_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_2_dilations_0 = const()[name = string("input_2_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_2_groups_0 = const()[name = string("input_2_groups_0"), val = int32(1)];
            tensor<fp16, [288, 288, 1]> sep_module_11_tcn_0_weight_to_fp16 = const()[name = string("sep_module_11_tcn_0_weight_to_fp16"), val = tensor<fp16, [288, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1311616)))];
            tensor<fp16, [1, 288, 8]> input_2_cast_fp16 = conv(dilations = input_2_dilations_0, groups = input_2_groups_0, pad = input_2_pad_0, pad_type = input_2_pad_type_0, strides = input_2_strides_0, weight = sep_module_11_tcn_0_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_2_cast_fp16")];
            fp32 var_736_alpha_1 = const()[name = string("op_736_alpha_1"), val = fp32(0x1.ffc642p-1)];
            tensor<fp16, [1, 288, 8]> var_736_cast_fp16 = leaky_relu(alpha = fp16(0x1.ffcp-1), x = input_2_cast_fp16);
            tensor<int32, [1]> mean_y_2_axes_0 = const()[name = string("mean_y_2_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_2_keep_dims_0 = const()[name = string("mean_y_2_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_2_cast_fp16 = reduce_mean(axes = mean_y_2_axes_0, keep_dims = mean_y_2_keep_dims_0, x = var_736_cast_fp16)[name = string("mean_y_2_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_742_cast_fp16 = sub(x = var_736_cast_fp16, y = mean_y_2_cast_fp16)[name = string("op_742_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_743_cast_fp16 = square(x = var_742_cast_fp16);
            tensor<int32, [1]> var_745_axes_0 = const()[name = string("op_745_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_745_keep_dims_0 = const()[name = string("op_745_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_745_cast_fp16 = reduce_mean(axes = var_745_axes_0, keep_dims = var_745_keep_dims_0, x = var_743_cast_fp16)[name = string("op_745_cast_fp16")];
            fp16 var_746_to_fp16 = const()[name = string("op_746_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_747_cast_fp16 = add(x = var_745_cast_fp16, y = var_746_to_fp16)[name = string("op_747_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_2_cast_fp16 = sqrt(x = var_747_cast_fp16)[name = string("std_y_2_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_11_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_11_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1477568)))];
            tensor<fp16, [1, 288, 8]> var_750_cast_fp16 = mul(x = sep_module_11_tcn_2_norm_gamma_to_fp16, y = var_742_cast_fp16)[name = string("op_750_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_751_cast_fp16 = real_div(x = var_750_cast_fp16, y = std_y_2_cast_fp16)[name = string("op_751_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_11_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_11_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1478208)))];
            tensor<fp16, [1, 288, 8]> input_4_cast_fp16 = add(x = var_751_cast_fp16, y = sep_module_11_tcn_2_norm_beta_to_fp16)[name = string("input_4_cast_fp16")];
            tensor<int32, [6]> input_6_pad_0 = const()[name = string("input_6_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            string input_6_mode_0 = const()[name = string("input_6_mode_0"), val = string("constant")];
            fp16 const_11_to_fp16 = const()[name = string("const_11_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 288, 72]> input_4_cast_fp16_state_input = read_state(input = input_4_cast_fp16_state);
            tensor<fp16, [1, 288, 72]> input_6_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 64]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input_4_cast_fp16, x = input_4_cast_fp16_state_input);
            write_state(data = input_6_cast_fp16, input = input_4_cast_fp16_state);
            string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_1_dilations_0 = const()[name = string("input_1_dilations_0"), val = tensor<int32, [1]>([32])];
            int32 input_1_groups_0 = const()[name = string("input_1_groups_0"), val = int32(288)];
            tensor<int32, [1]> input_1_strides_0 = const()[name = string("input_1_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [288, 1, 3]> sep_module_11_tcn_4_weight_to_fp16 = const()[name = string("sep_module_11_tcn_4_weight_to_fp16"), val = tensor<fp16, [288, 1, 3]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1478848)))];
            tensor<fp16, [1, 288, 8]> input_1_cast_fp16 = conv(dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = sep_module_11_tcn_4_weight_to_fp16, x = input_6_cast_fp16)[name = string("input_1_cast_fp16")];
            fp32 var_762_alpha_1 = const()[name = string("op_762_alpha_1"), val = fp32(0x1.003c5ap+0)];
            tensor<fp16, [1, 288, 8]> var_762_cast_fp16 = leaky_relu(alpha = fp16(0x1.004p+0), x = input_1_cast_fp16);
            tensor<int32, [1]> mean_y_1_axes_0 = const()[name = string("mean_y_1_axes_0"), val = tensor<int32, [1]>([1])];
            bool mean_y_1_keep_dims_0 = const()[name = string("mean_y_1_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> mean_y_1_cast_fp16 = reduce_mean(axes = mean_y_1_axes_0, keep_dims = mean_y_1_keep_dims_0, x = var_762_cast_fp16)[name = string("mean_y_1_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_768_cast_fp16 = sub(x = var_762_cast_fp16, y = mean_y_1_cast_fp16)[name = string("op_768_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_769_cast_fp16 = square(x = var_768_cast_fp16);
            tensor<int32, [1]> var_771_axes_0 = const()[name = string("op_771_axes_0"), val = tensor<int32, [1]>([1])];
            bool var_771_keep_dims_0 = const()[name = string("op_771_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 8]> var_771_cast_fp16 = reduce_mean(axes = var_771_axes_0, keep_dims = var_771_keep_dims_0, x = var_769_cast_fp16)[name = string("op_771_cast_fp16")];
            fp16 var_772_to_fp16 = const()[name = string("op_772_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 8]> var_773_cast_fp16 = add(x = var_771_cast_fp16, y = var_772_to_fp16)[name = string("op_773_cast_fp16")];
            tensor<fp16, [1, 1, 8]> std_y_1_cast_fp16 = sqrt(x = var_773_cast_fp16)[name = string("std_y_1_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_11_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_11_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1480640)))];
            tensor<fp16, [1, 288, 8]> var_776_cast_fp16 = mul(x = sep_module_11_tcn_6_norm_gamma_to_fp16, y = var_768_cast_fp16)[name = string("op_776_cast_fp16")];
            tensor<fp16, [1, 288, 8]> var_777_cast_fp16 = real_div(x = var_776_cast_fp16, y = std_y_1_cast_fp16)[name = string("op_777_cast_fp16")];
            tensor<fp16, [1, 288, 1]> sep_module_11_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_11_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 288, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1481280)))];
            tensor<fp16, [1, 288, 8]> y_1_cast_fp16 = add(x = var_777_cast_fp16, y = sep_module_11_tcn_6_norm_beta_to_fp16)[name = string("y_1_cast_fp16")];
            tensor<fp16, [1, 288, 8]> x_1_cast_fp16 = add(x = input_3_cast_fp16, y = y_1_cast_fp16)[name = string("x_1_cast_fp16")];
            tensor<int32, [1]> input_117_axes_0 = const()[name = string("input_117_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 288, 8]> input_117_cast_fp16 = expand_dims(axes = input_117_axes_0, x = x_1_cast_fp16)[name = string("input_117_cast_fp16")];
            string input0_1_pad_type_0 = const()[name = string("input0_1_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input0_1_pad_0 = const()[name = string("input0_1_pad_0"), val = tensor<int32, [4]>([80, 80, 0, 0])];
            tensor<int32, [2]> input0_1_strides_0 = const()[name = string("input0_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input0_1_dilations_0 = const()[name = string("input0_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input0_1_groups_0 = const()[name = string("input0_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 1, 225, 1]> mask_layer_weight_to_fp16 = const()[name = string("mask_layer_weight_to_fp16"), val = tensor<fp16, [1, 1, 225, 1]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1481920)))];
            tensor<fp16, [1, 1, 224, 8]> input0_1_cast_fp16 = conv(dilations = input0_1_dilations_0, groups = input0_1_groups_0, pad = input0_1_pad_0, pad_type = input0_1_pad_type_0, strides = input0_1_strides_0, weight = mask_layer_weight_to_fp16, x = input_117_cast_fp16)[name = string("input0_1_cast_fp16")];
            tensor<fp16, [1, 1, 224, 8]> var_793_cast_fp16 = sigmoid(x = input0_1_cast_fp16)[name = string("op_793_cast_fp16")];
            tensor<int32, [1]> var_794_axes_0 = const()[name = string("op_794_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 224, 8]> var_794_cast_fp16 = expand_dims(axes = var_794_axes_0, x = var_26_cast_fp16)[name = string("op_794_cast_fp16")];
            tensor<fp16, [1, 1, 224, 15]> var_794_cast_fp16_state_input = read_state(input = var_794_cast_fp16_state);
            tensor<fp16, [1, 1, 224, 15]> var_794_cast_fp16_state_updated = slice_update(begin = tensor<int32, [4]>([0, 0, 0, 7]), end = tensor<int32, [4]>([0, 0, 0, 0]), end_mask = tensor<bool, [4]>([true, true, true, true]), update = var_794_cast_fp16, x = var_794_cast_fp16_state_input);
            write_state(data = var_794_cast_fp16_state_updated, input = var_794_cast_fp16_state);
            tensor<fp16, [1, 1, 224, 8]> var_794_cast_fp16_delayed = slice_by_size(begin = tensor<int32, [4]>([0, 0, 0, 0]), size = tensor<int32, [4]>([1, 1, 224, 8]), x = var_794_cast_fp16_state_updated);
            tensor<fp16, [1, 1, 224, 8]> x0_1_cast_fp16 = mul(x = var_793_cast_fp16, y = var_794_cast_fp16_delayed)[name = string("x0_1_cast_fp16")];
            tensor<int32, [3]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [3]>([1, 224, -1])];
            tensor<fp16, [1, 224, 8]> input0_3_cast_fp16 = reshape(shape = concat_0x, x = x0_1_cast_fp16)[name = string("input0_3_cast_fp16")];
            string var_821_pad_type_0 = const()[name = string("op_821_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> var_821_pad_0 = const()[name = string("op_821_pad_0"), val = tensor<int32, [2]>([60, 60])];
            tensor<int32, [1]> var_821_strides_0 = const()[name = string("op_821_strides_0"), val = tensor<int32, [1]>([60])];
            tensor<int32, [1]> var_821_dilations_0 = const()[name = string("op_821_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_821_groups_0 = const()[name = string("op_821_groups_0"), val = int32(1)];
            tensor<fp16, [224, 1, 120]> resynthesizer_weight_to_fp16 = const()[name = string("resynthesizer_weight_to_fp16"), val = tensor<fp16, [224, 1, 120]>(BLOBFILE(path = string("@model_path/weights/sc-tpsup-nnet.weight.bin"), offset = uint64(1482496)))];
            tensor<fp16, [1, 224, 9]> input0_3_cast_fp16_state_input = read_state(input = input0_3_cast_fp16_state);
            tensor<fp16, [1, 224, 9]> tmp_0 = slice_update(begin = tensor<int32, [3]>([0, 0, 1]), end = tensor<int32, [3]>([0, 0, 0]), end_mask = tensor<bool, [3]>([true, true, true]), update = input0_3_cast_fp16, x = input0_3_cast_fp16_state_input);
            write_state(data = tmp_0, input = input0_3_cast_fp16_state);
            tensor<fp16, [1, 1, 480]> var_821_cast_fp16 = conv_transpose(dilations = var_821_dilations_0, groups = var_821_groups_0, pad = var_821_pad_0, pad_type = var_821_pad_type_0, strides = var_821_strides_0, weight = resynthesizer_weight_to_fp16, x = tmp_0);
            string var_821_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_821_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
            tensor<fp32, [1, 1, 480]> var_821 = cast(dtype = var_821_cast_fp16_to_fp32_dtype_0, x = var_821_cast_fp16)[name = string("cast_0")];
        } -> (var_821);
}