program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3400.17.1"}, {"coremlc-version", "3400.17.1"}}), mldb_token = string("mldb-7nwypn9a9o")]
{
    func main<ios18>(tensor<fp32, [1, 1, 1024]> audio, state<tensor<fp16, [1, 1, 1056]>> cast_1_state, state<tensor<fp16, [1, 256, 33]>> input1_1_cast_fp16_state, state<tensor<fp16, [1, 256, 36]>> input_107_cast_fp16_state, state<tensor<fp16, [1, 256, 40]>> input_117_cast_fp16_state, state<tensor<fp16, [1, 256, 48]>> input_127_cast_fp16_state, state<tensor<fp16, [1, 256, 64]>> input_137_cast_fp16_state, state<tensor<fp16, [1, 256, 34]>> input_13_cast_fp16_state, state<tensor<fp16, [1, 256, 96]>> input_147_cast_fp16_state, state<tensor<fp16, [1, 256, 160]>> input_157_cast_fp16_state, state<tensor<fp16, [1, 256, 288]>> input_167_cast_fp16_state, state<tensor<fp16, [1, 256, 544]>> input_177_cast_fp16_state, state<tensor<fp16, [1, 256, 36]>> input_17_cast_fp16_state, state<tensor<fp16, [1, 256, 34]>> input_187_cast_fp16_state, state<tensor<fp16, [1, 256, 36]>> input_197_cast_fp16_state, state<tensor<fp16, [1, 256, 40]>> input_207_cast_fp16_state, state<tensor<fp16, [1, 256, 48]>> input_217_cast_fp16_state, state<tensor<fp16, [1, 256, 64]>> input_227_cast_fp16_state, state<tensor<fp16, [1, 256, 96]>> input_237_cast_fp16_state, state<tensor<fp16, [1, 256, 36]>> input_23_cast_fp16_state, state<tensor<fp16, [1, 256, 160]>> input_247_cast_fp16_state, state<tensor<fp16, [1, 256, 288]>> input_257_cast_fp16_state, state<tensor<fp16, [1, 256, 544]>> input_267_cast_fp16_state, state<tensor<fp16, [1, 256, 34]>> input_277_cast_fp16_state, state<tensor<fp16, [1, 256, 40]>> input_27_cast_fp16_state, state<tensor<fp16, [1, 256, 36]>> input_287_cast_fp16_state, state<tensor<fp16, [1, 256, 40]>> input_297_cast_fp16_state, state<tensor<fp16, [1, 256, 48]>> input_307_cast_fp16_state, state<tensor<fp16, [1, 256, 64]>> input_317_cast_fp16_state, state<tensor<fp16, [1, 256, 96]>> input_327_cast_fp16_state, state<tensor<fp16, [1, 256, 160]>> input_337_cast_fp16_state, state<tensor<fp16, [1, 256, 40]>> input_33_cast_fp16_state, state<tensor<fp16, [1, 256, 288]>> input_347_cast_fp16_state, state<tensor<fp16, [1, 256, 33]>> input_357_cast_fp16_state, state<tensor<fp16, [1, 256, 48]>> input_37_cast_fp16_state, state<tensor<fp16, [1, 256, 64]>> input_47_cast_fp16_state, state<tensor<fp16, [1, 256, 544]>> input_4_cast_fp16_state, state<tensor<fp16, [1, 256, 96]>> input_57_cast_fp16_state, state<tensor<fp16, [1, 256, 160]>> input_67_cast_fp16_state, state<tensor<fp16, [1, 256, 288]>> input_77_cast_fp16_state, state<tensor<fp16, [1, 256, 34]>> input_7_cast_fp16_state, state<tensor<fp16, [1, 256, 544]>> input_87_cast_fp16_state, state<tensor<fp16, [1, 256, 34]>> input_97_cast_fp16_state, state<tensor<fp16, [1, 1, 256, 47]>> var_2212_cast_fp16_state) [BNNSOptions = dict<string, string>({{"StateMode", "Streaming"}}), FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"audio", [1, 1, 1024]}}), ("RangeDims", {{"audio", [[1, 1], [1, 1], [1024, 1024]]}}))), UserMetadata = dict<string, string>({{"iteration", "331980"}, {"taskid", "43neaiuuqy"}})] {
            int32 var_17 = const()[name = string("op_17"), val = int32(1)];
            tensor<int32, [1]> var_21 = const()[name = string("op_21"), val = tensor<int32, [1]>([32])];
            tensor<int32, [1]> var_23 = const()[name = string("op_23"), val = tensor<int32, [1]>([1])];
            string input0_5_pad_type_0 = const()[name = string("input0_5_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input0_5_pad_0 = const()[name = string("input0_5_pad_0"), val = tensor<int32, [2]>([32, 32])];
            string audio_to_fp16_dtype_0 = const()[name = string("audio_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [256, 1, 64]> front_end_0_weight_to_fp16 = const()[name = string("front_end_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 64]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(64)))];
            tensor<fp16, [1, 1, 1024]> cast_1 = cast(dtype = audio_to_fp16_dtype_0, x = audio)[name = string("cast_1")];
            tensor<fp16, [1, 1, 1056]> cast_1_state_input = read_state(input = cast_1_state);
            tensor<fp16, [1, 1, 1056]> cast_1_state_updated = slice_update(begin = tensor<int32, [3]>([0, 0, 32]), end = tensor<int32, [3]>([1, 1, 1056]), end_mask = tensor<bool, [3]>([false, false, false]), update = cast_1, x = cast_1_state_input);
            tensor<fp16, [1, 256, 32]> input0_5_cast_fp16 = conv(dilations = var_23, groups = var_17, pad = tensor<int32, [2]>([0, 0]), pad_type = input0_5_pad_type_0, strides = var_21, weight = front_end_0_weight_to_fp16, x = cast_1_state_updated);
            write_state(data = cast_1_state_updated, input = cast_1_state);
            tensor<fp16, [1, 256, 32]> var_26_cast_fp16 = relu(x = input0_5_cast_fp16)[name = string("op_26_cast_fp16")];
            bool var_29 = const()[name = string("op_29"), val = bool(true)];
            tensor<int32, [1]> var_34 = const()[name = string("op_34"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_4_cast_fp16 = reduce_mean(axes = var_34, keep_dims = var_29, x = var_26_cast_fp16)[name = string("mean_y_4_cast_fp16")];
            tensor<fp16, [1, 256, 32]> 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, 256, 32]> var_37_cast_fp16 = square(x = var_36_cast_fp16);
            tensor<int32, [1]> var_38 = const()[name = string("op_38"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_39_cast_fp16 = reduce_mean(axes = var_38, keep_dims = var_29, 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, 32]> 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, 32]> std_y_4_cast_fp16 = sqrt(x = var_41_cast_fp16)[name = string("std_y_4_cast_fp16")];
            tensor<fp16, [1, 256, 1]> front_norm_norm_gamma_to_fp16 = const()[name = string("front_norm_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(32896)))];
            tensor<fp16, [1, 256, 32]> 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, 256, 32]> 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, 256, 1]> front_norm_norm_beta_to_fp16 = const()[name = string("front_norm_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(33472)))];
            tensor<fp16, [1, 256, 32]> input_353_cast_fp16 = add(x = var_45_cast_fp16, y = front_norm_norm_beta_to_fp16)[name = string("input_353_cast_fp16")];
            int32 var_48 = const()[name = string("op_48"), val = int32(1)];
            tensor<int32, [1]> var_53 = const()[name = string("op_53"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_55 = const()[name = string("op_55"), val = tensor<int32, [1]>([1])];
            string input_357_pad_type_0 = const()[name = string("input_357_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_357_pad_0 = const()[name = string("input_357_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> to_latent_weight_to_fp16 = const()[name = string("to_latent_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(34048)))];
            tensor<fp16, [1, 256, 32]> input_357_cast_fp16 = conv(dilations = var_55, groups = var_48, pad = input_357_pad_0, pad_type = input_357_pad_type_0, strides = var_53, weight = to_latent_weight_to_fp16, x = input_353_cast_fp16)[name = string("input_357_cast_fp16")];
            int32 var_66 = const()[name = string("op_66"), val = int32(1)];
            int32 var_67 = const()[name = string("op_67"), val = int32(256)];
            bool var_70 = const()[name = string("op_70"), val = bool(true)];
            tensor<int32, [1]> var_118 = const()[name = string("op_118"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_120 = const()[name = string("op_120"), val = tensor<int32, [1]>([1])];
            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_0_tcn_0_weight_to_fp16 = const()[name = string("sep_module_0_tcn_0_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(165184)))];
            tensor<fp16, [1, 256, 32]> input_5_cast_fp16 = conv(dilations = var_120, groups = var_66, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = var_118, weight = sep_module_0_tcn_0_weight_to_fp16, x = input_357_cast_fp16)[name = string("input_5_cast_fp16")];
            fp32 var_124_alpha_1 = const()[name = string("op_124_alpha_1"), val = fp32(0x1.d078dp-2)];
            tensor<fp16, [1, 256, 32]> var_124_cast_fp16 = leaky_relu(alpha = var_124_alpha_1, x = input_5_cast_fp16)[name = string("op_124_cast_fp16")];
            tensor<int32, [1]> var_128 = const()[name = string("op_128"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_3_cast_fp16 = reduce_mean(axes = var_128, keep_dims = var_70, x = var_124_cast_fp16)[name = string("mean_y_3_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_130_cast_fp16 = sub(x = var_124_cast_fp16, y = mean_y_3_cast_fp16)[name = string("op_130_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_131_cast_fp16 = square(x = var_130_cast_fp16);
            tensor<int32, [1]> var_132 = const()[name = string("op_132"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_133_cast_fp16 = reduce_mean(axes = var_132, keep_dims = var_70, 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, 32]> 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, 32]> std_y_3_cast_fp16 = sqrt(x = var_135_cast_fp16)[name = string("std_y_3_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(296320)))];
            tensor<fp16, [1, 256, 32]> var_138_cast_fp16 = mul(x = sep_module_0_tcn_2_norm_gamma_to_fp16, y = var_130_cast_fp16)[name = string("op_138_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_139_cast_fp16 = real_div(x = var_138_cast_fp16, y = std_y_3_cast_fp16)[name = string("op_139_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(296896)))];
            tensor<fp16, [1, 256, 32]> input_7_cast_fp16 = add(x = var_139_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 input_9_constant_val_0_to_fp16 = const()[name = string("input_9_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 34]> input_7_cast_fp16_state_input = read_state(input = input_7_cast_fp16_state);
            tensor<fp16, [1, 256, 34]> input_9_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 2]), end = tensor<int32, [3]>([1, 256, 34]), end_mask = tensor<bool, [3]>([false, false, false]), 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);
            tensor<int32, [1]> var_144 = const()[name = string("op_144"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_146 = const()[name = string("op_146"), val = tensor<int32, [1]>([1])];
            string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_0_tcn_4_weight_to_fp16 = const()[name = string("sep_module_0_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(297472)))];
            tensor<fp16, [1, 256, 32]> input_11_cast_fp16 = conv(dilations = var_146, groups = var_67, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_144, weight = sep_module_0_tcn_4_weight_to_fp16, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")];
            fp32 var_150_alpha_1 = const()[name = string("op_150_alpha_1"), val = fp32(-0x1.0371f6p-5)];
            tensor<fp16, [1, 256, 32]> var_150_cast_fp16 = leaky_relu(alpha = var_150_alpha_1, x = input_11_cast_fp16)[name = string("op_150_cast_fp16")];
            tensor<int32, [1]> var_154 = const()[name = string("op_154"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_5_cast_fp16 = reduce_mean(axes = var_154, keep_dims = var_70, x = var_150_cast_fp16)[name = string("mean_y_5_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_156_cast_fp16 = sub(x = var_150_cast_fp16, y = mean_y_5_cast_fp16)[name = string("op_156_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_157_cast_fp16 = square(x = var_156_cast_fp16);
            tensor<int32, [1]> var_158 = const()[name = string("op_158"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_159_cast_fp16 = reduce_mean(axes = var_158, keep_dims = var_70, x = var_157_cast_fp16)[name = string("op_159_cast_fp16")];
            fp16 var_160_to_fp16 = const()[name = string("op_160_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_161_cast_fp16 = add(x = var_159_cast_fp16, y = var_160_to_fp16)[name = string("op_161_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_5_cast_fp16 = sqrt(x = var_161_cast_fp16)[name = string("std_y_5_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(299072)))];
            tensor<fp16, [1, 256, 32]> var_164_cast_fp16 = mul(x = sep_module_0_tcn_6_norm_gamma_to_fp16, y = var_156_cast_fp16)[name = string("op_164_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_165_cast_fp16 = real_div(x = var_164_cast_fp16, y = std_y_5_cast_fp16)[name = string("op_165_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(299648)))];
            tensor<fp16, [1, 256, 32]> y_2_cast_fp16 = add(x = var_165_cast_fp16, y = sep_module_0_tcn_6_norm_beta_to_fp16)[name = string("y_2_cast_fp16")];
            tensor<fp16, [1, 256, 33]> input_357_cast_fp16_state_input = read_state(input = input_357_cast_fp16_state);
            tensor<fp16, [1, 256, 33]> input_357_cast_fp16_state_updated = slice_update(begin = tensor<int32, [3]>([0, 0, 1]), end = tensor<int32, [3]>([1, 256, 33]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_357_cast_fp16, x = input_357_cast_fp16_state_input);
            write_state(data = input_357_cast_fp16_state_updated, input = input_357_cast_fp16_state);
            tensor<fp16, [1, 256, 32]> input_357_cast_fp16_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 256, 32]), x = input_357_cast_fp16_state_updated);
            tensor<fp16, [1, 256, 32]> input_13_cast_fp16 = add(x = input_357_cast_fp16_delayed, y = y_2_cast_fp16)[name = string("input_13_cast_fp16")];
            tensor<int32, [1]> var_176 = const()[name = string("op_176"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_178 = const()[name = string("op_178"), val = tensor<int32, [1]>([1])];
            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_1_tcn_0_weight_to_fp16 = const()[name = string("sep_module_1_tcn_0_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(300224)))];
            tensor<fp16, [1, 256, 32]> input_15_cast_fp16 = conv(dilations = var_178, groups = var_66, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = var_176, weight = sep_module_1_tcn_0_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")];
            fp32 var_182_alpha_1 = const()[name = string("op_182_alpha_1"), val = fp32(0x1.13f1cap-1)];
            tensor<fp16, [1, 256, 32]> var_182_cast_fp16 = leaky_relu(alpha = var_182_alpha_1, x = input_15_cast_fp16)[name = string("op_182_cast_fp16")];
            tensor<int32, [1]> var_186 = const()[name = string("op_186"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_7_cast_fp16 = reduce_mean(axes = var_186, keep_dims = var_70, x = var_182_cast_fp16)[name = string("mean_y_7_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_188_cast_fp16 = sub(x = var_182_cast_fp16, y = mean_y_7_cast_fp16)[name = string("op_188_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_189_cast_fp16 = square(x = var_188_cast_fp16);
            tensor<int32, [1]> var_190 = const()[name = string("op_190"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_191_cast_fp16 = reduce_mean(axes = var_190, keep_dims = var_70, 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, 32]> 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, 32]> std_y_7_cast_fp16 = sqrt(x = var_193_cast_fp16)[name = string("std_y_7_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(431360)))];
            tensor<fp16, [1, 256, 32]> var_196_cast_fp16 = mul(x = sep_module_1_tcn_2_norm_gamma_to_fp16, y = var_188_cast_fp16)[name = string("op_196_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_197_cast_fp16 = real_div(x = var_196_cast_fp16, y = std_y_7_cast_fp16)[name = string("op_197_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(431936)))];
            tensor<fp16, [1, 256, 32]> input_17_cast_fp16 = add(x = var_197_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 input_19_constant_val_0_to_fp16 = const()[name = string("input_19_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 36]> input_17_cast_fp16_state_input = read_state(input = input_17_cast_fp16_state);
            tensor<fp16, [1, 256, 36]> input_19_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 4]), end = tensor<int32, [3]>([1, 256, 36]), end_mask = tensor<bool, [3]>([false, false, false]), 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);
            tensor<int32, [1]> var_202 = const()[name = string("op_202"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_204 = const()[name = string("op_204"), val = tensor<int32, [1]>([2])];
            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_1_tcn_4_weight_to_fp16 = const()[name = string("sep_module_1_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(432512)))];
            tensor<fp16, [1, 256, 32]> input_21_cast_fp16 = conv(dilations = var_204, groups = var_67, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = var_202, weight = sep_module_1_tcn_4_weight_to_fp16, x = input_19_cast_fp16)[name = string("input_21_cast_fp16")];
            fp32 var_208_alpha_1 = const()[name = string("op_208_alpha_1"), val = fp32(0x1.2fa48p-4)];
            tensor<fp16, [1, 256, 32]> var_208_cast_fp16 = leaky_relu(alpha = var_208_alpha_1, x = input_21_cast_fp16)[name = string("op_208_cast_fp16")];
            tensor<int32, [1]> var_212 = const()[name = string("op_212"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_9_cast_fp16 = reduce_mean(axes = var_212, keep_dims = var_70, x = var_208_cast_fp16)[name = string("mean_y_9_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_214_cast_fp16 = sub(x = var_208_cast_fp16, y = mean_y_9_cast_fp16)[name = string("op_214_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_215_cast_fp16 = square(x = var_214_cast_fp16);
            tensor<int32, [1]> var_216 = const()[name = string("op_216"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_217_cast_fp16 = reduce_mean(axes = var_216, keep_dims = var_70, x = var_215_cast_fp16)[name = string("op_217_cast_fp16")];
            fp16 var_218_to_fp16 = const()[name = string("op_218_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_219_cast_fp16 = add(x = var_217_cast_fp16, y = var_218_to_fp16)[name = string("op_219_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_9_cast_fp16 = sqrt(x = var_219_cast_fp16)[name = string("std_y_9_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(434112)))];
            tensor<fp16, [1, 256, 32]> var_222_cast_fp16 = mul(x = sep_module_1_tcn_6_norm_gamma_to_fp16, y = var_214_cast_fp16)[name = string("op_222_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_223_cast_fp16 = real_div(x = var_222_cast_fp16, y = std_y_9_cast_fp16)[name = string("op_223_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(434688)))];
            tensor<fp16, [1, 256, 32]> y_4_cast_fp16 = add(x = var_223_cast_fp16, y = sep_module_1_tcn_6_norm_beta_to_fp16)[name = string("y_4_cast_fp16")];
            tensor<fp16, [1, 256, 34]> input_13_cast_fp16_state_input = read_state(input = input_13_cast_fp16_state);
            tensor<fp16, [1, 256, 34]> input_13_cast_fp16_state_updated = slice_update(begin = tensor<int32, [3]>([0, 0, 2]), end = tensor<int32, [3]>([1, 256, 34]), end_mask = tensor<bool, [3]>([false, false, false]), 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, 256, 32]> input_13_cast_fp16_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 256, 32]), x = input_13_cast_fp16_state_updated);
            tensor<fp16, [1, 256, 32]> input_23_cast_fp16 = add(x = input_13_cast_fp16_delayed, y = y_4_cast_fp16)[name = string("input_23_cast_fp16")];
            tensor<int32, [1]> var_234 = const()[name = string("op_234"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_236 = const()[name = string("op_236"), val = tensor<int32, [1]>([1])];
            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_2_tcn_0_weight_to_fp16 = const()[name = string("sep_module_2_tcn_0_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(435264)))];
            tensor<fp16, [1, 256, 32]> input_25_cast_fp16 = conv(dilations = var_236, groups = var_66, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = var_234, weight = sep_module_2_tcn_0_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")];
            fp32 var_240_alpha_1 = const()[name = string("op_240_alpha_1"), val = fp32(0x1.1a1afp-1)];
            tensor<fp16, [1, 256, 32]> var_240_cast_fp16 = leaky_relu(alpha = var_240_alpha_1, x = input_25_cast_fp16)[name = string("op_240_cast_fp16")];
            tensor<int32, [1]> var_244 = const()[name = string("op_244"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_11_cast_fp16 = reduce_mean(axes = var_244, keep_dims = var_70, x = var_240_cast_fp16)[name = string("mean_y_11_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_246_cast_fp16 = sub(x = var_240_cast_fp16, y = mean_y_11_cast_fp16)[name = string("op_246_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_247_cast_fp16 = square(x = var_246_cast_fp16);
            tensor<int32, [1]> var_248 = const()[name = string("op_248"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_249_cast_fp16 = reduce_mean(axes = var_248, keep_dims = var_70, 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, 32]> 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, 32]> std_y_11_cast_fp16 = sqrt(x = var_251_cast_fp16)[name = string("std_y_11_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(566400)))];
            tensor<fp16, [1, 256, 32]> var_254_cast_fp16 = mul(x = sep_module_2_tcn_2_norm_gamma_to_fp16, y = var_246_cast_fp16)[name = string("op_254_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_255_cast_fp16 = real_div(x = var_254_cast_fp16, y = std_y_11_cast_fp16)[name = string("op_255_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(566976)))];
            tensor<fp16, [1, 256, 32]> input_27_cast_fp16 = add(x = var_255_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 input_29_constant_val_0_to_fp16 = const()[name = string("input_29_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 40]> input_27_cast_fp16_state_input = read_state(input = input_27_cast_fp16_state);
            tensor<fp16, [1, 256, 40]> input_29_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 8]), end = tensor<int32, [3]>([1, 256, 40]), end_mask = tensor<bool, [3]>([false, false, false]), 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);
            tensor<int32, [1]> var_260 = const()[name = string("op_260"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_262 = const()[name = string("op_262"), val = tensor<int32, [1]>([4])];
            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_2_tcn_4_weight_to_fp16 = const()[name = string("sep_module_2_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(567552)))];
            tensor<fp16, [1, 256, 32]> input_31_cast_fp16 = conv(dilations = var_262, groups = var_67, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = var_260, weight = sep_module_2_tcn_4_weight_to_fp16, x = input_29_cast_fp16)[name = string("input_31_cast_fp16")];
            fp32 var_266_alpha_1 = const()[name = string("op_266_alpha_1"), val = fp32(-0x1.caf17p-3)];
            tensor<fp16, [1, 256, 32]> var_266_cast_fp16 = leaky_relu(alpha = var_266_alpha_1, x = input_31_cast_fp16)[name = string("op_266_cast_fp16")];
            tensor<int32, [1]> var_270 = const()[name = string("op_270"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_13_cast_fp16 = reduce_mean(axes = var_270, keep_dims = var_70, x = var_266_cast_fp16)[name = string("mean_y_13_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_272_cast_fp16 = sub(x = var_266_cast_fp16, y = mean_y_13_cast_fp16)[name = string("op_272_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_273_cast_fp16 = square(x = var_272_cast_fp16);
            tensor<int32, [1]> var_274 = const()[name = string("op_274"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_275_cast_fp16 = reduce_mean(axes = var_274, keep_dims = var_70, x = var_273_cast_fp16)[name = string("op_275_cast_fp16")];
            fp16 var_276_to_fp16 = const()[name = string("op_276_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_277_cast_fp16 = add(x = var_275_cast_fp16, y = var_276_to_fp16)[name = string("op_277_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_13_cast_fp16 = sqrt(x = var_277_cast_fp16)[name = string("std_y_13_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(569152)))];
            tensor<fp16, [1, 256, 32]> var_280_cast_fp16 = mul(x = sep_module_2_tcn_6_norm_gamma_to_fp16, y = var_272_cast_fp16)[name = string("op_280_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_281_cast_fp16 = real_div(x = var_280_cast_fp16, y = std_y_13_cast_fp16)[name = string("op_281_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(569728)))];
            tensor<fp16, [1, 256, 32]> y_6_cast_fp16 = add(x = var_281_cast_fp16, y = sep_module_2_tcn_6_norm_beta_to_fp16)[name = string("y_6_cast_fp16")];
            tensor<fp16, [1, 256, 36]> input_23_cast_fp16_state_input = read_state(input = input_23_cast_fp16_state);
            tensor<fp16, [1, 256, 36]> input_23_cast_fp16_state_updated = slice_update(begin = tensor<int32, [3]>([0, 0, 4]), end = tensor<int32, [3]>([1, 256, 36]), end_mask = tensor<bool, [3]>([false, false, false]), 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, 256, 32]> input_23_cast_fp16_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 256, 32]), x = input_23_cast_fp16_state_updated);
            tensor<fp16, [1, 256, 32]> input_33_cast_fp16 = add(x = input_23_cast_fp16_delayed, y = y_6_cast_fp16)[name = string("input_33_cast_fp16")];
            tensor<int32, [1]> var_292 = const()[name = string("op_292"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_294 = const()[name = string("op_294"), val = tensor<int32, [1]>([1])];
            string input_35_pad_type_0 = const()[name = string("input_35_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_3_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(570304))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(603136))))[name = string("sep_module_3_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_35_cast_fp16 = conv(dilations = var_294, groups = var_66, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = var_292, weight = sep_module_3_tcn_0_weight_to_fp16_palettized, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")];
            fp32 var_298_alpha_1 = const()[name = string("op_298_alpha_1"), val = fp32(0x1.62e9a2p-1)];
            tensor<fp16, [1, 256, 32]> var_298_cast_fp16 = leaky_relu(alpha = var_298_alpha_1, x = input_35_cast_fp16)[name = string("op_298_cast_fp16")];
            tensor<int32, [1]> var_302 = const()[name = string("op_302"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_15_cast_fp16 = reduce_mean(axes = var_302, keep_dims = var_70, x = var_298_cast_fp16)[name = string("mean_y_15_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_304_cast_fp16 = sub(x = var_298_cast_fp16, y = mean_y_15_cast_fp16)[name = string("op_304_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_305_cast_fp16 = square(x = var_304_cast_fp16);
            tensor<int32, [1]> var_306 = const()[name = string("op_306"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_307_cast_fp16 = reduce_mean(axes = var_306, keep_dims = var_70, 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, 32]> 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, 32]> std_y_15_cast_fp16 = sqrt(x = var_309_cast_fp16)[name = string("std_y_15_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(603264)))];
            tensor<fp16, [1, 256, 32]> var_312_cast_fp16 = mul(x = sep_module_3_tcn_2_norm_gamma_to_fp16, y = var_304_cast_fp16)[name = string("op_312_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_313_cast_fp16 = real_div(x = var_312_cast_fp16, y = std_y_15_cast_fp16)[name = string("op_313_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(603840)))];
            tensor<fp16, [1, 256, 32]> input_37_cast_fp16 = add(x = var_313_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, 8, 8])];
            string input_39_mode_0 = const()[name = string("input_39_mode_0"), val = string("constant")];
            fp16 input_39_constant_val_0_to_fp16 = const()[name = string("input_39_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 48]> input_37_cast_fp16_state_input = read_state(input = input_37_cast_fp16_state);
            tensor<fp16, [1, 256, 48]> input_39_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 16]), end = tensor<int32, [3]>([1, 256, 48]), end_mask = tensor<bool, [3]>([false, false, false]), 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);
            tensor<int32, [1]> var_318 = const()[name = string("op_318"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_320 = const()[name = string("op_320"), val = tensor<int32, [1]>([8])];
            string input_41_pad_type_0 = const()[name = string("input_41_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_3_tcn_4_weight_to_fp16 = const()[name = string("sep_module_3_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(604416)))];
            tensor<fp16, [1, 256, 32]> input_41_cast_fp16 = conv(dilations = var_320, groups = var_67, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = var_318, weight = sep_module_3_tcn_4_weight_to_fp16, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")];
            fp32 var_324_alpha_1 = const()[name = string("op_324_alpha_1"), val = fp32(-0x1.6d4c8cp-5)];
            tensor<fp16, [1, 256, 32]> var_324_cast_fp16 = leaky_relu(alpha = var_324_alpha_1, x = input_41_cast_fp16)[name = string("op_324_cast_fp16")];
            tensor<int32, [1]> var_328 = const()[name = string("op_328"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_17_cast_fp16 = reduce_mean(axes = var_328, keep_dims = var_70, x = var_324_cast_fp16)[name = string("mean_y_17_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_330_cast_fp16 = sub(x = var_324_cast_fp16, y = mean_y_17_cast_fp16)[name = string("op_330_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_331_cast_fp16 = square(x = var_330_cast_fp16);
            tensor<int32, [1]> var_332 = const()[name = string("op_332"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_333_cast_fp16 = reduce_mean(axes = var_332, keep_dims = var_70, x = var_331_cast_fp16)[name = string("op_333_cast_fp16")];
            fp16 var_334_to_fp16 = const()[name = string("op_334_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_335_cast_fp16 = add(x = var_333_cast_fp16, y = var_334_to_fp16)[name = string("op_335_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_17_cast_fp16 = sqrt(x = var_335_cast_fp16)[name = string("std_y_17_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(606016)))];
            tensor<fp16, [1, 256, 32]> var_338_cast_fp16 = mul(x = sep_module_3_tcn_6_norm_gamma_to_fp16, y = var_330_cast_fp16)[name = string("op_338_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_339_cast_fp16 = real_div(x = var_338_cast_fp16, y = std_y_17_cast_fp16)[name = string("op_339_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(606592)))];
            tensor<fp16, [1, 256, 32]> y_8_cast_fp16 = add(x = var_339_cast_fp16, y = sep_module_3_tcn_6_norm_beta_to_fp16)[name = string("y_8_cast_fp16")];
            tensor<fp16, [1, 256, 40]> input_33_cast_fp16_state_input = read_state(input = input_33_cast_fp16_state);
            tensor<fp16, [1, 256, 40]> input_33_cast_fp16_state_updated = slice_update(begin = tensor<int32, [3]>([0, 0, 8]), end = tensor<int32, [3]>([1, 256, 40]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_33_cast_fp16, x = input_33_cast_fp16_state_input);
            write_state(data = input_33_cast_fp16_state_updated, input = input_33_cast_fp16_state);
            tensor<fp16, [1, 256, 32]> input_33_cast_fp16_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 256, 32]), x = input_33_cast_fp16_state_updated);
            tensor<fp16, [1, 256, 32]> input_43_cast_fp16 = add(x = input_33_cast_fp16_delayed, y = y_8_cast_fp16)[name = string("input_43_cast_fp16")];
            tensor<int32, [1]> var_350 = const()[name = string("op_350"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_352 = const()[name = string("op_352"), val = tensor<int32, [1]>([1])];
            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_4_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(607168))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(640000))))[name = string("sep_module_4_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_45_cast_fp16 = conv(dilations = var_352, groups = var_66, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_350, weight = sep_module_4_tcn_0_weight_to_fp16_palettized, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")];
            fp32 var_356_alpha_1 = const()[name = string("op_356_alpha_1"), val = fp32(0x1.29f6fcp-1)];
            tensor<fp16, [1, 256, 32]> var_356_cast_fp16 = leaky_relu(alpha = var_356_alpha_1, x = input_45_cast_fp16)[name = string("op_356_cast_fp16")];
            tensor<int32, [1]> var_360 = const()[name = string("op_360"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_19_cast_fp16 = reduce_mean(axes = var_360, keep_dims = var_70, x = var_356_cast_fp16)[name = string("mean_y_19_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_362_cast_fp16 = sub(x = var_356_cast_fp16, y = mean_y_19_cast_fp16)[name = string("op_362_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_363_cast_fp16 = square(x = var_362_cast_fp16);
            tensor<int32, [1]> var_364 = const()[name = string("op_364"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_365_cast_fp16 = reduce_mean(axes = var_364, keep_dims = var_70, 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, 32]> 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, 32]> std_y_19_cast_fp16 = sqrt(x = var_367_cast_fp16)[name = string("std_y_19_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(640128)))];
            tensor<fp16, [1, 256, 32]> var_370_cast_fp16 = mul(x = sep_module_4_tcn_2_norm_gamma_to_fp16, y = var_362_cast_fp16)[name = string("op_370_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_371_cast_fp16 = real_div(x = var_370_cast_fp16, y = std_y_19_cast_fp16)[name = string("op_371_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(640704)))];
            tensor<fp16, [1, 256, 32]> input_47_cast_fp16 = add(x = var_371_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 input_49_constant_val_0_to_fp16 = const()[name = string("input_49_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 64]> input_47_cast_fp16_state_input = read_state(input = input_47_cast_fp16_state);
            tensor<fp16, [1, 256, 64]> input_49_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 32]), end = tensor<int32, [3]>([1, 256, 64]), end_mask = tensor<bool, [3]>([false, false, false]), 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);
            tensor<int32, [1]> var_376 = const()[name = string("op_376"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_378 = const()[name = string("op_378"), val = tensor<int32, [1]>([16])];
            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_4_tcn_4_weight_to_fp16 = const()[name = string("sep_module_4_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(641280)))];
            tensor<fp16, [1, 256, 32]> input_51_cast_fp16 = conv(dilations = var_378, groups = var_67, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = var_376, weight = sep_module_4_tcn_4_weight_to_fp16, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")];
            fp32 var_382_alpha_1 = const()[name = string("op_382_alpha_1"), val = fp32(-0x1.a18dfp-4)];
            tensor<fp16, [1, 256, 32]> var_382_cast_fp16 = leaky_relu(alpha = var_382_alpha_1, x = input_51_cast_fp16)[name = string("op_382_cast_fp16")];
            tensor<int32, [1]> var_386 = const()[name = string("op_386"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_21_cast_fp16 = reduce_mean(axes = var_386, keep_dims = var_70, x = var_382_cast_fp16)[name = string("mean_y_21_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_388_cast_fp16 = sub(x = var_382_cast_fp16, y = mean_y_21_cast_fp16)[name = string("op_388_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_389_cast_fp16 = square(x = var_388_cast_fp16);
            tensor<int32, [1]> var_390 = const()[name = string("op_390"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_391_cast_fp16 = reduce_mean(axes = var_390, keep_dims = var_70, x = var_389_cast_fp16)[name = string("op_391_cast_fp16")];
            fp16 var_392_to_fp16 = const()[name = string("op_392_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_393_cast_fp16 = add(x = var_391_cast_fp16, y = var_392_to_fp16)[name = string("op_393_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_21_cast_fp16 = sqrt(x = var_393_cast_fp16)[name = string("std_y_21_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(642880)))];
            tensor<fp16, [1, 256, 32]> var_396_cast_fp16 = mul(x = sep_module_4_tcn_6_norm_gamma_to_fp16, y = var_388_cast_fp16)[name = string("op_396_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_397_cast_fp16 = real_div(x = var_396_cast_fp16, y = std_y_21_cast_fp16)[name = string("op_397_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(643456)))];
            tensor<fp16, [1, 256, 32]> y_10_cast_fp16 = add(x = var_397_cast_fp16, y = sep_module_4_tcn_6_norm_beta_to_fp16)[name = string("y_10_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_53_cast_fp16 = add(x = input_43_cast_fp16, y = y_10_cast_fp16)[name = string("input_53_cast_fp16")];
            tensor<int32, [1]> var_408 = const()[name = string("op_408"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_410 = const()[name = string("op_410"), val = tensor<int32, [1]>([1])];
            string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_5_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(644032))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(676864))))[name = string("sep_module_5_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_55_cast_fp16 = conv(dilations = var_410, groups = var_66, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = var_408, weight = sep_module_5_tcn_0_weight_to_fp16_palettized, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")];
            fp32 var_414_alpha_1 = const()[name = string("op_414_alpha_1"), val = fp32(0x1.039708p-1)];
            tensor<fp16, [1, 256, 32]> var_414_cast_fp16 = leaky_relu(alpha = var_414_alpha_1, x = input_55_cast_fp16)[name = string("op_414_cast_fp16")];
            tensor<int32, [1]> var_418 = const()[name = string("op_418"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_23_cast_fp16 = reduce_mean(axes = var_418, keep_dims = var_70, x = var_414_cast_fp16)[name = string("mean_y_23_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_420_cast_fp16 = sub(x = var_414_cast_fp16, y = mean_y_23_cast_fp16)[name = string("op_420_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_421_cast_fp16 = square(x = var_420_cast_fp16);
            tensor<int32, [1]> var_422 = const()[name = string("op_422"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_423_cast_fp16 = reduce_mean(axes = var_422, keep_dims = var_70, 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, 32]> 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, 32]> std_y_23_cast_fp16 = sqrt(x = var_425_cast_fp16)[name = string("std_y_23_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(676992)))];
            tensor<fp16, [1, 256, 32]> var_428_cast_fp16 = mul(x = sep_module_5_tcn_2_norm_gamma_to_fp16, y = var_420_cast_fp16)[name = string("op_428_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_429_cast_fp16 = real_div(x = var_428_cast_fp16, y = std_y_23_cast_fp16)[name = string("op_429_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(677568)))];
            tensor<fp16, [1, 256, 32]> input_57_cast_fp16 = add(x = var_429_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 input_59_constant_val_0_to_fp16 = const()[name = string("input_59_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 96]> input_57_cast_fp16_state_input = read_state(input = input_57_cast_fp16_state);
            tensor<fp16, [1, 256, 96]> input_59_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 64]), end = tensor<int32, [3]>([1, 256, 96]), end_mask = tensor<bool, [3]>([false, false, false]), 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);
            tensor<int32, [1]> var_434 = const()[name = string("op_434"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_436 = const()[name = string("op_436"), val = tensor<int32, [1]>([32])];
            string input_61_pad_type_0 = const()[name = string("input_61_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_5_tcn_4_weight_to_fp16 = const()[name = string("sep_module_5_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(678144)))];
            tensor<fp16, [1, 256, 32]> input_61_cast_fp16 = conv(dilations = var_436, groups = var_67, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = var_434, weight = sep_module_5_tcn_4_weight_to_fp16, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")];
            fp32 var_440_alpha_1 = const()[name = string("op_440_alpha_1"), val = fp32(0x1.908434p-5)];
            tensor<fp16, [1, 256, 32]> var_440_cast_fp16 = leaky_relu(alpha = var_440_alpha_1, x = input_61_cast_fp16)[name = string("op_440_cast_fp16")];
            tensor<int32, [1]> var_444 = const()[name = string("op_444"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_25_cast_fp16 = reduce_mean(axes = var_444, keep_dims = var_70, x = var_440_cast_fp16)[name = string("mean_y_25_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_446_cast_fp16 = sub(x = var_440_cast_fp16, y = mean_y_25_cast_fp16)[name = string("op_446_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_447_cast_fp16 = square(x = var_446_cast_fp16);
            tensor<int32, [1]> var_448 = const()[name = string("op_448"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_449_cast_fp16 = reduce_mean(axes = var_448, keep_dims = var_70, x = var_447_cast_fp16)[name = string("op_449_cast_fp16")];
            fp16 var_450_to_fp16 = const()[name = string("op_450_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_451_cast_fp16 = add(x = var_449_cast_fp16, y = var_450_to_fp16)[name = string("op_451_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_25_cast_fp16 = sqrt(x = var_451_cast_fp16)[name = string("std_y_25_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(679744)))];
            tensor<fp16, [1, 256, 32]> var_454_cast_fp16 = mul(x = sep_module_5_tcn_6_norm_gamma_to_fp16, y = var_446_cast_fp16)[name = string("op_454_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_455_cast_fp16 = real_div(x = var_454_cast_fp16, y = std_y_25_cast_fp16)[name = string("op_455_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(680320)))];
            tensor<fp16, [1, 256, 32]> y_12_cast_fp16 = add(x = var_455_cast_fp16, y = sep_module_5_tcn_6_norm_beta_to_fp16)[name = string("y_12_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_63_cast_fp16 = add(x = input_53_cast_fp16, y = y_12_cast_fp16)[name = string("input_63_cast_fp16")];
            tensor<int32, [1]> var_466 = const()[name = string("op_466"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_468 = const()[name = string("op_468"), val = tensor<int32, [1]>([1])];
            string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_6_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(680896))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(713728))))[name = string("sep_module_6_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_65_cast_fp16 = conv(dilations = var_468, groups = var_66, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = var_466, weight = sep_module_6_tcn_0_weight_to_fp16_palettized, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")];
            fp32 var_472_alpha_1 = const()[name = string("op_472_alpha_1"), val = fp32(0x1.7c9ep-2)];
            tensor<fp16, [1, 256, 32]> var_472_cast_fp16 = leaky_relu(alpha = var_472_alpha_1, x = input_65_cast_fp16)[name = string("op_472_cast_fp16")];
            tensor<int32, [1]> var_476 = const()[name = string("op_476"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_27_cast_fp16 = reduce_mean(axes = var_476, keep_dims = var_70, x = var_472_cast_fp16)[name = string("mean_y_27_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_478_cast_fp16 = sub(x = var_472_cast_fp16, y = mean_y_27_cast_fp16)[name = string("op_478_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_479_cast_fp16 = square(x = var_478_cast_fp16);
            tensor<int32, [1]> var_480 = const()[name = string("op_480"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_481_cast_fp16 = reduce_mean(axes = var_480, keep_dims = var_70, 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, 32]> 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, 32]> std_y_27_cast_fp16 = sqrt(x = var_483_cast_fp16)[name = string("std_y_27_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(713856)))];
            tensor<fp16, [1, 256, 32]> var_486_cast_fp16 = mul(x = sep_module_6_tcn_2_norm_gamma_to_fp16, y = var_478_cast_fp16)[name = string("op_486_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_487_cast_fp16 = real_div(x = var_486_cast_fp16, y = std_y_27_cast_fp16)[name = string("op_487_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(714432)))];
            tensor<fp16, [1, 256, 32]> input_67_cast_fp16 = add(x = var_487_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, 128, 0])];
            string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("constant")];
            fp16 input_69_constant_val_0_to_fp16 = const()[name = string("input_69_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 160]> input_67_cast_fp16_state_input = read_state(input = input_67_cast_fp16_state);
            tensor<fp16, [1, 256, 160]> input_69_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 128]), end = tensor<int32, [3]>([1, 256, 160]), end_mask = tensor<bool, [3]>([false, false, false]), 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);
            tensor<int32, [1]> var_492 = const()[name = string("op_492"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_494 = const()[name = string("op_494"), val = tensor<int32, [1]>([64])];
            string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_6_tcn_4_weight_to_fp16 = const()[name = string("sep_module_6_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(715008)))];
            tensor<fp16, [1, 256, 32]> input_71_cast_fp16 = conv(dilations = var_494, groups = var_67, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = var_492, weight = sep_module_6_tcn_4_weight_to_fp16, x = input_69_cast_fp16)[name = string("input_71_cast_fp16")];
            fp32 var_498_alpha_1 = const()[name = string("op_498_alpha_1"), val = fp32(-0x1.0033acp-2)];
            tensor<fp16, [1, 256, 32]> var_498_cast_fp16 = leaky_relu(alpha = var_498_alpha_1, x = input_71_cast_fp16)[name = string("op_498_cast_fp16")];
            tensor<int32, [1]> var_502 = const()[name = string("op_502"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_29_cast_fp16 = reduce_mean(axes = var_502, keep_dims = var_70, x = var_498_cast_fp16)[name = string("mean_y_29_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_504_cast_fp16 = sub(x = var_498_cast_fp16, y = mean_y_29_cast_fp16)[name = string("op_504_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_505_cast_fp16 = square(x = var_504_cast_fp16);
            tensor<int32, [1]> var_506 = const()[name = string("op_506"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_507_cast_fp16 = reduce_mean(axes = var_506, keep_dims = var_70, x = var_505_cast_fp16)[name = string("op_507_cast_fp16")];
            fp16 var_508_to_fp16 = const()[name = string("op_508_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_509_cast_fp16 = add(x = var_507_cast_fp16, y = var_508_to_fp16)[name = string("op_509_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_29_cast_fp16 = sqrt(x = var_509_cast_fp16)[name = string("std_y_29_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(716608)))];
            tensor<fp16, [1, 256, 32]> var_512_cast_fp16 = mul(x = sep_module_6_tcn_6_norm_gamma_to_fp16, y = var_504_cast_fp16)[name = string("op_512_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_513_cast_fp16 = real_div(x = var_512_cast_fp16, y = std_y_29_cast_fp16)[name = string("op_513_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(717184)))];
            tensor<fp16, [1, 256, 32]> y_14_cast_fp16 = add(x = var_513_cast_fp16, y = sep_module_6_tcn_6_norm_beta_to_fp16)[name = string("y_14_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_73_cast_fp16 = add(x = input_63_cast_fp16, y = y_14_cast_fp16)[name = string("input_73_cast_fp16")];
            tensor<int32, [1]> var_524 = const()[name = string("op_524"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_526 = const()[name = string("op_526"), val = tensor<int32, [1]>([1])];
            string input_75_pad_type_0 = const()[name = string("input_75_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_75_pad_0 = const()[name = string("input_75_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_7_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(717760))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(750592))))[name = string("sep_module_7_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_75_cast_fp16 = conv(dilations = var_526, groups = var_66, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = var_524, weight = sep_module_7_tcn_0_weight_to_fp16_palettized, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")];
            fp32 var_530_alpha_1 = const()[name = string("op_530_alpha_1"), val = fp32(0x1.e87b74p-2)];
            tensor<fp16, [1, 256, 32]> var_530_cast_fp16 = leaky_relu(alpha = var_530_alpha_1, x = input_75_cast_fp16)[name = string("op_530_cast_fp16")];
            tensor<int32, [1]> var_534 = const()[name = string("op_534"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_31_cast_fp16 = reduce_mean(axes = var_534, keep_dims = var_70, x = var_530_cast_fp16)[name = string("mean_y_31_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_536_cast_fp16 = sub(x = var_530_cast_fp16, y = mean_y_31_cast_fp16)[name = string("op_536_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_537_cast_fp16 = square(x = var_536_cast_fp16);
            tensor<int32, [1]> var_538 = const()[name = string("op_538"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_539_cast_fp16 = reduce_mean(axes = var_538, keep_dims = var_70, 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, 32]> 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, 32]> std_y_31_cast_fp16 = sqrt(x = var_541_cast_fp16)[name = string("std_y_31_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(750720)))];
            tensor<fp16, [1, 256, 32]> var_544_cast_fp16 = mul(x = sep_module_7_tcn_2_norm_gamma_to_fp16, y = var_536_cast_fp16)[name = string("op_544_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_545_cast_fp16 = real_div(x = var_544_cast_fp16, y = std_y_31_cast_fp16)[name = string("op_545_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(751296)))];
            tensor<fp16, [1, 256, 32]> input_77_cast_fp16 = add(x = var_545_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, 256, 0])];
            string input_79_mode_0 = const()[name = string("input_79_mode_0"), val = string("constant")];
            fp16 input_79_constant_val_0_to_fp16 = const()[name = string("input_79_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 288]> input_77_cast_fp16_state_input = read_state(input = input_77_cast_fp16_state);
            tensor<fp16, [1, 256, 288]> input_79_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 256]), end = tensor<int32, [3]>([1, 256, 288]), end_mask = tensor<bool, [3]>([false, false, false]), 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);
            tensor<int32, [1]> var_550 = const()[name = string("op_550"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_552 = const()[name = string("op_552"), val = tensor<int32, [1]>([128])];
            string input_81_pad_type_0 = const()[name = string("input_81_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_81_pad_0 = const()[name = string("input_81_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_7_tcn_4_weight_to_fp16 = const()[name = string("sep_module_7_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(751872)))];
            tensor<fp16, [1, 256, 32]> input_81_cast_fp16 = conv(dilations = var_552, groups = var_67, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = var_550, weight = sep_module_7_tcn_4_weight_to_fp16, x = input_79_cast_fp16)[name = string("input_81_cast_fp16")];
            fp32 var_556_alpha_1 = const()[name = string("op_556_alpha_1"), val = fp32(0x1.5cd644p-6)];
            tensor<fp16, [1, 256, 32]> var_556_cast_fp16 = leaky_relu(alpha = var_556_alpha_1, x = input_81_cast_fp16)[name = string("op_556_cast_fp16")];
            tensor<int32, [1]> var_560 = const()[name = string("op_560"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_33_cast_fp16 = reduce_mean(axes = var_560, keep_dims = var_70, x = var_556_cast_fp16)[name = string("mean_y_33_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_562_cast_fp16 = sub(x = var_556_cast_fp16, y = mean_y_33_cast_fp16)[name = string("op_562_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_563_cast_fp16 = square(x = var_562_cast_fp16);
            tensor<int32, [1]> var_564 = const()[name = string("op_564"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_565_cast_fp16 = reduce_mean(axes = var_564, keep_dims = var_70, x = var_563_cast_fp16)[name = string("op_565_cast_fp16")];
            fp16 var_566_to_fp16 = const()[name = string("op_566_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_567_cast_fp16 = add(x = var_565_cast_fp16, y = var_566_to_fp16)[name = string("op_567_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_33_cast_fp16 = sqrt(x = var_567_cast_fp16)[name = string("std_y_33_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(753472)))];
            tensor<fp16, [1, 256, 32]> var_570_cast_fp16 = mul(x = sep_module_7_tcn_6_norm_gamma_to_fp16, y = var_562_cast_fp16)[name = string("op_570_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_571_cast_fp16 = real_div(x = var_570_cast_fp16, y = std_y_33_cast_fp16)[name = string("op_571_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(754048)))];
            tensor<fp16, [1, 256, 32]> y_16_cast_fp16 = add(x = var_571_cast_fp16, y = sep_module_7_tcn_6_norm_beta_to_fp16)[name = string("y_16_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_83_cast_fp16 = add(x = input_73_cast_fp16, y = y_16_cast_fp16)[name = string("input_83_cast_fp16")];
            tensor<int32, [1]> var_582 = const()[name = string("op_582"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_584 = const()[name = string("op_584"), val = tensor<int32, [1]>([1])];
            string input_85_pad_type_0 = const()[name = string("input_85_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_8_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(754624))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(787456))))[name = string("sep_module_8_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_85_cast_fp16 = conv(dilations = var_584, groups = var_66, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = var_582, weight = sep_module_8_tcn_0_weight_to_fp16_palettized, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")];
            fp32 var_588_alpha_1 = const()[name = string("op_588_alpha_1"), val = fp32(0x1.8d996cp-2)];
            tensor<fp16, [1, 256, 32]> var_588_cast_fp16 = leaky_relu(alpha = var_588_alpha_1, x = input_85_cast_fp16)[name = string("op_588_cast_fp16")];
            tensor<int32, [1]> var_592 = const()[name = string("op_592"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_35_cast_fp16 = reduce_mean(axes = var_592, keep_dims = var_70, x = var_588_cast_fp16)[name = string("mean_y_35_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_594_cast_fp16 = sub(x = var_588_cast_fp16, y = mean_y_35_cast_fp16)[name = string("op_594_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_595_cast_fp16 = square(x = var_594_cast_fp16);
            tensor<int32, [1]> var_596 = const()[name = string("op_596"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_597_cast_fp16 = reduce_mean(axes = var_596, keep_dims = var_70, 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, 32]> 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, 32]> std_y_35_cast_fp16 = sqrt(x = var_599_cast_fp16)[name = string("std_y_35_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(787584)))];
            tensor<fp16, [1, 256, 32]> var_602_cast_fp16 = mul(x = sep_module_8_tcn_2_norm_gamma_to_fp16, y = var_594_cast_fp16)[name = string("op_602_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_603_cast_fp16 = real_div(x = var_602_cast_fp16, y = std_y_35_cast_fp16)[name = string("op_603_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(788160)))];
            tensor<fp16, [1, 256, 32]> input_87_cast_fp16 = add(x = var_603_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, 512, 0])];
            string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("constant")];
            fp16 input_89_constant_val_0_to_fp16 = const()[name = string("input_89_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 544]> input_87_cast_fp16_state_input = read_state(input = input_87_cast_fp16_state);
            tensor<fp16, [1, 256, 544]> input_89_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 512]), end = tensor<int32, [3]>([1, 256, 544]), end_mask = tensor<bool, [3]>([false, false, false]), 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);
            tensor<int32, [1]> var_608 = const()[name = string("op_608"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_610 = const()[name = string("op_610"), val = tensor<int32, [1]>([256])];
            string input_91_pad_type_0 = const()[name = string("input_91_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_91_pad_0 = const()[name = string("input_91_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_8_tcn_4_weight_to_fp16 = const()[name = string("sep_module_8_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(788736)))];
            tensor<fp16, [1, 256, 32]> input_91_cast_fp16 = conv(dilations = var_610, groups = var_67, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = var_608, weight = sep_module_8_tcn_4_weight_to_fp16, x = input_89_cast_fp16)[name = string("input_91_cast_fp16")];
            fp32 var_614_alpha_1 = const()[name = string("op_614_alpha_1"), val = fp32(-0x1.ae100ap-3)];
            tensor<fp16, [1, 256, 32]> var_614_cast_fp16 = leaky_relu(alpha = var_614_alpha_1, x = input_91_cast_fp16)[name = string("op_614_cast_fp16")];
            tensor<int32, [1]> var_618 = const()[name = string("op_618"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_37_cast_fp16 = reduce_mean(axes = var_618, keep_dims = var_70, x = var_614_cast_fp16)[name = string("mean_y_37_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_620_cast_fp16 = sub(x = var_614_cast_fp16, y = mean_y_37_cast_fp16)[name = string("op_620_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_621_cast_fp16 = square(x = var_620_cast_fp16);
            tensor<int32, [1]> var_622 = const()[name = string("op_622"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_623_cast_fp16 = reduce_mean(axes = var_622, keep_dims = var_70, x = var_621_cast_fp16)[name = string("op_623_cast_fp16")];
            fp16 var_624_to_fp16 = const()[name = string("op_624_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_625_cast_fp16 = add(x = var_623_cast_fp16, y = var_624_to_fp16)[name = string("op_625_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_37_cast_fp16 = sqrt(x = var_625_cast_fp16)[name = string("std_y_37_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(790336)))];
            tensor<fp16, [1, 256, 32]> var_628_cast_fp16 = mul(x = sep_module_8_tcn_6_norm_gamma_to_fp16, y = var_620_cast_fp16)[name = string("op_628_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_629_cast_fp16 = real_div(x = var_628_cast_fp16, y = std_y_37_cast_fp16)[name = string("op_629_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(790912)))];
            tensor<fp16, [1, 256, 32]> y_18_cast_fp16 = add(x = var_629_cast_fp16, y = sep_module_8_tcn_6_norm_beta_to_fp16)[name = string("y_18_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_93_cast_fp16 = add(x = input_83_cast_fp16, y = y_18_cast_fp16)[name = string("input_93_cast_fp16")];
            tensor<int32, [1]> var_640 = const()[name = string("op_640"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_642 = const()[name = string("op_642"), val = tensor<int32, [1]>([1])];
            string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_9_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(791488))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(824320))))[name = string("sep_module_9_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_95_cast_fp16 = conv(dilations = var_642, groups = var_66, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = var_640, weight = sep_module_9_tcn_0_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = string("input_95_cast_fp16")];
            fp32 var_646_alpha_1 = const()[name = string("op_646_alpha_1"), val = fp32(0x1.6d4604p-3)];
            tensor<fp16, [1, 256, 32]> var_646_cast_fp16 = leaky_relu(alpha = var_646_alpha_1, x = input_95_cast_fp16)[name = string("op_646_cast_fp16")];
            tensor<int32, [1]> var_650 = const()[name = string("op_650"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_39_cast_fp16 = reduce_mean(axes = var_650, keep_dims = var_70, x = var_646_cast_fp16)[name = string("mean_y_39_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_652_cast_fp16 = sub(x = var_646_cast_fp16, y = mean_y_39_cast_fp16)[name = string("op_652_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_653_cast_fp16 = square(x = var_652_cast_fp16);
            tensor<int32, [1]> var_654 = const()[name = string("op_654"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_655_cast_fp16 = reduce_mean(axes = var_654, keep_dims = var_70, 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, 32]> 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, 32]> std_y_39_cast_fp16 = sqrt(x = var_657_cast_fp16)[name = string("std_y_39_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(824448)))];
            tensor<fp16, [1, 256, 32]> var_660_cast_fp16 = mul(x = sep_module_9_tcn_2_norm_gamma_to_fp16, y = var_652_cast_fp16)[name = string("op_660_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_661_cast_fp16 = real_div(x = var_660_cast_fp16, y = std_y_39_cast_fp16)[name = string("op_661_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(825024)))];
            tensor<fp16, [1, 256, 32]> input_97_cast_fp16 = add(x = var_661_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, 2, 0])];
            string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("constant")];
            fp16 input_99_constant_val_0_to_fp16 = const()[name = string("input_99_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 34]> input_97_cast_fp16_state_input = read_state(input = input_97_cast_fp16_state);
            tensor<fp16, [1, 256, 34]> input_99_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 2]), end = tensor<int32, [3]>([1, 256, 34]), end_mask = tensor<bool, [3]>([false, false, false]), 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);
            tensor<int32, [1]> var_666 = const()[name = string("op_666"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_668 = const()[name = string("op_668"), val = tensor<int32, [1]>([1])];
            string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_9_tcn_4_weight_to_fp16 = const()[name = string("sep_module_9_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(825600)))];
            tensor<fp16, [1, 256, 32]> input_101_cast_fp16 = conv(dilations = var_668, groups = var_67, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = var_666, weight = sep_module_9_tcn_4_weight_to_fp16, x = input_99_cast_fp16)[name = string("input_101_cast_fp16")];
            fp32 var_672_alpha_1 = const()[name = string("op_672_alpha_1"), val = fp32(0x1.77f43p-1)];
            tensor<fp16, [1, 256, 32]> var_672_cast_fp16 = leaky_relu(alpha = var_672_alpha_1, x = input_101_cast_fp16)[name = string("op_672_cast_fp16")];
            tensor<int32, [1]> var_676 = const()[name = string("op_676"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_41_cast_fp16 = reduce_mean(axes = var_676, keep_dims = var_70, x = var_672_cast_fp16)[name = string("mean_y_41_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_678_cast_fp16 = sub(x = var_672_cast_fp16, y = mean_y_41_cast_fp16)[name = string("op_678_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_679_cast_fp16 = square(x = var_678_cast_fp16);
            tensor<int32, [1]> var_680 = const()[name = string("op_680"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_681_cast_fp16 = reduce_mean(axes = var_680, keep_dims = var_70, x = var_679_cast_fp16)[name = string("op_681_cast_fp16")];
            fp16 var_682_to_fp16 = const()[name = string("op_682_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_683_cast_fp16 = add(x = var_681_cast_fp16, y = var_682_to_fp16)[name = string("op_683_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_41_cast_fp16 = sqrt(x = var_683_cast_fp16)[name = string("std_y_41_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(827200)))];
            tensor<fp16, [1, 256, 32]> var_686_cast_fp16 = mul(x = sep_module_9_tcn_6_norm_gamma_to_fp16, y = var_678_cast_fp16)[name = string("op_686_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_687_cast_fp16 = real_div(x = var_686_cast_fp16, y = std_y_41_cast_fp16)[name = string("op_687_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(827776)))];
            tensor<fp16, [1, 256, 32]> y_20_cast_fp16 = add(x = var_687_cast_fp16, y = sep_module_9_tcn_6_norm_beta_to_fp16)[name = string("y_20_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_103_cast_fp16 = add(x = input_93_cast_fp16, y = y_20_cast_fp16)[name = string("input_103_cast_fp16")];
            tensor<int32, [1]> var_698 = const()[name = string("op_698"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_700 = const()[name = string("op_700"), val = tensor<int32, [1]>([1])];
            string input_105_pad_type_0 = const()[name = string("input_105_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_105_pad_0 = const()[name = string("input_105_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_10_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(828352))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(861184))))[name = string("sep_module_10_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_105_cast_fp16 = conv(dilations = var_700, groups = var_66, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = var_698, weight = sep_module_10_tcn_0_weight_to_fp16_palettized, x = input_103_cast_fp16)[name = string("input_105_cast_fp16")];
            fp32 var_704_alpha_1 = const()[name = string("op_704_alpha_1"), val = fp32(-0x1.fc66b4p-3)];
            tensor<fp16, [1, 256, 32]> var_704_cast_fp16 = leaky_relu(alpha = var_704_alpha_1, x = input_105_cast_fp16)[name = string("op_704_cast_fp16")];
            tensor<int32, [1]> var_708 = const()[name = string("op_708"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_43_cast_fp16 = reduce_mean(axes = var_708, keep_dims = var_70, x = var_704_cast_fp16)[name = string("mean_y_43_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_710_cast_fp16 = sub(x = var_704_cast_fp16, y = mean_y_43_cast_fp16)[name = string("op_710_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_711_cast_fp16 = square(x = var_710_cast_fp16);
            tensor<int32, [1]> var_712 = const()[name = string("op_712"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_713_cast_fp16 = reduce_mean(axes = var_712, keep_dims = var_70, 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, 32]> 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, 32]> std_y_43_cast_fp16 = sqrt(x = var_715_cast_fp16)[name = string("std_y_43_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(861312)))];
            tensor<fp16, [1, 256, 32]> var_718_cast_fp16 = mul(x = sep_module_10_tcn_2_norm_gamma_to_fp16, y = var_710_cast_fp16)[name = string("op_718_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_719_cast_fp16 = real_div(x = var_718_cast_fp16, y = std_y_43_cast_fp16)[name = string("op_719_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(861888)))];
            tensor<fp16, [1, 256, 32]> input_107_cast_fp16 = add(x = var_719_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, 4, 0])];
            string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("constant")];
            fp16 input_109_constant_val_0_to_fp16 = const()[name = string("input_109_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 36]> input_107_cast_fp16_state_input = read_state(input = input_107_cast_fp16_state);
            tensor<fp16, [1, 256, 36]> input_109_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 4]), end = tensor<int32, [3]>([1, 256, 36]), end_mask = tensor<bool, [3]>([false, false, false]), 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);
            tensor<int32, [1]> var_724 = const()[name = string("op_724"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_726 = const()[name = string("op_726"), val = tensor<int32, [1]>([2])];
            string input_111_pad_type_0 = const()[name = string("input_111_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_111_pad_0 = const()[name = string("input_111_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_10_tcn_4_weight_to_fp16 = const()[name = string("sep_module_10_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(862464)))];
            tensor<fp16, [1, 256, 32]> input_111_cast_fp16 = conv(dilations = var_726, groups = var_67, pad = input_111_pad_0, pad_type = input_111_pad_type_0, strides = var_724, weight = sep_module_10_tcn_4_weight_to_fp16, x = input_109_cast_fp16)[name = string("input_111_cast_fp16")];
            fp32 var_730_alpha_1 = const()[name = string("op_730_alpha_1"), val = fp32(0x1.288e6p-1)];
            tensor<fp16, [1, 256, 32]> var_730_cast_fp16 = leaky_relu(alpha = var_730_alpha_1, x = input_111_cast_fp16)[name = string("op_730_cast_fp16")];
            tensor<int32, [1]> var_734 = const()[name = string("op_734"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_45_cast_fp16 = reduce_mean(axes = var_734, keep_dims = var_70, x = var_730_cast_fp16)[name = string("mean_y_45_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_736_cast_fp16 = sub(x = var_730_cast_fp16, y = mean_y_45_cast_fp16)[name = string("op_736_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_737_cast_fp16 = square(x = var_736_cast_fp16);
            tensor<int32, [1]> var_738 = const()[name = string("op_738"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_739_cast_fp16 = reduce_mean(axes = var_738, keep_dims = var_70, x = var_737_cast_fp16)[name = string("op_739_cast_fp16")];
            fp16 var_740_to_fp16 = const()[name = string("op_740_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_741_cast_fp16 = add(x = var_739_cast_fp16, y = var_740_to_fp16)[name = string("op_741_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_45_cast_fp16 = sqrt(x = var_741_cast_fp16)[name = string("std_y_45_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(864064)))];
            tensor<fp16, [1, 256, 32]> var_744_cast_fp16 = mul(x = sep_module_10_tcn_6_norm_gamma_to_fp16, y = var_736_cast_fp16)[name = string("op_744_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_745_cast_fp16 = real_div(x = var_744_cast_fp16, y = std_y_45_cast_fp16)[name = string("op_745_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(864640)))];
            tensor<fp16, [1, 256, 32]> y_22_cast_fp16 = add(x = var_745_cast_fp16, y = sep_module_10_tcn_6_norm_beta_to_fp16)[name = string("y_22_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_113_cast_fp16 = add(x = input_103_cast_fp16, y = y_22_cast_fp16)[name = string("input_113_cast_fp16")];
            tensor<int32, [1]> var_756 = const()[name = string("op_756"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_758 = const()[name = string("op_758"), val = tensor<int32, [1]>([1])];
            string input_115_pad_type_0 = const()[name = string("input_115_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_115_pad_0 = const()[name = string("input_115_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_11_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(865216))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(898048))))[name = string("sep_module_11_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_115_cast_fp16 = conv(dilations = var_758, groups = var_66, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = var_756, weight = sep_module_11_tcn_0_weight_to_fp16_palettized, x = input_113_cast_fp16)[name = string("input_115_cast_fp16")];
            fp32 var_762_alpha_1 = const()[name = string("op_762_alpha_1"), val = fp32(0x1.cacd54p-2)];
            tensor<fp16, [1, 256, 32]> var_762_cast_fp16 = leaky_relu(alpha = var_762_alpha_1, x = input_115_cast_fp16)[name = string("op_762_cast_fp16")];
            tensor<int32, [1]> var_766 = const()[name = string("op_766"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_47_cast_fp16 = reduce_mean(axes = var_766, keep_dims = var_70, x = var_762_cast_fp16)[name = string("mean_y_47_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_768_cast_fp16 = sub(x = var_762_cast_fp16, y = mean_y_47_cast_fp16)[name = string("op_768_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_769_cast_fp16 = square(x = var_768_cast_fp16);
            tensor<int32, [1]> var_770 = const()[name = string("op_770"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_771_cast_fp16 = reduce_mean(axes = var_770, keep_dims = var_70, 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, 32]> 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, 32]> std_y_47_cast_fp16 = sqrt(x = var_773_cast_fp16)[name = string("std_y_47_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(898176)))];
            tensor<fp16, [1, 256, 32]> var_776_cast_fp16 = mul(x = sep_module_11_tcn_2_norm_gamma_to_fp16, y = var_768_cast_fp16)[name = string("op_776_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_777_cast_fp16 = real_div(x = var_776_cast_fp16, y = std_y_47_cast_fp16)[name = string("op_777_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(898752)))];
            tensor<fp16, [1, 256, 32]> input_117_cast_fp16 = add(x = var_777_cast_fp16, y = sep_module_11_tcn_2_norm_beta_to_fp16)[name = string("input_117_cast_fp16")];
            tensor<int32, [6]> input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("constant")];
            fp16 input_119_constant_val_0_to_fp16 = const()[name = string("input_119_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 40]> input_117_cast_fp16_state_input = read_state(input = input_117_cast_fp16_state);
            tensor<fp16, [1, 256, 40]> input_119_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 8]), end = tensor<int32, [3]>([1, 256, 40]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_117_cast_fp16, x = input_117_cast_fp16_state_input);
            write_state(data = input_119_cast_fp16, input = input_117_cast_fp16_state);
            tensor<int32, [1]> var_782 = const()[name = string("op_782"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_784 = const()[name = string("op_784"), val = tensor<int32, [1]>([4])];
            string input_121_pad_type_0 = const()[name = string("input_121_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_121_pad_0 = const()[name = string("input_121_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_11_tcn_4_weight_to_fp16 = const()[name = string("sep_module_11_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(899328)))];
            tensor<fp16, [1, 256, 32]> input_121_cast_fp16 = conv(dilations = var_784, groups = var_67, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = var_782, weight = sep_module_11_tcn_4_weight_to_fp16, x = input_119_cast_fp16)[name = string("input_121_cast_fp16")];
            fp32 var_788_alpha_1 = const()[name = string("op_788_alpha_1"), val = fp32(-0x1.5d082ep-3)];
            tensor<fp16, [1, 256, 32]> var_788_cast_fp16 = leaky_relu(alpha = var_788_alpha_1, x = input_121_cast_fp16)[name = string("op_788_cast_fp16")];
            tensor<int32, [1]> var_792 = const()[name = string("op_792"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_49_cast_fp16 = reduce_mean(axes = var_792, keep_dims = var_70, x = var_788_cast_fp16)[name = string("mean_y_49_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_794_cast_fp16 = sub(x = var_788_cast_fp16, y = mean_y_49_cast_fp16)[name = string("op_794_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_795_cast_fp16 = square(x = var_794_cast_fp16);
            tensor<int32, [1]> var_796 = const()[name = string("op_796"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_797_cast_fp16 = reduce_mean(axes = var_796, keep_dims = var_70, x = var_795_cast_fp16)[name = string("op_797_cast_fp16")];
            fp16 var_798_to_fp16 = const()[name = string("op_798_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_799_cast_fp16 = add(x = var_797_cast_fp16, y = var_798_to_fp16)[name = string("op_799_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_49_cast_fp16 = sqrt(x = var_799_cast_fp16)[name = string("std_y_49_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(900928)))];
            tensor<fp16, [1, 256, 32]> var_802_cast_fp16 = mul(x = sep_module_11_tcn_6_norm_gamma_to_fp16, y = var_794_cast_fp16)[name = string("op_802_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_803_cast_fp16 = real_div(x = var_802_cast_fp16, y = std_y_49_cast_fp16)[name = string("op_803_cast_fp16")];
            tensor<fp16, [1, 256, 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, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(901504)))];
            tensor<fp16, [1, 256, 32]> y_24_cast_fp16 = add(x = var_803_cast_fp16, y = sep_module_11_tcn_6_norm_beta_to_fp16)[name = string("y_24_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_123_cast_fp16 = add(x = input_113_cast_fp16, y = y_24_cast_fp16)[name = string("input_123_cast_fp16")];
            tensor<int32, [1]> var_814 = const()[name = string("op_814"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_816 = const()[name = string("op_816"), val = tensor<int32, [1]>([1])];
            string input_125_pad_type_0 = const()[name = string("input_125_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_125_pad_0 = const()[name = string("input_125_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_12_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(902080))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(934912))))[name = string("sep_module_12_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_125_cast_fp16 = conv(dilations = var_816, groups = var_66, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = var_814, weight = sep_module_12_tcn_0_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = string("input_125_cast_fp16")];
            fp32 var_820_alpha_1 = const()[name = string("op_820_alpha_1"), val = fp32(0x1.c2f53p-2)];
            tensor<fp16, [1, 256, 32]> var_820_cast_fp16 = leaky_relu(alpha = var_820_alpha_1, x = input_125_cast_fp16)[name = string("op_820_cast_fp16")];
            tensor<int32, [1]> var_824 = const()[name = string("op_824"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_51_cast_fp16 = reduce_mean(axes = var_824, keep_dims = var_70, x = var_820_cast_fp16)[name = string("mean_y_51_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_826_cast_fp16 = sub(x = var_820_cast_fp16, y = mean_y_51_cast_fp16)[name = string("op_826_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_827_cast_fp16 = square(x = var_826_cast_fp16);
            tensor<int32, [1]> var_828 = const()[name = string("op_828"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_829_cast_fp16 = reduce_mean(axes = var_828, keep_dims = var_70, x = var_827_cast_fp16)[name = string("op_829_cast_fp16")];
            fp16 var_830_to_fp16 = const()[name = string("op_830_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_831_cast_fp16 = add(x = var_829_cast_fp16, y = var_830_to_fp16)[name = string("op_831_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_51_cast_fp16 = sqrt(x = var_831_cast_fp16)[name = string("std_y_51_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_12_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_12_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(935040)))];
            tensor<fp16, [1, 256, 32]> var_834_cast_fp16 = mul(x = sep_module_12_tcn_2_norm_gamma_to_fp16, y = var_826_cast_fp16)[name = string("op_834_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_835_cast_fp16 = real_div(x = var_834_cast_fp16, y = std_y_51_cast_fp16)[name = string("op_835_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_12_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_12_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(935616)))];
            tensor<fp16, [1, 256, 32]> input_127_cast_fp16 = add(x = var_835_cast_fp16, y = sep_module_12_tcn_2_norm_beta_to_fp16)[name = string("input_127_cast_fp16")];
            tensor<int32, [6]> input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("constant")];
            fp16 input_129_constant_val_0_to_fp16 = const()[name = string("input_129_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 48]> input_127_cast_fp16_state_input = read_state(input = input_127_cast_fp16_state);
            tensor<fp16, [1, 256, 48]> input_129_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 16]), end = tensor<int32, [3]>([1, 256, 48]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_127_cast_fp16, x = input_127_cast_fp16_state_input);
            write_state(data = input_129_cast_fp16, input = input_127_cast_fp16_state);
            tensor<int32, [1]> var_840 = const()[name = string("op_840"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_842 = const()[name = string("op_842"), val = tensor<int32, [1]>([8])];
            string input_131_pad_type_0 = const()[name = string("input_131_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_12_tcn_4_weight_to_fp16 = const()[name = string("sep_module_12_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(936192)))];
            tensor<fp16, [1, 256, 32]> input_131_cast_fp16 = conv(dilations = var_842, groups = var_67, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = var_840, weight = sep_module_12_tcn_4_weight_to_fp16, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")];
            fp32 var_846_alpha_1 = const()[name = string("op_846_alpha_1"), val = fp32(-0x1.0bd24p-2)];
            tensor<fp16, [1, 256, 32]> var_846_cast_fp16 = leaky_relu(alpha = var_846_alpha_1, x = input_131_cast_fp16)[name = string("op_846_cast_fp16")];
            tensor<int32, [1]> var_850 = const()[name = string("op_850"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_53_cast_fp16 = reduce_mean(axes = var_850, keep_dims = var_70, x = var_846_cast_fp16)[name = string("mean_y_53_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_852_cast_fp16 = sub(x = var_846_cast_fp16, y = mean_y_53_cast_fp16)[name = string("op_852_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_853_cast_fp16 = square(x = var_852_cast_fp16);
            tensor<int32, [1]> var_854 = const()[name = string("op_854"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_855_cast_fp16 = reduce_mean(axes = var_854, keep_dims = var_70, x = var_853_cast_fp16)[name = string("op_855_cast_fp16")];
            fp16 var_856_to_fp16 = const()[name = string("op_856_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_857_cast_fp16 = add(x = var_855_cast_fp16, y = var_856_to_fp16)[name = string("op_857_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_53_cast_fp16 = sqrt(x = var_857_cast_fp16)[name = string("std_y_53_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_12_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_12_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(937792)))];
            tensor<fp16, [1, 256, 32]> var_860_cast_fp16 = mul(x = sep_module_12_tcn_6_norm_gamma_to_fp16, y = var_852_cast_fp16)[name = string("op_860_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_861_cast_fp16 = real_div(x = var_860_cast_fp16, y = std_y_53_cast_fp16)[name = string("op_861_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_12_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_12_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(938368)))];
            tensor<fp16, [1, 256, 32]> y_26_cast_fp16 = add(x = var_861_cast_fp16, y = sep_module_12_tcn_6_norm_beta_to_fp16)[name = string("y_26_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_133_cast_fp16 = add(x = input_123_cast_fp16, y = y_26_cast_fp16)[name = string("input_133_cast_fp16")];
            tensor<int32, [1]> var_872 = const()[name = string("op_872"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_874 = const()[name = string("op_874"), val = tensor<int32, [1]>([1])];
            string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_13_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(938944))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(971776))))[name = string("sep_module_13_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_135_cast_fp16 = conv(dilations = var_874, groups = var_66, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = var_872, weight = sep_module_13_tcn_0_weight_to_fp16_palettized, x = input_133_cast_fp16)[name = string("input_135_cast_fp16")];
            fp32 var_878_alpha_1 = const()[name = string("op_878_alpha_1"), val = fp32(0x1.05adc8p-1)];
            tensor<fp16, [1, 256, 32]> var_878_cast_fp16 = leaky_relu(alpha = var_878_alpha_1, x = input_135_cast_fp16)[name = string("op_878_cast_fp16")];
            tensor<int32, [1]> var_882 = const()[name = string("op_882"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_55_cast_fp16 = reduce_mean(axes = var_882, keep_dims = var_70, x = var_878_cast_fp16)[name = string("mean_y_55_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_884_cast_fp16 = sub(x = var_878_cast_fp16, y = mean_y_55_cast_fp16)[name = string("op_884_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_885_cast_fp16 = square(x = var_884_cast_fp16);
            tensor<int32, [1]> var_886 = const()[name = string("op_886"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_887_cast_fp16 = reduce_mean(axes = var_886, keep_dims = var_70, x = var_885_cast_fp16)[name = string("op_887_cast_fp16")];
            fp16 var_888_to_fp16 = const()[name = string("op_888_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_889_cast_fp16 = add(x = var_887_cast_fp16, y = var_888_to_fp16)[name = string("op_889_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_55_cast_fp16 = sqrt(x = var_889_cast_fp16)[name = string("std_y_55_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_13_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_13_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(971904)))];
            tensor<fp16, [1, 256, 32]> var_892_cast_fp16 = mul(x = sep_module_13_tcn_2_norm_gamma_to_fp16, y = var_884_cast_fp16)[name = string("op_892_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_893_cast_fp16 = real_div(x = var_892_cast_fp16, y = std_y_55_cast_fp16)[name = string("op_893_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_13_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_13_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(972480)))];
            tensor<fp16, [1, 256, 32]> input_137_cast_fp16 = add(x = var_893_cast_fp16, y = sep_module_13_tcn_2_norm_beta_to_fp16)[name = string("input_137_cast_fp16")];
            tensor<int32, [6]> input_139_pad_0 = const()[name = string("input_139_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            string input_139_mode_0 = const()[name = string("input_139_mode_0"), val = string("constant")];
            fp16 input_139_constant_val_0_to_fp16 = const()[name = string("input_139_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 64]> input_137_cast_fp16_state_input = read_state(input = input_137_cast_fp16_state);
            tensor<fp16, [1, 256, 64]> input_139_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 32]), end = tensor<int32, [3]>([1, 256, 64]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_137_cast_fp16, x = input_137_cast_fp16_state_input);
            write_state(data = input_139_cast_fp16, input = input_137_cast_fp16_state);
            tensor<int32, [1]> var_898 = const()[name = string("op_898"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_900 = const()[name = string("op_900"), val = tensor<int32, [1]>([16])];
            string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_13_tcn_4_weight_to_fp16 = const()[name = string("sep_module_13_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(973056)))];
            tensor<fp16, [1, 256, 32]> input_141_cast_fp16 = conv(dilations = var_900, groups = var_67, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = var_898, weight = sep_module_13_tcn_4_weight_to_fp16, x = input_139_cast_fp16)[name = string("input_141_cast_fp16")];
            fp32 var_904_alpha_1 = const()[name = string("op_904_alpha_1"), val = fp32(-0x1.a9043p-5)];
            tensor<fp16, [1, 256, 32]> var_904_cast_fp16 = leaky_relu(alpha = var_904_alpha_1, x = input_141_cast_fp16)[name = string("op_904_cast_fp16")];
            tensor<int32, [1]> var_908 = const()[name = string("op_908"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_57_cast_fp16 = reduce_mean(axes = var_908, keep_dims = var_70, x = var_904_cast_fp16)[name = string("mean_y_57_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_910_cast_fp16 = sub(x = var_904_cast_fp16, y = mean_y_57_cast_fp16)[name = string("op_910_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_911_cast_fp16 = square(x = var_910_cast_fp16);
            tensor<int32, [1]> var_912 = const()[name = string("op_912"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_913_cast_fp16 = reduce_mean(axes = var_912, keep_dims = var_70, x = var_911_cast_fp16)[name = string("op_913_cast_fp16")];
            fp16 var_914_to_fp16 = const()[name = string("op_914_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_915_cast_fp16 = add(x = var_913_cast_fp16, y = var_914_to_fp16)[name = string("op_915_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_57_cast_fp16 = sqrt(x = var_915_cast_fp16)[name = string("std_y_57_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_13_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_13_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(974656)))];
            tensor<fp16, [1, 256, 32]> var_918_cast_fp16 = mul(x = sep_module_13_tcn_6_norm_gamma_to_fp16, y = var_910_cast_fp16)[name = string("op_918_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_919_cast_fp16 = real_div(x = var_918_cast_fp16, y = std_y_57_cast_fp16)[name = string("op_919_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_13_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_13_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(975232)))];
            tensor<fp16, [1, 256, 32]> y_28_cast_fp16 = add(x = var_919_cast_fp16, y = sep_module_13_tcn_6_norm_beta_to_fp16)[name = string("y_28_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_143_cast_fp16 = add(x = input_133_cast_fp16, y = y_28_cast_fp16)[name = string("input_143_cast_fp16")];
            tensor<int32, [1]> var_930 = const()[name = string("op_930"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_932 = const()[name = string("op_932"), val = tensor<int32, [1]>([1])];
            string input_145_pad_type_0 = const()[name = string("input_145_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_145_pad_0 = const()[name = string("input_145_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_14_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(975808))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1008640))))[name = string("sep_module_14_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_145_cast_fp16 = conv(dilations = var_932, groups = var_66, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = var_930, weight = sep_module_14_tcn_0_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = string("input_145_cast_fp16")];
            fp32 var_936_alpha_1 = const()[name = string("op_936_alpha_1"), val = fp32(0x1.712116p-2)];
            tensor<fp16, [1, 256, 32]> var_936_cast_fp16 = leaky_relu(alpha = var_936_alpha_1, x = input_145_cast_fp16)[name = string("op_936_cast_fp16")];
            tensor<int32, [1]> var_940 = const()[name = string("op_940"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_59_cast_fp16 = reduce_mean(axes = var_940, keep_dims = var_70, x = var_936_cast_fp16)[name = string("mean_y_59_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_942_cast_fp16 = sub(x = var_936_cast_fp16, y = mean_y_59_cast_fp16)[name = string("op_942_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_943_cast_fp16 = square(x = var_942_cast_fp16);
            tensor<int32, [1]> var_944 = const()[name = string("op_944"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_945_cast_fp16 = reduce_mean(axes = var_944, keep_dims = var_70, x = var_943_cast_fp16)[name = string("op_945_cast_fp16")];
            fp16 var_946_to_fp16 = const()[name = string("op_946_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_947_cast_fp16 = add(x = var_945_cast_fp16, y = var_946_to_fp16)[name = string("op_947_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_59_cast_fp16 = sqrt(x = var_947_cast_fp16)[name = string("std_y_59_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_14_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_14_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1008768)))];
            tensor<fp16, [1, 256, 32]> var_950_cast_fp16 = mul(x = sep_module_14_tcn_2_norm_gamma_to_fp16, y = var_942_cast_fp16)[name = string("op_950_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_951_cast_fp16 = real_div(x = var_950_cast_fp16, y = std_y_59_cast_fp16)[name = string("op_951_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_14_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_14_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1009344)))];
            tensor<fp16, [1, 256, 32]> input_147_cast_fp16 = add(x = var_951_cast_fp16, y = sep_module_14_tcn_2_norm_beta_to_fp16)[name = string("input_147_cast_fp16")];
            tensor<int32, [6]> input_149_pad_0 = const()[name = string("input_149_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("constant")];
            fp16 input_149_constant_val_0_to_fp16 = const()[name = string("input_149_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 96]> input_147_cast_fp16_state_input = read_state(input = input_147_cast_fp16_state);
            tensor<fp16, [1, 256, 96]> input_149_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 64]), end = tensor<int32, [3]>([1, 256, 96]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_147_cast_fp16, x = input_147_cast_fp16_state_input);
            write_state(data = input_149_cast_fp16, input = input_147_cast_fp16_state);
            tensor<int32, [1]> var_956 = const()[name = string("op_956"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_958 = const()[name = string("op_958"), val = tensor<int32, [1]>([32])];
            string input_151_pad_type_0 = const()[name = string("input_151_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_151_pad_0 = const()[name = string("input_151_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_14_tcn_4_weight_to_fp16 = const()[name = string("sep_module_14_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1009920)))];
            tensor<fp16, [1, 256, 32]> input_151_cast_fp16 = conv(dilations = var_958, groups = var_67, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = var_956, weight = sep_module_14_tcn_4_weight_to_fp16, x = input_149_cast_fp16)[name = string("input_151_cast_fp16")];
            fp32 var_962_alpha_1 = const()[name = string("op_962_alpha_1"), val = fp32(0x1.205eaap-2)];
            tensor<fp16, [1, 256, 32]> var_962_cast_fp16 = leaky_relu(alpha = var_962_alpha_1, x = input_151_cast_fp16)[name = string("op_962_cast_fp16")];
            tensor<int32, [1]> var_966 = const()[name = string("op_966"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_61_cast_fp16 = reduce_mean(axes = var_966, keep_dims = var_70, x = var_962_cast_fp16)[name = string("mean_y_61_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_968_cast_fp16 = sub(x = var_962_cast_fp16, y = mean_y_61_cast_fp16)[name = string("op_968_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_969_cast_fp16 = square(x = var_968_cast_fp16);
            tensor<int32, [1]> var_970 = const()[name = string("op_970"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_971_cast_fp16 = reduce_mean(axes = var_970, keep_dims = var_70, x = var_969_cast_fp16)[name = string("op_971_cast_fp16")];
            fp16 var_972_to_fp16 = const()[name = string("op_972_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_973_cast_fp16 = add(x = var_971_cast_fp16, y = var_972_to_fp16)[name = string("op_973_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_61_cast_fp16 = sqrt(x = var_973_cast_fp16)[name = string("std_y_61_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_14_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_14_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1011520)))];
            tensor<fp16, [1, 256, 32]> var_976_cast_fp16 = mul(x = sep_module_14_tcn_6_norm_gamma_to_fp16, y = var_968_cast_fp16)[name = string("op_976_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_977_cast_fp16 = real_div(x = var_976_cast_fp16, y = std_y_61_cast_fp16)[name = string("op_977_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_14_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_14_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1012096)))];
            tensor<fp16, [1, 256, 32]> y_30_cast_fp16 = add(x = var_977_cast_fp16, y = sep_module_14_tcn_6_norm_beta_to_fp16)[name = string("y_30_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_153_cast_fp16 = add(x = input_143_cast_fp16, y = y_30_cast_fp16)[name = string("input_153_cast_fp16")];
            tensor<int32, [1]> var_988 = const()[name = string("op_988"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_990 = const()[name = string("op_990"), val = tensor<int32, [1]>([1])];
            string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_15_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1012672))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1045504))))[name = string("sep_module_15_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_155_cast_fp16 = conv(dilations = var_990, groups = var_66, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = var_988, weight = sep_module_15_tcn_0_weight_to_fp16_palettized, x = input_153_cast_fp16)[name = string("input_155_cast_fp16")];
            fp32 var_994_alpha_1 = const()[name = string("op_994_alpha_1"), val = fp32(0x1.689df8p-3)];
            tensor<fp16, [1, 256, 32]> var_994_cast_fp16 = leaky_relu(alpha = var_994_alpha_1, x = input_155_cast_fp16)[name = string("op_994_cast_fp16")];
            tensor<int32, [1]> var_998 = const()[name = string("op_998"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_63_cast_fp16 = reduce_mean(axes = var_998, keep_dims = var_70, x = var_994_cast_fp16)[name = string("mean_y_63_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1000_cast_fp16 = sub(x = var_994_cast_fp16, y = mean_y_63_cast_fp16)[name = string("op_1000_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1001_cast_fp16 = square(x = var_1000_cast_fp16);
            tensor<int32, [1]> var_1002 = const()[name = string("op_1002"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1003_cast_fp16 = reduce_mean(axes = var_1002, keep_dims = var_70, x = var_1001_cast_fp16)[name = string("op_1003_cast_fp16")];
            fp16 var_1004_to_fp16 = const()[name = string("op_1004_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1005_cast_fp16 = add(x = var_1003_cast_fp16, y = var_1004_to_fp16)[name = string("op_1005_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_63_cast_fp16 = sqrt(x = var_1005_cast_fp16)[name = string("std_y_63_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_15_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_15_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1045632)))];
            tensor<fp16, [1, 256, 32]> var_1008_cast_fp16 = mul(x = sep_module_15_tcn_2_norm_gamma_to_fp16, y = var_1000_cast_fp16)[name = string("op_1008_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1009_cast_fp16 = real_div(x = var_1008_cast_fp16, y = std_y_63_cast_fp16)[name = string("op_1009_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_15_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_15_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1046208)))];
            tensor<fp16, [1, 256, 32]> input_157_cast_fp16 = add(x = var_1009_cast_fp16, y = sep_module_15_tcn_2_norm_beta_to_fp16)[name = string("input_157_cast_fp16")];
            tensor<int32, [6]> input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 128, 0])];
            string input_159_mode_0 = const()[name = string("input_159_mode_0"), val = string("constant")];
            fp16 input_159_constant_val_0_to_fp16 = const()[name = string("input_159_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 160]> input_157_cast_fp16_state_input = read_state(input = input_157_cast_fp16_state);
            tensor<fp16, [1, 256, 160]> input_159_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 128]), end = tensor<int32, [3]>([1, 256, 160]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_157_cast_fp16, x = input_157_cast_fp16_state_input);
            write_state(data = input_159_cast_fp16, input = input_157_cast_fp16_state);
            tensor<int32, [1]> var_1014 = const()[name = string("op_1014"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1016 = const()[name = string("op_1016"), val = tensor<int32, [1]>([64])];
            string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_15_tcn_4_weight_to_fp16 = const()[name = string("sep_module_15_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1046784)))];
            tensor<fp16, [1, 256, 32]> input_161_cast_fp16 = conv(dilations = var_1016, groups = var_67, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = var_1014, weight = sep_module_15_tcn_4_weight_to_fp16, x = input_159_cast_fp16)[name = string("input_161_cast_fp16")];
            fp32 var_1020_alpha_1 = const()[name = string("op_1020_alpha_1"), val = fp32(0x1.91271p-1)];
            tensor<fp16, [1, 256, 32]> var_1020_cast_fp16 = leaky_relu(alpha = var_1020_alpha_1, x = input_161_cast_fp16)[name = string("op_1020_cast_fp16")];
            tensor<int32, [1]> var_1024 = const()[name = string("op_1024"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_65_cast_fp16 = reduce_mean(axes = var_1024, keep_dims = var_70, x = var_1020_cast_fp16)[name = string("mean_y_65_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1026_cast_fp16 = sub(x = var_1020_cast_fp16, y = mean_y_65_cast_fp16)[name = string("op_1026_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1027_cast_fp16 = square(x = var_1026_cast_fp16);
            tensor<int32, [1]> var_1028 = const()[name = string("op_1028"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1029_cast_fp16 = reduce_mean(axes = var_1028, keep_dims = var_70, x = var_1027_cast_fp16)[name = string("op_1029_cast_fp16")];
            fp16 var_1030_to_fp16 = const()[name = string("op_1030_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1031_cast_fp16 = add(x = var_1029_cast_fp16, y = var_1030_to_fp16)[name = string("op_1031_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_65_cast_fp16 = sqrt(x = var_1031_cast_fp16)[name = string("std_y_65_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_15_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_15_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1048384)))];
            tensor<fp16, [1, 256, 32]> var_1034_cast_fp16 = mul(x = sep_module_15_tcn_6_norm_gamma_to_fp16, y = var_1026_cast_fp16)[name = string("op_1034_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1035_cast_fp16 = real_div(x = var_1034_cast_fp16, y = std_y_65_cast_fp16)[name = string("op_1035_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_15_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_15_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1048960)))];
            tensor<fp16, [1, 256, 32]> y_32_cast_fp16 = add(x = var_1035_cast_fp16, y = sep_module_15_tcn_6_norm_beta_to_fp16)[name = string("y_32_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_163_cast_fp16 = add(x = input_153_cast_fp16, y = y_32_cast_fp16)[name = string("input_163_cast_fp16")];
            tensor<int32, [1]> var_1046 = const()[name = string("op_1046"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1048 = const()[name = string("op_1048"), val = tensor<int32, [1]>([1])];
            string input_165_pad_type_0 = const()[name = string("input_165_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_165_pad_0 = const()[name = string("input_165_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_16_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1049536))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1082368))))[name = string("sep_module_16_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_165_cast_fp16 = conv(dilations = var_1048, groups = var_66, pad = input_165_pad_0, pad_type = input_165_pad_type_0, strides = var_1046, weight = sep_module_16_tcn_0_weight_to_fp16_palettized, x = input_163_cast_fp16)[name = string("input_165_cast_fp16")];
            fp32 var_1052_alpha_1 = const()[name = string("op_1052_alpha_1"), val = fp32(0x1.d95fc2p-2)];
            tensor<fp16, [1, 256, 32]> var_1052_cast_fp16 = leaky_relu(alpha = var_1052_alpha_1, x = input_165_cast_fp16)[name = string("op_1052_cast_fp16")];
            tensor<int32, [1]> var_1056 = const()[name = string("op_1056"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_67_cast_fp16 = reduce_mean(axes = var_1056, keep_dims = var_70, x = var_1052_cast_fp16)[name = string("mean_y_67_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1058_cast_fp16 = sub(x = var_1052_cast_fp16, y = mean_y_67_cast_fp16)[name = string("op_1058_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1059_cast_fp16 = square(x = var_1058_cast_fp16);
            tensor<int32, [1]> var_1060 = const()[name = string("op_1060"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1061_cast_fp16 = reduce_mean(axes = var_1060, keep_dims = var_70, x = var_1059_cast_fp16)[name = string("op_1061_cast_fp16")];
            fp16 var_1062_to_fp16 = const()[name = string("op_1062_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1063_cast_fp16 = add(x = var_1061_cast_fp16, y = var_1062_to_fp16)[name = string("op_1063_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_67_cast_fp16 = sqrt(x = var_1063_cast_fp16)[name = string("std_y_67_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_16_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_16_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1082496)))];
            tensor<fp16, [1, 256, 32]> var_1066_cast_fp16 = mul(x = sep_module_16_tcn_2_norm_gamma_to_fp16, y = var_1058_cast_fp16)[name = string("op_1066_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1067_cast_fp16 = real_div(x = var_1066_cast_fp16, y = std_y_67_cast_fp16)[name = string("op_1067_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_16_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_16_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1083072)))];
            tensor<fp16, [1, 256, 32]> input_167_cast_fp16 = add(x = var_1067_cast_fp16, y = sep_module_16_tcn_2_norm_beta_to_fp16)[name = string("input_167_cast_fp16")];
            tensor<int32, [6]> input_169_pad_0 = const()[name = string("input_169_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 0])];
            string input_169_mode_0 = const()[name = string("input_169_mode_0"), val = string("constant")];
            fp16 input_169_constant_val_0_to_fp16 = const()[name = string("input_169_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 288]> input_167_cast_fp16_state_input = read_state(input = input_167_cast_fp16_state);
            tensor<fp16, [1, 256, 288]> input_169_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 256]), end = tensor<int32, [3]>([1, 256, 288]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_167_cast_fp16, x = input_167_cast_fp16_state_input);
            write_state(data = input_169_cast_fp16, input = input_167_cast_fp16_state);
            tensor<int32, [1]> var_1072 = const()[name = string("op_1072"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1074 = const()[name = string("op_1074"), val = tensor<int32, [1]>([128])];
            string input_171_pad_type_0 = const()[name = string("input_171_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_171_pad_0 = const()[name = string("input_171_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_16_tcn_4_weight_to_fp16 = const()[name = string("sep_module_16_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1083648)))];
            tensor<fp16, [1, 256, 32]> input_171_cast_fp16 = conv(dilations = var_1074, groups = var_67, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_1072, weight = sep_module_16_tcn_4_weight_to_fp16, x = input_169_cast_fp16)[name = string("input_171_cast_fp16")];
            fp32 var_1078_alpha_1 = const()[name = string("op_1078_alpha_1"), val = fp32(0x1.fb7fa2p-3)];
            tensor<fp16, [1, 256, 32]> var_1078_cast_fp16 = leaky_relu(alpha = var_1078_alpha_1, x = input_171_cast_fp16)[name = string("op_1078_cast_fp16")];
            tensor<int32, [1]> var_1082 = const()[name = string("op_1082"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_69_cast_fp16 = reduce_mean(axes = var_1082, keep_dims = var_70, x = var_1078_cast_fp16)[name = string("mean_y_69_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1084_cast_fp16 = sub(x = var_1078_cast_fp16, y = mean_y_69_cast_fp16)[name = string("op_1084_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1085_cast_fp16 = square(x = var_1084_cast_fp16);
            tensor<int32, [1]> var_1086 = const()[name = string("op_1086"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1087_cast_fp16 = reduce_mean(axes = var_1086, keep_dims = var_70, x = var_1085_cast_fp16)[name = string("op_1087_cast_fp16")];
            fp16 var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1089_cast_fp16 = add(x = var_1087_cast_fp16, y = var_1088_to_fp16)[name = string("op_1089_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_69_cast_fp16 = sqrt(x = var_1089_cast_fp16)[name = string("std_y_69_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_16_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_16_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1085248)))];
            tensor<fp16, [1, 256, 32]> var_1092_cast_fp16 = mul(x = sep_module_16_tcn_6_norm_gamma_to_fp16, y = var_1084_cast_fp16)[name = string("op_1092_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1093_cast_fp16 = real_div(x = var_1092_cast_fp16, y = std_y_69_cast_fp16)[name = string("op_1093_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_16_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_16_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1085824)))];
            tensor<fp16, [1, 256, 32]> y_34_cast_fp16 = add(x = var_1093_cast_fp16, y = sep_module_16_tcn_6_norm_beta_to_fp16)[name = string("y_34_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_173_cast_fp16 = add(x = input_163_cast_fp16, y = y_34_cast_fp16)[name = string("input_173_cast_fp16")];
            tensor<int32, [1]> var_1104 = const()[name = string("op_1104"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1106 = const()[name = string("op_1106"), val = tensor<int32, [1]>([1])];
            string input_175_pad_type_0 = const()[name = string("input_175_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_175_pad_0 = const()[name = string("input_175_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_17_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1086400))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1119232))))[name = string("sep_module_17_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_175_cast_fp16 = conv(dilations = var_1106, groups = var_66, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = var_1104, weight = sep_module_17_tcn_0_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("input_175_cast_fp16")];
            fp32 var_1110_alpha_1 = const()[name = string("op_1110_alpha_1"), val = fp32(0x1.d92da6p-2)];
            tensor<fp16, [1, 256, 32]> var_1110_cast_fp16 = leaky_relu(alpha = var_1110_alpha_1, x = input_175_cast_fp16)[name = string("op_1110_cast_fp16")];
            tensor<int32, [1]> var_1114 = const()[name = string("op_1114"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_71_cast_fp16 = reduce_mean(axes = var_1114, keep_dims = var_70, x = var_1110_cast_fp16)[name = string("mean_y_71_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1116_cast_fp16 = sub(x = var_1110_cast_fp16, y = mean_y_71_cast_fp16)[name = string("op_1116_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1117_cast_fp16 = square(x = var_1116_cast_fp16);
            tensor<int32, [1]> var_1118 = const()[name = string("op_1118"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1119_cast_fp16 = reduce_mean(axes = var_1118, keep_dims = var_70, x = var_1117_cast_fp16)[name = string("op_1119_cast_fp16")];
            fp16 var_1120_to_fp16 = const()[name = string("op_1120_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1121_cast_fp16 = add(x = var_1119_cast_fp16, y = var_1120_to_fp16)[name = string("op_1121_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_71_cast_fp16 = sqrt(x = var_1121_cast_fp16)[name = string("std_y_71_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_17_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_17_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1119360)))];
            tensor<fp16, [1, 256, 32]> var_1124_cast_fp16 = mul(x = sep_module_17_tcn_2_norm_gamma_to_fp16, y = var_1116_cast_fp16)[name = string("op_1124_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1125_cast_fp16 = real_div(x = var_1124_cast_fp16, y = std_y_71_cast_fp16)[name = string("op_1125_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_17_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_17_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1119936)))];
            tensor<fp16, [1, 256, 32]> input_177_cast_fp16 = add(x = var_1125_cast_fp16, y = sep_module_17_tcn_2_norm_beta_to_fp16)[name = string("input_177_cast_fp16")];
            tensor<int32, [6]> input_179_pad_0 = const()[name = string("input_179_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 512, 0])];
            string input_179_mode_0 = const()[name = string("input_179_mode_0"), val = string("constant")];
            fp16 input_179_constant_val_0_to_fp16 = const()[name = string("input_179_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 544]> input_177_cast_fp16_state_input = read_state(input = input_177_cast_fp16_state);
            tensor<fp16, [1, 256, 544]> input_179_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 512]), end = tensor<int32, [3]>([1, 256, 544]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_177_cast_fp16, x = input_177_cast_fp16_state_input);
            write_state(data = input_179_cast_fp16, input = input_177_cast_fp16_state);
            tensor<int32, [1]> var_1130 = const()[name = string("op_1130"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1132 = const()[name = string("op_1132"), val = tensor<int32, [1]>([256])];
            string input_181_pad_type_0 = const()[name = string("input_181_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_17_tcn_4_weight_to_fp16 = const()[name = string("sep_module_17_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1120512)))];
            tensor<fp16, [1, 256, 32]> input_181_cast_fp16 = conv(dilations = var_1132, groups = var_67, pad = input_181_pad_0, pad_type = input_181_pad_type_0, strides = var_1130, weight = sep_module_17_tcn_4_weight_to_fp16, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")];
            fp32 var_1136_alpha_1 = const()[name = string("op_1136_alpha_1"), val = fp32(-0x1.28c2b8p-4)];
            tensor<fp16, [1, 256, 32]> var_1136_cast_fp16 = leaky_relu(alpha = var_1136_alpha_1, x = input_181_cast_fp16)[name = string("op_1136_cast_fp16")];
            tensor<int32, [1]> var_1140 = const()[name = string("op_1140"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_73_cast_fp16 = reduce_mean(axes = var_1140, keep_dims = var_70, x = var_1136_cast_fp16)[name = string("mean_y_73_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1142_cast_fp16 = sub(x = var_1136_cast_fp16, y = mean_y_73_cast_fp16)[name = string("op_1142_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1143_cast_fp16 = square(x = var_1142_cast_fp16);
            tensor<int32, [1]> var_1144 = const()[name = string("op_1144"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1145_cast_fp16 = reduce_mean(axes = var_1144, keep_dims = var_70, x = var_1143_cast_fp16)[name = string("op_1145_cast_fp16")];
            fp16 var_1146_to_fp16 = const()[name = string("op_1146_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1147_cast_fp16 = add(x = var_1145_cast_fp16, y = var_1146_to_fp16)[name = string("op_1147_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_73_cast_fp16 = sqrt(x = var_1147_cast_fp16)[name = string("std_y_73_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_17_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_17_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1122112)))];
            tensor<fp16, [1, 256, 32]> var_1150_cast_fp16 = mul(x = sep_module_17_tcn_6_norm_gamma_to_fp16, y = var_1142_cast_fp16)[name = string("op_1150_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1151_cast_fp16 = real_div(x = var_1150_cast_fp16, y = std_y_73_cast_fp16)[name = string("op_1151_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_17_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_17_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1122688)))];
            tensor<fp16, [1, 256, 32]> y_36_cast_fp16 = add(x = var_1151_cast_fp16, y = sep_module_17_tcn_6_norm_beta_to_fp16)[name = string("y_36_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_183_cast_fp16 = add(x = input_173_cast_fp16, y = y_36_cast_fp16)[name = string("input_183_cast_fp16")];
            tensor<int32, [1]> var_1162 = const()[name = string("op_1162"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1164 = const()[name = string("op_1164"), val = tensor<int32, [1]>([1])];
            string input_185_pad_type_0 = const()[name = string("input_185_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_185_pad_0 = const()[name = string("input_185_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_18_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1123264))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1156096))))[name = string("sep_module_18_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_185_cast_fp16 = conv(dilations = var_1164, groups = var_66, pad = input_185_pad_0, pad_type = input_185_pad_type_0, strides = var_1162, weight = sep_module_18_tcn_0_weight_to_fp16_palettized, x = input_183_cast_fp16)[name = string("input_185_cast_fp16")];
            fp32 var_1168_alpha_1 = const()[name = string("op_1168_alpha_1"), val = fp32(-0x1.0354dap-3)];
            tensor<fp16, [1, 256, 32]> var_1168_cast_fp16 = leaky_relu(alpha = var_1168_alpha_1, x = input_185_cast_fp16)[name = string("op_1168_cast_fp16")];
            tensor<int32, [1]> var_1172 = const()[name = string("op_1172"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_75_cast_fp16 = reduce_mean(axes = var_1172, keep_dims = var_70, x = var_1168_cast_fp16)[name = string("mean_y_75_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1174_cast_fp16 = sub(x = var_1168_cast_fp16, y = mean_y_75_cast_fp16)[name = string("op_1174_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1175_cast_fp16 = square(x = var_1174_cast_fp16);
            tensor<int32, [1]> var_1176 = const()[name = string("op_1176"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1177_cast_fp16 = reduce_mean(axes = var_1176, keep_dims = var_70, x = var_1175_cast_fp16)[name = string("op_1177_cast_fp16")];
            fp16 var_1178_to_fp16 = const()[name = string("op_1178_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1179_cast_fp16 = add(x = var_1177_cast_fp16, y = var_1178_to_fp16)[name = string("op_1179_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_75_cast_fp16 = sqrt(x = var_1179_cast_fp16)[name = string("std_y_75_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_18_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_18_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1156224)))];
            tensor<fp16, [1, 256, 32]> var_1182_cast_fp16 = mul(x = sep_module_18_tcn_2_norm_gamma_to_fp16, y = var_1174_cast_fp16)[name = string("op_1182_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1183_cast_fp16 = real_div(x = var_1182_cast_fp16, y = std_y_75_cast_fp16)[name = string("op_1183_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_18_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_18_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1156800)))];
            tensor<fp16, [1, 256, 32]> input_187_cast_fp16 = add(x = var_1183_cast_fp16, y = sep_module_18_tcn_2_norm_beta_to_fp16)[name = string("input_187_cast_fp16")];
            tensor<int32, [6]> input_189_pad_0 = const()[name = string("input_189_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
            string input_189_mode_0 = const()[name = string("input_189_mode_0"), val = string("constant")];
            fp16 input_189_constant_val_0_to_fp16 = const()[name = string("input_189_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 34]> input_187_cast_fp16_state_input = read_state(input = input_187_cast_fp16_state);
            tensor<fp16, [1, 256, 34]> input_189_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 2]), end = tensor<int32, [3]>([1, 256, 34]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_187_cast_fp16, x = input_187_cast_fp16_state_input);
            write_state(data = input_189_cast_fp16, input = input_187_cast_fp16_state);
            tensor<int32, [1]> var_1188 = const()[name = string("op_1188"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1190 = const()[name = string("op_1190"), val = tensor<int32, [1]>([1])];
            string input_191_pad_type_0 = const()[name = string("input_191_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_191_pad_0 = const()[name = string("input_191_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_18_tcn_4_weight_to_fp16 = const()[name = string("sep_module_18_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1157376)))];
            tensor<fp16, [1, 256, 32]> input_191_cast_fp16 = conv(dilations = var_1190, groups = var_67, pad = input_191_pad_0, pad_type = input_191_pad_type_0, strides = var_1188, weight = sep_module_18_tcn_4_weight_to_fp16, x = input_189_cast_fp16)[name = string("input_191_cast_fp16")];
            fp32 var_1194_alpha_1 = const()[name = string("op_1194_alpha_1"), val = fp32(0x1.bc8beap-1)];
            tensor<fp16, [1, 256, 32]> var_1194_cast_fp16 = leaky_relu(alpha = var_1194_alpha_1, x = input_191_cast_fp16)[name = string("op_1194_cast_fp16")];
            tensor<int32, [1]> var_1198 = const()[name = string("op_1198"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_77_cast_fp16 = reduce_mean(axes = var_1198, keep_dims = var_70, x = var_1194_cast_fp16)[name = string("mean_y_77_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1200_cast_fp16 = sub(x = var_1194_cast_fp16, y = mean_y_77_cast_fp16)[name = string("op_1200_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1201_cast_fp16 = square(x = var_1200_cast_fp16);
            tensor<int32, [1]> var_1202 = const()[name = string("op_1202"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1203_cast_fp16 = reduce_mean(axes = var_1202, keep_dims = var_70, x = var_1201_cast_fp16)[name = string("op_1203_cast_fp16")];
            fp16 var_1204_to_fp16 = const()[name = string("op_1204_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1205_cast_fp16 = add(x = var_1203_cast_fp16, y = var_1204_to_fp16)[name = string("op_1205_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_77_cast_fp16 = sqrt(x = var_1205_cast_fp16)[name = string("std_y_77_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_18_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_18_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1158976)))];
            tensor<fp16, [1, 256, 32]> var_1208_cast_fp16 = mul(x = sep_module_18_tcn_6_norm_gamma_to_fp16, y = var_1200_cast_fp16)[name = string("op_1208_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1209_cast_fp16 = real_div(x = var_1208_cast_fp16, y = std_y_77_cast_fp16)[name = string("op_1209_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_18_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_18_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1159552)))];
            tensor<fp16, [1, 256, 32]> y_38_cast_fp16 = add(x = var_1209_cast_fp16, y = sep_module_18_tcn_6_norm_beta_to_fp16)[name = string("y_38_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_193_cast_fp16 = add(x = input_183_cast_fp16, y = y_38_cast_fp16)[name = string("input_193_cast_fp16")];
            tensor<int32, [1]> var_1220 = const()[name = string("op_1220"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1222 = const()[name = string("op_1222"), val = tensor<int32, [1]>([1])];
            string input_195_pad_type_0 = const()[name = string("input_195_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_195_pad_0 = const()[name = string("input_195_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_19_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1160128))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1192960))))[name = string("sep_module_19_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_195_cast_fp16 = conv(dilations = var_1222, groups = var_66, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = var_1220, weight = sep_module_19_tcn_0_weight_to_fp16_palettized, x = input_193_cast_fp16)[name = string("input_195_cast_fp16")];
            fp32 var_1226_alpha_1 = const()[name = string("op_1226_alpha_1"), val = fp32(-0x1.7b4914p-3)];
            tensor<fp16, [1, 256, 32]> var_1226_cast_fp16 = leaky_relu(alpha = var_1226_alpha_1, x = input_195_cast_fp16)[name = string("op_1226_cast_fp16")];
            tensor<int32, [1]> var_1230 = const()[name = string("op_1230"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_79_cast_fp16 = reduce_mean(axes = var_1230, keep_dims = var_70, x = var_1226_cast_fp16)[name = string("mean_y_79_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1232_cast_fp16 = sub(x = var_1226_cast_fp16, y = mean_y_79_cast_fp16)[name = string("op_1232_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1233_cast_fp16 = square(x = var_1232_cast_fp16);
            tensor<int32, [1]> var_1234 = const()[name = string("op_1234"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1235_cast_fp16 = reduce_mean(axes = var_1234, keep_dims = var_70, x = var_1233_cast_fp16)[name = string("op_1235_cast_fp16")];
            fp16 var_1236_to_fp16 = const()[name = string("op_1236_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1237_cast_fp16 = add(x = var_1235_cast_fp16, y = var_1236_to_fp16)[name = string("op_1237_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_79_cast_fp16 = sqrt(x = var_1237_cast_fp16)[name = string("std_y_79_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_19_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_19_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1193088)))];
            tensor<fp16, [1, 256, 32]> var_1240_cast_fp16 = mul(x = sep_module_19_tcn_2_norm_gamma_to_fp16, y = var_1232_cast_fp16)[name = string("op_1240_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1241_cast_fp16 = real_div(x = var_1240_cast_fp16, y = std_y_79_cast_fp16)[name = string("op_1241_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_19_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_19_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1193664)))];
            tensor<fp16, [1, 256, 32]> input_197_cast_fp16 = add(x = var_1241_cast_fp16, y = sep_module_19_tcn_2_norm_beta_to_fp16)[name = string("input_197_cast_fp16")];
            tensor<int32, [6]> input_199_pad_0 = const()[name = string("input_199_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
            string input_199_mode_0 = const()[name = string("input_199_mode_0"), val = string("constant")];
            fp16 input_199_constant_val_0_to_fp16 = const()[name = string("input_199_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 36]> input_197_cast_fp16_state_input = read_state(input = input_197_cast_fp16_state);
            tensor<fp16, [1, 256, 36]> input_199_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 4]), end = tensor<int32, [3]>([1, 256, 36]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_197_cast_fp16, x = input_197_cast_fp16_state_input);
            write_state(data = input_199_cast_fp16, input = input_197_cast_fp16_state);
            tensor<int32, [1]> var_1246 = const()[name = string("op_1246"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1248 = const()[name = string("op_1248"), val = tensor<int32, [1]>([2])];
            string input_201_pad_type_0 = const()[name = string("input_201_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_19_tcn_4_weight_to_fp16 = const()[name = string("sep_module_19_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1194240)))];
            tensor<fp16, [1, 256, 32]> input_201_cast_fp16 = conv(dilations = var_1248, groups = var_67, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = var_1246, weight = sep_module_19_tcn_4_weight_to_fp16, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")];
            fp32 var_1252_alpha_1 = const()[name = string("op_1252_alpha_1"), val = fp32(0x1.9c6d2ap-1)];
            tensor<fp16, [1, 256, 32]> var_1252_cast_fp16 = leaky_relu(alpha = var_1252_alpha_1, x = input_201_cast_fp16)[name = string("op_1252_cast_fp16")];
            tensor<int32, [1]> var_1256 = const()[name = string("op_1256"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_81_cast_fp16 = reduce_mean(axes = var_1256, keep_dims = var_70, x = var_1252_cast_fp16)[name = string("mean_y_81_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1258_cast_fp16 = sub(x = var_1252_cast_fp16, y = mean_y_81_cast_fp16)[name = string("op_1258_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1259_cast_fp16 = square(x = var_1258_cast_fp16);
            tensor<int32, [1]> var_1260 = const()[name = string("op_1260"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1261_cast_fp16 = reduce_mean(axes = var_1260, keep_dims = var_70, x = var_1259_cast_fp16)[name = string("op_1261_cast_fp16")];
            fp16 var_1262_to_fp16 = const()[name = string("op_1262_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1263_cast_fp16 = add(x = var_1261_cast_fp16, y = var_1262_to_fp16)[name = string("op_1263_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_81_cast_fp16 = sqrt(x = var_1263_cast_fp16)[name = string("std_y_81_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_19_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_19_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1195840)))];
            tensor<fp16, [1, 256, 32]> var_1266_cast_fp16 = mul(x = sep_module_19_tcn_6_norm_gamma_to_fp16, y = var_1258_cast_fp16)[name = string("op_1266_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1267_cast_fp16 = real_div(x = var_1266_cast_fp16, y = std_y_81_cast_fp16)[name = string("op_1267_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_19_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_19_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1196416)))];
            tensor<fp16, [1, 256, 32]> y_40_cast_fp16 = add(x = var_1267_cast_fp16, y = sep_module_19_tcn_6_norm_beta_to_fp16)[name = string("y_40_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_203_cast_fp16 = add(x = input_193_cast_fp16, y = y_40_cast_fp16)[name = string("input_203_cast_fp16")];
            tensor<int32, [1]> var_1278 = const()[name = string("op_1278"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1280 = const()[name = string("op_1280"), val = tensor<int32, [1]>([1])];
            string input_205_pad_type_0 = const()[name = string("input_205_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_205_pad_0 = const()[name = string("input_205_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_20_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1196992))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1229824))))[name = string("sep_module_20_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_205_cast_fp16 = conv(dilations = var_1280, groups = var_66, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = var_1278, weight = sep_module_20_tcn_0_weight_to_fp16_palettized, x = input_203_cast_fp16)[name = string("input_205_cast_fp16")];
            fp32 var_1284_alpha_1 = const()[name = string("op_1284_alpha_1"), val = fp32(-0x1.0858acp-3)];
            tensor<fp16, [1, 256, 32]> var_1284_cast_fp16 = leaky_relu(alpha = var_1284_alpha_1, x = input_205_cast_fp16)[name = string("op_1284_cast_fp16")];
            tensor<int32, [1]> var_1288 = const()[name = string("op_1288"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_83_cast_fp16 = reduce_mean(axes = var_1288, keep_dims = var_70, x = var_1284_cast_fp16)[name = string("mean_y_83_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1290_cast_fp16 = sub(x = var_1284_cast_fp16, y = mean_y_83_cast_fp16)[name = string("op_1290_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1291_cast_fp16 = square(x = var_1290_cast_fp16);
            tensor<int32, [1]> var_1292 = const()[name = string("op_1292"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1293_cast_fp16 = reduce_mean(axes = var_1292, keep_dims = var_70, x = var_1291_cast_fp16)[name = string("op_1293_cast_fp16")];
            fp16 var_1294_to_fp16 = const()[name = string("op_1294_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1295_cast_fp16 = add(x = var_1293_cast_fp16, y = var_1294_to_fp16)[name = string("op_1295_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_83_cast_fp16 = sqrt(x = var_1295_cast_fp16)[name = string("std_y_83_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_20_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_20_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1229952)))];
            tensor<fp16, [1, 256, 32]> var_1298_cast_fp16 = mul(x = sep_module_20_tcn_2_norm_gamma_to_fp16, y = var_1290_cast_fp16)[name = string("op_1298_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1299_cast_fp16 = real_div(x = var_1298_cast_fp16, y = std_y_83_cast_fp16)[name = string("op_1299_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_20_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_20_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1230528)))];
            tensor<fp16, [1, 256, 32]> input_207_cast_fp16 = add(x = var_1299_cast_fp16, y = sep_module_20_tcn_2_norm_beta_to_fp16)[name = string("input_207_cast_fp16")];
            tensor<int32, [6]> input_209_pad_0 = const()[name = string("input_209_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            string input_209_mode_0 = const()[name = string("input_209_mode_0"), val = string("constant")];
            fp16 input_209_constant_val_0_to_fp16 = const()[name = string("input_209_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 40]> input_207_cast_fp16_state_input = read_state(input = input_207_cast_fp16_state);
            tensor<fp16, [1, 256, 40]> input_209_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 8]), end = tensor<int32, [3]>([1, 256, 40]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_207_cast_fp16, x = input_207_cast_fp16_state_input);
            write_state(data = input_209_cast_fp16, input = input_207_cast_fp16_state);
            tensor<int32, [1]> var_1304 = const()[name = string("op_1304"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1306 = const()[name = string("op_1306"), val = tensor<int32, [1]>([4])];
            string input_211_pad_type_0 = const()[name = string("input_211_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_211_pad_0 = const()[name = string("input_211_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_20_tcn_4_weight_to_fp16 = const()[name = string("sep_module_20_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1231104)))];
            tensor<fp16, [1, 256, 32]> input_211_cast_fp16 = conv(dilations = var_1306, groups = var_67, pad = input_211_pad_0, pad_type = input_211_pad_type_0, strides = var_1304, weight = sep_module_20_tcn_4_weight_to_fp16, x = input_209_cast_fp16)[name = string("input_211_cast_fp16")];
            fp32 var_1310_alpha_1 = const()[name = string("op_1310_alpha_1"), val = fp32(0x1.652e0ap-1)];
            tensor<fp16, [1, 256, 32]> var_1310_cast_fp16 = leaky_relu(alpha = var_1310_alpha_1, x = input_211_cast_fp16)[name = string("op_1310_cast_fp16")];
            tensor<int32, [1]> var_1314 = const()[name = string("op_1314"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_85_cast_fp16 = reduce_mean(axes = var_1314, keep_dims = var_70, x = var_1310_cast_fp16)[name = string("mean_y_85_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1316_cast_fp16 = sub(x = var_1310_cast_fp16, y = mean_y_85_cast_fp16)[name = string("op_1316_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1317_cast_fp16 = square(x = var_1316_cast_fp16);
            tensor<int32, [1]> var_1318 = const()[name = string("op_1318"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1319_cast_fp16 = reduce_mean(axes = var_1318, keep_dims = var_70, x = var_1317_cast_fp16)[name = string("op_1319_cast_fp16")];
            fp16 var_1320_to_fp16 = const()[name = string("op_1320_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1321_cast_fp16 = add(x = var_1319_cast_fp16, y = var_1320_to_fp16)[name = string("op_1321_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_85_cast_fp16 = sqrt(x = var_1321_cast_fp16)[name = string("std_y_85_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_20_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_20_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1232704)))];
            tensor<fp16, [1, 256, 32]> var_1324_cast_fp16 = mul(x = sep_module_20_tcn_6_norm_gamma_to_fp16, y = var_1316_cast_fp16)[name = string("op_1324_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1325_cast_fp16 = real_div(x = var_1324_cast_fp16, y = std_y_85_cast_fp16)[name = string("op_1325_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_20_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_20_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1233280)))];
            tensor<fp16, [1, 256, 32]> y_42_cast_fp16 = add(x = var_1325_cast_fp16, y = sep_module_20_tcn_6_norm_beta_to_fp16)[name = string("y_42_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_213_cast_fp16 = add(x = input_203_cast_fp16, y = y_42_cast_fp16)[name = string("input_213_cast_fp16")];
            tensor<int32, [1]> var_1336 = const()[name = string("op_1336"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1338 = const()[name = string("op_1338"), val = tensor<int32, [1]>([1])];
            string input_215_pad_type_0 = const()[name = string("input_215_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_215_pad_0 = const()[name = string("input_215_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_21_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1233856))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1266688))))[name = string("sep_module_21_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_215_cast_fp16 = conv(dilations = var_1338, groups = var_66, pad = input_215_pad_0, pad_type = input_215_pad_type_0, strides = var_1336, weight = sep_module_21_tcn_0_weight_to_fp16_palettized, x = input_213_cast_fp16)[name = string("input_215_cast_fp16")];
            fp32 var_1342_alpha_1 = const()[name = string("op_1342_alpha_1"), val = fp32(-0x1.8e777cp-3)];
            tensor<fp16, [1, 256, 32]> var_1342_cast_fp16 = leaky_relu(alpha = var_1342_alpha_1, x = input_215_cast_fp16)[name = string("op_1342_cast_fp16")];
            tensor<int32, [1]> var_1346 = const()[name = string("op_1346"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_87_cast_fp16 = reduce_mean(axes = var_1346, keep_dims = var_70, x = var_1342_cast_fp16)[name = string("mean_y_87_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1348_cast_fp16 = sub(x = var_1342_cast_fp16, y = mean_y_87_cast_fp16)[name = string("op_1348_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1349_cast_fp16 = square(x = var_1348_cast_fp16);
            tensor<int32, [1]> var_1350 = const()[name = string("op_1350"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1351_cast_fp16 = reduce_mean(axes = var_1350, keep_dims = var_70, x = var_1349_cast_fp16)[name = string("op_1351_cast_fp16")];
            fp16 var_1352_to_fp16 = const()[name = string("op_1352_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1353_cast_fp16 = add(x = var_1351_cast_fp16, y = var_1352_to_fp16)[name = string("op_1353_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_87_cast_fp16 = sqrt(x = var_1353_cast_fp16)[name = string("std_y_87_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_21_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_21_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1266816)))];
            tensor<fp16, [1, 256, 32]> var_1356_cast_fp16 = mul(x = sep_module_21_tcn_2_norm_gamma_to_fp16, y = var_1348_cast_fp16)[name = string("op_1356_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1357_cast_fp16 = real_div(x = var_1356_cast_fp16, y = std_y_87_cast_fp16)[name = string("op_1357_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_21_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_21_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1267392)))];
            tensor<fp16, [1, 256, 32]> input_217_cast_fp16 = add(x = var_1357_cast_fp16, y = sep_module_21_tcn_2_norm_beta_to_fp16)[name = string("input_217_cast_fp16")];
            tensor<int32, [6]> input_219_pad_0 = const()[name = string("input_219_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("constant")];
            fp16 input_219_constant_val_0_to_fp16 = const()[name = string("input_219_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 48]> input_217_cast_fp16_state_input = read_state(input = input_217_cast_fp16_state);
            tensor<fp16, [1, 256, 48]> input_219_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 16]), end = tensor<int32, [3]>([1, 256, 48]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_217_cast_fp16, x = input_217_cast_fp16_state_input);
            write_state(data = input_219_cast_fp16, input = input_217_cast_fp16_state);
            tensor<int32, [1]> var_1362 = const()[name = string("op_1362"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1364 = const()[name = string("op_1364"), val = tensor<int32, [1]>([8])];
            string input_221_pad_type_0 = const()[name = string("input_221_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_221_pad_0 = const()[name = string("input_221_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_21_tcn_4_weight_to_fp16 = const()[name = string("sep_module_21_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1267968)))];
            tensor<fp16, [1, 256, 32]> input_221_cast_fp16 = conv(dilations = var_1364, groups = var_67, pad = input_221_pad_0, pad_type = input_221_pad_type_0, strides = var_1362, weight = sep_module_21_tcn_4_weight_to_fp16, x = input_219_cast_fp16)[name = string("input_221_cast_fp16")];
            fp32 var_1368_alpha_1 = const()[name = string("op_1368_alpha_1"), val = fp32(0x1.62d116p-1)];
            tensor<fp16, [1, 256, 32]> var_1368_cast_fp16 = leaky_relu(alpha = var_1368_alpha_1, x = input_221_cast_fp16)[name = string("op_1368_cast_fp16")];
            tensor<int32, [1]> var_1372 = const()[name = string("op_1372"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_89_cast_fp16 = reduce_mean(axes = var_1372, keep_dims = var_70, x = var_1368_cast_fp16)[name = string("mean_y_89_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1374_cast_fp16 = sub(x = var_1368_cast_fp16, y = mean_y_89_cast_fp16)[name = string("op_1374_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1375_cast_fp16 = square(x = var_1374_cast_fp16);
            tensor<int32, [1]> var_1376 = const()[name = string("op_1376"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1377_cast_fp16 = reduce_mean(axes = var_1376, keep_dims = var_70, x = var_1375_cast_fp16)[name = string("op_1377_cast_fp16")];
            fp16 var_1378_to_fp16 = const()[name = string("op_1378_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1379_cast_fp16 = add(x = var_1377_cast_fp16, y = var_1378_to_fp16)[name = string("op_1379_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_89_cast_fp16 = sqrt(x = var_1379_cast_fp16)[name = string("std_y_89_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_21_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_21_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1269568)))];
            tensor<fp16, [1, 256, 32]> var_1382_cast_fp16 = mul(x = sep_module_21_tcn_6_norm_gamma_to_fp16, y = var_1374_cast_fp16)[name = string("op_1382_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1383_cast_fp16 = real_div(x = var_1382_cast_fp16, y = std_y_89_cast_fp16)[name = string("op_1383_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_21_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_21_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1270144)))];
            tensor<fp16, [1, 256, 32]> y_44_cast_fp16 = add(x = var_1383_cast_fp16, y = sep_module_21_tcn_6_norm_beta_to_fp16)[name = string("y_44_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_223_cast_fp16 = add(x = input_213_cast_fp16, y = y_44_cast_fp16)[name = string("input_223_cast_fp16")];
            tensor<int32, [1]> var_1394 = const()[name = string("op_1394"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1396 = const()[name = string("op_1396"), val = tensor<int32, [1]>([1])];
            string input_225_pad_type_0 = const()[name = string("input_225_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_225_pad_0 = const()[name = string("input_225_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_22_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1270720))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1303552))))[name = string("sep_module_22_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_225_cast_fp16 = conv(dilations = var_1396, groups = var_66, pad = input_225_pad_0, pad_type = input_225_pad_type_0, strides = var_1394, weight = sep_module_22_tcn_0_weight_to_fp16_palettized, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")];
            fp32 var_1400_alpha_1 = const()[name = string("op_1400_alpha_1"), val = fp32(-0x1.3b1edcp-2)];
            tensor<fp16, [1, 256, 32]> var_1400_cast_fp16 = leaky_relu(alpha = var_1400_alpha_1, x = input_225_cast_fp16)[name = string("op_1400_cast_fp16")];
            tensor<int32, [1]> var_1404 = const()[name = string("op_1404"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_91_cast_fp16 = reduce_mean(axes = var_1404, keep_dims = var_70, x = var_1400_cast_fp16)[name = string("mean_y_91_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1406_cast_fp16 = sub(x = var_1400_cast_fp16, y = mean_y_91_cast_fp16)[name = string("op_1406_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1407_cast_fp16 = square(x = var_1406_cast_fp16);
            tensor<int32, [1]> var_1408 = const()[name = string("op_1408"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1409_cast_fp16 = reduce_mean(axes = var_1408, keep_dims = var_70, x = var_1407_cast_fp16)[name = string("op_1409_cast_fp16")];
            fp16 var_1410_to_fp16 = const()[name = string("op_1410_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1411_cast_fp16 = add(x = var_1409_cast_fp16, y = var_1410_to_fp16)[name = string("op_1411_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_91_cast_fp16 = sqrt(x = var_1411_cast_fp16)[name = string("std_y_91_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_22_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_22_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1303680)))];
            tensor<fp16, [1, 256, 32]> var_1414_cast_fp16 = mul(x = sep_module_22_tcn_2_norm_gamma_to_fp16, y = var_1406_cast_fp16)[name = string("op_1414_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1415_cast_fp16 = real_div(x = var_1414_cast_fp16, y = std_y_91_cast_fp16)[name = string("op_1415_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_22_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_22_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1304256)))];
            tensor<fp16, [1, 256, 32]> input_227_cast_fp16 = add(x = var_1415_cast_fp16, y = sep_module_22_tcn_2_norm_beta_to_fp16)[name = string("input_227_cast_fp16")];
            tensor<int32, [6]> input_229_pad_0 = const()[name = string("input_229_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            string input_229_mode_0 = const()[name = string("input_229_mode_0"), val = string("constant")];
            fp16 input_229_constant_val_0_to_fp16 = const()[name = string("input_229_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 64]> input_227_cast_fp16_state_input = read_state(input = input_227_cast_fp16_state);
            tensor<fp16, [1, 256, 64]> input_229_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 32]), end = tensor<int32, [3]>([1, 256, 64]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_227_cast_fp16, x = input_227_cast_fp16_state_input);
            write_state(data = input_229_cast_fp16, input = input_227_cast_fp16_state);
            tensor<int32, [1]> var_1420 = const()[name = string("op_1420"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1422 = const()[name = string("op_1422"), val = tensor<int32, [1]>([16])];
            string input_231_pad_type_0 = const()[name = string("input_231_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_231_pad_0 = const()[name = string("input_231_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_22_tcn_4_weight_to_fp16 = const()[name = string("sep_module_22_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1304832)))];
            tensor<fp16, [1, 256, 32]> input_231_cast_fp16 = conv(dilations = var_1422, groups = var_67, pad = input_231_pad_0, pad_type = input_231_pad_type_0, strides = var_1420, weight = sep_module_22_tcn_4_weight_to_fp16, x = input_229_cast_fp16)[name = string("input_231_cast_fp16")];
            fp32 var_1426_alpha_1 = const()[name = string("op_1426_alpha_1"), val = fp32(0x1.3bf682p-1)];
            tensor<fp16, [1, 256, 32]> var_1426_cast_fp16 = leaky_relu(alpha = var_1426_alpha_1, x = input_231_cast_fp16)[name = string("op_1426_cast_fp16")];
            tensor<int32, [1]> var_1430 = const()[name = string("op_1430"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_93_cast_fp16 = reduce_mean(axes = var_1430, keep_dims = var_70, x = var_1426_cast_fp16)[name = string("mean_y_93_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1432_cast_fp16 = sub(x = var_1426_cast_fp16, y = mean_y_93_cast_fp16)[name = string("op_1432_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1433_cast_fp16 = square(x = var_1432_cast_fp16);
            tensor<int32, [1]> var_1434 = const()[name = string("op_1434"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1435_cast_fp16 = reduce_mean(axes = var_1434, keep_dims = var_70, x = var_1433_cast_fp16)[name = string("op_1435_cast_fp16")];
            fp16 var_1436_to_fp16 = const()[name = string("op_1436_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1437_cast_fp16 = add(x = var_1435_cast_fp16, y = var_1436_to_fp16)[name = string("op_1437_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_93_cast_fp16 = sqrt(x = var_1437_cast_fp16)[name = string("std_y_93_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_22_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_22_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1306432)))];
            tensor<fp16, [1, 256, 32]> var_1440_cast_fp16 = mul(x = sep_module_22_tcn_6_norm_gamma_to_fp16, y = var_1432_cast_fp16)[name = string("op_1440_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1441_cast_fp16 = real_div(x = var_1440_cast_fp16, y = std_y_93_cast_fp16)[name = string("op_1441_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_22_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_22_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1307008)))];
            tensor<fp16, [1, 256, 32]> y_46_cast_fp16 = add(x = var_1441_cast_fp16, y = sep_module_22_tcn_6_norm_beta_to_fp16)[name = string("y_46_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_233_cast_fp16 = add(x = input_223_cast_fp16, y = y_46_cast_fp16)[name = string("input_233_cast_fp16")];
            tensor<int32, [1]> var_1452 = const()[name = string("op_1452"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1454 = const()[name = string("op_1454"), val = tensor<int32, [1]>([1])];
            string input_235_pad_type_0 = const()[name = string("input_235_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_235_pad_0 = const()[name = string("input_235_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_23_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1307584))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1340416))))[name = string("sep_module_23_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_235_cast_fp16 = conv(dilations = var_1454, groups = var_66, pad = input_235_pad_0, pad_type = input_235_pad_type_0, strides = var_1452, weight = sep_module_23_tcn_0_weight_to_fp16_palettized, x = input_233_cast_fp16)[name = string("input_235_cast_fp16")];
            fp32 var_1458_alpha_1 = const()[name = string("op_1458_alpha_1"), val = fp32(-0x1.c497cep-4)];
            tensor<fp16, [1, 256, 32]> var_1458_cast_fp16 = leaky_relu(alpha = var_1458_alpha_1, x = input_235_cast_fp16)[name = string("op_1458_cast_fp16")];
            tensor<int32, [1]> var_1462 = const()[name = string("op_1462"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_95_cast_fp16 = reduce_mean(axes = var_1462, keep_dims = var_70, x = var_1458_cast_fp16)[name = string("mean_y_95_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1464_cast_fp16 = sub(x = var_1458_cast_fp16, y = mean_y_95_cast_fp16)[name = string("op_1464_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1465_cast_fp16 = square(x = var_1464_cast_fp16);
            tensor<int32, [1]> var_1466 = const()[name = string("op_1466"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1467_cast_fp16 = reduce_mean(axes = var_1466, keep_dims = var_70, x = var_1465_cast_fp16)[name = string("op_1467_cast_fp16")];
            fp16 var_1468_to_fp16 = const()[name = string("op_1468_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1469_cast_fp16 = add(x = var_1467_cast_fp16, y = var_1468_to_fp16)[name = string("op_1469_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_95_cast_fp16 = sqrt(x = var_1469_cast_fp16)[name = string("std_y_95_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_23_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_23_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1340544)))];
            tensor<fp16, [1, 256, 32]> var_1472_cast_fp16 = mul(x = sep_module_23_tcn_2_norm_gamma_to_fp16, y = var_1464_cast_fp16)[name = string("op_1472_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1473_cast_fp16 = real_div(x = var_1472_cast_fp16, y = std_y_95_cast_fp16)[name = string("op_1473_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_23_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_23_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1341120)))];
            tensor<fp16, [1, 256, 32]> input_237_cast_fp16 = add(x = var_1473_cast_fp16, y = sep_module_23_tcn_2_norm_beta_to_fp16)[name = string("input_237_cast_fp16")];
            tensor<int32, [6]> input_239_pad_0 = const()[name = string("input_239_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            string input_239_mode_0 = const()[name = string("input_239_mode_0"), val = string("constant")];
            fp16 input_239_constant_val_0_to_fp16 = const()[name = string("input_239_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 96]> input_237_cast_fp16_state_input = read_state(input = input_237_cast_fp16_state);
            tensor<fp16, [1, 256, 96]> input_239_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 64]), end = tensor<int32, [3]>([1, 256, 96]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_237_cast_fp16, x = input_237_cast_fp16_state_input);
            write_state(data = input_239_cast_fp16, input = input_237_cast_fp16_state);
            tensor<int32, [1]> var_1478 = const()[name = string("op_1478"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1480 = const()[name = string("op_1480"), val = tensor<int32, [1]>([32])];
            string input_241_pad_type_0 = const()[name = string("input_241_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_241_pad_0 = const()[name = string("input_241_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_23_tcn_4_weight_to_fp16 = const()[name = string("sep_module_23_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1341696)))];
            tensor<fp16, [1, 256, 32]> input_241_cast_fp16 = conv(dilations = var_1480, groups = var_67, pad = input_241_pad_0, pad_type = input_241_pad_type_0, strides = var_1478, weight = sep_module_23_tcn_4_weight_to_fp16, x = input_239_cast_fp16)[name = string("input_241_cast_fp16")];
            fp32 var_1484_alpha_1 = const()[name = string("op_1484_alpha_1"), val = fp32(0x1.54acbep-1)];
            tensor<fp16, [1, 256, 32]> var_1484_cast_fp16 = leaky_relu(alpha = var_1484_alpha_1, x = input_241_cast_fp16)[name = string("op_1484_cast_fp16")];
            tensor<int32, [1]> var_1488 = const()[name = string("op_1488"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_97_cast_fp16 = reduce_mean(axes = var_1488, keep_dims = var_70, x = var_1484_cast_fp16)[name = string("mean_y_97_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1490_cast_fp16 = sub(x = var_1484_cast_fp16, y = mean_y_97_cast_fp16)[name = string("op_1490_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1491_cast_fp16 = square(x = var_1490_cast_fp16);
            tensor<int32, [1]> var_1492 = const()[name = string("op_1492"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1493_cast_fp16 = reduce_mean(axes = var_1492, keep_dims = var_70, x = var_1491_cast_fp16)[name = string("op_1493_cast_fp16")];
            fp16 var_1494_to_fp16 = const()[name = string("op_1494_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1495_cast_fp16 = add(x = var_1493_cast_fp16, y = var_1494_to_fp16)[name = string("op_1495_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_97_cast_fp16 = sqrt(x = var_1495_cast_fp16)[name = string("std_y_97_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_23_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_23_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1343296)))];
            tensor<fp16, [1, 256, 32]> var_1498_cast_fp16 = mul(x = sep_module_23_tcn_6_norm_gamma_to_fp16, y = var_1490_cast_fp16)[name = string("op_1498_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1499_cast_fp16 = real_div(x = var_1498_cast_fp16, y = std_y_97_cast_fp16)[name = string("op_1499_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_23_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_23_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1343872)))];
            tensor<fp16, [1, 256, 32]> y_48_cast_fp16 = add(x = var_1499_cast_fp16, y = sep_module_23_tcn_6_norm_beta_to_fp16)[name = string("y_48_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_243_cast_fp16 = add(x = input_233_cast_fp16, y = y_48_cast_fp16)[name = string("input_243_cast_fp16")];
            tensor<int32, [1]> var_1510 = const()[name = string("op_1510"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1512 = const()[name = string("op_1512"), val = tensor<int32, [1]>([1])];
            string input_245_pad_type_0 = const()[name = string("input_245_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_245_pad_0 = const()[name = string("input_245_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_24_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1344448))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1377280))))[name = string("sep_module_24_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_245_cast_fp16 = conv(dilations = var_1512, groups = var_66, pad = input_245_pad_0, pad_type = input_245_pad_type_0, strides = var_1510, weight = sep_module_24_tcn_0_weight_to_fp16_palettized, x = input_243_cast_fp16)[name = string("input_245_cast_fp16")];
            fp32 var_1516_alpha_1 = const()[name = string("op_1516_alpha_1"), val = fp32(-0x1.a4cff6p-3)];
            tensor<fp16, [1, 256, 32]> var_1516_cast_fp16 = leaky_relu(alpha = var_1516_alpha_1, x = input_245_cast_fp16)[name = string("op_1516_cast_fp16")];
            tensor<int32, [1]> var_1520 = const()[name = string("op_1520"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_99_cast_fp16 = reduce_mean(axes = var_1520, keep_dims = var_70, x = var_1516_cast_fp16)[name = string("mean_y_99_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1522_cast_fp16 = sub(x = var_1516_cast_fp16, y = mean_y_99_cast_fp16)[name = string("op_1522_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1523_cast_fp16 = square(x = var_1522_cast_fp16);
            tensor<int32, [1]> var_1524 = const()[name = string("op_1524"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1525_cast_fp16 = reduce_mean(axes = var_1524, keep_dims = var_70, x = var_1523_cast_fp16)[name = string("op_1525_cast_fp16")];
            fp16 var_1526_to_fp16 = const()[name = string("op_1526_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1527_cast_fp16 = add(x = var_1525_cast_fp16, y = var_1526_to_fp16)[name = string("op_1527_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_99_cast_fp16 = sqrt(x = var_1527_cast_fp16)[name = string("std_y_99_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_24_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_24_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1377408)))];
            tensor<fp16, [1, 256, 32]> var_1530_cast_fp16 = mul(x = sep_module_24_tcn_2_norm_gamma_to_fp16, y = var_1522_cast_fp16)[name = string("op_1530_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1531_cast_fp16 = real_div(x = var_1530_cast_fp16, y = std_y_99_cast_fp16)[name = string("op_1531_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_24_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_24_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1377984)))];
            tensor<fp16, [1, 256, 32]> input_247_cast_fp16 = add(x = var_1531_cast_fp16, y = sep_module_24_tcn_2_norm_beta_to_fp16)[name = string("input_247_cast_fp16")];
            tensor<int32, [6]> input_249_pad_0 = const()[name = string("input_249_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 128, 0])];
            string input_249_mode_0 = const()[name = string("input_249_mode_0"), val = string("constant")];
            fp16 input_249_constant_val_0_to_fp16 = const()[name = string("input_249_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 160]> input_247_cast_fp16_state_input = read_state(input = input_247_cast_fp16_state);
            tensor<fp16, [1, 256, 160]> input_249_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 128]), end = tensor<int32, [3]>([1, 256, 160]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_247_cast_fp16, x = input_247_cast_fp16_state_input);
            write_state(data = input_249_cast_fp16, input = input_247_cast_fp16_state);
            tensor<int32, [1]> var_1536 = const()[name = string("op_1536"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1538 = const()[name = string("op_1538"), val = tensor<int32, [1]>([64])];
            string input_251_pad_type_0 = const()[name = string("input_251_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_24_tcn_4_weight_to_fp16 = const()[name = string("sep_module_24_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1378560)))];
            tensor<fp16, [1, 256, 32]> input_251_cast_fp16 = conv(dilations = var_1538, groups = var_67, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = var_1536, weight = sep_module_24_tcn_4_weight_to_fp16, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")];
            fp32 var_1542_alpha_1 = const()[name = string("op_1542_alpha_1"), val = fp32(0x1.48d4cp-1)];
            tensor<fp16, [1, 256, 32]> var_1542_cast_fp16 = leaky_relu(alpha = var_1542_alpha_1, x = input_251_cast_fp16)[name = string("op_1542_cast_fp16")];
            tensor<int32, [1]> var_1546 = const()[name = string("op_1546"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_101_cast_fp16 = reduce_mean(axes = var_1546, keep_dims = var_70, x = var_1542_cast_fp16)[name = string("mean_y_101_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1548_cast_fp16 = sub(x = var_1542_cast_fp16, y = mean_y_101_cast_fp16)[name = string("op_1548_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1549_cast_fp16 = square(x = var_1548_cast_fp16);
            tensor<int32, [1]> var_1550 = const()[name = string("op_1550"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1551_cast_fp16 = reduce_mean(axes = var_1550, keep_dims = var_70, x = var_1549_cast_fp16)[name = string("op_1551_cast_fp16")];
            fp16 var_1552_to_fp16 = const()[name = string("op_1552_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1553_cast_fp16 = add(x = var_1551_cast_fp16, y = var_1552_to_fp16)[name = string("op_1553_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_101_cast_fp16 = sqrt(x = var_1553_cast_fp16)[name = string("std_y_101_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_24_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_24_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1380160)))];
            tensor<fp16, [1, 256, 32]> var_1556_cast_fp16 = mul(x = sep_module_24_tcn_6_norm_gamma_to_fp16, y = var_1548_cast_fp16)[name = string("op_1556_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1557_cast_fp16 = real_div(x = var_1556_cast_fp16, y = std_y_101_cast_fp16)[name = string("op_1557_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_24_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_24_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1380736)))];
            tensor<fp16, [1, 256, 32]> y_50_cast_fp16 = add(x = var_1557_cast_fp16, y = sep_module_24_tcn_6_norm_beta_to_fp16)[name = string("y_50_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_253_cast_fp16 = add(x = input_243_cast_fp16, y = y_50_cast_fp16)[name = string("input_253_cast_fp16")];
            tensor<int32, [1]> var_1568 = const()[name = string("op_1568"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1570 = const()[name = string("op_1570"), val = tensor<int32, [1]>([1])];
            string input_255_pad_type_0 = const()[name = string("input_255_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_255_pad_0 = const()[name = string("input_255_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_25_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1381312))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1414144))))[name = string("sep_module_25_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_255_cast_fp16 = conv(dilations = var_1570, groups = var_66, pad = input_255_pad_0, pad_type = input_255_pad_type_0, strides = var_1568, weight = sep_module_25_tcn_0_weight_to_fp16_palettized, x = input_253_cast_fp16)[name = string("input_255_cast_fp16")];
            fp32 var_1574_alpha_1 = const()[name = string("op_1574_alpha_1"), val = fp32(-0x1.dd5cbap-4)];
            tensor<fp16, [1, 256, 32]> var_1574_cast_fp16 = leaky_relu(alpha = var_1574_alpha_1, x = input_255_cast_fp16)[name = string("op_1574_cast_fp16")];
            tensor<int32, [1]> var_1578 = const()[name = string("op_1578"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_103_cast_fp16 = reduce_mean(axes = var_1578, keep_dims = var_70, x = var_1574_cast_fp16)[name = string("mean_y_103_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1580_cast_fp16 = sub(x = var_1574_cast_fp16, y = mean_y_103_cast_fp16)[name = string("op_1580_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1581_cast_fp16 = square(x = var_1580_cast_fp16);
            tensor<int32, [1]> var_1582 = const()[name = string("op_1582"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1583_cast_fp16 = reduce_mean(axes = var_1582, keep_dims = var_70, x = var_1581_cast_fp16)[name = string("op_1583_cast_fp16")];
            fp16 var_1584_to_fp16 = const()[name = string("op_1584_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1585_cast_fp16 = add(x = var_1583_cast_fp16, y = var_1584_to_fp16)[name = string("op_1585_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_103_cast_fp16 = sqrt(x = var_1585_cast_fp16)[name = string("std_y_103_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_25_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_25_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1414272)))];
            tensor<fp16, [1, 256, 32]> var_1588_cast_fp16 = mul(x = sep_module_25_tcn_2_norm_gamma_to_fp16, y = var_1580_cast_fp16)[name = string("op_1588_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1589_cast_fp16 = real_div(x = var_1588_cast_fp16, y = std_y_103_cast_fp16)[name = string("op_1589_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_25_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_25_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1414848)))];
            tensor<fp16, [1, 256, 32]> input_257_cast_fp16 = add(x = var_1589_cast_fp16, y = sep_module_25_tcn_2_norm_beta_to_fp16)[name = string("input_257_cast_fp16")];
            tensor<int32, [6]> input_259_pad_0 = const()[name = string("input_259_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 0])];
            string input_259_mode_0 = const()[name = string("input_259_mode_0"), val = string("constant")];
            fp16 input_259_constant_val_0_to_fp16 = const()[name = string("input_259_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 288]> input_257_cast_fp16_state_input = read_state(input = input_257_cast_fp16_state);
            tensor<fp16, [1, 256, 288]> input_259_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 256]), end = tensor<int32, [3]>([1, 256, 288]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_257_cast_fp16, x = input_257_cast_fp16_state_input);
            write_state(data = input_259_cast_fp16, input = input_257_cast_fp16_state);
            tensor<int32, [1]> var_1594 = const()[name = string("op_1594"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1596 = const()[name = string("op_1596"), val = tensor<int32, [1]>([128])];
            string input_261_pad_type_0 = const()[name = string("input_261_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_261_pad_0 = const()[name = string("input_261_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_25_tcn_4_weight_to_fp16 = const()[name = string("sep_module_25_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1415424)))];
            tensor<fp16, [1, 256, 32]> input_261_cast_fp16 = conv(dilations = var_1596, groups = var_67, pad = input_261_pad_0, pad_type = input_261_pad_type_0, strides = var_1594, weight = sep_module_25_tcn_4_weight_to_fp16, x = input_259_cast_fp16)[name = string("input_261_cast_fp16")];
            fp32 var_1600_alpha_1 = const()[name = string("op_1600_alpha_1"), val = fp32(0x1.77fdfcp-1)];
            tensor<fp16, [1, 256, 32]> var_1600_cast_fp16 = leaky_relu(alpha = var_1600_alpha_1, x = input_261_cast_fp16)[name = string("op_1600_cast_fp16")];
            tensor<int32, [1]> var_1604 = const()[name = string("op_1604"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_105_cast_fp16 = reduce_mean(axes = var_1604, keep_dims = var_70, x = var_1600_cast_fp16)[name = string("mean_y_105_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1606_cast_fp16 = sub(x = var_1600_cast_fp16, y = mean_y_105_cast_fp16)[name = string("op_1606_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1607_cast_fp16 = square(x = var_1606_cast_fp16);
            tensor<int32, [1]> var_1608 = const()[name = string("op_1608"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1609_cast_fp16 = reduce_mean(axes = var_1608, keep_dims = var_70, x = var_1607_cast_fp16)[name = string("op_1609_cast_fp16")];
            fp16 var_1610_to_fp16 = const()[name = string("op_1610_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1611_cast_fp16 = add(x = var_1609_cast_fp16, y = var_1610_to_fp16)[name = string("op_1611_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_105_cast_fp16 = sqrt(x = var_1611_cast_fp16)[name = string("std_y_105_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_25_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_25_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1417024)))];
            tensor<fp16, [1, 256, 32]> var_1614_cast_fp16 = mul(x = sep_module_25_tcn_6_norm_gamma_to_fp16, y = var_1606_cast_fp16)[name = string("op_1614_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1615_cast_fp16 = real_div(x = var_1614_cast_fp16, y = std_y_105_cast_fp16)[name = string("op_1615_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_25_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_25_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1417600)))];
            tensor<fp16, [1, 256, 32]> y_52_cast_fp16 = add(x = var_1615_cast_fp16, y = sep_module_25_tcn_6_norm_beta_to_fp16)[name = string("y_52_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_263_cast_fp16 = add(x = input_253_cast_fp16, y = y_52_cast_fp16)[name = string("input_263_cast_fp16")];
            tensor<int32, [1]> var_1626 = const()[name = string("op_1626"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1628 = const()[name = string("op_1628"), val = tensor<int32, [1]>([1])];
            string input_265_pad_type_0 = const()[name = string("input_265_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_265_pad_0 = const()[name = string("input_265_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_26_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1418176))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1451008))))[name = string("sep_module_26_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_265_cast_fp16 = conv(dilations = var_1628, groups = var_66, pad = input_265_pad_0, pad_type = input_265_pad_type_0, strides = var_1626, weight = sep_module_26_tcn_0_weight_to_fp16_palettized, x = input_263_cast_fp16)[name = string("input_265_cast_fp16")];
            fp32 var_1632_alpha_1 = const()[name = string("op_1632_alpha_1"), val = fp32(-0x1.235418p-5)];
            tensor<fp16, [1, 256, 32]> var_1632_cast_fp16 = leaky_relu(alpha = var_1632_alpha_1, x = input_265_cast_fp16)[name = string("op_1632_cast_fp16")];
            tensor<int32, [1]> var_1636 = const()[name = string("op_1636"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_107_cast_fp16 = reduce_mean(axes = var_1636, keep_dims = var_70, x = var_1632_cast_fp16)[name = string("mean_y_107_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1638_cast_fp16 = sub(x = var_1632_cast_fp16, y = mean_y_107_cast_fp16)[name = string("op_1638_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1639_cast_fp16 = square(x = var_1638_cast_fp16);
            tensor<int32, [1]> var_1640 = const()[name = string("op_1640"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1641_cast_fp16 = reduce_mean(axes = var_1640, keep_dims = var_70, x = var_1639_cast_fp16)[name = string("op_1641_cast_fp16")];
            fp16 var_1642_to_fp16 = const()[name = string("op_1642_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1643_cast_fp16 = add(x = var_1641_cast_fp16, y = var_1642_to_fp16)[name = string("op_1643_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_107_cast_fp16 = sqrt(x = var_1643_cast_fp16)[name = string("std_y_107_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_26_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_26_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1451136)))];
            tensor<fp16, [1, 256, 32]> var_1646_cast_fp16 = mul(x = sep_module_26_tcn_2_norm_gamma_to_fp16, y = var_1638_cast_fp16)[name = string("op_1646_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1647_cast_fp16 = real_div(x = var_1646_cast_fp16, y = std_y_107_cast_fp16)[name = string("op_1647_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_26_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_26_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1451712)))];
            tensor<fp16, [1, 256, 32]> input_267_cast_fp16 = add(x = var_1647_cast_fp16, y = sep_module_26_tcn_2_norm_beta_to_fp16)[name = string("input_267_cast_fp16")];
            tensor<int32, [6]> input_269_pad_0 = const()[name = string("input_269_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 512, 0])];
            string input_269_mode_0 = const()[name = string("input_269_mode_0"), val = string("constant")];
            fp16 input_269_constant_val_0_to_fp16 = const()[name = string("input_269_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 544]> input_267_cast_fp16_state_input = read_state(input = input_267_cast_fp16_state);
            tensor<fp16, [1, 256, 544]> input_269_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 512]), end = tensor<int32, [3]>([1, 256, 544]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_267_cast_fp16, x = input_267_cast_fp16_state_input);
            write_state(data = input_269_cast_fp16, input = input_267_cast_fp16_state);
            tensor<int32, [1]> var_1652 = const()[name = string("op_1652"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1654 = const()[name = string("op_1654"), val = tensor<int32, [1]>([256])];
            string input_271_pad_type_0 = const()[name = string("input_271_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_271_pad_0 = const()[name = string("input_271_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_26_tcn_4_weight_to_fp16 = const()[name = string("sep_module_26_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1452288)))];
            tensor<fp16, [1, 256, 32]> input_271_cast_fp16 = conv(dilations = var_1654, groups = var_67, pad = input_271_pad_0, pad_type = input_271_pad_type_0, strides = var_1652, weight = sep_module_26_tcn_4_weight_to_fp16, x = input_269_cast_fp16)[name = string("input_271_cast_fp16")];
            fp32 var_1658_alpha_1 = const()[name = string("op_1658_alpha_1"), val = fp32(0x1.4c4f5p-1)];
            tensor<fp16, [1, 256, 32]> var_1658_cast_fp16 = leaky_relu(alpha = var_1658_alpha_1, x = input_271_cast_fp16)[name = string("op_1658_cast_fp16")];
            tensor<int32, [1]> var_1662 = const()[name = string("op_1662"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_109_cast_fp16 = reduce_mean(axes = var_1662, keep_dims = var_70, x = var_1658_cast_fp16)[name = string("mean_y_109_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1664_cast_fp16 = sub(x = var_1658_cast_fp16, y = mean_y_109_cast_fp16)[name = string("op_1664_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1665_cast_fp16 = square(x = var_1664_cast_fp16);
            tensor<int32, [1]> var_1666 = const()[name = string("op_1666"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1667_cast_fp16 = reduce_mean(axes = var_1666, keep_dims = var_70, x = var_1665_cast_fp16)[name = string("op_1667_cast_fp16")];
            fp16 var_1668_to_fp16 = const()[name = string("op_1668_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1669_cast_fp16 = add(x = var_1667_cast_fp16, y = var_1668_to_fp16)[name = string("op_1669_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_109_cast_fp16 = sqrt(x = var_1669_cast_fp16)[name = string("std_y_109_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_26_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_26_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1453888)))];
            tensor<fp16, [1, 256, 32]> var_1672_cast_fp16 = mul(x = sep_module_26_tcn_6_norm_gamma_to_fp16, y = var_1664_cast_fp16)[name = string("op_1672_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1673_cast_fp16 = real_div(x = var_1672_cast_fp16, y = std_y_109_cast_fp16)[name = string("op_1673_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_26_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_26_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1454464)))];
            tensor<fp16, [1, 256, 32]> y_54_cast_fp16 = add(x = var_1673_cast_fp16, y = sep_module_26_tcn_6_norm_beta_to_fp16)[name = string("y_54_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_273_cast_fp16 = add(x = input_263_cast_fp16, y = y_54_cast_fp16)[name = string("input_273_cast_fp16")];
            tensor<int32, [1]> var_1684 = const()[name = string("op_1684"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1686 = const()[name = string("op_1686"), val = tensor<int32, [1]>([1])];
            string input_275_pad_type_0 = const()[name = string("input_275_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_275_pad_0 = const()[name = string("input_275_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_27_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1455040))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1487872))))[name = string("sep_module_27_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_275_cast_fp16 = conv(dilations = var_1686, groups = var_66, pad = input_275_pad_0, pad_type = input_275_pad_type_0, strides = var_1684, weight = sep_module_27_tcn_0_weight_to_fp16_palettized, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")];
            fp32 var_1690_alpha_1 = const()[name = string("op_1690_alpha_1"), val = fp32(0x1.9465e4p-2)];
            tensor<fp16, [1, 256, 32]> var_1690_cast_fp16 = leaky_relu(alpha = var_1690_alpha_1, x = input_275_cast_fp16)[name = string("op_1690_cast_fp16")];
            tensor<int32, [1]> var_1694 = const()[name = string("op_1694"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_111_cast_fp16 = reduce_mean(axes = var_1694, keep_dims = var_70, x = var_1690_cast_fp16)[name = string("mean_y_111_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1696_cast_fp16 = sub(x = var_1690_cast_fp16, y = mean_y_111_cast_fp16)[name = string("op_1696_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1697_cast_fp16 = square(x = var_1696_cast_fp16);
            tensor<int32, [1]> var_1698 = const()[name = string("op_1698"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1699_cast_fp16 = reduce_mean(axes = var_1698, keep_dims = var_70, x = var_1697_cast_fp16)[name = string("op_1699_cast_fp16")];
            fp16 var_1700_to_fp16 = const()[name = string("op_1700_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1701_cast_fp16 = add(x = var_1699_cast_fp16, y = var_1700_to_fp16)[name = string("op_1701_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_111_cast_fp16 = sqrt(x = var_1701_cast_fp16)[name = string("std_y_111_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_27_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_27_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1488000)))];
            tensor<fp16, [1, 256, 32]> var_1704_cast_fp16 = mul(x = sep_module_27_tcn_2_norm_gamma_to_fp16, y = var_1696_cast_fp16)[name = string("op_1704_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1705_cast_fp16 = real_div(x = var_1704_cast_fp16, y = std_y_111_cast_fp16)[name = string("op_1705_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_27_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_27_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1488576)))];
            tensor<fp16, [1, 256, 32]> input_277_cast_fp16 = add(x = var_1705_cast_fp16, y = sep_module_27_tcn_2_norm_beta_to_fp16)[name = string("input_277_cast_fp16")];
            tensor<int32, [6]> input_279_pad_0 = const()[name = string("input_279_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
            string input_279_mode_0 = const()[name = string("input_279_mode_0"), val = string("constant")];
            fp16 input_279_constant_val_0_to_fp16 = const()[name = string("input_279_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 34]> input_277_cast_fp16_state_input = read_state(input = input_277_cast_fp16_state);
            tensor<fp16, [1, 256, 34]> input_279_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 2]), end = tensor<int32, [3]>([1, 256, 34]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_277_cast_fp16, x = input_277_cast_fp16_state_input);
            write_state(data = input_279_cast_fp16, input = input_277_cast_fp16_state);
            tensor<int32, [1]> var_1710 = const()[name = string("op_1710"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1712 = const()[name = string("op_1712"), val = tensor<int32, [1]>([1])];
            string input_281_pad_type_0 = const()[name = string("input_281_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_281_pad_0 = const()[name = string("input_281_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_27_tcn_4_weight_to_fp16 = const()[name = string("sep_module_27_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1489152)))];
            tensor<fp16, [1, 256, 32]> input_281_cast_fp16 = conv(dilations = var_1712, groups = var_67, pad = input_281_pad_0, pad_type = input_281_pad_type_0, strides = var_1710, weight = sep_module_27_tcn_4_weight_to_fp16, x = input_279_cast_fp16)[name = string("input_281_cast_fp16")];
            fp32 var_1716_alpha_1 = const()[name = string("op_1716_alpha_1"), val = fp32(0x1.c49ef4p-1)];
            tensor<fp16, [1, 256, 32]> var_1716_cast_fp16 = leaky_relu(alpha = var_1716_alpha_1, x = input_281_cast_fp16)[name = string("op_1716_cast_fp16")];
            tensor<int32, [1]> var_1720 = const()[name = string("op_1720"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_113_cast_fp16 = reduce_mean(axes = var_1720, keep_dims = var_70, x = var_1716_cast_fp16)[name = string("mean_y_113_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1722_cast_fp16 = sub(x = var_1716_cast_fp16, y = mean_y_113_cast_fp16)[name = string("op_1722_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1723_cast_fp16 = square(x = var_1722_cast_fp16);
            tensor<int32, [1]> var_1724 = const()[name = string("op_1724"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1725_cast_fp16 = reduce_mean(axes = var_1724, keep_dims = var_70, x = var_1723_cast_fp16)[name = string("op_1725_cast_fp16")];
            fp16 var_1726_to_fp16 = const()[name = string("op_1726_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1727_cast_fp16 = add(x = var_1725_cast_fp16, y = var_1726_to_fp16)[name = string("op_1727_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_113_cast_fp16 = sqrt(x = var_1727_cast_fp16)[name = string("std_y_113_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_27_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_27_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1490752)))];
            tensor<fp16, [1, 256, 32]> var_1730_cast_fp16 = mul(x = sep_module_27_tcn_6_norm_gamma_to_fp16, y = var_1722_cast_fp16)[name = string("op_1730_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1731_cast_fp16 = real_div(x = var_1730_cast_fp16, y = std_y_113_cast_fp16)[name = string("op_1731_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_27_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_27_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1491328)))];
            tensor<fp16, [1, 256, 32]> y_56_cast_fp16 = add(x = var_1731_cast_fp16, y = sep_module_27_tcn_6_norm_beta_to_fp16)[name = string("y_56_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_283_cast_fp16 = add(x = input_273_cast_fp16, y = y_56_cast_fp16)[name = string("input_283_cast_fp16")];
            tensor<int32, [1]> var_1742 = const()[name = string("op_1742"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1744 = const()[name = string("op_1744"), val = tensor<int32, [1]>([1])];
            string input_285_pad_type_0 = const()[name = string("input_285_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_285_pad_0 = const()[name = string("input_285_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_28_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1491904))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1524736))))[name = string("sep_module_28_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_285_cast_fp16 = conv(dilations = var_1744, groups = var_66, pad = input_285_pad_0, pad_type = input_285_pad_type_0, strides = var_1742, weight = sep_module_28_tcn_0_weight_to_fp16_palettized, x = input_283_cast_fp16)[name = string("input_285_cast_fp16")];
            fp32 var_1748_alpha_1 = const()[name = string("op_1748_alpha_1"), val = fp32(-0x1.aff076p-5)];
            tensor<fp16, [1, 256, 32]> var_1748_cast_fp16 = leaky_relu(alpha = var_1748_alpha_1, x = input_285_cast_fp16)[name = string("op_1748_cast_fp16")];
            tensor<int32, [1]> var_1752 = const()[name = string("op_1752"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_115_cast_fp16 = reduce_mean(axes = var_1752, keep_dims = var_70, x = var_1748_cast_fp16)[name = string("mean_y_115_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1754_cast_fp16 = sub(x = var_1748_cast_fp16, y = mean_y_115_cast_fp16)[name = string("op_1754_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1755_cast_fp16 = square(x = var_1754_cast_fp16);
            tensor<int32, [1]> var_1756 = const()[name = string("op_1756"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1757_cast_fp16 = reduce_mean(axes = var_1756, keep_dims = var_70, x = var_1755_cast_fp16)[name = string("op_1757_cast_fp16")];
            fp16 var_1758_to_fp16 = const()[name = string("op_1758_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1759_cast_fp16 = add(x = var_1757_cast_fp16, y = var_1758_to_fp16)[name = string("op_1759_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_115_cast_fp16 = sqrt(x = var_1759_cast_fp16)[name = string("std_y_115_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_28_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_28_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1524864)))];
            tensor<fp16, [1, 256, 32]> var_1762_cast_fp16 = mul(x = sep_module_28_tcn_2_norm_gamma_to_fp16, y = var_1754_cast_fp16)[name = string("op_1762_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1763_cast_fp16 = real_div(x = var_1762_cast_fp16, y = std_y_115_cast_fp16)[name = string("op_1763_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_28_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_28_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1525440)))];
            tensor<fp16, [1, 256, 32]> input_287_cast_fp16 = add(x = var_1763_cast_fp16, y = sep_module_28_tcn_2_norm_beta_to_fp16)[name = string("input_287_cast_fp16")];
            tensor<int32, [6]> input_289_pad_0 = const()[name = string("input_289_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
            string input_289_mode_0 = const()[name = string("input_289_mode_0"), val = string("constant")];
            fp16 input_289_constant_val_0_to_fp16 = const()[name = string("input_289_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 36]> input_287_cast_fp16_state_input = read_state(input = input_287_cast_fp16_state);
            tensor<fp16, [1, 256, 36]> input_289_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 4]), end = tensor<int32, [3]>([1, 256, 36]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_287_cast_fp16, x = input_287_cast_fp16_state_input);
            write_state(data = input_289_cast_fp16, input = input_287_cast_fp16_state);
            tensor<int32, [1]> var_1768 = const()[name = string("op_1768"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1770 = const()[name = string("op_1770"), val = tensor<int32, [1]>([2])];
            string input_291_pad_type_0 = const()[name = string("input_291_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_291_pad_0 = const()[name = string("input_291_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_28_tcn_4_weight_to_fp16 = const()[name = string("sep_module_28_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1526016)))];
            tensor<fp16, [1, 256, 32]> input_291_cast_fp16 = conv(dilations = var_1770, groups = var_67, pad = input_291_pad_0, pad_type = input_291_pad_type_0, strides = var_1768, weight = sep_module_28_tcn_4_weight_to_fp16, x = input_289_cast_fp16)[name = string("input_291_cast_fp16")];
            fp32 var_1774_alpha_1 = const()[name = string("op_1774_alpha_1"), val = fp32(0x1.953414p-1)];
            tensor<fp16, [1, 256, 32]> var_1774_cast_fp16 = leaky_relu(alpha = var_1774_alpha_1, x = input_291_cast_fp16)[name = string("op_1774_cast_fp16")];
            tensor<int32, [1]> var_1778 = const()[name = string("op_1778"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_117_cast_fp16 = reduce_mean(axes = var_1778, keep_dims = var_70, x = var_1774_cast_fp16)[name = string("mean_y_117_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1780_cast_fp16 = sub(x = var_1774_cast_fp16, y = mean_y_117_cast_fp16)[name = string("op_1780_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1781_cast_fp16 = square(x = var_1780_cast_fp16);
            tensor<int32, [1]> var_1782 = const()[name = string("op_1782"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1783_cast_fp16 = reduce_mean(axes = var_1782, keep_dims = var_70, x = var_1781_cast_fp16)[name = string("op_1783_cast_fp16")];
            fp16 var_1784_to_fp16 = const()[name = string("op_1784_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1785_cast_fp16 = add(x = var_1783_cast_fp16, y = var_1784_to_fp16)[name = string("op_1785_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_117_cast_fp16 = sqrt(x = var_1785_cast_fp16)[name = string("std_y_117_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_28_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_28_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1527616)))];
            tensor<fp16, [1, 256, 32]> var_1788_cast_fp16 = mul(x = sep_module_28_tcn_6_norm_gamma_to_fp16, y = var_1780_cast_fp16)[name = string("op_1788_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1789_cast_fp16 = real_div(x = var_1788_cast_fp16, y = std_y_117_cast_fp16)[name = string("op_1789_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_28_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_28_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1528192)))];
            tensor<fp16, [1, 256, 32]> y_58_cast_fp16 = add(x = var_1789_cast_fp16, y = sep_module_28_tcn_6_norm_beta_to_fp16)[name = string("y_58_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_293_cast_fp16 = add(x = input_283_cast_fp16, y = y_58_cast_fp16)[name = string("input_293_cast_fp16")];
            tensor<int32, [1]> var_1800 = const()[name = string("op_1800"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1802 = const()[name = string("op_1802"), val = tensor<int32, [1]>([1])];
            string input_295_pad_type_0 = const()[name = string("input_295_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_29_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1528768))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1561600))))[name = string("sep_module_29_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_295_cast_fp16 = conv(dilations = var_1802, groups = var_66, pad = input_295_pad_0, pad_type = input_295_pad_type_0, strides = var_1800, weight = sep_module_29_tcn_0_weight_to_fp16_palettized, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")];
            fp32 var_1806_alpha_1 = const()[name = string("op_1806_alpha_1"), val = fp32(0x1.fe9f2ap-4)];
            tensor<fp16, [1, 256, 32]> var_1806_cast_fp16 = leaky_relu(alpha = var_1806_alpha_1, x = input_295_cast_fp16)[name = string("op_1806_cast_fp16")];
            tensor<int32, [1]> var_1810 = const()[name = string("op_1810"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_119_cast_fp16 = reduce_mean(axes = var_1810, keep_dims = var_70, x = var_1806_cast_fp16)[name = string("mean_y_119_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1812_cast_fp16 = sub(x = var_1806_cast_fp16, y = mean_y_119_cast_fp16)[name = string("op_1812_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1813_cast_fp16 = square(x = var_1812_cast_fp16);
            tensor<int32, [1]> var_1814 = const()[name = string("op_1814"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1815_cast_fp16 = reduce_mean(axes = var_1814, keep_dims = var_70, x = var_1813_cast_fp16)[name = string("op_1815_cast_fp16")];
            fp16 var_1816_to_fp16 = const()[name = string("op_1816_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1817_cast_fp16 = add(x = var_1815_cast_fp16, y = var_1816_to_fp16)[name = string("op_1817_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_119_cast_fp16 = sqrt(x = var_1817_cast_fp16)[name = string("std_y_119_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_29_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_29_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1561728)))];
            tensor<fp16, [1, 256, 32]> var_1820_cast_fp16 = mul(x = sep_module_29_tcn_2_norm_gamma_to_fp16, y = var_1812_cast_fp16)[name = string("op_1820_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1821_cast_fp16 = real_div(x = var_1820_cast_fp16, y = std_y_119_cast_fp16)[name = string("op_1821_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_29_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_29_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1562304)))];
            tensor<fp16, [1, 256, 32]> input_297_cast_fp16 = add(x = var_1821_cast_fp16, y = sep_module_29_tcn_2_norm_beta_to_fp16)[name = string("input_297_cast_fp16")];
            tensor<int32, [6]> input_299_pad_0 = const()[name = string("input_299_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            string input_299_mode_0 = const()[name = string("input_299_mode_0"), val = string("constant")];
            fp16 input_299_constant_val_0_to_fp16 = const()[name = string("input_299_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 40]> input_297_cast_fp16_state_input = read_state(input = input_297_cast_fp16_state);
            tensor<fp16, [1, 256, 40]> input_299_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 8]), end = tensor<int32, [3]>([1, 256, 40]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_297_cast_fp16, x = input_297_cast_fp16_state_input);
            write_state(data = input_299_cast_fp16, input = input_297_cast_fp16_state);
            tensor<int32, [1]> var_1826 = const()[name = string("op_1826"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1828 = const()[name = string("op_1828"), val = tensor<int32, [1]>([4])];
            string input_301_pad_type_0 = const()[name = string("input_301_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_301_pad_0 = const()[name = string("input_301_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_29_tcn_4_weight_to_fp16 = const()[name = string("sep_module_29_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1562880)))];
            tensor<fp16, [1, 256, 32]> input_301_cast_fp16 = conv(dilations = var_1828, groups = var_67, pad = input_301_pad_0, pad_type = input_301_pad_type_0, strides = var_1826, weight = sep_module_29_tcn_4_weight_to_fp16, x = input_299_cast_fp16)[name = string("input_301_cast_fp16")];
            fp32 var_1832_alpha_1 = const()[name = string("op_1832_alpha_1"), val = fp32(0x1.5e0a6ap-1)];
            tensor<fp16, [1, 256, 32]> var_1832_cast_fp16 = leaky_relu(alpha = var_1832_alpha_1, x = input_301_cast_fp16)[name = string("op_1832_cast_fp16")];
            tensor<int32, [1]> var_1836 = const()[name = string("op_1836"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_121_cast_fp16 = reduce_mean(axes = var_1836, keep_dims = var_70, x = var_1832_cast_fp16)[name = string("mean_y_121_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1838_cast_fp16 = sub(x = var_1832_cast_fp16, y = mean_y_121_cast_fp16)[name = string("op_1838_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1839_cast_fp16 = square(x = var_1838_cast_fp16);
            tensor<int32, [1]> var_1840 = const()[name = string("op_1840"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1841_cast_fp16 = reduce_mean(axes = var_1840, keep_dims = var_70, x = var_1839_cast_fp16)[name = string("op_1841_cast_fp16")];
            fp16 var_1842_to_fp16 = const()[name = string("op_1842_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1843_cast_fp16 = add(x = var_1841_cast_fp16, y = var_1842_to_fp16)[name = string("op_1843_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_121_cast_fp16 = sqrt(x = var_1843_cast_fp16)[name = string("std_y_121_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_29_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_29_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1564480)))];
            tensor<fp16, [1, 256, 32]> var_1846_cast_fp16 = mul(x = sep_module_29_tcn_6_norm_gamma_to_fp16, y = var_1838_cast_fp16)[name = string("op_1846_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1847_cast_fp16 = real_div(x = var_1846_cast_fp16, y = std_y_121_cast_fp16)[name = string("op_1847_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_29_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_29_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1565056)))];
            tensor<fp16, [1, 256, 32]> y_60_cast_fp16 = add(x = var_1847_cast_fp16, y = sep_module_29_tcn_6_norm_beta_to_fp16)[name = string("y_60_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_303_cast_fp16 = add(x = input_293_cast_fp16, y = y_60_cast_fp16)[name = string("input_303_cast_fp16")];
            tensor<int32, [1]> var_1858 = const()[name = string("op_1858"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1860 = const()[name = string("op_1860"), val = tensor<int32, [1]>([1])];
            string input_305_pad_type_0 = const()[name = string("input_305_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_305_pad_0 = const()[name = string("input_305_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_30_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1565632))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1598464))))[name = string("sep_module_30_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_305_cast_fp16 = conv(dilations = var_1860, groups = var_66, pad = input_305_pad_0, pad_type = input_305_pad_type_0, strides = var_1858, weight = sep_module_30_tcn_0_weight_to_fp16_palettized, x = input_303_cast_fp16)[name = string("input_305_cast_fp16")];
            fp32 var_1864_alpha_1 = const()[name = string("op_1864_alpha_1"), val = fp32(0x1.ca13dp-2)];
            tensor<fp16, [1, 256, 32]> var_1864_cast_fp16 = leaky_relu(alpha = var_1864_alpha_1, x = input_305_cast_fp16)[name = string("op_1864_cast_fp16")];
            tensor<int32, [1]> var_1868 = const()[name = string("op_1868"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_123_cast_fp16 = reduce_mean(axes = var_1868, keep_dims = var_70, x = var_1864_cast_fp16)[name = string("mean_y_123_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1870_cast_fp16 = sub(x = var_1864_cast_fp16, y = mean_y_123_cast_fp16)[name = string("op_1870_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1871_cast_fp16 = square(x = var_1870_cast_fp16);
            tensor<int32, [1]> var_1872 = const()[name = string("op_1872"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1873_cast_fp16 = reduce_mean(axes = var_1872, keep_dims = var_70, x = var_1871_cast_fp16)[name = string("op_1873_cast_fp16")];
            fp16 var_1874_to_fp16 = const()[name = string("op_1874_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1875_cast_fp16 = add(x = var_1873_cast_fp16, y = var_1874_to_fp16)[name = string("op_1875_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_123_cast_fp16 = sqrt(x = var_1875_cast_fp16)[name = string("std_y_123_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_30_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_30_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1598592)))];
            tensor<fp16, [1, 256, 32]> var_1878_cast_fp16 = mul(x = sep_module_30_tcn_2_norm_gamma_to_fp16, y = var_1870_cast_fp16)[name = string("op_1878_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1879_cast_fp16 = real_div(x = var_1878_cast_fp16, y = std_y_123_cast_fp16)[name = string("op_1879_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_30_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_30_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1599168)))];
            tensor<fp16, [1, 256, 32]> input_307_cast_fp16 = add(x = var_1879_cast_fp16, y = sep_module_30_tcn_2_norm_beta_to_fp16)[name = string("input_307_cast_fp16")];
            tensor<int32, [6]> input_309_pad_0 = const()[name = string("input_309_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            string input_309_mode_0 = const()[name = string("input_309_mode_0"), val = string("constant")];
            fp16 input_309_constant_val_0_to_fp16 = const()[name = string("input_309_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 48]> input_307_cast_fp16_state_input = read_state(input = input_307_cast_fp16_state);
            tensor<fp16, [1, 256, 48]> input_309_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 16]), end = tensor<int32, [3]>([1, 256, 48]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_307_cast_fp16, x = input_307_cast_fp16_state_input);
            write_state(data = input_309_cast_fp16, input = input_307_cast_fp16_state);
            tensor<int32, [1]> var_1884 = const()[name = string("op_1884"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1886 = const()[name = string("op_1886"), val = tensor<int32, [1]>([8])];
            string input_311_pad_type_0 = const()[name = string("input_311_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_311_pad_0 = const()[name = string("input_311_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_30_tcn_4_weight_to_fp16 = const()[name = string("sep_module_30_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1599744)))];
            tensor<fp16, [1, 256, 32]> input_311_cast_fp16 = conv(dilations = var_1886, groups = var_67, pad = input_311_pad_0, pad_type = input_311_pad_type_0, strides = var_1884, weight = sep_module_30_tcn_4_weight_to_fp16, x = input_309_cast_fp16)[name = string("input_311_cast_fp16")];
            fp32 var_1890_alpha_1 = const()[name = string("op_1890_alpha_1"), val = fp32(0x1.10b10ep-1)];
            tensor<fp16, [1, 256, 32]> var_1890_cast_fp16 = leaky_relu(alpha = var_1890_alpha_1, x = input_311_cast_fp16)[name = string("op_1890_cast_fp16")];
            tensor<int32, [1]> var_1894 = const()[name = string("op_1894"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_125_cast_fp16 = reduce_mean(axes = var_1894, keep_dims = var_70, x = var_1890_cast_fp16)[name = string("mean_y_125_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1896_cast_fp16 = sub(x = var_1890_cast_fp16, y = mean_y_125_cast_fp16)[name = string("op_1896_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1897_cast_fp16 = square(x = var_1896_cast_fp16);
            tensor<int32, [1]> var_1898 = const()[name = string("op_1898"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1899_cast_fp16 = reduce_mean(axes = var_1898, keep_dims = var_70, x = var_1897_cast_fp16)[name = string("op_1899_cast_fp16")];
            fp16 var_1900_to_fp16 = const()[name = string("op_1900_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1901_cast_fp16 = add(x = var_1899_cast_fp16, y = var_1900_to_fp16)[name = string("op_1901_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_125_cast_fp16 = sqrt(x = var_1901_cast_fp16)[name = string("std_y_125_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_30_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_30_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1601344)))];
            tensor<fp16, [1, 256, 32]> var_1904_cast_fp16 = mul(x = sep_module_30_tcn_6_norm_gamma_to_fp16, y = var_1896_cast_fp16)[name = string("op_1904_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1905_cast_fp16 = real_div(x = var_1904_cast_fp16, y = std_y_125_cast_fp16)[name = string("op_1905_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_30_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_30_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1601920)))];
            tensor<fp16, [1, 256, 32]> y_62_cast_fp16 = add(x = var_1905_cast_fp16, y = sep_module_30_tcn_6_norm_beta_to_fp16)[name = string("y_62_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_313_cast_fp16 = add(x = input_303_cast_fp16, y = y_62_cast_fp16)[name = string("input_313_cast_fp16")];
            tensor<int32, [1]> var_1916 = const()[name = string("op_1916"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1918 = const()[name = string("op_1918"), val = tensor<int32, [1]>([1])];
            string input_315_pad_type_0 = const()[name = string("input_315_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_315_pad_0 = const()[name = string("input_315_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_31_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1602496))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1635328))))[name = string("sep_module_31_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_315_cast_fp16 = conv(dilations = var_1918, groups = var_66, pad = input_315_pad_0, pad_type = input_315_pad_type_0, strides = var_1916, weight = sep_module_31_tcn_0_weight_to_fp16_palettized, x = input_313_cast_fp16)[name = string("input_315_cast_fp16")];
            fp32 var_1922_alpha_1 = const()[name = string("op_1922_alpha_1"), val = fp32(0x1.c077aep-2)];
            tensor<fp16, [1, 256, 32]> var_1922_cast_fp16 = leaky_relu(alpha = var_1922_alpha_1, x = input_315_cast_fp16)[name = string("op_1922_cast_fp16")];
            tensor<int32, [1]> var_1926 = const()[name = string("op_1926"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_127_cast_fp16 = reduce_mean(axes = var_1926, keep_dims = var_70, x = var_1922_cast_fp16)[name = string("mean_y_127_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1928_cast_fp16 = sub(x = var_1922_cast_fp16, y = mean_y_127_cast_fp16)[name = string("op_1928_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1929_cast_fp16 = square(x = var_1928_cast_fp16);
            tensor<int32, [1]> var_1930 = const()[name = string("op_1930"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1931_cast_fp16 = reduce_mean(axes = var_1930, keep_dims = var_70, x = var_1929_cast_fp16)[name = string("op_1931_cast_fp16")];
            fp16 var_1932_to_fp16 = const()[name = string("op_1932_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1933_cast_fp16 = add(x = var_1931_cast_fp16, y = var_1932_to_fp16)[name = string("op_1933_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_127_cast_fp16 = sqrt(x = var_1933_cast_fp16)[name = string("std_y_127_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_31_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_31_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1635456)))];
            tensor<fp16, [1, 256, 32]> var_1936_cast_fp16 = mul(x = sep_module_31_tcn_2_norm_gamma_to_fp16, y = var_1928_cast_fp16)[name = string("op_1936_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1937_cast_fp16 = real_div(x = var_1936_cast_fp16, y = std_y_127_cast_fp16)[name = string("op_1937_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_31_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_31_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1636032)))];
            tensor<fp16, [1, 256, 32]> input_317_cast_fp16 = add(x = var_1937_cast_fp16, y = sep_module_31_tcn_2_norm_beta_to_fp16)[name = string("input_317_cast_fp16")];
            tensor<int32, [6]> input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            string input_319_mode_0 = const()[name = string("input_319_mode_0"), val = string("constant")];
            fp16 input_319_constant_val_0_to_fp16 = const()[name = string("input_319_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 64]> input_317_cast_fp16_state_input = read_state(input = input_317_cast_fp16_state);
            tensor<fp16, [1, 256, 64]> input_319_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 32]), end = tensor<int32, [3]>([1, 256, 64]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_317_cast_fp16, x = input_317_cast_fp16_state_input);
            write_state(data = input_319_cast_fp16, input = input_317_cast_fp16_state);
            tensor<int32, [1]> var_1942 = const()[name = string("op_1942"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1944 = const()[name = string("op_1944"), val = tensor<int32, [1]>([16])];
            string input_321_pad_type_0 = const()[name = string("input_321_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_321_pad_0 = const()[name = string("input_321_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_31_tcn_4_weight_to_fp16 = const()[name = string("sep_module_31_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1636608)))];
            tensor<fp16, [1, 256, 32]> input_321_cast_fp16 = conv(dilations = var_1944, groups = var_67, pad = input_321_pad_0, pad_type = input_321_pad_type_0, strides = var_1942, weight = sep_module_31_tcn_4_weight_to_fp16, x = input_319_cast_fp16)[name = string("input_321_cast_fp16")];
            fp32 var_1948_alpha_1 = const()[name = string("op_1948_alpha_1"), val = fp32(0x1.931358p-2)];
            tensor<fp16, [1, 256, 32]> var_1948_cast_fp16 = leaky_relu(alpha = var_1948_alpha_1, x = input_321_cast_fp16)[name = string("op_1948_cast_fp16")];
            tensor<int32, [1]> var_1952 = const()[name = string("op_1952"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_129_cast_fp16 = reduce_mean(axes = var_1952, keep_dims = var_70, x = var_1948_cast_fp16)[name = string("mean_y_129_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1954_cast_fp16 = sub(x = var_1948_cast_fp16, y = mean_y_129_cast_fp16)[name = string("op_1954_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1955_cast_fp16 = square(x = var_1954_cast_fp16);
            tensor<int32, [1]> var_1956 = const()[name = string("op_1956"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1957_cast_fp16 = reduce_mean(axes = var_1956, keep_dims = var_70, x = var_1955_cast_fp16)[name = string("op_1957_cast_fp16")];
            fp16 var_1958_to_fp16 = const()[name = string("op_1958_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1959_cast_fp16 = add(x = var_1957_cast_fp16, y = var_1958_to_fp16)[name = string("op_1959_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_129_cast_fp16 = sqrt(x = var_1959_cast_fp16)[name = string("std_y_129_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_31_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_31_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1638208)))];
            tensor<fp16, [1, 256, 32]> var_1962_cast_fp16 = mul(x = sep_module_31_tcn_6_norm_gamma_to_fp16, y = var_1954_cast_fp16)[name = string("op_1962_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1963_cast_fp16 = real_div(x = var_1962_cast_fp16, y = std_y_129_cast_fp16)[name = string("op_1963_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_31_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_31_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1638784)))];
            tensor<fp16, [1, 256, 32]> y_64_cast_fp16 = add(x = var_1963_cast_fp16, y = sep_module_31_tcn_6_norm_beta_to_fp16)[name = string("y_64_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_323_cast_fp16 = add(x = input_313_cast_fp16, y = y_64_cast_fp16)[name = string("input_323_cast_fp16")];
            tensor<int32, [1]> var_1974 = const()[name = string("op_1974"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1976 = const()[name = string("op_1976"), val = tensor<int32, [1]>([1])];
            string input_325_pad_type_0 = const()[name = string("input_325_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_325_pad_0 = const()[name = string("input_325_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_32_tcn_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1639360))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1672192))))[name = string("sep_module_32_tcn_0_weight_to_fp16_palettized")];
            tensor<fp16, [1, 256, 32]> input_325_cast_fp16 = conv(dilations = var_1976, groups = var_66, pad = input_325_pad_0, pad_type = input_325_pad_type_0, strides = var_1974, weight = sep_module_32_tcn_0_weight_to_fp16_palettized, x = input_323_cast_fp16)[name = string("input_325_cast_fp16")];
            fp32 var_1980_alpha_1 = const()[name = string("op_1980_alpha_1"), val = fp32(-0x1.685bfcp-4)];
            tensor<fp16, [1, 256, 32]> var_1980_cast_fp16 = leaky_relu(alpha = var_1980_alpha_1, x = input_325_cast_fp16)[name = string("op_1980_cast_fp16")];
            tensor<int32, [1]> var_1984 = const()[name = string("op_1984"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_131_cast_fp16 = reduce_mean(axes = var_1984, keep_dims = var_70, x = var_1980_cast_fp16)[name = string("mean_y_131_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1986_cast_fp16 = sub(x = var_1980_cast_fp16, y = mean_y_131_cast_fp16)[name = string("op_1986_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1987_cast_fp16 = square(x = var_1986_cast_fp16);
            tensor<int32, [1]> var_1988 = const()[name = string("op_1988"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_1989_cast_fp16 = reduce_mean(axes = var_1988, keep_dims = var_70, x = var_1987_cast_fp16)[name = string("op_1989_cast_fp16")];
            fp16 var_1990_to_fp16 = const()[name = string("op_1990_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_1991_cast_fp16 = add(x = var_1989_cast_fp16, y = var_1990_to_fp16)[name = string("op_1991_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_131_cast_fp16 = sqrt(x = var_1991_cast_fp16)[name = string("std_y_131_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_32_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_32_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1672320)))];
            tensor<fp16, [1, 256, 32]> var_1994_cast_fp16 = mul(x = sep_module_32_tcn_2_norm_gamma_to_fp16, y = var_1986_cast_fp16)[name = string("op_1994_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_1995_cast_fp16 = real_div(x = var_1994_cast_fp16, y = std_y_131_cast_fp16)[name = string("op_1995_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_32_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_32_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1672896)))];
            tensor<fp16, [1, 256, 32]> input_327_cast_fp16 = add(x = var_1995_cast_fp16, y = sep_module_32_tcn_2_norm_beta_to_fp16)[name = string("input_327_cast_fp16")];
            tensor<int32, [6]> input_329_pad_0 = const()[name = string("input_329_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            string input_329_mode_0 = const()[name = string("input_329_mode_0"), val = string("constant")];
            fp16 input_329_constant_val_0_to_fp16 = const()[name = string("input_329_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 96]> input_327_cast_fp16_state_input = read_state(input = input_327_cast_fp16_state);
            tensor<fp16, [1, 256, 96]> input_329_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 64]), end = tensor<int32, [3]>([1, 256, 96]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_327_cast_fp16, x = input_327_cast_fp16_state_input);
            write_state(data = input_329_cast_fp16, input = input_327_cast_fp16_state);
            tensor<int32, [1]> var_2000 = const()[name = string("op_2000"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2002 = const()[name = string("op_2002"), val = tensor<int32, [1]>([32])];
            string input_331_pad_type_0 = const()[name = string("input_331_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_32_tcn_4_weight_to_fp16 = const()[name = string("sep_module_32_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1673472)))];
            tensor<fp16, [1, 256, 32]> input_331_cast_fp16 = conv(dilations = var_2002, groups = var_67, pad = input_331_pad_0, pad_type = input_331_pad_type_0, strides = var_2000, weight = sep_module_32_tcn_4_weight_to_fp16, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")];
            fp32 var_2006_alpha_1 = const()[name = string("op_2006_alpha_1"), val = fp32(0x1.1a61acp-1)];
            tensor<fp16, [1, 256, 32]> var_2006_cast_fp16 = leaky_relu(alpha = var_2006_alpha_1, x = input_331_cast_fp16)[name = string("op_2006_cast_fp16")];
            tensor<int32, [1]> var_2010 = const()[name = string("op_2010"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_133_cast_fp16 = reduce_mean(axes = var_2010, keep_dims = var_70, x = var_2006_cast_fp16)[name = string("mean_y_133_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2012_cast_fp16 = sub(x = var_2006_cast_fp16, y = mean_y_133_cast_fp16)[name = string("op_2012_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2013_cast_fp16 = square(x = var_2012_cast_fp16);
            tensor<int32, [1]> var_2014 = const()[name = string("op_2014"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_2015_cast_fp16 = reduce_mean(axes = var_2014, keep_dims = var_70, x = var_2013_cast_fp16)[name = string("op_2015_cast_fp16")];
            fp16 var_2016_to_fp16 = const()[name = string("op_2016_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_2017_cast_fp16 = add(x = var_2015_cast_fp16, y = var_2016_to_fp16)[name = string("op_2017_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_133_cast_fp16 = sqrt(x = var_2017_cast_fp16)[name = string("std_y_133_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_32_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_32_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1675072)))];
            tensor<fp16, [1, 256, 32]> var_2020_cast_fp16 = mul(x = sep_module_32_tcn_6_norm_gamma_to_fp16, y = var_2012_cast_fp16)[name = string("op_2020_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2021_cast_fp16 = real_div(x = var_2020_cast_fp16, y = std_y_133_cast_fp16)[name = string("op_2021_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_32_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_32_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1675648)))];
            tensor<fp16, [1, 256, 32]> y_66_cast_fp16 = add(x = var_2021_cast_fp16, y = sep_module_32_tcn_6_norm_beta_to_fp16)[name = string("y_66_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_333_cast_fp16 = add(x = input_323_cast_fp16, y = y_66_cast_fp16)[name = string("input_333_cast_fp16")];
            tensor<int32, [1]> var_2032 = const()[name = string("op_2032"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2034 = const()[name = string("op_2034"), val = tensor<int32, [1]>([1])];
            string input_335_pad_type_0 = const()[name = string("input_335_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_335_pad_0 = const()[name = string("input_335_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_33_tcn_0_weight_to_fp16 = const()[name = string("sep_module_33_tcn_0_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1676224)))];
            tensor<fp16, [1, 256, 32]> input_335_cast_fp16 = conv(dilations = var_2034, groups = var_66, pad = input_335_pad_0, pad_type = input_335_pad_type_0, strides = var_2032, weight = sep_module_33_tcn_0_weight_to_fp16, x = input_333_cast_fp16)[name = string("input_335_cast_fp16")];
            fp32 var_2038_alpha_1 = const()[name = string("op_2038_alpha_1"), val = fp32(0x1.fd7fdp-4)];
            tensor<fp16, [1, 256, 32]> var_2038_cast_fp16 = leaky_relu(alpha = var_2038_alpha_1, x = input_335_cast_fp16)[name = string("op_2038_cast_fp16")];
            tensor<int32, [1]> var_2042 = const()[name = string("op_2042"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_135_cast_fp16 = reduce_mean(axes = var_2042, keep_dims = var_70, x = var_2038_cast_fp16)[name = string("mean_y_135_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2044_cast_fp16 = sub(x = var_2038_cast_fp16, y = mean_y_135_cast_fp16)[name = string("op_2044_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2045_cast_fp16 = square(x = var_2044_cast_fp16);
            tensor<int32, [1]> var_2046 = const()[name = string("op_2046"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_2047_cast_fp16 = reduce_mean(axes = var_2046, keep_dims = var_70, x = var_2045_cast_fp16)[name = string("op_2047_cast_fp16")];
            fp16 var_2048_to_fp16 = const()[name = string("op_2048_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_2049_cast_fp16 = add(x = var_2047_cast_fp16, y = var_2048_to_fp16)[name = string("op_2049_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_135_cast_fp16 = sqrt(x = var_2049_cast_fp16)[name = string("std_y_135_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_33_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_33_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1807360)))];
            tensor<fp16, [1, 256, 32]> var_2052_cast_fp16 = mul(x = sep_module_33_tcn_2_norm_gamma_to_fp16, y = var_2044_cast_fp16)[name = string("op_2052_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2053_cast_fp16 = real_div(x = var_2052_cast_fp16, y = std_y_135_cast_fp16)[name = string("op_2053_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_33_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_33_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1807936)))];
            tensor<fp16, [1, 256, 32]> input_337_cast_fp16 = add(x = var_2053_cast_fp16, y = sep_module_33_tcn_2_norm_beta_to_fp16)[name = string("input_337_cast_fp16")];
            tensor<int32, [6]> input_339_pad_0 = const()[name = string("input_339_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 128, 0])];
            string input_339_mode_0 = const()[name = string("input_339_mode_0"), val = string("constant")];
            fp16 input_339_constant_val_0_to_fp16 = const()[name = string("input_339_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 160]> input_337_cast_fp16_state_input = read_state(input = input_337_cast_fp16_state);
            tensor<fp16, [1, 256, 160]> input_339_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 128]), end = tensor<int32, [3]>([1, 256, 160]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_337_cast_fp16, x = input_337_cast_fp16_state_input);
            write_state(data = input_339_cast_fp16, input = input_337_cast_fp16_state);
            tensor<int32, [1]> var_2058 = const()[name = string("op_2058"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2060 = const()[name = string("op_2060"), val = tensor<int32, [1]>([64])];
            string input_341_pad_type_0 = const()[name = string("input_341_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_341_pad_0 = const()[name = string("input_341_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_33_tcn_4_weight_to_fp16 = const()[name = string("sep_module_33_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1808512)))];
            tensor<fp16, [1, 256, 32]> input_341_cast_fp16 = conv(dilations = var_2060, groups = var_67, pad = input_341_pad_0, pad_type = input_341_pad_type_0, strides = var_2058, weight = sep_module_33_tcn_4_weight_to_fp16, x = input_339_cast_fp16)[name = string("input_341_cast_fp16")];
            fp32 var_2064_alpha_1 = const()[name = string("op_2064_alpha_1"), val = fp32(0x1.395a32p-1)];
            tensor<fp16, [1, 256, 32]> var_2064_cast_fp16 = leaky_relu(alpha = var_2064_alpha_1, x = input_341_cast_fp16)[name = string("op_2064_cast_fp16")];
            tensor<int32, [1]> var_2068 = const()[name = string("op_2068"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_137_cast_fp16 = reduce_mean(axes = var_2068, keep_dims = var_70, x = var_2064_cast_fp16)[name = string("mean_y_137_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2070_cast_fp16 = sub(x = var_2064_cast_fp16, y = mean_y_137_cast_fp16)[name = string("op_2070_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2071_cast_fp16 = square(x = var_2070_cast_fp16);
            tensor<int32, [1]> var_2072 = const()[name = string("op_2072"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_2073_cast_fp16 = reduce_mean(axes = var_2072, keep_dims = var_70, x = var_2071_cast_fp16)[name = string("op_2073_cast_fp16")];
            fp16 var_2074_to_fp16 = const()[name = string("op_2074_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_2075_cast_fp16 = add(x = var_2073_cast_fp16, y = var_2074_to_fp16)[name = string("op_2075_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_137_cast_fp16 = sqrt(x = var_2075_cast_fp16)[name = string("std_y_137_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_33_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_33_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1810112)))];
            tensor<fp16, [1, 256, 32]> var_2078_cast_fp16 = mul(x = sep_module_33_tcn_6_norm_gamma_to_fp16, y = var_2070_cast_fp16)[name = string("op_2078_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2079_cast_fp16 = real_div(x = var_2078_cast_fp16, y = std_y_137_cast_fp16)[name = string("op_2079_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_33_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_33_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1810688)))];
            tensor<fp16, [1, 256, 32]> y_68_cast_fp16 = add(x = var_2079_cast_fp16, y = sep_module_33_tcn_6_norm_beta_to_fp16)[name = string("y_68_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_343_cast_fp16 = add(x = input_333_cast_fp16, y = y_68_cast_fp16)[name = string("input_343_cast_fp16")];
            tensor<int32, [1]> var_2090 = const()[name = string("op_2090"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2092 = const()[name = string("op_2092"), val = tensor<int32, [1]>([1])];
            string input_345_pad_type_0 = const()[name = string("input_345_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_34_tcn_0_weight_to_fp16 = const()[name = string("sep_module_34_tcn_0_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1811264)))];
            tensor<fp16, [1, 256, 32]> input_345_cast_fp16 = conv(dilations = var_2092, groups = var_66, pad = input_345_pad_0, pad_type = input_345_pad_type_0, strides = var_2090, weight = sep_module_34_tcn_0_weight_to_fp16, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")];
            fp32 var_2096_alpha_1 = const()[name = string("op_2096_alpha_1"), val = fp32(0x1.857e9p-1)];
            tensor<fp16, [1, 256, 32]> var_2096_cast_fp16 = leaky_relu(alpha = var_2096_alpha_1, x = input_345_cast_fp16)[name = string("op_2096_cast_fp16")];
            tensor<int32, [1]> var_2100 = const()[name = string("op_2100"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_139_cast_fp16 = reduce_mean(axes = var_2100, keep_dims = var_70, x = var_2096_cast_fp16)[name = string("mean_y_139_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2102_cast_fp16 = sub(x = var_2096_cast_fp16, y = mean_y_139_cast_fp16)[name = string("op_2102_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2103_cast_fp16 = square(x = var_2102_cast_fp16);
            tensor<int32, [1]> var_2104 = const()[name = string("op_2104"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_2105_cast_fp16 = reduce_mean(axes = var_2104, keep_dims = var_70, x = var_2103_cast_fp16)[name = string("op_2105_cast_fp16")];
            fp16 var_2106_to_fp16 = const()[name = string("op_2106_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_2107_cast_fp16 = add(x = var_2105_cast_fp16, y = var_2106_to_fp16)[name = string("op_2107_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_139_cast_fp16 = sqrt(x = var_2107_cast_fp16)[name = string("std_y_139_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_34_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_34_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1942400)))];
            tensor<fp16, [1, 256, 32]> var_2110_cast_fp16 = mul(x = sep_module_34_tcn_2_norm_gamma_to_fp16, y = var_2102_cast_fp16)[name = string("op_2110_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2111_cast_fp16 = real_div(x = var_2110_cast_fp16, y = std_y_139_cast_fp16)[name = string("op_2111_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_34_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_34_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1942976)))];
            tensor<fp16, [1, 256, 32]> input_347_cast_fp16 = add(x = var_2111_cast_fp16, y = sep_module_34_tcn_2_norm_beta_to_fp16)[name = string("input_347_cast_fp16")];
            tensor<int32, [6]> input_349_pad_0 = const()[name = string("input_349_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 0])];
            string input_349_mode_0 = const()[name = string("input_349_mode_0"), val = string("constant")];
            fp16 input_349_constant_val_0_to_fp16 = const()[name = string("input_349_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 288]> input_347_cast_fp16_state_input = read_state(input = input_347_cast_fp16_state);
            tensor<fp16, [1, 256, 288]> input_349_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 256]), end = tensor<int32, [3]>([1, 256, 288]), end_mask = tensor<bool, [3]>([false, false, false]), update = input_347_cast_fp16, x = input_347_cast_fp16_state_input);
            write_state(data = input_349_cast_fp16, input = input_347_cast_fp16_state);
            tensor<int32, [1]> var_2116 = const()[name = string("op_2116"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2118 = const()[name = string("op_2118"), val = tensor<int32, [1]>([128])];
            string input_351_pad_type_0 = const()[name = string("input_351_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_351_pad_0 = const()[name = string("input_351_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_34_tcn_4_weight_to_fp16 = const()[name = string("sep_module_34_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1943552)))];
            tensor<fp16, [1, 256, 32]> input_351_cast_fp16 = conv(dilations = var_2118, groups = var_67, pad = input_351_pad_0, pad_type = input_351_pad_type_0, strides = var_2116, weight = sep_module_34_tcn_4_weight_to_fp16, x = input_349_cast_fp16)[name = string("input_351_cast_fp16")];
            fp32 var_2122_alpha_1 = const()[name = string("op_2122_alpha_1"), val = fp32(0x1.b0c094p-1)];
            tensor<fp16, [1, 256, 32]> var_2122_cast_fp16 = leaky_relu(alpha = var_2122_alpha_1, x = input_351_cast_fp16)[name = string("op_2122_cast_fp16")];
            tensor<int32, [1]> var_2126 = const()[name = string("op_2126"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_141_cast_fp16 = reduce_mean(axes = var_2126, keep_dims = var_70, x = var_2122_cast_fp16)[name = string("mean_y_141_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2128_cast_fp16 = sub(x = var_2122_cast_fp16, y = mean_y_141_cast_fp16)[name = string("op_2128_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2129_cast_fp16 = square(x = var_2128_cast_fp16);
            tensor<int32, [1]> var_2130 = const()[name = string("op_2130"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_2131_cast_fp16 = reduce_mean(axes = var_2130, keep_dims = var_70, x = var_2129_cast_fp16)[name = string("op_2131_cast_fp16")];
            fp16 var_2132_to_fp16 = const()[name = string("op_2132_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_2133_cast_fp16 = add(x = var_2131_cast_fp16, y = var_2132_to_fp16)[name = string("op_2133_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_141_cast_fp16 = sqrt(x = var_2133_cast_fp16)[name = string("std_y_141_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_34_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_34_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1945152)))];
            tensor<fp16, [1, 256, 32]> var_2136_cast_fp16 = mul(x = sep_module_34_tcn_6_norm_gamma_to_fp16, y = var_2128_cast_fp16)[name = string("op_2136_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2137_cast_fp16 = real_div(x = var_2136_cast_fp16, y = std_y_141_cast_fp16)[name = string("op_2137_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_34_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_34_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1945728)))];
            tensor<fp16, [1, 256, 32]> y_70_cast_fp16 = add(x = var_2137_cast_fp16, y = sep_module_34_tcn_6_norm_beta_to_fp16)[name = string("y_70_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_3_cast_fp16 = add(x = input_343_cast_fp16, y = y_70_cast_fp16)[name = string("input_3_cast_fp16")];
            tensor<int32, [1]> var_2148 = const()[name = string("op_2148"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2150 = const()[name = string("op_2150"), val = tensor<int32, [1]>([1])];
            string input_2_pad_type_0 = const()[name = string("input_2_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_2_pad_0 = const()[name = string("input_2_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_35_tcn_0_weight_to_fp16 = const()[name = string("sep_module_35_tcn_0_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(1946304)))];
            tensor<fp16, [1, 256, 32]> input_2_cast_fp16 = conv(dilations = var_2150, groups = var_66, pad = input_2_pad_0, pad_type = input_2_pad_type_0, strides = var_2148, weight = sep_module_35_tcn_0_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_2_cast_fp16")];
            fp32 var_2154_alpha_1 = const()[name = string("op_2154_alpha_1"), val = fp32(0x1.ec3c5ep-1)];
            tensor<fp16, [1, 256, 32]> var_2154_cast_fp16 = leaky_relu(alpha = var_2154_alpha_1, x = input_2_cast_fp16)[name = string("op_2154_cast_fp16")];
            tensor<int32, [1]> var_2158 = const()[name = string("op_2158"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_2_cast_fp16 = reduce_mean(axes = var_2158, keep_dims = var_70, x = var_2154_cast_fp16)[name = string("mean_y_2_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2160_cast_fp16 = sub(x = var_2154_cast_fp16, y = mean_y_2_cast_fp16)[name = string("op_2160_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2161_cast_fp16 = square(x = var_2160_cast_fp16);
            tensor<int32, [1]> var_2162 = const()[name = string("op_2162"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_2163_cast_fp16 = reduce_mean(axes = var_2162, keep_dims = var_70, x = var_2161_cast_fp16)[name = string("op_2163_cast_fp16")];
            fp16 var_2164_to_fp16 = const()[name = string("op_2164_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_2165_cast_fp16 = add(x = var_2163_cast_fp16, y = var_2164_to_fp16)[name = string("op_2165_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_2_cast_fp16 = sqrt(x = var_2165_cast_fp16)[name = string("std_y_2_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_35_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_35_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(2077440)))];
            tensor<fp16, [1, 256, 32]> var_2168_cast_fp16 = mul(x = sep_module_35_tcn_2_norm_gamma_to_fp16, y = var_2160_cast_fp16)[name = string("op_2168_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2169_cast_fp16 = real_div(x = var_2168_cast_fp16, y = std_y_2_cast_fp16)[name = string("op_2169_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_35_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_35_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(2078016)))];
            tensor<fp16, [1, 256, 32]> input_4_cast_fp16 = add(x = var_2169_cast_fp16, y = sep_module_35_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, 512, 0])];
            string input_6_mode_0 = const()[name = string("input_6_mode_0"), val = string("constant")];
            fp16 input_6_constant_val_0_to_fp16 = const()[name = string("input_6_constant_val_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 544]> input_4_cast_fp16_state_input = read_state(input = input_4_cast_fp16_state);
            tensor<fp16, [1, 256, 544]> input_6_cast_fp16 = slice_update(begin = tensor<int32, [3]>([0, 0, 512]), end = tensor<int32, [3]>([1, 256, 544]), end_mask = tensor<bool, [3]>([false, false, false]), 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);
            tensor<int32, [1]> var_2174 = const()[name = string("op_2174"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2176 = const()[name = string("op_2176"), val = tensor<int32, [1]>([256])];
            string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_35_tcn_4_weight_to_fp16 = const()[name = string("sep_module_35_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(2078592)))];
            tensor<fp16, [1, 256, 32]> input_1_cast_fp16 = conv(dilations = var_2176, groups = var_67, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_2174, weight = sep_module_35_tcn_4_weight_to_fp16, x = input_6_cast_fp16)[name = string("input_1_cast_fp16")];
            fp32 var_2180_alpha_1 = const()[name = string("op_2180_alpha_1"), val = fp32(0x1.f92c04p-1)];
            tensor<fp16, [1, 256, 32]> var_2180_cast_fp16 = leaky_relu(alpha = var_2180_alpha_1, x = input_1_cast_fp16)[name = string("op_2180_cast_fp16")];
            tensor<int32, [1]> var_2184 = const()[name = string("op_2184"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> mean_y_1_cast_fp16 = reduce_mean(axes = var_2184, keep_dims = var_70, x = var_2180_cast_fp16)[name = string("mean_y_1_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2186_cast_fp16 = sub(x = var_2180_cast_fp16, y = mean_y_1_cast_fp16)[name = string("op_2186_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2187_cast_fp16 = square(x = var_2186_cast_fp16);
            tensor<int32, [1]> var_2188 = const()[name = string("op_2188"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 32]> var_2189_cast_fp16 = reduce_mean(axes = var_2188, keep_dims = var_70, x = var_2187_cast_fp16)[name = string("op_2189_cast_fp16")];
            fp16 var_2190_to_fp16 = const()[name = string("op_2190_to_fp16"), val = fp16(0x1p-14)];
            tensor<fp16, [1, 1, 32]> var_2191_cast_fp16 = add(x = var_2189_cast_fp16, y = var_2190_to_fp16)[name = string("op_2191_cast_fp16")];
            tensor<fp16, [1, 1, 32]> std_y_1_cast_fp16 = sqrt(x = var_2191_cast_fp16)[name = string("std_y_1_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_35_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_35_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(2080192)))];
            tensor<fp16, [1, 256, 32]> var_2194_cast_fp16 = mul(x = sep_module_35_tcn_6_norm_gamma_to_fp16, y = var_2186_cast_fp16)[name = string("op_2194_cast_fp16")];
            tensor<fp16, [1, 256, 32]> var_2195_cast_fp16 = real_div(x = var_2194_cast_fp16, y = std_y_1_cast_fp16)[name = string("op_2195_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_35_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_35_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(2080768)))];
            tensor<fp16, [1, 256, 32]> y_1_cast_fp16 = add(x = var_2195_cast_fp16, y = sep_module_35_tcn_6_norm_beta_to_fp16)[name = string("y_1_cast_fp16")];
            tensor<fp16, [1, 256, 32]> x_1_cast_fp16 = add(x = input_3_cast_fp16, y = y_1_cast_fp16)[name = string("x_1_cast_fp16")];
            tensor<int32, [1]> input0_3_axes_0 = const()[name = string("input0_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 32]> input0_3_cast_fp16 = expand_dims(axes = input0_3_axes_0, x = x_1_cast_fp16)[name = string("input0_3_cast_fp16")];
            int32 var_2201 = const()[name = string("op_2201"), val = int32(1)];
            tensor<int32, [2]> var_2206 = const()[name = string("op_2206"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2208 = const()[name = string("op_2208"), val = tensor<int32, [2]>([1, 1])];
            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]>([128, 128, 0, 0])];
            tensor<fp16, [1, 1, 257, 1]> mask_layer_weight_to_fp16 = const()[name = string("mask_layer_weight_to_fp16"), val = tensor<fp16, [1, 1, 257, 1]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(2081344)))];
            tensor<fp16, [1, 1, 256, 32]> input0_1_cast_fp16 = conv(dilations = var_2208, groups = var_2201, pad = input0_1_pad_0, pad_type = input0_1_pad_type_0, strides = var_2206, weight = mask_layer_weight_to_fp16, x = input0_3_cast_fp16)[name = string("input0_1_cast_fp16")];
            tensor<fp16, [1, 1, 256, 32]> var_2211_cast_fp16 = sigmoid(x = input0_1_cast_fp16)[name = string("op_2211_cast_fp16")];
            tensor<int32, [1]> var_2212_axes_0 = const()[name = string("op_2212_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 32]> var_2212_cast_fp16 = expand_dims(axes = var_2212_axes_0, x = var_26_cast_fp16)[name = string("op_2212_cast_fp16")];
            tensor<fp16, [1, 1, 256, 47]> var_2212_cast_fp16_state_input = read_state(input = var_2212_cast_fp16_state);
            tensor<fp16, [1, 1, 256, 47]> var_2212_cast_fp16_state_updated = slice_update(begin = tensor<int32, [4]>([0, 0, 0, 15]), end = tensor<int32, [4]>([1, 1, 256, 47]), end_mask = tensor<bool, [4]>([false, false, false, false]), update = var_2212_cast_fp16, x = var_2212_cast_fp16_state_input);
            write_state(data = var_2212_cast_fp16_state_updated, input = var_2212_cast_fp16_state);
            tensor<fp16, [1, 1, 256, 32]> var_2212_cast_fp16_delayed = slice_by_size(begin = tensor<int32, [4]>([0, 0, 0, 0]), size = tensor<int32, [4]>([1, 1, 256, 32]), x = var_2212_cast_fp16_state_updated);
            tensor<fp16, [1, 1, 256, 32]> x_11_cast_fp16 = mul(x = var_2211_cast_fp16, y = var_2212_cast_fp16_delayed)[name = string("x_11_cast_fp16")];
            tensor<int32, [3]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [3]>([1, 256, -1])];
            tensor<fp16, [1, 256, 32]> input1_1_cast_fp16 = reshape(shape = concat_0x, x = x_11_cast_fp16)[name = string("input1_1_cast_fp16")];
            int32 var_2229 = const()[name = string("op_2229"), val = int32(1)];
            tensor<int32, [1]> var_2235 = const()[name = string("op_2235"), val = tensor<int32, [1]>([32])];
            tensor<int32, [1]> var_2237 = const()[name = string("op_2237"), val = tensor<int32, [1]>([1])];
            string var_2239_pad_type_0 = const()[name = string("op_2239_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> var_2239_pad_0 = const()[name = string("op_2239_pad_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<fp16, [256, 1, 64]> resynthesizer_weight_to_fp16 = const()[name = string("resynthesizer_weight_to_fp16"), val = tensor<fp16, [256, 1, 64]>(BLOBFILE(path = string("@model_path/weights/vi-nnet.weight.bin"), offset = uint64(2081984)))];
            tensor<fp16, [1, 256, 33]> input1_1_cast_fp16_state_input = read_state(input = input1_1_cast_fp16_state);
            tensor<fp16, [1, 256, 33]> input1_1_cast_fp16_padded = slice_update(begin = tensor<int32, [3]>([0, 0, 1]), end = tensor<int32, [3]>([1, 256, 33]), end_mask = tensor<bool, [3]>([false, false, false]), update = input1_1_cast_fp16, x = input1_1_cast_fp16_state_input);
            write_state(data = input1_1_cast_fp16_padded, input = input1_1_cast_fp16_state);
            tensor<fp16, [1, 1, 1024]> var_2239_cast_fp16 = conv_transpose(dilations = var_2237, groups = var_2229, pad = var_2239_pad_0, pad_type = var_2239_pad_type_0, strides = var_2235, weight = resynthesizer_weight_to_fp16, x = input1_1_cast_fp16_padded);
            string var_2239_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_2239_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
            tensor<fp32, [1, 1, 1024]> var_2239 = cast(dtype = var_2239_cast_fp16_to_fp32_dtype_0, x = var_2239_cast_fp16)[name = string("cast_0")];
        } -> (var_2239);
}