Upload folder using huggingface_hub

segmentation-3.0-b32.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:129d4f2316a01d29c2636cb72fea64880086685250918bd6a89ea2b770286e68
+size 243

segmentation-3.0-b32.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac710fb9bcd0310d0c40fd82f2350ca5287b18596da33470bf5185be148aad81
+size 439

segmentation-3.0-b32.mlmodelc/model.mil

@@ -0,0 +1,219 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [?, 1, 160000]> input) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"input", [32, 1, 160000]}}), ("EnumeratedShapes", {{"047bedbd", {{"input", [24, 1, 160000]}}}, {"08383b0f", {{"input", [32, 1, 160000]}}}, {"146ea7a4", {{"input", [30, 1, 160000]}}}, {"14a6a9fa", {{"input", [27, 1, 160000]}}}, {"41d6af63", {{"input", [26, 1, 160000]}}}, {"4a349f6d", {{"input", [2, 1, 160000]}}}, {"4c2c6917", {{"input", [8, 1, 160000]}}}, {"4cb052b1", {{"input", [5, 1, 160000]}}}, {"4eab2425", {{"input", [23, 1, 160000]}}}, {"4f2b5bd2", {{"input", [14, 1, 160000]}}}, {"50b949f3", {{"input", [22, 1, 160000]}}}, {"5316ecea", {{"input", [1, 1, 160000]}}}, {"5d89881e", {{"input", [21, 1, 160000]}}}, {"693a1c76", {{"input", [19, 1, 160000]}}}, {"6ac4a6a4", {{"input", [29, 1, 160000]}}}, {"73f266d5", {{"input", [3, 1, 160000]}}}, {"73f43a1d", {{"input", [31, 1, 160000]}}}, {"7ee56056", {{"input", [18, 1, 160000]}}}, {"9035b52a", {{"input", [25, 1, 160000]}}}, {"94f7468c", {{"input", [20, 1, 160000]}}}, {"999a22b0", {{"input", [12, 1, 160000]}}}, {"9fad9511", {{"input", [4, 1, 160000]}}}, {"ab9dbd8c", {{"input", [9, 1, 160000]}}}, {"ae49a11c", {{"input", [16, 1, 160000]}}}, {"bf53b769", {{"input", [15, 1, 160000]}}}, {"c147bbba", {{"input", [11, 1, 160000]}}}, {"c32e6216", {{"input", [28, 1, 160000]}}}, {"d1a076a6", {{"input", [7, 1, 160000]}}}, {"dccf3050", {{"input", [17, 1, 160000]}}}, {"ef60c196", {{"input", [10, 1, 160000]}}}, {"fe5ae199", {{"input", [13, 1, 160000]}}}, {"ffc2aaa2", {{"input", [6, 1, 160000]}}}})))] {
+            tensor<fp32, [1]> sincnet_wav_norm1d_bias = const()[name = string("sincnet_wav_norm1d_bias"), val = tensor<fp32, [1]>([0x1.73505ep-5])];
+            tensor<fp32, [1]> sincnet_wav_norm1d_weight = const()[name = string("sincnet_wav_norm1d_weight"), val = tensor<fp32, [1]>([0x1.43f862p-7])];
+            tensor<fp32, [80]> sincnet_norm1d_0_bias = const()[name = string("sincnet_norm1d_0_bias"), val = tensor<fp32, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [80]> sincnet_norm1d_0_weight = const()[name = string("sincnet_norm1d_0_weight"), val = tensor<fp32, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448)))];
+            tensor<fp32, [60]> sincnet_conv1d_1_bias = const()[name = string("sincnet_conv1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(832)))];
+            tensor<fp32, [60, 80, 5]> sincnet_conv1d_1_weight = const()[name = string("sincnet_conv1d_1_weight"), val = tensor<fp32, [60, 80, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152)))];
+            tensor<fp32, [60]> sincnet_norm1d_1_bias = const()[name = string("sincnet_norm1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97216)))];
+            tensor<fp32, [60]> sincnet_norm1d_1_weight = const()[name = string("sincnet_norm1d_1_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97536)))];
+            tensor<fp32, [60]> sincnet_conv1d_2_bias = const()[name = string("sincnet_conv1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97856)))];
+            tensor<fp32, [60, 60, 5]> sincnet_conv1d_2_weight = const()[name = string("sincnet_conv1d_2_weight"), val = tensor<fp32, [60, 60, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98176)))];
+            tensor<fp32, [60]> sincnet_norm1d_2_bias = const()[name = string("sincnet_norm1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170240)))];
+            tensor<fp32, [60]> sincnet_norm1d_2_weight = const()[name = string("sincnet_norm1d_2_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170560)))];
+            tensor<fp32, [128]> linear_0_bias = const()[name = string("linear_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170880)))];
+            tensor<fp32, [128, 256]> linear_0_weight = const()[name = string("linear_0_weight"), val = tensor<fp32, [128, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171456)))];
+            tensor<fp32, [128]> linear_1_bias = const()[name = string("linear_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302592)))];
+            tensor<fp32, [128, 128]> linear_1_weight = const()[name = string("linear_1_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303168)))];
+            tensor<fp32, [7]> classifier_bias = const()[name = string("classifier_bias"), val = tensor<fp32, [7]>([-0x1.00e888p+0, 0x1.67cb52p-2, 0x1.3d87fp-1, 0x1.c8aa8p-2, -0x1.445f5ep-2, -0x1.591274p-1, -0x1.8fb70ep-2])];
+            tensor<fp32, [7, 128]> classifier_weight = const()[name = string("classifier_weight"), val = tensor<fp32, [7, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368768)))];
+            fp32 var_9 = const()[name = string("op_9"), val = fp32(0x1.47ae14p-7)];
+            fp32 var_24 = const()[name = string("op_24"), val = fp32(0x1.4f8b58p-17)];
+            tensor<fp32, [?, 1, 160000]> waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = input)[name = string("waveform")];
+            tensor<fp32, [80, 1, 251]> filters = const()[name = string("filters"), val = tensor<fp32, [80, 1, 251]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372416)))];
+            string outputs_pad_type_0 = const()[name = string("outputs_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> outputs_strides_0 = const()[name = string("outputs_strides_0"), val = tensor<int32, [1]>([10])];
+            tensor<int32, [2]> outputs_pad_0 = const()[name = string("outputs_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> outputs_dilations_0 = const()[name = string("outputs_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 outputs_groups_0 = const()[name = string("outputs_groups_0"), val = int32(1)];
+            tensor<fp32, [?, 80, 15975]> outputs = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = filters, x = waveform)[name = string("outputs")];
+            tensor<fp32, [?, 80, 15975]> input_1 = abs(x = outputs)[name = string("input_1")];
+            tensor<int32, [1]> var_119 = const()[name = string("op_119"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_120 = const()[name = string("op_120"), val = tensor<int32, [1]>([3])];
+            string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            bool input_3_ceil_mode_0 = const()[name = string("input_3_ceil_mode_0"), val = bool(false)];
+            tensor<fp32, [?, 80, 5325]> input_3 = max_pool(ceil_mode = input_3_ceil_mode_0, kernel_sizes = var_119, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_120, x = input_1)[name = string("input_3")];
+            tensor<fp32, [?, 80, 5325]> input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = string("input_5")];
+            tensor<fp32, [?, 80, 5325]> input_7 = leaky_relu(alpha = var_9, x = input_5)[name = string("input_7")];
+            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
+            tensor<fp32, [?, 60, 5321]> input_9 = conv(bias = sincnet_conv1d_1_bias, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = sincnet_conv1d_1_weight, x = input_7)[name = string("input_9")];
+            tensor<int32, [1]> var_135 = const()[name = string("op_135"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_136 = const()[name = string("op_136"), val = tensor<int32, [1]>([3])];
+            string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            bool input_11_ceil_mode_0 = const()[name = string("input_11_ceil_mode_0"), val = bool(false)];
+            tensor<fp32, [?, 60, 1773]> input_11 = max_pool(ceil_mode = input_11_ceil_mode_0, kernel_sizes = var_135, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_136, x = input_9)[name = string("input_11")];
+            tensor<fp32, [?, 60, 1773]> input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = string("input_13")];
+            tensor<fp32, [?, 60, 1773]> input_15 = leaky_relu(alpha = var_9, x = input_13)[name = string("input_15")];
+            string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(1)];
+            tensor<fp32, [?, 60, 1769]> input_17 = conv(bias = sincnet_conv1d_2_bias, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = sincnet_conv1d_2_weight, x = input_15)[name = string("input_17")];
+            tensor<int32, [1]> var_151 = const()[name = string("op_151"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_152 = const()[name = string("op_152"), val = tensor<int32, [1]>([3])];
+            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            bool input_19_ceil_mode_0 = const()[name = string("input_19_ceil_mode_0"), val = bool(false)];
+            tensor<fp32, [?, 60, 589]> input_19 = max_pool(ceil_mode = input_19_ceil_mode_0, kernel_sizes = var_151, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = var_152, x = input_17)[name = string("input_19")];
+            tensor<fp32, [?, 60, 589]> input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = string("input_21")];
+            tensor<fp32, [?, 60, 589]> x = leaky_relu(alpha = var_9, x = input_21)[name = string("x")];
+            tensor<int32, [3]> var_163 = const()[name = string("op_163"), val = tensor<int32, [3]>([0, 2, 1])];
+            int32 var_172 = const()[name = string("op_172"), val = int32(128)];
+            int32 var_173 = const()[name = string("op_173"), val = int32(8)];
+            tensor<fp32, [?, 589, 60]> input_23 = transpose(perm = var_163, x = x)[name = string("transpose_6")];
+            tensor<int32, [3]> var_207_shape = shape(x = input_23)[name = string("op_207_shape")];
+            int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)];
+            bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)];
+            int32 select_0 = const()[name = string("select_0"), val = int32(0)];
+            int32 gather_0_axis_1 = const()[name = string("gather_0_axis_1"), val = int32(0)];
+            int32 gather_0 = gather(axis = gather_0_axis_1, batch_dims = gather_0_batch_dims_0, indices = select_0, validate_indices = gather_0_validate_indices_0, x = var_207_shape)[name = string("gather_0")];
+            int32 concat_0_axis_0 = const()[name = string("concat_0_axis_0"), val = int32(0)];
+            bool concat_0_interleave_0 = const()[name = string("concat_0_interleave_0"), val = bool(false)];
+            tensor<int32, [3]> concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (var_173, gather_0, var_172))[name = string("concat_0")];
+            fp32 hx_1_value_0 = const()[name = string("hx_1_value_0"), val = fp32(0x0p+0)];
+            tensor<fp32, [8, ?, 128]> hx_1 = fill(shape = concat_0, value = hx_1_value_0)[name = string("hx_1")];
+            tensor<int32, [3]> input_23_batch_first_transpose_perm_0 = const()[name = string("input_23_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(4)];
+            int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)];
+            tensor<fp32, [2, ?, 128]> split_0_0, tensor<fp32, [2, ?, 128]> split_0_1, tensor<fp32, [2, ?, 128]> split_0_2, tensor<fp32, [2, ?, 128]> split_0_3 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = hx_1)[name = string("split_0")];
+            int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(4)];
+            int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)];
+            tensor<fp32, [2, ?, 128]> split_1_0, tensor<fp32, [2, ?, 128]> split_1_1, tensor<fp32, [2, ?, 128]> split_1_2, tensor<fp32, [2, ?, 128]> split_1_3 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = hx_1)[name = string("split_1")];
+            tensor<fp32, [512]> add_0 = const()[name = string("add_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(452800)))];
+            tensor<fp32, [512]> add_1 = const()[name = string("add_1"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454912)))];
+            tensor<fp32, [512, 60]> concat_6 = const()[name = string("concat_6"), val = tensor<fp32, [512, 60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457024)))];
+            tensor<fp32, [512, 128]> concat_7 = const()[name = string("concat_7"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(579968)))];
+            tensor<fp32, [512, 60]> concat_8 = const()[name = string("concat_8"), val = tensor<fp32, [512, 60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(842176)))];
+            tensor<fp32, [512, 128]> concat_9 = const()[name = string("concat_9"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(965120)))];
+            tensor<int32, [2]> split_10_split_sizes_0 = const()[name = string("split_10_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_10_axis_0 = const()[name = string("split_10_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_10_0, tensor<fp32, [1, ?, 128]> split_10_1 = split(axis = split_10_axis_0, split_sizes = split_10_split_sizes_0, x = split_0_0)[name = string("split_10")];
+            int32 concat_10_axis_0 = const()[name = string("concat_10_axis_0"), val = int32(2)];
+            bool concat_10_interleave_0 = const()[name = string("concat_10_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_10 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (split_10_0, split_10_1))[name = string("concat_10")];
+            tensor<int32, [1]> input_25_lstm_layer_0_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_0_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_0_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_0_lstm_h0_reshaped_axes_0, x = concat_10)[name = string("input_25_lstm_layer_0_lstm_h0_reshaped")];
+            tensor<int32, [2]> split_11_split_sizes_0 = const()[name = string("split_11_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_11_axis_0 = const()[name = string("split_11_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_11_0, tensor<fp32, [1, ?, 128]> split_11_1 = split(axis = split_11_axis_0, split_sizes = split_11_split_sizes_0, x = split_1_0)[name = string("split_11")];
+            int32 concat_11_axis_0 = const()[name = string("concat_11_axis_0"), val = int32(2)];
+            bool concat_11_interleave_0 = const()[name = string("concat_11_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_11 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (split_11_0, split_11_1))[name = string("concat_11")];
+            tensor<int32, [1]> input_25_lstm_layer_0_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_0_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_0_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_0_lstm_c0_reshaped_axes_0, x = concat_11)[name = string("input_25_lstm_layer_0_lstm_c0_reshaped")];
+            string input_25_lstm_layer_0_direction_0 = const()[name = string("input_25_lstm_layer_0_direction_0"), val = string("bidirectional")];
+            bool input_25_lstm_layer_0_output_sequence_0 = const()[name = string("input_25_lstm_layer_0_output_sequence_0"), val = bool(true)];
+            string input_25_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")];
+            string input_25_lstm_layer_0_cell_activation_0 = const()[name = string("input_25_lstm_layer_0_cell_activation_0"), val = string("tanh")];
+            string input_25_lstm_layer_0_activation_0 = const()[name = string("input_25_lstm_layer_0_activation_0"), val = string("tanh")];
+            tensor<fp32, [589, ?, 60]> input_23_batch_first_transpose = transpose(perm = input_23_batch_first_transpose_perm_0, x = input_23)[name = string("transpose_5")];
+            tensor<fp32, [589, ?, 256]> input_25_lstm_layer_0_0, tensor<fp32, [?, 256]> input_25_lstm_layer_0_1, tensor<fp32, [?, 256]> input_25_lstm_layer_0_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0, bias_back = add_1, cell_activation = input_25_lstm_layer_0_cell_activation_0, direction = input_25_lstm_layer_0_direction_0, initial_c = input_25_lstm_layer_0_lstm_c0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_7, weight_hh_back = concat_9, weight_ih = concat_6, weight_ih_back = concat_8, x = input_23_batch_first_transpose)[name = string("input_25_lstm_layer_0")];
+            tensor<fp32, [512]> add_2 = const()[name = string("add_2"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1227328)))];
+            tensor<fp32, [512]> add_3 = const()[name = string("add_3"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1229440)))];
+            tensor<fp32, [512, 256]> concat_16 = const()[name = string("concat_16"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1231552)))];
+            tensor<fp32, [512, 128]> concat_17 = const()[name = string("concat_17"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1755904)))];
+            tensor<fp32, [512, 256]> concat_18 = const()[name = string("concat_18"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2018112)))];
+            tensor<fp32, [512, 128]> concat_19 = const()[name = string("concat_19"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2542464)))];
+            tensor<int32, [2]> split_20_split_sizes_0 = const()[name = string("split_20_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_20_axis_0 = const()[name = string("split_20_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_20_0, tensor<fp32, [1, ?, 128]> split_20_1 = split(axis = split_20_axis_0, split_sizes = split_20_split_sizes_0, x = split_0_1)[name = string("split_20")];
+            int32 concat_20_axis_0 = const()[name = string("concat_20_axis_0"), val = int32(2)];
+            bool concat_20_interleave_0 = const()[name = string("concat_20_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_20 = concat(axis = concat_20_axis_0, interleave = concat_20_interleave_0, values = (split_20_0, split_20_1))[name = string("concat_20")];
+            tensor<int32, [1]> input_25_lstm_layer_1_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_1_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_1_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_1_lstm_h0_reshaped_axes_0, x = concat_20)[name = string("input_25_lstm_layer_1_lstm_h0_reshaped")];
+            tensor<int32, [2]> split_21_split_sizes_0 = const()[name = string("split_21_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_21_axis_0 = const()[name = string("split_21_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_21_0, tensor<fp32, [1, ?, 128]> split_21_1 = split(axis = split_21_axis_0, split_sizes = split_21_split_sizes_0, x = split_1_1)[name = string("split_21")];
+            int32 concat_21_axis_0 = const()[name = string("concat_21_axis_0"), val = int32(2)];
+            bool concat_21_interleave_0 = const()[name = string("concat_21_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_21 = concat(axis = concat_21_axis_0, interleave = concat_21_interleave_0, values = (split_21_0, split_21_1))[name = string("concat_21")];
+            tensor<int32, [1]> input_25_lstm_layer_1_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_1_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_1_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_1_lstm_c0_reshaped_axes_0, x = concat_21)[name = string("input_25_lstm_layer_1_lstm_c0_reshaped")];
+            string input_25_lstm_layer_1_direction_0 = const()[name = string("input_25_lstm_layer_1_direction_0"), val = string("bidirectional")];
+            bool input_25_lstm_layer_1_output_sequence_0 = const()[name = string("input_25_lstm_layer_1_output_sequence_0"), val = bool(true)];
+            string input_25_lstm_layer_1_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_1_recurrent_activation_0"), val = string("sigmoid")];
+            string input_25_lstm_layer_1_cell_activation_0 = const()[name = string("input_25_lstm_layer_1_cell_activation_0"), val = string("tanh")];
+            string input_25_lstm_layer_1_activation_0 = const()[name = string("input_25_lstm_layer_1_activation_0"), val = string("tanh")];
+            tensor<fp32, [589, ?, 256]> input_25_lstm_layer_1_0, tensor<fp32, [?, 256]> input_25_lstm_layer_1_1, tensor<fp32, [?, 256]> input_25_lstm_layer_1_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2, bias_back = add_3, cell_activation = input_25_lstm_layer_1_cell_activation_0, direction = input_25_lstm_layer_1_direction_0, initial_c = input_25_lstm_layer_1_lstm_c0_reshaped, initial_h = input_25_lstm_layer_1_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_17, weight_hh_back = concat_19, weight_ih = concat_16, weight_ih_back = concat_18, x = input_25_lstm_layer_0_0)[name = string("input_25_lstm_layer_1")];
+            tensor<fp32, [512]> add_4 = const()[name = string("add_4"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2804672)))];
+            tensor<fp32, [512]> add_5 = const()[name = string("add_5"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2806784)))];
+            tensor<fp32, [512, 256]> concat_26 = const()[name = string("concat_26"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2808896)))];
+            tensor<fp32, [512, 128]> concat_27 = const()[name = string("concat_27"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3333248)))];
+            tensor<fp32, [512, 256]> concat_28 = const()[name = string("concat_28"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3595456)))];
+            tensor<fp32, [512, 128]> concat_29 = const()[name = string("concat_29"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4119808)))];
+            tensor<int32, [2]> split_30_split_sizes_0 = const()[name = string("split_30_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_30_axis_0 = const()[name = string("split_30_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_30_0, tensor<fp32, [1, ?, 128]> split_30_1 = split(axis = split_30_axis_0, split_sizes = split_30_split_sizes_0, x = split_0_2)[name = string("split_30")];
+            int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(2)];
+            bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (split_30_0, split_30_1))[name = string("concat_30")];
+            tensor<int32, [1]> input_25_lstm_layer_2_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_2_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_2_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_2_lstm_h0_reshaped_axes_0, x = concat_30)[name = string("input_25_lstm_layer_2_lstm_h0_reshaped")];
+            tensor<int32, [2]> split_31_split_sizes_0 = const()[name = string("split_31_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_31_axis_0 = const()[name = string("split_31_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_31_0, tensor<fp32, [1, ?, 128]> split_31_1 = split(axis = split_31_axis_0, split_sizes = split_31_split_sizes_0, x = split_1_2)[name = string("split_31")];
+            int32 concat_31_axis_0 = const()[name = string("concat_31_axis_0"), val = int32(2)];
+            bool concat_31_interleave_0 = const()[name = string("concat_31_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_31 = concat(axis = concat_31_axis_0, interleave = concat_31_interleave_0, values = (split_31_0, split_31_1))[name = string("concat_31")];
+            tensor<int32, [1]> input_25_lstm_layer_2_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_2_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_2_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_2_lstm_c0_reshaped_axes_0, x = concat_31)[name = string("input_25_lstm_layer_2_lstm_c0_reshaped")];
+            string input_25_lstm_layer_2_direction_0 = const()[name = string("input_25_lstm_layer_2_direction_0"), val = string("bidirectional")];
+            bool input_25_lstm_layer_2_output_sequence_0 = const()[name = string("input_25_lstm_layer_2_output_sequence_0"), val = bool(true)];
+            string input_25_lstm_layer_2_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_2_recurrent_activation_0"), val = string("sigmoid")];
+            string input_25_lstm_layer_2_cell_activation_0 = const()[name = string("input_25_lstm_layer_2_cell_activation_0"), val = string("tanh")];
+            string input_25_lstm_layer_2_activation_0 = const()[name = string("input_25_lstm_layer_2_activation_0"), val = string("tanh")];
+            tensor<fp32, [589, ?, 256]> input_25_lstm_layer_2_0, tensor<fp32, [?, 256]> input_25_lstm_layer_2_1, tensor<fp32, [?, 256]> input_25_lstm_layer_2_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4, bias_back = add_5, cell_activation = input_25_lstm_layer_2_cell_activation_0, direction = input_25_lstm_layer_2_direction_0, initial_c = input_25_lstm_layer_2_lstm_c0_reshaped, initial_h = input_25_lstm_layer_2_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_27, weight_hh_back = concat_29, weight_ih = concat_26, weight_ih_back = concat_28, x = input_25_lstm_layer_1_0)[name = string("input_25_lstm_layer_2")];
+            tensor<fp32, [512]> add_6 = const()[name = string("add_6"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4382016)))];
+            tensor<fp32, [512]> add_7 = const()[name = string("add_7"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4384128)))];
+            tensor<fp32, [512, 256]> concat_36 = const()[name = string("concat_36"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4386240)))];
+            tensor<fp32, [512, 128]> concat_37 = const()[name = string("concat_37"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4910592)))];
+            tensor<fp32, [512, 256]> concat_38 = const()[name = string("concat_38"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5172800)))];
+            tensor<fp32, [512, 128]> concat_39 = const()[name = string("concat_39"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5697152)))];
+            tensor<int32, [2]> split_40_split_sizes_0 = const()[name = string("split_40_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_40_axis_0 = const()[name = string("split_40_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_40_0, tensor<fp32, [1, ?, 128]> split_40_1 = split(axis = split_40_axis_0, split_sizes = split_40_split_sizes_0, x = split_0_3)[name = string("split_40")];
+            int32 concat_40_axis_0 = const()[name = string("concat_40_axis_0"), val = int32(2)];
+            bool concat_40_interleave_0 = const()[name = string("concat_40_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_40 = concat(axis = concat_40_axis_0, interleave = concat_40_interleave_0, values = (split_40_0, split_40_1))[name = string("concat_40")];
+            tensor<int32, [1]> input_25_batch_first_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_batch_first_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_batch_first_lstm_h0_reshaped = squeeze(axes = input_25_batch_first_lstm_h0_reshaped_axes_0, x = concat_40)[name = string("input_25_batch_first_lstm_h0_reshaped")];
+            tensor<int32, [2]> split_41_split_sizes_0 = const()[name = string("split_41_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_41_axis_0 = const()[name = string("split_41_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_41_0, tensor<fp32, [1, ?, 128]> split_41_1 = split(axis = split_41_axis_0, split_sizes = split_41_split_sizes_0, x = split_1_3)[name = string("split_41")];
+            int32 concat_41_axis_0 = const()[name = string("concat_41_axis_0"), val = int32(2)];
+            bool concat_41_interleave_0 = const()[name = string("concat_41_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_41 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (split_41_0, split_41_1))[name = string("concat_41")];
+            tensor<int32, [1]> input_25_batch_first_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_batch_first_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_batch_first_lstm_c0_reshaped = squeeze(axes = input_25_batch_first_lstm_c0_reshaped_axes_0, x = concat_41)[name = string("input_25_batch_first_lstm_c0_reshaped")];
+            string input_25_batch_first_direction_0 = const()[name = string("input_25_batch_first_direction_0"), val = string("bidirectional")];
+            bool input_25_batch_first_output_sequence_0 = const()[name = string("input_25_batch_first_output_sequence_0"), val = bool(true)];
+            string input_25_batch_first_recurrent_activation_0 = const()[name = string("input_25_batch_first_recurrent_activation_0"), val = string("sigmoid")];
+            string input_25_batch_first_cell_activation_0 = const()[name = string("input_25_batch_first_cell_activation_0"), val = string("tanh")];
+            string input_25_batch_first_activation_0 = const()[name = string("input_25_batch_first_activation_0"), val = string("tanh")];
+            tensor<fp32, [589, ?, 256]> input_25_batch_first_0, tensor<fp32, [?, 256]> input_25_batch_first_1, tensor<fp32, [?, 256]> input_25_batch_first_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6, bias_back = add_7, cell_activation = input_25_batch_first_cell_activation_0, direction = input_25_batch_first_direction_0, initial_c = input_25_batch_first_lstm_c0_reshaped, initial_h = input_25_batch_first_lstm_h0_reshaped, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_37, weight_hh_back = concat_39, weight_ih = concat_36, weight_ih_back = concat_38, x = input_25_lstm_layer_2_0)[name = string("input_25_batch_first")];
+            tensor<int32, [3]> input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp32, [?, 589, 256]> input_25 = transpose(perm = input_25_perm_0, x = input_25_batch_first_0)[name = string("transpose_4")];
+            tensor<fp32, [?, 589, 128]> input_27 = linear(bias = linear_0_bias, weight = linear_0_weight, x = input_25)[name = string("linear_0")];
+            fp32 var_220 = const()[name = string("op_220"), val = fp32(0x1.47ae14p-7)];
+            tensor<fp32, [?, 589, 128]> input_29 = leaky_relu(alpha = var_220, x = input_27)[name = string("input_29")];
+            tensor<fp32, [?, 589, 128]> input_31 = linear(bias = linear_1_bias, weight = linear_1_weight, x = input_29)[name = string("linear_1")];
+            fp32 var_225 = const()[name = string("op_225"), val = fp32(0x1.47ae14p-7)];
+            tensor<fp32, [?, 589, 128]> input_33 = leaky_relu(alpha = var_225, x = input_31)[name = string("input_33")];
+            tensor<fp32, [?, 589, 7]> input_1_1 = linear(bias = classifier_bias, weight = classifier_weight, x = input_33)[name = string("linear_2")];
+            int32 var_231 = const()[name = string("op_231"), val = int32(-1)];
+            tensor<fp32, [?, 589, 7]> var_232_softmax = softmax(axis = var_231, x = input_1_1)[name = string("op_232_softmax")];
+            fp32 var_232_epsilon_0 = const()[name = string("op_232_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp32, [?, 589, 7]> output = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = string("op_232")];
+        } -> (output);
+}

segmentation-3.0-b32.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3189a64946c75bc24fcb98afe89ad78c52bdbadfdf65e857fb1b81e2cc9fbb2
+size 5959360

segmentation-3.0-b32.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:94deac93dacc90d3191511f87f6f4d8517e3b2f5e10a449a851bcbf0ba9cdc94
+size 6178495

segmentation-3.0.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:129d4f2316a01d29c2636cb72fea64880086685250918bd6a89ea2b770286e68
+size 243

segmentation-3.0.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac710fb9bcd0310d0c40fd82f2350ca5287b18596da33470bf5185be148aad81
+size 439

segmentation-3.0.mlmodelc/model.mil

@@ -0,0 +1,219 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [?, 1, 160000]> input) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"input", [32, 1, 160000]}}), ("EnumeratedShapes", {{"047bedbd", {{"input", [24, 1, 160000]}}}, {"08383b0f", {{"input", [32, 1, 160000]}}}, {"146ea7a4", {{"input", [30, 1, 160000]}}}, {"14a6a9fa", {{"input", [27, 1, 160000]}}}, {"41d6af63", {{"input", [26, 1, 160000]}}}, {"4a349f6d", {{"input", [2, 1, 160000]}}}, {"4c2c6917", {{"input", [8, 1, 160000]}}}, {"4cb052b1", {{"input", [5, 1, 160000]}}}, {"4eab2425", {{"input", [23, 1, 160000]}}}, {"4f2b5bd2", {{"input", [14, 1, 160000]}}}, {"50b949f3", {{"input", [22, 1, 160000]}}}, {"5316ecea", {{"input", [1, 1, 160000]}}}, {"5d89881e", {{"input", [21, 1, 160000]}}}, {"693a1c76", {{"input", [19, 1, 160000]}}}, {"6ac4a6a4", {{"input", [29, 1, 160000]}}}, {"73f266d5", {{"input", [3, 1, 160000]}}}, {"73f43a1d", {{"input", [31, 1, 160000]}}}, {"7ee56056", {{"input", [18, 1, 160000]}}}, {"9035b52a", {{"input", [25, 1, 160000]}}}, {"94f7468c", {{"input", [20, 1, 160000]}}}, {"999a22b0", {{"input", [12, 1, 160000]}}}, {"9fad9511", {{"input", [4, 1, 160000]}}}, {"ab9dbd8c", {{"input", [9, 1, 160000]}}}, {"ae49a11c", {{"input", [16, 1, 160000]}}}, {"bf53b769", {{"input", [15, 1, 160000]}}}, {"c147bbba", {{"input", [11, 1, 160000]}}}, {"c32e6216", {{"input", [28, 1, 160000]}}}, {"d1a076a6", {{"input", [7, 1, 160000]}}}, {"dccf3050", {{"input", [17, 1, 160000]}}}, {"ef60c196", {{"input", [10, 1, 160000]}}}, {"fe5ae199", {{"input", [13, 1, 160000]}}}, {"ffc2aaa2", {{"input", [6, 1, 160000]}}}})))] {
+            tensor<fp32, [1]> sincnet_wav_norm1d_bias = const()[name = string("sincnet_wav_norm1d_bias"), val = tensor<fp32, [1]>([0x1.73505ep-5])];
+            tensor<fp32, [1]> sincnet_wav_norm1d_weight = const()[name = string("sincnet_wav_norm1d_weight"), val = tensor<fp32, [1]>([0x1.43f862p-7])];
+            tensor<fp32, [80]> sincnet_norm1d_0_bias = const()[name = string("sincnet_norm1d_0_bias"), val = tensor<fp32, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [80]> sincnet_norm1d_0_weight = const()[name = string("sincnet_norm1d_0_weight"), val = tensor<fp32, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448)))];
+            tensor<fp32, [60]> sincnet_conv1d_1_bias = const()[name = string("sincnet_conv1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(832)))];
+            tensor<fp32, [60, 80, 5]> sincnet_conv1d_1_weight = const()[name = string("sincnet_conv1d_1_weight"), val = tensor<fp32, [60, 80, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152)))];
+            tensor<fp32, [60]> sincnet_norm1d_1_bias = const()[name = string("sincnet_norm1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97216)))];
+            tensor<fp32, [60]> sincnet_norm1d_1_weight = const()[name = string("sincnet_norm1d_1_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97536)))];
+            tensor<fp32, [60]> sincnet_conv1d_2_bias = const()[name = string("sincnet_conv1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97856)))];
+            tensor<fp32, [60, 60, 5]> sincnet_conv1d_2_weight = const()[name = string("sincnet_conv1d_2_weight"), val = tensor<fp32, [60, 60, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98176)))];
+            tensor<fp32, [60]> sincnet_norm1d_2_bias = const()[name = string("sincnet_norm1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170240)))];
+            tensor<fp32, [60]> sincnet_norm1d_2_weight = const()[name = string("sincnet_norm1d_2_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170560)))];
+            tensor<fp32, [128]> linear_0_bias = const()[name = string("linear_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170880)))];
+            tensor<fp32, [128, 256]> linear_0_weight = const()[name = string("linear_0_weight"), val = tensor<fp32, [128, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171456)))];
+            tensor<fp32, [128]> linear_1_bias = const()[name = string("linear_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302592)))];
+            tensor<fp32, [128, 128]> linear_1_weight = const()[name = string("linear_1_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303168)))];
+            tensor<fp32, [7]> classifier_bias = const()[name = string("classifier_bias"), val = tensor<fp32, [7]>([-0x1.00e888p+0, 0x1.67cb52p-2, 0x1.3d87fp-1, 0x1.c8aa8p-2, -0x1.445f5ep-2, -0x1.591274p-1, -0x1.8fb70ep-2])];
+            tensor<fp32, [7, 128]> classifier_weight = const()[name = string("classifier_weight"), val = tensor<fp32, [7, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(368768)))];
+            fp32 var_9 = const()[name = string("op_9"), val = fp32(0x1.47ae14p-7)];
+            fp32 var_24 = const()[name = string("op_24"), val = fp32(0x1.4f8b58p-17)];
+            tensor<fp32, [?, 1, 160000]> waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = input)[name = string("waveform")];
+            tensor<fp32, [80, 1, 251]> filters = const()[name = string("filters"), val = tensor<fp32, [80, 1, 251]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372416)))];
+            string outputs_pad_type_0 = const()[name = string("outputs_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> outputs_strides_0 = const()[name = string("outputs_strides_0"), val = tensor<int32, [1]>([10])];
+            tensor<int32, [2]> outputs_pad_0 = const()[name = string("outputs_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> outputs_dilations_0 = const()[name = string("outputs_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 outputs_groups_0 = const()[name = string("outputs_groups_0"), val = int32(1)];
+            tensor<fp32, [?, 80, 15975]> outputs = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = filters, x = waveform)[name = string("outputs")];
+            tensor<fp32, [?, 80, 15975]> input_1 = abs(x = outputs)[name = string("input_1")];
+            tensor<int32, [1]> var_119 = const()[name = string("op_119"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_120 = const()[name = string("op_120"), val = tensor<int32, [1]>([3])];
+            string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            bool input_3_ceil_mode_0 = const()[name = string("input_3_ceil_mode_0"), val = bool(false)];
+            tensor<fp32, [?, 80, 5325]> input_3 = max_pool(ceil_mode = input_3_ceil_mode_0, kernel_sizes = var_119, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_120, x = input_1)[name = string("input_3")];
+            tensor<fp32, [?, 80, 5325]> input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = string("input_5")];
+            tensor<fp32, [?, 80, 5325]> input_7 = leaky_relu(alpha = var_9, x = input_5)[name = string("input_7")];
+            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
+            tensor<fp32, [?, 60, 5321]> input_9 = conv(bias = sincnet_conv1d_1_bias, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = sincnet_conv1d_1_weight, x = input_7)[name = string("input_9")];
+            tensor<int32, [1]> var_135 = const()[name = string("op_135"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_136 = const()[name = string("op_136"), val = tensor<int32, [1]>([3])];
+            string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            bool input_11_ceil_mode_0 = const()[name = string("input_11_ceil_mode_0"), val = bool(false)];
+            tensor<fp32, [?, 60, 1773]> input_11 = max_pool(ceil_mode = input_11_ceil_mode_0, kernel_sizes = var_135, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_136, x = input_9)[name = string("input_11")];
+            tensor<fp32, [?, 60, 1773]> input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = string("input_13")];
+            tensor<fp32, [?, 60, 1773]> input_15 = leaky_relu(alpha = var_9, x = input_13)[name = string("input_15")];
+            string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(1)];
+            tensor<fp32, [?, 60, 1769]> input_17 = conv(bias = sincnet_conv1d_2_bias, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = sincnet_conv1d_2_weight, x = input_15)[name = string("input_17")];
+            tensor<int32, [1]> var_151 = const()[name = string("op_151"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_152 = const()[name = string("op_152"), val = tensor<int32, [1]>([3])];
+            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            bool input_19_ceil_mode_0 = const()[name = string("input_19_ceil_mode_0"), val = bool(false)];
+            tensor<fp32, [?, 60, 589]> input_19 = max_pool(ceil_mode = input_19_ceil_mode_0, kernel_sizes = var_151, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = var_152, x = input_17)[name = string("input_19")];
+            tensor<fp32, [?, 60, 589]> input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = string("input_21")];
+            tensor<fp32, [?, 60, 589]> x = leaky_relu(alpha = var_9, x = input_21)[name = string("x")];
+            tensor<int32, [3]> var_163 = const()[name = string("op_163"), val = tensor<int32, [3]>([0, 2, 1])];
+            int32 var_172 = const()[name = string("op_172"), val = int32(128)];
+            int32 var_173 = const()[name = string("op_173"), val = int32(8)];
+            tensor<fp32, [?, 589, 60]> input_23 = transpose(perm = var_163, x = x)[name = string("transpose_6")];
+            tensor<int32, [3]> var_207_shape = shape(x = input_23)[name = string("op_207_shape")];
+            int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)];
+            bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)];
+            int32 select_0 = const()[name = string("select_0"), val = int32(0)];
+            int32 gather_0_axis_1 = const()[name = string("gather_0_axis_1"), val = int32(0)];
+            int32 gather_0 = gather(axis = gather_0_axis_1, batch_dims = gather_0_batch_dims_0, indices = select_0, validate_indices = gather_0_validate_indices_0, x = var_207_shape)[name = string("gather_0")];
+            int32 concat_0_axis_0 = const()[name = string("concat_0_axis_0"), val = int32(0)];
+            bool concat_0_interleave_0 = const()[name = string("concat_0_interleave_0"), val = bool(false)];
+            tensor<int32, [3]> concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (var_173, gather_0, var_172))[name = string("concat_0")];
+            fp32 hx_1_value_0 = const()[name = string("hx_1_value_0"), val = fp32(0x0p+0)];
+            tensor<fp32, [8, ?, 128]> hx_1 = fill(shape = concat_0, value = hx_1_value_0)[name = string("hx_1")];
+            tensor<int32, [3]> input_23_batch_first_transpose_perm_0 = const()[name = string("input_23_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            int32 split_0_num_splits_0 = const()[name = string("split_0_num_splits_0"), val = int32(4)];
+            int32 split_0_axis_0 = const()[name = string("split_0_axis_0"), val = int32(0)];
+            tensor<fp32, [2, ?, 128]> split_0_0, tensor<fp32, [2, ?, 128]> split_0_1, tensor<fp32, [2, ?, 128]> split_0_2, tensor<fp32, [2, ?, 128]> split_0_3 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = hx_1)[name = string("split_0")];
+            int32 split_1_num_splits_0 = const()[name = string("split_1_num_splits_0"), val = int32(4)];
+            int32 split_1_axis_0 = const()[name = string("split_1_axis_0"), val = int32(0)];
+            tensor<fp32, [2, ?, 128]> split_1_0, tensor<fp32, [2, ?, 128]> split_1_1, tensor<fp32, [2, ?, 128]> split_1_2, tensor<fp32, [2, ?, 128]> split_1_3 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = hx_1)[name = string("split_1")];
+            tensor<fp32, [512]> add_0 = const()[name = string("add_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(452800)))];
+            tensor<fp32, [512]> add_1 = const()[name = string("add_1"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454912)))];
+            tensor<fp32, [512, 60]> concat_6 = const()[name = string("concat_6"), val = tensor<fp32, [512, 60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457024)))];
+            tensor<fp32, [512, 128]> concat_7 = const()[name = string("concat_7"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(579968)))];
+            tensor<fp32, [512, 60]> concat_8 = const()[name = string("concat_8"), val = tensor<fp32, [512, 60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(842176)))];
+            tensor<fp32, [512, 128]> concat_9 = const()[name = string("concat_9"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(965120)))];
+            tensor<int32, [2]> split_10_split_sizes_0 = const()[name = string("split_10_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_10_axis_0 = const()[name = string("split_10_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_10_0, tensor<fp32, [1, ?, 128]> split_10_1 = split(axis = split_10_axis_0, split_sizes = split_10_split_sizes_0, x = split_0_0)[name = string("split_10")];
+            int32 concat_10_axis_0 = const()[name = string("concat_10_axis_0"), val = int32(2)];
+            bool concat_10_interleave_0 = const()[name = string("concat_10_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_10 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (split_10_0, split_10_1))[name = string("concat_10")];
+            tensor<int32, [1]> input_25_lstm_layer_0_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_0_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_0_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_0_lstm_h0_reshaped_axes_0, x = concat_10)[name = string("input_25_lstm_layer_0_lstm_h0_reshaped")];
+            tensor<int32, [2]> split_11_split_sizes_0 = const()[name = string("split_11_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_11_axis_0 = const()[name = string("split_11_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_11_0, tensor<fp32, [1, ?, 128]> split_11_1 = split(axis = split_11_axis_0, split_sizes = split_11_split_sizes_0, x = split_1_0)[name = string("split_11")];
+            int32 concat_11_axis_0 = const()[name = string("concat_11_axis_0"), val = int32(2)];
+            bool concat_11_interleave_0 = const()[name = string("concat_11_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_11 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (split_11_0, split_11_1))[name = string("concat_11")];
+            tensor<int32, [1]> input_25_lstm_layer_0_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_0_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_0_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_0_lstm_c0_reshaped_axes_0, x = concat_11)[name = string("input_25_lstm_layer_0_lstm_c0_reshaped")];
+            string input_25_lstm_layer_0_direction_0 = const()[name = string("input_25_lstm_layer_0_direction_0"), val = string("bidirectional")];
+            bool input_25_lstm_layer_0_output_sequence_0 = const()[name = string("input_25_lstm_layer_0_output_sequence_0"), val = bool(true)];
+            string input_25_lstm_layer_0_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_0_recurrent_activation_0"), val = string("sigmoid")];
+            string input_25_lstm_layer_0_cell_activation_0 = const()[name = string("input_25_lstm_layer_0_cell_activation_0"), val = string("tanh")];
+            string input_25_lstm_layer_0_activation_0 = const()[name = string("input_25_lstm_layer_0_activation_0"), val = string("tanh")];
+            tensor<fp32, [589, ?, 60]> input_23_batch_first_transpose = transpose(perm = input_23_batch_first_transpose_perm_0, x = input_23)[name = string("transpose_5")];
+            tensor<fp32, [589, ?, 256]> input_25_lstm_layer_0_0, tensor<fp32, [?, 256]> input_25_lstm_layer_0_1, tensor<fp32, [?, 256]> input_25_lstm_layer_0_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0, bias_back = add_1, cell_activation = input_25_lstm_layer_0_cell_activation_0, direction = input_25_lstm_layer_0_direction_0, initial_c = input_25_lstm_layer_0_lstm_c0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_7, weight_hh_back = concat_9, weight_ih = concat_6, weight_ih_back = concat_8, x = input_23_batch_first_transpose)[name = string("input_25_lstm_layer_0")];
+            tensor<fp32, [512]> add_2 = const()[name = string("add_2"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1227328)))];
+            tensor<fp32, [512]> add_3 = const()[name = string("add_3"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1229440)))];
+            tensor<fp32, [512, 256]> concat_16 = const()[name = string("concat_16"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1231552)))];
+            tensor<fp32, [512, 128]> concat_17 = const()[name = string("concat_17"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1755904)))];
+            tensor<fp32, [512, 256]> concat_18 = const()[name = string("concat_18"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2018112)))];
+            tensor<fp32, [512, 128]> concat_19 = const()[name = string("concat_19"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2542464)))];
+            tensor<int32, [2]> split_20_split_sizes_0 = const()[name = string("split_20_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_20_axis_0 = const()[name = string("split_20_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_20_0, tensor<fp32, [1, ?, 128]> split_20_1 = split(axis = split_20_axis_0, split_sizes = split_20_split_sizes_0, x = split_0_1)[name = string("split_20")];
+            int32 concat_20_axis_0 = const()[name = string("concat_20_axis_0"), val = int32(2)];
+            bool concat_20_interleave_0 = const()[name = string("concat_20_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_20 = concat(axis = concat_20_axis_0, interleave = concat_20_interleave_0, values = (split_20_0, split_20_1))[name = string("concat_20")];
+            tensor<int32, [1]> input_25_lstm_layer_1_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_1_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_1_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_1_lstm_h0_reshaped_axes_0, x = concat_20)[name = string("input_25_lstm_layer_1_lstm_h0_reshaped")];
+            tensor<int32, [2]> split_21_split_sizes_0 = const()[name = string("split_21_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_21_axis_0 = const()[name = string("split_21_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_21_0, tensor<fp32, [1, ?, 128]> split_21_1 = split(axis = split_21_axis_0, split_sizes = split_21_split_sizes_0, x = split_1_1)[name = string("split_21")];
+            int32 concat_21_axis_0 = const()[name = string("concat_21_axis_0"), val = int32(2)];
+            bool concat_21_interleave_0 = const()[name = string("concat_21_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_21 = concat(axis = concat_21_axis_0, interleave = concat_21_interleave_0, values = (split_21_0, split_21_1))[name = string("concat_21")];
+            tensor<int32, [1]> input_25_lstm_layer_1_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_1_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_1_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_1_lstm_c0_reshaped_axes_0, x = concat_21)[name = string("input_25_lstm_layer_1_lstm_c0_reshaped")];
+            string input_25_lstm_layer_1_direction_0 = const()[name = string("input_25_lstm_layer_1_direction_0"), val = string("bidirectional")];
+            bool input_25_lstm_layer_1_output_sequence_0 = const()[name = string("input_25_lstm_layer_1_output_sequence_0"), val = bool(true)];
+            string input_25_lstm_layer_1_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_1_recurrent_activation_0"), val = string("sigmoid")];
+            string input_25_lstm_layer_1_cell_activation_0 = const()[name = string("input_25_lstm_layer_1_cell_activation_0"), val = string("tanh")];
+            string input_25_lstm_layer_1_activation_0 = const()[name = string("input_25_lstm_layer_1_activation_0"), val = string("tanh")];
+            tensor<fp32, [589, ?, 256]> input_25_lstm_layer_1_0, tensor<fp32, [?, 256]> input_25_lstm_layer_1_1, tensor<fp32, [?, 256]> input_25_lstm_layer_1_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2, bias_back = add_3, cell_activation = input_25_lstm_layer_1_cell_activation_0, direction = input_25_lstm_layer_1_direction_0, initial_c = input_25_lstm_layer_1_lstm_c0_reshaped, initial_h = input_25_lstm_layer_1_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_17, weight_hh_back = concat_19, weight_ih = concat_16, weight_ih_back = concat_18, x = input_25_lstm_layer_0_0)[name = string("input_25_lstm_layer_1")];
+            tensor<fp32, [512]> add_4 = const()[name = string("add_4"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2804672)))];
+            tensor<fp32, [512]> add_5 = const()[name = string("add_5"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2806784)))];
+            tensor<fp32, [512, 256]> concat_26 = const()[name = string("concat_26"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2808896)))];
+            tensor<fp32, [512, 128]> concat_27 = const()[name = string("concat_27"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3333248)))];
+            tensor<fp32, [512, 256]> concat_28 = const()[name = string("concat_28"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3595456)))];
+            tensor<fp32, [512, 128]> concat_29 = const()[name = string("concat_29"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4119808)))];
+            tensor<int32, [2]> split_30_split_sizes_0 = const()[name = string("split_30_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_30_axis_0 = const()[name = string("split_30_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_30_0, tensor<fp32, [1, ?, 128]> split_30_1 = split(axis = split_30_axis_0, split_sizes = split_30_split_sizes_0, x = split_0_2)[name = string("split_30")];
+            int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(2)];
+            bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (split_30_0, split_30_1))[name = string("concat_30")];
+            tensor<int32, [1]> input_25_lstm_layer_2_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_2_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_2_lstm_h0_reshaped = squeeze(axes = input_25_lstm_layer_2_lstm_h0_reshaped_axes_0, x = concat_30)[name = string("input_25_lstm_layer_2_lstm_h0_reshaped")];
+            tensor<int32, [2]> split_31_split_sizes_0 = const()[name = string("split_31_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_31_axis_0 = const()[name = string("split_31_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_31_0, tensor<fp32, [1, ?, 128]> split_31_1 = split(axis = split_31_axis_0, split_sizes = split_31_split_sizes_0, x = split_1_2)[name = string("split_31")];
+            int32 concat_31_axis_0 = const()[name = string("concat_31_axis_0"), val = int32(2)];
+            bool concat_31_interleave_0 = const()[name = string("concat_31_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_31 = concat(axis = concat_31_axis_0, interleave = concat_31_interleave_0, values = (split_31_0, split_31_1))[name = string("concat_31")];
+            tensor<int32, [1]> input_25_lstm_layer_2_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_lstm_layer_2_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_lstm_layer_2_lstm_c0_reshaped = squeeze(axes = input_25_lstm_layer_2_lstm_c0_reshaped_axes_0, x = concat_31)[name = string("input_25_lstm_layer_2_lstm_c0_reshaped")];
+            string input_25_lstm_layer_2_direction_0 = const()[name = string("input_25_lstm_layer_2_direction_0"), val = string("bidirectional")];
+            bool input_25_lstm_layer_2_output_sequence_0 = const()[name = string("input_25_lstm_layer_2_output_sequence_0"), val = bool(true)];
+            string input_25_lstm_layer_2_recurrent_activation_0 = const()[name = string("input_25_lstm_layer_2_recurrent_activation_0"), val = string("sigmoid")];
+            string input_25_lstm_layer_2_cell_activation_0 = const()[name = string("input_25_lstm_layer_2_cell_activation_0"), val = string("tanh")];
+            string input_25_lstm_layer_2_activation_0 = const()[name = string("input_25_lstm_layer_2_activation_0"), val = string("tanh")];
+            tensor<fp32, [589, ?, 256]> input_25_lstm_layer_2_0, tensor<fp32, [?, 256]> input_25_lstm_layer_2_1, tensor<fp32, [?, 256]> input_25_lstm_layer_2_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4, bias_back = add_5, cell_activation = input_25_lstm_layer_2_cell_activation_0, direction = input_25_lstm_layer_2_direction_0, initial_c = input_25_lstm_layer_2_lstm_c0_reshaped, initial_h = input_25_lstm_layer_2_lstm_h0_reshaped, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_27, weight_hh_back = concat_29, weight_ih = concat_26, weight_ih_back = concat_28, x = input_25_lstm_layer_1_0)[name = string("input_25_lstm_layer_2")];
+            tensor<fp32, [512]> add_6 = const()[name = string("add_6"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4382016)))];
+            tensor<fp32, [512]> add_7 = const()[name = string("add_7"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4384128)))];
+            tensor<fp32, [512, 256]> concat_36 = const()[name = string("concat_36"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4386240)))];
+            tensor<fp32, [512, 128]> concat_37 = const()[name = string("concat_37"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4910592)))];
+            tensor<fp32, [512, 256]> concat_38 = const()[name = string("concat_38"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5172800)))];
+            tensor<fp32, [512, 128]> concat_39 = const()[name = string("concat_39"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5697152)))];
+            tensor<int32, [2]> split_40_split_sizes_0 = const()[name = string("split_40_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_40_axis_0 = const()[name = string("split_40_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_40_0, tensor<fp32, [1, ?, 128]> split_40_1 = split(axis = split_40_axis_0, split_sizes = split_40_split_sizes_0, x = split_0_3)[name = string("split_40")];
+            int32 concat_40_axis_0 = const()[name = string("concat_40_axis_0"), val = int32(2)];
+            bool concat_40_interleave_0 = const()[name = string("concat_40_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_40 = concat(axis = concat_40_axis_0, interleave = concat_40_interleave_0, values = (split_40_0, split_40_1))[name = string("concat_40")];
+            tensor<int32, [1]> input_25_batch_first_lstm_h0_reshaped_axes_0 = const()[name = string("input_25_batch_first_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_batch_first_lstm_h0_reshaped = squeeze(axes = input_25_batch_first_lstm_h0_reshaped_axes_0, x = concat_40)[name = string("input_25_batch_first_lstm_h0_reshaped")];
+            tensor<int32, [2]> split_41_split_sizes_0 = const()[name = string("split_41_split_sizes_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 split_41_axis_0 = const()[name = string("split_41_axis_0"), val = int32(0)];
+            tensor<fp32, [1, ?, 128]> split_41_0, tensor<fp32, [1, ?, 128]> split_41_1 = split(axis = split_41_axis_0, split_sizes = split_41_split_sizes_0, x = split_1_3)[name = string("split_41")];
+            int32 concat_41_axis_0 = const()[name = string("concat_41_axis_0"), val = int32(2)];
+            bool concat_41_interleave_0 = const()[name = string("concat_41_interleave_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 256]> concat_41 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (split_41_0, split_41_1))[name = string("concat_41")];
+            tensor<int32, [1]> input_25_batch_first_lstm_c0_reshaped_axes_0 = const()[name = string("input_25_batch_first_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [?, 256]> input_25_batch_first_lstm_c0_reshaped = squeeze(axes = input_25_batch_first_lstm_c0_reshaped_axes_0, x = concat_41)[name = string("input_25_batch_first_lstm_c0_reshaped")];
+            string input_25_batch_first_direction_0 = const()[name = string("input_25_batch_first_direction_0"), val = string("bidirectional")];
+            bool input_25_batch_first_output_sequence_0 = const()[name = string("input_25_batch_first_output_sequence_0"), val = bool(true)];
+            string input_25_batch_first_recurrent_activation_0 = const()[name = string("input_25_batch_first_recurrent_activation_0"), val = string("sigmoid")];
+            string input_25_batch_first_cell_activation_0 = const()[name = string("input_25_batch_first_cell_activation_0"), val = string("tanh")];
+            string input_25_batch_first_activation_0 = const()[name = string("input_25_batch_first_activation_0"), val = string("tanh")];
+            tensor<fp32, [589, ?, 256]> input_25_batch_first_0, tensor<fp32, [?, 256]> input_25_batch_first_1, tensor<fp32, [?, 256]> input_25_batch_first_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6, bias_back = add_7, cell_activation = input_25_batch_first_cell_activation_0, direction = input_25_batch_first_direction_0, initial_c = input_25_batch_first_lstm_c0_reshaped, initial_h = input_25_batch_first_lstm_h0_reshaped, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_37, weight_hh_back = concat_39, weight_ih = concat_36, weight_ih_back = concat_38, x = input_25_lstm_layer_2_0)[name = string("input_25_batch_first")];
+            tensor<int32, [3]> input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp32, [?, 589, 256]> input_25 = transpose(perm = input_25_perm_0, x = input_25_batch_first_0)[name = string("transpose_4")];
+            tensor<fp32, [?, 589, 128]> input_27 = linear(bias = linear_0_bias, weight = linear_0_weight, x = input_25)[name = string("linear_0")];
+            fp32 var_220 = const()[name = string("op_220"), val = fp32(0x1.47ae14p-7)];
+            tensor<fp32, [?, 589, 128]> input_29 = leaky_relu(alpha = var_220, x = input_27)[name = string("input_29")];
+            tensor<fp32, [?, 589, 128]> input_31 = linear(bias = linear_1_bias, weight = linear_1_weight, x = input_29)[name = string("linear_1")];
+            fp32 var_225 = const()[name = string("op_225"), val = fp32(0x1.47ae14p-7)];
+            tensor<fp32, [?, 589, 128]> input_33 = leaky_relu(alpha = var_225, x = input_31)[name = string("input_33")];
+            tensor<fp32, [?, 589, 7]> input_1_1 = linear(bias = classifier_bias, weight = classifier_weight, x = input_33)[name = string("linear_2")];
+            int32 var_231 = const()[name = string("op_231"), val = int32(-1)];
+            tensor<fp32, [?, 589, 7]> var_232_softmax = softmax(axis = var_231, x = input_1_1)[name = string("op_232_softmax")];
+            fp32 var_232_epsilon_0 = const()[name = string("op_232_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp32, [?, 589, 7]> output = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = string("op_232")];
+        } -> (output);
+}

segmentation-3.0.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3189a64946c75bc24fcb98afe89ad78c52bdbadfdf65e857fb1b81e2cc9fbb2
+size 5959360

wespeaker-fbank-b32.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4a6a692403029bf4d417d17ba0e43ab89482e9d862d4ed7d897b309d7455910
+size 243

wespeaker-fbank-b32.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4241bd2543b6face59368f98e9cb7a049c46db8cc5ebd70a12814d3382324ede
+size 168

wespeaker-fbank-b32.mlmodelc/model.mil

@@ -0,0 +1,63 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [?, 1, 160000]> waveform) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"waveform", [32, 1, 160000]}}), ("EnumeratedShapes", {{"1decda00", {{"waveform", [1, 1, 160000]}}}, {"9025f589", {{"waveform", [32, 1, 160000]}}}})))] {
+            tensor<fp32, [257, 512]> dft_sin = const()[name = string("dft_sin"), val = tensor<fp32, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [257, 512]> dft_cos = const()[name = string("dft_cos"), val = tensor<fp32, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526464)))];
+            tensor<fp32, [400, 1, 400]> identity_kernel = const()[name = string("identity_kernel"), val = tensor<fp32, [400, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1052864)))];
+            tensor<int32, [3]> var_17_begin_0 = const()[name = string("op_17_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> var_17_end_0 = const()[name = string("op_17_end_0"), val = tensor<int32, [3]>([0, 1, 160000])];
+            tensor<bool, [3]> var_17_end_mask_0 = const()[name = string("op_17_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            tensor<fp32, [?, 1, 160000]> var_17 = slice_by_index(begin = var_17_begin_0, end = var_17_end_0, end_mask = var_17_end_mask_0, x = waveform)[name = string("op_17")];
+            fp32 var_23 = const()[name = string("op_23"), val = fp32(0x1p+15)];
+            tensor<fp32, [?, 1, 160000]> signal = mul(x = var_17, y = var_23)[name = string("signal")];
+            string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [2]> frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)];
+            tensor<fp32, [?, 400, 998]> frames_1 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = identity_kernel, x = signal)[name = string("frames_1")];
+            tensor<int32, [3]> var_44 = const()[name = string("op_44"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_50_axes_0 = const()[name = string("op_50_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_50_keep_dims_0 = const()[name = string("op_50_keep_dims_0"), val = bool(true)];
+            tensor<fp32, [?, 998, 400]> frames_3 = transpose(perm = var_44, x = frames_1)[name = string("transpose_3")];
+            tensor<fp32, [?, 998, 1]> var_50 = reduce_mean(axes = var_50_axes_0, keep_dims = var_50_keep_dims_0, x = frames_3)[name = string("op_50")];
+            tensor<fp32, [?, 998, 400]> input_1 = sub(x = frames_3, y = var_50)[name = string("input_1")];
+            fp32 const_0 = const()[name = string("const_0"), val = fp32(0x0p+0)];
+            tensor<int32, [6]> var_58_pad_0 = const()[name = string("op_58_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])];
+            string var_58_mode_0 = const()[name = string("op_58_mode_0"), val = string("replicate")];
+            tensor<fp32, [?, 998, 401]> var_58 = pad(constant_val = const_0, mode = var_58_mode_0, pad = var_58_pad_0, x = input_1)[name = string("op_58")];
+            tensor<int32, [3]> previous_begin_0 = const()[name = string("previous_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> previous_end_0 = const()[name = string("previous_end_0"), val = tensor<int32, [3]>([0, 998, 400])];
+            tensor<bool, [3]> previous_end_mask_0 = const()[name = string("previous_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<fp32, [?, 998, 400]> previous = slice_by_index(begin = previous_begin_0, end = previous_end_0, end_mask = previous_end_mask_0, x = var_58)[name = string("previous")];
+            fp32 var_64 = const()[name = string("op_64"), val = fp32(0x1.f0a3d8p-1)];
+            tensor<fp32, [?, 998, 400]> var_65 = mul(x = previous, y = var_64)[name = string("op_65")];
+            tensor<fp32, [?, 998, 400]> frames_5 = sub(x = input_1, y = var_65)[name = string("frames_5")];
+            tensor<fp32, [1, 1, 400]> var_72 = const()[name = string("op_72"), val = tensor<fp32, [1, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1692928)))];
+            tensor<fp32, [?, 998, 400]> input = mul(x = frames_5, y = var_72)[name = string("input")];
+            fp32 const_1 = const()[name = string("const_1"), val = fp32(0x0p+0)];
+            tensor<int32, [6]> frames_pad_0 = const()[name = string("frames_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 112])];
+            string frames_mode_0 = const()[name = string("frames_mode_0"), val = string("constant")];
+            tensor<fp32, [?, 998, 512]> frames = pad(constant_val = const_1, mode = frames_mode_0, pad = frames_pad_0, x = input)[name = string("frames")];
+            tensor<fp32, [257]> real_part_bias_0 = const()[name = string("real_part_bias_0"), val = tensor<fp32, [257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1694592)))];
+            tensor<fp32, [?, 998, 257]> real_part = linear(bias = real_part_bias_0, weight = dft_cos, x = frames)[name = string("real_part")];
+            tensor<fp32, [?, 998, 257]> imag_part = linear(bias = real_part_bias_0, weight = dft_sin, x = frames)[name = string("imag_part")];
+            fp32 var_84 = const()[name = string("op_84"), val = fp32(0x1p+1)];
+            tensor<fp32, [?, 998, 257]> var_85 = pow(x = real_part, y = var_84)[name = string("op_85")];
+            fp32 var_86 = const()[name = string("op_86"), val = fp32(0x1p+1)];
+            tensor<fp32, [?, 998, 257]> var_87 = pow(x = imag_part, y = var_86)[name = string("op_87")];
+            tensor<fp32, [?, 998, 257]> spectrum = add(x = var_85, y = var_87)[name = string("spectrum")];
+            tensor<fp32, [80, 257]> transpose_2 = const()[name = string("transpose_2"), val = tensor<fp32, [80, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1695744)))];
+            tensor<fp32, [80]> mel_1_bias_0 = const()[name = string("mel_1_bias_0"), val = tensor<fp32, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1778048)))];
+            tensor<fp32, [?, 998, 80]> mel_1 = linear(bias = mel_1_bias_0, weight = transpose_2, x = spectrum)[name = string("mel_1")];
+            fp32 const_3 = const()[name = string("const_3"), val = fp32(0x1p-23)];
+            tensor<fp32, [?, 998, 80]> var_102 = maximum(x = mel_1, y = const_3)[name = string("op_102")];
+            fp32 mel_3_epsilon_0 = const()[name = string("mel_3_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp32, [?, 998, 80]> mel_3 = log(epsilon = mel_3_epsilon_0, x = var_102)[name = string("mel_3")];
+            tensor<int32, [1]> var_108_axes_0 = const()[name = string("op_108_axes_0"), val = tensor<int32, [1]>([1])];
+            bool var_108_keep_dims_0 = const()[name = string("op_108_keep_dims_0"), val = bool(true)];
+            tensor<fp32, [?, 1, 80]> var_108 = reduce_mean(axes = var_108_axes_0, keep_dims = var_108_keep_dims_0, x = mel_3)[name = string("op_108")];
+            tensor<fp32, [?, 998, 80]> output = sub(x = mel_3, y = var_108)[name = string("op_110")];
+        } -> (output);
+}

wespeaker-fbank-b32.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:27396cb0afdd09164a9d6b2dbd10688bed15230948b3e0a6692ec490c03ae4d7
+size 1778432

wespeaker-fbank-b32.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5b1cc223098300b2d0a5693ff2e28984b7f593fa22525aed9e2537dfe6a342e3
+size 110518

wespeaker-fbank.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4a6a692403029bf4d417d17ba0e43ab89482e9d862d4ed7d897b309d7455910
+size 243

wespeaker-fbank.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4241bd2543b6face59368f98e9cb7a049c46db8cc5ebd70a12814d3382324ede
+size 168

wespeaker-fbank.mlmodelc/model.mil

@@ -0,0 +1,63 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [?, 1, 160000]> waveform) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"waveform", [32, 1, 160000]}}), ("EnumeratedShapes", {{"1decda00", {{"waveform", [1, 1, 160000]}}}, {"9025f589", {{"waveform", [32, 1, 160000]}}}})))] {
+            tensor<fp32, [257, 512]> dft_sin = const()[name = string("dft_sin"), val = tensor<fp32, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [257, 512]> dft_cos = const()[name = string("dft_cos"), val = tensor<fp32, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526464)))];
+            tensor<fp32, [400, 1, 400]> identity_kernel = const()[name = string("identity_kernel"), val = tensor<fp32, [400, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1052864)))];
+            tensor<int32, [3]> var_17_begin_0 = const()[name = string("op_17_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> var_17_end_0 = const()[name = string("op_17_end_0"), val = tensor<int32, [3]>([0, 1, 160000])];
+            tensor<bool, [3]> var_17_end_mask_0 = const()[name = string("op_17_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            tensor<fp32, [?, 1, 160000]> var_17 = slice_by_index(begin = var_17_begin_0, end = var_17_end_0, end_mask = var_17_end_mask_0, x = waveform)[name = string("op_17")];
+            fp32 var_23 = const()[name = string("op_23"), val = fp32(0x1p+15)];
+            tensor<fp32, [?, 1, 160000]> signal = mul(x = var_17, y = var_23)[name = string("signal")];
+            string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [2]> frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)];
+            tensor<fp32, [?, 400, 998]> frames_1 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = identity_kernel, x = signal)[name = string("frames_1")];
+            tensor<int32, [3]> var_44 = const()[name = string("op_44"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_50_axes_0 = const()[name = string("op_50_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_50_keep_dims_0 = const()[name = string("op_50_keep_dims_0"), val = bool(true)];
+            tensor<fp32, [?, 998, 400]> frames_3 = transpose(perm = var_44, x = frames_1)[name = string("transpose_3")];
+            tensor<fp32, [?, 998, 1]> var_50 = reduce_mean(axes = var_50_axes_0, keep_dims = var_50_keep_dims_0, x = frames_3)[name = string("op_50")];
+            tensor<fp32, [?, 998, 400]> input_1 = sub(x = frames_3, y = var_50)[name = string("input_1")];
+            fp32 const_0 = const()[name = string("const_0"), val = fp32(0x0p+0)];
+            tensor<int32, [6]> var_58_pad_0 = const()[name = string("op_58_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])];
+            string var_58_mode_0 = const()[name = string("op_58_mode_0"), val = string("replicate")];
+            tensor<fp32, [?, 998, 401]> var_58 = pad(constant_val = const_0, mode = var_58_mode_0, pad = var_58_pad_0, x = input_1)[name = string("op_58")];
+            tensor<int32, [3]> previous_begin_0 = const()[name = string("previous_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> previous_end_0 = const()[name = string("previous_end_0"), val = tensor<int32, [3]>([0, 998, 400])];
+            tensor<bool, [3]> previous_end_mask_0 = const()[name = string("previous_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<fp32, [?, 998, 400]> previous = slice_by_index(begin = previous_begin_0, end = previous_end_0, end_mask = previous_end_mask_0, x = var_58)[name = string("previous")];
+            fp32 var_64 = const()[name = string("op_64"), val = fp32(0x1.f0a3d8p-1)];
+            tensor<fp32, [?, 998, 400]> var_65 = mul(x = previous, y = var_64)[name = string("op_65")];
+            tensor<fp32, [?, 998, 400]> frames_5 = sub(x = input_1, y = var_65)[name = string("frames_5")];
+            tensor<fp32, [1, 1, 400]> var_72 = const()[name = string("op_72"), val = tensor<fp32, [1, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1692928)))];
+            tensor<fp32, [?, 998, 400]> input = mul(x = frames_5, y = var_72)[name = string("input")];
+            fp32 const_1 = const()[name = string("const_1"), val = fp32(0x0p+0)];
+            tensor<int32, [6]> frames_pad_0 = const()[name = string("frames_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 112])];
+            string frames_mode_0 = const()[name = string("frames_mode_0"), val = string("constant")];
+            tensor<fp32, [?, 998, 512]> frames = pad(constant_val = const_1, mode = frames_mode_0, pad = frames_pad_0, x = input)[name = string("frames")];
+            tensor<fp32, [257]> real_part_bias_0 = const()[name = string("real_part_bias_0"), val = tensor<fp32, [257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1694592)))];
+            tensor<fp32, [?, 998, 257]> real_part = linear(bias = real_part_bias_0, weight = dft_cos, x = frames)[name = string("real_part")];
+            tensor<fp32, [?, 998, 257]> imag_part = linear(bias = real_part_bias_0, weight = dft_sin, x = frames)[name = string("imag_part")];
+            fp32 var_84 = const()[name = string("op_84"), val = fp32(0x1p+1)];
+            tensor<fp32, [?, 998, 257]> var_85 = pow(x = real_part, y = var_84)[name = string("op_85")];
+            fp32 var_86 = const()[name = string("op_86"), val = fp32(0x1p+1)];
+            tensor<fp32, [?, 998, 257]> var_87 = pow(x = imag_part, y = var_86)[name = string("op_87")];
+            tensor<fp32, [?, 998, 257]> spectrum = add(x = var_85, y = var_87)[name = string("spectrum")];
+            tensor<fp32, [80, 257]> transpose_2 = const()[name = string("transpose_2"), val = tensor<fp32, [80, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1695744)))];
+            tensor<fp32, [80]> mel_1_bias_0 = const()[name = string("mel_1_bias_0"), val = tensor<fp32, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1778048)))];
+            tensor<fp32, [?, 998, 80]> mel_1 = linear(bias = mel_1_bias_0, weight = transpose_2, x = spectrum)[name = string("mel_1")];
+            fp32 const_3 = const()[name = string("const_3"), val = fp32(0x1p-23)];
+            tensor<fp32, [?, 998, 80]> var_102 = maximum(x = mel_1, y = const_3)[name = string("op_102")];
+            fp32 mel_3_epsilon_0 = const()[name = string("mel_3_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp32, [?, 998, 80]> mel_3 = log(epsilon = mel_3_epsilon_0, x = var_102)[name = string("mel_3")];
+            tensor<int32, [1]> var_108_axes_0 = const()[name = string("op_108_axes_0"), val = tensor<int32, [1]>([1])];
+            bool var_108_keep_dims_0 = const()[name = string("op_108_keep_dims_0"), val = bool(true)];
+            tensor<fp32, [?, 1, 80]> var_108 = reduce_mean(axes = var_108_axes_0, keep_dims = var_108_keep_dims_0, x = mel_3)[name = string("op_108")];
+            tensor<fp32, [?, 998, 80]> output = sub(x = mel_3, y = var_108)[name = string("op_110")];
+        } -> (output);
+}

wespeaker-fbank.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:27396cb0afdd09164a9d6b2dbd10688bed15230948b3e0a6692ec490c03ae4d7
+size 1778432

wespeaker-fbank.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7d2d4ad87789b178d75309d7418056b4d07cf408efcc994155fdd55f240102b5
+size 110518

wespeaker-voxceleb-resnet34-fused-b3-f16.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3fd492b62d5e26ad34278cea685f931276bb94ab564ca6db80d832f87135f69d
+size 243

wespeaker-voxceleb-resnet34-fused-b3-f16.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f279b672793ba4f1d246f69442be874b49fc275f6c9b08f6ab85b57dc0bbe68
+size 225

wespeaker-voxceleb-resnet34-fused-b3-f16.mlmodelc/model.mil

@@ -0,0 +1,468 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [?, 1, 160000]> waveform, tensor<fp32, [?, 589]> weights) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"waveform", [32, 1, 160000]}, {"weights", [32, 589]}}), ("EnumeratedShapes", {{"3c334fba", {{"waveform", [3, 1, 160000]}, {"weights", [3, 589]}}}, {"79c53add", {{"waveform", [1, 1, 160000]}, {"weights", [1, 589]}}}, {"cb01bf12", {{"waveform", [32, 1, 160000]}, {"weights", [32, 589]}}}})))] {
+            tensor<int32, [3]> var_27_begin_0 = const()[name = string("op_27_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> var_27_end_0 = const()[name = string("op_27_end_0"), val = tensor<int32, [3]>([0, 1, 160000])];
+            tensor<bool, [3]> var_27_end_mask_0 = const()[name = string("op_27_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            string waveform_to_fp16_dtype_0 = const()[name = string("waveform_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [?, 1, 160000]> waveform_to_fp16 = cast(dtype = waveform_to_fp16_dtype_0, x = waveform)[name = string("cast_10")];
+            tensor<fp16, [?, 1, 160000]> var_27_cast_fp16 = slice_by_index(begin = var_27_begin_0, end = var_27_end_0, end_mask = var_27_end_mask_0, x = waveform_to_fp16)[name = string("op_27_cast_fp16")];
+            fp16 var_29_to_fp16 = const()[name = string("op_29_to_fp16"), val = fp16(0x1p+15)];
+            tensor<fp16, [?, 1, 160000]> signal_cast_fp16 = mul(x = var_27_cast_fp16, y = var_29_to_fp16)[name = string("signal_cast_fp16")];
+            string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [2]> frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)];
+            tensor<fp16, [400, 1, 400]> fbank_identity_kernel_to_fp16 = const()[name = string("fbank_identity_kernel_to_fp16"), val = tensor<fp16, [400, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [?, 400, 998]> frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = fbank_identity_kernel_to_fp16, x = signal_cast_fp16)[name = string("frames_1_cast_fp16")];
+            tensor<int32, [3]> var_36 = const()[name = string("op_36"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_39_axes_0 = const()[name = string("op_39_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_39_keep_dims_0 = const()[name = string("op_39_keep_dims_0"), val = bool(true)];
+            tensor<fp16, [?, 998, 400]> frames_3_cast_fp16 = transpose(perm = var_36, x = frames_1_cast_fp16)[name = string("transpose_4")];
+            tensor<fp16, [?, 998, 1]> var_39_cast_fp16 = reduce_mean(axes = var_39_axes_0, keep_dims = var_39_keep_dims_0, x = frames_3_cast_fp16)[name = string("op_39_cast_fp16")];
+            tensor<fp16, [?, 998, 400]> input_1_cast_fp16 = sub(x = frames_3_cast_fp16, y = var_39_cast_fp16)[name = string("input_1_cast_fp16")];
+            tensor<int32, [6]> var_42_pad_0 = const()[name = string("op_42_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])];
+            string var_42_mode_0 = const()[name = string("op_42_mode_0"), val = string("replicate")];
+            fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [?, 998, 401]> var_42_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = var_42_mode_0, pad = var_42_pad_0, x = input_1_cast_fp16)[name = string("op_42_cast_fp16")];
+            tensor<int32, [3]> previous_begin_0 = const()[name = string("previous_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> previous_end_0 = const()[name = string("previous_end_0"), val = tensor<int32, [3]>([0, 998, 400])];
+            tensor<bool, [3]> previous_end_mask_0 = const()[name = string("previous_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<fp16, [?, 998, 400]> previous_cast_fp16 = slice_by_index(begin = previous_begin_0, end = previous_end_0, end_mask = previous_end_mask_0, x = var_42_cast_fp16)[name = string("previous_cast_fp16")];
+            fp16 var_44_to_fp16 = const()[name = string("op_44_to_fp16"), val = fp16(0x1.f0cp-1)];
+            tensor<fp16, [?, 998, 400]> var_45_cast_fp16 = mul(x = previous_cast_fp16, y = var_44_to_fp16)[name = string("op_45_cast_fp16")];
+            tensor<fp16, [?, 998, 400]> frames_5_cast_fp16 = sub(x = input_1_cast_fp16, y = var_45_cast_fp16)[name = string("frames_5_cast_fp16")];
+            tensor<fp16, [1, 1, 400]> var_48_to_fp16 = const()[name = string("op_48_to_fp16"), val = tensor<fp16, [1, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320128)))];
+            tensor<fp16, [?, 998, 400]> input_3_cast_fp16 = mul(x = frames_5_cast_fp16, y = var_48_to_fp16)[name = string("input_3_cast_fp16")];
+            tensor<int32, [6]> frames_7_pad_0 = const()[name = string("frames_7_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 112])];
+            string frames_7_mode_0 = const()[name = string("frames_7_mode_0"), val = string("constant")];
+            fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [?, 998, 512]> frames_7_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = frames_7_mode_0, pad = frames_7_pad_0, x = input_3_cast_fp16)[name = string("frames_7_cast_fp16")];
+            tensor<fp16, [257, 512]> fbank_dft_cos_to_fp16 = const()[name = string("fbank_dft_cos_to_fp16"), val = tensor<fp16, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321024)))];
+            tensor<fp16, [257]> real_part_bias_0_to_fp16 = const()[name = string("real_part_bias_0_to_fp16"), val = tensor<fp16, [257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(584256)))];
+            tensor<fp16, [?, 998, 257]> real_part_cast_fp16 = linear(bias = real_part_bias_0_to_fp16, weight = fbank_dft_cos_to_fp16, x = frames_7_cast_fp16)[name = string("real_part_cast_fp16")];
+            tensor<fp16, [257, 512]> fbank_dft_sin_to_fp16 = const()[name = string("fbank_dft_sin_to_fp16"), val = tensor<fp16, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(584896)))];
+            tensor<fp16, [?, 998, 257]> imag_part_cast_fp16 = linear(bias = real_part_bias_0_to_fp16, weight = fbank_dft_sin_to_fp16, x = frames_7_cast_fp16)[name = string("imag_part_cast_fp16")];
+            fp16 var_7_to_fp16 = const()[name = string("op_7_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [?, 998, 257]> var_56_cast_fp16 = pow(x = real_part_cast_fp16, y = var_7_to_fp16)[name = string("op_56_cast_fp16")];
+            tensor<fp16, [?, 998, 257]> var_57_cast_fp16 = pow(x = imag_part_cast_fp16, y = var_7_to_fp16)[name = string("op_57_cast_fp16")];
+            tensor<fp16, [?, 998, 257]> spectrum_cast_fp16 = add(x = var_56_cast_fp16, y = var_57_cast_fp16)[name = string("spectrum_cast_fp16")];
+            tensor<fp16, [80, 257]> transpose_2_to_fp16 = const()[name = string("transpose_2_to_fp16"), val = tensor<fp16, [80, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(848128)))];
+            tensor<fp16, [80]> mel_1_bias_0_to_fp16 = const()[name = string("mel_1_bias_0_to_fp16"), val = tensor<fp16, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889344)))];
+            tensor<fp16, [?, 998, 80]> mel_1_cast_fp16 = linear(bias = mel_1_bias_0_to_fp16, weight = transpose_2_to_fp16, x = spectrum_cast_fp16)[name = string("mel_1_cast_fp16")];
+            fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x1p-23)];
+            tensor<fp16, [?, 998, 80]> var_62_cast_fp16 = maximum(x = mel_1_cast_fp16, y = const_3_to_fp16)[name = string("op_62_cast_fp16")];
+            fp32 mel_3_epsilon_0 = const()[name = string("mel_3_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [?, 998, 80]> mel_3_cast_fp16 = log(epsilon = mel_3_epsilon_0, x = var_62_cast_fp16)[name = string("mel_3_cast_fp16")];
+            tensor<int32, [1]> var_65_axes_0 = const()[name = string("op_65_axes_0"), val = tensor<int32, [1]>([1])];
+            bool var_65_keep_dims_0 = const()[name = string("op_65_keep_dims_0"), val = bool(true)];
+            tensor<fp16, [?, 1, 80]> var_65_cast_fp16 = reduce_mean(axes = var_65_axes_0, keep_dims = var_65_keep_dims_0, x = mel_3_cast_fp16)[name = string("op_65_cast_fp16")];
+            tensor<fp16, [?, 998, 80]> fbank_1_cast_fp16 = sub(x = mel_3_cast_fp16, y = var_65_cast_fp16)[name = string("fbank_1_cast_fp16")];
+            int32 var_67 = const()[name = string("op_67"), val = int32(-1)];
+            tensor<int32, [3]> var_94 = const()[name = string("op_94"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [?, 80, 998]> fbank_3_cast_fp16 = transpose(perm = var_94, x = fbank_1_cast_fp16)[name = string("transpose_3")];
+            tensor<fp16, [?, 1, 80, 998]> input_5_cast_fp16 = expand_dims(axes = input_5_axes_0, x = fbank_3_cast_fp16)[name = string("input_5_cast_fp16")];
+            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 1, 3, 3]> const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = tensor<fp16, [32, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889600)))];
+            tensor<fp16, [32]> const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(890240)))];
+            tensor<fp16, [?, 32, 80, 998]> input_9_cast_fp16 = conv(bias = const_5_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = const_4_to_fp16, x = input_5_cast_fp16)[name = string("input_9_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = string("input_11_cast_fp16")];
+            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(890368)))];
+            tensor<fp16, [32]> const_7_to_fp16 = const()[name = string("const_7_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908864)))];
+            tensor<fp16, [?, 32, 80, 998]> input_15_cast_fp16 = conv(bias = const_7_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = const_6_to_fp16, x = input_11_cast_fp16)[name = string("input_15_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_17_cast_fp16 = relu(x = input_15_cast_fp16)[name = string("input_17_cast_fp16")];
+            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_19_strides_0 = const()[name = string("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_19_dilations_0 = const()[name = string("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_19_groups_0 = const()[name = string("input_19_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908992)))];
+            tensor<fp16, [32]> const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(927488)))];
+            tensor<fp16, [?, 32, 80, 998]> out_1_cast_fp16 = conv(bias = const_9_to_fp16, dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = const_8_to_fp16, x = input_17_cast_fp16)[name = string("out_1_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_21_cast_fp16 = add(x = out_1_cast_fp16, y = input_11_cast_fp16)[name = string("input_21_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_23_cast_fp16 = relu(x = input_21_cast_fp16)[name = string("input_23_cast_fp16")];
+            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(927616)))];
+            tensor<fp16, [32]> const_11_to_fp16 = const()[name = string("const_11_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(946112)))];
+            tensor<fp16, [?, 32, 80, 998]> input_27_cast_fp16 = conv(bias = const_11_to_fp16, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = const_10_to_fp16, x = input_23_cast_fp16)[name = string("input_27_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_29_cast_fp16 = relu(x = input_27_cast_fp16)[name = string("input_29_cast_fp16")];
+            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(946240)))];
+            tensor<fp16, [32]> const_13_to_fp16 = const()[name = string("const_13_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(964736)))];
+            tensor<fp16, [?, 32, 80, 998]> out_3_cast_fp16 = conv(bias = const_13_to_fp16, dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = const_12_to_fp16, x = input_29_cast_fp16)[name = string("out_3_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_33_cast_fp16 = add(x = out_3_cast_fp16, y = input_23_cast_fp16)[name = string("input_33_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_35_cast_fp16 = relu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")];
+            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_14_to_fp16 = const()[name = string("const_14_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(964864)))];
+            tensor<fp16, [32]> const_15_to_fp16 = const()[name = string("const_15_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(983360)))];
+            tensor<fp16, [?, 32, 80, 998]> input_39_cast_fp16 = conv(bias = const_15_to_fp16, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = const_14_to_fp16, x = input_35_cast_fp16)[name = string("input_39_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_41_cast_fp16 = relu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")];
+            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(983488)))];
+            tensor<fp16, [32]> const_17_to_fp16 = const()[name = string("const_17_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1001984)))];
+            tensor<fp16, [?, 32, 80, 998]> out_5_cast_fp16 = conv(bias = const_17_to_fp16, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = const_16_to_fp16, x = input_41_cast_fp16)[name = string("out_5_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_45_cast_fp16 = add(x = out_5_cast_fp16, y = input_35_cast_fp16)[name = string("input_45_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_47_cast_fp16 = relu(x = input_45_cast_fp16)[name = string("input_47_cast_fp16")];
+            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 32, 3, 3]> const_18_to_fp16 = const()[name = string("const_18_to_fp16"), val = tensor<fp16, [64, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1002112)))];
+            tensor<fp16, [64]> const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1039040)))];
+            tensor<fp16, [?, 64, 40, 499]> input_51_cast_fp16 = conv(bias = const_19_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_18_to_fp16, x = input_47_cast_fp16)[name = string("input_51_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_53_cast_fp16 = relu(x = input_51_cast_fp16)[name = string("input_53_cast_fp16")];
+            string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_55_strides_0 = const()[name = string("input_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_55_dilations_0 = const()[name = string("input_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_55_groups_0 = const()[name = string("input_55_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_20_to_fp16 = const()[name = string("const_20_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1039232)))];
+            tensor<fp16, [64]> const_21_to_fp16 = const()[name = string("const_21_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1113024)))];
+            tensor<fp16, [?, 64, 40, 499]> out_7_cast_fp16 = conv(bias = const_21_to_fp16, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = const_20_to_fp16, x = input_53_cast_fp16)[name = string("out_7_cast_fp16")];
+            string input_57_pad_type_0 = const()[name = string("input_57_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_57_strides_0 = const()[name = string("input_57_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_57_dilations_0 = const()[name = string("input_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_57_groups_0 = const()[name = string("input_57_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 32, 1, 1]> const_22_to_fp16 = const()[name = string("const_22_to_fp16"), val = tensor<fp16, [64, 32, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1113216)))];
+            tensor<fp16, [64]> const_23_to_fp16 = const()[name = string("const_23_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1117376)))];
+            tensor<fp16, [?, 64, 40, 499]> var_243_cast_fp16 = conv(bias = const_23_to_fp16, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_22_to_fp16, x = input_47_cast_fp16)[name = string("op_243_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_59_cast_fp16 = add(x = out_7_cast_fp16, y = var_243_cast_fp16)[name = string("input_59_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_61_cast_fp16 = relu(x = input_59_cast_fp16)[name = string("input_61_cast_fp16")];
+            string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1117568)))];
+            tensor<fp16, [64]> const_25_to_fp16 = const()[name = string("const_25_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1191360)))];
+            tensor<fp16, [?, 64, 40, 499]> input_65_cast_fp16 = conv(bias = const_25_to_fp16, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = const_24_to_fp16, x = input_61_cast_fp16)[name = string("input_65_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_67_cast_fp16 = relu(x = input_65_cast_fp16)[name = string("input_67_cast_fp16")];
+            string input_69_pad_type_0 = const()[name = string("input_69_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_69_pad_0 = const()[name = string("input_69_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_69_strides_0 = const()[name = string("input_69_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_69_dilations_0 = const()[name = string("input_69_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_69_groups_0 = const()[name = string("input_69_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1191552)))];
+            tensor<fp16, [64]> const_27_to_fp16 = const()[name = string("const_27_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265344)))];
+            tensor<fp16, [?, 64, 40, 499]> out_9_cast_fp16 = conv(bias = const_27_to_fp16, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = const_26_to_fp16, x = input_67_cast_fp16)[name = string("out_9_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_71_cast_fp16 = add(x = out_9_cast_fp16, y = input_61_cast_fp16)[name = string("input_71_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_73_cast_fp16 = relu(x = input_71_cast_fp16)[name = string("input_73_cast_fp16")];
+            string input_75_pad_type_0 = const()[name = string("input_75_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_75_pad_0 = const()[name = string("input_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_75_strides_0 = const()[name = string("input_75_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_75_dilations_0 = const()[name = string("input_75_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_75_groups_0 = const()[name = string("input_75_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265536)))];
+            tensor<fp16, [64]> const_29_to_fp16 = const()[name = string("const_29_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1339328)))];
+            tensor<fp16, [?, 64, 40, 499]> input_77_cast_fp16 = conv(bias = const_29_to_fp16, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = const_28_to_fp16, x = input_73_cast_fp16)[name = string("input_77_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_79_cast_fp16 = relu(x = input_77_cast_fp16)[name = string("input_79_cast_fp16")];
+            string input_81_pad_type_0 = const()[name = string("input_81_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_81_pad_0 = const()[name = string("input_81_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_81_strides_0 = const()[name = string("input_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_81_dilations_0 = const()[name = string("input_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_81_groups_0 = const()[name = string("input_81_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_30_to_fp16 = const()[name = string("const_30_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1339520)))];
+            tensor<fp16, [64]> const_31_to_fp16 = const()[name = string("const_31_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413312)))];
+            tensor<fp16, [?, 64, 40, 499]> out_11_cast_fp16 = conv(bias = const_31_to_fp16, dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = const_30_to_fp16, x = input_79_cast_fp16)[name = string("out_11_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_83_cast_fp16 = add(x = out_11_cast_fp16, y = input_73_cast_fp16)[name = string("input_83_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_85_cast_fp16 = relu(x = input_83_cast_fp16)[name = string("input_85_cast_fp16")];
+            string input_87_pad_type_0 = const()[name = string("input_87_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_87_pad_0 = const()[name = string("input_87_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_87_strides_0 = const()[name = string("input_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_87_dilations_0 = const()[name = string("input_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_87_groups_0 = const()[name = string("input_87_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413504)))];
+            tensor<fp16, [64]> const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1487296)))];
+            tensor<fp16, [?, 64, 40, 499]> input_89_cast_fp16 = conv(bias = const_33_to_fp16, dilations = input_87_dilations_0, groups = input_87_groups_0, pad = input_87_pad_0, pad_type = input_87_pad_type_0, strides = input_87_strides_0, weight = const_32_to_fp16, x = input_85_cast_fp16)[name = string("input_89_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_91_cast_fp16 = relu(x = input_89_cast_fp16)[name = string("input_91_cast_fp16")];
+            string input_93_pad_type_0 = const()[name = string("input_93_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_93_pad_0 = const()[name = string("input_93_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_93_strides_0 = const()[name = string("input_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_93_dilations_0 = const()[name = string("input_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_93_groups_0 = const()[name = string("input_93_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_34_to_fp16 = const()[name = string("const_34_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1487488)))];
+            tensor<fp16, [64]> const_35_to_fp16 = const()[name = string("const_35_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1561280)))];
+            tensor<fp16, [?, 64, 40, 499]> out_13_cast_fp16 = conv(bias = const_35_to_fp16, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = const_34_to_fp16, x = input_91_cast_fp16)[name = string("out_13_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_95_cast_fp16 = add(x = out_13_cast_fp16, y = input_85_cast_fp16)[name = string("input_95_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_97_cast_fp16 = relu(x = input_95_cast_fp16)[name = string("input_97_cast_fp16")];
+            string input_99_pad_type_0 = const()[name = string("input_99_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_99_pad_0 = const()[name = string("input_99_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_99_strides_0 = const()[name = string("input_99_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_99_dilations_0 = const()[name = string("input_99_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_99_groups_0 = const()[name = string("input_99_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 64, 3, 3]> const_36_to_fp16 = const()[name = string("const_36_to_fp16"), val = tensor<fp16, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1561472)))];
+            tensor<fp16, [128]> const_37_to_fp16 = const()[name = string("const_37_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1708992)))];
+            tensor<fp16, [?, 128, 20, 250]> input_101_cast_fp16 = conv(bias = const_37_to_fp16, dilations = input_99_dilations_0, groups = input_99_groups_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = const_36_to_fp16, x = input_97_cast_fp16)[name = string("input_101_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_103_cast_fp16 = relu(x = input_101_cast_fp16)[name = string("input_103_cast_fp16")];
+            string input_105_pad_type_0 = const()[name = string("input_105_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_105_pad_0 = const()[name = string("input_105_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_105_strides_0 = const()[name = string("input_105_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_105_dilations_0 = const()[name = string("input_105_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_105_groups_0 = const()[name = string("input_105_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_38_to_fp16 = const()[name = string("const_38_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1709312)))];
+            tensor<fp16, [128]> const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2004288)))];
+            tensor<fp16, [?, 128, 20, 250]> out_15_cast_fp16 = conv(bias = const_39_to_fp16, dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = const_38_to_fp16, x = input_103_cast_fp16)[name = string("out_15_cast_fp16")];
+            string input_107_pad_type_0 = const()[name = string("input_107_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_107_strides_0 = const()[name = string("input_107_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_107_dilations_0 = const()[name = string("input_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_107_groups_0 = const()[name = string("input_107_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 64, 1, 1]> const_40_to_fp16 = const()[name = string("const_40_to_fp16"), val = tensor<fp16, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2004608)))];
+            tensor<fp16, [128]> const_41_to_fp16 = const()[name = string("const_41_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2021056)))];
+            tensor<fp16, [?, 128, 20, 250]> var_379_cast_fp16 = conv(bias = const_41_to_fp16, dilations = input_107_dilations_0, groups = input_107_groups_0, pad = input_107_pad_0, pad_type = input_107_pad_type_0, strides = input_107_strides_0, weight = const_40_to_fp16, x = input_97_cast_fp16)[name = string("op_379_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_109_cast_fp16 = add(x = out_15_cast_fp16, y = var_379_cast_fp16)[name = string("input_109_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_111_cast_fp16 = relu(x = input_109_cast_fp16)[name = string("input_111_cast_fp16")];
+            string input_113_pad_type_0 = const()[name = string("input_113_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_113_pad_0 = const()[name = string("input_113_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_113_strides_0 = const()[name = string("input_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_113_dilations_0 = const()[name = string("input_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_113_groups_0 = const()[name = string("input_113_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_42_to_fp16 = const()[name = string("const_42_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2021376)))];
+            tensor<fp16, [128]> const_43_to_fp16 = const()[name = string("const_43_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2316352)))];
+            tensor<fp16, [?, 128, 20, 250]> input_115_cast_fp16 = conv(bias = const_43_to_fp16, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = const_42_to_fp16, x = input_111_cast_fp16)[name = string("input_115_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_117_cast_fp16 = relu(x = input_115_cast_fp16)[name = string("input_117_cast_fp16")];
+            string input_119_pad_type_0 = const()[name = string("input_119_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_119_strides_0 = const()[name = string("input_119_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_119_dilations_0 = const()[name = string("input_119_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_119_groups_0 = const()[name = string("input_119_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2316672)))];
+            tensor<fp16, [128]> const_45_to_fp16 = const()[name = string("const_45_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2611648)))];
+            tensor<fp16, [?, 128, 20, 250]> out_17_cast_fp16 = conv(bias = const_45_to_fp16, dilations = input_119_dilations_0, groups = input_119_groups_0, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = input_119_strides_0, weight = const_44_to_fp16, x = input_117_cast_fp16)[name = string("out_17_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_121_cast_fp16 = add(x = out_17_cast_fp16, y = input_111_cast_fp16)[name = string("input_121_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_123_cast_fp16 = relu(x = input_121_cast_fp16)[name = string("input_123_cast_fp16")];
+            string input_125_pad_type_0 = const()[name = string("input_125_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_125_pad_0 = const()[name = string("input_125_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_125_strides_0 = const()[name = string("input_125_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_125_dilations_0 = const()[name = string("input_125_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_125_groups_0 = const()[name = string("input_125_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2611968)))];
+            tensor<fp16, [128]> const_47_to_fp16 = const()[name = string("const_47_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2906944)))];
+            tensor<fp16, [?, 128, 20, 250]> input_127_cast_fp16 = conv(bias = const_47_to_fp16, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = const_46_to_fp16, x = input_123_cast_fp16)[name = string("input_127_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_129_cast_fp16 = relu(x = input_127_cast_fp16)[name = string("input_129_cast_fp16")];
+            string input_131_pad_type_0 = const()[name = string("input_131_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_131_strides_0 = const()[name = string("input_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_131_dilations_0 = const()[name = string("input_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_131_groups_0 = const()[name = string("input_131_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2907264)))];
+            tensor<fp16, [128]> const_49_to_fp16 = const()[name = string("const_49_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3202240)))];
+            tensor<fp16, [?, 128, 20, 250]> out_19_cast_fp16 = conv(bias = const_49_to_fp16, dilations = input_131_dilations_0, groups = input_131_groups_0, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = input_131_strides_0, weight = const_48_to_fp16, x = input_129_cast_fp16)[name = string("out_19_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_133_cast_fp16 = add(x = out_19_cast_fp16, y = input_123_cast_fp16)[name = string("input_133_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_135_cast_fp16 = relu(x = input_133_cast_fp16)[name = string("input_135_cast_fp16")];
+            string input_137_pad_type_0 = const()[name = string("input_137_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_137_pad_0 = const()[name = string("input_137_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_137_strides_0 = const()[name = string("input_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_137_dilations_0 = const()[name = string("input_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_137_groups_0 = const()[name = string("input_137_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3202560)))];
+            tensor<fp16, [128]> const_51_to_fp16 = const()[name = string("const_51_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3497536)))];
+            tensor<fp16, [?, 128, 20, 250]> input_139_cast_fp16 = conv(bias = const_51_to_fp16, dilations = input_137_dilations_0, groups = input_137_groups_0, pad = input_137_pad_0, pad_type = input_137_pad_type_0, strides = input_137_strides_0, weight = const_50_to_fp16, x = input_135_cast_fp16)[name = string("input_139_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_141_cast_fp16 = relu(x = input_139_cast_fp16)[name = string("input_141_cast_fp16")];
+            string input_143_pad_type_0 = const()[name = string("input_143_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_143_strides_0 = const()[name = string("input_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_143_dilations_0 = const()[name = string("input_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_143_groups_0 = const()[name = string("input_143_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3497856)))];
+            tensor<fp16, [128]> const_53_to_fp16 = const()[name = string("const_53_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3792832)))];
+            tensor<fp16, [?, 128, 20, 250]> out_21_cast_fp16 = conv(bias = const_53_to_fp16, dilations = input_143_dilations_0, groups = input_143_groups_0, pad = input_143_pad_0, pad_type = input_143_pad_type_0, strides = input_143_strides_0, weight = const_52_to_fp16, x = input_141_cast_fp16)[name = string("out_21_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_145_cast_fp16 = add(x = out_21_cast_fp16, y = input_135_cast_fp16)[name = string("input_145_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_147_cast_fp16 = relu(x = input_145_cast_fp16)[name = string("input_147_cast_fp16")];
+            string input_149_pad_type_0 = const()[name = string("input_149_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_149_pad_0 = const()[name = string("input_149_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_149_strides_0 = const()[name = string("input_149_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_149_dilations_0 = const()[name = string("input_149_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_149_groups_0 = const()[name = string("input_149_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_54_to_fp16 = const()[name = string("const_54_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3793152)))];
+            tensor<fp16, [128]> const_55_to_fp16 = const()[name = string("const_55_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4088128)))];
+            tensor<fp16, [?, 128, 20, 250]> input_151_cast_fp16 = conv(bias = const_55_to_fp16, dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = const_54_to_fp16, x = input_147_cast_fp16)[name = string("input_151_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_153_cast_fp16 = relu(x = input_151_cast_fp16)[name = string("input_153_cast_fp16")];
+            string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_56_to_fp16 = const()[name = string("const_56_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4088448)))];
+            tensor<fp16, [128]> const_57_to_fp16 = const()[name = string("const_57_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4383424)))];
+            tensor<fp16, [?, 128, 20, 250]> out_23_cast_fp16 = conv(bias = const_57_to_fp16, dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = const_56_to_fp16, x = input_153_cast_fp16)[name = string("out_23_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_157_cast_fp16 = add(x = out_23_cast_fp16, y = input_147_cast_fp16)[name = string("input_157_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_159_cast_fp16 = relu(x = input_157_cast_fp16)[name = string("input_159_cast_fp16")];
+            string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_161_strides_0 = const()[name = string("input_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_161_dilations_0 = const()[name = string("input_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_161_groups_0 = const()[name = string("input_161_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_58_to_fp16 = const()[name = string("const_58_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4383744)))];
+            tensor<fp16, [128]> const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4678720)))];
+            tensor<fp16, [?, 128, 20, 250]> input_163_cast_fp16 = conv(bias = const_59_to_fp16, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_58_to_fp16, x = input_159_cast_fp16)[name = string("input_163_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_165_cast_fp16 = relu(x = input_163_cast_fp16)[name = string("input_165_cast_fp16")];
+            string input_167_pad_type_0 = const()[name = string("input_167_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_167_pad_0 = const()[name = string("input_167_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_167_strides_0 = const()[name = string("input_167_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_167_dilations_0 = const()[name = string("input_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_167_groups_0 = const()[name = string("input_167_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_60_to_fp16 = const()[name = string("const_60_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4679040)))];
+            tensor<fp16, [128]> const_61_to_fp16 = const()[name = string("const_61_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4974016)))];
+            tensor<fp16, [?, 128, 20, 250]> out_25_cast_fp16 = conv(bias = const_61_to_fp16, dilations = input_167_dilations_0, groups = input_167_groups_0, pad = input_167_pad_0, pad_type = input_167_pad_type_0, strides = input_167_strides_0, weight = const_60_to_fp16, x = input_165_cast_fp16)[name = string("out_25_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_169_cast_fp16 = add(x = out_25_cast_fp16, y = input_159_cast_fp16)[name = string("input_169_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_171_cast_fp16 = relu(x = input_169_cast_fp16)[name = string("input_171_cast_fp16")];
+            string input_173_pad_type_0 = const()[name = string("input_173_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_173_pad_0 = const()[name = string("input_173_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_173_strides_0 = const()[name = string("input_173_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_173_dilations_0 = const()[name = string("input_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_173_groups_0 = const()[name = string("input_173_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 128, 3, 3]> const_62_to_fp16 = const()[name = string("const_62_to_fp16"), val = tensor<fp16, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4974336)))];
+            tensor<fp16, [256]> const_63_to_fp16 = const()[name = string("const_63_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5564224)))];
+            tensor<fp16, [?, 256, 10, 125]> input_175_cast_fp16 = conv(bias = const_63_to_fp16, dilations = input_173_dilations_0, groups = input_173_groups_0, pad = input_173_pad_0, pad_type = input_173_pad_type_0, strides = input_173_strides_0, weight = const_62_to_fp16, x = input_171_cast_fp16)[name = string("input_175_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_177_cast_fp16 = relu(x = input_175_cast_fp16)[name = string("input_177_cast_fp16")];
+            string input_179_pad_type_0 = const()[name = string("input_179_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_179_pad_0 = const()[name = string("input_179_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_179_strides_0 = const()[name = string("input_179_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_179_dilations_0 = const()[name = string("input_179_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_179_groups_0 = const()[name = string("input_179_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5564800)))];
+            tensor<fp16, [256]> const_65_to_fp16 = const()[name = string("const_65_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6744512)))];
+            tensor<fp16, [?, 256, 10, 125]> out_27_cast_fp16 = conv(bias = const_65_to_fp16, dilations = input_179_dilations_0, groups = input_179_groups_0, pad = input_179_pad_0, pad_type = input_179_pad_type_0, strides = input_179_strides_0, weight = const_64_to_fp16, x = input_177_cast_fp16)[name = string("out_27_cast_fp16")];
+            string input_181_pad_type_0 = const()[name = string("input_181_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_181_strides_0 = const()[name = string("input_181_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_181_dilations_0 = const()[name = string("input_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_181_groups_0 = const()[name = string("input_181_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 128, 1, 1]> const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = tensor<fp16, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6745088)))];
+            tensor<fp16, [256]> const_67_to_fp16 = const()[name = string("const_67_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6810688)))];
+            tensor<fp16, [?, 256, 10, 125]> var_570_cast_fp16 = conv(bias = const_67_to_fp16, dilations = input_181_dilations_0, groups = input_181_groups_0, pad = input_181_pad_0, pad_type = input_181_pad_type_0, strides = input_181_strides_0, weight = const_66_to_fp16, x = input_171_cast_fp16)[name = string("op_570_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_183_cast_fp16 = add(x = out_27_cast_fp16, y = var_570_cast_fp16)[name = string("input_183_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_185_cast_fp16 = relu(x = input_183_cast_fp16)[name = string("input_185_cast_fp16")];
+            string input_187_pad_type_0 = const()[name = string("input_187_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_187_pad_0 = const()[name = string("input_187_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_187_strides_0 = const()[name = string("input_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_187_dilations_0 = const()[name = string("input_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_187_groups_0 = const()[name = string("input_187_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_68_to_fp16 = const()[name = string("const_68_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6811264)))];
+            tensor<fp16, [256]> const_69_to_fp16 = const()[name = string("const_69_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7990976)))];
+            tensor<fp16, [?, 256, 10, 125]> input_189_cast_fp16 = conv(bias = const_69_to_fp16, dilations = input_187_dilations_0, groups = input_187_groups_0, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = input_187_strides_0, weight = const_68_to_fp16, x = input_185_cast_fp16)[name = string("input_189_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_191_cast_fp16 = relu(x = input_189_cast_fp16)[name = string("input_191_cast_fp16")];
+            string input_193_pad_type_0 = const()[name = string("input_193_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_193_pad_0 = const()[name = string("input_193_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_193_strides_0 = const()[name = string("input_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_193_dilations_0 = const()[name = string("input_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_193_groups_0 = const()[name = string("input_193_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7991552)))];
+            tensor<fp16, [256]> const_71_to_fp16 = const()[name = string("const_71_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9171264)))];
+            tensor<fp16, [?, 256, 10, 125]> out_29_cast_fp16 = conv(bias = const_71_to_fp16, dilations = input_193_dilations_0, groups = input_193_groups_0, pad = input_193_pad_0, pad_type = input_193_pad_type_0, strides = input_193_strides_0, weight = const_70_to_fp16, x = input_191_cast_fp16)[name = string("out_29_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_195_cast_fp16 = add(x = out_29_cast_fp16, y = input_185_cast_fp16)[name = string("input_195_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_197_cast_fp16 = relu(x = input_195_cast_fp16)[name = string("input_197_cast_fp16")];
+            string input_199_pad_type_0 = const()[name = string("input_199_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_199_pad_0 = const()[name = string("input_199_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_199_strides_0 = const()[name = string("input_199_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_199_dilations_0 = const()[name = string("input_199_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_199_groups_0 = const()[name = string("input_199_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_72_to_fp16 = const()[name = string("const_72_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9171840)))];
+            tensor<fp16, [256]> const_73_to_fp16 = const()[name = string("const_73_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10351552)))];
+            tensor<fp16, [?, 256, 10, 125]> input_201_cast_fp16 = conv(bias = const_73_to_fp16, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = const_72_to_fp16, x = input_197_cast_fp16)[name = string("input_201_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_203_cast_fp16 = relu(x = input_201_cast_fp16)[name = string("input_203_cast_fp16")];
+            string input_205_pad_type_0 = const()[name = string("input_205_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_205_pad_0 = const()[name = string("input_205_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_205_strides_0 = const()[name = string("input_205_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_205_dilations_0 = const()[name = string("input_205_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_205_groups_0 = const()[name = string("input_205_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_74_to_fp16 = const()[name = string("const_74_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10352128)))];
+            tensor<fp16, [256]> const_75_to_fp16 = const()[name = string("const_75_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11531840)))];
+            tensor<fp16, [?, 256, 10, 125]> out_cast_fp16 = conv(bias = const_75_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_74_to_fp16, x = input_203_cast_fp16)[name = string("out_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_207_cast_fp16 = add(x = out_cast_fp16, y = input_197_cast_fp16)[name = string("input_207_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> frames_cast_fp16 = relu(x = input_207_cast_fp16)[name = string("frames_cast_fp16")];
+            tensor<int32, [3]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp16, [?, 2560, 125]> sequences_cast_fp16 = reshape(shape = concat_0x, x = frames_cast_fp16)[name = string("sequences_cast_fp16")];
+            tensor<int32, [1]> input_209_axes_0 = const()[name = string("input_209_axes_0"), val = tensor<int32, [1]>([1])];
+            string weights_to_fp16_dtype_0 = const()[name = string("weights_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [?, 589]> weights_to_fp16 = cast(dtype = weights_to_fp16_dtype_0, x = weights)[name = string("cast_9")];
+            tensor<fp16, [?, 1, 589]> input_209_cast_fp16 = expand_dims(axes = input_209_axes_0, x = weights_to_fp16)[name = string("input_209_cast_fp16")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [?, 1, 589, 1]> expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = input_209_cast_fp16)[name = string("expand_dims_0_cast_fp16")];
+            fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)];
+            fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)];
+            tensor<fp16, [?, 1, 125, 1]> upsample_nearest_neighbor_0_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0_cast_fp16)[name = string("upsample_nearest_neighbor_0_cast_fp16")];
+            tensor<int32, [1]> weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [?, 1, 125]> weights_cast_fp16 = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0_cast_fp16)[name = string("weights_cast_fp16")];
+            tensor<int32, [1]> weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 1]> weight_sum_cast_fp16 = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights_cast_fp16)[name = string("weight_sum_cast_fp16")];
+            fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(0x0p+0)];
+            tensor<bool, [?, 1]> var_646_cast_fp16 = greater(x = weight_sum_cast_fp16, y = var_69_to_fp16)[name = string("op_646_cast_fp16")];
+            fp16 fill_like_0_value_0_to_fp16 = const()[name = string("fill_like_0_value_0_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [?, 1]> fill_like_0_cast_fp16 = fill_like(ref_tensor = weight_sum_cast_fp16, value = fill_like_0_value_0_to_fp16)[name = string("fill_like_0_cast_fp16")];
+            tensor<fp16, [?, 1]> safe_sum_cast_fp16 = select(a = weight_sum_cast_fp16, b = fill_like_0_cast_fp16, cond = var_646_cast_fp16)[name = string("safe_sum_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> var_649_cast_fp16 = mul(x = sequences_cast_fp16, y = weights_cast_fp16)[name = string("op_649_cast_fp16")];
+            tensor<int32, [1]> var_651_axes_0 = const()[name = string("op_651_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_651_keep_dims_0 = const()[name = string("op_651_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 2560]> var_651_cast_fp16 = reduce_sum(axes = var_651_axes_0, keep_dims = var_651_keep_dims_0, x = var_649_cast_fp16)[name = string("op_651_cast_fp16")];
+            tensor<fp16, [?, 2560]> mean_cast_fp16 = real_div(x = var_651_cast_fp16, y = safe_sum_cast_fp16)[name = string("mean_cast_fp16")];
+            tensor<int32, [1]> var_653_axes_0 = const()[name = string("op_653_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [?, 2560, 1]> var_653_cast_fp16 = expand_dims(axes = var_653_axes_0, x = mean_cast_fp16)[name = string("op_653_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> var_654_cast_fp16 = sub(x = sequences_cast_fp16, y = var_653_cast_fp16)[name = string("op_654_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> dx2_cast_fp16 = mul(x = var_654_cast_fp16, y = var_654_cast_fp16)[name = string("dx2_cast_fp16")];
+            tensor<fp16, [?, 1, 125]> var_656_cast_fp16 = mul(x = weights_cast_fp16, y = weights_cast_fp16)[name = string("op_656_cast_fp16")];
+            tensor<int32, [1]> weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 1]> weight_sq_sum_cast_fp16 = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_656_cast_fp16)[name = string("weight_sq_sum_cast_fp16")];
+            tensor<fp16, [?, 1]> var_659_cast_fp16 = real_div(x = weight_sq_sum_cast_fp16, y = safe_sum_cast_fp16)[name = string("op_659_cast_fp16")];
+            tensor<fp16, [?, 1]> var_660_cast_fp16 = sub(x = safe_sum_cast_fp16, y = var_659_cast_fp16)[name = string("op_660_cast_fp16")];
+            fp16 var_661_to_fp16 = const()[name = string("op_661_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [?, 1]> denom_cast_fp16 = add(x = var_660_cast_fp16, y = var_661_to_fp16)[name = string("denom_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> var_663_cast_fp16 = mul(x = dx2_cast_fp16, y = weights_cast_fp16)[name = string("op_663_cast_fp16")];
+            tensor<int32, [1]> var_665_axes_0 = const()[name = string("op_665_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_665_keep_dims_0 = const()[name = string("op_665_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 2560]> var_665_cast_fp16 = reduce_sum(axes = var_665_axes_0, keep_dims = var_665_keep_dims_0, x = var_663_cast_fp16)[name = string("op_665_cast_fp16")];
+            tensor<fp16, [?, 2560]> var_cast_fp16 = real_div(x = var_665_cast_fp16, y = denom_cast_fp16)[name = string("var_cast_fp16")];
+            fp16 var_68_to_fp16 = const()[name = string("op_68_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [?, 2560]> var_667_cast_fp16 = maximum(x = var_cast_fp16, y = var_68_to_fp16)[name = string("op_667_cast_fp16")];
+            tensor<fp16, [?, 2560]> std_cast_fp16 = sqrt(x = var_667_cast_fp16)[name = string("std_cast_fp16")];
+            bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)];
+            tensor<fp16, [?, 5120]> stats_cast_fp16 = concat(axis = var_67, interleave = stats_interleave_0, values = (mean_cast_fp16, std_cast_fp16))[name = string("stats_cast_fp16")];
+            tensor<fp16, [?, 2560]> sub_0_cast_fp16 = sub(x = mean_cast_fp16, y = mean_cast_fp16)[name = string("sub_0_cast_fp16")];
+            fp16 var_672_value_0_to_fp16 = const()[name = string("op_672_value_0_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [?, 2560]> var_672_cast_fp16 = fill_like(ref_tensor = std_cast_fp16, value = var_672_value_0_to_fp16)[name = string("op_672_cast_fp16")];
+            bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)];
+            tensor<fp16, [?, 5120]> zero_stats_cast_fp16 = concat(axis = var_67, interleave = zero_stats_interleave_0, values = (sub_0_cast_fp16, var_672_cast_fp16))[name = string("zero_stats_cast_fp16")];
+            tensor<bool, [?, 1]> var_675_cast_fp16 = less_equal(x = weight_sum_cast_fp16, y = var_69_to_fp16)[name = string("op_675_cast_fp16")];
+            tensor<int32, [2]> var_677 = const()[name = string("op_677"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [?, 5120]> zero_mask = tile(reps = var_677, x = var_675_cast_fp16)[name = string("zero_mask")];
+            tensor<fp16, [?, 5120]> input_cast_fp16 = select(a = zero_stats_cast_fp16, b = stats_cast_fp16, cond = zero_mask)[name = string("input_cast_fp16")];
+            tensor<fp16, [256, 5120]> tail_resnet_seg_1_weight_to_fp16 = const()[name = string("tail_resnet_seg_1_weight_to_fp16"), val = tensor<fp16, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11532416)))];
+            tensor<fp16, [256]> tail_resnet_seg_1_bias_to_fp16 = const()[name = string("tail_resnet_seg_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14153920)))];
+            tensor<fp16, [?, 256]> linear_0_cast_fp16 = linear(bias = tail_resnet_seg_1_bias_to_fp16, weight = tail_resnet_seg_1_weight_to_fp16, x = input_cast_fp16)[name = string("linear_0_cast_fp16")];
+            string linear_0_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_0_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [?, 256]> output = cast(dtype = linear_0_cast_fp16_to_fp32_dtype_0, x = linear_0_cast_fp16)[name = string("cast_8")];
+        } -> (output);
+}

wespeaker-voxceleb-resnet34-fused-b3-f16.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3f2a1aba33878f0388d8dc3ab7259af1978482145fb4931cd93876a3d875eef
+size 14154496

wespeaker-voxceleb-resnet34-fused-b3.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab9e15f213ba81809fd0abd6dd6d4c6b569dfa891f2dda282567819a732f301b
+size 243

wespeaker-voxceleb-resnet34-fused-b3.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f279b672793ba4f1d246f69442be874b49fc275f6c9b08f6ab85b57dc0bbe68
+size 225

wespeaker-voxceleb-resnet34-fused-b3.mlmodelc/model.mil

@@ -0,0 +1,462 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [?, 1, 160000]> waveform, tensor<fp32, [?, 589]> weights) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"waveform", [32, 1, 160000]}, {"weights", [32, 589]}}), ("EnumeratedShapes", {{"3c334fba", {{"waveform", [3, 1, 160000]}, {"weights", [3, 589]}}}, {"79c53add", {{"waveform", [1, 1, 160000]}, {"weights", [1, 589]}}}, {"cb01bf12", {{"waveform", [32, 1, 160000]}, {"weights", [32, 589]}}}})))] {
+            tensor<fp32, [257, 512]> fbank_dft_sin = const()[name = string("fbank_dft_sin"), val = tensor<fp32, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [257, 512]> fbank_dft_cos = const()[name = string("fbank_dft_cos"), val = tensor<fp32, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526464)))];
+            tensor<fp32, [400, 1, 400]> fbank_identity_kernel = const()[name = string("fbank_identity_kernel"), val = tensor<fp32, [400, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1052864)))];
+            tensor<fp32, [256]> tail_resnet_seg_1_bias = const()[name = string("tail_resnet_seg_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1692928)))];
+            tensor<fp32, [256, 5120]> tail_resnet_seg_1_weight = const()[name = string("tail_resnet_seg_1_weight"), val = tensor<fp32, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1694016)))];
+            fp32 var_7 = const()[name = string("op_7"), val = fp32(0x1p+1)];
+            tensor<int32, [3]> var_27_begin_0 = const()[name = string("op_27_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> var_27_end_0 = const()[name = string("op_27_end_0"), val = tensor<int32, [3]>([0, 1, 160000])];
+            tensor<bool, [3]> var_27_end_mask_0 = const()[name = string("op_27_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            tensor<fp32, [?, 1, 160000]> var_27 = slice_by_index(begin = var_27_begin_0, end = var_27_end_0, end_mask = var_27_end_mask_0, x = waveform)[name = string("op_27")];
+            fp32 var_29 = const()[name = string("op_29"), val = fp32(0x1p+15)];
+            tensor<fp32, [?, 1, 160000]> signal = mul(x = var_27, y = var_29)[name = string("signal")];
+            string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [2]> frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)];
+            tensor<fp32, [?, 400, 998]> frames_1 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = fbank_identity_kernel, x = signal)[name = string("frames_1")];
+            tensor<int32, [3]> var_36 = const()[name = string("op_36"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_39_axes_0 = const()[name = string("op_39_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_39_keep_dims_0 = const()[name = string("op_39_keep_dims_0"), val = bool(true)];
+            tensor<fp32, [?, 998, 400]> frames_3 = transpose(perm = var_36, x = frames_1)[name = string("transpose_4")];
+            tensor<fp32, [?, 998, 1]> var_39 = reduce_mean(axes = var_39_axes_0, keep_dims = var_39_keep_dims_0, x = frames_3)[name = string("op_39")];
+            tensor<fp32, [?, 998, 400]> input_1 = sub(x = frames_3, y = var_39)[name = string("input_1")];
+            fp32 const_0 = const()[name = string("const_0"), val = fp32(0x0p+0)];
+            tensor<int32, [6]> var_42_pad_0 = const()[name = string("op_42_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])];
+            string var_42_mode_0 = const()[name = string("op_42_mode_0"), val = string("replicate")];
+            tensor<fp32, [?, 998, 401]> var_42 = pad(constant_val = const_0, mode = var_42_mode_0, pad = var_42_pad_0, x = input_1)[name = string("op_42")];
+            tensor<int32, [3]> previous_begin_0 = const()[name = string("previous_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> previous_end_0 = const()[name = string("previous_end_0"), val = tensor<int32, [3]>([0, 998, 400])];
+            tensor<bool, [3]> previous_end_mask_0 = const()[name = string("previous_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<fp32, [?, 998, 400]> previous = slice_by_index(begin = previous_begin_0, end = previous_end_0, end_mask = previous_end_mask_0, x = var_42)[name = string("previous")];
+            fp32 var_44 = const()[name = string("op_44"), val = fp32(0x1.f0a3d8p-1)];
+            tensor<fp32, [?, 998, 400]> var_45 = mul(x = previous, y = var_44)[name = string("op_45")];
+            tensor<fp32, [?, 998, 400]> frames_5 = sub(x = input_1, y = var_45)[name = string("frames_5")];
+            tensor<fp32, [1, 1, 400]> var_48 = const()[name = string("op_48"), val = tensor<fp32, [1, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6936960)))];
+            tensor<fp32, [?, 998, 400]> input_3 = mul(x = frames_5, y = var_48)[name = string("input_3")];
+            fp32 const_1 = const()[name = string("const_1"), val = fp32(0x0p+0)];
+            tensor<int32, [6]> frames_7_pad_0 = const()[name = string("frames_7_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 112])];
+            string frames_7_mode_0 = const()[name = string("frames_7_mode_0"), val = string("constant")];
+            tensor<fp32, [?, 998, 512]> frames_7 = pad(constant_val = const_1, mode = frames_7_mode_0, pad = frames_7_pad_0, x = input_3)[name = string("frames_7")];
+            tensor<fp32, [257]> real_part_bias_0 = const()[name = string("real_part_bias_0"), val = tensor<fp32, [257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6938624)))];
+            tensor<fp32, [?, 998, 257]> real_part = linear(bias = real_part_bias_0, weight = fbank_dft_cos, x = frames_7)[name = string("real_part")];
+            tensor<fp32, [?, 998, 257]> imag_part = linear(bias = real_part_bias_0, weight = fbank_dft_sin, x = frames_7)[name = string("imag_part")];
+            tensor<fp32, [?, 998, 257]> var_56 = pow(x = real_part, y = var_7)[name = string("op_56")];
+            tensor<fp32, [?, 998, 257]> var_57 = pow(x = imag_part, y = var_7)[name = string("op_57")];
+            tensor<fp32, [?, 998, 257]> spectrum = add(x = var_56, y = var_57)[name = string("spectrum")];
+            tensor<fp32, [80, 257]> transpose_2 = const()[name = string("transpose_2"), val = tensor<fp32, [80, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6939776)))];
+            tensor<fp32, [80]> mel_1_bias_0 = const()[name = string("mel_1_bias_0"), val = tensor<fp32, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7022080)))];
+            tensor<fp32, [?, 998, 80]> mel_1 = linear(bias = mel_1_bias_0, weight = transpose_2, x = spectrum)[name = string("mel_1")];
+            fp32 const_3 = const()[name = string("const_3"), val = fp32(0x1p-23)];
+            tensor<fp32, [?, 998, 80]> var_62 = maximum(x = mel_1, y = const_3)[name = string("op_62")];
+            fp32 mel_3_epsilon_0 = const()[name = string("mel_3_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp32, [?, 998, 80]> mel_3 = log(epsilon = mel_3_epsilon_0, x = var_62)[name = string("mel_3")];
+            tensor<int32, [1]> var_65_axes_0 = const()[name = string("op_65_axes_0"), val = tensor<int32, [1]>([1])];
+            bool var_65_keep_dims_0 = const()[name = string("op_65_keep_dims_0"), val = bool(true)];
+            tensor<fp32, [?, 1, 80]> var_65 = reduce_mean(axes = var_65_axes_0, keep_dims = var_65_keep_dims_0, x = mel_3)[name = string("op_65")];
+            tensor<fp32, [?, 998, 80]> fbank_1 = sub(x = mel_3, y = var_65)[name = string("fbank_1")];
+            int32 var_67 = const()[name = string("op_67"), val = int32(-1)];
+            fp32 var_68 = const()[name = string("op_68"), val = fp32(0x1.b7cdfep-34)];
+            fp32 var_69 = const()[name = string("op_69"), val = fp32(0x0p+0)];
+            tensor<int32, [3]> var_94 = const()[name = string("op_94"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 80, 998]> fbank_3 = transpose(perm = var_94, x = fbank_1)[name = string("transpose_3")];
+            tensor<fp32, [?, 1, 80, 998]> input_5 = expand_dims(axes = input_5_axes_0, x = fbank_3)[name = string("input_5")];
+            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 1, 3, 3]> const_4 = const()[name = string("const_4"), val = tensor<fp32, [32, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7022464)))];
+            tensor<fp32, [32]> const_5 = const()[name = string("const_5"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7023680)))];
+            tensor<fp32, [?, 32, 80, 998]> input_9 = conv(bias = const_5, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = const_4, x = input_5)[name = string("input_9")];
+            tensor<fp32, [?, 32, 80, 998]> input_11 = relu(x = input_9)[name = string("input_11")];
+            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_6 = const()[name = string("const_6"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7023872)))];
+            tensor<fp32, [32]> const_7 = const()[name = string("const_7"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7060800)))];
+            tensor<fp32, [?, 32, 80, 998]> input_15 = conv(bias = const_7, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = const_6, x = input_11)[name = string("input_15")];
+            tensor<fp32, [?, 32, 80, 998]> input_17 = relu(x = input_15)[name = string("input_17")];
+            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_19_strides_0 = const()[name = string("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_19_dilations_0 = const()[name = string("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_19_groups_0 = const()[name = string("input_19_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_8 = const()[name = string("const_8"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7060992)))];
+            tensor<fp32, [32]> const_9 = const()[name = string("const_9"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7097920)))];
+            tensor<fp32, [?, 32, 80, 998]> out_1 = conv(bias = const_9, dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = const_8, x = input_17)[name = string("out_1")];
+            tensor<fp32, [?, 32, 80, 998]> input_21 = add(x = out_1, y = input_11)[name = string("input_21")];
+            tensor<fp32, [?, 32, 80, 998]> input_23 = relu(x = input_21)[name = string("input_23")];
+            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_10 = const()[name = string("const_10"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7098112)))];
+            tensor<fp32, [32]> const_11 = const()[name = string("const_11"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7135040)))];
+            tensor<fp32, [?, 32, 80, 998]> input_27 = conv(bias = const_11, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = const_10, x = input_23)[name = string("input_27")];
+            tensor<fp32, [?, 32, 80, 998]> input_29 = relu(x = input_27)[name = string("input_29")];
+            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_12 = const()[name = string("const_12"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7135232)))];
+            tensor<fp32, [32]> const_13 = const()[name = string("const_13"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7172160)))];
+            tensor<fp32, [?, 32, 80, 998]> out_3 = conv(bias = const_13, dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = const_12, x = input_29)[name = string("out_3")];
+            tensor<fp32, [?, 32, 80, 998]> input_33 = add(x = out_3, y = input_23)[name = string("input_33")];
+            tensor<fp32, [?, 32, 80, 998]> input_35 = relu(x = input_33)[name = string("input_35")];
+            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_14 = const()[name = string("const_14"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7172352)))];
+            tensor<fp32, [32]> const_15 = const()[name = string("const_15"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7209280)))];
+            tensor<fp32, [?, 32, 80, 998]> input_39 = conv(bias = const_15, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = const_14, x = input_35)[name = string("input_39")];
+            tensor<fp32, [?, 32, 80, 998]> input_41 = relu(x = input_39)[name = string("input_41")];
+            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_16 = const()[name = string("const_16"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7209472)))];
+            tensor<fp32, [32]> const_17 = const()[name = string("const_17"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7246400)))];
+            tensor<fp32, [?, 32, 80, 998]> out_5 = conv(bias = const_17, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = const_16, x = input_41)[name = string("out_5")];
+            tensor<fp32, [?, 32, 80, 998]> input_45 = add(x = out_5, y = input_35)[name = string("input_45")];
+            tensor<fp32, [?, 32, 80, 998]> input_47 = relu(x = input_45)[name = string("input_47")];
+            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 3, 3]> const_18 = const()[name = string("const_18"), val = tensor<fp32, [64, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7246592)))];
+            tensor<fp32, [64]> const_19 = const()[name = string("const_19"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7320384)))];
+            tensor<fp32, [?, 64, 40, 499]> input_51 = conv(bias = const_19, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_18, x = input_47)[name = string("input_51")];
+            tensor<fp32, [?, 64, 40, 499]> input_53 = relu(x = input_51)[name = string("input_53")];
+            string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_55_strides_0 = const()[name = string("input_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_55_dilations_0 = const()[name = string("input_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_55_groups_0 = const()[name = string("input_55_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_20 = const()[name = string("const_20"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7320704)))];
+            tensor<fp32, [64]> const_21 = const()[name = string("const_21"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7468224)))];
+            tensor<fp32, [?, 64, 40, 499]> out_7 = conv(bias = const_21, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = const_20, x = input_53)[name = string("out_7")];
+            string input_57_pad_type_0 = const()[name = string("input_57_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_57_strides_0 = const()[name = string("input_57_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_57_dilations_0 = const()[name = string("input_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_57_groups_0 = const()[name = string("input_57_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 1, 1]> const_22 = const()[name = string("const_22"), val = tensor<fp32, [64, 32, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7468544)))];
+            tensor<fp32, [64]> const_23 = const()[name = string("const_23"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7476800)))];
+            tensor<fp32, [?, 64, 40, 499]> var_243 = conv(bias = const_23, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_22, x = input_47)[name = string("op_243")];
+            tensor<fp32, [?, 64, 40, 499]> input_59 = add(x = out_7, y = var_243)[name = string("input_59")];
+            tensor<fp32, [?, 64, 40, 499]> input_61 = relu(x = input_59)[name = string("input_61")];
+            string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_24 = const()[name = string("const_24"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7477120)))];
+            tensor<fp32, [64]> const_25 = const()[name = string("const_25"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7624640)))];
+            tensor<fp32, [?, 64, 40, 499]> input_65 = conv(bias = const_25, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = const_24, x = input_61)[name = string("input_65")];
+            tensor<fp32, [?, 64, 40, 499]> input_67 = relu(x = input_65)[name = string("input_67")];
+            string input_69_pad_type_0 = const()[name = string("input_69_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_69_pad_0 = const()[name = string("input_69_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_69_strides_0 = const()[name = string("input_69_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_69_dilations_0 = const()[name = string("input_69_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_69_groups_0 = const()[name = string("input_69_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_26 = const()[name = string("const_26"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7624960)))];
+            tensor<fp32, [64]> const_27 = const()[name = string("const_27"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7772480)))];
+            tensor<fp32, [?, 64, 40, 499]> out_9 = conv(bias = const_27, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = const_26, x = input_67)[name = string("out_9")];
+            tensor<fp32, [?, 64, 40, 499]> input_71 = add(x = out_9, y = input_61)[name = string("input_71")];
+            tensor<fp32, [?, 64, 40, 499]> input_73 = relu(x = input_71)[name = string("input_73")];
+            string input_75_pad_type_0 = const()[name = string("input_75_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_75_pad_0 = const()[name = string("input_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_75_strides_0 = const()[name = string("input_75_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_75_dilations_0 = const()[name = string("input_75_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_75_groups_0 = const()[name = string("input_75_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_28 = const()[name = string("const_28"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7772800)))];
+            tensor<fp32, [64]> const_29 = const()[name = string("const_29"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7920320)))];
+            tensor<fp32, [?, 64, 40, 499]> input_77 = conv(bias = const_29, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = const_28, x = input_73)[name = string("input_77")];
+            tensor<fp32, [?, 64, 40, 499]> input_79 = relu(x = input_77)[name = string("input_79")];
+            string input_81_pad_type_0 = const()[name = string("input_81_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_81_pad_0 = const()[name = string("input_81_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_81_strides_0 = const()[name = string("input_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_81_dilations_0 = const()[name = string("input_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_81_groups_0 = const()[name = string("input_81_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_30 = const()[name = string("const_30"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7920640)))];
+            tensor<fp32, [64]> const_31 = const()[name = string("const_31"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8068160)))];
+            tensor<fp32, [?, 64, 40, 499]> out_11 = conv(bias = const_31, dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = const_30, x = input_79)[name = string("out_11")];
+            tensor<fp32, [?, 64, 40, 499]> input_83 = add(x = out_11, y = input_73)[name = string("input_83")];
+            tensor<fp32, [?, 64, 40, 499]> input_85 = relu(x = input_83)[name = string("input_85")];
+            string input_87_pad_type_0 = const()[name = string("input_87_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_87_pad_0 = const()[name = string("input_87_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_87_strides_0 = const()[name = string("input_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_87_dilations_0 = const()[name = string("input_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_87_groups_0 = const()[name = string("input_87_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_32 = const()[name = string("const_32"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8068480)))];
+            tensor<fp32, [64]> const_33 = const()[name = string("const_33"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8216000)))];
+            tensor<fp32, [?, 64, 40, 499]> input_89 = conv(bias = const_33, dilations = input_87_dilations_0, groups = input_87_groups_0, pad = input_87_pad_0, pad_type = input_87_pad_type_0, strides = input_87_strides_0, weight = const_32, x = input_85)[name = string("input_89")];
+            tensor<fp32, [?, 64, 40, 499]> input_91 = relu(x = input_89)[name = string("input_91")];
+            string input_93_pad_type_0 = const()[name = string("input_93_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_93_pad_0 = const()[name = string("input_93_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_93_strides_0 = const()[name = string("input_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_93_dilations_0 = const()[name = string("input_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_93_groups_0 = const()[name = string("input_93_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_34 = const()[name = string("const_34"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8216320)))];
+            tensor<fp32, [64]> const_35 = const()[name = string("const_35"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8363840)))];
+            tensor<fp32, [?, 64, 40, 499]> out_13 = conv(bias = const_35, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = const_34, x = input_91)[name = string("out_13")];
+            tensor<fp32, [?, 64, 40, 499]> input_95 = add(x = out_13, y = input_85)[name = string("input_95")];
+            tensor<fp32, [?, 64, 40, 499]> input_97 = relu(x = input_95)[name = string("input_97")];
+            string input_99_pad_type_0 = const()[name = string("input_99_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_99_pad_0 = const()[name = string("input_99_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_99_strides_0 = const()[name = string("input_99_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_99_dilations_0 = const()[name = string("input_99_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_99_groups_0 = const()[name = string("input_99_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 3, 3]> const_36 = const()[name = string("const_36"), val = tensor<fp32, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8364160)))];
+            tensor<fp32, [128]> const_37 = const()[name = string("const_37"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8659136)))];
+            tensor<fp32, [?, 128, 20, 250]> input_101 = conv(bias = const_37, dilations = input_99_dilations_0, groups = input_99_groups_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = const_36, x = input_97)[name = string("input_101")];
+            tensor<fp32, [?, 128, 20, 250]> input_103 = relu(x = input_101)[name = string("input_103")];
+            string input_105_pad_type_0 = const()[name = string("input_105_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_105_pad_0 = const()[name = string("input_105_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_105_strides_0 = const()[name = string("input_105_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_105_dilations_0 = const()[name = string("input_105_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_105_groups_0 = const()[name = string("input_105_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_38 = const()[name = string("const_38"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8659712)))];
+            tensor<fp32, [128]> const_39 = const()[name = string("const_39"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9249600)))];
+            tensor<fp32, [?, 128, 20, 250]> out_15 = conv(bias = const_39, dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = const_38, x = input_103)[name = string("out_15")];
+            string input_107_pad_type_0 = const()[name = string("input_107_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_107_strides_0 = const()[name = string("input_107_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_107_dilations_0 = const()[name = string("input_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_107_groups_0 = const()[name = string("input_107_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 1, 1]> const_40 = const()[name = string("const_40"), val = tensor<fp32, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9250176)))];
+            tensor<fp32, [128]> const_41 = const()[name = string("const_41"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9283008)))];
+            tensor<fp32, [?, 128, 20, 250]> var_379 = conv(bias = const_41, dilations = input_107_dilations_0, groups = input_107_groups_0, pad = input_107_pad_0, pad_type = input_107_pad_type_0, strides = input_107_strides_0, weight = const_40, x = input_97)[name = string("op_379")];
+            tensor<fp32, [?, 128, 20, 250]> input_109 = add(x = out_15, y = var_379)[name = string("input_109")];
+            tensor<fp32, [?, 128, 20, 250]> input_111 = relu(x = input_109)[name = string("input_111")];
+            string input_113_pad_type_0 = const()[name = string("input_113_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_113_pad_0 = const()[name = string("input_113_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_113_strides_0 = const()[name = string("input_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_113_dilations_0 = const()[name = string("input_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_113_groups_0 = const()[name = string("input_113_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_42 = const()[name = string("const_42"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9283584)))];
+            tensor<fp32, [128]> const_43 = const()[name = string("const_43"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9873472)))];
+            tensor<fp32, [?, 128, 20, 250]> input_115 = conv(bias = const_43, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = const_42, x = input_111)[name = string("input_115")];
+            tensor<fp32, [?, 128, 20, 250]> input_117 = relu(x = input_115)[name = string("input_117")];
+            string input_119_pad_type_0 = const()[name = string("input_119_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_119_strides_0 = const()[name = string("input_119_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_119_dilations_0 = const()[name = string("input_119_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_119_groups_0 = const()[name = string("input_119_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_44 = const()[name = string("const_44"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9874048)))];
+            tensor<fp32, [128]> const_45 = const()[name = string("const_45"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10463936)))];
+            tensor<fp32, [?, 128, 20, 250]> out_17 = conv(bias = const_45, dilations = input_119_dilations_0, groups = input_119_groups_0, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = input_119_strides_0, weight = const_44, x = input_117)[name = string("out_17")];
+            tensor<fp32, [?, 128, 20, 250]> input_121 = add(x = out_17, y = input_111)[name = string("input_121")];
+            tensor<fp32, [?, 128, 20, 250]> input_123 = relu(x = input_121)[name = string("input_123")];
+            string input_125_pad_type_0 = const()[name = string("input_125_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_125_pad_0 = const()[name = string("input_125_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_125_strides_0 = const()[name = string("input_125_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_125_dilations_0 = const()[name = string("input_125_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_125_groups_0 = const()[name = string("input_125_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_46 = const()[name = string("const_46"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10464512)))];
+            tensor<fp32, [128]> const_47 = const()[name = string("const_47"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11054400)))];
+            tensor<fp32, [?, 128, 20, 250]> input_127 = conv(bias = const_47, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = const_46, x = input_123)[name = string("input_127")];
+            tensor<fp32, [?, 128, 20, 250]> input_129 = relu(x = input_127)[name = string("input_129")];
+            string input_131_pad_type_0 = const()[name = string("input_131_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_131_strides_0 = const()[name = string("input_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_131_dilations_0 = const()[name = string("input_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_131_groups_0 = const()[name = string("input_131_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_48 = const()[name = string("const_48"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11054976)))];
+            tensor<fp32, [128]> const_49 = const()[name = string("const_49"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11644864)))];
+            tensor<fp32, [?, 128, 20, 250]> out_19 = conv(bias = const_49, dilations = input_131_dilations_0, groups = input_131_groups_0, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = input_131_strides_0, weight = const_48, x = input_129)[name = string("out_19")];
+            tensor<fp32, [?, 128, 20, 250]> input_133 = add(x = out_19, y = input_123)[name = string("input_133")];
+            tensor<fp32, [?, 128, 20, 250]> input_135 = relu(x = input_133)[name = string("input_135")];
+            string input_137_pad_type_0 = const()[name = string("input_137_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_137_pad_0 = const()[name = string("input_137_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_137_strides_0 = const()[name = string("input_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_137_dilations_0 = const()[name = string("input_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_137_groups_0 = const()[name = string("input_137_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_50 = const()[name = string("const_50"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11645440)))];
+            tensor<fp32, [128]> const_51 = const()[name = string("const_51"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12235328)))];
+            tensor<fp32, [?, 128, 20, 250]> input_139 = conv(bias = const_51, dilations = input_137_dilations_0, groups = input_137_groups_0, pad = input_137_pad_0, pad_type = input_137_pad_type_0, strides = input_137_strides_0, weight = const_50, x = input_135)[name = string("input_139")];
+            tensor<fp32, [?, 128, 20, 250]> input_141 = relu(x = input_139)[name = string("input_141")];
+            string input_143_pad_type_0 = const()[name = string("input_143_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_143_strides_0 = const()[name = string("input_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_143_dilations_0 = const()[name = string("input_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_143_groups_0 = const()[name = string("input_143_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_52 = const()[name = string("const_52"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12235904)))];
+            tensor<fp32, [128]> const_53 = const()[name = string("const_53"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12825792)))];
+            tensor<fp32, [?, 128, 20, 250]> out_21 = conv(bias = const_53, dilations = input_143_dilations_0, groups = input_143_groups_0, pad = input_143_pad_0, pad_type = input_143_pad_type_0, strides = input_143_strides_0, weight = const_52, x = input_141)[name = string("out_21")];
+            tensor<fp32, [?, 128, 20, 250]> input_145 = add(x = out_21, y = input_135)[name = string("input_145")];
+            tensor<fp32, [?, 128, 20, 250]> input_147 = relu(x = input_145)[name = string("input_147")];
+            string input_149_pad_type_0 = const()[name = string("input_149_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_149_pad_0 = const()[name = string("input_149_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_149_strides_0 = const()[name = string("input_149_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_149_dilations_0 = const()[name = string("input_149_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_149_groups_0 = const()[name = string("input_149_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_54 = const()[name = string("const_54"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12826368)))];
+            tensor<fp32, [128]> const_55 = const()[name = string("const_55"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13416256)))];
+            tensor<fp32, [?, 128, 20, 250]> input_151 = conv(bias = const_55, dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = const_54, x = input_147)[name = string("input_151")];
+            tensor<fp32, [?, 128, 20, 250]> input_153 = relu(x = input_151)[name = string("input_153")];
+            string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_56 = const()[name = string("const_56"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13416832)))];
+            tensor<fp32, [128]> const_57 = const()[name = string("const_57"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14006720)))];
+            tensor<fp32, [?, 128, 20, 250]> out_23 = conv(bias = const_57, dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = const_56, x = input_153)[name = string("out_23")];
+            tensor<fp32, [?, 128, 20, 250]> input_157 = add(x = out_23, y = input_147)[name = string("input_157")];
+            tensor<fp32, [?, 128, 20, 250]> input_159 = relu(x = input_157)[name = string("input_159")];
+            string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_161_strides_0 = const()[name = string("input_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_161_dilations_0 = const()[name = string("input_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_161_groups_0 = const()[name = string("input_161_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_58 = const()[name = string("const_58"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14007296)))];
+            tensor<fp32, [128]> const_59 = const()[name = string("const_59"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14597184)))];
+            tensor<fp32, [?, 128, 20, 250]> input_163 = conv(bias = const_59, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_58, x = input_159)[name = string("input_163")];
+            tensor<fp32, [?, 128, 20, 250]> input_165 = relu(x = input_163)[name = string("input_165")];
+            string input_167_pad_type_0 = const()[name = string("input_167_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_167_pad_0 = const()[name = string("input_167_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_167_strides_0 = const()[name = string("input_167_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_167_dilations_0 = const()[name = string("input_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_167_groups_0 = const()[name = string("input_167_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_60 = const()[name = string("const_60"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14597760)))];
+            tensor<fp32, [128]> const_61 = const()[name = string("const_61"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15187648)))];
+            tensor<fp32, [?, 128, 20, 250]> out_25 = conv(bias = const_61, dilations = input_167_dilations_0, groups = input_167_groups_0, pad = input_167_pad_0, pad_type = input_167_pad_type_0, strides = input_167_strides_0, weight = const_60, x = input_165)[name = string("out_25")];
+            tensor<fp32, [?, 128, 20, 250]> input_169 = add(x = out_25, y = input_159)[name = string("input_169")];
+            tensor<fp32, [?, 128, 20, 250]> input_171 = relu(x = input_169)[name = string("input_171")];
+            string input_173_pad_type_0 = const()[name = string("input_173_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_173_pad_0 = const()[name = string("input_173_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_173_strides_0 = const()[name = string("input_173_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_173_dilations_0 = const()[name = string("input_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_173_groups_0 = const()[name = string("input_173_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 3, 3]> const_62 = const()[name = string("const_62"), val = tensor<fp32, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15188224)))];
+            tensor<fp32, [256]> const_63 = const()[name = string("const_63"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16367936)))];
+            tensor<fp32, [?, 256, 10, 125]> input_175 = conv(bias = const_63, dilations = input_173_dilations_0, groups = input_173_groups_0, pad = input_173_pad_0, pad_type = input_173_pad_type_0, strides = input_173_strides_0, weight = const_62, x = input_171)[name = string("input_175")];
+            tensor<fp32, [?, 256, 10, 125]> input_177 = relu(x = input_175)[name = string("input_177")];
+            string input_179_pad_type_0 = const()[name = string("input_179_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_179_pad_0 = const()[name = string("input_179_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_179_strides_0 = const()[name = string("input_179_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_179_dilations_0 = const()[name = string("input_179_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_179_groups_0 = const()[name = string("input_179_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_64 = const()[name = string("const_64"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16369024)))];
+            tensor<fp32, [256]> const_65 = const()[name = string("const_65"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18728384)))];
+            tensor<fp32, [?, 256, 10, 125]> out_27 = conv(bias = const_65, dilations = input_179_dilations_0, groups = input_179_groups_0, pad = input_179_pad_0, pad_type = input_179_pad_type_0, strides = input_179_strides_0, weight = const_64, x = input_177)[name = string("out_27")];
+            string input_181_pad_type_0 = const()[name = string("input_181_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_181_strides_0 = const()[name = string("input_181_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_181_dilations_0 = const()[name = string("input_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_181_groups_0 = const()[name = string("input_181_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 1, 1]> const_66 = const()[name = string("const_66"), val = tensor<fp32, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18729472)))];
+            tensor<fp32, [256]> const_67 = const()[name = string("const_67"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18860608)))];
+            tensor<fp32, [?, 256, 10, 125]> var_570 = conv(bias = const_67, dilations = input_181_dilations_0, groups = input_181_groups_0, pad = input_181_pad_0, pad_type = input_181_pad_type_0, strides = input_181_strides_0, weight = const_66, x = input_171)[name = string("op_570")];
+            tensor<fp32, [?, 256, 10, 125]> input_183 = add(x = out_27, y = var_570)[name = string("input_183")];
+            tensor<fp32, [?, 256, 10, 125]> input_185 = relu(x = input_183)[name = string("input_185")];
+            string input_187_pad_type_0 = const()[name = string("input_187_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_187_pad_0 = const()[name = string("input_187_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_187_strides_0 = const()[name = string("input_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_187_dilations_0 = const()[name = string("input_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_187_groups_0 = const()[name = string("input_187_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_68 = const()[name = string("const_68"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18861696)))];
+            tensor<fp32, [256]> const_69 = const()[name = string("const_69"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21221056)))];
+            tensor<fp32, [?, 256, 10, 125]> input_189 = conv(bias = const_69, dilations = input_187_dilations_0, groups = input_187_groups_0, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = input_187_strides_0, weight = const_68, x = input_185)[name = string("input_189")];
+            tensor<fp32, [?, 256, 10, 125]> input_191 = relu(x = input_189)[name = string("input_191")];
+            string input_193_pad_type_0 = const()[name = string("input_193_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_193_pad_0 = const()[name = string("input_193_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_193_strides_0 = const()[name = string("input_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_193_dilations_0 = const()[name = string("input_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_193_groups_0 = const()[name = string("input_193_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_70 = const()[name = string("const_70"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21222144)))];
+            tensor<fp32, [256]> const_71 = const()[name = string("const_71"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23581504)))];
+            tensor<fp32, [?, 256, 10, 125]> out_29 = conv(bias = const_71, dilations = input_193_dilations_0, groups = input_193_groups_0, pad = input_193_pad_0, pad_type = input_193_pad_type_0, strides = input_193_strides_0, weight = const_70, x = input_191)[name = string("out_29")];
+            tensor<fp32, [?, 256, 10, 125]> input_195 = add(x = out_29, y = input_185)[name = string("input_195")];
+            tensor<fp32, [?, 256, 10, 125]> input_197 = relu(x = input_195)[name = string("input_197")];
+            string input_199_pad_type_0 = const()[name = string("input_199_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_199_pad_0 = const()[name = string("input_199_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_199_strides_0 = const()[name = string("input_199_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_199_dilations_0 = const()[name = string("input_199_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_199_groups_0 = const()[name = string("input_199_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_72 = const()[name = string("const_72"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23582592)))];
+            tensor<fp32, [256]> const_73 = const()[name = string("const_73"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25941952)))];
+            tensor<fp32, [?, 256, 10, 125]> input_201 = conv(bias = const_73, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = const_72, x = input_197)[name = string("input_201")];
+            tensor<fp32, [?, 256, 10, 125]> input_203 = relu(x = input_201)[name = string("input_203")];
+            string input_205_pad_type_0 = const()[name = string("input_205_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_205_pad_0 = const()[name = string("input_205_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_205_strides_0 = const()[name = string("input_205_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_205_dilations_0 = const()[name = string("input_205_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_205_groups_0 = const()[name = string("input_205_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_74 = const()[name = string("const_74"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25943040)))];
+            tensor<fp32, [256]> const_75 = const()[name = string("const_75"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28302400)))];
+            tensor<fp32, [?, 256, 10, 125]> out = conv(bias = const_75, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_74, x = input_203)[name = string("out")];
+            tensor<fp32, [?, 256, 10, 125]> input_207 = add(x = out, y = input_197)[name = string("input_207")];
+            tensor<fp32, [?, 256, 10, 125]> frames = relu(x = input_207)[name = string("frames")];
+            tensor<int32, [3]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp32, [?, 2560, 125]> sequences = reshape(shape = concat_0x, x = frames)[name = string("sequences")];
+            tensor<int32, [1]> input_209_axes_0 = const()[name = string("input_209_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 1, 589]> input_209 = expand_dims(axes = input_209_axes_0, x = weights)[name = string("input_209")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [?, 1, 589, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input_209)[name = string("expand_dims_0")];
+            fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)];
+            fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [?, 1, 125, 1]> upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")];
+            tensor<int32, [1]> weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [?, 1, 125]> weights_1 = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0)[name = string("weights")];
+            tensor<int32, [1]> weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 1]> weight_sum = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights_1)[name = string("weight_sum")];
+            tensor<bool, [?, 1]> var_646 = greater(x = weight_sum, y = var_69)[name = string("op_646")];
+            fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [?, 1]> fill_like_0 = fill_like(ref_tensor = weight_sum, value = fill_like_0_value_0)[name = string("fill_like_0")];
+            tensor<fp32, [?, 1]> safe_sum = select(a = weight_sum, b = fill_like_0, cond = var_646)[name = string("safe_sum")];
+            tensor<fp32, [?, 2560, 125]> var_649 = mul(x = sequences, y = weights_1)[name = string("op_649")];
+            tensor<int32, [1]> var_651_axes_0 = const()[name = string("op_651_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_651_keep_dims_0 = const()[name = string("op_651_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 2560]> var_651 = reduce_sum(axes = var_651_axes_0, keep_dims = var_651_keep_dims_0, x = var_649)[name = string("op_651")];
+            tensor<fp32, [?, 2560]> mean = real_div(x = var_651, y = safe_sum)[name = string("mean")];
+            tensor<int32, [1]> var_653_axes_0 = const()[name = string("op_653_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [?, 2560, 1]> var_653 = expand_dims(axes = var_653_axes_0, x = mean)[name = string("op_653")];
+            tensor<fp32, [?, 2560, 125]> var_654 = sub(x = sequences, y = var_653)[name = string("op_654")];
+            tensor<fp32, [?, 2560, 125]> dx2 = mul(x = var_654, y = var_654)[name = string("dx2")];
+            tensor<fp32, [?, 1, 125]> var_656 = mul(x = weights_1, y = weights_1)[name = string("op_656")];
+            tensor<int32, [1]> weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 1]> weight_sq_sum = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_656)[name = string("weight_sq_sum")];
+            tensor<fp32, [?, 1]> var_659 = real_div(x = weight_sq_sum, y = safe_sum)[name = string("op_659")];
+            tensor<fp32, [?, 1]> var_660 = sub(x = safe_sum, y = var_659)[name = string("op_660")];
+            fp32 var_661 = const()[name = string("op_661"), val = fp32(0x1.5798eep-27)];
+            tensor<fp32, [?, 1]> denom = add(x = var_660, y = var_661)[name = string("denom")];
+            tensor<fp32, [?, 2560, 125]> var_663 = mul(x = dx2, y = weights_1)[name = string("op_663")];
+            tensor<int32, [1]> var_665_axes_0 = const()[name = string("op_665_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_665_keep_dims_0 = const()[name = string("op_665_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 2560]> var_665 = reduce_sum(axes = var_665_axes_0, keep_dims = var_665_keep_dims_0, x = var_663)[name = string("op_665")];
+            tensor<fp32, [?, 2560]> var = real_div(x = var_665, y = denom)[name = string("var")];
+            tensor<fp32, [?, 2560]> var_667 = maximum(x = var, y = var_68)[name = string("op_667")];
+            tensor<fp32, [?, 2560]> std = sqrt(x = var_667)[name = string("std")];
+            bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)];
+            tensor<fp32, [?, 5120]> stats = concat(axis = var_67, interleave = stats_interleave_0, values = (mean, std))[name = string("stats")];
+            tensor<fp32, [?, 2560]> var_671 = sub(x = mean, y = mean)[name = string("sub_0")];
+            fp32 var_672_value_0 = const()[name = string("op_672_value_0"), val = fp32(0x1.4f8b58p-17)];
+            tensor<fp32, [?, 2560]> var_672 = fill_like(ref_tensor = std, value = var_672_value_0)[name = string("op_672")];
+            bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)];
+            tensor<fp32, [?, 5120]> zero_stats = concat(axis = var_67, interleave = zero_stats_interleave_0, values = (var_671, var_672))[name = string("zero_stats")];
+            tensor<bool, [?, 1]> var_675 = less_equal(x = weight_sum, y = var_69)[name = string("op_675")];
+            tensor<int32, [2]> var_677 = const()[name = string("op_677"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [?, 5120]> zero_mask = tile(reps = var_677, x = var_675)[name = string("zero_mask")];
+            tensor<fp32, [?, 5120]> input = select(a = zero_stats, b = stats, cond = zero_mask)[name = string("input")];
+            tensor<fp32, [?, 256]> output = linear(bias = tail_resnet_seg_1_bias, weight = tail_resnet_seg_1_weight, x = input)[name = string("linear_0")];
+        } -> (output);
+}

wespeaker-voxceleb-resnet34-fused-b3.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:741dd1d7fb08a55128b8e5aa0371bc3dd522f9f99d58f692b66b8733853b9b27
+size 28303488

wespeaker-voxceleb-resnet34-fused-b32-f16.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3fd492b62d5e26ad34278cea685f931276bb94ab564ca6db80d832f87135f69d
+size 243

wespeaker-voxceleb-resnet34-fused-b32-f16.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f279b672793ba4f1d246f69442be874b49fc275f6c9b08f6ab85b57dc0bbe68
+size 225

wespeaker-voxceleb-resnet34-fused-b32-f16.mlmodelc/model.mil

@@ -0,0 +1,468 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [?, 1, 160000]> waveform, tensor<fp32, [?, 589]> weights) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"waveform", [32, 1, 160000]}, {"weights", [32, 589]}}), ("EnumeratedShapes", {{"3c334fba", {{"waveform", [3, 1, 160000]}, {"weights", [3, 589]}}}, {"79c53add", {{"waveform", [1, 1, 160000]}, {"weights", [1, 589]}}}, {"cb01bf12", {{"waveform", [32, 1, 160000]}, {"weights", [32, 589]}}}})))] {
+            tensor<int32, [3]> var_27_begin_0 = const()[name = string("op_27_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> var_27_end_0 = const()[name = string("op_27_end_0"), val = tensor<int32, [3]>([0, 1, 160000])];
+            tensor<bool, [3]> var_27_end_mask_0 = const()[name = string("op_27_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            string waveform_to_fp16_dtype_0 = const()[name = string("waveform_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [?, 1, 160000]> waveform_to_fp16 = cast(dtype = waveform_to_fp16_dtype_0, x = waveform)[name = string("cast_10")];
+            tensor<fp16, [?, 1, 160000]> var_27_cast_fp16 = slice_by_index(begin = var_27_begin_0, end = var_27_end_0, end_mask = var_27_end_mask_0, x = waveform_to_fp16)[name = string("op_27_cast_fp16")];
+            fp16 var_29_to_fp16 = const()[name = string("op_29_to_fp16"), val = fp16(0x1p+15)];
+            tensor<fp16, [?, 1, 160000]> signal_cast_fp16 = mul(x = var_27_cast_fp16, y = var_29_to_fp16)[name = string("signal_cast_fp16")];
+            string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [2]> frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)];
+            tensor<fp16, [400, 1, 400]> fbank_identity_kernel_to_fp16 = const()[name = string("fbank_identity_kernel_to_fp16"), val = tensor<fp16, [400, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [?, 400, 998]> frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = fbank_identity_kernel_to_fp16, x = signal_cast_fp16)[name = string("frames_1_cast_fp16")];
+            tensor<int32, [3]> var_36 = const()[name = string("op_36"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_39_axes_0 = const()[name = string("op_39_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_39_keep_dims_0 = const()[name = string("op_39_keep_dims_0"), val = bool(true)];
+            tensor<fp16, [?, 998, 400]> frames_3_cast_fp16 = transpose(perm = var_36, x = frames_1_cast_fp16)[name = string("transpose_4")];
+            tensor<fp16, [?, 998, 1]> var_39_cast_fp16 = reduce_mean(axes = var_39_axes_0, keep_dims = var_39_keep_dims_0, x = frames_3_cast_fp16)[name = string("op_39_cast_fp16")];
+            tensor<fp16, [?, 998, 400]> input_1_cast_fp16 = sub(x = frames_3_cast_fp16, y = var_39_cast_fp16)[name = string("input_1_cast_fp16")];
+            tensor<int32, [6]> var_42_pad_0 = const()[name = string("op_42_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])];
+            string var_42_mode_0 = const()[name = string("op_42_mode_0"), val = string("replicate")];
+            fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [?, 998, 401]> var_42_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = var_42_mode_0, pad = var_42_pad_0, x = input_1_cast_fp16)[name = string("op_42_cast_fp16")];
+            tensor<int32, [3]> previous_begin_0 = const()[name = string("previous_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> previous_end_0 = const()[name = string("previous_end_0"), val = tensor<int32, [3]>([0, 998, 400])];
+            tensor<bool, [3]> previous_end_mask_0 = const()[name = string("previous_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<fp16, [?, 998, 400]> previous_cast_fp16 = slice_by_index(begin = previous_begin_0, end = previous_end_0, end_mask = previous_end_mask_0, x = var_42_cast_fp16)[name = string("previous_cast_fp16")];
+            fp16 var_44_to_fp16 = const()[name = string("op_44_to_fp16"), val = fp16(0x1.f0cp-1)];
+            tensor<fp16, [?, 998, 400]> var_45_cast_fp16 = mul(x = previous_cast_fp16, y = var_44_to_fp16)[name = string("op_45_cast_fp16")];
+            tensor<fp16, [?, 998, 400]> frames_5_cast_fp16 = sub(x = input_1_cast_fp16, y = var_45_cast_fp16)[name = string("frames_5_cast_fp16")];
+            tensor<fp16, [1, 1, 400]> var_48_to_fp16 = const()[name = string("op_48_to_fp16"), val = tensor<fp16, [1, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320128)))];
+            tensor<fp16, [?, 998, 400]> input_3_cast_fp16 = mul(x = frames_5_cast_fp16, y = var_48_to_fp16)[name = string("input_3_cast_fp16")];
+            tensor<int32, [6]> frames_7_pad_0 = const()[name = string("frames_7_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 112])];
+            string frames_7_mode_0 = const()[name = string("frames_7_mode_0"), val = string("constant")];
+            fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [?, 998, 512]> frames_7_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = frames_7_mode_0, pad = frames_7_pad_0, x = input_3_cast_fp16)[name = string("frames_7_cast_fp16")];
+            tensor<fp16, [257, 512]> fbank_dft_cos_to_fp16 = const()[name = string("fbank_dft_cos_to_fp16"), val = tensor<fp16, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321024)))];
+            tensor<fp16, [257]> real_part_bias_0_to_fp16 = const()[name = string("real_part_bias_0_to_fp16"), val = tensor<fp16, [257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(584256)))];
+            tensor<fp16, [?, 998, 257]> real_part_cast_fp16 = linear(bias = real_part_bias_0_to_fp16, weight = fbank_dft_cos_to_fp16, x = frames_7_cast_fp16)[name = string("real_part_cast_fp16")];
+            tensor<fp16, [257, 512]> fbank_dft_sin_to_fp16 = const()[name = string("fbank_dft_sin_to_fp16"), val = tensor<fp16, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(584896)))];
+            tensor<fp16, [?, 998, 257]> imag_part_cast_fp16 = linear(bias = real_part_bias_0_to_fp16, weight = fbank_dft_sin_to_fp16, x = frames_7_cast_fp16)[name = string("imag_part_cast_fp16")];
+            fp16 var_7_to_fp16 = const()[name = string("op_7_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [?, 998, 257]> var_56_cast_fp16 = pow(x = real_part_cast_fp16, y = var_7_to_fp16)[name = string("op_56_cast_fp16")];
+            tensor<fp16, [?, 998, 257]> var_57_cast_fp16 = pow(x = imag_part_cast_fp16, y = var_7_to_fp16)[name = string("op_57_cast_fp16")];
+            tensor<fp16, [?, 998, 257]> spectrum_cast_fp16 = add(x = var_56_cast_fp16, y = var_57_cast_fp16)[name = string("spectrum_cast_fp16")];
+            tensor<fp16, [80, 257]> transpose_2_to_fp16 = const()[name = string("transpose_2_to_fp16"), val = tensor<fp16, [80, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(848128)))];
+            tensor<fp16, [80]> mel_1_bias_0_to_fp16 = const()[name = string("mel_1_bias_0_to_fp16"), val = tensor<fp16, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889344)))];
+            tensor<fp16, [?, 998, 80]> mel_1_cast_fp16 = linear(bias = mel_1_bias_0_to_fp16, weight = transpose_2_to_fp16, x = spectrum_cast_fp16)[name = string("mel_1_cast_fp16")];
+            fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x1p-23)];
+            tensor<fp16, [?, 998, 80]> var_62_cast_fp16 = maximum(x = mel_1_cast_fp16, y = const_3_to_fp16)[name = string("op_62_cast_fp16")];
+            fp32 mel_3_epsilon_0 = const()[name = string("mel_3_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [?, 998, 80]> mel_3_cast_fp16 = log(epsilon = mel_3_epsilon_0, x = var_62_cast_fp16)[name = string("mel_3_cast_fp16")];
+            tensor<int32, [1]> var_65_axes_0 = const()[name = string("op_65_axes_0"), val = tensor<int32, [1]>([1])];
+            bool var_65_keep_dims_0 = const()[name = string("op_65_keep_dims_0"), val = bool(true)];
+            tensor<fp16, [?, 1, 80]> var_65_cast_fp16 = reduce_mean(axes = var_65_axes_0, keep_dims = var_65_keep_dims_0, x = mel_3_cast_fp16)[name = string("op_65_cast_fp16")];
+            tensor<fp16, [?, 998, 80]> fbank_1_cast_fp16 = sub(x = mel_3_cast_fp16, y = var_65_cast_fp16)[name = string("fbank_1_cast_fp16")];
+            int32 var_67 = const()[name = string("op_67"), val = int32(-1)];
+            tensor<int32, [3]> var_94 = const()[name = string("op_94"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [?, 80, 998]> fbank_3_cast_fp16 = transpose(perm = var_94, x = fbank_1_cast_fp16)[name = string("transpose_3")];
+            tensor<fp16, [?, 1, 80, 998]> input_5_cast_fp16 = expand_dims(axes = input_5_axes_0, x = fbank_3_cast_fp16)[name = string("input_5_cast_fp16")];
+            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 1, 3, 3]> const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = tensor<fp16, [32, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889600)))];
+            tensor<fp16, [32]> const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(890240)))];
+            tensor<fp16, [?, 32, 80, 998]> input_9_cast_fp16 = conv(bias = const_5_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = const_4_to_fp16, x = input_5_cast_fp16)[name = string("input_9_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = string("input_11_cast_fp16")];
+            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(890368)))];
+            tensor<fp16, [32]> const_7_to_fp16 = const()[name = string("const_7_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908864)))];
+            tensor<fp16, [?, 32, 80, 998]> input_15_cast_fp16 = conv(bias = const_7_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = const_6_to_fp16, x = input_11_cast_fp16)[name = string("input_15_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_17_cast_fp16 = relu(x = input_15_cast_fp16)[name = string("input_17_cast_fp16")];
+            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_19_strides_0 = const()[name = string("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_19_dilations_0 = const()[name = string("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_19_groups_0 = const()[name = string("input_19_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908992)))];
+            tensor<fp16, [32]> const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(927488)))];
+            tensor<fp16, [?, 32, 80, 998]> out_1_cast_fp16 = conv(bias = const_9_to_fp16, dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = const_8_to_fp16, x = input_17_cast_fp16)[name = string("out_1_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_21_cast_fp16 = add(x = out_1_cast_fp16, y = input_11_cast_fp16)[name = string("input_21_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_23_cast_fp16 = relu(x = input_21_cast_fp16)[name = string("input_23_cast_fp16")];
+            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(927616)))];
+            tensor<fp16, [32]> const_11_to_fp16 = const()[name = string("const_11_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(946112)))];
+            tensor<fp16, [?, 32, 80, 998]> input_27_cast_fp16 = conv(bias = const_11_to_fp16, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = const_10_to_fp16, x = input_23_cast_fp16)[name = string("input_27_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_29_cast_fp16 = relu(x = input_27_cast_fp16)[name = string("input_29_cast_fp16")];
+            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(946240)))];
+            tensor<fp16, [32]> const_13_to_fp16 = const()[name = string("const_13_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(964736)))];
+            tensor<fp16, [?, 32, 80, 998]> out_3_cast_fp16 = conv(bias = const_13_to_fp16, dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = const_12_to_fp16, x = input_29_cast_fp16)[name = string("out_3_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_33_cast_fp16 = add(x = out_3_cast_fp16, y = input_23_cast_fp16)[name = string("input_33_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_35_cast_fp16 = relu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")];
+            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_14_to_fp16 = const()[name = string("const_14_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(964864)))];
+            tensor<fp16, [32]> const_15_to_fp16 = const()[name = string("const_15_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(983360)))];
+            tensor<fp16, [?, 32, 80, 998]> input_39_cast_fp16 = conv(bias = const_15_to_fp16, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = const_14_to_fp16, x = input_35_cast_fp16)[name = string("input_39_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_41_cast_fp16 = relu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")];
+            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(983488)))];
+            tensor<fp16, [32]> const_17_to_fp16 = const()[name = string("const_17_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1001984)))];
+            tensor<fp16, [?, 32, 80, 998]> out_5_cast_fp16 = conv(bias = const_17_to_fp16, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = const_16_to_fp16, x = input_41_cast_fp16)[name = string("out_5_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_45_cast_fp16 = add(x = out_5_cast_fp16, y = input_35_cast_fp16)[name = string("input_45_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_47_cast_fp16 = relu(x = input_45_cast_fp16)[name = string("input_47_cast_fp16")];
+            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 32, 3, 3]> const_18_to_fp16 = const()[name = string("const_18_to_fp16"), val = tensor<fp16, [64, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1002112)))];
+            tensor<fp16, [64]> const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1039040)))];
+            tensor<fp16, [?, 64, 40, 499]> input_51_cast_fp16 = conv(bias = const_19_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_18_to_fp16, x = input_47_cast_fp16)[name = string("input_51_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_53_cast_fp16 = relu(x = input_51_cast_fp16)[name = string("input_53_cast_fp16")];
+            string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_55_strides_0 = const()[name = string("input_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_55_dilations_0 = const()[name = string("input_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_55_groups_0 = const()[name = string("input_55_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_20_to_fp16 = const()[name = string("const_20_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1039232)))];
+            tensor<fp16, [64]> const_21_to_fp16 = const()[name = string("const_21_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1113024)))];
+            tensor<fp16, [?, 64, 40, 499]> out_7_cast_fp16 = conv(bias = const_21_to_fp16, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = const_20_to_fp16, x = input_53_cast_fp16)[name = string("out_7_cast_fp16")];
+            string input_57_pad_type_0 = const()[name = string("input_57_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_57_strides_0 = const()[name = string("input_57_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_57_dilations_0 = const()[name = string("input_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_57_groups_0 = const()[name = string("input_57_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 32, 1, 1]> const_22_to_fp16 = const()[name = string("const_22_to_fp16"), val = tensor<fp16, [64, 32, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1113216)))];
+            tensor<fp16, [64]> const_23_to_fp16 = const()[name = string("const_23_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1117376)))];
+            tensor<fp16, [?, 64, 40, 499]> var_243_cast_fp16 = conv(bias = const_23_to_fp16, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_22_to_fp16, x = input_47_cast_fp16)[name = string("op_243_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_59_cast_fp16 = add(x = out_7_cast_fp16, y = var_243_cast_fp16)[name = string("input_59_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_61_cast_fp16 = relu(x = input_59_cast_fp16)[name = string("input_61_cast_fp16")];
+            string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1117568)))];
+            tensor<fp16, [64]> const_25_to_fp16 = const()[name = string("const_25_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1191360)))];
+            tensor<fp16, [?, 64, 40, 499]> input_65_cast_fp16 = conv(bias = const_25_to_fp16, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = const_24_to_fp16, x = input_61_cast_fp16)[name = string("input_65_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_67_cast_fp16 = relu(x = input_65_cast_fp16)[name = string("input_67_cast_fp16")];
+            string input_69_pad_type_0 = const()[name = string("input_69_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_69_pad_0 = const()[name = string("input_69_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_69_strides_0 = const()[name = string("input_69_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_69_dilations_0 = const()[name = string("input_69_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_69_groups_0 = const()[name = string("input_69_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1191552)))];
+            tensor<fp16, [64]> const_27_to_fp16 = const()[name = string("const_27_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265344)))];
+            tensor<fp16, [?, 64, 40, 499]> out_9_cast_fp16 = conv(bias = const_27_to_fp16, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = const_26_to_fp16, x = input_67_cast_fp16)[name = string("out_9_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_71_cast_fp16 = add(x = out_9_cast_fp16, y = input_61_cast_fp16)[name = string("input_71_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_73_cast_fp16 = relu(x = input_71_cast_fp16)[name = string("input_73_cast_fp16")];
+            string input_75_pad_type_0 = const()[name = string("input_75_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_75_pad_0 = const()[name = string("input_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_75_strides_0 = const()[name = string("input_75_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_75_dilations_0 = const()[name = string("input_75_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_75_groups_0 = const()[name = string("input_75_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265536)))];
+            tensor<fp16, [64]> const_29_to_fp16 = const()[name = string("const_29_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1339328)))];
+            tensor<fp16, [?, 64, 40, 499]> input_77_cast_fp16 = conv(bias = const_29_to_fp16, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = const_28_to_fp16, x = input_73_cast_fp16)[name = string("input_77_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_79_cast_fp16 = relu(x = input_77_cast_fp16)[name = string("input_79_cast_fp16")];
+            string input_81_pad_type_0 = const()[name = string("input_81_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_81_pad_0 = const()[name = string("input_81_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_81_strides_0 = const()[name = string("input_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_81_dilations_0 = const()[name = string("input_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_81_groups_0 = const()[name = string("input_81_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_30_to_fp16 = const()[name = string("const_30_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1339520)))];
+            tensor<fp16, [64]> const_31_to_fp16 = const()[name = string("const_31_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413312)))];
+            tensor<fp16, [?, 64, 40, 499]> out_11_cast_fp16 = conv(bias = const_31_to_fp16, dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = const_30_to_fp16, x = input_79_cast_fp16)[name = string("out_11_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_83_cast_fp16 = add(x = out_11_cast_fp16, y = input_73_cast_fp16)[name = string("input_83_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_85_cast_fp16 = relu(x = input_83_cast_fp16)[name = string("input_85_cast_fp16")];
+            string input_87_pad_type_0 = const()[name = string("input_87_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_87_pad_0 = const()[name = string("input_87_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_87_strides_0 = const()[name = string("input_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_87_dilations_0 = const()[name = string("input_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_87_groups_0 = const()[name = string("input_87_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413504)))];
+            tensor<fp16, [64]> const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1487296)))];
+            tensor<fp16, [?, 64, 40, 499]> input_89_cast_fp16 = conv(bias = const_33_to_fp16, dilations = input_87_dilations_0, groups = input_87_groups_0, pad = input_87_pad_0, pad_type = input_87_pad_type_0, strides = input_87_strides_0, weight = const_32_to_fp16, x = input_85_cast_fp16)[name = string("input_89_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_91_cast_fp16 = relu(x = input_89_cast_fp16)[name = string("input_91_cast_fp16")];
+            string input_93_pad_type_0 = const()[name = string("input_93_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_93_pad_0 = const()[name = string("input_93_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_93_strides_0 = const()[name = string("input_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_93_dilations_0 = const()[name = string("input_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_93_groups_0 = const()[name = string("input_93_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_34_to_fp16 = const()[name = string("const_34_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1487488)))];
+            tensor<fp16, [64]> const_35_to_fp16 = const()[name = string("const_35_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1561280)))];
+            tensor<fp16, [?, 64, 40, 499]> out_13_cast_fp16 = conv(bias = const_35_to_fp16, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = const_34_to_fp16, x = input_91_cast_fp16)[name = string("out_13_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_95_cast_fp16 = add(x = out_13_cast_fp16, y = input_85_cast_fp16)[name = string("input_95_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_97_cast_fp16 = relu(x = input_95_cast_fp16)[name = string("input_97_cast_fp16")];
+            string input_99_pad_type_0 = const()[name = string("input_99_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_99_pad_0 = const()[name = string("input_99_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_99_strides_0 = const()[name = string("input_99_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_99_dilations_0 = const()[name = string("input_99_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_99_groups_0 = const()[name = string("input_99_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 64, 3, 3]> const_36_to_fp16 = const()[name = string("const_36_to_fp16"), val = tensor<fp16, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1561472)))];
+            tensor<fp16, [128]> const_37_to_fp16 = const()[name = string("const_37_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1708992)))];
+            tensor<fp16, [?, 128, 20, 250]> input_101_cast_fp16 = conv(bias = const_37_to_fp16, dilations = input_99_dilations_0, groups = input_99_groups_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = const_36_to_fp16, x = input_97_cast_fp16)[name = string("input_101_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_103_cast_fp16 = relu(x = input_101_cast_fp16)[name = string("input_103_cast_fp16")];
+            string input_105_pad_type_0 = const()[name = string("input_105_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_105_pad_0 = const()[name = string("input_105_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_105_strides_0 = const()[name = string("input_105_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_105_dilations_0 = const()[name = string("input_105_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_105_groups_0 = const()[name = string("input_105_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_38_to_fp16 = const()[name = string("const_38_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1709312)))];
+            tensor<fp16, [128]> const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2004288)))];
+            tensor<fp16, [?, 128, 20, 250]> out_15_cast_fp16 = conv(bias = const_39_to_fp16, dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = const_38_to_fp16, x = input_103_cast_fp16)[name = string("out_15_cast_fp16")];
+            string input_107_pad_type_0 = const()[name = string("input_107_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_107_strides_0 = const()[name = string("input_107_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_107_dilations_0 = const()[name = string("input_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_107_groups_0 = const()[name = string("input_107_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 64, 1, 1]> const_40_to_fp16 = const()[name = string("const_40_to_fp16"), val = tensor<fp16, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2004608)))];
+            tensor<fp16, [128]> const_41_to_fp16 = const()[name = string("const_41_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2021056)))];
+            tensor<fp16, [?, 128, 20, 250]> var_379_cast_fp16 = conv(bias = const_41_to_fp16, dilations = input_107_dilations_0, groups = input_107_groups_0, pad = input_107_pad_0, pad_type = input_107_pad_type_0, strides = input_107_strides_0, weight = const_40_to_fp16, x = input_97_cast_fp16)[name = string("op_379_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_109_cast_fp16 = add(x = out_15_cast_fp16, y = var_379_cast_fp16)[name = string("input_109_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_111_cast_fp16 = relu(x = input_109_cast_fp16)[name = string("input_111_cast_fp16")];
+            string input_113_pad_type_0 = const()[name = string("input_113_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_113_pad_0 = const()[name = string("input_113_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_113_strides_0 = const()[name = string("input_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_113_dilations_0 = const()[name = string("input_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_113_groups_0 = const()[name = string("input_113_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_42_to_fp16 = const()[name = string("const_42_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2021376)))];
+            tensor<fp16, [128]> const_43_to_fp16 = const()[name = string("const_43_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2316352)))];
+            tensor<fp16, [?, 128, 20, 250]> input_115_cast_fp16 = conv(bias = const_43_to_fp16, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = const_42_to_fp16, x = input_111_cast_fp16)[name = string("input_115_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_117_cast_fp16 = relu(x = input_115_cast_fp16)[name = string("input_117_cast_fp16")];
+            string input_119_pad_type_0 = const()[name = string("input_119_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_119_strides_0 = const()[name = string("input_119_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_119_dilations_0 = const()[name = string("input_119_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_119_groups_0 = const()[name = string("input_119_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2316672)))];
+            tensor<fp16, [128]> const_45_to_fp16 = const()[name = string("const_45_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2611648)))];
+            tensor<fp16, [?, 128, 20, 250]> out_17_cast_fp16 = conv(bias = const_45_to_fp16, dilations = input_119_dilations_0, groups = input_119_groups_0, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = input_119_strides_0, weight = const_44_to_fp16, x = input_117_cast_fp16)[name = string("out_17_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_121_cast_fp16 = add(x = out_17_cast_fp16, y = input_111_cast_fp16)[name = string("input_121_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_123_cast_fp16 = relu(x = input_121_cast_fp16)[name = string("input_123_cast_fp16")];
+            string input_125_pad_type_0 = const()[name = string("input_125_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_125_pad_0 = const()[name = string("input_125_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_125_strides_0 = const()[name = string("input_125_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_125_dilations_0 = const()[name = string("input_125_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_125_groups_0 = const()[name = string("input_125_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2611968)))];
+            tensor<fp16, [128]> const_47_to_fp16 = const()[name = string("const_47_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2906944)))];
+            tensor<fp16, [?, 128, 20, 250]> input_127_cast_fp16 = conv(bias = const_47_to_fp16, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = const_46_to_fp16, x = input_123_cast_fp16)[name = string("input_127_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_129_cast_fp16 = relu(x = input_127_cast_fp16)[name = string("input_129_cast_fp16")];
+            string input_131_pad_type_0 = const()[name = string("input_131_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_131_strides_0 = const()[name = string("input_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_131_dilations_0 = const()[name = string("input_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_131_groups_0 = const()[name = string("input_131_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2907264)))];
+            tensor<fp16, [128]> const_49_to_fp16 = const()[name = string("const_49_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3202240)))];
+            tensor<fp16, [?, 128, 20, 250]> out_19_cast_fp16 = conv(bias = const_49_to_fp16, dilations = input_131_dilations_0, groups = input_131_groups_0, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = input_131_strides_0, weight = const_48_to_fp16, x = input_129_cast_fp16)[name = string("out_19_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_133_cast_fp16 = add(x = out_19_cast_fp16, y = input_123_cast_fp16)[name = string("input_133_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_135_cast_fp16 = relu(x = input_133_cast_fp16)[name = string("input_135_cast_fp16")];
+            string input_137_pad_type_0 = const()[name = string("input_137_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_137_pad_0 = const()[name = string("input_137_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_137_strides_0 = const()[name = string("input_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_137_dilations_0 = const()[name = string("input_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_137_groups_0 = const()[name = string("input_137_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3202560)))];
+            tensor<fp16, [128]> const_51_to_fp16 = const()[name = string("const_51_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3497536)))];
+            tensor<fp16, [?, 128, 20, 250]> input_139_cast_fp16 = conv(bias = const_51_to_fp16, dilations = input_137_dilations_0, groups = input_137_groups_0, pad = input_137_pad_0, pad_type = input_137_pad_type_0, strides = input_137_strides_0, weight = const_50_to_fp16, x = input_135_cast_fp16)[name = string("input_139_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_141_cast_fp16 = relu(x = input_139_cast_fp16)[name = string("input_141_cast_fp16")];
+            string input_143_pad_type_0 = const()[name = string("input_143_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_143_strides_0 = const()[name = string("input_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_143_dilations_0 = const()[name = string("input_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_143_groups_0 = const()[name = string("input_143_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3497856)))];
+            tensor<fp16, [128]> const_53_to_fp16 = const()[name = string("const_53_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3792832)))];
+            tensor<fp16, [?, 128, 20, 250]> out_21_cast_fp16 = conv(bias = const_53_to_fp16, dilations = input_143_dilations_0, groups = input_143_groups_0, pad = input_143_pad_0, pad_type = input_143_pad_type_0, strides = input_143_strides_0, weight = const_52_to_fp16, x = input_141_cast_fp16)[name = string("out_21_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_145_cast_fp16 = add(x = out_21_cast_fp16, y = input_135_cast_fp16)[name = string("input_145_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_147_cast_fp16 = relu(x = input_145_cast_fp16)[name = string("input_147_cast_fp16")];
+            string input_149_pad_type_0 = const()[name = string("input_149_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_149_pad_0 = const()[name = string("input_149_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_149_strides_0 = const()[name = string("input_149_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_149_dilations_0 = const()[name = string("input_149_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_149_groups_0 = const()[name = string("input_149_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_54_to_fp16 = const()[name = string("const_54_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3793152)))];
+            tensor<fp16, [128]> const_55_to_fp16 = const()[name = string("const_55_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4088128)))];
+            tensor<fp16, [?, 128, 20, 250]> input_151_cast_fp16 = conv(bias = const_55_to_fp16, dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = const_54_to_fp16, x = input_147_cast_fp16)[name = string("input_151_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_153_cast_fp16 = relu(x = input_151_cast_fp16)[name = string("input_153_cast_fp16")];
+            string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_56_to_fp16 = const()[name = string("const_56_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4088448)))];
+            tensor<fp16, [128]> const_57_to_fp16 = const()[name = string("const_57_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4383424)))];
+            tensor<fp16, [?, 128, 20, 250]> out_23_cast_fp16 = conv(bias = const_57_to_fp16, dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = const_56_to_fp16, x = input_153_cast_fp16)[name = string("out_23_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_157_cast_fp16 = add(x = out_23_cast_fp16, y = input_147_cast_fp16)[name = string("input_157_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_159_cast_fp16 = relu(x = input_157_cast_fp16)[name = string("input_159_cast_fp16")];
+            string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_161_strides_0 = const()[name = string("input_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_161_dilations_0 = const()[name = string("input_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_161_groups_0 = const()[name = string("input_161_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_58_to_fp16 = const()[name = string("const_58_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4383744)))];
+            tensor<fp16, [128]> const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4678720)))];
+            tensor<fp16, [?, 128, 20, 250]> input_163_cast_fp16 = conv(bias = const_59_to_fp16, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_58_to_fp16, x = input_159_cast_fp16)[name = string("input_163_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_165_cast_fp16 = relu(x = input_163_cast_fp16)[name = string("input_165_cast_fp16")];
+            string input_167_pad_type_0 = const()[name = string("input_167_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_167_pad_0 = const()[name = string("input_167_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_167_strides_0 = const()[name = string("input_167_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_167_dilations_0 = const()[name = string("input_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_167_groups_0 = const()[name = string("input_167_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_60_to_fp16 = const()[name = string("const_60_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4679040)))];
+            tensor<fp16, [128]> const_61_to_fp16 = const()[name = string("const_61_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4974016)))];
+            tensor<fp16, [?, 128, 20, 250]> out_25_cast_fp16 = conv(bias = const_61_to_fp16, dilations = input_167_dilations_0, groups = input_167_groups_0, pad = input_167_pad_0, pad_type = input_167_pad_type_0, strides = input_167_strides_0, weight = const_60_to_fp16, x = input_165_cast_fp16)[name = string("out_25_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_169_cast_fp16 = add(x = out_25_cast_fp16, y = input_159_cast_fp16)[name = string("input_169_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_171_cast_fp16 = relu(x = input_169_cast_fp16)[name = string("input_171_cast_fp16")];
+            string input_173_pad_type_0 = const()[name = string("input_173_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_173_pad_0 = const()[name = string("input_173_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_173_strides_0 = const()[name = string("input_173_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_173_dilations_0 = const()[name = string("input_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_173_groups_0 = const()[name = string("input_173_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 128, 3, 3]> const_62_to_fp16 = const()[name = string("const_62_to_fp16"), val = tensor<fp16, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4974336)))];
+            tensor<fp16, [256]> const_63_to_fp16 = const()[name = string("const_63_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5564224)))];
+            tensor<fp16, [?, 256, 10, 125]> input_175_cast_fp16 = conv(bias = const_63_to_fp16, dilations = input_173_dilations_0, groups = input_173_groups_0, pad = input_173_pad_0, pad_type = input_173_pad_type_0, strides = input_173_strides_0, weight = const_62_to_fp16, x = input_171_cast_fp16)[name = string("input_175_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_177_cast_fp16 = relu(x = input_175_cast_fp16)[name = string("input_177_cast_fp16")];
+            string input_179_pad_type_0 = const()[name = string("input_179_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_179_pad_0 = const()[name = string("input_179_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_179_strides_0 = const()[name = string("input_179_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_179_dilations_0 = const()[name = string("input_179_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_179_groups_0 = const()[name = string("input_179_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5564800)))];
+            tensor<fp16, [256]> const_65_to_fp16 = const()[name = string("const_65_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6744512)))];
+            tensor<fp16, [?, 256, 10, 125]> out_27_cast_fp16 = conv(bias = const_65_to_fp16, dilations = input_179_dilations_0, groups = input_179_groups_0, pad = input_179_pad_0, pad_type = input_179_pad_type_0, strides = input_179_strides_0, weight = const_64_to_fp16, x = input_177_cast_fp16)[name = string("out_27_cast_fp16")];
+            string input_181_pad_type_0 = const()[name = string("input_181_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_181_strides_0 = const()[name = string("input_181_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_181_dilations_0 = const()[name = string("input_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_181_groups_0 = const()[name = string("input_181_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 128, 1, 1]> const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = tensor<fp16, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6745088)))];
+            tensor<fp16, [256]> const_67_to_fp16 = const()[name = string("const_67_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6810688)))];
+            tensor<fp16, [?, 256, 10, 125]> var_570_cast_fp16 = conv(bias = const_67_to_fp16, dilations = input_181_dilations_0, groups = input_181_groups_0, pad = input_181_pad_0, pad_type = input_181_pad_type_0, strides = input_181_strides_0, weight = const_66_to_fp16, x = input_171_cast_fp16)[name = string("op_570_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_183_cast_fp16 = add(x = out_27_cast_fp16, y = var_570_cast_fp16)[name = string("input_183_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_185_cast_fp16 = relu(x = input_183_cast_fp16)[name = string("input_185_cast_fp16")];
+            string input_187_pad_type_0 = const()[name = string("input_187_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_187_pad_0 = const()[name = string("input_187_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_187_strides_0 = const()[name = string("input_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_187_dilations_0 = const()[name = string("input_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_187_groups_0 = const()[name = string("input_187_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_68_to_fp16 = const()[name = string("const_68_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6811264)))];
+            tensor<fp16, [256]> const_69_to_fp16 = const()[name = string("const_69_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7990976)))];
+            tensor<fp16, [?, 256, 10, 125]> input_189_cast_fp16 = conv(bias = const_69_to_fp16, dilations = input_187_dilations_0, groups = input_187_groups_0, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = input_187_strides_0, weight = const_68_to_fp16, x = input_185_cast_fp16)[name = string("input_189_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_191_cast_fp16 = relu(x = input_189_cast_fp16)[name = string("input_191_cast_fp16")];
+            string input_193_pad_type_0 = const()[name = string("input_193_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_193_pad_0 = const()[name = string("input_193_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_193_strides_0 = const()[name = string("input_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_193_dilations_0 = const()[name = string("input_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_193_groups_0 = const()[name = string("input_193_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7991552)))];
+            tensor<fp16, [256]> const_71_to_fp16 = const()[name = string("const_71_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9171264)))];
+            tensor<fp16, [?, 256, 10, 125]> out_29_cast_fp16 = conv(bias = const_71_to_fp16, dilations = input_193_dilations_0, groups = input_193_groups_0, pad = input_193_pad_0, pad_type = input_193_pad_type_0, strides = input_193_strides_0, weight = const_70_to_fp16, x = input_191_cast_fp16)[name = string("out_29_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_195_cast_fp16 = add(x = out_29_cast_fp16, y = input_185_cast_fp16)[name = string("input_195_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_197_cast_fp16 = relu(x = input_195_cast_fp16)[name = string("input_197_cast_fp16")];
+            string input_199_pad_type_0 = const()[name = string("input_199_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_199_pad_0 = const()[name = string("input_199_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_199_strides_0 = const()[name = string("input_199_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_199_dilations_0 = const()[name = string("input_199_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_199_groups_0 = const()[name = string("input_199_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_72_to_fp16 = const()[name = string("const_72_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9171840)))];
+            tensor<fp16, [256]> const_73_to_fp16 = const()[name = string("const_73_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10351552)))];
+            tensor<fp16, [?, 256, 10, 125]> input_201_cast_fp16 = conv(bias = const_73_to_fp16, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = const_72_to_fp16, x = input_197_cast_fp16)[name = string("input_201_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_203_cast_fp16 = relu(x = input_201_cast_fp16)[name = string("input_203_cast_fp16")];
+            string input_205_pad_type_0 = const()[name = string("input_205_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_205_pad_0 = const()[name = string("input_205_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_205_strides_0 = const()[name = string("input_205_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_205_dilations_0 = const()[name = string("input_205_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_205_groups_0 = const()[name = string("input_205_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_74_to_fp16 = const()[name = string("const_74_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10352128)))];
+            tensor<fp16, [256]> const_75_to_fp16 = const()[name = string("const_75_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11531840)))];
+            tensor<fp16, [?, 256, 10, 125]> out_cast_fp16 = conv(bias = const_75_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_74_to_fp16, x = input_203_cast_fp16)[name = string("out_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_207_cast_fp16 = add(x = out_cast_fp16, y = input_197_cast_fp16)[name = string("input_207_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> frames_cast_fp16 = relu(x = input_207_cast_fp16)[name = string("frames_cast_fp16")];
+            tensor<int32, [3]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp16, [?, 2560, 125]> sequences_cast_fp16 = reshape(shape = concat_0x, x = frames_cast_fp16)[name = string("sequences_cast_fp16")];
+            tensor<int32, [1]> input_209_axes_0 = const()[name = string("input_209_axes_0"), val = tensor<int32, [1]>([1])];
+            string weights_to_fp16_dtype_0 = const()[name = string("weights_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [?, 589]> weights_to_fp16 = cast(dtype = weights_to_fp16_dtype_0, x = weights)[name = string("cast_9")];
+            tensor<fp16, [?, 1, 589]> input_209_cast_fp16 = expand_dims(axes = input_209_axes_0, x = weights_to_fp16)[name = string("input_209_cast_fp16")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [?, 1, 589, 1]> expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = input_209_cast_fp16)[name = string("expand_dims_0_cast_fp16")];
+            fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)];
+            fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)];
+            tensor<fp16, [?, 1, 125, 1]> upsample_nearest_neighbor_0_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0_cast_fp16)[name = string("upsample_nearest_neighbor_0_cast_fp16")];
+            tensor<int32, [1]> weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [?, 1, 125]> weights_cast_fp16 = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0_cast_fp16)[name = string("weights_cast_fp16")];
+            tensor<int32, [1]> weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 1]> weight_sum_cast_fp16 = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights_cast_fp16)[name = string("weight_sum_cast_fp16")];
+            fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(0x0p+0)];
+            tensor<bool, [?, 1]> var_646_cast_fp16 = greater(x = weight_sum_cast_fp16, y = var_69_to_fp16)[name = string("op_646_cast_fp16")];
+            fp16 fill_like_0_value_0_to_fp16 = const()[name = string("fill_like_0_value_0_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [?, 1]> fill_like_0_cast_fp16 = fill_like(ref_tensor = weight_sum_cast_fp16, value = fill_like_0_value_0_to_fp16)[name = string("fill_like_0_cast_fp16")];
+            tensor<fp16, [?, 1]> safe_sum_cast_fp16 = select(a = weight_sum_cast_fp16, b = fill_like_0_cast_fp16, cond = var_646_cast_fp16)[name = string("safe_sum_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> var_649_cast_fp16 = mul(x = sequences_cast_fp16, y = weights_cast_fp16)[name = string("op_649_cast_fp16")];
+            tensor<int32, [1]> var_651_axes_0 = const()[name = string("op_651_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_651_keep_dims_0 = const()[name = string("op_651_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 2560]> var_651_cast_fp16 = reduce_sum(axes = var_651_axes_0, keep_dims = var_651_keep_dims_0, x = var_649_cast_fp16)[name = string("op_651_cast_fp16")];
+            tensor<fp16, [?, 2560]> mean_cast_fp16 = real_div(x = var_651_cast_fp16, y = safe_sum_cast_fp16)[name = string("mean_cast_fp16")];
+            tensor<int32, [1]> var_653_axes_0 = const()[name = string("op_653_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [?, 2560, 1]> var_653_cast_fp16 = expand_dims(axes = var_653_axes_0, x = mean_cast_fp16)[name = string("op_653_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> var_654_cast_fp16 = sub(x = sequences_cast_fp16, y = var_653_cast_fp16)[name = string("op_654_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> dx2_cast_fp16 = mul(x = var_654_cast_fp16, y = var_654_cast_fp16)[name = string("dx2_cast_fp16")];
+            tensor<fp16, [?, 1, 125]> var_656_cast_fp16 = mul(x = weights_cast_fp16, y = weights_cast_fp16)[name = string("op_656_cast_fp16")];
+            tensor<int32, [1]> weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 1]> weight_sq_sum_cast_fp16 = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_656_cast_fp16)[name = string("weight_sq_sum_cast_fp16")];
+            tensor<fp16, [?, 1]> var_659_cast_fp16 = real_div(x = weight_sq_sum_cast_fp16, y = safe_sum_cast_fp16)[name = string("op_659_cast_fp16")];
+            tensor<fp16, [?, 1]> var_660_cast_fp16 = sub(x = safe_sum_cast_fp16, y = var_659_cast_fp16)[name = string("op_660_cast_fp16")];
+            fp16 var_661_to_fp16 = const()[name = string("op_661_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [?, 1]> denom_cast_fp16 = add(x = var_660_cast_fp16, y = var_661_to_fp16)[name = string("denom_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> var_663_cast_fp16 = mul(x = dx2_cast_fp16, y = weights_cast_fp16)[name = string("op_663_cast_fp16")];
+            tensor<int32, [1]> var_665_axes_0 = const()[name = string("op_665_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_665_keep_dims_0 = const()[name = string("op_665_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 2560]> var_665_cast_fp16 = reduce_sum(axes = var_665_axes_0, keep_dims = var_665_keep_dims_0, x = var_663_cast_fp16)[name = string("op_665_cast_fp16")];
+            tensor<fp16, [?, 2560]> var_cast_fp16 = real_div(x = var_665_cast_fp16, y = denom_cast_fp16)[name = string("var_cast_fp16")];
+            fp16 var_68_to_fp16 = const()[name = string("op_68_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [?, 2560]> var_667_cast_fp16 = maximum(x = var_cast_fp16, y = var_68_to_fp16)[name = string("op_667_cast_fp16")];
+            tensor<fp16, [?, 2560]> std_cast_fp16 = sqrt(x = var_667_cast_fp16)[name = string("std_cast_fp16")];
+            bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)];
+            tensor<fp16, [?, 5120]> stats_cast_fp16 = concat(axis = var_67, interleave = stats_interleave_0, values = (mean_cast_fp16, std_cast_fp16))[name = string("stats_cast_fp16")];
+            tensor<fp16, [?, 2560]> sub_0_cast_fp16 = sub(x = mean_cast_fp16, y = mean_cast_fp16)[name = string("sub_0_cast_fp16")];
+            fp16 var_672_value_0_to_fp16 = const()[name = string("op_672_value_0_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [?, 2560]> var_672_cast_fp16 = fill_like(ref_tensor = std_cast_fp16, value = var_672_value_0_to_fp16)[name = string("op_672_cast_fp16")];
+            bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)];
+            tensor<fp16, [?, 5120]> zero_stats_cast_fp16 = concat(axis = var_67, interleave = zero_stats_interleave_0, values = (sub_0_cast_fp16, var_672_cast_fp16))[name = string("zero_stats_cast_fp16")];
+            tensor<bool, [?, 1]> var_675_cast_fp16 = less_equal(x = weight_sum_cast_fp16, y = var_69_to_fp16)[name = string("op_675_cast_fp16")];
+            tensor<int32, [2]> var_677 = const()[name = string("op_677"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [?, 5120]> zero_mask = tile(reps = var_677, x = var_675_cast_fp16)[name = string("zero_mask")];
+            tensor<fp16, [?, 5120]> input_cast_fp16 = select(a = zero_stats_cast_fp16, b = stats_cast_fp16, cond = zero_mask)[name = string("input_cast_fp16")];
+            tensor<fp16, [256, 5120]> tail_resnet_seg_1_weight_to_fp16 = const()[name = string("tail_resnet_seg_1_weight_to_fp16"), val = tensor<fp16, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11532416)))];
+            tensor<fp16, [256]> tail_resnet_seg_1_bias_to_fp16 = const()[name = string("tail_resnet_seg_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14153920)))];
+            tensor<fp16, [?, 256]> linear_0_cast_fp16 = linear(bias = tail_resnet_seg_1_bias_to_fp16, weight = tail_resnet_seg_1_weight_to_fp16, x = input_cast_fp16)[name = string("linear_0_cast_fp16")];
+            string linear_0_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_0_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [?, 256]> output = cast(dtype = linear_0_cast_fp16_to_fp32_dtype_0, x = linear_0_cast_fp16)[name = string("cast_8")];
+        } -> (output);
+}

wespeaker-voxceleb-resnet34-fused-b32-f16.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3f2a1aba33878f0388d8dc3ab7259af1978482145fb4931cd93876a3d875eef
+size 14154496

wespeaker-voxceleb-resnet34-fused-b32.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab9e15f213ba81809fd0abd6dd6d4c6b569dfa891f2dda282567819a732f301b
+size 243

wespeaker-voxceleb-resnet34-fused-b32.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f279b672793ba4f1d246f69442be874b49fc275f6c9b08f6ab85b57dc0bbe68
+size 225

wespeaker-voxceleb-resnet34-fused-b32.mlmodelc/model.mil

@@ -0,0 +1,462 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [?, 1, 160000]> waveform, tensor<fp32, [?, 589]> weights) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"waveform", [32, 1, 160000]}, {"weights", [32, 589]}}), ("EnumeratedShapes", {{"3c334fba", {{"waveform", [3, 1, 160000]}, {"weights", [3, 589]}}}, {"79c53add", {{"waveform", [1, 1, 160000]}, {"weights", [1, 589]}}}, {"cb01bf12", {{"waveform", [32, 1, 160000]}, {"weights", [32, 589]}}}})))] {
+            tensor<fp32, [257, 512]> fbank_dft_sin = const()[name = string("fbank_dft_sin"), val = tensor<fp32, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [257, 512]> fbank_dft_cos = const()[name = string("fbank_dft_cos"), val = tensor<fp32, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526464)))];
+            tensor<fp32, [400, 1, 400]> fbank_identity_kernel = const()[name = string("fbank_identity_kernel"), val = tensor<fp32, [400, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1052864)))];
+            tensor<fp32, [256]> tail_resnet_seg_1_bias = const()[name = string("tail_resnet_seg_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1692928)))];
+            tensor<fp32, [256, 5120]> tail_resnet_seg_1_weight = const()[name = string("tail_resnet_seg_1_weight"), val = tensor<fp32, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1694016)))];
+            fp32 var_7 = const()[name = string("op_7"), val = fp32(0x1p+1)];
+            tensor<int32, [3]> var_27_begin_0 = const()[name = string("op_27_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> var_27_end_0 = const()[name = string("op_27_end_0"), val = tensor<int32, [3]>([0, 1, 160000])];
+            tensor<bool, [3]> var_27_end_mask_0 = const()[name = string("op_27_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            tensor<fp32, [?, 1, 160000]> var_27 = slice_by_index(begin = var_27_begin_0, end = var_27_end_0, end_mask = var_27_end_mask_0, x = waveform)[name = string("op_27")];
+            fp32 var_29 = const()[name = string("op_29"), val = fp32(0x1p+15)];
+            tensor<fp32, [?, 1, 160000]> signal = mul(x = var_27, y = var_29)[name = string("signal")];
+            string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [2]> frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)];
+            tensor<fp32, [?, 400, 998]> frames_1 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = fbank_identity_kernel, x = signal)[name = string("frames_1")];
+            tensor<int32, [3]> var_36 = const()[name = string("op_36"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_39_axes_0 = const()[name = string("op_39_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_39_keep_dims_0 = const()[name = string("op_39_keep_dims_0"), val = bool(true)];
+            tensor<fp32, [?, 998, 400]> frames_3 = transpose(perm = var_36, x = frames_1)[name = string("transpose_4")];
+            tensor<fp32, [?, 998, 1]> var_39 = reduce_mean(axes = var_39_axes_0, keep_dims = var_39_keep_dims_0, x = frames_3)[name = string("op_39")];
+            tensor<fp32, [?, 998, 400]> input_1 = sub(x = frames_3, y = var_39)[name = string("input_1")];
+            fp32 const_0 = const()[name = string("const_0"), val = fp32(0x0p+0)];
+            tensor<int32, [6]> var_42_pad_0 = const()[name = string("op_42_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])];
+            string var_42_mode_0 = const()[name = string("op_42_mode_0"), val = string("replicate")];
+            tensor<fp32, [?, 998, 401]> var_42 = pad(constant_val = const_0, mode = var_42_mode_0, pad = var_42_pad_0, x = input_1)[name = string("op_42")];
+            tensor<int32, [3]> previous_begin_0 = const()[name = string("previous_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> previous_end_0 = const()[name = string("previous_end_0"), val = tensor<int32, [3]>([0, 998, 400])];
+            tensor<bool, [3]> previous_end_mask_0 = const()[name = string("previous_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<fp32, [?, 998, 400]> previous = slice_by_index(begin = previous_begin_0, end = previous_end_0, end_mask = previous_end_mask_0, x = var_42)[name = string("previous")];
+            fp32 var_44 = const()[name = string("op_44"), val = fp32(0x1.f0a3d8p-1)];
+            tensor<fp32, [?, 998, 400]> var_45 = mul(x = previous, y = var_44)[name = string("op_45")];
+            tensor<fp32, [?, 998, 400]> frames_5 = sub(x = input_1, y = var_45)[name = string("frames_5")];
+            tensor<fp32, [1, 1, 400]> var_48 = const()[name = string("op_48"), val = tensor<fp32, [1, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6936960)))];
+            tensor<fp32, [?, 998, 400]> input_3 = mul(x = frames_5, y = var_48)[name = string("input_3")];
+            fp32 const_1 = const()[name = string("const_1"), val = fp32(0x0p+0)];
+            tensor<int32, [6]> frames_7_pad_0 = const()[name = string("frames_7_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 112])];
+            string frames_7_mode_0 = const()[name = string("frames_7_mode_0"), val = string("constant")];
+            tensor<fp32, [?, 998, 512]> frames_7 = pad(constant_val = const_1, mode = frames_7_mode_0, pad = frames_7_pad_0, x = input_3)[name = string("frames_7")];
+            tensor<fp32, [257]> real_part_bias_0 = const()[name = string("real_part_bias_0"), val = tensor<fp32, [257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6938624)))];
+            tensor<fp32, [?, 998, 257]> real_part = linear(bias = real_part_bias_0, weight = fbank_dft_cos, x = frames_7)[name = string("real_part")];
+            tensor<fp32, [?, 998, 257]> imag_part = linear(bias = real_part_bias_0, weight = fbank_dft_sin, x = frames_7)[name = string("imag_part")];
+            tensor<fp32, [?, 998, 257]> var_56 = pow(x = real_part, y = var_7)[name = string("op_56")];
+            tensor<fp32, [?, 998, 257]> var_57 = pow(x = imag_part, y = var_7)[name = string("op_57")];
+            tensor<fp32, [?, 998, 257]> spectrum = add(x = var_56, y = var_57)[name = string("spectrum")];
+            tensor<fp32, [80, 257]> transpose_2 = const()[name = string("transpose_2"), val = tensor<fp32, [80, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6939776)))];
+            tensor<fp32, [80]> mel_1_bias_0 = const()[name = string("mel_1_bias_0"), val = tensor<fp32, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7022080)))];
+            tensor<fp32, [?, 998, 80]> mel_1 = linear(bias = mel_1_bias_0, weight = transpose_2, x = spectrum)[name = string("mel_1")];
+            fp32 const_3 = const()[name = string("const_3"), val = fp32(0x1p-23)];
+            tensor<fp32, [?, 998, 80]> var_62 = maximum(x = mel_1, y = const_3)[name = string("op_62")];
+            fp32 mel_3_epsilon_0 = const()[name = string("mel_3_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp32, [?, 998, 80]> mel_3 = log(epsilon = mel_3_epsilon_0, x = var_62)[name = string("mel_3")];
+            tensor<int32, [1]> var_65_axes_0 = const()[name = string("op_65_axes_0"), val = tensor<int32, [1]>([1])];
+            bool var_65_keep_dims_0 = const()[name = string("op_65_keep_dims_0"), val = bool(true)];
+            tensor<fp32, [?, 1, 80]> var_65 = reduce_mean(axes = var_65_axes_0, keep_dims = var_65_keep_dims_0, x = mel_3)[name = string("op_65")];
+            tensor<fp32, [?, 998, 80]> fbank_1 = sub(x = mel_3, y = var_65)[name = string("fbank_1")];
+            int32 var_67 = const()[name = string("op_67"), val = int32(-1)];
+            fp32 var_68 = const()[name = string("op_68"), val = fp32(0x1.b7cdfep-34)];
+            fp32 var_69 = const()[name = string("op_69"), val = fp32(0x0p+0)];
+            tensor<int32, [3]> var_94 = const()[name = string("op_94"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 80, 998]> fbank_3 = transpose(perm = var_94, x = fbank_1)[name = string("transpose_3")];
+            tensor<fp32, [?, 1, 80, 998]> input_5 = expand_dims(axes = input_5_axes_0, x = fbank_3)[name = string("input_5")];
+            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 1, 3, 3]> const_4 = const()[name = string("const_4"), val = tensor<fp32, [32, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7022464)))];
+            tensor<fp32, [32]> const_5 = const()[name = string("const_5"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7023680)))];
+            tensor<fp32, [?, 32, 80, 998]> input_9 = conv(bias = const_5, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = const_4, x = input_5)[name = string("input_9")];
+            tensor<fp32, [?, 32, 80, 998]> input_11 = relu(x = input_9)[name = string("input_11")];
+            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_6 = const()[name = string("const_6"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7023872)))];
+            tensor<fp32, [32]> const_7 = const()[name = string("const_7"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7060800)))];
+            tensor<fp32, [?, 32, 80, 998]> input_15 = conv(bias = const_7, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = const_6, x = input_11)[name = string("input_15")];
+            tensor<fp32, [?, 32, 80, 998]> input_17 = relu(x = input_15)[name = string("input_17")];
+            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_19_strides_0 = const()[name = string("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_19_dilations_0 = const()[name = string("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_19_groups_0 = const()[name = string("input_19_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_8 = const()[name = string("const_8"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7060992)))];
+            tensor<fp32, [32]> const_9 = const()[name = string("const_9"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7097920)))];
+            tensor<fp32, [?, 32, 80, 998]> out_1 = conv(bias = const_9, dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = const_8, x = input_17)[name = string("out_1")];
+            tensor<fp32, [?, 32, 80, 998]> input_21 = add(x = out_1, y = input_11)[name = string("input_21")];
+            tensor<fp32, [?, 32, 80, 998]> input_23 = relu(x = input_21)[name = string("input_23")];
+            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_10 = const()[name = string("const_10"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7098112)))];
+            tensor<fp32, [32]> const_11 = const()[name = string("const_11"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7135040)))];
+            tensor<fp32, [?, 32, 80, 998]> input_27 = conv(bias = const_11, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = const_10, x = input_23)[name = string("input_27")];
+            tensor<fp32, [?, 32, 80, 998]> input_29 = relu(x = input_27)[name = string("input_29")];
+            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_12 = const()[name = string("const_12"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7135232)))];
+            tensor<fp32, [32]> const_13 = const()[name = string("const_13"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7172160)))];
+            tensor<fp32, [?, 32, 80, 998]> out_3 = conv(bias = const_13, dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = const_12, x = input_29)[name = string("out_3")];
+            tensor<fp32, [?, 32, 80, 998]> input_33 = add(x = out_3, y = input_23)[name = string("input_33")];
+            tensor<fp32, [?, 32, 80, 998]> input_35 = relu(x = input_33)[name = string("input_35")];
+            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_14 = const()[name = string("const_14"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7172352)))];
+            tensor<fp32, [32]> const_15 = const()[name = string("const_15"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7209280)))];
+            tensor<fp32, [?, 32, 80, 998]> input_39 = conv(bias = const_15, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = const_14, x = input_35)[name = string("input_39")];
+            tensor<fp32, [?, 32, 80, 998]> input_41 = relu(x = input_39)[name = string("input_41")];
+            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_16 = const()[name = string("const_16"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7209472)))];
+            tensor<fp32, [32]> const_17 = const()[name = string("const_17"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7246400)))];
+            tensor<fp32, [?, 32, 80, 998]> out_5 = conv(bias = const_17, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = const_16, x = input_41)[name = string("out_5")];
+            tensor<fp32, [?, 32, 80, 998]> input_45 = add(x = out_5, y = input_35)[name = string("input_45")];
+            tensor<fp32, [?, 32, 80, 998]> input_47 = relu(x = input_45)[name = string("input_47")];
+            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 3, 3]> const_18 = const()[name = string("const_18"), val = tensor<fp32, [64, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7246592)))];
+            tensor<fp32, [64]> const_19 = const()[name = string("const_19"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7320384)))];
+            tensor<fp32, [?, 64, 40, 499]> input_51 = conv(bias = const_19, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_18, x = input_47)[name = string("input_51")];
+            tensor<fp32, [?, 64, 40, 499]> input_53 = relu(x = input_51)[name = string("input_53")];
+            string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_55_strides_0 = const()[name = string("input_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_55_dilations_0 = const()[name = string("input_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_55_groups_0 = const()[name = string("input_55_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_20 = const()[name = string("const_20"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7320704)))];
+            tensor<fp32, [64]> const_21 = const()[name = string("const_21"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7468224)))];
+            tensor<fp32, [?, 64, 40, 499]> out_7 = conv(bias = const_21, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = const_20, x = input_53)[name = string("out_7")];
+            string input_57_pad_type_0 = const()[name = string("input_57_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_57_strides_0 = const()[name = string("input_57_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_57_dilations_0 = const()[name = string("input_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_57_groups_0 = const()[name = string("input_57_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 1, 1]> const_22 = const()[name = string("const_22"), val = tensor<fp32, [64, 32, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7468544)))];
+            tensor<fp32, [64]> const_23 = const()[name = string("const_23"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7476800)))];
+            tensor<fp32, [?, 64, 40, 499]> var_243 = conv(bias = const_23, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_22, x = input_47)[name = string("op_243")];
+            tensor<fp32, [?, 64, 40, 499]> input_59 = add(x = out_7, y = var_243)[name = string("input_59")];
+            tensor<fp32, [?, 64, 40, 499]> input_61 = relu(x = input_59)[name = string("input_61")];
+            string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_24 = const()[name = string("const_24"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7477120)))];
+            tensor<fp32, [64]> const_25 = const()[name = string("const_25"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7624640)))];
+            tensor<fp32, [?, 64, 40, 499]> input_65 = conv(bias = const_25, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = const_24, x = input_61)[name = string("input_65")];
+            tensor<fp32, [?, 64, 40, 499]> input_67 = relu(x = input_65)[name = string("input_67")];
+            string input_69_pad_type_0 = const()[name = string("input_69_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_69_pad_0 = const()[name = string("input_69_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_69_strides_0 = const()[name = string("input_69_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_69_dilations_0 = const()[name = string("input_69_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_69_groups_0 = const()[name = string("input_69_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_26 = const()[name = string("const_26"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7624960)))];
+            tensor<fp32, [64]> const_27 = const()[name = string("const_27"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7772480)))];
+            tensor<fp32, [?, 64, 40, 499]> out_9 = conv(bias = const_27, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = const_26, x = input_67)[name = string("out_9")];
+            tensor<fp32, [?, 64, 40, 499]> input_71 = add(x = out_9, y = input_61)[name = string("input_71")];
+            tensor<fp32, [?, 64, 40, 499]> input_73 = relu(x = input_71)[name = string("input_73")];
+            string input_75_pad_type_0 = const()[name = string("input_75_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_75_pad_0 = const()[name = string("input_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_75_strides_0 = const()[name = string("input_75_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_75_dilations_0 = const()[name = string("input_75_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_75_groups_0 = const()[name = string("input_75_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_28 = const()[name = string("const_28"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7772800)))];
+            tensor<fp32, [64]> const_29 = const()[name = string("const_29"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7920320)))];
+            tensor<fp32, [?, 64, 40, 499]> input_77 = conv(bias = const_29, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = const_28, x = input_73)[name = string("input_77")];
+            tensor<fp32, [?, 64, 40, 499]> input_79 = relu(x = input_77)[name = string("input_79")];
+            string input_81_pad_type_0 = const()[name = string("input_81_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_81_pad_0 = const()[name = string("input_81_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_81_strides_0 = const()[name = string("input_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_81_dilations_0 = const()[name = string("input_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_81_groups_0 = const()[name = string("input_81_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_30 = const()[name = string("const_30"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7920640)))];
+            tensor<fp32, [64]> const_31 = const()[name = string("const_31"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8068160)))];
+            tensor<fp32, [?, 64, 40, 499]> out_11 = conv(bias = const_31, dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = const_30, x = input_79)[name = string("out_11")];
+            tensor<fp32, [?, 64, 40, 499]> input_83 = add(x = out_11, y = input_73)[name = string("input_83")];
+            tensor<fp32, [?, 64, 40, 499]> input_85 = relu(x = input_83)[name = string("input_85")];
+            string input_87_pad_type_0 = const()[name = string("input_87_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_87_pad_0 = const()[name = string("input_87_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_87_strides_0 = const()[name = string("input_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_87_dilations_0 = const()[name = string("input_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_87_groups_0 = const()[name = string("input_87_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_32 = const()[name = string("const_32"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8068480)))];
+            tensor<fp32, [64]> const_33 = const()[name = string("const_33"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8216000)))];
+            tensor<fp32, [?, 64, 40, 499]> input_89 = conv(bias = const_33, dilations = input_87_dilations_0, groups = input_87_groups_0, pad = input_87_pad_0, pad_type = input_87_pad_type_0, strides = input_87_strides_0, weight = const_32, x = input_85)[name = string("input_89")];
+            tensor<fp32, [?, 64, 40, 499]> input_91 = relu(x = input_89)[name = string("input_91")];
+            string input_93_pad_type_0 = const()[name = string("input_93_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_93_pad_0 = const()[name = string("input_93_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_93_strides_0 = const()[name = string("input_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_93_dilations_0 = const()[name = string("input_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_93_groups_0 = const()[name = string("input_93_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_34 = const()[name = string("const_34"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8216320)))];
+            tensor<fp32, [64]> const_35 = const()[name = string("const_35"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8363840)))];
+            tensor<fp32, [?, 64, 40, 499]> out_13 = conv(bias = const_35, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = const_34, x = input_91)[name = string("out_13")];
+            tensor<fp32, [?, 64, 40, 499]> input_95 = add(x = out_13, y = input_85)[name = string("input_95")];
+            tensor<fp32, [?, 64, 40, 499]> input_97 = relu(x = input_95)[name = string("input_97")];
+            string input_99_pad_type_0 = const()[name = string("input_99_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_99_pad_0 = const()[name = string("input_99_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_99_strides_0 = const()[name = string("input_99_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_99_dilations_0 = const()[name = string("input_99_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_99_groups_0 = const()[name = string("input_99_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 3, 3]> const_36 = const()[name = string("const_36"), val = tensor<fp32, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8364160)))];
+            tensor<fp32, [128]> const_37 = const()[name = string("const_37"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8659136)))];
+            tensor<fp32, [?, 128, 20, 250]> input_101 = conv(bias = const_37, dilations = input_99_dilations_0, groups = input_99_groups_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = const_36, x = input_97)[name = string("input_101")];
+            tensor<fp32, [?, 128, 20, 250]> input_103 = relu(x = input_101)[name = string("input_103")];
+            string input_105_pad_type_0 = const()[name = string("input_105_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_105_pad_0 = const()[name = string("input_105_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_105_strides_0 = const()[name = string("input_105_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_105_dilations_0 = const()[name = string("input_105_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_105_groups_0 = const()[name = string("input_105_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_38 = const()[name = string("const_38"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8659712)))];
+            tensor<fp32, [128]> const_39 = const()[name = string("const_39"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9249600)))];
+            tensor<fp32, [?, 128, 20, 250]> out_15 = conv(bias = const_39, dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = const_38, x = input_103)[name = string("out_15")];
+            string input_107_pad_type_0 = const()[name = string("input_107_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_107_strides_0 = const()[name = string("input_107_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_107_dilations_0 = const()[name = string("input_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_107_groups_0 = const()[name = string("input_107_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 1, 1]> const_40 = const()[name = string("const_40"), val = tensor<fp32, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9250176)))];
+            tensor<fp32, [128]> const_41 = const()[name = string("const_41"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9283008)))];
+            tensor<fp32, [?, 128, 20, 250]> var_379 = conv(bias = const_41, dilations = input_107_dilations_0, groups = input_107_groups_0, pad = input_107_pad_0, pad_type = input_107_pad_type_0, strides = input_107_strides_0, weight = const_40, x = input_97)[name = string("op_379")];
+            tensor<fp32, [?, 128, 20, 250]> input_109 = add(x = out_15, y = var_379)[name = string("input_109")];
+            tensor<fp32, [?, 128, 20, 250]> input_111 = relu(x = input_109)[name = string("input_111")];
+            string input_113_pad_type_0 = const()[name = string("input_113_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_113_pad_0 = const()[name = string("input_113_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_113_strides_0 = const()[name = string("input_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_113_dilations_0 = const()[name = string("input_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_113_groups_0 = const()[name = string("input_113_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_42 = const()[name = string("const_42"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9283584)))];
+            tensor<fp32, [128]> const_43 = const()[name = string("const_43"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9873472)))];
+            tensor<fp32, [?, 128, 20, 250]> input_115 = conv(bias = const_43, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = const_42, x = input_111)[name = string("input_115")];
+            tensor<fp32, [?, 128, 20, 250]> input_117 = relu(x = input_115)[name = string("input_117")];
+            string input_119_pad_type_0 = const()[name = string("input_119_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_119_strides_0 = const()[name = string("input_119_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_119_dilations_0 = const()[name = string("input_119_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_119_groups_0 = const()[name = string("input_119_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_44 = const()[name = string("const_44"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9874048)))];
+            tensor<fp32, [128]> const_45 = const()[name = string("const_45"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10463936)))];
+            tensor<fp32, [?, 128, 20, 250]> out_17 = conv(bias = const_45, dilations = input_119_dilations_0, groups = input_119_groups_0, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = input_119_strides_0, weight = const_44, x = input_117)[name = string("out_17")];
+            tensor<fp32, [?, 128, 20, 250]> input_121 = add(x = out_17, y = input_111)[name = string("input_121")];
+            tensor<fp32, [?, 128, 20, 250]> input_123 = relu(x = input_121)[name = string("input_123")];
+            string input_125_pad_type_0 = const()[name = string("input_125_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_125_pad_0 = const()[name = string("input_125_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_125_strides_0 = const()[name = string("input_125_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_125_dilations_0 = const()[name = string("input_125_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_125_groups_0 = const()[name = string("input_125_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_46 = const()[name = string("const_46"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10464512)))];
+            tensor<fp32, [128]> const_47 = const()[name = string("const_47"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11054400)))];
+            tensor<fp32, [?, 128, 20, 250]> input_127 = conv(bias = const_47, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = const_46, x = input_123)[name = string("input_127")];
+            tensor<fp32, [?, 128, 20, 250]> input_129 = relu(x = input_127)[name = string("input_129")];
+            string input_131_pad_type_0 = const()[name = string("input_131_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_131_strides_0 = const()[name = string("input_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_131_dilations_0 = const()[name = string("input_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_131_groups_0 = const()[name = string("input_131_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_48 = const()[name = string("const_48"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11054976)))];
+            tensor<fp32, [128]> const_49 = const()[name = string("const_49"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11644864)))];
+            tensor<fp32, [?, 128, 20, 250]> out_19 = conv(bias = const_49, dilations = input_131_dilations_0, groups = input_131_groups_0, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = input_131_strides_0, weight = const_48, x = input_129)[name = string("out_19")];
+            tensor<fp32, [?, 128, 20, 250]> input_133 = add(x = out_19, y = input_123)[name = string("input_133")];
+            tensor<fp32, [?, 128, 20, 250]> input_135 = relu(x = input_133)[name = string("input_135")];
+            string input_137_pad_type_0 = const()[name = string("input_137_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_137_pad_0 = const()[name = string("input_137_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_137_strides_0 = const()[name = string("input_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_137_dilations_0 = const()[name = string("input_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_137_groups_0 = const()[name = string("input_137_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_50 = const()[name = string("const_50"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11645440)))];
+            tensor<fp32, [128]> const_51 = const()[name = string("const_51"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12235328)))];
+            tensor<fp32, [?, 128, 20, 250]> input_139 = conv(bias = const_51, dilations = input_137_dilations_0, groups = input_137_groups_0, pad = input_137_pad_0, pad_type = input_137_pad_type_0, strides = input_137_strides_0, weight = const_50, x = input_135)[name = string("input_139")];
+            tensor<fp32, [?, 128, 20, 250]> input_141 = relu(x = input_139)[name = string("input_141")];
+            string input_143_pad_type_0 = const()[name = string("input_143_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_143_strides_0 = const()[name = string("input_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_143_dilations_0 = const()[name = string("input_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_143_groups_0 = const()[name = string("input_143_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_52 = const()[name = string("const_52"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12235904)))];
+            tensor<fp32, [128]> const_53 = const()[name = string("const_53"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12825792)))];
+            tensor<fp32, [?, 128, 20, 250]> out_21 = conv(bias = const_53, dilations = input_143_dilations_0, groups = input_143_groups_0, pad = input_143_pad_0, pad_type = input_143_pad_type_0, strides = input_143_strides_0, weight = const_52, x = input_141)[name = string("out_21")];
+            tensor<fp32, [?, 128, 20, 250]> input_145 = add(x = out_21, y = input_135)[name = string("input_145")];
+            tensor<fp32, [?, 128, 20, 250]> input_147 = relu(x = input_145)[name = string("input_147")];
+            string input_149_pad_type_0 = const()[name = string("input_149_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_149_pad_0 = const()[name = string("input_149_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_149_strides_0 = const()[name = string("input_149_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_149_dilations_0 = const()[name = string("input_149_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_149_groups_0 = const()[name = string("input_149_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_54 = const()[name = string("const_54"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12826368)))];
+            tensor<fp32, [128]> const_55 = const()[name = string("const_55"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13416256)))];
+            tensor<fp32, [?, 128, 20, 250]> input_151 = conv(bias = const_55, dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = const_54, x = input_147)[name = string("input_151")];
+            tensor<fp32, [?, 128, 20, 250]> input_153 = relu(x = input_151)[name = string("input_153")];
+            string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_56 = const()[name = string("const_56"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13416832)))];
+            tensor<fp32, [128]> const_57 = const()[name = string("const_57"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14006720)))];
+            tensor<fp32, [?, 128, 20, 250]> out_23 = conv(bias = const_57, dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = const_56, x = input_153)[name = string("out_23")];
+            tensor<fp32, [?, 128, 20, 250]> input_157 = add(x = out_23, y = input_147)[name = string("input_157")];
+            tensor<fp32, [?, 128, 20, 250]> input_159 = relu(x = input_157)[name = string("input_159")];
+            string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_161_strides_0 = const()[name = string("input_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_161_dilations_0 = const()[name = string("input_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_161_groups_0 = const()[name = string("input_161_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_58 = const()[name = string("const_58"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14007296)))];
+            tensor<fp32, [128]> const_59 = const()[name = string("const_59"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14597184)))];
+            tensor<fp32, [?, 128, 20, 250]> input_163 = conv(bias = const_59, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_58, x = input_159)[name = string("input_163")];
+            tensor<fp32, [?, 128, 20, 250]> input_165 = relu(x = input_163)[name = string("input_165")];
+            string input_167_pad_type_0 = const()[name = string("input_167_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_167_pad_0 = const()[name = string("input_167_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_167_strides_0 = const()[name = string("input_167_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_167_dilations_0 = const()[name = string("input_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_167_groups_0 = const()[name = string("input_167_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_60 = const()[name = string("const_60"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14597760)))];
+            tensor<fp32, [128]> const_61 = const()[name = string("const_61"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15187648)))];
+            tensor<fp32, [?, 128, 20, 250]> out_25 = conv(bias = const_61, dilations = input_167_dilations_0, groups = input_167_groups_0, pad = input_167_pad_0, pad_type = input_167_pad_type_0, strides = input_167_strides_0, weight = const_60, x = input_165)[name = string("out_25")];
+            tensor<fp32, [?, 128, 20, 250]> input_169 = add(x = out_25, y = input_159)[name = string("input_169")];
+            tensor<fp32, [?, 128, 20, 250]> input_171 = relu(x = input_169)[name = string("input_171")];
+            string input_173_pad_type_0 = const()[name = string("input_173_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_173_pad_0 = const()[name = string("input_173_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_173_strides_0 = const()[name = string("input_173_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_173_dilations_0 = const()[name = string("input_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_173_groups_0 = const()[name = string("input_173_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 3, 3]> const_62 = const()[name = string("const_62"), val = tensor<fp32, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15188224)))];
+            tensor<fp32, [256]> const_63 = const()[name = string("const_63"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16367936)))];
+            tensor<fp32, [?, 256, 10, 125]> input_175 = conv(bias = const_63, dilations = input_173_dilations_0, groups = input_173_groups_0, pad = input_173_pad_0, pad_type = input_173_pad_type_0, strides = input_173_strides_0, weight = const_62, x = input_171)[name = string("input_175")];
+            tensor<fp32, [?, 256, 10, 125]> input_177 = relu(x = input_175)[name = string("input_177")];
+            string input_179_pad_type_0 = const()[name = string("input_179_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_179_pad_0 = const()[name = string("input_179_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_179_strides_0 = const()[name = string("input_179_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_179_dilations_0 = const()[name = string("input_179_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_179_groups_0 = const()[name = string("input_179_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_64 = const()[name = string("const_64"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16369024)))];
+            tensor<fp32, [256]> const_65 = const()[name = string("const_65"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18728384)))];
+            tensor<fp32, [?, 256, 10, 125]> out_27 = conv(bias = const_65, dilations = input_179_dilations_0, groups = input_179_groups_0, pad = input_179_pad_0, pad_type = input_179_pad_type_0, strides = input_179_strides_0, weight = const_64, x = input_177)[name = string("out_27")];
+            string input_181_pad_type_0 = const()[name = string("input_181_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_181_strides_0 = const()[name = string("input_181_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_181_dilations_0 = const()[name = string("input_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_181_groups_0 = const()[name = string("input_181_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 1, 1]> const_66 = const()[name = string("const_66"), val = tensor<fp32, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18729472)))];
+            tensor<fp32, [256]> const_67 = const()[name = string("const_67"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18860608)))];
+            tensor<fp32, [?, 256, 10, 125]> var_570 = conv(bias = const_67, dilations = input_181_dilations_0, groups = input_181_groups_0, pad = input_181_pad_0, pad_type = input_181_pad_type_0, strides = input_181_strides_0, weight = const_66, x = input_171)[name = string("op_570")];
+            tensor<fp32, [?, 256, 10, 125]> input_183 = add(x = out_27, y = var_570)[name = string("input_183")];
+            tensor<fp32, [?, 256, 10, 125]> input_185 = relu(x = input_183)[name = string("input_185")];
+            string input_187_pad_type_0 = const()[name = string("input_187_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_187_pad_0 = const()[name = string("input_187_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_187_strides_0 = const()[name = string("input_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_187_dilations_0 = const()[name = string("input_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_187_groups_0 = const()[name = string("input_187_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_68 = const()[name = string("const_68"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18861696)))];
+            tensor<fp32, [256]> const_69 = const()[name = string("const_69"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21221056)))];
+            tensor<fp32, [?, 256, 10, 125]> input_189 = conv(bias = const_69, dilations = input_187_dilations_0, groups = input_187_groups_0, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = input_187_strides_0, weight = const_68, x = input_185)[name = string("input_189")];
+            tensor<fp32, [?, 256, 10, 125]> input_191 = relu(x = input_189)[name = string("input_191")];
+            string input_193_pad_type_0 = const()[name = string("input_193_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_193_pad_0 = const()[name = string("input_193_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_193_strides_0 = const()[name = string("input_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_193_dilations_0 = const()[name = string("input_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_193_groups_0 = const()[name = string("input_193_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_70 = const()[name = string("const_70"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21222144)))];
+            tensor<fp32, [256]> const_71 = const()[name = string("const_71"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23581504)))];
+            tensor<fp32, [?, 256, 10, 125]> out_29 = conv(bias = const_71, dilations = input_193_dilations_0, groups = input_193_groups_0, pad = input_193_pad_0, pad_type = input_193_pad_type_0, strides = input_193_strides_0, weight = const_70, x = input_191)[name = string("out_29")];
+            tensor<fp32, [?, 256, 10, 125]> input_195 = add(x = out_29, y = input_185)[name = string("input_195")];
+            tensor<fp32, [?, 256, 10, 125]> input_197 = relu(x = input_195)[name = string("input_197")];
+            string input_199_pad_type_0 = const()[name = string("input_199_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_199_pad_0 = const()[name = string("input_199_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_199_strides_0 = const()[name = string("input_199_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_199_dilations_0 = const()[name = string("input_199_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_199_groups_0 = const()[name = string("input_199_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_72 = const()[name = string("const_72"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23582592)))];
+            tensor<fp32, [256]> const_73 = const()[name = string("const_73"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25941952)))];
+            tensor<fp32, [?, 256, 10, 125]> input_201 = conv(bias = const_73, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = const_72, x = input_197)[name = string("input_201")];
+            tensor<fp32, [?, 256, 10, 125]> input_203 = relu(x = input_201)[name = string("input_203")];
+            string input_205_pad_type_0 = const()[name = string("input_205_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_205_pad_0 = const()[name = string("input_205_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_205_strides_0 = const()[name = string("input_205_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_205_dilations_0 = const()[name = string("input_205_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_205_groups_0 = const()[name = string("input_205_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_74 = const()[name = string("const_74"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25943040)))];
+            tensor<fp32, [256]> const_75 = const()[name = string("const_75"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28302400)))];
+            tensor<fp32, [?, 256, 10, 125]> out = conv(bias = const_75, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_74, x = input_203)[name = string("out")];
+            tensor<fp32, [?, 256, 10, 125]> input_207 = add(x = out, y = input_197)[name = string("input_207")];
+            tensor<fp32, [?, 256, 10, 125]> frames = relu(x = input_207)[name = string("frames")];
+            tensor<int32, [3]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp32, [?, 2560, 125]> sequences = reshape(shape = concat_0x, x = frames)[name = string("sequences")];
+            tensor<int32, [1]> input_209_axes_0 = const()[name = string("input_209_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 1, 589]> input_209 = expand_dims(axes = input_209_axes_0, x = weights)[name = string("input_209")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [?, 1, 589, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input_209)[name = string("expand_dims_0")];
+            fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)];
+            fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [?, 1, 125, 1]> upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")];
+            tensor<int32, [1]> weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [?, 1, 125]> weights_1 = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0)[name = string("weights")];
+            tensor<int32, [1]> weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 1]> weight_sum = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights_1)[name = string("weight_sum")];
+            tensor<bool, [?, 1]> var_646 = greater(x = weight_sum, y = var_69)[name = string("op_646")];
+            fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [?, 1]> fill_like_0 = fill_like(ref_tensor = weight_sum, value = fill_like_0_value_0)[name = string("fill_like_0")];
+            tensor<fp32, [?, 1]> safe_sum = select(a = weight_sum, b = fill_like_0, cond = var_646)[name = string("safe_sum")];
+            tensor<fp32, [?, 2560, 125]> var_649 = mul(x = sequences, y = weights_1)[name = string("op_649")];
+            tensor<int32, [1]> var_651_axes_0 = const()[name = string("op_651_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_651_keep_dims_0 = const()[name = string("op_651_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 2560]> var_651 = reduce_sum(axes = var_651_axes_0, keep_dims = var_651_keep_dims_0, x = var_649)[name = string("op_651")];
+            tensor<fp32, [?, 2560]> mean = real_div(x = var_651, y = safe_sum)[name = string("mean")];
+            tensor<int32, [1]> var_653_axes_0 = const()[name = string("op_653_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [?, 2560, 1]> var_653 = expand_dims(axes = var_653_axes_0, x = mean)[name = string("op_653")];
+            tensor<fp32, [?, 2560, 125]> var_654 = sub(x = sequences, y = var_653)[name = string("op_654")];
+            tensor<fp32, [?, 2560, 125]> dx2 = mul(x = var_654, y = var_654)[name = string("dx2")];
+            tensor<fp32, [?, 1, 125]> var_656 = mul(x = weights_1, y = weights_1)[name = string("op_656")];
+            tensor<int32, [1]> weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 1]> weight_sq_sum = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_656)[name = string("weight_sq_sum")];
+            tensor<fp32, [?, 1]> var_659 = real_div(x = weight_sq_sum, y = safe_sum)[name = string("op_659")];
+            tensor<fp32, [?, 1]> var_660 = sub(x = safe_sum, y = var_659)[name = string("op_660")];
+            fp32 var_661 = const()[name = string("op_661"), val = fp32(0x1.5798eep-27)];
+            tensor<fp32, [?, 1]> denom = add(x = var_660, y = var_661)[name = string("denom")];
+            tensor<fp32, [?, 2560, 125]> var_663 = mul(x = dx2, y = weights_1)[name = string("op_663")];
+            tensor<int32, [1]> var_665_axes_0 = const()[name = string("op_665_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_665_keep_dims_0 = const()[name = string("op_665_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 2560]> var_665 = reduce_sum(axes = var_665_axes_0, keep_dims = var_665_keep_dims_0, x = var_663)[name = string("op_665")];
+            tensor<fp32, [?, 2560]> var = real_div(x = var_665, y = denom)[name = string("var")];
+            tensor<fp32, [?, 2560]> var_667 = maximum(x = var, y = var_68)[name = string("op_667")];
+            tensor<fp32, [?, 2560]> std = sqrt(x = var_667)[name = string("std")];
+            bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)];
+            tensor<fp32, [?, 5120]> stats = concat(axis = var_67, interleave = stats_interleave_0, values = (mean, std))[name = string("stats")];
+            tensor<fp32, [?, 2560]> var_671 = sub(x = mean, y = mean)[name = string("sub_0")];
+            fp32 var_672_value_0 = const()[name = string("op_672_value_0"), val = fp32(0x1.4f8b58p-17)];
+            tensor<fp32, [?, 2560]> var_672 = fill_like(ref_tensor = std, value = var_672_value_0)[name = string("op_672")];
+            bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)];
+            tensor<fp32, [?, 5120]> zero_stats = concat(axis = var_67, interleave = zero_stats_interleave_0, values = (var_671, var_672))[name = string("zero_stats")];
+            tensor<bool, [?, 1]> var_675 = less_equal(x = weight_sum, y = var_69)[name = string("op_675")];
+            tensor<int32, [2]> var_677 = const()[name = string("op_677"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [?, 5120]> zero_mask = tile(reps = var_677, x = var_675)[name = string("zero_mask")];
+            tensor<fp32, [?, 5120]> input = select(a = zero_stats, b = stats, cond = zero_mask)[name = string("input")];
+            tensor<fp32, [?, 256]> output = linear(bias = tail_resnet_seg_1_bias, weight = tail_resnet_seg_1_weight, x = input)[name = string("linear_0")];
+        } -> (output);
+}

wespeaker-voxceleb-resnet34-fused-b32.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:741dd1d7fb08a55128b8e5aa0371bc3dd522f9f99d58f692b66b8733853b9b27
+size 28303488

wespeaker-voxceleb-resnet34-fused-f16.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3fd492b62d5e26ad34278cea685f931276bb94ab564ca6db80d832f87135f69d
+size 243

wespeaker-voxceleb-resnet34-fused-f16.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f279b672793ba4f1d246f69442be874b49fc275f6c9b08f6ab85b57dc0bbe68
+size 225

wespeaker-voxceleb-resnet34-fused-f16.mlmodelc/model.mil

@@ -0,0 +1,468 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [?, 1, 160000]> waveform, tensor<fp32, [?, 589]> weights) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"waveform", [32, 1, 160000]}, {"weights", [32, 589]}}), ("EnumeratedShapes", {{"3c334fba", {{"waveform", [3, 1, 160000]}, {"weights", [3, 589]}}}, {"79c53add", {{"waveform", [1, 1, 160000]}, {"weights", [1, 589]}}}, {"cb01bf12", {{"waveform", [32, 1, 160000]}, {"weights", [32, 589]}}}})))] {
+            tensor<int32, [3]> var_27_begin_0 = const()[name = string("op_27_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> var_27_end_0 = const()[name = string("op_27_end_0"), val = tensor<int32, [3]>([0, 1, 160000])];
+            tensor<bool, [3]> var_27_end_mask_0 = const()[name = string("op_27_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            string waveform_to_fp16_dtype_0 = const()[name = string("waveform_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [?, 1, 160000]> waveform_to_fp16 = cast(dtype = waveform_to_fp16_dtype_0, x = waveform)[name = string("cast_10")];
+            tensor<fp16, [?, 1, 160000]> var_27_cast_fp16 = slice_by_index(begin = var_27_begin_0, end = var_27_end_0, end_mask = var_27_end_mask_0, x = waveform_to_fp16)[name = string("op_27_cast_fp16")];
+            fp16 var_29_to_fp16 = const()[name = string("op_29_to_fp16"), val = fp16(0x1p+15)];
+            tensor<fp16, [?, 1, 160000]> signal_cast_fp16 = mul(x = var_27_cast_fp16, y = var_29_to_fp16)[name = string("signal_cast_fp16")];
+            string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [2]> frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)];
+            tensor<fp16, [400, 1, 400]> fbank_identity_kernel_to_fp16 = const()[name = string("fbank_identity_kernel_to_fp16"), val = tensor<fp16, [400, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [?, 400, 998]> frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = fbank_identity_kernel_to_fp16, x = signal_cast_fp16)[name = string("frames_1_cast_fp16")];
+            tensor<int32, [3]> var_36 = const()[name = string("op_36"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_39_axes_0 = const()[name = string("op_39_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_39_keep_dims_0 = const()[name = string("op_39_keep_dims_0"), val = bool(true)];
+            tensor<fp16, [?, 998, 400]> frames_3_cast_fp16 = transpose(perm = var_36, x = frames_1_cast_fp16)[name = string("transpose_4")];
+            tensor<fp16, [?, 998, 1]> var_39_cast_fp16 = reduce_mean(axes = var_39_axes_0, keep_dims = var_39_keep_dims_0, x = frames_3_cast_fp16)[name = string("op_39_cast_fp16")];
+            tensor<fp16, [?, 998, 400]> input_1_cast_fp16 = sub(x = frames_3_cast_fp16, y = var_39_cast_fp16)[name = string("input_1_cast_fp16")];
+            tensor<int32, [6]> var_42_pad_0 = const()[name = string("op_42_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])];
+            string var_42_mode_0 = const()[name = string("op_42_mode_0"), val = string("replicate")];
+            fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [?, 998, 401]> var_42_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = var_42_mode_0, pad = var_42_pad_0, x = input_1_cast_fp16)[name = string("op_42_cast_fp16")];
+            tensor<int32, [3]> previous_begin_0 = const()[name = string("previous_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> previous_end_0 = const()[name = string("previous_end_0"), val = tensor<int32, [3]>([0, 998, 400])];
+            tensor<bool, [3]> previous_end_mask_0 = const()[name = string("previous_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<fp16, [?, 998, 400]> previous_cast_fp16 = slice_by_index(begin = previous_begin_0, end = previous_end_0, end_mask = previous_end_mask_0, x = var_42_cast_fp16)[name = string("previous_cast_fp16")];
+            fp16 var_44_to_fp16 = const()[name = string("op_44_to_fp16"), val = fp16(0x1.f0cp-1)];
+            tensor<fp16, [?, 998, 400]> var_45_cast_fp16 = mul(x = previous_cast_fp16, y = var_44_to_fp16)[name = string("op_45_cast_fp16")];
+            tensor<fp16, [?, 998, 400]> frames_5_cast_fp16 = sub(x = input_1_cast_fp16, y = var_45_cast_fp16)[name = string("frames_5_cast_fp16")];
+            tensor<fp16, [1, 1, 400]> var_48_to_fp16 = const()[name = string("op_48_to_fp16"), val = tensor<fp16, [1, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320128)))];
+            tensor<fp16, [?, 998, 400]> input_3_cast_fp16 = mul(x = frames_5_cast_fp16, y = var_48_to_fp16)[name = string("input_3_cast_fp16")];
+            tensor<int32, [6]> frames_7_pad_0 = const()[name = string("frames_7_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 112])];
+            string frames_7_mode_0 = const()[name = string("frames_7_mode_0"), val = string("constant")];
+            fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [?, 998, 512]> frames_7_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = frames_7_mode_0, pad = frames_7_pad_0, x = input_3_cast_fp16)[name = string("frames_7_cast_fp16")];
+            tensor<fp16, [257, 512]> fbank_dft_cos_to_fp16 = const()[name = string("fbank_dft_cos_to_fp16"), val = tensor<fp16, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321024)))];
+            tensor<fp16, [257]> real_part_bias_0_to_fp16 = const()[name = string("real_part_bias_0_to_fp16"), val = tensor<fp16, [257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(584256)))];
+            tensor<fp16, [?, 998, 257]> real_part_cast_fp16 = linear(bias = real_part_bias_0_to_fp16, weight = fbank_dft_cos_to_fp16, x = frames_7_cast_fp16)[name = string("real_part_cast_fp16")];
+            tensor<fp16, [257, 512]> fbank_dft_sin_to_fp16 = const()[name = string("fbank_dft_sin_to_fp16"), val = tensor<fp16, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(584896)))];
+            tensor<fp16, [?, 998, 257]> imag_part_cast_fp16 = linear(bias = real_part_bias_0_to_fp16, weight = fbank_dft_sin_to_fp16, x = frames_7_cast_fp16)[name = string("imag_part_cast_fp16")];
+            fp16 var_7_to_fp16 = const()[name = string("op_7_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [?, 998, 257]> var_56_cast_fp16 = pow(x = real_part_cast_fp16, y = var_7_to_fp16)[name = string("op_56_cast_fp16")];
+            tensor<fp16, [?, 998, 257]> var_57_cast_fp16 = pow(x = imag_part_cast_fp16, y = var_7_to_fp16)[name = string("op_57_cast_fp16")];
+            tensor<fp16, [?, 998, 257]> spectrum_cast_fp16 = add(x = var_56_cast_fp16, y = var_57_cast_fp16)[name = string("spectrum_cast_fp16")];
+            tensor<fp16, [80, 257]> transpose_2_to_fp16 = const()[name = string("transpose_2_to_fp16"), val = tensor<fp16, [80, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(848128)))];
+            tensor<fp16, [80]> mel_1_bias_0_to_fp16 = const()[name = string("mel_1_bias_0_to_fp16"), val = tensor<fp16, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889344)))];
+            tensor<fp16, [?, 998, 80]> mel_1_cast_fp16 = linear(bias = mel_1_bias_0_to_fp16, weight = transpose_2_to_fp16, x = spectrum_cast_fp16)[name = string("mel_1_cast_fp16")];
+            fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x1p-23)];
+            tensor<fp16, [?, 998, 80]> var_62_cast_fp16 = maximum(x = mel_1_cast_fp16, y = const_3_to_fp16)[name = string("op_62_cast_fp16")];
+            fp32 mel_3_epsilon_0 = const()[name = string("mel_3_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [?, 998, 80]> mel_3_cast_fp16 = log(epsilon = mel_3_epsilon_0, x = var_62_cast_fp16)[name = string("mel_3_cast_fp16")];
+            tensor<int32, [1]> var_65_axes_0 = const()[name = string("op_65_axes_0"), val = tensor<int32, [1]>([1])];
+            bool var_65_keep_dims_0 = const()[name = string("op_65_keep_dims_0"), val = bool(true)];
+            tensor<fp16, [?, 1, 80]> var_65_cast_fp16 = reduce_mean(axes = var_65_axes_0, keep_dims = var_65_keep_dims_0, x = mel_3_cast_fp16)[name = string("op_65_cast_fp16")];
+            tensor<fp16, [?, 998, 80]> fbank_1_cast_fp16 = sub(x = mel_3_cast_fp16, y = var_65_cast_fp16)[name = string("fbank_1_cast_fp16")];
+            int32 var_67 = const()[name = string("op_67"), val = int32(-1)];
+            tensor<int32, [3]> var_94 = const()[name = string("op_94"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [?, 80, 998]> fbank_3_cast_fp16 = transpose(perm = var_94, x = fbank_1_cast_fp16)[name = string("transpose_3")];
+            tensor<fp16, [?, 1, 80, 998]> input_5_cast_fp16 = expand_dims(axes = input_5_axes_0, x = fbank_3_cast_fp16)[name = string("input_5_cast_fp16")];
+            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 1, 3, 3]> const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = tensor<fp16, [32, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889600)))];
+            tensor<fp16, [32]> const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(890240)))];
+            tensor<fp16, [?, 32, 80, 998]> input_9_cast_fp16 = conv(bias = const_5_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = const_4_to_fp16, x = input_5_cast_fp16)[name = string("input_9_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = string("input_11_cast_fp16")];
+            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(890368)))];
+            tensor<fp16, [32]> const_7_to_fp16 = const()[name = string("const_7_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908864)))];
+            tensor<fp16, [?, 32, 80, 998]> input_15_cast_fp16 = conv(bias = const_7_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = const_6_to_fp16, x = input_11_cast_fp16)[name = string("input_15_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_17_cast_fp16 = relu(x = input_15_cast_fp16)[name = string("input_17_cast_fp16")];
+            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_19_strides_0 = const()[name = string("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_19_dilations_0 = const()[name = string("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_19_groups_0 = const()[name = string("input_19_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908992)))];
+            tensor<fp16, [32]> const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(927488)))];
+            tensor<fp16, [?, 32, 80, 998]> out_1_cast_fp16 = conv(bias = const_9_to_fp16, dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = const_8_to_fp16, x = input_17_cast_fp16)[name = string("out_1_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_21_cast_fp16 = add(x = out_1_cast_fp16, y = input_11_cast_fp16)[name = string("input_21_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_23_cast_fp16 = relu(x = input_21_cast_fp16)[name = string("input_23_cast_fp16")];
+            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(927616)))];
+            tensor<fp16, [32]> const_11_to_fp16 = const()[name = string("const_11_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(946112)))];
+            tensor<fp16, [?, 32, 80, 998]> input_27_cast_fp16 = conv(bias = const_11_to_fp16, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = const_10_to_fp16, x = input_23_cast_fp16)[name = string("input_27_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_29_cast_fp16 = relu(x = input_27_cast_fp16)[name = string("input_29_cast_fp16")];
+            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(946240)))];
+            tensor<fp16, [32]> const_13_to_fp16 = const()[name = string("const_13_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(964736)))];
+            tensor<fp16, [?, 32, 80, 998]> out_3_cast_fp16 = conv(bias = const_13_to_fp16, dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = const_12_to_fp16, x = input_29_cast_fp16)[name = string("out_3_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_33_cast_fp16 = add(x = out_3_cast_fp16, y = input_23_cast_fp16)[name = string("input_33_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_35_cast_fp16 = relu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")];
+            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_14_to_fp16 = const()[name = string("const_14_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(964864)))];
+            tensor<fp16, [32]> const_15_to_fp16 = const()[name = string("const_15_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(983360)))];
+            tensor<fp16, [?, 32, 80, 998]> input_39_cast_fp16 = conv(bias = const_15_to_fp16, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = const_14_to_fp16, x = input_35_cast_fp16)[name = string("input_39_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_41_cast_fp16 = relu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")];
+            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(983488)))];
+            tensor<fp16, [32]> const_17_to_fp16 = const()[name = string("const_17_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1001984)))];
+            tensor<fp16, [?, 32, 80, 998]> out_5_cast_fp16 = conv(bias = const_17_to_fp16, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = const_16_to_fp16, x = input_41_cast_fp16)[name = string("out_5_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_45_cast_fp16 = add(x = out_5_cast_fp16, y = input_35_cast_fp16)[name = string("input_45_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_47_cast_fp16 = relu(x = input_45_cast_fp16)[name = string("input_47_cast_fp16")];
+            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 32, 3, 3]> const_18_to_fp16 = const()[name = string("const_18_to_fp16"), val = tensor<fp16, [64, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1002112)))];
+            tensor<fp16, [64]> const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1039040)))];
+            tensor<fp16, [?, 64, 40, 499]> input_51_cast_fp16 = conv(bias = const_19_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_18_to_fp16, x = input_47_cast_fp16)[name = string("input_51_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_53_cast_fp16 = relu(x = input_51_cast_fp16)[name = string("input_53_cast_fp16")];
+            string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_55_strides_0 = const()[name = string("input_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_55_dilations_0 = const()[name = string("input_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_55_groups_0 = const()[name = string("input_55_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_20_to_fp16 = const()[name = string("const_20_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1039232)))];
+            tensor<fp16, [64]> const_21_to_fp16 = const()[name = string("const_21_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1113024)))];
+            tensor<fp16, [?, 64, 40, 499]> out_7_cast_fp16 = conv(bias = const_21_to_fp16, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = const_20_to_fp16, x = input_53_cast_fp16)[name = string("out_7_cast_fp16")];
+            string input_57_pad_type_0 = const()[name = string("input_57_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_57_strides_0 = const()[name = string("input_57_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_57_dilations_0 = const()[name = string("input_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_57_groups_0 = const()[name = string("input_57_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 32, 1, 1]> const_22_to_fp16 = const()[name = string("const_22_to_fp16"), val = tensor<fp16, [64, 32, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1113216)))];
+            tensor<fp16, [64]> const_23_to_fp16 = const()[name = string("const_23_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1117376)))];
+            tensor<fp16, [?, 64, 40, 499]> var_243_cast_fp16 = conv(bias = const_23_to_fp16, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_22_to_fp16, x = input_47_cast_fp16)[name = string("op_243_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_59_cast_fp16 = add(x = out_7_cast_fp16, y = var_243_cast_fp16)[name = string("input_59_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_61_cast_fp16 = relu(x = input_59_cast_fp16)[name = string("input_61_cast_fp16")];
+            string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1117568)))];
+            tensor<fp16, [64]> const_25_to_fp16 = const()[name = string("const_25_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1191360)))];
+            tensor<fp16, [?, 64, 40, 499]> input_65_cast_fp16 = conv(bias = const_25_to_fp16, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = const_24_to_fp16, x = input_61_cast_fp16)[name = string("input_65_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_67_cast_fp16 = relu(x = input_65_cast_fp16)[name = string("input_67_cast_fp16")];
+            string input_69_pad_type_0 = const()[name = string("input_69_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_69_pad_0 = const()[name = string("input_69_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_69_strides_0 = const()[name = string("input_69_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_69_dilations_0 = const()[name = string("input_69_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_69_groups_0 = const()[name = string("input_69_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1191552)))];
+            tensor<fp16, [64]> const_27_to_fp16 = const()[name = string("const_27_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265344)))];
+            tensor<fp16, [?, 64, 40, 499]> out_9_cast_fp16 = conv(bias = const_27_to_fp16, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = const_26_to_fp16, x = input_67_cast_fp16)[name = string("out_9_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_71_cast_fp16 = add(x = out_9_cast_fp16, y = input_61_cast_fp16)[name = string("input_71_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_73_cast_fp16 = relu(x = input_71_cast_fp16)[name = string("input_73_cast_fp16")];
+            string input_75_pad_type_0 = const()[name = string("input_75_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_75_pad_0 = const()[name = string("input_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_75_strides_0 = const()[name = string("input_75_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_75_dilations_0 = const()[name = string("input_75_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_75_groups_0 = const()[name = string("input_75_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265536)))];
+            tensor<fp16, [64]> const_29_to_fp16 = const()[name = string("const_29_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1339328)))];
+            tensor<fp16, [?, 64, 40, 499]> input_77_cast_fp16 = conv(bias = const_29_to_fp16, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = const_28_to_fp16, x = input_73_cast_fp16)[name = string("input_77_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_79_cast_fp16 = relu(x = input_77_cast_fp16)[name = string("input_79_cast_fp16")];
+            string input_81_pad_type_0 = const()[name = string("input_81_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_81_pad_0 = const()[name = string("input_81_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_81_strides_0 = const()[name = string("input_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_81_dilations_0 = const()[name = string("input_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_81_groups_0 = const()[name = string("input_81_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_30_to_fp16 = const()[name = string("const_30_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1339520)))];
+            tensor<fp16, [64]> const_31_to_fp16 = const()[name = string("const_31_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413312)))];
+            tensor<fp16, [?, 64, 40, 499]> out_11_cast_fp16 = conv(bias = const_31_to_fp16, dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = const_30_to_fp16, x = input_79_cast_fp16)[name = string("out_11_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_83_cast_fp16 = add(x = out_11_cast_fp16, y = input_73_cast_fp16)[name = string("input_83_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_85_cast_fp16 = relu(x = input_83_cast_fp16)[name = string("input_85_cast_fp16")];
+            string input_87_pad_type_0 = const()[name = string("input_87_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_87_pad_0 = const()[name = string("input_87_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_87_strides_0 = const()[name = string("input_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_87_dilations_0 = const()[name = string("input_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_87_groups_0 = const()[name = string("input_87_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1413504)))];
+            tensor<fp16, [64]> const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1487296)))];
+            tensor<fp16, [?, 64, 40, 499]> input_89_cast_fp16 = conv(bias = const_33_to_fp16, dilations = input_87_dilations_0, groups = input_87_groups_0, pad = input_87_pad_0, pad_type = input_87_pad_type_0, strides = input_87_strides_0, weight = const_32_to_fp16, x = input_85_cast_fp16)[name = string("input_89_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_91_cast_fp16 = relu(x = input_89_cast_fp16)[name = string("input_91_cast_fp16")];
+            string input_93_pad_type_0 = const()[name = string("input_93_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_93_pad_0 = const()[name = string("input_93_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_93_strides_0 = const()[name = string("input_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_93_dilations_0 = const()[name = string("input_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_93_groups_0 = const()[name = string("input_93_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_34_to_fp16 = const()[name = string("const_34_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1487488)))];
+            tensor<fp16, [64]> const_35_to_fp16 = const()[name = string("const_35_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1561280)))];
+            tensor<fp16, [?, 64, 40, 499]> out_13_cast_fp16 = conv(bias = const_35_to_fp16, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = const_34_to_fp16, x = input_91_cast_fp16)[name = string("out_13_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_95_cast_fp16 = add(x = out_13_cast_fp16, y = input_85_cast_fp16)[name = string("input_95_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_97_cast_fp16 = relu(x = input_95_cast_fp16)[name = string("input_97_cast_fp16")];
+            string input_99_pad_type_0 = const()[name = string("input_99_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_99_pad_0 = const()[name = string("input_99_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_99_strides_0 = const()[name = string("input_99_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_99_dilations_0 = const()[name = string("input_99_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_99_groups_0 = const()[name = string("input_99_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 64, 3, 3]> const_36_to_fp16 = const()[name = string("const_36_to_fp16"), val = tensor<fp16, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1561472)))];
+            tensor<fp16, [128]> const_37_to_fp16 = const()[name = string("const_37_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1708992)))];
+            tensor<fp16, [?, 128, 20, 250]> input_101_cast_fp16 = conv(bias = const_37_to_fp16, dilations = input_99_dilations_0, groups = input_99_groups_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = const_36_to_fp16, x = input_97_cast_fp16)[name = string("input_101_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_103_cast_fp16 = relu(x = input_101_cast_fp16)[name = string("input_103_cast_fp16")];
+            string input_105_pad_type_0 = const()[name = string("input_105_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_105_pad_0 = const()[name = string("input_105_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_105_strides_0 = const()[name = string("input_105_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_105_dilations_0 = const()[name = string("input_105_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_105_groups_0 = const()[name = string("input_105_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_38_to_fp16 = const()[name = string("const_38_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1709312)))];
+            tensor<fp16, [128]> const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2004288)))];
+            tensor<fp16, [?, 128, 20, 250]> out_15_cast_fp16 = conv(bias = const_39_to_fp16, dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = const_38_to_fp16, x = input_103_cast_fp16)[name = string("out_15_cast_fp16")];
+            string input_107_pad_type_0 = const()[name = string("input_107_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_107_strides_0 = const()[name = string("input_107_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_107_dilations_0 = const()[name = string("input_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_107_groups_0 = const()[name = string("input_107_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 64, 1, 1]> const_40_to_fp16 = const()[name = string("const_40_to_fp16"), val = tensor<fp16, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2004608)))];
+            tensor<fp16, [128]> const_41_to_fp16 = const()[name = string("const_41_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2021056)))];
+            tensor<fp16, [?, 128, 20, 250]> var_379_cast_fp16 = conv(bias = const_41_to_fp16, dilations = input_107_dilations_0, groups = input_107_groups_0, pad = input_107_pad_0, pad_type = input_107_pad_type_0, strides = input_107_strides_0, weight = const_40_to_fp16, x = input_97_cast_fp16)[name = string("op_379_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_109_cast_fp16 = add(x = out_15_cast_fp16, y = var_379_cast_fp16)[name = string("input_109_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_111_cast_fp16 = relu(x = input_109_cast_fp16)[name = string("input_111_cast_fp16")];
+            string input_113_pad_type_0 = const()[name = string("input_113_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_113_pad_0 = const()[name = string("input_113_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_113_strides_0 = const()[name = string("input_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_113_dilations_0 = const()[name = string("input_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_113_groups_0 = const()[name = string("input_113_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_42_to_fp16 = const()[name = string("const_42_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2021376)))];
+            tensor<fp16, [128]> const_43_to_fp16 = const()[name = string("const_43_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2316352)))];
+            tensor<fp16, [?, 128, 20, 250]> input_115_cast_fp16 = conv(bias = const_43_to_fp16, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = const_42_to_fp16, x = input_111_cast_fp16)[name = string("input_115_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_117_cast_fp16 = relu(x = input_115_cast_fp16)[name = string("input_117_cast_fp16")];
+            string input_119_pad_type_0 = const()[name = string("input_119_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_119_strides_0 = const()[name = string("input_119_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_119_dilations_0 = const()[name = string("input_119_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_119_groups_0 = const()[name = string("input_119_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2316672)))];
+            tensor<fp16, [128]> const_45_to_fp16 = const()[name = string("const_45_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2611648)))];
+            tensor<fp16, [?, 128, 20, 250]> out_17_cast_fp16 = conv(bias = const_45_to_fp16, dilations = input_119_dilations_0, groups = input_119_groups_0, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = input_119_strides_0, weight = const_44_to_fp16, x = input_117_cast_fp16)[name = string("out_17_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_121_cast_fp16 = add(x = out_17_cast_fp16, y = input_111_cast_fp16)[name = string("input_121_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_123_cast_fp16 = relu(x = input_121_cast_fp16)[name = string("input_123_cast_fp16")];
+            string input_125_pad_type_0 = const()[name = string("input_125_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_125_pad_0 = const()[name = string("input_125_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_125_strides_0 = const()[name = string("input_125_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_125_dilations_0 = const()[name = string("input_125_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_125_groups_0 = const()[name = string("input_125_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2611968)))];
+            tensor<fp16, [128]> const_47_to_fp16 = const()[name = string("const_47_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2906944)))];
+            tensor<fp16, [?, 128, 20, 250]> input_127_cast_fp16 = conv(bias = const_47_to_fp16, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = const_46_to_fp16, x = input_123_cast_fp16)[name = string("input_127_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_129_cast_fp16 = relu(x = input_127_cast_fp16)[name = string("input_129_cast_fp16")];
+            string input_131_pad_type_0 = const()[name = string("input_131_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_131_strides_0 = const()[name = string("input_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_131_dilations_0 = const()[name = string("input_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_131_groups_0 = const()[name = string("input_131_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2907264)))];
+            tensor<fp16, [128]> const_49_to_fp16 = const()[name = string("const_49_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3202240)))];
+            tensor<fp16, [?, 128, 20, 250]> out_19_cast_fp16 = conv(bias = const_49_to_fp16, dilations = input_131_dilations_0, groups = input_131_groups_0, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = input_131_strides_0, weight = const_48_to_fp16, x = input_129_cast_fp16)[name = string("out_19_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_133_cast_fp16 = add(x = out_19_cast_fp16, y = input_123_cast_fp16)[name = string("input_133_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_135_cast_fp16 = relu(x = input_133_cast_fp16)[name = string("input_135_cast_fp16")];
+            string input_137_pad_type_0 = const()[name = string("input_137_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_137_pad_0 = const()[name = string("input_137_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_137_strides_0 = const()[name = string("input_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_137_dilations_0 = const()[name = string("input_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_137_groups_0 = const()[name = string("input_137_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3202560)))];
+            tensor<fp16, [128]> const_51_to_fp16 = const()[name = string("const_51_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3497536)))];
+            tensor<fp16, [?, 128, 20, 250]> input_139_cast_fp16 = conv(bias = const_51_to_fp16, dilations = input_137_dilations_0, groups = input_137_groups_0, pad = input_137_pad_0, pad_type = input_137_pad_type_0, strides = input_137_strides_0, weight = const_50_to_fp16, x = input_135_cast_fp16)[name = string("input_139_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_141_cast_fp16 = relu(x = input_139_cast_fp16)[name = string("input_141_cast_fp16")];
+            string input_143_pad_type_0 = const()[name = string("input_143_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_143_strides_0 = const()[name = string("input_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_143_dilations_0 = const()[name = string("input_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_143_groups_0 = const()[name = string("input_143_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3497856)))];
+            tensor<fp16, [128]> const_53_to_fp16 = const()[name = string("const_53_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3792832)))];
+            tensor<fp16, [?, 128, 20, 250]> out_21_cast_fp16 = conv(bias = const_53_to_fp16, dilations = input_143_dilations_0, groups = input_143_groups_0, pad = input_143_pad_0, pad_type = input_143_pad_type_0, strides = input_143_strides_0, weight = const_52_to_fp16, x = input_141_cast_fp16)[name = string("out_21_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_145_cast_fp16 = add(x = out_21_cast_fp16, y = input_135_cast_fp16)[name = string("input_145_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_147_cast_fp16 = relu(x = input_145_cast_fp16)[name = string("input_147_cast_fp16")];
+            string input_149_pad_type_0 = const()[name = string("input_149_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_149_pad_0 = const()[name = string("input_149_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_149_strides_0 = const()[name = string("input_149_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_149_dilations_0 = const()[name = string("input_149_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_149_groups_0 = const()[name = string("input_149_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_54_to_fp16 = const()[name = string("const_54_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3793152)))];
+            tensor<fp16, [128]> const_55_to_fp16 = const()[name = string("const_55_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4088128)))];
+            tensor<fp16, [?, 128, 20, 250]> input_151_cast_fp16 = conv(bias = const_55_to_fp16, dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = const_54_to_fp16, x = input_147_cast_fp16)[name = string("input_151_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_153_cast_fp16 = relu(x = input_151_cast_fp16)[name = string("input_153_cast_fp16")];
+            string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_56_to_fp16 = const()[name = string("const_56_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4088448)))];
+            tensor<fp16, [128]> const_57_to_fp16 = const()[name = string("const_57_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4383424)))];
+            tensor<fp16, [?, 128, 20, 250]> out_23_cast_fp16 = conv(bias = const_57_to_fp16, dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = const_56_to_fp16, x = input_153_cast_fp16)[name = string("out_23_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_157_cast_fp16 = add(x = out_23_cast_fp16, y = input_147_cast_fp16)[name = string("input_157_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_159_cast_fp16 = relu(x = input_157_cast_fp16)[name = string("input_159_cast_fp16")];
+            string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_161_strides_0 = const()[name = string("input_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_161_dilations_0 = const()[name = string("input_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_161_groups_0 = const()[name = string("input_161_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_58_to_fp16 = const()[name = string("const_58_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4383744)))];
+            tensor<fp16, [128]> const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4678720)))];
+            tensor<fp16, [?, 128, 20, 250]> input_163_cast_fp16 = conv(bias = const_59_to_fp16, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_58_to_fp16, x = input_159_cast_fp16)[name = string("input_163_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_165_cast_fp16 = relu(x = input_163_cast_fp16)[name = string("input_165_cast_fp16")];
+            string input_167_pad_type_0 = const()[name = string("input_167_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_167_pad_0 = const()[name = string("input_167_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_167_strides_0 = const()[name = string("input_167_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_167_dilations_0 = const()[name = string("input_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_167_groups_0 = const()[name = string("input_167_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_60_to_fp16 = const()[name = string("const_60_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4679040)))];
+            tensor<fp16, [128]> const_61_to_fp16 = const()[name = string("const_61_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4974016)))];
+            tensor<fp16, [?, 128, 20, 250]> out_25_cast_fp16 = conv(bias = const_61_to_fp16, dilations = input_167_dilations_0, groups = input_167_groups_0, pad = input_167_pad_0, pad_type = input_167_pad_type_0, strides = input_167_strides_0, weight = const_60_to_fp16, x = input_165_cast_fp16)[name = string("out_25_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_169_cast_fp16 = add(x = out_25_cast_fp16, y = input_159_cast_fp16)[name = string("input_169_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_171_cast_fp16 = relu(x = input_169_cast_fp16)[name = string("input_171_cast_fp16")];
+            string input_173_pad_type_0 = const()[name = string("input_173_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_173_pad_0 = const()[name = string("input_173_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_173_strides_0 = const()[name = string("input_173_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_173_dilations_0 = const()[name = string("input_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_173_groups_0 = const()[name = string("input_173_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 128, 3, 3]> const_62_to_fp16 = const()[name = string("const_62_to_fp16"), val = tensor<fp16, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4974336)))];
+            tensor<fp16, [256]> const_63_to_fp16 = const()[name = string("const_63_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5564224)))];
+            tensor<fp16, [?, 256, 10, 125]> input_175_cast_fp16 = conv(bias = const_63_to_fp16, dilations = input_173_dilations_0, groups = input_173_groups_0, pad = input_173_pad_0, pad_type = input_173_pad_type_0, strides = input_173_strides_0, weight = const_62_to_fp16, x = input_171_cast_fp16)[name = string("input_175_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_177_cast_fp16 = relu(x = input_175_cast_fp16)[name = string("input_177_cast_fp16")];
+            string input_179_pad_type_0 = const()[name = string("input_179_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_179_pad_0 = const()[name = string("input_179_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_179_strides_0 = const()[name = string("input_179_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_179_dilations_0 = const()[name = string("input_179_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_179_groups_0 = const()[name = string("input_179_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5564800)))];
+            tensor<fp16, [256]> const_65_to_fp16 = const()[name = string("const_65_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6744512)))];
+            tensor<fp16, [?, 256, 10, 125]> out_27_cast_fp16 = conv(bias = const_65_to_fp16, dilations = input_179_dilations_0, groups = input_179_groups_0, pad = input_179_pad_0, pad_type = input_179_pad_type_0, strides = input_179_strides_0, weight = const_64_to_fp16, x = input_177_cast_fp16)[name = string("out_27_cast_fp16")];
+            string input_181_pad_type_0 = const()[name = string("input_181_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_181_strides_0 = const()[name = string("input_181_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_181_dilations_0 = const()[name = string("input_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_181_groups_0 = const()[name = string("input_181_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 128, 1, 1]> const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = tensor<fp16, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6745088)))];
+            tensor<fp16, [256]> const_67_to_fp16 = const()[name = string("const_67_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6810688)))];
+            tensor<fp16, [?, 256, 10, 125]> var_570_cast_fp16 = conv(bias = const_67_to_fp16, dilations = input_181_dilations_0, groups = input_181_groups_0, pad = input_181_pad_0, pad_type = input_181_pad_type_0, strides = input_181_strides_0, weight = const_66_to_fp16, x = input_171_cast_fp16)[name = string("op_570_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_183_cast_fp16 = add(x = out_27_cast_fp16, y = var_570_cast_fp16)[name = string("input_183_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_185_cast_fp16 = relu(x = input_183_cast_fp16)[name = string("input_185_cast_fp16")];
+            string input_187_pad_type_0 = const()[name = string("input_187_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_187_pad_0 = const()[name = string("input_187_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_187_strides_0 = const()[name = string("input_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_187_dilations_0 = const()[name = string("input_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_187_groups_0 = const()[name = string("input_187_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_68_to_fp16 = const()[name = string("const_68_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6811264)))];
+            tensor<fp16, [256]> const_69_to_fp16 = const()[name = string("const_69_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7990976)))];
+            tensor<fp16, [?, 256, 10, 125]> input_189_cast_fp16 = conv(bias = const_69_to_fp16, dilations = input_187_dilations_0, groups = input_187_groups_0, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = input_187_strides_0, weight = const_68_to_fp16, x = input_185_cast_fp16)[name = string("input_189_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_191_cast_fp16 = relu(x = input_189_cast_fp16)[name = string("input_191_cast_fp16")];
+            string input_193_pad_type_0 = const()[name = string("input_193_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_193_pad_0 = const()[name = string("input_193_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_193_strides_0 = const()[name = string("input_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_193_dilations_0 = const()[name = string("input_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_193_groups_0 = const()[name = string("input_193_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7991552)))];
+            tensor<fp16, [256]> const_71_to_fp16 = const()[name = string("const_71_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9171264)))];
+            tensor<fp16, [?, 256, 10, 125]> out_29_cast_fp16 = conv(bias = const_71_to_fp16, dilations = input_193_dilations_0, groups = input_193_groups_0, pad = input_193_pad_0, pad_type = input_193_pad_type_0, strides = input_193_strides_0, weight = const_70_to_fp16, x = input_191_cast_fp16)[name = string("out_29_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_195_cast_fp16 = add(x = out_29_cast_fp16, y = input_185_cast_fp16)[name = string("input_195_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_197_cast_fp16 = relu(x = input_195_cast_fp16)[name = string("input_197_cast_fp16")];
+            string input_199_pad_type_0 = const()[name = string("input_199_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_199_pad_0 = const()[name = string("input_199_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_199_strides_0 = const()[name = string("input_199_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_199_dilations_0 = const()[name = string("input_199_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_199_groups_0 = const()[name = string("input_199_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_72_to_fp16 = const()[name = string("const_72_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9171840)))];
+            tensor<fp16, [256]> const_73_to_fp16 = const()[name = string("const_73_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10351552)))];
+            tensor<fp16, [?, 256, 10, 125]> input_201_cast_fp16 = conv(bias = const_73_to_fp16, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = const_72_to_fp16, x = input_197_cast_fp16)[name = string("input_201_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_203_cast_fp16 = relu(x = input_201_cast_fp16)[name = string("input_203_cast_fp16")];
+            string input_205_pad_type_0 = const()[name = string("input_205_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_205_pad_0 = const()[name = string("input_205_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_205_strides_0 = const()[name = string("input_205_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_205_dilations_0 = const()[name = string("input_205_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_205_groups_0 = const()[name = string("input_205_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_74_to_fp16 = const()[name = string("const_74_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10352128)))];
+            tensor<fp16, [256]> const_75_to_fp16 = const()[name = string("const_75_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11531840)))];
+            tensor<fp16, [?, 256, 10, 125]> out_cast_fp16 = conv(bias = const_75_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_74_to_fp16, x = input_203_cast_fp16)[name = string("out_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_207_cast_fp16 = add(x = out_cast_fp16, y = input_197_cast_fp16)[name = string("input_207_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> frames_cast_fp16 = relu(x = input_207_cast_fp16)[name = string("frames_cast_fp16")];
+            tensor<int32, [3]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp16, [?, 2560, 125]> sequences_cast_fp16 = reshape(shape = concat_0x, x = frames_cast_fp16)[name = string("sequences_cast_fp16")];
+            tensor<int32, [1]> input_209_axes_0 = const()[name = string("input_209_axes_0"), val = tensor<int32, [1]>([1])];
+            string weights_to_fp16_dtype_0 = const()[name = string("weights_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [?, 589]> weights_to_fp16 = cast(dtype = weights_to_fp16_dtype_0, x = weights)[name = string("cast_9")];
+            tensor<fp16, [?, 1, 589]> input_209_cast_fp16 = expand_dims(axes = input_209_axes_0, x = weights_to_fp16)[name = string("input_209_cast_fp16")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [?, 1, 589, 1]> expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = input_209_cast_fp16)[name = string("expand_dims_0_cast_fp16")];
+            fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)];
+            fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)];
+            tensor<fp16, [?, 1, 125, 1]> upsample_nearest_neighbor_0_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0_cast_fp16)[name = string("upsample_nearest_neighbor_0_cast_fp16")];
+            tensor<int32, [1]> weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [?, 1, 125]> weights_cast_fp16 = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0_cast_fp16)[name = string("weights_cast_fp16")];
+            tensor<int32, [1]> weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 1]> weight_sum_cast_fp16 = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights_cast_fp16)[name = string("weight_sum_cast_fp16")];
+            fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(0x0p+0)];
+            tensor<bool, [?, 1]> var_646_cast_fp16 = greater(x = weight_sum_cast_fp16, y = var_69_to_fp16)[name = string("op_646_cast_fp16")];
+            fp16 fill_like_0_value_0_to_fp16 = const()[name = string("fill_like_0_value_0_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [?, 1]> fill_like_0_cast_fp16 = fill_like(ref_tensor = weight_sum_cast_fp16, value = fill_like_0_value_0_to_fp16)[name = string("fill_like_0_cast_fp16")];
+            tensor<fp16, [?, 1]> safe_sum_cast_fp16 = select(a = weight_sum_cast_fp16, b = fill_like_0_cast_fp16, cond = var_646_cast_fp16)[name = string("safe_sum_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> var_649_cast_fp16 = mul(x = sequences_cast_fp16, y = weights_cast_fp16)[name = string("op_649_cast_fp16")];
+            tensor<int32, [1]> var_651_axes_0 = const()[name = string("op_651_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_651_keep_dims_0 = const()[name = string("op_651_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 2560]> var_651_cast_fp16 = reduce_sum(axes = var_651_axes_0, keep_dims = var_651_keep_dims_0, x = var_649_cast_fp16)[name = string("op_651_cast_fp16")];
+            tensor<fp16, [?, 2560]> mean_cast_fp16 = real_div(x = var_651_cast_fp16, y = safe_sum_cast_fp16)[name = string("mean_cast_fp16")];
+            tensor<int32, [1]> var_653_axes_0 = const()[name = string("op_653_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [?, 2560, 1]> var_653_cast_fp16 = expand_dims(axes = var_653_axes_0, x = mean_cast_fp16)[name = string("op_653_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> var_654_cast_fp16 = sub(x = sequences_cast_fp16, y = var_653_cast_fp16)[name = string("op_654_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> dx2_cast_fp16 = mul(x = var_654_cast_fp16, y = var_654_cast_fp16)[name = string("dx2_cast_fp16")];
+            tensor<fp16, [?, 1, 125]> var_656_cast_fp16 = mul(x = weights_cast_fp16, y = weights_cast_fp16)[name = string("op_656_cast_fp16")];
+            tensor<int32, [1]> weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 1]> weight_sq_sum_cast_fp16 = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_656_cast_fp16)[name = string("weight_sq_sum_cast_fp16")];
+            tensor<fp16, [?, 1]> var_659_cast_fp16 = real_div(x = weight_sq_sum_cast_fp16, y = safe_sum_cast_fp16)[name = string("op_659_cast_fp16")];
+            tensor<fp16, [?, 1]> var_660_cast_fp16 = sub(x = safe_sum_cast_fp16, y = var_659_cast_fp16)[name = string("op_660_cast_fp16")];
+            fp16 var_661_to_fp16 = const()[name = string("op_661_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [?, 1]> denom_cast_fp16 = add(x = var_660_cast_fp16, y = var_661_to_fp16)[name = string("denom_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> var_663_cast_fp16 = mul(x = dx2_cast_fp16, y = weights_cast_fp16)[name = string("op_663_cast_fp16")];
+            tensor<int32, [1]> var_665_axes_0 = const()[name = string("op_665_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_665_keep_dims_0 = const()[name = string("op_665_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 2560]> var_665_cast_fp16 = reduce_sum(axes = var_665_axes_0, keep_dims = var_665_keep_dims_0, x = var_663_cast_fp16)[name = string("op_665_cast_fp16")];
+            tensor<fp16, [?, 2560]> var_cast_fp16 = real_div(x = var_665_cast_fp16, y = denom_cast_fp16)[name = string("var_cast_fp16")];
+            fp16 var_68_to_fp16 = const()[name = string("op_68_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [?, 2560]> var_667_cast_fp16 = maximum(x = var_cast_fp16, y = var_68_to_fp16)[name = string("op_667_cast_fp16")];
+            tensor<fp16, [?, 2560]> std_cast_fp16 = sqrt(x = var_667_cast_fp16)[name = string("std_cast_fp16")];
+            bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)];
+            tensor<fp16, [?, 5120]> stats_cast_fp16 = concat(axis = var_67, interleave = stats_interleave_0, values = (mean_cast_fp16, std_cast_fp16))[name = string("stats_cast_fp16")];
+            tensor<fp16, [?, 2560]> sub_0_cast_fp16 = sub(x = mean_cast_fp16, y = mean_cast_fp16)[name = string("sub_0_cast_fp16")];
+            fp16 var_672_value_0_to_fp16 = const()[name = string("op_672_value_0_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [?, 2560]> var_672_cast_fp16 = fill_like(ref_tensor = std_cast_fp16, value = var_672_value_0_to_fp16)[name = string("op_672_cast_fp16")];
+            bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)];
+            tensor<fp16, [?, 5120]> zero_stats_cast_fp16 = concat(axis = var_67, interleave = zero_stats_interleave_0, values = (sub_0_cast_fp16, var_672_cast_fp16))[name = string("zero_stats_cast_fp16")];
+            tensor<bool, [?, 1]> var_675_cast_fp16 = less_equal(x = weight_sum_cast_fp16, y = var_69_to_fp16)[name = string("op_675_cast_fp16")];
+            tensor<int32, [2]> var_677 = const()[name = string("op_677"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [?, 5120]> zero_mask = tile(reps = var_677, x = var_675_cast_fp16)[name = string("zero_mask")];
+            tensor<fp16, [?, 5120]> input_cast_fp16 = select(a = zero_stats_cast_fp16, b = stats_cast_fp16, cond = zero_mask)[name = string("input_cast_fp16")];
+            tensor<fp16, [256, 5120]> tail_resnet_seg_1_weight_to_fp16 = const()[name = string("tail_resnet_seg_1_weight_to_fp16"), val = tensor<fp16, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11532416)))];
+            tensor<fp16, [256]> tail_resnet_seg_1_bias_to_fp16 = const()[name = string("tail_resnet_seg_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14153920)))];
+            tensor<fp16, [?, 256]> linear_0_cast_fp16 = linear(bias = tail_resnet_seg_1_bias_to_fp16, weight = tail_resnet_seg_1_weight_to_fp16, x = input_cast_fp16)[name = string("linear_0_cast_fp16")];
+            string linear_0_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_0_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [?, 256]> output = cast(dtype = linear_0_cast_fp16_to_fp32_dtype_0, x = linear_0_cast_fp16)[name = string("cast_8")];
+        } -> (output);
+}

wespeaker-voxceleb-resnet34-fused-f16.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3f2a1aba33878f0388d8dc3ab7259af1978482145fb4931cd93876a3d875eef
+size 14154496

wespeaker-voxceleb-resnet34-fused.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab9e15f213ba81809fd0abd6dd6d4c6b569dfa891f2dda282567819a732f301b
+size 243

wespeaker-voxceleb-resnet34-fused.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f279b672793ba4f1d246f69442be874b49fc275f6c9b08f6ab85b57dc0bbe68
+size 225

wespeaker-voxceleb-resnet34-fused.mlmodelc/model.mil

@@ -0,0 +1,462 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [?, 1, 160000]> waveform, tensor<fp32, [?, 589]> weights) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"waveform", [32, 1, 160000]}, {"weights", [32, 589]}}), ("EnumeratedShapes", {{"3c334fba", {{"waveform", [3, 1, 160000]}, {"weights", [3, 589]}}}, {"79c53add", {{"waveform", [1, 1, 160000]}, {"weights", [1, 589]}}}, {"cb01bf12", {{"waveform", [32, 1, 160000]}, {"weights", [32, 589]}}}})))] {
+            tensor<fp32, [257, 512]> fbank_dft_sin = const()[name = string("fbank_dft_sin"), val = tensor<fp32, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [257, 512]> fbank_dft_cos = const()[name = string("fbank_dft_cos"), val = tensor<fp32, [257, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526464)))];
+            tensor<fp32, [400, 1, 400]> fbank_identity_kernel = const()[name = string("fbank_identity_kernel"), val = tensor<fp32, [400, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1052864)))];
+            tensor<fp32, [256]> tail_resnet_seg_1_bias = const()[name = string("tail_resnet_seg_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1692928)))];
+            tensor<fp32, [256, 5120]> tail_resnet_seg_1_weight = const()[name = string("tail_resnet_seg_1_weight"), val = tensor<fp32, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1694016)))];
+            fp32 var_7 = const()[name = string("op_7"), val = fp32(0x1p+1)];
+            tensor<int32, [3]> var_27_begin_0 = const()[name = string("op_27_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> var_27_end_0 = const()[name = string("op_27_end_0"), val = tensor<int32, [3]>([0, 1, 160000])];
+            tensor<bool, [3]> var_27_end_mask_0 = const()[name = string("op_27_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            tensor<fp32, [?, 1, 160000]> var_27 = slice_by_index(begin = var_27_begin_0, end = var_27_end_0, end_mask = var_27_end_mask_0, x = waveform)[name = string("op_27")];
+            fp32 var_29 = const()[name = string("op_29"), val = fp32(0x1p+15)];
+            tensor<fp32, [?, 1, 160000]> signal = mul(x = var_27, y = var_29)[name = string("signal")];
+            string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [2]> frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)];
+            tensor<fp32, [?, 400, 998]> frames_1 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = fbank_identity_kernel, x = signal)[name = string("frames_1")];
+            tensor<int32, [3]> var_36 = const()[name = string("op_36"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_39_axes_0 = const()[name = string("op_39_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_39_keep_dims_0 = const()[name = string("op_39_keep_dims_0"), val = bool(true)];
+            tensor<fp32, [?, 998, 400]> frames_3 = transpose(perm = var_36, x = frames_1)[name = string("transpose_4")];
+            tensor<fp32, [?, 998, 1]> var_39 = reduce_mean(axes = var_39_axes_0, keep_dims = var_39_keep_dims_0, x = frames_3)[name = string("op_39")];
+            tensor<fp32, [?, 998, 400]> input_1 = sub(x = frames_3, y = var_39)[name = string("input_1")];
+            fp32 const_0 = const()[name = string("const_0"), val = fp32(0x0p+0)];
+            tensor<int32, [6]> var_42_pad_0 = const()[name = string("op_42_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])];
+            string var_42_mode_0 = const()[name = string("op_42_mode_0"), val = string("replicate")];
+            tensor<fp32, [?, 998, 401]> var_42 = pad(constant_val = const_0, mode = var_42_mode_0, pad = var_42_pad_0, x = input_1)[name = string("op_42")];
+            tensor<int32, [3]> previous_begin_0 = const()[name = string("previous_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> previous_end_0 = const()[name = string("previous_end_0"), val = tensor<int32, [3]>([0, 998, 400])];
+            tensor<bool, [3]> previous_end_mask_0 = const()[name = string("previous_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<fp32, [?, 998, 400]> previous = slice_by_index(begin = previous_begin_0, end = previous_end_0, end_mask = previous_end_mask_0, x = var_42)[name = string("previous")];
+            fp32 var_44 = const()[name = string("op_44"), val = fp32(0x1.f0a3d8p-1)];
+            tensor<fp32, [?, 998, 400]> var_45 = mul(x = previous, y = var_44)[name = string("op_45")];
+            tensor<fp32, [?, 998, 400]> frames_5 = sub(x = input_1, y = var_45)[name = string("frames_5")];
+            tensor<fp32, [1, 1, 400]> var_48 = const()[name = string("op_48"), val = tensor<fp32, [1, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6936960)))];
+            tensor<fp32, [?, 998, 400]> input_3 = mul(x = frames_5, y = var_48)[name = string("input_3")];
+            fp32 const_1 = const()[name = string("const_1"), val = fp32(0x0p+0)];
+            tensor<int32, [6]> frames_7_pad_0 = const()[name = string("frames_7_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 112])];
+            string frames_7_mode_0 = const()[name = string("frames_7_mode_0"), val = string("constant")];
+            tensor<fp32, [?, 998, 512]> frames_7 = pad(constant_val = const_1, mode = frames_7_mode_0, pad = frames_7_pad_0, x = input_3)[name = string("frames_7")];
+            tensor<fp32, [257]> real_part_bias_0 = const()[name = string("real_part_bias_0"), val = tensor<fp32, [257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6938624)))];
+            tensor<fp32, [?, 998, 257]> real_part = linear(bias = real_part_bias_0, weight = fbank_dft_cos, x = frames_7)[name = string("real_part")];
+            tensor<fp32, [?, 998, 257]> imag_part = linear(bias = real_part_bias_0, weight = fbank_dft_sin, x = frames_7)[name = string("imag_part")];
+            tensor<fp32, [?, 998, 257]> var_56 = pow(x = real_part, y = var_7)[name = string("op_56")];
+            tensor<fp32, [?, 998, 257]> var_57 = pow(x = imag_part, y = var_7)[name = string("op_57")];
+            tensor<fp32, [?, 998, 257]> spectrum = add(x = var_56, y = var_57)[name = string("spectrum")];
+            tensor<fp32, [80, 257]> transpose_2 = const()[name = string("transpose_2"), val = tensor<fp32, [80, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6939776)))];
+            tensor<fp32, [80]> mel_1_bias_0 = const()[name = string("mel_1_bias_0"), val = tensor<fp32, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7022080)))];
+            tensor<fp32, [?, 998, 80]> mel_1 = linear(bias = mel_1_bias_0, weight = transpose_2, x = spectrum)[name = string("mel_1")];
+            fp32 const_3 = const()[name = string("const_3"), val = fp32(0x1p-23)];
+            tensor<fp32, [?, 998, 80]> var_62 = maximum(x = mel_1, y = const_3)[name = string("op_62")];
+            fp32 mel_3_epsilon_0 = const()[name = string("mel_3_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp32, [?, 998, 80]> mel_3 = log(epsilon = mel_3_epsilon_0, x = var_62)[name = string("mel_3")];
+            tensor<int32, [1]> var_65_axes_0 = const()[name = string("op_65_axes_0"), val = tensor<int32, [1]>([1])];
+            bool var_65_keep_dims_0 = const()[name = string("op_65_keep_dims_0"), val = bool(true)];
+            tensor<fp32, [?, 1, 80]> var_65 = reduce_mean(axes = var_65_axes_0, keep_dims = var_65_keep_dims_0, x = mel_3)[name = string("op_65")];
+            tensor<fp32, [?, 998, 80]> fbank_1 = sub(x = mel_3, y = var_65)[name = string("fbank_1")];
+            int32 var_67 = const()[name = string("op_67"), val = int32(-1)];
+            fp32 var_68 = const()[name = string("op_68"), val = fp32(0x1.b7cdfep-34)];
+            fp32 var_69 = const()[name = string("op_69"), val = fp32(0x0p+0)];
+            tensor<int32, [3]> var_94 = const()[name = string("op_94"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 80, 998]> fbank_3 = transpose(perm = var_94, x = fbank_1)[name = string("transpose_3")];
+            tensor<fp32, [?, 1, 80, 998]> input_5 = expand_dims(axes = input_5_axes_0, x = fbank_3)[name = string("input_5")];
+            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 1, 3, 3]> const_4 = const()[name = string("const_4"), val = tensor<fp32, [32, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7022464)))];
+            tensor<fp32, [32]> const_5 = const()[name = string("const_5"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7023680)))];
+            tensor<fp32, [?, 32, 80, 998]> input_9 = conv(bias = const_5, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = const_4, x = input_5)[name = string("input_9")];
+            tensor<fp32, [?, 32, 80, 998]> input_11 = relu(x = input_9)[name = string("input_11")];
+            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_6 = const()[name = string("const_6"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7023872)))];
+            tensor<fp32, [32]> const_7 = const()[name = string("const_7"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7060800)))];
+            tensor<fp32, [?, 32, 80, 998]> input_15 = conv(bias = const_7, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = const_6, x = input_11)[name = string("input_15")];
+            tensor<fp32, [?, 32, 80, 998]> input_17 = relu(x = input_15)[name = string("input_17")];
+            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_19_strides_0 = const()[name = string("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_19_dilations_0 = const()[name = string("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_19_groups_0 = const()[name = string("input_19_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_8 = const()[name = string("const_8"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7060992)))];
+            tensor<fp32, [32]> const_9 = const()[name = string("const_9"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7097920)))];
+            tensor<fp32, [?, 32, 80, 998]> out_1 = conv(bias = const_9, dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = const_8, x = input_17)[name = string("out_1")];
+            tensor<fp32, [?, 32, 80, 998]> input_21 = add(x = out_1, y = input_11)[name = string("input_21")];
+            tensor<fp32, [?, 32, 80, 998]> input_23 = relu(x = input_21)[name = string("input_23")];
+            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_10 = const()[name = string("const_10"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7098112)))];
+            tensor<fp32, [32]> const_11 = const()[name = string("const_11"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7135040)))];
+            tensor<fp32, [?, 32, 80, 998]> input_27 = conv(bias = const_11, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = const_10, x = input_23)[name = string("input_27")];
+            tensor<fp32, [?, 32, 80, 998]> input_29 = relu(x = input_27)[name = string("input_29")];
+            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_12 = const()[name = string("const_12"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7135232)))];
+            tensor<fp32, [32]> const_13 = const()[name = string("const_13"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7172160)))];
+            tensor<fp32, [?, 32, 80, 998]> out_3 = conv(bias = const_13, dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = const_12, x = input_29)[name = string("out_3")];
+            tensor<fp32, [?, 32, 80, 998]> input_33 = add(x = out_3, y = input_23)[name = string("input_33")];
+            tensor<fp32, [?, 32, 80, 998]> input_35 = relu(x = input_33)[name = string("input_35")];
+            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_14 = const()[name = string("const_14"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7172352)))];
+            tensor<fp32, [32]> const_15 = const()[name = string("const_15"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7209280)))];
+            tensor<fp32, [?, 32, 80, 998]> input_39 = conv(bias = const_15, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = const_14, x = input_35)[name = string("input_39")];
+            tensor<fp32, [?, 32, 80, 998]> input_41 = relu(x = input_39)[name = string("input_41")];
+            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_16 = const()[name = string("const_16"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7209472)))];
+            tensor<fp32, [32]> const_17 = const()[name = string("const_17"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7246400)))];
+            tensor<fp32, [?, 32, 80, 998]> out_5 = conv(bias = const_17, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = const_16, x = input_41)[name = string("out_5")];
+            tensor<fp32, [?, 32, 80, 998]> input_45 = add(x = out_5, y = input_35)[name = string("input_45")];
+            tensor<fp32, [?, 32, 80, 998]> input_47 = relu(x = input_45)[name = string("input_47")];
+            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 3, 3]> const_18 = const()[name = string("const_18"), val = tensor<fp32, [64, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7246592)))];
+            tensor<fp32, [64]> const_19 = const()[name = string("const_19"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7320384)))];
+            tensor<fp32, [?, 64, 40, 499]> input_51 = conv(bias = const_19, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_18, x = input_47)[name = string("input_51")];
+            tensor<fp32, [?, 64, 40, 499]> input_53 = relu(x = input_51)[name = string("input_53")];
+            string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_55_strides_0 = const()[name = string("input_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_55_dilations_0 = const()[name = string("input_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_55_groups_0 = const()[name = string("input_55_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_20 = const()[name = string("const_20"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7320704)))];
+            tensor<fp32, [64]> const_21 = const()[name = string("const_21"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7468224)))];
+            tensor<fp32, [?, 64, 40, 499]> out_7 = conv(bias = const_21, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = const_20, x = input_53)[name = string("out_7")];
+            string input_57_pad_type_0 = const()[name = string("input_57_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_57_strides_0 = const()[name = string("input_57_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_57_dilations_0 = const()[name = string("input_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_57_groups_0 = const()[name = string("input_57_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 1, 1]> const_22 = const()[name = string("const_22"), val = tensor<fp32, [64, 32, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7468544)))];
+            tensor<fp32, [64]> const_23 = const()[name = string("const_23"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7476800)))];
+            tensor<fp32, [?, 64, 40, 499]> var_243 = conv(bias = const_23, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_22, x = input_47)[name = string("op_243")];
+            tensor<fp32, [?, 64, 40, 499]> input_59 = add(x = out_7, y = var_243)[name = string("input_59")];
+            tensor<fp32, [?, 64, 40, 499]> input_61 = relu(x = input_59)[name = string("input_61")];
+            string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_24 = const()[name = string("const_24"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7477120)))];
+            tensor<fp32, [64]> const_25 = const()[name = string("const_25"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7624640)))];
+            tensor<fp32, [?, 64, 40, 499]> input_65 = conv(bias = const_25, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = const_24, x = input_61)[name = string("input_65")];
+            tensor<fp32, [?, 64, 40, 499]> input_67 = relu(x = input_65)[name = string("input_67")];
+            string input_69_pad_type_0 = const()[name = string("input_69_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_69_pad_0 = const()[name = string("input_69_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_69_strides_0 = const()[name = string("input_69_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_69_dilations_0 = const()[name = string("input_69_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_69_groups_0 = const()[name = string("input_69_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_26 = const()[name = string("const_26"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7624960)))];
+            tensor<fp32, [64]> const_27 = const()[name = string("const_27"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7772480)))];
+            tensor<fp32, [?, 64, 40, 499]> out_9 = conv(bias = const_27, dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = const_26, x = input_67)[name = string("out_9")];
+            tensor<fp32, [?, 64, 40, 499]> input_71 = add(x = out_9, y = input_61)[name = string("input_71")];
+            tensor<fp32, [?, 64, 40, 499]> input_73 = relu(x = input_71)[name = string("input_73")];
+            string input_75_pad_type_0 = const()[name = string("input_75_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_75_pad_0 = const()[name = string("input_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_75_strides_0 = const()[name = string("input_75_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_75_dilations_0 = const()[name = string("input_75_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_75_groups_0 = const()[name = string("input_75_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_28 = const()[name = string("const_28"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7772800)))];
+            tensor<fp32, [64]> const_29 = const()[name = string("const_29"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7920320)))];
+            tensor<fp32, [?, 64, 40, 499]> input_77 = conv(bias = const_29, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = const_28, x = input_73)[name = string("input_77")];
+            tensor<fp32, [?, 64, 40, 499]> input_79 = relu(x = input_77)[name = string("input_79")];
+            string input_81_pad_type_0 = const()[name = string("input_81_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_81_pad_0 = const()[name = string("input_81_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_81_strides_0 = const()[name = string("input_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_81_dilations_0 = const()[name = string("input_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_81_groups_0 = const()[name = string("input_81_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_30 = const()[name = string("const_30"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7920640)))];
+            tensor<fp32, [64]> const_31 = const()[name = string("const_31"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8068160)))];
+            tensor<fp32, [?, 64, 40, 499]> out_11 = conv(bias = const_31, dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = const_30, x = input_79)[name = string("out_11")];
+            tensor<fp32, [?, 64, 40, 499]> input_83 = add(x = out_11, y = input_73)[name = string("input_83")];
+            tensor<fp32, [?, 64, 40, 499]> input_85 = relu(x = input_83)[name = string("input_85")];
+            string input_87_pad_type_0 = const()[name = string("input_87_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_87_pad_0 = const()[name = string("input_87_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_87_strides_0 = const()[name = string("input_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_87_dilations_0 = const()[name = string("input_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_87_groups_0 = const()[name = string("input_87_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_32 = const()[name = string("const_32"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8068480)))];
+            tensor<fp32, [64]> const_33 = const()[name = string("const_33"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8216000)))];
+            tensor<fp32, [?, 64, 40, 499]> input_89 = conv(bias = const_33, dilations = input_87_dilations_0, groups = input_87_groups_0, pad = input_87_pad_0, pad_type = input_87_pad_type_0, strides = input_87_strides_0, weight = const_32, x = input_85)[name = string("input_89")];
+            tensor<fp32, [?, 64, 40, 499]> input_91 = relu(x = input_89)[name = string("input_91")];
+            string input_93_pad_type_0 = const()[name = string("input_93_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_93_pad_0 = const()[name = string("input_93_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_93_strides_0 = const()[name = string("input_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_93_dilations_0 = const()[name = string("input_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_93_groups_0 = const()[name = string("input_93_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_34 = const()[name = string("const_34"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8216320)))];
+            tensor<fp32, [64]> const_35 = const()[name = string("const_35"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8363840)))];
+            tensor<fp32, [?, 64, 40, 499]> out_13 = conv(bias = const_35, dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = const_34, x = input_91)[name = string("out_13")];
+            tensor<fp32, [?, 64, 40, 499]> input_95 = add(x = out_13, y = input_85)[name = string("input_95")];
+            tensor<fp32, [?, 64, 40, 499]> input_97 = relu(x = input_95)[name = string("input_97")];
+            string input_99_pad_type_0 = const()[name = string("input_99_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_99_pad_0 = const()[name = string("input_99_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_99_strides_0 = const()[name = string("input_99_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_99_dilations_0 = const()[name = string("input_99_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_99_groups_0 = const()[name = string("input_99_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 3, 3]> const_36 = const()[name = string("const_36"), val = tensor<fp32, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8364160)))];
+            tensor<fp32, [128]> const_37 = const()[name = string("const_37"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8659136)))];
+            tensor<fp32, [?, 128, 20, 250]> input_101 = conv(bias = const_37, dilations = input_99_dilations_0, groups = input_99_groups_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = const_36, x = input_97)[name = string("input_101")];
+            tensor<fp32, [?, 128, 20, 250]> input_103 = relu(x = input_101)[name = string("input_103")];
+            string input_105_pad_type_0 = const()[name = string("input_105_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_105_pad_0 = const()[name = string("input_105_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_105_strides_0 = const()[name = string("input_105_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_105_dilations_0 = const()[name = string("input_105_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_105_groups_0 = const()[name = string("input_105_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_38 = const()[name = string("const_38"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8659712)))];
+            tensor<fp32, [128]> const_39 = const()[name = string("const_39"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9249600)))];
+            tensor<fp32, [?, 128, 20, 250]> out_15 = conv(bias = const_39, dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = const_38, x = input_103)[name = string("out_15")];
+            string input_107_pad_type_0 = const()[name = string("input_107_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_107_strides_0 = const()[name = string("input_107_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_107_dilations_0 = const()[name = string("input_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_107_groups_0 = const()[name = string("input_107_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 1, 1]> const_40 = const()[name = string("const_40"), val = tensor<fp32, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9250176)))];
+            tensor<fp32, [128]> const_41 = const()[name = string("const_41"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9283008)))];
+            tensor<fp32, [?, 128, 20, 250]> var_379 = conv(bias = const_41, dilations = input_107_dilations_0, groups = input_107_groups_0, pad = input_107_pad_0, pad_type = input_107_pad_type_0, strides = input_107_strides_0, weight = const_40, x = input_97)[name = string("op_379")];
+            tensor<fp32, [?, 128, 20, 250]> input_109 = add(x = out_15, y = var_379)[name = string("input_109")];
+            tensor<fp32, [?, 128, 20, 250]> input_111 = relu(x = input_109)[name = string("input_111")];
+            string input_113_pad_type_0 = const()[name = string("input_113_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_113_pad_0 = const()[name = string("input_113_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_113_strides_0 = const()[name = string("input_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_113_dilations_0 = const()[name = string("input_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_113_groups_0 = const()[name = string("input_113_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_42 = const()[name = string("const_42"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9283584)))];
+            tensor<fp32, [128]> const_43 = const()[name = string("const_43"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9873472)))];
+            tensor<fp32, [?, 128, 20, 250]> input_115 = conv(bias = const_43, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = const_42, x = input_111)[name = string("input_115")];
+            tensor<fp32, [?, 128, 20, 250]> input_117 = relu(x = input_115)[name = string("input_117")];
+            string input_119_pad_type_0 = const()[name = string("input_119_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_119_strides_0 = const()[name = string("input_119_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_119_dilations_0 = const()[name = string("input_119_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_119_groups_0 = const()[name = string("input_119_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_44 = const()[name = string("const_44"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9874048)))];
+            tensor<fp32, [128]> const_45 = const()[name = string("const_45"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10463936)))];
+            tensor<fp32, [?, 128, 20, 250]> out_17 = conv(bias = const_45, dilations = input_119_dilations_0, groups = input_119_groups_0, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = input_119_strides_0, weight = const_44, x = input_117)[name = string("out_17")];
+            tensor<fp32, [?, 128, 20, 250]> input_121 = add(x = out_17, y = input_111)[name = string("input_121")];
+            tensor<fp32, [?, 128, 20, 250]> input_123 = relu(x = input_121)[name = string("input_123")];
+            string input_125_pad_type_0 = const()[name = string("input_125_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_125_pad_0 = const()[name = string("input_125_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_125_strides_0 = const()[name = string("input_125_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_125_dilations_0 = const()[name = string("input_125_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_125_groups_0 = const()[name = string("input_125_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_46 = const()[name = string("const_46"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10464512)))];
+            tensor<fp32, [128]> const_47 = const()[name = string("const_47"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11054400)))];
+            tensor<fp32, [?, 128, 20, 250]> input_127 = conv(bias = const_47, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = const_46, x = input_123)[name = string("input_127")];
+            tensor<fp32, [?, 128, 20, 250]> input_129 = relu(x = input_127)[name = string("input_129")];
+            string input_131_pad_type_0 = const()[name = string("input_131_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_131_strides_0 = const()[name = string("input_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_131_dilations_0 = const()[name = string("input_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_131_groups_0 = const()[name = string("input_131_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_48 = const()[name = string("const_48"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11054976)))];
+            tensor<fp32, [128]> const_49 = const()[name = string("const_49"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11644864)))];
+            tensor<fp32, [?, 128, 20, 250]> out_19 = conv(bias = const_49, dilations = input_131_dilations_0, groups = input_131_groups_0, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = input_131_strides_0, weight = const_48, x = input_129)[name = string("out_19")];
+            tensor<fp32, [?, 128, 20, 250]> input_133 = add(x = out_19, y = input_123)[name = string("input_133")];
+            tensor<fp32, [?, 128, 20, 250]> input_135 = relu(x = input_133)[name = string("input_135")];
+            string input_137_pad_type_0 = const()[name = string("input_137_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_137_pad_0 = const()[name = string("input_137_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_137_strides_0 = const()[name = string("input_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_137_dilations_0 = const()[name = string("input_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_137_groups_0 = const()[name = string("input_137_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_50 = const()[name = string("const_50"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11645440)))];
+            tensor<fp32, [128]> const_51 = const()[name = string("const_51"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12235328)))];
+            tensor<fp32, [?, 128, 20, 250]> input_139 = conv(bias = const_51, dilations = input_137_dilations_0, groups = input_137_groups_0, pad = input_137_pad_0, pad_type = input_137_pad_type_0, strides = input_137_strides_0, weight = const_50, x = input_135)[name = string("input_139")];
+            tensor<fp32, [?, 128, 20, 250]> input_141 = relu(x = input_139)[name = string("input_141")];
+            string input_143_pad_type_0 = const()[name = string("input_143_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_143_strides_0 = const()[name = string("input_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_143_dilations_0 = const()[name = string("input_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_143_groups_0 = const()[name = string("input_143_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_52 = const()[name = string("const_52"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12235904)))];
+            tensor<fp32, [128]> const_53 = const()[name = string("const_53"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12825792)))];
+            tensor<fp32, [?, 128, 20, 250]> out_21 = conv(bias = const_53, dilations = input_143_dilations_0, groups = input_143_groups_0, pad = input_143_pad_0, pad_type = input_143_pad_type_0, strides = input_143_strides_0, weight = const_52, x = input_141)[name = string("out_21")];
+            tensor<fp32, [?, 128, 20, 250]> input_145 = add(x = out_21, y = input_135)[name = string("input_145")];
+            tensor<fp32, [?, 128, 20, 250]> input_147 = relu(x = input_145)[name = string("input_147")];
+            string input_149_pad_type_0 = const()[name = string("input_149_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_149_pad_0 = const()[name = string("input_149_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_149_strides_0 = const()[name = string("input_149_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_149_dilations_0 = const()[name = string("input_149_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_149_groups_0 = const()[name = string("input_149_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_54 = const()[name = string("const_54"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12826368)))];
+            tensor<fp32, [128]> const_55 = const()[name = string("const_55"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13416256)))];
+            tensor<fp32, [?, 128, 20, 250]> input_151 = conv(bias = const_55, dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = const_54, x = input_147)[name = string("input_151")];
+            tensor<fp32, [?, 128, 20, 250]> input_153 = relu(x = input_151)[name = string("input_153")];
+            string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_56 = const()[name = string("const_56"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13416832)))];
+            tensor<fp32, [128]> const_57 = const()[name = string("const_57"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14006720)))];
+            tensor<fp32, [?, 128, 20, 250]> out_23 = conv(bias = const_57, dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = const_56, x = input_153)[name = string("out_23")];
+            tensor<fp32, [?, 128, 20, 250]> input_157 = add(x = out_23, y = input_147)[name = string("input_157")];
+            tensor<fp32, [?, 128, 20, 250]> input_159 = relu(x = input_157)[name = string("input_159")];
+            string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_161_strides_0 = const()[name = string("input_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_161_dilations_0 = const()[name = string("input_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_161_groups_0 = const()[name = string("input_161_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_58 = const()[name = string("const_58"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14007296)))];
+            tensor<fp32, [128]> const_59 = const()[name = string("const_59"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14597184)))];
+            tensor<fp32, [?, 128, 20, 250]> input_163 = conv(bias = const_59, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_58, x = input_159)[name = string("input_163")];
+            tensor<fp32, [?, 128, 20, 250]> input_165 = relu(x = input_163)[name = string("input_165")];
+            string input_167_pad_type_0 = const()[name = string("input_167_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_167_pad_0 = const()[name = string("input_167_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_167_strides_0 = const()[name = string("input_167_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_167_dilations_0 = const()[name = string("input_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_167_groups_0 = const()[name = string("input_167_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_60 = const()[name = string("const_60"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14597760)))];
+            tensor<fp32, [128]> const_61 = const()[name = string("const_61"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15187648)))];
+            tensor<fp32, [?, 128, 20, 250]> out_25 = conv(bias = const_61, dilations = input_167_dilations_0, groups = input_167_groups_0, pad = input_167_pad_0, pad_type = input_167_pad_type_0, strides = input_167_strides_0, weight = const_60, x = input_165)[name = string("out_25")];
+            tensor<fp32, [?, 128, 20, 250]> input_169 = add(x = out_25, y = input_159)[name = string("input_169")];
+            tensor<fp32, [?, 128, 20, 250]> input_171 = relu(x = input_169)[name = string("input_171")];
+            string input_173_pad_type_0 = const()[name = string("input_173_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_173_pad_0 = const()[name = string("input_173_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_173_strides_0 = const()[name = string("input_173_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_173_dilations_0 = const()[name = string("input_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_173_groups_0 = const()[name = string("input_173_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 3, 3]> const_62 = const()[name = string("const_62"), val = tensor<fp32, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15188224)))];
+            tensor<fp32, [256]> const_63 = const()[name = string("const_63"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16367936)))];
+            tensor<fp32, [?, 256, 10, 125]> input_175 = conv(bias = const_63, dilations = input_173_dilations_0, groups = input_173_groups_0, pad = input_173_pad_0, pad_type = input_173_pad_type_0, strides = input_173_strides_0, weight = const_62, x = input_171)[name = string("input_175")];
+            tensor<fp32, [?, 256, 10, 125]> input_177 = relu(x = input_175)[name = string("input_177")];
+            string input_179_pad_type_0 = const()[name = string("input_179_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_179_pad_0 = const()[name = string("input_179_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_179_strides_0 = const()[name = string("input_179_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_179_dilations_0 = const()[name = string("input_179_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_179_groups_0 = const()[name = string("input_179_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_64 = const()[name = string("const_64"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16369024)))];
+            tensor<fp32, [256]> const_65 = const()[name = string("const_65"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18728384)))];
+            tensor<fp32, [?, 256, 10, 125]> out_27 = conv(bias = const_65, dilations = input_179_dilations_0, groups = input_179_groups_0, pad = input_179_pad_0, pad_type = input_179_pad_type_0, strides = input_179_strides_0, weight = const_64, x = input_177)[name = string("out_27")];
+            string input_181_pad_type_0 = const()[name = string("input_181_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_181_strides_0 = const()[name = string("input_181_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_181_dilations_0 = const()[name = string("input_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_181_groups_0 = const()[name = string("input_181_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 1, 1]> const_66 = const()[name = string("const_66"), val = tensor<fp32, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18729472)))];
+            tensor<fp32, [256]> const_67 = const()[name = string("const_67"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18860608)))];
+            tensor<fp32, [?, 256, 10, 125]> var_570 = conv(bias = const_67, dilations = input_181_dilations_0, groups = input_181_groups_0, pad = input_181_pad_0, pad_type = input_181_pad_type_0, strides = input_181_strides_0, weight = const_66, x = input_171)[name = string("op_570")];
+            tensor<fp32, [?, 256, 10, 125]> input_183 = add(x = out_27, y = var_570)[name = string("input_183")];
+            tensor<fp32, [?, 256, 10, 125]> input_185 = relu(x = input_183)[name = string("input_185")];
+            string input_187_pad_type_0 = const()[name = string("input_187_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_187_pad_0 = const()[name = string("input_187_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_187_strides_0 = const()[name = string("input_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_187_dilations_0 = const()[name = string("input_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_187_groups_0 = const()[name = string("input_187_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_68 = const()[name = string("const_68"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18861696)))];
+            tensor<fp32, [256]> const_69 = const()[name = string("const_69"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21221056)))];
+            tensor<fp32, [?, 256, 10, 125]> input_189 = conv(bias = const_69, dilations = input_187_dilations_0, groups = input_187_groups_0, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = input_187_strides_0, weight = const_68, x = input_185)[name = string("input_189")];
+            tensor<fp32, [?, 256, 10, 125]> input_191 = relu(x = input_189)[name = string("input_191")];
+            string input_193_pad_type_0 = const()[name = string("input_193_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_193_pad_0 = const()[name = string("input_193_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_193_strides_0 = const()[name = string("input_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_193_dilations_0 = const()[name = string("input_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_193_groups_0 = const()[name = string("input_193_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_70 = const()[name = string("const_70"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21222144)))];
+            tensor<fp32, [256]> const_71 = const()[name = string("const_71"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23581504)))];
+            tensor<fp32, [?, 256, 10, 125]> out_29 = conv(bias = const_71, dilations = input_193_dilations_0, groups = input_193_groups_0, pad = input_193_pad_0, pad_type = input_193_pad_type_0, strides = input_193_strides_0, weight = const_70, x = input_191)[name = string("out_29")];
+            tensor<fp32, [?, 256, 10, 125]> input_195 = add(x = out_29, y = input_185)[name = string("input_195")];
+            tensor<fp32, [?, 256, 10, 125]> input_197 = relu(x = input_195)[name = string("input_197")];
+            string input_199_pad_type_0 = const()[name = string("input_199_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_199_pad_0 = const()[name = string("input_199_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_199_strides_0 = const()[name = string("input_199_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_199_dilations_0 = const()[name = string("input_199_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_199_groups_0 = const()[name = string("input_199_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_72 = const()[name = string("const_72"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23582592)))];
+            tensor<fp32, [256]> const_73 = const()[name = string("const_73"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25941952)))];
+            tensor<fp32, [?, 256, 10, 125]> input_201 = conv(bias = const_73, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = const_72, x = input_197)[name = string("input_201")];
+            tensor<fp32, [?, 256, 10, 125]> input_203 = relu(x = input_201)[name = string("input_203")];
+            string input_205_pad_type_0 = const()[name = string("input_205_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_205_pad_0 = const()[name = string("input_205_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_205_strides_0 = const()[name = string("input_205_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_205_dilations_0 = const()[name = string("input_205_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_205_groups_0 = const()[name = string("input_205_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_74 = const()[name = string("const_74"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25943040)))];
+            tensor<fp32, [256]> const_75 = const()[name = string("const_75"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28302400)))];
+            tensor<fp32, [?, 256, 10, 125]> out = conv(bias = const_75, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_74, x = input_203)[name = string("out")];
+            tensor<fp32, [?, 256, 10, 125]> input_207 = add(x = out, y = input_197)[name = string("input_207")];
+            tensor<fp32, [?, 256, 10, 125]> frames = relu(x = input_207)[name = string("frames")];
+            tensor<int32, [3]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp32, [?, 2560, 125]> sequences = reshape(shape = concat_0x, x = frames)[name = string("sequences")];
+            tensor<int32, [1]> input_209_axes_0 = const()[name = string("input_209_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 1, 589]> input_209 = expand_dims(axes = input_209_axes_0, x = weights)[name = string("input_209")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [?, 1, 589, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input_209)[name = string("expand_dims_0")];
+            fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)];
+            fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [?, 1, 125, 1]> upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")];
+            tensor<int32, [1]> weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [?, 1, 125]> weights_1 = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0)[name = string("weights")];
+            tensor<int32, [1]> weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 1]> weight_sum = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights_1)[name = string("weight_sum")];
+            tensor<bool, [?, 1]> var_646 = greater(x = weight_sum, y = var_69)[name = string("op_646")];
+            fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [?, 1]> fill_like_0 = fill_like(ref_tensor = weight_sum, value = fill_like_0_value_0)[name = string("fill_like_0")];
+            tensor<fp32, [?, 1]> safe_sum = select(a = weight_sum, b = fill_like_0, cond = var_646)[name = string("safe_sum")];
+            tensor<fp32, [?, 2560, 125]> var_649 = mul(x = sequences, y = weights_1)[name = string("op_649")];
+            tensor<int32, [1]> var_651_axes_0 = const()[name = string("op_651_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_651_keep_dims_0 = const()[name = string("op_651_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 2560]> var_651 = reduce_sum(axes = var_651_axes_0, keep_dims = var_651_keep_dims_0, x = var_649)[name = string("op_651")];
+            tensor<fp32, [?, 2560]> mean = real_div(x = var_651, y = safe_sum)[name = string("mean")];
+            tensor<int32, [1]> var_653_axes_0 = const()[name = string("op_653_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [?, 2560, 1]> var_653 = expand_dims(axes = var_653_axes_0, x = mean)[name = string("op_653")];
+            tensor<fp32, [?, 2560, 125]> var_654 = sub(x = sequences, y = var_653)[name = string("op_654")];
+            tensor<fp32, [?, 2560, 125]> dx2 = mul(x = var_654, y = var_654)[name = string("dx2")];
+            tensor<fp32, [?, 1, 125]> var_656 = mul(x = weights_1, y = weights_1)[name = string("op_656")];
+            tensor<int32, [1]> weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 1]> weight_sq_sum = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_656)[name = string("weight_sq_sum")];
+            tensor<fp32, [?, 1]> var_659 = real_div(x = weight_sq_sum, y = safe_sum)[name = string("op_659")];
+            tensor<fp32, [?, 1]> var_660 = sub(x = safe_sum, y = var_659)[name = string("op_660")];
+            fp32 var_661 = const()[name = string("op_661"), val = fp32(0x1.5798eep-27)];
+            tensor<fp32, [?, 1]> denom = add(x = var_660, y = var_661)[name = string("denom")];
+            tensor<fp32, [?, 2560, 125]> var_663 = mul(x = dx2, y = weights_1)[name = string("op_663")];
+            tensor<int32, [1]> var_665_axes_0 = const()[name = string("op_665_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_665_keep_dims_0 = const()[name = string("op_665_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 2560]> var_665 = reduce_sum(axes = var_665_axes_0, keep_dims = var_665_keep_dims_0, x = var_663)[name = string("op_665")];
+            tensor<fp32, [?, 2560]> var = real_div(x = var_665, y = denom)[name = string("var")];
+            tensor<fp32, [?, 2560]> var_667 = maximum(x = var, y = var_68)[name = string("op_667")];
+            tensor<fp32, [?, 2560]> std = sqrt(x = var_667)[name = string("std")];
+            bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)];
+            tensor<fp32, [?, 5120]> stats = concat(axis = var_67, interleave = stats_interleave_0, values = (mean, std))[name = string("stats")];
+            tensor<fp32, [?, 2560]> var_671 = sub(x = mean, y = mean)[name = string("sub_0")];
+            fp32 var_672_value_0 = const()[name = string("op_672_value_0"), val = fp32(0x1.4f8b58p-17)];
+            tensor<fp32, [?, 2560]> var_672 = fill_like(ref_tensor = std, value = var_672_value_0)[name = string("op_672")];
+            bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)];
+            tensor<fp32, [?, 5120]> zero_stats = concat(axis = var_67, interleave = zero_stats_interleave_0, values = (var_671, var_672))[name = string("zero_stats")];
+            tensor<bool, [?, 1]> var_675 = less_equal(x = weight_sum, y = var_69)[name = string("op_675")];
+            tensor<int32, [2]> var_677 = const()[name = string("op_677"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [?, 5120]> zero_mask = tile(reps = var_677, x = var_675)[name = string("zero_mask")];
+            tensor<fp32, [?, 5120]> input = select(a = zero_stats, b = stats, cond = zero_mask)[name = string("input")];
+            tensor<fp32, [?, 256]> output = linear(bias = tail_resnet_seg_1_bias, weight = tail_resnet_seg_1_weight, x = input)[name = string("linear_0")];
+        } -> (output);
+}

wespeaker-voxceleb-resnet34-fused.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:741dd1d7fb08a55128b8e5aa0371bc3dd522f9f99d58f692b66b8733853b9b27
+size 28303488

wespeaker-voxceleb-resnet34-tail-b3-f16.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:45b464e43b0f1205603525d160d13f7789701c04010ead13d40d0fb2b31fdee6
+size 243

wespeaker-voxceleb-resnet34-tail-b3-f16.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e35919b7985082e3fe0fa8554679632383ef815f863f29a133a23a0bf17898a
+size 218

wespeaker-voxceleb-resnet34-tail-b3-f16.mlmodelc/model.mil

@@ -0,0 +1,414 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [?, 998, 80]> fbank, tensor<fp32, [?, 589]> weights) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"fbank", [32, 998, 80]}, {"weights", [32, 589]}}), ("EnumeratedShapes", {{"316ab78f", {{"fbank", [3, 998, 80]}, {"weights", [3, 589]}}}, {"f6770b54", {{"fbank", [1, 998, 80]}, {"weights", [1, 589]}}}, {"fd0b6e18", {{"fbank", [32, 998, 80]}, {"weights", [32, 589]}}}})))] {
+            tensor<int32, [3]> var_20 = const()[name = string("op_20"), val = tensor<int32, [3]>([0, 2, 1])];
+            string fbank_to_fp16_dtype_0 = const()[name = string("fbank_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<int32, [1]> input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [?, 998, 80]> fbank_to_fp16 = cast(dtype = fbank_to_fp16_dtype_0, x = fbank)[name = string("cast_9")];
+            tensor<fp16, [?, 80, 998]> fbank_cast_fp16 = transpose(perm = var_20, x = fbank_to_fp16)[name = string("transpose_0")];
+            tensor<fp16, [?, 1, 80, 998]> input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = fbank_cast_fp16)[name = string("input_1_cast_fp16")];
+            string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 1, 3, 3]> const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = tensor<fp16, [32, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [32]> const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(704)))];
+            tensor<fp16, [?, 32, 80, 998]> input_5_cast_fp16 = conv(bias = const_1_to_fp16, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = const_0_to_fp16, x = input_1_cast_fp16)[name = string("input_5_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = string("input_7_cast_fp16")];
+            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(832)))];
+            tensor<fp16, [32]> const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19328)))];
+            tensor<fp16, [?, 32, 80, 998]> input_11_cast_fp16 = conv(bias = const_3_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = const_2_to_fp16, x = input_7_cast_fp16)[name = string("input_11_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_13_cast_fp16 = relu(x = input_11_cast_fp16)[name = string("input_13_cast_fp16")];
+            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19456)))];
+            tensor<fp16, [32]> const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37952)))];
+            tensor<fp16, [?, 32, 80, 998]> out_1_cast_fp16 = conv(bias = const_5_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = const_4_to_fp16, x = input_13_cast_fp16)[name = string("out_1_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_17_cast_fp16 = add(x = out_1_cast_fp16, y = input_7_cast_fp16)[name = string("input_17_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_19_cast_fp16 = relu(x = input_17_cast_fp16)[name = string("input_19_cast_fp16")];
+            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38080)))];
+            tensor<fp16, [32]> const_7_to_fp16 = const()[name = string("const_7_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56576)))];
+            tensor<fp16, [?, 32, 80, 998]> input_23_cast_fp16 = conv(bias = const_7_to_fp16, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = const_6_to_fp16, x = input_19_cast_fp16)[name = string("input_23_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_25_cast_fp16 = relu(x = input_23_cast_fp16)[name = string("input_25_cast_fp16")];
+            string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56704)))];
+            tensor<fp16, [32]> const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75200)))];
+            tensor<fp16, [?, 32, 80, 998]> out_3_cast_fp16 = conv(bias = const_9_to_fp16, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = const_8_to_fp16, x = input_25_cast_fp16)[name = string("out_3_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_29_cast_fp16 = add(x = out_3_cast_fp16, y = input_19_cast_fp16)[name = string("input_29_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_31_cast_fp16 = relu(x = input_29_cast_fp16)[name = string("input_31_cast_fp16")];
+            string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75328)))];
+            tensor<fp16, [32]> const_11_to_fp16 = const()[name = string("const_11_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93824)))];
+            tensor<fp16, [?, 32, 80, 998]> input_35_cast_fp16 = conv(bias = const_11_to_fp16, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = const_10_to_fp16, x = input_31_cast_fp16)[name = string("input_35_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_37_cast_fp16 = relu(x = input_35_cast_fp16)[name = string("input_37_cast_fp16")];
+            string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)];
+            tensor<fp16, [32, 32, 3, 3]> const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93952)))];
+            tensor<fp16, [32]> const_13_to_fp16 = const()[name = string("const_13_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112448)))];
+            tensor<fp16, [?, 32, 80, 998]> out_5_cast_fp16 = conv(bias = const_13_to_fp16, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = const_12_to_fp16, x = input_37_cast_fp16)[name = string("out_5_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_41_cast_fp16 = add(x = out_5_cast_fp16, y = input_31_cast_fp16)[name = string("input_41_cast_fp16")];
+            tensor<fp16, [?, 32, 80, 998]> input_43_cast_fp16 = relu(x = input_41_cast_fp16)[name = string("input_43_cast_fp16")];
+            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 32, 3, 3]> const_14_to_fp16 = const()[name = string("const_14_to_fp16"), val = tensor<fp16, [64, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112576)))];
+            tensor<fp16, [64]> const_15_to_fp16 = const()[name = string("const_15_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149504)))];
+            tensor<fp16, [?, 64, 40, 499]> input_47_cast_fp16 = conv(bias = const_15_to_fp16, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = const_14_to_fp16, x = input_43_cast_fp16)[name = string("input_47_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_49_cast_fp16 = relu(x = input_47_cast_fp16)[name = string("input_49_cast_fp16")];
+            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149696)))];
+            tensor<fp16, [64]> const_17_to_fp16 = const()[name = string("const_17_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223488)))];
+            tensor<fp16, [?, 64, 40, 499]> out_7_cast_fp16 = conv(bias = const_17_to_fp16, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = const_16_to_fp16, x = input_49_cast_fp16)[name = string("out_7_cast_fp16")];
+            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 32, 1, 1]> const_18_to_fp16 = const()[name = string("const_18_to_fp16"), val = tensor<fp16, [64, 32, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223680)))];
+            tensor<fp16, [64]> const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227840)))];
+            tensor<fp16, [?, 64, 40, 499]> var_194_cast_fp16 = conv(bias = const_19_to_fp16, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = const_18_to_fp16, x = input_43_cast_fp16)[name = string("op_194_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_55_cast_fp16 = add(x = out_7_cast_fp16, y = var_194_cast_fp16)[name = string("input_55_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_57_cast_fp16 = relu(x = input_55_cast_fp16)[name = string("input_57_cast_fp16")];
+            string input_59_pad_type_0 = const()[name = string("input_59_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_59_pad_0 = const()[name = string("input_59_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_59_strides_0 = const()[name = string("input_59_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_59_dilations_0 = const()[name = string("input_59_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_59_groups_0 = const()[name = string("input_59_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_20_to_fp16 = const()[name = string("const_20_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228032)))];
+            tensor<fp16, [64]> const_21_to_fp16 = const()[name = string("const_21_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301824)))];
+            tensor<fp16, [?, 64, 40, 499]> input_61_cast_fp16 = conv(bias = const_21_to_fp16, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = const_20_to_fp16, x = input_57_cast_fp16)[name = string("input_61_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_63_cast_fp16 = relu(x = input_61_cast_fp16)[name = string("input_63_cast_fp16")];
+            string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_22_to_fp16 = const()[name = string("const_22_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302016)))];
+            tensor<fp16, [64]> const_23_to_fp16 = const()[name = string("const_23_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375808)))];
+            tensor<fp16, [?, 64, 40, 499]> out_9_cast_fp16 = conv(bias = const_23_to_fp16, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = const_22_to_fp16, x = input_63_cast_fp16)[name = string("out_9_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_67_cast_fp16 = add(x = out_9_cast_fp16, y = input_57_cast_fp16)[name = string("input_67_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_69_cast_fp16 = relu(x = input_67_cast_fp16)[name = string("input_69_cast_fp16")];
+            string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_71_strides_0 = const()[name = string("input_71_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_71_dilations_0 = const()[name = string("input_71_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_71_groups_0 = const()[name = string("input_71_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(376000)))];
+            tensor<fp16, [64]> const_25_to_fp16 = const()[name = string("const_25_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(449792)))];
+            tensor<fp16, [?, 64, 40, 499]> input_73_cast_fp16 = conv(bias = const_25_to_fp16, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = const_24_to_fp16, x = input_69_cast_fp16)[name = string("input_73_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_75_cast_fp16 = relu(x = input_73_cast_fp16)[name = string("input_75_cast_fp16")];
+            string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(449984)))];
+            tensor<fp16, [64]> const_27_to_fp16 = const()[name = string("const_27_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(523776)))];
+            tensor<fp16, [?, 64, 40, 499]> out_11_cast_fp16 = conv(bias = const_27_to_fp16, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = const_26_to_fp16, x = input_75_cast_fp16)[name = string("out_11_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_79_cast_fp16 = add(x = out_11_cast_fp16, y = input_69_cast_fp16)[name = string("input_79_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_81_cast_fp16 = relu(x = input_79_cast_fp16)[name = string("input_81_cast_fp16")];
+            string input_83_pad_type_0 = const()[name = string("input_83_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_83_pad_0 = const()[name = string("input_83_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_83_strides_0 = const()[name = string("input_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_83_dilations_0 = const()[name = string("input_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_83_groups_0 = const()[name = string("input_83_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(523968)))];
+            tensor<fp16, [64]> const_29_to_fp16 = const()[name = string("const_29_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(597760)))];
+            tensor<fp16, [?, 64, 40, 499]> input_85_cast_fp16 = conv(bias = const_29_to_fp16, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = const_28_to_fp16, x = input_81_cast_fp16)[name = string("input_85_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_87_cast_fp16 = relu(x = input_85_cast_fp16)[name = string("input_87_cast_fp16")];
+            string input_89_pad_type_0 = const()[name = string("input_89_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_89_pad_0 = const()[name = string("input_89_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_89_strides_0 = const()[name = string("input_89_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_89_dilations_0 = const()[name = string("input_89_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_89_groups_0 = const()[name = string("input_89_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> const_30_to_fp16 = const()[name = string("const_30_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(597952)))];
+            tensor<fp16, [64]> const_31_to_fp16 = const()[name = string("const_31_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(671744)))];
+            tensor<fp16, [?, 64, 40, 499]> out_13_cast_fp16 = conv(bias = const_31_to_fp16, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = const_30_to_fp16, x = input_87_cast_fp16)[name = string("out_13_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_91_cast_fp16 = add(x = out_13_cast_fp16, y = input_81_cast_fp16)[name = string("input_91_cast_fp16")];
+            tensor<fp16, [?, 64, 40, 499]> input_93_cast_fp16 = relu(x = input_91_cast_fp16)[name = string("input_93_cast_fp16")];
+            string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 64, 3, 3]> const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = tensor<fp16, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(671936)))];
+            tensor<fp16, [128]> const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(819456)))];
+            tensor<fp16, [?, 128, 20, 250]> input_97_cast_fp16 = conv(bias = const_33_to_fp16, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = const_32_to_fp16, x = input_93_cast_fp16)[name = string("input_97_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_99_cast_fp16 = relu(x = input_97_cast_fp16)[name = string("input_99_cast_fp16")];
+            string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_34_to_fp16 = const()[name = string("const_34_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(819776)))];
+            tensor<fp16, [128]> const_35_to_fp16 = const()[name = string("const_35_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1114752)))];
+            tensor<fp16, [?, 128, 20, 250]> out_15_cast_fp16 = conv(bias = const_35_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_34_to_fp16, x = input_99_cast_fp16)[name = string("out_15_cast_fp16")];
+            string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 64, 1, 1]> const_36_to_fp16 = const()[name = string("const_36_to_fp16"), val = tensor<fp16, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1115072)))];
+            tensor<fp16, [128]> const_37_to_fp16 = const()[name = string("const_37_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1131520)))];
+            tensor<fp16, [?, 128, 20, 250]> var_338_cast_fp16 = conv(bias = const_37_to_fp16, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = const_36_to_fp16, x = input_93_cast_fp16)[name = string("op_338_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_105_cast_fp16 = add(x = out_15_cast_fp16, y = var_338_cast_fp16)[name = string("input_105_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_107_cast_fp16 = relu(x = input_105_cast_fp16)[name = string("input_107_cast_fp16")];
+            string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_38_to_fp16 = const()[name = string("const_38_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1131840)))];
+            tensor<fp16, [128]> const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1426816)))];
+            tensor<fp16, [?, 128, 20, 250]> input_111_cast_fp16 = conv(bias = const_39_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_38_to_fp16, x = input_107_cast_fp16)[name = string("input_111_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_113_cast_fp16 = relu(x = input_111_cast_fp16)[name = string("input_113_cast_fp16")];
+            string input_115_pad_type_0 = const()[name = string("input_115_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_115_pad_0 = const()[name = string("input_115_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_115_strides_0 = const()[name = string("input_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_115_dilations_0 = const()[name = string("input_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_115_groups_0 = const()[name = string("input_115_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_40_to_fp16 = const()[name = string("const_40_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1427136)))];
+            tensor<fp16, [128]> const_41_to_fp16 = const()[name = string("const_41_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1722112)))];
+            tensor<fp16, [?, 128, 20, 250]> out_17_cast_fp16 = conv(bias = const_41_to_fp16, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = const_40_to_fp16, x = input_113_cast_fp16)[name = string("out_17_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_117_cast_fp16 = add(x = out_17_cast_fp16, y = input_107_cast_fp16)[name = string("input_117_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_119_cast_fp16 = relu(x = input_117_cast_fp16)[name = string("input_119_cast_fp16")];
+            string input_121_pad_type_0 = const()[name = string("input_121_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_121_pad_0 = const()[name = string("input_121_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_121_strides_0 = const()[name = string("input_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_121_dilations_0 = const()[name = string("input_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_121_groups_0 = const()[name = string("input_121_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_42_to_fp16 = const()[name = string("const_42_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1722432)))];
+            tensor<fp16, [128]> const_43_to_fp16 = const()[name = string("const_43_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2017408)))];
+            tensor<fp16, [?, 128, 20, 250]> input_123_cast_fp16 = conv(bias = const_43_to_fp16, dilations = input_121_dilations_0, groups = input_121_groups_0, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = input_121_strides_0, weight = const_42_to_fp16, x = input_119_cast_fp16)[name = string("input_123_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_125_cast_fp16 = relu(x = input_123_cast_fp16)[name = string("input_125_cast_fp16")];
+            string input_127_pad_type_0 = const()[name = string("input_127_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_127_pad_0 = const()[name = string("input_127_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_127_strides_0 = const()[name = string("input_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_127_dilations_0 = const()[name = string("input_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_127_groups_0 = const()[name = string("input_127_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2017728)))];
+            tensor<fp16, [128]> const_45_to_fp16 = const()[name = string("const_45_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2312704)))];
+            tensor<fp16, [?, 128, 20, 250]> out_19_cast_fp16 = conv(bias = const_45_to_fp16, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = const_44_to_fp16, x = input_125_cast_fp16)[name = string("out_19_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_129_cast_fp16 = add(x = out_19_cast_fp16, y = input_119_cast_fp16)[name = string("input_129_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_131_cast_fp16 = relu(x = input_129_cast_fp16)[name = string("input_131_cast_fp16")];
+            string input_133_pad_type_0 = const()[name = string("input_133_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_133_pad_0 = const()[name = string("input_133_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_133_strides_0 = const()[name = string("input_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_133_dilations_0 = const()[name = string("input_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_133_groups_0 = const()[name = string("input_133_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2313024)))];
+            tensor<fp16, [128]> const_47_to_fp16 = const()[name = string("const_47_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2608000)))];
+            tensor<fp16, [?, 128, 20, 250]> input_135_cast_fp16 = conv(bias = const_47_to_fp16, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = const_46_to_fp16, x = input_131_cast_fp16)[name = string("input_135_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_137_cast_fp16 = relu(x = input_135_cast_fp16)[name = string("input_137_cast_fp16")];
+            string input_139_pad_type_0 = const()[name = string("input_139_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_139_pad_0 = const()[name = string("input_139_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_139_strides_0 = const()[name = string("input_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_139_dilations_0 = const()[name = string("input_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_139_groups_0 = const()[name = string("input_139_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2608320)))];
+            tensor<fp16, [128]> const_49_to_fp16 = const()[name = string("const_49_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2903296)))];
+            tensor<fp16, [?, 128, 20, 250]> out_21_cast_fp16 = conv(bias = const_49_to_fp16, dilations = input_139_dilations_0, groups = input_139_groups_0, pad = input_139_pad_0, pad_type = input_139_pad_type_0, strides = input_139_strides_0, weight = const_48_to_fp16, x = input_137_cast_fp16)[name = string("out_21_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_141_cast_fp16 = add(x = out_21_cast_fp16, y = input_131_cast_fp16)[name = string("input_141_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_143_cast_fp16 = relu(x = input_141_cast_fp16)[name = string("input_143_cast_fp16")];
+            string input_145_pad_type_0 = const()[name = string("input_145_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_145_pad_0 = const()[name = string("input_145_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_145_strides_0 = const()[name = string("input_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_145_dilations_0 = const()[name = string("input_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_145_groups_0 = const()[name = string("input_145_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2903616)))];
+            tensor<fp16, [128]> const_51_to_fp16 = const()[name = string("const_51_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3198592)))];
+            tensor<fp16, [?, 128, 20, 250]> input_147_cast_fp16 = conv(bias = const_51_to_fp16, dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = const_50_to_fp16, x = input_143_cast_fp16)[name = string("input_147_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_149_cast_fp16 = relu(x = input_147_cast_fp16)[name = string("input_149_cast_fp16")];
+            string input_151_pad_type_0 = const()[name = string("input_151_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_151_pad_0 = const()[name = string("input_151_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_151_strides_0 = const()[name = string("input_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_151_dilations_0 = const()[name = string("input_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_151_groups_0 = const()[name = string("input_151_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3198912)))];
+            tensor<fp16, [128]> const_53_to_fp16 = const()[name = string("const_53_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3493888)))];
+            tensor<fp16, [?, 128, 20, 250]> out_23_cast_fp16 = conv(bias = const_53_to_fp16, dilations = input_151_dilations_0, groups = input_151_groups_0, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = input_151_strides_0, weight = const_52_to_fp16, x = input_149_cast_fp16)[name = string("out_23_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_153_cast_fp16 = add(x = out_23_cast_fp16, y = input_143_cast_fp16)[name = string("input_153_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_155_cast_fp16 = relu(x = input_153_cast_fp16)[name = string("input_155_cast_fp16")];
+            string input_157_pad_type_0 = const()[name = string("input_157_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_157_strides_0 = const()[name = string("input_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_157_dilations_0 = const()[name = string("input_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_157_groups_0 = const()[name = string("input_157_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_54_to_fp16 = const()[name = string("const_54_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3494208)))];
+            tensor<fp16, [128]> const_55_to_fp16 = const()[name = string("const_55_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3789184)))];
+            tensor<fp16, [?, 128, 20, 250]> input_159_cast_fp16 = conv(bias = const_55_to_fp16, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = const_54_to_fp16, x = input_155_cast_fp16)[name = string("input_159_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_161_cast_fp16 = relu(x = input_159_cast_fp16)[name = string("input_161_cast_fp16")];
+            string input_163_pad_type_0 = const()[name = string("input_163_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_163_pad_0 = const()[name = string("input_163_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_163_strides_0 = const()[name = string("input_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_163_dilations_0 = const()[name = string("input_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_163_groups_0 = const()[name = string("input_163_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> const_56_to_fp16 = const()[name = string("const_56_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3789504)))];
+            tensor<fp16, [128]> const_57_to_fp16 = const()[name = string("const_57_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4084480)))];
+            tensor<fp16, [?, 128, 20, 250]> out_25_cast_fp16 = conv(bias = const_57_to_fp16, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = const_56_to_fp16, x = input_161_cast_fp16)[name = string("out_25_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_165_cast_fp16 = add(x = out_25_cast_fp16, y = input_155_cast_fp16)[name = string("input_165_cast_fp16")];
+            tensor<fp16, [?, 128, 20, 250]> input_167_cast_fp16 = relu(x = input_165_cast_fp16)[name = string("input_167_cast_fp16")];
+            string input_169_pad_type_0 = const()[name = string("input_169_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_169_pad_0 = const()[name = string("input_169_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_169_strides_0 = const()[name = string("input_169_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_169_dilations_0 = const()[name = string("input_169_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_169_groups_0 = const()[name = string("input_169_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 128, 3, 3]> const_58_to_fp16 = const()[name = string("const_58_to_fp16"), val = tensor<fp16, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4084800)))];
+            tensor<fp16, [256]> const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4674688)))];
+            tensor<fp16, [?, 256, 10, 125]> input_171_cast_fp16 = conv(bias = const_59_to_fp16, dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = const_58_to_fp16, x = input_167_cast_fp16)[name = string("input_171_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_173_cast_fp16 = relu(x = input_171_cast_fp16)[name = string("input_173_cast_fp16")];
+            string input_175_pad_type_0 = const()[name = string("input_175_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_175_pad_0 = const()[name = string("input_175_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_175_strides_0 = const()[name = string("input_175_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_175_dilations_0 = const()[name = string("input_175_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_175_groups_0 = const()[name = string("input_175_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_60_to_fp16 = const()[name = string("const_60_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4675264)))];
+            tensor<fp16, [256]> const_61_to_fp16 = const()[name = string("const_61_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5854976)))];
+            tensor<fp16, [?, 256, 10, 125]> out_27_cast_fp16 = conv(bias = const_61_to_fp16, dilations = input_175_dilations_0, groups = input_175_groups_0, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = input_175_strides_0, weight = const_60_to_fp16, x = input_173_cast_fp16)[name = string("out_27_cast_fp16")];
+            string input_177_pad_type_0 = const()[name = string("input_177_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_177_strides_0 = const()[name = string("input_177_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_177_pad_0 = const()[name = string("input_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_177_dilations_0 = const()[name = string("input_177_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_177_groups_0 = const()[name = string("input_177_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 128, 1, 1]> const_62_to_fp16 = const()[name = string("const_62_to_fp16"), val = tensor<fp16, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5855552)))];
+            tensor<fp16, [256]> const_63_to_fp16 = const()[name = string("const_63_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5921152)))];
+            tensor<fp16, [?, 256, 10, 125]> var_537_cast_fp16 = conv(bias = const_63_to_fp16, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = const_62_to_fp16, x = input_167_cast_fp16)[name = string("op_537_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_179_cast_fp16 = add(x = out_27_cast_fp16, y = var_537_cast_fp16)[name = string("input_179_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_181_cast_fp16 = relu(x = input_179_cast_fp16)[name = string("input_181_cast_fp16")];
+            string input_183_pad_type_0 = const()[name = string("input_183_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_183_pad_0 = const()[name = string("input_183_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_183_strides_0 = const()[name = string("input_183_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_183_dilations_0 = const()[name = string("input_183_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_183_groups_0 = const()[name = string("input_183_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5921728)))];
+            tensor<fp16, [256]> const_65_to_fp16 = const()[name = string("const_65_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7101440)))];
+            tensor<fp16, [?, 256, 10, 125]> input_185_cast_fp16 = conv(bias = const_65_to_fp16, dilations = input_183_dilations_0, groups = input_183_groups_0, pad = input_183_pad_0, pad_type = input_183_pad_type_0, strides = input_183_strides_0, weight = const_64_to_fp16, x = input_181_cast_fp16)[name = string("input_185_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_187_cast_fp16 = relu(x = input_185_cast_fp16)[name = string("input_187_cast_fp16")];
+            string input_189_pad_type_0 = const()[name = string("input_189_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_189_pad_0 = const()[name = string("input_189_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_189_strides_0 = const()[name = string("input_189_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_189_dilations_0 = const()[name = string("input_189_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_189_groups_0 = const()[name = string("input_189_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7102016)))];
+            tensor<fp16, [256]> const_67_to_fp16 = const()[name = string("const_67_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8281728)))];
+            tensor<fp16, [?, 256, 10, 125]> out_29_cast_fp16 = conv(bias = const_67_to_fp16, dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = const_66_to_fp16, x = input_187_cast_fp16)[name = string("out_29_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_191_cast_fp16 = add(x = out_29_cast_fp16, y = input_181_cast_fp16)[name = string("input_191_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_193_cast_fp16 = relu(x = input_191_cast_fp16)[name = string("input_193_cast_fp16")];
+            string input_195_pad_type_0 = const()[name = string("input_195_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_195_pad_0 = const()[name = string("input_195_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_195_strides_0 = const()[name = string("input_195_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_195_dilations_0 = const()[name = string("input_195_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_195_groups_0 = const()[name = string("input_195_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_68_to_fp16 = const()[name = string("const_68_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8282304)))];
+            tensor<fp16, [256]> const_69_to_fp16 = const()[name = string("const_69_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9462016)))];
+            tensor<fp16, [?, 256, 10, 125]> input_197_cast_fp16 = conv(bias = const_69_to_fp16, dilations = input_195_dilations_0, groups = input_195_groups_0, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = input_195_strides_0, weight = const_68_to_fp16, x = input_193_cast_fp16)[name = string("input_197_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_199_cast_fp16 = relu(x = input_197_cast_fp16)[name = string("input_199_cast_fp16")];
+            string input_201_pad_type_0 = const()[name = string("input_201_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_201_strides_0 = const()[name = string("input_201_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_201_dilations_0 = const()[name = string("input_201_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_201_groups_0 = const()[name = string("input_201_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9462592)))];
+            tensor<fp16, [256]> const_71_to_fp16 = const()[name = string("const_71_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10642304)))];
+            tensor<fp16, [?, 256, 10, 125]> out_cast_fp16 = conv(bias = const_71_to_fp16, dilations = input_201_dilations_0, groups = input_201_groups_0, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = input_201_strides_0, weight = const_70_to_fp16, x = input_199_cast_fp16)[name = string("out_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> input_203_cast_fp16 = add(x = out_cast_fp16, y = input_193_cast_fp16)[name = string("input_203_cast_fp16")];
+            tensor<fp16, [?, 256, 10, 125]> frames_cast_fp16 = relu(x = input_203_cast_fp16)[name = string("frames_cast_fp16")];
+            tensor<int32, [3]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp16, [?, 2560, 125]> sequences_cast_fp16 = reshape(shape = concat_0x, x = frames_cast_fp16)[name = string("sequences_cast_fp16")];
+            tensor<int32, [1]> input_205_axes_0 = const()[name = string("input_205_axes_0"), val = tensor<int32, [1]>([1])];
+            string weights_to_fp16_dtype_0 = const()[name = string("weights_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [?, 589]> weights_to_fp16 = cast(dtype = weights_to_fp16_dtype_0, x = weights)[name = string("cast_8")];
+            tensor<fp16, [?, 1, 589]> input_205_cast_fp16 = expand_dims(axes = input_205_axes_0, x = weights_to_fp16)[name = string("input_205_cast_fp16")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [?, 1, 589, 1]> expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = input_205_cast_fp16)[name = string("expand_dims_0_cast_fp16")];
+            fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)];
+            fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)];
+            tensor<fp16, [?, 1, 125, 1]> upsample_nearest_neighbor_0_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0_cast_fp16)[name = string("upsample_nearest_neighbor_0_cast_fp16")];
+            tensor<int32, [1]> weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [?, 1, 125]> weights_cast_fp16 = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0_cast_fp16)[name = string("weights_cast_fp16")];
+            tensor<int32, [1]> weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 1]> weight_sum_cast_fp16 = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights_cast_fp16)[name = string("weight_sum_cast_fp16")];
+            fp16 var_627_to_fp16 = const()[name = string("op_627_to_fp16"), val = fp16(0x0p+0)];
+            tensor<bool, [?, 1]> var_628_cast_fp16 = greater(x = weight_sum_cast_fp16, y = var_627_to_fp16)[name = string("op_628_cast_fp16")];
+            fp16 fill_like_0_value_0_to_fp16 = const()[name = string("fill_like_0_value_0_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [?, 1]> fill_like_0_cast_fp16 = fill_like(ref_tensor = weight_sum_cast_fp16, value = fill_like_0_value_0_to_fp16)[name = string("fill_like_0_cast_fp16")];
+            tensor<fp16, [?, 1]> safe_sum_cast_fp16 = select(a = weight_sum_cast_fp16, b = fill_like_0_cast_fp16, cond = var_628_cast_fp16)[name = string("safe_sum_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> var_636_cast_fp16 = mul(x = sequences_cast_fp16, y = weights_cast_fp16)[name = string("op_636_cast_fp16")];
+            tensor<int32, [1]> var_641_axes_0 = const()[name = string("op_641_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_641_keep_dims_0 = const()[name = string("op_641_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 2560]> var_641_cast_fp16 = reduce_sum(axes = var_641_axes_0, keep_dims = var_641_keep_dims_0, x = var_636_cast_fp16)[name = string("op_641_cast_fp16")];
+            tensor<fp16, [?, 2560]> mean_cast_fp16 = real_div(x = var_641_cast_fp16, y = safe_sum_cast_fp16)[name = string("mean_cast_fp16")];
+            tensor<int32, [1]> var_644_axes_0 = const()[name = string("op_644_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [?, 2560, 1]> var_644_cast_fp16 = expand_dims(axes = var_644_axes_0, x = mean_cast_fp16)[name = string("op_644_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> var_646_cast_fp16 = sub(x = sequences_cast_fp16, y = var_644_cast_fp16)[name = string("op_646_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> dx2_cast_fp16 = mul(x = var_646_cast_fp16, y = var_646_cast_fp16)[name = string("dx2_cast_fp16")];
+            tensor<fp16, [?, 1, 125]> var_648_cast_fp16 = mul(x = weights_cast_fp16, y = weights_cast_fp16)[name = string("op_648_cast_fp16")];
+            tensor<int32, [1]> weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 1]> weight_sq_sum_cast_fp16 = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_648_cast_fp16)[name = string("weight_sq_sum_cast_fp16")];
+            tensor<fp16, [?, 1]> var_654_cast_fp16 = real_div(x = weight_sq_sum_cast_fp16, y = safe_sum_cast_fp16)[name = string("op_654_cast_fp16")];
+            tensor<fp16, [?, 1]> var_656_cast_fp16 = sub(x = safe_sum_cast_fp16, y = var_654_cast_fp16)[name = string("op_656_cast_fp16")];
+            fp16 var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [?, 1]> denom_cast_fp16 = add(x = var_656_cast_fp16, y = var_658_to_fp16)[name = string("denom_cast_fp16")];
+            tensor<fp16, [?, 2560, 125]> var_660_cast_fp16 = mul(x = dx2_cast_fp16, y = weights_cast_fp16)[name = string("op_660_cast_fp16")];
+            tensor<int32, [1]> var_665_axes_0 = const()[name = string("op_665_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_665_keep_dims_0 = const()[name = string("op_665_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [?, 2560]> var_665_cast_fp16 = reduce_sum(axes = var_665_axes_0, keep_dims = var_665_keep_dims_0, x = var_660_cast_fp16)[name = string("op_665_cast_fp16")];
+            tensor<fp16, [?, 2560]> var_cast_fp16 = real_div(x = var_665_cast_fp16, y = denom_cast_fp16)[name = string("var_cast_fp16")];
+            fp16 var_667_to_fp16 = const()[name = string("op_667_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [?, 2560]> var_668_cast_fp16 = maximum(x = var_cast_fp16, y = var_667_to_fp16)[name = string("op_668_cast_fp16")];
+            tensor<fp16, [?, 2560]> std_cast_fp16 = sqrt(x = var_668_cast_fp16)[name = string("std_cast_fp16")];
+            int32 var_671 = const()[name = string("op_671"), val = int32(-1)];
+            bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)];
+            tensor<fp16, [?, 5120]> stats_cast_fp16 = concat(axis = var_671, interleave = stats_interleave_0, values = (mean_cast_fp16, std_cast_fp16))[name = string("stats_cast_fp16")];
+            tensor<fp16, [?, 2560]> sub_0_cast_fp16 = sub(x = mean_cast_fp16, y = mean_cast_fp16)[name = string("sub_0_cast_fp16")];
+            fp16 var_685_value_0_to_fp16 = const()[name = string("op_685_value_0_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [?, 2560]> var_685_cast_fp16 = fill_like(ref_tensor = std_cast_fp16, value = var_685_value_0_to_fp16)[name = string("op_685_cast_fp16")];
+            int32 var_687 = const()[name = string("op_687"), val = int32(-1)];
+            bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)];
+            tensor<fp16, [?, 5120]> zero_stats_cast_fp16 = concat(axis = var_687, interleave = zero_stats_interleave_0, values = (sub_0_cast_fp16, var_685_cast_fp16))[name = string("zero_stats_cast_fp16")];
+            fp16 var_689_to_fp16 = const()[name = string("op_689_to_fp16"), val = fp16(0x0p+0)];
+            tensor<bool, [?, 1]> var_690_cast_fp16 = less_equal(x = weight_sum_cast_fp16, y = var_689_to_fp16)[name = string("op_690_cast_fp16")];
+            tensor<int32, [2]> var_696 = const()[name = string("op_696"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [?, 5120]> zero_mask = tile(reps = var_696, x = var_690_cast_fp16)[name = string("zero_mask")];
+            tensor<fp16, [?, 5120]> input_cast_fp16 = select(a = zero_stats_cast_fp16, b = stats_cast_fp16, cond = zero_mask)[name = string("input_cast_fp16")];
+            tensor<fp16, [256, 5120]> resnet_seg_1_weight_to_fp16 = const()[name = string("resnet_seg_1_weight_to_fp16"), val = tensor<fp16, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10642880)))];
+            tensor<fp16, [256]> resnet_seg_1_bias_to_fp16 = const()[name = string("resnet_seg_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13264384)))];
+            tensor<fp16, [?, 256]> linear_0_cast_fp16 = linear(bias = resnet_seg_1_bias_to_fp16, weight = resnet_seg_1_weight_to_fp16, x = input_cast_fp16)[name = string("linear_0_cast_fp16")];
+            string linear_0_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_0_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [?, 256]> output = cast(dtype = linear_0_cast_fp16_to_fp32_dtype_0, x = linear_0_cast_fp16)[name = string("cast_7")];
+        } -> (output);
+}

wespeaker-voxceleb-resnet34-tail-b3-f16.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6dba18a57a81b1e872802ca4def29541bb7900ccff430d9b2040092cadd7d688
+size 13264960

wespeaker-voxceleb-resnet34-tail-b3.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07abd12d7cdb8af793b6d439b40bd9e5c1f44b9eb69cbb3d3d272f494a77a556
+size 243

wespeaker-voxceleb-resnet34-tail-b3.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e35919b7985082e3fe0fa8554679632383ef815f863f29a133a23a0bf17898a
+size 218

wespeaker-voxceleb-resnet34-tail-b3.mlmodelc/model.mil

@@ -0,0 +1,408 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [?, 998, 80]> fbank, tensor<fp32, [?, 589]> weights) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"fbank", [32, 998, 80]}, {"weights", [32, 589]}}), ("EnumeratedShapes", {{"316ab78f", {{"fbank", [3, 998, 80]}, {"weights", [3, 589]}}}, {"f6770b54", {{"fbank", [1, 998, 80]}, {"weights", [1, 589]}}}, {"fd0b6e18", {{"fbank", [32, 998, 80]}, {"weights", [32, 589]}}}})))] {
+            tensor<fp32, [256]> resnet_seg_1_bias = const()[name = string("resnet_seg_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [256, 5120]> resnet_seg_1_weight = const()[name = string("resnet_seg_1_weight"), val = tensor<fp32, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152)))];
+            tensor<int32, [3]> var_20 = const()[name = string("op_20"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 80, 998]> fbank_1 = transpose(perm = var_20, x = fbank)[name = string("transpose_0")];
+            tensor<fp32, [?, 1, 80, 998]> input_1 = expand_dims(axes = input_1_axes_0, x = fbank_1)[name = string("input_1")];
+            string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 1, 3, 3]> const_0 = const()[name = string("const_0"), val = tensor<fp32, [32, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))];
+            tensor<fp32, [32]> const_1 = const()[name = string("const_1"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))];
+            tensor<fp32, [?, 32, 80, 998]> input_5 = conv(bias = const_1, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = const_0, x = input_1)[name = string("input_5")];
+            tensor<fp32, [?, 32, 80, 998]> input_7 = relu(x = input_5)[name = string("input_7")];
+            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_2 = const()[name = string("const_2"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))];
+            tensor<fp32, [32]> const_3 = const()[name = string("const_3"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))];
+            tensor<fp32, [?, 32, 80, 998]> input_11 = conv(bias = const_3, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = const_2, x = input_7)[name = string("input_11")];
+            tensor<fp32, [?, 32, 80, 998]> input_13 = relu(x = input_11)[name = string("input_13")];
+            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_4 = const()[name = string("const_4"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))];
+            tensor<fp32, [32]> const_5 = const()[name = string("const_5"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))];
+            tensor<fp32, [?, 32, 80, 998]> out_1 = conv(bias = const_5, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = const_4, x = input_13)[name = string("out_1")];
+            tensor<fp32, [?, 32, 80, 998]> input_17 = add(x = out_1, y = input_7)[name = string("input_17")];
+            tensor<fp32, [?, 32, 80, 998]> input_19 = relu(x = input_17)[name = string("input_19")];
+            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_6 = const()[name = string("const_6"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))];
+            tensor<fp32, [32]> const_7 = const()[name = string("const_7"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))];
+            tensor<fp32, [?, 32, 80, 998]> input_23 = conv(bias = const_7, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = const_6, x = input_19)[name = string("input_23")];
+            tensor<fp32, [?, 32, 80, 998]> input_25 = relu(x = input_23)[name = string("input_25")];
+            string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_8 = const()[name = string("const_8"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))];
+            tensor<fp32, [32]> const_9 = const()[name = string("const_9"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))];
+            tensor<fp32, [?, 32, 80, 998]> out_3 = conv(bias = const_9, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = const_8, x = input_25)[name = string("out_3")];
+            tensor<fp32, [?, 32, 80, 998]> input_29 = add(x = out_3, y = input_19)[name = string("input_29")];
+            tensor<fp32, [?, 32, 80, 998]> input_31 = relu(x = input_29)[name = string("input_31")];
+            string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_10 = const()[name = string("const_10"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))];
+            tensor<fp32, [32]> const_11 = const()[name = string("const_11"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))];
+            tensor<fp32, [?, 32, 80, 998]> input_35 = conv(bias = const_11, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = const_10, x = input_31)[name = string("input_35")];
+            tensor<fp32, [?, 32, 80, 998]> input_37 = relu(x = input_35)[name = string("input_37")];
+            string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_12 = const()[name = string("const_12"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))];
+            tensor<fp32, [32]> const_13 = const()[name = string("const_13"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))];
+            tensor<fp32, [?, 32, 80, 998]> out_5 = conv(bias = const_13, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = const_12, x = input_37)[name = string("out_5")];
+            tensor<fp32, [?, 32, 80, 998]> input_41 = add(x = out_5, y = input_31)[name = string("input_41")];
+            tensor<fp32, [?, 32, 80, 998]> input_43 = relu(x = input_41)[name = string("input_43")];
+            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 3, 3]> const_14 = const()[name = string("const_14"), val = tensor<fp32, [64, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))];
+            tensor<fp32, [64]> const_15 = const()[name = string("const_15"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))];
+            tensor<fp32, [?, 64, 40, 499]> input_47 = conv(bias = const_15, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = const_14, x = input_43)[name = string("input_47")];
+            tensor<fp32, [?, 64, 40, 499]> input_49 = relu(x = input_47)[name = string("input_49")];
+            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_16 = const()[name = string("const_16"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))];
+            tensor<fp32, [64]> const_17 = const()[name = string("const_17"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))];
+            tensor<fp32, [?, 64, 40, 499]> out_7 = conv(bias = const_17, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = const_16, x = input_49)[name = string("out_7")];
+            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 1, 1]> const_18 = const()[name = string("const_18"), val = tensor<fp32, [64, 32, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))];
+            tensor<fp32, [64]> const_19 = const()[name = string("const_19"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))];
+            tensor<fp32, [?, 64, 40, 499]> var_194 = conv(bias = const_19, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = const_18, x = input_43)[name = string("op_194")];
+            tensor<fp32, [?, 64, 40, 499]> input_55 = add(x = out_7, y = var_194)[name = string("input_55")];
+            tensor<fp32, [?, 64, 40, 499]> input_57 = relu(x = input_55)[name = string("input_57")];
+            string input_59_pad_type_0 = const()[name = string("input_59_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_59_pad_0 = const()[name = string("input_59_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_59_strides_0 = const()[name = string("input_59_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_59_dilations_0 = const()[name = string("input_59_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_59_groups_0 = const()[name = string("input_59_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_20 = const()[name = string("const_20"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))];
+            tensor<fp32, [64]> const_21 = const()[name = string("const_21"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))];
+            tensor<fp32, [?, 64, 40, 499]> input_61 = conv(bias = const_21, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = const_20, x = input_57)[name = string("input_61")];
+            tensor<fp32, [?, 64, 40, 499]> input_63 = relu(x = input_61)[name = string("input_63")];
+            string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_22 = const()[name = string("const_22"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))];
+            tensor<fp32, [64]> const_23 = const()[name = string("const_23"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))];
+            tensor<fp32, [?, 64, 40, 499]> out_9 = conv(bias = const_23, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = const_22, x = input_63)[name = string("out_9")];
+            tensor<fp32, [?, 64, 40, 499]> input_67 = add(x = out_9, y = input_57)[name = string("input_67")];
+            tensor<fp32, [?, 64, 40, 499]> input_69 = relu(x = input_67)[name = string("input_69")];
+            string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_71_strides_0 = const()[name = string("input_71_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_71_dilations_0 = const()[name = string("input_71_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_71_groups_0 = const()[name = string("input_71_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_24 = const()[name = string("const_24"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))];
+            tensor<fp32, [64]> const_25 = const()[name = string("const_25"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))];
+            tensor<fp32, [?, 64, 40, 499]> input_73 = conv(bias = const_25, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = const_24, x = input_69)[name = string("input_73")];
+            tensor<fp32, [?, 64, 40, 499]> input_75 = relu(x = input_73)[name = string("input_75")];
+            string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_26 = const()[name = string("const_26"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))];
+            tensor<fp32, [64]> const_27 = const()[name = string("const_27"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))];
+            tensor<fp32, [?, 64, 40, 499]> out_11 = conv(bias = const_27, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = const_26, x = input_75)[name = string("out_11")];
+            tensor<fp32, [?, 64, 40, 499]> input_79 = add(x = out_11, y = input_69)[name = string("input_79")];
+            tensor<fp32, [?, 64, 40, 499]> input_81 = relu(x = input_79)[name = string("input_81")];
+            string input_83_pad_type_0 = const()[name = string("input_83_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_83_pad_0 = const()[name = string("input_83_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_83_strides_0 = const()[name = string("input_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_83_dilations_0 = const()[name = string("input_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_83_groups_0 = const()[name = string("input_83_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_28 = const()[name = string("const_28"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))];
+            tensor<fp32, [64]> const_29 = const()[name = string("const_29"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))];
+            tensor<fp32, [?, 64, 40, 499]> input_85 = conv(bias = const_29, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = const_28, x = input_81)[name = string("input_85")];
+            tensor<fp32, [?, 64, 40, 499]> input_87 = relu(x = input_85)[name = string("input_87")];
+            string input_89_pad_type_0 = const()[name = string("input_89_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_89_pad_0 = const()[name = string("input_89_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_89_strides_0 = const()[name = string("input_89_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_89_dilations_0 = const()[name = string("input_89_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_89_groups_0 = const()[name = string("input_89_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_30 = const()[name = string("const_30"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))];
+            tensor<fp32, [64]> const_31 = const()[name = string("const_31"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))];
+            tensor<fp32, [?, 64, 40, 499]> out_13 = conv(bias = const_31, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = const_30, x = input_87)[name = string("out_13")];
+            tensor<fp32, [?, 64, 40, 499]> input_91 = add(x = out_13, y = input_81)[name = string("input_91")];
+            tensor<fp32, [?, 64, 40, 499]> input_93 = relu(x = input_91)[name = string("input_93")];
+            string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 3, 3]> const_32 = const()[name = string("const_32"), val = tensor<fp32, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))];
+            tensor<fp32, [128]> const_33 = const()[name = string("const_33"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))];
+            tensor<fp32, [?, 128, 20, 250]> input_97 = conv(bias = const_33, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = const_32, x = input_93)[name = string("input_97")];
+            tensor<fp32, [?, 128, 20, 250]> input_99 = relu(x = input_97)[name = string("input_99")];
+            string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_34 = const()[name = string("const_34"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))];
+            tensor<fp32, [128]> const_35 = const()[name = string("const_35"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))];
+            tensor<fp32, [?, 128, 20, 250]> out_15 = conv(bias = const_35, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_34, x = input_99)[name = string("out_15")];
+            string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 1, 1]> const_36 = const()[name = string("const_36"), val = tensor<fp32, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))];
+            tensor<fp32, [128]> const_37 = const()[name = string("const_37"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))];
+            tensor<fp32, [?, 128, 20, 250]> var_338 = conv(bias = const_37, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = const_36, x = input_93)[name = string("op_338")];
+            tensor<fp32, [?, 128, 20, 250]> input_105 = add(x = out_15, y = var_338)[name = string("input_105")];
+            tensor<fp32, [?, 128, 20, 250]> input_107 = relu(x = input_105)[name = string("input_107")];
+            string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_38 = const()[name = string("const_38"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))];
+            tensor<fp32, [128]> const_39 = const()[name = string("const_39"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))];
+            tensor<fp32, [?, 128, 20, 250]> input_111 = conv(bias = const_39, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_38, x = input_107)[name = string("input_111")];
+            tensor<fp32, [?, 128, 20, 250]> input_113 = relu(x = input_111)[name = string("input_113")];
+            string input_115_pad_type_0 = const()[name = string("input_115_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_115_pad_0 = const()[name = string("input_115_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_115_strides_0 = const()[name = string("input_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_115_dilations_0 = const()[name = string("input_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_115_groups_0 = const()[name = string("input_115_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_40 = const()[name = string("const_40"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))];
+            tensor<fp32, [128]> const_41 = const()[name = string("const_41"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))];
+            tensor<fp32, [?, 128, 20, 250]> out_17 = conv(bias = const_41, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = const_40, x = input_113)[name = string("out_17")];
+            tensor<fp32, [?, 128, 20, 250]> input_117 = add(x = out_17, y = input_107)[name = string("input_117")];
+            tensor<fp32, [?, 128, 20, 250]> input_119 = relu(x = input_117)[name = string("input_119")];
+            string input_121_pad_type_0 = const()[name = string("input_121_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_121_pad_0 = const()[name = string("input_121_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_121_strides_0 = const()[name = string("input_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_121_dilations_0 = const()[name = string("input_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_121_groups_0 = const()[name = string("input_121_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_42 = const()[name = string("const_42"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))];
+            tensor<fp32, [128]> const_43 = const()[name = string("const_43"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))];
+            tensor<fp32, [?, 128, 20, 250]> input_123 = conv(bias = const_43, dilations = input_121_dilations_0, groups = input_121_groups_0, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = input_121_strides_0, weight = const_42, x = input_119)[name = string("input_123")];
+            tensor<fp32, [?, 128, 20, 250]> input_125 = relu(x = input_123)[name = string("input_125")];
+            string input_127_pad_type_0 = const()[name = string("input_127_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_127_pad_0 = const()[name = string("input_127_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_127_strides_0 = const()[name = string("input_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_127_dilations_0 = const()[name = string("input_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_127_groups_0 = const()[name = string("input_127_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_44 = const()[name = string("const_44"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))];
+            tensor<fp32, [128]> const_45 = const()[name = string("const_45"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))];
+            tensor<fp32, [?, 128, 20, 250]> out_19 = conv(bias = const_45, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = const_44, x = input_125)[name = string("out_19")];
+            tensor<fp32, [?, 128, 20, 250]> input_129 = add(x = out_19, y = input_119)[name = string("input_129")];
+            tensor<fp32, [?, 128, 20, 250]> input_131 = relu(x = input_129)[name = string("input_131")];
+            string input_133_pad_type_0 = const()[name = string("input_133_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_133_pad_0 = const()[name = string("input_133_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_133_strides_0 = const()[name = string("input_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_133_dilations_0 = const()[name = string("input_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_133_groups_0 = const()[name = string("input_133_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_46 = const()[name = string("const_46"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))];
+            tensor<fp32, [128]> const_47 = const()[name = string("const_47"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))];
+            tensor<fp32, [?, 128, 20, 250]> input_135 = conv(bias = const_47, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = const_46, x = input_131)[name = string("input_135")];
+            tensor<fp32, [?, 128, 20, 250]> input_137 = relu(x = input_135)[name = string("input_137")];
+            string input_139_pad_type_0 = const()[name = string("input_139_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_139_pad_0 = const()[name = string("input_139_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_139_strides_0 = const()[name = string("input_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_139_dilations_0 = const()[name = string("input_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_139_groups_0 = const()[name = string("input_139_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_48 = const()[name = string("const_48"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))];
+            tensor<fp32, [128]> const_49 = const()[name = string("const_49"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))];
+            tensor<fp32, [?, 128, 20, 250]> out_21 = conv(bias = const_49, dilations = input_139_dilations_0, groups = input_139_groups_0, pad = input_139_pad_0, pad_type = input_139_pad_type_0, strides = input_139_strides_0, weight = const_48, x = input_137)[name = string("out_21")];
+            tensor<fp32, [?, 128, 20, 250]> input_141 = add(x = out_21, y = input_131)[name = string("input_141")];
+            tensor<fp32, [?, 128, 20, 250]> input_143 = relu(x = input_141)[name = string("input_143")];
+            string input_145_pad_type_0 = const()[name = string("input_145_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_145_pad_0 = const()[name = string("input_145_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_145_strides_0 = const()[name = string("input_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_145_dilations_0 = const()[name = string("input_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_145_groups_0 = const()[name = string("input_145_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_50 = const()[name = string("const_50"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))];
+            tensor<fp32, [128]> const_51 = const()[name = string("const_51"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))];
+            tensor<fp32, [?, 128, 20, 250]> input_147 = conv(bias = const_51, dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = const_50, x = input_143)[name = string("input_147")];
+            tensor<fp32, [?, 128, 20, 250]> input_149 = relu(x = input_147)[name = string("input_149")];
+            string input_151_pad_type_0 = const()[name = string("input_151_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_151_pad_0 = const()[name = string("input_151_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_151_strides_0 = const()[name = string("input_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_151_dilations_0 = const()[name = string("input_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_151_groups_0 = const()[name = string("input_151_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_52 = const()[name = string("const_52"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))];
+            tensor<fp32, [128]> const_53 = const()[name = string("const_53"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))];
+            tensor<fp32, [?, 128, 20, 250]> out_23 = conv(bias = const_53, dilations = input_151_dilations_0, groups = input_151_groups_0, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = input_151_strides_0, weight = const_52, x = input_149)[name = string("out_23")];
+            tensor<fp32, [?, 128, 20, 250]> input_153 = add(x = out_23, y = input_143)[name = string("input_153")];
+            tensor<fp32, [?, 128, 20, 250]> input_155 = relu(x = input_153)[name = string("input_155")];
+            string input_157_pad_type_0 = const()[name = string("input_157_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_157_strides_0 = const()[name = string("input_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_157_dilations_0 = const()[name = string("input_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_157_groups_0 = const()[name = string("input_157_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_54 = const()[name = string("const_54"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))];
+            tensor<fp32, [128]> const_55 = const()[name = string("const_55"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))];
+            tensor<fp32, [?, 128, 20, 250]> input_159 = conv(bias = const_55, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = const_54, x = input_155)[name = string("input_159")];
+            tensor<fp32, [?, 128, 20, 250]> input_161 = relu(x = input_159)[name = string("input_161")];
+            string input_163_pad_type_0 = const()[name = string("input_163_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_163_pad_0 = const()[name = string("input_163_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_163_strides_0 = const()[name = string("input_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_163_dilations_0 = const()[name = string("input_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_163_groups_0 = const()[name = string("input_163_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_56 = const()[name = string("const_56"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))];
+            tensor<fp32, [128]> const_57 = const()[name = string("const_57"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))];
+            tensor<fp32, [?, 128, 20, 250]> out_25 = conv(bias = const_57, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = const_56, x = input_161)[name = string("out_25")];
+            tensor<fp32, [?, 128, 20, 250]> input_165 = add(x = out_25, y = input_155)[name = string("input_165")];
+            tensor<fp32, [?, 128, 20, 250]> input_167 = relu(x = input_165)[name = string("input_167")];
+            string input_169_pad_type_0 = const()[name = string("input_169_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_169_pad_0 = const()[name = string("input_169_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_169_strides_0 = const()[name = string("input_169_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_169_dilations_0 = const()[name = string("input_169_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_169_groups_0 = const()[name = string("input_169_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 3, 3]> const_58 = const()[name = string("const_58"), val = tensor<fp32, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))];
+            tensor<fp32, [256]> const_59 = const()[name = string("const_59"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))];
+            tensor<fp32, [?, 256, 10, 125]> input_171 = conv(bias = const_59, dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = const_58, x = input_167)[name = string("input_171")];
+            tensor<fp32, [?, 256, 10, 125]> input_173 = relu(x = input_171)[name = string("input_173")];
+            string input_175_pad_type_0 = const()[name = string("input_175_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_175_pad_0 = const()[name = string("input_175_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_175_strides_0 = const()[name = string("input_175_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_175_dilations_0 = const()[name = string("input_175_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_175_groups_0 = const()[name = string("input_175_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_60 = const()[name = string("const_60"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))];
+            tensor<fp32, [256]> const_61 = const()[name = string("const_61"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))];
+            tensor<fp32, [?, 256, 10, 125]> out_27 = conv(bias = const_61, dilations = input_175_dilations_0, groups = input_175_groups_0, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = input_175_strides_0, weight = const_60, x = input_173)[name = string("out_27")];
+            string input_177_pad_type_0 = const()[name = string("input_177_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_177_strides_0 = const()[name = string("input_177_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_177_pad_0 = const()[name = string("input_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_177_dilations_0 = const()[name = string("input_177_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_177_groups_0 = const()[name = string("input_177_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 1, 1]> const_62 = const()[name = string("const_62"), val = tensor<fp32, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))];
+            tensor<fp32, [256]> const_63 = const()[name = string("const_63"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))];
+            tensor<fp32, [?, 256, 10, 125]> var_537 = conv(bias = const_63, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = const_62, x = input_167)[name = string("op_537")];
+            tensor<fp32, [?, 256, 10, 125]> input_179 = add(x = out_27, y = var_537)[name = string("input_179")];
+            tensor<fp32, [?, 256, 10, 125]> input_181 = relu(x = input_179)[name = string("input_181")];
+            string input_183_pad_type_0 = const()[name = string("input_183_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_183_pad_0 = const()[name = string("input_183_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_183_strides_0 = const()[name = string("input_183_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_183_dilations_0 = const()[name = string("input_183_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_183_groups_0 = const()[name = string("input_183_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_64 = const()[name = string("const_64"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))];
+            tensor<fp32, [256]> const_65 = const()[name = string("const_65"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))];
+            tensor<fp32, [?, 256, 10, 125]> input_185 = conv(bias = const_65, dilations = input_183_dilations_0, groups = input_183_groups_0, pad = input_183_pad_0, pad_type = input_183_pad_type_0, strides = input_183_strides_0, weight = const_64, x = input_181)[name = string("input_185")];
+            tensor<fp32, [?, 256, 10, 125]> input_187 = relu(x = input_185)[name = string("input_187")];
+            string input_189_pad_type_0 = const()[name = string("input_189_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_189_pad_0 = const()[name = string("input_189_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_189_strides_0 = const()[name = string("input_189_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_189_dilations_0 = const()[name = string("input_189_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_189_groups_0 = const()[name = string("input_189_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_66 = const()[name = string("const_66"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))];
+            tensor<fp32, [256]> const_67 = const()[name = string("const_67"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))];
+            tensor<fp32, [?, 256, 10, 125]> out_29 = conv(bias = const_67, dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = const_66, x = input_187)[name = string("out_29")];
+            tensor<fp32, [?, 256, 10, 125]> input_191 = add(x = out_29, y = input_181)[name = string("input_191")];
+            tensor<fp32, [?, 256, 10, 125]> input_193 = relu(x = input_191)[name = string("input_193")];
+            string input_195_pad_type_0 = const()[name = string("input_195_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_195_pad_0 = const()[name = string("input_195_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_195_strides_0 = const()[name = string("input_195_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_195_dilations_0 = const()[name = string("input_195_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_195_groups_0 = const()[name = string("input_195_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_68 = const()[name = string("const_68"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))];
+            tensor<fp32, [256]> const_69 = const()[name = string("const_69"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))];
+            tensor<fp32, [?, 256, 10, 125]> input_197 = conv(bias = const_69, dilations = input_195_dilations_0, groups = input_195_groups_0, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = input_195_strides_0, weight = const_68, x = input_193)[name = string("input_197")];
+            tensor<fp32, [?, 256, 10, 125]> input_199 = relu(x = input_197)[name = string("input_199")];
+            string input_201_pad_type_0 = const()[name = string("input_201_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_201_strides_0 = const()[name = string("input_201_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_201_dilations_0 = const()[name = string("input_201_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_201_groups_0 = const()[name = string("input_201_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_70 = const()[name = string("const_70"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))];
+            tensor<fp32, [256]> const_71 = const()[name = string("const_71"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))];
+            tensor<fp32, [?, 256, 10, 125]> out = conv(bias = const_71, dilations = input_201_dilations_0, groups = input_201_groups_0, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = input_201_strides_0, weight = const_70, x = input_199)[name = string("out")];
+            tensor<fp32, [?, 256, 10, 125]> input_203 = add(x = out, y = input_193)[name = string("input_203")];
+            tensor<fp32, [?, 256, 10, 125]> frames = relu(x = input_203)[name = string("frames")];
+            tensor<int32, [3]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp32, [?, 2560, 125]> sequences = reshape(shape = concat_0x, x = frames)[name = string("sequences")];
+            tensor<int32, [1]> input_205_axes_0 = const()[name = string("input_205_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 1, 589]> input_205 = expand_dims(axes = input_205_axes_0, x = weights)[name = string("input_205")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [?, 1, 589, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input_205)[name = string("expand_dims_0")];
+            fp32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = fp32(0x1.b2a2a4p-3)];
+            fp32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [?, 1, 125, 1]> upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")];
+            tensor<int32, [1]> weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [?, 1, 125]> weights_1 = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0)[name = string("weights")];
+            tensor<int32, [1]> weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 1]> weight_sum = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights_1)[name = string("weight_sum")];
+            fp32 var_627 = const()[name = string("op_627"), val = fp32(0x0p+0)];
+            tensor<bool, [?, 1]> var_628 = greater(x = weight_sum, y = var_627)[name = string("op_628")];
+            fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [?, 1]> fill_like_0 = fill_like(ref_tensor = weight_sum, value = fill_like_0_value_0)[name = string("fill_like_0")];
+            tensor<fp32, [?, 1]> safe_sum = select(a = weight_sum, b = fill_like_0, cond = var_628)[name = string("safe_sum")];
+            tensor<fp32, [?, 2560, 125]> var_636 = mul(x = sequences, y = weights_1)[name = string("op_636")];
+            tensor<int32, [1]> var_641_axes_0 = const()[name = string("op_641_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_641_keep_dims_0 = const()[name = string("op_641_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 2560]> var_641 = reduce_sum(axes = var_641_axes_0, keep_dims = var_641_keep_dims_0, x = var_636)[name = string("op_641")];
+            tensor<fp32, [?, 2560]> mean = real_div(x = var_641, y = safe_sum)[name = string("mean")];
+            tensor<int32, [1]> var_644_axes_0 = const()[name = string("op_644_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [?, 2560, 1]> var_644 = expand_dims(axes = var_644_axes_0, x = mean)[name = string("op_644")];
+            tensor<fp32, [?, 2560, 125]> var_646 = sub(x = sequences, y = var_644)[name = string("op_646")];
+            tensor<fp32, [?, 2560, 125]> dx2 = mul(x = var_646, y = var_646)[name = string("dx2")];
+            tensor<fp32, [?, 1, 125]> var_648 = mul(x = weights_1, y = weights_1)[name = string("op_648")];
+            tensor<int32, [1]> weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 1]> weight_sq_sum = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_648)[name = string("weight_sq_sum")];
+            tensor<fp32, [?, 1]> var_654 = real_div(x = weight_sq_sum, y = safe_sum)[name = string("op_654")];
+            tensor<fp32, [?, 1]> var_656 = sub(x = safe_sum, y = var_654)[name = string("op_656")];
+            fp32 var_658 = const()[name = string("op_658"), val = fp32(0x1.5798eep-27)];
+            tensor<fp32, [?, 1]> denom = add(x = var_656, y = var_658)[name = string("denom")];
+            tensor<fp32, [?, 2560, 125]> var_660 = mul(x = dx2, y = weights_1)[name = string("op_660")];
+            tensor<int32, [1]> var_665_axes_0 = const()[name = string("op_665_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_665_keep_dims_0 = const()[name = string("op_665_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 2560]> var_665 = reduce_sum(axes = var_665_axes_0, keep_dims = var_665_keep_dims_0, x = var_660)[name = string("op_665")];
+            tensor<fp32, [?, 2560]> var = real_div(x = var_665, y = denom)[name = string("var")];
+            fp32 var_667 = const()[name = string("op_667"), val = fp32(0x1.b7cdfep-34)];
+            tensor<fp32, [?, 2560]> var_668 = maximum(x = var, y = var_667)[name = string("op_668")];
+            tensor<fp32, [?, 2560]> std = sqrt(x = var_668)[name = string("std")];
+            int32 var_671 = const()[name = string("op_671"), val = int32(-1)];
+            bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)];
+            tensor<fp32, [?, 5120]> stats = concat(axis = var_671, interleave = stats_interleave_0, values = (mean, std))[name = string("stats")];
+            tensor<fp32, [?, 2560]> var_678 = sub(x = mean, y = mean)[name = string("sub_0")];
+            fp32 var_685_value_0 = const()[name = string("op_685_value_0"), val = fp32(0x1.4f8b58p-17)];
+            tensor<fp32, [?, 2560]> var_685 = fill_like(ref_tensor = std, value = var_685_value_0)[name = string("op_685")];
+            int32 var_687 = const()[name = string("op_687"), val = int32(-1)];
+            bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)];
+            tensor<fp32, [?, 5120]> zero_stats = concat(axis = var_687, interleave = zero_stats_interleave_0, values = (var_678, var_685))[name = string("zero_stats")];
+            fp32 var_689 = const()[name = string("op_689"), val = fp32(0x0p+0)];
+            tensor<bool, [?, 1]> var_690 = less_equal(x = weight_sum, y = var_689)[name = string("op_690")];
+            tensor<int32, [2]> var_696 = const()[name = string("op_696"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [?, 5120]> zero_mask = tile(reps = var_696, x = var_690)[name = string("zero_mask")];
+            tensor<fp32, [?, 5120]> input = select(a = zero_stats, b = stats, cond = zero_mask)[name = string("input")];
+            tensor<fp32, [?, 256]> output = linear(bias = resnet_seg_1_bias, weight = resnet_seg_1_weight, x = input)[name = string("linear_0")];
+        } -> (output);
+}