program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "5.27.1"}, {"coremlc-version", "1749.0.0.0.2"}, {"mldb_token", "mldb-loabjjpynk"}})]
{
    func main<ios15>(tensor<fp32, [1, 2, 480]> audio, tensor<fp16, [1, 2, 40]> cast_778_in_state, tensor<fp16, [1, 768, 1]> input1_2_cast_in_state, tensor<fp16, [1, 448, 4]> input_107_cast_in_state, tensor<fp16, [1, 448, 8]> input_117_cast_in_state, tensor<fp16, [1, 448, 16]> input_127_cast_in_state, tensor<fp16, [1, 448, 1]> input_12_cast_elementwise_in_state, tensor<fp16, [1, 448, 32]> input_137_cast_in_state, tensor<fp16, [1, 448, 2]> input_13_cast_elementwise_in_state, tensor<fp16, [1, 448, 64]> input_147_cast_in_state, tensor<fp16, [1, 448, 128]> input_157_cast_in_state, tensor<fp16, [1, 448, 256]> input_167_cast_in_state, tensor<fp16, [1, 448, 512]> input_177_cast_in_state, tensor<fp16, [1, 448, 4]> input_17_cast_in_state, tensor<fp16, [1, 448, 2]> input_187_cast_in_state, tensor<fp16, [1, 448, 4]> input_197_cast_in_state, tensor<fp16, [1, 448, 8]> input_207_cast_in_state, tensor<fp16, [1, 448, 16]> input_217_cast_in_state, tensor<fp16, [1, 448, 32]> input_227_cast_in_state, tensor<fp16, [1, 448, 64]> input_237_cast_in_state, tensor<fp16, [1, 448, 4]> input_23_cast_elementwise_in_state, tensor<fp16, [1, 448, 128]> input_247_cast_in_state, tensor<fp16, [1, 448, 256]> input_257_cast_in_state, tensor<fp16, [1, 448, 8]> input_27_cast_in_state, tensor<fp16, [1, 448, 16]> input_37_cast_in_state, tensor<fp16, [1, 448, 32]> input_47_cast_in_state, tensor<fp16, [1, 448, 512]> input_4_cast_in_state, tensor<fp16, [1, 448, 64]> input_57_cast_in_state, tensor<fp16, [1, 448, 128]> input_67_cast_in_state, tensor<fp16, [1, 448, 256]> input_77_cast_in_state, tensor<fp16, [1, 448, 2]> input_7_cast_in_state, tensor<fp16, [1, 448, 512]> input_87_cast_in_state, tensor<fp16, [1, 448, 2]> input_97_cast_in_state, tensor<fp16, [1, 1, 384, 7]> var_1661_cast_elementwise_in_state) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"audio", [1, 2, 480]}}), ("RangeDims", {{"audio", [[1, 1], [2, 2], [480, 480]]}}))), UserMetadata = dict<tensor<string, []>, tensor<string, []>>({{"iteration", "308880"}, {"taskid", "gqsiw4ipsm"}})] {
            tensor<int32, []> var_6 = const()[name = tensor<string, []>("op_6"), val = tensor<int32, []>(448)];
            tensor<bool, []> var_17 = const()[name = tensor<string, []>("op_17"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_19 = const()[name = tensor<string, []>("op_19"), val = tensor<int32, []>(2)];
            tensor<int32, []> var_21 = const()[name = tensor<string, []>("op_21"), val = tensor<int32, []>(1)];
            tensor<int32, [1]> var_31 = const()[name = tensor<string, []>("op_31"), val = tensor<int32, [1]>([40])];
            tensor<int32, [1]> var_33 = const()[name = tensor<string, []>("op_33"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input0_1_pad_type_0 = const()[name = tensor<string, []>("input0_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input0_1_pad_0 = const()[name = tensor<string, []>("input0_1_pad_0"), val = tensor<int32, [2]>([40, 40])];
            tensor<string, []> audio_to_fp16_dtype_0 = const()[name = tensor<string, []>("audio_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [384, 2, 80]> stem_front_end_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_front_end_0_weight_to_fp16"), val = tensor<fp16, [384, 2, 80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp16, [1, 2, 480]> cast_778 = cast(dtype = audio_to_fp16_dtype_0, x = audio);
            tensor<fp16, [1, 2, 520]> cast_778_expanded = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (cast_778_in_state, cast_778));
            tensor<fp16, [1, 2, 40]> cast_778_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -40]), size = tensor<int32, [3]>([1, 2, 40]), x = cast_778_expanded);
            tensor<fp16, [1, 384, 12]> input0_1_cast = conv(dilations = var_33, groups = var_21, pad = tensor<int32, [2]>([0, 0]), pad_type = tensor<string, []>("custom"), strides = var_31, weight = stem_front_end_0_weight_to_fp16, x = cast_778_expanded);
            tensor<fp16, [1, 384, 12]> var_36_cast = relu(x = input0_1_cast);
            tensor<int32, [1]> var_41 = const()[name = tensor<string, []>("op_41"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_4_cast = reduce_mean(axes = var_41, keep_dims = var_17, x = var_36_cast);
            tensor<fp16, [1, 384, 12]> var_43_cast = sub(x = var_36_cast, y = mean_y_4_cast);
            tensor<fp16, [1, 384, 12]> var_44_cast = square(x = var_43_cast);
            tensor<int32, [1]> var_45 = const()[name = tensor<string, []>("op_45"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_46_cast = reduce_mean(axes = var_45, keep_dims = var_17, x = var_44_cast);
            tensor<fp16, []> var_47_to_fp16 = const()[name = tensor<string, []>("op_47_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_48_cast = add(x = var_46_cast, y = var_47_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_4_cast = sqrt(x = var_48_cast);
            tensor<fp16, [1, 384, 1]> stem_front_norm_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_front_norm_norm_gamma_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(123008)))];
            tensor<fp16, [1, 384, 12]> var_51_cast = mul(x = stem_front_norm_norm_gamma_to_fp16, y = var_43_cast);
            tensor<fp16, [1, 384, 12]> var_52_cast = real_div(x = var_51_cast, y = std_y_4_cast);
            tensor<fp16, [1, 384, 1]> stem_front_norm_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_front_norm_norm_beta_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(123840)))];
            tensor<fp16, [1, 384, 12]> input_8_cast = add(x = var_52_cast, y = stem_front_norm_norm_beta_to_fp16);
            tensor<int32, [1]> var_55 = const()[name = tensor<string, []>("op_55"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_57 = const()[name = tensor<string, []>("op_57"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_12_pad_type_0 = const()[name = tensor<string, []>("input_12_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_12_pad_0 = const()[name = tensor<string, []>("input_12_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 384, 1]> stem_to_latent_weight_to_fp16 = const()[name = tensor<string, []>("stem_to_latent_weight_to_fp16"), val = tensor<fp16, [448, 384, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(124672)))];
            tensor<fp16, [1, 448, 12]> input_12_cast = conv(dilations = var_57, groups = var_21, pad = input_12_pad_0, pad_type = input_12_pad_type_0, strides = var_55, weight = stem_to_latent_weight_to_fp16, x = input_8_cast);
            tensor<int32, [1]> var_95 = const()[name = tensor<string, []>("op_95"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_97 = const()[name = tensor<string, []>("op_97"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_5_pad_type_0 = const()[name = tensor<string, []>("input_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_5_pad_0 = const()[name = tensor<string, []>("input_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_0_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_0_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(468800)))];
            tensor<fp16, [1, 448, 12]> input_5_cast = conv(dilations = var_97, groups = var_21, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = var_95, weight = stem_sep_module_0_tcn_0_weight_to_fp16, x = input_12_cast);
            tensor<fp16, []> var_101_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_101_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.35p-2)];
            tensor<fp16, [1, 448, 12]> var_101_cast = leaky_relu(alpha = var_101_alpha_1_to_fp16, x = input_5_cast);
            tensor<int32, [1]> var_105 = const()[name = tensor<string, []>("op_105"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_3_cast = reduce_mean(axes = var_105, keep_dims = var_17, x = var_101_cast);
            tensor<fp16, [1, 448, 12]> var_107_cast = sub(x = var_101_cast, y = mean_y_3_cast);
            tensor<fp16, [1, 448, 12]> var_108_cast = square(x = var_107_cast);
            tensor<int32, [1]> var_109 = const()[name = tensor<string, []>("op_109"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_110_cast = reduce_mean(axes = var_109, keep_dims = var_17, x = var_108_cast);
            tensor<fp16, []> var_111_to_fp16 = const()[name = tensor<string, []>("op_111_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_112_cast = add(x = var_110_cast, y = var_111_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_3_cast = sqrt(x = var_112_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_0_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_0_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(870272)))];
            tensor<fp16, [1, 448, 12]> var_115_cast = mul(x = stem_sep_module_0_tcn_2_norm_gamma_to_fp16, y = var_107_cast);
            tensor<fp16, [1, 448, 12]> var_116_cast = real_div(x = var_115_cast, y = std_y_3_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_0_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_0_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(871232)))];
            tensor<fp16, [1, 448, 12]> input_7_cast = add(x = var_116_cast, y = stem_sep_module_0_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 1])];
            tensor<string, []> input_9_mode_0 = const()[name = tensor<string, []>("input_9_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_9_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_9_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 14]> input_9_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_7_cast_in_state, input_7_cast));
            tensor<fp16, [1, 448, 2]> input_7_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 448, 2]), x = input_9_cast);
            tensor<int32, [1]> var_121 = const()[name = tensor<string, []>("op_121"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_123 = const()[name = tensor<string, []>("op_123"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_11_pad_type_0 = const()[name = tensor<string, []>("input_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_11_pad_0 = const()[name = tensor<string, []>("input_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_0_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_0_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(872192)))];
            tensor<fp16, [1, 448, 12]> input_11_cast = conv(dilations = var_123, groups = var_6, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_121, weight = stem_sep_module_0_tcn_4_weight_to_fp16, x = input_9_cast);
            tensor<fp16, []> var_127_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_127_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.478p-4)];
            tensor<fp16, [1, 448, 12]> var_127_cast = leaky_relu(alpha = var_127_alpha_1_to_fp16, x = input_11_cast);
            tensor<int32, [1]> var_131 = const()[name = tensor<string, []>("op_131"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_5_cast = reduce_mean(axes = var_131, keep_dims = var_17, x = var_127_cast);
            tensor<fp16, [1, 448, 12]> var_133_cast = sub(x = var_127_cast, y = mean_y_5_cast);
            tensor<fp16, [1, 448, 12]> var_134_cast = square(x = var_133_cast);
            tensor<int32, [1]> var_135 = const()[name = tensor<string, []>("op_135"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_136_cast = reduce_mean(axes = var_135, keep_dims = var_17, x = var_134_cast);
            tensor<fp16, []> var_137_to_fp16 = const()[name = tensor<string, []>("op_137_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_138_cast = add(x = var_136_cast, y = var_137_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_5_cast = sqrt(x = var_138_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_0_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_0_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(874944)))];
            tensor<fp16, [1, 448, 12]> var_141_cast = mul(x = stem_sep_module_0_tcn_6_norm_gamma_to_fp16, y = var_133_cast);
            tensor<fp16, [1, 448, 12]> var_142_cast = real_div(x = var_141_cast, y = std_y_5_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_0_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_0_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(875904)))];
            tensor<fp16, [1, 448, 12]> y_2_cast = add(x = var_142_cast, y = stem_sep_module_0_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 13]> input_12_cast_elementwise_expanded = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_12_cast_elementwise_in_state, input_12_cast));
            tensor<fp16, [1, 448, 12]> input_12_cast_elementwise_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 448, 12]), x = input_12_cast_elementwise_expanded);
            tensor<fp16, [1, 448, 1]> input_12_cast_elementwise_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -1]), size = tensor<int32, [3]>([1, 448, 1]), x = input_12_cast_elementwise_expanded);
            tensor<fp16, [1, 448, 12]> input_13_cast = add(x = input_12_cast_elementwise_delayed, y = y_2_cast);
            tensor<int32, [1]> var_153 = const()[name = tensor<string, []>("op_153"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_155 = const()[name = tensor<string, []>("op_155"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_15_pad_type_0 = const()[name = tensor<string, []>("input_15_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_15_pad_0 = const()[name = tensor<string, []>("input_15_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_1_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_1_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(876864)))];
            tensor<fp16, [1, 448, 12]> input_15_cast = conv(dilations = var_155, groups = var_21, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = var_153, weight = stem_sep_module_1_tcn_0_weight_to_fp16, x = input_13_cast);
            tensor<fp16, []> var_159_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_159_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.e6cp-2)];
            tensor<fp16, [1, 448, 12]> var_159_cast = leaky_relu(alpha = var_159_alpha_1_to_fp16, x = input_15_cast);
            tensor<int32, [1]> var_163 = const()[name = tensor<string, []>("op_163"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_7_cast = reduce_mean(axes = var_163, keep_dims = var_17, x = var_159_cast);
            tensor<fp16, [1, 448, 12]> var_165_cast = sub(x = var_159_cast, y = mean_y_7_cast);
            tensor<fp16, [1, 448, 12]> var_166_cast = square(x = var_165_cast);
            tensor<int32, [1]> var_167 = const()[name = tensor<string, []>("op_167"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_168_cast = reduce_mean(axes = var_167, keep_dims = var_17, x = var_166_cast);
            tensor<fp16, []> var_169_to_fp16 = const()[name = tensor<string, []>("op_169_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_170_cast = add(x = var_168_cast, y = var_169_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_7_cast = sqrt(x = var_170_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_1_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_1_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(1278336)))];
            tensor<fp16, [1, 448, 12]> var_173_cast = mul(x = stem_sep_module_1_tcn_2_norm_gamma_to_fp16, y = var_165_cast);
            tensor<fp16, [1, 448, 12]> var_174_cast = real_div(x = var_173_cast, y = std_y_7_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_1_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_1_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(1279296)))];
            tensor<fp16, [1, 448, 12]> input_17_cast = add(x = var_174_cast, y = stem_sep_module_1_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            tensor<string, []> input_19_mode_0 = const()[name = tensor<string, []>("input_19_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_19_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_19_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 16]> input_19_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_17_cast_in_state, input_17_cast));
            tensor<fp16, [1, 448, 4]> input_17_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 448, 4]), x = input_19_cast);
            tensor<int32, [1]> var_179 = const()[name = tensor<string, []>("op_179"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_181 = const()[name = tensor<string, []>("op_181"), val = tensor<int32, [1]>([2])];
            tensor<string, []> input_21_pad_type_0 = const()[name = tensor<string, []>("input_21_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_21_pad_0 = const()[name = tensor<string, []>("input_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_1_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_1_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(1280256)))];
            tensor<fp16, [1, 448, 12]> input_21_cast = conv(dilations = var_181, groups = var_6, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = var_179, weight = stem_sep_module_1_tcn_4_weight_to_fp16, x = input_19_cast);
            tensor<fp16, []> var_185_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_185_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.eb4p-3)];
            tensor<fp16, [1, 448, 12]> var_185_cast = leaky_relu(alpha = var_185_alpha_1_to_fp16, x = input_21_cast);
            tensor<int32, [1]> var_189 = const()[name = tensor<string, []>("op_189"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_9_cast = reduce_mean(axes = var_189, keep_dims = var_17, x = var_185_cast);
            tensor<fp16, [1, 448, 12]> var_191_cast = sub(x = var_185_cast, y = mean_y_9_cast);
            tensor<fp16, [1, 448, 12]> var_192_cast = square(x = var_191_cast);
            tensor<int32, [1]> var_193 = const()[name = tensor<string, []>("op_193"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_194_cast = reduce_mean(axes = var_193, keep_dims = var_17, x = var_192_cast);
            tensor<fp16, []> var_195_to_fp16 = const()[name = tensor<string, []>("op_195_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_196_cast = add(x = var_194_cast, y = var_195_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_9_cast = sqrt(x = var_196_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_1_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_1_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(1283008)))];
            tensor<fp16, [1, 448, 12]> var_199_cast = mul(x = stem_sep_module_1_tcn_6_norm_gamma_to_fp16, y = var_191_cast);
            tensor<fp16, [1, 448, 12]> var_200_cast = real_div(x = var_199_cast, y = std_y_9_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_1_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_1_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(1283968)))];
            tensor<fp16, [1, 448, 12]> y_4_cast = add(x = var_200_cast, y = stem_sep_module_1_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 14]> input_13_cast_elementwise_expanded = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_13_cast_elementwise_in_state, input_13_cast));
            tensor<fp16, [1, 448, 12]> input_13_cast_elementwise_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 448, 12]), x = input_13_cast_elementwise_expanded);
            tensor<fp16, [1, 448, 2]> input_13_cast_elementwise_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 448, 2]), x = input_13_cast_elementwise_expanded);
            tensor<fp16, [1, 448, 12]> input_23_cast = add(x = input_13_cast_elementwise_delayed, y = y_4_cast);
            tensor<int32, [1]> var_211 = const()[name = tensor<string, []>("op_211"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_213 = const()[name = tensor<string, []>("op_213"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_25_pad_type_0 = const()[name = tensor<string, []>("input_25_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_25_pad_0 = const()[name = tensor<string, []>("input_25_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_2_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_2_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(1284928)))];
            tensor<fp16, [1, 448, 12]> input_25_cast = conv(dilations = var_213, groups = var_21, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = var_211, weight = stem_sep_module_2_tcn_0_weight_to_fp16, x = input_23_cast);
            tensor<fp16, []> var_217_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_217_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.64p-1)];
            tensor<fp16, [1, 448, 12]> var_217_cast = leaky_relu(alpha = var_217_alpha_1_to_fp16, x = input_25_cast);
            tensor<int32, [1]> var_221 = const()[name = tensor<string, []>("op_221"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_11_cast = reduce_mean(axes = var_221, keep_dims = var_17, x = var_217_cast);
            tensor<fp16, [1, 448, 12]> var_223_cast = sub(x = var_217_cast, y = mean_y_11_cast);
            tensor<fp16, [1, 448, 12]> var_224_cast = square(x = var_223_cast);
            tensor<int32, [1]> var_225 = const()[name = tensor<string, []>("op_225"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_226_cast = reduce_mean(axes = var_225, keep_dims = var_17, x = var_224_cast);
            tensor<fp16, []> var_227_to_fp16 = const()[name = tensor<string, []>("op_227_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_228_cast = add(x = var_226_cast, y = var_227_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_11_cast = sqrt(x = var_228_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_2_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_2_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(1686400)))];
            tensor<fp16, [1, 448, 12]> var_231_cast = mul(x = stem_sep_module_2_tcn_2_norm_gamma_to_fp16, y = var_223_cast);
            tensor<fp16, [1, 448, 12]> var_232_cast = real_div(x = var_231_cast, y = std_y_11_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_2_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_2_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(1687360)))];
            tensor<fp16, [1, 448, 12]> input_27_cast = add(x = var_232_cast, y = stem_sep_module_2_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_29_pad_0 = const()[name = tensor<string, []>("input_29_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_29_mode_0 = const()[name = tensor<string, []>("input_29_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_29_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_29_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 20]> input_29_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_27_cast_in_state, input_27_cast));
            tensor<fp16, [1, 448, 8]> input_27_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 448, 8]), x = input_29_cast);
            tensor<int32, [1]> var_237 = const()[name = tensor<string, []>("op_237"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_239 = const()[name = tensor<string, []>("op_239"), val = tensor<int32, [1]>([4])];
            tensor<string, []> input_31_pad_type_0 = const()[name = tensor<string, []>("input_31_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_31_pad_0 = const()[name = tensor<string, []>("input_31_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_2_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_2_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(1688320)))];
            tensor<fp16, [1, 448, 12]> input_31_cast = conv(dilations = var_239, groups = var_6, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = var_237, weight = stem_sep_module_2_tcn_4_weight_to_fp16, x = input_29_cast);
            tensor<fp16, []> var_243_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_243_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.e4p-3)];
            tensor<fp16, [1, 448, 12]> var_243_cast = leaky_relu(alpha = var_243_alpha_1_to_fp16, x = input_31_cast);
            tensor<int32, [1]> var_247 = const()[name = tensor<string, []>("op_247"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_13_cast = reduce_mean(axes = var_247, keep_dims = var_17, x = var_243_cast);
            tensor<fp16, [1, 448, 12]> var_249_cast = sub(x = var_243_cast, y = mean_y_13_cast);
            tensor<fp16, [1, 448, 12]> var_250_cast = square(x = var_249_cast);
            tensor<int32, [1]> var_251 = const()[name = tensor<string, []>("op_251"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_252_cast = reduce_mean(axes = var_251, keep_dims = var_17, x = var_250_cast);
            tensor<fp16, []> var_253_to_fp16 = const()[name = tensor<string, []>("op_253_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_254_cast = add(x = var_252_cast, y = var_253_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_13_cast = sqrt(x = var_254_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_2_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_2_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(1691072)))];
            tensor<fp16, [1, 448, 12]> var_257_cast = mul(x = stem_sep_module_2_tcn_6_norm_gamma_to_fp16, y = var_249_cast);
            tensor<fp16, [1, 448, 12]> var_258_cast = real_div(x = var_257_cast, y = std_y_13_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_2_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_2_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(1692032)))];
            tensor<fp16, [1, 448, 12]> y_6_cast = add(x = var_258_cast, y = stem_sep_module_2_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 16]> input_23_cast_elementwise_expanded = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_23_cast_elementwise_in_state, input_23_cast));
            tensor<fp16, [1, 448, 12]> input_23_cast_elementwise_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 448, 12]), x = input_23_cast_elementwise_expanded);
            tensor<fp16, [1, 448, 4]> input_23_cast_elementwise_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 448, 4]), x = input_23_cast_elementwise_expanded);
            tensor<fp16, [1, 448, 12]> input_33_cast = add(x = input_23_cast_elementwise_delayed, y = y_6_cast);
            tensor<int32, [1]> var_269 = const()[name = tensor<string, []>("op_269"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_271 = const()[name = tensor<string, []>("op_271"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_35_pad_type_0 = const()[name = tensor<string, []>("input_35_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_35_pad_0 = const()[name = tensor<string, []>("input_35_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_3_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_3_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(1692992)))];
            tensor<fp16, [1, 448, 12]> input_35_cast = conv(dilations = var_271, groups = var_21, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = var_269, weight = stem_sep_module_3_tcn_0_weight_to_fp16, x = input_33_cast);
            tensor<fp16, []> var_275_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_275_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.5e4p-1)];
            tensor<fp16, [1, 448, 12]> var_275_cast = leaky_relu(alpha = var_275_alpha_1_to_fp16, x = input_35_cast);
            tensor<int32, [1]> var_279 = const()[name = tensor<string, []>("op_279"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_15_cast = reduce_mean(axes = var_279, keep_dims = var_17, x = var_275_cast);
            tensor<fp16, [1, 448, 12]> var_281_cast = sub(x = var_275_cast, y = mean_y_15_cast);
            tensor<fp16, [1, 448, 12]> var_282_cast = square(x = var_281_cast);
            tensor<int32, [1]> var_283 = const()[name = tensor<string, []>("op_283"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_284_cast = reduce_mean(axes = var_283, keep_dims = var_17, x = var_282_cast);
            tensor<fp16, []> var_285_to_fp16 = const()[name = tensor<string, []>("op_285_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_286_cast = add(x = var_284_cast, y = var_285_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_15_cast = sqrt(x = var_286_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_3_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_3_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2094464)))];
            tensor<fp16, [1, 448, 12]> var_289_cast = mul(x = stem_sep_module_3_tcn_2_norm_gamma_to_fp16, y = var_281_cast);
            tensor<fp16, [1, 448, 12]> var_290_cast = real_div(x = var_289_cast, y = std_y_15_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_3_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_3_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2095424)))];
            tensor<fp16, [1, 448, 12]> input_37_cast = add(x = var_290_cast, y = stem_sep_module_3_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_39_pad_0 = const()[name = tensor<string, []>("input_39_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            tensor<string, []> input_39_mode_0 = const()[name = tensor<string, []>("input_39_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_39_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_39_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 28]> input_39_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_37_cast_in_state, input_37_cast));
            tensor<fp16, [1, 448, 16]> input_37_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -16]), size = tensor<int32, [3]>([1, 448, 16]), x = input_39_cast);
            tensor<int32, [1]> var_295 = const()[name = tensor<string, []>("op_295"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_297 = const()[name = tensor<string, []>("op_297"), val = tensor<int32, [1]>([8])];
            tensor<string, []> input_41_pad_type_0 = const()[name = tensor<string, []>("input_41_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_41_pad_0 = const()[name = tensor<string, []>("input_41_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_3_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_3_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2096384)))];
            tensor<fp16, [1, 448, 12]> input_41_cast = conv(dilations = var_297, groups = var_6, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = var_295, weight = stem_sep_module_3_tcn_4_weight_to_fp16, x = input_39_cast);
            tensor<fp16, []> var_301_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_301_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.874p-3)];
            tensor<fp16, [1, 448, 12]> var_301_cast = leaky_relu(alpha = var_301_alpha_1_to_fp16, x = input_41_cast);
            tensor<int32, [1]> var_305 = const()[name = tensor<string, []>("op_305"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_17_cast = reduce_mean(axes = var_305, keep_dims = var_17, x = var_301_cast);
            tensor<fp16, [1, 448, 12]> var_307_cast = sub(x = var_301_cast, y = mean_y_17_cast);
            tensor<fp16, [1, 448, 12]> var_308_cast = square(x = var_307_cast);
            tensor<int32, [1]> var_309 = const()[name = tensor<string, []>("op_309"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_310_cast = reduce_mean(axes = var_309, keep_dims = var_17, x = var_308_cast);
            tensor<fp16, []> var_311_to_fp16 = const()[name = tensor<string, []>("op_311_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_312_cast = add(x = var_310_cast, y = var_311_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_17_cast = sqrt(x = var_312_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_3_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_3_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2099136)))];
            tensor<fp16, [1, 448, 12]> var_315_cast = mul(x = stem_sep_module_3_tcn_6_norm_gamma_to_fp16, y = var_307_cast);
            tensor<fp16, [1, 448, 12]> var_316_cast = real_div(x = var_315_cast, y = std_y_17_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_3_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_3_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2100096)))];
            tensor<fp16, [1, 448, 12]> y_8_cast = add(x = var_316_cast, y = stem_sep_module_3_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_43_cast = add(x = input_33_cast, y = y_8_cast);
            tensor<int32, [1]> var_327 = const()[name = tensor<string, []>("op_327"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_329 = const()[name = tensor<string, []>("op_329"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_45_pad_type_0 = const()[name = tensor<string, []>("input_45_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_45_pad_0 = const()[name = tensor<string, []>("input_45_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_4_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_4_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2101056)))];
            tensor<fp16, [1, 448, 12]> input_45_cast = conv(dilations = var_329, groups = var_21, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_327, weight = stem_sep_module_4_tcn_0_weight_to_fp16, x = input_43_cast);
            tensor<fp16, []> var_333_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_333_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.78p-1)];
            tensor<fp16, [1, 448, 12]> var_333_cast = leaky_relu(alpha = var_333_alpha_1_to_fp16, x = input_45_cast);
            tensor<int32, [1]> var_337 = const()[name = tensor<string, []>("op_337"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_19_cast = reduce_mean(axes = var_337, keep_dims = var_17, x = var_333_cast);
            tensor<fp16, [1, 448, 12]> var_339_cast = sub(x = var_333_cast, y = mean_y_19_cast);
            tensor<fp16, [1, 448, 12]> var_340_cast = square(x = var_339_cast);
            tensor<int32, [1]> var_341 = const()[name = tensor<string, []>("op_341"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_342_cast = reduce_mean(axes = var_341, keep_dims = var_17, x = var_340_cast);
            tensor<fp16, []> var_343_to_fp16 = const()[name = tensor<string, []>("op_343_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_344_cast = add(x = var_342_cast, y = var_343_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_19_cast = sqrt(x = var_344_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_4_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_4_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2502528)))];
            tensor<fp16, [1, 448, 12]> var_347_cast = mul(x = stem_sep_module_4_tcn_2_norm_gamma_to_fp16, y = var_339_cast);
            tensor<fp16, [1, 448, 12]> var_348_cast = real_div(x = var_347_cast, y = std_y_19_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_4_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_4_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2503488)))];
            tensor<fp16, [1, 448, 12]> input_47_cast = add(x = var_348_cast, y = stem_sep_module_4_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_49_pad_0 = const()[name = tensor<string, []>("input_49_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            tensor<string, []> input_49_mode_0 = const()[name = tensor<string, []>("input_49_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_49_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_49_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 44]> input_49_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_47_cast_in_state, input_47_cast));
            tensor<fp16, [1, 448, 32]> input_47_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -32]), size = tensor<int32, [3]>([1, 448, 32]), x = input_49_cast);
            tensor<int32, [1]> var_353 = const()[name = tensor<string, []>("op_353"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_355 = const()[name = tensor<string, []>("op_355"), val = tensor<int32, [1]>([16])];
            tensor<string, []> input_51_pad_type_0 = const()[name = tensor<string, []>("input_51_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_51_pad_0 = const()[name = tensor<string, []>("input_51_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_4_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_4_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2504448)))];
            tensor<fp16, [1, 448, 12]> input_51_cast = conv(dilations = var_355, groups = var_6, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = var_353, weight = stem_sep_module_4_tcn_4_weight_to_fp16, x = input_49_cast);
            tensor<fp16, []> var_359_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_359_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.2ecp-3)];
            tensor<fp16, [1, 448, 12]> var_359_cast = leaky_relu(alpha = var_359_alpha_1_to_fp16, x = input_51_cast);
            tensor<int32, [1]> var_363 = const()[name = tensor<string, []>("op_363"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_21_cast = reduce_mean(axes = var_363, keep_dims = var_17, x = var_359_cast);
            tensor<fp16, [1, 448, 12]> var_365_cast = sub(x = var_359_cast, y = mean_y_21_cast);
            tensor<fp16, [1, 448, 12]> var_366_cast = square(x = var_365_cast);
            tensor<int32, [1]> var_367 = const()[name = tensor<string, []>("op_367"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_368_cast = reduce_mean(axes = var_367, keep_dims = var_17, x = var_366_cast);
            tensor<fp16, []> var_369_to_fp16 = const()[name = tensor<string, []>("op_369_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_370_cast = add(x = var_368_cast, y = var_369_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_21_cast = sqrt(x = var_370_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_4_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_4_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2507200)))];
            tensor<fp16, [1, 448, 12]> var_373_cast = mul(x = stem_sep_module_4_tcn_6_norm_gamma_to_fp16, y = var_365_cast);
            tensor<fp16, [1, 448, 12]> var_374_cast = real_div(x = var_373_cast, y = std_y_21_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_4_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_4_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2508160)))];
            tensor<fp16, [1, 448, 12]> y_10_cast = add(x = var_374_cast, y = stem_sep_module_4_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_53_cast = add(x = input_43_cast, y = y_10_cast);
            tensor<int32, [1]> var_385 = const()[name = tensor<string, []>("op_385"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_387 = const()[name = tensor<string, []>("op_387"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_55_pad_type_0 = const()[name = tensor<string, []>("input_55_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_55_pad_0 = const()[name = tensor<string, []>("input_55_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_5_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_5_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2509120)))];
            tensor<fp16, [1, 448, 12]> input_55_cast = conv(dilations = var_387, groups = var_21, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = var_385, weight = stem_sep_module_5_tcn_0_weight_to_fp16, x = input_53_cast);
            tensor<fp16, []> var_391_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_391_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.14cp-1)];
            tensor<fp16, [1, 448, 12]> var_391_cast = leaky_relu(alpha = var_391_alpha_1_to_fp16, x = input_55_cast);
            tensor<int32, [1]> var_395 = const()[name = tensor<string, []>("op_395"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_23_cast = reduce_mean(axes = var_395, keep_dims = var_17, x = var_391_cast);
            tensor<fp16, [1, 448, 12]> var_397_cast = sub(x = var_391_cast, y = mean_y_23_cast);
            tensor<fp16, [1, 448, 12]> var_398_cast = square(x = var_397_cast);
            tensor<int32, [1]> var_399 = const()[name = tensor<string, []>("op_399"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_400_cast = reduce_mean(axes = var_399, keep_dims = var_17, x = var_398_cast);
            tensor<fp16, []> var_401_to_fp16 = const()[name = tensor<string, []>("op_401_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_402_cast = add(x = var_400_cast, y = var_401_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_23_cast = sqrt(x = var_402_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_5_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_5_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2910592)))];
            tensor<fp16, [1, 448, 12]> var_405_cast = mul(x = stem_sep_module_5_tcn_2_norm_gamma_to_fp16, y = var_397_cast);
            tensor<fp16, [1, 448, 12]> var_406_cast = real_div(x = var_405_cast, y = std_y_23_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_5_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_5_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2911552)))];
            tensor<fp16, [1, 448, 12]> input_57_cast = add(x = var_406_cast, y = stem_sep_module_5_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_59_pad_0 = const()[name = tensor<string, []>("input_59_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            tensor<string, []> input_59_mode_0 = const()[name = tensor<string, []>("input_59_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_59_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_59_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 76]> input_59_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_57_cast_in_state, input_57_cast));
            tensor<fp16, [1, 448, 64]> input_57_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 448, 64]), x = input_59_cast);
            tensor<int32, [1]> var_411 = const()[name = tensor<string, []>("op_411"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_413 = const()[name = tensor<string, []>("op_413"), val = tensor<int32, [1]>([32])];
            tensor<string, []> input_61_pad_type_0 = const()[name = tensor<string, []>("input_61_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_61_pad_0 = const()[name = tensor<string, []>("input_61_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_5_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_5_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2912512)))];
            tensor<fp16, [1, 448, 12]> input_61_cast = conv(dilations = var_413, groups = var_6, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = var_411, weight = stem_sep_module_5_tcn_4_weight_to_fp16, x = input_59_cast);
            tensor<fp16, []> var_417_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_417_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.234p-2)];
            tensor<fp16, [1, 448, 12]> var_417_cast = leaky_relu(alpha = var_417_alpha_1_to_fp16, x = input_61_cast);
            tensor<int32, [1]> var_421 = const()[name = tensor<string, []>("op_421"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_25_cast = reduce_mean(axes = var_421, keep_dims = var_17, x = var_417_cast);
            tensor<fp16, [1, 448, 12]> var_423_cast = sub(x = var_417_cast, y = mean_y_25_cast);
            tensor<fp16, [1, 448, 12]> var_424_cast = square(x = var_423_cast);
            tensor<int32, [1]> var_425 = const()[name = tensor<string, []>("op_425"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_426_cast = reduce_mean(axes = var_425, keep_dims = var_17, x = var_424_cast);
            tensor<fp16, []> var_427_to_fp16 = const()[name = tensor<string, []>("op_427_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_428_cast = add(x = var_426_cast, y = var_427_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_25_cast = sqrt(x = var_428_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_5_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_5_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2915264)))];
            tensor<fp16, [1, 448, 12]> var_431_cast = mul(x = stem_sep_module_5_tcn_6_norm_gamma_to_fp16, y = var_423_cast);
            tensor<fp16, [1, 448, 12]> var_432_cast = real_div(x = var_431_cast, y = std_y_25_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_5_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_5_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2916224)))];
            tensor<fp16, [1, 448, 12]> y_12_cast = add(x = var_432_cast, y = stem_sep_module_5_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_63_cast = add(x = input_53_cast, y = y_12_cast);
            tensor<int32, [1]> var_443 = const()[name = tensor<string, []>("op_443"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_445 = const()[name = tensor<string, []>("op_445"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_65_pad_type_0 = const()[name = tensor<string, []>("input_65_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_65_pad_0 = const()[name = tensor<string, []>("input_65_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_6_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_6_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(2917184)))];
            tensor<fp16, [1, 448, 12]> input_65_cast = conv(dilations = var_445, groups = var_21, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = var_443, weight = stem_sep_module_6_tcn_0_weight_to_fp16, x = input_63_cast);
            tensor<fp16, []> var_449_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_449_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.ae8p-2)];
            tensor<fp16, [1, 448, 12]> var_449_cast = leaky_relu(alpha = var_449_alpha_1_to_fp16, x = input_65_cast);
            tensor<int32, [1]> var_453 = const()[name = tensor<string, []>("op_453"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_27_cast = reduce_mean(axes = var_453, keep_dims = var_17, x = var_449_cast);
            tensor<fp16, [1, 448, 12]> var_455_cast = sub(x = var_449_cast, y = mean_y_27_cast);
            tensor<fp16, [1, 448, 12]> var_456_cast = square(x = var_455_cast);
            tensor<int32, [1]> var_457 = const()[name = tensor<string, []>("op_457"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_458_cast = reduce_mean(axes = var_457, keep_dims = var_17, x = var_456_cast);
            tensor<fp16, []> var_459_to_fp16 = const()[name = tensor<string, []>("op_459_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_460_cast = add(x = var_458_cast, y = var_459_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_27_cast = sqrt(x = var_460_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_6_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_6_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(3318656)))];
            tensor<fp16, [1, 448, 12]> var_463_cast = mul(x = stem_sep_module_6_tcn_2_norm_gamma_to_fp16, y = var_455_cast);
            tensor<fp16, [1, 448, 12]> var_464_cast = real_div(x = var_463_cast, y = std_y_27_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_6_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_6_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(3319616)))];
            tensor<fp16, [1, 448, 12]> input_67_cast = add(x = var_464_cast, y = stem_sep_module_6_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_69_pad_0 = const()[name = tensor<string, []>("input_69_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 128, 0])];
            tensor<string, []> input_69_mode_0 = const()[name = tensor<string, []>("input_69_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_69_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_69_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 140]> input_69_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_67_cast_in_state, input_67_cast));
            tensor<fp16, [1, 448, 128]> input_67_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -128]), size = tensor<int32, [3]>([1, 448, 128]), x = input_69_cast);
            tensor<int32, [1]> var_469 = const()[name = tensor<string, []>("op_469"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_471 = const()[name = tensor<string, []>("op_471"), val = tensor<int32, [1]>([64])];
            tensor<string, []> input_71_pad_type_0 = const()[name = tensor<string, []>("input_71_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_71_pad_0 = const()[name = tensor<string, []>("input_71_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_6_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_6_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(3320576)))];
            tensor<fp16, [1, 448, 12]> input_71_cast = conv(dilations = var_471, groups = var_6, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = var_469, weight = stem_sep_module_6_tcn_4_weight_to_fp16, x = input_69_cast);
            tensor<fp16, []> var_475_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_475_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.c6p-3)];
            tensor<fp16, [1, 448, 12]> var_475_cast = leaky_relu(alpha = var_475_alpha_1_to_fp16, x = input_71_cast);
            tensor<int32, [1]> var_479 = const()[name = tensor<string, []>("op_479"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_29_cast = reduce_mean(axes = var_479, keep_dims = var_17, x = var_475_cast);
            tensor<fp16, [1, 448, 12]> var_481_cast = sub(x = var_475_cast, y = mean_y_29_cast);
            tensor<fp16, [1, 448, 12]> var_482_cast = square(x = var_481_cast);
            tensor<int32, [1]> var_483 = const()[name = tensor<string, []>("op_483"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_484_cast = reduce_mean(axes = var_483, keep_dims = var_17, x = var_482_cast);
            tensor<fp16, []> var_485_to_fp16 = const()[name = tensor<string, []>("op_485_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_486_cast = add(x = var_484_cast, y = var_485_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_29_cast = sqrt(x = var_486_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_6_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_6_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(3323328)))];
            tensor<fp16, [1, 448, 12]> var_489_cast = mul(x = stem_sep_module_6_tcn_6_norm_gamma_to_fp16, y = var_481_cast);
            tensor<fp16, [1, 448, 12]> var_490_cast = real_div(x = var_489_cast, y = std_y_29_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_6_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_6_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(3324288)))];
            tensor<fp16, [1, 448, 12]> y_14_cast = add(x = var_490_cast, y = stem_sep_module_6_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_73_cast = add(x = input_63_cast, y = y_14_cast);
            tensor<int32, [1]> var_501 = const()[name = tensor<string, []>("op_501"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_503 = const()[name = tensor<string, []>("op_503"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_75_pad_type_0 = const()[name = tensor<string, []>("input_75_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_75_pad_0 = const()[name = tensor<string, []>("input_75_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_7_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_7_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(3325248)))];
            tensor<fp16, [1, 448, 12]> input_75_cast = conv(dilations = var_503, groups = var_21, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = var_501, weight = stem_sep_module_7_tcn_0_weight_to_fp16, x = input_73_cast);
            tensor<fp16, []> var_507_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_507_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.124p-1)];
            tensor<fp16, [1, 448, 12]> var_507_cast = leaky_relu(alpha = var_507_alpha_1_to_fp16, x = input_75_cast);
            tensor<int32, [1]> var_511 = const()[name = tensor<string, []>("op_511"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_31_cast = reduce_mean(axes = var_511, keep_dims = var_17, x = var_507_cast);
            tensor<fp16, [1, 448, 12]> var_513_cast = sub(x = var_507_cast, y = mean_y_31_cast);
            tensor<fp16, [1, 448, 12]> var_514_cast = square(x = var_513_cast);
            tensor<int32, [1]> var_515 = const()[name = tensor<string, []>("op_515"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_516_cast = reduce_mean(axes = var_515, keep_dims = var_17, x = var_514_cast);
            tensor<fp16, []> var_517_to_fp16 = const()[name = tensor<string, []>("op_517_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_518_cast = add(x = var_516_cast, y = var_517_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_31_cast = sqrt(x = var_518_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_7_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_7_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(3726720)))];
            tensor<fp16, [1, 448, 12]> var_521_cast = mul(x = stem_sep_module_7_tcn_2_norm_gamma_to_fp16, y = var_513_cast);
            tensor<fp16, [1, 448, 12]> var_522_cast = real_div(x = var_521_cast, y = std_y_31_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_7_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_7_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(3727680)))];
            tensor<fp16, [1, 448, 12]> input_77_cast = add(x = var_522_cast, y = stem_sep_module_7_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_79_pad_0 = const()[name = tensor<string, []>("input_79_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 0])];
            tensor<string, []> input_79_mode_0 = const()[name = tensor<string, []>("input_79_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_79_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_79_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 268]> input_79_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_77_cast_in_state, input_77_cast));
            tensor<fp16, [1, 448, 256]> input_77_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -256]), size = tensor<int32, [3]>([1, 448, 256]), x = input_79_cast);
            tensor<int32, [1]> var_527 = const()[name = tensor<string, []>("op_527"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_529 = const()[name = tensor<string, []>("op_529"), val = tensor<int32, [1]>([128])];
            tensor<string, []> input_81_pad_type_0 = const()[name = tensor<string, []>("input_81_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_81_pad_0 = const()[name = tensor<string, []>("input_81_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_7_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_7_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(3728640)))];
            tensor<fp16, [1, 448, 12]> input_81_cast = conv(dilations = var_529, groups = var_6, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = var_527, weight = stem_sep_module_7_tcn_4_weight_to_fp16, x = input_79_cast);
            tensor<fp16, []> var_533_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_533_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.93p-5)];
            tensor<fp16, [1, 448, 12]> var_533_cast = leaky_relu(alpha = var_533_alpha_1_to_fp16, x = input_81_cast);
            tensor<int32, [1]> var_537 = const()[name = tensor<string, []>("op_537"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_33_cast = reduce_mean(axes = var_537, keep_dims = var_17, x = var_533_cast);
            tensor<fp16, [1, 448, 12]> var_539_cast = sub(x = var_533_cast, y = mean_y_33_cast);
            tensor<fp16, [1, 448, 12]> var_540_cast = square(x = var_539_cast);
            tensor<int32, [1]> var_541 = const()[name = tensor<string, []>("op_541"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_542_cast = reduce_mean(axes = var_541, keep_dims = var_17, x = var_540_cast);
            tensor<fp16, []> var_543_to_fp16 = const()[name = tensor<string, []>("op_543_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_544_cast = add(x = var_542_cast, y = var_543_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_33_cast = sqrt(x = var_544_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_7_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_7_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(3731392)))];
            tensor<fp16, [1, 448, 12]> var_547_cast = mul(x = stem_sep_module_7_tcn_6_norm_gamma_to_fp16, y = var_539_cast);
            tensor<fp16, [1, 448, 12]> var_548_cast = real_div(x = var_547_cast, y = std_y_33_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_7_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_7_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(3732352)))];
            tensor<fp16, [1, 448, 12]> y_16_cast = add(x = var_548_cast, y = stem_sep_module_7_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_83_cast = add(x = input_73_cast, y = y_16_cast);
            tensor<int32, [1]> var_559 = const()[name = tensor<string, []>("op_559"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_561 = const()[name = tensor<string, []>("op_561"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_85_pad_type_0 = const()[name = tensor<string, []>("input_85_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_85_pad_0 = const()[name = tensor<string, []>("input_85_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_8_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_8_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(3733312)))];
            tensor<fp16, [1, 448, 12]> input_85_cast = conv(dilations = var_561, groups = var_21, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = var_559, weight = stem_sep_module_8_tcn_0_weight_to_fp16, x = input_83_cast);
            tensor<fp16, []> var_565_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_565_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.b68p-2)];
            tensor<fp16, [1, 448, 12]> var_565_cast = leaky_relu(alpha = var_565_alpha_1_to_fp16, x = input_85_cast);
            tensor<int32, [1]> var_569 = const()[name = tensor<string, []>("op_569"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_35_cast = reduce_mean(axes = var_569, keep_dims = var_17, x = var_565_cast);
            tensor<fp16, [1, 448, 12]> var_571_cast = sub(x = var_565_cast, y = mean_y_35_cast);
            tensor<fp16, [1, 448, 12]> var_572_cast = square(x = var_571_cast);
            tensor<int32, [1]> var_573 = const()[name = tensor<string, []>("op_573"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_574_cast = reduce_mean(axes = var_573, keep_dims = var_17, x = var_572_cast);
            tensor<fp16, []> var_575_to_fp16 = const()[name = tensor<string, []>("op_575_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_576_cast = add(x = var_574_cast, y = var_575_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_35_cast = sqrt(x = var_576_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_8_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_8_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4134784)))];
            tensor<fp16, [1, 448, 12]> var_579_cast = mul(x = stem_sep_module_8_tcn_2_norm_gamma_to_fp16, y = var_571_cast);
            tensor<fp16, [1, 448, 12]> var_580_cast = real_div(x = var_579_cast, y = std_y_35_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_8_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_8_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4135744)))];
            tensor<fp16, [1, 448, 12]> input_87_cast = add(x = var_580_cast, y = stem_sep_module_8_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_89_pad_0 = const()[name = tensor<string, []>("input_89_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 512, 0])];
            tensor<string, []> input_89_mode_0 = const()[name = tensor<string, []>("input_89_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_89_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_89_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 524]> input_89_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_87_cast_in_state, input_87_cast));
            tensor<fp16, [1, 448, 512]> input_87_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -512]), size = tensor<int32, [3]>([1, 448, 512]), x = input_89_cast);
            tensor<int32, [1]> var_585 = const()[name = tensor<string, []>("op_585"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_587 = const()[name = tensor<string, []>("op_587"), val = tensor<int32, [1]>([256])];
            tensor<string, []> input_91_pad_type_0 = const()[name = tensor<string, []>("input_91_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_91_pad_0 = const()[name = tensor<string, []>("input_91_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_8_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_8_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4136704)))];
            tensor<fp16, [1, 448, 12]> input_91_cast = conv(dilations = var_587, groups = var_6, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = var_585, weight = stem_sep_module_8_tcn_4_weight_to_fp16, x = input_89_cast);
            tensor<fp16, []> var_591_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_591_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.4ccp-3)];
            tensor<fp16, [1, 448, 12]> var_591_cast = leaky_relu(alpha = var_591_alpha_1_to_fp16, x = input_91_cast);
            tensor<int32, [1]> var_595 = const()[name = tensor<string, []>("op_595"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_37_cast = reduce_mean(axes = var_595, keep_dims = var_17, x = var_591_cast);
            tensor<fp16, [1, 448, 12]> var_597_cast = sub(x = var_591_cast, y = mean_y_37_cast);
            tensor<fp16, [1, 448, 12]> var_598_cast = square(x = var_597_cast);
            tensor<int32, [1]> var_599 = const()[name = tensor<string, []>("op_599"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_600_cast = reduce_mean(axes = var_599, keep_dims = var_17, x = var_598_cast);
            tensor<fp16, []> var_601_to_fp16 = const()[name = tensor<string, []>("op_601_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_602_cast = add(x = var_600_cast, y = var_601_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_37_cast = sqrt(x = var_602_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_8_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_8_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4139456)))];
            tensor<fp16, [1, 448, 12]> var_605_cast = mul(x = stem_sep_module_8_tcn_6_norm_gamma_to_fp16, y = var_597_cast);
            tensor<fp16, [1, 448, 12]> var_606_cast = real_div(x = var_605_cast, y = std_y_37_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_8_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_8_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4140416)))];
            tensor<fp16, [1, 448, 12]> y_18_cast = add(x = var_606_cast, y = stem_sep_module_8_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_93_cast = add(x = input_83_cast, y = y_18_cast);
            tensor<int32, [1]> var_617 = const()[name = tensor<string, []>("op_617"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_619 = const()[name = tensor<string, []>("op_619"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_95_pad_type_0 = const()[name = tensor<string, []>("input_95_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_95_pad_0 = const()[name = tensor<string, []>("input_95_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_9_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_9_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4141376)))];
            tensor<fp16, [1, 448, 12]> input_95_cast = conv(dilations = var_619, groups = var_21, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = var_617, weight = stem_sep_module_9_tcn_0_weight_to_fp16, x = input_93_cast);
            tensor<fp16, []> var_623_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_623_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.cccp-3)];
            tensor<fp16, [1, 448, 12]> var_623_cast = leaky_relu(alpha = var_623_alpha_1_to_fp16, x = input_95_cast);
            tensor<int32, [1]> var_627 = const()[name = tensor<string, []>("op_627"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_39_cast = reduce_mean(axes = var_627, keep_dims = var_17, x = var_623_cast);
            tensor<fp16, [1, 448, 12]> var_629_cast = sub(x = var_623_cast, y = mean_y_39_cast);
            tensor<fp16, [1, 448, 12]> var_630_cast = square(x = var_629_cast);
            tensor<int32, [1]> var_631 = const()[name = tensor<string, []>("op_631"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_632_cast = reduce_mean(axes = var_631, keep_dims = var_17, x = var_630_cast);
            tensor<fp16, []> var_633_to_fp16 = const()[name = tensor<string, []>("op_633_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_634_cast = add(x = var_632_cast, y = var_633_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_39_cast = sqrt(x = var_634_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_9_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_9_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4542848)))];
            tensor<fp16, [1, 448, 12]> var_637_cast = mul(x = stem_sep_module_9_tcn_2_norm_gamma_to_fp16, y = var_629_cast);
            tensor<fp16, [1, 448, 12]> var_638_cast = real_div(x = var_637_cast, y = std_y_39_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_9_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_9_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4543808)))];
            tensor<fp16, [1, 448, 12]> input_97_cast = add(x = var_638_cast, y = stem_sep_module_9_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_99_pad_0 = const()[name = tensor<string, []>("input_99_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
            tensor<string, []> input_99_mode_0 = const()[name = tensor<string, []>("input_99_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_99_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_99_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 14]> input_99_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_97_cast_in_state, input_97_cast));
            tensor<fp16, [1, 448, 2]> input_97_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 448, 2]), x = input_99_cast);
            tensor<int32, [1]> var_643 = const()[name = tensor<string, []>("op_643"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_645 = const()[name = tensor<string, []>("op_645"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_101_pad_type_0 = const()[name = tensor<string, []>("input_101_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_101_pad_0 = const()[name = tensor<string, []>("input_101_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_9_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_9_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4544768)))];
            tensor<fp16, [1, 448, 12]> input_101_cast = conv(dilations = var_645, groups = var_6, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = var_643, weight = stem_sep_module_9_tcn_4_weight_to_fp16, x = input_99_cast);
            tensor<fp16, []> var_649_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_649_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.ebp-1)];
            tensor<fp16, [1, 448, 12]> var_649_cast = leaky_relu(alpha = var_649_alpha_1_to_fp16, x = input_101_cast);
            tensor<int32, [1]> var_653 = const()[name = tensor<string, []>("op_653"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_41_cast = reduce_mean(axes = var_653, keep_dims = var_17, x = var_649_cast);
            tensor<fp16, [1, 448, 12]> var_655_cast = sub(x = var_649_cast, y = mean_y_41_cast);
            tensor<fp16, [1, 448, 12]> var_656_cast = square(x = var_655_cast);
            tensor<int32, [1]> var_657 = const()[name = tensor<string, []>("op_657"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_658_cast = reduce_mean(axes = var_657, keep_dims = var_17, x = var_656_cast);
            tensor<fp16, []> var_659_to_fp16 = const()[name = tensor<string, []>("op_659_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_660_cast = add(x = var_658_cast, y = var_659_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_41_cast = sqrt(x = var_660_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_9_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_9_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4547520)))];
            tensor<fp16, [1, 448, 12]> var_663_cast = mul(x = stem_sep_module_9_tcn_6_norm_gamma_to_fp16, y = var_655_cast);
            tensor<fp16, [1, 448, 12]> var_664_cast = real_div(x = var_663_cast, y = std_y_41_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_9_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_9_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4548480)))];
            tensor<fp16, [1, 448, 12]> y_20_cast = add(x = var_664_cast, y = stem_sep_module_9_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_103_cast = add(x = input_93_cast, y = y_20_cast);
            tensor<int32, [1]> var_675 = const()[name = tensor<string, []>("op_675"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_677 = const()[name = tensor<string, []>("op_677"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_105_pad_type_0 = const()[name = tensor<string, []>("input_105_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_105_pad_0 = const()[name = tensor<string, []>("input_105_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_10_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_10_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4549440)))];
            tensor<fp16, [1, 448, 12]> input_105_cast = conv(dilations = var_677, groups = var_21, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = var_675, weight = stem_sep_module_10_tcn_0_weight_to_fp16, x = input_103_cast);
            tensor<fp16, []> var_681_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_681_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.b08p-2)];
            tensor<fp16, [1, 448, 12]> var_681_cast = leaky_relu(alpha = var_681_alpha_1_to_fp16, x = input_105_cast);
            tensor<int32, [1]> var_685 = const()[name = tensor<string, []>("op_685"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_43_cast = reduce_mean(axes = var_685, keep_dims = var_17, x = var_681_cast);
            tensor<fp16, [1, 448, 12]> var_687_cast = sub(x = var_681_cast, y = mean_y_43_cast);
            tensor<fp16, [1, 448, 12]> var_688_cast = square(x = var_687_cast);
            tensor<int32, [1]> var_689 = const()[name = tensor<string, []>("op_689"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_690_cast = reduce_mean(axes = var_689, keep_dims = var_17, x = var_688_cast);
            tensor<fp16, []> var_691_to_fp16 = const()[name = tensor<string, []>("op_691_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_692_cast = add(x = var_690_cast, y = var_691_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_43_cast = sqrt(x = var_692_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_10_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_10_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4950912)))];
            tensor<fp16, [1, 448, 12]> var_695_cast = mul(x = stem_sep_module_10_tcn_2_norm_gamma_to_fp16, y = var_687_cast);
            tensor<fp16, [1, 448, 12]> var_696_cast = real_div(x = var_695_cast, y = std_y_43_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_10_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_10_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4951872)))];
            tensor<fp16, [1, 448, 12]> input_107_cast = add(x = var_696_cast, y = stem_sep_module_10_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_109_pad_0 = const()[name = tensor<string, []>("input_109_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
            tensor<string, []> input_109_mode_0 = const()[name = tensor<string, []>("input_109_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_109_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_109_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 16]> input_109_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_107_cast_in_state, input_107_cast));
            tensor<fp16, [1, 448, 4]> input_107_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 448, 4]), x = input_109_cast);
            tensor<int32, [1]> var_701 = const()[name = tensor<string, []>("op_701"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_703 = const()[name = tensor<string, []>("op_703"), val = tensor<int32, [1]>([2])];
            tensor<string, []> input_111_pad_type_0 = const()[name = tensor<string, []>("input_111_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_111_pad_0 = const()[name = tensor<string, []>("input_111_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_10_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_10_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4952832)))];
            tensor<fp16, [1, 448, 12]> input_111_cast = conv(dilations = var_703, groups = var_6, pad = input_111_pad_0, pad_type = input_111_pad_type_0, strides = var_701, weight = stem_sep_module_10_tcn_4_weight_to_fp16, x = input_109_cast);
            tensor<fp16, []> var_707_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_707_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.d5cp-1)];
            tensor<fp16, [1, 448, 12]> var_707_cast = leaky_relu(alpha = var_707_alpha_1_to_fp16, x = input_111_cast);
            tensor<int32, [1]> var_711 = const()[name = tensor<string, []>("op_711"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_45_cast = reduce_mean(axes = var_711, keep_dims = var_17, x = var_707_cast);
            tensor<fp16, [1, 448, 12]> var_713_cast = sub(x = var_707_cast, y = mean_y_45_cast);
            tensor<fp16, [1, 448, 12]> var_714_cast = square(x = var_713_cast);
            tensor<int32, [1]> var_715 = const()[name = tensor<string, []>("op_715"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_716_cast = reduce_mean(axes = var_715, keep_dims = var_17, x = var_714_cast);
            tensor<fp16, []> var_717_to_fp16 = const()[name = tensor<string, []>("op_717_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_718_cast = add(x = var_716_cast, y = var_717_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_45_cast = sqrt(x = var_718_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_10_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_10_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4955584)))];
            tensor<fp16, [1, 448, 12]> var_721_cast = mul(x = stem_sep_module_10_tcn_6_norm_gamma_to_fp16, y = var_713_cast);
            tensor<fp16, [1, 448, 12]> var_722_cast = real_div(x = var_721_cast, y = std_y_45_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_10_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_10_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4956544)))];
            tensor<fp16, [1, 448, 12]> y_22_cast = add(x = var_722_cast, y = stem_sep_module_10_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_113_cast = add(x = input_103_cast, y = y_22_cast);
            tensor<int32, [1]> var_733 = const()[name = tensor<string, []>("op_733"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_735 = const()[name = tensor<string, []>("op_735"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_115_pad_type_0 = const()[name = tensor<string, []>("input_115_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_115_pad_0 = const()[name = tensor<string, []>("input_115_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_11_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_11_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(4957504)))];
            tensor<fp16, [1, 448, 12]> input_115_cast = conv(dilations = var_735, groups = var_21, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = var_733, weight = stem_sep_module_11_tcn_0_weight_to_fp16, x = input_113_cast);
            tensor<fp16, []> var_739_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_739_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.5ap-2)];
            tensor<fp16, [1, 448, 12]> var_739_cast = leaky_relu(alpha = var_739_alpha_1_to_fp16, x = input_115_cast);
            tensor<int32, [1]> var_743 = const()[name = tensor<string, []>("op_743"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_47_cast = reduce_mean(axes = var_743, keep_dims = var_17, x = var_739_cast);
            tensor<fp16, [1, 448, 12]> var_745_cast = sub(x = var_739_cast, y = mean_y_47_cast);
            tensor<fp16, [1, 448, 12]> var_746_cast = square(x = var_745_cast);
            tensor<int32, [1]> var_747 = const()[name = tensor<string, []>("op_747"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_748_cast = reduce_mean(axes = var_747, keep_dims = var_17, x = var_746_cast);
            tensor<fp16, []> var_749_to_fp16 = const()[name = tensor<string, []>("op_749_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_750_cast = add(x = var_748_cast, y = var_749_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_47_cast = sqrt(x = var_750_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_11_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_11_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(5358976)))];
            tensor<fp16, [1, 448, 12]> var_753_cast = mul(x = stem_sep_module_11_tcn_2_norm_gamma_to_fp16, y = var_745_cast);
            tensor<fp16, [1, 448, 12]> var_754_cast = real_div(x = var_753_cast, y = std_y_47_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_11_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_11_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(5359936)))];
            tensor<fp16, [1, 448, 12]> input_117_cast = add(x = var_754_cast, y = stem_sep_module_11_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_119_pad_0 = const()[name = tensor<string, []>("input_119_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            tensor<string, []> input_119_mode_0 = const()[name = tensor<string, []>("input_119_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_119_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_119_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 20]> input_119_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_117_cast_in_state, input_117_cast));
            tensor<fp16, [1, 448, 8]> input_117_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 448, 8]), x = input_119_cast);
            tensor<int32, [1]> var_759 = const()[name = tensor<string, []>("op_759"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_761 = const()[name = tensor<string, []>("op_761"), val = tensor<int32, [1]>([4])];
            tensor<string, []> input_121_pad_type_0 = const()[name = tensor<string, []>("input_121_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_121_pad_0 = const()[name = tensor<string, []>("input_121_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_11_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_11_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(5360896)))];
            tensor<fp16, [1, 448, 12]> input_121_cast = conv(dilations = var_761, groups = var_6, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = var_759, weight = stem_sep_module_11_tcn_4_weight_to_fp16, x = input_119_cast);
            tensor<fp16, []> var_765_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_765_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.df4p-1)];
            tensor<fp16, [1, 448, 12]> var_765_cast = leaky_relu(alpha = var_765_alpha_1_to_fp16, x = input_121_cast);
            tensor<int32, [1]> var_769 = const()[name = tensor<string, []>("op_769"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_49_cast = reduce_mean(axes = var_769, keep_dims = var_17, x = var_765_cast);
            tensor<fp16, [1, 448, 12]> var_771_cast = sub(x = var_765_cast, y = mean_y_49_cast);
            tensor<fp16, [1, 448, 12]> var_772_cast = square(x = var_771_cast);
            tensor<int32, [1]> var_773 = const()[name = tensor<string, []>("op_773"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_774_cast = reduce_mean(axes = var_773, keep_dims = var_17, x = var_772_cast);
            tensor<fp16, []> var_775_to_fp16 = const()[name = tensor<string, []>("op_775_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_776_cast = add(x = var_774_cast, y = var_775_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_49_cast = sqrt(x = var_776_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_11_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_11_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(5363648)))];
            tensor<fp16, [1, 448, 12]> var_779_cast = mul(x = stem_sep_module_11_tcn_6_norm_gamma_to_fp16, y = var_771_cast);
            tensor<fp16, [1, 448, 12]> var_780_cast = real_div(x = var_779_cast, y = std_y_49_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_11_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_11_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(5364608)))];
            tensor<fp16, [1, 448, 12]> y_24_cast = add(x = var_780_cast, y = stem_sep_module_11_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_123_cast = add(x = input_113_cast, y = y_24_cast);
            tensor<int32, [1]> var_791 = const()[name = tensor<string, []>("op_791"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_793 = const()[name = tensor<string, []>("op_793"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_125_pad_type_0 = const()[name = tensor<string, []>("input_125_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_125_pad_0 = const()[name = tensor<string, []>("input_125_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_12_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_12_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(5365568)))];
            tensor<fp16, [1, 448, 12]> input_125_cast = conv(dilations = var_793, groups = var_21, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = var_791, weight = stem_sep_module_12_tcn_0_weight_to_fp16, x = input_123_cast);
            tensor<fp16, []> var_797_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_797_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.61cp-1)];
            tensor<fp16, [1, 448, 12]> var_797_cast = leaky_relu(alpha = var_797_alpha_1_to_fp16, x = input_125_cast);
            tensor<int32, [1]> var_801 = const()[name = tensor<string, []>("op_801"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_51_cast = reduce_mean(axes = var_801, keep_dims = var_17, x = var_797_cast);
            tensor<fp16, [1, 448, 12]> var_803_cast = sub(x = var_797_cast, y = mean_y_51_cast);
            tensor<fp16, [1, 448, 12]> var_804_cast = square(x = var_803_cast);
            tensor<int32, [1]> var_805 = const()[name = tensor<string, []>("op_805"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_806_cast = reduce_mean(axes = var_805, keep_dims = var_17, x = var_804_cast);
            tensor<fp16, []> var_807_to_fp16 = const()[name = tensor<string, []>("op_807_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_808_cast = add(x = var_806_cast, y = var_807_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_51_cast = sqrt(x = var_808_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_12_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_12_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(5767040)))];
            tensor<fp16, [1, 448, 12]> var_811_cast = mul(x = stem_sep_module_12_tcn_2_norm_gamma_to_fp16, y = var_803_cast);
            tensor<fp16, [1, 448, 12]> var_812_cast = real_div(x = var_811_cast, y = std_y_51_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_12_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_12_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(5768000)))];
            tensor<fp16, [1, 448, 12]> input_127_cast = add(x = var_812_cast, y = stem_sep_module_12_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_129_pad_0 = const()[name = tensor<string, []>("input_129_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            tensor<string, []> input_129_mode_0 = const()[name = tensor<string, []>("input_129_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_129_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_129_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 28]> input_129_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_127_cast_in_state, input_127_cast));
            tensor<fp16, [1, 448, 16]> input_127_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -16]), size = tensor<int32, [3]>([1, 448, 16]), x = input_129_cast);
            tensor<int32, [1]> var_817 = const()[name = tensor<string, []>("op_817"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_819 = const()[name = tensor<string, []>("op_819"), val = tensor<int32, [1]>([8])];
            tensor<string, []> input_131_pad_type_0 = const()[name = tensor<string, []>("input_131_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_131_pad_0 = const()[name = tensor<string, []>("input_131_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_12_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_12_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(5768960)))];
            tensor<fp16, [1, 448, 12]> input_131_cast = conv(dilations = var_819, groups = var_6, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = var_817, weight = stem_sep_module_12_tcn_4_weight_to_fp16, x = input_129_cast);
            tensor<fp16, []> var_823_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_823_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.cd4p-1)];
            tensor<fp16, [1, 448, 12]> var_823_cast = leaky_relu(alpha = var_823_alpha_1_to_fp16, x = input_131_cast);
            tensor<int32, [1]> var_827 = const()[name = tensor<string, []>("op_827"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_53_cast = reduce_mean(axes = var_827, keep_dims = var_17, x = var_823_cast);
            tensor<fp16, [1, 448, 12]> var_829_cast = sub(x = var_823_cast, y = mean_y_53_cast);
            tensor<fp16, [1, 448, 12]> var_830_cast = square(x = var_829_cast);
            tensor<int32, [1]> var_831 = const()[name = tensor<string, []>("op_831"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_832_cast = reduce_mean(axes = var_831, keep_dims = var_17, x = var_830_cast);
            tensor<fp16, []> var_833_to_fp16 = const()[name = tensor<string, []>("op_833_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_834_cast = add(x = var_832_cast, y = var_833_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_53_cast = sqrt(x = var_834_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_12_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_12_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(5771712)))];
            tensor<fp16, [1, 448, 12]> var_837_cast = mul(x = stem_sep_module_12_tcn_6_norm_gamma_to_fp16, y = var_829_cast);
            tensor<fp16, [1, 448, 12]> var_838_cast = real_div(x = var_837_cast, y = std_y_53_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_12_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_12_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(5772672)))];
            tensor<fp16, [1, 448, 12]> y_26_cast = add(x = var_838_cast, y = stem_sep_module_12_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_133_cast = add(x = input_123_cast, y = y_26_cast);
            tensor<int32, [1]> var_849 = const()[name = tensor<string, []>("op_849"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_851 = const()[name = tensor<string, []>("op_851"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_135_pad_type_0 = const()[name = tensor<string, []>("input_135_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_135_pad_0 = const()[name = tensor<string, []>("input_135_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_13_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_13_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(5773632)))];
            tensor<fp16, [1, 448, 12]> input_135_cast = conv(dilations = var_851, groups = var_21, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = var_849, weight = stem_sep_module_13_tcn_0_weight_to_fp16, x = input_133_cast);
            tensor<fp16, []> var_855_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_855_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.01p-2)];
            tensor<fp16, [1, 448, 12]> var_855_cast = leaky_relu(alpha = var_855_alpha_1_to_fp16, x = input_135_cast);
            tensor<int32, [1]> var_859 = const()[name = tensor<string, []>("op_859"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_55_cast = reduce_mean(axes = var_859, keep_dims = var_17, x = var_855_cast);
            tensor<fp16, [1, 448, 12]> var_861_cast = sub(x = var_855_cast, y = mean_y_55_cast);
            tensor<fp16, [1, 448, 12]> var_862_cast = square(x = var_861_cast);
            tensor<int32, [1]> var_863 = const()[name = tensor<string, []>("op_863"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_864_cast = reduce_mean(axes = var_863, keep_dims = var_17, x = var_862_cast);
            tensor<fp16, []> var_865_to_fp16 = const()[name = tensor<string, []>("op_865_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_866_cast = add(x = var_864_cast, y = var_865_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_55_cast = sqrt(x = var_866_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_13_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_13_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6175104)))];
            tensor<fp16, [1, 448, 12]> var_869_cast = mul(x = stem_sep_module_13_tcn_2_norm_gamma_to_fp16, y = var_861_cast);
            tensor<fp16, [1, 448, 12]> var_870_cast = real_div(x = var_869_cast, y = std_y_55_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_13_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_13_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6176064)))];
            tensor<fp16, [1, 448, 12]> input_137_cast = add(x = var_870_cast, y = stem_sep_module_13_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_139_pad_0 = const()[name = tensor<string, []>("input_139_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            tensor<string, []> input_139_mode_0 = const()[name = tensor<string, []>("input_139_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_139_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_139_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 44]> input_139_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_137_cast_in_state, input_137_cast));
            tensor<fp16, [1, 448, 32]> input_137_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -32]), size = tensor<int32, [3]>([1, 448, 32]), x = input_139_cast);
            tensor<int32, [1]> var_875 = const()[name = tensor<string, []>("op_875"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_877 = const()[name = tensor<string, []>("op_877"), val = tensor<int32, [1]>([16])];
            tensor<string, []> input_141_pad_type_0 = const()[name = tensor<string, []>("input_141_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_141_pad_0 = const()[name = tensor<string, []>("input_141_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_13_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_13_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6177024)))];
            tensor<fp16, [1, 448, 12]> input_141_cast = conv(dilations = var_877, groups = var_6, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = var_875, weight = stem_sep_module_13_tcn_4_weight_to_fp16, x = input_139_cast);
            tensor<fp16, []> var_881_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_881_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.03p-2)];
            tensor<fp16, [1, 448, 12]> var_881_cast = leaky_relu(alpha = var_881_alpha_1_to_fp16, x = input_141_cast);
            tensor<int32, [1]> var_885 = const()[name = tensor<string, []>("op_885"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_57_cast = reduce_mean(axes = var_885, keep_dims = var_17, x = var_881_cast);
            tensor<fp16, [1, 448, 12]> var_887_cast = sub(x = var_881_cast, y = mean_y_57_cast);
            tensor<fp16, [1, 448, 12]> var_888_cast = square(x = var_887_cast);
            tensor<int32, [1]> var_889 = const()[name = tensor<string, []>("op_889"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_890_cast = reduce_mean(axes = var_889, keep_dims = var_17, x = var_888_cast);
            tensor<fp16, []> var_891_to_fp16 = const()[name = tensor<string, []>("op_891_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_892_cast = add(x = var_890_cast, y = var_891_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_57_cast = sqrt(x = var_892_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_13_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_13_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6179776)))];
            tensor<fp16, [1, 448, 12]> var_895_cast = mul(x = stem_sep_module_13_tcn_6_norm_gamma_to_fp16, y = var_887_cast);
            tensor<fp16, [1, 448, 12]> var_896_cast = real_div(x = var_895_cast, y = std_y_57_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_13_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_13_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6180736)))];
            tensor<fp16, [1, 448, 12]> y_28_cast = add(x = var_896_cast, y = stem_sep_module_13_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_143_cast = add(x = input_133_cast, y = y_28_cast);
            tensor<int32, [1]> var_907 = const()[name = tensor<string, []>("op_907"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_909 = const()[name = tensor<string, []>("op_909"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_145_pad_type_0 = const()[name = tensor<string, []>("input_145_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_145_pad_0 = const()[name = tensor<string, []>("input_145_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_14_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_14_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6181696)))];
            tensor<fp16, [1, 448, 12]> input_145_cast = conv(dilations = var_909, groups = var_21, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = var_907, weight = stem_sep_module_14_tcn_0_weight_to_fp16, x = input_143_cast);
            tensor<fp16, []> var_913_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_913_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.6b8p-2)];
            tensor<fp16, [1, 448, 12]> var_913_cast = leaky_relu(alpha = var_913_alpha_1_to_fp16, x = input_145_cast);
            tensor<int32, [1]> var_917 = const()[name = tensor<string, []>("op_917"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_59_cast = reduce_mean(axes = var_917, keep_dims = var_17, x = var_913_cast);
            tensor<fp16, [1, 448, 12]> var_919_cast = sub(x = var_913_cast, y = mean_y_59_cast);
            tensor<fp16, [1, 448, 12]> var_920_cast = square(x = var_919_cast);
            tensor<int32, [1]> var_921 = const()[name = tensor<string, []>("op_921"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_922_cast = reduce_mean(axes = var_921, keep_dims = var_17, x = var_920_cast);
            tensor<fp16, []> var_923_to_fp16 = const()[name = tensor<string, []>("op_923_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_924_cast = add(x = var_922_cast, y = var_923_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_59_cast = sqrt(x = var_924_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_14_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_14_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6583168)))];
            tensor<fp16, [1, 448, 12]> var_927_cast = mul(x = stem_sep_module_14_tcn_2_norm_gamma_to_fp16, y = var_919_cast);
            tensor<fp16, [1, 448, 12]> var_928_cast = real_div(x = var_927_cast, y = std_y_59_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_14_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_14_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6584128)))];
            tensor<fp16, [1, 448, 12]> input_147_cast = add(x = var_928_cast, y = stem_sep_module_14_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_149_pad_0 = const()[name = tensor<string, []>("input_149_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            tensor<string, []> input_149_mode_0 = const()[name = tensor<string, []>("input_149_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_149_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_149_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 76]> input_149_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_147_cast_in_state, input_147_cast));
            tensor<fp16, [1, 448, 64]> input_147_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 448, 64]), x = input_149_cast);
            tensor<int32, [1]> var_933 = const()[name = tensor<string, []>("op_933"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_935 = const()[name = tensor<string, []>("op_935"), val = tensor<int32, [1]>([32])];
            tensor<string, []> input_151_pad_type_0 = const()[name = tensor<string, []>("input_151_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_151_pad_0 = const()[name = tensor<string, []>("input_151_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_14_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_14_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6585088)))];
            tensor<fp16, [1, 448, 12]> input_151_cast = conv(dilations = var_935, groups = var_6, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = var_933, weight = stem_sep_module_14_tcn_4_weight_to_fp16, x = input_149_cast);
            tensor<fp16, []> var_939_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_939_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.b4cp-1)];
            tensor<fp16, [1, 448, 12]> var_939_cast = leaky_relu(alpha = var_939_alpha_1_to_fp16, x = input_151_cast);
            tensor<int32, [1]> var_943 = const()[name = tensor<string, []>("op_943"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_61_cast = reduce_mean(axes = var_943, keep_dims = var_17, x = var_939_cast);
            tensor<fp16, [1, 448, 12]> var_945_cast = sub(x = var_939_cast, y = mean_y_61_cast);
            tensor<fp16, [1, 448, 12]> var_946_cast = square(x = var_945_cast);
            tensor<int32, [1]> var_947 = const()[name = tensor<string, []>("op_947"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_948_cast = reduce_mean(axes = var_947, keep_dims = var_17, x = var_946_cast);
            tensor<fp16, []> var_949_to_fp16 = const()[name = tensor<string, []>("op_949_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_950_cast = add(x = var_948_cast, y = var_949_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_61_cast = sqrt(x = var_950_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_14_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_14_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6587840)))];
            tensor<fp16, [1, 448, 12]> var_953_cast = mul(x = stem_sep_module_14_tcn_6_norm_gamma_to_fp16, y = var_945_cast);
            tensor<fp16, [1, 448, 12]> var_954_cast = real_div(x = var_953_cast, y = std_y_61_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_14_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_14_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6588800)))];
            tensor<fp16, [1, 448, 12]> y_30_cast = add(x = var_954_cast, y = stem_sep_module_14_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_153_cast = add(x = input_143_cast, y = y_30_cast);
            tensor<int32, [1]> var_965 = const()[name = tensor<string, []>("op_965"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_967 = const()[name = tensor<string, []>("op_967"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_155_pad_type_0 = const()[name = tensor<string, []>("input_155_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_155_pad_0 = const()[name = tensor<string, []>("input_155_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_15_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_15_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6589760)))];
            tensor<fp16, [1, 448, 12]> input_155_cast = conv(dilations = var_967, groups = var_21, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = var_965, weight = stem_sep_module_15_tcn_0_weight_to_fp16, x = input_153_cast);
            tensor<fp16, []> var_971_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_971_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.798p-2)];
            tensor<fp16, [1, 448, 12]> var_971_cast = leaky_relu(alpha = var_971_alpha_1_to_fp16, x = input_155_cast);
            tensor<int32, [1]> var_975 = const()[name = tensor<string, []>("op_975"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_63_cast = reduce_mean(axes = var_975, keep_dims = var_17, x = var_971_cast);
            tensor<fp16, [1, 448, 12]> var_977_cast = sub(x = var_971_cast, y = mean_y_63_cast);
            tensor<fp16, [1, 448, 12]> var_978_cast = square(x = var_977_cast);
            tensor<int32, [1]> var_979 = const()[name = tensor<string, []>("op_979"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_980_cast = reduce_mean(axes = var_979, keep_dims = var_17, x = var_978_cast);
            tensor<fp16, []> var_981_to_fp16 = const()[name = tensor<string, []>("op_981_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_982_cast = add(x = var_980_cast, y = var_981_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_63_cast = sqrt(x = var_982_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_15_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_15_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6991232)))];
            tensor<fp16, [1, 448, 12]> var_985_cast = mul(x = stem_sep_module_15_tcn_2_norm_gamma_to_fp16, y = var_977_cast);
            tensor<fp16, [1, 448, 12]> var_986_cast = real_div(x = var_985_cast, y = std_y_63_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_15_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_15_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6992192)))];
            tensor<fp16, [1, 448, 12]> input_157_cast = add(x = var_986_cast, y = stem_sep_module_15_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_159_pad_0 = const()[name = tensor<string, []>("input_159_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 128, 0])];
            tensor<string, []> input_159_mode_0 = const()[name = tensor<string, []>("input_159_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_159_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_159_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 140]> input_159_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_157_cast_in_state, input_157_cast));
            tensor<fp16, [1, 448, 128]> input_157_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -128]), size = tensor<int32, [3]>([1, 448, 128]), x = input_159_cast);
            tensor<int32, [1]> var_991 = const()[name = tensor<string, []>("op_991"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_993 = const()[name = tensor<string, []>("op_993"), val = tensor<int32, [1]>([64])];
            tensor<string, []> input_161_pad_type_0 = const()[name = tensor<string, []>("input_161_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_161_pad_0 = const()[name = tensor<string, []>("input_161_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_15_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_15_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6993152)))];
            tensor<fp16, [1, 448, 12]> input_161_cast = conv(dilations = var_993, groups = var_6, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = var_991, weight = stem_sep_module_15_tcn_4_weight_to_fp16, x = input_159_cast);
            tensor<fp16, []> var_997_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_997_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.eap-1)];
            tensor<fp16, [1, 448, 12]> var_997_cast = leaky_relu(alpha = var_997_alpha_1_to_fp16, x = input_161_cast);
            tensor<int32, [1]> var_1001 = const()[name = tensor<string, []>("op_1001"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_65_cast = reduce_mean(axes = var_1001, keep_dims = var_17, x = var_997_cast);
            tensor<fp16, [1, 448, 12]> var_1003_cast = sub(x = var_997_cast, y = mean_y_65_cast);
            tensor<fp16, [1, 448, 12]> var_1004_cast = square(x = var_1003_cast);
            tensor<int32, [1]> var_1005 = const()[name = tensor<string, []>("op_1005"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1006_cast = reduce_mean(axes = var_1005, keep_dims = var_17, x = var_1004_cast);
            tensor<fp16, []> var_1007_to_fp16 = const()[name = tensor<string, []>("op_1007_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1008_cast = add(x = var_1006_cast, y = var_1007_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_65_cast = sqrt(x = var_1008_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_15_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_15_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6995904)))];
            tensor<fp16, [1, 448, 12]> var_1011_cast = mul(x = stem_sep_module_15_tcn_6_norm_gamma_to_fp16, y = var_1003_cast);
            tensor<fp16, [1, 448, 12]> var_1012_cast = real_div(x = var_1011_cast, y = std_y_65_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_15_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_15_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6996864)))];
            tensor<fp16, [1, 448, 12]> y_32_cast = add(x = var_1012_cast, y = stem_sep_module_15_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_163_cast = add(x = input_153_cast, y = y_32_cast);
            tensor<int32, [1]> var_1023 = const()[name = tensor<string, []>("op_1023"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1025 = const()[name = tensor<string, []>("op_1025"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_165_pad_type_0 = const()[name = tensor<string, []>("input_165_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_165_pad_0 = const()[name = tensor<string, []>("input_165_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_16_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_16_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(6997824)))];
            tensor<fp16, [1, 448, 12]> input_165_cast = conv(dilations = var_1025, groups = var_21, pad = input_165_pad_0, pad_type = input_165_pad_type_0, strides = var_1023, weight = stem_sep_module_16_tcn_0_weight_to_fp16, x = input_163_cast);
            tensor<fp16, []> var_1029_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1029_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.5d4p-3)];
            tensor<fp16, [1, 448, 12]> var_1029_cast = leaky_relu(alpha = var_1029_alpha_1_to_fp16, x = input_165_cast);
            tensor<int32, [1]> var_1033 = const()[name = tensor<string, []>("op_1033"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_67_cast = reduce_mean(axes = var_1033, keep_dims = var_17, x = var_1029_cast);
            tensor<fp16, [1, 448, 12]> var_1035_cast = sub(x = var_1029_cast, y = mean_y_67_cast);
            tensor<fp16, [1, 448, 12]> var_1036_cast = square(x = var_1035_cast);
            tensor<int32, [1]> var_1037 = const()[name = tensor<string, []>("op_1037"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1038_cast = reduce_mean(axes = var_1037, keep_dims = var_17, x = var_1036_cast);
            tensor<fp16, []> var_1039_to_fp16 = const()[name = tensor<string, []>("op_1039_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1040_cast = add(x = var_1038_cast, y = var_1039_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_67_cast = sqrt(x = var_1040_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_16_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_16_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(7399296)))];
            tensor<fp16, [1, 448, 12]> var_1043_cast = mul(x = stem_sep_module_16_tcn_2_norm_gamma_to_fp16, y = var_1035_cast);
            tensor<fp16, [1, 448, 12]> var_1044_cast = real_div(x = var_1043_cast, y = std_y_67_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_16_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_16_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(7400256)))];
            tensor<fp16, [1, 448, 12]> input_167_cast = add(x = var_1044_cast, y = stem_sep_module_16_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_169_pad_0 = const()[name = tensor<string, []>("input_169_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 0])];
            tensor<string, []> input_169_mode_0 = const()[name = tensor<string, []>("input_169_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_169_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_169_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 268]> input_169_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_167_cast_in_state, input_167_cast));
            tensor<fp16, [1, 448, 256]> input_167_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -256]), size = tensor<int32, [3]>([1, 448, 256]), x = input_169_cast);
            tensor<int32, [1]> var_1049 = const()[name = tensor<string, []>("op_1049"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1051 = const()[name = tensor<string, []>("op_1051"), val = tensor<int32, [1]>([128])];
            tensor<string, []> input_171_pad_type_0 = const()[name = tensor<string, []>("input_171_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_171_pad_0 = const()[name = tensor<string, []>("input_171_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_16_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_16_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(7401216)))];
            tensor<fp16, [1, 448, 12]> input_171_cast = conv(dilations = var_1051, groups = var_6, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_1049, weight = stem_sep_module_16_tcn_4_weight_to_fp16, x = input_169_cast);
            tensor<fp16, []> var_1055_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1055_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.568p-1)];
            tensor<fp16, [1, 448, 12]> var_1055_cast = leaky_relu(alpha = var_1055_alpha_1_to_fp16, x = input_171_cast);
            tensor<int32, [1]> var_1059 = const()[name = tensor<string, []>("op_1059"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_69_cast = reduce_mean(axes = var_1059, keep_dims = var_17, x = var_1055_cast);
            tensor<fp16, [1, 448, 12]> var_1061_cast = sub(x = var_1055_cast, y = mean_y_69_cast);
            tensor<fp16, [1, 448, 12]> var_1062_cast = square(x = var_1061_cast);
            tensor<int32, [1]> var_1063 = const()[name = tensor<string, []>("op_1063"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1064_cast = reduce_mean(axes = var_1063, keep_dims = var_17, x = var_1062_cast);
            tensor<fp16, []> var_1065_to_fp16 = const()[name = tensor<string, []>("op_1065_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1066_cast = add(x = var_1064_cast, y = var_1065_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_69_cast = sqrt(x = var_1066_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_16_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_16_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(7403968)))];
            tensor<fp16, [1, 448, 12]> var_1069_cast = mul(x = stem_sep_module_16_tcn_6_norm_gamma_to_fp16, y = var_1061_cast);
            tensor<fp16, [1, 448, 12]> var_1070_cast = real_div(x = var_1069_cast, y = std_y_69_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_16_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_16_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(7404928)))];
            tensor<fp16, [1, 448, 12]> y_34_cast = add(x = var_1070_cast, y = stem_sep_module_16_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_173_cast = add(x = input_163_cast, y = y_34_cast);
            tensor<int32, [1]> var_1081 = const()[name = tensor<string, []>("op_1081"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1083 = const()[name = tensor<string, []>("op_1083"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_175_pad_type_0 = const()[name = tensor<string, []>("input_175_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_175_pad_0 = const()[name = tensor<string, []>("input_175_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_17_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_17_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(7405888)))];
            tensor<fp16, [1, 448, 12]> input_175_cast = conv(dilations = var_1083, groups = var_21, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = var_1081, weight = stem_sep_module_17_tcn_0_weight_to_fp16, x = input_173_cast);
            tensor<fp16, []> var_1087_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1087_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.50cp-4)];
            tensor<fp16, [1, 448, 12]> var_1087_cast = leaky_relu(alpha = var_1087_alpha_1_to_fp16, x = input_175_cast);
            tensor<int32, [1]> var_1091 = const()[name = tensor<string, []>("op_1091"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_71_cast = reduce_mean(axes = var_1091, keep_dims = var_17, x = var_1087_cast);
            tensor<fp16, [1, 448, 12]> var_1093_cast = sub(x = var_1087_cast, y = mean_y_71_cast);
            tensor<fp16, [1, 448, 12]> var_1094_cast = square(x = var_1093_cast);
            tensor<int32, [1]> var_1095 = const()[name = tensor<string, []>("op_1095"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1096_cast = reduce_mean(axes = var_1095, keep_dims = var_17, x = var_1094_cast);
            tensor<fp16, []> var_1097_to_fp16 = const()[name = tensor<string, []>("op_1097_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1098_cast = add(x = var_1096_cast, y = var_1097_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_71_cast = sqrt(x = var_1098_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_17_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_17_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(7807360)))];
            tensor<fp16, [1, 448, 12]> var_1101_cast = mul(x = stem_sep_module_17_tcn_2_norm_gamma_to_fp16, y = var_1093_cast);
            tensor<fp16, [1, 448, 12]> var_1102_cast = real_div(x = var_1101_cast, y = std_y_71_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_17_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_17_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(7808320)))];
            tensor<fp16, [1, 448, 12]> input_177_cast = add(x = var_1102_cast, y = stem_sep_module_17_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_179_pad_0 = const()[name = tensor<string, []>("input_179_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 512, 0])];
            tensor<string, []> input_179_mode_0 = const()[name = tensor<string, []>("input_179_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_179_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_179_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 524]> input_179_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_177_cast_in_state, input_177_cast));
            tensor<fp16, [1, 448, 512]> input_177_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -512]), size = tensor<int32, [3]>([1, 448, 512]), x = input_179_cast);
            tensor<int32, [1]> var_1107 = const()[name = tensor<string, []>("op_1107"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1109 = const()[name = tensor<string, []>("op_1109"), val = tensor<int32, [1]>([256])];
            tensor<string, []> input_181_pad_type_0 = const()[name = tensor<string, []>("input_181_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_181_pad_0 = const()[name = tensor<string, []>("input_181_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_17_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_17_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(7809280)))];
            tensor<fp16, [1, 448, 12]> input_181_cast = conv(dilations = var_1109, groups = var_6, pad = input_181_pad_0, pad_type = input_181_pad_type_0, strides = var_1107, weight = stem_sep_module_17_tcn_4_weight_to_fp16, x = input_179_cast);
            tensor<fp16, []> var_1113_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1113_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.154p-1)];
            tensor<fp16, [1, 448, 12]> var_1113_cast = leaky_relu(alpha = var_1113_alpha_1_to_fp16, x = input_181_cast);
            tensor<int32, [1]> var_1117 = const()[name = tensor<string, []>("op_1117"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_73_cast = reduce_mean(axes = var_1117, keep_dims = var_17, x = var_1113_cast);
            tensor<fp16, [1, 448, 12]> var_1119_cast = sub(x = var_1113_cast, y = mean_y_73_cast);
            tensor<fp16, [1, 448, 12]> var_1120_cast = square(x = var_1119_cast);
            tensor<int32, [1]> var_1121 = const()[name = tensor<string, []>("op_1121"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1122_cast = reduce_mean(axes = var_1121, keep_dims = var_17, x = var_1120_cast);
            tensor<fp16, []> var_1123_to_fp16 = const()[name = tensor<string, []>("op_1123_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1124_cast = add(x = var_1122_cast, y = var_1123_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_73_cast = sqrt(x = var_1124_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_17_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_17_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(7812032)))];
            tensor<fp16, [1, 448, 12]> var_1127_cast = mul(x = stem_sep_module_17_tcn_6_norm_gamma_to_fp16, y = var_1119_cast);
            tensor<fp16, [1, 448, 12]> var_1128_cast = real_div(x = var_1127_cast, y = std_y_73_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_17_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_17_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(7812992)))];
            tensor<fp16, [1, 448, 12]> y_36_cast = add(x = var_1128_cast, y = stem_sep_module_17_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_183_cast = add(x = input_173_cast, y = y_36_cast);
            tensor<int32, [1]> var_1139 = const()[name = tensor<string, []>("op_1139"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1141 = const()[name = tensor<string, []>("op_1141"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_185_pad_type_0 = const()[name = tensor<string, []>("input_185_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_185_pad_0 = const()[name = tensor<string, []>("input_185_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_18_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_18_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(7813952)))];
            tensor<fp16, [1, 448, 12]> input_185_cast = conv(dilations = var_1141, groups = var_21, pad = input_185_pad_0, pad_type = input_185_pad_type_0, strides = var_1139, weight = stem_sep_module_18_tcn_0_weight_to_fp16, x = input_183_cast);
            tensor<fp16, []> var_1145_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1145_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.36p+0)];
            tensor<fp16, [1, 448, 12]> var_1145_cast = leaky_relu(alpha = var_1145_alpha_1_to_fp16, x = input_185_cast);
            tensor<int32, [1]> var_1149 = const()[name = tensor<string, []>("op_1149"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_75_cast = reduce_mean(axes = var_1149, keep_dims = var_17, x = var_1145_cast);
            tensor<fp16, [1, 448, 12]> var_1151_cast = sub(x = var_1145_cast, y = mean_y_75_cast);
            tensor<fp16, [1, 448, 12]> var_1152_cast = square(x = var_1151_cast);
            tensor<int32, [1]> var_1153 = const()[name = tensor<string, []>("op_1153"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1154_cast = reduce_mean(axes = var_1153, keep_dims = var_17, x = var_1152_cast);
            tensor<fp16, []> var_1155_to_fp16 = const()[name = tensor<string, []>("op_1155_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1156_cast = add(x = var_1154_cast, y = var_1155_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_75_cast = sqrt(x = var_1156_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_18_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_18_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(8215424)))];
            tensor<fp16, [1, 448, 12]> var_1159_cast = mul(x = stem_sep_module_18_tcn_2_norm_gamma_to_fp16, y = var_1151_cast);
            tensor<fp16, [1, 448, 12]> var_1160_cast = real_div(x = var_1159_cast, y = std_y_75_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_18_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_18_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(8216384)))];
            tensor<fp16, [1, 448, 12]> input_187_cast = add(x = var_1160_cast, y = stem_sep_module_18_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_189_pad_0 = const()[name = tensor<string, []>("input_189_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
            tensor<string, []> input_189_mode_0 = const()[name = tensor<string, []>("input_189_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_189_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_189_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 14]> input_189_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_187_cast_in_state, input_187_cast));
            tensor<fp16, [1, 448, 2]> input_187_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 448, 2]), x = input_189_cast);
            tensor<int32, [1]> var_1165 = const()[name = tensor<string, []>("op_1165"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1167 = const()[name = tensor<string, []>("op_1167"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_191_pad_type_0 = const()[name = tensor<string, []>("input_191_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_191_pad_0 = const()[name = tensor<string, []>("input_191_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_18_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_18_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(8217344)))];
            tensor<fp16, [1, 448, 12]> input_191_cast = conv(dilations = var_1167, groups = var_6, pad = input_191_pad_0, pad_type = input_191_pad_type_0, strides = var_1165, weight = stem_sep_module_18_tcn_4_weight_to_fp16, x = input_189_cast);
            tensor<fp16, []> var_1171_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1171_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.9fcp-3)];
            tensor<fp16, [1, 448, 12]> var_1171_cast = leaky_relu(alpha = var_1171_alpha_1_to_fp16, x = input_191_cast);
            tensor<int32, [1]> var_1175 = const()[name = tensor<string, []>("op_1175"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_77_cast = reduce_mean(axes = var_1175, keep_dims = var_17, x = var_1171_cast);
            tensor<fp16, [1, 448, 12]> var_1177_cast = sub(x = var_1171_cast, y = mean_y_77_cast);
            tensor<fp16, [1, 448, 12]> var_1178_cast = square(x = var_1177_cast);
            tensor<int32, [1]> var_1179 = const()[name = tensor<string, []>("op_1179"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1180_cast = reduce_mean(axes = var_1179, keep_dims = var_17, x = var_1178_cast);
            tensor<fp16, []> var_1181_to_fp16 = const()[name = tensor<string, []>("op_1181_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1182_cast = add(x = var_1180_cast, y = var_1181_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_77_cast = sqrt(x = var_1182_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_18_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_18_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(8220096)))];
            tensor<fp16, [1, 448, 12]> var_1185_cast = mul(x = stem_sep_module_18_tcn_6_norm_gamma_to_fp16, y = var_1177_cast);
            tensor<fp16, [1, 448, 12]> var_1186_cast = real_div(x = var_1185_cast, y = std_y_77_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_18_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_18_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(8221056)))];
            tensor<fp16, [1, 448, 12]> y_38_cast = add(x = var_1186_cast, y = stem_sep_module_18_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_193_cast = add(x = input_183_cast, y = y_38_cast);
            tensor<int32, [1]> var_1197 = const()[name = tensor<string, []>("op_1197"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1199 = const()[name = tensor<string, []>("op_1199"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_195_pad_type_0 = const()[name = tensor<string, []>("input_195_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_195_pad_0 = const()[name = tensor<string, []>("input_195_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_19_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_19_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(8222016)))];
            tensor<fp16, [1, 448, 12]> input_195_cast = conv(dilations = var_1199, groups = var_21, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = var_1197, weight = stem_sep_module_19_tcn_0_weight_to_fp16, x = input_193_cast);
            tensor<fp16, []> var_1203_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1203_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.ee4p-2)];
            tensor<fp16, [1, 448, 12]> var_1203_cast = leaky_relu(alpha = var_1203_alpha_1_to_fp16, x = input_195_cast);
            tensor<int32, [1]> var_1207 = const()[name = tensor<string, []>("op_1207"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_79_cast = reduce_mean(axes = var_1207, keep_dims = var_17, x = var_1203_cast);
            tensor<fp16, [1, 448, 12]> var_1209_cast = sub(x = var_1203_cast, y = mean_y_79_cast);
            tensor<fp16, [1, 448, 12]> var_1210_cast = square(x = var_1209_cast);
            tensor<int32, [1]> var_1211 = const()[name = tensor<string, []>("op_1211"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1212_cast = reduce_mean(axes = var_1211, keep_dims = var_17, x = var_1210_cast);
            tensor<fp16, []> var_1213_to_fp16 = const()[name = tensor<string, []>("op_1213_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1214_cast = add(x = var_1212_cast, y = var_1213_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_79_cast = sqrt(x = var_1214_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_19_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_19_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(8623488)))];
            tensor<fp16, [1, 448, 12]> var_1217_cast = mul(x = stem_sep_module_19_tcn_2_norm_gamma_to_fp16, y = var_1209_cast);
            tensor<fp16, [1, 448, 12]> var_1218_cast = real_div(x = var_1217_cast, y = std_y_79_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_19_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_19_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(8624448)))];
            tensor<fp16, [1, 448, 12]> input_197_cast = add(x = var_1218_cast, y = stem_sep_module_19_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_199_pad_0 = const()[name = tensor<string, []>("input_199_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
            tensor<string, []> input_199_mode_0 = const()[name = tensor<string, []>("input_199_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_199_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_199_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 16]> input_199_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_197_cast_in_state, input_197_cast));
            tensor<fp16, [1, 448, 4]> input_197_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 448, 4]), x = input_199_cast);
            tensor<int32, [1]> var_1223 = const()[name = tensor<string, []>("op_1223"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1225 = const()[name = tensor<string, []>("op_1225"), val = tensor<int32, [1]>([2])];
            tensor<string, []> input_201_pad_type_0 = const()[name = tensor<string, []>("input_201_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_201_pad_0 = const()[name = tensor<string, []>("input_201_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_19_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_19_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(8625408)))];
            tensor<fp16, [1, 448, 12]> input_201_cast = conv(dilations = var_1225, groups = var_6, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = var_1223, weight = stem_sep_module_19_tcn_4_weight_to_fp16, x = input_199_cast);
            tensor<fp16, []> var_1229_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1229_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.f58p-1)];
            tensor<fp16, [1, 448, 12]> var_1229_cast = leaky_relu(alpha = var_1229_alpha_1_to_fp16, x = input_201_cast);
            tensor<int32, [1]> var_1233 = const()[name = tensor<string, []>("op_1233"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_81_cast = reduce_mean(axes = var_1233, keep_dims = var_17, x = var_1229_cast);
            tensor<fp16, [1, 448, 12]> var_1235_cast = sub(x = var_1229_cast, y = mean_y_81_cast);
            tensor<fp16, [1, 448, 12]> var_1236_cast = square(x = var_1235_cast);
            tensor<int32, [1]> var_1237 = const()[name = tensor<string, []>("op_1237"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1238_cast = reduce_mean(axes = var_1237, keep_dims = var_17, x = var_1236_cast);
            tensor<fp16, []> var_1239_to_fp16 = const()[name = tensor<string, []>("op_1239_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1240_cast = add(x = var_1238_cast, y = var_1239_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_81_cast = sqrt(x = var_1240_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_19_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_19_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(8628160)))];
            tensor<fp16, [1, 448, 12]> var_1243_cast = mul(x = stem_sep_module_19_tcn_6_norm_gamma_to_fp16, y = var_1235_cast);
            tensor<fp16, [1, 448, 12]> var_1244_cast = real_div(x = var_1243_cast, y = std_y_81_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_19_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_19_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(8629120)))];
            tensor<fp16, [1, 448, 12]> y_40_cast = add(x = var_1244_cast, y = stem_sep_module_19_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_203_cast = add(x = input_193_cast, y = y_40_cast);
            tensor<int32, [1]> var_1255 = const()[name = tensor<string, []>("op_1255"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1257 = const()[name = tensor<string, []>("op_1257"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_205_pad_type_0 = const()[name = tensor<string, []>("input_205_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_205_pad_0 = const()[name = tensor<string, []>("input_205_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_20_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_20_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(8630080)))];
            tensor<fp16, [1, 448, 12]> input_205_cast = conv(dilations = var_1257, groups = var_21, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = var_1255, weight = stem_sep_module_20_tcn_0_weight_to_fp16, x = input_203_cast);
            tensor<fp16, []> var_1261_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1261_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.7fp-2)];
            tensor<fp16, [1, 448, 12]> var_1261_cast = leaky_relu(alpha = var_1261_alpha_1_to_fp16, x = input_205_cast);
            tensor<int32, [1]> var_1265 = const()[name = tensor<string, []>("op_1265"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_83_cast = reduce_mean(axes = var_1265, keep_dims = var_17, x = var_1261_cast);
            tensor<fp16, [1, 448, 12]> var_1267_cast = sub(x = var_1261_cast, y = mean_y_83_cast);
            tensor<fp16, [1, 448, 12]> var_1268_cast = square(x = var_1267_cast);
            tensor<int32, [1]> var_1269 = const()[name = tensor<string, []>("op_1269"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1270_cast = reduce_mean(axes = var_1269, keep_dims = var_17, x = var_1268_cast);
            tensor<fp16, []> var_1271_to_fp16 = const()[name = tensor<string, []>("op_1271_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1272_cast = add(x = var_1270_cast, y = var_1271_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_83_cast = sqrt(x = var_1272_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_20_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_20_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9031552)))];
            tensor<fp16, [1, 448, 12]> var_1275_cast = mul(x = stem_sep_module_20_tcn_2_norm_gamma_to_fp16, y = var_1267_cast);
            tensor<fp16, [1, 448, 12]> var_1276_cast = real_div(x = var_1275_cast, y = std_y_83_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_20_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_20_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9032512)))];
            tensor<fp16, [1, 448, 12]> input_207_cast = add(x = var_1276_cast, y = stem_sep_module_20_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_209_pad_0 = const()[name = tensor<string, []>("input_209_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 8, 0])];
            tensor<string, []> input_209_mode_0 = const()[name = tensor<string, []>("input_209_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_209_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_209_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 20]> input_209_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_207_cast_in_state, input_207_cast));
            tensor<fp16, [1, 448, 8]> input_207_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 448, 8]), x = input_209_cast);
            tensor<int32, [1]> var_1281 = const()[name = tensor<string, []>("op_1281"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1283 = const()[name = tensor<string, []>("op_1283"), val = tensor<int32, [1]>([4])];
            tensor<string, []> input_211_pad_type_0 = const()[name = tensor<string, []>("input_211_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_211_pad_0 = const()[name = tensor<string, []>("input_211_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_20_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_20_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9033472)))];
            tensor<fp16, [1, 448, 12]> input_211_cast = conv(dilations = var_1283, groups = var_6, pad = input_211_pad_0, pad_type = input_211_pad_type_0, strides = var_1281, weight = stem_sep_module_20_tcn_4_weight_to_fp16, x = input_209_cast);
            tensor<fp16, []> var_1287_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1287_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.808p-2)];
            tensor<fp16, [1, 448, 12]> var_1287_cast = leaky_relu(alpha = var_1287_alpha_1_to_fp16, x = input_211_cast);
            tensor<int32, [1]> var_1291 = const()[name = tensor<string, []>("op_1291"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_85_cast = reduce_mean(axes = var_1291, keep_dims = var_17, x = var_1287_cast);
            tensor<fp16, [1, 448, 12]> var_1293_cast = sub(x = var_1287_cast, y = mean_y_85_cast);
            tensor<fp16, [1, 448, 12]> var_1294_cast = square(x = var_1293_cast);
            tensor<int32, [1]> var_1295 = const()[name = tensor<string, []>("op_1295"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1296_cast = reduce_mean(axes = var_1295, keep_dims = var_17, x = var_1294_cast);
            tensor<fp16, []> var_1297_to_fp16 = const()[name = tensor<string, []>("op_1297_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1298_cast = add(x = var_1296_cast, y = var_1297_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_85_cast = sqrt(x = var_1298_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_20_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_20_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9036224)))];
            tensor<fp16, [1, 448, 12]> var_1301_cast = mul(x = stem_sep_module_20_tcn_6_norm_gamma_to_fp16, y = var_1293_cast);
            tensor<fp16, [1, 448, 12]> var_1302_cast = real_div(x = var_1301_cast, y = std_y_85_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_20_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_20_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9037184)))];
            tensor<fp16, [1, 448, 12]> y_42_cast = add(x = var_1302_cast, y = stem_sep_module_20_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_213_cast = add(x = input_203_cast, y = y_42_cast);
            tensor<int32, [1]> var_1313 = const()[name = tensor<string, []>("op_1313"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1315 = const()[name = tensor<string, []>("op_1315"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_215_pad_type_0 = const()[name = tensor<string, []>("input_215_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_215_pad_0 = const()[name = tensor<string, []>("input_215_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_21_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_21_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9038144)))];
            tensor<fp16, [1, 448, 12]> input_215_cast = conv(dilations = var_1315, groups = var_21, pad = input_215_pad_0, pad_type = input_215_pad_type_0, strides = var_1313, weight = stem_sep_module_21_tcn_0_weight_to_fp16, x = input_213_cast);
            tensor<fp16, []> var_1319_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1319_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.68cp-2)];
            tensor<fp16, [1, 448, 12]> var_1319_cast = leaky_relu(alpha = var_1319_alpha_1_to_fp16, x = input_215_cast);
            tensor<int32, [1]> var_1323 = const()[name = tensor<string, []>("op_1323"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_87_cast = reduce_mean(axes = var_1323, keep_dims = var_17, x = var_1319_cast);
            tensor<fp16, [1, 448, 12]> var_1325_cast = sub(x = var_1319_cast, y = mean_y_87_cast);
            tensor<fp16, [1, 448, 12]> var_1326_cast = square(x = var_1325_cast);
            tensor<int32, [1]> var_1327 = const()[name = tensor<string, []>("op_1327"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1328_cast = reduce_mean(axes = var_1327, keep_dims = var_17, x = var_1326_cast);
            tensor<fp16, []> var_1329_to_fp16 = const()[name = tensor<string, []>("op_1329_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1330_cast = add(x = var_1328_cast, y = var_1329_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_87_cast = sqrt(x = var_1330_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_21_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_21_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9439616)))];
            tensor<fp16, [1, 448, 12]> var_1333_cast = mul(x = stem_sep_module_21_tcn_2_norm_gamma_to_fp16, y = var_1325_cast);
            tensor<fp16, [1, 448, 12]> var_1334_cast = real_div(x = var_1333_cast, y = std_y_87_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_21_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_21_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9440576)))];
            tensor<fp16, [1, 448, 12]> input_217_cast = add(x = var_1334_cast, y = stem_sep_module_21_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_219_pad_0 = const()[name = tensor<string, []>("input_219_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 16, 0])];
            tensor<string, []> input_219_mode_0 = const()[name = tensor<string, []>("input_219_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_219_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_219_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 28]> input_219_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_217_cast_in_state, input_217_cast));
            tensor<fp16, [1, 448, 16]> input_217_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -16]), size = tensor<int32, [3]>([1, 448, 16]), x = input_219_cast);
            tensor<int32, [1]> var_1339 = const()[name = tensor<string, []>("op_1339"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1341 = const()[name = tensor<string, []>("op_1341"), val = tensor<int32, [1]>([8])];
            tensor<string, []> input_221_pad_type_0 = const()[name = tensor<string, []>("input_221_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_221_pad_0 = const()[name = tensor<string, []>("input_221_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_21_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_21_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9441536)))];
            tensor<fp16, [1, 448, 12]> input_221_cast = conv(dilations = var_1341, groups = var_6, pad = input_221_pad_0, pad_type = input_221_pad_type_0, strides = var_1339, weight = stem_sep_module_21_tcn_4_weight_to_fp16, x = input_219_cast);
            tensor<fp16, []> var_1345_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1345_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.6bp-2)];
            tensor<fp16, [1, 448, 12]> var_1345_cast = leaky_relu(alpha = var_1345_alpha_1_to_fp16, x = input_221_cast);
            tensor<int32, [1]> var_1349 = const()[name = tensor<string, []>("op_1349"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_89_cast = reduce_mean(axes = var_1349, keep_dims = var_17, x = var_1345_cast);
            tensor<fp16, [1, 448, 12]> var_1351_cast = sub(x = var_1345_cast, y = mean_y_89_cast);
            tensor<fp16, [1, 448, 12]> var_1352_cast = square(x = var_1351_cast);
            tensor<int32, [1]> var_1353 = const()[name = tensor<string, []>("op_1353"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1354_cast = reduce_mean(axes = var_1353, keep_dims = var_17, x = var_1352_cast);
            tensor<fp16, []> var_1355_to_fp16 = const()[name = tensor<string, []>("op_1355_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1356_cast = add(x = var_1354_cast, y = var_1355_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_89_cast = sqrt(x = var_1356_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_21_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_21_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9444288)))];
            tensor<fp16, [1, 448, 12]> var_1359_cast = mul(x = stem_sep_module_21_tcn_6_norm_gamma_to_fp16, y = var_1351_cast);
            tensor<fp16, [1, 448, 12]> var_1360_cast = real_div(x = var_1359_cast, y = std_y_89_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_21_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_21_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9445248)))];
            tensor<fp16, [1, 448, 12]> y_44_cast = add(x = var_1360_cast, y = stem_sep_module_21_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_223_cast = add(x = input_213_cast, y = y_44_cast);
            tensor<int32, [1]> var_1371 = const()[name = tensor<string, []>("op_1371"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1373 = const()[name = tensor<string, []>("op_1373"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_225_pad_type_0 = const()[name = tensor<string, []>("input_225_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_225_pad_0 = const()[name = tensor<string, []>("input_225_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_22_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_22_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9446208)))];
            tensor<fp16, [1, 448, 12]> input_225_cast = conv(dilations = var_1373, groups = var_21, pad = input_225_pad_0, pad_type = input_225_pad_type_0, strides = var_1371, weight = stem_sep_module_22_tcn_0_weight_to_fp16, x = input_223_cast);
            tensor<fp16, []> var_1377_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1377_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.664p-3)];
            tensor<fp16, [1, 448, 12]> var_1377_cast = leaky_relu(alpha = var_1377_alpha_1_to_fp16, x = input_225_cast);
            tensor<int32, [1]> var_1381 = const()[name = tensor<string, []>("op_1381"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_91_cast = reduce_mean(axes = var_1381, keep_dims = var_17, x = var_1377_cast);
            tensor<fp16, [1, 448, 12]> var_1383_cast = sub(x = var_1377_cast, y = mean_y_91_cast);
            tensor<fp16, [1, 448, 12]> var_1384_cast = square(x = var_1383_cast);
            tensor<int32, [1]> var_1385 = const()[name = tensor<string, []>("op_1385"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1386_cast = reduce_mean(axes = var_1385, keep_dims = var_17, x = var_1384_cast);
            tensor<fp16, []> var_1387_to_fp16 = const()[name = tensor<string, []>("op_1387_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1388_cast = add(x = var_1386_cast, y = var_1387_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_91_cast = sqrt(x = var_1388_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_22_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_22_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9847680)))];
            tensor<fp16, [1, 448, 12]> var_1391_cast = mul(x = stem_sep_module_22_tcn_2_norm_gamma_to_fp16, y = var_1383_cast);
            tensor<fp16, [1, 448, 12]> var_1392_cast = real_div(x = var_1391_cast, y = std_y_91_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_22_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_22_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9848640)))];
            tensor<fp16, [1, 448, 12]> input_227_cast = add(x = var_1392_cast, y = stem_sep_module_22_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_229_pad_0 = const()[name = tensor<string, []>("input_229_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            tensor<string, []> input_229_mode_0 = const()[name = tensor<string, []>("input_229_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_229_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_229_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 44]> input_229_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_227_cast_in_state, input_227_cast));
            tensor<fp16, [1, 448, 32]> input_227_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -32]), size = tensor<int32, [3]>([1, 448, 32]), x = input_229_cast);
            tensor<int32, [1]> var_1397 = const()[name = tensor<string, []>("op_1397"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1399 = const()[name = tensor<string, []>("op_1399"), val = tensor<int32, [1]>([16])];
            tensor<string, []> input_231_pad_type_0 = const()[name = tensor<string, []>("input_231_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_231_pad_0 = const()[name = tensor<string, []>("input_231_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_22_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_22_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9849600)))];
            tensor<fp16, [1, 448, 12]> input_231_cast = conv(dilations = var_1399, groups = var_6, pad = input_231_pad_0, pad_type = input_231_pad_type_0, strides = var_1397, weight = stem_sep_module_22_tcn_4_weight_to_fp16, x = input_229_cast);
            tensor<fp16, []> var_1403_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1403_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.ddp-1)];
            tensor<fp16, [1, 448, 12]> var_1403_cast = leaky_relu(alpha = var_1403_alpha_1_to_fp16, x = input_231_cast);
            tensor<int32, [1]> var_1407 = const()[name = tensor<string, []>("op_1407"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_93_cast = reduce_mean(axes = var_1407, keep_dims = var_17, x = var_1403_cast);
            tensor<fp16, [1, 448, 12]> var_1409_cast = sub(x = var_1403_cast, y = mean_y_93_cast);
            tensor<fp16, [1, 448, 12]> var_1410_cast = square(x = var_1409_cast);
            tensor<int32, [1]> var_1411 = const()[name = tensor<string, []>("op_1411"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1412_cast = reduce_mean(axes = var_1411, keep_dims = var_17, x = var_1410_cast);
            tensor<fp16, []> var_1413_to_fp16 = const()[name = tensor<string, []>("op_1413_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1414_cast = add(x = var_1412_cast, y = var_1413_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_93_cast = sqrt(x = var_1414_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_22_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_22_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9852352)))];
            tensor<fp16, [1, 448, 12]> var_1417_cast = mul(x = stem_sep_module_22_tcn_6_norm_gamma_to_fp16, y = var_1409_cast);
            tensor<fp16, [1, 448, 12]> var_1418_cast = real_div(x = var_1417_cast, y = std_y_93_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_22_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_22_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9853312)))];
            tensor<fp16, [1, 448, 12]> y_46_cast = add(x = var_1418_cast, y = stem_sep_module_22_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_233_cast = add(x = input_223_cast, y = y_46_cast);
            tensor<int32, [1]> var_1429 = const()[name = tensor<string, []>("op_1429"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1431 = const()[name = tensor<string, []>("op_1431"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_235_pad_type_0 = const()[name = tensor<string, []>("input_235_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_235_pad_0 = const()[name = tensor<string, []>("input_235_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_23_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_23_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(9854272)))];
            tensor<fp16, [1, 448, 12]> input_235_cast = conv(dilations = var_1431, groups = var_21, pad = input_235_pad_0, pad_type = input_235_pad_type_0, strides = var_1429, weight = stem_sep_module_23_tcn_0_weight_to_fp16, x = input_233_cast);
            tensor<fp16, []> var_1435_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1435_alpha_1_to_fp16"), val = tensor<fp16, []>(-0x1.1c8p-2)];
            tensor<fp16, [1, 448, 12]> var_1435_cast = leaky_relu(alpha = var_1435_alpha_1_to_fp16, x = input_235_cast);
            tensor<int32, [1]> var_1439 = const()[name = tensor<string, []>("op_1439"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_95_cast = reduce_mean(axes = var_1439, keep_dims = var_17, x = var_1435_cast);
            tensor<fp16, [1, 448, 12]> var_1441_cast = sub(x = var_1435_cast, y = mean_y_95_cast);
            tensor<fp16, [1, 448, 12]> var_1442_cast = square(x = var_1441_cast);
            tensor<int32, [1]> var_1443 = const()[name = tensor<string, []>("op_1443"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1444_cast = reduce_mean(axes = var_1443, keep_dims = var_17, x = var_1442_cast);
            tensor<fp16, []> var_1445_to_fp16 = const()[name = tensor<string, []>("op_1445_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1446_cast = add(x = var_1444_cast, y = var_1445_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_95_cast = sqrt(x = var_1446_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_23_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_23_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(10255744)))];
            tensor<fp16, [1, 448, 12]> var_1449_cast = mul(x = stem_sep_module_23_tcn_2_norm_gamma_to_fp16, y = var_1441_cast);
            tensor<fp16, [1, 448, 12]> var_1450_cast = real_div(x = var_1449_cast, y = std_y_95_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_23_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_23_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(10256704)))];
            tensor<fp16, [1, 448, 12]> input_237_cast = add(x = var_1450_cast, y = stem_sep_module_23_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_239_pad_0 = const()[name = tensor<string, []>("input_239_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            tensor<string, []> input_239_mode_0 = const()[name = tensor<string, []>("input_239_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_239_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_239_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 76]> input_239_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_237_cast_in_state, input_237_cast));
            tensor<fp16, [1, 448, 64]> input_237_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 448, 64]), x = input_239_cast);
            tensor<int32, [1]> var_1455 = const()[name = tensor<string, []>("op_1455"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1457 = const()[name = tensor<string, []>("op_1457"), val = tensor<int32, [1]>([32])];
            tensor<string, []> input_241_pad_type_0 = const()[name = tensor<string, []>("input_241_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_241_pad_0 = const()[name = tensor<string, []>("input_241_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_23_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_23_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(10257664)))];
            tensor<fp16, [1, 448, 12]> input_241_cast = conv(dilations = var_1457, groups = var_6, pad = input_241_pad_0, pad_type = input_241_pad_type_0, strides = var_1455, weight = stem_sep_module_23_tcn_4_weight_to_fp16, x = input_239_cast);
            tensor<fp16, []> var_1461_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1461_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.d5cp-2)];
            tensor<fp16, [1, 448, 12]> var_1461_cast = leaky_relu(alpha = var_1461_alpha_1_to_fp16, x = input_241_cast);
            tensor<int32, [1]> var_1465 = const()[name = tensor<string, []>("op_1465"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_97_cast = reduce_mean(axes = var_1465, keep_dims = var_17, x = var_1461_cast);
            tensor<fp16, [1, 448, 12]> var_1467_cast = sub(x = var_1461_cast, y = mean_y_97_cast);
            tensor<fp16, [1, 448, 12]> var_1468_cast = square(x = var_1467_cast);
            tensor<int32, [1]> var_1469 = const()[name = tensor<string, []>("op_1469"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1470_cast = reduce_mean(axes = var_1469, keep_dims = var_17, x = var_1468_cast);
            tensor<fp16, []> var_1471_to_fp16 = const()[name = tensor<string, []>("op_1471_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1472_cast = add(x = var_1470_cast, y = var_1471_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_97_cast = sqrt(x = var_1472_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_23_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_23_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(10260416)))];
            tensor<fp16, [1, 448, 12]> var_1475_cast = mul(x = stem_sep_module_23_tcn_6_norm_gamma_to_fp16, y = var_1467_cast);
            tensor<fp16, [1, 448, 12]> var_1476_cast = real_div(x = var_1475_cast, y = std_y_97_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_23_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_23_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(10261376)))];
            tensor<fp16, [1, 448, 12]> y_48_cast = add(x = var_1476_cast, y = stem_sep_module_23_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_243_cast = add(x = input_233_cast, y = y_48_cast);
            tensor<int32, [1]> var_1487 = const()[name = tensor<string, []>("op_1487"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1489 = const()[name = tensor<string, []>("op_1489"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_245_pad_type_0 = const()[name = tensor<string, []>("input_245_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_245_pad_0 = const()[name = tensor<string, []>("input_245_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_24_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_24_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(10262336)))];
            tensor<fp16, [1, 448, 12]> input_245_cast = conv(dilations = var_1489, groups = var_21, pad = input_245_pad_0, pad_type = input_245_pad_type_0, strides = var_1487, weight = stem_sep_module_24_tcn_0_weight_to_fp16, x = input_243_cast);
            tensor<fp16, []> var_1493_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1493_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.004p+0)];
            tensor<fp16, [1, 448, 12]> var_1493_cast = leaky_relu(alpha = var_1493_alpha_1_to_fp16, x = input_245_cast);
            tensor<int32, [1]> var_1497 = const()[name = tensor<string, []>("op_1497"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_99_cast = reduce_mean(axes = var_1497, keep_dims = var_17, x = var_1493_cast);
            tensor<fp16, [1, 448, 12]> var_1499_cast = sub(x = var_1493_cast, y = mean_y_99_cast);
            tensor<fp16, [1, 448, 12]> var_1500_cast = square(x = var_1499_cast);
            tensor<int32, [1]> var_1501 = const()[name = tensor<string, []>("op_1501"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1502_cast = reduce_mean(axes = var_1501, keep_dims = var_17, x = var_1500_cast);
            tensor<fp16, []> var_1503_to_fp16 = const()[name = tensor<string, []>("op_1503_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1504_cast = add(x = var_1502_cast, y = var_1503_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_99_cast = sqrt(x = var_1504_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_24_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_24_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(10663808)))];
            tensor<fp16, [1, 448, 12]> var_1507_cast = mul(x = stem_sep_module_24_tcn_2_norm_gamma_to_fp16, y = var_1499_cast);
            tensor<fp16, [1, 448, 12]> var_1508_cast = real_div(x = var_1507_cast, y = std_y_99_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_24_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_24_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(10664768)))];
            tensor<fp16, [1, 448, 12]> input_247_cast = add(x = var_1508_cast, y = stem_sep_module_24_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_249_pad_0 = const()[name = tensor<string, []>("input_249_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 128, 0])];
            tensor<string, []> input_249_mode_0 = const()[name = tensor<string, []>("input_249_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_249_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_249_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 140]> input_249_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_247_cast_in_state, input_247_cast));
            tensor<fp16, [1, 448, 128]> input_247_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -128]), size = tensor<int32, [3]>([1, 448, 128]), x = input_249_cast);
            tensor<int32, [1]> var_1513 = const()[name = tensor<string, []>("op_1513"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1515 = const()[name = tensor<string, []>("op_1515"), val = tensor<int32, [1]>([64])];
            tensor<string, []> input_251_pad_type_0 = const()[name = tensor<string, []>("input_251_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_251_pad_0 = const()[name = tensor<string, []>("input_251_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_24_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_24_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(10665728)))];
            tensor<fp16, [1, 448, 12]> input_251_cast = conv(dilations = var_1515, groups = var_6, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = var_1513, weight = stem_sep_module_24_tcn_4_weight_to_fp16, x = input_249_cast);
            tensor<fp16, []> var_1519_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1519_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.004p+0)];
            tensor<fp16, [1, 448, 12]> var_1519_cast = leaky_relu(alpha = var_1519_alpha_1_to_fp16, x = input_251_cast);
            tensor<int32, [1]> var_1523 = const()[name = tensor<string, []>("op_1523"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_101_cast = reduce_mean(axes = var_1523, keep_dims = var_17, x = var_1519_cast);
            tensor<fp16, [1, 448, 12]> var_1525_cast = sub(x = var_1519_cast, y = mean_y_101_cast);
            tensor<fp16, [1, 448, 12]> var_1526_cast = square(x = var_1525_cast);
            tensor<int32, [1]> var_1527 = const()[name = tensor<string, []>("op_1527"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1528_cast = reduce_mean(axes = var_1527, keep_dims = var_17, x = var_1526_cast);
            tensor<fp16, []> var_1529_to_fp16 = const()[name = tensor<string, []>("op_1529_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1530_cast = add(x = var_1528_cast, y = var_1529_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_101_cast = sqrt(x = var_1530_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_24_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_24_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(10668480)))];
            tensor<fp16, [1, 448, 12]> var_1533_cast = mul(x = stem_sep_module_24_tcn_6_norm_gamma_to_fp16, y = var_1525_cast);
            tensor<fp16, [1, 448, 12]> var_1534_cast = real_div(x = var_1533_cast, y = std_y_101_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_24_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_24_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(10669440)))];
            tensor<fp16, [1, 448, 12]> y_50_cast = add(x = var_1534_cast, y = stem_sep_module_24_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_253_cast = add(x = input_243_cast, y = y_50_cast);
            tensor<int32, [1]> var_1545 = const()[name = tensor<string, []>("op_1545"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1547 = const()[name = tensor<string, []>("op_1547"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_255_pad_type_0 = const()[name = tensor<string, []>("input_255_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_255_pad_0 = const()[name = tensor<string, []>("input_255_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_25_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_25_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(10670400)))];
            tensor<fp16, [1, 448, 12]> input_255_cast = conv(dilations = var_1547, groups = var_21, pad = input_255_pad_0, pad_type = input_255_pad_type_0, strides = var_1545, weight = stem_sep_module_25_tcn_0_weight_to_fp16, x = input_253_cast);
            tensor<fp16, []> var_1551_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1551_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.ff8p-1)];
            tensor<fp16, [1, 448, 12]> var_1551_cast = leaky_relu(alpha = var_1551_alpha_1_to_fp16, x = input_255_cast);
            tensor<int32, [1]> var_1555 = const()[name = tensor<string, []>("op_1555"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_103_cast = reduce_mean(axes = var_1555, keep_dims = var_17, x = var_1551_cast);
            tensor<fp16, [1, 448, 12]> var_1557_cast = sub(x = var_1551_cast, y = mean_y_103_cast);
            tensor<fp16, [1, 448, 12]> var_1558_cast = square(x = var_1557_cast);
            tensor<int32, [1]> var_1559 = const()[name = tensor<string, []>("op_1559"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1560_cast = reduce_mean(axes = var_1559, keep_dims = var_17, x = var_1558_cast);
            tensor<fp16, []> var_1561_to_fp16 = const()[name = tensor<string, []>("op_1561_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1562_cast = add(x = var_1560_cast, y = var_1561_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_103_cast = sqrt(x = var_1562_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_25_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_25_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(11071872)))];
            tensor<fp16, [1, 448, 12]> var_1565_cast = mul(x = stem_sep_module_25_tcn_2_norm_gamma_to_fp16, y = var_1557_cast);
            tensor<fp16, [1, 448, 12]> var_1566_cast = real_div(x = var_1565_cast, y = std_y_103_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_25_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_25_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(11072832)))];
            tensor<fp16, [1, 448, 12]> input_257_cast = add(x = var_1566_cast, y = stem_sep_module_25_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_259_pad_0 = const()[name = tensor<string, []>("input_259_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 0])];
            tensor<string, []> input_259_mode_0 = const()[name = tensor<string, []>("input_259_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_259_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_259_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 268]> input_259_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_257_cast_in_state, input_257_cast));
            tensor<fp16, [1, 448, 256]> input_257_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -256]), size = tensor<int32, [3]>([1, 448, 256]), x = input_259_cast);
            tensor<int32, [1]> var_1571 = const()[name = tensor<string, []>("op_1571"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1573 = const()[name = tensor<string, []>("op_1573"), val = tensor<int32, [1]>([128])];
            tensor<string, []> input_261_pad_type_0 = const()[name = tensor<string, []>("input_261_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_261_pad_0 = const()[name = tensor<string, []>("input_261_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_25_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_25_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(11073792)))];
            tensor<fp16, [1, 448, 12]> input_261_cast = conv(dilations = var_1573, groups = var_6, pad = input_261_pad_0, pad_type = input_261_pad_type_0, strides = var_1571, weight = stem_sep_module_25_tcn_4_weight_to_fp16, x = input_259_cast);
            tensor<fp16, []> var_1577_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1577_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
            tensor<fp16, [1, 448, 12]> var_1577_cast = leaky_relu(alpha = var_1577_alpha_1_to_fp16, x = input_261_cast);
            tensor<int32, [1]> var_1581 = const()[name = tensor<string, []>("op_1581"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_105_cast = reduce_mean(axes = var_1581, keep_dims = var_17, x = var_1577_cast);
            tensor<fp16, [1, 448, 12]> var_1583_cast = sub(x = var_1577_cast, y = mean_y_105_cast);
            tensor<fp16, [1, 448, 12]> var_1584_cast = square(x = var_1583_cast);
            tensor<int32, [1]> var_1585 = const()[name = tensor<string, []>("op_1585"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1586_cast = reduce_mean(axes = var_1585, keep_dims = var_17, x = var_1584_cast);
            tensor<fp16, []> var_1587_to_fp16 = const()[name = tensor<string, []>("op_1587_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1588_cast = add(x = var_1586_cast, y = var_1587_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_105_cast = sqrt(x = var_1588_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_25_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_25_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(11076544)))];
            tensor<fp16, [1, 448, 12]> var_1591_cast = mul(x = stem_sep_module_25_tcn_6_norm_gamma_to_fp16, y = var_1583_cast);
            tensor<fp16, [1, 448, 12]> var_1592_cast = real_div(x = var_1591_cast, y = std_y_105_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_25_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_25_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(11077504)))];
            tensor<fp16, [1, 448, 12]> y_52_cast = add(x = var_1592_cast, y = stem_sep_module_25_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> input_3_cast = add(x = input_253_cast, y = y_52_cast);
            tensor<int32, [1]> var_1603 = const()[name = tensor<string, []>("op_1603"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1605 = const()[name = tensor<string, []>("op_1605"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_2_pad_type_0 = const()[name = tensor<string, []>("input_2_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_2_pad_0 = const()[name = tensor<string, []>("input_2_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 448, 1]> stem_sep_module_26_tcn_0_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_26_tcn_0_weight_to_fp16"), val = tensor<fp16, [448, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(11078464)))];
            tensor<fp16, [1, 448, 12]> input_2_cast = conv(dilations = var_1605, groups = var_21, pad = input_2_pad_0, pad_type = input_2_pad_type_0, strides = var_1603, weight = stem_sep_module_26_tcn_0_weight_to_fp16, x = input_3_cast);
            tensor<fp16, []> var_1609_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1609_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.99cp-2)];
            tensor<fp16, [1, 448, 12]> var_1609_cast = leaky_relu(alpha = var_1609_alpha_1_to_fp16, x = input_2_cast);
            tensor<int32, [1]> var_1613 = const()[name = tensor<string, []>("op_1613"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_2_cast = reduce_mean(axes = var_1613, keep_dims = var_17, x = var_1609_cast);
            tensor<fp16, [1, 448, 12]> var_1615_cast = sub(x = var_1609_cast, y = mean_y_2_cast);
            tensor<fp16, [1, 448, 12]> var_1616_cast = square(x = var_1615_cast);
            tensor<int32, [1]> var_1617 = const()[name = tensor<string, []>("op_1617"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1618_cast = reduce_mean(axes = var_1617, keep_dims = var_17, x = var_1616_cast);
            tensor<fp16, []> var_1619_to_fp16 = const()[name = tensor<string, []>("op_1619_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1620_cast = add(x = var_1618_cast, y = var_1619_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_2_cast = sqrt(x = var_1620_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_26_tcn_2_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_26_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(11479936)))];
            tensor<fp16, [1, 448, 12]> var_1623_cast = mul(x = stem_sep_module_26_tcn_2_norm_gamma_to_fp16, y = var_1615_cast);
            tensor<fp16, [1, 448, 12]> var_1624_cast = real_div(x = var_1623_cast, y = std_y_2_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_26_tcn_2_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_26_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(11480896)))];
            tensor<fp16, [1, 448, 12]> input_4_cast = add(x = var_1624_cast, y = stem_sep_module_26_tcn_2_norm_beta_to_fp16);
            tensor<int32, [6]> input_6_pad_0 = const()[name = tensor<string, []>("input_6_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 512, 0])];
            tensor<string, []> input_6_mode_0 = const()[name = tensor<string, []>("input_6_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> input_6_constant_val_0_to_fp16 = const()[name = tensor<string, []>("input_6_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 448, 524]> input_6_cast = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input_4_cast_in_state, input_4_cast));
            tensor<fp16, [1, 448, 512]> input_4_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -512]), size = tensor<int32, [3]>([1, 448, 512]), x = input_6_cast);
            tensor<int32, [1]> var_1629 = const()[name = tensor<string, []>("op_1629"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1631 = const()[name = tensor<string, []>("op_1631"), val = tensor<int32, [1]>([256])];
            tensor<string, []> input_1_pad_type_0 = const()[name = tensor<string, []>("input_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_1_pad_0 = const()[name = tensor<string, []>("input_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [448, 1, 3]> stem_sep_module_26_tcn_4_weight_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_26_tcn_4_weight_to_fp16"), val = tensor<fp16, [448, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(11481856)))];
            tensor<fp16, [1, 448, 12]> input_1_cast = conv(dilations = var_1631, groups = var_6, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_1629, weight = stem_sep_module_26_tcn_4_weight_to_fp16, x = input_6_cast);
            tensor<fp16, []> var_1635_alpha_1_to_fp16 = const()[name = tensor<string, []>("op_1635_alpha_1_to_fp16"), val = tensor<fp16, []>(0x1.9b8p-2)];
            tensor<fp16, [1, 448, 12]> var_1635_cast = leaky_relu(alpha = var_1635_alpha_1_to_fp16, x = input_1_cast);
            tensor<int32, [1]> var_1639 = const()[name = tensor<string, []>("op_1639"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> mean_y_1_cast = reduce_mean(axes = var_1639, keep_dims = var_17, x = var_1635_cast);
            tensor<fp16, [1, 448, 12]> var_1641_cast = sub(x = var_1635_cast, y = mean_y_1_cast);
            tensor<fp16, [1, 448, 12]> var_1642_cast = square(x = var_1641_cast);
            tensor<int32, [1]> var_1643 = const()[name = tensor<string, []>("op_1643"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 12]> var_1644_cast = reduce_mean(axes = var_1643, keep_dims = var_17, x = var_1642_cast);
            tensor<fp16, []> var_1645_to_fp16 = const()[name = tensor<string, []>("op_1645_to_fp16"), val = tensor<fp16, []>(0x1p-14)];
            tensor<fp16, [1, 1, 12]> var_1646_cast = add(x = var_1644_cast, y = var_1645_to_fp16);
            tensor<fp16, [1, 1, 12]> std_y_1_cast = sqrt(x = var_1646_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_26_tcn_6_norm_gamma_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_26_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(11484608)))];
            tensor<fp16, [1, 448, 12]> var_1649_cast = mul(x = stem_sep_module_26_tcn_6_norm_gamma_to_fp16, y = var_1641_cast);
            tensor<fp16, [1, 448, 12]> var_1650_cast = real_div(x = var_1649_cast, y = std_y_1_cast);
            tensor<fp16, [1, 448, 1]> stem_sep_module_26_tcn_6_norm_beta_to_fp16 = const()[name = tensor<string, []>("stem_sep_module_26_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 448, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(11485568)))];
            tensor<fp16, [1, 448, 12]> y_1_cast = add(x = var_1650_cast, y = stem_sep_module_26_tcn_6_norm_beta_to_fp16);
            tensor<fp16, [1, 448, 12]> x_1_cast = add(x = input_3_cast, y = y_1_cast);
            tensor<int32, [1]> input0_2_axes_0 = const()[name = tensor<string, []>("input0_2_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 448, 12]> input0_2_cast = expand_dims(axes = input0_2_axes_0, x = x_1_cast);
            tensor<int32, [2]> var_1655 = const()[name = tensor<string, []>("op_1655"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1657 = const()[name = tensor<string, []>("op_1657"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input1_1_pad_type_0 = const()[name = tensor<string, []>("input1_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input1_1_pad_0 = const()[name = tensor<string, []>("input1_1_pad_0"), val = tensor<int32, [4]>([160, 160, 0, 0])];
            tensor<fp16, [2, 1, 385, 1]> stem_mask_layer_weight_to_fp16 = const()[name = tensor<string, []>("stem_mask_layer_weight_to_fp16"), val = tensor<fp16, [2, 1, 385, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(11486528)))];
            tensor<fp16, [1, 2, 384, 12]> input1_1_cast = conv(dilations = var_1657, groups = var_21, pad = input1_1_pad_0, pad_type = input1_1_pad_type_0, strides = var_1655, weight = stem_mask_layer_weight_to_fp16, x = input0_2_cast);
            tensor<fp16, [1, 2, 384, 12]> var_1660_cast = sigmoid(x = input1_1_cast);
            tensor<int32, [1]> var_1661_axes_0 = const()[name = tensor<string, []>("op_1661_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 384, 12]> var_1661_cast = expand_dims(axes = var_1661_axes_0, x = var_36_cast);
            tensor<fp16, [1, 1, 384, 19]> var_1661_cast_elementwise_expanded = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (var_1661_cast_elementwise_in_state, var_1661_cast));
            tensor<fp16, [1, 1, 384, 12]> var_1661_cast_elementwise_delayed = slice_by_size(begin = tensor<int32, [4]>([0, 0, 0, 0]), size = tensor<int32, [4]>([1, 1, 384, 12]), x = var_1661_cast_elementwise_expanded);
            tensor<fp16, [1, 1, 384, 7]> var_1661_cast_elementwise_out_state = slice_by_size(begin = tensor<int32, [4]>([0, 0, 0, -7]), size = tensor<int32, [4]>([1, 1, 384, 7]), x = var_1661_cast_elementwise_expanded);
            tensor<fp16, [1, 2, 384, 12]> x_2_cast = mul(x = var_1660_cast, y = var_1661_cast_elementwise_delayed);
            tensor<int32, [3]> concat_0x = const()[name = tensor<string, []>("concat_0x"), val = tensor<int32, [3]>([1, 768, -1])];
            tensor<fp16, [1, 768, 12]> input1_2_cast = reshape(shape = concat_0x, x = x_2_cast);
            tensor<int32, [1]> var_1674 = const()[name = tensor<string, []>("op_1674"), val = tensor<int32, [1]>([40])];
            tensor<int32, [1]> var_1676 = const()[name = tensor<string, []>("op_1676"), val = tensor<int32, [1]>([1])];
            tensor<string, []> target_1_pad_type_0 = const()[name = tensor<string, []>("target_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> target_1_pad_0 = const()[name = tensor<string, []>("target_1_pad_0"), val = tensor<int32, [2]>([40, 40])];
            tensor<fp16, [768, 1, 80]> stem_resynthesizer_weight_to_fp16 = const()[name = tensor<string, []>("stem_resynthesizer_weight_to_fp16"), val = tensor<fp16, [768, 1, 80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/sc-vires-nnet.weight.bin"), offset = tensor<uint64, []>(11488192)))];
            tensor<fp16, [1, 768, 13]> input1_2_cast_padded = concat(axis = tensor<int32, []>(-1), interleave = tensor<bool, []>(false), values = (input1_2_cast_in_state, input1_2_cast));
            tensor<fp16, [1, 768, 1]> input1_2_cast_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -1]), size = tensor<int32, [3]>([1, 768, 1]), x = input1_2_cast_padded);
            tensor<fp16, [1, 2, 480]> target_1_cast = conv_transpose(dilations = var_1676, groups = var_19, pad = target_1_pad_0, pad_type = target_1_pad_type_0, strides = var_1674, weight = stem_resynthesizer_weight_to_fp16, x = input1_2_cast_padded);
            tensor<string, []> target_1_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("target_1_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [1, 2, 480]> target_1 = cast(dtype = target_1_cast_to_fp32_dtype_0, x = target_1_cast);
        } -> (target_1, input1_2_cast_out_state, var_1661_cast_elementwise_out_state, input_4_cast_out_state, input_257_cast_out_state, input_247_cast_out_state, input_237_cast_out_state, input_227_cast_out_state, input_217_cast_out_state, input_207_cast_out_state, input_197_cast_out_state, input_187_cast_out_state, input_177_cast_out_state, input_167_cast_out_state, input_157_cast_out_state, input_147_cast_out_state, input_137_cast_out_state, input_127_cast_out_state, input_117_cast_out_state, input_107_cast_out_state, input_97_cast_out_state, input_87_cast_out_state, input_77_cast_out_state, input_67_cast_out_state, input_57_cast_out_state, input_47_cast_out_state, input_37_cast_out_state, input_23_cast_elementwise_out_state, input_27_cast_out_state, input_13_cast_elementwise_out_state, input_17_cast_out_state, input_12_cast_elementwise_out_state, input_7_cast_out_state, cast_778_out_state);
}